[
  {
    "path": ".gitignore",
    "content": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\nwheels/\npip-wheel-metadata/\nshare/python-wheels/\n*.egg-info/\n.installed.cfg\n*.egg\nruns/\ntrain_imgs/\nMANIFEST\n\n# PyInstaller\n#  Usually these files are written by a python script from a template\n#  before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.nox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*.cover\n*.py,cover\n.hypothesis/\n.pytest_cache/\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\n*.log\nlocal_settings.py\ndb.sqlite3\ndb.sqlite3-journal\n\n# Flask stuff:\ninstance/\n.webassets-cache\n\n# Scrapy stuff:\n.scrapy\n\n# Sphinx documentation\ndocs/_build/\n\n# PyBuilder\ntarget/\n\n# Jupyter Notebook\n.ipynb_checkpoints\n\n# IPython\nprofile_default/\nipython_config.py\n\n#key\nChatGPT/config\n# pyenv\n.python-version\n\n# pipenv\n#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.\n#   However, in case of collaboration, if having platform-specific dependencies or dependencies\n#   having no cross-platform support, pipenv may install dependencies that don't work, or not\n#   install all needed dependencies.\n#Pipfile.lock\n\n# PEP 582; used by e.g. github.com/David-OConnor/pyflow\n__pypackages__/\n\n# Celery stuff\ncelerybeat-schedule\ncelerybeat.pid\n\n# SageMath parsed files\n*.sage.py\n\n# Environments\n.env\n.venv\nenv/\nvenv/\nENV/\nenv.bak/\nvenv.bak/\n\n# Spyder project settings\n.spyderproject\n.spyproject\n\n# Rope project settings\n.ropeproject\n\n# mkdocs documentation\n/site\n\n# mypy\n.mypy_cache/\n.dmypy.json\ndmypy.json\n\n# Pyre type checker\n.pyre/\n\n# checkpoint\n*.pth\noutputs/\ncheckpoints/\ngfpan/\nresults/\n\n.idea/\nweights/\nvoice_dir/\nSadTalker/\nVITS/\nconfig_private.py\nprivate_upload\ngpt_log"
  },
  {
    "path": ".gitmodules",
    "content": "[submodule \"VisualGLM_6B\"]\n\tpath = VisualGLM_6B\n\turl = https://github.com/positive666/VisualGLM_6B.git\n\n"
  },
  {
    "path": "LICENSE",
    "content": "                    GNU GENERAL PUBLIC LICENSE\n                       Version 3, 29 June 2007\n\n Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>\n Everyone is permitted to copy and distribute verbatim copies\n of this license document, but changing it is not allowed.\n\n                            Preamble\n\n  The GNU General Public License is a free, copyleft license for\nsoftware and other kinds of works.\n\n  The licenses for most software and other practical works are designed\nto take away your freedom to share and change the works.  By contrast,\nthe GNU General Public License is intended to guarantee your freedom to\nshare and change all versions of a program--to make sure it remains free\nsoftware for all its users.  We, the Free Software Foundation, use the\nGNU General Public License for most of our software; it applies also to\nany other work released this way by its authors.  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If your rights have been terminated and not permanently\nreinstated, you do not qualify to receive new licenses for the same\nmaterial under section 10.\n\n  9. Acceptance Not Required for Having Copies.\n\n  You are not required to accept this License in order to receive or\nrun a copy of the Program.  Ancillary propagation of a covered work\noccurring solely as a consequence of using peer-to-peer transmission\nto receive a copy likewise does not require acceptance.  However,\nnothing other than this License grants you permission to propagate or\nmodify any covered work.  These actions infringe copyright if you do\nnot accept this License.  Therefore, by modifying or propagating a\ncovered work, you indicate your acceptance of this License to do so.\n\n  10. Automatic Licensing of Downstream Recipients.\n\n  Each time you convey a covered work, the recipient automatically\nreceives a license from the original licensors, to run, modify and\npropagate that work, subject to this License.  You are not responsible\nfor enforcing compliance by third parties with this License.\n\n  An \"entity transaction\" is a transaction transferring control of an\norganization, or substantially all assets of one, or subdividing an\norganization, or merging organizations.  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Patents.\n\n  A \"contributor\" is a copyright holder who authorizes use under this\nLicense of the Program or a work on which the Program is based.  The\nwork thus licensed is called the contributor's \"contributor version\".\n\n  A contributor's \"essential patent claims\" are all patent claims\nowned or controlled by the contributor, whether already acquired or\nhereafter acquired, that would be infringed by some manner, permitted\nby this License, of making, using, or selling its contributor version,\nbut do not include claims that would be infringed only as a\nconsequence of further modification of the contributor version.  For\npurposes of this definition, \"control\" includes the right to grant\npatent sublicenses in a manner consistent with the requirements of\nthis License.\n\n  Each contributor grants you a non-exclusive, worldwide, royalty-free\npatent license under the contributor's essential patent claims, to\nmake, use, sell, offer for sale, import and otherwise run, modify and\npropagate the contents of its contributor version.\n\n  In the following three paragraphs, a \"patent license\" is any express\nagreement or commitment, however denominated, not to enforce a patent\n(such as an express permission to practice a patent or covenant not to\nsue for patent infringement).  To \"grant\" such a patent license to a\nparty means to make such an agreement or commitment not to enforce a\npatent against the party.\n\n  If you convey a covered work, knowingly relying on a patent license,\nand the Corresponding Source of the work is not available for anyone\nto copy, free of charge and under the terms of this License, through a\npublicly available network server or other readily accessible means,\nthen you must either (1) cause the Corresponding Source to be so\navailable, or (2) arrange to deprive yourself of the benefit of the\npatent license for this particular work, or (3) arrange, in a manner\nconsistent with the requirements of this License, to extend the patent\nlicense to downstream recipients.  \"Knowingly relying\" means you have\nactual knowledge that, but for the patent license, your conveying the\ncovered work in a country, or your recipient's use of the covered work\nin a country, would infringe one or more identifiable patents in that\ncountry that you have reason to believe are valid.\n\n  If, pursuant to or in connection with a single transaction or\narrangement, you convey, or propagate by procuring conveyance of, a\ncovered work, and grant a patent license to some of the parties\nreceiving the covered work authorizing them to use, propagate, modify\nor convey a specific copy of the covered work, then the patent license\nyou grant is automatically extended to all recipients of the covered\nwork and works based on it.\n\n  A patent license is \"discriminatory\" if it does not include within\nthe scope of its coverage, prohibits the exercise of, or is\nconditioned on the non-exercise of one or more of the rights that are\nspecifically granted under this License.  You may not convey a covered\nwork if you are a party to an arrangement with a third party that is\nin the business of distributing software, under which you make payment\nto the third party based on the extent of your activity of conveying\nthe work, and under which the third party grants, to any of the\nparties who would receive the covered work from you, a discriminatory\npatent license (a) in connection with copies of the covered work\nconveyed by you (or copies made from those copies), or (b) primarily\nfor and in connection with specific products or compilations that\ncontain the covered work, unless you entered into that arrangement,\nor that patent license was granted, prior to 28 March 2007.\n\n  Nothing in this License shall be construed as excluding or limiting\nany implied license or other defenses to infringement that may\notherwise be available to you under applicable patent law.\n\n  12. No Surrender of Others' Freedom.\n\n  If conditions are imposed on you (whether by court order, agreement or\notherwise) that contradict the conditions of this License, they do not\nexcuse you from the conditions of this License.  If you cannot convey a\ncovered work so as to satisfy simultaneously your obligations under this\nLicense and any other pertinent obligations, then as a consequence you may\nnot convey it at all.  For example, if you agree to terms that obligate you\nto collect a royalty for further conveying from those to whom you convey\nthe Program, the only way you could satisfy both those terms and this\nLicense would be to refrain entirely from conveying the Program.\n\n  13. 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  },
  {
    "path": "README.md",
    "content": "# Prompt-Can-Anything\n\n<p align=\"center\"> English | <a href=\"README_zh.md\">中文</a></p>\n\nThis is a gradio  library and research repository that combines SOTA AI applications. It can help you achieve anything - all you need to do is provide prompts and make one click. Through the prompts and creativity of SOTA models, you can do anything.You don't have to install all the features, you can install them according to the features you want to use.\n\n**Motivation**\n\nCurrently, the “Anything” AI intelligent agent backend has been accumulated for engineering and research. This requires the use of more multi-modal tasks and zero-shot models, not only to provide multi-modal AI processing web UI, but also to gradually enrich its functionality.\n\nYou can accomplish anything through this project! Let’s learn more about the development progress and plan of this project, and the final complete intelligent agent that combines the local GPT repository can help you call any AI task! Questions, stars, forks,You can also become a developer.\n\n## Feature\n\n 1. (YOCO) It is not just a tool that can prompt anything\n\n    🔥 Data Engine:\n    \n    In addition, we will introduce video, audio, and 3D annotations in the future. YOCO relies on integrated multimodal models and auxiliary generators such as ChatGPT. Of course, it is not omnipotent. Through effective fully automatic annotation and stable diffusion series methods to produce and control data that meet the requirements, we complete the “data engine” and generate customized label formats that facilitate the training of conventional models.\n    \n    🔥 Model Training:\n    \n    For each model, we not only need to use it, but also read its paper, fine-tuning methods, and communicate with the original author to try some development work for improvement and better training. We use fine-tune large models and customized label formats generated by YOCO to more efficiently train conventional models.\n\n<img src=\"asset/data_engine.png\" alt=\"structure\" style=\"zoom: 33%;\" />\n\n 2.  🚀 Interactive content creation and visual GPT\n\nIntegrate diversified GPT, mainly using the port of chatgpt, and use the open-source Tsinghua VISUALGLM to deploy and fine-tune localized GPT, as well as try to improve the model structure. Through multimodal application tools, we can conduct dialogues and content creation.\n\neasy example( asr->llM_model->tts->a2f app)\n\nhttps://github.com/positive666/Prompt-Can-Anything/assets/28972473/c9cc64af-939d-480f-a684-08d8db34b25f\n\n 3. ⭐ 3D && 2D Avatar(comming soon)\n\nComplete a role design interaction through a 3D Engine combined with multimodal tasks such as GPT;\n\nComplete a role design interaction through the Sadtalker open source project and multimodal tasks such as GPT.\n\n\n\n\n\n 4.  🔥🔥🚀 Unlimited potential “Anything”\n\nThrough continuous creativity and accumulation, we will integrate and learn from Sota AI. We will record each integrated model and provide a detailed explanation and summary in the article. The author will summarize all the AI-related knowledge reserves and engineering experience for the local large model (this part is the final development function and is planned).\n\n<img src=\"asset/v1.15.png\" alt=\"structure\" style=\"zoom: 33%;\" />\n\n\n<details open >\n<summary>⭐ Research🚀 project🔥 Inspiration（In preparation）</summary>\n\n\t  At research level, Zero-shot comparative learning is research trend, we hope to understand as much as possible the model design details of the project we are applying, so that we want to combine text, images, and audio to design a strong aligned backbone.\n\t  At project level, Tensorrt acceleration of the basic model accelerates efficiency.\n\n\n\n</details>\n\n### <div align=\"left\"> 🔥 [August , Update plan preview , Welcome fork] </div>\n\n- 🔥 add gpt_academic repo crazy functions and add langchain\\agent comming soon\n\n-  Optimization of speech problems and code logic optimization before optimization, add Gilgen\n\n- 🔥Official latest model integration test for Tag2text version 2 in early June,add RAM(Done)\n\n-  One-click fine-tuning button function, adding: visualglm  (Done)\n\n-  Voice text processing link GPT, joining chatglm   with a2f APP( Done)\n\n  \n\n  \n\n### <div align=\"left\">⭐[News list] </div>\n\n\n\t-【2023/8/7】   Fix bug with llm(chatglm2,gpt3.5 loads and improve gradio ui)\n\t\n\t-【2023/7/21】  update tag2text and ram with offical repo\n\t\n\t-【2023/6/7】   v1.15:add submodule SadTalker,update UI\n\t\n\t-【2023/6/6】   v1.15:environment installation problems and supplementary instructions, special models are called independently, and no need to install dependencies; Added the function of one-click fine-tuning of VisualGLM, considering machine configuration and video memory with caution\n\t\n\t-【2023/6/5】   v1.15 a vide demo and plan,fix asr bug ,chatgpt with asr and tts \n\t\n\t-【2023/5/31】  Fixed the already issue, add tts demo, the Linux platform is tested through all open features\n\t\n\t-【2023/5/23】  add web demo:Add VisualGLM ,chatgpt from [Academic-gpt](https://github.com/binary-husky/gpt_academic)\n\t\n\t-【2023/5/7】   add web demo:At present, the function of text generation, detection and segmentation of images or image folders on the website has been tested normally, and the program does not need to be restarted, and the last model loading configuration is remembered, and it will be continuously optimized in the future.\n\t\n\t-【2023/5/4】   add  semantic segmentatio label, add args(--color-flag --save-mask )\n\t\n\t-【2023/4/26】  YOCO,Automatic annotation TOOLS:Commit preliminary code ,For the input image or folder, you can obtain the results of detection, segmentation, and text annotation , optional chatgpt api.\n\n\n\n## Preliminary-Works\n\n- [VisualGLM-6B](https://github.com/THUDM/VisualGLM-6B) : Visual ChatGlm(6B) \n\n- [Segment Anything](https://github.com/facebookresearch/segment-anything) : Strong segmentation model. But it needs prompts (like boxes/points/text) to generate masks. \n\n- [Grounding DINO](https://github.com/IDEA-Research/GroundingDINO) :  Strong zero-shot detector which is capable of to generate high quality boxes and labels with free-form text. \n\n- [Stable-Diffusion](https://github.com/CompVis/stable-diffusion) :  Amazing strong text-to-image diffusion model.\n\n- [Tag2text](https://github.com/xinyu1205/Tag2Text) : Efficient and controllable vision-language model which can simultaneously output superior image captioning and image tagging.\n- [SadTalker](https://github.com/OpenTalker/SadTalker): Learning Realistic 3D Motion Coefficients for Stylized Audio-Driven Single Image Talking Face Animation\n- [lama](https://github.com/advimman/lama) :  Resolution-robust large mask Inpainting with Fourier Convolutions\n\n- [gpt_academic](https://github.com/binary-husky/gpt_academic) :  LLM tools.\n  \n  ## :hammer_and_wrench: YOCO: Quick Start\n\nFirst, Make sure you have a basic gpu deep learning environment.\n\n (Linux is recommended, Windows may have problems compiling Grounded-DINO Deformable- transformer operator, see [Grounding DINO](https://github.com/IDEA-Research/GroundingDINO) )\n\n```bash\ngit clone https://github.com/positive666/Prompt-Can-Anything\ncd Prompt-Can-Anything\n```\n\n**Install environment **\n\nInstallation of basic environment\n\n```\npip install -r requiremens  \nor  \npip install -i https://mirrors.aliyun.com/pypi/simple/ -r requirements.txt\n```\n\nInstallation of Ground detector (compiling)\n\n```bash\ncd model_cards\npip install -e .\n```\n\nInstallation of Tsinghua VisualGLM (optional, better to use LINUX system, installation plan will be updated after testing on Windows)\n\n```bash\ngit submodule update --init --recursive\ncd VisualGLM_6B && pip install -i https://mirrors.aliyun.com/pypi/simple/ -r requirements.txt\n```\n\nInstall SadTalker (optional )\n\n```bash\ngit clone https://github.com/Winfredy/SadTalker.git\ncd  SadTalker && pip install -i https://mirrors.aliyun.com/pypi/simple/ -r requirements.txt\n```\n\n​\t\tTips:create two directories, checkpoints and gfpgan, and place them in the root directory. Download the extracted weights from the official website and put them into two folders,\n\nInstallation of LAMA model (optional, not yet released):\n\nThis environment has a relatively strict requirement for the Python version, you may need to manually override the installation by version specified in the txt below:\n\n```\npip install -r model_cards/lama/requirements.txt\n```\n\nInstallation of diffuser (optional):\n\n```bash\npip install --upgrade diffusers[torch]\n```\n\nFor more content, you can check requirements, “pip install < your missing packages>”, if there is an installation version issue, please carefully look at the requirement version.\n\n**Linux environment issue**:\n\n1. for pyaudio\n\nMethod 1:\n\n pip may not be successful on the Linux platform, go to this page[pyaudio-wheels · PyPI](https://pypi.org/project/pyaudio-wheels/#files), select the version corresponding to your Python version, download it and pip install the whl file. Detailed instructions will be provided in the future.\n\nMethod 2:\n\n```\nsudo apt-get install portaudio19-dev\nsudo apt-get install python3-all-dev\npip install pyaudio\n```\n\n2. use qlora fine tune question\n\n   ```\n   pip install  bitsandbytes  -i https://mirrors.aliyun.com/pypi/simple\n   ```\n\n**Windows installation issue**\n\n​\t\tas Linux\n\nFor more content, you can check the requirements, “pip install < your missing packages>”, and if there are version installation issues, please check the version carefully in the requirements.\n\n\n\n**Run**\t\n\n1. downloads models weights\n\n   <!-- insert a table -->\n\n   <table>\n     <thead>\n       <tr style=\"text-align: left;\">\n         <th></th>\n         <th>name</th>\n          <th>backbone</th>\n         <th>Data</th>\n         <th>Checkpoint</th>\n           <th>model-config</th>\n       </tr>\n     </thead>\n     <tbody>\n       <tr>\n         <th>1</th>\n         <td>Tag2Text-Swin</td>\n         <td>Swin-Base</td>\n         <td>COCO, VG, SBU, CC-3M, CC-12M</td>\n         <td><a href=\"https://huggingface.co/spaces/xinyu1205/Tag2Text/blob/main/tag2text_swin_14m.pth\">Download  link</a></td>\n       <tr>\n         <th>2</th>\n         <td>Segment-anything</td>\n          <td>vit</td>\n           <td> </td>\n           <td><a href=\"https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth\">Download  link</a>| <a \n   <td><a href=\"https://dl.fbaipublicfiles.com/segment_anything/sam_vit_l_0b3195.pth\">Download  link</a>| <a \n       <td><a href=\"https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth\">Download  link</a></td>\n       <tr>\n         <th>3</th>\n         <td>Lama</td>\n           <td>FFC</td>\n            <td> </td>\n         <td><a href=\"https://disk.yandex.ru/d/ouP6l8VJ0HpMZg\">Download  link</a></td>\n       <tr>\n         <th>4</th>\n         <td>GroundingDINO-T</td>\n         <td>Swin-T</td>\n         <td>O365,GoldG,Cap4M</td>\n         <td><a href=\"https://github.com/IDEA-Research/GroundingDINO/releases/download/v0.1.0-alpha/groundingdino_swint_ogc.pth\">Github link</a> | <a href=\"https://huggingface.co/ShilongLiu/GroundingDINO/resolve/main/groundingdino_swint_ogc.pth\">HF link</a></td>\n         <td><a href=\"https://github.com/IDEA-Research/GroundingDINO/blob/main/groundingdino/config/GroundingDINO_SwinT_OGC.py\">link</a></td>\n       </tr>\n       <tr>\n         <th>5</th>\n         <td>GroundingDINO-B</td>\n         <td>Swin-B</td>\n         <td>COCO,O365,GoldG,Cap4M,OpenImage,ODinW-35,RefCOCO</td>\n         <td><a href=\"https://github.com/IDEA-Research/GroundingDINO/releases/download/v0.1.0-alpha2/groundingdino_swinb_cogcoor.pth\">Github link</a>  | <a href=\"https://huggingface.co/ShilongLiu/GroundingDINO/resolve/main/groundingdino_swinb_cogcoor.pth\">HF link</a> \n         <td><a href=\"https://github.com/IDEA-Research/GroundingDINO/blob/main/groundingdino/config/GroundingDINO_SwinB.cfg.py\">link</a></td>\n       </tr>\n     </tbody>\n   </table>\n   \n\n   \n\n2. Configure privacy files and parameters in config_private.py. After downloading the model, configure the path in the “MODEL_xxxx_PATH” variable. If using ChatGPT, configure its proxy and API key. (If there are networking issues with other services such as TTS during use on the web UI, first turn off the VPN connection and only open it when using ChatGPT).\n\n**🏃Demo**\n\n [Video demo 1 online on baidu clound ](https://pan.baidu.com/s/1AllUjuOVhzJh7abe71iCxg?pwd=c6v6）   \n [ Video demo 2 ] (https://pan.baidu.com/s/1jdP9mgUhyfLh_hz1W3pkeQ?pwd=c6v6)\n\n\n\n1. Auto-label\n\n```bash\n\"--input_prompt\" :  You can manually input a prompt. For example, if you only want to detect target categories that interest you, you can directly input the prompt to the grounded detection model, or input it to the Tag2Text model.\n'--color-flag': Using BOX’s tags, distinguish between category and instance segmentation: the category color of speech segmentation is distinguished using BOX’s tags.\n```\n\n\n   \tpython auto_lable_demo.py  --source <data path>  --save-txt  --save-mask --save-xml  --save_caption \n\n   Example:\n\n​    Support multi-tasks, such as :\n\n​                 default tasks include images understand /detect/instance segment .....(add methods for image generation and inpainting )\n\n<img src=\"asset/1.jpg\" style=\"zoom: 32%;\" />\n\n\"Prompt\" control models output, example\n\n​\t\t\t\t\t<img src=\"asset/d2.png\" style=\"zoom: 35%;\" >\n\n\n\n<img src=\"asset/image-20230427093103453.png\" alt=\"image-20230427093103453\" style=\"zoom: 33%;\" />\n\n2.   webui(all)\n\n```pyhton\n\t\tpython app.py\n```\n\n<img src=\"asset/default_all.png\" alt=\"image-20230508075845259\" style=\"zoom:33%;\" />\n\n​\t\t\t<img src=\"asset/demo1.png\" style=\"zoom:25%;\" />\n\n\n\n\n\n<img src=\"asset/v1.1_demo.png\" alt=\"image-20230527022556630\" style=\"zoom:50%;\" />\n\n​\t2.1 audio2face with llm model (Beta)\n\n​\t\t\tIn Fact, ASR\\TTS\\LLM ，They are all arbitrarily replaceable.\n\n​           this  is  a easy  example, support chatglm,chatgpt(you can use anything llm model,but you need custom )\n\n​           start asr&tts with audio2face \n\n​\t\t\tyou need  install audio2face in omniverse APP,see\n\nhttps://www.nvidia.cn/omniverse/\n\n​\t\t\tstep1. In audio2face，open a demo ,choose a Player ,auto build Trt engine ,（not support GTX10xx GPU），latest version support chinese!\n\n​\t\t\t\t\t\tget model pim path.\n\n<img src=\"asset/a2f.png\" alt=\"image-20230725122731372\" style=\"zoom: 33%;\"/>\n\n​\t\t\t\t\t\t\t\t\t\t<img src=\"asset/a2f2023.png\" alt=\"image-20230331372\" style=\"zoom: 33%;\"/>\t\n\n​\t\t\t\t\t\t\t\t\t\t ![image-20230725133326397](asset\\getpath.png) \n\n​    step 2. in webui , configure your Prim path \"Avatar_instance_A\" in config_private.py , click\"start system\" and\" Speech_system\"\n\n​\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t \t\t\t\t\t\t\t\t<img src=\"asset/start-chat.png\" style=\"zoom:67%;\">\n\n\n\n\n\n## 🔨To Do List\n\n- [x] Release demo and code.\n- [x] web ui  demo \n- [x] Support ChatGPT/VISUALGLM/ASR/TTS\n- [x]   YOCO labeling fine-tuning of VISUALGLM demo[next week]\n- [x] 3D && 2D avatar \n- [ ] Complete the planned AI combination “Anything”\n- [ ] Fine-tune the segmentation and ground detectors of SAM, and expand the input control of SAM\n- [ ] Release training methods\n- [ ] Knowledge cloning\n\n## :cupid: Acknowledgements\n\n- [gpt_academic](https://github.com/binary-husky/gpt_academic)\n- [Segment Anything](https://github.com/facebookresearch/segment-anything)\n- [Grounding DINO](https://github.com/IDEA-Research/GroundingDINO)\n- [Tag2text](https://github.com/xinyu1205/Tag2Text) \n- [SadTalker](https://github.com/OpenTalker/SadTalker)\n- [lama](https://github.com/advimman/lama) \n- [ VisualGLM-6B](https://github.com/THUDM/VisualGLM-6B.git)\n\n   Thanks for their great work!\n\n"
  },
  {
    "path": "README_zh.md",
    "content": "\n\n# Prompt-Can-Anything\n\n这是一个结合SOTA AI的应用web库以及研究的储备库，它能够帮你实现一切：你只需要提供提示！只需一次点击！通过SOTA模型的提示和创意，你可以做任何事情。\n\n**动机**\n\n当前：为工程和研究所积累的AI智能体后台”安尼森“，这需要使用更多的多模态任务以及zero-shot模型，不仅提供多模态的AI处理web UI，逐渐丰富的功能。\n\n目标：你可以通过它完成一切事情！让我们详细了解下该项目的开发进度和计划，最终完整的智能体结合本地储备的GPT可以帮你调用一切AI任务！欢迎提问、star和fork,以及伸出援助之手~\n\n## 特性\n\n1. （YOCO）它不仅是一个可以提示任何事情的工具\n\n   🔥 数据引擎：\n   \n   此外，我们将在未来引入视频、音频和3D注释，YOCO依赖于集成的多模态模型以及GPT等辅助生成，当然它并不是万能的，通过有效的全自动标注和stable diffusion系列的方法去生产和控制符合需求的数据，完成”数据引擎“，并且生成的定制化的标签格式，去便于训练常规模型。\n   \n   🔥 模型训练: \n   \n   对于每一个模型我们不仅要做到使用，还在阅读它的论文和微调方法以及和原作者交流，尝试一些改进和更好训练的开发工作，Fine-tune大模型和通过YOCO生成的定制化的标签格式，更高效地训练常规模型。\n   \n   \n\n<img src=\"asset/data_engine.png\" alt=\"structure\" style=\"zoom: 33%;\" />\n\n2. 🚀交互内容创作和视觉&&语音GPT\n\n​      集成多样化GPT，目前主要以chatgpt的端口为主，利用开源的清华VISUALGLM，我们实现本地化GPT的部署和微调，以及尝试改进模型结构，通过多模态的应用工具进行对话和内容创作，支持语音识别、语音合成、并发送Audio2face.\n\n​\t\t这是一个最简单的例子\n\n​\t\thttps://github.com/positive666/Prompt-Can-Anything/assets/28972473/c9cc64af-939d-480f-a684-08d8db34b25f\n\n\n\n3. ⭐ 应用角色扮演—— 3D &&2D 虚拟人（开发中）\n\n   通过3D引擎去结合GPT等多模态任务完成一个角色设计互动；\n\n   通过saldtalker开源项目去结合GPT等多模态任务完成一个角色设计互动；\n\n   \n\n4.  🔥🔥🚀无限的潜力“安尼森”\n\n​\t   不断的创意和积累，SOTA -AI的集成和学习，我们会通过记录每一个集成的模型，对它进行一次详解，总结在文章中。\n\n​\t   作者AI相关所有知识储备和工程经验总结给本地大模型（这部分是最终开发功能，计划中）\n\n<img src=\"asset/v1.15.png\" alt=\"structure\" style=\"zoom: 33%;\" />\n\n<details open>\n<summary>⭐ 研究 🚀 项目 🔥 灵感（筹备中）</summary>\n\n在研究层面上，零样本迁移比较学习是热门的研究趋势，我希望尽可能理解正在应用的项目的模型设计细节，这样我们想将文本、图像和音频相结合设计一个强大的对齐backbone。\n\n在项目层面上，可考虑Tensorrt加速基本模型或者其他的模型转换方式可以提高效率。\n\n</details>\n\n### <div align=\"left\">🔥 [8月更新预告，更新频繁，感兴趣关注]</div>\n\n\n-  修复了LLM调用相关的BUG和界面调整，正在更新langchains和Agent\n\n-  更新了ram&7tag2Text【Done】\n\n-  修复优化开源GLM的一些功能，一键微调按钮和各种微调模型\n\n-  语音文本处理链接gpt，加入chatglm    【Done】\n\n- Gilgen测试代码更新\n\n- ram&7tag2TexT等解析文章\n\n  \n\n\n\n\n\n</details>\n\n### <div align=\"left\">⭐ [更新列表]</div>\n\n\n-  【2023/8/7】  v1.2: 修复了界面已知BUG，分离了部分依赖，修复了chatglm2和多模型加载问题，完整添加了最新的学术GPT功能，并在更新langchains更多功能\n\n-  【2023/7/21】 v1.15: 更新了Tag2text和ram的代码，支持RAM，是一个中英识别标签的双模态模型\n\n-  【2023/6/7】 v1.15 :加入子项目SadTalker，更新UP界面，语音对话功能界面更新\n\n-  【2023/6/6】 v1.15版本:修复了已知的环境安装问题和补充说明，特殊的模型独立了调用，不需要可以不用安装依赖了；添加了一键微调VisualGLM的功能，考虑机器配置和显存慎用；\n\n-  【2023/6/5】 修复whisper asr的bug，内部可选模型，但是考虑显存不建议超过small,上传百度云一个介绍。\n\n- 【2023/5/31】添加Web演示：修复已知问题BUG，添加TTS模块（临时版本），LINUX系统上测试通过了所有开放的功能，补充一些说明和测试。（修改重载：每次勾选加载模型和释放模型后，因为太多的本地化的大模型，如果部署本地GPT显卡必须要20G+，但目前机制无法动态释放调节释放多个模型显存，这个按钮只能帮助你选择、组合串联cv模型的使用方式了）\n\n- 【2023/5/29】添加Web演示：加入了学术chatgpt部分功能，感谢他们的工作，其次添加了一键生成VisualGLM-6B数据集标注功能，后续可一键微调\n\n- 【2023/5/23】添加Web演示：加入清华的VisualGLM-6B版本\n\n- 【2023/5/7】添加Web演示：目前，已经测试了文本生成、图像或图像文件夹的检测和分割功能，程序无需重新启动，记住了最后的模型加载配置，并将在未来持续优化。\n- 【2023/5/4】添加语义分割标签，添加args（--color-flag --save-mask）\n- 【2023/4/26】YOCO，自动标注工具：提交初步代码，针对输入图像或文件夹，可以获得检测、分割和文本注释的结果，额外提供选择chatgpt api。\n\n**预备工作**\n\n- [VisualGLM-6B](https://github.com/THUDM/VisualGLM-6B.git) : Visual ChatGlm. \n\n- [Segment Anything](https://github.com/facebookresearch/segment-anything)：强大的分割模型。但它需要提示（如包围框/点/掩码、文本）来生成蒙版。\n\n- [Grounding DINO](https://github.com/IDEA-Research/GroundingDINO)：强大的零样本泛化检测器，能够使用自由格式文本生成高质量框和标签。\n\n- [Stable-Diffusion](https://github.com/CompVis/stable-diffusion)：文本-图像扩散模型。\n\n- [Tag2text](https://github.com/xinyu1205/Tag2Text)：高效可控的视觉-语言模型，可以同时输出优越的图像字幕和图像标记。\n\n- [SadTalker](https://github.com/OpenTalker/SadTalker):单图声音驱动人脸的方法\n\n- [lama](https://github.com/advimman/lama)：分辨率鲁棒的大屏蔽填充与傅立叶卷积\n\n- [gpt_academic](https://github.com/binary-husky/gpt_academic) :  丰富的LLM工具箱。\n\n  ## :hammer_and_wrench: YOCO: 快速入门\n\n首先，需要有基本的gpu深度学习环境。\n\n（强烈建议使用Linux，Windows可能在编译Grounded-DINO Deformable和配置Visualglm时候算子时出现问题，参见[Grounding DINO](https://github.com/IDEA-Research/GroundingDINO)）\n\n```bash\ngit clone https://github.com/positive666/Prompt-Can-Anything\ncd Prompt-Can-Anything\n```\n\n安装基本环境：\n\n```\npip install -r requirements\n或者\npip install -i https://mirrors.aliyun.com/pypi/simple/ -r requirements.txt\n```\n\n安装Ground检测器（编译）：\n\n```\ncd  model_cards\npip install -e .\n```\n\n安装清华智谱视觉VisualGLM（可选，最好用LINUX系统，window后面测试后补充安装方案）：\n\n```bash\ngit submodule update --init --recursive\ncd  VisualGLM_6B && pip install -i https://mirrors.aliyun.com/pypi/simple/ -r requirements.txt\n```\n\n安装SadTalker(optional )\n\n```bash\ngit clone https://github.com/Winfredy/SadTalker.git\ncd  SadTalker && pip install -i https://mirrors.aliyun.com/pypi/simple/ -r requirements.txt\n```\n\n​\t\t\tTips:创建checkpoints 和gfpgan两个目录，放置在根目录下。从官网下载解压的权重分别放进两个文件夹!!\n\n安装LAMA模型（可选还未发布）：\n\n​    这个环境对Python版本要求比较苛刻，可能需要按照下面txt的版本手动覆盖安装\n\n```\n pip install -r model_cards/lama/requirements.txt \n```\n\n安装扩散器（可选）：\n\n```bash\npip install --upgrade diffusers[torch]\n```\n\n更多内容，可以查看requirements, “pip install < your missing packages>”, 如果出现安装版本问题，请仔细看requirements的版本\n\n**Linux环境问题【易出现问题的库】**\n\n1. 对于pyaudio\n\n方法一：在Linux平台可能通过pip并不一定成功，进入这里[pyaudio-wheels · PyPI](https://pypi.org/project/pyaudio-wheels/#files)，选择对应你Python的版本，下载后pip安装whl，后续会详细补充。\n\n方法二：\n\n```\nsudo apt-get install portaudio19-dev\nsudo apt-get install python3-all-dev\npip install pyaudio\n```\n\n2.VisualGLM训练环境:使用qlora微调int4模型问题：\n\n```\npip install  bitsandbytes  -i https://mirrors.aliyun.com/pypi/simple\n```\n\n**Windows安装问题**\n\n目前除了LLM的加速和微调三方库，无特殊问题。\n\n运行\n\n1. 下载模型权重\n\n<!-- insert a table -->\n\n<table>\n  <thead>\n    <tr style=\"text-align: left;\">\n      <th></th>\n      <th>名称</th>\n       <th>骨干</th>\n      <th>数据</th>\n      <th>权重</th>\n        <th>模型配置</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>Tag2Text-Swin</td>\n      <td>Swin-Base</td>\n      <td>COCO、VG、SBU、CC-3M、CC-12M</td>\n      <td><a href=\"https://huggingface.co/spaces/xinyu1205/Tag2Text/blob/main/tag2text_swin_14m.pth\">下载链接</a></td>\n    <tr>\n      <th>2</th>\n      <td>Segment-anything</td>\n       <td>vit</td>\n        <td> </td>\n        <td><a href=\"https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth\">下载链接</a>| <a \n    <td><a href=\"https://dl.fbaipublicfiles.com/segment_anything/sam_vit_l_0b3195.pth\">下载链接</a>| <a \n    <td><a href=\"https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth\">下载链接</a></td>\n    <tr>\n      <th>3</th>\n      <td>Lama</td>\n        <td>FFC</td>\n         <td> </td>\n      <td><a href=\"https://disk.yandex.ru/d/ouP6l8VJ0HpMZg\">下载链接</a></td>\n    <tr>\n      <th>4</th>\n      <td>GroundingDINO-T</td>\n      <td>Swin-T</td>\n      <td>O365、GoldG、Cap4M</td>\n      <td><a href=\"https://github.com/IDEA-Research/GroundingDINO/releases/download/v0.1.0-alpha/groundingdino_swint_ogc.pth\">Github链接</a> | <a href=\"https://huggingface.co/ShilongLiu/GroundingDINO/resolve/main/groundingdino_swint_ogc.pth\">HF链接</a></td>\n      <td><a href=\"https://github.com/IDEA-Research/GroundingDINO/blob/main/groundingdino/config/GroundingDINO_SwinT_OGC.py\">链接</a></td>\n    </tr>\n    <tr>\n</table>\n   2. 配置隐私文件和参数在 config_private.py 下,下载模型后将路径配置在\" MODEL_xxxx_PATH“的变量  ,如果使用 chatgpt ,配置其代理和API密钥，可能在WEBUI使用过程中，如果其他服务如tts有联网问题，先关掉VPN链接，仅当使用Chatgpt时候打开。\n\n      \n\n## 🏃Demo\n\n [视频Demo介绍 ](https://pan.baidu.com/s/1AllUjuOVhzJh7abe71iCxg?pwd=c6v6）  \n [ Video demo 2 ] (https://pan.baidu.com/s/1jdP9mgUhyfLh_hz1W3pkeQ?pwd=c6v6)\n\n1. 自动标注的测试样例\n\n      ```bash\n   \"--input_prompt\" :  你可以手动输入prompt,比如你只想检测你感兴趣的目标类别，可以直接输入给grounded检测模型，也可以输入给tag2text\n   '--color-flag': 使用BOX的标签同类别和实例分割区别:语义分割的类别颜色\n   ```\n\n   支持多种任务，例如：\n   \n   默认任务包括图像理解/检测/实例分割…(以及后修添加图像生成和编辑的方法去制作新数据)\n   \n   <img src=\"asset/1.jpg\" style=\"zoom: 39%;\" />\n   \n   \"Prompt\" control models output, example\n   \n   ​\t\t\t\t\t<img src=\"asset/d2.png\" style=\"zoom: 57%;\" >\n   \n   \n   \tpython auto_label_demo.py  --source <data path>  --save-txt  --save-mask --save-xml  --save_caption \n   \n   \n\n<img src=\"asset/image-20230427093103453.png\" alt=\"image-20230427093103453\" style=\"zoom:25%;\" />\n\n2. webui\n\n   \n\n   ```pyhton\n   \t\tpython app.py\n   ```\n\n   <img src=\"asset/anything.png\" alt=\"image-20230527022556630\" style=\"zoom: 33%;\" />\n\n   \n\n   <img src=\"asset/default_all.png\" alt=\"image-20230508075845259\" style=\"zoom:33%;\" />\n\n   ​\t\t\t\n\n   \n   \n   <img src=\"asset/demo1.png\" alt=\"visual_chatglm\" style=\"zoom:33%;\" />\n   \n   2.语音大语言模型&&驱动a2f \n   \n   ​           这是一个简单的例子，实际上asr、tts\\llm_model\\这些组件是可以任意替换的，只要你具备基本的开发能力，通过语言模型和语音驱动去完成A2F的服务，你需要安装Omniverse软件和Audio2face的应用，GPU不能是比较旧的帕斯卡架构，详情可以看https://www.nvidia.cn/omniverse/\n   \n   ​\t\t\t步骤1.在Omniverse中，点击如图下的例子，安装一个Demo player,它会自动完成tensortt的构建，然后可以如下图中获取Player的路径Prim Path\n   \n   <img src=\"E:/code/git_code/Prompt-Can-Anything/asset/a2f.png\" alt=\"image-20230725122731372\" style=\"zoom:50%;\" />\n   \n   \n   \n   ​\t\t\t\t\t\t\t\t\t\t<img src=\"asset/a2f2023.png\" alt=\"image-20230331372\" style=\"zoom: 33%;\"/>\t\n   \n   ​\t\t\t\t\t\t\t\t\t\t ![image-20230725133326397](asset/getpath.png) \n   \n   ​\t     步骤2. 程序运行起来后，将上面获得的路径拷贝，填写在config_private的“Avatar_instance_A”，在web端如图下操作点击 ‘start system’后，点击加载“Speech_system”启动语音模式，但是注意TTS是网络服务。\n   \n   ​\t\t\t\t\t\t\t\t\t\t\t\t\t\t   \t\t\t\t\t   <img src=\"asset/start-chat.png\" style=\"zoom:50%;\" >\n   \n   \n\n## 🔨计划清单\n\n- [x] 释放初版\n- [x] web ui 界面调整 \n- [x] 支持chatgpt/VISUALGLM/ASR/TTS\n- [x] Yoco一键标注微调VISUALGLM Demo\n- [x] 3d &&2d avatvor\n- [ ] 完成计划的AI结合体“安尼森”\n- [ ] 微调sam分割器 and ground检测器 ,拓展SAM的输入控制\n- [ ] 释放训练方法.\n- [ ] 知识克隆\n\n\n## 参考工作 \n\n- [gpt_academic](https://github.com/binary-husky/gpt_academic)\n- [Segment Anything](https://github.com/facebookresearch/segment-anything)\n- [Grounding DINO](https://github.com/IDEA-Research/GroundingDINO)\n- [Tag2text](https://github.com/xinyu1205/Tag2Text) \n- [SadTalker](https://github.com/OpenTalker/SadTalker)\n- [lama](https://github.com/advimman/lama) \n- [VisualGLM-6B](https://github.com/THUDM/VisualGLM-6B.git) \n\n感谢他们的出色工作！\n\n"
  },
  {
    "path": "a2f.py",
    "content": "import argparse\nimport functools\nimport os\nimport yaml\nimport numpy as np\nimport ffmpeg\nimport grpc\nimport grpc\nimport audio2face_pb2\nimport audio2face_pb2_grpc\nfrom pydub import AudioSegment\nfrom pydub.silence import split_on_silence\nimport soundfile\nfrom audio2face_streaming_utils import push_audio_track_stream,push_audio_track,push_stream\nimport pyaudio\nimport wave\nfrom queue import Queue\nimport time\nimport whisper \nimport requests\n#from llm_cards.bridge_chatgpt import predict\nfrom config_private import API_KEY\nimport uuid\nimport re \nimport asyncio\nimport threading\n# 创建事件，用于线程间同步\nsend_event = threading.Event()   \n\n# 按秒截取音频\ndef get_part_wav(sound, start_time, end_time, part_wav_path):\n    save_path = os.path.dirname(part_wav_path)\n    if not os.path.exists(save_path):\n        os.makedirs(save_path)\n    start_time = int(start_time) * 1000\n    end_time = int(end_time) * 1000\n    word = sound[start_time:end_time]\n    word.export(part_wav_path, format=\"wav\")\n\n\ndef crop_wav(path, crop_len):\n    for src_wav_path in os.listdir(path):\n        wave_path = os.path.join(path, src_wav_path)\n        print(wave_path[-4:])\n        if wave_path[-4:] != '.wav':\n            continue\n        file = wave.open(wave_path)\n        # 帧总数\n        a = file.getparams().nframes\n        # 采样频率\n        f = file.getparams().framerate\n        # 获取音频时间长度\n        t = int(a / f)\n        print('总时长为 %d s' % t)\n        # 读取语音\n        sound = AudioSegment.from_wav(wave_path)\n        for start_time in range(0, t, crop_len):\n            save_path = os.path.join(path, os.path.basename(wave_path)[:-4], str(uuid.uuid1()) + '.wav')\n            get_part_wav(sound, start_time, start_time + crop_len, save_path)\n\nfrom concurrent.futures import ThreadPoolExecutor\ndef process_chunk(model, chunk, detect_language):\n    # make log-Mel spectrogram and move to the same device as the model\n    mel = whisper.log_mel_spectrogram(chunk).to(model.device)\n\n    # detect the spoken language\n    speech_language = 'zh'\n    if detect_language :\n        _, probs = model.detect_language(mel)\n        speech_language = max(probs, key=probs.get)\n\n    # decode the audio\n    options = whisper.DecodingOptions()\n    result = whisper.decode(model, mel, options)\n\n    return result.text, speech_language\n\ndef speech_recognition(inputs, model,stream_model=False,detect_language=False):\n    # whisper\n    all_result=''\n    speech_language='zh'\n    executor = ThreadPoolExecutor()\n    results = []\n    audio=None\n    if not stream_model:\n          audio,sr= soundfile.read(inputs, dtype='float32')\n    else:  \n          print('numpy  data')\n          sr,audio=inputs\n          data = audio / 65538\n          audio = data.astype(np.float32)\n    print(sr)\n    chunk_size= sr*30\n    print((audio))\n    for i in range(0, len(audio), chunk_size):\n        chunk_end = min(i + chunk_size, len(audio))\n        chunk = whisper.pad_or_trim(audio[i:chunk_end])\n        \n        # submit the chunk to the thread pool for processing\n        results.append(executor.submit(process_chunk, model, chunk, detect_language))\n\n    # print the recognized text and the detected language\n    for result in results:\n        text, language = result.result()\n        #print(text)\n        all_result += text\n        speech_language = language\n\n    # # print the recognized text\n    # all_result+=result.text\n    return all_result, speech_language\n\n\n\n\nAvatar_instance_A='/World/audio2face/PlayerStreaming'\na2f_url = 'localhost:50051' # The audio2face url by default\nsample_rate_Omniverse = 22050 # Audio frame rate\n# 录音参数\nCHUNK = 1024\nFORMAT = pyaudio.paInt16\nCHANNELS = 1\nRATE = 16000\nRECORD_SECONDS =5\naudio_file = \"F:\\\\VoiceprintRecognition-Pytorch-develop\\\\error001.wav\"\nbuffer_length=int(RATE / CHUNK * RECORD_SECONDS)\nrecord_file='record.wav'\np = pyaudio.PyAudio()  \n\ndef mic_audio(record_file=\"record.wav\"):\n     # 打开录音\n     import keyboard\n     stream = p.open(\n                input_device_index=1,\n                format=FORMAT,\n                channels=CHANNELS,\n                rate=RATE,\n                input=True,\n                frames_per_buffer=CHUNK)\n     print(\"Recording...\")\n     frames = []\n     while True:\n          data = stream.read(CHUNK)\n          frames.append(data)\n          if keyboard.is_pressed('s'):\n               break\n     stream.stop_stream()\n     stream.close()\n     p.terminate()\n\n     wf = wave.open(record_file, 'wb')\n     wf.setnchannels(CHANNELS)\n     wf.setsampwidth(p.get_sample_size(FORMAT))\n     wf.setframerate(RATE)\n     wf.writeframes(b''.join(frames))\n     wf.close()\n     return 'OK'\n\n\n\nimport edge_tts\n\nasync def tts_send(text,onmiverse=False,send_file='voice_dir/send_a2f.wav'):\n        if text is not None:\n            sentences = re.split(r'[！？。: ]', text)\n            sentences = [s.strip() for s in sentences if s.strip()]\n            sentences_len=len(sentences)\n            audio_chunks = {}\n            async def process_sentences():\n                tasks = []\n                for i, sentence in enumerate(sentences):\n                    if len(sentence) > 0:\n                    # 提交任务到协程池\n                        task = asyncio.create_task(speak(sentence, i % sentences_len))\n                        tasks.append(task) \n                await asyncio.gather(*tasks) \n                       \n            async def speak(sentence, worker_id):\n                    # 合成语音\n                print(worker_id)\n         \n                audio_stream =edge_tts.Communicate(sentence, voice='zh-CN-YunxiNeural', rate='+1%', volume='+1%').stream()\n                async for package in audio_stream:\n                    if package['type'] == 'audio':\n                        # 获取音频数据的字节流(chunk)\n                        audio_chunk = package['data']\n                        # 将音频数据添加到字典中\n                        if worker_id not in audio_chunks:\n                            audio_chunks[worker_id] = []\n                        audio_chunks[worker_id].append(audio_chunk)  \n                            \n            await process_sentences()\n            # 将每个协程合成的音频数据拼接起来\n            \n            audio_data = b''\n            for i in range(sentences_len):\n                if i in audio_chunks:\n                     for chunk in audio_chunks[i]:\n                         audio_data += chunk\n            with open(f'{send_file}', 'wb') as f:\n                  f.write(audio_data)  \n            if onmiverse:\n                audio_data, samplerate = soundfile.read(f'{send_file}', dtype=\"float32\")                \n                if len(audio_data.shape) > 1:\n                    audio_data = np.average(audio_data, axis=1)\n                push_audio_track_stream(a2f_url, audio_data, samplerate, Avatar_instance_A)  \n                \n                \nasync def tts_a2f(text):\n    import edge_tts\n    import soundfile as sf\n    import numpy as np\n    from  audio2face_streaming_utils import push_audio_track_stream\n    generate_wave = edge_tts.Communicate(text, voice='zh-CN-YunxiNeural', rate='-5%', volume='+1%')\n    await generate_wave.save('./voice_dir/send_frame.wav')  \n                                       \n    try:\n        audio_data, samplerate = sf.read('./voice_dir/send_frame.wav', dtype=\"float32\")\n        if len(audio_data.shape) > 1:\n            audio_data = np.average(audio_data, axis=1) \n        push_audio_track_stream(a2f_url, audio_data, samplerate , Avatar_instance_A) \n        print(\"send done\")\n        return 'Send Done!' \n    except Exception as e:\n        print(f\"检查是否开启omniverse!!!\")              \n              \n\ndef push_stream(url,player,dir=\"voice_dir/send_omniverse.wav\"): \n    from audio2face_streaming_utils import push_audio_track_stream\n    import soundfile    \n    import numpy as np\n    retry=0\n    while True:  \n        try:                    \n            audio_data,sr= soundfile.read(dir, dtype='float32');break    \n        except :\n            print(\"tts合成速度稍慢，等待....\")\n            retry += 1\n            print('正在重试')\n            if retry >=2: raise TimeoutError                   \n    if len(audio_data.shape) > 1:\n        audio_data = np.average(audio_data, axis=1)    \n    push_audio_track_stream(url, audio_data, sr, player)          \n             \n                \ndef audio_synthesis(gpt_replying_buffer,url,player):\n    import threading\n    threading.Thread(target=process_send_stream, args=(gpt_replying_buffer,url,player,)).start()\n       \ndef process_send_stream(gpt_replying_buffer,url,player):\n    import subprocess\n    dir=\"voice_dir/send_omniverse.wav\"\n    cmd = f'edge-tts --voice {\"zh-CN-YunxiNeural\"} --text \"{gpt_replying_buffer}\" --write-media {dir}   '\n    subprocess.run(cmd, shell=True) \n    time.sleep(0.5)\n    push_stream(url,player,dir)\n                \ndef receive_max(q,Text):\n     \n    global receive_flag \n    receive_flag=True\n    sentences = re.split(r'[！？。: ,]', Text)\n    sentences = [s.strip() for s in sentences if s.strip()] \n   # from VITS import \n    while True :\n        \n          if len(sentences)>0 :\n               \n               #audio_data=vit_tts(sentences.pop(0)\n               #audio_data=r'voice_dir/send_frame.wav'\n               audio_data=edge_tts.Communicate(sentences.pop(0), voice='zh-CN-YunxiNeural', rate='+1%', volume='+1%')\n               q.put((audio_data,True))\n               print('done')\n          else :\n               print('语音合成线程结束......')\n               receive_flag=False  \n               break \n      \n\n###--------线程：收集数据，中转处理源buffer收集后发送------------###                           \ndef send_stream2(q):\n    global mess      \n    global receive_flag\n    mess=False\n    with grpc.insecure_channel(a2f_url) as channel:\n        stub= audio2face_pb2_grpc.Audio2FaceStub(channel)    \n        def create_generator():\n            global mess\n            while True:\n                if not q.empty():       \n                                #取出队列中的音频文件路径和对应的发送标志位\n                                #print(\"检查缓存容量 :\",q.qsize()) \n                                #time.sleep(2)\n                                audio_data,send_flag = q.get()\n                                if not send_flag:\n                                    # TODO: 将音频文件发送出去\n                                    print(f'Sending audio...')                               \n                                    audio_data,sr= soundfile.read('voice_dir/send_framex.wav', dtype='float32')                      \n                                    if len(audio_data.shape) > 1:\n                                        audio_data = np.average(audio_data, axis=1)    \n                                                            \n                                    #yield audio2face_pb2.PushAudioStreamRequest(start_marker=Avatar_instance_A) \n                                    #for i in range(len(audio_data) // sr//10 + 1):      \n                                                       # chunk = audio_data[i * sr//10: i * sr//10+ sr//10]\n                                                        #yield audio2face_pb2.PushAudioStreamRequest(audio_data=chunk.astype(np.float32).tobytes()) \n                                    push_audio_track_stream(a2f_url, audio_data, sr, Avatar_instance_A)            \n                                    send_flag=True\n                                    # 重置事件状态\n                                    send_event.clear()  \n                                                                                                        \n                else: \n                                if not receive_flag:\n                                    print(\"发送线程结束\")\n                                    break \n                                else:\n                                    continue\n        stub.PushAudioStream(create_generator())                            \n\ndef audio_chatbot(text):\n     \n     q = Queue()\n     \n     t1 = threading.Thread(target=receive_max,args=(q,text))\n     t2 = threading.Thread(target=send_stream2,args=(q,))\n     t1.start()\n     t2.start()\n    # t1.join()\n     #t2.join()\n     global receive_flag\n     while True:\n          send_flag=True\n    # 从队列中取出音频文件路径和对应的发送标志位\n          audio, send_flag = q.get()\n          if not send_flag:\n               # 将音频文件路径放回队列（因为发送是在另一个线程中完成的）\n               q.put((audio,False))\n               # 设置事件，通知发送线程可以发送该音频\n               send_event.set()           \n          if not receive_flag:\n               break\n           \nif __name__ == \"__main__\":\n    \n    text = \"这里是一段较长的文本，需要拆分成多个句子来进行语音合成！句子也可以用问号来结尾吗？\\\n    当然可以。我要实现一个人工智能,这里是一段较长的文本，需要拆分成多个句子来进行语音合成！句子也可以用问号来结尾吗？当然可以。我要实现一个人工智能，但是我需要很多时间和精力完成\\\n        这里是一段较长的文本，需要拆分成多个句子来进行语音合成！句子也可以用问号来结尾吗？当然可以。我要实现一个人工智能，但是我需要很多时间和精力完成\"\n    # 启动主程序\n    audio_chatbot(text)\n#     t1=time.time()\n#     asyncio.run(tts_send(text))\n   \n    \n#     print(time.time()-t1)\n#   #  t1 = threading.Thread(target=send_stream)\n#     t1=time.time()\n#     #asyncio.run(tts_a2f(text))\n#     print(time.time()-t1)"
  },
  {
    "path": "app.py",
    "content": "from model_cards.autoback import AutoBackend\nimport argparse\nimport os\nimport platform\nimport sys\nfrom pathlib import Path\nimport numpy as np \nimport torch\nimport torch.backends.cudnn as cudnn\nimport matplotlib.pyplot as plt\nfrom PIL import Image,ImageDraw,ImageFont\nfrom utils.ops import (LOGGER, Profile, check_file, check_requirements, colorstr, cv2,\n                     dilate_mask, increment_path , scale_boxes, xyxy2xywh,save_format)\nfrom utils.plot import Annotator, save_one_box,show_box,show_mask,save_mask_data,Draw_img\nfrom config_private import *\nfrom llm_cards.bridge_all import predict_all,talk_all\nfrom llm_cards.bridge_chatgpt import Talk_with_app\nfrom llm_cards.core_functional import get_core_functions\nfrom utils.toolbox import format_io, find_free_port, on_file_uploaded, on_report_generated, get_conf, ArgsGeneralWrapper, load_chat_cookies, DummyWith\n\nfrom utils.torch_utils import select_device\nfrom utils import VID_FORMATS,IMG_FORMATS,write_categories\n\nimport gradio as gr\nimport random\nimport json\nimport multiprocessing as mp\nimport asyncio\nimport concurrent.futures\nfrom utils.colorful import *\n\nfunctional = get_core_functions()\n\nVisualGLM_dir=f\"VisualGLM_6B\"\nsys.path.append(VisualGLM_dir)\nFILE = Path(__file__).resolve()\nROOT = FILE.parents[0]  #  root directory\nif str(ROOT) not in sys.path:\n    sys.path.append(str(ROOT))  # add ROOT to PATH\nROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative\n\nglobal categories\ncategories = {}\nglobal category_colors\ncategory_colors={}\n# 初始对应类别编号\nclass_ids = []\nglobal speech_AI\nspeech_AI={'asr':{'whisper':None},'tts':{'tts_VITS':None,'tts_edge': None}} ## speech \nglobal models_config\nmodels_config = {'tag2text': None, 'ram': None,'lama': None,'sam': None,'grounded': None,'sd': None,    ## cv with text\n                 'visual_glm': None , 'trans_zh': None,'gligen': None}\nNUM_WORKERS=1\nJSON_DATASETS=[]\n\n\noperation_running = False\n\ndef toggle_operation(flag):\n\n        import whisper\n        from a2f import speech_recognition,mic_audio,keyboard\n        if speech_AI['asr']['whisper'] is  None:\n            speech_AI['asr']['whisper']=whisper.load_model(\"small\",\n                                download_root=\"weights\")\n        \n        print(\"asr加载完毕,开始录音!\")\n        text=[]\n        speech_text=''\n        while True:\n               # result_txt=\"你好我没有正确识别到结果\"\n                if keyboard.is_pressed('q'):  \n                    mic_audio('voice_dir/send_asr.wav')\n                    speech_text,__=speech_recognition('voice_dir/send_asr.wav',speech_AI['asr']['whisper'],False) \n                    break\n        print(speech_text) \n        text.append(speech_text)\n        return  text\n\nasync def sadtalker_demo(checkpoint_path,config_path,source_image,\n                            driven_audio,\n                            preprocess_type,\n                            is_still_mode,\n                            enhancer,\n                            batch_size,                            \n                            size_of_image,\n                            pose_style,\n                            exp_weight):\n        sys.path.append('SadTalker')\n        from SadTalker.app import SadTalker\n    \n        sadtaker_model=SadTalker(checkpoint_path, config_path, lazy_load=True)\n        output = await asyncio.to_thread(sadtaker_model.test, source_image,\n                            driven_audio,\n                            preprocess_type,\n                            is_still_mode,\n                            enhancer,\n                            batch_size,                            \n                            size_of_image,\n                            pose_style,\n                            exp_weight)\n        return output\n         \ndef train_visualGLM(name,model_size,mode,train_iters,resume_data,\n        max_source_length,max_target_length,lora_rank,layer_range_s,layer_range_e,pre_seq_len,\n       train_data,valid_data,distributed_backend,lr_decay_style,warmup,\n       checkpoint_activations,save_interval,eval_interval,save_path,\n       split,eval_iters,eval_batch_size ,zero_stage,\n       lr,batch_size,accumulation_steps,method_type):\n    \n    model_args=[max_source_length,max_target_length,lora_rank,layer_range_s,layer_range_e,pre_seq_len]\n    gpt_option=[name,int(model_size),mode,int(train_iters),resume_data, #23 \n       train_data,valid_data,distributed_backend,lr_decay_style,warmup, \n       checkpoint_activations,int(save_interval),int(eval_interval),save_path,\n       int(split),int(eval_iters),int(eval_batch_size),int(zero_stage),\n       lr,int(batch_size),int(accumulation_steps)]\n    \n    processes = []\n    for i in range(NUM_WORKERS):\n         p = mp.Process(target=start_finetuning_process, args=(gpt_option,model_args,method_type))\n         p.start()\n         processes.append(p)\n    for p in processes:\n        p.join()\n    return 'OK'    \n\n#具体参数待修复调整\ndef start_finetuning_process(gpt_option,model_args,method_type):\n    print('fine subprocess start')\n    script_path = os.path.abspath(__file__)\n    script_dir = os.path.dirname(script_path)\n    print(script_dir+'/'+VisualGLM_dir)\n    main_dir = os.path.dirname(script_dir)\n  \n    model_args = f'--max_source_length {model_args[0]} --max_target_length {model_args[1]} --lora_rank {model_args[2]} --layer_range {model_args[3]}  {model_args[4]} --pre_seq_len {model_args[5]}'\n    options_nccl = 'NCCL_DEBUG=info NCCL_IB_DISABLE=0 NCCL_NET_GDR_LEVEL=2'\n    host_file_path = 'hostfile_single'\n   \n    gpt_option_prefix=f\" \\\n       --experiment-name finetune-{gpt_option[0]} \\\n       --model-parallel-size {gpt_option[1]} \\\n       --mode {gpt_option[2]} \\\n       --train-iters {gpt_option[3]} \\\n       --resume-dataloader \\\n        {model_args} \\\n       --train-data {gpt_option[5]} \\\n       --valid-data {gpt_option[6]} \\\n       --distributed-backend {gpt_option[7]} \\\n       --lr-decay-style {gpt_option[8]}\\\n       --warmup {gpt_option[9]} \\\n       --checkpoint-activations \\\n       --save-interval {gpt_option[11]} \\\n       --eval-interval {gpt_option[12]} \\\n       --save {gpt_option[13]} \\\n       --split {gpt_option[14]}\\\n       --eval-iters {gpt_option[15]} \\\n       --eval-batch-size {gpt_option[16]}\\\n       --zero-stage {gpt_option[17]} \\\n       --lr {gpt_option[18]} \\\n       --batch-size {gpt_option[19]} \"\n    lora=f\"  \\\n       --skip-init  \\\n       --fp16  \\\n       --use_lora \"\n    qlora=f\"--gradient-accumulation-steps {gpt_option[20]} \\\n       --skip-init \\\n       --fp16 \\\n       --use_qlora\"\n    ptune=f\"  \\\n       --skip-init  \\\n       --fp16  \\\n       --use_ptuning\"  \n    if method_type=='use_qlora':\n        gpt_options=gpt_option_prefix+qlora\n    elif method_type=='use_lora':\n        gpt_options=gpt_option_prefix+lora\n    elif method_type=='use_ptuning':\n        gpt_options=gpt_option_prefix+ptune\n    else:    \n        LOGGER.info(\"没有选择训练方法！！！\")   \n        return   \n      \n    run_cmd = f'{options_nccl} deepspeed --master_port 16666 --hostfile {host_file_path} {VisualGLM_dir}/finetune_visualglm.py {gpt_options} '\n    os.system(run_cmd)\n\nasync def load_speech_model(asr_method,tts_method):\n        import whisper \n        global speech_AI\n        if asr_method=='whisper' :\n            speech_AI['asr']['whisper']=  whisper.load_model(\"small\",download_root=\"weights\")\n            LOGGER.info('loads whisper')\n            \n        elif not asr_method and speech_AI['asr']['whisper']:\n            LOGGER.info('free  memory')\n            speech_AI['asr']['whisper']=None  \n        else:    \n            LOGGER.info('pass')  \n          \n        if tts_method ==\"VITS\":\n             print('调试中，很快更新')\n        #          speech_AI['tts']['VITS'] =  \n        #    LOGGER.info('loads whisper')\n            \n        elif not tts_method:\n            LOGGER.info('pass')\n        return '语音识别记载完成'\n\n               \ndef save_text2img_data(prompt,label,img_name,zh_select):\n    global JSON_DATASETS\n    if not prompt :\n        prompt=f\"这张图片的背景里有什么内容?\"\n    if not zh_select:\n        prompt=f'What contents are present in the background of this picture?'\n    example = {\n        \"img\": f\"{img_name}\",\n        \"prompt\": prompt,\n        \"label\": label\n    }\n    JSON_DATASETS.append(example)\n            \nasync def load_auto_backend_models(lama, sam, det,tag2text,ram, trans_zh, visual_glm,device=0, quant=4, bar=None): \n    try:    \n        with concurrent.futures.ThreadPoolExecutor() as pool:\n                wait_coros =  asyncio.get_event_loop().run_in_executor(pool, load_auto_backend_model, lama, sam, det, tag2text,ram,trans_zh, visual_glm,device, quant, bar)\n                await asyncio.wait([wait_coros])\n        await asyncio.sleep(0.01) \n    except Exception as e:\n        LOGGER.info(\"An error occurred: \", e)\n        return 'windows可能会出现问题,请再次点击加载按钮，也可以检查后台'\n    return 'Loads Done !'\n   \n\ndef load_auto_backend_model(lama,sam,det,tag2text,ram,trans_zh,visual_glm,device,quant,bar):\n    \"\"\"\n    加载模型库\n    \"\"\"\n    # Load model    \n    \n    global models_config\n \n    if visual_glm and not models_config['visual_glm']:\n          from VisualGLM_6B.chatglm import  VisualGLM\n          models_config['visual_glm']=VisualGLM(gpu_device=int(device),quant=int(quant))\n          LOGGER.info(f'GPU{int(device)}———量化VisualGLM模型:int{int(quant)}')\n    elif not visual_glm:\n          LOGGER.info('no select visualGLM')\n          models_config['visual_glm']=None  \n    else:    \n          LOGGER.info('free or no visual_glm')      \n           \n    device = select_device(device)    \n    if tag2text and not models_config['tag2text']:\n                models_config['tag2text'] = AutoBackend(\"tag2text\",weights=Tag2Text_Model_Path,device=device)              \n    elif not tag2text  :\n            LOGGER.info('no tag2text')\n            models_config['tag2text'] =None \n    else :\n            LOGGER.info('free or tag2text pass')   \n             \n    if det and not models_config['grounded']:\n            models_config['grounded'] = AutoBackend(\"grounded-DINO\",weights=GROUNED_MODEL_TYPE['S'], device=device,\n            args_config= 'model_cards/groundingdino/config/GroundingDINO_SwinT_OGC.py')\n    elif not det  :\n            models_config['grounded'] =None \n    else :\n            LOGGER.info('free or grounded pass')\n            \n    if sam and not models_config['sam']:\n            models_config['sam']= AutoBackend(\"segment-anything\",weights=SAM_MODEL_TYPE['vit_h'] ,device=device)\n    elif not sam :\n            models_config['sam'] =None      \n    else:\n            LOGGER.info(\"PASS SAM\")\n\n    if ram and not models_config['ram']:\n            LOGGER.info(\"ram loads\")\n            models_config['ram']= AutoBackend('ram',weights=Ram_Model_Path ,device=device)\n    elif not ram :\n            models_config['ram'] =None      \n    else:\n            LOGGER.info(\"PASS ram\")       \n\n    if trans_zh and not models_config['trans_zh']:\n            from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM\n            cn_tokenizer = AutoTokenizer.from_pretrained(\"Helsinki-NLP/opus-mt-en-zh\",cache_dir='weights')\n            cn_model = AutoModelForSeq2SeqLM.from_pretrained(\"Helsinki-NLP/opus-mt-en-zh\",cache_dir='weights')\n            translator = pipeline(\"text2text-generation\", model=cn_model, tokenizer=cn_tokenizer)\n            models_config['trans_zh']= translator       \n    elif not trans_zh  :\n            models_config['trans_zh'] =None \n    else :\n            LOGGER.info('zh model pass')    \n        \n    if lama and not models_config['lama']:\n            models_config['lama']= AutoBackend(\"lama\",weights=None,args_config='model_cards/lama/configs/prediction/default.yaml',device=device)\n    elif not lama :\n            models_config['lama'] =None \n    else :\n            LOGGER.info('free or lama pass') \n   \n    return 'OK'\n    \ndef Auto_run(\n        source= 'data/images',  # file/dir/URL/glob, 0 for webcam\n        img_input='',\n        input_prompt=\"Anything in this image\",\n        conf_thres=0.3,  # confidence threshold\n        iou_thres=0.5,  # NMS IOU threshold\n        text_thres=0.2,\n        device='',  # cuda device, i.e. 0 or 0,1,2,3 or cpu   \n        quant=4,\n        save_conf=False,  # save confidences in --save-txt labels\n        img_save=False,  # do not save images/videos\n        visualize=False,  # visualize features\n        project=ROOT / 'runs/detect',  # save results to project/name\n        name='exp',  # save results to project/name\n        exist_ok=False,  # existing project/name ok, do not increment\n        lama=False,   # use lama models\n        sam=True,    # use segment-anythings\n        det=True,    # use grounded detect model with text\n        tag2text=False,\n        ram=False,\n        save_txt=False,  # save results to *.txt\n        save_xml=False,  # save results to *.xml\n        save_mask=False,\n        save_caption=False,\n        batch_process=False,\n        color_flag=False,\n        zh_select=False,\n        record_audio=None,\n        up_audio=None,\n        process_name=0,    \n        ):  \n            \n            global models_config\n            global category_colors\n            global JSON_DATASETS\n        \n            cls_index = -1        # 设置默认值为 -1\n            if img_input:\n                source =img_input\n            source = str(source)\n     \n            img_paths=None\n            if os.path.isdir(source):\n                img_paths = [os.path.join(source, f) for f in os.listdir(source) if\n                    Path(f).suffix[1:] in (IMG_FORMATS + VID_FORMATS)] \n            else:\n                img_paths = [source]    \n\n            # Directories\n            is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)\n          #  save_img = img_save and not source.endswith('.txt')  # save inference images\n            is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://'))\n            #webcam = source.isnumeric() or source.endswith('.streams') or (is_url )\n            if is_url and is_file:\n                source = check_file(source)  # download\n\n            save_dir = increment_path(Path(project) / name, exist_ok=exist_ok)  # increment run\n            \n            (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # make dir\n            (save_dir / 'xmls' if save_xml else save_dir).mkdir(parents=True, exist_ok=True)  # make dir\n            (save_dir / 'masks' if save_mask else save_dir).mkdir(parents=True, exist_ok=True)  # make dir\n            (save_dir / 'captions' if save_caption else save_dir).mkdir(parents=True, exist_ok=True)  # make dir\n            p = Path(str(save_dir) )  # to Path\n            seen=0\n            # loda data and inference\n            caption=None\n            for source in (img_paths): \n                    im = cv2.imread(source)\n                    name_p= source.split('/')[-1].split('.')[0]\n                    img_rgb = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)\n                    preds=None\n                    masks=[]\n                    prompt=input_prompt\n                    if tag2text:\n                        LOGGER.info(f'text_prompt:{prompt}')\n                        preds = models_config['tag2text'](im = img_rgb ,prompt=prompt,box_threshold=conf_thres,text_threshold=text_thres,iou_threshold=iou_thres)\n                    # Currently \", \" is better for detecting single tags\n                    # while \". \" is a little worse in some case\n                        prompt=preds[0].replace(' |', ',')\n                        caption=preds[2]\n                        LOGGER.info(f\"Caption: {caption}\")\n                        LOGGER.info(f\"Tags: {prompt}\")\n                        if zh_select and prompt :\n                            caption=models_config['trans_zh'](caption, max_length=1000, clean_up_tokenization_spaces=True)[0][\"generated_text\"]\n                        if save_caption:\n                            save_text2img_data(None, caption,name_p,zh_select)\n                            #save_format(label_format=\"txt\",save_path=f'{save_dir}/captions',img_name=name_p, results=caption)\n                    if ram:\n                        LOGGER.info(f'ram No need prompt:{prompt}')\n                        en_tag,zh_tag = models_config['ram'](im = img_rgb,prompt=prompt,box_threshold=conf_thres,text_threshold=text_thres,iou_threshold=iou_thres)\n                       \n                        prompt=en_tag.replace(' |', ',')\n                        zh_tag=zh_tag.replace(' |', ', ')\n                        #LOGGER.info(preds[1])\n                        LOGGER.info(f\"en_Tags: {prompt}\")\n                        print(f\"zh_Tags : {zh_tag}\")\n                        # if zh_select and prompt :\n                        #     caption=models_config['trans_zh'](caption, max_length=1000, clean_up_tokenization_spaces=True)[0][\"generated_text\"]\n                        # if save_caption:\n                        #     save_text2img_data(None, caption,name_p,zh_select)\n\n                    if det:\n                        if input_prompt:\n                            prompt=input_prompt\n                            LOGGER.info('your input prompt replace default:',prompt)\n                        preds= models_config['grounded'](im = img_rgb,prompt=prompt, box_threshold=conf_thres,text_threshold=text_thres, iou_threshold=iou_thres) \n                      \n                    if sam and det :        \n                        if preds[0].numel()>0:      \n                            masks= models_config['sam'](im = img_rgb, prompt=preds[0],box_threshold=conf_thres,text_threshold=text_thres, iou_threshold=iou_thres)\n                            if save_mask:\n                                save_mask_data(str(save_dir)+'/masks', caption, masks, preds[0], preds[2],name_p)\n                    # Write results\n                    \n                    if img_save:\n                        seen+=1\n                        plt.figure(figsize=(20,18))\n                        plt.imshow(img_rgb)\n                        if det:\n                            for box,label in zip(preds[0],preds[2]):\n                                    show_box(box.numpy(),plt.gca(),label)            \n                            if sam :              \n                                for mask in masks:         \n                                    show_mask(mask.cpu().numpy(),plt.gca(),random_color=True)\n                        if tag2text:\n                            plt.title('Captioning: ' + caption + '\\n' + 'Tagging:' + prompt + '\\n')    \n                        plt.axis('off')\n                        plt.savefig(f'{save_dir}/{seen}.jpg',bbox_iches='tight',dpi=600,pad_inches=0.0)     \n                        \n                    if lama and masks is not None :      \n                        masks_prompts= masks.detach().cpu().numpy().astype(np.uint8) * 255\n                        for idx, mask in enumerate(masks_prompts):   \n                            \n                            sub_mask = [dilate_mask(ma, 15) for ma in mask]\n                            img_inpainted_p= f'{save_dir}/mask_{idx}.png'\n                            idx=idx+1\n                            img_inpainted = models_config['lama'](\n                                    im=img_rgb, prompt=sub_mask[0])\n                            Image.fromarray(img_inpainted.astype(np.uint8)).save(img_inpainted_p)\n                            img_rgb=img_inpainted       \n                    for category in categories:\n                        if category not in category_colors:\n                            category_colors[category] = (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255))            \n                    gn = torch.tensor(im.shape)[[1, 0, 1, 0]]  # normalization gain whwh   \n                    \n                    if (color_flag or save_txt) and(det ) :\n                        seg_mask = np.zeros_like(img_rgb)  # img_array 为输入图像的数组表示\n                        category_color=[]\n                        for xyxy, conf, cls,mask in zip(preds[0],preds[1],preds[2],masks):       #per im boxes              \n                                xywh = (xyxy2xywh((xyxy).view(1,4)) / gn).view(-1).tolist()  # normalized xywh   \n                                if cls not in categories:\n                                    categories.update({\n                                            str(cls): len(categories)})        \n                                    write_categories(cls,f'{save_dir}/classes_id.txt')\n                                    cls_index = len(categories) - 1\n                                    category_colors.update({\n                                            str(cls):  (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255))}) \n                                    category_color=category_colors[str(cls)]\n                                else:   \n                                    cls_index = categories[str(cls)]\n                                    if str(cls) not in category_colors:\n                                        category_colors.update({\n                                            str(cls):  (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255))})\n                                    category_color=category_colors[str(cls)]\n                                line = (cls_index, xywh, conf) if save_conf else (cls_index, xywh)  # label format\n                                line = str(line).replace('[', '').replace(']', '').replace(\"(\",'').replace(\")\",\" \").replace(\",\", \" \" * 2)\n                                if save_mask:\n                                    h, w = mask.shape[-2:]\n                                    mask_color = np.array(category_color).reshape((1, 1, -1))  \n                                    seg_mask = seg_mask + mask.cpu().numpy().reshape(h, w, 1)  * mask_color  # add    \n                                if save_txt:                                 \n                                    save_format(label_format=\"txt\",save_path=f'{save_dir}/labels', img_name=name_p, results=line)\n                                   \n                        if save_mask:\n                            plt.figure(figsize=(10,10))\n                            plt.imshow(seg_mask)\n                            #plt.title('Captioning: ' + caption + '\\n' + 'Tagging:' + prompt + '\\n')    \n                            plt.axis('off')            \n                            plt.savefig(os.path.join(f'{save_dir}/masks', f'{name_p}_cls.jpg'), bbox_inches=\"tight\", dpi=300, pad_inches=0.0)                                                         \n                    if save_xml:    \n                            h,w=im.shape[:2]\n                            save_format(\"xml\",f'{save_dir}/xmls' ,name_p, Path(source).parent, preds, h, w)\n                    if det:\n                        img_rgb= Image.fromarray(np.uint8(img_rgb), mode='RGB') \n                        draw_img=ImageDraw.Draw(img_rgb) \n                        for box,label in zip(preds[0],preds[2]):   \n                            Draw_img( box, draw_img,'box',label,category_colors[str(label)] if color_flag else None)\n                    if sam:\n                        img_mask=Image.new('RGBA',img_rgb.size,color=(0,0,0,0)  )\n                        draw_mask=ImageDraw.Draw(img_mask)       \n\n                        for mask in masks:    \n                            Draw_img(mask[0].cpu().numpy(),draw_mask,'mask',None,category_colors[str(label)] if color_flag else None)\n                        img_rgb.paste(img_mask, mask=img_mask)  \n                    #img_rgb.save(f'{save_dir}/{seen}.jpg')    \n                    \n            if save_txt:\n                #class_ids.append(cls) \n                LOGGER.info(f\"Results saved to {colorstr('bold', save_dir)}/labels\")  \n            if save_xml:           \n                LOGGER.info(f\"Results saved to {colorstr('bold', save_dir)}/xmls\")\n            if save_caption:\n               with open(f'{save_dir}/captions/dataset.json', 'a',encoding='utf-8') as f: \n                    json.dump(JSON_DATASETS,f,ensure_ascii=False) \n                    f.write('\\n')\n                    LOGGER.info(f\"Results saved to {colorstr('bold', save_dir)}/captions\")\n            if save_mask:\n                LOGGER.info(f\"Results saved to {colorstr('bold', save_dir)}/masks\")\n            LOGGER.info('Done...')\n\n            return [[img_rgb],caption,prompt,len(categories)]\n\n\ndef visual_chat(prompt_input, temperature, top_p, image_prompt, result_text,record_audio,upload_audio,omniverse=False):\n  \n    global models_config\n    print(f\"是否连接omniverse:{omniverse}\")\n    if models_config['visual_glm']:\n          if image_prompt and  prompt_input:\n\n                __, result_text=(models_config['visual_glm'].request_model(prompt_input, temperature, top_p, image_prompt, result_text))\n                if omniverse: \n                        from a2f import tts_a2f       \n                        asyncio.run(tts_a2f(result_text[-1][-1]))\n                return \"\",result_text\n          else :\n               LOGGER.info(\"请检查你的输入格式和glm模型的参数配置！！！\")\n    else:                \n          return result_text,\"没有加载部署的VisualGLM模型!!!\"\n\ndef clear_fn_image(value):\n    return [(\"\", \"Hi, What do you want to know ?或者你想从图像中知道什么?\")]\n\nif __name__ == \"__main__\":\n         \n          #check_requirements(exclude=('tensorboard', 'thop'))\n          proxies, WEB_PORT, LLM_MODEL, CONCURRENT_COUNT, AUTHENTICATION, CHATBOT_HEIGHT, LAYOUT, AVAIL_LLM_MODELS, AUTO_CLEAR_TXT = \\\n          get_conf('proxies', 'WEB_PORT', 'LLM_MODEL', 'CONCURRENT_COUNT', 'AUTHENTICATION', 'CHATBOT_HEIGHT', 'LAYOUT', 'AVAIL_LLM_MODELS', 'AUTO_CLEAR_TXT')\n          AUTO_CLEAR_TXT = get_conf('AUTO_CLEAR_TXT')\n        # 如果WEB_PORT是-1, 则随机选取WEB端口\n          PORT = find_free_port() if WEB_PORT <= 0 else WEB_PORT\n          functional = get_core_functions()\n          from themes.theme import adjust_theme, advanced_css, theme_declaration\n            # 高级函数插件\n          from llm_cards.crazy_functional import get_crazy_functions\n          crazy_fns = get_crazy_functions()\n          import logging, uuid\n          os.makedirs(\"gpt_log\", exist_ok=True)\n          try:logging.basicConfig(filename=\"gpt_log/chat_secrets.log\", level=logging.INFO, encoding=\"utf-8\", format=\"%(asctime)s %(levelname)-8s %(message)s\", datefmt=\"%Y-%m-%d %H:%M:%S\")\n          except:logging.basicConfig(filename=\"gpt_log/chat_secrets.log\", level=logging.INFO,  format=\"%(asctime)s %(levelname)-8s %(message)s\", datefmt=\"%Y-%m-%d %H:%M:%S\")\n        # Disable logging output from the 'httpx' logger\n          logging.getLogger(\"httpx\").setLevel(logging.WARNING)\n          print(\"所有问询记录将自动保存在本地目录./gpt_log/chat_secrets.log, 请注意自我隐私保护哦！\")\n        # 处理markdown文本格式的转变\n          gr.Chatbot.postprocess = format_io\n          # 代理与自动更新\n          from utils.check_proxy import check_proxy, auto_update, warm_up_modules\n          proxy_info = check_proxy(proxies)\n          voice_dir='voice_dir'\n          if not os.path.exists(voice_dir):\n                os.mkdir(voice_dir)\n          inputxs=[]\n          outputs=[]\n          cancel_handles = []\n          \n          with gr.Blocks(title=\"Prompt-Can-Anythings\",reload=True, theme=adjust_theme(), analytics_enabled=False,full_width=True,css=advanced_css) as block:\n               gr.HTML( f\"<h1 align=\\\"center\\\"> Prompt-Can-Anythings_v1.15 (周更迭代中)</h1>\")\n               cookies = gr.State({'api_key': API_KEY, 'llm_model': LLM_MODEL})\n               with gr.Row().style(equal_height=False):\n                    with gr.Column(scale=1):\n                         with gr.Accordion('视觉模型配置', open=False):             \n                            with gr.TabItem('本地模型配置'):\n                                    box_threshold=gr.inputs.Number(label='Confidence Threshold', default=0.3)\n                                    iou_threshold=gr.inputs.Number(label='Iou Threshold', default=0.5)\n                                    text_threshold=gr.inputs.Number(label='Text Threshold', default=0.25)\n                                    device_input=gr.inputs.Textbox(label='device',default='0')\n                                    quant=gr.inputs.Number(label='quant levels',default=4)   \n                                     \n                            with gr.TabItem('其他【不需要修改】'):\n                                    option_inputs  = {\n                                    'Save Conf': gr.inputs.Checkbox(label='Save Conf',default=False),\n                                    'Save img': gr.inputs.Checkbox(label='Save img',default=False),\n                                    'Visualize': gr.inputs.Checkbox(label='Visualize',default=False),\n                                    'Project': gr.inputs.Textbox(label='Project:save dir_path',default='runs/detect'),\n                                    'Name': gr.inputs.Textbox(label='Name',default='exp'),\n                                    'Exist Ok': gr.inputs.Checkbox(label='Exist Ok',default=False)\n                                    }   \n                            \n                         inputxs.extend(list(option_inputs.values()))\n                         with gr.Accordion('Method_Options:free combo', open=True):                \n                                   \n                                methods_options={'Lama': gr.inputs.Checkbox(label='Lama model[近期更新测试中]',default=False), \n                                                'Sam': gr.inputs.Checkbox(label='Sam[当前仅支持检测器的BOX输入]',default=False),\n                                                'Det': gr.inputs.Checkbox(label='Grounded[可输入文本的检测器]',default=False), \n                                                'Tag2text': gr.inputs.Checkbox(label='Tag2text[图文理解]',default=False),\n                                                'ram': gr.inputs.Checkbox(label='ram[识别标签]',default=False)\n                                }\n                                \n                                visual_glm=gr.inputs.Checkbox(label='VisualGLM',default=False)\n                                chatgpt=gr.inputs.Checkbox(label='ChatGPT(目前为网络服务自动挂载)',default=True)\n                                \n                                loads_model_button=gr.Button('热重载模型',variant=\"primary\")\n                                loads_flag=gr.inputs.Textbox(label=\"加载模型进度\")\n                            \n                         list_methods=list(methods_options.values())\n                         inputxs.extend(list_methods)  \n                       \n                         with gr.Accordion('format Options', open=False):                \n                                   \n                                save_options={\n                                'Save txt': gr.inputs.Checkbox(label='Save txt [collect class nums]',default=False), \n                                'Save xml': gr.inputs.Checkbox(label='Save xml',default=False), \n                                'Save Mask': gr.inputs.Checkbox(label='Save Mask',default=False),  \n                                'Save Caption': gr.inputs.Checkbox(label='Save Caption',default=False), \n                                'Batch Process': gr.inputs.Checkbox(label='Batch Process[暂不支持]',default=False), \n                                'Color Flag': gr.inputs.Checkbox(label='Color Flag[标识语义]',default=False)\n                            }\n                         inputxs.extend(list(save_options.values()))\n                         dir_inputs =gr.inputs.Textbox(label='加载本地图像文件夹路径',default='train_imgs')\n                         with gr.Accordion('LLM模型配置', open=False):\n                            checkboxes = gr.CheckboxGroup([\"基础功能区\", \"函数插件区\", \"底部输入区\", \"输入清除键\", \"插件参数区\"], value=[\"基础功能区\", \"函数插件区\"], label=\"显示/隐藏功能区\")\n                            md_dropdown = gr.Dropdown(AVAIL_LLM_MODELS, value=LLM_MODEL, label=\"更换LLM模型源 [暂时仅支持chatgpt/glm2]\").style(container=False)\n                            max_length_sl = gr.Slider(minimum=256, maximum=4096, value=512, step=1, interactive=True, label=\"Local LLM MaxLength\")\n                            with gr.Row():\n                                quant_chatglm= gr.Dropdown(MODEL_QUANTIZE,value=None,label=\"llm quantize[chatglm] \").style(container=False)\n                                top_p = gr.Slider(minimum=-0, maximum=1.0, value=1.0, step=0.01,interactive=True, label=\"nucleus sampling\",)\n                                temperature = gr.Slider(minimum=-0, maximum=2.0, value=1.0, step=0.01, interactive=True, label=\"Temperature\",)\n                         with gr.Accordion('VisualGLM模型配置', open=False):\n                              visual_temperature = gr.Slider(maximum=1, value=0.8, minimum=0, label='VisualGLMTemperature')\n                              visual_top_p = gr.Slider(maximum=1, value=0.4, minimum=0, label='VisualGLM top_P')\n                         \n                         with gr.Accordion('语音模型配置', open=False):\n                                            with gr.Row():\n                                                asr_select = gr.Dropdown(ASR_METHOD,value='whisper', label=\"语音识别方法\").style(container=False)\n                                                tts_select = gr.Dropdown(TTS_METHOD,value='VITS', label=\"语音合成方法\").style(container=False)\n                                                asr_gpt = gr.inputs.Checkbox(label='ASR gpt [无需加载按钮]',default=False).style(height=1,width=1)\n                                                asr_button = gr.Button('Loads SPEECH_AI').style(height=5,width=5)  \n                         with gr.Accordion('大模型对话系统配置', open=True):\n                                            with gr.Row():                                        \n                                                chat_app = gr.inputs.Checkbox(label='start system',default=False).style(height=1,width=1)\n                                                chat_app_button = gr.Button('Speech_system').style(height=5,width=5)  \n                         with gr.Accordion('ViusalGLM训练配置', open=False):\n                                with gr.Row():\n                                    train_methods=gr.Dropdown(AVAIL_METHOD_FINETUNE,value=METHOD_FINETUNE, label=\"微调方法\").style(container=False)\n                                    visualglm_args=[                              \n                                    gr.inputs.Textbox(label=\"Experiment_Name\", default=\"visualglm-6b\"),\n                                    gr.inputs.Number(label=\"Model Parallel Size\", default=1),\n                                    gr.inputs.Textbox(label=\"mode\", default='finetune'),\n                                    gr.Slider(minimum=1, maximum=3000, value=300, step=1, interactive=True, label=\"train-iters\"),\n                                    gr.inputs.Checkbox(label=\"resume dataloader\", default=True),\n                                    gr.Slider(minimum=16, maximum=256, value=64, step=1, interactive=True, label=\"max_source_length\"),\n                                    gr.Slider(minimum=16, maximum=1024, value=256, step=1, interactive=True, label=\"max_target_length\"),\n                                    gr.Slider(minimum=1, maximum=100, value=10, step=1, interactive=True, label=\"lora_rank\"),\n                                    gr.Slider(minimum=0, maximum=256, value=0, step=1, interactive=True, label=\"layer_range_start\"),\n                                    gr.Slider(minimum=0, maximum=20, value=14, step=1, interactive=True, label=\"layer_range_end\"),\n                                    gr.Slider(minimum=1, maximum=60, value=4, step=1, interactive=True, label=\"pre_seq_len\"),          \n                                    gr.inputs.Textbox(label=\"Train Data\", default=\"fewshot-data/dataset.json\"),\n                                    gr.inputs.Textbox(label=\"Eval Data\", default=\"fewshot-data/dataset.json\"),\n                                    gr.inputs.Textbox(label=\"distributed backend\", default=\"nccl\"),\n                                    gr.inputs.Dropdown(label=\"lr decay style \", choices=[\"cosine\", \"linear\"], default=\"cosine\"),\n                                    gr.inputs.Number(label=\"warmup\", default=0.02),\n                                    gr.inputs.Checkbox(label=\"checkpoint-activations\", default=True) ,\n                                    gr.inputs.Number(label=\"Save Interval\", default=300),\n                                    gr.inputs.Number(label=\"Eval Interval\", default=10000),\n                                    gr.inputs.Textbox(label=\"Save Directory\", default=\"./checkpoints\"),\n                                    gr.inputs.Number(label=\"split\", default=1),\n                                    gr.inputs.Number(label=\"Eval Iters\", default=10),\n                                    gr.inputs.Number(label=\"Eval Batch Size\", default=8),\n                                    gr.inputs.Textbox(label='Zero Stage',default=1),\n                                    gr.inputs.Number(label=\"lr\", default=0.0001),\n                                    gr.inputs.Number(label=\"batch size\", default=4),\n                                    gr.inputs.Number(label=\"gradient accumulation steps\", default=4),\n                                    ]\n                         fine_tune=gr.Button('Finetune VisualGLM').style(height=5,width=5)   \n                                    \n                         with gr.Accordion('sadtakler配置', open=False):\n                            with gr.Tabs(elem_id=\"sadtalker_checkbox\"):\n                                with gr.TabItem('Settings'):\n                                    gr.Markdown(\"need help? please visit our [[best practice page](https://github.com/OpenTalker/SadTalker/blob/main/docs/best_practice.md)] for more detials\")\n                                    with gr.Column(variant='panel'):\n                                            # width = gr.Slider(minimum=64, elem_id=\"img2img_width\", maximum=2048, step=8, label=\"Manually Crop Width\", value=512) # img2img_width\n                                            # height = gr.Slider(minimum=64, elem_id=\"img2img_height\", maximum=2048, step=8, label=\"Manually Crop Height\", value=512) # img2img_width\n                                            with gr.Row():\n                                                pose_style = gr.Slider(minimum=0, maximum=46, step=1, label=\"Pose style\", value=0) #\n                                                exp_weight = gr.Slider(minimum=0, maximum=3, step=0.1, label=\"expression scale\", value=1) # \n                                            with gr.Row():\n                                                sadtalker_path=gr.inputs.Textbox(label=\"checkpoint path\", default=\"checkpoints\") \n                                                sadtalker_config=gr.inputs.Textbox(label=\"config path\", default=\"SadTalker/src/config\")\n                                            with gr.Row():\n                                                size_of_image = gr.Radio([256, 512], value=256, label='face model resolution', info=\"use 256/512 model?\") # \n                                                preprocess_type = gr.Radio(['crop', 'resize','full', 'extcrop', 'extfull'], value='crop', label='preprocess', info=\"How to handle input image?\")\n                                            \n                                            with gr.Row():\n                                                is_still_mode = gr.Checkbox(label=\"Still Mode (fewer hand motion, works with preprocess `full`)\")\n                                                batch_size = gr.Slider(label=\"batch size in generation\", step=1, maximum=10, value=2)\n                                                enhancer = gr.Checkbox(label=\"GFPGAN as Face enhancer\")\n                                               \n                                            sadtalker_submit = gr.Button('Generate_video', elem_id=\"sadtalker_generate\", variant='primary')\n                                       \n                    with gr.Column(variant='panel',scale=15):      \n                       \n                         with gr.Tabs(elem_id=\"Process_audio\"):\n                                with gr.TabItem('Upload OR TTS'):\n                                        \n                                        with gr.Column(variant='panel'):\n                                            with gr.Row():\n                                                record_audio = gr.Audio(label=\"record your voice\", source=\"microphone\",type='filepath')\n                                                #Recording_audio=gr.Button('Recording_asr',elem_id=\"speech2text\", variant='primary')\n                                                \n                                            with gr.Row():\n                                                upload_audio = gr.Audio(label=\"Input audio(./wav/.mp3)\", source=\"upload\",type='filepath').style(height=20,width=120)\n                                                input_text = gr.Textbox(label=\"Generating audio from text\", lines=2, placeholder=\"please enter some text here, we genreate the audio from  TTS.\")\n                                               \n                                            with gr.Row():\n                                                asr = gr.Button('Generate text',elem_id=\"text_generate\", variant='primary')\n                                                tts = gr.Button('Generate audio',elem_id=\"audio_generate\", variant='primary')   \n                                with gr.TabItem('Omniverse App'):   \n                                        with gr.Row():\n                                            omniverse_switch = gr.inputs.Checkbox(label='Omniverse A2F',default=False)\n                                            #audio_to_face=gr.Button('send a Audio to Omniverse ', variant='primary')  \n                                               \n                         def t2s(text,method):\n                                    from a2f import tts_send2\n                                    send_dir=f'{voice_dir}/send_a2f.wav'\n                                    if method=='VITS':\n                                        print('更新中，暂不支持')\n                                    elif method=='edge_tts'  :  \n                                        asyncio.run(tts_send2(text,False,send_dir))\n                                    return send_dir\n                                                    \n                         def s2t(speech_file,stream_mode=False):\n                            from a2f import speech_recognition\n                            speech_text, speech_language=speech_recognition(speech_file, speech_AI['asr']['whisper'],stream_mode)                             #\n                            return  speech_text  \n                        \n                         with gr.Tabs(elem_id=\"上传图像\"):\n                                with gr.TabItem('Upload image'):\n                                        with gr.Row():\n                                            image_prompt = gr.Image(label=\"Source image\", source=\"upload\", type=\"filepath\").style(height=200,width=180)\n                                      \n                         prompt_input=gr.inputs.Textbox(lines=2, label=\"prompt with image/仅与图像相关 : (Optional,注意每个功能请考虑在这个框里的TEXT提示词要不要先清空)\")\n                           \n                         inputs = [dir_inputs,image_prompt,prompt_input,box_threshold,iou_threshold,text_threshold,device_input,quant]\n                         inputs.extend(inputxs)\n\n                         with gr.Row():\n                                run_button = gr.Button('Run CV_Task',variant=\"primary\"); run_button.style(size=\"sm\")\n                                clear_button= gr.Button(\"清除文本\", variant=\"secondary\"); clear_button.style(size=\"sm\")\n                        \n                         with gr.Row():\n                                    resetBtn = gr.Button(\"重置\", variant=\"secondary\"); resetBtn.style(size=\"sm\")\n                                    stopBtn2 = gr.Button(\"停止\", variant=\"secondary\"); stopBtn2.style(size=\"sm\")\n                                    clearBtn = gr.Button(\"清除\", variant=\"secondary\", visible=False); clearBtn.style(size=\"sm\")\n                         with gr.Row():       \n                                status = gr.Markdown(f\"Tip: 按Enter提交, 按Shift+Enter换行。当前模型: {LLM_MODEL} \\n {proxy_info}\", elem_id=\"state-panel\")        \n                         with gr.Tabs(elem_id=\"Chatbox\"): \n                            with gr.TabItem('对话区'):  \n                                with gr.Accordion(\"输入区\", open=True, elem_id=\"input-panel\") as area_input_primary: \n                                    with gr.Row():  \n                                        chat_txt=gr.Textbox(lines=3,show_label=False, placeholder=\"question\").style(container=False)\n                         with gr.Accordion(\"备选输入区\", open=True, visible=False) as area_input_secondary:\n                            with gr.Row():\n                                txt = gr.Textbox(show_label=False, placeholder=\"Input question here.\", label=\"输入区2\").style(container=False)\n                         with gr.Row():\n                            run_button_chat = gr.Button('Chat_Sumbit',variant=\"primary\")\n                            run_button_2 = gr.Button('VisualGLM',variant=\"primary\")\n                         with gr.Accordion(\"学术ChatGPT基础功能\", open=False) as area_basic_fn:\n                              with gr.Row():\n                                for k in functional:\n                                    if (\"Visible\" in functional[k]) and (not functional[k][\"Visible\"]): continue\n                                    variant = functional[k][\"Color\"] if \"Color\" in functional[k] else \"secondary\"\n                                    functional[k][\"Button\"] = gr.Button(k, variant=variant)   \n                         with gr.Accordion(\"函数插件区\", open=False, elem_id=\"plugin-panel\") as area_crazy_fn:\n                            with gr.Row():\n                                gr.Markdown(\"插件可读取“输入区”文本/路径作为参数（上传文件自动修正路径）\")\n                            with gr.Row():\n                                for k in crazy_fns:\n                                    if not crazy_fns[k].get(\"AsButton\", True): continue\n                                    variant = crazy_fns[k][\"Color\"] if \"Color\" in crazy_fns[k] else \"secondary\"\n                                    crazy_fns[k][\"Button\"] = gr.Button(k, variant=variant)\n                                    crazy_fns[k][\"Button\"].style(size=\"sm\")\n                         with gr.Row():\n                            with gr.Accordion(\"更多函数插件\", open=False):\n                                # update\n                                dropdown_fn_list = crazy_fns.keys() \n                                with gr.Row():\n                                    dropdown = gr.Dropdown(dropdown_fn_list, value=r\"打开插件列表\", label=\"\", show_label=False).style(container=False)  \n                                with gr.Row():      \n                                    plugin_advanced_arg = gr.Textbox(show_label=True, label=\"高级参数输入区\", visible=False, \n                                                                 placeholder=\"特殊函数插件的高级参数输入区\").style(container=False)\n                                with gr.Row():\n                                    switchy_bt = gr.Button(r\"请先从插件列表中选择\", variant=\"secondary\")\n                         with gr.Row():\n                            with gr.Accordion(\"点击展开“文件上传区”。上传本地文件/压缩包供函数插件调用。\", open=False) as area_file_up:\n                                file_upload = gr.Files(label=\"任何文件, 但推荐上传压缩文件(zip, tar)\", file_count=\"multiple\")        \n                       \n                    with gr.Column(scale=20):\n                         with gr.Accordion('输出区', open=True):\n                            with gr.TabItem('图像输出'):   \n                                gallery = gr.Gallery(label=\"Generated images\",show_label=False,elem_id=\"gallery\",).style(preview=True, grid=2, object_fit=\"scale-down\")\n                            with gr.TabItem('视频输出'):  \n                                video_output = gr.Video(label=\"Generated video\", format=\"mp4\").style(width=600)\n                         with gr.TabItem('图文理解'):  \n                            with gr.Row(): \n                                output_text = gr.Textbox(label=\"tag2text\",lines=2)\n                                with gr.Row():\n                                        output_tag= gr.outputs.Textbox(label=\"Tag\").style(height=1)  \n                            with gr.Row():\n                               \n                                zh_select=gr.inputs.Checkbox(label='英译中 Tag2Text【选后需重载模型】',default=False).style(width=1) \n                                with gr.Row():\n                                    output_classes= gr.Textbox(label=\"Class Numbers \",lines=1,\n                                            placeholder=\"generate classes numbers,color flag or save_txt must be ture/你必须启动存储txt的功能，这个是全局的\").style(conatiner=False,width=1)\n                                        \n                         with gr.Row():\n                            with gr.Accordion(\"备选输入区\", open=True, visible=False) as area_input_secondary:\n                                 system_prompt = gr.Textbox(show_label=True, placeholder=f\"Chat Prompt\", label=\"下方输入对话支持图像和文本\", value=\"AI assistant.\")\n                                 #stopBtn2 = gr.Button(\"停止\", variant=\"secondary\"); stopBtn2.style(size=\"sm\")\n                                 clearBtn2 = gr.Button(\"清除\", variant=\"secondary\", visible=False); clearBtn2.style(size=\"sm\")           \n                         with gr.Row():\n                            with gr.Column(scale=2):\n                                result_text = gr.Chatbot(label=f'当前模型:{LLM_MODEL}', value=[(\"\", \"Hi, What do you want to know ?\")]).style(height=CHATBOT_HEIGHT)\n                                history = gr.State([])\n               \n               #Recording_audio.click(fn=toggle_operation,inputs=[asr_select],outputs=[input_text]) # 将 toggle_operation 函数绑定到按钮\n                  # 功能区显示开关与功能区的互动\n               def fn_area_visibility(a):\n                    ret = {}\n                    ret.update({area_basic_fn: gr.update(visible=(\"基础功能区\" in a))})\n                    ret.update({area_crazy_fn: gr.update(visible=(\"函数插件区\" in a))})\n                    ret.update({area_input_primary: gr.update(visible=(\"底部输入区\" not in a))})\n                    ret.update({area_input_secondary: gr.update(visible=(\"底部输入区\" in a))})\n                    ret.update({clearBtn: gr.update(visible=(\"输入清除键\" in a))})\n                    ret.update({clearBtn2: gr.update(visible=(\"输入清除键\" in a))})\n                    ret.update({plugin_advanced_arg: gr.update(visible=(\"插件参数区\" in a))})\n                    if \"底部输入区\" in a: ret.update({txt: gr.update(value=\"\")})\n                    return ret\n               checkboxes.select(fn_area_visibility, [checkboxes], [area_basic_fn, area_crazy_fn, area_input_primary, area_input_secondary, chat_txt,txt , clearBtn, clearBtn2, plugin_advanced_arg] )\n               sadtalker_submit.click(fn=sadtalker_demo,inputs=[sadtalker_path,sadtalker_config,image_prompt,upload_audio, preprocess_type,is_still_mode,enhancer,\n                                batch_size, size_of_image, pose_style, exp_weight],outputs=[video_output])\n               #audio_to_face.click(fn=t2s, inputs=[result_text,input_text,gr.State(True),omniverse_switch], outputs=[upload_audio] )                                 \n               asr_button.click(fn=load_speech_model,inputs=[asr_select,tts_select],outputs=[loads_flag])        \n               asr.click(fn=s2t, inputs=[upload_audio], outputs=[input_text])                    \n               tts.click(fn=t2s, inputs=[input_text,tts_select], outputs=[upload_audio]) \n               # fine tune VisualGLM  \n               visualglm_args.append(train_methods)   \n               fine_tune.click(fn=train_visualGLM,inputs=visualglm_args,outputs=[txt])   \n                 \n               # visualGLM inputs    \n               cs=[]                 \n               cs.extend(list_methods)  \n               cs.extend([zh_select, visual_glm,device_input, quant, loads_flag])\n               loads_model_button.click(fn=load_auto_backend_models,inputs=cs,outputs=[loads_flag])                     \n               inputs.append(zh_select)\n               \n               def on_md_dropdown_changed(k):\n                    return {result_text: gr.update(label=\"当前模型：\"+k)}\n               md_dropdown.select(on_md_dropdown_changed, [md_dropdown],[result_text])\n               \n               outputs = [gallery, output_text, output_tag,output_classes]             \n               input_combo = [cookies, max_length_sl, md_dropdown,chat_txt,txt,top_p, temperature, result_text, history,system_prompt,plugin_advanced_arg,omniverse_switch,record_audio,asr_gpt,quant_chatglm,chat_app]        \n               output_combo = [cookies, result_text, history, status]\n              # output_combo2=[result_text, history, status]\n               predict_args = dict(fn=ArgsGeneralWrapper(predict_all), inputs=input_combo, outputs=output_combo)  \n               chat_args=dict(fn=ArgsGeneralWrapper(talk_all), inputs=input_combo, outputs=output_combo)  \n               run_button.click(fn=Auto_run, inputs=inputs, outputs=outputs)\n                # 提交按钮、重置按钮\n               cancel_handles.append(chat_txt.submit(**predict_args))\n               cancel_handles.append(txt.submit(**predict_args))\n               cancel_handles.append(run_button_chat.click(**predict_args))\n               cancel_handles.append(run_button_2.click(**predict_args))\n               cancel_handles.append(chat_app_button.click(**chat_args)) \n               resetBtn.click(lambda: ([], [], \"已重置\"), None, [result_text, history, status])\n               stopBtn2.click(fn=None, inputs=None, outputs=None, cancels=cancel_handles)\n               clearBtn.click(lambda: (\"\",\"\"), None, [chat_txt,txt])\n               clearBtn2.click(lambda: (\"\",\"\"), None, [chat_txt,txt])\n               if AUTO_CLEAR_TXT:\n                    run_button_chat.click(lambda: (\"\",\"\"), None, [chat_txt,txt])\n                    run_button_2.click(lambda: (\"\",\"\"), None, [chat_txt,txt])\n                    chat_txt.submit(lambda: (\"\",\"\"), None, [chat_txt,txt])\n                    txt.submit(lambda: (\"\",\"\"), None, [chat_txt,txt])\n               for k in functional:\n                    if (\"Visible\" in functional[k]) and (not functional[k][\"Visible\"]): continue\n                    dict_args=dict(fn=ArgsGeneralWrapper(predict_all), inputs=[*input_combo, gr.State(True),gr.State(k)], outputs=output_combo)\n                    \n                    cancel_handles.append(functional[k][\"Button\"].click(**dict_args))\n               # 文件上传区，接收文件后与chatbot的互动\n               file_upload.upload(on_file_uploaded, [file_upload, result_text, chat_txt, txt, checkboxes], [result_text, chat_txt, txt])\n                # 函数插件-固定按钮区\n               for k in crazy_fns:\n                    print(f'检查插件名字{k}，是否载入')\n                    if not crazy_fns[k].get(\"AsButton\", True): continue\n                    click_handle = crazy_fns[k][\"Button\"].click(ArgsGeneralWrapper(crazy_fns[k][\"Function\"]), [*input_combo, gr.State(PORT)], output_combo)\n                    click_handle.then(on_report_generated, [cookies, file_upload, result_text], [cookies, file_upload, result_text])\n                    cancel_handles.append(click_handle)\n                # 函数插件-下拉菜单与随变按钮的互动\n               def on_dropdown_changed(k):\n                    variant = crazy_fns[k][\"Color\"] if \"Color\" in crazy_fns[k] else \"secondary\"\n                    ret = {switchy_bt: gr.update(value=k, variant=variant)}\n                    if crazy_fns[k].get(\"AdvancedArgs\", False): # 是否唤起高级插件参数区\n                        ret.update({plugin_advanced_arg: gr.update(visible=True,  label=f\"插件[{k}]的高级参数说明：\" + crazy_fns[k].get(\"ArgsReminder\", [f\"没有提供高级参数功能说明\"]))})\n                    else:\n                        ret.update({plugin_advanced_arg: gr.update(visible=False, label=f\"插件[{k}]不需要高级参数。\")})\n                    return ret\n               dropdown.select(on_dropdown_changed, [dropdown], [switchy_bt, plugin_advanced_arg] )\n               def on_md_dropdown_changed(k):\n                    return {result_text: gr.update(label=\"当前模型：\"+k)}\n               md_dropdown.select(on_md_dropdown_changed, [md_dropdown], [result_text] )\n                # 随变按钮的回调函数注册\n               def route(request: gr.Request, k, *args, **kwargs):\n                    if k in [r\"打开插件列表\", r\"请先从插件列表中选择\"]: return\n                    yield from ArgsGeneralWrapper(crazy_fns[k][\"Function\"])(request, *args, **kwargs)\n               click_handle = switchy_bt.click(route,[switchy_bt, *input_combo, gr.State(PORT)], output_combo)\n               click_handle.then(on_report_generated, [cookies, file_upload, result_text], [cookies, file_upload, result_text])\n               cancel_handles.append(click_handle)\n                # 终止按钮的回调函数注册\n             #  stopBtn.click(fn=None, inputs=None, outputs=None, cancels=cancel_handles)\n               stopBtn2.click(fn=None, inputs=None, outputs=None, cancels=cancel_handles)     \n               #VisualGLM run         \n               run_button_2.click(fn=visual_chat,inputs=[chat_txt, visual_temperature, visual_top_p, image_prompt,\n                                                         result_text,record_audio,upload_audio,omniverse_switch],\n                                outputs=[txt, result_text])\n               prompt_input.submit(fn=visual_chat,inputs=[chat_txt, visual_temperature, visual_top_p, image_prompt,\n                                                         result_text,record_audio,upload_audio,omniverse_switch],\n                                        outputs=[txt,result_text])\n               #upload_audio.upload(fn=clear_fn_image, inputs=clear_button, outputs=[result_text])\n               image_prompt.upload(fn=clear_fn_image, inputs=clear_button, outputs=[result_text])\n               clear_button.click(lambda: (\"\",\"\",\"\",\"\",\"\"), None, [prompt_input,result_text,txt, input_text,chat_txt])\n               image_prompt.clear(fn=clear_fn_image, inputs=clear_button, outputs=[result_text])\n            #    def init_cookie(cookies, chatbot):\n            #             # 为每一位访问的用户赋予一个独一无二的uuid编码\n            #             cookies.update({'uuid': uuid.uuid4()})\n            #             return cookies\n          def auto_opentab_delay(port=7586):\n                import threading, webbrowser, time\n                LOGGER.info(f\"\\n如果浏览器没有自动打开，请复制并转到以下URL：\")\n                LOGGER.info(f\"\\t（亮色主题）: http://localhost:{port}\")\n                LOGGER.info(f\"\\t（暗色主题）: http://localhost:{port}/?__theme=dark\")\n                def open():\n                    time.sleep(2)       # 打开浏览器\n                    DARK_MODE, = get_conf('DARK_MODE')\n                    if DARK_MODE: webbrowser.open_new_tab(f\"http://localhost:{port}/?__theme=dark\")\n                    else: webbrowser.open_new_tab(f\"http://localhost:{port}\")\n                threading.Thread(target=open, name=\"open-browser\", daemon=True).start()\n               #threading.Thread(target=auto_update, name=\"self-upgrade\", daemon=True).start()\n          auto_opentab_delay(7901)\n          block.queue(concurrency_count=CONCURRENT_COUNT).launch(server_name='0.0.0.0', server_port=7901,debug=True, share=False)"
  },
  {
    "path": "audio2face_pb2.py",
    "content": "# -*- coding: utf-8 -*-\n# Generated by the protocol buffer compiler.  DO NOT EDIT!\n# source: audio2face.proto\n\"\"\"Generated protocol buffer code.\"\"\"\nfrom google.protobuf import descriptor as _descriptor\nfrom google.protobuf import message as _message\nfrom google.protobuf import reflection as _reflection\nfrom google.protobuf import symbol_database as _symbol_database\n\n# @@protoc_insertion_point(imports)\n\n_sym_db = _symbol_database.Default()\n\n\nDESCRIPTOR = _descriptor.FileDescriptor(\n    name=\"audio2face.proto\",\n    package=\"nvidia.audio2face\",\n    syntax=\"proto3\",\n    serialized_options=None,\n    create_key=_descriptor._internal_create_key,\n    serialized_pb=b'\\n\\x10\\x61udio2face.proto\\x12\\x11nvidia.audio2face\"{\\n\\x10PushAudioRequest\\x12\\x15\\n\\rinstance_name\\x18\\x01 \\x01(\\t\\x12\\x12\\n\\nsamplerate\\x18\\x02 \\x01(\\x05\\x12\\x12\\n\\naudio_data\\x18\\x03 \\x01(\\x0c\\x12(\\n block_until_playback_is_finished\\x18\\x04 \\x01(\\x08\"5\\n\\x11PushAudioResponse\\x12\\x0f\\n\\x07success\\x18\\x01 \\x01(\\x08\\x12\\x0f\\n\\x07message\\x18\\x02 \\x01(\\t\"\\x85\\x01\\n\\x16PushAudioStreamRequest\\x12@\\n\\x0cstart_marker\\x18\\x01 \\x01(\\x0b\\x32(.nvidia.audio2face.PushAudioRequestStartH\\x00\\x12\\x14\\n\\naudio_data\\x18\\x02 \\x01(\\x0cH\\x00\\x42\\x13\\n\\x11streaming_request\"l\\n\\x15PushAudioRequestStart\\x12\\x15\\n\\rinstance_name\\x18\\x01 \\x01(\\t\\x12\\x12\\n\\nsamplerate\\x18\\x02 \\x01(\\x05\\x12(\\n block_until_playback_is_finished\\x18\\x03 \\x01(\\x08\";\\n\\x17PushAudioStreamResponse\\x12\\x0f\\n\\x07success\\x18\\x01 \\x01(\\x08\\x12\\x0f\\n\\x07message\\x18\\x02 \\x01(\\t2\\xd4\\x01\\n\\nAudio2Face\\x12X\\n\\tPushAudio\\x12#.nvidia.audio2face.PushAudioRequest\\x1a$.nvidia.audio2face.PushAudioResponse\"\\x00\\x12l\\n\\x0fPushAudioStream\\x12).nvidia.audio2face.PushAudioStreamRequest\\x1a*.nvidia.audio2face.PushAudioStreamResponse\"\\x00(\\x01\\x62\\x06proto3',\n)\n\n\n_PUSHAUDIOREQUEST = _descriptor.Descriptor(\n    name=\"PushAudioRequest\",\n    full_name=\"nvidia.audio2face.PushAudioRequest\",\n    filename=None,\n    file=DESCRIPTOR,\n    containing_type=None,\n    create_key=_descriptor._internal_create_key,\n    fields=[\n        _descriptor.FieldDescriptor(\n            name=\"instance_name\",\n            full_name=\"nvidia.audio2face.PushAudioRequest.instance_name\",\n            index=0,\n            number=1,\n            type=9,\n            cpp_type=9,\n            label=1,\n            has_default_value=False,\n            default_value=b\"\".decode(\"utf-8\"),\n            message_type=None,\n            enum_type=None,\n            containing_type=None,\n            is_extension=False,\n            extension_scope=None,\n            serialized_options=None,\n            file=DESCRIPTOR,\n            create_key=_descriptor._internal_create_key,\n        ),\n        _descriptor.FieldDescriptor(\n            name=\"samplerate\",\n            full_name=\"nvidia.audio2face.PushAudioRequest.samplerate\",\n            index=1,\n            number=2,\n            type=5,\n            cpp_type=1,\n            label=1,\n            has_default_value=False,\n            default_value=0,\n            message_type=None,\n            enum_type=None,\n            containing_type=None,\n            is_extension=False,\n            extension_scope=None,\n            serialized_options=None,\n            file=DESCRIPTOR,\n            create_key=_descriptor._internal_create_key,\n        ),\n        _descriptor.FieldDescriptor(\n            name=\"audio_data\",\n            full_name=\"nvidia.audio2face.PushAudioRequest.audio_data\",\n            index=2,\n            number=3,\n            type=12,\n            cpp_type=9,\n            label=1,\n            has_default_value=False,\n            default_value=b\"\",\n            message_type=None,\n            enum_type=None,\n            containing_type=None,\n            is_extension=False,\n            extension_scope=None,\n            serialized_options=None,\n            file=DESCRIPTOR,\n            create_key=_descriptor._internal_create_key,\n        ),\n        _descriptor.FieldDescriptor(\n            name=\"block_until_playback_is_finished\",\n            full_name=\"nvidia.audio2face.PushAudioRequest.block_until_playback_is_finished\",\n            index=3,\n            number=4,\n            type=8,\n            cpp_type=7,\n            label=1,\n            has_default_value=False,\n            default_value=False,\n            message_type=None,\n            enum_type=None,\n            containing_type=None,\n            is_extension=False,\n            extension_scope=None,\n            serialized_options=None,\n            file=DESCRIPTOR,\n            create_key=_descriptor._internal_create_key,\n        ),\n    ],\n    extensions=[],\n    nested_types=[],\n    enum_types=[],\n    serialized_options=None,\n    is_extendable=False,\n    syntax=\"proto3\",\n    extension_ranges=[],\n    oneofs=[],\n    serialized_start=39,\n    serialized_end=162,\n)\n\n\n_PUSHAUDIORESPONSE = _descriptor.Descriptor(\n    name=\"PushAudioResponse\",\n    full_name=\"nvidia.audio2face.PushAudioResponse\",\n    filename=None,\n    file=DESCRIPTOR,\n    containing_type=None,\n    create_key=_descriptor._internal_create_key,\n    fields=[\n        _descriptor.FieldDescriptor(\n            name=\"success\",\n            full_name=\"nvidia.audio2face.PushAudioResponse.success\",\n            index=0,\n            number=1,\n            type=8,\n            cpp_type=7,\n            label=1,\n            has_default_value=False,\n            default_value=False,\n            message_type=None,\n            enum_type=None,\n            containing_type=None,\n            is_extension=False,\n            extension_scope=None,\n            serialized_options=None,\n            file=DESCRIPTOR,\n            create_key=_descriptor._internal_create_key,\n        ),\n        _descriptor.FieldDescriptor(\n            name=\"message\",\n            full_name=\"nvidia.audio2face.PushAudioResponse.message\",\n            index=1,\n            number=2,\n            type=9,\n            cpp_type=9,\n            label=1,\n            has_default_value=False,\n            default_value=b\"\".decode(\"utf-8\"),\n            message_type=None,\n            enum_type=None,\n            containing_type=None,\n            is_extension=False,\n            extension_scope=None,\n            serialized_options=None,\n            file=DESCRIPTOR,\n            create_key=_descriptor._internal_create_key,\n        ),\n    ],\n    extensions=[],\n    nested_types=[],\n    enum_types=[],\n    serialized_options=None,\n    is_extendable=False,\n    syntax=\"proto3\",\n    extension_ranges=[],\n    oneofs=[],\n    serialized_start=164,\n    serialized_end=217,\n)\n\n\n_PUSHAUDIOSTREAMREQUEST = _descriptor.Descriptor(\n    name=\"PushAudioStreamRequest\",\n    full_name=\"nvidia.audio2face.PushAudioStreamRequest\",\n    filename=None,\n    file=DESCRIPTOR,\n    containing_type=None,\n    create_key=_descriptor._internal_create_key,\n    fields=[\n        _descriptor.FieldDescriptor(\n            name=\"start_marker\",\n            full_name=\"nvidia.audio2face.PushAudioStreamRequest.start_marker\",\n            index=0,\n            number=1,\n            type=11,\n            cpp_type=10,\n            label=1,\n            has_default_value=False,\n            default_value=None,\n            message_type=None,\n            enum_type=None,\n            containing_type=None,\n            is_extension=False,\n            extension_scope=None,\n            serialized_options=None,\n            file=DESCRIPTOR,\n            create_key=_descriptor._internal_create_key,\n        ),\n        _descriptor.FieldDescriptor(\n            name=\"audio_data\",\n            full_name=\"nvidia.audio2face.PushAudioStreamRequest.audio_data\",\n            index=1,\n            number=2,\n            type=12,\n            cpp_type=9,\n            label=1,\n            has_default_value=False,\n            default_value=b\"\",\n            message_type=None,\n            enum_type=None,\n            containing_type=None,\n            is_extension=False,\n            extension_scope=None,\n            serialized_options=None,\n            file=DESCRIPTOR,\n            create_key=_descriptor._internal_create_key,\n        ),\n    ],\n    extensions=[],\n    nested_types=[],\n    enum_types=[],\n    serialized_options=None,\n    is_extendable=False,\n    syntax=\"proto3\",\n    extension_ranges=[],\n    oneofs=[\n        _descriptor.OneofDescriptor(\n            name=\"streaming_request\",\n            full_name=\"nvidia.audio2face.PushAudioStreamRequest.streaming_request\",\n            index=0,\n            containing_type=None,\n            create_key=_descriptor._internal_create_key,\n            fields=[],\n        )\n    ],\n    serialized_start=220,\n    serialized_end=353,\n)\n\n\n_PUSHAUDIOREQUESTSTART = _descriptor.Descriptor(\n    name=\"PushAudioRequestStart\",\n    full_name=\"nvidia.audio2face.PushAudioRequestStart\",\n    filename=None,\n    file=DESCRIPTOR,\n    containing_type=None,\n    create_key=_descriptor._internal_create_key,\n    fields=[\n        _descriptor.FieldDescriptor(\n            name=\"instance_name\",\n            full_name=\"nvidia.audio2face.PushAudioRequestStart.instance_name\",\n            index=0,\n            number=1,\n            type=9,\n            cpp_type=9,\n            label=1,\n            has_default_value=False,\n            default_value=b\"\".decode(\"utf-8\"),\n            message_type=None,\n            enum_type=None,\n            containing_type=None,\n            is_extension=False,\n            extension_scope=None,\n            serialized_options=None,\n            file=DESCRIPTOR,\n            create_key=_descriptor._internal_create_key,\n        ),\n        _descriptor.FieldDescriptor(\n            name=\"samplerate\",\n            full_name=\"nvidia.audio2face.PushAudioRequestStart.samplerate\",\n            index=1,\n            number=2,\n            type=5,\n            cpp_type=1,\n            label=1,\n            has_default_value=False,\n            default_value=0,\n            message_type=None,\n            enum_type=None,\n            containing_type=None,\n            is_extension=False,\n            extension_scope=None,\n            serialized_options=None,\n            file=DESCRIPTOR,\n            create_key=_descriptor._internal_create_key,\n        ),\n        _descriptor.FieldDescriptor(\n            name=\"block_until_playback_is_finished\",\n            full_name=\"nvidia.audio2face.PushAudioRequestStart.block_until_playback_is_finished\",\n            index=2,\n            number=3,\n            type=8,\n            cpp_type=7,\n            label=1,\n            has_default_value=False,\n            default_value=False,\n            message_type=None,\n            enum_type=None,\n            containing_type=None,\n            is_extension=False,\n            extension_scope=None,\n            serialized_options=None,\n            file=DESCRIPTOR,\n            create_key=_descriptor._internal_create_key,\n        ),\n    ],\n    extensions=[],\n    nested_types=[],\n    enum_types=[],\n    serialized_options=None,\n    is_extendable=False,\n    syntax=\"proto3\",\n    extension_ranges=[],\n    oneofs=[],\n    serialized_start=355,\n    serialized_end=463,\n)\n\n\n_PUSHAUDIOSTREAMRESPONSE = _descriptor.Descriptor(\n    name=\"PushAudioStreamResponse\",\n    full_name=\"nvidia.audio2face.PushAudioStreamResponse\",\n    filename=None,\n    file=DESCRIPTOR,\n    containing_type=None,\n    create_key=_descriptor._internal_create_key,\n    fields=[\n        _descriptor.FieldDescriptor(\n            name=\"success\",\n            full_name=\"nvidia.audio2face.PushAudioStreamResponse.success\",\n            index=0,\n            number=1,\n            type=8,\n            cpp_type=7,\n            label=1,\n            has_default_value=False,\n            default_value=False,\n            message_type=None,\n            enum_type=None,\n            containing_type=None,\n            is_extension=False,\n            extension_scope=None,\n            serialized_options=None,\n            file=DESCRIPTOR,\n            create_key=_descriptor._internal_create_key,\n        ),\n        _descriptor.FieldDescriptor(\n            name=\"message\",\n            full_name=\"nvidia.audio2face.PushAudioStreamResponse.message\",\n            index=1,\n            number=2,\n            type=9,\n            cpp_type=9,\n            label=1,\n            has_default_value=False,\n            default_value=b\"\".decode(\"utf-8\"),\n            message_type=None,\n            enum_type=None,\n            containing_type=None,\n            is_extension=False,\n            extension_scope=None,\n            serialized_options=None,\n            file=DESCRIPTOR,\n            create_key=_descriptor._internal_create_key,\n        ),\n    ],\n    extensions=[],\n    nested_types=[],\n    enum_types=[],\n    serialized_options=None,\n    is_extendable=False,\n    syntax=\"proto3\",\n    extension_ranges=[],\n    oneofs=[],\n    serialized_start=465,\n    serialized_end=524,\n)\n\n_PUSHAUDIOSTREAMREQUEST.fields_by_name[\"start_marker\"].message_type = _PUSHAUDIOREQUESTSTART\n_PUSHAUDIOSTREAMREQUEST.oneofs_by_name[\"streaming_request\"].fields.append(\n    _PUSHAUDIOSTREAMREQUEST.fields_by_name[\"start_marker\"]\n)\n_PUSHAUDIOSTREAMREQUEST.fields_by_name[\"start_marker\"].containing_oneof = _PUSHAUDIOSTREAMREQUEST.oneofs_by_name[\n    \"streaming_request\"\n]\n_PUSHAUDIOSTREAMREQUEST.oneofs_by_name[\"streaming_request\"].fields.append(\n    _PUSHAUDIOSTREAMREQUEST.fields_by_name[\"audio_data\"]\n)\n_PUSHAUDIOSTREAMREQUEST.fields_by_name[\"audio_data\"].containing_oneof = _PUSHAUDIOSTREAMREQUEST.oneofs_by_name[\n    \"streaming_request\"\n]\nDESCRIPTOR.message_types_by_name[\"PushAudioRequest\"] = _PUSHAUDIOREQUEST\nDESCRIPTOR.message_types_by_name[\"PushAudioResponse\"] = _PUSHAUDIORESPONSE\nDESCRIPTOR.message_types_by_name[\"PushAudioStreamRequest\"] = _PUSHAUDIOSTREAMREQUEST\nDESCRIPTOR.message_types_by_name[\"PushAudioRequestStart\"] = _PUSHAUDIOREQUESTSTART\nDESCRIPTOR.message_types_by_name[\"PushAudioStreamResponse\"] = _PUSHAUDIOSTREAMRESPONSE\n_sym_db.RegisterFileDescriptor(DESCRIPTOR)\n\nPushAudioRequest = _reflection.GeneratedProtocolMessageType(\n    \"PushAudioRequest\",\n    (_message.Message,),\n    {\n        \"DESCRIPTOR\": _PUSHAUDIOREQUEST,\n        \"__module__\": \"audio2face_pb2\"\n        # @@protoc_insertion_point(class_scope:nvidia.audio2face.PushAudioRequest)\n    },\n)\n_sym_db.RegisterMessage(PushAudioRequest)\n\nPushAudioResponse = _reflection.GeneratedProtocolMessageType(\n    \"PushAudioResponse\",\n    (_message.Message,),\n    {\n        \"DESCRIPTOR\": _PUSHAUDIORESPONSE,\n        \"__module__\": \"audio2face_pb2\"\n        # @@protoc_insertion_point(class_scope:nvidia.audio2face.PushAudioResponse)\n    },\n)\n_sym_db.RegisterMessage(PushAudioResponse)\n\nPushAudioStreamRequest = _reflection.GeneratedProtocolMessageType(\n    \"PushAudioStreamRequest\",\n    (_message.Message,),\n    {\n        \"DESCRIPTOR\": _PUSHAUDIOSTREAMREQUEST,\n        \"__module__\": \"audio2face_pb2\"\n        # @@protoc_insertion_point(class_scope:nvidia.audio2face.PushAudioStreamRequest)\n    },\n)\n_sym_db.RegisterMessage(PushAudioStreamRequest)\n\nPushAudioRequestStart = _reflection.GeneratedProtocolMessageType(\n    \"PushAudioRequestStart\",\n    (_message.Message,),\n    {\n        \"DESCRIPTOR\": _PUSHAUDIOREQUESTSTART,\n        \"__module__\": \"audio2face_pb2\"\n        # @@protoc_insertion_point(class_scope:nvidia.audio2face.PushAudioRequestStart)\n    },\n)\n_sym_db.RegisterMessage(PushAudioRequestStart)\n\nPushAudioStreamResponse = _reflection.GeneratedProtocolMessageType(\n    \"PushAudioStreamResponse\",\n    (_message.Message,),\n    {\n        \"DESCRIPTOR\": _PUSHAUDIOSTREAMRESPONSE,\n        \"__module__\": \"audio2face_pb2\"\n        # @@protoc_insertion_point(class_scope:nvidia.audio2face.PushAudioStreamResponse)\n    },\n)\n_sym_db.RegisterMessage(PushAudioStreamResponse)\n\n\n_AUDIO2FACE = _descriptor.ServiceDescriptor(\n    name=\"Audio2Face\",\n    full_name=\"nvidia.audio2face.Audio2Face\",\n    file=DESCRIPTOR,\n    index=0,\n    serialized_options=None,\n    create_key=_descriptor._internal_create_key,\n    serialized_start=527,\n    serialized_end=739,\n    methods=[\n        _descriptor.MethodDescriptor(\n            name=\"PushAudio\",\n            full_name=\"nvidia.audio2face.Audio2Face.PushAudio\",\n            index=0,\n            containing_service=None,\n            input_type=_PUSHAUDIOREQUEST,\n            output_type=_PUSHAUDIORESPONSE,\n            serialized_options=None,\n            create_key=_descriptor._internal_create_key,\n        ),\n        _descriptor.MethodDescriptor(\n            name=\"PushAudioStream\",\n            full_name=\"nvidia.audio2face.Audio2Face.PushAudioStream\",\n            index=1,\n            containing_service=None,\n            input_type=_PUSHAUDIOSTREAMREQUEST,\n            output_type=_PUSHAUDIOSTREAMRESPONSE,\n            serialized_options=None,\n            create_key=_descriptor._internal_create_key,\n        ),\n    ],\n)\n_sym_db.RegisterServiceDescriptor(_AUDIO2FACE)\n\nDESCRIPTOR.services_by_name[\"Audio2Face\"] = _AUDIO2FACE\n\n# @@protoc_insertion_point(module_scope)\n"
  },
  {
    "path": "audio2face_pb2_grpc.py",
    "content": "# Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT!\n\"\"\"Client and server classes corresponding to protobuf-defined services.\"\"\"\nimport grpc\n\nimport audio2face_pb2  as audio2face__pb2\n\n\nclass Audio2FaceStub(object):\n    \"\"\"Missing associated documentation comment in .proto file.\"\"\"\n\n    def __init__(self, channel):\n        \"\"\"Constructor.\n\n        Args:\n            channel: A grpc.Channel.\n        \"\"\"\n        self.PushAudio = channel.unary_unary(\n            \"/nvidia.audio2face.Audio2Face/PushAudio\",\n            request_serializer=audio2face__pb2.PushAudioRequest.SerializeToString,\n            response_deserializer=audio2face__pb2.PushAudioResponse.FromString,\n        )\n        self.PushAudioStream = channel.stream_unary(\n            \"/nvidia.audio2face.Audio2Face/PushAudioStream\",\n            request_serializer=audio2face__pb2.PushAudioStreamRequest.SerializeToString,\n            response_deserializer=audio2face__pb2.PushAudioStreamResponse.FromString,\n        )\n\n\nclass Audio2FaceServicer(object):\n    \"\"\"Missing associated documentation comment in .proto file.\"\"\"\n\n    def PushAudio(self, request, context):\n        \"\"\"Missing associated documentation comment in .proto file.\"\"\"\n        context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n        context.set_details(\"Method not implemented!\")\n        raise NotImplementedError(\"Method not implemented!\")\n\n    def PushAudioStream(self, request_iterator, context):\n        \"\"\"Missing associated documentation comment in .proto file.\"\"\"\n        context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n        context.set_details(\"Method not implemented!\")\n        raise NotImplementedError(\"Method not implemented!\")\n\n\ndef add_Audio2FaceServicer_to_server(servicer, server):\n    rpc_method_handlers = {\n        \"PushAudio\": grpc.unary_unary_rpc_method_handler(\n            servicer.PushAudio,\n            request_deserializer=audio2face__pb2.PushAudioRequest.FromString,\n            response_serializer=audio2face__pb2.PushAudioResponse.SerializeToString,\n        ),\n        \"PushAudioStream\": grpc.stream_unary_rpc_method_handler(\n            servicer.PushAudioStream,\n            request_deserializer=audio2face__pb2.PushAudioStreamRequest.FromString,\n            response_serializer=audio2face__pb2.PushAudioStreamResponse.SerializeToString,\n        ),\n    }\n    generic_handler = grpc.method_handlers_generic_handler(\"nvidia.audio2face.Audio2Face\", rpc_method_handlers)\n    server.add_generic_rpc_handlers((generic_handler,))\n\n\n# This class is part of an EXPERIMENTAL API.\nclass Audio2Face(object):\n    \"\"\"Missing associated documentation comment in .proto file.\"\"\"\n\n    @staticmethod\n    def PushAudio(\n        request,\n        target,\n        options=(),\n        channel_credentials=None,\n        call_credentials=None,\n        insecure=False,\n        compression=None,\n        wait_for_ready=None,\n        timeout=None,\n        metadata=None,\n    ):\n        return grpc.experimental.unary_unary(\n            request,\n            target,\n            \"/nvidia.audio2face.Audio2Face/PushAudio\",\n            audio2face__pb2.PushAudioRequest.SerializeToString,\n            audio2face__pb2.PushAudioResponse.FromString,\n            options,\n            channel_credentials,\n            insecure,\n            call_credentials,\n            compression,\n            wait_for_ready,\n            timeout,\n            metadata,\n        )\n\n    @staticmethod\n    def PushAudioStream(\n        request_iterator,\n        target,\n        options=(),\n        channel_credentials=None,\n        call_credentials=None,\n        insecure=False,\n        compression=None,\n        wait_for_ready=None,\n        timeout=None,\n        metadata=None,\n    ):\n        return grpc.experimental.stream_unary(\n            request_iterator,\n            target,\n            \"/nvidia.audio2face.Audio2Face/PushAudioStream\",\n            audio2face__pb2.PushAudioStreamRequest.SerializeToString,\n            audio2face__pb2.PushAudioStreamResponse.FromString,\n            options,\n            channel_credentials,\n            insecure,\n            call_credentials,\n            compression,\n            wait_for_ready,\n            timeout,\n            metadata,\n        )\n"
  },
  {
    "path": "audio2face_streaming_utils.py",
    "content": "\"\"\"\nThis demo script shows how to send audio data to Audio2Face Streaming Audio Player via gRPC requests.\nThere are two options:\n * Send the whole track at once using PushAudioRequest()\n * Send the audio chunks seuqntially in a stream using PushAudioStreamRequest()\nFor the second option this script emulates the stream of chunks, generated by splitting an input WAV audio file.\nBut in a real application such stream of chunks may be aquired from some other streaming source:\n * streaming audio via internet, streaming Text-To-Speech, etc\ngRPC protocol details could be find in audio2face.proto\n\"\"\"\n\nimport sys\nimport grpc\nimport time\nimport numpy as np\nimport soundfile\n\n\nimport audio2face_pb2_grpc\nimport audio2face_pb2\n\ndef push_audio_track(url, audio_data, samplerate, instance_name):\n    \"\"\"\n    This function pushes the whole audio track at once via PushAudioRequest()\n    PushAudioRequest parameters:\n     * audio_data: bytes, containing audio data for the whole track, where each sample is encoded as 4 bytes (float32)\n     * samplerate: sampling rate for the audio data\n     * instance_name: prim path of the Audio2Face Streaming Audio Player on the stage, were to push the audio data\n     * block_until_playback_is_finished: if True, the gRPC request will be blocked until the playback of the pushed track is finished\n    The request is passed to PushAudio()\n    \"\"\"\n\n    block_until_playback_is_finished = True  # ADJUST\n    with grpc.insecure_channel(url) as channel:\n        stub = audio2face_pb2_grpc.Audio2FaceStub(channel)\n        request = audio2face_pb2.PushAudioRequest()\n        request.audio_data = audio_data.astype(np.float32).tobytes()\n        request.samplerate = samplerate\n        request.instance_name = instance_name\n        request.block_until_playback_is_finished = block_until_playback_is_finished\n        print(\"Sending audio data...\")\n        response = stub.PushAudio(request)\n        if response.success:\n            print(\"SUCCESS\")\n        else:\n            print(f\"ERROR: {response.message}\")\n    print(\"Closed channel\")\n\n\ndef push_audio_track_stream(url, audio_data, samplerate, instance_name):\n    \"\"\"\n    This function pushes audio chunks sequentially via PushAudioStreamRequest()\n    The function emulates the stream of chunks, generated by splitting input audio track.\n    But in a real application such stream of chunks may be aquired from some other streaming source.\n    The first message must contain start_marker field, containing only meta information (without audio data):\n     * samplerate: sampling rate for the audio data\n     * instance_name: prim path of the Audio2Face Streaming Audio Player on the stage, were to push the audio data\n     * block_until_playback_is_finished: if True, the gRPC request will be blocked until the playback of the pushed track is finished (after the last message)\n    Second and other messages must contain audio_data field:\n     * audio_data: bytes, containing audio data for an audio chunk, where each sample is encoded as 4 bytes (float32)\n    All messages are packed into a Python generator and passed to PushAudioStream()\n    \"\"\"\n    #print(type(audio_data))\n    \n    chunk_size = samplerate // 10  # ADJUST\n    sleep_between_chunks = 0.01  # ADJUST\n    block_until_playback_is_finished = True  # ADJUST\n    #print(type(audio_data))\n    with grpc.insecure_channel(url) as channel:\n        stub = audio2face_pb2_grpc.Audio2FaceStub(channel)\n\n        def make_generator():\n            start_marker = audio2face_pb2.PushAudioRequestStart(\n                samplerate=samplerate,\n                instance_name=instance_name,\n                block_until_playback_is_finished=block_until_playback_is_finished,\n            )\n            # At first, we send a message with start_marker\n            yield audio2face_pb2.PushAudioStreamRequest(start_marker=start_marker)\n            # Then we send messages with audio_data\n            for i in range(len(audio_data) // chunk_size + 1):\n                #time.sleep(sleep_between_chunks)\n                chunk = audio_data[i * chunk_size : i * chunk_size + chunk_size]\n                yield audio2face_pb2.PushAudioStreamRequest(audio_data=chunk.astype(np.float32).tobytes())\n\n        request_generator = make_generator()\n        print(\"Sending audio data...\")\n        response = stub.PushAudioStream(request_generator)\n        if response.success:\n            print(\"SUCCESS\")\n        else:\n            print(f\"ERROR: {response.message}\")\n    print(\"Channel closed\")\n    \ndef push_stream(url, audio_data, samplerate, instance_name):\n    \"\"\"\n    This function pushes audio chunks sequentially via PushAudioStreamRequest()\n    The function emulates the stream of chunks, generated by splitting input audio track.\n    But in a real application such stream of chunks may be aquired from some other streaming source.\n    The first message must contain start_marker field, containing only meta information (without audio data):\n     * samplerate: sampling rate for the audio data\n     * instance_name: prim path of the Audio2Face Streaming Audio Player on the stage, were to push the audio data\n     * block_until_playback_is_finished: if True, the gRPC request will be blocked until the playback of the pushed track is finished (after the last message)\n    Second and other messages must contain audio_data field:\n     * audio_data: bytes, containing audio data for an audio chunk, where each sample is encoded as 4 bytes (float32)\n    All messages are packed into a Python generator and passed to PushAudioStream()\n    \"\"\"\n\n    print(len(audio_data))\n    chunk_size = samplerate // 10  # ADJUST\n    sleep_between_chunks = 0.01  # ADJUST\n    block_until_playback_is_finished = True  # ADJUST\n    print(type(audio_data))\n    with grpc.insecure_channel(url) as channel:\n        print(\"Channel creadted\")\n        stub = audio2face_pb2_grpc.Audio2FaceStub(channel)\n\n        def make_generator():\n            start_marker = audio2face_pb2.PushAudioRequestStart(\n                samplerate=samplerate,\n                instance_name=instance_name,\n                block_until_playback_is_finished=block_until_playback_is_finished,\n            )\n            # At first, we send a message with start_marker\n            yield audio2face_pb2.PushAudioStreamRequest(start_marker=start_marker)\n            # Then we send messages with audio_data\n            for i in range(len(audio_data) // chunk_size + 1):\n                #time.sleep(sleep_between_chunks)\n                chunk = audio_data[i * chunk_size : i * chunk_size + chunk_size]\n                yield audio2face_pb2.PushAudioStreamRequest(audio_data=chunk.astype(np.float32).tobytes())\n\n        request_generator = make_generator()\n        print(\"Sending audio data...\")\n        response = stub.PushAudioStream(request_generator)\n        if response.success:\n            print(\"SUCCESS\")\n            return True \n        else:\n            print(f\"ERROR: {response.message}\")\n   # print(\"Channel closed\")"
  },
  {
    "path": "audio_segment.py",
    "content": "\n\nimport os\nimport gradio as gr\nfrom pydub import AudioSegment\n\n# function to crop audio according to the given start and end time\ndef crop_audio(file_path, start_time, end_time):\n    audio = AudioSegment.from_file(file_path)\n    cropped_audio = audio[start_time:end_time]\n    filename = os.path.splitext(os.path.basename(file_path))[0]\n    if not os.path.exists(\"cropped_audio\"):\n        os.makedirs(\"cropped_audio\")\n    cropped_file_path = os.path.join(\"cropped_audio\",f\"{filename}_{start_time//1000}_{end_time//1000}.wav\")\n    cropped_audio.export(cropped_file_path, format=\"wav\")\n    return cropped_file_path\n\n# function to split audio file into segments\ndef split_audio_file(file_path, output_path, segment_time=3000):\n    audio = AudioSegment.from_file(file_path)\n    file_name = os.path.splitext(os.path.basename(file_path))[0]\n\n    # Calculating total segments that will be created.\n    total_segments = int(audio.duration_seconds // (segment_time/1000)) + 1\n\n    # Creating each segment and saving to the output folder\n    for segment_number in range(total_segments):\n        start_time = segment_number * segment_time\n        end_time = start_time + segment_time\n        segment_file_path = os.path.join(output_path, f\"{file_name}_{start_time//1000}_{end_time//1000}.wav\")\n        segment = audio[start_time:end_time]\n        segment.export(segment_file_path, format=\"wav\")\n\n    return output_path\n\n# main function\ndef audio_processing(file_path, output_path, label):\n    # 分割音频文件\n    if not os.path.exists(output_path):\n        os.makedirs(output_path)\n    split_audio_file(file_path, output_path)\n\n    # 获取手动选择的音频段并裁剪\n    cropped_files_paths = []\n    for root, dirs, files in os.walk(output_path):\n        for file in files:\n            if file.endswith('.wav'):\n                file_path = os.path.abspath(os.path.join(root, file))\n                cropped_file_path = crop_audio(file_path, 0, 1000) # 注意此处仅提供示例裁剪了1s的音频 \n                cropped_files_paths.append(cropped_file_path)\n\n    # 生成txt文件\n    txt_file = open('file_labels.txt', 'a')\n    for index, cropped_file_path in enumerate(cropped_files_paths):\n        segment_label = label + '_' + str(index)\n        # 将文件路径和标签写入txt文件\n        txt_file.write(f\"{cropped_file_path}\\t{segment_label}\\n\")\n    txt_file.close()\n\n    print(\"处理完成！\")\n# 定义输入界面, 接收音频文件、输出文件夹和标签\niface = gr.Interface(\n            fn=audio_processing,\n            inputs=[gr.inputs.File(label=\"上传音频文件\"),\n                    gr.inputs.Textbox(label=\"输出文件夹路径\"),\n                    gr.inputs.Textbox(label=\"标签\")],\n            outputs=\"text\",\n            title=\"音频处理工具\",\n            description=\"通过鼠标点击音频的任意区间保存片段\")\n\niface.launch()    "
  },
  {
    "path": "auto_label_demo.py",
    "content": "from model_cards.autoback import AutoBackend\nimport argparse\nimport os\nimport platform\nimport sys\nfrom pathlib import Path\nimport  numpy as np \nimport torch\nimport torch.backends.cudnn as cudnn\nimport matplotlib.pyplot as plt\nfrom PIL import Image\nimport random\nFILE = Path(__file__).resolve()\nROOT = FILE.parents[0]  #  root directory\nif str(ROOT) not in sys.path:\n    sys.path.append(str(ROOT))  # add ROOT to PATH\nROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative\n\nfrom utils.ops import (LOGGER, Profile, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2,\n                     dilate_mask, increment_path, non_max_suppression ,print_args, scale_boxes, xyxy2xywh,save_format)\nfrom utils.plot import Annotator, save_one_box,show_box,show_mask,save_mask_data\n\n\nfrom utils.torch_utils import select_device\nfrom config_private import SAM_MODEL_TYPE,GROUNED_MODEL_TYPE,Tag2Text_Model_Path,GLIGEN_META_LIST\nfrom utils import VID_FORMATS,IMG_FORMATS,write_categories\nimport json\nimport xml.etree.cElementTree as ET\nfrom tqdm import tqdm\n\n\n\n\n# 初始已知类别列表\nglobal categories\ncategories = {}\nglobal category_colors\ncategory_colors={}\n# 初始对应类别编号\nclass_ids = []\nmodels_config = {'tag2text': None, 'lama': None,'sam': None,'grounded': None,'sd': None,'visual_glm': None,'trans_zh': None,'gilgen':None}\nJSON_DATASETS=[]\n\n\ndef save_text2img_data(output_dir, prompt,label,img_name):\n    global JSON_DATASETS\n    if not prompt:\n        prompt=f\"这张图片的背景里有什么内容?\"\n    example = {\n        \"img\": f\"{img_name}\",\n        \"prompt\": prompt,\n        \"label\": label\n    }\n    JSON_DATASETS.append((example))\ndef load_auto_backend_models(opt):\n    \"\"\"\n    加载多个模型\n    \"\"\"\n    # Load model\n    device = select_device(opt.device)\n    if opt.tag2text:\n        models_config['tag2text'] = AutoBackend(\"tag2text\",weights=Tag2Text_Model_Path,device=device, fp16=opt.half)\n    if opt.det:\n        models_config['grounded'] = AutoBackend(\"grounded-DINO\",weights=GROUNED_MODEL_TYPE['S'], device=device,\n        args_config= 'model_cards/groundingdino/config/GroundingDINO_SwinT_OGC.py', fp16=opt.half)\n    if opt.sam:\n        models_config['sam']= AutoBackend(\"segment-anything\",weights=SAM_MODEL_TYPE['vit_h'] ,device=device, fp16=opt.half)\n    if opt.lama:\n        models_config['lama']= AutoBackend(\"lama\",weights=None,args_config='model_cards/lama/configs/prediction/default.yaml',device=device)\n    if opt.gligen:\n        models_config['gligen']=AutoBackend(\"gligen\",weights=GLIGEN_META_LIST[0])\n        print('【loads models done】')\n        \ndef Auto_run(weights=ROOT / '',  # model.pt path(s)\n        source= 'data/images',  # file/dir/URL/glob, 0 for webcam\n        input_prompt=\"Anything in this image\",\n        data=ROOT / 'data/',  # dataset.yaml path\n        imgsz=(1920, 1080),  # inference size (height, width)\n        conf_thres=0.25,  # confidence threshold\n        iou_thres=0.45,  # NMS IOU threshold\n        text_thres=0.3,\n        max_det=1000,  # maximum detections per image\n        device='',  # cuda device, i.e. 0 or 0,1,2,3 or cpu\n        view_img=False,  # show results\n        save_txt=False,  # save results to *.txt\n        save_xml=False,  # save results to *.xml\n        save_conf=False,  # save confidences in --save-txt labels\n        save_crop=False,  # save cropped prediction boxes\n        nosave=False,  # do not save images/videos\n        classes=None,  # filter by class: --class 0, or --class 0 2 3\n        zh_select=False,\n        agnostic_nms=False,  # class-agnostic NMS\n        augment=False,  # augmented inference\n        visualize=False,  # visualize features\n        update=False,  # update all models\n        project=ROOT / 'runs/detect',  # save results to project/name\n        name='exp',  # save results to project/name\n        exist_ok=False,  # existing project/name ok, do not increment\n        line_thickness=3,  # bounding box thickness (pixels)\n        hide_labels=False,  # hide labels\n        hide_conf=False,  # hide confidences\n        half=False,  # use FP16 half-precision inference\n        trace=False,  # u\n        lama=False,   # use lama models\n        sam=True,    # use segment-anythings\n        det=True,    # use grounded detect model with text\n        tag2text=True,\n        save_mask=False,\n        save_caption=False,\n        batch_process=False,\n        color_flag=False,\n        process_name=0,\n        gligen=False,\n        ):  \n            global models_config\n            global category_colors\n            global JSON_DATASETS\n            LOGGER.info(f'当前的进程ID：{process_name},加载的模型列表：{models_config.keys()}')\n            cls_index = -1      # 设置默认值为 -1\n            source = str(source)\n            print(f'input:{source}')\n            img_paths=None\n            if os.path.isdir(source):\n                img_paths = [os.path.join(source, f) for f in os.listdir(source) if\n                    Path(f).suffix[1:] in (IMG_FORMATS + VID_FORMATS)] \n            elif os.path.isfile(source):\n                img_paths = [source]    \n            else:\n                 return False        \n            # 获取文件夹中的所有图像\n            is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)\n            save_img = not nosave and not source.endswith('.txt')  # save inference images\n            is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://'))\n            #webcam = source.isnumeric() or source.endswith('.streams') or (is_url )\n            if is_url and is_file:\n                source = check_file(source)  # download\n            # Directories\n            save_dir = increment_path(Path(project) / name, exist_ok=exist_ok)  # increment run\n            (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # make dir\n            (save_dir / 'xmls' if save_xml else save_dir).mkdir(parents=True, exist_ok=True)  # make dir\n            (save_dir / 'masks' if save_mask else save_dir).mkdir(parents=True, exist_ok=True)  # make dir\n            (save_dir / 'captions' if save_caption else save_dir).mkdir(parents=True, exist_ok=True)  # make dir\n\n            seen=0\n            # loda data and inference\n            caption=None\n            for source in tqdm(img_paths,desc=\"Processing\"): \n                    im = cv2.imread(source)\n                    name_p= source.split('/')[-1].split('.')[0]\n                    img_rgb = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)\n                    preds=None\n                    masks=[]\n                    prompt=input_prompt\n                    if tag2text:\n                        preds = models_config['tag2text'](im = img_rgb ,prompt=prompt,box_threshold=conf_thres,text_threshold=text_thres,iou_threshold=iou_thres)\n                    # Currently \", \" is better for detecting single tags\n                    # while \". \" is a little worse in some case\n                        prompt=preds[0].replace(' |', ',')\n                        caption=preds[2]\n                        print(f\"Caption: {caption}\")\n                        print(f\"Tags: {prompt}\")\n                        if zh_select:\n                            caption=models_config['trans_zh'](prompt, max_length=1000, clean_up_tokenization_spaces=True)[0][\"generated_text\"]\n                        if save_caption:\n                            save_format(label_format=\"txt\",save_path=f'{save_dir}/captions',img_name=name_p, results=caption)\n                            \n                    if det:\n                        if input_prompt:\n                            prompt=input_prompt\n                        print('grouned start input prompt:',prompt)\n                        preds= models_config['grounded'](im = img_rgb,prompt=prompt, box_threshold=conf_thres,text_threshold=text_thres, iou_threshold=iou_thres) \n                        \n                    if sam and det :        \n                        if preds[0].numel()>0:\n                            print('sam start input prompt:',preds[0])\n                            masks= models_config['sam'](im = img_rgb, prompt=preds[0],box_threshold=conf_thres,text_threshold=text_thres, iou_threshold=iou_thres)\n                            if save_mask:\n                                save_mask_data(str(save_dir)+'/masks', caption, masks, preds[0], preds[2],name_p)\n                    # Write results\n                    if save_img:\n                        seen+=1\n                        plt.figure(figsize=(10,10))\n                        plt.imshow(img_rgb)\n                        if det:\n                            for box,label in zip(preds[0],preds[2]):\n                                    show_box(box.numpy(),plt.gca(),label)\n                            for mask in masks:            \n                                show_mask(mask.cpu().numpy(),plt.gca(),random_color=True)\n                        if tag2text:\n                            plt.title('Captioning: ' + caption + '\\n' + 'Tagging:' + prompt + '\\n')    \n                        plt.axis('off')\n                        plt.savefig(f'{save_dir}/{seen}.png',bbox_iches='tight',dpi=300,pad_inches=0.0)     \n                        \n                    if lama and masks is not None :      \n                        masks_prompts= masks.detach().cpu().numpy().astype(np.uint8) * 255\n                        for idx, mask in enumerate(masks_prompts):   \n                            \n                            sub_mask = [dilate_mask(ma, 15) for ma in mask]\n                            img_inpainted_p= f'{save_dir}/mask_{idx}.png'\n                            idx=idx+1\n                            img_inpainted = models_config['lama'](\n                                    im=img_rgb, prompt=sub_mask[0])\n                            Image.fromarray(img_inpainted.astype(np.uint8)).save(img_inpainted_p)\n                            img_rgb=img_inpainted     \n                    for category in categories:\n                        if category not in category_colors:\n                            category_colors[category] = (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255))            \n                    gn = torch.tensor(im.shape)[[1, 0, 1, 0]]  # normalization gain whwh   \n                    \n                    if color_flag or save_txt:\n                        seg_mask = np.zeros_like(img_rgb)  # img_array \n                        category_color=[]\n                        for xyxy, conf, cls,mask in zip(preds[0],preds[1],preds[2],masks):       #per im boxes              \n                                xywh = (xyxy2xywh((xyxy).view(1,4)) / gn).view(-1).tolist()  # normalized xywh   \n                                if cls not in categories:\n                                # print(f'Add {cls} to categories: {categories}')\n                                    categories.update({\n                                            str(cls): len(categories)})        \n                                    write_categories(cls,f'{save_dir}/classes_id.txt')\n                                    cls_index = len(categories) - 1\n                                    category_colors.update({\n                                            str(cls):  (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255))}) \n                                    category_color=category_colors[str(cls)]\n                                else:   \n                                    cls_index = categories[str(cls)]\n                                    if str(cls) not in category_colors:\n                                        category_colors.update({\n                                            str(cls):  (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255))})\n                                    category_color=category_colors[str(cls)]\n                                line = (cls_index, xywh, conf) if save_conf else (cls_index, xywh)  # label format\n                                line = str(line).replace('[', '').replace(']', '').replace(\"(\",'').replace(\")\",\" \").replace(\",\", \" \" * 2)\n                                if save_mask:\n                                    h, w = mask.shape[-2:]\n                                    mask_color = np.array(category_color).reshape((1, 1, -1))  \n                                    seg_mask = seg_mask + mask.cpu().numpy().reshape(h, w, 1)  * mask_color  # add    \n                                if save_txt:                                 \n                                    save_format(label_format=\"txt\",save_path=f'{save_dir}/labels', img_name=name_p, results=line)\n                    if color_flag and save_mask:\n                            plt.figure(figsize=(10,10))\n                            plt.imshow(seg_mask)\n                            plt.title('Captioning: ' + caption + '\\n' + 'Tagging:' + prompt + '\\n')    \n                            plt.axis('off')            \n                            plt.savefig(os.path.join(f'{save_dir}/masks', f'{name_p}_cls.jpg'), bbox_inches=\"tight\", dpi=300, pad_inches=0.0)                                                         \n                    if save_xml:    \n                            h,w=im.shape[:2]\n                            save_format(\"xml\",f'{save_dir}/xmls' ,name_p, Path(source).parent,\n                                preds, h,w)\n            if save_txt:\n                #class_ids.append(cls) \n                LOGGER.info(f\"Results saved to {colorstr('bold', save_dir)}/labels\")  \n            if save_xml:           \n                LOGGER.info(f\"Results saved to {colorstr('bold', save_dir)}/xmls\")\n            if save_caption:     \n                with open(f'{save_dir}/dataset.json', 'a',encoding='utf-8') as f: \n                    json.dump(JSON_DATASETS,f,ensure_ascii=False) \n                    f.write('\\n')\n                    LOGGER.info(f\"Results saved to {colorstr('bold', save_dir)}/captions\")\n                LOGGER.info(f\"Results saved to {colorstr('bold', save_dir)}/captions\")\n            if save_mask:\n                LOGGER.info(f\"Results saved to {colorstr('bold', save_dir)}/masks\")\n                   \ndef run_do(shared_args,process_name=0):\n    \n    Auto_run(**vars(shared_args), process_name=process_name)\n\ndef parse_opt():\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'your model path', help='model path(s)')\n    parser.add_argument('--source', type=str, default=ROOT / 'train_imgs', help='file/dir/URL/glob, 0 for webcam')\n    parser.add_argument('--input_prompt', type=str, default='', help='provide prompt words')\n    parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='(optional) dataset.yaml path')\n    parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640], help='inference size h,w')\n    parser.add_argument('--conf-thres', type=float, default=0.25, help='confidence threshold')\n    parser.add_argument('--text-thres', type=float, default=0.3, help='confidence threshold')\n    parser.add_argument('--iou-thres', type=float, default=0.5, help='NMS IoU threshold')\n    parser.add_argument('--max-det', type=int, default=1000, help='maximum detections per image')\n    parser.add_argument('--device', default='0', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')\n    parser.add_argument('--view-img', action='store_true', help='show results')\n    parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')\n    parser.add_argument('--save-xml', action='store_true', help='save results to *.xml')\n    parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')\n    parser.add_argument('--save-crop', action='store_true', help='save cropped prediction boxes')\n    parser.add_argument('--nosave', action='store_true', help='do not save images/videos')\n    parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --classes 0, or --classes 0 2 3')\n    parser.add_argument('--zh_select', action='store_true', default=False)\n    parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')\n    parser.add_argument('--augment', action='store_true', help='augmented inference')\n    parser.add_argument('--visualize', action='store_true', help='visualize features')\n    parser.add_argument('--update', action='store_true', help='update all models')\n    parser.add_argument('--project', default=ROOT / 'runs/detect', help='save results to project/name')\n    parser.add_argument('--name', default='exp', help='save results to project/name')\n    parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')\n    parser.add_argument('--line-thickness', default=3, type=int, help='bounding box thickness (pixels)')\n    parser.add_argument('--hide-labels', default=False, action='store_true', help='hide labels')\n    parser.add_argument('--hide-conf', default=False, action='store_true', help='hide confidences')\n    parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')\n    parser.add_argument('--trace', action='store_true', help='trace model')\n    parser.add_argument('--lama',default=False, action='store_true', help='lama model')\n    parser.add_argument('--sam', default=False,action='store_true', help='seg model')\n    parser.add_argument('--det',default=False, action='store_true', help='det model')\n    parser.add_argument('--tag2text', default=True,action='store_true', help='tag2text model ')\n    parser.add_argument('--save-mask', default=False,action='store_true', help='mask save json')\n    parser.add_argument('--save-caption', default=True,action='store_true', help='caption ')\n    parser.add_argument('--batch-process', action='store_true', help='therads process file')\n    parser.add_argument('--color-flag', action='store_true', help='class-color ')\n    parser.add_argument('--gligen', action='store_true', help='class-color ')\n   \n    opt = parser.parse_args()\n    print_args(vars(opt))\n    return opt\n\nimport threading\nimport concurrent.futures\ndef main(opt):\n    \n    check_requirements(exclude=('tensorboard', 'thop'))\n    global models_config  \n\n    # if  not opt.input_prompt and opt.input_prompt=='':\n    #     LOGGER.info(' input prompt')\n    #     words_name= input(\"please your prompt words: \")\n    #     opt.input_prompt=words_name\n        \n    load_auto_backend_models(opt)\n    LOGGER.info(f\"模型加载成功{models_config.keys()}\")\n    if opt.batch_process and os.path.isdir(opt.source):\n        #检查目录是否存在以及检查是否为目录的操作\n        if not os.path.exists(opt.source):\n            LOGGER.info(f\"Error: Input directory {opt.source} does not exist.\")\n            return \n        seen=0\n       # output_dir=f'{opt.source}_subs{seen}'\n        segment_size =100\n        for file_name in opt.source:\n            file_path = os.path.join(opt.source, file_name)\n            # pass \n            if not  Path(file_path).suffix[1:] in (IMG_FORMATS + VID_FORMATS):\n                continue\n            # 使用Pillow库读取图像文件并将其转换为NumPy数组\n            img = Image.open(file_path)\n            img_array = np.asarray(img)\n\n            # 多线程处理每个图像段\n            with concurrent.futures.ThreadPoolExecutor() as executor:\n                futures = []  # 用于保存每个线程的未来对象\n\n                # 分段并发读取并进行处理\n                for i in range(0, img_array.shape[0], segment_size):\n                    start_row = i\n                    end_row = min(i + segment_size, img_array.shape[0])\n                    future = executor.submit(run_do, img_array, start_row, end_row)\n                    futures.append(future)\n\n                # 获取所有未来对象的结果\n                for future in concurrent.futures.as_completed(futures):\n                    segment = future.result()\n    else:\n        Auto_run(**vars(opt))\n\nif __name__ == \"__main__\":\n    opt = parse_opt()\n    main(opt)"
  },
  {
    "path": "batch_clean_gpu.txt",
    "content": "sudo fuser -v /dev/nvidia* |awk '{for(i=1;i<=NF;i++)print \"kill -9 \" $i;}' | sudo sh\n"
  },
  {
    "path": "crazy_functions/Langchain知识库.py",
    "content": "from utils.toolbox import CatchException, update_ui, ProxyNetworkActivate\nfrom .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive, get_files_from_everything\n\n\n\n@CatchException\ndef 知识库问答(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):\n    \"\"\"\n    txt             输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径\n    llm_kwargs      gpt模型参数, 如温度和top_p等, 一般原样传递下去就行\n    plugin_kwargs   插件模型的参数，暂时没有用武之地\n    chatbot         聊天显示框的句柄，用于显示给用户\n    history         聊天历史，前情提要\n    system_prompt   给gpt的静默提醒\n    web_port        当前软件运行的端口号\n    \"\"\"\n    history = []    # 清空历史，以免输入溢出\n    chatbot.append((\"这是什么功能？\", \"[Local Message] 从一批文件(txt, md, tex)中读取数据构建知识库, 然后进行问答。\"))\n    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n\n    # resolve deps\n    try:\n        from zh_langchain import construct_vector_store\n        from langchain.embeddings.huggingface import HuggingFaceEmbeddings\n        from .crazy_utils import knowledge_archive_interface\n    except Exception as e:\n        chatbot.append(\n            [\"依赖不足\", \n             \"导入依赖失败。正在尝试自动安装，请查看终端的输出或耐心等待...\"]\n        )\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        from .crazy_utils import try_install_deps\n        try_install_deps(['zh_langchain==0.2.1', 'pypinyin'])\n    \n    # < --------------------读取参数--------------- >\n    if (\"advanced_arg\" in plugin_kwargs) and (plugin_kwargs[\"advanced_arg\"] == \"\"): plugin_kwargs.pop(\"advanced_arg\")\n    kai_id = plugin_kwargs.get(\"advanced_arg\", 'default')\n\n    # < --------------------读取文件--------------- >\n    file_manifest = []\n    spl = [\"txt\", \"doc\", \"docx\", \"email\", \"epub\", \"html\", \"json\", \"md\", \"msg\", \"pdf\", \"ppt\", \"pptx\", \"rtf\"]\n    for sp in spl:\n        _, file_manifest_tmp, _ = get_files_from_everything(txt, type=f'.{sp}')\n        file_manifest += file_manifest_tmp\n    \n    if len(file_manifest) == 0:\n        chatbot.append([\"没有找到任何可读取文件\", \"当前支持的格式包括: txt, md, docx, pptx, pdf, json等\"])\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n    \n    # < -------------------预热文本向量化模组--------------- >\n    chatbot.append(['<br/>'.join(file_manifest), \"正在预热文本向量化模组, 如果是第一次运行, 将消耗较长时间下载中文向量化模型...\"])\n    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n    print('Checking Text2vec ...')\n    from langchain.embeddings.huggingface import HuggingFaceEmbeddings\n    with ProxyNetworkActivate():    # 临时地激活代理网络\n        HuggingFaceEmbeddings(model_name=\"GanymedeNil/text2vec-large-chinese\")\n\n    # < -------------------构建知识库--------------- >\n    chatbot.append(['<br/>'.join(file_manifest), \"正在构建知识库...\"])\n    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n    print('Establishing knowledge archive ...')\n    with ProxyNetworkActivate():    # 临时地激活代理网络\n        kai = knowledge_archive_interface()\n        kai.feed_archive(file_manifest=file_manifest, id=kai_id)\n    kai_files = kai.get_loaded_file()\n    kai_files = '<br/>'.join(kai_files)\n    # chatbot.append(['知识库构建成功', \"正在将知识库存储至cookie中\"])\n    # yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n    # chatbot._cookies['langchain_plugin_embedding'] = kai.get_current_archive_id()\n    # chatbot._cookies['lock_plugin'] = 'crazy_functions.Langchain知识库->读取知识库作答'\n    # chatbot.append(['完成', \"“根据知识库作答”函数插件已经接管问答系统, 提问吧! 但注意, 您接下来不能再使用其他插件了，刷新页面即可以退出知识库问答模式。\"])\n    chatbot.append(['构建完成', f\"当前知识库内的有效文件：\\n\\n---\\n\\n{kai_files}\\n\\n---\\n\\n请切换至“知识库问答”插件进行知识库访问, 或者使用此插件继续上传更多文件。\"])\n    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新\n\n@CatchException\ndef 读取知识库作答(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port=-1):\n    # resolve deps\n    try:\n        from zh_langchain import construct_vector_store\n        from langchain.embeddings.huggingface import HuggingFaceEmbeddings\n        from .crazy_utils import knowledge_archive_interface\n    except Exception as e:\n        chatbot.append([\"依赖不足\", \"导入依赖失败。正在尝试自动安装，请查看终端的输出或耐心等待...\"])\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        from .crazy_utils import try_install_deps\n        try_install_deps(['zh_langchain==0.2.1'])\n\n    # < -------------------  --------------- >\n    kai = knowledge_archive_interface()\n\n    if 'langchain_plugin_embedding' in chatbot._cookies:\n        resp, prompt = kai.answer_with_archive_by_id(txt, chatbot._cookies['langchain_plugin_embedding'])\n    else:\n        if (\"advanced_arg\" in plugin_kwargs) and (plugin_kwargs[\"advanced_arg\"] == \"\"): plugin_kwargs.pop(\"advanced_arg\")\n        kai_id = plugin_kwargs.get(\"advanced_arg\", 'default')\n        resp, prompt = kai.answer_with_archive_by_id(txt, kai_id)\n\n    chatbot.append((txt, '[Local Message] ' + prompt))\n    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新\n    gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(\n        inputs=prompt, inputs_show_user=txt, \n        llm_kwargs=llm_kwargs, chatbot=chatbot, history=[], \n        sys_prompt=system_prompt\n    )\n    history.extend((prompt, gpt_say))\n    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新\n"
  },
  {
    "path": "crazy_functions/Latex全文润色.py",
    "content": "from utils.toolbox import update_ui, trimmed_format_exc\nfrom utils.toolbox import CatchException, report_execption, write_results_to_file, zip_folder\n\n\nclass PaperFileGroup():\n    def __init__(self):\n        self.file_paths = []\n        self.file_contents = []\n        self.sp_file_contents = []\n        self.sp_file_index = []\n        self.sp_file_tag = []\n\n        # count_token\n        from llm_cards.bridge_all import model_info\n        enc = model_info[\"gpt-3.5-turbo\"]['tokenizer']\n        def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))\n        self.get_token_num = get_token_num\n\n    def run_file_split(self, max_token_limit=1900):\n        \"\"\"\n        将长文本分离开来\n        \"\"\"\n        for index, file_content in enumerate(self.file_contents):\n            if self.get_token_num(file_content) < max_token_limit:\n                self.sp_file_contents.append(file_content)\n                self.sp_file_index.append(index)\n                self.sp_file_tag.append(self.file_paths[index])\n            else:\n                from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf\n                segments = breakdown_txt_to_satisfy_token_limit_for_pdf(file_content, self.get_token_num, max_token_limit)\n                for j, segment in enumerate(segments):\n                    self.sp_file_contents.append(segment)\n                    self.sp_file_index.append(index)\n                    self.sp_file_tag.append(self.file_paths[index] + f\".part-{j}.tex\")\n\n        print('Segmentation: done')\n    def merge_result(self):\n        self.file_result = [\"\" for _ in range(len(self.file_paths))]\n        for r, k in zip(self.sp_file_result, self.sp_file_index):\n            self.file_result[k] += r\n\n    def write_result(self):\n        manifest = []\n        for path, res in zip(self.file_paths, self.file_result):\n            with open(path + '.polish.tex', 'w', encoding='utf8') as f:\n                manifest.append(path + '.polish.tex')\n                f.write(res)\n        return manifest\n    \n    def zip_result(self):\n        import os, time\n        folder = os.path.dirname(self.file_paths[0])\n        t = time.strftime(\"%Y-%m-%d-%H-%M-%S\", time.localtime())\n        zip_folder(folder, './gpt_log/', f'{t}-polished.zip')\n\n\ndef 多文件润色(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='en', mode='polish'):\n    import time, os, re\n    from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency\n\n\n    #  <-------- 读取Latex文件，删除其中的所有注释 ----------> \n    pfg = PaperFileGroup()\n\n    for index, fp in enumerate(file_manifest):\n        with open(fp, 'r', encoding='utf-8', errors='replace') as f:\n            file_content = f.read()\n            # 定义注释的正则表达式\n            comment_pattern = r'(?<!\\\\)%.*'\n            # 使用正则表达式查找注释，并替换为空字符串\n            clean_tex_content = re.sub(comment_pattern, '', file_content)\n            # 记录删除注释后的文本\n            pfg.file_paths.append(fp)\n            pfg.file_contents.append(clean_tex_content)\n\n    #  <-------- 拆分过长的latex文件 ----------> \n    pfg.run_file_split(max_token_limit=1024)\n    n_split = len(pfg.sp_file_contents)\n\n\n    #  <-------- 多线程润色开始 ----------> \n    if language == 'en':\n        if mode == 'polish':\n            inputs_array = [\"Below is a section from an academic paper, polish this section to meet the academic standard, \" + \n                            \"improve the grammar, clarity and overall readability, do not modify any latex command such as \\section, \\cite and equations:\" + \n                            f\"\\n\\n{frag}\" for frag in pfg.sp_file_contents]\n        else:\n            inputs_array = [r\"Below is a section from an academic paper, proofread this section.\" + \n                            r\"Do not modify any latex command such as \\section, \\cite, \\begin, \\item and equations. \" + \n                            r\"Answer me only with the revised text:\" + \n                        f\"\\n\\n{frag}\" for frag in pfg.sp_file_contents]\n        inputs_show_user_array = [f\"Polish {f}\" for f in pfg.sp_file_tag]\n        sys_prompt_array = [\"You are a professional academic paper writer.\" for _ in range(n_split)]\n    elif language == 'zh':\n        if mode == 'polish':\n            inputs_array = [f\"以下是一篇学术论文中的一段内容，请将此部分润色以满足学术标准，提高语法、清晰度和整体可读性，不要修改任何LaTeX命令，例如\\section，\\cite和方程式：\" + \n                            f\"\\n\\n{frag}\" for frag in pfg.sp_file_contents]\n        else:\n            inputs_array = [f\"以下是一篇学术论文中的一段内容，请对这部分内容进行语法矫正。不要修改任何LaTeX命令，例如\\section，\\cite和方程式：\" + \n                            f\"\\n\\n{frag}\" for frag in pfg.sp_file_contents] \n        inputs_show_user_array = [f\"润色 {f}\" for f in pfg.sp_file_tag]\n        sys_prompt_array=[\"你是一位专业的中文学术论文作家。\" for _ in range(n_split)]\n\n\n    gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(\n        inputs_array=inputs_array,\n        inputs_show_user_array=inputs_show_user_array,\n        llm_kwargs=llm_kwargs,\n        chatbot=chatbot,\n        history_array=[[\"\"] for _ in range(n_split)],\n        sys_prompt_array=sys_prompt_array,\n        # max_workers=5,  # 并行任务数量限制，最多同时执行5个，其他的排队等待\n        scroller_max_len = 80\n    )\n\n    #  <-------- 文本碎片重组为完整的tex文件，整理结果为压缩包 ----------> \n    try:\n        pfg.sp_file_result = []\n        for i_say, gpt_say in zip(gpt_response_collection[0::2], gpt_response_collection[1::2]):\n            pfg.sp_file_result.append(gpt_say)\n        pfg.merge_result()\n        pfg.write_result()\n        pfg.zip_result()\n    except:\n        print(trimmed_format_exc())\n\n    #  <-------- 整理结果，退出 ----------> \n    create_report_file_name = time.strftime(\"%Y-%m-%d-%H-%M-%S\", time.localtime()) + f\"-chatgpt.polish.md\"\n    res = write_results_to_file(gpt_response_collection, file_name=create_report_file_name)\n    history = gpt_response_collection\n    chatbot.append((f\"{fp}完成了吗？\", res))\n    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n\n\n@CatchException\ndef Latex英文润色(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):\n    # 基本信息：功能、贡献者\n    chatbot.append([\n        \"函数插件功能？\",\n        \"对整个Latex项目进行润色。函数插件贡献者: Binary-Husky\"])\n    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n\n    # 尝试导入依赖，如果缺少依赖，则给出安装建议\n    try:\n        import tiktoken\n    except:\n        report_execption(chatbot, history,\n                         a=f\"解析项目: {txt}\",\n                         b=f\"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade tiktoken```。\")\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n    history = []    # 清空历史，以免输入溢出\n    import glob, os\n    if os.path.exists(txt):\n        project_folder = txt\n    else:\n        if txt == \"\": txt = '空空如也的输入栏'\n        report_execption(chatbot, history, a = f\"解析项目: {txt}\", b = f\"找不到本地项目或无权访问: {txt}\")\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)]\n    if len(file_manifest) == 0:\n        report_execption(chatbot, history, a = f\"解析项目: {txt}\", b = f\"找不到任何.tex文件: {txt}\")\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n    yield from 多文件润色(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='en')\n\n\n\n\n\n\n@CatchException\ndef Latex中文润色(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):\n    # 基本信息：功能、贡献者\n    chatbot.append([\n        \"函数插件功能？\",\n        \"对整个Latex项目进行润色。函数插件贡献者: Binary-Husky\"])\n    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n\n    # 尝试导入依赖，如果缺少依赖，则给出安装建议\n    try:\n        import tiktoken\n    except:\n        report_execption(chatbot, history,\n                         a=f\"解析项目: {txt}\",\n                         b=f\"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade tiktoken```。\")\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n    history = []    # 清空历史，以免输入溢出\n    import glob, os\n    if os.path.exists(txt):\n        project_folder = txt\n    else:\n        if txt == \"\": txt = '空空如也的输入栏'\n        report_execption(chatbot, history, a = f\"解析项目: {txt}\", b = f\"找不到本地项目或无权访问: {txt}\")\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)]\n    if len(file_manifest) == 0:\n        report_execption(chatbot, history, a = f\"解析项目: {txt}\", b = f\"找不到任何.tex文件: {txt}\")\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n    yield from 多文件润色(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='zh')\n\n\n\n\n@CatchException\ndef Latex英文纠错(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):\n    # 基本信息：功能、贡献者\n    chatbot.append([\n        \"函数插件功能？\",\n        \"对整个Latex项目进行纠错。函数插件贡献者: Binary-Husky\"])\n    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n\n    # 尝试导入依赖，如果缺少依赖，则给出安装建议\n    try:\n        import tiktoken\n    except:\n        report_execption(chatbot, history,\n                         a=f\"解析项目: {txt}\",\n                         b=f\"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade tiktoken```。\")\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n    history = []    # 清空历史，以免输入溢出\n    import glob, os\n    if os.path.exists(txt):\n        project_folder = txt\n    else:\n        if txt == \"\": txt = '空空如也的输入栏'\n        report_execption(chatbot, history, a = f\"解析项目: {txt}\", b = f\"找不到本地项目或无权访问: {txt}\")\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)]\n    if len(file_manifest) == 0:\n        report_execption(chatbot, history, a = f\"解析项目: {txt}\", b = f\"找不到任何.tex文件: {txt}\")\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n    yield from 多文件润色(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='en', mode='proofread')\n\n\n\n"
  },
  {
    "path": "crazy_functions/Latex全文翻译.py",
    "content": "from utils.toolbox import update_ui\nfrom utils.toolbox import CatchException, report_execption, write_results_to_file\nfast_debug = False\n\nclass PaperFileGroup():\n    def __init__(self):\n        self.file_paths = []\n        self.file_contents = []\n        self.sp_file_contents = []\n        self.sp_file_index = []\n        self.sp_file_tag = []\n\n        # count_token\n        from llm_cards.bridge_all import model_info\n        enc = model_info[\"gpt-3.5-turbo\"]['tokenizer']\n        def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))\n        self.get_token_num = get_token_num\n\n    def run_file_split(self, max_token_limit=1900):\n        \"\"\"\n        将长文本分离开来\n        \"\"\"\n        for index, file_content in enumerate(self.file_contents):\n            if self.get_token_num(file_content) < max_token_limit:\n                self.sp_file_contents.append(file_content)\n                self.sp_file_index.append(index)\n                self.sp_file_tag.append(self.file_paths[index])\n            else:\n                from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf\n                segments = breakdown_txt_to_satisfy_token_limit_for_pdf(file_content, self.get_token_num, max_token_limit)\n                for j, segment in enumerate(segments):\n                    self.sp_file_contents.append(segment)\n                    self.sp_file_index.append(index)\n                    self.sp_file_tag.append(self.file_paths[index] + f\".part-{j}.tex\")\n\n        print('Segmentation: done')\n\ndef 多文件翻译(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='en'):\n    import time, os, re\n    from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency\n\n    #  <-------- 读取Latex文件，删除其中的所有注释 ----------> \n    pfg = PaperFileGroup()\n\n    for index, fp in enumerate(file_manifest):\n        with open(fp, 'r', encoding='utf-8', errors='replace') as f:\n            file_content = f.read()\n            # 定义注释的正则表达式\n            comment_pattern = r'(?<!\\\\)%.*'\n            # 使用正则表达式查找注释，并替换为空字符串\n            clean_tex_content = re.sub(comment_pattern, '', file_content)\n            # 记录删除注释后的文本\n            pfg.file_paths.append(fp)\n            pfg.file_contents.append(clean_tex_content)\n\n    #  <-------- 拆分过长的latex文件 ----------> \n    pfg.run_file_split(max_token_limit=1024)\n    n_split = len(pfg.sp_file_contents)\n\n    #  <-------- 抽取摘要 ----------> \n    # if language == 'en':\n    #     abs_extract_inputs = f\"Please write an abstract for this paper\"\n\n    # # 单线，获取文章meta信息\n    # paper_meta_info = yield from request_gpt_model_in_new_thread_with_ui_alive(\n    #     inputs=abs_extract_inputs,\n    #     inputs_show_user=f\"正在抽取摘要信息。\",\n    #     llm_kwargs=llm_kwargs,\n    #     chatbot=chatbot, history=[],\n    #     sys_prompt=\"Your job is to collect information from materials。\",\n    # )\n\n    #  <-------- 多线程润色开始 ----------> \n    if language == 'en->zh':\n        inputs_array = [\"Below is a section from an English academic paper, translate it into Chinese, do not modify any latex command such as \\section, \\cite and equations:\" + \n                        f\"\\n\\n{frag}\" for frag in pfg.sp_file_contents]\n        inputs_show_user_array = [f\"翻译 {f}\" for f in pfg.sp_file_tag]\n        sys_prompt_array = [\"You are a professional academic paper translator.\" for _ in range(n_split)]\n    elif language == 'zh->en':\n        inputs_array = [f\"Below is a section from a Chinese academic paper, translate it into English, do not modify any latex command such as \\section, \\cite and equations:\" + \n                        f\"\\n\\n{frag}\" for frag in pfg.sp_file_contents]\n        inputs_show_user_array = [f\"翻译 {f}\" for f in pfg.sp_file_tag]\n        sys_prompt_array = [\"You are a professional academic paper translator.\" for _ in range(n_split)]\n\n    gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(\n        inputs_array=inputs_array,\n        inputs_show_user_array=inputs_show_user_array,\n        llm_kwargs=llm_kwargs,\n        chatbot=chatbot,\n        history_array=[[\"\"] for _ in range(n_split)],\n        sys_prompt_array=sys_prompt_array,\n        # max_workers=5,  # OpenAI所允许的最大并行过载\n        scroller_max_len = 80\n    )\n\n    #  <-------- 整理结果，退出 ----------> \n    create_report_file_name = time.strftime(\"%Y-%m-%d-%H-%M-%S\", time.localtime()) + f\"-chatgpt.polish.md\"\n    res = write_results_to_file(gpt_response_collection, file_name=create_report_file_name)\n    history = gpt_response_collection\n    chatbot.append((f\"{fp}完成了吗？\", res))\n    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n\n\n\n\n\n@CatchException\ndef Latex英译中(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):\n    # 基本信息：功能、贡献者\n    chatbot.append([\n        \"函数插件功能？\",\n        \"对整个Latex项目进行翻译。函数插件贡献者: Binary-Husky\"])\n    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n\n    # 尝试导入依赖，如果缺少依赖，则给出安装建议\n    try:\n        import tiktoken\n    except:\n        report_execption(chatbot, history,\n                         a=f\"解析项目: {txt}\",\n                         b=f\"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade tiktoken```。\")\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n    history = []    # 清空历史，以免输入溢出\n    import glob, os\n    if os.path.exists(txt):\n        project_folder = txt\n    else:\n        if txt == \"\": txt = '空空如也的输入栏'\n        report_execption(chatbot, history, a = f\"解析项目: {txt}\", b = f\"找不到本地项目或无权访问: {txt}\")\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)]\n    if len(file_manifest) == 0:\n        report_execption(chatbot, history, a = f\"解析项目: {txt}\", b = f\"找不到任何.tex文件: {txt}\")\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n    yield from 多文件翻译(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='en->zh')\n\n\n\n\n\n@CatchException\ndef Latex中译英(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):\n    # 基本信息：功能、贡献者\n    chatbot.append([\n        \"函数插件功能？\",\n        \"对整个Latex项目进行翻译。函数插件贡献者: Binary-Husky\"])\n    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n\n    # 尝试导入依赖，如果缺少依赖，则给出安装建议\n    try:\n        import tiktoken\n    except:\n        report_execption(chatbot, history,\n                         a=f\"解析项目: {txt}\",\n                         b=f\"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade tiktoken```。\")\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n    history = []    # 清空历史，以免输入溢出\n    import glob, os\n    if os.path.exists(txt):\n        project_folder = txt\n    else:\n        if txt == \"\": txt = '空空如也的输入栏'\n        report_execption(chatbot, history, a = f\"解析项目: {txt}\", b = f\"找不到本地项目或无权访问: {txt}\")\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)]\n    if len(file_manifest) == 0:\n        report_execption(chatbot, history, a = f\"解析项目: {txt}\", b = f\"找不到任何.tex文件: {txt}\")\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n    yield from 多文件翻译(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='zh->en')"
  },
  {
    "path": "crazy_functions/Latex输出PDF结果.py",
    "content": "from utils.toolbox import update_ui, trimmed_format_exc, get_conf, objdump, objload, promote_file_to_downloadzone\nfrom utils.toolbox import CatchException, report_execption, update_ui_lastest_msg, zip_result, gen_time_str\nfrom functools import partial\nimport glob, os, requests, time\npj = os.path.join\nARXIV_CACHE_DIR = os.path.expanduser(f\"~/arxiv_cache/\")\n\n# =================================== 工具函数 ===============================================\n专业词汇声明  = 'If the term \"agent\" is used in this section, it should be translated to \"智能体\". '\ndef switch_prompt(pfg, mode, more_requirement):\n    \"\"\"\n    Generate prompts and system prompts based on the mode for proofreading or translating.\n    Args:\n    - pfg: Proofreader or Translator instance.\n    - mode: A string specifying the mode, either 'proofread' or 'translate_zh'.\n\n    Returns:\n    - inputs_array: A list of strings containing prompts for users to respond to.\n    - sys_prompt_array: A list of strings containing prompts for system prompts.\n    \"\"\"\n    n_split = len(pfg.sp_file_contents)\n    if mode == 'proofread_en':\n        inputs_array = [r\"Below is a section from an academic paper, proofread this section.\" + \n                        r\"Do not modify any latex command such as \\section, \\cite, \\begin, \\item and equations. \" + more_requirement +\n                        r\"Answer me only with the revised text:\" + \n                        f\"\\n\\n{frag}\" for frag in pfg.sp_file_contents]\n        sys_prompt_array = [\"You are a professional academic paper writer.\" for _ in range(n_split)]\n    elif mode == 'translate_zh':\n        inputs_array = [r\"Below is a section from an English academic paper, translate it into Chinese. \" + more_requirement + \n                        r\"Do not modify any latex command such as \\section, \\cite, \\begin, \\item and equations. \" + \n                        r\"Answer me only with the translated text:\" + \n                        f\"\\n\\n{frag}\" for frag in pfg.sp_file_contents]\n        sys_prompt_array = [\"You are a professional translator.\" for _ in range(n_split)]\n    else:\n        assert False, \"未知指令\"\n    return inputs_array, sys_prompt_array\n\ndef desend_to_extracted_folder_if_exist(project_folder):\n    \"\"\" \n    Descend into the extracted folder if it exists, otherwise return the original folder.\n\n    Args:\n    - project_folder: A string specifying the folder path.\n\n    Returns:\n    - A string specifying the path to the extracted folder, or the original folder if there is no extracted folder.\n    \"\"\"\n    maybe_dir = [f for f in glob.glob(f'{project_folder}/*') if os.path.isdir(f)]\n    if len(maybe_dir) == 0: return project_folder\n    if maybe_dir[0].endswith('.extract'): return maybe_dir[0]\n    return project_folder\n\ndef move_project(project_folder, arxiv_id=None):\n    \"\"\" \n    Create a new work folder and copy the project folder to it.\n\n    Args:\n    - project_folder: A string specifying the folder path of the project.\n\n    Returns:\n    - A string specifying the path to the new work folder.\n    \"\"\"\n    import shutil, time\n    time.sleep(2)   # avoid time string conflict\n    if arxiv_id is not None:\n        new_workfolder = pj(ARXIV_CACHE_DIR, arxiv_id, 'workfolder')\n    else:\n        new_workfolder = f'gpt_log/{gen_time_str()}'\n    try:\n        shutil.rmtree(new_workfolder)\n    except:\n        pass\n\n    # align subfolder if there is a folder wrapper\n    items = glob.glob(pj(project_folder,'*'))\n    if len(glob.glob(pj(project_folder,'*.tex'))) == 0 and len(items) == 1:\n        if os.path.isdir(items[0]): project_folder = items[0]\n\n    shutil.copytree(src=project_folder, dst=new_workfolder)\n    return new_workfolder\n\ndef arxiv_download(chatbot, history, txt):\n    def check_cached_translation_pdf(arxiv_id):\n        translation_dir = pj(ARXIV_CACHE_DIR, arxiv_id, 'translation')\n        if not os.path.exists(translation_dir):\n            os.makedirs(translation_dir)\n        target_file = pj(translation_dir, 'translate_zh.pdf')\n        if os.path.exists(target_file):\n            promote_file_to_downloadzone(target_file, rename_file=None, chatbot=chatbot)\n            return target_file\n        return False\n    def is_float(s):\n        try:\n            float(s)\n            return True\n        except ValueError:\n            return False\n    if ('.' in txt) and ('/' not in txt) and is_float(txt): # is arxiv ID\n        txt = 'https://arxiv.org/abs/' + txt.strip()\n    if ('.' in txt) and ('/' not in txt) and is_float(txt[:10]): # is arxiv ID\n        txt = 'https://arxiv.org/abs/' + txt[:10]\n    if not txt.startswith('https://arxiv.org'): \n        return txt, None\n    \n    # <-------------- inspect format ------------->\n    chatbot.append([f\"检测到arxiv文档连接\", '尝试下载 ...']) \n    yield from update_ui(chatbot=chatbot, history=history)\n    time.sleep(1) # 刷新界面\n\n    url_ = txt   # https://arxiv.org/abs/1707.06690\n    if not txt.startswith('https://arxiv.org/abs/'): \n        msg = f\"解析arxiv网址失败, 期望格式例如: https://arxiv.org/abs/1707.06690。实际得到格式: {url_}\"\n        yield from update_ui_lastest_msg(msg, chatbot=chatbot, history=history) # 刷新界面\n        return msg, None\n    # <-------------- set format ------------->\n    arxiv_id = url_.split('/abs/')[-1]\n    if 'v' in arxiv_id: arxiv_id = arxiv_id[:10]\n    cached_translation_pdf = check_cached_translation_pdf(arxiv_id)\n    if cached_translation_pdf: return cached_translation_pdf, arxiv_id\n\n    url_tar = url_.replace('/abs/', '/e-print/')\n    translation_dir = pj(ARXIV_CACHE_DIR, arxiv_id, 'e-print')\n    extract_dst = pj(ARXIV_CACHE_DIR, arxiv_id, 'extract')\n    os.makedirs(translation_dir, exist_ok=True)\n    \n    # <-------------- download arxiv source file ------------->\n    dst = pj(translation_dir, arxiv_id+'.tar')\n    if os.path.exists(dst):\n        yield from update_ui_lastest_msg(\"调用缓存\", chatbot=chatbot, history=history)  # 刷新界面\n    else:\n        yield from update_ui_lastest_msg(\"开始下载\", chatbot=chatbot, history=history)  # 刷新界面\n        proxies, = get_conf('proxies')\n        r = requests.get(url_tar, proxies=proxies)\n        with open(dst, 'wb+') as f:\n            f.write(r.content)\n    # <-------------- extract file ------------->\n    yield from update_ui_lastest_msg(\"下载完成\", chatbot=chatbot, history=history)  # 刷新界面\n    from utils.toolbox import extract_archive\n    extract_archive(file_path=dst, dest_dir=extract_dst)\n    return extract_dst, arxiv_id\n# ========================================= 插件主程序1 =====================================================    \n\n\n@CatchException\ndef Latex英文纠错加PDF对比(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):\n    # <-------------- information about this plugin ------------->\n    chatbot.append([ \"函数插件功能？\",\n        \"对整个Latex项目进行纠错, 用latex编译为PDF对修正处做高亮。函数插件贡献者: Binary-Husky。注意事项: 目前仅支持GPT3.5/GPT4，其他模型转化效果未知。目前对机器学习类文献转化效果最好，其他类型文献转化效果未知。仅在Windows系统进行了测试，其他操作系统表现未知。\"])\n    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n    \n    # <-------------- more requirements ------------->\n    if (\"advanced_arg\" in plugin_kwargs) and (plugin_kwargs[\"advanced_arg\"] == \"\"): plugin_kwargs.pop(\"advanced_arg\")\n    more_req = plugin_kwargs.get(\"advanced_arg\", \"\")\n    _switch_prompt_ = partial(switch_prompt, more_requirement=more_req)\n\n    # <-------------- check deps ------------->\n    try:\n        import glob, os, time, subprocess\n        subprocess.Popen(['pdflatex', '-version'])\n        from .latex_fns.latex_actions import Latex精细分解与转化, 编译Latex\n    except Exception as e:\n        chatbot.append([ f\"解析项目: {txt}\",\n            f\"尝试执行Latex指令失败。Latex没有安装, 或者不在环境变量PATH中。安装方法https://tug.org/texlive/。报错信息\\n\\n```\\n\\n{trimmed_format_exc()}\\n\\n```\\n\\n\"])\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n    \n\n    # <-------------- clear history and read input ------------->\n    history = []\n    if os.path.exists(txt):\n        project_folder = txt\n    else:\n        if txt == \"\": txt = '空空如也的输入栏'\n        report_execption(chatbot, history, a = f\"解析项目: {txt}\", b = f\"找不到本地项目或无权访问: {txt}\")\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)]\n    if len(file_manifest) == 0:\n        report_execption(chatbot, history, a = f\"解析项目: {txt}\", b = f\"找不到任何.tex文件: {txt}\")\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n    \n\n    # <-------------- if is a zip/tar file ------------->\n    project_folder = desend_to_extracted_folder_if_exist(project_folder)\n\n\n    # <-------------- move latex project away from temp folder ------------->\n    project_folder = move_project(project_folder, arxiv_id=None)\n\n\n    # <-------------- if merge_translate_zh is already generated, skip gpt req ------------->\n    if not os.path.exists(project_folder + '/merge_proofread_en.tex'):\n        yield from Latex精细分解与转化(file_manifest, project_folder, llm_kwargs, plugin_kwargs, \n                                chatbot, history, system_prompt, mode='proofread_en', switch_prompt=_switch_prompt_)\n\n\n    # <-------------- compile PDF ------------->\n    success = yield from 编译Latex(chatbot, history, main_file_original='merge', main_file_modified='merge_proofread_en', \n                             work_folder_original=project_folder, work_folder_modified=project_folder, work_folder=project_folder)\n    \n\n    # <-------------- zip PDF ------------->\n    zip_res = zip_result(project_folder)\n    if success:\n        chatbot.append((f\"成功啦\", '请查收结果（压缩包）...'))\n        yield from update_ui(chatbot=chatbot, history=history); time.sleep(1) # 刷新界面\n        promote_file_to_downloadzone(file=zip_res, chatbot=chatbot)\n    else:\n        chatbot.append((f\"失败了\", '虽然PDF生成失败了, 但请查收结果（压缩包）, 内含已经翻译的Tex文档, 也是可读的, 您可以到Github Issue区, 用该压缩包+对话历史存档进行反馈 ...'))\n        yield from update_ui(chatbot=chatbot, history=history); time.sleep(1) # 刷新界面\n        promote_file_to_downloadzone(file=zip_res, chatbot=chatbot)\n\n    # <-------------- we are done ------------->\n    return success\n\n\n# ========================================= 插件主程序2 =====================================================    \n\n@CatchException\ndef Latex翻译中文并重新编译PDF(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):\n    # <-------------- information about this plugin ------------->\n    chatbot.append([\n        \"函数插件功能？\",\n        \"对整个Latex项目进行翻译, 生成中文PDF。函数插件贡献者: Binary-Husky。注意事项: 此插件Windows支持最佳，Linux下必须使用Docker安装，详见项目主README.md。目前仅支持GPT3.5/GPT4，其他模型转化效果未知。目前对机器学习类文献转化效果最好，其他类型文献转化效果未知。\"])\n    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n\n    # <-------------- more requirements ------------->\n    if (\"advanced_arg\" in plugin_kwargs) and (plugin_kwargs[\"advanced_arg\"] == \"\"): plugin_kwargs.pop(\"advanced_arg\")\n    more_req = plugin_kwargs.get(\"advanced_arg\", \"\")\n    _switch_prompt_ = partial(switch_prompt, more_requirement=more_req)\n\n    # <-------------- check deps ------------->\n    try:\n        import glob, os, time, subprocess\n        subprocess.Popen(['pdflatex', '-version'])\n        from .latex_fns.latex_actions import Latex精细分解与转化, 编译Latex\n    except Exception as e:\n        chatbot.append([ f\"解析项目: {txt}\",\n            f\"尝试执行Latex指令失败。Latex没有安装, 或者不在环境变量PATH中。安装方法https://tug.org/texlive/。报错信息\\n\\n```\\n\\n{trimmed_format_exc()}\\n\\n```\\n\\n\"])\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n    \n\n    # <-------------- clear history and read input ------------->\n    history = []\n    txt, arxiv_id = yield from arxiv_download(chatbot, history, txt)\n    if txt.endswith('.pdf'):\n        report_execption(chatbot, history, a = f\"解析项目: {txt}\", b = f\"发现已经存在翻译好的PDF文档\")\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n    \n\n    if os.path.exists(txt):\n        project_folder = txt\n    else:\n        if txt == \"\": txt = '空空如也的输入栏'\n        report_execption(chatbot, history, a = f\"解析项目: {txt}\", b = f\"找不到本地项目或无权访问: {txt}\")\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n    \n    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)]\n    if len(file_manifest) == 0:\n        report_execption(chatbot, history, a = f\"解析项目: {txt}\", b = f\"找不到任何.tex文件: {txt}\")\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n    \n\n    # <-------------- if is a zip/tar file ------------->\n    project_folder = desend_to_extracted_folder_if_exist(project_folder)\n\n\n    # <-------------- move latex project away from temp folder ------------->\n    project_folder = move_project(project_folder, arxiv_id)\n\n\n    # <-------------- if merge_translate_zh is already generated, skip gpt req ------------->\n    if not os.path.exists(project_folder + '/merge_translate_zh.tex'):\n        yield from Latex精细分解与转化(file_manifest, project_folder, llm_kwargs, plugin_kwargs, \n                                chatbot, history, system_prompt, mode='translate_zh', switch_prompt=_switch_prompt_)\n\n\n    # <-------------- compile PDF ------------->\n    success = yield from 编译Latex(chatbot, history, main_file_original='merge', main_file_modified='merge_translate_zh', mode='translate_zh', \n                             work_folder_original=project_folder, work_folder_modified=project_folder, work_folder=project_folder)\n\n    # <-------------- zip PDF ------------->\n    zip_res = zip_result(project_folder)\n    if success:\n        chatbot.append((f\"成功啦\", '请查收结果（压缩包）...'))\n        yield from update_ui(chatbot=chatbot, history=history); time.sleep(1) # 刷新界面\n        promote_file_to_downloadzone(file=zip_res, chatbot=chatbot)\n    else:\n        chatbot.append((f\"失败了\", '虽然PDF生成失败了, 但请查收结果（压缩包）, 内含已经翻译的Tex文档, 也是可读的, 您可以到Github Issue区, 用该压缩包+对话历史存档进行反馈 ...'))\n        yield from update_ui(chatbot=chatbot, history=history); time.sleep(1) # 刷新界面\n        promote_file_to_downloadzone(file=zip_res, chatbot=chatbot)\n\n\n    # <-------------- we are done ------------->\n    return success\n"
  },
  {
    "path": "crazy_functions/__init__.py",
    "content": ""
  },
  {
    "path": "crazy_functions/chatglm微调工具.py",
    "content": "from utils.toolbox import CatchException, update_ui, promote_file_to_downloadzone\nfrom .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency\nimport datetime, json\n\ndef fetch_items(list_of_items, batch_size):\n    for i in range(0, len(list_of_items), batch_size):\n        yield list_of_items[i:i + batch_size]\n\ndef string_to_options(arguments):\n    import argparse\n    import shlex\n\n    # Create an argparse.ArgumentParser instance\n    parser = argparse.ArgumentParser()\n\n    # Add command-line arguments\n    parser.add_argument(\"--llm_to_learn\", type=str, help=\"LLM model to learn\", default=\"gpt-3.5-turbo\")\n    parser.add_argument(\"--prompt_prefix\", type=str, help=\"Prompt prefix\", default='')\n    parser.add_argument(\"--system_prompt\", type=str, help=\"System prompt\", default='')\n    parser.add_argument(\"--batch\", type=int, help=\"System prompt\", default=50)\n    parser.add_argument(\"--pre_seq_len\", type=int, help=\"pre_seq_len\", default=50)\n    parser.add_argument(\"--learning_rate\", type=float, help=\"learning_rate\", default=2e-2)\n    parser.add_argument(\"--num_gpus\", type=int, help=\"num_gpus\", default=1)\n    parser.add_argument(\"--json_dataset\", type=str, help=\"json_dataset\", default=\"\")\n    parser.add_argument(\"--ptuning_directory\", type=str, help=\"ptuning_directory\", default=\"\")\n\n\n\n    # Parse the arguments\n    args = parser.parse_args(shlex.split(arguments))\n\n    return args\n\n@CatchException\ndef 微调数据集生成(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):\n    \"\"\"\n    txt             输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径\n    llm_kwargs      gpt模型参数，如温度和top_p等，一般原样传递下去就行\n    plugin_kwargs   插件模型的参数\n    chatbot         聊天显示框的句柄，用于显示给用户\n    history         聊天历史，前情提要\n    system_prompt   给gpt的静默提醒\n    web_port        当前软件运行的端口号\n    \"\"\"\n    history = []    # 清空历史，以免输入溢出\n    chatbot.append((\"这是什么功能？\", \"[Local Message] 微调数据集生成\"))\n    if (\"advanced_arg\" in plugin_kwargs) and (plugin_kwargs[\"advanced_arg\"] == \"\"): plugin_kwargs.pop(\"advanced_arg\")\n    args = plugin_kwargs.get(\"advanced_arg\", None)\n    if args is None: \n        chatbot.append((\"没给定指令\", \"退出\"))\n        yield from update_ui(chatbot=chatbot, history=history); return\n    else:\n        arguments = string_to_options(arguments=args)\n\n    dat = []\n    with open(txt, 'r', encoding='utf8') as f:\n        for line in f.readlines():\n            json_dat = json.loads(line)\n            dat.append(json_dat[\"content\"])\n\n    llm_kwargs['llm_model'] = arguments.llm_to_learn\n    for batch in fetch_items(dat, arguments.batch):\n        res = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(\n            inputs_array=[f\"{arguments.prompt_prefix}\\n\\n{b}\" for b in (batch)],\n            inputs_show_user_array=[f\"Show Nothing\" for _ in (batch)],\n            llm_kwargs=llm_kwargs,\n            chatbot=chatbot,\n            history_array=[[] for _ in (batch)],\n            sys_prompt_array=[arguments.system_prompt for _ in (batch)],\n            max_workers=10  # OpenAI所允许的最大并行过载\n        )\n    \n        with open(txt+'.generated.json', 'a+', encoding='utf8') as f:\n            for b, r in zip(batch, res[1::2]):\n                f.write(json.dumps({\"content\":b, \"summary\":r}, ensure_ascii=False)+'\\n')\n\n    promote_file_to_downloadzone(txt+'.generated.json', rename_file='generated.json', chatbot=chatbot)\n    return\n\n\n\n@CatchException\ndef 启动微调(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):\n    \"\"\"\n    txt             输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径\n    llm_kwargs      gpt模型参数，如温度和top_p等，一般原样传递下去就行\n    plugin_kwargs   插件模型的参数\n    chatbot         聊天显示框的句柄，用于显示给用户\n    history         聊天历史，前情提要\n    system_prompt   给gpt的静默提醒\n    web_port        当前软件运行的端口号\n    \"\"\"\n    import subprocess\n    history = []    # 清空历史，以免输入溢出\n    chatbot.append((\"这是什么功能？\", \"[Local Message] 微调数据集生成\"))\n    if (\"advanced_arg\" in plugin_kwargs) and (plugin_kwargs[\"advanced_arg\"] == \"\"): plugin_kwargs.pop(\"advanced_arg\")\n    args = plugin_kwargs.get(\"advanced_arg\", None)\n    if args is None: \n        chatbot.append((\"没给定指令\", \"退出\"))\n        yield from update_ui(chatbot=chatbot, history=history); return\n    else:\n        arguments = string_to_options(arguments=args)\n      \n\n\n    pre_seq_len = arguments.pre_seq_len             # 128\n    learning_rate = arguments.learning_rate                               # 2e-2\n    num_gpus = arguments.num_gpus                   # 1\n    json_dataset = arguments.json_dataset                 # 't_code.json'\n    ptuning_directory = arguments.ptuning_directory       # '/home/hmp/ChatGLM2-6B/ptuning'\n\n    command = f\"torchrun --standalone --nnodes=1 --nproc-per-node={num_gpus} main.py \\\n        --do_train \\\n        --train_file AdvertiseGen/{json_dataset} \\\n        --validation_file AdvertiseGen/{json_dataset} \\\n        --preprocessing_num_workers 20 \\\n        --prompt_column content \\\n        --response_column summary \\\n        --overwrite_cache \\\n        --model_name_or_path THUDM/chatglm2-6b \\\n        --output_dir output/clothgen-chatglm2-6b-pt-{pre_seq_len}-{learning_rate} \\\n        --overwrite_output_dir \\\n        --max_source_length 256 \\\n        --max_target_length 256 \\\n        --per_device_train_batch_size 1 \\\n        --per_device_eval_batch_size 1 \\\n        --gradient_accumulation_steps 16 \\\n        --predict_with_generate \\\n        --max_steps 100 \\\n        --logging_steps 10 \\\n        --save_steps 20 \\\n        --learning_rate {learning_rate} \\\n        --pre_seq_len {pre_seq_len} \\\n        --quantization_bit 4\"\n\n    process = subprocess.Popen(command, shell=True, cwd=ptuning_directory)\n    try:\n        process.communicate(timeout=3600*24)\n    except subprocess.TimeoutExpired:\n        process.kill()\n    return\n"
  },
  {
    "path": "crazy_functions/crazy_functions_test.py",
    "content": "\"\"\"\n这是什么？\n    这个文件用于函数插件的单元测试\n    运行方法 python crazy_functions/crazy_functions_test.py\n\"\"\"\n    \n# ==============================================================================================================================\n\ndef validate_path():\n    import os, sys\n    dir_name = os.path.dirname(__file__)\n    root_dir_assume = os.path.abspath(os.path.dirname(__file__) +  '/..')\n    os.chdir(root_dir_assume)\n    sys.path.append(root_dir_assume)\nvalidate_path() # validate path so you can run from base directory\n\n# ==============================================================================================================================\n\nfrom utils.colorful import *\nfrom utils.toolbox import get_conf, ChatBotWithCookies\nimport contextlib\nimport os\nimport sys\nfrom functools import wraps\nproxies, WEB_PORT, LLM_MODEL, CONCURRENT_COUNT, AUTHENTICATION, CHATBOT_HEIGHT, LAYOUT, API_KEY = \\\n    get_conf('proxies', 'WEB_PORT', 'LLM_MODEL', 'CONCURRENT_COUNT', 'AUTHENTICATION', 'CHATBOT_HEIGHT', 'LAYOUT', 'API_KEY')\n\nllm_kwargs = {\n    'api_key': API_KEY,\n    'llm_model': LLM_MODEL,\n    'top_p':1.0, \n    'max_length': None,\n    'temperature':1.0,\n}\nplugin_kwargs = { }\nchatbot = ChatBotWithCookies(llm_kwargs)\nhistory = []\nsystem_prompt = \"Serve me as a writing and programming assistant.\"\nweb_port = 1024\n\n# ==============================================================================================================================\n\ndef silence_stdout(func):\n    @wraps(func)\n    def wrapper(*args, **kwargs):\n        _original_stdout = sys.stdout\n        sys.stdout = open(os.devnull, 'w')\n        for q in func(*args, **kwargs):\n            sys.stdout = _original_stdout\n            yield q\n            sys.stdout = open(os.devnull, 'w')\n        sys.stdout.close()\n        sys.stdout = _original_stdout\n    return wrapper\n\nclass CLI_Printer():\n    def __init__(self) -> None:\n        self.pre_buf = \"\"\n\n    def print(self, buf):\n        bufp = \"\"\n        for index, chat in enumerate(buf):\n            a, b = chat\n            bufp += sprint亮靛('[Me]:' + a) + '\\n'\n            bufp += '[GPT]:' + b\n            if index < len(buf)-1: \n                bufp += '\\n'\n\n        if self.pre_buf!=\"\" and bufp.startswith(self.pre_buf):\n            print(bufp[len(self.pre_buf):], end='')\n        else:\n            print('\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n'+bufp, end='')\n        self.pre_buf = bufp\n        return\n    \ncli_printer = CLI_Printer()\n# ==============================================================================================================================\ndef test_解析一个Python项目():\n    from crazy_functions.解析项目源代码 import 解析一个Python项目\n    txt = \"crazy_functions/test_project/python/dqn\"\n    for cookies, cb, hist, msg in 解析一个Python项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):\n        print(cb)\n\ndef test_解析一个Cpp项目():\n    from crazy_functions.解析项目源代码 import 解析一个C项目\n    txt = \"crazy_functions/test_project/cpp/cppipc\"\n    for cookies, cb, hist, msg in 解析一个C项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):\n        print(cb)\n\ndef test_Latex英文润色():\n    from crazy_functions.Latex全文润色 import Latex英文润色\n    txt = \"crazy_functions/test_project/latex/attention\"\n    for cookies, cb, hist, msg in Latex英文润色(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):\n        print(cb)\n\ndef test_Markdown中译英():\n    from crazy_functions.批量Markdown翻译 import Markdown中译英\n    txt = \"README.md\"\n    for cookies, cb, hist, msg in Markdown中译英(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):\n        print(cb)\n\ndef test_批量翻译PDF文档():\n    from crazy_functions.批量翻译PDF文档_多线程 import 批量翻译PDF文档\n    txt = \"crazy_functions/test_project/pdf_and_word\"\n    for cookies, cb, hist, msg in 批量翻译PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):\n        print(cb)\n\ndef test_谷歌检索小助手():\n    from crazy_functions.谷歌检索小助手 import 谷歌检索小助手\n    txt = \"https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=auto+reinforcement+learning&btnG=\"\n    for cookies, cb, hist, msg in 谷歌检索小助手(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):\n        print(cb)\n\ndef test_总结word文档():\n    from crazy_functions.总结word文档 import 总结word文档\n    txt = \"crazy_functions/test_project/pdf_and_word\"\n    for cookies, cb, hist, msg in 总结word文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):\n        print(cb)\n\ndef test_下载arxiv论文并翻译摘要():\n    from crazy_functions.下载arxiv论文翻译摘要 import 下载arxiv论文并翻译摘要\n    txt = \"1812.10695\"\n    for cookies, cb, hist, msg in 下载arxiv论文并翻译摘要(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):\n        print(cb)\n\ndef test_联网回答问题():\n    from crazy_functions.联网的ChatGPT import 连接网络回答问题\n    # txt = \"谁是应急食品？\"\n    # >>        '根据以上搜索结果可以得知，应急食品是“原神”游戏中的角色派蒙的外号。'\n    # txt = \"道路千万条，安全第一条。后面两句是？\"\n    # >>        '行车不规范，亲人两行泪。'\n    # txt = \"You should have gone for the head. What does that mean?\"\n    # >>        The phrase \"You should have gone for the head\" is a quote from the Marvel movies, Avengers: Infinity War and Avengers: Endgame. It was spoken by the character Thanos in Infinity War and by Thor in Endgame.\n    txt = \"AutoGPT是什么？\"\n    for cookies, cb, hist, msg in 连接网络回答问题(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port): \n        print(\"当前问答：\", cb[-1][-1].replace(\"\\n\",\" \"))\n    for i, it in enumerate(cb): print亮蓝(it[0]); print亮黄(it[1])\n\ndef test_解析ipynb文件():\n    from crazy_functions.解析JupyterNotebook import 解析ipynb文件\n    txt = \"crazy_functions/test_samples\"\n    for cookies, cb, hist, msg in 解析ipynb文件(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):\n        print(cb)\n\n\ndef test_数学动画生成manim():\n    from crazy_functions.数学动画生成manim import 动画生成\n    txt = \"A ball split into 2, and then split into 4, and finally split into 8.\"\n    for cookies, cb, hist, msg in 动画生成(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):\n        print(cb)\n\n\n\ndef test_Markdown多语言():\n    from crazy_functions.批量Markdown翻译 import Markdown翻译指定语言\n    txt = \"README.md\"\n    history = []\n    for lang in [\"English\", \"French\", \"Japanese\", \"Korean\", \"Russian\", \"Italian\", \"German\", \"Portuguese\", \"Arabic\"]:\n        plugin_kwargs = {\"advanced_arg\": lang}\n        for cookies, cb, hist, msg in Markdown翻译指定语言(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):\n            print(cb)\n\ndef test_Langchain知识库():\n    from crazy_functions.Langchain知识库 import 知识库问答\n    txt = \"./\"\n    chatbot = ChatBotWithCookies(llm_kwargs)\n    for cookies, cb, hist, msg in silence_stdout(知识库问答)(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):\n        cli_printer.print(cb)   #  print(cb)\n    \n    chatbot = ChatBotWithCookies(cookies)\n    from crazy_functions.Langchain知识库 import 读取知识库作答\n    txt = \"What is the installation method？\"\n    for cookies, cb, hist, msg in silence_stdout(读取知识库作答)(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):\n        cli_printer.print(cb)   #  print(cb)\n\ndef test_Langchain知识库读取():\n    from crazy_functions.Langchain知识库 import 读取知识库作答\n    txt = \"远程云服务器部署？\"\n    for cookies, cb, hist, msg in silence_stdout(读取知识库作答)(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):\n        cli_printer.print(cb)   #  print(cb)\n\ndef test_Latex():\n    from crazy_functions.Latex输出PDF结果 import Latex英文纠错加PDF对比, Latex翻译中文并重新编译PDF\n\n    # txt = r\"https://arxiv.org/abs/1706.03762\"\n    # txt = r\"https://arxiv.org/abs/1902.03185\"\n    # txt = r\"https://arxiv.org/abs/2305.18290\"\n    # txt = r\"https://arxiv.org/abs/2305.17608\"\n    # txt = r\"https://arxiv.org/abs/2211.16068\"                     #  ACE\n    # txt = r\"C:\\Users\\x\\arxiv_cache\\2211.16068\\workfolder\"  #  ACE\n    # txt = r\"https://arxiv.org/abs/2002.09253\"\n    # txt = r\"https://arxiv.org/abs/2306.07831\"\n    # txt = r\"https://arxiv.org/abs/2212.10156\"\n    # txt = r\"https://arxiv.org/abs/2211.11559\"\n    # txt = r\"https://arxiv.org/abs/2303.08774\"\n    # txt = r\"https://arxiv.org/abs/2303.12712\"\n    # txt = r\"C:\\Users\\fuqingxu\\arxiv_cache\\2303.12712\\workfolder\"\n    # txt = r\"2306.17157\" # 这个paper有个input命令文件名大小写错误！\n    # txt = \"https://arxiv.org/abs/2205.14135\"\n    # txt = r\"C:\\Users\\fuqingxu\\arxiv_cache\\2205.14135\\workfolder\"\n    # txt = r\"C:\\Users\\fuqingxu\\arxiv_cache\\2205.14135\\workfolder\"\n    txt = r\"2210.03629\"\n    txt = r\"2307.04964\"\n    for cookies, cb, hist, msg in (Latex翻译中文并重新编译PDF)(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):\n        cli_printer.print(cb)   #  print(cb)\n\n\n\n    # txt = \"2302.02948.tar\"\n    # print(txt)\n    # main_tex, work_folder = Latex预处理(txt)\n    # print('main tex:', main_tex)\n    # res = 编译Latex(main_tex, work_folder)\n    # # for cookies, cb, hist, msg in silence_stdout(编译Latex)(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):\n    #     cli_printer.print(cb)   #  print(cb)\n\ndef test_chatglm_finetune():\n    from crazy_functions.chatglm微调工具 import 微调数据集生成, 启动微调\n    txt = 'build/dev.json'\n    plugin_kwargs = {\"advanced_arg\":\"--llm_to_learn=gpt-3.5-turbo --prompt_prefix='根据下面的服装类型提示，想象一个穿着者，对这个人外貌、身处的环境、内心世界、人设进行描写。要求：100字以内，用第二人称。' --system_prompt=''\" }\n\n    # for cookies, cb, hist, msg in (微调数据集生成)(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):\n    #     cli_printer.print(cb)\n\n    plugin_kwargs = {\"advanced_arg\":\n    \"      --pre_seq_len=128 --learning_rate=2e-2 --num_gpus=1 --json_dataset='t_code.json' --ptuning_directory='/home/hmp/ChatGLM2-6B/ptuning'     \" }\n\n    for cookies, cb, hist, msg in (启动微调)(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):\n        cli_printer.print(cb)\n\n\nif __name__ == \"__main__\":\n    # test_解析一个Python项目()\n    # test_Latex英文润色()\n    # test_Markdown中译英()\n    # test_批量翻译PDF文档()\n    # test_谷歌检索小助手()\n    # test_总结word文档()\n    # test_下载arxiv论文并翻译摘要()\n    # test_解析一个Cpp项目()\n    # test_联网回答问题()\n    # test_解析ipynb文件()\n    # test_数学动画生成manim()\n    # test_Langchain知识库()\n    # test_Langchain知识库读取()\n    test_Latex()\n    # test_chatglm_finetune()\n    input(\"程序完成，回车退出。\")\n    print(\"退出。\")"
  },
  {
    "path": "crazy_functions/crazy_utils.py",
    "content": "from utils.toolbox import update_ui, get_conf, trimmed_format_exc\nimport threading\n\ndef input_clipping(inputs, history, max_token_limit):\n    import numpy as np\n    from llm_cards.bridge_all import model_info\n    enc = model_info[\"gpt-3.5-turbo\"]['tokenizer']\n    def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))\n\n    mode = 'input-and-history'\n    # 当 输入部分的token占比 小于 全文的一半时，只裁剪历史\n    input_token_num = get_token_num(inputs)\n    if input_token_num < max_token_limit//2: \n        mode = 'only-history'\n        max_token_limit = max_token_limit - input_token_num\n\n    everything = [inputs] if mode == 'input-and-history' else ['']\n    everything.extend(history)\n    n_token = get_token_num('\\n'.join(everything))\n    everything_token = [get_token_num(e) for e in everything]\n    delta = max(everything_token) // 16 # 截断时的颗粒度\n        \n    while n_token > max_token_limit:\n        where = np.argmax(everything_token)\n        encoded = enc.encode(everything[where], disallowed_special=())\n        clipped_encoded = encoded[:len(encoded)-delta]\n        everything[where] = enc.decode(clipped_encoded)[:-1]    # -1 to remove the may-be illegal char\n        everything_token[where] = get_token_num(everything[where])\n        n_token = get_token_num('\\n'.join(everything))\n\n    if mode == 'input-and-history':\n        inputs = everything[0]\n    else:\n        pass\n    history = everything[1:]\n    return inputs, history\n\ndef request_gpt_model_in_new_thread_with_ui_alive(\n        inputs, inputs_show_user, llm_kwargs, \n        chatbot, history, sys_prompt, refresh_interval=0.2,\n        handle_token_exceed=True, \n        retry_times_at_unknown_error=2,\n        ):\n    \"\"\"\n    Request GPT model，请求GPT模型同时维持用户界面活跃。\n\n    输入参数 Args （以_array结尾的输入变量都是列表，列表长度为子任务的数量，执行时，会把列表拆解，放到每个子线程中分别执行）:\n        inputs (string): List of inputs （输入）\n        inputs_show_user (string): List of inputs to show user（展现在报告中的输入，借助此参数，在汇总报告中隐藏啰嗦的真实输入，增强报告的可读性）\n        top_p (float): Top p value for sampling from model distribution （GPT参数，浮点数）\n        temperature (float): Temperature value for sampling from model distribution（GPT参数，浮点数）\n        chatbot: chatbot inputs and outputs （用户界面对话窗口句柄，用于数据流可视化）\n        history (list): List of chat history （历史，对话历史列表）\n        sys_prompt (string): List of system prompts （系统输入，列表，用于输入给GPT的前提提示，比如你是翻译官怎样怎样）\n        refresh_interval (float, optional): Refresh interval for UI (default: 0.2) （刷新时间间隔频率，建议低于1，不可高于3，仅仅服务于视觉效果）\n        handle_token_exceed：是否自动处理token溢出的情况，如果选择自动处理，则会在溢出时暴力截断，默认开启\n        retry_times_at_unknown_error：失败时的重试次数\n\n    输出 Returns:\n        future: 输出，GPT返回的结果\n    \"\"\"\n    import time\n    from concurrent.futures import ThreadPoolExecutor\n    from llm_cards.bridge_all import predict_no_ui_long_connection\n    # 用户反馈\n    chatbot.append([inputs_show_user, \"\"])\n    yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面\n    executor = ThreadPoolExecutor(max_workers=16)\n    mutable = [\"\", time.time(), \"\"]\n    def _req_gpt(inputs, history, sys_prompt):\n        retry_op = retry_times_at_unknown_error\n        exceeded_cnt = 0\n        while True:\n            # watchdog error\n            if len(mutable) >= 2 and (time.time()-mutable[1]) > 5: \n                raise RuntimeError(\"检测到程序终止。\")\n            try:\n                # 【第一种情况】：顺利完成\n                result = predict_no_ui_long_connection(\n                    inputs=inputs, llm_kwargs=llm_kwargs,\n                    history=history, sys_prompt=sys_prompt, observe_window=mutable)\n                return result\n            except ConnectionAbortedError as token_exceeded_error:\n                # 【第二种情况】：Token溢出\n                if handle_token_exceed:\n                    exceeded_cnt += 1\n                    # 【选择处理】 尝试计算比例，尽可能多地保留文本\n                    from utils.toolbox import get_reduce_token_percent\n                    p_ratio, n_exceed = get_reduce_token_percent(str(token_exceeded_error))\n                    MAX_TOKEN = 4096\n                    EXCEED_ALLO = 512 + 512 * exceeded_cnt\n                    inputs, history = input_clipping(inputs, history, max_token_limit=MAX_TOKEN-EXCEED_ALLO)\n                    mutable[0] += f'[Local Message] 警告，文本过长将进行截断，Token溢出数：{n_exceed}。\\n\\n'\n                    continue # 返回重试\n                else:\n                    # 【选择放弃】\n                    tb_str = '```\\n' + trimmed_format_exc() + '```'\n                    mutable[0] += f\"[Local Message] 警告，在执行过程中遭遇问题, Traceback：\\n\\n{tb_str}\\n\\n\"\n                    return mutable[0] # 放弃\n            except:\n                # 【第三种情况】：其他错误：重试几次\n                tb_str = '```\\n' + trimmed_format_exc() + '```'\n                print(tb_str)\n                mutable[0] += f\"[Local Message] 警告，在执行过程中遭遇问题, Traceback：\\n\\n{tb_str}\\n\\n\"\n                if retry_op > 0:\n                    retry_op -= 1\n                    mutable[0] += f\"[Local Message] 重试中，请稍等 {retry_times_at_unknown_error-retry_op}/{retry_times_at_unknown_error}：\\n\\n\"\n                    if (\"Rate limit reached\" in tb_str) or (\"Too Many Requests\" in tb_str):\n                        time.sleep(30)\n                    time.sleep(5)\n                    continue # 返回重试\n                else:\n                    time.sleep(5)\n                    return mutable[0] # 放弃\n\n    # 提交任务\n    future = executor.submit(_req_gpt, inputs, history, sys_prompt)\n    while True:\n        # yield一次以刷新前端页面\n        time.sleep(refresh_interval)\n        # “喂狗”（看门狗）\n        mutable[1] = time.time()\n        if future.done():\n            break\n        chatbot[-1] = [chatbot[-1][0], mutable[0]]\n        yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面\n\n    final_result = future.result()\n    chatbot[-1] = [chatbot[-1][0], final_result]\n    yield from update_ui(chatbot=chatbot, history=[]) # 如果最后成功了，则删除报错信息\n    return final_result\n\ndef can_multi_process(llm):\n    if llm.startswith('gpt-'): return True\n    if llm.startswith('api2d-'): return True\n    if llm.startswith('azure-'): return True\n    return False\n\ndef request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(\n        inputs_array, inputs_show_user_array, llm_kwargs, \n        chatbot, history_array, sys_prompt_array, \n        refresh_interval=0.2, max_workers=-1, scroller_max_len=30,\n        handle_token_exceed=True, show_user_at_complete=False,\n        retry_times_at_unknown_error=2,\n        ):\n    \"\"\"\n    Request GPT model using multiple threads with UI and high efficiency\n    请求GPT模型的[多线程]版。\n    具备以下功能：\n        实时在UI上反馈远程数据流\n        使用线程池，可调节线程池的大小避免openai的流量限制错误\n        处理中途中止的情况\n        网络等出问题时，会把traceback和已经接收的数据转入输出\n\n    输入参数 Args （以_array结尾的输入变量都是列表，列表长度为子任务的数量，执行时，会把列表拆解，放到每个子线程中分别执行）:\n        inputs_array (list): List of inputs （每个子任务的输入）\n        inputs_show_user_array (list): List of inputs to show user（每个子任务展现在报告中的输入，借助此参数，在汇总报告中隐藏啰嗦的真实输入，增强报告的可读性）\n        llm_kwargs: llm_kwargs参数\n        chatbot: chatbot （用户界面对话窗口句柄，用于数据流可视化）\n        history_array (list): List of chat history （历史对话输入，双层列表，第一层列表是子任务分解，第二层列表是对话历史）\n        sys_prompt_array (list): List of system prompts （系统输入，列表，用于输入给GPT的前提提示，比如你是翻译官怎样怎样）\n        refresh_interval (float, optional): Refresh interval for UI (default: 0.2) （刷新时间间隔频率，建议低于1，不可高于3，仅仅服务于视觉效果）\n        max_workers (int, optional): Maximum number of threads (default: see config.py) （最大线程数，如果子任务非常多，需要用此选项防止高频地请求openai导致错误）\n        scroller_max_len (int, optional): Maximum length for scroller (default: 30)（数据流的显示最后收到的多少个字符，仅仅服务于视觉效果）\n        handle_token_exceed (bool, optional): （是否在输入过长时，自动缩减文本）\n        handle_token_exceed：是否自动处理token溢出的情况，如果选择自动处理，则会在溢出时暴力截断，默认开启\n        show_user_at_complete (bool, optional): (在结束时，把完整输入-输出结果显示在聊天框)\n        retry_times_at_unknown_error：子任务失败时的重试次数\n\n    输出 Returns:\n        list: List of GPT model responses （每个子任务的输出汇总，如果某个子任务出错，response中会携带traceback报错信息，方便调试和定位问题。）\n    \"\"\"\n    import time, random\n    from concurrent.futures import ThreadPoolExecutor\n    from llm_cards.bridge_all import predict_no_ui_long_connection\n    assert len(inputs_array) == len(history_array)\n    assert len(inputs_array) == len(sys_prompt_array)\n    if max_workers == -1: # 读取配置文件\n        try: max_workers, = get_conf('DEFAULT_WORKER_NUM')\n        except: max_workers = 8\n        if max_workers <= 0: max_workers = 3\n    # 屏蔽掉 chatglm的多线程，可能会导致严重卡顿\n    if not can_multi_process(llm_kwargs['llm_model']):\n        max_workers = 1\n        \n    executor = ThreadPoolExecutor(max_workers=max_workers)\n    n_frag = len(inputs_array)\n    # 用户反馈\n    chatbot.append([\"请开始多线程操作。\", \"\"])\n    yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面\n    # 跨线程传递\n    mutable = [[\"\", time.time(), \"等待中\"] for _ in range(n_frag)]\n\n    # 子线程任务\n    def _req_gpt(index, inputs, history, sys_prompt):\n        gpt_say = \"\"\n        retry_op = retry_times_at_unknown_error\n        exceeded_cnt = 0\n        mutable[index][2] = \"执行中\"\n        while True:\n            # watchdog error\n            if len(mutable[index]) >= 2 and (time.time()-mutable[index][1]) > 5: \n                raise RuntimeError(\"检测到程序终止。\")\n            try:\n                # 【第一种情况】：顺利完成\n                # time.sleep(10); raise RuntimeError(\"测试\")\n                gpt_say = predict_no_ui_long_connection(\n                    inputs=inputs, llm_kwargs=llm_kwargs, history=history, \n                    sys_prompt=sys_prompt, observe_window=mutable[index], console_slience=True\n                )\n                mutable[index][2] = \"已成功\"\n                return gpt_say\n            except ConnectionAbortedError as token_exceeded_error:\n                # 【第二种情况】：Token溢出，\n                if handle_token_exceed:\n                    exceeded_cnt += 1\n                    # 【选择处理】 尝试计算比例，尽可能多地保留文本\n                    from utils.toolbox import get_reduce_token_percent\n                    p_ratio, n_exceed = get_reduce_token_percent(str(token_exceeded_error))\n                    MAX_TOKEN = 4096\n                    EXCEED_ALLO = 512 + 512 * exceeded_cnt\n                    inputs, history = input_clipping(inputs, history, max_token_limit=MAX_TOKEN-EXCEED_ALLO)\n                    gpt_say += f'[Local Message] 警告，文本过长将进行截断，Token溢出数：{n_exceed}。\\n\\n'\n                    mutable[index][2] = f\"截断重试\"\n                    continue # 返回重试\n                else:\n                    # 【选择放弃】\n                    tb_str = '```\\n' + trimmed_format_exc() + '```'\n                    gpt_say += f\"[Local Message] 警告，线程{index}在执行过程中遭遇问题, Traceback：\\n\\n{tb_str}\\n\\n\"\n                    if len(mutable[index][0]) > 0: gpt_say += \"此线程失败前收到的回答：\\n\\n\" + mutable[index][0]\n                    mutable[index][2] = \"输入过长已放弃\"\n                    return gpt_say # 放弃\n            except:\n                # 【第三种情况】：其他错误\n                tb_str = '```\\n' + trimmed_format_exc() + '```'\n                print(tb_str)\n                gpt_say += f\"[Local Message] 警告，线程{index}在执行过程中遭遇问题, Traceback：\\n\\n{tb_str}\\n\\n\"\n                if len(mutable[index][0]) > 0: gpt_say += \"此线程失败前收到的回答：\\n\\n\" + mutable[index][0]\n                if retry_op > 0: \n                    retry_op -= 1\n                    wait = random.randint(5, 20)\n                    if (\"Rate limit reached\" in tb_str) or (\"Too Many Requests\" in tb_str):\n                        wait = wait * 3\n                        fail_info = \"OpenAI绑定信用卡可解除频率限制 \"\n                    else:\n                        fail_info = \"\"\n                    # 也许等待十几秒后，情况会好转\n                    for i in range(wait):\n                        mutable[index][2] = f\"{fail_info}等待重试 {wait-i}\"; time.sleep(1)\n                    # 开始重试\n                    mutable[index][2] = f\"重试中 {retry_times_at_unknown_error-retry_op}/{retry_times_at_unknown_error}\"\n                    continue # 返回重试\n                else:\n                    mutable[index][2] = \"已失败\"\n                    wait = 5\n                    time.sleep(5)\n                    return gpt_say # 放弃\n\n    # 异步任务开始\n    futures = [executor.submit(_req_gpt, index, inputs, history, sys_prompt) for index, inputs, history, sys_prompt in zip(\n        range(len(inputs_array)), inputs_array, history_array, sys_prompt_array)]\n    cnt = 0\n    while True:\n        # yield一次以刷新前端页面\n        time.sleep(refresh_interval)\n        cnt += 1\n        worker_done = [h.done() for h in futures]\n        # 更好的UI视觉效果\n        observe_win = []\n        # 每个线程都要“喂狗”（看门狗）\n        for thread_index, _ in enumerate(worker_done):\n            mutable[thread_index][1] = time.time()\n        # 在前端打印些好玩的东西\n        for thread_index, _ in enumerate(worker_done):\n            print_something_really_funny = \"[ ...`\"+mutable[thread_index][0][-scroller_max_len:].\\\n                replace('\\n', '').replace('```', '...').replace(\n                    ' ', '.').replace('<br/>', '.....').replace('$', '.')+\"`... ]\"\n            observe_win.append(print_something_really_funny)\n        # 在前端打印些好玩的东西\n        stat_str = ''.join([f'`{mutable[thread_index][2]}`: {obs}\\n\\n' \n                            if not done else f'`{mutable[thread_index][2]}`\\n\\n' \n                            for thread_index, done, obs in zip(range(len(worker_done)), worker_done, observe_win)])\n        # 在前端打印些好玩的东西\n        chatbot[-1] = [chatbot[-1][0], f'多线程操作已经开始，完成情况: \\n\\n{stat_str}' + ''.join(['.']*(cnt % 10+1))]\n        yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面\n        if all(worker_done):\n            executor.shutdown()\n            break\n\n    # 异步任务结束\n    gpt_response_collection = []\n    for inputs_show_user, f in zip(inputs_show_user_array, futures):\n        gpt_res = f.result()\n        gpt_response_collection.extend([inputs_show_user, gpt_res])\n    \n    # 是否在结束时，在界面上显示结果\n    if show_user_at_complete:\n        for inputs_show_user, f in zip(inputs_show_user_array, futures):\n            gpt_res = f.result()\n            chatbot.append([inputs_show_user, gpt_res])\n            yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面\n            time.sleep(0.3)\n    return gpt_response_collection\n\n\ndef breakdown_txt_to_satisfy_token_limit(txt, get_token_fn, limit):\n    def cut(txt_tocut, must_break_at_empty_line):  # 递归\n        if get_token_fn(txt_tocut) <= limit:\n            return [txt_tocut]\n        else:\n            lines = txt_tocut.split('\\n')\n            estimated_line_cut = limit / get_token_fn(txt_tocut) * len(lines)\n            estimated_line_cut = int(estimated_line_cut)\n            for cnt in reversed(range(estimated_line_cut)):\n                if must_break_at_empty_line:\n                    if lines[cnt] != \"\":\n                        continue\n                print(cnt)\n                prev = \"\\n\".join(lines[:cnt])\n                post = \"\\n\".join(lines[cnt:])\n                if get_token_fn(prev) < limit:\n                    break\n            if cnt == 0:\n                raise RuntimeError(\"存在一行极长的文本！\")\n            # print(len(post))\n            # 列表递归接龙\n            result = [prev]\n            result.extend(cut(post, must_break_at_empty_line))\n            return result\n    try:\n        return cut(txt, must_break_at_empty_line=True)\n    except RuntimeError:\n        return cut(txt, must_break_at_empty_line=False)\n\n\ndef force_breakdown(txt, limit, get_token_fn):\n    \"\"\"\n    当无法用标点、空行分割时，我们用最暴力的方法切割\n    \"\"\"\n    for i in reversed(range(len(txt))):\n        if get_token_fn(txt[:i]) < limit:\n            return txt[:i], txt[i:]\n    return \"Tiktoken未知错误\", \"Tiktoken未知错误\"\n\ndef breakdown_txt_to_satisfy_token_limit_for_pdf(txt, get_token_fn, limit):\n    # 递归\n    def cut(txt_tocut, must_break_at_empty_line, break_anyway=False):  \n        if get_token_fn(txt_tocut) <= limit:\n            return [txt_tocut]\n        else:\n            lines = txt_tocut.split('\\n')\n            estimated_line_cut = limit / get_token_fn(txt_tocut) * len(lines)\n            estimated_line_cut = int(estimated_line_cut)\n            cnt = 0\n            for cnt in reversed(range(estimated_line_cut)):\n                if must_break_at_empty_line:\n                    if lines[cnt] != \"\":\n                        continue\n                prev = \"\\n\".join(lines[:cnt])\n                post = \"\\n\".join(lines[cnt:])\n                if get_token_fn(prev) < limit:\n                    break\n            if cnt == 0:\n                if break_anyway:\n                    prev, post = force_breakdown(txt_tocut, limit, get_token_fn)\n                else:\n                    raise RuntimeError(f\"存在一行极长的文本！{txt_tocut}\")\n            # print(len(post))\n            # 列表递归接龙\n            result = [prev]\n            result.extend(cut(post, must_break_at_empty_line, break_anyway=break_anyway))\n            return result\n    try:\n        # 第1次尝试，将双空行（\\n\\n）作为切分点\n        return cut(txt, must_break_at_empty_line=True)\n    except RuntimeError:\n        try:\n            # 第2次尝试，将单空行（\\n）作为切分点\n            return cut(txt, must_break_at_empty_line=False)\n        except RuntimeError:\n            try:\n                # 第3次尝试，将英文句号（.）作为切分点\n                res = cut(txt.replace('.', '。\\n'), must_break_at_empty_line=False) # 这个中文的句号是故意的，作为一个标识而存在\n                return [r.replace('。\\n', '.') for r in res]\n            except RuntimeError as e:\n                try:\n                    # 第4次尝试，将中文句号（。）作为切分点\n                    res = cut(txt.replace('。', '。。\\n'), must_break_at_empty_line=False)\n                    return [r.replace('。。\\n', '。') for r in res]\n                except RuntimeError as e:\n                    # 第5次尝试，没办法了，随便切一下敷衍吧\n                    return cut(txt, must_break_at_empty_line=False, break_anyway=True)\n\n\n\ndef read_and_clean_pdf_text(fp):\n    \"\"\"\n    这个函数用于分割pdf，用了很多trick，逻辑较乱，效果奇好\n\n    **输入参数说明**\n    - `fp`：需要读取和清理文本的pdf文件路径\n\n    **输出参数说明**\n    - `meta_txt`：清理后的文本内容字符串\n    - `page_one_meta`：第一页清理后的文本内容列表\n\n    **函数功能**\n    读取pdf文件并清理其中的文本内容，清理规则包括：\n    - 提取所有块元的文本信息，并合并为一个字符串\n    - 去除短块（字符数小于100）并替换为回车符\n    - 清理多余的空行\n    - 合并小写字母开头的段落块并替换为空格\n    - 清除重复的换行\n    - 将每个换行符替换为两个换行符，使每个段落之间有两个换行符分隔\n    \"\"\"\n    import fitz, copy\n    import re\n    import numpy as np\n    from utils.colorful import print亮黄, print亮绿\n    fc = 0  # Index 0 文本\n    fs = 1  # Index 1 字体\n    fb = 2  # Index 2 框框\n    REMOVE_FOOT_NOTE = True # 是否丢弃掉 不是正文的内容 （比正文字体小，如参考文献、脚注、图注等）\n    REMOVE_FOOT_FFSIZE_PERCENT = 0.95 # 小于正文的？时，判定为不是正文（有些文章的正文部分字体大小不是100%统一的，有肉眼不可见的小变化）\n    def primary_ffsize(l):\n        \"\"\"\n        提取文本块主字体\n        \"\"\"\n        fsize_statiscs = {}\n        for wtf in l['spans']:\n            if wtf['size'] not in fsize_statiscs: fsize_statiscs[wtf['size']] = 0\n            fsize_statiscs[wtf['size']] += len(wtf['text'])\n        return max(fsize_statiscs, key=fsize_statiscs.get)\n        \n    def ffsize_same(a,b):\n        \"\"\"\n        提取字体大小是否近似相等\n        \"\"\"\n        return abs((a-b)/max(a,b)) < 0.02\n\n    with fitz.open(fp) as doc:\n        meta_txt = []\n        meta_font = []\n\n        meta_line = []\n        meta_span = []\n        ############################## <第 1 步，搜集初始信息> ##################################\n        for index, page in enumerate(doc):\n            # file_content += page.get_text()\n            text_areas = page.get_text(\"dict\")  # 获取页面上的文本信息\n            for t in text_areas['blocks']:\n                if 'lines' in t:\n                    pf = 998\n                    for l in t['lines']:\n                        txt_line = \"\".join([wtf['text'] for wtf in l['spans']])\n                        if len(txt_line) == 0: continue\n                        pf = primary_ffsize(l)\n                        meta_line.append([txt_line, pf, l['bbox'], l])\n                        for wtf in l['spans']: # for l in t['lines']:\n                            meta_span.append([wtf['text'], wtf['size'], len(wtf['text'])])\n                    # meta_line.append([\"NEW_BLOCK\", pf])\n            # 块元提取                           for each word segment with in line                       for each line         cross-line words                          for each block\n            meta_txt.extend([\" \".join([\"\".join([wtf['text'] for wtf in l['spans']]) for l in t['lines']]).replace(\n                '- ', '') for t in text_areas['blocks'] if 'lines' in t])\n            meta_font.extend([np.mean([np.mean([wtf['size'] for wtf in l['spans']])\n                             for l in t['lines']]) for t in text_areas['blocks'] if 'lines' in t])\n            if index == 0:\n                page_one_meta = [\" \".join([\"\".join([wtf['text'] for wtf in l['spans']]) for l in t['lines']]).replace(\n                    '- ', '') for t in text_areas['blocks'] if 'lines' in t]\n                \n        ############################## <第 2 步，获取正文主字体> ##################################\n        fsize_statiscs = {}\n        for span in meta_span:\n            if span[1] not in fsize_statiscs: fsize_statiscs[span[1]] = 0\n            fsize_statiscs[span[1]] += span[2]\n        main_fsize = max(fsize_statiscs, key=fsize_statiscs.get)\n        if REMOVE_FOOT_NOTE:\n            give_up_fize_threshold = main_fsize * REMOVE_FOOT_FFSIZE_PERCENT\n\n        ############################## <第 3 步，切分和重新整合> ##################################\n        mega_sec = []\n        sec = []\n        for index, line in enumerate(meta_line):\n            if index == 0: \n                sec.append(line[fc])\n                continue\n            if REMOVE_FOOT_NOTE:\n                if meta_line[index][fs] <= give_up_fize_threshold:\n                    continue\n            if ffsize_same(meta_line[index][fs], meta_line[index-1][fs]):\n                # 尝试识别段落\n                if meta_line[index][fc].endswith('.') and\\\n                    (meta_line[index-1][fc] != 'NEW_BLOCK') and \\\n                    (meta_line[index][fb][2] - meta_line[index][fb][0]) < (meta_line[index-1][fb][2] - meta_line[index-1][fb][0]) * 0.7:\n                    sec[-1] += line[fc]\n                    sec[-1] += \"\\n\\n\"\n                else:\n                    sec[-1] += \" \"\n                    sec[-1] += line[fc]\n            else:\n                if (index+1 < len(meta_line)) and \\\n                    meta_line[index][fs] > main_fsize:\n                    # 单行 + 字体大\n                    mega_sec.append(copy.deepcopy(sec))\n                    sec = []\n                    sec.append(\"# \" + line[fc])\n                else:\n                    # 尝试识别section\n                    if meta_line[index-1][fs] > meta_line[index][fs]:\n                        sec.append(\"\\n\" + line[fc])\n                    else:\n                        sec.append(line[fc])\n        mega_sec.append(copy.deepcopy(sec))\n\n        finals = []\n        for ms in mega_sec:\n            final = \" \".join(ms)\n            final = final.replace('- ', ' ')\n            finals.append(final)\n        meta_txt = finals\n\n        ############################## <第 4 步，乱七八糟的后处理> ##################################\n        def 把字符太少的块清除为回车(meta_txt):\n            for index, block_txt in enumerate(meta_txt):\n                if len(block_txt) < 100:\n                    meta_txt[index] = '\\n'\n            return meta_txt\n        meta_txt = 把字符太少的块清除为回车(meta_txt)\n\n        def 清理多余的空行(meta_txt):\n            for index in reversed(range(1, len(meta_txt))):\n                if meta_txt[index] == '\\n' and meta_txt[index-1] == '\\n':\n                    meta_txt.pop(index)\n            return meta_txt\n        meta_txt = 清理多余的空行(meta_txt)\n\n        def 合并小写开头的段落块(meta_txt):\n            def starts_with_lowercase_word(s):\n                pattern = r\"^[a-z]+\"\n                match = re.match(pattern, s)\n                if match:\n                    return True\n                else:\n                    return False\n            for _ in range(100):\n                for index, block_txt in enumerate(meta_txt):\n                    if starts_with_lowercase_word(block_txt):\n                        if meta_txt[index-1] != '\\n':\n                            meta_txt[index-1] += ' '\n                        else:\n                            meta_txt[index-1] = ''\n                        meta_txt[index-1] += meta_txt[index]\n                        meta_txt[index] = '\\n'\n            return meta_txt\n        meta_txt = 合并小写开头的段落块(meta_txt)\n        meta_txt = 清理多余的空行(meta_txt)\n\n        meta_txt = '\\n'.join(meta_txt)\n        # 清除重复的换行\n        for _ in range(5):\n            meta_txt = meta_txt.replace('\\n\\n', '\\n')\n\n        # 换行 -> 双换行\n        meta_txt = meta_txt.replace('\\n', '\\n\\n')\n\n        ############################## <第 5 步，展示分割效果> ##################################\n        # for f in finals:\n        #    print亮黄(f)\n        #    print亮绿('***************************')\n\n    return meta_txt, page_one_meta\n\n\ndef get_files_from_everything(txt, type): # type='.md'\n    \"\"\"\n    这个函数是用来获取指定目录下所有指定类型（如.md）的文件，并且对于网络上的文件，也可以获取它。\n    下面是对每个参数和返回值的说明：\n    参数 \n    - txt: 路径或网址，表示要搜索的文件或者文件夹路径或网络上的文件。 \n    - type: 字符串，表示要搜索的文件类型。默认是.md。\n    返回值 \n    - success: 布尔值，表示函数是否成功执行。 \n    - file_manifest: 文件路径列表，里面包含以指定类型为后缀名的所有文件的绝对路径。 \n    - project_folder: 字符串，表示文件所在的文件夹路径。如果是网络上的文件，就是临时文件夹的路径。\n    该函数详细注释已添加，请确认是否满足您的需要。\n    \"\"\"\n    import glob, os\n\n    success = True\n    if txt.startswith('http'):\n        # 网络的远程文件\n        import requests\n        from utils.toolbox import get_conf\n        proxies, = get_conf('proxies')\n        r = requests.get(txt, proxies=proxies)\n        with open('./gpt_log/temp'+type, 'wb+') as f: f.write(r.content)\n        project_folder = './gpt_log/'\n        file_manifest = ['./gpt_log/temp'+type]\n    elif txt.endswith(type):\n        # 直接给定文件\n        file_manifest = [txt]\n        project_folder = os.path.dirname(txt)\n    elif os.path.exists(txt):\n        # 本地路径，递归搜索\n        project_folder = txt\n        file_manifest = [f for f in glob.glob(f'{project_folder}/**/*'+type, recursive=True)]\n        if len(file_manifest) == 0:\n            success = False\n    else:\n        project_folder = None\n        file_manifest = []\n        success = False\n\n    return success, file_manifest, project_folder\n\n\n\n\ndef Singleton(cls):\n    _instance = {}\n \n    def _singleton(*args, **kargs):\n        if cls not in _instance:\n            _instance[cls] = cls(*args, **kargs)\n        return _instance[cls]\n \n    return _singleton\n\n\n@Singleton\nclass knowledge_archive_interface():\n    def __init__(self) -> None:\n        self.threadLock = threading.Lock()\n        self.current_id = \"\"\n        self.kai_path = None\n        self.qa_handle = None\n        self.text2vec_large_chinese = None\n\n    def get_chinese_text2vec(self):\n        if self.text2vec_large_chinese is None:\n            # < -------------------预热文本向量化模组--------------- >\n            from utils.toolbox import ProxyNetworkActivate\n            print('Checking Text2vec ...')\n            from langchain.embeddings.huggingface import HuggingFaceEmbeddings\n            with ProxyNetworkActivate():    # 临时地激活代理网络\n                self.text2vec_large_chinese = HuggingFaceEmbeddings(model_name=\"GanymedeNil/text2vec-large-chinese\")\n\n        return self.text2vec_large_chinese\n\n\n    def feed_archive(self, file_manifest, id=\"default\"):\n        self.threadLock.acquire()\n        # import uuid\n        self.current_id = id\n        from zh_langchain import construct_vector_store\n        self.qa_handle, self.kai_path = construct_vector_store(   \n            vs_id=self.current_id, \n            files=file_manifest, \n            sentence_size=100,\n            history=[],\n            one_conent=\"\",\n            one_content_segmentation=\"\",\n            text2vec = self.get_chinese_text2vec(),\n        )\n        self.threadLock.release()\n\n    def get_current_archive_id(self):\n        return self.current_id\n    \n    def get_loaded_file(self):\n        return self.qa_handle.get_loaded_file()\n\n    def answer_with_archive_by_id(self, txt, id):\n        self.threadLock.acquire()\n        if not self.current_id == id:\n            self.current_id = id\n            from zh_langchain import construct_vector_store\n            self.qa_handle, self.kai_path = construct_vector_store(   \n                vs_id=self.current_id, \n                files=[], \n                sentence_size=100,\n                history=[],\n                one_conent=\"\",\n                one_content_segmentation=\"\",\n                text2vec = self.get_chinese_text2vec(),\n            )\n        VECTOR_SEARCH_SCORE_THRESHOLD = 0\n        VECTOR_SEARCH_TOP_K = 4\n        CHUNK_SIZE = 512\n        resp, prompt = self.qa_handle.get_knowledge_based_conent_test(\n            query = txt,\n            vs_path = self.kai_path,\n            score_threshold=VECTOR_SEARCH_SCORE_THRESHOLD,\n            vector_search_top_k=VECTOR_SEARCH_TOP_K, \n            chunk_conent=True,\n            chunk_size=CHUNK_SIZE,\n            text2vec = self.get_chinese_text2vec(),\n        )\n        self.threadLock.release()\n        return resp, prompt\n\ndef try_install_deps(deps):\n    for dep in deps:\n        import subprocess, sys\n        import platform\n        system = platform.system()\n\n        # 判断系统是否为Windows\n        if system == \"Windows\":\n            print(\"Windows please  conda install pycocotools \")\n            subprocess.check_call ([sys.executable, 'conda', 'install',  'pycocotools'])\n        subprocess.check_call([sys.executable, '-m', 'pip', 'install', '--user', dep])\n        \n\nclass construct_html():\n    def __init__(self) -> None:\n        self.css = \"\"\"\n.row {\n  display: flex;\n  flex-wrap: wrap;\n}\n\n.column {\n  flex: 1;\n  padding: 10px;\n}\n\n.table-header {\n  font-weight: bold;\n  border-bottom: 1px solid black;\n}\n\n.table-row {\n  border-bottom: 1px solid lightgray;\n}\n\n.table-cell {\n  padding: 5px;\n}\n        \"\"\"\n        self.html_string = f'<!DOCTYPE html><head><meta charset=\"utf-8\"><title>翻译结果</title><style>{self.css}</style></head>'\n\n\n    def add_row(self, a, b):\n        tmp = \"\"\"\n<div class=\"row table-row\">\n    <div class=\"column table-cell\">REPLACE_A</div>\n    <div class=\"column table-cell\">REPLACE_B</div>\n</div>\n        \"\"\"\n        from utils.toolbox import markdown_convertion\n        tmp = tmp.replace('REPLACE_A', markdown_convertion(a))\n        tmp = tmp.replace('REPLACE_B', markdown_convertion(b))\n        self.html_string += tmp\n\n\n    def save_file(self, file_name):\n        with open(f'./gpt_log/{file_name}', 'w', encoding='utf8') as f:\n            f.write(self.html_string.encode('utf-8', 'ignore').decode())\n\n\n# from typing import List, Tuple, Any, Union\n# from langchain.schema import AgentAction, AgentFinish\n# from langchain.agents import BaseSingleActionAgent\n# from langchain import LLMChain, PromptTemplate\n# from langchain.base_language import BaseLanguageModel\n\n\n# class IntentAgent(BaseSingleActionAgent):\n#     tools: List\n#     llm: BaseLanguageModel\n#     intent_template: str = \"\"\"\n#     现在有一些意图，类别为{intents}，你的任务是理解用户问题的意图，并判断该问题属于哪一类意图。\n#     回复的意图类别必须在提供的类别中，并且必须按格式回复：“意图类别：<>”。\n    \n#     举例：\n#     问题：什么是游戏角色皮卡丘？\n#     意图类别：游戏角色信息查询\n    \n\n#     问题：“{query}”\n#     \"\"\"\n#     prompt = PromptTemplate.from_template(intent_template)\n#     llm_chain: LLMChain = None\n\n#     def get_llm_chain(self):\n#         if not self.llm_chain:\n#             self.llm_chain = LLMChain(llm=self.llm, prompt=self.prompt)\n\n#     def choose_tools(self, query) -> List[str]:\n#         self.get_llm_chain()\n#         tool_names = [tool.name for tool in self.tools]\n#         resp = self.llm_chain.predict(intents=tool_names, query=query)\n#         select_tools = [(name, resp.index(name)) for name in tool_names if name in resp]\n#         select_tools.sort(key=lambda x:x[1])\n#         return [x[0] for x in select_tools]\n\n#     @property\n#     def input_keys(self):\n#         return [\"input\"]\n\n#     def plan(\n#             self, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any\n#     ) -> Union[AgentAction, AgentFinish]:\n#         # only for single tool\n#         tool_name = self.choose_tools(kwargs[\"input\"])[0]\n#         return AgentAction(tool=tool_name, tool_input=kwargs[\"input\"], log=\"\")\n\n#     async def aplan(\n#             self, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any\n#     ) -> Union[List[AgentAction], AgentFinish]:\n#         raise NotImplementedError(\"IntentAgent does not support async\")\n"
  },
  {
    "path": "crazy_functions/latex_fns/latex_actions.py",
    "content": "from utils.toolbox import update_ui, update_ui_lastest_msg    # 刷新Gradio前端界面\nfrom utils.toolbox import zip_folder, objdump, objload, promote_file_to_downloadzone\nfrom .latex_toolbox import PRESERVE, TRANSFORM\nfrom .latex_toolbox import set_forbidden_text, set_forbidden_text_begin_end, set_forbidden_text_careful_brace\nfrom .latex_toolbox import reverse_forbidden_text_careful_brace, reverse_forbidden_text, convert_to_linklist, post_process\nfrom .latex_toolbox import fix_content, find_main_tex_file, merge_tex_files, compile_latex_with_timeout\n\nimport os, shutil\nimport re\nimport numpy as np\n\npj = os.path.join\n\n\ndef split_subprocess(txt, project_folder, return_dict, opts):\n    \"\"\"\n    break down latex file to a linked list,\n    each node use a preserve flag to indicate whether it should\n    be proccessed by GPT.\n    \"\"\"\n    text = txt\n    mask = np.zeros(len(txt), dtype=np.uint8) + TRANSFORM\n\n    # 吸收title与作者以上的部分\n    text, mask = set_forbidden_text(text, mask, r\"^(.*?)\\\\maketitle\", re.DOTALL)\n    text, mask = set_forbidden_text(text, mask, r\"^(.*?)\\\\begin{document}\", re.DOTALL)\n    # 吸收iffalse注释\n    text, mask = set_forbidden_text(text, mask, r\"\\\\iffalse(.*?)\\\\fi\", re.DOTALL)\n    # 吸收在42行以内的begin-end组合\n    text, mask = set_forbidden_text_begin_end(text, mask, r\"\\\\begin\\{([a-z\\*]*)\\}(.*?)\\\\end\\{\\1\\}\", re.DOTALL, limit_n_lines=42)\n    # 吸收匿名公式\n    text, mask = set_forbidden_text(text, mask, [ r\"\\$\\$([^$]+)\\$\\$\",  r\"\\\\\\[.*?\\\\\\]\" ], re.DOTALL)\n    # 吸收其他杂项\n    text, mask = set_forbidden_text(text, mask, [ r\"\\\\section\\{(.*?)\\}\", r\"\\\\section\\*\\{(.*?)\\}\", r\"\\\\subsection\\{(.*?)\\}\", r\"\\\\subsubsection\\{(.*?)\\}\" ])\n    text, mask = set_forbidden_text(text, mask, [ r\"\\\\bibliography\\{(.*?)\\}\", r\"\\\\bibliographystyle\\{(.*?)\\}\" ])\n    text, mask = set_forbidden_text(text, mask, r\"\\\\begin\\{thebibliography\\}.*?\\\\end\\{thebibliography\\}\", re.DOTALL)\n    text, mask = set_forbidden_text(text, mask, r\"\\\\begin\\{lstlisting\\}(.*?)\\\\end\\{lstlisting\\}\", re.DOTALL)\n    text, mask = set_forbidden_text(text, mask, r\"\\\\begin\\{wraptable\\}(.*?)\\\\end\\{wraptable\\}\", re.DOTALL)\n    text, mask = set_forbidden_text(text, mask, r\"\\\\begin\\{algorithm\\}(.*?)\\\\end\\{algorithm\\}\", re.DOTALL)\n    text, mask = set_forbidden_text(text, mask, [r\"\\\\begin\\{wrapfigure\\}(.*?)\\\\end\\{wrapfigure\\}\", r\"\\\\begin\\{wrapfigure\\*\\}(.*?)\\\\end\\{wrapfigure\\*\\}\"], re.DOTALL)\n    text, mask = set_forbidden_text(text, mask, [r\"\\\\begin\\{figure\\}(.*?)\\\\end\\{figure\\}\", r\"\\\\begin\\{figure\\*\\}(.*?)\\\\end\\{figure\\*\\}\"], re.DOTALL)\n    text, mask = set_forbidden_text(text, mask, [r\"\\\\begin\\{multline\\}(.*?)\\\\end\\{multline\\}\", r\"\\\\begin\\{multline\\*\\}(.*?)\\\\end\\{multline\\*\\}\"], re.DOTALL)\n    text, mask = set_forbidden_text(text, mask, [r\"\\\\begin\\{table\\}(.*?)\\\\end\\{table\\}\", r\"\\\\begin\\{table\\*\\}(.*?)\\\\end\\{table\\*\\}\"], re.DOTALL)\n    text, mask = set_forbidden_text(text, mask, [r\"\\\\begin\\{minipage\\}(.*?)\\\\end\\{minipage\\}\", r\"\\\\begin\\{minipage\\*\\}(.*?)\\\\end\\{minipage\\*\\}\"], re.DOTALL)\n    text, mask = set_forbidden_text(text, mask, [r\"\\\\begin\\{align\\*\\}(.*?)\\\\end\\{align\\*\\}\", r\"\\\\begin\\{align\\}(.*?)\\\\end\\{align\\}\"], re.DOTALL)\n    text, mask = set_forbidden_text(text, mask, [r\"\\\\begin\\{equation\\}(.*?)\\\\end\\{equation\\}\", r\"\\\\begin\\{equation\\*\\}(.*?)\\\\end\\{equation\\*\\}\"], re.DOTALL)\n    text, mask = set_forbidden_text(text, mask, [r\"\\\\includepdf\\[(.*?)\\]\\{(.*?)\\}\", r\"\\\\clearpage\", r\"\\\\newpage\", r\"\\\\appendix\", r\"\\\\tableofcontents\", r\"\\\\include\\{(.*?)\\}\"])\n    text, mask = set_forbidden_text(text, mask, [r\"\\\\vspace\\{(.*?)\\}\", r\"\\\\hspace\\{(.*?)\\}\", r\"\\\\label\\{(.*?)\\}\", r\"\\\\begin\\{(.*?)\\}\", r\"\\\\end\\{(.*?)\\}\", r\"\\\\item \"])\n    text, mask = set_forbidden_text_careful_brace(text, mask, r\"\\\\hl\\{(.*?)\\}\", re.DOTALL)\n    # reverse 操作必须放在最后\n    text, mask = reverse_forbidden_text_careful_brace(text, mask, r\"\\\\caption\\{(.*?)\\}\", re.DOTALL, forbid_wrapper=True)\n    text, mask = reverse_forbidden_text_careful_brace(text, mask, r\"\\\\abstract\\{(.*?)\\}\", re.DOTALL, forbid_wrapper=True)\n    text, mask = reverse_forbidden_text(text, mask, r\"\\\\begin\\{abstract\\}(.*?)\\\\end\\{abstract\\}\", re.DOTALL, forbid_wrapper=True)\n    root = convert_to_linklist(text, mask)\n\n    # 最后一步处理，增强稳健性\n    root = post_process(root)\n\n    # 输出html调试文件，用红色标注处保留区（PRESERVE），用黑色标注转换区（TRANSFORM）\n    with open(pj(project_folder, 'debug_log.html'), 'w', encoding='utf8') as f:\n        segment_parts_for_gpt = []\n        nodes = []\n        node = root\n        while True:\n            nodes.append(node)\n            show_html = node.string.replace('\\n','<br/>')\n            if not node.preserve:\n                segment_parts_for_gpt.append(node.string)\n                f.write(f'<p style=\"color:black;\">#{node.range}{show_html}#</p>')\n            else:\n                f.write(f'<p style=\"color:red;\">{show_html}</p>')\n            node = node.next\n            if node is None: break\n\n    for n in nodes: n.next = None   # break\n    return_dict['nodes'] = nodes\n    return_dict['segment_parts_for_gpt'] = segment_parts_for_gpt\n    return return_dict\n\nclass LatexPaperSplit():\n    \"\"\"\n    break down latex file to a linked list,\n    each node use a preserve flag to indicate whether it should\n    be proccessed by GPT.\n    \"\"\"\n    def __init__(self) -> None:\n        self.nodes = None\n        self.msg = \"*{\\\\scriptsize\\\\textbf{警告：该PDF由GPT-Academic开源项目调用大语言模型+Latex翻译插件一键生成，\" + \\\n            \"版权归原文作者所有。翻译内容可靠性无保障，请仔细鉴别并以原文为准。\" + \\\n            \"项目Github地址 \\\\url{https://github.com/binary-husky/gpt_academic/}。\"\n        # 请您不要删除或修改这行警告，除非您是论文的原作者（如果您是论文原作者，欢迎加REAME中的QQ联系开发者）\n        self.msg_declare = \"为了防止大语言模型的意外谬误产生扩散影响，禁止移除或修改此警告。}}\\\\\\\\\" \n\n\n    def merge_result(self, arr, mode, msg, buggy_lines=[], buggy_line_surgery_n_lines=10):\n        \"\"\"\n        Merge the result after the GPT process completed\n        \"\"\"\n        result_string = \"\"\n        node_cnt = 0\n        line_cnt = 0\n        \n        for node in self.nodes:\n            if node.preserve:\n                line_cnt += node.string.count('\\n')\n                result_string += node.string\n            else:\n                translated_txt = fix_content(arr[node_cnt], node.string)\n                begin_line = line_cnt\n                end_line = line_cnt + translated_txt.count('\\n')\n\n                # reverse translation if any error\n                if any([begin_line-buggy_line_surgery_n_lines <= b_line <= end_line+buggy_line_surgery_n_lines for b_line in buggy_lines]):\n                    translated_txt = node.string\n\n                result_string += translated_txt\n                node_cnt += 1\n                line_cnt += translated_txt.count('\\n')\n\n        if mode == 'translate_zh':\n            pattern = re.compile(r'\\\\begin\\{abstract\\}.*\\n')\n            match = pattern.search(result_string)\n            if not match:\n                # match \\abstract{xxxx}\n                pattern_compile = re.compile(r\"\\\\abstract\\{(.*?)\\}\", flags=re.DOTALL)\n                match = pattern_compile.search(result_string)\n                position = match.regs[1][0]\n            else:\n                # match \\begin{abstract}xxxx\\end{abstract}\n                position = match.end()\n            result_string = result_string[:position] + self.msg + msg + self.msg_declare + result_string[position:]\n        return result_string\n\n\n    def split(self, txt, project_folder, opts): \n        \"\"\"\n        break down latex file to a linked list,\n        each node use a preserve flag to indicate whether it should\n        be proccessed by GPT.\n        P.S. use multiprocessing to avoid timeout error\n        \"\"\"\n        import multiprocessing\n        manager = multiprocessing.Manager()\n        return_dict = manager.dict()\n        p = multiprocessing.Process(\n            target=split_subprocess, \n            args=(txt, project_folder, return_dict, opts))\n        p.start()\n        p.join()\n        p.close()\n        self.nodes = return_dict['nodes']\n        self.sp = return_dict['segment_parts_for_gpt']\n        return self.sp\n\n\nclass LatexPaperFileGroup():\n    \"\"\"\n    use tokenizer to break down text according to max_token_limit\n    \"\"\"\n    def __init__(self):\n        self.file_paths = []\n        self.file_contents = []\n        self.sp_file_contents = []\n        self.sp_file_index = []\n        self.sp_file_tag = []\n\n        # count_token\n        from request_llm.bridge_all import model_info\n        enc = model_info[\"gpt-3.5-turbo\"]['tokenizer']\n        def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))\n        self.get_token_num = get_token_num\n\n    def run_file_split(self, max_token_limit=1900):\n        \"\"\"\n        use tokenizer to break down text according to max_token_limit\n        \"\"\"\n        for index, file_content in enumerate(self.file_contents):\n            if self.get_token_num(file_content) < max_token_limit:\n                self.sp_file_contents.append(file_content)\n                self.sp_file_index.append(index)\n                self.sp_file_tag.append(self.file_paths[index])\n            else:\n                from ..crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf\n                segments = breakdown_txt_to_satisfy_token_limit_for_pdf(file_content, self.get_token_num, max_token_limit)\n                for j, segment in enumerate(segments):\n                    self.sp_file_contents.append(segment)\n                    self.sp_file_index.append(index)\n                    self.sp_file_tag.append(self.file_paths[index] + f\".part-{j}.tex\")\n        print('Segmentation: done')\n\n    def merge_result(self):\n        self.file_result = [\"\" for _ in range(len(self.file_paths))]\n        for r, k in zip(self.sp_file_result, self.sp_file_index):\n            self.file_result[k] += r\n\n    def write_result(self):\n        manifest = []\n        for path, res in zip(self.file_paths, self.file_result):\n            with open(path + '.polish.tex', 'w', encoding='utf8') as f:\n                manifest.append(path + '.polish.tex')\n                f.write(res)\n        return manifest\n\n\ndef Latex精细分解与转化(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, mode='proofread', switch_prompt=None, opts=[]):\n    import time, os, re\n    from ..crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency\n    from .latex_actions import LatexPaperFileGroup, LatexPaperSplit\n\n    #  <-------- 寻找主tex文件 ----------> \n    maintex = find_main_tex_file(file_manifest, mode)\n    chatbot.append((f\"定位主Latex文件\", f'[Local Message] 分析结果：该项目的Latex主文件是{maintex}, 如果分析错误, 请立即终止程序, 删除或修改歧义文件, 然后重试。主程序即将开始, 请稍候。'))\n    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n    time.sleep(3)\n\n    #  <-------- 读取Latex文件, 将多文件tex工程融合为一个巨型tex ----------> \n    main_tex_basename = os.path.basename(maintex)\n    assert main_tex_basename.endswith('.tex')\n    main_tex_basename_bare = main_tex_basename[:-4]\n    may_exist_bbl = pj(project_folder, f'{main_tex_basename_bare}.bbl')\n    if os.path.exists(may_exist_bbl):\n        shutil.copyfile(may_exist_bbl, pj(project_folder, f'merge.bbl'))\n        shutil.copyfile(may_exist_bbl, pj(project_folder, f'merge_{mode}.bbl'))\n        shutil.copyfile(may_exist_bbl, pj(project_folder, f'merge_diff.bbl'))\n\n    with open(maintex, 'r', encoding='utf-8', errors='replace') as f:\n        content = f.read()\n        merged_content = merge_tex_files(project_folder, content, mode)\n\n    with open(project_folder + '/merge.tex', 'w', encoding='utf-8', errors='replace') as f:\n        f.write(merged_content)\n\n    #  <-------- 精细切分latex文件 ----------> \n    chatbot.append((f\"Latex文件融合完成\", f'[Local Message] 正在精细切分latex文件，这需要一段时间计算，文档越长耗时越长，请耐心等待。'))\n    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n    lps = LatexPaperSplit()\n    res = lps.split(merged_content, project_folder, opts) # 消耗时间的函数\n\n    #  <-------- 拆分过长的latex片段 ----------> \n    pfg = LatexPaperFileGroup()\n    for index, r in enumerate(res):\n        pfg.file_paths.append('segment-' + str(index))\n        pfg.file_contents.append(r)\n\n    pfg.run_file_split(max_token_limit=1024)\n    n_split = len(pfg.sp_file_contents)\n\n    #  <-------- 根据需要切换prompt ----------> \n    inputs_array, sys_prompt_array = switch_prompt(pfg, mode)\n    inputs_show_user_array = [f\"{mode} {f}\" for f in pfg.sp_file_tag]\n\n    if os.path.exists(pj(project_folder,'temp.pkl')):\n\n        #  <-------- 【仅调试】如果存在调试缓存文件，则跳过GPT请求环节 ----------> \n        pfg = objload(file=pj(project_folder,'temp.pkl'))\n\n    else:\n        #  <-------- gpt 多线程请求 ----------> \n        gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(\n            inputs_array=inputs_array,\n            inputs_show_user_array=inputs_show_user_array,\n            llm_kwargs=llm_kwargs,\n            chatbot=chatbot,\n            history_array=[[\"\"] for _ in range(n_split)],\n            sys_prompt_array=sys_prompt_array,\n            # max_workers=5,  # 并行任务数量限制, 最多同时执行5个, 其他的排队等待\n            scroller_max_len = 40\n        )\n\n        #  <-------- 文本碎片重组为完整的tex片段 ----------> \n        pfg.sp_file_result = []\n        for i_say, gpt_say, orig_content in zip(gpt_response_collection[0::2], gpt_response_collection[1::2], pfg.sp_file_contents):\n            pfg.sp_file_result.append(gpt_say)\n        pfg.merge_result()\n\n        # <-------- 临时存储用于调试 ----------> \n        pfg.get_token_num = None\n        objdump(pfg, file=pj(project_folder,'temp.pkl'))\n\n    write_html(pfg.sp_file_contents, pfg.sp_file_result, chatbot=chatbot, project_folder=project_folder)\n\n    #  <-------- 写出文件 ----------> \n    msg = f\"当前大语言模型: {llm_kwargs['llm_model']}，当前语言模型温度设定: {llm_kwargs['temperature']}。\"\n    final_tex = lps.merge_result(pfg.file_result, mode, msg)\n    objdump((lps, pfg.file_result, mode, msg), file=pj(project_folder,'merge_result.pkl'))\n\n    with open(project_folder + f'/merge_{mode}.tex', 'w', encoding='utf-8', errors='replace') as f:\n        if mode != 'translate_zh' or \"binary\" in final_tex: f.write(final_tex)\n        \n\n    #  <-------- 整理结果, 退出 ----------> \n    chatbot.append((f\"完成了吗？\", 'GPT结果已输出, 即将编译PDF'))\n    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n\n    #  <-------- 返回 ----------> \n    return project_folder + f'/merge_{mode}.tex'\n\n\ndef remove_buggy_lines(file_path, log_path, tex_name, tex_name_pure, n_fix, work_folder_modified, fixed_line=[]):\n    try:\n        with open(log_path, 'r', encoding='utf-8', errors='replace') as f:\n            log = f.read()\n        import re\n        buggy_lines = re.findall(tex_name+':([0-9]{1,5}):', log)\n        buggy_lines = [int(l) for l in buggy_lines]\n        buggy_lines = sorted(buggy_lines)\n        buggy_line = buggy_lines[0]-1\n        print(\"reversing tex line that has errors\", buggy_line)\n\n        # 重组，逆转出错的段落\n        if buggy_line not in fixed_line:\n            fixed_line.append(buggy_line)\n\n        lps, file_result, mode, msg = objload(file=pj(work_folder_modified,'merge_result.pkl'))\n        final_tex = lps.merge_result(file_result, mode, msg, buggy_lines=fixed_line, buggy_line_surgery_n_lines=5*n_fix)\n\n        with open(pj(work_folder_modified, f\"{tex_name_pure}_fix_{n_fix}.tex\"), 'w', encoding='utf-8', errors='replace') as f:\n            f.write(final_tex)\n\n        return True, f\"{tex_name_pure}_fix_{n_fix}\", buggy_lines\n    except:\n        print(\"Fatal error occurred, but we cannot identify error, please download zip, read latex log, and compile manually.\")\n        return False, -1, [-1]\n\n\ndef 编译Latex(chatbot, history, main_file_original, main_file_modified, work_folder_original, work_folder_modified, work_folder, mode='default'):\n    import os, time\n    n_fix = 1\n    fixed_line = []\n    max_try = 32\n    chatbot.append([f\"正在编译PDF文档\", f'编译已经开始。当前工作路径为{work_folder}，如果程序停顿5分钟以上，请直接去该路径下取回翻译结果，或者重启之后再度尝试 ...']); yield from update_ui(chatbot=chatbot, history=history)\n    chatbot.append([f\"正在编译PDF文档\", '...']); yield from update_ui(chatbot=chatbot, history=history); time.sleep(1); chatbot[-1] = list(chatbot[-1]) # 刷新界面\n    yield from update_ui_lastest_msg('编译已经开始...', chatbot, history)   # 刷新Gradio前端界面\n\n    while True:\n        import os\n        may_exist_bbl = pj(work_folder_modified, f'merge.bbl')\n        target_bbl = pj(work_folder_modified, f'{main_file_modified}.bbl')\n        if os.path.exists(may_exist_bbl) and not os.path.exists(target_bbl):\n            shutil.copyfile(may_exist_bbl, target_bbl)\n\n        # https://stackoverflow.com/questions/738755/dont-make-me-manually-abort-a-latex-compile-when-theres-an-error\n        yield from update_ui_lastest_msg(f'尝试第 {n_fix}/{max_try} 次编译, 编译原始PDF ...', chatbot, history)   # 刷新Gradio前端界面\n        ok = compile_latex_with_timeout(f'pdflatex -interaction=batchmode -file-line-error {main_file_original}.tex', work_folder_original)\n\n        yield from update_ui_lastest_msg(f'尝试第 {n_fix}/{max_try} 次编译, 编译转化后的PDF ...', chatbot, history)   # 刷新Gradio前端界面\n        ok = compile_latex_with_timeout(f'pdflatex -interaction=batchmode -file-line-error {main_file_modified}.tex', work_folder_modified)\n        \n        if ok and os.path.exists(pj(work_folder_modified, f'{main_file_modified}.pdf')):\n            # 只有第二步成功，才能继续下面的步骤\n            yield from update_ui_lastest_msg(f'尝试第 {n_fix}/{max_try} 次编译, 编译BibTex ...', chatbot, history)    # 刷新Gradio前端界面\n            if not os.path.exists(pj(work_folder_original, f'{main_file_original}.bbl')):\n                ok = compile_latex_with_timeout(f'bibtex  {main_file_original}.aux', work_folder_original)\n            if not os.path.exists(pj(work_folder_modified, f'{main_file_modified}.bbl')):\n                ok = compile_latex_with_timeout(f'bibtex  {main_file_modified}.aux', work_folder_modified)\n\n            yield from update_ui_lastest_msg(f'尝试第 {n_fix}/{max_try} 次编译, 编译文献交叉引用 ...', chatbot, history)  # 刷新Gradio前端界面\n            ok = compile_latex_with_timeout(f'pdflatex -interaction=batchmode -file-line-error {main_file_original}.tex', work_folder_original)\n            ok = compile_latex_with_timeout(f'pdflatex -interaction=batchmode -file-line-error {main_file_modified}.tex', work_folder_modified)\n            ok = compile_latex_with_timeout(f'pdflatex -interaction=batchmode -file-line-error {main_file_original}.tex', work_folder_original)\n            ok = compile_latex_with_timeout(f'pdflatex -interaction=batchmode -file-line-error {main_file_modified}.tex', work_folder_modified)\n\n            if mode!='translate_zh':\n                yield from update_ui_lastest_msg(f'尝试第 {n_fix}/{max_try} 次编译, 使用latexdiff生成论文转化前后对比 ...', chatbot, history) # 刷新Gradio前端界面\n                print(    f'latexdiff --encoding=utf8 --append-safecmd=subfile {work_folder_original}/{main_file_original}.tex  {work_folder_modified}/{main_file_modified}.tex --flatten > {work_folder}/merge_diff.tex')\n                ok = compile_latex_with_timeout(f'latexdiff --encoding=utf8 --append-safecmd=subfile {work_folder_original}/{main_file_original}.tex  {work_folder_modified}/{main_file_modified}.tex --flatten > {work_folder}/merge_diff.tex')\n\n                yield from update_ui_lastest_msg(f'尝试第 {n_fix}/{max_try} 次编译, 正在编译对比PDF ...', chatbot, history)   # 刷新Gradio前端界面\n                ok = compile_latex_with_timeout(f'pdflatex  -interaction=batchmode -file-line-error merge_diff.tex', work_folder)\n                ok = compile_latex_with_timeout(f'bibtex    merge_diff.aux', work_folder)\n                ok = compile_latex_with_timeout(f'pdflatex  -interaction=batchmode -file-line-error merge_diff.tex', work_folder)\n                ok = compile_latex_with_timeout(f'pdflatex  -interaction=batchmode -file-line-error merge_diff.tex', work_folder)\n\n        # <---------- 检查结果 ----------->\n        results_ = \"\"\n        original_pdf_success = os.path.exists(pj(work_folder_original, f'{main_file_original}.pdf'))\n        modified_pdf_success = os.path.exists(pj(work_folder_modified, f'{main_file_modified}.pdf'))\n        diff_pdf_success     = os.path.exists(pj(work_folder, f'merge_diff.pdf'))\n        results_ += f\"原始PDF编译是否成功: {original_pdf_success};\" \n        results_ += f\"转化PDF编译是否成功: {modified_pdf_success};\" \n        results_ += f\"对比PDF编译是否成功: {diff_pdf_success};\" \n        yield from update_ui_lastest_msg(f'第{n_fix}编译结束:<br/>{results_}...', chatbot, history) # 刷新Gradio前端界面\n\n        if diff_pdf_success:\n            result_pdf = pj(work_folder_modified, f'merge_diff.pdf')    # get pdf path\n            promote_file_to_downloadzone(result_pdf, rename_file=None, chatbot=chatbot)  # promote file to web UI\n        if modified_pdf_success:\n            yield from update_ui_lastest_msg(f'转化PDF编译已经成功, 即将退出 ...', chatbot, history)    # 刷新Gradio前端界面\n            result_pdf = pj(work_folder_modified, f'{main_file_modified}.pdf') # get pdf path\n            origin_pdf = pj(work_folder_original, f'{main_file_original}.pdf') # get pdf path\n            if os.path.exists(pj(work_folder, '..', 'translation')):\n                shutil.copyfile(result_pdf, pj(work_folder, '..', 'translation', 'translate_zh.pdf'))\n            promote_file_to_downloadzone(result_pdf, rename_file=None, chatbot=chatbot)  # promote file to web UI\n            # 将两个PDF拼接\n            if original_pdf_success: \n                try:\n                    from .latex_toolbox import merge_pdfs\n                    concat_pdf = pj(work_folder_modified, f'comparison.pdf')\n                    merge_pdfs(origin_pdf, result_pdf, concat_pdf)\n                    promote_file_to_downloadzone(concat_pdf, rename_file=None, chatbot=chatbot)  # promote file to web UI\n                except Exception as e:\n                    pass\n            return True # 成功啦\n        else:\n            if n_fix>=max_try: break\n            n_fix += 1\n            can_retry, main_file_modified, buggy_lines = remove_buggy_lines(\n                file_path=pj(work_folder_modified, f'{main_file_modified}.tex'), \n                log_path=pj(work_folder_modified, f'{main_file_modified}.log'),\n                tex_name=f'{main_file_modified}.tex',\n                tex_name_pure=f'{main_file_modified}',\n                n_fix=n_fix,\n                work_folder_modified=work_folder_modified,\n                fixed_line=fixed_line\n            )\n            yield from update_ui_lastest_msg(f'由于最为关键的转化PDF编译失败, 将根据报错信息修正tex源文件并重试, 当前报错的latex代码处于第{buggy_lines}行 ...', chatbot, history)   # 刷新Gradio前端界面\n            if not can_retry: break\n\n    return False # 失败啦\n\n\ndef write_html(sp_file_contents, sp_file_result, chatbot, project_folder):\n    # write html\n    try:\n        import shutil\n        from ..crazy_utils import construct_html\n        from toolbox import gen_time_str\n        ch = construct_html() \n        orig = \"\"\n        trans = \"\"\n        final = []\n        for c,r in zip(sp_file_contents, sp_file_result): \n            final.append(c)\n            final.append(r)\n        for i, k in enumerate(final): \n            if i%2==0:\n                orig = k\n            if i%2==1:\n                trans = k\n                ch.add_row(a=orig, b=trans)\n        create_report_file_name = f\"{gen_time_str()}.trans.html\"\n        ch.save_file(create_report_file_name)\n        shutil.copyfile(pj('./gpt_log/', create_report_file_name), pj(project_folder, create_report_file_name))\n        promote_file_to_downloadzone(file=f'./gpt_log/{create_report_file_name}', chatbot=chatbot)\n    except:\n        from toolbox import trimmed_format_exc\n        print('writing html result failed:', trimmed_format_exc())\n"
  },
  {
    "path": "crazy_functions/latex_fns/latex_toolbox.py",
    "content": "import os, shutil\nimport re\nimport numpy as np\nPRESERVE = 0\nTRANSFORM = 1\n\npj = os.path.join\n\nclass LinkedListNode():\n    \"\"\"\n    Linked List Node\n    \"\"\"\n    def __init__(self, string, preserve=True) -> None:\n        self.string = string\n        self.preserve = preserve\n        self.next = None\n        self.range = None\n        # self.begin_line = 0\n        # self.begin_char = 0\n\ndef convert_to_linklist(text, mask):\n    root = LinkedListNode(\"\", preserve=True)\n    current_node = root\n    for c, m, i in zip(text, mask, range(len(text))):\n        if (m==PRESERVE and current_node.preserve) \\\n            or (m==TRANSFORM and not current_node.preserve):\n            # add\n            current_node.string += c\n        else:\n            current_node.next = LinkedListNode(c, preserve=(m==PRESERVE))\n            current_node = current_node.next\n    return root\n\ndef post_process(root):\n    # 修复括号\n    node = root\n    while True:\n        string = node.string\n        if node.preserve: \n            node = node.next\n            if node is None: break\n            continue\n        def break_check(string):\n            str_stack = [\"\"] # (lv, index)\n            for i, c in enumerate(string):\n                if c == '{':\n                    str_stack.append('{')\n                elif c == '}':\n                    if len(str_stack) == 1:\n                        print('stack fix')\n                        return i\n                    str_stack.pop(-1)\n                else:\n                    str_stack[-1] += c\n            return -1\n        bp = break_check(string)\n\n        if bp == -1:\n            pass\n        elif bp == 0:\n            node.string = string[:1]\n            q = LinkedListNode(string[1:], False)\n            q.next = node.next\n            node.next = q\n        else:\n            node.string = string[:bp]\n            q = LinkedListNode(string[bp:], False)\n            q.next = node.next\n            node.next = q\n\n        node = node.next\n        if node is None: break\n\n    # 屏蔽空行和太短的句子\n    node = root\n    while True:\n        if len(node.string.strip('\\n').strip(''))==0: node.preserve = True\n        if len(node.string.strip('\\n').strip(''))<42: node.preserve = True\n        node = node.next\n        if node is None: break\n    node = root\n    while True:\n        if node.next and node.preserve and node.next.preserve:\n            node.string += node.next.string\n            node.next = node.next.next\n        node = node.next\n        if node is None: break\n\n    # 将前后断行符脱离\n    node = root\n    prev_node = None\n    while True:\n        if not node.preserve:\n            lstriped_ = node.string.lstrip().lstrip('\\n')\n            if (prev_node is not None) and (prev_node.preserve) and (len(lstriped_)!=len(node.string)):\n                prev_node.string += node.string[:-len(lstriped_)]\n                node.string = lstriped_\n            rstriped_ = node.string.rstrip().rstrip('\\n')\n            if (node.next is not None) and (node.next.preserve) and (len(rstriped_)!=len(node.string)):\n                node.next.string = node.string[len(rstriped_):] + node.next.string\n                node.string = rstriped_\n        # =====\n        prev_node = node\n        node = node.next\n        if node is None: break\n\n    # 标注节点的行数范围\n    node = root\n    n_line = 0\n    expansion = 2\n    while True:\n        n_l = node.string.count('\\n')\n        node.range = [n_line-expansion, n_line+n_l+expansion]   # 失败时，扭转的范围\n        n_line = n_line+n_l\n        node = node.next\n        if node is None: break\n    return root\n\n\n\"\"\"\n=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=\nLatex segmentation with a binary mask (PRESERVE=0, TRANSFORM=1)\n=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=\n\"\"\"\n\n\ndef set_forbidden_text(text, mask, pattern, flags=0):\n    \"\"\"\n    Add a preserve text area in this paper\n    e.g. with pattern = r\"\\\\begin\\{algorithm\\}(.*?)\\\\end\\{algorithm\\}\"\n    you can mask out (mask = PRESERVE so that text become untouchable for GPT) \n    everything between \"\\begin{equation}\" and \"\\end{equation}\"\n    \"\"\"\n    if isinstance(pattern, list): pattern = '|'.join(pattern)\n    pattern_compile = re.compile(pattern, flags)\n    for res in pattern_compile.finditer(text):\n        mask[res.span()[0]:res.span()[1]] = PRESERVE\n    return text, mask\n\ndef reverse_forbidden_text(text, mask, pattern, flags=0, forbid_wrapper=True):\n    \"\"\"\n    Move area out of preserve area (make text editable for GPT)\n    count the number of the braces so as to catch compelete text area. \n    e.g.\n    \\begin{abstract} blablablablablabla. \\end{abstract} \n    \"\"\"\n    if isinstance(pattern, list): pattern = '|'.join(pattern)\n    pattern_compile = re.compile(pattern, flags)\n    for res in pattern_compile.finditer(text):\n        if not forbid_wrapper:\n            mask[res.span()[0]:res.span()[1]] = TRANSFORM\n        else:\n            mask[res.regs[0][0]: res.regs[1][0]] = PRESERVE   # '\\\\begin{abstract}'\n            mask[res.regs[1][0]: res.regs[1][1]] = TRANSFORM   # abstract\n            mask[res.regs[1][1]: res.regs[0][1]] = PRESERVE   # abstract\n    return text, mask\n\ndef set_forbidden_text_careful_brace(text, mask, pattern, flags=0):\n    \"\"\"\n    Add a preserve text area in this paper (text become untouchable for GPT).\n    count the number of the braces so as to catch compelete text area. \n    e.g.\n    \\caption{blablablablabla\\texbf{blablabla}blablabla.} \n    \"\"\"\n    pattern_compile = re.compile(pattern, flags)\n    for res in pattern_compile.finditer(text):\n        brace_level = -1\n        p = begin = end = res.regs[0][0]\n        for _ in range(1024*16):\n            if text[p] == '}' and brace_level == 0: break\n            elif text[p] == '}':  brace_level -= 1\n            elif text[p] == '{':  brace_level += 1\n            p += 1\n        end = p+1\n        mask[begin:end] = PRESERVE\n    return text, mask\n\ndef reverse_forbidden_text_careful_brace(text, mask, pattern, flags=0, forbid_wrapper=True):\n    \"\"\"\n    Move area out of preserve area (make text editable for GPT)\n    count the number of the braces so as to catch compelete text area. \n    e.g.\n    \\caption{blablablablabla\\texbf{blablabla}blablabla.} \n    \"\"\"\n    pattern_compile = re.compile(pattern, flags)\n    for res in pattern_compile.finditer(text):\n        brace_level = 0\n        p = begin = end = res.regs[1][0]\n        for _ in range(1024*16):\n            if text[p] == '}' and brace_level == 0: break\n            elif text[p] == '}':  brace_level -= 1\n            elif text[p] == '{':  brace_level += 1\n            p += 1\n        end = p\n        mask[begin:end] = TRANSFORM\n        if forbid_wrapper:\n            mask[res.regs[0][0]:begin] = PRESERVE\n            mask[end:res.regs[0][1]] = PRESERVE\n    return text, mask\n\ndef set_forbidden_text_begin_end(text, mask, pattern, flags=0, limit_n_lines=42):\n    \"\"\"\n    Find all \\begin{} ... \\end{} text block that with less than limit_n_lines lines.\n    Add it to preserve area\n    \"\"\"\n    pattern_compile = re.compile(pattern, flags)\n    def search_with_line_limit(text, mask):\n        for res in pattern_compile.finditer(text):\n            cmd = res.group(1)  # begin{what}\n            this = res.group(2) # content between begin and end\n            this_mask = mask[res.regs[2][0]:res.regs[2][1]]\n            white_list = ['document', 'abstract', 'lemma', 'definition', 'sproof', \n                          'em', 'emph', 'textit', 'textbf', 'itemize', 'enumerate']\n            if (cmd in white_list) or this.count('\\n') >= limit_n_lines: # use a magical number 42\n                this, this_mask = search_with_line_limit(this, this_mask)\n                mask[res.regs[2][0]:res.regs[2][1]] = this_mask\n            else:\n                mask[res.regs[0][0]:res.regs[0][1]] = PRESERVE\n        return text, mask\n    return search_with_line_limit(text, mask) \n\n\n\n\"\"\"\n=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=\nLatex Merge File\n=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=\n\"\"\"\n\ndef find_main_tex_file(file_manifest, mode):\n    \"\"\"\n    在多Tex文档中，寻找主文件，必须包含documentclass，返回找到的第一个。\n    P.S. 但愿没人把latex模板放在里面传进来 (6.25 加入判定latex模板的代码)\n    \"\"\"\n    canidates = []\n    for texf in file_manifest:\n        if os.path.basename(texf).startswith('merge'):\n            continue\n        with open(texf, 'r', encoding='utf8', errors='ignore') as f:\n            file_content = f.read()\n        if r'\\documentclass' in file_content:\n            canidates.append(texf)\n        else:\n            continue\n\n    if len(canidates) == 0:\n        raise RuntimeError('无法找到一个主Tex文件（包含documentclass关键字）')\n    elif len(canidates) == 1:\n        return canidates[0]\n    else: # if len(canidates) >= 2 通过一些Latex模板中常见（但通常不会出现在正文）的单词，对不同latex源文件扣分，取评分最高者返回\n        canidates_score = []\n        # 给出一些判定模板文档的词作为扣分项\n        unexpected_words = ['\\LaTeX', 'manuscript', 'Guidelines', 'font', 'citations', 'rejected', 'blind review', 'reviewers']\n        expected_words = ['\\input', '\\ref', '\\cite']\n        for texf in canidates:\n            canidates_score.append(0)\n            with open(texf, 'r', encoding='utf8', errors='ignore') as f:\n                file_content = f.read()\n            for uw in unexpected_words:\n                if uw in file_content:\n                    canidates_score[-1] -= 1\n            for uw in expected_words:\n                if uw in file_content:\n                    canidates_score[-1] += 1\n        select = np.argmax(canidates_score) # 取评分最高者返回\n        return canidates[select]\n    \ndef rm_comments(main_file):\n    new_file_remove_comment_lines = []\n    for l in main_file.splitlines():\n        # 删除整行的空注释\n        if l.lstrip().startswith(\"%\"):\n            pass\n        else:\n            new_file_remove_comment_lines.append(l)\n    main_file = '\\n'.join(new_file_remove_comment_lines)\n    # main_file = re.sub(r\"\\\\include{(.*?)}\", r\"\\\\input{\\1}\", main_file)  # 将 \\include 命令转换为 \\input 命令\n    main_file = re.sub(r'(?<!\\\\)%.*', '', main_file)  # 使用正则表达式查找半行注释, 并替换为空字符串\n    return main_file\n\ndef find_tex_file_ignore_case(fp):\n    dir_name = os.path.dirname(fp)\n    base_name = os.path.basename(fp)\n    if not base_name.endswith('.tex'): base_name+='.tex'\n    if os.path.exists(pj(dir_name, base_name)): return pj(dir_name, base_name)\n    # go case in-sensitive\n    import glob\n    for f in glob.glob(dir_name+'/*.tex'):\n        base_name_s = os.path.basename(fp)\n        if base_name_s.lower() == base_name.lower(): return f\n    return None\n\ndef merge_tex_files_(project_foler, main_file, mode):\n    \"\"\"\n    Merge Tex project recrusively\n    \"\"\"\n    main_file = rm_comments(main_file)\n    for s in reversed([q for q in re.finditer(r\"\\\\input\\{(.*?)\\}\", main_file, re.M)]):\n        f = s.group(1)\n        fp = os.path.join(project_foler, f)\n        fp = find_tex_file_ignore_case(fp)\n        if fp:\n            with open(fp, 'r', encoding='utf-8', errors='replace') as fx: c = fx.read()\n        else:\n            raise RuntimeError(f'找不到{fp}，Tex源文件缺失！')\n        c = merge_tex_files_(project_foler, c, mode)\n        main_file = main_file[:s.span()[0]] + c + main_file[s.span()[1]:]\n    return main_file\n\ndef merge_tex_files(project_foler, main_file, mode):\n    \"\"\"\n    Merge Tex project recrusively\n    P.S. 顺便把CTEX塞进去以支持中文\n    P.S. 顺便把Latex的注释去除\n    \"\"\"\n    main_file = merge_tex_files_(project_foler, main_file, mode)\n    main_file = rm_comments(main_file)\n\n    if mode == 'translate_zh':\n        # find paper documentclass\n        pattern = re.compile(r'\\\\documentclass.*\\n')\n        match = pattern.search(main_file)\n        assert match is not None, \"Cannot find documentclass statement!\"\n        position = match.end()\n        add_ctex = '\\\\usepackage{ctex}\\n'\n        add_url = '\\\\usepackage{url}\\n' if '{url}' not in main_file else ''\n        main_file = main_file[:position] + add_ctex + add_url + main_file[position:]\n        # fontset=windows\n        import platform\n        main_file = re.sub(r\"\\\\documentclass\\[(.*?)\\]{(.*?)}\", r\"\\\\documentclass[\\1,fontset=windows,UTF8]{\\2}\",main_file)\n        main_file = re.sub(r\"\\\\documentclass{(.*?)}\", r\"\\\\documentclass[fontset=windows,UTF8]{\\1}\",main_file)\n        # find paper abstract\n        pattern_opt1 = re.compile(r'\\\\begin\\{abstract\\}.*\\n')\n        pattern_opt2 = re.compile(r\"\\\\abstract\\{(.*?)\\}\", flags=re.DOTALL)\n        match_opt1 = pattern_opt1.search(main_file)\n        match_opt2 = pattern_opt2.search(main_file)\n        assert (match_opt1 is not None) or (match_opt2 is not None), \"Cannot find paper abstract section!\"\n    return main_file\n\n\n\"\"\"\n=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=\nPost process\n=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=\n\"\"\"\ndef mod_inbraket(match):\n    \"\"\"\n    为啥chatgpt会把cite里面的逗号换成中文逗号呀 \n    \"\"\"\n    # get the matched string\n    cmd = match.group(1)\n    str_to_modify = match.group(2)\n    # modify the matched string\n    str_to_modify = str_to_modify.replace('：', ':')    # 前面是中文冒号，后面是英文冒号\n    str_to_modify = str_to_modify.replace('，', ',')    # 前面是中文逗号，后面是英文逗号\n    # str_to_modify = 'BOOM'\n    return \"\\\\\" + cmd + \"{\" + str_to_modify + \"}\"\n\ndef fix_content(final_tex, node_string):\n    \"\"\"\n    Fix common GPT errors to increase success rate\n    \"\"\"\n    final_tex = re.sub(r\"(?<!\\\\)%\", \"\\\\%\", final_tex)\n    final_tex = re.sub(r\"\\\\([a-z]{2,10})\\ \\{\", r\"\\\\\\1{\", string=final_tex)\n    final_tex = re.sub(r\"\\\\\\ ([a-z]{2,10})\\{\", r\"\\\\\\1{\", string=final_tex)\n    final_tex = re.sub(r\"\\\\([a-z]{2,10})\\{([^\\}]*?)\\}\", mod_inbraket, string=final_tex)\n\n    if \"Traceback\" in final_tex and \"[Local Message]\" in final_tex:\n        final_tex = node_string # 出问题了，还原原文\n    if node_string.count('\\\\begin') != final_tex.count('\\\\begin'):\n        final_tex = node_string # 出问题了，还原原文\n    if node_string.count('\\_') > 0 and node_string.count('\\_') > final_tex.count('\\_'):\n        # walk and replace any _ without \\\n        final_tex = re.sub(r\"(?<!\\\\)_\", \"\\\\_\", final_tex)\n\n    def compute_brace_level(string):\n        # this function count the number of { and }\n        brace_level = 0\n        for c in string:\n            if c == \"{\": brace_level += 1\n            elif c == \"}\": brace_level -= 1\n        return brace_level\n    def join_most(tex_t, tex_o):\n        # this function join translated string and original string when something goes wrong\n        p_t = 0\n        p_o = 0\n        def find_next(string, chars, begin):\n            p = begin\n            while p < len(string):\n                if string[p] in chars: return p, string[p]\n                p += 1\n            return None, None\n        while True:\n            res1, char = find_next(tex_o, ['{','}'], p_o)\n            if res1 is None: break\n            res2, char = find_next(tex_t, [char], p_t)\n            if res2 is None: break\n            p_o = res1 + 1\n            p_t = res2 + 1\n        return tex_t[:p_t] + tex_o[p_o:]\n\n    if compute_brace_level(final_tex) != compute_brace_level(node_string):\n        # 出问题了，还原部分原文，保证括号正确\n        final_tex = join_most(final_tex, node_string)\n    return final_tex\n    \ndef compile_latex_with_timeout(command, cwd, timeout=60):\n    import subprocess\n    process = subprocess.Popen(command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, cwd=cwd)\n    try:\n        stdout, stderr = process.communicate(timeout=timeout)\n    except subprocess.TimeoutExpired:\n        process.kill()\n        stdout, stderr = process.communicate()\n        print(\"Process timed out!\")\n        return False\n    return True\n\n\n\ndef merge_pdfs(pdf1_path, pdf2_path, output_path):\n    import PyPDF2\n    Percent = 0.8\n    # Open the first PDF file\n    with open(pdf1_path, 'rb') as pdf1_file:\n        pdf1_reader = PyPDF2.PdfFileReader(pdf1_file)\n        # Open the second PDF file\n        with open(pdf2_path, 'rb') as pdf2_file:\n            pdf2_reader = PyPDF2.PdfFileReader(pdf2_file)\n            # Create a new PDF file to store the merged pages\n            output_writer = PyPDF2.PdfFileWriter()\n            # Determine the number of pages in each PDF file\n            num_pages = max(pdf1_reader.numPages, pdf2_reader.numPages)\n            # Merge the pages from the two PDF files\n            for page_num in range(num_pages):\n                # Add the page from the first PDF file\n                if page_num < pdf1_reader.numPages:\n                    page1 = pdf1_reader.getPage(page_num)\n                else:\n                    page1 = PyPDF2.PageObject.createBlankPage(pdf1_reader)\n                # Add the page from the second PDF file\n                if page_num < pdf2_reader.numPages:\n                    page2 = pdf2_reader.getPage(page_num)\n                else:\n                    page2 = PyPDF2.PageObject.createBlankPage(pdf1_reader)\n                # Create a new empty page with double width\n                new_page = PyPDF2.PageObject.createBlankPage(\n                    width = int(int(page1.mediaBox.getWidth()) + int(page2.mediaBox.getWidth()) * Percent),\n                    height = max(page1.mediaBox.getHeight(), page2.mediaBox.getHeight())\n                )\n                new_page.mergeTranslatedPage(page1, 0, 0)\n                new_page.mergeTranslatedPage(page2, int(int(page1.mediaBox.getWidth())-int(page2.mediaBox.getWidth())* (1-Percent)), 0)\n                output_writer.addPage(new_page)\n            # Save the merged PDF file\n            with open(output_path, 'wb') as output_file:\n                output_writer.write(output_file)\n"
  },
  {
    "path": "crazy_functions/live_audio/aliyunASR.py",
    "content": "import time, threading, json\n\n\nclass AliyunASR():\n\n    def test_on_sentence_begin(self, message, *args):\n        # print(\"test_on_sentence_begin:{}\".format(message))\n        pass\n\n    def test_on_sentence_end(self, message, *args):\n        # print(\"test_on_sentence_end:{}\".format(message))\n        message = json.loads(message)\n        self.parsed_sentence = message['payload']['result']\n        self.event_on_entence_end.set()\n        print(self.parsed_sentence)\n\n    def test_on_start(self, message, *args):\n        # print(\"test_on_start:{}\".format(message))\n        pass\n\n    def test_on_error(self, message, *args):\n        print(\"on_error args=>{}\".format(args))\n        pass\n\n    def test_on_close(self, *args):\n        self.aliyun_service_ok = False\n        pass\n\n    def test_on_result_chg(self, message, *args):\n        # print(\"test_on_chg:{}\".format(message))\n        message = json.loads(message)\n        self.parsed_text = message['payload']['result']\n        self.event_on_result_chg.set()\n\n    def test_on_completed(self, message, *args):\n        # print(\"on_completed:args=>{} message=>{}\".format(args, message))\n        pass\n\n\n    def audio_convertion_thread(self, uuid):\n        # 在一个异步线程中采集音频\n        import nls  # pip install git+https://github.com/aliyun/alibabacloud-nls-python-sdk.git\n        import tempfile\n        from scipy import io\n        from toolbox import get_conf\n        from .audio_io import change_sample_rate\n        from .audio_io import RealtimeAudioDistribution\n        NEW_SAMPLERATE = 16000\n        rad = RealtimeAudioDistribution()\n        rad.clean_up()\n        temp_folder = tempfile.gettempdir()\n        TOKEN, APPKEY = get_conf('ALIYUN_TOKEN', 'ALIYUN_APPKEY')\n        if len(TOKEN) == 0:\n            TOKEN = self.get_token()\n        self.aliyun_service_ok = True\n        URL=\"wss://nls-gateway.aliyuncs.com/ws/v1\"\n        sr = nls.NlsSpeechTranscriber(\n                    url=URL,\n                    token=TOKEN,\n                    appkey=APPKEY,\n                    on_sentence_begin=self.test_on_sentence_begin,\n                    on_sentence_end=self.test_on_sentence_end,\n                    on_start=self.test_on_start,\n                    on_result_changed=self.test_on_result_chg,\n                    on_completed=self.test_on_completed,\n                    on_error=self.test_on_error,\n                    on_close=self.test_on_close,\n                    callback_args=[uuid.hex]\n                )\n\n        r = sr.start(aformat=\"pcm\",\n                enable_intermediate_result=True,\n                enable_punctuation_prediction=True,\n                enable_inverse_text_normalization=True)\n\n        while not self.stop:\n            # time.sleep(self.capture_interval)\n            audio = rad.read(uuid.hex) \n            if audio is not None:\n                # convert to pcm file\n                temp_file = f'{temp_folder}/{uuid.hex}.pcm' # \n                dsdata = change_sample_rate(audio, rad.rate, NEW_SAMPLERATE) # 48000 --> 16000\n                io.wavfile.write(temp_file, NEW_SAMPLERATE, dsdata)\n                # read pcm binary\n                with open(temp_file, \"rb\") as f: data = f.read()\n                # print('audio len:', len(audio), '\\t ds len:', len(dsdata), '\\t need n send:', len(data)//640)\n                slices = zip(*(iter(data),) * 640)    # 640个字节为一组\n                for i in slices: sr.send_audio(bytes(i))\n            else:\n                time.sleep(0.1)\n\n            if not self.aliyun_service_ok:\n                self.stop = True\n                self.stop_msg = 'Aliyun音频服务异常，请检查ALIYUN_TOKEN和ALIYUN_APPKEY是否过期。'\n        r = sr.stop()\n\n    def get_token(self):\n        from toolbox import get_conf\n        import json\n        from aliyunsdkcore.request import CommonRequest\n        from aliyunsdkcore.client import AcsClient\n        AccessKey_ID, AccessKey_secret = get_conf('ALIYUN_ACCESSKEY', 'ALIYUN_SECRET')\n\n        # 创建AcsClient实例\n        client = AcsClient(\n            AccessKey_ID,\n            AccessKey_secret,\n            \"cn-shanghai\"\n        )\n\n        # 创建request，并设置参数。\n        request = CommonRequest()\n        request.set_method('POST')\n        request.set_domain('nls-meta.cn-shanghai.aliyuncs.com')\n        request.set_version('2019-02-28')\n        request.set_action_name('CreateToken')\n\n        try:\n            response = client.do_action_with_exception(request)\n            print(response)\n            jss = json.loads(response)\n            if 'Token' in jss and 'Id' in jss['Token']:\n                token = jss['Token']['Id']\n                expireTime = jss['Token']['ExpireTime']\n                print(\"token = \" + token)\n                print(\"expireTime = \" + str(expireTime))\n        except Exception as e:\n            print(e)\n\n        return token\n"
  },
  {
    "path": "crazy_functions/live_audio/audio_io.py",
    "content": "import numpy as np\nfrom scipy import interpolate\n\ndef Singleton(cls):\n    _instance = {}\n \n    def _singleton(*args, **kargs):\n        if cls not in _instance:\n            _instance[cls] = cls(*args, **kargs)\n        return _instance[cls]\n \n    return _singleton\n\n\n@Singleton\nclass RealtimeAudioDistribution():\n    def __init__(self) -> None:\n        self.data = {}\n        self.max_len = 1024*1024\n        self.rate = 48000   # 只读，每秒采样数量\n\n    def clean_up(self):\n        self.data = {}\n\n    def feed(self, uuid, audio):\n        self.rate, audio_ = audio\n        # print('feed', len(audio_), audio_[-25:])\n        if uuid not in self.data:\n            self.data[uuid] = audio_\n        else:\n            new_arr = np.concatenate((self.data[uuid], audio_))\n            if len(new_arr) > self.max_len: new_arr = new_arr[-self.max_len:]\n            self.data[uuid] = new_arr\n\n    def read(self, uuid):\n        if uuid in self.data:\n            res = self.data.pop(uuid)\n            print('\\r read-', len(res), '-', max(res), end='', flush=True)\n        else:\n            res = None\n        return res\n    \ndef change_sample_rate(audio, old_sr, new_sr):\n    duration = audio.shape[0] / old_sr\n\n    time_old  = np.linspace(0, duration, audio.shape[0])\n    time_new  = np.linspace(0, duration, int(audio.shape[0] * new_sr / old_sr))\n\n    interpolator = interpolate.interp1d(time_old, audio.T)\n    new_audio = interpolator(time_new).T\n    return new_audio.astype(np.int16)"
  },
  {
    "path": "crazy_functions/test_project/cpp/cppipc/buffer.cpp",
    "content": "#include \"libipc/buffer.h\"\n#include \"libipc/utility/pimpl.h\"\n\n#include <cstring>\n\nnamespace ipc {\n\nbool operator==(buffer const & b1, buffer const & b2) {\n    return (b1.size() == b2.size()) && (std::memcmp(b1.data(), b2.data(), b1.size()) == 0);\n}\n\nbool operator!=(buffer const & b1, buffer const & b2) {\n    return !(b1 == b2);\n}\n\nclass buffer::buffer_ : public pimpl<buffer_> {\npublic:\n    void*       p_;\n    std::size_t s_;\n    void*       a_;\n    buffer::destructor_t d_;\n\n    buffer_(void* p, std::size_t s, buffer::destructor_t d, void* a)\n        : p_(p), s_(s), a_(a), d_(d) {\n    }\n\n    ~buffer_() {\n        if (d_ == nullptr) return;\n        d_((a_ == nullptr) ? p_ : a_, s_);\n    }\n};\n\nbuffer::buffer()\n    : buffer(nullptr, 0, nullptr, nullptr) {\n}\n\nbuffer::buffer(void* p, std::size_t s, destructor_t d)\n    : p_(p_->make(p, s, d, nullptr)) {\n}\n\nbuffer::buffer(void* p, std::size_t s, destructor_t d, void* additional)\n    : p_(p_->make(p, s, d, additional)) {\n}\n\nbuffer::buffer(void* p, std::size_t s)\n    : buffer(p, s, nullptr) {\n}\n\nbuffer::buffer(char const & c)\n    : buffer(const_cast<char*>(&c), 1) {\n}\n\nbuffer::buffer(buffer&& rhs)\n    : buffer() {\n    swap(rhs);\n}\n\nbuffer::~buffer() {\n    p_->clear();\n}\n\nvoid buffer::swap(buffer& rhs) {\n    std::swap(p_, rhs.p_);\n}\n\nbuffer& buffer::operator=(buffer rhs) {\n    swap(rhs);\n    return *this;\n}\n\nbool buffer::empty() const noexcept {\n    return (impl(p_)->p_ == nullptr) || (impl(p_)->s_ == 0);\n}\n\nvoid* buffer::data() noexcept {\n    return impl(p_)->p_;\n}\n\nvoid const * buffer::data() const noexcept {\n    return impl(p_)->p_;\n}\n\nstd::size_t buffer::size() const noexcept {\n    return impl(p_)->s_;\n}\n\n} // namespace ipc\n"
  },
  {
    "path": "crazy_functions/test_project/cpp/cppipc/ipc.cpp",
    "content": "\n#include <type_traits>\n#include <cstring>\n#include <algorithm>\n#include <utility>          // std::pair, std::move, std::forward\n#include <atomic>\n#include <type_traits>      // aligned_storage_t\n#include <string>\n#include <vector>\n#include <array>\n#include <cassert>\n\n#include \"libipc/ipc.h\"\n#include \"libipc/def.h\"\n#include \"libipc/shm.h\"\n#include \"libipc/pool_alloc.h\"\n#include \"libipc/queue.h\"\n#include \"libipc/policy.h\"\n#include \"libipc/rw_lock.h\"\n#include \"libipc/waiter.h\"\n\n#include \"libipc/utility/log.h\"\n#include \"libipc/utility/id_pool.h\"\n#include \"libipc/utility/scope_guard.h\"\n#include \"libipc/utility/utility.h\"\n\n#include \"libipc/memory/resource.h\"\n#include \"libipc/platform/detail.h\"\n#include \"libipc/circ/elem_array.h\"\n\nnamespace {\n\nusing msg_id_t = std::uint32_t;\nusing acc_t    = std::atomic<msg_id_t>;\n\ntemplate <std::size_t DataSize, std::size_t AlignSize>\nstruct msg_t;\n\ntemplate <std::size_t AlignSize>\nstruct msg_t<0, AlignSize> {\n    msg_id_t     cc_id_;\n    msg_id_t     id_;\n    std::int32_t remain_;\n    bool         storage_;\n};\n\ntemplate <std::size_t DataSize, std::size_t AlignSize>\nstruct msg_t : msg_t<0, AlignSize> {\n    std::aligned_storage_t<DataSize, AlignSize> data_ {};\n\n    msg_t() = default;\n    msg_t(msg_id_t cc_id, msg_id_t id, std::int32_t remain, void const * data, std::size_t size)\n        : msg_t<0, AlignSize> {cc_id, id, remain, (data == nullptr) || (size == 0)} {\n        if (this->storage_) {\n            if (data != nullptr) {\n                // copy storage-id\n                *reinterpret_cast<ipc::storage_id_t*>(&data_) =\n                     *static_cast<ipc::storage_id_t const *>(data);\n            }\n        }\n        else std::memcpy(&data_, data, size);\n    }\n};\n\ntemplate <typename T>\nipc::buff_t make_cache(T& data, std::size_t size) {\n    auto ptr = ipc::mem::alloc(size);\n    std::memcpy(ptr, &data, (ipc::detail::min)(sizeof(data), size));\n    return { ptr, size, ipc::mem::free };\n}\n\nstruct cache_t {\n    std::size_t fill_;\n    ipc::buff_t buff_;\n\n    cache_t(std::size_t f, ipc::buff_t && b)\n        : fill_(f), buff_(std::move(b))\n    {}\n\n    void append(void const * data, std::size_t size) {\n        if (fill_ >= buff_.size() || data == nullptr || size == 0) return;\n        auto new_fill = (ipc::detail::min)(fill_ + size, buff_.size());\n        std::memcpy(static_cast<ipc::byte_t*>(buff_.data()) + fill_, data, new_fill - fill_);\n        fill_ = new_fill;\n    }\n};\n\nauto cc_acc() {\n    static ipc::shm::handle acc_h(\"__CA_CONN__\", sizeof(acc_t));\n    return static_cast<acc_t*>(acc_h.get());\n}\n\nIPC_CONSTEXPR_ std::size_t align_chunk_size(std::size_t size) noexcept {\n    return (((size - 1) / ipc::large_msg_align) + 1) * ipc::large_msg_align;\n}\n\nIPC_CONSTEXPR_ std::size_t calc_chunk_size(std::size_t size) noexcept {\n    return ipc::make_align(alignof(std::max_align_t), align_chunk_size(\n           ipc::make_align(alignof(std::max_align_t), sizeof(std::atomic<ipc::circ::cc_t>)) + size));\n}\n\nstruct chunk_t {\n    std::atomic<ipc::circ::cc_t> &conns() noexcept {\n        return *reinterpret_cast<std::atomic<ipc::circ::cc_t> *>(this);\n    }\n\n    void *data() noexcept {\n        return reinterpret_cast<ipc::byte_t *>(this)\n             + ipc::make_align(alignof(std::max_align_t), sizeof(std::atomic<ipc::circ::cc_t>));\n    }\n};\n\nstruct chunk_info_t {\n    ipc::id_pool<> pool_;\n    ipc::spin_lock lock_;\n\n    IPC_CONSTEXPR_ static std::size_t chunks_mem_size(std::size_t chunk_size) noexcept {\n        return ipc::id_pool<>::max_count * chunk_size;\n    }\n\n    ipc::byte_t *chunks_mem() noexcept {\n        return reinterpret_cast<ipc::byte_t *>(this + 1);\n    }\n\n    chunk_t *at(std::size_t chunk_size, ipc::storage_id_t id) noexcept {\n        if (id < 0) return nullptr;\n        return reinterpret_cast<chunk_t *>(chunks_mem() + (chunk_size * id));\n    }\n};\n\nauto& chunk_storages() {\n    class chunk_handle_t {\n        ipc::shm::handle handle_;\n\n    public:\n        chunk_info_t *get_info(std::size_t chunk_size) {\n            if (!handle_.valid() &&\n                !handle_.acquire( (\"__CHUNK_INFO__\" + ipc::to_string(chunk_size)).c_str(), \n                                  sizeof(chunk_info_t) + chunk_info_t::chunks_mem_size(chunk_size) )) {\n                ipc::error(\"[chunk_storages] chunk_shm.id_info_.acquire failed: chunk_size = %zd\\n\", chunk_size);\n                return nullptr;\n            }\n            auto info = static_cast<chunk_info_t*>(handle_.get());\n            if (info == nullptr) {\n                ipc::error(\"[chunk_storages] chunk_shm.id_info_.get failed: chunk_size = %zd\\n\", chunk_size);\n                return nullptr;\n            }\n            return info;\n        }\n    };\n    static ipc::map<std::size_t, chunk_handle_t> chunk_hs;\n    return chunk_hs;\n}\n\nchunk_info_t *chunk_storage_info(std::size_t chunk_size) {\n    auto &storages = chunk_storages();\n    std::decay_t<decltype(storages)>::iterator it;\n    {\n        static ipc::rw_lock lock;\n        IPC_UNUSED_ std::shared_lock<ipc::rw_lock> guard {lock};\n        if ((it = storages.find(chunk_size)) == storages.end()) {\n            using chunk_handle_t = std::decay_t<decltype(storages)>::value_type::second_type;\n            guard.unlock();\n            IPC_UNUSED_ std::lock_guard<ipc::rw_lock> guard {lock};\n            it = storages.emplace(chunk_size, chunk_handle_t{}).first;\n        }\n    }\n    return it->second.get_info(chunk_size);\n}\n\nstd::pair<ipc::storage_id_t, void*> acquire_storage(std::size_t size, ipc::circ::cc_t conns) {\n    std::size_t chunk_size = calc_chunk_size(size);\n    auto info = chunk_storage_info(chunk_size);\n    if (info == nullptr) return {};\n\n    info->lock_.lock();\n    info->pool_.prepare();\n    // got an unique id\n    auto id = info->pool_.acquire();\n    info->lock_.unlock();\n\n    auto chunk = info->at(chunk_size, id);\n    if (chunk == nullptr) return {};\n    chunk->conns().store(conns, std::memory_order_relaxed);\n    return { id, chunk->data() };\n}\n\nvoid *find_storage(ipc::storage_id_t id, std::size_t size) {\n    if (id < 0) {\n        ipc::error(\"[find_storage] id is invalid: id = %ld, size = %zd\\n\", (long)id, size);\n        return nullptr;\n    }\n    std::size_t chunk_size = calc_chunk_size(size);\n    auto info = chunk_storage_info(chunk_size);\n    if (info == nullptr) return nullptr;\n    return info->at(chunk_size, id)->data();\n}\n\nvoid release_storage(ipc::storage_id_t id, std::size_t size) {\n    if (id < 0) {\n        ipc::error(\"[release_storage] id is invalid: id = %ld, size = %zd\\n\", (long)id, size);\n        return;\n    }\n    std::size_t chunk_size = calc_chunk_size(size);\n    auto info = chunk_storage_info(chunk_size);\n    if (info == nullptr) return;\n    info->lock_.lock();\n    info->pool_.release(id);\n    info->lock_.unlock();\n}\n\ntemplate <ipc::relat Rp, ipc::relat Rc>\nbool sub_rc(ipc::wr<Rp, Rc, ipc::trans::unicast>, \n            std::atomic<ipc::circ::cc_t> &/*conns*/, ipc::circ::cc_t /*curr_conns*/, ipc::circ::cc_t /*conn_id*/) noexcept {\n    return true;\n}\n\ntemplate <ipc::relat Rp, ipc::relat Rc>\nbool sub_rc(ipc::wr<Rp, Rc, ipc::trans::broadcast>, \n            std::atomic<ipc::circ::cc_t> &conns, ipc::circ::cc_t curr_conns, ipc::circ::cc_t conn_id) noexcept {\n    auto last_conns = curr_conns & ~conn_id;\n    for (unsigned k = 0;;) {\n        auto chunk_conns  = conns.load(std::memory_order_acquire);\n        if (conns.compare_exchange_weak(chunk_conns, chunk_conns & last_conns, std::memory_order_release)) {\n            return (chunk_conns & last_conns) == 0;\n        }\n        ipc::yield(k);\n    }\n}\n\ntemplate <typename Flag>\nvoid recycle_storage(ipc::storage_id_t id, std::size_t size, ipc::circ::cc_t curr_conns, ipc::circ::cc_t conn_id) {\n    if (id < 0) {\n        ipc::error(\"[recycle_storage] id is invalid: id = %ld, size = %zd\\n\", (long)id, size);\n        return;\n    }\n    std::size_t chunk_size = calc_chunk_size(size);\n    auto info = chunk_storage_info(chunk_size);\n    if (info == nullptr) return;\n\n    auto chunk = info->at(chunk_size, id);\n    if (chunk == nullptr) return;\n\n    if (!sub_rc(Flag{}, chunk->conns(), curr_conns, conn_id)) {\n        return;\n    }\n    info->lock_.lock();\n    info->pool_.release(id);\n    info->lock_.unlock();\n}\n\ntemplate <typename MsgT>\nbool clear_message(void* p) {\n    auto msg = static_cast<MsgT*>(p);\n    if (msg->storage_) {\n        std::int32_t r_size = static_cast<std::int32_t>(ipc::data_length) + msg->remain_;\n        if (r_size <= 0) {\n            ipc::error(\"[clear_message] invalid msg size: %d\\n\", (int)r_size);\n            return true;\n        }\n        release_storage(\n            *reinterpret_cast<ipc::storage_id_t*>(&msg->data_),\n            static_cast<std::size_t>(r_size));\n    }\n    return true;\n}\n\nstruct conn_info_head {\n\n    ipc::string name_;\n    msg_id_t    cc_id_; // connection-info id\n    ipc::detail::waiter cc_waiter_, wt_waiter_, rd_waiter_;\n    ipc::shm::handle acc_h_;\n\n    conn_info_head(char const * name)\n        : name_     {name}\n        , cc_id_    {(cc_acc() == nullptr) ? 0 : cc_acc()->fetch_add(1, std::memory_order_relaxed)}\n        , cc_waiter_{(\"__CC_CONN__\" + name_).c_str()}\n        , wt_waiter_{(\"__WT_CONN__\" + name_).c_str()}\n        , rd_waiter_{(\"__RD_CONN__\" + name_).c_str()}\n        , acc_h_    {(\"__AC_CONN__\" + name_).c_str(), sizeof(acc_t)} {\n    }\n\n    void quit_waiting() {\n        cc_waiter_.quit_waiting();\n        wt_waiter_.quit_waiting();\n        rd_waiter_.quit_waiting();\n    }\n\n    auto acc() {\n        return static_cast<acc_t*>(acc_h_.get());\n    }\n\n    auto& recv_cache() {\n        thread_local ipc::unordered_map<msg_id_t, cache_t> tls;\n        return tls;\n    }\n};\n\ntemplate <typename W, typename F>\nbool wait_for(W& waiter, F&& pred, std::uint64_t tm) {\n    if (tm == 0) return !pred();\n    for (unsigned k = 0; pred();) {\n        bool ret = true;\n        ipc::sleep(k, [&k, &ret, &waiter, &pred, tm] {\n            ret = waiter.wait_if(std::forward<F>(pred), tm);\n            k   = 0;\n        });\n        if (!ret) return false; // timeout or fail\n        if (k == 0) break; // k has been reset\n    }\n    return true;\n}\n\ntemplate <typename Policy,\n          std::size_t DataSize  = ipc::data_length,\n          std::size_t AlignSize = (ipc::detail::min)(DataSize, alignof(std::max_align_t))>\nstruct queue_generator {\n\n    using queue_t = ipc::queue<msg_t<DataSize, AlignSize>, Policy>;\n\n    struct conn_info_t : conn_info_head {\n        queue_t que_;\n\n        conn_info_t(char const * name)\n            : conn_info_head{name}\n            , que_{(\"__QU_CONN__\" +\n                    ipc::to_string(DataSize) + \"__\" +\n                    ipc::to_string(AlignSize) + \"__\" + name).c_str()} {\n        }\n\n        void disconnect_receiver() {\n            bool dis = que_.disconnect();\n            this->quit_waiting();\n            if (dis) {\n                this->recv_cache().clear();\n            }\n        }\n    };\n};\n\ntemplate <typename Policy>\nstruct detail_impl {\n\nusing policy_t    = Policy;\nusing flag_t      = typename policy_t::flag_t;\nusing queue_t     = typename queue_generator<policy_t>::queue_t;\nusing conn_info_t = typename queue_generator<policy_t>::conn_info_t;\n\nconstexpr static conn_info_t* info_of(ipc::handle_t h) noexcept {\n    return static_cast<conn_info_t*>(h);\n}\n\nconstexpr static queue_t* queue_of(ipc::handle_t h) noexcept {\n    return (info_of(h) == nullptr) ? nullptr : &(info_of(h)->que_);\n}\n\n/* API implementations */\n\nstatic void disconnect(ipc::handle_t h) {\n    auto que = queue_of(h);\n    if (que == nullptr) {\n        return;\n    }\n    que->shut_sending();\n    assert(info_of(h) != nullptr);\n    info_of(h)->disconnect_receiver();\n}\n\nstatic bool reconnect(ipc::handle_t * ph, bool start_to_recv) {\n    assert(ph != nullptr);\n    assert(*ph != nullptr);\n    auto que = queue_of(*ph);\n    if (que == nullptr) {\n        return false;\n    }\n    if (start_to_recv) {\n        que->shut_sending();\n        if (que->connect()) { // wouldn't connect twice\n            info_of(*ph)->cc_waiter_.broadcast();\n            return true;\n        }\n        return false;\n    }\n    // start_to_recv == false\n    if (que->connected()) {\n        info_of(*ph)->disconnect_receiver();\n    }\n    return que->ready_sending();\n}\n\nstatic bool connect(ipc::handle_t * ph, char const * name, bool start_to_recv) {\n    assert(ph != nullptr);\n    if (*ph == nullptr) {\n        *ph = ipc::mem::alloc<conn_info_t>(name);\n    }\n    return reconnect(ph, start_to_recv);\n}\n\nstatic void destroy(ipc::handle_t h) {\n    disconnect(h);\n    ipc::mem::free(info_of(h));\n}\n\nstatic std::size_t recv_count(ipc::handle_t h) noexcept {\n    auto que = queue_of(h);\n    if (que == nullptr) {\n        return ipc::invalid_value;\n    }\n    return que->conn_count();\n}\n\nstatic bool wait_for_recv(ipc::handle_t h, std::size_t r_count, std::uint64_t tm) {\n    auto que = queue_of(h);\n    if (que == nullptr) {\n        return false;\n    }\n    return wait_for(info_of(h)->cc_waiter_, [que, r_count] {\n        return que->conn_count() < r_count;\n    }, tm);\n}\n\ntemplate <typename F>\nstatic bool send(F&& gen_push, ipc::handle_t h, void const * data, std::size_t size) {\n    if (data == nullptr || size == 0) {\n        ipc::error(\"fail: send(%p, %zd)\\n\", data, size);\n        return false;\n    }\n    auto que = queue_of(h);\n    if (que == nullptr) {\n        ipc::error(\"fail: send, queue_of(h) == nullptr\\n\");\n        return false;\n    }\n    if (que->elems() == nullptr) {\n        ipc::error(\"fail: send, queue_of(h)->elems() == nullptr\\n\");\n        return false;\n    }\n    if (!que->ready_sending()) {\n        ipc::error(\"fail: send, que->ready_sending() == false\\n\");\n        return false;\n    }\n    ipc::circ::cc_t conns = que->elems()->connections(std::memory_order_relaxed);\n    if (conns == 0) {\n        ipc::error(\"fail: send, there is no receiver on this connection.\\n\");\n        return false;\n    }\n    // calc a new message id\n    auto acc = info_of(h)->acc();\n    if (acc == nullptr) {\n        ipc::error(\"fail: send, info_of(h)->acc() == nullptr\\n\");\n        return false;\n    }\n    auto msg_id   = acc->fetch_add(1, std::memory_order_relaxed);\n    auto try_push = std::forward<F>(gen_push)(info_of(h), que, msg_id);\n    if (size > ipc::large_msg_limit) {\n        auto   dat = acquire_storage(size, conns);\n        void * buf = dat.second;\n        if (buf != nullptr) {\n            std::memcpy(buf, data, size);\n            return try_push(static_cast<std::int32_t>(size) - \n                            static_cast<std::int32_t>(ipc::data_length), &(dat.first), 0);\n        }\n        // try using message fragment\n        //ipc::log(\"fail: shm::handle for big message. msg_id: %zd, size: %zd\\n\", msg_id, size);\n    }\n    // push message fragment\n    std::int32_t offset = 0;\n    for (std::int32_t i = 0; i < static_cast<std::int32_t>(size / ipc::data_length); ++i, offset += ipc::data_length) {\n        if (!try_push(static_cast<std::int32_t>(size) - offset - static_cast<std::int32_t>(ipc::data_length),\n                      static_cast<ipc::byte_t const *>(data) + offset, ipc::data_length)) {\n            return false;\n        }\n    }\n    // if remain > 0, this is the last message fragment\n    std::int32_t remain = static_cast<std::int32_t>(size) - offset;\n    if (remain > 0) {\n        if (!try_push(remain - static_cast<std::int32_t>(ipc::data_length),\n                      static_cast<ipc::byte_t const *>(data) + offset, \n                      static_cast<std::size_t>(remain))) {\n            return false;\n        }\n    }\n    return true;\n}\n\nstatic bool send(ipc::handle_t h, void const * data, std::size_t size, std::uint64_t tm) {\n    return send([tm](auto info, auto que, auto msg_id) {\n        return [tm, info, que, msg_id](std::int32_t remain, void const * data, std::size_t size) {\n            if (!wait_for(info->wt_waiter_, [&] {\n                    return !que->push(\n                        [](void*) { return true; },\n                        info->cc_id_, msg_id, remain, data, size);\n                }, tm)) {\n                ipc::log(\"force_push: msg_id = %zd, remain = %d, size = %zd\\n\", msg_id, remain, size);\n                if (!que->force_push(\n                        clear_message<typename queue_t::value_t>,\n                        info->cc_id_, msg_id, remain, data, size)) {\n                    return false;\n                }\n            }\n            info->rd_waiter_.broadcast();\n            return true;\n        };\n    }, h, data, size);\n}\n\nstatic bool try_send(ipc::handle_t h, void const * data, std::size_t size, std::uint64_t tm) {\n    return send([tm](auto info, auto que, auto msg_id) {\n        return [tm, info, que, msg_id](std::int32_t remain, void const * data, std::size_t size) {\n            if (!wait_for(info->wt_waiter_, [&] {\n                    return !que->push(\n                        [](void*) { return true; },\n                        info->cc_id_, msg_id, remain, data, size);\n                }, tm)) {\n                return false;\n            }\n            info->rd_waiter_.broadcast();\n            return true;\n        };\n    }, h, data, size);\n}\n\nstatic ipc::buff_t recv(ipc::handle_t h, std::uint64_t tm) {\n    auto que = queue_of(h);\n    if (que == nullptr) {\n        ipc::error(\"fail: recv, queue_of(h) == nullptr\\n\");\n        return {};\n    }\n    if (!que->connected()) {\n        // hasn't connected yet, just return.\n        return {};\n    }\n    auto& rc = info_of(h)->recv_cache();\n    for (;;) {\n        // pop a new message\n        typename queue_t::value_t msg;\n        if (!wait_for(info_of(h)->rd_waiter_, [que, &msg] {\n                return !que->pop(msg);\n            }, tm)) {\n            // pop failed, just return.\n            return {};\n        }\n        info_of(h)->wt_waiter_.broadcast();\n        if ((info_of(h)->acc() != nullptr) && (msg.cc_id_ == info_of(h)->cc_id_)) {\n            continue; // ignore message to self\n        }\n        // msg.remain_ may minus & abs(msg.remain_) < data_length\n        std::int32_t r_size = static_cast<std::int32_t>(ipc::data_length) + msg.remain_;\n        if (r_size <= 0) {\n            ipc::error(\"fail: recv, r_size = %d\\n\", (int)r_size);\n            return {};\n        }\n        std::size_t msg_size = static_cast<std::size_t>(r_size);\n        // large message\n        if (msg.storage_) {\n            ipc::storage_id_t buf_id = *reinterpret_cast<ipc::storage_id_t*>(&msg.data_);\n            void* buf = find_storage(buf_id, msg_size);\n            if (buf != nullptr) {\n                struct recycle_t {\n                    ipc::storage_id_t storage_id;\n                    ipc::circ::cc_t   curr_conns;\n                    ipc::circ::cc_t   conn_id;\n                } *r_info = ipc::mem::alloc<recycle_t>(recycle_t{\n                    buf_id, que->elems()->connections(std::memory_order_relaxed), que->connected_id()\n                });\n                if (r_info == nullptr) {\n                    ipc::log(\"fail: ipc::mem::alloc<recycle_t>.\\n\");\n                    return ipc::buff_t{buf, msg_size}; // no recycle\n                } else {\n                    return ipc::buff_t{buf, msg_size, [](void* p_info, std::size_t size) {\n                        auto r_info = static_cast<recycle_t *>(p_info);\n                        IPC_UNUSED_ auto finally = ipc::guard([r_info] {\n                            ipc::mem::free(r_info);\n                        });\n                        recycle_storage<flag_t>(r_info->storage_id, size, r_info->curr_conns, r_info->conn_id);\n                    }, r_info};\n                }\n            } else {\n                ipc::log(\"fail: shm::handle for large message. msg_id: %zd, buf_id: %zd, size: %zd\\n\", msg.id_, buf_id, msg_size);\n                continue;\n            }\n        }\n        // find cache with msg.id_\n        auto cac_it = rc.find(msg.id_);\n        if (cac_it == rc.end()) {\n            if (msg_size <= ipc::data_length) {\n                return make_cache(msg.data_, msg_size);\n            }\n            // gc\n            if (rc.size() > 1024) {\n                std::vector<msg_id_t> need_del;\n                for (auto const & pair : rc) {\n                    auto cmp = std::minmax(msg.id_, pair.first);\n                    if (cmp.second - cmp.first > 8192) {\n                        need_del.push_back(pair.first);\n                    }\n                }\n                for (auto id : need_del) rc.erase(id);\n            }\n            // cache the first message fragment\n            rc.emplace(msg.id_, cache_t { ipc::data_length, make_cache(msg.data_, msg_size) });\n        }\n        // has cached before this message\n        else {\n            auto& cac = cac_it->second;\n            // this is the last message fragment\n            if (msg.remain_ <= 0) {\n                cac.append(&(msg.data_), msg_size);\n                // finish this message, erase it from cache\n                auto buff = std::move(cac.buff_);\n                rc.erase(cac_it);\n                return buff;\n            }\n            // there are remain datas after this message\n            cac.append(&(msg.data_), ipc::data_length);\n        }\n    }\n}\n\nstatic ipc::buff_t try_recv(ipc::handle_t h) {\n    return recv(h, 0);\n}\n\n}; // detail_impl<Policy>\n\ntemplate <typename Flag>\nusing policy_t = ipc::policy::choose<ipc::circ::elem_array, Flag>;\n\n} // internal-linkage\n\nnamespace ipc {\n\ntemplate <typename Flag>\nipc::handle_t chan_impl<Flag>::inited() {\n    ipc::detail::waiter::init();\n    return nullptr;\n}\n\ntemplate <typename Flag>\nbool chan_impl<Flag>::connect(ipc::handle_t * ph, char const * name, unsigned mode) {\n    return detail_impl<policy_t<Flag>>::connect(ph, name, mode & receiver);\n}\n\ntemplate <typename Flag>\nbool chan_impl<Flag>::reconnect(ipc::handle_t * ph, unsigned mode) {\n    return detail_impl<policy_t<Flag>>::reconnect(ph, mode & receiver);\n}\n\ntemplate <typename Flag>\nvoid chan_impl<Flag>::disconnect(ipc::handle_t h) {\n    detail_impl<policy_t<Flag>>::disconnect(h);\n}\n\ntemplate <typename Flag>\nvoid chan_impl<Flag>::destroy(ipc::handle_t h) {\n    detail_impl<policy_t<Flag>>::destroy(h);\n}\n\ntemplate <typename Flag>\nchar const * chan_impl<Flag>::name(ipc::handle_t h) {\n    auto info = detail_impl<policy_t<Flag>>::info_of(h);\n    return (info == nullptr) ? nullptr : info->name_.c_str();\n}\n\ntemplate <typename Flag>\nstd::size_t chan_impl<Flag>::recv_count(ipc::handle_t h) {\n    return detail_impl<policy_t<Flag>>::recv_count(h);\n}\n\ntemplate <typename Flag>\nbool chan_impl<Flag>::wait_for_recv(ipc::handle_t h, std::size_t r_count, std::uint64_t tm) {\n    return detail_impl<policy_t<Flag>>::wait_for_recv(h, r_count, tm);\n}\n\ntemplate <typename Flag>\nbool chan_impl<Flag>::send(ipc::handle_t h, void const * data, std::size_t size, std::uint64_t tm) {\n    return detail_impl<policy_t<Flag>>::send(h, data, size, tm);\n}\n\ntemplate <typename Flag>\nbuff_t chan_impl<Flag>::recv(ipc::handle_t h, std::uint64_t tm) {\n    return detail_impl<policy_t<Flag>>::recv(h, tm);\n}\n\ntemplate <typename Flag>\nbool chan_impl<Flag>::try_send(ipc::handle_t h, void const * data, std::size_t size, std::uint64_t tm) {\n    return detail_impl<policy_t<Flag>>::try_send(h, data, size, tm);\n}\n\ntemplate <typename Flag>\nbuff_t chan_impl<Flag>::try_recv(ipc::handle_t h) {\n    return detail_impl<policy_t<Flag>>::try_recv(h);\n}\n\ntemplate struct chan_impl<ipc::wr<relat::single, relat::single, trans::unicast  >>;\n// template struct chan_impl<ipc::wr<relat::single, relat::multi , trans::unicast  >>; // TBD\n// template struct chan_impl<ipc::wr<relat::multi , relat::multi , trans::unicast  >>; // TBD\ntemplate struct chan_impl<ipc::wr<relat::single, relat::multi , trans::broadcast>>;\ntemplate struct chan_impl<ipc::wr<relat::multi , relat::multi , trans::broadcast>>;\n\n} // namespace ipc\n"
  },
  {
    "path": "crazy_functions/test_project/cpp/cppipc/policy.h",
    "content": "#pragma once\n\n#include <type_traits>\n\n#include \"libipc/def.h\"\n#include \"libipc/prod_cons.h\"\n\n#include \"libipc/circ/elem_array.h\"\n\nnamespace ipc {\nnamespace policy {\n\ntemplate <template <typename, std::size_t...> class Elems, typename Flag>\nstruct choose;\n\ntemplate <typename Flag>\nstruct choose<circ::elem_array, Flag> {\n    using flag_t = Flag;\n\n    template <std::size_t DataSize, std::size_t AlignSize>\n    using elems_t = circ::elem_array<ipc::prod_cons_impl<flag_t>, DataSize, AlignSize>;\n};\n\n} // namespace policy\n} // namespace ipc\n"
  },
  {
    "path": "crazy_functions/test_project/cpp/cppipc/pool_alloc.cpp",
    "content": "#include \"libipc/pool_alloc.h\"\n\n#include \"libipc/memory/resource.h\"\n\nnamespace ipc {\nnamespace mem {\n\nvoid* pool_alloc::alloc(std::size_t size) {\n    return async_pool_alloc::alloc(size);\n}\n\nvoid pool_alloc::free(void* p, std::size_t size) {\n    async_pool_alloc::free(p, size);\n}\n\n} // namespace mem\n} // namespace ipc\n"
  },
  {
    "path": "crazy_functions/test_project/cpp/cppipc/prod_cons.h",
    "content": "#pragma once\n\n#include <atomic>\n#include <utility>\n#include <cstring>\n#include <type_traits>\n#include <cstdint>\n\n#include \"libipc/def.h\"\n\n#include \"libipc/platform/detail.h\"\n#include \"libipc/circ/elem_def.h\"\n#include \"libipc/utility/log.h\"\n#include \"libipc/utility/utility.h\"\n\nnamespace ipc {\n\n////////////////////////////////////////////////////////////////\n/// producer-consumer implementation\n////////////////////////////////////////////////////////////////\n\ntemplate <typename Flag>\nstruct prod_cons_impl;\n\ntemplate <>\nstruct prod_cons_impl<wr<relat::single, relat::single, trans::unicast>> {\n\n    template <std::size_t DataSize, std::size_t AlignSize>\n    struct elem_t {\n        std::aligned_storage_t<DataSize, AlignSize> data_ {};\n    };\n\n    alignas(cache_line_size) std::atomic<circ::u2_t> rd_; // read index\n    alignas(cache_line_size) std::atomic<circ::u2_t> wt_; // write index\n\n    constexpr circ::u2_t cursor() const noexcept {\n        return 0;\n    }\n\n    template <typename W, typename F, typename E>\n    bool push(W* /*wrapper*/, F&& f, E* elems) {\n        auto cur_wt = circ::index_of(wt_.load(std::memory_order_relaxed));\n        if (cur_wt == circ::index_of(rd_.load(std::memory_order_acquire) - 1)) {\n            return false; // full\n        }\n        std::forward<F>(f)(&(elems[cur_wt].data_));\n        wt_.fetch_add(1, std::memory_order_release);\n        return true;\n    }\n\n    /**\n     * In single-single-unicast, 'force_push' means 'no reader' or 'the only one reader is dead'.\n     * So we could just disconnect all connections of receiver, and return false.\n    */\n    template <typename W, typename F, typename E>\n    bool force_push(W* wrapper, F&&, E*) {\n        wrapper->elems()->disconnect_receiver(~static_cast<circ::cc_t>(0u));\n        return false;\n    }\n\n    template <typename W, typename F, typename R, typename E>\n    bool pop(W* /*wrapper*/, circ::u2_t& /*cur*/, F&& f, R&& out, E* elems) {\n        auto cur_rd = circ::index_of(rd_.load(std::memory_order_relaxed));\n        if (cur_rd == circ::index_of(wt_.load(std::memory_order_acquire))) {\n            return false; // empty\n        }\n        std::forward<F>(f)(&(elems[cur_rd].data_));\n        std::forward<R>(out)(true);\n        rd_.fetch_add(1, std::memory_order_release);\n        return true;\n    }\n};\n\ntemplate <>\nstruct prod_cons_impl<wr<relat::single, relat::multi , trans::unicast>>\n     : prod_cons_impl<wr<relat::single, relat::single, trans::unicast>> {\n\n    template <typename W, typename F, typename E>\n    bool force_push(W* wrapper, F&&, E*) {\n        wrapper->elems()->disconnect_receiver(1);\n        return false;\n    }\n\n    template <typename W, typename F, typename R, \n              template <std::size_t, std::size_t> class E, std::size_t DS, std::size_t AS>\n    bool pop(W* /*wrapper*/, circ::u2_t& /*cur*/, F&& f, R&& out, E<DS, AS>* elems) {\n        byte_t buff[DS];\n        for (unsigned k = 0;;) {\n            auto cur_rd = rd_.load(std::memory_order_relaxed);\n            if (circ::index_of(cur_rd) ==\n                circ::index_of(wt_.load(std::memory_order_acquire))) {\n                return false; // empty\n            }\n            std::memcpy(buff, &(elems[circ::index_of(cur_rd)].data_), sizeof(buff));\n            if (rd_.compare_exchange_weak(cur_rd, cur_rd + 1, std::memory_order_release)) {\n                std::forward<F>(f)(buff);\n                std::forward<R>(out)(true);\n                return true;\n            }\n            ipc::yield(k);\n        }\n    }\n};\n\ntemplate <>\nstruct prod_cons_impl<wr<relat::multi , relat::multi, trans::unicast>>\n     : prod_cons_impl<wr<relat::single, relat::multi, trans::unicast>> {\n\n    using flag_t = std::uint64_t;\n\n    template <std::size_t DataSize, std::size_t AlignSize>\n    struct elem_t {\n        std::aligned_storage_t<DataSize, AlignSize> data_ {};\n        std::atomic<flag_t> f_ct_ { 0 }; // commit flag\n    };\n\n    alignas(cache_line_size) std::atomic<circ::u2_t> ct_; // commit index\n\n    template <typename W, typename F, typename E>\n    bool push(W* /*wrapper*/, F&& f, E* elems) {\n        circ::u2_t cur_ct, nxt_ct;\n        for (unsigned k = 0;;) {\n            cur_ct = ct_.load(std::memory_order_relaxed);\n            if (circ::index_of(nxt_ct = cur_ct + 1) ==\n                circ::index_of(rd_.load(std::memory_order_acquire))) {\n                return false; // full\n            }\n            if (ct_.compare_exchange_weak(cur_ct, nxt_ct, std::memory_order_acq_rel)) {\n                break;\n            }\n            ipc::yield(k);\n        }\n        auto* el = elems + circ::index_of(cur_ct);\n        std::forward<F>(f)(&(el->data_));\n        // set flag & try update wt\n        el->f_ct_.store(~static_cast<flag_t>(cur_ct), std::memory_order_release);\n        while (1) {\n            auto cac_ct = el->f_ct_.load(std::memory_order_acquire);\n            if (cur_ct != wt_.load(std::memory_order_relaxed)) {\n                return true;\n            }\n            if ((~cac_ct) != cur_ct) {\n                return true;\n            }\n            if (!el->f_ct_.compare_exchange_strong(cac_ct, 0, std::memory_order_relaxed)) {\n                return true;\n            }\n            wt_.store(nxt_ct, std::memory_order_release);\n            cur_ct = nxt_ct;\n            nxt_ct = cur_ct + 1;\n            el = elems + circ::index_of(cur_ct);\n        }\n        return true;\n    }\n\n    template <typename W, typename F, typename E>\n    bool force_push(W* wrapper, F&&, E*) {\n        wrapper->elems()->disconnect_receiver(1);\n        return false;\n    }\n\n    template <typename W, typename F, typename R, \n              template <std::size_t, std::size_t> class E, std::size_t DS, std::size_t AS>\n    bool pop(W* /*wrapper*/, circ::u2_t& /*cur*/, F&& f, R&& out, E<DS, AS>* elems) {\n        byte_t buff[DS];\n        for (unsigned k = 0;;) {\n            auto cur_rd = rd_.load(std::memory_order_relaxed);\n            auto cur_wt = wt_.load(std::memory_order_acquire);\n            auto id_rd  = circ::index_of(cur_rd);\n            auto id_wt  = circ::index_of(cur_wt);\n            if (id_rd == id_wt) {\n                auto* el = elems + id_wt;\n                auto cac_ct = el->f_ct_.load(std::memory_order_acquire);\n                if ((~cac_ct) != cur_wt) {\n                    return false; // empty\n                }\n                if (el->f_ct_.compare_exchange_weak(cac_ct, 0, std::memory_order_relaxed)) {\n                    wt_.store(cur_wt + 1, std::memory_order_release);\n                }\n                k = 0;\n            }\n            else {\n                std::memcpy(buff, &(elems[circ::index_of(cur_rd)].data_), sizeof(buff));\n                if (rd_.compare_exchange_weak(cur_rd, cur_rd + 1, std::memory_order_release)) {\n                    std::forward<F>(f)(buff);\n                    std::forward<R>(out)(true);\n                    return true;\n                }\n                ipc::yield(k);\n            }\n        }\n    }\n};\n\ntemplate <>\nstruct prod_cons_impl<wr<relat::single, relat::multi, trans::broadcast>> {\n\n    using rc_t = std::uint64_t;\n\n    enum : rc_t {\n        ep_mask = 0x00000000ffffffffull,\n        ep_incr = 0x0000000100000000ull\n    };\n\n    template <std::size_t DataSize, std::size_t AlignSize>\n    struct elem_t {\n        std::aligned_storage_t<DataSize, AlignSize> data_ {};\n        std::atomic<rc_t> rc_ { 0 }; // read-counter\n    };\n\n    alignas(cache_line_size) std::atomic<circ::u2_t> wt_;   // write index\n    alignas(cache_line_size) rc_t epoch_ { 0 };             // only one writer\n\n    circ::u2_t cursor() const noexcept {\n        return wt_.load(std::memory_order_acquire);\n    }\n\n    template <typename W, typename F, typename E>\n    bool push(W* wrapper, F&& f, E* elems) {\n        E* el;\n        for (unsigned k = 0;;) {\n            circ::cc_t cc = wrapper->elems()->connections(std::memory_order_relaxed);\n            if (cc == 0) return false; // no reader\n            el = elems + circ::index_of(wt_.load(std::memory_order_relaxed));\n            // check all consumers have finished reading this element\n            auto cur_rc = el->rc_.load(std::memory_order_acquire);\n            circ::cc_t rem_cc = cur_rc & ep_mask;\n            if ((cc & rem_cc) && ((cur_rc & ~ep_mask) == epoch_)) {\n                return false; // has not finished yet\n            }\n            // consider rem_cc to be 0 here\n            if (el->rc_.compare_exchange_weak(\n                        cur_rc, epoch_ | static_cast<rc_t>(cc), std::memory_order_release)) {\n                break;\n            }\n            ipc::yield(k);\n        }\n        std::forward<F>(f)(&(el->data_));\n        wt_.fetch_add(1, std::memory_order_release);\n        return true;\n    }\n\n    template <typename W, typename F, typename E>\n    bool force_push(W* wrapper, F&& f, E* elems) {\n        E* el;\n        epoch_ += ep_incr;\n        for (unsigned k = 0;;) {\n            circ::cc_t cc = wrapper->elems()->connections(std::memory_order_relaxed);\n            if (cc == 0) return false; // no reader\n            el = elems + circ::index_of(wt_.load(std::memory_order_relaxed));\n            // check all consumers have finished reading this element\n            auto cur_rc = el->rc_.load(std::memory_order_acquire);\n            circ::cc_t rem_cc = cur_rc & ep_mask;\n            if (cc & rem_cc) {\n                ipc::log(\"force_push: k = %u, cc = %u, rem_cc = %u\\n\", k, cc, rem_cc);\n                cc = wrapper->elems()->disconnect_receiver(rem_cc); // disconnect all invalid readers\n                if (cc == 0) return false; // no reader\n            }\n            // just compare & exchange\n            if (el->rc_.compare_exchange_weak(\n                        cur_rc, epoch_ | static_cast<rc_t>(cc), std::memory_order_release)) {\n                break;\n            }\n            ipc::yield(k);\n        }\n        std::forward<F>(f)(&(el->data_));\n        wt_.fetch_add(1, std::memory_order_release);\n        return true;\n    }\n\n    template <typename W, typename F, typename R, typename E>\n    bool pop(W* wrapper, circ::u2_t& cur, F&& f, R&& out, E* elems) {\n        if (cur == cursor()) return false; // acquire\n        auto* el = elems + circ::index_of(cur++);\n        std::forward<F>(f)(&(el->data_));\n        for (unsigned k = 0;;) {\n            auto cur_rc = el->rc_.load(std::memory_order_acquire);\n            if ((cur_rc & ep_mask) == 0) {\n                std::forward<R>(out)(true);\n                return true;\n            }\n            auto nxt_rc = cur_rc & ~static_cast<rc_t>(wrapper->connected_id());\n            if (el->rc_.compare_exchange_weak(cur_rc, nxt_rc, std::memory_order_release)) {\n                std::forward<R>(out)((nxt_rc & ep_mask) == 0);\n                return true;\n            }\n            ipc::yield(k);\n        }\n    }\n};\n\ntemplate <>\nstruct prod_cons_impl<wr<relat::multi, relat::multi, trans::broadcast>> {\n\n    using rc_t   = std::uint64_t;\n    using flag_t = std::uint64_t;\n\n    enum : rc_t {\n        rc_mask = 0x00000000ffffffffull,\n        ep_mask = 0x00ffffffffffffffull,\n        ep_incr = 0x0100000000000000ull,\n        ic_mask = 0xff000000ffffffffull,\n        ic_incr = 0x0000000100000000ull\n    };\n\n    template <std::size_t DataSize, std::size_t AlignSize>\n    struct elem_t {\n        std::aligned_storage_t<DataSize, AlignSize> data_ {};\n        std::atomic<rc_t  > rc_   { 0 }; // read-counter\n        std::atomic<flag_t> f_ct_ { 0 }; // commit flag\n    };\n\n    alignas(cache_line_size) std::atomic<circ::u2_t> ct_;   // commit index\n    alignas(cache_line_size) std::atomic<rc_t> epoch_ { 0 };\n\n    circ::u2_t cursor() const noexcept {\n        return ct_.load(std::memory_order_acquire);\n    }\n\n    constexpr static rc_t inc_rc(rc_t rc) noexcept {\n        return (rc & ic_mask) | ((rc + ic_incr) & ~ic_mask);\n    }\n\n    constexpr static rc_t inc_mask(rc_t rc) noexcept {\n        return inc_rc(rc) & ~rc_mask;\n    }\n\n    template <typename W, typename F, typename E>\n    bool push(W* wrapper, F&& f, E* elems) {\n        E* el;\n        circ::u2_t cur_ct;\n        rc_t epoch = epoch_.load(std::memory_order_acquire);\n        for (unsigned k = 0;;) {\n            circ::cc_t cc = wrapper->elems()->connections(std::memory_order_relaxed);\n            if (cc == 0) return false; // no reader\n            el = elems + circ::index_of(cur_ct = ct_.load(std::memory_order_relaxed));\n            // check all consumers have finished reading this element\n            auto cur_rc = el->rc_.load(std::memory_order_relaxed);\n            circ::cc_t rem_cc = cur_rc & rc_mask;\n            if ((cc & rem_cc) && ((cur_rc & ~ep_mask) == epoch)) {\n                return false; // has not finished yet\n            }\n            else if (!rem_cc) {\n                auto cur_fl = el->f_ct_.load(std::memory_order_acquire);\n                if ((cur_fl != cur_ct) && cur_fl) {\n                    return false; // full\n                }\n            }\n            // consider rem_cc to be 0 here\n            if (el->rc_.compare_exchange_weak(\n                        cur_rc, inc_mask(epoch | (cur_rc & ep_mask)) | static_cast<rc_t>(cc), std::memory_order_relaxed) &&\n                epoch_.compare_exchange_weak(epoch, epoch, std::memory_order_acq_rel)) {\n                break;\n            }\n            ipc::yield(k);\n        }\n        // only one thread/process would touch here at one time\n        ct_.store(cur_ct + 1, std::memory_order_release);\n        std::forward<F>(f)(&(el->data_));\n        // set flag & try update wt\n        el->f_ct_.store(~static_cast<flag_t>(cur_ct), std::memory_order_release);\n        return true;\n    }\n\n    template <typename W, typename F, typename E>\n    bool force_push(W* wrapper, F&& f, E* elems) {\n        E* el;\n        circ::u2_t cur_ct;\n        rc_t epoch = epoch_.fetch_add(ep_incr, std::memory_order_release) + ep_incr;\n        for (unsigned k = 0;;) {\n            circ::cc_t cc = wrapper->elems()->connections(std::memory_order_relaxed);\n            if (cc == 0) return false; // no reader\n            el = elems + circ::index_of(cur_ct = ct_.load(std::memory_order_relaxed));\n            // check all consumers have finished reading this element\n            auto cur_rc = el->rc_.load(std::memory_order_acquire);\n            circ::cc_t rem_cc = cur_rc & rc_mask;\n            if (cc & rem_cc) {\n                ipc::log(\"force_push: k = %u, cc = %u, rem_cc = %u\\n\", k, cc, rem_cc);\n                cc = wrapper->elems()->disconnect_receiver(rem_cc); // disconnect all invalid readers\n                if (cc == 0) return false; // no reader\n            }\n            // just compare & exchange\n            if (el->rc_.compare_exchange_weak(\n                        cur_rc, inc_mask(epoch | (cur_rc & ep_mask)) | static_cast<rc_t>(cc), std::memory_order_relaxed)) {\n                if (epoch == epoch_.load(std::memory_order_acquire)) {\n                    break;\n                }\n                else if (push(wrapper, std::forward<F>(f), elems)) {\n                    return true;\n                }\n                epoch = epoch_.fetch_add(ep_incr, std::memory_order_release) + ep_incr;\n            }\n            ipc::yield(k);\n        }\n        // only one thread/process would touch here at one time\n        ct_.store(cur_ct + 1, std::memory_order_release);\n        std::forward<F>(f)(&(el->data_));\n        // set flag & try update wt\n        el->f_ct_.store(~static_cast<flag_t>(cur_ct), std::memory_order_release);\n        return true;\n    }\n\n    template <typename W, typename F, typename R, typename E, std::size_t N>\n    bool pop(W* wrapper, circ::u2_t& cur, F&& f, R&& out, E(& elems)[N]) {\n        auto* el = elems + circ::index_of(cur);\n        auto cur_fl = el->f_ct_.load(std::memory_order_acquire);\n        if (cur_fl != ~static_cast<flag_t>(cur)) {\n            return false; // empty\n        }\n        ++cur;\n        std::forward<F>(f)(&(el->data_));\n        for (unsigned k = 0;;) {\n            auto cur_rc = el->rc_.load(std::memory_order_acquire);\n            if ((cur_rc & rc_mask) == 0) {\n                std::forward<R>(out)(true);\n                el->f_ct_.store(cur + N - 1, std::memory_order_release);\n                return true;\n            }\n            auto nxt_rc = inc_rc(cur_rc) & ~static_cast<rc_t>(wrapper->connected_id());\n            bool last_one = false;\n            if ((last_one = (nxt_rc & rc_mask) == 0)) {\n                el->f_ct_.store(cur + N - 1, std::memory_order_release);\n            }\n            if (el->rc_.compare_exchange_weak(cur_rc, nxt_rc, std::memory_order_release)) {\n                std::forward<R>(out)(last_one);\n                return true;\n            }\n            ipc::yield(k);\n        }\n    }\n};\n\n} // namespace ipc\n"
  },
  {
    "path": "crazy_functions/test_project/cpp/cppipc/queue.h",
    "content": "#pragma once\n\n#include <type_traits>\n#include <new>\n#include <utility>  // [[since C++14]]: std::exchange\n#include <algorithm>\n#include <atomic>\n#include <tuple>\n#include <thread>\n#include <chrono>\n#include <string>\n#include <cassert>  // assert\n\n#include \"libipc/def.h\"\n#include \"libipc/shm.h\"\n#include \"libipc/rw_lock.h\"\n\n#include \"libipc/utility/log.h\"\n#include \"libipc/platform/detail.h\"\n#include \"libipc/circ/elem_def.h\"\n\nnamespace ipc {\nnamespace detail {\n\nclass queue_conn {\nprotected:\n    circ::cc_t connected_ = 0;\n    shm::handle elems_h_;\n\n    template <typename Elems>\n    Elems* open(char const * name) {\n        if (name == nullptr || name[0] == '\\0') {\n            ipc::error(\"fail open waiter: name is empty!\\n\");\n            return nullptr;\n        }\n        if (!elems_h_.acquire(name, sizeof(Elems))) {\n            return nullptr;\n        }\n        auto elems = static_cast<Elems*>(elems_h_.get());\n        if (elems == nullptr) {\n            ipc::error(\"fail acquire elems: %s\\n\", name);\n            return nullptr;\n        }\n        elems->init();\n        return elems;\n    }\n\n    void close() {\n        elems_h_.release();\n    }\n\npublic:\n    queue_conn() = default;\n    queue_conn(const queue_conn&) = delete;\n    queue_conn& operator=(const queue_conn&) = delete;\n\n    bool connected() const noexcept {\n        return connected_ != 0;\n    }\n\n    circ::cc_t connected_id() const noexcept {\n        return connected_;\n    }\n\n    template <typename Elems>\n    auto connect(Elems* elems) noexcept\n                         /*needs 'optional' here*/\n     -> std::tuple<bool, bool, decltype(std::declval<Elems>().cursor())> {\n        if (elems == nullptr) return {};\n        // if it's already connected, just return\n        if (connected()) return {connected(), false, 0};\n        connected_ = elems->connect_receiver();\n        return {connected(), true, elems->cursor()};\n    }\n\n    template <typename Elems>\n    bool disconnect(Elems* elems) noexcept {\n        if (elems == nullptr) return false;\n        // if it's already disconnected, just return false\n        if (!connected()) return false;\n        elems->disconnect_receiver(std::exchange(connected_, 0));\n        return true;\n    }\n};\n\ntemplate <typename Elems>\nclass queue_base : public queue_conn {\n    using base_t = queue_conn;\n\npublic:\n    using elems_t  = Elems;\n    using policy_t = typename elems_t::policy_t;\n\nprotected:\n    elems_t * elems_ = nullptr;\n    decltype(std::declval<elems_t>().cursor()) cursor_ = 0;\n    bool sender_flag_ = false;\n\npublic:\n    using base_t::base_t;\n\n    queue_base() = default;\n\n    explicit queue_base(char const * name)\n        : queue_base{} {\n        elems_ = open<elems_t>(name);\n    }\n\n    explicit queue_base(elems_t * elems) noexcept\n        : queue_base{} {\n        assert(elems != nullptr);\n        elems_ = elems;\n    }\n\n    /* not virtual */ ~queue_base() {\n        base_t::close();\n    }\n\n    elems_t       * elems()       noexcept { return elems_; }\n    elems_t const * elems() const noexcept { return elems_; }\n\n    bool ready_sending() noexcept {\n        if (elems_ == nullptr) return false;\n        return sender_flag_ || (sender_flag_ = elems_->connect_sender());\n    }\n\n    void shut_sending() noexcept {\n        if (elems_ == nullptr) return;\n        if (!sender_flag_) return;\n        elems_->disconnect_sender();\n    }\n\n    bool connect() noexcept {\n        auto tp = base_t::connect(elems_);\n        if (std::get<0>(tp) && std::get<1>(tp)) {\n            cursor_ = std::get<2>(tp);\n            return true;\n        }\n        return std::get<0>(tp);\n    }\n\n    bool disconnect() noexcept {\n        return base_t::disconnect(elems_);\n    }\n\n    std::size_t conn_count() const noexcept {\n        return (elems_ == nullptr) ? static_cast<std::size_t>(invalid_value) : elems_->conn_count();\n    }\n\n    bool valid() const noexcept {\n        return elems_ != nullptr;\n    }\n\n    bool empty() const noexcept {\n        return !valid() || (cursor_ == elems_->cursor());\n    }\n\n    template <typename T, typename F, typename... P>\n    bool push(F&& prep, P&&... params) {\n        if (elems_ == nullptr) return false;\n        return elems_->push(this, [&](void* p) {\n            if (prep(p)) ::new (p) T(std::forward<P>(params)...);\n        });\n    }\n\n    template <typename T, typename F, typename... P>\n    bool force_push(F&& prep, P&&... params) {\n        if (elems_ == nullptr) return false;\n        return elems_->force_push(this, [&](void* p) {\n            if (prep(p)) ::new (p) T(std::forward<P>(params)...);\n        });\n    }\n\n    template <typename T, typename F>\n    bool pop(T& item, F&& out) {\n        if (elems_ == nullptr) {\n            return false;\n        }\n        return elems_->pop(this, &(this->cursor_), [&item](void* p) {\n            ::new (&item) T(std::move(*static_cast<T*>(p)));\n        }, std::forward<F>(out));\n    }\n};\n\n} // namespace detail\n\ntemplate <typename T, typename Policy>\nclass queue final : public detail::queue_base<typename Policy::template elems_t<sizeof(T), alignof(T)>> {\n    using base_t = detail::queue_base<typename Policy::template elems_t<sizeof(T), alignof(T)>>;\n\npublic:\n    using value_t = T;\n\n    using base_t::base_t;\n\n    template <typename... P>\n    bool push(P&&... params) {\n        return base_t::template push<T>(std::forward<P>(params)...);\n    }\n\n    template <typename... P>\n    bool force_push(P&&... params) {\n        return base_t::template force_push<T>(std::forward<P>(params)...);\n    }\n\n    bool pop(T& item) {\n        return base_t::pop(item, [](bool) {});\n    }\n\n    template <typename F>\n    bool pop(T& item, F&& out) {\n        return base_t::pop(item, std::forward<F>(out));\n    }\n};\n\n} // namespace ipc\n"
  },
  {
    "path": "crazy_functions/test_project/cpp/cppipc/shm.cpp",
    "content": "\n#include <string>\n#include <utility>\n\n#include \"libipc/shm.h\"\n\n#include \"libipc/utility/pimpl.h\"\n#include \"libipc/memory/resource.h\"\n\nnamespace ipc {\nnamespace shm {\n\nclass handle::handle_ : public pimpl<handle_> {\npublic:\n    shm::id_t id_ = nullptr;\n    void*     m_  = nullptr;\n\n    ipc::string n_;\n    std::size_t s_ = 0;\n};\n\nhandle::handle()\n    : p_(p_->make()) {\n}\n\nhandle::handle(char const * name, std::size_t size, unsigned mode)\n    : handle() {\n    acquire(name, size, mode);\n}\n\nhandle::handle(handle&& rhs)\n    : handle() {\n    swap(rhs);\n}\n\nhandle::~handle() {\n    release();\n    p_->clear();\n}\n\nvoid handle::swap(handle& rhs) {\n    std::swap(p_, rhs.p_);\n}\n\nhandle& handle::operator=(handle rhs) {\n    swap(rhs);\n    return *this;\n}\n\nbool handle::valid() const noexcept {\n    return impl(p_)->m_ != nullptr;\n}\n\nstd::size_t handle::size() const noexcept {\n    return impl(p_)->s_;\n}\n\nchar const * handle::name() const noexcept {\n    return impl(p_)->n_.c_str();\n}\n\nstd::int32_t handle::ref() const noexcept {\n    return shm::get_ref(impl(p_)->id_);\n}\n\nvoid handle::sub_ref() noexcept {\n    shm::sub_ref(impl(p_)->id_);\n}\n\nbool handle::acquire(char const * name, std::size_t size, unsigned mode) {\n    release();\n    impl(p_)->id_ = shm::acquire((impl(p_)->n_ = name).c_str(), size, mode);\n    impl(p_)->m_  = shm::get_mem(impl(p_)->id_, &(impl(p_)->s_));\n    return valid();\n}\n\nstd::int32_t handle::release() {\n    if (impl(p_)->id_ == nullptr) return -1;\n    return shm::release(detach());\n}\n\nvoid* handle::get() const {\n    return impl(p_)->m_;\n}\n\nvoid handle::attach(id_t id) {\n    if (id == nullptr) return;\n    release();\n    impl(p_)->id_ = id;\n    impl(p_)->m_  = shm::get_mem(impl(p_)->id_, &(impl(p_)->s_));\n}\n\nid_t handle::detach() {\n    auto old = impl(p_)->id_;\n    impl(p_)->id_ = nullptr;\n    impl(p_)->m_  = nullptr;\n    impl(p_)->s_  = 0;\n    impl(p_)->n_.clear();\n    return old;\n}\n\n} // namespace shm\n} // namespace ipc\n"
  },
  {
    "path": "crazy_functions/test_project/cpp/cppipc/waiter.h",
    "content": "#pragma once\n\n#include <utility>\n#include <string>\n#include <mutex>\n#include <atomic>\n\n#include \"libipc/def.h\"\n#include \"libipc/mutex.h\"\n#include \"libipc/condition.h\"\n#include \"libipc/platform/detail.h\"\n\nnamespace ipc {\nnamespace detail {\n\nclass waiter {\n    ipc::sync::condition cond_;\n    ipc::sync::mutex     lock_;\n    std::atomic<bool>    quit_ {false};\n\npublic:\n    static void init();\n\n    waiter() = default;\n    waiter(char const *name) {\n        open(name);\n    }\n\n    ~waiter() {\n        close();\n    }\n\n    bool valid() const noexcept {\n        return cond_.valid() && lock_.valid();\n    }\n\n    bool open(char const *name) noexcept {\n        quit_.store(false, std::memory_order_relaxed);\n        if (!cond_.open((std::string{\"_waiter_cond_\"} + name).c_str())) {\n            return false;\n        }\n        if (!lock_.open((std::string{\"_waiter_lock_\"} + name).c_str())) {\n            cond_.close();\n            return false;\n        }\n        return valid();\n    }\n\n    void close() noexcept {\n        cond_.close();\n        lock_.close();\n    }\n\n    template <typename F>\n    bool wait_if(F &&pred, std::uint64_t tm = ipc::invalid_value) noexcept {\n        IPC_UNUSED_ std::lock_guard<ipc::sync::mutex> guard {lock_};\n        while ([this, &pred] {\n                    return !quit_.load(std::memory_order_relaxed)\n                        && std::forward<F>(pred)();\n                }()) {\n            if (!cond_.wait(lock_, tm)) return false;\n        }\n        return true;\n    }\n\n    bool notify() noexcept {\n        std::lock_guard<ipc::sync::mutex>{lock_}; // barrier\n        return cond_.notify(lock_);\n    }\n\n    bool broadcast() noexcept {\n        std::lock_guard<ipc::sync::mutex>{lock_}; // barrier\n        return cond_.broadcast(lock_);\n    }\n\n    bool quit_waiting() {\n        quit_.store(true, std::memory_order_release);\n        return broadcast();\n    }\n};\n\n} // namespace detail\n} // namespace ipc\n"
  },
  {
    "path": "crazy_functions/test_project/cpp/cppipc/来源",
    "content": "https://github.com/mutouyun/cpp-ipc\n\nA high-performance inter-process communication library using shared memory on Linux/Windows."
  },
  {
    "path": "crazy_functions/test_project/cpp/libJPG/jpgd.cpp",
    "content": "// jpgd.cpp - C++ class for JPEG decompression.\n// Public domain, Rich Geldreich <richgel99@gmail.com>\n// Last updated Apr. 16, 2011\n// Alex Evans: Linear memory allocator (taken from jpge.h).\n//\n// Supports progressive and baseline sequential JPEG image files, and the most common chroma subsampling factors: Y, H1V1, H2V1, H1V2, and H2V2.\n//\n// Chroma upsampling quality: H2V2 is upsampled in the frequency domain, H2V1 and H1V2 are upsampled using point sampling.\n// Chroma upsampling reference: \"Fast Scheme for Image Size Change in the Compressed Domain\"\n// http://vision.ai.uiuc.edu/~dugad/research/dct/index.html\n\n#include \"jpgd.h\"\n#include <string.h>\n\n#include <assert.h>\n// BEGIN EPIC MOD\n#define JPGD_ASSERT(x) { assert(x); CA_ASSUME(x); } (void)0\n// END EPIC MOD\n\n#ifdef _MSC_VER\n#pragma warning (disable : 4611) // warning C4611: interaction between '_setjmp' and C++ object destruction is non-portable\n#endif\n\n// Set to 1 to enable freq. domain chroma upsampling on images using H2V2 subsampling (0=faster nearest neighbor sampling).\n// This is slower, but results in higher quality on images with highly saturated colors.\n#define JPGD_SUPPORT_FREQ_DOMAIN_UPSAMPLING 1\n\n#define JPGD_TRUE (1)\n#define JPGD_FALSE (0)\n\n#define JPGD_MAX(a,b) (((a)>(b)) ? (a) : (b))\n#define JPGD_MIN(a,b) (((a)<(b)) ? (a) : (b))\n\nnamespace jpgd {\n\n\tstatic inline void *jpgd_malloc(size_t nSize) { return FMemory::Malloc(nSize); }\n\tstatic inline void jpgd_free(void *p) { FMemory::Free(p); }\n\n// BEGIN EPIC MOD\n//@UE3 - use UE3 BGRA encoding instead of assuming RGBA\n\t// stolen from IImageWrapper.h\n\tenum ERGBFormatJPG\n\t{\n\t\tInvalid = -1,\n\t\tRGBA =  0,\n\t\tBGRA =  1,\n\t\tGray =  2,\n\t};\n\tstatic ERGBFormatJPG jpg_format;\n// END EPIC MOD\n\n\t// DCT coefficients are stored in this sequence.\n\tstatic int g_ZAG[64] = {  0,1,8,16,9,2,3,10,17,24,32,25,18,11,4,5,12,19,26,33,40,48,41,34,27,20,13,6,7,14,21,28,35,42,49,56,57,50,43,36,29,22,15,23,30,37,44,51,58,59,52,45,38,31,39,46,53,60,61,54,47,55,62,63 };\n\n\tenum JPEG_MARKER\n\t{\n\t\tM_SOF0  = 0xC0, M_SOF1  = 0xC1, M_SOF2  = 0xC2, M_SOF3  = 0xC3, M_SOF5  = 0xC5, M_SOF6  = 0xC6, M_SOF7  = 0xC7, M_JPG   = 0xC8,\n\t\tM_SOF9  = 0xC9, M_SOF10 = 0xCA, M_SOF11 = 0xCB, M_SOF13 = 0xCD, M_SOF14 = 0xCE, M_SOF15 = 0xCF, M_DHT   = 0xC4, M_DAC   = 0xCC,\n\t\tM_RST0  = 0xD0, M_RST1  = 0xD1, M_RST2  = 0xD2, M_RST3  = 0xD3, M_RST4  = 0xD4, M_RST5  = 0xD5, M_RST6  = 0xD6, M_RST7  = 0xD7,\n\t\tM_SOI   = 0xD8, M_EOI   = 0xD9, M_SOS   = 0xDA, M_DQT   = 0xDB, M_DNL   = 0xDC, M_DRI   = 0xDD, M_DHP   = 0xDE, M_EXP   = 0xDF,\n\t\tM_APP0  = 0xE0, M_APP15 = 0xEF, M_JPG0  = 0xF0, M_JPG13 = 0xFD, M_COM   = 0xFE, M_TEM   = 0x01, M_ERROR = 0x100, RST0   = 0xD0\n\t};\n\n\tenum JPEG_SUBSAMPLING { JPGD_GRAYSCALE = 0, JPGD_YH1V1, JPGD_YH2V1, JPGD_YH1V2, JPGD_YH2V2 };\n\n#define CONST_BITS  13\n#define PASS1_BITS  2\n#define SCALEDONE ((int32)1)\n\n#define FIX_0_298631336  ((int32)2446)        /* FIX(0.298631336) */\n#define FIX_0_390180644  ((int32)3196)        /* FIX(0.390180644) */\n#define FIX_0_541196100  ((int32)4433)        /* FIX(0.541196100) */\n#define FIX_0_765366865  ((int32)6270)        /* FIX(0.765366865) */\n#define FIX_0_899976223  ((int32)7373)        /* FIX(0.899976223) */\n#define FIX_1_175875602  ((int32)9633)        /* FIX(1.175875602) */\n#define FIX_1_501321110  ((int32)12299)       /* FIX(1.501321110) */\n#define FIX_1_847759065  ((int32)15137)       /* FIX(1.847759065) */\n#define FIX_1_961570560  ((int32)16069)       /* FIX(1.961570560) */\n#define FIX_2_053119869  ((int32)16819)       /* FIX(2.053119869) */\n#define FIX_2_562915447  ((int32)20995)       /* FIX(2.562915447) */\n#define FIX_3_072711026  ((int32)25172)       /* FIX(3.072711026) */\n\n#define DESCALE(x,n)  (((x) + (SCALEDONE << ((n)-1))) >> (n))\n#define DESCALE_ZEROSHIFT(x,n)  (((x) + (128 << (n)) + (SCALEDONE << ((n)-1))) >> (n))\n\n#define MULTIPLY(var, cnst)  ((var) * (cnst))\n\n#define CLAMP(i) ((static_cast<uint>(i) > 255) ? (((~i) >> 31) & 0xFF) : (i))\n\n\t// Compiler creates a fast path 1D IDCT for X non-zero columns\n\ttemplate <int NONZERO_COLS>\n\tstruct Row\n\t{\n\t\tstatic void idct(int* pTemp, const jpgd_block_t* pSrc)\n\t\t{\n\t\t\t// ACCESS_COL() will be optimized at compile time to either an array access, or 0.\n#define ACCESS_COL(x) (((x) < NONZERO_COLS) ? (int)pSrc[x] : 0)\n\n\t\t\tconst int z2 = ACCESS_COL(2), z3 = ACCESS_COL(6);\n\n\t\t\tconst int z1 = MULTIPLY(z2 + z3, FIX_0_541196100);\n\t\t\tconst int tmp2 = z1 + MULTIPLY(z3, - FIX_1_847759065);\n\t\t\tconst int tmp3 = z1 + MULTIPLY(z2, FIX_0_765366865);\n\n\t\t\tconst int tmp0 = (ACCESS_COL(0) + ACCESS_COL(4)) << CONST_BITS;\n\t\t\tconst int tmp1 = (ACCESS_COL(0) - ACCESS_COL(4)) << CONST_BITS;\n\n\t\t\tconst int tmp10 = tmp0 + tmp3, tmp13 = tmp0 - tmp3, tmp11 = tmp1 + tmp2, tmp12 = tmp1 - tmp2;\n\n\t\t\tconst int atmp0 = ACCESS_COL(7), atmp1 = ACCESS_COL(5), atmp2 = ACCESS_COL(3), atmp3 = ACCESS_COL(1);\n\n\t\t\tconst int bz1 = atmp0 + atmp3, bz2 = atmp1 + atmp2, bz3 = atmp0 + atmp2, bz4 = atmp1 + atmp3;\n\t\t\tconst int bz5 = MULTIPLY(bz3 + bz4, FIX_1_175875602);\n\n\t\t\tconst int az1 = MULTIPLY(bz1, - FIX_0_899976223);\n\t\t\tconst int az2 = MULTIPLY(bz2, - FIX_2_562915447);\n\t\t\tconst int az3 = MULTIPLY(bz3, - FIX_1_961570560) + bz5;\n\t\t\tconst int az4 = MULTIPLY(bz4, - FIX_0_390180644) + bz5;\n\n\t\t\tconst int btmp0 = MULTIPLY(atmp0, FIX_0_298631336) + az1 + az3;\n\t\t\tconst int btmp1 = MULTIPLY(atmp1, FIX_2_053119869) + az2 + az4;\n\t\t\tconst int btmp2 = MULTIPLY(atmp2, FIX_3_072711026) + az2 + az3;\n\t\t\tconst int btmp3 = MULTIPLY(atmp3, FIX_1_501321110) + az1 + az4;\n\n\t\t\tpTemp[0] = DESCALE(tmp10 + btmp3, CONST_BITS-PASS1_BITS);\n\t\t\tpTemp[7] = DESCALE(tmp10 - btmp3, CONST_BITS-PASS1_BITS);\n\t\t\tpTemp[1] = DESCALE(tmp11 + btmp2, CONST_BITS-PASS1_BITS);\n\t\t\tpTemp[6] = DESCALE(tmp11 - btmp2, CONST_BITS-PASS1_BITS);\n\t\t\tpTemp[2] = DESCALE(tmp12 + btmp1, CONST_BITS-PASS1_BITS);\n\t\t\tpTemp[5] = DESCALE(tmp12 - btmp1, CONST_BITS-PASS1_BITS);\n\t\t\tpTemp[3] = DESCALE(tmp13 + btmp0, CONST_BITS-PASS1_BITS);\n\t\t\tpTemp[4] = DESCALE(tmp13 - btmp0, CONST_BITS-PASS1_BITS);\n\t\t}\n\t};\n\n\ttemplate <>\n\tstruct Row<0>\n\t{\n\t\tstatic void idct(int* pTemp, const jpgd_block_t* pSrc)\n\t\t{\n#ifdef _MSC_VER\n\t\t\tpTemp; pSrc;\n#endif\n\t\t}\n\t};\n\n\ttemplate <>\n\tstruct Row<1>\n\t{\n\t\tstatic void idct(int* pTemp, const jpgd_block_t* pSrc)\n\t\t{\n\t\t\tconst int dcval = (pSrc[0] << PASS1_BITS);\n\n\t\t\tpTemp[0] = dcval;\n\t\t\tpTemp[1] = dcval;\n\t\t\tpTemp[2] = dcval;\n\t\t\tpTemp[3] = dcval;\n\t\t\tpTemp[4] = dcval;\n\t\t\tpTemp[5] = dcval;\n\t\t\tpTemp[6] = dcval;\n\t\t\tpTemp[7] = dcval;\n\t\t}\n\t};\n\n\t// Compiler creates a fast path 1D IDCT for X non-zero rows\n\ttemplate <int NONZERO_ROWS>\n\tstruct Col\n\t{\n\t\tstatic void idct(uint8* pDst_ptr, const int* pTemp)\n\t\t{\n\t\t\t// ACCESS_ROW() will be optimized at compile time to either an array access, or 0.\n#define ACCESS_ROW(x) (((x) < NONZERO_ROWS) ? pTemp[x * 8] : 0)\n\n\t\t\tconst int z2 = ACCESS_ROW(2);\n\t\t\tconst int z3 = ACCESS_ROW(6);\n\n\t\t\tconst int z1 = MULTIPLY(z2 + z3, FIX_0_541196100);\n\t\t\tconst int tmp2 = z1 + MULTIPLY(z3, - FIX_1_847759065);\n\t\t\tconst int tmp3 = z1 + MULTIPLY(z2, FIX_0_765366865);\n\n\t\t\tconst int tmp0 = (ACCESS_ROW(0) + ACCESS_ROW(4)) << CONST_BITS;\n\t\t\tconst int tmp1 = (ACCESS_ROW(0) - ACCESS_ROW(4)) << CONST_BITS;\n\n\t\t\tconst int tmp10 = tmp0 + tmp3, tmp13 = tmp0 - tmp3, tmp11 = tmp1 + tmp2, tmp12 = tmp1 - tmp2;\n\n\t\t\tconst int atmp0 = ACCESS_ROW(7), atmp1 = ACCESS_ROW(5), atmp2 = ACCESS_ROW(3), atmp3 = ACCESS_ROW(1);\n\n\t\t\tconst int bz1 = atmp0 + atmp3, bz2 = atmp1 + atmp2, bz3 = atmp0 + atmp2, bz4 = atmp1 + atmp3;\n\t\t\tconst int bz5 = MULTIPLY(bz3 + bz4, FIX_1_175875602);\n\n\t\t\tconst int az1 = MULTIPLY(bz1, - FIX_0_899976223);\n\t\t\tconst int az2 = MULTIPLY(bz2, - FIX_2_562915447);\n\t\t\tconst int az3 = MULTIPLY(bz3, - FIX_1_961570560) + bz5;\n\t\t\tconst int az4 = MULTIPLY(bz4, - FIX_0_390180644) + bz5;\n\n\t\t\tconst int btmp0 = MULTIPLY(atmp0, FIX_0_298631336) + az1 + az3;\n\t\t\tconst int btmp1 = MULTIPLY(atmp1, FIX_2_053119869) + az2 + az4;\n\t\t\tconst int btmp2 = MULTIPLY(atmp2, FIX_3_072711026) + az2 + az3;\n\t\t\tconst int btmp3 = MULTIPLY(atmp3, FIX_1_501321110) + az1 + az4;\n\n\t\t\tint i = DESCALE_ZEROSHIFT(tmp10 + btmp3, CONST_BITS+PASS1_BITS+3);\n\t\t\tpDst_ptr[8*0] = (uint8)CLAMP(i);\n\n\t\t\ti = DESCALE_ZEROSHIFT(tmp10 - btmp3, CONST_BITS+PASS1_BITS+3);\n\t\t\tpDst_ptr[8*7] = (uint8)CLAMP(i);\n\n\t\t\ti = DESCALE_ZEROSHIFT(tmp11 + btmp2, CONST_BITS+PASS1_BITS+3);\n\t\t\tpDst_ptr[8*1] = (uint8)CLAMP(i);\n\n\t\t\ti = DESCALE_ZEROSHIFT(tmp11 - btmp2, CONST_BITS+PASS1_BITS+3);\n\t\t\tpDst_ptr[8*6] = (uint8)CLAMP(i);\n\n\t\t\ti = DESCALE_ZEROSHIFT(tmp12 + btmp1, CONST_BITS+PASS1_BITS+3);\n\t\t\tpDst_ptr[8*2] = (uint8)CLAMP(i);\n\n\t\t\ti = DESCALE_ZEROSHIFT(tmp12 - btmp1, CONST_BITS+PASS1_BITS+3);\n\t\t\tpDst_ptr[8*5] = (uint8)CLAMP(i);\n\n\t\t\ti = DESCALE_ZEROSHIFT(tmp13 + btmp0, CONST_BITS+PASS1_BITS+3);\n\t\t\tpDst_ptr[8*3] = (uint8)CLAMP(i);\n\n\t\t\ti = DESCALE_ZEROSHIFT(tmp13 - btmp0, CONST_BITS+PASS1_BITS+3);\n\t\t\tpDst_ptr[8*4] = (uint8)CLAMP(i);\n\t\t}\n\t};\n\n\ttemplate <>\n\tstruct Col<1>\n\t{\n\t\tstatic void idct(uint8* pDst_ptr, const int* pTemp)\n\t\t{\n\t\t\tint dcval = DESCALE_ZEROSHIFT(pTemp[0], PASS1_BITS+3);\n\t\t\tconst uint8 dcval_clamped = (uint8)CLAMP(dcval);\n\t\t\tpDst_ptr[0*8] = dcval_clamped;\n\t\t\tpDst_ptr[1*8] = dcval_clamped;\n\t\t\tpDst_ptr[2*8] = dcval_clamped;\n\t\t\tpDst_ptr[3*8] = dcval_clamped;\n\t\t\tpDst_ptr[4*8] = dcval_clamped;\n\t\t\tpDst_ptr[5*8] = dcval_clamped;\n\t\t\tpDst_ptr[6*8] = dcval_clamped;\n\t\t\tpDst_ptr[7*8] = dcval_clamped;\n\t\t}\n\t};\n\n\tstatic const uint8 s_idct_row_table[] =\n\t{\n\t\t1,0,0,0,0,0,0,0, 2,0,0,0,0,0,0,0, 2,1,0,0,0,0,0,0, 2,1,1,0,0,0,0,0, 2,2,1,0,0,0,0,0, 3,2,1,0,0,0,0,0, 4,2,1,0,0,0,0,0, 4,3,1,0,0,0,0,0,\n\t\t4,3,2,0,0,0,0,0, 4,3,2,1,0,0,0,0, 4,3,2,1,1,0,0,0, 4,3,2,2,1,0,0,0, 4,3,3,2,1,0,0,0, 4,4,3,2,1,0,0,0, 5,4,3,2,1,0,0,0, 6,4,3,2,1,0,0,0,\n\t\t6,5,3,2,1,0,0,0, 6,5,4,2,1,0,0,0, 6,5,4,3,1,0,0,0, 6,5,4,3,2,0,0,0, 6,5,4,3,2,1,0,0, 6,5,4,3,2,1,1,0, 6,5,4,3,2,2,1,0, 6,5,4,3,3,2,1,0,\n\t\t6,5,4,4,3,2,1,0, 6,5,5,4,3,2,1,0, 6,6,5,4,3,2,1,0, 7,6,5,4,3,2,1,0, 8,6,5,4,3,2,1,0, 8,7,5,4,3,2,1,0, 8,7,6,4,3,2,1,0, 8,7,6,5,3,2,1,0,\n\t\t8,7,6,5,4,2,1,0, 8,7,6,5,4,3,1,0, 8,7,6,5,4,3,2,0, 8,7,6,5,4,3,2,1, 8,7,6,5,4,3,2,2, 8,7,6,5,4,3,3,2, 8,7,6,5,4,4,3,2, 8,7,6,5,5,4,3,2,\n\t\t8,7,6,6,5,4,3,2, 8,7,7,6,5,4,3,2, 8,8,7,6,5,4,3,2, 8,8,8,6,5,4,3,2, 8,8,8,7,5,4,3,2, 8,8,8,7,6,4,3,2, 8,8,8,7,6,5,3,2, 8,8,8,7,6,5,4,2,\n\t\t8,8,8,7,6,5,4,3, 8,8,8,7,6,5,4,4, 8,8,8,7,6,5,5,4, 8,8,8,7,6,6,5,4, 8,8,8,7,7,6,5,4, 8,8,8,8,7,6,5,4, 8,8,8,8,8,6,5,4, 8,8,8,8,8,7,5,4,\n\t\t8,8,8,8,8,7,6,4, 8,8,8,8,8,7,6,5, 8,8,8,8,8,7,6,6, 8,8,8,8,8,7,7,6, 8,8,8,8,8,8,7,6, 8,8,8,8,8,8,8,6, 8,8,8,8,8,8,8,7, 8,8,8,8,8,8,8,8,\n\t};\n\n\tstatic const uint8 s_idct_col_table[] = { 1, 1, 2, 3, 3, 3, 3, 3, 3, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8 };\n\n\tvoid idct(const jpgd_block_t* pSrc_ptr, uint8* pDst_ptr, int block_max_zag)\n\t{\n\t\tJPGD_ASSERT(block_max_zag >= 1);\n\t\tJPGD_ASSERT(block_max_zag <= 64);\n\n\t\tif (block_max_zag == 1)\n\t\t{\n\t\t\tint k = ((pSrc_ptr[0] + 4) >> 3) + 128;\n\t\t\tk = CLAMP(k);\n\t\t\tk = k | (k<<8);\n\t\t\tk = k | (k<<16);\n\n\t\t\tfor (int i = 8; i > 0; i--)\n\t\t\t{\n\t\t\t\t*(int*)&pDst_ptr[0] = k;\n\t\t\t\t*(int*)&pDst_ptr[4] = k;\n\t\t\t\tpDst_ptr += 8;\n\t\t\t}\n\t\t\treturn;\n\t\t}\n\n\t\tint temp[64];\n\n\t\tconst jpgd_block_t* pSrc = pSrc_ptr;\n\t\tint* pTemp = temp;\n\n\t\tconst uint8* pRow_tab = &s_idct_row_table[(block_max_zag - 1) * 8];\n\t\tint i;\n\t\tfor (i = 8; i > 0; i--, pRow_tab++)\n\t\t{\n\t\t\tswitch (*pRow_tab)\n\t\t\t{\n\t\t\tcase 0: Row<0>::idct(pTemp, pSrc); break;\n\t\t\tcase 1: Row<1>::idct(pTemp, pSrc); break;\n\t\t\tcase 2: Row<2>::idct(pTemp, pSrc); break;\n\t\t\tcase 3: Row<3>::idct(pTemp, pSrc); break;\n\t\t\tcase 4: Row<4>::idct(pTemp, pSrc); break;\n\t\t\tcase 5: Row<5>::idct(pTemp, pSrc); break;\n\t\t\tcase 6: Row<6>::idct(pTemp, pSrc); break;\n\t\t\tcase 7: Row<7>::idct(pTemp, pSrc); break;\n\t\t\tcase 8: Row<8>::idct(pTemp, pSrc); break;\n\t\t\t}\n\n\t\t\tpSrc += 8;\n\t\t\tpTemp += 8;\n\t\t}\n\n\t\tpTemp = temp;\n\n\t\tconst int nonzero_rows = s_idct_col_table[block_max_zag - 1];\n\t\tfor (i = 8; i > 0; i--)\n\t\t{\n\t\t\tswitch (nonzero_rows)\n\t\t\t{\n\t\t\tcase 1: Col<1>::idct(pDst_ptr, pTemp); break;\n\t\t\tcase 2: Col<2>::idct(pDst_ptr, pTemp); break;\n\t\t\tcase 3: Col<3>::idct(pDst_ptr, pTemp); break;\n\t\t\tcase 4: Col<4>::idct(pDst_ptr, pTemp); break;\n\t\t\tcase 5: Col<5>::idct(pDst_ptr, pTemp); break;\n\t\t\tcase 6: Col<6>::idct(pDst_ptr, pTemp); break;\n\t\t\tcase 7: Col<7>::idct(pDst_ptr, pTemp); break;\n\t\t\tcase 8: Col<8>::idct(pDst_ptr, pTemp); break;\n\t\t\t}\n\n\t\t\tpTemp++;\n\t\t\tpDst_ptr++;\n\t\t}\n\t}\n\n\tvoid idct_4x4(const jpgd_block_t* pSrc_ptr, uint8* pDst_ptr)\n\t{\n\t\tint temp[64];\n\t\tint* pTemp = temp;\n\t\tconst jpgd_block_t* pSrc = pSrc_ptr;\n\n\t\tfor (int i = 4; i > 0; i--)\n\t\t{\n\t\t\tRow<4>::idct(pTemp, pSrc);\n\t\t\tpSrc += 8;\n\t\t\tpTemp += 8;\n\t\t}\n\n\t\tpTemp = temp;\n\t\tfor (int i = 8; i > 0; i--)\n\t\t{\n\t\t\tCol<4>::idct(pDst_ptr, pTemp);\n\t\t\tpTemp++;\n\t\t\tpDst_ptr++;\n\t\t}\n\t}\n\n\t// Retrieve one character from the input stream.\n\tinline uint jpeg_decoder::get_char()\n\t{\n\t\t// Any bytes remaining in buffer?\n\t\tif (!m_in_buf_left)\n\t\t{\n\t\t\t// Try to get more bytes.\n\t\t\tprep_in_buffer();\n\t\t\t// Still nothing to get?\n\t\t\tif (!m_in_buf_left)\n\t\t\t{\n\t\t\t\t// Pad the end of the stream with 0xFF 0xD9 (EOI marker)\n\t\t\t\tint t = m_tem_flag;\n\t\t\t\tm_tem_flag ^= 1;\n\t\t\t\tif (t)\n\t\t\t\t\treturn 0xD9;\n\t\t\t\telse\n\t\t\t\t\treturn 0xFF;\n\t\t\t}\n\t\t}\n\n\t\tuint c = *m_pIn_buf_ofs++;\n\t\tm_in_buf_left--;\n\n\t\treturn c;\n\t}\n\n\t// Same as previous method, except can indicate if the character is a pad character or not.\n\tinline uint jpeg_decoder::get_char(bool *pPadding_flag)\n\t{\n\t\tif (!m_in_buf_left)\n\t\t{\n\t\t\tprep_in_buffer();\n\t\t\tif (!m_in_buf_left)\n\t\t\t{\n\t\t\t\t*pPadding_flag = true;\n\t\t\t\tint t = m_tem_flag;\n\t\t\t\tm_tem_flag ^= 1;\n\t\t\t\tif (t)\n\t\t\t\t\treturn 0xD9;\n\t\t\t\telse\n\t\t\t\t\treturn 0xFF;\n\t\t\t}\n\t\t}\n\n\t\t*pPadding_flag = false;\n\n\t\tuint c = *m_pIn_buf_ofs++;\n\t\tm_in_buf_left--;\n\n\t\treturn c;\n\t}\n\n\t// Inserts a previously retrieved character back into the input buffer.\n\tinline void jpeg_decoder::stuff_char(uint8 q)\n\t{\n\t\t*(--m_pIn_buf_ofs) = q;\n\t\tm_in_buf_left++;\n\t}\n\n\t// Retrieves one character from the input stream, but does not read past markers. Will continue to return 0xFF when a marker is encountered.\n\tinline uint8 jpeg_decoder::get_octet()\n\t{\n\t\tbool padding_flag;\n\t\tint c = get_char(&padding_flag);\n\n\t\tif (c == 0xFF)\n\t\t{\n\t\t\tif (padding_flag)\n\t\t\t\treturn 0xFF;\n\n\t\t\tc = get_char(&padding_flag);\n\t\t\tif (padding_flag)\n\t\t\t{\n\t\t\t\tstuff_char(0xFF);\n\t\t\t\treturn 0xFF;\n\t\t\t}\n\n\t\t\tif (c == 0x00)\n\t\t\t\treturn 0xFF;\n\t\t\telse\n\t\t\t{\n\t\t\t\tstuff_char(static_cast<uint8>(c));\n\t\t\t\tstuff_char(0xFF);\n\t\t\t\treturn 0xFF;\n\t\t\t}\n\t\t}\n\n\t\treturn static_cast<uint8>(c);\n\t}\n\n\t// Retrieves a variable number of bits from the input stream. Does not recognize markers.\n\tinline uint jpeg_decoder::get_bits(int num_bits)\n\t{\n\t\tif (!num_bits)\n\t\t\treturn 0;\n\n\t\tuint i = m_bit_buf >> (32 - num_bits);\n\n\t\tif ((m_bits_left -= num_bits) <= 0)\n\t\t{\n\t\t\tm_bit_buf <<= (num_bits += m_bits_left);\n\n\t\t\tuint c1 = get_char();\n\t\t\tuint c2 = get_char();\n\t\t\tm_bit_buf = (m_bit_buf & 0xFFFF0000) | (c1 << 8) | c2;\n\n\t\t\tm_bit_buf <<= -m_bits_left;\n\n\t\t\tm_bits_left += 16;\n\n\t\t\tJPGD_ASSERT(m_bits_left >= 0);\n\t\t}\n\t\telse\n\t\t\tm_bit_buf <<= num_bits;\n\n\t\treturn i;\n\t}\n\n\t// Retrieves a variable number of bits from the input stream. Markers will not be read into the input bit buffer. Instead, an infinite number of all 1's will be returned when a marker is encountered.\n\tinline uint jpeg_decoder::get_bits_no_markers(int num_bits)\n\t{\n\t\tif (!num_bits)\n\t\t\treturn 0;\n\n\t\tuint i = m_bit_buf >> (32 - num_bits);\n\n\t\tif ((m_bits_left -= num_bits) <= 0)\n\t\t{\n\t\t\tm_bit_buf <<= (num_bits += m_bits_left);\n\n\t\t\tif ((m_in_buf_left < 2) || (m_pIn_buf_ofs[0] == 0xFF) || (m_pIn_buf_ofs[1] == 0xFF))\n\t\t\t{\n\t\t\t\tuint c1 = get_octet();\n\t\t\t\tuint c2 = get_octet();\n\t\t\t\tm_bit_buf |= (c1 << 8) | c2;\n\t\t\t}\n\t\t\telse\n\t\t\t{\n\t\t\t\tm_bit_buf |= ((uint)m_pIn_buf_ofs[0] << 8) | m_pIn_buf_ofs[1];\n\t\t\t\tm_in_buf_left -= 2;\n\t\t\t\tm_pIn_buf_ofs += 2;\n\t\t\t}\n\n\t\t\tm_bit_buf <<= -m_bits_left;\n\n\t\t\tm_bits_left += 16;\n\n\t\t\tJPGD_ASSERT(m_bits_left >= 0);\n\t\t}\n\t\telse\n\t\t\tm_bit_buf <<= num_bits;\n\n\t\treturn i;\n\t}\n\n\t// Decodes a Huffman encoded symbol.\n\tinline int jpeg_decoder::huff_decode(huff_tables *pH)\n\t{\n\t\tint symbol;\n\n\t\t// Check first 8-bits: do we have a complete symbol?\n\t\tif ((symbol = pH->look_up[m_bit_buf >> 24]) < 0)\n\t\t{\n\t\t\t// Decode more bits, use a tree traversal to find symbol.\n\t\t\tint ofs = 23;\n\t\t\tdo\n\t\t\t{\n\t\t\t\tsymbol = pH->tree[-(int)(symbol + ((m_bit_buf >> ofs) & 1))];\n\t\t\t\tofs--;\n\t\t\t} while (symbol < 0);\n\n\t\t\tget_bits_no_markers(8 + (23 - ofs));\n\t\t}\n\t\telse\n\t\t\tget_bits_no_markers(pH->code_size[symbol]);\n\n\t\treturn symbol;\n\t}\n\n\t// Decodes a Huffman encoded symbol.\n\tinline int jpeg_decoder::huff_decode(huff_tables *pH, int& extra_bits)\n\t{\n\t\tint symbol;\n\n\t\t// Check first 8-bits: do we have a complete symbol?\n\t\tif ((symbol = pH->look_up2[m_bit_buf >> 24]) < 0)\n\t\t{\n\t\t\t// Use a tree traversal to find symbol.\n\t\t\tint ofs = 23;\n\t\t\tdo\n\t\t\t{\n\t\t\t\tsymbol = pH->tree[-(int)(symbol + ((m_bit_buf >> ofs) & 1))];\n\t\t\t\tofs--;\n\t\t\t} while (symbol < 0);\n\n\t\t\tget_bits_no_markers(8 + (23 - ofs));\n\n\t\t\textra_bits = get_bits_no_markers(symbol & 0xF);\n\t\t}\n\t\telse\n\t\t{\n\t\t\tJPGD_ASSERT(((symbol >> 8) & 31) == pH->code_size[symbol & 255] + ((symbol & 0x8000) ? (symbol & 15) : 0));\n\n\t\t\tif (symbol & 0x8000)\n\t\t\t{\n\t\t\t\tget_bits_no_markers((symbol >> 8) & 31);\n\t\t\t\textra_bits = symbol >> 16;\n\t\t\t}\n\t\t\telse\n\t\t\t{\n\t\t\t\tint code_size = (symbol >> 8) & 31;\n\t\t\t\tint num_extra_bits = symbol & 0xF;\n\t\t\t\tint bits = code_size + num_extra_bits;\n\t\t\t\tif (bits <= (m_bits_left + 16))\n\t\t\t\t\textra_bits = get_bits_no_markers(bits) & ((1 << num_extra_bits) - 1);\n\t\t\t\telse\n\t\t\t\t{\n\t\t\t\t\tget_bits_no_markers(code_size);\n\t\t\t\t\textra_bits = get_bits_no_markers(num_extra_bits);\n\t\t\t\t}\n\t\t\t}\n\n\t\t\tsymbol &= 0xFF;\n\t\t}\n\n\t\treturn symbol;\n\t}\n\n\t// Tables and macro used to fully decode the DPCM differences.\n\tstatic const int s_extend_test[16] = { 0, 0x0001, 0x0002, 0x0004, 0x0008, 0x0010, 0x0020, 0x0040, 0x0080, 0x0100, 0x0200, 0x0400, 0x0800, 0x1000, 0x2000, 0x4000 };\n\tstatic const int s_extend_offset[16] = { 0, -1, -3, -7, -15, -31, -63, -127, -255, -511, -1023, -2047, -4095, -8191, -16383, -32767 };\n\tstatic const int s_extend_mask[] = { 0, (1<<0), (1<<1), (1<<2), (1<<3), (1<<4), (1<<5), (1<<6), (1<<7), (1<<8), (1<<9), (1<<10), (1<<11), (1<<12), (1<<13), (1<<14), (1<<15), (1<<16) };\n#define HUFF_EXTEND(x,s) ((x) < s_extend_test[s] ? (x) + s_extend_offset[s] : (x))\n\n\t// Clamps a value between 0-255.\n\tinline uint8 jpeg_decoder::clamp(int i)\n\t{\n\t\tif (static_cast<uint>(i) > 255)\n\t\t\ti = (((~i) >> 31) & 0xFF);\n\n\t\treturn static_cast<uint8>(i);\n\t}\n\n\tnamespace DCT_Upsample\n\t{\n\t\tstruct Matrix44\n\t\t{\n\t\t\ttypedef int Element_Type;\n\t\t\tenum { NUM_ROWS = 4, NUM_COLS = 4 };\n\n\t\t\tElement_Type v[NUM_ROWS][NUM_COLS];\n\n\t\t\tinline int rows() const { return NUM_ROWS; }\n\t\t\tinline int cols() const { return NUM_COLS; }\n\n\t\t\tinline const Element_Type & at(int r, int c) const { return v[r][c]; }\n\t\t\tinline       Element_Type & at(int r, int c)       { return v[r][c]; }\n\n\t\t\tinline Matrix44() { }\n\n\t\t\tinline Matrix44& operator += (const Matrix44& a)\n\t\t\t{\n\t\t\t\tfor (int r = 0; r < NUM_ROWS; r++)\n\t\t\t\t{\n\t\t\t\t\tat(r, 0) += a.at(r, 0);\n\t\t\t\t\tat(r, 1) += a.at(r, 1);\n\t\t\t\t\tat(r, 2) += a.at(r, 2);\n\t\t\t\t\tat(r, 3) += a.at(r, 3);\n\t\t\t\t}\n\t\t\t\treturn *this;\n\t\t\t}\n\n\t\t\tinline Matrix44& operator -= (const Matrix44& a)\n\t\t\t{\n\t\t\t\tfor (int r = 0; r < NUM_ROWS; r++)\n\t\t\t\t{\n\t\t\t\t\tat(r, 0) -= a.at(r, 0);\n\t\t\t\t\tat(r, 1) -= a.at(r, 1);\n\t\t\t\t\tat(r, 2) -= a.at(r, 2);\n\t\t\t\t\tat(r, 3) -= a.at(r, 3);\n\t\t\t\t}\n\t\t\t\treturn *this;\n\t\t\t}\n\n\t\t\tfriend inline Matrix44 operator + (const Matrix44& a, const Matrix44& b)\n\t\t\t{\n\t\t\t\tMatrix44 ret;\n\t\t\t\tfor (int r = 0; r < NUM_ROWS; r++)\n\t\t\t\t{\n\t\t\t\t\tret.at(r, 0) = a.at(r, 0) + b.at(r, 0);\n\t\t\t\t\tret.at(r, 1) = a.at(r, 1) + b.at(r, 1);\n\t\t\t\t\tret.at(r, 2) = a.at(r, 2) + b.at(r, 2);\n\t\t\t\t\tret.at(r, 3) = a.at(r, 3) + b.at(r, 3);\n\t\t\t\t}\n\t\t\t\treturn ret;\n\t\t\t}\n\n\t\t\tfriend inline Matrix44 operator - (const Matrix44& a, const Matrix44& b)\n\t\t\t{\n\t\t\t\tMatrix44 ret;\n\t\t\t\tfor (int r = 0; r < NUM_ROWS; r++)\n\t\t\t\t{\n\t\t\t\t\tret.at(r, 0) = a.at(r, 0) - b.at(r, 0);\n\t\t\t\t\tret.at(r, 1) = a.at(r, 1) - b.at(r, 1);\n\t\t\t\t\tret.at(r, 2) = a.at(r, 2) - b.at(r, 2);\n\t\t\t\t\tret.at(r, 3) = a.at(r, 3) - b.at(r, 3);\n\t\t\t\t}\n\t\t\t\treturn ret;\n\t\t\t}\n\n\t\t\tstatic inline void add_and_store(jpgd_block_t* pDst, const Matrix44& a, const Matrix44& b)\n\t\t\t{\n\t\t\t\tfor (int r = 0; r < 4; r++)\n\t\t\t\t{\n\t\t\t\t\tpDst[0*8 + r] = static_cast<jpgd_block_t>(a.at(r, 0) + b.at(r, 0));\n\t\t\t\t\tpDst[1*8 + r] = static_cast<jpgd_block_t>(a.at(r, 1) + b.at(r, 1));\n\t\t\t\t\tpDst[2*8 + r] = static_cast<jpgd_block_t>(a.at(r, 2) + b.at(r, 2));\n\t\t\t\t\tpDst[3*8 + r] = static_cast<jpgd_block_t>(a.at(r, 3) + b.at(r, 3));\n\t\t\t\t}\n\t\t\t}\n\n\t\t\tstatic inline void sub_and_store(jpgd_block_t* pDst, const Matrix44& a, const Matrix44& b)\n\t\t\t{\n\t\t\t\tfor (int r = 0; r < 4; r++)\n\t\t\t\t{\n\t\t\t\t\tpDst[0*8 + r] = static_cast<jpgd_block_t>(a.at(r, 0) - b.at(r, 0));\n\t\t\t\t\tpDst[1*8 + r] = static_cast<jpgd_block_t>(a.at(r, 1) - b.at(r, 1));\n\t\t\t\t\tpDst[2*8 + r] = static_cast<jpgd_block_t>(a.at(r, 2) - b.at(r, 2));\n\t\t\t\t\tpDst[3*8 + r] = static_cast<jpgd_block_t>(a.at(r, 3) - b.at(r, 3));\n\t\t\t\t}\n\t\t\t}\n\t\t};\n\n\t\tconst int FRACT_BITS = 10;\n\t\tconst int SCALE = 1 << FRACT_BITS;\n\n\t\ttypedef int Temp_Type;\n#define D(i) (((i) + (SCALE >> 1)) >> FRACT_BITS)\n#define F(i) ((int)((i) * SCALE + .5f))\n\n\t\t// Any decent C++ compiler will optimize this at compile time to a 0, or an array access.\n#define AT(c, r) ((((c)>=NUM_COLS)||((r)>=NUM_ROWS)) ? 0 : pSrc[(c)+(r)*8])\n\n\t\t// NUM_ROWS/NUM_COLS = # of non-zero rows/cols in input matrix\n\t\ttemplate<int NUM_ROWS, int NUM_COLS>\n\t\tstruct P_Q\n\t\t{\n\t\t\tstatic void calc(Matrix44& P, Matrix44& Q, const jpgd_block_t* pSrc)\n\t\t\t{\n\t\t\t\t// 4x8 = 4x8 times 8x8, matrix 0 is constant\n\t\t\t\tconst Temp_Type X000 = AT(0, 0);\n\t\t\t\tconst Temp_Type X001 = AT(0, 1);\n\t\t\t\tconst Temp_Type X002 = AT(0, 2);\n\t\t\t\tconst Temp_Type X003 = AT(0, 3);\n\t\t\t\tconst Temp_Type X004 = AT(0, 4);\n\t\t\t\tconst Temp_Type X005 = AT(0, 5);\n\t\t\t\tconst Temp_Type X006 = AT(0, 6);\n\t\t\t\tconst Temp_Type X007 = AT(0, 7);\n\t\t\t\tconst Temp_Type X010 = D(F(0.415735f) * AT(1, 0) + F(0.791065f) * AT(3, 0) + F(-0.352443f) * AT(5, 0) + F(0.277785f) * AT(7, 0));\n\t\t\t\tconst Temp_Type X011 = D(F(0.415735f) * AT(1, 1) + F(0.791065f) * AT(3, 1) + F(-0.352443f) * AT(5, 1) + F(0.277785f) * AT(7, 1));\n\t\t\t\tconst Temp_Type X012 = D(F(0.415735f) * AT(1, 2) + F(0.791065f) * AT(3, 2) + F(-0.352443f) * AT(5, 2) + F(0.277785f) * AT(7, 2));\n\t\t\t\tconst Temp_Type X013 = D(F(0.415735f) * AT(1, 3) + F(0.791065f) * AT(3, 3) + F(-0.352443f) * AT(5, 3) + F(0.277785f) * AT(7, 3));\n\t\t\t\tconst Temp_Type X014 = D(F(0.415735f) * AT(1, 4) + F(0.791065f) * AT(3, 4) + F(-0.352443f) * AT(5, 4) + F(0.277785f) * AT(7, 4));\n\t\t\t\tconst Temp_Type X015 = D(F(0.415735f) * AT(1, 5) + F(0.791065f) * AT(3, 5) + F(-0.352443f) * AT(5, 5) + F(0.277785f) * AT(7, 5));\n\t\t\t\tconst Temp_Type X016 = D(F(0.415735f) * AT(1, 6) + F(0.791065f) * AT(3, 6) + F(-0.352443f) * AT(5, 6) + F(0.277785f) * AT(7, 6));\n\t\t\t\tconst Temp_Type X017 = D(F(0.415735f) * AT(1, 7) + F(0.791065f) * AT(3, 7) + F(-0.352443f) * AT(5, 7) + F(0.277785f) * AT(7, 7));\n\t\t\t\tconst Temp_Type X020 = AT(4, 0);\n\t\t\t\tconst Temp_Type X021 = AT(4, 1);\n\t\t\t\tconst Temp_Type X022 = AT(4, 2);\n\t\t\t\tconst Temp_Type X023 = AT(4, 3);\n\t\t\t\tconst Temp_Type X024 = AT(4, 4);\n\t\t\t\tconst Temp_Type X025 = AT(4, 5);\n\t\t\t\tconst Temp_Type X026 = AT(4, 6);\n\t\t\t\tconst Temp_Type X027 = AT(4, 7);\n\t\t\t\tconst Temp_Type X030 = D(F(0.022887f) * AT(1, 0) + F(-0.097545f) * AT(3, 0) + F(0.490393f) * AT(5, 0) + F(0.865723f) * AT(7, 0));\n\t\t\t\tconst Temp_Type X031 = D(F(0.022887f) * AT(1, 1) + F(-0.097545f) * AT(3, 1) + F(0.490393f) * AT(5, 1) + F(0.865723f) * AT(7, 1));\n\t\t\t\tconst Temp_Type X032 = D(F(0.022887f) * AT(1, 2) + F(-0.097545f) * AT(3, 2) + F(0.490393f) * AT(5, 2) + F(0.865723f) * AT(7, 2));\n\t\t\t\tconst Temp_Type X033 = D(F(0.022887f) * AT(1, 3) + F(-0.097545f) * AT(3, 3) + F(0.490393f) * AT(5, 3) + F(0.865723f) * AT(7, 3));\n\t\t\t\tconst Temp_Type X034 = D(F(0.022887f) * AT(1, 4) + F(-0.097545f) * AT(3, 4) + F(0.490393f) * AT(5, 4) + F(0.865723f) * AT(7, 4));\n\t\t\t\tconst Temp_Type X035 = D(F(0.022887f) * AT(1, 5) + F(-0.097545f) * AT(3, 5) + F(0.490393f) * AT(5, 5) + F(0.865723f) * AT(7, 5));\n\t\t\t\tconst Temp_Type X036 = D(F(0.022887f) * AT(1, 6) + F(-0.097545f) * AT(3, 6) + F(0.490393f) * AT(5, 6) + F(0.865723f) * AT(7, 6));\n\t\t\t\tconst Temp_Type X037 = D(F(0.022887f) * AT(1, 7) + F(-0.097545f) * AT(3, 7) + F(0.490393f) * AT(5, 7) + F(0.865723f) * AT(7, 7));\n\n\t\t\t\t// 4x4 = 4x8 times 8x4, matrix 1 is constant\n\t\t\t\tP.at(0, 0) = X000;\n\t\t\t\tP.at(0, 1) = D(X001 * F(0.415735f) + X003 * F(0.791065f) + X005 * F(-0.352443f) + X007 * F(0.277785f));\n\t\t\t\tP.at(0, 2) = X004;\n\t\t\t\tP.at(0, 3) = D(X001 * F(0.022887f) + X003 * F(-0.097545f) + X005 * F(0.490393f) + X007 * F(0.865723f));\n\t\t\t\tP.at(1, 0) = X010;\n\t\t\t\tP.at(1, 1) = D(X011 * F(0.415735f) + X013 * F(0.791065f) + X015 * F(-0.352443f) + X017 * F(0.277785f));\n\t\t\t\tP.at(1, 2) = X014;\n\t\t\t\tP.at(1, 3) = D(X011 * F(0.022887f) + X013 * F(-0.097545f) + X015 * F(0.490393f) + X017 * F(0.865723f));\n\t\t\t\tP.at(2, 0) = X020;\n\t\t\t\tP.at(2, 1) = D(X021 * F(0.415735f) + X023 * F(0.791065f) + X025 * F(-0.352443f) + X027 * F(0.277785f));\n\t\t\t\tP.at(2, 2) = X024;\n\t\t\t\tP.at(2, 3) = D(X021 * F(0.022887f) + X023 * F(-0.097545f) + X025 * F(0.490393f) + X027 * F(0.865723f));\n\t\t\t\tP.at(3, 0) = X030;\n\t\t\t\tP.at(3, 1) = D(X031 * F(0.415735f) + X033 * F(0.791065f) + X035 * F(-0.352443f) + X037 * F(0.277785f));\n\t\t\t\tP.at(3, 2) = X034;\n\t\t\t\tP.at(3, 3) = D(X031 * F(0.022887f) + X033 * F(-0.097545f) + X035 * F(0.490393f) + X037 * F(0.865723f));\n\t\t\t\t// 40 muls 24 adds\n\n\t\t\t\t// 4x4 = 4x8 times 8x4, matrix 1 is constant\n\t\t\t\tQ.at(0, 0) = D(X001 * F(0.906127f) + X003 * F(-0.318190f) + X005 * F(0.212608f) + X007 * F(-0.180240f));\n\t\t\t\tQ.at(0, 1) = X002;\n\t\t\t\tQ.at(0, 2) = D(X001 * F(-0.074658f) + X003 * F(0.513280f) + X005 * F(0.768178f) + X007 * F(-0.375330f));\n\t\t\t\tQ.at(0, 3) = X006;\n\t\t\t\tQ.at(1, 0) = D(X011 * F(0.906127f) + X013 * F(-0.318190f) + X015 * F(0.212608f) + X017 * F(-0.180240f));\n\t\t\t\tQ.at(1, 1) = X012;\n\t\t\t\tQ.at(1, 2) = D(X011 * F(-0.074658f) + X013 * F(0.513280f) + X015 * F(0.768178f) + X017 * F(-0.375330f));\n\t\t\t\tQ.at(1, 3) = X016;\n\t\t\t\tQ.at(2, 0) = D(X021 * F(0.906127f) + X023 * F(-0.318190f) + X025 * F(0.212608f) + X027 * F(-0.180240f));\n\t\t\t\tQ.at(2, 1) = X022;\n\t\t\t\tQ.at(2, 2) = D(X021 * F(-0.074658f) + X023 * F(0.513280f) + X025 * F(0.768178f) + X027 * F(-0.375330f));\n\t\t\t\tQ.at(2, 3) = X026;\n\t\t\t\tQ.at(3, 0) = D(X031 * F(0.906127f) + X033 * F(-0.318190f) + X035 * F(0.212608f) + X037 * F(-0.180240f));\n\t\t\t\tQ.at(3, 1) = X032;\n\t\t\t\tQ.at(3, 2) = D(X031 * F(-0.074658f) + X033 * F(0.513280f) + X035 * F(0.768178f) + X037 * F(-0.375330f));\n\t\t\t\tQ.at(3, 3) = X036;\n\t\t\t\t// 40 muls 24 adds\n\t\t\t}\n\t\t};\n\n\t\ttemplate<int NUM_ROWS, int NUM_COLS>\n\t\tstruct R_S\n\t\t{\n\t\t\tstatic void calc(Matrix44& R, Matrix44& S, const jpgd_block_t* pSrc)\n\t\t\t{\n\t\t\t\t// 4x8 = 4x8 times 8x8, matrix 0 is constant\n\t\t\t\tconst Temp_Type X100 = D(F(0.906127f) * AT(1, 0) + F(-0.318190f) * AT(3, 0) + F(0.212608f) * AT(5, 0) + F(-0.180240f) * AT(7, 0));\n\t\t\t\tconst Temp_Type X101 = D(F(0.906127f) * AT(1, 1) + F(-0.318190f) * AT(3, 1) + F(0.212608f) * AT(5, 1) + F(-0.180240f) * AT(7, 1));\n\t\t\t\tconst Temp_Type X102 = D(F(0.906127f) * AT(1, 2) + F(-0.318190f) * AT(3, 2) + F(0.212608f) * AT(5, 2) + F(-0.180240f) * AT(7, 2));\n\t\t\t\tconst Temp_Type X103 = D(F(0.906127f) * AT(1, 3) + F(-0.318190f) * AT(3, 3) + F(0.212608f) * AT(5, 3) + F(-0.180240f) * AT(7, 3));\n\t\t\t\tconst Temp_Type X104 = D(F(0.906127f) * AT(1, 4) + F(-0.318190f) * AT(3, 4) + F(0.212608f) * AT(5, 4) + F(-0.180240f) * AT(7, 4));\n\t\t\t\tconst Temp_Type X105 = D(F(0.906127f) * AT(1, 5) + F(-0.318190f) * AT(3, 5) + F(0.212608f) * AT(5, 5) + F(-0.180240f) * AT(7, 5));\n\t\t\t\tconst Temp_Type X106 = D(F(0.906127f) * AT(1, 6) + F(-0.318190f) * AT(3, 6) + F(0.212608f) * AT(5, 6) + F(-0.180240f) * AT(7, 6));\n\t\t\t\tconst Temp_Type X107 = D(F(0.906127f) * AT(1, 7) + F(-0.318190f) * AT(3, 7) + F(0.212608f) * AT(5, 7) + F(-0.180240f) * AT(7, 7));\n\t\t\t\tconst Temp_Type X110 = AT(2, 0);\n\t\t\t\tconst Temp_Type X111 = AT(2, 1);\n\t\t\t\tconst Temp_Type X112 = AT(2, 2);\n\t\t\t\tconst Temp_Type X113 = AT(2, 3);\n\t\t\t\tconst Temp_Type X114 = AT(2, 4);\n\t\t\t\tconst Temp_Type X115 = AT(2, 5);\n\t\t\t\tconst Temp_Type X116 = AT(2, 6);\n\t\t\t\tconst Temp_Type X117 = AT(2, 7);\n\t\t\t\tconst Temp_Type X120 = D(F(-0.074658f) * AT(1, 0) + F(0.513280f) * AT(3, 0) + F(0.768178f) * AT(5, 0) + F(-0.375330f) * AT(7, 0));\n\t\t\t\tconst Temp_Type X121 = D(F(-0.074658f) * AT(1, 1) + F(0.513280f) * AT(3, 1) + F(0.768178f) * AT(5, 1) + F(-0.375330f) * AT(7, 1));\n\t\t\t\tconst Temp_Type X122 = D(F(-0.074658f) * AT(1, 2) + F(0.513280f) * AT(3, 2) + F(0.768178f) * AT(5, 2) + F(-0.375330f) * AT(7, 2));\n\t\t\t\tconst Temp_Type X123 = D(F(-0.074658f) * AT(1, 3) + F(0.513280f) * AT(3, 3) + F(0.768178f) * AT(5, 3) + F(-0.375330f) * AT(7, 3));\n\t\t\t\tconst Temp_Type X124 = D(F(-0.074658f) * AT(1, 4) + F(0.513280f) * AT(3, 4) + F(0.768178f) * AT(5, 4) + F(-0.375330f) * AT(7, 4));\n\t\t\t\tconst Temp_Type X125 = D(F(-0.074658f) * AT(1, 5) + F(0.513280f) * AT(3, 5) + F(0.768178f) * AT(5, 5) + F(-0.375330f) * AT(7, 5));\n\t\t\t\tconst Temp_Type X126 = D(F(-0.074658f) * AT(1, 6) + F(0.513280f) * AT(3, 6) + F(0.768178f) * AT(5, 6) + F(-0.375330f) * AT(7, 6));\n\t\t\t\tconst Temp_Type X127 = D(F(-0.074658f) * AT(1, 7) + F(0.513280f) * AT(3, 7) + F(0.768178f) * AT(5, 7) + F(-0.375330f) * AT(7, 7));\n\t\t\t\tconst Temp_Type X130 = AT(6, 0);\n\t\t\t\tconst Temp_Type X131 = AT(6, 1);\n\t\t\t\tconst Temp_Type X132 = AT(6, 2);\n\t\t\t\tconst Temp_Type X133 = AT(6, 3);\n\t\t\t\tconst Temp_Type X134 = AT(6, 4);\n\t\t\t\tconst Temp_Type X135 = AT(6, 5);\n\t\t\t\tconst Temp_Type X136 = AT(6, 6);\n\t\t\t\tconst Temp_Type X137 = AT(6, 7);\n\t\t\t\t// 80 muls 48 adds\n\n\t\t\t\t// 4x4 = 4x8 times 8x4, matrix 1 is constant\n\t\t\t\tR.at(0, 0) = X100;\n\t\t\t\tR.at(0, 1) = D(X101 * F(0.415735f) + X103 * F(0.791065f) + X105 * F(-0.352443f) + X107 * F(0.277785f));\n\t\t\t\tR.at(0, 2) = X104;\n\t\t\t\tR.at(0, 3) = D(X101 * F(0.022887f) + X103 * F(-0.097545f) + X105 * F(0.490393f) + X107 * F(0.865723f));\n\t\t\t\tR.at(1, 0) = X110;\n\t\t\t\tR.at(1, 1) = D(X111 * F(0.415735f) + X113 * F(0.791065f) + X115 * F(-0.352443f) + X117 * F(0.277785f));\n\t\t\t\tR.at(1, 2) = X114;\n\t\t\t\tR.at(1, 3) = D(X111 * F(0.022887f) + X113 * F(-0.097545f) + X115 * F(0.490393f) + X117 * F(0.865723f));\n\t\t\t\tR.at(2, 0) = X120;\n\t\t\t\tR.at(2, 1) = D(X121 * F(0.415735f) + X123 * F(0.791065f) + X125 * F(-0.352443f) + X127 * F(0.277785f));\n\t\t\t\tR.at(2, 2) = X124;\n\t\t\t\tR.at(2, 3) = D(X121 * F(0.022887f) + X123 * F(-0.097545f) + X125 * F(0.490393f) + X127 * F(0.865723f));\n\t\t\t\tR.at(3, 0) = X130;\n\t\t\t\tR.at(3, 1) = D(X131 * F(0.415735f) + X133 * F(0.791065f) + X135 * F(-0.352443f) + X137 * F(0.277785f));\n\t\t\t\tR.at(3, 2) = X134;\n\t\t\t\tR.at(3, 3) = D(X131 * F(0.022887f) + X133 * F(-0.097545f) + X135 * F(0.490393f) + X137 * F(0.865723f));\n\t\t\t\t// 40 muls 24 adds\n\t\t\t\t// 4x4 = 4x8 times 8x4, matrix 1 is constant\n\t\t\t\tS.at(0, 0) = D(X101 * F(0.906127f) + X103 * F(-0.318190f) + X105 * F(0.212608f) + X107 * F(-0.180240f));\n\t\t\t\tS.at(0, 1) = X102;\n\t\t\t\tS.at(0, 2) = D(X101 * F(-0.074658f) + X103 * F(0.513280f) + X105 * F(0.768178f) + X107 * F(-0.375330f));\n\t\t\t\tS.at(0, 3) = X106;\n\t\t\t\tS.at(1, 0) = D(X111 * F(0.906127f) + X113 * F(-0.318190f) + X115 * F(0.212608f) + X117 * F(-0.180240f));\n\t\t\t\tS.at(1, 1) = X112;\n\t\t\t\tS.at(1, 2) = D(X111 * F(-0.074658f) + X113 * F(0.513280f) + X115 * F(0.768178f) + X117 * F(-0.375330f));\n\t\t\t\tS.at(1, 3) = X116;\n\t\t\t\tS.at(2, 0) = D(X121 * F(0.906127f) + X123 * F(-0.318190f) + X125 * F(0.212608f) + X127 * F(-0.180240f));\n\t\t\t\tS.at(2, 1) = X122;\n\t\t\t\tS.at(2, 2) = D(X121 * F(-0.074658f) + X123 * F(0.513280f) + X125 * F(0.768178f) + X127 * F(-0.375330f));\n\t\t\t\tS.at(2, 3) = X126;\n\t\t\t\tS.at(3, 0) = D(X131 * F(0.906127f) + X133 * F(-0.318190f) + X135 * F(0.212608f) + X137 * F(-0.180240f));\n\t\t\t\tS.at(3, 1) = X132;\n\t\t\t\tS.at(3, 2) = D(X131 * F(-0.074658f) + X133 * F(0.513280f) + X135 * F(0.768178f) + X137 * F(-0.375330f));\n\t\t\t\tS.at(3, 3) = X136;\n\t\t\t\t// 40 muls 24 adds\n\t\t\t}\n\t\t};\n\t} // end namespace DCT_Upsample\n\n\t// Unconditionally frees all allocated m_blocks.\n\tvoid jpeg_decoder::free_all_blocks()\n\t{\n\t\tm_pStream = NULL;\n\t\tfor (mem_block *b = m_pMem_blocks; b; )\n\t\t{\n\t\t\tmem_block *n = b->m_pNext;\n\t\t\tjpgd_free(b);\n\t\t\tb = n;\n\t\t}\n\t\tm_pMem_blocks = NULL;\n\t}\n\n\t// This method handles all errors.\n\t// It could easily be changed to use C++ exceptions.\n\tvoid jpeg_decoder::stop_decoding(jpgd_status status)\n\t{\n\t\tm_error_code = status;\n\t\tfree_all_blocks();\n\t\tlongjmp(m_jmp_state, status);\n\n\t\t// we shouldn't get here as longjmp shouldn't return, but we put it here to make it explicit\n\t\t// that this function doesn't return, otherwise we get this error:\n\t\t// \n\t\t// error : function declared 'noreturn' should not return\n\t\texit(1);\n\t}\n\n\tvoid *jpeg_decoder::alloc(size_t nSize, bool zero)\n\t{\n\t\tnSize = (JPGD_MAX(nSize, 1) + 3) & ~3;\n\t\tchar *rv = NULL;\n\t\tfor (mem_block *b = m_pMem_blocks; b; b = b->m_pNext)\n\t\t{\n\t\t\tif ((b->m_used_count + nSize) <= b->m_size)\n\t\t\t{\n\t\t\t\trv = b->m_data + b->m_used_count;\n\t\t\t\tb->m_used_count += nSize;\n\t\t\t\tbreak;\n\t\t\t}\n\t\t}\n\t\tif (!rv)\n\t\t{\n\t\t\tint capacity = JPGD_MAX(32768 - 256, (nSize + 2047) & ~2047);\n\t\t\tmem_block *b = (mem_block*)jpgd_malloc(sizeof(mem_block) + capacity);\n\t\t\tif (!b) stop_decoding(JPGD_NOTENOUGHMEM);\n\t\t\tb->m_pNext = m_pMem_blocks; m_pMem_blocks = b;\n\t\t\tb->m_used_count = nSize;\n\t\t\tb->m_size = capacity;\n\t\t\trv = b->m_data;\n\t\t}\n\t\tif (zero) memset(rv, 0, nSize);\n\t\treturn rv;\n\t}\n\n\tvoid jpeg_decoder::word_clear(void *p, uint16 c, uint n)\n\t{\n\t\tuint8 *pD = (uint8*)p;\n\t\tconst uint8 l = c & 0xFF, h = (c >> 8) & 0xFF;\n\t\twhile (n)\n\t\t{\n\t\t\tpD[0] = l; pD[1] = h; pD += 2;\n\t\t\tn--;\n\t\t}\n\t}\n\n\t// Refill the input buffer.\n\t// This method will sit in a loop until (A) the buffer is full or (B)\n\t// the stream's read() method reports and end of file condition.\n\tvoid jpeg_decoder::prep_in_buffer()\n\t{\n\t\tm_in_buf_left = 0;\n\t\tm_pIn_buf_ofs = m_in_buf;\n\n\t\tif (m_eof_flag)\n\t\t\treturn;\n\n\t\tdo\n\t\t{\n\t\t\tint bytes_read = m_pStream->read(m_in_buf + m_in_buf_left, JPGD_IN_BUF_SIZE - m_in_buf_left, &m_eof_flag);\n\t\t\tif (bytes_read == -1)\n\t\t\t\tstop_decoding(JPGD_STREAM_READ);\n\n\t\t\tm_in_buf_left += bytes_read;\n\t\t} while ((m_in_buf_left < JPGD_IN_BUF_SIZE) && (!m_eof_flag));\n\n\t\tm_total_bytes_read += m_in_buf_left;\n\n\t\t// Pad the end of the block with M_EOI (prevents the decompressor from going off the rails if the stream is invalid).\n\t\t// (This dates way back to when this decompressor was written in C/asm, and the all-asm Huffman decoder did some fancy things to increase perf.)\n\t\tword_clear(m_pIn_buf_ofs + m_in_buf_left, 0xD9FF, 64);\n\t}\n\n\t// Read a Huffman code table.\n\tvoid jpeg_decoder::read_dht_marker()\n\t{\n\t\tint i, index, count;\n\t\tuint8 huff_num[17];\n\t\tuint8 huff_val[256];\n\n\t\tuint num_left = get_bits(16);\n\n\t\tif (num_left < 2)\n\t\t\tstop_decoding(JPGD_BAD_DHT_MARKER);\n\n\t\tnum_left -= 2;\n\n\t\twhile (num_left)\n\t\t{\n\t\t\tindex = get_bits(8);\n\n\t\t\thuff_num[0] = 0;\n\n\t\t\tcount = 0;\n\n\t\t\tfor (i = 1; i <= 16; i++)\n\t\t\t{\n\t\t\t\thuff_num[i] = static_cast<uint8>(get_bits(8));\n\t\t\t\tcount += huff_num[i];\n\t\t\t}\n\n\t\t\tif (count > 255)\n\t\t\t\tstop_decoding(JPGD_BAD_DHT_COUNTS);\n\n\t\t\tfor (i = 0; i < count; i++)\n\t\t\t\thuff_val[i] = static_cast<uint8>(get_bits(8));\n\n\t\t\ti = 1 + 16 + count;\n\n\t\t\tif (num_left < (uint)i)\n\t\t\t\tstop_decoding(JPGD_BAD_DHT_MARKER);\n\n\t\t\tnum_left -= i;\n\n\t\t\tif ((index & 0x10) > 0x10)\n\t\t\t\tstop_decoding(JPGD_BAD_DHT_INDEX);\n\n\t\t\tindex = (index & 0x0F) + ((index & 0x10) >> 4) * (JPGD_MAX_HUFF_TABLES >> 1);\n\n\t\t\tif (index >= JPGD_MAX_HUFF_TABLES)\n\t\t\t\tstop_decoding(JPGD_BAD_DHT_INDEX);\n\n\t\t\tif (!m_huff_num[index])\n\t\t\t\tm_huff_num[index] = (uint8 *)alloc(17);\n\n\t\t\tif (!m_huff_val[index])\n\t\t\t\tm_huff_val[index] = (uint8 *)alloc(256);\n\n\t\t\tm_huff_ac[index] = (index & 0x10) != 0;\n\t\t\tmemcpy(m_huff_num[index], huff_num, 17);\n\t\t\tmemcpy(m_huff_val[index], huff_val, 256);\n\t\t}\n\t}\n\n\t// Read a quantization table.\n\tvoid jpeg_decoder::read_dqt_marker()\n\t{\n\t\tint n, i, prec;\n\t\tuint num_left;\n\t\tuint temp;\n\n\t\tnum_left = get_bits(16);\n\n\t\tif (num_left < 2)\n\t\t\tstop_decoding(JPGD_BAD_DQT_MARKER);\n\n\t\tnum_left -= 2;\n\n\t\twhile (num_left)\n\t\t{\n\t\t\tn = get_bits(8);\n\t\t\tprec = n >> 4;\n\t\t\tn &= 0x0F;\n\n\t\t\tif (n >= JPGD_MAX_QUANT_TABLES)\n\t\t\t\tstop_decoding(JPGD_BAD_DQT_TABLE);\n\n\t\t\tif (!m_quant[n])\n\t\t\t\tm_quant[n] = (jpgd_quant_t *)alloc(64 * sizeof(jpgd_quant_t));\n\n\t\t\t// read quantization entries, in zag order\n\t\t\tfor (i = 0; i < 64; i++)\n\t\t\t{\n\t\t\t\ttemp = get_bits(8);\n\n\t\t\t\tif (prec)\n\t\t\t\t\ttemp = (temp << 8) + get_bits(8);\n\n\t\t\t\tm_quant[n][i] = static_cast<jpgd_quant_t>(temp);\n\t\t\t}\n\n\t\t\ti = 64 + 1;\n\n\t\t\tif (prec)\n\t\t\t\ti += 64;\n\n\t\t\tif (num_left < (uint)i)\n\t\t\t\tstop_decoding(JPGD_BAD_DQT_LENGTH);\n\n\t\t\tnum_left -= i;\n\t\t}\n\t}\n\n\t// Read the start of frame (SOF) marker.\n\tvoid jpeg_decoder::read_sof_marker()\n\t{\n\t\tint i;\n\t\tuint num_left;\n\n\t\tnum_left = get_bits(16);\n\n\t\tif (get_bits(8) != 8)   /* precision: sorry, only 8-bit precision is supported right now */\n\t\t\tstop_decoding(JPGD_BAD_PRECISION);\n\n\t\tm_image_y_size = get_bits(16);\n\n\t\tif ((m_image_y_size < 1) || (m_image_y_size > JPGD_MAX_HEIGHT))\n\t\t\tstop_decoding(JPGD_BAD_HEIGHT);\n\n\t\tm_image_x_size = get_bits(16);\n\n\t\tif ((m_image_x_size < 1) || (m_image_x_size > JPGD_MAX_WIDTH))\n\t\t\tstop_decoding(JPGD_BAD_WIDTH);\n\n\t\tm_comps_in_frame = get_bits(8);\n\n\t\tif (m_comps_in_frame > JPGD_MAX_COMPONENTS)\n\t\t\tstop_decoding(JPGD_TOO_MANY_COMPONENTS);\n\n\t\tif (num_left != (uint)(m_comps_in_frame * 3 + 8))\n\t\t\tstop_decoding(JPGD_BAD_SOF_LENGTH);\n\n\t\tfor (i = 0; i < m_comps_in_frame; i++)\n\t\t{\n\t\t\tm_comp_ident[i]  = get_bits(8);\n\t\t\tm_comp_h_samp[i] = get_bits(4);\n\t\t\tm_comp_v_samp[i] = get_bits(4);\n\t\t\tm_comp_quant[i]  = get_bits(8);\n\t\t}\n\t}\n\n\t// Used to skip unrecognized markers.\n\tvoid jpeg_decoder::skip_variable_marker()\n\t{\n\t\tuint num_left;\n\n\t\tnum_left = get_bits(16);\n\n\t\tif (num_left < 2)\n\t\t\tstop_decoding(JPGD_BAD_VARIABLE_MARKER);\n\n\t\tnum_left -= 2;\n\n\t\twhile (num_left)\n\t\t{\n\t\t\tget_bits(8);\n\t\t\tnum_left--;\n\t\t}\n\t}\n\n\t// Read a define restart interval (DRI) marker.\n\tvoid jpeg_decoder::read_dri_marker()\n\t{\n\t\tif (get_bits(16) != 4)\n\t\t\tstop_decoding(JPGD_BAD_DRI_LENGTH);\n\n\t\tm_restart_interval = get_bits(16);\n\t}\n\n\t// Read a start of scan (SOS) marker.\n\tvoid jpeg_decoder::read_sos_marker()\n\t{\n\t\tuint num_left;\n\t\tint i, ci, n, c, cc;\n\n\t\tnum_left = get_bits(16);\n\n\t\tn = get_bits(8);\n\n\t\tm_comps_in_scan = n;\n\n\t\tnum_left -= 3;\n\n\t\tif ( (num_left != (uint)(n * 2 + 3)) || (n < 1) || (n > JPGD_MAX_COMPS_IN_SCAN) )\n\t\t\tstop_decoding(JPGD_BAD_SOS_LENGTH);\n\n\t\tfor (i = 0; i < n; i++)\n\t\t{\n\t\t\tcc = get_bits(8);\n\t\t\tc = get_bits(8);\n\t\t\tnum_left -= 2;\n\n\t\t\tfor (ci = 0; ci < m_comps_in_frame; ci++)\n\t\t\t\tif (cc == m_comp_ident[ci])\n\t\t\t\t\tbreak;\n\n\t\t\tif (ci >= m_comps_in_frame)\n\t\t\t\tstop_decoding(JPGD_BAD_SOS_COMP_ID);\n\n\t\t\tm_comp_list[i]    = ci;\n\t\t\tm_comp_dc_tab[ci] = (c >> 4) & 15;\n\t\t\tm_comp_ac_tab[ci] = (c & 15) + (JPGD_MAX_HUFF_TABLES >> 1);\n\t\t}\n\n\t\tm_spectral_start  = get_bits(8);\n\t\tm_spectral_end    = get_bits(8);\n\t\tm_successive_high = get_bits(4);\n\t\tm_successive_low  = get_bits(4);\n\n\t\tif (!m_progressive_flag)\n\t\t{\n\t\t\tm_spectral_start = 0;\n\t\t\tm_spectral_end = 63;\n\t\t}\n\n\t\tnum_left -= 3;\n\n\t\twhile (num_left)                  /* read past whatever is num_left */\n\t\t{\n\t\t\tget_bits(8);\n\t\t\tnum_left--;\n\t\t}\n\t}\n\n\t// Finds the next marker.\n\tint jpeg_decoder::next_marker()\n\t{\n\t\tuint c, bytes;\n\n\t\tbytes = 0;\n\n\t\tdo\n\t\t{\n\t\t\tdo\n\t\t\t{\n\t\t\t\tbytes++;\n\t\t\t\tc = get_bits(8);\n\t\t\t} while (c != 0xFF);\n\n\t\t\tdo\n\t\t\t{\n\t\t\t\tc = get_bits(8);\n\t\t\t} while (c == 0xFF);\n\n\t\t} while (c == 0);\n\n\t\t// If bytes > 0 here, there where extra bytes before the marker (not good).\n\n\t\treturn c;\n\t}\n\n\t// Process markers. Returns when an SOFx, SOI, EOI, or SOS marker is\n\t// encountered.\n\tint jpeg_decoder::process_markers()\n\t{\n\t\tint c;\n\n\t\tfor ( ; ; )\n\t\t{\n\t\t\tc = next_marker();\n\n\t\t\tswitch (c)\n\t\t\t{\n\t\t\tcase M_SOF0:\n\t\t\tcase M_SOF1:\n\t\t\tcase M_SOF2:\n\t\t\tcase M_SOF3:\n\t\t\tcase M_SOF5:\n\t\t\tcase M_SOF6:\n\t\t\tcase M_SOF7:\n\t\t\t\t//      case M_JPG:\n\t\t\tcase M_SOF9:\n\t\t\tcase M_SOF10:\n\t\t\tcase M_SOF11:\n\t\t\tcase M_SOF13:\n\t\t\tcase M_SOF14:\n\t\t\tcase M_SOF15:\n\t\t\tcase M_SOI:\n\t\t\tcase M_EOI:\n\t\t\tcase M_SOS:\n\t\t\t\t{\n\t\t\t\t\treturn c;\n\t\t\t\t}\n\t\t\tcase M_DHT:\n\t\t\t\t{\n\t\t\t\t\tread_dht_marker();\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\t\t// No arithmitic support - dumb patents!\n\t\t\tcase M_DAC:\n\t\t\t\t{\n\t\t\t\t\tstop_decoding(JPGD_NO_ARITHMITIC_SUPPORT);\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\tcase M_DQT:\n\t\t\t\t{\n\t\t\t\t\tread_dqt_marker();\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\tcase M_DRI:\n\t\t\t\t{\n\t\t\t\t\tread_dri_marker();\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\t\t//case M_APP0:  /* no need to read the JFIF marker */\n\n\t\t\tcase M_JPG:\n\t\t\tcase M_RST0:    /* no parameters */\n\t\t\tcase M_RST1:\n\t\t\tcase M_RST2:\n\t\t\tcase M_RST3:\n\t\t\tcase M_RST4:\n\t\t\tcase M_RST5:\n\t\t\tcase M_RST6:\n\t\t\tcase M_RST7:\n\t\t\tcase M_TEM:\n\t\t\t\t{\n\t\t\t\t\tstop_decoding(JPGD_UNEXPECTED_MARKER);\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\tdefault:    /* must be DNL, DHP, EXP, APPn, JPGn, COM, or RESn or APP0 */\n\t\t\t\t{\n\t\t\t\t\tskip_variable_marker();\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\n\t// Finds the start of image (SOI) marker.\n\t// This code is rather defensive: it only checks the first 512 bytes to avoid\n\t// false positives.\n\tvoid jpeg_decoder::locate_soi_marker()\n\t{\n\t\tuint lastchar, thischar;\n\t\tuint bytesleft;\n\n\t\tlastchar = get_bits(8);\n\n\t\tthischar = get_bits(8);\n\n\t\t/* ok if it's a normal JPEG file without a special header */\n\n\t\tif ((lastchar == 0xFF) && (thischar == M_SOI))\n\t\t\treturn;\n\n\t\tbytesleft = 4096; //512;\n\n\t\tfor ( ; ; )\n\t\t{\n\t\t\tif (--bytesleft == 0)\n\t\t\t\tstop_decoding(JPGD_NOT_JPEG);\n\n\t\t\tlastchar = thischar;\n\n\t\t\tthischar = get_bits(8);\n\n\t\t\tif (lastchar == 0xFF)\n\t\t\t{\n\t\t\t\tif (thischar == M_SOI)\n\t\t\t\t\tbreak;\n\t\t\t\telse if (thischar == M_EOI) // get_bits will keep returning M_EOI if we read past the end\n\t\t\t\t\tstop_decoding(JPGD_NOT_JPEG);\n\t\t\t}\n\t\t}\n\n\t\t// Check the next character after marker: if it's not 0xFF, it can't be the start of the next marker, so the file is bad.\n\t\tthischar = (m_bit_buf >> 24) & 0xFF;\n\n\t\tif (thischar != 0xFF)\n\t\t\tstop_decoding(JPGD_NOT_JPEG);\n\t}\n\n\t// Find a start of frame (SOF) marker.\n\tvoid jpeg_decoder::locate_sof_marker()\n\t{\n\t\tlocate_soi_marker();\n\n\t\tint c = process_markers();\n\n\t\tswitch (c)\n\t\t{\n\t\tcase M_SOF2:\n\t\t\tm_progressive_flag = JPGD_TRUE;\n\t\tcase M_SOF0:  /* baseline DCT */\n\t\tcase M_SOF1:  /* extended sequential DCT */\n\t\t\t{\n\t\t\t\tread_sof_marker();\n\t\t\t\tbreak;\n\t\t\t}\n\t\tcase M_SOF9:  /* Arithmitic coding */\n\t\t\t{\n\t\t\t\tstop_decoding(JPGD_NO_ARITHMITIC_SUPPORT);\n\t\t\t\tbreak;\n\t\t\t}\n\t\tdefault:\n\t\t\t{\n\t\t\t\tstop_decoding(JPGD_UNSUPPORTED_MARKER);\n\t\t\t\tbreak;\n\t\t\t}\n\t\t}\n\t}\n\n\t// Find a start of scan (SOS) marker.\n\tint jpeg_decoder::locate_sos_marker()\n\t{\n\t\tint c;\n\n\t\tc = process_markers();\n\n\t\tif (c == M_EOI)\n\t\t\treturn JPGD_FALSE;\n\t\telse if (c != M_SOS)\n\t\t\tstop_decoding(JPGD_UNEXPECTED_MARKER);\n\n\t\tread_sos_marker();\n\n\t\treturn JPGD_TRUE;\n\t}\n\n\t// Reset everything to default/uninitialized state.\n\tvoid jpeg_decoder::init(jpeg_decoder_stream *pStream)\n\t{\n\t\tm_pMem_blocks = NULL;\n\t\tm_error_code = JPGD_SUCCESS;\n\t\tm_ready_flag = false;\n\t\tm_image_x_size = m_image_y_size = 0;\n\t\tm_pStream = pStream;\n\t\tm_progressive_flag = JPGD_FALSE;\n\n\t\tmemset(m_huff_ac, 0, sizeof(m_huff_ac));\n\t\tmemset(m_huff_num, 0, sizeof(m_huff_num));\n\t\tmemset(m_huff_val, 0, sizeof(m_huff_val));\n\t\tmemset(m_quant, 0, sizeof(m_quant));\n\n\t\tm_scan_type = 0;\n\t\tm_comps_in_frame = 0;\n\n\t\tmemset(m_comp_h_samp, 0, sizeof(m_comp_h_samp));\n\t\tmemset(m_comp_v_samp, 0, sizeof(m_comp_v_samp));\n\t\tmemset(m_comp_quant, 0, sizeof(m_comp_quant));\n\t\tmemset(m_comp_ident, 0, sizeof(m_comp_ident));\n\t\tmemset(m_comp_h_blocks, 0, sizeof(m_comp_h_blocks));\n\t\tmemset(m_comp_v_blocks, 0, sizeof(m_comp_v_blocks));\n\n\t\tm_comps_in_scan = 0;\n\t\tmemset(m_comp_list, 0, sizeof(m_comp_list));\n\t\tmemset(m_comp_dc_tab, 0, sizeof(m_comp_dc_tab));\n\t\tmemset(m_comp_ac_tab, 0, sizeof(m_comp_ac_tab));\n\n\t\tm_spectral_start = 0;\n\t\tm_spectral_end = 0;\n\t\tm_successive_low = 0;\n\t\tm_successive_high = 0;\n\t\tm_max_mcu_x_size = 0;\n\t\tm_max_mcu_y_size = 0;\n\t\tm_blocks_per_mcu = 0;\n\t\tm_max_blocks_per_row = 0;\n\t\tm_mcus_per_row = 0;\n\t\tm_mcus_per_col = 0;\n\t\tm_expanded_blocks_per_component = 0;\n\t\tm_expanded_blocks_per_mcu = 0;\n\t\tm_expanded_blocks_per_row = 0;\n\t\tm_freq_domain_chroma_upsample = false;\n\n\t\tmemset(m_mcu_org, 0, sizeof(m_mcu_org));\n\n\t\tm_total_lines_left = 0;\n\t\tm_mcu_lines_left = 0;\n\t\tm_real_dest_bytes_per_scan_line = 0;\n\t\tm_dest_bytes_per_scan_line = 0;\n\t\tm_dest_bytes_per_pixel = 0;\n\n\t\tmemset(m_pHuff_tabs, 0, sizeof(m_pHuff_tabs));\n\n\t\tmemset(m_dc_coeffs, 0, sizeof(m_dc_coeffs));\n\t\tmemset(m_ac_coeffs, 0, sizeof(m_ac_coeffs));\n\t\tmemset(m_block_y_mcu, 0, sizeof(m_block_y_mcu));\n\n\t\tm_eob_run = 0;\n\n\t\tmemset(m_block_y_mcu, 0, sizeof(m_block_y_mcu));\n\n\t\tm_pIn_buf_ofs = m_in_buf;\n\t\tm_in_buf_left = 0;\n\t\tm_eof_flag = false;\n\t\tm_tem_flag = 0;\n\n\t\tmemset(m_in_buf_pad_start, 0, sizeof(m_in_buf_pad_start));\n\t\tmemset(m_in_buf, 0, sizeof(m_in_buf));\n\t\tmemset(m_in_buf_pad_end, 0, sizeof(m_in_buf_pad_end));\n\n\t\tm_restart_interval = 0;\n\t\tm_restarts_left    = 0;\n\t\tm_next_restart_num = 0;\n\n\t\tm_max_mcus_per_row = 0;\n\t\tm_max_blocks_per_mcu = 0;\n\t\tm_max_mcus_per_col = 0;\n\n\t\tmemset(m_last_dc_val, 0, sizeof(m_last_dc_val));\n\t\tm_pMCU_coefficients = NULL;\n\t\tm_pSample_buf = NULL;\n\n\t\tm_total_bytes_read = 0;\n\n\t\tm_pScan_line_0 = NULL;\n\t\tm_pScan_line_1 = NULL;\n\n\t\t// Ready the input buffer.\n\t\tprep_in_buffer();\n\n\t\t// Prime the bit buffer.\n\t\tm_bits_left = 16;\n\t\tm_bit_buf = 0;\n\n\t\tget_bits(16);\n\t\tget_bits(16);\n\n\t\tfor (int i = 0; i < JPGD_MAX_BLOCKS_PER_MCU; i++)\n\t\t\tm_mcu_block_max_zag[i] = 64;\n\t}\n\n#define SCALEBITS 16\n#define ONE_HALF  ((int) 1 << (SCALEBITS-1))\n#define FIX(x)    ((int) ((x) * (1L<<SCALEBITS) + 0.5f))\n\n\t// Create a few tables that allow us to quickly convert YCbCr to RGB.\n\tvoid jpeg_decoder::create_look_ups()\n\t{\n\t\tfor (int i = 0; i <= 255; i++)\n\t\t{\n\t\t\tint k = i - 128;\n\t\t\tm_crr[i] = ( FIX(1.40200f)  * k + ONE_HALF) >> SCALEBITS;\n\t\t\tm_cbb[i] = ( FIX(1.77200f)  * k + ONE_HALF) >> SCALEBITS;\n\t\t\tm_crg[i] = (-FIX(0.71414f)) * k;\n\t\t\tm_cbg[i] = (-FIX(0.34414f)) * k + ONE_HALF;\n\t\t}\n\t}\n\n\t// This method throws back into the stream any bytes that where read\n\t// into the bit buffer during initial marker scanning.\n\tvoid jpeg_decoder::fix_in_buffer()\n\t{\n\t\t// In case any 0xFF's where pulled into the buffer during marker scanning.\n\t\tJPGD_ASSERT((m_bits_left & 7) == 0);\n\n\t\tif (m_bits_left == 16)\n\t\t\tstuff_char( (uint8)(m_bit_buf & 0xFF));\n\n\t\tif (m_bits_left >= 8)\n\t\t\tstuff_char( (uint8)((m_bit_buf >> 8) & 0xFF));\n\n\t\tstuff_char((uint8)((m_bit_buf >> 16) & 0xFF));\n\t\tstuff_char((uint8)((m_bit_buf >> 24) & 0xFF));\n\n\t\tm_bits_left = 16;\n\t\tget_bits_no_markers(16);\n\t\tget_bits_no_markers(16);\n\t}\n\n\tvoid jpeg_decoder::transform_mcu(int mcu_row)\n\t{\n\t\tjpgd_block_t* pSrc_ptr = m_pMCU_coefficients;\n\t\tuint8* pDst_ptr = m_pSample_buf + mcu_row * m_blocks_per_mcu * 64;\n\n\t\tfor (int mcu_block = 0; mcu_block < m_blocks_per_mcu; mcu_block++)\n\t\t{\n\t\t\tidct(pSrc_ptr, pDst_ptr, m_mcu_block_max_zag[mcu_block]);\n\t\t\tpSrc_ptr += 64;\n\t\t\tpDst_ptr += 64;\n\t\t}\n\t}\n\n\tstatic const uint8 s_max_rc[64] =\n\t{\n\t\t17, 18, 34, 50, 50, 51, 52, 52, 52, 68, 84, 84, 84, 84, 85, 86, 86, 86, 86, 86,\n\t\t102, 118, 118, 118, 118, 118, 118, 119, 120, 120, 120, 120, 120, 120, 120, 136,\n\t\t136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136,\n\t\t136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136\n\t};\n\n\tvoid jpeg_decoder::transform_mcu_expand(int mcu_row)\n\t{\n\t\tjpgd_block_t* pSrc_ptr = m_pMCU_coefficients;\n\t\tuint8* pDst_ptr = m_pSample_buf + mcu_row * m_expanded_blocks_per_mcu * 64;\n\n\t\t// Y IDCT\n\t\tint mcu_block;\n\t\tfor (mcu_block = 0; mcu_block < m_expanded_blocks_per_component; mcu_block++)\n\t\t{\n\t\t\tidct(pSrc_ptr, pDst_ptr, m_mcu_block_max_zag[mcu_block]);\n\t\t\tpSrc_ptr += 64;\n\t\t\tpDst_ptr += 64;\n\t\t}\n\n\t\t// Chroma IDCT, with upsampling\n\t\tjpgd_block_t temp_block[64];\n\n\t\tfor (int i = 0; i < 2; i++)\n\t\t{\n\t\t\tDCT_Upsample::Matrix44 P, Q, R, S;\n\n\t\t\tJPGD_ASSERT(m_mcu_block_max_zag[mcu_block] >= 1);\n\t\t\tJPGD_ASSERT(m_mcu_block_max_zag[mcu_block] <= 64);\n\n\t\t\tswitch (s_max_rc[m_mcu_block_max_zag[mcu_block++] - 1])\n\t\t\t{\n\t\t\tcase 1*16+1:\n\t\t\t\tDCT_Upsample::P_Q<1, 1>::calc(P, Q, pSrc_ptr);\n\t\t\t\tDCT_Upsample::R_S<1, 1>::calc(R, S, pSrc_ptr);\n\t\t\t\tbreak;\n\t\t\tcase 1*16+2:\n\t\t\t\tDCT_Upsample::P_Q<1, 2>::calc(P, Q, pSrc_ptr);\n\t\t\t\tDCT_Upsample::R_S<1, 2>::calc(R, S, pSrc_ptr);\n\t\t\t\tbreak;\n\t\t\tcase 2*16+2:\n\t\t\t\tDCT_Upsample::P_Q<2, 2>::calc(P, Q, pSrc_ptr);\n\t\t\t\tDCT_Upsample::R_S<2, 2>::calc(R, S, pSrc_ptr);\n\t\t\t\tbreak;\n\t\t\tcase 3*16+2:\n\t\t\t\tDCT_Upsample::P_Q<3, 2>::calc(P, Q, pSrc_ptr);\n\t\t\t\tDCT_Upsample::R_S<3, 2>::calc(R, S, pSrc_ptr);\n\t\t\t\tbreak;\n\t\t\tcase 3*16+3:\n\t\t\t\tDCT_Upsample::P_Q<3, 3>::calc(P, Q, pSrc_ptr);\n\t\t\t\tDCT_Upsample::R_S<3, 3>::calc(R, S, pSrc_ptr);\n\t\t\t\tbreak;\n\t\t\tcase 3*16+4:\n\t\t\t\tDCT_Upsample::P_Q<3, 4>::calc(P, Q, pSrc_ptr);\n\t\t\t\tDCT_Upsample::R_S<3, 4>::calc(R, S, pSrc_ptr);\n\t\t\t\tbreak;\n\t\t\tcase 4*16+4:\n\t\t\t\tDCT_Upsample::P_Q<4, 4>::calc(P, Q, pSrc_ptr);\n\t\t\t\tDCT_Upsample::R_S<4, 4>::calc(R, S, pSrc_ptr);\n\t\t\t\tbreak;\n\t\t\tcase 5*16+4:\n\t\t\t\tDCT_Upsample::P_Q<5, 4>::calc(P, Q, pSrc_ptr);\n\t\t\t\tDCT_Upsample::R_S<5, 4>::calc(R, S, pSrc_ptr);\n\t\t\t\tbreak;\n\t\t\tcase 5*16+5:\n\t\t\t\tDCT_Upsample::P_Q<5, 5>::calc(P, Q, pSrc_ptr);\n\t\t\t\tDCT_Upsample::R_S<5, 5>::calc(R, S, pSrc_ptr);\n\t\t\t\tbreak;\n\t\t\tcase 5*16+6:\n\t\t\t\tDCT_Upsample::P_Q<5, 6>::calc(P, Q, pSrc_ptr);\n\t\t\t\tDCT_Upsample::R_S<5, 6>::calc(R, S, pSrc_ptr);\n\t\t\t\tbreak;\n\t\t\tcase 6*16+6:\n\t\t\t\tDCT_Upsample::P_Q<6, 6>::calc(P, Q, pSrc_ptr);\n\t\t\t\tDCT_Upsample::R_S<6, 6>::calc(R, S, pSrc_ptr);\n\t\t\t\tbreak;\n\t\t\tcase 7*16+6:\n\t\t\t\tDCT_Upsample::P_Q<7, 6>::calc(P, Q, pSrc_ptr);\n\t\t\t\tDCT_Upsample::R_S<7, 6>::calc(R, S, pSrc_ptr);\n\t\t\t\tbreak;\n\t\t\tcase 7*16+7:\n\t\t\t\tDCT_Upsample::P_Q<7, 7>::calc(P, Q, pSrc_ptr);\n\t\t\t\tDCT_Upsample::R_S<7, 7>::calc(R, S, pSrc_ptr);\n\t\t\t\tbreak;\n\t\t\tcase 7*16+8:\n\t\t\t\tDCT_Upsample::P_Q<7, 8>::calc(P, Q, pSrc_ptr);\n\t\t\t\tDCT_Upsample::R_S<7, 8>::calc(R, S, pSrc_ptr);\n\t\t\t\tbreak;\n\t\t\tcase 8*16+8:\n\t\t\t\tDCT_Upsample::P_Q<8, 8>::calc(P, Q, pSrc_ptr);\n\t\t\t\tDCT_Upsample::R_S<8, 8>::calc(R, S, pSrc_ptr);\n\t\t\t\tbreak;\n\t\t\tdefault:\n\t\t\t\tJPGD_ASSERT(false);\n\t\t\t}\n\n\t\t\tDCT_Upsample::Matrix44 a(P + Q); P -= Q;\n\t\t\tDCT_Upsample::Matrix44& b = P;\n\t\t\tDCT_Upsample::Matrix44 c(R + S); R -= S;\n\t\t\tDCT_Upsample::Matrix44& d = R;\n\n\t\t\tDCT_Upsample::Matrix44::add_and_store(temp_block, a, c);\n\t\t\tidct_4x4(temp_block, pDst_ptr);\n\t\t\tpDst_ptr += 64;\n\n\t\t\tDCT_Upsample::Matrix44::sub_and_store(temp_block, a, c);\n\t\t\tidct_4x4(temp_block, pDst_ptr);\n\t\t\tpDst_ptr += 64;\n\n\t\t\tDCT_Upsample::Matrix44::add_and_store(temp_block, b, d);\n\t\t\tidct_4x4(temp_block, pDst_ptr);\n\t\t\tpDst_ptr += 64;\n\n\t\t\tDCT_Upsample::Matrix44::sub_and_store(temp_block, b, d);\n\t\t\tidct_4x4(temp_block, pDst_ptr);\n\t\t\tpDst_ptr += 64;\n\n\t\t\tpSrc_ptr += 64;\n\t\t}\n\t}\n\n\t// Loads and dequantizes the next row of (already decoded) coefficients.\n\t// Progressive images only.\n\tvoid jpeg_decoder::load_next_row()\n\t{\n\t\tint i;\n\t\tjpgd_block_t *p;\n\t\tjpgd_quant_t *q;\n\t\tint mcu_row, mcu_block, row_block = 0;\n\t\tint component_num, component_id;\n\t\tint block_x_mcu[JPGD_MAX_COMPONENTS];\n\n\t\tmemset(block_x_mcu, 0, JPGD_MAX_COMPONENTS * sizeof(int));\n\n\t\tfor (mcu_row = 0; mcu_row < m_mcus_per_row; mcu_row++)\n\t\t{\n\t\t\tint block_x_mcu_ofs = 0, block_y_mcu_ofs = 0;\n\n\t\t\tfor (mcu_block = 0; mcu_block < m_blocks_per_mcu; mcu_block++)\n\t\t\t{\n\t\t\t\tcomponent_id = m_mcu_org[mcu_block];\n\t\t\t\tq = m_quant[m_comp_quant[component_id]];\n\n\t\t\t\tp = m_pMCU_coefficients + 64 * mcu_block;\n\n\t\t\t\tjpgd_block_t* pAC = coeff_buf_getp(m_ac_coeffs[component_id], block_x_mcu[component_id] + block_x_mcu_ofs, m_block_y_mcu[component_id] + block_y_mcu_ofs);\n\t\t\t\tjpgd_block_t* pDC = coeff_buf_getp(m_dc_coeffs[component_id], block_x_mcu[component_id] + block_x_mcu_ofs, m_block_y_mcu[component_id] + block_y_mcu_ofs);\n\t\t\t\tp[0] = pDC[0];\n\t\t\t\tmemcpy(&p[1], &pAC[1], 63 * sizeof(jpgd_block_t));\n\n\t\t\t\tfor (i = 63; i > 0; i--)\n\t\t\t\t\tif (p[g_ZAG[i]])\n\t\t\t\t\t\tbreak;\n\n\t\t\t\tm_mcu_block_max_zag[mcu_block] = i + 1;\n\n\t\t\t\tfor ( ; i >= 0; i--)\n\t\t\t\t\tif (p[g_ZAG[i]])\n\t\t\t\t\t\tp[g_ZAG[i]] = static_cast<jpgd_block_t>(p[g_ZAG[i]] * q[i]);\n\n\t\t\t\trow_block++;\n\n\t\t\t\tif (m_comps_in_scan == 1)\n\t\t\t\t\tblock_x_mcu[component_id]++;\n\t\t\t\telse\n\t\t\t\t{\n\t\t\t\t\tif (++block_x_mcu_ofs == m_comp_h_samp[component_id])\n\t\t\t\t\t{\n\t\t\t\t\t\tblock_x_mcu_ofs = 0;\n\n\t\t\t\t\t\tif (++block_y_mcu_ofs == m_comp_v_samp[component_id])\n\t\t\t\t\t\t{\n\t\t\t\t\t\t\tblock_y_mcu_ofs = 0;\n\n\t\t\t\t\t\t\tblock_x_mcu[component_id] += m_comp_h_samp[component_id];\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\n\t\t\tif (m_freq_domain_chroma_upsample)\n\t\t\t\ttransform_mcu_expand(mcu_row);\n\t\t\telse\n\t\t\t\ttransform_mcu(mcu_row);\n\t\t}\n\n\t\tif (m_comps_in_scan == 1)\n\t\t\tm_block_y_mcu[m_comp_list[0]]++;\n\t\telse\n\t\t{\n\t\t\tfor (component_num = 0; component_num < m_comps_in_scan; component_num++)\n\t\t\t{\n\t\t\t\tcomponent_id = m_comp_list[component_num];\n\n\t\t\t\tm_block_y_mcu[component_id] += m_comp_v_samp[component_id];\n\t\t\t}\n\t\t}\n\t}\n\n\t// Restart interval processing.\n\tvoid jpeg_decoder::process_restart()\n\t{\n\t\tint i;\n\t\tint c = 0;\n\n\t\t// Align to a byte boundry\n\t\t// FIXME: Is this really necessary? get_bits_no_markers() never reads in markers!\n\t\t//get_bits_no_markers(m_bits_left & 7);\n\n\t\t// Let's scan a little bit to find the marker, but not _too_ far.\n\t\t// 1536 is a \"fudge factor\" that determines how much to scan.\n\t\tfor (i = 1536; i > 0; i--)\n\t\t\tif (get_char() == 0xFF)\n\t\t\t\tbreak;\n\n\t\tif (i == 0)\n\t\t\tstop_decoding(JPGD_BAD_RESTART_MARKER);\n\n\t\tfor ( ; i > 0; i--)\n\t\t\tif ((c = get_char()) != 0xFF)\n\t\t\t\tbreak;\n\n\t\tif (i == 0)\n\t\t\tstop_decoding(JPGD_BAD_RESTART_MARKER);\n\n\t\t// Is it the expected marker? If not, something bad happened.\n\t\tif (c != (m_next_restart_num + M_RST0))\n\t\t\tstop_decoding(JPGD_BAD_RESTART_MARKER);\n\n\t\t// Reset each component's DC prediction values.\n\t\tmemset(&m_last_dc_val, 0, m_comps_in_frame * sizeof(uint));\n\n\t\tm_eob_run = 0;\n\n\t\tm_restarts_left = m_restart_interval;\n\n\t\tm_next_restart_num = (m_next_restart_num + 1) & 7;\n\n\t\t// Get the bit buffer going again...\n\n\t\tm_bits_left = 16;\n\t\tget_bits_no_markers(16);\n\t\tget_bits_no_markers(16);\n\t}\n\n\tstatic inline int dequantize_ac(int c, int q) {\tc *= q;\treturn c; }\n\n\t// Decodes and dequantizes the next row of coefficients.\n\tvoid jpeg_decoder::decode_next_row()\n\t{\n\t\tint row_block = 0;\n\n\t\tfor (int mcu_row = 0; mcu_row < m_mcus_per_row; mcu_row++)\n\t\t{\n\t\t\tif ((m_restart_interval) && (m_restarts_left == 0))\n\t\t\t\tprocess_restart();\n\n\t\t\tjpgd_block_t* p = m_pMCU_coefficients;\n\t\t\tfor (int mcu_block = 0; mcu_block < m_blocks_per_mcu; mcu_block++, p += 64)\n\t\t\t{\n\t\t\t\tint component_id = m_mcu_org[mcu_block];\n\t\t\t\tjpgd_quant_t* q = m_quant[m_comp_quant[component_id]];\n\n\t\t\t\tint r, s;\n\t\t\t\ts = huff_decode(m_pHuff_tabs[m_comp_dc_tab[component_id]], r);\n\t\t\t\ts = HUFF_EXTEND(r, s);\n\n\t\t\t\tm_last_dc_val[component_id] = (s += m_last_dc_val[component_id]);\n\n\t\t\t\tp[0] = static_cast<jpgd_block_t>(s * q[0]);\n\n\t\t\t\tint prev_num_set = m_mcu_block_max_zag[mcu_block];\n\n\t\t\t\thuff_tables *pH = m_pHuff_tabs[m_comp_ac_tab[component_id]];\n\n\t\t\t\tint k;\n\t\t\t\tfor (k = 1; k < 64; k++)\n\t\t\t\t{\n\t\t\t\t\tint extra_bits;\n\t\t\t\t\ts = huff_decode(pH, extra_bits);\n\n\t\t\t\t\tr = s >> 4;\n\t\t\t\t\ts &= 15;\n\n\t\t\t\t\tif (s)\n\t\t\t\t\t{\n\t\t\t\t\t\tif (r)\n\t\t\t\t\t\t{\n\t\t\t\t\t\t\tif ((k + r) > 63)\n\t\t\t\t\t\t\t\tstop_decoding(JPGD_DECODE_ERROR);\n\n\t\t\t\t\t\t\tif (k < prev_num_set)\n\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\tint n = JPGD_MIN(r, prev_num_set - k);\n\t\t\t\t\t\t\t\tint kt = k;\n\t\t\t\t\t\t\t\twhile (n--)\n\t\t\t\t\t\t\t\t\tp[g_ZAG[kt++]] = 0;\n\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\tk += r;\n\t\t\t\t\t\t}\n\n\t\t\t\t\t\ts = HUFF_EXTEND(extra_bits, s);\n\n\t\t\t\t\t\tJPGD_ASSERT(k < 64);\n\n\t\t\t\t\t\tp[g_ZAG[k]] = static_cast<jpgd_block_t>(dequantize_ac(s, q[k])); //s * q[k];\n\t\t\t\t\t}\n\t\t\t\t\telse\n\t\t\t\t\t{\n\t\t\t\t\t\tif (r == 15)\n\t\t\t\t\t\t{\n\t\t\t\t\t\t\tif ((k + 16) > 64)\n\t\t\t\t\t\t\t\tstop_decoding(JPGD_DECODE_ERROR);\n\n\t\t\t\t\t\t\tif (k < prev_num_set)\n\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\tint n = JPGD_MIN(16, prev_num_set - k);\n\t\t\t\t\t\t\t\tint kt = k;\n\t\t\t\t\t\t\t\twhile (n--)\n\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\tJPGD_ASSERT(kt <= 63);\n\t\t\t\t\t\t\t\t\tp[g_ZAG[kt++]] = 0;\n\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\tk += 16 - 1; // - 1 because the loop counter is k\n\t\t\t\t\t\t\t// BEGIN EPIC MOD\n\t\t\t\t\t\t\tJPGD_ASSERT(k < 64 && p[g_ZAG[k]] == 0);\n\t\t\t\t\t\t\t// END EPIC MOD\n\t\t\t\t\t\t}\n\t\t\t\t\t\telse\n\t\t\t\t\t\t\tbreak;\n\t\t\t\t\t}\n\t\t\t\t}\n\n\t\t\t\tif (k < prev_num_set)\n\t\t\t\t{\n\t\t\t\t\tint kt = k;\n\t\t\t\t\twhile (kt < prev_num_set)\n\t\t\t\t\t\tp[g_ZAG[kt++]] = 0;\n\t\t\t\t}\n\n\t\t\t\tm_mcu_block_max_zag[mcu_block] = k;\n\n\t\t\t\trow_block++;\n\t\t\t}\n\n\t\t\tif (m_freq_domain_chroma_upsample)\n\t\t\t\ttransform_mcu_expand(mcu_row);\n\t\t\telse\n\t\t\t\ttransform_mcu(mcu_row);\n\n\t\t\tm_restarts_left--;\n\t\t}\n\t}\n\n\t// YCbCr H1V1 (1x1:1:1, 3 m_blocks per MCU) to RGB\n\tvoid jpeg_decoder::H1V1Convert()\n\t{\n\t\tint row = m_max_mcu_y_size - m_mcu_lines_left;\n\t\tuint8 *d = m_pScan_line_0;\n\t\tuint8 *s = m_pSample_buf + row * 8;\n\n\t\tfor (int i = m_max_mcus_per_row; i > 0; i--)\n\t\t{\n\t\t\tfor (int j = 0; j < 8; j++)\n\t\t\t{\n\t\t\t\tint y = s[j];\n\t\t\t\tint cb = s[64+j];\n\t\t\t\tint cr = s[128+j];\n\n\t\t\t\tif (jpg_format == ERGBFormatJPG::BGRA)\n\t\t\t\t{\n\t\t\t\t\td[0] = clamp(y + m_cbb[cb]);\n\t\t\t\t\td[1] = clamp(y + ((m_crg[cr] + m_cbg[cb]) >> 16));\n\t\t\t\t\td[2] = clamp(y + m_crr[cr]);\n\t\t\t\t\td[3] = 255;\n\t\t\t\t}\n\t\t\t\telse\n\t\t\t\t{\n\t\t\t\t\td[0] = clamp(y + m_crr[cr]);\n\t\t\t\t\td[1] = clamp(y + ((m_crg[cr] + m_cbg[cb]) >> 16));\n\t\t\t\t\td[2] = clamp(y + m_cbb[cb]);\n\t\t\t\t\td[3] = 255;\n\t\t\t\t}\n\t\t\t\td += 4;\n\t\t\t}\n\n\t\t\ts += 64*3;\n\t\t}\n\t}\n\n\t// YCbCr H2V1 (2x1:1:1, 4 m_blocks per MCU) to RGB\n\tvoid jpeg_decoder::H2V1Convert()\n\t{\n\t\tint row = m_max_mcu_y_size - m_mcu_lines_left;\n\t\tuint8 *d0 = m_pScan_line_0;\n\t\tuint8 *y = m_pSample_buf + row * 8;\n\t\tuint8 *c = m_pSample_buf + 2*64 + row * 8;\n\n\t\tfor (int i = m_max_mcus_per_row; i > 0; i--)\n\t\t{\n\t\t\tfor (int l = 0; l < 2; l++)\n\t\t\t{\n\t\t\t\tfor (int j = 0; j < 4; j++)\n\t\t\t\t{\n\t\t\t\t\tint cb = c[0];\n\t\t\t\t\tint cr = c[64];\n\n\t\t\t\t\tint rc = m_crr[cr];\n\t\t\t\t\tint gc = ((m_crg[cr] + m_cbg[cb]) >> 16);\n\t\t\t\t\tint bc = m_cbb[cb];\n\n\t\t\t\t\tint yy = y[j<<1];\n\t\t\t\t\tif (jpg_format == ERGBFormatJPG::BGRA)\n\t\t\t\t\t{\n\t\t\t\t\t\td0[0] = clamp(yy+bc);\n\t\t\t\t\t\td0[1] = clamp(yy+gc);\n\t\t\t\t\t\td0[2] = clamp(yy+rc);\n\t\t\t\t\t\td0[3] = 255;\n\t\t\t\t\t\tyy = y[(j<<1)+1];\n\t\t\t\t\t\td0[4] = clamp(yy+bc);\n\t\t\t\t\t\td0[5] = clamp(yy+gc);\n\t\t\t\t\t\td0[6] = clamp(yy+rc);\n\t\t\t\t\t\td0[7] = 255;\n\t\t\t\t\t}\n\t\t\t\t\telse\n\t\t\t\t\t{\n\t\t\t\t\t\td0[0] = clamp(yy+rc);\n\t\t\t\t\t\td0[1] = clamp(yy+gc);\n\t\t\t\t\t\td0[2] = clamp(yy+bc);\n\t\t\t\t\t\td0[3] = 255;\n\t\t\t\t\t\tyy = y[(j<<1)+1];\n\t\t\t\t\t\td0[4] = clamp(yy+rc);\n\t\t\t\t\t\td0[5] = clamp(yy+gc);\n\t\t\t\t\t\td0[6] = clamp(yy+bc);\n\t\t\t\t\t\td0[7] = 255;\n\t\t\t\t\t}\n\n\t\t\t\t\td0 += 8;\n\n\t\t\t\t\tc++;\n\t\t\t\t}\n\t\t\t\ty += 64;\n\t\t\t}\n\n\t\t\ty += 64*4 - 64*2;\n\t\t\tc += 64*4 - 8;\n\t\t}\n\t}\n\n\t// YCbCr H2V1 (1x2:1:1, 4 m_blocks per MCU) to RGB\n\tvoid jpeg_decoder::H1V2Convert()\n\t{\n\t\tint row = m_max_mcu_y_size - m_mcu_lines_left;\n\t\tuint8 *d0 = m_pScan_line_0;\n\t\tuint8 *d1 = m_pScan_line_1;\n\t\tuint8 *y;\n\t\tuint8 *c;\n\n\t\tif (row < 8)\n\t\t\ty = m_pSample_buf + row * 8;\n\t\telse\n\t\t\ty = m_pSample_buf + 64*1 + (row & 7) * 8;\n\n\t\tc = m_pSample_buf + 64*2 + (row >> 1) * 8;\n\n\t\tfor (int i = m_max_mcus_per_row; i > 0; i--)\n\t\t{\n\t\t\tfor (int j = 0; j < 8; j++)\n\t\t\t{\n\t\t\t\tint cb = c[0+j];\n\t\t\t\tint cr = c[64+j];\n\n\t\t\t\tint rc = m_crr[cr];\n\t\t\t\tint gc = ((m_crg[cr] + m_cbg[cb]) >> 16);\n\t\t\t\tint bc = m_cbb[cb];\n\n\t\t\t\tint yy = y[j];\n\t\t\t\tif (jpg_format == ERGBFormatJPG::BGRA)\n\t\t\t\t{\n\t\t\t\t\td0[0] = clamp(yy+bc);\n\t\t\t\t\td0[1] = clamp(yy+gc);\n\t\t\t\t\td0[2] = clamp(yy+rc);\n\t\t\t\t\td0[3] = 255;\n\t\t\t\t\tyy = y[8+j];\n\t\t\t\t\td1[0] = clamp(yy+bc);\n\t\t\t\t\td1[1] = clamp(yy+gc);\n\t\t\t\t\td1[2] = clamp(yy+rc);\n\t\t\t\t\td1[3] = 255;\n\t\t\t\t}\n\t\t\t\telse\n\t\t\t\t{\n\t\t\t\t\td0[0] = clamp(yy+rc);\n\t\t\t\t\td0[1] = clamp(yy+gc);\n\t\t\t\t\td0[2] = clamp(yy+bc);\n\t\t\t\t\td0[3] = 255;\n\t\t\t\t\tyy = y[8+j];\n\t\t\t\t\td1[0] = clamp(yy+rc);\n\t\t\t\t\td1[1] = clamp(yy+gc);\n\t\t\t\t\td1[2] = clamp(yy+bc);\n\t\t\t\t\td1[3] = 255;\n\t\t\t\t}\n\n\t\t\t\td0 += 4;\n\t\t\t\td1 += 4;\n\t\t\t}\n\n\t\t\ty += 64*4;\n\t\t\tc += 64*4;\n\t\t}\n\t}\n\n\t// YCbCr H2V2 (2x2:1:1, 6 m_blocks per MCU) to RGB\n\tvoid jpeg_decoder::H2V2Convert()\n\t{\n\t\tint row = m_max_mcu_y_size - m_mcu_lines_left;\n\t\tuint8 *d0 = m_pScan_line_0;\n\t\tuint8 *d1 = m_pScan_line_1;\n\t\tuint8 *y;\n\t\tuint8 *c;\n\n\t\tif (row < 8)\n\t\t\ty = m_pSample_buf + row * 8;\n\t\telse\n\t\t\ty = m_pSample_buf + 64*2 + (row & 7) * 8;\n\n\t\tc = m_pSample_buf + 64*4 + (row >> 1) * 8;\n\n\t\tfor (int i = m_max_mcus_per_row; i > 0; i--)\n\t\t{\n\t\t\tfor (int l = 0; l < 2; l++)\n\t\t\t{\n\t\t\t\tfor (int j = 0; j < 8; j += 2)\n\t\t\t\t{\n\t\t\t\t\tint cb = c[0];\n\t\t\t\t\tint cr = c[64];\n\n\t\t\t\t\tint rc = m_crr[cr];\n\t\t\t\t\tint gc = ((m_crg[cr] + m_cbg[cb]) >> 16);\n\t\t\t\t\tint bc = m_cbb[cb];\n\n\t\t\t\t\tint yy = y[j];\n\t\t\t\t\tif (jpg_format == ERGBFormatJPG::BGRA)\n\t\t\t\t\t{\n\t\t\t\t\t\td0[0] = clamp(yy+bc);\n\t\t\t\t\t\td0[1] = clamp(yy+gc);\n\t\t\t\t\t\td0[2] = clamp(yy+rc);\n\t\t\t\t\t\td0[3] = 255;\n\t\t\t\t\t\tyy = y[j+1];\n\t\t\t\t\t\td0[4] = clamp(yy+bc);\n\t\t\t\t\t\td0[5] = clamp(yy+gc);\n\t\t\t\t\t\td0[6] = clamp(yy+rc);\n\t\t\t\t\t\td0[7] = 255;\n\t\t\t\t\t\tyy = y[j+8];\n\t\t\t\t\t\td1[0] = clamp(yy+bc);\n\t\t\t\t\t\td1[1] = clamp(yy+gc);\n\t\t\t\t\t\td1[2] = clamp(yy+rc);\n\t\t\t\t\t\td1[3] = 255;\n\t\t\t\t\t\tyy = y[j+8+1];\n\t\t\t\t\t\td1[4] = clamp(yy+bc);\n\t\t\t\t\t\td1[5] = clamp(yy+gc);\n\t\t\t\t\t\td1[6] = clamp(yy+rc);\n\t\t\t\t\t\td1[7] = 255;\n\t\t\t\t\t}\n\t\t\t\t\telse\n\t\t\t\t\t{\n\t\t\t\t\t\td0[0] = clamp(yy+rc);\n\t\t\t\t\t\td0[1] = clamp(yy+gc);\n\t\t\t\t\t\td0[2] = clamp(yy+bc);\n\t\t\t\t\t\td0[3] = 255;\n\t\t\t\t\t\tyy = y[j+1];\n\t\t\t\t\t\td0[4] = clamp(yy+rc);\n\t\t\t\t\t\td0[5] = clamp(yy+gc);\n\t\t\t\t\t\td0[6] = clamp(yy+bc);\n\t\t\t\t\t\td0[7] = 255;\n\t\t\t\t\t\tyy = y[j+8];\n\t\t\t\t\t\td1[0] = clamp(yy+rc);\n\t\t\t\t\t\td1[1] = clamp(yy+gc);\n\t\t\t\t\t\td1[2] = clamp(yy+bc);\n\t\t\t\t\t\td1[3] = 255;\n\t\t\t\t\t\tyy = y[j+8+1];\n\t\t\t\t\t\td1[4] = clamp(yy+rc);\n\t\t\t\t\t\td1[5] = clamp(yy+gc);\n\t\t\t\t\t\td1[6] = clamp(yy+bc);\n\t\t\t\t\t\td1[7] = 255;\n\t\t\t\t\t}\n\n\t\t\t\t\td0 += 8;\n\t\t\t\t\td1 += 8;\n\n\t\t\t\t\tc++;\n\t\t\t\t}\n\t\t\t\ty += 64;\n\t\t\t}\n\n\t\t\ty += 64*6 - 64*2;\n\t\t\tc += 64*6 - 8;\n\t\t}\n\t}\n\n\t// Y (1 block per MCU) to 8-bit grayscale\n\tvoid jpeg_decoder::gray_convert()\n\t{\n\t\tint row = m_max_mcu_y_size - m_mcu_lines_left;\n\t\tuint8 *d = m_pScan_line_0;\n\t\tuint8 *s = m_pSample_buf + row * 8;\n\n\t\tfor (int i = m_max_mcus_per_row; i > 0; i--)\n\t\t{\n\t\t\t*(uint *)d = *(uint *)s;\n\t\t\t*(uint *)(&d[4]) = *(uint *)(&s[4]);\n\n\t\t\ts += 64;\n\t\t\td += 8;\n\t\t}\n\t}\n\n\tvoid jpeg_decoder::expanded_convert()\n\t{\n\t\tint row = m_max_mcu_y_size - m_mcu_lines_left;\n\n\t\tuint8* Py = m_pSample_buf + (row / 8) * 64 * m_comp_h_samp[0] + (row & 7) * 8;\n\n\t\tuint8* d = m_pScan_line_0;\n\n\t\tfor (int i = m_max_mcus_per_row; i > 0; i--)\n\t\t{\n\t\t\tfor (int k = 0; k < m_max_mcu_x_size; k += 8)\n\t\t\t{\n\t\t\t\tconst int Y_ofs = k * 8;\n\t\t\t\tconst int Cb_ofs = Y_ofs + 64 * m_expanded_blocks_per_component;\n\t\t\t\tconst int Cr_ofs = Y_ofs + 64 * m_expanded_blocks_per_component * 2;\n\t\t\t\tfor (int j = 0; j < 8; j++)\n\t\t\t\t{\n\t\t\t\t\tint y = Py[Y_ofs + j];\n\t\t\t\t\tint cb = Py[Cb_ofs + j];\n\t\t\t\t\tint cr = Py[Cr_ofs + j];\n\n\t\t\t\t\tif (jpg_format == ERGBFormatJPG::BGRA)\n\t\t\t\t\t{\n\t\t\t\t\t\td[0] = clamp(y + m_cbb[cb]);\n\t\t\t\t\t\td[1] = clamp(y + ((m_crg[cr] + m_cbg[cb]) >> 16));\n\t\t\t\t\t\td[2] = clamp(y + m_crr[cr]);\n\t\t\t\t\t\td[3] = 255;\n\t\t\t\t\t}\n\t\t\t\t\telse\n\t\t\t\t\t{\n\t\t\t\t\t\td[0] = clamp(y + m_crr[cr]);\n\t\t\t\t\t\td[1] = clamp(y + ((m_crg[cr] + m_cbg[cb]) >> 16));\n\t\t\t\t\t\td[2] = clamp(y + m_cbb[cb]);\n\t\t\t\t\t\td[3] = 255;\n\t\t\t\t\t}\n\n\t\t\t\t\td += 4;\n\t\t\t\t}\n\t\t\t}\n\n\t\t\tPy += 64 * m_expanded_blocks_per_mcu;\n\t\t}\n\t}\n\n\t// Find end of image (EOI) marker, so we can return to the user the exact size of the input stream.\n\tvoid jpeg_decoder::find_eoi()\n\t{\n\t\tif (!m_progressive_flag)\n\t\t{\n\t\t\t// Attempt to read the EOI marker.\n\t\t\t//get_bits_no_markers(m_bits_left & 7);\n\n\t\t\t// Prime the bit buffer\n\t\t\tm_bits_left = 16;\n\t\t\tget_bits(16);\n\t\t\tget_bits(16);\n\n\t\t\t// The next marker _should_ be EOI\n\t\t\tprocess_markers();\n\t\t}\n\n\t\tm_total_bytes_read -= m_in_buf_left;\n\t}\n\n\tint jpeg_decoder::decode(const void** pScan_line, uint* pScan_line_len)\n\t{\n\t\tif ((m_error_code) || (!m_ready_flag))\n\t\t\treturn JPGD_FAILED;\n\n\t\tif (m_total_lines_left == 0)\n\t\t\treturn JPGD_DONE;\n\n\t\tif (m_mcu_lines_left == 0)\n\t\t{\n\t\t\tif (setjmp(m_jmp_state))\n\t\t\t\treturn JPGD_FAILED;\n\n\t\t\tif (m_progressive_flag)\n\t\t\t\tload_next_row();\n\t\t\telse\n\t\t\t\tdecode_next_row();\n\n\t\t\t// Find the EOI marker if that was the last row.\n\t\t\tif (m_total_lines_left <= m_max_mcu_y_size)\n\t\t\t\tfind_eoi();\n\n\t\t\tm_mcu_lines_left = m_max_mcu_y_size;\n\t\t}\n\n\t\tif (m_freq_domain_chroma_upsample)\n\t\t{\n\t\t\texpanded_convert();\n\t\t\t*pScan_line = m_pScan_line_0;\n\t\t}\n\t\telse\n\t\t{\n\t\t\tswitch (m_scan_type)\n\t\t\t{\n\t\t\tcase JPGD_YH2V2:\n\t\t\t\t{\n\t\t\t\t\tif ((m_mcu_lines_left & 1) == 0)\n\t\t\t\t\t{\n\t\t\t\t\t\tH2V2Convert();\n\t\t\t\t\t\t*pScan_line = m_pScan_line_0;\n\t\t\t\t\t}\n\t\t\t\t\telse\n\t\t\t\t\t\t*pScan_line = m_pScan_line_1;\n\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\tcase JPGD_YH2V1:\n\t\t\t\t{\n\t\t\t\t\tH2V1Convert();\n\t\t\t\t\t*pScan_line = m_pScan_line_0;\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\tcase JPGD_YH1V2:\n\t\t\t\t{\n\t\t\t\t\tif ((m_mcu_lines_left & 1) == 0)\n\t\t\t\t\t{\n\t\t\t\t\t\tH1V2Convert();\n\t\t\t\t\t\t*pScan_line = m_pScan_line_0;\n\t\t\t\t\t}\n\t\t\t\t\telse\n\t\t\t\t\t\t*pScan_line = m_pScan_line_1;\n\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\tcase JPGD_YH1V1:\n\t\t\t\t{\n\t\t\t\t\tH1V1Convert();\n\t\t\t\t\t*pScan_line = m_pScan_line_0;\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\tcase JPGD_GRAYSCALE:\n\t\t\t\t{\n\t\t\t\t\tgray_convert();\n\t\t\t\t\t*pScan_line = m_pScan_line_0;\n\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\t*pScan_line_len = m_real_dest_bytes_per_scan_line;\n\n\t\tm_mcu_lines_left--;\n\t\tm_total_lines_left--;\n\n\t\treturn JPGD_SUCCESS;\n\t}\n\n\t// Creates the tables needed for efficient Huffman decoding.\n\tvoid jpeg_decoder::make_huff_table(int index, huff_tables *pH)\n\t{\n\t\tint p, i, l, si;\n\t\tuint8 huffsize[257];\n\t\tuint huffcode[257];\n\t\tuint code;\n\t\tuint subtree;\n\t\tint code_size;\n\t\tint lastp;\n\t\tint nextfreeentry;\n\t\tint currententry;\n\n\t\tpH->ac_table = m_huff_ac[index] != 0;\n\n\t\tp = 0;\n\n\t\tfor (l = 1; l <= 16; l++)\n\t\t{\n\t\t\tfor (i = 1; i <= m_huff_num[index][l]; i++)\n\t\t\t\thuffsize[p++] = static_cast<uint8>(l);\n\t\t}\n\n\t\thuffsize[p] = 0;\n\n\t\tlastp = p;\n\n\t\tcode = 0;\n\t\tsi = huffsize[0];\n\t\tp = 0;\n\n\t\twhile (huffsize[p])\n\t\t{\n\t\t\twhile (huffsize[p] == si)\n\t\t\t{\n\t\t\t\thuffcode[p++] = code;\n\t\t\t\tcode++;\n\t\t\t}\n\n\t\t\tcode <<= 1;\n\t\t\tsi++;\n\t\t}\n\n\t\tmemset(pH->look_up, 0, sizeof(pH->look_up));\n\t\tmemset(pH->look_up2, 0, sizeof(pH->look_up2));\n\t\tmemset(pH->tree, 0, sizeof(pH->tree));\n\t\tmemset(pH->code_size, 0, sizeof(pH->code_size));\n\n\t\tnextfreeentry = -1;\n\n\t\tp = 0;\n\n\t\twhile (p < lastp)\n\t\t{\n\t\t\ti = m_huff_val[index][p];\n\t\t\tcode = huffcode[p];\n\t\t\tcode_size = huffsize[p];\n\n\t\t\tpH->code_size[i] = static_cast<uint8>(code_size);\n\n\t\t\tif (code_size <= 8)\n\t\t\t{\n\t\t\t\tcode <<= (8 - code_size);\n\n\t\t\t\tfor (l = 1 << (8 - code_size); l > 0; l--)\n\t\t\t\t{\n\t\t\t\t\tJPGD_ASSERT(i < 256);\n\n\t\t\t\t\tpH->look_up[code] = i;\n\n\t\t\t\t\tbool has_extrabits = false;\n\t\t\t\t\tint extra_bits = 0;\n\t\t\t\t\tint num_extra_bits = i & 15;\n\n\t\t\t\t\tint bits_to_fetch = code_size;\n\t\t\t\t\tif (num_extra_bits)\n\t\t\t\t\t{\n\t\t\t\t\t\tint total_codesize = code_size + num_extra_bits;\n\t\t\t\t\t\tif (total_codesize <= 8)\n\t\t\t\t\t\t{\n\t\t\t\t\t\t\thas_extrabits = true;\n\t\t\t\t\t\t\textra_bits = ((1 << num_extra_bits) - 1) & (code >> (8 - total_codesize));\n\t\t\t\t\t\t\tJPGD_ASSERT(extra_bits <= 0x7FFF);\n\t\t\t\t\t\t\tbits_to_fetch += num_extra_bits;\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\n\t\t\t\t\tif (!has_extrabits)\n\t\t\t\t\t\tpH->look_up2[code] = i | (bits_to_fetch << 8);\n\t\t\t\t\telse\n\t\t\t\t\t\tpH->look_up2[code] = i | 0x8000 | (extra_bits << 16) | (bits_to_fetch << 8);\n\n\t\t\t\t\tcode++;\n\t\t\t\t}\n\t\t\t}\n\t\t\telse\n\t\t\t{\n\t\t\t\tsubtree = (code >> (code_size - 8)) & 0xFF;\n\n\t\t\t\tcurrententry = pH->look_up[subtree];\n\n\t\t\t\tif (currententry == 0)\n\t\t\t\t{\n\t\t\t\t\tpH->look_up[subtree] = currententry = nextfreeentry;\n\t\t\t\t\tpH->look_up2[subtree] = currententry = nextfreeentry;\n\n\t\t\t\t\tnextfreeentry -= 2;\n\t\t\t\t}\n\n\t\t\t\tcode <<= (16 - (code_size - 8));\n\n\t\t\t\tfor (l = code_size; l > 9; l--)\n\t\t\t\t{\n\t\t\t\t\tif ((code & 0x8000) == 0)\n\t\t\t\t\t\tcurrententry--;\n\n\t\t\t\t\tif (pH->tree[-currententry - 1] == 0)\n\t\t\t\t\t{\n\t\t\t\t\t\tpH->tree[-currententry - 1] = nextfreeentry;\n\n\t\t\t\t\t\tcurrententry = nextfreeentry;\n\n\t\t\t\t\t\tnextfreeentry -= 2;\n\t\t\t\t\t}\n\t\t\t\t\telse\n\t\t\t\t\t\tcurrententry = pH->tree[-currententry - 1];\n\n\t\t\t\t\tcode <<= 1;\n\t\t\t\t}\n\n\t\t\t\tif ((code & 0x8000) == 0)\n\t\t\t\t\tcurrententry--;\n\n\t\t\t\tpH->tree[-currententry - 1] = i;\n\t\t\t}\n\n\t\t\tp++;\n\t\t}\n\t}\n\n\t// Verifies the quantization tables needed for this scan are available.\n\tvoid jpeg_decoder::check_quant_tables()\n\t{\n\t\tfor (int i = 0; i < m_comps_in_scan; i++)\n\t\t\tif (m_quant[m_comp_quant[m_comp_list[i]]] == NULL)\n\t\t\t\tstop_decoding(JPGD_UNDEFINED_QUANT_TABLE);\n\t}\n\n\t// Verifies that all the Huffman tables needed for this scan are available.\n\tvoid jpeg_decoder::check_huff_tables()\n\t{\n\t\tfor (int i = 0; i < m_comps_in_scan; i++)\n\t\t{\n\t\t\tif ((m_spectral_start == 0) && (m_huff_num[m_comp_dc_tab[m_comp_list[i]]] == NULL))\n\t\t\t\tstop_decoding(JPGD_UNDEFINED_HUFF_TABLE);\n\n\t\t\tif ((m_spectral_end > 0) && (m_huff_num[m_comp_ac_tab[m_comp_list[i]]] == NULL))\n\t\t\t\tstop_decoding(JPGD_UNDEFINED_HUFF_TABLE);\n\t\t}\n\n\t\tfor (int i = 0; i < JPGD_MAX_HUFF_TABLES; i++)\n\t\t\tif (m_huff_num[i])\n\t\t\t{\n\t\t\t\tif (!m_pHuff_tabs[i])\n\t\t\t\t\tm_pHuff_tabs[i] = (huff_tables *)alloc(sizeof(huff_tables));\n\n\t\t\t\tmake_huff_table(i, m_pHuff_tabs[i]);\n\t\t\t}\n\t}\n\n\t// Determines the component order inside each MCU.\n\t// Also calcs how many MCU's are on each row, etc.\n\tvoid jpeg_decoder::calc_mcu_block_order()\n\t{\n\t\tint component_num, component_id;\n\t\tint max_h_samp = 0, max_v_samp = 0;\n\n\t\tfor (component_id = 0; component_id < m_comps_in_frame; component_id++)\n\t\t{\n\t\t\tif (m_comp_h_samp[component_id] > max_h_samp)\n\t\t\t\tmax_h_samp = m_comp_h_samp[component_id];\n\n\t\t\tif (m_comp_v_samp[component_id] > max_v_samp)\n\t\t\t\tmax_v_samp = m_comp_v_samp[component_id];\n\t\t}\n\n\t\tfor (component_id = 0; component_id < m_comps_in_frame; component_id++)\n\t\t{\n\t\t\tm_comp_h_blocks[component_id] = ((((m_image_x_size * m_comp_h_samp[component_id]) + (max_h_samp - 1)) / max_h_samp) + 7) / 8;\n\t\t\tm_comp_v_blocks[component_id] = ((((m_image_y_size * m_comp_v_samp[component_id]) + (max_v_samp - 1)) / max_v_samp) + 7) / 8;\n\t\t}\n\n\t\tif (m_comps_in_scan == 1)\n\t\t{\n\t\t\tm_mcus_per_row = m_comp_h_blocks[m_comp_list[0]];\n\t\t\tm_mcus_per_col = m_comp_v_blocks[m_comp_list[0]];\n\t\t}\n\t\telse\n\t\t{\n\t\t\tm_mcus_per_row = (((m_image_x_size + 7) / 8) + (max_h_samp - 1)) / max_h_samp;\n\t\t\tm_mcus_per_col = (((m_image_y_size + 7) / 8) + (max_v_samp - 1)) / max_v_samp;\n\t\t}\n\n\t\tif (m_comps_in_scan == 1)\n\t\t{\n\t\t\tm_mcu_org[0] = m_comp_list[0];\n\n\t\t\tm_blocks_per_mcu = 1;\n\t\t}\n\t\telse\n\t\t{\n\t\t\tm_blocks_per_mcu = 0;\n\n\t\t\tfor (component_num = 0; component_num < m_comps_in_scan; component_num++)\n\t\t\t{\n\t\t\t\tint num_blocks;\n\n\t\t\t\tcomponent_id = m_comp_list[component_num];\n\n\t\t\t\tnum_blocks = m_comp_h_samp[component_id] * m_comp_v_samp[component_id];\n\n\t\t\t\twhile (num_blocks--)\n\t\t\t\t\tm_mcu_org[m_blocks_per_mcu++] = component_id;\n\t\t\t}\n\t\t}\n\t}\n\n\t// Starts a new scan.\n\tint jpeg_decoder::init_scan()\n\t{\n\t\tif (!locate_sos_marker())\n\t\t\treturn JPGD_FALSE;\n\n\t\tcalc_mcu_block_order();\n\n\t\tcheck_huff_tables();\n\n\t\tcheck_quant_tables();\n\n\t\tmemset(m_last_dc_val, 0, m_comps_in_frame * sizeof(uint));\n\n\t\tm_eob_run = 0;\n\n\t\tif (m_restart_interval)\n\t\t{\n\t\t\tm_restarts_left = m_restart_interval;\n\t\t\tm_next_restart_num = 0;\n\t\t}\n\n\t\tfix_in_buffer();\n\n\t\treturn JPGD_TRUE;\n\t}\n\n\t// Starts a frame. Determines if the number of components or sampling factors\n\t// are supported.\n\tvoid jpeg_decoder::init_frame()\n\t{\n\t\tint i;\n\n\t\tif (m_comps_in_frame == 1)\n\t\t{\n\t\t\tif ((m_comp_h_samp[0] != 1) || (m_comp_v_samp[0] != 1))\n\t\t\t\tstop_decoding(JPGD_UNSUPPORTED_SAMP_FACTORS);\n\n\t\t\tm_scan_type = JPGD_GRAYSCALE;\n\t\t\tm_max_blocks_per_mcu = 1;\n\t\t\tm_max_mcu_x_size = 8;\n\t\t\tm_max_mcu_y_size = 8;\n\t\t}\n\t\telse if (m_comps_in_frame == 3)\n\t\t{\n\t\t\tif ( ((m_comp_h_samp[1] != 1) || (m_comp_v_samp[1] != 1)) ||\n\t\t\t\t((m_comp_h_samp[2] != 1) || (m_comp_v_samp[2] != 1)) )\n\t\t\t\tstop_decoding(JPGD_UNSUPPORTED_SAMP_FACTORS);\n\n\t\t\tif ((m_comp_h_samp[0] == 1) && (m_comp_v_samp[0] == 1))\n\t\t\t{\n\t\t\t\tm_scan_type = JPGD_YH1V1;\n\n\t\t\t\tm_max_blocks_per_mcu = 3;\n\t\t\t\tm_max_mcu_x_size = 8;\n\t\t\t\tm_max_mcu_y_size = 8;\n\t\t\t}\n\t\t\telse if ((m_comp_h_samp[0] == 2) && (m_comp_v_samp[0] == 1))\n\t\t\t{\n\t\t\t\tm_scan_type = JPGD_YH2V1;\n\t\t\t\tm_max_blocks_per_mcu = 4;\n\t\t\t\tm_max_mcu_x_size = 16;\n\t\t\t\tm_max_mcu_y_size = 8;\n\t\t\t}\n\t\t\telse if ((m_comp_h_samp[0] == 1) && (m_comp_v_samp[0] == 2))\n\t\t\t{\n\t\t\t\tm_scan_type = JPGD_YH1V2;\n\t\t\t\tm_max_blocks_per_mcu = 4;\n\t\t\t\tm_max_mcu_x_size = 8;\n\t\t\t\tm_max_mcu_y_size = 16;\n\t\t\t}\n\t\t\telse if ((m_comp_h_samp[0] == 2) && (m_comp_v_samp[0] == 2))\n\t\t\t{\n\t\t\t\tm_scan_type = JPGD_YH2V2;\n\t\t\t\tm_max_blocks_per_mcu = 6;\n\t\t\t\tm_max_mcu_x_size = 16;\n\t\t\t\tm_max_mcu_y_size = 16;\n\t\t\t}\n\t\t\telse\n\t\t\t\tstop_decoding(JPGD_UNSUPPORTED_SAMP_FACTORS);\n\t\t}\n\t\telse\n\t\t\tstop_decoding(JPGD_UNSUPPORTED_COLORSPACE);\n\n\t\tm_max_mcus_per_row = (m_image_x_size + (m_max_mcu_x_size - 1)) / m_max_mcu_x_size;\n\t\tm_max_mcus_per_col = (m_image_y_size + (m_max_mcu_y_size - 1)) / m_max_mcu_y_size;\n\n\t\t// These values are for the *destination* pixels: after conversion.\n\t\tif (m_scan_type == JPGD_GRAYSCALE)\n\t\t\tm_dest_bytes_per_pixel = 1;\n\t\telse\n\t\t\tm_dest_bytes_per_pixel = 4;\n\n\t\tm_dest_bytes_per_scan_line = ((m_image_x_size + 15) & 0xFFF0) * m_dest_bytes_per_pixel;\n\n\t\tm_real_dest_bytes_per_scan_line = (m_image_x_size * m_dest_bytes_per_pixel);\n\n\t\t// Initialize two scan line buffers.\n\t\tm_pScan_line_0 = (uint8 *)alloc(m_dest_bytes_per_scan_line, true);\n\t\tif ((m_scan_type == JPGD_YH1V2) || (m_scan_type == JPGD_YH2V2))\n\t\t\tm_pScan_line_1 = (uint8 *)alloc(m_dest_bytes_per_scan_line, true);\n\n\t\tm_max_blocks_per_row = m_max_mcus_per_row * m_max_blocks_per_mcu;\n\n\t\t// Should never happen\n\t\tif (m_max_blocks_per_row > JPGD_MAX_BLOCKS_PER_ROW)\n\t\t\tstop_decoding(JPGD_ASSERTION_ERROR);\n\n\t\t// Allocate the coefficient buffer, enough for one MCU\n\t\tm_pMCU_coefficients = (jpgd_block_t*)alloc(m_max_blocks_per_mcu * 64 * sizeof(jpgd_block_t));\n\n\t\tfor (i = 0; i < m_max_blocks_per_mcu; i++)\n\t\t\tm_mcu_block_max_zag[i] = 64;\n\n\t\tm_expanded_blocks_per_component = m_comp_h_samp[0] * m_comp_v_samp[0];\n\t\tm_expanded_blocks_per_mcu = m_expanded_blocks_per_component * m_comps_in_frame;\n\t\tm_expanded_blocks_per_row = m_max_mcus_per_row * m_expanded_blocks_per_mcu;\n\t\t// Freq. domain chroma upsampling is only supported for H2V2 subsampling factor.\n// BEGIN EPIC MOD\n#if JPGD_SUPPORT_FREQ_DOMAIN_UPSAMPLING\n\t\tm_freq_domain_chroma_upsample = (m_expanded_blocks_per_mcu == 4*3);\n#else\n\t\tm_freq_domain_chroma_upsample = 0;\n#endif\n// END EPIC MOD\n\n\t\tif (m_freq_domain_chroma_upsample)\n\t\t\tm_pSample_buf = (uint8 *)alloc(m_expanded_blocks_per_row * 64);\n\t\telse\n\t\t\tm_pSample_buf = (uint8 *)alloc(m_max_blocks_per_row * 64);\n\n\t\tm_total_lines_left = m_image_y_size;\n\n\t\tm_mcu_lines_left = 0;\n\n\t\tcreate_look_ups();\n\t}\n\n\t// The coeff_buf series of methods originally stored the coefficients\n\t// into a \"virtual\" file which was located in EMS, XMS, or a disk file. A cache\n\t// was used to make this process more efficient. Now, we can store the entire\n\t// thing in RAM.\n\tjpeg_decoder::coeff_buf* jpeg_decoder::coeff_buf_open(int block_num_x, int block_num_y, int block_len_x, int block_len_y)\n\t{\n\t\tcoeff_buf* cb = (coeff_buf*)alloc(sizeof(coeff_buf));\n\n\t\tcb->block_num_x = block_num_x;\n\t\tcb->block_num_y = block_num_y;\n\t\tcb->block_len_x = block_len_x;\n\t\tcb->block_len_y = block_len_y;\n\t\tcb->block_size = (block_len_x * block_len_y) * sizeof(jpgd_block_t);\n\t\tcb->pData = (uint8 *)alloc(cb->block_size * block_num_x * block_num_y, true);\n\t\treturn cb;\n\t}\n\n\tinline jpgd_block_t *jpeg_decoder::coeff_buf_getp(coeff_buf *cb, int block_x, int block_y)\n\t{\n\t\tJPGD_ASSERT((block_x < cb->block_num_x) && (block_y < cb->block_num_y));\n\t\treturn (jpgd_block_t *)(cb->pData + block_x * cb->block_size + block_y * (cb->block_size * cb->block_num_x));\n\t}\n\n\t// The following methods decode the various types of m_blocks encountered\n\t// in progressively encoded images.\n\tvoid jpeg_decoder::decode_block_dc_first(jpeg_decoder *pD, int component_id, int block_x, int block_y)\n\t{\n\t\tint s, r;\n\t\tjpgd_block_t *p = pD->coeff_buf_getp(pD->m_dc_coeffs[component_id], block_x, block_y);\n\n\t\tif ((s = pD->huff_decode(pD->m_pHuff_tabs[pD->m_comp_dc_tab[component_id]])) != 0)\n\t\t{\n\t\t\tr = pD->get_bits_no_markers(s);\n\t\t\ts = HUFF_EXTEND(r, s);\n\t\t}\n\n\t\tpD->m_last_dc_val[component_id] = (s += pD->m_last_dc_val[component_id]);\n\n\t\tp[0] = static_cast<jpgd_block_t>(s << pD->m_successive_low);\n\t}\n\n\tvoid jpeg_decoder::decode_block_dc_refine(jpeg_decoder *pD, int component_id, int block_x, int block_y)\n\t{\n\t\tif (pD->get_bits_no_markers(1))\n\t\t{\n\t\t\tjpgd_block_t *p = pD->coeff_buf_getp(pD->m_dc_coeffs[component_id], block_x, block_y);\n\n\t\t\tp[0] |= (1 << pD->m_successive_low);\n\t\t}\n\t}\n\n\tvoid jpeg_decoder::decode_block_ac_first(jpeg_decoder *pD, int component_id, int block_x, int block_y)\n\t{\n\t\tint k, s, r;\n\n\t\tif (pD->m_eob_run)\n\t\t{\n\t\t\tpD->m_eob_run--;\n\t\t\treturn;\n\t\t}\n\n\t\tjpgd_block_t *p = pD->coeff_buf_getp(pD->m_ac_coeffs[component_id], block_x, block_y);\n\n\t\tfor (k = pD->m_spectral_start; k <= pD->m_spectral_end; k++)\n\t\t{\n\t\t\ts = pD->huff_decode(pD->m_pHuff_tabs[pD->m_comp_ac_tab[component_id]]);\n\n\t\t\tr = s >> 4;\n\t\t\ts &= 15;\n\n\t\t\tif (s)\n\t\t\t{\n\t\t\t\tif ((k += r) > 63)\n\t\t\t\t\tpD->stop_decoding(JPGD_DECODE_ERROR);\n\n\t\t\t\tr = pD->get_bits_no_markers(s);\n\t\t\t\ts = HUFF_EXTEND(r, s);\n\n\t\t\t\tp[g_ZAG[k]] = static_cast<jpgd_block_t>(s << pD->m_successive_low);\n\t\t\t}\n\t\t\telse\n\t\t\t{\n\t\t\t\tif (r == 15)\n\t\t\t\t{\n\t\t\t\t\tif ((k += 15) > 63)\n\t\t\t\t\t\tpD->stop_decoding(JPGD_DECODE_ERROR);\n\t\t\t\t}\n\t\t\t\telse\n\t\t\t\t{\n\t\t\t\t\tpD->m_eob_run = 1 << r;\n\n\t\t\t\t\tif (r)\n\t\t\t\t\t\tpD->m_eob_run += pD->get_bits_no_markers(r);\n\n\t\t\t\t\tpD->m_eob_run--;\n\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\n\tvoid jpeg_decoder::decode_block_ac_refine(jpeg_decoder *pD, int component_id, int block_x, int block_y)\n\t{\n\t\tint s, k, r;\n\t\tint p1 = 1 << pD->m_successive_low;\n\t\tint m1 = (-1) << pD->m_successive_low;\n\t\tjpgd_block_t *p = pD->coeff_buf_getp(pD->m_ac_coeffs[component_id], block_x, block_y);\n\n\t\tk = pD->m_spectral_start;\n\n\t\tif (pD->m_eob_run == 0)\n\t\t{\n\t\t\tfor ( ; k <= pD->m_spectral_end; k++)\n\t\t\t{\n\t\t\t\ts = pD->huff_decode(pD->m_pHuff_tabs[pD->m_comp_ac_tab[component_id]]);\n\n\t\t\t\tr = s >> 4;\n\t\t\t\ts &= 15;\n\n\t\t\t\tif (s)\n\t\t\t\t{\n\t\t\t\t\tif (s != 1)\n\t\t\t\t\t\tpD->stop_decoding(JPGD_DECODE_ERROR);\n\n\t\t\t\t\tif (pD->get_bits_no_markers(1))\n\t\t\t\t\t\ts = p1;\n\t\t\t\t\telse\n\t\t\t\t\t\ts = m1;\n\t\t\t\t}\n\t\t\t\telse\n\t\t\t\t{\n\t\t\t\t\tif (r != 15)\n\t\t\t\t\t{\n\t\t\t\t\t\tpD->m_eob_run = 1 << r;\n\n\t\t\t\t\t\tif (r)\n\t\t\t\t\t\t\tpD->m_eob_run += pD->get_bits_no_markers(r);\n\n\t\t\t\t\t\tbreak;\n\t\t\t\t\t}\n\t\t\t\t}\n\n\t\t\t\tdo\n\t\t\t\t{\n\t\t\t\t\t// BEGIN EPIC MOD\n\t\t\t\t\tJPGD_ASSERT(k < 64);\n\t\t\t\t\t// END EPIC MOD\n\n\t\t\t\t\tjpgd_block_t *this_coef = p + g_ZAG[k];\n\n\t\t\t\t\tif (*this_coef != 0)\n\t\t\t\t\t{\n\t\t\t\t\t\tif (pD->get_bits_no_markers(1))\n\t\t\t\t\t\t{\n\t\t\t\t\t\t\tif ((*this_coef & p1) == 0)\n\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\tif (*this_coef >= 0)\n\t\t\t\t\t\t\t\t\t*this_coef = static_cast<jpgd_block_t>(*this_coef + p1);\n\t\t\t\t\t\t\t\telse\n\t\t\t\t\t\t\t\t\t*this_coef = static_cast<jpgd_block_t>(*this_coef + m1);\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t\telse\n\t\t\t\t\t{\n\t\t\t\t\t\tif (--r < 0)\n\t\t\t\t\t\t\tbreak;\n\t\t\t\t\t}\n\n\t\t\t\t\tk++;\n\n\t\t\t\t} while (k <= pD->m_spectral_end);\n\n\t\t\t\tif ((s) && (k < 64))\n\t\t\t\t{\n\t\t\t\t\tp[g_ZAG[k]] = static_cast<jpgd_block_t>(s);\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\tif (pD->m_eob_run > 0)\n\t\t{\n\t\t\tfor ( ; k <= pD->m_spectral_end; k++)\n\t\t\t{\n\t\t\t\t// BEGIN EPIC MOD\n\t\t\t\tJPGD_ASSERT(k < 64);\n\t\t\t\t// END EPIC MOD\n\n\t\t\t\tjpgd_block_t *this_coef = p + g_ZAG[k];\n\n\t\t\t\tif (*this_coef != 0)\n\t\t\t\t{\n\t\t\t\t\tif (pD->get_bits_no_markers(1))\n\t\t\t\t\t{\n\t\t\t\t\t\tif ((*this_coef & p1) == 0)\n\t\t\t\t\t\t{\n\t\t\t\t\t\t\tif (*this_coef >= 0)\n\t\t\t\t\t\t\t\t*this_coef = static_cast<jpgd_block_t>(*this_coef + p1);\n\t\t\t\t\t\t\telse\n\t\t\t\t\t\t\t\t*this_coef = static_cast<jpgd_block_t>(*this_coef + m1);\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\n\t\t\tpD->m_eob_run--;\n\t\t}\n\t}\n\n\t// Decode a scan in a progressively encoded image.\n\tvoid jpeg_decoder::decode_scan(pDecode_block_func decode_block_func)\n\t{\n\t\tint mcu_row, mcu_col, mcu_block;\n\t\tint block_x_mcu[JPGD_MAX_COMPONENTS], m_block_y_mcu[JPGD_MAX_COMPONENTS];\n\n\t\tmemset(m_block_y_mcu, 0, sizeof(m_block_y_mcu));\n\n\t\tfor (mcu_col = 0; mcu_col < m_mcus_per_col; mcu_col++)\n\t\t{\n\t\t\tint component_num, component_id;\n\n\t\t\tmemset(block_x_mcu, 0, sizeof(block_x_mcu));\n\n\t\t\tfor (mcu_row = 0; mcu_row < m_mcus_per_row; mcu_row++)\n\t\t\t{\n\t\t\t\tint block_x_mcu_ofs = 0, block_y_mcu_ofs = 0;\n\n\t\t\t\tif ((m_restart_interval) && (m_restarts_left == 0))\n\t\t\t\t\tprocess_restart();\n\n\t\t\t\tfor (mcu_block = 0; mcu_block < m_blocks_per_mcu; mcu_block++)\n\t\t\t\t{\n\t\t\t\t\tcomponent_id = m_mcu_org[mcu_block];\n\n\t\t\t\t\tdecode_block_func(this, component_id, block_x_mcu[component_id] + block_x_mcu_ofs, m_block_y_mcu[component_id] + block_y_mcu_ofs);\n\n\t\t\t\t\tif (m_comps_in_scan == 1)\n\t\t\t\t\t\tblock_x_mcu[component_id]++;\n\t\t\t\t\telse\n\t\t\t\t\t{\n\t\t\t\t\t\tif (++block_x_mcu_ofs == m_comp_h_samp[component_id])\n\t\t\t\t\t\t{\n\t\t\t\t\t\t\tblock_x_mcu_ofs = 0;\n\n\t\t\t\t\t\t\tif (++block_y_mcu_ofs == m_comp_v_samp[component_id])\n\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\tblock_y_mcu_ofs = 0;\n\t\t\t\t\t\t\t\tblock_x_mcu[component_id] += m_comp_h_samp[component_id];\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\n\t\t\t\tm_restarts_left--;\n\t\t\t}\n\n\t\t\tif (m_comps_in_scan == 1)\n\t\t\t\tm_block_y_mcu[m_comp_list[0]]++;\n\t\t\telse\n\t\t\t{\n\t\t\t\tfor (component_num = 0; component_num < m_comps_in_scan; component_num++)\n\t\t\t\t{\n\t\t\t\t\tcomponent_id = m_comp_list[component_num];\n\t\t\t\t\tm_block_y_mcu[component_id] += m_comp_v_samp[component_id];\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\n\t// Decode a progressively encoded image.\n\tvoid jpeg_decoder::init_progressive()\n\t{\n\t\tint i;\n\n\t\tif (m_comps_in_frame == 4)\n\t\t\tstop_decoding(JPGD_UNSUPPORTED_COLORSPACE);\n\n\t\t// Allocate the coefficient buffers.\n\t\tfor (i = 0; i < m_comps_in_frame; i++)\n\t\t{\n\t\t\tm_dc_coeffs[i] = coeff_buf_open(m_max_mcus_per_row * m_comp_h_samp[i], m_max_mcus_per_col * m_comp_v_samp[i], 1, 1);\n\t\t\tm_ac_coeffs[i] = coeff_buf_open(m_max_mcus_per_row * m_comp_h_samp[i], m_max_mcus_per_col * m_comp_v_samp[i], 8, 8);\n\t\t}\n\n\t\tfor ( ; ; )\n\t\t{\n\t\t\tint dc_only_scan, refinement_scan;\n\t\t\tpDecode_block_func decode_block_func;\n\n\t\t\tif (!init_scan())\n\t\t\t\tbreak;\n\n\t\t\tdc_only_scan = (m_spectral_start == 0);\n\t\t\trefinement_scan = (m_successive_high != 0);\n\n\t\t\tif ((m_spectral_start > m_spectral_end) || (m_spectral_end > 63))\n\t\t\t\tstop_decoding(JPGD_BAD_SOS_SPECTRAL);\n\n\t\t\tif (dc_only_scan)\n\t\t\t{\n\t\t\t\tif (m_spectral_end)\n\t\t\t\t\tstop_decoding(JPGD_BAD_SOS_SPECTRAL);\n\t\t\t}\n\t\t\telse if (m_comps_in_scan != 1)  /* AC scans can only contain one component */\n\t\t\t\tstop_decoding(JPGD_BAD_SOS_SPECTRAL);\n\n\t\t\tif ((refinement_scan) && (m_successive_low != m_successive_high - 1))\n\t\t\t\tstop_decoding(JPGD_BAD_SOS_SUCCESSIVE);\n\n\t\t\tif (dc_only_scan)\n\t\t\t{\n\t\t\t\tif (refinement_scan)\n\t\t\t\t\tdecode_block_func = decode_block_dc_refine;\n\t\t\t\telse\n\t\t\t\t\tdecode_block_func = decode_block_dc_first;\n\t\t\t}\n\t\t\telse\n\t\t\t{\n\t\t\t\tif (refinement_scan)\n\t\t\t\t\tdecode_block_func = decode_block_ac_refine;\n\t\t\t\telse\n\t\t\t\t\tdecode_block_func = decode_block_ac_first;\n\t\t\t}\n\n\t\t\tdecode_scan(decode_block_func);\n\n\t\t\tm_bits_left = 16;\n\t\t\tget_bits(16);\n\t\t\tget_bits(16);\n\t\t}\n\n\t\tm_comps_in_scan = m_comps_in_frame;\n\n\t\tfor (i = 0; i < m_comps_in_frame; i++)\n\t\t\tm_comp_list[i] = i;\n\n\t\tcalc_mcu_block_order();\n\t}\n\n\tvoid jpeg_decoder::init_sequential()\n\t{\n\t\tif (!init_scan())\n\t\t\tstop_decoding(JPGD_UNEXPECTED_MARKER);\n\t}\n\n\tvoid jpeg_decoder::decode_start()\n\t{\n\t\tinit_frame();\n\n\t\tif (m_progressive_flag)\n\t\t\tinit_progressive();\n\t\telse\n\t\t\tinit_sequential();\n\t}\n\n\tvoid jpeg_decoder::decode_init(jpeg_decoder_stream *pStream)\n\t{\n\t\tinit(pStream);\n\t\tlocate_sof_marker();\n\t}\n\n\tjpeg_decoder::jpeg_decoder(jpeg_decoder_stream *pStream)\n\t{\n\t\tif (setjmp(m_jmp_state))\n\t\t\treturn;\n\t\tdecode_init(pStream);\n\t}\n\n\tint jpeg_decoder::begin_decoding()\n\t{\n\t\tif (m_ready_flag)\n\t\t\treturn JPGD_SUCCESS;\n\n\t\tif (m_error_code)\n\t\t\treturn JPGD_FAILED;\n\n\t\tif (setjmp(m_jmp_state))\n\t\t\treturn JPGD_FAILED;\n\n\t\tdecode_start();\n\n\t\tm_ready_flag = true;\n\n\t\treturn JPGD_SUCCESS;\n\t}\n\n\tjpeg_decoder::~jpeg_decoder()\n\t{\n\t\tfree_all_blocks();\n\t}\n\n\tjpeg_decoder_file_stream::jpeg_decoder_file_stream()\n\t{\n\t\tm_pFile = NULL;\n\t\tm_eof_flag = false;\n\t\tm_error_flag = false;\n\t}\n\n\tvoid jpeg_decoder_file_stream::close()\n\t{\n\t\tif (m_pFile)\n\t\t{\n\t\t\tfclose(m_pFile);\n\t\t\tm_pFile = NULL;\n\t\t}\n\n\t\tm_eof_flag = false;\n\t\tm_error_flag = false;\n\t}\n\n\tjpeg_decoder_file_stream::~jpeg_decoder_file_stream()\n\t{\n\t\tclose();\n\t}\n\n\tbool jpeg_decoder_file_stream::open(const char *Pfilename)\n\t{\n\t\tclose();\n\n\t\tm_eof_flag = false;\n\t\tm_error_flag = false;\n\n#if defined(_MSC_VER)\n\t\tm_pFile = NULL;\n\t\tfopen_s(&m_pFile, Pfilename, \"rb\");\n#else\n\t\tm_pFile = fopen(Pfilename, \"rb\");\n#endif\n\t\treturn m_pFile != NULL;\n\t}\n\n\tint jpeg_decoder_file_stream::read(uint8 *pBuf, int max_bytes_to_read, bool *pEOF_flag)\n\t{\n\t\tif (!m_pFile)\n\t\t\treturn -1;\n\n\t\tif (m_eof_flag)\n\t\t{\n\t\t\t*pEOF_flag = true;\n\t\t\treturn 0;\n\t\t}\n\n\t\tif (m_error_flag)\n\t\t\treturn -1;\n\n\t\tint bytes_read = static_cast<int>(fread(pBuf, 1, max_bytes_to_read, m_pFile));\n\t\tif (bytes_read < max_bytes_to_read)\n\t\t{\n\t\t\tif (ferror(m_pFile))\n\t\t\t{\n\t\t\t\tm_error_flag = true;\n\t\t\t\treturn -1;\n\t\t\t}\n\n\t\t\tm_eof_flag = true;\n\t\t\t*pEOF_flag = true;\n\t\t}\n\n\t\treturn bytes_read;\n\t}\n\n\tbool jpeg_decoder_mem_stream::open(const uint8 *pSrc_data, uint size)\n\t{\n\t\tclose();\n\t\tm_pSrc_data = pSrc_data;\n\t\tm_ofs = 0;\n\t\tm_size = size;\n\t\treturn true;\n\t}\n\n\tint jpeg_decoder_mem_stream::read(uint8 *pBuf, int max_bytes_to_read, bool *pEOF_flag)\n\t{\n\t\t*pEOF_flag = false;\n\n\t\tif (!m_pSrc_data)\n\t\t\treturn -1;\n\n\t\tuint bytes_remaining = m_size - m_ofs;\n\t\tif ((uint)max_bytes_to_read > bytes_remaining)\n\t\t{\n\t\t\tmax_bytes_to_read = bytes_remaining;\n\t\t\t*pEOF_flag = true;\n\t\t}\n\n\t\tmemcpy(pBuf, m_pSrc_data + m_ofs, max_bytes_to_read);\n\t\tm_ofs += max_bytes_to_read;\n\n\t\treturn max_bytes_to_read;\n\t}\n\n\tunsigned char *decompress_jpeg_image_from_stream(jpeg_decoder_stream *pStream, int *width, int *height, int *actual_comps, int req_comps)\n\t{\n\t\tif (!actual_comps)\n\t\t\treturn NULL;\n\t\t*actual_comps = 0;\n\n\t\tif ((!pStream) || (!width) || (!height) || (!req_comps))\n\t\t\treturn NULL;\n\n\t\tif ((req_comps != 1) && (req_comps != 3) && (req_comps != 4))\n\t\t\treturn NULL;\n\n\t\tjpeg_decoder decoder(pStream);\n\t\tif (decoder.get_error_code() != JPGD_SUCCESS)\n\t\t\treturn NULL;\n\n\t\tconst int image_width = decoder.get_width(), image_height = decoder.get_height();\n\t\t*width = image_width;\n\t\t*height = image_height;\n\t\t*actual_comps = decoder.get_num_components();\n\n\t\tif (decoder.begin_decoding() != JPGD_SUCCESS)\n\t\t\treturn NULL;\n\n\t\tconst int dst_bpl = image_width * req_comps;\n\n\t\tuint8 *pImage_data = (uint8*)jpgd_malloc(dst_bpl * image_height);\n\t\tif (!pImage_data)\n\t\t\treturn NULL;\n\n\t\tfor (int y = 0; y < image_height; y++)\n\t\t{\n\t\t\tconst uint8* pScan_line = 0;\n\t\t\tuint scan_line_len;\n\t\t\tif (decoder.decode((const void**)&pScan_line, &scan_line_len) != JPGD_SUCCESS)\n\t\t\t{\n\t\t\t\tjpgd_free(pImage_data);\n\t\t\t\treturn NULL;\n\t\t\t}\n\n\t\t\tuint8 *pDst = pImage_data + y * dst_bpl;\n\n\t\t\tif (((req_comps == 4) && (decoder.get_num_components() == 3)) ||\n\t\t\t\t((req_comps == 1) && (decoder.get_num_components() == 1)))\n\t\t\t{\n\t\t\t\tmemcpy(pDst, pScan_line, dst_bpl);\n\t\t\t}\n\t\t\telse if (decoder.get_num_components() == 1)\n\t\t\t{\n\t\t\t\tif (req_comps == 3)\n\t\t\t\t{\n\t\t\t\t\tfor (int x = 0; x < image_width; x++)\n\t\t\t\t\t{\n\t\t\t\t\t\tuint8 luma = pScan_line[x];\n\t\t\t\t\t\tpDst[0] = luma;\n\t\t\t\t\t\tpDst[1] = luma;\n\t\t\t\t\t\tpDst[2] = luma;\n\t\t\t\t\t\tpDst += 3;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\telse\n\t\t\t\t{\n\t\t\t\t\tfor (int x = 0; x < image_width; x++)\n\t\t\t\t\t{\n\t\t\t\t\t\tuint8 luma = pScan_line[x];\n\t\t\t\t\t\tpDst[0] = luma;\n\t\t\t\t\t\tpDst[1] = luma;\n\t\t\t\t\t\tpDst[2] = luma;\n\t\t\t\t\t\tpDst[3] = 255;\n\t\t\t\t\t\tpDst += 4;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t\telse if (decoder.get_num_components() == 3)\n\t\t\t{\n\t\t\t\tif (req_comps == 1)\n\t\t\t\t{\n\t\t\t\t\tconst int YR = 19595, YG = 38470, YB = 7471;\n\t\t\t\t\tfor (int x = 0; x < image_width; x++)\n\t\t\t\t\t{\n\t\t\t\t\t\tint r = pScan_line[x*4+0];\n\t\t\t\t\t\tint g = pScan_line[x*4+1];\n\t\t\t\t\t\tint b = pScan_line[x*4+2];\n\t\t\t\t\t\t*pDst++ = static_cast<uint8>((r * YR + g * YG + b * YB + 32768) >> 16);\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\telse\n\t\t\t\t{\n\t\t\t\t\tfor (int x = 0; x < image_width; x++)\n\t\t\t\t\t{\n\t\t\t\t\t\tpDst[0] = pScan_line[x*4+0];\n\t\t\t\t\t\tpDst[1] = pScan_line[x*4+1];\n\t\t\t\t\t\tpDst[2] = pScan_line[x*4+2];\n\t\t\t\t\t\tpDst += 3;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\treturn pImage_data;\n\t}\n\n// BEGIN EPIC MOD\n\tunsigned char *decompress_jpeg_image_from_memory(const unsigned char *pSrc_data, int src_data_size, int *width, int *height, int *actual_comps, int req_comps, int format)\n\t{\n\t\tjpg_format = (ERGBFormatJPG)format;\n// EMD EPIC MOD\n\t\tjpgd::jpeg_decoder_mem_stream mem_stream(pSrc_data, src_data_size);\n\t\treturn decompress_jpeg_image_from_stream(&mem_stream, width, height, actual_comps, req_comps);\n\t}\n\n\tunsigned char *decompress_jpeg_image_from_file(const char *pSrc_filename, int *width, int *height, int *actual_comps, int req_comps)\n\t{\n\t\tjpgd::jpeg_decoder_file_stream file_stream;\n\t\tif (!file_stream.open(pSrc_filename))\n\t\t\treturn NULL;\n\t\treturn decompress_jpeg_image_from_stream(&file_stream, width, height, actual_comps, req_comps);\n\t}\n\n} // namespace jpgd\n"
  },
  {
    "path": "crazy_functions/test_project/cpp/libJPG/jpgd.h",
    "content": "// jpgd.h - C++ class for JPEG decompression.\n// Public domain, Rich Geldreich <richgel99@gmail.com>\n#ifndef JPEG_DECODER_H\n#define JPEG_DECODER_H\n\n#include <stdlib.h>\n#include <stdio.h>\n#include <setjmp.h>\n\nnamespace jpgd\n{\n  typedef unsigned char  uint8;\n  typedef   signed short int16;\n  typedef unsigned short uint16;\n  typedef unsigned int   uint;\n  typedef   signed int   int32;\n\n  // Loads a JPEG image from a memory buffer or a file.\n  // req_comps can be 1 (grayscale), 3 (RGB), or 4 (RGBA).\n  // On return, width/height will be set to the image's dimensions, and actual_comps will be set to the either 1 (grayscale) or 3 (RGB).\n  // Notes: For more control over where and how the source data is read, see the decompress_jpeg_image_from_stream() function below, or call the jpeg_decoder class directly.\n  // Requesting a 8 or 32bpp image is currently a little faster than 24bpp because the jpeg_decoder class itself currently always unpacks to either 8 or 32bpp.\n// BEGIN EPIC MOD\n//unsigned char *decompress_jpeg_image_from_memory(const unsigned char *pSrc_data, int src_data_size, int *width, int *height, int *actual_comps, int req_comps);\n  unsigned char *decompress_jpeg_image_from_memory(const unsigned char *pSrc_data, int src_data_size, int *width, int *height, int *actual_comps, int req_comps, int format);\n// END EPIC MOD\n  unsigned char *decompress_jpeg_image_from_file(const char *pSrc_filename, int *width, int *height, int *actual_comps, int req_comps);\n\n  // Success/failure error codes.\n  enum jpgd_status\n  {\n    JPGD_SUCCESS = 0, JPGD_FAILED = -1, JPGD_DONE = 1,\n    JPGD_BAD_DHT_COUNTS = -256, JPGD_BAD_DHT_INDEX, JPGD_BAD_DHT_MARKER, JPGD_BAD_DQT_MARKER, JPGD_BAD_DQT_TABLE, \n    JPGD_BAD_PRECISION, JPGD_BAD_HEIGHT, JPGD_BAD_WIDTH, JPGD_TOO_MANY_COMPONENTS, \n    JPGD_BAD_SOF_LENGTH, JPGD_BAD_VARIABLE_MARKER, JPGD_BAD_DRI_LENGTH, JPGD_BAD_SOS_LENGTH,\n    JPGD_BAD_SOS_COMP_ID, JPGD_W_EXTRA_BYTES_BEFORE_MARKER, JPGD_NO_ARITHMITIC_SUPPORT, JPGD_UNEXPECTED_MARKER,\n    JPGD_NOT_JPEG, JPGD_UNSUPPORTED_MARKER, JPGD_BAD_DQT_LENGTH, JPGD_TOO_MANY_BLOCKS,\n    JPGD_UNDEFINED_QUANT_TABLE, JPGD_UNDEFINED_HUFF_TABLE, JPGD_NOT_SINGLE_SCAN, JPGD_UNSUPPORTED_COLORSPACE,\n    JPGD_UNSUPPORTED_SAMP_FACTORS, JPGD_DECODE_ERROR, JPGD_BAD_RESTART_MARKER, JPGD_ASSERTION_ERROR,\n    JPGD_BAD_SOS_SPECTRAL, JPGD_BAD_SOS_SUCCESSIVE, JPGD_STREAM_READ, JPGD_NOTENOUGHMEM\n  };\n    \n  // Input stream interface.\n  // Derive from this class to read input data from sources other than files or memory. Set m_eof_flag to true when no more data is available.\n  // The decoder is rather greedy: it will keep on calling this method until its internal input buffer is full, or until the EOF flag is set.\n  // It the input stream contains data after the JPEG stream's EOI (end of image) marker it will probably be pulled into the internal buffer.\n  // Call the get_total_bytes_read() method to determine the actual size of the JPEG stream after successful decoding.\n  class jpeg_decoder_stream\n  {\n  public:\n    jpeg_decoder_stream() { }\n    virtual ~jpeg_decoder_stream() { }\n\n    // The read() method is called when the internal input buffer is empty.\n    // Parameters:\n    // pBuf - input buffer\n    // max_bytes_to_read - maximum bytes that can be written to pBuf\n    // pEOF_flag - set this to true if at end of stream (no more bytes remaining)\n    // Returns -1 on error, otherwise return the number of bytes actually written to the buffer (which may be 0).\n    // Notes: This method will be called in a loop until you set *pEOF_flag to true or the internal buffer is full.\n    virtual int read(uint8 *pBuf, int max_bytes_to_read, bool *pEOF_flag) = 0;\n  };\n\n  // stdio FILE stream class.\n  class jpeg_decoder_file_stream : public jpeg_decoder_stream\n  {\n    jpeg_decoder_file_stream(const jpeg_decoder_file_stream &);\n    jpeg_decoder_file_stream &operator =(const jpeg_decoder_file_stream &);\n\n    FILE *m_pFile;\n    bool m_eof_flag, m_error_flag;\n\n  public:\n    jpeg_decoder_file_stream();\n    virtual ~jpeg_decoder_file_stream();\n    \n    bool open(const char *Pfilename);\n    void close();\n\n    virtual int read(uint8 *pBuf, int max_bytes_to_read, bool *pEOF_flag);\n  };\n\n  // Memory stream class.\n  class jpeg_decoder_mem_stream : public jpeg_decoder_stream\n  {\n    const uint8 *m_pSrc_data;\n    uint m_ofs, m_size;\n\n  public:\n    jpeg_decoder_mem_stream() : m_pSrc_data(NULL), m_ofs(0), m_size(0) { }\n    jpeg_decoder_mem_stream(const uint8 *pSrc_data, uint size) : m_pSrc_data(pSrc_data), m_ofs(0), m_size(size) { }\n\n    virtual ~jpeg_decoder_mem_stream() { }\n\n    bool open(const uint8 *pSrc_data, uint size);\n    void close() { m_pSrc_data = NULL; m_ofs = 0; m_size = 0; }\n    \n    virtual int read(uint8 *pBuf, int max_bytes_to_read, bool *pEOF_flag);\n  };\n\n  // Loads JPEG file from a jpeg_decoder_stream.\n  unsigned char *decompress_jpeg_image_from_stream(jpeg_decoder_stream *pStream, int *width, int *height, int *actual_comps, int req_comps);\n\n  enum \n  { \n    JPGD_IN_BUF_SIZE = 8192, JPGD_MAX_BLOCKS_PER_MCU = 10, JPGD_MAX_HUFF_TABLES = 8, JPGD_MAX_QUANT_TABLES = 4, \n    JPGD_MAX_COMPONENTS = 4, JPGD_MAX_COMPS_IN_SCAN = 4, JPGD_MAX_BLOCKS_PER_ROW = 8192, JPGD_MAX_HEIGHT = 16384, JPGD_MAX_WIDTH = 16384 \n  };\n          \n  typedef int16 jpgd_quant_t;\n  typedef int16 jpgd_block_t;\n\n  class jpeg_decoder\n  {\n  public:\n    // Call get_error_code() after constructing to determine if the stream is valid or not. You may call the get_width(), get_height(), etc.\n    // methods after the constructor is called. You may then either destruct the object, or begin decoding the image by calling begin_decoding(), then decode() on each scanline.\n    jpeg_decoder(jpeg_decoder_stream *pStream);\n\n    ~jpeg_decoder();\n\n    // Call this method after constructing the object to begin decompression.\n    // If JPGD_SUCCESS is returned you may then call decode() on each scanline.\n    int begin_decoding();\n\n    // Returns the next scan line.\n    // For grayscale images, pScan_line will point to a buffer containing 8-bit pixels (get_bytes_per_pixel() will return 1). \n    // Otherwise, it will always point to a buffer containing 32-bit RGBA pixels (A will always be 255, and get_bytes_per_pixel() will return 4).\n    // Returns JPGD_SUCCESS if a scan line has been returned.\n    // Returns JPGD_DONE if all scan lines have been returned.\n    // Returns JPGD_FAILED if an error occurred. Call get_error_code() for a more info.\n    int decode(const void** pScan_line, uint* pScan_line_len);\n    \n    inline jpgd_status get_error_code() const { return m_error_code; }\n\n    inline int get_width() const { return m_image_x_size; }\n    inline int get_height() const { return m_image_y_size; }\n\n    inline int get_num_components() const { return m_comps_in_frame; }\n\n    inline int get_bytes_per_pixel() const { return m_dest_bytes_per_pixel; }\n    inline int get_bytes_per_scan_line() const { return m_image_x_size * get_bytes_per_pixel(); }\n\n    // Returns the total number of bytes actually consumed by the decoder (which should equal the actual size of the JPEG file).\n    inline int get_total_bytes_read() const { return m_total_bytes_read; }\n    \n  private:\n    jpeg_decoder(const jpeg_decoder &);\n    jpeg_decoder &operator =(const jpeg_decoder &);\n\n    typedef void (*pDecode_block_func)(jpeg_decoder *, int, int, int);\n\n    struct huff_tables\n    {\n      bool ac_table;\n      uint  look_up[256];\n      uint  look_up2[256];\n      uint8 code_size[256];\n      uint  tree[512];\n    };\n\n    struct coeff_buf\n    {\n      uint8 *pData;\n      int block_num_x, block_num_y;\n      int block_len_x, block_len_y;\n      int block_size;\n    };\n\n    struct mem_block\n    {\n      mem_block *m_pNext;\n      size_t m_used_count;\n      size_t m_size;\n      char m_data[1];\n    };\n\n    jmp_buf m_jmp_state;\n    mem_block *m_pMem_blocks;\n    int m_image_x_size;\n    int m_image_y_size;\n    jpeg_decoder_stream *m_pStream;\n    int m_progressive_flag;\n    uint8 m_huff_ac[JPGD_MAX_HUFF_TABLES];\n    uint8* m_huff_num[JPGD_MAX_HUFF_TABLES];      // pointer to number of Huffman codes per bit size\n    uint8* m_huff_val[JPGD_MAX_HUFF_TABLES];      // pointer to Huffman codes per bit size\n    jpgd_quant_t* m_quant[JPGD_MAX_QUANT_TABLES]; // pointer to quantization tables\n    int m_scan_type;                              // Gray, Yh1v1, Yh1v2, Yh2v1, Yh2v2 (CMYK111, CMYK4114 no longer supported)\n    int m_comps_in_frame;                         // # of components in frame\n    int m_comp_h_samp[JPGD_MAX_COMPONENTS];       // component's horizontal sampling factor\n    int m_comp_v_samp[JPGD_MAX_COMPONENTS];       // component's vertical sampling factor\n    int m_comp_quant[JPGD_MAX_COMPONENTS];        // component's quantization table selector\n    int m_comp_ident[JPGD_MAX_COMPONENTS];        // component's ID\n    int m_comp_h_blocks[JPGD_MAX_COMPONENTS];\n    int m_comp_v_blocks[JPGD_MAX_COMPONENTS];\n    int m_comps_in_scan;                          // # of components in scan\n    int m_comp_list[JPGD_MAX_COMPS_IN_SCAN];      // components in this scan\n    int m_comp_dc_tab[JPGD_MAX_COMPONENTS];       // component's DC Huffman coding table selector\n    int m_comp_ac_tab[JPGD_MAX_COMPONENTS];       // component's AC Huffman coding table selector\n    int m_spectral_start;                         // spectral selection start\n    int m_spectral_end;                           // spectral selection end\n    int m_successive_low;                         // successive approximation low\n    int m_successive_high;                        // successive approximation high\n    int m_max_mcu_x_size;                         // MCU's max. X size in pixels\n    int m_max_mcu_y_size;                         // MCU's max. Y size in pixels\n    int m_blocks_per_mcu;\n    int m_max_blocks_per_row;\n    int m_mcus_per_row, m_mcus_per_col;\n    int m_mcu_org[JPGD_MAX_BLOCKS_PER_MCU];\n    int m_total_lines_left;                       // total # lines left in image\n    int m_mcu_lines_left;                         // total # lines left in this MCU\n    int m_real_dest_bytes_per_scan_line;\n    int m_dest_bytes_per_scan_line;               // rounded up\n    int m_dest_bytes_per_pixel;                   // 4 (RGB) or 1 (Y)\n    huff_tables* m_pHuff_tabs[JPGD_MAX_HUFF_TABLES];\n    coeff_buf* m_dc_coeffs[JPGD_MAX_COMPONENTS];\n    coeff_buf* m_ac_coeffs[JPGD_MAX_COMPONENTS];\n    int m_eob_run;\n    int m_block_y_mcu[JPGD_MAX_COMPONENTS];\n    uint8* m_pIn_buf_ofs;\n    int m_in_buf_left;\n    int m_tem_flag;\n    bool m_eof_flag;\n    uint8 m_in_buf_pad_start[128];\n    uint8 m_in_buf[JPGD_IN_BUF_SIZE + 128];\n    uint8 m_in_buf_pad_end[128];\n    int m_bits_left;\n    uint m_bit_buf;\n    int m_restart_interval;\n    int m_restarts_left;\n    int m_next_restart_num;\n    int m_max_mcus_per_row;\n    int m_max_blocks_per_mcu;\n    int m_expanded_blocks_per_mcu;\n    int m_expanded_blocks_per_row;\n    int m_expanded_blocks_per_component;\n    bool  m_freq_domain_chroma_upsample;\n    int m_max_mcus_per_col;\n    uint m_last_dc_val[JPGD_MAX_COMPONENTS];\n    jpgd_block_t* m_pMCU_coefficients;\n    int m_mcu_block_max_zag[JPGD_MAX_BLOCKS_PER_MCU];\n    uint8* m_pSample_buf;\n    int m_crr[256];\n    int m_cbb[256];\n    int m_crg[256];\n    int m_cbg[256];\n    uint8* m_pScan_line_0;\n    uint8* m_pScan_line_1;\n    jpgd_status m_error_code;\n    bool m_ready_flag;\n    int m_total_bytes_read;\n\n    void free_all_blocks();\n    // BEGIN EPIC MOD\n    UE_NORETURN void stop_decoding(jpgd_status status);\n    // END EPIC MOD\n    void *alloc(size_t n, bool zero = false);\n    void word_clear(void *p, uint16 c, uint n);\n    void prep_in_buffer();\n    void read_dht_marker();\n    void read_dqt_marker();\n    void read_sof_marker();\n    void skip_variable_marker();\n    void read_dri_marker();\n    void read_sos_marker();\n    int next_marker();\n    int process_markers();\n    void locate_soi_marker();\n    void locate_sof_marker();\n    int locate_sos_marker();\n    void init(jpeg_decoder_stream * pStream);\n    void create_look_ups();\n    void fix_in_buffer();\n    void transform_mcu(int mcu_row);\n    void transform_mcu_expand(int mcu_row);\n    coeff_buf* coeff_buf_open(int block_num_x, int block_num_y, int block_len_x, int block_len_y);\n    inline jpgd_block_t *coeff_buf_getp(coeff_buf *cb, int block_x, int block_y);\n    void load_next_row();\n    void decode_next_row();\n    void make_huff_table(int index, huff_tables *pH);\n    void check_quant_tables();\n    void check_huff_tables();\n    void calc_mcu_block_order();\n    int init_scan();\n    void init_frame();\n    void process_restart();\n    void decode_scan(pDecode_block_func decode_block_func);\n    void init_progressive();\n    void init_sequential();\n    void decode_start();\n    void decode_init(jpeg_decoder_stream * pStream);\n    void H2V2Convert();\n    void H2V1Convert();\n    void H1V2Convert();\n    void H1V1Convert();\n    void gray_convert();\n    void expanded_convert();\n    void find_eoi();\n    inline uint get_char();\n    inline uint get_char(bool *pPadding_flag);\n    inline void stuff_char(uint8 q);\n    inline uint8 get_octet();\n    inline uint get_bits(int num_bits);\n    inline uint get_bits_no_markers(int numbits);\n    inline int huff_decode(huff_tables *pH);\n    inline int huff_decode(huff_tables *pH, int& extrabits);\n    static inline uint8 clamp(int i);\n    static void decode_block_dc_first(jpeg_decoder *pD, int component_id, int block_x, int block_y);\n    static void decode_block_dc_refine(jpeg_decoder *pD, int component_id, int block_x, int block_y);\n    static void decode_block_ac_first(jpeg_decoder *pD, int component_id, int block_x, int block_y);\n    static void decode_block_ac_refine(jpeg_decoder *pD, int component_id, int block_x, int block_y);\n  };\n  \n} // namespace jpgd\n\n#endif // JPEG_DECODER_H\n"
  },
  {
    "path": "crazy_functions/test_project/cpp/libJPG/jpge.cpp",
    "content": "// jpge.cpp - C++ class for JPEG compression.\n// Public domain, Rich Geldreich <richgel99@gmail.com>\n// v1.01, Dec. 18, 2010 - Initial release\n// v1.02, Apr. 6, 2011 - Removed 2x2 ordered dither in H2V1 chroma subsampling method load_block_16_8_8(). (The rounding factor was 2, when it should have been 1. Either way, it wasn't helping.)\n// v1.03, Apr. 16, 2011 - Added support for optimized Huffman code tables, optimized dynamic memory allocation down to only 1 alloc.\n//                        Also from Alex Evans: Added RGBA support, linear memory allocator (no longer needed in v1.03).\n// v1.04, May. 19, 2012: Forgot to set m_pFile ptr to NULL in cfile_stream::close(). Thanks to Owen Kaluza for reporting this bug.\n//                       Code tweaks to fix VS2008 static code analysis warnings (all looked harmless).\n//                       Code review revealed method load_block_16_8_8() (used for the non-default H2V1 sampling mode to downsample chroma) somehow didn't get the rounding factor fix from v1.02.\n\n#include \"jpge.h\"\n\n#include <stdlib.h>\n#include <string.h>\n#if PLATFORM_WINDOWS\n#include <malloc.h>\n#endif\n\n#define JPGE_MAX(a,b) (((a)>(b))?(a):(b))\n#define JPGE_MIN(a,b) (((a)<(b))?(a):(b))\n\nnamespace jpge {\n\nstatic inline void *jpge_malloc(size_t nSize) { return FMemory::Malloc(nSize); }\nstatic inline void jpge_free(void *p) { FMemory::Free(p);; }\n\n// Various JPEG enums and tables.\nenum { M_SOF0 = 0xC0, M_DHT = 0xC4, M_SOI = 0xD8, M_EOI = 0xD9, M_SOS = 0xDA, M_DQT = 0xDB, M_APP0 = 0xE0 };\nenum { DC_LUM_CODES = 12, AC_LUM_CODES = 256, DC_CHROMA_CODES = 12, AC_CHROMA_CODES = 256, MAX_HUFF_SYMBOLS = 257, MAX_HUFF_CODESIZE = 32 };\n\nstatic uint8 s_zag[64] = { 0,1,8,16,9,2,3,10,17,24,32,25,18,11,4,5,12,19,26,33,40,48,41,34,27,20,13,6,7,14,21,28,35,42,49,56,57,50,43,36,29,22,15,23,30,37,44,51,58,59,52,45,38,31,39,46,53,60,61,54,47,55,62,63 };\nstatic int16 s_std_lum_quant[64] = { 16,11,12,14,12,10,16,14,13,14,18,17,16,19,24,40,26,24,22,22,24,49,35,37,29,40,58,51,61,60,57,51,56,55,64,72,92,78,64,68,87,69,55,56,80,109,81,87,95,98,103,104,103,62,77,113,121,112,100,120,92,101,103,99 };\nstatic int16 s_std_croma_quant[64] = { 17,18,18,24,21,24,47,26,26,47,99,66,56,66,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99 };\nstatic uint8 s_dc_lum_bits[17] = { 0,0,1,5,1,1,1,1,1,1,0,0,0,0,0,0,0 };\nstatic uint8 s_dc_lum_val[DC_LUM_CODES] = { 0,1,2,3,4,5,6,7,8,9,10,11 };\nstatic uint8 s_ac_lum_bits[17] = { 0,0,2,1,3,3,2,4,3,5,5,4,4,0,0,1,0x7d };\nstatic uint8 s_ac_lum_val[AC_LUM_CODES]  =\n{\n  0x01,0x02,0x03,0x00,0x04,0x11,0x05,0x12,0x21,0x31,0x41,0x06,0x13,0x51,0x61,0x07,0x22,0x71,0x14,0x32,0x81,0x91,0xa1,0x08,0x23,0x42,0xb1,0xc1,0x15,0x52,0xd1,0xf0,\n  0x24,0x33,0x62,0x72,0x82,0x09,0x0a,0x16,0x17,0x18,0x19,0x1a,0x25,0x26,0x27,0x28,0x29,0x2a,0x34,0x35,0x36,0x37,0x38,0x39,0x3a,0x43,0x44,0x45,0x46,0x47,0x48,0x49,\n  0x4a,0x53,0x54,0x55,0x56,0x57,0x58,0x59,0x5a,0x63,0x64,0x65,0x66,0x67,0x68,0x69,0x6a,0x73,0x74,0x75,0x76,0x77,0x78,0x79,0x7a,0x83,0x84,0x85,0x86,0x87,0x88,0x89,\n  0x8a,0x92,0x93,0x94,0x95,0x96,0x97,0x98,0x99,0x9a,0xa2,0xa3,0xa4,0xa5,0xa6,0xa7,0xa8,0xa9,0xaa,0xb2,0xb3,0xb4,0xb5,0xb6,0xb7,0xb8,0xb9,0xba,0xc2,0xc3,0xc4,0xc5,\n  0xc6,0xc7,0xc8,0xc9,0xca,0xd2,0xd3,0xd4,0xd5,0xd6,0xd7,0xd8,0xd9,0xda,0xe1,0xe2,0xe3,0xe4,0xe5,0xe6,0xe7,0xe8,0xe9,0xea,0xf1,0xf2,0xf3,0xf4,0xf5,0xf6,0xf7,0xf8,\n  0xf9,0xfa\n};\nstatic uint8 s_dc_chroma_bits[17] = { 0,0,3,1,1,1,1,1,1,1,1,1,0,0,0,0,0 };\nstatic uint8 s_dc_chroma_val[DC_CHROMA_CODES]  = { 0,1,2,3,4,5,6,7,8,9,10,11 };\nstatic uint8 s_ac_chroma_bits[17] = { 0,0,2,1,2,4,4,3,4,7,5,4,4,0,1,2,0x77 };\nstatic uint8 s_ac_chroma_val[AC_CHROMA_CODES] =\n{\n  0x00,0x01,0x02,0x03,0x11,0x04,0x05,0x21,0x31,0x06,0x12,0x41,0x51,0x07,0x61,0x71,0x13,0x22,0x32,0x81,0x08,0x14,0x42,0x91,0xa1,0xb1,0xc1,0x09,0x23,0x33,0x52,0xf0,\n  0x15,0x62,0x72,0xd1,0x0a,0x16,0x24,0x34,0xe1,0x25,0xf1,0x17,0x18,0x19,0x1a,0x26,0x27,0x28,0x29,0x2a,0x35,0x36,0x37,0x38,0x39,0x3a,0x43,0x44,0x45,0x46,0x47,0x48,\n  0x49,0x4a,0x53,0x54,0x55,0x56,0x57,0x58,0x59,0x5a,0x63,0x64,0x65,0x66,0x67,0x68,0x69,0x6a,0x73,0x74,0x75,0x76,0x77,0x78,0x79,0x7a,0x82,0x83,0x84,0x85,0x86,0x87,\n  0x88,0x89,0x8a,0x92,0x93,0x94,0x95,0x96,0x97,0x98,0x99,0x9a,0xa2,0xa3,0xa4,0xa5,0xa6,0xa7,0xa8,0xa9,0xaa,0xb2,0xb3,0xb4,0xb5,0xb6,0xb7,0xb8,0xb9,0xba,0xc2,0xc3,\n  0xc4,0xc5,0xc6,0xc7,0xc8,0xc9,0xca,0xd2,0xd3,0xd4,0xd5,0xd6,0xd7,0xd8,0xd9,0xda,0xe2,0xe3,0xe4,0xe5,0xe6,0xe7,0xe8,0xe9,0xea,0xf2,0xf3,0xf4,0xf5,0xf6,0xf7,0xf8,\n  0xf9,0xfa\n};\n\n// Low-level helper functions.\ntemplate <class T> inline void clear_obj(T &obj) { memset(&obj, 0, sizeof(obj)); }\n\nconst int YR = 19595, YG = 38470, YB = 7471, CB_R = -11059, CB_G = -21709, CB_B = 32768, CR_R = 32768, CR_G = -27439, CR_B = -5329;\nstatic inline uint8 clamp(int i) { if (static_cast<uint>(i) > 255U) { if (i < 0) i = 0; else if (i > 255) i = 255; } return static_cast<uint8>(i); }\n\nstatic void RGB_to_YCC(uint8* pDst, const uint8 *pSrc, int num_pixels)\n{\n  for ( ; num_pixels; pDst += 3, pSrc += 3, num_pixels--)\n  {\n    const int r = pSrc[0], g = pSrc[1], b = pSrc[2];\n    pDst[0] = static_cast<uint8>((r * YR + g * YG + b * YB + 32768) >> 16);\n    pDst[1] = clamp(128 + ((r * CB_R + g * CB_G + b * CB_B + 32768) >> 16));\n    pDst[2] = clamp(128 + ((r * CR_R + g * CR_G + b * CR_B + 32768) >> 16));\n  }\n}\n\nstatic void RGB_to_Y(uint8* pDst, const uint8 *pSrc, int num_pixels)\n{\n  for ( ; num_pixels; pDst++, pSrc += 3, num_pixels--)\n    pDst[0] = static_cast<uint8>((pSrc[0] * YR + pSrc[1] * YG + pSrc[2] * YB + 32768) >> 16);\n}\n\nstatic void RGBA_to_YCC(uint8* pDst, const uint8 *pSrc, int num_pixels)\n{\n  for ( ; num_pixels; pDst += 3, pSrc += 4, num_pixels--)\n  {\n    const int r = pSrc[0], g = pSrc[1], b = pSrc[2];\n    pDst[0] = static_cast<uint8>((r * YR + g * YG + b * YB + 32768) >> 16);\n    pDst[1] = clamp(128 + ((r * CB_R + g * CB_G + b * CB_B + 32768) >> 16));\n    pDst[2] = clamp(128 + ((r * CR_R + g * CR_G + b * CR_B + 32768) >> 16));\n  }\n}\n\nstatic void RGBA_to_Y(uint8* pDst, const uint8 *pSrc, int num_pixels)\n{\n  for ( ; num_pixels; pDst++, pSrc += 4, num_pixels--)\n    pDst[0] = static_cast<uint8>((pSrc[0] * YR + pSrc[1] * YG + pSrc[2] * YB + 32768) >> 16);\n}\n\nstatic void Y_to_YCC(uint8* pDst, const uint8* pSrc, int num_pixels)\n{\n  for( ; num_pixels; pDst += 3, pSrc++, num_pixels--) { pDst[0] = pSrc[0]; pDst[1] = 128; pDst[2] = 128; }\n}\n\n// Forward DCT - DCT derived from jfdctint.\n#define CONST_BITS  13\n#define ROW_BITS    2\n#define DCT_DESCALE(x, n) (((x) + (((int32)1) << ((n) - 1))) >> (n))\n#define DCT_MUL(var, c) (static_cast<int16>(var) * static_cast<int32>(c))\n#define DCT1D(s0, s1, s2, s3, s4, s5, s6, s7) \\\n  int32 t0 = s0 + s7, t7 = s0 - s7, t1 = s1 + s6, t6 = s1 - s6, t2 = s2 + s5, t5 = s2 - s5, t3 = s3 + s4, t4 = s3 - s4; \\\n  int32 t10 = t0 + t3, t13 = t0 - t3, t11 = t1 + t2, t12 = t1 - t2; \\\n  int32 u1 = DCT_MUL(t12 + t13, 4433); \\\n  s2 = u1 + DCT_MUL(t13, 6270); \\\n  s6 = u1 + DCT_MUL(t12, -15137); \\\n  u1 = t4 + t7; \\\n  int32 u2 = t5 + t6, u3 = t4 + t6, u4 = t5 + t7; \\\n  int32 z5 = DCT_MUL(u3 + u4, 9633); \\\n  t4 = DCT_MUL(t4, 2446); t5 = DCT_MUL(t5, 16819); \\\n  t6 = DCT_MUL(t6, 25172); t7 = DCT_MUL(t7, 12299); \\\n  u1 = DCT_MUL(u1, -7373); u2 = DCT_MUL(u2, -20995); \\\n  u3 = DCT_MUL(u3, -16069); u4 = DCT_MUL(u4, -3196); \\\n  u3 += z5; u4 += z5; \\\n  s0 = t10 + t11; s1 = t7 + u1 + u4; s3 = t6 + u2 + u3; s4 = t10 - t11; s5 = t5 + u2 + u4; s7 = t4 + u1 + u3;\n\nstatic void DCT2D(int32 *p)\n{\n  int32 c, *q = p;\n  for (c = 7; c >= 0; c--, q += 8)\n  {\n    int32 s0 = q[0], s1 = q[1], s2 = q[2], s3 = q[3], s4 = q[4], s5 = q[5], s6 = q[6], s7 = q[7];\n    DCT1D(s0, s1, s2, s3, s4, s5, s6, s7);\n    q[0] = s0 << ROW_BITS; q[1] = DCT_DESCALE(s1, CONST_BITS-ROW_BITS); q[2] = DCT_DESCALE(s2, CONST_BITS-ROW_BITS); q[3] = DCT_DESCALE(s3, CONST_BITS-ROW_BITS);\n    q[4] = s4 << ROW_BITS; q[5] = DCT_DESCALE(s5, CONST_BITS-ROW_BITS); q[6] = DCT_DESCALE(s6, CONST_BITS-ROW_BITS); q[7] = DCT_DESCALE(s7, CONST_BITS-ROW_BITS);\n  }\n  for (q = p, c = 7; c >= 0; c--, q++)\n  {\n    int32 s0 = q[0*8], s1 = q[1*8], s2 = q[2*8], s3 = q[3*8], s4 = q[4*8], s5 = q[5*8], s6 = q[6*8], s7 = q[7*8];\n    DCT1D(s0, s1, s2, s3, s4, s5, s6, s7);\n    q[0*8] = DCT_DESCALE(s0, ROW_BITS+3); q[1*8] = DCT_DESCALE(s1, CONST_BITS+ROW_BITS+3); q[2*8] = DCT_DESCALE(s2, CONST_BITS+ROW_BITS+3); q[3*8] = DCT_DESCALE(s3, CONST_BITS+ROW_BITS+3);\n    q[4*8] = DCT_DESCALE(s4, ROW_BITS+3); q[5*8] = DCT_DESCALE(s5, CONST_BITS+ROW_BITS+3); q[6*8] = DCT_DESCALE(s6, CONST_BITS+ROW_BITS+3); q[7*8] = DCT_DESCALE(s7, CONST_BITS+ROW_BITS+3);\n  }\n}\n\nstruct sym_freq { uint m_key, m_sym_index; };\n\n// Radix sorts sym_freq[] array by 32-bit key m_key. Returns ptr to sorted values.\nstatic inline sym_freq* radix_sort_syms(uint num_syms, sym_freq* pSyms0, sym_freq* pSyms1)\n{\n  const uint cMaxPasses = 4;\n  uint32 hist[256 * cMaxPasses]; clear_obj(hist);\n  for (uint i = 0; i < num_syms; i++) { uint freq = pSyms0[i].m_key; hist[freq & 0xFF]++; hist[256 + ((freq >> 8) & 0xFF)]++; hist[256*2 + ((freq >> 16) & 0xFF)]++; hist[256*3 + ((freq >> 24) & 0xFF)]++; }\n  sym_freq* pCur_syms = pSyms0, *pNew_syms = pSyms1;\n  uint total_passes = cMaxPasses; while ((total_passes > 1) && (num_syms == hist[(total_passes - 1) * 256])) total_passes--;\n  for (uint pass_shift = 0, pass = 0; pass < total_passes; pass++, pass_shift += 8)\n  {\n    const uint32* pHist = &hist[pass << 8];\n    uint offsets[256], cur_ofs = 0;\n    for (uint i = 0; i < 256; i++) { offsets[i] = cur_ofs; cur_ofs += pHist[i]; }\n    for (uint i = 0; i < num_syms; i++)\n      pNew_syms[offsets[(pCur_syms[i].m_key >> pass_shift) & 0xFF]++] = pCur_syms[i];\n    sym_freq* t = pCur_syms; pCur_syms = pNew_syms; pNew_syms = t;\n  }\n  return pCur_syms;\n}\n\n// calculate_minimum_redundancy() originally written by: Alistair Moffat, alistair@cs.mu.oz.au, Jyrki Katajainen, jyrki@diku.dk, November 1996.\nstatic void calculate_minimum_redundancy(sym_freq *A, int n)\n{\n  int root, leaf, next, avbl, used, dpth;\n  if (n==0) return; else if (n==1) { A[0].m_key = 1; return; }\n  A[0].m_key += A[1].m_key; root = 0; leaf = 2;\n  for (next=1; next < n-1; next++)\n  {\n    if (leaf>=n || A[root].m_key<A[leaf].m_key) { A[next].m_key = A[root].m_key; A[root++].m_key = next; } else A[next].m_key = A[leaf++].m_key;\n    if (leaf>=n || (root<next && A[root].m_key<A[leaf].m_key)) { A[next].m_key += A[root].m_key; A[root++].m_key = next; } else A[next].m_key += A[leaf++].m_key;\n  }\n  A[n-2].m_key = 0;\n  for (next=n-3; next>=0; next--) A[next].m_key = A[A[next].m_key].m_key+1;\n  avbl = 1; used = dpth = 0; root = n-2; next = n-1;\n  while (avbl>0)\n  {\n    while (root>=0 && (int)A[root].m_key==dpth) { used++; root--; }\n    while (avbl>used) { A[next--].m_key = dpth; avbl--; }\n    avbl = 2*used; dpth++; used = 0;\n  }\n}\n\n// Limits canonical Huffman code table's max code size to max_code_size.\nstatic void huffman_enforce_max_code_size(int *pNum_codes, int code_list_len, int max_code_size)\n{\n  if (code_list_len <= 1) return;\n\n  for (int i = max_code_size + 1; i <= MAX_HUFF_CODESIZE; i++) pNum_codes[max_code_size] += pNum_codes[i];\n\n  uint32 total = 0;\n  for (int i = max_code_size; i > 0; i--)\n    total += (((uint32)pNum_codes[i]) << (max_code_size - i));\n\n  while (total != (1UL << max_code_size))\n  {\n    pNum_codes[max_code_size]--;\n    for (int i = max_code_size - 1; i > 0; i--)\n    {\n      if (pNum_codes[i]) { pNum_codes[i]--; pNum_codes[i + 1] += 2; break; }\n    }\n    total--;\n  }\n}\n\n// Generates an optimized offman table.\nvoid jpeg_encoder::optimize_huffman_table(int table_num, int table_len)\n{\n  sym_freq syms0[MAX_HUFF_SYMBOLS], syms1[MAX_HUFF_SYMBOLS];\n  syms0[0].m_key = 1; syms0[0].m_sym_index = 0;  // dummy symbol, assures that no valid code contains all 1's\n  int num_used_syms = 1;\n  const uint32 *pSym_count = &m_huff_count[table_num][0];\n  for (int i = 0; i < table_len; i++)\n    if (pSym_count[i]) { syms0[num_used_syms].m_key = pSym_count[i]; syms0[num_used_syms++].m_sym_index = i + 1; }\n  sym_freq* pSyms = radix_sort_syms(num_used_syms, syms0, syms1);\n  calculate_minimum_redundancy(pSyms, num_used_syms);\n\n  // Count the # of symbols of each code size.\n  int num_codes[1 + MAX_HUFF_CODESIZE]; clear_obj(num_codes);\n  for (int i = 0; i < num_used_syms; i++)\n    num_codes[pSyms[i].m_key]++;\n\n  const uint JPGE_CODE_SIZE_LIMIT = 16; // the maximum possible size of a JPEG Huffman code (valid range is [9,16] - 9 vs. 8 because of the dummy symbol)\n  huffman_enforce_max_code_size(num_codes, num_used_syms, JPGE_CODE_SIZE_LIMIT);\n\n  // Compute m_huff_bits array, which contains the # of symbols per code size.\n  clear_obj(m_huff_bits[table_num]);\n  for (int i = 1; i <= (int)JPGE_CODE_SIZE_LIMIT; i++)\n    m_huff_bits[table_num][i] = static_cast<uint8>(num_codes[i]);\n\n  // Remove the dummy symbol added above, which must be in largest bucket.\n  for (int i = JPGE_CODE_SIZE_LIMIT; i >= 1; i--)\n  {\n    if (m_huff_bits[table_num][i]) { m_huff_bits[table_num][i]--; break; }\n  }\n\n  // Compute the m_huff_val array, which contains the symbol indices sorted by code size (smallest to largest).\n  for (int i = num_used_syms - 1; i >= 1; i--)\n    m_huff_val[table_num][num_used_syms - 1 - i] = static_cast<uint8>(pSyms[i].m_sym_index - 1);\n}\n\n// JPEG marker generation.\nvoid jpeg_encoder::emit_byte(uint8 i)\n{\n  m_all_stream_writes_succeeded = m_all_stream_writes_succeeded && m_pStream->put_obj(i);\n}\n\nvoid jpeg_encoder::emit_word(uint i)\n{\n  emit_byte(uint8(i >> 8)); emit_byte(uint8(i & 0xFF));\n}\n\nvoid jpeg_encoder::emit_marker(int marker)\n{\n  emit_byte(uint8(0xFF)); emit_byte(uint8(marker));\n}\n\n// Emit JFIF marker\nvoid jpeg_encoder::emit_jfif_app0()\n{\n  emit_marker(M_APP0);\n  emit_word(2 + 4 + 1 + 2 + 1 + 2 + 2 + 1 + 1);\n  emit_byte(0x4A); emit_byte(0x46); emit_byte(0x49); emit_byte(0x46); /* Identifier: ASCII \"JFIF\" */\n  emit_byte(0);\n  emit_byte(1);      /* Major version */\n  emit_byte(1);      /* Minor version */\n  emit_byte(0);      /* Density unit */\n  emit_word(1);\n  emit_word(1);\n  emit_byte(0);      /* No thumbnail image */\n  emit_byte(0);\n}\n\n// Emit quantization tables\nvoid jpeg_encoder::emit_dqt()\n{\n  for (int i = 0; i < ((m_num_components == 3) ? 2 : 1); i++)\n  {\n    emit_marker(M_DQT);\n    emit_word(64 + 1 + 2);\n    emit_byte(static_cast<uint8>(i));\n    for (int j = 0; j < 64; j++)\n      emit_byte(static_cast<uint8>(m_quantization_tables[i][j]));\n  }\n}\n\n// Emit start of frame marker\nvoid jpeg_encoder::emit_sof()\n{\n  emit_marker(M_SOF0);                           /* baseline */\n  emit_word(3 * m_num_components + 2 + 5 + 1);\n  emit_byte(8);                                  /* precision */\n  emit_word(m_image_y);\n  emit_word(m_image_x);\n  emit_byte(m_num_components);\n  for (int i = 0; i < m_num_components; i++)\n  {\n    emit_byte(static_cast<uint8>(i + 1));                                   /* component ID     */\n    emit_byte((m_comp_h_samp[i] << 4) + m_comp_v_samp[i]);  /* h and v sampling */\n    emit_byte(i > 0);                                   /* quant. table num */\n  }\n}\n\n// Emit Huffman table.\nvoid jpeg_encoder::emit_dht(uint8 *bits, uint8 *val, int index, bool ac_flag)\n{\n  emit_marker(M_DHT);\n\n  int length = 0;\n  for (int i = 1; i <= 16; i++)\n    length += bits[i];\n\n  emit_word(length + 2 + 1 + 16);\n  emit_byte(static_cast<uint8>(index + (ac_flag << 4)));\n\n  for (int i = 1; i <= 16; i++)\n    emit_byte(bits[i]);\n\n  for (int i = 0; i < length; i++)\n    emit_byte(val[i]);\n}\n\n// Emit all Huffman tables.\nvoid jpeg_encoder::emit_dhts()\n{\n  emit_dht(m_huff_bits[0+0], m_huff_val[0+0], 0, false);\n  emit_dht(m_huff_bits[2+0], m_huff_val[2+0], 0, true);\n  if (m_num_components == 3)\n  {\n    emit_dht(m_huff_bits[0+1], m_huff_val[0+1], 1, false);\n    emit_dht(m_huff_bits[2+1], m_huff_val[2+1], 1, true);\n  }\n}\n\n// emit start of scan\nvoid jpeg_encoder::emit_sos()\n{\n  emit_marker(M_SOS);\n  emit_word(2 * m_num_components + 2 + 1 + 3);\n  emit_byte(m_num_components);\n  for (int i = 0; i < m_num_components; i++)\n  {\n    emit_byte(static_cast<uint8>(i + 1));\n    if (i == 0)\n      emit_byte((0 << 4) + 0);\n    else\n      emit_byte((1 << 4) + 1);\n  }\n  emit_byte(0);     /* spectral selection */\n  emit_byte(63);\n  emit_byte(0);\n}\n\n// Emit all markers at beginning of image file.\nvoid jpeg_encoder::emit_markers()\n{\n  emit_marker(M_SOI);\n  emit_jfif_app0();\n  emit_dqt();\n  emit_sof();\n  emit_dhts();\n  emit_sos();\n}\n\n// Compute the actual canonical Huffman codes/code sizes given the JPEG huff bits and val arrays.\nvoid jpeg_encoder::compute_huffman_table(uint *codes, uint8 *code_sizes, uint8 *bits, uint8 *val)\n{\n  int i, l, last_p, si;\n  uint8 huff_size[257];\n  uint huff_code[257];\n  uint code;\n\n  int p = 0;\n  for (l = 1; l <= 16; l++)\n    for (i = 1; i <= bits[l]; i++)\n      huff_size[p++] = (char)l;\n\n  huff_size[p] = 0; last_p = p; // write sentinel\n\n  code = 0; si = huff_size[0]; p = 0;\n\n  while (huff_size[p])\n  {\n    while (huff_size[p] == si)\n      huff_code[p++] = code++;\n    code <<= 1;\n    si++;\n  }\n\n  memset(codes, 0, sizeof(codes[0])*256);\n  memset(code_sizes, 0, sizeof(code_sizes[0])*256);\n  for (p = 0; p < last_p; p++)\n  {\n    codes[val[p]]      = huff_code[p];\n    code_sizes[val[p]] = huff_size[p];\n  }\n}\n\n// Quantization table generation.\nvoid jpeg_encoder::compute_quant_table(int32 *pDst, int16 *pSrc)\n{\n  int32 q;\n  if (m_params.m_quality < 50)\n    q = 5000 / m_params.m_quality;\n  else\n    q = 200 - m_params.m_quality * 2;\n  for (int i = 0; i < 64; i++)\n  {\n    int32 j = *pSrc++; j = (j * q + 50L) / 100L;\n    *pDst++ = JPGE_MIN(JPGE_MAX(j, 1), 255);\n  }\n}\n\n// Higher-level methods.\nvoid jpeg_encoder::first_pass_init()\n{\n  m_bit_buffer = 0; m_bits_in = 0;\n  memset(m_last_dc_val, 0, 3 * sizeof(m_last_dc_val[0]));\n  m_mcu_y_ofs = 0;\n  m_pass_num = 1;\n}\n\nbool jpeg_encoder::second_pass_init()\n{\n  compute_huffman_table(&m_huff_codes[0+0][0], &m_huff_code_sizes[0+0][0], m_huff_bits[0+0], m_huff_val[0+0]);\n  compute_huffman_table(&m_huff_codes[2+0][0], &m_huff_code_sizes[2+0][0], m_huff_bits[2+0], m_huff_val[2+0]);\n  if (m_num_components > 1)\n  {\n    compute_huffman_table(&m_huff_codes[0+1][0], &m_huff_code_sizes[0+1][0], m_huff_bits[0+1], m_huff_val[0+1]);\n    compute_huffman_table(&m_huff_codes[2+1][0], &m_huff_code_sizes[2+1][0], m_huff_bits[2+1], m_huff_val[2+1]);\n  }\n  first_pass_init();\n  emit_markers();\n  m_pass_num = 2;\n  return true;\n}\n\nbool jpeg_encoder::jpg_open(int p_x_res, int p_y_res, int src_channels)\n{\n  m_num_components = 3;\n  switch (m_params.m_subsampling)\n  {\n    case Y_ONLY:\n    {\n      m_num_components = 1;\n      m_comp_h_samp[0] = 1; m_comp_v_samp[0] = 1;\n      m_mcu_x          = 8; m_mcu_y          = 8;\n      break;\n    }\n    case H1V1:\n    {\n      m_comp_h_samp[0] = 1; m_comp_v_samp[0] = 1;\n      m_comp_h_samp[1] = 1; m_comp_v_samp[1] = 1;\n      m_comp_h_samp[2] = 1; m_comp_v_samp[2] = 1;\n      m_mcu_x          = 8; m_mcu_y          = 8;\n      break;\n    }\n    case H2V1:\n    {\n      m_comp_h_samp[0] = 2; m_comp_v_samp[0] = 1;\n      m_comp_h_samp[1] = 1; m_comp_v_samp[1] = 1;\n      m_comp_h_samp[2] = 1; m_comp_v_samp[2] = 1;\n      m_mcu_x          = 16; m_mcu_y         = 8;\n      break;\n    }\n    case H2V2:\n    {\n      m_comp_h_samp[0] = 2; m_comp_v_samp[0] = 2;\n      m_comp_h_samp[1] = 1; m_comp_v_samp[1] = 1;\n      m_comp_h_samp[2] = 1; m_comp_v_samp[2] = 1;\n      m_mcu_x          = 16; m_mcu_y         = 16;\n    }\n  }\n\n  m_image_x        = p_x_res; m_image_y = p_y_res;\n  m_image_bpp      = src_channels;\n  m_image_bpl      = m_image_x * src_channels;\n  m_image_x_mcu    = (m_image_x + m_mcu_x - 1) & (~(m_mcu_x - 1));\n  m_image_y_mcu    = (m_image_y + m_mcu_y - 1) & (~(m_mcu_y - 1));\n  m_image_bpl_xlt  = m_image_x * m_num_components;\n  m_image_bpl_mcu  = m_image_x_mcu * m_num_components;\n  m_mcus_per_row   = m_image_x_mcu / m_mcu_x;\n\n  if ((m_mcu_lines[0] = static_cast<uint8*>(jpge_malloc(m_image_bpl_mcu * m_mcu_y))) == NULL) return false;\n  for (int i = 1; i < m_mcu_y; i++)\n    m_mcu_lines[i] = m_mcu_lines[i-1] + m_image_bpl_mcu;\n\n  compute_quant_table(m_quantization_tables[0], s_std_lum_quant);\n  compute_quant_table(m_quantization_tables[1], m_params.m_no_chroma_discrim_flag ? s_std_lum_quant : s_std_croma_quant);\n\n  m_out_buf_left = JPGE_OUT_BUF_SIZE;\n  m_pOut_buf = m_out_buf;\n\n  if (m_params.m_two_pass_flag)\n  {\n    clear_obj(m_huff_count);\n    first_pass_init();\n  }\n  else\n  {\n    memcpy(m_huff_bits[0+0], s_dc_lum_bits, 17);    memcpy(m_huff_val [0+0], s_dc_lum_val, DC_LUM_CODES);\n    memcpy(m_huff_bits[2+0], s_ac_lum_bits, 17);    memcpy(m_huff_val [2+0], s_ac_lum_val, AC_LUM_CODES);\n    memcpy(m_huff_bits[0+1], s_dc_chroma_bits, 17); memcpy(m_huff_val [0+1], s_dc_chroma_val, DC_CHROMA_CODES);\n    memcpy(m_huff_bits[2+1], s_ac_chroma_bits, 17); memcpy(m_huff_val [2+1], s_ac_chroma_val, AC_CHROMA_CODES);\n    if (!second_pass_init()) return false;   // in effect, skip over the first pass\n  }\n  return m_all_stream_writes_succeeded;\n}\n\nvoid jpeg_encoder::load_block_8_8_grey(int x)\n{\n  uint8 *pSrc;\n  sample_array_t *pDst = m_sample_array;\n  x <<= 3;\n  for (int i = 0; i < 8; i++, pDst += 8)\n  {\n    pSrc = m_mcu_lines[i] + x;\n    pDst[0] = pSrc[0] - 128; pDst[1] = pSrc[1] - 128; pDst[2] = pSrc[2] - 128; pDst[3] = pSrc[3] - 128;\n    pDst[4] = pSrc[4] - 128; pDst[5] = pSrc[5] - 128; pDst[6] = pSrc[6] - 128; pDst[7] = pSrc[7] - 128;\n  }\n}\n\nvoid jpeg_encoder::load_block_8_8(int x, int y, int c)\n{\n  uint8 *pSrc;\n  sample_array_t *pDst = m_sample_array;\n  x = (x * (8 * 3)) + c;\n  y <<= 3;\n  for (int i = 0; i < 8; i++, pDst += 8)\n  {\n    pSrc = m_mcu_lines[y + i] + x;\n    pDst[0] = pSrc[0 * 3] - 128; pDst[1] = pSrc[1 * 3] - 128; pDst[2] = pSrc[2 * 3] - 128; pDst[3] = pSrc[3 * 3] - 128;\n    pDst[4] = pSrc[4 * 3] - 128; pDst[5] = pSrc[5 * 3] - 128; pDst[6] = pSrc[6 * 3] - 128; pDst[7] = pSrc[7 * 3] - 128;\n  }\n}\n\nvoid jpeg_encoder::load_block_16_8(int x, int c)\n{\n  uint8 *pSrc1, *pSrc2;\n  sample_array_t *pDst = m_sample_array;\n  x = (x * (16 * 3)) + c;\n  int a = 0, b = 2;\n  for (int i = 0; i < 16; i += 2, pDst += 8)\n  {\n    pSrc1 = m_mcu_lines[i + 0] + x;\n    pSrc2 = m_mcu_lines[i + 1] + x;\n    pDst[0] = ((pSrc1[ 0 * 3] + pSrc1[ 1 * 3] + pSrc2[ 0 * 3] + pSrc2[ 1 * 3] + a) >> 2) - 128; pDst[1] = ((pSrc1[ 2 * 3] + pSrc1[ 3 * 3] + pSrc2[ 2 * 3] + pSrc2[ 3 * 3] + b) >> 2) - 128;\n    pDst[2] = ((pSrc1[ 4 * 3] + pSrc1[ 5 * 3] + pSrc2[ 4 * 3] + pSrc2[ 5 * 3] + a) >> 2) - 128; pDst[3] = ((pSrc1[ 6 * 3] + pSrc1[ 7 * 3] + pSrc2[ 6 * 3] + pSrc2[ 7 * 3] + b) >> 2) - 128;\n    pDst[4] = ((pSrc1[ 8 * 3] + pSrc1[ 9 * 3] + pSrc2[ 8 * 3] + pSrc2[ 9 * 3] + a) >> 2) - 128; pDst[5] = ((pSrc1[10 * 3] + pSrc1[11 * 3] + pSrc2[10 * 3] + pSrc2[11 * 3] + b) >> 2) - 128;\n    pDst[6] = ((pSrc1[12 * 3] + pSrc1[13 * 3] + pSrc2[12 * 3] + pSrc2[13 * 3] + a) >> 2) - 128; pDst[7] = ((pSrc1[14 * 3] + pSrc1[15 * 3] + pSrc2[14 * 3] + pSrc2[15 * 3] + b) >> 2) - 128;\n    int temp = a; a = b; b = temp;\n  }\n}\n\nvoid jpeg_encoder::load_block_16_8_8(int x, int c)\n{\n  uint8 *pSrc1;\n  sample_array_t *pDst = m_sample_array;\n  x = (x * (16 * 3)) + c;\n  for (int i = 0; i < 8; i++, pDst += 8)\n  {\n    pSrc1 = m_mcu_lines[i + 0] + x;\n    pDst[0] = ((pSrc1[ 0 * 3] + pSrc1[ 1 * 3]) >> 1) - 128; pDst[1] = ((pSrc1[ 2 * 3] + pSrc1[ 3 * 3]) >> 1) - 128;\n    pDst[2] = ((pSrc1[ 4 * 3] + pSrc1[ 5 * 3]) >> 1) - 128; pDst[3] = ((pSrc1[ 6 * 3] + pSrc1[ 7 * 3]) >> 1) - 128;\n    pDst[4] = ((pSrc1[ 8 * 3] + pSrc1[ 9 * 3]) >> 1) - 128; pDst[5] = ((pSrc1[10 * 3] + pSrc1[11 * 3]) >> 1) - 128;\n    pDst[6] = ((pSrc1[12 * 3] + pSrc1[13 * 3]) >> 1) - 128; pDst[7] = ((pSrc1[14 * 3] + pSrc1[15 * 3]) >> 1) - 128;\n  }\n}\n\nvoid jpeg_encoder::load_quantized_coefficients(int component_num)\n{\n  int32 *q = m_quantization_tables[component_num > 0];\n  int16 *pDst = m_coefficient_array;\n  for (int i = 0; i < 64; i++)\n  {\n    sample_array_t j = m_sample_array[s_zag[i]];\n    if (j < 0)\n    {\n      if ((j = -j + (*q >> 1)) < *q)\n        *pDst++ = 0;\n      else\n        *pDst++ = static_cast<int16>(-(j / *q));\n    }\n    else\n    {\n      if ((j = j + (*q >> 1)) < *q)\n        *pDst++ = 0;\n      else\n        *pDst++ = static_cast<int16>((j / *q));\n    }\n    q++;\n  }\n}\n\nvoid jpeg_encoder::flush_output_buffer()\n{\n  if (m_out_buf_left != JPGE_OUT_BUF_SIZE)\n    m_all_stream_writes_succeeded = m_all_stream_writes_succeeded && m_pStream->put_buf(m_out_buf, JPGE_OUT_BUF_SIZE - m_out_buf_left);\n  m_pOut_buf = m_out_buf;\n  m_out_buf_left = JPGE_OUT_BUF_SIZE;\n}\n\nvoid jpeg_encoder::put_bits(uint bits, uint len)\n{\n  m_bit_buffer |= ((uint32)bits << (24 - (m_bits_in += len)));\n  while (m_bits_in >= 8)\n  {\n    uint8 c;\n    #define JPGE_PUT_BYTE(c) { *m_pOut_buf++ = (c); if (--m_out_buf_left == 0) flush_output_buffer(); }\n    JPGE_PUT_BYTE(c = (uint8)((m_bit_buffer >> 16) & 0xFF));\n    if (c == 0xFF) JPGE_PUT_BYTE(0);\n    m_bit_buffer <<= 8;\n    m_bits_in -= 8;\n  }\n}\n\nvoid jpeg_encoder::code_coefficients_pass_one(int component_num)\n{\n  if (component_num >= 3) return; // just to shut up static analysis\n  int i, run_len, nbits, temp1;\n  int16 *src = m_coefficient_array;\n  uint32 *dc_count = component_num ? m_huff_count[0 + 1] : m_huff_count[0 + 0], *ac_count = component_num ? m_huff_count[2 + 1] : m_huff_count[2 + 0];\n\n  temp1 = src[0] - m_last_dc_val[component_num];\n  m_last_dc_val[component_num] = src[0];\n  if (temp1 < 0) temp1 = -temp1;\n\n  nbits = 0;\n  while (temp1)\n  {\n    nbits++; temp1 >>= 1;\n  }\n\n  dc_count[nbits]++;\n  for (run_len = 0, i = 1; i < 64; i++)\n  {\n    if ((temp1 = m_coefficient_array[i]) == 0)\n      run_len++;\n    else\n    {\n      while (run_len >= 16)\n      {\n        ac_count[0xF0]++;\n        run_len -= 16;\n      }\n      if (temp1 < 0) temp1 = -temp1;\n      nbits = 1;\n      while (temp1 >>= 1) nbits++;\n      ac_count[(run_len << 4) + nbits]++;\n      run_len = 0;\n    }\n  }\n  if (run_len) ac_count[0]++;\n}\n\nvoid jpeg_encoder::code_coefficients_pass_two(int component_num)\n{\n  int i, j, run_len, nbits, temp1, temp2;\n  int16 *pSrc = m_coefficient_array;\n  uint *codes[2];\n  uint8 *code_sizes[2];\n\n  if (component_num == 0)\n  {\n    codes[0] = m_huff_codes[0 + 0]; codes[1] = m_huff_codes[2 + 0];\n    code_sizes[0] = m_huff_code_sizes[0 + 0]; code_sizes[1] = m_huff_code_sizes[2 + 0];\n  }\n  else\n  {\n    codes[0] = m_huff_codes[0 + 1]; codes[1] = m_huff_codes[2 + 1];\n    code_sizes[0] = m_huff_code_sizes[0 + 1]; code_sizes[1] = m_huff_code_sizes[2 + 1];\n  }\n\n  temp1 = temp2 = pSrc[0] - m_last_dc_val[component_num];\n  m_last_dc_val[component_num] = pSrc[0];\n\n  if (temp1 < 0)\n  {\n    temp1 = -temp1; temp2--;\n  }\n\n  nbits = 0;\n  while (temp1)\n  {\n    nbits++; temp1 >>= 1;\n  }\n\n  put_bits(codes[0][nbits], code_sizes[0][nbits]);\n  if (nbits) put_bits(temp2 & ((1 << nbits) - 1), nbits);\n\n  for (run_len = 0, i = 1; i < 64; i++)\n  {\n    if ((temp1 = m_coefficient_array[i]) == 0)\n      run_len++;\n    else\n    {\n      while (run_len >= 16)\n      {\n        put_bits(codes[1][0xF0], code_sizes[1][0xF0]);\n        run_len -= 16;\n      }\n      if ((temp2 = temp1) < 0)\n      {\n        temp1 = -temp1;\n        temp2--;\n      }\n      nbits = 1;\n      while (temp1 >>= 1)\n        nbits++;\n      j = (run_len << 4) + nbits;\n      put_bits(codes[1][j], code_sizes[1][j]);\n      put_bits(temp2 & ((1 << nbits) - 1), nbits);\n      run_len = 0;\n    }\n  }\n  if (run_len)\n    put_bits(codes[1][0], code_sizes[1][0]);\n}\n\nvoid jpeg_encoder::code_block(int component_num)\n{\n  DCT2D(m_sample_array);\n  load_quantized_coefficients(component_num);\n  if (m_pass_num == 1)\n    code_coefficients_pass_one(component_num);\n  else\n    code_coefficients_pass_two(component_num);\n}\n\nvoid jpeg_encoder::process_mcu_row()\n{\n  if (m_num_components == 1)\n  {\n    for (int i = 0; i < m_mcus_per_row; i++)\n    {\n      load_block_8_8_grey(i); code_block(0);\n    }\n  }\n  else if ((m_comp_h_samp[0] == 1) && (m_comp_v_samp[0] == 1))\n  {\n    for (int i = 0; i < m_mcus_per_row; i++)\n    {\n      load_block_8_8(i, 0, 0); code_block(0); load_block_8_8(i, 0, 1); code_block(1); load_block_8_8(i, 0, 2); code_block(2);\n    }\n  }\n  else if ((m_comp_h_samp[0] == 2) && (m_comp_v_samp[0] == 1))\n  {\n    for (int i = 0; i < m_mcus_per_row; i++)\n    {\n      load_block_8_8(i * 2 + 0, 0, 0); code_block(0); load_block_8_8(i * 2 + 1, 0, 0); code_block(0);\n      load_block_16_8_8(i, 1); code_block(1); load_block_16_8_8(i, 2); code_block(2);\n    }\n  }\n  else if ((m_comp_h_samp[0] == 2) && (m_comp_v_samp[0] == 2))\n  {\n    for (int i = 0; i < m_mcus_per_row; i++)\n    {\n      load_block_8_8(i * 2 + 0, 0, 0); code_block(0); load_block_8_8(i * 2 + 1, 0, 0); code_block(0);\n      load_block_8_8(i * 2 + 0, 1, 0); code_block(0); load_block_8_8(i * 2 + 1, 1, 0); code_block(0);\n      load_block_16_8(i, 1); code_block(1); load_block_16_8(i, 2); code_block(2);\n    }\n  }\n}\n\nbool jpeg_encoder::terminate_pass_one()\n{\n  optimize_huffman_table(0+0, DC_LUM_CODES); optimize_huffman_table(2+0, AC_LUM_CODES);\n  if (m_num_components > 1)\n  {\n    optimize_huffman_table(0+1, DC_CHROMA_CODES); optimize_huffman_table(2+1, AC_CHROMA_CODES);\n  }\n  return second_pass_init();\n}\n\nbool jpeg_encoder::terminate_pass_two()\n{\n  put_bits(0x7F, 7);\n  flush_output_buffer();\n  emit_marker(M_EOI);\n  m_pass_num++; // purposely bump up m_pass_num, for debugging\n  return true;\n}\n\nbool jpeg_encoder::process_end_of_image()\n{\n  if (m_mcu_y_ofs)\n  {\n    if (m_mcu_y_ofs < 16) // check here just to shut up static analysis\n    {\n      for (int i = m_mcu_y_ofs; i < m_mcu_y; i++)\n        memcpy(m_mcu_lines[i], m_mcu_lines[m_mcu_y_ofs - 1], m_image_bpl_mcu);\n    }\n\n    process_mcu_row();\n  }\n\n  if (m_pass_num == 1)\n    return terminate_pass_one();\n  else\n    return terminate_pass_two();\n}\n\nvoid jpeg_encoder::load_mcu(const void *pSrc)\n{\n  const uint8* Psrc = reinterpret_cast<const uint8*>(pSrc);\n\n  uint8* pDst = m_mcu_lines[m_mcu_y_ofs]; // OK to write up to m_image_bpl_xlt bytes to pDst\n\n  if (m_num_components == 1)\n  {\n    if (m_image_bpp == 4)\n      RGBA_to_Y(pDst, Psrc, m_image_x);\n    else if (m_image_bpp == 3)\n      RGB_to_Y(pDst, Psrc, m_image_x);\n    else\n      memcpy(pDst, Psrc, m_image_x);\n  }\n  else\n  {\n    if (m_image_bpp == 4)\n      RGBA_to_YCC(pDst, Psrc, m_image_x);\n    else if (m_image_bpp == 3)\n      RGB_to_YCC(pDst, Psrc, m_image_x);\n    else\n      Y_to_YCC(pDst, Psrc, m_image_x);\n  }\n\n  // Possibly duplicate pixels at end of scanline if not a multiple of 8 or 16\n  if (m_num_components == 1)\n    memset(m_mcu_lines[m_mcu_y_ofs] + m_image_bpl_xlt, pDst[m_image_bpl_xlt - 1], m_image_x_mcu - m_image_x);\n  else\n  {\n    const uint8 y = pDst[m_image_bpl_xlt - 3 + 0], cb = pDst[m_image_bpl_xlt - 3 + 1], cr = pDst[m_image_bpl_xlt - 3 + 2];\n    uint8 *q = m_mcu_lines[m_mcu_y_ofs] + m_image_bpl_xlt;\n    for (int i = m_image_x; i < m_image_x_mcu; i++)\n    {\n      *q++ = y; *q++ = cb; *q++ = cr;\n    }\n  }\n\n  if (++m_mcu_y_ofs == m_mcu_y)\n  {\n    process_mcu_row();\n    m_mcu_y_ofs = 0;\n  }\n}\n\nvoid jpeg_encoder::clear()\n{\n  m_mcu_lines[0] = NULL;\n  m_pass_num = 0;\n  m_all_stream_writes_succeeded = true;\n}\n\njpeg_encoder::jpeg_encoder()\n{\n  clear();\n}\n\njpeg_encoder::~jpeg_encoder()\n{\n  deinit();\n}\n\nbool jpeg_encoder::init(output_stream *pStream, int64_t width, int64_t height, int64_t src_channels, const params &comp_params)\n{\n  deinit();\n  if (((!pStream) || (width < 1) || (height < 1)) || ((src_channels != 1) && (src_channels != 3) && (src_channels != 4)) || (!comp_params.check_valid())) return false;\n  m_pStream = pStream;\n  m_params = comp_params;\n  return jpg_open(width, height, src_channels);\n}\n\nvoid jpeg_encoder::deinit()\n{\n  jpge_free(m_mcu_lines[0]);\n  clear();\n}\n\nbool jpeg_encoder::process_scanline(const void* pScanline)\n{\n  if ((m_pass_num < 1) || (m_pass_num > 2)) return false;\n  if (m_all_stream_writes_succeeded)\n  {\n    if (!pScanline)\n    {\n      if (!process_end_of_image()) return false;\n    }\n    else\n    {\n      load_mcu(pScanline);\n    }\n  }\n  return m_all_stream_writes_succeeded;\n}\n\n// Higher level wrappers/examples (optional).\n#include <stdio.h>\n\nclass cfile_stream : public output_stream\n{\n   cfile_stream(const cfile_stream &);\n   cfile_stream &operator= (const cfile_stream &);\n\n   FILE* m_pFile;\n   bool m_bStatus;\n\npublic:\n   cfile_stream() : m_pFile(NULL), m_bStatus(false) { }\n\n   virtual ~cfile_stream()\n   {\n      close();\n   }\n\n   bool open(const char *pFilename)\n   {\n      close();\n#if defined(_MSC_VER)\n      if (fopen_s(&m_pFile, pFilename, \"wb\") != 0)\n\t  {\n\t\t  return false;\n\t  }\n#else\n      m_pFile = fopen(pFilename, \"wb\");\n#endif\n      m_bStatus = (m_pFile != NULL);\n      return m_bStatus;\n   }\n\n   bool close()\n   {\n      if (m_pFile)\n      {\n         if (fclose(m_pFile) == EOF)\n         {\n            m_bStatus = false;\n         }\n         m_pFile = NULL;\n      }\n      return m_bStatus;\n   }\n\n   virtual bool put_buf(const void* pBuf, int64_t len)\n   {\n      m_bStatus = m_bStatus && (fwrite(pBuf, len, 1, m_pFile) == 1);\n      return m_bStatus;\n   }\n\n   uint get_size() const\n   {\n      return m_pFile ? ftell(m_pFile) : 0;\n   }\n};\n\n// Writes JPEG image to file.\nbool compress_image_to_jpeg_file(const char *pFilename, int64_t width, int64_t height, int64_t num_channels, const uint8 *pImage_data, const params &comp_params)\n{\n  cfile_stream dst_stream;\n  if (!dst_stream.open(pFilename))\n    return false;\n\n  jpge::jpeg_encoder dst_image;\n  if (!dst_image.init(&dst_stream, width, height, num_channels, comp_params))\n    return false;\n\n  for (uint pass_index = 0; pass_index < dst_image.get_total_passes(); pass_index++)\n  {\n    for (int64_t i = 0; i < height; i++)\n    {\n\t\t// i, width, and num_channels are all 64bit\n       const uint8* pBuf = pImage_data + i * width * num_channels;\n       if (!dst_image.process_scanline(pBuf))\n          return false;\n    }\n    if (!dst_image.process_scanline(NULL))\n       return false;\n  }\n\n  dst_image.deinit();\n\n  return dst_stream.close();\n}\n\nclass memory_stream : public output_stream\n{\n   memory_stream(const memory_stream &);\n   memory_stream &operator= (const memory_stream &);\n\n   uint8 *m_pBuf;\n   uint64_t m_buf_size, m_buf_ofs;\n\npublic:\n   memory_stream(void *pBuf, uint64_t buf_size) : m_pBuf(static_cast<uint8*>(pBuf)), m_buf_size(buf_size), m_buf_ofs(0) { }\n\n   virtual ~memory_stream() { }\n\n   virtual bool put_buf(const void* pBuf, int64_t len)\n   {\n      uint64_t buf_remaining = m_buf_size - m_buf_ofs;\n      if ((uint64_t)len > buf_remaining)\n         return false;\n      memcpy(m_pBuf + m_buf_ofs, pBuf, len);\n      m_buf_ofs += len;\n      return true;\n   }\n\n   uint64_t get_size() const\n   {\n      return m_buf_ofs;\n   }\n};\n\nbool compress_image_to_jpeg_file_in_memory(void *pDstBuf, int64_t &buf_size, int64_t width, int64_t height, int64_t num_channels, const uint8 *pImage_data, const params &comp_params)\n{\n   if ((!pDstBuf) || (!buf_size))\n      return false;\n\n   memory_stream dst_stream(pDstBuf, buf_size);\n\n   buf_size = 0;\n\n   jpge::jpeg_encoder dst_image;\n   if (!dst_image.init(&dst_stream, width, height, num_channels, comp_params))\n      return false;\n\n   for (uint pass_index = 0; pass_index < dst_image.get_total_passes(); pass_index++)\n   {\n     for (int64_t i = 0; i < height; i++)\n     {\n        const uint8* pScanline = pImage_data + i * width * num_channels;\n        if (!dst_image.process_scanline(pScanline))\n           return false;\n     }\n     if (!dst_image.process_scanline(NULL))\n        return false;\n   }\n\n   dst_image.deinit();\n\n   buf_size = dst_stream.get_size();\n   return true;\n}\n\n} // namespace jpge"
  },
  {
    "path": "crazy_functions/test_project/cpp/libJPG/jpge.h",
    "content": "\n// jpge.h - C++ class for JPEG compression.\n// Public domain, Rich Geldreich <richgel99@gmail.com>\n// Alex Evans: Added RGBA support, linear memory allocator.\n#ifndef JPEG_ENCODER_H\n#define JPEG_ENCODER_H\n\n#include <stdint.h>\n\nnamespace jpge\n{\n  typedef unsigned char  uint8;\n  typedef signed short   int16;\n  typedef signed int     int32;\n  typedef unsigned short uint16;\n  typedef unsigned int   uint32;\n  typedef unsigned int   uint;\n\n  // JPEG chroma subsampling factors. Y_ONLY (grayscale images) and H2V2 (color images) are the most common.\n  enum subsampling_t { Y_ONLY = 0, H1V1 = 1, H2V1 = 2, H2V2 = 3 };\n\n  // JPEG compression parameters structure.\n  struct params\n  {\n    inline params() : m_quality(85), m_subsampling(H2V2), m_no_chroma_discrim_flag(false), m_two_pass_flag(false) { }\n\n    inline bool check_valid() const\n    {\n      if ((m_quality < 1) || (m_quality > 100)) return false;\n      if ((uint)m_subsampling > (uint)H2V2) return false;\n      return true;\n    }\n\n    // Quality: 1-100, higher is better. Typical values are around 50-95.\n    int m_quality;\n\n    // m_subsampling:\n    // 0 = Y (grayscale) only\n    // 1 = YCbCr, no subsampling (H1V1, YCbCr 1x1x1, 3 blocks per MCU)\n    // 2 = YCbCr, H2V1 subsampling (YCbCr 2x1x1, 4 blocks per MCU)\n    // 3 = YCbCr, H2V2 subsampling (YCbCr 4x1x1, 6 blocks per MCU-- very common)\n    subsampling_t m_subsampling;\n\n    // Disables CbCr discrimination - only intended for testing.\n    // If true, the Y quantization table is also used for the CbCr channels.\n    bool m_no_chroma_discrim_flag;\n\n    bool m_two_pass_flag;\n  };\n  \n  // Writes JPEG image to a file. \n  // num_channels must be 1 (Y) or 3 (RGB), image pitch must be width*num_channels.\n  bool compress_image_to_jpeg_file(const char *pFilename, int64_t width, int64_t height, int64_t num_channels, const uint8 *pImage_data, const params &comp_params = params());\n\n  // Writes JPEG image to memory buffer. \n  // On entry, buf_size is the size of the output buffer pointed at by pBuf, which should be at least ~1024 bytes. \n  // If return value is true, buf_size will be set to the size of the compressed data.\n  bool compress_image_to_jpeg_file_in_memory(void *pBuf, int64_t &buf_size, int64_t width, int64_t height, int64_t num_channels, const uint8 *pImage_data, const params &comp_params = params());\n    \n  // Output stream abstract class - used by the jpeg_encoder class to write to the output stream. \n  // put_buf() is generally called with len==JPGE_OUT_BUF_SIZE bytes, but for headers it'll be called with smaller amounts.\n  class output_stream\n  {\n  public:\n    virtual ~output_stream() { };\n    virtual bool put_buf(const void* Pbuf, int64_t len) = 0;\n    template<class T> inline bool put_obj(const T& obj) { return put_buf(&obj, sizeof(T)); }\n  };\n    \n  // Lower level jpeg_encoder class - useful if more control is needed than the above helper functions.\n  class jpeg_encoder\n  {\n  public:\n    jpeg_encoder();\n    ~jpeg_encoder();\n\n    // Initializes the compressor.\n    // pStream: The stream object to use for writing compressed data.\n    // params - Compression parameters structure, defined above.\n    // width, height  - Image dimensions.\n    // channels - May be 1, or 3. 1 indicates grayscale, 3 indicates RGB source data.\n    // Returns false on out of memory or if a stream write fails.\n    bool init(output_stream *pStream, int64_t width, int64_t height, int64_t src_channels, const params &comp_params = params());\n    \n    const params &get_params() const { return m_params; }\n    \n    // Deinitializes the compressor, freeing any allocated memory. May be called at any time.\n    void deinit();\n\n    uint get_total_passes() const { return m_params.m_two_pass_flag ? 2 : 1; }\n    inline uint get_cur_pass() { return m_pass_num; }\n\n    // Call this method with each source scanline.\n    // width * src_channels bytes per scanline is expected (RGB or Y format).\n    // You must call with NULL after all scanlines are processed to finish compression.\n    // Returns false on out of memory or if a stream write fails.\n    bool process_scanline(const void* pScanline);\n        \n  private:\n    jpeg_encoder(const jpeg_encoder &);\n    jpeg_encoder &operator =(const jpeg_encoder &);\n\n    typedef int32 sample_array_t;\n        \n    output_stream *m_pStream;\n    params m_params;\n    uint8 m_num_components;\n    uint8 m_comp_h_samp[3], m_comp_v_samp[3];\n    int m_image_x, m_image_y, m_image_bpp, m_image_bpl;\n    int m_image_x_mcu, m_image_y_mcu;\n    int m_image_bpl_xlt, m_image_bpl_mcu;\n    int m_mcus_per_row;\n    int m_mcu_x, m_mcu_y;\n    uint8 *m_mcu_lines[16];\n    uint8 m_mcu_y_ofs;\n    sample_array_t m_sample_array[64];\n    int16 m_coefficient_array[64];\n    int32 m_quantization_tables[2][64];\n    uint m_huff_codes[4][256];\n    uint8 m_huff_code_sizes[4][256];\n    uint8 m_huff_bits[4][17];\n    uint8 m_huff_val[4][256];\n    uint32 m_huff_count[4][256];\n    int m_last_dc_val[3];\n    enum { JPGE_OUT_BUF_SIZE = 2048 };\n    uint8 m_out_buf[JPGE_OUT_BUF_SIZE];\n    uint8 *m_pOut_buf;\n    uint m_out_buf_left;\n    uint32 m_bit_buffer;\n    uint m_bits_in;\n    uint8 m_pass_num;\n    bool m_all_stream_writes_succeeded;\n        \n    void optimize_huffman_table(int table_num, int table_len);\n    void emit_byte(uint8 i);\n    void emit_word(uint i);\n    void emit_marker(int marker);\n    void emit_jfif_app0();\n    void emit_dqt();\n    void emit_sof();\n    void emit_dht(uint8 *bits, uint8 *val, int index, bool ac_flag);\n    void emit_dhts();\n    void emit_sos();\n    void emit_markers();\n    void compute_huffman_table(uint *codes, uint8 *code_sizes, uint8 *bits, uint8 *val);\n    void compute_quant_table(int32 *dst, int16 *src);\n    void adjust_quant_table(int32 *dst, int32 *src);\n    void first_pass_init();\n    bool second_pass_init();\n    bool jpg_open(int p_x_res, int p_y_res, int src_channels);\n    void load_block_8_8_grey(int x);\n    void load_block_8_8(int x, int y, int c);\n    void load_block_16_8(int x, int c);\n    void load_block_16_8_8(int x, int c);\n    void load_quantized_coefficients(int component_num);\n    void flush_output_buffer();\n    void put_bits(uint bits, uint len);\n    void code_coefficients_pass_one(int component_num);\n    void code_coefficients_pass_two(int component_num);\n    void code_block(int component_num);\n    void process_mcu_row();\n    bool terminate_pass_one();\n    bool terminate_pass_two();\n    bool process_end_of_image();\n    void load_mcu(const void* src);\n    void clear();\n    void init();\n  };\n\n} // namespace jpge\n\n#endif // JPEG_ENCODER"
  },
  {
    "path": "crazy_functions/test_project/cpp/libJPG/来源",
    "content": "jpge.h - C++ class for JPEG compression.\nPublic domain, Rich Geldreich <richgel99@gmail.com>\nAlex Evans: Added RGBA support, linear memory allocator."
  },
  {
    "path": "crazy_functions/test_project/cpp/longcode/jpgd.cpp",
    "content": "// jpgd.cpp - C++ class for JPEG decompression.\n// Public domain, Rich Geldreich <richgel99@gmail.com>\n// Last updated Apr. 16, 2011\n// Alex Evans: Linear memory allocator (taken from jpge.h).\n//\n// Supports progressive and baseline sequential JPEG image files, and the most common chroma subsampling factors: Y, H1V1, H2V1, H1V2, and H2V2.\n//\n// Chroma upsampling quality: H2V2 is upsampled in the frequency domain, H2V1 and H1V2 are upsampled using point sampling.\n// Chroma upsampling reference: \"Fast Scheme for Image Size Change in the Compressed Domain\"\n// http://vision.ai.uiuc.edu/~dugad/research/dct/index.html\n\n#include \"jpgd.h\"\n#include <string.h>\n\n#include <assert.h>\n// BEGIN EPIC MOD\n#define JPGD_ASSERT(x) { assert(x); CA_ASSUME(x); } (void)0\n// END EPIC MOD\n\n#ifdef _MSC_VER\n#pragma warning (disable : 4611) // warning C4611: interaction between '_setjmp' and C++ object destruction is non-portable\n#endif\n\n// Set to 1 to enable freq. domain chroma upsampling on images using H2V2 subsampling (0=faster nearest neighbor sampling).\n// This is slower, but results in higher quality on images with highly saturated colors.\n#define JPGD_SUPPORT_FREQ_DOMAIN_UPSAMPLING 1\n\n#define JPGD_TRUE (1)\n#define JPGD_FALSE (0)\n\n#define JPGD_MAX(a,b) (((a)>(b)) ? (a) : (b))\n#define JPGD_MIN(a,b) (((a)<(b)) ? (a) : (b))\n\nnamespace jpgd {\n\n\tstatic inline void *jpgd_malloc(size_t nSize) { return FMemory::Malloc(nSize); }\n\tstatic inline void jpgd_free(void *p) { FMemory::Free(p); }\n\n// BEGIN EPIC MOD\n//@UE3 - use UE3 BGRA encoding instead of assuming RGBA\n\t// stolen from IImageWrapper.h\n\tenum ERGBFormatJPG\n\t{\n\t\tInvalid = -1,\n\t\tRGBA =  0,\n\t\tBGRA =  1,\n\t\tGray =  2,\n\t};\n\tstatic ERGBFormatJPG jpg_format;\n// END EPIC MOD\n\n\t// DCT coefficients are stored in this sequence.\n\tstatic int g_ZAG[64] = {  0,1,8,16,9,2,3,10,17,24,32,25,18,11,4,5,12,19,26,33,40,48,41,34,27,20,13,6,7,14,21,28,35,42,49,56,57,50,43,36,29,22,15,23,30,37,44,51,58,59,52,45,38,31,39,46,53,60,61,54,47,55,62,63 };\n\n\tenum JPEG_MARKER\n\t{\n\t\tM_SOF0  = 0xC0, M_SOF1  = 0xC1, M_SOF2  = 0xC2, M_SOF3  = 0xC3, M_SOF5  = 0xC5, M_SOF6  = 0xC6, M_SOF7  = 0xC7, M_JPG   = 0xC8,\n\t\tM_SOF9  = 0xC9, M_SOF10 = 0xCA, M_SOF11 = 0xCB, M_SOF13 = 0xCD, M_SOF14 = 0xCE, M_SOF15 = 0xCF, M_DHT   = 0xC4, M_DAC   = 0xCC,\n\t\tM_RST0  = 0xD0, M_RST1  = 0xD1, M_RST2  = 0xD2, M_RST3  = 0xD3, M_RST4  = 0xD4, M_RST5  = 0xD5, M_RST6  = 0xD6, M_RST7  = 0xD7,\n\t\tM_SOI   = 0xD8, M_EOI   = 0xD9, M_SOS   = 0xDA, M_DQT   = 0xDB, M_DNL   = 0xDC, M_DRI   = 0xDD, M_DHP   = 0xDE, M_EXP   = 0xDF,\n\t\tM_APP0  = 0xE0, M_APP15 = 0xEF, M_JPG0  = 0xF0, M_JPG13 = 0xFD, M_COM   = 0xFE, M_TEM   = 0x01, M_ERROR = 0x100, RST0   = 0xD0\n\t};\n\n\tenum JPEG_SUBSAMPLING { JPGD_GRAYSCALE = 0, JPGD_YH1V1, JPGD_YH2V1, JPGD_YH1V2, JPGD_YH2V2 };\n\n#define CONST_BITS  13\n#define PASS1_BITS  2\n#define SCALEDONE ((int32)1)\n\n#define FIX_0_298631336  ((int32)2446)        /* FIX(0.298631336) */\n#define FIX_0_390180644  ((int32)3196)        /* FIX(0.390180644) */\n#define FIX_0_541196100  ((int32)4433)        /* FIX(0.541196100) */\n#define FIX_0_765366865  ((int32)6270)        /* FIX(0.765366865) */\n#define FIX_0_899976223  ((int32)7373)        /* FIX(0.899976223) */\n#define FIX_1_175875602  ((int32)9633)        /* FIX(1.175875602) */\n#define FIX_1_501321110  ((int32)12299)       /* FIX(1.501321110) */\n#define FIX_1_847759065  ((int32)15137)       /* FIX(1.847759065) */\n#define FIX_1_961570560  ((int32)16069)       /* FIX(1.961570560) */\n#define FIX_2_053119869  ((int32)16819)       /* FIX(2.053119869) */\n#define FIX_2_562915447  ((int32)20995)       /* FIX(2.562915447) */\n#define FIX_3_072711026  ((int32)25172)       /* FIX(3.072711026) */\n\n#define DESCALE(x,n)  (((x) + (SCALEDONE << ((n)-1))) >> (n))\n#define DESCALE_ZEROSHIFT(x,n)  (((x) + (128 << (n)) + (SCALEDONE << ((n)-1))) >> (n))\n\n#define MULTIPLY(var, cnst)  ((var) * (cnst))\n\n#define CLAMP(i) ((static_cast<uint>(i) > 255) ? (((~i) >> 31) & 0xFF) : (i))\n\n\t// Compiler creates a fast path 1D IDCT for X non-zero columns\n\ttemplate <int NONZERO_COLS>\n\tstruct Row\n\t{\n\t\tstatic void idct(int* pTemp, const jpgd_block_t* pSrc)\n\t\t{\n\t\t\t// ACCESS_COL() will be optimized at compile time to either an array access, or 0.\n#define ACCESS_COL(x) (((x) < NONZERO_COLS) ? (int)pSrc[x] : 0)\n\n\t\t\tconst int z2 = ACCESS_COL(2), z3 = ACCESS_COL(6);\n\n\t\t\tconst int z1 = MULTIPLY(z2 + z3, FIX_0_541196100);\n\t\t\tconst int tmp2 = z1 + MULTIPLY(z3, - FIX_1_847759065);\n\t\t\tconst int tmp3 = z1 + MULTIPLY(z2, FIX_0_765366865);\n\n\t\t\tconst int tmp0 = (ACCESS_COL(0) + ACCESS_COL(4)) << CONST_BITS;\n\t\t\tconst int tmp1 = (ACCESS_COL(0) - ACCESS_COL(4)) << CONST_BITS;\n\n\t\t\tconst int tmp10 = tmp0 + tmp3, tmp13 = tmp0 - tmp3, tmp11 = tmp1 + tmp2, tmp12 = tmp1 - tmp2;\n\n\t\t\tconst int atmp0 = ACCESS_COL(7), atmp1 = ACCESS_COL(5), atmp2 = ACCESS_COL(3), atmp3 = ACCESS_COL(1);\n\n\t\t\tconst int bz1 = atmp0 + atmp3, bz2 = atmp1 + atmp2, bz3 = atmp0 + atmp2, bz4 = atmp1 + atmp3;\n\t\t\tconst int bz5 = MULTIPLY(bz3 + bz4, FIX_1_175875602);\n\n\t\t\tconst int az1 = MULTIPLY(bz1, - FIX_0_899976223);\n\t\t\tconst int az2 = MULTIPLY(bz2, - FIX_2_562915447);\n\t\t\tconst int az3 = MULTIPLY(bz3, - FIX_1_961570560) + bz5;\n\t\t\tconst int az4 = MULTIPLY(bz4, - FIX_0_390180644) + bz5;\n\n\t\t\tconst int btmp0 = MULTIPLY(atmp0, FIX_0_298631336) + az1 + az3;\n\t\t\tconst int btmp1 = MULTIPLY(atmp1, FIX_2_053119869) + az2 + az4;\n\t\t\tconst int btmp2 = MULTIPLY(atmp2, FIX_3_072711026) + az2 + az3;\n\t\t\tconst int btmp3 = MULTIPLY(atmp3, FIX_1_501321110) + az1 + az4;\n\n\t\t\tpTemp[0] = DESCALE(tmp10 + btmp3, CONST_BITS-PASS1_BITS);\n\t\t\tpTemp[7] = DESCALE(tmp10 - btmp3, CONST_BITS-PASS1_BITS);\n\t\t\tpTemp[1] = DESCALE(tmp11 + btmp2, CONST_BITS-PASS1_BITS);\n\t\t\tpTemp[6] = DESCALE(tmp11 - btmp2, CONST_BITS-PASS1_BITS);\n\t\t\tpTemp[2] = DESCALE(tmp12 + btmp1, CONST_BITS-PASS1_BITS);\n\t\t\tpTemp[5] = DESCALE(tmp12 - btmp1, CONST_BITS-PASS1_BITS);\n\t\t\tpTemp[3] = DESCALE(tmp13 + btmp0, CONST_BITS-PASS1_BITS);\n\t\t\tpTemp[4] = DESCALE(tmp13 - btmp0, CONST_BITS-PASS1_BITS);\n\t\t}\n\t};\n\n\ttemplate <>\n\tstruct Row<0>\n\t{\n\t\tstatic void idct(int* pTemp, const jpgd_block_t* pSrc)\n\t\t{\n#ifdef _MSC_VER\n\t\t\tpTemp; pSrc;\n#endif\n\t\t}\n\t};\n\n\ttemplate <>\n\tstruct Row<1>\n\t{\n\t\tstatic void idct(int* pTemp, const jpgd_block_t* pSrc)\n\t\t{\n\t\t\tconst int dcval = (pSrc[0] << PASS1_BITS);\n\n\t\t\tpTemp[0] = dcval;\n\t\t\tpTemp[1] = dcval;\n\t\t\tpTemp[2] = dcval;\n\t\t\tpTemp[3] = dcval;\n\t\t\tpTemp[4] = dcval;\n\t\t\tpTemp[5] = dcval;\n\t\t\tpTemp[6] = dcval;\n\t\t\tpTemp[7] = dcval;\n\t\t}\n\t};\n\n\t// Compiler creates a fast path 1D IDCT for X non-zero rows\n\ttemplate <int NONZERO_ROWS>\n\tstruct Col\n\t{\n\t\tstatic void idct(uint8* pDst_ptr, const int* pTemp)\n\t\t{\n\t\t\t// ACCESS_ROW() will be optimized at compile time to either an array access, or 0.\n#define ACCESS_ROW(x) (((x) < NONZERO_ROWS) ? pTemp[x * 8] : 0)\n\n\t\t\tconst int z2 = ACCESS_ROW(2);\n\t\t\tconst int z3 = ACCESS_ROW(6);\n\n\t\t\tconst int z1 = MULTIPLY(z2 + z3, FIX_0_541196100);\n\t\t\tconst int tmp2 = z1 + MULTIPLY(z3, - FIX_1_847759065);\n\t\t\tconst int tmp3 = z1 + MULTIPLY(z2, FIX_0_765366865);\n\n\t\t\tconst int tmp0 = (ACCESS_ROW(0) + ACCESS_ROW(4)) << CONST_BITS;\n\t\t\tconst int tmp1 = (ACCESS_ROW(0) - ACCESS_ROW(4)) << CONST_BITS;\n\n\t\t\tconst int tmp10 = tmp0 + tmp3, tmp13 = tmp0 - tmp3, tmp11 = tmp1 + tmp2, tmp12 = tmp1 - tmp2;\n\n\t\t\tconst int atmp0 = ACCESS_ROW(7), atmp1 = ACCESS_ROW(5), atmp2 = ACCESS_ROW(3), atmp3 = ACCESS_ROW(1);\n\n\t\t\tconst int bz1 = atmp0 + atmp3, bz2 = atmp1 + atmp2, bz3 = atmp0 + atmp2, bz4 = atmp1 + atmp3;\n\t\t\tconst int bz5 = MULTIPLY(bz3 + bz4, FIX_1_175875602);\n\n\t\t\tconst int az1 = MULTIPLY(bz1, - FIX_0_899976223);\n\t\t\tconst int az2 = MULTIPLY(bz2, - FIX_2_562915447);\n\t\t\tconst int az3 = MULTIPLY(bz3, - FIX_1_961570560) + bz5;\n\t\t\tconst int az4 = MULTIPLY(bz4, - FIX_0_390180644) + bz5;\n\n\t\t\tconst int btmp0 = MULTIPLY(atmp0, FIX_0_298631336) + az1 + az3;\n\t\t\tconst int btmp1 = MULTIPLY(atmp1, FIX_2_053119869) + az2 + az4;\n\t\t\tconst int btmp2 = MULTIPLY(atmp2, FIX_3_072711026) + az2 + az3;\n\t\t\tconst int btmp3 = MULTIPLY(atmp3, FIX_1_501321110) + az1 + az4;\n\n\t\t\tint i = DESCALE_ZEROSHIFT(tmp10 + btmp3, CONST_BITS+PASS1_BITS+3);\n\t\t\tpDst_ptr[8*0] = (uint8)CLAMP(i);\n\n\t\t\ti = DESCALE_ZEROSHIFT(tmp10 - btmp3, CONST_BITS+PASS1_BITS+3);\n\t\t\tpDst_ptr[8*7] = (uint8)CLAMP(i);\n\n\t\t\ti = DESCALE_ZEROSHIFT(tmp11 + btmp2, CONST_BITS+PASS1_BITS+3);\n\t\t\tpDst_ptr[8*1] = (uint8)CLAMP(i);\n\n\t\t\ti = DESCALE_ZEROSHIFT(tmp11 - btmp2, CONST_BITS+PASS1_BITS+3);\n\t\t\tpDst_ptr[8*6] = (uint8)CLAMP(i);\n\n\t\t\ti = DESCALE_ZEROSHIFT(tmp12 + btmp1, CONST_BITS+PASS1_BITS+3);\n\t\t\tpDst_ptr[8*2] = (uint8)CLAMP(i);\n\n\t\t\ti = DESCALE_ZEROSHIFT(tmp12 - btmp1, CONST_BITS+PASS1_BITS+3);\n\t\t\tpDst_ptr[8*5] = (uint8)CLAMP(i);\n\n\t\t\ti = DESCALE_ZEROSHIFT(tmp13 + btmp0, CONST_BITS+PASS1_BITS+3);\n\t\t\tpDst_ptr[8*3] = (uint8)CLAMP(i);\n\n\t\t\ti = DESCALE_ZEROSHIFT(tmp13 - btmp0, CONST_BITS+PASS1_BITS+3);\n\t\t\tpDst_ptr[8*4] = (uint8)CLAMP(i);\n\t\t}\n\t};\n\n\ttemplate <>\n\tstruct Col<1>\n\t{\n\t\tstatic void idct(uint8* pDst_ptr, const int* pTemp)\n\t\t{\n\t\t\tint dcval = DESCALE_ZEROSHIFT(pTemp[0], PASS1_BITS+3);\n\t\t\tconst uint8 dcval_clamped = (uint8)CLAMP(dcval);\n\t\t\tpDst_ptr[0*8] = dcval_clamped;\n\t\t\tpDst_ptr[1*8] = dcval_clamped;\n\t\t\tpDst_ptr[2*8] = dcval_clamped;\n\t\t\tpDst_ptr[3*8] = dcval_clamped;\n\t\t\tpDst_ptr[4*8] = dcval_clamped;\n\t\t\tpDst_ptr[5*8] = dcval_clamped;\n\t\t\tpDst_ptr[6*8] = dcval_clamped;\n\t\t\tpDst_ptr[7*8] = dcval_clamped;\n\t\t}\n\t};\n\n\tstatic const uint8 s_idct_row_table[] =\n\t{\n\t\t1,0,0,0,0,0,0,0, 2,0,0,0,0,0,0,0, 2,1,0,0,0,0,0,0, 2,1,1,0,0,0,0,0, 2,2,1,0,0,0,0,0, 3,2,1,0,0,0,0,0, 4,2,1,0,0,0,0,0, 4,3,1,0,0,0,0,0,\n\t\t4,3,2,0,0,0,0,0, 4,3,2,1,0,0,0,0, 4,3,2,1,1,0,0,0, 4,3,2,2,1,0,0,0, 4,3,3,2,1,0,0,0, 4,4,3,2,1,0,0,0, 5,4,3,2,1,0,0,0, 6,4,3,2,1,0,0,0,\n\t\t6,5,3,2,1,0,0,0, 6,5,4,2,1,0,0,0, 6,5,4,3,1,0,0,0, 6,5,4,3,2,0,0,0, 6,5,4,3,2,1,0,0, 6,5,4,3,2,1,1,0, 6,5,4,3,2,2,1,0, 6,5,4,3,3,2,1,0,\n\t\t6,5,4,4,3,2,1,0, 6,5,5,4,3,2,1,0, 6,6,5,4,3,2,1,0, 7,6,5,4,3,2,1,0, 8,6,5,4,3,2,1,0, 8,7,5,4,3,2,1,0, 8,7,6,4,3,2,1,0, 8,7,6,5,3,2,1,0,\n\t\t8,7,6,5,4,2,1,0, 8,7,6,5,4,3,1,0, 8,7,6,5,4,3,2,0, 8,7,6,5,4,3,2,1, 8,7,6,5,4,3,2,2, 8,7,6,5,4,3,3,2, 8,7,6,5,4,4,3,2, 8,7,6,5,5,4,3,2,\n\t\t8,7,6,6,5,4,3,2, 8,7,7,6,5,4,3,2, 8,8,7,6,5,4,3,2, 8,8,8,6,5,4,3,2, 8,8,8,7,5,4,3,2, 8,8,8,7,6,4,3,2, 8,8,8,7,6,5,3,2, 8,8,8,7,6,5,4,2,\n\t\t8,8,8,7,6,5,4,3, 8,8,8,7,6,5,4,4, 8,8,8,7,6,5,5,4, 8,8,8,7,6,6,5,4, 8,8,8,7,7,6,5,4, 8,8,8,8,7,6,5,4, 8,8,8,8,8,6,5,4, 8,8,8,8,8,7,5,4,\n\t\t8,8,8,8,8,7,6,4, 8,8,8,8,8,7,6,5, 8,8,8,8,8,7,6,6, 8,8,8,8,8,7,7,6, 8,8,8,8,8,8,7,6, 8,8,8,8,8,8,8,6, 8,8,8,8,8,8,8,7, 8,8,8,8,8,8,8,8,\n\t};\n\n\tstatic const uint8 s_idct_col_table[] = { 1, 1, 2, 3, 3, 3, 3, 3, 3, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8 };\n\n\tvoid idct(const jpgd_block_t* pSrc_ptr, uint8* pDst_ptr, int block_max_zag)\n\t{\n\t\tJPGD_ASSERT(block_max_zag >= 1);\n\t\tJPGD_ASSERT(block_max_zag <= 64);\n\n\t\tif (block_max_zag == 1)\n\t\t{\n\t\t\tint k = ((pSrc_ptr[0] + 4) >> 3) + 128;\n\t\t\tk = CLAMP(k);\n\t\t\tk = k | (k<<8);\n\t\t\tk = k | (k<<16);\n\n\t\t\tfor (int i = 8; i > 0; i--)\n\t\t\t{\n\t\t\t\t*(int*)&pDst_ptr[0] = k;\n\t\t\t\t*(int*)&pDst_ptr[4] = k;\n\t\t\t\tpDst_ptr += 8;\n\t\t\t}\n\t\t\treturn;\n\t\t}\n\n\t\tint temp[64];\n\n\t\tconst jpgd_block_t* pSrc = pSrc_ptr;\n\t\tint* pTemp = temp;\n\n\t\tconst uint8* pRow_tab = &s_idct_row_table[(block_max_zag - 1) * 8];\n\t\tint i;\n\t\tfor (i = 8; i > 0; i--, pRow_tab++)\n\t\t{\n\t\t\tswitch (*pRow_tab)\n\t\t\t{\n\t\t\tcase 0: Row<0>::idct(pTemp, pSrc); break;\n\t\t\tcase 1: Row<1>::idct(pTemp, pSrc); break;\n\t\t\tcase 2: Row<2>::idct(pTemp, pSrc); break;\n\t\t\tcase 3: Row<3>::idct(pTemp, pSrc); break;\n\t\t\tcase 4: Row<4>::idct(pTemp, pSrc); break;\n\t\t\tcase 5: Row<5>::idct(pTemp, pSrc); break;\n\t\t\tcase 6: Row<6>::idct(pTemp, pSrc); break;\n\t\t\tcase 7: Row<7>::idct(pTemp, pSrc); break;\n\t\t\tcase 8: Row<8>::idct(pTemp, pSrc); break;\n\t\t\t}\n\n\t\t\tpSrc += 8;\n\t\t\tpTemp += 8;\n\t\t}\n\n\t\tpTemp = temp;\n\n\t\tconst int nonzero_rows = s_idct_col_table[block_max_zag - 1];\n\t\tfor (i = 8; i > 0; i--)\n\t\t{\n\t\t\tswitch (nonzero_rows)\n\t\t\t{\n\t\t\tcase 1: Col<1>::idct(pDst_ptr, pTemp); break;\n\t\t\tcase 2: Col<2>::idct(pDst_ptr, pTemp); break;\n\t\t\tcase 3: Col<3>::idct(pDst_ptr, pTemp); break;\n\t\t\tcase 4: Col<4>::idct(pDst_ptr, pTemp); break;\n\t\t\tcase 5: Col<5>::idct(pDst_ptr, pTemp); break;\n\t\t\tcase 6: Col<6>::idct(pDst_ptr, pTemp); break;\n\t\t\tcase 7: Col<7>::idct(pDst_ptr, pTemp); break;\n\t\t\tcase 8: Col<8>::idct(pDst_ptr, pTemp); break;\n\t\t\t}\n\n\t\t\tpTemp++;\n\t\t\tpDst_ptr++;\n\t\t}\n\t}\n\n\tvoid idct_4x4(const jpgd_block_t* pSrc_ptr, uint8* pDst_ptr)\n\t{\n\t\tint temp[64];\n\t\tint* pTemp = temp;\n\t\tconst jpgd_block_t* pSrc = pSrc_ptr;\n\n\t\tfor (int i = 4; i > 0; i--)\n\t\t{\n\t\t\tRow<4>::idct(pTemp, pSrc);\n\t\t\tpSrc += 8;\n\t\t\tpTemp += 8;\n\t\t}\n\n\t\tpTemp = temp;\n\t\tfor (int i = 8; i > 0; i--)\n\t\t{\n\t\t\tCol<4>::idct(pDst_ptr, pTemp);\n\t\t\tpTemp++;\n\t\t\tpDst_ptr++;\n\t\t}\n\t}\n\n\t// Retrieve one character from the input stream.\n\tinline uint jpeg_decoder::get_char()\n\t{\n\t\t// Any bytes remaining in buffer?\n\t\tif (!m_in_buf_left)\n\t\t{\n\t\t\t// Try to get more bytes.\n\t\t\tprep_in_buffer();\n\t\t\t// Still nothing to get?\n\t\t\tif (!m_in_buf_left)\n\t\t\t{\n\t\t\t\t// Pad the end of the stream with 0xFF 0xD9 (EOI marker)\n\t\t\t\tint t = m_tem_flag;\n\t\t\t\tm_tem_flag ^= 1;\n\t\t\t\tif (t)\n\t\t\t\t\treturn 0xD9;\n\t\t\t\telse\n\t\t\t\t\treturn 0xFF;\n\t\t\t}\n\t\t}\n\n\t\tuint c = *m_pIn_buf_ofs++;\n\t\tm_in_buf_left--;\n\n\t\treturn c;\n\t}\n\n\t// Same as previous method, except can indicate if the character is a pad character or not.\n\tinline uint jpeg_decoder::get_char(bool *pPadding_flag)\n\t{\n\t\tif (!m_in_buf_left)\n\t\t{\n\t\t\tprep_in_buffer();\n\t\t\tif (!m_in_buf_left)\n\t\t\t{\n\t\t\t\t*pPadding_flag = true;\n\t\t\t\tint t = m_tem_flag;\n\t\t\t\tm_tem_flag ^= 1;\n\t\t\t\tif (t)\n\t\t\t\t\treturn 0xD9;\n\t\t\t\telse\n\t\t\t\t\treturn 0xFF;\n\t\t\t}\n\t\t}\n\n\t\t*pPadding_flag = false;\n\n\t\tuint c = *m_pIn_buf_ofs++;\n\t\tm_in_buf_left--;\n\n\t\treturn c;\n\t}\n\n\t// Inserts a previously retrieved character back into the input buffer.\n\tinline void jpeg_decoder::stuff_char(uint8 q)\n\t{\n\t\t*(--m_pIn_buf_ofs) = q;\n\t\tm_in_buf_left++;\n\t}\n\n\t// Retrieves one character from the input stream, but does not read past markers. Will continue to return 0xFF when a marker is encountered.\n\tinline uint8 jpeg_decoder::get_octet()\n\t{\n\t\tbool padding_flag;\n\t\tint c = get_char(&padding_flag);\n\n\t\tif (c == 0xFF)\n\t\t{\n\t\t\tif (padding_flag)\n\t\t\t\treturn 0xFF;\n\n\t\t\tc = get_char(&padding_flag);\n\t\t\tif (padding_flag)\n\t\t\t{\n\t\t\t\tstuff_char(0xFF);\n\t\t\t\treturn 0xFF;\n\t\t\t}\n\n\t\t\tif (c == 0x00)\n\t\t\t\treturn 0xFF;\n\t\t\telse\n\t\t\t{\n\t\t\t\tstuff_char(static_cast<uint8>(c));\n\t\t\t\tstuff_char(0xFF);\n\t\t\t\treturn 0xFF;\n\t\t\t}\n\t\t}\n\n\t\treturn static_cast<uint8>(c);\n\t}\n\n\t// Retrieves a variable number of bits from the input stream. Does not recognize markers.\n\tinline uint jpeg_decoder::get_bits(int num_bits)\n\t{\n\t\tif (!num_bits)\n\t\t\treturn 0;\n\n\t\tuint i = m_bit_buf >> (32 - num_bits);\n\n\t\tif ((m_bits_left -= num_bits) <= 0)\n\t\t{\n\t\t\tm_bit_buf <<= (num_bits += m_bits_left);\n\n\t\t\tuint c1 = get_char();\n\t\t\tuint c2 = get_char();\n\t\t\tm_bit_buf = (m_bit_buf & 0xFFFF0000) | (c1 << 8) | c2;\n\n\t\t\tm_bit_buf <<= -m_bits_left;\n\n\t\t\tm_bits_left += 16;\n\n\t\t\tJPGD_ASSERT(m_bits_left >= 0);\n\t\t}\n\t\telse\n\t\t\tm_bit_buf <<= num_bits;\n\n\t\treturn i;\n\t}\n\n\t// Retrieves a variable number of bits from the input stream. Markers will not be read into the input bit buffer. Instead, an infinite number of all 1's will be returned when a marker is encountered.\n\tinline uint jpeg_decoder::get_bits_no_markers(int num_bits)\n\t{\n\t\tif (!num_bits)\n\t\t\treturn 0;\n\n\t\tuint i = m_bit_buf >> (32 - num_bits);\n\n\t\tif ((m_bits_left -= num_bits) <= 0)\n\t\t{\n\t\t\tm_bit_buf <<= (num_bits += m_bits_left);\n\n\t\t\tif ((m_in_buf_left < 2) || (m_pIn_buf_ofs[0] == 0xFF) || (m_pIn_buf_ofs[1] == 0xFF))\n\t\t\t{\n\t\t\t\tuint c1 = get_octet();\n\t\t\t\tuint c2 = get_octet();\n\t\t\t\tm_bit_buf |= (c1 << 8) | c2;\n\t\t\t}\n\t\t\telse\n\t\t\t{\n\t\t\t\tm_bit_buf |= ((uint)m_pIn_buf_ofs[0] << 8) | m_pIn_buf_ofs[1];\n\t\t\t\tm_in_buf_left -= 2;\n\t\t\t\tm_pIn_buf_ofs += 2;\n\t\t\t}\n\n\t\t\tm_bit_buf <<= -m_bits_left;\n\n\t\t\tm_bits_left += 16;\n\n\t\t\tJPGD_ASSERT(m_bits_left >= 0);\n\t\t}\n\t\telse\n\t\t\tm_bit_buf <<= num_bits;\n\n\t\treturn i;\n\t}\n\n\t// Decodes a Huffman encoded symbol.\n\tinline int jpeg_decoder::huff_decode(huff_tables *pH)\n\t{\n\t\tint symbol;\n\n\t\t// Check first 8-bits: do we have a complete symbol?\n\t\tif ((symbol = pH->look_up[m_bit_buf >> 24]) < 0)\n\t\t{\n\t\t\t// Decode more bits, use a tree traversal to find symbol.\n\t\t\tint ofs = 23;\n\t\t\tdo\n\t\t\t{\n\t\t\t\tsymbol = pH->tree[-(int)(symbol + ((m_bit_buf >> ofs) & 1))];\n\t\t\t\tofs--;\n\t\t\t} while (symbol < 0);\n\n\t\t\tget_bits_no_markers(8 + (23 - ofs));\n\t\t}\n\t\telse\n\t\t\tget_bits_no_markers(pH->code_size[symbol]);\n\n\t\treturn symbol;\n\t}\n\n\t// Decodes a Huffman encoded symbol.\n\tinline int jpeg_decoder::huff_decode(huff_tables *pH, int& extra_bits)\n\t{\n\t\tint symbol;\n\n\t\t// Check first 8-bits: do we have a complete symbol?\n\t\tif ((symbol = pH->look_up2[m_bit_buf >> 24]) < 0)\n\t\t{\n\t\t\t// Use a tree traversal to find symbol.\n\t\t\tint ofs = 23;\n\t\t\tdo\n\t\t\t{\n\t\t\t\tsymbol = pH->tree[-(int)(symbol + ((m_bit_buf >> ofs) & 1))];\n\t\t\t\tofs--;\n\t\t\t} while (symbol < 0);\n\n\t\t\tget_bits_no_markers(8 + (23 - ofs));\n\n\t\t\textra_bits = get_bits_no_markers(symbol & 0xF);\n\t\t}\n\t\telse\n\t\t{\n\t\t\tJPGD_ASSERT(((symbol >> 8) & 31) == pH->code_size[symbol & 255] + ((symbol & 0x8000) ? (symbol & 15) : 0));\n\n\t\t\tif (symbol & 0x8000)\n\t\t\t{\n\t\t\t\tget_bits_no_markers((symbol >> 8) & 31);\n\t\t\t\textra_bits = symbol >> 16;\n\t\t\t}\n\t\t\telse\n\t\t\t{\n\t\t\t\tint code_size = (symbol >> 8) & 31;\n\t\t\t\tint num_extra_bits = symbol & 0xF;\n\t\t\t\tint bits = code_size + num_extra_bits;\n\t\t\t\tif (bits <= (m_bits_left + 16))\n\t\t\t\t\textra_bits = get_bits_no_markers(bits) & ((1 << num_extra_bits) - 1);\n\t\t\t\telse\n\t\t\t\t{\n\t\t\t\t\tget_bits_no_markers(code_size);\n\t\t\t\t\textra_bits = get_bits_no_markers(num_extra_bits);\n\t\t\t\t}\n\t\t\t}\n\n\t\t\tsymbol &= 0xFF;\n\t\t}\n\n\t\treturn symbol;\n\t}\n\n\t// Tables and macro used to fully decode the DPCM differences.\n\tstatic const int s_extend_test[16] = { 0, 0x0001, 0x0002, 0x0004, 0x0008, 0x0010, 0x0020, 0x0040, 0x0080, 0x0100, 0x0200, 0x0400, 0x0800, 0x1000, 0x2000, 0x4000 };\n\tstatic const int s_extend_offset[16] = { 0, -1, -3, -7, -15, -31, -63, -127, -255, -511, -1023, -2047, -4095, -8191, -16383, -32767 };\n\tstatic const int s_extend_mask[] = { 0, (1<<0), (1<<1), (1<<2), (1<<3), (1<<4), (1<<5), (1<<6), (1<<7), (1<<8), (1<<9), (1<<10), (1<<11), (1<<12), (1<<13), (1<<14), (1<<15), (1<<16) };\n#define HUFF_EXTEND(x,s) ((x) < s_extend_test[s] ? (x) + s_extend_offset[s] : (x))\n\n\t// Clamps a value between 0-255.\n\tinline uint8 jpeg_decoder::clamp(int i)\n\t{\n\t\tif (static_cast<uint>(i) > 255)\n\t\t\ti = (((~i) >> 31) & 0xFF);\n\n\t\treturn static_cast<uint8>(i);\n\t}\n\n\tnamespace DCT_Upsample\n\t{\n\t\tstruct Matrix44\n\t\t{\n\t\t\ttypedef int Element_Type;\n\t\t\tenum { NUM_ROWS = 4, NUM_COLS = 4 };\n\n\t\t\tElement_Type v[NUM_ROWS][NUM_COLS];\n\n\t\t\tinline int rows() const { return NUM_ROWS; }\n\t\t\tinline int cols() const { return NUM_COLS; }\n\n\t\t\tinline const Element_Type & at(int r, int c) const { return v[r][c]; }\n\t\t\tinline       Element_Type & at(int r, int c)       { return v[r][c]; }\n\n\t\t\tinline Matrix44() { }\n\n\t\t\tinline Matrix44& operator += (const Matrix44& a)\n\t\t\t{\n\t\t\t\tfor (int r = 0; r < NUM_ROWS; r++)\n\t\t\t\t{\n\t\t\t\t\tat(r, 0) += a.at(r, 0);\n\t\t\t\t\tat(r, 1) += a.at(r, 1);\n\t\t\t\t\tat(r, 2) += a.at(r, 2);\n\t\t\t\t\tat(r, 3) += a.at(r, 3);\n\t\t\t\t}\n\t\t\t\treturn *this;\n\t\t\t}\n\n\t\t\tinline Matrix44& operator -= (const Matrix44& a)\n\t\t\t{\n\t\t\t\tfor (int r = 0; r < NUM_ROWS; r++)\n\t\t\t\t{\n\t\t\t\t\tat(r, 0) -= a.at(r, 0);\n\t\t\t\t\tat(r, 1) -= a.at(r, 1);\n\t\t\t\t\tat(r, 2) -= a.at(r, 2);\n\t\t\t\t\tat(r, 3) -= a.at(r, 3);\n\t\t\t\t}\n\t\t\t\treturn *this;\n\t\t\t}\n\n\t\t\tfriend inline Matrix44 operator + (const Matrix44& a, const Matrix44& b)\n\t\t\t{\n\t\t\t\tMatrix44 ret;\n\t\t\t\tfor (int r = 0; r < NUM_ROWS; r++)\n\t\t\t\t{\n\t\t\t\t\tret.at(r, 0) = a.at(r, 0) + b.at(r, 0);\n\t\t\t\t\tret.at(r, 1) = a.at(r, 1) + b.at(r, 1);\n\t\t\t\t\tret.at(r, 2) = a.at(r, 2) + b.at(r, 2);\n\t\t\t\t\tret.at(r, 3) = a.at(r, 3) + b.at(r, 3);\n\t\t\t\t}\n\t\t\t\treturn ret;\n\t\t\t}\n\n\t\t\tfriend inline Matrix44 operator - (const Matrix44& a, const Matrix44& b)\n\t\t\t{\n\t\t\t\tMatrix44 ret;\n\t\t\t\tfor (int r = 0; r < NUM_ROWS; r++)\n\t\t\t\t{\n\t\t\t\t\tret.at(r, 0) = a.at(r, 0) - b.at(r, 0);\n\t\t\t\t\tret.at(r, 1) = a.at(r, 1) - b.at(r, 1);\n\t\t\t\t\tret.at(r, 2) = a.at(r, 2) - b.at(r, 2);\n\t\t\t\t\tret.at(r, 3) = a.at(r, 3) - b.at(r, 3);\n\t\t\t\t}\n\t\t\t\treturn ret;\n\t\t\t}\n\n\t\t\tstatic inline void add_and_store(jpgd_block_t* pDst, const Matrix44& a, const Matrix44& b)\n\t\t\t{\n\t\t\t\tfor (int r = 0; r < 4; r++)\n\t\t\t\t{\n\t\t\t\t\tpDst[0*8 + r] = static_cast<jpgd_block_t>(a.at(r, 0) + b.at(r, 0));\n\t\t\t\t\tpDst[1*8 + r] = static_cast<jpgd_block_t>(a.at(r, 1) + b.at(r, 1));\n\t\t\t\t\tpDst[2*8 + r] = static_cast<jpgd_block_t>(a.at(r, 2) + b.at(r, 2));\n\t\t\t\t\tpDst[3*8 + r] = static_cast<jpgd_block_t>(a.at(r, 3) + b.at(r, 3));\n\t\t\t\t}\n\t\t\t}\n\n\t\t\tstatic inline void sub_and_store(jpgd_block_t* pDst, const Matrix44& a, const Matrix44& b)\n\t\t\t{\n\t\t\t\tfor (int r = 0; r < 4; r++)\n\t\t\t\t{\n\t\t\t\t\tpDst[0*8 + r] = static_cast<jpgd_block_t>(a.at(r, 0) - b.at(r, 0));\n\t\t\t\t\tpDst[1*8 + r] = static_cast<jpgd_block_t>(a.at(r, 1) - b.at(r, 1));\n\t\t\t\t\tpDst[2*8 + r] = static_cast<jpgd_block_t>(a.at(r, 2) - b.at(r, 2));\n\t\t\t\t\tpDst[3*8 + r] = static_cast<jpgd_block_t>(a.at(r, 3) - b.at(r, 3));\n\t\t\t\t}\n\t\t\t}\n\t\t};\n\n\t\tconst int FRACT_BITS = 10;\n\t\tconst int SCALE = 1 << FRACT_BITS;\n\n\t\ttypedef int Temp_Type;\n#define D(i) (((i) + (SCALE >> 1)) >> FRACT_BITS)\n#define F(i) ((int)((i) * SCALE + .5f))\n\n\t\t// Any decent C++ compiler will optimize this at compile time to a 0, or an array access.\n#define AT(c, r) ((((c)>=NUM_COLS)||((r)>=NUM_ROWS)) ? 0 : pSrc[(c)+(r)*8])\n\n\t\t// NUM_ROWS/NUM_COLS = # of non-zero rows/cols in input matrix\n\t\ttemplate<int NUM_ROWS, int NUM_COLS>\n\t\tstruct P_Q\n\t\t{\n\t\t\tstatic void calc(Matrix44& P, Matrix44& Q, const jpgd_block_t* pSrc)\n\t\t\t{\n\t\t\t\t// 4x8 = 4x8 times 8x8, matrix 0 is constant\n\t\t\t\tconst Temp_Type X000 = AT(0, 0);\n\t\t\t\tconst Temp_Type X001 = AT(0, 1);\n\t\t\t\tconst Temp_Type X002 = AT(0, 2);\n\t\t\t\tconst Temp_Type X003 = AT(0, 3);\n\t\t\t\tconst Temp_Type X004 = AT(0, 4);\n\t\t\t\tconst Temp_Type X005 = AT(0, 5);\n\t\t\t\tconst Temp_Type X006 = AT(0, 6);\n\t\t\t\tconst Temp_Type X007 = AT(0, 7);\n\t\t\t\tconst Temp_Type X010 = D(F(0.415735f) * AT(1, 0) + F(0.791065f) * AT(3, 0) + F(-0.352443f) * AT(5, 0) + F(0.277785f) * AT(7, 0));\n\t\t\t\tconst Temp_Type X011 = D(F(0.415735f) * AT(1, 1) + F(0.791065f) * AT(3, 1) + F(-0.352443f) * AT(5, 1) + F(0.277785f) * AT(7, 1));\n\t\t\t\tconst Temp_Type X012 = D(F(0.415735f) * AT(1, 2) + F(0.791065f) * AT(3, 2) + F(-0.352443f) * AT(5, 2) + F(0.277785f) * AT(7, 2));\n\t\t\t\tconst Temp_Type X013 = D(F(0.415735f) * AT(1, 3) + F(0.791065f) * AT(3, 3) + F(-0.352443f) * AT(5, 3) + F(0.277785f) * AT(7, 3));\n\t\t\t\tconst Temp_Type X014 = D(F(0.415735f) * AT(1, 4) + F(0.791065f) * AT(3, 4) + F(-0.352443f) * AT(5, 4) + F(0.277785f) * AT(7, 4));\n\t\t\t\tconst Temp_Type X015 = D(F(0.415735f) * AT(1, 5) + F(0.791065f) * AT(3, 5) + F(-0.352443f) * AT(5, 5) + F(0.277785f) * AT(7, 5));\n\t\t\t\tconst Temp_Type X016 = D(F(0.415735f) * AT(1, 6) + F(0.791065f) * AT(3, 6) + F(-0.352443f) * AT(5, 6) + F(0.277785f) * AT(7, 6));\n\t\t\t\tconst Temp_Type X017 = D(F(0.415735f) * AT(1, 7) + F(0.791065f) * AT(3, 7) + F(-0.352443f) * AT(5, 7) + F(0.277785f) * AT(7, 7));\n\t\t\t\tconst Temp_Type X020 = AT(4, 0);\n\t\t\t\tconst Temp_Type X021 = AT(4, 1);\n\t\t\t\tconst Temp_Type X022 = AT(4, 2);\n\t\t\t\tconst Temp_Type X023 = AT(4, 3);\n\t\t\t\tconst Temp_Type X024 = AT(4, 4);\n\t\t\t\tconst Temp_Type X025 = AT(4, 5);\n\t\t\t\tconst Temp_Type X026 = AT(4, 6);\n\t\t\t\tconst Temp_Type X027 = AT(4, 7);\n\t\t\t\tconst Temp_Type X030 = D(F(0.022887f) * AT(1, 0) + F(-0.097545f) * AT(3, 0) + F(0.490393f) * AT(5, 0) + F(0.865723f) * AT(7, 0));\n\t\t\t\tconst Temp_Type X031 = D(F(0.022887f) * AT(1, 1) + F(-0.097545f) * AT(3, 1) + F(0.490393f) * AT(5, 1) + F(0.865723f) * AT(7, 1));\n\t\t\t\tconst Temp_Type X032 = D(F(0.022887f) * AT(1, 2) + F(-0.097545f) * AT(3, 2) + F(0.490393f) * AT(5, 2) + F(0.865723f) * AT(7, 2));\n\t\t\t\tconst Temp_Type X033 = D(F(0.022887f) * AT(1, 3) + F(-0.097545f) * AT(3, 3) + F(0.490393f) * AT(5, 3) + F(0.865723f) * AT(7, 3));\n\t\t\t\tconst Temp_Type X034 = D(F(0.022887f) * AT(1, 4) + F(-0.097545f) * AT(3, 4) + F(0.490393f) * AT(5, 4) + F(0.865723f) * AT(7, 4));\n\t\t\t\tconst Temp_Type X035 = D(F(0.022887f) * AT(1, 5) + F(-0.097545f) * AT(3, 5) + F(0.490393f) * AT(5, 5) + F(0.865723f) * AT(7, 5));\n\t\t\t\tconst Temp_Type X036 = D(F(0.022887f) * AT(1, 6) + F(-0.097545f) * AT(3, 6) + F(0.490393f) * AT(5, 6) + F(0.865723f) * AT(7, 6));\n\t\t\t\tconst Temp_Type X037 = D(F(0.022887f) * AT(1, 7) + F(-0.097545f) * AT(3, 7) + F(0.490393f) * AT(5, 7) + F(0.865723f) * AT(7, 7));\n\n\t\t\t\t// 4x4 = 4x8 times 8x4, matrix 1 is constant\n\t\t\t\tP.at(0, 0) = X000;\n\t\t\t\tP.at(0, 1) = D(X001 * F(0.415735f) + X003 * F(0.791065f) + X005 * F(-0.352443f) + X007 * F(0.277785f));\n\t\t\t\tP.at(0, 2) = X004;\n\t\t\t\tP.at(0, 3) = D(X001 * F(0.022887f) + X003 * F(-0.097545f) + X005 * F(0.490393f) + X007 * F(0.865723f));\n\t\t\t\tP.at(1, 0) = X010;\n\t\t\t\tP.at(1, 1) = D(X011 * F(0.415735f) + X013 * F(0.791065f) + X015 * F(-0.352443f) + X017 * F(0.277785f));\n\t\t\t\tP.at(1, 2) = X014;\n\t\t\t\tP.at(1, 3) = D(X011 * F(0.022887f) + X013 * F(-0.097545f) + X015 * F(0.490393f) + X017 * F(0.865723f));\n\t\t\t\tP.at(2, 0) = X020;\n\t\t\t\tP.at(2, 1) = D(X021 * F(0.415735f) + X023 * F(0.791065f) + X025 * F(-0.352443f) + X027 * F(0.277785f));\n\t\t\t\tP.at(2, 2) = X024;\n\t\t\t\tP.at(2, 3) = D(X021 * F(0.022887f) + X023 * F(-0.097545f) + X025 * F(0.490393f) + X027 * F(0.865723f));\n\t\t\t\tP.at(3, 0) = X030;\n\t\t\t\tP.at(3, 1) = D(X031 * F(0.415735f) + X033 * F(0.791065f) + X035 * F(-0.352443f) + X037 * F(0.277785f));\n\t\t\t\tP.at(3, 2) = X034;\n\t\t\t\tP.at(3, 3) = D(X031 * F(0.022887f) + X033 * F(-0.097545f) + X035 * F(0.490393f) + X037 * F(0.865723f));\n\t\t\t\t// 40 muls 24 adds\n\n\t\t\t\t// 4x4 = 4x8 times 8x4, matrix 1 is constant\n\t\t\t\tQ.at(0, 0) = D(X001 * F(0.906127f) + X003 * F(-0.318190f) + X005 * F(0.212608f) + X007 * F(-0.180240f));\n\t\t\t\tQ.at(0, 1) = X002;\n\t\t\t\tQ.at(0, 2) = D(X001 * F(-0.074658f) + X003 * F(0.513280f) + X005 * F(0.768178f) + X007 * F(-0.375330f));\n\t\t\t\tQ.at(0, 3) = X006;\n\t\t\t\tQ.at(1, 0) = D(X011 * F(0.906127f) + X013 * F(-0.318190f) + X015 * F(0.212608f) + X017 * F(-0.180240f));\n\t\t\t\tQ.at(1, 1) = X012;\n\t\t\t\tQ.at(1, 2) = D(X011 * F(-0.074658f) + X013 * F(0.513280f) + X015 * F(0.768178f) + X017 * F(-0.375330f));\n\t\t\t\tQ.at(1, 3) = X016;\n\t\t\t\tQ.at(2, 0) = D(X021 * F(0.906127f) + X023 * F(-0.318190f) + X025 * F(0.212608f) + X027 * F(-0.180240f));\n\t\t\t\tQ.at(2, 1) = X022;\n\t\t\t\tQ.at(2, 2) = D(X021 * F(-0.074658f) + X023 * F(0.513280f) + X025 * F(0.768178f) + X027 * F(-0.375330f));\n\t\t\t\tQ.at(2, 3) = X026;\n\t\t\t\tQ.at(3, 0) = D(X031 * F(0.906127f) + X033 * F(-0.318190f) + X035 * F(0.212608f) + X037 * F(-0.180240f));\n\t\t\t\tQ.at(3, 1) = X032;\n\t\t\t\tQ.at(3, 2) = D(X031 * F(-0.074658f) + X033 * F(0.513280f) + X035 * F(0.768178f) + X037 * F(-0.375330f));\n\t\t\t\tQ.at(3, 3) = X036;\n\t\t\t\t// 40 muls 24 adds\n\t\t\t}\n\t\t};\n\n\t\ttemplate<int NUM_ROWS, int NUM_COLS>\n\t\tstruct R_S\n\t\t{\n\t\t\tstatic void calc(Matrix44& R, Matrix44& S, const jpgd_block_t* pSrc)\n\t\t\t{\n\t\t\t\t// 4x8 = 4x8 times 8x8, matrix 0 is constant\n\t\t\t\tconst Temp_Type X100 = D(F(0.906127f) * AT(1, 0) + F(-0.318190f) * AT(3, 0) + F(0.212608f) * AT(5, 0) + F(-0.180240f) * AT(7, 0));\n\t\t\t\tconst Temp_Type X101 = D(F(0.906127f) * AT(1, 1) + F(-0.318190f) * AT(3, 1) + F(0.212608f) * AT(5, 1) + F(-0.180240f) * AT(7, 1));\n\t\t\t\tconst Temp_Type X102 = D(F(0.906127f) * AT(1, 2) + F(-0.318190f) * AT(3, 2) + F(0.212608f) * AT(5, 2) + F(-0.180240f) * AT(7, 2));\n\t\t\t\tconst Temp_Type X103 = D(F(0.906127f) * AT(1, 3) + F(-0.318190f) * AT(3, 3) + F(0.212608f) * AT(5, 3) + F(-0.180240f) * AT(7, 3));\n\t\t\t\tconst Temp_Type X104 = D(F(0.906127f) * AT(1, 4) + F(-0.318190f) * AT(3, 4) + F(0.212608f) * AT(5, 4) + F(-0.180240f) * AT(7, 4));\n\t\t\t\tconst Temp_Type X105 = D(F(0.906127f) * AT(1, 5) + F(-0.318190f) * AT(3, 5) + F(0.212608f) * AT(5, 5) + F(-0.180240f) * AT(7, 5));\n\t\t\t\tconst Temp_Type X106 = D(F(0.906127f) * AT(1, 6) + F(-0.318190f) * AT(3, 6) + F(0.212608f) * AT(5, 6) + F(-0.180240f) * AT(7, 6));\n\t\t\t\tconst Temp_Type X107 = D(F(0.906127f) * AT(1, 7) + F(-0.318190f) * AT(3, 7) + F(0.212608f) * AT(5, 7) + F(-0.180240f) * AT(7, 7));\n\t\t\t\tconst Temp_Type X110 = AT(2, 0);\n\t\t\t\tconst Temp_Type X111 = AT(2, 1);\n\t\t\t\tconst Temp_Type X112 = AT(2, 2);\n\t\t\t\tconst Temp_Type X113 = AT(2, 3);\n\t\t\t\tconst Temp_Type X114 = AT(2, 4);\n\t\t\t\tconst Temp_Type X115 = AT(2, 5);\n\t\t\t\tconst Temp_Type X116 = AT(2, 6);\n\t\t\t\tconst Temp_Type X117 = AT(2, 7);\n\t\t\t\tconst Temp_Type X120 = D(F(-0.074658f) * AT(1, 0) + F(0.513280f) * AT(3, 0) + F(0.768178f) * AT(5, 0) + F(-0.375330f) * AT(7, 0));\n\t\t\t\tconst Temp_Type X121 = D(F(-0.074658f) * AT(1, 1) + F(0.513280f) * AT(3, 1) + F(0.768178f) * AT(5, 1) + F(-0.375330f) * AT(7, 1));\n\t\t\t\tconst Temp_Type X122 = D(F(-0.074658f) * AT(1, 2) + F(0.513280f) * AT(3, 2) + F(0.768178f) * AT(5, 2) + F(-0.375330f) * AT(7, 2));\n\t\t\t\tconst Temp_Type X123 = D(F(-0.074658f) * AT(1, 3) + F(0.513280f) * AT(3, 3) + F(0.768178f) * AT(5, 3) + F(-0.375330f) * AT(7, 3));\n\t\t\t\tconst Temp_Type X124 = D(F(-0.074658f) * AT(1, 4) + F(0.513280f) * AT(3, 4) + F(0.768178f) * AT(5, 4) + F(-0.375330f) * AT(7, 4));\n\t\t\t\tconst Temp_Type X125 = D(F(-0.074658f) * AT(1, 5) + F(0.513280f) * AT(3, 5) + F(0.768178f) * AT(5, 5) + F(-0.375330f) * AT(7, 5));\n\t\t\t\tconst Temp_Type X126 = D(F(-0.074658f) * AT(1, 6) + F(0.513280f) * AT(3, 6) + F(0.768178f) * AT(5, 6) + F(-0.375330f) * AT(7, 6));\n\t\t\t\tconst Temp_Type X127 = D(F(-0.074658f) * AT(1, 7) + F(0.513280f) * AT(3, 7) + F(0.768178f) * AT(5, 7) + F(-0.375330f) * AT(7, 7));\n\t\t\t\tconst Temp_Type X130 = AT(6, 0);\n\t\t\t\tconst Temp_Type X131 = AT(6, 1);\n\t\t\t\tconst Temp_Type X132 = AT(6, 2);\n\t\t\t\tconst Temp_Type X133 = AT(6, 3);\n\t\t\t\tconst Temp_Type X134 = AT(6, 4);\n\t\t\t\tconst Temp_Type X135 = AT(6, 5);\n\t\t\t\tconst Temp_Type X136 = AT(6, 6);\n\t\t\t\tconst Temp_Type X137 = AT(6, 7);\n\t\t\t\t// 80 muls 48 adds\n\n\t\t\t\t// 4x4 = 4x8 times 8x4, matrix 1 is constant\n\t\t\t\tR.at(0, 0) = X100;\n\t\t\t\tR.at(0, 1) = D(X101 * F(0.415735f) + X103 * F(0.791065f) + X105 * F(-0.352443f) + X107 * F(0.277785f));\n\t\t\t\tR.at(0, 2) = X104;\n\t\t\t\tR.at(0, 3) = D(X101 * F(0.022887f) + X103 * F(-0.097545f) + X105 * F(0.490393f) + X107 * F(0.865723f));\n\t\t\t\tR.at(1, 0) = X110;\n\t\t\t\tR.at(1, 1) = D(X111 * F(0.415735f) + X113 * F(0.791065f) + X115 * F(-0.352443f) + X117 * F(0.277785f));\n\t\t\t\tR.at(1, 2) = X114;\n\t\t\t\tR.at(1, 3) = D(X111 * F(0.022887f) + X113 * F(-0.097545f) + X115 * F(0.490393f) + X117 * F(0.865723f));\n\t\t\t\tR.at(2, 0) = X120;\n\t\t\t\tR.at(2, 1) = D(X121 * F(0.415735f) + X123 * F(0.791065f) + X125 * F(-0.352443f) + X127 * F(0.277785f));\n\t\t\t\tR.at(2, 2) = X124;\n\t\t\t\tR.at(2, 3) = D(X121 * F(0.022887f) + X123 * F(-0.097545f) + X125 * F(0.490393f) + X127 * F(0.865723f));\n\t\t\t\tR.at(3, 0) = X130;\n\t\t\t\tR.at(3, 1) = D(X131 * F(0.415735f) + X133 * F(0.791065f) + X135 * F(-0.352443f) + X137 * F(0.277785f));\n\t\t\t\tR.at(3, 2) = X134;\n\t\t\t\tR.at(3, 3) = D(X131 * F(0.022887f) + X133 * F(-0.097545f) + X135 * F(0.490393f) + X137 * F(0.865723f));\n\t\t\t\t// 40 muls 24 adds\n\t\t\t\t// 4x4 = 4x8 times 8x4, matrix 1 is constant\n\t\t\t\tS.at(0, 0) = D(X101 * F(0.906127f) + X103 * F(-0.318190f) + X105 * F(0.212608f) + X107 * F(-0.180240f));\n\t\t\t\tS.at(0, 1) = X102;\n\t\t\t\tS.at(0, 2) = D(X101 * F(-0.074658f) + X103 * F(0.513280f) + X105 * F(0.768178f) + X107 * F(-0.375330f));\n\t\t\t\tS.at(0, 3) = X106;\n\t\t\t\tS.at(1, 0) = D(X111 * F(0.906127f) + X113 * F(-0.318190f) + X115 * F(0.212608f) + X117 * F(-0.180240f));\n\t\t\t\tS.at(1, 1) = X112;\n\t\t\t\tS.at(1, 2) = D(X111 * F(-0.074658f) + X113 * F(0.513280f) + X115 * F(0.768178f) + X117 * F(-0.375330f));\n\t\t\t\tS.at(1, 3) = X116;\n\t\t\t\tS.at(2, 0) = D(X121 * F(0.906127f) + X123 * F(-0.318190f) + X125 * F(0.212608f) + X127 * F(-0.180240f));\n\t\t\t\tS.at(2, 1) = X122;\n\t\t\t\tS.at(2, 2) = D(X121 * F(-0.074658f) + X123 * F(0.513280f) + X125 * F(0.768178f) + X127 * F(-0.375330f));\n\t\t\t\tS.at(2, 3) = X126;\n\t\t\t\tS.at(3, 0) = D(X131 * F(0.906127f) + X133 * F(-0.318190f) + X135 * F(0.212608f) + X137 * F(-0.180240f));\n\t\t\t\tS.at(3, 1) = X132;\n\t\t\t\tS.at(3, 2) = D(X131 * F(-0.074658f) + X133 * F(0.513280f) + X135 * F(0.768178f) + X137 * F(-0.375330f));\n\t\t\t\tS.at(3, 3) = X136;\n\t\t\t\t// 40 muls 24 adds\n\t\t\t}\n\t\t};\n\t} // end namespace DCT_Upsample\n\n\t// Unconditionally frees all allocated m_blocks.\n\tvoid jpeg_decoder::free_all_blocks()\n\t{\n\t\tm_pStream = NULL;\n\t\tfor (mem_block *b = m_pMem_blocks; b; )\n\t\t{\n\t\t\tmem_block *n = b->m_pNext;\n\t\t\tjpgd_free(b);\n\t\t\tb = n;\n\t\t}\n\t\tm_pMem_blocks = NULL;\n\t}\n\n\t// This method handles all errors.\n\t// It could easily be changed to use C++ exceptions.\n\tvoid jpeg_decoder::stop_decoding(jpgd_status status)\n\t{\n\t\tm_error_code = status;\n\t\tfree_all_blocks();\n\t\tlongjmp(m_jmp_state, status);\n\n\t\t// we shouldn't get here as longjmp shouldn't return, but we put it here to make it explicit\n\t\t// that this function doesn't return, otherwise we get this error:\n\t\t// \n\t\t// error : function declared 'noreturn' should not return\n\t\texit(1);\n\t}\n\n\tvoid *jpeg_decoder::alloc(size_t nSize, bool zero)\n\t{\n\t\tnSize = (JPGD_MAX(nSize, 1) + 3) & ~3;\n\t\tchar *rv = NULL;\n\t\tfor (mem_block *b = m_pMem_blocks; b; b = b->m_pNext)\n\t\t{\n\t\t\tif ((b->m_used_count + nSize) <= b->m_size)\n\t\t\t{\n\t\t\t\trv = b->m_data + b->m_used_count;\n\t\t\t\tb->m_used_count += nSize;\n\t\t\t\tbreak;\n\t\t\t}\n\t\t}\n\t\tif (!rv)\n\t\t{\n\t\t\tint capacity = JPGD_MAX(32768 - 256, (nSize + 2047) & ~2047);\n\t\t\tmem_block *b = (mem_block*)jpgd_malloc(sizeof(mem_block) + capacity);\n\t\t\tif (!b) stop_decoding(JPGD_NOTENOUGHMEM);\n\t\t\tb->m_pNext = m_pMem_blocks; m_pMem_blocks = b;\n\t\t\tb->m_used_count = nSize;\n\t\t\tb->m_size = capacity;\n\t\t\trv = b->m_data;\n\t\t}\n\t\tif (zero) memset(rv, 0, nSize);\n\t\treturn rv;\n\t}\n\n\tvoid jpeg_decoder::word_clear(void *p, uint16 c, uint n)\n\t{\n\t\tuint8 *pD = (uint8*)p;\n\t\tconst uint8 l = c & 0xFF, h = (c >> 8) & 0xFF;\n\t\twhile (n)\n\t\t{\n\t\t\tpD[0] = l; pD[1] = h; pD += 2;\n\t\t\tn--;\n\t\t}\n\t}\n\n\t// Refill the input buffer.\n\t// This method will sit in a loop until (A) the buffer is full or (B)\n\t// the stream's read() method reports and end of file condition.\n\tvoid jpeg_decoder::prep_in_buffer()\n\t{\n\t\tm_in_buf_left = 0;\n\t\tm_pIn_buf_ofs = m_in_buf;\n\n\t\tif (m_eof_flag)\n\t\t\treturn;\n\n\t\tdo\n\t\t{\n\t\t\tint bytes_read = m_pStream->read(m_in_buf + m_in_buf_left, JPGD_IN_BUF_SIZE - m_in_buf_left, &m_eof_flag);\n\t\t\tif (bytes_read == -1)\n\t\t\t\tstop_decoding(JPGD_STREAM_READ);\n\n\t\t\tm_in_buf_left += bytes_read;\n\t\t} while ((m_in_buf_left < JPGD_IN_BUF_SIZE) && (!m_eof_flag));\n\n\t\tm_total_bytes_read += m_in_buf_left;\n\n\t\t// Pad the end of the block with M_EOI (prevents the decompressor from going off the rails if the stream is invalid).\n\t\t// (This dates way back to when this decompressor was written in C/asm, and the all-asm Huffman decoder did some fancy things to increase perf.)\n\t\tword_clear(m_pIn_buf_ofs + m_in_buf_left, 0xD9FF, 64);\n\t}\n\n\t// Read a Huffman code table.\n\tvoid jpeg_decoder::read_dht_marker()\n\t{\n\t\tint i, index, count;\n\t\tuint8 huff_num[17];\n\t\tuint8 huff_val[256];\n\n\t\tuint num_left = get_bits(16);\n\n\t\tif (num_left < 2)\n\t\t\tstop_decoding(JPGD_BAD_DHT_MARKER);\n\n\t\tnum_left -= 2;\n\n\t\twhile (num_left)\n\t\t{\n\t\t\tindex = get_bits(8);\n\n\t\t\thuff_num[0] = 0;\n\n\t\t\tcount = 0;\n\n\t\t\tfor (i = 1; i <= 16; i++)\n\t\t\t{\n\t\t\t\thuff_num[i] = static_cast<uint8>(get_bits(8));\n\t\t\t\tcount += huff_num[i];\n\t\t\t}\n\n\t\t\tif (count > 255)\n\t\t\t\tstop_decoding(JPGD_BAD_DHT_COUNTS);\n\n\t\t\tfor (i = 0; i < count; i++)\n\t\t\t\thuff_val[i] = static_cast<uint8>(get_bits(8));\n\n\t\t\ti = 1 + 16 + count;\n\n\t\t\tif (num_left < (uint)i)\n\t\t\t\tstop_decoding(JPGD_BAD_DHT_MARKER);\n\n\t\t\tnum_left -= i;\n\n\t\t\tif ((index & 0x10) > 0x10)\n\t\t\t\tstop_decoding(JPGD_BAD_DHT_INDEX);\n\n\t\t\tindex = (index & 0x0F) + ((index & 0x10) >> 4) * (JPGD_MAX_HUFF_TABLES >> 1);\n\n\t\t\tif (index >= JPGD_MAX_HUFF_TABLES)\n\t\t\t\tstop_decoding(JPGD_BAD_DHT_INDEX);\n\n\t\t\tif (!m_huff_num[index])\n\t\t\t\tm_huff_num[index] = (uint8 *)alloc(17);\n\n\t\t\tif (!m_huff_val[index])\n\t\t\t\tm_huff_val[index] = (uint8 *)alloc(256);\n\n\t\t\tm_huff_ac[index] = (index & 0x10) != 0;\n\t\t\tmemcpy(m_huff_num[index], huff_num, 17);\n\t\t\tmemcpy(m_huff_val[index], huff_val, 256);\n\t\t}\n\t}\n\n\t// Read a quantization table.\n\tvoid jpeg_decoder::read_dqt_marker()\n\t{\n\t\tint n, i, prec;\n\t\tuint num_left;\n\t\tuint temp;\n\n\t\tnum_left = get_bits(16);\n\n\t\tif (num_left < 2)\n\t\t\tstop_decoding(JPGD_BAD_DQT_MARKER);\n\n\t\tnum_left -= 2;\n\n\t\twhile (num_left)\n\t\t{\n\t\t\tn = get_bits(8);\n\t\t\tprec = n >> 4;\n\t\t\tn &= 0x0F;\n\n\t\t\tif (n >= JPGD_MAX_QUANT_TABLES)\n\t\t\t\tstop_decoding(JPGD_BAD_DQT_TABLE);\n\n\t\t\tif (!m_quant[n])\n\t\t\t\tm_quant[n] = (jpgd_quant_t *)alloc(64 * sizeof(jpgd_quant_t));\n\n\t\t\t// read quantization entries, in zag order\n\t\t\tfor (i = 0; i < 64; i++)\n\t\t\t{\n\t\t\t\ttemp = get_bits(8);\n\n\t\t\t\tif (prec)\n\t\t\t\t\ttemp = (temp << 8) + get_bits(8);\n\n\t\t\t\tm_quant[n][i] = static_cast<jpgd_quant_t>(temp);\n\t\t\t}\n\n\t\t\ti = 64 + 1;\n\n\t\t\tif (prec)\n\t\t\t\ti += 64;\n\n\t\t\tif (num_left < (uint)i)\n\t\t\t\tstop_decoding(JPGD_BAD_DQT_LENGTH);\n\n\t\t\tnum_left -= i;\n\t\t}\n\t}\n\n\t// Read the start of frame (SOF) marker.\n\tvoid jpeg_decoder::read_sof_marker()\n\t{\n\t\tint i;\n\t\tuint num_left;\n\n\t\tnum_left = get_bits(16);\n\n\t\tif (get_bits(8) != 8)   /* precision: sorry, only 8-bit precision is supported right now */\n\t\t\tstop_decoding(JPGD_BAD_PRECISION);\n\n\t\tm_image_y_size = get_bits(16);\n\n\t\tif ((m_image_y_size < 1) || (m_image_y_size > JPGD_MAX_HEIGHT))\n\t\t\tstop_decoding(JPGD_BAD_HEIGHT);\n\n\t\tm_image_x_size = get_bits(16);\n\n\t\tif ((m_image_x_size < 1) || (m_image_x_size > JPGD_MAX_WIDTH))\n\t\t\tstop_decoding(JPGD_BAD_WIDTH);\n\n\t\tm_comps_in_frame = get_bits(8);\n\n\t\tif (m_comps_in_frame > JPGD_MAX_COMPONENTS)\n\t\t\tstop_decoding(JPGD_TOO_MANY_COMPONENTS);\n\n\t\tif (num_left != (uint)(m_comps_in_frame * 3 + 8))\n\t\t\tstop_decoding(JPGD_BAD_SOF_LENGTH);\n\n\t\tfor (i = 0; i < m_comps_in_frame; i++)\n\t\t{\n\t\t\tm_comp_ident[i]  = get_bits(8);\n\t\t\tm_comp_h_samp[i] = get_bits(4);\n\t\t\tm_comp_v_samp[i] = get_bits(4);\n\t\t\tm_comp_quant[i]  = get_bits(8);\n\t\t}\n\t}\n\n\t// Used to skip unrecognized markers.\n\tvoid jpeg_decoder::skip_variable_marker()\n\t{\n\t\tuint num_left;\n\n\t\tnum_left = get_bits(16);\n\n\t\tif (num_left < 2)\n\t\t\tstop_decoding(JPGD_BAD_VARIABLE_MARKER);\n\n\t\tnum_left -= 2;\n\n\t\twhile (num_left)\n\t\t{\n\t\t\tget_bits(8);\n\t\t\tnum_left--;\n\t\t}\n\t}\n\n\t// Read a define restart interval (DRI) marker.\n\tvoid jpeg_decoder::read_dri_marker()\n\t{\n\t\tif (get_bits(16) != 4)\n\t\t\tstop_decoding(JPGD_BAD_DRI_LENGTH);\n\n\t\tm_restart_interval = get_bits(16);\n\t}\n\n\t// Read a start of scan (SOS) marker.\n\tvoid jpeg_decoder::read_sos_marker()\n\t{\n\t\tuint num_left;\n\t\tint i, ci, n, c, cc;\n\n\t\tnum_left = get_bits(16);\n\n\t\tn = get_bits(8);\n\n\t\tm_comps_in_scan = n;\n\n\t\tnum_left -= 3;\n\n\t\tif ( (num_left != (uint)(n * 2 + 3)) || (n < 1) || (n > JPGD_MAX_COMPS_IN_SCAN) )\n\t\t\tstop_decoding(JPGD_BAD_SOS_LENGTH);\n\n\t\tfor (i = 0; i < n; i++)\n\t\t{\n\t\t\tcc = get_bits(8);\n\t\t\tc = get_bits(8);\n\t\t\tnum_left -= 2;\n\n\t\t\tfor (ci = 0; ci < m_comps_in_frame; ci++)\n\t\t\t\tif (cc == m_comp_ident[ci])\n\t\t\t\t\tbreak;\n\n\t\t\tif (ci >= m_comps_in_frame)\n\t\t\t\tstop_decoding(JPGD_BAD_SOS_COMP_ID);\n\n\t\t\tm_comp_list[i]    = ci;\n\t\t\tm_comp_dc_tab[ci] = (c >> 4) & 15;\n\t\t\tm_comp_ac_tab[ci] = (c & 15) + (JPGD_MAX_HUFF_TABLES >> 1);\n\t\t}\n\n\t\tm_spectral_start  = get_bits(8);\n\t\tm_spectral_end    = get_bits(8);\n\t\tm_successive_high = get_bits(4);\n\t\tm_successive_low  = get_bits(4);\n\n\t\tif (!m_progressive_flag)\n\t\t{\n\t\t\tm_spectral_start = 0;\n\t\t\tm_spectral_end = 63;\n\t\t}\n\n\t\tnum_left -= 3;\n\n\t\twhile (num_left)                  /* read past whatever is num_left */\n\t\t{\n\t\t\tget_bits(8);\n\t\t\tnum_left--;\n\t\t}\n\t}\n\n\t// Finds the next marker.\n\tint jpeg_decoder::next_marker()\n\t{\n\t\tuint c, bytes;\n\n\t\tbytes = 0;\n\n\t\tdo\n\t\t{\n\t\t\tdo\n\t\t\t{\n\t\t\t\tbytes++;\n\t\t\t\tc = get_bits(8);\n\t\t\t} while (c != 0xFF);\n\n\t\t\tdo\n\t\t\t{\n\t\t\t\tc = get_bits(8);\n\t\t\t} while (c == 0xFF);\n\n\t\t} while (c == 0);\n\n\t\t// If bytes > 0 here, there where extra bytes before the marker (not good).\n\n\t\treturn c;\n\t}\n\n\t// Process markers. Returns when an SOFx, SOI, EOI, or SOS marker is\n\t// encountered.\n\tint jpeg_decoder::process_markers()\n\t{\n\t\tint c;\n\n\t\tfor ( ; ; )\n\t\t{\n\t\t\tc = next_marker();\n\n\t\t\tswitch (c)\n\t\t\t{\n\t\t\tcase M_SOF0:\n\t\t\tcase M_SOF1:\n\t\t\tcase M_SOF2:\n\t\t\tcase M_SOF3:\n\t\t\tcase M_SOF5:\n\t\t\tcase M_SOF6:\n\t\t\tcase M_SOF7:\n\t\t\t\t//      case M_JPG:\n\t\t\tcase M_SOF9:\n\t\t\tcase M_SOF10:\n\t\t\tcase M_SOF11:\n\t\t\tcase M_SOF13:\n\t\t\tcase M_SOF14:\n\t\t\tcase M_SOF15:\n\t\t\tcase M_SOI:\n\t\t\tcase M_EOI:\n\t\t\tcase M_SOS:\n\t\t\t\t{\n\t\t\t\t\treturn c;\n\t\t\t\t}\n\t\t\tcase M_DHT:\n\t\t\t\t{\n\t\t\t\t\tread_dht_marker();\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\t\t// No arithmitic support - dumb patents!\n\t\t\tcase M_DAC:\n\t\t\t\t{\n\t\t\t\t\tstop_decoding(JPGD_NO_ARITHMITIC_SUPPORT);\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\tcase M_DQT:\n\t\t\t\t{\n\t\t\t\t\tread_dqt_marker();\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\tcase M_DRI:\n\t\t\t\t{\n\t\t\t\t\tread_dri_marker();\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\t\t//case M_APP0:  /* no need to read the JFIF marker */\n\n\t\t\tcase M_JPG:\n\t\t\tcase M_RST0:    /* no parameters */\n\t\t\tcase M_RST1:\n\t\t\tcase M_RST2:\n\t\t\tcase M_RST3:\n\t\t\tcase M_RST4:\n\t\t\tcase M_RST5:\n\t\t\tcase M_RST6:\n\t\t\tcase M_RST7:\n\t\t\tcase M_TEM:\n\t\t\t\t{\n\t\t\t\t\tstop_decoding(JPGD_UNEXPECTED_MARKER);\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\tdefault:    /* must be DNL, DHP, EXP, APPn, JPGn, COM, or RESn or APP0 */\n\t\t\t\t{\n\t\t\t\t\tskip_variable_marker();\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\n\t// Finds the start of image (SOI) marker.\n\t// This code is rather defensive: it only checks the first 512 bytes to avoid\n\t// false positives.\n\tvoid jpeg_decoder::locate_soi_marker()\n\t{\n\t\tuint lastchar, thischar;\n\t\tuint bytesleft;\n\n\t\tlastchar = get_bits(8);\n\n\t\tthischar = get_bits(8);\n\n\t\t/* ok if it's a normal JPEG file without a special header */\n\n\t\tif ((lastchar == 0xFF) && (thischar == M_SOI))\n\t\t\treturn;\n\n\t\tbytesleft = 4096; //512;\n\n\t\tfor ( ; ; )\n\t\t{\n\t\t\tif (--bytesleft == 0)\n\t\t\t\tstop_decoding(JPGD_NOT_JPEG);\n\n\t\t\tlastchar = thischar;\n\n\t\t\tthischar = get_bits(8);\n\n\t\t\tif (lastchar == 0xFF)\n\t\t\t{\n\t\t\t\tif (thischar == M_SOI)\n\t\t\t\t\tbreak;\n\t\t\t\telse if (thischar == M_EOI) // get_bits will keep returning M_EOI if we read past the end\n\t\t\t\t\tstop_decoding(JPGD_NOT_JPEG);\n\t\t\t}\n\t\t}\n\n\t\t// Check the next character after marker: if it's not 0xFF, it can't be the start of the next marker, so the file is bad.\n\t\tthischar = (m_bit_buf >> 24) & 0xFF;\n\n\t\tif (thischar != 0xFF)\n\t\t\tstop_decoding(JPGD_NOT_JPEG);\n\t}\n\n\t// Find a start of frame (SOF) marker.\n\tvoid jpeg_decoder::locate_sof_marker()\n\t{\n\t\tlocate_soi_marker();\n\n\t\tint c = process_markers();\n\n\t\tswitch (c)\n\t\t{\n\t\tcase M_SOF2:\n\t\t\tm_progressive_flag = JPGD_TRUE;\n\t\tcase M_SOF0:  /* baseline DCT */\n\t\tcase M_SOF1:  /* extended sequential DCT */\n\t\t\t{\n\t\t\t\tread_sof_marker();\n\t\t\t\tbreak;\n\t\t\t}\n\t\tcase M_SOF9:  /* Arithmitic coding */\n\t\t\t{\n\t\t\t\tstop_decoding(JPGD_NO_ARITHMITIC_SUPPORT);\n\t\t\t\tbreak;\n\t\t\t}\n\t\tdefault:\n\t\t\t{\n\t\t\t\tstop_decoding(JPGD_UNSUPPORTED_MARKER);\n\t\t\t\tbreak;\n\t\t\t}\n\t\t}\n\t}\n\n\t// Find a start of scan (SOS) marker.\n\tint jpeg_decoder::locate_sos_marker()\n\t{\n\t\tint c;\n\n\t\tc = process_markers();\n\n\t\tif (c == M_EOI)\n\t\t\treturn JPGD_FALSE;\n\t\telse if (c != M_SOS)\n\t\t\tstop_decoding(JPGD_UNEXPECTED_MARKER);\n\n\t\tread_sos_marker();\n\n\t\treturn JPGD_TRUE;\n\t}\n\n\t// Reset everything to default/uninitialized state.\n\tvoid jpeg_decoder::init(jpeg_decoder_stream *pStream)\n\t{\n\t\tm_pMem_blocks = NULL;\n\t\tm_error_code = JPGD_SUCCESS;\n\t\tm_ready_flag = false;\n\t\tm_image_x_size = m_image_y_size = 0;\n\t\tm_pStream = pStream;\n\t\tm_progressive_flag = JPGD_FALSE;\n\n\t\tmemset(m_huff_ac, 0, sizeof(m_huff_ac));\n\t\tmemset(m_huff_num, 0, sizeof(m_huff_num));\n\t\tmemset(m_huff_val, 0, sizeof(m_huff_val));\n\t\tmemset(m_quant, 0, sizeof(m_quant));\n\n\t\tm_scan_type = 0;\n\t\tm_comps_in_frame = 0;\n\n\t\tmemset(m_comp_h_samp, 0, sizeof(m_comp_h_samp));\n\t\tmemset(m_comp_v_samp, 0, sizeof(m_comp_v_samp));\n\t\tmemset(m_comp_quant, 0, sizeof(m_comp_quant));\n\t\tmemset(m_comp_ident, 0, sizeof(m_comp_ident));\n\t\tmemset(m_comp_h_blocks, 0, sizeof(m_comp_h_blocks));\n\t\tmemset(m_comp_v_blocks, 0, sizeof(m_comp_v_blocks));\n\n\t\tm_comps_in_scan = 0;\n\t\tmemset(m_comp_list, 0, sizeof(m_comp_list));\n\t\tmemset(m_comp_dc_tab, 0, sizeof(m_comp_dc_tab));\n\t\tmemset(m_comp_ac_tab, 0, sizeof(m_comp_ac_tab));\n\n\t\tm_spectral_start = 0;\n\t\tm_spectral_end = 0;\n\t\tm_successive_low = 0;\n\t\tm_successive_high = 0;\n\t\tm_max_mcu_x_size = 0;\n\t\tm_max_mcu_y_size = 0;\n\t\tm_blocks_per_mcu = 0;\n\t\tm_max_blocks_per_row = 0;\n\t\tm_mcus_per_row = 0;\n\t\tm_mcus_per_col = 0;\n\t\tm_expanded_blocks_per_component = 0;\n\t\tm_expanded_blocks_per_mcu = 0;\n\t\tm_expanded_blocks_per_row = 0;\n\t\tm_freq_domain_chroma_upsample = false;\n\n\t\tmemset(m_mcu_org, 0, sizeof(m_mcu_org));\n\n\t\tm_total_lines_left = 0;\n\t\tm_mcu_lines_left = 0;\n\t\tm_real_dest_bytes_per_scan_line = 0;\n\t\tm_dest_bytes_per_scan_line = 0;\n\t\tm_dest_bytes_per_pixel = 0;\n\n\t\tmemset(m_pHuff_tabs, 0, sizeof(m_pHuff_tabs));\n\n\t\tmemset(m_dc_coeffs, 0, sizeof(m_dc_coeffs));\n\t\tmemset(m_ac_coeffs, 0, sizeof(m_ac_coeffs));\n\t\tmemset(m_block_y_mcu, 0, sizeof(m_block_y_mcu));\n\n\t\tm_eob_run = 0;\n\n\t\tmemset(m_block_y_mcu, 0, sizeof(m_block_y_mcu));\n\n\t\tm_pIn_buf_ofs = m_in_buf;\n\t\tm_in_buf_left = 0;\n\t\tm_eof_flag = false;\n\t\tm_tem_flag = 0;\n\n\t\tmemset(m_in_buf_pad_start, 0, sizeof(m_in_buf_pad_start));\n\t\tmemset(m_in_buf, 0, sizeof(m_in_buf));\n\t\tmemset(m_in_buf_pad_end, 0, sizeof(m_in_buf_pad_end));\n\n\t\tm_restart_interval = 0;\n\t\tm_restarts_left    = 0;\n\t\tm_next_restart_num = 0;\n\n\t\tm_max_mcus_per_row = 0;\n\t\tm_max_blocks_per_mcu = 0;\n\t\tm_max_mcus_per_col = 0;\n\n\t\tmemset(m_last_dc_val, 0, sizeof(m_last_dc_val));\n\t\tm_pMCU_coefficients = NULL;\n\t\tm_pSample_buf = NULL;\n\n\t\tm_total_bytes_read = 0;\n\n\t\tm_pScan_line_0 = NULL;\n\t\tm_pScan_line_1 = NULL;\n\n\t\t// Ready the input buffer.\n\t\tprep_in_buffer();\n\n\t\t// Prime the bit buffer.\n\t\tm_bits_left = 16;\n\t\tm_bit_buf = 0;\n\n\t\tget_bits(16);\n\t\tget_bits(16);\n\n\t\tfor (int i = 0; i < JPGD_MAX_BLOCKS_PER_MCU; i++)\n\t\t\tm_mcu_block_max_zag[i] = 64;\n\t}\n\n#define SCALEBITS 16\n#define ONE_HALF  ((int) 1 << (SCALEBITS-1))\n#define FIX(x)    ((int) ((x) * (1L<<SCALEBITS) + 0.5f))\n\n\t// Create a few tables that allow us to quickly convert YCbCr to RGB.\n\tvoid jpeg_decoder::create_look_ups()\n\t{\n\t\tfor (int i = 0; i <= 255; i++)\n\t\t{\n\t\t\tint k = i - 128;\n\t\t\tm_crr[i] = ( FIX(1.40200f)  * k + ONE_HALF) >> SCALEBITS;\n\t\t\tm_cbb[i] = ( FIX(1.77200f)  * k + ONE_HALF) >> SCALEBITS;\n\t\t\tm_crg[i] = (-FIX(0.71414f)) * k;\n\t\t\tm_cbg[i] = (-FIX(0.34414f)) * k + ONE_HALF;\n\t\t}\n\t}\n\n\t// This method throws back into the stream any bytes that where read\n\t// into the bit buffer during initial marker scanning.\n\tvoid jpeg_decoder::fix_in_buffer()\n\t{\n\t\t// In case any 0xFF's where pulled into the buffer during marker scanning.\n\t\tJPGD_ASSERT((m_bits_left & 7) == 0);\n\n\t\tif (m_bits_left == 16)\n\t\t\tstuff_char( (uint8)(m_bit_buf & 0xFF));\n\n\t\tif (m_bits_left >= 8)\n\t\t\tstuff_char( (uint8)((m_bit_buf >> 8) & 0xFF));\n\n\t\tstuff_char((uint8)((m_bit_buf >> 16) & 0xFF));\n\t\tstuff_char((uint8)((m_bit_buf >> 24) & 0xFF));\n\n\t\tm_bits_left = 16;\n\t\tget_bits_no_markers(16);\n\t\tget_bits_no_markers(16);\n\t}\n\n\tvoid jpeg_decoder::transform_mcu(int mcu_row)\n\t{\n\t\tjpgd_block_t* pSrc_ptr = m_pMCU_coefficients;\n\t\tuint8* pDst_ptr = m_pSample_buf + mcu_row * m_blocks_per_mcu * 64;\n\n\t\tfor (int mcu_block = 0; mcu_block < m_blocks_per_mcu; mcu_block++)\n\t\t{\n\t\t\tidct(pSrc_ptr, pDst_ptr, m_mcu_block_max_zag[mcu_block]);\n\t\t\tpSrc_ptr += 64;\n\t\t\tpDst_ptr += 64;\n\t\t}\n\t}\n\n\tstatic const uint8 s_max_rc[64] =\n\t{\n\t\t17, 18, 34, 50, 50, 51, 52, 52, 52, 68, 84, 84, 84, 84, 85, 86, 86, 86, 86, 86,\n\t\t102, 118, 118, 118, 118, 118, 118, 119, 120, 120, 120, 120, 120, 120, 120, 136,\n\t\t136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136,\n\t\t136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136\n\t};\n\n\tvoid jpeg_decoder::transform_mcu_expand(int mcu_row)\n\t{\n\t\tjpgd_block_t* pSrc_ptr = m_pMCU_coefficients;\n\t\tuint8* pDst_ptr = m_pSample_buf + mcu_row * m_expanded_blocks_per_mcu * 64;\n\n\t\t// Y IDCT\n\t\tint mcu_block;\n\t\tfor (mcu_block = 0; mcu_block < m_expanded_blocks_per_component; mcu_block++)\n\t\t{\n\t\t\tidct(pSrc_ptr, pDst_ptr, m_mcu_block_max_zag[mcu_block]);\n\t\t\tpSrc_ptr += 64;\n\t\t\tpDst_ptr += 64;\n\t\t}\n\n\t\t// Chroma IDCT, with upsampling\n\t\tjpgd_block_t temp_block[64];\n\n\t\tfor (int i = 0; i < 2; i++)\n\t\t{\n\t\t\tDCT_Upsample::Matrix44 P, Q, R, S;\n\n\t\t\tJPGD_ASSERT(m_mcu_block_max_zag[mcu_block] >= 1);\n\t\t\tJPGD_ASSERT(m_mcu_block_max_zag[mcu_block] <= 64);\n\n\t\t\tswitch (s_max_rc[m_mcu_block_max_zag[mcu_block++] - 1])\n\t\t\t{\n\t\t\tcase 1*16+1:\n\t\t\t\tDCT_Upsample::P_Q<1, 1>::calc(P, Q, pSrc_ptr);\n\t\t\t\tDCT_Upsample::R_S<1, 1>::calc(R, S, pSrc_ptr);\n\t\t\t\tbreak;\n\t\t\tcase 1*16+2:\n\t\t\t\tDCT_Upsample::P_Q<1, 2>::calc(P, Q, pSrc_ptr);\n\t\t\t\tDCT_Upsample::R_S<1, 2>::calc(R, S, pSrc_ptr);\n\t\t\t\tbreak;\n\t\t\tcase 2*16+2:\n\t\t\t\tDCT_Upsample::P_Q<2, 2>::calc(P, Q, pSrc_ptr);\n\t\t\t\tDCT_Upsample::R_S<2, 2>::calc(R, S, pSrc_ptr);\n\t\t\t\tbreak;\n\t\t\tcase 3*16+2:\n\t\t\t\tDCT_Upsample::P_Q<3, 2>::calc(P, Q, pSrc_ptr);\n\t\t\t\tDCT_Upsample::R_S<3, 2>::calc(R, S, pSrc_ptr);\n\t\t\t\tbreak;\n\t\t\tcase 3*16+3:\n\t\t\t\tDCT_Upsample::P_Q<3, 3>::calc(P, Q, pSrc_ptr);\n\t\t\t\tDCT_Upsample::R_S<3, 3>::calc(R, S, pSrc_ptr);\n\t\t\t\tbreak;\n\t\t\tcase 3*16+4:\n\t\t\t\tDCT_Upsample::P_Q<3, 4>::calc(P, Q, pSrc_ptr);\n\t\t\t\tDCT_Upsample::R_S<3, 4>::calc(R, S, pSrc_ptr);\n\t\t\t\tbreak;\n\t\t\tcase 4*16+4:\n\t\t\t\tDCT_Upsample::P_Q<4, 4>::calc(P, Q, pSrc_ptr);\n\t\t\t\tDCT_Upsample::R_S<4, 4>::calc(R, S, pSrc_ptr);\n\t\t\t\tbreak;\n\t\t\tcase 5*16+4:\n\t\t\t\tDCT_Upsample::P_Q<5, 4>::calc(P, Q, pSrc_ptr);\n\t\t\t\tDCT_Upsample::R_S<5, 4>::calc(R, S, pSrc_ptr);\n\t\t\t\tbreak;\n\t\t\tcase 5*16+5:\n\t\t\t\tDCT_Upsample::P_Q<5, 5>::calc(P, Q, pSrc_ptr);\n\t\t\t\tDCT_Upsample::R_S<5, 5>::calc(R, S, pSrc_ptr);\n\t\t\t\tbreak;\n\t\t\tcase 5*16+6:\n\t\t\t\tDCT_Upsample::P_Q<5, 6>::calc(P, Q, pSrc_ptr);\n\t\t\t\tDCT_Upsample::R_S<5, 6>::calc(R, S, pSrc_ptr);\n\t\t\t\tbreak;\n\t\t\tcase 6*16+6:\n\t\t\t\tDCT_Upsample::P_Q<6, 6>::calc(P, Q, pSrc_ptr);\n\t\t\t\tDCT_Upsample::R_S<6, 6>::calc(R, S, pSrc_ptr);\n\t\t\t\tbreak;\n\t\t\tcase 7*16+6:\n\t\t\t\tDCT_Upsample::P_Q<7, 6>::calc(P, Q, pSrc_ptr);\n\t\t\t\tDCT_Upsample::R_S<7, 6>::calc(R, S, pSrc_ptr);\n\t\t\t\tbreak;\n\t\t\tcase 7*16+7:\n\t\t\t\tDCT_Upsample::P_Q<7, 7>::calc(P, Q, pSrc_ptr);\n\t\t\t\tDCT_Upsample::R_S<7, 7>::calc(R, S, pSrc_ptr);\n\t\t\t\tbreak;\n\t\t\tcase 7*16+8:\n\t\t\t\tDCT_Upsample::P_Q<7, 8>::calc(P, Q, pSrc_ptr);\n\t\t\t\tDCT_Upsample::R_S<7, 8>::calc(R, S, pSrc_ptr);\n\t\t\t\tbreak;\n\t\t\tcase 8*16+8:\n\t\t\t\tDCT_Upsample::P_Q<8, 8>::calc(P, Q, pSrc_ptr);\n\t\t\t\tDCT_Upsample::R_S<8, 8>::calc(R, S, pSrc_ptr);\n\t\t\t\tbreak;\n\t\t\tdefault:\n\t\t\t\tJPGD_ASSERT(false);\n\t\t\t}\n\n\t\t\tDCT_Upsample::Matrix44 a(P + Q); P -= Q;\n\t\t\tDCT_Upsample::Matrix44& b = P;\n\t\t\tDCT_Upsample::Matrix44 c(R + S); R -= S;\n\t\t\tDCT_Upsample::Matrix44& d = R;\n\n\t\t\tDCT_Upsample::Matrix44::add_and_store(temp_block, a, c);\n\t\t\tidct_4x4(temp_block, pDst_ptr);\n\t\t\tpDst_ptr += 64;\n\n\t\t\tDCT_Upsample::Matrix44::sub_and_store(temp_block, a, c);\n\t\t\tidct_4x4(temp_block, pDst_ptr);\n\t\t\tpDst_ptr += 64;\n\n\t\t\tDCT_Upsample::Matrix44::add_and_store(temp_block, b, d);\n\t\t\tidct_4x4(temp_block, pDst_ptr);\n\t\t\tpDst_ptr += 64;\n\n\t\t\tDCT_Upsample::Matrix44::sub_and_store(temp_block, b, d);\n\t\t\tidct_4x4(temp_block, pDst_ptr);\n\t\t\tpDst_ptr += 64;\n\n\t\t\tpSrc_ptr += 64;\n\t\t}\n\t}\n\n\t// Loads and dequantizes the next row of (already decoded) coefficients.\n\t// Progressive images only.\n\tvoid jpeg_decoder::load_next_row()\n\t{\n\t\tint i;\n\t\tjpgd_block_t *p;\n\t\tjpgd_quant_t *q;\n\t\tint mcu_row, mcu_block, row_block = 0;\n\t\tint component_num, component_id;\n\t\tint block_x_mcu[JPGD_MAX_COMPONENTS];\n\n\t\tmemset(block_x_mcu, 0, JPGD_MAX_COMPONENTS * sizeof(int));\n\n\t\tfor (mcu_row = 0; mcu_row < m_mcus_per_row; mcu_row++)\n\t\t{\n\t\t\tint block_x_mcu_ofs = 0, block_y_mcu_ofs = 0;\n\n\t\t\tfor (mcu_block = 0; mcu_block < m_blocks_per_mcu; mcu_block++)\n\t\t\t{\n\t\t\t\tcomponent_id = m_mcu_org[mcu_block];\n\t\t\t\tq = m_quant[m_comp_quant[component_id]];\n\n\t\t\t\tp = m_pMCU_coefficients + 64 * mcu_block;\n\n\t\t\t\tjpgd_block_t* pAC = coeff_buf_getp(m_ac_coeffs[component_id], block_x_mcu[component_id] + block_x_mcu_ofs, m_block_y_mcu[component_id] + block_y_mcu_ofs);\n\t\t\t\tjpgd_block_t* pDC = coeff_buf_getp(m_dc_coeffs[component_id], block_x_mcu[component_id] + block_x_mcu_ofs, m_block_y_mcu[component_id] + block_y_mcu_ofs);\n\t\t\t\tp[0] = pDC[0];\n\t\t\t\tmemcpy(&p[1], &pAC[1], 63 * sizeof(jpgd_block_t));\n\n\t\t\t\tfor (i = 63; i > 0; i--)\n\t\t\t\t\tif (p[g_ZAG[i]])\n\t\t\t\t\t\tbreak;\n\n\t\t\t\tm_mcu_block_max_zag[mcu_block] = i + 1;\n\n\t\t\t\tfor ( ; i >= 0; i--)\n\t\t\t\t\tif (p[g_ZAG[i]])\n\t\t\t\t\t\tp[g_ZAG[i]] = static_cast<jpgd_block_t>(p[g_ZAG[i]] * q[i]);\n\n\t\t\t\trow_block++;\n\n\t\t\t\tif (m_comps_in_scan == 1)\n\t\t\t\t\tblock_x_mcu[component_id]++;\n\t\t\t\telse\n\t\t\t\t{\n\t\t\t\t\tif (++block_x_mcu_ofs == m_comp_h_samp[component_id])\n\t\t\t\t\t{\n\t\t\t\t\t\tblock_x_mcu_ofs = 0;\n\n\t\t\t\t\t\tif (++block_y_mcu_ofs == m_comp_v_samp[component_id])\n\t\t\t\t\t\t{\n\t\t\t\t\t\t\tblock_y_mcu_ofs = 0;\n\n\t\t\t\t\t\t\tblock_x_mcu[component_id] += m_comp_h_samp[component_id];\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\n\t\t\tif (m_freq_domain_chroma_upsample)\n\t\t\t\ttransform_mcu_expand(mcu_row);\n\t\t\telse\n\t\t\t\ttransform_mcu(mcu_row);\n\t\t}\n\n\t\tif (m_comps_in_scan == 1)\n\t\t\tm_block_y_mcu[m_comp_list[0]]++;\n\t\telse\n\t\t{\n\t\t\tfor (component_num = 0; component_num < m_comps_in_scan; component_num++)\n\t\t\t{\n\t\t\t\tcomponent_id = m_comp_list[component_num];\n\n\t\t\t\tm_block_y_mcu[component_id] += m_comp_v_samp[component_id];\n\t\t\t}\n\t\t}\n\t}\n\n\t// Restart interval processing.\n\tvoid jpeg_decoder::process_restart()\n\t{\n\t\tint i;\n\t\tint c = 0;\n\n\t\t// Align to a byte boundry\n\t\t// FIXME: Is this really necessary? get_bits_no_markers() never reads in markers!\n\t\t//get_bits_no_markers(m_bits_left & 7);\n\n\t\t// Let's scan a little bit to find the marker, but not _too_ far.\n\t\t// 1536 is a \"fudge factor\" that determines how much to scan.\n\t\tfor (i = 1536; i > 0; i--)\n\t\t\tif (get_char() == 0xFF)\n\t\t\t\tbreak;\n\n\t\tif (i == 0)\n\t\t\tstop_decoding(JPGD_BAD_RESTART_MARKER);\n\n\t\tfor ( ; i > 0; i--)\n\t\t\tif ((c = get_char()) != 0xFF)\n\t\t\t\tbreak;\n\n\t\tif (i == 0)\n\t\t\tstop_decoding(JPGD_BAD_RESTART_MARKER);\n\n\t\t// Is it the expected marker? If not, something bad happened.\n\t\tif (c != (m_next_restart_num + M_RST0))\n\t\t\tstop_decoding(JPGD_BAD_RESTART_MARKER);\n\n\t\t// Reset each component's DC prediction values.\n\t\tmemset(&m_last_dc_val, 0, m_comps_in_frame * sizeof(uint));\n\n\t\tm_eob_run = 0;\n\n\t\tm_restarts_left = m_restart_interval;\n\n\t\tm_next_restart_num = (m_next_restart_num + 1) & 7;\n\n\t\t// Get the bit buffer going again...\n\n\t\tm_bits_left = 16;\n\t\tget_bits_no_markers(16);\n\t\tget_bits_no_markers(16);\n\t}\n\n\tstatic inline int dequantize_ac(int c, int q) {\tc *= q;\treturn c; }\n\n\t// Decodes and dequantizes the next row of coefficients.\n\tvoid jpeg_decoder::decode_next_row()\n\t{\n\t\tint row_block = 0;\n\n\t\tfor (int mcu_row = 0; mcu_row < m_mcus_per_row; mcu_row++)\n\t\t{\n\t\t\tif ((m_restart_interval) && (m_restarts_left == 0))\n\t\t\t\tprocess_restart();\n\n\t\t\tjpgd_block_t* p = m_pMCU_coefficients;\n\t\t\tfor (int mcu_block = 0; mcu_block < m_blocks_per_mcu; mcu_block++, p += 64)\n\t\t\t{\n\t\t\t\tint component_id = m_mcu_org[mcu_block];\n\t\t\t\tjpgd_quant_t* q = m_quant[m_comp_quant[component_id]];\n\n\t\t\t\tint r, s;\n\t\t\t\ts = huff_decode(m_pHuff_tabs[m_comp_dc_tab[component_id]], r);\n\t\t\t\ts = HUFF_EXTEND(r, s);\n\n\t\t\t\tm_last_dc_val[component_id] = (s += m_last_dc_val[component_id]);\n\n\t\t\t\tp[0] = static_cast<jpgd_block_t>(s * q[0]);\n\n\t\t\t\tint prev_num_set = m_mcu_block_max_zag[mcu_block];\n\n\t\t\t\thuff_tables *pH = m_pHuff_tabs[m_comp_ac_tab[component_id]];\n\n\t\t\t\tint k;\n\t\t\t\tfor (k = 1; k < 64; k++)\n\t\t\t\t{\n\t\t\t\t\tint extra_bits;\n\t\t\t\t\ts = huff_decode(pH, extra_bits);\n\n\t\t\t\t\tr = s >> 4;\n\t\t\t\t\ts &= 15;\n\n\t\t\t\t\tif (s)\n\t\t\t\t\t{\n\t\t\t\t\t\tif (r)\n\t\t\t\t\t\t{\n\t\t\t\t\t\t\tif ((k + r) > 63)\n\t\t\t\t\t\t\t\tstop_decoding(JPGD_DECODE_ERROR);\n\n\t\t\t\t\t\t\tif (k < prev_num_set)\n\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\tint n = JPGD_MIN(r, prev_num_set - k);\n\t\t\t\t\t\t\t\tint kt = k;\n\t\t\t\t\t\t\t\twhile (n--)\n\t\t\t\t\t\t\t\t\tp[g_ZAG[kt++]] = 0;\n\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\tk += r;\n\t\t\t\t\t\t}\n\n\t\t\t\t\t\ts = HUFF_EXTEND(extra_bits, s);\n\n\t\t\t\t\t\tJPGD_ASSERT(k < 64);\n\n\t\t\t\t\t\tp[g_ZAG[k]] = static_cast<jpgd_block_t>(dequantize_ac(s, q[k])); //s * q[k];\n\t\t\t\t\t}\n\t\t\t\t\telse\n\t\t\t\t\t{\n\t\t\t\t\t\tif (r == 15)\n\t\t\t\t\t\t{\n\t\t\t\t\t\t\tif ((k + 16) > 64)\n\t\t\t\t\t\t\t\tstop_decoding(JPGD_DECODE_ERROR);\n\n\t\t\t\t\t\t\tif (k < prev_num_set)\n\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\tint n = JPGD_MIN(16, prev_num_set - k);\n\t\t\t\t\t\t\t\tint kt = k;\n\t\t\t\t\t\t\t\twhile (n--)\n\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\tJPGD_ASSERT(kt <= 63);\n\t\t\t\t\t\t\t\t\tp[g_ZAG[kt++]] = 0;\n\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\tk += 16 - 1; // - 1 because the loop counter is k\n\t\t\t\t\t\t\t// BEGIN EPIC MOD\n\t\t\t\t\t\t\tJPGD_ASSERT(k < 64 && p[g_ZAG[k]] == 0);\n\t\t\t\t\t\t\t// END EPIC MOD\n\t\t\t\t\t\t}\n\t\t\t\t\t\telse\n\t\t\t\t\t\t\tbreak;\n\t\t\t\t\t}\n\t\t\t\t}\n\n\t\t\t\tif (k < prev_num_set)\n\t\t\t\t{\n\t\t\t\t\tint kt = k;\n\t\t\t\t\twhile (kt < prev_num_set)\n\t\t\t\t\t\tp[g_ZAG[kt++]] = 0;\n\t\t\t\t}\n\n\t\t\t\tm_mcu_block_max_zag[mcu_block] = k;\n\n\t\t\t\trow_block++;\n\t\t\t}\n\n\t\t\tif (m_freq_domain_chroma_upsample)\n\t\t\t\ttransform_mcu_expand(mcu_row);\n\t\t\telse\n\t\t\t\ttransform_mcu(mcu_row);\n\n\t\t\tm_restarts_left--;\n\t\t}\n\t}\n\n\t// YCbCr H1V1 (1x1:1:1, 3 m_blocks per MCU) to RGB\n\tvoid jpeg_decoder::H1V1Convert()\n\t{\n\t\tint row = m_max_mcu_y_size - m_mcu_lines_left;\n\t\tuint8 *d = m_pScan_line_0;\n\t\tuint8 *s = m_pSample_buf + row * 8;\n\n\t\tfor (int i = m_max_mcus_per_row; i > 0; i--)\n\t\t{\n\t\t\tfor (int j = 0; j < 8; j++)\n\t\t\t{\n\t\t\t\tint y = s[j];\n\t\t\t\tint cb = s[64+j];\n\t\t\t\tint cr = s[128+j];\n\n\t\t\t\tif (jpg_format == ERGBFormatJPG::BGRA)\n\t\t\t\t{\n\t\t\t\t\td[0] = clamp(y + m_cbb[cb]);\n\t\t\t\t\td[1] = clamp(y + ((m_crg[cr] + m_cbg[cb]) >> 16));\n\t\t\t\t\td[2] = clamp(y + m_crr[cr]);\n\t\t\t\t\td[3] = 255;\n\t\t\t\t}\n\t\t\t\telse\n\t\t\t\t{\n\t\t\t\t\td[0] = clamp(y + m_crr[cr]);\n\t\t\t\t\td[1] = clamp(y + ((m_crg[cr] + m_cbg[cb]) >> 16));\n\t\t\t\t\td[2] = clamp(y + m_cbb[cb]);\n\t\t\t\t\td[3] = 255;\n\t\t\t\t}\n\t\t\t\td += 4;\n\t\t\t}\n\n\t\t\ts += 64*3;\n\t\t}\n\t}\n\n\t// YCbCr H2V1 (2x1:1:1, 4 m_blocks per MCU) to RGB\n\tvoid jpeg_decoder::H2V1Convert()\n\t{\n\t\tint row = m_max_mcu_y_size - m_mcu_lines_left;\n\t\tuint8 *d0 = m_pScan_line_0;\n\t\tuint8 *y = m_pSample_buf + row * 8;\n\t\tuint8 *c = m_pSample_buf + 2*64 + row * 8;\n\n\t\tfor (int i = m_max_mcus_per_row; i > 0; i--)\n\t\t{\n\t\t\tfor (int l = 0; l < 2; l++)\n\t\t\t{\n\t\t\t\tfor (int j = 0; j < 4; j++)\n\t\t\t\t{\n\t\t\t\t\tint cb = c[0];\n\t\t\t\t\tint cr = c[64];\n\n\t\t\t\t\tint rc = m_crr[cr];\n\t\t\t\t\tint gc = ((m_crg[cr] + m_cbg[cb]) >> 16);\n\t\t\t\t\tint bc = m_cbb[cb];\n\n\t\t\t\t\tint yy = y[j<<1];\n\t\t\t\t\tif (jpg_format == ERGBFormatJPG::BGRA)\n\t\t\t\t\t{\n\t\t\t\t\t\td0[0] = clamp(yy+bc);\n\t\t\t\t\t\td0[1] = clamp(yy+gc);\n\t\t\t\t\t\td0[2] = clamp(yy+rc);\n\t\t\t\t\t\td0[3] = 255;\n\t\t\t\t\t\tyy = y[(j<<1)+1];\n\t\t\t\t\t\td0[4] = clamp(yy+bc);\n\t\t\t\t\t\td0[5] = clamp(yy+gc);\n\t\t\t\t\t\td0[6] = clamp(yy+rc);\n\t\t\t\t\t\td0[7] = 255;\n\t\t\t\t\t}\n\t\t\t\t\telse\n\t\t\t\t\t{\n\t\t\t\t\t\td0[0] = clamp(yy+rc);\n\t\t\t\t\t\td0[1] = clamp(yy+gc);\n\t\t\t\t\t\td0[2] = clamp(yy+bc);\n\t\t\t\t\t\td0[3] = 255;\n\t\t\t\t\t\tyy = y[(j<<1)+1];\n\t\t\t\t\t\td0[4] = clamp(yy+rc);\n\t\t\t\t\t\td0[5] = clamp(yy+gc);\n\t\t\t\t\t\td0[6] = clamp(yy+bc);\n\t\t\t\t\t\td0[7] = 255;\n\t\t\t\t\t}\n\n\t\t\t\t\td0 += 8;\n\n\t\t\t\t\tc++;\n\t\t\t\t}\n\t\t\t\ty += 64;\n\t\t\t}\n\n\t\t\ty += 64*4 - 64*2;\n\t\t\tc += 64*4 - 8;\n\t\t}\n\t}\n\n\t// YCbCr H2V1 (1x2:1:1, 4 m_blocks per MCU) to RGB\n\tvoid jpeg_decoder::H1V2Convert()\n\t{\n\t\tint row = m_max_mcu_y_size - m_mcu_lines_left;\n\t\tuint8 *d0 = m_pScan_line_0;\n\t\tuint8 *d1 = m_pScan_line_1;\n\t\tuint8 *y;\n\t\tuint8 *c;\n\n\t\tif (row < 8)\n\t\t\ty = m_pSample_buf + row * 8;\n\t\telse\n\t\t\ty = m_pSample_buf + 64*1 + (row & 7) * 8;\n\n\t\tc = m_pSample_buf + 64*2 + (row >> 1) * 8;\n\n\t\tfor (int i = m_max_mcus_per_row; i > 0; i--)\n\t\t{\n\t\t\tfor (int j = 0; j < 8; j++)\n\t\t\t{\n\t\t\t\tint cb = c[0+j];\n\t\t\t\tint cr = c[64+j];\n\n\t\t\t\tint rc = m_crr[cr];\n\t\t\t\tint gc = ((m_crg[cr] + m_cbg[cb]) >> 16);\n\t\t\t\tint bc = m_cbb[cb];\n\n\t\t\t\tint yy = y[j];\n\t\t\t\tif (jpg_format == ERGBFormatJPG::BGRA)\n\t\t\t\t{\n\t\t\t\t\td0[0] = clamp(yy+bc);\n\t\t\t\t\td0[1] = clamp(yy+gc);\n\t\t\t\t\td0[2] = clamp(yy+rc);\n\t\t\t\t\td0[3] = 255;\n\t\t\t\t\tyy = y[8+j];\n\t\t\t\t\td1[0] = clamp(yy+bc);\n\t\t\t\t\td1[1] = clamp(yy+gc);\n\t\t\t\t\td1[2] = clamp(yy+rc);\n\t\t\t\t\td1[3] = 255;\n\t\t\t\t}\n\t\t\t\telse\n\t\t\t\t{\n\t\t\t\t\td0[0] = clamp(yy+rc);\n\t\t\t\t\td0[1] = clamp(yy+gc);\n\t\t\t\t\td0[2] = clamp(yy+bc);\n\t\t\t\t\td0[3] = 255;\n\t\t\t\t\tyy = y[8+j];\n\t\t\t\t\td1[0] = clamp(yy+rc);\n\t\t\t\t\td1[1] = clamp(yy+gc);\n\t\t\t\t\td1[2] = clamp(yy+bc);\n\t\t\t\t\td1[3] = 255;\n\t\t\t\t}\n\n\t\t\t\td0 += 4;\n\t\t\t\td1 += 4;\n\t\t\t}\n\n\t\t\ty += 64*4;\n\t\t\tc += 64*4;\n\t\t}\n\t}\n\n\t// YCbCr H2V2 (2x2:1:1, 6 m_blocks per MCU) to RGB\n\tvoid jpeg_decoder::H2V2Convert()\n\t{\n\t\tint row = m_max_mcu_y_size - m_mcu_lines_left;\n\t\tuint8 *d0 = m_pScan_line_0;\n\t\tuint8 *d1 = m_pScan_line_1;\n\t\tuint8 *y;\n\t\tuint8 *c;\n\n\t\tif (row < 8)\n\t\t\ty = m_pSample_buf + row * 8;\n\t\telse\n\t\t\ty = m_pSample_buf + 64*2 + (row & 7) * 8;\n\n\t\tc = m_pSample_buf + 64*4 + (row >> 1) * 8;\n\n\t\tfor (int i = m_max_mcus_per_row; i > 0; i--)\n\t\t{\n\t\t\tfor (int l = 0; l < 2; l++)\n\t\t\t{\n\t\t\t\tfor (int j = 0; j < 8; j += 2)\n\t\t\t\t{\n\t\t\t\t\tint cb = c[0];\n\t\t\t\t\tint cr = c[64];\n\n\t\t\t\t\tint rc = m_crr[cr];\n\t\t\t\t\tint gc = ((m_crg[cr] + m_cbg[cb]) >> 16);\n\t\t\t\t\tint bc = m_cbb[cb];\n\n\t\t\t\t\tint yy = y[j];\n\t\t\t\t\tif (jpg_format == ERGBFormatJPG::BGRA)\n\t\t\t\t\t{\n\t\t\t\t\t\td0[0] = clamp(yy+bc);\n\t\t\t\t\t\td0[1] = clamp(yy+gc);\n\t\t\t\t\t\td0[2] = clamp(yy+rc);\n\t\t\t\t\t\td0[3] = 255;\n\t\t\t\t\t\tyy = y[j+1];\n\t\t\t\t\t\td0[4] = clamp(yy+bc);\n\t\t\t\t\t\td0[5] = clamp(yy+gc);\n\t\t\t\t\t\td0[6] = clamp(yy+rc);\n\t\t\t\t\t\td0[7] = 255;\n\t\t\t\t\t\tyy = y[j+8];\n\t\t\t\t\t\td1[0] = clamp(yy+bc);\n\t\t\t\t\t\td1[1] = clamp(yy+gc);\n\t\t\t\t\t\td1[2] = clamp(yy+rc);\n\t\t\t\t\t\td1[3] = 255;\n\t\t\t\t\t\tyy = y[j+8+1];\n\t\t\t\t\t\td1[4] = clamp(yy+bc);\n\t\t\t\t\t\td1[5] = clamp(yy+gc);\n\t\t\t\t\t\td1[6] = clamp(yy+rc);\n\t\t\t\t\t\td1[7] = 255;\n\t\t\t\t\t}\n\t\t\t\t\telse\n\t\t\t\t\t{\n\t\t\t\t\t\td0[0] = clamp(yy+rc);\n\t\t\t\t\t\td0[1] = clamp(yy+gc);\n\t\t\t\t\t\td0[2] = clamp(yy+bc);\n\t\t\t\t\t\td0[3] = 255;\n\t\t\t\t\t\tyy = y[j+1];\n\t\t\t\t\t\td0[4] = clamp(yy+rc);\n\t\t\t\t\t\td0[5] = clamp(yy+gc);\n\t\t\t\t\t\td0[6] = clamp(yy+bc);\n\t\t\t\t\t\td0[7] = 255;\n\t\t\t\t\t\tyy = y[j+8];\n\t\t\t\t\t\td1[0] = clamp(yy+rc);\n\t\t\t\t\t\td1[1] = clamp(yy+gc);\n\t\t\t\t\t\td1[2] = clamp(yy+bc);\n\t\t\t\t\t\td1[3] = 255;\n\t\t\t\t\t\tyy = y[j+8+1];\n\t\t\t\t\t\td1[4] = clamp(yy+rc);\n\t\t\t\t\t\td1[5] = clamp(yy+gc);\n\t\t\t\t\t\td1[6] = clamp(yy+bc);\n\t\t\t\t\t\td1[7] = 255;\n\t\t\t\t\t}\n\n\t\t\t\t\td0 += 8;\n\t\t\t\t\td1 += 8;\n\n\t\t\t\t\tc++;\n\t\t\t\t}\n\t\t\t\ty += 64;\n\t\t\t}\n\n\t\t\ty += 64*6 - 64*2;\n\t\t\tc += 64*6 - 8;\n\t\t}\n\t}\n\n\t// Y (1 block per MCU) to 8-bit grayscale\n\tvoid jpeg_decoder::gray_convert()\n\t{\n\t\tint row = m_max_mcu_y_size - m_mcu_lines_left;\n\t\tuint8 *d = m_pScan_line_0;\n\t\tuint8 *s = m_pSample_buf + row * 8;\n\n\t\tfor (int i = m_max_mcus_per_row; i > 0; i--)\n\t\t{\n\t\t\t*(uint *)d = *(uint *)s;\n\t\t\t*(uint *)(&d[4]) = *(uint *)(&s[4]);\n\n\t\t\ts += 64;\n\t\t\td += 8;\n\t\t}\n\t}\n\n\tvoid jpeg_decoder::expanded_convert()\n\t{\n\t\tint row = m_max_mcu_y_size - m_mcu_lines_left;\n\n\t\tuint8* Py = m_pSample_buf + (row / 8) * 64 * m_comp_h_samp[0] + (row & 7) * 8;\n\n\t\tuint8* d = m_pScan_line_0;\n\n\t\tfor (int i = m_max_mcus_per_row; i > 0; i--)\n\t\t{\n\t\t\tfor (int k = 0; k < m_max_mcu_x_size; k += 8)\n\t\t\t{\n\t\t\t\tconst int Y_ofs = k * 8;\n\t\t\t\tconst int Cb_ofs = Y_ofs + 64 * m_expanded_blocks_per_component;\n\t\t\t\tconst int Cr_ofs = Y_ofs + 64 * m_expanded_blocks_per_component * 2;\n\t\t\t\tfor (int j = 0; j < 8; j++)\n\t\t\t\t{\n\t\t\t\t\tint y = Py[Y_ofs + j];\n\t\t\t\t\tint cb = Py[Cb_ofs + j];\n\t\t\t\t\tint cr = Py[Cr_ofs + j];\n\n\t\t\t\t\tif (jpg_format == ERGBFormatJPG::BGRA)\n\t\t\t\t\t{\n\t\t\t\t\t\td[0] = clamp(y + m_cbb[cb]);\n\t\t\t\t\t\td[1] = clamp(y + ((m_crg[cr] + m_cbg[cb]) >> 16));\n\t\t\t\t\t\td[2] = clamp(y + m_crr[cr]);\n\t\t\t\t\t\td[3] = 255;\n\t\t\t\t\t}\n\t\t\t\t\telse\n\t\t\t\t\t{\n\t\t\t\t\t\td[0] = clamp(y + m_crr[cr]);\n\t\t\t\t\t\td[1] = clamp(y + ((m_crg[cr] + m_cbg[cb]) >> 16));\n\t\t\t\t\t\td[2] = clamp(y + m_cbb[cb]);\n\t\t\t\t\t\td[3] = 255;\n\t\t\t\t\t}\n\n\t\t\t\t\td += 4;\n\t\t\t\t}\n\t\t\t}\n\n\t\t\tPy += 64 * m_expanded_blocks_per_mcu;\n\t\t}\n\t}\n\n\t// Find end of image (EOI) marker, so we can return to the user the exact size of the input stream.\n\tvoid jpeg_decoder::find_eoi()\n\t{\n\t\tif (!m_progressive_flag)\n\t\t{\n\t\t\t// Attempt to read the EOI marker.\n\t\t\t//get_bits_no_markers(m_bits_left & 7);\n\n\t\t\t// Prime the bit buffer\n\t\t\tm_bits_left = 16;\n\t\t\tget_bits(16);\n\t\t\tget_bits(16);\n\n\t\t\t// The next marker _should_ be EOI\n\t\t\tprocess_markers();\n\t\t}\n\n\t\tm_total_bytes_read -= m_in_buf_left;\n\t}\n\n\tint jpeg_decoder::decode(const void** pScan_line, uint* pScan_line_len)\n\t{\n\t\tif ((m_error_code) || (!m_ready_flag))\n\t\t\treturn JPGD_FAILED;\n\n\t\tif (m_total_lines_left == 0)\n\t\t\treturn JPGD_DONE;\n\n\t\tif (m_mcu_lines_left == 0)\n\t\t{\n\t\t\tif (setjmp(m_jmp_state))\n\t\t\t\treturn JPGD_FAILED;\n\n\t\t\tif (m_progressive_flag)\n\t\t\t\tload_next_row();\n\t\t\telse\n\t\t\t\tdecode_next_row();\n\n\t\t\t// Find the EOI marker if that was the last row.\n\t\t\tif (m_total_lines_left <= m_max_mcu_y_size)\n\t\t\t\tfind_eoi();\n\n\t\t\tm_mcu_lines_left = m_max_mcu_y_size;\n\t\t}\n\n\t\tif (m_freq_domain_chroma_upsample)\n\t\t{\n\t\t\texpanded_convert();\n\t\t\t*pScan_line = m_pScan_line_0;\n\t\t}\n\t\telse\n\t\t{\n\t\t\tswitch (m_scan_type)\n\t\t\t{\n\t\t\tcase JPGD_YH2V2:\n\t\t\t\t{\n\t\t\t\t\tif ((m_mcu_lines_left & 1) == 0)\n\t\t\t\t\t{\n\t\t\t\t\t\tH2V2Convert();\n\t\t\t\t\t\t*pScan_line = m_pScan_line_0;\n\t\t\t\t\t}\n\t\t\t\t\telse\n\t\t\t\t\t\t*pScan_line = m_pScan_line_1;\n\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\tcase JPGD_YH2V1:\n\t\t\t\t{\n\t\t\t\t\tH2V1Convert();\n\t\t\t\t\t*pScan_line = m_pScan_line_0;\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\tcase JPGD_YH1V2:\n\t\t\t\t{\n\t\t\t\t\tif ((m_mcu_lines_left & 1) == 0)\n\t\t\t\t\t{\n\t\t\t\t\t\tH1V2Convert();\n\t\t\t\t\t\t*pScan_line = m_pScan_line_0;\n\t\t\t\t\t}\n\t\t\t\t\telse\n\t\t\t\t\t\t*pScan_line = m_pScan_line_1;\n\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\tcase JPGD_YH1V1:\n\t\t\t\t{\n\t\t\t\t\tH1V1Convert();\n\t\t\t\t\t*pScan_line = m_pScan_line_0;\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\tcase JPGD_GRAYSCALE:\n\t\t\t\t{\n\t\t\t\t\tgray_convert();\n\t\t\t\t\t*pScan_line = m_pScan_line_0;\n\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\t*pScan_line_len = m_real_dest_bytes_per_scan_line;\n\n\t\tm_mcu_lines_left--;\n\t\tm_total_lines_left--;\n\n\t\treturn JPGD_SUCCESS;\n\t}\n\n\t// Creates the tables needed for efficient Huffman decoding.\n\tvoid jpeg_decoder::make_huff_table(int index, huff_tables *pH)\n\t{\n\t\tint p, i, l, si;\n\t\tuint8 huffsize[257];\n\t\tuint huffcode[257];\n\t\tuint code;\n\t\tuint subtree;\n\t\tint code_size;\n\t\tint lastp;\n\t\tint nextfreeentry;\n\t\tint currententry;\n\n\t\tpH->ac_table = m_huff_ac[index] != 0;\n\n\t\tp = 0;\n\n\t\tfor (l = 1; l <= 16; l++)\n\t\t{\n\t\t\tfor (i = 1; i <= m_huff_num[index][l]; i++)\n\t\t\t\thuffsize[p++] = static_cast<uint8>(l);\n\t\t}\n\n\t\thuffsize[p] = 0;\n\n\t\tlastp = p;\n\n\t\tcode = 0;\n\t\tsi = huffsize[0];\n\t\tp = 0;\n\n\t\twhile (huffsize[p])\n\t\t{\n\t\t\twhile (huffsize[p] == si)\n\t\t\t{\n\t\t\t\thuffcode[p++] = code;\n\t\t\t\tcode++;\n\t\t\t}\n\n\t\t\tcode <<= 1;\n\t\t\tsi++;\n\t\t}\n\n\t\tmemset(pH->look_up, 0, sizeof(pH->look_up));\n\t\tmemset(pH->look_up2, 0, sizeof(pH->look_up2));\n\t\tmemset(pH->tree, 0, sizeof(pH->tree));\n\t\tmemset(pH->code_size, 0, sizeof(pH->code_size));\n\n\t\tnextfreeentry = -1;\n\n\t\tp = 0;\n\n\t\twhile (p < lastp)\n\t\t{\n\t\t\ti = m_huff_val[index][p];\n\t\t\tcode = huffcode[p];\n\t\t\tcode_size = huffsize[p];\n\n\t\t\tpH->code_size[i] = static_cast<uint8>(code_size);\n\n\t\t\tif (code_size <= 8)\n\t\t\t{\n\t\t\t\tcode <<= (8 - code_size);\n\n\t\t\t\tfor (l = 1 << (8 - code_size); l > 0; l--)\n\t\t\t\t{\n\t\t\t\t\tJPGD_ASSERT(i < 256);\n\n\t\t\t\t\tpH->look_up[code] = i;\n\n\t\t\t\t\tbool has_extrabits = false;\n\t\t\t\t\tint extra_bits = 0;\n\t\t\t\t\tint num_extra_bits = i & 15;\n\n\t\t\t\t\tint bits_to_fetch = code_size;\n\t\t\t\t\tif (num_extra_bits)\n\t\t\t\t\t{\n\t\t\t\t\t\tint total_codesize = code_size + num_extra_bits;\n\t\t\t\t\t\tif (total_codesize <= 8)\n\t\t\t\t\t\t{\n\t\t\t\t\t\t\thas_extrabits = true;\n\t\t\t\t\t\t\textra_bits = ((1 << num_extra_bits) - 1) & (code >> (8 - total_codesize));\n\t\t\t\t\t\t\tJPGD_ASSERT(extra_bits <= 0x7FFF);\n\t\t\t\t\t\t\tbits_to_fetch += num_extra_bits;\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\n\t\t\t\t\tif (!has_extrabits)\n\t\t\t\t\t\tpH->look_up2[code] = i | (bits_to_fetch << 8);\n\t\t\t\t\telse\n\t\t\t\t\t\tpH->look_up2[code] = i | 0x8000 | (extra_bits << 16) | (bits_to_fetch << 8);\n\n\t\t\t\t\tcode++;\n\t\t\t\t}\n\t\t\t}\n\t\t\telse\n\t\t\t{\n\t\t\t\tsubtree = (code >> (code_size - 8)) & 0xFF;\n\n\t\t\t\tcurrententry = pH->look_up[subtree];\n\n\t\t\t\tif (currententry == 0)\n\t\t\t\t{\n\t\t\t\t\tpH->look_up[subtree] = currententry = nextfreeentry;\n\t\t\t\t\tpH->look_up2[subtree] = currententry = nextfreeentry;\n\n\t\t\t\t\tnextfreeentry -= 2;\n\t\t\t\t}\n\n\t\t\t\tcode <<= (16 - (code_size - 8));\n\n\t\t\t\tfor (l = code_size; l > 9; l--)\n\t\t\t\t{\n\t\t\t\t\tif ((code & 0x8000) == 0)\n\t\t\t\t\t\tcurrententry--;\n\n\t\t\t\t\tif (pH->tree[-currententry - 1] == 0)\n\t\t\t\t\t{\n\t\t\t\t\t\tpH->tree[-currententry - 1] = nextfreeentry;\n\n\t\t\t\t\t\tcurrententry = nextfreeentry;\n\n\t\t\t\t\t\tnextfreeentry -= 2;\n\t\t\t\t\t}\n\t\t\t\t\telse\n\t\t\t\t\t\tcurrententry = pH->tree[-currententry - 1];\n\n\t\t\t\t\tcode <<= 1;\n\t\t\t\t}\n\n\t\t\t\tif ((code & 0x8000) == 0)\n\t\t\t\t\tcurrententry--;\n\n\t\t\t\tpH->tree[-currententry - 1] = i;\n\t\t\t}\n\n\t\t\tp++;\n\t\t}\n\t}\n\n\t// Verifies the quantization tables needed for this scan are available.\n\tvoid jpeg_decoder::check_quant_tables()\n\t{\n\t\tfor (int i = 0; i < m_comps_in_scan; i++)\n\t\t\tif (m_quant[m_comp_quant[m_comp_list[i]]] == NULL)\n\t\t\t\tstop_decoding(JPGD_UNDEFINED_QUANT_TABLE);\n\t}\n\n\t// Verifies that all the Huffman tables needed for this scan are available.\n\tvoid jpeg_decoder::check_huff_tables()\n\t{\n\t\tfor (int i = 0; i < m_comps_in_scan; i++)\n\t\t{\n\t\t\tif ((m_spectral_start == 0) && (m_huff_num[m_comp_dc_tab[m_comp_list[i]]] == NULL))\n\t\t\t\tstop_decoding(JPGD_UNDEFINED_HUFF_TABLE);\n\n\t\t\tif ((m_spectral_end > 0) && (m_huff_num[m_comp_ac_tab[m_comp_list[i]]] == NULL))\n\t\t\t\tstop_decoding(JPGD_UNDEFINED_HUFF_TABLE);\n\t\t}\n\n\t\tfor (int i = 0; i < JPGD_MAX_HUFF_TABLES; i++)\n\t\t\tif (m_huff_num[i])\n\t\t\t{\n\t\t\t\tif (!m_pHuff_tabs[i])\n\t\t\t\t\tm_pHuff_tabs[i] = (huff_tables *)alloc(sizeof(huff_tables));\n\n\t\t\t\tmake_huff_table(i, m_pHuff_tabs[i]);\n\t\t\t}\n\t}\n\n\t// Determines the component order inside each MCU.\n\t// Also calcs how many MCU's are on each row, etc.\n\tvoid jpeg_decoder::calc_mcu_block_order()\n\t{\n\t\tint component_num, component_id;\n\t\tint max_h_samp = 0, max_v_samp = 0;\n\n\t\tfor (component_id = 0; component_id < m_comps_in_frame; component_id++)\n\t\t{\n\t\t\tif (m_comp_h_samp[component_id] > max_h_samp)\n\t\t\t\tmax_h_samp = m_comp_h_samp[component_id];\n\n\t\t\tif (m_comp_v_samp[component_id] > max_v_samp)\n\t\t\t\tmax_v_samp = m_comp_v_samp[component_id];\n\t\t}\n\n\t\tfor (component_id = 0; component_id < m_comps_in_frame; component_id++)\n\t\t{\n\t\t\tm_comp_h_blocks[component_id] = ((((m_image_x_size * m_comp_h_samp[component_id]) + (max_h_samp - 1)) / max_h_samp) + 7) / 8;\n\t\t\tm_comp_v_blocks[component_id] = ((((m_image_y_size * m_comp_v_samp[component_id]) + (max_v_samp - 1)) / max_v_samp) + 7) / 8;\n\t\t}\n\n\t\tif (m_comps_in_scan == 1)\n\t\t{\n\t\t\tm_mcus_per_row = m_comp_h_blocks[m_comp_list[0]];\n\t\t\tm_mcus_per_col = m_comp_v_blocks[m_comp_list[0]];\n\t\t}\n\t\telse\n\t\t{\n\t\t\tm_mcus_per_row = (((m_image_x_size + 7) / 8) + (max_h_samp - 1)) / max_h_samp;\n\t\t\tm_mcus_per_col = (((m_image_y_size + 7) / 8) + (max_v_samp - 1)) / max_v_samp;\n\t\t}\n\n\t\tif (m_comps_in_scan == 1)\n\t\t{\n\t\t\tm_mcu_org[0] = m_comp_list[0];\n\n\t\t\tm_blocks_per_mcu = 1;\n\t\t}\n\t\telse\n\t\t{\n\t\t\tm_blocks_per_mcu = 0;\n\n\t\t\tfor (component_num = 0; component_num < m_comps_in_scan; component_num++)\n\t\t\t{\n\t\t\t\tint num_blocks;\n\n\t\t\t\tcomponent_id = m_comp_list[component_num];\n\n\t\t\t\tnum_blocks = m_comp_h_samp[component_id] * m_comp_v_samp[component_id];\n\n\t\t\t\twhile (num_blocks--)\n\t\t\t\t\tm_mcu_org[m_blocks_per_mcu++] = component_id;\n\t\t\t}\n\t\t}\n\t}\n\n\t// Starts a new scan.\n\tint jpeg_decoder::init_scan()\n\t{\n\t\tif (!locate_sos_marker())\n\t\t\treturn JPGD_FALSE;\n\n\t\tcalc_mcu_block_order();\n\n\t\tcheck_huff_tables();\n\n\t\tcheck_quant_tables();\n\n\t\tmemset(m_last_dc_val, 0, m_comps_in_frame * sizeof(uint));\n\n\t\tm_eob_run = 0;\n\n\t\tif (m_restart_interval)\n\t\t{\n\t\t\tm_restarts_left = m_restart_interval;\n\t\t\tm_next_restart_num = 0;\n\t\t}\n\n\t\tfix_in_buffer();\n\n\t\treturn JPGD_TRUE;\n\t}\n\n\t// Starts a frame. Determines if the number of components or sampling factors\n\t// are supported.\n\tvoid jpeg_decoder::init_frame()\n\t{\n\t\tint i;\n\n\t\tif (m_comps_in_frame == 1)\n\t\t{\n\t\t\tif ((m_comp_h_samp[0] != 1) || (m_comp_v_samp[0] != 1))\n\t\t\t\tstop_decoding(JPGD_UNSUPPORTED_SAMP_FACTORS);\n\n\t\t\tm_scan_type = JPGD_GRAYSCALE;\n\t\t\tm_max_blocks_per_mcu = 1;\n\t\t\tm_max_mcu_x_size = 8;\n\t\t\tm_max_mcu_y_size = 8;\n\t\t}\n\t\telse if (m_comps_in_frame == 3)\n\t\t{\n\t\t\tif ( ((m_comp_h_samp[1] != 1) || (m_comp_v_samp[1] != 1)) ||\n\t\t\t\t((m_comp_h_samp[2] != 1) || (m_comp_v_samp[2] != 1)) )\n\t\t\t\tstop_decoding(JPGD_UNSUPPORTED_SAMP_FACTORS);\n\n\t\t\tif ((m_comp_h_samp[0] == 1) && (m_comp_v_samp[0] == 1))\n\t\t\t{\n\t\t\t\tm_scan_type = JPGD_YH1V1;\n\n\t\t\t\tm_max_blocks_per_mcu = 3;\n\t\t\t\tm_max_mcu_x_size = 8;\n\t\t\t\tm_max_mcu_y_size = 8;\n\t\t\t}\n\t\t\telse if ((m_comp_h_samp[0] == 2) && (m_comp_v_samp[0] == 1))\n\t\t\t{\n\t\t\t\tm_scan_type = JPGD_YH2V1;\n\t\t\t\tm_max_blocks_per_mcu = 4;\n\t\t\t\tm_max_mcu_x_size = 16;\n\t\t\t\tm_max_mcu_y_size = 8;\n\t\t\t}\n\t\t\telse if ((m_comp_h_samp[0] == 1) && (m_comp_v_samp[0] == 2))\n\t\t\t{\n\t\t\t\tm_scan_type = JPGD_YH1V2;\n\t\t\t\tm_max_blocks_per_mcu = 4;\n\t\t\t\tm_max_mcu_x_size = 8;\n\t\t\t\tm_max_mcu_y_size = 16;\n\t\t\t}\n\t\t\telse if ((m_comp_h_samp[0] == 2) && (m_comp_v_samp[0] == 2))\n\t\t\t{\n\t\t\t\tm_scan_type = JPGD_YH2V2;\n\t\t\t\tm_max_blocks_per_mcu = 6;\n\t\t\t\tm_max_mcu_x_size = 16;\n\t\t\t\tm_max_mcu_y_size = 16;\n\t\t\t}\n\t\t\telse\n\t\t\t\tstop_decoding(JPGD_UNSUPPORTED_SAMP_FACTORS);\n\t\t}\n\t\telse\n\t\t\tstop_decoding(JPGD_UNSUPPORTED_COLORSPACE);\n\n\t\tm_max_mcus_per_row = (m_image_x_size + (m_max_mcu_x_size - 1)) / m_max_mcu_x_size;\n\t\tm_max_mcus_per_col = (m_image_y_size + (m_max_mcu_y_size - 1)) / m_max_mcu_y_size;\n\n\t\t// These values are for the *destination* pixels: after conversion.\n\t\tif (m_scan_type == JPGD_GRAYSCALE)\n\t\t\tm_dest_bytes_per_pixel = 1;\n\t\telse\n\t\t\tm_dest_bytes_per_pixel = 4;\n\n\t\tm_dest_bytes_per_scan_line = ((m_image_x_size + 15) & 0xFFF0) * m_dest_bytes_per_pixel;\n\n\t\tm_real_dest_bytes_per_scan_line = (m_image_x_size * m_dest_bytes_per_pixel);\n\n\t\t// Initialize two scan line buffers.\n\t\tm_pScan_line_0 = (uint8 *)alloc(m_dest_bytes_per_scan_line, true);\n\t\tif ((m_scan_type == JPGD_YH1V2) || (m_scan_type == JPGD_YH2V2))\n\t\t\tm_pScan_line_1 = (uint8 *)alloc(m_dest_bytes_per_scan_line, true);\n\n\t\tm_max_blocks_per_row = m_max_mcus_per_row * m_max_blocks_per_mcu;\n\n\t\t// Should never happen\n\t\tif (m_max_blocks_per_row > JPGD_MAX_BLOCKS_PER_ROW)\n\t\t\tstop_decoding(JPGD_ASSERTION_ERROR);\n\n\t\t// Allocate the coefficient buffer, enough for one MCU\n\t\tm_pMCU_coefficients = (jpgd_block_t*)alloc(m_max_blocks_per_mcu * 64 * sizeof(jpgd_block_t));\n\n\t\tfor (i = 0; i < m_max_blocks_per_mcu; i++)\n\t\t\tm_mcu_block_max_zag[i] = 64;\n\n\t\tm_expanded_blocks_per_component = m_comp_h_samp[0] * m_comp_v_samp[0];\n\t\tm_expanded_blocks_per_mcu = m_expanded_blocks_per_component * m_comps_in_frame;\n\t\tm_expanded_blocks_per_row = m_max_mcus_per_row * m_expanded_blocks_per_mcu;\n\t\t// Freq. domain chroma upsampling is only supported for H2V2 subsampling factor.\n// BEGIN EPIC MOD\n#if JPGD_SUPPORT_FREQ_DOMAIN_UPSAMPLING\n\t\tm_freq_domain_chroma_upsample = (m_expanded_blocks_per_mcu == 4*3);\n#else\n\t\tm_freq_domain_chroma_upsample = 0;\n#endif\n// END EPIC MOD\n\n\t\tif (m_freq_domain_chroma_upsample)\n\t\t\tm_pSample_buf = (uint8 *)alloc(m_expanded_blocks_per_row * 64);\n\t\telse\n\t\t\tm_pSample_buf = (uint8 *)alloc(m_max_blocks_per_row * 64);\n\n\t\tm_total_lines_left = m_image_y_size;\n\n\t\tm_mcu_lines_left = 0;\n\n\t\tcreate_look_ups();\n\t}\n\n\t// The coeff_buf series of methods originally stored the coefficients\n\t// into a \"virtual\" file which was located in EMS, XMS, or a disk file. A cache\n\t// was used to make this process more efficient. Now, we can store the entire\n\t// thing in RAM.\n\tjpeg_decoder::coeff_buf* jpeg_decoder::coeff_buf_open(int block_num_x, int block_num_y, int block_len_x, int block_len_y)\n\t{\n\t\tcoeff_buf* cb = (coeff_buf*)alloc(sizeof(coeff_buf));\n\n\t\tcb->block_num_x = block_num_x;\n\t\tcb->block_num_y = block_num_y;\n\t\tcb->block_len_x = block_len_x;\n\t\tcb->block_len_y = block_len_y;\n\t\tcb->block_size = (block_len_x * block_len_y) * sizeof(jpgd_block_t);\n\t\tcb->pData = (uint8 *)alloc(cb->block_size * block_num_x * block_num_y, true);\n\t\treturn cb;\n\t}\n\n\tinline jpgd_block_t *jpeg_decoder::coeff_buf_getp(coeff_buf *cb, int block_x, int block_y)\n\t{\n\t\tJPGD_ASSERT((block_x < cb->block_num_x) && (block_y < cb->block_num_y));\n\t\treturn (jpgd_block_t *)(cb->pData + block_x * cb->block_size + block_y * (cb->block_size * cb->block_num_x));\n\t}\n\n\t// The following methods decode the various types of m_blocks encountered\n\t// in progressively encoded images.\n\tvoid jpeg_decoder::decode_block_dc_first(jpeg_decoder *pD, int component_id, int block_x, int block_y)\n\t{\n\t\tint s, r;\n\t\tjpgd_block_t *p = pD->coeff_buf_getp(pD->m_dc_coeffs[component_id], block_x, block_y);\n\n\t\tif ((s = pD->huff_decode(pD->m_pHuff_tabs[pD->m_comp_dc_tab[component_id]])) != 0)\n\t\t{\n\t\t\tr = pD->get_bits_no_markers(s);\n\t\t\ts = HUFF_EXTEND(r, s);\n\t\t}\n\n\t\tpD->m_last_dc_val[component_id] = (s += pD->m_last_dc_val[component_id]);\n\n\t\tp[0] = static_cast<jpgd_block_t>(s << pD->m_successive_low);\n\t}\n\n\tvoid jpeg_decoder::decode_block_dc_refine(jpeg_decoder *pD, int component_id, int block_x, int block_y)\n\t{\n\t\tif (pD->get_bits_no_markers(1))\n\t\t{\n\t\t\tjpgd_block_t *p = pD->coeff_buf_getp(pD->m_dc_coeffs[component_id], block_x, block_y);\n\n\t\t\tp[0] |= (1 << pD->m_successive_low);\n\t\t}\n\t}\n\n\tvoid jpeg_decoder::decode_block_ac_first(jpeg_decoder *pD, int component_id, int block_x, int block_y)\n\t{\n\t\tint k, s, r;\n\n\t\tif (pD->m_eob_run)\n\t\t{\n\t\t\tpD->m_eob_run--;\n\t\t\treturn;\n\t\t}\n\n\t\tjpgd_block_t *p = pD->coeff_buf_getp(pD->m_ac_coeffs[component_id], block_x, block_y);\n\n\t\tfor (k = pD->m_spectral_start; k <= pD->m_spectral_end; k++)\n\t\t{\n\t\t\ts = pD->huff_decode(pD->m_pHuff_tabs[pD->m_comp_ac_tab[component_id]]);\n\n\t\t\tr = s >> 4;\n\t\t\ts &= 15;\n\n\t\t\tif (s)\n\t\t\t{\n\t\t\t\tif ((k += r) > 63)\n\t\t\t\t\tpD->stop_decoding(JPGD_DECODE_ERROR);\n\n\t\t\t\tr = pD->get_bits_no_markers(s);\n\t\t\t\ts = HUFF_EXTEND(r, s);\n\n\t\t\t\tp[g_ZAG[k]] = static_cast<jpgd_block_t>(s << pD->m_successive_low);\n\t\t\t}\n\t\t\telse\n\t\t\t{\n\t\t\t\tif (r == 15)\n\t\t\t\t{\n\t\t\t\t\tif ((k += 15) > 63)\n\t\t\t\t\t\tpD->stop_decoding(JPGD_DECODE_ERROR);\n\t\t\t\t}\n\t\t\t\telse\n\t\t\t\t{\n\t\t\t\t\tpD->m_eob_run = 1 << r;\n\n\t\t\t\t\tif (r)\n\t\t\t\t\t\tpD->m_eob_run += pD->get_bits_no_markers(r);\n\n\t\t\t\t\tpD->m_eob_run--;\n\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\n\tvoid jpeg_decoder::decode_block_ac_refine(jpeg_decoder *pD, int component_id, int block_x, int block_y)\n\t{\n\t\tint s, k, r;\n\t\tint p1 = 1 << pD->m_successive_low;\n\t\tint m1 = (-1) << pD->m_successive_low;\n\t\tjpgd_block_t *p = pD->coeff_buf_getp(pD->m_ac_coeffs[component_id], block_x, block_y);\n\n\t\tk = pD->m_spectral_start;\n\n\t\tif (pD->m_eob_run == 0)\n\t\t{\n\t\t\tfor ( ; k <= pD->m_spectral_end; k++)\n\t\t\t{\n\t\t\t\ts = pD->huff_decode(pD->m_pHuff_tabs[pD->m_comp_ac_tab[component_id]]);\n\n\t\t\t\tr = s >> 4;\n\t\t\t\ts &= 15;\n\n\t\t\t\tif (s)\n\t\t\t\t{\n\t\t\t\t\tif (s != 1)\n\t\t\t\t\t\tpD->stop_decoding(JPGD_DECODE_ERROR);\n\n\t\t\t\t\tif (pD->get_bits_no_markers(1))\n\t\t\t\t\t\ts = p1;\n\t\t\t\t\telse\n\t\t\t\t\t\ts = m1;\n\t\t\t\t}\n\t\t\t\telse\n\t\t\t\t{\n\t\t\t\t\tif (r != 15)\n\t\t\t\t\t{\n\t\t\t\t\t\tpD->m_eob_run = 1 << r;\n\n\t\t\t\t\t\tif (r)\n\t\t\t\t\t\t\tpD->m_eob_run += pD->get_bits_no_markers(r);\n\n\t\t\t\t\t\tbreak;\n\t\t\t\t\t}\n\t\t\t\t}\n\n\t\t\t\tdo\n\t\t\t\t{\n\t\t\t\t\t// BEGIN EPIC MOD\n\t\t\t\t\tJPGD_ASSERT(k < 64);\n\t\t\t\t\t// END EPIC MOD\n\n\t\t\t\t\tjpgd_block_t *this_coef = p + g_ZAG[k];\n\n\t\t\t\t\tif (*this_coef != 0)\n\t\t\t\t\t{\n\t\t\t\t\t\tif (pD->get_bits_no_markers(1))\n\t\t\t\t\t\t{\n\t\t\t\t\t\t\tif ((*this_coef & p1) == 0)\n\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\tif (*this_coef >= 0)\n\t\t\t\t\t\t\t\t\t*this_coef = static_cast<jpgd_block_t>(*this_coef + p1);\n\t\t\t\t\t\t\t\telse\n\t\t\t\t\t\t\t\t\t*this_coef = static_cast<jpgd_block_t>(*this_coef + m1);\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t\telse\n\t\t\t\t\t{\n\t\t\t\t\t\tif (--r < 0)\n\t\t\t\t\t\t\tbreak;\n\t\t\t\t\t}\n\n\t\t\t\t\tk++;\n\n\t\t\t\t} while (k <= pD->m_spectral_end);\n\n\t\t\t\tif ((s) && (k < 64))\n\t\t\t\t{\n\t\t\t\t\tp[g_ZAG[k]] = static_cast<jpgd_block_t>(s);\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\tif (pD->m_eob_run > 0)\n\t\t{\n\t\t\tfor ( ; k <= pD->m_spectral_end; k++)\n\t\t\t{\n\t\t\t\t// BEGIN EPIC MOD\n\t\t\t\tJPGD_ASSERT(k < 64);\n\t\t\t\t// END EPIC MOD\n\n\t\t\t\tjpgd_block_t *this_coef = p + g_ZAG[k];\n\n\t\t\t\tif (*this_coef != 0)\n\t\t\t\t{\n\t\t\t\t\tif (pD->get_bits_no_markers(1))\n\t\t\t\t\t{\n\t\t\t\t\t\tif ((*this_coef & p1) == 0)\n\t\t\t\t\t\t{\n\t\t\t\t\t\t\tif (*this_coef >= 0)\n\t\t\t\t\t\t\t\t*this_coef = static_cast<jpgd_block_t>(*this_coef + p1);\n\t\t\t\t\t\t\telse\n\t\t\t\t\t\t\t\t*this_coef = static_cast<jpgd_block_t>(*this_coef + m1);\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\n\t\t\tpD->m_eob_run--;\n\t\t}\n\t}\n\n\t// Decode a scan in a progressively encoded image.\n\tvoid jpeg_decoder::decode_scan(pDecode_block_func decode_block_func)\n\t{\n\t\tint mcu_row, mcu_col, mcu_block;\n\t\tint block_x_mcu[JPGD_MAX_COMPONENTS], m_block_y_mcu[JPGD_MAX_COMPONENTS];\n\n\t\tmemset(m_block_y_mcu, 0, sizeof(m_block_y_mcu));\n\n\t\tfor (mcu_col = 0; mcu_col < m_mcus_per_col; mcu_col++)\n\t\t{\n\t\t\tint component_num, component_id;\n\n\t\t\tmemset(block_x_mcu, 0, sizeof(block_x_mcu));\n\n\t\t\tfor (mcu_row = 0; mcu_row < m_mcus_per_row; mcu_row++)\n\t\t\t{\n\t\t\t\tint block_x_mcu_ofs = 0, block_y_mcu_ofs = 0;\n\n\t\t\t\tif ((m_restart_interval) && (m_restarts_left == 0))\n\t\t\t\t\tprocess_restart();\n\n\t\t\t\tfor (mcu_block = 0; mcu_block < m_blocks_per_mcu; mcu_block++)\n\t\t\t\t{\n\t\t\t\t\tcomponent_id = m_mcu_org[mcu_block];\n\n\t\t\t\t\tdecode_block_func(this, component_id, block_x_mcu[component_id] + block_x_mcu_ofs, m_block_y_mcu[component_id] + block_y_mcu_ofs);\n\n\t\t\t\t\tif (m_comps_in_scan == 1)\n\t\t\t\t\t\tblock_x_mcu[component_id]++;\n\t\t\t\t\telse\n\t\t\t\t\t{\n\t\t\t\t\t\tif (++block_x_mcu_ofs == m_comp_h_samp[component_id])\n\t\t\t\t\t\t{\n\t\t\t\t\t\t\tblock_x_mcu_ofs = 0;\n\n\t\t\t\t\t\t\tif (++block_y_mcu_ofs == m_comp_v_samp[component_id])\n\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\tblock_y_mcu_ofs = 0;\n\t\t\t\t\t\t\t\tblock_x_mcu[component_id] += m_comp_h_samp[component_id];\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\n\t\t\t\tm_restarts_left--;\n\t\t\t}\n\n\t\t\tif (m_comps_in_scan == 1)\n\t\t\t\tm_block_y_mcu[m_comp_list[0]]++;\n\t\t\telse\n\t\t\t{\n\t\t\t\tfor (component_num = 0; component_num < m_comps_in_scan; component_num++)\n\t\t\t\t{\n\t\t\t\t\tcomponent_id = m_comp_list[component_num];\n\t\t\t\t\tm_block_y_mcu[component_id] += m_comp_v_samp[component_id];\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\n\t// Decode a progressively encoded image.\n\tvoid jpeg_decoder::init_progressive()\n\t{\n\t\tint i;\n\n\t\tif (m_comps_in_frame == 4)\n\t\t\tstop_decoding(JPGD_UNSUPPORTED_COLORSPACE);\n\n\t\t// Allocate the coefficient buffers.\n\t\tfor (i = 0; i < m_comps_in_frame; i++)\n\t\t{\n\t\t\tm_dc_coeffs[i] = coeff_buf_open(m_max_mcus_per_row * m_comp_h_samp[i], m_max_mcus_per_col * m_comp_v_samp[i], 1, 1);\n\t\t\tm_ac_coeffs[i] = coeff_buf_open(m_max_mcus_per_row * m_comp_h_samp[i], m_max_mcus_per_col * m_comp_v_samp[i], 8, 8);\n\t\t}\n\n\t\tfor ( ; ; )\n\t\t{\n\t\t\tint dc_only_scan, refinement_scan;\n\t\t\tpDecode_block_func decode_block_func;\n\n\t\t\tif (!init_scan())\n\t\t\t\tbreak;\n\n\t\t\tdc_only_scan = (m_spectral_start == 0);\n\t\t\trefinement_scan = (m_successive_high != 0);\n\n\t\t\tif ((m_spectral_start > m_spectral_end) || (m_spectral_end > 63))\n\t\t\t\tstop_decoding(JPGD_BAD_SOS_SPECTRAL);\n\n\t\t\tif (dc_only_scan)\n\t\t\t{\n\t\t\t\tif (m_spectral_end)\n\t\t\t\t\tstop_decoding(JPGD_BAD_SOS_SPECTRAL);\n\t\t\t}\n\t\t\telse if (m_comps_in_scan != 1)  /* AC scans can only contain one component */\n\t\t\t\tstop_decoding(JPGD_BAD_SOS_SPECTRAL);\n\n\t\t\tif ((refinement_scan) && (m_successive_low != m_successive_high - 1))\n\t\t\t\tstop_decoding(JPGD_BAD_SOS_SUCCESSIVE);\n\n\t\t\tif (dc_only_scan)\n\t\t\t{\n\t\t\t\tif (refinement_scan)\n\t\t\t\t\tdecode_block_func = decode_block_dc_refine;\n\t\t\t\telse\n\t\t\t\t\tdecode_block_func = decode_block_dc_first;\n\t\t\t}\n\t\t\telse\n\t\t\t{\n\t\t\t\tif (refinement_scan)\n\t\t\t\t\tdecode_block_func = decode_block_ac_refine;\n\t\t\t\telse\n\t\t\t\t\tdecode_block_func = decode_block_ac_first;\n\t\t\t}\n\n\t\t\tdecode_scan(decode_block_func);\n\n\t\t\tm_bits_left = 16;\n\t\t\tget_bits(16);\n\t\t\tget_bits(16);\n\t\t}\n\n\t\tm_comps_in_scan = m_comps_in_frame;\n\n\t\tfor (i = 0; i < m_comps_in_frame; i++)\n\t\t\tm_comp_list[i] = i;\n\n\t\tcalc_mcu_block_order();\n\t}\n\n\tvoid jpeg_decoder::init_sequential()\n\t{\n\t\tif (!init_scan())\n\t\t\tstop_decoding(JPGD_UNEXPECTED_MARKER);\n\t}\n\n\tvoid jpeg_decoder::decode_start()\n\t{\n\t\tinit_frame();\n\n\t\tif (m_progressive_flag)\n\t\t\tinit_progressive();\n\t\telse\n\t\t\tinit_sequential();\n\t}\n\n\tvoid jpeg_decoder::decode_init(jpeg_decoder_stream *pStream)\n\t{\n\t\tinit(pStream);\n\t\tlocate_sof_marker();\n\t}\n\n\tjpeg_decoder::jpeg_decoder(jpeg_decoder_stream *pStream)\n\t{\n\t\tif (setjmp(m_jmp_state))\n\t\t\treturn;\n\t\tdecode_init(pStream);\n\t}\n\n\tint jpeg_decoder::begin_decoding()\n\t{\n\t\tif (m_ready_flag)\n\t\t\treturn JPGD_SUCCESS;\n\n\t\tif (m_error_code)\n\t\t\treturn JPGD_FAILED;\n\n\t\tif (setjmp(m_jmp_state))\n\t\t\treturn JPGD_FAILED;\n\n\t\tdecode_start();\n\n\t\tm_ready_flag = true;\n\n\t\treturn JPGD_SUCCESS;\n\t}\n\n\tjpeg_decoder::~jpeg_decoder()\n\t{\n\t\tfree_all_blocks();\n\t}\n\n\tjpeg_decoder_file_stream::jpeg_decoder_file_stream()\n\t{\n\t\tm_pFile = NULL;\n\t\tm_eof_flag = false;\n\t\tm_error_flag = false;\n\t}\n\n\tvoid jpeg_decoder_file_stream::close()\n\t{\n\t\tif (m_pFile)\n\t\t{\n\t\t\tfclose(m_pFile);\n\t\t\tm_pFile = NULL;\n\t\t}\n\n\t\tm_eof_flag = false;\n\t\tm_error_flag = false;\n\t}\n\n\tjpeg_decoder_file_stream::~jpeg_decoder_file_stream()\n\t{\n\t\tclose();\n\t}\n\n\tbool jpeg_decoder_file_stream::open(const char *Pfilename)\n\t{\n\t\tclose();\n\n\t\tm_eof_flag = false;\n\t\tm_error_flag = false;\n\n#if defined(_MSC_VER)\n\t\tm_pFile = NULL;\n\t\tfopen_s(&m_pFile, Pfilename, \"rb\");\n#else\n\t\tm_pFile = fopen(Pfilename, \"rb\");\n#endif\n\t\treturn m_pFile != NULL;\n\t}\n\n\tint jpeg_decoder_file_stream::read(uint8 *pBuf, int max_bytes_to_read, bool *pEOF_flag)\n\t{\n\t\tif (!m_pFile)\n\t\t\treturn -1;\n\n\t\tif (m_eof_flag)\n\t\t{\n\t\t\t*pEOF_flag = true;\n\t\t\treturn 0;\n\t\t}\n\n\t\tif (m_error_flag)\n\t\t\treturn -1;\n\n\t\tint bytes_read = static_cast<int>(fread(pBuf, 1, max_bytes_to_read, m_pFile));\n\t\tif (bytes_read < max_bytes_to_read)\n\t\t{\n\t\t\tif (ferror(m_pFile))\n\t\t\t{\n\t\t\t\tm_error_flag = true;\n\t\t\t\treturn -1;\n\t\t\t}\n\n\t\t\tm_eof_flag = true;\n\t\t\t*pEOF_flag = true;\n\t\t}\n\n\t\treturn bytes_read;\n\t}\n\n\tbool jpeg_decoder_mem_stream::open(const uint8 *pSrc_data, uint size)\n\t{\n\t\tclose();\n\t\tm_pSrc_data = pSrc_data;\n\t\tm_ofs = 0;\n\t\tm_size = size;\n\t\treturn true;\n\t}\n\n\tint jpeg_decoder_mem_stream::read(uint8 *pBuf, int max_bytes_to_read, bool *pEOF_flag)\n\t{\n\t\t*pEOF_flag = false;\n\n\t\tif (!m_pSrc_data)\n\t\t\treturn -1;\n\n\t\tuint bytes_remaining = m_size - m_ofs;\n\t\tif ((uint)max_bytes_to_read > bytes_remaining)\n\t\t{\n\t\t\tmax_bytes_to_read = bytes_remaining;\n\t\t\t*pEOF_flag = true;\n\t\t}\n\n\t\tmemcpy(pBuf, m_pSrc_data + m_ofs, max_bytes_to_read);\n\t\tm_ofs += max_bytes_to_read;\n\n\t\treturn max_bytes_to_read;\n\t}\n\n\tunsigned char *decompress_jpeg_image_from_stream(jpeg_decoder_stream *pStream, int *width, int *height, int *actual_comps, int req_comps)\n\t{\n\t\tif (!actual_comps)\n\t\t\treturn NULL;\n\t\t*actual_comps = 0;\n\n\t\tif ((!pStream) || (!width) || (!height) || (!req_comps))\n\t\t\treturn NULL;\n\n\t\tif ((req_comps != 1) && (req_comps != 3) && (req_comps != 4))\n\t\t\treturn NULL;\n\n\t\tjpeg_decoder decoder(pStream);\n\t\tif (decoder.get_error_code() != JPGD_SUCCESS)\n\t\t\treturn NULL;\n\n\t\tconst int image_width = decoder.get_width(), image_height = decoder.get_height();\n\t\t*width = image_width;\n\t\t*height = image_height;\n\t\t*actual_comps = decoder.get_num_components();\n\n\t\tif (decoder.begin_decoding() != JPGD_SUCCESS)\n\t\t\treturn NULL;\n\n\t\tconst int dst_bpl = image_width * req_comps;\n\n\t\tuint8 *pImage_data = (uint8*)jpgd_malloc(dst_bpl * image_height);\n\t\tif (!pImage_data)\n\t\t\treturn NULL;\n\n\t\tfor (int y = 0; y < image_height; y++)\n\t\t{\n\t\t\tconst uint8* pScan_line = 0;\n\t\t\tuint scan_line_len;\n\t\t\tif (decoder.decode((const void**)&pScan_line, &scan_line_len) != JPGD_SUCCESS)\n\t\t\t{\n\t\t\t\tjpgd_free(pImage_data);\n\t\t\t\treturn NULL;\n\t\t\t}\n\n\t\t\tuint8 *pDst = pImage_data + y * dst_bpl;\n\n\t\t\tif (((req_comps == 4) && (decoder.get_num_components() == 3)) ||\n\t\t\t\t((req_comps == 1) && (decoder.get_num_components() == 1)))\n\t\t\t{\n\t\t\t\tmemcpy(pDst, pScan_line, dst_bpl);\n\t\t\t}\n\t\t\telse if (decoder.get_num_components() == 1)\n\t\t\t{\n\t\t\t\tif (req_comps == 3)\n\t\t\t\t{\n\t\t\t\t\tfor (int x = 0; x < image_width; x++)\n\t\t\t\t\t{\n\t\t\t\t\t\tuint8 luma = pScan_line[x];\n\t\t\t\t\t\tpDst[0] = luma;\n\t\t\t\t\t\tpDst[1] = luma;\n\t\t\t\t\t\tpDst[2] = luma;\n\t\t\t\t\t\tpDst += 3;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\telse\n\t\t\t\t{\n\t\t\t\t\tfor (int x = 0; x < image_width; x++)\n\t\t\t\t\t{\n\t\t\t\t\t\tuint8 luma = pScan_line[x];\n\t\t\t\t\t\tpDst[0] = luma;\n\t\t\t\t\t\tpDst[1] = luma;\n\t\t\t\t\t\tpDst[2] = luma;\n\t\t\t\t\t\tpDst[3] = 255;\n\t\t\t\t\t\tpDst += 4;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t\telse if (decoder.get_num_components() == 3)\n\t\t\t{\n\t\t\t\tif (req_comps == 1)\n\t\t\t\t{\n\t\t\t\t\tconst int YR = 19595, YG = 38470, YB = 7471;\n\t\t\t\t\tfor (int x = 0; x < image_width; x++)\n\t\t\t\t\t{\n\t\t\t\t\t\tint r = pScan_line[x*4+0];\n\t\t\t\t\t\tint g = pScan_line[x*4+1];\n\t\t\t\t\t\tint b = pScan_line[x*4+2];\n\t\t\t\t\t\t*pDst++ = static_cast<uint8>((r * YR + g * YG + b * YB + 32768) >> 16);\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\telse\n\t\t\t\t{\n\t\t\t\t\tfor (int x = 0; x < image_width; x++)\n\t\t\t\t\t{\n\t\t\t\t\t\tpDst[0] = pScan_line[x*4+0];\n\t\t\t\t\t\tpDst[1] = pScan_line[x*4+1];\n\t\t\t\t\t\tpDst[2] = pScan_line[x*4+2];\n\t\t\t\t\t\tpDst += 3;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\treturn pImage_data;\n\t}\n\n// BEGIN EPIC MOD\n\tunsigned char *decompress_jpeg_image_from_memory(const unsigned char *pSrc_data, int src_data_size, int *width, int *height, int *actual_comps, int req_comps, int format)\n\t{\n\t\tjpg_format = (ERGBFormatJPG)format;\n// EMD EPIC MOD\n\t\tjpgd::jpeg_decoder_mem_stream mem_stream(pSrc_data, src_data_size);\n\t\treturn decompress_jpeg_image_from_stream(&mem_stream, width, height, actual_comps, req_comps);\n\t}\n\n\tunsigned char *decompress_jpeg_image_from_file(const char *pSrc_filename, int *width, int *height, int *actual_comps, int req_comps)\n\t{\n\t\tjpgd::jpeg_decoder_file_stream file_stream;\n\t\tif (!file_stream.open(pSrc_filename))\n\t\t\treturn NULL;\n\t\treturn decompress_jpeg_image_from_stream(&file_stream, width, height, actual_comps, req_comps);\n\t}\n\n} // namespace jpgd\n"
  },
  {
    "path": "crazy_functions/test_project/cpp/longcode/jpge.cpp",
    "content": "// jpge.cpp - C++ class for JPEG compression.\n// Public domain, Rich Geldreich <richgel99@gmail.com>\n// v1.01, Dec. 18, 2010 - Initial release\n// v1.02, Apr. 6, 2011 - Removed 2x2 ordered dither in H2V1 chroma subsampling method load_block_16_8_8(). (The rounding factor was 2, when it should have been 1. Either way, it wasn't helping.)\n// v1.03, Apr. 16, 2011 - Added support for optimized Huffman code tables, optimized dynamic memory allocation down to only 1 alloc.\n//                        Also from Alex Evans: Added RGBA support, linear memory allocator (no longer needed in v1.03).\n// v1.04, May. 19, 2012: Forgot to set m_pFile ptr to NULL in cfile_stream::close(). Thanks to Owen Kaluza for reporting this bug.\n//                       Code tweaks to fix VS2008 static code analysis warnings (all looked harmless).\n//                       Code review revealed method load_block_16_8_8() (used for the non-default H2V1 sampling mode to downsample chroma) somehow didn't get the rounding factor fix from v1.02.\n\n#include \"jpge.h\"\n\n#include <stdlib.h>\n#include <string.h>\n#if PLATFORM_WINDOWS\n#include <malloc.h>\n#endif\n\n#define JPGE_MAX(a,b) (((a)>(b))?(a):(b))\n#define JPGE_MIN(a,b) (((a)<(b))?(a):(b))\n\nnamespace jpge {\n\nstatic inline void *jpge_malloc(size_t nSize) { return FMemory::Malloc(nSize); }\nstatic inline void jpge_free(void *p) { FMemory::Free(p);; }\n\n// Various JPEG enums and tables.\nenum { M_SOF0 = 0xC0, M_DHT = 0xC4, M_SOI = 0xD8, M_EOI = 0xD9, M_SOS = 0xDA, M_DQT = 0xDB, M_APP0 = 0xE0 };\nenum { DC_LUM_CODES = 12, AC_LUM_CODES = 256, DC_CHROMA_CODES = 12, AC_CHROMA_CODES = 256, MAX_HUFF_SYMBOLS = 257, MAX_HUFF_CODESIZE = 32 };\n\nstatic uint8 s_zag[64] = { 0,1,8,16,9,2,3,10,17,24,32,25,18,11,4,5,12,19,26,33,40,48,41,34,27,20,13,6,7,14,21,28,35,42,49,56,57,50,43,36,29,22,15,23,30,37,44,51,58,59,52,45,38,31,39,46,53,60,61,54,47,55,62,63 };\nstatic int16 s_std_lum_quant[64] = { 16,11,12,14,12,10,16,14,13,14,18,17,16,19,24,40,26,24,22,22,24,49,35,37,29,40,58,51,61,60,57,51,56,55,64,72,92,78,64,68,87,69,55,56,80,109,81,87,95,98,103,104,103,62,77,113,121,112,100,120,92,101,103,99 };\nstatic int16 s_std_croma_quant[64] = { 17,18,18,24,21,24,47,26,26,47,99,66,56,66,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99 };\nstatic uint8 s_dc_lum_bits[17] = { 0,0,1,5,1,1,1,1,1,1,0,0,0,0,0,0,0 };\nstatic uint8 s_dc_lum_val[DC_LUM_CODES] = { 0,1,2,3,4,5,6,7,8,9,10,11 };\nstatic uint8 s_ac_lum_bits[17] = { 0,0,2,1,3,3,2,4,3,5,5,4,4,0,0,1,0x7d };\nstatic uint8 s_ac_lum_val[AC_LUM_CODES]  =\n{\n  0x01,0x02,0x03,0x00,0x04,0x11,0x05,0x12,0x21,0x31,0x41,0x06,0x13,0x51,0x61,0x07,0x22,0x71,0x14,0x32,0x81,0x91,0xa1,0x08,0x23,0x42,0xb1,0xc1,0x15,0x52,0xd1,0xf0,\n  0x24,0x33,0x62,0x72,0x82,0x09,0x0a,0x16,0x17,0x18,0x19,0x1a,0x25,0x26,0x27,0x28,0x29,0x2a,0x34,0x35,0x36,0x37,0x38,0x39,0x3a,0x43,0x44,0x45,0x46,0x47,0x48,0x49,\n  0x4a,0x53,0x54,0x55,0x56,0x57,0x58,0x59,0x5a,0x63,0x64,0x65,0x66,0x67,0x68,0x69,0x6a,0x73,0x74,0x75,0x76,0x77,0x78,0x79,0x7a,0x83,0x84,0x85,0x86,0x87,0x88,0x89,\n  0x8a,0x92,0x93,0x94,0x95,0x96,0x97,0x98,0x99,0x9a,0xa2,0xa3,0xa4,0xa5,0xa6,0xa7,0xa8,0xa9,0xaa,0xb2,0xb3,0xb4,0xb5,0xb6,0xb7,0xb8,0xb9,0xba,0xc2,0xc3,0xc4,0xc5,\n  0xc6,0xc7,0xc8,0xc9,0xca,0xd2,0xd3,0xd4,0xd5,0xd6,0xd7,0xd8,0xd9,0xda,0xe1,0xe2,0xe3,0xe4,0xe5,0xe6,0xe7,0xe8,0xe9,0xea,0xf1,0xf2,0xf3,0xf4,0xf5,0xf6,0xf7,0xf8,\n  0xf9,0xfa\n};\nstatic uint8 s_dc_chroma_bits[17] = { 0,0,3,1,1,1,1,1,1,1,1,1,0,0,0,0,0 };\nstatic uint8 s_dc_chroma_val[DC_CHROMA_CODES]  = { 0,1,2,3,4,5,6,7,8,9,10,11 };\nstatic uint8 s_ac_chroma_bits[17] = { 0,0,2,1,2,4,4,3,4,7,5,4,4,0,1,2,0x77 };\nstatic uint8 s_ac_chroma_val[AC_CHROMA_CODES] =\n{\n  0x00,0x01,0x02,0x03,0x11,0x04,0x05,0x21,0x31,0x06,0x12,0x41,0x51,0x07,0x61,0x71,0x13,0x22,0x32,0x81,0x08,0x14,0x42,0x91,0xa1,0xb1,0xc1,0x09,0x23,0x33,0x52,0xf0,\n  0x15,0x62,0x72,0xd1,0x0a,0x16,0x24,0x34,0xe1,0x25,0xf1,0x17,0x18,0x19,0x1a,0x26,0x27,0x28,0x29,0x2a,0x35,0x36,0x37,0x38,0x39,0x3a,0x43,0x44,0x45,0x46,0x47,0x48,\n  0x49,0x4a,0x53,0x54,0x55,0x56,0x57,0x58,0x59,0x5a,0x63,0x64,0x65,0x66,0x67,0x68,0x69,0x6a,0x73,0x74,0x75,0x76,0x77,0x78,0x79,0x7a,0x82,0x83,0x84,0x85,0x86,0x87,\n  0x88,0x89,0x8a,0x92,0x93,0x94,0x95,0x96,0x97,0x98,0x99,0x9a,0xa2,0xa3,0xa4,0xa5,0xa6,0xa7,0xa8,0xa9,0xaa,0xb2,0xb3,0xb4,0xb5,0xb6,0xb7,0xb8,0xb9,0xba,0xc2,0xc3,\n  0xc4,0xc5,0xc6,0xc7,0xc8,0xc9,0xca,0xd2,0xd3,0xd4,0xd5,0xd6,0xd7,0xd8,0xd9,0xda,0xe2,0xe3,0xe4,0xe5,0xe6,0xe7,0xe8,0xe9,0xea,0xf2,0xf3,0xf4,0xf5,0xf6,0xf7,0xf8,\n  0xf9,0xfa\n};\n\n// Low-level helper functions.\ntemplate <class T> inline void clear_obj(T &obj) { memset(&obj, 0, sizeof(obj)); }\n\nconst int YR = 19595, YG = 38470, YB = 7471, CB_R = -11059, CB_G = -21709, CB_B = 32768, CR_R = 32768, CR_G = -27439, CR_B = -5329;\nstatic inline uint8 clamp(int i) { if (static_cast<uint>(i) > 255U) { if (i < 0) i = 0; else if (i > 255) i = 255; } return static_cast<uint8>(i); }\n\nstatic void RGB_to_YCC(uint8* pDst, const uint8 *pSrc, int num_pixels)\n{\n  for ( ; num_pixels; pDst += 3, pSrc += 3, num_pixels--)\n  {\n    const int r = pSrc[0], g = pSrc[1], b = pSrc[2];\n    pDst[0] = static_cast<uint8>((r * YR + g * YG + b * YB + 32768) >> 16);\n    pDst[1] = clamp(128 + ((r * CB_R + g * CB_G + b * CB_B + 32768) >> 16));\n    pDst[2] = clamp(128 + ((r * CR_R + g * CR_G + b * CR_B + 32768) >> 16));\n  }\n}\n\nstatic void RGB_to_Y(uint8* pDst, const uint8 *pSrc, int num_pixels)\n{\n  for ( ; num_pixels; pDst++, pSrc += 3, num_pixels--)\n    pDst[0] = static_cast<uint8>((pSrc[0] * YR + pSrc[1] * YG + pSrc[2] * YB + 32768) >> 16);\n}\n\nstatic void RGBA_to_YCC(uint8* pDst, const uint8 *pSrc, int num_pixels)\n{\n  for ( ; num_pixels; pDst += 3, pSrc += 4, num_pixels--)\n  {\n    const int r = pSrc[0], g = pSrc[1], b = pSrc[2];\n    pDst[0] = static_cast<uint8>((r * YR + g * YG + b * YB + 32768) >> 16);\n    pDst[1] = clamp(128 + ((r * CB_R + g * CB_G + b * CB_B + 32768) >> 16));\n    pDst[2] = clamp(128 + ((r * CR_R + g * CR_G + b * CR_B + 32768) >> 16));\n  }\n}\n\nstatic void RGBA_to_Y(uint8* pDst, const uint8 *pSrc, int num_pixels)\n{\n  for ( ; num_pixels; pDst++, pSrc += 4, num_pixels--)\n    pDst[0] = static_cast<uint8>((pSrc[0] * YR + pSrc[1] * YG + pSrc[2] * YB + 32768) >> 16);\n}\n\nstatic void Y_to_YCC(uint8* pDst, const uint8* pSrc, int num_pixels)\n{\n  for( ; num_pixels; pDst += 3, pSrc++, num_pixels--) { pDst[0] = pSrc[0]; pDst[1] = 128; pDst[2] = 128; }\n}\n\n// Forward DCT - DCT derived from jfdctint.\n#define CONST_BITS  13\n#define ROW_BITS    2\n#define DCT_DESCALE(x, n) (((x) + (((int32)1) << ((n) - 1))) >> (n))\n#define DCT_MUL(var, c) (static_cast<int16>(var) * static_cast<int32>(c))\n#define DCT1D(s0, s1, s2, s3, s4, s5, s6, s7) \\\n  int32 t0 = s0 + s7, t7 = s0 - s7, t1 = s1 + s6, t6 = s1 - s6, t2 = s2 + s5, t5 = s2 - s5, t3 = s3 + s4, t4 = s3 - s4; \\\n  int32 t10 = t0 + t3, t13 = t0 - t3, t11 = t1 + t2, t12 = t1 - t2; \\\n  int32 u1 = DCT_MUL(t12 + t13, 4433); \\\n  s2 = u1 + DCT_MUL(t13, 6270); \\\n  s6 = u1 + DCT_MUL(t12, -15137); \\\n  u1 = t4 + t7; \\\n  int32 u2 = t5 + t6, u3 = t4 + t6, u4 = t5 + t7; \\\n  int32 z5 = DCT_MUL(u3 + u4, 9633); \\\n  t4 = DCT_MUL(t4, 2446); t5 = DCT_MUL(t5, 16819); \\\n  t6 = DCT_MUL(t6, 25172); t7 = DCT_MUL(t7, 12299); \\\n  u1 = DCT_MUL(u1, -7373); u2 = DCT_MUL(u2, -20995); \\\n  u3 = DCT_MUL(u3, -16069); u4 = DCT_MUL(u4, -3196); \\\n  u3 += z5; u4 += z5; \\\n  s0 = t10 + t11; s1 = t7 + u1 + u4; s3 = t6 + u2 + u3; s4 = t10 - t11; s5 = t5 + u2 + u4; s7 = t4 + u1 + u3;\n\nstatic void DCT2D(int32 *p)\n{\n  int32 c, *q = p;\n  for (c = 7; c >= 0; c--, q += 8)\n  {\n    int32 s0 = q[0], s1 = q[1], s2 = q[2], s3 = q[3], s4 = q[4], s5 = q[5], s6 = q[6], s7 = q[7];\n    DCT1D(s0, s1, s2, s3, s4, s5, s6, s7);\n    q[0] = s0 << ROW_BITS; q[1] = DCT_DESCALE(s1, CONST_BITS-ROW_BITS); q[2] = DCT_DESCALE(s2, CONST_BITS-ROW_BITS); q[3] = DCT_DESCALE(s3, CONST_BITS-ROW_BITS);\n    q[4] = s4 << ROW_BITS; q[5] = DCT_DESCALE(s5, CONST_BITS-ROW_BITS); q[6] = DCT_DESCALE(s6, CONST_BITS-ROW_BITS); q[7] = DCT_DESCALE(s7, CONST_BITS-ROW_BITS);\n  }\n  for (q = p, c = 7; c >= 0; c--, q++)\n  {\n    int32 s0 = q[0*8], s1 = q[1*8], s2 = q[2*8], s3 = q[3*8], s4 = q[4*8], s5 = q[5*8], s6 = q[6*8], s7 = q[7*8];\n    DCT1D(s0, s1, s2, s3, s4, s5, s6, s7);\n    q[0*8] = DCT_DESCALE(s0, ROW_BITS+3); q[1*8] = DCT_DESCALE(s1, CONST_BITS+ROW_BITS+3); q[2*8] = DCT_DESCALE(s2, CONST_BITS+ROW_BITS+3); q[3*8] = DCT_DESCALE(s3, CONST_BITS+ROW_BITS+3);\n    q[4*8] = DCT_DESCALE(s4, ROW_BITS+3); q[5*8] = DCT_DESCALE(s5, CONST_BITS+ROW_BITS+3); q[6*8] = DCT_DESCALE(s6, CONST_BITS+ROW_BITS+3); q[7*8] = DCT_DESCALE(s7, CONST_BITS+ROW_BITS+3);\n  }\n}\n\nstruct sym_freq { uint m_key, m_sym_index; };\n\n// Radix sorts sym_freq[] array by 32-bit key m_key. Returns ptr to sorted values.\nstatic inline sym_freq* radix_sort_syms(uint num_syms, sym_freq* pSyms0, sym_freq* pSyms1)\n{\n  const uint cMaxPasses = 4;\n  uint32 hist[256 * cMaxPasses]; clear_obj(hist);\n  for (uint i = 0; i < num_syms; i++) { uint freq = pSyms0[i].m_key; hist[freq & 0xFF]++; hist[256 + ((freq >> 8) & 0xFF)]++; hist[256*2 + ((freq >> 16) & 0xFF)]++; hist[256*3 + ((freq >> 24) & 0xFF)]++; }\n  sym_freq* pCur_syms = pSyms0, *pNew_syms = pSyms1;\n  uint total_passes = cMaxPasses; while ((total_passes > 1) && (num_syms == hist[(total_passes - 1) * 256])) total_passes--;\n  for (uint pass_shift = 0, pass = 0; pass < total_passes; pass++, pass_shift += 8)\n  {\n    const uint32* pHist = &hist[pass << 8];\n    uint offsets[256], cur_ofs = 0;\n    for (uint i = 0; i < 256; i++) { offsets[i] = cur_ofs; cur_ofs += pHist[i]; }\n    for (uint i = 0; i < num_syms; i++)\n      pNew_syms[offsets[(pCur_syms[i].m_key >> pass_shift) & 0xFF]++] = pCur_syms[i];\n    sym_freq* t = pCur_syms; pCur_syms = pNew_syms; pNew_syms = t;\n  }\n  return pCur_syms;\n}\n\n// calculate_minimum_redundancy() originally written by: Alistair Moffat, alistair@cs.mu.oz.au, Jyrki Katajainen, jyrki@diku.dk, November 1996.\nstatic void calculate_minimum_redundancy(sym_freq *A, int n)\n{\n  int root, leaf, next, avbl, used, dpth;\n  if (n==0) return; else if (n==1) { A[0].m_key = 1; return; }\n  A[0].m_key += A[1].m_key; root = 0; leaf = 2;\n  for (next=1; next < n-1; next++)\n  {\n    if (leaf>=n || A[root].m_key<A[leaf].m_key) { A[next].m_key = A[root].m_key; A[root++].m_key = next; } else A[next].m_key = A[leaf++].m_key;\n    if (leaf>=n || (root<next && A[root].m_key<A[leaf].m_key)) { A[next].m_key += A[root].m_key; A[root++].m_key = next; } else A[next].m_key += A[leaf++].m_key;\n  }\n  A[n-2].m_key = 0;\n  for (next=n-3; next>=0; next--) A[next].m_key = A[A[next].m_key].m_key+1;\n  avbl = 1; used = dpth = 0; root = n-2; next = n-1;\n  while (avbl>0)\n  {\n    while (root>=0 && (int)A[root].m_key==dpth) { used++; root--; }\n    while (avbl>used) { A[next--].m_key = dpth; avbl--; }\n    avbl = 2*used; dpth++; used = 0;\n  }\n}\n\n// Limits canonical Huffman code table's max code size to max_code_size.\nstatic void huffman_enforce_max_code_size(int *pNum_codes, int code_list_len, int max_code_size)\n{\n  if (code_list_len <= 1) return;\n\n  for (int i = max_code_size + 1; i <= MAX_HUFF_CODESIZE; i++) pNum_codes[max_code_size] += pNum_codes[i];\n\n  uint32 total = 0;\n  for (int i = max_code_size; i > 0; i--)\n    total += (((uint32)pNum_codes[i]) << (max_code_size - i));\n\n  while (total != (1UL << max_code_size))\n  {\n    pNum_codes[max_code_size]--;\n    for (int i = max_code_size - 1; i > 0; i--)\n    {\n      if (pNum_codes[i]) { pNum_codes[i]--; pNum_codes[i + 1] += 2; break; }\n    }\n    total--;\n  }\n}\n\n// Generates an optimized offman table.\nvoid jpeg_encoder::optimize_huffman_table(int table_num, int table_len)\n{\n  sym_freq syms0[MAX_HUFF_SYMBOLS], syms1[MAX_HUFF_SYMBOLS];\n  syms0[0].m_key = 1; syms0[0].m_sym_index = 0;  // dummy symbol, assures that no valid code contains all 1's\n  int num_used_syms = 1;\n  const uint32 *pSym_count = &m_huff_count[table_num][0];\n  for (int i = 0; i < table_len; i++)\n    if (pSym_count[i]) { syms0[num_used_syms].m_key = pSym_count[i]; syms0[num_used_syms++].m_sym_index = i + 1; }\n  sym_freq* pSyms = radix_sort_syms(num_used_syms, syms0, syms1);\n  calculate_minimum_redundancy(pSyms, num_used_syms);\n\n  // Count the # of symbols of each code size.\n  int num_codes[1 + MAX_HUFF_CODESIZE]; clear_obj(num_codes);\n  for (int i = 0; i < num_used_syms; i++)\n    num_codes[pSyms[i].m_key]++;\n\n  const uint JPGE_CODE_SIZE_LIMIT = 16; // the maximum possible size of a JPEG Huffman code (valid range is [9,16] - 9 vs. 8 because of the dummy symbol)\n  huffman_enforce_max_code_size(num_codes, num_used_syms, JPGE_CODE_SIZE_LIMIT);\n\n  // Compute m_huff_bits array, which contains the # of symbols per code size.\n  clear_obj(m_huff_bits[table_num]);\n  for (int i = 1; i <= (int)JPGE_CODE_SIZE_LIMIT; i++)\n    m_huff_bits[table_num][i] = static_cast<uint8>(num_codes[i]);\n\n  // Remove the dummy symbol added above, which must be in largest bucket.\n  for (int i = JPGE_CODE_SIZE_LIMIT; i >= 1; i--)\n  {\n    if (m_huff_bits[table_num][i]) { m_huff_bits[table_num][i]--; break; }\n  }\n\n  // Compute the m_huff_val array, which contains the symbol indices sorted by code size (smallest to largest).\n  for (int i = num_used_syms - 1; i >= 1; i--)\n    m_huff_val[table_num][num_used_syms - 1 - i] = static_cast<uint8>(pSyms[i].m_sym_index - 1);\n}\n\n// JPEG marker generation.\nvoid jpeg_encoder::emit_byte(uint8 i)\n{\n  m_all_stream_writes_succeeded = m_all_stream_writes_succeeded && m_pStream->put_obj(i);\n}\n\nvoid jpeg_encoder::emit_word(uint i)\n{\n  emit_byte(uint8(i >> 8)); emit_byte(uint8(i & 0xFF));\n}\n\nvoid jpeg_encoder::emit_marker(int marker)\n{\n  emit_byte(uint8(0xFF)); emit_byte(uint8(marker));\n}\n\n// Emit JFIF marker\nvoid jpeg_encoder::emit_jfif_app0()\n{\n  emit_marker(M_APP0);\n  emit_word(2 + 4 + 1 + 2 + 1 + 2 + 2 + 1 + 1);\n  emit_byte(0x4A); emit_byte(0x46); emit_byte(0x49); emit_byte(0x46); /* Identifier: ASCII \"JFIF\" */\n  emit_byte(0);\n  emit_byte(1);      /* Major version */\n  emit_byte(1);      /* Minor version */\n  emit_byte(0);      /* Density unit */\n  emit_word(1);\n  emit_word(1);\n  emit_byte(0);      /* No thumbnail image */\n  emit_byte(0);\n}\n\n// Emit quantization tables\nvoid jpeg_encoder::emit_dqt()\n{\n  for (int i = 0; i < ((m_num_components == 3) ? 2 : 1); i++)\n  {\n    emit_marker(M_DQT);\n    emit_word(64 + 1 + 2);\n    emit_byte(static_cast<uint8>(i));\n    for (int j = 0; j < 64; j++)\n      emit_byte(static_cast<uint8>(m_quantization_tables[i][j]));\n  }\n}\n\n// Emit start of frame marker\nvoid jpeg_encoder::emit_sof()\n{\n  emit_marker(M_SOF0);                           /* baseline */\n  emit_word(3 * m_num_components + 2 + 5 + 1);\n  emit_byte(8);                                  /* precision */\n  emit_word(m_image_y);\n  emit_word(m_image_x);\n  emit_byte(m_num_components);\n  for (int i = 0; i < m_num_components; i++)\n  {\n    emit_byte(static_cast<uint8>(i + 1));                                   /* component ID     */\n    emit_byte((m_comp_h_samp[i] << 4) + m_comp_v_samp[i]);  /* h and v sampling */\n    emit_byte(i > 0);                                   /* quant. table num */\n  }\n}\n\n// Emit Huffman table.\nvoid jpeg_encoder::emit_dht(uint8 *bits, uint8 *val, int index, bool ac_flag)\n{\n  emit_marker(M_DHT);\n\n  int length = 0;\n  for (int i = 1; i <= 16; i++)\n    length += bits[i];\n\n  emit_word(length + 2 + 1 + 16);\n  emit_byte(static_cast<uint8>(index + (ac_flag << 4)));\n\n  for (int i = 1; i <= 16; i++)\n    emit_byte(bits[i]);\n\n  for (int i = 0; i < length; i++)\n    emit_byte(val[i]);\n}\n\n// Emit all Huffman tables.\nvoid jpeg_encoder::emit_dhts()\n{\n  emit_dht(m_huff_bits[0+0], m_huff_val[0+0], 0, false);\n  emit_dht(m_huff_bits[2+0], m_huff_val[2+0], 0, true);\n  if (m_num_components == 3)\n  {\n    emit_dht(m_huff_bits[0+1], m_huff_val[0+1], 1, false);\n    emit_dht(m_huff_bits[2+1], m_huff_val[2+1], 1, true);\n  }\n}\n\n// emit start of scan\nvoid jpeg_encoder::emit_sos()\n{\n  emit_marker(M_SOS);\n  emit_word(2 * m_num_components + 2 + 1 + 3);\n  emit_byte(m_num_components);\n  for (int i = 0; i < m_num_components; i++)\n  {\n    emit_byte(static_cast<uint8>(i + 1));\n    if (i == 0)\n      emit_byte((0 << 4) + 0);\n    else\n      emit_byte((1 << 4) + 1);\n  }\n  emit_byte(0);     /* spectral selection */\n  emit_byte(63);\n  emit_byte(0);\n}\n\n// Emit all markers at beginning of image file.\nvoid jpeg_encoder::emit_markers()\n{\n  emit_marker(M_SOI);\n  emit_jfif_app0();\n  emit_dqt();\n  emit_sof();\n  emit_dhts();\n  emit_sos();\n}\n\n// Compute the actual canonical Huffman codes/code sizes given the JPEG huff bits and val arrays.\nvoid jpeg_encoder::compute_huffman_table(uint *codes, uint8 *code_sizes, uint8 *bits, uint8 *val)\n{\n  int i, l, last_p, si;\n  uint8 huff_size[257];\n  uint huff_code[257];\n  uint code;\n\n  int p = 0;\n  for (l = 1; l <= 16; l++)\n    for (i = 1; i <= bits[l]; i++)\n      huff_size[p++] = (char)l;\n\n  huff_size[p] = 0; last_p = p; // write sentinel\n\n  code = 0; si = huff_size[0]; p = 0;\n\n  while (huff_size[p])\n  {\n    while (huff_size[p] == si)\n      huff_code[p++] = code++;\n    code <<= 1;\n    si++;\n  }\n\n  memset(codes, 0, sizeof(codes[0])*256);\n  memset(code_sizes, 0, sizeof(code_sizes[0])*256);\n  for (p = 0; p < last_p; p++)\n  {\n    codes[val[p]]      = huff_code[p];\n    code_sizes[val[p]] = huff_size[p];\n  }\n}\n\n// Quantization table generation.\nvoid jpeg_encoder::compute_quant_table(int32 *pDst, int16 *pSrc)\n{\n  int32 q;\n  if (m_params.m_quality < 50)\n    q = 5000 / m_params.m_quality;\n  else\n    q = 200 - m_params.m_quality * 2;\n  for (int i = 0; i < 64; i++)\n  {\n    int32 j = *pSrc++; j = (j * q + 50L) / 100L;\n    *pDst++ = JPGE_MIN(JPGE_MAX(j, 1), 255);\n  }\n}\n\n// Higher-level methods.\nvoid jpeg_encoder::first_pass_init()\n{\n  m_bit_buffer = 0; m_bits_in = 0;\n  memset(m_last_dc_val, 0, 3 * sizeof(m_last_dc_val[0]));\n  m_mcu_y_ofs = 0;\n  m_pass_num = 1;\n}\n\nbool jpeg_encoder::second_pass_init()\n{\n  compute_huffman_table(&m_huff_codes[0+0][0], &m_huff_code_sizes[0+0][0], m_huff_bits[0+0], m_huff_val[0+0]);\n  compute_huffman_table(&m_huff_codes[2+0][0], &m_huff_code_sizes[2+0][0], m_huff_bits[2+0], m_huff_val[2+0]);\n  if (m_num_components > 1)\n  {\n    compute_huffman_table(&m_huff_codes[0+1][0], &m_huff_code_sizes[0+1][0], m_huff_bits[0+1], m_huff_val[0+1]);\n    compute_huffman_table(&m_huff_codes[2+1][0], &m_huff_code_sizes[2+1][0], m_huff_bits[2+1], m_huff_val[2+1]);\n  }\n  first_pass_init();\n  emit_markers();\n  m_pass_num = 2;\n  return true;\n}\n\nbool jpeg_encoder::jpg_open(int p_x_res, int p_y_res, int src_channels)\n{\n  m_num_components = 3;\n  switch (m_params.m_subsampling)\n  {\n    case Y_ONLY:\n    {\n      m_num_components = 1;\n      m_comp_h_samp[0] = 1; m_comp_v_samp[0] = 1;\n      m_mcu_x          = 8; m_mcu_y          = 8;\n      break;\n    }\n    case H1V1:\n    {\n      m_comp_h_samp[0] = 1; m_comp_v_samp[0] = 1;\n      m_comp_h_samp[1] = 1; m_comp_v_samp[1] = 1;\n      m_comp_h_samp[2] = 1; m_comp_v_samp[2] = 1;\n      m_mcu_x          = 8; m_mcu_y          = 8;\n      break;\n    }\n    case H2V1:\n    {\n      m_comp_h_samp[0] = 2; m_comp_v_samp[0] = 1;\n      m_comp_h_samp[1] = 1; m_comp_v_samp[1] = 1;\n      m_comp_h_samp[2] = 1; m_comp_v_samp[2] = 1;\n      m_mcu_x          = 16; m_mcu_y         = 8;\n      break;\n    }\n    case H2V2:\n    {\n      m_comp_h_samp[0] = 2; m_comp_v_samp[0] = 2;\n      m_comp_h_samp[1] = 1; m_comp_v_samp[1] = 1;\n      m_comp_h_samp[2] = 1; m_comp_v_samp[2] = 1;\n      m_mcu_x          = 16; m_mcu_y         = 16;\n    }\n  }\n\n  m_image_x        = p_x_res; m_image_y = p_y_res;\n  m_image_bpp      = src_channels;\n  m_image_bpl      = m_image_x * src_channels;\n  m_image_x_mcu    = (m_image_x + m_mcu_x - 1) & (~(m_mcu_x - 1));\n  m_image_y_mcu    = (m_image_y + m_mcu_y - 1) & (~(m_mcu_y - 1));\n  m_image_bpl_xlt  = m_image_x * m_num_components;\n  m_image_bpl_mcu  = m_image_x_mcu * m_num_components;\n  m_mcus_per_row   = m_image_x_mcu / m_mcu_x;\n\n  if ((m_mcu_lines[0] = static_cast<uint8*>(jpge_malloc(m_image_bpl_mcu * m_mcu_y))) == NULL) return false;\n  for (int i = 1; i < m_mcu_y; i++)\n    m_mcu_lines[i] = m_mcu_lines[i-1] + m_image_bpl_mcu;\n\n  compute_quant_table(m_quantization_tables[0], s_std_lum_quant);\n  compute_quant_table(m_quantization_tables[1], m_params.m_no_chroma_discrim_flag ? s_std_lum_quant : s_std_croma_quant);\n\n  m_out_buf_left = JPGE_OUT_BUF_SIZE;\n  m_pOut_buf = m_out_buf;\n\n  if (m_params.m_two_pass_flag)\n  {\n    clear_obj(m_huff_count);\n    first_pass_init();\n  }\n  else\n  {\n    memcpy(m_huff_bits[0+0], s_dc_lum_bits, 17);    memcpy(m_huff_val [0+0], s_dc_lum_val, DC_LUM_CODES);\n    memcpy(m_huff_bits[2+0], s_ac_lum_bits, 17);    memcpy(m_huff_val [2+0], s_ac_lum_val, AC_LUM_CODES);\n    memcpy(m_huff_bits[0+1], s_dc_chroma_bits, 17); memcpy(m_huff_val [0+1], s_dc_chroma_val, DC_CHROMA_CODES);\n    memcpy(m_huff_bits[2+1], s_ac_chroma_bits, 17); memcpy(m_huff_val [2+1], s_ac_chroma_val, AC_CHROMA_CODES);\n    if (!second_pass_init()) return false;   // in effect, skip over the first pass\n  }\n  return m_all_stream_writes_succeeded;\n}\n\nvoid jpeg_encoder::load_block_8_8_grey(int x)\n{\n  uint8 *pSrc;\n  sample_array_t *pDst = m_sample_array;\n  x <<= 3;\n  for (int i = 0; i < 8; i++, pDst += 8)\n  {\n    pSrc = m_mcu_lines[i] + x;\n    pDst[0] = pSrc[0] - 128; pDst[1] = pSrc[1] - 128; pDst[2] = pSrc[2] - 128; pDst[3] = pSrc[3] - 128;\n    pDst[4] = pSrc[4] - 128; pDst[5] = pSrc[5] - 128; pDst[6] = pSrc[6] - 128; pDst[7] = pSrc[7] - 128;\n  }\n}\n\nvoid jpeg_encoder::load_block_8_8(int x, int y, int c)\n{\n  uint8 *pSrc;\n  sample_array_t *pDst = m_sample_array;\n  x = (x * (8 * 3)) + c;\n  y <<= 3;\n  for (int i = 0; i < 8; i++, pDst += 8)\n  {\n    pSrc = m_mcu_lines[y + i] + x;\n    pDst[0] = pSrc[0 * 3] - 128; pDst[1] = pSrc[1 * 3] - 128; pDst[2] = pSrc[2 * 3] - 128; pDst[3] = pSrc[3 * 3] - 128;\n    pDst[4] = pSrc[4 * 3] - 128; pDst[5] = pSrc[5 * 3] - 128; pDst[6] = pSrc[6 * 3] - 128; pDst[7] = pSrc[7 * 3] - 128;\n  }\n}\n\nvoid jpeg_encoder::load_block_16_8(int x, int c)\n{\n  uint8 *pSrc1, *pSrc2;\n  sample_array_t *pDst = m_sample_array;\n  x = (x * (16 * 3)) + c;\n  int a = 0, b = 2;\n  for (int i = 0; i < 16; i += 2, pDst += 8)\n  {\n    pSrc1 = m_mcu_lines[i + 0] + x;\n    pSrc2 = m_mcu_lines[i + 1] + x;\n    pDst[0] = ((pSrc1[ 0 * 3] + pSrc1[ 1 * 3] + pSrc2[ 0 * 3] + pSrc2[ 1 * 3] + a) >> 2) - 128; pDst[1] = ((pSrc1[ 2 * 3] + pSrc1[ 3 * 3] + pSrc2[ 2 * 3] + pSrc2[ 3 * 3] + b) >> 2) - 128;\n    pDst[2] = ((pSrc1[ 4 * 3] + pSrc1[ 5 * 3] + pSrc2[ 4 * 3] + pSrc2[ 5 * 3] + a) >> 2) - 128; pDst[3] = ((pSrc1[ 6 * 3] + pSrc1[ 7 * 3] + pSrc2[ 6 * 3] + pSrc2[ 7 * 3] + b) >> 2) - 128;\n    pDst[4] = ((pSrc1[ 8 * 3] + pSrc1[ 9 * 3] + pSrc2[ 8 * 3] + pSrc2[ 9 * 3] + a) >> 2) - 128; pDst[5] = ((pSrc1[10 * 3] + pSrc1[11 * 3] + pSrc2[10 * 3] + pSrc2[11 * 3] + b) >> 2) - 128;\n    pDst[6] = ((pSrc1[12 * 3] + pSrc1[13 * 3] + pSrc2[12 * 3] + pSrc2[13 * 3] + a) >> 2) - 128; pDst[7] = ((pSrc1[14 * 3] + pSrc1[15 * 3] + pSrc2[14 * 3] + pSrc2[15 * 3] + b) >> 2) - 128;\n    int temp = a; a = b; b = temp;\n  }\n}\n\nvoid jpeg_encoder::load_block_16_8_8(int x, int c)\n{\n  uint8 *pSrc1;\n  sample_array_t *pDst = m_sample_array;\n  x = (x * (16 * 3)) + c;\n  for (int i = 0; i < 8; i++, pDst += 8)\n  {\n    pSrc1 = m_mcu_lines[i + 0] + x;\n    pDst[0] = ((pSrc1[ 0 * 3] + pSrc1[ 1 * 3]) >> 1) - 128; pDst[1] = ((pSrc1[ 2 * 3] + pSrc1[ 3 * 3]) >> 1) - 128;\n    pDst[2] = ((pSrc1[ 4 * 3] + pSrc1[ 5 * 3]) >> 1) - 128; pDst[3] = ((pSrc1[ 6 * 3] + pSrc1[ 7 * 3]) >> 1) - 128;\n    pDst[4] = ((pSrc1[ 8 * 3] + pSrc1[ 9 * 3]) >> 1) - 128; pDst[5] = ((pSrc1[10 * 3] + pSrc1[11 * 3]) >> 1) - 128;\n    pDst[6] = ((pSrc1[12 * 3] + pSrc1[13 * 3]) >> 1) - 128; pDst[7] = ((pSrc1[14 * 3] + pSrc1[15 * 3]) >> 1) - 128;\n  }\n}\n\nvoid jpeg_encoder::load_quantized_coefficients(int component_num)\n{\n  int32 *q = m_quantization_tables[component_num > 0];\n  int16 *pDst = m_coefficient_array;\n  for (int i = 0; i < 64; i++)\n  {\n    sample_array_t j = m_sample_array[s_zag[i]];\n    if (j < 0)\n    {\n      if ((j = -j + (*q >> 1)) < *q)\n        *pDst++ = 0;\n      else\n        *pDst++ = static_cast<int16>(-(j / *q));\n    }\n    else\n    {\n      if ((j = j + (*q >> 1)) < *q)\n        *pDst++ = 0;\n      else\n        *pDst++ = static_cast<int16>((j / *q));\n    }\n    q++;\n  }\n}\n\nvoid jpeg_encoder::flush_output_buffer()\n{\n  if (m_out_buf_left != JPGE_OUT_BUF_SIZE)\n    m_all_stream_writes_succeeded = m_all_stream_writes_succeeded && m_pStream->put_buf(m_out_buf, JPGE_OUT_BUF_SIZE - m_out_buf_left);\n  m_pOut_buf = m_out_buf;\n  m_out_buf_left = JPGE_OUT_BUF_SIZE;\n}\n\nvoid jpeg_encoder::put_bits(uint bits, uint len)\n{\n  m_bit_buffer |= ((uint32)bits << (24 - (m_bits_in += len)));\n  while (m_bits_in >= 8)\n  {\n    uint8 c;\n    #define JPGE_PUT_BYTE(c) { *m_pOut_buf++ = (c); if (--m_out_buf_left == 0) flush_output_buffer(); }\n    JPGE_PUT_BYTE(c = (uint8)((m_bit_buffer >> 16) & 0xFF));\n    if (c == 0xFF) JPGE_PUT_BYTE(0);\n    m_bit_buffer <<= 8;\n    m_bits_in -= 8;\n  }\n}\n\nvoid jpeg_encoder::code_coefficients_pass_one(int component_num)\n{\n  if (component_num >= 3) return; // just to shut up static analysis\n  int i, run_len, nbits, temp1;\n  int16 *src = m_coefficient_array;\n  uint32 *dc_count = component_num ? m_huff_count[0 + 1] : m_huff_count[0 + 0], *ac_count = component_num ? m_huff_count[2 + 1] : m_huff_count[2 + 0];\n\n  temp1 = src[0] - m_last_dc_val[component_num];\n  m_last_dc_val[component_num] = src[0];\n  if (temp1 < 0) temp1 = -temp1;\n\n  nbits = 0;\n  while (temp1)\n  {\n    nbits++; temp1 >>= 1;\n  }\n\n  dc_count[nbits]++;\n  for (run_len = 0, i = 1; i < 64; i++)\n  {\n    if ((temp1 = m_coefficient_array[i]) == 0)\n      run_len++;\n    else\n    {\n      while (run_len >= 16)\n      {\n        ac_count[0xF0]++;\n        run_len -= 16;\n      }\n      if (temp1 < 0) temp1 = -temp1;\n      nbits = 1;\n      while (temp1 >>= 1) nbits++;\n      ac_count[(run_len << 4) + nbits]++;\n      run_len = 0;\n    }\n  }\n  if (run_len) ac_count[0]++;\n}\n\nvoid jpeg_encoder::code_coefficients_pass_two(int component_num)\n{\n  int i, j, run_len, nbits, temp1, temp2;\n  int16 *pSrc = m_coefficient_array;\n  uint *codes[2];\n  uint8 *code_sizes[2];\n\n  if (component_num == 0)\n  {\n    codes[0] = m_huff_codes[0 + 0]; codes[1] = m_huff_codes[2 + 0];\n    code_sizes[0] = m_huff_code_sizes[0 + 0]; code_sizes[1] = m_huff_code_sizes[2 + 0];\n  }\n  else\n  {\n    codes[0] = m_huff_codes[0 + 1]; codes[1] = m_huff_codes[2 + 1];\n    code_sizes[0] = m_huff_code_sizes[0 + 1]; code_sizes[1] = m_huff_code_sizes[2 + 1];\n  }\n\n  temp1 = temp2 = pSrc[0] - m_last_dc_val[component_num];\n  m_last_dc_val[component_num] = pSrc[0];\n\n  if (temp1 < 0)\n  {\n    temp1 = -temp1; temp2--;\n  }\n\n  nbits = 0;\n  while (temp1)\n  {\n    nbits++; temp1 >>= 1;\n  }\n\n  put_bits(codes[0][nbits], code_sizes[0][nbits]);\n  if (nbits) put_bits(temp2 & ((1 << nbits) - 1), nbits);\n\n  for (run_len = 0, i = 1; i < 64; i++)\n  {\n    if ((temp1 = m_coefficient_array[i]) == 0)\n      run_len++;\n    else\n    {\n      while (run_len >= 16)\n      {\n        put_bits(codes[1][0xF0], code_sizes[1][0xF0]);\n        run_len -= 16;\n      }\n      if ((temp2 = temp1) < 0)\n      {\n        temp1 = -temp1;\n        temp2--;\n      }\n      nbits = 1;\n      while (temp1 >>= 1)\n        nbits++;\n      j = (run_len << 4) + nbits;\n      put_bits(codes[1][j], code_sizes[1][j]);\n      put_bits(temp2 & ((1 << nbits) - 1), nbits);\n      run_len = 0;\n    }\n  }\n  if (run_len)\n    put_bits(codes[1][0], code_sizes[1][0]);\n}\n\nvoid jpeg_encoder::code_block(int component_num)\n{\n  DCT2D(m_sample_array);\n  load_quantized_coefficients(component_num);\n  if (m_pass_num == 1)\n    code_coefficients_pass_one(component_num);\n  else\n    code_coefficients_pass_two(component_num);\n}\n\nvoid jpeg_encoder::process_mcu_row()\n{\n  if (m_num_components == 1)\n  {\n    for (int i = 0; i < m_mcus_per_row; i++)\n    {\n      load_block_8_8_grey(i); code_block(0);\n    }\n  }\n  else if ((m_comp_h_samp[0] == 1) && (m_comp_v_samp[0] == 1))\n  {\n    for (int i = 0; i < m_mcus_per_row; i++)\n    {\n      load_block_8_8(i, 0, 0); code_block(0); load_block_8_8(i, 0, 1); code_block(1); load_block_8_8(i, 0, 2); code_block(2);\n    }\n  }\n  else if ((m_comp_h_samp[0] == 2) && (m_comp_v_samp[0] == 1))\n  {\n    for (int i = 0; i < m_mcus_per_row; i++)\n    {\n      load_block_8_8(i * 2 + 0, 0, 0); code_block(0); load_block_8_8(i * 2 + 1, 0, 0); code_block(0);\n      load_block_16_8_8(i, 1); code_block(1); load_block_16_8_8(i, 2); code_block(2);\n    }\n  }\n  else if ((m_comp_h_samp[0] == 2) && (m_comp_v_samp[0] == 2))\n  {\n    for (int i = 0; i < m_mcus_per_row; i++)\n    {\n      load_block_8_8(i * 2 + 0, 0, 0); code_block(0); load_block_8_8(i * 2 + 1, 0, 0); code_block(0);\n      load_block_8_8(i * 2 + 0, 1, 0); code_block(0); load_block_8_8(i * 2 + 1, 1, 0); code_block(0);\n      load_block_16_8(i, 1); code_block(1); load_block_16_8(i, 2); code_block(2);\n    }\n  }\n}\n\nbool jpeg_encoder::terminate_pass_one()\n{\n  optimize_huffman_table(0+0, DC_LUM_CODES); optimize_huffman_table(2+0, AC_LUM_CODES);\n  if (m_num_components > 1)\n  {\n    optimize_huffman_table(0+1, DC_CHROMA_CODES); optimize_huffman_table(2+1, AC_CHROMA_CODES);\n  }\n  return second_pass_init();\n}\n\nbool jpeg_encoder::terminate_pass_two()\n{\n  put_bits(0x7F, 7);\n  flush_output_buffer();\n  emit_marker(M_EOI);\n  m_pass_num++; // purposely bump up m_pass_num, for debugging\n  return true;\n}\n\nbool jpeg_encoder::process_end_of_image()\n{\n  if (m_mcu_y_ofs)\n  {\n    if (m_mcu_y_ofs < 16) // check here just to shut up static analysis\n    {\n      for (int i = m_mcu_y_ofs; i < m_mcu_y; i++)\n        memcpy(m_mcu_lines[i], m_mcu_lines[m_mcu_y_ofs - 1], m_image_bpl_mcu);\n    }\n\n    process_mcu_row();\n  }\n\n  if (m_pass_num == 1)\n    return terminate_pass_one();\n  else\n    return terminate_pass_two();\n}\n\nvoid jpeg_encoder::load_mcu(const void *pSrc)\n{\n  const uint8* Psrc = reinterpret_cast<const uint8*>(pSrc);\n\n  uint8* pDst = m_mcu_lines[m_mcu_y_ofs]; // OK to write up to m_image_bpl_xlt bytes to pDst\n\n  if (m_num_components == 1)\n  {\n    if (m_image_bpp == 4)\n      RGBA_to_Y(pDst, Psrc, m_image_x);\n    else if (m_image_bpp == 3)\n      RGB_to_Y(pDst, Psrc, m_image_x);\n    else\n      memcpy(pDst, Psrc, m_image_x);\n  }\n  else\n  {\n    if (m_image_bpp == 4)\n      RGBA_to_YCC(pDst, Psrc, m_image_x);\n    else if (m_image_bpp == 3)\n      RGB_to_YCC(pDst, Psrc, m_image_x);\n    else\n      Y_to_YCC(pDst, Psrc, m_image_x);\n  }\n\n  // Possibly duplicate pixels at end of scanline if not a multiple of 8 or 16\n  if (m_num_components == 1)\n    memset(m_mcu_lines[m_mcu_y_ofs] + m_image_bpl_xlt, pDst[m_image_bpl_xlt - 1], m_image_x_mcu - m_image_x);\n  else\n  {\n    const uint8 y = pDst[m_image_bpl_xlt - 3 + 0], cb = pDst[m_image_bpl_xlt - 3 + 1], cr = pDst[m_image_bpl_xlt - 3 + 2];\n    uint8 *q = m_mcu_lines[m_mcu_y_ofs] + m_image_bpl_xlt;\n    for (int i = m_image_x; i < m_image_x_mcu; i++)\n    {\n      *q++ = y; *q++ = cb; *q++ = cr;\n    }\n  }\n\n  if (++m_mcu_y_ofs == m_mcu_y)\n  {\n    process_mcu_row();\n    m_mcu_y_ofs = 0;\n  }\n}\n\nvoid jpeg_encoder::clear()\n{\n  m_mcu_lines[0] = NULL;\n  m_pass_num = 0;\n  m_all_stream_writes_succeeded = true;\n}\n\njpeg_encoder::jpeg_encoder()\n{\n  clear();\n}\n\njpeg_encoder::~jpeg_encoder()\n{\n  deinit();\n}\n\nbool jpeg_encoder::init(output_stream *pStream, int64_t width, int64_t height, int64_t src_channels, const params &comp_params)\n{\n  deinit();\n  if (((!pStream) || (width < 1) || (height < 1)) || ((src_channels != 1) && (src_channels != 3) && (src_channels != 4)) || (!comp_params.check_valid())) return false;\n  m_pStream = pStream;\n  m_params = comp_params;\n  return jpg_open(width, height, src_channels);\n}\n\nvoid jpeg_encoder::deinit()\n{\n  jpge_free(m_mcu_lines[0]);\n  clear();\n}\n\nbool jpeg_encoder::process_scanline(const void* pScanline)\n{\n  if ((m_pass_num < 1) || (m_pass_num > 2)) return false;\n  if (m_all_stream_writes_succeeded)\n  {\n    if (!pScanline)\n    {\n      if (!process_end_of_image()) return false;\n    }\n    else\n    {\n      load_mcu(pScanline);\n    }\n  }\n  return m_all_stream_writes_succeeded;\n}\n\n// Higher level wrappers/examples (optional).\n#include <stdio.h>\n\nclass cfile_stream : public output_stream\n{\n   cfile_stream(const cfile_stream &);\n   cfile_stream &operator= (const cfile_stream &);\n\n   FILE* m_pFile;\n   bool m_bStatus;\n\npublic:\n   cfile_stream() : m_pFile(NULL), m_bStatus(false) { }\n\n   virtual ~cfile_stream()\n   {\n      close();\n   }\n\n   bool open(const char *pFilename)\n   {\n      close();\n#if defined(_MSC_VER)\n      if (fopen_s(&m_pFile, pFilename, \"wb\") != 0)\n\t  {\n\t\t  return false;\n\t  }\n#else\n      m_pFile = fopen(pFilename, \"wb\");\n#endif\n      m_bStatus = (m_pFile != NULL);\n      return m_bStatus;\n   }\n\n   bool close()\n   {\n      if (m_pFile)\n      {\n         if (fclose(m_pFile) == EOF)\n         {\n            m_bStatus = false;\n         }\n         m_pFile = NULL;\n      }\n      return m_bStatus;\n   }\n\n   virtual bool put_buf(const void* pBuf, int64_t len)\n   {\n      m_bStatus = m_bStatus && (fwrite(pBuf, len, 1, m_pFile) == 1);\n      return m_bStatus;\n   }\n\n   uint get_size() const\n   {\n      return m_pFile ? ftell(m_pFile) : 0;\n   }\n};\n\n// Writes JPEG image to file.\nbool compress_image_to_jpeg_file(const char *pFilename, int64_t width, int64_t height, int64_t num_channels, const uint8 *pImage_data, const params &comp_params)\n{\n  cfile_stream dst_stream;\n  if (!dst_stream.open(pFilename))\n    return false;\n\n  jpge::jpeg_encoder dst_image;\n  if (!dst_image.init(&dst_stream, width, height, num_channels, comp_params))\n    return false;\n\n  for (uint pass_index = 0; pass_index < dst_image.get_total_passes(); pass_index++)\n  {\n    for (int64_t i = 0; i < height; i++)\n    {\n\t\t// i, width, and num_channels are all 64bit\n       const uint8* pBuf = pImage_data + i * width * num_channels;\n       if (!dst_image.process_scanline(pBuf))\n          return false;\n    }\n    if (!dst_image.process_scanline(NULL))\n       return false;\n  }\n\n  dst_image.deinit();\n\n  return dst_stream.close();\n}\n\nclass memory_stream : public output_stream\n{\n   memory_stream(const memory_stream &);\n   memory_stream &operator= (const memory_stream &);\n\n   uint8 *m_pBuf;\n   uint64_t m_buf_size, m_buf_ofs;\n\npublic:\n   memory_stream(void *pBuf, uint64_t buf_size) : m_pBuf(static_cast<uint8*>(pBuf)), m_buf_size(buf_size), m_buf_ofs(0) { }\n\n   virtual ~memory_stream() { }\n\n   virtual bool put_buf(const void* pBuf, int64_t len)\n   {\n      uint64_t buf_remaining = m_buf_size - m_buf_ofs;\n      if ((uint64_t)len > buf_remaining)\n         return false;\n      memcpy(m_pBuf + m_buf_ofs, pBuf, len);\n      m_buf_ofs += len;\n      return true;\n   }\n\n   uint64_t get_size() const\n   {\n      return m_buf_ofs;\n   }\n};\n\nbool compress_image_to_jpeg_file_in_memory(void *pDstBuf, int64_t &buf_size, int64_t width, int64_t height, int64_t num_channels, const uint8 *pImage_data, const params &comp_params)\n{\n   if ((!pDstBuf) || (!buf_size))\n      return false;\n\n   memory_stream dst_stream(pDstBuf, buf_size);\n\n   buf_size = 0;\n\n   jpge::jpeg_encoder dst_image;\n   if (!dst_image.init(&dst_stream, width, height, num_channels, comp_params))\n      return false;\n\n   for (uint pass_index = 0; pass_index < dst_image.get_total_passes(); pass_index++)\n   {\n     for (int64_t i = 0; i < height; i++)\n     {\n        const uint8* pScanline = pImage_data + i * width * num_channels;\n        if (!dst_image.process_scanline(pScanline))\n           return false;\n     }\n     if (!dst_image.process_scanline(NULL))\n        return false;\n   }\n\n   dst_image.deinit();\n\n   buf_size = dst_stream.get_size();\n   return true;\n}\n\n} // namespace jpge"
  },
  {
    "path": "crazy_functions/test_project/cpp/longcode/prod_cons.h",
    "content": "#pragma once\n\n#include <atomic>\n#include <utility>\n#include <cstring>\n#include <type_traits>\n#include <cstdint>\n\n#include \"libipc/def.h\"\n\n#include \"libipc/platform/detail.h\"\n#include \"libipc/circ/elem_def.h\"\n#include \"libipc/utility/log.h\"\n#include \"libipc/utility/utility.h\"\n\nnamespace ipc {\n\n////////////////////////////////////////////////////////////////\n/// producer-consumer implementation\n////////////////////////////////////////////////////////////////\n\ntemplate <typename Flag>\nstruct prod_cons_impl;\n\ntemplate <>\nstruct prod_cons_impl<wr<relat::single, relat::single, trans::unicast>> {\n\n    template <std::size_t DataSize, std::size_t AlignSize>\n    struct elem_t {\n        std::aligned_storage_t<DataSize, AlignSize> data_ {};\n    };\n\n    alignas(cache_line_size) std::atomic<circ::u2_t> rd_; // read index\n    alignas(cache_line_size) std::atomic<circ::u2_t> wt_; // write index\n\n    constexpr circ::u2_t cursor() const noexcept {\n        return 0;\n    }\n\n    template <typename W, typename F, typename E>\n    bool push(W* /*wrapper*/, F&& f, E* elems) {\n        auto cur_wt = circ::index_of(wt_.load(std::memory_order_relaxed));\n        if (cur_wt == circ::index_of(rd_.load(std::memory_order_acquire) - 1)) {\n            return false; // full\n        }\n        std::forward<F>(f)(&(elems[cur_wt].data_));\n        wt_.fetch_add(1, std::memory_order_release);\n        return true;\n    }\n\n    /**\n     * In single-single-unicast, 'force_push' means 'no reader' or 'the only one reader is dead'.\n     * So we could just disconnect all connections of receiver, and return false.\n    */\n    template <typename W, typename F, typename E>\n    bool force_push(W* wrapper, F&&, E*) {\n        wrapper->elems()->disconnect_receiver(~static_cast<circ::cc_t>(0u));\n        return false;\n    }\n\n    template <typename W, typename F, typename R, typename E>\n    bool pop(W* /*wrapper*/, circ::u2_t& /*cur*/, F&& f, R&& out, E* elems) {\n        auto cur_rd = circ::index_of(rd_.load(std::memory_order_relaxed));\n        if (cur_rd == circ::index_of(wt_.load(std::memory_order_acquire))) {\n            return false; // empty\n        }\n        std::forward<F>(f)(&(elems[cur_rd].data_));\n        std::forward<R>(out)(true);\n        rd_.fetch_add(1, std::memory_order_release);\n        return true;\n    }\n};\n\ntemplate <>\nstruct prod_cons_impl<wr<relat::single, relat::multi , trans::unicast>>\n     : prod_cons_impl<wr<relat::single, relat::single, trans::unicast>> {\n\n    template <typename W, typename F, typename E>\n    bool force_push(W* wrapper, F&&, E*) {\n        wrapper->elems()->disconnect_receiver(1);\n        return false;\n    }\n\n    template <typename W, typename F, typename R, \n              template <std::size_t, std::size_t> class E, std::size_t DS, std::size_t AS>\n    bool pop(W* /*wrapper*/, circ::u2_t& /*cur*/, F&& f, R&& out, E<DS, AS>* elems) {\n        byte_t buff[DS];\n        for (unsigned k = 0;;) {\n            auto cur_rd = rd_.load(std::memory_order_relaxed);\n            if (circ::index_of(cur_rd) ==\n                circ::index_of(wt_.load(std::memory_order_acquire))) {\n                return false; // empty\n            }\n            std::memcpy(buff, &(elems[circ::index_of(cur_rd)].data_), sizeof(buff));\n            if (rd_.compare_exchange_weak(cur_rd, cur_rd + 1, std::memory_order_release)) {\n                std::forward<F>(f)(buff);\n                std::forward<R>(out)(true);\n                return true;\n            }\n            ipc::yield(k);\n        }\n    }\n};\n\ntemplate <>\nstruct prod_cons_impl<wr<relat::multi , relat::multi, trans::unicast>>\n     : prod_cons_impl<wr<relat::single, relat::multi, trans::unicast>> {\n\n    using flag_t = std::uint64_t;\n\n    template <std::size_t DataSize, std::size_t AlignSize>\n    struct elem_t {\n        std::aligned_storage_t<DataSize, AlignSize> data_ {};\n        std::atomic<flag_t> f_ct_ { 0 }; // commit flag\n    };\n\n    alignas(cache_line_size) std::atomic<circ::u2_t> ct_; // commit index\n\n    template <typename W, typename F, typename E>\n    bool push(W* /*wrapper*/, F&& f, E* elems) {\n        circ::u2_t cur_ct, nxt_ct;\n        for (unsigned k = 0;;) {\n            cur_ct = ct_.load(std::memory_order_relaxed);\n            if (circ::index_of(nxt_ct = cur_ct + 1) ==\n                circ::index_of(rd_.load(std::memory_order_acquire))) {\n                return false; // full\n            }\n            if (ct_.compare_exchange_weak(cur_ct, nxt_ct, std::memory_order_acq_rel)) {\n                break;\n            }\n            ipc::yield(k);\n        }\n        auto* el = elems + circ::index_of(cur_ct);\n        std::forward<F>(f)(&(el->data_));\n        // set flag & try update wt\n        el->f_ct_.store(~static_cast<flag_t>(cur_ct), std::memory_order_release);\n        while (1) {\n            auto cac_ct = el->f_ct_.load(std::memory_order_acquire);\n            if (cur_ct != wt_.load(std::memory_order_relaxed)) {\n                return true;\n            }\n            if ((~cac_ct) != cur_ct) {\n                return true;\n            }\n            if (!el->f_ct_.compare_exchange_strong(cac_ct, 0, std::memory_order_relaxed)) {\n                return true;\n            }\n            wt_.store(nxt_ct, std::memory_order_release);\n            cur_ct = nxt_ct;\n            nxt_ct = cur_ct + 1;\n            el = elems + circ::index_of(cur_ct);\n        }\n        return true;\n    }\n\n    template <typename W, typename F, typename E>\n    bool force_push(W* wrapper, F&&, E*) {\n        wrapper->elems()->disconnect_receiver(1);\n        return false;\n    }\n\n    template <typename W, typename F, typename R, \n              template <std::size_t, std::size_t> class E, std::size_t DS, std::size_t AS>\n    bool pop(W* /*wrapper*/, circ::u2_t& /*cur*/, F&& f, R&& out, E<DS, AS>* elems) {\n        byte_t buff[DS];\n        for (unsigned k = 0;;) {\n            auto cur_rd = rd_.load(std::memory_order_relaxed);\n            auto cur_wt = wt_.load(std::memory_order_acquire);\n            auto id_rd  = circ::index_of(cur_rd);\n            auto id_wt  = circ::index_of(cur_wt);\n            if (id_rd == id_wt) {\n                auto* el = elems + id_wt;\n                auto cac_ct = el->f_ct_.load(std::memory_order_acquire);\n                if ((~cac_ct) != cur_wt) {\n                    return false; // empty\n                }\n                if (el->f_ct_.compare_exchange_weak(cac_ct, 0, std::memory_order_relaxed)) {\n                    wt_.store(cur_wt + 1, std::memory_order_release);\n                }\n                k = 0;\n            }\n            else {\n                std::memcpy(buff, &(elems[circ::index_of(cur_rd)].data_), sizeof(buff));\n                if (rd_.compare_exchange_weak(cur_rd, cur_rd + 1, std::memory_order_release)) {\n                    std::forward<F>(f)(buff);\n                    std::forward<R>(out)(true);\n                    return true;\n                }\n                ipc::yield(k);\n            }\n        }\n    }\n};\n\ntemplate <>\nstruct prod_cons_impl<wr<relat::single, relat::multi, trans::broadcast>> {\n\n    using rc_t = std::uint64_t;\n\n    enum : rc_t {\n        ep_mask = 0x00000000ffffffffull,\n        ep_incr = 0x0000000100000000ull\n    };\n\n    template <std::size_t DataSize, std::size_t AlignSize>\n    struct elem_t {\n        std::aligned_storage_t<DataSize, AlignSize> data_ {};\n        std::atomic<rc_t> rc_ { 0 }; // read-counter\n    };\n\n    alignas(cache_line_size) std::atomic<circ::u2_t> wt_;   // write index\n    alignas(cache_line_size) rc_t epoch_ { 0 };             // only one writer\n\n    circ::u2_t cursor() const noexcept {\n        return wt_.load(std::memory_order_acquire);\n    }\n\n    template <typename W, typename F, typename E>\n    bool push(W* wrapper, F&& f, E* elems) {\n        E* el;\n        for (unsigned k = 0;;) {\n            circ::cc_t cc = wrapper->elems()->connections(std::memory_order_relaxed);\n            if (cc == 0) return false; // no reader\n            el = elems + circ::index_of(wt_.load(std::memory_order_relaxed));\n            // check all consumers have finished reading this element\n            auto cur_rc = el->rc_.load(std::memory_order_acquire);\n            circ::cc_t rem_cc = cur_rc & ep_mask;\n            if ((cc & rem_cc) && ((cur_rc & ~ep_mask) == epoch_)) {\n                return false; // has not finished yet\n            }\n            // consider rem_cc to be 0 here\n            if (el->rc_.compare_exchange_weak(\n                        cur_rc, epoch_ | static_cast<rc_t>(cc), std::memory_order_release)) {\n                break;\n            }\n            ipc::yield(k);\n        }\n        std::forward<F>(f)(&(el->data_));\n        wt_.fetch_add(1, std::memory_order_release);\n        return true;\n    }\n\n    template <typename W, typename F, typename E>\n    bool force_push(W* wrapper, F&& f, E* elems) {\n        E* el;\n        epoch_ += ep_incr;\n        for (unsigned k = 0;;) {\n            circ::cc_t cc = wrapper->elems()->connections(std::memory_order_relaxed);\n            if (cc == 0) return false; // no reader\n            el = elems + circ::index_of(wt_.load(std::memory_order_relaxed));\n            // check all consumers have finished reading this element\n            auto cur_rc = el->rc_.load(std::memory_order_acquire);\n            circ::cc_t rem_cc = cur_rc & ep_mask;\n            if (cc & rem_cc) {\n                ipc::log(\"force_push: k = %u, cc = %u, rem_cc = %u\\n\", k, cc, rem_cc);\n                cc = wrapper->elems()->disconnect_receiver(rem_cc); // disconnect all invalid readers\n                if (cc == 0) return false; // no reader\n            }\n            // just compare & exchange\n            if (el->rc_.compare_exchange_weak(\n                        cur_rc, epoch_ | static_cast<rc_t>(cc), std::memory_order_release)) {\n                break;\n            }\n            ipc::yield(k);\n        }\n        std::forward<F>(f)(&(el->data_));\n        wt_.fetch_add(1, std::memory_order_release);\n        return true;\n    }\n\n    template <typename W, typename F, typename R, typename E>\n    bool pop(W* wrapper, circ::u2_t& cur, F&& f, R&& out, E* elems) {\n        if (cur == cursor()) return false; // acquire\n        auto* el = elems + circ::index_of(cur++);\n        std::forward<F>(f)(&(el->data_));\n        for (unsigned k = 0;;) {\n            auto cur_rc = el->rc_.load(std::memory_order_acquire);\n            if ((cur_rc & ep_mask) == 0) {\n                std::forward<R>(out)(true);\n                return true;\n            }\n            auto nxt_rc = cur_rc & ~static_cast<rc_t>(wrapper->connected_id());\n            if (el->rc_.compare_exchange_weak(cur_rc, nxt_rc, std::memory_order_release)) {\n                std::forward<R>(out)((nxt_rc & ep_mask) == 0);\n                return true;\n            }\n            ipc::yield(k);\n        }\n    }\n};\n\ntemplate <>\nstruct prod_cons_impl<wr<relat::multi, relat::multi, trans::broadcast>> {\n\n    using rc_t   = std::uint64_t;\n    using flag_t = std::uint64_t;\n\n    enum : rc_t {\n        rc_mask = 0x00000000ffffffffull,\n        ep_mask = 0x00ffffffffffffffull,\n        ep_incr = 0x0100000000000000ull,\n        ic_mask = 0xff000000ffffffffull,\n        ic_incr = 0x0000000100000000ull\n    };\n\n    template <std::size_t DataSize, std::size_t AlignSize>\n    struct elem_t {\n        std::aligned_storage_t<DataSize, AlignSize> data_ {};\n        std::atomic<rc_t  > rc_   { 0 }; // read-counter\n        std::atomic<flag_t> f_ct_ { 0 }; // commit flag\n    };\n\n    alignas(cache_line_size) std::atomic<circ::u2_t> ct_;   // commit index\n    alignas(cache_line_size) std::atomic<rc_t> epoch_ { 0 };\n\n    circ::u2_t cursor() const noexcept {\n        return ct_.load(std::memory_order_acquire);\n    }\n\n    constexpr static rc_t inc_rc(rc_t rc) noexcept {\n        return (rc & ic_mask) | ((rc + ic_incr) & ~ic_mask);\n    }\n\n    constexpr static rc_t inc_mask(rc_t rc) noexcept {\n        return inc_rc(rc) & ~rc_mask;\n    }\n\n    template <typename W, typename F, typename E>\n    bool push(W* wrapper, F&& f, E* elems) {\n        E* el;\n        circ::u2_t cur_ct;\n        rc_t epoch = epoch_.load(std::memory_order_acquire);\n        for (unsigned k = 0;;) {\n            circ::cc_t cc = wrapper->elems()->connections(std::memory_order_relaxed);\n            if (cc == 0) return false; // no reader\n            el = elems + circ::index_of(cur_ct = ct_.load(std::memory_order_relaxed));\n            // check all consumers have finished reading this element\n            auto cur_rc = el->rc_.load(std::memory_order_relaxed);\n            circ::cc_t rem_cc = cur_rc & rc_mask;\n            if ((cc & rem_cc) && ((cur_rc & ~ep_mask) == epoch)) {\n                return false; // has not finished yet\n            }\n            else if (!rem_cc) {\n                auto cur_fl = el->f_ct_.load(std::memory_order_acquire);\n                if ((cur_fl != cur_ct) && cur_fl) {\n                    return false; // full\n                }\n            }\n            // consider rem_cc to be 0 here\n            if (el->rc_.compare_exchange_weak(\n                        cur_rc, inc_mask(epoch | (cur_rc & ep_mask)) | static_cast<rc_t>(cc), std::memory_order_relaxed) &&\n                epoch_.compare_exchange_weak(epoch, epoch, std::memory_order_acq_rel)) {\n                break;\n            }\n            ipc::yield(k);\n        }\n        // only one thread/process would touch here at one time\n        ct_.store(cur_ct + 1, std::memory_order_release);\n        std::forward<F>(f)(&(el->data_));\n        // set flag & try update wt\n        el->f_ct_.store(~static_cast<flag_t>(cur_ct), std::memory_order_release);\n        return true;\n    }\n\n    template <typename W, typename F, typename E>\n    bool force_push(W* wrapper, F&& f, E* elems) {\n        E* el;\n        circ::u2_t cur_ct;\n        rc_t epoch = epoch_.fetch_add(ep_incr, std::memory_order_release) + ep_incr;\n        for (unsigned k = 0;;) {\n            circ::cc_t cc = wrapper->elems()->connections(std::memory_order_relaxed);\n            if (cc == 0) return false; // no reader\n            el = elems + circ::index_of(cur_ct = ct_.load(std::memory_order_relaxed));\n            // check all consumers have finished reading this element\n            auto cur_rc = el->rc_.load(std::memory_order_acquire);\n            circ::cc_t rem_cc = cur_rc & rc_mask;\n            if (cc & rem_cc) {\n                ipc::log(\"force_push: k = %u, cc = %u, rem_cc = %u\\n\", k, cc, rem_cc);\n                cc = wrapper->elems()->disconnect_receiver(rem_cc); // disconnect all invalid readers\n                if (cc == 0) return false; // no reader\n            }\n            // just compare & exchange\n            if (el->rc_.compare_exchange_weak(\n                        cur_rc, inc_mask(epoch | (cur_rc & ep_mask)) | static_cast<rc_t>(cc), std::memory_order_relaxed)) {\n                if (epoch == epoch_.load(std::memory_order_acquire)) {\n                    break;\n                }\n                else if (push(wrapper, std::forward<F>(f), elems)) {\n                    return true;\n                }\n                epoch = epoch_.fetch_add(ep_incr, std::memory_order_release) + ep_incr;\n            }\n            ipc::yield(k);\n        }\n        // only one thread/process would touch here at one time\n        ct_.store(cur_ct + 1, std::memory_order_release);\n        std::forward<F>(f)(&(el->data_));\n        // set flag & try update wt\n        el->f_ct_.store(~static_cast<flag_t>(cur_ct), std::memory_order_release);\n        return true;\n    }\n\n    template <typename W, typename F, typename R, typename E, std::size_t N>\n    bool pop(W* wrapper, circ::u2_t& cur, F&& f, R&& out, E(& elems)[N]) {\n        auto* el = elems + circ::index_of(cur);\n        auto cur_fl = el->f_ct_.load(std::memory_order_acquire);\n        if (cur_fl != ~static_cast<flag_t>(cur)) {\n            return false; // empty\n        }\n        ++cur;\n        std::forward<F>(f)(&(el->data_));\n        for (unsigned k = 0;;) {\n            auto cur_rc = el->rc_.load(std::memory_order_acquire);\n            if ((cur_rc & rc_mask) == 0) {\n                std::forward<R>(out)(true);\n                el->f_ct_.store(cur + N - 1, std::memory_order_release);\n                return true;\n            }\n            auto nxt_rc = inc_rc(cur_rc) & ~static_cast<rc_t>(wrapper->connected_id());\n            bool last_one = false;\n            if ((last_one = (nxt_rc & rc_mask) == 0)) {\n                el->f_ct_.store(cur + N - 1, std::memory_order_release);\n            }\n            if (el->rc_.compare_exchange_weak(cur_rc, nxt_rc, std::memory_order_release)) {\n                std::forward<R>(out)(last_one);\n                return true;\n            }\n            ipc::yield(k);\n        }\n    }\n};\n\n} // namespace ipc\n"
  },
  {
    "path": "crazy_functions/test_project/latex/attention/background.tex",
    "content": "The goal of reducing sequential computation also forms the foundation of the Extended Neural GPU \\citep{extendedngpu}, ByteNet \\citep{NalBytenet2017} and ConvS2S \\citep{JonasFaceNet2017}, all of which use convolutional neural networks as basic building block, computing hidden representations in parallel for all input and output positions. In these models, the number of operations required to relate signals from two arbitrary input or output positions grows in the distance between positions, linearly for ConvS2S and logarithmically for ByteNet. This makes it more difficult to learn dependencies between distant positions \\citep{hochreiter2001gradient}. In the Transformer this is reduced to a constant number of operations, albeit at the cost of reduced effective resolution due to averaging attention-weighted positions, an effect we counteract with Multi-Head Attention as described in section~\\ref{sec:attention}. \n\nSelf-attention, sometimes called intra-attention is an attention mechanism relating different positions of a single sequence in order to compute a representation of the sequence. Self-attention has been used successfully in a variety of tasks including reading comprehension, abstractive summarization, textual entailment and learning task-independent sentence representations \\citep{cheng2016long, decomposableAttnModel, paulus2017deep, lin2017structured}.\n\nEnd-to-end memory networks are based on a recurrent attention mechanism instead of sequence-aligned recurrence and have been shown to perform well on simple-language question answering and language modeling tasks \\citep{sukhbaatar2015}.\n\nTo the best of our knowledge, however, the Transformer is the first transduction model relying entirely on self-attention to compute representations of its input and output without using sequence-aligned RNNs or convolution.\nIn the following sections, we will describe the Transformer, motivate self-attention and discuss its advantages over models such as \\citep{neural_gpu, NalBytenet2017} and \\citep{JonasFaceNet2017}.\n\n\n%\\citep{JonasFaceNet2017} report new SOTA on machine translation for English-to-German (EnDe), Enlish-to-French (EnFr) and English-to-Romanian language pairs. \n\n%For example,! in MT, we must draw information from both input and previous output words to translate an output word accurately. An attention layer \\citep{bahdanau2014neural} can connect a very large number of positions at low computation cost, making it an essential ingredient in competitive recurrent models for machine translation.\n\n%A natural question to ask then is, \"Could we replace recurrence with attention?\". \\marginpar{Don't know if it's the most natural question to ask given the previous statements. Also, need to say that the complexity table summarizes these statements} Such a model would be blessed with the computational efficiency of attention and the power of cross-positional communication. In this work, show that pure attention models work remarkably well for MT, achieving new SOTA results on EnDe and EnFr, and can be trained in under $2$ days on xyz architecture. \n\n%After the seminal models introduced in \\citep{sutskever14, bahdanau2014neural, cho2014learning}, recurrent models have become the dominant solution for both sequence modeling and sequence-to-sequence transduction. Many efforts such as \\citep{wu2016google,luong2015effective,jozefowicz2016exploring} have pushed the boundaries of machine translation (MT) and language modeling with recurrent endoder-decoder and recurrent language models. Recent effort \\citep{shazeer2017outrageously} has successfully combined the power of conditional computation with sequence models to train very large models for MT, pushing SOTA at lower computational cost.\n\n%Recurrent models compute a vector of hidden states $h_t$, for each time step $t$ of computation. $h_t$ is a function of both the input at time $t$ and the previous hidden state $h_t$. This dependence on the previous hidden state precludes processing all timesteps at once, instead requiring long sequences of sequential operations.  In practice, this results in greatly reduced computational efficiency, as on modern computing hardware, a single operation on a large batch is much faster than a large number of operations on small batches.  The problem gets worse at longer sequence lengths. Although sequential computation is not a severe bottleneck at inference time, as autoregressively generating each output requires all previous outputs, the inability to compute scores at all output positions at once hinders us from rapidly training our models over large datasets. Although impressive work such as \\citep{Kuchaiev2017Factorization} is able to significantly accelerate the training of LSTMs with factorization tricks, we are still bound by the linear dependence on sequence length.\n\n%If the model could compute hidden states at each time step using only the inputs and outputs,  it would be liberated from the dependence on results from previous time steps during training. This line of thought is the foundation of recent efforts such as the Markovian neural GPU \\citep{neural_gpu}, ByteNet \\citep{NalBytenet2017} and ConvS2S \\citep{JonasFaceNet2017}, all of which use convolutional neural networks as a building block to compute hidden representations simultaneously for all timesteps, resulting in $O(1)$ sequential time complexity. \\citep{JonasFaceNet2017} report new SOTA on machine translation for English-to-German (EnDe), Enlish-to-French (EnFr) and English-to-Romanian language pairs. \n\n%A crucial component for accurate sequence prediction is modeling cross-positional communication. For example, in MT, we must draw information from both input and previous output words to translate an output word accurately. An attention layer \\citep{bahdanau2014neural} can connect a very large number of positions at a low computation cost, also $O(1)$ sequential time complexity, making it an essential ingredient in recurrent encoder-decoder architectures for MT. A natural question to ask then is, \"Could we replace recurrence with attention?\". \\marginpar{Don't know if it's the most natural question to ask given the previous statements. Also, need to say that the complexity table summarizes these statements} Such a model would be blessed with the computational efficiency of attention and the power of cross-positional communication. In this work, show that pure attention models work remarkably well for MT, achieving new SOTA results on EnDe and EnFr, and can be trained in under $2$ days on xyz architecture. \n\n\n\n%Note: Facebook model is no better than RNNs in this regard, since it requires a number of layers proportional to the distance you want to communicate.  Bytenet is more promising, since it requires a logarithmnic number of layers (does bytenet have SOTA results)?   \n\n%Note: An attention  layer can connect a very large number of positions at a low computation cost in O(1) sequential operations.  This is why encoder-decoder attention has been so successful in seq-to-seq models so far.  It is only natural, then, to also use attention to connect the timesteps of the same sequence.\n\n%Note: I wouldn't say that long sequences are not a problem during inference.  It would be great if we could infer with no long sequences.  We could just say later on that, while our training graph is constant-depth, our model still requires sequential operations in the decoder part during inference due to the autoregressive nature of the model.   \n\n%\\begin{table}[h!]\n%\\caption{Attention models are quite efficient for cross-positional communications when sequence length is smaller than channel depth. $n$ represents the sequence length and $d$ represents the channel depth.}\n%\\label{tab:op_complexities}\n%\\begin{center}\n%\\vspace{-5pt}\n%\\scalebox{0.75}{\n\n%\\begin{tabular}{l|c|c|c}\n%\\hline \\hline\n%Layer Type & Receptive & Complexity & Sequential  \\\\\n%           & Field     &            & Operations  \\\\\n%\\hline\n%Pointwise Feed-Forward & $1$ & $O(n \\cdot d^2)$ & $O(1)$ \\\\\n%\\hline\n%Recurrent & $n$ & $O(n \\cdot d^2)$ & $O(n)$ \\\\\n%\\hline\n%Convolutional & $r$ & $O(r \\cdot n \\cdot d^2)$ & $O(1)$ \\\\\n%\\hline\n%Convolutional (separable) & $r$ & $O(r \\cdot n \\cdot d + n %\\cdot d^2)$ & $O(1)$ \\\\\n%\\hline\n%Attention & $r$ & $O(r \\cdot n \\cdot d)$ & $O(1)$ \\\\\n%\\hline \\hline\n%\\end{tabular}\n%}\n%\\end{center}\n%\\end{table}"
  },
  {
    "path": "crazy_functions/test_project/latex/attention/introduction.tex",
    "content": "Recurrent neural networks, long short-term memory \\citep{hochreiter1997} and gated recurrent \\citep{gruEval14} neural networks in particular, have been firmly established as state of the art approaches in sequence modeling and transduction problems such as language modeling and machine translation \\citep{sutskever14, bahdanau2014neural, cho2014learning}. Numerous efforts have since continued to push the boundaries of recurrent language models and encoder-decoder architectures \\citep{wu2016google,luong2015effective,jozefowicz2016exploring}.\n\nRecurrent models typically factor computation along the symbol positions of the input and output sequences. Aligning the positions to steps in computation time, they generate a sequence of hidden states $h_t$, as a function of the previous hidden state $h_{t-1}$ and the input for position $t$. This inherently sequential nature precludes parallelization within training examples, which becomes critical at longer sequence lengths, as memory constraints limit batching across examples.\n%\\marginpar{not sure if the memory constraints are understandable here}\nRecent work has achieved significant improvements in computational efficiency through factorization tricks \\citep{Kuchaiev2017Factorization} and conditional computation \\citep{shazeer2017outrageously}, while also improving model performance in case of the latter. The fundamental constraint of sequential computation, however, remains.\n\n%\\marginpar{@all: there is work on analyzing what attention really does in seq2seq models, couldn't find it right away} \n\nAttention mechanisms have become an integral part of compelling sequence modeling and transduction models in various tasks, allowing modeling of dependencies without regard to their distance in the input or output sequences \\citep{bahdanau2014neural, structuredAttentionNetworks}. In all but a few cases \\citep{decomposableAttnModel}, however, such attention mechanisms are used in conjunction with a recurrent network.\n\n%\\marginpar{not sure if \"cross-positional communication\" is understandable without explanation}\n%\\marginpar{insert exact training times and stats for the model that reaches sota earliest, maybe even a single GPU model?}\n\nIn this work we propose the Transformer, a model architecture eschewing recurrence and instead relying entirely on an attention mechanism to draw global dependencies between input and output. The Transformer allows for significantly more parallelization and can reach a new state of the art in translation quality after being trained for as little as twelve hours on eight P100 GPUs. \n%\\marginpar{you removed the constant number of repetitions part. I wrote it because I wanted to make it clear that the model does not only perform attention once, while it's also not recurrent. I thought that might be important to get across early.}\n\n% Just a standard paragraph with citations, rewrite.\n%After the seminal papers of \\citep{sutskever14}, \\citep{bahdanau2014neural}, and \\citep{cho2014learning}, recurrent models have become the dominant solution for both sequence modeling and sequence-to-sequence transduction. Many efforts such as \\citep{wu2016google,luong2015effective,jozefowicz2016exploring} have pushed the boundaries of machine translation and language modeling with recurrent sequence models. Recent effort \\citep{shazeer2017outrageously} has combined the power of conditional computation with sequence models to train very large models for machine translation, pushing SOTA at lower computational cost. Recurrent models compute a vector of hidden states $h_t$, for each time step $t$ of computation. $h_t$ is a function of both the input at time $t$ and the previous hidden state $h_t$. This dependence on the previous hidden state encumbers recurrnet models to process multiple inputs at once, and their time complexity is a linear function of the length of the input and output, both during training and inference. [What I want to say here is that although this is fine during decoding, at training time, we are given both input and output and this linear nature does not allow the RNN to process all inputs and outputs simultaneously and haven't been used on datasets that are the of the scale of the web. What's the largest dataset we have ? . Talk about Nividia and possibly other's effors to speed up things, and possibly other efforts that alleviate this, but are still limited by it's comptuational nature]. Rest of the intro: What if you could construct the state based on the actual inputs and outputs, then you could construct them all at once. This has been the foundation of many promising recent efforts, bytenet,facenet (Also talk about quasi rnn here). Now we talk about attention!! Along with cell architectures such as long short-term meory (LSTM) \\citep{hochreiter1997}, and gated recurrent units (GRUs) \\citep{cho2014learning}, attention has emerged as an essential ingredient in successful sequence models, in particular for machine translation. In recent years, many, if not all, state-of-the-art (SOTA) results in machine translation have been achieved with attention-based sequence models \\citep{wu2016google,luong2015effective,jozefowicz2016exploring}. Talk about the neon work on how it played with attention to do self attention! Then talk about what we do."
  },
  {
    "path": "crazy_functions/test_project/latex/attention/model_architecture.tex",
    "content": "\n\\begin{figure}\n  \\centering\n  \\includegraphics[scale=0.6]{Figures/ModalNet-21}\n  \\caption{The Transformer - model architecture.}\n  \\label{fig:model-arch}\n\\end{figure}\n\n% Although the primary workhorse of our model is attention, \n%Our model maintains the encoder-decoder structure that is common to many so-called sequence-to-sequence models \\citep{bahdanau2014neural,sutskever14}.  As in all such architectures, the encoder computes a representation of the input sequence, and the decoder consumes these representations along with the output tokens to autoregressively produce the output sequence.  Where, traditionally, the encoder and decoder contain stacks of recurrent or convolutional layers, our encoder and decoder stacks are composed of attention layers and position-wise feed-forward layers (Figure~\\ref{fig:model-arch}).  The following sections describe the gross architecture and these particular components in detail.\n\nMost competitive neural sequence transduction models have an encoder-decoder structure \\citep{cho2014learning,bahdanau2014neural,sutskever14}. Here, the encoder maps an input sequence of symbol representations $(x_1, ..., x_n)$ to a sequence of continuous representations $\\mathbf{z} = (z_1, ..., z_n)$. Given $\\mathbf{z}$, the decoder then generates an output sequence $(y_1,...,y_m)$ of symbols one element at a time. At each step the model is auto-regressive \\citep{graves2013generating}, consuming the previously generated symbols as additional input when generating the next.\n\nThe Transformer follows this overall architecture using stacked self-attention and point-wise, fully connected layers for both the encoder and decoder, shown in the left and right halves of Figure~\\ref{fig:model-arch}, respectively.\n\n\\subsection{Encoder and Decoder Stacks}\n\n\\paragraph{Encoder:}The encoder is composed of a stack of $N=6$ identical layers. Each layer has two sub-layers. The first is a multi-head self-attention mechanism, and the second is a simple, position-wise fully connected feed-forward network.   We employ a residual connection \\citep{he2016deep} around each of the two sub-layers, followed by layer normalization \\cite{layernorm2016}.  That is, the output of each sub-layer is $\\mathrm{LayerNorm}(x + \\mathrm{Sublayer}(x))$, where $\\mathrm{Sublayer}(x)$ is the function implemented by the sub-layer itself.  To facilitate these residual connections, all sub-layers in the model, as well as the embedding layers, produce outputs of dimension $\\dmodel=512$.\n\n\\paragraph{Decoder:}The decoder is also composed of a stack of $N=6$ identical layers.  In addition to the two sub-layers in each encoder layer, the decoder inserts a third sub-layer, which performs multi-head attention over the output of the encoder stack.  Similar to the encoder, we employ residual connections around each of the sub-layers, followed by layer normalization.  We also modify the self-attention sub-layer in the decoder stack to prevent positions from attending to subsequent positions.  This masking, combined with fact that the output embeddings are offset by one position, ensures that the predictions for position $i$ can depend only on the known outputs at positions less than $i$.\n\n% In our model (Figure~\\ref{fig:model-arch}), the encoder and decoder are composed of stacks of alternating self-attention layers (for cross-positional communication) and position-wise feed-forward layers (for in-place computation).  In addition, the decoder stack contains encoder-decoder attention layers.  Since attention is agnostic to the distances between words, our model requires a \"positional encoding\" to be added to the encoder and decoder input. The following sections describe all of these components in detail.\n\n\\subsection{Attention} \\label{sec:attention}\nAn attention function can be described as mapping a query and a set of key-value pairs to an output, where the query, keys, values, and output are all vectors.  The output is computed as a weighted sum of the values, where the weight assigned to each value is computed by a compatibility function of the query with the corresponding key.\n\n\\subsubsection{Scaled Dot-Product Attention} \\label{sec:scaled-dot-prod}\n\n% \\begin{figure}\n%   \\centering\n%   \\includegraphics[scale=0.6]{Figures/ModalNet-19}\n%   \\caption{Scaled Dot-Product Attention.}\n%   \\label{fig:multi-head-att}\n% \\end{figure}\n\nWe call our particular attention \"Scaled Dot-Product Attention\" (Figure~\\ref{fig:multi-head-att}).   The input consists of queries and keys of dimension $d_k$, and values of dimension $d_v$.  We compute the dot products of the query with all keys, divide each by $\\sqrt{d_k}$, and apply a softmax function to obtain the weights on the values.\n\nIn practice, we compute the attention function on a set of queries simultaneously, packed together into a matrix $Q$.   The keys and values are also packed together into matrices $K$ and $V$.  We compute the matrix of outputs as:\n\n\\begin{equation}\n   \\mathrm{Attention}(Q, K, V) = \\mathrm{softmax}(\\frac{QK^T}{\\sqrt{d_k}})V\n\\end{equation}\n\nThe two most commonly used attention functions are additive attention \\citep{bahdanau2014neural}, and dot-product (multiplicative) attention.  Dot-product attention is identical to our algorithm, except for the scaling factor of $\\frac{1}{\\sqrt{d_k}}$. Additive attention computes the compatibility function using a feed-forward network with a single hidden layer.  While the two are similar in theoretical complexity, dot-product attention is much faster and more space-efficient in practice, since it can be implemented using highly optimized matrix multiplication code. \n\n%We scale the dot products by $1/\\sqrt{d_k}$ to limit the magnitude of the dot products, which works well in practice. Otherwise, we found applying the softmax to often result in weights very close to 0 or 1, and hence minuscule gradients.\n\n% Already described in the subsequent section\n%When used as part of decoder self-attention, an optional mask function is applied just before the softmax to prevent positions from attending to subsequent positions.   This mask simply sets the logits corresponding to all illegal connections (those outside of the lower triangle) to $-\\infty$.\n\n%\\paragraph{Comparison to Additive Attention: } We choose dot product attention over additive attention \\citep{bahdanau2014neural} since it can be computed using highly optimized matrix multiplication code.  This optimization is particularly important to us, as we employ many attention layers in our model.\n\nWhile for small values of $d_k$ the two mechanisms perform similarly, additive attention outperforms dot product attention without scaling for larger values of $d_k$ \\citep{DBLP:journals/corr/BritzGLL17}. We suspect that for large values of $d_k$, the dot products grow large in magnitude, pushing the softmax function into regions where it has extremely small gradients  \\footnote{To illustrate why the dot products get large, assume that the components of $q$ and $k$ are independent random variables with mean $0$ and variance $1$.  Then their dot product, $q \\cdot k = \\sum_{i=1}^{d_k} q_ik_i$, has mean $0$ and variance $d_k$.}. To counteract this effect, we scale the dot products by $\\frac{1}{\\sqrt{d_k}}$.\n\n\n%We suspect this to be caused by the dot products growing too large in magnitude to result in useful gradients after applying the softmax function.  To counteract this, we scale the dot product by $1/\\sqrt{d_k}$.\n\n\n\\subsubsection{Multi-Head Attention} \\label{sec:multihead}\n\n\\begin{figure}\n\\begin{minipage}[t]{0.5\\textwidth}\n  \\centering\n  Scaled Dot-Product Attention \\\\\n  \\vspace{0.5cm}\n  \\includegraphics[scale=0.6]{Figures/ModalNet-19}\n\\end{minipage}\n\\begin{minipage}[t]{0.5\\textwidth}\n  \\centering \n  Multi-Head Attention \\\\\n  \\vspace{0.1cm}\n  \\includegraphics[scale=0.6]{Figures/ModalNet-20}  \n\\end{minipage}\n\n\n  % \\centering\n\n  \\caption{(left) Scaled Dot-Product Attention. (right) Multi-Head Attention consists of several attention layers running in parallel.}\n  \\label{fig:multi-head-att}\n\\end{figure}\n\nInstead of performing a single attention function with $\\dmodel$-dimensional keys, values and queries, we found it beneficial to linearly project the queries, keys and values $h$ times with different, learned linear projections to $d_k$, $d_k$ and $d_v$ dimensions, respectively.\nOn each of these projected versions of queries, keys and values we then perform the attention function in parallel, yielding $d_v$-dimensional output values. These are concatenated and once again projected, resulting in the final values, as depicted in Figure~\\ref{fig:multi-head-att}.\n\nMulti-head attention allows the model to jointly attend to information from different representation subspaces at different positions. With a single attention head, averaging inhibits this.\n\n\\begin{align*}\n    \\mathrm{MultiHead}(Q, K, V) &= \\mathrm{Concat}(\\mathrm{head_1}, ..., \\mathrm{head_h})W^O\\\\\n%    \\mathrm{where} \\mathrm{head_i} &= \\mathrm{Attention}(QW_Q_i^{\\dmodel \\times d_q}, KW_K_i^{\\dmodel \\times d_k}, VW^V_i^{\\dmodel \\times d_v})\\\\\n    \\text{where}~\\mathrm{head_i} &= \\mathrm{Attention}(QW^Q_i, KW^K_i, VW^V_i)\\\\\n\\end{align*}\n\nWhere the projections are parameter matrices $W^Q_i \\in \\mathbb{R}^{\\dmodel \\times d_k}$, $W^K_i \\in \\mathbb{R}^{\\dmodel \\times d_k}$, $W^V_i \\in \\mathbb{R}^{\\dmodel \\times d_v}$ and $W^O \\in \\mathbb{R}^{hd_v \\times \\dmodel}$.\n\n\n%find it better (and no more expensive) to have multiple parallel attention layers (each over the full set of positions) with proportionally lower-dimensional keys, values and queries.  We call this \"Multi-Head Attention\" (Figure~\\ref{fig:multi-head-att}).  The keys, values, and queries for each of these parallel attention layers are computed by learned linear transformations of the inputs to the multi-head attention.  We use different linear transformations across different parallel attention layers.  The output of the parallel attention layers are concatenated, and then passed through a final learned linear transformation. \n\nIn this work we employ $h=8$ parallel attention layers, or heads. For each of these we use $d_k=d_v=\\dmodel/h=64$.\nDue to the reduced dimension of each head, the total computational cost is similar to that of single-head attention with full dimensionality.\n\n\\subsubsection{Applications of Attention in our Model}\n\nThe Transformer uses multi-head attention in three different ways: \n\\begin{itemize}\n \\item In \"encoder-decoder attention\" layers, the queries come from the previous decoder layer, and the memory keys and values come from the output of the encoder.   This allows every position in the decoder to attend over all positions in the input sequence.  This mimics the typical encoder-decoder attention mechanisms in sequence-to-sequence models such as \\citep{wu2016google, bahdanau2014neural,JonasFaceNet2017}.\n\n \\item The encoder contains self-attention layers.  In a self-attention layer all of the keys, values and queries come from the same place, in this case, the output of the previous layer in the encoder.   Each position in the encoder can attend to all positions in the previous layer of the encoder.\n\n \\item Similarly, self-attention layers in the decoder allow each position in the decoder to attend to all positions in the decoder up to and including that position.  We need to prevent leftward information flow in the decoder to preserve the auto-regressive property.  We implement this inside of scaled dot-product attention by masking out (setting to $-\\infty$) all values in the input of the softmax which correspond to illegal connections.  See Figure~\\ref{fig:multi-head-att}.\n\n\\end{itemize}\n\n\\subsection{Position-wise Feed-Forward Networks}\\label{sec:ffn}\n\nIn addition to attention sub-layers, each of the layers in our encoder and decoder contains a fully connected feed-forward network, which is applied to each position separately and identically.  This consists of two linear transformations with a ReLU activation in between.\n\n\\begin{equation}\n   \\mathrm{FFN}(x)=\\max(0, xW_1 + b_1) W_2 + b_2\n\\end{equation}\n\nWhile the linear transformations are the same across different positions, they use different parameters from layer to layer. Another way of describing this is as two convolutions with kernel size 1.  The dimensionality of input and output is $\\dmodel=512$, and the inner-layer has dimensionality $d_{ff}=2048$.\n\n\n\n%In the appendix, we describe how the position-wise feed-forward network can also be seen as a form of attention.\n\n%from Jakob: The number of operations required for the model to relate signals from two arbitrary input or output positions grows in the distance between positions in input or output, linearly for ConvS2S and logarithmically for ByteNet, making it harder to learn dependencies between these positions \\citep{hochreiter2001gradient}. In the transformer this is reduced to a constant number of operations, albeit at the cost of effective resolution caused by averaging attention-weighted positions, an effect we aim to counteract with multi-headed attention.\n\n\n%Figure~\\ref{fig:simple-att} presents a simple attention function, $A$, with a single head, that forms the basis of our multi-head attention. $A$ takes a query key vector $\\kq$, matrices of memory keys $\\km$ and memory values $\\vm$ ,and produces a query value vector $\\vq$ as \n%\\begin{equation*} \\label{eq:attention}\n%    A(\\kq, \\km, \\vm) = {\\vm}^T (Softmax(\\km \\kq).\n%\\end{equation*}\n%We linearly transform $\\kq,\\,\\km$, and $\\vm$ with learned matrices ${\\Wkq \\text{,} \\, \\Wkm}$, and ${\\Wvm}$ before calling the attention function, and transform the output query with $\\Wvq$ before handing it to the feed forward layer. Each attention layer has it's own set of transformation matrices, which are shared across all query positions. $A$ is applied in parallel for each query position, and is implemented very efficiently as a batch of matrix multiplies. The self-attention and encoder-decoder attention layers use $A$, but with different arguments. For example, in encdoder self-attention, queries in encoder layer $i$ attention to memories in encoder layer $i-1$. To ensure that decoder self-attention layers do not look at future words, we add $- \\inf$ to the softmax logits in positions $j+1$ to query length for query position $l$.  \n\n%In simple attention, the query value is a weighted combination of the memory values where the attention weights sum to one. Although this function performs well in practice, the constraint on attention weights can restrict the amount of information that flows from memories to queries because the query cannot focus on multiple memory positions at once, which might be desirable when translating long sequences. \\marginpar{@usz, could you think of an example of this ?} We remedy this by maintaining multiple attention heads at each query position that attend to all memory positions in parallel, with a different set of parameters  per attention head $h$. \n%\\marginpar{}\n\n\\subsection{Embeddings and Softmax}\nSimilarly to other sequence transduction models, we use learned embeddings to convert the input tokens and output tokens to vectors of dimension $\\dmodel$.  We also use the usual learned linear transformation and softmax function to convert the decoder output to predicted next-token probabilities.  In our model, we share the same weight matrix between the two embedding layers and the pre-softmax linear transformation, similar to \\citep{press2016using}.   In the embedding layers, we multiply those weights by $\\sqrt{\\dmodel}$.\n\n\n\\subsection{Positional Encoding}\nSince our model contains no recurrence and no convolution, in order for the model to make use of the order of the sequence, we must inject some information about the relative or absolute position of the tokens in the sequence.  To this end, we add \"positional encodings\" to the input embeddings at the bottoms of the encoder and decoder stacks.  The positional encodings have the same dimension $\\dmodel$ as the embeddings, so that the two can be summed.   There are many choices of positional encodings, learned and fixed \\citep{JonasFaceNet2017}.\n\nIn this work, we use sine and cosine functions of different frequencies:\n\n\\begin{align*}\n    PE_{(pos,2i)} = sin(pos / 10000^{2i/\\dmodel}) \\\\\n    PE_{(pos,2i+1)} = cos(pos / 10000^{2i/\\dmodel})\n\\end{align*}\n\nwhere $pos$ is the position and $i$ is the dimension.  That is, each dimension of the positional encoding corresponds to a sinusoid.  The wavelengths form a geometric progression from $2\\pi$ to $10000 \\cdot 2\\pi$.  We chose this function because we hypothesized it would allow the model to easily learn to attend by relative positions, since for any fixed offset $k$, $PE_{pos+k}$ can be represented as a linear function of $PE_{pos}$.\n\nWe also experimented with using learned positional embeddings \\citep{JonasFaceNet2017} instead, and found that the two versions produced nearly identical results (see Table~\\ref{tab:variations} row (E)).  We chose the sinusoidal version because it may allow the model to extrapolate to sequence lengths longer than the ones encountered during training.\n"
  },
  {
    "path": "crazy_functions/test_project/latex/attention/parameter_attention.tex",
    "content": "\\pagebreak\n\\section*{Two Feed-Forward Layers = Attention over Parameters}\\label{sec:parameter_attention}\n\nIn addition to attention layers, our model contains position-wise feed-forward networks (Section \\ref{sec:ffn}), which consist of two linear transformations with a ReLU activation in between.  In fact, these networks too can be seen as a form of attention.  Compare the formula for such a network with the formula for a simple dot-product attention layer (biases and scaling factors omitted):\n\n\\begin{align*}\n    FFN(x, W_1, W_2) = ReLU(xW_1)W_2 \\\\\n    A(q, K, V) = Softmax(qK^T)V\n\\end{align*}\n\nBased on the similarity of these formulae, the two-layer feed-forward network can be seen as a kind of attention, where the keys and values are the rows of the trainable parameter matrices $W_1$ and $W_2$, and where we use ReLU instead of Softmax in the compatibility function.\n\n%the compatablity function is $compat(q, k_i) = ReLU(q \\cdot k_i)$ instead of $Softmax(qK_T)_i$.\n\nGiven this similarity, we experimented with replacing the position-wise feed-forward networks with attention layers similar to the ones we use everywhere else our model. The multi-head-attention-over-parameters sublayer is identical to the multi-head attention described in \\ref{sec:multihead}, except that the \"keys\" and \"values\" inputs to each attention head are trainable model parameters, as opposed to being linear projections of a previous layer.  These parameters are scaled up by a factor of $\\sqrt{d_{model}}$ in order to be more similar to activations.\n\nIn our first experiment, we replaced each position-wise feed-forward network with a multi-head-attention-over-parameters sublayer with $h_p=8$ heads, key-dimensionality $d_{pk}=64$, and value-dimensionality $d_{pv}=64$, using $n_p=1536$ key-value pairs for each attention head.  The sublayer has a total of $2097152$ parameters, including the parameters in the query projection and the output projection.  This matches the number of parameters in the position-wise feed-forward network that we replaced.  While the theoretical amount of computation is also the same, in practice, the attention version caused the step times to be about 30\\% longer.\n\nIn our second experiment, we used $h_p=8$ heads, and $n_p=512$ key-value pairs for each attention head, again matching the total number of parameters in the base model.\n\nResults for the first experiment were slightly worse than for the base model, and results for the second experiment were slightly better, see Table~\\ref{tab:parameter_attention}.\n\n\\begin{table}[h]\n\\caption{Replacing the position-wise feed-forward networks with multihead-attention-over-parameters produces similar results to the base model.  All metrics are on the English-to-German translation development set, newstest2013.}\n\\label{tab:parameter_attention}\n\\begin{center}\n\\vspace{-2mm}\n%\\scalebox{1.0}{\n\\begin{tabular}{c|cccccc|cccc}\n\\hline\\rule{0pt}{2.0ex}\n & \\multirow{2}{*}{$\\dmodel$} & \\multirow{2}{*}{$\\dff$} &\n\\multirow{2}{*}{$h_p$} & \\multirow{2}{*}{$d_{pk}$} & \\multirow{2}{*}{$d_{pv}$} &\n \\multirow{2}{*}{$n_p$} &\n PPL & BLEU & params & training\\\\\n & & & & & &  & (dev) & (dev) & $\\times10^6$ & time \\\\\n\\hline\\rule{0pt}{2.0ex}\nbase & 512 & 2048 & & & & & 4.92 & 25.8 & 65 & 12 hours\\\\\n\\hline\\rule{0pt}{2.0ex}\nAOP$_1$ & 512 & & 8 & 64 & 64 & 1536 & 4.92& 25.5  & 65 & 16 hours\\\\\nAOP$_2$ & 512 & & 16 & 64 & 64 & 512 & \\textbf{4.86} & \\textbf{25.9}  & 65 & 16 hours \\\\\n\\hline\n\\end{tabular}\n%}\n\\end{center}\n\\end{table}\n"
  },
  {
    "path": "crazy_functions/test_project/latex/attention/来源",
    "content": "chatgpt的老祖宗《Attention is all you need》\n\nAshish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin\n\n真实的摘要如下\nThe dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.\n\nhttps://arxiv.org/abs/1706.03762"
  },
  {
    "path": "crazy_functions/test_project/python/dqn/__init__.py",
    "content": "from stable_baselines3.dqn.dqn import DQN\nfrom stable_baselines3.dqn.policies import CnnPolicy, MlpPolicy\n"
  },
  {
    "path": "crazy_functions/test_project/python/dqn/dqn.py",
    "content": "from typing import Any, Dict, List, Optional, Tuple, Type, Union\n\nimport gym\nimport numpy as np\nimport torch as th\nfrom torch.nn import functional as F\n\nfrom stable_baselines3.common import logger\nfrom stable_baselines3.common.off_policy_algorithm import OffPolicyAlgorithm\nfrom stable_baselines3.common.preprocessing import maybe_transpose\nfrom stable_baselines3.common.type_aliases import GymEnv, MaybeCallback, Schedule\nfrom stable_baselines3.common.utils import get_linear_fn, is_vectorized_observation, polyak_update\nfrom stable_baselines3.dqn.policies import DQNPolicy\n\n\nclass DQN(OffPolicyAlgorithm):\n    \"\"\"\n    Deep Q-Network (DQN)\n\n    Paper: https://arxiv.org/abs/1312.5602, https://www.nature.com/articles/nature14236\n    Default hyperparameters are taken from the nature paper,\n    except for the optimizer and learning rate that were taken from Stable Baselines defaults.\n\n    :param policy: The policy model to use (MlpPolicy, CnnPolicy, ...)\n    :param env: The environment to learn from (if registered in Gym, can be str)\n    :param learning_rate: The learning rate, it can be a function\n        of the current progress remaining (from 1 to 0)\n    :param buffer_size: size of the replay buffer\n    :param learning_starts: how many steps of the model to collect transitions for before learning starts\n    :param batch_size: Minibatch size for each gradient update\n    :param tau: the soft update coefficient (\"Polyak update\", between 0 and 1) default 1 for hard update\n    :param gamma: the discount factor\n    :param train_freq: Update the model every ``train_freq`` steps. Alternatively pass a tuple of frequency and unit\n        like ``(5, \"step\")`` or ``(2, \"episode\")``.\n    :param gradient_steps: How many gradient steps to do after each rollout (see ``train_freq``)\n        Set to ``-1`` means to do as many gradient steps as steps done in the environment\n        during the rollout.\n    :param optimize_memory_usage: Enable a memory efficient variant of the replay buffer\n        at a cost of more complexity.\n        See https://github.com/DLR-RM/stable-baselines3/issues/37#issuecomment-637501195\n    :param target_update_interval: update the target network every ``target_update_interval``\n        environment steps.\n    :param exploration_fraction: fraction of entire training period over which the exploration rate is reduced\n    :param exploration_initial_eps: initial value of random action probability\n    :param exploration_final_eps: final value of random action probability\n    :param max_grad_norm: The maximum value for the gradient clipping\n    :param tensorboard_log: the log location for tensorboard (if None, no logging)\n    :param create_eval_env: Whether to create a second environment that will be\n        used for evaluating the agent periodically. (Only available when passing string for the environment)\n    :param policy_kwargs: additional arguments to be passed to the policy on creation\n    :param verbose: the verbosity level: 0 no output, 1 info, 2 debug\n    :param seed: Seed for the pseudo random generators\n    :param device: Device (cpu, cuda, ...) on which the code should be run.\n        Setting it to auto, the code will be run on the GPU if possible.\n    :param _init_setup_model: Whether or not to build the network at the creation of the instance\n    \"\"\"\n\n    def __init__(\n        self,\n        policy: Union[str, Type[DQNPolicy]],\n        env: Union[GymEnv, str],\n        learning_rate: Union[float, Schedule] = 1e-4,\n        buffer_size: int = 1000000,\n        learning_starts: int = 50000,\n        batch_size: Optional[int] = 32,\n        tau: float = 1.0,\n        gamma: float = 0.99,\n        train_freq: Union[int, Tuple[int, str]] = 4,\n        gradient_steps: int = 1,\n        optimize_memory_usage: bool = False,\n        target_update_interval: int = 10000,\n        exploration_fraction: float = 0.1,\n        exploration_initial_eps: float = 1.0,\n        exploration_final_eps: float = 0.05,\n        max_grad_norm: float = 10,\n        tensorboard_log: Optional[str] = None,\n        create_eval_env: bool = False,\n        policy_kwargs: Optional[Dict[str, Any]] = None,\n        verbose: int = 0,\n        seed: Optional[int] = None,\n        device: Union[th.device, str] = \"auto\",\n        _init_setup_model: bool = True,\n    ):\n\n        super(DQN, self).__init__(\n            policy,\n            env,\n            DQNPolicy,\n            learning_rate,\n            buffer_size,\n            learning_starts,\n            batch_size,\n            tau,\n            gamma,\n            train_freq,\n            gradient_steps,\n            action_noise=None,  # No action noise\n            policy_kwargs=policy_kwargs,\n            tensorboard_log=tensorboard_log,\n            verbose=verbose,\n            device=device,\n            create_eval_env=create_eval_env,\n            seed=seed,\n            sde_support=False,\n            optimize_memory_usage=optimize_memory_usage,\n            supported_action_spaces=(gym.spaces.Discrete,),\n        )\n\n        self.exploration_initial_eps = exploration_initial_eps\n        self.exploration_final_eps = exploration_final_eps\n        self.exploration_fraction = exploration_fraction\n        self.target_update_interval = target_update_interval\n        self.max_grad_norm = max_grad_norm\n        # \"epsilon\" for the epsilon-greedy exploration\n        self.exploration_rate = 0.0\n        # Linear schedule will be defined in `_setup_model()`\n        self.exploration_schedule = None\n        self.q_net, self.q_net_target = None, None\n\n        if _init_setup_model:\n            self._setup_model()\n\n    def _setup_model(self) -> None:\n        super(DQN, self)._setup_model()\n        self._create_aliases()\n        self.exploration_schedule = get_linear_fn(\n            self.exploration_initial_eps, self.exploration_final_eps, self.exploration_fraction\n        )\n\n    def _create_aliases(self) -> None:\n        self.q_net = self.policy.q_net\n        self.q_net_target = self.policy.q_net_target\n\n    def _on_step(self) -> None:\n        \"\"\"\n        Update the exploration rate and target network if needed.\n        This method is called in ``collect_rollouts()`` after each step in the environment.\n        \"\"\"\n        if self.num_timesteps % self.target_update_interval == 0:\n            polyak_update(self.q_net.parameters(), self.q_net_target.parameters(), self.tau)\n\n        self.exploration_rate = self.exploration_schedule(self._current_progress_remaining)\n        logger.record(\"rollout/exploration rate\", self.exploration_rate)\n\n    def train(self, gradient_steps: int, batch_size: int = 100) -> None:\n        # Update learning rate according to schedule\n        self._update_learning_rate(self.policy.optimizer)\n\n        losses = []\n        for _ in range(gradient_steps):\n            # Sample replay buffer\n            replay_data = self.replay_buffer.sample(batch_size, env=self._vec_normalize_env)\n\n            with th.no_grad():\n                # Compute the next Q-values using the target network\n                next_q_values = self.q_net_target(replay_data.next_observations)\n                # Follow greedy policy: use the one with the highest value\n                next_q_values, _ = next_q_values.max(dim=1)\n                # Avoid potential broadcast issue\n                next_q_values = next_q_values.reshape(-1, 1)\n                # 1-step TD target\n                target_q_values = replay_data.rewards + (1 - replay_data.dones) * self.gamma * next_q_values\n\n            # Get current Q-values estimates\n            current_q_values = self.q_net(replay_data.observations)\n\n            # Retrieve the q-values for the actions from the replay buffer\n            current_q_values = th.gather(current_q_values, dim=1, index=replay_data.actions.long())\n\n            # Compute Huber loss (less sensitive to outliers)\n            loss = F.smooth_l1_loss(current_q_values, target_q_values)\n            losses.append(loss.item())\n\n            # Optimize the policy\n            self.policy.optimizer.zero_grad()\n            loss.backward()\n            # Clip gradient norm\n            th.nn.utils.clip_grad_norm_(self.policy.parameters(), self.max_grad_norm)\n            self.policy.optimizer.step()\n\n        # Increase update counter\n        self._n_updates += gradient_steps\n\n        logger.record(\"train/n_updates\", self._n_updates, exclude=\"tensorboard\")\n        logger.record(\"train/loss\", np.mean(losses))\n\n    def predict(\n        self,\n        observation: np.ndarray,\n        state: Optional[np.ndarray] = None,\n        mask: Optional[np.ndarray] = None,\n        deterministic: bool = False,\n    ) -> Tuple[np.ndarray, Optional[np.ndarray]]:\n        \"\"\"\n        Overrides the base_class predict function to include epsilon-greedy exploration.\n\n        :param observation: the input observation\n        :param state: The last states (can be None, used in recurrent policies)\n        :param mask: The last masks (can be None, used in recurrent policies)\n        :param deterministic: Whether or not to return deterministic actions.\n        :return: the model's action and the next state\n            (used in recurrent policies)\n        \"\"\"\n        if not deterministic and np.random.rand() < self.exploration_rate:\n            if is_vectorized_observation(maybe_transpose(observation, self.observation_space), self.observation_space):\n                n_batch = observation.shape[0]\n                action = np.array([self.action_space.sample() for _ in range(n_batch)])\n            else:\n                action = np.array(self.action_space.sample())\n        else:\n            action, state = self.policy.predict(observation, state, mask, deterministic)\n        return action, state\n\n    def learn(\n        self,\n        total_timesteps: int,\n        callback: MaybeCallback = None,\n        log_interval: int = 4,\n        eval_env: Optional[GymEnv] = None,\n        eval_freq: int = -1,\n        n_eval_episodes: int = 5,\n        tb_log_name: str = \"DQN\",\n        eval_log_path: Optional[str] = None,\n        reset_num_timesteps: bool = True,\n    ) -> OffPolicyAlgorithm:\n\n        return super(DQN, self).learn(\n            total_timesteps=total_timesteps,\n            callback=callback,\n            log_interval=log_interval,\n            eval_env=eval_env,\n            eval_freq=eval_freq,\n            n_eval_episodes=n_eval_episodes,\n            tb_log_name=tb_log_name,\n            eval_log_path=eval_log_path,\n            reset_num_timesteps=reset_num_timesteps,\n        )\n\n    def _excluded_save_params(self) -> List[str]:\n        return super(DQN, self)._excluded_save_params() + [\"q_net\", \"q_net_target\"]\n\n    def _get_torch_save_params(self) -> Tuple[List[str], List[str]]:\n        state_dicts = [\"policy\", \"policy.optimizer\"]\n\n        return state_dicts, []\n"
  },
  {
    "path": "crazy_functions/test_project/python/dqn/policies.py",
    "content": "from typing import Any, Dict, List, Optional, Type\n\nimport gym\nimport torch as th\nfrom torch import nn\n\nfrom stable_baselines3.common.policies import BasePolicy, register_policy\nfrom stable_baselines3.common.torch_layers import BaseFeaturesExtractor, FlattenExtractor, NatureCNN, create_mlp\nfrom stable_baselines3.common.type_aliases import Schedule\n\n\nclass QNetwork(BasePolicy):\n    \"\"\"\n    Action-Value (Q-Value) network for DQN\n\n    :param observation_space: Observation space\n    :param action_space: Action space\n    :param net_arch: The specification of the policy and value networks.\n    :param activation_fn: Activation function\n    :param normalize_images: Whether to normalize images or not,\n         dividing by 255.0 (True by default)\n    \"\"\"\n\n    def __init__(\n        self,\n        observation_space: gym.spaces.Space,\n        action_space: gym.spaces.Space,\n        features_extractor: nn.Module,\n        features_dim: int,\n        net_arch: Optional[List[int]] = None,\n        activation_fn: Type[nn.Module] = nn.ReLU,\n        normalize_images: bool = True,\n    ):\n        super(QNetwork, self).__init__(\n            observation_space,\n            action_space,\n            features_extractor=features_extractor,\n            normalize_images=normalize_images,\n        )\n\n        if net_arch is None:\n            net_arch = [64, 64]\n\n        self.net_arch = net_arch\n        self.activation_fn = activation_fn\n        self.features_extractor = features_extractor\n        self.features_dim = features_dim\n        self.normalize_images = normalize_images\n        action_dim = self.action_space.n  # number of actions\n        q_net = create_mlp(self.features_dim, action_dim, self.net_arch, self.activation_fn)\n        self.q_net = nn.Sequential(*q_net)\n\n    def forward(self, obs: th.Tensor) -> th.Tensor:\n        \"\"\"\n        Predict the q-values.\n\n        :param obs: Observation\n        :return: The estimated Q-Value for each action.\n        \"\"\"\n        return self.q_net(self.extract_features(obs))\n\n    def _predict(self, observation: th.Tensor, deterministic: bool = True) -> th.Tensor:\n        q_values = self.forward(observation)\n        # Greedy action\n        action = q_values.argmax(dim=1).reshape(-1)\n        return action\n\n    def _get_constructor_parameters(self) -> Dict[str, Any]:\n        data = super()._get_constructor_parameters()\n\n        data.update(\n            dict(\n                net_arch=self.net_arch,\n                features_dim=self.features_dim,\n                activation_fn=self.activation_fn,\n                features_extractor=self.features_extractor,\n            )\n        )\n        return data\n\n\nclass DQNPolicy(BasePolicy):\n    \"\"\"\n    Policy class with Q-Value Net and target net for DQN\n\n    :param observation_space: Observation space\n    :param action_space: Action space\n    :param lr_schedule: Learning rate schedule (could be constant)\n    :param net_arch: The specification of the policy and value networks.\n    :param activation_fn: Activation function\n    :param features_extractor_class: Features extractor to use.\n    :param features_extractor_kwargs: Keyword arguments\n        to pass to the features extractor.\n    :param normalize_images: Whether to normalize images or not,\n         dividing by 255.0 (True by default)\n    :param optimizer_class: The optimizer to use,\n        ``th.optim.Adam`` by default\n    :param optimizer_kwargs: Additional keyword arguments,\n        excluding the learning rate, to pass to the optimizer\n    \"\"\"\n\n    def __init__(\n        self,\n        observation_space: gym.spaces.Space,\n        action_space: gym.spaces.Space,\n        lr_schedule: Schedule,\n        net_arch: Optional[List[int]] = None,\n        activation_fn: Type[nn.Module] = nn.ReLU,\n        features_extractor_class: Type[BaseFeaturesExtractor] = FlattenExtractor,\n        features_extractor_kwargs: Optional[Dict[str, Any]] = None,\n        normalize_images: bool = True,\n        optimizer_class: Type[th.optim.Optimizer] = th.optim.Adam,\n        optimizer_kwargs: Optional[Dict[str, Any]] = None,\n    ):\n        super(DQNPolicy, self).__init__(\n            observation_space,\n            action_space,\n            features_extractor_class,\n            features_extractor_kwargs,\n            optimizer_class=optimizer_class,\n            optimizer_kwargs=optimizer_kwargs,\n        )\n\n        if net_arch is None:\n            if features_extractor_class == FlattenExtractor:\n                net_arch = [64, 64]\n            else:\n                net_arch = []\n\n        self.net_arch = net_arch\n        self.activation_fn = activation_fn\n        self.normalize_images = normalize_images\n\n        self.net_args = {\n            \"observation_space\": self.observation_space,\n            \"action_space\": self.action_space,\n            \"net_arch\": self.net_arch,\n            \"activation_fn\": self.activation_fn,\n            \"normalize_images\": normalize_images,\n        }\n\n        self.q_net, self.q_net_target = None, None\n        self._build(lr_schedule)\n\n    def _build(self, lr_schedule: Schedule) -> None:\n        \"\"\"\n        Create the network and the optimizer.\n\n        :param lr_schedule: Learning rate schedule\n            lr_schedule(1) is the initial learning rate\n        \"\"\"\n\n        self.q_net = self.make_q_net()\n        self.q_net_target = self.make_q_net()\n        self.q_net_target.load_state_dict(self.q_net.state_dict())\n\n        # Setup optimizer with initial learning rate\n        self.optimizer = self.optimizer_class(self.parameters(), lr=lr_schedule(1), **self.optimizer_kwargs)\n\n    def make_q_net(self) -> QNetwork:\n        # Make sure we always have separate networks for features extractors etc\n        net_args = self._update_features_extractor(self.net_args, features_extractor=None)\n        return QNetwork(**net_args).to(self.device)\n\n    def forward(self, obs: th.Tensor, deterministic: bool = True) -> th.Tensor:\n        return self._predict(obs, deterministic=deterministic)\n\n    def _predict(self, obs: th.Tensor, deterministic: bool = True) -> th.Tensor:\n        return self.q_net._predict(obs, deterministic=deterministic)\n\n    def _get_constructor_parameters(self) -> Dict[str, Any]:\n        data = super()._get_constructor_parameters()\n\n        data.update(\n            dict(\n                net_arch=self.net_args[\"net_arch\"],\n                activation_fn=self.net_args[\"activation_fn\"],\n                lr_schedule=self._dummy_schedule,  # dummy lr schedule, not needed for loading policy alone\n                optimizer_class=self.optimizer_class,\n                optimizer_kwargs=self.optimizer_kwargs,\n                features_extractor_class=self.features_extractor_class,\n                features_extractor_kwargs=self.features_extractor_kwargs,\n            )\n        )\n        return data\n\n\nMlpPolicy = DQNPolicy\n\n\nclass CnnPolicy(DQNPolicy):\n    \"\"\"\n    Policy class for DQN when using images as input.\n\n    :param observation_space: Observation space\n    :param action_space: Action space\n    :param lr_schedule: Learning rate schedule (could be constant)\n    :param net_arch: The specification of the policy and value networks.\n    :param activation_fn: Activation function\n    :param features_extractor_class: Features extractor to use.\n    :param normalize_images: Whether to normalize images or not,\n         dividing by 255.0 (True by default)\n    :param optimizer_class: The optimizer to use,\n        ``th.optim.Adam`` by default\n    :param optimizer_kwargs: Additional keyword arguments,\n        excluding the learning rate, to pass to the optimizer\n    \"\"\"\n\n    def __init__(\n        self,\n        observation_space: gym.spaces.Space,\n        action_space: gym.spaces.Space,\n        lr_schedule: Schedule,\n        net_arch: Optional[List[int]] = None,\n        activation_fn: Type[nn.Module] = nn.ReLU,\n        features_extractor_class: Type[BaseFeaturesExtractor] = NatureCNN,\n        features_extractor_kwargs: Optional[Dict[str, Any]] = None,\n        normalize_images: bool = True,\n        optimizer_class: Type[th.optim.Optimizer] = th.optim.Adam,\n        optimizer_kwargs: Optional[Dict[str, Any]] = None,\n    ):\n        super(CnnPolicy, self).__init__(\n            observation_space,\n            action_space,\n            lr_schedule,\n            net_arch,\n            activation_fn,\n            features_extractor_class,\n            features_extractor_kwargs,\n            normalize_images,\n            optimizer_class,\n            optimizer_kwargs,\n        )\n\n\nregister_policy(\"MlpPolicy\", MlpPolicy)\nregister_policy(\"CnnPolicy\", CnnPolicy)\n"
  },
  {
    "path": "crazy_functions/test_project/python/dqn/来源",
    "content": "github stablebaseline3\nhttps://github.com/DLR-RM/stable-baselines3"
  },
  {
    "path": "crazy_functions/test_project/其他测试",
    "content": "\"In practice, we found that a high-entropy initial state is more likely to increase the speed of training.\nThe entropy is calculated by:\n$$H=-\\sum_{k= 1}^{n_k} p(k) \\cdot \\log p(k), p(k)=\\frac{|A_k|}{|\\mathcal{A}|}$$\nwhere $H$ is the entropy, $|A_k|$ is the number of agent nodes in $k$-th cluster, $|\\mathcal{A}|$ is the total number of agents.\nTo ensure the Cooperation Graph initialization has higher entropy,\nwe will randomly generate multiple initial states, \nrank by their entropy and then pick the one with maximum $H$.\"\n\n```\nFROM ubuntu:latest\n\nRUN apt-get update && \\\n    apt-get install -y python3 python3-pip && \\\n    rm -rf /var/lib/apt/lists/*\n\nRUN echo '[global]' > /etc/pip.conf && \\\n    echo 'index-url = https://mirrors.aliyun.com/pypi/simple/' >> /etc/pip.conf && \\\n    echo 'trusted-host = mirrors.aliyun.com' >> /etc/pip.conf\n\nRUN pip3 install gradio requests[socks] mdtex2html\n\nCOPY . /gpt\nWORKDIR /gpt\n\n\nCMD [\"python3\", \"main.py\"]\n```\n"
  },
  {
    "path": "crazy_functions/下载arxiv论文翻译摘要.py",
    "content": "from utils.toolbox import update_ui\nfrom utils.toolbox import CatchException, report_execption, write_results_to_file, get_conf\nimport re, requests, unicodedata, os\nfrom .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive\ndef download_arxiv_(url_pdf):\n    if 'arxiv.org' not in url_pdf:\n        if ('.' in url_pdf) and ('/' not in url_pdf):\n            new_url = 'https://arxiv.org/abs/'+url_pdf\n            print('下载编号：', url_pdf, '自动定位：', new_url)\n            # download_arxiv_(new_url)\n            return download_arxiv_(new_url)\n        else:\n            print('不能识别的URL！')\n            return None\n    if 'abs' in url_pdf:\n        url_pdf = url_pdf.replace('abs', 'pdf')\n        url_pdf = url_pdf + '.pdf'\n\n    url_abs = url_pdf.replace('.pdf', '').replace('pdf', 'abs')\n    title, other_info = get_name(_url_=url_abs)\n\n    paper_id = title.split()[0]  # '[1712.00559]'\n    if '2' in other_info['year']:\n        title = other_info['year'] + ' ' + title\n\n    known_conf = ['NeurIPS', 'NIPS', 'Nature', 'Science', 'ICLR', 'AAAI']\n    for k in known_conf:\n        if k in other_info['comment']:\n            title = k + ' ' + title\n\n    download_dir = './gpt_log/arxiv/'\n    os.makedirs(download_dir, exist_ok=True)\n\n    title_str = title.replace('?', '？')\\\n        .replace(':', '：')\\\n        .replace('\\\"', '“')\\\n        .replace('\\n', '')\\\n        .replace('  ', ' ')\\\n        .replace('  ', ' ')\n\n    requests_pdf_url = url_pdf\n    file_path = download_dir+title_str\n    # if os.path.exists(file_path):\n    #     print('返回缓存文件')\n    #     return './gpt_log/arxiv/'+title_str\n\n    print('下载中')\n    proxies, = get_conf('proxies')\n    r = requests.get(requests_pdf_url, proxies=proxies)\n    with open(file_path, 'wb+') as f:\n        f.write(r.content)\n    print('下载完成')\n\n    # print('输出下载命令：','aria2c -o \\\"%s\\\" %s'%(title_str,url_pdf))\n    # subprocess.call('aria2c --all-proxy=\\\"172.18.116.150:11084\\\" -o \\\"%s\\\" %s'%(download_dir+title_str,url_pdf), shell=True)\n\n    x = \"%s  %s %s.bib\" % (paper_id, other_info['year'], other_info['authors'])\n    x = x.replace('?', '？')\\\n        .replace(':', '：')\\\n        .replace('\\\"', '“')\\\n        .replace('\\n', '')\\\n        .replace('  ', ' ')\\\n        .replace('  ', ' ')\n    return './gpt_log/arxiv/'+title_str, other_info\n\n\ndef get_name(_url_):\n    import os\n    from bs4 import BeautifulSoup\n    print('正在获取文献名！')\n    print(_url_)\n\n    # arxiv_recall = {}\n    # if os.path.exists('./arxiv_recall.pkl'):\n    #     with open('./arxiv_recall.pkl', 'rb') as f:\n    #         arxiv_recall = pickle.load(f)\n\n    # if _url_ in arxiv_recall:\n    #     print('在缓存中')\n    #     return arxiv_recall[_url_]\n\n    proxies, = get_conf('proxies')\n    res = requests.get(_url_, proxies=proxies)\n\n    bs = BeautifulSoup(res.text, 'html.parser')\n    other_details = {}\n\n    # get year\n    try:\n        year = bs.find_all(class_='dateline')[0].text\n        year = re.search(r'(\\d{4})', year, re.M | re.I).group(1)\n        other_details['year'] = year\n        abstract = bs.find_all(class_='abstract mathjax')[0].text\n        other_details['abstract'] = abstract\n    except:\n        other_details['year'] = ''\n        print('年份获取失败')\n\n    # get author\n    try:\n        authors = bs.find_all(class_='authors')[0].text\n        authors = authors.split('Authors:')[1]\n        other_details['authors'] = authors\n    except:\n        other_details['authors'] = ''\n        print('authors获取失败')\n\n    # get comment\n    try:\n        comment = bs.find_all(class_='metatable')[0].text\n        real_comment = None\n        for item in comment.replace('\\n', ' ').split('   '):\n            if 'Comments' in item:\n                real_comment = item\n        if real_comment is not None:\n            other_details['comment'] = real_comment\n        else:\n            other_details['comment'] = ''\n    except:\n        other_details['comment'] = ''\n        print('年份获取失败')\n\n    title_str = BeautifulSoup(\n        res.text, 'html.parser').find('title').contents[0]\n    print('获取成功：', title_str)\n    # arxiv_recall[_url_] = (title_str+'.pdf', other_details)\n    # with open('./arxiv_recall.pkl', 'wb') as f:\n    #     pickle.dump(arxiv_recall, f)\n\n    return title_str+'.pdf', other_details\n\n\n\n@CatchException\ndef 下载arxiv论文并翻译摘要(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):\n\n    CRAZY_FUNCTION_INFO = \"下载arxiv论文并翻译摘要，函数插件作者[binary-husky]。正在提取摘要并下载PDF文档……\"\n    import glob\n    import os\n\n    # 基本信息：功能、贡献者\n    chatbot.append([\"函数插件功能？\", CRAZY_FUNCTION_INFO])\n    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n\n    # 尝试导入依赖，如果缺少依赖，则给出安装建议\n    try:\n        import bs4\n    except:\n        report_execption(chatbot, history, \n            a = f\"解析项目: {txt}\", \n            b = f\"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade beautifulsoup4```。\")\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n\n    # 清空历史，以免输入溢出\n    history = []\n\n    # 提取摘要，下载PDF文档\n    try:\n        pdf_path, info = download_arxiv_(txt)\n    except:\n        report_execption(chatbot, history, \n            a = f\"解析项目: {txt}\", \n            b = f\"下载pdf文件未成功\")\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n    \n    # 翻译摘要等\n    i_say =            f\"请你阅读以下学术论文相关的材料，提取摘要，翻译为中文。材料如下：{str(info)}\"\n    i_say_show_user =  f'请你阅读以下学术论文相关的材料，提取摘要，翻译为中文。论文：{pdf_path}'\n    chatbot.append((i_say_show_user, \"[Local Message] waiting gpt response.\"))\n    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n    msg = '正常'\n    # ** gpt request **\n    # 单线，获取文章meta信息\n    gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(\n        inputs=i_say,\n        inputs_show_user=i_say_show_user,\n        llm_kwargs=llm_kwargs,\n        chatbot=chatbot, history=[],\n        sys_prompt=\"Your job is to collect information from materials and translate to Chinese。\",\n    )\n\n    chatbot[-1] = (i_say_show_user, gpt_say)\n    history.append(i_say_show_user); history.append(gpt_say)\n    yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面\n    # 写入文件\n    import shutil\n    # 重置文件的创建时间\n    shutil.copyfile(pdf_path, f'./gpt_log/{os.path.basename(pdf_path)}'); os.remove(pdf_path)\n    res = write_results_to_file(history)\n    chatbot.append((\"完成了吗？\", res + \"\\n\\nPDF文件也已经下载\"))\n    yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面\n\n"
  },
  {
    "path": "crazy_functions/交互功能函数模板.py",
    "content": "from utils.toolbox import CatchException, update_ui\nfrom .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive\n\n\n@CatchException\ndef 交互功能模板函数(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):\n    \"\"\"\n    txt             输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径\n    llm_kwargs      gpt模型参数, 如温度和top_p等, 一般原样传递下去就行\n    plugin_kwargs   插件模型的参数, 如温度和top_p等, 一般原样传递下去就行\n    chatbot         聊天显示框的句柄，用于显示给用户\n    history         聊天历史，前情提要\n    system_prompt   给gpt的静默提醒\n    web_port        当前软件运行的端口号\n    \"\"\"\n    history = []    # 清空历史，以免输入溢出\n    chatbot.append((\"这是什么功能？\", \"交互功能函数模板。在执行完成之后, 可以将自身的状态存储到cookie中, 等待用户的再次调用。\"))\n    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n\n    state = chatbot._cookies.get('plugin_state_0001', None) # 初始化插件状态\n\n    if state is None:\n        chatbot._cookies['lock_plugin'] = 'crazy_functions.交互功能函数模板->交互功能模板函数'      # 赋予插件锁定 锁定插件回调路径，当下一次用户提交时，会直接转到该函数\n        chatbot._cookies['plugin_state_0001'] = 'wait_user_keyword'                              # 赋予插件状态\n\n        chatbot.append((\"第一次调用：\", \"请输入关键词, 我将为您查找相关壁纸, 建议使用英文单词, 插件锁定中，请直接提交即可。\"))\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n\n    if state == 'wait_user_keyword':\n        chatbot._cookies['lock_plugin'] = None          # 解除插件锁定，避免遗忘导致死锁\n        chatbot._cookies['plugin_state_0001'] = None    # 解除插件状态，避免遗忘导致死锁\n\n        # 解除插件锁定\n        chatbot.append((f\"获取关键词：{txt}\", \"\"))\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        page_return = get_image_page_by_keyword(txt)\n        inputs=inputs_show_user=f\"Extract all image urls in this html page, pick the first 5 images and show them with markdown format: \\n\\n {page_return}\"\n        gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(\n            inputs=inputs, inputs_show_user=inputs_show_user,\n            llm_kwargs=llm_kwargs, chatbot=chatbot, history=[], \n            sys_prompt=\"When you want to show an image, use markdown format. e.g. ![image_description](image_url). If there are no image url provided, answer 'no image url provided'\"\n        )\n        chatbot[-1] = [chatbot[-1][0], gpt_say]\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n\n\n\n# ---------------------------------------------------------------------------------\n\ndef get_image_page_by_keyword(keyword):\n    import requests\n    from bs4 import BeautifulSoup\n    response = requests.get(f'https://wallhaven.cc/search?q={keyword}', timeout=2)\n    res = \"image urls: \\n\"\n    for image_element in BeautifulSoup(response.content, 'html.parser').findAll(\"img\"):\n        try:\n            res += image_element[\"data-src\"]\n            res += \"\\n\"\n        except:\n            pass\n    return res\n"
  },
  {
    "path": "crazy_functions/代码重写为全英文_多线程.py",
    "content": "import threading\nfrom llm_cards.bridge_all import predict_no_ui_long_connection\nfrom utils.toolbox import update_ui\nfrom utils.toolbox import CatchException, write_results_to_file, report_execption\nfrom .crazy_utils import breakdown_txt_to_satisfy_token_limit\n\ndef extract_code_block_carefully(txt):\n    splitted = txt.split('```')\n    n_code_block_seg = len(splitted) - 1\n    if n_code_block_seg <= 1: return txt\n    # 剩下的情况都开头除去 ``` 结尾除去一次 ```\n    txt_out = '```'.join(splitted[1:-1])\n    return txt_out\n\n\n\ndef break_txt_into_half_at_some_linebreak(txt):\n    lines = txt.split('\\n')\n    n_lines = len(lines)\n    pre = lines[:(n_lines//2)]\n    post = lines[(n_lines//2):]\n    return \"\\n\".join(pre), \"\\n\".join(post)\n\n\n@CatchException\ndef 全项目切换英文(txt, llm_kwargs, plugin_kwargs, chatbot, history, sys_prompt, web_port):\n    # 第1步：清空历史，以免输入溢出\n    history = []\n\n    # 第2步：尝试导入依赖，如果缺少依赖，则给出安装建议\n    try:\n        import tiktoken\n    except:\n        report_execption(chatbot, history, \n            a = f\"解析项目: {txt}\", \n            b = f\"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade tiktoken```。\")\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n\n    # 第3步：集合文件\n    import time, glob, os, shutil, re\n    os.makedirs('gpt_log/generated_english_version', exist_ok=True)\n    os.makedirs('gpt_log/generated_english_version/crazy_functions', exist_ok=True)\n    file_manifest = [f for f in glob.glob('./*.py') if ('test_project' not in f) and ('gpt_log' not in f)] + \\\n                    [f for f in glob.glob('./crazy_functions/*.py') if ('test_project' not in f) and ('gpt_log' not in f)]\n    # file_manifest = ['./toolbox.py']\n    i_say_show_user_buffer = []\n\n    # 第4步：随便显示点什么防止卡顿的感觉\n    for index, fp in enumerate(file_manifest):\n        # if 'test_project' in fp: continue\n        with open(fp, 'r', encoding='utf-8', errors='replace') as f:\n            file_content = f.read()\n        i_say_show_user =f'[{index}/{len(file_manifest)}] 接下来请将以下代码中包含的所有中文转化为英文，只输出转化后的英文代码，请用代码块输出代码: {os.path.abspath(fp)}'\n        i_say_show_user_buffer.append(i_say_show_user)\n        chatbot.append((i_say_show_user, \"[Local Message] 等待多线程操作，中间过程不予显示.\"))\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n\n\n    # 第5步：Token限制下的截断与处理\n    MAX_TOKEN = 3000\n    from llm_cards.bridge_all import model_info\n    enc = model_info[\"gpt-3.5-turbo\"]['tokenizer']\n    def get_token_fn(txt): return len(enc.encode(txt, disallowed_special=()))\n\n\n    # 第6步：任务函数\n    mutable_return = [None for _ in file_manifest]\n    observe_window = [[\"\"] for _ in file_manifest]\n    def thread_worker(fp,index):\n        if index > 10: \n            time.sleep(60)\n            print('Openai 限制免费用户每分钟20次请求，降低请求频率中。')\n        with open(fp, 'r', encoding='utf-8', errors='replace') as f:\n            file_content = f.read()\n        i_say_template = lambda fp, file_content: f'接下来请将以下代码中包含的所有中文转化为英文，只输出代码，文件名是{fp}，文件代码是 ```{file_content}```'\n        try:\n            gpt_say = \"\"\n            # 分解代码文件\n            file_content_breakdown = breakdown_txt_to_satisfy_token_limit(file_content, get_token_fn, MAX_TOKEN)\n            for file_content_partial in file_content_breakdown:\n                i_say = i_say_template(fp, file_content_partial)\n                # # ** gpt request **\n                gpt_say_partial = predict_no_ui_long_connection(inputs=i_say, llm_kwargs=llm_kwargs, history=[], sys_prompt=sys_prompt, observe_window=observe_window[index])\n                gpt_say_partial = extract_code_block_carefully(gpt_say_partial)\n                gpt_say += gpt_say_partial\n            mutable_return[index] = gpt_say\n        except ConnectionAbortedError as token_exceed_err:\n            print('至少一个线程任务Token溢出而失败', e)\n        except Exception as e:\n            print('至少一个线程任务意外失败', e)\n\n    # 第7步：所有线程同时开始执行任务函数\n    handles = [threading.Thread(target=thread_worker, args=(fp,index)) for index, fp in enumerate(file_manifest)]\n    for h in handles:\n        h.daemon = True\n        h.start()\n    chatbot.append(('开始了吗？', f'多线程操作已经开始'))\n    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n\n    # 第8步：循环轮询各个线程是否执行完毕\n    cnt = 0\n    while True:\n        cnt += 1\n        time.sleep(0.2)\n        th_alive = [h.is_alive() for h in handles]\n        if not any(th_alive): break\n        # 更好的UI视觉效果\n        observe_win = []\n        for thread_index, alive in enumerate(th_alive): \n            observe_win.append(\"[ ...\"+observe_window[thread_index][0][-60:].replace('\\n','').replace('```','...').replace(' ','.').replace('<br/>','.....').replace('$','.')+\"... ]\")\n        stat = [f'执行中: {obs}\\n\\n' if alive else '已完成\\n\\n' for alive, obs in zip(th_alive, observe_win)]\n        stat_str = ''.join(stat)\n        chatbot[-1] = (chatbot[-1][0], f'多线程操作已经开始，完成情况: \\n\\n{stat_str}' + ''.join(['.']*(cnt%10+1)))\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n\n    # 第9步：把结果写入文件\n    for index, h in enumerate(handles):\n        h.join() # 这里其实不需要join了，肯定已经都结束了\n        fp = file_manifest[index]\n        gpt_say = mutable_return[index]\n        i_say_show_user = i_say_show_user_buffer[index]\n\n        where_to_relocate = f'gpt_log/generated_english_version/{fp}'\n        if gpt_say is not None:\n            with open(where_to_relocate, 'w+', encoding='utf-8') as f:  \n                f.write(gpt_say)\n        else:  # 失败\n            shutil.copyfile(file_manifest[index], where_to_relocate)\n        chatbot.append((i_say_show_user, f'[Local Message] 已完成{os.path.abspath(fp)}的转化，\\n\\n存入{os.path.abspath(where_to_relocate)}'))\n        history.append(i_say_show_user); history.append(gpt_say)\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        time.sleep(1)\n\n    # 第10步：备份一个文件\n    res = write_results_to_file(history)\n    chatbot.append((\"生成一份任务执行报告\", res))\n    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n"
  },
  {
    "path": "crazy_functions/图片生成.py",
    "content": "from utils.toolbox import CatchException, update_ui, get_conf, select_api_key\nfrom .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive\nimport datetime\n\n\ndef gen_image(llm_kwargs, prompt, resolution=\"256x256\"):\n    import requests, json, time, os\n    from llm_cards.bridge_all import model_info\n\n    proxies, = get_conf('proxies')\n    # Set up OpenAI API key and model \n    api_key = select_api_key(llm_kwargs['api_key'], llm_kwargs['llm_model'])\n    chat_endpoint = model_info[llm_kwargs['llm_model']]['endpoint']\n    # 'https://api.openai.com/v1/chat/completions'\n    img_endpoint = chat_endpoint.replace('chat/completions','images/generations')\n    # # Generate the image\n    url = img_endpoint\n    headers = {\n        'Authorization': f\"Bearer {api_key}\",\n        'Content-Type': 'application/json'\n    }\n    data = {\n        'prompt': prompt,\n        'n': 1,\n        'size': resolution,\n        'response_format': 'url'\n    }\n    response = requests.post(url, headers=headers, json=data, proxies=proxies)\n    print(response.content)\n    try:\n        image_url = json.loads(response.content.decode('utf8'))['data'][0]['url']\n    except:\n        raise RuntimeError(response.content.decode())\n    # 文件保存到本地\n    r = requests.get(image_url, proxies=proxies)\n    file_path = 'gpt_log/image_gen/'\n    os.makedirs(file_path, exist_ok=True)\n    file_name = 'Image' + time.strftime(\"%Y-%m-%d-%H-%M-%S\", time.localtime()) + '.png'\n    with open(file_path+file_name, 'wb+') as f: f.write(r.content)\n\n\n    return image_url, file_path+file_name\n\n\n\n@CatchException\ndef 图片生成(prompt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):\n    \"\"\"\n    txt             输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径\n    llm_kwargs      gpt模型参数，如温度和top_p等，一般原样传递下去就行\n    plugin_kwargs   插件模型的参数，暂时没有用武之地\n    chatbot         聊天显示框的句柄，用于显示给用户\n    history         聊天历史，前情提要\n    system_prompt   给gpt的静默提醒\n    web_port        当前软件运行的端口号\n    \"\"\"\n    history = []    # 清空历史，以免输入溢出\n    chatbot.append((\"这是什么功能？\", \"[Local Message] 生成图像, 请先把模型切换至gpt-*或者api2d-*。如果中文效果不理想, 请尝试英文Prompt。正在处理中 .....\"))\n    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新\n    if (\"advanced_arg\" in plugin_kwargs) and (plugin_kwargs[\"advanced_arg\"] == \"\"): plugin_kwargs.pop(\"advanced_arg\")\n    resolution = plugin_kwargs.get(\"advanced_arg\", '256x256')\n    image_url, image_path = gen_image(llm_kwargs, prompt, resolution)\n    chatbot.append([prompt,  \n        f'图像中转网址: <br/>`{image_url}`<br/>'+\n        f'中转网址预览: <br/><div align=\"center\"><img src=\"{image_url}\"></div>'\n        f'本地文件地址: <br/>`{image_path}`<br/>'+\n        f'本地文件预览: <br/><div align=\"center\"><img src=\"file={image_path}\"></div>'\n    ])\n    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新\n"
  },
  {
    "path": "crazy_functions/对话历史存档.py",
    "content": "from utils.toolbox import CatchException, update_ui, promote_file_to_downloadzone\nfrom .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive\nimport re\n\ndef write_chat_to_file(chatbot, history=None, file_name=None):\n    \"\"\"\n    将对话记录history以Markdown格式写入文件中。如果没有指定文件名，则使用当前时间生成文件名。\n    \"\"\"\n    import os\n    import time\n    if file_name is None:\n        file_name = 'chatGPT对话历史' + time.strftime(\"%Y-%m-%d-%H-%M-%S\", time.localtime()) + '.html'\n    os.makedirs('./gpt_log/', exist_ok=True)\n    with open(f'./gpt_log/{file_name}', 'w', encoding='utf8') as f:\n        from themes.theme import advanced_css\n        f.write(f'<!DOCTYPE html><head><meta charset=\"utf-8\"><title>对话历史</title><style>{advanced_css}</style></head>')\n        for i, contents in enumerate(chatbot):\n            for j, content in enumerate(contents):\n                try:    # 这个bug没找到触发条件，暂时先这样顶一下\n                    if type(content) != str: content = str(content)\n                except:\n                    continue\n                f.write(content)\n                if j == 0:\n                    f.write('<hr style=\"border-top: dotted 3px #ccc;\">')\n            f.write('<hr color=\"red\"> \\n\\n')\n        f.write('<hr color=\"blue\"> \\n\\n raw chat context:\\n')\n        f.write('<code>')\n        for h in history:\n            f.write(\"\\n>>>\" + h)\n        f.write('</code>')\n    promote_file_to_downloadzone(f'./gpt_log/{file_name}', rename_file=file_name, chatbot=chatbot)\n    return '对话历史写入：' + os.path.abspath(f'./gpt_log/{file_name}')\n\ndef gen_file_preview(file_name):\n    try:\n        with open(file_name, 'r', encoding='utf8') as f:\n            file_content = f.read()\n        # pattern to match the text between <head> and </head>\n        pattern = re.compile(r'<head>.*?</head>', flags=re.DOTALL)\n        file_content = re.sub(pattern, '', file_content)\n        html, history = file_content.split('<hr color=\"blue\"> \\n\\n raw chat context:\\n')\n        history = history.strip('<code>')\n        history = history.strip('</code>')\n        history = history.split(\"\\n>>>\")\n        return list(filter(lambda x:x!=\"\", history))[0][:100]\n    except:\n        return \"\"\n\ndef read_file_to_chat(chatbot, history, file_name):\n    with open(file_name, 'r', encoding='utf8') as f:\n        file_content = f.read()\n    # pattern to match the text between <head> and </head>\n    pattern = re.compile(r'<head>.*?</head>', flags=re.DOTALL)\n    file_content = re.sub(pattern, '', file_content)\n    html, history = file_content.split('<hr color=\"blue\"> \\n\\n raw chat context:\\n')\n    history = history.strip('<code>')\n    history = history.strip('</code>')\n    history = history.split(\"\\n>>>\")\n    history = list(filter(lambda x:x!=\"\", history))\n    html = html.split('<hr color=\"red\"> \\n\\n')\n    html = list(filter(lambda x:x!=\"\", html))\n    chatbot.clear()\n    for i, h in enumerate(html):\n        i_say, gpt_say = h.split('<hr style=\"border-top: dotted 3px #ccc;\">')\n        chatbot.append([i_say, gpt_say])\n    chatbot.append([f\"存档文件详情？\", f\"[Local Message] 载入对话{len(html)}条，上下文{len(history)}条。\"])\n    return chatbot, history    \n\n@CatchException\ndef 对话历史存档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):\n    \"\"\"\n    txt             输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径\n    llm_kwargs      gpt模型参数，如温度和top_p等，一般原样传递下去就行\n    plugin_kwargs   插件模型的参数，暂时没有用武之地\n    chatbot         聊天显示框的句柄，用于显示给用户\n    history         聊天历史，前情提要\n    system_prompt   给gpt的静默提醒\n    web_port        当前软件运行的端口号\n    \"\"\"\n\n    chatbot.append((\"保存当前对话\", \n        f\"[Local Message] {write_chat_to_file(chatbot, history)}，您可以调用“载入对话历史存档”还原当下的对话。\\n警告！被保存的对话历史可以被使用该系统的任何人查阅。\"))\n    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新\n\ndef hide_cwd(str):\n    import os\n    current_path = os.getcwd()\n    replace_path = \".\"\n    return str.replace(current_path, replace_path)\n\n@CatchException\ndef 载入对话历史存档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):\n    \"\"\"\n    txt             输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径\n    llm_kwargs      gpt模型参数，如温度和top_p等，一般原样传递下去就行\n    plugin_kwargs   插件模型的参数，暂时没有用武之地\n    chatbot         聊天显示框的句柄，用于显示给用户\n    history         聊天历史，前情提要\n    system_prompt   给gpt的静默提醒\n    web_port        当前软件运行的端口号\n    \"\"\"\n    from .crazy_utils import get_files_from_everything\n    success, file_manifest, _ = get_files_from_everything(txt, type='.html')\n\n    if not success:\n        if txt == \"\": txt = '空空如也的输入栏'\n        import glob\n        local_history = \"<br/>\".join([\"`\"+hide_cwd(f)+f\" ({gen_file_preview(f)})\"+\"`\" for f in glob.glob(f'gpt_log/**/chatGPT对话历史*.html', recursive=True)])\n        chatbot.append([f\"正在查找对话历史文件（html格式）: {txt}\", f\"找不到任何html文件: {txt}。但本地存储了以下历史文件，您可以将任意一个文件路径粘贴到输入区，然后重试：<br/>{local_history}\"])\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n\n    try:\n        chatbot, history = read_file_to_chat(chatbot, history, file_manifest[0])\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n    except:\n        chatbot.append([f\"载入对话历史文件\", f\"对话历史文件损坏！\"])\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n\n@CatchException\ndef 删除所有本地对话历史记录(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):\n    \"\"\"\n    txt             输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径\n    llm_kwargs      gpt模型参数，如温度和top_p等，一般原样传递下去就行\n    plugin_kwargs   插件模型的参数，暂时没有用武之地\n    chatbot         聊天显示框的句柄，用于显示给用户\n    history         聊天历史，前情提要\n    system_prompt   给gpt的静默提醒\n    web_port        当前软件运行的端口号\n    \"\"\"\n\n    import glob, os\n    local_history = \"<br/>\".join([\"`\"+hide_cwd(f)+\"`\" for f in glob.glob(f'gpt_log/**/chatGPT对话历史*.html', recursive=True)])\n    for f in glob.glob(f'gpt_log/**/chatGPT对话历史*.html', recursive=True):\n        os.remove(f)\n    chatbot.append([f\"删除所有历史对话文件\", f\"已删除<br/>{local_history}\"])\n    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n    return\n\n\n"
  },
  {
    "path": "crazy_functions/总结word文档.py",
    "content": "from utils.toolbox import update_ui\nfrom utils.toolbox import CatchException, report_execption, write_results_to_file\nfrom .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive\nfast_debug = False\n\n\ndef 解析docx(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):\n    import time, os\n    # pip install python-docx 用于docx格式，跨平台\n    # pip install pywin32 用于doc格式，仅支持Win平台\n    for index, fp in enumerate(file_manifest):\n        if fp.split(\".\")[-1] == \"docx\":\n            from docx import Document\n            doc = Document(fp)\n            file_content = \"\\n\".join([para.text for para in doc.paragraphs])\n        else:\n            try:\n                import win32com.client\n                word = win32com.client.Dispatch(\"Word.Application\")\n                word.visible = False\n                # 打开文件\n                doc = word.Documents.Open(os.getcwd() + '/' + fp)\n                # file_content = doc.Content.Text\n                doc = word.ActiveDocument\n                file_content = doc.Range().Text\n                doc.Close()\n                word.Quit()\n            except:\n                raise RuntimeError('请先将.doc文档转换为.docx文档。')\n\n        print(file_content)\n        # private_upload里面的文件名在解压zip后容易出现乱码（rar和7z格式正常），故可以只分析文章内容，不输入文件名\n        from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf\n        from llm_cards.bridge_all import model_info\n        max_token = model_info[llm_kwargs['llm_model']]['max_token']\n        TOKEN_LIMIT_PER_FRAGMENT = max_token * 3 // 4\n        paper_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(\n            txt=file_content,  \n            get_token_fn=model_info[llm_kwargs['llm_model']]['token_cnt'], \n            limit=TOKEN_LIMIT_PER_FRAGMENT\n        )\n        this_paper_history = []\n        for i, paper_frag in enumerate(paper_fragments):\n            i_say = f'请对下面的文章片段用中文做概述，文件名是{os.path.relpath(fp, project_folder)}，文章内容是 ```{paper_frag}```'\n            i_say_show_user = f'请对下面的文章片段做概述: {os.path.abspath(fp)}的第{i+1}/{len(paper_fragments)}个片段。'\n            gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(\n                inputs=i_say, \n                inputs_show_user=i_say_show_user, \n                llm_kwargs=llm_kwargs,\n                chatbot=chatbot, \n                history=[],\n                sys_prompt=\"总结文章。\"\n            )\n\n            chatbot[-1] = (i_say_show_user, gpt_say)\n            history.extend([i_say_show_user,gpt_say])\n            this_paper_history.extend([i_say_show_user,gpt_say])\n\n        # 已经对该文章的所有片段总结完毕，如果文章被切分了，\n        if len(paper_fragments) > 1:\n            i_say = f\"根据以上的对话，总结文章{os.path.abspath(fp)}的主要内容。\"\n            gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(\n                inputs=i_say, \n                inputs_show_user=i_say, \n                llm_kwargs=llm_kwargs,\n                chatbot=chatbot, \n                history=this_paper_history,\n                sys_prompt=\"总结文章。\"\n            )\n\n            history.extend([i_say,gpt_say])\n            this_paper_history.extend([i_say,gpt_say])\n\n        res = write_results_to_file(history)\n        chatbot.append((\"完成了吗？\", res))\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n\n    res = write_results_to_file(history)\n    chatbot.append((\"所有文件都总结完成了吗？\", res))\n    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n\n\n@CatchException\ndef 总结word文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):\n    import glob, os\n\n    # 基本信息：功能、贡献者\n    chatbot.append([\n        \"函数插件功能？\",\n        \"批量总结Word文档。函数插件贡献者: JasonGuo1。注意, 如果是.doc文件, 请先转化为.docx格式。\"])\n    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n\n    # 尝试导入依赖，如果缺少依赖，则给出安装建议\n    try:\n        from docx import Document\n    except:\n        report_execption(chatbot, history,\n                         a=f\"解析项目: {txt}\",\n                         b=f\"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade python-docx pywin32```。\")\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n\n    # 清空历史，以免输入溢出\n    history = []\n\n    # 检测输入参数，如没有给定输入参数，直接退出\n    if os.path.exists(txt):\n        project_folder = txt\n    else:\n        if txt == \"\": txt = '空空如也的输入栏'\n        report_execption(chatbot, history, a=f\"解析项目: {txt}\", b=f\"找不到本地项目或无权访问: {txt}\")\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n\n    # 搜索需要处理的文件清单\n    if txt.endswith('.docx') or txt.endswith('.doc'):\n        file_manifest = [txt]\n    else:\n        file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.docx', recursive=True)] + \\\n                        [f for f in glob.glob(f'{project_folder}/**/*.doc', recursive=True)]\n\n    # 如果没找到任何文件\n    if len(file_manifest) == 0:\n        report_execption(chatbot, history, a=f\"解析项目: {txt}\", b=f\"找不到任何.docx或doc文件: {txt}\")\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n\n    # 开始正式执行任务\n    yield from 解析docx(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)\n"
  },
  {
    "path": "crazy_functions/总结音视频.py",
    "content": "from utils.toolbox import CatchException, report_execption, select_api_key, update_ui, write_results_to_file, get_conf\nfrom .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive\n\ndef split_audio_file(filename, split_duration=1000):\n    \"\"\"\n    根据给定的切割时长将音频文件切割成多个片段。\n\n    Args:\n        filename (str): 需要被切割的音频文件名。\n        split_duration (int, optional): 每个切割音频片段的时长（以秒为单位）。默认值为1000。\n\n    Returns:\n        filelist (list): 一个包含所有切割音频片段文件路径的列表。\n\n    \"\"\"\n    from moviepy.editor import AudioFileClip\n    import os\n    os.makedirs('gpt_log/mp3/cut/', exist_ok=True)  # 创建存储切割音频的文件夹\n\n    # 读取音频文件\n    audio = AudioFileClip(filename)\n\n    # 计算文件总时长和切割点\n    total_duration = audio.duration\n    split_points = list(range(0, int(total_duration), split_duration))\n    split_points.append(int(total_duration))\n    filelist = []\n\n    # 切割音频文件\n    for i in range(len(split_points) - 1):\n        start_time = split_points[i]\n        end_time = split_points[i + 1]\n        split_audio = audio.subclip(start_time, end_time)\n        split_audio.write_audiofile(f\"gpt_log/mp3/cut/{filename[0]}_{i}.mp3\")\n        filelist.append(f\"gpt_log/mp3/cut/{filename[0]}_{i}.mp3\")\n\n    audio.close()\n    return filelist\n\ndef AnalyAudio(parse_prompt, file_manifest, llm_kwargs, chatbot, history):\n    import os, requests\n    from moviepy.editor import AudioFileClip\n    from llm_cards.bridge_all import model_info\n\n    # 设置OpenAI密钥和模型\n    api_key = select_api_key(llm_kwargs['api_key'], llm_kwargs['llm_model'])\n    chat_endpoint = model_info[llm_kwargs['llm_model']]['endpoint']\n\n    whisper_endpoint = chat_endpoint.replace('chat/completions', 'audio/transcriptions')\n    url = whisper_endpoint\n    headers = {\n        'Authorization': f\"Bearer {api_key}\"\n    }\n\n    os.makedirs('gpt_log/mp3/', exist_ok=True)\n    for index, fp in enumerate(file_manifest):\n        audio_history = []\n        # 提取文件扩展名\n        ext = os.path.splitext(fp)[1]\n        # 提取视频中的音频\n        if ext not in [\".mp3\", \".wav\", \".m4a\", \".mpga\"]:\n            audio_clip = AudioFileClip(fp)\n            audio_clip.write_audiofile(f'gpt_log/mp3/output{index}.mp3')\n            fp = f'gpt_log/mp3/output{index}.mp3'\n        # 调用whisper模型音频转文字\n        voice = split_audio_file(fp)\n        for j, i in enumerate(voice):\n            with open(i, 'rb') as f:\n                file_content = f.read()  # 读取文件内容到内存\n                files = {\n                    'file': (os.path.basename(i), file_content),\n                }\n                data = {\n                    \"model\": \"whisper-1\",\n                    \"prompt\": parse_prompt,\n                    'response_format': \"text\"\n                }\n\n            chatbot.append([f\"将 {i} 发送到openai音频解析终端 (whisper)，当前参数：{parse_prompt}\", \"正在处理 ...\"])\n            yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面\n            proxies, = get_conf('proxies')\n            response = requests.post(url, headers=headers, files=files, data=data, proxies=proxies).text\n\n            chatbot.append([\"音频解析结果\", response])\n            history.extend([\"音频解析结果\", response])\n            yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面\n\n            i_say = f'请对下面的音频片段做概述，音频内容是 ```{response}```'\n            i_say_show_user = f'第{index + 1}段音频的第{j + 1} / {len(voice)}片段。'\n            gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(\n                inputs=i_say,\n                inputs_show_user=i_say_show_user,\n                llm_kwargs=llm_kwargs,\n                chatbot=chatbot,\n                history=[],\n                sys_prompt=f\"总结音频。音频文件名{fp}\"\n            )\n\n            chatbot[-1] = (i_say_show_user, gpt_say)\n            history.extend([i_say_show_user, gpt_say])\n            audio_history.extend([i_say_show_user, gpt_say])\n\n        # 已经对该文章的所有片段总结完毕，如果文章被切分了\n        result = \"\".join(audio_history)\n        if len(audio_history) > 1:\n            i_say = f\"根据以上的对话，使用中文总结音频“{result}”的主要内容。\"\n            i_say_show_user = f'第{index + 1}段音频的主要内容：'\n            gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(\n                inputs=i_say,\n                inputs_show_user=i_say_show_user,\n                llm_kwargs=llm_kwargs,\n                chatbot=chatbot,\n                history=audio_history,\n                sys_prompt=\"总结文章。\"\n            )\n\n            history.extend([i_say, gpt_say])\n            audio_history.extend([i_say, gpt_say])\n\n        res = write_results_to_file(history)\n        chatbot.append((f\"第{index + 1}段音频完成了吗？\", res))\n        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面\n\n    # 删除中间文件夹\n    import shutil\n    shutil.rmtree('gpt_log/mp3')\n    res = write_results_to_file(history)\n    chatbot.append((\"所有音频都总结完成了吗？\", res))\n    yield from update_ui(chatbot=chatbot, history=history)\n\n\n@CatchException\ndef 总结音视频(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, WEB_PORT):\n    import glob, os\n\n    # 基本信息：功能、贡献者\n    chatbot.append([\n        \"函数插件功能？\",\n        \"总结音视频内容，函数插件贡献者: dalvqw & BinaryHusky\"])\n    yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面\n\n    try:\n        from moviepy.editor import AudioFileClip\n    except:\n        report_execption(chatbot, history,\n                         a=f\"解析项目: {txt}\",\n                         b=f\"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade moviepy```。\")\n        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面\n        return\n\n    # 清空历史，以免输入溢出\n    history = []\n\n    # 检测输入参数，如没有给定输入参数，直接退出\n    if os.path.exists(txt):\n        project_folder = txt\n    else:\n        if txt == \"\": txt = '空空如也的输入栏'\n        report_execption(chatbot, history, a=f\"解析项目: {txt}\", b=f\"找不到本地项目或无权访问: {txt}\")\n        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面\n        return\n\n    # 搜索需要处理的文件清单\n    extensions = ['.mp4', '.m4a', '.wav', '.mpga', '.mpeg', '.mp3', '.avi', '.mkv', '.flac', '.aac']\n\n    if txt.endswith(tuple(extensions)):\n        file_manifest = [txt]\n    else:\n        file_manifest = []\n        for extension in extensions:\n            file_manifest.extend(glob.glob(f'{project_folder}/**/*{extension}', recursive=True))\n\n    # 如果没找到任何文件\n    if len(file_manifest) == 0:\n        report_execption(chatbot, history, a=f\"解析项目: {txt}\", b=f\"找不到任何音频或视频文件: {txt}\")\n        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面\n        return\n\n    # 开始正式执行任务\n    if (\"advanced_arg\" in plugin_kwargs) and (plugin_kwargs[\"advanced_arg\"] == \"\"): plugin_kwargs.pop(\"advanced_arg\")\n    parse_prompt = plugin_kwargs.get(\"advanced_arg\", '将音频解析为简体中文')\n    yield from AnalyAudio(parse_prompt, file_manifest, llm_kwargs, chatbot, history)\n\n    yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面\n"
  },
  {
    "path": "crazy_functions/批量Markdown翻译.py",
    "content": "import glob, time, os, re\nfrom utils.toolbox import update_ui, trimmed_format_exc, gen_time_str, disable_auto_promotion\nfrom utils.toolbox import CatchException, report_execption, write_history_to_file\nfrom utils.toolbox import promote_file_to_downloadzone, get_log_folder\nfast_debug = False\n\nclass PaperFileGroup():\n    def __init__(self):\n        self.file_paths = []\n        self.file_contents = []\n        self.sp_file_contents = []\n        self.sp_file_index = []\n        self.sp_file_tag = []\n\n        # count_token\n        from llm_cards.bridge_all import model_info\n        enc = model_info[\"gpt-3.5-turbo\"]['tokenizer']\n        def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))\n        self.get_token_num = get_token_num\n\n    def run_file_split(self, max_token_limit=1900):\n        \"\"\"\n        将长文本分离开来\n        \"\"\"\n        for index, file_content in enumerate(self.file_contents):\n            if self.get_token_num(file_content) < max_token_limit:\n                self.sp_file_contents.append(file_content)\n                self.sp_file_index.append(index)\n                self.sp_file_tag.append(self.file_paths[index])\n            else:\n                from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf\n                segments = breakdown_txt_to_satisfy_token_limit_for_pdf(file_content, self.get_token_num, max_token_limit)\n                for j, segment in enumerate(segments):\n                    self.sp_file_contents.append(segment)\n                    self.sp_file_index.append(index)\n                    self.sp_file_tag.append(self.file_paths[index] + f\".part-{j}.md\")\n        print('Segmentation: done')\n\n    def merge_result(self):\n        self.file_result = [\"\" for _ in range(len(self.file_paths))]\n        for r, k in zip(self.sp_file_result, self.sp_file_index):\n            self.file_result[k] += r\n\n    def write_result(self, language):\n        manifest = []\n        for path, res in zip(self.file_paths, self.file_result):\n            dst_file = os.path.join(get_log_folder(), f'{gen_time_str()}.md')\n            with open(dst_file, 'w', encoding='utf8') as f:\n                manifest.append(dst_file)\n                f.write(res)\n        return manifest\n\ndef 多文件翻译(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='en'):\n    from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency\n\n    #  <-------- 读取Markdown文件，删除其中的所有注释 ----------> \n    pfg = PaperFileGroup()\n\n    for index, fp in enumerate(file_manifest):\n        with open(fp, 'r', encoding='utf-8', errors='replace') as f:\n            file_content = f.read()\n            # 记录删除注释后的文本\n            pfg.file_paths.append(fp)\n            pfg.file_contents.append(file_content)\n\n    #  <-------- 拆分过长的Markdown文件 ----------> \n    pfg.run_file_split(max_token_limit=1500)\n    n_split = len(pfg.sp_file_contents)\n\n    #  <-------- 多线程翻译开始 ----------> \n    if language == 'en->zh':\n        inputs_array = [\"This is a Markdown file, translate it into Chinese, do not modify any existing Markdown commands:\" + \n                        f\"\\n\\n{frag}\" for frag in pfg.sp_file_contents]\n        inputs_show_user_array = [f\"翻译 {f}\" for f in pfg.sp_file_tag]\n        sys_prompt_array = [\"You are a professional academic paper translator.\" for _ in range(n_split)]\n    elif language == 'zh->en':\n        inputs_array = [f\"This is a Markdown file, translate it into English, do not modify any existing Markdown commands:\" + \n                        f\"\\n\\n{frag}\" for frag in pfg.sp_file_contents]\n        inputs_show_user_array = [f\"翻译 {f}\" for f in pfg.sp_file_tag]\n        sys_prompt_array = [\"You are a professional academic paper translator.\" for _ in range(n_split)]\n    else:\n        inputs_array = [f\"This is a Markdown file, translate it into {language}, do not modify any existing Markdown commands, only answer me with translated results:\" + \n                        f\"\\n\\n{frag}\" for frag in pfg.sp_file_contents]\n        inputs_show_user_array = [f\"翻译 {f}\" for f in pfg.sp_file_tag]\n        sys_prompt_array = [\"You are a professional academic paper translator.\" for _ in range(n_split)]\n\n    gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(\n        inputs_array=inputs_array,\n        inputs_show_user_array=inputs_show_user_array,\n        llm_kwargs=llm_kwargs,\n        chatbot=chatbot,\n        history_array=[[\"\"] for _ in range(n_split)],\n        sys_prompt_array=sys_prompt_array,\n        # max_workers=5,  # OpenAI所允许的最大并行过载\n        scroller_max_len = 80\n    )\n    try:\n        pfg.sp_file_result = []\n        for i_say, gpt_say in zip(gpt_response_collection[0::2], gpt_response_collection[1::2]):\n            pfg.sp_file_result.append(gpt_say)\n        pfg.merge_result()\n        pfg.write_result(language)\n    except:\n        print(trimmed_format_exc())\n\n    #  <-------- 整理结果，退出 ----------> \n    create_report_file_name = gen_time_str() + f\"-chatgpt.md\"\n    res = write_history_to_file(gpt_response_collection, file_basename=create_report_file_name)\n    promote_file_to_downloadzone(res, chatbot=chatbot)\n    history = gpt_response_collection\n    chatbot.append((f\"{fp}完成了吗？\", res))\n    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n\n\ndef get_files_from_everything(txt, preference=''):\n    if txt == \"\": return False, None, None\n    success = True\n    if txt.startswith('http'):\n        import requests\n        from toolbox import get_conf\n        proxies, = get_conf('proxies')\n        # 网络的远程文件\n        if preference == 'Github':\n            print('正在从github下载资源 ...')\n            if not txt.endswith('.md'):\n                # Make a request to the GitHub API to retrieve the repository information\n                url = txt.replace(\"https://github.com/\", \"https://api.github.com/repos/\") + '/readme'\n                response = requests.get(url, proxies=proxies)\n                txt = response.json()['download_url']\n            else:\n                txt = txt.replace(\"https://github.com/\", \"https://raw.githubusercontent.com/\")\n                txt = txt.replace(\"/blob/\", \"/\")\n\n        r = requests.get(txt, proxies=proxies)\n        download_local = f'{get_log_folder(plugin_name=\"批量Markdown翻译\")}/raw-readme-{gen_time_str()}.md'\n        project_folder = f'{get_log_folder(plugin_name=\"批量Markdown翻译\")}'\n        with open(download_local, 'wb+') as f: f.write(r.content)\n        file_manifest = [download_local]\n    elif txt.endswith('.md'):\n        # 直接给定文件\n        file_manifest = [txt]\n        project_folder = os.path.dirname(txt)\n    elif os.path.exists(txt):\n        # 本地路径，递归搜索\n        project_folder = txt\n        file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.md', recursive=True)]\n    else:\n        success = False\n\n    return success, file_manifest, project_folder\n\n\n@CatchException\ndef Markdown英译中(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):\n    # 基本信息：功能、贡献者\n    chatbot.append([\n        \"函数插件功能？\",\n        \"对整个Markdown项目进行翻译。函数插件贡献者: Binary-Husky\"])\n    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n    disable_auto_promotion(chatbot)\n\n    # 尝试导入依赖，如果缺少依赖，则给出安装建议\n    try:\n        import tiktoken\n    except:\n        report_execption(chatbot, history,\n                         a=f\"解析项目: {txt}\",\n                         b=f\"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade tiktoken```。\")\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n    history = []    # 清空历史，以免输入溢出\n\n    success, file_manifest, project_folder = get_files_from_everything(txt, preference=\"Github\")\n\n    if not success:\n        # 什么都没有\n        if txt == \"\": txt = '空空如也的输入栏'\n        report_execption(chatbot, history, a = f\"解析项目: {txt}\", b = f\"找不到本地项目或无权访问: {txt}\")\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n\n    if len(file_manifest) == 0:\n        report_execption(chatbot, history, a = f\"解析项目: {txt}\", b = f\"找不到任何.md文件: {txt}\")\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n\n    yield from 多文件翻译(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='en->zh')\n\n\n\n\n\n@CatchException\ndef Markdown中译英(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):\n    # 基本信息：功能、贡献者\n    chatbot.append([\n        \"函数插件功能？\",\n        \"对整个Markdown项目进行翻译。函数插件贡献者: Binary-Husky\"])\n    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n    disable_auto_promotion(chatbot)\n\n    # 尝试导入依赖，如果缺少依赖，则给出安装建议\n    try:\n        import tiktoken\n    except:\n        report_execption(chatbot, history,\n                         a=f\"解析项目: {txt}\",\n                         b=f\"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade tiktoken```。\")\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n    history = []    # 清空历史，以免输入溢出\n    success, file_manifest, project_folder = get_files_from_everything(txt)\n    if not success:\n        # 什么都没有\n        if txt == \"\": txt = '空空如也的输入栏'\n        report_execption(chatbot, history, a = f\"解析项目: {txt}\", b = f\"找不到本地项目或无权访问: {txt}\")\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n    if len(file_manifest) == 0:\n        report_execption(chatbot, history, a = f\"解析项目: {txt}\", b = f\"找不到任何.md文件: {txt}\")\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n    yield from 多文件翻译(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='zh->en')\n\n\n@CatchException\ndef Markdown翻译指定语言(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):\n    # 基本信息：功能、贡献者\n    chatbot.append([\n        \"函数插件功能？\",\n        \"对整个Markdown项目进行翻译。函数插件贡献者: Binary-Husky\"])\n    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n    disable_auto_promotion(chatbot)\n\n    # 尝试导入依赖，如果缺少依赖，则给出安装建议\n    try:\n        import tiktoken\n    except:\n        report_execption(chatbot, history,\n                         a=f\"解析项目: {txt}\",\n                         b=f\"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade tiktoken```。\")\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n    history = []    # 清空历史，以免输入溢出\n    success, file_manifest, project_folder = get_files_from_everything(txt)\n    if not success:\n        # 什么都没有\n        if txt == \"\": txt = '空空如也的输入栏'\n        report_execption(chatbot, history, a = f\"解析项目: {txt}\", b = f\"找不到本地项目或无权访问: {txt}\")\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n    if len(file_manifest) == 0:\n        report_execption(chatbot, history, a = f\"解析项目: {txt}\", b = f\"找不到任何.md文件: {txt}\")\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n    \n    if (\"advanced_arg\" in plugin_kwargs) and (plugin_kwargs[\"advanced_arg\"] == \"\"): plugin_kwargs.pop(\"advanced_arg\")\n    language = plugin_kwargs.get(\"advanced_arg\", 'Chinese')\n    yield from 多文件翻译(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language=language)"
  },
  {
    "path": "crazy_functions/批量总结PDF文档.py",
    "content": "from utils.toolbox import update_ui, promote_file_to_downloadzone, gen_time_str\nfrom utils.toolbox import CatchException, report_execption, write_results_to_file\nfrom .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive\nfrom .crazy_utils import read_and_clean_pdf_text\nfrom .crazy_utils import input_clipping\n\n\n\ndef 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):\n    file_write_buffer = []\n    for file_name in file_manifest:\n        print('begin analysis on:', file_name)\n        ############################## <第 0 步，切割PDF> ##################################\n        # 递归地切割PDF文件，每一块（尽量是完整的一个section，比如introduction，experiment等，必要时再进行切割）\n        # 的长度必须小于 2500 个 Token\n        file_content, page_one = read_and_clean_pdf_text(file_name) # （尝试）按照章节切割PDF\n        file_content = file_content.encode('utf-8', 'ignore').decode()   # avoid reading non-utf8 chars\n        page_one = str(page_one).encode('utf-8', 'ignore').decode()  # avoid reading non-utf8 chars\n        \n        TOKEN_LIMIT_PER_FRAGMENT = 2500\n\n        from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf\n        from llm_cards.bridge_all import model_info\n        enc = model_info[\"gpt-3.5-turbo\"]['tokenizer']\n        def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))\n        paper_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(\n            txt=file_content,  get_token_fn=get_token_num, limit=TOKEN_LIMIT_PER_FRAGMENT)\n        page_one_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(\n            txt=str(page_one), get_token_fn=get_token_num, limit=TOKEN_LIMIT_PER_FRAGMENT//4)\n        # 为了更好的效果，我们剥离Introduction之后的部分（如果有）\n        paper_meta = page_one_fragments[0].split('introduction')[0].split('Introduction')[0].split('INTRODUCTION')[0]\n        \n        ############################## <第 1 步，从摘要中提取高价值信息，放到history中> ##################################\n        final_results = []\n        final_results.append(paper_meta)\n\n        ############################## <第 2 步，迭代地历遍整个文章，提取精炼信息> ##################################\n        i_say_show_user = f'首先你在中文语境下通读整篇论文。'; gpt_say = \"[Local Message] 收到。\"           # 用户提示\n        chatbot.append([i_say_show_user, gpt_say]); yield from update_ui(chatbot=chatbot, history=[])    # 更新UI\n\n        iteration_results = []\n        last_iteration_result = paper_meta  # 初始值是摘要\n        MAX_WORD_TOTAL = 4096 * 0.7\n        n_fragment = len(paper_fragments)\n        if n_fragment >= 20: print('文章极长，不能达到预期效果')\n        for i in range(n_fragment):\n            NUM_OF_WORD = MAX_WORD_TOTAL // n_fragment\n            i_say = f\"Read this section, recapitulate the content of this section with less than {NUM_OF_WORD} Chinese characters: {paper_fragments[i]}\"\n            i_say_show_user = f\"[{i+1}/{n_fragment}] Read this section, recapitulate the content of this section with less than {NUM_OF_WORD} Chinese characters: {paper_fragments[i][:200]}\"\n            gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(i_say, i_say_show_user,  # i_say=真正给chatgpt的提问， i_say_show_user=给用户看的提问\n                                                                                llm_kwargs, chatbot, \n                                                                                history=[\"The main idea of the previous section is?\", last_iteration_result], # 迭代上一次的结果\n                                                                                sys_prompt=\"Extract the main idea of this section with Chinese.\"  # 提示\n                                                                                ) \n            iteration_results.append(gpt_say)\n            last_iteration_result = gpt_say\n\n        ############################## <第 3 步，整理history，提取总结> ##################################\n        final_results.extend(iteration_results)\n        final_results.append(f'Please conclude this paper discussed above。')\n        # This prompt is from https://github.com/kaixindelele/ChatPaper/blob/main/chat_paper.py\n        NUM_OF_WORD = 1000\n        i_say = \"\"\"\n1. Mark the title of the paper (with Chinese translation)\n2. list all the authors' names (use English)\n3. mark the first author's affiliation (output Chinese translation only)\n4. mark the keywords of this article (use English)\n5. link to the paper, Github code link (if available, fill in Github:None if not)\n6. summarize according to the following four points.Be sure to use Chinese answers (proper nouns need to be marked in English)\n    - (1):What is the research background of this article?\n    - (2):What are the past methods? What are the problems with them? Is the approach well motivated?\n    - (3):What is the research methodology proposed in this paper?\n    - (4):On what task and what performance is achieved by the methods in this paper? Can the performance support their goals?\nFollow the format of the output that follows:                  \n1. Title: xxx\\n\\n\n2. Authors: xxx\\n\\n\n3. Affiliation: xxx\\n\\n\n4. Keywords: xxx\\n\\n\n5. Urls: xxx or xxx , xxx \\n\\n\n6. Summary: \\n\\n\n    - (1):xxx;\\n \n    - (2):xxx;\\n \n    - (3):xxx;\\n\n    - (4):xxx.\\n\\n\nBe sure to use Chinese answers (proper nouns need to be marked in English), statements as concise and academic as possible,\ndo not have too much repetitive information, numerical values using the original numbers.\n        \"\"\"\n        # This prompt is from https://github.com/kaixindelele/ChatPaper/blob/main/chat_paper.py\n        file_write_buffer.extend(final_results)\n        i_say, final_results = input_clipping(i_say, final_results, max_token_limit=2000)\n        gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(\n            inputs=i_say, inputs_show_user='开始最终总结', \n            llm_kwargs=llm_kwargs, chatbot=chatbot, history=final_results, \n            sys_prompt= f\"Extract the main idea of this paper with less than {NUM_OF_WORD} Chinese characters\"\n        )\n        final_results.append(gpt_say)\n        file_write_buffer.extend([i_say, gpt_say])\n        ############################## <第 4 步，设置一个token上限> ##################################\n        _, final_results = input_clipping(\"\", final_results, max_token_limit=3200)\n        yield from update_ui(chatbot=chatbot, history=final_results) # 注意这里的历史记录被替代了\n\n    res = write_results_to_file(file_write_buffer, file_name=gen_time_str())\n    promote_file_to_downloadzone(res.split('\\t')[-1], chatbot=chatbot)\n    yield from update_ui(chatbot=chatbot, history=final_results) # 刷新界面\n\n\n@CatchException\ndef 批量总结PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):\n    import glob, os\n\n    # 基本信息：功能、贡献者\n    chatbot.append([\n        \"函数插件功能？\",\n        \"批量总结PDF文档。函数插件贡献者: ValeriaWong，Eralien\"])\n    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n\n    # 尝试导入依赖，如果缺少依赖，则给出安装建议\n    try:\n        import fitz\n    except:\n        report_execption(chatbot, history, \n            a = f\"解析项目: {txt}\", \n            b = f\"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade pymupdf```。\")\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n\n    # 清空历史，以免输入溢出\n    history = []\n\n    # 检测输入参数，如没有给定输入参数，直接退出\n    if os.path.exists(txt):\n        project_folder = txt\n    else:\n        if txt == \"\": txt = '空空如也的输入栏'\n        report_execption(chatbot, history, a = f\"解析项目: {txt}\", b = f\"找不到本地项目或无权访问: {txt}\")\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n\n    # 搜索需要处理的文件清单\n    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.pdf', recursive=True)]\n    \n    # 如果没找到任何文件\n    if len(file_manifest) == 0:\n        report_execption(chatbot, history, a = f\"解析项目: {txt}\", b = f\"找不到任何.tex或.pdf文件: {txt}\")\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n\n    # 开始正式执行任务\n    yield from 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)\n"
  },
  {
    "path": "crazy_functions/批量总结PDF文档pdfminer.py",
    "content": "from utils.toolbox import update_ui\nfrom utils.toolbox import CatchException, report_execption, write_results_to_file\nfrom .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive\n\nfast_debug = False\n\ndef readPdf(pdfPath):\n    \"\"\"\n    读取pdf文件，返回文本内容\n    \"\"\"\n    import pdfminer\n    from pdfminer.pdfparser import PDFParser\n    from pdfminer.pdfdocument import PDFDocument\n    from pdfminer.pdfpage import PDFPage, PDFTextExtractionNotAllowed\n    from pdfminer.pdfinterp import PDFResourceManager, PDFPageInterpreter\n    from pdfminer.pdfdevice import PDFDevice\n    from pdfminer.layout import LAParams\n    from pdfminer.converter import PDFPageAggregator\n\n    fp = open(pdfPath, 'rb')\n\n    # Create a PDF parser object associated with the file object\n    parser = PDFParser(fp)\n\n    # Create a PDF document object that stores the document structure.\n    # Password for initialization as 2nd parameter\n    document = PDFDocument(parser)\n    # Check if the document allows text extraction. If not, abort.\n    if not document.is_extractable:\n        raise PDFTextExtractionNotAllowed\n\n    # Create a PDF resource manager object that stores shared resources.\n    rsrcmgr = PDFResourceManager()\n\n    # Create a PDF device object.\n    # device = PDFDevice(rsrcmgr)\n\n    # BEGIN LAYOUT ANALYSIS.\n    # Set parameters for analysis.\n    laparams = LAParams(\n        char_margin=10.0,\n        line_margin=0.2,\n        boxes_flow=0.2,\n        all_texts=False,\n    )\n    # Create a PDF page aggregator object.\n    device = PDFPageAggregator(rsrcmgr, laparams=laparams)\n    # Create a PDF interpreter object.\n    interpreter = PDFPageInterpreter(rsrcmgr, device)\n\n    # loop over all pages in the document\n    outTextList = []\n    for page in PDFPage.create_pages(document):\n        # read the page into a layout object\n        interpreter.process_page(page)\n        layout = device.get_result()\n        for obj in layout._objs:\n            if isinstance(obj, pdfminer.layout.LTTextBoxHorizontal):\n                # print(obj.get_text())\n                outTextList.append(obj.get_text())\n\n    return outTextList\n\n\ndef 解析Paper(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):\n    import time, glob, os\n    from bs4 import BeautifulSoup\n    print('begin analysis on:', file_manifest)\n    for index, fp in enumerate(file_manifest):\n        if \".tex\" in fp:\n            with open(fp, 'r', encoding='utf-8', errors='replace') as f:\n                file_content = f.read()\n        if \".pdf\" in fp.lower():\n            file_content = readPdf(fp)\n            file_content = BeautifulSoup(''.join(file_content), features=\"lxml\").body.text.encode('gbk', 'ignore').decode('gbk')\n\n        prefix = \"接下来请你逐文件分析下面的论文文件，概括其内容\" if index==0 else \"\"\n        i_say = prefix + f'请对下面的文章片段用中文做一个概述，文件名是{os.path.relpath(fp, project_folder)}，文章内容是 ```{file_content}```'\n        i_say_show_user = prefix + f'[{index}/{len(file_manifest)}] 请对下面的文章片段做一个概述: {os.path.abspath(fp)}'\n        chatbot.append((i_say_show_user, \"[Local Message] waiting gpt response.\"))\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n\n        if not fast_debug:\n            msg = '正常'\n            # ** gpt request **\n            gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(\n                inputs=i_say, \n                inputs_show_user=i_say_show_user, \n                llm_kwargs=llm_kwargs,\n                chatbot=chatbot, \n                history=[],\n                sys_prompt=\"总结文章。\"\n            )  # 带超时倒计时\n            chatbot[-1] = (i_say_show_user, gpt_say)\n            history.append(i_say_show_user); history.append(gpt_say)\n            yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面\n            if not fast_debug: time.sleep(2)\n\n    all_file = ', '.join([os.path.relpath(fp, project_folder) for index, fp in enumerate(file_manifest)])\n    i_say = f'根据以上你自己的分析，对全文进行概括，用学术性语言写一段中文摘要，然后再写一段英文摘要（包括{all_file}）。'\n    chatbot.append((i_say, \"[Local Message] waiting gpt response.\"))\n    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n\n    if not fast_debug:\n        msg = '正常'\n        # ** gpt request **\n        gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(\n            inputs=i_say, \n            inputs_show_user=i_say, \n            llm_kwargs=llm_kwargs,\n            chatbot=chatbot, \n            history=history,\n            sys_prompt=\"总结文章。\"\n        )  # 带超时倒计时\n        chatbot[-1] = (i_say, gpt_say)\n        history.append(i_say); history.append(gpt_say)\n        yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面\n        res = write_results_to_file(history)\n        chatbot.append((\"完成了吗？\", res))\n        yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面\n\n\n\n@CatchException\ndef 批量总结PDF文档pdfminer(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):\n    history = []    # 清空历史，以免输入溢出\n    import glob, os\n\n    # 基本信息：功能、贡献者\n    chatbot.append([\n        \"函数插件功能？\",\n        \"批量总结PDF文档，此版本使用pdfminer插件，带token约简功能。函数插件贡献者: Euclid-Jie。\"])\n    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n\n    # 尝试导入依赖，如果缺少依赖，则给出安装建议\n    try:\n        import pdfminer, bs4\n    except:\n        report_execption(chatbot, history, \n            a = f\"解析项目: {txt}\", \n            b = f\"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade pdfminer beautifulsoup4```。\")\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n    if os.path.exists(txt):\n        project_folder = txt\n    else:\n        if txt == \"\": txt = '空空如也的输入栏'\n        report_execption(chatbot, history, a = f\"解析项目: {txt}\", b = f\"找不到本地项目或无权访问: {txt}\")\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)] + \\\n                    [f for f in glob.glob(f'{project_folder}/**/*.pdf', recursive=True)] # + \\\n                    # [f for f in glob.glob(f'{project_folder}/**/*.cpp', recursive=True)] + \\\n                    # [f for f in glob.glob(f'{project_folder}/**/*.c', recursive=True)]\n    if len(file_manifest) == 0:\n        report_execption(chatbot, history, a = f\"解析项目: {txt}\", b = f\"找不到任何.tex或pdf文件: {txt}\")\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n    yield from 解析Paper(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)\n\n"
  },
  {
    "path": "crazy_functions/批量翻译PDF文档_多线程.py",
    "content": "from utils.toolbox import CatchException, report_execption, write_results_to_file\nfrom utils.toolbox import update_ui, promote_file_to_downloadzone\nfrom .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive\nfrom .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency\nfrom .crazy_utils import read_and_clean_pdf_text\nfrom utils.colorful import *\n\n@CatchException\ndef 批量翻译PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, sys_prompt, web_port):\n    import glob\n    import os\n\n    # 基本信息：功能、贡献者\n    chatbot.append([\n        \"函数插件功能？\",\n        \"批量翻译PDF文档。函数插件贡献者: Binary-Husky\"])\n    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n\n    # 尝试导入依赖，如果缺少依赖，则给出安装建议\n    try:\n        import fitz\n        import tiktoken\n    except:\n        report_execption(chatbot, history,\n                         a=f\"解析项目: {txt}\",\n                         b=f\"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade pymupdf tiktoken```。\")\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n\n    # 清空历史，以免输入溢出\n    history = []\n\n    # 检测输入参数，如没有给定输入参数，直接退出\n    if os.path.exists(txt):\n        project_folder = txt\n    else:\n        if txt == \"\":\n            txt = '空空如也的输入栏'\n        report_execption(chatbot, history,\n                         a=f\"解析项目: {txt}\", b=f\"找不到本地项目或无权访问: {txt}\")\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n\n    # 搜索需要处理的文件清单\n    file_manifest = [f for f in glob.glob(\n        f'{project_folder}/**/*.pdf', recursive=True)]\n\n    # 如果没找到任何文件\n    if len(file_manifest) == 0:\n        report_execption(chatbot, history,\n                         a=f\"解析项目: {txt}\", b=f\"找不到任何.tex或.pdf文件: {txt}\")\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n\n    # 开始正式执行任务\n    yield from 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, sys_prompt)\n\n\ndef 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, sys_prompt):\n    import os\n    import copy\n    import tiktoken\n    TOKEN_LIMIT_PER_FRAGMENT = 1280\n    generated_conclusion_files = []\n    generated_html_files = []\n    for index, fp in enumerate(file_manifest):\n\n        # 读取PDF文件\n        file_content, page_one = read_and_clean_pdf_text(fp)\n        file_content = file_content.encode('utf-8', 'ignore').decode()   # avoid reading non-utf8 chars\n        page_one = str(page_one).encode('utf-8', 'ignore').decode()      # avoid reading non-utf8 chars\n        # 递归地切割PDF文件\n        from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf\n        from request_llm.bridge_all import model_info\n        enc = model_info[\"gpt-3.5-turbo\"]['tokenizer']\n        def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))\n        paper_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(\n            txt=file_content,  get_token_fn=get_token_num, limit=TOKEN_LIMIT_PER_FRAGMENT)\n        page_one_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(\n            txt=page_one, get_token_fn=get_token_num, limit=TOKEN_LIMIT_PER_FRAGMENT//4)\n\n        # 为了更好的效果，我们剥离Introduction之后的部分（如果有）\n        paper_meta = page_one_fragments[0].split('introduction')[0].split('Introduction')[0].split('INTRODUCTION')[0]\n        \n        # 单线，获取文章meta信息\n        paper_meta_info = yield from request_gpt_model_in_new_thread_with_ui_alive(\n            inputs=f\"以下是一篇学术论文的基础信息，请从中提取出“标题”、“收录会议或期刊”、“作者”、“摘要”、“编号”、“作者邮箱”这六个部分。请用markdown格式输出，最后用中文翻译摘要部分。请提取：{paper_meta}\",\n            inputs_show_user=f\"请从{fp}中提取出“标题”、“收录会议或期刊”等基本信息。\",\n            llm_kwargs=llm_kwargs,\n            chatbot=chatbot, history=[],\n            sys_prompt=\"Your job is to collect information from materials。\",\n        )\n\n        # 多线，翻译\n        gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(\n            inputs_array=[\n                f\"你需要翻译以下内容：\\n{frag}\" for frag in paper_fragments],\n            inputs_show_user_array=[f\"\\n---\\n 原文： \\n\\n {frag.replace('#', '')}  \\n---\\n 翻译：\\n \" for frag in paper_fragments],\n            llm_kwargs=llm_kwargs,\n            chatbot=chatbot,\n            history_array=[[paper_meta] for _ in paper_fragments],\n            sys_prompt_array=[\n                \"请你作为一个学术翻译，负责把学术论文准确翻译成中文。注意文章中的每一句话都要翻译。\" for _ in paper_fragments],\n            # max_workers=5  # OpenAI所允许的最大并行过载\n        )\n        gpt_response_collection_md = copy.deepcopy(gpt_response_collection)\n        # 整理报告的格式\n        for i,k in enumerate(gpt_response_collection_md): \n            if i%2==0:\n                gpt_response_collection_md[i] = f\"\\n\\n---\\n\\n ## 原文[{i//2}/{len(gpt_response_collection_md)//2}]： \\n\\n {paper_fragments[i//2].replace('#', '')}  \\n\\n---\\n\\n ## 翻译[{i//2}/{len(gpt_response_collection_md)//2}]：\\n \"\n            else:\n                gpt_response_collection_md[i] = gpt_response_collection_md[i]\n        final = [\"一、论文概况\\n\\n---\\n\\n\", paper_meta_info.replace('# ', '### ') + '\\n\\n---\\n\\n', \"二、论文翻译\", \"\"]\n        final.extend(gpt_response_collection_md)\n        create_report_file_name = f\"{os.path.basename(fp)}.trans.md\"\n        res = write_results_to_file(final, file_name=create_report_file_name)\n\n        # 更新UI\n        generated_conclusion_files.append(f'./gpt_log/{create_report_file_name}')\n        chatbot.append((f\"{fp}完成了吗？\", res))\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n\n        # write html\n        try:\n            ch = construct_html() \n            orig = \"\"\n            trans = \"\"\n            gpt_response_collection_html = copy.deepcopy(gpt_response_collection)\n            for i,k in enumerate(gpt_response_collection_html): \n                if i%2==0:\n                    gpt_response_collection_html[i] = paper_fragments[i//2].replace('#', '')\n                else:\n                    gpt_response_collection_html[i] = gpt_response_collection_html[i]\n            final = [\"论文概况\", paper_meta_info.replace('# ', '### '),  \"二、论文翻译\",  \"\"]\n            final.extend(gpt_response_collection_html)\n            for i, k in enumerate(final): \n                if i%2==0:\n                    orig = k\n                if i%2==1:\n                    trans = k\n                    ch.add_row(a=orig, b=trans)\n            create_report_file_name = f\"{os.path.basename(fp)}.trans.html\"\n            ch.save_file(create_report_file_name)\n            generated_html_files.append(f'./gpt_log/{create_report_file_name}')\n        except:\n            from toolbox import trimmed_format_exc\n            print('writing html result failed:', trimmed_format_exc())\n\n    # 准备文件的下载\n    for pdf_path in generated_conclusion_files:\n        # 重命名文件\n        rename_file = f'翻译-{os.path.basename(pdf_path)}'\n        promote_file_to_downloadzone(pdf_path, rename_file=rename_file, chatbot=chatbot)\n    for html_path in generated_html_files:\n        # 重命名文件\n        rename_file = f'翻译-{os.path.basename(html_path)}'\n        promote_file_to_downloadzone(html_path, rename_file=rename_file, chatbot=chatbot)\n    chatbot.append((\"给出输出文件清单\", str(generated_conclusion_files + generated_html_files)))\n    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n\n\nclass construct_html():\n    def __init__(self) -> None:\n        self.css = \"\"\"\n.row {\n  display: flex;\n  flex-wrap: wrap;\n}\n\n.column {\n  flex: 1;\n  padding: 10px;\n}\n\n.table-header {\n  font-weight: bold;\n  border-bottom: 1px solid black;\n}\n\n.table-row {\n  border-bottom: 1px solid lightgray;\n}\n\n.table-cell {\n  padding: 5px;\n}\n        \"\"\"\n        self.html_string = f'<!DOCTYPE html><head><meta charset=\"utf-8\"><title>翻译结果</title><style>{self.css}</style></head>'\n\n\n    def add_row(self, a, b):\n        tmp = \"\"\"\n<div class=\"row table-row\">\n    <div class=\"column table-cell\">REPLACE_A</div>\n    <div class=\"column table-cell\">REPLACE_B</div>\n</div>\n        \"\"\"\n        from toolbox import markdown_convertion\n        tmp = tmp.replace('REPLACE_A', markdown_convertion(a))\n        tmp = tmp.replace('REPLACE_B', markdown_convertion(b))\n        self.html_string += tmp\n\n\n    def save_file(self, file_name):\n        with open(f'./gpt_log/{file_name}', 'w', encoding='utf8') as f:\n            f.write(self.html_string.encode('utf-8', 'ignore').decode())\n\n"
  },
  {
    "path": "crazy_functions/数学动画生成manim.py",
    "content": "from utils.toolbox import CatchException, update_ui, gen_time_str\nfrom .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive\nfrom .crazy_utils import input_clipping\n\ndef inspect_dependency(chatbot, history):\n    # 尝试导入依赖，如果缺少依赖，则给出安装建议\n    try:\n        import manim\n        return True\n    except:\n        chatbot.append([\"导入依赖失败\", \"使用该模块需要额外依赖，安装方法:```pip install manim manimgl```\"])\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return False\n\ndef eval_manim(code):\n    import subprocess, sys, os, shutil\n\n    with open('gpt_log/MyAnimation.py', 'w', encoding='utf8') as f:\n        f.write(code)\n\n    def get_class_name(class_string):\n        import re\n        # Use regex to extract the class name\n        class_name = re.search(r'class (\\w+)\\(', class_string).group(1)\n        return class_name\n\n    class_name = get_class_name(code)\n\n    try: \n        subprocess.check_output([sys.executable, '-c', f\"from gpt_log.MyAnimation import {class_name}; {class_name}().render()\"])\n        shutil.move('media/videos/1080p60/{class_name}.mp4', f'gpt_log/{class_name}-{gen_time_str()}.mp4')\n        return f'gpt_log/{gen_time_str()}.mp4'\n    except subprocess.CalledProcessError as e:\n        output = e.output.decode()\n        print(f\"Command returned non-zero exit status {e.returncode}: {output}.\")\n        return f\"Evaluating python script failed: {e.output}.\"\n    except: \n        print('generating mp4 failed')\n        return \"Generating mp4 failed.\"\n\n\ndef get_code_block(reply):\n    import re\n    pattern = r\"```([\\s\\S]*?)```\" # regex pattern to match code blocks\n    matches = re.findall(pattern, reply) # find all code blocks in text\n    if len(matches) != 1: \n        raise RuntimeError(\"GPT is not generating proper code.\")\n    return matches[0].strip('python') #  code block\n\n@CatchException\ndef 动画生成(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):\n    \"\"\"\n    txt             输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径\n    llm_kwargs      gpt模型参数，如温度和top_p等，一般原样传递下去就行\n    plugin_kwargs   插件模型的参数，暂时没有用武之地\n    chatbot         聊天显示框的句柄，用于显示给用户\n    history         聊天历史，前情提要\n    system_prompt   给gpt的静默提醒\n    web_port        当前软件运行的端口号\n    \"\"\"\n    # 清空历史，以免输入溢出\n    history = []    \n\n    # 基本信息：功能、贡献者\n    chatbot.append([\n        \"函数插件功能？\",\n        \"生成数学动画, 此插件处于开发阶段, 建议暂时不要使用, 作者: binary-husky, 插件初始化中 ...\"\n    ])\n    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n\n    # 尝试导入依赖, 如果缺少依赖, 则给出安装建议\n    dep_ok = yield from inspect_dependency(chatbot=chatbot, history=history) # 刷新界面\n    if not dep_ok: return\n    \n    # 输入\n    i_say = f'Generate a animation to show: ' + txt\n    demo = [\"Here is some examples of manim\", examples_of_manim()]\n    _, demo = input_clipping(inputs=\"\", history=demo, max_token_limit=2560)\n    # 开始\n    gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(\n        inputs=i_say, inputs_show_user=i_say, \n        llm_kwargs=llm_kwargs, chatbot=chatbot, history=demo, \n        sys_prompt=\n        r\"Write a animation script with 3blue1brown's manim. \"+\n        r\"Please begin with `from manim import *`. \" + \n        r\"Answer me with a code block wrapped by ```.\"\n    )\n    chatbot.append([\"开始生成动画\", \"...\"])\n    history.extend([i_say, gpt_say])\n    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新\n    \n    # 将代码转为动画\n    code = get_code_block(gpt_say)\n    res = eval_manim(code)\n\n    chatbot.append((\"生成的视频文件路径\", res))\n    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新\n\n# 在这里放一些网上搜集的demo，辅助gpt生成代码\ndef examples_of_manim():\n    return r\"\"\"\n\n\n```\n\nclass MovingGroupToDestination(Scene):\n    def construct(self):\n        group = VGroup(Dot(LEFT), Dot(ORIGIN), Dot(RIGHT, color=RED), Dot(2 * RIGHT)).scale(1.4)\n        dest = Dot([4, 3, 0], color=YELLOW)\n        self.add(group, dest)\n        self.play(group.animate.shift(dest.get_center() - group[2].get_center()))\n        self.wait(0.5)\n\n```\n\n\n```\n\nclass LatexWithMovingFramebox(Scene):\n    def construct(self):\n        text=MathTex(\n            \"\\\\frac{d}{dx}f(x)g(x)=\",\"f(x)\\\\frac{d}{dx}g(x)\",\"+\",\n            \"g(x)\\\\frac{d}{dx}f(x)\"\n        )\n        self.play(Write(text))\n        framebox1 = SurroundingRectangle(text[1], buff = .1)\n        framebox2 = SurroundingRectangle(text[3], buff = .1)\n        self.play(\n            Create(framebox1),\n        )\n        self.wait()\n        self.play(\n            ReplacementTransform(framebox1,framebox2),\n        )\n        self.wait()\n\n```\n\n\n\n```\n\nclass PointWithTrace(Scene):\n    def construct(self):\n        path = VMobject()\n        dot = Dot()\n        path.set_points_as_corners([dot.get_center(), dot.get_center()])\n        def update_path(path):\n            previous_path = path.copy()\n            previous_path.add_points_as_corners([dot.get_center()])\n            path.become(previous_path)\n        path.add_updater(update_path)\n        self.add(path, dot)\n        self.play(Rotating(dot, radians=PI, about_point=RIGHT, run_time=2))\n        self.wait()\n        self.play(dot.animate.shift(UP))\n        self.play(dot.animate.shift(LEFT))\n        self.wait()\n\n```\n\n```\n\n# do not use get_graph, this funciton is deprecated\n\nclass ExampleFunctionGraph(Scene):\n    def construct(self):\n        cos_func = FunctionGraph(\n            lambda t: np.cos(t) + 0.5 * np.cos(7 * t) + (1 / 7) * np.cos(14 * t),\n            color=RED,\n        )\n\n        sin_func_1 = FunctionGraph(\n            lambda t: np.sin(t) + 0.5 * np.sin(7 * t) + (1 / 7) * np.sin(14 * t),\n            color=BLUE,\n        )\n\n        sin_func_2 = FunctionGraph(\n            lambda t: np.sin(t) + 0.5 * np.sin(7 * t) + (1 / 7) * np.sin(14 * t),\n            x_range=[-4, 4],\n            color=GREEN,\n        ).move_to([0, 1, 0])\n\n        self.add(cos_func, sin_func_1, sin_func_2)\n\n```\n\"\"\""
  },
  {
    "path": "crazy_functions/理解PDF文档内容.py",
    "content": "from utils.toolbox import update_ui\nfrom utils.toolbox import CatchException, report_execption\nfrom .crazy_utils import read_and_clean_pdf_text\nfrom .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive\nfast_debug = False\n\n\ndef 解析PDF(file_name, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):\n    import tiktoken\n    print('begin analysis on:', file_name)\n\n    ############################## <第 0 步，切割PDF> ##################################\n    # 递归地切割PDF文件，每一块（尽量是完整的一个section，比如introduction，experiment等，必要时再进行切割）\n    # 的长度必须小于 2500 个 Token\n    file_content, page_one = read_and_clean_pdf_text(file_name) # （尝试）按照章节切割PDF\n    file_content = file_content.encode('utf-8', 'ignore').decode()   # avoid reading non-utf8 chars\n    page_one = str(page_one).encode('utf-8', 'ignore').decode()  # avoid reading non-utf8 chars\n    \n    TOKEN_LIMIT_PER_FRAGMENT = 2500\n\n    from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf\n    from llm_cards.bridge_all import model_info\n    enc = model_info[\"gpt-3.5-turbo\"]['tokenizer']\n    def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))\n    paper_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(\n        txt=file_content,  get_token_fn=get_token_num, limit=TOKEN_LIMIT_PER_FRAGMENT)\n    page_one_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(\n        txt=str(page_one), get_token_fn=get_token_num, limit=TOKEN_LIMIT_PER_FRAGMENT//4)\n    # 为了更好的效果，我们剥离Introduction之后的部分（如果有）\n    paper_meta = page_one_fragments[0].split('introduction')[0].split('Introduction')[0].split('INTRODUCTION')[0]\n    \n    ############################## <第 1 步，从摘要中提取高价值信息，放到history中> ##################################\n    final_results = []\n    final_results.append(paper_meta)\n\n    ############################## <第 2 步，迭代地历遍整个文章，提取精炼信息> ##################################\n    i_say_show_user = f'首先你在英文语境下通读整篇论文。'; gpt_say = \"[Local Message] 收到。\"           # 用户提示\n    chatbot.append([i_say_show_user, gpt_say]); yield from update_ui(chatbot=chatbot, history=[])    # 更新UI\n\n    iteration_results = []\n    last_iteration_result = paper_meta  # 初始值是摘要\n    MAX_WORD_TOTAL = 4096\n    n_fragment = len(paper_fragments)\n    if n_fragment >= 20: print('文章极长，不能达到预期效果')\n    for i in range(n_fragment):\n        NUM_OF_WORD = MAX_WORD_TOTAL // n_fragment\n        i_say = f\"Read this section, recapitulate the content of this section with less than {NUM_OF_WORD} words: {paper_fragments[i]}\"\n        i_say_show_user = f\"[{i+1}/{n_fragment}] Read this section, recapitulate the content of this section with less than {NUM_OF_WORD} words: {paper_fragments[i][:200]}\"\n        gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(i_say, i_say_show_user,  # i_say=真正给chatgpt的提问， i_say_show_user=给用户看的提问\n                                                                           llm_kwargs, chatbot, \n                                                                           history=[\"The main idea of the previous section is?\", last_iteration_result], # 迭代上一次的结果\n                                                                           sys_prompt=\"Extract the main idea of this section.\"  # 提示\n                                                                        ) \n        iteration_results.append(gpt_say)\n        last_iteration_result = gpt_say\n\n    ############################## <第 3 步，整理history> ##################################\n    final_results.extend(iteration_results)\n    final_results.append(f'接下来，你是一名专业的学术教授，利用以上信息，使用中文回答我的问题。')\n    # 接下来两句话只显示在界面上，不起实际作用\n    i_say_show_user = f'接下来，你是一名专业的学术教授，利用以上信息，使用中文回答我的问题。'; gpt_say = \"[Local Message] 收到。\"\n    chatbot.append([i_say_show_user, gpt_say])\n\n    ############################## <第 4 步，设置一个token上限，防止回答时Token溢出> ##################################\n    from .crazy_utils import input_clipping\n    _, final_results = input_clipping(\"\", final_results, max_token_limit=3200)\n    yield from update_ui(chatbot=chatbot, history=final_results) # 注意这里的历史记录被替代了\n\n\n@CatchException\ndef 理解PDF文档内容标准文件输入(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):\n    import glob, os\n\n    # 基本信息：功能、贡献者\n    chatbot.append([\n        \"函数插件功能？\",\n        \"理解PDF论文内容，并且将结合上下文内容，进行学术解答。函数插件贡献者: Hanzoe, binary-husky\"])\n    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n\n    # 尝试导入依赖，如果缺少依赖，则给出安装建议\n    try:\n        import fitz\n    except:\n        report_execption(chatbot, history, \n            a = f\"解析项目: {txt}\", \n            b = f\"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade pymupdf```。\")\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n\n    # 清空历史，以免输入溢出\n    history = []\n\n    # 检测输入参数，如没有给定输入参数，直接退出\n    if os.path.exists(txt):\n        project_folder = txt\n    else:\n        if txt == \"\":\n            txt = '空空如也的输入栏'\n        report_execption(chatbot, history,\n                         a=f\"解析项目: {txt}\", b=f\"找不到本地项目或无权访问: {txt}\")\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n\n    # 搜索需要处理的文件清单\n    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.pdf', recursive=True)]\n    # 如果没找到任何文件\n    if len(file_manifest) == 0:\n        report_execption(chatbot, history,\n                         a=f\"解析项目: {txt}\", b=f\"找不到任何.tex或.pdf文件: {txt}\")\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n    txt = file_manifest[0]\n    # 开始正式执行任务\n    yield from 解析PDF(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)\n"
  },
  {
    "path": "crazy_functions/生成函数注释.py",
    "content": "from utils.toolbox import update_ui\nfrom utils.toolbox import CatchException, report_execption, write_results_to_file\nfrom .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive\nfast_debug = False\n\ndef 生成函数注释(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):\n    import time, os\n    print('begin analysis on:', file_manifest)\n    for index, fp in enumerate(file_manifest):\n        with open(fp, 'r', encoding='utf-8', errors='replace') as f:\n            file_content = f.read()\n\n        i_say = f'请对下面的程序文件做一个概述，并对文件中的所有函数生成注释，使用markdown表格输出结果，文件名是{os.path.relpath(fp, project_folder)}，文件内容是 ```{file_content}```'\n        i_say_show_user = f'[{index}/{len(file_manifest)}] 请对下面的程序文件做一个概述，并对文件中的所有函数生成注释: {os.path.abspath(fp)}'\n        chatbot.append((i_say_show_user, \"[Local Message] waiting gpt response.\"))\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n\n        if not fast_debug: \n            msg = '正常'\n            # ** gpt request **\n            gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(\n                i_say, i_say_show_user, llm_kwargs, chatbot, history=[], sys_prompt=system_prompt)   # 带超时倒计时\n\n            chatbot[-1] = (i_say_show_user, gpt_say)\n            history.append(i_say_show_user); history.append(gpt_say)\n            yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面\n            if not fast_debug: time.sleep(2)\n\n    if not fast_debug: \n        res = write_results_to_file(history)\n        chatbot.append((\"完成了吗？\", res))\n        yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面\n\n\n\n@CatchException\ndef 批量生成函数注释(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):\n    history = []    # 清空历史，以免输入溢出\n    import glob, os\n    if os.path.exists(txt):\n        project_folder = txt\n    else:\n        if txt == \"\": txt = '空空如也的输入栏'\n        report_execption(chatbot, history, a = f\"解析项目: {txt}\", b = f\"找不到本地项目或无权访问: {txt}\")\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.py', recursive=True)] + \\\n                    [f for f in glob.glob(f'{project_folder}/**/*.cpp', recursive=True)]\n\n    if len(file_manifest) == 0:\n        report_execption(chatbot, history, a = f\"解析项目: {txt}\", b = f\"找不到任何.tex文件: {txt}\")\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n    yield from 生成函数注释(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)\n"
  },
  {
    "path": "crazy_functions/联网的ChatGPT.py",
    "content": "from utils.toolbox import CatchException, update_ui\nfrom .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive, input_clipping\nimport requests\nfrom bs4 import BeautifulSoup\nfrom llm_cards.bridge_all import model_info\n\ndef google(query, proxies):\n    query = query # 在此处替换您要搜索的关键词\n    url = f\"https://www.google.com/search?q={query}\"\n    headers = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/94.0.4606.61 Safari/537.36'}\n    response = requests.get(url, headers=headers, proxies=proxies)\n    soup = BeautifulSoup(response.content, 'html.parser')\n    results = []\n    for g in soup.find_all('div', class_='g'):\n        anchors = g.find_all('a')\n        if anchors:\n            link = anchors[0]['href']\n            if link.startswith('/url?q='):\n                link = link[7:]\n            if not link.startswith('http'):\n                continue\n            title = g.find('h3').text\n            item = {'title': title, 'link': link}\n            results.append(item)\n\n    for r in results:\n        print(r['link'])\n    return results\n\ndef scrape_text(url, proxies) -> str:\n    \"\"\"Scrape text from a webpage\n\n    Args:\n        url (str): The URL to scrape text from\n\n    Returns:\n        str: The scraped text\n    \"\"\"\n    headers = {\n        'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/94.0.4606.61 Safari/537.36',\n        'Content-Type': 'text/plain',\n    }\n    try: \n        response = requests.get(url, headers=headers, proxies=proxies, timeout=8)\n        if response.encoding == \"ISO-8859-1\": response.encoding = response.apparent_encoding\n    except: \n        return \"无法连接到该网页\"\n    soup = BeautifulSoup(response.text, \"html.parser\")\n    for script in soup([\"script\", \"style\"]):\n        script.extract()\n    text = soup.get_text()\n    lines = (line.strip() for line in text.splitlines())\n    chunks = (phrase.strip() for line in lines for phrase in line.split(\"  \"))\n    text = \"\\n\".join(chunk for chunk in chunks if chunk)\n    return text\n\n@CatchException\ndef 连接网络回答问题(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):\n    \"\"\"\n    txt             输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径\n    llm_kwargs      gpt模型参数，如温度和top_p等，一般原样传递下去就行\n    plugin_kwargs   插件模型的参数，暂时没有用武之地\n    chatbot         聊天显示框的句柄，用于显示给用户\n    history         聊天历史，前情提要\n    system_prompt   给gpt的静默提醒\n    web_port        当前软件运行的端口号\n    \"\"\"\n    history = []    # 清空历史，以免输入溢出\n    chatbot.append((f\"请结合互联网信息回答以下问题：{txt}\", \n                    \"[Local Message] 请注意，您正在调用一个[函数插件]的模板，该模板可以实现ChatGPT联网信息综合。该函数面向希望实现更多有趣功能的开发者，它可以作为创建新功能函数的模板。您若希望分享新的功能模组，请不吝PR！\"))\n    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新\n\n    # ------------- < 第1步：爬取搜索引擎的结果 > -------------\n    from utils.toolbox import get_conf\n    proxies, = get_conf('proxies')\n    urls = google(txt, proxies)\n    history = []\n\n    # ------------- < 第2步：依次访问网页 > -------------\n    max_search_result = 5   # 最多收纳多少个网页的结果\n    for index, url in enumerate(urls[:max_search_result]):\n        res = scrape_text(url['link'], proxies)\n        history.extend([f\"第{index}份搜索结果：\", res])\n        chatbot.append([f\"第{index}份搜索结果：\", res[:500]+\"......\"])\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新\n\n    # ------------- < 第3步：ChatGPT综合 > -------------\n    i_say = f\"从以上搜索结果中抽取信息，然后回答问题：{txt}\"\n    i_say, history = input_clipping(    # 裁剪输入，从最长的条目开始裁剪，防止爆token\n        inputs=i_say, \n        history=history, \n        max_token_limit=model_info[llm_kwargs['llm_model']]['max_token']*3//4\n    )\n    gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(\n        inputs=i_say, inputs_show_user=i_say, \n        llm_kwargs=llm_kwargs, chatbot=chatbot, history=history, \n        sys_prompt=\"请从给定的若干条搜索结果中抽取信息，对最相关的两个搜索结果进行总结，然后回答问题。\"\n    )\n    chatbot[-1] = (i_say, gpt_say)\n    history.append(i_say);history.append(gpt_say)\n    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新\n\n"
  },
  {
    "path": "crazy_functions/联网的ChatGPT_bing版.py",
    "content": "from utils.toolbox import CatchException, update_ui\nfrom .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive, input_clipping\nimport requests\nfrom bs4 import BeautifulSoup\nfrom llm_cards.bridge_all import model_info\n\n\ndef bing_search(query, proxies=None):\n    query = query\n    url = f\"https://cn.bing.com/search?q={query}\"\n    headers = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/94.0.4606.61 Safari/537.36'}\n    response = requests.get(url, headers=headers, proxies=proxies)\n    soup = BeautifulSoup(response.content, 'html.parser')\n    results = []\n    for g in soup.find_all('li', class_='b_algo'):\n        anchors = g.find_all('a')\n        if anchors:\n            link = anchors[0]['href']\n            if not link.startswith('http'):\n                continue\n            title = g.find('h2').text\n            item = {'title': title, 'link': link}\n            results.append(item)\n\n    for r in results:\n        print(r['link'])\n    return results\n\n\ndef scrape_text(url, proxies) -> str:\n    \"\"\"Scrape text from a webpage\n\n    Args:\n        url (str): The URL to scrape text from\n\n    Returns:\n        str: The scraped text\n    \"\"\"\n    headers = {\n        'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/94.0.4606.61 Safari/537.36',\n        'Content-Type': 'text/plain',\n    }\n    try:\n        response = requests.get(url, headers=headers, proxies=proxies, timeout=8)\n        if response.encoding == \"ISO-8859-1\": response.encoding = response.apparent_encoding\n    except:\n        return \"无法连接到该网页\"\n    soup = BeautifulSoup(response.text, \"html.parser\")\n    for script in soup([\"script\", \"style\"]):\n        script.extract()\n    text = soup.get_text()\n    lines = (line.strip() for line in text.splitlines())\n    chunks = (phrase.strip() for line in lines for phrase in line.split(\"  \"))\n    text = \"\\n\".join(chunk for chunk in chunks if chunk)\n    return text\n\n@CatchException\ndef 连接bing搜索回答问题(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):\n    \"\"\"\n    txt             输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径\n    llm_kwargs      gpt模型参数，如温度和top_p等，一般原样传递下去就行\n    plugin_kwargs   插件模型的参数，暂时没有用武之地\n    chatbot         聊天显示框的句柄，用于显示给用户\n    history         聊天历史，前情提要\n    system_prompt   给gpt的静默提醒\n    web_port        当前软件运行的端口号\n    \"\"\"\n    history = []    # 清空历史，以免输入溢出\n    chatbot.append((f\"请结合互联网信息回答以下问题：{txt}\",\n                    \"[Local Message] 请注意，您正在调用一个[函数插件]的模板，该模板可以实现ChatGPT联网信息综合。该函数面向希望实现更多有趣功能的开发者，它可以作为创建新功能函数的模板。您若希望分享新的功能模组，请不吝PR！\"))\n    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新\n\n    # ------------- < 第1步：爬取搜索引擎的结果 > -------------\n    from utils.toolbox import get_conf\n    proxies, = get_conf('proxies')\n    urls = bing_search(txt, proxies)\n    history = []\n\n    # ------------- < 第2步：依次访问网页 > -------------\n    max_search_result = 8   # 最多收纳多少个网页的结果\n    for index, url in enumerate(urls[:max_search_result]):\n        res = scrape_text(url['link'], proxies)\n        history.extend([f\"第{index}份搜索结果：\", res])\n        chatbot.append([f\"第{index}份搜索结果：\", res[:500]+\"......\"])\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新\n\n    # ------------- < 第3步：ChatGPT综合 > -------------\n    i_say = f\"从以上搜索结果中抽取信息，然后回答问题：{txt}\"\n    i_say, history = input_clipping(    # 裁剪输入，从最长的条目开始裁剪，防止爆token\n        inputs=i_say,\n        history=history,\n        max_token_limit=model_info[llm_kwargs['llm_model']]['max_token']*3//4\n    )\n    gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(\n        inputs=i_say, inputs_show_user=i_say,\n        llm_kwargs=llm_kwargs, chatbot=chatbot, history=history,\n        sys_prompt=\"请从给定的若干条搜索结果中抽取信息，对最相关的两个搜索结果进行总结，然后回答问题。\"\n    )\n    chatbot[-1] = (i_say, gpt_say)\n    history.append(i_say);history.append(gpt_say)\n    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新\n\n"
  },
  {
    "path": "crazy_functions/虚空终端.py",
    "content": "from toolbox import CatchException, update_ui, gen_time_str\nfrom .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive\nfrom .crazy_utils import input_clipping\n\n\nprompt = \"\"\"\nI have to achieve some functionalities by calling one of the functions below.\nYour job is to find the correct funtion to use to satisfy my requirement,\nand then write python code to call this function with correct parameters.\n\nThese are functions you are allowed to choose from:\n1. \n    功能描述: 总结音视频内容\n    调用函数: ConcludeAudioContent(txt, llm_kwargs)\n    参数说明: \n            txt: 音频文件的路径\n            llm_kwargs: 模型参数, 永远给定None\n2. \n    功能描述: 将每次对话记录写入Markdown格式的文件中\n    调用函数: WriteMarkdown()\n3.\n    功能描述: 将指定目录下的PDF文件从英文翻译成中文\n    调用函数: BatchTranslatePDFDocuments_MultiThreaded(txt, llm_kwargs)\n    参数说明: \n            txt: PDF文件所在的路径\n            llm_kwargs: 模型参数, 永远给定None\n4.\n    功能描述: 根据文本使用GPT模型生成相应的图像\n    调用函数: ImageGeneration(txt, llm_kwargs)\n    参数说明: \n            txt: 图像生成所用到的提示文本\n            llm_kwargs: 模型参数, 永远给定None\n5.\n    功能描述: 对输入的word文档进行摘要生成 \n    调用函数: SummarizingWordDocuments(input_path, output_path)\n    参数说明: \n            input_path: 待处理的word文档路径\n            output_path: 摘要生成后的文档路径\n\n\nYou should always anwser with following format:\n----------------\nCode:\n```\nclass AutoAcademic(object):\n    def __init__(self):\n        self.selected_function = \"FILL_CORRECT_FUNCTION_HERE\"      # e.g., \"GenerateImage\"\n        self.txt = \"FILL_MAIN_PARAMETER_HERE\"      # e.g., \"荷叶上的蜻蜓\"\n        self.llm_kwargs = None\n```\nExplanation:\n只有GenerateImage和生成图像相关, 因此选择GenerateImage函数。\n----------------\n\nNow, this is my requirement: \n\n\"\"\"\ndef get_fn_lib():\n    return {\n        \"BatchTranslatePDFDocuments_MultiThreaded\": (\"crazy_functions.批量翻译PDF文档_多线程\",  \"批量翻译PDF文档\"),\n        \"SummarizingWordDocuments\": (\"crazy_functions.总结word文档\",  \"总结word文档\"),\n        \"ImageGeneration\": (\"crazy_functions.图片生成\",  \"图片生成\"),\n        \"TranslateMarkdownFromEnglishToChinese\": (\"crazy_functions.批量Markdown翻译\",  \"Markdown中译英\"),\n        \"SummaryAudioVideo\": (\"crazy_functions.总结音视频\",  \"总结音视频\"),\n    }\n\ndef inspect_dependency(chatbot, history):\n    return True\n\ndef eval_code(code, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):\n    import subprocess, sys, os, shutil, importlib\n\n    with open('gpt_log/void_terminal_runtime.py', 'w', encoding='utf8') as f:\n        f.write(code)\n\n    try:\n        AutoAcademic = getattr(importlib.import_module('gpt_log.void_terminal_runtime', 'AutoAcademic'), 'AutoAcademic')\n        # importlib.reload(AutoAcademic)\n        auto_dict = AutoAcademic()\n        selected_function = auto_dict.selected_function\n        txt = auto_dict.txt\n        fp, fn = get_fn_lib()[selected_function]\n        fn_plugin = getattr(importlib.import_module(fp, fn), fn)\n        yield from fn_plugin(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port)\n    except:\n        from toolbox import trimmed_format_exc\n        chatbot.append([\"执行错误\", f\"\\n```\\n{trimmed_format_exc()}\\n```\\n\"])\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n\ndef get_code_block(reply):\n    import re\n    pattern = r\"```([\\s\\S]*?)```\" # regex pattern to match code blocks\n    matches = re.findall(pattern, reply) # find all code blocks in text\n    if len(matches) != 1: \n        raise RuntimeError(\"GPT is not generating proper code.\")\n    return matches[0].strip('python') #  code block\n\n@CatchException\ndef 终端(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):\n    \"\"\"\n    txt             输入栏用户输入的文本, 例如需要翻译的一段话, 再例如一个包含了待处理文件的路径\n    llm_kwargs      gpt模型参数, 如温度和top_p等, 一般原样传递下去就行\n    plugin_kwargs   插件模型的参数, 暂时没有用武之地\n    chatbot         聊天显示框的句柄, 用于显示给用户\n    history         聊天历史, 前情提要\n    system_prompt   给gpt的静默提醒\n    web_port        当前软件运行的端口号\n    \"\"\"\n    # 清空历史, 以免输入溢出\n    history = []    \n\n    # 基本信息：功能、贡献者\n    chatbot.append([\"函数插件功能？\", \"根据自然语言执行插件命令, 作者: binary-husky, 插件初始化中 ...\"])\n    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n\n    # # 尝试导入依赖, 如果缺少依赖, 则给出安装建议\n    # dep_ok = yield from inspect_dependency(chatbot=chatbot, history=history) # 刷新界面\n    # if not dep_ok: return\n    \n    # 输入\n    i_say = prompt + txt\n    # 开始\n    gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(\n        inputs=i_say, inputs_show_user=txt, \n        llm_kwargs=llm_kwargs, chatbot=chatbot, history=[], \n        sys_prompt=\"\"\n    )\n\n    # 将代码转为动画\n    code = get_code_block(gpt_say)\n    yield from eval_code(code, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port)\n"
  },
  {
    "path": "crazy_functions/解析JupyterNotebook.py",
    "content": "from utils.toolbox import update_ui\nfrom utils.toolbox import CatchException, report_execption, write_results_to_file\nfast_debug = True\n\n\nclass PaperFileGroup():\n    def __init__(self):\n        self.file_paths = []\n        self.file_contents = []\n        self.sp_file_contents = []\n        self.sp_file_index = []\n        self.sp_file_tag = []\n\n        # count_token\n        from llm_cards.bridge_all import model_info\n        enc = model_info[\"gpt-3.5-turbo\"]['tokenizer']\n        def get_token_num(txt): return len(\n            enc.encode(txt, disallowed_special=()))\n        self.get_token_num = get_token_num\n\n    def run_file_split(self, max_token_limit=1900):\n        \"\"\"\n        将长文本分离开来\n        \"\"\"\n        for index, file_content in enumerate(self.file_contents):\n            if self.get_token_num(file_content) < max_token_limit:\n                self.sp_file_contents.append(file_content)\n                self.sp_file_index.append(index)\n                self.sp_file_tag.append(self.file_paths[index])\n            else:\n                from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf\n                segments = breakdown_txt_to_satisfy_token_limit_for_pdf(\n                    file_content, self.get_token_num, max_token_limit)\n                for j, segment in enumerate(segments):\n                    self.sp_file_contents.append(segment)\n                    self.sp_file_index.append(index)\n                    self.sp_file_tag.append(\n                        self.file_paths[index] + f\".part-{j}.txt\")\n\n\n\ndef parseNotebook(filename, enable_markdown=1):\n    import json\n\n    CodeBlocks = []\n    with open(filename, 'r', encoding='utf-8', errors='replace') as f:\n        notebook = json.load(f)\n    for cell in notebook['cells']:\n        if cell['cell_type'] == 'code' and cell['source']:\n            # remove blank lines\n            cell['source'] = [line for line in cell['source'] if line.strip()\n                              != '']\n            CodeBlocks.append(\"\".join(cell['source']))\n        elif enable_markdown and cell['cell_type'] == 'markdown' and cell['source']:\n            cell['source'] = [line for line in cell['source'] if line.strip()\n                              != '']\n            CodeBlocks.append(\"Markdown:\"+\"\".join(cell['source']))\n\n    Code = \"\"\n    for idx, code in enumerate(CodeBlocks):\n        Code += f\"This is {idx+1}th code block: \\n\"\n        Code += code+\"\\n\"\n\n    return Code \n\n\ndef ipynb解释(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):\n    from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency\n\n    if (\"advanced_arg\" in plugin_kwargs) and (plugin_kwargs[\"advanced_arg\"] == \"\"): plugin_kwargs.pop(\"advanced_arg\")\n    enable_markdown = plugin_kwargs.get(\"advanced_arg\", \"1\")\n    try:\n        enable_markdown = int(enable_markdown)\n    except ValueError:\n        enable_markdown = 1\n\n    pfg = PaperFileGroup()\n\n    for fp in file_manifest:\n        file_content = parseNotebook(fp, enable_markdown=enable_markdown)\n        pfg.file_paths.append(fp)\n        pfg.file_contents.append(file_content)\n\n    #  <-------- 拆分过长的IPynb文件 ---------->\n    pfg.run_file_split(max_token_limit=1024)\n    n_split = len(pfg.sp_file_contents)\n\n    inputs_array = [r\"This is a Jupyter Notebook file, tell me about Each Block in Chinese. Focus Just On Code.\" +\n                    r\"If a block starts with `Markdown` which means it's a markdown block in ipynbipynb. \" +\n                    r\"Start a new line for a block and block num use Chinese.\" +\n                    f\"\\n\\n{frag}\" for frag in pfg.sp_file_contents]\n    inputs_show_user_array = [f\"{f}的分析如下\" for f in pfg.sp_file_tag]\n    sys_prompt_array = [\"You are a professional programmer.\"] * n_split\n\n    gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(\n        inputs_array=inputs_array,\n        inputs_show_user_array=inputs_show_user_array,\n        llm_kwargs=llm_kwargs,\n        chatbot=chatbot,\n        history_array=[[\"\"] for _ in range(n_split)],\n        sys_prompt_array=sys_prompt_array,\n        # max_workers=5,  # OpenAI所允许的最大并行过载\n        scroller_max_len=80\n    )\n\n    #  <-------- 整理结果，退出 ---------->\n    block_result = \"  \\n\".join(gpt_response_collection)\n    chatbot.append((\"解析的结果如下\", block_result))\n    history.extend([\"解析的结果如下\", block_result])\n    yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面\n\n    #  <-------- 写入文件，退出 ---------->\n    res = write_results_to_file(history)\n    chatbot.append((\"完成了吗？\", res))\n    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n\n@CatchException\ndef 解析ipynb文件(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):\n    chatbot.append([\n        \"函数插件功能？\",\n        \"对IPynb文件进行解析。Contributor: codycjy.\"])\n    yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面\n\n    history = []    # 清空历史\n    import glob\n    import os\n    if os.path.exists(txt):\n        project_folder = txt\n    else:\n        if txt == \"\":\n            txt = '空空如也的输入栏'\n        report_execption(chatbot, history,\n                         a=f\"解析项目: {txt}\", b=f\"找不到本地项目或无权访问: {txt}\")\n        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面\n        return\n    if txt.endswith('.ipynb'):\n        file_manifest = [txt]\n    else:\n        file_manifest = [f for f in glob.glob(\n            f'{project_folder}/**/*.ipynb', recursive=True)]\n    if len(file_manifest) == 0:\n        report_execption(chatbot, history,\n                         a=f\"解析项目: {txt}\", b=f\"找不到任何.ipynb文件: {txt}\")\n        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面\n        return\n    yield from ipynb解释(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, )\n"
  },
  {
    "path": "crazy_functions/解析项目源代码.py",
    "content": "from utils.toolbox import update_ui\nfrom utils.toolbox import CatchException, report_execption, write_results_to_file\nfrom .crazy_utils import input_clipping\n\ndef 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):\n    import os, copy\n    from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency\n    from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive\n    msg = '正常'\n    summary_batch_isolation = True\n    inputs_array = []\n    inputs_show_user_array = []\n    history_array = []\n    sys_prompt_array = []\n    report_part_1 = []\n\n    assert len(file_manifest) <= 512, \"源文件太多（超过512个）, 请缩减输入文件的数量。或者，您也可以选择删除此行警告，并修改代码拆分file_manifest列表，从而实现分批次处理。\"\n    ############################## <第一步，逐个文件分析，多线程> ##################################\n    for index, fp in enumerate(file_manifest):\n        # 读取文件\n        with open(fp, 'r', encoding='utf-8', errors='replace') as f:\n            file_content = f.read()\n        prefix = \"接下来请你逐文件分析下面的工程\" if index==0 else \"\"\n        i_say = prefix + f'请对下面的程序文件做一个概述文件名是{os.path.relpath(fp, project_folder)}，文件代码是 ```{file_content}```'\n        i_say_show_user = prefix + f'[{index}/{len(file_manifest)}] 请对下面的程序文件做一个概述: {os.path.abspath(fp)}'\n        # 装载请求内容\n        inputs_array.append(i_say)\n        inputs_show_user_array.append(i_say_show_user)\n        history_array.append([])\n        sys_prompt_array.append(\"你是一个程序架构分析师，正在分析一个源代码项目。你的回答必须简单明了。\")\n\n    # 文件读取完成，对每一个源代码文件，生成一个请求线程，发送到chatgpt进行分析\n    gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(\n        inputs_array = inputs_array,\n        inputs_show_user_array = inputs_show_user_array,\n        history_array = history_array,\n        sys_prompt_array = sys_prompt_array,\n        llm_kwargs = llm_kwargs,\n        chatbot = chatbot,\n        show_user_at_complete = True\n    )\n\n    # 全部文件解析完成，结果写入文件，准备对工程源代码进行汇总分析\n    report_part_1 = copy.deepcopy(gpt_response_collection)\n    history_to_return = report_part_1\n    res = write_results_to_file(report_part_1)\n    chatbot.append((\"完成？\", \"逐个文件分析已完成。\" + res + \"\\n\\n正在开始汇总。\"))\n    yield from update_ui(chatbot=chatbot, history=history_to_return) # 刷新界面\n\n    ############################## <第二步，综合，单线程，分组+迭代处理> ##################################\n    batchsize = 16  # 10个文件为一组\n    report_part_2 = []\n    previous_iteration_files = []\n    last_iteration_result = \"\"\n    while True:\n        if len(file_manifest) == 0: break\n        this_iteration_file_manifest = file_manifest[:batchsize]\n        this_iteration_gpt_response_collection = gpt_response_collection[:batchsize*2]\n        file_rel_path = [os.path.relpath(fp, project_folder) for index, fp in enumerate(this_iteration_file_manifest)]\n        # 把“请对下面的程序文件做一个概述” 替换成 精简的 \"文件名：{all_file[index]}\"\n        for index, content in enumerate(this_iteration_gpt_response_collection):\n            if index%2==0: this_iteration_gpt_response_collection[index] = f\"{file_rel_path[index//2]}\" # 只保留文件名节省token\n        this_iteration_files = [os.path.relpath(fp, project_folder) for index, fp in enumerate(this_iteration_file_manifest)]\n        previous_iteration_files.extend(this_iteration_files)\n        previous_iteration_files_string = ', '.join(previous_iteration_files)\n        current_iteration_focus = ', '.join(this_iteration_files)\n        if summary_batch_isolation: focus = current_iteration_focus\n        else:                       focus = previous_iteration_files_string\n        i_say = f'用一张Markdown表格简要描述以下文件的功能：{focus}。根据以上分析，用一句话概括程序的整体功能。'\n        if last_iteration_result != \"\":\n            sys_prompt_additional = \"已知某些代码的局部作用是:\" + last_iteration_result + \"\\n请继续分析其他源代码，从而更全面地理解项目的整体功能。\"\n        else:\n            sys_prompt_additional = \"\"\n        inputs_show_user = f'根据以上分析，对程序的整体功能和构架重新做出概括，由于输入长度限制，可能需要分组处理，本组文件为 {current_iteration_focus} + 已经汇总的文件组。'\n        this_iteration_history = copy.deepcopy(this_iteration_gpt_response_collection)\n        this_iteration_history.append(last_iteration_result)\n        # 裁剪input\n        inputs, this_iteration_history_feed = input_clipping(inputs=i_say, history=this_iteration_history, max_token_limit=2560)\n        result = yield from request_gpt_model_in_new_thread_with_ui_alive(\n            inputs=inputs, inputs_show_user=inputs_show_user, llm_kwargs=llm_kwargs, chatbot=chatbot,\n            history=this_iteration_history_feed,   # 迭代之前的分析\n            sys_prompt=\"你是一个程序架构分析师，正在分析一个项目的源代码。\" + sys_prompt_additional)\n        \n        summary = \"请用一句话概括这些文件的整体功能\"\n        summary_result = yield from request_gpt_model_in_new_thread_with_ui_alive(\n            inputs=summary, \n            inputs_show_user=summary, \n            llm_kwargs=llm_kwargs, \n            chatbot=chatbot,\n            history=[i_say, result],   # 迭代之前的分析\n            sys_prompt=\"你是一个程序架构分析师，正在分析一个项目的源代码。\" + sys_prompt_additional)\n\n        report_part_2.extend([i_say, result])\n        last_iteration_result = summary_result\n        file_manifest = file_manifest[batchsize:]\n        gpt_response_collection = gpt_response_collection[batchsize*2:]\n\n    ############################## <END> ##################################\n    history_to_return.extend(report_part_2)\n    res = write_results_to_file(history_to_return)\n    chatbot.append((\"完成了吗？\", res))\n    yield from update_ui(chatbot=chatbot, history=history_to_return) # 刷新界面\n\n\n@CatchException\ndef 解析项目本身(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):\n    history = []    # 清空历史，以免输入溢出\n    import glob\n    file_manifest = [f for f in glob.glob('./*.py') if ('test_project' not in f) and ('gpt_log' not in f)] + \\\n                    [f for f in glob.glob('./crazy_functions/*.py') if ('test_project' not in f) and ('gpt_log' not in f)]+ \\\n                    [f for f in glob.glob('./request_llm/*.py') if ('test_project' not in f) and ('gpt_log' not in f)]\n    project_folder = './'\n    if len(file_manifest) == 0:\n        report_execption(chatbot, history, a = f\"解析项目: {txt}\", b = f\"找不到任何python文件: {txt}\")\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n    yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)\n\n@CatchException\ndef 解析一个Python项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):\n    history = []    # 清空历史，以免输入溢出\n    import glob, os\n    if os.path.exists(txt):\n        project_folder = txt\n    else:\n        if txt == \"\": txt = '空空如也的输入栏'\n        report_execption(chatbot, history, a = f\"解析项目: {txt}\", b = f\"找不到本地项目或无权访问: {txt}\")\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.py', recursive=True)]\n    if len(file_manifest) == 0:\n        report_execption(chatbot, history, a = f\"解析项目: {txt}\", b = f\"找不到任何python文件: {txt}\")\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n    yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)\n\n\n@CatchException\ndef 解析一个C项目的头文件(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):\n    history = []    # 清空历史，以免输入溢出\n    import glob, os\n    if os.path.exists(txt):\n        project_folder = txt\n    else:\n        if txt == \"\": txt = '空空如也的输入栏'\n        report_execption(chatbot, history, a = f\"解析项目: {txt}\", b = f\"找不到本地项目或无权访问: {txt}\")\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.h', recursive=True)]  + \\\n                    [f for f in glob.glob(f'{project_folder}/**/*.hpp', recursive=True)] #+ \\\n                    # [f for f in glob.glob(f'{project_folder}/**/*.c', recursive=True)]\n    if len(file_manifest) == 0:\n        report_execption(chatbot, history, a = f\"解析项目: {txt}\", b = f\"找不到任何.h头文件: {txt}\")\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n    yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)\n\n@CatchException\ndef 解析一个C项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):\n    history = []    # 清空历史，以免输入溢出\n    import glob, os\n    if os.path.exists(txt):\n        project_folder = txt\n    else:\n        if txt == \"\": txt = '空空如也的输入栏'\n        report_execption(chatbot, history, a = f\"解析项目: {txt}\", b = f\"找不到本地项目或无权访问: {txt}\")\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.h', recursive=True)]  + \\\n                    [f for f in glob.glob(f'{project_folder}/**/*.cpp', recursive=True)] + \\\n                    [f for f in glob.glob(f'{project_folder}/**/*.hpp', recursive=True)] + \\\n                    [f for f in glob.glob(f'{project_folder}/**/*.c', recursive=True)]\n    if len(file_manifest) == 0:\n        report_execption(chatbot, history, a = f\"解析项目: {txt}\", b = f\"找不到任何.h头文件: {txt}\")\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n    yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)\n\n\n@CatchException\ndef 解析一个Java项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):\n    history = []  # 清空历史，以免输入溢出\n    import glob, os\n    if os.path.exists(txt):\n        project_folder = txt\n    else:\n        if txt == \"\": txt = '空空如也的输入栏'\n        report_execption(chatbot, history, a=f\"解析项目: {txt}\", b=f\"找不到本地项目或无权访问: {txt}\")\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.java', recursive=True)] + \\\n                    [f for f in glob.glob(f'{project_folder}/**/*.jar', recursive=True)] + \\\n                    [f for f in glob.glob(f'{project_folder}/**/*.xml', recursive=True)] + \\\n                    [f for f in glob.glob(f'{project_folder}/**/*.sh', recursive=True)]\n    if len(file_manifest) == 0:\n        report_execption(chatbot, history, a=f\"解析项目: {txt}\", b=f\"找不到任何java文件: {txt}\")\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n    yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)\n\n\n@CatchException\ndef 解析一个前端项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):\n    history = []  # 清空历史，以免输入溢出\n    import glob, os\n    if os.path.exists(txt):\n        project_folder = txt\n    else:\n        if txt == \"\": txt = '空空如也的输入栏'\n        report_execption(chatbot, history, a=f\"解析项目: {txt}\", b=f\"找不到本地项目或无权访问: {txt}\")\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.ts', recursive=True)] + \\\n                    [f for f in glob.glob(f'{project_folder}/**/*.tsx', recursive=True)] + \\\n                    [f for f in glob.glob(f'{project_folder}/**/*.json', recursive=True)] + \\\n                    [f for f in glob.glob(f'{project_folder}/**/*.js', recursive=True)] + \\\n                    [f for f in glob.glob(f'{project_folder}/**/*.vue', recursive=True)] + \\\n                    [f for f in glob.glob(f'{project_folder}/**/*.less', recursive=True)] + \\\n                    [f for f in glob.glob(f'{project_folder}/**/*.sass', recursive=True)] + \\\n                    [f for f in glob.glob(f'{project_folder}/**/*.wxml', recursive=True)] + \\\n                    [f for f in glob.glob(f'{project_folder}/**/*.wxss', recursive=True)] + \\\n                    [f for f in glob.glob(f'{project_folder}/**/*.css', recursive=True)] + \\\n                    [f for f in glob.glob(f'{project_folder}/**/*.jsx', recursive=True)]\n    if len(file_manifest) == 0:\n        report_execption(chatbot, history, a=f\"解析项目: {txt}\", b=f\"找不到任何前端相关文件: {txt}\")\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n    yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)\n\n\n@CatchException\ndef 解析一个Golang项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):\n    history = []  # 清空历史，以免输入溢出\n    import glob, os\n    if os.path.exists(txt):\n        project_folder = txt\n    else:\n        if txt == \"\": txt = '空空如也的输入栏'\n        report_execption(chatbot, history, a=f\"解析项目: {txt}\", b=f\"找不到本地项目或无权访问: {txt}\")\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.go', recursive=True)] + \\\n                    [f for f in glob.glob(f'{project_folder}/**/go.mod', recursive=True)] + \\\n                    [f for f in glob.glob(f'{project_folder}/**/go.sum', recursive=True)] + \\\n                    [f for f in glob.glob(f'{project_folder}/**/go.work', recursive=True)]\n    if len(file_manifest) == 0:\n        report_execption(chatbot, history, a=f\"解析项目: {txt}\", b=f\"找不到任何golang文件: {txt}\")\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n    yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)\n\n@CatchException\ndef 解析一个Rust项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):\n    history = []  # 清空历史，以免输入溢出\n    import glob, os\n    if os.path.exists(txt):\n        project_folder = txt\n    else:\n        if txt == \"\": txt = '空空如也的输入栏'\n        report_execption(chatbot, history, a=f\"解析项目: {txt}\", b=f\"找不到本地项目或无权访问: {txt}\")\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.rs', recursive=True)] + \\\n                    [f for f in glob.glob(f'{project_folder}/**/*.toml', recursive=True)] + \\\n                    [f for f in glob.glob(f'{project_folder}/**/*.lock', recursive=True)]\n    if len(file_manifest) == 0:\n        report_execption(chatbot, history, a=f\"解析项目: {txt}\", b=f\"找不到任何golang文件: {txt}\")\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n    yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)\n\n@CatchException\ndef 解析一个Lua项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):\n    history = []    # 清空历史，以免输入溢出\n    import glob, os\n    if os.path.exists(txt):\n        project_folder = txt\n    else:\n        if txt == \"\": txt = '空空如也的输入栏'\n        report_execption(chatbot, history, a = f\"解析项目: {txt}\", b = f\"找不到本地项目或无权访问: {txt}\")\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.lua', recursive=True)] + \\\n                    [f for f in glob.glob(f'{project_folder}/**/*.xml', recursive=True)] + \\\n                    [f for f in glob.glob(f'{project_folder}/**/*.json', recursive=True)] + \\\n                    [f for f in glob.glob(f'{project_folder}/**/*.toml', recursive=True)]\n    if len(file_manifest) == 0:\n        report_execption(chatbot, history, a = f\"解析项目: {txt}\", b = f\"找不到任何lua文件: {txt}\")\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n    yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)\n\n\n@CatchException\ndef 解析一个CSharp项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):\n    history = []    # 清空历史，以免输入溢出\n    import glob, os\n    if os.path.exists(txt):\n        project_folder = txt\n    else:\n        if txt == \"\": txt = '空空如也的输入栏'\n        report_execption(chatbot, history, a = f\"解析项目: {txt}\", b = f\"找不到本地项目或无权访问: {txt}\")\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.cs', recursive=True)] + \\\n                    [f for f in glob.glob(f'{project_folder}/**/*.csproj', recursive=True)]\n    if len(file_manifest) == 0:\n        report_execption(chatbot, history, a = f\"解析项目: {txt}\", b = f\"找不到任何CSharp文件: {txt}\")\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n    yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)\n\n\n@CatchException\ndef 解析任意code项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):\n    txt_pattern = plugin_kwargs.get(\"advanced_arg\")\n    txt_pattern = txt_pattern.replace(\"，\", \",\")\n    # 将要匹配的模式(例如: *.c, *.cpp, *.py, config.toml)\n    pattern_include = [_.lstrip(\" ,\").rstrip(\" ,\") for _ in txt_pattern.split(\",\") if _ != \"\" and not _.strip().startswith(\"^\")]\n    if not pattern_include: pattern_include = [\"*\"] # 不输入即全部匹配\n    # 将要忽略匹配的文件后缀(例如: ^*.c, ^*.cpp, ^*.py)\n    pattern_except_suffix = [_.lstrip(\" ^*.,\").rstrip(\" ,\") for _ in txt_pattern.split(\" \") if _ != \"\" and _.strip().startswith(\"^*.\")]\n    pattern_except_suffix += ['zip', 'rar', '7z', 'tar', 'gz'] # 避免解析压缩文件\n    # 将要忽略匹配的文件名(例如: ^README.md)\n    pattern_except_name = [_.lstrip(\" ^*,\").rstrip(\" ,\").replace(\".\", \"\\.\") for _ in txt_pattern.split(\" \") if _ != \"\" and _.strip().startswith(\"^\") and not _.strip().startswith(\"^*.\")]\n    # 生成正则表达式\n    pattern_except = '/[^/]+\\.(' + \"|\".join(pattern_except_suffix) + ')$'\n    pattern_except += '|/(' + \"|\".join(pattern_except_name) + ')$' if pattern_except_name != [] else ''\n\n    history.clear()\n    import glob, os, re\n    if os.path.exists(txt):\n        project_folder = txt\n    else:\n        if txt == \"\": txt = '空空如也的输入栏'\n        report_execption(chatbot, history, a = f\"解析项目: {txt}\", b = f\"找不到本地项目或无权访问: {txt}\")\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n    # 若上传压缩文件, 先寻找到解压的文件夹路径, 从而避免解析压缩文件\n    maybe_dir = [f for f in glob.glob(f'{project_folder}/*') if os.path.isdir(f)]\n    if len(maybe_dir)>0 and maybe_dir[0].endswith('.extract'):\n        extract_folder_path = maybe_dir[0]\n    else:\n        extract_folder_path = project_folder\n    # 按输入的匹配模式寻找上传的非压缩文件和已解压的文件\n    file_manifest = [f for pattern in pattern_include for f in glob.glob(f'{extract_folder_path}/**/{pattern}', recursive=True) if \"\" != extract_folder_path and \\\n                      os.path.isfile(f) and (not re.search(pattern_except, f) or pattern.endswith('.' + re.search(pattern_except, f).group().split('.')[-1]))]\n    if len(file_manifest) == 0:\n        report_execption(chatbot, history, a = f\"解析项目: {txt}\", b = f\"找不到任何文件: {txt}\")\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n    yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)"
  },
  {
    "path": "crazy_functions/询问多个大语言模型.py",
    "content": "from utils.toolbox import CatchException, update_ui\nfrom .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive\nimport datetime\n@CatchException\ndef 同时问询(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):\n    \"\"\"\n    txt             输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径\n    llm_kwargs      gpt模型参数，如温度和top_p等，一般原样传递下去就行\n    plugin_kwargs   插件模型的参数，用于灵活调整复杂功能的各种参数\n    chatbot         聊天显示框的句柄，用于显示给用户\n    history         聊天历史，前情提要\n    system_prompt   给gpt的静默提醒\n    web_port        当前软件运行的端口号\n    \"\"\"\n    history = []    # 清空历史，以免输入溢出\n    chatbot.append((txt, \"正在同时咨询ChatGPT和ChatGLM……\"))\n    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新\n\n    # llm_kwargs['llm_model'] = 'chatglm&gpt-3.5-turbo&api2d-gpt-3.5-turbo' # 支持任意数量的llm接口，用&符号分隔\n    llm_kwargs['llm_model'] = 'chatglm&gpt-3.5-turbo' # 支持任意数量的llm接口，用&符号分隔\n    gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(\n        inputs=txt, inputs_show_user=txt, \n        llm_kwargs=llm_kwargs, chatbot=chatbot, history=history, \n        sys_prompt=system_prompt,\n        retry_times_at_unknown_error=0\n    )\n\n    history.append(txt)\n    history.append(gpt_say)\n    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新\n\n\n@CatchException\ndef 同时问询_指定模型(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):\n    \"\"\"\n    txt             输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径\n    llm_kwargs      gpt模型参数，如温度和top_p等，一般原样传递下去就行\n    plugin_kwargs   插件模型的参数，用于灵活调整复杂功能的各种参数\n    chatbot         聊天显示框的句柄，用于显示给用户\n    history         聊天历史，前情提要\n    system_prompt   给gpt的静默提醒\n    web_port        当前软件运行的端口号\n    \"\"\"\n    history = []    # 清空历史，以免输入溢出\n    chatbot.append((txt, \"正在同时咨询ChatGPT和ChatGLM……\"))\n    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新\n\n    if (\"advanced_arg\" in plugin_kwargs) and (plugin_kwargs[\"advanced_arg\"] == \"\"): plugin_kwargs.pop(\"advanced_arg\")\n    # llm_kwargs['llm_model'] = 'chatglm&gpt-3.5-turbo&api2d-gpt-3.5-turbo' # 支持任意数量的llm接口，用&符号分隔\n    llm_kwargs['llm_model'] = plugin_kwargs.get(\"advanced_arg\", 'chatglm&gpt-3.5-turbo') # 'chatglm&gpt-3.5-turbo' # 支持任意数量的llm接口，用&符号分隔\n    gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(\n        inputs=txt, inputs_show_user=txt, \n        llm_kwargs=llm_kwargs, chatbot=chatbot, history=history, \n        sys_prompt=system_prompt,\n        retry_times_at_unknown_error=0\n    )\n\n    history.append(txt)\n    history.append(gpt_say)\n    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新"
  },
  {
    "path": "crazy_functions/语音助手.py",
    "content": "from utils.toolbox import update_ui\nfrom utils.toolbox import CatchException, get_conf, markdown_convertion\nfrom crazy_functions.crazy_utils import input_clipping\nfrom llm_cards.bridge_all import predict_no_ui_long_connection\nimport threading, time\nimport numpy as np\nfrom .live_audio.aliyunASR import AliyunASR\nimport json\n\nclass WatchDog():\n    def __init__(self, timeout, bark_fn, interval=3, msg=\"\") -> None:\n        self.last_feed = None\n        self.timeout = timeout\n        self.bark_fn = bark_fn\n        self.interval = interval\n        self.msg = msg\n        self.kill_dog = False\n    \n    def watch(self):\n        while True:\n            if self.kill_dog: break\n            if time.time() - self.last_feed > self.timeout:\n                if len(self.msg) > 0: print(self.msg)\n                self.bark_fn()\n                break\n            time.sleep(self.interval)\n\n    def begin_watch(self):\n        self.last_feed = time.time()\n        th = threading.Thread(target=self.watch)\n        th.daemon = True\n        th.start()\n\n    def feed(self):\n        self.last_feed = time.time()\n\ndef chatbot2history(chatbot):\n    history = []\n    for c in chatbot:\n        for q in c:\n            if q not in [\"[请讲话]\", \"[等待GPT响应]\", \"[正在等您说完问题]\"]:\n                history.append(q.strip('<div class=\"markdown-body\">').strip('</div>').strip('<p>').strip('</p>'))\n    return history\n\nclass AsyncGptTask():\n    def __init__(self) -> None:\n        self.observe_future = []\n        self.observe_future_chatbot_index = []\n\n    def gpt_thread_worker(self, i_say, llm_kwargs, history, sys_prompt, observe_window, index):\n        try:\n            MAX_TOKEN_ALLO = 2560\n            i_say, history = input_clipping(i_say, history, max_token_limit=MAX_TOKEN_ALLO)\n            gpt_say_partial = predict_no_ui_long_connection(inputs=i_say, llm_kwargs=llm_kwargs, history=history, sys_prompt=sys_prompt, \n                                                            observe_window=observe_window[index], console_slience=True)\n        except ConnectionAbortedError as token_exceed_err:\n            print('至少一个线程任务Token溢出而失败', e)\n        except Exception as e:\n            print('至少一个线程任务意外失败', e)\n\n    def add_async_gpt_task(self, i_say, chatbot_index, llm_kwargs, history, system_prompt):\n        self.observe_future.append([\"\"])\n        self.observe_future_chatbot_index.append(chatbot_index)\n        cur_index = len(self.observe_future)-1\n        th_new = threading.Thread(target=self.gpt_thread_worker, args=(i_say, llm_kwargs, history, system_prompt, self.observe_future, cur_index))\n        th_new.daemon = True\n        th_new.start()\n\n    def update_chatbot(self, chatbot):\n        for of, ofci in zip(self.observe_future, self.observe_future_chatbot_index):\n            try:\n                chatbot[ofci] = list(chatbot[ofci])\n                chatbot[ofci][1] = markdown_convertion(of[0])\n            except:\n                self.observe_future = []\n                self.observe_future_chatbot_index = []\n        return chatbot\n\nclass InterviewAssistant(AliyunASR):\n    def __init__(self):\n        self.capture_interval = 0.5 # second\n        self.stop = False\n        self.parsed_text = \"\"\n        self.parsed_sentence = \"\"\n        self.buffered_sentence = \"\"\n        self.event_on_result_chg = threading.Event()\n        self.event_on_entence_end = threading.Event()\n        self.event_on_commit_question = threading.Event()\n\n    def __del__(self):\n        self.stop = True\n        self.stop_msg = \"\"\n        self.commit_wd.kill_dog = True\n        self.plugin_wd.kill_dog = True\n\n    def init(self, chatbot):\n        # 初始化音频采集线程\n        self.captured_audio = np.array([])\n        self.keep_latest_n_second = 10\n        self.commit_after_pause_n_second = 2.0\n        self.ready_audio_flagment = None\n        self.stop = False\n        self.plugin_wd = WatchDog(timeout=5, bark_fn=self.__del__, msg=\"程序终止\")\n        self.aut = threading.Thread(target=self.audio_convertion_thread, args=(chatbot._cookies['uuid'],))\n        self.aut.daemon = True\n        self.aut.start()\n        # th2 = threading.Thread(target=self.audio2txt_thread, args=(chatbot._cookies['uuid'],))\n        # th2.daemon = True\n        # th2.start()\n\n    def no_audio_for_a_while(self):\n        if len(self.buffered_sentence) < 7: # 如果一句话小于7个字，暂不提交\n            self.commit_wd.begin_watch()\n        else:\n            self.event_on_commit_question.set()\n\n    def begin(self, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):\n        # main plugin function\n        self.init(chatbot)\n        chatbot.append([\"[请讲话]\", \"[正在等您说完问题]\"])\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        self.plugin_wd.begin_watch()\n        self.agt = AsyncGptTask()\n        self.commit_wd = WatchDog(timeout=self.commit_after_pause_n_second, bark_fn=self.no_audio_for_a_while, interval=0.2)\n        self.commit_wd.begin_watch()\n\n        while not self.stop:\n            self.event_on_result_chg.wait(timeout=0.25)  # run once every 0.25 second\n            chatbot = self.agt.update_chatbot(chatbot)   # 将子线程的gpt结果写入chatbot\n            history = chatbot2history(chatbot)\n            yield from update_ui(chatbot=chatbot, history=history)      # 刷新界面\n            self.plugin_wd.feed()\n\n            if self.event_on_result_chg.is_set(): \n                # update audio decode result\n                self.event_on_result_chg.clear()\n                chatbot[-1] = list(chatbot[-1])\n                chatbot[-1][0] = self.buffered_sentence + self.parsed_text\n                history = chatbot2history(chatbot)\n                yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面\n                self.commit_wd.feed()\n\n            if self.event_on_entence_end.is_set():\n                # called when a sentence has ended\n                self.event_on_entence_end.clear()\n                self.parsed_text = self.parsed_sentence\n                self.buffered_sentence += self.parsed_sentence\n\n            if self.event_on_commit_question.is_set():\n                # called when a question should be commited\n                self.event_on_commit_question.clear()\n                if len(self.buffered_sentence) == 0: raise RuntimeError\n\n                self.commit_wd.begin_watch()\n                chatbot[-1] = list(chatbot[-1])\n                chatbot[-1] = [self.buffered_sentence, \"[等待GPT响应]\"]\n                yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n                # add gpt task 创建子线程请求gpt，避免线程阻塞\n                history = chatbot2history(chatbot)\n                self.agt.add_async_gpt_task(self.buffered_sentence, len(chatbot)-1, llm_kwargs, history, system_prompt)\n                \n                self.buffered_sentence = \"\"\n                chatbot.append([\"[请讲话]\", \"[正在等您说完问题]\"])\n                yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n\n        if len(self.stop_msg) != 0:\n            raise RuntimeError(self.stop_msg)\n\n\n\n@CatchException\ndef 语音助手(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):\n    # pip install -U openai-whisper\n    chatbot.append([\"对话助手函数插件：使用时，双手离开鼠标键盘吧\", \"音频助手, 正在听您讲话（点击“停止”键可终止程序）...\"])\n    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n\n    # 尝试导入依赖，如果缺少依赖，则给出安装建议\n    try:\n        import nls\n        from scipy import io\n    except:\n        chatbot.append([\"导入依赖失败\", \"使用该模块需要额外依赖, 安装方法:```pip install --upgrade aliyun-python-sdk-core==2.13.3 pyOpenSSL scipy git+https://github.com/aliyun/alibabacloud-nls-python-sdk.git```\"])\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n\n    APPKEY = get_conf('ALIYUN_APPKEY')\n    if APPKEY == \"\":\n        chatbot.append([\"导入依赖失败\", \"没有阿里云语音识别APPKEY和TOKEN, 详情见https://help.aliyun.com/document_detail/450255.html\"])\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n\n    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n    ia = InterviewAssistant()\n    yield from ia.begin(llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)\n\n"
  },
  {
    "path": "crazy_functions/读文章写摘要.py",
    "content": "from utils.toolbox import update_ui\nfrom utils.toolbox import CatchException, report_execption, write_results_to_file\nfrom .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive\nfast_debug = False\n\n\ndef 解析Paper(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):\n    import time, glob, os\n    print('begin analysis on:', file_manifest)\n    for index, fp in enumerate(file_manifest):\n        with open(fp, 'r', encoding='utf-8', errors='replace') as f:\n            file_content = f.read()\n\n        prefix = \"接下来请你逐文件分析下面的论文文件，概括其内容\" if index==0 else \"\"\n        i_say = prefix + f'请对下面的文章片段用中文做一个概述，文件名是{os.path.relpath(fp, project_folder)}，文章内容是 ```{file_content}```'\n        i_say_show_user = prefix + f'[{index}/{len(file_manifest)}] 请对下面的文章片段做一个概述: {os.path.abspath(fp)}'\n        chatbot.append((i_say_show_user, \"[Local Message] waiting gpt response.\"))\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n\n        if not fast_debug: \n            msg = '正常'\n            # ** gpt request **\n            gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(i_say, i_say_show_user, llm_kwargs, chatbot, history=[], sys_prompt=system_prompt)   # 带超时倒计时\n\n            chatbot[-1] = (i_say_show_user, gpt_say)\n            history.append(i_say_show_user); history.append(gpt_say)\n            yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面\n            if not fast_debug: time.sleep(2)\n\n    all_file = ', '.join([os.path.relpath(fp, project_folder) for index, fp in enumerate(file_manifest)])\n    i_say = f'根据以上你自己的分析，对全文进行概括，用学术性语言写一段中文摘要，然后再写一段英文摘要（包括{all_file}）。'\n    chatbot.append((i_say, \"[Local Message] waiting gpt response.\"))\n    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n\n    if not fast_debug: \n        msg = '正常'\n        # ** gpt request **\n        gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(i_say, i_say, llm_kwargs, chatbot, history=history, sys_prompt=system_prompt)   # 带超时倒计时\n\n        chatbot[-1] = (i_say, gpt_say)\n        history.append(i_say); history.append(gpt_say)\n        yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面\n        res = write_results_to_file(history)\n        chatbot.append((\"完成了吗？\", res))\n        yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面\n\n\n\n@CatchException\ndef 读文章写摘要(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):\n    history = []    # 清空历史，以免输入溢出\n    import glob, os\n    if os.path.exists(txt):\n        project_folder = txt\n    else:\n        if txt == \"\": txt = '空空如也的输入栏'\n        report_execption(chatbot, history, a = f\"解析项目: {txt}\", b = f\"找不到本地项目或无权访问: {txt}\")\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)] # + \\\n                    # [f for f in glob.glob(f'{project_folder}/**/*.cpp', recursive=True)] + \\\n                    # [f for f in glob.glob(f'{project_folder}/**/*.c', recursive=True)]\n    if len(file_manifest) == 0:\n        report_execption(chatbot, history, a = f\"解析项目: {txt}\", b = f\"找不到任何.tex文件: {txt}\")\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n    yield from 解析Paper(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)\n"
  },
  {
    "path": "crazy_functions/谷歌检索小助手.py",
    "content": "from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive\nfrom utils.toolbox import CatchException, report_execption, write_results_to_file\nfrom utils.toolbox import update_ui\n\ndef get_meta_information(url, chatbot, history):\n    import requests\n    import arxiv\n    import difflib\n    from bs4 import BeautifulSoup\n    from utils.toolbox import get_conf\n    proxies, = get_conf('proxies')\n    headers = {\n        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/105.0.0.0 Safari/537.36',\n    }\n    # 发送 GET 请求\n    response = requests.get(url, proxies=proxies, headers=headers)\n\n    # 解析网页内容\n    soup = BeautifulSoup(response.text, \"html.parser\")\n\n    def string_similar(s1, s2):\n        return difflib.SequenceMatcher(None, s1, s2).quick_ratio()\n\n    profile = []\n    # 获取所有文章的标题和作者\n    for result in soup.select(\".gs_ri\"):\n        title = result.a.text.replace('\\n', ' ').replace('  ', ' ')\n        author = result.select_one(\".gs_a\").text\n        try:\n            citation = result.select_one(\".gs_fl > a[href*='cites']\").text  # 引用次数是链接中的文本，直接取出来\n        except:\n            citation = 'cited by 0'\n        abstract = result.select_one(\".gs_rs\").text.strip()  # 摘要在 .gs_rs 中的文本，需要清除首尾空格\n        search = arxiv.Search(\n            query = title,\n            max_results = 1,\n            sort_by = arxiv.SortCriterion.Relevance,\n        )\n        try:\n            paper = next(search.results())\n            if string_similar(title, paper.title) > 0.90: # same paper\n                abstract = paper.summary.replace('\\n', ' ')\n                is_paper_in_arxiv = True\n            else:   # different paper\n                abstract = abstract\n                is_paper_in_arxiv = False\n            paper = next(search.results())\n        except:\n            abstract = abstract\n            is_paper_in_arxiv = False\n        print(title)\n        print(author)\n        print(citation)\n        profile.append({\n            'title':title,\n            'author':author,\n            'citation':citation,\n            'abstract':abstract,\n            'is_paper_in_arxiv':is_paper_in_arxiv,\n        })\n\n        chatbot[-1] = [chatbot[-1][0], title + f'\\n\\n是否在arxiv中（不在arxiv中无法获取完整摘要）:{is_paper_in_arxiv}\\n\\n' + abstract]\n        yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面\n    return profile\n\n@CatchException\ndef 谷歌检索小助手(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):\n    # 基本信息：功能、贡献者\n    chatbot.append([\n        \"函数插件功能？\",\n        \"分析用户提供的谷歌学术（google scholar）搜索页面中，出现的所有文章: binary-husky，插件初始化中...\"])\n    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n\n    # 尝试导入依赖，如果缺少依赖，则给出安装建议\n    try:\n        import arxiv\n        import math\n        from bs4 import BeautifulSoup\n    except:\n        report_execption(chatbot, history, \n            a = f\"解析项目: {txt}\", \n            b = f\"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade beautifulsoup4 arxiv```。\")\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面\n        return\n\n    # 清空历史，以免输入溢出\n    history = []\n    meta_paper_info_list = yield from get_meta_information(txt, chatbot, history)\n    batchsize = 5\n    for batch in range(math.ceil(len(meta_paper_info_list)/batchsize)):\n        if len(meta_paper_info_list[:batchsize]) > 0:\n            i_say = \"下面是一些学术文献的数据，提取出以下内容：\" + \\\n            \"1、英文题目；2、中文题目翻译；3、作者；4、arxiv公开（is_paper_in_arxiv）；4、引用数量（cite）；5、中文摘要翻译。\" + \\\n            f\"以下是信息源：{str(meta_paper_info_list[:batchsize])}\" \n\n            inputs_show_user = f\"请分析此页面中出现的所有文章：{txt}，这是第{batch+1}批\"\n            gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(\n                inputs=i_say, inputs_show_user=inputs_show_user,\n                llm_kwargs=llm_kwargs, chatbot=chatbot, history=[],\n                sys_prompt=\"你是一个学术翻译，请从数据中提取信息。你必须使用Markdown表格。你必须逐个文献进行处理。\"\n            )\n\n            history.extend([ f\"第{batch+1}批\", gpt_say ])\n            meta_paper_info_list = meta_paper_info_list[batchsize:]\n\n    chatbot.append([\"状态？\", \n        \"已经全部完成，您可以试试让AI写一个Related Works，例如您可以继续输入Write a \\\"Related Works\\\" section about \\\"你搜索的研究领域\\\" for me.\"])\n    msg = '正常'\n    yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面\n    res = write_results_to_file(history)\n    chatbot.append((\"完成了吗？\", res)); \n    yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面\n"
  },
  {
    "path": "crazy_functions/辅助回答.py",
    "content": "# encoding: utf-8\n# @Time   : 2023/4/19\n# @Author : Spike\n# @Descr   :\nfrom utils.toolbox import update_ui\nfrom utils.toolbox import CatchException, report_execption, write_results_to_file\nfrom crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive\n\n\n@CatchException\ndef 猜你想问(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):\n    if txt:\n        show_say = txt\n        prompt = txt+'\\n回答完问题后，再列出用户可能提出的三个问题。'\n    else:\n        prompt = history[-1]+\"\\n分析上述回答，再列出用户可能提出的三个问题。\"\n        show_say = '分析上述回答，再列出用户可能提出的三个问题。'\n    gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(\n        inputs=prompt,\n        inputs_show_user=show_say,\n        llm_kwargs=llm_kwargs,\n        chatbot=chatbot,\n        history=history,\n        sys_prompt=system_prompt\n    )\n    chatbot[-1] = (show_say, gpt_say)\n    history.extend([show_say, gpt_say])\n    yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面"
  },
  {
    "path": "crazy_functions/高级功能函数模板.py",
    "content": "from utils.toolbox import CatchException, update_ui\nfrom .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive\nimport datetime\n@CatchException\ndef 高阶功能模板函数(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):\n    \"\"\"\n    txt             输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径\n    llm_kwargs      gpt模型参数，如温度和top_p等，一般原样传递下去就行\n    plugin_kwargs   插件模型的参数，用于灵活调整复杂功能的各种参数\n    chatbot         聊天显示框的句柄，用于显示给用户\n    history         聊天历史，前情提要\n    system_prompt   给gpt的静默提醒\n    web_port        当前软件运行的端口号\n    \"\"\"\n    history = []    # 清空历史，以免输入溢出\n    chatbot.append((\"这是什么功能？\", \"[Local Message] 请注意，您正在调用一个[函数插件]的模板，该函数面向希望实现更多有趣功能的开发者，它可以作为创建新功能函数的模板（该函数只有20多行代码）。此外我们也提供可同步处理大量文件的多线程Demo供您参考。您若希望分享新的功能模组，请不吝PR！\"))\n    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新\n    for i in range(5):\n        currentMonth = (datetime.date.today() + datetime.timedelta(days=i)).month\n        currentDay = (datetime.date.today() + datetime.timedelta(days=i)).day\n        i_say = f'历史中哪些事件发生在{currentMonth}月{currentDay}日？列举两条并发送相关图片。发送图片时，请使用Markdown，将Unsplash API中的PUT_YOUR_QUERY_HERE替换成描述该事件的一个最重要的单词。'\n        gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(\n            inputs=i_say, inputs_show_user=i_say, \n            llm_kwargs=llm_kwargs, chatbot=chatbot, history=[], \n            sys_prompt=\"当你想发送一张照片时，请使用Markdown, 并且不要有反斜线, 不要用代码块。使用 Unsplash API (https://source.unsplash.com/1280x720/? < PUT_YOUR_QUERY_HERE >)。\"\n        )\n        chatbot[-1] = (i_say, gpt_say)\n        history.append(i_say);history.append(gpt_say)\n        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新"
  },
  {
    "path": "gradio_demo.py",
    "content": "from model_cards.autoback import AutoBackend\nimport argparse\nimport os\nimport platform\nimport sys\nfrom pathlib import Path\nimport  numpy as np \nimport torch\nimport torch.backends.cudnn as cudnn\nimport matplotlib.pyplot as plt\nfrom PIL import Image,ImageDraw,ImageFont\nfrom utils.ops import (LOGGER, Profile, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2,\n                     dilate_mask, increment_path, non_max_suppression ,print_args, scale_boxes, xyxy2xywh,save_format)\nfrom utils.plot import Annotator, save_one_box,show_box,show_mask,save_mask_data,Draw_img\n\n\nfrom utils.torch_utils import select_device\nfrom utils.conf import SAM_MODEL_TYPE,GROUNED_MODEL_TYPE,Tag2Text_Model_Path,NUM_WORKS\nfrom utils import VID_FORMATS,IMG_FORMATS,write_categories\nfrom VisualGLM_6B.chatglm import *\nimport multiprocessing\nimport xml.etree.cElementTree as ET\nimport gradio as gr\nfrom gradio.inputs import File\nimport random\n\nFILE = Path(__file__).resolve()\nROOT = FILE.parents[0]  #  root directory\nif str(ROOT) not in sys.path:\n    sys.path.append(str(ROOT))  # add ROOT to PATH\nROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative\nglobal chatbot\nglobal categories\ncategories = {}\nglobal category_colors\ncategory_colors={}\n# 初始对应类别编号\nclass_ids = []\nmodels_config = {'tag2text': None, 'lama': None,'sam': None,'grounded': None,'sd': None,'chat_glm': None}\n\ndef auto_opentab_delay(port=7585):\n               import threading, webbrowser, time\n               print(f\"如果浏览器没有自动打开，请复制并转到以下URL：\")\n               print(f\"\\t（orgstyle）: http://localhost:{port}, (Darkstyle）: http://localhost:{port}/?__dark-theme=true\")\n               def open(): \n                    time.sleep(2)                                           # 打开浏览器\n                    webbrowser.open_new_tab(f\"http://localhost:{port}/?__dark-theme=true\")\n               threading.Thread(target=open, name=\"open-browser\", daemon=True).start()\n\ndef load_auto_backend_models(lama,sam,det,tag2text,device):\n    \"\"\"\n    加载多个模型\n    \"\"\"\n    # Load model\n    \n    device = select_device(device)\n    if tag2text and not models_config['tag2text']:\n        models_config['tag2text'] = AutoBackend(\"tag2text\",weights=Tag2Text_Model_Path,device=device)\n    elif not tag2text  :\n        models_config['tag2text'] =None \n    else :\n        print('tag2text pass')    \n    if det and not models_config['grounded']:\n        models_config['grounded'] = AutoBackend(\"grounded-DINO\",weights=GROUNED_MODEL_TYPE['S'], device=device,\n        args_config= 'model_cards/groundingdino/config/GroundingDINO_SwinT_OGC.py')\n    elif not det  :\n        models_config['grounded'] =None \n    else :\n        print('grounded pass')\n        \n    if sam and not models_config['sam']:\n        models_config['sam']= AutoBackend(\"segment-anything\",weights=SAM_MODEL_TYPE['vit_h'] ,device=device)\n    elif not sam :\n        models_config['sam'] =None \n    else :\n        print(' sam pass')\n        \n    if lama and not models_config['lama']:\n        models_config['lama']= AutoBackend(\"lama\",weights=None,args_config='model_cards/lama/configs/prediction/default.yaml',device=device)\n    elif not lama :\n        models_config['lama'] =None \n    else :\n        print(' lama pass')   \n\ndef Auto_run(\n        source= 'data/images',  # file/dir/URL/glob, 0 for webcam\n        img_input='',\n        input_prompt=\"Anything in this image\",\n        conf_thres=0.3,  # confidence threshold\n        iou_thres=0.5,  # NMS IOU threshold\n        text_thres=0.2,\n        device='',  # cuda device, i.e. 0 or 0,1,2,3 or cpu   \n        save_conf=False,  # save confidences in --save-txt labels\n        img_save=False,  # do not save images/videos\n        chatgpt=False,\n        visualize=False,  # visualize features\n        project=ROOT / 'runs/detect',  # save results to project/name\n        name='exp',  # save results to project/name\n        exist_ok=False,  # existing project/name ok, do not increment\n        lama=False,   # use lama models\n        sam=True,    # use segment-anythings\n        det=True,    # use grounded detect model with text\n        tag2text=True,\n        save_txt=False,  # save results to *.txt\n        save_xml=False,  # save results to *.xml\n        save_mask=False,\n        save_caption=False,\n        batch_process=False,\n        color_flag=False,\n        process_name=0,\n        ):  \n\n            global models_config\n            global category_colors\n            # if not memory_model  \n            load_auto_backend_models(lama,sam,det,tag2text,device)\n            # memory_model=True\n            LOGGER.info (f'proceess ID：{process_name},loads model list ：{models_config.keys()}')\n            if chatgpt:\n               # global chatbot\n                chatbot = Chatbot(api_key=API_KEY,proxy=PROXIES,engine=\"gpt-3.5-turbo\")\n            cls_index = -1        # 设置默认值为 -1\n            if img_input:\n                source =img_input\n            source = str(source)\n            \n            print(f'input:{source}')\n            img_paths=None\n            if os.path.isdir(source):\n                img_paths = [os.path.join(source, f) for f in os.listdir(source) if\n                    Path(f).suffix[1:] in (IMG_FORMATS + VID_FORMATS)] \n            else:\n                img_paths = [source]    \n\n            # Directories\n            is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)\n          #  save_img = img_save and not source.endswith('.txt')  # save inference images\n            is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://'))\n            #webcam = source.isnumeric() or source.endswith('.streams') or (is_url )\n            if is_url and is_file:\n                source = check_file(source)  # download\n\n            save_dir = increment_path(Path(project) / name, exist_ok=exist_ok)  # increment run\n            \n            (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # make dir\n            (save_dir / 'xmls' if save_xml else save_dir).mkdir(parents=True, exist_ok=True)  # make dir\n            (save_dir / 'masks' if save_mask else save_dir).mkdir(parents=True, exist_ok=True)  # make dir\n            (save_dir / 'captions' if save_caption else save_dir).mkdir(parents=True, exist_ok=True)  # make dir\n            p = Path(str(save_dir) )  # to Path\n            seen=0\n            # loda data and inference\n            caption=None\n            for source in (img_paths): \n                    im = cv2.imread(source)\n                    name_p= source.split('/')[-1].split('.')[0]\n                    img_rgb = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)\n                    preds=None\n                    masks=[]\n                    prompt=input_prompt\n                    if tag2text:\n                        print(f'text_prompt:{prompt}')\n                        preds = models_config['tag2text'](im = img_rgb ,prompt=prompt,box_threshold=conf_thres,text_threshold=text_thres,iou_threshold=iou_thres)\n                    # Currently \", \" is better for detecting single tags\n                    # while \". \" is a little worse in some case\n                        prompt=preds[0].replace(' |', ',')\n                        caption=preds[2]\n                        print(f\"Caption: {caption}\")\n                        print(f\"Tags: {prompt}\")\n                        if save_caption:\n                            save_format(label_format=\"txt\",save_path=f'{save_dir}/captions',img_name=name_p, results=caption)\n                    if det:\n                        if input_prompt:\n                            prompt=input_prompt\n                            print('your input prompt replace default:',prompt)\n                        preds= models_config['grounded'](im = img_rgb,prompt=prompt, box_threshold=conf_thres,text_threshold=text_thres, iou_threshold=iou_thres) \n                        if chatgpt:\n                            from gpt_demo import check_caption\n                            caption=check_caption(caption, preds[2], chatbot)\n                    if sam and det :        \n                        if preds[0].numel()>0:      \n                            masks= models_config['sam'](im = img_rgb, prompt=preds[0],box_threshold=conf_thres,text_threshold=text_thres, iou_threshold=iou_thres)\n                            if save_mask:\n                                save_mask_data(str(save_dir)+'/masks', caption, masks, preds[0], preds[2],name_p)\n                    # Write results\n                    \n                    if img_save:\n                        seen+=1\n                        plt.figure(figsize=(20,18))\n                        plt.imshow(img_rgb)\n                        if det:\n                            for box,label in zip(preds[0],preds[2]):\n                                    show_box(box.numpy(),plt.gca(),label)            \n                            if sam :              \n                                for mask in masks:         \n                                    show_mask(mask.cpu().numpy(),plt.gca(),random_color=True)\n                        if tag2text:\n                            plt.title('Captioning: ' + caption + '\\n' + 'Tagging:' + prompt + '\\n')    \n                        plt.axis('off')\n                        plt.savefig(f'{save_dir}/{seen}.jpg',bbox_iches='tight',dpi=600,pad_inches=0.0)     \n                        \n                    if lama and masks is not None :      \n                        masks_prompts= masks.detach().cpu().numpy().astype(np.uint8) * 255\n                        for idx, mask in enumerate(masks_prompts):   \n                            \n                            sub_mask = [dilate_mask(ma, 15) for ma in mask]\n                            img_inpainted_p= f'{save_dir}/mask_{idx}.png'\n                            idx=idx+1\n                            img_inpainted = models_config['lama'](\n                                    im=img_rgb, prompt=sub_mask[0])\n                            Image.fromarray(img_inpainted.astype(np.uint8)).save(img_inpainted_p)\n                            img_rgb=img_inpainted       \n                    for category in categories:\n                        if category not in category_colors:\n                            category_colors[category] = (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255))            \n                    gn = torch.tensor(im.shape)[[1, 0, 1, 0]]  # normalization gain whwh   \n                    \n                    if (color_flag or save_txt) and(det ) :\n                        seg_mask = np.zeros_like(img_rgb)  # img_array 为输入图像的数组表示\n                        category_color=[]\n                        for xyxy, conf, cls,mask in zip(preds[0],preds[1],preds[2],masks):       #per im boxes              \n                                xywh = (xyxy2xywh((xyxy).view(1,4)) / gn).view(-1).tolist()  # normalized xywh   \n                                if cls not in categories:\n                                    categories.update({\n                                            str(cls): len(categories)})        \n                                    write_categories(cls,f'{save_dir}/classes_id.txt')\n                                    cls_index = len(categories) - 1\n                                    category_colors.update({\n                                            str(cls):  (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255))}) \n                                    category_color=category_colors[str(cls)]\n                                else:   \n                                    cls_index = categories[str(cls)]\n                                    if str(cls) not in category_colors:\n                                        category_colors.update({\n                                            str(cls):  (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255))})\n                                    category_color=category_colors[str(cls)]\n                                line = (cls_index, xywh, conf) if save_conf else (cls_index, xywh)  # label format\n                                line = str(line).replace('[', '').replace(']', '').replace(\"(\",'').replace(\")\",\" \").replace(\",\", \" \" * 2)\n                                if save_mask:\n                                    h, w = mask.shape[-2:]\n                                    mask_color = np.array(category_color).reshape((1, 1, -1))  \n                                    seg_mask = seg_mask + mask.cpu().numpy().reshape(h, w, 1)  * mask_color  # add    \n                                if save_txt:                                 \n                                    save_format(label_format=\"txt\",save_path=f'{save_dir}/labels', img_name=name_p, results=line)\n                                   \n                        if save_mask:\n                            plt.figure(figsize=(10,10))\n                            plt.imshow(seg_mask)\n                            #plt.title('Captioning: ' + caption + '\\n' + 'Tagging:' + prompt + '\\n')    \n                            plt.axis('off')            \n                            plt.savefig(os.path.join(f'{save_dir}/masks', f'{name_p}_cls.jpg'), bbox_inches=\"tight\", dpi=300, pad_inches=0.0)                                                         \n                    if save_xml:    \n                            h,w=im.shape[:2]\n                            save_format(\"xml\",f'{save_dir}/xmls' ,name_p, Path(source).parent, preds, h, w)\n                    if det:\n                        img_rgb= Image.fromarray(np.uint8(img_rgb), mode='RGB') \n                        draw_img=ImageDraw.Draw(img_rgb) \n                        for box,label in zip(preds[0],preds[2]):   \n                            Draw_img( box, draw_img,'box',label,category_colors[str(label)] if color_flag else None)\n                    if sam:\n                        img_mask=Image.new('RGBA',img_rgb.size,color=(0,0,0,0)  )\n                        draw_mask=ImageDraw.Draw(img_mask)       \n\n                        for mask in masks:    \n                            Draw_img(mask[0].cpu().numpy(),draw_mask,'mask',None,category_colors[str(label)] if color_flag else None)\n                        img_rgb.paste(img_mask, mask=img_mask)  \n                    #img_rgb.save(f'{save_dir}/{seen}.jpg')    \n                    \n            if save_txt:\n                #class_ids.append(cls) \n                LOGGER.info(f\"Results saved to {colorstr('bold', save_dir)}/labels\")  \n            if save_xml:           \n                LOGGER.info(f\"Results saved to {colorstr('bold', save_dir)}/xmls\")\n            if save_caption:\n                LOGGER.info(f\"Results saved to {colorstr('bold', save_dir)}/captions\")\n            if save_mask:\n                LOGGER.info(f\"Results saved to {colorstr('bold', save_dir)}/masks\")\n            LOGGER.info('Done...')\n            #re=Image.open(f'{save_dir}/{seen}.jpg') \n\n            return [[img_rgb],caption,prompt,len(categories)]\n            \n\n\ndef main(args):\n      \n          #check_requirements(exclude=('tensorboard', 'thop'))\n          global models_config,tokenizer_glm\n          if args.chat_glm:\n                 models_config['chat_glm']=VisualGLM(quant=args.quant)\n          block=gr.Blocks()\n          inputxs=[]\n          outputs=[]\n          block=block.queue()\n          with block:\n               with gr.Row():\n                    with gr.Column():\n                         with gr.Accordion('Grounded-DINO threshold Options', open=False):\n                            box_threshold=gr.inputs.Number(label='Confidence Threshold', default=0.3)\n                            iou_threshold=gr.inputs.Number(label='ioue Threshold', default=0.5)\n                            text_threshold=gr.inputs.Number(label='TEXT Threshold', default=0.25)\n                            device_input=gr.inputs.Textbox(label='device',default='0')                             \n                         with gr.Accordion('others Options', open=False):\n                            option_inputs  = {\n                            'Save Conf': gr.inputs.Checkbox(label='Save Conf',default=False),\n                            'Save img': gr.inputs.Checkbox(label='Save img',default=False),\n                            'Chat GPT': gr.inputs.Checkbox(label='ChatGPT',default=False),\n                            'Visualize': gr.inputs.Checkbox(label='Visualize',default=False),\n                            'Project': gr.inputs.Textbox(label='Project:save dir_path',default='runs/detect'),\n                            'Name': gr.inputs.Textbox(label='Name',default='exp'),\n                            'Exist Ok': gr.inputs.Checkbox(label='Exist Ok',default=False)\n                            }   \n                           \n                         inputxs.extend(list(option_inputs.values()))\n                         with gr.Accordion('Method_Options:free combo', open=True):                \n                                   \n                            methods_options={'Lama': gr.inputs.Checkbox(label='Lama model',default=False), 'Sam': gr.inputs.Checkbox(label='Sam model',default=False),\n                                'Det': gr.inputs.Checkbox(label='Grounded',default=False), \n                                'Tag2text': gr.inputs.Checkbox(label='Tag2text',default=False), \n                            }\n                    \n                         inputxs.extend(list( methods_options.values()))      \n                         with gr.Accordion('format Options', open=False):                \n                                   \n                                save_options={\n                                'Save txt': gr.inputs.Checkbox(label='Save txt',default=False), \n                                'Save xml': gr.inputs.Checkbox(label='Save xml',default=False), \n                                'Save Mask': gr.inputs.Checkbox(label='Save Mask',default=False),  \n                                'Save Caption': gr.inputs.Checkbox(label='Save Caption',default=False),  \n                                'Batch Process': gr.inputs.Checkbox(label='Batch Process',default=False), \n                                'Color Flag': gr.inputs.Checkbox(label='Color Flag : classes mask',default=False)\n                            }\n                         inputxs.extend(list( save_options.values()))\n                         \n                         API_KEY=gr.inputs.Textbox(label='OPENAI_kety',default='')  \n                         dir_inputs =gr.inputs.Textbox(label='dir_path',default='train_imgs')\n                         prompt_input=gr.inputs.Textbox(lines=3, label=\"Prompt: User Specified Tags (Optional, Enter with commas)\")\n                         run_button = gr.Button('Run')\n                         image_prompt = gr.Image(type=\"filepath\", label=\"Image Prompt\", value=None)\n                         inputs = [dir_inputs,image_prompt,prompt_input,box_threshold,iou_threshold,text_threshold,device_input]\n                         inputs.extend(inputxs)\n                         \n                         if models_config['chat_glm']:\n                            with gr.Row():\n                                run_button_2 = gr.Button('Send')\n                                clear_button = gr.Button('Clear')\n                            with gr.Row():\n                                temperature = gr.Slider(maximum=1, value=0.8, minimum=0, label='Temperature')\n                                top_p = gr.Slider(maximum=1, value=0.4, minimum=0, label='Top P')\n                            with gr.Group():\n                                with gr.Row():\n                                    maintenance_notice = gr.Markdown(MAINTENANCE_NOTICE1)   \n                    with gr.Column(scale=1.5):\n                         gallery = gr.Gallery(label=\"Generated images\",show_label=False,elem_id=\"gallery\",).style(preview=True, grid=2, object_fit=\"scale-down\")\n                         output_text = gr.Textbox(label=\"Caption\",lines=2)\n                         output_classes= gr.Textbox(label=\"Class_numbers:auto generate classes numbers, 【color flag】 or 【save_txt】 must be ture \")\n                         output_tag= gr.outputs.Textbox(label=\"Tag\")\n                         outputs = [gallery, output_text, output_tag,output_classes]\n                         if models_config['chat_glm']:   \n                            result_text = gr.components.Chatbot(label='Multi-round conversation History', value=[(\"\", \"Hi, What do you want to know about this image?\")]).style(height=550)\n               \n               run_button.click(fn=Auto_run, inputs=inputs, outputs=outputs)\n               if  models_config['chat_glm']:   \n                            \n                    run_button_2.click(fn=models_config['chat_glm'].request_model,inputs=[prompt_input, temperature, top_p, image_prompt, result_text],\n                                outputs=[prompt_input, result_text])\n                    prompt_input.submit(fn=models_config['chat_glm'].request_model,inputs=[prompt_input, temperature, top_p, image_prompt, result_text],\n                                        outputs=[prompt_input, result_text])\n                    clear_button.click(fn=clear_fn, inputs=clear_button, outputs=[prompt_input, result_text, image_prompt])\n                    image_prompt.upload(fn=clear_fn2, inputs=clear_button, outputs=[result_text])\n                    image_prompt.clear(fn=clear_fn2, inputs=clear_button, outputs=[result_text])\n        \n          auto_opentab_delay()\n          block.queue(concurrency_count=100)\n          block.launch(server_name='0.0.0.0', server_port=7585, debug=True, share=False)\n     \nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"--quant\", choices=[8, 4], type=int, default=4)\n    parser.add_argument(\"--share\", action=\"store_true\")\n    parser.add_argument(\"--chat_glm\", default=False,action=\"store_true\")\n    args = parser.parse_args()\n    main(args)\n    \n     "
  },
  {
    "path": "llm_cards/bridge_all.py",
    "content": "\n\"\"\"\n    该文件中主要包含2个函数，是所有LLM的通用接口，它们会继续向下调用更底层的LLM模型，处理多模型并行等细节\n\n    不具备多线程能力的函数：正常对话时使用，具备完备的交互功能，不可多线程\n    1. predict(...)\n\n    具备多线程调用能力的函数：在函数插件中被调用，灵活而简洁\n    2. predict_no_ui_long_connection(...)\n\"\"\"\nimport tiktoken\nfrom functools import lru_cache\nfrom concurrent.futures import ThreadPoolExecutor\nfrom utils.toolbox import get_conf, trimmed_format_exc\n\nfrom llm_cards.bridge_chatgpt import predict_no_ui_long_connection as chatgpt_noui\nfrom llm_cards.bridge_chatgpt import predict as chatgpt_ui\nfrom llm_cards.bridge_chatgpt import Talk_with_app as talk_chatgpt\n\nfrom llm_cards.bridge_chatglm import predict_no_ui_long_connection as chatglm_noui\nfrom llm_cards.bridge_chatglm import predict as chatglm_ui\nfrom llm_cards.bridge_chatglm import Talk_with_app as talk_chatglm\n# from .bridge_tgui import predict_no_ui_long_connection as tgui_noui\n# from .bridge_tgui import predict as tgui_ui   \n\ncolors = ['#FF00FF', '#00FFFF', '#FF0000', '#990099', '#009999', '#990044']\n\nclass LazyloadTiktoken(object):\n    def __init__(self, model):\n        self.model = model\n\n    @staticmethod\n    @lru_cache(maxsize=128)\n    def get_encoder(model):\n        print('正在加载tokenizer，如果是第一次运行，可能需要一点时间下载参数')\n        tmp = tiktoken.encoding_for_model(model)\n        print('加载tokenizer完毕')\n        return tmp\n    \n    def encode(self, *args, **kwargs):\n        encoder = self.get_encoder(self.model) \n        return encoder.encode(*args, **kwargs)\n    \n    def decode(self, *args, **kwargs):\n        encoder = self.get_encoder(self.model) \n        return encoder.decode(*args, **kwargs)\n\n# Endpoint 重定向\nAPI_URL_REDIRECT, = get_conf(\"API_URL_REDIRECT\")\nopenai_endpoint = \"https://api.openai.com/v1/chat/completions\"\napi2d_endpoint = \"https://openai.api2d.net/v1/chat/completions\"\nnewbing_endpoint = \"wss://sydney.bing.com/sydney/ChatHub\"\n# 兼容旧版的配置\ntry:\n    API_URL, = get_conf(\"API_URL\")\n    if API_URL != \"https://api.openai.com/v1/chat/completions\": \n        openai_endpoint = API_URL\n        print(\"警告！API_URL配置选项将被弃用，请更换为API_URL_REDIRECT配置\")\nexcept:\n    pass\n# 新版配置\nif openai_endpoint in API_URL_REDIRECT: openai_endpoint = API_URL_REDIRECT[openai_endpoint]\nif api2d_endpoint in API_URL_REDIRECT: api2d_endpoint = API_URL_REDIRECT[api2d_endpoint]\nif newbing_endpoint in API_URL_REDIRECT: newbing_endpoint = API_URL_REDIRECT[newbing_endpoint]\n\n\n# 获取tokenizer\ntokenizer_gpt35 = LazyloadTiktoken(\"gpt-3.5-turbo\")\ntokenizer_gpt4 = LazyloadTiktoken(\"gpt-4\")\nget_token_num_gpt35 = lambda txt: len(tokenizer_gpt35.encode(txt, disallowed_special=()))\nget_token_num_gpt4 = lambda txt: len(tokenizer_gpt4.encode(txt, disallowed_special=()))\n\n\n\nmodel_info = {\n    # openai\n    \"gpt-3.5-turbo\": {\n        \"fn_with_ui\": chatgpt_ui,\n        \"fn_without_ui\": chatgpt_noui,\n        \"fn_talk\": talk_chatgpt,\n        \"endpoint\": openai_endpoint,\n        \"max_token\": 4096,\n        \"tokenizer\": tokenizer_gpt35,\n        \"token_cnt\": get_token_num_gpt35,\n    },\n\n    \"gpt-4\": {\n        \"fn_with_ui\": chatgpt_ui,\n        \"fn_without_ui\": chatgpt_noui,\n        \"fn_talk\": talk_chatgpt,\n        \"endpoint\": openai_endpoint,\n        \"max_token\": 8192,\n        \"tokenizer\": tokenizer_gpt4,\n        \"token_cnt\": get_token_num_gpt4,\n    },\n\n    # api_2d\n    \"api2d-gpt-3.5-turbo\": {\n        \"fn_with_ui\": chatgpt_ui,\n        \"fn_without_ui\": chatgpt_noui,\n        \"fn_talk\": talk_chatgpt,\n        \"endpoint\": api2d_endpoint,\n        \"max_token\": 4096,\n        \"tokenizer\": tokenizer_gpt35,\n        \"token_cnt\": get_token_num_gpt35,\n    },\n\n    \"api2d-gpt-4\": {\n        \"fn_with_ui\": chatgpt_ui,\n        \"fn_without_ui\": chatgpt_noui,\n        \"fn_talk\": talk_chatgpt,\n        \"endpoint\": api2d_endpoint,\n        \"max_token\": 8192,\n        \"tokenizer\": tokenizer_gpt4,\n        \"token_cnt\": get_token_num_gpt4,\n    },\n\n    # chatglm\n    \"chatglm\": {\n        \"fn_with_ui\": chatglm_ui,\n        \"fn_without_ui\": chatglm_noui,\n        \"fn_talk\": talk_chatglm,\n        \"endpoint\": None,\n        \"max_token\": 1024,\n        \"tokenizer\": tokenizer_gpt35,\n        \"token_cnt\": get_token_num_gpt35,\n    },\n     \"chatglm2\": {\n        \"fn_with_ui\": chatglm_ui,\n        \"fn_without_ui\": chatglm_noui,\n        \"fn_talk\": talk_chatglm,\n        \"endpoint\": None,\n        \"max_token\": 1024,\n        \"tokenizer\": tokenizer_gpt35,\n        \"token_cnt\": get_token_num_gpt35,\n    },\n    # newbing\n    # \"newbing\": {\n    #     \"fn_with_ui\": newbing_ui,\n    #     \"fn_without_ui\": newbing_noui,\n    #     \"endpoint\": newbing_endpoint,\n    #     \"max_token\": 4096,\n    #     \"tokenizer\": tokenizer_gpt35,\n    #     \"token_cnt\": get_token_num_gpt35,\n    # },\n\n}\n\n\nAVAIL_LLM_MODELS, = get_conf(\"AVAIL_LLM_MODELS\")\n# if \"jittorllms_rwkv\" in AVAIL_LLM_MODELS:\n#     from .bridge_jittorllms_rwkv import predict_no_ui_long_connection as rwkv_noui\n#     from .bridge_jittorllms_rwkv import predict as rwkv_ui\n#     model_info.update({\n#         \"jittorllms_rwkv\": {\n#             \"fn_with_ui\": rwkv_ui,\n#             \"fn_without_ui\": rwkv_noui,\n#             \"endpoint\": None,\n#             \"max_token\": 1024,\n#             \"tokenizer\": tokenizer_gpt35,\n#             \"token_cnt\": get_token_num_gpt35,\n#         },\n#   })\n# if \"jittorllms_llama\" in AVAIL_LLM_MODELS:\n#     from .bridge_jittorllms_llama import predict_no_ui_long_connection as llama_noui\n#     from .bridge_jittorllms_llama import predict as llama_ui\n#     model_info.update({\n#         \"jittorllms_llama\": {\n#             \"fn_with_ui\": llama_ui,\n#             \"fn_without_ui\": llama_noui,\n#             \"endpoint\": None,\n#             \"max_token\": 1024,\n#             \"tokenizer\": tokenizer_gpt35,\n#             \"token_cnt\": get_token_num_gpt35,\n#         },\n#     })\n# if \"jittorllms_pangualpha\" in AVAIL_LLM_MODELS:\n#     from .bridge_jittorllms_pangualpha import predict_no_ui_long_connection as pangualpha_noui\n#     from .bridge_jittorllms_pangualpha import predict as pangualpha_ui\n#     model_info.update({\n#         \"jittorllms_pangualpha\": {\n#             \"fn_with_ui\": pangualpha_ui,\n#             \"fn_without_ui\": pangualpha_noui,\n#             \"endpoint\": None,\n#             \"max_token\": 1024,\n#             \"tokenizer\": tokenizer_gpt35,\n#             \"token_cnt\": get_token_num_gpt35,\n#         },\n#     })\n# if \"moss\" in AVAIL_LLM_MODELS:\n#     from .bridge_moss import predict_no_ui_long_connection as moss_noui\n#     from .bridge_moss import predict as moss_ui\n#     model_info.update({\n#         \"moss\": {\n#             \"fn_with_ui\": moss_ui,\n#             \"fn_without_ui\": moss_noui,\n#             \"endpoint\": None,\n#             \"max_token\": 1024,\n#             \"tokenizer\": tokenizer_gpt35,\n#             \"token_cnt\": get_token_num_gpt35,\n#         },\n#     })\nif \"stack-claude\" in AVAIL_LLM_MODELS:\n    from .bridge_stackclaude import predict_no_ui_long_connection as claude_noui\n    from .bridge_stackclaude import predict as claude_ui\n    # claude\n    model_info.update({\n        \"stack-claude\": {\n            \"fn_with_ui\": claude_ui,\n            \"fn_without_ui\": claude_noui,\n            \"endpoint\": None,\n            \"max_token\": 8192,\n            \"tokenizer\": tokenizer_gpt35,\n            \"token_cnt\": get_token_num_gpt35,\n        }\n    })\n# if \"newbing-free\" in AVAIL_LLM_MODELS:\n#     try:\n#         from .bridge_newbingfree import predict_no_ui_long_connection as newbingfree_noui\n#         from .bridge_newbingfree import predict as newbingfree_ui\n#         # claude\n#         model_info.update({\n#             \"newbing-free\": {\n#                 \"fn_with_ui\": newbingfree_ui,\n#                 \"fn_without_ui\": newbingfree_noui,\n#                 \"endpoint\": newbing_endpoint,\n#                 \"max_token\": 4096,\n#                 \"tokenizer\": tokenizer_gpt35,\n#                 \"token_cnt\": get_token_num_gpt35,\n#             }\n#         })\n    # except:\n    #     print(trimmed_format_exc())\n\ndef LLM_CATCH_EXCEPTION(f):\n    \"\"\"\n    装饰器函数，将错误显示出来\n    \"\"\"\n    def decorated(inputs, llm_kwargs, history, sys_prompt, observe_window, console_slience):\n        try:\n            return f(inputs, llm_kwargs, history, sys_prompt, observe_window, console_slience)\n        except Exception as e:\n            tb_str = '\\n```\\n' + trimmed_format_exc() + '\\n```\\n'\n            observe_window[0] = tb_str\n            return tb_str\n    return decorated\n\n\ndef predict_no_ui_long_connection(inputs, llm_kwargs, history, sys_prompt, observe_window, console_slience=False):\n    \"\"\"\n    发送至LLM，等待回复，一次性完成，不显示中间过程。但内部用stream的方法避免中途网线被掐。\n    inputs：\n        是本次问询的输入\n    sys_prompt:\n        系统静默prompt\n    llm_kwargs：\n        LLM的内部调优参数\n    history：\n        是之前的对话列表\n    observe_window = None：\n        用于负责跨越线程传递已经输出的部分，大部分时候仅仅为了fancy的视觉效果，留空即可。observe_window[0]：观测窗。observe_window[1]：看门狗\n    \"\"\"\n    import threading, time, copy\n\n    model = llm_kwargs['llm_model']\n    n_model = 1\n    if '&' not in model:\n        assert not model.startswith(\"tgui\"), \"TGUI不支持函数插件的实现\"\n\n        # 如果只询问1个大语言模型：\n        method = model_info[model][\"fn_without_ui\"]\n        return method(inputs, llm_kwargs, history, sys_prompt, observe_window, console_slience)\n    else:\n        # 如果同时询问多个大语言模型：\n        executor = ThreadPoolExecutor(max_workers=4)\n        models = model.split('&')\n        n_model = len(models)\n        \n        window_len = len(observe_window)\n        assert window_len==3\n        window_mutex = [[\"\", time.time(), \"\"] for _ in range(n_model)] + [True]\n\n        futures = []\n        for i in range(n_model):\n            model = models[i]\n            method = model_info[model][\"fn_without_ui\"]\n            llm_kwargs_feedin = copy.deepcopy(llm_kwargs)\n            llm_kwargs_feedin['llm_model'] = model\n            future = executor.submit(LLM_CATCH_EXCEPTION(method), inputs, llm_kwargs_feedin, history, sys_prompt, window_mutex[i], console_slience)\n            futures.append(future)\n\n        def mutex_manager(window_mutex, observe_window):\n            while True:\n                time.sleep(0.25)\n                if not window_mutex[-1]: break\n                # 看门狗（watchdog）\n                for i in range(n_model): \n                    window_mutex[i][1] = observe_window[1]\n                # 观察窗（window）\n                chat_string = []\n                for i in range(n_model):\n                    chat_string.append( f\"【{str(models[i])} 说】: <font color=\\\"{colors[i]}\\\"> {window_mutex[i][0]} </font>\" )\n                res = '<br/><br/>\\n\\n---\\n\\n'.join(chat_string)\n                # # # # # # # # # # #\n                observe_window[0] = res\n\n        t_model = threading.Thread(target=mutex_manager, args=(window_mutex, observe_window), daemon=True)\n        t_model.start()\n\n        return_string_collect = []\n        while True:\n            worker_done = [h.done() for h in futures]\n            if all(worker_done):\n                executor.shutdown()\n                break\n            time.sleep(1)\n\n        for i, future in enumerate(futures):  # wait and get\n            return_string_collect.append( f\"【{str(models[i])} 说】: <font color=\\\"{colors[i]}\\\"> {future.result()} </font>\" )\n\n        window_mutex[-1] = False # stop mutex thread\n        res = '<br/><br/>\\n\\n---\\n\\n'.join(return_string_collect)\n        return res\n\n\ndef predict_all(inputs, llm_kwargs, *args, **kwargs):\n    \"\"\"\n    发送至LLM，流式获取输出。\n    用于基础的对话功能。\n    inputs 是本次问询的输入\n    top_p, temperature是LLM的内部调优参数\n    history 是之前的对话列表（注意无论是inputs还是history，内容太长了都会触发token数量溢出的错误）\n    chatbot 为WebUI中显示的对话列表，修改它，然后yeild出去，可以直接修改对话界面内容\n    additional_fn代表点击的哪个按钮，按钮见functional.py\n    \"\"\"\n    print('quantize:',llm_kwargs['quantize'])\n    method = model_info[llm_kwargs['llm_model']][\"fn_with_ui\"]\n    yield from method(inputs, llm_kwargs, *args, **kwargs)\n\ndef talk_all(inputs, llm_kwargs, *args, **kwargs):\n    \n    method = model_info[llm_kwargs['llm_model']][\"fn_talk\"]\n    yield from method(inputs, llm_kwargs, *args, **kwargs)"
  },
  {
    "path": "llm_cards/bridge_chatglm.py",
    "content": "\nfrom transformers import AutoModel, AutoTokenizer\nimport time\nimport threading\nimport importlib\nfrom utils.toolbox import update_ui, get_conf\nfrom multiprocessing import Process, Pipe\nimport multiprocessing as mp\nimport torch \nload_message = \"ChatGLM尚未加载，加载需要一段时间。注意，取决于`config.py`的配置，ChatGLM消耗大量的内存（CPU）或显存（GPU），也许会导致低配计算机卡死 ……\"\n\n#################################################################################\nclass GetGLMHandle(Process):\n    def __init__(self,quantize=None):\n        super().__init__(daemon=True)\n        self.parent, self.child = Pipe()\n        self.chatglm_model = None\n        self.chatglm_tokenizer = None\n        self.info = \"\"\n        mp.set_start_method('spawn')\n        self.success = True\n        self.quantize=quantize\n        self.check_dependency()\n        self.start()\n        self.threadLock = threading.Lock()\n        \n    def check_dependency(self):\n        try:\n            import sentencepiece\n            self.info = \"依赖检测通过\"\n            self.success = True\n        except:\n            self.info = \"缺少ChatGLM的依赖，如果要使用ChatGLM，除了基础的pip依赖以外，您还需要运行`pip install -r request_llm/requirements_chatglm.txt`安装ChatGLM的依赖。\"\n            self.success = False\n\n    def ready(self):\n        return self.chatglm_model is not None\n\n    def run(self):\n        # 子进程执行\n        # 第一次运行，加载参数\n        torch.cuda.init()\n        retry = 0\n        \n        LOCAL_MODEL_QUANT, device,LOCAL_MODEL_PATH = get_conf('LOCAL_MODEL_QUANT', 'LOCAL_MODEL_DEVICE','LOCAL_MODEL_PATH')\n        if LOCAL_MODEL_QUANT == \"INT4\":         # INT4\n            _model_name_ = \"THUDM/chatglm2-6b-int4\"\n        elif LOCAL_MODEL_QUANT == \"INT8\":       # INT8\n            _model_name_ = \"THUDM/chatglm2-6b-int8\"\n        else:\n            _model_name_ = \"THUDM/chatglm2-6b\"  # FP16\n        while True:\n            try:\n                if LOCAL_MODEL_PATH is not None:\n                    print('检测到本地模型')\n                    _model_name_=LOCAL_MODEL_PATH\n                if self.chatglm_model is None:\n                    self.chatglm_tokenizer = AutoTokenizer.from_pretrained(_model_name_, trust_remote_code=True,cache_dir='weights')\n                    if device=='cpu':\n                        self.chatglm_model = AutoModel.from_pretrained(_model_name_, trust_remote_code=True,cache_dir='weights').float()\n                    else:\n                        if int(self.quantize)==8 or int((self.quantize))==4 :\n                            print(f'{_model_name_}---在线量化 {self.quantize}')\n                            self.chatglm_model = AutoModel.from_pretrained(_model_name_, trust_remote_code=True,cache_dir='weights').quantize(int(self.quantize)).half().cuda()\n                        else :\n                                self.chatglm_model = AutoModel.from_pretrained(_model_name_, trust_remote_code=True,cache_dir='weights').half().cuda()   \n                    self.chatglm_model = self.chatglm_model.eval()\n                    break\n                #elif:\n                else:\n                    break\n            except:\n                retry += 1\n                if retry > 3: \n                    self.child.send('[Local Message] Call ChatGLM fail 不能正常加载ChatGLM的参数。')\n                    raise RuntimeError(\"不能正常加载ChatGLM的参数！\")\n\n        while True:\n            # 进入任务等待状态\n            kwargs = self.child.recv()\n            # 收到消息，开始请求\n            try:\n                for response, history in self.chatglm_model.stream_chat(self.chatglm_tokenizer, **kwargs):\n                    self.child.send(response)\n                    # # 中途接收可能的终止指令（如果有的话）\n                    # if self.child.poll(): \n                    #     command = self.child.recv()\n                    #     if command == '[Terminate]': break\n            except:\n                from utils.toolbox import trimmed_format_exc\n                self.child.send('[Local Message] Call ChatGLM fail.' + '\\n```\\n' + trimmed_format_exc() + '\\n```\\n')\n            # 请求处理结束，开始下一个循环\n            self.child.send('[Finish]')\n\n    def stream_chat(self, **kwargs):\n        # 主进程执行\n        self.threadLock.acquire()\n        self.parent.send(kwargs)\n        while True:\n            res = self.parent.recv()\n            if res != '[Finish]':\n                yield res\n            else:\n                break\n        self.threadLock.release()\n    \nglobal glm_handle\n\nglm_handle = None\n#################################################################################\ndef predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt=\"\", observe_window=[], console_slience=False):\n    \"\"\"\n        多线程方法\n        函数的说明请见 request_llm/bridge_all.py\n    \"\"\"\n    global glm_handle\n    if glm_handle is None:\n        glm_handle = GetGLMHandle(quantize=llm_kwargs.get('quantize', False) )\n        if len(observe_window) >= 1: observe_window[0] = load_message + \"\\n\\n\" + glm_handle.info\n        if not glm_handle.success: \n            error = glm_handle.info\n            glm_handle = None\n            raise RuntimeError(error)\n\n    # chatglm 没有 sys_prompt 接口，因此把prompt加入 history\n    history_feedin = []\n    history_feedin.append([\"What can I do?\", sys_prompt])\n    for i in range(len(history)//2):\n        history_feedin.append([history[2*i], history[2*i+1]] )\n\n    watch_dog_patience = 5 # 看门狗 (watchdog) 的耐心, 设置5秒即可\n    response = \"\"\n    for response in glm_handle.stream_chat(query=inputs, history=history_feedin, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']):\n        if len(observe_window) >= 1:  observe_window[0] = response\n        if len(observe_window) >= 2:  \n            if (time.time()-observe_window[1]) > watch_dog_patience:\n                raise RuntimeError(\"程序终止。\")\n    return response\n\nimport threading \nimport queue\n\ndef asr_with_gpt(transcriber,text_queue, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):\n    print('开启识别线程')\n   \n    while True:\n            if transcriber.transcript_changed_event.is_set():\n                start_time = time.time()\n                transcriber.transcript_changed_event.clear() \n                transcript_string=''\n                if transcriber.two_ways:\n                    transcript_string = transcriber.get_transcript()\n                else:\n                    #print(transcriber.transcript_data[\"You\"])\n                    transcript_string=transcriber.transcript_data[\"You\"] \n                    #transcript_string=transcript_string[0][0][len('You: [') : -len(']\\n\\n')]\n                if transcript_string!=\" \":\n                    response=predict_no_ui_long_connection(str(transcript_string), llm_kwargs, history, system_prompt, None,False) \n                    end_time = time.time()  # Measure end time\n                    execution_time = end_time - start_time  # Calculate the time it took to execute the function\n                    \n                    text_queue.put((transcript_string,response))\n                    remaining_time = 2 - execution_time\n                    if remaining_time > 0:\n                        time.sleep(remaining_time)\n            \n            else:\n                time.sleep(0.02) # 允许事件循环在这里运行，避免耗费 CPU                     \n        \n\nasync def send_ui(text_queue,chatbot, history): \n    #time.sleep(0.01)\n    while  True:\n       #     print('updata ui',text_queue.qsize())\n        if text_queue.qsize()>0:\n                iss,ans=text_queue.get()\n                chatbot.append((iss,ans))\n                \n                history.extend([iss,ans])\n                \n                print('更新界面中。。。。。')\n             \n                await chatbot.get_cookies(),chatbot, history, 'done'\n        else:\n            print('队列为空')\n        \ndef Talk_with_app(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):\n        import threading \n        from utils import AudioRecorder \n        from utils import AudioTrans\n        import whisper\n        \n        chat_app=llm_kwargs.get('chat_app', False)\n        print(f'是否开启自动对话系统：{chat_app}')\n        model = whisper.load_model('small',download_root='weights')  \n        # 在主线程中初始化 loop\n        #loop = asyncio.get_event_loop() \n        \n        if(chat_app):\n            chatbot.append((\"开启自动对话系统\", \"等待效应开启语音识别服务\"))\n            #history.append(\"\")\n            yield from update_ui(chatbot=chatbot, history=history, msg=\"等待响应\") # 刷新界面\n            audio_queue = queue.Queue()\n            text_queue=queue.Queue()\n            user_audio_recorder = AudioRecorder.DefaultMicRecorder()\n            user_audio_recorder.record_into_queue(audio_queue)\n\n            time.sleep(2)\n            #speaker_audio_recorder = AudioRecorder.DefaultSpeakerRecorder()\n            #speaker_audio_recorder.record_into_queue(audio_queue)\n            print('开始语音识别服务........')\n        \n            transcriber = AudioTrans.AudioTranscriber(user_audio_recorder.source, None, model)\n            transcribe = threading.Thread(target=transcriber.transcribe_audio_queue, args=(audio_queue,))\n            transcribe.daemon=True\n            transcribe.start()\n            t1=threading.Thread(target=asr_with_gpt, args=(transcriber, text_queue,llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, stream , additional_fn,))\n            t1.daemon=True\n            t1.start()\n        \n            while  True:              \n                while text_queue.qsize()>0:\n                        try:\n                            iss,ans=text_queue.get()\n                            chatbot.append((iss,ans))                   \n                            history.extend([iss,ans])\n\n                           # print('更新界面中。。。。。')\n                            yield from update_ui(chatbot=chatbot, history=history, msg=\"Done\") \n                        except Exception as e:  \n                            yield from update_ui(chatbot=chatbot, history=history, msg=\"Error\") \n                            return \n                time.sleep(0.02)  \n                yield from update_ui(chatbot=chatbot, history=history, msg=\"等待响应\") # 刷新界面\n             \n        chatbot[-1]=('退出对话系统',\"已结束对话系统\")   \n        yield from update_ui(chatbot=chatbot, history=history, msg=\"远程返回:\") # 刷新界面\n\ndef predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):\n    \"\"\"\n        单线程方法\n        函数的说明请见 request_llm/bridge_all.py\n    \"\"\"\n    chatbot.append((inputs, \"\"))\n\n    global glm_handle\n    if glm_handle is None:\n        glm_handle = GetGLMHandle(quantize=llm_kwargs.get('quantize', False))\n        chatbot[-1] = (inputs, load_message + \"\\n\\n\" + glm_handle.info)\n        yield from update_ui(chatbot=chatbot, history=[])\n        if not glm_handle.success: \n            glm_handle = None\n            return\n\n    if additional_fn is not None:\n        from core_functional import handle_core_functionality\n        inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)\n\n    # 处理历史信息\n    history_feedin = []\n    history_feedin.append([\"What can I do?\", system_prompt] )\n    for i in range(len(history)//2):\n        history_feedin.append([history[2*i], history[2*i+1]] )\n\n    # 开始接收chatglm的回复\n    response = \"[Local Message]: 等待ChatGLM响应中 ...\"\n    for response in glm_handle.stream_chat(query=inputs, history=history_feedin, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']):\n        chatbot[-1] = (inputs, response)\n        yield from update_ui(chatbot=chatbot, history=history)\n\n    # 总结输出\n    if response == \"[Local Message]: 等待ChatGLM响应中 ...\":\n        response = \"[Local Message]: ChatGLM响应异常 ...\"\n    history.extend([inputs, response])\n    yield from update_ui(chatbot=chatbot, history=history)"
  },
  {
    "path": "llm_cards/bridge_chatgpt.py",
    "content": "# 借鉴了 https://github.com/GaiZhenbiao/ChuanhuChatGPT 项目\n\n\"\"\"\n    该文件中主要包含三个函数\n\n    不具备多线程能力的函数：\n    1. predict: 正常对话时使用，具备完备的交互功能，不可多线程\n\n    具备多线程调用能力的函数\n    2. predict_no_ui：高级实验性功能模块调用，不会实时显示在界面上，参数简单，可以多线程并行，方便实现复杂的功能逻辑\n    3. predict_no_ui_long_connection：在实验过程中发现调用predict_no_ui处理长文档时，和openai的连接容易断掉，这个函数用stream的方式解决这个问题，同样支持多线程\n\"\"\"\n\nimport json\nimport time\nimport gradio as gr\nimport logging\nimport traceback\nimport requests\nimport importlib\n\n# config_private.py放自己的秘密如API和代理网址\n# 读取时首先看是否存在私密的config_private配置文件（不受git管控），如果有，则覆盖原config文件\nfrom utils.toolbox import get_conf, update_ui, is_any_api_key, select_api_key, what_keys, clip_history, trimmed_format_exc\nproxies, API_KEY, TIMEOUT_SECONDS, MAX_RETRY,A2F_URL,Avatar_instance_A = \\\n    get_conf('proxies', 'API_KEY', 'TIMEOUT_SECONDS', 'MAX_RETRY','A2F_URL','Avatar_instance_A')\n\ntimeout_bot_msg = '[Local Message] Request timeout. Network error. Please check proxy settings in config.py.' + \\\n                  '网络错误，检查代理服务器是否可用，以及代理设置的格式是否正确，格式须是[协议]://[地址]:[端口]，缺一不可。'\n\ndef get_full_error(chunk, stream_response):\n    \"\"\"\n        获取完整的从Openai返回的报错\n    \"\"\"\n    while True:\n        try:\n            chunk += next(stream_response)\n        except:\n            break\n    return chunk\n\n\ndef predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt=\"\", observe_window=None, console_slience=False):\n    \"\"\"\n    发送至chatGPT，等待回复，一次性完成，不显示中间过程。但内部用stream的方法避免中途网线被掐。\n    inputs：\n        是本次问询的输入\n    sys_prompt:\n        系统静默prompt\n    llm_kwargs：\n        chatGPT的内部调优参数\n    history：\n        是之前的对话列表\n    observe_window = None：\n        用于负责跨越线程传递已经输出的部分，大部分时候仅仅为了fancy的视觉效果，留空即可。observe_window[0]：观测窗。observe_window[1]：看门狗\n    \"\"\"\n    watch_dog_patience = 5 # 看门狗的耐心, 设置5秒即可\n    headers, payload = generate_payload(inputs, llm_kwargs, history, system_prompt=sys_prompt, stream=True)\n    retry = 0\n    while True:\n        try:\n            # make a POST request to the API endpoint, stream=False\n            from llm_cards.bridge_all import model_info\n            endpoint = model_info[llm_kwargs['llm_model']]['endpoint']\n            response = requests.post(endpoint, headers=headers, proxies=proxies,\n                                    json=payload, stream=True, timeout=TIMEOUT_SECONDS); break\n        except requests.exceptions.ReadTimeout as e:\n            retry += 1\n            traceback.print_exc()\n            if retry > MAX_RETRY: raise TimeoutError\n            if MAX_RETRY!=0: print(f'请求超时，正在重试 ({retry}/{MAX_RETRY}) ……')\n\n    stream_response =  response.iter_lines()\n    result = ''\n    while True:\n        try: chunk = next(stream_response).decode()\n        except StopIteration: \n            break\n        except requests.exceptions.ConnectionError:\n            chunk = next(stream_response).decode() # 失败了，重试一次？再失败就没办法了。\n        if len(chunk)==0: continue\n        if not chunk.startswith('data:'): \n            error_msg = get_full_error(chunk.encode('utf8'), stream_response).decode()\n            if \"reduce the length\" in error_msg:\n                raise ConnectionAbortedError(\"OpenAI拒绝了请求:\" + error_msg)\n            else:\n                raise RuntimeError(\"OpenAI拒绝了请求：\" + error_msg)\n        if ('data: [DONE]' in chunk): break # api2d 正常完成\n        json_data = json.loads(chunk.lstrip('data:'))['choices'][0]\n        delta = json_data[\"delta\"]\n        if len(delta) == 0: break\n        if \"role\" in delta: continue\n        if \"content\" in delta: \n            result += delta[\"content\"]\n            if not console_slience: print(delta[\"content\"], end='')\n            if observe_window is not None: \n                # 观测窗，把已经获取的数据显示出去\n                if len(observe_window) >= 1: observe_window[0] += delta[\"content\"]\n                # 看门狗，如果超过期限没有喂狗，则终止\n                if len(observe_window) >= 2:  \n                    if (time.time()-observe_window[1]) > watch_dog_patience:\n                        raise RuntimeError(\"用户取消了程序。\")\n        else: raise RuntimeError(\"意外Json结构：\"+delta)\n    if json_data['finish_reason'] == 'length':\n        raise ConnectionAbortedError(\"正常结束，但显示Token不足，导致输出不完整，请削减单次输入的文本量。\")\n    return result\n\nglobal asr_handle\nasr_handle=None\nimport queue\ndef asr_with_gpt(transcriber,text_queue, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):\n    print('开启识别线程')\n    history_text=''\n    while True:\n            if transcriber.transcript_changed_event.is_set():\n                start_time = time.time()\n                transcriber.transcript_changed_event.clear() \n                transcript_string=''\n                if transcriber.two_ways:\n                    transcript_string = transcriber.get_transcript()\n                else:\n                    #print(transcriber.transcript_data[\"You\"])\n                    transcript_string=transcriber.transcript_data[\"You\"] \n                    #transcript_string=transcript_string[0][0][len('You: [') : -len(']\\n\\n')]\n                if transcript_string!=\" \"and transcript_string!= history_text :\n                    response=predict_no_ui_long_connection(str(transcript_string), llm_kwargs, history, system_prompt, None,False) \n                    end_time = time.time()  # Measure end time\n                    execution_time = end_time - start_time  # Calculate the time it took to execute the function\n                    \n                    text_queue.put((transcript_string,response))\n                    remaining_time = 2 - execution_time\n                    if remaining_time > 0:\n                        time.sleep(remaining_time)\n            \n            else:\n                time.sleep(0.02) # 允许事件循环在这里运行，避免耗费 CPU                     \n        \ndef Talk_with_app(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):\n        import threading \n        from a2f import audio_synthesis\n        from utils import AudioRecorder \n        from utils import AudioTrans\n        import whisper\n        \n        chat_app=llm_kwargs.get('chat_app', False)\n        print(f'是否开启自动对话系统：{chat_app}')\n        model = whisper.load_model('small',download_root='weights')  \n        \n        if(chat_app):\n            chatbot.append((\"开启自动对话系统\", \"等待效应开启语音识别服务\"))\n            yield from update_ui(chatbot=chatbot, history=history, msg=\"等待响应\") # 刷新界面\n            audio_queue = queue.Queue()\n            text_queue=queue.Queue()\n            user_audio_recorder = AudioRecorder.DefaultMicRecorder()\n            user_audio_recorder.record_into_queue(audio_queue)\n            time.sleep(2)\n            #speaker_audio_recorder = AudioRecorder.DefaultSpeakerRecorder()\n            #speaker_audio_recorder.record_into_queue(audio_queue)\n            print('开始语音识别服务........')\n        \n            transcriber = AudioTrans.AudioTranscriber(user_audio_recorder.source, None, model)\n            transcribe = threading.Thread(target=transcriber.transcribe_audio_queue, args=(audio_queue,))\n            transcribe.daemon=True\n            transcribe.start()\n            t1=threading.Thread(target=asr_with_gpt, args=(transcriber, text_queue,llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, stream , additional_fn,))\n            t1.daemon=True\n            t1.start()\n            \n            while True:              \n                while text_queue.qsize()>0:\n                        try:\n                            iss,ans=text_queue.get()\n                            chatbot.append((iss,ans))                   \n                            history.extend([iss,ans])\n                            audio_synthesis(ans,A2F_URL,Avatar_instance_A)\n                            yield from update_ui(chatbot=chatbot, history=history, msg=\"Done\") \n                        except Exception as e:\n                            chatbot[-1][-1]='超时错误'  \n                            print(e)\n                            yield from update_ui(chatbot=chatbot, history=history, msg=\"Error\") \n                            return \n                time.sleep(0.01)  \n                yield from update_ui(chatbot=chatbot, history=history, msg=\"等待响应\") # 刷新界面\n        \n        chatbot[-1]=('退出对话系统',\"已结束对话系统\")   \n        yield from update_ui(chatbot=chatbot, history=history, msg=\"远程返回:\") # 刷新界面\n    \n    \n# async def Talk_with_app(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):\n#     await asyncio.gather(talk_with_app(inputs, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, stream, additional_fn), send_ui )\ndef predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):\n    \"\"\"\n    发送至chatGPT，流式获取输出。\n    用于基础的对话功能。\n    inputs 是本次问询的输入\n    top_p, temperature是chatGPT的内部调优参数\n    history 是之前的对话列表（注意无论是inputs还是history，内容太长了都会触发token数量溢出的错误）\n    chatbot 为WebUI中显示的对话列表，修改它，然后yeild出去，可以直接修改对话界面内容\n    additional_fn代表点击的哪个按钮，按钮见functional.py\n    \"\"\"\n    omniverse_state = llm_kwargs.get('omniverse', False)\n    record_file=llm_kwargs.get('record_audio', False)\n    asr=llm_kwargs.get('asr', False)\n    if asr:     \n        import whisper\n        global asr_handle    \n        if asr_handle is  None:\n            print('loads asf model..')\n            asr_handle=whisper.load_model(\"small\",\n                                 download_root=\"weights\")\n           \n        result = asr_handle.transcribe(record_file)\n        inputs=result[\"text\"]\n        print('result:',inputs) \n     \n    if is_any_api_key(inputs):\n        chatbot._cookies['api_key'] = inputs\n        chatbot.append((\"输入已识别为openai的api_key\", what_keys(inputs)))\n        yield from update_ui(chatbot=chatbot, history=history, msg=\"api_key已导入\") # 刷新界面\n        return\n    elif not is_any_api_key(chatbot._cookies['api_key']):\n        chatbot.append((inputs, \"缺少api_key。\\n\\n1. 临时解决方案：直接在输入区键入api_key，然后回车提交。\\n\\n2. 长效解决方案：在config.py中配置。\"))\n        yield from update_ui(chatbot=chatbot, history=history, msg=\"缺少api_key\") # 刷新界面\n        return\n    \n    if additional_fn is not None:\n        from llm_cards import core_functional\n        importlib.reload(core_functional)    # 热更新prompt\n        core_functional = core_functional.get_core_functions()\n        if \"PreProcess\" in core_functional[additional_fn]: inputs = core_functional[additional_fn][\"PreProcess\"](inputs)  # 获取预处理函数（如果有的话）\n        inputs = core_functional[additional_fn][\"Prefix\"] + inputs + core_functional[additional_fn][\"Suffix\"]\n  \n        \n    raw_input = inputs\n    logging.info(f'[raw_input] {raw_input}')\n    chatbot.append((inputs, \"\"))\n    yield from update_ui(chatbot=chatbot, history=history, msg=\"等待响应\") # 刷新界面\n\n    try:\n        headers, payload = generate_payload(inputs, llm_kwargs, history, system_prompt, stream)\n    except RuntimeError as e:\n        chatbot[-1] = (inputs, f\"您提供的api-key不满足要求，不包含任何可用于{llm_kwargs['llm_model']}的api-key。您可能选择了错误的模型或请求源。\")\n        yield from update_ui(chatbot=chatbot, history=history, msg=\"api-key不满足要求\") # 刷新界面\n        return\n        \n    history.append(inputs); history.append(\"\")\n    retry = 0\n    while True:\n        try:\n            # make a POST request to the API endpoint, stream=True\n            from llm_cards.bridge_all import model_info\n            endpoint = model_info[llm_kwargs['llm_model']]['endpoint']\n            response = requests.post(endpoint, headers=headers, proxies=proxies,\n                                    json=payload, stream=True, timeout=TIMEOUT_SECONDS);break\n        except:\n            retry += 1\n            chatbot[-1] = ((chatbot[-1][0], timeout_bot_msg))\n            retry_msg = f\"，正在重试 ({retry}/{MAX_RETRY}) ……\" if MAX_RETRY > 0 else \"\"\n            yield from update_ui(chatbot=chatbot, history=history, msg=\"请求超时\"+retry_msg) # 刷新界面\n            if retry > MAX_RETRY: raise TimeoutError\n\n    gpt_replying_buffer = \"\"\n    count=0\n    is_head_of_the_stream = True\n    if stream:\n        stream_response =  response.iter_lines()\n        while True:\n            try:\n                chunk = next(stream_response)\n            except StopIteration:\n                # 非OpenAI官方接口的出现这样的报错，OpenAI和API2D不会走这里\n                from utils.toolbox import regular_txt_to_markdown; tb_str = '```\\n' + trimmed_format_exc() + '```'\n                chatbot[-1] = (chatbot[-1][0], f\"[Local Message] 远程返回错误: \\n\\n{tb_str} \\n\\n{regular_txt_to_markdown(chunk.decode())}\")\n                yield from update_ui(chatbot=chatbot, history=history, msg=\"远程返回错误:\" + chunk.decode()) # 刷新界面\n                return\n            \n            if is_head_of_the_stream and (r'\"object\":\"error\"' not in chunk.decode()):\n                # 数据流的第一帧不携带content\n                is_head_of_the_stream = False; continue\n            \n            if chunk:\n                try:\n                    chunk_decoded = chunk.decode()\n                    # 前者API2D的\n                    if ('data: [DONE]' in chunk_decoded) or (len(json.loads(chunk_decoded[6:])['choices'][0][\"delta\"]) == 0):\n                        # 判定为数据流的结束，gpt_replying_buffer也写完了\n                        logging.info(f'[response] {gpt_replying_buffer}')\n                        break\n                    # 处理数据流的主体\n                    chunkjson = json.loads(chunk_decoded[6:])\n                    status_text = f\"finish_reason: {chunkjson['choices'][0]['finish_reason']}\"\n                    # 如果这里抛出异常，一般是文本过长，详情见get_full_error的输出\n                    new_buffer=json.loads(chunk_decoded[6:])['choices'][0][\"delta\"][\"content\"]\n                    gpt_replying_buffer = gpt_replying_buffer + new_buffer\n                    history[-1] = gpt_replying_buffer\n                    # if omniverse_state and gpt_replying_buffer[-1] in('。'):\n\n                    chatbot[-1] = (history[-2], history[-1])                  \n             \n                    yield from update_ui(chatbot=chatbot, history=history, msg=status_text) # 刷新界面\n                    \n                \n                except Exception as e:\n                    traceback.print_exc()\n                    yield from update_ui(chatbot=chatbot, history=history, msg=\"Json解析不合常规\") # 刷新界面\n                    chunk = get_full_error(chunk, stream_response)\n                    chunk_decoded = chunk.decode()\n                    error_msg = chunk_decoded\n                    if \"reduce the length\" in error_msg:\n                        if len(history) >= 2: history[-1] = \"\"; history[-2] = \"\" # 清除当前溢出的输入：history[-2] 是本次输入, history[-1] 是本次输出\n                        history = clip_history(inputs=inputs, history=history, tokenizer=model_info[llm_kwargs['llm_model']]['tokenizer'], \n                                               max_token_limit=(model_info[llm_kwargs['llm_model']]['max_token'])) # history至少释放二分之一\n                        chatbot[-1] = (chatbot[-1][0], \"[Local Message] Reduce the length. 本次输入过长, 或历史数据过长. 历史缓存数据已部分释放, 您可以请再次尝试. (若再次失败则更可能是因为输入过长.)\")\n                        # history = []    # 清除历史\n                    elif \"does not exist\" in error_msg:\n                        chatbot[-1] = (chatbot[-1][0], f\"[Local Message] Model {llm_kwargs['llm_model']} does not exist. 模型不存在, 或者您没有获得体验资格.\")\n                    elif \"Incorrect API key\" in error_msg:\n                        chatbot[-1] = (chatbot[-1][0], \"[Local Message] Incorrect API key. OpenAI以提供了不正确的API_KEY为由, 拒绝服务.\")\n                    elif \"exceeded your current quota\" in error_msg:\n                        chatbot[-1] = (chatbot[-1][0], \"[Local Message] You exceeded your current quota. OpenAI以账户额度不足为由, 拒绝服务.\")\n                    elif \"bad forward key\" in error_msg:\n                        chatbot[-1] = (chatbot[-1][0], \"[Local Message] Bad forward key. API2D账户额度不足.\")\n                    elif \"Not enough point\" in error_msg:\n                        chatbot[-1] = (chatbot[-1][0], \"[Local Message] Not enough point. API2D账户点数不足.\")\n                    else:\n                        from utils.toolbox import regular_txt_to_markdown\n                        tb_str = '```\\n' + trimmed_format_exc() + '```'\n                        chatbot[-1] = (chatbot[-1][0], f\"[Local Message] 异常 \\n\\n{tb_str} \\n\\n{regular_txt_to_markdown(chunk_decoded)}\")\n                    yield from update_ui(chatbot=chatbot, history=history, msg=\"Json异常\" + error_msg) # 刷新界面\n                    return\n           \n        if omniverse_state:\n            from a2f import tts_a2f,audio_synthesis\n            audio_synthesis(chatbot[-1][-1],A2F_URL,Avatar_instance_A)   \n           #asyncio.run(tts_a2f(chatbot[-1][-1]))\n\n        \ndef generate_payload(inputs, llm_kwargs, history, system_prompt, stream):\n    \"\"\"\n    整合所有信息，选择LLM模型，生成http请求，为发送请求做准备\n    \"\"\"\n    if not is_any_api_key(llm_kwargs['api_key']):\n        raise AssertionError(\"你提供了错误的API_KEY。\\n\\n1. 临时解决方案：直接在输入区键入api_key，然后回车提交。\\n\\n2. 长效解决方案：在config.py中配置。\")\n\n    api_key = select_api_key(llm_kwargs['api_key'], llm_kwargs['llm_model'])\n\n    headers = {\n        \"Content-Type\": \"application/json\",\n        \"Authorization\": f\"Bearer {api_key}\"\n    }\n\n    conversation_cnt = len(history) // 2\n\n    messages = [{\"role\": \"system\", \"content\": system_prompt}]\n    if conversation_cnt:\n        for index in range(0, 2*conversation_cnt, 2):\n            what_i_have_asked = {}\n            what_i_have_asked[\"role\"] = \"user\"\n            what_i_have_asked[\"content\"] = history[index]\n            what_gpt_answer = {}\n            what_gpt_answer[\"role\"] = \"assistant\"\n            what_gpt_answer[\"content\"] = history[index+1]\n            if what_i_have_asked[\"content\"] != \"\":\n                if what_gpt_answer[\"content\"] == \"\": continue\n                if what_gpt_answer[\"content\"] == timeout_bot_msg: continue\n                messages.append(what_i_have_asked)\n                messages.append(what_gpt_answer)\n            else:\n                messages[-1]['content'] = what_gpt_answer['content']\n\n    what_i_ask_now = {}\n    what_i_ask_now[\"role\"] = \"user\"\n    what_i_ask_now[\"content\"] = inputs\n    messages.append(what_i_ask_now)\n\n    payload = {\n        \"model\": llm_kwargs['llm_model'].strip('api2d-'),\n        \"messages\": messages, \n        \"temperature\": llm_kwargs['temperature'],  # 1.0,\n        \"top_p\": llm_kwargs['top_p'],  # 1.0,\n        \"n\": 1,\n        \"stream\": stream,\n        \"presence_penalty\": 0,\n        \"frequency_penalty\": 0,\n    }\n    try:\n        print(f\" {llm_kwargs['llm_model']} : {conversation_cnt} : {inputs[:100]} ..........\")\n    except:\n        print('输入中可能存在乱码。')\n    return headers,payload\n\n\n"
  },
  {
    "path": "llm_cards/bridge_stackclaude.py",
    "content": "#from llm_.bridge_newbing import preprocess_newbing_out, preprocess_newbing_out_simple\nfrom multiprocessing import Process, Pipe\nfrom utils.toolbox import update_ui, get_conf, trimmed_format_exc\nimport threading\nimport importlib\nimport logging\nimport time\nfrom utils.toolbox import get_conf\nimport asyncio\nload_message = \"正在加载Claude组件，请稍候...\"\n\ntry:\n    \"\"\"\n    ========================================================================\n    第一部分：Slack API Client\n    https://github.com/yokonsan/claude-in-slack-api\n    ========================================================================\n    \"\"\"\n\n    from slack_sdk.errors import SlackApiError\n    from slack_sdk.web.async_client import AsyncWebClient\n\n    class SlackClient(AsyncWebClient):\n        \"\"\"SlackClient类用于与Slack API进行交互，实现消息发送、接收等功能。\n\n            属性：\n            - CHANNEL_ID：str类型，表示频道ID。\n\n            方法：\n            - open_channel()：异步方法。通过调用conversations_open方法打开一个频道，并将返回的频道ID保存在属性CHANNEL_ID中。\n            - chat(text: str)：异步方法。向已打开的频道发送一条文本消息。\n            - get_slack_messages()：异步方法。获取已打开频道的最新消息并返回消息列表，目前不支持历史消息查询。\n            - get_reply()：异步方法。循环监听已打开频道的消息，如果收到\"Typing…_\"结尾的消息说明Claude还在继续输出，否则结束循环。\n\n        \"\"\"\n        CHANNEL_ID = None\n\n        async def open_channel(self):\n            response = await self.conversations_open(users=get_conf('SLACK_CLAUDE_BOT_ID')[0])\n            self.CHANNEL_ID = response[\"channel\"][\"id\"]\n\n        async def chat(self, text):\n            if not self.CHANNEL_ID:\n                raise Exception(\"Channel not found.\")\n\n            resp = await self.chat_postMessage(channel=self.CHANNEL_ID, text=text)\n            self.LAST_TS = resp[\"ts\"]\n\n        async def get_slack_messages(self):\n            try:\n                # TODO：暂时不支持历史消息，因为在同一个频道里存在多人使用时历史消息渗透问题\n                resp = await self.conversations_history(channel=self.CHANNEL_ID, oldest=self.LAST_TS, limit=1)\n                msg = [msg for msg in resp[\"messages\"]\n                    if msg.get(\"user\") == get_conf('SLACK_CLAUDE_BOT_ID')[0]]\n                return msg\n            except (SlackApiError, KeyError) as e:\n                raise RuntimeError(f\"获取Slack消息失败。\")\n        \n        async def get_reply(self):\n            while True:\n                slack_msgs = await self.get_slack_messages()\n                if len(slack_msgs) == 0:\n                    await asyncio.sleep(0.5)\n                    continue\n                \n                msg = slack_msgs[-1]\n                if msg[\"text\"].endswith(\"Typing…_\"):\n                    yield False, msg[\"text\"]\n                else:\n                    yield True, msg[\"text\"]\n                    break\nexcept:\n    pass\n\n\"\"\"\n========================================================================\n第二部分：子进程Worker（调用主体）\n========================================================================\n\"\"\"\n\n\nclass ClaudeHandle(Process):\n    def __init__(self):\n        super().__init__(daemon=True)\n        self.parent, self.child = Pipe()\n        self.claude_model = None\n        self.info = \"\"\n        self.success = True\n        self.local_history = []\n        self.check_dependency()\n        if self.success: \n            self.start()\n            self.threadLock = threading.Lock()\n\n    def check_dependency(self):\n        try:\n            self.success = False\n            import slack_sdk\n            self.info = \"依赖检测通过，等待Claude响应。注意目前不能多人同时调用Claude接口（有线程锁），否则将导致每个人的Claude问询历史互相渗透。调用Claude时，会自动使用已配置的代理。\"\n            self.success = True\n        except:\n            self.info = \"缺少的依赖，如果要使用Claude，除了基础的pip依赖以外，您还需要运行`pip install -r request_llm/requirements_slackclaude.txt`安装Claude的依赖，然后重启程序。\"\n            self.success = False\n\n    def ready(self):\n        return self.claude_model is not None    \n    \n    async def async_run(self):\n        await self.claude_model.open_channel()\n        while True:\n            # 等待\n            kwargs = self.child.recv()\n            question = kwargs['query']\n            history = kwargs['history']\n\n            # 开始问问题\n            prompt = \"\"\n\n            # 问题\n            prompt += question\n            print('question:', prompt)\n\n            # 提交\n            await self.claude_model.chat(prompt)\n            \n            # 获取回复\n            async for final, response in self.claude_model.get_reply():                \n                if not final:\n                    print(response)\n                    self.child.send(str(response))\n                else:\n                    # 防止丢失最后一条消息\n                    slack_msgs = await self.claude_model.get_slack_messages()\n                    last_msg = slack_msgs[-1][\"text\"] if slack_msgs and len(slack_msgs) > 0 else \"\"\n                    if last_msg:\n                        self.child.send(last_msg)\n                    print('-------- receive final ---------')\n                    self.child.send('[Finish]')\n                    \n    def run(self):\n        \"\"\"\n        这个函数运行在子进程\n        \"\"\"\n        # 第一次运行，加载参数\n        self.success = False\n        self.local_history = []\n        if (self.claude_model is None) or (not self.success):\n            # 代理设置\n            proxies, = get_conf('proxies')\n            if proxies is None:\n                self.proxies_https = None\n            else:\n                self.proxies_https = proxies['https']\n\n            try:\n                SLACK_CLAUDE_USER_TOKEN, = get_conf('SLACK_CLAUDE_USER_TOKEN')\n                self.claude_model = SlackClient(token=SLACK_CLAUDE_USER_TOKEN, proxy=self.proxies_https)\n                print('Claude组件初始化成功。')\n            except:\n                self.success = False\n                tb_str = '\\n```\\n' + trimmed_format_exc() + '\\n```\\n'\n                self.child.send(f'[Local Message] 不能加载Claude组件。{tb_str}')\n                self.child.send('[Fail]')\n                self.child.send('[Finish]')\n                raise RuntimeError(f\"不能加载Claude组件。\")\n\n        self.success = True\n        try:\n            # 进入任务等待状态\n            asyncio.run(self.async_run())\n        except Exception:\n            tb_str = '\\n```\\n' + trimmed_format_exc() + '\\n```\\n'\n            self.child.send(f'[Local Message] Claude失败 {tb_str}.')\n            self.child.send('[Fail]')\n            self.child.send('[Finish]')\n\n    def stream_chat(self, **kwargs):\n        \"\"\"\n        这个函数运行在主进程\n        \"\"\"\n        self.threadLock.acquire()\n        self.parent.send(kwargs)    # 发送请求到子进程\n        while True:\n            res = self.parent.recv()    # 等待Claude回复的片段\n            if res == '[Finish]':\n                break       # 结束\n            elif res == '[Fail]':\n                self.success = False\n                break\n            else:\n                yield res   # Claude回复的片段\n        self.threadLock.release()\n\n\n\"\"\"\n========================================================================\n第三部分：主进程统一调用函数接口\n========================================================================\n\"\"\"\nglobal claude_handle\nclaude_handle = None\n\n\ndef predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt=\"\", observe_window=None, console_slience=False):\n    \"\"\"\n        多线程方法\n        函数的说明请见 request_llm/bridge_all.py\n    \"\"\"\n    global claude_handle\n    if (claude_handle is None) or (not claude_handle.success):\n        claude_handle = ClaudeHandle()\n        observe_window[0] = load_message + \"\\n\\n\" + claude_handle.info\n        if not claude_handle.success:\n            error = claude_handle.info\n            claude_handle = None\n            raise RuntimeError(error)\n\n    # 没有 sys_prompt 接口，因此把prompt加入 history\n    history_feedin = []\n    for i in range(len(history)//2):\n        history_feedin.append([history[2*i], history[2*i+1]])\n\n    watch_dog_patience = 5  # 看门狗 (watchdog) 的耐心, 设置5秒即可\n    response = \"\"\n    observe_window[0] = \"[Local Message]: 等待Claude响应中 ...\"\n    for response in claude_handle.stream_chat(query=inputs, history=history_feedin, system_prompt=sys_prompt, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']):\n        observe_window[0] = preprocess_newbing_out_simple(response)\n        if len(observe_window) >= 2:\n            if (time.time()-observe_window[1]) > watch_dog_patience:\n                raise RuntimeError(\"程序终止。\")\n    return preprocess_newbing_out_simple(response)\n\n\ndef predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream=True, additional_fn=None):\n    \"\"\"\n        单线程方法\n        函数的说明请见 request_llm/bridge_all.py\n    \"\"\"\n    chatbot.append((inputs, \"[Local Message]: 等待Claude响应中 ...\"))\n\n    global claude_handle\n    if (claude_handle is None) or (not claude_handle.success):\n        claude_handle = ClaudeHandle()\n        chatbot[-1] = (inputs, load_message + \"\\n\\n\" + claude_handle.info)\n        yield from update_ui(chatbot=chatbot, history=[])\n        if not claude_handle.success:\n            claude_handle = None\n            return\n\n    if additional_fn is not None:\n        import core_functional\n        importlib.reload(core_functional)    # 热更新prompt\n        core_functional = core_functional.get_core_functions()\n        if \"PreProcess\" in core_functional[additional_fn]:\n            inputs = core_functional[additional_fn][\"PreProcess\"](\n                inputs)  # 获取预处理函数（如果有的话）\n        inputs = core_functional[additional_fn][\"Prefix\"] + \\\n            inputs + core_functional[additional_fn][\"Suffix\"]\n\n    history_feedin = []\n    for i in range(len(history)//2):\n        history_feedin.append([history[2*i], history[2*i+1]])\n\n    chatbot[-1] = (inputs, \"[Local Message]: 等待Claude响应中 ...\")\n    response = \"[Local Message]: 等待Claude响应中 ...\"\n    yield from update_ui(chatbot=chatbot, history=history, msg=\"Claude响应缓慢，尚未完成全部响应，请耐心完成后再提交新问题。\")\n    for response in claude_handle.stream_chat(query=inputs, history=history_feedin, system_prompt=system_prompt):\n        chatbot[-1] = (inputs, preprocess_newbing_out(response))\n        yield from update_ui(chatbot=chatbot, history=history, msg=\"Claude响应缓慢，尚未完成全部响应，请耐心完成后再提交新问题。\")\n    if response == \"[Local Message]: 等待Claude响应中 ...\":\n        response = \"[Local Message]: Claude响应异常，请刷新界面重试 ...\"\n    history.extend([inputs, response])\n    logging.info(f'[raw_input] {inputs}')\n    logging.info(f'[response] {response}')\n    yield from update_ui(chatbot=chatbot, history=history, msg=\"完成全部响应，请提交新问题。\")\n"
  },
  {
    "path": "llm_cards/core_functional.py",
    "content": "# 'primary' 颜色对应 theme.py 中的 primary_hue\n# 'secondary' 颜色对应 theme.py 中的 neutral_hue\n# 'stop' 颜色对应 theme.py 中的 color_er\n# 默认按钮颜色是 secondary\nimport importlib\nfrom utils.toolbox import clear_line_break\n\n\ndef get_core_functions():\n    return {\n        \"英语学术润色\": {\n            # 前言\n            \"Prefix\":   r\"Below is a paragraph from an academic paper. Polish the writing to meet the academic style, \" +\n                        r\"improve the spelling, grammar, clarity, concision and overall readability. When necessary, rewrite the whole sentence. \" +\n                        r\"Furthermore, list all modification and explain the reasons to do so in markdown table.\" + \"\\n\\n\",\n            # 后语\n            \"Suffix\":   r\"\",\n            \"Color\":    r\"secondary\",    # 按钮颜色\n        },\n        \"中文学术润色\": {\n            \"Prefix\":   r\"作为一名中文学术论文写作改进助理，你的任务是改进所提供文本的拼写、语法、清晰、简洁和整体可读性，\" +\n                        r\"同时分解长句，减少重复，并提供改进建议。请只提供文本的更正版本，避免包括解释。请编辑以下文本\" + \"\\n\\n\",\n            \"Suffix\":   r\"\",\n        },\n        \"查找语法错误\": {\n            \"Prefix\":   r\"Can you help me ensure that the grammar and the spelling is correct? \" +\n                        r\"Do not try to polish the text, if no mistake is found, tell me that this paragraph is good.\" +\n                        r\"If you find grammar or spelling mistakes, please list mistakes you find in a two-column markdown table, \" +\n                        r\"put the original text the first column, \" +\n                        r\"put the corrected text in the second column and highlight the key words you fixed.\"\"\\n\"\n                        r\"Example:\"\"\\n\"\n                        r\"Paragraph: How is you? Do you knows what is it?\"\"\\n\"\n                        r\"| Original sentence | Corrected sentence |\"\"\\n\"\n                        r\"| :--- | :--- |\"\"\\n\"\n                        r\"| How **is** you? | How **are** you? |\"\"\\n\"\n                        r\"| Do you **knows** what **is** **it**? | Do you **know** what **it** **is** ? |\"\"\\n\"\n                        r\"Below is a paragraph from an academic paper. \"\n                        r\"You need to report all grammar and spelling mistakes as the example before.\"\n                        + \"\\n\\n\",\n            \"Suffix\":   r\"\",\n            \"PreProcess\": clear_line_break,    # 预处理：清除换行符\n        },\n        \"中译英\": {\n            \"Prefix\":   r\"Please translate following sentence to English:\" + \"\\n\\n\",\n            \"Suffix\":   r\"\",\n        },\n        \"学术中英互译\": {\n            \"Prefix\":   r\"I want you to act as a scientific English-Chinese translator, \" +\n                        r\"I will provide you with some paragraphs in one language \" +\n                        r\"and your task is to accurately and academically translate the paragraphs only into the other language. \" +\n                        r\"Do not repeat the original provided paragraphs after translation. \" +\n                        r\"You should use artificial intelligence tools, \" +\n                        r\"such as natural language processing, and rhetorical knowledge \" +\n                        r\"and experience about effective writing techniques to reply. \" +\n                        r\"I'll give you my paragraphs as follows, tell me what language it is written in, and then translate:\" + \"\\n\\n\",\n            \"Suffix\": \"\",\n            \"Color\": \"secondary\",\n        },\n        \"英译中\": {\n            \"Prefix\":   r\"翻译成地道的中文：\" + \"\\n\\n\",\n            \"Suffix\":   r\"\",\n        },\n        \"找图片\": {\n            \"Prefix\":   r\"我需要你找一张网络图片。使用Unsplash API(https://source.unsplash.com/960x640/?<英语关键词>)获取图片URL，\" +\n                        r\"然后请使用Markdown格式封装，并且不要有反斜线，不要用代码块。现在，请按以下描述给我发送图片：\" + \"\\n\\n\",\n            \"Suffix\":   r\"\",\n        },\n        \"解释代码\": {\n            \"Prefix\":   r\"请解释以下代码：\" + \"\\n```\\n\",\n            \"Suffix\":   \"\\n```\\n\",\n        },\n        \"参考文献转Bib\": {\n            \"Prefix\":   r\"Here are some bibliography items, please transform them into bibtex style.\" +\n                        r\"Note that, reference styles maybe more than one kind, you should transform each item correctly.\" +\n                        r\"Items need to be transformed:\",\n            \"Suffix\":   r\"\",\n            \"Visible\": False,\n        }\n    }\n\ndef handle_core_functionality(additional_fn, inputs, history, chatbot):\n    import core_functional\n    importlib.reload(core_functional)    # 热更新prompt\n    core_functional = core_functional.get_core_functions()\n    if \"PreProcess\" in core_functional[additional_fn]: inputs = core_functional[additional_fn][\"PreProcess\"](inputs)  # 获取预处理函数（如果有的话）\n    inputs = core_functional[additional_fn][\"Prefix\"] + inputs + core_functional[additional_fn][\"Suffix\"]\n    if core_functional[additional_fn].get(\"AutoClearHistory\", False):\n        history = []\n    return inputs, history"
  },
  {
    "path": "llm_cards/crazy_functional.py",
    "content": "from utils.toolbox import HotReload  # HotReload 的意思是热更新，修改函数插件后，不需要重启程序，代码直接生效\n\n\ndef get_crazy_functions():\n    ###################### 第一组插件 ###########################\n    from crazy_functions.读文章写摘要 import 读文章写摘要\n    from crazy_functions.生成函数注释 import 批量生成函数注释\n    from crazy_functions.解析项目源代码 import 解析项目本身\n    from crazy_functions.解析项目源代码 import 解析一个Python项目\n    from crazy_functions.解析项目源代码 import 解析一个C项目的头文件\n    from crazy_functions.解析项目源代码 import 解析一个C项目\n    from crazy_functions.解析项目源代码 import 解析一个Golang项目\n    from crazy_functions.解析项目源代码 import 解析一个Rust项目\n    from crazy_functions.解析项目源代码 import 解析一个Java项目\n    from crazy_functions.解析项目源代码 import 解析一个前端项目\n    from crazy_functions.高级功能函数模板 import 高阶功能模板函数\n    from crazy_functions.代码重写为全英文_多线程 import 全项目切换英文\n    from crazy_functions.Latex全文润色 import Latex英文润色\n    from crazy_functions.询问多个大语言模型 import 同时问询\n    from crazy_functions.解析项目源代码 import 解析一个Lua项目\n    from crazy_functions.解析项目源代码 import 解析一个CSharp项目\n    from crazy_functions.总结word文档 import 总结word文档\n    from crazy_functions.解析JupyterNotebook import 解析ipynb文件\n    from crazy_functions.对话历史存档 import 对话历史存档\n    from crazy_functions.对话历史存档 import 载入对话历史存档\n    from crazy_functions.对话历史存档 import 删除所有本地对话历史记录\n    \n    from crazy_functions.批量Markdown翻译 import Markdown英译中\n    function_plugins = {\n        \"解析整个Python项目\": {\n            \"Color\": \"stop\",    # 按钮颜色\n            \"Function\": HotReload(解析一个Python项目)\n        },\n        \"载入对话历史存档（先上传存档或输入路径）\": {\n            \"Color\": \"stop\",\n            \"AsButton\":False,\n            \"Function\": HotReload(载入对话历史存档)\n        },\n        \"删除所有本地对话历史记录（请谨慎操作）\": {\n            \"AsButton\":False,\n            \"Function\": HotReload(删除所有本地对话历史记录)\n        },\n        \"[测试功能] 解析Jupyter Notebook文件\": {\n            \"Color\": \"stop\",\n            \"AsButton\":False,\n            \"Function\": HotReload(解析ipynb文件),\n            \"AdvancedArgs\": True, # 调用时，唤起高级参数输入区（默认False）\n            \"ArgsReminder\": \"若输入0，则不解析notebook中的Markdown块\", # 高级参数输入区的显示提示\n        },\n        \"批量总结Word文档\": {\n            \"Color\": \"stop\",\n            \"Function\": HotReload(总结word文档)\n        },\n        \"解析整个C++项目头文件\": {\n            \"Color\": \"stop\",    # 按钮颜色\n            \"AsButton\": False,  # 加入下拉菜单中\n            \"Function\": HotReload(解析一个C项目的头文件)\n        },\n        \"解析整个C++项目（.cpp/.hpp/.c/.h）\": {\n            \"Color\": \"stop\",    # 按钮颜色\n            \"AsButton\": False,  # 加入下拉菜单中\n            \"Function\": HotReload(解析一个C项目)\n        },\n        \"解析整个Go项目\": {\n            \"Color\": \"stop\",    # 按钮颜色\n            \"AsButton\": False,  # 加入下拉菜单中\n            \"Function\": HotReload(解析一个Golang项目)\n        },\n        \"解析整个Rust项目\": {\n            \"Color\": \"stop\",    # 按钮颜色\n            \"AsButton\": False,  # 加入下拉菜单中\n            \"Function\": HotReload(解析一个Rust项目)\n        },\n        \"解析整个Java项目\": {\n            \"Color\": \"stop\",  # 按钮颜色\n            \"AsButton\": False,  # 加入下拉菜单中\n            \"Function\": HotReload(解析一个Java项目)\n        },\n        \"解析整个前端项目（js,ts,css等）\": {\n            \"Color\": \"stop\",  # 按钮颜色\n            \"AsButton\": False,  # 加入下拉菜单中\n            \"Function\": HotReload(解析一个前端项目)\n        },\n        \"解析整个Lua项目\": {\n            \"Color\": \"stop\",    # 按钮颜色\n            \"AsButton\": False,  # 加入下拉菜单中\n            \"Function\": HotReload(解析一个Lua项目)\n        },\n        \"解析整个CSharp项目\": {\n            \"Color\": \"stop\",    # 按钮颜色\n            \"AsButton\": False,  # 加入下拉菜单中\n            \"Function\": HotReload(解析一个CSharp项目)\n        },\n        \"读Tex论文写摘要\": {\n            \"Color\": \"stop\",    # 按钮颜色\n            \"Function\": HotReload(读文章写摘要)\n        },\n        \"Markdown/Readme英译中\": {\n            # HotReload 的意思是热更新，修改函数插件代码后，不需要重启程序，代码直接生效\n            \"Color\": \"stop\",\n            \"Function\": HotReload(Markdown英译中)\n        },\n        \"批量生成函数注释\": {\n            \"Color\": \"stop\",    # 按钮颜色\n            \"AsButton\": False,  # 加入下拉菜单中\n            \"Function\": HotReload(批量生成函数注释)\n        },\n        \"保存当前的对话\": {\n            \"Function\": HotReload(对话历史存档)\n        },\n        \"[多线程Demo] 解析此项目本身（源码自译解）\": {\n            \"AsButton\": False,  # 加入下拉菜单中\n            \"Function\": HotReload(解析项目本身)\n        },\n        # \"[老旧的Demo] 把本项目源代码切换成全英文\": {\n        #     # HotReload 的意思是热更新，修改函数插件代码后，不需要重启程序，代码直接生效\n        #     \"AsButton\": False,  # 加入下拉菜单中\n        #     \"Function\": HotReload(全项目切换英文)\n        # },\n        \"[插件demo] 历史上的今天\": {\n            # HotReload 的意思是热更新，修改函数插件代码后，不需要重启程序，代码直接生效\n            \"Function\": HotReload(高阶功能模板函数)\n        },\n\n    }\n    ###################### 第二组插件 ###########################\n    # [第二组插件]: 经过充分测试\n    from crazy_functions.批量总结PDF文档 import 批量总结PDF文档\n    # from crazy_functions.批量总结PDF文档pdfminer import 批量总结PDF文档pdfminer\n    from crazy_functions.批量翻译PDF文档_多线程 import 批量翻译PDF文档\n    from crazy_functions.谷歌检索小助手 import 谷歌检索小助手\n    from crazy_functions.理解PDF文档内容 import 理解PDF文档内容标准文件输入\n    from crazy_functions.Latex全文润色 import Latex中文润色\n    from crazy_functions.Latex全文润色 import Latex英文纠错\n    from crazy_functions.Latex全文翻译 import Latex中译英\n    from crazy_functions.Latex全文翻译 import Latex英译中\n    from crazy_functions.批量Markdown翻译 import Markdown中译英\n\n    function_plugins.update({\n        \"批量翻译PDF文档（多线程）\": {\n            \"Color\": \"stop\",\n            \"AsButton\": True,  # 加入下拉菜单中\n            \"Function\": HotReload(批量翻译PDF文档)\n        },\n        \"询问多个GPT模型\": {\n            \"Color\": \"stop\",    # 按钮颜色\n            \"Function\": HotReload(同时问询)\n        },\n        \"[测试功能] 批量总结PDF文档\": {\n            \"Color\": \"stop\",\n            \"AsButton\": False,  # 加入下拉菜单中\n            # HotReload 的意思是热更新，修改函数插件代码后，不需要重启程序，代码直接生效\n            \"Function\": HotReload(批量总结PDF文档)\n        },\n        # \"[测试功能] 批量总结PDF文档pdfminer\": {\n        #     \"Color\": \"stop\",\n        #     \"AsButton\": False,  # 加入下拉菜单中\n        #     \"Function\": HotReload(批量总结PDF文档pdfminer)\n        # },\n        \"谷歌学术检索助手（输入谷歌学术搜索页url）\": {\n            \"Color\": \"stop\",\n            \"AsButton\": False,  # 加入下拉菜单中\n            \"Function\": HotReload(谷歌检索小助手)\n        },\n        \"理解PDF文档内容 （模仿ChatPDF）\": {\n            # HotReload 的意思是热更新，修改函数插件代码后，不需要重启程序，代码直接生效\n            \"Color\": \"stop\",\n            \"AsButton\": False,  # 加入下拉菜单中\n            \"Function\": HotReload(理解PDF文档内容标准文件输入)\n        },\n        \"英文Latex项目全文润色（输入路径或上传压缩包）\": {\n            # HotReload 的意思是热更新，修改函数插件代码后，不需要重启程序，代码直接生效\n            \"Color\": \"stop\",\n            \"AsButton\": False,  # 加入下拉菜单中\n            \"Function\": HotReload(Latex英文润色)\n        },\n        \"英文Latex项目全文纠错（输入路径或上传压缩包）\": {\n            # HotReload 的意思是热更新，修改函数插件代码后，不需要重启程序，代码直接生效\n            \"Color\": \"stop\",\n            \"AsButton\": False,  # 加入下拉菜单中\n            \"Function\": HotReload(Latex英文纠错)\n        },\n        \"中文Latex项目全文润色（输入路径或上传压缩包）\": {\n            # HotReload 的意思是热更新，修改函数插件代码后，不需要重启程序，代码直接生效\n            \"Color\": \"stop\",\n            \"AsButton\": False,  # 加入下拉菜单中\n            \"Function\": HotReload(Latex中文润色)\n        },\n        \"Latex项目全文中译英（输入路径或上传压缩包）\": {\n            # HotReload 的意思是热更新，修改函数插件代码后，不需要重启程序，代码直接生效\n            \"Color\": \"stop\",\n            \"AsButton\": False,  # 加入下拉菜单中\n            \"Function\": HotReload(Latex中译英)\n        },\n        \"Latex项目全文英译中（输入路径或上传压缩包）\": {\n            # HotReload 的意思是热更新，修改函数插件代码后，不需要重启程序，代码直接生效\n            \"Color\": \"stop\",\n            \"AsButton\": False,  # 加入下拉菜单中\n            \"Function\": HotReload(Latex英译中)\n        },\n        \"批量Markdown中译英（输入路径或上传压缩包）\": {\n            # HotReload 的意思是热更新，修改函数插件代码后，不需要重启程序，代码直接生效\n            \"Color\": \"stop\",\n            \"AsButton\": False,  # 加入下拉菜单中\n            \"Function\": HotReload(Markdown中译英)\n        },\n\n\n    })\n\n    ###################### 第三组插件 ###########################\n    # [第三组插件]: 尚未充分测试的函数插件\n\n    try:\n        from crazy_functions.下载arxiv论文翻译摘要 import 下载arxiv论文并翻译摘要\n        function_plugins.update({\n            \"一键下载arxiv论文并翻译摘要（先在input输入编号，如1812.10695）\": {\n                \"Color\": \"stop\",\n                \"AsButton\": False,  # 加入下拉菜单中\n                \"Function\": HotReload(下载arxiv论文并翻译摘要)\n            }\n        })\n    except:\n        print('Load function plugin failed')\n\n    try:\n        from crazy_functions.联网的ChatGPT import 连接网络回答问题\n        function_plugins.update({\n            \"连接网络回答问题（输入问题后点击该插件，需要访问谷歌）\": {\n                \"Color\": \"stop\",\n                \"AsButton\": False,  # 加入下拉菜单中\n                \"Function\": HotReload(连接网络回答问题)\n            }\n        })\n        from crazy_functions.联网的ChatGPT_bing版 import 连接bing搜索回答问题\n        function_plugins.update({\n            \"连接网络回答问题（中文Bing版，输入问题后点击该插件）\": {\n                \"Color\": \"stop\",\n                \"AsButton\": False,  # 加入下拉菜单中\n                \"Function\": HotReload(连接bing搜索回答问题)\n            }\n        })\n    except:\n        print('Load function plugin failed')\n\n    try:\n        from crazy_functions.解析项目源代码 import 解析任意code项目\n        function_plugins.update({\n            \"解析项目源代码（手动指定和筛选源代码文件类型）\": {\n                \"Color\": \"stop\",\n                \"AsButton\": False,\n                \"AdvancedArgs\": True, # 调用时，唤起高级参数输入区（默认False）\n                \"ArgsReminder\": \"输入时用逗号隔开, *代表通配符, 加了^代表不匹配; 不输入代表全部匹配。例如: \\\"*.c, ^*.cpp, config.toml, ^*.toml\\\"\", # 高级参数输入区的显示提示\n                \"Function\": HotReload(解析任意code项目)\n            },\n        })\n    except:\n        print('Load function plugin failed')\n\n    try:\n        from crazy_functions.询问多个大语言模型 import 同时问询_指定模型\n        function_plugins.update({\n            \"询问多个GPT模型（手动指定询问哪些模型）\": {\n                \"Color\": \"stop\",\n                \"AsButton\": False,\n                \"AdvancedArgs\": True, # 调用时，唤起高级参数输入区（默认False）\n                \"ArgsReminder\": \"支持任意数量的llm接口，用&符号分隔。例如chatglm&gpt-3.5-turbo&api2d-gpt-4\", # 高级参数输入区的显示提示\n                \"Function\": HotReload(同时问询_指定模型)\n            },\n        })\n    except:\n        print('Load function plugin failed')\n\n    try:\n        from crazy_functions.图片生成 import 图片生成\n        function_plugins.update({\n            \"图片生成（先切换模型到openai或api2d）\": {\n                \"Color\": \"stop\",\n                \"AsButton\": False,\n                \"AdvancedArgs\": True, # 调用时，唤起高级参数输入区（默认False）\n                \"ArgsReminder\": \"在这里输入分辨率, 如256x256（默认）\", # 高级参数输入区的显示提示\n                \"Function\": HotReload(图片生成)\n            },\n        })\n    except:\n        print('Load function plugin failed')\n\n    try:\n        from crazy_functions.总结音视频 import 总结音视频\n        function_plugins.update({\n            \"批量总结音视频（输入路径或上传压缩包）\": {\n                \"Color\": \"stop\",\n                \"AsButton\": False,\n                \"AdvancedArgs\": True,\n                \"ArgsReminder\": \"调用openai api 使用whisper-1模型, 目前支持的格式:mp4, m4a, wav, mpga, mpeg, mp3。此处可以输入解析提示，例如：解析为简体中文（默认）。\",\n                \"Function\": HotReload(总结音视频)\n            }\n        })\n    except:\n        print('Load function plugin failed')\n\n    try:\n        from crazy_functions.数学动画生成manim import 动画生成\n        function_plugins.update({\n            \"数学动画生成（Manim）\": {\n                \"Color\": \"stop\",\n                \"AsButton\": False,\n                \"Function\": HotReload(动画生成)\n            }\n        })\n    except:\n        print('Load function plugin failed')\n\n    try:\n        from crazy_functions.批量Markdown翻译 import Markdown翻译指定语言\n        function_plugins.update({\n            \"Markdown翻译（手动指定语言）\": {\n                \"Color\": \"stop\",\n                \"AsButton\": False,\n                \"AdvancedArgs\": True,\n                \"ArgsReminder\": \"请输入要翻译成哪种语言，默认为Chinese。\",\n                \"Function\": HotReload(Markdown翻译指定语言)\n            }\n        })\n    except:\n        print('Load function plugin failed')\n\n    try:\n        from crazy_functions.Langchain知识库 import 知识库问答\n        function_plugins.update({\n            \"[功能尚不稳定] 构建知识库（请先上传文件素材）\": {\n                \"Color\": \"stop\",\n                \"AsButton\": True,\n                \"AdvancedArgs\": True,\n                \"ArgsReminder\": \"待注入的知识库名称id, 默认为default\",\n                \"Function\": HotReload(知识库问答)\n            }\n        })\n    except:\n        print('Load{知识库问答} function plugin failed')\n\n    try:\n        from crazy_functions.Langchain知识库 import 读取知识库作答\n        function_plugins.update({\n            \"[功能尚不稳定] 知识库问答\": {\n                \"Color\": \"stop\",\n                \"AsButton\": True,\n                \"AdvancedArgs\": True,\n                \"ArgsReminder\": \"待提取的知识库名称id, 默认为default, 您需要首先调用构建知识库\",\n                \"Function\": HotReload(读取知识库作答)\n            }\n        })\n    except:\n        print('Load function plugin failed')\n        \n    try:\n        from crazy_functions.交互功能函数模板 import 交互功能模板函数\n        function_plugins.update({\n            \"交互功能模板函数\": {\n                \"Color\": \"stop\",\n                \"AsButton\": False,\n                \"Function\": HotReload(交互功能模板函数)\n            }\n        })\n    except:\n        print('Load function plugin failed')\n\n    # try:\n    #     from crazy_functions.chatglm微调工具 import 微调数据集生成\n    #     function_plugins.update({\n    #         \"黑盒模型学习: 微调数据集生成 (先上传数据集)\": {\n    #             \"Color\": \"stop\",\n    #             \"AsButton\": False,\n    #             \"AdvancedArgs\": True,\n    #             \"ArgsReminder\": \"针对数据集输入（如 绿帽子*深蓝色衬衫*黑色运动裤）给出指令，例如您可以将以下命令复制到下方: --llm_to_learn=azure-gpt-3.5 --prompt_prefix='根据下面的服装类型提示，想象一个穿着者，对这个人外貌、身处的环境、内心世界、过去经历进行描写。要求：100字以内，用第二人称。' --system_prompt=''\",\n    #             \"Function\": HotReload(微调数据集生成)\n    #         }\n    #     })\n    # except:\n    #     print('Load function plugin failed')\n\n    try:\n        from crazy_functions.Latex输出PDF结果 import Latex英文纠错加PDF对比\n        function_plugins.update({\n            \"Latex英文纠错+高亮修正位置 [需Latex]\": {\n                \"Color\": \"stop\",\n                \"AsButton\": False,\n                \"AdvancedArgs\": True,\n                \"ArgsReminder\": \"如果有必要, 请在此处追加更细致的矫错指令（使用英文）。\",\n                \"Function\": HotReload(Latex英文纠错加PDF对比)\n            }\n        })\n        from crazy_functions.Latex输出PDF结果 import Latex翻译中文并重新编译PDF\n        function_plugins.update({\n            \"Arixv论文精细翻译（输入arxivID）[需Latex]\": {\n                \"Color\": \"stop\",\n                \"AsButton\": False,\n                \"AdvancedArgs\": True,\n                \"ArgsReminder\": \n                    \"如果有必要, 请在此处给出自定义翻译命令, 解决部分词汇翻译不准确的问题。 \"+ \n                    \"例如当单词'agent'翻译不准确时, 请尝试把以下指令复制到高级参数区: \" + 'If the term \"agent\" is used in this section, it should be translated to \"智能体\". ',\n                \"Function\": HotReload(Latex翻译中文并重新编译PDF)\n            }\n        })\n        function_plugins.update({\n            \"本地Latex论文精细翻译（上传Latex项目）[需Latex]\": {\n                \"Color\": \"stop\",\n                \"AsButton\": False,\n                \"AdvancedArgs\": True,\n                \"ArgsReminder\": \n                    \"如果有必要, 请在此处给出自定义翻译命令, 解决部分词汇翻译不准确的问题。 \"+ \n                    \"例如当单词'agent'翻译不准确时, 请尝试把以下指令复制到高级参数区: \" + 'If the term \"agent\" is used in this section, it should be translated to \"智能体\". ',\n                \"Function\": HotReload(Latex翻译中文并重新编译PDF)\n            }\n        })\n    except:\n        print('Load function plugin failed')\n\n\n    try:\n        from toolbox import get_conf\n        ENABLE_AUDIO, = get_conf('ENABLE_AUDIO')\n        if ENABLE_AUDIO:\n            from crazy_functions.语音助手 import 语音助手\n            function_plugins.update({\n                \"实时音频采集\": {\n                    \"Color\": \"stop\",\n                    \"AsButton\": True,\n                    \"Function\": HotReload(语音助手)\n                }\n            })\n    except:\n        print('Load function plugin failed')\n        \n    # try:\n    #     from crazy_functions.虚空终端 import 终端\n    #     function_plugins.update({\n    #         \"超级终端\": {\n    #             \"Color\": \"stop\",\n    #             \"AsButton\": False,\n    #             # \"AdvancedArgs\": True,\n    #             # \"ArgsReminder\": \"\",\n    #             \"Function\": HotReload(终端)\n    #         }\n    #     })\n    # except:\n    #     print('Load function plugin failed')\n\n    return function_plugins\n"
  },
  {
    "path": "llm_cards/requirements_chatglm.txt",
    "content": "protobuf\ntransformers==4.27.1\ncpm_kernels\ntorch>=1.10\nmdtex2html\nsentencepiece"
  },
  {
    "path": "llm_cards/requirements_slackclaude.txt",
    "content": "slack-sdk==3.21.3"
  },
  {
    "path": "model_cards/Tag2Text/MANIFEST.in",
    "content": "include ram/configs/*.json\ninclude ram/configs/swin/*.json\ninclude ram/data/*.txt\n"
  },
  {
    "path": "model_cards/Tag2Text/batch_inference.py",
    "content": "from argparse import ArgumentParser\nfrom pathlib import Path\nfrom typing import Dict, List, Optional, TextIO, Tuple\n\nimport torch\nfrom PIL import Image, UnidentifiedImageError\nfrom torch import Tensor\nfrom torch.nn import Module, Parameter\nfrom torch.nn.functional import relu, sigmoid\nfrom torch.utils.data import DataLoader, Dataset\nfrom tqdm import tqdm\n\nfrom ram import get_transform\nfrom ram.models import ram, tag2text\nfrom ram.utils import build_openset_label_embedding, get_mAP, get_PR\n\ndevice = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n\n\ndef parse_args():\n    parser = ArgumentParser()\n    # model\n    parser.add_argument(\"--model-type\",\n                        type=str,\n                        choices=(\"ram\", \"tag2text\"),\n                        required=True)\n    parser.add_argument(\"--checkpoint\",\n                        type=str,\n                        required=True)\n    parser.add_argument(\"--backbone\",\n                        type=str,\n                        choices=(\"swin_l\", \"swin_b\"),\n                        default=None,\n                        help=\"If `None`, will judge from `--model-type`\")\n    parser.add_argument(\"--open-set\",\n                        action=\"store_true\",\n                        help=(\n                            \"Treat all categories in the taglist file as \"\n                            \"unseen and perform open-set classification. Only \"\n                            \"works with RAM.\"\n                        ))\n    # data\n    parser.add_argument(\"--dataset\",\n                        type=str,\n                        choices=(\n                            \"openimages_common_214\",\n                            \"openimages_rare_200\"\n                        ),\n                        required=True)\n    parser.add_argument(\"--input-size\",\n                        type=int,\n                        default=384)\n    # threshold\n    group = parser.add_mutually_exclusive_group()\n    group.add_argument(\"--threshold\",\n                       type=float,\n                       default=None,\n                       help=(\n                           \"Use custom threshold for all classes. Mutually \"\n                           \"exclusive with `--threshold-file`. If both \"\n                           \"`--threshold` and `--threshold-file` is `None`, \"\n                           \"will use a default threshold setting.\"\n                       ))\n    group.add_argument(\"--threshold-file\",\n                       type=str,\n                       default=None,\n                       help=(\n                           \"Use custom class-wise thresholds by providing a \"\n                           \"text file. Each line is a float-type threshold, \"\n                           \"following the order of the tags in taglist file. \"\n                           \"See `ram/data/ram_tag_list_threshold.txt` as an \"\n                           \"example. Mutually exclusive with `--threshold`. \"\n                           \"If both `--threshold` and `--threshold-file` is \"\n                           \"`None`, will use default threshold setting.\"\n                       ))\n    # miscellaneous\n    parser.add_argument(\"--output-dir\", type=str, default=\"./outputs\")\n    parser.add_argument(\"--batch-size\", type=int, default=128)\n    parser.add_argument(\"--num-workers\", type=int, default=4)\n\n    args = parser.parse_args()\n\n    # post process and validity check\n    args.model_type = args.model_type.lower()\n\n    assert not (args.model_type == \"tag2text\" and args.open_set)\n\n    if args.backbone is None:\n        args.backbone = \"swin_l\" if args.model_type == \"ram\" else \"swin_b\"\n\n    return args\n\n\ndef load_dataset(\n    dataset: str,\n    model_type: str,\n    input_size: int,\n    batch_size: int,\n    num_workers: int\n) -> Tuple[DataLoader, Dict]:\n    dataset_root = str(Path(__file__).resolve().parent / \"datasets\" / dataset)\n    img_root = dataset_root + \"/imgs\"\n    # Label system of tag2text contains duplicate tag texts, like\n    # \"train\" (noun) and \"train\" (verb). Therefore, for tag2text, we use\n    # `tagid` instead of `tag`.\n    if model_type == \"ram\":\n        tag_file = dataset_root + f\"/{dataset}_ram_taglist.txt\"\n        annot_file = dataset_root + f\"/{dataset}_{model_type}_annots.txt\"\n    else:\n        tag_file = dataset_root + f\"/{dataset}_tag2text_tagidlist.txt\"\n        annot_file = dataset_root + f\"/{dataset}_{model_type}_idannots.txt\"\n\n    with open(tag_file, \"r\", encoding=\"utf-8\") as f:\n        taglist = [line.strip() for line in f]\n\n    with open(annot_file, \"r\", encoding=\"utf-8\") as f:\n        imglist = [img_root + \"/\" + line.strip().split(\",\")[0] for line in f]\n\n    class _Dataset(Dataset):\n        def __init__(self):\n            self.transform = get_transform(input_size)\n\n        def __len__(self):\n            return len(imglist)\n\n        def __getitem__(self, index):\n            try:\n                img = Image.open(imglist[index])\n            except (OSError, FileNotFoundError, UnidentifiedImageError):\n                img = Image.new('RGB', (10, 10), 0)\n                print(\"Error loading image:\", imglist[index])\n            return self.transform(img)\n\n    loader = DataLoader(\n        dataset=_Dataset(),\n        shuffle=False,\n        drop_last=False,\n        pin_memory=True,\n        batch_size=batch_size,\n        num_workers=num_workers\n    )\n    info = {\n        \"taglist\": taglist,\n        \"imglist\": imglist,\n        \"annot_file\": annot_file,\n        \"img_root\": img_root\n    }\n    return loader, info\n\n\ndef get_class_idxs(\n    model_type: str,\n    open_set: bool,\n    taglist: List[str]\n) -> Optional[List[int]]:\n    \"\"\"Get indices of required categories in the label system.\"\"\"\n    if model_type == \"ram\":\n        if not open_set:\n            model_taglist_file = \"ram/data/ram_tag_list.txt\"\n            with open(model_taglist_file, \"r\", encoding=\"utf-8\") as f:\n                model_taglist = [line.strip() for line in f]\n            return [model_taglist.index(tag) for tag in taglist]\n        else:\n            return None\n    else:  # for tag2text, we directly use tagid instead of text-form of tag.\n        # here tagid equals to tag index.\n        return [int(tag) for tag in taglist]\n\n\ndef load_thresholds(\n    threshold: Optional[float],\n    threshold_file: Optional[str],\n    model_type: str,\n    open_set: bool,\n    class_idxs: List[int],\n    num_classes: int,\n) -> List[float]:\n    \"\"\"Decide what threshold(s) to use.\"\"\"\n    if not threshold_file and not threshold:  # use default\n        if model_type == \"ram\":\n            if not open_set:  # use class-wise tuned thresholds\n                ram_threshold_file = \"ram/data/ram_tag_list_threshold.txt\"\n                with open(ram_threshold_file, \"r\", encoding=\"utf-8\") as f:\n                    idx2thre = {\n                        idx: float(line.strip()) for idx, line in enumerate(f)\n                    }\n                    return [idx2thre[idx] for idx in class_idxs]\n            else:\n                return [0.5] * num_classes\n        else:\n            return [0.68] * num_classes\n    elif threshold_file:\n        with open(threshold_file, \"r\", encoding=\"utf-8\") as f:\n            thresholds = [float(line.strip()) for line in f]\n        assert len(thresholds) == num_classes\n        return thresholds\n    else:\n        return [threshold] * num_classes\n\n\ndef gen_pred_file(\n    imglist: List[str],\n    tags: List[List[str]],\n    img_root: str,\n    pred_file: str\n) -> None:\n    \"\"\"Generate text file of tag prediction results.\"\"\"\n    with open(pred_file, \"w\", encoding=\"utf-8\") as f:\n        for image, tag in zip(imglist, tags):\n            # should be relative to img_root to match the gt file.\n            s = str(Path(image).relative_to(img_root))\n            if tag:\n                s = s + \",\" + \",\".join(tag)\n            f.write(s + \"\\n\")\n\n\ndef load_ram(\n    backbone: str,\n    checkpoint: str,\n    input_size: int,\n    taglist: List[str],\n    open_set: bool,\n    class_idxs: List[int],\n) -> Module:\n    model = ram(pretrained=checkpoint, image_size=input_size, vit=backbone)\n    # trim taglist for faster inference\n    if open_set:\n        print(\"Building tag embeddings ...\")\n        label_embed, _ = build_openset_label_embedding(taglist)\n        model.label_embed = Parameter(label_embed.float())\n    else:\n        model.label_embed = Parameter(model.label_embed[class_idxs, :])\n    return model.to(device).eval()\n\n\ndef load_tag2text(\n    backbone: str,\n    checkpoint: str,\n    input_size: int\n) -> Module:\n    model = tag2text(\n        pretrained=checkpoint,\n        image_size=input_size,\n        vit=backbone\n    )\n    return model.to(device).eval()\n\n\n@torch.no_grad()\ndef forward_ram(model: Module, imgs: Tensor) -> Tensor:\n    image_embeds = model.image_proj(model.visual_encoder(imgs.to(device)))\n    image_atts = torch.ones(\n        image_embeds.size()[:-1], dtype=torch.long).to(device)\n    label_embed = relu(model.wordvec_proj(model.label_embed)).unsqueeze(0)\\\n        .repeat(imgs.shape[0], 1, 1)\n    tagging_embed, _ = model.tagging_head(\n        encoder_embeds=label_embed,\n        encoder_hidden_states=image_embeds,\n        encoder_attention_mask=image_atts,\n        return_dict=False,\n        mode='tagging',\n    )\n    return sigmoid(model.fc(tagging_embed).squeeze(-1))\n\n\n@torch.no_grad()\ndef forward_tag2text(\n    model: Module,\n    class_idxs: List[int],\n    imgs: Tensor\n) -> Tensor:\n    image_embeds = model.visual_encoder(imgs.to(device))\n    image_atts = torch.ones(\n        image_embeds.size()[:-1], dtype=torch.long).to(device)\n    label_embed = model.label_embed.weight.unsqueeze(0)\\\n        .repeat(imgs.shape[0], 1, 1)\n    tagging_embed, _ = model.tagging_head(\n        encoder_embeds=label_embed,\n        encoder_hidden_states=image_embeds,\n        encoder_attention_mask=image_atts,\n        return_dict=False,\n        mode='tagging',\n    )\n    return sigmoid(model.fc(tagging_embed))[:, class_idxs]\n\n\ndef print_write(f: TextIO, s: str):\n    print(s)\n    f.write(s + \"\\n\")\n\n\nif __name__ == \"__main__\":\n    args = parse_args()\n\n    # set up output paths\n    output_dir = args.output_dir\n    Path(output_dir).mkdir(parents=True, exist_ok=True)\n    pred_file, pr_file, ap_file, summary_file, logit_file = [\n        output_dir + \"/\" + name for name in\n        (\"pred.txt\", \"pr.txt\", \"ap.txt\", \"summary.txt\", \"logits.pth\")\n    ]\n    with open(summary_file, \"w\", encoding=\"utf-8\") as f:\n        print_write(f, \"****************\")\n        for key in (\n            \"model_type\", \"backbone\", \"checkpoint\", \"open_set\",\n            \"dataset\", \"input_size\",\n            \"threshold\", \"threshold_file\",\n            \"output_dir\", \"batch_size\", \"num_workers\"\n        ):\n            print_write(f, f\"{key}: {getattr(args, key)}\")\n        print_write(f, \"****************\")\n\n    # prepare data\n    loader, info = load_dataset(\n        dataset=args.dataset,\n        model_type=args.model_type,\n        input_size=args.input_size,\n        batch_size=args.batch_size,\n        num_workers=args.num_workers\n    )\n    taglist, imglist, annot_file, img_root = \\\n        info[\"taglist\"], info[\"imglist\"], info[\"annot_file\"], info[\"img_root\"]\n\n    # get class idxs\n    class_idxs = get_class_idxs(\n        model_type=args.model_type,\n        open_set=args.open_set,\n        taglist=taglist\n    )\n\n    # set up threshold(s)\n    thresholds = load_thresholds(\n        threshold=args.threshold,\n        threshold_file=args.threshold_file,\n        model_type=args.model_type,\n        open_set=args.open_set,\n        class_idxs=class_idxs,\n        num_classes=len(taglist)\n    )\n\n    # inference\n    if Path(logit_file).is_file():\n\n        logits = torch.load(logit_file)\n\n    else:\n\n        # load model\n        if args.model_type == \"ram\":\n            model = load_ram(\n                backbone=args.backbone,\n                checkpoint=args.checkpoint,\n                input_size=args.input_size,\n                taglist=taglist,\n                open_set=args.open_set,\n                class_idxs=class_idxs\n            )\n        else:\n            model = load_tag2text(\n                backbone=args.backbone,\n                checkpoint=args.checkpoint,\n                input_size=args.input_size\n            )\n\n        # inference\n        logits = torch.empty(len(imglist), len(taglist))\n        pos = 0\n        for imgs in tqdm(loader, desc=\"inference\"):\n            if args.model_type == \"ram\":\n                out = forward_ram(model, imgs)\n            else:\n                out = forward_tag2text(model, class_idxs, imgs)\n            bs = imgs.shape[0]\n            logits[pos:pos+bs, :] = out.cpu()\n            pos += bs\n\n        # save logits, making threshold-tuning super fast\n        torch.save(logits, logit_file)\n\n    # filter with thresholds\n    pred_tags = []\n    for scores in logits.tolist():\n        pred_tags.append([\n            taglist[i] for i, s in enumerate(scores) if s >= thresholds[i]\n        ])\n\n    # generate result file\n    gen_pred_file(imglist, pred_tags, img_root, pred_file)\n\n    # evaluate and record\n    mAP, APs = get_mAP(logits.numpy(), annot_file, taglist)\n    CP, CR, Ps, Rs = get_PR(pred_file, annot_file, taglist)\n\n    with open(ap_file, \"w\", encoding=\"utf-8\") as f:\n        f.write(\"Tag,AP\\n\")\n        for tag, AP in zip(taglist, APs):\n            f.write(f\"{tag},{AP*100.0:.2f}\\n\")\n\n    with open(pr_file, \"w\", encoding=\"utf-8\") as f:\n        f.write(\"Tag,Precision,Recall\\n\")\n        for tag, P, R in zip(taglist, Ps, Rs):\n            f.write(f\"{tag},{P*100.0:.2f},{R*100.0:.2f}\\n\")\n\n    with open(summary_file, \"w\", encoding=\"utf-8\") as f:\n        print_write(f, f\"mAP: {mAP*100.0}\")\n        print_write(f, f\"CP: {CP*100.0}\")\n        print_write(f, f\"CR: {CR*100.0}\")\n"
  },
  {
    "path": "model_cards/Tag2Text/datasets/openimages_common_214/imgs/.gitkeep",
    "content": ""
  },
  {
    "path": "model_cards/Tag2Text/datasets/openimages_common_214/openimages_common_214_ram_annots.txt",
    "content": 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light\ntest/fea0dc3ebd3945b8,microphone\ntest/fea164e2c9c833f4,pet,dog\ntest/fea1982623b76eef,blueberry\ntest/fea21422f194d74a,flower\ntest/fea3ba65b8e136ec,butterfly\ntest/fea6b17858faad64,car\ntest/fea8643e569cbd92,guitar\ntest/fea87d5a8dc5d8e6,piano\ntest/fea8f73817514d0d,car\ntest/fea945a44c88a838,flower\ntest/fea9b802820030ac,car\ntest/feaaa351b35d0130,street art\ntest/feac0cb6ee53fb9e,washing machine\ntest/feac6add0c4c401a,car\ntest/feac99f643e34842,cave\ntest/fead4a028e052d27,crab\ntest/feae7a1d4d2ec3fe,car\ntest/feaea23148f4aa29,car\ntest/feaf3fa758355797,car\ntest/feb175e9e8a19faa,barbecue\ntest/feb4a83bec26362b,car\ntest/feb4d2be5707ea06,winter\ntest/feb57db3d0415154,flower\ntest/feb799d28992a98a,car\ntest/feb926dd9e92ae8a,frog\ntest/feb970f49296d982,car\ntest/feb9996b45e8ef8a,christmas,candle\ntest/feba2aae64284e51,car\ntest/febb10312d57ae3d,cheese,hamburger\ntest/febbf0a781a9c5fd,car\ntest/febde420109b9002,palm 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station\ntest/fec3ffc4871d1467,sea\ntest/fec4bbd3dd410872,pumpkin\ntest/fec74e113ee2af1a,cannon\ntest/fec94ddba7e14b21,flower\ntest/fec991192ca25081,car\ntest/fecaaafd703e59c4,cat,chicken,pet\ntest/fecb05feac0d8904,horse\ntest/fecbe8d8d5cda40a,laptop\ntest/fecc1de970fa8cc0,salad\ntest/fecc39d4ffca9bf6,mosque\ntest/fecd042450ea83a8,drum\ntest/fecd5098dd505692,cat,sea,pet\ntest/fecd65bc5f32a73c,truck\ntest/fecd67c1a9168b3b,barbecue,fire\ntest/fecdc179a0a116b1,mushroom\ntest/fecedc722710bd79,sparrow\ntest/fed0cd319bb996f2,car\ntest/fed3315ececa8061,car\ntest/fed351824d1f0c34,car\ntest/fed4f894ebd59208,pet,dog\ntest/fed6f2243d199d7b,plane\ntest/fed7008307a9d9ff,sea\ntest/feda5759a4972ca4,flower\ntest/fedb2deee4a64424,car\ntest/fedb621a1efe02f0,flower\ntest/fedbe36628395f93,flower\ntest/fedd9df6a0102d87,car\ntest/fedeb80086558f46,car\ntest/feded158596f2e76,flower\ntest/fee15f1f24504701,wheat\ntest/fee199a9ff8a3c9d,car\ntest/fee2769e77184a90,cat,pet\ntest/fee4045430cf19bc,car\ntest/fee4efd9c5786e22,car\ntest/fee5f3cee0209776,pet,dog\ntest/fee97b8fe8898e82,pet,dog\ntest/feecb7c8822d8a56,car\ntest/feecb94338fe3dc8,bicycle\ntest/feefa11fd6caa389,camping\ntest/feefb6267d68e599,cat,pet\ntest/fef0b90c78358132,truck,car\ntest/fef53ce63faad3c7,flower\ntest/fef584f81617b821,deer\ntest/fef730f455a11a5a,flower\ntest/fef894675d18b57d,bikini\ntest/fef9e1438b3dd8b2,pet,dog\ntest/fefa9892944f8c73,car\ntest/fefaf576edbce7e0,chef\ntest/fefb3d634d567ad4,blackberry\ntest/fefb68c6a13c4e6f,car\ntest/fefb9e6725fe3504,flower\ntest/ff009b4f452f3a7a,desert\ntest/ff0194495344e81c,pet,dog\ntest/ff025b2ab3ed9942,train\ntest/ff028ee1eed85411,flower\ntest/ff02b1fbf7098153,mask\ntest/ff0609ba267912f9,snake\ntest/ff07efd86389f0b8,bus,winter,car\ntest/ff081c8e65a9a346,plane\ntest/ff08a9a30787a421,mouse\ntest/ff0a420679d38eb0,horse\ntest/ff0c950ea5ce1c76,car\ntest/ff0e12845c7b51c7,guitar,drum\ntest/ff0e93390598c074,restroom\ntest/ff1034526f4cae9b,car\ntest/ff1080551cfc1d1f,shrine\ntest/ff108dccd569ac76,moon\ntest/ff1180c80422af02,winter,wolf,dog,pet\ntest/ff11b5c5345d25af,car\ntest/ff1270a5fbfc97d1,flower\ntest/ff14517858e4a06e,football\ntest/ff154cd5dcf8766e,toy\ntest/ff17fb24d7b1f412,flower\ntest/ff18b8a051b50aaa,bed\ntest/ff1b9a76032517de,microphone\ntest/ff1de49664e5ea16,car\ntest/ff1f99ff1ee0df82,fire\ntest/ff1fe2086556ca21,flower\ntest/ff205975f1bb450b,flower\ntest/ff236ee609cd025c,car\ntest/ff23fb3d56566ad3,accordion,piano\ntest/ff245dfd1acf4509,toy,dog\ntest/ff26a8164f385f54,plane\ntest/ff2961d40002cb20,car\ntest/ff2ba25a7fbd97ca,bikini\ntest/ff2be552a7b1c1ad,bridge\ntest/ff2ce21ee2fbeda7,pebble\ntest/ff2e1c1ece92c6e8,flower\ntest/ff2f26a77c4e6eb1,flower\ntest/ff2fc4aa858c935f,sushi\ntest/ff30c7ed7af40f8e,statue\ntest/ff32f2a3db72e122,bicycle\ntest/ff33440e242b43f0,car\ntest/ff346db842c3b644,car\ntest/ff35507823b9d6ea,toy\ntest/ff3551cb04fdc815,car\ntest/ff35668ac449c2e3,car\ntest/ff35946bd84afc68,dog\ntest/ff35ca641681f222,moon\ntest/ff35d994782a47a3,map\ntest/ff364a9c03696687,car\ntest/ff3a45fce6a841b5,chicken\ntest/ff3d2cdc7bdb29e3,flower\ntest/ff3ef51d2a40dab5,restroom\ntest/ff3fa7fc7d279894,lion\ntest/ff40ed2e935f6729,dragonfly\ntest/ff418ae7ec73db80,car\ntest/ff41cce27b27cbb2,candle\ntest/ff43542609ba948f,car\ntest/ff43f22a6af260fa,car\ntest/ff4476fe270082e0,plane\ntest/ff447d8212b05555,car\ntest/ff45db2d8346bcd0,sushi\ntest/ff475721d72f128d,car\ntest/ff47a2c697c7fa94,car\ntest/ff48198ec353f5e3,windmill\ntest/ff4f2b87432d6d17,pet,dog\ntest/ff4fd116f5f8ed1c,cucumber\ntest/ff51b642c4427f6b,gas stove\ntest/ff51b6f2da8b1d60,flower\ntest/ff54998769300f10,car\ntest/ff55f82136245e4a,firework\ntest/ff58e2fa223caa16,car\ntest/ff596e141936a452,poodle,pet,dog\ntest/ff59efc44f003e17,football\ntest/ff5a650ecf4d5c7d,pet,wolf,dog\ntest/ff5ad4b9740d8425,lily,flower\ntest/ff5f0a364f2f2555,winter,glacier\ntest/ff5f42b8905e8dc7,bicycle\ntest/ff5fd9c3c624b7dc,apple\ntest/ff6028743dd914c0,car\ntest/ff62f44cf16d07a5,lily\ntest/ff644aec413dcfdb,halloween\ntest/ff651f26b76452d2,salad\ntest/ff678695f8c19f82,helicopter\ntest/ff681c0b9bab4461,pig\ntest/ff6914b96eea007e,car\ntest/ff6940a1d4876d66,stage\ntest/ff69e5ebb58b9435,train\ntest/ff6ad4faeff0abc5,handbag\ntest/ff70c26d99b5c672,truck,car\ntest/ff71a89c7ee6c46d,car\ntest/ff71ecb0daa2f3b6,car\ntest/ff765a1ed355572e,flower\ntest/ff79f4b1cdd9b4a5,car\ntest/ff7a137ff7ff2efa,salad\ntest/ff7a370a28055089,helicopter,plane\ntest/ff7abc612e454eae,tiger\ntest/ff7ce5360fe49a17,run\ntest/ff7de4c6525019a7,dolphin\ntest/ff80d5b3f1720202,plane\ntest/ff81426d24251fc7,paper\ntest/ff815e0f52ec28b9,car\ntest/ff816168aa4abf51,sea\ntest/ff81b9c81ec53d2a,flower\ntest/ff81cbffa1928948,poodle,pet,dog\ntest/ff8211b8c4774343,car\ntest/ff82f678e804fb7c,flower\ntest/ff85280227704db6,auditorium\ntest/ff871ba07e733a84,toy\ntest/ff87bb7f07ebba4f,fire\ntest/ff886a826b6dbbd9,car\ntest/ff8aac0df786eea0,palm tree\ntest/ff8ca56c5e08671e,truck\ntest/ff8cada6a9e8f69e,car\ntest/ff8ced4884deec83,helicopter\ntest/ff8dcdaf9a62633b,car\ntest/ff8f88cda7a50d15,lizard\ntest/ff9017811cf1342a,pet,dog\ntest/ff913093c0fb6898,microphone\ntest/ff91bef410900002,pet,dog\ntest/ff92c94a3728f686,chicken\ntest/ff9446801cc5d781,sea\ntest/ff94d46fcedd8f78,sea\ntest/ff950903e4824055,trampoline\ntest/ff954b8968205aae\ntest/ff984272d363823a,bicycle\ntest/ff9bfb7d963dbfc8,salad\ntest/ffa382be7e2deb98,backpack\ntest/ffa49c59d1b8d9ef,flower\ntest/ffa4a81954f448cb,car\ntest/ffa5cfd66c304aaa,flower\ntest/ffa6046da250fb12,pebble\ntest/ffa8c11683ff692a,car\ntest/ffa8f320b1a34079,snake\ntest/ffa97731387762a3,microphone,stage\ntest/ffab8139a9f872ed,plane\ntest/ffac37793c887398,pasta\ntest/ffacad54e7db0e6e,cup\ntest/ffacc4c2cef88f2f,car\ntest/ffacf8502615260e,bicycle\ntest/ffad3bc70c7f51f8,surfboard,sea\ntest/ffad69b9c3e27965,lobster\ntest/ffae623038c101f3,pet,dog\ntest/ffb023607751cf52,train\ntest/ffb0bef7456f21f1,ceiling,auditorium,stage\ntest/ffb14012aabac6ab,flower\ntest/ffb24b3739e8157b,car\ntest/ffb251259eb97e04,flower\ntest/ffb260bc6cfd8829,car\ntest/ffb45837ba6ceae2,plane,toy\ntest/ffb48f7196a5eb02,fridge\ntest/ffb57bb12eaeca73,flower\ntest/ffb5a44fefc97698,bus\ntest/ffb691bda7dd5ad3,car\ntest/ffb9355688220e13,dolphin,sea\ntest/ffb99038ac073014,car\ntest/ffbbee74080d045b,car\ntest/ffbbfb33b0400e76,pet,dog\ntest/ffbc58b07824784e,tattoo\ntest/ffbc8a8574c5c7fa,truck\ntest/ffbd21d9b35154ea,car\ntest/ffbd482239c19828,car\ntest/ffbd66aacc8acd35,winter\ntest/ffbd9ea8bb0656e8,car\ntest/ffc07e8d58296898,flower\ntest/ffc0d139c2511283,lizard\ntest/ffc13cdf01c0d535,car\ntest/ffc279def2723031,winter\ntest/ffc2e5bf258ebd55,car\ntest/ffc3564c2bdd0d73,hummingbird\ntest/ffc37d5108196fb2,car\ntest/ffc470ea77933c70,christmas\ntest/ffc4e88b6c8f798a,leg\ntest/ffc6e937b03c1662,pet,dog\ntest/ffc6f3dfe464c70a,pet,dog\ntest/ffc82cebfe88a946,dog,horse\ntest/ffca393640f8b551,car\ntest/ffcbacdb03acf225,flower\ntest/ffcc0f9435bcf699,tattoo\ntest/ffcc9a45c9e4ac0a,flower\ntest/ffcccde260f5bfb7,car\ntest/ffcfd462236ec78a,flower\ntest/ffd1b531dedb0fb5,dolphin\ntest/ffd2e4929586c86c,flower\ntest/ffd466526fa9f42d,guitar\ntest/ffd7ba6f9f22ee1e,plane\ntest/ffd7df7e560714a3,sea\ntest/ffdaf6e0f75aae1a,toy\ntest/ffdaff09f5fe7db1,dog\ntest/ffdb7556405ef576,mushroom\ntest/ffdc80f71e756075,winter\ntest/ffdec351cea041e5,birthday cake\ntest/ffdf9548c06d79a0,plane\ntest/ffe04404f471a3e2,toast\ntest/ffe27ebea6866a6f,car\ntest/ffe3c978a2f27cd9,truck\ntest/ffe527b8457da787,car\ntest/ffe5ee5d81d1cb9b,truck\ntest/ffe7a4ef946c7f5b,car\ntest/ffe809fcb5b25602,car\ntest/ffe8d89197cb7ad0,car\ntest/ffe9a0db54e36f37,toy\ntest/ffea1b30f38a85ec,toy\ntest/ffea9e311adf9953,pet,dog\ntest/ffec3aa222dfc2d1,guitar\ntest/ffecfc3edf922417,flower\ntest/ffee883088882976,candle\ntest/ffef8fa6031038f9,car\ntest/fff23a84a63a1acd,courtyard\ntest/fff3c65641e4925e,pet,winter,dog\ntest/fff419b5ba84d9b5,plane\ntest/fff42624ea5737d9,bicycle\ntest/fff81e4812e78a4c,dog\ntest/fff8f2372f82fa8d,dragonfly\ntest/fff930f3d412848b,pet,dog\ntest/fffc2ecd02883c6f,halloween\ntest/fffc6543b32da1dd,horse\n"
  },
  {
    "path": "model_cards/Tag2Text/datasets/openimages_common_214/openimages_common_214_ram_taglist.txt",
    "content": "accident\naccordion\nplane\nairport\nantelope\napple\nart gallery\neggplant\nauditorium\nautumn\nbaboon\nbackpack\nbakery\nbamboo\nbanana\nbarbecue\nbed\nbedroom\nclock\nbicycle\nbikini\nbirthday cake\nblackberry\nblueberry\npig\nbookcase\nbridge\nbroccoli\nbus\nbutterfly\ncalculator\ncalendar\ncamping\ncandle\ncandy\ncannon\ncanyon\ncar\ncarousel\ncat\ncave\nceiling\ncheese\ncheetah\nchef\nchicken\nchristmas\nchristmas tree\nclover\ncoral\ncorn\ncourtyard\ncrab\nlobster\ncrocodile\ncrosswalk\ncrow\ncucumber\ncup\ncurrency\ndachshund\ndeer\ndesert\ndie\ndinosaur\ndog\ndolphin\ndoodle\ndragonfly\ndrum\nduck\ndumbbell\neaster egg\negg\nelephant\nfaucet\nferris wheel\nfire\nfireman\nfirework\nflamingo\nflower\nfootball\nfountain\nfox\nfridge\nfrog\nham\ngas stove\ngiraffe\nglacier\nglove\ngoat\ngoose\ngorilla\ngrape\nguitar\ngull\ngym\nhalloween\nhamburger\nhamster\nhandbag\nhedgehog\nhelicopter\nhorse\nhummingbird\njellyfish\nkangaroo\nkimono\nkite\nladybird\nlaptop\nleg\nmailbox\nlibrary\nlightning\nlily\nlion\nlizard\nluggage\nmannequin\nmap\nmask\nmattress\nmicrophone\nmicrowave\nmonkey\nmoon\nmosque\nmouse\nmushroom\nnebula\nsea\nostrich\npalm tree\npaper\npasta\npatient\npavilion\npear\npebble\npenguin\npet\npiano\npicture frame\npine\npineapple\npizza\npolice car\npomegranate\npoodle\npopcorn\nstamp\npower station\nprinter\npumpkin\nraccoon\nrainbow\nrat\nrestroom\nring\nrun\nsalad\nsandwich\nsausage\nshark\nsheet music\nshrine\nsnowboard\nsnake\nsparrow\nsquirrel\nstage\nstarfish\nstatue\nsteering wheel\nstream\nstreet art\nstreet light\nsubmarine\nsuite\nsurfboard\nsushi\nswan\ntattoo\nteddy\ntennis court\ntennis racket\ntiger\ntoast\ntoilet bowl\ntoy\ntractor\ntrain\ntrampoline\ntreadmill\ntruck\ntunnel\nturkey\nvending machine\nwaffle\nwalnut\nwashing machine\nwater buffalo\nwaterfall\nwatermelon\nwheat\nwheelchair\nwindmill\nwinter\nwolf\nwoodpecker\nzebra\n"
  },
  {
    "path": "model_cards/Tag2Text/datasets/openimages_common_214/openimages_common_214_tag2text_idannots.txt",
    "content": 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  {
    "path": "model_cards/Tag2Text/datasets/openimages_rare_200/openimages_rare_200_ram_annots.txt",
    "content": "test/0000c64e1253d68f,Auto racing\ntest/0002cc8afaf1b611,Residential area\ntest/0003d84e0165d630,Lawn game\ntest/00045d609ca3f4eb,Calabaza\ntest/0006acf221a3e1d1,Junk food,Fried food\ntest/00090369f46e7b9e,Microcontroller\ntest/00094d5e8b3cb038,Combat sport\ntest/000a546e910f0a6b,Residential area\ntest/000aa0b1c8fd5ddf,Luxury vehicle\ntest/000b14e4ee4a2b2b,Luxury vehicle\ntest/000cf5859025877f,Arthropod,Close-up\ntest/001341ae9926808d,Bovine\ntest/0013a0927e6bbefc,Motorcycling\ntest/001547e7ef44a7d8,Luxury vehicle,Antique car\ntest/00156ac2ee0c3d58,Ball game,Team sport\ntest/00167b2ac2394762,Junk food\ntest/0017da7a364de4a5,Luxury vehicle,Sport utility vehicle\ntest/00186cd93b82866e,Compact car,Performance car,Antique car\ntest/0019ac4eb659f57e,Senior citizen,Childbirth\ntest/001c31a8ace249e9,Fried noodles\ntest/001cfa87e56b76a1,Sport utility vehicle\ntest/001dc1a88f4bac12,Compact car,Luxury vehicle,Antique car\ntest/001eb42ffe74be81,Luxury vehicle,Antique car\ntest/001efdd4bdffbfc7,Luxury vehicle\ntest/001f95237e94b3db,Luxury vehicle,Antique car\ntest/001fdc4bd0dd96f1,Vegetarian food\ntest/00203708377265a0,Firearm\ntest/002200be72145198,Boating,Rowing\ntest/00220bebad614827,Luxury vehicle,Antique car\ntest/0022601d70752de3,Compact car,Luxury vehicle,Antique car\ntest/00229645efaf204f,Combat sport\ntest/00233e61f68461f5,Compact car\ntest/0025a6cccec41134,Boating,Personal water craft\ntest/00277e220e6c1e2b,Wallaby\ntest/00284b14d2aebb4d,Auto racing,Luxury vehicle\ntest/00290e34951731ac,Frozen yogurt\ntest/002934e5712ba154,Luxury vehicle,Antique car\ntest/002a46e8cbba77fe,Plant stem\ntest/002afe9e08cc03b1,Luxury vehicle\ntest/002b7b427862f33d,Classical sculpture\ntest/002bd0ca1e119db7,Combat sport\ntest/002d966db9db634c,Equestrianism\ntest/002f5082ace5d2b0,Sport utility vehicle\ntest/0030c22c0c3e0ed1,Modern dance\ntest/003173245832d0cb,Close-up\ntest/0035b9006c333719,Close-up\ntest/003625d619697583,Sport utility vehicle\ntest/003802a692d96647,Aircraft engine\ntest/0038b12aeccdcb7e,Musical theatre\ntest/0039d1a14d295dee,Vegetarian food,Corn on the cob\ntest/003b350846f7bdf5,Black-and-white\ntest/003c2b6816ba9d22,Road cycling\ntest/003cc40208f5abff,Arthropod\ntest/003d4acb635f05b7,Heavy cruiser\ntest/003de84dbb04cea3,Plant stem,Close-up\ntest/0040b9648db8bee2,Calabaza\ntest/0041c772f8b9aef6,Chordophone\ntest/0044247496fe4a6d,Ball game,Team sport\ntest/0044df245dafe994,Barechested\ntest/00474165314cd9cf,Rural area,Bovine\ntest/0048300945516b38,Senior citizen,Close-up\ntest/0048c8734bd9116f,Close-up\ntest/0049980696be0a34,Cosmetics\ntest/004b17294aff80dc,Luxury vehicle\ntest/004be65d692692b4,Residential area,Aerial photography\ntest/004c0d4a277cc9a7,Blond,Close-up\ntest/004cf12589884a42,Touring car,Antique car\ntest/005060d96c235319,Compact car,Sport utility vehicle\ntest/0050642479c69116,Luxury vehicle\ntest/005071c765575897,Close-up\ntest/0053c3b693c2b8c4,Whiskers\ntest/0055371c2eb7e9dd,Black-and-white\ntest/005589a94cadf725,Bovine\ntest/0055b74192ea3ab6,Coral reef fish\ntest/0055c8f0164b3b2d,Compact car,Luxury vehicle\ntest/00567dcce4a5f7d9,Domestic short-haired cat,Whiskers\ntest/00577e58c62f30f0,Comics\ntest/005ba66e075763db,Rural area\ntest/005d27bb11ed423e,Jeans\ntest/005db64b7bfc26bb,Auto racing,Performance car\ntest/005e297eed629e8b,Ball game\ntest/005ed150c8f09ea3,Compact car\ntest/0061d4f01e42f908,Compact car,Luxury vehicle\ntest/00629fe556f50e83,Plant stem\ntest/006526e6627b8b84,Antique car\ntest/00672f3c5d893df4,Black-and-white\ntest/0068e3cd743ca5e9,Auto racing\ntest/0069d54248bee069,Great white shark\ntest/006e56e504ff449c,Black-and-white,Portrait photography\ntest/006f315121023f91,Auto racing\ntest/0071cbcfa234fe57,Arthropod,Close-up\ntest/00733b95ebfa566a,Compact car\ntest/00741fb6fb4c7209,Military vehicle,Gun turret\ntest/0075896c03c35c38,Close-up\ntest/0075e5ecd0728123,Jeans\ntest/0078db9683122177,Black-and-white,Close-up\ntest/0079ae4be5f70d5b,Vegetarian food\ntest/007a4d234b26981c,Close-up\ntest/007a517cacd7f053,Sport utility vehicle\ntest/007b2b754dc72382,Auto racing\ntest/007b9cad3e996e06,Ball game,Team sport\ntest/007bddc8f1277b76,Ball game\ntest/007cae31ad106eac,Military vehicle\ntest/007dc13386ae3d9c,Lumpia\ntest/007de8bd0c439055,Plant stem,Close-up\ntest/007de8d56e59900a,Black-and-white,Modern dance\ntest/00835f0fbe950715,Aircraft engine\ntest/00859454439212be,Close-up\ntest/0089c490282ac05f,Compact car\ntest/0089fabf617de1e6,Luxury vehicle,Performance car\ntest/008aa959e28d602b,Vegetarian food\ntest/008acff2aa16a95a,Close-up\ntest/008af1d34142a5bb,Close-up\ntest/008e0dc6b90fd509,Barbecue chicken,Fried food\ntest/008ed2884d0fab85,Cosmetics\ntest/008f009f3c83b480,Luxury vehicle\ntest/008f80eb30f8ef54,Compact car,Luxury vehicle\ntest/0090a04851ebcc71,Junk food\ntest/0094cf85e4c9b6e2,Combat sport,Grappling\ntest/009b9d9957155405,Close-up,Scaled reptile\ntest/009c6ebee7e06d25,Close-up\ntest/009e9c8ee5bbe1fb,Black-and-white\ntest/009efa1fb9ab9a08,Modern dance,Musical theatre\ntest/00a011632563dae2,Ball game,Team sport\ntest/00a4e2347de87aea,Compact car,Luxury vehicle,Antique car\ntest/00a5679940461f10,Jeans\ntest/00a5c9469db810ee,Barechested\ntest/00a5f08a8fdc1cdb,Black-and-white,Arthropod,Close-up\ntest/00a8014df2207b5d,Luxury vehicle,Sport utility vehicle\ntest/00a805f794d91315,Close-up\ntest/00a8361fbe82a0a7,Siberian husky\ntest/00a841f0611c28fb,Compact car,Luxury vehicle,Antique car\ntest/00a9ab66f8431dd6,Luxury vehicle\ntest/00a9f1817594f581,Luxury vehicle,Antique car\ntest/00aa934b6ad5297a,Compact car\ntest/00ac7414de3f47d1,Close-up\ntest/00acbd16792f2b25,Auto racing,Performance car\ntest/00ade0071518a9fa,Hair coloring\ntest/00ae02779702fe2a,Steamed rice\ntest/00aea52c2f4e5a03,Compact car,Luxury vehicle\ntest/00b3bf5fff874866,Freight transport\ntest/00b55305793d2e36,Steamed rice\ntest/00b5f2b0ade593ed,Orator,Public speaking\ntest/00b6be538644be0b,Whiskers\ntest/00b96ff2060fcfa2,Combat sport\ntest/00bac5ba29b06beb,Close-up\ntest/00bb584b34600c9c,Close-up\ntest/00bf4d1c58f26838,Close-up\ntest/00bf63ce253deb33,Arthropod,Close-up\ntest/00c0e3b734506745,Figure skating\ntest/00c64e1acd3a586b,Scaled reptile\ntest/00c8891d88ff36c1,Antique car\ntest/00c99a6abc376fed,Modern dance\ntest/00ca02eef9994568,Woodwind instrument\ntest/00ca18f0b632408b,Road cycling\ntest/00cc7876bb94db6a,Fried food\ntest/00cd3b2e7135ac47,Black-and-white\ntest/00cd932265611688,Water polo\ntest/00cde7c878ddba55,Black-and-white\ntest/00d0d3f1a60604af,Rural area\ntest/00d5e1b2b962aad8,Luxury vehicle\ntest/00d66c079e4acaff,Luxury vehicle\ntest/00d9419e2faa59fa,Whiskers\ntest/00dababf772c58ec,Antique car,Performance car\ntest/00dbabf445242ba3,Goats\ntest/00dd6d717c50e78b,Whiskers\ntest/00de91d2063cbcbc,Cosmetics,Black-and-white,Close-up\ntest/00e20fa6098f2577,Computer speaker\ntest/00e789d671a484c6,Equestrianism\ntest/00e7d613216e482e,Luxury vehicle,Performance car\ntest/00e98b97ea9bfbc1,Bovine\ntest/00ea4ebcea489abd,Ball game\ntest/00f0dfc5e83813af,Plant stem\ntest/00f181a154b68ab5,Fried food\ntest/00f29fb46f0626d1,Close-up\ntest/00f64af0069b56d5,Antique car\ntest/00f65f6f374c5f35,Solar energy\ntest/00f8135662b9e6e6,Jeans\ntest/00f8b4d7bc4c74c9,Stemware\ntest/00fa86219a15b740,Jeans,Extreme sport\ntest/00fa9f753c8c35b3,Performance car\ntest/00faa3c80e7aa721,Plant stem\ntest/00fae90cf0d27dcf,Luxury vehicle\ntest/00fb03e524c40d66,Compact car,Luxury vehicle,Antique car\ntest/00fb1f6166e2cc59,Black-and-white,Modern dance\ntest/00fc655c61361b4e,Black-and-white\ntest/00fc6fca3c76f9a4,Floristry\ntest/00fcce0ef479d5b1,Computer speaker\ntest/00fd547449716c1a,Whiskers,Domestic rabbit\ntest/0103045f398f8cd2,Military vehicle,Luxury vehicle\ntest/0106cda91c80b149,Roller skating\ntest/01097588cd739a63,Frozen yogurt\ntest/01097eaa96dcfb20,Equitation,Equestrianism\ntest/010986f44f36214a,Performance car\ntest/0109b91ed80bf617,Black-and-white\ntest/0109d93bece91540,Prosciutto\ntest/010e9b4243809546,Compact car\ntest/0110e8ec47d60bda,Team sport\ntest/0110eb4e1207f360,Breadboard\ntest/01122addafb07701,Vegetarian food,Frying,Junk food,Fried food\ntest/01127efd2be0a028,Close-up\ntest/0113b62272e9fada,Luxury vehicle,Performance car\ntest/0113c330c2fa1779,Antique car\ntest/0116b69b0eaedaaf,Antique car\ntest/01186b60a302eaa8,Residential area,Aerial photography\ntest/0118804d8fedc568,High-speed rail\ntest/0119cbb6204b410b,Backlighting\ntest/01203fe50f8dcab0,Close-up\ntest/012082521ae560e7,Middle ages\ntest/01223353c774b4f4,Performance car\ntest/0123ece59e4a2e2e,Cosmetics\ntest/0124e516da4fe039,Vegetarian food,Corn on the cob\ntest/0124ed4b5973567e,Rural area\ntest/0125f15ad93205bf,Aircraft engine\ntest/0125fb0d572611a3,Luxury vehicle\ntest/0129b2246ff9d5e6,Team sport\ntest/012b40f0d9ccba39,Domestic short-haired cat,Close-up,Whiskers\ntest/012c4003cd9eafe5,Modern dance,Musical theatre\ntest/012d00735745567e,Close-up,Whiskers\ntest/012fff5ad5a5bc9d,Jeans\ntest/013087a0a86316c1,Team sport\ntest/013386bbe4c5aade,Rural area\ntest/013393625a45a044,Auto racing,Sport utility vehicle\ntest/01343a37e871fd09,Aircraft engine\ntest/0134e4c71ff2dd8a,Extreme sport,Road cycling\ntest/01379cf46eeb6b97,Performance car\ntest/0138492cec634649,Ball game,Team sport\ntest/0138fb77f3a3f70d,Nile crocodile,Crocodilia\ntest/013908ead6313e68,Arthropod\ntest/013fa6325ed13380,Extreme sport\ntest/01410fe3dbca7a01,Luxury vehicle,Performance car\ntest/0142d1fc46883a7b,Black swan\ntest/0144e6262e62d196,Performance car\ntest/01451f644c30d470,Aircraft engine\ntest/01455d4ee3e71577,Ball game\ntest/01462ad475d84dc7,Close-up\ntest/0147886b3a949343,Arthropod,Close-up\ntest/0149d723dbc0d724,Whiskers\ntest/014bcc2a3590db99,Halter\ntest/014ce4294afbd04e,Black-and-white\ntest/014ed795e501662f,Motorcycling\ntest/014fa0c316679154,Leaf vegetable\ntest/0150ef8e30b2f283,Hound\ntest/01536681f764849f,Digital camera\ntest/01537bd4806aea21,Ball game,Team sport\ntest/0157306d7db1820c,Beef tenderloin\ntest/01577d7ab6092248,Athletic shoe,Outdoor shoe\ntest/015abd5b60829e3c,Floristry\ntest/015b51526d6813b5,Hound\ntest/015c3c4a93dbf669,Monoplane\ntest/015df3fbcf17d6fe,Antique car\ntest/015ee313d5aca6fd,Luxury vehicle,Sport utility vehicle\ntest/0160e0d0dfa49184,Monoplane,Aircraft engine\ntest/0161f344e3253bed,Luxury vehicle,Performance car\ntest/01625315516089e8,Black-and-white\ntest/0162547d90955919,Fried food\ntest/01632af92cff6284,Perching bird,Close-up\ntest/0166bf8f6b1ab38a,Luxury vehicle,Performance car\ntest/016b22aad93f2fae,Watercolor paint\ntest/016b450fe116e01d,Luxury vehicle\ntest/016f4eb81a38a5cc,Luxury vehicle,Performance car\ntest/016feb7bf9748149,Musical theatre\ntest/0172fc5c492396c3,Rural area\ntest/0174a89d14cf1dc5,Black-and-white\ntest/0175b6c71de4f27f,Erinaceidae\ntest/01768e3dbae6df15,Microcontroller\ntest/0176b9bcd8aca034,Fried noodles\ntest/0177d4fc1dd643c0,Close-up\ntest/0178045107080d21,Black-and-white,Luxury vehicle\ntest/01794fce76f527f3,Compact car\ntest/017960d44010694e,Ball game,Team sport\ntest/0179ee390ed1d47a,Performance car\ntest/017ae4de3271e9a9,Bird of prey\ntest/017b66301f769755,Close-up\ntest/017be8f77fbcf77d,Close-up\ntest/017c5a5507ec70b8,Dog walking\ntest/017c776a67956dd9,Close-up\ntest/017fdd2b45d070bf,Herding,Goats\ntest/0181ee7c5c6a7a7f,Luxury vehicle\ntest/0182142868e2ff55,Hound\ntest/018376cd9ec14643,Domestic short-haired cat,Close-up,Whiskers\ntest/0184d0eeb3b32f29,Black-and-white\ntest/01874c92fdcb9199,Auto racing,Luxury vehicle,Performance car\ntest/0187938d277188fa,Bird of prey\ntest/01883fb28906dbb3,Antique car\ntest/018c62ef8e0a3b57,Compact car,Antique car\ntest/018d66fb024c0d25,Ball game,Team sport\ntest/01905f180fe41311,Close-up,Whiskers\ntest/0192deb7b7fade4c,Close-up\ntest/0194705d5bea5956,Auto racing\ntest/0195bb9bc177c5b3,Senior citizen\ntest/0195f04d4c2cc69b,Extreme sport,Divemaster,Scuba diving,Underwater diving\ntest/01981fb00cd5d947,Nile crocodile,Crocodilia\ntest/019c24bc78bf8285,Aircraft engine\ntest/019c95b2274106f5,Rural area\ntest/019ccd1486492513,Junk food\ntest/019e31d8de913484,Siberian husky\ntest/019fca579b95b65a,Close-up\ntest/01a0508ae2fc1d69,Luxury vehicle,Performance car\ntest/01a07e591ac3aeff,Residential area\ntest/01a0cb6c76d4bb10,Sport utility vehicle\ntest/01a2823fc6452bef,Blond,Portrait photography,Close-up\ntest/01a36c1263e1caf0,Jeans\ntest/01a3de0001daa248,Team sport\ntest/01a471db31e88727,Whiskers\ntest/01a54a8db55760b7,Black-and-white\ntest/01a5651107113ae7,Compact car,Antique car\ntest/01a81a65d70b05cb,Black-and-white\ntest/01a978c9a171c107,Equestrianism\ntest/01ab4be3f275d44c,Ball game,Team sport\ntest/01ab5f6e750830f5,Luxury vehicle\ntest/01ac026097ecc699,Close-up\ntest/01aca1d798e3815b,Luxury vehicle\ntest/01adef7d05949f16,Musical theatre\ntest/01ae716bfbe9be25,Close-up\ntest/01aea3e322b0f0be,Sailboat racing\ntest/01af4b786d06164d,Luxury vehicle,Performance car\ntest/01afd648a4376160,Jeans,Lawn game,Ball game\ntest/01afe156484bb08e,Close-up,Whiskers\ntest/01b52943f00323a0,Luxury vehicle,Performance car\ntest/01b53aaa04bf4c0d,Firearm\ntest/01b6066f2247bd4b,Pork chop\ntest/01b7625045c9161a,Musical theatre\ntest/01b7c6079846210d,Close-up\ntest/01b7daa699be4fc2,Rural area,Canola\ntest/01b908c385d1cd61,Ball game,Team sport\ntest/01b9c2a6c0f7945a,Luxury vehicle,Antique car\ntest/01b9d625ef5c404d,Luxury vehicle,Antique car\ntest/01c0e4565bf7fdcc,Jeans\ntest/01c30f593bb8a32a,Military vehicle\ntest/01c72aaaa6ef0aed,Sport utility vehicle\ntest/01c8a5b1c31cd6a5,Luxury vehicle,Performance car\ntest/01c9edfa13e2eb24,Luxury vehicle,Antique car\ntest/01ca1add8d9f3338,Close-up,Scaled reptile\ntest/01ca6f4314ae7c11,Compact car\ntest/01cc02e595039de0,Nest box\ntest/01cc07838fc28a68,Domestic short-haired cat,Close-up,Whiskers\ntest/01d1c8c162ae025d,Luxury vehicle\ntest/01d2f429ff6e02a9,Senior citizen\ntest/01d4fc65683b0798,Arthropod,Close-up\ntest/01d793461b34ef42,Auto racing\ntest/01d7ea3ad5224b2c,Cobblestone\ntest/01d831fbda6fa27e,Close-up\ntest/01d938785ed631ee,Compact car,Luxury vehicle\ntest/01d9849de01fe67f,Residential area\ntest/01d9c3326766da09,Watercolor paint\ntest/01da72c595315646,Compact car,Luxury vehicle,Antique car\ntest/01dab9f10bba0485,Watercolor paint\ntest/01dba6037e5672a3,Aerial photography\ntest/01dc579858cc791b,Luxury vehicle,Antique car\ntest/01dcb225875427a5,Junk food,Cookies and crackers\ntest/01dd3f9ed4b274d2,Primate\ntest/01dd6bc460954dea,Fried food\ntest/01dde2016d147b23,Ramen\ntest/01e0331b346f1ba0,Antique car\ntest/01e1b1405489ff61,Sport utility vehicle\ntest/01e260e2d519a7ec,Billiards\ntest/01e368998a68ff28,Domestic short-haired cat,Whiskers\ntest/01e3940e07aa73c9,Luxury vehicle\ntest/01e3cb2dabd82145,Luxury vehicle,Antique car\ntest/01e511ae1e11f71b,Wind wave\ntest/01e6d37711b1f80b,Luxury vehicle\ntest/01e836367cfb0c76,Whiskers\ntest/01e85ae4cda2e644,Brochette,Fried food\ntest/01e8e6894bf88777,Close-up\ntest/01ec193289b6a668,Black-and-white\ntest/01ec502a9b3aabf0,Ball game\ntest/01ed0c6b81dbbbe3,Black-and-white\ntest/01edc08633c6dceb,Ball game\ntest/01edd7f9ca531e1b,Brisket\ntest/01ee79a4f50483ca,Dobermann\ntest/01ee85b236b57b7f,Siberian husky\ntest/01ef6cf9f6db33f4,Black-and-white\ntest/01f3b18950c85336,Electronic instrument\ntest/01f4314757621e8c,Luxury vehicle,Performance car\ntest/01f4905ec765dca8,Close-up,Scaled reptile\ntest/01f4f8fb9121bbc4,Vegetarian food\ntest/01f7f875dfc7a893,Close-up\ntest/01f8ae9fafd9942d,Military vehicle,Sport utility vehicle\ntest/01faacb9a100e850,Roller skating\ntest/01fbe8482c24948c,Portrait photography\ntest/01fbf977a317ae22,Close-up\ntest/01fdd867ec430d2b,Close-up\ntest/01fdf7be4d4dbc6a,Vegetarian food\ntest/0201c3ace7ff0524,Compact car\ntest/0203bb91c43ad34c,Full moon\ntest/020418f41a5dde99,Domestic short-haired cat,Whiskers\ntest/02053bc389142b78,Close-up\ntest/0209cd9e676d4933,Luxury vehicle,Antique car\ntest/020c658cb50a664c,Close-up\ntest/020f72181beb2ae9,Performance car\ntest/020fa3d6becf6888,Stemware\ntest/021195ffb271f051,Bovine\ntest/0211a018a96eb924,Figure skating\ntest/0212693ac61d83a0,Luxury vehicle,Performance car\ntest/0214ec0124a44812,Compact car,Luxury vehicle\ntest/0216a440cf310e77,Brisket\ntest/02177c7b318e1d5c,Military person\ntest/021a22be2558a2ec,Fried noodles\ntest/021b038e2fb6da93,Close-up,Scaled reptile\ntest/021b5961963bc8d0,Black-and-white\ntest/021c0b30df0ebd1f,Luxury vehicle,Antique car\ntest/021c6ec8ac6c1712,Residential area\ntest/021cb59ddabe7b5a,Extreme sport,Surfing Equipment,Wind wave\ntest/021d18d5777aafec,Antique car\ntest/021d7613df35e468,Luxury vehicle\ntest/021d9739adae9f9c,Military vehicle\ntest/02208a6a6d4b110c,Rural area\ntest/02211e9fd1fd1a0b,Road cycling\ntest/02214e9e87af8091,Ball game\ntest/02234034b470426f,Leaf vegetable\ntest/0229ffe8b26cd421,Compact car,Performance car\ntest/022ae6e2bf3b5aaf,Luxury vehicle\ntest/022d642a36f4a551,Whiskers\ntest/0230faae1c2e77bb,Close-up\ntest/0231ba6852a4fb3c,Close-up\ntest/0234466938f727ba,Luxury vehicle,Performance car\ntest/0235e003a894e001,Auto racing,Compact car\ntest/023618f3769799c5,Plant stem,Close-up\ntest/02391f10e857dc8c,Ball game\ntest/02394a3449862b85,Combat sport\ntest/023a0a6c28bd4930,Jeans\ntest/023bb48250738229,Luxury vehicle,Performance car\ntest/023bc52e922f1e59,Close-up,Whiskers\ntest/023be51fb66fe232,Close-up\ntest/023da766dc2f8fea,Fried food\ntest/023e0c016fb05e7f,Sledding\ntest/023e0d78c1165bb2,Rural area\ntest/023ef3b8248a9409,Team sport\ntest/024145e8b5cde39a,Aloe,Plant stem\ntest/024508e426be0712,Forklift truck\ntest/0245d310f25a6d7f,Forklift truck\ntest/02496ae1fa86b346,Sport utility vehicle\ntest/024c1c43837c0f97,Monoplane,Gliding\ntest/024d645b0d362cfa,Luxury vehicle,Sport utility vehicle\ntest/024e2d83066758fe,Angling\ntest/024e8cf1a7aa084f,Watercolor paint\ntest/024eb057d35c64b4,Auto racing\ntest/024f05451e4942bd,Firearm\ntest/024fb208337d48ad,Boating\ntest/0250382927a9c897,Boating\ntest/0250fb7a56c8c934,Modern dance\ntest/02530be4f2f64f16,Domestic short-haired cat,Whiskers\ntest/02531119801140b3,Close-up\ntest/02539d32a56b0a52,Residential area\ntest/0255da3e954f3ff9,Whiskers\ntest/02575c8316daf514,Compact car\ntest/025c246aeb6d6b02,Close-up\ntest/025c9ca1125dca0a,Extreme sport\ntest/025dfa9ea18bcb18,Close-up\ntest/025e41b66ba52da4,Extreme sport,Sport climbing\ntest/025ec7dc8c2263b7,Horse and buggy\ntest/02608889587fb53e,Luxury vehicle\ntest/02610378c3e83091,Go-kart\ntest/0262cdd43cd34ac6,Close-up\ntest/0263c0df6ce14f8d,Luxury vehicle,Antique car\ntest/02676fc72e6c6a44,Close-up\ntest/02690f65ea2a2223,Combat sport\ntest/026aeb38ed48e08d,Great white shark\ntest/026b234177cbe6fe,Luxury vehicle,Antique car\ntest/026dfb42b3a71793,Great white shark\ntest/026ee00405c54eb1,Luxury vehicle\ntest/027058d4930184f7,Close-up\ntest/0270f0ee4df0beed,Blond\ntest/027221b0eeb3d56d,Close-up\ntest/02724ca84ff65cca,Aerial photography\ntest/027282a8efd3300b,Blond,Close-up\ntest/0272df32be511f18,Close-up,Plant stem\ntest/02737291abb464c9,Close-up\ntest/0274d781e2a2b3b1,Black-and-white\ntest/0275cd995b9ae47c,Fried food\ntest/027609cac4cf342b,Comics\ntest/0278e4dca5d4b40f,Modern dance\ntest/027b16382feaca69,Musical theatre\ntest/027b59daf7d1f4a1,Woodwind instrument\ntest/027bb34abcfe7557,Close-up,Plant stem\ntest/027c9d8e256cb9a1,Performance car\ntest/027dafd1b0d6b215,Electric piano\ntest/027eed09ac62674b,Ramen,Soba\ntest/027f9265229d6e40,Luxury vehicle,Performance car\ntest/02829caf73470bd1,Briefs\ntest/0282e807adf2ffd0,Black-and-white\ntest/0284916969f261ee,High-speed rail\ntest/02853f4a52af73b8,Close-up\ntest/02862c4d9df2787d,Luxury vehicle,Sport utility vehicle\ntest/02866b5332267a21,Ramen\ntest/02888127ec7b0333,Jeans\ntest/028980f8813db2c1,Close-up\ntest/028a8c039865f9a5,Luxury vehicle,Performance car\ntest/028b4e49b14c17d5,Extreme sport\ntest/028b81e5b5f9cd34,Aerial photography\ntest/028ce14d18fe5571,Scaled reptile\ntest/028d604264df1e8c,Aircraft engine\ntest/028db9af9e92a79c,Rural area\ntest/028fe4c3f3ffd473,Military person\ntest/029096bb7f199843,Miniature Poodle\ntest/02913ee7cd486680,Steamed rice\ntest/02916d7fbe44cb62,Arthropod,Close-up\ntest/02922cb8eb7c41bf,Combat sport\ntest/02968452eb79fceb,Backlighting\ntest/02978dd4984331db,Luxury vehicle,Performance car\ntest/0298c70279b6e842,Ball game,Team sport\ntest/029a799730eb8b30,Siberian husky\ntest/029e1ec61528e4d5,Compact car,Luxury vehicle,Antique car\ntest/029ebef52328c6bb,Luxury vehicle\ntest/029f570710b1a117,Luxury vehicle\ntest/029f8110e3dea2d8,Luxury vehicle\ntest/02a0f5dd0749b1a5,Luxury vehicle,Sport utility vehicle\ntest/02a23e38a9d39c1d,Sport utility vehicle\ntest/02a2d593bc324526,Antique car\ntest/02a4ba545c91c8a0,Luxury vehicle\ntest/02a5b41524b4ba4b,Bovine\ntest/02a63ebed0f709ce,Jeans,Chordophone\ntest/02a6be65118e1910,Sport utility vehicle\ntest/02a874da512add8d,Luxury vehicle,Performance car\ntest/02a8b53abeb5f015,Monoplane\ntest/02ac0011bab35ad1,Compact car\ntest/02ad64cff13f5ccd,Antique car,Performance car\ntest/02ad8d3ff0c7ffd9,Microcontroller,Breadboard\ntest/02b336d12bc20674,Close-up\ntest/02b5ac344dea8b8e,Luxury vehicle,Performance car\ntest/02b7501d5064f0cc,Lawn game,Ball game\ntest/02b82f2d79903633,Close-up\ntest/02b8cbb00f69108b,Whiskers,Domestic rabbit\ntest/02ba1a016d97490b,Luxury vehicle\ntest/02ba70a37a4cba80,Leaf vegetable\ntest/02ba71c35fa1520a,Close-up\ntest/02bc1d7afa790ea8,Antique car\ntest/02bcc561da13fd77,Luxury vehicle,Sport utility vehicle\ntest/02be878bca422ceb,Compact car,Antique car\ntest/02c0ef08e87bca1c,Luxury vehicle\ntest/02c2ae7ddc5b76a7,Black-and-white\ntest/02c2baa6fa5f87f8,Performance car\ntest/02c41bf9d19fbae4,Extreme sport\ntest/02c6f4b87790edca,Sport utility vehicle\ntest/02c722c808abae39,Luxury vehicle,Sport utility vehicle\ntest/02c74f2be91fd19a,Perching bird,Close-up\ntest/02c813bfb0eaa12d,Blond\ntest/02c9d4cea24edb66,Microcontroller\ntest/02ca31bbd4429ab5,Pork chop,Beef tenderloin\ntest/02ca9adbdaa8fdfa,Fried food\ntest/02cb36ec9fb3a58f,Close-up,Whiskers\ntest/02cc288776541f07,Close-up\ntest/02ce1fafcee090b2,Luxury vehicle,Performance car\ntest/02d55fa9a71dab16,Microcontroller\ntest/02d6c90cad03c054,Chordophone\ntest/02d7bd1e53346a11,Close-up\ntest/02d9a40bf2fb0a09,Medical imaging\ntest/02dab2c15bd9ca8b,Close-up\ntest/02e368d6af6b7d84,Close-up\ntest/02e42cfb4cd69621,Orator,Public speaking\ntest/02e5c8c76cc2cdf0,Monoplane\ntest/02e669a35cac41d8,Firearm\ntest/02e7b256852ef262,Microcontroller\ntest/02e87642778864e2,Luxury vehicle\ntest/02e94b92d16c65de,Domestic short-haired cat,Close-up,Whiskers\ntest/02e9e72c47a5a669,Luxury vehicle,Performance car\ntest/02ea0c95efbb4a91,Jeans,Luxury vehicle,Performance car\ntest/02ebbbe4b401859f,Jeans\ntest/02ecda99aed5218a,Siberian husky\ntest/02ed619dd04d1c00,Auto racing,Luxury vehicle,Antique car\ntest/02eec2c28f8cdb8e,Melee weapon\ntest/02efb3675cfb1c8f,Close-up\ntest/02f2198e364994b9,Close-up\ntest/02f2eb66e7befc62,Jeans,Compact car,Luxury vehicle\ntest/02f31a30707c8ea4,Antique car\ntest/02f38c8870910ae8,Luxury vehicle,Antique car\ntest/02f3bbf1da724ef6,Bovine\ntest/02f594ec73c1ea25,Extreme sport,Parachuting\ntest/02f9c4aa3e4247bf,Combat sport\ntest/02fbdccb15dfbec8,Close-up\ntest/02fbefc50b95e50e,Flatbread\ntest/02fc77ab00cea91a,Firearm\ntest/02fcd677ba652117,Close-up\ntest/02fdaac90cdf6593,Luxury vehicle\ntest/02fe43145b06bb57,Performance car\ntest/02ff509712130d4b,Black-and-white\ntest/0300b94913926939,Sport utility vehicle\ntest/0300e59281a86403,Aircraft engine\ntest/03014954973d3eb7,Sport utility vehicle\ntest/030212548c1656e6,Domestic rabbit\ntest/030265ffe2e90f50,Double-decker bus\ntest/030280fa1d88bfcd,Combat sport\ntest/03046c4977b897f7,Floristry\ntest/03063cd8d3ab6530,Portrait photography,Close-up\ntest/030712a09dfea6e0,Road cycling\ntest/03079e80275857b8,Antique car\ntest/0307aaccb1aa277f,Arthropod,Close-up\ntest/030828a333d5fa98,Auto racing\ntest/030b15c1452382f6,Residential area\ntest/030cbf50e8725f6d,Junk food\ntest/0310048b057262ad,Boating\ntest/0310e61953ef7115,Compact car\ntest/03151c1cc6a6d7e1,Arthropod,Close-up\ntest/0317e581988fb533,Aircraft engine\ntest/0317f5abdc721e11,Luxury vehicle,Antique car\ntest/0319a2749920f37b,Close-up\ntest/0319b683fcc1f9d5,Floristry\ntest/031b6acf8f2b55da,Portrait photography\ntest/031d6107792ecdfa,Aircraft engine\ntest/031da453527750ea,Junk food\ntest/031de3300e00b7cb,Hound\ntest/031e6e5b1a8b8014,Plant stem,Close-up\ntest/032026a9f3bfd4fa,Nile crocodile,Close-up,Crocodilia\ntest/03209838640791fc,Military vehicle\ntest/032804e8b0b7c298,Black-and-white\ntest/032912c229586966,Black-and-white\ntest/0329708e66a64efb,Domestic short-haired cat,Close-up,Whiskers\ntest/032a45aae71ab704,Aircraft engine\ntest/032bd9374ead2331,Luxury vehicle,Performance car\ntest/032e9504bb539be4,Luxury vehicle,Antique car\ntest/0334271ebc0263d6,Steamed rice\ntest/033467b2560aa67c,Military vehicle\ntest/03370058aa1d1ced,Amphibian,Scaled reptile\ntest/033797e7b2ae595b,Whiskers\ntest/0339b44aeaea68fc,Close-up\ntest/0339d9cff7ea0af4,Cosmetics\ntest/033c662851c65e27,Black-and-white,Modern dance\ntest/033d3b9fef531887,Molluscs\ntest/033ea4e8238523ee,Compact car,Luxury vehicle,Antique car\ntest/033ed26506d3768b,Arthropod,Close-up\ntest/033fc1bba2ca7014,Close-up\ntest/034022685178eda1,Auto racing\ntest/0340c9cf050faab6,Luxury vehicle\ntest/0341cea7eaab7b46,Compact car,Luxury vehicle\ntest/034496d1dd5f89f4,Black-and-white\ntest/03462a2854682813,Microcontroller\ntest/0349efca6e822e22,Outdoor shoe\ntest/034a2ea7a2cc265f,Fried food\ntest/034a9fec4c5e38a4,Ball game\ntest/034b8a81b11cdfe1,Combat sport\ntest/034f9dae4cbce7c3,Freight transport\ntest/03504848e90a3706,Horse racing\ntest/03522c7d66b0feba,Jackfruit\ntest/03548c37b8c63199,Luxury vehicle\ntest/03553650f907c2c6,Luxury vehicle\ntest/0357f53b5a6cab65,Compact car,Luxury vehicle,Antique car\ntest/0359ecff838be6a4,Musical theatre\ntest/035b7e93038893e1,Flatbread\ntest/035e98110f55b4a5,Compact car,Antique car\ntest/035fa8ad64dcaf64,Aerial photography\ntest/03610c1b5000066d,Close-up\ntest/0361460769c2f49f,Compact car,Luxury vehicle\ntest/03623cdca375c425,Orator,Public speaking\ntest/03624a5223c4f732,Vegetarian food,Fried food\ntest/03625c9b6b49d192,Luxury vehicle\ntest/0362d35a6d9d0024,Compact car,Luxury vehicle\ntest/036577fa0421c5c6,Ramen\ntest/03674739d455f92e,Performance car\ntest/0369718fb44ffd1b,Sport utility vehicle\ntest/036e139964de52c7,Close-up\ntest/036e20088d819641,Luxury vehicle,Sport utility vehicle\ntest/036e9ea1bfec9990,Luxury vehicle,Antique car\ntest/036f172343c1caff,Black-and-white\ntest/03701ec409b90abd,Close-up\ntest/037139519b0044b3,Luxury vehicle,Performance car\ntest/03715c7dd19b1f36,Bird of prey\ntest/03726844adb2e67a,Horse and buggy\ntest/0374581b0677f389,Luxury vehicle,Performance car\ntest/037553015fec6d93,Jeans\ntest/03767ec056b8994c,Peafowl\ntest/0377e3375829aba3,Close-up\ntest/0378fe468292e979,Headphones\ntest/037b967dff7b97b9,Close-up\ntest/037d01ea79f33a44,Rural area\ntest/0382ece019b27f26,Close-up\ntest/0385d1bbc6fbeff0,Close-up\ntest/038765c1dd37e491,Billiard room,Billiards\ntest/038a6ba9f84e64d0,Black-and-white\ntest/038a86cdd8b09dcd,Arthropod,Close-up\ntest/038b00c019d334cd,Cookies and crackers\ntest/038e6d79ac8c033e,Senior citizen\ntest/038f7e0e31c51740,Perching bird\ntest/03907120d0b9824b,Frying,Junk food\ntest/0391d35cc2a92100,Plant stem,Close-up\ntest/039237c49fd32bc1,Road cycling\ntest/0393cdd8161598e4,Gun turret,Military vehicle\ntest/039446bf525bece8,Close-up,Plant stem\ntest/0395bf272b8fadba,Aircraft engine\ntest/0397e41deb089f40,Arthropod,Close-up\ntest/03986dfedf9f294b,Wallaby\ntest/0399820d18cbcef5,Luxury vehicle,Performance car\ntest/039c34214daae952,Coral reef fish\ntest/039c752e5de51b8a,Whiskers\ntest/039d314bad5403d7,Brochette\ntest/039d5b2842e0252e,Electronic instrument\ntest/039d7c690d5a3b53,Arthropod,Close-up\ntest/039ef180aad94e03,Jeans\ntest/03a3cdf4b87d1a34,Compact car\ntest/03a3dad019405e17,Whiskers\ntest/03a63d67052c0e17,Drums\ntest/03a94aa9d877ca40,Rural area\ntest/03a9ad066b595102,Compact car\ntest/03abf85fbae6ceaa,Floristry\ntest/03ac8046540333bb,Galleon\ntest/03b00ca1310f3ca7,Military vehicle,Gun turret\ntest/03b1b26651878b5f,Briefs\ntest/03b21fb42485372c,Wind wave\ntest/03b3a8c125195709,Close-up\ntest/03b62fd6305aafab,Antique car\ntest/03b72eb9b233effd,Vegetarian food\ntest/03b74dc692aa7795,Fire apparatus\ntest/03b9630d0ba5749d,Close-up,Whiskers\ntest/03ba49dd29f08dc4,Molluscs,Close-up\ntest/03bacd7be83b721e,Close-up,Whiskers\ntest/03bb15edb87b7821,Ball game\ntest/03c0d701c94b620c,Jeans,Blond\ntest/03c1d088165dd456,Falafel,Fried food\ntest/03c21fcddd961819,Luxury vehicle,Sport utility vehicle\ntest/03c3dfc7e998b856,Compact car\ntest/03c54a7aafcf5c21,Luxury vehicle\ntest/03ca6ad93a6b77f2,Chordophone\ntest/03cb47e1461da9e6,Boating\ntest/03cbb24c7bf02c60,Junk food,Fried food\ntest/03cbbda01c6410c8,Luxury vehicle,Sport utility vehicle\ntest/03cf8209e6c60fcd,Antique car\ntest/03d4045662b16f6d,Combat sport,Grappling\ntest/03d9913e461d3837,Amphibian\ntest/03dd3bda57442dcc,Ribs\ntest/03de2a83847fb559,Ball game,Team sport\ntest/03e20bf7e49d402a,Compact car\ntest/03e288996840786f,Luxury vehicle\ntest/03e3a964997cfc60,Luxury vehicle\ntest/03e3be44dddc6389,Wind wave\ntest/03e58544fa71b708,Black-and-white,Whiskers\ntest/03e5d3d1d576fd23,Motorcycling\ntest/03e8f7b0e42ff0b8,Black-and-white\ntest/03ec41090309801c,Residential area\ntest/03eeb77e2166c5d6,Close-up\ntest/03f02e8458339791,Aerial photography\ntest/03f1a449c6a3b683,Sport utility vehicle\ntest/03f4adcebcf3f439,Dishware\ntest/03fa0d1d74894166,Rural area\ntest/03fa2ae52c8bebc4,Brisket,Ribs\ntest/03fb5628ea9fc281,Boating\ntest/03fdc309c3405fd0,Luxury vehicle,Performance car\ntest/03fe1fa2636afc61,Luxury vehicle\ntest/03fede72e591b58c,Luxury vehicle,Antique car\ntest/03fee7da50651397,Cookies and crackers\ntest/0400d1f291245b6d,Toy block\ntest/0400edaf34d63ec7,Backlighting\ntest/0402361d506a21f5,Combat sport\ntest/0403852aa5f28483,Black-and-white,Kitesurfing,Wind wave\ntest/040413c2cb1b2abd,Blond\ntest/0404bea3ac7150ca,Jeans\ntest/040a328027bcde54,Forklift truck\ntest/040b620bef12198a,Extreme sport,Divemaster,Scuba diving,Underwater diving\ntest/040d22e91306e905,Luxury vehicle\ntest/040e11da35343a5e,Close-up\ntest/040e3416220f1f68,Galleon,Frigate\ntest/040f589253f2c3ce,Aerial photography\ntest/040fe6debb1992c5,Black-and-white\ntest/04100bc0866036ce,Perching bird,Close-up\ntest/041029edc8089ff5,Plant stem,Close-up\ntest/0410eff2d01dd540,Whiskers\ntest/0411b1e268aa052b,Divemaster,Scuba diving,Underwater diving\ntest/04122078df16b077,Team sport\ntest/04148d451d34d720,Headphones\ntest/04163677359d192e,Flatbread\ntest/0416ff9bd589ef4f,Chordophone\ntest/0419a8858234cb92,Lumpia\ntest/041a28a56ec73f41,Outdoor shoe\ntest/041ad9ea598c47ad,Equitation,Equestrianism\ntest/041c23fc23d30894,Luxury vehicle\ntest/041cb2f958f04d82,Perching bird,Close-up\ntest/041ed18c2584a0d4,Luxury vehicle\ntest/0423752f180ef665,Ramen\ntest/042676f6b5eba097,Extreme sport,Motorcycling\ntest/0427f1eeadbc26de,Extreme sport\ntest/042ddd2bb03774d6,Black-and-white\ntest/042e3e2f40bd00b8,Modern dance\ntest/04303137a0a9dd29,Aerial photography\ntest/04318cb343f80860,Whiskers\ntest/0431a8b3d425aa38,Rural area\ntest/04325567e6039b7c,Luxury vehicle\ntest/043304e924cb0bc4,Luxury vehicle\ntest/04339ab3afbe34e8,Plant stem,Close-up\ntest/0433ddce791bfc68,Black-and-white\ntest/0433f2e5decb0702,Residential area\ntest/0436592a0ac34908,Plant stem\ntest/0436c13e9dc73c13,Luxury vehicle,Performance car\ntest/043bc5bc1d1c6216,Extreme sport\ntest/043d38140a8252f1,Close-up,Whiskers\ntest/043d8fd4001ffca9,Amphibian,Close-up\ntest/043e49649e58ac7f,Luxury vehicle,Sport utility vehicle\ntest/04423d3f6f5b8e74,Close-up\ntest/0442e2e7d30dae02,Black-and-white,Close-up\ntest/0443405892fa306a,Hunting knife\ntest/0443e2e47bd82496,Touring car,Luxury vehicle,Antique car\ntest/0446927cb5f3e6fd,Close-up,Scaled reptile\ntest/04475e014c97f62b,Electronic instrument\ntest/0447d6428b6c2cf0,Whiskers\ntest/044b0ab97d4f68ff,Miniature Poodle\ntest/044bd671db897463,Vegetarian food\ntest/044ca32c36798fc2,Jeans\ntest/0452a774133e15eb,Close-up\ntest/04540b7b101578c6,Microcontroller\ntest/0458034c81d6f267,Close-up\ntest/04585bec00db964f,Close-up\ntest/045b90e4131c890e,Luxury vehicle,Antique car\ntest/045bac53f3de2402,Close-up,Plant stem\ntest/045bc018aa9648d8,Luxury vehicle\ntest/045cffd17afd6f71,Antique car\ntest/045d2643c3b62bc6,Ball game\ntest/04618d3d003a1a5c,Luxury vehicle,Performance car\ntest/0461f5305cac6365,Pit bull\ntest/0462dce081edfda4,Close-up\ntest/0463153ffd483c48,Firearm\ntest/04636a03350f48aa,Residential area\ntest/0463db3e8fe4c409,Sport utility vehicle\ntest/046540f0da8b9f71,Luxury vehicle\ntest/0467903150956d3e,Close-up\ntest/0468ec09bc89221b,Luxury vehicle,Performance car\ntest/046c99f5b14dbfd2,Classical sculpture\ntest/046d6a4aa30d74f7,Close-up\ntest/046ef3cc6acbbc53,Luxury vehicle\ntest/046f3ef93350a486,Fried noodles,Soba\ntest/046f48d5c2c8d898,Domestic short-haired cat,Whiskers\ntest/047451a957b464d7,Computer speaker\ntest/047533618b78e7ff,Hurdling\ntest/0475b943a4b9f23a,Chordophone,Electric guitar\ntest/04773839db040761,Leaf vegetable\ntest/047874dbf193f56c,Close-up\ntest/0478fbe401407a19,Floristry\ntest/04791bdedcd9bbea,Antique car\ntest/047a35dea9779e67,Luxury vehicle\ntest/047b1fbbf332c15c,Extreme sport,Sport climbing\ntest/047c02edc7043382,Luxury vehicle,Performance car\ntest/047dd97e08aeead5,Hair coloring\ntest/047e01dd70db61b3,Luxury vehicle\ntest/047e27b8589517a4,Extreme sport\ntest/047ebacecd412dee,Luxury vehicle,Performance car\ntest/047f1a572e4069e5,Double-decker bus\ntest/0483d3c6a9351648,Camera operator\ntest/04851c9cbeba109f,Bovine\ntest/048553ddce4f5148,Luxury vehicle,Antique car\ntest/04859b3cc008fe27,Black-and-white\ntest/04874a101b837f2f,Pit bull\ntest/0489614a02e7e335,Performance car\ntest/048c5a3cccc35143,Domestic short-haired cat,Whiskers\ntest/048d6043d659b0de,Arthropod,Close-up\ntest/048e17bbc1107dfa,Electric piano,Black-and-white\ntest/048ef636880e3163,Rural area\ntest/04916a30e99034bb,Luxury vehicle\ntest/0492b18d44d6680a,Arthropod,Close-up\ntest/04941fae01f9c6cf,Performance car\ntest/0498a0a0893b20f1,Close-up\ntest/049aa19c83ab2bef,Ball game,Team sport\ntest/049c96ec7fbdcfa5,Compact car,Luxury vehicle\ntest/049d05502c21067e,Frozen yogurt\ntest/049d99faa622b032,Bottled water\ntest/049dc497254412a2,Residential area\ntest/049f211dcba7d9a1,Close-up\ntest/04a1e38417533231,Machine tool\ntest/04a44fe8a65e182c,Performance car\ntest/04a79ce6de6e3719,Bird of prey\ntest/04a7deccf126c2fd,Antique car\ntest/04a8593690b9efea,Black-and-white,Horse and buggy\ntest/04abf880fdee279d,Firearm\ntest/04ad09b4882c6cf4,Luxury vehicle,Antique car\ntest/04af13c8a282bd4f,Monoplane\ntest/04b36ee25565036d,Luxury vehicle\ntest/04b86928b5e54b39,Domestic rabbit\ntest/04b8cbde6dc65cd3,Jeans\ntest/04b9067d15b37d4b,Luxury vehicle\ntest/04bb16bd202e17df,Luxury vehicle,Antique car\ntest/04bbf8cd411bd1ae,Close-up\ntest/04bc6b643d1551ba,Stemware\ntest/04bd02346a23482a,Domestic short-haired cat,Whiskers\ntest/04c20ed653143606,Close-up\ntest/04c36af8852c18bc,Jeans\ntest/04c78b2958786cd7,Close-up\ntest/04c96ce577b79243,Sailboat racing\ntest/04cc7b5666664f3e,Close-up,Plant stem\ntest/04cd7b82cb2a7d85,Luxury vehicle,Performance car\ntest/04cdb317d08d15a4,Compact car\ntest/04ce76414e1f2e25,Close-up\ntest/04cec64d91c6cc4d,Drums\ntest/04cf089cb8cef87d,Luxury vehicle\ntest/04cfbb7f73bc0c80,Ball game,Team sport\ntest/04d288ed94a91eae,Luxury vehicle\ntest/04d39d27fa4c9e34,Close-up\ntest/04d65e97b4d0461b,Arthropod,Close-up\ntest/04d881a900ecfefa,Performance car\ntest/04d9cced0dd42e6c,Bovine,Close-up\ntest/04db1e231cce67fe,Residential area,Aerial photography\ntest/04e0964c06156a89,Extreme sport,Boating\ntest/04e16e2a1ce9b92b,Close-up\ntest/04e353c1d8d726b4,Flatbread\ntest/04e59378fa44de12,Black-and-white\ntest/04e7d91649d4c403,Performance car\ntest/04e7f6eb60ef0e23,Coral reef fish\ntest/04e97bd36b9415ef,Compact car,Luxury vehicle\ntest/04ed1889ed405082,Forklift truck\ntest/04ef633c41462641,Road cycling\ntest/04f15b46938269a5,Full moon\ntest/04f367c1a6f2e1ae,Sport utility vehicle\ntest/04f547800aa0cdc3,Jeans\ntest/04f7503bc5b28f83,Rural area,Goats,Herding\ntest/04f7b3a3cc694c52,Rural area\ntest/04f7ebda00efe00a,Frying,Junk food,Fried food\ntest/04f906addbdeaf1c,Floristry\ntest/04f97f3db69a9aa4,Figure skating\ntest/04fa5516eabf38d7,Luxury vehicle\ntest/04fb867df7ccacb0,Close-up\ntest/04fbce0724a6271a,Steamed rice,Fried food\ntest/04fbe16b718816c0,Ball game\ntest/04fd3d60a4470e2d,Road cycling\ntest/04ffafd84aa1be26,Combat sport\ntest/04ffba697e012a72,Compact car,Luxury vehicle\ntest/050661e7e8a16525,Prosciutto\ntest/050766a6f10f6b65,Black-and-white\ntest/050850ece13f02fd,Zeppelin\ntest/050872032f21495d,Khinkali,Jiaozi,Pelmeni\ntest/0508cd8e63941ff1,Close-up\ntest/0508f4396fb4e144,Ball game,Team sport\ntest/050adcfe87fa563e,Molluscs\ntest/050b7c4534e9b37f,Luxury vehicle\ntest/050d89cfd37ab14f,Nile crocodile,Crocodilia\ntest/050e60d19948aea3,Shinto shrine\ntest/051545e07a1f8eaa,Melee weapon,Hunting knife\ntest/05158f46ea26e302,Fried noodles\ntest/051bdbe0f1bbd7bd,Compact car,Luxury vehicle\ntest/051d5f29e283fb24,Combat sport,Grappling\ntest/051d7ef8b9480b4f,Rural area\ntest/051df4cae715fc09,Luxury vehicle,Performance car\ntest/051fc8d27871042d,Performance car\ntest/0521763b0aa4b4bb,Close-up\ntest/0521fb640b0e3e5e,Close-up\ntest/0522864448b9b751,Compact car\ntest/05230faa3cbfee42,Black-and-white\ntest/0524443ce8d771fb,Domestic short-haired cat,Whiskers\ntest/0527df0015507693,Outdoor shoe\ntest/052964bf50e12727,Ball game\ntest/05297ab209c10dc3,Luxury vehicle,Antique car\ntest/052a21ccdad5760c,Computer speaker\ntest/052ba4e02d432ea4,Close-up\ntest/052d319f64400d03,Divemaster,Scuba diving,Underwater diving\ntest/052db8036ae62961,Luxury vehicle\ntest/053023ec3851309b,Compact car,Luxury vehicle\ntest/0533af66d3b78f90,Cosmetics\ntest/05345cabb6a8c12f,Close-up,Scaled reptile\ntest/05356d5521d983e2,Fried food\ntest/0536436b72e5fefc,Public speaking\ntest/0536deb7d8972154,Gliding\ntest/0536e53169dede5b,Antique car\ntest/05373b7f9363f1b5,Close-up\ntest/053a77b349ed1e9d,Aircraft engine\ntest/053ab9bcb0f05d96,Rural area\ntest/053b0d9a04e3c562,Luxury vehicle,Performance car\ntest/053cabdc5756955d,Luxury vehicle,Antique car\ntest/053d462d63998627,Luxury vehicle,Performance car\ntest/0540c5d031611551,Freight transport\ntest/054139c742f8ddf6,Black-and-white\ntest/05420655d6f2728f,Great white shark\ntest/0542b1fa6919428b,Combat sport\ntest/05432767bcd5e816,Extreme sport\ntest/0543fc451dad39b6,Boating\ntest/0545b5da2d5dbb56,Compact car\ntest/054a9b1e2fba7e0e,Flatbread\ntest/054bf826743affe8,Close-up\ntest/054c60519b92e6e7,Plant stem,Close-up\ntest/054d7962c6403f8f,Close-up\ntest/054fc099ca876402,Luxury vehicle,Performance car\ntest/05500ffb072df581,Vegetarian food,Fried food\ntest/055194c672845d3c,Luxury vehicle,Performance car\ntest/0551ef1d3b60a1ca,Freight transport\ntest/05533d702833a0ec,Jeans\ntest/055340c50841e031,Calabaza\ntest/0553c15c104909b3,Luxury vehicle\ntest/0554a7ce8a4bdfcf,Close-up\ntest/0555377341030c80,Luxury vehicle\ntest/0555911ee31d049f,Close-up\ntest/0555c7f8d767cdae,Domestic short-haired cat,Close-up,Whiskers\ntest/05560751a4c6dd2c,Luxury vehicle\ntest/0558cb98d2d6e940,Performance car\ntest/055a37924d1b05d0,Arcade game\ntest/055b3b56ed924815,Headphones\ntest/05619ff3579b51ce,Luxury vehicle,Sport utility vehicle\ntest/05627792c1ce7cb7,Arthropod,Close-up\ntest/0562810c11db3492,Sledding\ntest/05638c868cecf3db,Aircraft engine\ntest/05641d6762c67c52,Ball game\ntest/05656f08e4aa5564,Luxury vehicle\ntest/05657d2986a98591,Luxury vehicle\ntest/05661687100e9e02,Galleon\ntest/0568a2dbc5388947,Sport utility vehicle\ntest/056d5deb1682629b,Bovine\ntest/056e01b97acb3eca,Luxury vehicle\ntest/05710bca0967c2b4,Vegetarian food\ntest/0571923ee6c67a60,Black-and-white\ntest/0574e8fd2b179f22,Luxury vehicle,Sport utility vehicle\ntest/0575185e94f4c727,Luxury vehicle\ntest/05759ff84b21370d,Bovine\ntest/0579dc08d5951958,Floristry\ntest/057af1014ed4f696,Watercolor paint\ntest/057bc52803e6d81c,Auto racing\ntest/057bce56bfbbf5d6,Boating\ntest/057c27cb2af2acd2,Black-and-white,Close-up\ntest/057cef35365dfe64,Black-and-white\ntest/057e4cbfd6c187f2,Sport utility vehicle\ntest/0580778092059843,Close-up\ntest/0583e7310fc03182,Steamed rice\ntest/0584d798831cb140,Luxury vehicle,Sport utility vehicle\ntest/0584ee1c9812ced1,Black-and-white\ntest/058567362019b68c,Ball game,Team sport\ntest/058569265bb1dbe2,Blond\ntest/058b385c43e8cb9a,Auto racing\ntest/058cb4cebdf97c2c,Nordic skiing\ntest/058dd0dbcc6b6ea0,Microcontroller\ntest/059001702b9b91e5,Combat sport\ntest/059108da497d5b23,Calabaza\ntest/0593290e211d4f5c,Luxury vehicle,Antique car\ntest/0594e6dbc06f21a1,Jeans,Luxury vehicle,Performance car\ntest/059520433a9809d0,Ball game,Team sport\ntest/0595edf7ea6d9c3b,Vegetarian food\ntest/059636004bc2b8d4,Vegetarian food\ntest/0599e98df4d8fec7,Boating\ntest/059b6516487aae47,Compact car\ntest/059c1d759638dd88,Compact car,Antique car\ntest/059fb348d8c6875c,Black-and-white\ntest/05a19b260e7d5128,Luxury vehicle,Performance car\ntest/05a49562f874890b,Leaf vegetable,Vegetarian food\ntest/05a561e9109066f7,Arthropod,Close-up\ntest/05a5fae34ce088b5,Erinaceidae\ntest/05a61be815fd985e,Machine tool\ntest/05a86145092ca60f,Ball game\ntest/05a95a5278890387,Compact car\ntest/05aa63d0810e2ec9,Fried food\ntest/05ab8cb823b8df06,Glacial landform\ntest/05accaeaa0b77567,Junk food\ntest/05ad012d0ccab5cb,Ball game,Team sport\ntest/05ada3c22d33ca57,Flatbread\ntest/05b07b8c766a8a81,Vegetarian food\ntest/05b15ae341cd6ec5,Performance car\ntest/05b17e472fa7c4ad,Briefs\ntest/05b1b435fe12eea6,Vegetarian food,Cookies and crackers\ntest/05b2665be4d7f5a8,Auto racing\ntest/05b4438a0456a1e7,Jeans\ntest/05b47d2c71c4eff4,Ball game\ntest/05b8cb33917d9b22,Antique car\ntest/05b8d33f50c4fd23,Black-and-white\ntest/05b94a7d5c3e2203,Coral reef fish\ntest/05bf8ef77f675896,Hound\ntest/05c12c1407bd678c,Modern dance\ntest/05c18f2bbcf72545,Sport utility vehicle\ntest/05c3a552f76056b0,Peafowl,Close-up\ntest/05c749224fc18b42,Miniature Poodle\ntest/05c77dd04223955b,Ball game,Team sport\ntest/05ca3422aa670ef0,Cosmetics\ntest/05cfe6dfdb381b34,Flatbread\ntest/05d15c16dc244659,Bovine\ntest/05d1d0de1fcd4253,Frying,Fried food\ntest/05d4ff6e4b3031d6,Arthropod\ntest/05d5c65185a16b86,Performance car\ntest/05d64884459abee8,Performance car\ntest/05d6b805449696c3,Ball game,Team sport\ntest/05d6c9b8d140ea0e,Close-up\ntest/05d9cbaa98d33f8d,Black-and-white,Full moon\ntest/05d9d537060881b6,Horse and buggy\ntest/05da9a3a550ceeed,Close-up\ntest/05daa49f411c5ef1,Close-up\ntest/05dcb6b7795de81c,Electronic instrument\ntest/05ddac1ea53ebab1,Orator,Public speaking\ntest/05de8434c0a6d6a5,Dobermann\ntest/05dee62f08851380,Black-and-white\ntest/05e3226855dfe11c,Antique car\ntest/05e3298104cd9386,Boating\ntest/05e4e47723232f65,Compact car\ntest/05e7ad10493ecace,Ball game,Team sport\ntest/05ec1df46929fa2c,Compact car,Luxury vehicle\ntest/05ec828698b84114,Residential area\ntest/05ee5fa7aa8120cd,Compact car\ntest/05ef5702ffaf3cba,Jeans,Dog walking\ntest/05effda41591fc8e,Luxury vehicle,Antique car\ntest/05f06ff3a425e788,Outdoor shoe\ntest/05f0aa43a4bdd395,Equestrianism\ntest/05f15577ecec2078,Athletic shoe\ntest/05f4f513997754d4,Aircraft engine\ntest/05f64242984ff159,Sport utility vehicle\ntest/05fb404d4c55b6da,Flatbread\ntest/05fe0725636fe840,Floristry\ntest/05fe9c0ff44beb3a,Compact car,Luxury vehicle,Sport utility vehicle\ntest/05ffabe12fd44b94,Arcade game\ntest/05ffb4c3314b4162,Luxury vehicle\ntest/05ffb59dbad9f6c1,Boating\ntest/060018b4c636a9ff,Luxury vehicle,Performance car\ntest/06019a1f0f9e31a5,Perching bird\ntest/0603031082e92497,Luxury vehicle\ntest/0607775125d48030,Plant stem\ntest/060871e66b31ee84,Extreme sport\ntest/060a3f248974b14b,Antique car\ntest/060b5a6d1225052a,Bonbon\ntest/060bc355400e77ef,Military person\ntest/060c96023ddd4232,Close-up,Plant stem\ntest/060e84486c7a58e3,Vegetarian food\ntest/06102cb30ec0f3d3,Luxury vehicle\ntest/061083a9aebf7503,Miniature Poodle\ntest/0611459e91fbdc57,Portrait photography\ntest/0611b8c66651d127,Aerial photography\ntest/061331ad6774a187,Chordophone\ntest/06134855d4f6e0ec,Close-up\ntest/0614aeb7d1a707d7,Digital camera\ntest/06171c6d90f7c087,Compact car\ntest/06190a18fc37887b,Black-and-white,Modern dance\ntest/0619efcc5be11030,Luxury vehicle\ntest/061be1d696201b24,Floristry\ntest/061fd2203035b9de,Luxury vehicle\ntest/0620fe4c9e29ab20,Bird of prey\ntest/062190eba90512f7,Ball game\ntest/062261a4ce80965e,Extreme sport\ntest/06227614ca0641a6,Coral reef fish\ntest/06260504ebf8e19b,Luxury vehicle\ntest/0627dbe2bd362bec,Compact car,Luxury vehicle,Antique car\ntest/0628d98a36cdb01a,Arthropod,Close-up\ntest/0629209fd06b5cba,Black-and-white\ntest/062a59f60d47e917,Close-up\ntest/062d4bfa12a29cb0,Cookies and crackers\ntest/062f0b898c109f3f,Compact car,Luxury vehicle,Antique car\ntest/0632cd44102a148f,Dog walking\ntest/06338e1b92ae7a4f,Hair coloring\ntest/0634825fc1dcc96b,Chordophone\ntest/063568b8ff6f8caa,Compact car,Luxury vehicle\ntest/06358abb2ea99449,Residential area\ntest/0638a0788ef2f739,Canola\ntest/0639fe17a3b59a1f,Black-and-white\ntest/063a6077ed0b6d36,Bonbon,Cookies and crackers\ntest/063b8b5d71e526b4,Auto racing\ntest/063c12da246784d9,Luxury vehicle,Sport utility vehicle\ntest/063f9b7d709267d0,Performance car\ntest/06418e33c65f9600,Jeans,Camera operator\ntest/0642b6fbe50c96fb,Calabaza\ntest/064728f5860d55f4,Compact car,Luxury vehicle\ntest/064a2fa9cf69b203,Angling\ntest/064d22aca79564e9,Parachuting\ntest/064f4efd6576d6b7,Extreme sport\ntest/064fe962dadac560,Close-up,Whiskers\ntest/06507b9821346770,Arthropod,Close-up\ntest/0650cc04bd4c93c1,Ball game\ntest/06512068645af874,Performance car\ntest/065144a1124c8e97,Luxury vehicle,Antique car\ntest/0651c18f6bf8205e,Luxury vehicle\ntest/06568bb0d9f2cf08,Performance car\ntest/0656d7a24212668b,Close-up\ntest/065719da5d654b87,Hound\ntest/0657517e8b803902,Roman temple\ntest/0657ea93e09eeab8,Whiskers\ntest/06582c36034ca464,Luxury vehicle,Antique car\ntest/065dc37cbbfcd59c,Molluscs,Close-up\ntest/0662bba230de26be,Black-and-white,Extreme sport,Roller skating\ntest/0663c05983eb1488,Primate\ntest/0664e168198cede3,Digital camera,Black-and-white\ntest/06665644cdd87b03,Arthropod,Close-up\ntest/066684885e4ef366,Plant stem,Close-up\ntest/0666a429dbbc9268,Outdoor shoe\ntest/06671976a526104a,Leaf vegetable,Vegetarian food\ntest/0668ae891d7671a9,Close-up\ntest/066e465a19ef6713,Molluscs\ntest/066f4e7245ffecad,Close-up\ntest/0670e5a41dfbbe9e,Antique car\ntest/0674328470a04ed6,Jeans\ntest/0674f3c8a72a2ed5,Luxury vehicle\ntest/0675c64e78c43bd4,Domestic short-haired cat,Whiskers\ntest/0676e8ad53e7d44b,Aerial photography\ntest/0679834e656a26d6,Luxury vehicle\ntest/0679f48ef1495fa3,Combat sport\ntest/067bedeff76e6083,Close-up\ntest/067e31ac1d7266d9,Blond,Hair coloring,Portrait photography,Close-up\ntest/067e927567bf59ff,Monoplane\ntest/068479f64fc145e8,Frying,Fried food\ntest/06854112d9fe065d,Jeans\ntest/068632a3623c99e6,Bonbon\ntest/0686600b8c674930,Luxury vehicle,Antique car\ntest/068752ec03d8e54b,Compact car,Luxury vehicle,Antique car\ntest/068ae45a7078d042,Close-up,Shallot\ntest/068ae72a574526b8,Luxury vehicle,Antique car\ntest/068c4d9dc3fb4846,Close-up\ntest/068f374dc9caf37c,Brochette\ntest/069199a017a73d5a,Vegetarian food\ntest/06925ae18d97f657,Monoplane\ntest/0693d6cc6510491e,Compact car,Luxury vehicle\ntest/0694f233116e0dd6,Sport utility vehicle\ntest/069604fb13b0be48,Electric piano,Electronic instrument\ntest/0697a09d8bd405c9,Vegetarian food\ntest/06990ec93a9c1b16,Aircraft engine\ntest/06992f45e5194e23,Luxury vehicle\ntest/069ac2ad4505a93b,Extreme sport\ntest/069b725fba6dd67d,Wind wave,Glacial landform\ntest/069e7ab239603084,Antique car\ntest/069e7b3be3545f27,Pasta salad\ntest/069f891a912808f6,Luxury vehicle,Antique car\ntest/06a1522c64e19408,Compact car\ntest/06a16c3c40f61fbc,Whiskers,Domestic rabbit\ntest/06a1d798c81bad8a,Black-and-white\ntest/06a1e53fc2d9374d,Luxury vehicle\ntest/06a2d531f7a2a243,Plant stem\ntest/06a330184f863997,Great white shark\ntest/06a5f3f53ded298b,Rural area\ntest/06a6ce6ec5b2724b,Scaled reptile\ntest/06a7472acf2d57fa,Close-up\ntest/06a7b2033f099595,Modern dance\ntest/06a7e15f0bde2a2e,Extreme sport,Personal water craft,Boating\ntest/06adb7e02e058a2e,Close-up,Scaled reptile\ntest/06adfe6a2984a580,Steamed rice\ntest/06ae457a819d1e81,Luxury vehicle,Performance car\ntest/06ae6dee71abfb0e,Luxury vehicle,Antique car\ntest/06afae8665b2dab7,Close-up\ntest/06b2069f091ee596,Close-up,Whiskers\ntest/06b2e9d58a80d78d,Jackfruit\ntest/06bc505edf461bfd,Jiaozi\ntest/06bcc09321656cc5,Bird of prey\ntest/06beb6f904e40dc8,Steamed rice\ntest/06bebda0d6da6cbd,Blond\ntest/06bfd5af15c7a58a,Antique car,Performance car\ntest/06c0d3758c86dd9c,Black-and-white,Zeppelin\ntest/06c1529b93dbc973,Electronic instrument,Electric piano\ntest/06c2bd3083b46f1e,Woodwind instrument\ntest/06c3a4d6d83885cc,Auto racing,Luxury vehicle,Performance car\ntest/06c6aa6db9b0c81b,Plant stem\ntest/06c76f075ba3eff7,Monoplane\ntest/06c8c10769b125b2,Auto racing\ntest/06c9cc5fd907e7cd,Perching bird,Close-up\ntest/06c9e26673e79c93,Antique car\ntest/06c9eaf2f2af8e53,Extreme sport,Bouldering,Sport climbing\ntest/06cb8a63fdb2b3d8,Luxury vehicle,Performance car\ntest/06cdcb90974a94fe,Electric piano,Electronic instrument\ntest/06ce8a3a659b585c,Blond\ntest/06d3086953fe445e,Chordophone\ntest/06d67ce06ae11ab3,Jeans\ntest/06d79636ef2e7b27,Halter,Equestrianism\ntest/06d91fd2da071f63,Luxury vehicle\ntest/06d95502b04faad2,Coral reef fish\ntest/06d96d8db1696f79,Microcontroller\ntest/06dbd3f4f59bd495,Scaled reptile\ntest/06dc0f036188af5a,Close-up\ntest/06dde783da447451,Shallot\ntest/06de6c4d3f9669e3,Rural area\ntest/06df742df246d6b2,Luxury vehicle,Performance car\ntest/06e00c5b327efe22,Performance car\ntest/06e17c5a87476f5c,Boating,Rowing\ntest/06e26d5da8f1deff,Frying,Junk food,Fried food\ntest/06e5917bdd3291af,Shinto shrine\ntest/06e61ebc07e5eebc,Camera operator\ntest/06e64210b31f477d,Black-and-white\ntest/06e65f19fde56f05,Athletic shoe,Outdoor shoe\ntest/06e9b526a94fb4ef,Whiskers\ntest/06ea105de5e427f6,Antique car\ntest/06ea10dbc0157665,Antique car,Performance car\ntest/06eb329da2a438df,Boating\ntest/06f1d75cc0f21a20,Arthropod,Close-up\ntest/06f35dce44b6c701,Blond\ntest/06f451d0c449de97,Compact car\ntest/06f4f4d2b8029701,Extreme sport,Surfing Equipment\ntest/06f9dac862b108ee,Luxury vehicle,Performance car\ntest/06fa758a6fb29701,Plant stem\ntest/06fe3959e77d65e2,Antique car\ntest/06ff1bb32dd9c8aa,Amphibian,Close-up\ntest/06ffd088c92ce1a9,Antique car\ntest/07005d39850827ee,Hurdling\ntest/07029d4497060a02,Luxury vehicle,Sport utility vehicle\ntest/0704fa4e5aab2b7f,Luxury vehicle,Sport utility vehicle\ntest/0706dc2e5e908ddf,Outdoor shoe\ntest/070796cae7b21655,Domestic short-haired cat,Whiskers\ntest/0708106d7a746d78,Close-up\ntest/0708923badf2ff5d,Chordophone\ntest/0709f9212101d231,Junk food\ntest/070afdbe165adba7,Whiskers\ntest/070b9e99bbccb9e3,Sailboat racing\ntest/070bd664d0a7d8af,Compact car,Luxury vehicle,Antique car\ntest/070bd95cc3aea0a4,Close-up\ntest/0712378f73c93d8e,Senior citizen\ntest/0715496590c6f5d0,Compact car,Antique car\ntest/0715bd2f5c0a24f1,Compact car,Luxury vehicle\ntest/0716f55d7debe300,Arthropod,Close-up\ntest/07174a860468f7c0,Jeans\ntest/0717791fb6820334,Luxury vehicle\ntest/07185a85a77c79c3,Sport utility vehicle\ntest/0719d20e0f49ccb1,Middle ages\ntest/071ae934f12449aa,Black-and-white\ntest/071ce2b7b62aea70,Compact car\ntest/071df718bc0d3315,Luxury vehicle,Performance car\ntest/0721182e6eccb131,Water polo,Ball game,Team sport\ntest/07226bb068118da0,Luxury vehicle\ntest/0722a8a895aaa5fd,Junk food,Fried food\ntest/0722f82dc4957e4f,Combat sport\ntest/07238b4f592007e4,Luxury vehicle,Antique car\ntest/072425567fc12b0c,Chordophone\ntest/07264e57e3607661,Ball game,Team sport\ntest/07294fe7db23d288,Ball game\ntest/072a8545e84e26ad,Compact car,Luxury vehicle\ntest/072b8d37d81d59cd,Digital camera\ntest/072b8fd82919ab3e,Whiskers,Domestic rabbit\ntest/072c960231be1bdf,Pork chop\ntest/072f0c6ea2ca3db4,Aircraft engine\ntest/07301815c8038143,Bird of prey\ntest/0732f70d9b9f10f4,Aircraft engine\ntest/0734fde135338a35,Vegetarian food\ntest/073511a9ae654577,Close-up\ntest/0735faa8f3ccc282,Luxury vehicle,Performance car\ntest/07366b653d9b9e51,Luxury vehicle,Antique car\ntest/073b8fa46b601901,Filling station\ntest/073be21786470123,Ball game,Team sport\ntest/073df346eca9fa5d,Antique car\ntest/07419d5a6774bd0c,Monoplane\ntest/0743cb95ebef2863,Military vehicle\ntest/07444f92cdb8b4c6,Dishware\ntest/0745af351683e689,Junk food\ntest/0745beb7a3b3d4e3,Motorcycling\ntest/07460a2d88db9df5,Aircraft engine\ntest/0746a91524220f3c,Barechested\ntest/0746d3ade7ff9d72,Aircraft engine\ntest/0746d9b7174a350e,Performance car\ntest/074852295d65855d,Ball game,Team sport\ntest/0748adba07b6eeec,Computer speaker\ntest/074908680cccee4e,Close-up\ntest/074a382df6735522,Luxury vehicle\ntest/074b1436b6395d3b,Close-up\ntest/074b798a5be12095,Luxury vehicle,Antique car\ntest/074d0cafef643dac,Antique car\ntest/074e1392f782bcf5,Amphibian,Close-up\ntest/075068fc9cf138f3,Sport utility vehicle\ntest/07510743cf79a800,Compact car,Luxury vehicle,Antique car\ntest/075a1c04ffd19dae,Close-up\ntest/075a41768b55fc6d,Amphibian\ntest/075c817150e16013,Vegetarian food\ntest/075c8b26363a07d9,Galleon\ntest/075cdfae3950872d,Luxury vehicle,Sport utility vehicle\ntest/075e8ff2794191fa,Close-up\ntest/076078740387b572,Roller skating\ntest/0761631fdc930da9,Primate\ntest/07616a9b9efc0d51,Luxury vehicle\ntest/0762129ae2d90f20,Military vehicle\ntest/076386a0cce74c40,Rural area,Glacial landform\ntest/07666e4de98ab757,Luxury vehicle\ntest/076785b6f7757717,Luxury vehicle\ntest/07681b9ce5190cfa,Aerial photography\ntest/076882ae7439dcfc,Close-up,Whiskers\ntest/076ab3ca490e62c8,Frozen yogurt\ntest/076be09a72dd9b80,Canola\ntest/076cf19f929c0935,Luxury vehicle,Sport utility vehicle\ntest/076d5d45da95de9c,Close-up\ntest/076ecfc48304bd9a,Sport utility vehicle\ntest/07701a8ec09b44e3,Compact car\ntest/0771b992d86dd861,Close-up,Plant stem\ntest/0774cb0bae3ae3c1,Whiskers\ntest/07773db4dc87c54c,Close-up,Plant stem\ntest/077bd4823e186017,Compact car\ntest/077f91eb83819e7c,Frozen yogurt\ntest/078006dc7f56dd34,Fried food\ntest/07803f80075a7f01,Siberian husky\ntest/0784e1c83250b77f,Close-up,Plant stem\ntest/0787ad13d1b011a7,Plant stem\ntest/078a155dac9c9ba4,Extreme sport,Kitesurfing,Wind wave\ntest/078a3fd83fac864c,Ball game\ntest/078b53d8a454894d,Boating\ntest/078bce3ad453e0ec,Aircraft engine\ntest/078c4e9914ad77e2,Woodwind instrument\ntest/078d135bd925d03b,Arthropod,Locust,Plant stem,Close-up\ntest/078d6895558bb7d7,Residential area\ntest/078dd812269c5143,Blond\ntest/078fa6bec8dd4a99,Amphibian,Close-up\ntest/07938a3ec06e26af,Bovine\ntest/0794988847ff4530,Erinaceidae\ntest/07979c56d8cb27d5,Auto racing,Luxury vehicle\ntest/0798314e05a07724,Cobblestone\ntest/07989d9f1b11a786,Antique car\ntest/079908756140cf32,Vegetarian food\ntest/0799bc053265b0ef,Pit bull\ntest/079b491eab3a93ae,Senior citizen\ntest/07a33fc1a0ae6236,Chordophone\ntest/07a41b00866f81d9,Aircraft engine\ntest/07a562e6df1ef0af,Plant stem,Close-up\ntest/07a5bee7f3ee1597,Portrait photography\ntest/07a737d04e2cbfdb,Luxury vehicle,Performance car\ntest/07a9ef247e338641,Jackfruit\ntest/07ac8debad975ca1,Close-up\ntest/07ada01467904f04,Primate\ntest/07adde55744da629,Whiskers\ntest/07afb7bbd5628c3c,Luxury vehicle\ntest/07afbe28e286087e,Close-up\ntest/07b06449065bb322,Black-and-white\ntest/07b1d9a2e5a5673a,Luxury vehicle\ntest/07b2d0f2b1fcd959,Steamed rice\ntest/07b30794f220e634,Zeppelin\ntest/07b38472c737cfd0,Close-up,Scaled reptile\ntest/07b49d7bec31a699,Leaf vegetable\ntest/07b79ed486dbaebf,Shinto shrine\ntest/07b883e6c0a9a4cd,Military vehicle\ntest/07ba05ae63327355,Go-kart\ntest/07bc013c92a141c0,Compact car\ntest/07bc2e2024b8a949,Erinaceidae\ntest/07bca8ba39274665,Military vehicle\ntest/07bd85e00308ab96,Performance car\ntest/07bdad692a27b235,Domestic short-haired cat,Whiskers\ntest/07beee3b309752a8,Close-up\ntest/07c302b371a347f0,Fire apparatus\ntest/07c336f69a11c8ae,Military person\ntest/07c38a5af6c2d19a,Microcontroller\ntest/07c42e5882a169d5,Combat sport\ntest/07c590255de615e0,Close-up\ntest/07c9c45a25a08433,Compact car,Luxury vehicle,Antique car\ntest/07ca9c828b21440d,Fried food\ntest/07ccb9a1ca8fe714,Nordic skiing\ntest/07d16cbfbf5a2c46,Luxury vehicle,Sport utility vehicle\ntest/07d37a0f6a0db311,Close-up,Plant stem\ntest/07d37babca0a3736,Close-up\ntest/07d44ec8e68818c8,Rural area\ntest/07d4bdcea907ab25,Monoplane\ntest/07d4da9ff4908e0b,Vegetarian food,Corn on the cob\ntest/07d69099b981f303,Junk food\ntest/07d7b9e31b21c02b,Sailboat racing\ntest/07d8788ca0b038b5,Ball game\ntest/07d9048a6e0dc007,Close-up,Whiskers\ntest/07d94732a721145a,Luxury vehicle\ntest/07dc373d2d9944a1,Luxury vehicle,Performance car\ntest/07de30f884c537ef,Close-up\ntest/07e02444758e34fd,Arthropod,Plant stem,Close-up\ntest/07e03a94edbedac5,Machine tool\ntest/07e0670a620921d4,Blond\ntest/07e246880f46b860,Frozen yogurt\ntest/07e4a0bb583c95eb,Dobermann\ntest/07e5eac0ec5ca022,Antique car,Luxury vehicle,Performance car\ntest/07e851446f46a17e,Black-and-white,Full moon\ntest/07e98e72093730c4,Assault rifle,Firearm\ntest/07eae3ce00fdeea6,Black-and-white\ntest/07ebc8f0229341eb,Blond\ntest/07ec57e5a4f2c9c3,Luxury vehicle,Antique car\ntest/07f0bbd3fd9f3448,Whiskers\ntest/07f7044b01c9bf80,Close-up\ntest/07f8bd7aedb7fc95,Black-and-white,Close-up\ntest/07f8ddcec3a88e33,Antique car\ntest/07fbef9715e97c53,Extreme sport,Parachuting\ntest/07fdbdbc01520b79,Compact car,Luxury vehicle,Antique car\ntest/07feb6cc3655dc87,Close-up,Whiskers\ntest/0801a96c85dab03c,Gliding\ntest/0802c02a8feb794d,Vegetarian food,Corn on the cob\ntest/0803ccee1731fcb8,Junk food,Fried food\ntest/08062a892d05b9e5,Jeans\ntest/0807a9b5522242cd,Black-and-white,Close-up\ntest/08081ff1f0986fb3,Frying,Junk food,Fried food\ntest/08082ce94a10bc89,Close-up\ntest/0808bb2b3a4c4806,Arthropod,Close-up\ntest/080a43360edbe44e,Digital camera\ntest/080ded1bc2a72d81,Close-up\ntest/08129ae11e7afa1e,Ball game,Team sport\ntest/0812c925577193d4,Black-and-white,Portrait photography\ntest/0817d02a826b8fe1,Close-up\ntest/081870fc659cb82a,Outdoor shoe\ntest/08190d15e1d6d695,Close-up,Scaled reptile\ntest/08192d5cc57518d3,Arthropod,Close-up\ntest/081a8e7de0d61490,Aircraft engine\ntest/081b7accad413ae8,Antique car\ntest/081e4cfeda09b93c,Camera operator\ntest/08203182501139bc,Portrait photography,Close-up\ntest/08219bbac0e053d3,Team sport\ntest/08220b9e332eba77,Fried noodles\ntest/08254b4c56b55d2e,Antique car\ntest/08257849f3e4d2ff,Close-up,Plant stem\ntest/0825b033a2378bf1,Luxury vehicle,Performance car\ntest/08289b52b2727914,Coral reef fish\ntest/082b7d6b6d5f73d9,Luxury vehicle\ntest/082b808d2f13d13f,Luxury vehicle\ntest/082be64878422f6b,Black-and-white\ntest/082dcf9d4f696e4c,Nest box\ntest/08309ff4082be74f,Close-up\ntest/083327ccfc4b1684,Lawn game,Ball game\ntest/08360cb5ad873a7d,Extreme sport\ntest/0837a0944e6a12cb,Ball game\ntest/0837c952aff1abce,Luxury vehicle,Performance car\ntest/0837e116ebc0a9be,Luxury vehicle,Sport utility vehicle\ntest/0838c179ef57b86c,Machine tool\ntest/08392c290ecc9d2a,Watercolor paint\ntest/083a5e6da19c6fa9,Blond\ntest/083b3dd9b07547cb,Erinaceidae\ntest/083ec984c2a9b14c,Comics,Black-and-white\ntest/083f32565444dcda,Fried food\ntest/083f66d49bbf5d9d,Close-up\ntest/0840bd81ce2bc722,Watercolor paint\ntest/0841354904c2f687,Compact car,Luxury vehicle,Antique car\ntest/08419556e5550ef9,Close-up\ntest/0841c34acb1e449f,Residential area,Aerial photography\ntest/0842c3058b6a264f,Luxury vehicle\ntest/0844963f5234481e,Performance car\ntest/08466d6f545a38c5,Auto racing\ntest/0846bbc1d5a2dbb9,Cookies and crackers\ntest/0847947b615b7b0b,Extreme sport,Wind wave,Surfing Equipment\ntest/0847e846af1c94b8,Sledding\ntest/084a37b8ba5c4a30,Ball game\ntest/084ad8a09ffc27c9,Compact car,Luxury vehicle,Antique car\ntest/084af7e4e02f1cb3,Close-up\ntest/084b363c71a7f958,Bottled water\ntest/084d9b0d21b5de75,Ball game,Team sport\ntest/084e91aca075899f,Close-up\ntest/0852dddbdffe15e3,Aircraft engine\ntest/0853fd3ee4478f5f,Plant stem\ntest/0856c2fbc4b2db9a,Miniature Poodle\ntest/085714d40ae0cd09,Freight transport\ntest/085801a888180779,Close-up\ntest/0858733c806b5b5a,Fried noodles\ntest/0858ec43587967bf,Cosmetics\ntest/085b7c5d27c9d2ec,Rural area\ntest/085d41b59aaaa3a4,Compact car,Luxury vehicle\ntest/0861576f4b6747f0,Close-up\ntest/0861698242ff9464,Compact car,Antique car\ntest/08621d070f3baa45,Canola\ntest/086275d4aad6cd42,Rear-view mirror\ntest/0863080ec9fc4d6c,Bagpipes\ntest/086361bbf78d0bdf,Plant stem,Close-up\ntest/0864ce9bdf78d6b7,Jeans\ntest/0864d7fc4f1adb31,Extreme sport,Nordic skiing\ntest/086537bd4dfa6e50,Luxury vehicle,Performance car\ntest/0865f1777c74161e,Bird of prey\ntest/0865f7a8c38e9757,Dog walking\ntest/08662526a8889805,Luxury vehicle\ntest/0867138f7fb3dc5b,Luxury vehicle,Performance car\ntest/08675994e6743b52,Domestic short-haired cat,Whiskers\ntest/086768e806e0eefa,Luxury vehicle,Performance car\ntest/08692ebdd3e18729,Performance car,Luxury vehicle,Antique car\ntest/086a5a9d00b65e94,Close-up\ntest/086ae640b9f067db,Ball game,Team sport\ntest/086b4c628d1f6eb4,Antique car\ntest/086b4c663597bbfc,Compact car\ntest/086c106d74f1bca1,Orator,Public speaking\ntest/086c30c2c19ad078,Aircraft engine\ntest/086c327931e0a69c,Whiskers\ntest/086ce3d00285071d,Blond,Hair coloring\ntest/086d9146953c8364,Luxury vehicle\ntest/086de43b81481279,Black-and-white,Close-up\ntest/086e00e384e8419f,Black-and-white\ntest/086e20b26cefa816,Close-up\ntest/08729259aef5f6b6,Yakitori,Fried food\ntest/0876506af8192522,Miniature Poodle\ntest/0876ccfcb408596b,Sport utility vehicle\ntest/0878bd099f9ee463,Residential area\ntest/08795fa83e456cc6,Luxury vehicle\ntest/087972c8b894ab66,Boating\ntest/0879decdc6079d14,Compact car\ntest/087a0fcdf626a8e1,Peafowl\ntest/087b1062dc719626,Road cycling\ntest/087e24da3a4fb200,Luxury vehicle\ntest/088113ebdef163a6,Floristry\ntest/0882dcd634f6bb03,Sport utility vehicle\ntest/0884ef11050b5cef,Coral reef fish\ntest/0885b5fdd2581c11,Luxury vehicle,Antique car\ntest/0889586f04ea4433,Compact car,Luxury vehicle,Antique car\ntest/088a1a9a5b12df70,Extreme sport\ntest/088c3bc4ff38948c,Plant stem,Close-up\ntest/088e12fd31e0fa82,Rural area\ntest/08900915d2529e50,Close-up\ntest/08941c442ff98916,Aircraft engine\ntest/08955b217ed540d0,Ball game,Team sport\ntest/08961d239c774be1,Performance car\ntest/0896cabb5ce7a20a,Lechon\ntest/0896fc2334e7d594,Molluscs\ntest/0897ce251842b8a7,Close-up\ntest/089bce8c559de264,Antique car\ntest/089cef3acc866627,Bovine\ntest/089ed123cb0eee58,Hunting knife\ntest/08a04a0702af8c89,Luxury vehicle\ntest/08a2bfa186fb2e89,Luxury vehicle\ntest/08a3f4151684cb4e,Luxury vehicle\ntest/08a51a6e7eaf084b,Whiskers\ntest/08a64a2b69df40b0,Figure skating\ntest/08a67848b41f0511,Motorcycling\ntest/08a6986e8f9ad9bc,Frozen yogurt\ntest/08a87a3402445742,Black-and-white\ntest/08abee2f1dd564b1,Rice ball\ntest/08ad804598536a4f,Close-up\ntest/08ae030230003b95,Leaf vegetable\ntest/08b1957204d84dfd,Rural area\ntest/08b1f56a4a6afab5,Luxury vehicle\ntest/08b2c397d88c85d1,Prosciutto\ntest/08b33e22ca2eed03,Residential area\ntest/08b4fca5cceef044,Aircraft engine\ntest/08b744e9a5d68567,Medical imaging\ntest/08b8fc5fb98a7138,Jeans\ntest/08b90c26448c5ea5,Divemaster,Scuba diving,Underwater diving\ntest/08bb8135ecfc9add,Close-up\ntest/08bd8eec421b0ef2,Molluscs\ntest/08be1b157f5982f9,Close-up,Scaled reptile\ntest/08c232cdd85f698e,Flatbread\ntest/08c29932253e0f81,Backlighting,Black-and-white,Portrait photography,Close-up\ntest/08c49e65222c87a2,Ribs\ntest/08c6b847085abed5,Luxury vehicle,Antique car\ntest/08c8ad054cf80f34,Jeans,Luxury vehicle\ntest/08c8cb90eb39c217,Plant stem\ntest/08c8de437da48427,Arcade game\ntest/08cb9e3e06e69294,Luxury vehicle,Antique car\ntest/08cbd567dde78394,Antique car\ntest/08cdf2975921faeb,Outdoor shoe\ntest/08cf3645808b56b4,Arthropod,Close-up\ntest/08d11f0a431f8ebc,Rural area\ntest/08d20bcb869f6e13,Luxury vehicle,Antique car\ntest/08d3314312c69620,Equitation,Equestrianism\ntest/08d76f2a788d813a,Primate\ntest/08dc0de88f95ccfc,Amphibian\ntest/08dc30f9ae6fa3c3,Close-up,Plant stem\ntest/08de7f817fc138a9,Black-and-white\ntest/08df6158b3e00ab3,Antique car\ntest/08e0a62e92643799,Inflatable\ntest/08e305c9897827dd,Whiskers\ntest/08e355282dec878a,Vegetarian food\ntest/08e3bd71231faa7c,Whiskers\ntest/08e51ffc23dab9a7,Compact car,Luxury vehicle,Antique car\ntest/08e6e64f51a5e87d,Extreme sport,Surfing Equipment,Wind wave\ntest/08ea61be8f17aa9c,Extreme sport,Parachuting\ntest/08eb9697d767c481,Extreme sport,Motorcycling\ntest/08eef8249bb1c096,Whiskers\ntest/08eefe80375a10fe,Close-up\ntest/08f0e23313c11e1b,Close-up\ntest/08f2bc490a2ed244,Close-up\ntest/08f5080cd126a1f7,Jeans,Antique car\ntest/08f7a350a7aa91c9,Close-up\ntest/08f8d8b95e0e3b52,Water polo,Ball game,Team sport\ntest/08f92e8243e13686,Stemware,Close-up\ntest/08f96c6b05f7d894,Close-up\ntest/08fa7048cf4303fa,Close-up\ntest/08fa8e3b8af7bf26,Luxury vehicle,Antique car\ntest/08fb28cd818cc4bf,Black-and-white,Close-up\ntest/08fc16bf8409624d,Jeans\ntest/08fc51dfdb0b8b65,Water polo,Ball game,Team sport\ntest/08fced60b9800b21,Racewalking\ntest/08fe50a41855ab68,Musical theatre\ntest/0905339d91a318d3,Luxury vehicle\ntest/0906e6060a150ed5,High-speed rail\ntest/0906f81784391ecc,Compact car\ntest/090914b26473256e,Junk food\ntest/090931d594da6859,Junk food,Fried food\ntest/0909d870459792b8,Luxury vehicle,Sport utility vehicle\ntest/090a48eb33173908,Fried food\ntest/090d42391fab5ee0,Compact car,Antique car\ntest/091263fd8fb6becf,Equestrianism\ntest/0912f320184a8f1b,Plant stem\ntest/0913640030c7608e,Ramen\ntest/0913c45f872b0038,Musical theatre\ntest/091607261c083036,Arthropod,Close-up\ntest/09175b8d00551f38,Ball game\ntest/09176ed12ad6a926,Antique car\ntest/091e747be8f80c04,Arthropod,Close-up\ntest/09206ca2a6681a44,Divemaster,Scuba diving,Underwater diving\ntest/0922a6d8749c77cb,Rural area\ntest/0925b96b77309a2f,Rural area,Herding\ntest/09280c41852d7b14,Luxury vehicle\ntest/092b2ebea2296cca,Antique car\ntest/092c9d963499b5c4,Hound\ntest/092d1ec0225476ee,Bird of prey\ntest/092d1fcb6f2cca69,Arthropod\ntest/092d91b5e46f6fc2,Black-and-white\ntest/092daa3fec3416f4,Athletic shoe,Outdoor shoe\ntest/092e53e136b92240,Wind wave\ntest/092f2c528ede24c2,Compact car\ntest/0930345451d77e60,Bird of prey\ntest/09326a78ad69259c,Miniature Poodle\ntest/0934e9d5a4109706,Close-up\ntest/09350bd3c653bf8a,Amphibian\ntest/0935913f38921350,Blond\ntest/0937ef8bb2b601ee,Luxury vehicle\ntest/0938caf264ee3088,Close-up\ntest/093a1df9d216bdcf,Luxury vehicle\ntest/093a4861abe6e023,Bird of prey\ntest/093ab55e46fe2e60,Close-up\ntest/093b2be9fc1a77a2,Whiskers\ntest/093eca4058859686,Backlighting\ntest/0941a5d380131f37,Luxury vehicle,Antique car\ntest/0943227e7ef8a53a,Musical theatre\ntest/094334ac0e2f0d9d,Whiskers\ntest/09436f1b6377c9d5,Ball game,Team sport\ntest/094777ba90eef690,Chordophone\ntest/09478cad2847057b,Compact car,Sport utility vehicle\ntest/094819cc87d2abf2,Billiards\ntest/094a11af1c6d031c,Frigate\ntest/094abf5ea889971c,Close-up\ntest/094ac501ddda2c7f,Compact car\ntest/094ba5d827093d86,Luxury vehicle,Performance car\ntest/094f7f671cd066a6,Sport utility vehicle\ntest/094fec6cd7f4bb7e,Luxury vehicle,Performance car\ntest/0951692a3e88cf3a,Close-up\ntest/095179fca0af93ca,Compact car\ntest/0951a175c79519eb,Senior citizen\ntest/0951e7d928bb210f,Combat sport\ntest/09538f85b566d3e0,Luxury vehicle\ntest/09545a79a4e3cdb6,Senior citizen\ntest/09554693d2ff94c0,Close-up\ntest/09559c676569a5dc,Water polo,Ball game,Team sport\ntest/0957f84aecdf874d,Compact car\ntest/09598149cb9a5f78,Close-up\ntest/095a1537fe049a51,Sport utility vehicle\ntest/095b4fdbb07f4dbd,Arcade game\ntest/095bce682fcc2c8e,Boating\ntest/095eeaffc2a95164,Molluscs\ntest/0960f4d25c2d8e84,Team sport\ntest/0962b832b3090472,Luxury vehicle,Performance car\ntest/0964a481122a1c1f,Leaf vegetable\ntest/09698840b009022c,Aircraft engine\ntest/096c1aa2a76cde2c,Luxury vehicle,Performance car\ntest/096c59c88315927c,Compact car\ntest/096e6652bdf28eba,Extreme sport\ntest/096f339c63794009,Plant stem\ntest/09721fc21e6a8adb,Residential area,Aerial photography\ntest/09729ef7ea077267,Luxury vehicle,Performance car\ntest/0977359a93902547,Compact car\ntest/0979e18ffbfc53c4,Luxury vehicle,Antique car\ntest/097dbbc1971163f8,Close-up\ntest/097e069fce54de57,Plant stem,Close-up\ntest/098055c5133befa9,Chordophone,Electric guitar\ntest/09852431c7ff17d8,Hair coloring\ntest/09855ca3c9066d27,Vegetarian food,Fried food\ntest/0987750876b29932,Extreme sport,Wind wave\ntest/098894a1b82b9b81,Extreme sport,Sport climbing\ntest/0988a94fcbb49a1b,Luxury vehicle,Performance car\ntest/098f0d2a1e49ad7a,Luxury vehicle,Performance car\ntest/09979d1f4fb884ed,Machine tool\ntest/099862b891b82c9a,Glacial landform\ntest/0998f953f99bf925,Luxury vehicle\ntest/099a2ece4ca5c2e3,Auto racing\ntest/099a481ee7c5bb74,Boating\ntest/099b4942c6a592e5,Luxury vehicle\ntest/099d79a1168e95a2,Domestic short-haired cat,Whiskers\ntest/099fff17bf3f9a79,Khinkali\ntest/09a0b85d35f0f546,Ball game\ntest/09a268448ae38657,Close-up\ntest/09a3eb691dbf8aa8,Close-up\ntest/09a409f01363f3e0,Close-up\ntest/09a4ea8c11ee50ad,Stemware\ntest/09a4fd91bccf481e,Auto racing\ntest/09a6326deac0db22,Frozen yogurt\ntest/09a678dea47de774,Luxury vehicle,Performance car\ntest/09a727503e753c5d,Whiskers\ntest/09a790b8702f56f3,Luxury vehicle,Performance car\ntest/09a80a7e88da0e00,Soba\ntest/09a8d2742012c383,Monoplane\ntest/09ab0fcb74b86405,Black-and-white\ntest/09ab444e2f716dad,Close-up\ntest/09ae0e457a55533c,Luxury vehicle,Sport utility vehicle\ntest/09b0493af2e0ad65,Floristry\ntest/09b4460bbe237750,Rural area\ntest/09b9636e13e3722b,Military person\ntest/09baea0062dc8c4c,Luxury vehicle,Antique car\ntest/09bb6a0d7b1b9ab7,Dobermann\ntest/09bbf6eebbd71c2e,Military person\ntest/09bd95bfed0e3f42,Boating\ntest/09bfcd982ad9f59b,Assault rifle,Firearm\ntest/09bff5f615f67d3f,Combat sport\ntest/09c1c18e1ca3f7c3,Whiskers\ntest/09c48ceb3b1dbaf9,Domestic short-haired cat,Whiskers\ntest/09c63a120d899404,Boating\ntest/09c73711349d53f1,Close-up\ntest/09c7b67f1dffce0b,Luxury vehicle,Performance car\ntest/09c7d9199170dff9,Aircraft engine\ntest/09c86aeeacffefd6,Black-and-white\ntest/09caa31ba83c14fa,Luxury vehicle,Performance car\ntest/09cc66c3ce06c937,Combat sport\ntest/09ce2868686be56f,Compact car,Antique car\ntest/09cf1a9281995f6a,Hound,Close-up\ntest/09cf1ba2bd950cc2,Drums,Electronic instrument\ntest/09cf47e3f484edbf,Antique car\ntest/09cfa33619d9e63b,Barbecue chicken\ntest/09d068cb1caded1d,Close-up,Anole,Scaled reptile\ntest/09d0920b8a937698,Luxury vehicle,Performance car\ntest/09d0f9e983d23b58,Luxury vehicle\ntest/09d12b2c4a6bd8e6,Dobermann\ntest/09d21724c6c739d7,Chordophone\ntest/09d2374843129c2a,Jeans\ntest/09d25d275b804014,Extreme sport\ntest/09d32f6139e2be23,Brochette\ntest/09d45a5f275728e3,Combat sport\ntest/09d58c082f651e93,Auto racing\ntest/09d67da0e071c027,Figure skating\ntest/09db3800b249e10b,Shooting range,Firearm\ntest/09db444f904f79a1,Domestic rabbit\ntest/09ded7f57fbb294c,Hound\ntest/09df6c9fb15a42da,Perching bird\ntest/09e617098f3a6a5b,Close-up\ntest/09e88364989501ab,Luxury vehicle,Antique car\ntest/09e88e4d977e1329,Bird of prey\ntest/09eb3a64ff84690a,Arthropod\ntest/09ecc579416988ba,Black-and-white,Luxury vehicle\ntest/09f081dd5f98d5da,Fried food\ntest/09f10f0327e0d2e4,Hurdling\ntest/09f1acedd7de21fe,Arthropod,Close-up\ntest/09f220bfef78ac7b,Hound\ntest/09f24bb736f7ab0d,Black-and-white\ntest/09f4a89b4180cd87,Ball game,Team sport\ntest/09f645d279bf9b10,Fried food\ntest/09f729bdd6529f70,Luxury vehicle\ntest/09fe664d0aa852f6,Close-up\ntest/09fece734d1279a0,Lifebuoy\ntest/09feea00236890b2,Aerial photography\ntest/0a0224155926e572,Bovine\ntest/0a052fa399407a16,Compact car,Sport utility vehicle\ntest/0a0626eacf656261,Black-and-white,Medical imaging\ntest/0a07c15f90208a8d,Black-and-white\ntest/0a07c84c3e315339,Full moon\ntest/0a09761ee6dc42bb,Leaf vegetable,Vegetarian food\ntest/0a0a87b74e2142b4,Jeans\ntest/0a0b3287f3246d49,Extreme sport,Boating\ntest/0a0c2a9d29adcc27,Ramen\ntest/0a0c3ee5e0b97dd8,Ball game\ntest/0a0c94f67dd7ab3c,Vegetarian food\ntest/0a1061eb963e48a2,Digital camera,Close-up\ntest/0a10772cdd3a21db,Watercolor paint\ntest/0a10e5741162ef98,Close-up,Whiskers\ntest/0a13e0401b30a8ab,Rural area\ntest/0a1473ec73692c5a,Luxury vehicle\ntest/0a14baea6027eea5,Aloe\ntest/0a1526b77ea36fd4,Middle ages\ntest/0a16016b833fc07f,Close-up\ntest/0a19785b18b12331,Combat sport\ntest/0a1a9b5ccbde4949,Flatbread\ntest/0a1dae8f71065201,Ball game,Team sport\ntest/0a1e1ed43c0cbe88,Ball game,Team sport\ntest/0a1fcc37060caeb9,Close-up\ntest/0a20cd63d242dab2,Antique car\ntest/0a21c6fa25edac47,Electronic instrument\ntest/0a259c7378596ed5,Jeans\ntest/0a2615da6f5891da,Musical theatre\ntest/0a2851919500e261,Compact car\ntest/0a2c1f8f5d520a2c,Rural area\ntest/0a2c54742050f097,Ale\ntest/0a2ca221284ab461,Black-and-white\ntest/0a382d095b381670,Rural area\ntest/0a38dff25243cb94,Scaled reptile,Close-up,Anole\ntest/0a39441668695e05,Aircraft engine\ntest/0a397c6757b20695,Domestic short-haired cat,Whiskers\ntest/0a398a6311419953,Black-and-white\ntest/0a39b5f0c2c0c625,Medical imaging\ntest/0a3f73165751b5fd,Aircraft engine\ntest/0a411d151f978818,Black swan\ntest/0a4944d22e6d21fe,Floristry\ntest/0a4b7bad3287d0dc,Close-up\ntest/0a4c15c28f16e4ab,Luxury vehicle\ntest/0a4d46a98790f3a5,Arthropod\ntest/0a4ecb2f027209f6,Close-up\ntest/0a4ed4f127bcedbc,Compact car,Luxury vehicle\ntest/0a4f0099934b081c,Luxury vehicle\ntest/0a5341aca048c7a9,Residential area\ntest/0a53a6076b08597c,Auto racing\ntest/0a53b25fdd55704b,Luxury vehicle\ntest/0a53c887621511b2,Arthropod,Close-up\ntest/0a593d23f391ce70,Musical theatre\ntest/0a59f15b9aa83bab,Luxury vehicle\ntest/0a5c6d7b6d4cd42c,Arthropod,Close-up\ntest/0a5dd95c5d7c64a4,Senior citizen\ntest/0a5f6925b7af0423,Luxury vehicle,Sport utility vehicle\ntest/0a638e9d9a924f5f,Close-up\ntest/0a652c84fb45f9a9,Close-up,Whiskers\ntest/0a65c6bcbb229000,Cookies and crackers\ntest/0a66241b96f97649,Athletic shoe,Outdoor shoe\ntest/0a668900826d2793,Antique car,Compact car,Performance car\ntest/0a681c7f88e74460,Close-up\ntest/0a6874845ad648e1,Close-up,Whiskers\ntest/0a69315e797961de,Luxury vehicle\ntest/0a6c82232d2ca916,Arthropod,Close-up\ntest/0a6c95dd96e2318c,Close-up\ntest/0a70f739f5470c48,Blond,Close-up\ntest/0a737ef9ae94052d,Ball game\ntest/0a74367f0c70fb25,Antique car\ntest/0a75b03e0d1d7fe4,Musical theatre\ntest/0a77e5dee4c414a4,Whiskers,Domestic rabbit\ntest/0a788c6a95398ad5,Blond\ntest/0a79d9e657af9a3c,Performance car\ntest/0a7bf24fd9ac2bcf,Water polo,Ball game,Team sport\ntest/0a7c7f278529d0e0,Extreme sport\ntest/0a7ca7a959a8d536,Arthropod,Close-up\ntest/0a80a49f9b0bc08f,Aircraft engine\ntest/0a81738cd51205e4,Luxury vehicle\ntest/0a81990b49b6d7b6,Scaled reptile\ntest/0a8534b73a498c33,Compact car,Luxury vehicle\ntest/0a86f8a861301669,Rural area\ntest/0a88f90d336de327,Sailboat racing\ntest/0a8b19b15ec09495,Machine tool\ntest/0a8b367d8983474a,Domestic rabbit\ntest/0a8bec44cb217572,Wind wave\ntest/0a8bf26de6d8e7e3,Black-and-white\ntest/0a8bf9c7a090a29d,Floristry\ntest/0a8c7bbae99db4df,Black-and-white,Halter\ntest/0a8d6f855dad280d,Compact car\ntest/0a8da32e8ee3c54f,Motorcycling\ntest/0a8ef573281c5f2d,Close-up\ntest/0a9033f96755177c,Team sport\ntest/0a9049018772784d,Luxury vehicle\ntest/0a9277cb49a4544d,Close-up\ntest/0a94b3b3b5575f66,Drums\ntest/0a963a30f2a9cc8f,Luxury vehicle\ntest/0a965f8769b16abe,Luxury vehicle\ntest/0a9675f1b60f1786,Luxury vehicle\ntest/0a9879cdba2f1864,Blond\ntest/0a9aeda1f89a6e91,Aerial photography\ntest/0a9c5de841b5efb5,Briefs\ntest/0a9cae46a2fe661b,Ball game,Team sport\ntest/0a9e8662b278b821,Luxury vehicle,Performance car\ntest/0a9e86c0b0426639,Jeans\ntest/0aa0df5add17ca30,Compact car,Luxury vehicle,Antique car\ntest/0aa11585e6feebc8,Luxury vehicle,Antique car\ntest/0aa1648b3cbae942,Arthropod,Close-up\ntest/0aa20025142ff434,Middle ages\ntest/0aa6336623e2d715,Close-up\ntest/0aa93bcbfe7b5201,Luxury vehicle,Performance car\ntest/0aab7455155030cf,Road cycling\ntest/0aad99ecc431be62,Wind wave\ntest/0aadfc74281a7ada,Luxury vehicle\ntest/0aae87f4fa55cc79,Jeans,Luxury vehicle,Performance car\ntest/0ab012ddddb2b11a,Sport utility vehicle\ntest/0ab01646adecb909,Black-and-white,Military person\ntest/0ab0df1065520be2,Whiskers\ntest/0ab14ba1f4ef9c3a,Compact car,Luxury vehicle,Antique car\ntest/0ab1fc229a95b1b6,Luxury vehicle,Antique car\ntest/0ab20f50ffd8fabe,Aircraft engine\ntest/0ab2720184db780f,Nuclear power plant\ntest/0ab29ba1cde933ac,Watercolor paint\ntest/0ab2ea5a701de570,Luxury vehicle,Antique car\ntest/0ab36cca023d63a9,Black-and-white\ntest/0ab766268ce939cc,Luxury vehicle,Performance car\ntest/0ab9830cc46f22b2,Aerial photography\ntest/0abacba89cf3c98c,Filling station\ntest/0abd9b5a17d54942,Extreme sport\ntest/0abe5e4f09bc1262,Whiskers\ntest/0ac0323383dd6611,Scaled reptile\ntest/0ac066dc375dcbf0,Close-up\ntest/0ac092abdc4d77d9,Rural area,Canola\ntest/0ac1717140554997,Fried food\ntest/0ac1ba3381e07b9b,Compact car,Luxury vehicle\ntest/0ac3653a9625cd9e,Luxury vehicle,Performance car\ntest/0ac41185991e4fd7,Siberian husky\ntest/0ac51026a0d5caf1,Antique car\ntest/0ac5eb806af21f8e,Jeans\ntest/0aca4d8a382ce0ef,Plant stem\ntest/0acc4f413cb08639,Blond,Close-up\ntest/0acd1d11251595c9,Ball game\ntest/0acdb6f3f02439df,Close-up\ntest/0ace571897b33699,Arthropod,Close-up\ntest/0ad1c6b84907d2ad,Close-up\ntest/0ad263b131ee4c7a,Hound,Close-up\ntest/0ad542aa4e7f49c5,Compact car,Luxury vehicle\ntest/0ad70dcae4230c60,Frozen yogurt\ntest/0ad7537b1bd1c6e1,Athletic shoe,Outdoor shoe\ntest/0ad7693198e95a40,Sailboat racing\ntest/0ad78b4755eb6535,Digital camera\ntest/0adb355aab49d993,Digital camera\ntest/0adebe1718baff25,Luxury vehicle\ntest/0adecc4f361ba1ba,Black-and-white\ntest/0ae2888ec25ba342,Scaled reptile\ntest/0ae7ebd7a8876389,Hound\ntest/0aea452ca8e904e7,Team sport\ntest/0aecdda329ce4a4f,Combat sport,Sumo\ntest/0aeec418f212ef2c,Arthropod,Close-up\ntest/0aef58955b126057,Luxury vehicle\ntest/0af081b3e5cc94ef,Rural area\ntest/0af5ba2fbc9fe829,Close-up\ntest/0af5cc28d27c8dc2,Performance car,Antique car\ntest/0af65fe5bc971236,Auto racing\ntest/0af7135b5cff11c0,Close-up\ntest/0af732c1546f9dcc,Compact car,Antique car\ntest/0af7495ce8c2f098,Extreme sport\ntest/0af7ea4cedd9727f,Extreme sport\ntest/0af88c7a8b7195c7,Close-up,Whiskers\ntest/0af88e0948630469,Whiskers\ntest/0af9da3da5287168,Extreme sport,Wind wave\ntest/0afaaf880d2f2b5f,Portrait photography\ntest/0afc7719da632962,Boating,Rowing\ntest/0afe8ae2f520cd02,Close-up,Whiskers\ntest/0afed95b79ac15e3,Headphones\ntest/0b0121cc9c10ad1a,Ball game,Team sport\ntest/0b01a8b7ffa2f3fd,Compact car,Antique car\ntest/0b055d85bc052e7e,Luxury vehicle,Antique car\ntest/0b061ceda8f590e7,Domestic short-haired cat\ntest/0b09e7f163c5cac6,Antique car\ntest/0b0a920b2a8d2cf9,Luxury vehicle\ntest/0b0cd341a2d24e5f,Chordophone\ntest/0b11498ebc764c27,Antique car\ntest/0b1166e05c7e2705,Domestic short-haired cat,Close-up,Whiskers\ntest/0b13e843e7175016,Antique car\ntest/0b14c5e33b318cd0,Luxury vehicle,Antique car\ntest/0b15f98cd53b7b19,Glacial landform\ntest/0b162fcc2cb20057,Perching bird\ntest/0b17d5cb644ef36b,Halter\ntest/0b17e346d5b0c944,Luxury vehicle,Antique car\ntest/0b1865633d629ed4,Antique car\ntest/0b19644e5ae13266,Rural area\ntest/0b19b57b99ea7d55,Firearm\ntest/0b1bc5867264f4a1,Close-up\ntest/0b1d8641c0a629e5,Domestic short-haired cat,Whiskers\ntest/0b1e6c026ce3d667,Firearm\ntest/0b1e7e85dfe50c1a,Close-up\ntest/0b1ec01b1063caac,Boating\ntest/0b1fc684b559cbb8,Sport utility vehicle\ntest/0b2114c445adaf18,Close-up\ntest/0b2154403006f7ec,Bovine\ntest/0b217bae9d60c52d,Luxury vehicle,Antique car\ntest/0b236d0c3c194aae,Outdoor shoe\ntest/0b237ed8f2da94bf,Leaf vegetable\ntest/0b23a940144435e8,Portrait photography\ntest/0b24ae3baf6ac907,Bird of prey\ntest/0b25da06565c65de,Leaf vegetable\ntest/0b262e5ed192428e,Luxury vehicle,Performance car\ntest/0b282e3b5dde64ca,Antique car\ntest/0b2a37690aab7045,Monoplane\ntest/0b2b42f5a298a7f4,Compact car,Antique car\ntest/0b2bad4670b077cd,Antique car\ntest/0b2f80825b40c10a,Arthropod,Close-up\ntest/0b313fd8350f2754,Hound\ntest/0b336fc59253f5cc,Luxury vehicle,Antique car\ntest/0b34f3dc93b9e430,Military vehicle\ntest/0b35fc31c3c6b33f,Compact car\ntest/0b3640f4df0ad590,Close-up\ntest/0b3740b6e5bf6550,Domestic short-haired cat,Close-up,Whiskers\ntest/0b3adc57b2e8a750,Shinto shrine\ntest/0b3b798a8f0b8af7,Antique car\ntest/0b3c9498755e2560,Close-up\ntest/0b3cee81b907aa24,Motorcycling\ntest/0b4159410adc183a,Compact car,Luxury vehicle\ntest/0b43d59b3d4da66c,Rural area\ntest/0b45431e579e3f6e,Peafowl\ntest/0b45d3851786a180,Luxury vehicle\ntest/0b46956ac04aa2ac,Monoplane,Aircraft engine\ntest/0b46f810aa9ef38c,Divemaster,Scuba diving,Underwater diving\ntest/0b4854f95ff858c7,Extreme sport,Motorcycling\ntest/0b4f76675243c81d,Milky way\ntest/0b54080a7159dfb2,Residential area,Aerial photography\ntest/0b59ee0b31a5ab4e,Domestic short-haired cat,Whiskers\ntest/0b5aa14a3f270688,Luxury vehicle\ntest/0b5df476f1e540dd,Aircraft engine\ntest/0b5f21a7446b148d,Close-up\ntest/0b5faf743305c39a,Close-up\ntest/0b6063f056787270,Black-and-white\ntest/0b61c951b4047e9e,Electric piano\ntest/0b6232bad6152c13,Black-and-white,Luxury vehicle,Performance car\ntest/0b62f6f140c3192d,Floristry\ntest/0b67381f171391d7,Microcontroller\ntest/0b6740d15303b587,Watercolor paint\ntest/0b679ffe819c1799,Aircraft engine\ntest/0b680ddf8d7e4d9d,Jeans\ntest/0b6870b626967559,Molluscs,Close-up\ntest/0b68d490a91827a9,Close-up\ntest/0b69a552a94a2ead,Combat sport\ntest/0b69c66bb20a8d69,Narcissus\ntest/0b69f9dd6735fe93,Angling\ntest/0b6a4d503a022c74,Blond\ntest/0b6e66252480f379,Luxury vehicle\ntest/0b719c5cb9de62ed,Performance car\ntest/0b72b821c0e1af25,Musical theatre\ntest/0b73807345d840eb,Luxury vehicle,Sport utility vehicle\ntest/0b7394791c14de59,Chordophone\ntest/0b748340e89165f4,Domestic short-haired cat,Whiskers\ntest/0b76d851a16a5b54,Luxury vehicle,Performance car\ntest/0b7703708dce49d3,Luxury vehicle\ntest/0b7761337e00ca1d,Antique car\ntest/0b7983fdfc159032,Junk food\ntest/0b7a27de22397643,Close-up,Scaled reptile\ntest/0b7ca6b5e09bae3e,Close-up\ntest/0b7ccba5154ea64e,Black-and-white\ntest/0b7d043822c6db85,Bird of prey\ntest/0b8024808dcd7382,Cookies and crackers\ntest/0b83ee6f754b476a,Gumbo\ntest/0b83f1552e97bfdf,Antique car\ntest/0b83fc33016c00f5,Luxury vehicle\ntest/0b84a70c2ac0c874,Ball game\ntest/0b86bef0f8249609,Luxury vehicle,Antique car\ntest/0b8868d6d1435d4c,Rural area\ntest/0b8b0b38eb09efea,Luxury vehicle\ntest/0b8ca4cb39bc4cc1,Divemaster,Scuba diving,Underwater diving\ntest/0b8d7fb48d4f17b9,Cookies and crackers\ntest/0b8e63bbe0d65c37,Close-up\ntest/0b93e142e8a2742c,Chordophone\ntest/0b95ae7723da5e40,Extreme sport\ntest/0b96cfacc3d900a0,Glacial landform\ntest/0b9967cdd42d76d3,Close-up\ntest/0b9ab10605b0ead8,Close-up\ntest/0b9ee80fb57d207d,Black-and-white,Close-up\ntest/0ba02529d0169e05,Vegetarian food,Fried food\ntest/0ba2756bae006392,Close-up\ntest/0ba2b4ac3de10c3e,Auto racing\ntest/0ba46b6a4075f595,Close-up,Plant stem\ntest/0ba4a702f67ab14d,Extreme sport,Motorcycling\ntest/0ba4cfe3255da676,Compact car,Luxury vehicle,Antique car\ntest/0ba8dea9487e3833,Arthropod,Close-up\ntest/0ba9a71318cf03d5,Steamed rice\ntest/0ba9df96617af631,Vegetarian food\ntest/0baa9f6bc2044416,Luxury vehicle\ntest/0bb03b3cad0efb39,Whiskers\ntest/0bb24c98e61ec7b2,Equestrianism\ntest/0bb28cc935d9c950,Auto racing\ntest/0bb52be26622d55d,Drums\ntest/0bb61915282231e2,Scaled reptile\ntest/0bb6c84550fcbc87,Microcontroller\ntest/0bbb41e28f4dc599,Auto racing\ntest/0bbd1d51fb00274b,Sport utility vehicle\ntest/0bbe49c8e3daaaba,Antique car\ntest/0bc325731b1570c2,Jeans\ntest/0bca58a6b6fbe770,Close-up\ntest/0bcabf76c4e8c6cf,Assault rifle,Firearm\ntest/0bcb060911706e2a,Vegetarian food\ntest/0bcb992975eca8c2,Coral reef fish\ntest/0bcc1d9f92e418b4,Whiskers\ntest/0bce192cc498b0d1,Compact car\ntest/0bcf01690c21e2a0,Jeans\ntest/0bd09b6952adb90c,Close-up\ntest/0bd1ab4f429100ca,Luxury vehicle,Antique car\ntest/0bd2106d8f72ef32,Floristry\ntest/0bd2695383a2c295,Sport utility vehicle\ntest/0bd3d413eb90a326,Rural area\ntest/0bd639cae82eb91c,Equestrianism\ntest/0bd67f4467711438,Arthropod,Close-up,Plant stem\ntest/0bd7249cd8e061ba,Close-up,Whiskers\ntest/0bd97cc657f4c3f4,Ball game,Team sport\ntest/0bda92d12a746b5e,Sport utility vehicle\ntest/0bdada492e85aa0e,Close-up\ntest/0bdb7bdd6ca4f983,Scaled reptile\ntest/0bdc378272c98063,Road cycling\ntest/0bdd5e5efec0d5bc,Jeans\ntest/0be1e2bfc18c0226,Close-up\ntest/0be24a051ab73ebb,Compact car,Luxury vehicle\ntest/0be2d33cb3284846,Performance car\ntest/0be31040b2e8c187,Boating\ntest/0be39faa9d07e343,Hair coloring\ntest/0be68cb951208dc2,Aircraft engine\ntest/0be849ce296f5dec,Luxury vehicle\ntest/0be934cf1b004d8b,Molluscs\ntest/0bea584b249ee4b3,Compact car,Luxury vehicle\ntest/0beb12cbe3054883,Ball game\ntest/0bec5f7868b7354b,Luxury vehicle,Sport utility vehicle\ntest/0bf0f85a86ad212f,Domestic short-haired cat\ntest/0bf150dc6bd7b026,Luxury vehicle\ntest/0bf337c59de77ffe,Luxury vehicle,Performance car\ntest/0bf35accf04af9f9,Luxury vehicle\ntest/0bf5ae78b74f061f,Luxury vehicle,Antique car\ntest/0bf64e55d046a453,Antique car,Performance car\ntest/0bf80b98b70d97f0,Combat sport\ntest/0bf98a7543287fbb,Touring car,Luxury vehicle,Antique car\ntest/0bf99714ce063ab6,Floristry\ntest/0bfa36f9dce28dd1,Compact car\ntest/0bfa4e63f7c90cb7,Rural area\ntest/0bfa55a61a2cfa53,Ball game,Team sport\ntest/0bfb11c43d935c5a,Luxury vehicle\ntest/0bfc98098fd6198e,Plant stem\ntest/0bfd8ef96228aeee,Herding,Goats,Bovine\ntest/0bfecfa8bde92a97,Hound\ntest/0bffd4a376d8c39a,Auto racing\ntest/0c00ecaf8d9fed00,Sport utility vehicle\ntest/0c010adb0be155cf,Arthropod\ntest/0c02b9504d2d3843,Fried food\ntest/0c03982e7c148192,Ball game\ntest/0c03f722a2244d71,Cosmetics\ntest/0c04767d5c29923c,Road cycling\ntest/0c0496a01aedcd59,Leaf vegetable\ntest/0c06df711b194b07,Luxury vehicle\ntest/0c076cefb6c37743,Luxury vehicle\ntest/0c0a68d70f850506,Blond\ntest/0c119fb0a2f0d63b,Extreme sport,Motorcycling\ntest/0c1254529b7633f6,Sport utility vehicle\ntest/0c13fc8f316fbf64,Close-up,Whiskers\ntest/0c140d146143e70f,Luxury vehicle,Sport utility vehicle\ntest/0c14f96467fd7ba1,Close-up\ntest/0c15cedc8716a24b,Domestic short-haired cat,Whiskers\ntest/0c17c58d5e8e689b,Bovine\ntest/0c1acbe0a14e2e51,Close-up\ntest/0c1e67462f844c81,Steamed rice\ntest/0c21cb5ad76b6c47,Musical theatre\ntest/0c24efae3dded0bd,Senior citizen\ntest/0c281ae0dbc5dfeb,Compact car\ntest/0c283b812397fe48,Comics\ntest/0c2a57b94489ddcd,Close-up,Scaled reptile\ntest/0c3228734c66d31b,Combat sport\ntest/0c3240ecec541bfc,Freight transport,Heavy cruiser,Frigate\ntest/0c32874b02effa3f,Jeans\ntest/0c328d61dc9605d6,Luxury vehicle\ntest/0c3470dd5854c7b4,Briefs\ntest/0c34a2f75a195db1,Jeans\ntest/0c359d3dae1532a8,Close-up\ntest/0c360e0a55d0acac,Machine tool\ntest/0c36e1b19a01bf7e,Luxury vehicle,Performance car\ntest/0c380be170720aa2,Arthropod,Close-up\ntest/0c3b00ba25829145,Arthropod,Close-up\ntest/0c3b0cc143337eaf,Sport utility vehicle\ntest/0c3c2470dae73fa2,Jeans\ntest/0c3e0316409f1565,Scaled reptile\ntest/0c3e57001355bb9b,Blond,Portrait photography\ntest/0c3f6a02c518052d,Aircraft engine\ntest/0c41dfb15f58e558,Electronic instrument\ntest/0c4378005de94a91,Hound\ntest/0c43ab75f99b8316,Electronic instrument,Electric piano\ntest/0c45c53219ceb5a6,Amphibian,Close-up\ntest/0c46a2452e597bfc,Comics\ntest/0c473049e26c0ffb,Close-up\ntest/0c4a6ecfba08bf9e,Military person\ntest/0c4aaa96a8254e66,Close-up\ntest/0c4b3742fea6ccd2,Junk food\ntest/0c4b47db0a89d8ab,Extreme sport\ntest/0c4b5545f7fe261f,Luxury vehicle,Antique car\ntest/0c4c5d934ddbd76f,Luxury vehicle\ntest/0c4cc12a33c9c349,Compact car,Antique car\ntest/0c4e291e151205c8,Ball game\ntest/0c5438e0d6ec07db,Luxury vehicle\ntest/0c5b8a6505fb3082,Wind wave\ntest/0c5c8fa9d6cd9d67,Blond,Hair coloring,Close-up\ntest/0c5ec8f8c83a28ef,Close-up\ntest/0c5f65371652e673,Peafowl\ntest/0c5fec42a6bc3c7b,Close-up\ntest/0c6218aae7a8d579,Boating\ntest/0c63e26e4963eb2c,Firearm\ntest/0c641ef1ca8168ad,Antique car,Luxury vehicle,Performance car\ntest/0c65119022122afc,Procyonidae,Close-up,Whiskers\ntest/0c65e368f3e3d414,Extreme sport,Motorcycling\ntest/0c6631c1b55a395e,Compact car,Luxury vehicle,Sport utility vehicle\ntest/0c67e296102635cc,Close-up\ntest/0c68233761347875,Extreme sport,Boating\ntest/0c68315d4dd99c4a,Extreme sport\ntest/0c6b1d6003dd3726,Luxury vehicle\ntest/0c6bc20d000a3e6c,Rural area,Nuclear power plant\ntest/0c6c040ef01c9e10,Monoplane,Aircraft engine\ntest/0c6f829fcef11595,Compact car,Antique car\ntest/0c71c300e14a7040,Vegetarian food\ntest/0c783b33f01f7cac,Whiskers\ntest/0c78eab0bc0b64cb,Leaf vegetable\ntest/0c793ae1b75b1d38,Luxury vehicle\ntest/0c79a0995798a379,Ball game,Team sport\ntest/0c7a5b1dea4d17a9,Whiskers\ntest/0c7a6741eb221a69,Luxury vehicle,Performance car\ntest/0c7b37d0bd3417b9,Close-up\ntest/0c7b5e604caf1b18,Aircraft engine\ntest/0c7c64ee73f9a974,Milky way\ntest/0c7c6c2292006bd0,Plant stem\ntest/0c7d64b2e02adb24,Luxury vehicle,Antique car\ntest/0c8018afa7029c2b,Full moon\ntest/0c805afd8718c328,Bovine\ntest/0c82b569016cb88b,Luxury vehicle,Sport utility vehicle\ntest/0c86e9b679f89ce9,Aircraft engine\ntest/0c87c5394666da7b,Amphibian\ntest/0c88431f0fc1ba48,Extreme sport,Parachuting\ntest/0c8898ac6d39a526,Rural area\ntest/0c8962eed4925008,Close-up\ntest/0c8a3a65845d19e5,Close-up\ntest/0c8ab9a6e31fcf52,Close-up\ntest/0c8c7e644722e941,Floristry\ntest/0c902454c851deba,Extreme sport\ntest/0c92e6bfd7df2d20,Luxury vehicle,Antique car\ntest/0c92e9417b1a336c,Erinaceidae\ntest/0c94c1d1108c3b38,Ball game,Team sport\ntest/0c9617655fb3a76b,Ball game,Team sport\ntest/0c96676d3cc5df27,Rural area,Aerial photography\ntest/0c9780f9e66d8d67,Vegetarian food\ntest/0c98e559f5677700,Performance car\ntest/0c995cca833f6514,Luxury vehicle,Performance car\ntest/0c9dfc7a13459c6c,Rear-view mirror\ntest/0ca2db447051276e,Dobermann,Hound\ntest/0ca3663d4e237556,Figure skating\ntest/0ca38be01f41843c,Luxury vehicle\ntest/0ca4113218413093,Ball game\ntest/0ca74c6eeddc0f1a,Ball game,Team sport\ntest/0ca85fa00dc8b332,High-speed rail\ntest/0ca861c495f683a9,Firearm\ntest/0ca8a79b3d0f0ae6,Aircraft engine\ntest/0ca8f16196bf09d9,Luxury vehicle,Antique car\ntest/0ca96e946074d366,Luxury vehicle,Performance car\ntest/0caa2900d51d68dc,Rice ball\ntest/0caafe50e3258dd3,Luxury vehicle\ntest/0cacac7d1bf5d528,Ball game,Team sport\ntest/0cb031c3a1e83c1e,Antique car\ntest/0cb08dc109160b64,Pork chop\ntest/0cb1f8f87d9136c4,Boating\ntest/0cb2bf13a8219a02,Scaled reptile\ntest/0cb4f551f181b498,Fried food\ntest/0cb561aef1e16beb,Team sport\ntest/0cb65a9cb4479bb6,Junk food\ntest/0cb79d784ef50752,Auto racing,Luxury vehicle,Performance car\ntest/0cb849f73e0f1a4a,Comics\ntest/0cb8808f7615904e,Domestic short-haired cat,Whiskers\ntest/0cba449d27ea91d5,Inflatable,Boating\ntest/0cbaa459f95aee37,Luxury vehicle\ntest/0cbc154250fd7c76,Close-up\ntest/0cbe2febe4b166c1,Aerial photography,Wind wave\ntest/0cbf705bacaea864,Plant stem\ntest/0cbf8493b636ce37,Domestic short-haired cat,Close-up,Whiskers\ntest/0cc072bcbf9d1cd3,Sport utility vehicle\ntest/0cc2dc882099443a,Military vehicle,Luxury vehicle,Sport utility vehicle\ntest/0cc326880c31b4b3,Roman temple\ntest/0cc4722ca906f86c,Close-up\ntest/0cc5f4f336a34105,Close-up\ntest/0cc601928e03555b,Blond\ntest/0cc6886934df7401,Outdoor shoe\ntest/0cc87dd793dc925a,Black-and-white\ntest/0cc91a1ee964030b,Close-up\ntest/0ccb324679704c73,Gliding\ntest/0ccbb2436b065590,Inflatable\ntest/0cce4b162239793f,Fire apparatus\ntest/0ccf0f96cd86ad53,Fried noodles\ntest/0ccf7dd044e99c30,Machine tool\ntest/0ccf83d65ea3f8fa,Black-and-white\ntest/0cd0d53628ad7d29,Close-up\ntest/0cd4c277f2b0cea1,Sport utility vehicle\ntest/0cd742a89392c75b,Luxury vehicle,Performance car\ntest/0cd7694544c896db,Road cycling\ntest/0cd81ce27b8344c9,Luxury vehicle,Sport utility vehicle\ntest/0cd8ed0bbaa2cb46,Medical imaging\ntest/0cd8f1f92170d450,Performance car\ntest/0cdae528456599ed,Luxury vehicle,Sport utility vehicle\ntest/0cdc92d24827cb9e,Performance car\ntest/0cdcbed35382917a,Performance car\ntest/0cde56167d08a8aa,Ball game,Team sport\ntest/0ce3a7cc54814851,Brochette\ntest/0ce423f38795dd52,Close-up\ntest/0ce5c71e66ac9e3c,Antique car\ntest/0ce61cc878540ace,Flatbread\ntest/0ce6c3376d3cf47c,Luxury vehicle,Performance car\ntest/0ceacfd214ddb209,Horse racing,Equestrianism\ntest/0ced03eed2ab7ad1,Compact car\ntest/0cee0d104ef10f13,Aircraft engine\ntest/0cee7ea334feb5d5,Luxury vehicle,Performance car\ntest/0cef49f8794d431c,Blond,Portrait photography,Close-up\ntest/0cf00bfadfd3f23f,Close-up\ntest/0cf45dc3b53ea8b6,Ball game,Team sport\ntest/0cfb9153a6342635,Frying,Fried food\ntest/0cfba6ce57f28629,Close-up\ntest/0cfd4e0fc4f1bf56,Domestic short-haired cat,Whiskers\ntest/0cfdb305f4db53a4,Antique car\ntest/0cff680b8bbb1689,Luxury vehicle\ntest/0d0089bdb4a8df30,Trampolining\ntest/0d02fff427c106f7,Close-up,Scaled reptile\ntest/0d0468dd805ec3cf,Monoplane\ntest/0d04a6ac9f82d487,Domestic short-haired cat,Whiskers\ntest/0d0742f5fc27ee6b,Assault rifle,Firearm\ntest/0d0831ef0676677d,Musical theatre\ntest/0d084c3676864cd1,Fried food\ntest/0d0bdcc1dcd64c95,Arthropod,Close-up\ntest/0d13b218a8701fff,Compact car,Luxury vehicle\ntest/0d13c2b46b9c7937,Jeans\ntest/0d14ca3c11f49a00,Extreme sport,Wind wave\ntest/0d16eaef22469129,Nuclear power plant\ntest/0d1872648dd54dd6,Close-up\ntest/0d1902a8a0ccab84,Road cycling\ntest/0d19a2508a6ea0aa,Antique car,Compact car,Luxury vehicle,Performance car\ntest/0d19c09c197dc4b0,Arthropod,Close-up\ntest/0d1b88b1cfc5c168,Close-up\ntest/0d1c0cbd7ba4a93b,Aircraft engine\ntest/0d2069678ded738e,Toy block\ntest/0d216056660d6fbb,Jeans\ntest/0d2486e1b0367db4,Whiskers\ntest/0d263cacb9ed343d,Cobblestone\ntest/0d29ccf5758a52b2,Luxury vehicle,Antique car\ntest/0d29d4e611acacfe,Perching bird\ntest/0d29efb397bbee39,Black-and-white\ntest/0d2c5b1589550b25,Scaled reptile\ntest/0d2d33b55c9faad4,Outdoor shoe\ntest/0d2eeaccda7e4c1b,Combat sport\ntest/0d2f8fae469b7c58,Close-up\ntest/0d30c64b29ff3b40,Luxury vehicle\ntest/0d3119fe1227332d,Combat sport,Grappling\ntest/0d34d7e02c4b8e3c,Close-up\ntest/0d3521ddce39d1b6,Luxury vehicle,Antique car\ntest/0d3592657b2b9e21,Luxury vehicle,Performance car\ntest/0d3786516a4d335e,Arthropod\ntest/0d386e3c0ee5275b,Compact car,Luxury vehicle\ntest/0d39d9e21db3be83,Ball game,Team sport\ntest/0d3a728e4c5703a4,Antique car\ntest/0d3cd0fdea635b66,Molluscs\ntest/0d3cdf2f07d96895,Performance car\ntest/0d3de6abf533c29c,Divemaster,Underwater diving\ntest/0d41d737d89d7f3e,Halter,Equestrianism\ntest/0d4448f456d6bfaf,Fried noodles\ntest/0d460329f4684193,Luxury vehicle\ntest/0d462b204ccfb9af,Auto racing\ntest/0d491b770c084be9,Road cycling\ntest/0d4becbb921d522c,Jeans\ntest/0d4f502c46d9d3b6,Compact car\ntest/0d4fc05d933ea095,Aerial photography\ntest/0d4fd94cda8a8fdb,Comics\ntest/0d5074b9d59e87ff,Auto racing\ntest/0d5439fdbc907f03,Vegetarian food,Fried food\ntest/0d5461cc341ca8c6,Ball game,Team sport\ntest/0d548e87655648aa,Boating\ntest/0d556269099a2d09,High-speed rail,Luxury vehicle\ntest/0d574962cba4afb9,Compact car\ntest/0d59e56b02d1ce9e,Close-up\ntest/0d5a16efb7b92b38,Compact car,Antique car\ntest/0d5bba48ede8007d,Compact car,Luxury vehicle,Antique car\ntest/0d5f29db5a6cd05b,Black-and-white\ntest/0d5f410dd2689842,Aircraft engine\ntest/0d5f48a7920119ab,Roman temple\ntest/0d60f649edf02d5d,Chordophone\ntest/0d62287562a09758,Luxury vehicle\ntest/0d65ce6c62c9e332,Sport utility vehicle\ntest/0d6607be1e0a79d2,Extreme sport,Sport climbing\ntest/0d6a527e509d7e6e,Wind wave,Glacial landform\ntest/0d6de82d00476771,Luxury vehicle\ntest/0d6df23796aec890,Extreme sport\ntest/0d6edf4c1c909875,Outdoor shoe\ntest/0d6eedcb49503a92,Ball game,Team sport\ntest/0d6f84ed4a7b8234,Wind wave\ntest/0d7001fe5ee6ab57,Luxury vehicle\ntest/0d75c2f9bfa400f3,Aircraft engine\ntest/0d76fa17adca869d,Wind wave\ntest/0d7c38c48b8b7bff,Ball game,Team sport\ntest/0d7c7be2ebe4df10,Billiard room,Billiards\ntest/0d80097107e54404,Blond,Hair coloring\ntest/0d801a3887bb3ed7,Arthropod,Close-up\ntest/0d82f1449851a093,Fried food\ntest/0d846e2dba40b8cf,Performance car,Luxury vehicle,Antique car\ntest/0d84a7c2908c9e58,Luxury vehicle,Performance car\ntest/0d858ac3856244ee,Headphones\ntest/0d85aa6868af45d4,Touring car,Luxury vehicle,Antique car\ntest/0d8898f445c9b7b9,Black-and-white\ntest/0d8a9cb939b72745,Compact car,Luxury vehicle,Antique car\ntest/0d8af2d90f9679e2,Motorcycling\ntest/0d8ec3e32d268799,Luxury vehicle,Performance car\ntest/0d8f0c44294cf97b,Compact car\ntest/0d8f8d49e29ead3c,Luxury vehicle,Sport utility vehicle\ntest/0d8ff4b567c12aff,Jeans\ntest/0d91401ca0527139,Close-up,Plant stem\ntest/0d916a137c4fd373,Close-up,Plant stem\ntest/0d95de414d78b220,Scaled reptile\ntest/0d9ba34848489310,Compact car\ntest/0d9bd0863b98998c,Black-and-white\ntest/0d9bfd132c9713ef,Arthropod,Close-up\ntest/0d9e87aa850251ed,Close-up\ntest/0d9eb36c20213402,Compact car\ntest/0d9ebb4a5b2b4429,Anole,Scaled reptile\ntest/0da021059927e742,Outdoor shoe\ntest/0da05893555cecb9,Aircraft engine\ntest/0da066484e199ed1,Luxury vehicle\ntest/0da0786d3afd67e7,Cookies and crackers\ntest/0da08033b803bce3,Extreme sport,Boating\ntest/0da0be71b583ac38,Luxury vehicle\ntest/0da29d130f68b405,Black-and-white\ntest/0da319c0204ce1ff,Microcontroller,Breadboard\ntest/0da362d88278c784,Corn on the cob\ntest/0da3c1aaaa84f2da,Perching bird\ntest/0da4ef69c56c81a8,Wind wave\ntest/0da811072e417bb0,Luxury vehicle\ntest/0da92f48a10f95c0,Road cycling\ntest/0daacededdc780cd,Luxury vehicle\ntest/0dae295abfdff1ea,Team sport\ntest/0db1b418bc599cf3,Jeans\ntest/0db1fbeb67982cb9,Boating\ntest/0db39790f9b5ee36,Aircraft engine\ntest/0db3a4908be98445,Junk food,Fried food\ntest/0db95a235d3266ba,Luxury vehicle\ntest/0db989cab12302ac,Bratwurst\ntest/0db9ff9ca926de45,Luxury vehicle,Sport utility vehicle\ntest/0dbaeb5a47715025,Monoplane\ntest/0dbdc67fa69e71a4,Luxury vehicle,Performance car\ntest/0dbdcd1c2c695470,Close-up\ntest/0dbde104a29605e9,Close-up\ntest/0dc06d6e1e940749,Extreme sport\ntest/0dc0f3466796b3f4,Extreme sport\ntest/0dc54be86ae10086,Performance car\ntest/0dc57c6d646a1d67,Auto racing\ntest/0dc66ee65d3e9b3d,Arthropod,Close-up\ntest/0dc83e967e270234,Luxury vehicle,Performance car\ntest/0dc8a72d14b31de6,Luxury vehicle\ntest/0dcc93144a0d16cf,Close-up,Crocodilia\ntest/0dccda36775c66b5,Athletic shoe,Outdoor shoe\ntest/0dce5d2bfcfc90cb,Close-up\ntest/0dcfd78853e9b3b6,Holding hands,Close-up\ntest/0dd11fa8dbb654a7,Close-up\ntest/0dd1a135e3123a4f,Military person\ntest/0dd2a07c2d55fe29,Black-and-white\ntest/0dd363da69c58e7c,Auto racing\ntest/0dd4c580c14df3d1,Ball game\ntest/0dd8097e0a31946b,Plant stem\ntest/0dd879f75903b087,Luxury vehicle,Performance car\ntest/0dd94901ce5c65e0,Bird of prey\ntest/0dd9ad8ad6ea549c,Arthropod,Close-up\ntest/0dd9c6243b7fe780,Luxury vehicle,Performance car\ntest/0ddc1e6cc5addd67,Scaled reptile\ntest/0ddf0df5cd03474e,Luxury vehicle\ntest/0de152d7131f801a,Close-up\ntest/0de1dc90c6c9f5d9,Close-up,Plant stem\ntest/0de6da38397a8a45,Monoplane\ntest/0de6e7cc50d9e0c2,Luxury vehicle\ntest/0de810360aa4ab15,Goats\ntest/0dea8b2b4c567dbe,Antique car\ntest/0dea8b5f046435f4,Vegetarian food\ntest/0deb7a66a1cc7390,Luxury vehicle,Performance car\ntest/0debe68fda27731a,Luxury vehicle,Performance car\ntest/0dec518ada218425,Hound\ntest/0dedb420629a1757,Close-up\ntest/0deee1bb956b2216,Combat sport,Sumo\ntest/0def43d0bd4744e3,Equitation,Equestrianism\ntest/0df00916435c3a47,Close-up,Scaled reptile\ntest/0df0988922510361,Combat sport\ntest/0df1d34eabcd1a6a,Performance car\ntest/0df1fdab2f6bc5b9,Luxury vehicle,Antique car\ntest/0df2fffe03617f26,Luxury vehicle,Performance car\ntest/0df40db10dbc69fe,Close-up\ntest/0df4392de7e612be,Antique car\ntest/0df6b62fd043d682,Military person\ntest/0df8e4a7ef7d2acd,Auto racing\ntest/0dfbdc2140debd33,Luxury vehicle,Performance car\ntest/0dfc27324cdae14b,Ramen\ntest/0dfdec9b9000d5a4,Antique car\ntest/0dff7f2d4826f8cc,Wallaby\ntest/0e0269e5e18326f3,Cookies and crackers\ntest/0e0455cb519b020c,Steamed rice\ntest/0e050d07ae96ff1d,Close-up\ntest/0e0625e9e1bd200d,Luxury vehicle,Sport utility vehicle\ntest/0e0702ca20d0397a,Luxury vehicle,Antique car\ntest/0e079a697f9329ef,Touring car,Luxury vehicle,Antique car\ntest/0e07ac97469b38d7,Billiards\ntest/0e085be1388be9ff,Roller skating\ntest/0e0b385c589f378c,Compact car,Antique car\ntest/0e0c5273ed65df4a,Dishware\ntest/0e0f6b07659af19a,Brisket,Beef tenderloin\ntest/0e109773463d66cd,Luxury vehicle,Sport utility vehicle\ntest/0e1863cf7e29cc0e,Luxury vehicle,Antique car\ntest/0e1a3aa443aabcf3,Fried food\ntest/0e1aae342ed70c04,Pit bull\ntest/0e1af943035b2f80,Fried food\ntest/0e1b430c3b8a0e31,Microcontroller\ntest/0e1e111c408bbec9,Luxury vehicle,Performance car\ntest/0e1ebe9ec4d249d1,Close-up\ntest/0e2004910f2bf847,Rural area,Bovine\ntest/0e20f6e1360d8df0,Vegetarian food\ntest/0e21bea107856941,Luxury vehicle\ntest/0e21eced359138f2,Luxury vehicle\ntest/0e23e318b29104b4,Fried food\ntest/0e25acb2c58850a8,Leaf vegetable\ntest/0e2641a229642fed,Vegetarian food,Falafel,Fried food\ntest/0e2715e7343f6ec3,Domestic short-haired cat,Close-up,Whiskers\ntest/0e283f5098587c37,Close-up\ntest/0e2865a812f2d8cb,Close-up\ntest/0e2878aa394cb0f2,Luxury vehicle,Antique car\ntest/0e2b8f41fc72b8d9,Luxury vehicle,Antique car\ntest/0e2be3ccc72ba890,Luxury vehicle\ntest/0e2e5369865ba607,Domestic short-haired cat,Whiskers\ntest/0e308146f8ce7573,Close-up\ntest/0e31ebad0d8cd2ae,Extreme sport\ntest/0e32b18f1f85abe0,Black-and-white\ntest/0e339658d17b8711,Goats,Bovine\ntest/0e33e0d79a3b416a,Luxury vehicle\ntest/0e351d08e34c2b51,Jeans\ntest/0e353d379a6de4b2,Galleon,Frigate\ntest/0e38458beddddca7,Compact car,Sport utility vehicle\ntest/0e3ec9eda547398c,Boating,Rowing\ntest/0e3fc50a46e054f5,Extreme sport,Motorcycling\ntest/0e428d64951ff7db,Jeans\ntest/0e42cdd2bba3323f,Extreme sport,Parachuting\ntest/0e4301dae8aac327,Extreme sport\ntest/0e43a894a701cd8e,Close-up\ntest/0e43f9803b11b988,Close-up\ntest/0e45c6e68cfc3980,Antique car\ntest/0e462ccea01a29af,Electronic instrument\ntest/0e47cf48693a0bd4,Domestic short-haired cat,Whiskers\ntest/0e48e83fe4bed47e,Luxury vehicle\ntest/0e490e59ab1ceb8a,Plant stem\ntest/0e49727ce449e8b8,Vegetarian food\ntest/0e4a123770f4e835,Surfing Equipment\ntest/0e4a3e360d28f816,Close-up\ntest/0e4aea130eb44ce5,Extreme sport,Parachuting\ntest/0e4b6483a1d236de,Dobermann\ntest/0e4d3bfa258a7930,Luxury vehicle\ntest/0e51fef7465e75ab,Extreme sport\ntest/0e531b8ac95a7fa2,Compact car,Luxury vehicle,Antique car\ntest/0e5464185ce5f64c,Extreme sport,Boating\ntest/0e55070bf1b887b8,Compact car,Luxury vehicle\ntest/0e568caa07e50452,Floristry\ntest/0e5751f7547334b1,Rear-view mirror\ntest/0e5832e7aa233b7d,Luxury vehicle,Performance car\ntest/0e59d5d7e1c635c8,Luxury vehicle\ntest/0e5b538e5355a784,Extreme sport,Nordic skiing\ntest/0e5d7a682ac979d9,Floristry\ntest/0e61ee3614881ca1,Ball game,Team sport\ntest/0e6275396999530d,Firearm\ntest/0e6327d050b285ae,Close-up\ntest/0e640ea432df23b3,Flatbread\ntest/0e6d4b910307a401,Plant stem\ntest/0e6dd2b743b089da,Church bell\ntest/0e6e0d780fe26eca,Gumbo\ntest/0e707f5205a18253,Scaled reptile\ntest/0e718e294eb0a3e0,Auto racing,Antique car\ntest/0e729e742c3ad6c5,Full moon\ntest/0e74587664064f71,Vegetarian food,Corn on the cob\ntest/0e777aa5b1fabc08,Toy block\ntest/0e7971fde96b86b3,Team sport\ntest/0e798fe3aeb6d568,Gumbo\ntest/0e7bb3dfec62264c,Compact car\ntest/0e7cccb7542d4530,Medical imaging\ntest/0e7d1a54b6edfef5,Antique car\ntest/0e7f1af2ef104d70,Athletic shoe\ntest/0e81257465697166,Senior citizen\ntest/0e824cfbec18080a,Extreme sport\ntest/0e850878b8388c0a,Luxury vehicle\ntest/0e867264d95e4dfe,Flatbread\ntest/0e86e5b11fdb3eab,Compact car,Luxury vehicle,Antique car\ntest/0e86f900641162b0,Extreme sport,Parachuting\ntest/0e883c0f1ef93d72,Antique car\ntest/0e8a11094943678e,Luxury vehicle,Performance car\ntest/0e8fb6dc54c31fbd,Close-up\ntest/0e90728969117b6f,Amphibian\ntest/0e91059fdef8e134,Luxury vehicle,Antique car\ntest/0e94f6d1982af1f8,Antique car\ntest/0e9501643c02356c,Luxury vehicle\ntest/0e95cbdb0176a0ac,Ball game\ntest/0e9791407fd5f5a6,Blond\ntest/0e98c960728b9e3f,Luxury vehicle\ntest/0e99f0c66dbb0ad4,Drums\ntest/0e9c519e9db14b1d,Ramen\ntest/0e9d7928364dce7e,Roller skating\ntest/0e9df919f514bf25,Black-and-white\ntest/0e9fa269cf9b3419,Black-and-white\ntest/0ea39a2dcc1d75cd,Team sport\ntest/0ea4852b0c090058,Domestic short-haired cat,Close-up,Whiskers\ntest/0ea5c4f2c7ce5427,Bonbon\ntest/0ea669fe836ef8fa,Aircraft engine\ntest/0ea6b6cf2c7b78c7,Orator,Public speaking\ntest/0ea75ebeec219b2a,Leaf vegetable\ntest/0ea78a8ea4e50ab1,Black swan\ntest/0eab1cf76590b0ac,Close-up\ntest/0eab34694419b470,Luxury vehicle\ntest/0eac0048edc9ecf2,Divemaster,Scuba diving,Underwater diving\ntest/0eada9ecdf1a6536,Vegetarian food\ntest/0eae73dd0db1c189,Cookies and crackers\ntest/0eb06a642b8dd9f8,Compact car,Luxury vehicle,Antique car\ntest/0eb32611b755d91c,Bottled water\ntest/0eb6b41e680aa10b,Performance car\ntest/0eb720b70b4f7e13,Steamed rice\ntest/0eb83454d3dc6023,Frying,Junk food,Fried food\ntest/0eb8b09abf51a4d3,Antique car,Luxury vehicle,Performance car\ntest/0eba28fc91e7b593,Team sport\ntest/0ebb49849b2634fb,Antique car,Luxury vehicle,Performance car\ntest/0ebbc76dc573f42a,Jeans\ntest/0ebc08df9323a51e,Boating\ntest/0ebd6a0200dfa59c,Electronic instrument\ntest/0ebf3dcf61ed8f0f,Performance car\ntest/0ebf7bb90c689045,Plant stem,Close-up\ntest/0ebfdc63e2177013,Luxury vehicle,Performance car\ntest/0ec002d432560064,Auto racing\ntest/0ec0f4949fff9e7e,Luxury vehicle,Antique car\ntest/0ec3a2bff9fb0c52,Underwater diving\ntest/0ec47c9461da1d85,Luxury vehicle,Performance car\ntest/0ec543415e3af6fc,Luxury vehicle,Performance car\ntest/0ec72f6d689912bc,Electronic instrument\ntest/0ec78b454ca76477,Drums\ntest/0ec89de392847498,Ball game,Team sport\ntest/0ecb836ca3cf0cce,Close-up\ntest/0ecca251dcaefc54,Close-up\ntest/0ecf525f7d74c6c3,Luxury vehicle,Performance car\ntest/0ed19bb3ea0bb133,Antique car\ntest/0ed2ce28c4d654bd,Whiskers\ntest/0ed3415658fb70c8,Junk food\ntest/0ed4fba0df47ea54,Luxury vehicle\ntest/0ed58454ae1d1707,Arthropod,Close-up\ntest/0ed973d8cc8a1d89,Close-up\ntest/0edd1eeda60a45ed,Sledding\ntest/0edd8f0501350781,Motorcycling\ntest/0ede512c28b7ae1a,Close-up\ntest/0edfb5688d514faf,Machine tool\ntest/0ee1ca00afcc9072,Ball game,Team sport\ntest/0ee4a25285a6d10d,Black-and-white\ntest/0ee4f974260a1454,Rural area\ntest/0ee6c01f76db86fb,Pasta salad\ntest/0ee8132092017afc,Luxury vehicle\ntest/0ee9097b0c090b41,Nest box\ntest/0ee9f11ca2f91346,Whiskers\ntest/0eea4870c161ce9e,Compact car,Sport utility vehicle\ntest/0eeb46fad6102dad,Coral reef fish\ntest/0eec66f9dde326bb,Plant stem\ntest/0ef1c8ab97fe373f,Aircraft engine\ntest/0ef2193d70191a39,Auto racing,Touring car,Luxury vehicle,Performance car\ntest/0ef2e0426cbabb22,Compact car,Luxury vehicle\ntest/0ef30cd6bbf348fc,Scaled reptile\ntest/0ef777de9c874efd,Performance car\ntest/0efdf583495552e7,Compact car,Luxury vehicle,Antique car\ntest/0efec0cd0d591d69,Vegetarian food\ntest/0effee2e45db387c,Compact car,Antique car\ntest/0f01334daaf48274,Chordophone\ntest/0f01ebb453db6d44,Machine tool\ntest/0f025e53e2a46fa8,Compact car\ntest/0f0474e2b2ba845a,Vegetarian food\ntest/0f04b2d3e7dec005,Steamed rice,Soba\ntest/0f077a281f8be3c9,Electric piano,Electronic instrument\ntest/0f08708a1ed61fc7,Residential area\ntest/0f08b38cf3929d24,Residential area\ntest/0f09ed7aacd74c59,Filling station\ntest/0f0c1b2c365f72c3,Luxury vehicle\ntest/0f0c51d8f6bc91be,Luxury vehicle\ntest/0f0d8f5fcf7a606a,Steamed rice\ntest/0f0e7604e2447bf6,Portrait photography,Close-up\ntest/0f0ef965a5ae2fcb,Combat sport\ntest/0f0ffd971d4869bc,Dishware\ntest/0f10496fcd1ec766,Drums,Electronic instrument\ntest/0f10ae6fe314c2dd,Arthropod,Close-up\ntest/0f11ba02176ac0b4,Wind wave\ntest/0f12b0dc35b04267,Close-up\ntest/0f15b11611a9f495,Compact car,Luxury vehicle\ntest/0f16a97f747f0f55,Combat sport\ntest/0f191d6423c9881c,Luxury vehicle,Sport utility vehicle\ntest/0f1a4506c1ada576,Road cycling\ntest/0f1aeeda05649af7,Military vehicle,Antique car\ntest/0f1b3fc347bd3d82,Sport utility vehicle\ntest/0f1d7c90cf739c45,Residential area\ntest/0f1d8becfd07e953,Ball game\ntest/0f217bc9bbd6440b,Close-up\ntest/0f22f3af31263255,Military vehicle\ntest/0f2307654f8baca0,Cookies and crackers\ntest/0f28b00c63af4595,Compact car,Luxury vehicle,Antique car\ntest/0f28c1e81e08ed9a,Sport utility vehicle\ntest/0f29e6dcd8efa172,Motorcycling\ntest/0f2a98ecb311c693,Dishware\ntest/0f2c3f5f9de97b17,Blond\ntest/0f2c6de9d0c3dc59,Ball game\ntest/0f306d115a513170,Compact car\ntest/0f31a210a6919097,Compact car,Luxury vehicle\ntest/0f3240f6dc2b3f4b,Aircraft engine\ntest/0f32590d3fe27c44,Sport utility vehicle\ntest/0f3291490a5b0d44,Luxury vehicle,Performance car\ntest/0f33008c516e302b,Arthropod,Close-up\ntest/0f35be6267df295a,Boating\ntest/0f36642e3668a273,Chordophone\ntest/0f3742c65d7bb522,Extreme sport,Wind wave\ntest/0f3a8d79829e0af1,Melee weapon\ntest/0f3ac3564d2fc5cc,Luxury vehicle,Antique car\ntest/0f3b0af905775dd7,Stemware\ntest/0f3c9e70fa6e32a3,Close-up\ntest/0f3ec41eb8551e9b,Drums\ntest/0f4036ef99eb11d7,Coca-cola\ntest/0f4299dbe0140484,Aircraft engine\ntest/0f43c88a94f619c5,Performance car\ntest/0f4789ab2d01e421,Pork chop,Beef tenderloin\ntest/0f4c65b41bfb4128,Vegetarian food\ntest/0f4e511fc23b18ff,Bird of prey\ntest/0f5277cf716ec80e,Compact car\ntest/0f5457c1d86ab7b4,Road cycling\ntest/0f57ec1cd2589dbb,Outdoor shoe\ntest/0f58911c512fd6e0,Compact car,Luxury vehicle,Antique car\ntest/0f58d026caa3d044,Compact car,Luxury vehicle\ntest/0f59c562e644eab4,Electronic instrument\ntest/0f59ea29ab0453e8,Jeans,Headphones\ntest/0f5a97c59b5fcbc9,Nuclear power plant\ntest/0f5c3055ee6f01ec,Monoplane\ntest/0f5ca336cdafeab1,Pit bull\ntest/0f5ddac77ae7bdbf,Digital camera,Black-and-white\ntest/0f5eec3235d8a3df,Firearm\ntest/0f60f591d0722f2e,Luxury vehicle,Performance car\ntest/0f6367eb190d0505,Ball game,Team sport\ntest/0f65fc2b426776a4,Military person\ntest/0f68fcf0320c9c85,Outdoor shoe\ntest/0f6acc9dfc071de5,Amphibian\ntest/0f6b2ec7411c3dce,Beef tenderloin\ntest/0f6bd19582991f43,Compact car,Antique car\ntest/0f6c567a33cc62c0,Plant stem\ntest/0f6deca6dd38d0e3,Junk food\ntest/0f6dfa13e7395dd3,Touring car,Luxury vehicle,Antique car\ntest/0f6e331808f9220c,Luxury vehicle\ntest/0f716229076bf9f6,Close-up\ntest/0f71f5fc1473f7d4,Extreme sport,Wind wave\ntest/0f73482fe830d119,Luxury vehicle\ntest/0f785c74cc2c0795,Prosciutto\ntest/0f78fbcf48b24438,Sport utility vehicle\ntest/0f7b5fade6eceafa,Black-and-white\ntest/0f7bb12a4307944e,Black-and-white\ntest/0f7e8a5b8667b1d2,Compact car,Luxury vehicle,Antique car\ntest/0f80c72f7d04feff,Performance car\ntest/0f82ee68e51747d4,Sport utility vehicle\ntest/0f8323ed50815e1e,Full moon\ntest/0f85225850026490,Luxury vehicle,Antique car\ntest/0f8549f66e05d39e,Modern dance\ntest/0f88b4af20ed211c,Sport utility vehicle\ntest/0f8a38312499d209,Close-up\ntest/0f8a98444d3b9379,Compact car\ntest/0f8b7f940986d98b,Motorcycling\ntest/0f8e9ce37dcb2428,Zeppelin\ntest/0f9293c5fded1dea,Aerial photography\ntest/0f92aa6babdb5149,Headphones\ntest/0f92e7070a513ebd,Close-up\ntest/0f933c3de35704fc,Blond,Hair coloring\ntest/0f946237ff672b36,Residential area,Aerial photography\ntest/0f949420862cb302,Whiskers\ntest/0f94aed2bb5398d1,Angling\ntest/0f95ac241fe29d69,Extreme sport\ntest/0f9659d6162de32f,Close-up\ntest/0f96680fdd9951ed,Combat sport\ntest/0f97088f6695f3b6,Compact car\ntest/0f975470afc926bf,Compact car\ntest/0f97925a260c5958,Vegetarian food,Lumpia\ntest/0f97dacc5e87346e,Cookies and crackers\ntest/0f9839797a1be6df,Rural area,Nest box\ntest/0f9966ced9ba1c91,Double-decker bus\ntest/0f9a5d99da0cb00e,Sport utility vehicle\ntest/0f9aa07e5fd7454f,Luxury vehicle\ntest/0f9d64f2982ceeb1,Close-up\ntest/0f9d7f82f512a1f3,Monoplane\ntest/0fa0631492204626,Close-up\ntest/0fa06449bf0d41b2,Compact car,Luxury vehicle\ntest/0fa0987153e72e74,Luxury vehicle,Performance car\ntest/0fa154cc9ce85b6b,Close-up\ntest/0fa1969585743b54,Luxury vehicle\ntest/0fa68436fdb3364a,Military person\ntest/0fa75cfa82c9615b,Sport utility vehicle\ntest/0fa7c9a018d8b45a,Rural area\ntest/0faaa82e7546df40,Vegetarian food,Corn on the cob\ntest/0faaacdbe6194755,Frying,Fried food\ntest/0facc8f3887d5648,Dishware\ntest/0fadee7155199581,Close-up,Anole,Scaled reptile\ntest/0fafae9764b485a1,Close-up,Whiskers\ntest/0fafce9f67fbb76b,Jiaozi,Fried food\ntest/0fb656f0d8e27c9a,Horse and buggy\ntest/0fb762f3dd026d91,Rural area\ntest/0fb95610ed14680e,Luxury vehicle\ntest/0fb984cec1a344de,Compact car,Antique car\ntest/0fb9d436e33e58ae,Firearm\ntest/0fba6ffd66b452db,Close-up\ntest/0fbaf2e2db331109,Rural area\ntest/0fbdca0ebe930ffe,Combat sport\ntest/0fc0097be2976acb,Vegetarian food,Pasta salad\ntest/0fc25f22162a0784,Outdoor shoe\ntest/0fc70d3067def51b,Domestic short-haired cat,Close-up,Whiskers\ntest/0fc7e3448e0b0f53,Extreme sport\ntest/0fcae685b41eef67,Luxury vehicle,Performance car\ntest/0fcaf53443bd7a03,Monoplane\ntest/0fcde33acdaf5795,Compact car\ntest/0fd251203f3dfcd7,Close-up\ntest/0fd2b33fa114271f,Close-up\ntest/0fd62f89fd22c6b9,Residential area\ntest/0fd72579946be783,Portrait photography,Digital camera,Close-up,Camera operator,Black-and-white\ntest/0fdc84573f6330ea,Luxury vehicle,Performance car\ntest/0fdc8616bc760047,Luxury vehicle\ntest/0fdc9fbb67709f01,Luxury vehicle,Performance car\ntest/0fdd3d1eceb9e2b2,Sport utility vehicle\ntest/0fdd4416d20dadad,Fried food\ntest/0fe0276c4f131674,Comics\ntest/0fe02c60a556ec5b,Military person\ntest/0fe1f1d49da210fa,Arthropod,Locust\ntest/0fe323469058ccfa,Wind wave\ntest/0fe34b21e79de867,Whiskers\ntest/0fe3b6e7cbe31a90,Compact car,Luxury vehicle\ntest/0fe3f79c9523477a,Combat sport\ntest/0fe60cbc820222ee,Ball game\ntest/0fe7fe66dfe1cbe5,Compact car,Luxury vehicle,Antique car\ntest/0fe80cdfef74daff,Luxury vehicle\ntest/0fe95644115e8829,Ball game,Team sport\ntest/0feb3dc71936230f,Close-up\ntest/0febb6fee08a7470,Ball game,Team sport\ntest/0fed318b0664de44,Close-up\ntest/0fefa5d8f8dbe8e3,Extreme sport,Boating\ntest/0ff0afad0834e579,Jeans\ntest/0ff1094e7364eccd,Flatbread\ntest/0ff391d738cd9f5a,Luxury vehicle,Performance car\ntest/0ff6228a79b046ba,Luxury vehicle\ntest/0ff91acd327a7a9d,Double-decker bus\ntest/0ffa5ed1c5e9eeae,Close-up,Whiskers\ntest/0ffc9bb1e54039f4,Close-up,Whiskers\ntest/0fff06272cf4da8b,Domestic short-haired cat,Close-up,Whiskers\ntest/0fff956ea9fe6584,Arthropod,Close-up\ntest/1004708828396a84,Coral reef fish\ntest/1004e907fe619c3e,Close-up,Plant stem\ntest/10050ae3c7122b14,Compact car,Antique car\ntest/10070f2690635e50,Jeans\ntest/1008e9d1007dae55,Luxury vehicle,Antique car\ntest/100990c77937b284,Monoplane\ntest/100c3ed79f108ccd,Luxury vehicle,Sport utility vehicle\ntest/100c8d06f8bf57b4,Electronic instrument\ntest/100d6bbd6f81fe6f,Motorcycling\ntest/100d7f168b8f0ac5,Residential area\ntest/100e638dbf556ae9,Vegetarian food\ntest/1011d7b4979f212a,Frying,Fried food\ntest/10123b788670c79c,Black-and-white\ntest/1013804128c328af,Blond\ntest/101734470f5fdc04,Residential area\ntest/1018329e4a64eb8b,Arthropod,Close-up\ntest/1018423367ef25bf,Black-and-white\ntest/1019beb7e9cbaa9f,Close-up\ntest/101a8b32fdf9d98c,Compact car,Luxury vehicle\ntest/101a8f0b9a0ad565,Ramen,Soba\ntest/101b7afc4770d3ab,Cookies and crackers\ntest/1021400e7dc9996d,Compact car\ntest/10214ca8b5a6aa93,Domestic short-haired cat,Close-up,Whiskers\ntest/1022934d0289a7f1,Military vehicle\ntest/102513e3b527d2a3,Black-and-white,Modern dance\ntest/10252184ee1b2c08,Close-up,Whiskers\ntest/1025ae696aff49b8,Whiskers\ntest/10260af843cb42bf,Junk food,Cookies and crackers\ntest/102668940a1a2d9a,Plant stem\ntest/102864680bfe6666,Hound\ntest/102900a8d8048e4a,Boating\ntest/10295d3373cd30d0,Military vehicle\ntest/10299ece71297b2e,Luxury vehicle,Performance car\ntest/102b2e8397b39107,Barechested\ntest/102b7552af9477b1,Hound\ntest/102c4ee72cab2b88,Ball game\ntest/102c7ecc4e7b8012,Compact car,Antique car\ntest/102e9fefe472e7a4,Bird of prey,Close-up\ntest/1030ac5cdc409d2b,Military person\ntest/103210451eebd2f5,Rural area\ntest/103237e28e17f06b,Hair coloring,Close-up\ntest/1032dac59415354c,Perching bird,Close-up\ntest/10391dde8f47bdec,Blond\ntest/103a9bec7300e6a3,Close-up\ntest/103b032b62e6f3a2,Solar energy\ntest/103c7d5e627a9c7d,Pelmeni\ntest/103e1b4f013853ba,Frying,Junk food,Fried food\ntest/103e846bace25edc,Antique car\ntest/104080803cf71688,Plant stem\ntest/1042f6f4890bc877,Luxury vehicle,Antique car\ntest/10443d90526a4d34,Compact car\ntest/104649fd9ccfd04a,Luxury vehicle\ntest/10470490a0bee26a,Rural area\ntest/104c541de52b3347,Arthropod,Close-up\ntest/104d5490d60f5052,Luxury vehicle\ntest/10501403678d8cc2,Close-up\ntest/105045ca1600113b,Extreme sport\ntest/1051c5923a24bdfb,Cookies and crackers\ntest/1053bcb68ea98a6b,Rear-view mirror\ntest/10592d998cd5b6a4,Compact car,Luxury vehicle\ntest/105968f7d1e9d10e,Close-up\ntest/105c151cd18d06c9,Aerial photography\ntest/105f061e143be0ba,Extreme sport\ntest/10626368043a3bd2,Vegetarian food\ntest/1062c3956dcff70c,Briefs\ntest/1062e03e6a18de3d,Hound\ntest/10636103fb39805b,Bratwurst\ntest/10637d8609954a59,Compact car\ntest/1064b8242aeda1d6,Cosmetics\ntest/1065c89607a395cb,Luxury vehicle\ntest/1065d81ece2a22f4,Compact car\ntest/1066f24dc9d25a3b,Flatbread\ntest/10670dbfdbc91097,Close-up\ntest/1067819924c14459,Black-and-white\ntest/106a0cebeca62a0c,Luxury vehicle,Sport utility vehicle\ntest/106a71475c38d177,Domestic short-haired cat,Close-up,Whiskers\ntest/106d95603f72a740,Performance car,Antique car\ntest/1071dd36dec0bfca,Water polo,Ball game,Team sport\ntest/1072994585827ea7,Close-up\ntest/1076c8f635bc2c47,Junk food,Fried food\ntest/1079052e02163b61,Domestic short-haired cat,Close-up,Whiskers\ntest/107abe3685256796,Firearm\ntest/107c3faf303363de,Falafel\ntest/107e32176599faa2,Blond,Hair coloring\ntest/10802a3111e8ddd6,Ball game\ntest/10826f368b060d67,Close-up\ntest/10832955e9afee68,Miniature Poodle\ntest/1084e8a6a1af3dd5,Bottled water\ntest/1088e2b8dbf961d5,Shinto shrine\ntest/10895bc3d7562b66,Combat sport\ntest/108a2de3b5a3674e,Close-up\ntest/108a7549556b5354,Close-up\ntest/108fe902d5b95d15,Residential area\ntest/1091c1c5a90fba65,Sport utility vehicle\ntest/1093c06b321f8137,Aircraft engine\ntest/10946fa016098d27,Compact car\ntest/109625c7f228a35f,Compact car\ntest/109754ea6e523d5b,Motorcycling\ntest/1098559cebaafd9e,Headphones\ntest/10995a5edcea92a8,Ball game,Team sport\ntest/109afa7b2942a9dd,Antique car\ntest/109b1dbb9ba0de34,Antique car\ntest/109cfea2513fe9aa,Antique car,Performance car\ntest/109d6046a9a44c26,Close-up\ntest/109fb678aae7caa8,Black-and-white,Close-up,Whiskers\ntest/10a02d382464269e,Sport utility vehicle\ntest/10a5df25d8d8ce4d,Auto racing\ntest/10a9e87b8ccf5a6a,Electronic instrument\ntest/10aaaa8fba1b79be,Luxury vehicle,Performance car\ntest/10ad08e600986f5d,Lumpia\ntest/10ae438e597928f6,Arthropod\ntest/10b33fee9db8ff8d,Close-up\ntest/10b6950811956620,Compact car,Antique car\ntest/10b762e2918ba435,Blond\ntest/10b80145df20b87a,Coral reef fish,Close-up\ntest/10b92361aeea667d,Canola\ntest/10baaed13b699731,Sport utility vehicle\ntest/10badb8911e48b79,Luxury vehicle\ntest/10bcfd772a0a6d6b,Residential area\ntest/10be7cb997986a7b,Compact car,Luxury vehicle,Antique car\ntest/10bf5f17f29243e7,Athletic shoe,Outdoor shoe\ntest/10c6a29dabd387f3,Boating\ntest/10c6e79ac7a2ca33,Sport utility vehicle\ntest/10c6f146fe957be4,Luxury vehicle,Performance car\ntest/10ca0c743f825dde,Close-up,Chordophone\ntest/10cb72ed97d5160c,Newsagent's shop\ntest/10cdaca6d67dd98c,Ball game,Team sport\ntest/10ce08a5eaef8f9a,Close-up\ntest/10cf66a427f3ac64,Ball game,Team sport\ntest/10d0507c88ea2dc0,Boating\ntest/10d082d9eb4fdc36,Shinto shrine\ntest/10d0d782f3e71a79,Close-up\ntest/10d3664ec0bb4dc9,Whiskers\ntest/10d47e98ea9ae24f,Barechested\ntest/10d5e98d83e2325c,Close-up\ntest/10d96aea95b1d6e3,Luxury vehicle,Performance car\ntest/10d9e982144a639b,Fried food\ntest/10dec13f53cbf93a,Athletic shoe,Outdoor shoe\ntest/10e2f25e6d5b66ce,Jeans\ntest/10e5b1b9d7ac9443,Extreme sport,Divemaster,Underwater diving\ntest/10ea8880c9ca33ce,Luxury vehicle\ntest/10ebec1071c3ef8b,Close-up\ntest/10eccca317b9fa28,Performance car\ntest/10ed292d62ca54b6,Floristry\ntest/10ee3f4b2a57a2f1,Digital camera,Black-and-white,Close-up,Camera operator\ntest/10ef1c793f640537,Arcade game\ntest/10f0aa3135eb54c1,Electronic instrument\ntest/10f0b6cfe8e9c4c5,Auto racing\ntest/10f2ecb632162624,Residential area\ntest/10f5669b0c77295b,Arthropod\ntest/10f6ba6e66e99e1d,Glacial landform\ntest/10f8edae04861e4a,Senior citizen\ntest/10fb94f4632fed64,Cookies and crackers\ntest/10fba3ec1125f370,Close-up\ntest/10fbf38f15f0211b,Auto racing\ntest/10fc94fb4227962e,Team sport\ntest/10fe1cd8f10a42f1,Hound\ntest/10ff2912b920c650,Jeans\ntest/1103d346dd64975c,Sport utility vehicle\ntest/11054714c0694379,Black-and-white,Close-up\ntest/1105b96bdacc6f2e,Watercolor paint\ntest/11090ec088d1b79e,Luxury vehicle,Performance car\ntest/1109ead9c403fb65,Luxury vehicle,Performance car\ntest/110aafc383c42a70,Close-up\ntest/110ab88f24a8707b,Vegetarian food\ntest/110bd1ae92182988,Compact car,Luxury vehicle\ntest/110c3d46f72b6dd3,Firearm\ntest/110cd2400b7d13d7,Amphibian,Scaled reptile\ntest/110e028bedca6f7d,Performance car\ntest/11126aa5d8c1021a,Black swan,Black-and-white,Close-up\ntest/111402ab571f72fa,Luxury vehicle\ntest/11159cd115a905db,Black-and-white\ntest/111cfd5c4134407b,Military person\ntest/1120a37009c3a498,Close-up\ntest/11217e5f56e7abbe,Military person\ntest/11231b644e9e8bff,Floristry\ntest/11243eb6fdd829db,Close-up\ntest/1125f7cfcc8fab82,Hound\ntest/112892b16978fc5a,Sport utility vehicle\ntest/112938a7ac5ddfa0,Stemware\ntest/112db86eb8995cbd,Modern dance\ntest/112f56bfff7c599f,Boating\ntest/11303f8914fe5deb,Ball game\ntest/11319cc516e2a351,Vegetarian food\ntest/1135f38ed6d20380,Compact car\ntest/113e9ba6124b8f5b,Luxury vehicle\ntest/11406691ea6c50e9,Narcissus\ntest/11424b836caa2419,Great white shark\ntest/1144357227d92d7a,Scaled reptile\ntest/1144cf9d48f6d1b2,Junk food,Fried food\ntest/1146181f077b38ac,Compact car\ntest/11475450c4639acd,Aircraft engine\ntest/114c785d8a10063a,Extreme sport,Parachuting\ntest/114ca6daff03c377,Close-up\ntest/114ccbeca294381b,Sport utility vehicle\ntest/1151776de3707445,Sport utility vehicle\ntest/1152b921a2229700,High-speed rail\ntest/11535f32b87c0948,Luxury vehicle\ntest/11546ba8e4571084,Residential area\ntest/1156cc77a92c1921,Monoplane\ntest/11576c45895da4ba,Whiskers\ntest/1157c1dfbe7c033f,Aircraft engine\ntest/1158e85e58b879d2,Bird of prey\ntest/115a0808c9e190a5,Plant stem\ntest/115a0c15320caf8b,Bagpipes\ntest/115ab2c588c2552d,Wind wave\ntest/115ab620a30a259c,Chordophone\ntest/115d3b5f3e6aa335,Compact car,Sport utility vehicle\ntest/115daaafa920105c,Wind wave\ntest/115fb0d010f91828,Luxury vehicle,Performance car\ntest/11604c19460e314d,Electric piano,Black-and-white\ntest/116585728cdc0657,Vegetarian food,Corn on the cob\ntest/1166adb6f45e08c8,Aircraft engine\ntest/1167186ec702bc1b,Auto racing\ntest/11672b4fe3584614,Luxury vehicle,Performance car\ntest/116891eaf0cb0d4a,Luxury vehicle\ntest/116b7e072589a6ef,Junk food\ntest/116bb15314194b96,Pit bull\ntest/116d9c4a88c66a34,Team sport\ntest/11708c1dd7f50ccf,Arthropod,Close-up\ntest/117257b3386f145b,Hurdling\ntest/1172ba5fda6fbfb6,Firearm,Shooting range\ntest/1176d60362eb2895,Extreme sport,Motorcycling\ntest/117761706a01ad2b,Luxury vehicle,Performance car\ntest/11780aff4017e22b,Rural area\ntest/11790705bf64bbe9,Luxury vehicle\ntest/1179a4c1249fc4d4,Fried food\ntest/117afa77ab304552,Antique car\ntest/117cdc8826c6fb7a,Bird of prey\ntest/1189de964011365e,Close-up\ntest/118a7aa0c1112e2d,Aircraft engine\ntest/118d1e4fa537eb28,Lechon\ntest/118e34b1af1942e8,Ball game,Team sport\ntest/118fc9e3ad50b20e,Aircraft engine\ntest/119066c869793188,Fried food\ntest/11906f971e8ac49a,Luxury vehicle\ntest/1194132584fa4ba2,Melee weapon,Hunting knife\ntest/11969b45e5ca8a25,Modern dance\ntest/1199ccc230fb9090,Close-up\ntest/119aacdbc280d107,Rural area\ntest/119ac7be28ae3a61,Outdoor shoe\ntest/119b5160de14245b,Close-up\ntest/119c1d2743e613e3,Auto racing\ntest/119fd7a102c23481,Close-up\ntest/11a0be6132a198da,Luxury vehicle,Performance car\ntest/11a166bee60957ef,Full moon\ntest/11a1bf6aca83b7ee,Roman temple\ntest/11a303c2b9360432,Orator,Public speaking\ntest/11a4c1e7c453c7d3,Extreme sport\ntest/11a68f88c668fcf7,Luxury vehicle,Antique car\ntest/11a7ed9997a22521,Arthropod\ntest/11a80d52a6ed8e39,Black-and-white\ntest/11a9c9ae64178d75,Antique car\ntest/11a9fff2c7a076bf,Ball game\ntest/11ae5dcbd4c257b4,Luxury vehicle,Antique car\ntest/11af4080e0584a89,Ball game\ntest/11b089c5e498372c,Military person\ntest/11b470b6a254f23b,Performance car\ntest/11b4c249c577010e,Racewalking\ntest/11ba9ac09fe7d8b6,Nile crocodile,Close-up,Crocodilia\ntest/11bdddffa5f5f672,Plant stem\ntest/11c01215f96223fc,Motorcycling\ntest/11c05bcba9794149,Coral reef fish\ntest/11c0a6d39ff3b884,Hound,Close-up\ntest/11c527338056397e,Aerial photography\ntest/11c54ddf730f283a,Steamed rice\ntest/11cbfbf53c875989,Luxury vehicle,Performance car\ntest/11cc452e1e27d0c9,Black-and-white,Cobblestone\ntest/11d033fb6defb810,Auto racing\ntest/11d2621d4c7c79d4,Rural area\ntest/11d354080039844c,Arthropod,Close-up\ntest/11d77610008c4f73,Compact car\ntest/11d7eb4f71e2dff0,Antique car\ntest/11d9ebec7ec1b649,Close-up,Nile crocodile,Crocodilia,Scaled reptile\ntest/11db06550649574a,Compact car\ntest/11ddbc3dde3e0dd1,Outdoor shoe\ntest/11e139518a4b10d9,Auto racing\ntest/11e3189eee39131e,Gun turret,Military vehicle\ntest/11e4b33e7445aa65,Stemware,Close-up\ntest/11e6ceb92b47d1f7,Luxury vehicle\ntest/11e8385ee216a9c8,Close-up\ntest/11e90f43dd1b906f,Molluscs\ntest/11ea4694dc106510,Compact car\ntest/11ea913a02e026bc,Black-and-white\ntest/11ef3cb3ffb457be,Luxury vehicle,Performance car\ntest/11f1e728dcc74737,Jiaozi\ntest/11f275a3ab45eb1f,Chordophone\ntest/11f3bf6ff9f6120d,Black-and-white\ntest/11f3dc5dbf6ddeac,Luxury vehicle,Antique car\ntest/11f4313c24c2e27b,Horse and buggy\ntest/11f4e7f8b769573c,Close-up\ntest/11fae0e1bfc95d4a,Close-up\ntest/11fb2517ba2d2240,Plant stem\ntest/11fc7942c5190339,Roman temple\ntest/11fd59524ff95701,Luxury vehicle,Performance car\ntest/11fdd25a4b8b85d7,Horse racing\ntest/11ffa1b958cfaba0,Luxury vehicle,Antique car\ntest/1202b1fb9176ea8f,Combat sport\ntest/12032d88a0aac30f,Black-and-white\ntest/120366298e4ebe93,Close-up\ntest/120383993bf1971c,Bovine\ntest/1203af37e4dc67ac,Firearm\ntest/1203cfff1c5b2e02,Rear-view mirror\ntest/120459fc4eb1b1ec,Black-and-white,Close-up\ntest/120a01ef5a123ef1,Horse and buggy\ntest/120efec9ee4c7772,Frozen yogurt\ntest/120ffe9f13ddf1c3,Residential area\ntest/1210635a6fe16762,Aircraft engine\ntest/121093bb460f5ad8,Frying,Fried food\ntest/1211205fec11d85c,Close-up\ntest/1213f82316448015,Dishware\ntest/1216493d1fe4913d,Compact car\ntest/12171f16df2a583c,Combat sport\ntest/121794a37b3aab8b,Extreme sport,Sport climbing\ntest/12187f4525ff316d,Ball game\ntest/12194e430e4db193,Auto racing,Performance car\ntest/121b5d8027dc5b6a,Blond\ntest/121cb977d66bc3ac,Dishware\ntest/121d0ecc5ac0a27d,Black-and-white\ntest/121e210b62aeaad9,Luxury vehicle,Performance car\ntest/121ff7a516317e66,Digital camera\ntest/12210ff91bf67e45,Cookies and crackers\ntest/1221a5d42ed9a2ec,Fried food\ntest/122366cdc350c6f7,Team sport\ntest/1224dc67c1543df6,Shallot\ntest/12271f7cd213508e,Luxury vehicle\ntest/122cbe069b17af5a,Comics\ntest/122d5ce1ed1b7135,Bratwurst\ntest/122ea3f60b9b4f84,Luxury vehicle,Performance car\ntest/122f51b968cfbd13,Blond\ntest/122fec1d0d96cf61,Plant stem,Close-up\ntest/1231078a63cdebc1,Sport utility vehicle\ntest/1233762e6e9db7d6,Dobermann,Hound\ntest/1233f9bfe534bfec,Extreme sport\ntest/1234cb08c1483f94,Close-up\ntest/1235a08a7259008c,Boating,Personal water craft\ntest/1235b2941dc591e4,Luxury vehicle,Performance car\ntest/1236a48432938c84,Compact car,Luxury vehicle\ntest/123806b8b67d50c7,Arthropod,Close-up\ntest/12396ac87d2dbb05,Close-up\ntest/1239eb1e14825b9b,Flatbread\ntest/1239fa40eecbb506,Firearm\ntest/123b018bf069b759,Whiskers\ntest/123b517385bd10be,Hound\ntest/123f4d08d92aee21,Antique car\ntest/12401b1c579e3be6,Plant stem\ntest/1244696b5daaae29,Antique car\ntest/1244fd65e80e9c2e,Auto racing\ntest/124a3dda76c0273d,Aircraft engine\ntest/124ab4a389ab9036,Luxury vehicle\ntest/124babd7b2a22a9a,Combat sport\ntest/124c72fb1f8f3bc3,Electronic instrument\ntest/124e49d1ca0d38e9,Close-up\ntest/124e5520f91c8157,Perching bird\ntest/124e77d54dca3eff,Performance car\ntest/1253baf5755d43aa,Bird of prey,Close-up\ntest/12542a6cc45d52e3,Dishware\ntest/1255e2365c7a18cf,Extreme sport,Parachuting\ntest/125720d273e2ca1d,Plant stem\ntest/1257b18ca801d601,Senior citizen\ntest/1257db98da916382,Extreme sport,Boating\ntest/12583676ef985f95,Senior citizen\ntest/1258c62537115412,Blond,Hair coloring\ntest/1258c71fad813557,Sport utility vehicle\ntest/125a7690f196ac60,Close-up\ntest/125c783110eb4592,Compact car,Sport utility vehicle\ntest/125d7bc0b4ad1b43,Frigate\ntest/12603a0d397aa5a9,Steamed rice\ntest/1265a59916050f73,Vegetarian food\ntest/126a5f1bd6fa27e9,Boating\ntest/126b247a62a8d573,Luxury vehicle\ntest/126b43309ee38ea9,Performance car,Luxury vehicle,Antique car\ntest/126c4e457fcc8040,Portrait photography,Close-up\ntest/126cab8e28f78d29,Firearm,Shooting range\ntest/126ccdf649cd0a8b,Compact car,Luxury vehicle,Performance car\ntest/126dd5f3af4932e3,Luxury vehicle\ntest/126e6022bfbecc4b,Close-up\ntest/126ebc35bcc6e423,Bird of prey\ntest/126fa257c384c16f,Extreme sport,Boating,Surfing Equipment,Wind wave\ntest/1274a313c3ea9ebf,Luxury vehicle\ntest/127519517e80431d,Cookies and crackers\ntest/1278072a04a2302d,Compact car\ntest/127a513e93396e5f,Combat sport\ntest/127baac599c45712,Close-up\ntest/127dc41f87cf9e22,Blond\ntest/127e3d7e04f9c5f6,Ball game\ntest/127e9706e6f2b617,Close-up\ntest/1280afb8e109c7a9,Luxury vehicle,Antique car\ntest/12871380c3035cfd,Sport utility vehicle\ntest/12871fd807b0eca0,Plant stem,Close-up\ntest/1287c3594a174f6b,Luxury vehicle\ntest/1288b7bb34719e54,Rural area\ntest/128c93eeddb45299,Stemware\ntest/128d2f7b9eed698c,Corn on the cob\ntest/128df2ffb15fb73c,Compact car,Luxury vehicle\ntest/128df4d8d7780f12,Brochette\ntest/128e5b53298e2d54,Close-up\ntest/128effcd5bc60e5e,Rural area\ntest/128f733f517f5858,Black-and-white\ntest/12904a676daf54c6,Luxury vehicle,Performance car\ntest/1290d01d689d5cd3,Combat sport\ntest/1291ff65e9dabde6,Cookies and crackers\ntest/1295e3493032542a,Blond\ntest/12969fbd4cbb32b3,Luxury vehicle,Sport utility vehicle\ntest/1297b6d1a741ad0a,Firearm,Shooting range\ntest/129a216e2ae54f29,Blond\ntest/129b7a5d6370c128,Whiskers\ntest/129ddf1a0e918943,Luxury vehicle,Antique car\ntest/129eedde1ee45a21,Black-and-white\ntest/12a15f78b30922c4,Jiaozi\ntest/12a162541f137238,Close-up\ntest/12a1e11921565977,Performance car\ntest/12a35c4da6de98a9,Close-up\ntest/12a5558913784cd9,Luxury vehicle\ntest/12a5bbc425a25fd2,Black-and-white\ntest/12a7515937bf96db,Close-up\ntest/12aa32840cff5ed6,Leaf vegetable\ntest/12aa9a0d7f5f7c18,Bird of prey,Close-up\ntest/12adf09b8be4f512,Whiskers\ntest/12ae13cee8a172a0,Shallot\ntest/12ae3e29de392848,Dishware\ntest/12af3e2960248132,Blond,Hair coloring\ntest/12b10237b012f7c0,Arthropod,Close-up\ntest/12b1f929640f1227,Close-up\ntest/12b1fd0cc4c4fa3e,Vegetarian food\ntest/12b8421dd557480a,Fried food\ntest/12bdf6cd470c29d9,Chordophone,Electric guitar\ntest/12bfab94760359b6,Ball game,Team sport\ntest/12c0f0524d441a3b,Aircraft engine\ntest/12c4537e10e0f1f3,Black-and-white,Woodwind instrument\ntest/12c5e6411050a03d,Molluscs\ntest/12c96c1aa3d44aee,Musical theatre\ntest/12cff10bc6b6ddf7,Rural area\ntest/12d04c28a2909709,Close-up,Plant stem\ntest/12d12e41c4eb84ea,Military person\ntest/12d1d6cb01a346b6,Road cycling\ntest/12d23628822d875f,Whiskers\ntest/12d3529230a76f23,Sport utility vehicle\ntest/12d411b8aa57ea1d,Domestic short-haired cat,Whiskers\ntest/12d579adbfbed27b,Luxury vehicle,Antique car\ntest/12d5eac451e61842,Luxury vehicle\ntest/12d63b7cfb633408,Sport utility vehicle\ntest/12d8c0e031b0a447,Road cycling\ntest/12daee2ec40b286a,Sport utility vehicle\ntest/12db725787411264,Arcade game\ntest/12dce90f0f0f5617,Performance car\ntest/12e22862f3b7e934,Auto racing\ntest/12e234eba04c7888,Whiskers\ntest/12e262615d71d0d8,Domestic short-haired cat,Close-up,Whiskers\ntest/12e27aab527ecc81,Close-up\ntest/12e362a481f36ae8,Goats\ntest/12e3a191ec6d32a6,Domestic short-haired cat,Whiskers\ntest/12e98875901645b4,Middle ages\ntest/12eaa784fd528c62,Luxury vehicle,Performance car\ntest/12eb26a6c1a41893,Auto racing,Performance car,Antique car\ntest/12ecc1f2047f0d67,Luxury vehicle\ntest/12ee320943f26417,Sport utility vehicle\ntest/12ee630539c5d0f6,Arthropod,Close-up\ntest/12ef8ed584f5af6f,Rear-view mirror\ntest/12f6da7e72a3d1ca,Luxury vehicle,Antique car\ntest/12f71fdd4070a01d,Compact car,Luxury vehicle,Antique car\ntest/12f7ba232fffbc91,Close-up\ntest/12fbc0dfd241cc18,Sport utility vehicle\ntest/1300f17ffa4e8833,Residential area,Aerial photography\ntest/1301abd7df04425b,Ball game,Team sport\ntest/130405d9f3a269d1,Stemware\ntest/1304f1e77a1f3cf0,Luxury vehicle\ntest/130832eba348c2e9,Jeans\ntest/1308c8e64cbaf036,Gliding\ntest/130920fac04eefb3,Sport utility vehicle\ntest/130a95434f790985,Close-up\ntest/130e1147e9fdca34,Compact car,Luxury vehicle\ntest/130e43fd24bdc915,Floristry\ntest/13127308dc561f10,Monoplane,Gliding\ntest/13132139bd3b6766,Plant stem\ntest/131350d5a7be04c2,Chordophone\ntest/13154d30ea9a4069,Performance car\ntest/1317f4022e2fae9b,Black-and-white\ntest/131a113ff3ba4e2e,Angling\ntest/131a248c706aba89,Jeans\ntest/131bd55a97947f26,Calabaza\ntest/131c8077544ca677,Auto racing,Performance car\ntest/13226bcf0bc85e7a,Aircraft engine\ntest/13253d29a3ecad33,Black-and-white\ntest/132a5e52a7b123b0,Compact car,Antique car\ntest/132ea71249bbd839,Arthropod,Locust,Close-up\ntest/132f261af97a8428,Gumbo\ntest/132f7aa4604175e0,Floristry\ntest/133129b17d3535d7,Floristry\ntest/13323904b51f5644,Chordophone,Electric guitar\ntest/1332f29c1d58a3b6,Luxury vehicle\ntest/1333eb5cde9e40ad,Compact car,Luxury vehicle,Antique car\ntest/13356ab3cb5d617d,Hair coloring\ntest/133619f8304ace80,Boating\ntest/133c042afc008b6e,Whiskers\ntest/133c2608531812ef,Hound\ntest/133dbe3c65de7b5b,Luxury vehicle,Antique car\ntest/13445ba7a57f7bf8,Vegetarian food\ntest/1344a3eb9d6bac9a,Frigate\ntest/1344bf0230cdffab,Pit bull\ntest/134509a70aea83bb,Extreme sport\ntest/1346e022f56db2df,Microcontroller\ntest/13497fe692221c57,Performance car,Luxury vehicle,Antique car\ntest/134a5003332b3f2b,Prosciutto\ntest/134aaf516889ab97,Jeans\ntest/134bf4d0af67e527,Motorcycling\ntest/134e7aae3e2561c9,Roller skating\ntest/1350ab1322a76fc6,Compact car\ntest/135207e3613e7b55,Black swan\ntest/1356967d306c775d,Arthropod,Close-up\ntest/13572fd94134b130,Antique car\ntest/1358956ea77ea43a,Machine tool\ntest/1358b4dfe7daf744,Compact car,Antique car\ntest/1359ef05ec5f96de,Arthropod,Close-up\ntest/135c2d66ca782aaf,Boating,Rowing\ntest/135cb7738046d333,Luxury vehicle,Antique car\ntest/135d5a13fa06063e,Close-up,Whiskers\ntest/135e966ac92a109e,Ball game,Team sport\ntest/135fb2a9eebaf6f2,Luxury vehicle\ntest/135ffb39fe40a831,Close-up\ntest/1360dddb08185aea,Roman temple\ntest/13628720bc962136,Compact car,Luxury vehicle\ntest/13632793d5f682d7,Drums\ntest/13664a20e8627308,Blond,Portrait photography,Close-up\ntest/136683ec2990429c,Galleon\ntest/1366ba3108da6171,Close-up,Whiskers\ntest/13682e72b508c3b4,Nordic skiing\ntest/136935de00da41bc,Boating\ntest/1369df17d4b764d1,Ball game,Team sport\ntest/136a7c94de7c61dd,Nile crocodile,Crocodilia\ntest/136c227b9143bfcd,Luxury vehicle\ntest/136df4a2c6facaaa,Compact car,Antique car\ntest/1370178fe80b6723,Amphibian,Close-up\ntest/1370240d2ec109cf,Canola\ntest/13713cf0537d0ecc,Luxury vehicle,Antique car\ntest/1372e0a2956b3112,Close-up,Whiskers\ntest/13738deed137e0ae,Residential area,Aerial photography\ntest/1375ce5429f60c8c,Ball game\ntest/1376dfa7c2f908a8,Jeans\ntest/137723758f0b8a72,Junk food,Fried food\ntest/137898e7570113ab,Motorcycling\ntest/137c76ceb4301c72,Luxury vehicle,Performance car\ntest/138083e552355446,Military vehicle,Sport utility vehicle\ntest/1381a8e6159da648,Cobblestone\ntest/1382bc3a48090686,Barechested\ntest/138323ddc170f97e,Whiskers\ntest/1384c29253cf885d,Luxury vehicle,Sport utility vehicle\ntest/138540d55171eaec,Arthropod,Close-up\ntest/1386ae8204e1a150,Black-and-white\ntest/138868ae7bb1ed2b,Outdoor shoe\ntest/1388ad991e9feef5,Antique car,Luxury vehicle,Performance car\ntest/138a8b06d3dd6ac9,Outdoor shoe\ntest/138b4e9b33f7adc5,Machine tool\ntest/138bce62418c0d11,Bottled water\ntest/138ccb498bcba6d1,Frozen yogurt\ntest/138d3a3de57ba86e,Rural area\ntest/138ea2ee825b8b9a,Domestic short-haired cat,Close-up,Whiskers\ntest/138f925b550fa946,Electronic instrument\ntest/13902d974799efba,Arthropod,Locust,Close-up\ntest/13913a1010a38f30,Close-up,Scaled reptile\ntest/1391dc7289ef16d3,Antique car\ntest/13998fa14da472bf,Luxury vehicle,Performance car\ntest/139b58bf9981102e,Monoplane,Gliding\ntest/139d065755085c0a,Blond\ntest/139e0da12625e7ff,Compact car,Luxury vehicle,Antique car\ntest/139f22576cf878c8,Close-up\ntest/139fc5b708a64fdd,Compact car\ntest/139fc8ce97f40264,Close-up\ntest/13a2e509eb136221,Musical theatre\ntest/13a31a9bf56d6bda,Barquentine\ntest/13a4f24dddd569e9,Computer speaker\ntest/13a597a295ca95a7,Close-up\ntest/13a687f057a0212e,Nest box\ntest/13a7e5b0ebb15fe6,Close-up\ntest/13a8ed5225aa5659,Compact car\ntest/13a9861ac17f21e3,Antique car\ntest/13aad8cea4c37ca6,Close-up\ntest/13aaf4003bdfa89d,Scaled reptile\ntest/13ab0b1d1d50460a,Aircraft engine\ntest/13ab1f156cb93ef4,Jeans,Chordophone\ntest/13aeaf3df3f7b9a7,Cosmetics\ntest/13aff5159c382ef5,Compact car\ntest/13b541db844da38d,Compact car,Luxury vehicle,Antique car\ntest/13b68e34a71aefdb,Luxury vehicle\ntest/13b77272a45d3cae,Antique car\ntest/13be61af6a26e101,Aloe\ntest/13c15d09480b7db7,Plant stem\ntest/13c415158034a69b,Military person\ntest/13c568997bebd595,Portrait photography,Close-up\ntest/13c877a7a832585c,Digital camera\ntest/13c888ae1ffe317c,Plant stem\ntest/13c8bca7bd9455ed,Antique car,Performance car\ntest/13c8ecfeb484b336,Extreme sport,Parachuting\ntest/13cac3caeb8915d8,Scaled reptile\ntest/13cce467c51b0660,Luxury vehicle,Antique car\ntest/13d2d78c98b8448d,Electronic instrument\ntest/13d3215eb983f7ad,Bird of prey\ntest/13d3287c4cac61c1,Machine tool\ntest/13d3833c98ab3d3e,Compact car,Sport utility vehicle\ntest/13d3e7f511e6b52f,Stemware\ntest/13d52e95adba29db,Close-up\ntest/13d58db06463ba77,Close-up\ntest/13d5bdf54c090680,Luxury vehicle\ntest/13d5e5ff8050608b,Close-up\ntest/13d6758d16846543,Aircraft engine\ntest/13da9efcce31f9fe,Close-up\ntest/13db08adb7521cda,Outdoor shoe\ntest/13dc3c532d6d2ac3,Luxury vehicle,Antique car\ntest/13dd66b5cd0b7481,Auto racing\ntest/13dd75f0f417dcca,Compact car\ntest/13de545f099ea325,Antique car,Performance car\ntest/13e10d554aaa9c0d,Sport utility vehicle\ntest/13e181d73ff7d918,Close-up\ntest/13e2a7342d445cb5,Arthropod,Locust,Close-up\ntest/13e32db51e07b8a3,Middle ages\ntest/13e4e923e54b7b63,Luxury vehicle\ntest/13e8e235504ac488,Arthropod,Close-up,Plant stem\ntest/13e97b4eb10a430d,Calabaza\ntest/13eaa1219788ced5,Stemware\ntest/13ecf652a65aed8d,Close-up\ntest/13ee0efbfc21d99f,Outdoor shoe\ntest/13efbc989ebe544e,Luxury vehicle\ntest/13f0edd3707f6107,Hound\ntest/13f2bce8ac40e427,Luxury vehicle,Antique car\ntest/13f39fd1be11c0a0,Extreme sport,Wind wave\ntest/13f3fd85f104eabf,Aircraft engine\ntest/13f58df4575380c7,Luxury vehicle,Sport utility vehicle\ntest/13f6864d0f650d6c,Chordophone,Electric guitar\ntest/13f8a57a12298e3f,Close-up,Whiskers\ntest/13fa18569e7294f7,Divemaster,Scuba diving,Underwater diving\ntest/13fbf4d8a571de45,Combat sport,Grappling\ntest/13fceeabb52cec9c,Residential area\ntest/13fe4458680e8d25,Compact car,Luxury vehicle\ntest/13fea215fbd053f0,Close-up\ntest/13feb5be8090ab00,Coral reef fish\ntest/13feeee628813aeb,Microcontroller\ntest/1401042858c48810,Close-up,Whiskers\ntest/1403a1f33f54ad3a,Military person\ntest/1403fc3f757dd86d,Aircraft engine\ntest/1405806c633c1175,Black-and-white\ntest/1405e8c2634168b6,Outdoor shoe\ntest/14067077e695b7d5,Amphibian,Close-up\ntest/1408a84f993004fd,Luxury vehicle,Antique car\ntest/1408fddce6fde129,Jeans\ntest/140a388242413992,Close-up\ntest/140ac79794c3d978,Luxury vehicle,Antique car\ntest/140ad20c487e1a55,Extreme sport,Boating\ntest/141110e5bd24b885,Compact car\ntest/1413d3df775ccc81,Close-up,Whiskers\ntest/1416172afbaeef27,Luxury vehicle,Performance car\ntest/141a89e2de02312b,Bovine\ntest/141f13dac6a824b7,Auto racing,Performance car\ntest/141f70633704fdac,Luxury vehicle,Performance car\ntest/142089a775e5c5de,Military vehicle,Antique car,Sport utility vehicle\ntest/1420d7e09988db90,Compact car\ntest/14232bde007b6c5c,Luxury vehicle,Performance car\ntest/1423ce7696c763e7,Perching bird\ntest/14245c1db04c5aa9,Black-and-white\ntest/14265d5c5ddddbd2,Close-up\ntest/1427a6fd38a9725c,Pasta salad\ntest/1429673685a074a9,Jeans,Luxury vehicle\ntest/142c5f0ecdeb5d63,Auto racing\ntest/142e4ad497ec7b8a,Compact car,Antique car\ntest/142f34f028914693,High-speed rail\ntest/1431422309330f42,Close-up\ntest/143169de563f01e0,Ribs\ntest/1431a78a8634ffb9,Floristry\ntest/143250acbccc1154,Luxury vehicle\ntest/1435f6e523debb81,Performance car\ntest/143619d2f1a67349,Sport utility vehicle\ntest/1437c062ffadd25f,Domestic short-haired cat,Whiskers\ntest/1438aa09e6d7e1b2,Luxury vehicle,Performance car\ntest/14395d55f47d37ee,Close-up\ntest/14398c3c86b3f174,Compact car\ntest/143b92c76c0eb82e,Luxury vehicle\ntest/143d9667e7d8b172,Antique car\ntest/143f348fb699ebf1,Close-up\ntest/143f3997df5b17e7,Plant stem,Close-up\ntest/14463c635c1ba418,Compact car,Luxury vehicle,Antique car\ntest/1447059b7e0c5c95,Frigate\ntest/144726e8b7c7d383,Ball game,Team sport\ntest/1447466da752b7a7,Aerial photography\ntest/144746e06c2ac45f,Briefs\ntest/1447fb6bef233a38,Sailboat racing,Wind wave\ntest/144836c21ae4a2c2,Compact car\ntest/144a56f9e9aa44bc,Electronic instrument\ntest/144e956148ec6655,Aircraft engine\ntest/144ed45fd4c709d8,Fire apparatus\ntest/144f47169f1935a2,Ball game\ntest/144fc946310cef8d,Boating\ntest/14546c375701c6f5,Compact car,Luxury vehicle,Antique car\ntest/1457c3c3d19f6d3d,Whiskers\ntest/14598b984f20b339,Fried food,Cookies and crackers\ntest/145a1f2673a7a76f,Perching bird\ntest/145e02a1608f2a7c,Orator\ntest/145f3aa6c3717090,Black-and-white\ntest/1461593063b5631d,Scaled reptile,Close-up,Anole\ntest/1462b97d57d54851,Ball game,Team sport\ntest/146381f16e4a2e27,Compact car,Sport utility vehicle\ntest/146b77f7e0d38f76,Close-up\ntest/146cc84eed462d7b,Performance car\ntest/146cfb1191ad7e1f,Luxury vehicle\ntest/146f44f0223687f9,Auto racing\ntest/14711e2cf3e9f16e,Compact car,Luxury vehicle,Sport utility vehicle\ntest/1472d13f4ac1e87d,Firearm\ntest/14757a9926ed13f3,Luxury vehicle\ntest/147697422ccd96b6,Performance car\ntest/14789c46e8855069,Chordophone\ntest/147919e40286eca6,Residential area,Filling station\ntest/14793250e31867f3,Scaled reptile,Anole\ntest/147941e15f0b3d14,Scaled reptile\ntest/14797a526b6709c6,Rural area\ntest/147c58a978643153,Backlighting\ntest/147e7b359a4968f9,Luxury vehicle\ntest/148017eb02ee6f3e,Extreme sport\ntest/1481fa562ea0dd5a,Close-up\ntest/1483e4876457ba92,Frigate\ntest/14857eda90de0088,Domestic short-haired cat,Whiskers\ntest/14882d65e1cfb6b3,Black-and-white\ntest/148869d02f42ba29,Digital camera\ntest/148929fe15f3af10,Black-and-white,Luxury vehicle,Antique car\ntest/148d23bae036cb45,Sport utility vehicle\ntest/1492c0db9912af4d,Jeans\ntest/149303ebe1cdbc15,Molluscs,Close-up\ntest/1493702d499fb731,Church bell\ntest/14947c4d93d03ad1,Black-and-white,Luxury vehicle,Antique car\ntest/1497766e308f1dcf,Fried food\ntest/14986e1675a7fa9b,Antique car\ntest/149a459813317be1,Antique car\ntest/149a8420df256faf,Chordophone\ntest/149a8d56fc1bc9ed,Amphibian,Scaled reptile\ntest/149ac0bc5f8bd257,Combat sport\ntest/149b9d5885bb9c7b,Childbirth\ntest/149be5da8f359def,Boating\ntest/149ebccfe8a6c900,Luxury vehicle,Antique car\ntest/14a5b4ad25e54b55,Vegetarian food\ntest/14a661b3189c8310,Athletic shoe,Outdoor shoe\ntest/14a6fc43df81f3e2,Luxury vehicle,Sport utility vehicle\ntest/14a77de0bda87d46,Luxury vehicle,Sport utility vehicle\ntest/14a839093eaba2d3,Roller skating\ntest/14a8976d555decb1,Compact car\ntest/14ad8b4142e0da49,Athletic shoe,Outdoor shoe\ntest/14addb385b7f6585,Lawn game,Ball game\ntest/14b1ad3070b4dad1,Plant stem,Close-up\ntest/14b3f44f081b1c5b,Luxury vehicle,Performance car\ntest/14b503571fdefce2,Junk food\ntest/14bb792d16b32bc6,Gumbo\ntest/14bba626a2508a34,Black-and-white\ntest/14bc2016e50c2235,Forklift truck\ntest/14be4541bbbbf520,Luxury vehicle\ntest/14c020ff31f8957b,Close-up\ntest/14c02c26910468d0,Billiards\ntest/14c043dad2f9a905,Close-up\ntest/14c081814f992962,Luxury vehicle\ntest/14c2892e19a96789,Siberian husky\ntest/14c39c068949c91d,Luxury vehicle,Sport utility vehicle\ntest/14c3a23b2ad74ac2,Junk food,Cookies and crackers\ntest/14c3f7defd3776fd,Chordophone,Electric guitar\ntest/14c653641449a3d8,Close-up,Plant stem\ntest/14c685eb2b5952c2,Whiskers\ntest/14c8454c6f84a8dd,Frozen yogurt\ntest/14c877417af7911b,Close-up\ntest/14c88380f7a64764,Inflatable\ntest/14c9cbca115c2f4b,Shinto shrine\ntest/14c9fb317ba6b92c,Bovine\ntest/14cad93b8dfdea38,Luxury vehicle\ntest/14cc7caf86337077,Luxury vehicle\ntest/14d50708c856e29a,Wind wave\ntest/14d61c69e482707c,Close-up\ntest/14da3cbe7b9bd730,Arthropod,Close-up\ntest/14dba23948cf4ecd,Whiskers\ntest/14dd2b5740f8ca70,Erinaceidae\ntest/14df12f60d409fab,Black swan\ntest/14e10a53bf1aa3c7,Miniature Poodle\ntest/14e28b8ea5c59032,Bovine\ntest/14e7131592f6e521,Luxury vehicle\ntest/14e8ca73ef667098,Ale\ntest/14eaab08037c5b17,Headphones\ntest/14ec3d9df18467d6,Ball game\ntest/14ecc4c4b4283f5c,Luxury vehicle,Sport utility vehicle\ntest/14ee8e8500db236e,Electronic instrument,Drums\ntest/14f015db1fdedb36,Luxury vehicle,Performance car\ntest/14f100eb85e81b95,Whiskers\ntest/14f33ba14df22e40,Frying,Fried food\ntest/14f33f4cba229992,Luxury vehicle,Sport utility vehicle\ntest/14f40096c24c2a7c,Luxury vehicle,Antique car\ntest/14f48e1dad61f7c6,Great white shark\ntest/14f5a5af8248c889,Digital camera\ntest/14f6cc448adf1489,Aircraft engine\ntest/14f6e0abac25e540,Barbecue chicken\ntest/14f802ed3b3867ca,Luxury vehicle\ntest/14f987036a71296d,Luxury vehicle,Performance car\ntest/14f99fff2f7833da,Close-up\ntest/14fce647401ac7c6,Close-up\ntest/14fceb7a490249a0,Floristry\ntest/14fcf7b5356be7ae,Luxury vehicle\ntest/14ff95d3d1df841c,Jeans\ntest/15002d09874abd95,Luxury vehicle,Antique car,Sport utility vehicle\ntest/1500b5ac246ce0a6,Plant stem\ntest/1504029bef1d4819,Equestrianism\ntest/15049fc166c9fe45,Aerial photography\ntest/1506e28a14198d7a,Bovine\ntest/150732f945ecd55c,Military vehicle,Sport utility vehicle\ntest/150a0a4623ed8a91,Domestic short-haired cat,Whiskers\ntest/150aa7a0506b003b,Chordophone\ntest/150d0cc4d0650fde,Luxury vehicle,Antique car\ntest/150f6204cd021059,Luxury vehicle,Antique car\ntest/15107ca86d05e7e8,Close-up\ntest/15112b2548af8225,Extreme sport\ntest/1517f32b03a8b35f,Combat sport,Sumo\ntest/151c5d2b4e52f8a9,Boating\ntest/151d317ed7e9c9eb,Senior citizen\ntest/15206cdb4d5acff2,Ball game,Team sport\ntest/15216c464ecedf1a,Arthropod\ntest/1521f8e16a5f7fdd,Whiskers\ntest/1521f993a99f8738,Black-and-white\ntest/1524236cfd3f175f,Siberian husky\ntest/15251aff1e01e347,Close-up\ntest/1527d2b1ddb82cce,Residential area,Aerial photography\ntest/152961ba09eff9aa,Aircraft engine\ntest/15298fef5dd83c4d,Luxury vehicle,Performance car\ntest/152c9af0dd86c225,Close-up\ntest/152cd818914ab323,Aircraft engine\ntest/152e2e7b13c3bd3a,Auto racing\ntest/152e565de3971edd,Childbirth\ntest/152ec03ea2186f82,Close-up\ntest/15308a7115e99573,Divemaster,Scuba diving,Underwater diving\ntest/1535606642920023,Underwater diving\ntest/15356a193428fd5d,Middle ages\ntest/1537f41b7a4fc016,Ball game,Team sport\ntest/1539a5edc6016394,Compact car\ntest/1539de1f19383daa,Close-up\ntest/153a026e1dcdf1fd,Electronic instrument\ntest/153c07148ed76343,Sport utility vehicle\ntest/153c7be28811c488,Bird of prey\ntest/153d164e827a6716,Goats\ntest/153d7ae4c75d9581,Close-up\ntest/153de975414db97f,Amphibian,Close-up\ntest/153e56e86931e922,Steamed rice\ntest/154067c4f163369d,Road cycling\ntest/154177dae36ac3ef,Performance car,Luxury vehicle,Antique car\ntest/1542122be4dcc76f,Narcissus\ntest/1543541a3e0ae9d1,Gumbo\ntest/15435853088a1962,Cosmetics\ntest/1543a8639bbc0e3a,Luxury vehicle\ntest/15441f45d3ec4ef2,Close-up,Whiskers\ntest/1546707ca6fb4ad4,Vegetarian food\ntest/1547065d2e0250fa,Close-up\ntest/15475dc266aaed7a,Arthropod,Locust\ntest/1547c76f5c13b2f8,Compact car,Luxury vehicle,Antique car\ntest/154cd0a8c596af31,Equestrianism\ntest/154d94193be21813,Hound\ntest/154e6083804b4ffc,Calabaza\ntest/154fba598a905b23,Luxury vehicle,Performance car\ntest/1550ac9e08b46b8d,Ramen,Soba\ntest/1554824fd4ebdc5f,Close-up\ntest/1554f6fd493281ec,Road cycling\ntest/1556c73a624590dc,Aircraft engine\ntest/15571ab27b3fae85,Luxury vehicle,Antique car\ntest/15579f372ee27e1c,Military vehicle\ntest/1557a9c31e4832ac,Lawn game\ntest/1557b56b0a01ceea,Antique car\ntest/15588d2079a35327,Motorcycling\ntest/15597982cbbf59c9,Arthropod,Close-up\ntest/155e426007fde989,Luxury vehicle,Sport utility vehicle\ntest/155f65d5e601fef0,Luxury vehicle,Performance car\ntest/155fadedae2caf64,Whiskers\ntest/1561ee74a4f7b731,Rural area,Bovine\ntest/1562e20aaeadf456,Team sport\ntest/15636dfbe9a76d15,Military person\ntest/1564ad0dd401481c,Arthropod,Close-up\ntest/156503ed6d16b73b,Rural area,Canola\ntest/1565109ce8dd6714,Drums\ntest/1569b337f4a0c7f6,Close-up\ntest/1569d5d54261fc3f,Floristry,Plant stem\ntest/156bb81bf55f3469,Microcontroller,Breadboard\ntest/156e19aaa8ad66dd,Motorcycling\ntest/1571768f086b1eea,Close-up,Scaled reptile\ntest/157267045406df82,Digital camera\ntest/15738a7c84583031,Antique car,Luxury vehicle,Performance car\ntest/1578e758dd98f548,Steamed rice\ntest/157921071bed1360,Sport utility vehicle\ntest/157b51b4c8c3573a,Luxury vehicle\ntest/157c94139ab54042,Luxury vehicle,Performance car\ntest/157cf96d22c4bf51,Military vehicle\ntest/158007f40b3bb576,Performance car,Antique car\ntest/15804491e8470a0c,Close-up\ntest/15826e408ef990a4,Residential area\ntest/15836f8793021800,Vegetarian food,Junk food\ntest/1583b3e785efabea,Luxury vehicle,Sport utility vehicle\ntest/1584e6666f47189d,Domestic rabbit\ntest/15859ba2e74bcdc6,Domestic short-haired cat,Whiskers\ntest/158aee7a7959f647,Close-up\ntest/158b25958539864a,Outdoor shoe\ntest/158bf11b09e7cd6e,Bottled water\ntest/158c1bdd9d3d1d1b,Fried food\ntest/158c71d2a1a9a10a,Boating\ntest/158e935adc152b45,Close-up\ntest/158fa83bb8d0d521,Aircraft engine\ntest/1591be25c63634b8,Luxury vehicle,Performance car\ntest/1592a510d9d21541,Close-up\ntest/15945566b3fba67e,Primate\ntest/159574653180b03e,Close-up\ntest/159c9a08c775c23c,Luxury vehicle\ntest/159de001bfb786cc,Junk food\ntest/159f27d238933ad0,Residential area\ntest/15a040baf4d6b6c8,Musical theatre\ntest/15a157eacff932ec,Black-and-white,Medical imaging\ntest/15a1988c5841b7d3,Close-up\ntest/15a31fcb17bda9ed,Water polo\ntest/15a47d46cad705a8,Figure skating\ntest/15a5de5a0c020a80,Whiskers\ntest/15a62f3893d1d214,Luxury vehicle\ntest/15abd805fabd13c1,Auto racing\ntest/15acd0471c9bd37a,Antique car\ntest/15ad1a8c516ebe5b,Goats,Bovine\ntest/15ad5476f536cddb,Blond\ntest/15adda5264364cdd,Close-up\ntest/15af9b72490c2251,Close-up\ntest/15b1a40cfe9a034d,Luxury vehicle\ntest/15b3135fd8e76a5f,Residential area\ntest/15b33a9b68d8ad5c,Chordophone\ntest/15b35f82340e9505,Blond\ntest/15b376e472318a69,Cookies and crackers\ntest/15b6fce72a3f17be,Whiskers\ntest/15b730309e3aa980,Sport utility vehicle\ntest/15b9363e46d70584,Close-up,Scaled reptile\ntest/15bc121e6e48c920,Compact car,Luxury vehicle,Antique car\ntest/15bd06319ee85c9c,Antique car\ntest/15bd19577d99d9a6,Black-and-white\ntest/15bf0e35241018c5,Domestic short-haired cat,Whiskers\ntest/15bf64e7714d0f85,Ball game,Team sport\ntest/15c4b23be4c9acff,Junk food\ntest/15c6c2da36abaa96,Rural area\ntest/15c931de424cb9ca,Dog walking\ntest/15ca0b4700e8f702,Extreme sport,Sledding\ntest/15cb145cde7d09c6,Luxury vehicle,Sport utility vehicle\ntest/15cb4a5fe1888e45,Vegetarian food\ntest/15cc2d4a022d1ef2,Hound\ntest/15cc4b7066271dcb,Ball game,Team sport\ntest/15cc70b7d0e87a7a,Luxury vehicle\ntest/15cdf068f1cc0c01,Ball game,Team sport\ntest/15ce90566c6bfff8,Rural area\ntest/15d044562f3a5dd0,Domestic short-haired cat\ntest/15d174bf9c01797c,Billiards\ntest/15d19218d699cee0,Close-up\ntest/15d1c8132be1d7f6,Whiskers\ntest/15d2a2db1489a896,Compact car,Antique car\ntest/15d668614ad67109,Luxury vehicle,Performance car\ntest/15d8e3954c05cdb3,Shinto shrine\ntest/15da176334e49bd6,Aircraft engine\ntest/15db33aed78114c5,Blond,Hair coloring\ntest/15db48b907d1725f,Bottled water\ntest/15db624efc45e95e,Plant stem,Close-up\ntest/15dc537989226851,Microcontroller\ntest/15dc82988a8053dd,Leaf vegetable\ntest/15dcaad607a81970,Military vehicle,Gun turret\ntest/15dd606bdc7b862b,Blond\ntest/15dde4d04a5bec09,Extreme sport\ntest/15dedf2f5cce5705,Auto racing,Touring car\ntest/15dee7a4f501d2a5,Boating\ntest/15e070117a54221d,Luxury vehicle\ntest/15e15f8c2ac6ae59,Auto racing,Compact car\ntest/15e2b2ed6f76a698,Luxury vehicle,Antique car\ntest/15e3c1041df9edeb,Jeans,Antique car\ntest/15e72063cccfefa2,Ramen\ntest/15e7271f8c235dbd,Close-up\ntest/15e98f0c10db5648,Machine tool\ntest/15eb219cffc3480a,Compact car\ntest/15eb54a5520717f9,Jeans\ntest/15eece3f93c56839,Close-up\ntest/15f1135c3e1c520f,Peafowl\ntest/15f580d7bc2692b3,Vegetarian food\ntest/15f8d68f9f6b72eb,Hair coloring\ntest/15f9d031455a7d1e,Microcontroller\ntest/15fb215666bb5782,Wind wave\ntest/15fba4beb52be52e,Black-and-white,Close-up\ntest/15feca9963419b34,Residential area\ntest/16004ac082c516af,Frying,Junk food,Fried food\ntest/160158cc7d3653f9,Shallot\ntest/160199a5ba842c8e,Electronic instrument\ntest/1604b7e11612b4b0,Arthropod,Close-up\ntest/1605d3f4a099266d,Close-up\ntest/16063545bd8b69f1,Firearm\ntest/16071596acd527cd,Close-up\ntest/1607e0acd8276ed6,Luxury vehicle\ntest/160843efc966ad2d,Close-up\ntest/16091945fda9d6a8,Ball game,Team sport\ntest/160c31f7298968a2,Bovine\ntest/160ca0b13511e97b,Luxury vehicle,Performance car\ntest/160ddf4ee655003f,Extreme sport,Boating\ntest/160df747d990e5b3,Black-and-white\ntest/1610d8da1b00d4f6,Athletic shoe,Outdoor shoe\ntest/1614a792fa7641b8,Portrait photography\ntest/1614fbd6482d111d,Luxury vehicle\ntest/161681cdbf004bf5,Plant stem\ntest/161767f64d78b66f,Arthropod,Plant stem,Close-up\ntest/161cf9219acff976,Close-up\ntest/16207d46f3b92b11,Melee weapon\ntest/162449d590357ff7,Bird of prey\ntest/1625835b1a7eaf5f,Compact car,Luxury vehicle\ntest/162ac22fcb7abe76,Gliding\ntest/162b86f5d2da9f6b,Auto racing,Performance car\ntest/162dcc942aef3ec5,Arthropod,Close-up,Plant stem\ntest/162ea758225d2dc5,Black-and-white,Medical imaging\ntest/162ec76abd5d27fb,Arthropod,Close-up\ntest/162f5cd16f96c478,Plant stem\ntest/162ff37e3a13b508,Close-up\ntest/1632a6608e59b26e,Angling\ntest/1632c4f5e5663616,Amphibian,Close-up,Scaled reptile\ntest/1632f2dac1d05a85,Ale\ntest/16352334e9f9dbc3,Close-up\ntest/16375f4a54946a90,Water polo,Ball game,Team sport\ntest/1637b4103ed3d5fa,Compact car,Luxury vehicle,Antique car\ntest/1637be87851edd97,Black-and-white,Close-up\ntest/1638e87d5e6eec91,Domestic short-haired cat,Whiskers\ntest/1638eb11a2439f9d,Close-up\ntest/1638f5dd4f28ea12,Electric piano,Electronic instrument\ntest/163972fc3fdc3612,Antique car\ntest/163af5233d03ed36,Close-up\ntest/163bc9299de6da58,Luxury vehicle,Antique car\ntest/163d87a7655d6887,Cookies and crackers\ntest/163e2e81945feb55,Great white shark\ntest/16440c58564b3432,Assault rifle,Firearm\ntest/16462ddae6a3055a,Melee weapon,Hunting knife\ntest/1648f9a633618567,Floristry\ntest/164a8e45bc699350,Fire apparatus\ntest/164b7cc7819deff4,Luxury vehicle\ntest/164e11a8bf2279fa,Racewalking\ntest/164ed5375f6ad610,Chordophone\ntest/164fb6565ef49ad4,Residential area\ntest/1650987494baf4d9,Equestrianism\ntest/1652367c424bc834,Black-and-white,Domestic short-haired cat,Close-up,Whiskers\ntest/1652d9d474fce936,Electronic instrument\ntest/165478b07691ee17,Rural area\ntest/1655eac20a451055,Luxury vehicle,Antique car\ntest/16586c5c46b04e89,Boating\ntest/1658b71c17f9c074,Auto racing\ntest/1658ee700cc5cf6e,Kitesurfing,Extreme sport,Wind wave\ntest/1659b2a2b3a6e519,Firearm\ntest/1659bc29e2e7300c,Hound\ntest/165b1856e160e75f,Plant stem\ntest/165b2c00479ed3c6,Antique car,Performance car\ntest/165c112e27ad791e,Watercolor paint\ntest/165dccb545a86cdc,Fried food\ntest/166328c1e36e4ad3,Military vehicle\ntest/166565c3be1e7a03,Luxury vehicle,Antique car\ntest/166c696dc8512207,Auto racing,Go-kart\ntest/166d1e3cb95d5270,Compact car,Luxury vehicle\ntest/166dc48c8ae7cdea,Luxury vehicle,Performance car\ntest/166f70ea6f71c41d,Domestic short-haired cat,Close-up,Whiskers\ntest/166fc335da7e2ba9,Luxury vehicle\ntest/166fdec014182306,Close-up\ntest/1671793afd2648dc,Sport utility vehicle\ntest/1671d5fef36ddad5,Aircraft engine\ntest/1672a3a808c94d1f,Whiskers\ntest/1673c89d3cba115d,Outdoor shoe\ntest/1678bb8be6fd2304,Close-up,Whiskers\ntest/16793262928d5263,Black-and-white\ntest/1679b6bb52a8a870,Ball game,Team sport\ntest/167c96258210796f,Primate\ntest/167ec397840bd0c3,Chordophone\ntest/16829d1cc125b697,Comics\ntest/1684878a21b5676c,Antique car\ntest/16856ab62573ee37,Middle ages\ntest/16857de08193111a,Close-up\ntest/1687aa113895ea5c,Erinaceidae\ntest/16882cb327bbc5fd,Auto racing,Go-kart\ntest/168f167615d49016,Extreme sport\ntest/1693a3cf6afba7aa,Black-and-white,Dishware\ntest/169422668d95e100,Jeans,Dog walking\ntest/1694475f19095bbb,Frigate\ntest/1695f70b9861d10d,Close-up\ntest/16966961b4f3dcd6,Close-up\ntest/169b7c17ad6932d1,Firearm\ntest/169bfc3250613901,Horse and buggy\ntest/169d43e0333b36da,Luxury vehicle\ntest/169d5f1145904f86,Arthropod,Close-up\ntest/169e2224a0a652b1,Luxury vehicle,Performance car\ntest/16a47ce33f379f2b,Close-up\ntest/16a618f9c5b0c3fb,Ball game\ntest/16a69e54ed68316d,Bird of prey\ntest/16a6efc582ed6144,Nordic skiing\ntest/16ab45388103b596,Luxury vehicle,Antique car\ntest/16ad88ff343d76b7,Close-up\ntest/16af828150a688d4,Residential area\ntest/16af9a24ba435d23,Compact car\ntest/16b4dbdda5be2a27,Luxury vehicle,Antique car\ntest/16b663dcd7aea31f,Middle ages\ntest/16b7da9735eede5e,Modern dance\ntest/16b93ae3b35f6cfa,Floristry\ntest/16b969fa9fc6633c,Luxury vehicle,Performance car\ntest/16bb181e80b3a80a,Performance car\ntest/16bb798e98391b22,Rural area\ntest/16c04101e6d68b07,Close-up\ntest/16c5b6a7418c8754,Luxury vehicle\ntest/16c5dc6b366afc64,Ball game\ntest/16c661604dca83b0,Black-and-white,Close-up\ntest/16c6a5aad7455216,Heavy cruiser,Frigate\ntest/16c6ff878cf35297,Go-kart\ntest/16c8203b592dbdd7,Machine tool\ntest/16c912d7bfb98b66,Residential area\ntest/16cb09c726d58d20,Nordic skiing\ntest/16cbb8d73b9859ac,Flatbread\ntest/16ce2d19ca5302a6,Domestic short-haired cat,Whiskers\ntest/16d0b90ad282fe65,Blond\ntest/16d1694424fb8000,Ball game,Team sport\ntest/16d23e2065d0ad23,Auto racing,Go-kart\ntest/16d2753cfcf851fc,Dishware\ntest/16d2d33440f08b4c,Cookies and crackers\ntest/16d45220c1145f8b,Senior citizen\ntest/16d71dfe9023ffb7,Close-up,Whiskers\ntest/16d76fde3c3c0b1e,Performance car\ntest/16d9b319fb9807e6,Luxury vehicle,Performance car\ntest/16dcf63ec1c3b10f,Compact car\ntest/16dd92c381a11cb8,Hound\ntest/16dee798c83fc088,Luxury vehicle\ntest/16e0044a9dce8b92,Goats\ntest/16e074805fac7a8a,Luxury vehicle,Performance car\ntest/16e0c168fff864b2,Vegetarian food,Cookies and crackers\ntest/16e29884cee9cac8,Coral reef fish\ntest/16e3545ceb87cefe,Wind wave\ntest/16e3af47788c1fcf,Junk food\ntest/16e3e31ff7e9ae1c,Compact car\ntest/16e3ee82eb0dabbd,Team sport\ntest/16e6a65d84366da5,Stemware\ntest/16e72a6ba83f6d0e,Luxury vehicle,Performance car\ntest/16e942cd3f23c02f,Close-up\ntest/16ea82f070fcc1b0,Miniature Poodle\ntest/16eb0bd2257a5ed5,Microcontroller\ntest/16ebfd113853420f,Watercolor paint\ntest/16ede62934ddbf72,Extreme sport\ntest/16ee7fc11080938d,Bird of prey,Perching bird\ntest/16ee9d9809c9ed80,Monoplane,Aircraft engine\ntest/16ef9e1aebe5ce1a,Luxury vehicle,Sport utility vehicle\ntest/16f0305dcdc596f4,Luxury vehicle\ntest/16f0c1ec4bb2a163,Aerial photography\ntest/16f232ef22ae62fe,Aircraft engine\ntest/16f51edf6ef907f5,Roller skating\ntest/16f5f484b07a50f0,Compact car\ntest/16f8a879d09ef4cb,Vegetarian food\ntest/16f99168b74cff1a,Equestrianism\ntest/16fa9f31bf3a56b9,Trampolining\ntest/16fd908fafb45beb,Luxury vehicle\ntest/170112ac1a34dd80,Combat sport,Grappling\ntest/1701c9c2a847b1bd,Close-up,Whiskers\ntest/170386b3b00ae03e,High-speed rail\ntest/1703fd7cba20c956,Scaled reptile\ntest/1704af4347261838,Luxury vehicle\ntest/17068abe1d5a39b5,Antique car\ntest/1709210a837479ef,Close-up\ntest/170bd096294bc6bf,Outdoor shoe\ntest/171048d2abe2f9c3,Residential area\ntest/17125ed9dfa33467,Aircraft engine\ntest/17142ca7e354c9a8,Leaf vegetable\ntest/1716f6fe00268aa4,Dobermann\ntest/1716f8960d888979,Compact car\ntest/1717d58edcd16675,Close-up\ntest/171b328f1c63c1d6,Extreme sport\ntest/171dabf3664818ab,Khinkali,Jiaozi\ntest/171db4cdc543cf4d,Combat sport\ntest/171f9ad84533c469,Luxury vehicle,Antique car\ntest/17218047facd07b5,Antique car\ntest/172366e146a054c3,Toy block\ntest/172a979ab50c5a4e,Amphibian\ntest/17313d091d17976a,Close-up\ntest/173151175f7dc16b,Vegetarian food\ntest/1732344e020b0c55,Arthropod,Close-up\ntest/173451e60de05894,Compact car,Performance car\ntest/1735493112c45d6d,Chordophone,Electric guitar\ntest/1738f6f74b2692a4,Figure skating\ntest/1739f0bad537e75c,Close-up,Anole,Scaled reptile\ntest/173a1ede79528025,Close-up\ntest/173b42ec116db415,Bratwurst\ntest/173ba1e8c1b0427d,Touring car,Luxury vehicle,Antique car\ntest/173e86c4f787b341,Antique car\ntest/17430f282286dfa9,Ball game\ntest/17438f3c649a737b,Jeans\ntest/174505d1cf7e7e09,Close-up,Plant stem\ntest/1745d7241bcdff81,Compact car,Luxury vehicle\ntest/1747347966d7b606,Hound\ntest/1748b273998217d3,Luxury vehicle\ntest/174965cae4fc5158,Cookies and crackers\ntest/1749e2a0f2ff20dc,Compact car,Luxury vehicle\ntest/17511c4ccf39e47d,Fire apparatus\ntest/1752701b5a0fcdcf,Luxury vehicle,Performance car\ntest/17565097f017e5de,Frying,Junk food,Fried food\ntest/17576c3ace83d9ce,Aircraft engine\ntest/17584e2c65324570,Full moon\ntest/17586fe8f66ee444,Close-up\ntest/175a7dfaffccfceb,Camera operator\ntest/175af666d6f72b05,Luxury vehicle,Sport utility vehicle\ntest/175c53a33364887b,Coral reef fish\ntest/175cf5b1897589dc,Bratwurst\ntest/175f7a7f62803a1a,Amphibian,Close-up\ntest/1761704bc5f1eeea,Luxury vehicle,Performance car\ntest/1761aa011fcede3b,Performance car\ntest/1761db9937a0e000,Auto racing\ntest/17627758c3303abb,Black-and-white\ntest/1762c0e4fa90fcfa,Plant stem\ntest/1763fce0076e07d5,Luxury vehicle,Sport utility vehicle\ntest/1765c1cb2f93e7cb,Jeans\ntest/1766bf861f517394,Cookies and crackers\ntest/176cfe12ce04514c,Black-and-white\ntest/176e9ba9dac21807,Performance car\ntest/176ea6ef14a07b8d,Performance car,Luxury vehicle,Antique car\ntest/176fd687e38bab23,Blond\ntest/1770dc32f7e47ff4,Antique car,Luxury vehicle,Performance car\ntest/177351a9b4f0356a,Bovine\ntest/1778f0abba56391a,Luxury vehicle,Performance car\ntest/177980d4cad87dc7,Luxury vehicle\ntest/177ace40d6d7a564,Antique car\ntest/177b07564e5e03a9,Road cycling\ntest/177b1dc142b81631,Middle ages\ntest/177bdb5e85084141,Luxury vehicle,Antique car\ntest/177c54c01ba79655,Electronic instrument,Black-and-white\ntest/177ef3b9cda6de0c,Residential area\ntest/17807c4ac898b91b,Luxury vehicle\ntest/1782961a8cba35cd,Angling\ntest/1783ad10903fba33,Whiskers\ntest/178910e402ae8caf,Compact car\ntest/1789e596514349f6,Domestic short-haired cat,Close-up,Whiskers\ntest/178b831fe87baa4e,Black-and-white\ntest/178cc8fd612ebf0c,Corn on the cob\ntest/178df841a2e226af,Herding,Bovine,Rural area\ntest/178e5290d3d1deba,Rural area\ntest/178fec5ad7217521,Jeans\ntest/17967adb1aaae380,Close-up\ntest/179739688abea47f,Microcontroller\ntest/1798f8e52818936f,Whiskers\ntest/179a3225b919261e,Performance car,Luxury vehicle,Antique car\ntest/179b57cd6d877970,Close-up\ntest/179c191aac94cdc8,Bovine\ntest/179c43424454734d,Close-up\ntest/179d81a4aa8edc99,Arthropod\ntest/179f7a9b1af96cc7,Sport utility vehicle\ntest/17a1aa347b8b29ae,Miniature Poodle\ntest/17a29173d5b42a5c,Camera operator\ntest/17a331b6896e888b,Beef tenderloin\ntest/17a5b99be5ae9bf0,Scaled reptile\ntest/17a5f5b1255d4646,Modern dance,Musical theatre\ntest/17ae3688ee96be25,Plant stem,Close-up\ntest/17b0bafcb4fef861,Antique car\ntest/17b1cab83303d41f,Black swan,Close-up\ntest/17b49e8c450a47e4,Floristry\ntest/17b72f094211173f,Close-up\ntest/17b8182b98a12bde,Antique car\ntest/17b895b873a3dae6,Erinaceidae\ntest/17b8ad9b50b4a902,Ball game\ntest/17b8fb5211a6c510,Luxury vehicle,Antique car\ntest/17b95a8dc4f90024,Jeans\ntest/17b9eb3b18a86ac4,Compact car\ntest/17bcfecbd8e82f51,Equestrianism\ntest/17bd8dbf3a446017,Aircraft engine\ntest/17bed7e41af2934f,Equitation,Equestrianism\ntest/17befa9f1b5fb758,Ball game\ntest/17c0feb1d5a00d1a,Scaled reptile\ntest/17c148d252531f2f,Miniature Poodle\ntest/17c3954e290af921,Luxury vehicle\ntest/17c3a2ecdb762b00,Bratwurst\ntest/17c3d4049c7ee5a4,Close-up\ntest/17c3e016c831433e,Jeans,Luxury vehicle\ntest/17c4523fd65eb9a4,Rural area\ntest/17c7ab3d02e77dcf,Compact car,Performance car\ntest/17c8c54dcff5c729,Compact car,Antique car\ntest/17c938cdc20f4174,Pork chop\ntest/17ccd5a8bf88375d,Compact car\ntest/17cce6d5f6cc6601,Hurdling\ntest/17cceae78dbeff46,Compact car\ntest/17cd5fe768e07127,Rural area\ntest/17d286a973db5a6f,Close-up\ntest/17d3b50ee2026fdd,Dog walking\ntest/17d602988ee3a631,Amphibian\ntest/17d8b89e9848df64,Ball game,Team sport\ntest/17d9e22cb472d3ce,Luxury vehicle,Performance car\ntest/17da54c4ef6af808,Junk food\ntest/17dc2f3aa0c87e39,Floristry\ntest/17dc8293d14697a0,Close-up\ntest/17df70074db67118,Jeans\ntest/17dfc891e3988bc6,Fried food\ntest/17e02f7da9786c6c,Luxury vehicle\ntest/17e0536b00f5a2fc,Chordophone\ntest/17e3f2c3081ff6ed,Performance car,Antique car\ntest/17e6bac927b180b1,Senior citizen,Close-up\ntest/17ea41dd37cf678a,Close-up\ntest/17eec5ba5de33e4e,Auto racing,Compact car\ntest/17efc002c49c6da1,Scaled reptile\ntest/17f06c6830a26d13,Antique car\ntest/17f26634ac0d0e4d,Leaf vegetable\ntest/17f448148be12d79,Luxury vehicle,Sport utility vehicle\ntest/17f5327961cebd07,Digital camera\ntest/17f5e0e8f0e94c58,Hound\ntest/17f62e751c0f28a9,Luxury vehicle\ntest/17f6e8ddc732f015,Road cycling\ntest/17f81586ffe1128b,Siberian husky\ntest/17f8fadc9201ecdf,Boating\ntest/17fe38872fedd0de,Outdoor shoe\ntest/17fe54b6ef70ca19,Black-and-white\ntest/17fe6f256c3e0b5f,Blond\ntest/17fff3df2bd0d9ae,Aircraft engine\ntest/1802ccc3aeeebd36,Roller skating\ntest/1804a75d643afdcd,Blond,Close-up\ntest/1805515d0239c29e,Sport utility vehicle\ntest/1805dc51e061ee3f,Ball game,Team sport\ntest/18094636c2d7542c,Close-up,Scaled reptile\ntest/180cf9419e379c58,Black-and-white,Close-up,Whiskers\ntest/180e560e220dd89d,Barbecue chicken\ntest/180f385261f2c726,Blond,Close-up\ntest/180fe142345753b0,Milky way\ntest/1810130430eeb31a,Flatbread\ntest/18144416b0c583d8,Ball game\ntest/1814aa55a7be69c0,Sport utility vehicle\ntest/1815ef2670315eb0,Luxury vehicle,Performance car\ntest/18167fd8433c0db8,Halter\ntest/1817ab0d85ea5d20,Close-up\ntest/181cb27f0595dc45,Bovine\ntest/182205061e929470,Aircraft engine\ntest/1823321103dde8fc,Scaled reptile\ntest/1824904e260955f6,Rural area\ntest/1826927f69c02e74,Compact car\ntest/182925d0f1c98a3f,Antique car\ntest/182b97064a1ba78b,Residential area\ntest/182c0df69b3f1fa9,Luxury vehicle\ntest/182c0e42f11053ee,Compact car,Luxury vehicle,Antique car\ntest/182c66d744f0f930,Luxury vehicle\ntest/182c92adb510f958,Luxury vehicle,Performance car\ntest/182eece372d89e0a,Compact car\ntest/182f32a7e06361bd,Siberian husky\ntest/182f8c415e97b719,Military vehicle\ntest/182fefc4b920b424,Performance car\ntest/183292adab325198,Bovine\ntest/183585b882c7dafc,Sport utility vehicle\ntest/1835e9a99b43823a,Compact car\ntest/183a6bef0be4de52,Road cycling\ntest/183ff22bb455d82f,Lawn game\ntest/18430e8ab2e5c1ae,Angling\ntest/184433993225f065,Whiskers\ntest/18450a5d1f230be9,Vegetarian food\ntest/1845503f52d15d0e,Close-up\ntest/1845e3e71e482d62,Luxury vehicle,Antique car\ntest/1849895bb9eb153a,Soba\ntest/184ac1358bd6e599,Black-and-white\ntest/184affca053776ea,Extreme sport,Wind wave\ntest/184b0a617ba375bb,Gun turret\ntest/184c820ada55747d,Nest box\ntest/184d3f9866e529c6,Cookies and crackers\ntest/184f877f71e05873,Team sport\ntest/1850d0e51705ac0d,Black-and-white\ntest/18519499b1b03986,Antique car\ntest/1851e76f3b9a8d30,Close-up\ntest/18534b1fd0a0dd43,Senior citizen\ntest/185429f3cb22d4b3,Luxury vehicle,Performance car\ntest/185441e091ac22fa,Compact car,Antique car\ntest/1855ef8935d980da,Luxury vehicle\ntest/185724a040c74eaf,Luxury vehicle,Performance car\ntest/18584e1a058ee8de,Jeans\ntest/1858f05255e4bf62,Motorcycling\ntest/185989fbdebf529c,Whiskers\ntest/185b9e32bd08e26c,Blond\ntest/185c781e259f22df,Computer speaker\ntest/185fa5de90d26d79,Luxury vehicle,Performance car\ntest/1860ca756579258f,Rural area,Bovine\ntest/18618e468c9683f6,Antique car\ntest/1861fd6106562f0a,Luxury vehicle\ntest/186761841e01f9c7,Halter\ntest/18676d9a50f96b4a,Close-up\ntest/186859bf9a89b4af,Arcade game\ntest/18695ae26d89f270,Black-and-white\ntest/186bc24a4e455a9e,Molluscs\ntest/186bf789a984fe07,Close-up\ntest/186cedc2546345e5,Fried food\ntest/186e35aafc8a0d42,Rural area\ntest/18732493c7da2b3d,Antique car\ntest/1875e60f7a735345,Close-up\ntest/1876d48e55183f74,Compact car\ntest/187826aaeb9d38d9,Arcade game\ntest/1879109a1567d1c8,Arcade game\ntest/18793068c9b6f934,Aircraft engine\ntest/187cc1423e0db692,Sport utility vehicle\ntest/187db17be13bdc8d,Antique car\ntest/188072da22a1ac67,Jeans,Barechested\ntest/1881aaa3cb54d23b,Luxury vehicle\ntest/18831919db92f3a7,Military person\ntest/1883dcb8108c0655,Combat sport\ntest/1884e2efce51cbbb,Erinaceidae\ntest/18850236a98ac831,Antique car\ntest/1889ae54b4298f7a,Boating\ntest/188a257fdf123a36,Gumbo\ntest/188ad6178236bf74,Coral reef fish\ntest/188aebbcb4398ce8,Black-and-white\ntest/188af3229f0c762a,Antique car\ntest/188b03e4ea34c5e1,Horse racing,Equitation,Equestrianism\ntest/188cb94cccd06616,Portrait photography,Close-up\ntest/188e6b8e67624ddc,Aerial photography\ntest/188fcb471d077f6b,Ball game\ntest/18902b332eb8c624,Extreme sport\ntest/18982022f280a52b,Luxury vehicle,Performance car\ntest/1898f5c850f25d16,Luxury vehicle,Performance car\ntest/189ba219080a70f6,Firearm\ntest/189f2b0b825a651d,Horse racing\ntest/189faddbdffef535,Sport utility vehicle\ntest/18a2a984e7de0d35,Plant stem\ntest/18a3714d5af4f17b,Close-up\ntest/18a395740c0c52a7,Antique car\ntest/18a3d98da46ed185,Ball game\ntest/18a4677df68197c2,Compact car,Luxury vehicle\ntest/18a4e7f15fef686e,Luxury vehicle,Antique car\ntest/18a80a7f980f7250,Compact car\ntest/18a8419272191128,Close-up\ntest/18a99af900a6cc0c,Digital camera,Black-and-white\ntest/18aacac3898d464e,Rural area\ntest/18ac5e9bfbe752d7,Primate\ntest/18ac79e24348d6f7,Galleon,Frigate\ntest/18af6c9a5da97063,Luxury vehicle\ntest/18b30e5de9201c54,Compact car\ntest/18b552a1dee2ee13,Black-and-white,Whiskers\ntest/18b74e7f2a75e271,Sport utility vehicle\ntest/18b83cf883a7f1eb,Luxury vehicle,Performance car\ntest/18bd7a6b5c618058,Compact car\ntest/18bdee7348320cfc,Luxury vehicle\ntest/18bed2a09181a499,Plant stem\ntest/18c0a74f5f72e5f4,Antique car\ntest/18c0f42f042b1e52,Black-and-white\ntest/18c2a760d9194a75,Wind wave\ntest/18c394d63b0445c5,Luxury vehicle,Performance car\ntest/18c3df8325f43647,Military person\ntest/18c3ffeca497c0d5,Stemware\ntest/18c620982f888c5d,Aerial photography\ntest/18c68265cf55300c,Fried food\ntest/18c6916fd88b5280,Black-and-white,Close-up\ntest/18c6abfa5511721d,Jeans\ntest/18c87dc29bf6439b,Perching bird,Close-up\ntest/18ccb91b950314a5,Combat sport\ntest/18cd6cdfef304c9b,Performance car\ntest/18cebb5bb472fb6d,Floristry\ntest/18d0c3a274e257dc,Compact car,Luxury vehicle\ntest/18d0ec111e3dd228,Solar energy\ntest/18d333ba8e2e3dd7,Antique car\ntest/18d38b3414b8145a,Luxury vehicle\ntest/18d412aba3d2ef56,Luxury vehicle,Antique car\ntest/18d43aaed0d378a4,Church bell\ntest/18d6032b6f706057,Perching bird\ntest/18d6bb96f024127c,Lawn game\ntest/18d796c3b576a969,Portrait photography\ntest/18d810db85567286,Rural area\ntest/18d84198b563152b,Church bell\ntest/18d962c7db9f1953,Jeans,Equestrianism\ntest/18da8947813f35e3,Vegetarian food,Falafel,Fried food\ntest/18dcbc544ca2ae8f,Horse and buggy\ntest/18de08de5f689381,Black-and-white\ntest/18de0c150fd7672c,Luxury vehicle,Performance car\ntest/18dfbab066702204,Close-up\ntest/18e05a280e5f0edd,Arcade game\ntest/18e164b534a238ea,Compact car\ntest/18e1eb2d2d2e1042,Amphibian\ntest/18e1fe880d899997,Compact car\ntest/18e5dbb67158438c,Halter\ntest/18e672debeeed02d,Inflatable\ntest/18e6c85767ef9904,Boating\ntest/18e7cc9637d1820b,Extreme sport,Divemaster,Scuba diving,Underwater diving\ntest/18ea670169234b7c,Arthropod,Close-up\ntest/18edfd2eff60217a,Scaled reptile\ntest/18ee36d71fc38d01,Performance car\ntest/18ee9613fa5c24d3,Junk food\ntest/18ee9c56590461f7,Whiskers\ntest/18eedb7aeb04344a,Ale\ntest/18ef3c3b44398222,Team sport\ntest/18efb8770f3fa0e1,Lifebuoy\ntest/18f2af57c9d05ddc,Bird of prey\ntest/18f2dee19ab7b491,Chordophone\ntest/18f639da1166edbf,Close-up,Chordophone\ntest/18f670783fe2bf90,Team sport\ntest/18fabcc80e6073a1,Luxury vehicle\ntest/1900615aeb71fa6d,Luxury vehicle,Performance car\ntest/19014dbdfdd675af,Close-up\ntest/19026b4f50e9e85c,Whiskers\ntest/1903334c73ad3c52,Luxury vehicle\ntest/1903b0fc6ec5e7a8,Boating\ntest/19048e993e624c9b,Auto racing,Touring car\ntest/1904fdee2aee797d,Close-up\ntest/190678c040e3d62c,Fried food\ntest/190789f89dc52d70,Bovine\ntest/1907aaf46dbc094e,Black-and-white,Medical imaging\ntest/19080decd815f25e,Close-up\ntest/1908b811b1ccaa12,Close-up\ntest/190a2c84dec938bf,Equitation,Equestrianism\ntest/190caf9eeec9b058,Floristry\ntest/190d1af3cb805f47,Orator,Public speaking\ntest/190fd4843bdfe76a,Boating\ntest/190fe8c9c849e291,Close-up\ntest/1910dc141b9651c9,Steamed rice\ntest/191288636ea17064,Cosmetics\ntest/1912c6cccf53b30f,Arthropod,Close-up\ntest/1912e83ff784d438,Plant stem\ntest/1913d60e4311e419,Motorcycling\ntest/1915170e60429f44,Extreme sport\ntest/19168fdacc0ae4c2,Hound\ntest/19183e8d6bee2d54,Nordic skiing\ntest/1919d4b8a22736a6,Luxury vehicle\ntest/191b02f379ef8020,Flatbread,Junk food\ntest/191cb144149ec3ed,Sport utility vehicle\ntest/191cf82c28cc4e0a,Middle ages\ntest/191cfce3b194233f,Boating\ntest/191f0c64646ceabb,Luxury vehicle,Sport utility vehicle\ntest/19231f744490f9ef,Amphibian\ntest/19236c7246a7c659,Military vehicle,Black-and-white,Sport utility vehicle\ntest/1924a03e00fd1c4d,Compact car\ntest/1928189562dc619a,Close-up\ntest/192c104f856407e9,Performance car\ntest/192c3e482444f853,Ball game,Team sport\ntest/192eef454ad7aad5,Luxury vehicle,Performance car\ntest/192f201f096577b5,Compact car,Antique car\ntest/192fdfb24f14e845,Glacial landform\ntest/192fe9516d605295,Microcontroller,Breadboard\ntest/193128b2e2c9e305,Molluscs\ntest/193400c3baa55fd1,Ball game,Team sport\ntest/1934433c56fef022,Sailboat racing\ntest/193445837185b78e,Procyonidae,Whiskers\ntest/1934acc3a7bae52d,Fried noodles\ntest/19367d3a04a77389,Antique car\ntest/19387757d5d7d04b,Scaled reptile,Close-up,Anole\ntest/1938df647ef1fd2a,Monoplane\ntest/193b1c641b7d904a,Cosmetics\ntest/193c3b1480a99fb2,Flatbread\ntest/193df57341b61383,Black-and-white\ntest/193f3b1d54c75033,Milky way\ntest/193f8bde1ef795e7,Domestic short-haired cat,Whiskers\ntest/19407c3be5902c49,Auto racing\ntest/19430aa55fce2ef3,Whiskers,Domestic rabbit\ntest/194344716c9b7d5e,Antique car\ntest/19456dc8fd5d5e46,Perching bird,Close-up\ntest/194b4aebce687554,Luxury vehicle\ntest/194c0796217cb704,Whiskers,Domestic rabbit\ntest/194f812bc0f9dceb,Herding\ntest/195088533bfc38e7,Ball game\ntest/1950d33f2dfde69d,Ball game\ntest/19521245e8ecc1ee,Nordic skiing\ntest/1957b2ed3b8bc682,Arthropod,Close-up\ntest/19591d5b5d1d9dc9,Antique car\ntest/195996109e06df38,Luxury vehicle,Performance car\ntest/195b78b640eb81f1,Combat sport\ntest/195bd955b9f2dce9,Portrait photography,Close-up\ntest/195f696e87b53772,Close-up,Whiskers\ntest/195fed6b684c89ee,Athletic shoe,Outdoor shoe\ntest/1961fa651cfeb926,Close-up\ntest/196489de46bc6431,Scaled reptile\ntest/196a4797ef8575cb,Performance car\ntest/196ac8a365bc3f48,Close-up\ntest/196c2272f6b38b37,Sport utility vehicle\ntest/196de82ff3ad6696,Coral reef fish\ntest/196eadadf8bbce12,Extreme sport\ntest/196ef44480b7b304,Assault rifle,Firearm\ntest/196fd7c76d14ea82,Aircraft engine\ntest/1973c97e061970e9,Luxury vehicle\ntest/197576630912def6,Luxury vehicle,Antique car\ntest/1979192da02a1441,Brochette,Yakitori\ntest/197ce01c327681dc,Close-up\ntest/19845b7c9bb1f9fc,Plant stem\ntest/19853857c2bda175,Leaf vegetable\ntest/1985c90d978080b0,Compact car\ntest/1986f7d6b549327c,Close-up\ntest/198b358e894561c0,Equestrianism\ntest/198e059fec5e0e25,Fried food\ntest/198e70dc30eca5e6,Ball game,Team sport\ntest/198f7a195d94d491,Performance car\ntest/1990471fd6461402,Roman temple\ntest/1991eb178640cc36,Angling\ntest/1993005e3e47a261,Jeans\ntest/1996e1c01e09208e,Cookies and crackers\ntest/1998cd59c5ef0e02,Bratwurst\ntest/1999bc0cf02e127c,Auto racing\ntest/199b1ca3e5a1946d,Team sport\ntest/199c3aaca6ce7b59,Black-and-white\ntest/199c8ee7aa4dfc84,Black-and-white\ntest/199d0445a2d8c43c,Jeans\ntest/19a07cb24ab74dcc,Luxury vehicle\ntest/19a13bd48977752f,Wind wave,Barechested\ntest/19a596682cf64122,Rear-view mirror\ntest/19a724471009dada,Plant stem\ntest/19aa4b70ba7b0a68,Luxury vehicle,Sport utility vehicle\ntest/19ad73a14d324952,Ball game,Team sport\ntest/19af50899c23ac53,Compact car\ntest/19b008bd4ce8dcda,Medical imaging\ntest/19b0f0b1476f37c6,Ribs,Bratwurst\ntest/19b383918e412877,Plant stem,Close-up\ntest/19b5d71b5a5a72f7,Extreme sport\ntest/19b68201d2cac55b,Compact car,Sport utility vehicle\ntest/19b9be1b6e3b543a,Bird of prey\ntest/19bcee3dde24f11d,Ball game\ntest/19c11d975db7651c,Black-and-white\ntest/19c11f321bf2d476,Extreme sport,Gliding\ntest/19c4a0cc64449c25,Domestic short-haired cat,Whiskers\ntest/19c4f01ffeac476b,Extreme sport,Parachuting\ntest/19c5338d49c97e52,Domestic rabbit\ntest/19c66ebb43651480,Luxury vehicle\ntest/19ca3529055ec0e7,Rural area\ntest/19cabd37d8d61013,Electronic instrument,Electric piano\ntest/19cb2b801f2ffc2a,Luxury vehicle\ntest/19cb83f68238b004,Luxury vehicle\ntest/19cb85ad5350c107,Nordic skiing\ntest/19cd352493c1d29f,Combat sport\ntest/19ce47859fe84403,Ball game,Team sport\ntest/19ceb7c03c626f0f,Black-and-white\ntest/19d00d9e17c1f484,Double-decker bus\ntest/19d043a424db9b39,Rural area\ntest/19d0793040e595c9,Extreme sport,Divemaster,Scuba diving,Underwater diving\ntest/19d10e54089299bb,Compact car\ntest/19d4e28fbe354407,Inflatable\ntest/19dc3a8cbc4b90a9,Jeans\ntest/19decfe14e3b73b7,Close-up\ntest/19e234612551167b,Rural area\ntest/19e3305598e6400a,Luxury vehicle,Antique car\ntest/19e35624a55fc9c4,Vegetarian food\ntest/19e3dd12958445ac,Close-up\ntest/19e46a3441e5b141,Woodwind instrument\ntest/19eb8383ea718d57,Close-up\ntest/19ec294c724b8d10,Outdoor shoe\ntest/19ed10f087eb9b06,Domestic short-haired cat,Whiskers\ntest/19ef168d3eaa64e2,Ball game\ntest/19f0006cd6bf857a,Canola\ntest/19f1845c407ce846,Perching bird,Close-up\ntest/19f18af642afb573,Luxury vehicle,Performance car\ntest/19f33b3b7c4168ad,Black swan\ntest/19f38dda74288489,Vegetarian food\ntest/19f66f5ca3437e5c,Boating\ntest/19f6fcc3327556fe,Performance car\ntest/19f7080486f7c8a0,Bovine\ntest/19fa5f23271f8526,Ball game,Team sport\ntest/19fba38a11d125a6,Aerial photography\ntest/19fbff8d5ec1e4ac,Arthropod,Close-up\ntest/19fdc71102055d00,Close-up\ntest/19fdfa47315995c7,Close-up\ntest/19ffc3c0097eb030,Black-and-white\ntest/1a045056473e8261,Firearm\ntest/1a0462f6eb3b68bf,Arthropod,Close-up\ntest/1a04c4ed30eee935,Bovine\ntest/1a0734e1d6dad26d,Extreme sport\ntest/1a088883e69e1c47,Domestic short-haired cat,Whiskers\ntest/1a0892c41b625802,Coral reef fish\ntest/1a0c451b336bbe89,Luxury vehicle,Sport utility vehicle\ntest/1a0ddc415f959c99,Luxury vehicle\ntest/1a0e40ee0c385961,Domestic short-haired cat,Whiskers\ntest/1a0ff3a6d0d73f9e,Luxury vehicle,Performance car\ntest/1a0ff6c0abdda459,Gumbo,Steamed rice\ntest/1a1016b1c1a6fffb,Close-up\ntest/1a10628cb19bd4a1,Luxury vehicle\ntest/1a139fea13b1defd,Plant stem\ntest/1a1623e8a6a8d14a,Team sport\ntest/1a16c2bf594dc55d,Close-up\ntest/1a16f673052bbbfc,Close-up\ntest/1a1981b078e2d554,Blond,Close-up\ntest/1a1a89a8265a44a0,Close-up\ntest/1a1e1784e97b4e7c,Floristry\ntest/1a1e2226fd55e24b,Auto racing\ntest/1a1edba7eec08bbd,Compact car,Luxury vehicle,Antique car\ntest/1a1f29f419a0d590,Whiskers\ntest/1a1f557c804afc3a,Monoplane\ntest/1a1fb878502a9782,Aircraft engine\ntest/1a21202f785a6df7,Digital camera\ntest/1a21d2427de840c5,Barquentine\ntest/1a22f99a981ca26f,Portrait photography\ntest/1a2361a14bf163e7,Road cycling\ntest/1a276f9d83ecafaf,Extreme sport,Boating\ntest/1a28abc78b380600,Ball game,Team sport\ntest/1a28cf0ea88b8520,Luxury vehicle,Antique car\ntest/1a2a2ac059b13bf9,Jeans,Black-and-white,Cobblestone\ntest/1a2b0dd0dcf31379,Luxury vehicle\ntest/1a2d77e59fcd210a,Combat sport\ntest/1a30bc4b72bf51f2,Black-and-white\ntest/1a3291536cbc0f8c,Antique car\ntest/1a348041bf56a205,Black-and-white\ntest/1a34ebd033a29428,Touring car,Luxury vehicle,Antique car\ntest/1a363f9daddcac43,Digital camera\ntest/1a3938e06c1a9284,Blond\ntest/1a3a139801ca5d0a,Luxury vehicle,Antique car\ntest/1a3d511e5a350fb2,Close-up\ntest/1a3de849d42c3045,Lechon\ntest/1a41fdbf88f26b58,Black-and-white\ntest/1a427464f981a9ba,Ball game,Team sport\ntest/1a43f299ea762e77,Ball game\ntest/1a44f42dfe7e5a4f,Floristry\ntest/1a4714c094bce862,Computer speaker\ntest/1a48fd117e788139,Luxury vehicle,Antique car\ntest/1a491ad37db87764,Electronic instrument\ntest/1a4a6966a1e4cca2,Close-up\ntest/1a4fcccd5868871f,Luxury vehicle,Performance car\ntest/1a5043d058071166,Plant stem,Close-up\ntest/1a5312c9679fc85b,Plant stem\ntest/1a559942e5bdd1fc,Horse and buggy\ntest/1a55ceae86bd0205,Hound\ntest/1a57c9f6151c7fa1,Plant stem,Close-up\ntest/1a58d23ccfd25499,Machine tool\ntest/1a5b72476865532a,Close-up\ntest/1a5b8facdc9d607f,Luxury vehicle\ntest/1a5cb893af7b7257,Black-and-white\ntest/1a5f515be50a4b19,Close-up,Nile crocodile,Crocodilia\ntest/1a5f9439263855d9,Arthropod,Close-up\ntest/1a5ffa8417496b77,Athletic shoe,Outdoor shoe\ntest/1a64299a190a050f,Luxury vehicle\ntest/1a664371891db2eb,Luxury vehicle\ntest/1a675b0addf2d53f,Boating\ntest/1a698e1ae36aa478,Ramen\ntest/1a6d457c283fc0ff,Close-up\ntest/1a6f2e036ac09388,Portrait photography,Close-up\ntest/1a6f8d034513aaf5,Close-up\ntest/1a70014e04f0422b,Team sport\ntest/1a70e6cfc0030038,Aircraft engine\ntest/1a712bd3cb4fc1da,Hound\ntest/1a7203f7cdfe99a7,Close-up,Whiskers,Domestic rabbit\ntest/1a741ef128a60b47,Compact car\ntest/1a744d32b746c848,Amphibian,Scaled reptile\ntest/1a74fc2219db1a49,Ball game\ntest/1a75900918483d0d,Bird of prey\ntest/1a763572bc101798,Extreme sport\ntest/1a783a1ccae88c66,Luxury vehicle\ntest/1a7948af40273945,Compact car\ntest/1a79982956ddbfa9,Luxury vehicle\ntest/1a7a7a2aad92e5cd,Computer speaker\ntest/1a7a899988454c84,Equitation,Equestrianism\ntest/1a7aebd6509579b4,Arthropod\ntest/1a7c76c31198452d,Luxury vehicle\ntest/1a7cfce24ceaf96b,Antique car\ntest/1a7d01409409376a,Cosmetics\ntest/1a7dc66be1870153,Black-and-white\ntest/1a7e041d54ad896a,Close-up\ntest/1a7f82b933082d75,Ball game,Team sport\ntest/1a83f10460653416,Luxury vehicle\ntest/1a84b6a246fbfa02,Vegetarian food\ntest/1a85705da7b27d22,Close-up\ntest/1a869f39c61bb131,Luxury vehicle\ntest/1a885ee4bdb896ad,Firearm\ntest/1a888a8e477747b5,Luxury vehicle,Antique car\ntest/1a89795c35f89b6a,Close-up\ntest/1a8b9de0efe0270f,Coral reef fish\ntest/1a8c785e9acff5e7,Compact car\ntest/1a8d5cc70cc061a0,Portrait photography\ntest/1a8e42049a0f2712,Luxury vehicle,Performance car\ntest/1a8fa29b71a3660f,Monoplane,Aircraft engine\ntest/1a9344215f981140,Close-up\ntest/1a9a16cdd2f797c4,Auto racing\ntest/1a9a2b5799765af8,Aerial photography\ntest/1a9ab7e910d89c0a,Black-and-white\ntest/1a9b4abb67b7a7b4,Close-up\ntest/1a9c245885186b5b,Close-up\ntest/1a9c69e1cd2d41d8,Molluscs,Close-up\ntest/1a9ce8012788d22c,Siberian husky,Close-up\ntest/1a9d0ccdada0f2a7,Close-up\ntest/1a9ded32c7162733,Compact car\ntest/1a9e2a971958d988,Whiskers,Domestic rabbit\ntest/1a9efca0edeee490,Hound\ntest/1a9f816b290661a0,Close-up\ntest/1a9fd766e3406195,Compact car\ntest/1aa0058200a56fc2,Sport utility vehicle\ntest/1aa10fee2d5f8aa0,Extreme sport\ntest/1aa34c192bdcde36,Luxury vehicle\ntest/1aa3c928a4b63951,Equestrianism\ntest/1aa69ccac396f81b,Gumbo\ntest/1aa7786e191872a9,Vegetarian food\ntest/1aa8fc4f89a04c51,Compact car,Luxury vehicle,Antique car\ntest/1aac258107cef22b,Team sport\ntest/1aadfafad885f2a1,Nile crocodile,Crocodilia\ntest/1aaeaeea7890442a,Cookies and crackers\ntest/1ab15940e162299c,Ball game,Team sport\ntest/1ab1c9b7871ad8f4,Close-up\ntest/1ab25b7ca17b9f5e,Whiskers\ntest/1ab283bbe1714513,Compact car\ntest/1ab29c9f6680bc90,Luxury vehicle\ntest/1ab31b69206fbb64,Perching bird\ntest/1ab586408df2f061,Great white shark\ntest/1ab5d3b05ef07e9c,Cookies and crackers\ntest/1ab6a33dfc63552e,Vegetarian food\ntest/1ab914d97e771222,Arcade game\ntest/1abb56bb9394b068,Floristry\ntest/1abb76b981afa0c3,Luxury vehicle\ntest/1abdd18b5988ef40,Miniature Poodle\ntest/1abe88d03b643282,Headphones\ntest/1ac1b08ac7f416b1,Military vehicle,Luxury vehicle,Sport utility vehicle\ntest/1ac3455bd4d43962,Hair coloring\ntest/1ac42772f4e5081e,Domestic short-haired cat,Whiskers\ntest/1ac50a515382a1db,Falafel,Fried food\ntest/1ac514e97fb32c4c,Luxury vehicle\ntest/1ac9f65cb0a2f36a,Close-up\ntest/1acc579000367503,Heavy cruiser\ntest/1acce38f3bbf7219,Close-up\ntest/1acf0ade811c10bc,Close-up\ntest/1acffd2b8683edce,Luxury vehicle\ntest/1ad03c0542449be2,Figure skating\ntest/1ad26da6f2bce9a3,Luxury vehicle\ntest/1ad44b841eb5bed0,Extreme sport,Parachuting\ntest/1ad662910d8d6d5d,Horse and buggy\ntest/1ad8455c275fc955,Nuclear power plant\ntest/1adbac6adfec38b8,Nest box\ntest/1adc002bef1599cb,Floristry\ntest/1addf23792c6f3af,Jeans\ntest/1ade34f8ba871068,Rural area\ntest/1adea24879fd2136,Close-up\ntest/1ae1414fed2b4fce,Luxury vehicle,Antique car\ntest/1ae39a800dc79c01,Musical theatre\ntest/1ae6c0298fb4fccd,Close-up\ntest/1ae818ce669d076a,Miniature Poodle\ntest/1ae990d4e335aaf9,Luxury vehicle,Performance car\ntest/1aeb38f9f1598849,Leaf vegetable\ntest/1aebc9ff128ed6b2,Luxury vehicle\ntest/1aec1f0b94d01ca5,Close-up\ntest/1aed875e8be64acd,Close-up\ntest/1aeea7ce956b887e,Arthropod,Close-up\ntest/1aeebf46169f423b,Domestic short-haired cat,Close-up,Whiskers\ntest/1aef2196f8508302,Luxury vehicle\ntest/1aefbd0194192d18,Brisket,Ribs\ntest/1af1ef59d2891ae6,Road cycling\ntest/1af360e9af35c978,Molluscs\ntest/1af39790f0957012,Close-up\ntest/1af51178e84a4350,Close-up,Whiskers\ntest/1af65e8b45d92c88,Blond\ntest/1af752b509c87694,Residential area\ntest/1af87181bf79b4a7,Ball game,Team sport\ntest/1afb3bdc8e1ac06e,Close-up\ntest/1afbad5775ef2040,Close-up\ntest/1afc36bb97170dde,Luxury vehicle,Sport utility vehicle\ntest/1afcf554e94b4a0c,Close-up\ntest/1afcf89743755fce,Close-up\ntest/1afd15fa221e5a60,Plant stem\ntest/1afd5c38b4554cd2,Auto racing\ntest/1aff1100f881e7a6,Coral reef fish\ntest/1affa8ec02f9e2bf,Close-up\ntest/1b00d69489c291e4,Billiards,Billiard room\ntest/1b00dd518444b7f8,Domestic short-haired cat,Whiskers\ntest/1b0144b75a56f67f,Senior citizen,Close-up\ntest/1b02187c0fa53257,Milky way\ntest/1b03eba134d76885,Electronic instrument,Black-and-white\ntest/1b0423ff9bdff2b9,Extreme sport\ntest/1b060de9c578bf37,Black-and-white\ntest/1b07126014e9b208,Blond,Hair coloring\ntest/1b0a4107b3dd2a0a,Go-kart\ntest/1b0d19bef9f7997b,Jeans\ntest/1b0d1eb707a60b12,Shallot\ntest/1b0dffe793c8d02b,Wind wave\ntest/1b0e17f99c605f67,Chordophone\ntest/1b10af1479d027dc,Hound,Close-up\ntest/1b11b12e978fd74a,Vegetarian food\ntest/1b150bcc6898a21d,Jeans\ntest/1b153b4ebae85924,Close-up,Whiskers\ntest/1b161984a998d2ad,Drums\ntest/1b17f136f7561574,Portrait photography,Close-up\ntest/1b187e6873da7890,Aircraft engine\ntest/1b19b23b9e8cd7c8,Chordophone\ntest/1b1c45e2f77c97ec,Fried food\ntest/1b1e98b5f9cc8b50,Combat sport\ntest/1b1ec74ad2005f06,Ball game,Team sport\ntest/1b200c38fef9f3b2,Compact car,Luxury vehicle,Antique car\ntest/1b216de6f087f9c0,Luxury vehicle,Antique car\ntest/1b22124bf2b9ee28,Close-up\ntest/1b234a3e32c93406,Antique car\ntest/1b24dc7c665b9a71,Luxury vehicle\ntest/1b25b3c8f6bbda16,Portrait photography,Close-up\ntest/1b25e0ca8a528c27,Auto racing,Touring car,Performance car\ntest/1b26c78b6b5d7f25,Compact car,Luxury vehicle\ntest/1b26fc265b072033,Black-and-white\ntest/1b271e3dd1c01551,Milky way\ntest/1b29bc823433cc1e,Chordophone\ntest/1b29fddd487d9551,Jeans\ntest/1b2c77c4c7785762,Aircraft engine\ntest/1b2d94cfd2653a97,Boating\ntest/1b2dd0860d146823,Chordophone,Electric guitar\ntest/1b2e84b69b6dc01c,Luxury vehicle,Performance car\ntest/1b311a22d440a195,Compact car\ntest/1b3170a37d67e648,Jeans\ntest/1b3172fff052569c,Boating\ntest/1b32e788da559845,Jeans\ntest/1b3688c2f9bf3778,Compact car,Luxury vehicle\ntest/1b37aea12758f302,Drums\ntest/1b384ca6c9b5950f,Perching bird\ntest/1b3934c060e2fb34,Outdoor shoe\ntest/1b3ae58cb2adb9af,Electronic instrument\ntest/1b3b104c032ae8cb,Horse and buggy\ntest/1b3e506655b96551,Dishware\ntest/1b3edcc2f878666f,Antique car\ntest/1b3f6c0270871f72,Jeans,Luxury vehicle,Performance car\ntest/1b40a0b006ca32f0,Close-up,Plant stem\ntest/1b41dbf0c5fbb41b,Fried food\ntest/1b4217c6dc0e96b0,Road cycling\ntest/1b439ea34238b7c5,Ball game\ntest/1b43c7d518bafcdc,Bratwurst\ntest/1b43e01eb7e678e3,Arthropod,Close-up\ntest/1b4407c1406fca3f,Ball game\ntest/1b45ef606a021e2b,Cookies and crackers\ntest/1b4662c7e7361f92,Plant stem\ntest/1b46db55889c588c,Luxury vehicle\ntest/1b479c13bcb59942,Military vehicle,Sport utility vehicle\ntest/1b4b613a2b4ec3d0,Sport utility vehicle\ntest/1b4b82975296804d,Luxury vehicle\ntest/1b4cac62cc5a978f,Arthropod,Close-up\ntest/1b4d13dac6d9bb02,Compact car,Luxury vehicle,Antique car\ntest/1b4da2f18d18d98f,Plant stem,Close-up\ntest/1b4fb1ea450414d1,Flatbread\ntest/1b517ab485a16770,Bratwurst\ntest/1b518847be62a792,Antique car\ntest/1b51facadc9adbbe,Sport utility vehicle\ntest/1b528d7e2c671687,Close-up,Plant stem\ntest/1b5308a78f459cf5,Monoplane,Aircraft engine\ntest/1b5448ea9556d368,Luxury vehicle\ntest/1b54f10c3b429a41,Bovine\ntest/1b5c3d52b4ef1f5a,Aircraft engine\ntest/1b5fc175efef47ae,Aerial photography\ntest/1b634ed56943d0ad,Chordophone\ntest/1b635fbeb141de20,Close-up\ntest/1b642ac517ab10b5,Close-up,Scaled reptile\ntest/1b64ab538b22f095,Junk food\ntest/1b64c5112a5457b0,Luxury vehicle,Performance car\ntest/1b6508b00d183a30,Plant stem\ntest/1b65171463b431b6,Luxury vehicle\ntest/1b68702fff4df69a,Blond\ntest/1b6df57a6b470ccc,Luxury vehicle,Performance car\ntest/1b6e44e58c6b4ed0,Luxury vehicle\ntest/1b6ecb85482bfba7,Great white shark\ntest/1b70125d51112b8e,Luxury vehicle,Antique car\ntest/1b7076f650620e3e,Close-up\ntest/1b70fd05db10b18d,Locust,Close-up\ntest/1b721a0a469e90fe,Team sport\ntest/1b7b64081fd5e50a,Luxury vehicle\ntest/1b7c804d135420a9,Plant stem,Shallot\ntest/1b8119fc8be7e352,Cookies and crackers\ntest/1b826fdeaed2d55e,Close-up\ntest/1b849b67febda7fd,Digital camera\ntest/1b862211adb17351,Toy block\ntest/1b875eeea0f8a7bc,Compact car,Antique car\ntest/1b887d2232b01738,Military person\ntest/1b89712fe47f3b85,Close-up\ntest/1b89e8f39ffbbb44,Rural area\ntest/1b8d55cb449b81b0,Compact car\ntest/1b8e2948e1145831,Luxury vehicle,Antique car\ntest/1b8e65b36612b6ac,Hair coloring,Close-up\ntest/1b8eba711f3aed2d,Luxury vehicle,Sport utility vehicle\ntest/1b8efd65311c4854,Wind wave\ntest/1b9046b55107bedc,Performance car\ntest/1b914966aa34c638,Luxury vehicle,Performance car\ntest/1b92a2b422100e41,Team sport\ntest/1b92adf7b16d4fe6,Black-and-white,Close-up\ntest/1b98df4eee100b8a,Close-up\ntest/1b9a532bc44e9e32,Compact car\ntest/1b9b0207526aa058,Luxury vehicle,Performance car\ntest/1b9bb5575bd2aa27,Auto racing\ntest/1b9c7e12abbe940e,Roller skating\ntest/1b9ce9487064caa6,Luxury vehicle,Antique car\ntest/1b9cecca4c524a02,Forklift truck\ntest/1b9f2531caa96c63,Perching bird\ntest/1b9f6483abdde330,Extreme sport,Parachuting\ntest/1ba01a316a9d1480,Black-and-white\ntest/1ba13c0562599248,Electronic instrument\ntest/1ba1e12a719566f7,Go-kart\ntest/1ba2b58a77a0349b,Compact car,Antique car\ntest/1ba2b5c1287de1cc,Perching bird\ntest/1ba4133211378554,Whiskers\ntest/1ba4c6e2ce46dd91,Domestic rabbit\ntest/1ba57a1a0053c096,Luxury vehicle,Antique car\ntest/1ba9927eacb91077,Auto racing\ntest/1baa67b7a05a87c6,Bovine\ntest/1badb52e6037ff7f,Jeans\ntest/1bae65c688644728,Toy block\ntest/1baea94bc473b7e9,Plant stem\ntest/1baee04d5caba88c,Compact car,Luxury vehicle,Antique car\ntest/1baff11a188d7352,Outdoor shoe\ntest/1bb0563156fc0ede,Compact car\ntest/1bb06313eeb015fd,Antique car\ntest/1bb3bba414e9145c,Close-up\ntest/1bb3e0016b62a4cc,Molluscs\ntest/1bb4880cada742d1,Luxury vehicle,Performance car\ntest/1bb4f4b7da96b819,Luxury vehicle\ntest/1bb56aa38aff70b8,Compact car\ntest/1bb650046adf915c,Blond,Portrait photography\ntest/1bb8314389bbd2b5,Combat sport,Sumo\ntest/1bbd0159453195e7,Compact car,Luxury vehicle\ntest/1bbf793bebaf6ccb,Black-and-white\ntest/1bc029dc91911dee,Residential area\ntest/1bc0be8c5ab3d9f1,Auto racing\ntest/1bc28ea035406cf1,Vegetarian food\ntest/1bc4a429e14d0f45,Luxury vehicle,Antique car\ntest/1bc583f5f7487251,Close-up\ntest/1bc85ab5c8284cc2,Scaled reptile\ntest/1bc87374987751ad,Domestic short-haired cat,Whiskers\ntest/1bc9b4f39ed2474c,Aerial photography\ntest/1bcb602c6ccada9d,Ball game\ntest/1bce1d7719ba8bcc,Black-and-white\ntest/1bcfaae59625d70f,Antique car,Performance car\ntest/1bd2246171c68b42,Luxury vehicle\ntest/1bd4bae8ae384f19,Shooting range,Firearm\ntest/1bd594979b6d22bc,Close-up,Plant stem\ntest/1bd5ff88338e9b94,Compact car\ntest/1bd698636829d1f3,Plant stem\ntest/1bd7f392b25e0eac,Middle ages\ntest/1bd982243d02348c,Combat sport\ntest/1bd9a8c2a20ac1fd,Sport utility vehicle\ntest/1bda0f875f95f7fe,Compact car,Antique car\ntest/1bdb699e7952718f,Firearm\ntest/1bdd43df7eca88c3,Equitation,Equestrianism\ntest/1bdea3deee2129d9,Boating\ntest/1be0020212fe7a1e,Antique car\ntest/1be6c0c872aa4191,Wind wave\ntest/1be7602226017388,Antique car,Luxury vehicle,Performance car\ntest/1be81ca653fca78b,Luxury vehicle,Performance car\ntest/1be98808c983a9bb,Close-up,Whiskers\ntest/1bed86a9ae67fff5,Close-up\ntest/1bee31075f7f5397,Black-and-white\ntest/1beed0e4b19a79ee,Close-up\ntest/1bf0a18dbf3408a8,Scaled reptile,Close-up,Anole\ntest/1bf2fb72703f24c9,Outdoor shoe\ntest/1bf38d56ef2b80ac,Luxury vehicle,Sport utility vehicle\ntest/1bf38ea41d7b6220,Luxury vehicle,Sport utility vehicle\ntest/1bf3a8b02b207d32,Compact car,Antique car\ntest/1bf4530e4fd618c6,Luxury vehicle,Performance car\ntest/1bf46c40cf838a98,Boating,Rowing\ntest/1bf51ec72143c3f7,Compact car\ntest/1bf7b18e9816e5c6,Ball game\ntest/1bf81d7459bb43af,Close-up\ntest/1bf9895846159376,Whiskers\ntest/1bfa1d83da378171,Outdoor shoe\ntest/1bfa54e4ac9b4295,Extreme sport,Parachuting\ntest/1bfca20e4fb1d6f2,Fried food\ntest/1bfd3443b3afebfc,Auto racing,Go-kart\ntest/1bfd365238e1fedd,Bovine\ntest/1bfe02223a38116b,Luxury vehicle,Antique car\ntest/1bff8f72211a9c0f,Bovine\ntest/1c0456b1d3d0c5a4,Briefs\ntest/1c07f2fd7d471fcf,Luxury vehicle\ntest/1c07f8e9f7f4b2f3,Canola\ntest/1c090df2d69ed603,Black-and-white,Woodwind instrument\ntest/1c09d76fb3463793,Auto racing\ntest/1c0af6c5eee6f70b,Black-and-white\ntest/1c0c9989e9816923,Billiards\ntest/1c0e6655e424bc9d,Khinkali,Pelmeni\ntest/1c0f8687e2a0239b,Fried food\ntest/1c0fda423b25bf78,Ale\ntest/1c10a1394edcb76f,Team sport\ntest/1c10b33281fe583e,Extreme sport\ntest/1c114bcd0a808e97,Close-up\ntest/1c12b5481d2c041d,Luxury vehicle,Antique car\ntest/1c169ca0ae17a9bd,Goats,Bovine\ntest/1c16ce8b64639ada,Perching bird,Close-up\ntest/1c172d065cc39b11,Modern dance,Musical theatre\ntest/1c1b3e6cd2823c99,Close-up\ntest/1c1b794aebe623ea,Luxury vehicle,Performance car\ntest/1c1cad24cbe2fed3,Black-and-white,Luxury vehicle,Antique car\ntest/1c1cc705d42d16b6,Solar energy\ntest/1c1ebe134bbb76cc,Milky way\ntest/1c1f957496c8d830,Chordophone\ntest/1c20c6045d4c91c5,Close-up\ntest/1c223f7fc1478f86,Auto racing,Performance car\ntest/1c229f344ffe2700,Pork chop,Beef tenderloin\ntest/1c22deed66734f8a,Coral reef fish\ntest/1c232927a506a47e,Antique car\ntest/1c23595ed3431ceb,Childbirth\ntest/1c24c6a46afe1a57,Firearm,Shooting range\ntest/1c27353042d7a2bf,Floristry\ntest/1c2961e7cd76b04c,Luxury vehicle\ntest/1c2b0838f50a0007,Hunting knife\ntest/1c2c5c287c0df601,Blond\ntest/1c2d2340667e189a,Close-up\ntest/1c2d99cb643b8b10,Luxury vehicle\ntest/1c3003f6082ce8ea,Compact car,Luxury vehicle,Antique car\ntest/1c329e4f78d936b4,Blond,Close-up\ntest/1c3546ef6c5ab43e,Close-up\ntest/1c3a8ef37a9d3c65,Compact car,Sport utility vehicle\ntest/1c3cc52a97a52bc4,Roman temple\ntest/1c4480ad669f2a7d,Black-and-white\ntest/1c44ba78a3d741d0,Cookies and crackers\ntest/1c44be5c36ef95a1,Close-up,Whiskers\ntest/1c462da2b9bee128,Assault rifle,Firearm\ntest/1c46ac9b28859b12,Athletic shoe,Outdoor shoe\ntest/1c48e607b2107a3c,Scaled reptile\ntest/1c4a74e55fa0c04f,Luxury vehicle,Performance car\ntest/1c4aefca80b40118,Ball game\ntest/1c4afcabc8aab2a6,Luxury vehicle,Antique car\ntest/1c4caaaf495355f6,Jeans\ntest/1c4d2af59436a4d4,Luxury vehicle\ntest/1c4d697a82c13e13,Antique car\ntest/1c4d76f4dc92008d,Arthropod\ntest/1c4d94b3d35b22d3,Performance car\ntest/1c5161f870466236,Vegetarian food\ntest/1c5406f7bbd7f3ee,Luxury vehicle,Antique car\ntest/1c549b3a48274224,Inflatable\ntest/1c554515caf6f365,Extreme sport,Sport climbing\ntest/1c5547c670a5bc7d,Luxury vehicle\ntest/1c5aca126a0d7d59,Close-up,Whiskers\ntest/1c5af1d984d53333,Extreme sport\ntest/1c5cbf7248e2c7f9,Performance car\ntest/1c5d24b5bf763587,Compact car\ntest/1c5f8abde16868f6,Steamed rice\ntest/1c5fc11fd05f3534,Firearm\ntest/1c618757d7fbe472,Middle ages\ntest/1c61fb555751a174,Sledding\ntest/1c66b8c9eec3c766,Headphones\ntest/1c66de627dbe1ed6,Khinkali\ntest/1c66e5c3344a6997,Luxury vehicle,Performance car\ntest/1c6821844a923104,Performance car,Antique car\ntest/1c68a2ddcfd55e56,Close-up\ntest/1c68b3d8443dc722,Extreme sport,Boating\ntest/1c68d9eb5a921a43,Bratwurst\ntest/1c69b3460aa9f8c1,Compact car\ntest/1c6a7fdd2ee9af29,Machine tool\ntest/1c6aea767dce8384,Luxury vehicle,Antique car\ntest/1c6b3019abf15d4d,Breadboard\ntest/1c6b578ed548a788,Close-up,Scaled reptile\ntest/1c6dd42d52ea9ece,Portrait photography\ntest/1c728b61a17bb2cf,Rural area\ntest/1c74943b6f40af0f,Floristry\ntest/1c768a3e60a54b40,Auto racing\ntest/1c769448097c9d20,Sailboat racing\ntest/1c7acf5e900ba294,Aircraft engine\ntest/1c7b169c065e3753,Erinaceidae,Whiskers\ntest/1c81d0d9adcca8ea,Luxury vehicle,Performance car\ntest/1c83c6fbfff6f768,Orator\ntest/1c84bdd61fa3b883,Close-up,Floristry\ntest/1c8549f8325a8161,Residential area\ntest/1c86b50515bd977c,Compact car,Luxury vehicle,Antique car\ntest/1c86ea4f38b2daf3,Electronic instrument\ntest/1c89d2ef5fd62c42,Luxury vehicle,Performance car\ntest/1c8a4ceac03d980d,Residential area,Aerial photography\ntest/1c8a75de9611dfb2,Luxury vehicle,Performance car\ntest/1c8b4a6c7bf9a95d,Barechested\ntest/1c8d8cbc653e1ff3,Black-and-white\ntest/1c8f0176f39861be,Extreme sport,Gliding\ntest/1c90709ccd437067,Hurdling\ntest/1c91ef015c1e2a06,Luxury vehicle,Antique car\ntest/1c94f14cac958357,Bovine\ntest/1c961e6fd1f04e8e,Close-up\ntest/1c98ac0bb8966391,Modern dance\ntest/1c9a5b9c3cf42d03,Luxury vehicle\ntest/1c9a8cce764c7e53,Jeans,Newsagent's shop\ntest/1c9c0fb1020119b3,Blond,Hair coloring\ntest/1c9cfbc2631a37c2,Calabaza\ntest/1c9d007faef3411e,Antique car\ntest/1c9d66ae8d351518,Siberian husky\ntest/1c9f9e65645b144e,Fried noodles\ntest/1c9f9e769b02d785,Roller skating\ntest/1ca066a1376c51eb,Luxury vehicle,Performance car\ntest/1ca215c164af921d,Antique car\ntest/1ca37fb497926764,Classical sculpture\ntest/1ca479cd4b1dbfd3,Close-up\ntest/1ca8584cf2d06c95,Woodwind instrument\ntest/1ca9a1665408a7e8,Whiskers,Domestic rabbit\ntest/1caa2e4a5368d83b,Vegetarian food\ntest/1cad43f78ea99a4d,Plant stem,Close-up\ntest/1cad441b3754d959,Luxury vehicle,Performance car\ntest/1cada60c1c46c71c,Extreme sport\ntest/1cb086da2d0f605f,Domestic short-haired cat,Whiskers\ntest/1cb176b5ab13ff99,Electronic instrument,Headphones\ntest/1cb4e916a7b86339,Khinkali,Pelmeni,Jiaozi\ntest/1cba79ad0f04cbf5,Close-up\ntest/1cbac8c8f5628181,Ball game\ntest/1cc1f002042689a0,Luxury vehicle\ntest/1cc2e24499046e3e,Close-up,Chordophone\ntest/1cc53c8e090ba2f1,Ball game,Team sport\ntest/1cc6326c2e3f71a8,Residential area\ntest/1cc65f309cf46775,Monoplane\ntest/1cc72accbf66b279,Ball game,Team sport\ntest/1cc76415fe0c05f1,Steamed rice\ntest/1cd1a4dcd90e3066,Domestic short-haired cat,Whiskers\ntest/1cd43ad4470349a4,Bonbon\ntest/1cdacb276ec4ec2c,Boating\ntest/1cdb03c91439e267,Auto racing,Compact car,Antique car\ntest/1cdbdae8949c6a87,Close-up\ntest/1cdccd0244bd847d,Luxury vehicle\ntest/1cde9249b9dd72bd,Luxury vehicle\ntest/1cdef0c47eb885f9,Performance car\ntest/1cdf54e23522721f,Close-up,Whiskers\ntest/1cdf76b257d39bbd,Miniature Poodle\ntest/1ce08f347f58bd3d,Arthropod\ntest/1ce1aa61a150ef37,Arthropod,Locust\ntest/1ce2fb02e7f57be7,Compact car,Antique car\ntest/1ce2fffb1f89d127,Performance car\ntest/1ce44aaf9f5a05eb,Senior citizen,Close-up\ntest/1ce5b88b49f25281,Blond,Hair coloring\ntest/1ce724435658dd46,Musical theatre\ntest/1ce90baf12b5a1f7,Black-and-white\ntest/1ce9289474737824,Arthropod,Close-up\ntest/1cea6438c8ecea44,High-speed rail\ntest/1ceadea8c27e235c,Chordophone\ntest/1cec8021ad41e6ea,Close-up\ntest/1cef3ac8d7ce1a00,Residential area\ntest/1cf189b134324734,Compact car\ntest/1cf287be0ecf5b99,Luxury vehicle,Performance car\ntest/1cf4d247c70fcb94,Equestrianism\ntest/1cf579a3c09864cf,Luxury vehicle,Sport utility vehicle\ntest/1cf85089a6963a1e,Black-and-white\ntest/1cf8fa4c1c6bab19,Close-up\ntest/1cf995e483f70b0b,Close-up\ntest/1cfa108d4739d800,Ball game\ntest/1cfc18d13e57ce38,Black swan\ntest/1cfdb83e25e9dc00,Arthropod,Close-up\ntest/1cfdf4f39aea80e2,Luxury vehicle\ntest/1cfe9a7dcc2517ed,Auto racing,Go-kart\ntest/1cff3478f4c24ad2,Ball game,Team sport\ntest/1d0319610f9a200c,Middle ages\ntest/1d04a0ab760b66c6,Public speaking\ntest/1d051c2560602703,Antique car\ntest/1d055090c5631b0d,Firearm\ntest/1d058090eacb29aa,Athletic shoe,Outdoor shoe\ntest/1d073276028b90ac,Close-up\ntest/1d081010ddcde001,Close-up\ntest/1d0c214d00d201a5,Peafowl\ntest/1d0cdeabd5c8c909,Close-up\ntest/1d0d7ec692725ef1,Antique car\ntest/1d120be482b07b6c,Vegetarian food\ntest/1d126b60a5bce773,Pelmeni,Jiaozi\ntest/1d159178f4866bb4,Arthropod\ntest/1d192991d03f8e3d,Luxury vehicle,Performance car\ntest/1d1a0d1983f0fe38,Coral reef fish\ntest/1d1b30322990d0cd,Close-up\ntest/1d200c7187275bfd,Luxury vehicle\ntest/1d25fcbd3f263df2,Compact car\ntest/1d264faae97aa3d8,Residential area\ntest/1d265f42ca96c4c8,Equitation,Equestrianism\ntest/1d35405b6eabcab3,Aircraft engine\ntest/1d380784a165a5fe,Plant stem,Close-up\ntest/1d38469f2ce5fca2,Arthropod,Close-up\ntest/1d392dd3ea6a6d31,Antique car\ntest/1d3ace252d1f8c5c,Sport utility vehicle\ntest/1d3c2578e51aba85,Antique car\ntest/1d3d19b89e87072e,Antique car\ntest/1d3d75f8a62c89cb,Junk food\ntest/1d3e9a2ec8e35c09,Luxury vehicle\ntest/1d3f5efeec856fc1,Luxury vehicle\ntest/1d3f8a7b5c94e736,Road cycling\ntest/1d41eff341eeda5c,Close-up\ntest/1d427f709b2f5e21,Hair coloring\ntest/1d4349a1d5d56eef,Road cycling\ntest/1d4389e84eba2180,Milky way\ntest/1d4614baaa513506,Galleon\ntest/1d4856e314afee3f,Boating,Rowing\ntest/1d4890552a5b20df,Barechested\ntest/1d491ee48b02b4b5,Outdoor shoe\ntest/1d49edb100fe8acf,Extreme sport,Wind wave\ntest/1d4b07c80c2b83ab,Luxury vehicle,Performance car\ntest/1d4b48d226731c8a,Domestic rabbit\ntest/1d4c21d7d30793ea,Floristry\ntest/1d507080489d381d,Motorcycling\ntest/1d5179fd7bea6cc4,Wind wave\ntest/1d53b6e42c47cd41,Gumbo\ntest/1d551fc25fe3acf3,Black-and-white\ntest/1d5797f281d145b9,Fire apparatus\ntest/1d58ac3223120699,Luxury vehicle,Performance car\ntest/1d59db2495553468,Great white shark\ntest/1d5aeaec241c8266,Luxury vehicle\ntest/1d5ddee6d8479416,Peafowl\ntest/1d5ed362920a7043,Perching bird\ntest/1d5f2b7764f7444b,Brochette\ntest/1d5f4c873ded89bb,Aircraft engine\ntest/1d617e33b2974afc,Luxury vehicle\ntest/1d6596b7c8bfbfde,Close-up\ntest/1d668c3b72fb36f8,Jeans\ntest/1d678efef655d9c7,Luxury vehicle\ntest/1d6a25ab670f1f91,Vegetarian food\ntest/1d6ae21865b18561,Close-up\ntest/1d6dc00cc2e2841e,Electronic instrument\ntest/1d702f38d3b4c870,Close-up\ntest/1d710682f5b9bd8c,Perching bird,Close-up\ntest/1d72502e1212d73b,Luxury vehicle\ntest/1d72539d0364e219,Rural area,Dog walking,Herding\ntest/1d7600135bd7a591,Steamed rice\ntest/1d77b91971816466,Boating,Rowing\ntest/1d79f37d9a69e1b6,Compact car\ntest/1d7a815c586ce595,Vegetarian food,Fried food\ntest/1d7b66777f82e0e1,Jeans\ntest/1d7c5eee6b4affeb,Close-up\ntest/1d7e201bfe3702ca,Flatbread\ntest/1d7f079d3d46a579,Barechested\ntest/1d803eb47f38ed3d,Luxury vehicle\ntest/1d805560723345de,Equitation,Equestrianism\ntest/1d858df24125b871,Luxury vehicle\ntest/1d86b11d5cbc4871,Boating,Rowing\ntest/1d8807f34b587464,Combat sport\ntest/1d88e4206828a09d,Compact car,Luxury vehicle,Antique car\ntest/1d89428c1bd8aba9,Arthropod,Close-up\ntest/1d8a17c5368ba73c,Backlighting\ntest/1d8b7f2172005bf3,Floristry\ntest/1d8da495240e9e46,Auto racing\ntest/1d8eb9f8529b960e,Black-and-white,Luxury vehicle\ntest/1d8f062cf52fa7b5,Ball game\ntest/1d8f32185903c9cf,Luxury vehicle,Performance car\ntest/1d94d19d0a97f045,Jeans,Outdoor shoe\ntest/1d986f774b7f471c,Performance car\ntest/1d9911ad9759473d,Halter\ntest/1d9d4566ff97773b,Leaf vegetable\ntest/1d9e92b61632d18e,Drums\ntest/1d9ebd65d760fe90,Close-up\ntest/1d9f479190d82379,Leaf vegetable\ntest/1da02284a267ee75,Luxury vehicle,Performance car\ntest/1da09bcb2672bfbf,Close-up\ntest/1da259c1cdd806c6,Firearm\ntest/1da35ffdf63ced85,Jeans\ntest/1da679e45c45da55,Auto racing\ntest/1da7fa0fa6f6b326,Luxury vehicle,Antique car\ntest/1dac133de44dbc48,Close-up\ntest/1dae6711336da425,Close-up\ntest/1db160c6663edde4,Luxury vehicle,Performance car\ntest/1db3bd4f87969269,Compact car,Luxury vehicle\ntest/1db3d6cf71d0ee41,Sport utility vehicle\ntest/1db696390ef25366,Performance car\ntest/1db772d6e4983f9e,Auto racing,Performance car\ntest/1dbc0b2e1bb52d73,Melee weapon,Hunting knife\ntest/1dbc0e0c76ea8c19,Molluscs\ntest/1dc158293f7f7258,Performance car\ntest/1dc2344f47b2fcae,Close-up\ntest/1dc9d232ddf0ff71,Luxury vehicle\ntest/1dc9ead0945515c7,Luxury vehicle,Sport utility vehicle\ntest/1dcc972cbbd940d7,Classical sculpture\ntest/1dcd139ac68671a4,Luxury vehicle,Antique car\ntest/1dcd40584ba42ba4,Jeans\ntest/1dcd5e68f3c4bccf,Close-up\ntest/1dcf37c0c965b592,Jiaozi\ntest/1dcf37ffd5ec1842,Amphibian,Scaled reptile\ntest/1dd046bb6945bfc1,Luxury vehicle\ntest/1dd15f3acc55fb6a,Close-up\ntest/1dd1b52b63606fd1,Luxury vehicle\ntest/1dd224155d69a29f,Luxury vehicle\ntest/1dd2fc897f9f645f,Luxury vehicle\ntest/1dd4631474d5479c,Cosmetics,Close-up\ntest/1dd640c244772274,Fire apparatus\ntest/1dd84208709ff226,Jeans\ntest/1dd89cc117854f46,Extreme sport,Wind wave\ntest/1dd9dbfdebecd24d,Plant stem,Close-up\ntest/1ddac2d5fa67989d,Black-and-white\ntest/1ddbdbe2e7677143,Cookies and crackers\ntest/1ddd41cfad3b431e,Leaf vegetable\ntest/1dde631324a6d329,Bovine\ntest/1dde6a0c162b1a50,Luxury vehicle\ntest/1ddfb71530f1f31f,Military vehicle\ntest/1de0ccc9b2cfefd6,Wind wave\ntest/1de0f24a996ecada,Auto racing\ntest/1de16946261e8a00,Black-and-white\ntest/1de34715d64d7c9e,Luxury vehicle\ntest/1de68e44f26155a7,Close-up\ntest/1de73ef98d641e2c,Team sport\ntest/1de882e70d3cff5a,Luxury vehicle\ntest/1de92d0cd2207627,Coral reef fish\ntest/1debee287981d8d0,Plant stem\ntest/1ded071c54b324eb,Digital camera\ntest/1ded29144b374691,Luxury vehicle\ntest/1ded8e6b919074f0,Bovine\ntest/1dedc41351ecf83d,Compact car,Antique car\ntest/1defca26846fce97,Shinto shrine\ntest/1df001d673af08b9,Antique car\ntest/1df14d9212601466,Luxury vehicle,Antique car\ntest/1df310adb3451f90,Compact car,Antique car\ntest/1df42c052402c8bc,Trampolining\ntest/1df62fe536aae753,Wallaby\ntest/1df8c7cd3532617b,Close-up,Plant stem\ntest/1df97312728313f5,Compact car\ntest/1dfa642fe9b12681,Compact car\ntest/1dfb41cf7f3a74ea,Hunting knife\ntest/1dfc784c3efb9ea8,Vegetarian food\ntest/1dfca24963687690,Floristry\ntest/1dfe513b4f76bc11,Primate\ntest/1dff599d60167c6c,Close-up\ntest/1e00dd7503bc1b3e,Luxury vehicle,Performance car\ntest/1e02653555e498ed,Luxury vehicle,Antique car\ntest/1e0464894c2802b7,Gumbo\ntest/1e055b20b9f4eb3a,Boating\ntest/1e05dff220863b72,Black-and-white\ntest/1e071b5dd50aa4f5,Luxury vehicle,Antique car\ntest/1e07548d6a2038fb,Bird of prey\ntest/1e08bb16ad49a13e,Auto racing\ntest/1e0e575e519ce6ba,Blond\ntest/1e0e5fdaa9492423,Close-up\ntest/1e0ea2c624b06f15,Auto racing\ntest/1e0f728da5a602f0,Equestrianism\ntest/1e116f903657e017,Close-up\ntest/1e136158d2f0aba3,Arthropod,Close-up\ntest/1e195f9a98e163b3,Extreme sport,Sport climbing\ntest/1e196a69ca240e51,Crocodilia,Nile crocodile,Scaled reptile\ntest/1e1b24035061608f,Luxury vehicle,Performance car\ntest/1e1c93b4b0130d52,Erinaceidae,Close-up,Whiskers\ntest/1e1fe858ebe7fa8b,Close-up\ntest/1e24db2af2eb547a,Plant stem,Close-up\ntest/1e277471368a3f7a,Luxury vehicle,Antique car\ntest/1e287c826cd65b66,Antique car\ntest/1e2966342d7da386,Close-up\ntest/1e29c8d7c80eb519,Compact car\ntest/1e2a4fcc508d3660,Fire apparatus\ntest/1e2b9b8c69d4acf5,Jackfruit\ntest/1e2e0ccfac3a56e8,Canola\ntest/1e30242b3fd13ad9,Perching bird\ntest/1e31254283fbd3ec,Fried food\ntest/1e3130a2976f9d37,Jeans\ntest/1e31da6b37d1173a,Rural area\ntest/1e347d339d0aa738,Close-up\ntest/1e3abe29e35739a9,Scaled reptile,Nile crocodile,Crocodilia\ntest/1e3c8f312cb93644,Rural area,Horse and buggy\ntest/1e3ce0b21b2704b3,Gun turret\ntest/1e3d6bf388b2245a,Luxury vehicle\ntest/1e41d5d343a25b64,Floristry\ntest/1e43ebc3c7e4e10b,Milky way\ntest/1e45221af701bed4,Boating\ntest/1e459ba77c621f1e,Rural area\ntest/1e45dacea15116eb,Sport utility vehicle\ntest/1e46c9753185a092,Senior citizen\ntest/1e48dbdb996e4887,Black-and-white\ntest/1e48dfee7b6d5c8f,Antique car\ntest/1e4c95731f75a79d,Zeppelin\ntest/1e4d6d39aeb79611,Domestic short-haired cat,Whiskers\ntest/1e4df0f7812dbba3,Whiskers\ntest/1e4e31d44d617139,Whiskers,Domestic rabbit\ntest/1e4eaa8c959f7be6,Combat sport,Barechested\ntest/1e50d2e4bc09ee7a,Antique car\ntest/1e55b8233563673b,Hound\ntest/1e579cb0105ea680,Sport utility vehicle\ntest/1e581c1bf8e37f55,Microcontroller\ntest/1e59629ecef00de6,Boating\ntest/1e5bde720e4b2226,Frozen yogurt\ntest/1e5d358ae59daf2b,Scaled reptile\ntest/1e5e0ac7f3c82f2f,Ball game\ntest/1e5e2f2d86f8ea6d,Close-up\ntest/1e5fd0008ca1fe2b,Luxury vehicle\ntest/1e60067337b7d1eb,Boating,Wind wave\ntest/1e60aa45c07c9b91,Ball game,Team sport\ntest/1e60fefd60f33b7a,Vegetarian food,Junk food\ntest/1e6306d5d5130301,Erinaceidae\ntest/1e633c642e27b2f4,Close-up\ntest/1e638fa0815aa25d,Vegetarian food,Corn on the cob\ntest/1e64d52fa55e5f0d,Vegetarian food\ntest/1e667172abb25fc4,Black-and-white\ntest/1e6688403ba19194,Senior citizen\ntest/1e6add7b7eda7f53,Domestic short-haired cat,Whiskers\ntest/1e6b8f97cbc836d3,Combat sport\ntest/1e6b945fe36a9fc8,Close-up\ntest/1e6bc8bd4e4b6dc0,Close-up,Scaled reptile\ntest/1e6c196566908c2d,Luxury vehicle,Performance car\ntest/1e6cc6ab29e0652b,Arcade game\ntest/1e6f1985109fb835,Close-up\ntest/1e6ff643189efb63,Musical theatre\ntest/1e712c4af5c7efeb,Antique car,Performance car\ntest/1e716b94c797ed16,Close-up\ntest/1e728158d7fe164a,Close-up\ntest/1e72be4cbc59d37c,Cookies and crackers\ntest/1e73bfffb5a0f950,Cosmetics\ntest/1e75bc0e77d384d9,Auto racing\ntest/1e77e36490ba8249,Residential area\ntest/1e79712e5e418b7f,Luxury vehicle\ntest/1e7b6f4d7136d11d,Close-up\ntest/1e7d217c46289d44,Junk food\ntest/1e7d68b0f4a68a12,Luxury vehicle\ntest/1e7d92d724d5f558,Bird of prey\ntest/1e7f9d700d8d34d2,Military person\ntest/1e80c251ed20609c,Performance car\ntest/1e832a96437b8e89,Arthropod,Close-up\ntest/1e83cb6a2263d7bc,Aloe\ntest/1e843839619a06cf,Aerial photography\ntest/1e847f9bfaed1c11,Lechon\ntest/1e85c9b874a607eb,Compact car,Luxury vehicle\ntest/1e8657e7c30024c1,Black-and-white\ntest/1e867c2a56675689,Black-and-white,Close-up\ntest/1e8694c8727302fe,Wind wave\ntest/1e880a08e282465b,Ball game\ntest/1e8ab2be5d510972,Luxury vehicle\ntest/1e8bf1420b634603,Black-and-white\ntest/1e8e97ae16cca52e,Ball game\ntest/1e8ec426c4a420fa,Arthropod,Locust,Close-up\ntest/1e8f59208f75891f,Siberian husky\ntest/1e91a33399dd4bc3,Luxury vehicle,Performance car\ntest/1e92f20d13952104,Cookies and crackers\ntest/1e935dc4fcac7e73,Close-up\ntest/1e9940f3864e8d40,Ball game,Team sport\ntest/1e9be10cb23df23a,Bovine\ntest/1e9d73eaf588e689,Auto racing\ntest/1e9f05dce6a6cc46,Luxury vehicle,Performance car\ntest/1ea03aeb364482d6,Arthropod,Locust,Close-up\ntest/1ea0d861d77b3f17,Black-and-white,Luxury vehicle\ntest/1ea48c8d1fbac6eb,Antique car\ntest/1ea4a3fad9693ad1,Military person\ntest/1ea4cac4f1806b2b,Luxury vehicle\ntest/1ea583f11f988097,Black-and-white\ntest/1ea59cb17f77a82a,Close-up\ntest/1ea5f4a04f27caad,Compact car\ntest/1ea710a0987751d7,Billiards\ntest/1ea7e3c249f359b2,Domestic short-haired cat,Whiskers\ntest/1eaaf4a98b02a24b,Antique car\ntest/1eace30dcaf318e9,Luxury vehicle,Performance car\ntest/1ead5312df1f0710,Miniature Poodle\ntest/1eb09080f83bf4cb,Black-and-white\ntest/1eb4d731f329a85f,Jeans\ntest/1eb52131099a38ce,Auto racing,Performance car\ntest/1eb7b7890c6cc215,Plant stem\ntest/1eb84de8f34ed0fc,Sport utility vehicle\ntest/1eb98a586c7acfd4,Khinkali\ntest/1eba9c52f784a2d1,Luxury vehicle,Performance car\ntest/1ebb36368847d8c9,Close-up,Whiskers\ntest/1ebc7d046286b3d1,Close-up,Scaled reptile\ntest/1ebd1f341db1d19a,Melee weapon,Hunting knife\ntest/1ebe463a48083656,Sport utility vehicle\ntest/1ebf119e21e05ea4,Senior citizen\ntest/1ec0f2535c6ede5c,Arthropod,Close-up\ntest/1ec1b0e3b1b6c4c4,Compact car,Luxury vehicle\ntest/1ec232c2a2315258,Luxury vehicle,Performance car\ntest/1ec5f22b48f04e32,Military vehicle\ntest/1ec7e2b8221cda42,Close-up\ntest/1ec9c759c89b6fbb,Microcontroller\ntest/1ecb5e0c6fd6d98d,Aerial photography\ntest/1ecdfb08fe7878e2,Siberian husky\ntest/1ece14dbc32021fe,Luxury vehicle,Sport utility vehicle\ntest/1ece30d87436a6be,Classical sculpture,Close-up\ntest/1ed16042d8833b73,High-speed rail\ntest/1ed18490d7dfa277,Close-up,Plant stem\ntest/1ed1ba50b7c535d2,Compact car\ntest/1ed1fae0b668cedf,Compact car,Luxury vehicle\ntest/1ed32f9b21e2f5c5,Close-up,Whiskers\ntest/1ed358c1c79b44a6,Blond\ntest/1ed58bd10d7d23fb,Perching bird\ntest/1ed5e97f0e6252cd,Domestic short-haired cat,Whiskers\ntest/1ed6d4dd080a69e8,Luxury vehicle,Performance car\ntest/1ed82892a336561f,Luxury vehicle\ntest/1ed9e0b5a8f9ee3f,Nest box\ntest/1ed9e75377a13e76,Luxury vehicle,Performance car\ntest/1eda326e3532eafa,Junk food\ntest/1edc32c0b3f87069,Extreme sport\ntest/1edc83427d71b2ed,Luxury vehicle,Performance car\ntest/1ede81394e48aa4e,Black-and-white\ntest/1ee3066a476fb619,Close-up,Scaled reptile\ntest/1ee458e3b5224cd3,Cookies and crackers\ntest/1ee52034f39f9f9c,Vegetarian food\ntest/1ee5da5ad95fb6b0,Auto racing,Performance car\ntest/1ee8dbeb388cfc80,Ball game,Team sport\ntest/1eea8a04f89d0dcb,Compact car\ntest/1eed1261fe1a8b33,Residential area\ntest/1eee0c9d175f6d13,Antique car\ntest/1eeeb871ec3332aa,Equestrianism\ntest/1eef9e817a74047e,Motorcycling\ntest/1ef2dc2541f67071,Shooting range\ntest/1ef3d7572ee56a6d,Blond,Portrait photography,Close-up\ntest/1ef3e9e2fadcecf6,Aerial photography\ntest/1ef4332964335573,Luxury vehicle,Performance car\ntest/1ef4fbfc390caf08,Ball game,Team sport\ntest/1ef6a2b88e284c33,Pork chop\ntest/1ef724fc669078d4,Vegetarian food,Fried food\ntest/1ef83e6f24ed19fd,Residential area\ntest/1efa71c4396a77d2,Ball game,Team sport\ntest/1efd08fa9bfee48f,Boating\ntest/1efdbec2cdc79cc0,Extreme sport\ntest/1efde74ff07fbbc7,Goats\ntest/1efe29a0b0041999,Jeans\ntest/1eff0c50ff795494,Steamed rice\ntest/1eff89766bf76828,Close-up\ntest/1f01fa36d7fe27a6,Chordophone\ntest/1f0327cbcfc48e79,Bonbon\ntest/1f03bd4447b50024,Portrait photography,Close-up\ntest/1f04f9fd6e264a4e,Calabaza\ntest/1f05fe3fe163ca58,Black-and-white\ntest/1f083ee05f49a156,Arthropod,Close-up\ntest/1f0a9b8baee09d57,Auto racing\ntest/1f0ae016f7d50d95,Ball game,Team sport\ntest/1f0be8d27b417bb4,Horse racing,Equitation,Equestrianism\ntest/1f0de89f6bc0ccfd,Aircraft engine\ntest/1f0fa34ac2f0a0de,Luxury vehicle\ntest/1f10974ad024d292,Galleon\ntest/1f10bf027dd61af4,Extreme sport\ntest/1f118f1359caddc0,Close-up\ntest/1f11a2e3e7406e39,Bovine,Close-up\ntest/1f123d0bee392801,Close-up\ntest/1f12afa8f83f3d74,Plant stem,Close-up\ntest/1f143e9ac9a202fa,Vegetarian food\ntest/1f149f1cb10e8a1f,Close-up\ntest/1f16b6a2201798ea,Floristry\ntest/1f173efd32c32fc1,Steamed rice,Fried food\ntest/1f18a21429ee7548,Performance car\ntest/1f18f7093bba01cd,Close-up\ntest/1f1d59f60848f072,Auto racing\ntest/1f1ea0b9905b5afa,Close-up\ntest/1f22cd26caae1ab3,Ramen\ntest/1f2315c2f0d3a576,Compact car,Luxury vehicle\ntest/1f2458b246802775,Plant stem\ntest/1f24c8eb249fea2a,Digital camera\ntest/1f25c121b9a46a73,Close-up\ntest/1f27f9dfea55fe4e,Luxury vehicle\ntest/1f286c4bb30960f5,Bovine,Close-up\ntest/1f28ecbefd6a7fc7,Ball game,Team sport\ntest/1f295c5fa8cb5741,Firearm\ntest/1f2afd4a5761e55e,Motorcycling\ntest/1f2d2958035d4c30,Portrait photography,Close-up\ntest/1f2e61e27366f25a,Compact car,Luxury vehicle\ntest/1f2fd232a8c16e9f,Luxury vehicle,Antique car\ntest/1f35c0f795b1b413,Black-and-white,Close-up\ntest/1f3e93aa001ae0e9,Compact car\ntest/1f40eabfb57dfc9d,Nordic skiing\ntest/1f41c866f60b8714,Jeans\ntest/1f42819016987869,Musical theatre\ntest/1f4495cae0b8ba72,Floristry\ntest/1f48a16f48968fb6,Vegetarian food\ntest/1f4ad1cfa133f0d2,Whiskers\ntest/1f4d3096c12c0644,Luxury vehicle,Antique car\ntest/1f4ffb341629af6b,Antique car\ntest/1f50585e8a1566c9,Rural area\ntest/1f52d67dce5e7c21,Close-up\ntest/1f56dcdd1ac184bf,Sport utility vehicle\ntest/1f591524f225e25a,Combat sport\ntest/1f59155e3805b4f2,Fire apparatus\ntest/1f5938065ec4ff92,Luxury vehicle\ntest/1f5b5919fb37d93b,Combat sport\ntest/1f5e0ea74a222d8f,Jeans,Luxury vehicle,Performance car\ntest/1f60b3da9413c839,Rural area\ntest/1f69f7a65cccf320,Luxury vehicle,Antique car\ntest/1f6a8ad0f43bd4e5,Luxury vehicle,Performance car\ntest/1f6afc26ef5413c5,Arthropod,Close-up\ntest/1f6bf944a22e9834,Fried food\ntest/1f6e3d1e95c4b95e,Black-and-white\ntest/1f6f71e3daacf793,Blond\ntest/1f6f7cfd35820b8f,Close-up\ntest/1f7049f4815c7605,Close-up\ntest/1f70a2a743b02464,Ball game\ntest/1f71b4b68438b7c1,Pork chop\ntest/1f7502272eb5fa2a,Vegetarian food\ntest/1f75ab9fa6baa13a,Close-up\ntest/1f75ed1df33b3f50,Steamed rice,Fried food\ntest/1f781551baa01a35,Luxury vehicle,Antique car\ntest/1f785c5e1c97a186,Black-and-white\ntest/1f79f0ffe86e4ac3,Close-up,Whiskers\ntest/1f7ede9ba41004d4,Compact car,Antique car\ntest/1f7f0f1bf84e38cc,Plant stem\ntest/1f7f90c53aa03f9f,Black swan,Close-up\ntest/1f802000c3721b9f,Wind wave\ntest/1f83f815737224dc,Antique car,Luxury vehicle,Performance car\ntest/1f84430da97715e5,Luxury vehicle\ntest/1f8487ef4e7934c0,Arthropod\ntest/1f85348359d39e11,Glacial landform\ntest/1f87a69e15d6c53e,Luxury vehicle\ntest/1f8be9f4e2488c19,Whiskers\ntest/1f8c7b5798fc5545,Road cycling\ntest/1f8ca7b48d4a299b,Electronic instrument\ntest/1f908b884ec4c71a,Luxury vehicle,Antique car\ntest/1f91373d7bdc33dd,Arthropod,Close-up\ntest/1f91c9280e8b6ebd,Electric piano,Electronic instrument\ntest/1f92541b4f439e51,Combat sport\ntest/1f93eb9aceeaf52e,Military vehicle\ntest/1f9741351567d71d,Junk food,Fried food\ntest/1f99f99ba1ac1bc5,Luxury vehicle\ntest/1f9d2b68c6803f37,Dishware\ntest/1f9e21fd822b6921,Black-and-white\ntest/1f9f9f01cd666897,Extreme sport,Wind wave\ntest/1fa0f59f1d337441,Sailboat racing\ntest/1fa100d2d8067940,Luxury vehicle\ntest/1fa203101e83d731,Extreme sport,Parachuting\ntest/1fa97cc1ed206edb,Backlighting,Close-up\ntest/1fabf3f0313bf428,Black-and-white\ntest/1facadab743c8fce,Auto racing\ntest/1facae10d7781d2a,Plant stem\ntest/1facf3b79b412cf8,Residential area\ntest/1fad399f6aafa3d9,Close-up\ntest/1fae5137be6214fd,Wallaby\ntest/1fb2a23be21cdeb9,Monoplane\ntest/1fb36e32029f2171,Vegetarian food\ntest/1fb38520dcc3a34f,Ball game\ntest/1fb67ad46c9d11ef,Antique car\ntest/1fbdcfcb723efb54,Scaled reptile\ntest/1fbe0fc830675ce6,Arcade game\ntest/1fc34ce984d68f1d,Luxury vehicle\ntest/1fc4c32ce17b6563,Shooting range,Firearm\ntest/1fc4e068ed581f98,Luxury vehicle,Performance car\ntest/1fc5f01403865d82,Close-up\ntest/1fc666576d6f6363,Outdoor shoe\ntest/1fc787c0b78cd516,Machine tool\ntest/1fc8691285c6d6e1,Dog walking\ntest/1fceba3b223d6475,Pork chop,Beef tenderloin\ntest/1fcf88c9565ae898,Camera operator\ntest/1fd085b89abeafb3,Arthropod\ntest/1fd0ded3274a3097,Fried noodles\ntest/1fd12418bd1a2216,Close-up\ntest/1fd1e5229d548c76,Miniature Poodle\ntest/1fd44c302ceb5f8d,Outdoor shoe\ntest/1fd5da615855ea11,Hair coloring\ntest/1fd783e47a5a621a,Orator,Public speaking\ntest/1fd90a675043ffad,Gun turret\ntest/1fdb682770a1f854,Plant stem,Close-up\ntest/1fdc9867ab20a3f2,Performance car\ntest/1fdd112ea9a43486,Performance car,Luxury vehicle,Antique car\ntest/1fe13e4910edf386,Luxury vehicle,Antique car\ntest/1fe26e2a7eca0583,Coral reef fish\ntest/1fe43470b6489c7d,Whiskers\ntest/1fe4aa75afb72d14,Close-up\ntest/1fe5e0806ea2ca63,Luxury vehicle\ntest/1fe7a99c23f64885,Herding,Goats\ntest/1fe7e3014a53e3fa,Cookies and crackers\ntest/1fe831f0f4dd1b8b,Extreme sport\ntest/1fe8f95a557ab626,Musical theatre\ntest/1fe972098b09caea,Sport utility vehicle\ntest/1fe98ccf4134f002,Drums\ntest/1fe9ad8c6f0d5ff5,Rural area\ntest/1feabca6f66e4eb5,Close-up\ntest/1feef3f34c32662b,Sledding\ntest/1ff0669530fe1142,Rural area\ntest/1ff27a55871e30db,Orator,Public speaking\ntest/1ff506783be170b7,Compact car\ntest/1ffa3af3bfa43642,Glacial landform\ntest/1ffafe2db54bc173,High-speed rail\ntest/1ffb31da4400bbbc,Plant stem\ntest/1ffbb09a5594f8af,Aerial photography\ntest/1ffbc2e1d66bbd62,Compact car,Luxury vehicle\ntest/1ffcfeb20fd180b8,Whiskers\ntest/1ffd7360581fb804,Floristry\ntest/20026dbc93033472,Black-and-white\ntest/2002be05d1450f63,Plant stem\ntest/2006a682c15e0359,Antique car\ntest/200a9a5bed954c2a,Luxury vehicle\ntest/200c905e9b48d334,Machine tool\ntest/200cc83349a73f1f,Luxury vehicle,Sport utility vehicle\ntest/200cf602bb107ea5,Freight transport\ntest/200e6c0462d778bf,Plant stem,Close-up\ntest/200eaae66a594057,Jeans\ntest/200f58cb82e64a15,Antique car\ntest/20109d7d6ac63835,Luxury vehicle,Performance car\ntest/2014f56ce49de98a,Combat sport\ntest/20150a1ef8b6cc92,Luxury vehicle\ntest/2016cb4ecc6ec4a7,Black-and-white\ntest/2016fee92e951f88,Wind wave\ntest/20177caa12589cfc,Roller skating\ntest/20184a92247560f9,Hound\ntest/2018a0b761f2823b,Coral reef fish,Close-up\ntest/2019c9c8552d1996,Rural area\ntest/201afb83b9376b9c,Frying,Fried food\ntest/201e54721602e002,Luxury vehicle\ntest/201f23c3a3ba555c,Compact car,Luxury vehicle,Antique car\ntest/202168d8fe01c900,Sport utility vehicle\ntest/2021d753e684ca45,Residential area\ntest/2023abf04887541f,Senior citizen\ntest/2023c2a99d0473ba,Electronic instrument\ntest/20245be435bcf137,Aircraft engine\ntest/2025d38b7de25f45,Sport utility vehicle\ntest/20263e31e7f52ccf,Performance car\ntest/2026424af5f5dbc3,Luxury vehicle,Antique car\ntest/202931d29e9d3ec8,Luxury vehicle,Sport utility vehicle\ntest/202963cc6d2757b1,Luxury vehicle\ntest/202a3f7915f8a07c,Luxury vehicle\ntest/202ac4870224522f,Luxury vehicle,Performance car\ntest/202c129d07a73255,Compact car\ntest/202c66e9118f3ab5,Plant stem,Close-up\ntest/202e1f51b24148f5,Wind wave\ntest/202fcb8806930222,Luxury vehicle,Performance car\ntest/203050447809dbfc,Close-up\ntest/20329a2981ee263c,Luxury vehicle\ntest/20330665dc86ee54,Luxury vehicle,Antique car\ntest/20352c133ed0610a,Jeans\ntest/2035c70f13be07df,Luxury vehicle,Performance car\ntest/2035eb215f1abb07,Luxury vehicle,Sport utility vehicle\ntest/2038df1e7d5b6623,Freight transport\ntest/2039a06e6936f735,Canola\ntest/203b7cfdabfc70f8,Herding,Bovine\ntest/203c25c97459d34f,Fried food\ntest/203c6de0f0eb9dec,Equestrianism\ntest/203c72655aefef99,Hound\ntest/203cb8124a9f6873,Electronic instrument,Electric piano\ntest/203cf7ea55248f23,Jeans\ntest/203df1440f7b403c,Arthropod\ntest/203e207615af212f,Close-up,Scaled reptile\ntest/203f962f5c530990,Team sport\ntest/20461bac51cdc0b5,Jeans\ntest/2046494e85a3eb0c,Auto racing\ntest/20470c06ed3156a3,Combat sport\ntest/2049c59f88fcbdd8,Ramen\ntest/204c43de80a5e8d2,Residential area\ntest/204db8a542a415e0,Drums\ntest/204f33cb5b52c6ba,Close-up\ntest/204ff8fc04ab8725,Outdoor shoe\ntest/2051a17f75aaa11b,Cosmetics\ntest/20528f68b2dbe2e7,Miniature Poodle\ntest/205539863fde420c,Blond\ntest/205581cd78e90ea5,Luxury vehicle\ntest/20558bf613c5546c,Chordophone\ntest/2055d85188bd3eb5,Performance car,Luxury vehicle,Antique car\ntest/2056dc42c19aab70,Black-and-white,Close-up\ntest/20571a860e333836,Extreme sport\ntest/2057c968a05ab2b6,Close-up\ntest/2058cd1214c33fa5,Auto racing,Performance car\ntest/205aecc8b0337359,Jeans\ntest/205af195d7af2f66,Close-up\ntest/205cf59bab90644f,Black-and-white\ntest/205f1524391ae50d,Antique car\ntest/206044b19cc709af,Lawn game,Ball game\ntest/2060e25a653b9656,Modern dance,Musical theatre\ntest/2061812654a731ee,Leaf vegetable\ntest/2061937fe90d4e71,Close-up,Whiskers\ntest/2063cc4372428468,Black-and-white\ntest/206ac44d0240b801,Jeans\ntest/206ca984898cc992,Performance car\ntest/206e46d7f7618d64,Luxury vehicle\ntest/206e62077f463d5b,Hair coloring\ntest/206f16d2a0ebd435,Equestrianism\ntest/2071402188e7711e,Black-and-white\ntest/20748afe659814a6,Combat sport\ntest/2074b4d02b5ed1ce,Arthropod\ntest/2075e67666c2fca3,Arthropod\ntest/20795f5bd53e4859,Black-and-white\ntest/207c99da5d42f34b,Luxury vehicle,Performance car\ntest/207d45d161a5049f,Extreme sport,Motorcycling\ntest/207f94aa3b611e4a,Residential area\ntest/2083921340b9ff0a,Compact car,Luxury vehicle,Sport utility vehicle\ntest/2083cb29bfaf3d3b,Arthropod,Close-up\ntest/208645235380974f,Vegetarian food\ntest/2087913606004ed4,Luxury vehicle\ntest/208866e686cf5347,Luxury vehicle\ntest/2089d5f7ca7b5a37,Luxury vehicle\ntest/208c73132c9b9c03,Close-up\ntest/208ecbee03fbe852,Microcontroller\ntest/208f5eb2152d3e3d,Perching bird\ntest/20939105f0d86b8e,Soba\ntest/2093a6acd21d19a7,Luxury vehicle\ntest/2093d4716c70126b,Luxury vehicle,Antique car\ntest/2094ce28fdc83060,Whiskers\ntest/209759020819a65d,Compact car,Antique car\ntest/2097820b3624d29f,Luxury vehicle\ntest/2098bf6d0f77d7b9,Fried food\ntest/2098f8ed3234d00a,Luxury vehicle,Antique car\ntest/209a730031941da7,Close-up,Whiskers\ntest/209a7414a3eff0ad,Arthropod,Close-up\ntest/209cee58523a1890,Senior citizen\ntest/209fd6865ebad6cc,Black-and-white,Whiskers\ntest/20a2a0e9bcc32080,Close-up,Plant stem\ntest/20a344055fb3cc84,Chordophone\ntest/20a36d79e8559305,Chordophone\ntest/20a737b9c41e52f5,Hound\ntest/20a8ef4b1867143a,Rural area\ntest/20aaef1bee4ba7a6,Dog walking\ntest/20aed1447121c1d6,Combat sport\ntest/20aed6a3ca8cfc1f,Close-up\ntest/20b2ac86a7f3ad94,Luxury vehicle\ntest/20b49d2f736c62c2,Chordophone,Electric guitar\ntest/20b4ac46eebdde75,Dog walking\ntest/20b593ae8ed6df02,Military person\ntest/20b7b0786e6e6c2d,Antique car\ntest/20ba405843b2a1d8,Auto racing\ntest/20baf66382041716,Team sport\ntest/20bb1cd67c8df80b,Vegetarian food\ntest/20be7ac714787f58,Military person\ntest/20be7dc4008e7cb7,Jeans\ntest/20bf31d6c3dca876,Extreme sport\ntest/20c25df116a12983,Extreme sport\ntest/20c3ecd4cebd3c32,Military person\ntest/20c4a6b6a8a0cafb,Jeans\ntest/20c603ad7ff542d9,Compact car,Antique car\ntest/20c6b46541474c05,Close-up\ntest/20c9177b62290e49,Close-up\ntest/20ca9eac95952b17,Close-up\ntest/20cae1fc4813e0c9,Performance car\ntest/20cb47361e6f884a,Close-up,Whiskers\ntest/20cb6d6bb2b163c1,Jiaozi\ntest/20cdc64be5e7ead0,Close-up\ntest/20cf43e0e7e529b7,Close-up\ntest/20cfed9f5647b131,Luxury vehicle\ntest/20d2b7cf6287aa08,Arcade game\ntest/20d303549da5cebe,Boating,Rowing\ntest/20d3e9b9561f3f96,Compact car\ntest/20d4f0df8669e24e,Close-up\ntest/20d50fc921fe8829,Digital camera\ntest/20d570b0003760c2,Performance car\ntest/20d5b89bf2e22480,Domestic short-haired cat,Whiskers\ntest/20d5e256ac11a7af,Luxury vehicle\ntest/20d698247b464e07,Drums\ntest/20d6e5b4e3b796fd,Black-and-white\ntest/20d769a0d33141c9,Perching bird,Close-up\ntest/20d7a43ad58bd205,Miniature Poodle\ntest/20dc214293b58c53,Coral reef fish\ntest/20dc9e39ad44ea99,Dishware\ntest/20e04b5395c1ee8c,Black-and-white\ntest/20e17d6054c41ac0,Luxury vehicle,Antique car\ntest/20e3ff850a5d4373,Electronic instrument,Black-and-white\ntest/20e4287856122a06,Luxury vehicle,Performance car\ntest/20e6a34e83f08d3a,Luxury vehicle,Antique car\ntest/20e6b6077a95a36a,Scaled reptile,Close-up,Anole\ntest/20e7208201ef2633,Luxury vehicle,Performance car\ntest/20e8555a08042165,Aircraft engine\ntest/20e8e60ed10a262e,Luxury vehicle\ntest/20ea1483e1d0d361,Fried food\ntest/20ee7445160b217e,Portrait photography,Close-up\ntest/20f1a6c4e65e40d8,Antique car\ntest/20f26eaaee4bc77c,Combat sport\ntest/20f8465cd2a78305,Luxury vehicle\ntest/20faa52e7314007a,Blond,Barechested\ntest/20fe368757b0e11e,Luxury vehicle\ntest/2100988986f985d7,Monoplane,Aircraft engine\ntest/21014a9f96d11487,Monoplane\ntest/2101d698602b05ca,Bratwurst\ntest/2101dac233812083,Monoplane\ntest/2104ab144a99f4bd,Antique car\ntest/210524666ed3b890,Glacial landform\ntest/2106471942f53c86,Fried food\ntest/210663999940977c,Watercolor paint\ntest/210734f80256d6a4,Bovine,Close-up\ntest/2108401d7bfda63e,Cookies and crackers\ntest/210bf50976b197e3,Rear-view mirror\ntest/210d6bfedf48356d,Chordophone,Electric guitar\ntest/210d7ba9bd3587e8,Bovine\ntest/210f814cf87a66e9,Combat sport\ntest/210fccc9699e4bf4,Horse and buggy\ntest/2110f8c401886784,Combat sport\ntest/2111181499c8ba37,Rear-view mirror\ntest/2114833d011827a1,Luxury vehicle,Sport utility vehicle\ntest/2117a21f2420dc58,Boating\ntest/21186d67b22008cf,Black-and-white\ntest/2118d57aea760ee1,Luxury vehicle\ntest/2118df2d96e41530,Fried food\ntest/2118ed2e68e9bee2,Leaf vegetable\ntest/21199a79300d61ff,Domestic short-haired cat,Whiskers\ntest/211d400eaa6cd950,Compact car,Luxury vehicle\ntest/211dd6ed6a57588d,Black-and-white,Close-up\ntest/211e016655b75f5e,Black-and-white\ntest/211f3e4ce7fe9225,High-speed rail\ntest/2122be77ad370df0,Luxury vehicle,Performance car\ntest/2123c82463b9ff08,Auto racing\ntest/2125dd91f3e7b499,Soba\ntest/2126a898dc2b9b9d,Arthropod,Close-up\ntest/2127d7ef68b24e37,Hound\ntest/212826a636f7b231,Extreme sport,Parachuting\ntest/21287eb360587897,Outdoor shoe\ntest/2128bdf4fb435d1c,Team sport\ntest/21292b9080946908,Lifebuoy\ntest/2129f31c4c4fa890,Fried food\ntest/212a787f1f5a21d2,Compact car\ntest/212c0358ed156e13,Extreme sport\ntest/212f668a6cbb78b0,Close-up\ntest/2130d594542aa3ab,Compact car\ntest/21312bc4852900a2,Halter,Equestrianism\ntest/213174a9eecde5d8,Aircraft engine\ntest/21326e0dd8efde02,Compact car,Antique car\ntest/213313f9f9fe0f8a,Domestic short-haired cat,Whiskers\ntest/2133a2bfc22087e2,Luxury vehicle\ntest/21346035f9ffed30,Domestic short-haired cat,Whiskers\ntest/2137000d2df169d5,Chordophone\ntest/21371b829f00c65f,Sport utility vehicle\ntest/213b4bdcae65854b,Compact car,Luxury vehicle\ntest/2142b6d245fe11e2,Canola\ntest/21437aee78f572e4,Luxury vehicle\ntest/21439a470ab0211e,Monoplane,Aircraft engine\ntest/2145c2e6ac5f4016,Close-up\ntest/21485b12402ed312,Divemaster,Scuba diving,Underwater diving\ntest/214c43d9d905cb4a,Black-and-white\ntest/214e79cb64e3b244,Narcissus\ntest/2151237462109ab2,Roman temple\ntest/215163598b2005a4,Compact car\ntest/21533e6dc184a4b5,Briefs\ntest/21558302dad60aaf,Dog walking\ntest/2156d7a95f17f3f1,Extreme sport,Personal water craft,Boating\ntest/215868acb93c400e,Bird of prey\ntest/21598449334efe2d,Luxury vehicle\ntest/215dc68b2c525959,Ball game,Team sport\ntest/215df18e0b3b2deb,Compact car,Luxury vehicle\ntest/215df3e72bb322c4,Ball game,Team sport\ntest/215f0488d6efaf0d,Watercolor paint\ntest/216430a5c140a34b,Melee weapon,Hunting knife\ntest/2167755de02e7112,Melee weapon,Hunting knife\ntest/216820b69c308538,Double-decker bus\ntest/216b3859aaa7fa44,Junk food,Fried food\ntest/216bc07e57184a95,Medical imaging\ntest/216cec1ca6c17b64,Antique car\ntest/216fa21aa1f7e137,Ball game\ntest/2170a464c709e9e9,Close-up\ntest/2172329fc8cdca52,Dog walking\ntest/21728991558c1e4e,Close-up\ntest/217543128e798ff7,Luxury vehicle,Sport utility vehicle\ntest/21758b4cb5094a9d,Arthropod,Close-up\ntest/21770b85a2029249,Compact car\ntest/2178bd102665219e,Compact car,Luxury vehicle\ntest/2179db1a79b10502,Compact car,Luxury vehicle\ntest/217e1587dacaa440,Compact car\ntest/217f0f187c5ee569,Close-up\ntest/217fbeaaecfeed81,Shinto shrine\ntest/218103627de15d1a,Luxury vehicle,Antique car\ntest/218643b968bfcfb7,Luxury vehicle\ntest/2188390e889587ae,Luxury vehicle\ntest/2189cf2194e78b75,Luxury vehicle\ntest/218b0009fe9e90d6,Motorcycling\ntest/218b09923bbbad97,Luxury vehicle\ntest/21908c18b3abf09f,Drums\ntest/2190dbba242efd0f,Luxury vehicle\ntest/21911d28dcd8d6d5,Performance car\ntest/21927149ba444277,Extreme sport,Wind wave\ntest/219353c8cf8a194f,Modern dance\ntest/2193e38791bba561,Monoplane,Aircraft engine\ntest/21940d53559d7959,Cookies and crackers\ntest/2197342351942b0b,Flatbread\ntest/21974a495e0f6bb6,Musical theatre\ntest/2198994a2c201762,Sailboat racing\ntest/219911c303bbd09a,Outdoor shoe\ntest/219b0f0958647475,Arthropod\ntest/219cf488799c5fd0,Luxury vehicle,Performance car\ntest/219d3cfda03c6ccd,Bovine\ntest/21a0f298d98f0cb7,Frigate\ntest/21a85b9356ccf050,Extreme sport,Wind wave\ntest/21a8f8693e1047ea,Junk food\ntest/21a939a7f7e78842,Close-up\ntest/21aced3111b0e5b1,Compact car,Luxury vehicle\ntest/21aee0de29921164,Close-up\ntest/21af7acbaa409d83,Vegetarian food\ntest/21afc87d48812530,Luxury vehicle,Sport utility vehicle\ntest/21b0e66ea8cfa628,Cookies and crackers\ntest/21b235f5d1895b6a,Arthropod,Locust,Close-up\ntest/21b5eddd0209cbfa,Luxury vehicle,Antique car\ntest/21b829570dcc46b3,Close-up\ntest/21b8da431a48b485,Luxury vehicle,Performance car\ntest/21bbfa3a26f25fb8,Military vehicle,Sport utility vehicle\ntest/21bc68f567ac688a,Close-up\ntest/21c02130a795652b,Fried food\ntest/21c56fe835393ca1,Close-up\ntest/21c5ce67cb7c7da0,Auto racing,Performance car\ntest/21c7032f77359cec,Jeans\ntest/21cb5b758324c2b1,Sport utility vehicle\ntest/21cd4f34a2b20805,Military person\ntest/21d224deb1a44f68,Dog walking\ntest/21d2398a870b30e9,Whiskers,Domestic rabbit\ntest/21d69afbbec7597b,Black-and-white\ntest/21d7d2d92f813eb4,Road cycling\ntest/21e0be7788a6a769,Close-up,Whiskers\ntest/21e23458d8596122,Close-up\ntest/21e68792737a07d9,Close-up\ntest/21e6ef769e90ac7e,Rear-view mirror\ntest/21e9800470b96d9a,Close-up\ntest/21e9dd6c7413c7a0,Luxury vehicle,Antique car\ntest/21ea23b48cfc4138,Luxury vehicle,Performance car\ntest/21ebad7c95e1c634,Luxury vehicle\ntest/21ec967c36c7cdde,Jeans,Musical theatre\ntest/21ecaab6b219b33e,Luxury vehicle,Antique car\ntest/21ed35c277cb28d2,Military vehicle\ntest/21f28ce49c38faf9,Performance car\ntest/21f313479a6663cd,Close-up\ntest/21f39a1828e9f534,Luxury vehicle\ntest/21f3f9130de75534,Luxury vehicle\ntest/21f48932eacc2580,Luxury vehicle,Sport utility vehicle\ntest/21f506acd35edb16,Luxury vehicle\ntest/21f59f339c6a573f,Close-up\ntest/21f8833e8906b2bd,Close-up\ntest/21f89e98c95fa67f,Auto racing\ntest/21f985355a08c311,Close-up\ntest/21fa945315692f7a,Camera operator\ntest/21fa9ce5bcade459,Luxury vehicle,Antique car\ntest/21fb2f47fb8aa9a3,Vegetarian food\ntest/21fc397c4450ab01,Miniature Poodle\ntest/21fcc11e22c8ce00,Prosciutto\ntest/21ff77980ba2ef7b,Antique car\ntest/21ff7ba8c2dee195,Blond\ntest/2200dad40047e8f6,Close-up,Crocodilia\ntest/2203ffffa380d1df,Close-up,Whiskers\ntest/22048b93a752e2b7,Close-up\ntest/22066bc00132ea16,Vegetarian food,Fried noodles\ntest/2207456257f94a04,Church bell\ntest/220a8ae6f5045492,Leaf vegetable\ntest/220d07e135e3a313,Luxury vehicle,Sport utility vehicle\ntest/220e03f2bad93baa,Close-up\ntest/220e93f1abe33b66,Luxury vehicle\ntest/220effd28096ff0f,Bovine\ntest/220f05897de83a43,Black-and-white,Portrait photography\ntest/2213513e0dc9ad20,Angling\ntest/2213cc87243a6642,Ball game,Team sport\ntest/221494d75409c8d0,Compact car,Luxury vehicle,Antique car\ntest/22149dd8ba6fde6d,Performance car\ntest/22160e69a2a1f717,Chordophone\ntest/22167158a836d6bd,Luxury vehicle\ntest/221a233ec1ba04c9,Jeans\ntest/221ebab83b1eb4ef,Close-up\ntest/2222f47f869947c0,Church bell\ntest/2225226381120a08,Sport utility vehicle\ntest/2225b2c55ccdb2d6,Blond\ntest/2226ccdd617cca18,Close-up\ntest/22296b5de1e96dca,Peafowl\ntest/22323e18fde62452,Childbirth\ntest/22374608e68c2c98,Domestic short-haired cat,Close-up,Whiskers\ntest/2237f9bf4ec64d92,Luxury vehicle,Performance car\ntest/22389935686071bc,Military vehicle,Sport utility vehicle\ntest/2239bef3e25c1f56,Briefs,Barechested\ntest/223ffab99cddb346,Whiskers\ntest/22436eb0e686d56c,Luxury vehicle\ntest/2247e2cbf1f19816,Luxury vehicle,Performance car\ntest/22494b3459c5276e,Gliding\ntest/2249e10410948d71,Junk food\ntest/224e8ae49051778b,Domestic short-haired cat,Whiskers\ntest/225246c88d4e8099,Senior citizen\ntest/22537aa3ff33ba50,Compact car,Luxury vehicle\ntest/2253dc754a5f2338,Close-up\ntest/225441472d54443c,Luxury vehicle\ntest/2254ccf8cea540c7,Luxury vehicle\ntest/2255c16e65508d34,Shinto shrine\ntest/2256ddf949a325c0,Close-up,Nile crocodile,Crocodilia\ntest/22574e9a0b6e6bba,Luxury vehicle\ntest/2259a6c737ccafa4,Nile crocodile,Crocodilia\ntest/225ba91908037ec9,Childbirth\ntest/225c020947b4f487,Aircraft engine\ntest/225d436f25ef6ffb,Electronic instrument,Electric piano\ntest/225e21e4484c313e,Luxury vehicle,Antique car\ntest/225f3e2cb4312172,Extreme sport,Sledding\ntest/225fb4ef4078b324,Aircraft engine\ntest/226309e16adc568c,Blond,Hair coloring\ntest/226594d9f3d1067d,Freight transport\ntest/226670293e9a0ff3,Arthropod\ntest/2267b76b03b25be0,Luxury vehicle\ntest/22686e72ce1125b1,Close-up\ntest/226aa3f3fb576d7d,Wallaby\ntest/226be24eca06e771,Close-up\ntest/226c260eb70850d1,Luxury vehicle\ntest/226d284e986f5641,Sport utility vehicle\ntest/226d2c2a25bdba82,Wind wave\ntest/226d6dfc981e79be,Jeans\ntest/226dbc04db999b1b,Rural area\ntest/22700367d3a84933,Close-up\ntest/22702f4b63138b03,Team sport\ntest/227151c498157741,Close-up\ntest/22726c4fe674202c,Luxury vehicle\ntest/22742737b1072fa4,Luxury vehicle\ntest/22742c0abe0acc31,Jeans\ntest/22745409919d76a3,Luxury vehicle,Performance car\ntest/22761097a9119636,Rural area\ntest/2276116c941f0607,Boating\ntest/2279455bd3b4666a,Luxury vehicle,Sport utility vehicle\ntest/227cd48103512b06,Close-up\ntest/227dce40c40b25ad,Rural area\ntest/2280f7c192f6007b,Close-up,Whiskers\ntest/22892edb542d47c7,Black-and-white\ntest/2289453d8b5a4252,Antique car\ntest/2289b6f7b8ae95d7,Frigate\ntest/2289d81a78c6945a,Watercolor paint\ntest/228a876f29cca13b,Luxury vehicle,Performance car\ntest/228cce97c5ec2e8d,Nile crocodile,Crocodilia\ntest/228ffe26d6f13ca3,Luxury vehicle,Sport utility vehicle\ntest/22902f98b24b705c,Military vehicle\ntest/229180d77afe88ed,Luxury vehicle\ntest/2291aba7282708b9,Plant stem\ntest/2291c493fc0e8026,Ball game\ntest/22943369f00a5ea8,Khinkali,Pelmeni\ntest/22968740ea849809,Scaled reptile\ntest/229cf0e23b9371b9,Luxury vehicle\ntest/229e77f2c0736dbf,Pork chop\ntest/229e7aaf7f735688,Trampolining\ntest/229f33ca89679422,Forklift truck\ntest/22a329f187bf8d5c,Luxury vehicle,Performance car\ntest/22a34bd204a7d0ff,Luxury vehicle,Performance car\ntest/22a5a1c3958b323b,Leaf vegetable\ntest/22a7443a77b1f968,Monoplane\ntest/22a7fd00ed84617f,Compact car,Antique car\ntest/22a91f6b4eca416c,Jeans\ntest/22ab19a65ef15394,Glacial landform\ntest/22ac236b8a16cf5b,Team sport\ntest/22ad17b033c096f0,Luxury vehicle\ntest/22adc76d5e1cc500,Luxury vehicle\ntest/22b1ef7412184557,Luxury vehicle\ntest/22b2945b2564a281,Luxury vehicle,Antique car\ntest/22b295529a20bfd4,Chordophone,Electric guitar\ntest/22b4f3be8dfd172e,Compact car,Luxury vehicle\ntest/22b5e38ed312205a,Digital camera\ntest/22b75fa8b24a95a9,Close-up\ntest/22b80c494e5ec077,Luxury vehicle,Sport utility vehicle\ntest/22b818497fc431cd,Luxury vehicle\ntest/22b91be64a9d8643,Close-up\ntest/22b9de1ca10f631a,Digital camera,Close-up\ntest/22c11d10e12699ef,Close-up,Plant stem\ntest/22c17fb1411ee176,Black swan\ntest/22c2211f9a09c5ee,Frigate\ntest/22c7265b675cd0cd,Antique car\ntest/22c785c33fbd03f5,Machine tool\ntest/22c87ece684fe8cd,Roman temple\ntest/22c98d53c98be237,Blond,Hair coloring\ntest/22ca797f1ca23150,Compact car,Luxury vehicle,Antique car\ntest/22cb787284c85595,Whiskers\ntest/22cbd7a80ea2537e,Bird of prey\ntest/22cc709b6b43e018,Close-up\ntest/22cc9505f2c49f6a,Chordophone\ntest/22d035859c384fe9,Plant stem\ntest/22d0ab143002af35,Luxury vehicle\ntest/22d640e5b25886ba,Luxury vehicle\ntest/22d6b1f96cbbaa83,Black-and-white,Full moon\ntest/22d6c250a4df9236,Ball game\ntest/22dbad5eb45122c3,Luxury vehicle,Performance car\ntest/22dcfe2378377d32,Luxury vehicle,Performance car\ntest/22dd1e06fc2e28a1,Ball game,Team sport\ntest/22dd2c6704a8494f,Close-up\ntest/22dd2cfda3502a8d,Touring car,Antique car\ntest/22deb87614a920e6,Luxury vehicle\ntest/22dee337369b5627,Microcontroller\ntest/22dfa5eeeaf89c4f,Chordophone,Electric guitar\ntest/22e217e9df156a37,Antique car\ntest/22e21d377f197daf,Military vehicle\ntest/22e30d8637f53665,Chordophone\ntest/22e3cb9950856c81,Extreme sport,Parachuting\ntest/22e6d8df21fd90fd,Molluscs\ntest/22e7713bbacb3ff6,Luxury vehicle,Sport utility vehicle\ntest/22ece953014bbf2b,Hair coloring\ntest/22ef4bcb5bd2f7bd,Shinto shrine\ntest/22ef71e5c86153b6,Machine tool\ntest/22f2b91b0786bff2,Zeppelin\ntest/22f39d19ed20a814,Leaf vegetable\ntest/22f3edfa092f010e,Domestic short-haired cat,Whiskers\ntest/22f60650322f1dfe,Luxury vehicle\ntest/22f6d70a8626322c,Extreme sport,Sport climbing\ntest/22f8ca9c07a5dfaf,Luxury vehicle\ntest/22fa484831e7c314,Aircraft engine\ntest/22fa860f70c6f856,Electronic instrument\ntest/22fd7e17260b32d0,Luxury vehicle,Antique car\ntest/22ffa71ff21a2f11,Sport utility vehicle\ntest/22fface1a0bddcb0,Junk food\ntest/230043e5ee02728f,Close-up\ntest/2301d2a50402781f,Melee weapon\ntest/2303d1531f78e27e,Electronic instrument\ntest/2305b2eaf51d2693,Ribs\ntest/2307eec35c8cd29f,Musical theatre\ntest/2308b79956b85995,Residential area,Aerial photography\ntest/230a13d8ebe7d459,Firearm\ntest/230bb6e506d609bd,Aircraft engine\ntest/230c3eb8f22cee77,Horse and buggy\ntest/230d8fbd06f306c8,Performance car\ntest/230e5cdf01661097,Perching bird\ntest/23105606e468ea96,Halter,Equestrianism\ntest/2310b023281e7103,Compact car\ntest/23121265aff341e1,Prosciutto\ntest/231300092e40b91e,Extreme sport\ntest/2314dcd2c040d8b3,Luxury vehicle,Antique car\ntest/2316f65a43254b27,Compact car\ntest/231770820ef571db,Vegetarian food\ntest/2317e10ab559413d,Boating\ntest/23180090fe91a4d4,Antique car\ntest/231894ec38813c82,Brisket,Beef tenderloin\ntest/231a05c0ba762467,Bird of prey\ntest/231cdce458ee17bd,Rural area\ntest/231f047848f50f65,Briefs\ntest/2322f164cbebec9e,Auto racing,Luxury vehicle,Performance car\ntest/2327eb3c17c018d0,Combat sport,Grappling\ntest/232d7dcdbb251a3d,Close-up\ntest/2332e2a655aae32d,Close-up\ntest/2333631da2efaeee,Close-up\ntest/2333ac90234d7d50,Chordophone\ntest/2333fcfe55208f23,Close-up,Whiskers\ntest/2334c6bce2c760cf,Antique car\ntest/2334fac41ef084a2,Bovine\ntest/23369c78a134f9f0,Luxury vehicle,Performance car\ntest/23371b59d4d98dee,Luxury vehicle\ntest/2337210fa8b0397e,Close-up,Scaled reptile\ntest/2339605c43ba1a35,Fried noodles\ntest/2339d0afd4f28a54,Performance car\ntest/233f33a26637abb4,Close-up\ntest/2340283d8ffa8b46,Compact car\ntest/2343212ab72aafb2,Black swan\ntest/23439e2bab87d9e2,Extreme sport,Boating\ntest/234400819f72a80c,Combat sport\ntest/23487f4bf993138c,Extreme sport\ntest/2348ce3ed704ec67,Compact car,Antique car\ntest/234a212f89464a84,Luxury vehicle\ntest/234b31ecfc87d494,Luxury vehicle,Antique car\ntest/234bab8874869a9b,Luxury vehicle,Sport utility vehicle\ntest/234c6486ee73203c,Shinto shrine\ntest/234c99a77708ef74,Luxury vehicle,Performance car\ntest/234d72b20100b070,Combat sport\ntest/234d74e2e64ba81b,Middle ages\ntest/234ea2006635a292,Rural area\ntest/2350f9a52a3dbab1,Compact car,Luxury vehicle,Antique car\ntest/2351e8ef7d60e0a3,Luxury vehicle,Antique car\ntest/2352cc300e8508d9,Close-up\ntest/235338a368d8d88b,Barquentine,Frigate\ntest/235398a412d8eb82,Ball game,Team sport\ntest/2354568f7f71f052,Close-up\ntest/2354581a88550dde,Monoplane,Gliding\ntest/2355de8a62514ed9,Close-up\ntest/23575d63f138876c,Auto racing,Luxury vehicle\ntest/235a893889371e10,Performance car\ntest/235ac11ca4355b89,Ball game\ntest/235bf27404953f30,Luxury vehicle,Antique car\ntest/235c7ec4b713124c,Brisket\ntest/2361a08f1c0dc30f,Vegetarian food\ntest/2361db114e4d5926,Junk food\ntest/23639b02e6a606bf,Monoplane,Aircraft engine\ntest/236517caece01274,Black-and-white\ntest/2367e800f450e969,Ramen\ntest/2368e91f2557f6fc,Drums\ntest/236a87eaaaf7cb09,Galleon\ntest/236f0054c149afbc,Newsagent's shop\ntest/237302d5580cc2fd,Close-up\ntest/2375a153edd26b8a,Whiskers,Erinaceidae\ntest/23764de1fb9e13f8,Aircraft engine\ntest/23764fb06762d63e,Luxury vehicle\ntest/2377e225be4e135b,Luxury vehicle,Performance car\ntest/2378c77762a171bf,Luxury vehicle,Antique car\ntest/23792738738cad29,Military person\ntest/2379a5bd4a5d8232,Close-up\ntest/237b305ed165cc41,Aerial photography\ntest/237db00f0b6af025,Luxury vehicle,Antique car\ntest/2380fcabcd3fdbbf,Luxury vehicle,Antique car\ntest/23834dbebebe3d8c,Combat sport\ntest/23865844647eb33a,Junk food,Fried food\ntest/2387e7cf7d5390ad,Close-up\ntest/2389878e316820e7,Arthropod\ntest/238c51bf7836024e,Plant stem\ntest/238cb11beafab493,Luxury vehicle,Performance car\ntest/238ebe782377430f,Pit bull\ntest/23909572b1433120,Luxury vehicle,Performance car\ntest/2391a2bec497020b,Black-and-white,Luxury vehicle,Performance car\ntest/23926613eb9df89c,Compact car\ntest/239430b65d36d4cd,Watercolor paint\ntest/23957626ccae0c1d,Bovine\ntest/23975300c50aad9e,Luxury vehicle,Performance car\ntest/239ab11a1cfe19a4,Scaled reptile\ntest/239e84a3a2fb5c08,Antique car\ntest/23a4de103323418d,Black-and-white\ntest/23a7abc974a1c2bd,Jeans\ntest/23a7dcd30a882144,Compact car,Luxury vehicle\ntest/23a8c5b81db0c4b6,Comics\ntest/23a9162f4356427c,Combat sport\ntest/23a9efeb0d35490f,Domestic short-haired cat,Close-up,Whiskers\ntest/23ab22b183cb37cd,Melee weapon,Hunting knife\ntest/23ac916f2c4ef2e6,Modern dance,Musical theatre\ntest/23ad6da04b47a0ef,Glacial landform\ntest/23b1281e47cb1e9e,Rural area\ntest/23b3f130b818113c,Siberian husky\ntest/23b43d43a750986c,Domestic short-haired cat,Whiskers\ntest/23b4d9444a3e49c9,Auto racing\ntest/23b6a4f1f5215207,Sport utility vehicle\ntest/23b80cc797192507,Luxury vehicle\ntest/23ba1f10ecc5e5aa,Sport utility vehicle\ntest/23c1c177ac56bfc6,Combat sport\ntest/23c29a00db75ca0f,Luxury vehicle\ntest/23c4246164e94251,Black-and-white\ntest/23c49420f96a39ad,Halter\ntest/23c62c68d9bb64d6,Electronic instrument\ntest/23c69bb9b2f6491a,Luxury vehicle,Performance car\ntest/23ca5f20f6013807,Solar energy\ntest/23caf49eea404c46,Cookies and crackers\ntest/23cce5b013ff8367,Arthropod\ntest/23cdb13a12fec82a,Aircraft engine\ntest/23cfebbda826c8d5,Antique car\ntest/23d08aa36f1c432c,Auto racing\ntest/23d2696fcf6eef54,Gliding\ntest/23d43a0639b981ce,Compact car,Antique car\ntest/23d4d55f46bbae4f,Wind wave\ntest/23d5b79e997e6d1f,Arthropod,Close-up\ntest/23d654d92ffd65ec,Jeans,Dog walking\ntest/23d848ff6d7a063c,Boating\ntest/23de0d9896de8119,Cookies and crackers\ntest/23de8cb6a268c131,Luxury vehicle\ntest/23e1828c18dfd5b2,Wind wave\ntest/23e34a25a90ac1dd,Luxury vehicle,Performance car\ntest/23e35db320de2a3f,Close-up\ntest/23e4ddb53ec81951,Luxury vehicle,Performance car\ntest/23e50046eb1dc035,Luxury vehicle\ntest/23e5032190c55ee4,Filling station\ntest/23e64d7bc5d094a9,Luxury vehicle,Performance car\ntest/23e994fecc4f4909,Pork chop\ntest/23ee455213f9e085,Fried noodles\ntest/23f1035b418aaad3,Sport utility vehicle\ntest/23f2f84f2f5b0d5b,Great white shark\ntest/23f6df28daa9d736,Military person\ntest/23f9c12ebaad4fd7,Close-up\ntest/23fbad366a43b91a,Close-up\ntest/23fc1da34373a877,Extreme sport\ntest/23fcb4277a8ee317,Steamed rice\ntest/23feb4a4e6b7b5fd,Luxury vehicle,Antique car\ntest/23ff9fd0ad7fe7be,Luxury vehicle,Antique car\ntest/2400a8b66e63b90d,Rural area,Bovine\ntest/240166240fdc8a87,Antique car\ntest/2401adad9dfdfb42,Compact car\ntest/2401b70bab4f5fbd,Erinaceidae\ntest/2401e305e558d676,Flatbread\ntest/24021d1216eb4750,Equestrianism\ntest/2403f240e3f160e3,Frigate\ntest/240478b43b2d977a,Blond\ntest/2407b8a94a67a15f,Luxury vehicle\ntest/2407c73d6d594e53,Luxury vehicle,Performance car\ntest/240b357421cd7ea3,Luxury vehicle,Performance car\ntest/240d2e0a9b4cc7b0,Bovine\ntest/240f29245bf79988,Hound,Close-up\ntest/24104a81c4773790,Performance car,Luxury vehicle,Antique car\ntest/2410b4c85d561d32,Nordic skiing\ntest/2411fc04e8df6a11,Close-up,Scaled reptile\ntest/241474a1f06e03d0,Arthropod,Locust,Close-up\ntest/2415f9c2b9b49131,Black-and-white,Close-up,Whiskers\ntest/2419513367878e48,Ball game,Team sport\ntest/241cfb1cc8dd0bd4,Luxury vehicle,Performance car\ntest/241cfb79b3abf95e,Pit bull\ntest/241d78b6be815350,Luxury vehicle,Performance car\ntest/241e352973d2a8bf,Amphibian\ntest/241e3919bb08057f,Arthropod,Plant stem,Close-up\ntest/241ea955e4af6613,Black-and-white,Combat sport\ntest/241f4648caa3d22b,Ball game,Team sport\ntest/2422941251e13a0e,Domestic short-haired cat,Whiskers\ntest/242645a67b001e51,Luxury vehicle\ntest/24266fd4fc37a114,Compact car,Luxury vehicle,Antique car\ntest/24267c797cc1ff5a,Luxury vehicle\ntest/242823dd421d3715,Rural area,Aerial photography\ntest/2428997f838e2dd6,Chordophone\ntest/242935ad5d700ea1,Stemware\ntest/2429c8f6cae64fb3,Surfing Equipment\ntest/2431efd24f13c1ae,Luxury vehicle\ntest/2433583514678951,Close-up\ntest/243481f611ce0048,Senior citizen\ntest/24351f1ad66aa70a,Blond\ntest/243955ac1ccf710a,Close-up\ntest/243beae50a282f3b,Close-up\ntest/243c36d85b2dd881,Luxury vehicle\ntest/243cb9f462917e8a,Residential area\ntest/243d96c1ce8ae1c8,Ball game,Team sport\ntest/243db1c32790436a,Luxury vehicle\ntest/243ebde4a6118a0e,Rural area\ntest/243f6c252c25e680,Pit bull\ntest/2441a7a6ef2dddf6,Motorcycling\ntest/2446734e4ddddbdc,Performance car,Luxury vehicle,Antique car\ntest/244922a0c3a53657,Boating,Sailboat racing\ntest/24492bd9fe36432b,Plant stem\ntest/2449fa7170c0d780,Compact car\ntest/244b79e1e29e152c,Compact car,Luxury vehicle,Sport utility vehicle\ntest/244b89b78325dc21,Arthropod,Close-up\ntest/244bb1efb5fd014b,Luxury vehicle\ntest/244bb4bd36612915,Blond,Hair coloring\ntest/244bec80e51ac203,Sport utility vehicle\ntest/244c8472b8756551,Antique car,Performance car\ntest/244d041e96164f93,Close-up\ntest/244de5012a615d80,Modern dance\ntest/244ff97dae202e62,Close-up,Whiskers,Domestic rabbit\ntest/2450ad282b7a3663,Cookies and crackers\ntest/2450bcfff0acb351,Blond,Close-up\ntest/24512725cc084951,Drums,Electronic instrument\ntest/2451c0d78d9b4712,Close-up\ntest/24532ef3859724ed,Firearm\ntest/245458df2777df9d,Bovine\ntest/2459d8968322c9a5,Luxury vehicle\ntest/245d0f5a4c7bb1ed,Digital camera\ntest/245ed41d82032398,Sport utility vehicle\ntest/2462263331741e89,Firearm\ntest/2465068f1c5a448b,Ball game\ntest/24663096cf37f912,Junk food\ntest/2467f223c1688a1d,Ramen\ntest/246a912ddd39e8bb,Molluscs,Close-up\ntest/246ae86e4a2319be,Senior citizen,Childbirth\ntest/246d73584c7e6c5c,Ball game,Team sport\ntest/2470434d0358e64c,Close-up\ntest/2472a5a2b30f68ac,Scaled reptile\ntest/2473f17f3c22ce06,Aircraft engine\ntest/24756e2ed9add6e5,Black-and-white,Luxury vehicle,Sport utility vehicle\ntest/24770a64bb8b0263,Motorcycling\ntest/2477a84196c1d987,Firearm\ntest/24793a7acd4e8041,Close-up\ntest/247981329c028d8b,Luxury vehicle\ntest/247dfe75ecb71c6d,Aircraft engine\ntest/247ea372b6d275ea,Horse racing,Equestrianism\ntest/247ed9ca9a89f388,Goats\ntest/247fb95e0febbc5a,Ale\ntest/24801f2150aef1ca,Monoplane\ntest/248118f6c897b26f,Equitation,Equestrianism\ntest/2482ed9bf781223a,Luxury vehicle\ntest/248358dfb41cc2ae,Boating\ntest/24837d6da923a794,Sport utility vehicle\ntest/2486c6db2f19aec7,Plant stem\ntest/24876490d8687729,Close-up\ntest/2489061f0050cd28,Black-and-white\ntest/248abcc6bc2fb1ad,Close-up\ntest/248daaca7359977a,Plant stem,Close-up\ntest/248e19f3b7c34c71,Billiards\ntest/249049fb777c4880,Luxury vehicle,Antique car\ntest/249151a311b5b4f0,Flatbread\ntest/24918dde6b452bd8,Luxury vehicle,Antique car\ntest/24921780289fb702,Luxury vehicle,Sport utility vehicle\ntest/2492643345e51dd9,Leaf vegetable\ntest/249277e4b071a7cc,Luxury vehicle,Sport utility vehicle\ntest/2493a0607d45c8e4,Close-up\ntest/2495579490caad33,Soba\ntest/249577d828bae7e8,Black-and-white\ntest/2497346e5ffe0608,Luxury vehicle,Sport utility vehicle\ntest/2497b8203b1a9007,Boating\ntest/24980b7617757797,Backlighting\ntest/2499621a46019680,Boating\ntest/249b08fd010a89d5,Black-and-white,Close-up\ntest/249b726dec40527d,Luxury vehicle,Antique car\ntest/249f655af433fc41,Bovine\ntest/24a06bd7180e4150,Aircraft engine\ntest/24a3284b229a1050,Luxury vehicle,Antique car\ntest/24a469b43e4fd260,Close-up,Plant stem\ntest/24a4e793c65aae01,Performance car\ntest/24a5873645121f0d,Ball game\ntest/24a5ad5eef8e9088,Rural area\ntest/24a5eb34995b41b6,Boating\ntest/24aa6af4f025a068,Luxury vehicle,Antique car\ntest/24ab311b8ad8db90,Close-up\ntest/24aec5a155a6930c,Close-up\ntest/24afb10a5187cf5b,Close-up\ntest/24b071acd866c847,Rural area\ntest/24b1cdadf4873295,Antique car,Luxury vehicle,Performance car\ntest/24b1e9fea8895d4a,Compact car\ntest/24b3db2236fd9a1a,Microcontroller\ntest/24b5070ba7c200af,Luxury vehicle,Antique car\ntest/24b72ec37fb7809d,Combat sport\ntest/24b84ea30fada461,Ball game\ntest/24b995d60b4af490,High-speed rail\ntest/24ba8ee6b6327485,Close-up\ntest/24bac564f58b3c84,Milky way\ntest/24bacf68bd8feb11,Scaled reptile\ntest/24bad3bd398e13a4,Arthropod,Close-up\ntest/24bb5ac14ae7d78a,Compact car,Antique car\ntest/24bccfcf3fc1982d,Luxury vehicle,Performance car\ntest/24bd167261752281,Classical sculpture\ntest/24bfd7c114cd0d85,Calabaza\ntest/24c05bcad9859e09,Luxury vehicle\ntest/24c1fc0bf63da815,Luxury vehicle,Sport utility vehicle\ntest/24c2cce9b8252793,Residential area\ntest/24c48055b699ca80,Close-up\ntest/24c516844321586c,Extreme sport,Boating\ntest/24c7a9812a4bd521,Compact car\ntest/24c91cb91e950f5c,Arthropod,Locust,Close-up\ntest/24c9c612c593f371,Leaf vegetable\ntest/24cce6356a911ac5,Compact car,Luxury vehicle\ntest/24ce1003a4028a5f,Jeans\ntest/24d024afb3389c9b,Barechested\ntest/24d1b908afdf798d,Coral reef fish\ntest/24d219b849c3a04c,Fried food\ntest/24d38da7a036e8fd,Extreme sport\ntest/24d4478ef0caa6f9,Close-up\ntest/24d562e29a375a9d,Outdoor shoe\ntest/24d72bbbe13b3059,Luxury vehicle,Performance car\ntest/24d8c762e8ef36ed,Luxury vehicle\ntest/24d96cc1e21d161f,Ball game\ntest/24d9f6f5d1faef2c,Auto racing,Performance car\ntest/24daa60b410dc973,Luxury vehicle,Performance car\ntest/24db26b8984c7731,Aircraft engine\ntest/24dd72f8b0dff2c6,Luxury vehicle,Performance car\ntest/24ddcedfdec1a533,Black-and-white\ntest/24e50559c3635aeb,Senior citizen\ntest/24e607898ab7179f,Steamed rice\ntest/24e608a1ab709c6c,Electronic instrument\ntest/24e64037f5e8ebcf,Hound\ntest/24e645fed17860ac,Barquentine\ntest/24e74c25eb287f63,Luxury vehicle,Sport utility vehicle\ntest/24e7e4e20ecfaf97,Firearm\ntest/24e7eeb66bc6e49b,Luxury vehicle\ntest/24e8237d92637a4a,Luxury vehicle\ntest/24e901013f75824a,Luxury vehicle\ntest/24eca5e10d7977d7,Sport utility vehicle\ntest/24ee556ce434175d,Whiskers\ntest/24ef2da760e32fb2,Bovine\ntest/24efc7715654fb0c,Luxury vehicle\ntest/24eff9ae81897718,Bratwurst\ntest/24f263c2e01787f2,Extreme sport,Wind wave,Personal water craft,Boating\ntest/24f323ca8f1c447b,Plant stem,Close-up\ntest/24f32fd263ed45d1,Roman temple\ntest/24f4de9112c443fa,Auto racing\ntest/24f79ac6857cd1e5,Close-up\ntest/24fbc440d3a805a9,Sport utility vehicle\ntest/24fc00c8ae0f4721,Luxury vehicle,Antique car\ntest/24fc07682625b5ea,Figure skating\ntest/24fc757087c9e4a4,Divemaster,Scuba diving,Underwater diving\ntest/24fdd026fabc5ac5,Angling\ntest/24fec97108fe00a9,Plant stem,Close-up\ntest/24ffe4d864ff900f,Ball game,Team sport\ntest/250006666523cc6a,Close-up\ntest/2500be2324c7ea57,Cosmetics\ntest/250101aecee34e23,Vegetarian food\ntest/25049f8ecf407dea,Compact car\ntest/2504b9635a2476d2,Watercolor paint\ntest/2504dc70e27c98fb,Close-up\ntest/25078e6ff4afb291,Luxury vehicle\ntest/2509b4b6414f04d3,Combat sport\ntest/250c33fbb6114d04,Boating\ntest/250d6580646b10bf,Luxury vehicle\ntest/250db2c5941911a8,Luxury vehicle\ntest/250f70481b410851,Close-up\ntest/2512b96823218b95,Close-up\ntest/2512fe655d44ad1e,Close-up\ntest/25138f1a564f25e4,Whiskers\ntest/2514c17dc1857222,Luxury vehicle\ntest/2517436035e10d02,Junk food\ntest/25176b521dad07df,Luxury vehicle\ntest/251945826f13aa32,Residential area\ntest/251b5a66e1221e38,Microcontroller\ntest/251b8812efcfbb23,Vegetarian food\ntest/251e4c842963ad62,Jeans\ntest/251e861dbf438e9c,Residential area\ntest/251ec9cd855d3626,Compact car,Antique car\ntest/251f40f36b559ba8,Dishware\ntest/2520b85a3da7ecb2,Luxury vehicle,Performance car\ntest/252178560c248743,Compact car,Luxury vehicle,Antique car\ntest/2522ff0b989e2011,Arthropod\ntest/25268d0bd76fb1eb,Rural area\ntest/2526a58bb34fb208,Antique car\ntest/252a7d72b9ec994d,Plant stem,Narcissus,Close-up\ntest/252be15e9d37d379,Compact car\ntest/252d8f156da4d5a9,Black-and-white,Close-up\ntest/252eb4af3f5a1b0c,Blond\ntest/253425099d3393de,Jeans\ntest/2534461df861856c,Rural area\ntest/2534f71afbccaa2b,Compact car\ntest/25359ab72515f20a,Cobblestone\ntest/2539dc4249f3d479,Vegetarian food\ntest/253bff3c8c548266,Luxury vehicle,Performance car\ntest/253c533e6d7783be,Close-up\ntest/253ec416050934d0,Nordic skiing\ntest/2540127b1eb1a0ac,Compact car,Luxury vehicle,Antique car\ntest/2541c4b7feb06ddd,Rural area\ntest/2543607db0abeb8e,Luxury vehicle,Performance car\ntest/254443963507febd,Church bell\ntest/2544b8d63412da6a,Luxury vehicle,Performance car\ntest/2547fd525129f51a,Compact car\ntest/254f04535d601c61,Black-and-white\ntest/254f8a1021845534,Close-up\ntest/25505a8fa0cb9a1c,Rural area\ntest/2551c9d2ee2594d0,Combat sport\ntest/2555f95f13dd138c,Cosmetics\ntest/2556062762c5bfe7,Compact car,Luxury vehicle\ntest/2556577260b2c623,Luxury vehicle\ntest/255be523512026cd,Luxury vehicle,Performance car\ntest/255c982b55539912,Close-up,Plant stem\ntest/2565f4a062b7adb8,Sport utility vehicle\ntest/256711f30ef75cdc,Trampolining\ntest/256797cba4dd2b41,Close-up\ntest/2568940c59d1b918,Close-up\ntest/256a54a3b5462725,Residential area,Aerial photography\ntest/256a6ca812e06ea1,Team sport\ntest/256bc8abfd93b9cb,Domestic short-haired cat,Close-up,Whiskers\ntest/256bdbf6d1c879f4,Luxury vehicle\ntest/256ea4d489f55300,Auto racing,Performance car\ntest/256f1eeaf5cb433c,Extreme sport\ntest/256f54b410b0b0e9,Black-and-white\ntest/257148fe8df94563,Assault rifle,Firearm\ntest/2573d379e262efd2,Inflatable,Lifebuoy\ntest/257488c24a43d01c,Erinaceidae,Whiskers\ntest/2577a244a90c30d6,Luxury vehicle,Antique car\ntest/257aaa751ea37f12,Luxury vehicle\ntest/257da78c643c2d71,Jeans\ntest/257e5d5231025484,Whiskers\ntest/257f4ea050ba3d9e,Briefs,Barechested\ntest/257fd9aba9358a57,Antique car\ntest/257fe9a75684849f,Luxury vehicle\ntest/258273d4d2fd9197,Plant stem\ntest/25834531247d18b8,Rear-view mirror\ntest/2583c9a038218a16,Close-up\ntest/2583e2130ce6361d,Aircraft engine\ntest/25849dc02a360551,Close-up\ntest/2584bc1ee334a03f,Electric piano,Electronic instrument\ntest/2584c5c084a94cc9,Residential area\ntest/2586525e1856b040,Team sport\ntest/2586f7de8af0ac19,Close-up\ntest/2587d012dd160285,Fried food\ntest/25880c4f38d01747,Bird of prey\ntest/258b1202db97669e,Jiaozi\ntest/258bb4bd49e63b3a,Chordophone\ntest/258dbfd15a52c9c7,Compact car\ntest/258e9932344a5fd1,Senior citizen,Close-up\ntest/258f7cc321f38fd1,Luxury vehicle\ntest/2590233957645742,Sport utility vehicle\ntest/2590c171f1e25ad1,Equitation,Equestrianism\ntest/25918419866556bb,Compact car\ntest/2591a5bb3531992e,Boating\ntest/2591f478e47fb852,Blond\ntest/25957ed7a7771031,Luxury vehicle,Antique car\ntest/2598d424d4253ca0,Military person\ntest/2599494df89a77c8,Close-up\ntest/259ae479fd004754,Outdoor shoe\ntest/259cb7f2166ac18e,Outdoor shoe\ntest/259cf1e68d3f7f5b,Fried food\ntest/259d2a69da8045e9,Fried noodles\ntest/259d3f522293b91a,Close-up\ntest/259e0efa8b490cc3,Close-up\ntest/25a0c0e84422bae2,Rural area,Bovine\ntest/25a24a0bb12c594c,Compact car,Luxury vehicle\ntest/25a3ec10ea282066,Combat sport\ntest/25a5276c5b2104f0,Military vehicle,Sport utility vehicle\ntest/25a7ee8269898a83,Rural area\ntest/25a80145cafb0633,Luxury vehicle\ntest/25a9ad7cc8084d87,Monoplane,Gliding\ntest/25aa5ce596e825d0,Luxury vehicle,Performance car\ntest/25abbd495340b033,Floristry,Close-up\ntest/25ad88cb03154069,Ball game,Team sport\ntest/25ae6f2d2e2a4b41,Domestic short-haired cat,Whiskers\ntest/25b0f7419320a4d2,Scaled reptile\ntest/25b5469a3bc9b9c9,Rice ball\ntest/25b8fdc68b6e9e23,Luxury vehicle\ntest/25b9d47c838717f4,Fried food\ntest/25bf6547e6db583d,Close-up\ntest/25c09243e33d27cf,Jeans,Motorcycling\ntest/25c10d0c61aeed94,Antique car\ntest/25c191da5f059b0a,Luxury vehicle\ntest/25c1ba4b9caff840,Extreme sport\ntest/25c2e9cbfaad1869,Cosmetics\ntest/25c3b4bcbb64752c,Luxury vehicle,Sport utility vehicle\ntest/25c3c14e02aa2c0f,Aircraft engine\ntest/25c98bd2944d1116,Extreme sport\ntest/25ca9a4f13ccb823,Musical theatre\ntest/25cad7e7af017a47,Fried noodles,Soba\ntest/25cb070558671c38,Whiskers\ntest/25cfe36c5346410a,Scaled reptile\ntest/25cff8b926b869aa,Close-up\ntest/25d3cda45ae02de3,Close-up\ntest/25d90a8db7d0fe07,Black-and-white,Medical imaging\ntest/25d9a8e2b93f1680,Close-up\ntest/25da71eb8e08685b,Wind wave\ntest/25db010eb3c1e6a4,Miniature Poodle\ntest/25dee932443f9a64,Team sport\ntest/25e1b497d34a03c5,Plant stem\ntest/25e466b8dca5dddf,Bird of prey\ntest/25e5da5c6f83d70b,Luxury vehicle\ntest/25ea0e5ba54bda95,Black-and-white\ntest/25f35453f7c8cd14,Close-up\ntest/25f3c6a3367c0e5c,Pit bull,Black-and-white\ntest/25f9f48bf5151f37,Microcontroller\ntest/25fbb8f2617b9c7b,Black-and-white\ntest/25fc7cecfd64e216,Shinto shrine\ntest/25fedf1dd01702cb,Jeans\ntest/2600729c583d5821,Canola,Close-up\ntest/26013788f7ac12e4,Water polo\ntest/26017ae86ac044ed,Compact car\ntest/2601b6101882da17,Combat sport,Sumo,Grappling\ntest/26029c6c44ee5452,Combat sport\ntest/2602c8401f75bb17,Frozen yogurt\ntest/260515260b462180,Military vehicle\ntest/26098937334bfe59,Fried noodles\ntest/260e2acfd04a450e,Luxury vehicle\ntest/260e2dfef5ba4729,Amphibian,Close-up\ntest/260f026615c86219,Cookies and crackers\ntest/260f0d4f701f6ae6,Luxury vehicle,Performance car\ntest/260fba254dffc19f,Luxury vehicle,Performance car\ntest/261116ab4b051910,Floristry\ntest/2611d2e7db140a46,Extreme sport,Roller skating\ntest/2613b1a5bc57fcad,Black-and-white\ntest/2613ccddca33df02,Great white shark\ntest/2615ef09b0b953cf,Rural area,Aerial photography\ntest/2616ee9c0d7ea624,Aircraft engine\ntest/2618ee6f2c570a14,Whiskers\ntest/2619ae919740ce54,Close-up\ntest/261aaae4c64fff03,Plant stem\ntest/261b2db1983a700a,Compact car\ntest/261fca27a4be131c,Boating\ntest/2620fe00fee84e0b,Portrait photography\ntest/26219d3104d163f7,Antique car\ntest/26221d8a54fab01d,Close-up\ntest/2623906471385e0d,Vegetarian food\ntest/26257694e3659e81,Close-up\ntest/262708714a3c63f8,Combat sport\ntest/262780f2211e3819,Close-up\ntest/262b6c12e8f51c35,Antique car\ntest/262cf0fd1f5c4f33,Frigate\ntest/262ef00a19fcc320,Black-and-white\ntest/262f10efffb492ef,Athletic shoe,Outdoor shoe\ntest/262fb67efc614885,Black swan\ntest/2632783abd11bb0d,Jeans,Dog walking\ntest/2632f8f09139fa9a,Monoplane\ntest/2635e0de3b0f5c0c,Compact car\ntest/2636d64b4e7d3148,Close-up\ntest/263d7ee1ebc25c48,Nordic skiing\ntest/263e02fa1a32c227,Black-and-white\ntest/26407df923f45ca0,Cookies and crackers\ntest/26411e3ba638f46e,Microcontroller\ntest/264281e21805301d,Blond,Hair coloring\ntest/2644da6e54736a2c,Aircraft engine\ntest/26464b11e297bb30,Sport utility vehicle\ntest/26476a8a49696843,Close-up\ntest/2648007e007742bd,Chordophone\ntest/26490f9d898822ba,Vegetarian food\ntest/264ba51f3f45cd67,Domestic short-haired cat,Whiskers\ntest/264f45695c9f50b9,Chordophone\ntest/264f6f1a3610be46,Close-up\ntest/264f7c1608b13f83,Briefs\ntest/264fc65ba0cab98b,Performance car\ntest/265229ecf7ef7c04,Compact car\ntest/26537ed72cbbfe3f,Close-up\ntest/2653d1ffc451caf6,Close-up,Whiskers\ntest/2656e5d001446570,Antique car\ntest/2658c46d72cb5b14,Plant stem\ntest/2658cc09572412c5,Luxury vehicle\ntest/26592f984bbb666f,Portrait photography,Close-up\ntest/265aff5671d74cc1,Close-up\ntest/265fa8ce6c7dba41,Plant stem\ntest/26602328f43be34d,Compact car,Antique car\ntest/26610e4eb829732e,Close-up\ntest/2661f3da847ed1b6,Watercolor paint\ntest/26628cecf4bb1d95,Coca-cola\ntest/26632d51df7c6c5a,Luxury vehicle,Antique car\ntest/2663488abe4343d4,Double-decker bus\ntest/266356c78c3e2197,Scaled reptile\ntest/2664ce031523e2f9,Floristry\ntest/26676281f57c6bef,Sport utility vehicle\ntest/26687c1d7107d5cf,Senior citizen,Close-up\ntest/2669e21fadd5a6b6,Jeans\ntest/266bbb9429bad4ed,Jeans\ntest/266be369c11906aa,Briefs\ntest/266cc54a03c2238c,Extreme sport,Sledding\ntest/266d6da6031630cd,Close-up\ntest/266da5c9859d2cfc,Chordophone\ntest/266df9bf5f5d3186,Compact car\ntest/2670d0241af81b94,Compact car,Luxury vehicle\ntest/2670faae7c272de0,Close-up\ntest/267211afdc947906,Roller skating\ntest/2672a7ab928a2733,Musical theatre\ntest/267495119381bce3,Bird of prey\ntest/26749ca4386979ef,Fried food\ntest/26771a604af18984,Luxury vehicle,Performance car\ntest/26776f6816443445,Portrait photography\ntest/267a34018ba10881,Ball game\ntest/267f3644f43055e2,Sport utility vehicle\ntest/267fb809c612137c,Close-up\ntest/2681374f3a7471f6,Athletic shoe,Outdoor shoe\ntest/26824b17ee478dd9,Compact car,Luxury vehicle\ntest/26855989c926ab7d,Chordophone\ntest/268790ddba25e7c4,Hair coloring\ntest/2688931a58bb10b0,Compact car,Luxury vehicle,Antique car\ntest/268a3df339dfc79f,Bovine\ntest/268c5e32b4f225bb,Flatbread\ntest/268f0d26ef015e40,Figure skating\ntest/268ffc5aa4a17a4f,Close-up,Scaled reptile\ntest/26912ba548456bd8,Residential area\ntest/2691314b3f922f8a,Arthropod,Close-up\ntest/2691e44f223a784a,Residential area\ntest/26924b8890254a85,Black-and-white\ntest/269429f1842a2393,Blond\ntest/2694d5db3b02746a,Arcade game\ntest/269522dea616e644,Freight transport\ntest/2695824376168622,Junk food\ntest/2697312f27e440a9,Close-up\ntest/269942e86c4cc168,Close-up\ntest/269a0c74a17a5bfc,Fried food\ntest/269a5a04df01371a,Close-up,Plant stem\ntest/269aa6e87201904a,Black-and-white\ntest/269c7491faf33144,Plant stem\ntest/26a3a1f0c0ce4fe1,Compact car\ntest/26a3abc464178116,Boating\ntest/26a3d7690e768094,Black-and-white\ntest/26a455a839899dc2,Dog walking\ntest/26a4a2b93f9f78b4,Senior citizen\ntest/26a586c485204de3,Motorcycling\ntest/26a5bb39fc465b5f,Close-up\ntest/26a97d148e6939ba,Antique car\ntest/26ab3457f925cde4,Rural area\ntest/26ae973ecf2528fe,Aircraft engine\ntest/26b0ccf55903ff82,Bratwurst\ntest/26b2085c44c6a9cf,Auto racing,Luxury vehicle,Performance car\ntest/26b2babf2ec90d7b,Compact car\ntest/26b45e4572605eb1,Ball game,Team sport\ntest/26b5d57956344e99,Sport utility vehicle\ntest/26b8a97e8bb250aa,Motorcycling\ntest/26b93b15d7a5daf4,Sport utility vehicle\ntest/26b975b292c40443,Arcade game\ntest/26bab478910dc7af,Close-up,Whiskers\ntest/26bab82911620b39,Close-up\ntest/26bb359e39d13021,Dog walking\ntest/26bb663672c57292,Performance car\ntest/26bc4a57e9eb13ec,Equestrianism\ntest/26bca4cfc9a49755,Arthropod,Close-up\ntest/26bd929fca4c0523,Fried noodles\ntest/26c1b8c2d7074a93,Auto racing,Compact car\ntest/26c21cbe576d86e9,Lifebuoy\ntest/26c4446a0d22a969,Sport utility vehicle\ntest/26c7dfe1510469ef,Luxury vehicle,Antique car\ntest/26c881a4fad723e9,Ball game,Team sport\ntest/26c98ede8beac2a4,Performance car,Antique car\ntest/26cbd42692298f5e,Close-up,Whiskers\ntest/26cbdb9a654d6a9c,Close-up\ntest/26d3adc38ecbd63e,Junk food\ntest/26d53addb71b193c,Vegetarian food\ntest/26d5876a1ec99ed7,Luxury vehicle,Performance car\ntest/26d6263c214a6444,Hound\ntest/26d6c3b1c6a9dc4c,Bird of prey\ntest/26daf165ff814102,Perching bird\ntest/26db472e2dfc57fd,Luxury vehicle\ntest/26dbc26f819a0d68,Ball game,Team sport\ntest/26dbde35666a810a,Close-up\ntest/26dcc5a786b544dc,Headphones\ntest/26dddc9cfe1cad65,Compact car\ntest/26de010ff979cd08,Middle ages\ntest/26ded9ba9436b552,Luxury vehicle,Sport utility vehicle\ntest/26deef1ec5c487f1,Sport utility vehicle\ntest/26dff505256728ff,Firearm\ntest/26e0267dd33e2b1e,Close-up,Whiskers\ntest/26e451c06fc274a8,Sport utility vehicle\ntest/26e54483dff56879,Arthropod,Close-up\ntest/26e6ffec1a22bb01,Divemaster,Scuba diving,Underwater diving\ntest/26ea34575d7faa54,Firearm\ntest/26eba60bf2828382,Combat sport\ntest/26ec60d664e578db,Fire apparatus\ntest/26ecbaf87d1736d9,Auto racing\ntest/26ed450961730c81,Ball game,Team sport\ntest/26edf9711fa8fc61,Team sport\ntest/26eef7291d4ecc3f,Arthropod,Close-up\ntest/26f14c8492ade708,Dog walking\ntest/26f1e5aa7d1d7596,Floristry\ntest/26f2d478b61d6384,Close-up,Plant stem\ntest/26f63af11e5737c2,Luxury vehicle,Performance car\ntest/26f8c451dbf8d164,Luxury vehicle,Performance car\ntest/26faffecf50e53c7,Close-up\ntest/26fc772e71d2e6f8,Whiskers\ntest/26fc958ad08dabce,Antique car\ntest/26fe4763d054a29c,Bird of prey\ntest/26feb9693c01201e,Luxury vehicle,Performance car\ntest/270028df6092e5b2,Luxury vehicle\ntest/2705935bd7012868,Close-up\ntest/2705a992ce042258,Domestic short-haired cat,Close-up,Whiskers\ntest/2706d483dade2f7d,Luxury vehicle,Antique car\ntest/270711d8e87ba6bd,Leaf vegetable\ntest/270c4419cc535ca7,Rural area\ntest/270dc18691036565,Monoplane\ntest/270e8e2f30d198fe,Rural area,Aerial photography\ntest/270f22b95b1cf6db,Arthropod,Close-up\ntest/270fa62994b8164d,Luxury vehicle,Performance car\ntest/271124b70204e4f2,Athletic shoe,Outdoor shoe\ntest/27128f6d960279d2,Antique car\ntest/2714931d52826149,Rural area\ntest/2714b054d4e7d4ed,Close-up\ntest/27156b73affc8a86,Compact car,Luxury vehicle\ntest/27190bf75f0322bb,Fried food\ntest/271b9250f740b9be,Close-up\ntest/271dd5864fda01c0,Plant stem\ntest/271df469c2e3a031,Domestic short-haired cat,Whiskers\ntest/271e555e88756d96,Vegetarian food\ntest/2720cd190e78b0aa,Luxury vehicle,Performance car\ntest/2723dcc2b6434450,Luxury vehicle,Sport utility vehicle\ntest/2725048b6c10ba18,Middle ages\ntest/27266488d4ea4203,Dishware\ntest/272b78187d758d0e,Scaled reptile\ntest/272bbabec626642b,Electronic instrument\ntest/272c1b3c17ca31f0,Coral reef fish\ntest/272cad8f81c379f4,Ball game\ntest/272ed87d393817fc,Rear-view mirror\ntest/27300379cfe9809a,Herding,Bovine,Rural area\ntest/27356bb5941a73d7,Luxury vehicle\ntest/273774a8a5ffb97f,Nordic skiing\ntest/2738af3d7b128a7e,Luxury vehicle,Performance car\ntest/273bf1093f0db01c,Melee weapon,Hunting knife\ntest/273e5fd3aaca56ad,Black-and-white\ntest/273ec8863a86711c,Team sport\ntest/27425067f67e5db8,Black-and-white,Close-up\ntest/274261d31c04dc02,Luxury vehicle\ntest/27439d0af65ca73e,Close-up\ntest/2745297aa6963ca5,Digital camera,Close-up\ntest/27456637d6871c46,Rural area\ntest/2747a601c7d16fc7,Lawn game,Ball game\ntest/2748b62e153da0e4,Underwater diving\ntest/2748b750790468e0,Pork chop\ntest/274adf14714f521b,Jeans\ntest/274c6dc736cba68b,Whiskers,Domestic rabbit\ntest/274f57f229955744,Close-up\ntest/275227bb2100ba06,Close-up\ntest/2757633296e76b81,Lawn game,Ball game\ntest/27593569e92772d6,Luxury vehicle,Sport utility vehicle\ntest/2759665ea086956a,Close-up,Whiskers\ntest/2759c3bd2b58be7c,Close-up\ntest/275abf90e8d5ee54,Soba\ntest/275c9a407d3b14db,Luxury vehicle,Sport utility vehicle\ntest/275cfd4ecf1eeba3,Rural area\ntest/275ea6c26604802d,Luxury vehicle,Antique car\ntest/27622c023dc85d0e,Dishware\ntest/2762fe8e14de8d66,Plant stem\ntest/27644b1fba5ad04a,Luxury vehicle\ntest/276633227874b101,Hound\ntest/27667a1a11f501de,Leaf vegetable,Vegetarian food\ntest/27676a7727605410,Luxury vehicle\ntest/2767d6e6ad433afe,Luxury vehicle,Performance car\ntest/276ae418038d5142,Compact car,Luxury vehicle\ntest/276af5b2610f74bb,Military vehicle\ntest/276cbaf58bf7489f,Rear-view mirror,Luxury vehicle\ntest/276cbe89df664bd2,Boating\ntest/276ea88d95ca9277,Domestic short-haired cat,Close-up,Whiskers\ntest/27702544b54f3a98,Halter\ntest/2771a2e19786682b,Cosmetics\ntest/2772617ba6812dcb,Luxury vehicle,Performance car\ntest/27756742d38528c7,Ball game,Team sport\ntest/2777080ba6c4081f,Fried food\ntest/2777cb254d703c5e,Vegetarian food\ntest/2778381073cfa801,Floristry\ntest/277846a648b9aa85,Ball game\ntest/277967d5237a1147,Musical theatre\ntest/27798064661dfa0c,Compact car,Luxury vehicle,Antique car\ntest/277c2d455f443dda,Coral reef fish,Close-up\ntest/277ef1d7a5079076,Black-and-white\ntest/277fa0db5fc6bffd,Luxury vehicle\ntest/27803e0aebf9fba8,Luxury vehicle\ntest/2780dd25363cfb14,Melee weapon\ntest/2782277c2768a99b,Auto racing\ntest/2782cb73098baa43,Monoplane\ntest/2782d6f864b8c9bf,Luxury vehicle\ntest/2782dae5db5eeba2,Vegetarian food,Corn on the cob\ntest/278349f85fc7b337,Extreme sport\ntest/2784496ab2f91c44,Compact car\ntest/2787515f33e2c576,Bird of prey\ntest/2787da45c6d68943,Compact car,Antique car\ntest/2788d379e36dd384,Frozen yogurt\ntest/278a1ee5a8e9494c,Dog walking\ntest/278d7517f5533a8c,Residential area\ntest/278e8a805970f410,Rural area\ntest/27900572a1848b27,Fried noodles\ntest/279022213508f30b,Outdoor shoe\ntest/279133853151263e,Performance car\ntest/2791dcc32686d095,Sport utility vehicle\ntest/279450e0720aaff0,Luxury vehicle,Sport utility vehicle\ntest/27948ff87498ad30,Close-up,Whiskers\ntest/2794b16c82e5e766,Close-up\ntest/2796e6531053fa22,Ball game\ntest/279ca908e642fd7a,Close-up\ntest/279e511b00280b70,Portrait photography,Close-up\ntest/279e976b73642657,Electronic instrument\ntest/279f6794cd0b00e2,Fried noodles\ntest/27a119ff0e9517db,Luxury vehicle,Performance car\ntest/27a122b353a2c941,Auto racing\ntest/27a1e519d47748f9,Luxury vehicle\ntest/27a6329f12cadac0,Shallot\ntest/27a64e62360c6962,Residential area\ntest/27a9ea86ba138a4e,Luxury vehicle\ntest/27ac6b7dabfc26e9,Luxury vehicle\ntest/27acd8964e7325eb,Electronic instrument\ntest/27af2ae8a9012963,Motorcycling\ntest/27b0a05c17c17b30,Luxury vehicle,Antique car\ntest/27b21a539da308ea,Portrait photography\ntest/27b80b04f2ec6afc,Black-and-white\ntest/27b872caa0ce8db2,Performance car\ntest/27b8d830ff39932a,Auto racing\ntest/27b9afc8c23a9613,Luxury vehicle\ntest/27baa5614590585f,Aircraft engine\ntest/27bb3dc172948f53,Headphones\ntest/27bbfba5b9ff91a4,Close-up\ntest/27bd6c008aa821ca,Team sport\ntest/27be611bc90e79c8,Performance car,Antique car\ntest/27be859c2269adda,Luxury vehicle\ntest/27bf3c30cad7fe81,Microcontroller\ntest/27c5a74ee11be7fe,Cookies and crackers\ntest/27c668de0d65c367,Compact car,Performance car\ntest/27c81e07446979d4,Vegetarian food\ntest/27c9cc113b4af4a8,Amphibian,Close-up,Scaled reptile\ntest/27cadce7612e5433,Domestic short-haired cat,Close-up,Whiskers\ntest/27cc09779518a960,Whiskers\ntest/27ce58684dfb54fc,Compact car,Luxury vehicle\ntest/27cfb600f3b1e908,Black-and-white\ntest/27d10b696123cae2,Compact car,Luxury vehicle,Antique car\ntest/27d1d9bd0848b352,Bottled water\ntest/27d34dcf828f31f3,Luxury vehicle,Antique car\ntest/27d38a51b42d8174,Extreme sport,Parachuting\ntest/27d58a52d226cbde,Arthropod,Close-up\ntest/27d6239c4f409d2f,Aircraft engine\ntest/27d9403d97bf591b,Microcontroller\ntest/27da9e1438f43747,Military person\ntest/27db187a5f7c1605,Luxury vehicle\ntest/27dd6ae1618f17cf,Aerial photography\ntest/27e16f0c4e6df95d,Ball game,Team sport\ntest/27e51193fd8a4c66,Ball game,Team sport\ntest/27e57a00ee735872,Arthropod,Close-up\ntest/27e8991bcac7ff38,Sport utility vehicle\ntest/27e8fb1d10c59f1a,Close-up,Whiskers\ntest/27e99d29adbb2489,Sport utility vehicle\ntest/27ea4c890ce291c8,Vegetarian food\ntest/27eba73ea412df32,Close-up\ntest/27eea2d0cf6618aa,Close-up,Whiskers\ntest/27f103fc9b187521,Motorcycling\ntest/27f1c036cde16edb,Luxury vehicle\ntest/27f48223387386d9,Chordophone,Electric guitar\ntest/27f6112d6d25196d,Bovine\ntest/27f6b4aa31115a6f,Black-and-white\ntest/27f72e959ea8d576,Arthropod,Close-up\ntest/27f7382a21d9f2bf,Sport utility vehicle\ntest/27f9c40c6ceb6958,Glacial landform\ntest/27fc7631a96018e5,Underwater diving\ntest/27fee09188b4fcc7,Modern dance\ntest/2800879bd8b6377e,Residential area\ntest/28026e0b10a545e0,Close-up\ntest/2802df2d306fd144,Miniature Poodle\ntest/2803c774c44240e1,Amphibian,Close-up\ntest/28051d9622b04480,Ball game\ntest/2805847d68e45008,Luxury vehicle\ntest/2807437d7a3120c6,Blond\ntest/28077f608e6f2536,Roman temple\ntest/2807d80c525eb5a3,Extreme sport,Sport climbing\ntest/28082650466037be,Luxury vehicle\ntest/280876b5da4c2421,Close-up,Scaled reptile\ntest/2809d4c3b836c38c,Close-up\ntest/280b09698bd146ac,Brochette\ntest/280d53da6064de5d,Luxury vehicle,Performance car\ntest/280e190b0807274a,Milky way\ntest/280f69632aa5906c,Luxury vehicle\ntest/2810b3fd0360ec77,Ball game,Team sport\ntest/2812fc680318836e,Breadboard\ntest/2813d243c28538a4,Close-up\ntest/2815132c67ce11c1,Luxury vehicle,Performance car\ntest/2815458002b89f5b,Residential area\ntest/281549637b63394b,Close-up\ntest/2815e1c5b01c9a65,Whiskers\ntest/281a1d09ed1e650d,Jeans\ntest/281bd3c50a0f0fe0,Microcontroller\ntest/281c7f5653721d12,Black-and-white\ntest/281c89065dadc66c,Jeans\ntest/281f810c14f66193,Arthropod\ntest/2820a72adfcf73a6,Electric piano,Black-and-white,Electronic instrument\ntest/2823424a947e47aa,Hound\ntest/2823c8ffca4688b0,Boating,Rowing\ntest/282603123ac7a9bf,Compact car\ntest/2826b4b29d9b5a04,Close-up\ntest/282804269c348461,Filling station\ntest/2828f96c7dc84f73,Aircraft engine\ntest/282a91eeeed74439,Compact car,Luxury vehicle\ntest/282b752407d7791e,Luxury vehicle\ntest/282c9ef3356c9493,Close-up\ntest/282d9601835bf648,Black-and-white,Close-up\ntest/282fc3042377cdf2,Aircraft engine\ntest/2832310cce0fd783,Blond\ntest/28339f846986fb94,Close-up\ntest/283428010c7e336e,Ball game,Team sport\ntest/2838286d929483f0,Road cycling\ntest/2839f70d37156c3c,Fried food\ntest/283bc0f82939595f,Aircraft engine\ntest/283c1b234fb2cf16,Dishware\ntest/28421ba3c0bfe66a,Vegetarian food\ntest/2844d19ccabd333d,Musical theatre\ntest/2845cf8d499dde6b,Luxury vehicle\ntest/28461166e51f8217,Combat sport\ntest/28470824866704f6,Arthropod,Locust,Close-up\ntest/28479aed80add7d2,Close-up\ntest/2847b40c81780f47,Jeans\ntest/28483078060e5c0e,Luxury vehicle,Performance car\ntest/28493917194e1d24,Extreme sport,Divemaster,Scuba diving,Underwater diving\ntest/2849ddc3a98d5e73,Microcontroller\ntest/284a002a07e2bf5f,Leaf vegetable\ntest/284c4d158b331f31,Bovine\ntest/284f6aeb452c4de7,Floristry\ntest/285065f26c1e8ec8,Billiards,Billiard room\ntest/285170dd8e23627d,Compact car,Luxury vehicle\ntest/2851ef148c520fdf,Vegetarian food\ntest/2852b1b76635a2a5,Auto racing,Compact car,Luxury vehicle\ntest/28533f4197925ee5,Close-up\ntest/2853fca693966b8e,Close-up\ntest/28546eaf68ceb737,Ball game\ntest/285636c369173887,Combat sport\ntest/2858cfe204fcc513,Luxury vehicle\ntest/2859a6a4134d92e7,High-speed rail\ntest/285e4d472cdb3312,Compact car\ntest/285e8ec0a470ba6f,Auto racing,Luxury vehicle,Performance car\ntest/285f56a93052bd42,Roller skating\ntest/286117cfebaecbd0,Black-and-white,Close-up\ntest/2861f4f9c5d09a6e,Frying,Junk food,Fried food\ntest/28624d709084a93c,Domestic short-haired cat,Close-up,Whiskers\ntest/2863b2680d53a451,Monoplane,Aircraft engine\ntest/286476ed15922b06,Luxury vehicle,Sport utility vehicle\ntest/2864871ece627c11,Auto racing\ntest/28664bec74bba3fe,Boating\ntest/28664ed293c2961a,Arthropod,Close-up\ntest/2867dbf1e6cdd84e,Luxury vehicle,Performance car\ntest/28690e933a017025,Black-and-white,Close-up\ntest/28696c4e01b60605,Arthropod,Close-up\ntest/286dec0c962bd051,Luxury vehicle\ntest/2871e243fc527d3a,Ball game\ntest/287290160a77736c,Extreme sport,Parachuting\ntest/28735e68348358c3,Road cycling\ntest/28754359b971612f,Luxury vehicle,Antique car\ntest/28759ea3b0286081,Coral reef fish\ntest/28776d7a122da7be,Jeans\ntest/287a0ccfa93ad499,Black-and-white,Woodwind instrument\ntest/287aae0caf17a6e6,Luxury vehicle,Performance car\ntest/287c6686c652da6a,Ball game,Team sport\ntest/287d1a6207b6ceeb,Figure skating\ntest/287d515a070a5bdc,Monoplane\ntest/288243359918981e,Monoplane\ntest/288380ce94481ac7,Auto racing\ntest/28844106bf5deb91,Combat sport\ntest/2885f804611788ab,Aircraft engine\ntest/288668be64a204cd,Ball game\ntest/2887ce9a983955b3,Close-up\ntest/288a28fdd99647c4,Floristry\ntest/288ad886029dfe44,Equitation,Equestrianism\ntest/288d051552f4cfdc,Auto racing,Performance car\ntest/288df6bde749a12f,Ale\ntest/288e2484a57420f8,Close-up\ntest/288f117ace624a04,Herding,Goats\ntest/2892e2a87bdf44ab,Ball game\ntest/28946d61981fc697,Aircraft engine\ntest/28947b81111c828b,Black-and-white\ntest/289538b468e26347,Luxury vehicle,Performance car\ntest/28971eb57e9c7f6d,Black-and-white,Close-up\ntest/2898ce33bc6ab98a,High-speed rail\ntest/289a1baf052c6acd,Close-up\ntest/289ae09e2fea0a09,Scaled reptile\ntest/289ba8b8bc647ebd,Residential area\ntest/289e09b89bfb66f6,Close-up\ntest/289e4496c2f9b6cd,Hound\ntest/289ed3f95a168ffa,Aircraft engine\ntest/28a35476b9dcac43,Black-and-white,Close-up\ntest/28a36531dbb1a5b3,Glacial landform\ntest/28a43984f725b4e6,Wallaby\ntest/28a5af092bdc61a7,Extreme sport,Motorcycling\ntest/28a7cc12fcbb1e66,Blond,Close-up\ntest/28a87fb603cda31b,Luxury vehicle,Performance car\ntest/28a897fdb8b29f14,Frigate\ntest/28a933b3cbeb5bba,Vegetarian food,Pasta salad\ntest/28a9c704b3ff5ef8,Athletic shoe,Outdoor shoe\ntest/28aaa8ab2a96b3b2,Close-up\ntest/28acf2dd2971eafe,Close-up\ntest/28ad3dd2696d2d7f,Compact car\ntest/28adbe6869dee4fa,Black-and-white,Barechested\ntest/28afb5f04c2b2098,Perching bird,Nest box\ntest/28b0b4ca877cdf42,Close-up\ntest/28b0e70d8364da6a,Portrait photography,Close-up\ntest/28b152f87c076d68,Plant stem\ntest/28b240f7fdcaaead,Compact car\ntest/28b34aa5f38432c0,Residential area\ntest/28b404db192bc9d9,Luxury vehicle\ntest/28b4309d71b41abd,Auto racing\ntest/28b5637f8a1bf55f,Sport utility vehicle\ntest/28b608c67cc7dd32,Close-up\ntest/28b6374c630eca5a,Whiskers\ntest/28b758acbbd6acf1,Compact car\ntest/28bb013804866ce1,Chordophone\ntest/28bbf3fb9a795233,Aloe\ntest/28bc6c41aa791f32,Roman temple\ntest/28bc96627be030ff,Scaled reptile,Close-up,Anole\ntest/28bf12d1411d6f40,Wallaby\ntest/28bf59412f377981,Luxury vehicle,Performance car\ntest/28bf6f317bdeeae8,Cosmetics\ntest/28bfa2b57c62434f,Jeans\ntest/28bfb0e59b29f7fa,Musical theatre\ntest/28c1742dab03a143,Ball game,Team sport\ntest/28c3091a621160de,Black-and-white\ntest/28c4471e22235dab,Compact car\ntest/28c5cf02a3a04dfa,Sailboat racing\ntest/28c76d0a7af641d1,Bovine\ntest/28cb18ee740dffc8,Bovine\ntest/28cb85599270ee8a,Ball game,Team sport\ntest/28cc5b4872c98e34,Vegetarian food\ntest/28cd47011eabb00e,Steamed rice\ntest/28cfc2f65a6e8e3d,Halter\ntest/28d3d7264f0c8864,Hound\ntest/28d40cce09f8ff1f,Luxury vehicle\ntest/28d498b2aa1c2d77,Electronic instrument,Electric piano\ntest/28d5d30a2bf2c0e8,Musical theatre\ntest/28d74d9b8823ad3f,Luxury vehicle,Performance car\ntest/28d8e105046ce98a,Luxury vehicle,Performance car\ntest/28d91cd2ffa54006,Black-and-white\ntest/28d9faec55aaf22c,Sport utility vehicle\ntest/28db05e5435a614f,Luxury vehicle\ntest/28dbb3f8e62bab67,Black-and-white\ntest/28dbbfb4afc6b559,Ball game,Team sport\ntest/28dc9a7334d76326,Luxury vehicle,Antique car\ntest/28de6494bcd6c376,Horse racing\ntest/28decc1830d4e391,Combat sport,Grappling\ntest/28df744c28703fdb,Ball game\ntest/28dfaf5656221462,Luxury vehicle\ntest/28e1e7733bc85966,Electronic instrument,Electric piano\ntest/28e3525d81cccaba,Whiskers\ntest/28e54464de07beb4,Miniature Poodle\ntest/28e60e9fb35d820e,Plant stem\ntest/28e7f7f86d3ce461,Roller skating\ntest/28e86b6206e15ad9,Leaf vegetable\ntest/28ea6ec5cd5fd5d5,Blond,Close-up\ntest/28ebb4a069a52619,Military person\ntest/28eda5d92e68547e,Rural area\ntest/28ee2766d78af486,Close-up\ntest/28ee567d7dc8e457,Middle ages\ntest/28ef3262da22d1c7,Extreme sport,Divemaster,Scuba diving,Underwater diving\ntest/28f23d49b5e120e1,Perching bird\ntest/28f30313bd324ee9,Sport utility vehicle\ntest/28f3c8abd33dbe89,Modern dance\ntest/28f4482b6212f83c,Narcissus\ntest/28f47550b0b33ae7,Modern dance,Musical theatre\ntest/28f493d822504aec,Luxury vehicle\ntest/28f5d6ef901c54c2,Residential area\ntest/28f7c69b387da59a,Close-up\ntest/28f833d448d9dc6c,Close-up\ntest/28f87cca95efc25a,Sport utility vehicle\ntest/28fa7a1994701ca8,Aircraft engine\ntest/28fc7dd2abab4c7b,Auto racing,Luxury vehicle,Performance car\ntest/28fcd6e6f0f5677d,Rural area\ntest/28fd47c9f1a39388,Plant stem,Close-up\ntest/28fdb361d4d51701,Bird of prey\ntest/28fdc44f0d9674f6,Water polo,Ball game,Team sport\ntest/28fed98d58b388ab,Outdoor shoe\ntest/28ffb56a9aa02e16,Close-up\ntest/2901b64e6e715d5e,Boating\ntest/29020280d96d8ddf,Junk food\ntest/29054b0813c6c7d1,Sport utility vehicle\ntest/2905fc0fed5a057c,Black-and-white\ntest/29062708c7cb1434,Luxury vehicle,Sport utility vehicle\ntest/2908a2fbaeceeb44,Luxury vehicle,Antique car\ntest/29090adbf9c0d1f9,Combat sport\ntest/290b969f5e51c161,Backlighting\ntest/290d3bfc65be167d,Steamed rice\ntest/290eb559827ac015,Black-and-white\ntest/290f12e4cda36b2f,Amphibian\ntest/291084ad082a77d3,Luxury vehicle,Sport utility vehicle\ntest/2913f798a0dcb474,Hound\ntest/2915ab6e39e63dff,Floristry,Close-up\ntest/291630c572b8857e,Close-up\ntest/2916d352c9c849b4,Ball game\ntest/2916faf494541976,Vegetarian food,Falafel,Fried food\ntest/2917f80591b39e58,Auto racing,Luxury vehicle\ntest/291b1f613ed3138d,Auto racing\ntest/291b27eda7e00d41,Luxury vehicle,Performance car\ntest/291bd98f6d4a94ba,Luxury vehicle\ntest/291cc6e149041e79,Outdoor shoe\ntest/291d5c3706e4a0f6,Performance car\ntest/291ebfd9d4387d66,Compact car,Luxury vehicle\ntest/292082ab912847ec,Close-up,Whiskers\ntest/292200f2f7fb1ce7,Jeans\ntest/2922526174fb467c,Luxury vehicle,Sport utility vehicle\ntest/2926bb88e30fbda2,Scaled reptile\ntest/2927f5d0ea4a8390,Extreme sport\ntest/2928cc038b0e7646,Performance car\ntest/29295313a2514442,Close-up\ntest/292b9dd551abef63,Plant stem\ntest/292bbfe276f317ea,Luxury vehicle,Sport utility vehicle\ntest/292d2749d0119202,Aircraft engine\ntest/292d44781f122ca0,Scaled reptile\ntest/292e1ade3a1a1107,Domestic short-haired cat,Whiskers\ntest/29333a252813c5bf,Hound\ntest/29366b386e7bdfb5,Jeans\ntest/29392e45dc5cdc3f,Close-up\ntest/293a79980a598262,Close-up,Plant stem\ntest/293a9a08e3647a22,Arthropod\ntest/293b52a12ec70c55,Black-and-white\ntest/293bc8980a8ab81c,Vegetarian food\ntest/293c8f260c88a8ac,Extreme sport,Kitesurfing\ntest/293d9d8bd60c53a3,Fried food\ntest/293e3a8252dfbd90,Compact car,Luxury vehicle\ntest/293e7967885b12e7,Black-and-white\ntest/293f56dc17a59733,Ball game,Team sport\ntest/29405829e2c45494,Aircraft engine\ntest/29417c5a723e5ab7,Black-and-white\ntest/294193a8b994a82a,Compact car,Luxury vehicle,Antique car\ntest/2942b001dc6b34e1,Luxury vehicle,Antique car\ntest/29435cb93519e9eb,Assault rifle,Firearm\ntest/2943d0b287192baa,Goats\ntest/2943f5d676014d39,Close-up\ntest/2944cd830042417e,Ball game,Team sport\ntest/2945281418548d18,Rural area\ntest/2945c2b3e7ad1980,Erinaceidae\ntest/29460210f154c876,Orator\ntest/294c66a73e54c130,Coral reef fish\ntest/294d857d7ef6717b,Compact car,Luxury vehicle,Antique car\ntest/294e091da9db02ec,Ball game,Team sport\ntest/294e7eb7765ca5ca,Modern dance\ntest/2951b7f2f54d5bd6,Jeans\ntest/2952d46dadc6fb72,Luxury vehicle,Sport utility vehicle\ntest/2952dbb1af445d27,Boating\ntest/2954ed0730f32851,Close-up\ntest/2955ae97394623f8,Boating\ntest/295756b99724559d,Vegetarian food\ntest/295bfa48a0613738,Luxury vehicle,Performance car\ntest/295f424f80e779b2,Comics\ntest/29604731701461e4,Performance car\ntest/2963170cd6851f9f,Outdoor shoe\ntest/2963b974e5d10eee,Luxury vehicle,Performance car\ntest/2964d77947c25d7d,Luxury vehicle,Performance car\ntest/2965824a31666a7e,Amphibian,Scaled reptile\ntest/2965b9e3e47b55a0,Calabaza\ntest/29665fadcbd508a8,Luxury vehicle\ntest/29678f9b5bdbcc48,Melee weapon,Hunting knife\ntest/296b97e12992cab4,Junk food,Fried food\ntest/296c5b5f386b0df8,Scuba diving,Underwater diving\ntest/296c60a205b5fa2f,Luxury vehicle\ntest/296cd91a10d4fa12,Close-up,Scaled reptile\ntest/29741e5031a40a20,Luxury vehicle\ntest/2974868dd9aa50d4,Close-up\ntest/297522d392b8ad0f,Close-up\ntest/297953fedfe0a2c0,Close-up\ntest/2979dc9cee5b26fc,Vegetarian food\ntest/297bd5a3215d7a5e,Watercolor paint\ntest/2984a98ea4d008d1,Freight transport\ntest/2986ebddc768011b,Arthropod,Close-up\ntest/29884f389ebf989b,Auto racing\ntest/298b62adb129006f,Coral reef fish,Close-up\ntest/298d98974f4afd49,Ball game\ntest/298dccaf265ee304,Racewalking\ntest/298e973a683f13e8,Molluscs\ntest/298fa09ad409960c,Luxury vehicle\ntest/298faf98247decc5,Luxury vehicle\ntest/29943d127f1dfb3b,Blond\ntest/29962a2aa4dc3cf9,Performance car\ntest/29962ff9be724997,Bird of prey\ntest/299746d7a0688990,Hound,Close-up\ntest/299add6391df3b96,Luxury vehicle,Performance car\ntest/299b56d23bd1a283,Billiards,Billiard room\ntest/299cc67b813f68d6,Military person\ntest/299fa2638c63458f,Team sport\ntest/299fdf71e863d13b,Extreme sport,Boating\ntest/29a1a54ef6b8f3b6,Military vehicle\ntest/29a5bd1c5b838fbf,Whiskers\ntest/29a6ba0c0c3efaeb,Luxury vehicle,Sport utility vehicle\ntest/29a806aadc8dcd50,Close-up\ntest/29a8aeb6297bb4bb,Black-and-white\ntest/29ab07bcabb98c99,Close-up,Scaled reptile\ntest/29ab1e76122f1613,Cobblestone\ntest/29ac0d849fc8cd50,Boating\ntest/29ac3e7e5c591885,Whiskers\ntest/29ace28b867048de,Close-up,Plant stem\ntest/29ae20aefa45059f,Luxury vehicle,Sport utility vehicle\ntest/29aea51b69856373,Ball game\ntest/29aec29ee4256cfb,Luxury vehicle\ntest/29b09eaa10d4a57b,Aircraft engine\ntest/29b258604ce98d20,Milky way\ntest/29b4d6c63088ba2a,Luxury vehicle,Antique car\ntest/29b54c87517e48ea,Close-up\ntest/29b7277bb88e78b6,Solar energy\ntest/29b9b42e92313b1a,Antique car\ntest/29bb36a378256c24,Compact car,Luxury vehicle\ntest/29bbfc67055fed59,Plant stem\ntest/29bc7cfebdd5c030,Blond\ntest/29bdf9869b26a276,Bratwurst\ntest/29beb43419c93f58,Fried food\ntest/29c0c263f648380c,Vegetarian food\ntest/29c2b535aeb9e465,Close-up\ntest/29c331379d184362,Rural area\ntest/29c85854d5d1d298,Billiards\ntest/29cd299c966b6acb,Compact car\ntest/29ce02b8b3dd10ca,Sport utility vehicle\ntest/29cfbca636e6ba50,Equitation,Equestrianism\ntest/29d470fce985e2d0,Aircraft engine\ntest/29d5a4cc9410e2ab,Aircraft engine\ntest/29d66cb28ef209e1,Rural area\ntest/29d994bda531c4bf,Compact car,Luxury vehicle\ntest/29db42e72fb4a0cd,Compact car\ntest/29dbcefe4f73672e,Close-up\ntest/29dbd9ec1697a80d,Extreme sport\ntest/29dc47138fedf7e8,Bovine\ntest/29dd3ff3adc3483f,Gun turret\ntest/29dd7618f41378d5,Jeans\ntest/29de3d4c2d434b73,Ball game\ntest/29e071843507bcd3,Sledding\ntest/29e4312571967df1,Whiskers\ntest/29e50555233c7cc8,Childbirth\ntest/29e54d503a5f6e5a,Beef tenderloin\ntest/29e58756caa30da3,Siberian husky\ntest/29e8c90291e4eb29,Dishware\ntest/29ebc75270c61cd9,Boating,Rowing\ntest/29ebcc5a5715e3d2,Close-up,Primate\ntest/29ecf37cdccf5639,Aircraft engine\ntest/29ef95e7359bdef7,Bonbon\ntest/29f06bd18afbcfa5,Bird of prey\ntest/29f06ccb584f7553,Ale\ntest/29f06f5321e9e89c,Whiskers\ntest/29f223a25c7eb061,Luxury vehicle\ntest/29f3ca44a736375c,Luxury vehicle,Performance car\ntest/29f431b123c581a9,Performance car\ntest/29f6f5ec95cf98e2,Luxury vehicle,Antique car\ntest/29f719c3451bb37b,Compact car,Antique car\ntest/29f7226e4a7acffc,Luxury vehicle\ntest/29f72a5d92b2393b,Luxury vehicle,Performance car\ntest/29f758145728723a,Black-and-white,Close-up,Whiskers,Domestic rabbit\ntest/29f836c804c4994c,Luxury vehicle,Antique car\ntest/29f9688d873a71ea,Luxury vehicle\ntest/29fb2071f7eade3b,Black-and-white\ntest/29fc1db600b87338,Luxury vehicle\ntest/29fc3b8c50ff018d,Road cycling\ntest/29fd79c525b8621b,Ramen\ntest/29fea8b7c3386f6f,Cookies and crackers\ntest/29ff1fa11f6c7995,Black-and-white,Portrait photography,Close-up\ntest/2a001de4476fdbce,Lawn game\ntest/2a0026c072f05045,Blond\ntest/2a0540dc37d134d2,Compact car,Luxury vehicle,Sport utility vehicle\ntest/2a056b7f83a8a995,Arthropod,Close-up\ntest/2a0881f57e31e863,Leaf vegetable\ntest/2a0cf6d4fff03fab,Black-and-white\ntest/2a0e8fde10d44da9,Calabaza\ntest/2a11049d3a19a738,Erinaceidae\ntest/2a128893f34521db,Compact car\ntest/2a1379ca61b390d3,Modern dance\ntest/2a1408b9adb98b24,Close-up\ntest/2a144cde613eaf5d,Sport utility vehicle\ntest/2a15aaf4e733ca9d,Auto racing,Performance car\ntest/2a1a0353c7dab0e1,Road cycling\ntest/2a1b3845b6b0e624,Extreme sport,Kitesurfing,Wind wave\ntest/2a1c2c46081b2e24,Luxury vehicle\ntest/2a1cd1a767901733,Close-up\ntest/2a1f6ba8c457858d,Nuclear power plant\ntest/2a20a7b5db2cd869,Extreme sport\ntest/2a210c6b77d68b61,Antique car,Luxury vehicle,Performance car\ntest/2a216a642abef236,Boating\ntest/2a21ce7b99301cce,Sledding\ntest/2a2286776e84fbcd,Fried food\ntest/2a23e1e3cbe0cf77,Close-up\ntest/2a256f0928315ef5,Antique car\ntest/2a271cff6bd430d5,Nile crocodile,Crocodilia\ntest/2a282cce1c6a09cc,Leaf vegetable\ntest/2a28eabff35bd2f7,Ball game,Team sport\ntest/2a29484193434660,Close-up,Whiskers,Domestic rabbit\ntest/2a2a1d0ab1895bff,Shooting range,Firearm\ntest/2a2ab0da01952c0b,Military vehicle,Sport utility vehicle\ntest/2a2b66048a8c94b5,Wind wave\ntest/2a2ba4efe2d04309,Compact car,Luxury vehicle,Antique car\ntest/2a2ec8fae1245ced,Compact car\ntest/2a2f9b3ce21c8923,Barechested\ntest/2a335109596eab80,Blond,Close-up\ntest/2a365eb62e797e20,Monoplane,Aircraft engine\ntest/2a37790408c27c0e,Luxury vehicle,Performance car\ntest/2a38f8e0ea0715c7,Boating\ntest/2a39dcf3e96e2acd,Luxury vehicle,Performance car\ntest/2a3ac18e1d550b35,Electronic instrument\ntest/2a3dc1cc5f0426bd,Pit bull\ntest/2a3de7186acb59b9,Close-up\ntest/2a406521d4a12f03,Black-and-white,Miniature Poodle\ntest/2a442719d5ff9720,Cosmetics\ntest/2a44f90486c74b78,Domestic short-haired cat,Close-up,Whiskers\ntest/2a45f68a109fda99,Nordic skiing\ntest/2a462ebe50640e3e,Luxury vehicle,Performance car\ntest/2a4858800efd0ae6,Compact car,Antique car\ntest/2a4ac890edc4b712,Chordophone\ntest/2a4cbea562084397,Senior citizen\ntest/2a4e8ff5cbf047f5,Cobblestone\ntest/2a4eb604acfc9d32,Musical theatre\ntest/2a4ec1ae69245809,Close-up\ntest/2a4eed6e4fa87fff,Close-up\ntest/2a510aa2d2bfcacc,Auto racing\ntest/2a5226e1afc22ea9,Pelmeni\ntest/2a529b749f9cd128,Bovine\ntest/2a537b54cac82147,Compact car\ntest/2a5382403a9c8392,Roller skating\ntest/2a53f4a7f1304cd6,Combat sport\ntest/2a5411ef710bd034,Vegetarian food\ntest/2a5865456ba547df,Junk food\ntest/2a58811c3075544d,Bird of prey\ntest/2a592d73ad5eecc2,Extreme sport\ntest/2a5c0f58364a3adc,Luxury vehicle,Performance car\ntest/2a5d289ddd25d2fe,Residential area\ntest/2a5f3b33e6460e1e,Rural area\ntest/2a66f5a6a7cb1779,Siberian husky\ntest/2a6d30d3ae8f0080,Electronic instrument\ntest/2a714cc4d19f2775,Sport utility vehicle\ntest/2a74ba24e3477818,Glacial landform\ntest/2a750ea63d74fcfd,Black-and-white\ntest/2a7678ee4573a6a1,Luxury vehicle,Antique car\ntest/2a76afe52dfa9f31,Luxury vehicle,Performance car\ntest/2a771d364db380e2,Fried food\ntest/2a7862c5c20bf762,Luxury vehicle\ntest/2a7920526b4e7b58,Rural area\ntest/2a7d32e4352c4a14,Domestic short-haired cat,Whiskers\ntest/2a7e565549de1ab0,Cosmetics\ntest/2a81e7673bd4fcb0,Whiskers\ntest/2a81eee7ffc1bd06,Arthropod,Locust\ntest/2a82eb9bf2a4cbb0,Lumpia,Fried food\ntest/2a8677f366f94b11,Brochette,Yakitori\ntest/2a87aa4b6898ca8e,Hound\ntest/2a8913b197448020,Performance car,Luxury vehicle,Antique car\ntest/2a8ac2d049ab3aa7,Junk food,Fried food\ntest/2a8b98593bf42138,Luxury vehicle\ntest/2a8cc999b9c5ef66,Ball game\ntest/2a8d0d8b5693a045,Arthropod,Close-up\ntest/2a8e8b8b2f24a7cd,Filling station\ntest/2a91228abaaa6f57,Luxury vehicle\ntest/2a93109c1f66f4df,Luxury vehicle,Antique car\ntest/2a9384091bb40850,Auto racing\ntest/2a95fd2a4dbbe7a5,Watercolor paint\ntest/2a9603a9afd16362,Close-up\ntest/2a98634704a0ad28,Extreme sport\ntest/2a9918bdaee353f0,Aircraft engine\ntest/2a9ab0fc11603988,Brochette\ntest/2a9abb146448d656,Close-up\ntest/2a9c95d441f4958e,Extreme sport\ntest/2a9caba20f7b7dfd,Close-up\ntest/2aa0939e6d7c768d,Motorcycling\ntest/2aa0f26a074b47e9,Musical theatre\ntest/2aa12e095e1b1e3a,Zeppelin\ntest/2aa169346113ac0f,Brisket\ntest/2aa321ac42e0d5c4,Luxury vehicle\ntest/2aa39413c4495b0c,Compact car,Luxury vehicle,Sport utility vehicle\ntest/2aa6415fea174bde,Miniature Poodle\ntest/2aa719c465bad970,Luxury vehicle,Performance car\ntest/2aa8af4eb9560def,Auto racing,Antique car\ntest/2aaaeb30ebd0aead,Leaf vegetable\ntest/2aabbd844b6ac7a0,Antique car,Luxury vehicle,Performance car\ntest/2aad33bb125b10b3,Black-and-white\ntest/2aad95cbdd042cec,Close-up,Chordophone\ntest/2aadf56ce5614fb2,Plant stem,Close-up\ntest/2aae08bc633f8181,Equestrianism\ntest/2aae32b5587ab35a,Compact car,Antique car\ntest/2ab46c0e5af16fa7,Cookies and crackers\ntest/2ab4c6b99428d282,Luxury vehicle,Performance car\ntest/2ab4cdb19b8a65ba,Black-and-white\ntest/2ab51a6962b816f8,Close-up\ntest/2ab62594440fab78,Aircraft engine\ntest/2ab79587653b04a4,Luxury vehicle,Antique car\ntest/2ab825c78b5c77eb,Black-and-white\ntest/2abb6db7d546efc5,Ball game\ntest/2abc7c59e30e4a9f,Auto racing,Performance car\ntest/2abd6031c2c59b94,Plant stem,Close-up\ntest/2abfd797ac3e092c,Black-and-white\ntest/2ac0176d3b5ab294,Close-up\ntest/2ac1463cd8475e57,Arthropod\ntest/2ac1997a5ac5017f,Orator\ntest/2ac399c5a0624f7f,Motorcycling\ntest/2ac4bca34e365b88,Sport utility vehicle\ntest/2ac56e0194cceea3,Senior citizen\ntest/2ac59352b41d1495,Auto racing,Performance car\ntest/2ac5cdd2ae73083b,Fried noodles\ntest/2ac633f15e0c0d0c,Auto racing,Touring car,Luxury vehicle,Performance car\ntest/2ac663e3386d66f8,Melee weapon,Hunting knife\ntest/2ac6857abf349315,Boating,Rowing\ntest/2ac68a64d7cc67e8,Luxury vehicle\ntest/2ac755d0fca2422f,Close-up\ntest/2ac84de82ec38128,Arthropod\ntest/2aca22c3c4b01381,Cookies and crackers\ntest/2aca7479a250da09,Antique car\ntest/2acaef39f3d18626,Compact car\ntest/2acce1e18e5630b8,Luxury vehicle,Performance car\ntest/2acf31d17969d4d0,Horse and buggy\ntest/2ad290dda8f3343e,Boating\ntest/2ad2c3c633e1eeb0,Residential area\ntest/2ad2e3e74d0ae6a3,Whiskers\ntest/2ad3b44b58d2933f,Extreme sport,Boating\ntest/2ad47208da10f4e6,Cobblestone\ntest/2ad685e4feb29d00,Aircraft engine\ntest/2ad6eb87890b8578,Aircraft engine\ntest/2ad7f0a1bc47ed82,Halter\ntest/2ada90ea9142333b,Outdoor shoe\ntest/2adbb444d4f303c4,Performance car\ntest/2adc8874cb4284e8,Luxury vehicle\ntest/2adf2ae7c736e410,Dobermann\ntest/2adf377ee3579a3c,Close-up\ntest/2ae3bead0004f9b9,Compact car,Performance car\ntest/2ae4caa772dfd6db,Black swan\ntest/2ae532da1478e448,Luxury vehicle\ntest/2ae5575f39119c7d,Dishware\ntest/2ae8bac1fb3ef7e2,Compact car\ntest/2ae96e5ecea21c6d,Chordophone\ntest/2aeb6e6b9524dade,Luxury vehicle\ntest/2aec17e5d18c80b2,Close-up,Plant stem\ntest/2aec21cab43ad9db,Luxury vehicle,Sport utility vehicle\ntest/2af1bb4af66722d4,Black-and-white,Close-up\ntest/2af45fbcb655070a,Luxury vehicle\ntest/2af4c1bc5022c428,Luxury vehicle\ntest/2afa5620059f5723,Sailboat racing\ntest/2afb388b11fe3513,Auto racing\ntest/2afb87771477b63c,Blond\ntest/2afd808d6b89d993,Auto racing\ntest/2b001e038ff09b8c,Amphibian,Close-up\ntest/2b01936f878184d4,Sport utility vehicle\ntest/2b02db10ae96e323,Auto racing\ntest/2b052ace77875b86,Steamed rice\ntest/2b059b6861d628b1,Luxury vehicle,Performance car\ntest/2b05ec6b8c6ca93f,Jeans\ntest/2b06197d1a63319c,Compact car\ntest/2b06f997a70d980b,Blond\ntest/2b08282fb15a689b,Rural area\ntest/2b09ceedb8061ad3,Machine tool\ntest/2b0a700504d8e6c4,Close-up\ntest/2b0a79c66cfb31b6,Compact car\ntest/2b0b048958fc6c97,Antique car\ntest/2b0b7e48055585da,Equestrianism\ntest/2b0beed6d79b375e,Luxury vehicle\ntest/2b0da6db4ee3bfd6,Hair coloring,Portrait photography,Close-up\ntest/2b0e046628b7742b,Close-up\ntest/2b120629caef81eb,Domestic short-haired cat,Close-up,Whiskers\ntest/2b12863d6c34b398,Team sport\ntest/2b133f3d1c4c1f4d,Close-up\ntest/2b134ec4e8990fb1,Miniature Poodle\ntest/2b162562c3dab2ee,Whiskers\ntest/2b166f9eb754bd38,Residential area\ntest/2b16aa3ad37dfc7a,Arthropod,Close-up\ntest/2b192a0e46f9e1bd,Athletic shoe,Outdoor shoe\ntest/2b1a1f55b4797626,Comics\ntest/2b1c7b13db232e94,Blond,Hair coloring\ntest/2b1d157e740485b2,Whiskers\ntest/2b217a403318dfbb,Domestic short-haired cat,Whiskers\ntest/2b22f65e0ec07063,Antique car,Performance car\ntest/2b23a440fb259004,Performance car\ntest/2b243d5bfcd4e764,Compact car,Luxury vehicle\ntest/2b246f49c9cab7e5,Ball game,Team sport\ntest/2b25928e2136698d,Antique car\ntest/2b2693c2d6b8182a,Combat sport\ntest/2b2a7d79cbcba5c8,Luxury vehicle,Performance car\ntest/2b2c2917eb0fbc61,Fried noodles\ntest/2b2d5bf2cf18f538,Luxury vehicle,Antique car\ntest/2b2e698ab79a03ee,Boating\ntest/2b2e9f6725c0af25,Ball game,Team sport\ntest/2b2ebcdecca94de1,Close-up\ntest/2b33864cca07d231,Blond\ntest/2b34e312e241b910,Domestic short-haired cat,Whiskers\ntest/2b36af6dd51bb0a1,Steamed rice\ntest/2b3750218562b581,Outdoor shoe\ntest/2b391a3d9e658d59,Fried food\ntest/2b3d0fda529a488d,Chordophone\ntest/2b3e2b1968f4d2cb,Canola\ntest/2b3e864ae7ae6bb9,Freight transport\ntest/2b3eb67bb9de78bf,Fire apparatus\ntest/2b3f540b16479409,Antique car\ntest/2b3fc527b47a799c,Close-up\ntest/2b42d317e810c33f,Aircraft engine\ntest/2b42f3325e3be31b,Close-up\ntest/2b43b739c25de875,Boating\ntest/2b44b48ce1fcce96,Aloe\ntest/2b46acfd85573458,Rural area\ntest/2b483e2c59fb59cd,Digital camera\ntest/2b49a53da8ce5613,High-speed rail\ntest/2b4a6088b668b677,Divemaster,Scuba diving,Underwater diving\ntest/2b4b6a724e217e6a,Antique car\ntest/2b4bd2dbe8c3455f,Vegetarian food,Pasta salad\ntest/2b4c16d3405ad16a,Amphibian\ntest/2b4d16e4ea02e35d,Bird of prey\ntest/2b4ea830bb9f3de8,Close-up\ntest/2b4ec649d2a5ef27,Hound\ntest/2b4fe86bbab86172,Luxury vehicle,Performance car\ntest/2b4fe90ce66aa772,Bird of prey\ntest/2b516cbd5a766a83,Luxury vehicle,Performance car\ntest/2b52085f4ca118e1,Arthropod\ntest/2b5521d1609fb27f,Luxury vehicle,Antique car\ntest/2b5696ff903fa7fb,Monoplane\ntest/2b5698e77d44188e,Sport utility vehicle\ntest/2b5ac2cd85087493,Compact car\ntest/2b5cb704619889f7,Boating\ntest/2b5d2e58b3aa4d24,Compact car,Luxury vehicle\ntest/2b607717ec492bc2,Figure skating\ntest/2b609689af76518d,Miniature Poodle\ntest/2b6163cadbb675e9,Luxury vehicle,Antique car\ntest/2b64d4253cce953f,Boating\ntest/2b69ad153117d2ef,Jeans,Luxury vehicle\ntest/2b6aa6acaba75fd8,Luxury vehicle,Sport utility vehicle\ntest/2b6f9e1f6a46b5ba,Solar energy\ntest/2b704ddf7cb761e7,Luxury vehicle,Antique car\ntest/2b7287a062799833,Electronic instrument\ntest/2b72fe752df24b9c,Compact car,Luxury vehicle,Antique car\ntest/2b7533c5d2bec01e,Musical theatre\ntest/2b757019dc70148e,Whiskers\ntest/2b75d68c4df44042,Military person\ntest/2b76725b3a9b9188,Close-up\ntest/2b787f90b2319ae8,Steamed rice\ntest/2b78f0cba75b4de1,Coral reef fish\ntest/2b79ecfee4d532b7,Antique car\ntest/2b7bd6c9209263c1,Luxury vehicle,Antique car\ntest/2b7bf58f2dd55ba6,Antique car,Luxury vehicle,Performance car\ntest/2b8439629d73ef5d,Dog walking\ntest/2b8773c6950ad004,Black-and-white\ntest/2b8be270d7a19382,Musical theatre\ntest/2b8c2ec994078713,Fried food\ntest/2b8d6656c490df81,Microcontroller\ntest/2b8dc27f0e2f8afe,Team sport\ntest/2b8ea533e0d11bc8,Luxury vehicle\ntest/2b90c808b687cd24,Luxury vehicle\ntest/2b9145475b5819fd,Bird of prey\ntest/2b91cf96dc9e506c,Military person\ntest/2b9284b4c8229444,Blond,Hair coloring\ntest/2b935231bc53ea86,Frigate\ntest/2b936cb164edc441,Coca-cola\ntest/2b93bd8480b8b115,Close-up\ntest/2b944ee33df7ebe7,Blond\ntest/2b96c29af68b1289,Ball game,Team sport\ntest/2b987a0d211b62aa,Milky way\ntest/2b992f806c387624,Bird of prey\ntest/2b99715ae06d18a9,Luxury vehicle\ntest/2b9ed3488bcb5f77,Ball game,Team sport\ntest/2b9f0ac2f5065e13,Modern dance\ntest/2b9fdf2bd4d1d6e2,Close-up\ntest/2ba071dd771dabe3,Compact car,Luxury vehicle,Antique car\ntest/2ba0bb18a26906e2,Boating\ntest/2ba103751dc8d146,Luxury vehicle\ntest/2ba1863a2d1f6a35,Compact car,Antique car\ntest/2ba58e205417e616,Aerial photography\ntest/2ba68ba0eb94f114,Microcontroller\ntest/2ba7469f8081a2d8,Black-and-white\ntest/2bac4cbaa3859a6b,Junk food\ntest/2bb178ec21b48876,Close-up\ntest/2bb2c179d9a12ea5,Extreme sport,Parachuting\ntest/2bb32ed3ceb25527,Whiskers,Domestic rabbit\ntest/2bb3d0318044baf0,Luxury vehicle\ntest/2bb43acdccc33c6f,Glacial landform\ntest/2bb7d54aecf53850,Jeans\ntest/2bb8b427cfdc8c91,Falafel,Fried food\ntest/2bb947c300f1be13,Domestic short-haired cat,Whiskers\ntest/2bbbe1085188d624,Barquentine\ntest/2bbc6adf1cff3456,Fried food\ntest/2bbf772a17b38f70,Sailboat racing\ntest/2bbf95791cff985d,Close-up\ntest/2bc1923569c1e683,Milky way\ntest/2bc387b927d7241d,Portrait photography\ntest/2bc46b3b7c30c970,Watercolor paint\ntest/2bc4d9783aeab861,Watercolor paint,Middle ages\ntest/2bc559da8ad9f78b,Compact car,Luxury vehicle,Antique car\ntest/2bc6e9482ff56652,Black-and-white\ntest/2bc6fded1a1ff77f,Bird of prey\ntest/2bc953ae1edd35f7,Fried food\ntest/2bc9be6bcf576760,Auto racing,Luxury vehicle,Performance car\ntest/2bca6554fa7e04a5,Rural area\ntest/2bccc1f7aeb5ca92,Scaled reptile\ntest/2bcd472f6e2732ed,Compact car\ntest/2bce6a4255fb4579,Arthropod,Close-up\ntest/2bd04cdee286a589,Extreme sport\ntest/2bd0b03794f7f3a2,Close-up\ntest/2bd4dc0eaf6ed398,Horse racing\ntest/2bd6206114824f94,Motorcycling\ntest/2bd70d24d4f50ce8,Close-up\ntest/2bdc2d38b8d1c8d5,Close-up\ntest/2bdc6b0d948c0576,Fire apparatus\ntest/2bdd180a14741b37,Firearm,Shooting range\ntest/2bdee534abd836f4,Auto racing,Performance car\ntest/2bdefa6c6b45b495,Extreme sport\ntest/2be140d24cf11bc7,Steamed rice\ntest/2be39a809b2afc3a,Sport utility vehicle\ntest/2be3a23f96d29514,Drums\ntest/2be857954380a63f,Compact car,Antique car\ntest/2be8f0738a9a1c97,Ball game,Team sport\ntest/2beabd9ac1d00365,Luxury vehicle,Antique car\ntest/2beaee087df542a6,Luxury vehicle,Antique car\ntest/2beb012ddd2bc35d,Chordophone\ntest/2bebc4733a3af42d,Domestic short-haired cat,Whiskers\ntest/2bec3459c499e248,Boating\ntest/2bee7c60a4d6bb60,Black-and-white\ntest/2befbdfbd838eeb1,Sport utility vehicle\ntest/2befd1c39206a5aa,Performance car\ntest/2bf0bfe90dec79fe,Firearm\ntest/2bf3f9e1089d19cc,Combat sport\ntest/2bf43743f521a07e,Compact car,Luxury vehicle\ntest/2bf4546394471e47,Compact car,Luxury vehicle,Antique car\ntest/2bf6a8ac329714bd,Luxury vehicle,Performance car\ntest/2bf6e7d6d96bcce6,Domestic short-haired cat,Whiskers\ntest/2bfcf14d50324894,Close-up\ntest/2bfdbdb53da01caa,Auto racing,Luxury vehicle,Performance car\ntest/2bfdf94b4744b4e6,Luxury vehicle,Performance car\ntest/2bff62944e1a23e6,Ball game\ntest/2c00ed1e0f3d37bf,Sport utility vehicle\ntest/2c0296d2e6e890b0,Equestrianism\ntest/2c0421eed7bd2b6a,Classical sculpture\ntest/2c05cee2600624a8,Arthropod\ntest/2c0791bcdf997424,Close-up\ntest/2c081839ba742d99,Auto racing\ntest/2c0e26e487ba2e85,Close-up\ntest/2c0fcfef56bf116e,Pit bull\ntest/2c100b7d84ea8833,Pasta salad\ntest/2c162dd1c1fab5b1,Luxury vehicle,Antique car\ntest/2c1660abddce85b1,Shinto shrine\ntest/2c16c400530f7d9d,Amphibian,Close-up\ntest/2c16ed1f160a6dca,Whiskers,Domestic rabbit\ntest/2c18bbf543eca7fe,Perching bird\ntest/2c19201e01a8a336,Luxury vehicle,Antique car\ntest/2c1b26b8e9535964,Ball game\ntest/2c1b96fc6764061a,Luxury vehicle\ntest/2c1cc70dbaf5416c,Ball game\ntest/2c2194565e1c3286,Wind wave\ntest/2c234ca5ca627104,Performance car\ntest/2c270491761170a4,Military person\ntest/2c27df2d3400c34b,Floristry\ntest/2c280b22a9fe94ec,Arthropod,Close-up\ntest/2c281d0d32a82824,Plant stem\ntest/2c2a122869f04581,Hound\ntest/2c2b2e5e3fa238db,Black-and-white\ntest/2c2b52e0341b12bd,Great white shark\ntest/2c2bfba54c54e890,Barechested\ntest/2c2d5dc63559b6c2,Leaf vegetable\ntest/2c2dd1bf70b3da72,Jeans\ntest/2c2ec8030feba378,Rural area\ntest/2c2f08522befd4dc,Close-up\ntest/2c2f5b8cd2a68e03,Barbecue chicken,Fried food\ntest/2c2fd627148571fc,Childbirth\ntest/2c30745abf18a05f,Compact car\ntest/2c320065591076e9,Floristry,Close-up\ntest/2c32af368cb057b3,Close-up\ntest/2c336d2f595bbc92,Drums,Black-and-white,Close-up\ntest/2c339d2d0bfbd97d,Arthropod,Close-up\ntest/2c33b9d1d60c0ffa,Compact car\ntest/2c365c9b42ca2d87,Floristry\ntest/2c36daaa1887bd99,Aerial photography\ntest/2c37a357dddfc0f0,Cosmetics,Close-up\ntest/2c37a57fc83e6790,Domestic rabbit\ntest/2c38bf9fcf61ae33,Hair coloring\ntest/2c38ee29880b42eb,Shooting range,Firearm\ntest/2c3b054592fe2033,Combat sport\ntest/2c3d140883187462,Close-up\ntest/2c3e0ea3ae5c8116,Aircraft engine\ntest/2c3ffd7b9a8f9627,Whiskers\ntest/2c41faad4c1f65b5,Team sport\ntest/2c429e5a812c72f1,Aerial photography,Glacial landform\ntest/2c4492ae2cd5b0fa,Jeans\ntest/2c45739245799a6b,Jeans\ntest/2c45bc130bfa453a,Outdoor shoe\ntest/2c47c8cd5ec07c3a,Performance car\ntest/2c4851f355863023,Boating,Rowing\ntest/2c49502cec2ae2f9,Compact car\ntest/2c4b2abb82587fa1,Horse racing,Equestrianism\ntest/2c4b4207b0b76571,Close-up,Plant stem\ntest/2c4ca22d1b247fb5,Domestic short-haired cat,Whiskers\ntest/2c4f0213cb08854a,Luxury vehicle\ntest/2c4feb4662b29e32,Luxury vehicle,Antique car\ntest/2c5011f90e46013d,Extreme sport\ntest/2c5111540b4cafd2,Luxury vehicle\ntest/2c524727afd76e5d,Vegetarian food\ntest/2c5306d932882ba6,Luxury vehicle,Antique car\ntest/2c536c2d7b3e331c,Frigate\ntest/2c545fde3284b462,Dishware\ntest/2c582a54a63cb447,Chordophone\ntest/2c585fc1e1ef9f19,Luxury vehicle\ntest/2c5ac11198236460,Woodwind instrument\ntest/2c5b611f3a46d978,Black-and-white\ntest/2c5bb0c0cbadbab1,Lifebuoy\ntest/2c5cf6abb01748fe,Performance car\ntest/2c5ef7f62b9a1a10,Extreme sport,Kitesurfing\ntest/2c60896d95d9c149,Blond\ntest/2c63067e80fbbd62,Auto racing,Performance car\ntest/2c6565d8edc7567d,Luxury vehicle,Antique car\ntest/2c68d8a2e6c66524,Sport utility vehicle\ntest/2c698781f31c8e42,Luxury vehicle\ntest/2c6b472a3ddd5dd7,Rural area\ntest/2c6db97caa171ded,Compact car\ntest/2c6ddfdb97f3867d,Luxury vehicle\ntest/2c70c376aeb96743,Domestic short-haired cat,Whiskers\ntest/2c716fe6536d46a6,Floristry\ntest/2c72a86827b9e801,Close-up\ntest/2c73d8969c276392,Aircraft engine\ntest/2c74e9a24c554d51,Orator\ntest/2c77a1b68cf8b83e,Close-up\ntest/2c78689ddfd3cda3,Extreme sport,Aerial photography\ntest/2c786b0ae3d358c1,Junk food\ntest/2c788bf165f7ee2c,Nile crocodile,Crocodilia\ntest/2c79ca18e37c6db5,Arthropod,Close-up\ntest/2c7b789d4a23d46d,Ramen\ntest/2c7b7ba5afe3cbff,Luxury vehicle,Sport utility vehicle\ntest/2c80bbf539e6885b,Horse and buggy\ntest/2c82270bd7e72244,Boating\ntest/2c83b5d28e96e1b2,Black-and-white\ntest/2c868cc17039a1f0,Aircraft engine\ntest/2c876896fc126711,Rural area\ntest/2c87ac7c909646a0,Prosciutto\ntest/2c8971af30610fd1,Performance car\ntest/2c8fca931f78ae4c,Arthropod,Close-up\ntest/2c96234f808faf0d,Luxury vehicle\ntest/2c966cdfaab5d7b0,Perching bird\ntest/2c96e97288ba96bd,Rural area\ntest/2c978b1912a52880,Amphibian\ntest/2c9a5c68fde9d9dd,Roller skating\ntest/2c9c358a705e10d6,Close-up\ntest/2c9c364e20cc7fed,Bagpipes\ntest/2c9ccbfc67d466ad,Boating\ntest/2c9e37295c1736d2,Monoplane,Aircraft engine\ntest/2ca07cd856c3767f,Combat sport\ntest/2ca0e4eb5eb1ab62,Close-up\ntest/2ca1882a87e72f50,Bratwurst\ntest/2ca1c6097e06ec8a,Close-up\ntest/2ca336be88512333,Ball game\ntest/2ca618ff8b3f22c7,Halter,Equestrianism\ntest/2caa505dcafda819,Black-and-white\ntest/2cadc00cdddc87cc,Double-decker bus\ntest/2cae3e3332996b13,Domestic rabbit\ntest/2caecc9cbe1a2ab1,Flatbread\ntest/2cb04582cd406c49,Boating\ntest/2cb0647d44acf56c,Steamed rice\ntest/2cb0d2a18de68bca,Auto racing,Luxury vehicle,Performance car\ntest/2cb1eb61c074a73c,Black-and-white,Luxury vehicle\ntest/2cb51268007c8b8b,Wind wave\ntest/2cb608d3dee43688,Nile crocodile,Crocodilia\ntest/2cb9e855ba8ec872,Perching bird\ntest/2cb9ec4d40cfe923,Chordophone,Electric guitar\ntest/2cba1672bca71ece,Luxury vehicle\ntest/2cbb0020aa47cd35,Jeans,Siberian husky\ntest/2cbb67a064f58abc,Sport utility vehicle\ntest/2cbbce71f64cb0fc,Luxury vehicle,Sport utility vehicle\ntest/2cbd4b706af1961b,Road cycling\ntest/2cbd74a2e8e1ae1c,Boating\ntest/2cc09565ffaa1b16,Luxury vehicle,Sport utility vehicle\ntest/2cc1c19a8397a348,Fried food\ntest/2cc29cbfe882ad48,Rural area\ntest/2cc511fa46b55b76,Road cycling\ntest/2cc5407c837e81cd,Arcade game\ntest/2cc8523e95e38277,Fried food\ntest/2cc94d71584aefdf,Chordophone\ntest/2ccb4cd72870846f,Ball game,Team sport\ntest/2ccc26dbf7170541,Ball game\ntest/2ccc81b6260aaa19,Leaf vegetable\ntest/2ccd168c2636de3c,Senior citizen,Portrait photography\ntest/2ccd59d6eaf65d32,Equestrianism\ntest/2ccf1735a087be28,Close-up\ntest/2cd47239c750233f,Antique car\ntest/2cd4c282651892cb,Close-up\ntest/2cd4d7c4100918c2,Childbirth\ntest/2cd5134eba73e7df,Close-up\ntest/2cd532a53ff7da11,Water polo\ntest/2cd5a536b5eaa84a,Antique car\ntest/2cd68ff5b6a117a6,Machine tool\ntest/2cd812121494bacd,Close-up\ntest/2cd82a7e32a00371,Frying,Fried food\ntest/2cd91366f96ac996,Auto racing,Luxury vehicle,Performance car\ntest/2cddeafb7f5bef6b,Black-and-white,Sport utility vehicle\ntest/2ce0a83328d93a10,Compact car\ntest/2ce0ef6ba410366a,Senior citizen\ntest/2ce47419f4f91921,Luxury vehicle,Performance car\ntest/2ce478bf847d3e2f,Residential area\ntest/2ce572f23a8d187a,Drums\ntest/2ce71afb36f46048,Sport utility vehicle\ntest/2ce8f37438d3f2ba,Performance car\ntest/2ceacefd5e0d144e,Team sport\ntest/2ceb97045b16c6cf,Performance car,Antique car\ntest/2cec45ce984443b2,Auto racing\ntest/2ced9224eefcde09,Performance car\ntest/2cee1f8a4b004b43,Military person\ntest/2cee7b68b3c88750,Hair coloring\ntest/2cef16702ed65436,Luxury vehicle\ntest/2cef5ff7d45234ef,Great white shark\ntest/2cf0c63550cfcf25,Rural area\ntest/2cf28cf5d5ad44e6,Luxury vehicle\ntest/2cf31ecf4775b34a,Jeans\ntest/2cf36c03c7bac1b3,Aerial photography\ntest/2cf4026f212bf8e4,Vegetarian food\ntest/2cf4754271aaad4f,Luxury vehicle,Performance car\ntest/2cf4a9a868d53231,Luxury vehicle\ntest/2cf5576d24f472e1,Luxury vehicle\ntest/2cf8b0c5cef1e6c7,Junk food\ntest/2cf9b4e26a17da8d,Divemaster,Scuba diving,Underwater diving\ntest/2cf9c4ea32681575,Plant stem\ntest/2cfa23add9a58a57,Luxury vehicle,Antique car\ntest/2cfbdbb6517a5af7,Microcontroller\ntest/2cfd50b13db602b6,Close-up\ntest/2cfd81ec4e02e7c0,Rear-view mirror,Luxury vehicle\ntest/2cfe2cd86fcf96b1,Billiards,Billiard room\ntest/2cfe416507c412f1,Antique car\ntest/2d0077b56566925d,Black-and-white\ntest/2d0145829320fb07,Antique car\ntest/2d06a397773a3876,Vegetarian food,Junk food,Fried food\ntest/2d07128c9cfdcc05,Compact car\ntest/2d07f0ae6f1afd71,Khinkali,Pelmeni,Jiaozi\ntest/2d08fd379017b6ca,Ball game,Team sport\ntest/2d09485548da127a,Luxury vehicle\ntest/2d09c5a75d338268,Close-up\ntest/2d0afef01308abec,Rural area\ntest/2d0b2e9fe39a9f06,Rural area\ntest/2d0c547d9eaed799,Black-and-white,Portrait photography\ntest/2d0e558f582e9c47,Extreme sport\ntest/2d10e5e8e8f0764a,Close-up\ntest/2d118bfc4ff8a804,Boating\ntest/2d11a4149dbec52d,Military vehicle,Sport utility vehicle\ntest/2d11ac330b334fde,Filling station\ntest/2d15358a9c6aad15,Black-and-white,Horse and buggy\ntest/2d15e2771cf5b8dd,Horse and buggy\ntest/2d179c5d6bc2874c,Jeans\ntest/2d181634e1f80fbd,Antique car\ntest/2d1929b2d471ddc0,Extreme sport,Road cycling\ntest/2d1c5a113ef95572,Residential area,Aerial photography\ntest/2d1f194ffe483ab1,Close-up,Scaled reptile\ntest/2d20b2f21380fb5c,Wallaby\ntest/2d21b50ce97c6fed,Goats\ntest/2d247c6c85e9db4f,Ball game,Team sport\ntest/2d252781f0dc980d,Electric piano,Electronic instrument\ntest/2d2a93e9c3a6b534,Luxury vehicle,Performance car\ntest/2d2c6f3a116786c0,Bird of prey\ntest/2d2c9c238b0330a1,Luxury vehicle,Performance car\ntest/2d2da4e280f88f23,Ball game\ntest/2d2f03acf98f8500,Portrait photography,Close-up\ntest/2d30f6f69bc2450c,Arthropod,Close-up\ntest/2d337dc5bdecb45c,Team sport\ntest/2d36a90cf0b00775,Brochette,Yakitori\ntest/2d37d1d585e5ce5b,Close-up,Plant stem\ntest/2d394caf550b8c75,Scaled reptile\ntest/2d3bacbad868a416,Molluscs,Close-up\ntest/2d3bb997a7afa7af,Luxury vehicle,Sport utility vehicle\ntest/2d3d2970f4667731,Sport utility vehicle\ntest/2d3f598b9241e9e6,Military vehicle,Luxury vehicle,Sport utility vehicle\ntest/2d41c7ce10057d0c,Rural area\ntest/2d4216bc37f6bd21,Electronic instrument\ntest/2d43d7dd9ad73a30,Residential area\ntest/2d459f80dcb8e481,Kitesurfing,Extreme sport,Wind wave\ntest/2d47873f3dd75e20,Goats\ntest/2d48457dc1879cdd,Luxury vehicle\ntest/2d4a0430816a7023,Luxury vehicle,Antique car\ntest/2d505555ea9759fc,Portrait photography,Close-up\ntest/2d53f7c8f5263764,Luxury vehicle,Antique car\ntest/2d5424960ac071fe,Miniature Poodle\ntest/2d56c86887e8da66,Bovine\ntest/2d58462098b0f3de,Antique car\ntest/2d5b11cf16d56de1,Performance car,Antique car\ntest/2d5c0006af62de08,Vegetarian food,Junk food,Fried food\ntest/2d5dc410c4d5f31a,Close-up\ntest/2d5e6f8e18f870da,Black-and-white,Close-up\ntest/2d60394ee9447dce,Rural area\ntest/2d629622578b3cc2,Falafel,Fried food\ntest/2d63bf3c1687e5a9,Luxury vehicle,Antique car\ntest/2d657194871d782d,Antique car\ntest/2d66a2282db22f20,Steamed rice,Rice ball\ntest/2d6b28fd9df0dc91,Microcontroller\ntest/2d6c9adbc7e8edd8,Black-and-white\ntest/2d6de39a8ea7c80b,Close-up\ntest/2d7080dfda584673,Luxury vehicle\ntest/2d729c3986b5d0a5,Sport utility vehicle\ntest/2d73ce0da603e44b,Luxury vehicle,Antique car\ntest/2d74cbea72efad59,Luxury vehicle,Sport utility vehicle\ntest/2d772737923e9e6e,Black swan\ntest/2d778619f12c8aa1,Jeans,Black-and-white\ntest/2d7b55c937cafb0c,Zeppelin\ntest/2d7c226429bbce79,Antique car\ntest/2d7c61c30edff67e,Compact car\ntest/2d7ca7d56d50ee8b,Wallaby\ntest/2d7d234acae1c74f,Backlighting,Black-and-white\ntest/2d7d596cab2a6983,Black-and-white\ntest/2d7dfd160bc30f26,Close-up\ntest/2d7eff6ebdbd94c0,Auto racing,Performance car\ntest/2d80b4fb5bc6d238,Rural area\ntest/2d82dfa93f221fed,Antique car\ntest/2d833ba9829a0cb6,Luxury vehicle\ntest/2d852ba2497fc6c3,Luxury vehicle,Performance car\ntest/2d8779fce3dee239,Vegetarian food\ntest/2d87d0935b22d910,Monoplane\ntest/2d8849e30b82f465,Antique car\ntest/2d89a600ff13925c,Full moon\ntest/2d8a2670b133e75c,Rear-view mirror,Luxury vehicle\ntest/2d8e0ed06405b1d9,Ball game,Team sport\ntest/2d901b87353f710e,Compact car\ntest/2d922c1406bd543c,Ball game,Team sport\ntest/2d931cb5d88c45bd,Machine tool\ntest/2d962c11541ca750,Computer speaker\ntest/2d96b1901d8e6314,Frozen yogurt\ntest/2d97d6b1a7f9e8e4,Ball game\ntest/2d9a587e289c7f41,Performance car\ntest/2d9b082d122e0a7c,Antique car\ntest/2d9b30dbbd1d45a1,Amphibian\ntest/2d9dc5348b6178f2,Luxury vehicle,Antique car\ntest/2d9f5278329d9678,Close-up\ntest/2da2ba47aee1cd88,Aerial photography\ntest/2da2c22f1bd3d367,Ball game\ntest/2da44155aa979668,Residential area\ntest/2da60ebeb2657017,Steamed rice\ntest/2da7e474766c123f,Luxury vehicle\ntest/2da8ed2071ed6587,Ball game\ntest/2da9ca2cecec245a,Church bell\ntest/2dae5de02ff0730a,Extreme sport,Parachuting\ntest/2db13583339ce2b5,Scaled reptile\ntest/2db14430232e66bb,Blond\ntest/2db18b20d40d22a0,Whiskers\ntest/2db25d6cecc1a5fd,Compact car\ntest/2db3645295731775,Luxury vehicle\ntest/2db6f48858c4fab7,Close-up\ntest/2db7496e50971732,Extreme sport\ntest/2db78c0c977724da,Vegetarian food\ntest/2db9d47108213e88,Black-and-white\ntest/2dbb5329dce1d870,Hair coloring\ntest/2dbc06d9a808623c,Arthropod,Close-up\ntest/2dbc56d490d58361,Residential area\ntest/2dbde90a0af4079c,Close-up\ntest/2dc14280d962e327,Arthropod,Close-up\ntest/2dc3d6a8b2bbe91d,Close-up\ntest/2dc42e199f5d2251,Assault rifle,Firearm\ntest/2dcbc1468db622c3,Black-and-white\ntest/2dcbd21d897e7dc1,Aircraft engine\ntest/2dcbf78dec5204e6,Plant stem,Close-up\ntest/2dcd1c1325eae7f6,Black-and-white\ntest/2dce05d0bddbd134,Pit bull\ntest/2dd3af0ce5bfd890,Barechested\ntest/2dd5cdc8193cf6e0,Floristry\ntest/2dd6871011dfc7c8,Milky way\ntest/2dd6b19c881dabdc,Fried food\ntest/2dd7a5f2136c3254,Junk food,Fried food\ntest/2dd800c66dab4797,Close-up\ntest/2dddc0a2c0f2b488,Canola\ntest/2de26547ae0c4a3e,Close-up,Whiskers\ntest/2de3fd3804321add,Ball game,Team sport\ntest/2de57c403e8ab550,Close-up\ntest/2de7dcc8a1e9ac59,Luxury vehicle\ntest/2de82f18a10ce4e2,Military vehicle\ntest/2de84f12bd388986,Close-up,Floristry\ntest/2de8dfac07e58ba8,High-speed rail\ntest/2dec1b2db2ad0ab0,Vegetarian food,Pasta salad\ntest/2dec4ad0053237a5,Luxury vehicle,Sport utility vehicle\ntest/2def2eb52e74839a,Luxury vehicle\ntest/2def81511a8025a3,Cosmetics\ntest/2df001a9b3128ee5,Close-up\ntest/2df50abe11713656,Luxury vehicle,Antique car\ntest/2df61684e084dabf,Luxury vehicle\ntest/2df83097d4e10f78,Firearm\ntest/2df88cb6741b4125,Luxury vehicle,Performance car\ntest/2df9d8758e02faf5,Billiards,Close-up\ntest/2dfb08e566999729,Comics\ntest/2e0082a815e02f2c,Luxury vehicle,Antique car\ntest/2e0097ca01b1e5ea,Luxury vehicle\ntest/2e00af9f4507f534,Close-up\ntest/2e010a0689357d90,Boating,Rowing\ntest/2e01171f39037152,Watercolor paint\ntest/2e023507e722cce8,Childbirth\ntest/2e03c52062983c0a,Black-and-white\ntest/2e04afe4b2565cf2,Close-up\ntest/2e065fbeeea9bfa3,Headphones\ntest/2e0e07f495b3fc85,Sport utility vehicle\ntest/2e1098e873718d95,Ball game,Team sport\ntest/2e143c06d049b3e4,Close-up\ntest/2e14b6ff5d765312,Boating\ntest/2e176595e8c0e96e,Luxury vehicle\ntest/2e189d9a4156e10e,Luxury vehicle\ntest/2e1b94045efbd40c,Luxury vehicle\ntest/2e1c9aff8c471c9e,Combat sport\ntest/2e1ee30bd03852fe,Herding,Bovine\ntest/2e206a2635c56cd1,Close-up\ntest/2e2230f6ecd71978,Monoplane\ntest/2e235e878311f4f5,Luxury vehicle,Performance car\ntest/2e2479902e6f352c,Luxury vehicle,Performance car\ntest/2e25ad928848a033,Plant stem\ntest/2e25c3922fe40bc0,Arthropod,Close-up\ntest/2e286ff2a4c9c61b,Close-up,Plant stem\ntest/2e29476a8d09a7de,Motorcycling\ntest/2e2abbbe302d5486,Luxury vehicle\ntest/2e2af85fa7e3b9f3,Luxury vehicle,Performance car\ntest/2e2afe7c26177208,Close-up\ntest/2e2b7ef590b6724f,Compact car\ntest/2e2be0aad24a685e,Compact car,Antique car\ntest/2e2c9b97d816c772,Perching bird\ntest/2e2f634f607658c3,Aircraft engine\ntest/2e316d18c2172e46,Close-up\ntest/2e3219d598545a96,Compact car\ntest/2e3411f2a721faf2,Close-up,Whiskers\ntest/2e34d282a15dce3e,Ball game\ntest/2e36fbd1436e7c50,Luxury vehicle\ntest/2e385e0245eb4214,Floristry\ntest/2e389fc22c910374,Close-up\ntest/2e38eb8daa07c52a,Residential area\ntest/2e39078fc2ed0bef,Lifebuoy\ntest/2e3b5c1057507128,Close-up\ntest/2e3d3b0ceae06037,Comics\ntest/2e3d5f4ee37b1b4f,Fried food\ntest/2e3ddc8145d5afe3,Touring car\ntest/2e3ec3271c61954c,Compact car,Sport utility vehicle\ntest/2e3fe22bed8b71dd,Modern dance\ntest/2e4169d29fd631e5,Luxury vehicle,Antique car\ntest/2e4286c2fa24eafa,Luxury vehicle\ntest/2e43be445df93f26,Lifebuoy\ntest/2e45b2652f7e1f7f,Racewalking\ntest/2e46fb48cd7a9c82,Auto racing\ntest/2e481a95075f0d23,Aircraft engine\ntest/2e4bb65b0c7a4f8c,Computer speaker\ntest/2e4bbe1263443fa5,Racewalking\ntest/2e4ca94d4c6ad1de,Close-up\ntest/2e50646f5820a64a,Extreme sport,Wind wave\ntest/2e548b13352f4127,Bovine\ntest/2e55dad8ee8a544a,Luxury vehicle,Sport utility vehicle\ntest/2e5744f7805dea81,Arthropod,Close-up\ntest/2e5855becec70602,Outdoor shoe\ntest/2e591d91901ca753,Luxury vehicle\ntest/2e596a71dd93fb1d,Plant stem\ntest/2e596ebfd2e5c4db,Chordophone\ntest/2e59a87f7708e0b7,Peafowl\ntest/2e59d914764e44b9,Sport utility vehicle\ntest/2e5a39c2ae5a0e3a,Luxury vehicle,Performance car\ntest/2e5ab09910ccf41f,Black-and-white\ntest/2e5b1a58c8da708a,Arthropod\ntest/2e5c237bcce24a1a,Close-up,Plant stem\ntest/2e5cd531f6b4680e,Close-up,Whiskers\ntest/2e5d8df1d08fc655,Close-up\ntest/2e5da704722de81a,Musical theatre\ntest/2e62254ec913b202,Jeans\ntest/2e65eff06cc80014,Monoplane\ntest/2e6ce7083662558d,Combat sport\ntest/2e6d5ed298df7337,Black-and-white\ntest/2e6d87c87781a8d2,Motorcycling\ntest/2e6df1cefddeb607,Orator,Public speaking\ntest/2e6e0a48e1f5a336,Luxury vehicle\ntest/2e72ea1f77ecca5b,Hound\ntest/2e7437661a014a5f,Coral reef fish\ntest/2e7545f97b85c291,Bratwurst\ntest/2e759f336c613666,Luxury vehicle\ntest/2e76039b9660e847,Luxury vehicle,Antique car\ntest/2e7636f4a945129e,Close-up,Whiskers\ntest/2e7ad35ddcb94064,Luxury vehicle,Antique car\ntest/2e7c8ac130906525,Figure skating\ntest/2e7ce3229bc96a76,Close-up\ntest/2e7d444da42cb002,Erinaceidae\ntest/2e7d5e3f3cee6bc4,Antique car\ntest/2e815e4330cb123d,Rural area\ntest/2e87e6293429878a,Luxury vehicle,Antique car\ntest/2e8847958b5c1dbd,Barechested\ntest/2e89b6830d277669,Aircraft engine\ntest/2e89fe5ae1dace28,Luxury vehicle\ntest/2e8bd117813b83b8,Ball game,Team sport\ntest/2e8d3fd54b22ec56,Close-up\ntest/2e8e13102c8483e2,Bovine\ntest/2e8ef556a273c563,Luxury vehicle\ntest/2e8ffaf9fc2dfa8b,Ball game\ntest/2e908d2d78ca538d,Close-up,Whiskers\ntest/2e93e2885c0827d2,Domestic short-haired cat,Close-up,Whiskers\ntest/2e93eb8659a3ec46,Compact car\ntest/2e940ff5b4ce0c3b,Outdoor shoe\ntest/2e95ba08a29433a0,Luxury vehicle,Performance car\ntest/2e9655bc76be10ca,Residential area\ntest/2e97c38c45ce46cb,Extreme sport,Sport climbing\ntest/2e9801df17a26578,Jeans,Dog walking\ntest/2e9c576b44e8d27e,Luxury vehicle\ntest/2e9cce9498c82e28,Close-up\ntest/2e9dfe279f12c972,Black-and-white,Classical sculpture\ntest/2e9faa434fe1118f,Close-up\ntest/2ea05aeb0628a70d,Compact car\ntest/2ea13b4f7343ef39,Auto racing\ntest/2ea18fd2a5e9d97a,Military person\ntest/2ea59f40650d8861,Ball game,Team sport\ntest/2ea5f27b96c2e50e,Close-up\ntest/2ea7cd3f327764c9,Wind wave\ntest/2ea7f21e727fb8a2,Close-up\ntest/2ea8ea28f7503202,Residential area\ntest/2ea9a9d65a29bf64,Plant stem\ntest/2eaa92a025560d99,Luxury vehicle,Antique car\ntest/2eaaea67871098a6,Fried food\ntest/2eae1ae4971f7b03,Black-and-white\ntest/2eaffc2d0b54ef9d,Cosmetics\ntest/2eb13f6babe0ddb8,Ball game\ntest/2eb2b0a52b7e3c49,Boating,Rowing\ntest/2eb3cde5f83eea06,Portrait photography,Close-up\ntest/2eb535a7213b93c6,Digital camera\ntest/2eb62e24b9a1f99a,Luxury vehicle,Antique car\ntest/2eb96656e9bd4a53,Billiard room,Billiards\ntest/2ebc03e06d6a1abd,Luxury vehicle,Sport utility vehicle\ntest/2ebddc9d577deb34,Luxury vehicle,Performance car\ntest/2ec7463d470f2db5,Luxury vehicle,Antique car\ntest/2ec93b6b70e95344,Luxury vehicle\ntest/2ec94d0f6032542c,Orator\ntest/2ecd724394c7899c,Bird of prey\ntest/2ecdb1f7e844d276,Military vehicle,Gun turret\ntest/2ed06040b3fbd1c1,Primate\ntest/2ed1338f5186d353,Bovine\ntest/2ed17b738c178c83,Roller skating\ntest/2ed1fabfb5a2627c,Floristry\ntest/2ed30ad68259b02e,Junk food\ntest/2ed4789e985fc6d0,High-speed rail,Residential area\ntest/2ed4b2c714f2837d,Junk food\ntest/2ed4ddc2c8058656,Boating\ntest/2ed662b4d320061a,Amphibian\ntest/2ed6b312a0e9af3d,Portrait photography\ntest/2ed894e512cf8058,Watercolor paint\ntest/2ed8a1825ed36fef,Ball game\ntest/2ed9927f383371a3,Classical sculpture\ntest/2ed9e77c97542f51,Close-up\ntest/2eda40fb417a6ea8,Domestic short-haired cat,Close-up,Whiskers\ntest/2edd0c5ead316c01,Jeans,Chordophone\ntest/2edd39dd2ef74edf,Wind wave\ntest/2edf009d87f5d8d1,Ball game\ntest/2ee0d6da6882e418,Close-up\ntest/2ee0ef3382707ec3,Blond\ntest/2ee12d1f4e837c93,Plant stem\ntest/2ee1c389ce79f4f8,Jeans\ntest/2ee1da9e4489a712,Sport utility vehicle\ntest/2ee62e4d957c8ff5,Jeans\ntest/2ee8ab305463e901,Shallot\ntest/2ee91dd085c7e4da,Close-up\ntest/2eeb6e943d510826,Domestic short-haired cat,Whiskers\ntest/2ef0faceeea5a6f9,Compact car\ntest/2ef30330d04287b9,Luxury vehicle\ntest/2ef6378b90c343de,Halter\ntest/2ef72f8f1effd414,Luxury vehicle,Performance car\ntest/2ef78eb0a439a5c3,Compact car,Performance car\ntest/2ef8117c2042c5c3,Bovine\ntest/2ef9b93fdbb6c404,Aircraft engine\ntest/2efa6130ee952b09,Close-up\ntest/2efa918db55f9bc8,Close-up\ntest/2efae192612ba0c2,Black-and-white\ntest/2efbabf7b3a2a888,Erinaceidae\ntest/2efbd8430ea30061,Ball game,Team sport\ntest/2efc3200cc7805c5,Luxury vehicle,Antique car\ntest/2efdb55f2cf4e4f1,Arthropod,Close-up\ntest/2efe80f19ec25028,Sport utility vehicle\ntest/2eff2e9b401f5020,Bratwurst\ntest/2eff81c47a48596b,Shallot\ntest/2f00545e0a5f8544,Falafel\ntest/2f0093c453a9f23a,Domestic rabbit\ntest/2f0168cfa29633a2,Close-up\ntest/2f0393c8d53611a5,Antique car\ntest/2f0481f4fa5d6541,Black-and-white\ntest/2f04f8f22e7897c6,Performance car,Luxury vehicle,Antique car\ntest/2f05bd32dd1b9446,Jeans\ntest/2f06018578a7a576,Compact car\ntest/2f0692df6a63367e,Auto racing\ntest/2f08e4a46c3bcfd4,Arthropod,Close-up\ntest/2f099bceb961aba0,Plant stem\ntest/2f0a4f7fc284420a,Luxury vehicle,Antique car\ntest/2f0cd935147d2cda,Road cycling\ntest/2f0d46c3843374d9,Close-up\ntest/2f0d7c9c717e8fc7,Amphibian\ntest/2f0f75ebf6557557,Vegetarian food\ntest/2f0f7f677d19470f,Extreme sport\ntest/2f0f8ca4b69a8fcd,Siberian husky\ntest/2f15df57aaaece9c,Auto racing\ntest/2f165f16b4282539,Extreme sport,Parachuting\ntest/2f167693000876aa,Steamed rice\ntest/2f170d8b435b0145,Whiskers\ntest/2f194117eb6dc535,Ball game,Team sport\ntest/2f1a58272e1b0436,Calabaza\ntest/2f1aede2ffb80d72,Luxury vehicle\ntest/2f1da0f9164714fa,Extreme sport,Motorcycling\ntest/2f1e28be1960dbc4,Compact car,Luxury vehicle,Antique car\ntest/2f203a459d29bb09,Residential area\ntest/2f210e645d9231ef,Electronic instrument\ntest/2f2111ed9fa13e05,Jeans\ntest/2f28b563187591d3,Close-up\ntest/2f2c0984cb427e81,Close-up,Whiskers\ntest/2f2c6984d26e916e,Electric piano,Electronic instrument\ntest/2f2db6aa32f705b5,Pit bull\ntest/2f2ee1c5ee8bf6ba,Luxury vehicle,Antique car\ntest/2f31b80d1679464c,Close-up,Whiskers\ntest/2f32a5395756ecdf,Luxury vehicle\ntest/2f33cec19367bffd,Antique car\ntest/2f33ebb01699fc65,Herding\ntest/2f38d9fdbafdacff,Auto racing\ntest/2f38fa96344cb05d,Sport utility vehicle\ntest/2f3a4d100beceb4a,Performance car\ntest/2f3b5b4e8b566b0d,Luxury vehicle,Antique car\ntest/2f3be2c586c57b71,Jeans\ntest/2f3c922efd630eed,Antique car\ntest/2f3e245617d5a7bc,Antique car\ntest/2f3f96552be22ba2,Performance car\ntest/2f3f9a017f450bad,Black-and-white\ntest/2f40a1b19c553ac7,Luxury vehicle\ntest/2f4497d3051b6e49,Ball game,Team sport\ntest/2f454e51efcdd489,Glacial landform\ntest/2f4558866410e94e,Close-up\ntest/2f4673217176127a,Billiard room,Billiards\ntest/2f4aacd9d255fd7b,Combat sport\ntest/2f4aeb6289829f18,Compact car\ntest/2f4cf557c1a13dd1,Machine tool\ntest/2f4ef573016f0778,Jeans\ntest/2f4fab3629ee6190,Whiskers\ntest/2f54b455c008be53,Touring car,Luxury vehicle,Antique car\ntest/2f54fd48ec7e5e27,Blond\ntest/2f5553228ebcdc37,Luxury vehicle\ntest/2f55b82648664c1a,Digital camera\ntest/2f565a0331dd276b,Luxury vehicle,Performance car\ntest/2f570ab37a6e3e69,Luxury vehicle\ntest/2f571e5da293cd15,Arthropod,Close-up\ntest/2f57b961f42ce010,Whiskers\ntest/2f5bfd8416ef6c2f,Rice ball\ntest/2f5c592cd62b3b90,Auto racing,Performance car\ntest/2f626f0fb1a84fbf,Firearm\ntest/2f63e5659bb9ed43,Machine tool\ntest/2f645c884a282fd3,Ball game,Team sport\ntest/2f6680e9facf9d84,Cobblestone\ntest/2f69d81477982195,Antique car\ntest/2f6bc7764eafa8ef,Arthropod,Close-up\ntest/2f6c348fe08d4bd5,Peafowl,Close-up\ntest/2f6ca4f850d8aa34,Goats\ntest/2f6ce20f6f244efe,Rural area\ntest/2f712bf8d436b885,Floristry\ntest/2f71adddaec18bf0,Close-up\ntest/2f739ba133149fac,Jeans\ntest/2f75ac74eb288d54,Luxury vehicle\ntest/2f75c267eaae09c1,Close-up\ntest/2f75c4549b695351,Luxury vehicle,Antique car\ntest/2f783016bb99cc98,Compact car,Antique car\ntest/2f784535ef870ee7,Monoplane,Aircraft engine\ntest/2f7883217ce8ac95,Road cycling\ntest/2f7891262f6feff7,Luxury vehicle\ntest/2f7b50a9010d55cd,Arthropod,Close-up\ntest/2f7e046fe595dc5c,Arthropod,Close-up\ntest/2f81e4ef746c7d85,Aircraft engine\ntest/2f823f61672c9629,Extreme sport\ntest/2f82d030e758f5e7,Digital camera\ntest/2f84474b2948000d,Hound\ntest/2f8619ea74707dc2,Arthropod,Locust,Close-up\ntest/2f8646054639f943,Roller skating\ntest/2f8710feaeaf9598,Ball game,Team sport\ntest/2f87353d1f412052,Watercolor paint\ntest/2f889215f187b4d1,Steamed rice\ntest/2f8bcd18df748dcb,Halter\ntest/2f8c0dac35d31f94,Black-and-white\ntest/2f8e56374ce4b95d,Blond\ntest/2f90f8190ddb521e,Horse and buggy\ntest/2f9102f99962bf07,Luxury vehicle,Performance car\ntest/2f91f554d71bc1b1,Luxury vehicle,Antique car\ntest/2f93b26773ade304,Cookies and crackers\ntest/2f98a9520c6ca619,Luxury vehicle,Sport utility vehicle\ntest/2f9a340aa2a83a7d,Firearm\ntest/2f9ac1f11e4f79c7,Close-up\ntest/2f9af471a2ba7592,Plant stem\ntest/2f9be2ba6d3e3569,Gumbo\ntest/2f9ee9cf1521df80,Hound\ntest/2fa0242371280246,Extreme sport\ntest/2fa2f89d90f43623,Plant stem\ntest/2fa3c865ec00ce9f,Close-up,Plant stem\ntest/2fa493f69c0ad568,Black-and-white,Briefs\ntest/2fa4d728947841cd,Rural area,Bovine\ntest/2fa4dea7d96e9431,Close-up\ntest/2fa642e4e9389b1e,Solar energy\ntest/2fa6af3456340a91,Rear-view mirror\ntest/2fa8f8f9af952605,Fried food\ntest/2faec3ce69fc47bc,Herding,Goats,Bovine\ntest/2fb039be05fe633d,Combat sport\ntest/2fb0a8a195f211cf,Luxury vehicle\ntest/2fb0e01472a46c69,Hair coloring\ntest/2fb0f7ab7ab9c7f2,Black-and-white\ntest/2fb107dd7079415d,Whiskers\ntest/2fb1bd2044619e2d,Close-up\ntest/2fb5213a43afaec6,Hound\ntest/2fb5abb878966924,Antique car\ntest/2fb69c996d9fbcc6,Antique car\ntest/2fb96382132054e4,Luxury vehicle\ntest/2fba1aa52b8daaf6,Barquentine\ntest/2fbb321b6f378149,Close-up,Scaled reptile\ntest/2fbbaf56575bf54b,Luxury vehicle,Sport utility vehicle\ntest/2fbc967bf61e87b6,Water polo,Ball game,Team sport\ntest/2fbd0ffd345a68fd,Arthropod\ntest/2fbda9ba5e960bc8,Herding,Bovine,Rural area\ntest/2fbded9ce32e8dc6,Antique car\ntest/2fbf9abdc7ca6005,Arthropod\ntest/2fc1ca3d9751c048,Scaled reptile\ntest/2fc1fc08190a3217,Crocodilia,Nile crocodile,Close-up,Scaled reptile\ntest/2fc3055389510dbc,Amphibian\ntest/2fc33f14c91f74d7,Close-up,Whiskers\ntest/2fc4be27854ae19e,Close-up\ntest/2fc815db5f60e390,Team sport\ntest/2fc84e44c233e461,Filling station\ntest/2fcccb91c647caf7,Dishware\ntest/2fcf03d08f9ca463,Comics\ntest/2fcf37a00cd2f802,Jeans,Inflatable\ntest/2fd1948266c82802,Combat sport\ntest/2fd2b62cd3bfe33e,Compact car\ntest/2fd31c6532c1bc40,Luxury vehicle\ntest/2fd3aebb7c2e07fb,Luxury vehicle\ntest/2fd3eb1cc3d5d7d7,Rural area\ntest/2fd58bf18006c265,Sport utility vehicle\ntest/2fd91d447d744038,Combat sport\ntest/2fdb25f2d29f9efa,Jeans,Extreme sport\ntest/2fdc3d8e12203935,Luxury vehicle,Antique car\ntest/2fdcde4bd97a6d7d,Ball game,Team sport\ntest/2fded5aa70a15645,Close-up\ntest/2fdf26204c2aeae3,Luxury vehicle,Antique car\ntest/2fdfa82495a41240,Compact car,Luxury vehicle\ntest/2fe0371d4c51b70b,Ball game\ntest/2fe1689614535c1b,Luxury vehicle,Antique car\ntest/2fe2997d4eb89936,Compact car\ntest/2fe413aaca704ac0,Antique car\ntest/2fe430563929baff,Close-up,Outdoor shoe\ntest/2fe43fbffe6f8115,Luxury vehicle,Performance car\ntest/2fe6a58a21fdab3c,Luxury vehicle,Performance car\ntest/2fe836368e81aa35,Close-up\ntest/2fea42d3b18e43c9,Gun turret\ntest/2fed7615b48b8713,Monoplane\ntest/2ff5ed4d058bb3e9,Horse racing,Equestrianism\ntest/2ff608db5f10fa1a,Compact car\ntest/2ff618f5747cb15a,Whiskers\ntest/2ff6ef0ab236c98c,Compact car,Luxury vehicle\ntest/2ff8b20aa4ae2a3c,Black-and-white\ntest/2ff8cc6c32d1d07a,Compact car,Luxury vehicle\ntest/2ffdc6c5bdfb3d9d,Luxury vehicle,Performance car\ntest/2ffdf5fd42594f8b,Wind wave\ntest/30010d7dbab97495,Black-and-white\ntest/3003ba9e01a80a67,Luxury vehicle,Antique car\ntest/300494a02fefa5a2,Molluscs\ntest/3007e4245e6121bf,Close-up\ntest/300adbb4372d52c9,Close-up,Plant stem\ntest/300f2ccac834f9fe,Gliding\ntest/300f64f3d7896ace,Barquentine\ntest/300f94ffb34bb142,Arthropod,Close-up\ntest/300fbc235f6b7c79,Auto racing\ntest/301059d511323712,Blond,Hair coloring\ntest/301065396f32393d,Sport utility vehicle\ntest/3010e8373a200753,Aircraft engine\ntest/3017b1b6302635ef,Plant stem,Close-up\ntest/3018b0fbb93261c1,Performance car\ntest/301ac5562a37835f,Aerial photography\ntest/301b467afcbbe806,Close-up\ntest/301b9454368536a2,Pit bull\ntest/301c0830ea9b8ede,Freight transport\ntest/301d275af4c743cc,Close-up,Outdoor shoe\ntest/301d3c785f70f555,Modern dance\ntest/301e2493d40f3d69,Cobblestone\ntest/301fb6b07fa9c471,Ball game\ntest/301fcbb590c7fe46,Jeans\ntest/301fe0c932b89b5d,Modern dance,Musical theatre\ntest/302092115b718284,Luxury vehicle,Antique car\ntest/302105537ea307e1,Compact car,Luxury vehicle\ntest/3021cb833589ecdf,Close-up\ntest/3023153d781a36fb,Luxury vehicle\ntest/30238f953e080d2e,Close-up\ntest/3024d23311fc7e67,Hound\ntest/302702f7bebac30f,Close-up,Whiskers\ntest/30272be4b215c07d,Residential area\ntest/30283b31c3af1e58,Perching bird,Close-up\ntest/3028d17ebbade99d,Amphibian\ntest/3029f45368b5a2f6,Blond\ntest/302b2630d6cc2531,Luxury vehicle,Sport utility vehicle\ntest/302d170ae5d8334a,Antique car\ntest/302dcc08947e8b09,Close-up,Scaled reptile\ntest/30304367420de244,Compact car,Antique car\ntest/3032883aa14bf556,Close-up\ntest/30356464a4e001d8,Aircraft engine\ntest/30376950ed921d29,Calabaza\ntest/303769c94ffe7ddb,Luxury vehicle,Sport utility vehicle\ntest/3037bb1c74b06d00,Combat sport\ntest/3037fe7b526d0c27,Chordophone,Electric guitar\ntest/303a3afd931dc81c,Floristry\ntest/303a5cd4923e915b,Aerial photography\ntest/303b2fb21e4847d3,Chordophone,Electric guitar\ntest/303b6e55b374b06d,Luxury vehicle,Performance car\ntest/303b8b32816a92c2,Ball game\ntest/303bd173ea3d52f7,Jeans,Sledding\ntest/303bd81a53e689d6,Auto racing\ntest/303dc74f0d4cd951,Luxury vehicle\ntest/303ec2ee922365b6,Scaled reptile\ntest/303f4e66e87209e9,Sport utility vehicle\ntest/3041c8230d6f65d9,Nile crocodile,Crocodilia\ntest/304887fd92b7c360,Fried food\ntest/30498c67b74d168f,Auto racing,Performance car\ntest/3049e5534d5fadc4,Close-up,Whiskers\ntest/304abb4d2d1a4841,Equestrianism\ntest/304b022f917ed615,Close-up,Whiskers\ntest/304dd64012197b39,Anole,Scaled reptile\ntest/305350570c487c60,Vegetarian food,Corn on the cob\ntest/305399814fc5ddb0,Amphibian\ntest/3057b679f83b3080,Luxury vehicle\ntest/3058959a8b43c5cf,Luxury vehicle\ntest/3058e669cdaa5dc4,Compact car,Luxury vehicle,Antique car\ntest/30591d93d3531bc1,Luxury vehicle,Performance car\ntest/305ab6522ff22de9,Close-up\ntest/305ac2f729123abc,Close-up,Whiskers\ntest/305ca78705f393e3,Fried noodles\ntest/305ec1cf791c6f60,Steamed rice,Rice ball\ntest/306104c8a3d93e44,Stemware\ntest/306290e921145ed1,Luxury vehicle,Antique car\ntest/30635c36d1c05fc2,Chordophone,Electric guitar\ntest/3064de65bb02dc26,Boating\ntest/30651417a9420d88,Jeans\ntest/306603d8826c8ac1,Cookies and crackers\ntest/30669cca04b4b0e0,Close-up\ntest/30671e6d58fcafaa,Vegetarian food\ntest/30679f45bb261529,Extreme sport\ntest/3069fb364e3cbf78,Jeans\ntest/306bf743fe02e49c,Close-up\ntest/306c6b75a8ec2bec,Prosciutto\ntest/306fd7635441d86d,Flatbread\ntest/307476875274a73d,Junk food\ntest/3075e899a8d2ef86,Whiskers\ntest/30784e3376c6f0f6,Black-and-white,Close-up\ntest/307a4b83ad012f71,Luxury vehicle\ntest/307dd3482f39b31b,Senior citizen\ntest/307e507a1827d301,Bovine\ntest/307ef895dfd65e07,Performance car\ntest/308056cdb91e3813,Portrait photography\ntest/308142a1e24c53df,Barechested\ntest/30833916c4bb8637,Antique car\ntest/308467964f386972,Residential area\ntest/30860d88239c23e6,Compact car\ntest/308615a4d4bc27ab,Black-and-white\ntest/308a68262e09bb34,Aircraft engine\ntest/308dc291f4cf64fa,Miniature Poodle\ntest/3091580b995ff595,Arthropod\ntest/30928a68aaa26a5c,Monoplane\ntest/3092a179ff3fec6d,Aircraft engine\ntest/3092f31582f4d637,Machine tool\ntest/3096e7d4ef153a61,Luxury vehicle\ntest/3099a9d563a4a991,Shinto shrine\ntest/3099f17d24a6742e,Woodwind instrument\ntest/309d85a99db7731e,Gun turret\ntest/309dab42f8022af6,Luxury vehicle,Performance car\ntest/309e4663134c39b7,Residential area\ntest/309f5aad72650421,Ball game\ntest/30a187d92f32e5ff,Miniature Poodle\ntest/30a25c6210f3ed64,Black-and-white\ntest/30a47201b848e650,Black-and-white,Aerial photography\ntest/30a546b433144732,Boating,Rowing\ntest/30a9a3fd18621b00,Combat sport\ntest/30a9afdc96ed7a91,Whiskers\ntest/30abafaa408ef621,Luxury vehicle,Performance car\ntest/30b0855e90ca2678,Military person\ntest/30b28112e86c4687,Close-up\ntest/30b2c4a7d5aa1695,Arthropod,Close-up\ntest/30b34043f319eef1,Equestrianism\ntest/30b54b940617059f,Sport utility vehicle\ntest/30b6c0eb195dbaad,Vegetarian food\ntest/30b9d82080c08258,Arthropod,Close-up\ntest/30ba23cb3a2d6ac8,Domestic short-haired cat,Whiskers\ntest/30bb25b32c8ad53a,Compact car\ntest/30bc408216348408,Aircraft engine\ntest/30bfd3f1379114dd,Luxury vehicle,Performance car\ntest/30c1ebfea4f41b7f,Domestic short-haired cat,Whiskers\ntest/30c36436ff1c7e14,Jeans,Dog walking\ntest/30c40fd42cac7292,Classical sculpture\ntest/30c4990126b79364,Black-and-white\ntest/30c8e36253d9f6a1,Compact car,Luxury vehicle,Antique car\ntest/30cc909d083fc57b,Compact car\ntest/30cd0ebe6c88ecf8,Luxury vehicle\ntest/30cda36e69893125,Cosmetics\ntest/30cf34b03b97f963,Luxury vehicle\ntest/30d02d2abb291244,Close-up,Whiskers\ntest/30d22263f1a40a5a,Ball game\ntest/30d4c2b0bd25a136,Roman temple\ntest/30d58f7a4d6f0943,Bovine\ntest/30d7ae13bfbb0bb2,Stemware\ntest/30d8032481959844,Road cycling\ntest/30df72cc38492a3e,Miniature Poodle\ntest/30e13fc00b9c1732,Ball game\ntest/30e15713103581c7,Close-up,Whiskers\ntest/30e265d6147ef0b7,Boating,Rowing\ntest/30e73e5a1201fd5a,Black-and-white\ntest/30e7507a09aa7a66,Luxury vehicle\ntest/30e855c7efee4246,Corn on the cob\ntest/30e88581d3469aa0,Aircraft engine\ntest/30e901f597a1eaf6,Luxury vehicle\ntest/30e9866cddac9ab1,Rural area,Bovine\ntest/30ea1069f8ebf42f,Miniature Poodle\ntest/30eb7ed70c8f497f,Team sport\ntest/30ebbfac77011c7f,Bovine,Close-up\ntest/30ed70d6cd682462,Luxury vehicle,Antique car\ntest/30ede3c1fc68d878,Close-up\ntest/30ee8097adcbae53,Modern dance\ntest/30f1319a6fbedc23,Firearm\ntest/30f2e72d00bbb4b4,Compact car\ntest/30f3e73f947d4bab,Full moon\ntest/30f3fbc2b4979121,Close-up\ntest/30f7303fd7198537,Ale\ntest/30f995291fce0d8c,Boating\ntest/30fad0268a3f6904,Close-up,Whiskers\ntest/3100c5caabdf2815,Jeans\ntest/3101a242919ccc83,Luxury vehicle,Performance car\ntest/3104d0246ec09734,Luxury vehicle,Performance car\ntest/31050890b074e03d,Luxury vehicle\ntest/310537222f4d8c1d,Firearm\ntest/31053b9286cf06da,Close-up,Whiskers\ntest/3107d31505f0143d,Drums\ntest/31082b4dab8ea78b,Performance car\ntest/310a96c27c0cee0b,Performance car\ntest/310ab2eee54f5561,Auto racing\ntest/310fc8a6ffadaafb,Compact car,Luxury vehicle\ntest/3110985e7f8935e7,Arthropod,Close-up\ntest/3111da87d271cbd5,Performance car\ntest/3115394e2289c18e,Auto racing\ntest/31164141feddcd3b,Microcontroller\ntest/31180b42f93706b5,Close-up\ntest/3118b7968521d6dd,Close-up,Whiskers\ntest/3118d25fa6431f14,Military vehicle\ntest/311914ba1a61886c,Compact car\ntest/3119bc9b02b9a5e4,Black-and-white\ntest/311da0d9af9395be,Sport utility vehicle\ntest/3120dc28effd89b7,Bovine\ntest/312169ac0ec0d1fb,Monoplane,Gliding\ntest/31224d3ef2fd3906,Whiskers\ntest/312553eb97160242,Melee weapon,Hunting knife\ntest/312652ee3add5274,Pit bull\ntest/3128629a6f88d42f,Compact car,Luxury vehicle,Antique car\ntest/3128fe1a9a78e152,Coral reef fish\ntest/312953fd455deea8,Residential area\ntest/3129a2d3e7e435f8,Luxury vehicle,Antique car\ntest/312ef60aaa31d713,Freight transport\ntest/312ff0d2e81b5f77,Compact car,Luxury vehicle,Antique car\ntest/31314e23c6f65018,Close-up,Whiskers\ntest/31315f4df7ede550,Firearm,Headphones,Shooting range\ntest/31317f93b96ba25a,Aircraft engine\ntest/3132bd3f757514ad,Close-up\ntest/313302a30230b267,Flatbread\ntest/31342a7cb524db32,Luxury vehicle,Performance car\ntest/31357cdca819ab8b,Steamed rice\ntest/313866fa2e3dac6b,Military vehicle,Gun turret\ntest/3139e33f530d0f40,Rural area\ntest/313a0aef1345ff88,Bovine\ntest/313de6b0553ae1a7,Wind wave\ntest/313e3ff66d04b28a,Nordic skiing\ntest/3141f07231da29d8,Auto racing,Performance car\ntest/31432ba0fb229010,Black-and-white\ntest/3143458f8dd124e8,Gun turret,Black-and-white,Military vehicle\ntest/31446febffd4f24b,Plant stem\ntest/31468240d2e07e9c,Shooting range,Firearm\ntest/3148a96e7b323e9b,Rear-view mirror,Luxury vehicle\ntest/314984e691084553,Plant stem\ntest/314b21d8a651e0e9,Luxury vehicle\ntest/314beb420046d7ac,Dishware\ntest/314d45f043fed728,Plant stem\ntest/314d5a1196867389,Equitation,Equestrianism\ntest/314dc0a553f66d94,Boating,Rowing\ntest/314de4cc44e3882a,Vegetarian food\ntest/314e9085c3ff5613,Close-up,Scaled reptile\ntest/31514afef2f5bcc4,Equitation,Equestrianism\ntest/3151e58fbb51e604,Firearm\ntest/315317fce57d3276,Assault rifle,Firearm\ntest/31539e74696bf48f,Luxury vehicle\ntest/3155105407ef3902,Outdoor shoe\ntest/315c7b8439a8066d,Lawn game,Ball game\ntest/315d479f30cebdbc,Siberian husky\ntest/315d771554dae603,Blond,Close-up\ntest/315e0e7f1f83980c,Hunting knife\ntest/315e235218bca84b,Barechested\ntest/315e324eda8c98cd,Luxury vehicle\ntest/315f40a860ff69b8,Blond\ntest/31608f22e49fae99,Vegetarian food\ntest/31622269b0e9fe20,Luxury vehicle,Performance car\ntest/31631ead663dd5f3,Falafel\ntest/3163ee3bc37d5180,Cosmetics\ntest/3168f730e8488399,Fried food\ntest/316af044aec03d7a,Ball game\ntest/316bd77d83e98a37,Sport utility vehicle\ntest/316bdec054697719,Ball game,Team sport\ntest/316c79766dc073aa,Goats\ntest/316e43ad673ef80a,Black-and-white\ntest/316fbb26213c4851,Frozen yogurt\ntest/31704c4bf3f82eae,Black-and-white,Bovine\ntest/3170dd1ffbb2af8b,Luxury vehicle\ntest/317185e71e0551f4,Compact car\ntest/317304f8012d124d,Blond\ntest/3176e7cb69461bfd,Vegetarian food\ntest/31783b5638b90e26,Steamed rice,Rice ball\ntest/31798d57abad6ae4,Performance car\ntest/3182187225778112,Lawn game\ntest/31836b05d9a08de8,Close-up\ntest/318aab086661b4cf,Pork chop,Fried food\ntest/318abe97e77585c5,Luxury vehicle\ntest/318b0d16c233f163,Close-up\ntest/318b9a26697c1909,Cosmetics\ntest/318d29f966013282,Close-up\ntest/318d95508e86e2b6,Boating,Sailboat racing\ntest/318de47ffbb5192c,Luxury vehicle,Sport utility vehicle\ntest/318e1e29b08de4b2,Compact car\ntest/318f46edc6dadd19,Senior citizen\ntest/31903e180b32b03a,Hound\ntest/3194984f32cd8ae7,Black-and-white,Luxury vehicle\ntest/3194e35acbfa72f3,Extreme sport\ntest/3199eb2b43f351eb,Compact car,Luxury vehicle\ntest/319a80a8d3144d2c,Angling\ntest/319b401c09b20d28,Close-up\ntest/319c06a95e1fc4ff,High-speed rail\ntest/319c11db943af2ee,Luxury vehicle,Performance car\ntest/31a219997a139c5d,Leaf vegetable\ntest/31a4f41f27d64f67,Vegetarian food\ntest/31af267b3eb45d21,Close-up\ntest/31b0b3805a9021aa,Extreme sport,Parachuting\ntest/31b0f5a1250b0125,Dishware\ntest/31b235a49c56998f,Antique car\ntest/31b323e3c8e7c68e,Luxury vehicle,Sport utility vehicle\ntest/31b586615bb77735,Vegetarian food,Fried food\ntest/31b5b86f24a5d7a8,Close-up\ntest/31b6d82c84f5b1af,Close-up\ntest/31b8bde7705940d5,Woodwind instrument,Close-up\ntest/31bc5c65201c1594,Dishware\ntest/31bff1fb4067c515,Arthropod,Plant stem,Close-up\ntest/31c27d240c93c72a,Arcade game\ntest/31c291da11976123,Scaled reptile\ntest/31c32a62be2ca002,Compact car,Luxury vehicle\ntest/31c623fadcdd01b3,Close-up\ntest/31c66704f5623e96,Bovine\ntest/31c6d664692108aa,Extreme sport\ntest/31cd8eb5f8a79931,Bird of prey\ntest/31d0c27ae1208cd8,Luxury vehicle,Performance car\ntest/31d11a65df195a0b,Blond\ntest/31d2f5f31a8437d8,Black-and-white\ntest/31db46556874b156,Compact car,Luxury vehicle\ntest/31dc42b5a5ce770f,Amphibian,Close-up\ntest/31dee840f4863c03,Wind wave\ntest/31e59e20d945d211,Close-up\ntest/31e76123b6a3388b,Ball game,Team sport\ntest/31e7cea2f99c91c1,Close-up\ntest/31e9e28fd5dd60c7,Motorcycling\ntest/31ec7d11304123b4,Sailboat racing\ntest/31ee8053ccffc14b,Close-up\ntest/31ef96a888c4c2e3,Compact car,Luxury vehicle\ntest/31f0819dcaec0b16,Jeans,Luxury vehicle,Antique car\ntest/31f1d62eec215098,Military vehicle,Gun turret\ntest/31f3337ee4ccdaa0,Chordophone\ntest/31f3b4b392b5cdf2,Rural area\ntest/31f6be8719687909,Bratwurst\ntest/31fafa54a0b78ea5,Ball game,Team sport\ntest/31fc5927deae22ac,Arthropod\ntest/31fe26ec2176a514,Aircraft engine\ntest/31ff1710caa40002,Road cycling\ntest/32015d91ae70466a,Bottled water\ntest/320320e0a5ae5375,Close-up,Whiskers\ntest/320399a7d886ad10,Coral reef fish\ntest/3204a2edec44f064,Luxury vehicle,Performance car\ntest/3204d11c9daddb97,Frying\ntest/3205bdb5bf767046,Cosmetics\ntest/3208d2f08f6f9d42,Luxury vehicle\ntest/320913f1c657c382,Jeans\ntest/320eb9a2eb696a07,Luxury vehicle\ntest/320f8024741e95fc,Road cycling\ntest/320f8cb169ba85b9,Whiskers\ntest/3210797f65ea49eb,Jeans,Barechested\ntest/3211543f7f5fa899,Ball game,Team sport\ntest/3211e39e656e32b7,Perching bird,Close-up\ntest/321252c648c3abd2,Close-up\ntest/321db13fd08669f0,Ball game\ntest/321e326fc4a9a566,Goats\ntest/3220a637910ccfa2,Compact car\ntest/322159370cc59c9a,Horse and buggy\ntest/3221d1954eefe9a7,Monoplane,Gliding\ntest/3221e96aa6ff166a,Performance car\ntest/3221f665b5c19b3c,Boating\ntest/3222245684f73207,Scaled reptile\ntest/322360499485af3b,Gliding\ntest/3229b81b2193d069,Scaled reptile,Close-up,Anole\ntest/322b333fe29aa546,Luxury vehicle,Sport utility vehicle\ntest/322c62a76ee4021a,Dishware\ntest/322e7fbef074cc21,Extreme sport,Motorcycling\ntest/322ef4e8be0454fc,Miniature Poodle\ntest/322f14b3667b96d9,Canola\ntest/323375da1ecef6ed,Junk food\ntest/32360caac3d5d9c4,Ball game,Team sport\ntest/3236b029d13fe7cf,Compact car,Luxury vehicle\ntest/3236f9f47b25dfaa,Siberian husky\ntest/32376acfd5d8caf9,Antique car\ntest/3237d36241ae17fb,Jeans\ntest/323bb398b14f1185,Electronic instrument\ntest/323bceaf7704434b,Perching bird,Close-up\ntest/323c1f6359ae2416,Brochette\ntest/323c5c511b5bbccc,Luxury vehicle,Performance car\ntest/323c617bd3a1c27a,Luxury vehicle,Antique car\ntest/323d775085c0cd5e,Jeans\ntest/323e1e0b0e45aeea,Close-up,Whiskers\ntest/3240a1f6a77c0ee5,Luxury vehicle,Sport utility vehicle\ntest/32457ef7e0b8d1dd,Equitation,Equestrianism\ntest/3247dcd9fc0542bb,Bovine\ntest/3248d0189a2f0f53,Monoplane\ntest/324bb26fe27f3b5e,Residential area\ntest/324d576428b1b639,Floristry,Plant stem\ntest/324e3160e3acae6e,Woodwind instrument,Close-up\ntest/324fd2a1134706c0,Military person\ntest/325347155c8e87a6,Plant stem,Close-up\ntest/3254bc540ca2a86e,Melee weapon,Hunting knife\ntest/3255e6314c0ee283,Compact car,Luxury vehicle\ntest/325660774cdd0c68,Arthropod,Close-up\ntest/32576fb8950e529e,Close-up\ntest/3259794f71a31d9b,Musical theatre\ntest/3259979768cf4a1e,Cosmetics\ntest/325a9df80074eb3a,Arthropod\ntest/325aa2c5478e9ba0,Close-up\ntest/325aada36ec79f60,Steamed rice\ntest/325ad6a174ac6a73,Arthropod\ntest/325b8e6fac179407,Arthropod\ntest/325ba47fab9cd14b,Compact car\ntest/325bf4fe43e5e31c,Fried noodles,Soba\ntest/325f72efe052b075,Floristry\ntest/32600673c765c1fd,Childbirth\ntest/32625483d4e746b6,Freight transport\ntest/3263956dac0887fb,Close-up\ntest/326a806137afa329,Pork chop\ntest/326df69cfa6a56ff,Antique car\ntest/32713a818daa6988,Digital camera\ntest/327236c0ef04c7f0,Angling\ntest/32759aa0fbf4a564,Black-and-white,Headphones\ntest/32762d59e7e96b3b,Ball game\ntest/327645d06064fe1e,Middle ages\ntest/3276a458f56562f8,Watercolor paint\ntest/327a3b672986f047,Close-up,Scaled reptile\ntest/327a3e228cb5fcca,Arthropod\ntest/327af9da84f20d28,Barbecue chicken\ntest/327cad612544b111,Medical imaging\ntest/327d502b44d03695,Boating\ntest/327d6dd170ce769b,Black-and-white\ntest/327e1b865bc69c9d,Backlighting\ntest/327efe74fb6aa463,Jeans\ntest/327fd875c50c5473,Junk food,Fried food\ntest/328052e1bfbdc84c,Luxury vehicle,Performance car\ntest/328816ba6efb5597,Divemaster,Scuba diving,Underwater diving\ntest/328bc5910013e48f,Antique car\ntest/328cddf772f7a388,Machine tool\ntest/328e4b6ca39fa0f1,Arthropod\ntest/328fcd3c6e7b436c,Zeppelin\ntest/32964b7331d69693,Sport utility vehicle\ntest/329851d575c7f74e,Close-up,Whiskers\ntest/329aa6f2de5b33c7,Vegetarian food\ntest/329bcdeadf3bf80b,Extreme sport\ntest/329c823283bfb150,Jeans\ntest/329c89f705f91d35,Combat sport\ntest/329cf80dffcb43d9,Woodwind instrument\ntest/329db644fabfd141,Auto racing\ntest/329dc8c64c1760a6,Blond,Hair coloring,Close-up\ntest/32a1dd217294057e,Halter,Equestrianism\ntest/32a250979a8bef70,Modern dance,Musical theatre\ntest/32a48cc5926b84f3,Trampolining\ntest/32a6c3c37ca2cac5,Residential area\ntest/32a70ecdbf132831,Combat sport,Sumo,Grappling\ntest/32ad2caec200f008,Combat sport\ntest/32ae91389cd9e180,Black-and-white\ntest/32b01892d733507f,Sport utility vehicle\ntest/32b1e0b83b6eb330,Close-up\ntest/32b23050117abcbb,Extreme sport\ntest/32b63342a1e484a0,Arthropod,Close-up,Plant stem\ntest/32b8d99c7366fe5a,Team sport\ntest/32b93bb025e6a02b,Antique car\ntest/32b96d9533b8722f,Jeans\ntest/32bb5ce4dc081e99,Molluscs,Close-up\ntest/32bbb8d6b60f537f,Black-and-white\ntest/32bbdf6c82ca359b,Antique car,Performance car\ntest/32bc2bf5b6a3583e,Digital camera\ntest/32bdca935c6cb54d,Firearm\ntest/32bdcd6bf52bdcd3,Miniature Poodle\ntest/32bfe7799ee37cef,Sport utility vehicle\ntest/32c379679360d41c,Luxury vehicle\ntest/32c51e0e0ec2a5bf,Ball game\ntest/32c56835333a9179,Bagpipes\ntest/32c6509596353201,Sport utility vehicle\ntest/32c999f57de048d0,Gliding\ntest/32c9c807238cae84,Luxury vehicle\ntest/32ce63e607220691,Lumpia\ntest/32ceb8eb06a3af09,Arthropod,Close-up\ntest/32cebad428009a99,Whiskers\ntest/32d165f11e6f918c,Black-and-white,Military person\ntest/32d382647dbefab0,Woodwind instrument\ntest/32d3aa0208fad7cb,Dobermann\ntest/32d55a54435b86c7,Close-up\ntest/32d572d42aef2421,Boating\ntest/32d5bb70564d1d2a,Close-up,Whiskers\ntest/32d6a28778e47d86,Khinkali,Jiaozi\ntest/32d71f452dc947a2,Fire apparatus\ntest/32d8938a73135571,Rural area\ntest/32dac0d02a4ba81e,Luxury vehicle,Sport utility vehicle\ntest/32dcd9d940e25aa0,Rural area\ntest/32de943e506f5f39,Billiard room\ntest/32def2d6cad69d95,Ramen\ntest/32e2dcca2699ff6d,Black-and-white\ntest/32e4ca5bc2214424,Siberian husky\ntest/32e4cc36013d7395,Shallot\ntest/32e6aa33a873cbfb,Leaf vegetable\ntest/32e96cadd5f42c61,Siberian husky\ntest/32e98043f06ffb28,Coral reef fish\ntest/32ea1cad75b429f0,Comics\ntest/32ee1f7223150807,Firearm\ntest/32efd2d20e55e056,Jeans\ntest/32f219cabf6abd90,Gun turret\ntest/32f442cc469100c5,Blond,Portrait photography\ntest/32fe47ff3bf918fa,Erinaceidae\ntest/330270bc18499671,Close-up\ntest/330271a01731ae79,Stemware\ntest/3304216844bd78f1,Vegetarian food\ntest/33049138f170cc2d,Luxury vehicle,Performance car\ntest/330778af40df9ce9,Divemaster,Scuba diving,Underwater diving\ntest/33082f5aba390e54,Luxury vehicle\ntest/33086f5c68b4f263,Military vehicle,Sport utility vehicle\ntest/33087cd6680bf27d,Hair coloring\ntest/330cc6d167b6399f,Compact car,Luxury vehicle,Antique car\ntest/330de4ba637044a7,Divemaster,Scuba diving,Underwater diving\ntest/330fef47255728ac,Childbirth\ntest/3312156c1a389e3b,Flatbread\ntest/33147c3b1f6ae047,Aircraft engine\ntest/3314edfec10ce4d5,Antique car\ntest/33161723d7d424da,Arthropod,Close-up\ntest/331748f9e389c526,Close-up\ntest/331855139c68cd55,Luxury vehicle\ntest/331b9bab6bd0f064,Antique car\ntest/331c941c2b0d6b87,Luxury vehicle,Performance car\ntest/331dd4b8ee6fe3bc,Musical theatre\ntest/33202a93719b55f3,Luxury vehicle\ntest/3320a7f75fb70ac3,Blond\ntest/332140594154811c,Peafowl\ntest/3321913736376006,Bird of prey\ntest/33225f2ad5574a4e,Microcontroller\ntest/3322a68b4daf3fd0,Cobblestone\ntest/3324b578fef55087,Junk food\ntest/33266d17c6eadfc0,Hound\ntest/33275cea5dfbeb34,Extreme sport\ntest/332879b562e5b966,Arthropod,Close-up\ntest/33293d8221848703,Vegetarian food,Fried noodles\ntest/332be4d18f99252b,Plant stem\ntest/332c78de7efe4345,Flatbread\ntest/332d749d592d1ce7,Close-up\ntest/332e4005af89490b,Close-up\ntest/332f38fdd0b0b55f,Arthropod,Close-up\ntest/33313da60a7d4f50,Chordophone\ntest/333140f4c1ade6f0,Luxury vehicle,Antique car\ntest/333278179a1ebb57,Residential area,Aerial photography\ntest/3332ebdcbfa3d3e7,Black-and-white,Portrait photography\ntest/3336248fb42c5466,Horse and buggy\ntest/333aac66d63015d9,Ramen\ntest/333c6366df166914,Leaf vegetable\ntest/333dcac99f948537,High-speed rail\ntest/333dd5ee53ec12b7,Luxury vehicle\ntest/333fa8a87596b927,Plant stem\ntest/33421437927f7fed,Plant stem\ntest/33422f0f2f2bd75f,Close-up\ntest/33424781103f96c0,Compact car,Luxury vehicle\ntest/334350abb2556929,Close-up\ntest/3343d05671a6c0fb,Sailboat racing\ntest/3344228e585ccd6a,Close-up\ntest/3348dd2dc6e8d290,Close-up\ntest/3349082e5d9e6619,Blond\ntest/334935679cddcce7,Modern dance\ntest/334bcac436ae3c4e,Team sport\ntest/334d4cd10f6a1406,Compact car,Luxury vehicle\ntest/3351a095d4d2f669,Luxury vehicle,Performance car\ntest/3353159d2b966ff8,Solar energy\ntest/335417380026d2ea,Shinto shrine\ntest/33584db8c02de4cb,Frozen yogurt\ntest/335863e3f4c389d8,Coral reef fish\ntest/335ac4b48d0bd74b,Scaled reptile\ntest/335c7670cc439acf,Luxury vehicle\ntest/335d18240b64cf5c,Performance car,Antique car\ntest/335eb45678a545cc,Dishware\ntest/335f16a44a45b80d,Military person\ntest/336036490e882aa3,Close-up\ntest/3360b31124348fb2,Antique car\ntest/33614f5f4bb76083,Blond\ntest/33630763f4c1c4e7,Luxury vehicle,Sport utility vehicle\ntest/3364360221337fb5,Luxury vehicle,Sport utility vehicle\ntest/3365e0b2f6383ebd,Luxury vehicle,Performance car\ntest/33689b850e70d668,Compact car,Antique car\ntest/3369bd061d925f7e,Floristry\ntest/336c8ad9fa28f1ca,Domestic short-haired cat,Whiskers\ntest/336eadd3732962f9,Arthropod\ntest/336fefc28c75e241,Jeans,Luxury vehicle,Sport utility vehicle\ntest/33738f93e7c2e547,Close-up\ntest/33774a436ffb74c2,Performance car\ntest/3378906cdf833b8f,Black-and-white,Close-up\ntest/337e5285e360e2d2,Floristry\ntest/3382477be7b54019,Antique car,Luxury vehicle,Performance car\ntest/33830f5a50fec349,Forklift truck\ntest/338394c249ad9772,Black-and-white\ntest/3383dc50eacd2a5b,Blond,Hair coloring\ntest/33841d8146644ae7,Auto racing,Compact car\ntest/33859354415e22bf,Galleon,Black-and-white,Barquentine\ntest/33880da6ef5d3a91,Aircraft engine\ntest/33881f031110303c,Luxury vehicle\ntest/3389fbf5ce58d4b0,Close-up\ntest/338a4bec7e195ba4,Blond,Hair coloring\ntest/338c25de67be5df2,Luxury vehicle,Performance car\ntest/338f83a672288121,Military person\ntest/3391495a25bfe6d0,Equitation,Equestrianism\ntest/33965e5abdd51bb7,Black-and-white\ntest/33969504e65ae999,Jeans,Prosciutto\ntest/3397865da9f7bb83,Fried food\ntest/3399988665f866a2,Close-up\ntest/339af7ca084783b7,Scaled reptile,Close-up,Anole\ntest/339c34e6374634eb,Cookies and crackers\ntest/339d5fd5a2c993b1,Boating\ntest/339de428ada8cf52,Freight transport\ntest/33a1c5afbd9e4bfe,Compact car,Antique car\ntest/33a27cbf400f9eef,Military vehicle\ntest/33aa31174dd3f944,Plant stem\ntest/33ab6cf5e8d03fc8,Military person\ntest/33ab9cc743d85f14,Compact car\ntest/33abc64b0ce51eb1,Wind wave\ntest/33ac96b0cb903a03,Military person\ntest/33ad2eef16711ec4,Dog walking\ntest/33afc331732aa69c,Arthropod,Close-up\ntest/33b065ff034fd571,Ball game\ntest/33b28420f3e4977e,Luxury vehicle\ntest/33b34e00006422c8,Boating,Rowing\ntest/33b3a545565fee5f,Performance car\ntest/33b3f7bf102addb6,Shooting range,Firearm\ntest/33b4a365c786bf95,Compact car,Antique car\ntest/33b68c1564686873,Fried food\ntest/33b813190bc88bd7,Close-up\ntest/33b885dbc932f43c,Barechested\ntest/33ba03ffb81e25a4,Antique car\ntest/33bab3105b23818c,Boating\ntest/33bda38cc13e5cde,Double-decker bus\ntest/33c0a0d8accccae5,Blond,Portrait photography\ntest/33c1d6b4c2db0337,Blond\ntest/33c68d1c49cbe976,Lawn game,Ball game\ntest/33c7169f6c220128,Luxury vehicle\ntest/33c9681b449075c0,Blond\ntest/33cad7edb21d5bea,Bratwurst\ntest/33caf4f33d8ca5f1,Ball game\ntest/33cf3c483aa968c3,Steamed rice\ntest/33d46829f7a1cdd5,Residential area\ntest/33d4d69704f23157,Bratwurst\ntest/33d6c97ea13a399d,Machine tool\ntest/33d8e425411e792d,Auto racing,Performance car\ntest/33dda917c954a902,Plant stem\ntest/33df220600c5193a,Luxury vehicle,Performance car\ntest/33e1c4783bc3d82f,Compact car,Luxury vehicle\ntest/33e688b968f4af6f,Compact car\ntest/33e704d954c8b0d0,Auto racing\ntest/33e727b4af267632,Black swan\ntest/33e767940295502d,Luxury vehicle\ntest/33ee34212dcdecd4,Military vehicle,Sport utility vehicle\ntest/33f2032dedbb33f8,Close-up,Plant stem\ntest/33f2aca2da2ed55a,Auto racing,Performance car\ntest/33f3047edfc30dbf,Compact car,Luxury vehicle\ntest/33f38e00c42ae3f7,Extreme sport,Parachuting\ntest/33f50b1b600e546d,Athletic shoe,Outdoor shoe\ntest/33f577e844eb3e26,Ball game\ntest/33fb9a931cf4b4fd,Close-up\ntest/33fc9f0f76aeff22,Bovine\ntest/33fcd32311b32e6d,Cookies and crackers\ntest/34005ecc4503683a,Arthropod,Plant stem,Close-up\ntest/340089a6496ff3c0,Leaf vegetable\ntest/34012c59c928d625,Domestic rabbit\ntest/34014613771bd94e,Brisket\ntest/34014c5c3b7ed527,Shallot\ntest/3405397885b0fc1a,Chordophone\ntest/340b74944f315105,Computer speaker\ntest/340dc067c411fd64,Plant stem,Narcissus,Close-up\ntest/340f429a3d6dfbab,Domestic short-haired cat,Whiskers\ntest/340f4385b79cb69a,Luxury vehicle,Performance car\ntest/34127d355ab65a0a,Close-up\ntest/3413970850cac8a7,Inflatable\ntest/341505f8fbf5ef46,Close-up\ntest/34156013ca616278,Musical theatre\ntest/341b61521e33ee28,Luxury vehicle,Sport utility vehicle\ntest/341dbd49048c1341,Blond,Hair coloring\ntest/341f18dbf18abeba,Blond\ntest/3420d74584998cca,Rural area\ntest/3420da6c83041c88,Heavy cruiser\ntest/3420f6e13712517a,Sport utility vehicle\ntest/34213e6070c4bf9b,Dobermann\ntest/342158093ffb11e0,Luxury vehicle\ntest/342243252c9da929,Close-up\ntest/34238ba0c8ee9f48,Plant stem,Close-up\ntest/342719d0e7eec50c,Residential area\ntest/3427b2cf6603a4d0,Vegetarian food\ntest/3427b359a0780f6f,Luxury vehicle\ntest/3429b16d0451c62c,Fried food\ntest/342be9f705b90975,Luxury vehicle,Antique car\ntest/342c0c4f38532117,Domestic short-haired cat,Whiskers\ntest/342c8dfd1324dfe5,Plant stem\ntest/342fbf5c7de70a44,Rural area\ntest/3430c3b375ede35a,Fried food\ntest/34312054ecd2f642,Antique car\ntest/3431b93439a6e56c,Cosmetics\ntest/343756c6930bb8c1,Shinto shrine\ntest/3438c5722c086942,Musical theatre\ntest/34392730fd7528b0,Aerial photography\ntest/343ae7754c9ca054,Extreme sport,Wind wave\ntest/343be1023d631d9f,Jeans\ntest/343d40534f90b646,Close-up\ntest/343df9289f1f30ab,Jeans\ntest/343fc9fbc12f8308,Luxury vehicle\ntest/3440d213ce316ec7,Sport utility vehicle\ntest/34467b945a5c0048,Electronic instrument\ntest/344fc03e1615b1cb,Scaled reptile\ntest/34504bf6c22bd726,Luxury vehicle,Antique car\ntest/345087c2b892c55e,Ball game\ntest/3451dcf063142c48,Compact car,Luxury vehicle,Sport utility vehicle\ntest/34525a762f710c33,Close-up,Plant stem\ntest/3452651dffc9243e,Ball game\ntest/3452ab13dfd7cd45,Close-up,Whiskers\ntest/3453c28216a1175d,Aerial photography\ntest/3453d983e8f492a2,Luxury vehicle\ntest/345522803aaa84eb,Chordophone\ntest/3455be006d3442ae,Luxury vehicle\ntest/3455eeef98a29f66,Auto racing\ntest/345ae6d6c31fe8db,Gun turret,Military vehicle\ntest/345e86b45404b8af,Luxury vehicle,Antique car\ntest/3460dc99c01248c1,Firearm\ntest/34645aba8fc08675,Black-and-white\ntest/34652d27f43c0305,Luxury vehicle\ntest/3465d36860fc3b58,Blond\ntest/34663787a290bcbf,Luxury vehicle,Antique car\ntest/3469661f79445c77,Antique car\ntest/34699137ab6d2134,Ball game,Team sport\ntest/346e1b92b1e31016,Ball game,Team sport\ntest/346e4fd5ed509c6b,Glacial landform\ntest/346e55e4d8cd645c,Medical imaging\ntest/346e9f6eb71b334c,Aircraft engine\ntest/346fbc98f7e793a8,Ball game\ntest/346fd6ea7872b6f4,Close-up\ntest/34703eb67995c98b,Hound\ntest/347221a32e893e8e,Arthropod,Close-up\ntest/3472859190e25aa6,Antique car\ntest/3473b51f90126e85,Performance car\ntest/3473f295183ff39e,Extreme sport\ntest/34756561c9a1a2ce,Antique car,Performance car\ntest/347567711dc555ec,Extreme sport,Wind wave\ntest/34766858672c3d95,Compact car\ntest/34771b15a6093897,Gumbo\ntest/347c40ab10051661,Bovine\ntest/347d0c229ccc55bb,Auto racing,Touring car,Luxury vehicle,Performance car\ntest/347d400d92484130,Senior citizen\ntest/347f22f6b2429b05,Goats\ntest/34804a2e03ba8c62,Microcontroller\ntest/3480a1c284096e2e,Auto racing,Compact car,Luxury vehicle\ntest/3484e7b9e2d9284e,Rural area\ntest/34856ea1af0714e4,Military person\ntest/3486f12067d3d1cd,Antique car\ntest/34874899460bd257,Vegetarian food\ntest/34878bab72806863,Fried food\ntest/348824dd8c0c74e6,Luxury vehicle\ntest/34899c4304c4d1d7,Luxury vehicle\ntest/348bdc0d851e3d34,Close-up\ntest/348d24496f4d9ce1,Vegetarian food\ntest/348d9473b71a2bbd,Whiskers\ntest/348e82dcae24ccb1,Luxury vehicle\ntest/348fba599faf3ffe,Portrait photography\ntest/348fd4e0d97dc02a,Milky way\ntest/3493abfed6a2f1a4,Boating,Rowing\ntest/349423eea5c3e27a,Outdoor shoe\ntest/34961cea9977c655,Extreme sport,Sport climbing\ntest/3497b7c0d122f336,Domestic short-haired cat,Whiskers\ntest/3498137c4bb434fe,Aircraft engine\ntest/34994bdadfd91181,Outdoor shoe\ntest/349a6eb44f16bc38,Performance car\ntest/349b8bbb35eb5eee,Antique car\ntest/349bdb5a7ccde92b,Luxury vehicle\ntest/349c0583b966f772,Jeans\ntest/349c803b1bd26c7b,Electronic instrument\ntest/349cdf60be2a96bb,Ball game\ntest/349ddd72027bce9f,Drums\ntest/349f1539fc2644f6,Luxury vehicle,Sport utility vehicle\ntest/34a1641f725d6e4a,Close-up\ntest/34a209d16cfe63bf,Arthropod\ntest/34a22ce252b87cf6,Plant stem\ntest/34a6c58a11d2a3a8,Sport utility vehicle\ntest/34a6d3ee57971fd0,Auto racing\ntest/34a77c7c3575dfbf,Extreme sport,Parachuting\ntest/34a89a70b4345a60,Black-and-white\ntest/34a95e77be6d8712,Sport utility vehicle\ntest/34ac720a2f869f6e,Luxury vehicle\ntest/34ad0f6bc8e389df,Luxury vehicle,Sport utility vehicle\ntest/34ae17b372db0aab,Compact car,Luxury vehicle,Antique car\ntest/34b007530bf1bc4e,Hound\ntest/34b0fb7c57c11e3b,Domestic short-haired cat,Close-up,Whiskers\ntest/34b1971b0707d644,Aircraft engine\ntest/34b3072cb9fed2bb,Vegetarian food\ntest/34b3bfee5b1ba32b,Cookies and crackers\ntest/34b41fd53af8f4a1,Whiskers\ntest/34b71c5daa8585ff,Frozen yogurt\ntest/34bab4693f8b5447,Antique car\ntest/34bd517e94f1429d,Wind wave\ntest/34bdd7ef498e3c31,Aerial photography\ntest/34c111a3e3d029d9,Amphibian,Scaled reptile\ntest/34c2e9da0b51f345,Goats,Close-up\ntest/34c3009fd4ed965a,Close-up\ntest/34c54f78650ff52e,Performance car,Antique car\ntest/34c7812a516960a7,Close-up\ntest/34c9bb5b7b7c8552,Close-up,Whiskers,Domestic rabbit\ntest/34cb0cdcc6a97c2f,Military person\ntest/34cc56a75b4c1a36,Close-up\ntest/34cce31b3bb54cb4,Close-up\ntest/34cda2b85d772248,Floristry\ntest/34ced78d459c87e6,Miniature Poodle\ntest/34d0279738eebd96,Close-up\ntest/34d24671b567b03b,Aerial photography\ntest/34d39b8766b10d92,Aircraft engine\ntest/34d85b650969044e,Compact car\ntest/34d9ee0ec37f5078,Cookies and crackers\ntest/34da803c6cae973c,Headphones\ntest/34dc1a641b258217,Close-up\ntest/34dc4da71ebf8d60,Combat sport\ntest/34dcc977365f73e0,Auto racing\ntest/34df8b50ecc7ca7d,Performance car\ntest/34e09edd60c9afb6,Ball game,Team sport\ntest/34e16c5c17291648,Equitation,Equestrianism\ntest/34e1cb261e343ea4,Luxury vehicle,Antique car\ntest/34e24696edc53ab3,Extreme sport,Wind wave,Personal water craft\ntest/34e2df7245c9e61a,Luxury vehicle,Performance car\ntest/34e3372ce754adb0,Luxury vehicle,Performance car\ntest/34e3a3602aff3c7d,Domestic short-haired cat,Whiskers\ntest/34eab484403d1af1,Frigate\ntest/34eb6f26ccc34a65,Close-up\ntest/34ebf4323b7a2265,Extreme sport,Boating,Personal water craft\ntest/34ec26237ed8a415,Combat sport,Sumo\ntest/34ec3a579dbea003,Aircraft engine\ntest/34edc093b0031cd7,Close-up,Scaled reptile\ntest/34eed22782cced86,Close-up\ntest/34f021f5f48c29ee,Headphones\ntest/34f046aaf4e67239,Siberian husky\ntest/34f24d2374c50c25,Shinto shrine\ntest/34f25b843f1e136c,Aerial photography\ntest/34f32ec47126cf9d,Luxury vehicle,Performance car\ntest/34f8781f587c4bfe,Auto racing,Luxury vehicle\ntest/34f920cd181e5a9d,Ball game\ntest/34fc4def0ea5013d,Luxury vehicle\ntest/34fdf6eb47bbf4fd,Bonbon\ntest/34fe162fb5cae91b,Microcontroller\ntest/34fea7643aeb1a1d,Wind wave\ntest/350146445a1383f8,Rural area\ntest/350212082d0f2842,Athletic shoe,Outdoor shoe\ntest/3505a62b0a7097ff,Cookies and crackers\ntest/350608dc7f96b2ad,Blond\ntest/3506726069f02c04,Close-up\ntest/3506b9bf31cf46f7,Domestic short-haired cat,Whiskers\ntest/3507cdf6db87a8aa,Portrait photography\ntest/35091ea9d4b3cb73,Close-up\ntest/350a350ef98b2200,Flatbread\ntest/350c627132f9bfa1,Antique car\ntest/350cae9897344bd7,Sport utility vehicle\ntest/350d7a0fe55b2508,Compact car,Luxury vehicle\ntest/350ef0703098fb24,Bovine,Halter\ntest/35105640da50ad81,Bratwurst\ntest/3516ffd0eb5ba846,Jeans\ntest/3517b8fa2e690418,Luxury vehicle\ntest/351875e42115f7bf,Plant stem,Close-up\ntest/351ae4a6b639d03c,Luxury vehicle\ntest/351ca981cf3c8459,Cosmetics,Close-up\ntest/3520f34e5302edd0,Nordic skiing\ntest/35229bc33362376e,Luxury vehicle\ntest/3524279cc137309a,Classical sculpture\ntest/35248bcbb2643cc9,Sport utility vehicle\ntest/35272b4a510d3791,Arcade game\ntest/352b47a259c022db,Sailboat racing\ntest/352bc50bba7eb5f4,Vegetarian food,Pasta salad\ntest/352e1c7cfff08142,Jeans,Black-and-white\ntest/3530d2f687049a05,Luxury vehicle,Performance car\ntest/3532d139f028fa4a,Dishware\ntest/3533a213b4f5dfa8,Luxury vehicle,Performance car\ntest/3534cfd5aac12f69,Close-up\ntest/3539ca53a8a72ace,Compact car\ntest/353bd154107ffafd,Backlighting\ntest/353d6b05a9a00901,Close-up\ntest/353e6034a9ee268c,Compact car,Luxury vehicle\ntest/353ea483d2cdf8a4,Woodwind instrument\ntest/353f5c74f4c59954,Arthropod\ntest/35401f2bf3549eea,Vegetarian food,Steamed rice\ntest/3541300f13eaff11,Monoplane\ntest/3541cc9b48b0e149,Luxury vehicle,Performance car\ntest/3542231e42dc54cf,Extreme sport\ntest/35429b6a4f7309f5,Luxury vehicle,Performance car\ntest/3548aee1f290bbb3,Orator,Public speaking\ntest/35492aa0224b4fc3,Arthropod\ntest/35497db215177124,Compact car,Antique car\ntest/354ba3d19ec459a6,Arthropod,Close-up\ntest/354bcea8efc6eb0d,Digital camera\ntest/354ce0535934fdec,Vegetarian food,Junk food\ntest/354d55c53f434272,Digital camera\ntest/354d7a327bbcc152,Vegetarian food\ntest/354eed33b5e0547b,Boating\ntest/3553113e37631df3,Bovine\ntest/35533660e847d141,Luxury vehicle,Sport utility vehicle\ntest/35550fc7bc4659c9,Black-and-white\ntest/35570ee4a17d832b,Extreme sport\ntest/35581e7e4c809ca6,Close-up\ntest/355bc1ce582f0e96,Extreme sport,Road cycling\ntest/355ce449fbf0b48a,Wind wave,Glacial landform\ntest/35604bc10d23a7e0,Luxury vehicle\ntest/3563028b43d0a320,Close-up\ntest/35642dfb12a5e8a7,Amphibian,Close-up\ntest/356448c55a5ce0e7,Performance car\ntest/3565b78a51ebeef0,Extreme sport\ntest/35669675caf4ebe9,Ball game,Team sport\ntest/35675293ed63c1f8,Close-up,Plant stem,Floristry\ntest/356a691c7a0a61cc,Blond,Hair coloring,Close-up\ntest/356ad23dcb9811f3,Dishware\ntest/356fadf373084424,Ball game\ntest/3575853cbbd9c7d2,Close-up\ntest/357659baba435768,Close-up\ntest/357d8e182e29ae1b,Ball game,Team sport\ntest/357f7b4916c6f319,Close-up\ntest/358206e3798f86bd,Rear-view mirror\ntest/358568781fbb0489,Luxury vehicle,Sport utility vehicle\ntest/358574370432cc12,Compact car,Luxury vehicle\ntest/35863564f8ce39bb,Ball game,Team sport\ntest/3586d809f73f02cd,Rural area\ntest/35878fcc102a1475,Antique car\ntest/35887d79359c04cf,Dobermann\ntest/3588b97984e43e5c,Aerial photography\ntest/3589551e3554915f,Military vehicle,Sport utility vehicle\ntest/358a730ac285d72e,Inflatable\ntest/358aee318fdadd0a,Rear-view mirror\ntest/358c26a6c5fbea77,Chordophone\ntest/358d03312681943c,Machine tool\ntest/3590099add9f7c50,Frying,Junk food,Fried food\ntest/359072742b2fcad5,Scaled reptile\ntest/3590ed015de687c4,Luxury vehicle\ntest/3591fceeddffb25f,Junk food\ntest/359221ef9b7e0514,Extreme sport,Boating,Wind wave,Personal water craft\ntest/3593119841138bdb,Close-up\ntest/35942289a7c46c42,Sport utility vehicle\ntest/359707f190701e8b,Sport utility vehicle\ntest/3597f808ff0e08ff,Frying,Junk food,Fried food\ntest/359b0876a80f34ab,Brochette\ntest/359b32ccbe889a69,Close-up,Chordophone\ntest/359b47b29df463f8,Black-and-white\ntest/359cd5c045d65d09,Electronic instrument\ntest/359d6cfd9023c6e0,Auto racing,Compact car\ntest/359df0fcfd3c52fc,Blond\ntest/359eab1562aef6a1,Vegetarian food\ntest/359ee5f482872afd,Domestic short-haired cat,Whiskers\ntest/359f192e90f78b1c,Luxury vehicle,Performance car\ntest/359fdb9b03c3ba8b,Compact car\ntest/35a18adce7eadc8a,Orator\ntest/35a4846f66a36126,Cookies and crackers\ntest/35a49125c89bcbdf,Beef tenderloin\ntest/35a4ccaad2f96a75,Compact car\ntest/35a59c63aa24d545,Bratwurst\ntest/35a82af8c9c765df,Luxury vehicle\ntest/35a8783a3b607e2b,Scaled reptile\ntest/35abdcbd62e7f8a4,Rural area\ntest/35ad7945eb83d60f,Luxury vehicle,Performance car\ntest/35af308c5dcd2b75,Steamed rice\ntest/35b1f01964d271c7,Combat sport\ntest/35b2f63535b4a061,Luxury vehicle,Performance car\ntest/35b43c6c81761932,Ball game\ntest/35b49e8efc80cf8c,Boating\ntest/35b4ce3e30b238b7,Luxury vehicle\ntest/35b6761f2ddaebc6,Luxury vehicle\ntest/35bdcf736452174f,Luxury vehicle,Performance car\ntest/35c2c804706e7349,Ball game\ntest/35c4ffd8c00a99d1,Auto racing,Performance car\ntest/35c68448eac11a1c,Monoplane,Aircraft engine\ntest/35cb03cec4153727,Primate\ntest/35cb09e419c3c980,Luxury vehicle,Sport utility vehicle\ntest/35cb366bfbfc2c07,Residential area\ntest/35cbdcf622195336,Luxury vehicle,Antique car\ntest/35cce933c79ed058,Extreme sport\ntest/35cd692de448c717,Close-up,Whiskers\ntest/35ce1cbe20beef8a,Ball game,Team sport\ntest/35d01b4104bc4406,Rural area,Aerial photography\ntest/35d1378f197dc461,Vegetarian food,Junk food,Fried food\ntest/35d1a161e0b77736,Cosmetics\ntest/35d1aa832cffc11d,Fried food\ntest/35d1d0d24f02c532,Sport utility vehicle\ntest/35d28c8fcc98ae4d,Residential area\ntest/35d34bb3e85861f2,Electronic instrument,Electric piano\ntest/35d7234da98670be,Dobermann\ntest/35de01ca801de196,Watercolor paint\ntest/35e1edb7c015752e,Perching bird,Close-up\ntest/35e262ef02bb3e04,Whiskers\ntest/35e2c4b92694e748,Motorcycling\ntest/35e3a57f351ee6cf,Luxury vehicle,Performance car\ntest/35e8351f4df8f51e,Close-up\ntest/35e9d7b413412f96,Black-and-white\ntest/35eb4c4428e24c9d,Antique car,Performance car\ntest/35ecaf5814a47471,Galleon,Frigate\ntest/35ed0a188319981d,Ball game\ntest/35ef44a1edd16954,Miniature Poodle\ntest/35f078ff506bf147,Athletic shoe,Outdoor shoe\ntest/35f160e304753ffe,Luxury vehicle,Antique car\ntest/35f7d88c5cee1ebd,Black-and-white\ntest/35f887d56e261341,Extreme sport\ntest/35f9e1e71fa21300,Domestic rabbit\ntest/35fc7f25153737d5,Athletic shoe,Outdoor shoe\ntest/35fc9c29ed8fb5f6,Headphones\ntest/35fcd9f4c486deeb,Luxury vehicle,Antique car\ntest/35fd2db3fa5d2d51,Black-and-white\ntest/35ff65e19d289366,Performance car,Antique car\ntest/3601922acfc80330,Domestic short-haired cat,Close-up,Whiskers\ntest/3601de93a0e5fd67,Ball game,Team sport\ntest/360470f4329e7d94,Compact car,Luxury vehicle\ntest/3605042931bdaa95,Military person\ntest/3606c17a0bf6aeb6,Arthropod,Close-up\ntest/3608fb77697ec36f,Close-up\ntest/360b7c6ba8b5d86d,Fried noodles\ntest/360bef0121bd136f,Close-up\ntest/360cd3be4602c4e8,Microcontroller,Breadboard\ntest/360e5b2668719ff3,Rural area\ntest/360ef8487057c774,Luxury vehicle,Sport utility vehicle\ntest/360f2fec6936bb53,Close-up\ntest/360fc1faeddf8ea1,Public speaking\ntest/3610576dc07c04a2,Roman temple\ntest/361270ef3ae65e3a,Compact car,Luxury vehicle\ntest/3618749238199b6e,Close-up\ntest/361aaf5773f02c40,Jiaozi\ntest/361af9c5aba02d33,Vegetarian food\ntest/361cd4bcb5b27feb,Siberian husky\ntest/361db7bd1eea66d9,Compact car\ntest/361de053f8f9b9ae,Inflatable,Lifebuoy\ntest/361e85289196f54e,Ball game,Team sport\ntest/361ed9a26e316c89,Luxury vehicle,Antique car\ntest/361ef7ba14378b78,Chordophone\ntest/361fe41e63e9af0a,Aerial photography\ntest/3622167b7dcaee8b,Black-and-white\ntest/3623f736e228da04,Extreme sport,Motorcycling\ntest/3625d7f31899c93e,Luxury vehicle,Performance car\ntest/3626eb664fe862b8,Black-and-white,Military person\ntest/36290134d7481e01,Luxury vehicle,Performance car\ntest/36296aefe846a82e,Bovine\ntest/362b8d825e26363e,Electronic instrument\ntest/362e032f36f5de9b,Luxury vehicle,Sport utility vehicle\ntest/362e3b2a136fa652,Domestic short-haired cat,Whiskers\ntest/362f60f4da2ebc83,Close-up,Plant stem\ntest/36301bb736d8fd11,Bovine\ntest/3632975eeb91cae5,Luxury vehicle,Sport utility vehicle\ntest/3632bb0cee4277af,Close-up\ntest/3635195106ada65d,Sport utility vehicle\ntest/363574964a6e183d,Auto racing\ntest/3637c4312252ca2e,Leaf vegetable\ntest/36398b6df35a85da,Classical sculpture\ntest/363c48cd4991cfe4,Extreme sport\ntest/363e77ebb7b1182f,Close-up\ntest/363ee1969e754662,Close-up\ntest/363f12ccdfe0c7b0,Black-and-white\ntest/363fc13cbbaa23fa,Sport utility vehicle\ntest/3640bbd6b94b3485,Luxury vehicle\ntest/36427690d94df9b4,Close-up,Plant stem\ntest/364609e3d40bf73c,Lawn game\ntest/36467583ed42a701,Auto racing,Luxury vehicle,Performance car\ntest/3646cab88495313f,Extreme sport\ntest/364718c2d7997669,Luxury vehicle\ntest/3648dfe24cc97ec1,Whiskers\ntest/364c778bf832d7a7,Hound\ntest/364cc01e7bce2c7c,Close-up\ntest/364d825637d19d99,Ball game\ntest/364e60c9e1919802,Compact car,Antique car\ntest/364eb966f2f04bbc,Extreme sport\ntest/3654db6e6aebc591,Extreme sport,Parachuting\ntest/3656bdc0cb0537c4,Combat sport\ntest/36570201a42c3e04,Close-up\ntest/365b1fd4dbe92e6a,Close-up\ntest/365ed10eaae7141a,Close-up,Whiskers\ntest/36602eb518fe1498,Compact car\ntest/3660a02e7180a3f0,Luxury vehicle\ntest/36627ebcd2e013d2,Go-kart\ntest/36656a159a3f2fb6,Close-up\ntest/3666a80ecd1c5315,Luxury vehicle,Antique car\ntest/366733535966b06b,Close-up\ntest/366783861627c7e0,Rural area\ntest/3667a2395a23ae4b,Sailboat racing\ntest/366897860f58034e,Luxury vehicle,Performance car\ntest/366c03b2cb025632,Close-up\ntest/366c681e63d08667,Motorcycling\ntest/366db28477652f46,Antique car\ntest/366dd56a935eb4d1,Vegetarian food\ntest/366fab0410ec186f,Close-up\ntest/3670dd79c5c323aa,Luxury vehicle\ntest/367204f9e7deeed5,Coca-cola\ntest/3672822e63341e17,Close-up\ntest/36745709cb4a3c4f,Extreme sport,Parachuting\ntest/3677514c199c0d07,Luxury vehicle,Performance car\ntest/36799b6d06c2807a,Portrait photography,Close-up\ntest/3679a749e1179f8b,Luxury vehicle\ntest/367a90bfc0f2b7c5,Luxury vehicle\ntest/367bf6d127770094,Vegetarian food,Corn on the cob\ntest/367f958dfdca68ce,Luxury vehicle,Performance car\ntest/368083218f1f178c,Black-and-white\ntest/3681cf88f55cdacc,Steamed rice\ntest/36854dc098675acb,Jeans,Antique car,Luxury vehicle,Performance car\ntest/3688be1fde1dc688,Compact car,Luxury vehicle\ntest/3689b2a1f4d3c7fb,Close-up\ntest/368b586beec4c1a4,Close-up\ntest/368b8f3b2cb38f37,Leaf vegetable\ntest/368c9764c28b9b90,Boating\ntest/368d6c99470221a6,Sport utility vehicle\ntest/368e26b0a14adc0e,Scaled reptile\ntest/368e7c7ce4700519,Shinto shrine\ntest/368e9ac11d892bcc,Close-up\ntest/368ecb3a0cf58a9d,Compact car,Antique car\ntest/368fa5d92013278d,Sport utility vehicle\ntest/3691f16915123a65,Combat sport\ntest/3692da33dbc79bfc,Close-up,Scaled reptile\ntest/3694328fb7b351fc,Luxury vehicle,Performance car\ntest/36963c8e1dee2486,Boating,Rowing\ntest/3697d765e4a2da5b,Milky way\ntest/3699fada5cfca35c,Chordophone\ntest/369a37f83fdec87d,Antique car\ntest/369aa8c7c3deb121,Ball game\ntest/369be1ab3e98c34a,Antique car\ntest/369ca978d1fc4aaa,Firearm\ntest/369ce3d910752808,Antique car\ntest/369d9e481a1482ea,Chordophone,Electric guitar\ntest/36a059aa5faab323,Close-up,Whiskers\ntest/36a2001f92918a1a,Wind wave\ntest/36a248302a9995cd,Rural area\ntest/36a67cbd82dd3e8f,Luxury vehicle,Sport utility vehicle\ntest/36a6d6b934ddc333,Ball game\ntest/36a8249fa44acc62,Luxury vehicle,Performance car\ntest/36a8935e3b98fda6,Black-and-white,Portrait photography\ntest/36ab4a78d53e7784,Electronic instrument\ntest/36ae10355dcb5a4e,Luxury vehicle\ntest/36afa0b2f7cbfced,Microcontroller\ntest/36b1367a917c2e55,Peafowl\ntest/36b16f5d9c244dd3,Luxury vehicle\ntest/36b228dd1c72ccb8,Luxury vehicle,Performance car\ntest/36b5161bbe5b6813,Close-up\ntest/36b9051d8f76c524,Antique car\ntest/36b9526376fece8a,Bovine\ntest/36ba3cc7a5ddf43c,Hair coloring\ntest/36bbeca0b98a6cba,Extreme sport,Nordic skiing\ntest/36bd69dc9a56933d,Monoplane,Aircraft engine\ntest/36bd906d1ed455ac,Woodwind instrument\ntest/36bf3911a33da15c,Luxury vehicle\ntest/36c0c1a1c39aa496,Frigate\ntest/36c30b7439e420a5,Compact car\ntest/36c95204291d6081,Perching bird,Close-up\ntest/36c9c3257fa63be7,Luxury vehicle\ntest/36ca14b6a7812791,Vegetarian food,Corn on the cob\ntest/36cc44ceaadd1bb9,Outdoor shoe\ntest/36d28a2fa1bd3223,Black-and-white,Arthropod,Close-up\ntest/36d35476667b50ae,Melee weapon,Hunting knife\ntest/36d5deceee5deb1d,Flatbread\ntest/36d5e64518c07687,Close-up\ntest/36d80fbc65b4328d,Watercolor paint\ntest/36d9ea0694fee196,Luxury vehicle,Antique car\ntest/36db89e481d6b26f,Close-up,Whiskers\ntest/36db9782b69c22c0,Black-and-white,Close-up\ntest/36ddcaa7432e2008,Close-up\ntest/36dfef84792bf057,Plant stem\ntest/36e3da074bfcdf0f,Compact car,Luxury vehicle\ntest/36e452835979bb67,Luxury vehicle\ntest/36e58b5d9ab9f2d8,Close-up\ntest/36e675ec2416261f,Close-up\ntest/36ea3a8b9c54f9de,Close-up,Scaled reptile\ntest/36ebc8ce99353602,Performance car\ntest/36ef4beccf3b6aa7,Plant stem\ntest/36f29ffcb961c7d1,Ball game,Team sport\ntest/36f3c39ac4e65481,Performance car\ntest/36f4b1543639eb64,Senior citizen\ntest/36f6612dafdf0975,Nest box\ntest/36f86a1bce70b76a,Luxury vehicle\ntest/36f8cf968f7031b2,Junk food\ntest/36fb0dc3339604ff,Compact car\ntest/36fb60de113a8b65,Luxury vehicle,Performance car\ntest/36fcd280d9158b49,Microcontroller\ntest/36fe92e9ea45cf4f,Amphibian,Close-up\ntest/36feae2ecd2af479,Perching bird\ntest/3701e340918df43d,Prosciutto\ntest/3702aa8dc7aabd75,Perching bird\ntest/37036437f1e2371e,Antique car\ntest/3703ce795cc2ed50,Lawn game,Ball game\ntest/3704664761758284,Close-up\ntest/37050813d0f7cc0a,Close-up\ntest/370646c7ed8771a4,Close-up,Whiskers\ntest/3706a01f3ae43eaa,Military person\ntest/37077a022eed812f,Junk food,Cookies and crackers\ntest/37079d846e250a6f,Black-and-white\ntest/3708e5ac4f5fe250,Team sport\ntest/370ae3ac60b1a4e4,Close-up\ntest/370afdfc46eaa248,Extreme sport\ntest/370b44115a1ec643,Combat sport\ntest/370cbf35a66c742a,Jeans\ntest/370e6b7ffc0e839d,Auto racing\ntest/3710ec8aa173c0f3,Domestic short-haired cat,Whiskers\ntest/37143a4020e87cdb,Bratwurst\ntest/371450e60e79f68e,Primate\ntest/37150d85ed803436,Black-and-white,Close-up\ntest/3716432343a184b2,Arcade game\ntest/3716b87ade52a28a,Antique car\ntest/3717d537f6577676,Cosmetics\ntest/3717f14b92c42eec,Close-up\ntest/371904a3912af660,Black-and-white\ntest/3719c9c23c6f99d9,Close-up\ntest/371a75595affadc0,Bovine\ntest/371c0f41bb073e7d,Close-up\ntest/371e81f8e429eb85,Luxury vehicle,Performance car\ntest/371ed60bb917ba96,Close-up\ntest/371ff2b472495f8a,Luxury vehicle\ntest/3721450cca3ed932,Blond\ntest/3721d8953ffec09d,Bird of prey\ntest/3724a2107a12db1b,Orator,Public speaking\ntest/372624864025e362,Compact car,Sport utility vehicle\ntest/372899b0ba9a0494,Auto racing\ntest/372b6044aa4fa545,Extreme sport,Boating\ntest/372c942bc114069d,Antique car\ntest/372f5345e3a96d9e,Arthropod,Locust\ntest/373163ac385c97b1,Nile crocodile,Crocodilia\ntest/37360cbe3d1fecc4,Jeans\ntest/3736765d36c8d8cf,Road cycling\ntest/3737310d78f3085e,Machine tool\ntest/37374536fb65712f,Close-up\ntest/3737624600104693,Close-up\ntest/3737f180bd297f86,Chordophone\ntest/373ade27b0da4629,Luxury vehicle,Sport utility vehicle\ntest/373e59ee2382300e,Modern dance\ntest/37413da2dfb20864,Luxury vehicle\ntest/3742d70c0bfce4d0,Black-and-white\ntest/37468de3e9a3151b,Compact car\ntest/37476a6398374276,Plant stem,Close-up\ntest/374efef2bd3dbe8a,Luxury vehicle\ntest/3750075e3b01b1f9,Goats\ntest/37510ffe72539c23,Bird of prey\ntest/37512771591840bc,Nest box\ntest/3751eecda61d7473,Jackfruit\ntest/375516d4ebccdedb,Black-and-white,Close-up\ntest/3755a4afdf4285e4,Compact car,Luxury vehicle,Antique car\ntest/3755a693f9e79369,Extreme sport,Parachuting\ntest/37570c8d6a5cd5f3,Luxury vehicle,Performance car\ntest/37593d8ee416f425,Rural area\ntest/375967a350f6a262,Luxury vehicle\ntest/3759693c0ac14c96,Milky way\ntest/3759ff41bdc098bc,Black-and-white\ntest/375aa674afa7ee0e,Auto racing\ntest/375b691ea851fc69,Black-and-white\ntest/375b7171838f8a83,Antique car\ntest/375ce485344e0654,Ale\ntest/375df782b7e7ad1b,Luxury vehicle\ntest/375e84d28f0d3cbc,Extreme sport\ntest/375fa2adaac5da69,Close-up\ntest/37609166693075cb,Compact car,Luxury vehicle\ntest/3762028007831a8e,Auto racing,Compact car\ntest/376277227547ab4e,Close-up\ntest/3762909187196947,Jeans\ntest/3764eb05d94c8d78,Close-up\ntest/376637b52e824afd,Close-up\ntest/3766e8bca49cfae2,Hound\ntest/37698299a10d6225,Lumpia\ntest/376c5fc5f5d7b944,Auto racing,Luxury vehicle,Performance car\ntest/376ca81693b4b792,Luxury vehicle,Antique car\ntest/376dc812c975b28e,Luxury vehicle,Antique car\ntest/376dc8e606e252e7,Junk food,Fried food\ntest/376dfa2482e163c5,Black-and-white,Headphones\ntest/376eb0646b2eb373,Domestic short-haired cat,Close-up,Whiskers\ntest/376f840f12de50fd,Luxury vehicle\ntest/376fbc2b28733a1d,Scaled reptile\ntest/377502704651a56c,Divemaster,Scuba diving,Underwater diving\ntest/3775cdfd56d9e28a,Antique car,Luxury vehicle,Performance car\ntest/3776fccee9c92f2c,Ball game\ntest/3777688ead696f4d,Compact car\ntest/377b0319d4812d12,Toy block\ntest/377d07428b236150,Arthropod,Close-up\ntest/377e82c1713bb7f9,Fried food\ntest/377fdbd422a31fa8,Extreme sport,Nordic skiing\ntest/378083e548bc8afa,Motorcycling\ntest/378095ddf439fbe1,Senior citizen\ntest/3781d9f85eb31235,Ball game\ntest/37838ec82ceea001,Whiskers\ntest/3786f0af17241a18,Horse and buggy\ntest/378778f5bad685cf,Performance car,Luxury vehicle,Antique car\ntest/3787f32b04f23410,Luxury vehicle,Performance car\ntest/3788700dd1edbefe,Extreme sport,Parachuting\ntest/3788915eee288ce7,Solar energy\ntest/3789d2f6a70407cc,Luxury vehicle\ntest/378a2787f233f767,Military vehicle,Black-and-white\ntest/378d6ccecb776747,Lumpia\ntest/37901124c0527d2a,Leaf vegetable,Vegetarian food\ntest/3791371109921bd1,Parachuting\ntest/379281341b6faf8b,Whiskers\ntest/3792b07cda7b981b,Middle ages\ntest/37940a5ed82d2c4e,Rural area\ntest/3794abc04175ce82,Auto racing\ntest/37964f19fe457be9,Middle ages\ntest/37967b0224d1a752,Rural area,Canola\ntest/37968c5f1b3ac4fa,Whiskers\ntest/37984c2ac0f52716,Gumbo\ntest/3798ad908e517f29,Luxury vehicle\ntest/379a4e1f3a6bb549,Close-up,Whiskers\ntest/379bce4c8b7c0fc8,Electric piano\ntest/379c65a65747e8d4,Scaled reptile\ntest/379cb46171d89b29,Aircraft engine\ntest/379cd5ed693f70a9,Ball game,Team sport\ntest/379e38a7674a0b51,Antique car,Luxury vehicle,Performance car\ntest/379e99e697815d0c,Luxury vehicle,Performance car\ntest/379faac72f397844,Luxury vehicle,Performance car\ntest/379ff9ef35a16c30,Close-up\ntest/37a26644c8ec10c4,Arthropod,Close-up\ntest/37a3e9a52852b5df,Blond\ntest/37a945bee51c8c4d,Luxury vehicle,Sport utility vehicle\ntest/37aa5347face64ac,Luxury vehicle,Antique car\ntest/37aae17410666a9e,Bovine\ntest/37afddf7216016f7,Road cycling\ntest/37b01a489f487545,Military person\ntest/37b1e334f768e262,Camera operator\ntest/37b2fae346eaa12c,Woodwind instrument\ntest/37b41771313c0601,Barechested\ntest/37b477a9d05805c2,Filling station\ntest/37b69a3647573701,Close-up\ntest/37b8d81b66a9892a,Ball game,Team sport\ntest/37b94b9f68f50be0,Black-and-white\ntest/37b9dff15f791826,Extreme sport\ntest/37ba13f66c1bcfd7,Bird of prey\ntest/37bb0af19ffec5c7,Close-up,Scaled reptile\ntest/37bc9ea98c048d97,Luxury vehicle\ntest/37bcbdee9f74c31e,Luxury vehicle,Performance car\ntest/37bd7968b9c82953,Pork chop,Fried food\ntest/37bdba18bb3a9524,Close-up\ntest/37bf431337662a49,Close-up\ntest/37bf7aaf6fb9bdc0,Siberian husky\ntest/37c1f9016ef95123,Close-up\ntest/37c3966a81ffaacf,Black-and-white\ntest/37c47d238bd1c95a,Jiaozi\ntest/37c72202188f2430,Military person\ntest/37c98b8409ef5ee6,Close-up,Scaled reptile\ntest/37c9ad6633306fe9,Musical theatre\ntest/37cbe7ffa4cbe4f5,Ball game\ntest/37ccd6224a81e2d3,Auto racing\ntest/37cd9c953167ec47,Hair coloring\ntest/37cefbc4007562c5,Black-and-white,Boating\ntest/37d0a36c5b549ff7,Ball game,Team sport\ntest/37d12eb57d0c78b9,Sport utility vehicle\ntest/37d2edf5173fd7f9,Vegetarian food,Junk food\ntest/37d3869a3b9ced62,Cosmetics\ntest/37d4b962333df3d3,Luxury vehicle,Sport utility vehicle\ntest/37d596945558e1c9,Plant stem,Close-up\ntest/37d61ec2fe4d411e,Zeppelin\ntest/37d67dd606e4fea9,Luxury vehicle\ntest/37d7d195d28a8f24,Junk food\ntest/37d8669cf7981031,Rural area\ntest/37d8cc6074089795,Luxury vehicle,Antique car\ntest/37d8f468504ad26c,Melee weapon\ntest/37dab17b065434ae,Luxury vehicle\ntest/37dae147c8bb7cb5,Close-up,Whiskers\ntest/37dbd8be727a42bb,Black-and-white\ntest/37dc260d557214b1,Compact car,Antique car\ntest/37dd310aae1ae57d,Luxury vehicle\ntest/37dd94623bc33e92,Luxury vehicle\ntest/37de787a151088b5,Extreme sport\ntest/37e01c451c69789c,Luxury vehicle\ntest/37e4274812e666f1,Close-up\ntest/37e5130ccd52bb87,Monoplane\ntest/37e7cb71a36be134,Combat sport\ntest/37e82bd589c7e90f,Luxury vehicle,Antique car\ntest/37e86c314d76e221,Vegetarian food\ntest/37e87ec0bbee8a40,Sport utility vehicle\ntest/37ebbd0ee680f589,Domestic short-haired cat,Whiskers\ntest/37ed9abd3ace47d2,Close-up\ntest/37f105b9cb0bd119,Rural area\ntest/37f3b9b732550a1b,Close-up,Plant stem\ntest/37f6e51254189445,Close-up\ntest/37f7e6c11f66b05e,Rural area\ntest/37f89802ed90aef8,Road cycling\ntest/37fa2724e558c7af,Chordophone\ntest/37fa2abda1bf39bd,Whiskers\ntest/37fb1c26588ef67f,Black-and-white\ntest/37fe28cc757fe8c5,Vegetarian food\ntest/37ff4df985cd0575,Luxury vehicle,Performance car\ntest/37ffe0013e93b961,Digital camera\ntest/3802d2e268c884f1,Military person\ntest/38057e36fce4d356,Double-decker bus\ntest/3805aeb0f0ee2feb,Luxury vehicle,Performance car\ntest/3806811367ff68d6,Scaled reptile\ntest/38078678edc20549,Luxury vehicle,Sport utility vehicle\ntest/380c2e4fce6c9cb7,Backlighting,Close-up\ntest/380e5e9d4f4abc7d,Close-up\ntest/380f32af489aa027,Cookies and crackers\ntest/380fd3836d21c285,Luxury vehicle\ntest/38108b50fef3d885,Cookies and crackers\ntest/3810b89382b78e63,Cosmetics\ntest/3811c83a6c390734,Comics\ntest/38136bcb443a564a,Close-up\ntest/3817f531701560c0,Performance car,Antique car\ntest/3818602cac1a110c,Luxury vehicle,Antique car\ntest/381ab745a3ba180c,Black-and-white\ntest/381d4a3443741f1d,Church bell\ntest/381e9a45a6ab1043,Wind wave\ntest/381fbb7533c4fc7c,Steamed rice\ntest/381fedc7b6a8d58b,Pit bull\ntest/38203f98ddc7c854,Headphones\ntest/382145bc22a7f3c9,Cookies and crackers\ntest/38230736237f3b35,Compact car\ntest/3824ee6f4023cfa6,Luxury vehicle,Sport utility vehicle\ntest/3826983c70705027,Ball game,Team sport\ntest/3826add472304655,Close-up\ntest/3826bfa97cb5f8cf,Luxury vehicle,Antique car\ntest/3826e2fcee5bdeee,Close-up\ntest/38290901921e751c,Bovine,Close-up\ntest/382d2b4ac8f5cde0,Prosciutto\ntest/382d3227b0d1fdf6,Luxury vehicle\ntest/382e9153db420c38,Great white shark\ntest/382fb103b0eff10d,Vegetarian food,Fried food\ntest/3833a0bee4b6da86,Bratwurst\ntest/383430a4bbf4d321,Luxury vehicle,Sport utility vehicle\ntest/38371965d46a69c4,Compact car,Luxury vehicle\ntest/38377a9cb3110386,Flatbread\ntest/3837b7c544916eaf,Close-up\ntest/3838943641cb2639,Residential area\ntest/38390b62b80acddc,Drums\ntest/383ae31dc916a8f5,Antique car,Luxury vehicle,Performance car\ntest/383b0ffbe7edeaef,Luxury vehicle\ntest/383c34a30155cac1,Whiskers\ntest/383f679fd7fe3445,Ball game\ntest/3840eb8ad464e143,Close-up,Whiskers\ntest/3841d1fcf2edf01c,Blond,Close-up\ntest/384229f885156319,Jeans\ntest/38429590ed3e13ff,Arthropod,Close-up\ntest/3845477510ea75c8,Vegetarian food,Flatbread\ntest/384643758c6f7362,Nest box\ntest/38485db61e9ca278,Ball game,Team sport\ntest/384a45625d4ecd5b,Black-and-white\ntest/384ae770b26bc956,Luxury vehicle,Sport utility vehicle\ntest/384b171002fb1ac5,Cookies and crackers\ntest/384c32a640272513,Bird of prey\ntest/384e8bf9611319bb,Ball game\ntest/384ee8b43087f622,Roller skating\ntest/384ef8914929dd79,Microcontroller,Breadboard\ntest/3855519f82bc7cf5,Sport utility vehicle\ntest/38595150ee6b23a2,Auto racing,Performance car\ntest/385964f683dba920,Bagpipes\ntest/385a59f808ff3838,Ball game,Team sport\ntest/385af1598c36aad2,Rural area\ntest/385bd61df2a3ee07,Inflatable\ntest/385c81d042501ee6,Chordophone\ntest/385d5c76e6066987,Close-up\ntest/385f443253134fe7,Luxury vehicle,Performance car\ntest/38623672bfe3b06b,Jeans,Chordophone\ntest/3862e9b090b3f3bb,Hair coloring\ntest/3867c43194dd43ed,Ball game,Team sport\ntest/38682ced0ad2c17c,Luxury vehicle\ntest/38691287d020c02f,Rural area\ntest/3869899f4ce617ca,Molluscs\ntest/3869dd2b281f34fb,Arthropod,Close-up\ntest/386a81e65f7ba8a3,Boating\ntest/386b2b63d0f24278,Sport utility vehicle\ntest/386bd46a905da978,Antique car\ntest/386cb1c8d65936d9,Compact car,Luxury vehicle\ntest/386df61eb273220f,Luxury vehicle\ntest/386eddc1e4098b7d,Classical sculpture\ntest/3870d0f5543f94be,Residential area,Aerial photography\ntest/387195aa900a4769,Performance car,Antique car\ntest/387334490073c9c9,Close-up\ntest/38747313317ded50,Motorcycling\ntest/387ac74b3a860b1a,Frigate\ntest/387cc971718eb525,Luxury vehicle,Sport utility vehicle\ntest/387d419f6d1f6582,Whiskers\ntest/387df186bf558ed0,Luxury vehicle\ntest/387e9198a177fc85,Antique car\ntest/387f6eb68d632bff,Dishware\ntest/38803d0bee189f85,Fried noodles\ntest/3881dd572c5903ae,Extreme sport,Glacial landform\ntest/3882488f2a861a76,Machine tool\ntest/3882d04d74843f37,Scaled reptile\ntest/3885da46d7c71a80,Luxury vehicle,Antique car\ntest/38862cd57c62309a,Compact car,Luxury vehicle\ntest/38865b16077d589d,Performance car,Luxury vehicle,Antique car\ntest/388743ef88faad50,Vegetarian food,Lumpia\ntest/38884e2048f2c7db,Backlighting,Black-and-white\ntest/388887293c85e1c5,Close-up\ntest/388a77ccc172ef5c,Junk food,Fried food\ntest/388bf060b93bbba4,Close-up\ntest/388bf5893ff44211,Floristry\ntest/388d191ef4a34bf8,Residential area,Rural area\ntest/388d4a08bed0b7ab,Musical theatre\ntest/388da41e917517d5,Black-and-white\ntest/388e1aac589e09e6,Team sport\ntest/38918f7724954503,Luxury vehicle,Antique car\ntest/389435292fbf9732,Microcontroller\ntest/389527bfe196148c,Performance car\ntest/3897a9c62eb3b8f1,Drums\ntest/389803f97883b606,Luxury vehicle\ntest/38a1618cc4563507,Floristry\ntest/38a1e05f06cefe54,Luxury vehicle,Antique car\ntest/38a4a5334beb3884,Jeans\ntest/38a56f622cc4251b,Junk food\ntest/38a8ce3e88872f05,Touring car,Luxury vehicle,Antique car\ntest/38a9f9e69361bbb5,Antique car\ntest/38ad43a236c5846c,Compact car,Antique car\ntest/38aff2a21197d3e8,Outdoor shoe\ntest/38b455042e4a321c,Aerial photography\ntest/38b8a0f26b1d016e,Close-up\ntest/38bb1b5c50027c5b,Vegetarian food\ntest/38bc640210e2c07b,Senior citizen\ntest/38bc8eee59ec6ebf,Black-and-white\ntest/38bfae6e178c2ca8,Wind wave\ntest/38c094572f37fb6f,Domestic short-haired cat,Close-up,Whiskers\ntest/38c336aad3f192e9,Steamed rice\ntest/38c4504d426fd2ac,Black-and-white\ntest/38c45e9c3657b92b,Close-up,Whiskers\ntest/38c6668e60cfe264,Close-up\ntest/38c6a73c3e1fc3d2,Angling\ntest/38c7959d3055c344,Luxury vehicle,Performance car\ntest/38c7a2e6bfa870ef,Compact car\ntest/38c811ce9ab74337,Military vehicle\ntest/38c9524f441ddb43,Luxury vehicle\ntest/38c9e1fc7d2acd7a,Compact car,Luxury vehicle,Sport utility vehicle\ntest/38ca88d64a409f78,Perching bird\ntest/38caa15a15dd11e7,Bird of prey\ntest/38caf074140eae34,Performance car\ntest/38cbb0e353f4928e,Dobermann,Jeans\ntest/38cc1427c033a7fa,Luxury vehicle\ntest/38cc2364d6626a10,Antique car\ntest/38ceba2979c1f9f8,Compact car,Luxury vehicle\ntest/38cef7976ead8501,Extreme sport,Rowing,Boating\ntest/38d118f8033c9b4e,Close-up\ntest/38d36290079def0c,Compact car,Luxury vehicle\ntest/38d6b9ed6e61d8a3,Cosmetics\ntest/38d6e4332c8893f3,Microcontroller\ntest/38e148a27254c701,Black-and-white,Motorcycling\ntest/38e19a435e326237,Extreme sport\ntest/38e1df6198f402b0,Sport utility vehicle\ntest/38e2fd46759d3ea5,Military person\ntest/38e332a3ffab5d05,Steamed rice\ntest/38e45138ca50ea22,Close-up\ntest/38e6bff68a7a6269,Whiskers,Domestic rabbit\ntest/38e7a6e6e1cdbe67,Machine tool\ntest/38e7f3cd3eaac611,Close-up\ntest/38e7fa7b7158ffde,Compact car,Luxury vehicle\ntest/38ea6076b351f9d5,Electronic instrument,Black-and-white\ntest/38eb947f73eff69e,Luxury vehicle\ntest/38ed4d4e3886660f,Electric piano\ntest/38ee7068800994f3,Compact car,Luxury vehicle\ntest/38ef89d95f94c4ac,Miniature Poodle\ntest/38efc5b05ce7b107,Extreme sport,Sport climbing\ntest/38f14afc69aede39,Luxury vehicle,Performance car\ntest/38f23dc36b08f446,Luxury vehicle,Antique car\ntest/38f8ea585840fa11,Blond\ntest/38fb7f570422b0a1,Domestic rabbit\ntest/38fd0906c8083fc8,Auto racing,Performance car\ntest/38fe21d58940cd7d,Luxury vehicle\ntest/38fef787fc8ba39f,Black-and-white,Luxury vehicle,Performance car\ntest/38ff328a1a4ac4f6,Arthropod,Close-up\ntest/38ffab22f3c941f4,Arthropod,Close-up\ntest/39004b6a8a9dead8,Firearm\ntest/3904097ad46c2bda,Luxury vehicle,Performance car\ntest/3905ec57010825ac,Canola\ntest/3907f9531d6b2174,Whiskers\ntest/3908785ab06c4f24,Close-up,Plant stem\ntest/390f54376217d771,Close-up\ntest/391087fb083ab11e,Luxury vehicle,Sport utility vehicle\ntest/391116638aef048c,Luxury vehicle\ntest/391435636f22daca,Frigate\ntest/3914adf3e31cdce5,Newsagent's shop\ntest/391538671c923c1f,Plant stem\ntest/3915a12316f48cf7,Luxury vehicle,Sport utility vehicle\ntest/3917bce7e445a16a,Performance car\ntest/3917e05f98a500a9,Luxury vehicle,Performance car\ntest/3919fdc39fe0d182,Trampolining\ntest/391aabcdfa296b0f,Ball game\ntest/391b288aa2e9a5cd,Compact car,Luxury vehicle\ntest/391b4dab408cdc4a,Close-up\ntest/391b941cb0d41a6c,Close-up\ntest/391dcfbdce1648a0,Arthropod,Close-up\ntest/391df3a382a631db,Floristry\ntest/391e8f182e73a8c2,Luxury vehicle\ntest/391eddb71bb8181c,Lechon\ntest/391f1ec1c3ccf4bf,Luxury vehicle,Performance car\ntest/391f49f584f7d0d7,Sport utility vehicle\ntest/392089bb62ada865,Black-and-white\ntest/39213daecab09778,Team sport\ntest/39224d0d713cb866,Scaled reptile\ntest/392350de89a8caf4,Antique car\ntest/3924992327afd1e2,Ball game\ntest/3924cb274b664f76,Performance car\ntest/39266888cec0a7b6,Domestic short-haired cat,Whiskers\ntest/39282867ccd9e81d,Amphibian\ntest/39293762972eda51,Breadboard\ntest/392a371fe00b1328,Cosmetics\ntest/392ab53d5dca470a,Close-up,Scaled reptile\ntest/392ba896ca63c856,Siberian husky\ntest/392bc5d09988058a,Microcontroller\ntest/392c2deed6b98e89,Luxury vehicle,Antique car\ntest/392c8c13374ad383,Pasta salad\ntest/392cee5ac06e17b6,Extreme sport,Kitesurfing\ntest/392f07b11b16ab47,Halter\ntest/392fe9a3b1a86e7d,Auto racing\ntest/39328463c34a9a3e,Junk food\ntest/3933336a1daa6fe4,Luxury vehicle\ntest/39333ba6ae02ea21,Dog walking\ntest/39343adfda1f3574,Shinto shrine\ntest/3934f114f4d7556a,Performance car\ntest/3938da9276d95451,Close-up\ntest/393bfd0076ce5ce9,Monoplane,Aircraft engine\ntest/393cbe121b4f5fcc,Arthropod\ntest/393cf2588d1b7f0f,Jeans\ntest/393d053b9d567930,Arthropod,Locust,Close-up\ntest/393d4cb18394b44d,Chordophone\ntest/393d627fecca194e,Whiskers\ntest/393d8a72d057463b,Blond\ntest/393ec1f81daf8abd,Compact car,Sport utility vehicle\ntest/393f1602babb2ddf,Close-up\ntest/394161d072604c3f,Residential area\ntest/3942282675a54ce6,Close-up\ntest/3942b3d0cc85e58e,Inflatable\ntest/39432118603faf0c,Black-and-white\ntest/3943bdf208358d5a,Jeans\ntest/39468b81eaa62ba7,Equitation,Equestrianism\ntest/3946a5946fc2630d,Luxury vehicle\ntest/394769014034a20b,Lumpia\ntest/3949a8af0bc727f4,Sport utility vehicle\ntest/394abd99234a0a50,Auto racing\ntest/394b63a673533a29,Close-up\ntest/394b80ba49685130,Arthropod,Plant stem,Close-up\ntest/394d728dfc71e6db,Portrait photography\ntest/394df68605f8361e,Headphones\ntest/3951546fd59a457c,Barechested\ntest/39516613b0b556e2,Luxury vehicle,Performance car\ntest/39555bc95e8fae49,Combat sport\ntest/3955d7a1a7d23543,Bird of prey,Close-up\ntest/395871c197d32280,Luxury vehicle,Performance car\ntest/39592e65e915f480,Black-and-white,Holding hands,Close-up\ntest/395945e2b5467c02,Combat sport\ntest/395a6601cacf3c5c,Compact car,Antique car\ntest/395aa1766050836c,Melee weapon,Hunting knife\ntest/395bd78fd211987f,Luxury vehicle\ntest/395d3371a2c38c32,Luxury vehicle\ntest/395ef840dad123a1,Close-up\ntest/3965464d67a5a1d4,Arthropod\ntest/3967e4e392f187c3,Ramen\ntest/39690dc1b4397f88,Extreme sport\ntest/39701bd48d00e331,Close-up\ntest/39712519f523e767,Jeans\ntest/3971aad961b384de,Close-up\ntest/3972275e119eacd8,Luxury vehicle\ntest/3972ecaa8d08c9a0,Plant stem\ntest/3974f503366bcd92,Angling\ntest/39766e6adb5f956d,Close-up\ntest/397d5162aab9082e,Close-up\ntest/397f6e2e0816d5d2,Black-and-white,Portrait photography\ntest/3980b36b63d89171,Aircraft engine\ntest/3981eb7ed635fc3d,Miniature Poodle\ntest/3982a3b7512709f5,Extreme sport,Divemaster,Underwater diving\ntest/3982e9cda647fbfb,Black-and-white,Close-up,Whiskers\ntest/3982edd51a204a3b,Firearm\ntest/39892a1b7d515d99,Luxury vehicle,Performance car\ntest/398ce3213b0d68a5,Sport utility vehicle\ntest/398ecd25bebd227d,Coral reef fish\ntest/39907ac01fc30239,Plant stem\ntest/3991dffc9d0378e3,Electronic instrument\ntest/39928fa21e57e60b,Newsagent's shop\ntest/399464e83f89864a,Black-and-white,Ball game\ntest/3994f5f8bc61b6c3,Ball game\ntest/39957cebd62856bf,Arthropod\ntest/39969240ced50cbe,Goats\ntest/3996d61fca26d737,Auto racing\ntest/3997b93326945d2c,Luxury vehicle\ntest/3999d1ce124bfe1e,Team sport\ntest/399ce226d2ddfb08,Boating,Rowing\ntest/399d702eac2a2be7,Luxury vehicle,Antique car\ntest/399ddcbe9f2ca163,Rural area\ntest/399f9a752ec7e071,Sport utility vehicle\ntest/39a0bb1aaacd921e,Close-up\ntest/39a47183bfb3cd2a,Floristry\ntest/39a75fe3a86cb54c,Close-up\ntest/39acff426c2fe26d,Close-up\ntest/39ad3bccbac840c4,Antique car\ntest/39af0ad2e28c5300,Black-and-white,Portrait photography\ntest/39afb206a3180648,Close-up\ntest/39b0c1c91c56e0f9,Close-up,Floristry\ntest/39b26c612e19b7f8,Trampolining\ntest/39b69baeb6d748b8,Ball game,Team sport\ntest/39b73828a8ac32fc,Portrait photography\ntest/39b7635e6fd4a2c9,Luxury vehicle,Performance car\ntest/39b7844c71471bd7,Brochette\ntest/39bae3d26d682465,Compact car,Luxury vehicle\ntest/39be5c25fbe3cca4,Monoplane,Aircraft engine\ntest/39bf4e6fc6837e3b,Aircraft engine\ntest/39c2a2c5d470995c,Barechested\ntest/39c67a9608440d02,Arthropod,Close-up\ntest/39c7059baaf7502d,Black-and-white,Portrait photography,Close-up\ntest/39cbaab2ac223d94,Luxury vehicle\ntest/39cc26d2359b6474,Luxury vehicle,Performance car\ntest/39cce790b1b2e343,Halter\ntest/39cd376007cc2ad4,Fried noodles\ntest/39cdfaa7fb57e49a,Luxury vehicle,Antique car\ntest/39ce0a18637f2656,Vegetarian food\ntest/39ce8608956a4ba1,Motorcycling\ntest/39ce9fedcfced39a,Close-up\ntest/39d1bfbcfa37fd51,Antique car,Performance car\ntest/39d2bb543303c337,Digital camera,Close-up,Camera operator\ntest/39d47f47e9eb177b,Lawn game,Ball game\ntest/39d69d555c387688,Antique car\ntest/39d84c6b7460580b,Aircraft engine\ntest/39d8e887e95af56f,Leaf vegetable,Pasta salad\ntest/39dd87e00183ab3d,Arthropod\ntest/39df9f0dd9a662e8,Combat sport\ntest/39dffad841d0ca8b,Luxury vehicle\ntest/39e1df5a5ffbf1fb,Performance car\ntest/39e2078c670c6f67,Team sport,Equestrianism\ntest/39e3336fc0d65f91,Close-up\ntest/39e33e92be8b0bf7,Antique car\ntest/39e39937b3e4884c,Bottled water\ntest/39e4a2ebf3b5cdfb,Fire apparatus\ntest/39e51af7f25b3577,Digital camera\ntest/39e53b46268b9d09,Classical sculpture\ntest/39e8f949de7132a1,Luxury vehicle,Sport utility vehicle\ntest/39e97b23f2a0643f,Performance car,Antique car\ntest/39eb679fc9ba8a0f,Great white shark\ntest/39ee4065e7813eee,Plant stem,Close-up\ntest/39eebcbc0b106816,Blond\ntest/39ef08a878c895e1,Compact car\ntest/39f062e9ede41ad8,Luxury vehicle\ntest/39f088fd1cbeff9d,Rural area\ntest/39f3f1d58bdfb443,Fried food\ntest/39f6cb9f1987e409,Compact car,Luxury vehicle\ntest/39f825b4fd1a3da9,Domestic short-haired cat,Close-up,Whiskers\ntest/39fa1f59b8d609d7,Headphones\ntest/39fa373d4519508b,Compact car,Luxury vehicle,Antique car\ntest/39fa79f2c7ae7bca,Compact car\ntest/39fad66a70a9bc23,Combat sport\ntest/39fed8c010d5f982,Close-up\ntest/39ff2f892d33ec41,Sport utility vehicle\ntest/3a007070ff705fd5,Performance car\ntest/3a00a0865540be65,Luxury vehicle\ntest/3a00e5c21b2834a8,Compact car,Antique car\ntest/3a0226d356a7a681,Compact car\ntest/3a023d2117259c89,Black-and-white,Close-up\ntest/3a03c563ed835d09,Equestrianism\ntest/3a0771a2acf96b7f,Senior citizen\ntest/3a078b9702ff27c7,Melee weapon,Hunting knife\ntest/3a07b01bc9f7c168,Chordophone,Electric guitar\ntest/3a08ee40c954227a,Luxury vehicle\ntest/3a0938822ab0fecd,Flatbread\ntest/3a096a39832ce47c,Coca-cola\ntest/3a0c14247a77b435,Luxury vehicle\ntest/3a0dd9714467ed2e,Junk food\ntest/3a0deda67180ed2f,Boating\ntest/3a1029c25a35bd18,Steamed rice\ntest/3a11b0dcb9c29aa2,Zeppelin\ntest/3a1cf35857ec7454,Barechested\ntest/3a20b76081264078,Black-and-white\ntest/3a20c6efd49a9577,Junk food,Fried food\ntest/3a219cd30903a59d,Luxury vehicle,Performance car\ntest/3a227bc867f7754d,Close-up\ntest/3a254293f4e1ec07,Plant stem\ntest/3a25f2b1e9bcfc8d,Rear-view mirror\ntest/3a26d3870fcfd9d6,Coral reef fish\ntest/3a274504daf65d52,Close-up\ntest/3a2a2e9b95557cfb,Motorcycling\ntest/3a2a5c3cd20044de,Stemware\ntest/3a2c72e9bddf1aef,Boating\ntest/3a2c82ded3cee3fd,Luxury vehicle,Performance car\ntest/3a2dd92e271812ff,Domestic short-haired cat,Close-up,Whiskers\ntest/3a2e8339ff6f74a9,Blond\ntest/3a2e8e350a6ed5ac,Luxury vehicle\ntest/3a2f19d0581bcecd,Residential area\ntest/3a33dddee5dcc364,Combat sport\ntest/3a34e6c9c6f001f6,Close-up\ntest/3a35a5fffaf9c572,Athletic shoe,Outdoor shoe\ntest/3a35bf6afbb2eecd,Chordophone\ntest/3a36a259c53fd1ac,Fried food\ntest/3a3797bf9b4ce82c,Compact car,Luxury vehicle\ntest/3a37fd7156f45919,Compact car,Luxury vehicle\ntest/3a39cafa58ca5a7c,Domestic rabbit\ntest/3a3d584117a9287b,Athletic shoe,Outdoor shoe\ntest/3a3e65b46768e54f,Compact car,Antique car\ntest/3a3ef6f1e1e3f4b7,Newsagent's shop\ntest/3a48ac5f3be6f717,Sumo\ntest/3a49d7a1b4ea6bd1,Compact car\ntest/3a49e6251d094893,Close-up\ntest/3a4db2ff67d6ad20,Arthropod,Close-up\ntest/3a4ee8bf205ca8be,Portrait photography\ntest/3a53f81d4ab63e5b,Luxury vehicle\ntest/3a54b5a73b139f63,Luxury vehicle\ntest/3a55a398a876026b,Close-up\ntest/3a5612e8f0c938f4,Antique car\ntest/3a57cd173b8f522c,Rural area\ntest/3a590d62e778a7ee,Digital camera,Black-and-white\ntest/3a5c341b2446bade,Sport utility vehicle\ntest/3a5cfc535a052337,Hound\ntest/3a5e2fe56f74de88,Hound\ntest/3a619a3d32f4cb9f,Close-up\ntest/3a637c8c129ca21c,Close-up\ntest/3a687890e967e639,Close-up\ntest/3a68d84da6e7e7ba,Nile crocodile,Crocodilia\ntest/3a6a0368ccfbcc53,Close-up,Scaled reptile\ntest/3a6a1b6ca509e2e3,Molluscs,Close-up\ntest/3a6bc1848e9edfde,Luxury vehicle,Antique car\ntest/3a6c18b8f0808dd7,Rice ball\ntest/3a6e867a5df848b0,Coral reef fish\ntest/3a704629f5b347ad,Luxury vehicle,Antique car\ntest/3a70aae5e3a11a02,Luxury vehicle\ntest/3a7213e9cadbdaeb,Solar energy\ntest/3a74169b65c57b31,Luxury vehicle\ntest/3a75198b0dffb389,Rural area,Bovine,Herding\ntest/3a7615e64f9e2e33,Bagpipes\ntest/3a77b78f01236750,Extreme sport,Parachuting\ntest/3a793a6c773c6bb2,Peafowl\ntest/3a7ae116a29da652,Luxury vehicle,Performance car\ntest/3a7c89ae2d352f10,Chordophone\ntest/3a8029498df81b9c,Floristry\ntest/3a80773c08342850,Firearm,Shooting range\ntest/3a80fd5cc3e2eec4,Equitation,Equestrianism\ntest/3a8144684043167b,Luxury vehicle,Antique car\ntest/3a815162b6732fa3,Aircraft engine\ntest/3a85cb3b9a215f82,Briefs,Barechested\ntest/3a8677de9eaf0a59,Auto racing,Luxury vehicle\ntest/3a86bdf2c5fe54e7,Luxury vehicle\ntest/3a87b2a55c230146,Senior citizen,Close-up\ntest/3a87f8872f1b4234,Crocodilia,Nile crocodile,Scaled reptile\ntest/3a899a92bfedaeb6,Luxury vehicle,Performance car\ntest/3a89e09879d75f13,Heavy cruiser\ntest/3a8bfc1945408d51,Vegetarian food,Pasta salad\ntest/3a8d123abc28d4dc,Extreme sport\ntest/3a8e20135ca9a6d0,Compact car\ntest/3a9218614f5f537b,Wind wave\ntest/3a92c5550a707b71,Performance car\ntest/3a93514dbceb4b4b,Stemware\ntest/3a940e9fe33cdf92,Extreme sport,Motorcycling\ntest/3a94cf9e2b838ec7,Luxury vehicle,Performance car\ntest/3a95431d20afc8dc,Jeans,Miniature Poodle\ntest/3a967a46879a5e46,Whiskers\ntest/3a98760d7fc5630f,Equestrianism\ntest/3a992fe19a58dc61,Woodwind instrument\ntest/3a993fe05ef36754,Antique car,Performance car\ntest/3a997fb613da7217,Aircraft engine\ntest/3a9984dfabe30b9f,Team sport,Firearm\ntest/3a9bcf9c7c0d3566,Compact car,Sport utility vehicle\ntest/3a9c53c421db7c0d,Close-up\ntest/3a9cb3c4436843c2,Plant stem\ntest/3a9f9e058de4e843,Whiskers\ntest/3aa00c87122f009c,Digital camera\ntest/3aa1ad70721a340a,Water polo,Ball game\ntest/3aa44651c046fd7d,Coral reef fish\ntest/3aa81f9741ba9a9f,Team sport\ntest/3aa8d8155b63eb9d,Leaf vegetable\ntest/3aa9245db6d538c3,Frying,Fried food\ntest/3aa9c33b750e6cb8,Residential area\ntest/3aac43377c6c5dd3,Antique car\ntest/3aac849385af72f2,Brochette,Yakitori\ntest/3aad740e8f9a751c,Luxury vehicle,Performance car\ntest/3aafa6f869c68807,Senior citizen\ntest/3ab009a685b1b018,Compact car,Antique car\ntest/3ab31cabe6e14f57,Floristry\ntest/3ab39248aab724a8,Billiard room\ntest/3ab69d9177c21de1,Auto racing,Antique car,Luxury vehicle,Performance car\ntest/3ab70931985f806a,Blond\ntest/3ab71e49cb77d47b,Rural area\ntest/3ab7a6f8ebc6e0a9,Compact car,Luxury vehicle,Antique car\ntest/3ab8e42af654191f,Close-up\ntest/3abb138054950f0c,Comics\ntest/3abb65d0079dcb1b,Ball game\ntest/3abeb6464b619022,Ball game,Team sport\ntest/3abf8c716190e5ff,Vegetarian food\ntest/3abfb2778ce41990,Close-up\ntest/3abffd182d72c47d,Digital camera,Close-up\ntest/3ac041ee5a33df7d,Close-up\ntest/3ac15fbcbbc1e843,Zeppelin\ntest/3ac4b8457879efaf,Antique car\ntest/3ac88f08dbdbb853,Steamed rice\ntest/3ac92bf465c46eff,Brochette,Fried food\ntest/3aca46115fee5439,Athletic shoe,Outdoor shoe\ntest/3acb9312bc31dcae,Team sport\ntest/3acbc5899abfc79f,Luxury vehicle\ntest/3acc9d22a542e023,Luxury vehicle,Performance car\ntest/3acda210b110abc7,Jeans\ntest/3ace63ff14cec809,Luxury vehicle\ntest/3acf3a249eec7ff3,Close-up\ntest/3acf988b135f7f11,Ball game,Team sport\ntest/3ad1e4110ba3d9c0,Black-and-white\ntest/3ad5c371bb15dab4,Road cycling\ntest/3ad6b92165fa39de,Extreme sport,Divemaster,Scuba diving,Underwater diving\ntest/3ad6c39cd14f89b2,Barquentine\ntest/3ad95a278408067e,Sport utility vehicle\ntest/3ada123ed70c649a,Luxury vehicle,Antique car\ntest/3adc7021e4640b38,Vegetarian food\ntest/3ae1f171299c1e01,Close-up\ntest/3ae317493e94faa4,Close-up,Whiskers\ntest/3ae432176001fcca,Close-up\ntest/3ae4d7cadb6c3fe8,Close-up\ntest/3ae8bb68b2e90376,Middle ages\ntest/3ae9dfb8313079b8,Luxury vehicle,Sport utility vehicle\ntest/3aebe24bfab55392,Auto racing,Compact car\ntest/3af0de80c5636a7b,Close-up,Whiskers\ntest/3af2621012b7845a,Lumpia\ntest/3af3b841c56fa9d0,Bovine,Close-up\ntest/3afdc663f87fcd35,Halter\ntest/3afeef5c9397b23c,Combat sport\ntest/3aff11d62b991ee7,Zeppelin\ntest/3aff75be9d5ecce2,Luxury vehicle,Performance car\ntest/3b03953c3d67cffd,Bird of prey,Close-up\ntest/3b04aeabec312115,Close-up\ntest/3b0990f228c4835a,Brisket,Beef tenderloin\ntest/3b0ce70c57c39e3c,Auto racing\ntest/3b0e651e3e5e9ff7,Luxury vehicle,Sport utility vehicle\ntest/3b103cb5f17203ca,Luxury vehicle,Performance car\ntest/3b1087354de8ed84,Black-and-white\ntest/3b11cab6188cd0e8,Domestic short-haired cat,Close-up,Whiskers\ntest/3b1221232cc30b23,Black-and-white\ntest/3b13a484cc1669c1,Blond\ntest/3b1480c8d2f8b71c,Flatbread\ntest/3b157f4874030e07,Canola\ntest/3b1815e38464503d,Lawn game\ntest/3b19a008df14dedb,Equitation,Equestrianism\ntest/3b1bfd34f28f62de,Chordophone\ntest/3b1cc321e308bdcd,Close-up\ntest/3b1fb425f2ccadc9,Nest box\ntest/3b2002929231b152,Luxury vehicle\ntest/3b209593d86c607b,Jeans\ntest/3b22c89e7bd9dd12,Fire apparatus\ntest/3b23c9d7127548ac,Melee weapon,Hunting knife\ntest/3b2503535f77084e,Compact car,Antique car\ntest/3b2863654627beaa,Boating\ntest/3b2d6b53c81d4758,Compact car\ntest/3b3006dc6f72f9dc,Scaled reptile\ntest/3b335505711736c8,Stemware\ntest/3b340da48c7da344,Close-up,Whiskers\ntest/3b345a205a452c36,Close-up\ntest/3b34d3d4d25a2ae2,Compact car,Luxury vehicle\ntest/3b37ef5b3d0e3efb,Rural area\ntest/3b38a3cc26911fac,Black-and-white\ntest/3b3b748b6d6ea1c0,Arthropod,Close-up\ntest/3b3c74bda2228692,Residential area\ntest/3b3fe1351f9c185e,Fried food\ntest/3b411449688d6da2,Black-and-white\ntest/3b419069bad38bf3,Sport utility vehicle\ntest/3b42eb869925a5ef,Luxury vehicle,Antique car\ntest/3b4593a6bcbc2f25,High-speed rail\ntest/3b4709fdb92fd603,Jeans\ntest/3b4720872e76dd59,Wind wave\ntest/3b491bef51a11623,Plant stem\ntest/3b4a775038284863,Antique car\ntest/3b4b6d626a7834a2,Comics\ntest/3b4bc34797729d1d,Goats\ntest/3b4ef0835ccf0024,Extreme sport,Sport climbing\ntest/3b5250ea7b3cdfe9,Watercolor paint\ntest/3b52726fd1fbd0f6,Luxury vehicle,Performance car\ntest/3b52d80a3bde1d29,Luxury vehicle\ntest/3b53bd9c7b09bab8,Luxury vehicle\ntest/3b541676bf7c1102,Close-up\ntest/3b5679415e0002ac,Close-up\ntest/3b56b9556058c064,Briefs\ntest/3b59847b506cbfeb,Motorcycling\ntest/3b5be744707e5e60,Roller skating\ntest/3b5c64b8d4fa2c13,Luxury vehicle\ntest/3b5d68f17a2cd733,Domestic short-haired cat,Whiskers\ntest/3b6481dee89dffa5,Microcontroller\ntest/3b64ead3315a4170,Senior citizen\ntest/3b66da2d83c87d51,Luxury vehicle,Antique car\ntest/3b6749514ab90f20,Plant stem\ntest/3b6819e309d6459c,Domestic rabbit\ntest/3b690f63e74cb161,Amphibian,Scaled reptile\ntest/3b6ad7a035027cb1,Pelmeni,Jiaozi\ntest/3b6b1c72a095e224,Arthropod,Close-up\ntest/3b6b7e47e005e7cb,Jeans\ntest/3b6cc668fe76f835,Compact car,Luxury vehicle,Antique car\ntest/3b6fec0736353f09,Roller skating\ntest/3b7016d04808a23c,Luxury vehicle\ntest/3b705af5704aa60f,Luxury vehicle\ntest/3b727441da9834b4,Close-up,Scaled reptile\ntest/3b73011ea41b151a,Close-up\ntest/3b74f2d7065943ce,Black-and-white\ntest/3b751805fcfcdca8,Luxury vehicle,Sport utility vehicle\ntest/3b78b44f88ae6c53,Extreme sport,Parachuting\ntest/3b7979775b1ba5df,Compact car,Luxury vehicle\ntest/3b7b62bfe7915fcf,Erinaceidae\ntest/3b7c393e36fd5dee,Nordic skiing\ntest/3b7ea91bd904993c,Close-up\ntest/3b7f5cb96d8a69fd,Luxury vehicle,Sport utility vehicle\ntest/3b8023b122fe56f8,Luxury vehicle,Performance car\ntest/3b8115290f94de5d,Dishware\ntest/3b81cd90ad5f7ef6,Performance car\ntest/3b8245f15a874f64,Performance car\ntest/3b831ec4dadb301c,Luxury vehicle,Antique car\ntest/3b85e1c7a7320c1d,Scaled reptile\ntest/3b8ac5abad0f4106,Horse racing\ntest/3b8bda12d6200cfe,Plant stem,Close-up\ntest/3b8bee1b571394ae,Headphones\ntest/3b8c56a26abf57c5,Auto racing,Go-kart\ntest/3b8c7696b3865a60,Electronic instrument\ntest/3b8e29c4eb25df11,Aircraft engine\ntest/3b8e476104237396,Hair coloring\ntest/3b8ededb02e8c998,Electronic instrument\ntest/3b8f323c74ececc0,Extreme sport,Parachuting\ntest/3b953530d59d434c,Domestic short-haired cat,Whiskers\ntest/3b957ad961e979c0,Military person\ntest/3b961e9805030dbe,Black-and-white\ntest/3b972dab08544e66,Aircraft engine\ntest/3b97543808179667,Black-and-white\ntest/3b987ae69b7a1a2c,Rural area\ntest/3b9ab6c9e9d7445d,Compact car\ntest/3b9b0fdad212a21c,Antique car\ntest/3b9c924f65c25212,Luxury vehicle\ntest/3b9f427e9519dd2a,Black-and-white\ntest/3b9fa358ebe3a223,Luxury vehicle\ntest/3ba00e3814b25fb4,Performance car\ntest/3ba316de0af5dfb4,Blond\ntest/3ba3b750a7a86522,Ball game,Team sport\ntest/3ba4b5aaa57037a2,Boating\ntest/3ba9f7cf68c652e4,Horse and buggy\ntest/3babe18a47396a3e,Arthropod,Close-up\ntest/3bae359a2a298752,Luxury vehicle,Sport utility vehicle\ntest/3bae48482126aec7,Compact car,Luxury vehicle,Antique car\ntest/3baf87f4a872e9af,Fried noodles\ntest/3bafcfd41ab26a79,Camera operator\ntest/3baff22b3f9fcd5c,Blond\ntest/3bb1a462a78be260,Equestrianism\ntest/3bb22764f27e9d30,Compact car,Luxury vehicle,Antique car\ntest/3bb3e224dbbcf1e1,Close-up\ntest/3bb4084cde9b0366,Black-and-white\ntest/3bb5e5ae5f472fb4,Ball game,Team sport\ntest/3bb66cc4cd569f11,Military person\ntest/3bb7558c430c06de,Black-and-white,Whiskers\ntest/3bb7845abdd09750,Black-and-white\ntest/3bb787f7a5420bd6,Ball game,Team sport\ntest/3bb8c1b5dadd4c62,Electronic instrument,Electric piano\ntest/3bbd0cdeae09a6af,Performance car\ntest/3bbe049c666fc3d8,High-speed rail\ntest/3bbeb293527b2ba0,Senior citizen\ntest/3bc06a4c8021ab05,Luxury vehicle\ntest/3bc407d24c3cd346,Compact car,Antique car\ntest/3bc4358d0edbb0f6,Fried food\ntest/3bc4ad17a44d0074,Luxury vehicle\ntest/3bc62d8142bb44e9,Amphibian\ntest/3bc6e1d6676c3dd5,Digital camera\ntest/3bc7e07e892b3001,Compact car,Antique car\ntest/3bc8548a69bedbe9,Extreme sport,Parachuting\ntest/3bc9f6d0ae8a5c6d,Cookies and crackers\ntest/3bcb3c4138e97baf,Brisket\ntest/3bcbd6c5cf1f87dc,Computer speaker\ntest/3bcd0ec13f1fdf76,Close-up\ntest/3bceb66a0a62804b,Luxury vehicle\ntest/3bd245e9ac96a77f,Luxury vehicle\ntest/3bd303a2dfacb5a3,Close-up\ntest/3bd5b509bb6263f4,Melee weapon,Hunting knife\ntest/3bd8b01aa95bb33d,Whiskers\ntest/3bd95969aac0b88d,Vegetarian food,Fried food\ntest/3bd9e5d93333befd,Extreme sport,Wind wave\ntest/3bda55be7f19ac77,Luxury vehicle,Antique car\ntest/3bdb508115890d02,Extreme sport,Sport climbing\ntest/3bdbc371c1e9190e,Extreme sport,Wind wave\ntest/3bdc8fb3c5ff9f6a,Whiskers\ntest/3bde15c8de9b87b9,Black-and-white,Chordophone\ntest/3bdea04c16fa88a2,Ball game,Team sport\ntest/3be114d684703d4d,Close-up\ntest/3be14ce4034d9e71,Coral reef fish\ntest/3be4ae5ac2dd1cd0,Extreme sport,Divemaster,Scuba diving,Underwater diving\ntest/3be5f67b4b174e02,Close-up,Plant stem\ntest/3beb36bb4051a5df,Compact car,Luxury vehicle,Antique car\ntest/3bed3c51b5c3f0db,Sailboat racing\ntest/3bf0876bb470724d,Ball game\ntest/3bf2db12540358c6,Freight transport\ntest/3bf4e5aa7932299b,Aircraft engine\ntest/3bf5c145c94a4800,Compact car,Luxury vehicle,Antique car\ntest/3bf758ac1f8f3b81,Rural area\ntest/3bf93688cb550b7f,Shooting range,Firearm\ntest/3bf985c5f7e90b7d,Close-up\ntest/3bf9a6193754e10f,Luxury vehicle,Antique car\ntest/3bfa3617c08dc3db,Billiards,Billiard room\ntest/3bfa370238e38db0,Close-up\ntest/3bfb6a671de95a29,Soba\ntest/3bfb6b2a0bab05ca,Hound\ntest/3bfbbfb5bae906f0,Dishware\ntest/3bfc02bba0269b34,Luxury vehicle\ntest/3bfcc72555037aaf,Middle ages\ntest/3bfda0b89ceaa8d7,Junk food,Fried food\ntest/3bff4427e3a4f070,Church bell\ntest/3bffcd221ab21864,Firearm\ntest/3c049d05677bfe6b,Miniature Poodle\ntest/3c06b81586b93605,Sport utility vehicle\ntest/3c0894fd59c03700,Luxury vehicle\ntest/3c099f35b33b293b,Compact car,Luxury vehicle\ntest/3c0a3a12abac3652,Close-up\ntest/3c0aea50f0d2ae05,Scuba diving,Underwater diving\ntest/3c0cbc510d68196d,Luxury vehicle,Performance car\ntest/3c0da3ce35c60cd9,Vegetarian food,Fried food\ntest/3c113d6f8b634a39,Antique car\ntest/3c14f9af71394770,Steamed rice\ntest/3c15954fb3641aa0,Luxury vehicle,Performance car\ntest/3c17c875ffb0f5e6,Luxury vehicle\ntest/3c1826ac450981e7,Black-and-white\ntest/3c1a72891137bb2b,Combat sport\ntest/3c1bb5f4fce831fb,Rural area\ntest/3c1c731af8c30e4c,Luxury vehicle\ntest/3c1cbb8789984ccd,Close-up\ntest/3c1d9a9fcd09c8d0,Close-up\ntest/3c1de63b9e849644,Touring car,Antique car\ntest/3c1e403bea8afafb,Luxury vehicle,Antique car\ntest/3c1e48533c99cbfa,Rural area,Canola\ntest/3c1f45983fd0a74a,Luxury vehicle,Performance car\ntest/3c224d7882fb34be,Melee weapon,Hunting knife\ntest/3c228745006977e1,Team sport\ntest/3c26e68877b3e3d9,Fried noodles\ntest/3c2848073370c6ac,Bovine\ntest/3c28a7ba8c0aca3f,Bagpipes\ntest/3c28da73c29a056f,Floristry\ntest/3c29b29aa911ad55,Close-up\ntest/3c2a6a6f9cbad23c,Luxury vehicle\ntest/3c2c0aabcf986677,Frigate\ntest/3c2c579ec7c98671,Luxury vehicle\ntest/3c2d8e66accf402b,Vegetarian food,Fried food\ntest/3c2dd620f9591226,Close-up\ntest/3c2dd7f83da2f386,Luxury vehicle\ntest/3c2e84373c70bf30,Compact car\ntest/3c2ff67b21c444c0,Trampolining\ntest/3c336bf95a3cc855,Jeans\ntest/3c349e239f51c969,Close-up\ntest/3c350c6874b3787e,Close-up\ntest/3c355f08c61daa02,Extreme sport,Boating\ntest/3c36c0058b20d31a,Luxury vehicle,Antique car\ntest/3c383a5e1cc97a8b,Extreme sport,Gliding\ntest/3c391538795ed28b,Auto racing\ntest/3c3b2d958499087b,Luxury vehicle,Sport utility vehicle\ntest/3c403e161f58e712,Close-up\ntest/3c406e428fdea97f,Arthropod,Locust,Close-up\ntest/3c4332c50c547b95,Fried noodles\ntest/3c442fe1b5f4c8e8,Freight transport\ntest/3c4558963eec17e0,Jeans\ntest/3c47713b89e7e25a,Equitation,Equestrianism\ntest/3c47a23eb4113f52,Bird of prey\ntest/3c4887add4bff7f2,Black swan\ntest/3c492dce44eccbf4,Compact car,Antique car\ntest/3c4dc2b4bafb288e,Sport utility vehicle\ntest/3c4e294f8d735775,Ball game,Team sport\ntest/3c4eb718d21251e9,Arthropod,Close-up\ntest/3c4f0d194107dea2,Close-up\ntest/3c50cbf4b4b62d94,Military person\ntest/3c516b93884e854a,Herding,Goats\ntest/3c53da46c5731363,Ball game,Team sport\ntest/3c54959998c1f720,Coral reef fish\ntest/3c57382ff8bbd289,Digital camera\ntest/3c582d0c29d52952,Close-up\ntest/3c5ead1b607b180b,Rice ball\ntest/3c5f7e49f6f3df9f,Vegetarian food,Corn on the cob\ntest/3c604f0e7431716b,Close-up,Plant stem\ntest/3c6223b543dd3a29,Arthropod,Close-up\ntest/3c6434f8f4994e4c,Luxury vehicle\ntest/3c65b420ddbecd65,Military person\ntest/3c660d94767b9472,Close-up\ntest/3c68b3a823858fd0,Aircraft engine\ntest/3c69d6134a716b29,Luxury vehicle\ntest/3c6a639945b7ff05,Luxury vehicle\ntest/3c6e5cb6f18c0365,Jeans,Luxury vehicle,Antique car\ntest/3c6e6d2ac001fa76,Modern dance,Musical theatre\ntest/3c7047721e67a88b,Ball game\ntest/3c71c28ebdf50bd4,Performance car\ntest/3c738dc9f5571930,Goats,Bovine\ntest/3c73acd5ff2935cc,Equitation,Equestrianism\ntest/3c7450f680683032,Luxury vehicle,Performance car\ntest/3c7710e4c8a2e539,Compact car\ntest/3c7a7145e11798b6,Luxury vehicle,Antique car\ntest/3c7cedf5e86f741c,Rural area\ntest/3c7e64a8bb34ee38,Luxury vehicle,Performance car\ntest/3c7eaab60d7be4fa,Plant stem,Close-up\ntest/3c7f5b1b314b0bf4,Heavy cruiser\ntest/3c7f8bf93fbb917f,Antique car,Luxury vehicle,Performance car\ntest/3c821c71ff8de76f,Boating\ntest/3c824c38f611d75d,Galleon,Barquentine,Frigate\ntest/3c8254187041e3f0,Amphibian\ntest/3c83d72150d66de7,Blond\ntest/3c8464983d37b4b8,Cookies and crackers\ntest/3c878b9b010cda33,Floristry\ntest/3c8790b819c1e6d2,Compact car,Luxury vehicle\ntest/3c8bb3ee6014d710,Chordophone\ntest/3c8c918709d969e8,Arthropod,Close-up\ntest/3c8e2daa821cbb3d,Auto racing\ntest/3c8f2711c9508f21,Luxury vehicle,Performance car\ntest/3c8fa3b5e2e8bb02,Scaled reptile\ntest/3c91b618df8704be,Bird of prey\ntest/3c91e12ecdb5780f,Perching bird\ntest/3c97753de228514d,Cookies and crackers,Fried food\ntest/3c9845b40a374fd5,Close-up\ntest/3c9a8acb5a091cc1,Residential area\ntest/3c9d30269a0c7790,Vegetarian food\ntest/3c9d87a896c25cb1,Trampolining,Jeans\ntest/3ca058aaa56d1b9c,Brochette\ntest/3ca0d66f57af0cbb,Jeans\ntest/3ca22df648943609,Domestic short-haired cat,Whiskers\ntest/3ca275ac2aa3a1b1,Leaf vegetable\ntest/3ca6751695c43775,Compact car\ntest/3ca7f04576ff3ecd,Road cycling\ntest/3ca970ec48b3b883,Compact car\ntest/3caccc9bbebeb248,Blond,Close-up\ntest/3cb5fa8dfb3aaef7,Close-up\ntest/3cb639ea309aa04e,Arthropod,Close-up\ntest/3cba3ead83252192,Arthropod\ntest/3cbbc2f0009ca569,Aircraft engine\ntest/3cbc1c88ec064bf8,Molluscs,Close-up\ntest/3cbce9bda991da64,Compact car,Luxury vehicle\ntest/3cc1e533ae53c363,Black-and-white,Modern dance\ntest/3cc315914a8a88b5,Modern dance,Musical theatre\ntest/3cc50bf2037a74bb,Plant stem\ntest/3cc56da8ffe0ee1b,Black-and-white\ntest/3cc678bcb7fba129,Billiard room,Billiards\ntest/3ccaf258f76698cc,Roman temple\ntest/3ccbdb60e4baab56,Bovine\ntest/3ccbfc3391697fd5,Compact car\ntest/3ccec8358dbe7ab2,Microcontroller,Breadboard\ntest/3cd08e23d1a1b350,Sport utility vehicle\ntest/3cd1f76ad7a24f35,Shooting range,Firearm\ntest/3cd2b0ec91bdd1fc,Digital camera\ntest/3cd321ee565d8bbc,Ball game\ntest/3cd4f29b60b7a671,Extreme sport,Motorcycling\ntest/3cd741b8d45a2f53,Woodwind instrument\ntest/3cd812b6440cc94a,Compact car,Luxury vehicle\ntest/3cdbc333bf0b8c89,Auto racing\ntest/3cdc3ef9a1c5f660,Floristry\ntest/3cdf51b4a3904c8b,Fire apparatus\ntest/3ce0e70a5dd591a0,Hound\ntest/3ce230d645d81127,Compact car,Luxury vehicle\ntest/3ce43644fb36abf1,Arthropod,Close-up\ntest/3ce4e223826915e8,Whiskers,Domestic rabbit\ntest/3ce662058baefe8f,Luxury vehicle\ntest/3ce833ab856e766a,Monoplane\ntest/3ceb7c0a31ad7e33,Zeppelin\ntest/3cec7712c22ed98e,Close-up\ntest/3ced2ec5692f25e8,Close-up,Halter\ntest/3ced644959df5b47,Outdoor shoe\ntest/3ced856aa40b0660,Blond,Hair coloring,Close-up\ntest/3ceefee7b1143cf5,Whiskers\ntest/3cf0b8cc4e386633,Hound\ntest/3cf0ed4864de286b,Black-and-white\ntest/3cf11a3f8890607c,Jeans\ntest/3cf3697f58431a47,Luxury vehicle\ntest/3cf4654a7ef991d5,Sport utility vehicle\ntest/3cf4793b46f70b38,Coral reef fish\ntest/3cf67b16cfd35992,Luxury vehicle,Antique car\ntest/3cf680fefe3167d3,Barechested\ntest/3cf71211946ede49,Blond\ntest/3cf754707ea0c65b,Jeans\ntest/3cf84e8fbf9a8a84,Equitation,Equestrianism\ntest/3cf9bf3c8d096c02,Rural area\ntest/3cf9eed4e5d80760,Performance car,Antique car\ntest/3cfac799e351993e,Luxury vehicle\ntest/3cfc238fdf9052ee,Luxury vehicle\ntest/3cfc57e8addcd3fb,Chordophone\ntest/3cfe4a2c74ea128a,Jeans\ntest/3cff1223349e02ed,Antique car\ntest/3d0369ffe42bc952,Close-up\ntest/3d03af05af1fc72b,Nest box\ntest/3d06dba580883021,Cookies and crackers\ntest/3d0700a5632a9f39,Scaled reptile\ntest/3d0c5ec5cad8fc1b,Arthropod,Close-up\ntest/3d0c8a684c950c94,Luxury vehicle,Performance car\ntest/3d0ccd0a68590dc9,Glacial landform\ntest/3d0ddfdbd00a7d81,Domestic short-haired cat,Close-up,Whiskers\ntest/3d0e627b8b3999ff,Antique car\ntest/3d0f41a041e53d03,Bovine\ntest/3d0f99060a7625a8,Whiskers\ntest/3d10113e04cbbeae,Gumbo\ntest/3d10444295f5c402,Luxury vehicle,Performance car\ntest/3d115e4f2ee1e092,Hair coloring\ntest/3d12b751330aa6ea,Sport utility vehicle\ntest/3d142c1a416848ef,Black swan\ntest/3d149698e30cfc3c,Aircraft engine\ntest/3d14e93e48ea9364,Close-up\ntest/3d15f9f0d5ecae65,Arthropod,Close-up\ntest/3d171fc429011828,Rural area\ntest/3d173b027a9be7de,Antique car\ntest/3d1899fb5c716c2b,Arthropod,Close-up\ntest/3d19ccb9d6716a4e,Calabaza\ntest/3d1ac6d8ed30b9b9,Wallaby\ntest/3d1b3980be2abf5a,Scaled reptile\ntest/3d1b6bd50096c604,Luxury vehicle,Antique car\ntest/3d1c64892eeae743,Performance car\ntest/3d1c826d4a05cfd5,Close-up\ntest/3d1f33b44c44bd52,Luxury vehicle\ntest/3d1f9983f53242a7,Compact car,Luxury vehicle\ntest/3d20ed4e0a748d00,Bovine,Close-up\ntest/3d22eabbdc36b765,Junk food,Fried food\ntest/3d281cd4e2eac55d,Monoplane\ntest/3d28af34ee80c59c,Sailboat racing\ntest/3d28ba8256eb1cec,Close-up\ntest/3d293ef21a87a427,Close-up\ntest/3d2b3b45f963414e,Ball game,Team sport\ntest/3d2bb758ff98a8bc,Middle ages\ntest/3d2ccfc262de39d0,Compact car,Luxury vehicle,Antique car\ntest/3d2d002f45304b4c,Luxury vehicle,Performance car\ntest/3d2d7d09f269f250,Vegetarian food\ntest/3d2ee88f3ae0f615,Vegetarian food,Corn on the cob\ntest/3d309d62cdc7fec1,Sport utility vehicle\ntest/3d313508b4de09d3,Backlighting,Black-and-white\ntest/3d3276f82525871a,Close-up\ntest/3d340401137fcc6a,Luxury vehicle,Antique car\ntest/3d3797d8489a4ff8,Auto racing\ntest/3d37a6b6be146c12,Rural area,Goats\ntest/3d39153c288ec333,Military vehicle\ntest/3d392a01132fda90,Chordophone\ntest/3d3a53dc179b5739,Fried food\ntest/3d3afe8be12eea11,Arthropod,Locust,Plant stem,Close-up\ntest/3d3b2a05e766be9f,Close-up,Scaled reptile\ntest/3d3b4b9efc103569,Woodwind instrument\ntest/3d3cd3139b793c16,Luxury vehicle,Performance car\ntest/3d40b17e650f1e90,Boating\ntest/3d4589c7b9c4e86a,Junk food\ntest/3d458b5ccde38d14,Motorcycling\ntest/3d466d1dee6a2774,Auto racing\ntest/3d46d98438cd15fe,Luxury vehicle,Antique car\ntest/3d470bc35e497b2f,Auto racing,Touring car\ntest/3d49d915bd14ac05,Close-up\ntest/3d4ababe6272ea2a,Close-up\ntest/3d4f2ce25a369f2a,Close-up\ntest/3d4fe40985d01bed,Compact car,Luxury vehicle\ntest/3d539d437815b7e0,Close-up\ntest/3d53f0ca65c8c41f,Luxury vehicle,Performance car\ntest/3d542322096c1f84,Luxury vehicle,Antique car\ntest/3d54742d0b5d5e86,Luxury vehicle,Antique car\ntest/3d5510bb1764322b,Performance car,Luxury vehicle,Antique car\ntest/3d5587deeccd0fb4,Black-and-white\ntest/3d5a79d8d9afe2eb,Luxury vehicle,Performance car\ntest/3d5faa8191319aaf,Luxury vehicle\ntest/3d5fb8c58c942e03,Sport utility vehicle\ntest/3d617d0415a96590,Scaled reptile\ntest/3d618cfeb0702862,Vegetarian food\ntest/3d63fa94b5e4e759,Extreme sport\ntest/3d65d996402763d1,Extreme sport\ntest/3d65f4a594af4d22,Luxury vehicle\ntest/3d65f847c75504f9,Equestrianism\ntest/3d66b9e626d7d9c1,Glacial landform\ntest/3d686cb8752cfa1a,Luxury vehicle,Antique car\ntest/3d6974b30cbdb0e6,Close-up\ntest/3d699d6e3e763333,Whiskers\ntest/3d6ceb786fb08605,Hound\ntest/3d6d6d8d9e81a01e,Shooting range,Firearm\ntest/3d70aa37f4cecb84,Blond,Hair coloring\ntest/3d7234f01ac96fd8,Black-and-white,Close-up,Whiskers\ntest/3d74ffd695baa6b3,Black-and-white,Arthropod,Close-up\ntest/3d7691020986595a,Luxury vehicle\ntest/3d7833c699b966c9,Fried food\ntest/3d7b7d1dd640e846,Compact car\ntest/3d7cd81521573b7b,Luxury vehicle,Performance car\ntest/3d806c1c4647329a,Plant stem,Close-up\ntest/3d80af4eee1ddd3b,Nordic skiing\ntest/3d81c45ad48ae3e4,Plant stem\ntest/3d829e3c0fe687ca,Miniature Poodle\ntest/3d8480136e6640bb,Close-up\ntest/3d8515c50454f04a,Jeans,Lawn game\ntest/3d854a7ba616cfd8,Portrait photography,Close-up\ntest/3d8a7de8ac049408,Dog walking\ntest/3d8b56aee4f87373,Luxury vehicle\ntest/3d8d1580be1deab5,Ball game,Team sport\ntest/3d8d8e230fb0ccdd,Ball game\ntest/3d8df48cdec3ebbd,Compact car\ntest/3d8f90e8fa34060b,Freight transport\ntest/3d8fd48a41e8b430,Arthropod,Close-up\ntest/3d9059ccf0a54a03,Luxury vehicle,Sport utility vehicle\ntest/3d90f9501f98729d,Auto racing\ntest/3d9192fbe0bf8b9a,Bird of prey\ntest/3d91dbac40ef9300,Luxury vehicle\ntest/3d92cd804b358a74,Bovine\ntest/3d930f11eee5d446,Ball game\ntest/3d9317f48c65a39f,Luxury vehicle,Performance car\ntest/3d9353708308d9a2,Cosmetics\ntest/3d97ad5896e752e6,Rural area,Aerial photography\ntest/3d9a403a96e220d1,Performance car\ntest/3d9aa1a7fb189176,Combat sport\ntest/3d9caeab1ec3876a,Dishware\ntest/3d9e709fca8ae47f,Bird of prey\ntest/3d9fbba086653aa5,Bovine\ntest/3da0737e89bd1e10,Fried food\ntest/3da0db0b1d4b9253,Horse and buggy\ntest/3da2f5303219ada7,Luxury vehicle,Antique car\ntest/3da38cace48280c7,Horse racing,Equestrianism\ntest/3da6278dd618215c,Black swan\ntest/3da81459d63a0e10,Cookies and crackers\ntest/3dac163abeddcad2,Whiskers\ntest/3dad27f92386e120,Team sport\ntest/3dad98c66cf02992,Halter,Equestrianism\ntest/3daee3abbde90a42,Gumbo\ntest/3db19b7e69769af5,Sport utility vehicle\ntest/3db21597772850ec,Extreme sport\ntest/3db3e392b0859e01,Blond\ntest/3db880f36461913c,Close-up\ntest/3db8a0205351cbe9,Compact car,Luxury vehicle\ntest/3db8aee5fb946b7c,Luxury vehicle\ntest/3dbaff41fdf04a86,Fried food\ntest/3dbc4215166afab8,Close-up\ntest/3dbc9e9e9d93b732,Close-up\ntest/3dbd632868086e23,Luxury vehicle,Antique car\ntest/3dc217d78747fa15,Ball game,Team sport\ntest/3dc319b793a0d43d,Scaled reptile\ntest/3dc633a852e0e8cd,Luxury vehicle,Performance car\ntest/3dc9a079bf3cedfe,Luxury vehicle\ntest/3dc9d763270c26cc,Sport utility vehicle\ntest/3dca7ca7f9b06ef6,Vegetarian food,Gumbo\ntest/3dcd950096edfe18,Luxury vehicle\ntest/3dcebd5ba0495fe1,Whiskers\ntest/3dd0f9e5f72587ec,Fried noodles\ntest/3dd1150dbe78c22f,Sport utility vehicle\ntest/3dd155d9e88e42c2,Steamed rice\ntest/3dd23a9728bf9121,Luxury vehicle,Antique car\ntest/3dd41a6604e7b42a,Steamed rice\ntest/3dd427ab1dc5a7ad,Roller skating\ntest/3dd56713402e5428,Jeans\ntest/3dd853b7f88d9255,Arcade game\ntest/3dd8b785033994c0,Close-up\ntest/3dd8f503402750a3,Luxury vehicle,Performance car\ntest/3dda8e972e3e0f3f,Compact car\ntest/3dde313b5f98c150,Steamed rice\ntest/3de1a842e4d52305,Motorcycling\ntest/3de1c1285bf24945,Jeans\ntest/3de2941fe9081480,Cookies and crackers\ntest/3de3b70fae4cfcf4,Compact car,Antique car\ntest/3de3f959939337ed,Jeans,Auto racing\ntest/3de592c7bd817725,Close-up\ntest/3de6591a47e8c6d4,Black-and-white\ntest/3de6d36dad83aa53,Close-up\ntest/3de6ee9ad71064bb,Luxury vehicle\ntest/3de78810650e129d,Backlighting,Portrait photography\ntest/3de938f00cbf354d,Close-up\ntest/3dec520eb7141bcc,Military person\ntest/3decea27461a6932,Blond\ntest/3defe49ef85eedb5,Close-up\ntest/3df1a7320cb2aecd,Close-up\ntest/3df1cdb5e13289d9,Bratwurst\ntest/3df2b6a098712fee,Whiskers\ntest/3df2ba4b2182019f,Ball game\ntest/3df336f270c16428,Motorcycling\ntest/3df448230a2b06e4,Blond\ntest/3df553d663038934,Ball game,Team sport\ntest/3df74ff05bd44b5a,Close-up\ntest/3df75c6fa3e70361,Luxury vehicle,Performance car\ntest/3df8aff8b21491ad,Fried food\ntest/3df97fcb497b94c1,Bovine\ntest/3df993452d8769d8,Luxury vehicle,Performance car\ntest/3dfa9bd413e7eb4b,Performance car\ntest/3dfbadcf824ce8d4,Jeans\ntest/3dff3acab07cdf13,Ball game\ntest/3dff3fb317ff6bc1,Domestic short-haired cat,Whiskers\ntest/3dffe5685ada8943,Extreme sport\ntest/3e05383a2ab010c0,Arthropod,Close-up\ntest/3e07f36d2ca11d65,Combat sport\ntest/3e08e58c44209ac6,Luxury vehicle\ntest/3e091b7d127033b8,Arthropod,Close-up\ntest/3e0a4177c79fdae4,Shinto shrine\ntest/3e0b288b36da5696,Glacial landform\ntest/3e0bad4c6068a528,Close-up\ntest/3e0d85a959ce493e,Close-up\ntest/3e10c5654841d9c6,Luxury vehicle,Antique car\ntest/3e110836f9c189b4,Antique car\ntest/3e125f48b12192f7,Ball game\ntest/3e13cad7e50fc467,Ball game,Team sport\ntest/3e148490301a23ef,Close-up\ntest/3e151a2cc955b201,Luxury vehicle\ntest/3e15547e388bb54a,Close-up\ntest/3e18d190b26b28ab,Equitation,Equestrianism\ntest/3e190ce70990bf3f,Luxury vehicle\ntest/3e1b91bff735c60d,Brochette,Yakitori\ntest/3e1bc4c1e48948e2,Khinkali,Jiaozi,Pelmeni\ntest/3e1dcab99baa5c41,Ball game\ntest/3e1e097b6e5501f8,Ball game,Team sport\ntest/3e1e3b414dcd04a0,Close-up\ntest/3e1f3816298d285a,Close-up\ntest/3e200ae37d510114,Portrait photography,Close-up\ntest/3e218fa8b023599d,Equitation,Equestrianism\ntest/3e229812c1c9cc9d,Luxury vehicle,Antique car\ntest/3e2367cb7a78fc3b,Arthropod,Close-up\ntest/3e24ebf2fe5880b8,Aircraft engine\ntest/3e254886e26e5d42,Black-and-white\ntest/3e25c699605eb379,Goats\ntest/3e261705c7be71fb,Racewalking\ntest/3e2755bc07b99537,Close-up\ntest/3e282472aeb73171,Close-up\ntest/3e282b9291321ae9,Miniature Poodle\ntest/3e28ffc80e2553c4,Goats\ntest/3e2e4cb8f4355607,Military vehicle\ntest/3e300779a5207bc8,Ball game,Team sport\ntest/3e311b05d6d11e44,Church bell\ntest/3e3169071c91e1cf,Team sport\ntest/3e322efbcc95d043,Monoplane\ntest/3e3273b0c29501c0,Watercolor paint\ntest/3e32e7bab925c99f,Rural area\ntest/3e344a30ff53e37f,Arthropod,Close-up\ntest/3e34c57f75e0ee3a,Sport utility vehicle\ntest/3e357afa071ee4db,Anole,Scaled reptile\ntest/3e378bcb6ad1f06b,Classical sculpture\ntest/3e37be4a9dc3f7fd,Amphibian\ntest/3e37fdef983faef3,Herding\ntest/3e38f00bbfc74ce5,Chordophone\ntest/3e39a225c07591e9,Ball game,Team sport\ntest/3e39e10715b3caf8,Underwater diving\ntest/3e3a318a23753ea6,Luxury vehicle,Performance car\ntest/3e3a47cba939f293,Leaf vegetable,Vegetarian food\ntest/3e3e4d4ccc3d52d2,Compact car,Luxury vehicle\ntest/3e409846beff7683,Nest box\ntest/3e41aba45629e2bb,Antique car,Luxury vehicle,Performance car\ntest/3e425877861d0ade,Extreme sport\ntest/3e42726a046c7c20,Floristry\ntest/3e43d1212ec66d2b,Horse racing\ntest/3e47dd5fa541e4d0,Arthropod,Locust,Close-up\ntest/3e48ec08bb56cdd9,Black-and-white,Horse and buggy\ntest/3e48eee90c40c6a8,Lifebuoy\ntest/3e4a2a7109888088,Luxury vehicle,Sport utility vehicle\ntest/3e4a8eea23c96cfc,Luxury vehicle\ntest/3e4a944217ed8689,Jeans\ntest/3e4d9389e2de8635,Sailboat racing\ntest/3e4e0c0453c81325,Close-up\ntest/3e4e99bc628cff0d,Musical theatre\ntest/3e4ed88797c52b49,Compact car,Luxury vehicle\ntest/3e52cbd9956b3670,Arcade game\ntest/3e5394a77b36c7a2,Milky way\ntest/3e53cdd091bbde4a,Compact car,Antique car\ntest/3e53e819d861e8cb,Aircraft engine\ntest/3e54cd530d9774d7,Drums\ntest/3e586640765607ef,Compact car\ntest/3e59ce0eb7059935,Close-up\ntest/3e5ac077b81053ad,Close-up\ntest/3e5f100921df9852,Sport utility vehicle\ntest/3e6161e638c59073,Compact car\ntest/3e61fd5c30c63605,Close-up\ntest/3e6213abda0aae64,Fried food\ntest/3e62cd6f5ed48bd3,Roman temple\ntest/3e64e1a7a2a56caf,Sport utility vehicle\ntest/3e652a25e3f1a793,Molluscs,Close-up\ntest/3e658010153a8d4e,Luxury vehicle,Antique car\ntest/3e70529d3fdae4e2,Luxury vehicle\ntest/3e706dcba227a110,Performance car\ntest/3e707011e8ea1a82,Close-up\ntest/3e71a31b6796cf1e,Black-and-white,Headphones\ntest/3e73d205365e7928,Antique car\ntest/3e7437af6b15b9df,Domestic short-haired cat,Whiskers\ntest/3e76e800cdc38a57,Rural area\ntest/3e77c5d2e5f40893,Plant stem\ntest/3e7a4075b06c7ea6,Compact car\ntest/3e7a5c94f70c5444,Rural area,Canola\ntest/3e7ac4235ace17ab,Athletic shoe,Outdoor shoe\ntest/3e7b8ebd2dcbba32,Close-up\ntest/3e7cc0328c515d1a,Performance car\ntest/3e7d2612132ca197,Jackfruit\ntest/3e7da6881d895254,Luxury vehicle,Performance car\ntest/3e7de50ec8b70f64,Rear-view mirror,Luxury vehicle,Sport utility vehicle\ntest/3e7ecfc8a80995d2,Performance car,Antique car\ntest/3e7f0e95e8c2bd5c,Ball game,Team sport\ntest/3e81bd998d1e4f09,Vegetarian food\ntest/3e83b8e2e48e6d70,Compact car\ntest/3e85f144e0e222bc,Rural area,Canola\ntest/3e87f9e472fd16b2,Residential area\ntest/3e8d7162388f8143,Rural area\ntest/3e903e79527b7eb5,Luxury vehicle,Performance car\ntest/3e90ceb4a1145066,Close-up\ntest/3e91cfc6bea44280,Rural area\ntest/3e9373a8beee79a8,Compact car,Luxury vehicle,Antique car\ntest/3e9432104b00f0ba,Extreme sport,Gliding\ntest/3e9684aca8e7c049,Close-up\ntest/3e96c189a3ab15e5,Wind wave\ntest/3e9817cd1b4942a9,Sport utility vehicle\ntest/3e982c8446379643,Boating\ntest/3e98b8cb9986a4ab,Chordophone,Electric guitar\ntest/3e99f7ae87bb7d73,Luxury vehicle,Performance car\ntest/3e9bfbc0c9c257cd,Fire apparatus\ntest/3e9d956c088d405f,Luxury vehicle,Antique car\ntest/3e9e062bfda3e3e7,Jeans\ntest/3e9fc8ce3def4482,Combat sport\ntest/3e9fd947a93160cb,Auto racing,Luxury vehicle,Performance car\ntest/3ea0eda239680404,Luxury vehicle\ntest/3ea11b55cb51d9a6,Luxury vehicle,Sport utility vehicle\ntest/3ea3e3613c2940a0,Flatbread\ntest/3ea4b67e4cb71082,Microcontroller\ntest/3eaa4afa04a2168b,Rural area\ntest/3eac3b54781dfb7a,Dog walking\ntest/3eac739699be128a,Leaf vegetable\ntest/3eacb3976766535f,Sport utility vehicle\ntest/3eb564882dd566d6,Aerial photography\ntest/3eb658ea4b4ec760,Performance car,Luxury vehicle,Antique car\ntest/3eb6ba71c17bd30d,Close-up\ntest/3eb74579fe6dfa1d,Dishware\ntest/3eb8feff9b225777,Vegetarian food\ntest/3ebecb5d7dd0dc1d,Bovine\ntest/3ebfa702fe065724,Leaf vegetable\ntest/3ec0f221a1de56e9,Halter\ntest/3ec58be6ff3c1a4c,Road cycling\ntest/3ec642c0f46ee3aa,Aircraft engine\ntest/3ec73f71779ab245,Camera operator\ntest/3ecb99110ffdb628,Ball game\ntest/3ecbf39111c34aad,Vegetarian food\ntest/3ecc8dd591d2496d,Combat sport\ntest/3ece9f0ebee01915,Close-up\ntest/3ed149999a22632e,Dishware\ntest/3ed3c286529cbe6e,Ball game\ntest/3ed4241054b89db2,Close-up\ntest/3ed59b9c9ed096ae,Black-and-white,Barechested\ntest/3ed5ab8da6142020,Close-up\ntest/3ed709956faf6746,Plant stem\ntest/3eda2c81c78ce88b,Luxury vehicle,Performance car\ntest/3edd3c8140fb1990,Close-up\ntest/3ee15aa26a7d7286,Figure skating\ntest/3ee4c89725360bf3,Dobermann\ntest/3ee5072aa6ffc6b7,Aircraft engine\ntest/3ee7744c3ce98dd8,Gumbo\ntest/3ee7acfa4e5d71e2,Road cycling\ntest/3ee982bd4bdf62c2,Bird of prey\ntest/3eea8fd6ce09754f,Plant stem\ntest/3eeed51e266ab276,Roller skating\ntest/3eefc87b39fc86a6,Domestic short-haired cat,Close-up,Whiskers\ntest/3eefd4f70411c4b6,Fried food\ntest/3ef24666d64b4f40,Antique car\ntest/3ef40a842efd6cda,Arthropod,Close-up\ntest/3ef83fdb8602626c,Bovine\ntest/3ef8a1b9314a679b,Leaf vegetable\ntest/3ef8fc50c24dc96b,Compact car\ntest/3ef9eeb286adf2ec,Canola\ntest/3efb338c47f885a0,Residential area,Rural area\ntest/3efe995dab0f5d20,Aerial photography\ntest/3f007791440904fa,Domestic short-haired cat,Whiskers\ntest/3f00caea84121cd3,Whiskers\ntest/3f022da24d538782,Road cycling\ntest/3f0262f20163a5e2,Microcontroller\ntest/3f034364b7081628,Aircraft engine\ntest/3f04583c93abd0c9,Rural area\ntest/3f0a616b91cad80a,Luxury vehicle\ntest/3f0ad47f825f860f,Compact car,Luxury vehicle,Antique car\ntest/3f0c7acc17cf5092,Extreme sport\ntest/3f0e337ffe611d14,Luxury vehicle\ntest/3f0e66024ff3e665,Performance car\ntest/3f1128802a6ef9ae,Electronic instrument\ntest/3f158754252dd710,Close-up,Plant stem\ntest/3f16a685e82e516f,Vegetarian food\ntest/3f17e8aa8c0f8171,Vegetarian food\ntest/3f17f4002b89c32f,Luxury vehicle\ntest/3f18498b871d51ba,Ball game,Team sport\ntest/3f1b15cebe0ed6fe,Racewalking\ntest/3f1b7e433c499500,Bovine\ntest/3f200dad29f3a453,Close-up,Barechested\ntest/3f20d8ce9219fa2f,Close-up\ntest/3f21b96e6c08525e,Compact car,Luxury vehicle\ntest/3f2283b1beb6ca60,Scaled reptile\ntest/3f238bc7b786772b,Briefs\ntest/3f23f7479a415c17,Close-up\ntest/3f2492f3bcf88740,Luxury vehicle,Performance car\ntest/3f257afbe5c53332,Antique car,Performance car\ntest/3f26ce150c548b41,Luxury vehicle\ntest/3f26f416662d8733,Miniature Poodle\ntest/3f27c8d95000c301,Digital camera\ntest/3f290d107adb26cb,Close-up\ntest/3f2939ee824bb83e,Black-and-white,Close-up\ntest/3f300c38cdc770f0,Rural area\ntest/3f3078e09107b4a7,Residential area\ntest/3f30e50000f2b948,Bird of prey\ntest/3f326f6eb10eeb1f,Arthropod\ntest/3f3ed5bfa9d642ab,Auto racing\ntest/3f3f30f236cdd7b7,Antique car\ntest/3f40ed7dc9047ba5,Luxury vehicle,Performance car\ntest/3f41982f9781ed11,Luxury vehicle,Performance car\ntest/3f438311cdc841cf,Ball game\ntest/3f438ca66da4a1b5,Close-up,Plant stem\ntest/3f44fc59b8b02996,Inflatable\ntest/3f45e3023be88309,Compact car\ntest/3f48865f920e17fb,Jeans\ntest/3f489ad9054b83e7,Blond,Portrait photography,Hair coloring,Close-up\ntest/3f48de9fc0ac6c7c,Bovine\ntest/3f4b8c74f2dbea6e,Road cycling\ntest/3f4c09ad187c47fa,Luxury vehicle,Sport utility vehicle\ntest/3f4c7f5c63e82ab5,Cosmetics\ntest/3f4d1342c84892f8,Backlighting\ntest/3f52f738494f2de6,Residential area\ntest/3f539bae21993573,Antique car\ntest/3f540f99ce460201,Black-and-white\ntest/3f546aff62a3d95f,Auto racing\ntest/3f55dc99f0be8257,Jeans\ntest/3f5666b262871a3b,Luxury vehicle\ntest/3f56b2045a0c1cab,Black-and-white\ntest/3f58f5a329c8429d,Luxury vehicle\ntest/3f5999ad73fed928,Ball game\ntest/3f5be995afff7a2b,Domestic rabbit\ntest/3f5beb805b0e8f9b,Luxury vehicle,Performance car\ntest/3f5bf46c1455f0be,Antique car,Performance car\ntest/3f5c4cb899f15174,Close-up\ntest/3f5d3cff1869dd3c,Antique car\ntest/3f5da220b64c436f,Leaf vegetable\ntest/3f5db0238046a3ce,Close-up\ntest/3f64086c493bcca1,Frozen yogurt\ntest/3f66f34fb35fdb59,Rural area\ntest/3f68094e65938c6c,Luxury vehicle,Performance car\ntest/3f686593d3a8e3c4,Close-up\ntest/3f6a6b8b55c151f9,Vegetarian food,Falafel,Cookies and crackers\ntest/3f6aee9c6116e01c,Musical theatre\ntest/3f6b4d72f064653b,Auto racing,Performance car\ntest/3f6bbca6f95c7be2,Ball game,Team sport\ntest/3f6dd191c36355b5,Luxury vehicle,Antique car\ntest/3f6fda0bbbff93c6,Arthropod,Close-up\ntest/3f705a8cb7729c74,Hound\ntest/3f7507edcc8c75f8,Sport utility vehicle\ntest/3f7564e7c4b8b450,Whiskers\ntest/3f76a42a97f324a9,Compact car\ntest/3f77979bde72545c,Dog walking\ntest/3f77ea1c06f21611,Extreme sport\ntest/3f7a05d29f567a8d,Black-and-white,Machine tool\ntest/3f7b7b56d1906bbc,Luxury vehicle\ntest/3f7c6105f99dcafe,Plant stem\ntest/3f7c741f9b39d240,Luxury vehicle,Sport utility vehicle\ntest/3f7ee95820c5dfab,Military person\ntest/3f7fce670dab8197,Compact car,Antique car\ntest/3f816da1e49b01bc,Woodwind instrument\ntest/3f837e629f05c1e0,Black-and-white\ntest/3f83888b21c147e4,Jeans\ntest/3f83cf8360c1d5f9,Machine tool\ntest/3f8486084e1a7097,Luxury vehicle,Performance car\ntest/3f861014ef9fd3ac,Arthropod\ntest/3f890dba7a50c475,Compact car,Luxury vehicle,Sport utility vehicle\ntest/3f8a6a2347cf72c9,Jeans\ntest/3f8b03e9e503aa32,Steamed rice\ntest/3f8c2bfc0136506e,Jeans\ntest/3f8d0d4b296801fb,Domestic short-haired cat,Whiskers\ntest/3f8da9f59b44b1c3,Sport utility vehicle\ntest/3f91213574e54524,Luxury vehicle\ntest/3f9373ffd45576ec,Plant stem\ntest/3f937eb7b3882872,Luxury vehicle\ntest/3f9408b1b9c80f15,Combat sport\ntest/3f959210582d5b1c,Close-up\ntest/3f96e8df4e6bd3b0,Monoplane\ntest/3f9816611946eec6,Luxury vehicle\ntest/3f98fcabab996478,Microcontroller\ntest/3f99b162e7bdf531,Ball game,Team sport\ntest/3f9b16be1dbf5461,Performance car\ntest/3f9d2e73e1732e03,Machine tool\ntest/3f9da50204b29417,Luxury vehicle,Performance car\ntest/3fa1f3cc5c34b8d1,Close-up\ntest/3fa529c0458b6ccf,Sport utility vehicle\ntest/3fa6819854b27685,Whiskers\ntest/3fab277a98a4d0c7,Luxury vehicle\ntest/3fab3deffc6e7ddc,Luxury vehicle,Performance car\ntest/3fabf63c0578e008,Rear-view mirror\ntest/3faebca343c0918b,Luxury vehicle,Performance car\ntest/3fb06d5a23d42873,Cosmetics\ntest/3fb1a7c5f4e2cdcc,Electronic instrument,Black-and-white,Electric piano\ntest/3fb346dac0f92539,Compact car\ntest/3fb4047b4ce15af3,Close-up\ntest/3fb40ba58639bc9f,Aircraft engine\ntest/3fb445f35e64e88c,Boating\ntest/3fb485b51641a454,Boating\ntest/3fb6068517eb7ab2,Digital camera\ntest/3fb8124c39245df4,Miniature Poodle\ntest/3fb9e1417f331882,Drums\ntest/3fba8028665b6340,Perching bird\ntest/3fbb59a39708db71,Extreme sport\ntest/3fbf37d1b961ce3a,Domestic short-haired cat,Whiskers\ntest/3fc2b51f790affce,Compact car,Sport utility vehicle\ntest/3fc303e25fba693d,Nordic skiing\ntest/3fc432a2546ddfe8,Arthropod\ntest/3fc4c0f1a3ef27f2,Auto racing,Performance car\ntest/3fc7d8479d06b80d,Blond,Portrait photography,Close-up\ntest/3fc86d560ae6919b,Close-up\ntest/3fcd6ed1c3cf2d9b,Bovine,Close-up\ntest/3fd0b7c26ba526e1,Blond,Hair coloring\ntest/3fd19f7f5a01c758,Performance car\ntest/3fd35b907ad165e2,Plant stem,Close-up\ntest/3fd3d926076919ed,Antique car,Performance car\ntest/3fd3fd8840a8d2c8,Electric piano,Electronic instrument\ntest/3fd411ae0693e84b,Rural area\ntest/3fd4e4d2a42c7e65,Church bell\ntest/3fd6b3ada484fbde,Close-up\ntest/3fd94c5a9738421e,Close-up\ntest/3fdb62d0965cf42c,Portrait photography,Close-up\ntest/3fdd65f5a841e233,Residential area\ntest/3fddc8fe3c35100a,Chordophone\ntest/3fde0b2bc41cb963,Boating\ntest/3fde2ba8389b3e30,Team sport\ntest/3fdfe506da22acaa,Coral reef fish\ntest/3fe0b0680527ba77,Junk food\ntest/3fe1cc342fbea9c6,Bovine,Close-up\ntest/3fe3a2dc448d59a0,Fried food\ntest/3fe4c4a4164fb8f8,Close-up\ntest/3fe7029acc6a02c2,Luxury vehicle\ntest/3fe7795ea62511ea,Luxury vehicle,Antique car\ntest/3fe8ed3526f70b9b,Headphones,Camera operator\ntest/3fea8afa22b2ab18,Bird of prey\ntest/3feaa87cdce32091,Wind wave\ntest/3feb43a5976cfcb6,Headphones\ntest/3fed32556d016531,Blond\ntest/3fedfcb948a32cfa,Domestic short-haired cat,Whiskers\ntest/3fee241ab462d146,Luxury vehicle,Performance car\ntest/3ff051f46928cde7,Sport utility vehicle\ntest/3ff1795c3db0f538,Bird of prey\ntest/3ff28b60167c7709,Dog walking\ntest/3ff4b82c5c7bb153,Antique car\ntest/3ff56da98093083b,Domestic short-haired cat,Whiskers\ntest/3ff5f1295d5293b1,Black-and-white,Close-up\ntest/3ff86196f0f6d262,Athletic shoe,Outdoor shoe\ntest/3ff88044031b3c49,Close-up\ntest/3ffb1e94bda32ba3,Bovine\ntest/3ffcb82dab9d7fb3,Black-and-white\ntest/3ffff81bdd46c425,Performance car\ntest/40003df857c97972,Goats,Close-up\ntest/4002d6808080e535,Luxury vehicle,Antique car\ntest/400371c67bfc5e49,Whiskers\ntest/400674d2ff25f428,Electronic instrument\ntest/400b0b5ea91d7375,Watercolor paint\ntest/400b8e6960a67be0,Dishware\ntest/400cb5f5319f43bf,Jeans\ntest/400f0b9b2c452332,Sport utility vehicle\ntest/400f627708e78b72,Black-and-white\ntest/40118e68097b1a16,Whiskers\ntest/4011e0167edc7889,Extreme sport,Boating,Wind wave,Personal water craft\ntest/40121807e91e15d4,Leaf vegetable\ntest/4012d510c036446a,Team sport\ntest/4012db4437e6a0e8,Road cycling\ntest/4015364bf5d761c4,Luxury vehicle,Antique car\ntest/40154cb41d274ee7,Pork chop,Ribs\ntest/4017573231cccc73,Blond\ntest/401805c63cb32ba3,Close-up,Scaled reptile\ntest/40189859e760fe3a,Scaled reptile\ntest/401a4fc3425ec575,Close-up\ntest/401aebe8824777b7,Black-and-white\ntest/401d75ec9e691dd7,Close-up\ntest/401e1e81991be10a,Close-up\ntest/401e6e7558da3984,Black-and-white\ntest/402019ea973cbdcd,Lawn game,Ball game\ntest/4020d84c986e220f,Compact car\ntest/402158f10c0b8ff1,Close-up\ntest/4021cd06bd10df14,Performance car\ntest/40229e4170e3f571,Hound\ntest/4026299db2691ec6,Prosciutto\ntest/4026314886cb0ca8,Domestic short-haired cat,Whiskers\ntest/402690289399b88d,Arthropod,Close-up\ntest/4026cbf58702dad4,Plant stem\ntest/4027e0a280c74548,Leaf vegetable\ntest/40289f30b35e6d1b,Jeans,Luxury vehicle,Antique car\ntest/40296ed5d0115476,Auto racing,Luxury vehicle,Performance car\ntest/402e62394da42ea3,Combat sport\ntest/402ec7eb19a9b990,Domestic short-haired cat,Close-up,Whiskers\ntest/4031a3cf20ca4015,Hound\ntest/4034fde1a2f0b75e,Luxury vehicle,Antique car\ntest/4038601ef6236a44,Floristry\ntest/40394dfd84bda27c,Luxury vehicle,Sport utility vehicle\ntest/403c177aae93b7e5,Black-and-white\ntest/403c6374e20e7f04,Pork chop\ntest/403cfa4fdf863966,Toy block\ntest/403e02ecc283bfde,Close-up\ntest/403f55a196f61327,Extreme sport\ntest/40431c7c29d959b5,Black-and-white\ntest/4046943918d6d6f9,Close-up\ntest/4046f9584b32cc56,Vegetarian food\ntest/40479e7fd3263832,Jeans\ntest/40486a6ba4e31b2a,Machine tool\ntest/4048a3ef98ddabe4,Whiskers\ntest/40494a1c56f566c1,Close-up\ntest/404ccd45b7d32aff,Aircraft engine\ntest/404d8a0972a635e4,Luxury vehicle,Antique car\ntest/404e586df348cd30,Plant stem,Close-up\ntest/405099210ef848b6,Cosmetics\ntest/40523a20e47372bb,Inflatable\ntest/40558571e68e6180,Firearm\ntest/405d3c9b93690e8c,Drums\ntest/405e2484235251fe,Extreme sport\ntest/4061f6c6368e6675,Luxury vehicle,Antique car\ntest/4062f2e2c5377ca0,Close-up\ntest/4064142c2d98f147,Flatbread\ntest/406700cf1a04794b,Luxury vehicle,Antique car\ntest/406811b82cbc182e,Road cycling\ntest/4068613c551caec4,Briefs\ntest/406ab0cb19832098,Hound\ntest/406cb05f8fc88f71,Compact car\ntest/406de0c684a92aa7,Performance car\ntest/406fddd7e43eae28,Close-up\ntest/4071ecc6ef3b8b16,Electronic instrument,Computer speaker\ntest/4072ac68b0b1b13a,Plant stem,Close-up\ntest/4072b1b7330494ca,Luxury vehicle\ntest/4073e3f2864039aa,Compact car,Luxury vehicle\ntest/4076add50ddf454d,Plant stem,Close-up\ntest/4076f110109a463e,Rural area\ntest/4078114019cef857,Compact car\ntest/40783be9a947059d,Close-up\ntest/40799a0d99db2eb0,Musical theatre\ntest/407c513bd73d9129,Monoplane\ntest/407e517c4744ec7d,Computer speaker\ntest/407e5c907ccc6941,Auto racing\ntest/40802fce7708757a,Jackfruit\ntest/408217808e318824,Microcontroller\ntest/4084360b5f7ae15b,Aircraft engine\ntest/40844445b814a590,Close-up\ntest/40851ad56d39de8e,Molluscs\ntest/4086284b9e934070,Arthropod,Close-up\ntest/40897d656916f774,Luxury vehicle,Antique car\ntest/408c0c645424ba7a,Watercolor paint\ntest/408f0280ace84d5d,Sport utility vehicle\ntest/408f23e2f15b31d4,Whiskers\ntest/408fdbf30979ba84,Bird of prey\ntest/409113678e55f03d,Luxury vehicle,Antique car\ntest/4093530e4f7aebf9,Close-up\ntest/4095053e7ad2e501,Monoplane\ntest/4095e6f5434cf1ef,Ball game,Team sport\ntest/409666a87d97011f,Monoplane\ntest/40986fd3bd0962c1,Hair coloring\ntest/4098c000a57a36a9,Close-up\ntest/4099846505a86e59,Sport utility vehicle\ntest/409b08e050218cfe,Cookies and crackers\ntest/409b2cf2bfeb8902,Luxury vehicle\ntest/409cbde945143888,Rural area\ntest/40a10d13b55794bd,Shooting range,Firearm\ntest/40a651d6efb2ca05,Gumbo,Steamed rice\ntest/40a6c305f92456b1,Vegetarian food\ntest/40a9bf9db2bdafa3,Jeans\ntest/40aa0ea42204685f,Arcade game\ntest/40aa4a58f58fb91b,Gun turret,Military vehicle\ntest/40abb6ed6d7eca9a,Close-up\ntest/40addd98fa8e6f39,Scaled reptile\ntest/40b0aa76b00fcda9,Combat sport\ntest/40b0e2db513fc029,Luxury vehicle\ntest/40b1417ebf9f5fc0,Firearm\ntest/40b1c6b1c2aaf7e5,Close-up,Plant stem\ntest/40b1e60462b975ee,Luxury vehicle\ntest/40b21dfdc5d93dce,Luxury vehicle\ntest/40b43d22df2df166,Compact car,Antique car\ntest/40b4aa8da7ca740c,Compact car\ntest/40b53f33c9b869de,Vegetarian food\ntest/40b788667cff3c6a,Chordophone\ntest/40b8ac9c32fbae39,Junk food\ntest/40b910d5f7a2a3f9,Ball game,Team sport\ntest/40ba97198d0786f1,Antique car\ntest/40bbf0d367ae613f,Close-up\ntest/40bc03bdddd567c3,Headphones\ntest/40bdbecae6eb55bc,Jeans,Close-up\ntest/40c1ace6cf2dfe1a,Ball game\ntest/40c331e8b55acb73,Cookies and crackers\ntest/40c40a7ec4dd7a5e,Ball game,Team sport\ntest/40c4a9692e47d57a,Residential area\ntest/40c4e67cba015c35,Close-up\ntest/40c662b3e4354666,Ball game\ntest/40c7529df35acab6,Shallot\ntest/40c944183f41861c,Boating\ntest/40cac37cd1f1b0f5,Vegetarian food,Fried food\ntest/40cb6009487b11fd,Rural area,Bovine\ntest/40d05a5b20589060,Close-up\ntest/40d06f1bc517184a,Black-and-white\ntest/40d074b0ff703d49,Extreme sport,Boating\ntest/40d083eeffce44c7,Antique car\ntest/40d0901b6d3cbd22,Black-and-white\ntest/40d1a39184af9d3d,Luxury vehicle,Performance car\ntest/40d561a18ec315e5,Boating\ntest/40d64e811f5e9363,Blond\ntest/40d836a8e6150c69,Assault rifle,Firearm\ntest/40d9e696988d18f0,Arthropod,Close-up\ntest/40dacbae18c11643,Nest box\ntest/40dc22e9be0fb04b,Vegetarian food\ntest/40dc8711f14c5bcd,Whiskers,Domestic rabbit\ntest/40dd54c9729ce8b1,Luxury vehicle\ntest/40df7f5ba95ae7b0,Wind wave\ntest/40e03b8660a630aa,Extreme sport,Wind wave\ntest/40e17003c62445ed,Barechested\ntest/40e1d529bf566d98,Plant stem,Close-up\ntest/40e291706b043e0b,Arcade game\ntest/40e33bfdfcb4bf4c,Wallaby\ntest/40e417634c1ac879,Hair coloring\ntest/40e54778f24f3109,Compact car,Antique car\ntest/40e693f83d1ea222,Aerial photography\ntest/40e77858b36e2d36,Arthropod,Close-up\ntest/40e8fa4018a6d31b,Barquentine,Frigate\ntest/40e94a256fa457e2,Electronic instrument,Microcontroller\ntest/40eac7106223f45c,Brisket\ntest/40ef9cb74e3f36cc,Perching bird\ntest/40efe0a3a12ca301,Stemware,Close-up\ntest/40f0137523ed1e2e,Hound\ntest/40f0e0a0e5a7b049,Hound\ntest/40f21f9125edcf3e,Close-up\ntest/40f25bdb74e87b3a,Molluscs,Close-up\ntest/40f7feedebf14c29,Jeans\ntest/40f80a1bbf5f5944,Luxury vehicle\ntest/40fbf63d1fb76204,Canola\ntest/40ffbf2979d8b0d9,Domestic short-haired cat,Whiskers\ntest/4100144252cc8698,Compact car,Luxury vehicle\ntest/4101d0dab6935552,Boating\ntest/41030c5ad4da8e32,Bovine\ntest/4103b6bd0f714856,Brisket,Beef tenderloin\ntest/4103ea751231e8e9,Junk food,Fried food\ntest/4104ea3ee725779f,Floristry\ntest/410690d34ff17eb4,Vegetarian food\ntest/4107e02232d1efc5,Electronic instrument\ntest/410b6cd3a0657bbe,Domestic short-haired cat,Whiskers\ntest/4110b4c92fad87cd,Close-up\ntest/4111dfdd6358ce86,Miniature Poodle\ntest/411382fb3e127ac6,Military vehicle,Luxury vehicle,Sport utility vehicle\ntest/411606cc34732dfd,Cosmetics\ntest/4116f7b3b59e071d,Siberian husky\ntest/411826b3411e6a2b,Luxury vehicle\ntest/4118e9f9ca5633d6,Luxury vehicle\ntest/4119a374b4eed076,Athletic shoe,Outdoor shoe\ntest/411a782bd139f030,Luxury vehicle\ntest/411c0996c54a70d0,Leaf vegetable\ntest/411ebc4159432cab,Black-and-white\ntest/411f5cd9103e84a4,Monoplane\ntest/4120e25a1bb5eb66,Electronic instrument\ntest/41216de19cfa3b1d,Fried food\ntest/4123b598f18155d3,Extreme sport,Motorcycling\ntest/4125ed39946f0756,Auto racing\ntest/4126144e9a936bae,Bagpipes\ntest/4126de6c9a70b449,Jeans\ntest/41274b210309f94a,Bovine\ntest/412ca9c067749719,Pit bull\ntest/4132d17a2e5b289e,Plant stem\ntest/41380d836a025e9b,Performance car\ntest/4139655932505e0a,Close-up,Scaled reptile\ntest/4139fdeca75acc3c,Luxury vehicle,Sport utility vehicle\ntest/413ad917dc366c21,Luxury vehicle\ntest/413b85ed4de93ca3,Residential area,Rural area\ntest/413f9ec724894083,Luxury vehicle\ntest/41417bab20219c0f,Performance car,Antique car\ntest/41452318db1f195a,Residential area,Aerial photography\ntest/4147233ec01f5eb6,Prosciutto\ntest/4147acf3642ebb73,Ball game,Team sport\ntest/4149b401f6187430,Luxury vehicle\ntest/414a14d4cf6045b6,Coral reef fish\ntest/414a7712b1d6d9e0,Ball game\ntest/414abf4caa4448da,Calabaza\ntest/414bcdc18f8de650,Galleon,Barquentine,Frigate\ntest/414c00bd9e5b150f,Pork chop,Beef tenderloin\ntest/414dca4504526deb,Rural area\ntest/414f2d575892a0db,Combat sport\ntest/415050103eaec991,Equitation,Equestrianism\ntest/4152871216f2a904,Floristry\ntest/41557e6c68cf4566,Flatbread\ntest/4157bf1d97ab218c,Compact car\ntest/415b37287aef5e8f,Digital camera\ntest/415b5200d0ce4fb7,Extreme sport,Wind wave\ntest/415b5f3658be41e5,Bird of prey\ntest/415c839e61ff03cf,Boating\ntest/415c947da7cd64a1,Luxury vehicle,Performance car\ntest/415ebf0bcd276216,Bagpipes\ntest/415ecc55a2e8df81,Plant stem,Close-up\ntest/41602345a0adc1ab,Arthropod,Close-up\ntest/416063a120a70932,Black-and-white\ntest/4163315b5ee39d3b,Close-up\ntest/416545cc6736cc11,Nordic skiing\ntest/4169c12fe2416c6d,Full moon\ntest/416afb9920bce870,Black-and-white\ntest/416ddc6595e1a9c8,Falafel,Fried food\ntest/416ee42ea3a027b7,Melee weapon\ntest/41720eedb792bbde,Close-up,Whiskers\ntest/4175a65c37da404f,Ball game,Team sport\ntest/417e3697c26f14fb,Antique car,Performance car\ntest/417eac68b2085fdf,Extreme sport,Parachuting\ntest/418121d5af21e923,Miniature Poodle\ntest/41839661a842f4c5,Glacial landform\ntest/4183a0b5568f18d3,Military person\ntest/4183db60a60bdb3f,Ball game\ntest/4183ebfdd42dc79f,Luxury vehicle\ntest/4184e430d7a80829,Shooting range,Firearm\ntest/41892f2e26f403e1,Jeans\ntest/418b00411e6e267d,Luxury vehicle,Performance car\ntest/418b7fd60bb95519,Luxury vehicle,Performance car\ntest/418bb69638774c2b,Vegetarian food,Corn on the cob\ntest/418c5ccd80f19e48,High-speed rail\ntest/418c903b47fc446c,Cobblestone\ntest/418d24a1f7671cd6,Team sport\ntest/418d2a894a179019,Billiards\ntest/418e139a3db3387d,Wallaby\ntest/4191193b13f43ec9,Close-up\ntest/4193b11b5206bc1b,Electric piano,Electronic instrument\ntest/41942266d33cc6b8,Antique car\ntest/419655144ed51b45,Luxury vehicle,Performance car\ntest/419770d542dee2ce,Antique car\ntest/419a2e7d0d2f9102,Compact car,Luxury vehicle,Antique car\ntest/419ada1190594de1,Miniature Poodle\ntest/419c9c15cdc91271,Musical theatre\ntest/419d2c58fe31286f,Luxury vehicle,Antique car\ntest/419d742a1169d5c4,Calabaza\ntest/419d93959e2e7bfc,Cosmetics\ntest/419dc862e4a04429,Classical sculpture\ntest/419ddbefc6b84898,Chordophone\ntest/419f00ac86cfbab2,Briefs\ntest/419f8a15e8c72891,Compact car\ntest/41a14fa73dfbc941,Antique car,Luxury vehicle,Performance car\ntest/41a152823d763b7a,Close-up\ntest/41a29669c0ed3beb,Residential area\ntest/41a35357459af514,Combat sport\ntest/41a4b20253fce6d5,Luxury vehicle,Performance car\ntest/41a4d885f120a3e4,Floristry\ntest/41a68265ea64b5a4,Black-and-white\ntest/41a6c2170621fd20,Vegetarian food,Pasta salad\ntest/41a7f2d2a71358ce,Close-up\ntest/41a8fb1a4079e803,Close-up\ntest/41a9071dcb7d993e,Luxury vehicle,Sport utility vehicle\ntest/41a914d4f94579ae,Bagpipes\ntest/41a92aa9ae0de444,Sport utility vehicle\ntest/41a931d03549a54d,Luxury vehicle,Antique car\ntest/41ab54be56f080ea,Vegetarian food\ntest/41abc182cd0ecaca,Chordophone\ntest/41abcb3ca937a8bd,Compact car,Antique car\ntest/41ac37f570e9dff9,Close-up,Whiskers\ntest/41af3efaefe26242,Roller skating\ntest/41b188a87b9cb7d9,Close-up\ntest/41b1d155155252d5,Close-up\ntest/41b2b378cc0816b6,Luxury vehicle,Antique car\ntest/41b5b96be53d4e52,Bonbon\ntest/41b63277831da765,Boating,Sailboat racing\ntest/41b7b4386206b787,Glacial landform\ntest/41b8e47200872ffb,Floristry\ntest/41b8ea0127f8cf6d,Blond\ntest/41b9103314aa4628,Domestic short-haired cat,Whiskers\ntest/41b95ebd5baf0904,Junk food,Fried food\ntest/41b9b54580fc6497,Fried food\ntest/41b9cfaa382f83bb,Floristry\ntest/41bc0d78507fd5c3,Rural area\ntest/41bff5b5ceae23f8,Compact car\ntest/41c090fff4c0a884,Blond,Hair coloring\ntest/41c31aca0f568845,Domestic short-haired cat,Close-up,Whiskers\ntest/41c32132fb6d57e0,Compact car\ntest/41c38d6fc671e189,Performance car\ntest/41c4a0c694769087,Plant stem\ntest/41c580ca35d6a9ce,Aircraft engine\ntest/41c5962734aa74ab,Team sport\ntest/41c6d29d4b76127a,Antique car\ntest/41c735fefcfaeba7,Ball game\ntest/41c9a881abbd32d6,Close-up\ntest/41c9b09ef3da724d,Drums\ntest/41cb695195f705e3,Domestic short-haired cat,Whiskers\ntest/41cda2a28807dd75,Auto racing\ntest/41cdcfe08deed277,Fried food\ntest/41cdf73ee1e06ee8,Junk food\ntest/41cf50883d2b23b4,Miniature Poodle\ntest/41d3de542bb00c0a,Leaf vegetable,Vegetarian food\ntest/41d3e11e67cd7d1f,Shinto shrine\ntest/41d55e70db0d19e9,Performance car,Luxury vehicle,Antique car\ntest/41d764174e4c1c7b,Barechested\ntest/41d7e6d62445b116,Blond\ntest/41d9fae4c79a8c7b,Cookies and crackers\ntest/41db2388ecbb82a4,Antique car\ntest/41dd35e57d1b031a,Ball game\ntest/41de918de8db9701,Scaled reptile\ntest/41ded726698162ba,Luxury vehicle\ntest/41e0a514c67b442e,Luxury vehicle\ntest/41e1bfea84eb0de0,Floristry\ntest/41e2bcd58f9d9413,Close-up\ntest/41e3204c509e9587,Auto racing,Performance car\ntest/41e6ee53092edd0d,Compact car\ntest/41e8b495221c60de,Domestic short-haired cat,Whiskers\ntest/41e9102986b5d9c8,Fried food\ntest/41eb24437a6287ab,Performance car,Antique car\ntest/41ecbb354b8351a5,Performance car\ntest/41ee46ec2a715f9d,Luxury vehicle\ntest/41f0529743759095,Performance car\ntest/41f0af6bd5f6c932,Black-and-white,Full moon\ntest/41f0b7a663f6dfc8,Arthropod,Close-up\ntest/41f301cd60049938,Luxury vehicle\ntest/41f318b186a418cc,Jeans\ntest/41f540040b1c4757,Fried noodles\ntest/41f6af75cdf05f2c,Close-up\ntest/41f7b4a93baed5ba,Arthropod,Close-up\ntest/41f7ff043e4bfa5c,Compact car,Antique car\ntest/41f997880865f16e,Bratwurst\ntest/41fa004148c34232,Black-and-white\ntest/41fa4653f3bd9acc,Compact car,Luxury vehicle\ntest/41fa4668645abbea,Antique car\ntest/41fa51c16a16a9e2,Close-up\ntest/41fba216b7e1283a,Combat sport\ntest/41fc7a303a5e4bc4,Black-and-white,Headphones,Close-up\ntest/41fec22a8e7f2bb1,Senior citizen,Close-up\ntest/42002a4841f6ad99,Rural area\ntest/4201c40f3c1127cf,Cobblestone\ntest/42024d5dc3af8173,Compact car\ntest/420373053f6471ed,Flatbread\ntest/4203915b79cf4e0e,Boating\ntest/420781f846e31998,Goats\ntest/4208d64380709fef,Floristry\ntest/42097639702aa462,Luxury vehicle,Performance car\ntest/420a4b10913545f8,Luxury vehicle\ntest/420bec9b2802ec5b,Extreme sport,Wind wave\ntest/420e2b66fa295a55,Close-up\ntest/4211b2a300991531,Aircraft engine\ntest/4212bbcc1cb32236,Junk food\ntest/421418590fb228b6,Rural area\ntest/421688a299d8a45b,Divemaster,Scuba diving,Underwater diving\ntest/421c098428ca97f8,Luxury vehicle,Performance car\ntest/421ca0ce7f932962,Luxury vehicle\ntest/421e37caf6de5bc2,Fire apparatus\ntest/421f920e9e525b6a,Brisket,Beef tenderloin\ntest/4220318df912c89a,Black-and-white\ntest/42219b28724c0f07,Woodwind instrument\ntest/42227dba9297a75b,Luxury vehicle\ntest/42253923c03a7116,Aircraft engine\ntest/422617861cbade39,Close-up,Scaled reptile\ntest/422b9f766e9e2e63,Luxury vehicle\ntest/422cad1d459436f4,Black-and-white\ntest/422d0309caf67c51,Arcade game\ntest/422e5a2532b175c7,Boating\ntest/422ecc568f41b157,Sport utility vehicle\ntest/4230170c98d9d730,Close-up\ntest/4230b661fedc3519,Luxury vehicle,Performance car\ntest/4232b5a73365477c,Racewalking\ntest/42348966fdbbb5da,Chordophone\ntest/423724c7618433d8,Close-up\ntest/42378a0ed8caa54b,Woodwind instrument\ntest/4237c823b4f8ea49,Ball game,Team sport\ntest/42383bce18c1bd8c,Luxury vehicle\ntest/4239abf64bfde416,Compact car\ntest/423b6383fdd456c6,Close-up\ntest/423d43bf7f1d170b,Billiards,Billiard room\ntest/423e10320add5905,Luxury vehicle,Performance car\ntest/4240d41b7269f6e4,Musical theatre\ntest/42428b64f9625d04,Performance car,Luxury vehicle,Antique car\ntest/4242e8acea2a27ee,Luxury vehicle\ntest/42458a66ee90266f,Portrait photography,Close-up\ntest/4247ac1a0cb8fa78,Modern dance,Musical theatre\ntest/42486741c290969b,Modern dance\ntest/424992fbd868cfe3,Extreme sport\ntest/4249ac270d072538,Rural area\ntest/424b39fea0f631fb,Close-up\ntest/424d963290eb3187,Close-up,Whiskers\ntest/424da963c0744dfe,Senior citizen\ntest/424f9f857acf76d5,Perching bird\ntest/4250703d5f3b54b2,Melee weapon,Hunting knife\ntest/4251014ce30966d0,Sport utility vehicle\ntest/42512efdfb32012b,Close-up\ntest/425274b5d39a4d2b,Sport utility vehicle\ntest/42556fa9984cfd38,Compact car,Luxury vehicle\ntest/425594bf570c78f5,Fried food\ntest/425b4162a65b3ae6,Electric piano,Electronic instrument\ntest/425c0d346e99377d,Luxury vehicle\ntest/425c20985d157b6e,Luxury vehicle,Performance car\ntest/426009da7dfa1d80,Antique car\ntest/42606c1b48750ce7,Shinto shrine\ntest/4260d3f60474550e,Arcade game\ntest/4262e2d5da082581,Close-up\ntest/42640caa0a8187d0,Close-up\ntest/42675436b2669503,Compact car,Luxury vehicle\ntest/4267abd73e23657c,Luxury vehicle,Performance car\ntest/4268c6e19edfcf89,Sailboat racing\ntest/42693c2df18aa621,Glacial landform\ntest/426ac47c01800fd5,Residential area\ntest/426b7bd8d4e7c3fb,Close-up\ntest/426c0b7250ba6dcc,Amphibian,Close-up\ntest/426f6b665d06fbaf,Extreme sport\ntest/426fa63a2dc0d700,Close-up\ntest/4270c1238fce3631,Middle ages\ntest/4271538b10fdc221,Jeans\ntest/4271c166ed5e21b3,Compact car,Luxury vehicle,Antique car\ntest/4271f3714e9e4be6,Black-and-white\ntest/42723de933617c65,Close-up\ntest/4273e96173b5b03c,Boating,Wind wave,Personal water craft\ntest/427788510467b477,Auto racing,Sport utility vehicle\ntest/4277ebc30397cff9,Blond\ntest/4278f4b89c962177,Luxury vehicle\ntest/4279f3e81a73a74f,Junk food\ntest/427a320e331bbe30,Luxury vehicle\ntest/427bead5bd6bcc66,Compact car\ntest/427d5baa72858704,Auto racing\ntest/4280e8178128f9d9,Ale\ntest/428120ffb0efcb40,Chordophone\ntest/42817add057faa15,Senior citizen,Close-up\ntest/42864d7f81d90b21,Black-and-white,Close-up\ntest/42874237907fd0f4,Luxury vehicle,Performance car\ntest/428764c4e47a64ac,Ball game,Team sport\ntest/428b9f59d34c80ec,Luxury vehicle\ntest/428be8281d9e4831,Combat sport\ntest/428e62bf95013026,Firearm\ntest/428e9fb57c3ebde6,Close-up\ntest/42900a970f2e108d,Domestic short-haired cat,Whiskers\ntest/42908de727c86895,Ball game,Team sport\ntest/42936873d7e8ebd9,Close-up,Scaled reptile\ntest/4294362b4359f7ef,Aerial photography\ntest/42950ebb65ea8862,Plant stem,Close-up\ntest/42953b568bd02dfa,Auto racing\ntest/4295a59bd92c017d,Fried food\ntest/429724a29c51f9f8,Scaled reptile,Close-up,Anole\ntest/42978d3421fc7348,Equitation,Equestrianism\ntest/4299e2ebf9c58542,Combat sport\ntest/429a45a2e0af2ea3,Antique car,Luxury vehicle,Performance car\ntest/429a6327f94af647,Arthropod,Close-up\ntest/429ac042ac66fa02,Fried noodles\ntest/429ae2ce1900893c,Rural area\ntest/429f2d0b1195aa29,Close-up\ntest/42a491afa6ed52ff,Aircraft engine\ntest/42a59960351e9cdb,Luxury vehicle\ntest/42abc12afe013322,Junk food\ntest/42ac6cb070685a2a,Aircraft engine\ntest/42ac84dffe98c484,Vegetarian food\ntest/42ad1d0da63d78d0,Electronic instrument\ntest/42ae05c8eab2b13c,Luxury vehicle\ntest/42aeae5ab78a1d75,Compact car,Luxury vehicle,Antique car\ntest/42aeb423839eef56,Drums\ntest/42b9bfa409ce6ed5,Close-up,Scaled reptile\ntest/42bd4695f70fd925,Surfing Equipment\ntest/42be6bfa20f01c2b,Divemaster,Scuba diving,Underwater diving\ntest/42c11946d23060e8,High-speed rail\ntest/42c4b0a5b46cc4a8,Road cycling\ntest/42c4c99657172bd8,Close-up\ntest/42c6724f764e083a,Close-up\ntest/42c7f740c47625c0,Black-and-white\ntest/42c9df2d7e393885,Ball game,Team sport\ntest/42ca45961253c8c4,Toy block\ntest/42cb4d43781e5462,Rural area\ntest/42cc26dd872160fb,Roller skating\ntest/42cc532616b4da8e,Luxury vehicle,Performance car\ntest/42cc9b56beb235b8,Vegetarian food,Flatbread\ntest/42ce0e541e865b81,Ball game,Team sport\ntest/42cfb4f23fc22be8,Military vehicle\ntest/42d22e1f0b762ed5,Motorcycling\ntest/42d442fd19b72df7,Rural area\ntest/42d4498abb2bb7b5,Close-up\ntest/42d6b48cf68049b9,Arthropod,Close-up,Plant stem\ntest/42da52cc85d9095a,Blond\ntest/42da9ed555932d7a,Compact car\ntest/42da9fe26a251d14,Auto racing\ntest/42dad66eac0118bc,Machine tool\ntest/42daecc83058cdc9,Rural area\ntest/42db35ac1f6a3294,Ball game\ntest/42de279936a7f438,Close-up\ntest/42e0d60a1feef960,Peafowl\ntest/42e26b99f4cc6b7d,Athletic shoe,Outdoor shoe\ntest/42e4f4af9f01b88b,Black-and-white\ntest/42e7d0b153641d1c,Close-up\ntest/42e8124f64e78d37,Nile crocodile,Crocodilia\ntest/42e94fc9c1953f29,Luxury vehicle,Performance car\ntest/42f2ecfd0c2d00fd,Luxury vehicle\ntest/42f4b58b21cbe526,Team sport\ntest/42f66f9c572b865a,Close-up\ntest/42f90657f501a723,Boating,Rowing\ntest/42fabdbb40d74b25,Barechested\ntest/42fafbbf25dc485f,Luxury vehicle,Performance car\ntest/42ffb88674f40e62,Domestic short-haired cat,Close-up,Whiskers\ntest/43009247663c482b,Ball game,Team sport\ntest/4301c7499283f917,Black-and-white\ntest/43028c395ed649ef,Extreme sport\ntest/430294ae5f3282de,Black-and-white\ntest/43040e6a6a1d4376,Close-up\ntest/43048432edef7c9b,Jeans,Black-and-white\ntest/43075dd1aa81e11d,Modern dance,Figure skating\ntest/43096a6166e280c7,Black-and-white\ntest/4309a90748458320,Barechested,Close-up\ntest/430bc5b07c55d02f,Bovine,Close-up\ntest/430bde7175269de1,Water polo,Ball game,Team sport\ntest/430ed03f39291479,Athletic shoe,Outdoor shoe\ntest/430f5a60a8b1eb45,Hair coloring\ntest/430f8ce092d7ce81,Compact car,Luxury vehicle\ntest/430fd27214d7dec1,Roman temple\ntest/4311a68f5eeafc07,Performance car\ntest/4312be76028ca603,Ball game\ntest/4314ac29ea06925f,Junk food,Cookies and crackers\ntest/4315725b4b99e096,Plant stem\ntest/4317bde5ab9ea55a,Aircraft engine\ntest/431924bd56ebb5b7,Close-up,Whiskers\ntest/43199d95788967f5,Equitation,Equestrianism\ntest/431ad791c327017f,Hound\ntest/431af2928d56fa2d,Close-up\ntest/431cd9ddbaaa52d7,Luxury vehicle\ntest/431cf78e4f2c581f,Hair coloring\ntest/431ec17290b0f970,Combat sport,Sumo\ntest/4320c932293594d7,Bird of prey,Close-up\ntest/4321107856f9b9dd,Luxury vehicle\ntest/4321acb8be83308e,Scaled reptile\ntest/432218a6182f5111,Scaled reptile\ntest/43237c956f0ddf33,Antique car,Performance car\ntest/432410e6487f58c5,Close-up\ntest/43244e2690946b8b,Black-and-white,Touring car,Luxury vehicle,Antique car\ntest/4325ed4e3da00ef1,Close-up\ntest/432629394d40e315,Amphibian,Scaled reptile\ntest/4326f59925a359c3,Auto racing,Luxury vehicle\ntest/432777ea07e6a11c,Domestic short-haired cat,Whiskers\ntest/4328db648cd93ce3,Arthropod,Close-up\ntest/4329eef0fc8c0773,Luxury vehicle,Performance car\ntest/432e9320b42ffc1c,Whiskers,Domestic rabbit\ntest/432f135df3ea2371,Performance car\ntest/432fc731168c1cba,Luxury vehicle,Antique car\ntest/43301df0ce61d282,Halter,Equestrianism\ntest/4331e077d015164f,Combat sport\ntest/4338f3dcf90f4648,Compact car,Luxury vehicle,Antique car\ntest/433a75b557ea66fc,Rural area\ntest/433ad3a6a7e20267,Boating,Rowing\ntest/433b1019e2b38c86,Domestic short-haired cat,Close-up,Whiskers\ntest/433db732f2ad8104,Military person\ntest/433e61a83533e315,Luxury vehicle\ntest/433f2ab939c7dad3,Whiskers\ntest/43416c69b3546755,Performance car\ntest/4341e5c65a4a5c43,Sport utility vehicle\ntest/43424a9a4a51312d,Peafowl\ntest/434324b18714711d,Luxury vehicle\ntest/434a26d95fcb7c74,Road cycling\ntest/434a69668ea5a433,Digital camera\ntest/434e7a41f957cdc6,Jeans\ntest/434ec5ed241fe26d,Vegetarian food,Junk food\ntest/4352409425f4b6cf,Bratwurst\ntest/4352fc28c7b99078,Arcade game\ntest/43584f66a3181a1e,Compact car\ntest/435b5932d4137f40,Fried food\ntest/435b7143fc0d2129,Portrait photography,Close-up\ntest/435b94bef89be70e,Luxury vehicle\ntest/435c26890aad47b0,Close-up,Whiskers\ntest/435dce19797f3024,Luxury vehicle\ntest/435ddafb9f06b2d7,Black-and-white\ntest/435e4a19092643ca,Extreme sport,Motorcycling\ntest/435e6075a9f9e701,Luxury vehicle,Antique car\ntest/435e86c27d00d613,Aircraft engine\ntest/435fd7a5be5ad77a,Luxury vehicle,Performance car\ntest/4364d63e316e6ae5,Goats,Bovine\ntest/4364f950bebfde0d,Performance car\ntest/436770ae6f4a8f76,Auto racing\ntest/4368f6a3b52db39e,Sport utility vehicle\ntest/4368fc10d4318c87,Bonbon\ntest/4369e65b84b6594c,Close-up\ntest/436bacf25263257f,Frying,Fried food\ntest/436bf8c499ecd231,Sport utility vehicle\ntest/436dc60472022f84,Extreme sport\ntest/436e5ed6450deb9d,Military person\ntest/436e7be987e2f126,Senior citizen\ntest/4370e236a4ffe3e2,Leaf vegetable\ntest/4370f8e884615887,Arthropod,Plant stem,Close-up\ntest/4371060935a24ac7,Luxury vehicle,Sport utility vehicle\ntest/4372bea1ba4c07bf,Black-and-white,Close-up\ntest/43754d66299b6a87,Aircraft engine\ntest/4375832526f1149e,Luxury vehicle\ntest/4377fcd757033b6b,Close-up\ntest/43780b475de45751,Headphones\ntest/437842e60ee975b3,Wind wave\ntest/4378e12df400b920,Road cycling\ntest/437b858365ba06c4,Close-up\ntest/437d6323bc5a06b6,Fried food\ntest/437d6964a6ff7b2e,Ball game,Team sport\ntest/437dfc6959f8c042,Luxury vehicle,Antique car\ntest/437f53bc37520795,Rural area\ntest/4380188c19d09ce4,Coral reef fish,Close-up\ntest/4381188a824a88e8,Extreme sport\ntest/438323343203d97a,Fire apparatus\ntest/4383539c387da855,Wallaby\ntest/4383c45dbbfa3da2,Compact car\ntest/4384e27645092dad,Plant stem,Close-up\ntest/4386478f86c10c0b,Luxury vehicle,Sport utility vehicle\ntest/4386615d53621174,Combat sport\ntest/438825a96d75b3d4,Antique car,Performance car\ntest/43890ef1f074af02,Compact car\ntest/438a06167df517dd,Jeans\ntest/438a1efddf141c1a,Luxury vehicle\ntest/438ee43c44c1e02c,Luxury vehicle,Antique car\ntest/438eefb651c7ae26,Antique car\ntest/439384c0fc560dfd,Luxury vehicle\ntest/4394fd19d29eaa73,Extreme sport\ntest/43955231708ece52,Close-up\ntest/4397de125a1ede14,Equestrianism\ntest/43992bcd8ef2b617,Luxury vehicle,Performance car\ntest/439aec3dbf9d8d0f,Extreme sport\ntest/439b41a6ba2c872a,Sport utility vehicle\ntest/439c97a9922ff036,Frozen yogurt\ntest/439e21de21f1e611,Ball game,Team sport\ntest/439ebadaf6b46694,Luxury vehicle,Sport utility vehicle\ntest/43a04bca5363f935,Close-up\ntest/43a0563f55cbd083,Close-up,Whiskers\ntest/43a06c62c6f072ba,Extreme sport\ntest/43a30dcc1166444f,Compact car,Luxury vehicle\ntest/43a3cfd09c052363,Luxury vehicle\ntest/43a5437f4b3832eb,Horse and buggy\ntest/43a83bd438b6faf9,Whiskers\ntest/43a943bd032f14e7,Arthropod,Close-up\ntest/43aa964c1a2eb7d8,Compact car,Luxury vehicle,Performance car\ntest/43ad2fe3cd9a81fa,Luxury vehicle,Performance car\ntest/43ae2b3a6f329a30,Amphibian,Close-up\ntest/43b1ef5be2061649,Ball game,Team sport\ntest/43b2197799a1d59f,Close-up,Scaled reptile\ntest/43b21f1d844c86d1,Cookies and crackers\ntest/43b24afebd40efa3,Bird of prey\ntest/43b3da44901f1303,Hair coloring\ntest/43b5058beec85ee4,Close-up\ntest/43b6243cd8211888,Amphibian\ntest/43b68e7ed30fdaef,Luxury vehicle\ntest/43b8cd118701effc,Drums\ntest/43b8ef7dbaea924b,Sledding\ntest/43b9327f6fb9e495,Frying,Fried food\ntest/43bb9d63d72beb93,Compact car\ntest/43bbc230a10686b2,Compact car,Luxury vehicle\ntest/43bbe95f4e6ef4d7,Molluscs\ntest/43bd10b7d4319e74,Residential area,Aerial photography\ntest/43be2239184a1d02,Senior citizen\ntest/43be9bf26d70110e,Close-up\ntest/43c0751269a742b9,Great white shark\ntest/43c0dc0bb1059747,Floristry\ntest/43c5105b32725dea,Vegetarian food\ntest/43c591f4941ee1cf,Fried food\ntest/43c9ff79a3ff86ae,Close-up\ntest/43caa906c2494890,Military person\ntest/43caee8f2fba5c99,Outdoor shoe\ntest/43cd3274846a2ec4,Compact car\ntest/43cd5c8ea0c5522f,Musical theatre\ntest/43cdeba6c4f4dec1,Compact car,Luxury vehicle\ntest/43cf967f30c00367,Whiskers\ntest/43d2b10b6ee27c73,Cosmetics\ntest/43d4e1592c4d2eaf,Extreme sport,Parachuting\ntest/43d5633035f1f7a5,Combat sport,Grappling\ntest/43d809075c0cb839,Motorcycling\ntest/43da250f3db74385,Aircraft engine\ntest/43de0526ee6131a1,Rural area\ntest/43defd9b2304d753,Auto racing,Go-kart\ntest/43dfcbb00ad51b59,Pork chop,Fried food\ntest/43e1ac17fcbd97bf,Vegetarian food\ntest/43e27227e00ba268,Team sport\ntest/43e5bfdc52175b2f,Luxury vehicle\ntest/43e5e38aad5f053e,Blond\ntest/43e67f5c1f80db7c,Luxury vehicle,Antique car\ntest/43e6cde811610e7a,Sport utility vehicle\ntest/43e6dda21da422c3,Drums\ntest/43e839190cc1bb5b,Steamed rice\ntest/43e93c751680f781,Monoplane\ntest/43e9b187a1c6f866,Close-up\ntest/43e9d4576f7f5570,Stemware\ntest/43ea06e46d2d0f29,Close-up,Whiskers\ntest/43ef0d9ecd6dd4be,High-speed rail\ntest/43ef7e202598f506,Jeans\ntest/43f0c92c88e51a84,Domestic short-haired cat,Whiskers\ntest/43f127928a2ed3be,Luxury vehicle\ntest/43f3387c86ff305b,Close-up\ntest/43f4399d1aab60de,Luxury vehicle,Antique car\ntest/43f504ca8b6e8f36,Compact car,Antique car\ntest/43f520f2eb946d80,Firearm\ntest/43f65318a7ec8d6c,Wind wave\ntest/43fac3708f5805b9,Luxury vehicle,Antique car\ntest/43fb19f2e0075931,Luxury vehicle,Performance car\ntest/43fc7f123680ba25,Close-up\ntest/43fed13a478171f8,Luxury vehicle\ntest/43ff3450c9014131,Compact car,Luxury vehicle\ntest/43ff5499b476f970,Monoplane\ntest/440103fce5d95f00,Anole,Scaled reptile\ntest/4401a5b086fbc8c0,Luxury vehicle,Performance car\ntest/440252ac50748cdb,Luxury vehicle,Performance car\ntest/44025bb957689cd4,Antique car\ntest/4406af2414150959,Close-up\ntest/4409d6a5cbc5b110,Black-and-white\ntest/440aa8e30b5012d2,Compact car\ntest/440b48d2c61a0d15,Close-up\ntest/440b74caebc7b895,Hurdling\ntest/440bd6291eb7d8c9,Luxury vehicle\ntest/440bd7af383d8601,Heavy cruiser\ntest/440f11eb74cea5d0,Ball game\ntest/440ff15c29b85aaf,Frozen yogurt\ntest/44154ecf53ae1171,Miniature Poodle\ntest/441823d0a7a3f900,Blond\ntest/44199f5ff0c15453,Ball game\ntest/441ac7d90c069254,Whiskers,Domestic rabbit\ntest/441d7c7ab95f7c36,Equitation,Equestrianism\ntest/441d9a7195d9fd2c,Black-and-white\ntest/441e034b789f3e6a,Brochette\ntest/441e93465cee4392,Bird of prey,Close-up\ntest/441f0dbb8feb805f,Luxury vehicle,Performance car\ntest/44209608a8b3ea6e,Combat sport,Grappling\ntest/44223069de85849c,Close-up\ntest/4424264d478d2e97,Wind wave\ntest/44247d4a220a8a21,Close-up\ntest/4426b270a4c4eafe,Blond,Hair coloring\ntest/4426fa0e3b515179,Ball game,Team sport\ntest/442cf084e3f8e16f,Brisket\ntest/442ee8876ca0e52d,Close-up\ntest/442f548760b6a3be,Bovine\ntest/44342a60232e50cb,Assault rifle,Firearm\ntest/4434350826f0b00e,Close-up\ntest/4435e803dad4f11d,Fried food\ntest/4436f448b54f90d5,Extreme sport,Sport climbing\ntest/4437b8470a7332a1,Luxury vehicle,Antique car\ntest/4437cf34873e5a23,Microcontroller,Breadboard\ntest/4439f4dda1d52630,Chordophone\ntest/443a5090fd33bcee,Blond\ntest/443a9440de14df47,Close-up\ntest/443b9f2537f618db,Close-up\ntest/443c7a051241c500,Equestrianism\ntest/443e77c94e6391d6,Bovine\ntest/443e9c702eaf49a5,Luxury vehicle,Performance car\ntest/444698882f74641b,Whiskers\ntest/44477af35b046528,Whiskers\ntest/444a9dc17d604e71,Military vehicle\ntest/444afc387def318d,Luxury vehicle,Antique car\ntest/444d0167acbc9732,Performance car\ntest/444e6a063f318b25,Luxury vehicle,Performance car\ntest/444f2282b717a557,Close-up\ntest/4450a8445a60dda8,Arthropod,Close-up\ntest/4450e962c6974af6,Black-and-white\ntest/44518f84e812a57a,Luxury vehicle\ntest/445468753782b643,Whiskers\ntest/4457dd01c97a71d4,Gumbo\ntest/4457f8e171d138e6,Close-up\ntest/44580b543be03f25,Extreme sport,Boating\ntest/445b0a89f73f74da,Auto racing,Performance car\ntest/445c548bd7ca8f2d,Freight transport\ntest/445e76f2b7d5173a,Luxury vehicle\ntest/445f7845d17b05fb,Luxury vehicle\ntest/445fbb2509a57e07,Perching bird\ntest/4465a078a64a93d4,Blond\ntest/4468b40b69625dce,Scaled reptile\ntest/446a311db31eff1e,Boating\ntest/446c2d87febb93da,Luxury vehicle,Sport utility vehicle\ntest/446caf33319e2924,Trampolining\ntest/446d54c5b34d1aa7,Modern dance,Musical theatre\ntest/44700a596963fa2a,Luxury vehicle,Antique car\ntest/4470ae99d9bf7e96,Roman temple\ntest/4471031f24ca593c,Sailboat racing\ntest/4471e79b5426dd99,Whiskers\ntest/4474841751e50631,Antique car\ntest/4477b8e0bfcefb64,Cosmetics,Close-up\ntest/447a4308e611129c,Luxury vehicle\ntest/447b4376a2efada3,Ball game,Team sport\ntest/447c5af4846a30b1,Luxury vehicle,Performance car\ntest/4482fc0aacd76bce,Plant stem,Close-up\ntest/44835f984c3e96f4,Monoplane\ntest/4483bbdd3241f11a,Cookies and crackers\ntest/44892588d22ff775,Water polo,Ball game\ntest/448a3b6f36dcc893,Performance car,Luxury vehicle,Antique car\ntest/448c7db1af3c82cf,Nest box\ntest/448c884853bde0f4,Brochette,Yakitori\ntest/448ea2e5b2f79228,Monoplane,Aircraft engine\ntest/4491c573d64e2179,Plant stem,Close-up\ntest/449214ea0eccb9c1,Brisket,Beef tenderloin\ntest/4493c8397d6a636b,Extreme sport,Wind wave,Boating\ntest/44983b5d8ce57023,Brochette\ntest/449a04a76c7da4d7,Sport utility vehicle\ntest/449a111aec4637e0,Cookies and crackers\ntest/449ab524ebf56e19,Luxury vehicle,Performance car\ntest/449ae8dbb0130f31,Close-up,Whiskers\ntest/449d4dc27edec0f3,Breadboard\ntest/449ebec139eaafa0,Luxury vehicle,Performance car\ntest/449f33208665f2ab,Compact car\ntest/44a2789dc372263e,Extreme sport,Boating,Wind wave\ntest/44a33020a77a41e1,Close-up\ntest/44a33a54937f3fd6,Brisket,Pork chop\ntest/44a3e79bcd82150e,Combat sport\ntest/44a3f0e77ef474fa,Close-up\ntest/44a43759466ab8cf,Shallot\ntest/44a4d009057a3d6d,Close-up,Plant stem\ntest/44a4eed65a52d6d6,Close-up\ntest/44a5f3a2b936ab9b,Water polo,Ball game,Team sport\ntest/44a62c4b4f62911b,Aircraft engine\ntest/44a87667dff41919,Performance car\ntest/44a8ad50433192f7,Comics\ntest/44a91451e35ca541,Modern dance\ntest/44a9991210c2e2e0,Extreme sport\ntest/44abefe145f32ad9,Compact car\ntest/44ac890f6e71201b,Jeans\ntest/44aecbff4dc28e2c,Plant stem\ntest/44af6964922188bb,Close-up\ntest/44affe539b13d6d3,Middle ages\ntest/44b1676d0b6105ed,Scaled reptile\ntest/44b1d9ea53911b80,Team sport\ntest/44b223d56f487107,Coral reef fish\ntest/44b3ffee10c9098a,Arthropod,Close-up\ntest/44b5f7da770971e6,Close-up\ntest/44b60609231a929b,Bird of prey\ntest/44b9f1a12d49492c,Nordic skiing\ntest/44bb5b7fbcdf7bf5,Arthropod,Close-up\ntest/44bb759ad2c9e3f9,Luxury vehicle,Antique car\ntest/44bc92599fe92c44,Luxury vehicle\ntest/44bcc2b0c8baca6d,Luxury vehicle,Performance car\ntest/44bd6b397ac17d23,Auto racing\ntest/44bd6ef72c1c8c29,Close-up\ntest/44bec510ae6e5f38,Jeans\ntest/44bfdd700993cdef,Headphones\ntest/44c01ba03899c33b,Blond,Hair coloring\ntest/44c07ac38db30880,Plant stem\ntest/44c1be6c12629828,Arthropod,Close-up\ntest/44c21b93dccc235a,Aircraft engine\ntest/44c7cc94f9e94cfc,Plant stem,Close-up\ntest/44c849389dd74798,Close-up\ntest/44c873d4bc7c43a5,Black-and-white\ntest/44c8e15a43652bd5,Ball game\ntest/44c95e801b1e6506,Computer speaker\ntest/44cd1596bc7e5137,Luxury vehicle,Antique car\ntest/44cf79388419ae37,Antique car\ntest/44d04767a05c15ee,Flatbread\ntest/44d04ee305388dc8,Black-and-white\ntest/44d1bbc771002894,Luxury vehicle,Antique car\ntest/44d3082b4a1481a1,Amphibian\ntest/44d3c378cfc80d7d,Extreme sport\ntest/44d67929e68587b9,Luxury vehicle,Antique car\ntest/44d6c8275ffec7b8,Ale\ntest/44d9fc01a61739f0,Luxury vehicle,Performance car\ntest/44da855f8db7d75f,Ball game,Team sport\ntest/44db168654f96bf7,Sport utility vehicle\ntest/44de04e6fc334650,Luxury vehicle,Performance car\ntest/44ded67ca695b0a7,Electronic instrument,Chordophone\ntest/44e0c942620c3651,Black-and-white\ntest/44e1ea37f834249b,Frozen yogurt\ntest/44e25cbd9f1277c9,Floristry\ntest/44e30de74ada07a4,Bird of prey\ntest/44e56731abf54604,Compact car,Antique car\ntest/44e56eb925aa9dca,Fried food\ntest/44e64af23fff6617,Luxury vehicle,Performance car\ntest/44e8e2cc400050a9,Amphibian,Close-up\ntest/44eaa4c39b59facd,Frying,Fried food\ntest/44ecfef3b31b50a8,Jeans\ntest/44ed0e944eaf9a63,Extreme sport,Divemaster,Scuba diving,Underwater diving\ntest/44f0c729c0bfb0b3,Halter\ntest/44f152cdde75981c,Close-up\ntest/44f27f3fc2b29430,Luxury vehicle,Performance car\ntest/44f2bb83bc2947e7,Combat sport\ntest/44f83da93c3cb937,Arthropod\ntest/44f93cdab0781dd3,Close-up\ntest/44f98f1bbe82960e,Luxury vehicle\ntest/44f9e6fc3d0aeeb3,Compact car,Antique car\ntest/44fa908e157ea75c,Performance car\ntest/44fbc46a57311ee9,Combat sport\ntest/44fecb44945373b6,Jeans\ntest/44ff0c0e2a05c5b6,Lawn game\ntest/4502ca73ce142152,Wind wave\ntest/4503e905b9ec5113,Luxury vehicle\ntest/4506fd655fa2b914,Chordophone,Electric guitar\ntest/4507b5ed7f555e55,Antique car\ntest/4508d7aecded627b,Close-up\ntest/450ae5edfe49131a,Aircraft engine\ntest/450ea951ffb51051,Antique car,Luxury vehicle,Performance car\ntest/450f117e5a38e8ad,Luxury vehicle,Antique car\ntest/450f18ba8939e75d,Close-up,Whiskers\ntest/450fc4e27a15811a,Performance car\ntest/451110d3872396bb,Chordophone\ntest/45117780de31459a,Coca-cola\ntest/4511e24e009459df,Combat sport\ntest/45124b4c48d444a7,Lumpia\ntest/4512676e2c6d07be,Luxury vehicle,Antique car\ntest/45138cd0be46c9ef,Blond,Black-and-white\ntest/4514606a8984cf3d,Luxury vehicle\ntest/45148ea7896032e0,Rural area\ntest/4515e64fdf064b0e,Roman temple\ntest/45160b06977adcc8,Arthropod,Close-up\ntest/4516ce473238977e,Fried noodles\ntest/45171b72009719f7,Performance car,Luxury vehicle,Antique car\ntest/45181872bca9b32e,Luxury vehicle\ntest/45187d19af958c77,Musical theatre\ntest/4524a11f7c28d616,Close-up\ntest/45257c01ac658a16,Jeans\ntest/45267af16408f487,Arthropod,Close-up\ntest/452afc2650eb6731,Sport utility vehicle\ntest/452f1b252009e890,Aerial photography\ntest/4530f176df97f115,Close-up\ntest/4533af379719a989,Plant stem,Close-up\ntest/4534e0a6f9170ad2,Briefs\ntest/45361ea05eaf2d42,Close-up\ntest/4537a77e99caf82c,Motorcycling\ntest/453aec4dd98bbe53,Outdoor shoe\ntest/453c0d84e43d36ec,Military vehicle\ntest/453eb7cc601b91a0,Arthropod,Close-up\ntest/453f086030188352,Compact car\ntest/4542df480df863cf,Melee weapon,Hunting knife\ntest/454a34e538bc81fe,Compact car,Luxury vehicle\ntest/454b598468f57b34,Horse racing,Equestrianism\ntest/454b63eed80cdea5,Goats\ntest/454c277e7caff876,Close-up\ntest/454f2de5fc4ae34e,Forklift truck\ntest/45501d76a62df6f0,Black-and-white\ntest/45511cf107227568,Motorcycling\ntest/4551449356e928d5,Bovine\ntest/4552c00558dc1bb8,Close-up\ntest/4553739901ab8904,Close-up\ntest/4554471bb782a3db,Luxury vehicle\ntest/4554e16ec7a11af6,Black-and-white,Figure skating\ntest/4555976244d0a406,Extreme sport,Parachuting\ntest/4555b8698ca652f5,Extreme sport\ntest/4559be6e8da45106,Melee weapon,Hunting knife\ntest/455d0f161942ceab,High-speed rail\ntest/455e3bf252fa2172,Steamed rice\ntest/455f101eb08d474e,Extreme sport,Parachuting\ntest/4561177c54896898,Sport utility vehicle\ntest/4562af34b4c767cd,Compact car,Antique car\ntest/45635371183c1483,Luxury vehicle,Performance car\ntest/4563895bd04fc864,Double-decker bus\ntest/456554233f51cb14,Headphones\ntest/4565d412fbe5d4b5,Lifebuoy\ntest/45666fd0660a65de,Pasta salad\ntest/456bd061d07ceb9b,Military vehicle\ntest/456d9e5aa1f2c05e,Vegetarian food\ntest/456ee8a2bad82451,Musical theatre\ntest/456f0aa216ccebfa,Arthropod,Close-up\ntest/4571c8f151a2c15c,Antique car\ntest/457238ccd0967597,Orator\ntest/45740729e7feda6e,Close-up,Plant stem\ntest/4574572a6f0bb9e7,Plant stem\ntest/4576bfd8bd506dcb,Portrait photography\ntest/4579a53ad940399d,Ball game\ntest/457adee3ee9d6717,Go-kart\ntest/457e99581a3ae273,Close-up\ntest/457ea730c9ba5ee0,Luxury vehicle,Antique car\ntest/457ef0f2cdd249d6,Compact car\ntest/457ff1b17f74721a,Close-up\ntest/45813f77c657e328,Whiskers\ntest/4581f50d0f50da0a,Close-up\ntest/45881b752041627c,Whiskers\ntest/45898f68167eac67,Filling station\ntest/458a971cb3886ffe,Rural area\ntest/458e37892cbaa264,Blond,Hair coloring\ntest/4590b9aa33c0f9f7,Luxury vehicle,Sport utility vehicle\ntest/4592779415ef37ff,Prosciutto\ntest/459282a09f4277de,Sport utility vehicle\ntest/4592a320b99460e2,Combat sport\ntest/4595690ed2ade823,Dishware\ntest/4599cc4eb8568c82,Molluscs,Close-up\ntest/4599cc6214542924,Digital camera,Close-up\ntest/459cfce95550e228,Luxury vehicle\ntest/459e970b89b34794,Performance car\ntest/459ef29b78c4ca51,Freight transport\ntest/459f93453f7fbdca,Rural area\ntest/459f9c770b308d5a,Compact car,Luxury vehicle,Antique car\ntest/459fda51d8fc8b45,Jeans\ntest/45a01956eab7f0e9,Aircraft engine\ntest/45a0c499c7bfae2b,Compact car,Luxury vehicle,Antique car\ntest/45a0ef7f05d48cff,Ball game\ntest/45a23d6b28c34952,Close-up\ntest/45a3264050202ffa,Luxury vehicle\ntest/45a44a569447ce63,Miniature Poodle\ntest/45a6de0e41f1f5b2,Close-up\ntest/45a8fd8fbdb5be00,Firearm\ntest/45af201edc760367,Hound\ntest/45afbaca1e58504f,Goats\ntest/45b010308fe76a9f,Digital camera\ntest/45b01babe03a377f,Sport utility vehicle\ntest/45b3e44ce43ba6a6,Plant stem\ntest/45b3eb40d95f2e81,Herding,Goats\ntest/45b40a4a709481d0,Aircraft engine\ntest/45b60633513a63c5,Auto racing\ntest/45b82b457a673e3f,Glacial landform\ntest/45b88fb978d91d15,Bovine\ntest/45bbd04a049ddc75,Angling\ntest/45bd81b7b7741f4f,Frying,Fried food\ntest/45be485654e7c067,Equitation,Equestrianism\ntest/45be68c7d8e7b2b0,Jeans\ntest/45bf08566775da14,Antique car\ntest/45bf6f80b9b1ed9b,Chordophone\ntest/45c042ef2ab5c2a0,Close-up,Plant stem\ntest/45c383db65bca523,Fried food\ntest/45c6bf665bca9785,Residential area\ntest/45c6e1533a017b4e,Luxury vehicle,Performance car\ntest/45c7b17f2ababd78,Pork chop\ntest/45c82a03e7ab0ac3,Combat sport\ntest/45ca54f41ca3b8ba,Close-up,Plant stem\ntest/45cb1ca0d978fd3d,Machine tool\ntest/45cce0004bfa5715,Luxury vehicle,Performance car\ntest/45cce86945d92ed3,Antique car\ntest/45cdb780d849d2f7,Bottled water\ntest/45cfa9a3868b78e3,Rural area,Canola\ntest/45cffce95a39b7e5,Antique car\ntest/45d23ceefb7f0c5f,Close-up\ntest/45d680d2a9cece72,Luxury vehicle,Performance car\ntest/45d9da0e81d28415,Close-up\ntest/45dac57ae165662f,Frigate\ntest/45dd9677f65f50e8,Drums\ntest/45de9c962daf58e1,Cookies and crackers\ntest/45dffac623f1a4aa,Digital camera,Camera operator\ntest/45e08a7e52203ffa,Arthropod,Close-up\ntest/45e163d5a4585b38,Team sport\ntest/45e3ef238ecca2cc,Extreme sport,Divemaster,Scuba diving,Underwater diving\ntest/45e65a9749db7088,Luxury vehicle,Performance car\ntest/45e9821dbce1b858,Zeppelin\ntest/45ee7254fadab4f6,Luxury vehicle\ntest/45efc27be26308ba,Black-and-white\ntest/45f0e2517d61f757,Crocodilia\ntest/45f3e7055d621ae2,Luxury vehicle\ntest/45f53f4d96c0f25f,Close-up\ntest/45f6577596021b1f,Luxury vehicle,Antique car\ntest/45f8e2605dd60afa,Vegetarian food,Fried food\ntest/45fee17a8207077d,Luxury vehicle,Performance car\ntest/45ffbf2b7b9c395c,Outdoor shoe\ntest/45ffd709049c4717,Blond,Hair coloring\ntest/460014764aa3f3a3,Close-up\ntest/4601370dc4383352,Boating\ntest/4601bbed913e54b2,Extreme sport,Boating\ntest/4605bf01dde43943,Boating\ntest/460638456eca25e8,Gumbo\ntest/4608a1f0dc0b4d8b,Ball game\ntest/46093027e86c2982,Black-and-white\ntest/460a97a1742f99f4,Arthropod,Close-up\ntest/460d3eaa5990832f,Aircraft engine\ntest/460d6950d7a8f0a4,Trampolining\ntest/460d8444f9427ed0,Cookies and crackers\ntest/460e918ab3171b5c,Jeans\ntest/460ff52af21a2fef,Coral reef fish\ntest/46140a0838f563bf,Compact car,Luxury vehicle\ntest/461721567b02ecb0,Arthropod,Close-up\ntest/461848d365335ecf,Jeans\ntest/46274eaf67e67370,Antique car,Performance car\ntest/4627d1feeeb3856d,Heavy cruiser\ntest/4629260894773ba2,Firearm\ntest/462aa20e35f9252d,Close-up\ntest/462d550f6f68d453,Jeans\ntest/462fa024f3147723,Falafel,Fried food\ntest/46304a006a41bb13,Auto racing\ntest/463214e11bee0e8c,Domestic short-haired cat,Whiskers\ntest/46322858099cda96,Equitation,Equestrianism\ntest/463527290b383ef3,Cookies and crackers\ntest/4635f3e2bdf60603,Close-up,Chordophone\ntest/46387e3927c53a78,Compact car,Antique car\ntest/463885dc5090c6a7,Equestrianism\ntest/46389cbd82558fcc,Molluscs\ntest/4639fbbc1aa3d9cf,Auto racing,Touring car\ntest/463c44a928a72079,Close-up\ntest/463dc8c49938cf61,Luxury vehicle\ntest/463ddcf1e24a6273,Siberian husky\ntest/46402d9ffda2af96,Blond,Portrait photography,Close-up\ntest/46407bbda2dbeb12,Sport utility vehicle\ntest/4641664a48cad826,Black-and-white\ntest/4641b14a68c0b01b,Close-up\ntest/46432051f711fec0,Microcontroller\ntest/4644ec0d9408de24,Holding hands,Close-up\ntest/464783af54e49099,Compact car,Antique car\ntest/464f3565913121a9,Black-and-white\ntest/464f960f32835403,Whiskers\ntest/465272244811c325,Sport utility vehicle\ntest/46539e2fbb5178f0,Luxury vehicle\ntest/465438dbf0e27ea6,Fried food\ntest/4656464d55472d7f,Combat sport,Grappling\ntest/46568356dc857723,Military person\ntest/4656b09ef4b8451b,Portrait photography,Close-up\ntest/4657a4bc8356c65e,Luxury vehicle\ntest/4657cb72c5292ad4,Fire apparatus\ntest/4657cbe9230bccb8,Close-up\ntest/465a9b45a5a1dbef,Close-up\ntest/465e10af9b6e3ef5,Ball game,Team sport\ntest/465edb6bb8873411,Performance car\ntest/465ee3331a0eaf6c,Luxury vehicle,Antique car\ntest/465f0dd80b8b9da7,Close-up,Whiskers\ntest/4661efb44b8490cf,Portrait photography\ntest/46647b78ec49c8ad,Vegetarian food\ntest/4667ff3280d539a2,Sport utility vehicle\ntest/466819eb9b751aa5,Luxury vehicle,Performance car\ntest/46688742d32bdfe3,Close-up,Whiskers,Domestic rabbit\ntest/4669ecf5627622a2,Luxury vehicle,Performance car\ntest/466cf5f99af92100,Sport utility vehicle\ntest/466d28e6ef43ae07,Junk food\ntest/466d42bdb18830ee,Bovine\ntest/466e41c35e268820,Domestic short-haired cat,Whiskers\ntest/4670be426bdfb903,Fried food\ntest/4672c7bdbde4d670,Close-up\ntest/46737af5c34b29a2,Cookies and crackers\ntest/4673df09ad329371,Luxury vehicle,Performance car\ntest/4673ecd77f4543dc,Close-up\ntest/467419f740793f1d,Residential area\ntest/467453770f2b9226,Boating\ntest/4675bb2f1b4f88a9,Luxury vehicle,Antique car\ntest/467622d96075358c,Blond,Hair coloring\ntest/467a01982b7fb831,Close-up\ntest/467b84cba18ecc4b,Road cycling\ntest/467cbb6ae8c07462,Jeans\ntest/467d6ad5d32bf003,Fried food\ntest/467ee12038be2626,Arthropod,Locust\ntest/467fb4bbd613e5de,Backlighting,Black-and-white\ntest/46800f411123504c,Luxury vehicle\ntest/46807d6c696a8db9,Aerial photography\ntest/468091a307b3028d,Firearm\ntest/4686428862a78156,Close-up\ntest/4688dd03238f2ae9,Luxury vehicle,Antique car\ntest/46891cbc6c8a3c3a,Coral reef fish\ntest/46893b102fec3afc,Black-and-white\ntest/468a520c2133fd54,Luxury vehicle\ntest/468b9f5a3fa41b5f,Floristry\ntest/468c4c8cd63650f7,Compact car,Antique car\ntest/468d986346308a8c,Black-and-white\ntest/468f202ee45fba31,Close-up\ntest/4690b67e2a062f85,Luxury vehicle,Antique car\ntest/4690d1ba421b36f9,Middle ages\ntest/46949d097e0762a0,Equestrianism\ntest/4694cf3e625e0328,Combat sport\ntest/46984ac611f7d1ed,Digital camera\ntest/46994c17472e20e8,Plant stem,Close-up\ntest/469a260d52786ae7,Luxury vehicle,Performance car\ntest/469afc0da2077b9b,Chordophone,Electric guitar\ntest/469cbc243247f59d,Luxury vehicle\ntest/469cf4ddd3449c68,Black-and-white,Angling\ntest/469d42fb467bef46,Blond,Close-up\ntest/46a0ec9903adf8ef,Microcontroller\ntest/46a1d95bdde6cbf4,Domestic short-haired cat,Whiskers\ntest/46a1e6d61ffc8fa2,Compact car,Luxury vehicle,Antique car\ntest/46a242756bb7e1a6,Ball game,Team sport\ntest/46a433068f7aa763,Close-up\ntest/46a46cdc0f5b2359,Boating,Rowing\ntest/46a71f18a52d8857,Black-and-white\ntest/46a739a7d6d7c7e1,Hound\ntest/46a74a1b5bc0c0f7,Ball game,Team sport\ntest/46ada412736c65f9,Stemware\ntest/46b16f9ba0d449ec,Arthropod,Locust,Close-up\ntest/46b26e90cb5ed895,Ball game,Team sport\ntest/46b3c0f1e449dd39,Sledding\ntest/46b7d35aab0f81f6,Solar energy\ntest/46bad8dbea057675,Sport utility vehicle\ntest/46be742f03739846,Close-up\ntest/46bf2383f30a369d,Compact car\ntest/46bfcb40966db21a,Fried food\ntest/46c07b7a7e0dd19b,Jeans,Antique car\ntest/46c20fd487b25a4e,Luxury vehicle\ntest/46c2b8444dc99ecd,Portrait photography\ntest/46c58854468171af,Electric piano\ntest/46c745b62fc102e0,Close-up\ntest/46cc02e19877e491,Luxury vehicle,Antique car\ntest/46cc22b5436014cc,Ramen\ntest/46cd2e8468293902,Team sport\ntest/46cd4233a80976fd,Vegetarian food\ntest/46ce5238f487199d,Billiard room,Billiards\ntest/46d0612f04ebc800,Glacial landform\ntest/46d19e3449aad4bb,Jeans\ntest/46d1d26e2f113a4f,Compact car,Sport utility vehicle\ntest/46d2ff895db778c9,Luxury vehicle,Performance car\ntest/46d7634732e06065,Perching bird,Close-up\ntest/46d91202d0431329,Firearm\ntest/46da5418a428b8fa,Ball game,Team sport\ntest/46dab3d03db3a847,Motorcycling\ntest/46de9b4236def57e,Close-up\ntest/46e14e6dc320ffc2,Close-up\ntest/46e22d56deeb5ec8,Black-and-white\ntest/46e35958132d1f99,Compact car,Luxury vehicle,Antique car\ntest/46e3dfd73c4ff58c,Combat sport\ntest/46e6632ee11c08b3,Junk food,Fried food\ntest/46e67fe678ab7e6e,Close-up\ntest/46e7164ec9798540,Luxury vehicle,Sport utility vehicle\ntest/46e99093b5a6822b,Scaled reptile\ntest/46eb26549959fd9c,Compact car,Sport utility vehicle\ntest/46ec5daffc4a7c2d,Close-up\ntest/46ec9d030563ce2f,Luxury vehicle\ntest/46edcb3fe6d7b784,Khinkali,Jiaozi,Pelmeni\ntest/46efe326d812a325,Blond\ntest/46f1b84bcc47e61f,Blond\ntest/46f5bfac4cfcf958,Antique car\ntest/46f67cc594fa8566,Antique car\ntest/46f6e6806c6905c2,Close-up\ntest/46f8b65056c88f5b,Auto racing\ntest/46fb4ca5831511c4,Miniature Poodle\ntest/46fb4f1e1692935a,Equestrianism\ntest/46fc50890b21bb87,Cosmetics\ntest/46ff159ae0b34796,Horse and buggy\ntest/46ffbadf7a80fdf1,Luxury vehicle,Antique car\ntest/4701e0acb21f2e68,Barechested\ntest/4702863713307bc1,Close-up\ntest/47051f71515a8827,Jeans\ntest/4706e81ec1be7fc3,Luxury vehicle,Sport utility vehicle\ntest/470987d12b050b74,Auto racing\ntest/470b298d96d8ba4e,Close-up\ntest/470daaa9e75ba977,Vegetarian food,Junk food\ntest/470ed9c49466aa58,Team sport\ntest/47109ad871f512c1,Ball game,Team sport\ntest/47109ec0e47bc063,Shallot\ntest/4710b2de9d1b7e37,Domestic short-haired cat,Whiskers\ntest/4710db6f6459a131,Bouldering\ntest/471337eb334dd3d0,Bovine\ntest/4715549145a2eaff,Luxury vehicle,Performance car\ntest/47157907eb29a2d0,Auto racing\ntest/4716e1893496f0ff,Close-up\ntest/4717278bb7a2bc02,Luxury vehicle\ntest/4717743ce4fb412f,Close-up\ntest/471b1a51218844fb,Sport utility vehicle\ntest/471daa72508767ce,Whiskers\ntest/471eaf661376dcbb,Compact car,Luxury vehicle\ntest/47207099c4513c70,Antique car\ntest/47238a555fed5f8d,Outdoor shoe\ntest/4726ada1fc262312,Performance car\ntest/472cd243603c3162,Outdoor shoe\ntest/472da2ccded1899a,Antique car,Luxury vehicle,Performance car\ntest/472e4f8c1a593d78,Extreme sport,Parachuting\ntest/472f07013fe1fb53,Whiskers\ntest/472f59e9593a8c7b,Falafel,Fried food\ntest/472f851b97f8e1af,Performance car,Luxury vehicle,Antique car\ntest/473015509db3da2c,Luxury vehicle\ntest/47316e9c89960236,Performance car\ntest/47325af402d88c6a,Luxury vehicle,Performance car\ntest/4735e67f4ee5b8d1,Miniature Poodle\ntest/47369e7a1cf0b099,Bird of prey\ntest/4737ae093ce27c3a,Arthropod,Close-up\ntest/47393d20b383539c,Extreme sport,Bouldering,Sport climbing\ntest/4739fd443eee213e,Sport utility vehicle\ntest/473d09bdea5aceac,Combat sport\ntest/473d9ce022b1f23e,Antique car\ntest/473f886a4981c9ec,Domestic short-haired cat,Whiskers\ntest/47412831355f7cf2,Close-up\ntest/47419aa0a4893ddf,Ball game,Team sport\ntest/4742446d06df30b7,Senior citizen\ntest/474657174aa4e08d,Extreme sport\ntest/4747015237a8fc61,Electronic instrument\ntest/4747764231770264,Luxury vehicle\ntest/4748023c4a6115e1,Luxury vehicle,Antique car\ntest/4748309f11936b8d,Auto racing\ntest/47497b996e58639d,Black-and-white,Close-up\ntest/474a08d794fafe8d,Horse racing\ntest/474a9e3822b79d90,Sport utility vehicle\ntest/474b8a3834aabee9,Luxury vehicle\ntest/474b8d6588cc3bdc,Luxury vehicle\ntest/474eeca95b61a700,Extreme sport,Kitesurfing\ntest/4751f5c22036d4ff,Blond\ntest/47531b483faf2db4,Close-up\ntest/475363d16d70a270,Motorcycling\ntest/47559f521a8910fa,Amphibian\ntest/4756fee0fea57693,Floristry\ntest/47577d3f4cf304d9,Auto racing\ntest/4757aebe700d4238,Ball game,Team sport\ntest/4758c40917249124,Close-up\ntest/475bc6723ef32569,Combat sport\ntest/475de07c59d35a04,Luxury vehicle\ntest/4760d47dbcd19989,Bird of prey\ntest/4761c839f37f8b41,Close-up\ntest/47621123da93e746,Extreme sport\ntest/476265a736c553c1,Compact car,Luxury vehicle,Antique car\ntest/4762e92d599d9840,Luxury vehicle,Performance car\ntest/476387299cd19cde,Black-and-white\ntest/47642b86abdce83e,Portrait photography,Close-up\ntest/4765a1e683e4665a,Compact car\ntest/4765ca9c5ab26b14,Domestic short-haired cat,Whiskers\ntest/47678024f63ea7fd,Equitation,Equestrianism\ntest/4768574374ff9bc7,Luxury vehicle,Antique car\ntest/47688aaf2248d5af,Ball game\ntest/476894070ca68a7f,Sport utility vehicle\ntest/476c645f60e3f600,Compact car,Luxury vehicle\ntest/476c9d44c7e17ded,Luxury vehicle\ntest/476ef3cae9ced21c,Luxury vehicle,Performance car\ntest/47704aa13542c625,Plant stem\ntest/4770e9bc3023065a,Domestic short-haired cat,Whiskers\ntest/477108e964a03f61,Close-up\ntest/47717f797f478601,Musical theatre\ntest/4772f99fab32b7ae,Arthropod,Close-up\ntest/47778a964acbb683,Luxury vehicle\ntest/47799e11052301dc,Ball game,Team sport\ntest/477d13c28ece4366,Antique car\ntest/477e1a8b50437bf6,Freight transport\ntest/477ff908049a5951,Jeans\ntest/4781cbbd8dd0f5b7,Whiskers\ntest/4782c8b3c691979f,Close-up\ntest/4784a8e4665ffc70,Whiskers\ntest/4784ab333a28402a,Luxury vehicle,Performance car\ntest/47856da5a73dfbd7,Domestic short-haired cat,Close-up,Whiskers\ntest/478a2390aac92520,Brochette,Yakitori\ntest/478a6fcaf1a1266b,Performance car\ntest/478d29361fd7af82,Ball game\ntest/478e06c587fd4416,Filling station\ntest/478f13fa608b3cb8,Luxury vehicle\ntest/478fbd26e27a76e9,Road cycling\ntest/478fe0d13760fdd9,Watercolor paint\ntest/47903a4f9989d18d,Compact car,Luxury vehicle,Antique car\ntest/4790738c0a5c7826,Luxury vehicle\ntest/47917252e6db3e4d,Boating\ntest/47926af6ee60d14e,Luxury vehicle,Performance car\ntest/47935c73d1275e51,Antique car\ntest/4794d79f00e51054,Machine tool\ntest/47955a1c7354efdd,Jeans\ntest/479f47d8b3601917,Extreme sport,Gliding\ntest/47a00d45f1b1a4fe,Blond\ntest/47a01ae352237ed4,Whiskers\ntest/47a038791f9f97fa,Close-up\ntest/47a256c18ae5102d,Motorcycling\ntest/47a33be3b08a3f86,Luxury vehicle,Sport utility vehicle\ntest/47a7fe9abcb5108b,Jeans\ntest/47a963e34c20fe54,Plant stem\ntest/47ac6209de897aa0,Boating\ntest/47b0cb359162e593,Luxury vehicle\ntest/47b32d9b609933af,Black-and-white\ntest/47b4288c1e5a6bcf,Close-up,Scaled reptile\ntest/47b58fc3a23e1bd0,Black-and-white\ntest/47b5d5ac38d3c714,Calabaza\ntest/47b79f59739ba5f8,Compact car,Antique car\ntest/47b81ed691434c8d,Sport utility vehicle\ntest/47bbed337bd25d50,Childbirth\ntest/47bf14d20a0ea25f,Residential area\ntest/47bf2db76af1636f,Sport utility vehicle\ntest/47c041b5eb94eeaa,Rear-view mirror\ntest/47c138441c6f3d30,Chordophone\ntest/47c3961d00123424,Auto racing,Luxury vehicle\ntest/47c3c79be6aa0974,Combat sport\ntest/47c72fd629dc64df,Wind wave\ntest/47c7d0b08a8dd1e5,Compact car,Antique car\ntest/47cb7a06d3376787,Floristry\ntest/47cc64fa6001bb9d,Digital camera\ntest/47cd7fa3ef6f249e,Arthropod,Close-up\ntest/47ce058b5832818c,Close-up\ntest/47ce8740473b9954,Luxury vehicle\ntest/47d1979c30930ea7,Antique car\ntest/47d2055558824d32,Black-and-white\ntest/47d78b102913256a,Whiskers\ntest/47d8fa77467d7adf,Luxury vehicle,Antique car\ntest/47ddfaeb3743d50f,Antique car\ntest/47de8830a9ae56be,Auto racing\ntest/47df0141c648afbb,Jeans\ntest/47e21d3b62fafdc8,Aircraft engine\ntest/47e38a7ad17203bd,Flatbread\ntest/47e3c30f7ba83dc9,Black-and-white\ntest/47e754c6403bd7ae,Machine tool\ntest/47efca82ee7fd54a,Calabaza\ntest/47f039a640786099,Aircraft engine\ntest/47f118de6a3212fb,Nile crocodile,Crocodilia\ntest/47f1b562a8034895,Close-up\ntest/47f2df417e10dd7f,Bovine,Close-up\ntest/47f2e1f1cdc193c1,Luxury vehicle\ntest/47f6905bae2806ce,Perching bird,Close-up\ntest/47f6fe4d88ab19ff,Vegetarian food\ntest/47f7e57d39903960,Close-up\ntest/47f94bdd50a4d2f1,Arthropod,Close-up\ntest/47fbb2075e5635f6,Auto racing,Compact car,Antique car\ntest/47fc6c8cfce93d95,Hound\ntest/47fee212dd8ffe3c,Compact car,Luxury vehicle\ntest/48003ae00789cd38,Compact car,Antique car\ntest/4802baa6b23d1970,Close-up\ntest/480303b40066fa5c,Luxury vehicle\ntest/4804ba9ffef64899,Boating,Angling\ntest/4804dc22d641f8ef,Vegetarian food\ntest/4807e90086dee505,Scaled reptile\ntest/480b3d4d16ee46b6,Luxury vehicle,Performance car\ntest/480e350f7f83c1c0,Machine tool\ntest/480f3a46b5d118ac,Freight transport\ntest/48106431fc4dfb15,Compact car,Luxury vehicle,Antique car\ntest/48107c4d3526446f,Ball game\ntest/4812bbbfc003ebcb,Aerial photography\ntest/4812fea4cc8476bf,Close-up\ntest/48172407df7b3faa,Ball game,Team sport\ntest/48176f6c78eca231,Monoplane,Aircraft engine\ntest/48181f06fe7c3b81,Fried noodles\ntest/48194256874de07a,Extreme sport\ntest/482207a0894e69db,Compact car\ntest/4822428345fa33ef,Luxury vehicle\ntest/4822f0fd3c084654,Blond,Hair coloring\ntest/4824d933293bec58,Arthropod,Close-up\ntest/4825fedac89b9861,Black-and-white\ntest/482740a9d7277773,Narcissus\ntest/4827fc19d878e9e4,Nordic skiing\ntest/48281a6a3eddd841,Luxury vehicle,Performance car\ntest/48298fc955eaba7c,Plant stem,Close-up\ntest/482c1b62496261cb,Floristry\ntest/4830624c5c75e1b4,Flatbread\ntest/4833f0dac61ca2eb,Luxury vehicle\ntest/4834f70c6f17259d,Luxury vehicle\ntest/483b6edf21c3ae6b,Pit bull\ntest/483c2b237cebbbe4,Cookies and crackers\ntest/483f23ab11ae24d6,Performance car\ntest/484115b41108da9b,Middle ages\ntest/484232a8c5b36478,Electronic instrument,Drums\ntest/4843c1f67be67f28,Horse racing,Equestrianism\ntest/48471dc0bad9b6b4,Compact car\ntest/4848c65c78a35040,Luxury vehicle\ntest/4848f2bdcdd4db06,Compact car\ntest/4849a8a866eb0285,Compact car,Luxury vehicle,Antique car\ntest/4850725ff04e6a83,Vegetarian food\ntest/48525dca6707e3db,Trampolining\ntest/4852eaafb0b97c25,Ball game,Team sport\ntest/48541b191c5b0a33,Rear-view mirror\ntest/485464ceb04704a7,Antique car,Performance car\ntest/48548b6f6c256aa8,Antique car\ntest/4857f30971bcf20b,Close-up\ntest/485962746d2bc271,Barbecue chicken\ntest/485ca1d986c7e12d,Blond\ntest/485f31a69d63734c,Musical theatre\ntest/48622a8a719379f1,Compact car,Antique car\ntest/4862630a9d215c7b,Ball game\ntest/486608035d7b83e8,Canola\ntest/48664363f392237a,Black-and-white,Close-up,Chordophone\ntest/4867831ae0228f7c,Ball game,Team sport\ntest/486d354509b56502,Holding hands,Close-up\ntest/486d8dfd7e04c90f,Gumbo\ntest/486dfee175092ddd,Sport utility vehicle\ntest/486fe557d01d4d12,Boating\ntest/48713b96c96c14b0,Perching bird,Nest box\ntest/4871d4169c8f00f4,Compact car\ntest/4872fc5dd42cda92,Plant stem,Close-up\ntest/487407aa95b9d8b3,Ball game\ntest/4879a9ab29967698,Cookies and crackers\ntest/487ce3edf1f9f5e6,Luxury vehicle,Sport utility vehicle\ntest/487d04094c0da025,Steamed rice\ntest/487d137e1bef43ab,Military person\ntest/487ff4bf773258ab,Jeans\ntest/4880a0b779c6ae13,Auto racing,Performance car\ntest/4882018bdf0acb27,Firearm\ntest/4882db1a7d20bbe3,Combat sport\ntest/48840c460bff6d9d,Ball game,Team sport\ntest/4888ed1dc71b0293,Luxury vehicle,Antique car\ntest/488924fdbe266aa7,Cosmetics\ntest/488a827680e7897a,Brochette\ntest/488aa8f88363ee14,Arthropod,Close-up\ntest/488bf392e2b6009f,Black-and-white\ntest/489544fa899c64a0,Close-up\ntest/489724347aa8cf60,Compact car,Antique car\ntest/4897cd12684d3898,Luxury vehicle\ntest/4898eb2a939f012d,Motorcycling\ntest/489a5f4749a1b849,Black-and-white\ntest/489bd55061cc5bc4,Compact car,Luxury vehicle,Antique car\ntest/489cadb96cee3d66,Black-and-white,Military person\ntest/489e1efa871debb4,Close-up,Whiskers\ntest/489ea5fd79ff75bd,Sport utility vehicle\ntest/48a2cbd7fd08eb0b,Jeans\ntest/48a450c3d4707cad,Close-up\ntest/48a5c7bbbd8c5e51,Black-and-white\ntest/48a5fcf45d76305c,Luxury vehicle,Antique car\ntest/48a9b97b45164c6a,Ball game,Team sport\ntest/48aa3098ae6c8fd7,Aircraft engine\ntest/48aa3895bdad3482,Aerial photography\ntest/48aad5d878afb644,Aerial photography\ntest/48acf954097905f6,Performance car\ntest/48ae0ba2c55f2b1c,Close-up,Scaled reptile\ntest/48b138f239921a5e,Vegetarian food\ntest/48b1dbd33bf0485d,Whiskers\ntest/48b20b614b5c5409,Black-and-white\ntest/48b226303839da84,Senior citizen\ntest/48b229b2940aec50,Motorcycling\ntest/48b2da0e429fa6f8,Outdoor shoe\ntest/48b65d74573eef74,Bird of prey\ntest/48b7e7c819bf6e40,Ball game\ntest/48ba1d20a3d27e48,Woodwind instrument\ntest/48bb77f664e0b836,Athletic shoe\ntest/48bb978cd3a34348,Fried food\ntest/48c1613f5a7fe876,Leaf vegetable\ntest/48c23f7419aed24d,Auto racing\ntest/48c3b99664fd97c9,Luxury vehicle\ntest/48c3ff421b757988,Black-and-white,Wind wave\ntest/48c75d855b2f4333,Jeans\ntest/48c9777c54939648,Arthropod,Close-up\ntest/48cd1b412ccdf54e,Black swan\ntest/48cd1c1a3ef60302,Close-up,Whiskers\ntest/48cd63fb535043dc,Boating\ntest/48ce3adaa18c8988,Jackfruit\ntest/48cf0a097ce330dc,Bovine\ntest/48cf36af4ceedaa6,Luxury vehicle\ntest/48cfced96d65d21d,Dishware\ntest/48cfd394b5db1ec7,Sport utility vehicle\ntest/48d1324355471f44,Close-up,Whiskers\ntest/48d55fbc0dee82f7,Arthropod,Locust,Close-up\ntest/48d65695796fcabc,Sport utility vehicle\ntest/48d6d688e5010246,Bovine\ntest/48d9a30fa3ee0d4d,Close-up\ntest/48d9b22755e588d9,Rural area,Canola\ntest/48da9025bee74e53,Vegetarian food\ntest/48dda9ba7abfa26a,Luxury vehicle\ntest/48e093f07c0442fe,Vegetarian food\ntest/48e12511ffc3e336,Fried food\ntest/48e1538af4ad037e,Auto racing\ntest/48e1af119f878356,Scaled reptile\ntest/48e654943fbc9701,Rural area\ntest/48e6e53f10f6d0c4,Chordophone,Electric guitar\ntest/48e864e07f504c8d,Machine tool\ntest/48ecdb0f5aefb1f5,Luxury vehicle\ntest/48ee859c9bde0035,Plant stem,Close-up\ntest/48efb260b96ec4ae,Luxury vehicle,Antique car\ntest/48f1452c11232b15,Equitation,Equestrianism\ntest/48f14f79a9686d8c,Rural area,Aerial photography\ntest/48f25208f8bb1178,Close-up\ntest/48f511a17ea6cd77,Leaf vegetable\ntest/48f569bba4b314d3,Luxury vehicle,Sport utility vehicle\ntest/48f7964c32223e89,Luxury vehicle\ntest/48f866fbadaedc43,Dishware\ntest/48f95912a4ea40b9,Plant stem,Close-up\ntest/48f999020662917a,Close-up\ntest/48fb251427817cd7,Hound\ntest/48fcb617cac1cef4,Perching bird\ntest/48fcf0680c8afd43,Close-up\ntest/48ff05f928f37cf4,Black-and-white\ntest/48ff5caa866ee09f,Vegetarian food\ntest/4900139d351d52c7,Black-and-white\ntest/4901bc0a2bdf07b9,Holding hands,Close-up\ntest/4901cbbac783a7b2,Ball game,Team sport\ntest/4903b0a0f1d1346d,Ball game\ntest/4906f418a67a8d87,Backlighting\ntest/49081a0c140a622f,Team sport\ntest/49093f71aba15d3e,Vegetarian food\ntest/490950e4c4a2f44b,Close-up\ntest/490b8f013f4dbe52,Black-and-white\ntest/490c4984cbdadad1,Rice ball\ntest/490e13625ac524c9,Close-up\ntest/490e48673a08f25a,Ball game,Team sport\ntest/49116665eb72e7ee,Rear-view mirror\ntest/4911b648374de077,Modern dance,Musical theatre\ntest/491215ae6ff5c751,Sport utility vehicle\ntest/4912d11081cc976f,Performance car\ntest/4914616bba9e434c,Monoplane\ntest/4914b7f912fce9b3,Close-up\ntest/4914d2e7f311bca2,Ball game\ntest/4917625f032af0b4,Close-up\ntest/4917fd4736839c9c,Auto racing\ntest/491d0c0e06f8e0ee,Steamed rice\ntest/491d3142d2c2ad8d,Shallot\ntest/49230c85add223fe,Fried food\ntest/4924a9470daa28c4,Ball game,Team sport\ntest/4924ca0259185302,Close-up,Plant stem\ntest/49258f1dfee28815,Boating\ntest/4926a51780be7c79,Luxury vehicle,Performance car\ntest/492abb9968cb4320,Vegetarian food,Fried food\ntest/492f280bc0ffe4e7,Luxury vehicle,Performance car\ntest/492f6d607aae58c1,Luxury vehicle\ntest/492f88481e95a110,Chordophone\ntest/493686469f22394f,Leaf vegetable\ntest/49386353fd3cde9a,Junk food\ntest/4938eb6cc04183a7,Compact car,Luxury vehicle,Antique car\ntest/493a47d484594d42,Extreme sport,Wind wave\ntest/493a4ed11e0ecf7e,Blond\ntest/493cf54cb5246dd1,Roller skating\ntest/493f6e0801992421,Microcontroller,Breadboard\ntest/493fc196e97038ee,Melee weapon\ntest/49403830a0e19d9b,Auto racing\ntest/49428f7b3d33f965,Vegetarian food,Corn on the cob\ntest/4942de20284610a3,Wind wave\ntest/4946625414db5390,Dog walking\ntest/494816d435a0e241,Vegetarian food\ntest/494976356ad8215d,Boating\ntest/494a40b0aff80999,Chordophone\ntest/494b3eb9d37be7de,Rural area\ntest/494c64d373b55ef2,Luxury vehicle,Performance car\ntest/494ec37a2b634e2d,Sport utility vehicle\ntest/494fef07a21e85f0,Khinkali,Jiaozi,Pelmeni\ntest/4952ab6b41b2816b,Scaled reptile\ntest/4954057273e459a6,Church bell\ntest/49562d44b9625dd8,Auto racing\ntest/49567f8a088fb7e7,Barechested\ntest/495806c10cd84cf0,Close-up\ntest/495a138222cfcb7d,Close-up\ntest/495a61feeb30f418,Boating\ntest/495a876d857dc370,Goats,Close-up\ntest/495b434b38a66129,Bovine\ntest/495bce11d526882b,Billiards\ntest/495d3bca74f3813e,Close-up\ntest/4960ea9eed6d10b8,Rural area\ntest/49654b70ef5869c1,Close-up\ntest/4965e5c0a8c6186b,Coral reef fish\ntest/496a490b3ad9bc83,Auto racing\ntest/496d781dab88ead8,Plant stem\ntest/496d823ddcea9d53,Electronic instrument,Chordophone\ntest/496dda72a44c9822,Auto racing,Performance car\ntest/496e308ec9fca105,Bird of prey\ntest/496ffec9d2da032a,Motorcycling\ntest/497073406802f51e,Close-up\ntest/4970c7258c43950d,Antique car\ntest/49729affa14eed80,Extreme sport,Parachuting\ntest/4972f2dc995d9370,Blond,Hair coloring\ntest/4973c7c5961f1d14,Sport utility vehicle\ntest/4974326cdb9f8ad0,Brochette,Yakitori\ntest/4974463de5f31e4c,Drums\ntest/497bb58a5e1c83a0,Steamed rice\ntest/497e65bcf825f555,Wind wave\ntest/497ec1f562917a9a,Luxury vehicle,Sport utility vehicle\ntest/4985fc247e2c095e,Vegetarian food,Junk food\ntest/498692c905144932,Luxury vehicle\ntest/498a59707bfbc955,Rural area\ntest/498ae254e1ce2c98,Close-up\ntest/498c952c65d71bd7,Ball game,Team sport\ntest/498d31593a10f9c7,Performance car\ntest/498ec7d07f110bec,Close-up\ntest/498fa686db60b4d7,Residential area,Aerial photography\ntest/499688a54b724ff2,Close-up\ntest/49974aee3e809e6f,Monoplane,Aircraft engine\ntest/4998b1ab69fd73f9,Black-and-white\ntest/4999686b0e420878,Glacial landform\ntest/4999b42740542f69,Ball game,Team sport\ntest/4999ef8069f6f0ac,Compact car\ntest/499a28065fc84ec3,Black-and-white,Horse and buggy\ntest/499abaaef6cca9e1,Amphibian,Close-up\ntest/499cb99590588935,Monoplane\ntest/499e98ebd2515f1d,Aircraft engine\ntest/49a1d8163e280eca,Compact car\ntest/49a606c648110825,Ball game,Team sport\ntest/49a6600ce34158bb,Ball game\ntest/49a8c0307447a951,Antique car\ntest/49ac86dccc0c6caa,Luxury vehicle,Sport utility vehicle\ntest/49ace425c0ae487c,Aircraft engine\ntest/49aefaffd97e345f,Black swan\ntest/49b19721abeccc5e,Luxury vehicle\ntest/49b5655d76b69134,Black-and-white\ntest/49b633e93305ab9c,Black-and-white\ntest/49b7a8eb562f01a0,Antique car\ntest/49b85a3fe1cbc4d4,Compact car\ntest/49b8f21eea318e66,Portrait photography\ntest/49b8f40d3cc801ad,Domestic short-haired cat,Whiskers\ntest/49ba0e9579db5270,Combat sport\ntest/49ba8d286586b8be,Black-and-white,Modern dance\ntest/49bb9d97c793e70c,Wind wave\ntest/49bee9a12f582b3f,Compact car,Luxury vehicle\ntest/49bf04a42c9d83c1,Ball game,Team sport\ntest/49c0c0cf5e2fff3d,Compact car,Antique car\ntest/49c1273e86112fe1,Combat sport\ntest/49c71dedebe45f15,Domestic short-haired cat,Close-up,Whiskers\ntest/49c798fcdd3231f1,Shinto shrine\ntest/49c98a099e9e0834,Luxury vehicle\ntest/49cc326da6f56fa6,Auto racing\ntest/49cd2bbc078454f7,Arthropod,Close-up\ntest/49cda52ef563eabe,Blond,Portrait photography,Close-up\ntest/49cf480cf7eaf9ed,Miniature Poodle\ntest/49cf56a0bb725ce9,Sport utility vehicle\ntest/49cf612d4db3d72a,Whiskers\ntest/49d1bec9e1fa54e2,Extreme sport,Boating\ntest/49d3204f23c128d3,Floristry\ntest/49d42e686422801d,Surfing Equipment\ntest/49d8f9a9b51a708d,Auto racing\ntest/49d942e35e59ce70,Dishware\ntest/49d9590f60056c84,Woodwind instrument\ntest/49df30c823856660,Angling\ntest/49e1086572bec63c,Aircraft engine\ntest/49e2f528ebd2bc28,Fried food\ntest/49e3436bd38b4453,Arthropod,Close-up\ntest/49e40ccefbef1c86,Assault rifle,Firearm\ntest/49e48849d8174c20,Chordophone\ntest/49e52fbdce3dad07,Melee weapon,Hunting knife\ntest/49e712b7361a97ff,Freight transport\ntest/49e8aeec44ad3236,Close-up\ntest/49e8cd6f0b5ac2ed,Luxury vehicle,Performance car\ntest/49e8f34540030ecf,Performance car\ntest/49f093a5b0eba52e,Close-up\ntest/49f1950dc01efc97,Luxury vehicle\ntest/49f31f3d88309f5f,Black-and-white\ntest/49f509e047f0d3fd,Outdoor shoe\ntest/49f5880d3697bc14,Compact car\ntest/49fb65ee3ad2a03f,Lumpia,Fried food\ntest/49fc430dfe36374f,Black-and-white\ntest/49fc86c0c1d93d7e,Auto racing,Performance car\ntest/49ffd9138785baca,Outdoor shoe\ntest/4a0089de6eeb58b3,Close-up\ntest/4a01249f8aca87ed,Machine tool\ntest/4a03c84c91f24eda,Performance car\ntest/4a03d0d72109539f,Leaf vegetable\ntest/4a043afde5294156,Black-and-white\ntest/4a06792fbb1c28fa,Equitation,Equestrianism\ntest/4a069e31a4108fe3,Compact car\ntest/4a0781de068c8ac6,Dishware\ntest/4a0b4f3fdca09a2f,Freight transport\ntest/4a0e123a5ae7c664,Close-up\ntest/4a1034b331087feb,Galleon,Barquentine\ntest/4a10ce9b4d31fade,Blond\ntest/4a14b7ef316a1079,Luxury vehicle\ntest/4a1aaadff34febb4,Electric piano,Electronic instrument\ntest/4a299d2f25a2fc67,Fire apparatus\ntest/4a2ac6e0c85727c7,Goats\ntest/4a2d9e17c05529d8,Bird of prey\ntest/4a2e3925bc5e8832,Luxury vehicle\ntest/4a2e5077e8551fbe,Close-up,Anole,Scaled reptile\ntest/4a2e6f27d10132bf,Sledding\ntest/4a2f67b712b5bfeb,Athletic shoe,Outdoor shoe\ntest/4a303a5043600777,Compact car\ntest/4a304fa1654b5d1c,Luxury vehicle\ntest/4a323648b813baae,Luxury vehicle\ntest/4a328b62bb294abe,Close-up\ntest/4a381be32c28b7ae,Jeans\ntest/4a38eb81b413b8c6,Antique car\ntest/4a3b0991e5b3c438,Aerial photography\ntest/4a3ebc53d174823e,Firearm\ntest/4a3fd9e3da033bc1,Compact car,Antique car\ntest/4a403f3a85ce09a3,Cosmetics\ntest/4a409b78e355d89b,Equitation,Equestrianism\ntest/4a4123abc6225c69,Black-and-white\ntest/4a426770f515d6a2,Bovine,Close-up\ntest/4a42bf9f1f232733,Shooting range,Firearm\ntest/4a4492482bdd2e3a,Jeans\ntest/4a44d71670e8e97e,Close-up\ntest/4a46af2d7cd1b8b5,Close-up\ntest/4a49eefe47305392,Great white shark\ntest/4a4bbef70c1ffcc7,Aircraft engine\ntest/4a4f67d2b2eb0748,Extreme sport\ntest/4a501ac470c8966e,Luxury vehicle,Performance car\ntest/4a5125a7a06dc74b,Aircraft engine\ntest/4a51ce1d516915d4,Aircraft engine\ntest/4a54311bd3d3ae07,Newsagent's shop\ntest/4a5acff97665fc38,Ball game\ntest/4a5b1f32960b98cb,Luxury vehicle,Antique car\ntest/4a5c0164e147855d,Luxury vehicle,Performance car\ntest/4a5e84d299a0d093,Compact car,Luxury vehicle,Antique car\ntest/4a5f3a0ef13fcaa1,Military person\ntest/4a6215292a538784,Plant stem\ntest/4a62439da5e0facb,Luxury vehicle,Performance car\ntest/4a635ce134b50473,Aircraft engine\ntest/4a67328d12d72388,Equestrianism\ntest/4a6740281c7c8358,Blond\ntest/4a679f933c9d5f8a,Military person\ntest/4a67ec4f4cb5e41f,Performance car\ntest/4a67fba68e1a12e6,Extreme sport,Parachuting\ntest/4a687ef8a22deb99,Shooting range,Firearm\ntest/4a6cefb3a5cc3fd4,Aircraft engine\ntest/4a6d080bce509065,Vegetarian food\ntest/4a6ee11eccb4485c,Canola\ntest/4a6f68bece8394f7,Black-and-white,Close-up\ntest/4a6fcc166323c5c0,Close-up\ntest/4a7063f4672ab0ac,Sport utility vehicle\ntest/4a70ec3349e60eaf,Luxury vehicle,Performance car\ntest/4a72651aff218838,Coca-cola\ntest/4a739a8c00a6ef4f,Luxury vehicle,Performance car\ntest/4a73c858d8170027,Shallot\ntest/4a77a1da26202d46,Compact car,Antique car\ntest/4a7a47bff0ed563c,Church bell\ntest/4a7f3005309767dc,Scaled reptile\ntest/4a80e86f77aadc18,Luxury vehicle\ntest/4a87a57e8401efdf,Domestic short-haired cat,Whiskers\ntest/4a890975ec0065cd,Medical imaging\ntest/4a892844af006352,Frigate\ntest/4a8b4f8413c09aa5,Military person\ntest/4a8b527943e5866e,Luxury vehicle\ntest/4a8b6ebc5c4ceffb,Luxury vehicle,Performance car\ntest/4a8bf5abcef0bd44,Church bell\ntest/4a8ced3571991d1d,Monoplane,Aircraft engine\ntest/4a8edc595c63d186,Wind wave\ntest/4a91679d63ad7500,Sport utility vehicle\ntest/4a927aae62da877f,Black-and-white,Firearm\ntest/4a92a63561a44aaf,Rural area\ntest/4a948549a6d5119a,Luxury vehicle,Antique car\ntest/4a954c6a2a329783,Boating\ntest/4a961fe02b1e4fb3,Black-and-white,Barechested\ntest/4a974f9af39bd873,Luxury vehicle\ntest/4a975b5d21b036c8,Team sport\ntest/4a99c45d16bb87a4,Senior citizen\ntest/4a9c26ef716bd656,Close-up\ntest/4a9d143d301b73ba,Performance car\ntest/4a9d71b0f8a223a6,Rural area\ntest/4a9f18cc0479bcad,Leaf vegetable\ntest/4aa4866a2b469896,Close-up,Scaled reptile\ntest/4aa4c725802fb7ee,Trampolining\ntest/4aa6be594e009e42,Ball game,Team sport\ntest/4aa716064db6707e,Rice ball\ntest/4aa7410bf983ec47,Brochette\ntest/4aa92086efd578bc,Close-up\ntest/4aa9d1039a2ede88,Luxury vehicle\ntest/4aad0846783a6aeb,Black-and-white\ntest/4ab380e4bebd0e75,Luxury vehicle,Performance car\ntest/4ab48f729603bc3e,Team sport\ntest/4ab4ea7f98f74f3a,Aircraft engine\ntest/4ab8c0727a8a9ca1,Luxury vehicle,Antique car\ntest/4aba0110daeaec0c,Extreme sport,Sport climbing\ntest/4abb6c9133add215,Microcontroller\ntest/4abf61c7d2596232,Residential area,Aerial photography\ntest/4abf9565c4aa80b6,Performance car\ntest/4abfc3df08a6a47a,Black-and-white\ntest/4ac143fdbbb2be88,Black-and-white\ntest/4ac2fe9bbe851320,Antique car\ntest/4ac4bac22384f384,Combat sport\ntest/4ac51fdd348ed2e0,Hound\ntest/4ac6131c58c188cd,Luxury vehicle\ntest/4ac86e40d55fb5e7,Microcontroller\ntest/4ac879f4c9fbd910,Close-up\ntest/4ac9495d4bf2c719,Luxury vehicle,Antique car\ntest/4ac9d4382583cda5,Sport utility vehicle\ntest/4acf46e6c278b598,Microcontroller,Breadboard\ntest/4acfe910411eeb6e,Black-and-white\ntest/4ad2cab3fa2577e9,Aircraft engine\ntest/4ad44e987b014e08,Shallot\ntest/4ad59f46e11c412f,Surfing Equipment\ntest/4ad68b83785dc2ee,Aerial photography\ntest/4ad77d06fa730c26,Luxury vehicle\ntest/4ad9295d442500a7,Comics\ntest/4ada3ffd2955a0a7,Residential area\ntest/4adacb8e8293691c,Blond,Hair coloring\ntest/4adaf99870705b25,Whiskers\ntest/4adc174693cc7f52,Luxury vehicle\ntest/4adc85d8d85b4281,Monoplane,Gliding\ntest/4ade369361bc0568,Luxury vehicle,Antique car\ntest/4ae17175433a3f35,Floristry,Plant stem\ntest/4ae25f86875e45c5,Antique car\ntest/4ae378764a9737f9,Microcontroller\ntest/4ae486e5d6c9b58b,Vegetarian food\ntest/4ae51e207f6a73dd,Performance car\ntest/4aede51b6160616e,Military vehicle,Luxury vehicle,Sport utility vehicle\ntest/4aef96c357e105bf,Luxury vehicle,Antique car\ntest/4af190d940233905,Compact car,Luxury vehicle\ntest/4af1b5d099e60207,Black-and-white,Close-up,Whiskers\ntest/4af2181979363c9c,Performance car,Luxury vehicle,Antique car\ntest/4af2290400edd0bb,Forklift truck\ntest/4af5408df64b5588,Arthropod,Close-up\ntest/4af970d95ff5c44a,Black-and-white,Barechested\ntest/4af97d6829b28adb,Luxury vehicle\ntest/4af99e46a1567995,Luxury vehicle,Performance car\ntest/4afbf2f592d0ce9c,Luxury vehicle,Performance car\ntest/4afc51eb5365c5f0,Sledding\ntest/4afcf5e3a08bfda9,Plant stem\ntest/4afd86685cdd842e,Hound\ntest/4aff857345545fc0,Blond,Close-up\ntest/4b00124e855b04b5,Performance car,Antique car\ntest/4b003b3899719951,Perching bird,Close-up\ntest/4b02b6a25b45a51d,Close-up\ntest/4b0470fce88d4d67,Dishware\ntest/4b04cb6e6c0543cd,Close-up\ntest/4b053e72cc3d9fa2,Aerial photography\ntest/4b09edb7ad068305,Jeans\ntest/4b0a4bdf53cdade6,Black-and-white\ntest/4b0a91418047c4cf,Chordophone\ntest/4b0c53161ad9a06c,Blond\ntest/4b1198d48a2c7e4d,Blond\ntest/4b12b0c42ea2dfd5,Outdoor shoe\ntest/4b15951730a6afa1,Performance car\ntest/4b1662c80f753b39,Luxury vehicle,Performance car\ntest/4b16cf46eef31169,Watercolor paint\ntest/4b190c171acfeb63,Whiskers\ntest/4b1a686a8a90ce4b,Ball game,Team sport\ntest/4b1dd9640e7e9a05,Inflatable\ntest/4b20e0276fda94af,Bovine\ntest/4b2573de9c3e90fe,Auto racing,Touring car,Luxury vehicle,Performance car\ntest/4b2659d361e9edab,Jeans\ntest/4b2700acc13cbf11,Black-and-white,Portrait photography\ntest/4b27d1e938a0e918,Luxury vehicle\ntest/4b293bffc76bdbed,Ball game\ntest/4b30981c2e320d74,Full moon\ntest/4b3355e13b0a97af,Close-up\ntest/4b356e74c4f14ef8,Close-up,Whiskers\ntest/4b360852c30c8173,Sport utility vehicle\ntest/4b37a287adb9aedc,Aircraft engine\ntest/4b381113ff164a61,Auto racing\ntest/4b391ccf8c5954b5,Outdoor shoe\ntest/4b39721d1c50a4f6,Chordophone\ntest/4b3b20e30267e876,Close-up\ntest/4b3e48df7ec05a5a,Plant stem,Close-up\ntest/4b3f07f4d211f01b,Coral reef fish,Close-up\ntest/4b402148e3c21eda,Roller skating\ntest/4b409f7e47924435,Compact car,Luxury vehicle,Sport utility vehicle\ntest/4b471ba786693c20,Ball game\ntest/4b479a572b0888cb,Shallot\ntest/4b486dc3a1021f25,Molluscs\ntest/4b4a673e07f9381e,Luxury vehicle,Performance car\ntest/4b4c071b0c37997c,Close-up\ntest/4b4c86855566bded,Luxury vehicle,Antique car\ntest/4b4dfd6efdcd4fb9,Erinaceidae\ntest/4b4e13563b6e6af0,Bovine\ntest/4b53ff43a0414f83,Compact car\ntest/4b547397a2497cf4,Hair coloring\ntest/4b54d4d3f2c7ccfd,Black-and-white\ntest/4b55d43063251f6e,Ball game,Team sport\ntest/4b56d981842f8a2a,Childbirth\ntest/4b571758fc443b7d,Boating,Rowing\ntest/4b598bd1076bbccf,Melee weapon,Hunting knife\ntest/4b5d81fe034b416e,Rural area,Aerial photography\ntest/4b6033182c704d42,Compact car\ntest/4b609172bb574a7d,Close-up\ntest/4b6207e7f8753627,Jackfruit\ntest/4b66845bcd5c7441,Cobblestone\ntest/4b670b5755a6ee32,Compact car\ntest/4b68c98e046f0bef,Close-up\ntest/4b69ab0fde5e4d62,Performance car\ntest/4b6a90fc6acf8843,Vegetarian food,Fried noodles\ntest/4b6cb8274302abe6,Ball game,Team sport\ntest/4b6ce97509d5bcac,Electronic instrument,Black-and-white\ntest/4b6f40646b9b90c3,Luxury vehicle,Sport utility vehicle\ntest/4b6f7eaab588a580,Musical theatre\ntest/4b70315b416da11f,Computer speaker\ntest/4b714bc3f5545611,Ball game,Team sport\ntest/4b760ffdb08976fe,Antique car\ntest/4b76b35cd4d63ce3,Road cycling\ntest/4b7a543d167acd97,Compact car\ntest/4b7b421850783365,Extreme sport\ntest/4b7d62035c127c8f,Compact car\ntest/4b824dc9b898f21c,Trampolining\ntest/4b84cdd79cf1d477,Portrait photography,Close-up\ntest/4b8578d0edc114c5,Black-and-white\ntest/4b8a70afa276a842,Close-up\ntest/4b8bddefcec27f07,Monoplane,Gliding\ntest/4b8f015c907f87f5,Vegetarian food\ntest/4b8f1f9d1eda4f9d,Aircraft engine\ntest/4b8fe9ef78727bf3,Whiskers\ntest/4b919f8d5d18e239,Luxury vehicle,Antique car\ntest/4b961ac47fbd8694,Whiskers\ntest/4b96297f616591fa,Compact car\ntest/4b96afd6895318e5,Dishware\ntest/4b97a375e1510eff,Compact car,Luxury vehicle\ntest/4b9a1731f0f27a0c,Brisket\ntest/4b9a3d8b98aa46ec,Halter\ntest/4b9c9aaaa30703cf,Ball game,Team sport\ntest/4b9cd6e94f5e9bb5,Amphibian,Scaled reptile\ntest/4b9de8ee382cbc45,Luxury vehicle,Performance car\ntest/4b9ef3ce2685ee32,Backlighting,Black-and-white\ntest/4b9f075ff708afec,Close-up\ntest/4ba02c52fa4b0779,Close-up\ntest/4ba0d4a04f4a7e0e,Vegetarian food\ntest/4ba1bca113d6a4b4,Combat sport\ntest/4ba213cdd15e193d,Wind wave\ntest/4ba4d91275fc27c9,Close-up\ntest/4ba5e1457feefa18,Military person\ntest/4ba6db7d69962f32,Extreme sport\ntest/4ba79dee2c6ac645,Luxury vehicle,Sport utility vehicle\ntest/4ba875064f8417e9,Goats,Bovine\ntest/4ba930a0e91caf21,Extreme sport\ntest/4baa63084d31788b,Hair coloring\ntest/4bab37ae973b1216,Luxury vehicle,Sport utility vehicle\ntest/4badf9417cad1b91,Electronic instrument\ntest/4bb0966e8e85dbd0,Black-and-white\ntest/4bb15733cd8b95bb,Luxury vehicle\ntest/4bb456b84cb1e782,Wind wave\ntest/4bb50dd1e6f25def,Performance car\ntest/4bb890d3c0e758f9,Junk food\ntest/4bb897e00a933697,Aerial photography\ntest/4bb905ef751daa4a,Black-and-white,Whiskers\ntest/4bb91b1fb6ad9c2b,Roman temple\ntest/4bba5818fc7ae3b0,Luxury vehicle\ntest/4bbb2164e12af1bb,Halter\ntest/4bbcda69d788404f,Team sport\ntest/4bbe456081c4db6c,Luxury vehicle\ntest/4bbfa1b1e368d252,Black-and-white\ntest/4bbfd901937eed1f,Hound\ntest/4bc1d5c50eff4c69,Middle ages\ntest/4bc46462f1b2c07a,Luxury vehicle,Performance car\ntest/4bc5711bafd2ea03,Residential area,Aerial photography\ntest/4bc78fb3b2d38229,Black-and-white\ntest/4bc9b8613705e12d,Close-up\ntest/4bcb14d4692d6c67,Combat sport\ntest/4bcc5659be88d83b,Domestic rabbit\ntest/4bcdf2ddc291d802,Luxury vehicle\ntest/4bd01e70888103a3,Residential area,Aerial photography\ntest/4bd0b3680597da7c,Cookies and crackers\ntest/4bd184e05ea88e1d,Luxury vehicle\ntest/4bd3837a5811c377,Close-up\ntest/4bd47b0a19f71fec,Boating\ntest/4bd57b4baf5ffc9f,Close-up,Floristry\ntest/4bd5de37f25a38f2,Luxury vehicle,Performance car\ntest/4bd6398679feb8b4,Junk food\ntest/4bd660fabab94078,Close-up\ntest/4bd79e28e72cf4aa,Bagpipes\ntest/4bd8325532b23cbc,Frying,Junk food,Fried food\ntest/4bdac54732218285,Compact car,Antique car\ntest/4bdbefe2e4420146,Sport utility vehicle\ntest/4bdc019656bb4c94,Modern dance\ntest/4be102cded2b9274,Jeans\ntest/4be19ce00cb4a802,Black-and-white\ntest/4be3b19834ad939b,Black-and-white\ntest/4be5127d26f9db5f,Close-up\ntest/4be6609488f3f34c,Luxury vehicle\ntest/4be740d5c8f99b99,Watercolor paint\ntest/4be8288260a564c6,Rural area\ntest/4be917ab4b844b2f,Antique car\ntest/4be91b2f445e4ec1,Luxury vehicle,Sport utility vehicle\ntest/4bea804a553f896a,Brisket,Ribs\ntest/4bec48d442add788,Luxury vehicle,Performance car\ntest/4bee44348bae4289,Rural area\ntest/4bf20ac4061f4be0,Cookies and crackers\ntest/4bf210a1e02664cb,Domestic rabbit\ntest/4bf2b0e46a972d3a,Close-up\ntest/4bf2f23c250fe3ee,Residential area\ntest/4bf404ff9213f4b2,Sport utility vehicle\ntest/4bf5f6dd6ff10db2,Cookies and crackers\ntest/4bf5ff26d5b8cef3,Crocodilia,Scaled reptile\ntest/4bf6b3b0fef538c9,Ball game\ntest/4bf766d6039e7e27,Rural area\ntest/4bf9b03a2cf409df,Antique car,Performance car\ntest/4bfaefcfaf07b07b,Childbirth\ntest/4bfb01403d42f8cd,Aircraft engine\ntest/4bfd94726a2bbc57,Antique car\ntest/4bfd9e282c4b23bf,Rural area\ntest/4bfe76e0f515cce8,Domestic rabbit\ntest/4bfefee10cf27e59,Monoplane\ntest/4bff02ff12c4380a,Jeans\ntest/4bff40eb4ab80f8c,Electronic instrument\ntest/4c018eaa878d7a66,Boating,Wind wave\ntest/4c01bdeb9827d13a,Pit bull\ntest/4c0a784065138975,Luxury vehicle,Sport utility vehicle\ntest/4c10aee7c1801d10,Close-up\ntest/4c143d44e7835bcc,Military vehicle\ntest/4c1614eb6db4d317,Floristry\ntest/4c174e0321010f49,Sport utility vehicle\ntest/4c1a145799e3f9a6,Close-up\ntest/4c1b97ed3b07be7e,Bovine\ntest/4c1ff90dea31e9d7,Close-up,Anole,Scaled reptile\ntest/4c20007b62afd81c,Compact car\ntest/4c2126c046ef6bba,Rice ball\ntest/4c2133bd1f1d7b47,Calabaza\ntest/4c2382f351d885bc,Arthropod,Close-up\ntest/4c27b453f57b60fe,Residential area,Aerial photography\ntest/4c289ea0745b43a1,Luxury vehicle\ntest/4c2b8ace7c7491d4,Extreme sport,Parachuting\ntest/4c2deaff275266c7,Domestic short-haired cat,Whiskers\ntest/4c2e51d2d3cbb33a,Residential area\ntest/4c2f2d788d43f1cf,Scaled reptile\ntest/4c2f482892ce3a3b,Luxury vehicle,Sport utility vehicle\ntest/4c3035b99ab446e9,Boating\ntest/4c304052b695f81a,Luxury vehicle,Antique car\ntest/4c31c9d9da8a670f,Fire apparatus\ntest/4c32899cae50ec1d,Whiskers,Domestic rabbit\ntest/4c3514ac72704785,Machine tool\ntest/4c38c698faea1bdf,Luxury vehicle,Performance car\ntest/4c3c6b2679c8b509,Extreme sport,Wind wave\ntest/4c3f9cccf44919bd,Luxury vehicle\ntest/4c3fc234ad6c8758,Black-and-white\ntest/4c41513fb1c9bf62,Goats\ntest/4c42b5948b15922b,Amphibian\ntest/4c491567842197fd,Extreme sport\ntest/4c4c78d18cc8c755,Outdoor shoe\ntest/4c4f519539ec281f,Luxury vehicle,Sport utility vehicle\ntest/4c4fe375367fa611,Luxury vehicle\ntest/4c503aae894a0623,Motorcycling\ntest/4c516d3fc321727e,Black swan\ntest/4c56547806680783,Luxury vehicle,Antique car\ntest/4c57d90f3285fd75,Auto racing\ntest/4c5c8d2dd92caca2,Rural area\ntest/4c5cdda405c7d100,Performance car\ntest/4c5f2cbee68263bf,Close-up\ntest/4c5f3c9fbf36f99c,Close-up,Whiskers\ntest/4c5f71ac17f0ca5b,Flatbread\ntest/4c6327e077f0a065,Auto racing\ntest/4c654d7f4a8c2ccf,Combat sport\ntest/4c677abd4576bf60,Chordophone,Electric guitar\ntest/4c67e988a61ebfda,Luxury vehicle,Performance car\ntest/4c6a1b83df36da5c,Melee weapon,Hunting knife\ntest/4c6ab3eaf5bccb51,Road cycling\ntest/4c6e1a6b70e2008e,Arthropod,Close-up\ntest/4c718f26d4843151,Rural area\ntest/4c71e00a7d759ca6,Aerial photography\ntest/4c72955a7174a175,Luxury vehicle\ntest/4c737d1b499d2733,Close-up\ntest/4c7525afa256f650,Jeans\ntest/4c755c75e0086a04,Luxury vehicle,Performance car\ntest/4c75b2bb65d8b8d5,Compact car,Luxury vehicle\ntest/4c770440a4d0b5d9,Close-up\ntest/4c79ddeed8cc796c,Aircraft engine\ntest/4c7a12ee5a5c9300,Ball game\ntest/4c7b5bbf41a0d0bb,Vegetarian food,Junk food\ntest/4c7f6bd4a7aa0f7a,Close-up\ntest/4c7fee3327b52edb,Close-up\ntest/4c817c742671e28e,Performance car\ntest/4c82a46dee5a2554,Black-and-white\ntest/4c82ff25e487d76a,Luxury vehicle,Sport utility vehicle\ntest/4c8347572113df49,Compact car,Antique car\ntest/4c86946e9838e473,Close-up\ntest/4c882f7e53f4f35a,Gliding\ntest/4c8b0c4a510b5a9a,Leaf vegetable,Vegetarian food\ntest/4c8c63a8c0e50345,Heavy cruiser\ntest/4c8cfad96ace11c5,Luxury vehicle\ntest/4c8e87e89f368313,Black-and-white,Camera operator\ntest/4c8ff866ff9519c6,Close-up\ntest/4c909cb97a9d01ba,Equitation,Equestrianism\ntest/4c9243651be29c11,Roller skating\ntest/4c92fc7710438bac,Rural area\ntest/4c94a42f629af251,Monoplane,Aircraft engine\ntest/4c95623e3d408643,Black-and-white\ntest/4c9770d8e47923f7,Arthropod,Close-up\ntest/4c98cffd26b8c22f,Perching bird\ntest/4c9a520baadf044a,Luxury vehicle,Antique car\ntest/4c9b8a3225074a24,Auto racing\ntest/4c9c6e2bde3a90a3,Hound\ntest/4c9d2f2047a0134e,Dishware\ntest/4c9de75d6ce07eb3,Dog walking\ntest/4ca20eaeffbf98ae,Freight transport\ntest/4ca3431d3b50a948,Luxury vehicle\ntest/4ca5b82782903f1c,Sport utility vehicle\ntest/4ca747a5ac139572,Black-and-white\ntest/4ca7d792f2deeeda,Luxury vehicle\ntest/4caa152e3113502d,Milky way\ntest/4caa687f83db1df7,Luxury vehicle\ntest/4cad18d62d8e4d5a,Rural area\ntest/4cadd1aa81dfcaf0,Luxury vehicle,Performance car\ntest/4cafc381cba77240,Black-and-white\ntest/4cb0af474de7ca54,Luxury vehicle\ntest/4cb1fd1da966e0b3,Close-up\ntest/4cb37e2142dd0f64,Arthropod,Close-up\ntest/4cb3d95883d91a48,Ball game\ntest/4cb3df657baf0a42,Close-up\ntest/4cb4eb3abd0c2e1d,Close-up\ntest/4cb5c6dc3f71c9d4,Close-up,Whiskers\ntest/4cb6c76e449a3675,Close-up\ntest/4cb71771d133b96e,Antique car\ntest/4cb7e50ad8a1f401,Musical theatre\ntest/4cb8712376a6babd,Luxury vehicle\ntest/4cbac998b5eb1ae4,Dishware\ntest/4cbbc0a34c6db938,Boating\ntest/4cbc3d2c9833bdf7,Luxury vehicle\ntest/4cbc6cef51132d5f,Domestic short-haired cat,Close-up,Whiskers\ntest/4cbd8ec9ffd335fb,Jeans,Holding hands\ntest/4cbe26c664c4fa80,Chordophone,Electric guitar\ntest/4cc17ff04b6c1277,Trampolining\ntest/4cc49fb499f5efcc,Leaf vegetable\ntest/4cc4aae8a1ab1ed4,Black-and-white\ntest/4cc562e5ce7b00a2,Chordophone\ntest/4cc5986403f5d736,Herding,Bovine\ntest/4cc6594e5a81162a,Close-up\ntest/4cc6eac0745d2991,Ball game\ntest/4cc743401a4d1077,Luxury vehicle,Performance car\ntest/4cc82a839de0c832,Sport utility vehicle\ntest/4cca0331051eb601,Extreme sport,Wind wave,Boating\ntest/4cce0bddb1686d73,Luxury vehicle\ntest/4ccf70c190f9286a,Black-and-white\ntest/4cd1a142aacd6d32,Plant stem\ntest/4cd328fd14bfc1d9,Team sport\ntest/4cd461ad6865ec10,Electronic instrument\ntest/4cd6e1dc7945bb90,Computer speaker\ntest/4cd819bad039dbd8,Pasta salad\ntest/4cd8a784072d538b,Residential area\ntest/4cdd0d8fb395ba75,Jeans\ntest/4cdd16dad6b81b7b,Military person\ntest/4cdf416ac6bf1681,Ball game,Team sport\ntest/4ce01f67942c4ee6,Outdoor shoe\ntest/4ce03cfef343060f,Performance car\ntest/4ce1c67195107c09,Luxury vehicle\ntest/4ce5b5974ec869f8,Scaled reptile,Close-up,Anole\ntest/4ce61a09a47cdd82,Compact car\ntest/4ce6f064969dd163,Hurdling\ntest/4cea6bd80fe011fa,Vegetarian food\ntest/4ceb7ffd3c8e63cf,Modern dance,Musical theatre\ntest/4cebd6bb15055fe4,Halter,Equestrianism\ntest/4cec3130aebd1559,Compact car,Luxury vehicle,Antique car\ntest/4ced8ef1db96eff2,Luxury vehicle\ntest/4cefb5a8e1575b6a,High-speed rail\ntest/4cf200c4526f44ff,Ball game,Team sport\ntest/4cf233ee3b7786d1,Luxury vehicle,Antique car\ntest/4cf303fef1627eb9,Luxury vehicle\ntest/4cf367ef60a8a8ce,Sailboat racing\ntest/4cf3d95bccb1f6ee,Close-up,Whiskers\ntest/4cf5fff19c45abb3,Blond,Hair coloring\ntest/4cf7cf586c6831c2,Musical theatre\ntest/4cf9f4d0bd62f252,Jackfruit\ntest/4cfcbd11639b43d0,Close-up\ntest/4cfd587b633449fc,Outdoor shoe\ntest/4cfdc0d8360e78a2,Equestrianism\ntest/4cff953ad1460fbc,Jeans\ntest/4d009153793b34b1,Blond,Portrait photography,Close-up\ntest/4d01ed55688f1f6b,Modern dance,Musical theatre\ntest/4d0298c23afc0ab0,Black-and-white\ntest/4d02f1eefd6b5d0a,Performance car\ntest/4d0369f83e97f19e,Coca-cola\ntest/4d05c5eff296f5da,Residential area\ntest/4d0693f8f6dca2e3,Auto racing,Performance car\ntest/4d06a8060ec3c585,Firearm\ntest/4d0894c4bdb35cff,Luxury vehicle,Antique car\ntest/4d08ae351ec89484,Ball game\ntest/4d09c8703b3eb391,Rural area\ntest/4d0dd9046773ebc1,Luxury vehicle,Antique car\ntest/4d0f941950cb4a44,Combat sport\ntest/4d0fb41887704ca9,Arthropod,Close-up\ntest/4d109e10d934a66c,Pork chop\ntest/4d1138b9005b7641,Auto racing\ntest/4d168ffc0ca2f6cd,Close-up\ntest/4d1772e4aef70e44,Combat sport\ntest/4d18b168d2a61ed3,Close-up\ntest/4d1b2ad4b2d6d56f,Sport utility vehicle\ntest/4d1d4786ffd5edd6,Compact car,Luxury vehicle,Performance car\ntest/4d1df92d9db324b5,Roller skating\ntest/4d1ea0257d5a1622,Equestrianism\ntest/4d1ef73cf1c31621,Arthropod,Close-up\ntest/4d1f682537ff88bb,Angling\ntest/4d20806e8e8e2f9f,Close-up,Whiskers\ntest/4d20d35e91eb9624,Close-up\ntest/4d211ac255f269a6,Bovine\ntest/4d22789d96c32112,Close-up\ntest/4d242b0375c3c395,Aircraft engine\ntest/4d2597e8810dc4be,Compact car,Luxury vehicle,Antique car\ntest/4d261b265acab360,Luxury vehicle\ntest/4d2786e4d0239451,Whiskers\ntest/4d28ae509fee9e9d,Luxury vehicle\ntest/4d2ae18a244766aa,Performance car\ntest/4d2b6b0c2d86a2ff,Luxury vehicle,Performance car\ntest/4d2dc77799cae067,Luxury vehicle,Performance car\ntest/4d2ee21bd1a76b75,Ball game,Team sport\ntest/4d2f895b27ac8a19,High-speed rail\ntest/4d325aa5d811bd65,Senior citizen\ntest/4d3432fbc3383d33,Bonbon\ntest/4d356c8da663c5f2,Domestic short-haired cat,Whiskers\ntest/4d38e47c9c74abb5,Rural area\ntest/4d3b8fcda565397c,Domestic short-haired cat,Close-up,Whiskers\ntest/4d3d3d15380d0417,Erinaceidae\ntest/4d3e65ed952951d9,Aircraft engine\ntest/4d3efc08c7f202a0,Performance car\ntest/4d3f828e7a8a3025,Modern dance,Musical theatre\ntest/4d4495d9eebd7ebe,Compact car,Luxury vehicle\ntest/4d48cc485cd4f9fb,Compact car,Luxury vehicle,Antique car\ntest/4d4b2d6f110f8bfb,Steamed rice\ntest/4d4b4f850df89aaf,Floristry\ntest/4d4c77e1bdaec0ad,Bird of prey\ntest/4d502dcca1d0c4de,Junk food\ntest/4d51b08a4f17a99e,Close-up,Whiskers\ntest/4d53a61bbe2f302c,Chordophone,Electric guitar\ntest/4d540cbb08b3ce7c,Compact car\ntest/4d549d4da1caa102,Close-up\ntest/4d5602aab0d78c86,Close-up\ntest/4d579a56bf0f632b,Black-and-white\ntest/4d5d7b41d0eb43d3,Close-up\ntest/4d5ea45956808585,Bratwurst\ntest/4d5f7f8462748392,Glacial landform\ntest/4d611f3cde8df464,Sport utility vehicle\ntest/4d6450a573249744,Black-and-white\ntest/4d6669f64416bb32,Zeppelin\ntest/4d68719dde7fbf1f,Black-and-white\ntest/4d6931e24f3ec5e4,Auto racing\ntest/4d69a2ffe7c4ed4a,Aircraft engine\ntest/4d6e55bacc3ae59b,Extreme sport\ntest/4d6ffd6a2907fd2f,Floristry\ntest/4d707bd91074aa95,Bratwurst\ntest/4d716e392db18599,Team sport\ntest/4d71c003e4ad1479,Luxury vehicle,Antique car\ntest/4d7209ae718d6c10,Blond\ntest/4d747dacff4bf433,Close-up\ntest/4d767e4da491f4b1,Team sport\ntest/4d76e673330e61fa,Senior citizen\ntest/4d775eba6f273d96,Extreme sport,Parachuting\ntest/4d78016e9c21192c,Junk food,Fried food\ntest/4d78cffd3eae5c17,Dishware\ntest/4d79006d77c86b89,Fried noodles\ntest/4d79b12d6976a6e9,Antique car,Luxury vehicle,Performance car\ntest/4d7ca9ce4672e1f8,Galleon,Frigate\ntest/4d841600d50dbea6,Luxury vehicle\ntest/4d86b8b723c69496,Luxury vehicle,Performance car\ntest/4d879d39ed605eb1,Cosmetics\ntest/4d88b1700872153b,Luxury vehicle,Performance car\ntest/4d8c0ee4fa6eda5d,Molluscs\ntest/4d8ed288cee74b9c,Luxury vehicle,Antique car\ntest/4d8fd04d309db107,Close-up\ntest/4d93e3b37c79c8ff,Monoplane\ntest/4d9599e89f5b17f4,Black-and-white\ntest/4d95b874bc7801ef,Chordophone\ntest/4d9776bd1357a00a,Extreme sport\ntest/4d979645114f016e,Boating\ntest/4d9c6b38e730a6e1,Close-up\ntest/4d9cc067a8cac70d,Bovine\ntest/4d9d873f1d080e6a,Black-and-white,Close-up\ntest/4da0dbb349cf5d8b,Musical theatre\ntest/4da179219a784596,Close-up\ntest/4da202492a89f906,Blond,Portrait photography,Close-up\ntest/4da278f5f330adb6,Solar energy\ntest/4da4f5e22c21aeae,Fried food\ntest/4da5228bc6dd9e69,Ball game,Team sport\ntest/4da543202aa1cba3,Luxury vehicle\ntest/4da76d1e63381601,Bird of prey,Close-up\ntest/4daa58c661b8525b,Steamed rice\ntest/4daaf1b4d432cd0b,Roman temple\ntest/4daaf9ef1d7a5ce2,Luxury vehicle\ntest/4dab3e4089e42118,Close-up\ntest/4dac3249a9c23b52,Siberian husky\ntest/4dacecb089de06d8,Black-and-white\ntest/4dad2752f339902b,Luxury vehicle,Performance car\ntest/4dadc272128a5f0e,Close-up\ntest/4daea91077b1c357,Portrait photography,Close-up\ntest/4daf200e9387f8a8,Bagpipes\ntest/4db006901b2b5cbb,Milky way\ntest/4db35519c6c42324,Rural area\ntest/4db3b31db89842b2,Machine tool\ntest/4db4d76b07c39d42,Gliding\ntest/4db8a9f22b59ee69,Vegetarian food\ntest/4db8d7ea38a7efde,Sport utility vehicle\ntest/4dbadd6c67565a08,Portrait photography,Close-up\ntest/4dbba809d9f337dc,Luxury vehicle,Antique car\ntest/4dbbe3c8efec0378,Arthropod,Close-up\ntest/4dbd5d84f6573d6a,Close-up\ntest/4dc1bea84e5ea678,Ball game\ntest/4dc46cb76bbc7bcf,Rural area\ntest/4dc5d6c869f367a8,Divemaster,Underwater diving\ntest/4dc7944975586549,Black-and-white,Close-up\ntest/4dc81b302227219b,Whiskers\ntest/4dc9d022e175ceb9,Auto racing\ntest/4dca11642dbb03e6,Jeans\ntest/4dcb04f2ee8fdd24,Close-up,Whiskers\ntest/4dcedce59fd8166e,Blond\ntest/4dd3f6dd30333828,Black-and-white\ntest/4dd4ecd2137e4e18,Dobermann\ntest/4dd74bde9000db04,Musical theatre\ntest/4dd7fbd10982b2b6,Chordophone\ntest/4dd8e91e70d3983d,Antique car\ntest/4dd95204d4340ab4,Ramen,Soba\ntest/4ddbda7cfe9e96f3,Luxury vehicle\ntest/4ddd95bf3212036a,Auto racing,Motorcycling\ntest/4dde214bff6ef27d,Dog walking\ntest/4dde84abe3b984ff,Ball game,Team sport\ntest/4ddee860c5ad7824,Luxury vehicle\ntest/4de049ed5cdbdb09,Roller skating\ntest/4de0a154b2a49795,Fried food\ntest/4de1246fab9f9431,Black swan\ntest/4de2c67b555ec4a4,Plant stem\ntest/4de327d410fe70d3,Arthropod,Locust,Close-up\ntest/4de4094938b6d56f,Roman temple\ntest/4de444686df1b772,Close-up\ntest/4de7ced9233ace37,Arthropod\ntest/4de92047fe5db781,Close-up\ntest/4de9a5d4beafe82d,Military person\ntest/4de9a5fd858093b7,Luxury vehicle,Antique car\ntest/4de9b70295af1a29,Black-and-white,Military person\ntest/4dec3d71b439acd4,Extreme sport,Boating\ntest/4ded81f9585e65d0,Ball game,Team sport\ntest/4dedd7625413da0c,Black-and-white,Close-up\ntest/4def3a9dd498a5b1,Galleon\ntest/4def43e292219a30,Black-and-white\ntest/4df20a55a756fd56,Motorcycling\ntest/4df59b387b10f876,Combat sport\ntest/4dfad9aed4f6de37,Amphibian,Close-up\ntest/4dfafa952df21aab,Plant stem,Close-up\ntest/4dfc4be31dc8901e,Perching bird\ntest/4dfda12a5890a410,Cookies and crackers\ntest/4dfe32a553898142,Arthropod,Close-up\ntest/4dfe5cd60fb2f45a,Nile crocodile,Crocodilia\ntest/4dfe89a63212b3ce,Nordic skiing\ntest/4dffda05f2be005f,Coca-cola\ntest/4e03f0bd7fb944d1,Arthropod,Close-up\ntest/4e05b7f23a629cef,Dishware\ntest/4e05e89ed1e9fdf7,Close-up\ntest/4e067f4c0073808b,Wallaby\ntest/4e07bd5d873e1125,Whiskers\ntest/4e099f35dfed22e3,Fried food\ntest/4e0af94e531e6f7c,Fried food\ntest/4e0cbfe4bf8c2465,Compact car,Luxury vehicle,Performance car\ntest/4e0ccad0c2e0cbeb,Black-and-white\ntest/4e0cd0734916b02a,Electronic instrument\ntest/4e100010eae293f2,Luxury vehicle,Performance car\ntest/4e103dcf2c5239c7,Bovine\ntest/4e10bd0610ed094d,Electric piano,Electronic instrument\ntest/4e129dfaa06713e7,Close-up\ntest/4e16f3d33d6219b0,Leaf vegetable\ntest/4e1bc1af9b141c21,Luxury vehicle,Performance car\ntest/4e1c51d0acff5945,Luxury vehicle\ntest/4e1f22434cd0a722,Aircraft engine\ntest/4e216ef73e0ef865,Compact car,Antique car\ntest/4e22650de59cc651,Horse and buggy\ntest/4e227a224666072b,Freight transport\ntest/4e25884a099c56a3,Compact car,Antique car\ntest/4e265038420a1f43,Black-and-white\ntest/4e26ada1a6d124b3,Luxury vehicle\ntest/4e26ba402306726a,Inflatable\ntest/4e2713ded627230d,Close-up\ntest/4e27778df5f846e9,Sport utility vehicle\ntest/4e2cbde3cb73495f,Kitesurfing,Extreme sport,Wind wave,Surfing Equipment\ntest/4e2f1966226da890,Frigate\ntest/4e2fdb65105dc5b9,Luxury vehicle\ntest/4e30f75839fae913,Rural area,Bovine\ntest/4e30fc7f539f6c7f,Antique car\ntest/4e32787fcec72f92,Dog walking\ntest/4e32bd0f95092ea3,Sledding\ntest/4e357cc07a4a5410,Close-up\ntest/4e35841ab9713e84,Black-and-white,Close-up\ntest/4e36c305ad063f7b,Close-up\ntest/4e381dea84272ae3,Luxury vehicle\ntest/4e39f298ab64e9c7,Newsagent's shop\ntest/4e3d02df0801534e,Jeans,Compact car,Antique car\ntest/4e3d5de6e81bc3d3,Residential area\ntest/4e3ff50f646842cd,Extreme sport\ntest/4e413e613737ad7f,Luxury vehicle,Antique car\ntest/4e416d4066440a8f,Jeans,Luxury vehicle,Performance car\ntest/4e4203ea0f20fff8,Luxury vehicle\ntest/4e42aa800298a9f6,Cookies and crackers\ntest/4e4385fd5c7b37eb,Bovine\ntest/4e44723b000d7060,Jeans\ntest/4e4574841337b7d1,Drums\ntest/4e47f74919fa9a0a,Jeans\ntest/4e49c68f9f61f019,Ball game,Team sport\ntest/4e4d691fa74adf87,Plant stem\ntest/4e4e4add46b69b95,Close-up,Whiskers\ntest/4e5009a91a45f17d,Floristry\ntest/4e55b35294fe0629,Miniature Poodle\ntest/4e5eeac8b27bd6fa,Floristry\ntest/4e6427b93872d1cb,Freight transport\ntest/4e662713d76fe489,Extreme sport,Boating\ntest/4e675afed302dfd1,Rural area\ntest/4e6a00f050cbd347,Black-and-white\ntest/4e6b038b85cd66de,Close-up\ntest/4e6b32b2d1132112,Arthropod,Close-up\ntest/4e6b35599fa07e59,Compact car,Luxury vehicle,Antique car\ntest/4e6f75d798d3df94,Jeans\ntest/4e72da23e283d619,Luxury vehicle\ntest/4e72f6df7efa0d4a,Close-up\ntest/4e7391a8753a7af0,Antique car\ntest/4e73cdf1068beb80,Boating\ntest/4e741f761926d95c,Rural area\ntest/4e74ef873d7120dc,Luxury vehicle,Antique car\ntest/4e763503061c9b50,Luxury vehicle\ntest/4e77b644e00bbc41,Woodwind instrument\ntest/4e7af02f2bd7c2a1,Close-up\ntest/4e7b8b871832b7b1,Compact car,Luxury vehicle\ntest/4e7dce2a7f094a23,Black swan\ntest/4e7e96c4b5f5bf82,Close-up\ntest/4e7f5ae4f89f83de,Luxury vehicle\ntest/4e82d04d12476eae,Black-and-white\ntest/4e830bcf62b28ac8,Microcontroller\ntest/4e83da561f7d138b,Hound\ntest/4e841a9234735a1f,Team sport\ntest/4e8422fbc628ea80,Close-up\ntest/4e856269a7f10934,Watercolor paint\ntest/4e893a048022a7b3,Newsagent's shop\ntest/4e8967ecd31255aa,Nest box\ntest/4e8b9cae1cf53cfb,Close-up\ntest/4e8dab4eaf08f2d0,Close-up\ntest/4e8e204fe5e8c92c,Public speaking\ntest/4e8f91d4f890305f,Gumbo\ntest/4e901f9b22a5beef,Divemaster,Scuba diving,Underwater diving\ntest/4e90d267069ad337,Luxury vehicle,Performance car\ntest/4e90ee65041e95e0,Watercolor paint\ntest/4e911045135aa347,Close-up\ntest/4e9155deb93ca304,Zeppelin\ntest/4e92e6122b01e788,Backlighting,Black-and-white\ntest/4e9380983decb230,Luxury vehicle\ntest/4e965aac69fd4a98,Shallot\ntest/4e9682b11dfae9e1,Aircraft engine\ntest/4e9984dc2f3bfad5,Floristry\ntest/4e99bd1bbe3c1cd0,Rural area\ntest/4e99c8ecd5c4b5f0,Shallot\ntest/4e9d4da4797b799c,Compact car,Antique car\ntest/4e9dd61a1e4cc244,Barquentine,Frigate\ntest/4e9df759ce180a15,Ball game,Team sport\ntest/4e9e4c8910cd4d5e,Medical imaging\ntest/4ea3986df3da0e9c,Luxury vehicle\ntest/4ea3b7cd166115fa,Luxury vehicle,Sport utility vehicle\ntest/4ea4df51347cb91e,Extreme sport,Kitesurfing\ntest/4ea69a51203cec83,Compact car,Antique car\ntest/4ea72fa2da9b02fb,Stemware\ntest/4ea831c8f965d578,Combat sport\ntest/4eade5989cfc2e21,Luxury vehicle,Performance car\ntest/4eafbe215ce3c896,Pasta salad\ntest/4eb0fb0557e1ce8c,Calabaza\ntest/4eb3b440f6afa5a3,Luxury vehicle\ntest/4eb3fcb2a8c6a5e5,Hound\ntest/4eb4d2caba6b0313,Compact car,Luxury vehicle\ntest/4eb51b7d8ae26f43,Watercolor paint\ntest/4eb822efa46f274b,Compact car,Luxury vehicle\ntest/4ebb2a9da6de0b5d,Compact car,Antique car\ntest/4ebb36fb6d735406,Steamed rice\ntest/4ebf3f7b0b934264,Brisket,Beef tenderloin\ntest/4ec0af45479eb095,Vegetarian food\ntest/4ec11d8c34ac4c4d,Black-and-white,Domestic short-haired cat,Whiskers\ntest/4ec1ceff96b1a3e1,Extreme sport,Boating\ntest/4ec1cf492cde29a1,Touring car,Antique car\ntest/4ec34c5aabcf7fbd,Close-up\ntest/4ec36f70e1265862,Electronic instrument\ntest/4ec3cad62aac5a43,Extreme sport,Boating\ntest/4ec4ffe8de332eec,Team sport\ntest/4ec71e8bdf23fd45,Whiskers\ntest/4ec7b90c482db508,Steamed rice,Fried food\ntest/4ec87c248471eb91,Boating,Rowing\ntest/4ec9d899bb80e3ba,Luxury vehicle,Performance car\ntest/4eca09abb1a2d024,Sport utility vehicle\ntest/4eca5b7910d69107,Amphibian,Close-up\ntest/4ecd85a37f97c6b2,Flatbread\ntest/4ed0d695cb8728db,Arthropod\ntest/4ed0f12c80bc5052,Luxury vehicle\ntest/4ed5262bf691bba2,Boating\ntest/4eda9b0013b9045d,Close-up\ntest/4edbfa732af2ddad,Luxury vehicle,Performance car\ntest/4edc28ec5e25c324,Residential area\ntest/4edce00ba2f3c9c9,Ball game,Team sport\ntest/4edd7975f07bbe35,Extreme sport,Parachuting\ntest/4edf8c162c88cbbc,Hair coloring\ntest/4ee1213b0bc2774a,Luxury vehicle,Antique car\ntest/4ee30f20fd17e8da,Electronic instrument\ntest/4ee4878a39f3b30a,Zeppelin\ntest/4ee536c0a6e3931f,Amphibian,Close-up\ntest/4ee70a1191e49ede,Domestic short-haired cat,Whiskers\ntest/4eea48390f92233f,Lifebuoy\ntest/4eeac8423317bd3c,Close-up\ntest/4eead96ccbf7a62b,Rural area\ntest/4eeb051099b2c95e,Boating\ntest/4ef03696007ad64f,Black-and-white,Dishware\ntest/4ef0c3ed5729d678,Black-and-white\ntest/4ef5006344041d22,Fried food\ntest/4ef5126dbc5ebcc3,Leaf vegetable\ntest/4ef5de0079de0c72,Ball game\ntest/4ef6c5ee6a8d6864,Digital camera\ntest/4ef7bb00af337ae9,Outdoor shoe\ntest/4ef9e7b1af656336,Auto racing,Luxury vehicle\ntest/4f00491ac698b955,Luxury vehicle\ntest/4f00fa5d6b6671bd,Ball game,Team sport\ntest/4f017a092253be4d,Luxury vehicle,Sport utility vehicle\ntest/4f033dd740bfa441,Perching bird\ntest/4f05b5196686db83,Perching bird,Close-up\ntest/4f089c143310e8f6,Miniature Poodle\ntest/4f09be4c97799216,Ball game,Team sport\ntest/4f09dc25a724848c,Extreme sport\ntest/4f0a5ca8cac15cc0,Junk food\ntest/4f0c5f21a0500157,Luxury vehicle\ntest/4f0d36fba1c47d44,Arthropod,Close-up\ntest/4f0fc9cacb659cab,Falafel,Fried food\ntest/4f104194146c96fb,Wind wave\ntest/4f11845b97d0bedf,Compact car\ntest/4f11fe0da87b4cc9,Luxury vehicle\ntest/4f13d6ba9a48a5dc,Ball game,Team sport\ntest/4f13fec7e242430c,Hair coloring\ntest/4f1435237046c7e6,Floristry\ntest/4f14ff5908df600d,Compact car,Luxury vehicle\ntest/4f16635d961665f5,Close-up\ntest/4f16f6a73d784710,Compact car\ntest/4f18611bb07cbed7,Close-up\ntest/4f18c7010cc3f63a,Arthropod\ntest/4f1b16594140644f,Modern dance\ntest/4f1c59bd17b33118,Boating,Rowing\ntest/4f1d325a39811c38,Black-and-white\ntest/4f217eee30c07d91,Bovine\ntest/4f2343b8281ea366,Ball game,Team sport\ntest/4f2530c9a2c4ef0d,Boating\ntest/4f25c5dd6bd91d1b,Rural area,Bovine\ntest/4f2c8c4c528abe2e,Drums\ntest/4f2e0cbddf9c2401,Blond\ntest/4f2ffae85d9ce07b,Rural area\ntest/4f34f5a11a50dca7,Sport utility vehicle\ntest/4f3779c04d9330b2,Stemware\ntest/4f38f044dc2c429d,Horse racing,Equestrianism\ntest/4f3ab32d4bccbe4d,Auto racing\ntest/4f3d3b32ff4db0ba,Luxury vehicle,Antique car\ntest/4f3d69349dd52359,Fried food\ntest/4f41fa55c9e8f30b,Blond\ntest/4f43494bc64ec72b,Luxury vehicle,Antique car\ntest/4f443bae34545b88,Aircraft engine\ntest/4f4858c360d9d34c,Close-up\ntest/4f4ae1baabc991da,Blond\ntest/4f4b15188a7174be,Rural area\ntest/4f4b45a2ffc0dcfa,Cookies and crackers\ntest/4f4c507ecdd4e471,Bratwurst\ntest/4f4cfcd4fcfd2f69,Ball game\ntest/4f4dd7d537695312,Rural area\ntest/4f50a4ee0ad5d24d,Cookies and crackers\ntest/4f52a8070f72830b,Compact car\ntest/4f58294ea24be943,Close-up,Plant stem\ntest/4f58cbf15489e942,Compact car\ntest/4f59e60f54eaacec,Compact car\ntest/4f5aefdeffb4eaef,Compact car\ntest/4f5fcdbccb70fe17,Combat sport\ntest/4f6493f147630cd6,Boating,Rowing\ntest/4f6693f49ed63ca2,Flatbread\ntest/4f66d184298b29ea,Close-up\ntest/4f6703acf653af5d,Junk food,Fried food\ntest/4f6beb761bf142a4,Luxury vehicle,Antique car\ntest/4f6c4a9b7d43c43f,Luxury vehicle,Performance car\ntest/4f6e1f415f584340,Luxury vehicle\ntest/4f6ef624650e368f,Close-up\ntest/4f70cccb77f9ba59,Rural area\ntest/4f722070b0c5410f,Compact car,Luxury vehicle\ntest/4f758d320a48dcc0,Locust,Close-up\ntest/4f7970b006d04795,Residential area\ntest/4f7a11470b7e0b56,Luxury vehicle\ntest/4f7b105622fbf39f,Luxury vehicle,Performance car\ntest/4f7b110ae85d0984,Lawn game\ntest/4f7b8be0bb20984c,Arthropod,Locust,Close-up\ntest/4f7d6239c0e5cf67,Team sport\ntest/4f80e411112f20c9,Hair coloring\ntest/4f81057026ba768c,Digital camera,Black-and-white\ntest/4f86aef2c34d1a5a,Luxury vehicle,Performance car\ntest/4f86fc31efe4da03,Steamed rice\ntest/4f87191ba1277d75,Classical sculpture\ntest/4f87dfcdb9e570c8,Close-up\ntest/4f8b7cee35da352a,Aircraft engine\ntest/4f8e21a5de3e7bfe,Extreme sport\ntest/4f8eb3a4315daadb,Portrait photography,Close-up\ntest/4f8fa07b17e1f8dd,Close-up,Plant stem\ntest/4f90ab19176c4124,Black-and-white,Close-up\ntest/4f928442971b85ab,Antique car\ntest/4f9402f21ae61530,Luxury vehicle,Antique car\ntest/4f954736968fabb1,Luxury vehicle,Performance car\ntest/4f959c0056429af5,Black-and-white\ntest/4f9752bdba925be1,Vegetarian food\ntest/4f999b541dd5d487,Combat sport\ntest/4f9aedd1219875d2,Equestrianism\ntest/4f9e03ec26cc92a9,Vegetarian food\ntest/4f9e6fb292a604fa,Luxury vehicle\ntest/4f9edbca00c22045,Hair coloring\ntest/4f9fcd70307f871e,Bird of prey\ntest/4fa04a0d40bc46df,Extreme sport\ntest/4fa077506273d0fc,Monoplane\ntest/4fa0b83534b59922,Domestic short-haired cat,Whiskers\ntest/4fa17a32e39bd6f1,Luxury vehicle\ntest/4fa3146e8daa7346,Performance car\ntest/4fa579c46d3494b2,Blond\ntest/4fa5ff5e529abaec,Extreme sport,Parachuting\ntest/4fa8e06fc7b99ae4,Firearm\ntest/4fa90d9a73a5efe9,Sport utility vehicle\ntest/4fa93a985fed857a,Domestic short-haired cat,Close-up,Whiskers\ntest/4fa9c0522bd8c62c,Military vehicle,Sport utility vehicle\ntest/4faa54491fa0d97c,Coral reef fish,Close-up\ntest/4faa59238d44bcad,Jeans\ntest/4fabb465c99243cd,Black-and-white\ntest/4fabc49caa0668d6,Beef tenderloin\ntest/4fabd72ee9dc6983,Headphones\ntest/4fadbd089d0af298,Luxury vehicle,Performance car\ntest/4faf11025951e83d,Luxury vehicle\ntest/4faf5bfa0190e692,Plant stem\ntest/4fb045521e239eb6,Whiskers\ntest/4fb15d0fe7338f32,Compact car,Luxury vehicle\ntest/4fb39172beb43600,Arthropod\ntest/4fb44e07b50a06b4,Compact car\ntest/4fb5ccd011eb9156,Modern dance,Musical theatre\ntest/4fb678da0e4fc446,Medical imaging\ntest/4fb726733c133680,Luxury vehicle\ntest/4fb73409c68c30b6,Compact car,Luxury vehicle,Antique car\ntest/4fb933a7f77dbf7b,Jeans,Halter\ntest/4fb99911591545db,Luxury vehicle,Antique car\ntest/4fb9e52892a01f31,Luxury vehicle,Performance car\ntest/4fbd92848a9065ea,Military vehicle,Gun turret\ntest/4fbdb7140b68de8e,Luxury vehicle,Performance car\ntest/4fbe994df544252d,Figure skating\ntest/4fc0cca8d3944127,Blond\ntest/4fc0f33852f57fe7,Aircraft engine\ntest/4fc39af2ab9a279a,Blond,Hair coloring\ntest/4fc48d5dede42138,Bonbon\ntest/4fc4bbb1cfa6c2cc,Boating,Rowing\ntest/4fc6994977cbcbfc,Assault rifle,Firearm\ntest/4fc7564e6cf930f4,Digital camera\ntest/4fc7a53d08632b8c,Plant stem\ntest/4fc927bc8108634c,Black-and-white,Modern dance\ntest/4fc9f41fb9914612,Steamed rice,Fried food\ntest/4fcbe62fdabb8924,Luxury vehicle\ntest/4fccb5e52ab0dadc,Ball game,Team sport\ntest/4fccb99977dd04bc,Close-up\ntest/4fd1d6d995fa5153,Junk food\ntest/4fd28ec1a2314dd3,Aircraft engine\ntest/4fd745cd6d2a7cb7,Solar energy\ntest/4fd74cec978320bb,Arthropod,Close-up\ntest/4fd83912b9c912b4,Luxury vehicle,Performance car\ntest/4fda5ea488d7f835,Aerial photography,Glacial landform\ntest/4fdc2ff483c533b4,Senior citizen,Close-up\ntest/4fdc68013eb887a7,Extreme sport,Gliding\ntest/4fdccd83a33d40d1,Close-up\ntest/4fe11b3bb187e037,Boating\ntest/4fe5f429ad9e3ca7,Luxury vehicle\ntest/4fe6232730de9f6d,Middle ages\ntest/4fe6a07b42b1bd54,Compact car\ntest/4fe7154ed5b339e3,Equestrianism\ntest/4fe9a17cd72c7ff8,Wind wave\ntest/4fea184a533da5e6,Extreme sport\ntest/4fed6bc63d6c586d,Antique car\ntest/4fee5f7675cf75d8,Sport utility vehicle\ntest/4fee7c8de797afb6,Luxury vehicle\ntest/4feeaa38aebabd41,Luxury vehicle\ntest/4fef0af226528a85,Close-up\ntest/4fef7fa586126e24,Motorcycling\ntest/4ff17979c5725f48,Close-up\ntest/4ff2306dab883e42,Frozen yogurt\ntest/4ff3b4afd8317da7,Erinaceidae,Close-up,Whiskers\ntest/4ff73ea2a880da9a,Black-and-white,Close-up\ntest/4ff79076428f2f9d,Coca-cola\ntest/4ff82473f0141fcb,Wallaby\ntest/4ffd4d6f3d12253b,Close-up\ntest/4ffdce749792b79a,Black-and-white\ntest/4ffdf890fa0a921c,Close-up,Whiskers\ntest/4fff65d496b510ff,Luxury vehicle,Antique car\ntest/50004b537dea9680,Goats,Close-up\ntest/5000ad88b14e31ae,Bird of prey,Close-up\ntest/5000f4bccd37a7d5,Antique car\ntest/5002256231a6425e,Arthropod,Close-up\ntest/50057fbd728876e6,Bird of prey\ntest/500aef050e54a23f,Close-up\ntest/500d8b373d35fcdc,Coral reef fish\ntest/500e293bd825c04d,Close-up,Plant stem\ntest/500f3cf59fa4df19,Freight transport\ntest/500fa0a23e3a365a,Church bell\ntest/501004da4062546c,Halter\ntest/5011bf64c04b3f0a,Close-up\ntest/5011ce6961a06d38,Fried food\ntest/5012c81012d56761,Close-up\ntest/50136b75fdf0a0b5,Luxury vehicle,Performance car\ntest/5014eea2fd09a6c8,Luxury vehicle,Antique car\ntest/50152080c0ecfb56,Road cycling\ntest/5016b8056f086029,Close-up\ntest/5017b27280f0c203,Hound\ntest/50195fa810e9ca99,Chordophone\ntest/5019995626f38d53,Floristry\ntest/501aab90f13cbefe,Compact car,Luxury vehicle,Antique car\ntest/501afe517530a883,Close-up\ntest/501f7d181fa4c2b1,Black-and-white,Military person\ntest/50245aaec179b326,Bovine\ntest/502616751aa75c3b,Plant stem,Close-up\ntest/5028516744d45f1a,Close-up\ntest/502917b37e7a5545,Fried food\ntest/502b226e5e810e09,Boating\ntest/502d0019cd445d62,Sport utility vehicle\ntest/502dcac5ec753663,Antique car\ntest/502e5faf2ff7a088,Auto racing\ntest/5031af80538a1acd,Briefs\ntest/50326bca2476148c,Arthropod,Close-up\ntest/5032999589cccfcd,Close-up\ntest/50339e04a107aa9a,Ball game,Team sport\ntest/50347b5667128fe0,Compact car,Luxury vehicle\ntest/50377e1fcc56d0b0,Sport utility vehicle\ntest/5038a8326e39b682,Jeans\ntest/5038b1b052e34f8d,Electronic instrument\ntest/503c349d6fc567ef,Luxury vehicle,Performance car\ntest/503df99640378ddf,Fried food\ntest/503e94e33623c42f,Performance car\ntest/503f32f74400be45,Barquentine,Frigate\ntest/503f43092021faa1,Domestic short-haired cat,Whiskers\ntest/504167fd3ede9734,Floristry\ntest/5042ca0bd0c63f49,Luxury vehicle\ntest/5043f4dd0ab8cb38,Arthropod,Locust,Close-up\ntest/504467ea0029d820,Close-up,Scaled reptile\ntest/5045249bbb45f886,Close-up,Chordophone\ntest/504528c051c06fd1,Compact car\ntest/504735463c27a5ca,Performance car\ntest/504875e5b3868657,Steamed rice\ntest/504c3498eff3c512,Lumpia\ntest/504d4679c95131dd,Go-kart\ntest/504f6dd2d032c6b3,Steamed rice\ntest/504fb616786d46d3,Close-up\ntest/505295bbeb6e3987,Rural area\ntest/5052cf12a35ad13a,Jeans,Black-and-white\ntest/50590bbbd717ae0e,Luxury vehicle\ntest/5059cb8fdcfeee5d,Pelmeni\ntest/505a7e3a8e196fc6,Hound\ntest/505c9999688d973f,Boating\ntest/505ccff90e56cdf6,Bovine\ntest/505d60df9f11aa9c,Luxury vehicle\ntest/505de7f244120a41,Close-up\ntest/505e85d67c510bce,Nordic skiing\ntest/5062025bd783f4c4,Nuclear power plant\ntest/50644931a33f48ea,Sport utility vehicle\ntest/5064f5f818889f9c,Close-up\ntest/506538c37f4d16bb,Jeans\ntest/50665aafb8ea8b94,Close-up,Chordophone\ntest/5067593d51a8dad3,Monoplane\ntest/5069503d96d61c83,Close-up,Floristry\ntest/5069d2a610a2081c,Close-up,Whiskers\ntest/506a490a8486cc3f,Close-up\ntest/506e1c27a7e3778c,Plant stem\ntest/506e6a7802329dbb,Compact car\ntest/50701bdc0b1c8ef8,Close-up\ntest/5070e19a6ff72240,Antique car\ntest/5072ac32be563afa,Drums,Chordophone\ntest/5074ca91fb51d7b9,Antique car\ntest/507716af1c037357,Luxury vehicle\ntest/50777068b1720513,Military person\ntest/50778748dff1a744,Fried food\ntest/507b7f195c368a0b,Cookies and crackers\ntest/507d9a811ecf500e,Fried food\ntest/507f6080f0af26ce,Compact car\ntest/507fce7992745eb5,Peafowl,Close-up\ntest/508120afa6298df8,Whiskers\ntest/5081ae7d7bd1a83a,Ball game,Team sport\ntest/5084c71af0c5ec55,Scaled reptile\ntest/5084e921b30d3154,Compact car\ntest/50854d33809a228e,Close-up\ntest/50879bd476ef5eb4,Extreme sport\ntest/50884e9ce1896087,Junk food\ntest/5088ad8f995d4423,Luxury vehicle,Performance car\ntest/508a8c99dbc63c40,Dishware\ntest/508bd8d56bd69781,Motorcycling\ntest/508cc7005fdb5fc1,Camera operator\ntest/508d5e9e5ec34182,Chordophone\ntest/508e797901f240dd,Close-up,Whiskers\ntest/509024e07e217431,Glacial landform\ntest/50906e855eb5cff9,Close-up\ntest/5090acd79151e5d2,Bovine,Equestrianism\ntest/5097ff6b3a06ec57,Fried food\ntest/509d5158f4eb0a39,Domestic short-haired cat,Whiskers\ntest/509f1919fc44ced5,Floristry\ntest/50a0f66a39f2378e,Close-up\ntest/50a2e60138edeff9,Luxury vehicle\ntest/50a913719ae18a20,Bonbon\ntest/50a91483c0a6c743,Compact car\ntest/50ab4f602b25792c,Compact car\ntest/50ac058c46584cb1,Jeans,Siberian husky\ntest/50adec84343b43c1,Ball game,Team sport\ntest/50ae49368365e9bd,Luxury vehicle,Performance car\ntest/50af3007649302a8,Rural area\ntest/50afe1fc61726944,Bagpipes\ntest/50b16878e779e34c,Close-up\ntest/50b23a8e8a263f57,Pork chop\ntest/50b2dfb6495ef539,Frigate\ntest/50b33c5159f0759d,Close-up\ntest/50b4bbedbc563868,Close-up\ntest/50b6c1de5a25ce2f,Compact car\ntest/50b7061841b81488,Computer speaker\ntest/50b97c23205b8e04,Boating\ntest/50b992e7bb018782,Whiskers,Domestic rabbit\ntest/50ba919bb452f7d1,Backlighting\ntest/50bb62d664042d33,Dishware\ntest/50bdf4705ae7f144,Antique car,Luxury vehicle,Performance car\ntest/50beb47e587685f6,Compact car\ntest/50bf790cf497dd92,Domestic short-haired cat,Close-up,Whiskers\ntest/50c0695cc2198e19,Compact car\ntest/50c4418fdbec63d2,Close-up\ntest/50c622b9ef57d90c,Combat sport\ntest/50c647d96b5c3d93,Plant stem\ntest/50c654f86a15d150,Performance car\ntest/50c66ffa1144b6bd,Compact car\ntest/50c8b544b846a69b,Rural area,Bovine,Herding\ntest/50ca168345d6d24e,Luxury vehicle,Sport utility vehicle\ntest/50ca44af093f5733,Extreme sport\ntest/50cb32d7762fc5e1,Roman temple\ntest/50cbad8bc322d435,Black-and-white,Close-up\ntest/50cf9aea7e1ebfa3,Roman temple\ntest/50cfa45ccc89ee7a,Breadboard\ntest/50d208414727c0b4,Cobblestone\ntest/50d2f99882b67c1f,Ball game,Team sport\ntest/50d52d8b40e8e223,Fried noodles\ntest/50d9645d59b41cc6,High-speed rail\ntest/50dbb118034df443,Barechested\ntest/50e0f83e12d61fb0,Performance car,Antique car\ntest/50e19f05256b03f5,Compact car\ntest/50e829459a9e639f,Black-and-white,Woodwind instrument\ntest/50e8e6dd5f6e86e3,Auto racing\ntest/50ec37f1126d7c86,Close-up\ntest/50ec7720c1bde0d8,Fried food\ntest/50ef37e05b2b6452,Compact car,Luxury vehicle\ntest/50ef808aa89eec7a,Boating\ntest/50f0f5333857f0e5,Luxury vehicle,Sport utility vehicle\ntest/50f2bf380850dc5f,Luxury vehicle,Performance car\ntest/50f2db0dd1ddddb8,Luxury vehicle\ntest/50f787e4f824904e,Barquentine\ntest/50f93b86cbf78108,Luxury vehicle\ntest/50fccc08fb18d454,Close-up\ntest/50fd8344af420fbd,Luxury vehicle\ntest/50fe2e20f9451256,Fried noodles\ntest/50fe44af129eac35,Luxury vehicle,Antique car\ntest/50ff2e6b67562ffd,Compact car,Luxury vehicle,Antique car\ntest/50fff3903eea7e81,Performance car\ntest/51000b62215a8e55,Close-up,Scaled reptile\ntest/510086d3f2b163b0,Ball game,Team sport\ntest/510283e85b12f863,Close-up\ntest/51036a5675a9effd,Rural area\ntest/5103e161ffc26bfa,Vegetarian food,Fried food\ntest/5105ce06b1731e4c,Jeans\ntest/51090a67f7d954bc,Sport utility vehicle\ntest/510962e3d6c25aa1,Black-and-white\ntest/510cd4e37388f3bf,Watercolor paint\ntest/510e32f67044933f,Boating\ntest/511062532edbfc98,Plant stem,Close-up\ntest/5111370d5ef62961,Arthropod,Close-up\ntest/511242d3bbc472bf,Compact car,Luxury vehicle,Antique car\ntest/5112a3327b4cea53,Luxury vehicle\ntest/5112b639bbf015fa,Leaf vegetable,Vegetarian food\ntest/5114566b4b94ec7c,Close-up\ntest/5114d905ca1bfea3,Barbecue chicken\ntest/5117a87350b5d289,High-speed rail\ntest/5118b5f18517537b,Whiskers\ntest/5119bfb5aa7b070e,Performance car,Luxury vehicle,Antique car\ntest/511a0d70638edc7c,Road cycling\ntest/511cf87eaafffa24,Luxury vehicle,Antique car\ntest/511d3f6782eaedf2,Close-up,Whiskers\ntest/511e3c874661ab0e,Luxury vehicle\ntest/5120dd8f6e680f06,Fried food\ntest/5126cfba828c8ad6,Luxury vehicle,Antique car\ntest/51274638d03f07df,Fried food\ntest/5128a7f51a5f79e8,Flatbread\ntest/512937f444e4472d,Monoplane,Gliding\ntest/512a1adf52ce81b4,Chordophone,Electric guitar\ntest/512adba2e3bb8e15,Luxury vehicle\ntest/512b60ded86fc881,Compact car,Luxury vehicle,Antique car\ntest/512c618b8e32c107,Luxury vehicle\ntest/512d0bd83dd7bc6c,Luxury vehicle,Antique car\ntest/512d7982601ef838,Extreme sport,Parachuting\ntest/512f9dd48a5aff53,Aerial photography\ntest/5135f567c8083097,Luxury vehicle\ntest/513609e102f85698,Orator,Public speaking\ntest/51372436c0ff3cf2,Performance car\ntest/5139e961d448887a,Combat sport,Sumo\ntest/513b13d817a6c139,Close-up,Floristry\ntest/513bdc19fc6298c7,Hair coloring\ntest/513ee6db5eb695d9,Chordophone\ntest/5140ebbc03d02f44,Close-up\ntest/5142594963a76ac8,Compact car,Luxury vehicle,Antique car\ntest/51441d385bf0f35d,Rural area\ntest/514428973df647c8,Stemware\ntest/5144967602ba58b3,Luxury vehicle,Antique car\ntest/5144a3f99541f059,Sport utility vehicle\ntest/51482565513e0282,Fried noodles\ntest/514a01f200c4bec9,Nile crocodile,Crocodilia\ntest/5150817bd0f56ef0,Briefs\ntest/5151b9caec4c44ec,Machine tool\ntest/515206140b13337b,Gun turret\ntest/5154ec1a9b641234,Outdoor shoe\ntest/515bcadc8a37f915,Herding,Goats,Bovine\ntest/515d03d5e9070438,Amphibian,Close-up\ntest/515d048fd43ce0e3,Nest box\ntest/515eb34a016f6c61,Bovine\ntest/515eb61979b3326a,Plant stem\ntest/515ed193a1755253,Vegetarian food,Flatbread\ntest/515f73f736410792,Rural area\ntest/516037605f2493b7,Whiskers\ntest/51612dfb734cc08a,Close-up\ntest/51615559624b75cb,Domestic short-haired cat,Whiskers\ntest/5163d54541e76c7c,Barechested,Briefs\ntest/5165d647ac20bfa6,Luxury vehicle\ntest/5165fa044e8b4c2a,Shinto shrine\ntest/5169485f97303e20,Close-up\ntest/5169e497062da589,Aerial photography\ntest/516a38c9225e2a02,Extreme sport,Bouldering\ntest/516a5736c88b5dbd,Chordophone\ntest/516afa1958dc901d,Road cycling\ntest/516f3ae8a4d53614,Compact car,Luxury vehicle\ntest/516f6c3074792f2d,Close-up\ntest/516f6e145233a8ec,Sport utility vehicle\ntest/5172d3ce76a34ff2,Close-up,Scaled reptile\ntest/5172f19f0a512134,Arthropod,Close-up\ntest/5172f64419a18df1,Pit bull\ntest/5176cca60e245e3a,Arthropod,Close-up\ntest/5177e60703647c34,Chordophone\ntest/51786f1929198048,Sport utility vehicle\ntest/51798cd7e894d239,Bratwurst\ntest/517ba37d28cb7ae3,Falafel\ntest/517ddc1a0974b01f,Perching bird\ntest/517e3058894e13d9,Leaf vegetable\ntest/51805540ea007494,Galleon,Barquentine,Frigate\ntest/518516c70e2568e8,Close-up\ntest/518583e58c869a2a,Goats\ntest/5186e47698ea3204,Aerial photography\ntest/51886d3e3f1d1c94,Cobblestone\ntest/518b94e0a897cf3e,Combat sport\ntest/518f55afd1f073c8,Modern dance,Musical theatre\ntest/519112ecd9518c25,Jeans\ntest/51928591fc5c7632,Ball game,Team sport\ntest/5193f364f08bb156,Firearm\ntest/51945e187ef50dff,Close-up\ntest/519496f297a73a84,Luxury vehicle\ntest/5195e381ad92106e,Close-up\ntest/5198c820d3b83737,Black-and-white,Headphones\ntest/519a602536f4f557,Coral reef fish\ntest/51a07706ce0b0a77,Luxury vehicle\ntest/51a1afd42be02364,Jeans\ntest/51a383c60242872e,Floristry\ntest/51a4b9f656f4a575,Molluscs\ntest/51a4d507ceec4146,Combat sport\ntest/51a52150c60cbc64,Close-up\ntest/51a605606b058c1d,Inflatable\ntest/51a60fc656f3b392,Compact car,Luxury vehicle\ntest/51a6feae5aa39119,Peafowl\ntest/51ad18d91e041765,Whiskers\ntest/51ae02f6c585c444,Halter,Equestrianism\ntest/51b161ab59278930,Vegetarian food\ntest/51b1d473e22baa22,Rural area,Bovine\ntest/51b255d25a69f807,Compact car,Performance car,Luxury vehicle,Antique car\ntest/51b2a814946ed725,Sport utility vehicle\ntest/51b2b8f1bb837c7c,Leaf vegetable\ntest/51b2d23bdf89a58a,Military person\ntest/51b6e437af3c8523,Heavy cruiser,Frigate\ntest/51b70f8acdea7b49,Close-up\ntest/51b7609e07adc2cc,Rural area,Aerial photography\ntest/51b76f0a290b1e46,Arthropod,Close-up\ntest/51b8d7767bb37198,Microcontroller\ntest/51b95545a2a738bf,Compact car\ntest/51ba70702955ea39,Frozen yogurt\ntest/51c1009715dfb56c,Black-and-white,Plant stem,Close-up\ntest/51c14491dd8f589c,Goats\ntest/51c4eacd1a5ad260,Drums,Black-and-white\ntest/51c5966a0c871e2d,Flatbread\ntest/51c9915e089e0aa3,Compact car,Antique car\ntest/51cc0b0ccdc759a7,Leaf vegetable\ntest/51cd761c44ec0a20,Close-up\ntest/51cdfa8fd8839900,Bird of prey\ntest/51cea6d0b892e042,Hound,Close-up\ntest/51cf3228c57eaba7,Luxury vehicle\ntest/51cf394a7c911ef1,Luxury vehicle,Performance car\ntest/51d067cee1e0021e,Amphibian\ntest/51d1a4c3f92885aa,Compact car,Luxury vehicle\ntest/51d3796d187aa4df,Performance car,Luxury vehicle,Antique car\ntest/51d47e8d418e8b60,Residential area\ntest/51d49accf9bdd7c9,Rural area\ntest/51d50da4e2400402,Close-up\ntest/51d69e83d665c989,Shallot\ntest/51d7ac4e58029887,Compact car\ntest/51dcd316cb557f94,Fried food\ntest/51dde51858426f1f,Extreme sport,Road cycling\ntest/51ded978b3d09be1,Bird of prey\ntest/51df1b51a062b34e,Wind wave\ntest/51e1fc68f66f0881,Arcade game\ntest/51e35362c770ae50,Compact car\ntest/51e5ba65d1cbeb19,Combat sport\ntest/51e64f6ceabb4ce6,Camera operator\ntest/51e6598174bb98da,Black-and-white\ntest/51e6956bc27fe3a4,Blond\ntest/51e77a8c8bafe7cd,Blond,Close-up\ntest/51eb6ee1ea543ccd,Water polo,Ball game,Team sport\ntest/51f048056053a99a,Close-up\ntest/51f08c7fcc42126a,Bagpipes\ntest/51f152b015cb7dca,Extreme sport\ntest/51f17f0ecd40ee02,Luxury vehicle\ntest/51f267c900224be4,Compact car,Luxury vehicle,Antique car\ntest/51f28f7232925084,Boating\ntest/51f2f61586d76319,Vegetarian food,Flatbread\ntest/51f3af93c339fe15,Cosmetics\ntest/51f4157d5dd585ab,Perching bird\ntest/51fa18f9d7e1063a,Compact car,Luxury vehicle,Antique car\ntest/51fa9f12f0ac5f54,Close-up\ntest/51fb49a3488b5b17,Stemware\ntest/51fb71c678fbf5ac,Compact car\ntest/51fce7ef78f850ed,Ball game\ntest/51fee9452ab4189c,Close-up\ntest/51ff4611c45f4dab,Black-and-white\ntest/52004797cd00026a,Close-up\ntest/52005653c7e96e3c,Frigate\ntest/52031e63cf67fc03,Close-up\ntest/5203cea3ffe3693a,Close-up\ntest/52041deac3313fd2,Molluscs,Close-up\ntest/52075e5165b6b32e,Close-up\ntest/520761d87e87bd7f,Luxury vehicle,Antique car\ntest/5207d5aab11928f3,Close-up\ntest/52080617389a5e12,Whiskers\ntest/520818d4b6b21426,Extreme sport,Road cycling\ntest/5208be3750a33c40,Arthropod,Close-up\ntest/520938b2c3a0bd8e,Extreme sport,Boating\ntest/520a279514ad68ce,Cosmetics\ntest/520ac5f11ebb926f,Ball game,Team sport\ntest/520bfaffa01fe25b,Fried food\ntest/520c49b1c674437d,Close-up\ntest/520da7d8e8965d0f,Compact car,Luxury vehicle,Antique car\ntest/520e00a44297c989,Floristry\ntest/520e2532f06bf73e,Close-up\ntest/5212f32fd05be8e1,Luxury vehicle\ntest/52130936328eb89e,Outdoor shoe\ntest/521775fa3627eb27,Ball game,Team sport\ntest/521b8d854149021c,Monoplane,Gliding\ntest/521c78bf7a315c94,Arthropod,Close-up\ntest/521ce7cd732d5f65,Close-up\ntest/521d729019a0156d,Rural area\ntest/521e0585890bac79,Senior citizen\ntest/52208c53a30ef8e5,Close-up\ntest/522770057beeb1c3,Jeans\ntest/522782c37630dba3,Arthropod,Close-up\ntest/5227c39dc8c26c8b,Military vehicle,Gun turret\ntest/522834a9f3b54d03,Trampolining\ntest/5228e63f624fc54a,Jeans\ntest/522a58b85d85935c,Luxury vehicle,Performance car\ntest/522f9f221b963079,Vegetarian food\ntest/5230b00272204a5a,Close-up\ntest/5230e4ca7d570346,Luxury vehicle\ntest/523155ed656b2d78,Leaf vegetable\ntest/523354bab9b5a044,Blond,Close-up\ntest/52339ad3c2d3f867,Coral reef fish\ntest/523a7e16da172ed3,Residential area\ntest/523ba9a5488d3c08,Arthropod,Close-up\ntest/523bbee8914848f9,Combat sport,Sumo\ntest/52417c6af327cf31,Close-up\ntest/524247d6943a3da9,Boating\ntest/52435fac7ad9f225,Horse racing,Equestrianism\ntest/524478471f869688,Rural area\ntest/5244864612b0d730,Extreme sport\ntest/5246436b12460224,Perching bird\ntest/52486e3737d01dc2,Chordophone\ntest/52491dfdb5474272,Residential area\ntest/524bbb4d0ceadc59,Compact car,Sport utility vehicle\ntest/524c8d43b8ac8dfb,Close-up\ntest/52516ef9567805c5,Luxury vehicle\ntest/5252794a6ef42a7d,Close-up\ntest/5254edd7dc91c3a2,Pit bull\ntest/5259f8408fef52d6,Monoplane\ntest/525bae8d5d9c1992,Siberian husky\ntest/525d96de125ddcd4,Arthropod,Close-up\ntest/525e9ebd77494559,Aircraft engine\ntest/525f429bd95d6614,Musical theatre\ntest/526367592a8cf77c,Rural area\ntest/5265f65769a09081,Milky way\ntest/52672e3d5837effc,Firearm\ntest/5267a5c1474b83a1,Coral reef fish\ntest/5269cd6304acc3e5,Residential area,Aerial photography\ntest/526a72a3dbc0f9a4,Aircraft engine\ntest/526d9bdc6d16563d,Leaf vegetable,Vegetarian food\ntest/526e0b4f63c578d5,Luxury vehicle,Performance car\ntest/5271f13bce339d09,Vegetarian food\ntest/5275f57e5f87e0ef,Jiaozi,Pelmeni\ntest/52761c518b72be19,Roman temple\ntest/5276c5b60c7d9ba8,Close-up,Plant stem\ntest/5276ea14fbb4c0d0,Vegetarian food,Fried noodles,Soba\ntest/5276ea5e17503834,Aerial photography\ntest/5276ea5e873ec191,Coca-cola\ntest/52772cb60ec8b604,Goats,Close-up\ntest/527767ec6b17ae59,Arthropod,Close-up\ntest/5277e28dc8e120a7,Compact car\ntest/527bf64302583525,Amphibian,Scaled reptile\ntest/527c5821fba5ac98,Dishware\ntest/527cb64336c84605,Domestic short-haired cat,Whiskers\ntest/527ec070017d5fa8,Childbirth\ntest/52806385350cb39a,Motorcycling\ntest/528219663af7c0d2,Jeans\ntest/5283ae8e53787e68,Residential area\ntest/5284406c794d015f,Wind wave\ntest/5289786095f2a292,Angling\ntest/528c232ac8d175ae,Billiards\ntest/528db7f88ac0b2ea,Extreme sport,Parachuting\ntest/528f777d7d6da589,Extreme sport,Divemaster,Underwater diving\ntest/528fa60d7653f4ec,Combat sport\ntest/528fcc3f588361ee,Firearm,Headphones,Shooting range\ntest/5290189ef5ba5068,Rural area\ntest/529034157650f328,Compact car,Luxury vehicle,Antique car\ntest/52907cd37481f342,Antique car\ntest/52907ed836761a3b,Aircraft engine\ntest/529168296b92e46b,Microcontroller\ntest/52921830cfc31c74,Antique car\ntest/529328eed662dddc,Close-up\ntest/5293f023720f99aa,Billiards,Billiard room\ntest/52976a224ab6e5d8,Khinkali,Jiaozi\ntest/529a22c7fcb67273,Molluscs,Close-up\ntest/529ab9ae3a718c06,Compact car\ntest/529c3b784a86e1d6,Boating,Rowing\ntest/529d9d2284a92b02,Fried food\ntest/529e87eb15187d69,Close-up\ntest/529f08cf13da6248,Fried food\ntest/529f8f87a44f4136,Scaled reptile\ntest/52a1f660b27a6627,Road cycling\ntest/52a669147685a282,Drums\ntest/52a6972d1b6562bc,Lawn game,Ball game\ntest/52a6fbb46c8f41a9,Compact car,Sport utility vehicle\ntest/52a73c151b7c12e5,Luxury vehicle,Performance car\ntest/52a778269d49ca6c,Black-and-white,Performance car\ntest/52a8bbf93f7b2316,Plant stem,Close-up\ntest/52a974c80f715309,Close-up\ntest/52a9dbfefe0e6863,Luxury vehicle,Antique car\ntest/52aa452196a739af,Whiskers\ntest/52ab7fa27adc2770,Sport utility vehicle\ntest/52b223bf5fc70c68,Perching bird\ntest/52b29d80b2bdc937,Billiard room,Billiards\ntest/52b2e103f4e22120,Antique car\ntest/52b6473b40f51829,Military person\ntest/52b68da31fb21948,Close-up\ntest/52b763f6f940f97e,Domestic short-haired cat,Whiskers\ntest/52b7efa51db8a13a,Arthropod,Close-up\ntest/52b81283fca3dbe4,Close-up\ntest/52b8428114aab942,Black-and-white\ntest/52b9d82c2ffb3bec,Solar energy\ntest/52ba200dc17e2010,Luxury vehicle\ntest/52baa4f64e63e33d,Floristry\ntest/52bad2facb26fe1d,Luxury vehicle\ntest/52bb4940f00571be,Aircraft engine\ntest/52bb540c7fa53382,Sport utility vehicle\ntest/52bbce12671c89f1,Bovine\ntest/52bd0e62d9b5a396,Microcontroller\ntest/52c007607fdabe01,Residential area\ntest/52c1179625225c9a,Auto racing,Compact car\ntest/52c1444f43fb1fdc,Bovine,Close-up\ntest/52c1bcfe01c9f8ba,Nuclear power plant\ntest/52c20e81bc434b1d,Compact car\ntest/52c2bbeafb9d3960,Rural area\ntest/52c2fa27971849a0,Luxury vehicle\ntest/52c375c8509b25b6,Compact car\ntest/52c747f8dd6883f8,Extreme sport\ntest/52ca4b0ec26e6c53,Performance car\ntest/52cb64bd1659ca38,Luxury vehicle\ntest/52d0a27b4d01ded7,Luxury vehicle,Antique car\ntest/52d0d1864f4d3b3f,Military person\ntest/52d0dbe8c5bca588,Black-and-white\ntest/52d57d0ad3897023,Close-up\ntest/52d57fae3e27fa6e,Great white shark\ntest/52d6421dbe9e7100,Ball game,Team sport\ntest/52d72b49b9f2199c,Compact car,Antique car\ntest/52d789485fa377bc,Close-up\ntest/52d7d724c6537274,Cookies and crackers\ntest/52d8d620c33c91ca,Performance car\ntest/52d8fce8c32fe306,Motorcycling\ntest/52de09bb9290476d,Musical theatre\ntest/52df3d1e8ff2d069,Ramen\ntest/52df50395818c34f,Close-up\ntest/52e08be356ce0d94,Great white shark\ntest/52e1dee36431aba5,Luxury vehicle\ntest/52e26989461da58f,Hound\ntest/52e281a760aca5a3,Vegetarian food\ntest/52e60e612f88d1e8,Luxury vehicle\ntest/52e6852b032c0d86,Arthropod,Close-up\ntest/52e6880947120d4e,Boating\ntest/52e6a08bf714ab3e,Black-and-white,Modern dance\ntest/52e8b26047df25ad,Luxury vehicle,Performance car\ntest/52eb7580bd0c5a7a,Gumbo\ntest/52ef0fe8690eb546,Arthropod,Close-up\ntest/52f4e7f5c93e3d75,Close-up\ntest/52f58b6de8359281,Forklift truck\ntest/52f66d4e69681d7c,Close-up\ntest/52f893eff0af91ed,Close-up\ntest/52fa5965331066b3,Luxury vehicle,Antique car\ntest/52faa2e96f46635d,Boating\ntest/53013970a90ad067,Compact car\ntest/530164caac4b51ce,Steamed rice\ntest/530390b8c8a1b1c3,Close-up,Whiskers\ntest/53047c39cfc90468,Cookies and crackers\ntest/53055211a9b8b62c,Ball game\ntest/5305a594e5f192ae,Briefs\ntest/530847a6644c0351,Rear-view mirror,Luxury vehicle\ntest/53088f2e76186bf1,Fried food\ntest/530a59073149a771,Domestic short-haired cat,Whiskers\ntest/530b21132a80bf72,Close-up\ntest/530d318c6e3ec8b4,Plant stem\ntest/530dbbce49e60180,Luxury vehicle\ntest/530e63ed6ca61a02,Arthropod,Close-up\ntest/530f05dad528fae4,Gun turret,Firearm\ntest/530f353831c8c3dc,Glacial landform\ntest/530f3f2288485d18,Aircraft engine\ntest/530fc13710a9bc8d,Sport utility vehicle\ntest/530fdbb984f3b8ad,Arthropod,Close-up\ntest/53115880e251f514,Scaled reptile\ntest/53135f202f4340ab,Luxury vehicle,Performance car\ntest/5315c7800831731e,Antique car\ntest/5317d0c01e318117,Stemware\ntest/5318cca5a379c31f,Canola\ntest/531a01c57573d6b9,Bird of prey\ntest/531ab3c0a13ceaa8,Scaled reptile\ntest/531b4599eead24f7,Luxury vehicle,Performance car\ntest/531f97420b6c4c1c,Compact car\ntest/5322a91a9960e0f1,Portrait photography,Hair coloring,Close-up\ntest/5322e39247c22625,Military person\ntest/53251d79be9df50e,Dishware\ntest/532631c4b8d57382,Ale\ntest/532b6a445345fdc7,Arthropod,Close-up\ntest/532cd2e9ec45a281,Residential area\ntest/532dbca6ece38f6f,Performance car\ntest/532fe4c89e7b86d2,Ramen\ntest/533191aeb64c6ee7,Aerial photography\ntest/53327c1402167a4a,Shinto shrine\ntest/533599004b77b18f,Electronic instrument\ntest/53366387d26f0828,Close-up\ntest/5336ca923a6851e5,Sport utility vehicle\ntest/5337a4dae75449bd,Luxury vehicle\ntest/533a000347d4b072,Luxury vehicle,Antique car\ntest/533a4c0b1724cf33,Aircraft engine\ntest/533a7db481ec9584,Luxury vehicle,Antique car\ntest/533b85f1b088732d,Combat sport\ntest/533cffbd85bfc953,Luxury vehicle\ntest/533e4ef4ba2343f1,Equitation,Equestrianism\ntest/53400ecd6f0d7799,Luxury vehicle,Antique car\ntest/53412649822f0ffc,Performance car,Luxury vehicle,Antique car\ntest/53419a2099424b78,Luxury vehicle\ntest/5341a5ff33c978ca,Close-up\ntest/5341fb6b85a37908,Luxury vehicle,Performance car\ntest/5343b64732666414,Ball game,Team sport\ntest/5343d149163b5ac0,Hound\ntest/5344a0c65e79b59b,Melee weapon,Hunting knife\ntest/5348c94b98c1fd89,Luxury vehicle\ntest/5349834742daa3e6,Luxury vehicle\ntest/534c092808753c05,Jeans\ntest/5352585e2f11ef00,Plant stem\ntest/53533646e17edc94,Close-up\ntest/53537dfc83cd5cb4,Freight transport\ntest/5354286f9d07f704,Roller skating\ntest/5355554fa743f81f,Gumbo\ntest/53559ecd646f8d60,Close-up\ntest/535647e16bac1bf5,Black-and-white\ntest/5356c849811400d4,Black-and-white,Whiskers\ntest/53574a190c16d6b8,Headphones\ntest/535980ecc222713e,Ball game,Team sport\ntest/535a38748a74791e,Electronic instrument\ntest/535acf82fc510572,Black-and-white,Close-up\ntest/535d48571574db22,Luxury vehicle,Sport utility vehicle\ntest/535d488f00e610ac,Machine tool\ntest/535e2ee3a70f19ca,Bovine\ntest/535faf210f5e4859,Wind wave,Glacial landform\ntest/536035d02e738ea7,Close-up\ntest/536065f7987dd43c,Sport utility vehicle\ntest/5362510dd1d55b2c,Compact car\ntest/5364e809157b7b53,Residential area\ntest/5365fdeefa3bb8a0,Bovine\ntest/5366aa400f071f24,Close-up\ntest/536ac7f6b820d235,Close-up\ntest/536c172341df7ac6,Compact car\ntest/536ee3f2c1e4b2a6,Luxury vehicle,Performance car\ntest/5370111c15d2e3ef,Luxury vehicle,Performance car\ntest/5371acadf8060f53,Luxury vehicle,Antique car\ntest/5376fee27c87f8eb,Boating\ntest/53782d016d97266b,Portrait photography\ntest/537b1f719e70b553,Luxury vehicle\ntest/537b374be496631d,Luxury vehicle\ntest/537c4be8bd3a4226,Coca-cola\ntest/537f53dce6e8a2cb,Close-up\ntest/53821fa455b7f309,Extreme sport,Boating,Rowing\ntest/5382dd1a8ed81cb3,Compact car\ntest/53890ab1e897205a,Cookies and crackers\ntest/538a47a35495d040,Microcontroller\ntest/538ac1a4ddf9ed1f,Goats,Bovine\ntest/538d2b624415a556,Frigate\ntest/53906d62232929ae,Vegetarian food\ntest/53955d7f74eeacb4,Close-up,Plant stem\ntest/53965ca7bf460a89,Melee weapon,Hunting knife\ntest/539687a3112244b8,Jeans,Dog walking\ntest/539981c8f7dd5ded,Compact car,Antique car\ntest/539cb6a9ed973752,Close-up\ntest/53a2b2cc432fb8a7,Senior citizen,Close-up\ntest/53a512b347189400,Close-up\ntest/53a5f3302c16c99d,Frozen yogurt\ntest/53a72941e3f1ea69,Luxury vehicle,Performance car\ntest/53aafd13ad214ef3,Close-up,Anole,Scaled reptile\ntest/53ab0f932f3011e4,Luxury vehicle\ntest/53ac2a0e209e98df,Compact car\ntest/53aee704a43fda9a,Close-up\ntest/53af58f3d39c9a7c,Bovine\ntest/53b13f34405ecde5,Luxury vehicle\ntest/53b1a76ea7f284d9,Close-up,Whiskers\ntest/53b3c297086367c5,Middle ages\ntest/53b61207cf3cf17a,Military person\ntest/53b67bd585d7e27f,Aircraft engine\ntest/53b6cacc653d526e,Whiskers\ntest/53b90870a9d68079,Monoplane,Aircraft engine\ntest/53b9504a03c6d74a,Floristry\ntest/53bade2cd7bf1a8c,Close-up,Anole,Scaled reptile\ntest/53bb01b8b617b733,Cosmetics,Close-up\ntest/53bb7b316bcf14a1,Domestic short-haired cat,Whiskers\ntest/53be837a071c3146,Extreme sport,Sport climbing\ntest/53be8b8769947f9a,Frozen yogurt\ntest/53bff7c64a80dc45,Compact car,Antique car\ntest/53c30690c8a57783,Bovine\ntest/53c45089c05bb7a8,Sport utility vehicle\ntest/53c6c9ca0c6403bf,Angling\ntest/53c7dfaeb9a07bc2,Luxury vehicle\ntest/53c8101d223f87ba,Luxury vehicle\ntest/53cc348aa9dee00f,Vegetarian food\ntest/53cf8d6898955b33,Compact car,Luxury vehicle\ntest/53cfb2da58f92c82,Siberian husky\ntest/53d27bc47ca03f5f,Luxury vehicle,Performance car\ntest/53d29263f287be81,Ball game,Team sport\ntest/53d2aa2cf344d739,Close-up\ntest/53d33b00668669e2,Outdoor shoe\ntest/53d45851fe30c76e,Aircraft engine\ntest/53d47554c794b628,Extreme sport,Gliding\ntest/53d674784ad6e307,Forklift truck\ntest/53d6c8da460a8411,Hair coloring\ntest/53d7046e0fca9e40,Primate\ntest/53d913b85d43dffc,Antique car\ntest/53da5fb2805695b9,Junk food\ntest/53dbd33bed83bca8,Compact car,Luxury vehicle\ntest/53dcd5e9e9cc9255,Combat sport\ntest/53dd667a34149fe8,Jeans,Extreme sport\ntest/53ddb510582f4a6f,Luxury vehicle,Sport utility vehicle\ntest/53de264315f31cdc,Extreme sport\ntest/53dfee234216c5c8,Hound\ntest/53e0dfafabca7ec0,Molluscs\ntest/53e181f93e7c5041,Cobblestone\ntest/53e19901290661c3,Roller skating\ntest/53e1fe153e32150a,Residential area\ntest/53e351ed48972f7c,Extreme sport\ntest/53e3a036a2440629,Close-up\ntest/53e3db3e12f0e4e2,Sport utility vehicle\ntest/53e48e08339ef516,Luxury vehicle,Antique car\ntest/53e6f1f751a1ead7,Plant stem\ntest/53e9c3a5beafc452,Close-up\ntest/53eb0db89bde5907,Close-up\ntest/53ec0b414bb0cc75,Chordophone,Electric guitar\ntest/53ed581dda907c8b,Ball game\ntest/53edeabdb9f15eb8,Rural area\ntest/53f048a179f1ad5f,Compact car,Luxury vehicle,Antique car\ntest/53f17465ad28e73a,Black-and-white\ntest/53f24280b8390b1e,Barechested\ntest/53f24f232b83551e,Junk food\ntest/53f4f8da569a115b,Luxury vehicle\ntest/53f53f165a525995,Equitation,Equestrianism\ntest/53f751cb24159757,Cookies and crackers\ntest/53f7c44aa2f84928,Stemware,Black-and-white\ntest/53f9504422285ef1,Combat sport\ntest/53fa3e6c2ba127d7,Wallaby\ntest/5400eb865db6fc7f,Close-up\ntest/5401a4859dd7601c,Close-up\ntest/5402ee8ce5f8040a,Freight transport\ntest/5403becf6bb6ba29,Close-up\ntest/54098d326c7166f6,Gumbo\ntest/540cdf51dc58f289,Compact car,Luxury vehicle\ntest/54108a825272ba64,Microcontroller\ntest/5413d28944682a92,Close-up\ntest/54143ddd6c5923d6,Auto racing\ntest/5414644851e52ffc,Monoplane,Aircraft engine\ntest/54179d9003268b5f,Firearm\ntest/541905fe1903f8cc,Miniature Poodle\ntest/541aa26264afe915,Pork chop\ntest/541af90c79a9f3ac,Close-up\ntest/541b7e2b44820492,Whiskers\ntest/541c7dea9031cdd3,Close-up,Floristry\ntest/54209bda90cff9d7,Compact car,Luxury vehicle,Antique car\ntest/542202fb5000a2e2,Floristry\ntest/5423bfc2695075ac,Close-up\ntest/542428a652c8fd39,Ball game\ntest/542429bec668190a,Rural area\ntest/5424d2b8c96278cc,Performance car\ntest/5424e4ae74ff92b9,Close-up\ntest/5425f9a51ce286ee,Jeans\ntest/54281d353cad6bb5,Close-up\ntest/542998844c62a57a,Monoplane,Gliding\ntest/5429d33b06ddb3d6,Ball game,Team sport\ntest/542b334d1238c1e7,Plant stem\ntest/542c57a976b78b1e,Luxury vehicle,Performance car\ntest/542c9a54f5399550,Plant stem\ntest/542d37057e58d615,Black-and-white,Close-up\ntest/5430cc1f3574a3f2,Rural area,Bovine\ntest/5430d8e0639360d2,Aircraft engine\ntest/5431222168f2904f,Ball game,Team sport\ntest/5433e83451b049d2,Antique car\ntest/5434eecb6354c1a9,Luxury vehicle,Performance car\ntest/5436209329533b7e,Close-up\ntest/543647fe89b48b8a,Chordophone\ntest/5436599517cc861a,Auto racing,Luxury vehicle,Performance car\ntest/5436c813b09ea248,Ramen\ntest/5437a1d263c8321d,Extreme sport,Boating\ntest/543891472aa019b6,Close-up,Whiskers\ntest/543949b01a10ddc9,Arthropod,Close-up\ntest/543af4ad8db1e9d4,Wind wave\ntest/543c4f23580e8cd7,Close-up\ntest/543cda9104aa4d6b,Flatbread\ntest/543d8fa693fad6b6,Antique car\ntest/543ea58f7d61d101,Antique car\ntest/543f3c1c8b45bc3a,Close-up\ntest/5442a2e57242aa85,Bird of prey,Close-up\ntest/5442ae1483d2a403,Close-up\ntest/5442f4a7ab5ea97d,Outdoor shoe\ntest/54449ae537017b6e,Close-up\ntest/5444e9a99d923bba,Antique car\ntest/544643b224fee193,Scaled reptile\ntest/54466689acbad3fb,Blond\ntest/5448cb1397fefc0b,Hound\ntest/544a27a0a758dae4,Cookies and crackers\ntest/544abb2ee3a81467,Monoplane\ntest/544b43e6179c299b,Luxury vehicle,Performance car\ntest/544dae9ce775725d,Performance car,Luxury vehicle,Antique car\ntest/544de5e99ff664a4,Close-up\ntest/544fd7a2940550a0,Middle ages\ntest/545351037227b15e,Jeans\ntest/5453cfd4849ece8b,Luxury vehicle,Performance car\ntest/5453e0e7b4bfffa7,Primate\ntest/54562212592e237a,Luxury vehicle\ntest/5458125a838f44db,Rural area,Canola\ntest/545891d274f8cbb7,Luxury vehicle,Antique car\ntest/5458b967d06e3fee,Compact car,Antique car\ntest/5459e4047c565495,Compact car,Luxury vehicle,Performance car\ntest/545b6cd6f1f33f37,Barquentine\ntest/545d6bd6c8b65836,Close-up\ntest/545d7b04c8571fda,Military person\ntest/545f8d0f379756df,Floristry\ntest/54615aebca828bac,Compact car\ntest/5463b2e0a69fcd20,Modern dance\ntest/5463e0924826b2d6,Peafowl\ntest/5464228260c298e5,Plant stem,Close-up\ntest/5465f3d7c1cd2d69,Luxury vehicle,Antique car\ntest/546873f48b8d73d0,Flatbread\ntest/54687cfa54c9c6f2,Close-up\ntest/54695995d2aac8fa,Extreme sport,Wind wave\ntest/546e97952eccb3df,Ball game\ntest/546fc9324167bee6,Cosmetics\ntest/5473be254b823641,Firearm\ntest/54753e7d6b5ff909,Black-and-white\ntest/5475e278db0bfb6e,Falafel,Fried food\ntest/5477925eb10dd8ba,Antique car,Performance car\ntest/547c844fb958fe34,Luxury vehicle\ntest/547ce4cf05f6e827,Halter\ntest/547dcc786aba4b63,Gliding\ntest/54835db1ae4e1cc6,Rear-view mirror\ntest/5483fb0188a2d89a,Fried food\ntest/54842a4cc8b9876a,Antique car\ntest/5485cf00a255ae51,Shinto shrine\ntest/548708a029462935,Jeans\ntest/5488d9042cc1ae5b,Sport utility vehicle\ntest/54892468d7219577,Sport utility vehicle\ntest/548cddfad3fd96b6,Monoplane\ntest/548da1130b39f407,Sport utility vehicle\ntest/54906e95b973086b,Ball game\ntest/5491473d6b7b24aa,Black-and-white\ntest/5492602caa3b12c8,Compact car\ntest/5492b9a0fd0df6ad,Close-up\ntest/54945a20c7c9550e,Luxury vehicle,Performance car\ntest/54949abba5a843b3,Microcontroller\ntest/54963c2c56a28668,Brochette\ntest/54983eb0bd0b14ab,Compact car,Antique car\ntest/549876eb9515644b,Luxury vehicle,Performance car\ntest/549b30f19389eb73,Whiskers,Domestic rabbit\ntest/549b90481f940adf,Luxury vehicle,Performance car\ntest/549d46c596635f0b,Horse and buggy\ntest/549dac0a3c2fbb3f,Billiards,Close-up\ntest/549ecd12424c6f8b,Arthropod,Close-up,Plant stem\ntest/549ef44c3eb779c6,Close-up,Whiskers\ntest/549fedffe5d59c07,Flatbread\ntest/54a00e36e3f8db9c,Luxury vehicle,Antique car\ntest/54a32032e9453617,Khinkali,Jiaozi\ntest/54a6f4906ceb62bc,Black-and-white\ntest/54a9214ebc9cb1c3,Sport utility vehicle\ntest/54a9c18523daceb5,Luxury vehicle,Performance car\ntest/54aac0cfc747e7bc,Barechested\ntest/54ab0905917941a5,Close-up\ntest/54abd8a584bdb947,Luxury vehicle\ntest/54ac5e813836a2df,Hair coloring\ntest/54ae60ada8a3761d,Compact car,Luxury vehicle\ntest/54aee2171052de27,Close-up,Scaled reptile\ntest/54afc762f0f19a06,Military vehicle,Luxury vehicle,Sport utility vehicle\ntest/54b092b43eda9f9a,Luxury vehicle,Performance car\ntest/54b1221080a3301e,Roller skating\ntest/54b3300d44c630b1,Machine tool\ntest/54b382d6e842ea3e,Floristry\ntest/54b3c83e3b520e95,Luxury vehicle,Sport utility vehicle\ntest/54b57ffd1f9208fa,Road cycling\ntest/54b9d0707e05629a,Close-up\ntest/54ba1ec0d85d45b1,Floristry\ntest/54bcb1d5726b465b,Jeans\ntest/54bea4d9e1931394,Plant stem\ntest/54bee8af515dcf54,Arthropod,Close-up\ntest/54c11fc29f9db40b,Luxury vehicle\ntest/54c38b5ac361461c,Motorcycling\ntest/54c6a21f8df9cb6a,Orator,Public speaking\ntest/54c6e3b6845dc2b5,Aircraft engine\ntest/54c8bed6b0b0af23,Scaled reptile\ntest/54c8cfbca2fcaba1,Bovine\ntest/54c8e345c80e9544,Musical theatre\ntest/54c909dd5b3155ad,Close-up\ntest/54cbeaf7fbb5a121,Close-up\ntest/54cc9ce2621aee71,Frozen yogurt\ntest/54ce6485e8b144da,Close-up\ntest/54cf7f44010c28f9,Heavy cruiser,Frigate\ntest/54cfeeaaabcff4c2,Jeans\ntest/54d04f44171aeeaa,Microcontroller\ntest/54d1938b52d58f5d,Bovine,Close-up\ntest/54d2cad494fb79b8,Luxury vehicle\ntest/54d5216920a5df23,Luxury vehicle,Antique car\ntest/54d8ec6bdf56d71f,Molluscs,Close-up\ntest/54d92d1e31068481,Close-up\ntest/54dab9eaec121a56,Ball game\ntest/54dbfdadde0e2fe6,Black-and-white,Chordophone\ntest/54dcf1670bfc5035,Arthropod,Close-up\ntest/54dda77e88911b34,Modern dance\ntest/54e2cc36bda153ce,Residential area\ntest/54e2d426fced2fae,Vegetarian food,Junk food\ntest/54e2ff91b82e4474,Black-and-white\ntest/54e4020daeed57dc,Ball game,Team sport\ntest/54e602a326c437a6,Senior citizen\ntest/54e6bc44835076d8,Ball game\ntest/54e75a41358e1e53,Gumbo\ntest/54e8a7d2705598be,Performance car\ntest/54eb04cc46cf8d07,Close-up,Whiskers\ntest/54ec558e3e1b88dd,Jeans\ntest/54ed5b6486f63acd,Antique car\ntest/54ee79a3116ebf8d,Residential area\ntest/54ee808cd2e1f74d,Auto racing\ntest/54f049c072de2e0f,Arthropod,Close-up\ntest/54f4027880a15d51,Ball game,Team sport\ntest/54f55316141b3f13,Hair coloring\ntest/54f5e66e3cb039b4,Luxury vehicle\ntest/54f96bd28b8a9a32,Boating\ntest/54fb030ac1b8df9b,Junk food\ntest/54fb86c5fe1585c3,Aerial photography\ntest/54fbc840ecb47c6c,Aircraft engine\ntest/54fc487d7158bdfe,Barechested\ntest/54fcf298c3a38fc4,Divemaster,Scuba diving,Underwater diving\ntest/54fd9ab7e68870e3,Arthropod,Close-up\ntest/54feab6f80febb5d,Antique car\ntest/54febe9584a8dfc8,Electronic instrument\ntest/55022a7668936838,Pit bull\ntest/550305e3b510418e,Orator,Public speaking\ntest/5503cd6a39df1da6,Bird of prey\ntest/55054d74313433d4,Close-up,Plant stem\ntest/55058ff6fd431281,Chordophone\ntest/5505a990dab6b1c7,Lumpia\ntest/5507066b1eab1558,Portrait photography\ntest/55076cc6edd1b1af,Floristry\ntest/550b3a9e874134de,Extreme sport\ntest/550dda855c68ce6d,Equestrianism\ntest/550f697177cd1f20,Combat sport\ntest/550f9e2e779d8598,Domestic rabbit\ntest/5511a097ed075f36,Cookies and crackers\ntest/5511a4367bece2c5,Antique car\ntest/5512787f74b2d996,Military vehicle,Gun turret\ntest/551334fdf1dca901,Close-up,Whiskers\ntest/55138a9032e7c7d7,Military vehicle\ntest/5513de3c10ae65ae,Black-and-white\ntest/551431c06387dca5,Luxury vehicle\ntest/5517797d85a28e07,Luxury vehicle,Performance car\ntest/5518f803ae838bd0,Close-up\ntest/551c13595d987201,Rural area,Bovine\ntest/551c2d620c9da1b6,Luxury vehicle\ntest/552117413588d300,Woodwind instrument\ntest/5522939e56136ec3,Arcade game\ntest/55229893484ce19c,Compact car,Luxury vehicle,Sport utility vehicle\ntest/55244ab16c31f869,Luxury vehicle,Antique car\ntest/5526f66a41afdc4c,Senior citizen\ntest/552ac2f0a39aee71,Sport utility vehicle\ntest/552cf561f0ed2b52,Ale\ntest/552d9e78b2704cf2,Sport utility vehicle\ntest/552f04a6261e8c36,Plant stem\ntest/552fdeb747cdba52,Aloe\ntest/55306d0dc752bd12,Luxury vehicle\ntest/5531a3359ab88b96,Hair coloring\ntest/55320a387ffd9874,Vegetarian food,Cookies and crackers\ntest/5534d5df40bf1142,Firearm\ntest/553592716aafde3f,Luxury vehicle\ntest/553a103ab71e5ae0,Bovine\ntest/553a62f5b620993d,Fire apparatus\ntest/553bd3e7df754596,Floristry\ntest/553d98998d90d9dd,Aerial photography\ntest/5540b1af6082d5e4,Rural area\ntest/5542e60ae0d8d2c0,Motorcycling\ntest/5543c8154cad5b80,Bovine\ntest/5545b045a6206303,Scaled reptile\ntest/554c659917c427ad,Compact car,Luxury vehicle\ntest/554db6a25626c101,Arthropod,Plant stem,Close-up\ntest/55523ae453ad633b,Athletic shoe\ntest/5555daa9ec4e3e4d,Newsagent's shop\ntest/5555f2fde1a810e0,Compact car,Luxury vehicle\ntest/5556177dc4151acc,Combat sport\ntest/55568851a9b89e15,Close-up,Plant stem\ntest/5557d4973d26cddc,Rural area\ntest/5559515ef5c965fa,Firearm,Shooting range\ntest/555d9f122d7a5d58,Combat sport,Barechested\ntest/55611b3b13e2d819,Boating,Wind wave\ntest/55613ea6c970d918,Boating\ntest/556189130b0755e4,Luxury vehicle,Antique car\ntest/5561b7447db0bcda,Cookies and crackers\ntest/55649a5d54e2bd7a,Domestic short-haired cat,Whiskers\ntest/55660908bae60ffe,Auto racing\ntest/5566d4119c556e6d,Horse racing,Equestrianism\ntest/556751c626f1ff7c,Peafowl\ntest/5567d60bfbabec86,Aerial photography\ntest/5568baffb6280b92,Military vehicle\ntest/556bb0121ea38a7d,Luxury vehicle,Performance car\ntest/556bd03439595136,Luxury vehicle\ntest/556cc865e15020a7,Close-up\ntest/556d97297e1e7b33,Assault rifle,Firearm\ntest/556df9e8adad364a,Blond,Portrait photography\ntest/556f22bb5de3d31a,Nile crocodile,Crocodilia\ntest/5570e4a637e0e11c,Ball game,Team sport\ntest/55720b2ef71dadc3,Compact car\ntest/5574922045dcb165,Luxury vehicle,Performance car\ntest/5574a6871f76535b,Close-up\ntest/557691aa0d8fde2c,Antique car,Luxury vehicle,Performance car\ntest/55772e5576afb6eb,Molluscs\ntest/5577908961049505,Firearm\ntest/557993b0e044fa51,Luxury vehicle\ntest/557c21de07ec6fb0,Molluscs\ntest/557d65bdfcc13c1d,Luxury vehicle\ntest/55802fcfac7a3a69,Black-and-white\ntest/55814c23dbaac7ee,Close-up\ntest/5581a0901cc2c9f1,Black-and-white\ntest/5581dbb28f59bf20,Compact car\ntest/55830fa9fd02f3a6,Rear-view mirror\ntest/5585c53d10e82289,Lumpia\ntest/5585e5d9d2fe9556,Compact car,Luxury vehicle,Antique car\ntest/5585e8287954e652,Sport utility vehicle\ntest/55860a85aa3f4887,Luxury vehicle,Performance car\ntest/5587190c0ae23f65,Close-up,Barechested\ntest/558a8a7dc5cca11d,Close-up\ntest/558cff1b408247f1,Auto racing\ntest/558d0d4426b6c6e3,Billiards\ntest/558e865659a9137a,Antique car\ntest/558f40dda9b678ab,Jeans\ntest/55918a3d6caf1001,Miniature Poodle\ntest/55919e857d4a496e,Black-and-white,Outdoor shoe\ntest/5591cd950228d712,Miniature Poodle\ntest/55938ca508393b8f,Close-up\ntest/55967d085ca0a4f5,Vegetarian food,Fried food\ntest/5596af3b2d8b2c26,Vegetarian food\ntest/55977d5de1b57012,Boating\ntest/5598f725fbe3ee57,Hair coloring\ntest/5599680e67be2bf5,Procyonidae,Whiskers\ntest/5599f715c2640c17,Military person\ntest/559efcf329d90fa6,Vegetarian food\ntest/559f5e4607bd9c8b,Digital camera\ntest/55a0365006673b68,Close-up\ntest/55a14d4d92bd51f8,Plant stem,Close-up\ntest/55a3722ccb984767,Siberian husky,Close-up\ntest/55a5a3e49f6c93ed,Lechon\ntest/55a868fc6126cdc5,Close-up\ntest/55a9836160838de2,Plant stem\ntest/55ab9cfb51c6c2c1,Performance car\ntest/55ac2956b91d396d,Blond\ntest/55ad6fcb8c69f96e,Boating\ntest/55b13ed7a6f7f7db,Sport utility vehicle\ntest/55b1c1b71f9ca790,Close-up\ntest/55b6aa65a52e7e3c,Ball game,Team sport\ntest/55b6c48a828aaa3a,Plant stem,Close-up\ntest/55b6cb85f4ef3d7e,Black-and-white\ntest/55b7eb0d3a964c3f,Whiskers\ntest/55ba02f2cb61edda,Ball game,Team sport\ntest/55ba0a2d71686894,Close-up\ntest/55bafb36efe71cb3,Leaf vegetable\ntest/55bb5d7d297af847,Arcade game\ntest/55beeaa5d4e36bff,Nile crocodile,Crocodilia\ntest/55bf96c2d8b87868,Luxury vehicle,Performance car\ntest/55c027699db26a11,Sport utility vehicle\ntest/55c7dbfc14b37c97,Luxury vehicle\ntest/55c9651a4564d4b3,Pit bull\ntest/55ca146a68404a61,Luxury vehicle,Performance car\ntest/55cb37944eb0619e,Compact car\ntest/55ce25ec7e16fe8b,Extreme sport,Surfing Equipment\ntest/55cee84266d126b2,Black-and-white\ntest/55d02cc739230273,Luxury vehicle,Antique car\ntest/55d03e3ae98bd62a,Drums\ntest/55d36ea82507708b,Fried food\ntest/55d3bd5899b26889,Electronic instrument\ntest/55d61e29399b8f73,Bovine\ntest/55d6af701acb534c,Luxury vehicle,Performance car\ntest/55d7e1a9d1be87a0,Vegetarian food\ntest/55d81f1a109a15d3,Luxury vehicle\ntest/55db358e236e72ea,Extreme sport\ntest/55db5db2f3d40e7e,Black-and-white,Close-up\ntest/55dc54cec4fa1f2f,Ball game,Team sport\ntest/55ddae1c842f48d1,Compact car\ntest/55de6ad49c668e0a,Motorcycling\ntest/55ded3ac2764685c,Compact car\ntest/55e0dd4c48c97d07,Junk food\ntest/55e1b92026b71698,Holding hands,Close-up\ntest/55e30abf8011a5a0,Luxury vehicle,Antique car\ntest/55e3782d68472013,Luxury vehicle,Antique car\ntest/55e3fb35856e5b88,Black-and-white,Close-up\ntest/55e41d4e06a4fcaa,Sport utility vehicle\ntest/55e5245962ccbdb9,Floristry,Close-up\ntest/55e6b683c2245067,Compact car,Antique car\ntest/55e97ff3c5a90c18,Hound\ntest/55ebced128beb95c,Auto racing,Performance car\ntest/55ec5752d9e00e83,Residential area\ntest/55ee7137f67ed954,Arthropod,Close-up\ntest/55ef7b5629ffa944,Ball game,Team sport\ntest/55f340c672bf9c65,Rural area\ntest/55f3c8a8c68f28ec,Black-and-white\ntest/55f556941314c765,Military vehicle,Sport utility vehicle\ntest/55f820ed45933b43,Antique car\ntest/55f83cd92fa2a2f0,Sport utility vehicle\ntest/55f9b932d3d76d1e,Close-up\ntest/55fb06c2bbbc07d6,Hound\ntest/55fc3911626e062d,Frying,Fried food\ntest/55fd696113af3ac8,Stemware\ntest/55fdf470ce4215f9,Sport utility vehicle\ntest/55fe2b9dc4945746,Frying,Junk food,Fried food\ntest/56008c4c0d0f079f,Compact car\ntest/5602106eb46dcdfe,Close-up\ntest/5602d30c11f97ebb,Close-up\ntest/56040e8f80004c40,Flatbread\ntest/5609c39bfeeb7387,Antique car\ntest/560c9b88d3b3fdf9,Luxury vehicle,Antique car\ntest/560f27cdc8dbf679,Yakitori\ntest/561075ea72a2ea48,Electronic instrument\ntest/56107dc511048e9a,Bovine\ntest/5612bbded0995f28,Touring car,Antique car\ntest/561361a2c0377804,Compact car,Luxury vehicle\ntest/56138f48feb8a612,Aircraft engine\ntest/56147a62cba9e7d6,Firearm\ntest/5616cd666e3e6e54,Cosmetics\ntest/5616d8ae404edd2e,Ball game\ntest/56185111751953d2,Ball game,Team sport\ntest/5618f72bdbac4529,Domestic short-haired cat,Close-up,Whiskers\ntest/561c0531849452b9,Compact car\ntest/561d610b797ca2f9,Vegetarian food,Junk food\ntest/561d9c231ce439db,Military person\ntest/561dddeb34d7808a,Luxury vehicle\ntest/562094366324875f,Boating\ntest/5621a13db6ee42d8,Close-up\ntest/5621ee64f384470c,Luxury vehicle,Antique car\ntest/562268e12b5a00fd,Arthropod,Close-up\ntest/56229768adface48,Military vehicle,Luxury vehicle,Sport utility vehicle\ntest/5622ee40476d57a6,Chordophone\ntest/5623fc0a9e8b5aa3,Jiaozi\ntest/5624e319bc507fea,Sport utility vehicle\ntest/56264d1cda0c8571,Peafowl\ntest/562947e9d01117bf,Aircraft engine\ntest/5629a3da92fddf86,Blond\ntest/5629ca76e6f74b64,Computer speaker,Black-and-white\ntest/562b11ae54103420,Sport utility vehicle\ntest/562bb5aba47f73dd,Cobblestone\ntest/562bdb60bc9ee131,Compact car,Luxury vehicle,Antique car\ntest/562c8b7b1692128a,Close-up,Whiskers\ntest/562e32813cb107c9,Extreme sport,Wind wave\ntest/56304880ae9216fb,Flatbread\ntest/5630a545c8b2ace9,Fried food\ntest/563a2f26872d0312,Monoplane\ntest/563a44e667e19513,Amphibian\ntest/56410dff94295af6,Soba\ntest/5641455545707153,Angling\ntest/5643159febb8762a,Vegetarian food\ntest/5643a8739f875f58,Steamed rice\ntest/56454c8a286693b4,Extreme sport,Parachuting\ntest/56456b3536821aca,Arcade game\ntest/5646068deb7b5331,Cosmetics\ntest/564618df8585d334,Pork chop,Beef tenderloin\ntest/564799f4195b8d6b,Pit bull\ntest/5648bde7fdfd9766,Bovine\ntest/5649603ea9bba27b,Luxury vehicle,Antique car\ntest/564abd25bea73fb6,Auto racing\ntest/564baccf777e1ec7,Black swan,Close-up\ntest/564c1c4bf7473a88,Luxury vehicle\ntest/564d36df1e23c297,Jeans\ntest/564df7bac6ea6ae1,Luxury vehicle\ntest/564e527a2fe89dfa,Luxury vehicle,Performance car\ntest/564e99315a4c8664,Touring car,Luxury vehicle,Antique car\ntest/564eb99e621b8cf2,Equestrianism\ntest/564efee8782cdb54,Monoplane\ntest/564f45bd414c8636,Luxury vehicle,Performance car\ntest/5652662e6d5e7d91,Combat sport\ntest/56532d6cffe0c917,Bovine,Close-up\ntest/565362524f1fe004,Luxury vehicle,Performance car\ntest/56559d3399abe5c0,Luxury vehicle,Sport utility vehicle\ntest/5656956e8b60bfbd,Antique car,Performance car\ntest/565d2b0ed47da866,Luxury vehicle,Performance car\ntest/565ff835e200e5d8,Domestic rabbit\ntest/5660c6d5a11830a0,Close-up\ntest/5660c8f854f6d85e,Compact car,Luxury vehicle\ntest/56621f7b66dc018b,Luxury vehicle,Performance car\ntest/5662867280c8beb9,Melee weapon,Hunting knife\ntest/5662e12c1cad04f2,Hound\ntest/566437c52a1ae86b,Aircraft engine\ntest/56687ab1c990c97b,Compact car,Sport utility vehicle\ntest/5669cfd8ea34f957,Luxury vehicle\ntest/5669d38a4bc6c25d,Cosmetics\ntest/566a21a1ec1e2a47,Domestic short-haired cat,Whiskers\ntest/566ae4e1807d3445,Compact car,Luxury vehicle,Sport utility vehicle\ntest/566b558350db81af,Motorcycling\ntest/5670693c530ce8d1,Residential area\ntest/5671095ddcde8b3f,Blond,Portrait photography\ntest/5671e983f3496917,Equitation,Equestrianism\ntest/5672f96796f63f7b,Black-and-white\ntest/567325cdc4b943d7,Close-up\ntest/5674e2873e8da356,Close-up\ntest/567543b618c1b38e,Road cycling\ntest/56770050ea8dba24,Team sport\ntest/5677b1ecffe26e06,Plant stem\ntest/5677daedf1b7dbe7,Gun turret\ntest/567a7a8d94cbdf13,Luxury vehicle\ntest/567a86beae419011,Yakitori\ntest/567b70bb493c8036,Luxury vehicle,Performance car\ntest/567e05ac12229f4f,Auto racing\ntest/567e233b1b9dfccb,Domestic rabbit\ntest/567f184e5b24f067,Melee weapon\ntest/567fba0ae8f5cad5,Boating\ntest/568050b3b94bedb8,Portrait photography,Close-up\ntest/5683d2e8916ec42f,Double-decker bus\ntest/568524ddc5f72c12,Barechested\ntest/5685999a0dd3ea25,Luxury vehicle,Performance car\ntest/56868f6d0267be58,Antique car\ntest/5686b18c3dc7a373,Aircraft engine\ntest/5686fb45c1572793,Lumpia\ntest/568791b23af2f2fb,Luxury vehicle,Performance car\ntest/568b3eb5af98a8f7,Auto racing\ntest/568cce20f7e9bea0,Auto racing\ntest/568e9c1ca8fb37d9,Road cycling\ntest/569143ba4c037440,Team sport\ntest/56914983b7777be2,Close-up\ntest/569228da6e79ecee,Firearm\ntest/569427e1f5d8e207,Rural area\ntest/56951536c6458cad,Luxury vehicle\ntest/5697d6e5c34f3cde,Close-up\ntest/569aa2e16bb07dbd,Domestic short-haired cat,Whiskers\ntest/569c1bd7d75c977e,Luxury vehicle\ntest/56a614015e929724,Whiskers\ntest/56a8d737e345b6d6,Aircraft engine\ntest/56a903e6c95aa589,Cookies and crackers\ntest/56aa614b315ff621,High-speed rail\ntest/56ab2365a1efc28d,Compact car\ntest/56ac0091b0b5142e,Auto racing\ntest/56aef76a378d9c29,Pork chop\ntest/56af8762f1f58404,Combat sport\ntest/56b04b590a0aa688,Compact car\ntest/56b098cdc332b967,Antique car\ntest/56b2643401e6e046,Floristry\ntest/56b2eccec4a39617,Close-up,Whiskers\ntest/56b42d211e340263,Compact car\ntest/56b9005e804e39b5,Domestic short-haired cat,Whiskers\ntest/56b9c03ba400a624,Compact car\ntest/56bc2e2b56c69aef,Luxury vehicle,Performance car\ntest/56bc31139effa7da,Close-up\ntest/56bc9763d4dd26cd,Luxury vehicle,Performance car\ntest/56c3b2d15b498b5e,Compact car\ntest/56c50fe568054a56,Close-up,Whiskers\ntest/56c8e6f1b804c529,Roller skating\ntest/56cd5ecc2d041785,Close-up\ntest/56ce7a3ecff14ab7,Close-up\ntest/56cedf27babf5294,Bovine\ntest/56cf7c470fb11b1d,Junk food\ntest/56d10dfb9e8c7cf8,Antique car\ntest/56d1d8aca15ae219,Compact car,Antique car\ntest/56d252d1e2909d96,Whiskers\ntest/56d44333347974f0,Monoplane\ntest/56d73d73ca9a0442,Ball game,Team sport,Equestrianism\ntest/56d884461fce73da,Coca-cola\ntest/56da737a6a164832,Medical imaging\ntest/56daca6620cf4f59,Floristry\ntest/56daea46a6bfc88e,Close-up\ntest/56e04f8aa44f63a6,Close-up\ntest/56e4b0bb3ef38b14,Ball game,Team sport\ntest/56e5fac70f85bbba,Close-up\ntest/56e64bf16a61cee4,Coral reef fish\ntest/56e741d8f93e15eb,Toy block\ntest/56e8068cf57ec6e8,Aircraft engine\ntest/56e8f3ce90e7cb1a,Close-up,Scaled reptile\ntest/56eb57f2920866b2,Perching bird\ntest/56eb9650c27e620a,Frigate\ntest/56eb9c23173043ab,Compact car,Luxury vehicle,Antique car\ntest/56eba344274b5ac6,Residential area\ntest/56ed8e79caa2ea15,Luxury vehicle\ntest/56eda2cd94a289f4,Sailboat racing\ntest/56ee27ad60207e74,Electronic instrument\ntest/56eea45de317f038,Close-up\ntest/56efa114937e2f63,Surfing Equipment\ntest/56efde3453d6f5ba,Vegetarian food,Fried noodles\ntest/56f184605e749fcc,Close-up\ntest/56f37ac48523a978,Luxury vehicle\ntest/56f4517c5612eab1,Close-up\ntest/56f4d8d873ed18d2,Drums\ntest/56f4daa8f5b60159,Bovine\ntest/56f4e8c8422a1d67,Senior citizen\ntest/56f713525b044dbb,Close-up\ntest/56f8a8a6882332b4,Luxury vehicle,Sport utility vehicle\ntest/56faf63600778748,Domestic short-haired cat,Whiskers\ntest/56fb91c275abe0de,Arthropod\ntest/56fc5813dceeb503,Plant stem\ntest/57004cfc21f1e4aa,Black-and-white\ntest/5707ac6785daf9c4,Solar energy\ntest/5708b1aba5282e54,Performance car\ntest/570c09ae902fc41e,Aircraft engine\ntest/570d4019d83ea63d,Antique car\ntest/570e89b0937f01d2,Billiards\ntest/5711d33ac640a70d,Luxury vehicle,Performance car\ntest/5712d134fff063f4,Luxury vehicle,Performance car\ntest/571329428bd57cd4,Extreme sport\ntest/571474b9a7a3de9e,Outdoor shoe\ntest/5714933b4e94d795,Domestic short-haired cat,Close-up,Whiskers\ntest/5715abac3a348dff,Nest box\ntest/5716633bbf55edf0,Domestic short-haired cat,Whiskers\ntest/571807382a62b7b8,Compact car,Luxury vehicle\ntest/57193cb462096168,Compact car\ntest/571abfa0a95e675a,Road cycling\ntest/571b5eecff5600dc,Rural area,Bovine\ntest/571f8b2b2af7154a,Ball game,Team sport\ntest/5721c51a5d325fd7,Close-up,Whiskers,Domestic rabbit\ntest/5723c6f4c260e3fa,Compact car,Antique car\ntest/5724438d7ba31a08,Cosmetics\ntest/57251d1cf556fa5e,Ball game,Team sport\ntest/572a42e143564a87,Close-up\ntest/572ba5487747c485,Portrait photography,Hair coloring,Close-up\ntest/572df46f67c22185,Steamed rice\ntest/572e46e0829206fb,Ball game,Team sport\ntest/573212192cb8b4f3,Modern dance\ntest/57369851c89db0bc,Scaled reptile\ntest/57370d36110080f6,Close-up,Whiskers\ntest/573723bd53e04fec,Close-up\ntest/57379efb33867859,Vegetarian food\ntest/573824c1f149c864,Compact car\ntest/573bd1bb17371417,Residential area\ntest/573cd2480457e201,Great white shark\ntest/573daeeca50d6bea,Brisket\ntest/573e055c79e6b01b,Luxury vehicle,Performance car\ntest/573e4212cfecf1b3,Vegetarian food\ntest/573eba1519e367d3,Hound\ntest/573ed7b5e31efa7c,Aircraft engine\ntest/573faf015cbe1768,Firearm\ntest/5741b23eb2711d31,Jiaozi,Pelmeni\ntest/574343e171cb4347,Arthropod,Close-up\ntest/57438360106e1d4f,Close-up\ntest/574613ec6e9f54e3,Scaled reptile\ntest/57474bf30cf0d541,Luxury vehicle\ntest/574901d78c34e0a8,Ball game,Team sport\ntest/5749bc49945b5580,Ball game,Team sport\ntest/5750000aaba7acee,Close-up\ntest/5751796933f5c77a,Black-and-white\ntest/5751876d0222ff02,Scaled reptile\ntest/57552b84f2e558e7,Aircraft engine\ntest/5759fc9848faf349,Luxury vehicle,Performance car\ntest/575aa6985160c19c,Close-up\ntest/575ac0d1c7dee28f,Extreme sport,Wind wave\ntest/575c7fc8bd52b369,Aircraft engine\ntest/575f6ea1e6843692,Luxury vehicle,Antique car\ntest/575fed4a5f15b617,Antique car,Luxury vehicle,Performance car\ntest/576265d44ad7d934,Vegetarian food\ntest/5762a0ce1549ed54,Compact car\ntest/576349a03b599011,Close-up,Whiskers\ntest/5763c18dcc1fa1e4,Close-up\ntest/5763c23d887b1b6e,Antique car,Luxury vehicle,Performance car\ntest/57650e93d94f4da6,Beef tenderloin\ntest/5769aa844329b93c,Black-and-white\ntest/576ac414c675d27d,Bovine\ntest/576b746caff4c58c,Billiards,Billiard room\ntest/576b9440cd30e972,Auto racing\ntest/576ceb7bc7604eba,Luxury vehicle\ntest/576f64d0f2d906b6,Luxury vehicle,Antique car\ntest/576ffeeaf4366e7e,Luxury vehicle\ntest/57727dc8490da39f,Floristry\ntest/5772bbd05cb7aeba,Boating\ntest/57732441b82bc8d7,Rural area\ntest/577371098a41bbbb,Monoplane,Aircraft engine\ntest/5773e12036445bb8,Leaf vegetable\ntest/5773e9438a981e8c,Athletic shoe,Outdoor shoe\ntest/5775ceb3ea4c9e7a,Close-up\ntest/577638569201b670,Close-up,Scaled reptile\ntest/577a17c2b7862705,Close-up\ntest/577b2a23617150ff,Modern dance\ntest/577c81731be6bb2d,Black-and-white\ntest/577ce3c1d1cb9981,Rural area\ntest/577dd20d0d47b917,Headphones\ntest/577f77b326ff65e7,Ball game,Team sport\ntest/577f8eb0588733e1,Molluscs\ntest/577fcf27206aca23,Black-and-white,Close-up\ntest/57809970fd4480fc,Digital camera,Black-and-white,Close-up\ntest/578560afb0e80112,Compact car\ntest/57858123debc3734,Luxury vehicle,Antique car\ntest/578634318ade953d,Close-up,Whiskers\ntest/5786df2cb196e53d,Modern dance\ntest/5787f519826f31ad,Goats\ntest/5789304f5ccd057d,Combat sport\ntest/578ad933dfe20828,Vegetarian food,Fried food\ntest/578c2c142ac686d9,Antique car\ntest/578e2acf96e57985,Antique car\ntest/578e70a886a18b27,Plant stem\ntest/57919e5df1e6f5d6,Extreme sport\ntest/5796df1844959d45,Portrait photography,Close-up\ntest/57997812f319c5c4,Plant stem\ntest/5799e36a89fe27d4,Sport utility vehicle\ntest/579b974f27685ffb,Antique car\ntest/579babcf555b622a,Close-up\ntest/579c0baa92fbeca6,Ball game,Team sport\ntest/579c5def9788dbaf,Antique car\ntest/579c6a3fe214d0db,Close-up\ntest/579d9dca7ec92cf8,Auto racing,Performance car\ntest/579fe35050d8aa1c,Antique car\ntest/57a02610898d25d8,Glacial landform\ntest/57a027b0fa5ddec9,Black-and-white\ntest/57a2a06b200f9741,Hair coloring\ntest/57a5eedd702c3ff1,Floristry\ntest/57a60cb9821ff7f9,Rural area\ntest/57a7254dcf8e0b18,Combat sport\ntest/57a855f61d2662e9,Aircraft engine\ntest/57a8f443ab6e40a0,Rural area,Aerial photography\ntest/57b0288f4731b7c3,Luxury vehicle\ntest/57b028e15cd795e2,Arthropod,Close-up\ntest/57b1d6640957aa52,Ball game\ntest/57b2598b5a956584,Antique car,Sport utility vehicle\ntest/57b668951c866bdb,Luxury vehicle\ntest/57b6e3f1453dd70a,Jeans,Close-up\ntest/57b93e5f1266a89b,Close-up\ntest/57b96b20170126ad,Black-and-white\ntest/57bb944528663273,Compact car\ntest/57bbbf4fcc4bb318,Close-up\ntest/57c01f01cfa94928,Amphibian,Close-up,Scaled reptile\ntest/57c36ad1fbed4f6d,Luxury vehicle,Performance car\ntest/57c3a60967191667,Horse and buggy\ntest/57c6a2cd43ef823e,Compact car\ntest/57c7975ca6ab3a13,Auto racing\ntest/57c828905bbba4d4,Fried food\ntest/57c8a0f45d8249a9,Brisket\ntest/57cb0eb6f9819c7d,Arthropod,Close-up\ntest/57cca73e2afe2550,Close-up\ntest/57cfc70956fc4b75,Melee weapon,Hunting knife\ntest/57d00dfc02878077,Military person\ntest/57d3e1293b1c79b8,Compact car\ntest/57d4519126b76760,Sailboat racing\ntest/57d466387058d21c,Arthropod,Close-up\ntest/57d693a719a188dd,Close-up\ntest/57d7181f655a897e,Boating\ntest/57d8936514bc7b5a,Compact car,Luxury vehicle\ntest/57d89b038b345c98,Luxury vehicle,Performance car\ntest/57d8e4a298725869,Close-up,Scaled reptile\ntest/57d90a125dae5ea3,Black-and-white\ntest/57d9906180d7a87d,Extreme sport,Wind wave\ntest/57dfbf0bbcd74a1d,Whiskers\ntest/57e2d16faf0df82b,Compact car\ntest/57e4af56c6d1e5a5,Performance car\ntest/57e4f12386d73a49,Cookies and crackers\ntest/57e7a1083e6c4b35,Military vehicle\ntest/57e9252089e556a1,Scaled reptile,Anole\ntest/57eae1588c265448,Leaf vegetable\ntest/57eb7061e34b0987,Luxury vehicle\ntest/57eca2510bcc3192,Leaf vegetable\ntest/57ed66adf418222a,Arthropod\ntest/57f066abe1f1924c,Road cycling\ntest/57f06d0f4217e496,Bovine\ntest/57f1960554b4cb9b,Luxury vehicle,Antique car\ntest/57f1dbfaac9fc684,Luxury vehicle,Antique car\ntest/57f201de2313d9bc,Procyonidae\ntest/57f2f8e2a31721e5,Fried food\ntest/57f3dc351d8805d1,Compact car,Luxury vehicle\ntest/57f412193323df30,Aircraft engine\ntest/57f42bc5624a5876,Plant stem\ntest/57f5d55427023be8,Athletic shoe,Outdoor shoe\ntest/57f8bbbb930af0f1,Luxury vehicle\ntest/57f97c662491589c,Luxury vehicle,Sport utility vehicle\ntest/57f9ad908a3abdbe,Close-up\ntest/57fe97c7079f0bfe,Whiskers\ntest/57fee275751553c1,Leaf vegetable\ntest/57ff578f78174cc2,Boating\ntest/580308b950c6d0eb,Close-up\ntest/58036927568e4ddb,Compact car\ntest/5803cbe86e64e828,Combat sport\ntest/58046a0e372ce2fa,Plant stem\ntest/5804be6aa22dff87,Luxury vehicle,Antique car\ntest/5804dc4b2c20403e,Ball game\ntest/5807548c98054d4b,Residential area\ntest/5808edd984674c43,Plant stem\ntest/580995a742f4655c,Close-up\ntest/5809b265a4af0189,Close-up,Scaled reptile\ntest/580a6e349524ad85,Horse racing\ntest/580b47394fd82d78,Aircraft engine\ntest/580c7fb7fcbf9728,Leaf vegetable\ntest/580d70043969d2d5,Luxury vehicle,Antique car\ntest/580e3dcff0b2ed8b,Antique car\ntest/580e667e706a2ac6,Plant stem,Close-up\ntest/580e92a1e2758c62,Siberian husky\ntest/5810024768221bb4,Bovine\ntest/58114d7816f20d2b,Compact car,Luxury vehicle\ntest/58117aca5395244f,Ball game,Team sport\ntest/5811ae295902baee,Aerial photography\ntest/58122de2269d9ff7,Jeans,Luxury vehicle\ntest/58138b4ad1e4edfd,Aircraft engine\ntest/58156b82c899e639,Combat sport\ntest/58163a9942a9d221,Bovine\ntest/58166c7bc1c7ca1b,Dobermann\ntest/5816e7186d1c97af,Luxury vehicle,Sport utility vehicle\ntest/5817a42fe06e674d,Auto racing,Luxury vehicle\ntest/5819376dcc8dc145,Black-and-white,Modern dance\ntest/581d378f75c4feea,Rural area\ntest/581e3e1368950941,Equestrianism\ntest/581f2d15d6bacfa2,Performance car\ntest/5820ed345d002587,Close-up\ntest/5820f73a951372ee,Close-up\ntest/582254fd99353bde,Zeppelin\ntest/582705688f39ebe9,Rural area\ntest/582948e123cc9be2,Luxury vehicle,Performance car\ntest/582e3a9242ad84c3,Close-up,Whiskers\ntest/582fa530f3dc4864,Close-up\ntest/5830df4a4edf04a9,Fried food\ntest/5831561671ad4986,Gumbo\ntest/58348ddbf9eb2e84,Electronic instrument\ntest/5834fe8663a57c77,Machine tool\ntest/58352e16717cb79d,Fried noodles\ntest/583531699129404d,Antique car\ntest/583539197b8a821e,Frigate\ntest/583640876ac258e2,Arthropod,Close-up\ntest/583690f27741c698,Freight transport\ntest/5836f83d243059b3,Plant stem\ntest/5837839b63abcbe4,Black-and-white\ntest/5838181cef90c83d,Luxury vehicle\ntest/5838d8862dc1100a,Luxury vehicle,Performance car\ntest/583a8987df920c32,Junk food\ntest/583e570709f2ee48,Floristry\ntest/583ea161043cec35,Erinaceidae\ntest/5842d87d75249fb1,Extreme sport,Nordic skiing\ntest/5844a16c444b2906,Close-up\ntest/584590f6d3e26573,Firearm\ntest/58469930243d0514,Residential area\ntest/584d79dd595df3c4,Team sport\ntest/584df6fb4f1e10eb,Luxury vehicle,Sport utility vehicle\ntest/584f5b3bb1cd748c,Blond\ntest/585121ea3724ab63,Personal water craft,Extreme sport,Boating,Surfing Equipment,Wind wave\ntest/5855782749c3ed0b,Billiards\ntest/5855c47371aa4b49,Luxury vehicle\ntest/5855f6e0ad162581,Sport utility vehicle\ntest/585639c709bbe21e,Jeans\ntest/58579ac4bd80a19f,Arthropod,Close-up\ntest/5857fc9b37a2d4f1,Antique car\ntest/585c224819a7f826,Black-and-white\ntest/585cd1826977df44,Black-and-white\ntest/585e5a9747c1b0cf,Jeans\ntest/585eff8e14da3568,Zeppelin\ntest/585f3bef3d2af10b,Close-up,Whiskers\ntest/586280f79c236007,Bird of prey\ntest/5862ad7242fc2a45,Luxury vehicle\ntest/5863482ed1066ded,Church bell\ntest/58651adaad1c1676,Auto racing\ntest/5865d49b965f830f,Electric piano\ntest/586696460ce68dd2,Ball game\ntest/58686f46a9435e52,Luxury vehicle\ntest/5869801ccdd85bea,Compact car,Luxury vehicle,Antique car\ntest/586cdc448bce0d55,Steamed rice\ntest/586d0db92a3c200b,Luxury vehicle\ntest/5870506e96463983,Aerial photography\ntest/5870db7e694340ba,Shallot\ntest/58723eaa50e0c6cf,Junk food\ntest/5875bb24076e0403,Residential area\ntest/58767d66e732cec4,Jeans\ntest/5878aa13e1fa6420,Fried noodles,Soba\ntest/587da4d284b5d230,Leaf vegetable\ntest/587e950580cb6b3d,Domestic short-haired cat,Whiskers\ntest/587f6f9997ed4638,Arthropod,Close-up\ntest/58804158f092d0b8,Close-up\ntest/5880d7671fd08f95,Luxury vehicle\ntest/58841df9ecbb0b97,Pelmeni,Jiaozi\ntest/5886c110a8453d7d,Chordophone\ntest/58889694e2790bb2,Close-up\ntest/5888ad426ad4424f,Luxury vehicle\ntest/588a275d94780d0f,Close-up\ntest/588a9e8f69c6f7b0,Beef tenderloin\ntest/5892e73f8b38e01c,Luxury vehicle\ntest/5895ee289a82f3ff,Luxury vehicle\ntest/5897666102d5379a,Bovine\ntest/58980a87b9dad1d5,Luxury vehicle,Performance car\ntest/589847ace74936c3,Boating,Rowing\ntest/589a81ef18040b8c,Black-and-white\ntest/589ad995c7b21ef6,Blond\ntest/589f42036837dfd6,Close-up\ntest/589f46c586552628,Rural area\ntest/589f99191f2c03a7,Extreme sport,Parachuting\ntest/589f9933684521e7,Perching bird\ntest/58a0c796e16d767e,Floristry\ntest/58a0cfdfd118f9b6,Luxury vehicle\ntest/58a29a3e863e20a4,Antique car\ntest/58a36c0f09e8e5d8,Close-up\ntest/58a81dce6d1807a2,Close-up\ntest/58abb67ba32052bf,Scaled reptile\ntest/58aef885acbce01e,Luxury vehicle,Antique car\ntest/58af9dcd4c81a958,Peafowl\ntest/58aff0007d610239,Watercolor paint\ntest/58b084887fae33fb,Chordophone\ntest/58b0b1a726be5d9d,Antique car\ntest/58b0e55bda100b83,Extreme sport,Road cycling\ntest/58b140764193be39,Compact car,Luxury vehicle\ntest/58b14f524c34b404,Siberian husky\ntest/58b247268188f7ef,Aircraft engine\ntest/58b3e7442eb25fb9,Sport utility vehicle\ntest/58b46d76ff90abcc,Sport utility vehicle\ntest/58b5c75f094b200c,Vegetarian food\ntest/58b6066ac6628e6a,Bird of prey,Close-up\ntest/58b6aaa173189d5a,Luxury vehicle\ntest/58b938d08ed0de5f,Residential area\ntest/58ba430ae870acff,Musical theatre\ntest/58bac0dd7d3e7a51,Dishware\ntest/58bbbbe4e45b58ee,Ramen,Soba\ntest/58bdebef50f61def,Luxury vehicle\ntest/58be630532d87ba1,Close-up\ntest/58beb8de88574d5e,Frozen yogurt\ntest/58bfa6acf035a8ab,Close-up,Whiskers\ntest/58bfaaf6a14ffd07,Black-and-white\ntest/58c161a224e5049e,Close-up\ntest/58c19d66c4a3435b,Drums,Electronic instrument\ntest/58c27d6066ef6af0,Domestic short-haired cat,Close-up,Whiskers\ntest/58c3d4795824fe03,Jeans\ntest/58c4548131cefffc,Antique car,Performance car\ntest/58c58660bbc234ac,Luxury vehicle,Performance car\ntest/58cbaaea8bce5227,Hair coloring\ntest/58d1693ead449c0a,Sport utility vehicle\ntest/58d3cc60b1415406,Extreme sport,Sport climbing,Bouldering\ntest/58d6af92083ea6f5,Arthropod,Locust,Close-up\ntest/58d84120b9ba841b,Luxury vehicle,Sport utility vehicle\ntest/58d882295df9ad96,Jeans\ntest/58d887bd0b9faa3a,Rural area\ntest/58d9cd84bb14467b,Blond,Close-up\ntest/58da2c647314a029,Close-up\ntest/58db5d8a84495252,Domestic short-haired cat,Whiskers\ntest/58db8b9cd6b56201,Monoplane\ntest/58dbf6c25a1998f2,Ball game\ntest/58dc8aad7c6584f2,Frigate\ntest/58de387aab313a57,Plant stem,Close-up\ntest/58e2f6ed5c9b366a,Arthropod,Close-up\ntest/58e32b1be1a8b5b4,Domestic short-haired cat,Close-up,Whiskers\ntest/58e35cab58c93064,Rural area\ntest/58e4c8a04dcccd2a,Luxury vehicle\ntest/58e51d11ee20bb54,Ball game,Team sport\ntest/58e5cbcc17acc523,Arthropod\ntest/58e60b7bc460899d,Arthropod\ntest/58e7d7b411669873,Dobermann\ntest/58e83db6c31e6a50,Black-and-white\ntest/58e8a90a2424bd7f,Close-up\ntest/58e8d921a5125067,Luxury vehicle\ntest/58ea5d2c92230eb8,Luxury vehicle,Antique car\ntest/58ece78149360cc9,Divemaster,Scuba diving,Underwater diving\ntest/58ece9136d954b11,Blond\ntest/58f392c16282ffe9,Sport utility vehicle\ntest/58f39e514fdf8d7c,Luxury vehicle,Performance car\ntest/58f5ea390c335e13,Rural area\ntest/58f7bc8d284c156e,Lifebuoy\ntest/58f8851a05d65e3c,Auto racing\ntest/58fac116d0c0baa6,Touring car,Luxury vehicle,Compact car\ntest/58fad8ae668b466b,Scaled reptile\ntest/58fb8ef3be5d6b26,Close-up\ntest/58fc261209e20ea3,Whiskers\ntest/58fd3011ada40b07,Vegetarian food\ntest/58fd80819e59e2d5,Black-and-white\ntest/58fe2322d73805c5,Modern dance\ntest/59010a3cd511c126,Close-up\ntest/59018356663d45a6,Microcontroller\ntest/59019afc66c008dc,Luxury vehicle,Sport utility vehicle\ntest/5901aba7d0a740ea,Junk food\ntest/59023a69e101e147,Shinto shrine\ntest/59052de944ecd608,Arthropod,Close-up\ntest/590538ec7fc4a971,Vegetarian food\ntest/5907f467a34f01f2,Perching bird\ntest/59082ac43682b53c,Chordophone\ntest/59091a4372a9366d,Vegetarian food,Junk food,Fried food\ntest/590a2951d67435ae,Motorcycling\ntest/590a87bed3d3e4be,Antique car,Performance car\ntest/590b799bde160691,Luxury vehicle\ntest/590c930fb4690e08,Domestic short-haired cat,Whiskers\ntest/590cbfda89c25a09,Whiskers\ntest/590e8e645962f19e,Rural area,Horse and buggy\ntest/590f9b00905fe5d8,Combat sport\ntest/590fa1c204513173,Jeans\ntest/590fbbf614722c04,Military vehicle\ntest/5914ac34174e2d58,Close-up,Scaled reptile\ntest/5917eeb74ee336db,Close-up,Whiskers\ntest/59192755111dbd0b,Compact car,Luxury vehicle\ntest/5919bd120681d678,Auto racing\ntest/591f0f9c5c0dade6,Bovine\ntest/591f896bd51861f1,Leaf vegetable\ntest/5920d5ccd59ac4ba,Athletic shoe,Outdoor shoe\ntest/5920d60880a39b4e,Luxury vehicle,Performance car\ntest/5921c34f8b31ea0f,Residential area\ntest/5923deeb8ad10320,Close-up\ntest/59259de154d3e3ec,Compact car\ntest/5927c5346002d7af,Luxury vehicle,Performance car\ntest/592855e78f963f8d,Arcade game\ntest/592baf6183b5f90c,Lumpia\ntest/592bf7ae460cd22f,Roller skating\ntest/592c3035012a3f3e,Close-up\ntest/5930cf7cfbc53f20,Luxury vehicle,Sport utility vehicle\ntest/593296146cce0d7d,Luxury vehicle,Performance car\ntest/5932a24e197e1cb8,Scaled reptile\ntest/5932b1b019f0f256,Vegetarian food\ntest/5932ce03b3846659,Miniature Poodle\ntest/5932dbd3a8e000eb,Close-up,Plant stem\ntest/5934c0bb0c89f9ce,Extreme sport\ntest/5935180b36e4a738,Luxury vehicle,Performance car\ntest/59373bff73e59611,Fried food\ntest/59392dfb207ded4b,Wind wave\ntest/593992a64a6b3fcc,Aircraft engine\ntest/5939ba90dd94017f,Close-up\ntest/593b8df87d035306,Luxury vehicle,Performance car\ntest/593e5b4255c36ad6,Luxury vehicle\ntest/5944b0327ed6c1b6,Close-up\ntest/594571cb9a0327f0,Wind wave\ntest/5947bf838b981038,Close-up,Plant stem\ntest/5949b8f5637699b8,Digital camera,Black-and-white,Close-up,Camera operator\ntest/5949fb3499bfc24f,Bird of prey\ntest/594b3e8ff94fcf53,Erinaceidae\ntest/594be2b637f3069e,Black-and-white,Chordophone\ntest/594feecc83499712,Brochette\ntest/59510e3343b2438b,Close-up\ntest/59523cdee4cfcfaa,Arthropod,Close-up\ntest/5952c45a2d19674c,Ball game,Team sport\ntest/59534c233316d62e,Boating\ntest/5954eb294bff2b23,Compact car,Antique car\ntest/59555443eed28bf2,Scaled reptile\ntest/59557d161995d3f8,Sport utility vehicle\ntest/595839014f60f4f1,Close-up,Whiskers\ntest/59590484821d1a99,Boating\ntest/595963498cd394b3,Performance car\ntest/595aee12ac193767,Vegetarian food,Steamed rice\ntest/595aefba4db8aeb3,Floristry\ntest/595e9492b2ec5bc8,Aircraft engine\ntest/595ec04d6835fac0,Antique car\ntest/596177f964cd3d6a,Headphones\ntest/59636e87fa2482d8,Melee weapon,Hunting knife\ntest/5968debfda20b92a,Luxury vehicle,Performance car\ntest/596a5bf89b3a0228,Antique car\ntest/596affd29cf0e8ba,Luxury vehicle,Antique car\ntest/596e06f5ef188eaa,Close-up,Plant stem\ntest/59714fd03691d2af,Close-up,Plant stem\ntest/5972f362fe1aff0d,Close-up\ntest/59743453d684f826,Close-up\ntest/59767c6657a5e36e,Boating,Rowing\ntest/5979732f13c5fb6d,Luxury vehicle\ntest/597a9705f68368ac,Residential area\ntest/597bb1b5a554539f,Vegetarian food\ntest/597cf0cf75a7786e,Black-and-white\ntest/597ec9234f0b6faf,Luxury vehicle,Antique car\ntest/597ed30c8d64f748,Luxury vehicle\ntest/5986a3e438f9bba4,Sport utility vehicle\ntest/59904f941b59254e,Fried food\ntest/5997ab23b6437198,Bratwurst\ntest/59986b20806f9433,Black-and-white,Close-up\ntest/59989f079f201a21,Close-up\ntest/599a4b0f676e2719,Antique car\ntest/599ae29d7d0c9f5d,Boating\ntest/599b924ee2758321,Rural area\ntest/599baa7e6930d440,Team sport\ntest/599c5414c05f190b,Luxury vehicle\ntest/599f02e84a39d7e8,Aircraft engine\ntest/59a19997257862e6,Antique car\ntest/59a415ded9c6882f,Compact car,Luxury vehicle,Sport utility vehicle\ntest/59a5453726c89816,Rural area\ntest/59a583de5c3fb97b,Close-up\ntest/59a5a68e74de4e5e,Close-up\ntest/59a60b49281b2889,Digital camera\ntest/59a85bb32634eb74,Pit bull\ntest/59a873e7325c39fe,Black-and-white\ntest/59aa3c2e75337c53,Ball game,Team sport\ntest/59aa4800d5d6696d,Jiaozi,Fried food\ntest/59aa9008cd32c788,Ball game\ntest/59aa9470c67e78b5,Fried noodles\ntest/59abd1c9b0425080,Military person\ntest/59ada8bb2dbea123,Close-up\ntest/59ae07171f4b6ef4,Luxury vehicle\ntest/59b073399ece1fb6,Close-up\ntest/59b2a36f812f9a26,Close-up\ntest/59b3dc40f540b2d4,Full moon\ntest/59b493994d17ecb4,Performance car\ntest/59bcbc47cadce558,Close-up,Chordophone,Electric guitar\ntest/59bea45cb136cea6,Extreme sport,Boating\ntest/59c1d2bd208a9cae,Wind wave\ntest/59c4767777e3d818,Whiskers\ntest/59c6e5be9c8e269f,Vegetarian food\ntest/59c7c3b9cf9c2b88,Cosmetics\ntest/59c83c82b4f09cc2,Close-up\ntest/59c83e9798d44aa0,Sport utility vehicle\ntest/59cb4c85a8a11dde,Luxury vehicle\ntest/59cc1081ab1e4b50,Military person\ntest/59cd3fef5786c925,Brisket,Beef tenderloin\ntest/59cdea466a71f186,Vegetarian food\ntest/59ceb630665f7758,Team sport\ntest/59d11b2be61b838e,Performance car\ntest/59d14bbd23e4db57,Close-up\ntest/59d44d30a11250cb,Compact car,Luxury vehicle,Sport utility vehicle\ntest/59d73f2c3c7dfceb,Close-up\ntest/59d87ce106d4a1d0,Sport utility vehicle\ntest/59d94036f0f03227,Arthropod,Close-up\ntest/59d9f1800452e401,Hound\ntest/59daeaa177e6f1ae,Close-up\ntest/59db7c25e2074393,Ball game,Team sport\ntest/59dd196222ff4ced,Wind wave\ntest/59df8d32beb648ba,Fried food\ntest/59e1043a6319d102,Black-and-white\ntest/59e17f47007da39e,Melee weapon\ntest/59e1a5712929a786,Luxury vehicle,Performance car\ntest/59e1c06cbcefef44,Compact car\ntest/59e2c1ce2fe69212,Ball game,Team sport\ntest/59e2d740cbc194ef,Luxury vehicle,Antique car\ntest/59e43ba4a482a73e,Perching bird\ntest/59e47a7977c48a3b,Drums\ntest/59e59ed512dd59fa,Extreme sport\ntest/59e985466226396e,Fried food\ntest/59ea6adc8f093fa0,Whiskers\ntest/59eaf0d88f271f45,Portrait photography\ntest/59ec480590cfe719,Whiskers,Domestic rabbit\ntest/59ecba47e50b32e6,Woodwind instrument\ntest/59ed67c9a95bcadc,Gliding\ntest/59ee77ca3207be29,Luxury vehicle\ntest/59eee22dbf030ee7,Backlighting\ntest/59eeec3ba9cd8bcd,Double-decker bus\ntest/59eeecff87334cd1,Equitation,Equestrianism\ntest/59efeac52a711955,Bottled water\ntest/59f222e5c314ef9e,Auto racing,Luxury vehicle,Performance car\ntest/59f300115469f2cf,Plant stem\ntest/59f330bdd8a7eccd,Scaled reptile\ntest/59f3adba90dd9673,Trampolining\ntest/59f3bcbb3fd741bd,Close-up\ntest/59f46976c43cbeab,Black-and-white\ntest/59f46f699d6823de,Black-and-white,Close-up\ntest/59f5f3c21236f604,Black-and-white,Close-up\ntest/59f662a31d7788ea,Musical theatre\ntest/59f718e6f64d6ac5,Domestic short-haired cat,Whiskers\ntest/59f73fcbda3bac54,Close-up\ntest/59fe110e7bead3ea,Close-up,Scaled reptile\ntest/59fe5f93b59e56eb,Black-and-white\ntest/5a0057779a053549,Luxury vehicle,Antique car\ntest/5a03de5ead159691,Luxury vehicle,Antique car\ntest/5a0452b69618d37c,Jeans\ntest/5a0510ad444df148,Close-up\ntest/5a054054cb930e39,Performance car\ntest/5a05436021a5a48e,Close-up,Whiskers\ntest/5a06cc680775df9c,Road cycling\ntest/5a07d77be19ad0d4,Compact car\ntest/5a0812f54e3b97af,Compact car\ntest/5a09b3f710c78e1b,Milky way\ntest/5a0aa1e5f7b6ff5f,Close-up\ntest/5a0c010d27b88ced,Luxury vehicle\ntest/5a0c62d6f033caf0,Close-up\ntest/5a10bb37c5dba742,Combat sport\ntest/5a12f482fa8e6d9d,Bottled water\ntest/5a139be6c8da63ed,Rural area\ntest/5a145bd70ce36602,Auto racing\ntest/5a17d3b7c260342a,Antique car\ntest/5a18597109f5e8a7,Junk food\ntest/5a1ac0ebba9a872e,Extreme sport,Wind wave\ntest/5a1df4bfe299ede7,Dishware\ntest/5a1e185b4c7a8649,Frozen yogurt\ntest/5a22e6e7bcdc4bba,Rural area\ntest/5a22f3d920d6eed6,Arthropod\ntest/5a2450030d0d7d39,Perching bird\ntest/5a247b7851af5b38,Residential area,Rural area,Aerial photography\ntest/5a24f769f5394398,Molluscs,Close-up\ntest/5a25b98980cc4cad,Scuba diving,Underwater diving\ntest/5a264997a5c3c070,Electronic instrument,Electric piano\ntest/5a26c909a910c9d9,Plant stem\ntest/5a2785fcdcba0341,Leaf vegetable\ntest/5a290810e1e37dd4,Close-up\ntest/5a295624cf352f20,Church bell\ntest/5a2ae40b5d169160,Digital camera,Close-up\ntest/5a2e9d6d47fdeda5,Portrait photography,Close-up\ntest/5a313b946c6c3241,Luxury vehicle\ntest/5a337fb0235ae0e1,Compact car,Luxury vehicle,Antique car\ntest/5a3984f5135accc1,Luxury vehicle,Performance car\ntest/5a3a86b34e03463a,Musical theatre\ntest/5a3dab9ee4ba5473,Electronic instrument\ntest/5a40f71acc3a2400,Close-up,Nile crocodile,Crocodilia\ntest/5a417ca06db04737,Luxury vehicle,Performance car\ntest/5a44701f5c62ed69,Wind wave\ntest/5a46a8c6bc4847e0,Scaled reptile\ntest/5a470b0e4a02100b,Dog walking\ntest/5a492d825ff90c50,Orator,Public speaking\ntest/5a4b92dc3ff7bdf0,Close-up\ntest/5a4e8cbcfc2045f1,Fried food\ntest/5a4f8f9f45b2d1f0,Musical theatre\ntest/5a4fc14161a53bb4,Ball game,Team sport\ntest/5a500d095cdca299,Freight transport\ntest/5a50cabc742c9d29,Fire apparatus\ntest/5a54887790a664ce,Perching bird,Close-up\ntest/5a54b12b092b514c,Close-up\ntest/5a5937d6f0cf2e8e,Luxury vehicle,Performance car\ntest/5a59a428e60ee787,Hound\ntest/5a5bb3dfd86df4da,Touring car,Antique car\ntest/5a5df278205fc1b0,Billiards,Billiard room\ntest/5a601d0e0b2e91bc,Compact car,Antique car\ntest/5a6038d53fd62af1,Rural area\ntest/5a60419f35f77a76,Close-up\ntest/5a61820116260175,Jeans\ntest/5a61f0fd06a87b2a,Ball game\ntest/5a659bd701b156e5,Domestic short-haired cat,Whiskers\ntest/5a666f769bd5f046,Sport utility vehicle\ntest/5a697bdcaf277159,Leaf vegetable\ntest/5a6c7a3e0247735d,Luxury vehicle,Performance car\ntest/5a6ddf7290514724,Luxury vehicle\ntest/5a6f7297ade39887,Extreme sport,Motorcycling\ntest/5a706229b0fa45de,Luxury vehicle,Performance car\ntest/5a71db307230880e,Black-and-white\ntest/5a71e1630f1ac23c,Whiskers\ntest/5a72fb8841ccd99d,Auto racing\ntest/5a731797259d74e4,Rural area\ntest/5a733eb92334b37a,Aircraft engine\ntest/5a7366f570f68a14,Close-up\ntest/5a7593e1519f4b58,Molluscs,Close-up\ntest/5a7837ad632fc9cf,Digital camera\ntest/5a78424627ed1482,Residential area,Cobblestone\ntest/5a80e5785fdb6b55,Extreme sport,Nordic skiing\ntest/5a81fe2331003138,Arthropod,Close-up\ntest/5a82b29382d32edf,Antique car\ntest/5a82d800245bb065,Black-and-white\ntest/5a8446fe82bc4196,Machine tool\ntest/5a87fda9f322d18f,Compact car\ntest/5a88fce670bc8380,Luxury vehicle\ntest/5a891e91ee233df6,Plant stem\ntest/5a8cf3aea60e9c24,Arthropod,Locust\ntest/5a8d9af59865bd00,Middle ages\ntest/5a8de35bd8254d01,Compact car,Luxury vehicle,Antique car\ntest/5a8e36d14c6cd51e,Chordophone\ntest/5a8fd52f09179cef,Luxury vehicle\ntest/5a903ce8f3f67a64,Whiskers\ntest/5a9137a09cd65d70,Microcontroller,Breadboard\ntest/5a97e8180dab9ad7,Melee weapon,Hunting knife\ntest/5a990d32ad3b4094,Aerial photography\ntest/5a9929db58ba8ab3,Rural area\ntest/5a997a05314ad368,Bagpipes\ntest/5a9ce21b340b2ba4,Fried noodles\ntest/5a9e989cf0905faf,Luxury vehicle,Performance car\ntest/5a9f3a4abcab041e,Floristry\ntest/5aa1b66b4896974a,Vegetarian food\ntest/5aa61973bd62d025,Scaled reptile\ntest/5aa6c6db7458bb89,Inflatable\ntest/5aa6fc85c7677ec3,Coral reef fish\ntest/5aadcedd8fedc9f0,Close-up\ntest/5aadcef9db522f86,Aircraft engine\ntest/5aafaab4566d8d78,Freight transport\ntest/5ab0465e7dbfeb8d,Performance car,Antique car\ntest/5ab293e3ecf23857,Antique car\ntest/5ab46c192ce3dcb7,Luxury vehicle,Performance car\ntest/5ab5293c9f67fa72,Luxury vehicle\ntest/5ab6178c09eb7f2d,Black-and-white\ntest/5abc822ca60cdb64,Aerial photography\ntest/5abdfe2997d30ea8,Rural area,Aerial photography\ntest/5abea138d966fa40,Jeans,Antique car\ntest/5ac02f423b20b1ea,Nest box\ntest/5ac03067a3b1c4b7,Close-up,Scaled reptile\ntest/5ac078e9022fddce,Performance car\ntest/5ac24473c9037fa0,Luxury vehicle\ntest/5acb0e40dbaddb95,Luxury vehicle\ntest/5acc1882b30b33a6,Extreme sport,Boating\ntest/5acc475d69917afa,Scaled reptile,Close-up,Anole\ntest/5aced6b66dd5e806,Luxury vehicle\ntest/5ad2de0e0e0c44f9,Luxury vehicle,Performance car\ntest/5ad4a95fc72c63d0,Close-up\ntest/5ad4cb971c06c237,Luxury vehicle\ntest/5ad7445156bad85a,Luxury vehicle\ntest/5ad952fd8b7a5422,Herding,Goats\ntest/5ada41b10dbdf030,Bouldering\ntest/5ada7c74d1976a59,Barechested\ntest/5adaf5902a0aa3d7,Arthropod\ntest/5ae00fb6608f0757,Compact car,Luxury vehicle\ntest/5ae0bcacd32075d3,Whiskers\ntest/5ae0dae278b07325,Bratwurst\ntest/5ae342f308f52ec9,Plant stem\ntest/5ae35b582cbe045a,Domestic short-haired cat,Whiskers\ntest/5ae4648046c08b55,Khinkali,Jiaozi\ntest/5ae4aea6842677e1,Performance car\ntest/5ae4b2be93dadadf,Antique car\ntest/5ae4d284357b54c5,Luxury vehicle\ntest/5ae62c917944b243,Extreme sport,Parachuting\ntest/5ae8dc22bf8ce4d8,Close-up\ntest/5ae9aeb2ece23c7a,Military person\ntest/5aea0b0b8fc06a45,Machine tool\ntest/5aea12e07dd46282,Jeans\ntest/5aea7a114438e6b1,Watercolor paint\ntest/5aec3ebd56448dd0,Antique car\ntest/5aeffc2cbb7a4257,Aircraft engine\ntest/5af1537ef62d452e,Residential area,Aerial photography\ntest/5af1866d5af92271,Computer speaker\ntest/5af3a22bebb61020,Black-and-white\ntest/5af46ac5d796f985,Ball game,Team sport\ntest/5af47c9384ce91fb,Divemaster,Scuba diving,Underwater diving\ntest/5af4a291e93e7aa1,Luxury vehicle,Antique car\ntest/5af4fc20c78b4a85,Bovine\ntest/5af58667d0ca906a,Black-and-white\ntest/5af70bc6497e422c,Extreme sport\ntest/5af73a99b89054b4,Arcade game\ntest/5af7b12cb1e77ea7,Amphibian\ntest/5af9b4a7cfce4e2d,Arthropod,Plant stem\ntest/5afb5a47645aa0c4,Beef tenderloin\ntest/5afe66a77c9e3ee8,Luxury vehicle,Performance car\ntest/5affd97129e476dd,Close-up\ntest/5b0368c71a16031e,Luxury vehicle\ntest/5b037fc9857cae49,Bovine\ntest/5b067a2d4f4c4145,Close-up\ntest/5b06e6a021b8d77d,Freight transport\ntest/5b079c0762fe8e03,Performance car\ntest/5b0c9af2803addc2,Domestic short-haired cat,Whiskers\ntest/5b0d3b8631b8c647,Junk food\ntest/5b0f9dedfeba3892,Black-and-white\ntest/5b1086ed3dd6a04b,Performance car,Luxury vehicle,Antique car\ntest/5b12cc90b9126389,Blond,Close-up\ntest/5b13599a74202a8c,Compact car\ntest/5b15bbbbe0594fa9,Bird of prey\ntest/5b160c0aca593187,Aircraft engine\ntest/5b1656fb5592d586,Luxury vehicle,Performance car\ntest/5b16c3dd599f9492,Perching bird\ntest/5b17d36f53198e16,Close-up\ntest/5b1947f3adf87320,Fried food\ntest/5b19c76f1b2c55e9,Close-up\ntest/5b1c17cb6d748fa0,Jiaozi\ntest/5b1df16f3bae5b5a,Boating\ntest/5b1fa3c639aa0743,Luxury vehicle\ntest/5b29138cc7e800d3,Whiskers\ntest/5b29dcc3de45d313,Military vehicle,Sport utility vehicle\ntest/5b2a8da2dcc7e310,Sport utility vehicle\ntest/5b2b181026d3e90b,Close-up\ntest/5b2f18c1689eb6b8,Performance car\ntest/5b2feb8bcdbf5632,Whiskers\ntest/5b311e6a650a6208,Luxury vehicle\ntest/5b32b01397d22733,Close-up\ntest/5b336820a1e8baed,Team sport\ntest/5b35e35d63e1590c,Arthropod,Close-up,Plant stem\ntest/5b36005f62cf5aed,Jeans\ntest/5b374fa89c4ffc97,Molluscs,Close-up\ntest/5b38cdfe13b0f855,Plant stem,Close-up\ntest/5b38ce8e0a227c16,Drums,Electronic instrument\ntest/5b38ea225a76406c,Luxury vehicle\ntest/5b38ffd062caf1a6,Procyonidae,Whiskers\ntest/5b39e8b0c33fc205,Compact car,Antique car\ntest/5b3a1f0401e5ed7e,Close-up\ntest/5b3af0e65b0fba42,Compact car\ntest/5b3b6b3de18f6726,Bovine\ntest/5b3cbab4f9d04db8,Bratwurst\ntest/5b3d2c131c7f9fec,Equitation,Equestrianism\ntest/5b3d8eacb52b1468,Team sport\ntest/5b3f8338c2c17ed2,Equestrianism\ntest/5b41026aea97c548,Dishware\ntest/5b4129d967860915,Performance car,Luxury vehicle,Antique car\ntest/5b42afa8ac5c1fc9,Junk food\ntest/5b434cd7c95f4d29,Performance car,Luxury vehicle,Antique car\ntest/5b4570380c6e4ae3,Milky way\ntest/5b464bf6516cccfe,Arthropod\ntest/5b4c846e1b55469c,Luxury vehicle\ntest/5b4c857b7614b671,Plant stem\ntest/5b4f96aaa66d2836,Close-up\ntest/5b4fedc81bed1bee,Divemaster,Scuba diving,Underwater diving\ntest/5b565ea96ff622c4,Gun turret,Military vehicle\ntest/5b569c4cd6425b4c,Hair coloring\ntest/5b59cb39f53cf892,Black-and-white,Close-up\ntest/5b5c939f308c934b,Miniature Poodle\ntest/5b5dc8993c17f37e,Vegetarian food\ntest/5b5e02353f5734d7,Close-up,Plant stem\ntest/5b5e5614e196b919,Goats\ntest/5b5f359f40cb3649,Luxury vehicle,Antique car\ntest/5b60c646c5ac495a,Luxury vehicle\ntest/5b610377eabe8f09,Luxury vehicle\ntest/5b63376cf57a4179,Vegetarian food,Junk food\ntest/5b643325542515f0,Arthropod,Close-up\ntest/5b659e26c9164fba,Close-up\ntest/5b67067b2c96f867,Arthropod,Locust,Close-up\ntest/5b69237f0ee35107,Halter,Equestrianism\ntest/5b6fbdeeb05fce56,Residential area\ntest/5b73e83e2a46f83a,Soba\ntest/5b74464e441a26a2,Kitesurfing\ntest/5b75170f015c9665,Luxury vehicle,Performance car\ntest/5b76bee33542598b,Miniature Poodle\ntest/5b78da33aa829291,Rural area\ntest/5b7ad959f3a142f0,Vegetarian food,Fried food\ntest/5b7b58b5eab43e92,Ball game,Team sport\ntest/5b7cc90f5bf964eb,Luxury vehicle,Antique car\ntest/5b7f1b470c3bb30f,Arthropod,Plant stem,Close-up\ntest/5b81976dad564143,Orator\ntest/5b825eb5727f4218,Ball game,Team sport\ntest/5b837f2cd121296c,Cosmetics\ntest/5b87fe3dc049e0d3,Close-up,Whiskers\ntest/5b8bdf721b23316d,Rural area\ntest/5b8c90c48dd5810f,Combat sport\ntest/5b8d2cb84d663ee9,Aircraft engine\ntest/5b8e20f28f228d2e,Antique car\ntest/5b8eabcfe27b55a2,Whiskers\ntest/5b8ed5335cc72095,Luxury vehicle\ntest/5b8f8d5396b196e3,Church bell\ntest/5b8ff5713968aec3,Performance car\ntest/5b90207f3a3713f7,Luxury vehicle,Performance car\ntest/5b922460ac391884,Luxury vehicle,Antique car\ntest/5b937b0484133d2f,Sport utility vehicle\ntest/5b93c61d21121dea,Extreme sport,Wind wave\ntest/5b9445e918f4c383,Black-and-white,Close-up\ntest/5b947e365ea0a5da,Toy block\ntest/5b9591f0f81b6429,Luxury vehicle,Performance car\ntest/5b9b0b60d04aae25,Arthropod,Close-up\ntest/5b9b67a681391e81,Jackfruit\ntest/5b9c6ef802830997,Ball game,Team sport\ntest/5ba4cac7365da894,Arthropod,Close-up\ntest/5ba5365f174f6e2d,Sport utility vehicle\ntest/5ba609e21eaba56a,Gumbo\ntest/5ba6ddc560a95b0a,Brochette,Yakitori\ntest/5ba9cb0d0289bc9c,Bovine\ntest/5bab5cab27498b37,Horse racing\ntest/5babf7875314ae7b,Brochette,Yakitori\ntest/5bad3df54486b069,Fried food\ntest/5badbb0d1b50727e,Gun turret,Military vehicle\ntest/5bae8a04c1cde49f,Black-and-white,Portrait photography\ntest/5baefd09456e8bc4,Bratwurst\ntest/5baf526313e1258c,Machine tool\ntest/5bb18286cc13196e,Team sport\ntest/5bb58061737d4876,Plant stem,Close-up\ntest/5bb583fa67879860,Luxury vehicle\ntest/5bb60a6018b3e701,Auto racing,Compact car\ntest/5bb66d9a9ce284ff,Chordophone\ntest/5bb6d70d8f07ea3e,Goats\ntest/5bb7f640aaa0796f,Luxury vehicle,Sport utility vehicle\ntest/5bb86a607e8fa6ff,Luxury vehicle,Performance car\ntest/5bbd9261928d5708,Luxury vehicle\ntest/5bc1c93b9cae6873,Close-up\ntest/5bc21601ccdc7544,Gumbo\ntest/5bc39f31483b8f1e,Auto racing\ntest/5bc477c1c8bbf9fd,Microcontroller\ntest/5bc4a8db47ee62a5,Compact car\ntest/5bc55ddb812c22f5,Arcade game\ntest/5bc62e785be2c4f3,Compact car,Antique car\ntest/5bc6c21832176c26,Jeans\ntest/5bc75a34cfc75dfe,Digital camera,Black-and-white,Close-up\ntest/5bc92013182b5644,Extreme sport\ntest/5bc9e5d31b44ae10,Close-up\ntest/5bcaaacb0876e9f2,Arthropod,Close-up\ntest/5bcb428b66faf924,Ball game,Team sport\ntest/5bcc03add109c439,Digital camera,Black-and-white\ntest/5bcf1ad79dda113b,Goats\ntest/5bd399a8e71b82bc,Performance car\ntest/5bd45db2489e9f67,Close-up\ntest/5bd6210fb8dab791,Aircraft engine\ntest/5bd797a64f6457fd,Extreme sport\ntest/5bd8baa93721f6a1,Jeans\ntest/5bd8df91d26ccc91,Rear-view mirror\ntest/5bd9deffc58921be,Bovine\ntest/5bdb7c514f0f00df,Outdoor shoe\ntest/5bdc72e8c0bcfdc0,Boating\ntest/5bdee5bdf1b2b1eb,Close-up\ntest/5bdf0979b68df502,Compact car\ntest/5be0cc8d7f4875a1,Close-up\ntest/5be23820cdaaf08c,Close-up\ntest/5be33b5029b3b073,Stemware\ntest/5be61ff693d252a0,Close-up\ntest/5be67e4788cd740e,Aircraft engine\ntest/5be6cdd50e0b0abf,Hound\ntest/5be7702e011dce85,Khinkali\ntest/5be7b53616a99404,Luxury vehicle\ntest/5beaa48a613be275,Dobermann,Hound,Close-up\ntest/5beb61e9820fb6e9,Coral reef fish\ntest/5bed6fc231e0168f,Close-up\ntest/5bef605a59c63370,Machine tool\ntest/5bf044dbd92ee5e7,Whiskers\ntest/5bf182c14ade8c4f,Horse and buggy\ntest/5bf19602276aa712,Boating\ntest/5bf4226c6340f704,Aircraft engine\ntest/5bf4853aca123d5b,Compact car,Antique car\ntest/5bf4b0d24338689d,Close-up\ntest/5bf625e3811120f9,Whiskers\ntest/5bf79ad490d765d4,Bird of prey,Close-up\ntest/5bf854071f8d39ca,Close-up\ntest/5bf869169c71f52a,Extreme sport\ntest/5bf86c8813289fd0,Sport utility vehicle\ntest/5bf9c04abfed05f1,Close-up\ntest/5bfac34ddfcc79b5,Compact car,Luxury vehicle,Antique car\ntest/5bfdf5cb8f1b1204,Bagpipes\ntest/5bff555db19f3954,Compact car,Luxury vehicle\ntest/5c0196c33bf50789,Outdoor shoe\ntest/5c0202b8d36d48d3,Whiskers\ntest/5c02727c91bd6b33,Luxury vehicle\ntest/5c0682bc347eaf4c,Musical theatre\ntest/5c0847c5fa82bbcf,Luxury vehicle,Performance car\ntest/5c0882d88c80ab1c,Rural area\ntest/5c08f28848847497,Close-up\ntest/5c0ae2acdcfde7a5,Drums\ntest/5c0b21c7ff4cc77d,Plant stem,Close-up\ntest/5c0b8bff3154641c,Outdoor shoe\ntest/5c0c4b270c37cc02,Luxury vehicle,Antique car\ntest/5c0dd9888e0173c0,Luxury vehicle,Antique car\ntest/5c0f3537e45e9af1,Shinto shrine\ntest/5c0f574e1021ea6a,Combat sport\ntest/5c0f851222e38928,Compact car\ntest/5c103aa7684a31a2,Domestic short-haired cat,Whiskers\ntest/5c10d610aac84184,Extreme sport,Sport climbing\ntest/5c17d4f7366ca277,Close-up\ntest/5c180ffdd3f09409,Sport utility vehicle\ntest/5c18d88dd7b1d236,Antique car\ntest/5c19f6ccdb4256ae,Antique car\ntest/5c1a1a40cd3c2db2,Briefs\ntest/5c1a2d0748163047,Wind wave\ntest/5c1a8464cbf39af2,Firearm\ntest/5c1d33ccb9554f51,Black-and-white\ntest/5c1e4dbcedf1801c,Close-up\ntest/5c1e62fad3279058,Performance car\ntest/5c1ef6e7a08e267c,Aircraft engine\ntest/5c1fb724db1bf150,Close-up\ntest/5c2064b9201c2eda,Luxury vehicle,Performance car\ntest/5c20b6a6091705df,Arthropod,Close-up\ntest/5c20c723f6e6c746,Combat sport,Sumo\ntest/5c2125fd92fd11d8,Luxury vehicle,Antique car\ntest/5c219dea7bb12d2a,Performance car\ntest/5c2477c697f3be9f,Digital camera\ntest/5c25dc258f21d205,Luxury vehicle,Sport utility vehicle\ntest/5c2b8415b3621521,Junk food\ntest/5c2c82226da2e910,Antique car\ntest/5c2ecd8c712d74a5,Monoplane\ntest/5c2fae44ea381f33,Sport utility vehicle\ntest/5c3291d284419ec1,Close-up\ntest/5c356a07d8901162,Hound\ntest/5c35922cacbedc06,High-speed rail\ntest/5c364ffad76cd044,Close-up\ntest/5c3808d4d99ce6d8,Musical theatre\ntest/5c39cc7f5baa921b,Compact car,Luxury vehicle\ntest/5c3a0d1db40ba538,Close-up\ntest/5c3b3dd1f4c4a681,Luxury vehicle\ntest/5c3b9e45e865f388,Black swan,Close-up\ntest/5c3d7e1e55326af8,Combat sport\ntest/5c3da71885a0b491,Luxury vehicle\ntest/5c3e0b97573b61f8,Compact car\ntest/5c3e0dd01b24d346,Black-and-white\ntest/5c3e2379ef0ddbc7,Water polo,Ball game,Team sport\ntest/5c3e678e5beaa052,Nordic skiing\ntest/5c3e7ce1110c2ef5,Cosmetics\ntest/5c40a023f3abbbda,Performance car\ntest/5c414796cff585f5,Luxury vehicle\ntest/5c41d0cacc8754c6,Equestrianism\ntest/5c43cf63624d7e81,Scaled reptile\ntest/5c43f73a2f9a2bc9,Floristry\ntest/5c447670f1137d97,Luxury vehicle,Antique car\ntest/5c46432f977c05d6,Rural area,Glacial landform\ntest/5c469e79f4f536f8,Cobblestone\ntest/5c46c073dc88059d,Aircraft engine\ntest/5c48131543470378,Sport utility vehicle\ntest/5c48e4091cb02c9e,Outdoor shoe\ntest/5c4ac78e1ecc93ee,Floristry\ntest/5c4b23a70a201527,Toy block\ntest/5c51b818c315fad3,Close-up\ntest/5c5278b5d1b34288,Team sport\ntest/5c5425b7e930feef,Luxury vehicle\ntest/5c544330d831e406,Compact car,Luxury vehicle\ntest/5c54ce45b8eb3211,Close-up\ntest/5c558d90cf32049a,Rural area\ntest/5c566328c90446ba,Wind wave\ntest/5c57e4212f0b3484,Cookies and crackers\ntest/5c587338b3d2fa9f,Athletic shoe,Outdoor shoe\ntest/5c5d8b8fb3b860d5,Antique car,Performance car\ntest/5c5d8c1812766d24,Domestic short-haired cat,Whiskers\ntest/5c5f0ea14f4291b8,Goats\ntest/5c5f398a70c66c90,Whiskers\ntest/5c5f65d48dcb5f68,Compact car,Antique car\ntest/5c63b5a91e93947a,Vegetarian food\ntest/5c6410d176dd746c,Boating\ntest/5c6587f3a4f33462,Halter,Equitation,Equestrianism\ntest/5c66caa561c87523,Vegetarian food\ntest/5c673acbbc08b0e8,Military person\ntest/5c69ba95f184e8a2,Auto racing\ntest/5c6afbf283d1b1be,Close-up,Whiskers\ntest/5c6bd24a91b713fa,Hound\ntest/5c6e6b896b16a3ec,Close-up\ntest/5c70a4df9b10d0d0,Sport utility vehicle\ntest/5c70cf8084c52f6f,Compact car,Antique car\ntest/5c74ea394a56a322,Luxury vehicle,Antique car\ntest/5c7686dd7a4d72b3,Luxury vehicle\ntest/5c77b16775d19385,Briefs\ntest/5c7801b766648c36,Perching bird\ntest/5c78ce9e64586b86,Domestic short-haired cat,Whiskers\ntest/5c78e368c18c849e,Rural area\ntest/5c7cf8db846a0acb,Antique car\ntest/5c7e771ec1e302f3,Vegetarian food\ntest/5c7ebf80948eb24c,Floristry,Plant stem\ntest/5c82e206d805f8dd,Rural area\ntest/5c843a1746c3b1e8,Luxury vehicle,Performance car\ntest/5c855761119d8b0f,Cosmetics,Close-up,Whiskers\ntest/5c895b2582e14b64,Performance car\ntest/5c8a002736854302,Roman temple\ntest/5c8cc5219621ed51,Performance car\ntest/5c8f6cf93dd897be,Close-up,Primate\ntest/5c9028b60f17ffb8,Luxury vehicle,Antique car\ntest/5c902f4a20a73fe2,Ball game\ntest/5c90e4679a49d10a,Pork chop\ntest/5c9443ecc6fd7aaf,Arthropod,Close-up\ntest/5c955e3a7555aa23,Combat sport\ntest/5c9a6bb71d8d4e59,Outdoor shoe\ntest/5ca0d863c3f21728,Galleon,Black-and-white,Barquentine,Frigate\ntest/5ca0eb51ec8c5d47,Antique car\ntest/5ca163ab0cf88450,Arthropod,Locust,Close-up\ntest/5ca2a796d405ed49,Cookies and crackers\ntest/5ca4918f102bafce,Brochette\ntest/5ca5574ff0ff551f,Fried food\ntest/5ca655917acee913,Domestic short-haired cat,Whiskers\ntest/5ca7adbe0412a12a,Performance car\ntest/5ca9b17f4623ffaa,Black-and-white\ntest/5ca9fdb8188a47ba,Arthropod,Close-up\ntest/5caba8229d7ec861,Luxury vehicle,Performance car\ntest/5cad25e9249d6bda,Goats\ntest/5cae33d8939ffaa0,Auto racing\ntest/5caf923cecbb1c6d,Team sport\ntest/5cb08f231cf4874a,Compact car,Luxury vehicle\ntest/5cb74f59db31b577,Close-up,Whiskers\ntest/5cba038fdd8dc915,Luxury vehicle,Performance car\ntest/5cba1341876f8f6c,Close-up\ntest/5cbc14f3dcd8327a,Black-and-white\ntest/5cbddd1315c5440e,Pit bull\ntest/5cbe4a08bf82c6e6,Domestic rabbit\ntest/5cbfe6f4b592a4b3,Fire apparatus\ntest/5cc24893907e91e4,Siberian husky\ntest/5cc28e04b8e40226,Close-up\ntest/5cc3dcb2620c620a,Toy block\ntest/5cc5ad3b2d3431cf,Wind wave,Glacial landform\ntest/5cc7b3dab198d50d,Black-and-white,Headphones,Close-up\ntest/5ccd48b9764cacf5,Domestic short-haired cat,Whiskers\ntest/5ccd871518b953f6,Close-up\ntest/5cd186f73deb2393,Jeans\ntest/5cd1a5f21e30b40d,Luxury vehicle,Antique car\ntest/5cd3cb5d66c81030,Close-up\ntest/5cd4933c42d34488,Extreme sport,Wind wave,Surfing Equipment\ntest/5cd4c033bdb6903b,Luxury vehicle\ntest/5cd7337ad9062f90,Compact car\ntest/5cd736f5fb2927d7,Scaled reptile\ntest/5cd75133d345dac5,Bovine\ntest/5cd95f3b0c33eba0,Portrait photography\ntest/5cdf163a938037df,Close-up\ntest/5ce09a558ffd7edd,Compact car,Luxury vehicle\ntest/5ce0c4c38cc670ac,Close-up\ntest/5ce0d1e24521996b,Close-up\ntest/5ce0e23ac6e059ba,Machine tool\ntest/5ce148e88aa9dd87,Combat sport\ntest/5ce150bc38e60701,Performance car\ntest/5ce15936b682eedb,Ball game,Team sport\ntest/5ce2f96a113f8874,Performance car\ntest/5ce2f9b216d70ef1,Hound\ntest/5ce3fffb138be2fc,Wind wave\ntest/5ce91da1da246204,Sport utility vehicle\ntest/5ce9216feff668ac,Scaled reptile\ntest/5ce9cc15b77a1e13,Blond\ntest/5ceb1c347b001861,Whiskers\ntest/5cecfa5cd28d1baf,Antique car,Luxury vehicle,Performance car\ntest/5cedc72a3446feff,Compact car\ntest/5cf0d661bc5cc7cb,Jackfruit\ntest/5cf16fdbb6b90abf,Black-and-white\ntest/5cf17548730afcbd,Close-up\ntest/5cf1fd022d57a8ce,Domestic short-haired cat,Whiskers\ntest/5cf3db02f0301cc0,Vegetarian food\ntest/5cf5cab262f1eb18,Compact car,Luxury vehicle\ntest/5cf775420031edb7,Middle ages\ntest/5cf7ada370a0991f,Arthropod,Close-up\ntest/5cf84df2b7b3572a,Pit bull\ntest/5cfdfd67eed7c2c8,Siberian husky\ntest/5cfec64eb10c1888,Sport utility vehicle\ntest/5cfffe7d409a220c,Toy block\ntest/5d00cfcb4eef021a,Luxury vehicle\ntest/5d01c1953f1d6887,Ball game,Team sport\ntest/5d01d87e543231d4,Compact car\ntest/5d03087470692afc,Extreme sport\ntest/5d049dc4bf4fb743,Galleon,Frigate\ntest/5d05cc43c74d91e5,Luxury vehicle,Antique car\ntest/5d0731438606392b,Arthropod\ntest/5d0a13ff423fc6e9,Close-up\ntest/5d0c1bc65f1034d6,Arthropod,Close-up\ntest/5d0c69dfdc672f5b,Fried food\ntest/5d0e64f5422582bb,Close-up,Scaled reptile\ntest/5d0fd9eee81e50d9,Wind wave\ntest/5d1273a20d5a38a6,Luxury vehicle,Performance car\ntest/5d1582f23167aa07,Sport utility vehicle\ntest/5d1586dc64adc2c7,Close-up,Plant stem\ntest/5d19bda9cab0cf10,Auto racing,Compact car\ntest/5d1a16322d826c12,Aircraft engine\ntest/5d1cea56003777b6,Aerial photography\ntest/5d1ec49663953999,Beef tenderloin\ntest/5d2394dcaba76205,Compact car\ntest/5d2787cf4046fe0d,Steamed rice\ntest/5d2a505a7a482e21,Vegetarian food\ntest/5d2c986d0c8a24f3,Aircraft engine\ntest/5d2f6da3f978c329,Sledding\ntest/5d2ff5756eb26fbc,Luxury vehicle,Performance car\ntest/5d302a1a4afea9d0,Ball game,Team sport\ntest/5d32562836a905dc,Residential area\ntest/5d35f725796f810f,Close-up\ntest/5d3ac93b35e63391,Luxury vehicle\ntest/5d3d33e4cdd07e2a,Close-up\ntest/5d3d5b123a75750e,Luxury vehicle\ntest/5d3dddad70e8e798,Luxury vehicle\ntest/5d3ea6261a315656,Microcontroller\ntest/5d3f13adde833bc8,Ball game,Team sport\ntest/5d3fb2fbf88a6617,Antique car\ntest/5d3ff418315ad094,Fried food\ntest/5d40bab8c2182cf0,Plant stem\ntest/5d41f32b14fc70e0,Musical theatre\ntest/5d426a2670c76247,Ball game,Team sport\ntest/5d42803fa334d34a,Arthropod\ntest/5d48529c5c109119,Arthropod,Close-up\ntest/5d48a2abd2561be8,Hair coloring\ntest/5d48cff0d37f92d1,Gun turret\ntest/5d4b44e7f53906dc,Auto racing\ntest/5d4c006b53677917,Flatbread\ntest/5d4f1369b5b60e3f,Residential area,Aerial photography\ntest/5d5167eac6db8b75,Antique car\ntest/5d52e12dd2c6ac3d,Black-and-white\ntest/5d53624af91bcd95,Performance car\ntest/5d57435ea1776c51,Luxury vehicle\ntest/5d579f57ead82296,Blond\ntest/5d57fdc7534a749d,Figure skating\ntest/5d5c568735778ea0,Water polo,Ball game,Team sport\ntest/5d5d3a3290633813,Outdoor shoe\ntest/5d5dec691d0834f5,Luxury vehicle,Performance car\ntest/5d5ed416ac22f9a1,Nest box\ntest/5d5f2378d6fe407c,Watercolor paint\ntest/5d5fdab537a1d97e,Full moon\ntest/5d61eac1650231cf,Ball game\ntest/5d64de232c93d474,Rural area,Horse and buggy\ntest/5d6515c18af0be20,Fried food\ntest/5d657f793a08f926,Black-and-white\ntest/5d6620c49140a5ee,Lawn game,Ball game\ntest/5d66caf5918ee36d,Dobermann\ntest/5d677d77474e6a9f,Black-and-white,Holding hands,Close-up\ntest/5d67ce8409d8046b,Auto racing,Antique car\ntest/5d67f07fa89b8103,Sport utility vehicle\ntest/5d6a71db1523ce6b,Luxury vehicle,Performance car\ntest/5d6c121a8f2148c3,Compact car,Luxury vehicle\ntest/5d6dc5613faa6a05,Pelmeni\ntest/5d6f9921716b3c40,Compact car\ntest/5d70ca1b4f35dcc6,Road cycling\ntest/5d710ad40e8c84fa,Arcade game\ntest/5d710ca24ba724ec,Galleon,Black-and-white\ntest/5d73995297c76104,Sport utility vehicle\ntest/5d73dd24ff20ab0d,Canola,Close-up\ntest/5d76ce9fe7d79b10,Peafowl\ntest/5d78159a10efbecd,Motorcycling\ntest/5d798477cc015a65,Luxury vehicle,Sport utility vehicle\ntest/5d7a6acce8b83d3d,Close-up\ntest/5d7b05501e1b3a62,Goats\ntest/5d7b4bdc9510ae23,Luxury vehicle\ntest/5d7eb634114862d9,Plant stem,Close-up\ntest/5d7f2e3a0a583247,Sport utility vehicle\ntest/5d80e7a8afa0cef9,Vegetarian food,Pasta salad\ntest/5d823c7fc4318594,Team sport\ntest/5d828effc4474aa2,Compact car\ntest/5d86cb2a81191083,Galleon\ntest/5d877512f2295956,Luxury vehicle,Antique car\ntest/5d87ec17dd120d05,Blond\ntest/5d87f99de45c0bf7,Compact car,Luxury vehicle,Antique car\ntest/5d89c7c6ffa10a0e,Black-and-white\ntest/5d8b344861d80418,Luxury vehicle,Performance car\ntest/5d8bb85bb03d05a4,Modern dance,Musical theatre\ntest/5d8d232ccf29ae82,Blond\ntest/5d8fe2c5b2193078,Watercolor paint\ntest/5d916dd2259f6ae0,Shinto shrine\ntest/5d927b791ed979bd,Extreme sport\ntest/5d92a2cdee08b741,Aircraft engine\ntest/5d92cee37412fc79,Stemware\ntest/5d9910f6c0fce24e,Musical theatre\ntest/5d99436c45dbe1ab,Whiskers\ntest/5d9b01217ef8315b,Compact car,Sport utility vehicle\ntest/5d9e84c2271f4c93,Portrait photography,Close-up\ntest/5d9ea670c23f508d,Luxury vehicle,Antique car\ntest/5da04da0b4633df5,Luxury vehicle,Performance car\ntest/5da21d43cd5f05a4,Combat sport\ntest/5da7a35ec02305d9,High-speed rail\ntest/5da9044f57fa019b,Extreme sport\ntest/5dab7cbee95c5695,Bird of prey\ntest/5dabd9617d38b92e,Bird of prey\ntest/5dada8a656ffbd9a,Miniature Poodle\ntest/5dafb90559f26341,Luxury vehicle,Sport utility vehicle\ntest/5db2ddf5cf95cd2e,Close-up\ntest/5db44fbffbb5ea48,Musical theatre\ntest/5db6b6f8808f2ff8,Luxury vehicle\ntest/5db8ceff66ce240e,Horse and buggy\ntest/5dbb65e7f08a6c39,Vegetarian food\ntest/5dbf14e940ec32b6,Luxury vehicle,Sport utility vehicle\ntest/5dbf53dae38fa9c3,Electronic instrument\ntest/5dbfb079d8db2c72,Black-and-white\ntest/5dbfcb4943692d9e,Musical theatre\ntest/5dc4f3358a12cc6c,Aerial photography\ntest/5dc568b2ac90582d,Luxury vehicle,Performance car\ntest/5dc63a64e7e910c5,Miniature Poodle\ntest/5dc682c0795d651f,Barechested\ntest/5dc7d04e6613c8af,Amphibian\ntest/5dc8096f0bd718d6,Luxury vehicle,Performance car\ntest/5dc8a3b813dddaca,Sport utility vehicle\ntest/5dc9465b0bad69fc,Residential area\ntest/5dca9e0492bb814e,Leaf vegetable\ntest/5dcb47330531fc5f,Digital camera,Black-and-white,Close-up\ntest/5dcc6ed2c29bcbe7,Hound\ntest/5dce30e7bd51d911,Jeans\ntest/5dce44d0724fe25c,Luxury vehicle\ntest/5dceccf926c3c3a0,Compact car,Luxury vehicle\ntest/5dd31355f767bec0,Antique car\ntest/5dd6d76d3a402eef,Compact car,Luxury vehicle,Antique car\ntest/5dd73b73ca59cb84,Vegetarian food\ntest/5dd77b1499cc1611,Horse and buggy\ntest/5dd9ea57ca394747,Jeans\ntest/5ddabc043dfbd2ef,Close-up\ntest/5ddf1c6dbb7be7b3,Chordophone\ntest/5de3759a0fafd6f7,Molluscs\ntest/5de492b5d2f21416,Domestic short-haired cat,Whiskers\ntest/5de589999e944c10,Milky way\ntest/5de6b8490e5f7bce,Outdoor shoe\ntest/5de73c0a8a2f1ca9,Whiskers,Domestic rabbit\ntest/5de89f3299e1a620,Computer speaker\ntest/5de8f9b1f5e47230,Sport utility vehicle\ntest/5deaf6d5afd374d7,Ball game,Team sport\ntest/5deb45ba711d4f08,Combat sport\ntest/5debc5093e43bfe7,Rural area\ntest/5dec7648444166da,Luxury vehicle,Performance car\ntest/5decd0e6c55a5a83,Black-and-white\ntest/5dedba73c70ffd3f,Cosmetics\ntest/5dedbc0c49c07804,Close-up\ntest/5def33c7523933d2,Close-up\ntest/5df0572c7ba420fc,Senior citizen\ntest/5df062ee23a15705,Combat sport\ntest/5df06fd7290852a7,Luxury vehicle\ntest/5df381207c5b15b2,Luxury vehicle,Performance car\ntest/5df4b20a6a74dc49,Middle ages\ntest/5df4b396783179b2,Jiaozi\ntest/5df544eb51e5b789,Blond,Hair coloring\ntest/5df5dd4e32f5bc52,Assault rifle,Firearm\ntest/5df82312769b64a8,Sport utility vehicle\ntest/5df8350a0ed5ac7d,Luxury vehicle\ntest/5dfc82c845a25b03,Blond\ntest/5e00a27bcfecf3f1,Bird of prey,Close-up\ntest/5e00c7455ffa8a15,Close-up\ntest/5e045f5fe9203752,Close-up\ntest/5e068a252d2fe7c1,Close-up\ntest/5e0736b618abfe78,Auto racing\ntest/5e073882386413ef,Arthropod,Close-up\ntest/5e083b5b1c9ccda8,Close-up\ntest/5e096cecd6699cc4,Auto racing\ntest/5e0a6def2bc044c6,Electronic instrument\ntest/5e0a83c49171149d,Luxury vehicle\ntest/5e0ac4b1002e0d11,Sport utility vehicle\ntest/5e0b9d0d94ed2064,Amphibian\ntest/5e0c36b04f07e293,Luxury vehicle,Sport utility vehicle\ntest/5e1032bf8ee2b80f,Black-and-white\ntest/5e10c220f5356821,Road cycling\ntest/5e12d6a3662074c3,Outdoor shoe\ntest/5e147e154a4e1d10,Extreme sport\ntest/5e178f4fa510c145,Brisket,Pork chop\ntest/5e18f58140e49d08,Portrait photography,Close-up\ntest/5e19da890acfb8ca,Compact car,Luxury vehicle\ntest/5e1e5238070f44a2,Filling station\ntest/5e1ed800ce888691,Roman temple\ntest/5e219f839fe2b69a,Luxury vehicle\ntest/5e2219285fc501c2,Combat sport\ntest/5e22d75fbcc48a45,Luxury vehicle,Antique car\ntest/5e25bb93819d7e8e,Domestic rabbit\ntest/5e27ae26953f07d8,Firearm\ntest/5e28f1ab40439d0e,Senior citizen\ntest/5e2b80a6346834ce,Prosciutto\ntest/5e2dfb4f3f067e6b,Arthropod,Close-up\ntest/5e2e67a513ac68e7,Electronic instrument\ntest/5e2ebbfdf3d3c8a0,Extreme sport,Nordic skiing\ntest/5e2f20d38038599b,Luxury vehicle\ntest/5e2fafbc78227368,Bird of prey,Close-up\ntest/5e318bac58dbc0b3,Close-up\ntest/5e331c2db50a32e5,Calabaza\ntest/5e3368880d1870ef,Compact car\ntest/5e35e828e59598ff,Luxury vehicle,Antique car\ntest/5e35f9017e0c5d95,Close-up\ntest/5e38e447b792d568,Luxury vehicle\ntest/5e38eaa878a0d4f1,Black-and-white\ntest/5e3a978ab9c582de,Ball game,Team sport\ntest/5e3d1ca2b836f7b5,Senior citizen\ntest/5e3df92691ecb037,Fried food\ntest/5e3ec75bba882fbf,Ball game\ntest/5e459faa0dd2c6bd,Racewalking\ntest/5e470b900d5bc3dd,Performance car\ntest/5e495ba43be93289,Billiard room,Billiards\ntest/5e4dcf4ae3cf32c8,Luxury vehicle,Sport utility vehicle\ntest/5e4ecd012e2a6f16,Medical imaging\ntest/5e4eeb6c4317a491,Cookies and crackers\ntest/5e4f5ce8f707a572,Rural area\ntest/5e51715bcc9b2275,Digital camera\ntest/5e53cd04fca4cd57,Amphibian\ntest/5e541c551dd45547,Close-up\ntest/5e5b1ea5ab28656e,Luxury vehicle,Performance car\ntest/5e5f81d0bff3df44,Close-up\ntest/5e6419f6bb60569a,Plant stem\ntest/5e64d92af9d94f35,Rural area\ntest/5e66a8cb60fbacf5,Close-up\ntest/5e6707029a5f48f9,Whiskers,Domestic rabbit\ntest/5e6739d0adaa88c5,Close-up\ntest/5e6aa33bf49c870b,Domestic short-haired cat,Whiskers\ntest/5e6af72990c34b8c,Luxury vehicle\ntest/5e6bb18c8ba4f2c4,Auto racing\ntest/5e6c552af2411071,Miniature Poodle\ntest/5e6d0525d1e50b49,Whiskers\ntest/5e6d2e9cc6ecd1bd,Close-up\ntest/5e6d3574fd621c28,Luxury vehicle\ntest/5e708a98b3fc813c,Ball game,Team sport\ntest/5e7360f30870cb28,Luxury vehicle\ntest/5e73bf4787b949a4,Fried food\ntest/5e753e3b250c5c92,Luxury vehicle\ntest/5e7615a772a066b3,Fried noodles\ntest/5e76e6bf7fe86714,Luxury vehicle,Antique car\ntest/5e786004a6fcd342,Luxury vehicle,Performance car\ntest/5e786650c54071da,Close-up\ntest/5e78c0027532dea2,Steamed rice\ntest/5e7c976ba0202005,Close-up\ntest/5e7fcf5aa956e991,Luxury vehicle,Antique car\ntest/5e80eedb97b19c9c,Nest box\ntest/5e82701edaf1ffdb,Compact car\ntest/5e8291e30b3ff7ac,Luxury vehicle\ntest/5e82c17b7ea68c5a,Aircraft engine\ntest/5e864c3afb065b07,Senior citizen\ntest/5e873c7ddcbaf1b8,Frying,Fried food\ntest/5e874c7508a9c1c0,Performance car\ntest/5e8a3842cfb0faa2,Middle ages\ntest/5e8b5f467b13bc9b,Auto racing,Luxury vehicle\ntest/5e8d8630d8bb1063,Bratwurst\ntest/5e8d998e7fd4033e,Machine tool\ntest/5e8e9b93ac6b6cbf,Boating\ntest/5e8fa0b455efc7e0,Ball game,Team sport\ntest/5e90635844b31982,Whiskers\ntest/5e972eff804f4370,Close-up\ntest/5e99d307339cecae,Domestic short-haired cat,Whiskers\ntest/5e9b9e39bcfd0f9c,Extreme sport\ntest/5e9bc08e47a70ef7,Military vehicle\ntest/5e9bc5928bdf3ca7,Luxury vehicle\ntest/5e9bce86874ccc1a,Auto racing,Touring car,Luxury vehicle,Performance car\ntest/5e9bcecd815ae548,Residential area\ntest/5e9fa5e1c1ef83ac,Equestrianism\ntest/5ea3f4d404cbedb0,Auto racing\ntest/5ea435ee0fdce806,Luxury vehicle\ntest/5ea8f82960389d81,Fried food\ntest/5eab3fd9f8611eb9,Steamed rice\ntest/5eaceca26b145463,Monoplane\ntest/5eae04c1637ccd0b,Luxury vehicle\ntest/5eae631d4ff71e89,Junk food\ntest/5eafc28ca81884f3,Coral reef fish,Close-up\ntest/5eaffb4e578833a1,Compact car,Touring car\ntest/5eb01f9bdae4a5d3,Bovine\ntest/5eb021dc2b5a0368,Gliding\ntest/5eb417b0f8b4e902,Plant stem,Close-up\ntest/5ebb030dfa9e064b,Sport utility vehicle\ntest/5ebc5ad91a93a596,Wind wave\ntest/5ec13a013cfb967f,Military person\ntest/5ec56fb46be62ffe,Luxury vehicle,Performance car\ntest/5ec5af7ba5430fba,Glacial landform\ntest/5ec677ec5e387271,Dishware\ntest/5ec6ced4c9214f0f,Perching bird,Close-up\ntest/5ec8da85254071f2,Close-up\ntest/5eca10f8b334ecd6,Combat sport\ntest/5eca4159ef3328cd,Luxury vehicle\ntest/5ecf2d731fb518a2,Close-up\ntest/5ecff14d4085663c,Extreme sport,Road cycling\ntest/5ed25cfe5dac33b5,Jeans\ntest/5ed38f9252c8ff13,Herding,Goats,Rural area\ntest/5ed64a971d929058,Sport utility vehicle\ntest/5edd1770b34b6bcb,Luxury vehicle,Antique car\ntest/5edf95e09ce033ac,Luxury vehicle\ntest/5ee20eb9b298282e,Compact car,Luxury vehicle,Antique car\ntest/5ee26698ce9da636,Flatbread\ntest/5ee362add3ef5d33,Computer speaker\ntest/5ee473e9f4abe37a,Combat sport\ntest/5ee550add5b7419f,Auto racing\ntest/5ee66378183618fb,Close-up\ntest/5ee6a9ef1061249f,Compact car,Luxury vehicle\ntest/5ee8fbdbf22459b9,Electronic instrument\ntest/5ee994ee070b7a72,Close-up\ntest/5eea55b769935917,Combat sport\ntest/5eeac83fcb466041,Performance car\ntest/5eeb3f93a07fb208,Sport utility vehicle\ntest/5eec0662c0acaaaa,Floristry\ntest/5eec15d34d6d0cb6,Vegetarian food\ntest/5eef3ffc0d3c267b,Luxury vehicle,Performance car\ntest/5ef00b088d73e9c5,Domestic short-haired cat,Whiskers\ntest/5ef40a6a415812b6,Performance car\ntest/5ef45c160ceabe0d,Plant stem\ntest/5ef630fe9e2294a8,Luxury vehicle\ntest/5ef68e65f06fea71,Plant stem\ntest/5ef6c9918c6f3cea,Jeans\ntest/5ef7350e09afade3,Angling\ntest/5ef8b8beab6e1174,Fried food\ntest/5efc84904acb7a16,Leaf vegetable\ntest/5efffa8fb11dfcda,Luxury vehicle\ntest/5f01f142c072021c,Bovine\ntest/5f052c328ea047f3,Arthropod,Plant stem,Close-up\ntest/5f056d734749a734,Extreme sport,Gliding\ntest/5f05941e222ffeb7,Roman temple\ntest/5f0633f9399b4b20,Senior citizen\ntest/5f071b5a24690abb,Blond\ntest/5f0bc9917dc56aa8,Close-up\ntest/5f0c3f3aaeb64232,Ball game,Team sport\ntest/5f0c89ed7aae1421,Luxury vehicle\ntest/5f0ca2886b5c2018,Hair coloring\ntest/5f0f6ea3dd8ea14f,Black-and-white\ntest/5f0f9c6659959b52,Compact car\ntest/5f11eef72913e5e5,Athletic shoe,Outdoor shoe\ntest/5f121ff1df400943,Black-and-white,Horse and buggy\ntest/5f136aa49463ba0f,Luxury vehicle\ntest/5f179412affded02,Pasta salad\ntest/5f17fbabef1291fa,Horse racing\ntest/5f194dcab48ac715,Bovine,Close-up\ntest/5f1ab80e9c6daed6,Luxury vehicle,Antique car\ntest/5f1d22461b905190,Combat sport\ntest/5f1d766b2934897e,Athletic shoe\ntest/5f1df282f65e3ca4,Close-up\ntest/5f1f8ab047054334,Close-up\ntest/5f1fe36f063a89ff,Black-and-white\ntest/5f20f344285ad1f5,Plant stem,Close-up\ntest/5f210cce617f0757,Microcontroller\ntest/5f239067be321a7a,Chordophone,Electric guitar\ntest/5f241948307463e4,Luxury vehicle\ntest/5f26656b16d1e5b7,Fried food\ntest/5f26b3d25abddff0,Extreme sport\ntest/5f27ec22b2d2caa3,Black-and-white\ntest/5f283fcb49a786fe,Close-up\ntest/5f29420164df556f,Calabaza\ntest/5f29544fc4b2f579,Ball game,Team sport\ntest/5f2bbb6f7ed5d122,Arthropod,Close-up\ntest/5f2c1418087a0384,Luxury vehicle\ntest/5f2d5171d46676df,Firearm\ntest/5f2d72435a860e23,Close-up\ntest/5f2e81481303ed22,Lifebuoy\ntest/5f34b6588f79ea63,Steamed rice\ntest/5f3996223e3d6bc4,Luxury vehicle\ntest/5f3a3ea23d222a20,Performance car\ntest/5f3a4fb083eb0730,Close-up\ntest/5f3fd0440923f93c,Aircraft engine\ntest/5f477599886233bb,Close-up\ntest/5f4b6bb22102d69c,Auto racing\ntest/5f4e42b26c4861fc,Vegetarian food,Junk food\ntest/5f4f35268622e7e8,Military person\ntest/5f52e2911671bb6b,Arcade game\ntest/5f54689fde47f25a,Domestic short-haired cat,Whiskers\ntest/5f5533dde7424831,Ball game,Team sport\ntest/5f55c25f2bf30107,Church bell\ntest/5f589d38c63d228b,Road cycling\ntest/5f596d2e091f2489,Motorcycling\ntest/5f59d48550b8f244,Black-and-white,Military person\ntest/5f5c87649c069ba5,Divemaster,Scuba diving,Underwater diving\ntest/5f5e084e2c782feb,Close-up\ntest/5f62458459465987,Compact car\ntest/5f62b48958717d19,Close-up\ntest/5f652b8eb217d4ec,Luxury vehicle\ntest/5f653cdfce5f686e,Leaf vegetable\ntest/5f6926bf655b4c45,Angling\ntest/5f698f939e5a8262,Close-up\ntest/5f69d72d756b240b,Team sport,Water polo,Wind wave,Ball game\ntest/5f69fdc70e3f0876,Cookies and crackers\ntest/5f6bd7fc023e2f91,Coral reef fish\ntest/5f6c61098afa8659,Monoplane\ntest/5f70dc8c95e82b5e,Fried food\ntest/5f721372d331a03d,Calabaza,Floristry\ntest/5f72b104e4acf0b6,Vegetarian food\ntest/5f7300a39be59363,Extreme sport,Wind wave\ntest/5f79e9d2ffa64ba1,Luxury vehicle,Performance car\ntest/5f7ca8efe41bc14b,Machine tool\ntest/5f7d5f73bb22aec8,Luxury vehicle,Performance car\ntest/5f7f5b1f16aa8acf,Antique car\ntest/5f8041459028cae5,Perching bird\ntest/5f80ff6e85c6aa53,Vegetarian food\ntest/5f82f6b47a5a39e8,Lumpia\ntest/5f84971e3fe33531,Luxury vehicle\ntest/5f86a3e33180fe15,Close-up\ntest/5f8992c19cf8e731,Scaled reptile\ntest/5f89bd795d074f14,Arthropod,Close-up\ntest/5f8be3dab571298a,Rural area\ntest/5f8c8145f6faf224,Ball game,Team sport\ntest/5f8ca849ee7adfff,Luxury vehicle,Antique car\ntest/5f8ceb0bbc0fadb1,Rural area\ntest/5f8cfb1c585cb896,Camera operator\ntest/5f9083587b8fd3d6,Portrait photography,Close-up\ntest/5f91cda91b6a87e0,Freight transport\ntest/5f9653e22a63eaf8,Dishware\ntest/5f9be376ec8b270f,Whiskers\ntest/5f9c90b20a372b00,Close-up\ntest/5f9e00a8e2162386,Miniature Poodle\ntest/5f9eec8ea0109185,Road cycling\ntest/5fa662701a2abe2c,Wind wave\ntest/5fa6e6dd0b72aef6,Freight transport\ntest/5fa8d143f1940591,Military person\ntest/5fab97f9d2bbdc58,Great white shark\ntest/5fad2096f4204266,Luxury vehicle,Performance car\ntest/5fada3d2c30100df,Black-and-white\ntest/5faec91298c04f79,Equestrianism\ntest/5faf9320e4f31f4f,Blond,Portrait photography\ntest/5fb14675a56c0134,Whiskers\ntest/5fb1657ab52a6c4b,Vegetarian food\ntest/5fb7425122c6280c,Fried food\ntest/5fb9bd00ded17364,Sport utility vehicle\ntest/5fbb710534a3948a,Luxury vehicle,Antique car\ntest/5fbee917b9d43ae9,Close-up,Scaled reptile\ntest/5fbffd26519406dd,Athletic shoe,Outdoor shoe\ntest/5fc131efa7988d86,Roller skating\ntest/5fc2902d81319015,Luxury vehicle\ntest/5fc43e8b135857e6,Performance car\ntest/5fc55b8ccbbfe4ba,Headphones\ntest/5fc7caf61b51ab5e,Black-and-white\ntest/5fcac6ae38b7574f,Hound\ntest/5fcaeaeac722cfa0,Hound\ntest/5fcbcd657b89fa8b,Double-decker bus\ntest/5fce7e328257e7b1,Combat sport\ntest/5fcf05778e908f98,Luxury vehicle,Antique car\ntest/5fd17fca86d25c5f,Rural area\ntest/5fd2be53e962258f,Close-up\ntest/5fd5b30a0b51c5ae,Cookies and crackers\ntest/5fd723be9062aa64,Performance car,Luxury vehicle,Antique car\ntest/5fd8e74b3fe855b3,Flatbread\ntest/5fd9eddcfc084859,Bovine\ntest/5fddd41b1a10188c,Close-up\ntest/5fdde4228b7a967f,Amphibian\ntest/5fde255962ea14ff,Black-and-white\ntest/5fdee4a3f5e3bb28,Extreme sport\ntest/5fdefe27709355a4,Close-up\ntest/5fe1d425ed6230a3,Fire apparatus\ntest/5fe2556c02de784b,Close-up,Whiskers\ntest/5fe26dd6b862a833,Road cycling\ntest/5fe27c69d07f3970,Luxury vehicle\ntest/5fe282aa7fa267d1,Close-up\ntest/5fe2bb05a90424f4,Extreme sport,Nordic skiing\ntest/5fe345edbafce437,Perching bird\ntest/5fe40850d6e18a31,Arthropod,Close-up\ntest/5fe4a993bf7b4de0,Compact car,Luxury vehicle,Antique car\ntest/5fe4d8ff3e131ce2,Cookies and crackers\ntest/5fe64e9a614528f9,Boating,Luxury vehicle,Performance car\ntest/5fea94d62638582a,Rural area,Canola\ntest/5fed2a26689e3e8b,Compact car\ntest/5fefaa26310360c6,Antique car,Performance car\ntest/5ff23038fa3640d3,Luxury vehicle\ntest/5ff5a42c619b54a1,Medical imaging\ntest/5ff69a89609d349c,Arthropod\ntest/5ff81a0db10c04e1,Performance car\ntest/5ffe4e9339858b0e,Plant stem\ntest/5ffeffc15eb1de28,Ball game,Team sport\ntest/5fff9af74d724bdf,Close-up\ntest/5fffec0fb8237b1a,Auto racing\ntest/6001536a033088d5,Aerial photography\ntest/600319dc8ebae115,Blond,Portrait photography,Close-up\ntest/60043e2aee0b857b,Scuba diving,Underwater diving\ntest/6005da2f0f5410d7,Aircraft engine\ntest/6006b16b1ad085e4,Antique car\ntest/600807d2231b5f3e,Close-up\ntest/6009239f956e9e72,Arthropod,Close-up\ntest/600b35f0916b8091,Cobblestone\ntest/6011478695d8eb7b,Vegetarian food\ntest/60124c8a3ec9601a,Wallaby,Domestic rabbit\ntest/6013cfd4cbff9746,Jeans\ntest/6018e0403ce86656,Shinto shrine\ntest/60190dfd6548e676,Close-up\ntest/601b6b630cd4d90f,Bovine\ntest/601d9755aa39e043,Senior citizen\ntest/601e87f5737800d2,Frozen yogurt\ntest/601f519aef0f6c80,Halter,Equestrianism\ntest/6020e81bcb40ad33,Amphibian\ntest/6021c7a7b1a4df68,Divemaster,Scuba diving,Underwater diving\ntest/6025dcd7ae98ae38,Arthropod,Close-up\ntest/60261c64294a38d7,Scaled reptile\ntest/6027d8a5394dcd1a,Rural area\ntest/602ab27809f27805,Leaf vegetable,Vegetarian food\ntest/602be7ed4d2a3077,Combat sport\ntest/602fb49b8578d194,Whiskers\ntest/6030fd9d48ebdf1d,Close-up\ntest/60314364956afe7e,Rural area\ntest/6031e06fc418219c,Underwater diving\ntest/6032d42bd1906536,Close-up\ntest/6036e9b4ded88044,Flatbread\ntest/6037367c1720f107,Vegetarian food\ntest/6038f2fb22df82be,Close-up\ntest/6039d0542a2073e9,Close-up,Whiskers\ntest/603bb98d00d2ab37,Luxury vehicle,Performance car\ntest/603bd0f27aa853f5,Whiskers\ntest/603cc308957e8b09,Close-up\ntest/603d85dfcfc6671c,Floristry\ntest/603dcd92a8523b0d,Rural area\ntest/603e72571fb1c33e,Ball game\ntest/603fe8a811480298,Heavy cruiser\ntest/604038b3e16cae4e,Luxury vehicle,Antique car\ntest/6040ba02c506c458,Portrait photography,Close-up\ntest/6042a4e49dc84043,Sport utility vehicle\ntest/604546966837bdb6,Domestic short-haired cat,Whiskers\ntest/60488f95fff4a0cf,Boating\ntest/60496ae664f41943,Close-up\ntest/604a47bb0ccef2d6,Senior citizen\ntest/604c71b199ff1054,Gliding\ntest/604e665c6d312415,Blond,Hair coloring,Close-up\ntest/605037351108b258,Luxury vehicle\ntest/605085da05a7e75c,Compact car\ntest/6053966a36d1286a,Compact car,Luxury vehicle,Antique car\ntest/60546e237e6b1425,Arthropod,Plant stem,Close-up\ntest/605b570da5155d1a,Sport utility vehicle\ntest/605c6d41397f6730,Luxury vehicle\ntest/605f67a5d6895cdf,Domestic short-haired cat,Close-up,Whiskers\ntest/6061513d55c63981,Military person\ntest/6061da4aa8b3beb3,Luxury vehicle,Performance car\ntest/6064325315b63c57,Machine tool\ntest/6064ffc7815edf27,Black-and-white\ntest/60651fdd0a34870e,Arthropod,Close-up\ntest/60653f2bc1788af1,Boating\ntest/6066408c8757fe60,Combat sport\ntest/6066e0f1f1a29052,Domestic rabbit\ntest/60677f98082e6aa2,Arthropod\ntest/606a19bbf7dad278,Roman temple\ntest/606a28a97e634e38,Blond,Hair coloring\ntest/606cdd0f44fb92d6,Scaled reptile\ntest/606d54429c6406c9,Close-up\ntest/606da392618aad18,Rear-view mirror\ntest/606fa76c00daa0a2,Close-up,Plant stem\ntest/6070f447c7714384,Boating\ntest/6072d2623e631f94,Residential area\ntest/60798b8cb77b6a64,Luxury vehicle,Performance car\ntest/607be4416c763fbb,Newsagent's shop\ntest/607c31e91bbcbd55,Goats\ntest/607d76198f62b2a6,Sport utility vehicle\ntest/607f31994480b357,Sport utility vehicle\ntest/607f6fbde129b324,Domestic rabbit\ntest/607fc30becccf762,Rural area\ntest/6080b176733fc8a1,Electronic instrument\ntest/608114d98a8b1c6a,Close-up\ntest/608160c42f78b2d5,Extreme sport\ntest/60852b4c7e5aabf5,Close-up\ntest/6085e04d7beed09a,Black-and-white\ntest/608686b9c301ed3f,Vegetarian food\ntest/608858d1542ead2c,Auto racing\ntest/6088fd6ae034ee68,Close-up\ntest/608a18df064b58f2,Close-up\ntest/608db3664caf4a20,Domestic short-haired cat,Close-up,Whiskers\ntest/60901b6d68ecd91e,Close-up\ntest/6090e6494a62e411,Close-up\ntest/60937062cd8ce2a8,Boating,Rowing\ntest/609436f4db95b92a,Close-up\ntest/6095517f61a8dc53,Black-and-white\ntest/6097f1154aa5e3ad,Hair coloring\ntest/60998ea0301b13bb,Close-up\ntest/6099d78dbece1bdb,Auto racing,Compact car\ntest/609b63bd8415dcac,Dishware\ntest/609c8909d1cb74f6,Performance car\ntest/60a42c42bb568d92,Combat sport,Grappling\ntest/60a715433ef7ba49,Firearm\ntest/60a9c78dc53aeda3,Rural area\ntest/60a9d1a9e1013b1f,Luxury vehicle\ntest/60adcd51c5612f49,Vegetarian food,Corn on the cob\ntest/60b11e81d471649c,Luxury vehicle\ntest/60b19c2405efb3cc,Aircraft engine\ntest/60b20221492a50be,Domestic short-haired cat,Whiskers\ntest/60b23372417d57a3,Compact car,Luxury vehicle,Performance car\ntest/60b3aa53b08f11fd,High-speed rail\ntest/60b87855808a995b,Ale\ntest/60b8b7aaa7649bfe,Figure skating\ntest/60bbf2c3115f6115,Aircraft engine\ntest/60bcfb326146c128,Lechon\ntest/60bd624e3b81f963,Inflatable\ntest/60bf9a3510305ffb,Arthropod,Close-up\ntest/60c0f5448b442582,Racewalking\ntest/60c223bd8c07b49b,Auto racing\ntest/60c3c0ed33eece09,Motorcycling\ntest/60c40e5dd6e8f2e3,Compact car,Luxury vehicle\ntest/60c69059b4301b19,Auto racing\ntest/60caa6db3c2965f1,Arthropod,Close-up\ntest/60ce86cf8971295e,Luxury vehicle,Performance car\ntest/60ceb7f9be874511,Boating\ntest/60cf5a23bbf4823b,Black-and-white\ntest/60d21d5cf5cdd755,Close-up\ntest/60d4652976177416,Molluscs,Close-up\ntest/60d646efa5ea958e,Luxury vehicle\ntest/60d6972c846e420c,Racewalking\ntest/60d731d22e91a8f9,Flatbread\ntest/60d7ca964c2112db,Vegetarian food,Fried food\ntest/60d9653cf5a9dcd8,Close-up\ntest/60db6f002eef4c2f,Close-up,Barechested\ntest/60db7a88a08ec396,Ball game,Team sport\ntest/60dd7070c63f56a3,Luxury vehicle\ntest/60ddd79bf61ab933,Luxury vehicle\ntest/60e20252060f3f1f,Luxury vehicle,Antique car\ntest/60e45011a44bd185,Black-and-white,Close-up\ntest/60e46afcc4474e4c,Performance car\ntest/60e50a97f3a37593,Jeans\ntest/60e5def1f29cf964,Plant stem\ntest/60e5e99b3321d54e,Junk food\ntest/60e7e837158ce736,Steamed rice\ntest/60e7ef0acf23e7d5,Black-and-white\ntest/60e903f6809ccc72,Close-up\ntest/60ea1234f5b08996,Close-up\ntest/60eb7524ed12fdc5,Auto racing\ntest/60ec498eb6b9b8e6,Performance car\ntest/60edd0c79e405d63,Black-and-white,Close-up\ntest/60eec9203c4b9999,Close-up\ntest/60ef96175703dda7,Compact car,Luxury vehicle,Antique car\ntest/60f2f656398fc0a9,Arthropod,Close-up\ntest/60f3a5da181f22c6,Soba\ntest/60f4752a2fdb1664,Whiskers\ntest/60f4f0c9b84ccaef,Close-up\ntest/60f56870671b52e0,Luxury vehicle\ntest/60f56bbfe2662779,Sailboat racing\ntest/60f65fcc20bb4d0c,Machine tool\ntest/60f68509da7c982f,Luxury vehicle\ntest/60f69fe99aeb563d,Domestic short-haired cat,Whiskers\ntest/60f79177b1066959,Antique car\ntest/60fe4322c4664487,Cosmetics\ntest/61002e7388fa9d79,Domestic rabbit\ntest/6103de4879154e7f,Close-up\ntest/61052fcae53fe1d1,Team sport\ntest/6106b2f3aca386a4,Black-and-white\ntest/6107ddfd2e5696e9,Close-up,Primate\ntest/6107fe707d74fb4b,Zeppelin\ntest/6108beeaf78c0bd4,Combat sport\ntest/610caa8709ab6060,Compact car\ntest/610e8e59dbeea6e4,Junk food\ntest/610e8ebaaa7c6482,Luxury vehicle,Antique car\ntest/610fdd32b4eccfba,Microcontroller\ntest/61104c6ff5025840,Equestrianism\ntest/6112f0e8b2c4b96f,Blond\ntest/61132cd1e83a7a82,Luxury vehicle\ntest/61143c14f268af6d,Compact car,Luxury vehicle\ntest/61179e58948a32d2,Arthropod,Close-up\ntest/611a9f748c9a64b7,Close-up\ntest/611b64215baba099,Horse and buggy\ntest/611b8aa5f8de7641,Aircraft engine\ntest/611d05f445ee3ac2,Domestic short-haired cat,Whiskers\ntest/61221072da222cd1,Boating\ntest/61221f198a1826e5,Musical theatre\ntest/6122c7b36a0e030f,Microcontroller\ntest/61241a32cd0e613d,Close-up,Plant stem\ntest/61259fc780039c11,Jeans,Luxury vehicle,Antique car\ntest/612c5ef7ea401fc0,Performance car\ntest/612d66cbc906f29d,Bird of prey,Close-up\ntest/612f8c0c59f8ff5e,Rural area\ntest/612fbf04a644bb2e,Computer speaker\ntest/613262a2d4bc7a56,Leaf vegetable\ntest/6132ec2c6bf637e2,Jeans,Black-and-white\ntest/6134bf2c4b7a47c1,Miniature Poodle\ntest/61366d71d4cb2898,Great white shark\ntest/613924478d0d6ed7,Vegetarian food,Fried food\ntest/613a59d150cd6df8,Domestic short-haired cat,Close-up,Whiskers\ntest/613b9b2fa5a0d0af,Ball game,Team sport\ntest/613cb2a17a0dc356,Jeans\ntest/613d9627cf42c116,Equestrianism\ntest/613f76c779e21e47,Compact car,Antique car\ntest/614205ce0d0e4ab1,Shinto shrine\ntest/6142922d5809e5de,Ball game,Team sport\ntest/6142e93ddb595fe0,Compact car\ntest/61446e7de51ba2ab,Sport utility vehicle\ntest/6145e37d1e17eb22,Luxury vehicle,Performance car\ntest/614849685882fbae,Arthropod,Close-up\ntest/6149524fd9256a49,Gun turret\ntest/6149ab354348273f,Plant stem,Close-up\ntest/6149b5a7fa89aa87,Soba\ntest/614b86d8ca214b93,Black-and-white,Close-up\ntest/614d0a9911587f78,Compact car,Antique car\ntest/614d36a4fadcec56,Luxury vehicle,Antique car\ntest/614d7a6b3ef1e0a3,Cookies and crackers\ntest/6151802f8e40aa82,Performance car\ntest/6153c8e0e2678c08,Luxury vehicle,Sport utility vehicle\ntest/6156547813b0e326,Boating\ntest/6156bf8c83db2ead,Boating\ntest/6157e5f78ebf3456,Bird of prey,Close-up\ntest/615ebbbadcb96863,Boating,Rowing\ntest/615f126d9cab63d7,Boating\ntest/615ffcdc12b59391,Close-up\ntest/61600b8970f91b6b,Ball game\ntest/61601462c7635b78,Cosmetics\ntest/616498c96a3a9c7d,Coral reef fish\ntest/616525484aa44900,Junk food\ntest/616672acf5d316da,Coca-cola\ntest/616886beed04d992,Floristry\ntest/616991bc6e95e86e,Molluscs\ntest/616a02b8227ef507,Jiaozi,Pelmeni\ntest/616d89911ccc525a,Luxury vehicle,Performance car\ntest/616d98172488f7f9,Rural area\ntest/616e17fe2096d001,Jeans\ntest/616e6e60872c1f83,Senior citizen\ntest/616e723191360f93,Antique car\ntest/6170540c38e71d37,Jeans\ntest/6175a72b2c179732,Luxury vehicle,Performance car\ntest/6175ff16efcda3f6,Sport utility vehicle\ntest/617601e9000d0d6a,Vegetarian food\ntest/61765757695540c1,Close-up\ntest/617940bd38060922,Chordophone\ntest/617b630f7c48bf1f,Leaf vegetable\ntest/617b6f6a1d29be75,Arthropod,Close-up\ntest/617edb3bd6aa3967,Compact car,Luxury vehicle\ntest/617fc0b9ec5ea213,Luxury vehicle,Performance car\ntest/618543b6b3ffd2f0,Vegetarian food\ntest/61859f75a931dafe,Vegetarian food\ntest/618775e4ea2c093d,Chordophone\ntest/618a6ac5b888fbe4,Close-up,Scaled reptile\ntest/618adb4f4c0847ea,Compact car,Luxury vehicle\ntest/618e7432b384b5d5,Ball game,Team sport\ntest/6192c3b20ae0a942,Close-up\ntest/6193a13cf97667b7,Whiskers\ntest/61945903dab27d9e,Military vehicle\ntest/6194d1a015a289a0,Roman temple\ntest/61953d7fee0e2cf7,Compact car\ntest/61965c0fd44e573f,Close-up\ntest/6197c107f709b9d6,Black-and-white\ntest/619981c39811370e,Black-and-white\ntest/619a71121793485f,Luxury vehicle\ntest/619b93e77f77a09c,Close-up\ntest/619dec1c847a678e,Close-up\ntest/619ec3d8bf33e361,Chordophone\ntest/619f3c085189aa88,Close-up\ntest/61a0aedba74bced8,Floristry\ntest/61a33c4c7c5df7bd,Boating\ntest/61a35449a8db6b2c,Musical theatre\ntest/61a9bc2760ccb333,Close-up\ntest/61ab41895a0c1601,Close-up\ntest/61aba4520b1c1ddf,Close-up\ntest/61b0089732e9c54b,Sport utility vehicle\ntest/61b1d48f59a13381,Black-and-white,Combat sport\ntest/61b38cf56f354df5,Rural area,Bovine,Herding\ntest/61b534fc4ac2ee19,Close-up\ntest/61b88cdb22a8cdce,Luxury vehicle,Antique car\ntest/61b8a57a28449a0e,Road cycling\ntest/61ba2f3b3295ef24,Compact car\ntest/61ba8f06e95329bf,Close-up\ntest/61baa89ed9e0ee4d,Extreme sport,Surfing Equipment,Wind wave\ntest/61baaaff6e0a278f,Close-up\ntest/61be9529139ed232,Military vehicle\ntest/61beccc9bf155c5d,Aircraft engine\ntest/61c245e2173955a8,Junk food,Cookies and crackers\ntest/61c3df2b43f1d147,Luxury vehicle\ntest/61c4b944bf6c9a3f,Close-up,Plant stem\ntest/61c4c081871c8b14,Sport utility vehicle\ntest/61c62f6e662e3de4,Compact car,Luxury vehicle,Antique car\ntest/61c63df82719beac,Scaled reptile\ntest/61c7277d03fabd57,Machine tool\ntest/61c8c6fdde957ef4,Compact car\ntest/61ca48872c771cd1,Luxury vehicle\ntest/61caa5def9bb003f,Luxury vehicle,Performance car\ntest/61cb30fc061b5c8e,Plant stem\ntest/61ce065f55df410a,Close-up\ntest/61d0d9c310ac84c5,Locust\ntest/61d2c016076fb21e,Antique car\ntest/61d3666c8fee93ce,Shinto shrine\ntest/61d4162f5c1fdac6,Luxury vehicle\ntest/61d6818c4928573b,Sport utility vehicle\ntest/61d697e1310f77b2,Sport utility vehicle\ntest/61d6bb6503fadf61,Luxury vehicle,Performance car\ntest/61d6d44ed3bdced2,Electric piano\ntest/61d6dcaf18ef8899,Gun turret,Military vehicle\ntest/61da58883548c458,Ball game,Team sport\ntest/61da7de3fc6c6e0b,Luxury vehicle,Performance car\ntest/61daa008de8b54dc,Compact car,Luxury vehicle,Sport utility vehicle\ntest/61dbad8de93db950,Junk food,Cookies and crackers\ntest/61dc2d1ae0e5cd14,Fried food\ntest/61dcd49d83fd3d67,Black-and-white\ntest/61dd4a96071c519a,Auto racing\ntest/61de1f9a67b1d354,Blond\ntest/61de3c63e0d6286b,Luxury vehicle\ntest/61de966199991fed,Orator\ntest/61e067093e492fc3,Plant stem\ntest/61e2548747d893c4,Floristry\ntest/61e2af2655a85f8e,Jeans\ntest/61e2cb4fbd945adf,Close-up,Plant stem\ntest/61e38615a76d050c,Luxury vehicle\ntest/61e643eb5fe79fa4,Luxury vehicle,Performance car\ntest/61e8b50ffcc4cfaf,Gumbo\ntest/61ea5d35a14e4f04,Boating,Rowing\ntest/61eacc12c2c1fa16,Compact car,Antique car\ntest/61ead547e7fb5972,Combat sport\ntest/61eaea8d4b211b3b,Fried food\ntest/61eb9af23de930ce,Close-up,Whiskers\ntest/61ec90f2fa27e8e5,Performance car\ntest/61ed3a8e5e1b744a,Headphones\ntest/61ed426ef9fdb272,Domestic rabbit\ntest/61ed4a80fec576de,Close-up,Scaled reptile\ntest/61f0b5c2ec2c7f7e,Scuba diving,Underwater diving\ntest/61f14982aba489b0,Close-up\ntest/61f14a25b7345294,Compact car\ntest/61f1636c4edd2a8a,Plant stem\ntest/61f229e095c1885b,Electric piano\ntest/61f23895ab23dcf9,Rural area\ntest/61f241bfe4ff5558,Electronic instrument,Black-and-white\ntest/61f24c617025cf83,Leaf vegetable\ntest/61f26e3c6546d11d,Divemaster,Scuba diving,Underwater diving\ntest/61f926c80e00f39a,Auto racing,Luxury vehicle,Performance car\ntest/61f9e2ac8b438a2f,Close-up,Whiskers\ntest/61fa5d1ef1130ce4,Ball game\ntest/61fb7b5fdc0684d1,Microcontroller\ntest/61fdfe9530d1d160,Compact car\ntest/61ffc7189ae62763,Molluscs,Close-up\ntest/62032d035b49efe4,Solar energy\ntest/6205db7131e925d2,Plant stem\ntest/6209276da2efacf2,Close-up,Plant stem\ntest/620b38ebb15d03c3,Rural area\ntest/620ba23dbf44e8de,Fried food,Cookies and crackers\ntest/620d6ece0e099991,Auto racing,Touring car,Performance car\ntest/620e6cb5bea9f2ff,Briefs,Barechested\ntest/621047bef3d1b5eb,Luxury vehicle\ntest/621063834c1becfc,Blond,Close-up\ntest/6211a2c9fc78ef61,Close-up\ntest/6213dbc069ffbd5b,Luxury vehicle,Antique car\ntest/621438379adf4181,Performance car\ntest/6214a8987e7646e9,Close-up\ntest/6217237211d49dc0,Close-up\ntest/621864587e59e56a,Combat sport\ntest/6218a59ab66ae050,Close-up\ntest/6219666cf3a78b2a,Flatbread\ntest/621b5587f1c0fe46,Figure skating\ntest/6221a25e372505b5,Lawn game,Ball game\ntest/6224fde331332912,Arthropod,Close-up\ntest/622531da2d3783fd,Auto racing,Compact car\ntest/622983a2faffa06d,Black-and-white\ntest/6229abb44ed5f834,Black-and-white\ntest/6229b755a2bda325,Luxury vehicle,Performance car\ntest/622b13927b118fb7,Sport utility vehicle\ntest/622b5f46ffdce90f,Luxury vehicle,Performance car\ntest/622ca2d9fdb08d11,Jeans\ntest/622ea5af74d18285,Touring car\ntest/622ec7f83caabbeb,Digital camera\ntest/622ed181c6725075,Chordophone\ntest/622f5888d35ad326,Auto racing\ntest/622fcd7d0f6eb4d4,Black-and-white\ntest/6230c5167c5a401a,Close-up\ntest/62310966e679996c,Siberian husky,Close-up\ntest/6232164e229b73f1,Khinkali\ntest/6232bd80906dffc7,Domestic short-haired cat,Whiskers\ntest/623386ff91f3c818,Black-and-white\ntest/6233f3bd0fb4a7a4,Musical theatre\ntest/6234eabb4900619b,Antique car\ntest/62361b01f01ddb9a,Luxury vehicle\ntest/623b6c4fb6fdfdab,Coca-cola\ntest/623c81167e991660,Bovine\ntest/623ca655576b09e3,Watercolor paint\ntest/624108ddbabd78e4,Close-up,Scaled reptile\ntest/6241295092d8c26d,Water polo,Ball game,Team sport\ntest/624382dcb82bace0,Military vehicle\ntest/624491471e910c7b,Fried noodles\ntest/62460acbb0e8fc45,Performance car\ntest/62461214aeb24b63,Military vehicle\ntest/624860d2c1d529cf,Double-decker bus\ntest/624c0a10de5cacc4,Performance car,Antique car\ntest/624e01b2b05766ef,Luxury vehicle,Antique car\ntest/624f87c9a1a3eb3e,Extreme sport\ntest/625039581f4e8e13,Ball game,Team sport\ntest/6250f6797bf34d80,Luxury vehicle,Sport utility vehicle\ntest/6252a6e5ed824752,Military person\ntest/6252b23a3aadef54,Aircraft engine\ntest/6253532be47c296f,Compact car,Luxury vehicle\ntest/6256836f01f444bb,Woodwind instrument\ntest/62570d2491432cf2,Sport utility vehicle\ntest/6259050a44462d22,Bird of prey\ntest/625a1919162c46c1,Luxury vehicle,Performance car\ntest/625b6c7f33353877,Close-up\ntest/625bc3c8e30cc703,Compact car\ntest/6260df2ad0b1b37e,Arthropod,Close-up\ntest/626572b823872f85,Extreme sport,Parachuting\ntest/6265eed8dd7f6652,Close-up\ntest/6267acf846495997,Close-up\ntest/626a1ed343d2067d,Domestic short-haired cat,Close-up,Whiskers\ntest/626afde82efd5d24,Bird of prey\ntest/626c2a3f5930a63e,Auto racing,Touring car,Performance car\ntest/626feb1f9900d94c,Compact car,Performance car,Antique car\ntest/62701908a6ba8caa,Luxury vehicle,Antique car\ntest/6272f9bcf7abc2c5,Luxury vehicle\ntest/627570701e7cfc34,Luxury vehicle,Sport utility vehicle\ntest/627702c69afed65b,Black-and-white\ntest/6277ac9b7bda4692,Arthropod,Close-up\ntest/6279daa206ad7304,Close-up\ntest/627b96e2310f037a,Arthropod,Close-up\ntest/6282814b49ce89ba,Stemware\ntest/6283f13f9c32994e,Bird of prey\ntest/628581a67039f245,Whiskers\ntest/6288585e695fd165,Compact car,Luxury vehicle,Antique car\ntest/6288a1dfa18d029d,Jeans\ntest/628a5d5812f1af80,Vegetarian food\ntest/628c2b7421525cd2,Bovine\ntest/628faaf24d8e0308,Auto racing\ntest/629116131a53109a,Orator\ntest/62914e5ec77b32bf,Antique car\ntest/62917b7b82eb90c9,Black-and-white\ntest/62925767b07331f7,Luxury vehicle\ntest/62933104fa9e8c99,Performance car,Luxury vehicle,Antique car\ntest/62934486da2cef22,Vegetarian food\ntest/629a2b86f8de39d5,Black-and-white\ntest/629b80e1bad0e17f,Assault rifle,Firearm\ntest/629b84da160dc0c9,Auto racing\ntest/62a0bc551220e417,Domestic short-haired cat,Close-up,Whiskers\ntest/62a138860815224a,Extreme sport\ntest/62a375de34955c5c,Jiaozi\ntest/62a4f7c069767a1e,Luxury vehicle\ntest/62a5371857b9a53e,Extreme sport\ntest/62a6522be1e7c2ad,Heavy cruiser,Frigate\ntest/62a6a4cf2b9781e6,Military person\ntest/62a7d202c7ab5f5d,Sailboat racing\ntest/62ab063a5e0a7396,Sport utility vehicle\ntest/62ab47a590d33ac6,Arthropod\ntest/62ada8ec50ed8d80,Combat sport\ntest/62ae4ead6e037fce,Domestic short-haired cat,Whiskers\ntest/62afa3eb34da4ad0,Antique car\ntest/62b5dcc4bc0f0e60,Close-up\ntest/62b75a5fd8586a98,Close-up\ntest/62b8396d8ae8b906,Leaf vegetable\ntest/62bc7ea32da1ee3f,Leaf vegetable\ntest/62bd587b3e380564,Microcontroller\ntest/62bd75d61eb49c82,Rural area\ntest/62beedda61cd9d3a,Hurdling\ntest/62c12e4a1f8e1563,Amphibian\ntest/62c155d596eb133f,Domestic short-haired cat,Whiskers\ntest/62c2b66071bc8a54,Modern dance,Musical theatre\ntest/62c3b210c0559cb1,Jeans\ntest/62c438cd41a39196,Molluscs,Close-up\ntest/62c593f035946ebc,Blond\ntest/62c64d5696e88bbe,Arthropod,Close-up\ntest/62c75e993beb5e37,Combat sport\ntest/62c7ce7ef3753a2d,Close-up\ntest/62c9261e91e53009,Peafowl\ntest/62ca5c8f5dc8d26f,Monoplane\ntest/62cd085818cfaf8e,Ball game,Team sport\ntest/62ce27b2fca7ec3c,Compact car,Luxury vehicle\ntest/62d1b2aa15c96905,Musical theatre\ntest/62d5df6b00aab6c1,Sport utility vehicle\ntest/62d84462223ae64f,Luxury vehicle,Sport utility vehicle\ntest/62dae7df3e5c31f5,Extreme sport,Sledding\ntest/62dbc4ce1107d955,Arthropod,Close-up\ntest/62dd0483fbf0672c,Arthropod,Close-up\ntest/62e94f7ee23ff7c2,Luxury vehicle,Antique car\ntest/62eab486ff2753a7,Antique car,Luxury vehicle,Performance car\ntest/62eb678e10223b2a,Black-and-white\ntest/62f3dd0847ffbcba,Luxury vehicle\ntest/62f4e73dfca8b8d8,Equitation,Equestrianism\ntest/62f6b164f654a2eb,Luxury vehicle,Antique car\ntest/6301762bb246bb9c,Luxury vehicle\ntest/63025d527888af32,Compact car\ntest/6302fe85dbabf0a5,Compact car,Luxury vehicle\ntest/6303488fb6448e29,Black-and-white,Close-up\ntest/63068265c8e32e6a,Newsagent's shop\ntest/6307e447c32d4aaf,Sport utility vehicle\ntest/63092b4acd1cc8b2,Scaled reptile\ntest/630bf13ffc9dc860,Floristry\ntest/630f3372d54b54a8,Gliding\ntest/6311151934067c6b,Orator\ntest/63112acf0a4a8d62,Fried food\ntest/6312ecae9ba1eff3,Fried noodles\ntest/6313ec737af9431e,Black-and-white,Compact car,Luxury vehicle\ntest/6313f63faea960f2,Luxury vehicle,Antique car\ntest/6314462a19fb92f9,Modern dance\ntest/63162f4154cf1ff3,Underwater diving\ntest/6317244ea5917ec0,Rear-view mirror\ntest/63175debb375bdf5,Vegetarian food\ntest/63195ea2187d353e,Sport utility vehicle\ntest/63199c23e2c487d6,Compact car\ntest/631bed77a566d13e,Aerial photography\ntest/631e03862f779dbe,Luxury vehicle\ntest/631e1ea3d3308391,Steamed rice\ntest/631fe4ac13f7fb0c,Wind wave\ntest/6324d542fe790806,Auto racing\ntest/632672fd76dc44eb,Vegetarian food\ntest/632d56f50b343881,Wind wave\ntest/63307c4ea3807797,Aircraft engine\ntest/63313f0b2894a23e,Aircraft engine\ntest/6331854af0e22ef7,Toy block\ntest/633198c1212bccab,Close-up\ntest/6331e554afde0b06,Ball game,Team sport\ntest/63321b7b803c9a0f,Antique car\ntest/63373c02c7203e5b,Extreme sport,Parachuting\ntest/63386e0be460ef2d,Boating,Rowing\ntest/6339c2385578e654,Luxury vehicle,Performance car\ntest/633bc71f4d1d522a,Barechested\ntest/633f7dfaba69714a,Arthropod,Locust,Close-up\ntest/6342c9f7b4f1f22e,Compact car,Performance car,Luxury vehicle,Antique car\ntest/63448ea5140d5c67,High-speed rail\ntest/6345344620b9d424,Performance car\ntest/63479eea718fdfae,Luxury vehicle\ntest/63483185d5c0eeeb,Cookies and crackers\ntest/634bc52fdf7373fa,Arthropod,Close-up\ntest/634c0184dc085265,Gun turret,Military vehicle\ntest/634d03df9a50730e,Luxury vehicle,Performance car\ntest/634d3b6757f181bb,Floristry\ntest/634fb47920ba0a0b,Luxury vehicle,Antique car\ntest/63506907e4ec1578,Luxury vehicle\ntest/6352314dbea91bd8,Auto racing,Performance car\ntest/635585fb106c8d48,Auto racing\ntest/63594e96120c3f49,Bird of prey\ntest/635b3bfc224763c5,Close-up\ntest/6360ecb12524fc37,Close-up\ntest/63631ec63b3db03c,Aircraft engine\ntest/6363aeb7944bd6fd,Jeans\ntest/636622418b0981bb,Pork chop\ntest/6366d9daad870c7c,Modern dance,Musical theatre\ntest/6367ea94531945ff,Flatbread\ntest/6369f80b519987f2,Luxury vehicle\ntest/636bc3829c212177,Luxury vehicle,Performance car\ntest/636f1687e51c4768,Equestrianism\ntest/6370fbb3697d21eb,Whiskers\ntest/6372cd350be85d6c,Billiards,Billiard room\ntest/6372eb1218e9ef34,Luxury vehicle,Sport utility vehicle\ntest/6372ed251856bb28,Close-up\ntest/637364d5847bbad9,Whiskers\ntest/6374f394f3412011,Close-up\ntest/63756eeaa2ddae37,Vegetarian food\ntest/63761d107e0da723,Military vehicle,Sport utility vehicle\ntest/6378152d063c4633,Blond,Portrait photography,Close-up\ntest/63785df6655da1d6,Luxury vehicle,Performance car\ntest/6378fabd522e9f1d,Blond\ntest/637b68f4e76f09f7,Extreme sport,Divemaster,Scuba diving,Underwater diving\ntest/637bd7e807a742a8,Luxury vehicle,Antique car\ntest/637c9c4942c0b059,Auto racing\ntest/637d3c76f4df0772,Sport utility vehicle\ntest/63804f84296371dd,Luxury vehicle,Performance car\ntest/6380a44ad0665a5a,Monoplane\ntest/6382cc5e4eca1764,Arthropod,Locust,Close-up\ntest/6382d68d279991b8,Sport utility vehicle\ntest/6388248195e6982a,Close-up\ntest/638a2a729c374890,Antique car,Compact car,Performance car\ntest/638ad2c2107013ee,Sport utility vehicle\ntest/638b350e69873253,Black-and-white\ntest/638b3eda3daf5468,Luxury vehicle,Sport utility vehicle\ntest/638d25ee24deea0c,Motorcycling\ntest/638e274a69713ae3,Close-up\ntest/638ef45c2d5ed026,Floristry\ntest/638fe582a7700f2e,Close-up\ntest/6391575a7f287f57,Cookies and crackers\ntest/6393e5f873aa5de5,Close-up,Whiskers\ntest/63942da5cfb15839,Compact car,Luxury vehicle,Antique car\ntest/639515b29879a242,Melee weapon,Hunting knife\ntest/63960cee64aa8b8d,Freight transport\ntest/63968e2b53c21267,Arthropod,Close-up\ntest/6396e36d1e1fd0dc,Plant stem,Close-up,Scaled reptile\ntest/63972fa0ad2d81c5,Combat sport\ntest/6399c88efd10b0f6,Luxury vehicle\ntest/639e79130c222a69,Billiard room,Billiards\ntest/63a0f878570701e6,Close-up\ntest/63a156acab71b738,Pasta salad\ntest/63a159cd665abdf9,Aircraft engine\ntest/63a204bc68ac009b,Black-and-white\ntest/63a257fa007c14cd,Luxury vehicle,Performance car\ntest/63a26dca270409fd,Close-up\ntest/63a2fc7536bf0dcd,Luxury vehicle\ntest/63a79a56f6d02cce,Sport utility vehicle\ntest/63a9ee98f15b6354,Hound\ntest/63abcb9453f974af,Compact car\ntest/63aee9be65435f3f,Auto racing,Antique car,Performance car\ntest/63b22be6cc2fda9e,Extreme sport\ntest/63b27b406ffd9240,Compact car,Luxury vehicle,Antique car\ntest/63b4b694f00812c1,Compact car\ntest/63b4de1130a1ce25,Rural area,Herding\ntest/63b513cdbab70548,Aircraft engine\ntest/63b5adbb9c09b8f8,Dishware\ntest/63b5ee708f399f98,Close-up\ntest/63b73543f6d6a23b,Bird of prey\ntest/63b8e2f5ccd550a1,Jeans\ntest/63ba82a9b52b20da,Horse racing,Equitation,Equestrianism\ntest/63bba43a3b13bd48,Flatbread\ntest/63bc998b7e482ac4,Luxury vehicle,Sport utility vehicle\ntest/63bd0359e2fa3692,Luxury vehicle\ntest/63bd054a68448c3b,Luxury vehicle\ntest/63be31ee09f01fa0,Luxury vehicle,Antique car\ntest/63bea283db3c000d,Black-and-white,Close-up\ntest/63c1282e218d47e1,Coral reef fish,Close-up\ntest/63c18560f2db8382,Bird of prey\ntest/63c22ab38987a284,Compact car,Luxury vehicle,Antique car\ntest/63c30282629170a3,Jeans\ntest/63c359e03ced559a,Ball game\ntest/63c7a69e2ca8f893,Middle ages\ntest/63c84e465a7a30b6,Plant stem\ntest/63c9e8419fcf45cf,Boating\ntest/63cacbc5e6e9247a,Black-and-white\ntest/63cb39d5401e1a6e,Blond\ntest/63cbeba5233632ad,Residential area\ntest/63cdf8c23f197ee5,Double-decker bus\ntest/63ce4ae94bce413a,Frigate\ntest/63ce9f47b276deec,Black-and-white\ntest/63d0fdd8bdcdfc05,Cookies and crackers\ntest/63d174734e949957,Road cycling\ntest/63d2181c2276c644,Compact car\ntest/63d5085346ff661b,Close-up\ntest/63d5340c699845b4,Steamed rice\ntest/63d666d93970eeb4,Boating\ntest/63d75002a6ac0eb0,Luxury vehicle,Sport utility vehicle\ntest/63d868f5d1ee0437,Flatbread\ntest/63d927ffc07b98c9,Great white shark\ntest/63d954c5987e619c,Close-up\ntest/63daabf9997e0c85,Jeans\ntest/63dc006452589cd2,Floristry\ntest/63dcc44dbf8560f7,Monoplane\ntest/63de1c2142b160d6,Bovine\ntest/63de2458b37d8c78,Jeans,Extreme sport,Sport climbing\ntest/63e3f6b596b413c9,Outdoor shoe\ntest/63e6cae5fec61ff4,Close-up\ntest/63e87fe6d9dc10f7,Luxury vehicle\ntest/63e9dcc40d43d16d,Luxury vehicle,Antique car\ntest/63e9f08480ef04a8,Military person\ntest/63ec9d62d4d9985a,Performance car\ntest/63edb96bf47923b6,Luxury vehicle,Sport utility vehicle\ntest/63eea08ae3c49842,Close-up,Whiskers\ntest/63f368fb384f608d,Rear-view mirror\ntest/63f5afc104ac0260,Coca-cola\ntest/63f724a8af6787d4,Luxury vehicle,Antique car\ntest/63fa34715154d5a0,Close-up\ntest/63fb443a4f6b9a04,Shinto shrine\ntest/63fc636b015ed8fa,Nest box\ntest/63fcfec30d1346ed,Close-up\ntest/63fe0c7335bfe8ba,Sport utility vehicle\ntest/63fe907c848873b0,Luxury vehicle\ntest/63fe98606b3003ea,Antique car\ntest/6400751b8a7dbd36,Modern dance\ntest/64027a4778e137cf,Black-and-white\ntest/6403808a572448c2,Black-and-white\ntest/640449da33ab685c,Ale\ntest/6404875c7857cf5b,Luxury vehicle,Performance car\ntest/640527846bff272d,Compact car\ntest/6406b46968f10e67,Sport utility vehicle\ntest/6406e3baa385fad3,Bovine\ntest/6407aeab1a0122ad,Wind wave\ntest/6409844bdce73fa4,Luxury vehicle\ntest/6409f047ec4e520f,Touring car,Luxury vehicle,Antique car\ntest/640baf0d9ffbd32a,Brochette\ntest/640c414c8598cc89,Rural area\ntest/640cc4209379d6d9,Senior citizen\ntest/640cd7b6297d305c,Fried food\ntest/640de5e23beef372,Whiskers\ntest/640fa4221d5e0e1d,Bird of prey\ntest/64103c4b84b9a880,Domestic short-haired cat,Whiskers\ntest/641058ae7dd56289,Cookies and crackers\ntest/64115d907f3d8339,Close-up,Scaled reptile\ntest/64123ed3680fa703,Luxury vehicle\ntest/64137edbce51745b,Auto racing,Compact car\ntest/6414ba9dc49fa297,Close-up\ntest/64153332024189ae,Black-and-white\ntest/64155979d95053c3,Blond\ntest/641a70ddbd31c845,Extreme sport\ntest/641aa92b8e98b60c,Performance car\ntest/641d3e07620570c3,Domestic short-haired cat,Close-up,Whiskers\ntest/64200f46a979ba75,Digital camera\ntest/64224a597cc35c01,Close-up\ntest/6422e6e22029fef1,Junk food\ntest/64241a9d9dabcf02,Chordophone\ntest/64262ba87b4433a7,Compact car\ntest/6426e22ca40a289a,Ball game\ntest/64285e96f2931de6,Performance car\ntest/64294d928f12d0b9,Road cycling\ntest/6429840992e71216,Leaf vegetable\ntest/642de541a82cc3db,Luxury vehicle\ntest/642fd9cc0fb0ae04,Auto racing\ntest/642fe9910d8381cf,Monoplane\ntest/6430d2cee0067301,Luxury vehicle\ntest/6431de9bf59b2c8a,Aircraft engine\ntest/64335c1205b9ae5b,Compact car\ntest/643a31c8fc7635fa,Leaf vegetable,Vegetarian food\ntest/643b87594b036acb,Wind wave\ntest/643c637a6610d436,Ball game,Team sport\ntest/643ca7be965a31e2,Auto racing,Performance car\ntest/643de553bdb1b209,Bovine\ntest/643e62553db3a7d8,Assault rifle,Firearm\ntest/643ebf025cbe3257,Fried noodles\ntest/64407f8099747e3a,Nile crocodile,Crocodilia\ntest/6441c35b6aa66c23,Domestic short-haired cat,Close-up,Whiskers\ntest/64427521d776d7f0,Performance car\ntest/64427ea6fbfa6652,Plant stem\ntest/6442d6e692fef0c5,Jeans\ntest/6443df5adaece28e,Compact car,Luxury vehicle,Antique car\ntest/6446325073d9c356,Bovine\ntest/6446ffed64c9a72a,Arthropod,Locust\ntest/64483e3aaf432855,Black-and-white,Luxury vehicle\ntest/64488be828df2ba1,Leaf vegetable\ntest/64495d7c867042e1,Calabaza\ntest/644ae58bce24c5c7,Melee weapon,Hunting knife\ntest/644b57181f9e48b5,Close-up\ntest/644b62d943c5d612,Luxury vehicle,Performance car\ntest/644c564094c8c4b9,Military person\ntest/644c5dadeadfaa7e,Touring car,Luxury vehicle,Antique car\ntest/644d6dea734acd24,Computer speaker\ntest/644e425bf3cbb7f2,Compact car,Luxury vehicle\ntest/64500774b41b1fe5,Luxury vehicle,Performance car\ntest/645025eb02be40fc,Team sport\ntest/6450ff4b67915fb9,Close-up\ntest/6454ab09df4a3691,Narcissus\ntest/6454cbf93e37721d,Close-up\ntest/6455367d9a974936,Chordophone\ntest/64560cc1dbd3bf51,Toy block\ntest/645922562d3de125,Rural area\ntest/645b5ca371bc5d8f,Fire apparatus\ntest/645d5386d4ccf49f,Close-up\ntest/645f5a8b0e4d17f0,Backlighting\ntest/6461546a6fb51da3,Close-up\ntest/646302c85c1e3b99,Black-and-white\ntest/646399acebfe4b0a,Team sport\ntest/6463e6f1f09ef232,Luxury vehicle,Performance car\ntest/6464c61ec39dfac4,Luxury vehicle,Sport utility vehicle\ntest/6466ee18fc7498bc,Luxury vehicle\ntest/646736bdc93a9190,Scaled reptile\ntest/646857ac606ed374,Luxury vehicle\ntest/6468a7289d860196,Flatbread\ntest/646bdd129de65796,Close-up\ntest/646d8a60016446aa,Luxury vehicle\ntest/646db2f4684bb8a6,Plant stem\ntest/646dbdf7f64fba40,Black-and-white,Portrait photography,Close-up\ntest/64740ed112f54404,Close-up\ntest/6474edae837e1ab9,Compact car,Luxury vehicle,Sport utility vehicle\ntest/6477f9e987608be3,Luxury vehicle\ntest/647c66d2aab6467f,Bovine\ntest/647f0dd2f14ab6b2,Luxury vehicle,Antique car\ntest/6484089b0cb3d829,Briefs\ntest/64866f4e159a2163,Boating,Rowing\ntest/64894dac4213b048,Fried food\ntest/64899676e7486238,Sumo\ntest/648bed883625d4a7,Amphibian,Close-up,Scaled reptile\ntest/648dd687621743c5,Compact car\ntest/648e596a33ed7ddf,Outdoor shoe\ntest/648f074c8972f92b,Comics\ntest/64912ca4f6bf95ff,Luxury vehicle,Performance car\ntest/6491328b5dfcfa8c,Luxury vehicle,Antique car\ntest/64925e8c3b423139,Molluscs\ntest/649344786dd57623,Compact car\ntest/64939e0a8b7afc92,Glacial landform\ntest/6493e873f68b77a3,Antique car\ntest/6494026741b7189c,Childbirth\ntest/6495adeea5cb4e5d,Monoplane,Aircraft engine\ntest/64961eae2ac44027,Plant stem\ntest/6496f80282bbff28,Arthropod\ntest/6497ad29e485f1c4,Flatbread\ntest/649aaed43438a42a,Electronic instrument\ntest/649d58998d13af10,Rural area\ntest/64a0168aeaec357e,Vegetarian food\ntest/64a0a507c2483e45,Aircraft engine\ntest/64a29d411fc54c55,Aircraft engine\ntest/64a3a5307884bd5d,Kitesurfing,Wind wave\ntest/64a40ab63e094fc1,Equestrianism\ntest/64a48cc22a4eb6b8,Luxury vehicle\ntest/64a53af2dccd7dbb,Close-up\ntest/64a7976a73b47293,Hurdling\ntest/64ab3e76d43e4a1e,Boating\ntest/64ab4adfd21c30f9,Aircraft engine\ntest/64b03272db6cb86e,Close-up\ntest/64b0e4fb38df21b8,Bovine,Close-up\ntest/64b36560fa73fb5d,Military person\ntest/64b43c8e7e18f9f7,Steamed rice\ntest/64b7374f701b2e50,Shinto shrine\ntest/64b79a950275ed6d,Arthropod,Close-up\ntest/64bbc4ea3418781d,Chordophone\ntest/64bcb8b7e97a9fcf,Electronic instrument\ntest/64bece8d8d24eb2c,Close-up,Primate\ntest/64c05cb08566841b,Monoplane,Aircraft engine\ntest/64c19199581e513b,Rear-view mirror\ntest/64c1a677d13d61b0,Luxury vehicle,Performance car\ntest/64c715785e44dcd3,Close-up,Plant stem\ntest/64c8266beefad094,Fried noodles,Soba\ntest/64cb0d9408ba1900,Aircraft engine\ntest/64cb26d9712fc38d,Arthropod,Close-up\ntest/64cb8f3976662d36,Close-up\ntest/64ccff1f5d7c83c1,Vegetarian food\ntest/64ceec357d0377ed,Boating,Rowing\ntest/64d059e86cb94f56,Touring car,Luxury vehicle,Antique car\ntest/64d07970fbb95753,Luxury vehicle,Antique car\ntest/64d1623b60b88ed9,Fried food\ntest/64d194c0fe3c22ec,Close-up\ntest/64d1a7ad40ac5a3d,Aircraft engine\ntest/64d24bb96c3fe0cb,Floristry\ntest/64d3b927fbddc3e4,Portrait photography,Close-up\ntest/64d4560840d0303a,Close-up\ntest/64d68bacc008d1a8,Sport utility vehicle\ntest/64d6eb8f74c300df,Ball game\ntest/64d8cda85473393f,Military person\ntest/64d9d0773c67dfd2,Melee weapon,Hunting knife\ntest/64d9f615bca2457f,Blond\ntest/64daade132c9be50,Ball game\ntest/64dbc921847fea7e,Close-up\ntest/64dbd460ca9dcd66,Chordophone\ntest/64dc0f518cf93f6d,Coral reef fish\ntest/64dce1223b57d036,Luxury vehicle,Sport utility vehicle\ntest/64e2045c549b9b55,Aircraft engine\ntest/64e243fe6ac3f62c,Extreme sport\ntest/64e3df30e1fa4b03,Luxury vehicle\ntest/64e3e9df59c4e766,Firearm\ntest/64e6a7061cdd51b6,Aircraft engine\ntest/64e76d346ac6eb1a,Hair coloring,Close-up\ntest/64e88d31b5da804a,Billiard room,Billiards\ntest/64eb0a8f3f5e36a7,Steamed rice\ntest/64ed0e6a462b18ca,Electronic instrument\ntest/64ed633938598692,Dishware\ntest/64f030d41a224229,Close-up\ntest/64f09efbf6bc1d10,Luxury vehicle,Antique car\ntest/64f0ab075765b996,Luxury vehicle,Sport utility vehicle\ntest/64f372f4fe3b8e98,Luxury vehicle,Antique car\ntest/64f53d68b7aeffe4,Goats\ntest/64f67dc2128b15d7,Close-up\ntest/64f90cbe1b62c644,Close-up\ntest/64fc3a4bdd1d2ad1,Luxury vehicle,Antique car\ntest/64fc9026b42e614a,Rural area\ntest/64fd8b325a361624,Compact car,Luxury vehicle,Antique car\ntest/64feabb8d7ffc563,Domestic short-haired cat,Whiskers\ntest/6500c6ac16133865,Floristry\ntest/650278103bf5006d,Luxury vehicle\ntest/6503facb30b4386c,Close-up,Scaled reptile\ntest/6504b88bb2fd52d3,Luxury vehicle,Antique car\ntest/6504d2593a36d4af,Luxury vehicle\ntest/6504fb5bf47fa57a,Vegetarian food\ntest/6505484a4998afcc,Ramen\ntest/6505e2fa5d1ec7bb,Rural area\ntest/6506a1c87c264e29,Performance car\ntest/650a376f5da6f958,Briefs\ntest/650b7dbd1ac61ab2,Aerial photography\ntest/650d14b24312f5a6,Compact car\ntest/650f322092461837,Bird of prey\ntest/65109da6c1be77ce,Woodwind instrument\ntest/65135dc6d31e2fe6,Military vehicle,Sport utility vehicle\ntest/6513afd257938970,Great white shark\ntest/65155a90bf9cc6e8,Junk food\ntest/6517c0d6be2c55f5,Close-up\ntest/6517fdd4582e75b4,Luxury vehicle\ntest/6518fce105f9e19c,Close-up,Whiskers\ntest/651b51988955951f,Luxury vehicle,Antique car\ntest/651fd3bc88fe13af,Luxury vehicle\ntest/6520279c853247d5,Luxury vehicle\ntest/6530ab035a87a5ee,Black-and-white\ntest/6531397a2380a8ec,Antique car,Performance car\ntest/6531ae4b52225a8e,Blond\ntest/65339ba7ecc20a0e,Drums\ntest/6535f82efabb2596,Extreme sport\ntest/653642eeeb0ea36f,Aloe\ntest/6537dba646c81be8,Antique car\ntest/653aa2be61bce778,Luxury vehicle,Performance car\ntest/653c109b71f6fae0,Compact car,Antique car\ntest/653d59100ae70f96,Close-up\ntest/653d68c526feb17c,Portrait photography,Close-up\ntest/653de68b330dd1a1,Molluscs\ntest/653e3433477680ea,Compact car,Antique car\ntest/653e8cbffa7b21c9,Leaf vegetable\ntest/653f10382f03e16f,Milky way\ntest/653f52da3e38c1a9,Combat sport,Grappling\ntest/65400a9ef3354940,Close-up\ntest/65404436ae62b0a7,Close-up\ntest/65421d2f9a8e78f6,Bouldering\ntest/65425936a2fbd359,Modern dance\ntest/65433c5a09f63551,Luxury vehicle,Sport utility vehicle\ntest/65438bf3c159a5e3,Compact car\ntest/65448a7351e1a86b,Arthropod,Close-up\ntest/65457372f5b583f3,Domestic short-haired cat,Whiskers\ntest/654811280cc1756d,Compact car\ntest/654a2551bca71eec,Scaled reptile\ntest/654cbde78593fe80,Roller skating\ntest/654d741e1d80fd43,Team sport\ntest/654fc93cd36f6ebd,Bovine\ntest/65506639dda39863,Domestic short-haired cat,Whiskers\ntest/6550baee0b5831bc,Compact car,Luxury vehicle,Antique car\ntest/655202a83b34ca93,Scaled reptile\ntest/6552aa45a7cc6d22,Soba\ntest/6552ef9b91968350,Luxury vehicle,Antique car\ntest/6553115a4a13ad81,Aircraft engine\ntest/655467ebc819b644,Luxury vehicle,Sport utility vehicle\ntest/6554fba1fe669843,Jeans\ntest/6555c7252117a095,Athletic shoe\ntest/6557313c60d57bb4,Luxury vehicle\ntest/6559a7290b35fd35,Luxury vehicle\ntest/6559af5756690df5,Compact car,Luxury vehicle,Antique car\ntest/655ef3577ec30853,Extreme sport,Personal water craft\ntest/655f50f43998c2cd,Compact car\ntest/65648ea243d01317,Sailboat racing\ntest/656b80cdd869d2d4,Scaled reptile\ntest/656ed34a8bfd2e9d,Luxury vehicle,Performance car\ntest/65704399bc54a790,Arthropod,Close-up\ntest/6570bf23f2bbf734,Close-up\ntest/657299cc5c044737,Luxury vehicle\ntest/6572bb556ef35370,Close-up\ntest/6574a88bee35864d,Close-up\ntest/65755d157c8e5ec7,Coral reef fish\ntest/6576042e1bc0f15e,Close-up\ntest/657606e2f1c62742,Luxury vehicle,Performance car\ntest/657788b72ef144ac,Sport utility vehicle\ntest/6579a9ae48e5c837,Jeans\ntest/657a4930ad651757,Close-up\ntest/657d915e5720b620,Compact car,Luxury vehicle,Performance car\ntest/65805c98c1ac965b,Vegetarian food,Junk food\ntest/6581250accb252b5,Bovine\ntest/6582426662ed4ef2,Brochette\ntest/65824c486f3b0dfa,Jeans\ntest/6582d3d330001649,Arthropod,Close-up\ntest/6582ec2c7ed1361a,Bovine\ntest/65837a7425d32f88,Ball game,Team sport\ntest/6584094e9f134656,Woodwind instrument\ntest/658415cc2966971c,Compact car\ntest/6585d0bef7e26df6,Molluscs,Close-up\ntest/65864f6c4dcb87fc,Luxury vehicle\ntest/65869ed6d6a6ba7f,Black-and-white\ntest/6588158397a21f58,Close-up,Plant stem\ntest/6588c7e400ff6f42,Close-up\ntest/658a22379313d9f7,Close-up\ntest/658a56a6ffb052cd,Black-and-white\ntest/658caa10d97df8b0,Whiskers\ntest/658f6f982fe22507,Amphibian,Close-up\ntest/659141ffeb472af6,Performance car\ntest/6591623f57655a61,Extreme sport,Sledding\ntest/6591f78987835679,Close-up\ntest/65937f11a6a76c12,Compact car,Luxury vehicle,Antique car\ntest/659930c803700986,Compact car\ntest/6599be04dfdd8b13,Rural area,Aerial photography\ntest/659b1f5daf0b59a0,Performance car\ntest/659cb2ab9c59b6f8,Antique car\ntest/659cd3e0aacaff4c,Black-and-white\ntest/65a00768a0fb6d17,Performance car\ntest/65a112db2dbd02e1,Close-up,Plant stem\ntest/65a196064ae3773d,Senior citizen\ntest/65a231d6e54ce844,Cobblestone\ntest/65a26be71b525a4a,Compact car,Antique car\ntest/65a32cdcd3bee720,Portrait photography\ntest/65a3a19200ac68ec,Luxury vehicle,Performance car\ntest/65a426cd35a5e968,Luxury vehicle,Performance car\ntest/65a754a5a7ea0a1f,Close-up\ntest/65a8db7a59c68763,Close-up,Whiskers\ntest/65aa46691be44e14,Sport utility vehicle\ntest/65ab7dde17a514e7,Compact car\ntest/65acfe89a5593d2b,Scaled reptile\ntest/65b06000814f6ee7,Sport utility vehicle\ntest/65b18205c1497fec,Close-up\ntest/65b26d69707769a3,Luxury vehicle,Sport utility vehicle\ntest/65b3751f8d953e23,Aerial photography\ntest/65b410839323ec3d,Ball game,Team sport\ntest/65b5f7c7458bb716,Motorcycling\ntest/65b7e4384fa07fe4,Chordophone\ntest/65be698e9bedbe8c,Rural area\ntest/65c0530640b44c5a,Close-up,Plant stem\ntest/65c0affa16ef1dcf,Military vehicle,Gun turret\ntest/65c0bc75563d2a3e,Motorcycling\ntest/65c4097e9e44b3a0,Sport utility vehicle\ntest/65c6b3faecea5316,Hunting knife\ntest/65c7e4940e60aaaf,Close-up,Barechested\ntest/65ca608ce3286775,Divemaster,Scuba diving,Underwater diving\ntest/65cb41c439b66c98,Plant stem,Close-up\ntest/65cc7367a1e9d0fe,Luxury vehicle,Performance car\ntest/65ccaf8a6552910b,Zeppelin\ntest/65ccb86c41e4fe39,Luxury vehicle\ntest/65cd94fa03fa5538,Cookies and crackers\ntest/65cefa7a884823bc,Toy block\ntest/65cf129347a7f0c5,Close-up\ntest/65d04213885c63c4,Performance car\ntest/65d043efaa706ae7,Luxury vehicle,Antique car\ntest/65d14733a0eb7518,Jeans,Black-and-white\ntest/65d23bb65822de62,Luxury vehicle\ntest/65d2957356dc08c5,Locust,Arthropod,Close-up,Plant stem\ntest/65d3974d7c82156f,Close-up\ntest/65d4bd0269e032b1,Sport utility vehicle\ntest/65d541b370f26977,Coral reef fish\ntest/65d707a37d182f21,Domestic short-haired cat,Close-up,Whiskers\ntest/65d960a737ce0013,Aerial photography\ntest/65d9bb351d361a84,Extreme sport\ntest/65da3a65c48ae0c5,Close-up\ntest/65dbb37790b422e0,Luxury vehicle\ntest/65dd88090c2f2c90,Close-up\ntest/65deb57bfbfeb500,Black-and-white\ntest/65dfa1b7e85a6b31,Compact car\ntest/65e16f4eea967f4b,Sport utility vehicle\ntest/65e18e3f65bf89ae,Portrait photography,Close-up\ntest/65e1a979c09a102e,Close-up\ntest/65e297831c751675,Black swan,Black-and-white\ntest/65e3b64a81765929,Close-up\ntest/65e44c8e96a300ee,Khinkali\ntest/65e520c18584e3d8,Senior citizen\ntest/65e62ba82798f770,Luxury vehicle,Sport utility vehicle\ntest/65e6d6fac4b07123,Wind wave\ntest/65e7919964d74f4c,Aircraft engine\ntest/65e84857b21aa894,Leaf vegetable,Vegetarian food\ntest/65e9ce80e2d59a8a,Luxury vehicle,Performance car\ntest/65ed0e1c53def5d7,Ball game,Team sport\ntest/65ee857fbdb7697d,Luxury vehicle,Antique car\ntest/65ef05f7a31236d2,Blond,Close-up\ntest/65ef0d214cd8e5be,Close-up\ntest/65f02ab7c7feccf8,Performance car\ntest/65f18f2d4774daf6,Blond\ntest/65f3e212c61987a2,Luxury vehicle,Sport utility vehicle\ntest/65f4d54f97e2759c,Steamed rice\ntest/65f5a6276b0b72f6,Wind wave\ntest/65f64140a3f5326e,Luxury vehicle,Performance car\ntest/65f857604188e814,Frying,Junk food,Fried food\ntest/65f92ec8b7fe9c02,Dishware\ntest/65fbccdd5c1071a3,Jeans\ntest/65fc7a762c1781e7,Bonbon,Close-up\ntest/65fcfc534b7b06e9,Compact car\ntest/6600f199bc8901a0,Ball game\ntest/66013840fbc02a52,Fried food\ntest/66031a885c67a8f7,Luxury vehicle,Antique car\ntest/6603a5a832750478,Brisket,Ribs\ntest/66065723000335cf,Aircraft engine\ntest/6606e6ea7f8b820e,Antique car,Performance car\ntest/66082f142a2a5a86,Blond,Close-up\ntest/660a8fca141fca66,Gumbo\ntest/6610b190b93c2aac,Brisket,Beef tenderloin\ntest/66144e8ccb6114bc,Luxury vehicle\ntest/6614c34676b13a3d,Combat sport,Grappling\ntest/6614e5a1de4d0a3e,Team sport\ntest/6616226a7733c336,Comics\ntest/66165620fc875e10,Halter\ntest/661905434688189e,Rural area\ntest/6619d817549cb465,Extreme sport,Parachuting\ntest/6619e2e97cabf9a3,Close-up\ntest/661d6a87925cb8ef,Close-up\ntest/661e7bd4c8371210,Arthropod,Close-up\ntest/661ec1f758c5d254,Military vehicle,Gun turret\ntest/6620bb8f46f5a22a,Black-and-white,Boating\ntest/662338a0d4e6c4aa,Rural area,Aerial photography\ntest/662441ab724aae16,Auto racing,Touring car,Luxury vehicle,Performance car\ntest/66266cc464c923b3,Antique car,Performance car\ntest/6627f7a03726159d,Flatbread\ntest/662afbaa9016504d,Luxury vehicle,Performance car\ntest/662bd6c164e5470e,Sport utility vehicle\ntest/662c394319ed2cac,Vegetarian food\ntest/662cbb09ac351d36,Rural area\ntest/662ce01030dc8b18,Antique car\ntest/662da9e9b5c774bd,Compact car\ntest/6631c0cde106bad2,Athletic shoe\ntest/663223fcee474d6a,Luxury vehicle,Performance car\ntest/66369d37f3c6a8d1,Dobermann\ntest/6637aeda749e3614,Domestic short-haired cat,Whiskers\ntest/66394e8bfd8a46b3,Close-up\ntest/663a09f000ce74b8,Extreme sport\ntest/663da5b34c76f6f8,Frying,Junk food,Fried food\ntest/663f0b63f7115c4e,Bagpipes\ntest/66430d0bdfac46dd,Scaled reptile\ntest/66449e223b80a0a5,Billiards\ntest/66485628c46a0481,Cookies and crackers\ntest/664bac60867a5aae,Electronic instrument,Electric piano\ntest/664cd80613c7e748,Breadboard\ntest/665433fff0d2b278,Close-up\ntest/6655d4e82b785755,Ball game,Team sport\ntest/66563d681a539862,Woodwind instrument\ntest/6657cd2b34087445,Close-up\ntest/6658dee0cc18855a,Close-up\ntest/665a63ee484ef3ba,Extreme sport\ntest/665b9dc32d3029ec,Monoplane\ntest/665f16172030104c,Molluscs,Close-up\ntest/6660289fc8981cb8,Rural area,Aerial photography\ntest/666091a9a8d0a5a9,Black-and-white,Luxury vehicle,Antique car\ntest/6666022e501776fb,Chordophone\ntest/6666fa2ee285baf4,Luxury vehicle,Antique car\ntest/666ba098d1db05d1,Luxury vehicle\ntest/666fa46fc2d94d95,Extreme sport,Wind wave\ntest/66766460b7fe224b,Compact car\ntest/667b7f950a1ea1cb,Close-up\ntest/667c5fb551a56724,Coral reef fish\ntest/667d187f48112eef,Aerial photography\ntest/667ea012d9995afb,Close-up\ntest/668081e722674002,Performance car,Antique car\ntest/66822e0d47f42875,Blond\ntest/668293342dade0ff,Performance car\ntest/66837d01d8a01317,Close-up\ntest/66848534633d470a,Sport utility vehicle\ntest/6684ab292a4223fe,Antique car\ntest/668975628935f386,Close-up\ntest/668b3a6bc6f904aa,Electric piano\ntest/668b4313af410f75,Luxury vehicle\ntest/668e9ee296ad96bf,Luxury vehicle,Antique car\ntest/669303cfd564ffce,Pit bull\ntest/669372c2d821aaa2,Luxury vehicle\ntest/6693f61ce55cc2ca,Floristry\ntest/66941b7019902793,Vegetarian food\ntest/66944723e83b207e,Luxury vehicle\ntest/6695462611c56fb3,Close-up\ntest/66999815553ea105,Brochette,Yakitori\ntest/669a8ffa714e7ed4,Chordophone,Electric guitar\ntest/669bfdf3a0254c42,Scaled reptile\ntest/66a0ba73e9c22168,Vegetarian food\ntest/66a408369c1a5719,Rice ball\ntest/66a72c4fdd12b21f,Jeans\ntest/66a8ab1ac8fc0afb,Aircraft engine\ntest/66aa1a7440a70ed5,Ball game,Team sport\ntest/66aa8a11caf50c37,Crocodilia,Nile crocodile,Scaled reptile\ntest/66ac1bcbc8ffa63c,Close-up,Scaled reptile\ntest/66ae02d208ebea23,Performance car\ntest/66b2cb0ad28b7c7e,Auto racing\ntest/66b32d1758deecd9,Athletic shoe,Outdoor shoe\ntest/66b5696ee995b863,Luxury vehicle\ntest/66ba2d21e81adf20,Auto racing\ntest/66ba7291c73b3a14,Close-up\ntest/66bc2f6f149bc511,Antique car\ntest/66bc8b0b5351fd4c,Blond,Hair coloring\ntest/66bca8bd7ccd9e7f,Luxury vehicle,Antique car\ntest/66c0e0abccc5c1c2,Shooting range,Firearm\ntest/66c105fa2f71fdf7,Close-up\ntest/66c30b862ae004b3,Nile crocodile,Crocodilia\ntest/66c4bc0b6869ae70,Vegetarian food\ntest/66c56024ce870922,Cookies and crackers\ntest/66c6c454c46872d0,Rural area\ntest/66c91ddaf9643686,Lifebuoy\ntest/66cab177657a16f3,Galleon\ntest/66cbccb753cc78c7,Assault rifle,Firearm\ntest/66ccafe9120659f5,Compact car\ntest/66ccd7bf32e7167a,Luxury vehicle\ntest/66ce4c2137dfcf6f,Arthropod\ntest/66ceee0747aff0d7,Luxury vehicle,Performance car\ntest/66cfee5fed691c46,Compact car,Sport utility vehicle\ntest/66d1e3edcf98fb15,Luxury vehicle\ntest/66d3c5d73ce24bcb,Jiaozi\ntest/66d860618c0b62c4,Extreme sport,Wind wave\ntest/66d9dbe00d954aee,Close-up\ntest/66dedacdbfca4efd,Pork chop\ntest/66df26442be2e560,Luxury vehicle,Antique car\ntest/66df4780e7da146e,Luxury vehicle,Performance car\ntest/66e346369cbe9e53,Arthropod,Close-up\ntest/66e3f03ad2f26dcf,Luxury vehicle\ntest/66e4dc4035959e19,Luxury vehicle\ntest/66e52bb5c7bce9b2,Luxury vehicle,Performance car\ntest/66e5e2e0ff4b83c2,Compact car,Antique car\ntest/66e65762c01aa8e4,Dobermann,Hound\ntest/66e6faf7d7fcbac9,Close-up\ntest/66e95eeb2daaf014,Close-up\ntest/66e9924ba26db960,Compact car,Luxury vehicle\ntest/66e9d024ea9701c5,Close-up\ntest/66ebe29050953b9a,Boating,Rowing\ntest/66ecdaa4f3b75eb8,Plant stem\ntest/66ee4dd765690f75,Angling\ntest/66f12bfc93bb8738,Black swan\ntest/66f1329195abee0d,Luxury vehicle,Performance car\ntest/66f14bcb353790bf,Luxury vehicle,Antique car\ntest/66f2b0409205e27d,Aircraft engine\ntest/66f40a22c995f4b5,Plant stem\ntest/66f74e747a6448d3,Ribs\ntest/66f7a45cf53b5900,Black-and-white\ntest/66f8178c5be35800,Combat sport\ntest/66f83c7554a6fcc3,Arthropod,Close-up\ntest/66fe70b495c1d69c,Billiards\ntest/66ffa52d49945b0d,Close-up\ntest/67021826c4c473b2,Close-up\ntest/670261ccbea3af37,Electronic instrument\ntest/67041911e5595769,Portrait photography\ntest/6709df9cffa2af74,Musical theatre\ntest/670a8414a766d52d,Blond,Hair coloring\ntest/670ab4b3bcb58758,Plant stem\ntest/670ac4df8586c308,Jeans\ntest/670b845b038d2740,Rural area\ntest/67104693b206c803,Scaled reptile\ntest/6710cbc6c79da333,Luxury vehicle,Performance car\ntest/67114ed7e529ac8c,Luxury vehicle\ntest/6711d5b0851a3b38,Luxury vehicle\ntest/6711e328f351ddca,Solar energy\ntest/6713f7c78621dca4,Military vehicle\ntest/671400c7ee842160,Freight transport\ntest/67166b724a3c703b,Close-up\ntest/671a688f3ff2ca49,Bratwurst\ntest/671b5b58fa8607f1,Compact car,Luxury vehicle\ntest/671b6dff28ea8e65,Cobblestone\ntest/671e1f46225f8159,Briefs\ntest/672094b10a1b6110,Vegetarian food\ntest/67247d9e48a4c719,Domestic short-haired cat,Whiskers\ntest/6727f1225b2fe1ba,Fried food\ntest/672a13d630acd6a1,Extreme sport\ntest/672e2b9a5da56766,Dishware\ntest/67308b5cc43e7ca5,Steamed rice\ntest/6730f2a67b33c160,Compact car,Sport utility vehicle\ntest/67310c81574cfaba,Machine tool\ntest/6731e21781304bc6,Extreme sport,Road cycling\ntest/67337b2ce73e3f48,Rural area\ntest/6733b7fc27b869b9,Auto racing,Touring car\ntest/6734dcf6fbf79533,Modern dance\ntest/67382f752b11df92,Close-up\ntest/6738d4e8019a3d69,Blond\ntest/673a3e5dfb622ca4,Military vehicle\ntest/673bd676a894d8b7,Close-up,Plant stem\ntest/673d255e1266c527,Plant stem\ntest/673d5a6c1444f149,Cookies and crackers\ntest/673de5c8b66ab733,Microcontroller\ntest/673e9a78b5adfe3e,Backlighting\ntest/673f42f64a7d6397,Computer speaker\ntest/673fdb873d91b684,Luxury vehicle,Performance car\ntest/674245331a522e91,Luxury vehicle,Performance car\ntest/6745bbd8fc8df1a8,Hound\ntest/6747eeba1bad0d08,Ball game,Team sport\ntest/67489fc7459dcd92,Shooting range\ntest/674917b736ea1911,Leaf vegetable\ntest/674cc2da3339c261,Ball game\ntest/674d9b5f52aee8a6,Horse and buggy\ntest/67511f33e8bee7de,Jeans\ntest/6753836f3d724e35,Luxury vehicle\ntest/67540fee1e000291,Close-up\ntest/675735c27425d437,Residential area,Aerial photography\ntest/67590971e51443f1,Computer speaker\ntest/6759a6632e3d4114,Luxury vehicle,Performance car\ntest/6759e4f47f665f99,Close-up\ntest/675d77a85b4f026b,Luxury vehicle,Sport utility vehicle\ntest/676210a14b100ae4,Luxury vehicle\ntest/676247c82f554a8d,Classical sculpture\ntest/67629b26f57a12f0,Monoplane\ntest/6766c11caf4ca2f9,Ball game,Team sport\ntest/6766dc661c6b58d5,Close-up\ntest/67674243ebdcc37d,Arthropod,Close-up\ntest/676987a7157c7758,Black-and-white\ntest/676aa4d4163ed075,Ball game,Team sport\ntest/676abc6775195762,Luxury vehicle,Antique car\ntest/676bd646795c72f1,Aerial photography\ntest/677079e079c4564f,Ale\ntest/677585175dbf1166,Calabaza\ntest/677721da5ce76ee5,Black-and-white,Domestic short-haired cat,Whiskers\ntest/67799ad164f8e70f,Ball game,Team sport\ntest/6779db415f36edd5,Hurdling\ntest/677dfa252571761b,Compact car\ntest/677f20d1afd00552,Close-up\ntest/6780478011d9ca74,Jeans\ntest/678152ba207d88e1,Luxury vehicle\ntest/67840c2c16b7fe3f,Chordophone\ntest/67846482e74812ee,Vegetarian food\ntest/67855c7ad297a90a,Domestic short-haired cat,Whiskers\ntest/6785bef2ab687093,Close-up\ntest/678b781094248cc2,Auto racing\ntest/678cb0057e555513,Luxury vehicle,Performance car\ntest/678e2419ad34af24,Dog walking\ntest/678f09e79bca80ea,Luxury vehicle\ntest/678f5c941e9f776b,Antique car\ntest/67903e241d62137e,Blond,Close-up\ntest/67924482ec412221,Sport utility vehicle\ntest/679866fa252289c1,Compact car,Luxury vehicle\ntest/679c8aeb4e750146,Military person\ntest/679d31a061946ecd,Close-up\ntest/679d578e6a0f0256,Compact car\ntest/679debd6fcde6410,Senior citizen,Close-up\ntest/679e36aeecdc5e35,Compact car,Sport utility vehicle\ntest/67a2f15d8c54684d,Jeans,Performance car,Antique car\ntest/67a3a6729b57887e,Boating\ntest/67a3eabfd2db81a6,Performance car\ntest/67a423fd89d2bd8e,Close-up\ntest/67a67086b3cb460c,Scuba diving,Underwater diving\ntest/67a74e3bee39eac6,Plant stem,Close-up\ntest/67a7e9f6d8e7bfdd,Jeans,Blond\ntest/67a861b04cc027be,Close-up\ntest/67a89421542964a2,Blond\ntest/67a8ddb5e2d4b2fc,Sport utility vehicle\ntest/67ae19d6151d4c2c,Black-and-white,Goats\ntest/67af262a89dca2e1,Combat sport,Grappling\ntest/67afc3b1fca6e1fa,Ball game\ntest/67b01491e25f36b6,Jeans\ntest/67b0ff998af6bdc9,Aircraft engine\ntest/67b24b94f31f02b3,Firearm\ntest/67b2da50f70992b6,Luxury vehicle\ntest/67b3f845101a90fc,Performance car,Antique car\ntest/67b46bf90c5e2d01,Computer speaker\ntest/67b4cd5e00031789,Luxury vehicle\ntest/67b5efadab090dab,Compact car,Sport utility vehicle\ntest/67b61505281f8f91,Motorcycling\ntest/67b687e9b924ebdd,Sport utility vehicle\ntest/67b71de6a6305025,Extreme sport\ntest/67b7939d3db60d4e,Aircraft engine\ntest/67b849cb83bd2ea4,Performance car\ntest/67b8b1950399039c,Ball game,Team sport\ntest/67ba21bb58c84185,Auto racing,Performance car\ntest/67ba5fcc0074cbbc,Whiskers\ntest/67bb389e6a4c1f5d,Black-and-white\ntest/67bb44146421bef8,Extreme sport\ntest/67bc98ca0b6e4a0e,Molluscs,Close-up\ntest/67bd85c84bb10f8c,Watercolor paint,Whiskers\ntest/67be3c762691bf68,Melee weapon,Hunting knife\ntest/67bf0814e8aed69c,Sport utility vehicle\ntest/67c0dc64d0a4f4dc,Black-and-white\ntest/67c18d4084e0d0e3,Close-up\ntest/67c21912b1ab83dc,Luxury vehicle,Sport utility vehicle\ntest/67c4763f585892ce,Arthropod,Locust,Close-up\ntest/67c56bba07555825,Rural area\ntest/67c5fcdfd7c1cb8d,Compact car\ntest/67c6fbf86acba8d3,Team sport\ntest/67c98db8cf15467f,Headphones\ntest/67cac965604e9838,Ball game,Team sport\ntest/67cb4d8750ad13a3,Goats\ntest/67cca5865fb03146,Nordic skiing\ntest/67ce71ed4f6ad88a,Musical theatre\ntest/67d1a5218dbc1627,Sport utility vehicle\ntest/67d20c3e511ad346,Wind wave\ntest/67d28af3b6edcb97,Glacial landform\ntest/67d2b79435da0312,Close-up\ntest/67d381ff78d3a080,Whiskers\ntest/67d54308446d1149,Close-up\ntest/67d88ec06bed43aa,Close-up\ntest/67d8f3c13a13124f,Rural area\ntest/67d940c4c54662bb,Modern dance\ntest/67da83c5dd3a6bea,Modern dance\ntest/67dd52b7190e9f19,Amphibian,Anole\ntest/67e095377087f122,Scaled reptile\ntest/67e36f8fa656cce9,Blond,Hair coloring\ntest/67e487a24170cb80,Cookies and crackers\ntest/67e8e539b123747e,Assault rifle,Firearm\ntest/67e983b1785907d1,Extreme sport\ntest/67eb3619729b8893,Ball game,Team sport\ntest/67f02a6ed6891a3c,Extreme sport,Parachuting\ntest/67f1971d837973b6,Compact car,Antique car\ntest/67f1edb58f60a8ee,Compact car\ntest/67f24f231b93a0af,Wallaby\ntest/67f55c8d83f60871,Wallaby\ntest/67f8cd62f0523452,Aircraft engine\ntest/67fbf44fe867c787,Steamed rice\ntest/67fc00cbfdf7252d,Extreme sport\ntest/67fc4b696c6fee54,Vegetarian food\ntest/67fd7251d9ce4de7,Antique car\ntest/67fe259c40a87dcc,Luxury vehicle,Sport utility vehicle\ntest/67fef27c97fa417f,Electric piano,Electronic instrument\ntest/6800bfecec8b98d8,Bovine\ntest/6800dcd8d5bc075a,Arthropod,Locust,Close-up\ntest/6805657a52d30677,Computer speaker,Headphones,Electronic instrument\ntest/680597fc57d29159,Bovine\ntest/6805df4b4231d01e,Luxury vehicle,Antique car\ntest/6806421c4271cf9a,Luxury vehicle,Performance car\ntest/68087a5ff61991c1,Luxury vehicle,Antique car\ntest/6808a60a2519560d,Cookies and crackers\ntest/680952775237478b,Vegetarian food\ntest/680a06824a32b354,Arthropod,Close-up\ntest/680a27a805e63300,Boating,Personal water craft\ntest/680d0e5a388acc0e,Luxury vehicle\ntest/680dd9cfaa12c20c,Performance car\ntest/680e62a5feca3c20,Nuclear power plant\ntest/6812634c1ad9704e,Close-up\ntest/6817a43dfdbd57ff,Extreme sport,Parachuting\ntest/6818f79a237d0b9f,Cookies and crackers\ntest/681982f287bfdb11,Vegetarian food\ntest/681b5c6764d4e1df,Close-up\ntest/681b9d18e064e882,Roller skating\ntest/681c1882cba16556,Jeans\ntest/681d7c8691489853,Luxury vehicle,Performance car\ntest/681f991ea67b1bb4,Brochette\ntest/681fc2e8d5237f2a,Arthropod,Close-up\ntest/6820266c9d0ed771,Lawn game,Ball game\ntest/68227cd6c7be266f,Performance car\ntest/6822b693fdd52242,Close-up,Plant stem\ntest/6823748e04bf358b,Compact car,Luxury vehicle,Antique car\ntest/68258bbc151657d3,Luxury vehicle\ntest/6828160501ec418f,Combat sport\ntest/68282c6f594bba3e,Ball game,Team sport\ntest/68284266d24126aa,Whiskers\ntest/682b39a2fda58045,Musical theatre\ntest/682b99230ab505da,Amphibian\ntest/682c1449e4d358e0,Close-up\ntest/682e2f8e4e282590,Motorcycling\ntest/682fc58fe004eff9,Vegetarian food\ntest/682ff21cccb12af1,Luxury vehicle\ntest/6830ecc710b9804a,Compact car,Luxury vehicle\ntest/68310168e1da5913,Ball game\ntest/6831144a5d3e99de,Luxury vehicle\ntest/68361d6d44394db8,Rural area\ntest/6837867630623be9,Dishware\ntest/6837cb47d1cf10a8,Stemware\ntest/683878838f79000b,Close-up\ntest/6838cd297ac0d18b,Senior citizen\ntest/6838e38aa5867290,Antique car\ntest/683a70a92941e0d7,Domestic short-haired cat,Whiskers\ntest/683a8088c61143ea,Bovine\ntest/683c8b479764e6f6,Close-up\ntest/683de1d6adff3b7a,Nile crocodile,Crocodilia\ntest/683fc814ec0432d7,Luxury vehicle,Antique car\ntest/6842e26ab1573289,Miniature Poodle\ntest/68436467ce8a3e47,Jiaozi,Pelmeni\ntest/6845a7aba31d523a,Monoplane,Aircraft engine\ntest/6846362f1886223e,Extreme sport\ntest/684683e20876c667,Close-up\ntest/68474d792e528f57,Dishware\ntest/6849c210eb98602c,Luxury vehicle\ntest/684ad6f118ce64aa,Antique car\ntest/684b5fbfedae9a42,Jeans,Road cycling\ntest/684b784b67cfa461,Black-and-white\ntest/684d2ea8949d31e4,Luxury vehicle\ntest/684e82bbf5ca3224,Domestic short-haired cat,Whiskers\ntest/684fab52d1c8c761,Ball game\ntest/685037cd9be377dd,Sport utility vehicle\ntest/685064b22903e481,Aircraft engine\ntest/6852e6363c23c4e3,Black-and-white\ntest/68552f013effcf91,Sport utility vehicle\ntest/685592457e10e398,Luxury vehicle\ntest/685680aada3a79cc,Close-up\ntest/6857e85afd0865a2,Luxury vehicle,Antique car\ntest/68581b769c79bef9,Hair coloring\ntest/685a39b1c79d65ba,Plant stem\ntest/685c59468727bff2,Modern dance\ntest/685f6ab75b62b8c8,Jeans\ntest/6863397f785c0aea,Close-up\ntest/6864321d2ab38b8c,Bagpipes\ntest/686471f20915c676,Athletic shoe,Outdoor shoe\ntest/6864a30114c2acdc,Domestic rabbit\ntest/6866076d2eca6184,Black-and-white\ntest/6867c10422ff2d33,Luxury vehicle,Antique car\ntest/686936e0a0125ebf,Compact car\ntest/686961050be8fad9,Amphibian\ntest/686c681cb172bcc3,Aircraft engine\ntest/686c70a922b75262,Antique car\ntest/686d44e402b3b70d,Pork chop\ntest/686dfceffc75b4d8,Auto racing\ntest/686e20f004edc9f7,Military vehicle\ntest/686eda10fc51c841,Middle ages\ntest/686f31aed9f3334d,Glacial landform\ntest/6870ab795f3fb742,Herding,Bovine,Rural area\ntest/6870ebe0bed5b701,Peafowl\ntest/687287d6d38668a3,Steamed rice\ntest/6874c333d0117774,Extreme sport\ntest/6877056277efe7c5,Ball game,Team sport\ntest/6878ee39b36f966b,Luxury vehicle,Antique car\ntest/687b504f9b020a02,Compact car,Luxury vehicle\ntest/687c67afe4559c41,Aircraft engine\ntest/68818bcdb159bf47,Luxury vehicle\ntest/688195dab1699a44,Nuclear power plant\ntest/688214377e886ea9,Extreme sport\ntest/6885ecce8f5917fa,Black-and-white\ntest/688684d39b56f0c5,Black-and-white,Close-up\ntest/6889815bee7d3ba7,Compact car,Luxury vehicle\ntest/688bc63e38fba7ff,Luxury vehicle,Performance car\ntest/688edd921a5fa5f1,Perching bird,Close-up\ntest/6890fcd4b647a7d4,Ball game,Team sport\ntest/6891d798737dbea3,Cosmetics\ntest/68928e80c69f47f4,Leaf vegetable,Vegetarian food\ntest/6892a978b052373a,Sport utility vehicle\ntest/689300488bffcd1b,Auto racing\ntest/689703043bf4ce05,Athletic shoe,Outdoor shoe\ntest/689a106df150a803,Ball game,Team sport\ntest/689ae1de21ba3d7c,Vegetarian food\ntest/689cfe4cf41eaace,Compact car,Luxury vehicle\ntest/689e17ccd947491c,Whiskers\ntest/68a1c26f3926a103,Black-and-white\ntest/68a2511c5a01bd02,Extreme sport,Wind wave\ntest/68a32d28ef629d9c,Military vehicle,Antique car\ntest/68a52a77ee7cc031,Rear-view mirror\ntest/68a6e5ee216d5625,Rural area\ntest/68af3907c65c6063,Luxury vehicle,Sport utility vehicle\ntest/68afc1b7225dfb28,Aerial photography\ntest/68b27d7dc76b4aac,Luxury vehicle,Performance car\ntest/68b3adf5bc2a4acf,Arthropod,Locust\ntest/68b67c9b866ffaa8,Athletic shoe,Outdoor shoe\ntest/68b6b634551b115d,Jeans\ntest/68b7b352e297f202,Boating\ntest/68bccb04e68da1cb,Shinto shrine\ntest/68c11ad4b642f1fc,Galleon\ntest/68c3071c9126e5d5,Ball game\ntest/68c65d100be40574,Hair coloring\ntest/68c688f4a1f3f22c,Road cycling\ntest/68c7e610799ac7f2,Luxury vehicle\ntest/68c87e823cd65e82,Extreme sport,Boating\ntest/68c9263cfde6a7c3,Sport utility vehicle\ntest/68d0503ad28cb545,Black-and-white\ntest/68d10e214482cd82,Luxury vehicle,Antique car\ntest/68d36a3fa8d2aa63,Motorcycling\ntest/68d688764bf8be0e,Compact car,Luxury vehicle,Antique car\ntest/68d6c68a585d5d47,Luxury vehicle\ntest/68d6fdcec78a5b4e,Calabaza\ntest/68d7eafe0febd706,Luxury vehicle,Sport utility vehicle\ntest/68d85f23acf08e5a,Team sport\ntest/68dd3c8561d18ca5,Extreme sport,Motorcycling\ntest/68deae7a44a02d90,Performance car\ntest/68dede7c8f35449d,Luxury vehicle\ntest/68dff0f21af2fde9,Gumbo\ntest/68e0451af1ddcd46,Fried food\ntest/68e14b88797af282,Luxury vehicle\ntest/68e27e7613dc9149,Extreme sport\ntest/68e2c6cc5498e3da,Arthropod\ntest/68e2e0ed173941f4,Aircraft engine\ntest/68e4cbb8bb60d80c,Extreme sport\ntest/68e5128dc6413a44,Dog walking\ntest/68e8544a698de0ab,Luxury vehicle,Performance car\ntest/68e8c2619a39e4b1,Nordic skiing\ntest/68e9176387cdc383,Machine tool\ntest/68e949bc07242752,Luxury vehicle\ntest/68e993e8bf8767ee,Leaf vegetable\ntest/68eb3bca6468d3a0,Sport utility vehicle\ntest/68ec6fe28a223060,Ball game,Team sport\ntest/68efdedeb8a1e9b3,Luxury vehicle,Sport utility vehicle\ntest/68f0f10d03391a4b,Sport utility vehicle\ntest/68f2dd057464428e,Auto racing,Performance car\ntest/68f57951104d266e,Rural area\ntest/68f60c6dd519aaa7,Close-up\ntest/68f66f90c8bd14b8,Aircraft engine\ntest/68f695d426d944a8,Camera operator\ntest/68fc56b5b09c8545,Close-up,Whiskers\ntest/68fc8f60ae88602c,Hair coloring\ntest/68fd91293d49b0d3,Compact car\ntest/68fe0a55028bc7cb,Close-up\ntest/68ff0b39dbc6574b,Orator,Public speaking\ntest/6901e63fa671a560,Luxury vehicle,Antique car\ntest/6904fd6dbcad110c,Arthropod,Close-up\ntest/6905b3a9f9d1adf2,Rural area\ntest/6905e899329b6088,Black swan\ntest/690713bce44a6beb,Leaf vegetable\ntest/6908ead10689925e,Motorcycling\ntest/690d9cb2a253749a,Close-up\ntest/690fd08f9b5af89c,Road cycling\ntest/690ff8b027449c60,Close-up\ntest/69115c6c9e33a271,Arthropod\ntest/6911803621170192,Cookies and crackers\ntest/69126cd48945f952,Close-up,Scaled reptile\ntest/691351efc7364b69,Luxury vehicle\ntest/6913b4564615285a,Horse and buggy\ntest/6914b2116958d3ae,Close-up\ntest/691855a4be516b06,Leaf vegetable\ntest/69197b77d9ced593,Close-up\ntest/691a0d7267f7eb99,Antique car,Luxury vehicle,Performance car\ntest/691a52600f88671d,Compact car,Luxury vehicle\ntest/691afdfd83fac743,Combat sport\ntest/691da901fd0fee25,Extreme sport,Personal water craft,Boating\ntest/691e29e939f8845b,Ball game\ntest/691f84d689f115a9,Luxury vehicle,Performance car\ntest/6920a79020b2ecc1,Antique car,Performance car\ntest/69212a3be11bf8aa,Close-up\ntest/6921d9c3f29e9a19,Extreme sport\ntest/6922ca7b8b8b5b74,Bratwurst\ntest/69240b8d8143246c,Close-up\ntest/6925b842e041b91f,Computer speaker\ntest/6926686bd23227e0,Black-and-white\ntest/6929758665b6c721,Compact car\ntest/6929cc04cd3c93ae,Ball game,Team sport\ntest/692a625ef8a209a6,Sport utility vehicle\ntest/693006f7e4168052,Shallot\ntest/6931012dc5da9510,Jeans\ntest/69356f7c0fe37289,Filling station\ntest/69389ae41761014c,Luxury vehicle,Antique car\ntest/6938a48f99027a5a,Steamed rice\ntest/6938acc7f0940976,Chordophone\ntest/693b6cfcbe799ea4,Close-up,Whiskers\ntest/693b7e641b213273,Black-and-white\ntest/693b85a1c6f42ce1,Fried food\ntest/693ced039973dbbe,Floristry\ntest/693d4aba732d2789,Jeans\ntest/693db91f26bd867d,Sport utility vehicle\ntest/693fcbae2da30060,Performance car\ntest/69409713958604c2,Molluscs\ntest/69412b77c5451883,Cookies and crackers\ntest/6944532c45b779a4,Bovine\ntest/6944c4a13edc9eb1,Monoplane\ntest/69473a831086e572,Roller skating\ntest/694b4b8345e7b039,Brochette\ntest/694bda7284777ac1,Rural area\ntest/694c655147f6812c,Black-and-white,Close-up\ntest/694c732ffc00ed9e,Luxury vehicle\ntest/694d2cee8c96bc3a,Blond,Close-up\ntest/6950ea4e693d4034,Auto racing,Performance car\ntest/69518d812027669d,Luxury vehicle\ntest/6951d91c9d75c17a,Luxury vehicle,Performance car\ntest/69527747026688cb,Coca-cola\ntest/6952dac6207ef51f,Sport utility vehicle\ntest/69535cbe03133735,Extreme sport\ntest/6953c1bc1232b2ed,Cosmetics\ntest/695648c6ac5e7ef3,Luxury vehicle,Sport utility vehicle\ntest/6956eee4557abeb9,Aircraft engine\ntest/69572bbc3cbcc7cf,Performance car\ntest/695822f51c5b8839,Sledding\ntest/695983cb65e1deee,Black-and-white\ntest/695b2f8b5b51fd34,Road cycling\ntest/695d408df6594100,Compact car,Luxury vehicle\ntest/695e51cbc5f1c31e,Military vehicle\ntest/695e950607e5266e,Bird of prey\ntest/6961d9e9dd30a367,Freight transport\ntest/6962f90f46c4d987,Arthropod,Close-up\ntest/69644022f9540ba6,Luxury vehicle\ntest/6964f157b51d4272,Wind wave\ntest/69660236b20cb2ae,Aerial photography\ntest/696a2f93b2124ed5,Vegetarian food\ntest/696a5f83e08c3798,Miniature Poodle\ntest/696bdc8962ab1d82,Luxury vehicle,Performance car\ntest/696f5fd6a2c9aa63,Antique car\ntest/696f831bbd1bd19c,Billiards\ntest/6972a179f5b37a11,Jeans\ntest/69743be11b6b926a,Jeans\ntest/69757bb560514cf8,Leaf vegetable\ntest/69787ec7be793ad9,High-speed rail\ntest/69790496f370b01f,Hound\ntest/697918b27c87dafb,Close-up,Whiskers\ntest/697ba5a67e19f77a,Luxury vehicle\ntest/697d8c867478c813,Plant stem\ntest/697e77d50b730880,Close-up\ntest/697f7782cbdad848,Black-and-white\ntest/69812068f1d6cf0e,Luxury vehicle\ntest/6983262abece8039,Miniature Poodle\ntest/6983bede3febf2f8,Luxury vehicle,Performance car\ntest/6983e1b2c3d31487,Arthropod,Close-up\ntest/69854a03f13852fe,Compact car,Luxury vehicle,Sport utility vehicle\ntest/6986416dd4ecd0d9,Boating\ntest/69890a0e89b96a56,Jiaozi,Fried food\ntest/698b336fb57b2864,Water polo\ntest/698eb840727be062,Aerial photography\ntest/698eeecc52ea6893,Performance car,Antique car\ntest/6990a6bd0db41122,Ball game,Team sport\ntest/69914216c2d2a1f4,Erinaceidae\ntest/69916abbfd4952ba,Woodwind instrument\ntest/6992b3851c4e3181,Vegetarian food\ntest/699495c1e899a064,Luxury vehicle,Sport utility vehicle\ntest/69956927ae5cbf72,Compact car\ntest/6996d3bcf2449fad,Compact car\ntest/699c2e752b03d440,Antique car\ntest/699d771861a0a105,Luxury vehicle\ntest/699daa083260f70a,Rural area\ntest/699ed0366ad39614,Billiards,Billiard room\ntest/69a36d376a05d809,Frying,Junk food,Fried food\ntest/69a3ceb527ef3ce3,Luxury vehicle,Sport utility vehicle\ntest/69a76ad77ea06ce6,Arthropod,Close-up\ntest/69a8d87e1141ee8a,Electronic instrument\ntest/69a985506a4cca1d,Nile crocodile,Crocodilia\ntest/69a9c8b064aa5948,Aerial photography\ntest/69ab5ed9f1480aff,Scaled reptile\ntest/69ab9f3fe434dbc5,Extreme sport\ntest/69abb5959da5722c,Auto racing\ntest/69ada6e82c8553d9,Beef tenderloin\ntest/69b10a5f11fdd7bf,Microcontroller\ntest/69b2a7db7118c608,Antique car\ntest/69b2dd64466c32c1,Equitation,Equestrianism\ntest/69b35e92d763ced2,Electronic instrument\ntest/69b389b59a7c89dc,Antique car\ntest/69b5783e2a39e03a,Fried food\ntest/69b8cfca3dee9994,Jeans,Black-and-white\ntest/69b92368c9caf919,Monoplane\ntest/69b9d5f10de61287,Ball game\ntest/69ba3c74d72d4529,Sailboat racing\ntest/69ba5e35347bd4ee,Bovine\ntest/69bb15bb6b06a217,Close-up\ntest/69bc1a31527b5a09,Luxury vehicle,Performance car\ntest/69bc2a198e0b647e,Floristry\ntest/69bc4b2e52e4f9c6,Compact car\ntest/69bcd71dd6ed84e3,Compact car\ntest/69c2a7cc76892c1e,Auto racing\ntest/69c2c189e248fe19,Blond,Briefs\ntest/69c39363f746a7f9,Performance car,Antique car\ntest/69c5d09b0ec8346c,Leaf vegetable\ntest/69c6f9f5e80cf21e,Close-up\ntest/69c83e7740ebeddc,Antique car,Performance car\ntest/69c9ed908634a53b,Solar energy\ntest/69ca229f38ee8df3,Aircraft engine\ntest/69cdb1ea7269fecb,Extreme sport,Sport climbing\ntest/69cf1abd845f6dbe,Luxury vehicle\ntest/69d20e8a830043dd,Close-up\ntest/69d32504800238e1,Ball game\ntest/69d4387d491bccf9,Musical theatre\ntest/69d4b6de2e2b3d30,Luxury vehicle\ntest/69d4bbbc5358b064,Performance car,Antique car\ntest/69d4c0bf941a127c,Junk food,Cookies and crackers\ntest/69d58532aca1402f,Performance car,Antique car\ntest/69d619fe153038ba,Close-up\ntest/69d7e06b6733f6a3,Arthropod,Plant stem,Close-up\ntest/69d82cc4d581f81d,Close-up\ntest/69d957e1bc748655,Compact car,Luxury vehicle\ntest/69d9aaac8429e005,Black-and-white,Medical imaging\ntest/69d9df0defa9a4ae,High-speed rail\ntest/69db83fa9fb6bdbf,Chordophone,Electric guitar\ntest/69dc923ad680bc80,Close-up\ntest/69e11d5cba60b499,Compact car,Antique car\ntest/69e343aac8193300,Luxury vehicle\ntest/69e4f210d2d694e3,Close-up\ntest/69e525466c48dfe9,Plant stem\ntest/69e5cfd39bedf484,Blond,Portrait photography\ntest/69e670d7ecb21849,Close-up\ntest/69e7b900eca6c610,Blond\ntest/69e8bba972867f96,Rural area\ntest/69e9f5451f4854d5,Close-up\ntest/69ebf1893ec9592b,Ale\ntest/69f01c3ba009ac96,Close-up,Whiskers\ntest/69f044286c8fd6f8,Rural area\ntest/69f058316999135b,Antique car,Sport utility vehicle\ntest/69f0a6d5855d3ae4,Chordophone,Electric guitar\ntest/69f164474f92a4be,Domestic short-haired cat,Whiskers\ntest/69f206e0ce9165dc,Ball game,Team sport\ntest/69f22fea9da0590d,Compact car,Luxury vehicle,Antique car\ntest/69f25249aa5bbfd1,Coral reef fish\ntest/69f3086e46e7dbb6,Hurdling\ntest/69f3326be773030b,Fried food\ntest/69f79465ffa096f2,Luxury vehicle,Performance car\ntest/69f7b6270f245ba5,Luxury vehicle,Sport utility vehicle\ntest/69f7def2b491339f,Residential area\ntest/69f955b20c4aae30,Domestic short-haired cat,Whiskers\ntest/69fa34f9380ca170,Compact car\ntest/69fa6be5f0ecb7d9,Outdoor shoe\ntest/69fafbd6f209e52f,Compact car\ntest/69fca175d043f227,Performance car\ntest/69fd26df1c3d857e,Cookies and crackers\ntest/69fe88a79d6c220b,Motorcycling\ntest/69ff15add2a30111,Close-up\ntest/69ff376c92b69dc0,Double-decker bus\ntest/6a01b949ef0b3a3a,Goats,Close-up\ntest/6a01ed60ea28c962,Black-and-white\ntest/6a022a9e9f413293,Stemware\ntest/6a02328e9eb99ac0,Antique car\ntest/6a02d9994f89c7c9,Luxury vehicle\ntest/6a02f1340c1b0220,Black-and-white\ntest/6a0380a0f5d60f3e,Luxury vehicle\ntest/6a0690e18df55ce9,Extreme sport\ntest/6a07611b2fa889ca,Luxury vehicle\ntest/6a0b731a40edca9b,Plant stem\ntest/6a0bea314bb99b45,Luxury vehicle,Antique car\ntest/6a0ea0037b4d27f1,Jeans\ntest/6a0ea300d03570a1,Chordophone,Electric guitar\ntest/6a0ea669069b26a3,Boating\ntest/6a0eb466b550a71f,Antique car\ntest/6a10ebde7b0464a9,Stemware\ntest/6a12ebca5bbeeb4e,Auto racing,Compact car\ntest/6a15f6d942e7228a,Close-up\ntest/6a174537746145b1,Amphibian,Close-up\ntest/6a17c7fd56af8fae,Compact car,Luxury vehicle\ntest/6a1a954a97d9fd55,Fried food\ntest/6a1f55a52e0b14c4,Auto racing\ntest/6a1fed998aae9775,Coral reef fish\ntest/6a2013d3372a6262,Equitation,Equestrianism\ntest/6a22f838ac210fe6,Wind wave\ntest/6a255d214af18554,Jeans\ntest/6a275e7426b5c4f2,Close-up,Plant stem\ntest/6a29caa85151f256,Vegetarian food\ntest/6a2ae05459a1576e,Milky way\ntest/6a2b3b4ceba97b82,Fried food\ntest/6a309e95ae691574,Compact car,Luxury vehicle\ntest/6a3336ec45a6cb2c,Close-up\ntest/6a347a1cb0b401d1,Pit bull\ntest/6a34a8bdc0f0e839,High-speed rail,Black-and-white\ntest/6a34adf788d504f7,Hound\ntest/6a36cc5ff9ff083a,Extreme sport,Boating\ntest/6a3706bea327427e,Residential area\ntest/6a3773db1a99673a,Luxury vehicle,Performance car\ntest/6a385151935a5b9f,Touring car,Luxury vehicle,Antique car\ntest/6a39def4ca81a7c9,Vegetarian food\ntest/6a3b57a9da66934d,Compact car\ntest/6a3e9969c245d528,Boating\ntest/6a42fa79ac46d68f,Computer speaker\ntest/6a462fcaef83f8d2,Jiaozi,Pelmeni\ntest/6a4703928e62e348,Coral reef fish\ntest/6a49b9690202c5ef,Portrait photography\ntest/6a4d52a3786a10c6,Bovine\ntest/6a4dfb733918bdb7,Horse and buggy\ntest/6a501c781cc467d0,Close-up\ntest/6a53e938f8ee0c9b,Boating\ntest/6a5513d7a6eaae3b,Plant stem,Close-up\ntest/6a55188c5b91b553,Perching bird,Close-up\ntest/6a59c5402e3f461e,Aerial photography\ntest/6a5bd6de83c44856,Sport utility vehicle\ntest/6a5c3803a85ec675,Close-up\ntest/6a5d246c3e2bc194,Black-and-white\ntest/6a5f00f1d5a3f8d3,Compact car\ntest/6a5ffb03f58aa037,Vegetarian food,Corn on the cob\ntest/6a60106810b580ed,Close-up\ntest/6a615b3fe2d6249d,Aircraft engine\ntest/6a61da5ffe0a2205,Military person\ntest/6a624ae558b088fe,Rear-view mirror\ntest/6a6845c60819e24b,Close-up,Whiskers\ntest/6a69ad582894b353,Sport utility vehicle\ntest/6a69da675a60eef9,Luxury vehicle,Performance car\ntest/6a6c5596f138cb1f,Ball game,Team sport\ntest/6a6c63a50a0c1931,Jeans\ntest/6a6d49336578881d,Close-up\ntest/6a6e42637a86b80c,Plant stem\ntest/6a70483c7b7e3b4e,Extreme sport,Wind wave\ntest/6a725a25bb2ad286,Leaf vegetable,Vegetarian food\ntest/6a73b4c0173054bf,Sport utility vehicle\ntest/6a742895128c18e5,Close-up\ntest/6a74ac616faffa9a,Arthropod,Close-up\ntest/6a75652895905810,Luxury vehicle\ntest/6a7745c9f562a645,Vegetarian food\ntest/6a78fe5a3d94a64f,Modern dance,Musical theatre\ntest/6a7936e299c3d198,Antique car\ntest/6a7d11d3f0dbcea6,Steamed rice\ntest/6a7feba262ff859f,Cookies and crackers\ntest/6a82985a81d074e8,Galleon,Barquentine,Frigate\ntest/6a844cb211cbaa11,Amphibian,Close-up\ntest/6a85e01469bf40db,Sport utility vehicle\ntest/6a8885bbdfdbc5f7,Chordophone\ntest/6a896e1c6e98756e,Bird of prey\ntest/6a8a82c16615d73c,Inflatable\ntest/6a8a8abfbe719696,Floristry\ntest/6a8ae584f41b3616,Brochette,Yakitori\ntest/6a8cbe4949e538c2,Vegetarian food,Corn on the cob\ntest/6a8ed5ccba0ed83a,Luxury vehicle\ntest/6a8ef5b34717d849,Hound\ntest/6a9094c620014dca,Headphones\ntest/6a9307b291a0ba04,Performance car,Luxury vehicle,Antique car\ntest/6a932c293ee35ce6,Digital camera\ntest/6a9677a61c57b2e6,Luxury vehicle,Performance car\ntest/6a96b115d99633ea,Molluscs,Close-up\ntest/6a98702a836e1c71,Auto racing\ntest/6a989dc454ea8c83,Lumpia\ntest/6a98e204ff926bcf,Combat sport\ntest/6a9bb06320a811de,Water polo,Ball game,Team sport\ntest/6a9dc620a00b5c4b,Close-up\ntest/6aa01cd6310a6a6b,Close-up\ntest/6aa0534f9572b5de,Monoplane,Aircraft engine\ntest/6aa11ff4834e6732,Antique car\ntest/6aa2743bc86f7ca8,Jeans\ntest/6aa407f3acf1c0cc,Domestic short-haired cat,Whiskers\ntest/6aa5310e7a227717,Close-up\ntest/6aa56a9a2c851e04,Sport utility vehicle\ntest/6aa585480d70c24a,Close-up\ntest/6aa67b4aef29c578,Aerial photography\ntest/6aa6ff3a5907e254,Dobermann\ntest/6aa779c772a4db45,Extreme sport\ntest/6aa93283292ba6f3,Performance car\ntest/6aac1291d2dac220,Racewalking\ntest/6aac917ad0c61103,Melee weapon,Hunting knife\ntest/6aad3e4fe92487f3,Chordophone\ntest/6ab5c62117de2848,Luxury vehicle,Antique car\ntest/6ab7e9716b0720f9,Athletic shoe\ntest/6ab83b4eb68c92e2,Extreme sport\ntest/6abda28b0e6820fb,Compact car,Luxury vehicle\ntest/6abdb7bb1cfc449c,Leaf vegetable\ntest/6abe4ea67d908b55,Boating\ntest/6abe7dc211efbef5,Black-and-white\ntest/6ac0782824334fb5,Close-up\ntest/6ac07c7f1033137c,Hurdling\ntest/6ac3142b2b29ab6b,Road cycling\ntest/6ac5f1fbb13a8ba3,Luxury vehicle,Sport utility vehicle\ntest/6aceb827422a0a07,Sledding\ntest/6acf1b95f9c41e9a,Lawn game,Ball game\ntest/6acf51bfa246f79e,Perching bird,Close-up\ntest/6ad2dd5d738b2155,Ball game\ntest/6ad2fb9e0739e8d0,Compact car,Luxury vehicle,Sport utility vehicle\ntest/6ad5881023a7de6f,Luxury vehicle\ntest/6ad5d54fbca92f55,Luxury vehicle\ntest/6ad899899bd312be,Hair coloring\ntest/6ad8b2896b439e60,Black-and-white,Compact car,Luxury vehicle\ntest/6ad98b978a85c64d,Close-up\ntest/6adb138c43ff4e2a,Rural area\ntest/6add52345afb8feb,Combat sport\ntest/6adf4d6e9f7a8f44,Compact car,Antique car\ntest/6ae041dbe56f6f17,Aerial photography\ntest/6ae11f063590decb,Ball game,Team sport\ntest/6ae1a5e0973c0547,Arthropod,Close-up\ntest/6ae2bd9a29bf3286,Luxury vehicle\ntest/6ae470a7c9ec20fd,Chordophone\ntest/6ae4778af509cefa,Solar energy\ntest/6ae5571b26ce1802,Hound,Close-up\ntest/6ae70dc3f1611664,Miniature Poodle\ntest/6aea64be3ea593ec,Hound\ntest/6aedcc9fb9eff6ff,Close-up\ntest/6aef206e9405ce5f,Miniature Poodle\ntest/6aef7a11d43bf213,Ale\ntest/6aefabece2ae4d18,Close-up\ntest/6af12fc57f36ad33,Rural area\ntest/6af230a48ae7b94a,Luxury vehicle,Sport utility vehicle\ntest/6af24def008e6ddf,Steamed rice\ntest/6af297ba77031013,Sport utility vehicle\ntest/6af388242e17d331,Close-up\ntest/6af3af47ef671b80,Vegetarian food\ntest/6af47bfc27322f41,Plant stem\ntest/6af5b34dccbab966,Compact car,Antique car\ntest/6af602f233bc7cf6,Fried food\ntest/6af69ec1aa5723d6,Close-up\ntest/6af80a4505830f95,Electronic instrument\ntest/6af85937fe2c6780,Close-up\ntest/6af9c037d67c1e5e,Sport utility vehicle\ntest/6afb2c69e20048c9,Luxury vehicle\ntest/6aff0c022d63e8d2,Sport utility vehicle\ntest/6b0282b114983489,Khinkali\ntest/6b052e0c096b65a1,Monoplane\ntest/6b0532e284787555,Aircraft engine\ntest/6b07f56d896a3994,Luxury vehicle\ntest/6b085c6b4c3027b1,Double-decker bus\ntest/6b09aaa99dfb6b41,Luxury vehicle,Antique car\ntest/6b0adf9cd28d9a28,Flatbread\ntest/6b0e64a1356cf530,Scaled reptile\ntest/6b0e6ae78da0d206,Luxury vehicle\ntest/6b0fffbc5d37d8e9,Performance car\ntest/6b14464c6deb997b,Solar energy\ntest/6b149d9b21cf50e2,Ball game,Team sport\ntest/6b16f19c8b6b1b1c,Roller skating\ntest/6b170f280fa50242,Jeans\ntest/6b1863bfc699f208,Auto racing\ntest/6b1c8059e8d9b4a0,Domestic short-haired cat,Whiskers\ntest/6b1d456b0c16af51,Halter\ntest/6b1d46ad55e7cf4d,Electronic instrument\ntest/6b1dda155f632ae1,Plant stem,Close-up\ntest/6b1e8ade1d0411f4,Ball game,Team sport\ntest/6b1fe271379bc466,Fried noodles\ntest/6b238cb46e747d16,Luxury vehicle,Performance car\ntest/6b243472d75334b0,Rural area\ntest/6b27b3ffb904f51f,Figure skating\ntest/6b281aa376598ac3,Middle ages\ntest/6b28c54e8973a8a5,Gliding\ntest/6b2bece903fba48c,Close-up\ntest/6b2c8099b8d547af,Arthropod\ntest/6b2cc9f6e066b0d6,Performance car\ntest/6b2cf8a135975ea3,Scaled reptile,Close-up,Anole\ntest/6b2e063c1f7c38c9,Luxury vehicle,Antique car\ntest/6b2ec5d4e2a7a303,Black-and-white,Modern dance\ntest/6b2f5478b1fffdee,Luxury vehicle\ntest/6b30c17c967ff393,Antique car\ntest/6b32ac9e0480997d,Sport utility vehicle\ntest/6b33ca942224341c,Blond,Portrait photography,Close-up\ntest/6b363cff743436bd,Luxury vehicle,Performance car\ntest/6b3789b733016f7a,Military person\ntest/6b398dd323ee7ca7,Black-and-white\ntest/6b3b4395d7546e65,Jeans\ntest/6b403735ff45b370,Aircraft engine\ntest/6b40381efa03c346,Close-up\ntest/6b40b0e181843c6e,Monoplane\ntest/6b43582b6443928b,Goats\ntest/6b43a9f00479b61c,Antique car,Performance car\ntest/6b469da3c495d920,Luxury vehicle,Sport utility vehicle\ntest/6b484a5f6066d4a5,Junk food\ntest/6b487a6c8b60c175,Combat sport\ntest/6b492b5536ecb9a3,Digital camera\ntest/6b4a560cbc52d8f7,Procyonidae\ntest/6b4aefbfefae8e89,Watercolor paint\ntest/6b4baf2e15a8891b,Aircraft engine\ntest/6b4bfc3ed3218a9c,Chordophone\ntest/6b4c1535ec7357b9,Extreme sport\ntest/6b4f103abe0d6a9b,Close-up,Floristry\ntest/6b50347941951b77,Extreme sport\ntest/6b50eea699564204,Close-up\ntest/6b510b7cac74a1bb,Junk food,Fried food\ntest/6b511e7d95b2d182,Outdoor shoe\ntest/6b536423dc8f6ddd,Bratwurst\ntest/6b5631c83c023881,Arthropod,Close-up\ntest/6b569fa82af25787,Melee weapon\ntest/6b56eac1651d355f,Barechested\ntest/6b57495d7701a540,Compact car\ntest/6b598e4d45e3c8a3,Watercolor paint\ntest/6b5b2368a1927619,Jeans\ntest/6b5b9b103c6e9eba,Vegetarian food\ntest/6b5bca9940344f7d,Whiskers\ntest/6b5c7e7b5aef8d62,Aerial photography\ntest/6b5d8a12644716f5,Sport utility vehicle\ntest/6b5f96b35bfd81fb,Fried noodles\ntest/6b5fb52f21e969a2,Luxury vehicle,Antique car\ntest/6b6114e958b20063,Galleon,Barquentine\ntest/6b61aa038cc8da54,Arthropod\ntest/6b62d95178daddd9,Sport utility vehicle\ntest/6b63946119978b5e,Breadboard\ntest/6b67474eacb1a9fa,Roman temple\ntest/6b683671a7668aeb,Divemaster,Scuba diving,Underwater diving\ntest/6b6a5e1dda006547,Luxury vehicle,Antique car\ntest/6b6a96adc59ce730,Bagpipes\ntest/6b6a9e01ab46404c,Double-decker bus\ntest/6b6db1f14a58e02a,Horse racing,Equestrianism\ntest/6b6db40698526d92,Luxury vehicle,Performance car\ntest/6b6e5a284a1acf86,Close-up\ntest/6b74fd610991df9e,Performance car,Antique car\ntest/6b75773a76a78f7e,Antique car\ntest/6b757be86ab51366,High-speed rail\ntest/6b792f8539a62e67,Bovine\ntest/6b7b01d58c37094a,Motorcycling\ntest/6b7b532fbac2bbe9,Black-and-white\ntest/6b7c09cc768e8537,Ball game,Team sport\ntest/6b7d15cfd5f9feaa,Halter,Equestrianism\ntest/6b7d1a48d1b4f80d,Close-up\ntest/6b7d3fba64355352,Hound\ntest/6b7fb7d9c4d4db6e,Chordophone\ntest/6b802794958f09b7,Luxury vehicle,Antique car\ntest/6b838559a6fd6444,Motorcycling\ntest/6b838b30a5086a83,Black-and-white,Firearm\ntest/6b856f2c4e3a4a18,Senior citizen\ntest/6b86e4b2f29df0c6,Close-up\ntest/6b877c3d20738b20,Shallot\ntest/6b88e34464fe754d,Blond\ntest/6b8ae8962a05a5dd,Sport utility vehicle\ntest/6b8b19b9c7cfeeee,Nuclear power plant\ntest/6b8d3d4ced312fc6,Fried food\ntest/6b8e2694fbda986c,Microcontroller\ntest/6b8f5518cfb9cbf0,Machine tool\ntest/6b8f8c8a3831df9c,Black-and-white\ntest/6b9199557938af32,Black-and-white,Extreme sport,Motorcycling\ntest/6b923d8500500e5b,Luxury vehicle,Performance car\ntest/6b934c2fedcf1518,Luxury vehicle\ntest/6b93d9ebec1d6590,Newsagent's shop\ntest/6b9683cd4dbde49f,Luxury vehicle,Performance car\ntest/6b9937ce8b652cd1,Compact car,Antique car\ntest/6b99760f0923ea9a,Woodwind instrument\ntest/6b9ad0ba2e7e9cbf,Military vehicle,Luxury vehicle\ntest/6b9b309d8cf86ec8,Black-and-white\ntest/6b9b7d90162eeb65,Luxury vehicle\ntest/6b9c790a7ca6be69,Luxury vehicle,Performance car\ntest/6b9c9e5de59abfe1,Antique car\ntest/6b9d3856e1f5b1cc,Domestic short-haired cat,Close-up,Whiskers\ntest/6b9e10b39030d201,Luxury vehicle,Performance car\ntest/6b9f18b0fd1ae1a9,Hurdling\ntest/6b9f626b44f01415,Close-up\ntest/6b9f8b4117859a7e,Ball game,Team sport\ntest/6ba151ddbe81a3db,Luxury vehicle,Performance car\ntest/6ba18bfae61c930a,Sport utility vehicle\ntest/6ba47b78b9cb3bfe,Close-up\ntest/6ba4d7b14ad15ad0,Extreme sport,Parachuting\ntest/6ba813d62f036273,Close-up,Whiskers\ntest/6ba919d4793a19d1,Stemware,Close-up\ntest/6bab517eef89e4fa,Antique car,Performance car\ntest/6bac09629b0acced,Ball game,Team sport\ntest/6bac7d988efa7665,Musical theatre\ntest/6bb09465f9cc26ae,Luxury vehicle\ntest/6bb3124e76b4b1c8,Sport utility vehicle\ntest/6bb6ee6923afb055,Rural area\ntest/6bb8dd205265411e,Performance car,Antique car\ntest/6bba14e1ab2e587f,Dishware\ntest/6bbaf69a77dbd8ab,Luxury vehicle,Performance car\ntest/6bbc465b0d511e30,Arthropod,Close-up\ntest/6bbda8eb1a5309d7,Close-up\ntest/6bc89b9744a6a054,Ball game,Team sport\ntest/6bcb424a8d7eb968,Close-up\ntest/6bcbc288a16710f3,Luxury vehicle,Antique car\ntest/6bcc7f361068d715,Close-up,Whiskers\ntest/6bcd84559ccfde60,Toy block\ntest/6bd0a5a3b3753144,Close-up\ntest/6bd1c08f75828ef6,Compact car,Luxury vehicle\ntest/6bd24424266ea366,Luxury vehicle\ntest/6bd30c5468094263,Sledding\ntest/6bd567e027ea8283,Auto racing\ntest/6bd60a7ecc87efd4,Compact car,Luxury vehicle,Antique car\ntest/6bd777dccd366d3b,Luxury vehicle,Sport utility vehicle\ntest/6bda020500530e35,Fried food\ntest/6bdc776787b8b254,Plant stem\ntest/6be10f49422ebddf,Close-up\ntest/6be337c7a1a03b5d,Compact car,Luxury vehicle,Antique car\ntest/6be7bfce8dc43d56,Hound\ntest/6be850f81492f89c,Auto racing,Performance car\ntest/6be976a467d8e660,Close-up\ntest/6bea8127ccde5dab,Black-and-white\ntest/6bed385616414925,Computer speaker\ntest/6beff463643cb774,Fried noodles,Soba\ntest/6bf05bb5f4e201e7,Hurdling\ntest/6bf13ba5d719c24a,Auto racing,Touring car,Luxury vehicle,Performance car\ntest/6bf47a5d62845984,Woodwind instrument\ntest/6bf70ee8d2e4400d,Black-and-white,Machine tool\ntest/6bfaddfe83cfdba6,Equitation,Equestrianism\ntest/6bfb9333886876e4,Luxury vehicle,Performance car\ntest/6bfc0e774a5266d3,Black-and-white\ntest/6bfe9d26db84585a,Close-up\ntest/6c00eba5d56766cc,Equestrianism\ntest/6c061ac3528b5e05,Antique car\ntest/6c06ee5ef941b715,Electronic instrument\ntest/6c076d7afb8bc1dc,Close-up\ntest/6c0c4302f8fa01e7,Stemware,Black-and-white\ntest/6c0ed3ddee948768,Luxury vehicle,Sport utility vehicle\ntest/6c117afd587f5339,Computer speaker\ntest/6c12755022583a68,Luxury vehicle,Sport utility vehicle\ntest/6c14b78b245c2e16,Racewalking\ntest/6c15e5fada1dd51a,Compact car\ntest/6c169e5107009a91,Luxury vehicle,Performance car\ntest/6c195e7c6aa1c0e5,Domestic short-haired cat,Whiskers\ntest/6c1990cdbac97393,Luxury vehicle,Sport utility vehicle\ntest/6c19a16fb7940aa2,Military person\ntest/6c217757caa59d1a,Ball game,Team sport\ntest/6c2259924e6160c1,Whiskers\ntest/6c26bcfb0676c076,Sport utility vehicle\ntest/6c277649b0f8b9c0,Black-and-white\ntest/6c27f89d9081572e,Jeans,Extreme sport\ntest/6c29933ec8698de5,Domestic short-haired cat,Whiskers\ntest/6c299f32b8c3c70e,Whiskers\ntest/6c2df42b38dc6eee,Wind wave\ntest/6c2e0846677932ff,Vegetarian food\ntest/6c2f9bdd3fa7ed4f,Luxury vehicle,Antique car\ntest/6c30a118b52c2617,Black-and-white\ntest/6c328074006812c7,Residential area\ntest/6c330cd63eda8b18,Luxury vehicle,Performance car\ntest/6c34c8de8e14302a,Racewalking\ntest/6c35a6627a159052,Gumbo\ntest/6c36403731e595c3,Musical theatre\ntest/6c36575e675df78a,Close-up\ntest/6c36a837bae096ac,Newsagent's shop\ntest/6c373099abfd16c8,Extreme sport\ntest/6c379725ff7a9219,Briefs\ntest/6c3ad653b3048dfc,Close-up\ntest/6c3cec29b6573779,Public speaking\ntest/6c3d173b53d55980,Billiards\ntest/6c3d805ec8b0d5ff,Close-up\ntest/6c3eabdaa7522755,Team sport\ntest/6c421ecb0cb2ab77,Close-up\ntest/6c434c2c219f5bb1,Dog walking\ntest/6c43d3a95d1e7a37,Close-up\ntest/6c448c8175631fba,Vegetarian food\ntest/6c46adc07d5542e2,Military person\ntest/6c4bb0d588a0f93b,Plant stem\ntest/6c4d3de5550c2e36,Close-up,Plant stem\ntest/6c4fcc13ed75234a,Close-up\ntest/6c53c0c19d806810,Compact car,Luxury vehicle,Antique car\ntest/6c5447588492e6b7,Close-up\ntest/6c5595ad5b08b309,Close-up\ntest/6c5816441b74cdca,Compact car,Luxury vehicle,Antique car\ntest/6c5822a462dfaff2,Luxury vehicle,Performance car\ntest/6c589d53cb814566,Extreme sport,Boating\ntest/6c58e82313c30c4f,Amphibian\ntest/6c59a4657bce74f0,Newsagent's shop\ntest/6c5b9f62085d1f26,Luxury vehicle\ntest/6c5c5154e99df06a,Milky way\ntest/6c5d7718865dfd69,Aircraft engine\ntest/6c5e5c29ea556692,Sport utility vehicle\ntest/6c5ee16be8efc2a9,Arthropod,Close-up\ntest/6c60b9b5c4c21d77,Gumbo\ntest/6c6247f104e167ba,Trampolining\ntest/6c62d80e3ee868dd,Residential area\ntest/6c644e0b8f0b858e,Boating,Rowing\ntest/6c64f9658923b307,Dishware\ntest/6c6815408ed9880a,Aircraft engine\ntest/6c684b41ef9e4453,Portrait photography\ntest/6c688879812afe2c,Horse and buggy\ntest/6c68d98230d366fd,Leaf vegetable,Fried food\ntest/6c698618125b663b,Miniature Poodle\ntest/6c6b2d7f1aca47c4,Auto racing,Antique car,Performance car\ntest/6c6d48a7c30374fe,Milky way\ntest/6c6fd62f3906e988,Close-up\ntest/6c704aba837f9065,Combat sport\ntest/6c709b175fc6415a,Black-and-white\ntest/6c72c113aaa46606,Ball game\ntest/6c74fe675195d596,Cookies and crackers\ntest/6c76464773df1d6d,Plant stem\ntest/6c776e4e67498fe2,Combat sport\ntest/6c797721287c8b40,Flatbread\ntest/6c7d470e3ad922a3,Close-up\ntest/6c7dfc6377d291e2,Canola\ntest/6c7e18c6a226cd3b,Canola\ntest/6c7f63b9decf780d,Ball game\ntest/6c7fd3e5f753e3ab,Luxury vehicle\ntest/6c8173433817d319,Antique car\ntest/6c8335f0aea8d14f,Luxury vehicle,Performance car\ntest/6c834f1a1ba3b936,Scaled reptile\ntest/6c83a915e56e27d2,Aircraft engine\ntest/6c85486e9ea2274d,Extreme sport,Wind wave\ntest/6c85ab12f412e728,Sport utility vehicle\ntest/6c86689ec98b771d,Close-up,Whiskers\ntest/6c871ac98cd21d04,Melee weapon\ntest/6c88c160d7491cca,Close-up\ntest/6c89562b7921361f,Whiskers\ntest/6c8bf19ed2f39b97,Comics\ntest/6c8c6592b1a55c01,Close-up\ntest/6c8d0a096e9a496e,Vegetarian food,Junk food\ntest/6c8e234440f22261,Arthropod,Close-up\ntest/6c8eb8305d94e4f4,Sport utility vehicle\ntest/6c90066d76b73d46,Forklift truck\ntest/6c906fe112f15285,Ball game,Team sport\ntest/6c9096097de1cfb6,Wind wave\ntest/6c9144d1bbcbafdf,Ball game,Team sport\ntest/6c940bebdf7f8c8d,Close-up\ntest/6c953580a13d5841,Plant stem\ntest/6c95cfdfe77272d3,Equestrianism\ntest/6c9b4dc892973965,Black-and-white,Full moon\ntest/6c9d0a040153e941,Close-up\ntest/6c9f82c673b24f5e,Close-up\ntest/6c9fed4dca104e14,Luxury vehicle\ntest/6ca118a6e509be03,Compact car,Luxury vehicle\ntest/6ca15c8a5ed0009d,Close-up\ntest/6ca27b11eb161cbc,Boating\ntest/6ca313e89614215c,Extreme sport,Scuba diving\ntest/6ca3c1e2063f6132,Close-up\ntest/6ca3c788b59abe88,Arthropod,Close-up\ntest/6ca5fb4d1a5cfe53,Briefs\ntest/6ca63ee185856c3d,Military person\ntest/6caa8f25d1018be4,Team sport\ntest/6cae6a34a43a1906,Black-and-white\ntest/6cafd486c06d92dc,Auto racing\ntest/6cb10e425b25ba0b,Stemware\ntest/6cb12b25befa7ea1,Sailboat racing\ntest/6cb3211b31b2b71c,Arthropod,Close-up\ntest/6cb392edde17e717,Close-up\ntest/6cb393bb6dbf6c5b,Luxury vehicle\ntest/6cb3efeb423acf89,Compact car,Luxury vehicle\ntest/6cb53a3ebe5017f0,Milky way\ntest/6cb54fd6e4b8ecbe,Combat sport,Grappling\ntest/6cb6914aa7d5398b,Compact car,Luxury vehicle\ntest/6cb72b8ac80c1206,Bovine\ntest/6cb77d0b00b00628,Whiskers\ntest/6cba8ed184ca214b,Glacial landform\ntest/6cba995f9ed2ff4b,Black-and-white\ntest/6cbcbf962f236015,Sport utility vehicle\ntest/6cbcf2110acf60fd,Luxury vehicle,Sport utility vehicle\ntest/6cbdc8e8ea1fb25e,Peafowl\ntest/6cc2c40fcb60f8ca,Senior citizen\ntest/6cc2fd15035e7f4e,Extreme sport\ntest/6cc390c1f049e222,Antique car\ntest/6cc4737e0b22b264,Milky way\ntest/6cc4b38c9bd04039,Ball game,Team sport\ntest/6cc502fd78954a32,Auto racing\ntest/6cc774366b716a86,Black-and-white\ntest/6cc9dfe1f699a541,Ball game\ntest/6ccb56f5d1771ea7,Luxury vehicle,Performance car\ntest/6ccbfb5957a90399,Luxury vehicle,Performance car\ntest/6ccc0a819da117fc,Compact car,Luxury vehicle\ntest/6ccee4bb5c575f25,Compact car,Luxury vehicle\ntest/6cd030e1b5327150,Rural area\ntest/6cd2310c79f0b7b2,Roller skating\ntest/6cd25b20bd07d57b,Military vehicle\ntest/6cd2a2bfc3a3e4ca,Blond\ntest/6cd3fc517d3fbc88,Close-up,Whiskers\ntest/6cd538ab997fd9ab,Combat sport\ntest/6cd6e11a21d43c51,Leaf vegetable\ntest/6cd74e976b1e05e0,Rural area\ntest/6cd8f477b281b98c,Wind wave\ntest/6cdc010aa9c2624d,Black swan\ntest/6cdca5874deddd08,Jeans\ntest/6cdde1fb1f3df0e9,Performance car\ntest/6cde52c46db9c7e5,Cookies and crackers\ntest/6ce0315c9931e69c,Compact car\ntest/6ce2d149d4ed4a8d,Bouldering\ntest/6ce4c4caf965efcc,Bovine\ntest/6ce5ff1aa48db788,Horse racing,Equestrianism\ntest/6ce6b6ec08cadef6,Auto racing\ntest/6ce9abff2ba3648b,Newsagent's shop\ntest/6ceaea9eabe82410,Close-up\ntest/6ced667851f25d08,Close-up,Scaled reptile\ntest/6cef34afd01686c7,Bouldering\ntest/6cf012cdecdb5f17,Close-up,Whiskers\ntest/6cf06720076b3c31,Perching bird,Close-up\ntest/6cf278d2bbbfe1da,Rural area\ntest/6cf34dedaff9cc09,Black-and-white\ntest/6cf42e1eff41dd3d,Bovine\ntest/6cf7354c2db339cb,Plant stem,Close-up\ntest/6cf94cec5127602a,Gun turret,Military vehicle\ntest/6cfa77fb7c882b37,Bovine\ntest/6cfa82793f18ca47,Close-up,Whiskers\ntest/6cfb699acbe33fdb,Close-up\ntest/6cfbdb9cb78f48e5,Drums\ntest/6cfc3558ee2a589d,Sport utility vehicle\ntest/6cfdc82553a6f1bb,Boating\ntest/6cff66dd03838663,Close-up\ntest/6cff77f4d3407708,Jiaozi\ntest/6cffd73cf05fce05,Bottled water\ntest/6d017b7529818cf1,Microcontroller\ntest/6d021d2e1ae341dc,Luxury vehicle\ntest/6d06263c728e39ef,Luxury vehicle,Performance car\ntest/6d06b3ae603b0923,Ball game\ntest/6d077ca8e739b067,High-speed rail\ntest/6d08d4c3a79c2e78,Boating\ntest/6d0afdd3b0b7b166,Luxury vehicle,Performance car\ntest/6d0d5204713112cd,Leaf vegetable,Vegetarian food\ntest/6d0dc6079d38aed9,Performance car\ntest/6d0f05a15ce90911,Nile crocodile,Crocodilia\ntest/6d0f4825d18f902b,Arthropod,Close-up\ntest/6d1119fc4c5346dc,Antique car,Sport utility vehicle\ntest/6d11f445304c9a88,Close-up\ntest/6d12a9c77871a9bd,Extreme sport,Boating\ntest/6d1403feb60a7aba,Boating\ntest/6d159fc5f9f11b7b,Luxury vehicle,Performance car\ntest/6d16ccefa6becb31,Breadboard\ntest/6d17849d8c5f4df7,Senior citizen\ntest/6d1a0a451e1e1146,Compact car,Luxury vehicle,Sport utility vehicle\ntest/6d1b5b0e19590947,Luxury vehicle,Performance car\ntest/6d1c47cd6c428702,Freight transport\ntest/6d1ecdb50d64c24d,Blond,Hair coloring\ntest/6d1f521753d1d734,Luxury vehicle,Antique car\ntest/6d1ff8af9ed655f5,Backlighting,Black-and-white,Close-up\ntest/6d260580f1146f1b,Machine tool\ntest/6d2676d2f53457f5,Performance car\ntest/6d26fd1ef52917cd,Luxury vehicle,Sport utility vehicle\ntest/6d2a9f74233d4744,Steamed rice\ntest/6d2cd958365d4ea2,Machine tool\ntest/6d2e2055bb9a571a,Black-and-white\ntest/6d326bae1ea99ba7,Extreme sport,Boating,Wind wave\ntest/6d32e9d0ea35e5e2,Extreme sport\ntest/6d32edd4c583b764,Luxury vehicle,Sport utility vehicle\ntest/6d3456dda80c7077,Jeans\ntest/6d34f0dd5193946b,Luxury vehicle\ntest/6d3610017d813763,Wind wave\ntest/6d367ec97e3deac8,Digital camera,Close-up\ntest/6d3a2e122f329e33,Aircraft engine\ntest/6d3acd65a0552aa9,Sport utility vehicle\ntest/6d3bb50c1e56c871,Orator,Public speaking\ntest/6d3bbb17090e1564,Luxury vehicle\ntest/6d3d2c18af972f61,Blond,Close-up\ntest/6d3eb7541d8363d0,Compact car,Luxury vehicle\ntest/6d3ef337064a8630,Vegetarian food\ntest/6d404f688154f1e8,Luxury vehicle,Sport utility vehicle\ntest/6d4544cb1efcf3b1,Auto racing\ntest/6d47c03430250cf5,Modern dance\ntest/6d48b3ac912a62a3,Whiskers\ntest/6d491dfdeb7b5e60,Luxury vehicle\ntest/6d4b4e22adcc0427,Performance car\ntest/6d4cf5bc38749e7f,Luxury vehicle,Antique car\ntest/6d5230b809b37451,Luxury vehicle,Performance car\ntest/6d557989c7e5adfd,Floristry\ntest/6d5673f9955d274d,Black swan\ntest/6d579e382e92aa7f,Halter\ntest/6d589f834c547d0c,Ball game,Team sport\ntest/6d5908ef2828e1b7,Antique car\ntest/6d5a83b6b98217d3,Assault rifle,Firearm\ntest/6d5ba86564ae78ee,Athletic shoe,Outdoor shoe\ntest/6d5bf1a3c9e945f9,Molluscs,Close-up\ntest/6d5c9d425fe3b1f7,Military person\ntest/6d5da2f066820638,Cobblestone\ntest/6d5da3b6e235bd4c,Molluscs\ntest/6d5de9cd496f9186,Close-up\ntest/6d5f15c18421e359,Hurdling\ntest/6d66e31fa765ff40,Compact car\ntest/6d678425e47c9d68,Performance car,Antique car\ntest/6d6a48fd8168ca15,Compact car,Luxury vehicle\ntest/6d6bc97dea67b58d,Close-up\ntest/6d6c9829a7f4522b,Luxury vehicle,Antique car\ntest/6d6cb6e9ba3c42ff,Boating\ntest/6d6cee37981e50ea,Plant stem,Close-up\ntest/6d7164285911d5ca,Luxury vehicle,Performance car\ntest/6d717c219226d63d,Rural area\ntest/6d71d6e329fd8e80,Luxury vehicle\ntest/6d74fd7ff415964d,Black-and-white\ntest/6d759dff4bc28cbc,Close-up\ntest/6d7606813484f5be,Combat sport\ntest/6d765c78cdac0079,Ball game,Team sport\ntest/6d7a550fd01af3c1,Luxury vehicle,Sport utility vehicle\ntest/6d7bf93c32d4c5cf,Luxury vehicle\ntest/6d7c04ac67cf6eb4,Hound\ntest/6d7d563f66ce458f,Vegetarian food\ntest/6d7f19275b8367a6,Performance car,Antique car\ntest/6d7fd5c58e76c4ba,Luxury vehicle,Antique car\ntest/6d8101778981f2d4,Plant stem,Close-up\ntest/6d818a83a8d5b4f8,Arthropod,Close-up\ntest/6d82f3c9ac9f6dc6,Blond\ntest/6d8870b1f318c730,Luxury vehicle\ntest/6d896d1a03891415,Plant stem\ntest/6d8cedc5bb4a7b68,Close-up,Whiskers\ntest/6d8dc2c64f1dde36,Rural area,Canola\ntest/6d8fb87ebb17e36c,Close-up\ntest/6d9181ed90b0de22,Compact car\ntest/6d967b7e631cc5c0,Aircraft engine\ntest/6d96fad800cdfb06,Luxury vehicle,Performance car\ntest/6d9887fa4bc7cd0d,Luxury vehicle\ntest/6d9e0d4bc8557d0b,Luxury vehicle,Performance car\ntest/6d9f4c824da557e6,High-speed rail\ntest/6da1defc54bc75b6,Luxury vehicle,Performance car\ntest/6da2be23f0c14cbc,Luxury vehicle\ntest/6da4e51abfcec66a,Boating,Rowing\ntest/6da680f8ff7d9c90,Close-up\ntest/6da7d635e04ca683,Glacial landform\ntest/6da8b0f3d25938af,Auto racing\ntest/6da98e451ccc6836,Luxury vehicle,Antique car\ntest/6dab9ca7c7f514d3,Halter\ntest/6dad94078e293a80,Senior citizen\ntest/6dae830325b6a956,Luxury vehicle\ntest/6dafc7659eb68ba2,Galleon\ntest/6db7a304438ca4b4,Scaled reptile\ntest/6db82cf9163b19c4,Drums\ntest/6db893e3dd2049fa,Toy block\ntest/6db9c2898d2c5d5a,Computer speaker\ntest/6dbdaa3b5ee02478,Equitation,Equestrianism\ntest/6dbfe6633f91b28e,Close-up\ntest/6dc05f9fe003f7a7,Sport utility vehicle\ntest/6dc27f6c7d0023ad,Falafel\ntest/6dc2972b7d7d5c86,Rural area\ntest/6dc30259336b628f,Cookies and crackers\ntest/6dc31807d96aa741,Close-up\ntest/6dc7ee8cd3b2b37f,Athletic shoe,Outdoor shoe\ntest/6dc99f3894b86d20,Black-and-white,Close-up,Whiskers\ntest/6dca6affa9ba10e9,Fried noodles\ntest/6dca9eeb498da6bc,Blond\ntest/6dcf219f281f17ff,Outdoor shoe\ntest/6dd20e0be8768e2f,Sport climbing\ntest/6dd2166970e4e962,Compact car,Luxury vehicle,Antique car\ntest/6dd395baaa48a330,Luxury vehicle\ntest/6dd3c532e4e3bc76,Luxury vehicle\ntest/6dd5223b982d10e4,Luxury vehicle\ntest/6dd53fde73d50de6,Domestic rabbit\ntest/6dd958a851bbcf37,Coral reef fish,Close-up\ntest/6dd981d40001a7fb,Blond,Hair coloring\ntest/6ddb12ed9a1e6c44,Erinaceidae\ntest/6ddde7a28c680948,Extreme sport,Sport climbing\ntest/6dde85fe00b08036,Luxury vehicle\ntest/6ddf3f2c83917221,Compact car\ntest/6ddff1431ba16ce1,Compact car\ntest/6de4936e593d493c,Hair coloring\ntest/6de58c6310d52d13,Siberian husky\ntest/6de78cee6f3022d7,Close-up\ntest/6deb171be0e905fe,Orator\ntest/6deba2e3b420fcfe,Extreme sport,Parachuting\ntest/6dec2005cf18dbfe,Boating\ntest/6defbc6b78e9d624,Antique car\ntest/6df11ffc97f22ac9,Luxury vehicle,Antique car\ntest/6df509a7c10e2029,Luxury vehicle,Performance car\ntest/6df60d05be205150,Vegetarian food\ntest/6df84d2d04ab4663,Black-and-white,Close-up\ntest/6df8d9c2cfefe892,Extreme sport,Wind wave\ntest/6df93b0d0c03684f,Luxury vehicle,Antique car\ntest/6df98350a427b9e2,Whiskers\ntest/6df9c1678660ed4b,Cosmetics\ntest/6dfa8aa7070600a3,Jeans\ntest/6dfba1af6843e23e,Luxury vehicle\ntest/6dfc1458fb681cc0,Close-up\ntest/6dfc5474f46f1f38,Antique car\ntest/6dfce7cd6ebf610a,Plant stem,Close-up\ntest/6dfde06978ed1734,Compact car,Luxury vehicle,Antique car\ntest/6dfe0452f14d1eb0,Antique car\ntest/6dffe6a3ee1ab2be,Luxury vehicle\ntest/6e01f7dfb14692b9,Boating\ntest/6e0332ecef409a34,Senior citizen,Close-up\ntest/6e0508cc0bb0ab36,Electronic instrument\ntest/6e05133db1efcab7,Ball game\ntest/6e06a7072788b2ca,Blond\ntest/6e0755f288716d25,Arthropod,Plant stem,Close-up\ntest/6e08eec11fe1f8c9,Angling\ntest/6e0f3895aca685af,Electronic instrument\ntest/6e1084940c6bd8eb,Compact car\ntest/6e1186594bf63867,Roller skating\ntest/6e12311169bcb24c,Black-and-white\ntest/6e160a32c1d8c2ba,Close-up\ntest/6e174f0711173220,Close-up\ntest/6e1855bcde8b119b,Close-up\ntest/6e18c5975cdd2208,Black-and-white,Modern dance\ntest/6e1b6e3b83476361,Fried food\ntest/6e1bb432b783a9cd,Cosmetics\ntest/6e1cdebe46820163,Vegetarian food\ntest/6e1d2d4e6b8278a6,Sport utility vehicle\ntest/6e1de29256989456,Luxury vehicle,Sport utility vehicle\ntest/6e1f6f0e20e8a01c,Residential area\ntest/6e1fc729948b4933,Aircraft engine\ntest/6e208fb248668bbe,Close-up\ntest/6e217565290f2986,Luxury vehicle\ntest/6e2276528e6363e2,Luxury vehicle\ntest/6e24e6768b319a9b,Frying,Fried food\ntest/6e26531916c95b43,Luxury vehicle\ntest/6e2ae438b18dd8b0,Ball game,Team sport\ntest/6e3047670c9afd13,Cosmetics\ntest/6e307ca7ec8517b1,Close-up\ntest/6e30c5c181246144,Black-and-white,Woodwind instrument\ntest/6e311950f4d486d3,Ball game,Team sport\ntest/6e31a74abf58bbbe,Jeans\ntest/6e33b06fbfa32c80,Arthropod\ntest/6e33c2c2e5b862b6,Ball game,Team sport\ntest/6e34f61d84f46cc8,Solar energy\ntest/6e35ced81f3ae6a3,Aerial photography\ntest/6e366c9b0f29108b,Luxury vehicle,Sport utility vehicle\ntest/6e399611dac777df,Sport utility vehicle\ntest/6e3a7a6bba27265a,Chordophone\ntest/6e4225bf312818a8,Close-up,Plant stem\ntest/6e43f0b8ae5df00c,Close-up\ntest/6e44f722ad3f90d7,Luxury vehicle\ntest/6e453b9e06f1ea09,Luxury vehicle\ntest/6e45a9b8d645a3e1,Microcontroller\ntest/6e4657b5da071cd2,Chordophone\ntest/6e46bd8c8376084c,Luxury vehicle,Performance car\ntest/6e476377df9cab13,Close-up,Whiskers\ntest/6e47767c2f50c846,Senior citizen\ntest/6e49311e863f7b3e,Fire apparatus\ntest/6e49634b79f447d6,Scaled reptile\ntest/6e498a38ee127737,Glacial landform\ntest/6e4b87aa3e554aef,Extreme sport\ntest/6e4bfaae84b8d107,Whiskers\ntest/6e4c43968cdada66,Jeans\ntest/6e4d904abd81f225,Black-and-white,Barquentine\ntest/6e4d95eb3aea0dad,Aircraft engine\ntest/6e4f428020bf73fb,Hound\ntest/6e4fe8b045b3c1b0,Close-up\ntest/6e500fa526d4f7cf,Whiskers\ntest/6e5166a1c1dabfb8,Khinkali\ntest/6e51b4eec4a59579,Combat sport\ntest/6e54a5b41e5b2754,Flatbread\ntest/6e5713c26d9d937c,Vegetarian food\ntest/6e5802cd81799f07,Antique car\ntest/6e5b37f434dad6ca,Aerial photography\ntest/6e5c2eeaee84581f,Inflatable\ntest/6e5d1eb71a14e980,Aircraft engine\ntest/6e5de5566a621335,Blond\ntest/6e605d9f8d1b7904,Black-and-white\ntest/6e60ad4518aa4be0,Auto racing,Compact car\ntest/6e60fb4130f8e49c,Performance car\ntest/6e61de6f215d65e4,Electronic instrument\ntest/6e61f6003387a6ba,Close-up\ntest/6e62d319a8d514a3,Performance car\ntest/6e649ca47b0b6560,Combat sport\ntest/6e64c4d9bcc8e14a,Boating\ntest/6e6796761f186570,Aircraft engine\ntest/6e68c002592a6a1c,Luxury vehicle,Performance car\ntest/6e69790a76a99a53,Barechested\ntest/6e70dbf1fafc26af,Antique car\ntest/6e72f7613585dd7a,Machine tool\ntest/6e736277cb856604,Hound\ntest/6e7522db5d41057f,Close-up\ntest/6e76a7e65cc0cdb5,Leaf vegetable,Vegetarian food\ntest/6e798a1184a5bcbf,Auto racing,Antique car\ntest/6e7ae85877836d7f,Heavy cruiser,Frigate\ntest/6e7c135ae902ea0c,Musical theatre\ntest/6e7c847e12279b9c,Cookies and crackers\ntest/6e7d2c7fa546d48b,Compact car,Sport utility vehicle\ntest/6e7e118c21e54066,Performance car\ntest/6e7e68a85d286afa,Antique car,Luxury vehicle,Performance car\ntest/6e7eee60b85925db,Vegetarian food\ntest/6e7fbb17c2aeadbb,Rural area,Bovine\ntest/6e7ffb832600068f,Domestic rabbit\ntest/6e822a6fe017b645,Close-up\ntest/6e8416e20b4c7668,Domestic short-haired cat,Whiskers\ntest/6e854f6f97127ce5,Molluscs\ntest/6e859b7e97b0d097,Ball game\ntest/6e863f0fd79a678b,Hound\ntest/6e8704b6c83c03aa,Close-up\ntest/6e8b9ae2e27665d4,Black-and-white\ntest/6e8c6186c5a7c606,Sport utility vehicle\ntest/6e8d991f33cf4d9a,Leaf vegetable\ntest/6e8dc57bd7c7afde,Extreme sport,Parachuting\ntest/6e8df2cbc8bdbe15,Blond\ntest/6e8ff04c3ea00140,Sailboat racing\ntest/6e91fa884d801165,Luxury vehicle,Antique car\ntest/6e91fe6432c7d547,Blond,Hair coloring\ntest/6e93a382ea1d058a,Compact car,Luxury vehicle,Antique car\ntest/6e95d6623fec0029,Extreme sport,Sledding\ntest/6e96ed90b429c780,Calabaza\ntest/6e981247e5c5e65e,Sport utility vehicle\ntest/6e9817571d9df3ec,Cosmetics\ntest/6e9b63dfe4ae4b74,Combat sport,Sumo,Grappling\ntest/6e9b6fed90fe3c06,Antique car\ntest/6e9bf7ba144d4689,Siberian husky\ntest/6e9c59efb7f5174f,Leaf vegetable\ntest/6e9d0895a6d840de,Military person\ntest/6ea2f80b8332d477,Luxury vehicle,Performance car\ntest/6ea83165e7c4ac80,Luxury vehicle\ntest/6ea883874bc980b0,Close-up\ntest/6ea8fe28ece4f670,Water polo,Ball game,Team sport\ntest/6eadff2bf0e03e3e,Close-up\ntest/6eae89325254d43c,Luxury vehicle,Performance car\ntest/6eafe362add6f0b3,Touring car,Antique car\ntest/6eb03384f25d3f01,Black-and-white,Headphones\ntest/6eb08c2105d19538,Extreme sport\ntest/6eb27b05943021ff,Blond\ntest/6eb2c1c2e058fd66,Black-and-white\ntest/6eb3c44955175861,Monoplane,Gliding\ntest/6eb5a77d9dbdb7a4,Boating\ntest/6eb6b995735aad75,Bird of prey,Close-up\ntest/6eb721564bc89255,Aircraft engine\ntest/6ec3ebeefa462f0a,Close-up\ntest/6ec57c3fbe4d0867,Black-and-white\ntest/6ec5de698dec805a,Ramen,Soba\ntest/6ec6f9c9fab0d0de,Milky way\ntest/6ec8ef0786640566,Arthropod,Close-up\ntest/6ecc06b4adb82bf6,Luxury vehicle,Sport utility vehicle\ntest/6ecd2cbf237c4ecf,Childbirth\ntest/6ecdcd6ffe893c48,Rural area,Aerial photography\ntest/6ece87397c75e6b7,Modern dance,Musical theatre\ntest/6ed0303fedb92c35,Luxury vehicle\ntest/6ed0506da01e37e8,Motorcycling\ntest/6ed1cf3031b3cba7,Vegetarian food,Junk food,Fried food\ntest/6ed1e345c88025cc,Close-up\ntest/6ed52ac49b70d778,Glacial landform\ntest/6ed58c724d1ca69b,Luxury vehicle,Antique car\ntest/6ed6c97a0fb4ba57,Aircraft engine\ntest/6ed7686e79a91735,Black-and-white\ntest/6ed7d9bffc89ec5a,Close-up\ntest/6ed94f5eca66a629,Compact car\ntest/6ed9dc03acbd33b2,Performance car\ntest/6ede26db1c5db142,Close-up\ntest/6edfa6492fdfce5e,Chordophone\ntest/6ee0c6e53f2b42ab,Plant stem\ntest/6ee223630d8e9852,Barechested\ntest/6ee28283b622bba9,Arthropod,Close-up\ntest/6ee480b2414f3e47,Rural area\ntest/6ee605e86e56e0e3,Team sport\ntest/6ee722ac5d5075a4,Arthropod,Close-up\ntest/6ee7465e7185776a,Scaled reptile\ntest/6ee748fe67445382,Dog walking\ntest/6ee7eaf71ce6b061,Rural area\ntest/6ee81505f359dded,Domestic short-haired cat,Whiskers\ntest/6ee81ce5690db6be,Luxury vehicle\ntest/6ee903844af63636,Close-up\ntest/6eeb1ed4ca9ddba5,Close-up,Whiskers\ntest/6eec7fc67071f1d8,Compact car\ntest/6ef44b1452d07d8d,Sailboat racing\ntest/6ef60ff9ad0a1f5d,Extreme sport,Wind wave\ntest/6ef792ed14959ceb,Arthropod,Close-up\ntest/6ef86905a0cd1da0,Zeppelin\ntest/6ef87829f5302fad,Close-up\ntest/6ef88231dcd3869e,Antique car\ntest/6ef88d5a5d8edbc9,Cobblestone\ntest/6ef89b8f3e99fc26,Black-and-white\ntest/6ef8b3aef7e974a4,Combat sport,Grappling\ntest/6efa62bd4e984d56,Classical sculpture\ntest/6efb985b5ff74515,Luxury vehicle,Performance car\ntest/6efb9af7c3a3d6bf,Close-up\ntest/6efd84f454f894f8,Antique car\ntest/6eff87494d747a66,Blond\ntest/6f0489ff7408f513,Luxury vehicle\ntest/6f08600b92c6ccc3,Antique car\ntest/6f08b5650d037848,Cobblestone\ntest/6f0afde62effaeb1,Close-up\ntest/6f0b3f3032a11d02,Close-up\ntest/6f0e0608c6cbe3e9,Rural area\ntest/6f0f56d7eeaf45fb,Great white shark\ntest/6f0f875ad1cbf880,Luxury vehicle,Performance car\ntest/6f105bb5cb7fc4bf,Chordophone\ntest/6f12f79742e125dd,Luxury vehicle,Performance car\ntest/6f14ec453234b66f,Bottled water\ntest/6f1612dcb69bd2fc,Milky way\ntest/6f16ddf7a007dfc1,Black-and-white,Holding hands,Close-up\ntest/6f1802ab546410fd,Luxury vehicle\ntest/6f18d37dfb948e75,Auto racing\ntest/6f199438a49f90eb,Motorcycling\ntest/6f19ec1f1afb688f,Glacial landform\ntest/6f1aa57e4836d30e,Black-and-white\ntest/6f1aeba0404509dd,Leaf vegetable\ntest/6f1c4ea262598dcf,Ball game,Team sport\ntest/6f1d44be870d6780,Black-and-white\ntest/6f21618269292469,Compact car\ntest/6f223475e49946dd,Musical theatre\ntest/6f226ab0cd1ebf11,Compact car\ntest/6f22ebb0cd6fad85,Boating\ntest/6f23189ee71df010,Luxury vehicle\ntest/6f24da2b7208715b,Firearm\ntest/6f261c80f7e3a71f,Compact car,Luxury vehicle,Antique car\ntest/6f278aec6a3b147c,Team sport\ntest/6f2b224acbd822d2,Scaled reptile\ntest/6f2d648b68f1033a,Luxury vehicle,Performance car\ntest/6f2f4197e6f566bd,Boating\ntest/6f2f510cf592e1a3,Fried food\ntest/6f310c9e0cd76c65,Fried food\ntest/6f32668f51ffdfa0,Vegetarian food,Frying,Junk food,Fried food\ntest/6f361bca28294325,Leaf vegetable\ntest/6f36485718c9aca7,Combat sport\ntest/6f3651ac3d9c752f,Luxury vehicle,Antique car\ntest/6f38a65b87573871,Aerial photography\ntest/6f39677623bc925d,Close-up,Scaled reptile\ntest/6f3cef84f92bf363,Sport utility vehicle\ntest/6f3e6969a945e15e,Lifebuoy\ntest/6f3f12bf5f8e8e80,Miniature Poodle\ntest/6f419ca3b2193151,Luxury vehicle\ntest/6f436044c2b31399,Black-and-white,Modern dance\ntest/6f465c71e553ff55,Compact car,Performance car,Luxury vehicle,Antique car\ntest/6f46bf96d4be94d6,Black-and-white\ntest/6f4849cb2a40a962,Portrait photography,Close-up\ntest/6f49155aa02875b8,Newsagent's shop\ntest/6f4a12d557d7b049,Performance car\ntest/6f4dbc1016792a87,Floristry\ntest/6f50b08c4448a070,Team sport\ntest/6f50beea2a3c8e39,Domestic short-haired cat,Whiskers\ntest/6f510c0e4d09aa3a,Arthropod,Close-up\ntest/6f52e0e94bf20b40,Aerial photography\ntest/6f531489ef8259ed,Compact car\ntest/6f5390057a0207e4,Sport utility vehicle\ntest/6f5878ae76b31f8b,Molluscs,Close-up\ntest/6f5ab188a01f0558,Luxury vehicle,Antique car\ntest/6f5af3c9669b20b4,Monoplane\ntest/6f5b1b82d5fed970,Nile crocodile,Crocodilia\ntest/6f5b4f3f60ab2606,Vegetarian food\ntest/6f5c99e38aaec8aa,Antique car\ntest/6f5ebb007ea7248c,Fried food\ntest/6f5ff291e66b5066,Ball game,Team sport\ntest/6f6183dd619aa149,Close-up\ntest/6f6299f57c83e7d2,Aircraft engine\ntest/6f657d459ec466fb,Close-up\ntest/6f66217f8ffdbeea,Bird of prey\ntest/6f66256f27d08541,Vegetarian food\ntest/6f665969cf3f52f4,Compact car\ntest/6f67ed0ef44bcd83,Jeans\ntest/6f6826ec0f1ba416,Luxury vehicle,Sport utility vehicle\ntest/6f68c04b56476d1c,Ball game,Team sport\ntest/6f6a3502764f8ca2,Bovine\ntest/6f6e5a61e34f508f,Compact car\ntest/6f6eabdc497f847d,Aircraft engine\ntest/6f6ed5db479c8f51,Compact car,Luxury vehicle,Sport utility vehicle\ntest/6f739e2ad445bc72,Perching bird\ntest/6f76a052f44d35dc,Coca-cola\ntest/6f7933cbf4f01fae,Bratwurst\ntest/6f7a392ad1fbac46,Luxury vehicle,Performance car\ntest/6f7c3ee87c0c553b,Steamed rice\ntest/6f7e8835e76f7fda,Bird of prey\ntest/6f7eff880585232c,Jeans\ntest/6f7ff78d81bf79cb,Arthropod,Close-up\ntest/6f800eb80c5a9fd7,Antique car\ntest/6f82654a40e0ccbd,Ball game\ntest/6f8530e8b467ad3f,Whiskers\ntest/6f89c6ff426f5d0c,Compact car,Luxury vehicle,Antique car\ntest/6f8a1acdbbfb30c0,Ball game\ntest/6f8f4097a2f50e91,Jeans\ntest/6f90b605f942ee42,Close-up\ntest/6f92494437a3f93e,Luxury vehicle,Performance car\ntest/6f924d7105d438f4,Close-up\ntest/6f9293de30c7b0ed,Luxury vehicle\ntest/6f9b176d1e4b5ef1,Residential area\ntest/6f9d7a5776695ab1,Ball game\ntest/6f9d9c6b1bd6e077,Ball game\ntest/6f9ddc944a3d43ce,Amphibian\ntest/6f9f1697dd9c4f25,Steamed rice\ntest/6f9f7a78fb3929ea,Amphibian\ntest/6fa14c2ee5b3d0ba,Miniature Poodle\ntest/6fa2edb6d88db7bd,Chordophone\ntest/6fa3618f5c349941,Domestic short-haired cat,Close-up,Whiskers\ntest/6fa3f73142ae7b17,Holding hands,Close-up\ntest/6fa64079d13794ad,Church bell\ntest/6fa76e27ab83a10c,Compact car,Luxury vehicle,Performance car\ntest/6fa7e98a9fe64f09,Luxury vehicle\ntest/6faa277565ef32b2,Luxury vehicle,Performance car\ntest/6faa40cb1dc8223b,Plant stem\ntest/6fad57ef3fc5465f,Musical theatre\ntest/6faf93f6c39e4af9,Close-up,Scaled reptile\ntest/6fb1dae096ef191e,Military vehicle\ntest/6fb5cd75c6b5f400,Perching bird\ntest/6fb996eb11a829c5,Galleon\ntest/6fb9aaa7f432e287,Antique car\ntest/6fbac1b5c66b9459,Hound,Close-up\ntest/6fbbe8f510f221cc,Auto racing\ntest/6fbc73ab9de3d0e4,Performance car\ntest/6fbd5e0e163e3c9f,Khinkali\ntest/6fbd7bf080e2bfd6,Middle ages\ntest/6fbe91bf568e36fa,Plant stem\ntest/6fbf618d248a7115,Ball game\ntest/6fc105c6743a690b,Domestic short-haired cat,Whiskers\ntest/6fc11229415ef26a,Close-up\ntest/6fc554c05d2c92bc,Boating\ntest/6fc575bcd49b32fa,Jeans\ntest/6fc7263626f67ba2,Steamed rice\ntest/6fc730cb7e1d7244,Floristry\ntest/6fca0f044d2838a5,Hound\ntest/6fca519105417a52,Firearm\ntest/6fcb0c573729820a,Aerial photography\ntest/6fcf333638a40037,Dishware\ntest/6fd29cd2ede93f51,Chordophone\ntest/6fd33990386cbf33,Domestic rabbit\ntest/6fd66f1878065fa0,Luxury vehicle\ntest/6fd6e5e9e912ef2e,Close-up\ntest/6fdb310f382c4fa3,Close-up\ntest/6fddf2e6c5f40cf6,Rural area,Bovine,Herding\ntest/6fdf3e305f99860c,Luxury vehicle,Performance car\ntest/6fdf872952bb653a,Arthropod,Close-up\ntest/6fdff51a742326e2,Residential area\ntest/6fe17703a10866b8,Equestrianism\ntest/6fe23c0838b52e89,Antique car\ntest/6fe363b36429a6ab,Siberian husky\ntest/6fe45cc64847a2a9,Aircraft engine\ntest/6fe5382f1bf3f598,Luxury vehicle,Performance car\ntest/6fe57191fc50e1a9,Galleon,Barquentine\ntest/6fea7c5e647d69fc,Beef tenderloin\ntest/6fecb14618cecba6,Close-up\ntest/6fef9edccc6faac1,Gumbo\ntest/6fefdac8c871c84b,Rural area\ntest/6ff40b74e7c693e2,Dishware\ntest/6ff97752a4b6ea10,Fried food\ntest/6ff9c6510f6102bf,Middle ages,Equestrianism\ntest/6ffbbdde01f7f07b,Whiskers\ntest/6ffc78b29b639837,Luxury vehicle\ntest/6ffd1389733c2809,Blond\ntest/7000e7e299ae61e4,Rural area\ntest/70014e72dcc7c031,Compact car,Luxury vehicle,Sport utility vehicle\ntest/700162e4f1a98ef6,Black-and-white,Hound\ntest/70052de6a66dd92b,Luxury vehicle\ntest/7006ad22589335ef,Arthropod,Close-up\ntest/700758ee4f215b6c,Jeans\ntest/70076e640ca95ef5,Bird of prey,Close-up\ntest/70087c4fc53f8459,Aircraft engine\ntest/700a58ec3217005e,Machine tool\ntest/700a734a65c29fcd,Close-up\ntest/700b93c056dabb50,Monoplane,Gliding\ntest/700bb625238b9941,Jackfruit\ntest/700d589f9bf94b22,Performance car\ntest/700fa1def7fa0ac2,Extreme sport,Wind wave,Boating\ntest/700ffdcc15eec4a5,Black-and-white\ntest/700ffe6948cf72ed,Frozen yogurt\ntest/70101dd41f2313ad,Close-up\ntest/7012fa11485297d0,Arthropod,Close-up\ntest/70132a9098c554ca,Residential area\ntest/7013d06e41f62d35,Sport utility vehicle\ntest/701666b11179ff86,Whiskers\ntest/7017a27b9b3b0511,Close-up\ntest/7019c5bd962c3461,Luxury vehicle,Antique car\ntest/701ae6b5d6bb16fa,Blond\ntest/701bb3170369841e,Aerial photography\ntest/701c7c5aa245589c,Compact car,Antique car\ntest/701d5958b1c65738,Luxury vehicle,Sport utility vehicle\ntest/701d9d6ae09d5c41,Vegetarian food\ntest/7027360a7a2884a4,Close-up\ntest/7030a7e4d37cb1fe,Pork chop\ntest/70322884d0678f9c,Bratwurst\ntest/7032803be6031888,Compact car,Antique car\ntest/70334a76f67850c4,Ball game\ntest/70339f8e30109c6e,Firearm\ntest/7039ab8136f9b1ee,Sport utility vehicle\ntest/703c951d9aa83e7e,Luxury vehicle\ntest/703d8b6f63950d06,Scaled reptile,Close-up,Anole\ntest/703db4ca9467a900,Calabaza\ntest/703dc7c534dd5465,Luxury vehicle,Performance car\ntest/70414ba0fbd34cdc,Military vehicle\ntest/70434fbb135fdd11,Luxury vehicle\ntest/7045a2b7dd8f249b,Plant stem,Close-up\ntest/704701cf7e2fa8d6,Close-up,Scaled reptile\ntest/70470f22b97eb2a0,Bird of prey\ntest/70496b2b4ad32f1f,Goats\ntest/704ae0ee2b02f8ae,Digital camera,Black-and-white,Portrait photography\ntest/704af5aa4b942576,Compact car\ntest/704c163105ed14f7,Luxury vehicle,Antique car\ntest/704f083a97ecd205,Rural area\ntest/7050aa9297ef8243,Luxury vehicle,Performance car\ntest/7050d88a2f0ec747,Close-up\ntest/70522f1212e3afff,Antique car\ntest/7053ea6437a81f91,Ball game,Team sport\ntest/7056344a5e8ea8c1,Electronic instrument\ntest/70566da620c041b8,Rural area\ntest/7059a34f9f3d9d92,Black-and-white\ntest/7059c16a1c41f888,Musical theatre\ntest/705a0570411252a3,Fried noodles\ntest/705a892a2cf9c03f,Hound\ntest/705d73fc455a2466,Cookies and crackers\ntest/7060f0f0a1786ec2,Performance car\ntest/706214bbe9143ccc,Bird of prey\ntest/70643cc1adff8be9,Jeans\ntest/7064ef755ea5279a,Team sport\ntest/7064f08a23f73dac,Road cycling\ntest/706757dfe1a63cd2,Performance car\ntest/70694e6765ef9d22,Aircraft engine\ntest/706a67cca26a38d4,Leaf vegetable\ntest/706b2b8fc80678ff,Toy block\ntest/706b4b593f24d664,Bonbon\ntest/706b622f7b7c9817,Frozen yogurt\ntest/706cdab32dfb16e1,Solar energy\ntest/706d9b511b27e68c,Antique car\ntest/706e84baab4c25ea,Vegetarian food,Jiaozi,Pelmeni\ntest/706f6edd0d12f0f4,Boating\ntest/7071dc6f4b2fac66,Close-up\ntest/7072b8a657158ade,Fried noodles\ntest/7074da4f484d0aea,Compact car,Antique car\ntest/7076006c53441f32,Military vehicle,Black-and-white\ntest/70769e6e06cb1c4d,Domestic short-haired cat,Close-up,Whiskers\ntest/707893ee80815d0b,Luxury vehicle\ntest/707948c95b85372c,Fried food\ntest/707a01d16778422c,Compact car\ntest/707abebe50ebce8d,Combat sport\ntest/707d446e5f0afb64,Arthropod,Locust\ntest/707e1cd1eba3309e,Residential area\ntest/7080a0e4bcf2c84a,Bovine,Close-up\ntest/7080c7526ac858ec,Arthropod,Close-up\ntest/70816eb0ea903775,Drums,Black-and-white\ntest/7081cf129e98d4f4,Double-decker bus\ntest/708285b09b26bf85,Sport utility vehicle\ntest/7084245887cbed40,Luxury vehicle,Antique car\ntest/7084782c5b66673a,Wind wave\ntest/708497c146cab004,Blond\ntest/70877c696568b1ac,Road cycling\ntest/70878bd6dcf73fc4,Extreme sport\ntest/7088da96955de2ec,Ball game,Team sport\ntest/7089d2c682cc735a,Extreme sport,Parachuting\ntest/708bc1f88e1360ab,Cookies and crackers\ntest/708d9a1cc4e4d71b,Rural area\ntest/708de1ccd49b90ff,Ale\ntest/708e2db000d80949,Performance car,Luxury vehicle,Antique car\ntest/70916cd9f040e277,Combat sport\ntest/709324d88f73f041,Middle ages\ntest/7095f261203bdc11,Primate\ntest/7096ae1a7842e1f1,Arthropod,Close-up,Plant stem\ntest/7098d37c03784fc6,Molluscs\ntest/709938f21ae13ca4,Close-up\ntest/709a0c12c7e33a2b,Plant stem,Close-up\ntest/709b391ac2b8e9dc,Plant stem\ntest/709bb69695f093b1,Close-up\ntest/709d02f1e781374f,Plant stem,Close-up\ntest/709f524f0980400c,Vegetarian food,Fried noodles\ntest/709f6dba991dd350,Compact car\ntest/709fa2ca6dca0d85,Close-up\ntest/70a067589525af87,Bratwurst\ntest/70a29dac6058618e,Procyonidae\ntest/70a32230188b9f52,Close-up\ntest/70a3ede8ab39e51e,Blond\ntest/70a52659a0cf54d3,Domestic short-haired cat,Whiskers\ntest/70a700b1f10df0e0,Luxury vehicle\ntest/70a7ab302e3eccc2,Black-and-white,Sport utility vehicle\ntest/70a977dda2e4cb3d,Inflatable\ntest/70ab714d2d0ed764,Antique car\ntest/70b238702a036ba8,Bird of prey,Close-up\ntest/70b3e6abed4a6dd5,Whiskers\ntest/70b41551773e27b6,Vegetarian food,Falafel,Fried food\ntest/70b48cb95e16e69f,Compact car,Antique car\ntest/70b4b0ca7e45ccc6,Boating\ntest/70b4f0c4852e2b81,Luxury vehicle,Performance car\ntest/70bb58bf62de8b6c,Compact car\ntest/70bd59e227910c24,Close-up,Whiskers,Domestic rabbit\ntest/70c3763f439e5a18,Dishware\ntest/70c38b74fb3aa6ab,Antique car\ntest/70c6680f75f68093,Forklift truck\ntest/70c73dbf84f4cb17,Vegetarian food,Fried food\ntest/70c7fd4bd71de84c,Plant stem\ntest/70ccb19ed521023a,Close-up\ntest/70cf89b6bd473e7c,Close-up\ntest/70d51ad0885a5d4a,Rural area\ntest/70d5855ce880a423,Luxury vehicle,Antique car\ntest/70d5fe10da2c273e,Middle ages\ntest/70d670f7d0ba0d67,Sport utility vehicle\ntest/70d7d04e13c36110,Luxury vehicle\ntest/70d82a0855d7db1e,Residential area\ntest/70db1b2825f4088c,Extreme sport\ntest/70df666469d2fec8,Compact car,Antique car\ntest/70e1c8f456c1d2d7,Luxury vehicle\ntest/70e2727df0b6403d,Bagpipes\ntest/70e3de4ba2489b2f,Close-up\ntest/70e4afa97e364814,Plant stem,Floristry\ntest/70e4ec21d9ad1b13,Compact car,Luxury vehicle\ntest/70e6c76dad29e20a,Close-up\ntest/70e7883b83d64232,Lifebuoy\ntest/70e7aeeba778eba2,Luxury vehicle,Performance car\ntest/70e7cbd3f5a70bf4,Flatbread\ntest/70e87a068892c60c,Khinkali,Jiaozi\ntest/70e9070805b038e9,Chordophone,Electric guitar\ntest/70eb4bebf89d2d4e,Arthropod,Close-up\ntest/70ebf79467939e66,Vegetarian food\ntest/70ec6432684f6a7c,Canola\ntest/70ee2bc1efbc4773,Jeans\ntest/70ef54759e2a68e3,Aerial photography\ntest/70f14d082d077f1d,Luxury vehicle,Sport utility vehicle\ntest/70f27b64fc8b2abf,Dishware\ntest/70f3d007fe98a0b9,Nuclear power plant\ntest/70f3d7df07695d09,Whiskers\ntest/70f4a7d68de41094,Athletic shoe\ntest/70f57516cea7666a,Luxury vehicle,Sport utility vehicle\ntest/70f594789a90aaf7,Performance car\ntest/70f7438d477b9a9c,Molluscs,Close-up\ntest/70fab5ee10e3f66a,Close-up\ntest/70fb228cbde64b30,Arthropod,Close-up\ntest/70fb53e5978f8c5f,Close-up,Scaled reptile\ntest/70fb78d8ad307b8f,Military person\ntest/70fe11fcc72c5ae5,Athletic shoe,Outdoor shoe\ntest/70fe167d09329cc8,Close-up\ntest/70ff58bda7bd2dac,Extreme sport,Wind wave\ntest/71003330cf6b9fd1,Vegetarian food\ntest/71008b1666bec726,Compact car\ntest/710113a2a417da97,Bovine\ntest/710171da9d301b46,Luxury vehicle,Sport utility vehicle\ntest/71021310c8223d44,Compact car,Luxury vehicle\ntest/71039c970eb4a435,Digital camera\ntest/7105cc6829fcbb98,Jeans\ntest/71069c2c3b2b92b4,Sport utility vehicle\ntest/710739531bcb47f9,Wind wave\ntest/710739ed9065e9a7,Billiard room,Billiards\ntest/71087068bb0e70a4,Black-and-white,Woodwind instrument\ntest/71095d1ef7b69548,Plant stem\ntest/710983db6d561330,Ribs\ntest/710a6946859927d9,Blond,Portrait photography,Close-up\ntest/710d401a87d1da6b,Vegetarian food,Junk food\ntest/710da835b42be60f,Coca-cola\ntest/710f84bf43ca161d,Coca-cola\ntest/711012ff91bddbe5,Road cycling\ntest/711111dfec4d01d4,Jeans\ntest/71121266a9d49b9d,Extreme sport,Wind wave\ntest/7113cb1e20a60bd0,Close-up\ntest/7114129305c6f128,Black-and-white\ntest/71183b38da43a76b,Black-and-white\ntest/711901fd3604f87e,Aerial photography\ntest/711a0e63cede1141,Close-up\ntest/711a37b7cef502ce,Close-up\ntest/711aded625e758ab,Pork chop,Beef tenderloin\ntest/711c3f16c873b828,Ball game,Team sport\ntest/711d51608a8e35a5,Luxury vehicle,Antique car\ntest/711d741752d501ab,Sport utility vehicle\ntest/711e517f1d735c14,Close-up\ntest/711f4f1b1ec55951,Rural area,Goats\ntest/71211e51618fe76f,Close-up\ntest/7121a66d01cdd3e2,Middle ages\ntest/71232357605d5059,Aerial photography\ntest/71292659fa121a41,Black-and-white,Whiskers\ntest/712a4145fb190e96,Extreme sport,Kitesurfing\ntest/712b0cb33ff6ed42,Inflatable\ntest/712e55c702f9ba5f,Extreme sport,Parachuting\ntest/7131ad423a39b007,Zeppelin\ntest/7131c7891d507369,Close-up\ntest/7133557a275914c1,Dishware\ntest/7134e5f58928f074,Bird of prey\ntest/7135d2a7c930d5b7,Close-up\ntest/7137693c4e0b2766,Heavy cruiser\ntest/71380e21c298ef5d,Sport utility vehicle\ntest/713d733f78b82d47,Domestic short-haired cat,Whiskers\ntest/7140978142f4a1be,Black-and-white\ntest/7142c3550d6cb5ef,Luxury vehicle,Performance car\ntest/714583b378cdc9f4,Auto racing\ntest/7149d80773da32c7,Plant stem\ntest/714b5730265abe1b,Jeans,Dobermann\ntest/714bc03dac3c9376,Beef tenderloin\ntest/714c1b75de37e165,Luxury vehicle,Performance car\ntest/714d3e5902426480,Close-up\ntest/7151187b70a7a9a0,Compact car,Luxury vehicle,Antique car\ntest/7151940845badcfa,Luxury vehicle,Performance car\ntest/7153cca97afeabcc,Team sport\ntest/71547206400be20c,Military person\ntest/7155204980a38620,Close-up\ntest/71573b94fa42a38e,Horse and buggy\ntest/7157ae8eb87d2b14,Compact car\ntest/71596cdc48476c2c,Close-up\ntest/715b491ddad5227d,Black-and-white\ntest/715c8f435605a1be,Fried food\ntest/715d11000fddac74,Hound\ntest/715d8eae123e6ff0,High-speed rail\ntest/71625aee1e942dca,Close-up\ntest/7163890145aacb4c,Sport utility vehicle\ntest/716439ce49ae9b80,Narcissus\ntest/7164a77a4ca7140b,Residential area,Aerial photography\ntest/71652e2a240e0c33,Close-up\ntest/7165caabe66ede4f,Ball game,Team sport\ntest/7166ec99342d8c7b,Domestic rabbit\ntest/71695d80e282be09,Rural area\ntest/7169e8656dd2b2cf,Arthropod,Close-up\ntest/716bb03575e9fd3a,Luxury vehicle,Performance car\ntest/716dc1c003c48853,Luxury vehicle\ntest/716f71310be46af8,Luxury vehicle,Performance car\ntest/716fe06c18c66ded,Headphones\ntest/7170cecaa57a0e67,Arthropod,Close-up\ntest/7177d326d194f4b2,Luxury vehicle,Performance car\ntest/7179410be3ea63ea,Auto racing,Touring car,Performance car\ntest/717a200a9c865a50,Portrait photography,Hair coloring\ntest/717aca545f914a89,Luxury vehicle\ntest/717afb8d468ccaf3,Middle ages\ntest/717b02ed63771dda,Stemware\ntest/717b26a167c4a9ee,Toy block\ntest/717b396012deba08,Combat sport\ntest/717c291baffd5de0,Luxury vehicle\ntest/717d982a139f8906,Black-and-white,Boating\ntest/7182a2412e8b7958,Athletic shoe,Outdoor shoe\ntest/71860d59e2cff2d7,Black-and-white,Holding hands,Close-up\ntest/718ab777e444acf5,Comics\ntest/718bc627ac62028b,Black-and-white\ntest/718cb970743d60ca,Fried food\ntest/718cf39ac500bd7f,Blond\ntest/7191442e1415ad9c,Close-up,Whiskers\ntest/7193bf1fe4e0f9da,Outdoor shoe\ntest/71968b741c722b00,Aerial photography\ntest/71984699c99cd2f8,Luxury vehicle,Sport utility vehicle\ntest/7198f17e8406314f,Jackfruit\ntest/719912f48cfecba2,Close-up\ntest/719924f0f9d3e23e,Luxury vehicle\ntest/7199cd6d5fc22a4a,Jeans,Luxury vehicle\ntest/719a11a62e07e971,Close-up\ntest/719bacb2bf1908ba,Jeans\ntest/719be2be3a8fd8f2,Luxury vehicle,Sport utility vehicle\ntest/719c01ae73ef729c,Luxury vehicle,Antique car\ntest/719c259d6c12362e,Luxury vehicle\ntest/719d511b89435268,Close-up,Whiskers\ntest/719ecd5a753739d3,Bonbon\ntest/71a1e43ad32d130a,Close-up\ntest/71a5dc61c773746f,Close-up\ntest/71a6262fda2b739d,Perching bird\ntest/71a8b0e5b7782c58,Blond,Portrait photography,Close-up\ntest/71aac90ca3124125,Horse racing\ntest/71addee57d361ea2,Extreme sport,Motorcycling\ntest/71ae5670e0d4b66b,Boating,Rowing\ntest/71ae73fb6b6065f3,Sailboat racing\ntest/71b31bd459ebdea2,Cosmetics\ntest/71b3dd2f5c247abc,Black-and-white,Extreme sport\ntest/71b4b535d8df9e64,Coral reef fish\ntest/71b66d3ad245c793,Black-and-white\ntest/71b7fc6522575582,Extreme sport,Sport climbing\ntest/71b828357f8f3fdf,Black-and-white\ntest/71b8f5807d60acac,Dishware\ntest/71ba1f5f57ba152a,Luxury vehicle,Performance car\ntest/71bd1c4fca53ea9a,Aerial photography\ntest/71bd86f56c9f1eff,Combat sport\ntest/71bf4b4eb046062b,Galleon\ntest/71c19790be7c8093,Close-up\ntest/71c2801771cdd709,Middle ages\ntest/71c3a2ecf0e43107,Electronic instrument\ntest/71c5808bfd69c13d,Bratwurst\ntest/71c5a55f7010c16d,Rural area\ntest/71c5f240ac0f7ee8,Luxury vehicle\ntest/71c71473338d948b,Arthropod,Close-up\ntest/71c870b3aec95919,Hound\ntest/71c9910d6f21443d,Blond\ntest/71c9a3c0671d8896,Luxury vehicle,Antique car\ntest/71ca5a65bb6dee89,Compact car\ntest/71cb73b02f9104a9,Extreme sport\ntest/71cccf45e4bf7c2b,Black-and-white\ntest/71cd2134079e8318,Ball game\ntest/71cde9c771a2aabb,Bovine\ntest/71d00d3ff49f0068,Miniature Poodle\ntest/71d0af112571e863,Amphibian,Close-up\ntest/71d0d53c2b1f09e0,Residential area,Aerial photography\ntest/71d13d72999119b9,Domestic short-haired cat,Whiskers\ntest/71d1ca8e46314676,Arthropod,Close-up\ntest/71d4f62725c17c43,Rural area\ntest/71d61626e98070e5,Close-up\ntest/71d680b4617c78de,Outdoor shoe\ntest/71d6df9a6b3079fa,Combat sport\ntest/71d800429e699ce8,Water polo,Ball game,Team sport\ntest/71dafd52a2821386,Junk food\ntest/71db4bdad912bd52,Dishware\ntest/71dcefb316911d6b,Close-up\ntest/71dea8264c152123,Sport utility vehicle\ntest/71e5e87c36a67906,Whiskers\ntest/71e61c1183f5bfba,Ball game,Team sport\ntest/71e8261faccdf0a5,Luxury vehicle,Performance car\ntest/71e9e241a27c6052,Arthropod,Close-up\ntest/71ea3106bf0f7452,Arthropod\ntest/71eb5552456c9942,Black swan\ntest/71eb79ae10f7fdc0,Team sport\ntest/71edd77fe70e1d11,Compact car,Antique car\ntest/71f0c023c6feee21,Electronic instrument\ntest/71f127a8d81a336b,Blond\ntest/71f69ad4427e9cbd,Compact car\ntest/71f6cd8d22ec07a6,Galleon\ntest/71f7d5aea5b214a8,Extreme sport\ntest/71f82b1d16bbd835,Forklift truck\ntest/71f845216176789c,Motorcycling\ntest/71f8c8a0ce35d9a1,Close-up,Whiskers\ntest/71fa7154b0ffacc3,Plant stem\ntest/71ff2a544c2d074a,Shinto shrine\ntest/72015bbf9f11fc86,Luxury vehicle,Antique car\ntest/7201e6fb40c3cbe2,Musical theatre\ntest/72034702ed98349f,Vegetarian food\ntest/720541210bfc07a4,Vegetarian food\ntest/7206c804bf55502b,Close-up\ntest/72092a22a14fe238,Antique car\ntest/72097de21720c65b,Roller skating\ntest/720cbd43a1caa11d,Chordophone\ntest/720d627bc86e7d74,Whiskers\ntest/721088a6a6b69cff,Plant stem\ntest/7210d6bca33359f3,Boating,Personal water craft\ntest/7212aa7b22ef847b,Close-up\ntest/7212cd31fd8f4530,Close-up\ntest/72137bef3590bac5,Antique car\ntest/7214b3d165f6f448,Domestic short-haired cat,Whiskers\ntest/7214e37a9d50b58f,Ball game\ntest/7217537d27457d2d,Goats\ntest/721a9fcb4b51b055,Auto racing\ntest/721af9cf082c7467,Sport utility vehicle\ntest/721e1fb7dd7a69ea,Luxury vehicle,Performance car\ntest/72209c6ce88e64f6,Luxury vehicle,Performance car\ntest/7223683cccca059b,Antique car\ntest/7225cfb578d5aff5,Cookies and crackers\ntest/72294c60e5c6aa21,Jeans\ntest/722a48b380425683,Close-up\ntest/722a8e744e86168c,Black-and-white,Close-up\ntest/722c68d73746bf78,Plant stem\ntest/722c7aedec720cd8,Compact car\ntest/722e60b9a3421211,Luxury vehicle\ntest/722f6770ad49110a,Arthropod,Close-up\ntest/72325b7f8e0ba525,Close-up\ntest/7232725365be6924,Ball game,Team sport\ntest/7232ad84a7f14aa5,Antique car,Luxury vehicle,Performance car\ntest/7233414401e918ac,Luxury vehicle,Sport utility vehicle\ntest/7233afa5d24a22e2,Luxury vehicle,Performance car\ntest/7236064c35684dba,Ball game\ntest/7237406c562f2e2f,Ball game\ntest/7237ac52d4c1f870,Luxury vehicle\ntest/723930ef6cdd412a,Shooting range\ntest/723adfb3ad8d0c6a,Fried noodles\ntest/723d770e3a8be876,Rural area\ntest/723f2b49abcdfd93,Compact car\ntest/72411493d13225a7,Sport utility vehicle\ntest/7245d76c14e3aee3,Monoplane\ntest/7246abd927c8bc55,Boating\ntest/7246f3d0feeeaded,Aerial photography\ntest/7247106ba8280837,Computer speaker\ntest/7247dcf186a435d7,Ball game\ntest/724aeb75c3473e4c,Vegetarian food\ntest/724b36b1d3887203,Black-and-white,Close-up\ntest/724bd0b6d535ff23,Black-and-white\ntest/7250bf1310324380,Performance car\ntest/72513ac4beeedee5,Extreme sport,Surfing Equipment,Wind wave\ntest/72520502afe2869e,Ball game,Team sport\ntest/7252d9727bc1d7b8,Compact car\ntest/725330c88270c0ba,Black-and-white\ntest/7254120dac4c0fed,Whiskers\ntest/725452f715c0ad57,Compact car,Antique car\ntest/72590e756fb9dd06,Shinto shrine\ntest/725961d007bd365b,Jeans\ntest/725a640eb10b596f,Cookies and crackers\ntest/725c7e070821925e,Arthropod,Close-up\ntest/725ccacf90ec4c17,Rural area\ntest/725d3f358832af0f,Blond,Portrait photography,Close-up\ntest/725ec093b4da5183,Luxury vehicle,Performance car\ntest/725ef64a68987e2a,Performance car\ntest/726041c63f975a5d,Luxury vehicle\ntest/7261079c3353c9e7,Chordophone\ntest/726321d349eeaf4a,Close-up,Plant stem\ntest/7263c5cdb70492b4,Bird of prey\ntest/726410e7070f6dba,Fried food\ntest/7269590e4161b34b,Firearm\ntest/7269819a36a6b11c,Shallot\ntest/726a0ceec9d97f2e,Performance car\ntest/726a9bccb60e50a6,Black-and-white,Antique car\ntest/726c8b511e79d235,Luxury vehicle,Performance car\ntest/726d6108d31da15d,Ball game\ntest/72722a5b90c2951a,Compact car,Antique car\ntest/7272754986ddb596,Outdoor shoe\ntest/7274ddf690ea3c34,Whiskers,Domestic rabbit\ntest/72765d61c40aaf5e,Arthropod,Close-up\ntest/7277e8622bab11b5,Fried food\ntest/7278be99b13cf494,Arcade game\ntest/727a47fa5fc58a25,Performance car,Luxury vehicle,Antique car\ntest/727ba87b0c0b4db8,Close-up\ntest/727d536b904ed9c1,Zeppelin\ntest/727ed6eccae8a665,Extreme sport\ntest/7280e706c19c72d1,Luxury vehicle,Performance car\ntest/7282fe0167b50121,Luxury vehicle,Performance car\ntest/7287236799c8878a,Galleon\ntest/72876eefc5bfea31,Roller skating\ntest/728864d00681f9f4,Herding,Bovine\ntest/7289c49d1f24996d,Performance car\ntest/7289c86f09060de4,Luxury vehicle\ntest/728b855d2f07d352,Rural area\ntest/728c5e391f46a332,Close-up\ntest/728eb41d09d1b2fa,Floristry\ntest/7290de72c5751133,Leaf vegetable\ntest/72918b708228ea4b,Sport utility vehicle\ntest/7292f8e72fcf7734,Luxury vehicle\ntest/729372df411aa030,Auto racing\ntest/7298fb34091ccc7b,Performance car\ntest/729afbc89172154f,Sport utility vehicle\ntest/729b5250b2e514df,Compact car\ntest/729eac4ebd6350d9,Extreme sport,Boating\ntest/72a07ebbe014d85b,Classical sculpture\ntest/72a50745442eeac7,Milky way\ntest/72a52d43dae05d75,Luxury vehicle,Performance car\ntest/72a60fb96001576f,Boating\ntest/72a9928a7e27d7a8,Chordophone\ntest/72aa21a12a435369,Black-and-white\ntest/72aa321edf5922c7,Close-up\ntest/72aa93400eeb012c,Performance car\ntest/72aaa6f4f1dcbb51,Ball game\ntest/72ac5746f4ef6612,Performance car\ntest/72ac684d985ea627,Aircraft engine\ntest/72ad5821613afdde,Ball game,Team sport\ntest/72afcb26ce746a0f,Headphones\ntest/72b13ee060653856,Crocodilia\ntest/72b37bd06fe477b2,Chordophone,Electric guitar\ntest/72b38e51b80da3ad,Aircraft engine\ntest/72b3d000cec65a91,Black-and-white\ntest/72b442c357a1f4ef,Nordic skiing\ntest/72b4ee839ad2fd48,Khinkali\ntest/72b60b5197e6cbad,Coral reef fish\ntest/72b6900b714a7479,Plant stem\ntest/72b7174bbfb7deee,Lawn game,Ball game\ntest/72b71ad0d242920c,Orator\ntest/72b7822ebc274052,Close-up\ntest/72b7da7442126254,Luxury vehicle,Antique car\ntest/72b803f8194748b6,Extreme sport\ntest/72b80744567dc557,Close-up,Scaled reptile\ntest/72b9c326fa84cb89,Combat sport\ntest/72ba4b2f85653ccc,Black-and-white\ntest/72c015d6fab19b91,Arthropod,Close-up\ntest/72c4d4b771034d04,Auto racing,Luxury vehicle,Performance car\ntest/72c4f27bbfbc557e,Monoplane\ntest/72c54440d56fe695,Luxury vehicle,Performance car\ntest/72c594cd073ec2b2,Compact car,Antique car\ntest/72c7f6d9221c2a1b,Molluscs\ntest/72c912225f1cb8cf,Computer speaker\ntest/72c9afad11968300,Compact car,Luxury vehicle,Antique car\ntest/72ca6110d4188079,Luxury vehicle,Performance car\ntest/72cf24c5ba0c890e,Compact car,Sport utility vehicle\ntest/72d1ad356df0c0dc,Black-and-white\ntest/72d3845fceb33c5c,Hair coloring,Close-up\ntest/72d42e127d82a71e,Frozen yogurt\ntest/72d4b5360008b43f,Black-and-white\ntest/72d81fdbc71c42c1,Performance car\ntest/72da1486fea778c5,Fried food\ntest/72def26fbf2b371e,Blond\ntest/72df81f46b85a34b,Cosmetics,Close-up\ntest/72e0dff03b54ca3b,Close-up,Scaled reptile\ntest/72e20b630cfa258f,Performance car\ntest/72e333f47a2f6a12,Compact car,Antique car\ntest/72e6266bcafa2c57,Close-up,Whiskers\ntest/72e65794f9f8b127,Boating\ntest/72e84cbfa8a73795,Luxury vehicle\ntest/72e94f9f0b96a342,Close-up\ntest/72eb1dddf45348c9,Luxury vehicle\ntest/72ec9abcb25d073c,Extreme sport,Wind wave\ntest/72ed9db05e6409d2,Vegetarian food\ntest/72ef684879ed6f7a,Boating\ntest/72ef6890766dedcb,Luxury vehicle,Performance car\ntest/72f0fbff67cae641,Pelmeni,Jiaozi\ntest/72f47bb5f0c2186a,Dishware\ntest/72f4e8294a385ec5,Close-up\ntest/72f5df019673edb9,Equestrianism\ntest/72f759c3179b4c7f,Sport utility vehicle\ntest/72fa86042a07bb1f,Ball game,Team sport\ntest/72fdd744b2405808,Aerial photography\ntest/73047a472e335d87,Hair coloring\ntest/7304a390ccfa7013,Bovine\ntest/7305b79f2a5cec77,Arthropod,Locust,Close-up\ntest/7307f14a0b2cf2f3,Luxury vehicle\ntest/73094503fe7581f5,Bratwurst\ntest/730aab52013c2234,Military vehicle\ntest/730b2c629b5f6f8a,Close-up,Plant stem\ntest/730d2930189277d8,Extreme sport\ntest/730dbc4fc4c4e1be,Public speaking\ntest/730e2ac2e168cfb2,Motorcycling\ntest/730fea95d093ef28,Luxury vehicle\ntest/7310184927241d65,Ball game,Team sport\ntest/73109e7dc8306168,Arthropod,Close-up\ntest/731121a927c15cbe,Aircraft engine\ntest/73143a71107a806c,Compact car,Luxury vehicle,Antique car\ntest/73152b0e97956df8,Auto racing\ntest/73170ab3dd86d24d,Black-and-white\ntest/73178fcac392b3d0,Firearm\ntest/73190ce1847adf3c,Bonbon\ntest/73195fb971fbf8c8,Close-up\ntest/731ce2ec3c74ecd8,Wind wave\ntest/731df0b10841411b,Arthropod\ntest/732096ec230d4784,Lawn game\ntest/7322499605a17cae,Outdoor shoe\ntest/7324c78c25fde9a7,Compact car\ntest/732ae036db712376,Coca-cola\ntest/732c11a847312879,Modern dance,Musical theatre\ntest/732dabb3a6571cd2,Close-up,Whiskers\ntest/73301ba02a34c27f,Luxury vehicle\ntest/7333a07e5074b72a,Luxury vehicle,Sport utility vehicle\ntest/73350eea8f22b9a3,Luxury vehicle,Antique car\ntest/73362d0ce7707b36,Whiskers\ntest/733833f82741c834,Luxury vehicle,Performance car\ntest/73390352338e864d,Bonbon\ntest/733a3267085683cb,Motorcycling\ntest/733a55e93c501b8c,Digital camera\ntest/733b3a6efad570d0,Vegetarian food,Junk food,Fried food\ntest/733d8541dc76b9a6,Floristry\ntest/733eb9cb20d77d69,Blond,Portrait photography\ntest/734249d6ee42a9c0,Luxury vehicle\ntest/734383af76ed746b,Hound\ntest/7343c80fbbc1fd9c,Woodwind instrument\ntest/7346029758a4633b,Extreme sport,Sledding\ntest/7346f739610027a4,Horse and buggy\ntest/734a3f44d9e8960a,Plant stem,Close-up\ntest/734afe10bab98803,Perching bird\ntest/734bf3a36e9adb71,Luxury vehicle\ntest/734c3705592405e9,Plant stem\ntest/734c700f94c15790,Jeans\ntest/734c9d4b07cecd73,Flatbread\ntest/734db6fb9b4707a0,Watercolor paint,Bovine\ntest/7350b402b9b3a934,Blond\ntest/7351faf9c1cd3732,Cobblestone\ntest/73533d414125d53c,Performance car\ntest/73539cc87624ae1d,Blond\ntest/7353ae519715bb74,Auto racing,Luxury vehicle,Performance car\ntest/73543af3882cb336,Barechested\ntest/7354f94be163aa73,Fried noodles\ntest/73582a1642f7faf0,Breadboard\ntest/7359bba50b23b2d7,Black-and-white\ntest/735ab25e35335449,Close-up\ntest/735bd4f1cdffb38a,Vegetarian food\ntest/735d847ce5aff8d1,Rural area\ntest/735e501c5ffaab55,Portrait photography,Close-up\ntest/735ee422374abd0b,Ball game\ntest/7360ccc352bc587c,Black-and-white,Close-up\ntest/7361791a4294a34b,Aircraft engine\ntest/73626ce9656eae2b,Extreme sport\ntest/73646ba8e48f0b1b,Extreme sport,Boating\ntest/7365a6c049e8efa1,Compact car\ntest/73668864a4fe23ca,Close-up\ntest/73670f6d8df27781,Ball game\ntest/73680637c3de974e,Frying,Fried food\ntest/736865256b6733d5,Herding,Goats,Rural area\ntest/73699dc111fe53e6,Close-up\ntest/736b012bf366e038,Roller skating\ntest/736d05a2f04971d1,Arthropod,Close-up\ntest/736e79ad6c15b027,Compact car\ntest/7374bdf14bcaad6d,Hound\ntest/7375b975594e9bcd,Vegetarian food\ntest/7377674792efdc68,Rural area\ntest/737867d37f3367dc,Auto racing,Performance car\ntest/7378b01d091019de,Glacial landform\ntest/73794ae66c02a1a5,Luxury vehicle,Sport utility vehicle\ntest/737c33e427640f3f,Luxury vehicle\ntest/73861c8fba6caa15,Team sport\ntest/7387984d1da27064,Luxury vehicle,Performance car\ntest/73889cf8df1bee9a,Close-up\ntest/738d3093d47ccd10,Aircraft engine\ntest/738d8dea6ce900de,Luxury vehicle,Antique car\ntest/738e836ee5b22bb6,Plant stem,Close-up\ntest/738f60668cc1f099,Miniature Poodle\ntest/73904b07c3f24fd7,Close-up\ntest/7390da0e9cf64e54,Arthropod,Locust,Close-up\ntest/7391cca780819678,Auto racing,Touring car\ntest/7391dcbb186659d1,Auto racing\ntest/73962155511b47f4,Compact car\ntest/73989c26f81d0939,Rural area\ntest/7398df0f8b497645,Arthropod,Close-up\ntest/7398faa41eddc268,Combat sport\ntest/73991e5c3c0899e1,Amphibian,Close-up\ntest/739baabab8f58a94,Machine tool\ntest/739cdeb3e73fd1f1,Close-up,Scaled reptile\ntest/739f1d3a0b700a42,Luxury vehicle,Performance car\ntest/739f9ba07d1c6cdb,Residential area\ntest/73a189dcd15397b9,Combat sport,Grappling\ntest/73a1ffb704222608,Floristry\ntest/73a2c86f3d28c3cb,Compact car,Luxury vehicle\ntest/73a3679518f59c1f,Close-up\ntest/73a3effe4f390263,Aircraft engine\ntest/73a4bb5a75451521,Arthropod,Close-up\ntest/73a6d574b96b5bec,Close-up\ntest/73a7361d7b5306c6,Luxury vehicle,Antique car\ntest/73a768d6526ed30b,Whiskers\ntest/73aa40ea11e5aa92,Domestic short-haired cat,Whiskers\ntest/73aa458152cfe0bb,Rural area\ntest/73aa6ef6638f8425,Bovine\ntest/73aa845a4dc0d13e,Aerial photography\ntest/73adb376f8c9b5e3,Plant stem\ntest/73ae2abca4dda419,Black-and-white\ntest/73af5ab6dc9adbe2,Whiskers\ntest/73afe2c376a6238d,Aircraft engine\ntest/73b01dff156bd457,Floristry\ntest/73b079455786ab17,Coral reef fish\ntest/73b267ec4d6dba1b,Luxury vehicle,Performance car\ntest/73b3a03ed9dc3455,Performance car\ntest/73b54f5d11bfc705,Black-and-white,Close-up\ntest/73ba785f43da9ea2,Vegetarian food\ntest/73bb460f39aaedcb,Luxury vehicle\ntest/73bba7d7f439b76f,Luxury vehicle\ntest/73bc60f2db2f1e0f,Military person\ntest/73bf01ae5d9aea95,Road cycling\ntest/73bf3716b8cd5627,Motorcycling\ntest/73c0befcbb51053c,Beef tenderloin\ntest/73c1b704f48bc73b,Luxury vehicle\ntest/73c2fdbefeea8857,Frying,Junk food,Fried food\ntest/73c455552f1b6f3a,Antique car\ntest/73c4ff3138c123df,Nile crocodile,Crocodilia\ntest/73c7163405057519,Close-up\ntest/73caeb335f576904,Domestic short-haired cat,Whiskers\ntest/73cc83643fe3a0a5,Ball game,Team sport\ntest/73cdda08ec6f43c1,Close-up\ntest/73ce935fd729e5c4,Combat sport\ntest/73d0117ca4c0c50f,Steamed rice,Rice ball\ntest/73d0fb45c34b1503,Electronic instrument\ntest/73d506296012725b,Close-up\ntest/73d586ef622602a3,Performance car\ntest/73d88484754131ff,Molluscs\ntest/73da2cb976eab362,Junk food\ntest/73e1648efe4801f5,Compact car,Antique car\ntest/73e190e924b6a60e,Black-and-white\ntest/73e2ddabd76d5ed5,Close-up,Whiskers\ntest/73e40b22520f5b3b,Luxury vehicle,Performance car\ntest/73e6d4271ae39f00,Luxury vehicle\ntest/73e78593584462e5,Military vehicle,Gun turret\ntest/73e7f75ff0a81fc3,Close-up\ntest/73e971c0caf9de52,Luxury vehicle,Performance car\ntest/73e9b392dff4b07c,Close-up,Plant stem\ntest/73ec7a7f02460c3a,Musical theatre\ntest/73eeb6d43a94dc47,Pasta salad\ntest/73ef896ab275b791,Road cycling\ntest/73f02d3c0a38a956,Glacial landform\ntest/73f0b2657415a0be,Leaf vegetable,Vegetarian food\ntest/73f1f69d506d4122,Frozen yogurt\ntest/73f31bfb0e5c786e,Close-up\ntest/73f67f4713914f12,Chordophone\ntest/73f6e538006a40da,Blond,Close-up\ntest/73f884e7d0d9c0b6,Combat sport\ntest/73face3beb18e0c7,Luxury vehicle,Antique car\ntest/740055bea96541c6,Luxury vehicle,Sport utility vehicle\ntest/74016e4e451aa292,Black-and-white,Close-up\ntest/7402a07683d9475f,Whiskers\ntest/7402eddf9fb8c9ec,Dishware\ntest/740341a8283e07d4,Cosmetics\ntest/74040f7871e1d240,Ball game\ntest/740592b963ed3717,Boating\ntest/7407c9c7ab5ad42d,Vegetarian food,Fried food\ntest/7409fc10b381d980,Portrait photography,Close-up\ntest/740c8e911deb1b19,Gumbo\ntest/740ed2c6e03171cc,Antique car\ntest/7410ef6eaffb0b58,Arthropod,Locust\ntest/7410f9ae5a06a995,Equestrianism\ntest/7412a1e7b4edf786,Extreme sport\ntest/74148597d9fd6d08,Perching bird\ntest/741575902dca251d,Luxury vehicle,Antique car\ntest/7416c8cd552e937a,Vegetarian food,Corn on the cob\ntest/74171cf32542d132,Luxury vehicle\ntest/741923ea73a03dcd,Rural area\ntest/7419a38df58790ad,Jeans\ntest/7419eb86959033c9,Performance car,Antique car\ntest/741b570ea3e0922e,Arthropod,Close-up\ntest/741b8d71263aaaba,Cookies and crackers\ntest/7420a66f45dd7fe2,Jeans\ntest/74226dea2d824592,Boating\ntest/7423a122d8ce58bc,Close-up,Plant stem\ntest/7429db4fcf1e0855,Luxury vehicle,Performance car\ntest/742b585b230c3317,Auto racing\ntest/742b6bb537ab5244,Jeans\ntest/742e52216d449adf,Bird of prey\ntest/7430e5b70e64c4a4,Luxury vehicle\ntest/7432f3ab6703522e,Ball game,Team sport\ntest/743554b910a198a0,Rural area\ntest/743583b44bdaea43,Close-up\ntest/7436009411db585a,Antique car\ntest/7436762ea3f09735,Close-up\ntest/74373c3f097fab97,Portrait photography\ntest/7439c95b3a7f58c5,Childbirth\ntest/743a675fcb61095b,Bovine\ntest/743a7fe316c0dc96,Luxury vehicle,Performance car\ntest/743ab88b509d6576,Bratwurst\ntest/743be0b407e20d14,Frigate\ntest/743c0c83d50b9560,Compact car,Luxury vehicle,Antique car\ntest/743c478f595c307b,Vegetarian food\ntest/743cf00fda5a6618,Bovine,Halter\ntest/743e89d62813e5cd,Plant stem\ntest/743f42219a1966d6,Extreme sport\ntest/743fbd0c4c3a167b,Nile crocodile,Crocodilia\ntest/7443ae9bb9305845,Compact car,Luxury vehicle\ntest/7448c261e96505bf,Ball game\ntest/744b0279f2a223ac,Monoplane\ntest/744b16b41ae1878e,Compact car,Luxury vehicle,Antique car\ntest/744cd8daf41282a9,Luxury vehicle,Sport utility vehicle\ntest/744d4922a533d4d3,Performance car,Antique car\ntest/744ef921345b6301,Luxury vehicle,Sport utility vehicle\ntest/7450e65a4633467b,Antique car\ntest/7453e5afbc70d378,Antique car\ntest/7455e034ec1f4a71,Woodwind instrument\ntest/745653fc0559e1c7,Luxury vehicle\ntest/7458b24ec89312db,Barbecue chicken,Fried food\ntest/745bfed170a4a3c4,Junk food\ntest/745c3a00f4200313,Perching bird,Close-up\ntest/745dac0978281efd,Performance car\ntest/74625aa1f3df88bc,Luxury vehicle,Performance car\ntest/7463c3e770a9b28a,Shallot\ntest/7463fc67372e7b81,Plant stem,Close-up\ntest/746a3a178f5f6ecc,Hound\ntest/746a4a63a3c381be,Compact car\ntest/746c049dda3ab724,Close-up,Scaled reptile\ntest/746eb5fb99ccda9d,Ball game,Team sport\ntest/746f0d77045a4629,Bovine\ntest/746fb1fe04a7e8da,Motorcycling\ntest/7471dfa436e626da,Sport utility vehicle\ntest/747278c9be7e4885,Black-and-white\ntest/74731a1a10de171f,Vegetarian food\ntest/74736870a82c410e,Aircraft engine\ntest/74756d14c902f9b3,Black-and-white\ntest/74764943784830df,Leaf vegetable\ntest/747731547153335f,Calabaza\ntest/74777199be95a651,Black-and-white\ntest/7478905c2a3f52cd,Backlighting\ntest/7479913654581f3a,Touring car,Luxury vehicle,Antique car\ntest/74825d4e2850cae3,Wallaby\ntest/7484492dbd50fa5d,Athletic shoe\ntest/7484ed556416b527,Computer speaker\ntest/7485c4b58f125b7a,Black-and-white\ntest/7486a045630d8489,Residential area\ntest/74874b8d5afb80e1,Rural area\ntest/7487c025a81d08b4,Compact car\ntest/748b809e80e4e97b,Rural area\ntest/748c43f3c411e4b3,Compact car,Luxury vehicle\ntest/748d5c8e2da76356,Close-up\ntest/748fceb9de4976f8,Bagpipes\ntest/74928a586bccc9ef,Vegetarian food\ntest/74936adc221e0f6e,Close-up\ntest/7496c5db6d27fa56,Luxury vehicle\ntest/7497a8639503f260,Zeppelin\ntest/7497c74324998e8f,Whiskers\ntest/7498d1458ca47b0f,Coral reef fish\ntest/749efa9bb08a3515,Close-up\ntest/74a06d433b355eca,Extreme sport,Sledding\ntest/74a0a352a44cca78,Firearm\ntest/74a1e2d4adb8d9b1,Ball game,Team sport\ntest/74a292e2acc23d10,Auto racing,Performance car\ntest/74a3760cadd647bc,Aircraft engine\ntest/74a42706898a65b7,Luxury vehicle\ntest/74a6667ccdc56a46,Antique car\ntest/74a83b5c59eb00ab,Antique car,Luxury vehicle,Performance car\ntest/74a924d988d5b722,Vegetarian food\ntest/74aa413e84cc47d7,Bovine\ntest/74aa6d15949cdc1c,Close-up\ntest/74ab244ce8d11cc9,Modern dance,Musical theatre\ntest/74ab8e763ec49a28,Compact car,Antique car\ntest/74ac771b842ccc77,Performance car\ntest/74ac9cfe6b254dbc,Perching bird,Close-up\ntest/74ae828b2b1ae102,Close-up\ntest/74b0aaece2a9bbc4,Performance car,Antique car\ntest/74b355f41899bd94,Ball game\ntest/74b5621b40aaf632,Ball game,Team sport\ntest/74b5f91f7f459440,Flatbread\ntest/74b8a8da46a94023,Bovine\ntest/74b8f07d7d81129b,Wind wave\ntest/74baf177cb822e09,Military person\ntest/74bb0aeedf0c431b,Road cycling\ntest/74bbdffaf4cd90fd,Cosmetics\ntest/74bf4f1b219d83b1,Close-up\ntest/74bf5da0822ce9cc,Military person\ntest/74c2de3f8c681c74,Drums\ntest/74c33b28a82a93a1,Extreme sport,Boating\ntest/74c38d899eca1fdd,Floristry\ntest/74c3a540181e76cb,Procyonidae\ntest/74c56303db5999ef,Luxury vehicle,Performance car\ntest/74c683dc8519c1e0,Antique car\ntest/74c6af70e4e98eb9,Aircraft engine\ntest/74c8dfe88500a821,Residential area\ntest/74ca0cc75ff0433e,Combat sport\ntest/74ca13767167e6d1,Black-and-white\ntest/74caa32d8370a118,Modern dance\ntest/74cbfabf13032b84,Cookies and crackers\ntest/74ce2558df44fee1,Extreme sport\ntest/74cf3187247b76a2,Flatbread\ntest/74d3cc141abcd4f3,Comics\ntest/74d44f0c0c3ab149,Modern dance,Musical theatre\ntest/74da4d02071b3a01,Steamed rice\ntest/74dc080cc148a500,Rural area\ntest/74ddf7bcf6b7f4f3,Watercolor paint\ntest/74de475dbf20b8c3,Coral reef fish,Close-up\ntest/74e0234a68b40180,Performance car,Antique car\ntest/74e0ed65b75d0abf,Chordophone\ntest/74e8d31994d74a8e,Luxury vehicle,Antique car\ntest/74e990312b41516a,Digital camera\ntest/74eb24f78e519655,Holding hands\ntest/74ee6770538bd61a,Jeans\ntest/74f0137e03f8d1e2,Fried food\ntest/74f1ce13a4c4eeeb,Brisket,Ribs\ntest/74f330f62bc02ee6,Close-up\ntest/74f39cedfd9bd002,Compact car\ntest/74f4da21ba08c373,Cookies and crackers\ntest/74f555ea65fcd681,Black-and-white\ntest/74f850977093dda9,Luxury vehicle\ntest/74f94963ee1e135d,Middle ages\ntest/74fb614c54566820,Whiskers\ntest/74fd49e4f4397913,Close-up\ntest/74feef3cb849091c,Jeans\ntest/7502c8e0da1eba81,Compact car\ntest/750663ff03d5a065,Medical imaging\ntest/750aaaa6a2881257,Ball game\ntest/750d10185f00c37f,Luxury vehicle,Antique car\ntest/750da1961c6927ab,Luxury vehicle,Sport utility vehicle\ntest/750f08793ae3050d,Residential area\ntest/7515766906041713,Close-up\ntest/751597f0aa439789,Chordophone\ntest/7516ad714d412fb0,Perching bird\ntest/75185bae25f4dae9,Leaf vegetable\ntest/751993340dda78f9,Inflatable\ntest/751ad7417acc7152,Black-and-white,Close-up\ntest/751e1a128111bacf,Extreme sport,Divemaster,Scuba diving,Underwater diving\ntest/7520937e0ed6cbd2,Close-up\ntest/7521ac3a7dddd45a,Digital camera\ntest/7521b35930983316,Sport utility vehicle\ntest/75225fbb49b63da4,Coral reef fish\ntest/7522bdaa1a4bd0dd,Luxury vehicle\ntest/752761107714ec65,Compact car,Luxury vehicle\ntest/75293c83e6c5ca24,Combat sport\ntest/7529447018400e31,Portrait photography\ntest/7529517097b1e4a1,Barquentine\ntest/752f25c02ed2806b,Water polo,Ball game,Team sport\ntest/752f4d6f39f930ea,Luxury vehicle,Antique car\ntest/753077f1d48eb120,Close-up\ntest/7531339407966c17,Close-up,Whiskers\ntest/75326d1b2377051a,Compact car\ntest/7533cacae7489b58,Performance car,Antique car\ntest/7535de3b54c7427a,Compact car,Luxury vehicle\ntest/75377332394b2bfa,Close-up\ntest/7537da47a11f8932,Military person\ntest/753909dec7a729cc,Jeans\ntest/75393041b235cb66,Luxury vehicle\ntest/753acf44af225360,Luxury vehicle\ntest/753c907898e0fc89,Floristry,Close-up\ntest/753d4963aaec596a,Luxury vehicle\ntest/753db503f5cadbb8,Chordophone\ntest/753e69f17675e835,Bird of prey\ntest/753e8821c4813c8e,Bratwurst\ntest/753ece4020ebfcc2,Luxury vehicle,Sport utility vehicle\ntest/753f4def4738bbde,Combat sport,Grappling\ntest/754050deda2a0334,Whiskers\ntest/7542f12b9f06682b,Bovine\ntest/75457713cd98fec0,Touring car,Luxury vehicle\ntest/7548cf08f38ff648,Black-and-white\ntest/754d776a9ac067ff,Luxury vehicle,Antique car\ntest/754e57ee1b5bbf50,Drums,Close-up\ntest/754ffb26c0742854,Portrait photography\ntest/75503a0df145fd0d,Gumbo\ntest/7550609f30d78b1e,Close-up,Scaled reptile\ntest/7552195c4c503e96,Coral reef fish\ntest/7552c9ae3028df46,Fried noodles\ntest/75584fde8b20196e,Gumbo,Steamed rice\ntest/755b697e7d2cdf72,Siberian husky\ntest/755d5e4fe0ecaf63,Whiskers\ntest/755f7258edc1b169,Fried food\ntest/755facaee572eeee,Firearm\ntest/755fec05aa3d7163,Rural area\ntest/75614899e4ffb672,Performance car,Luxury vehicle,Antique car\ntest/7562cb13ae5e4262,Ball game,Team sport\ntest/75639a8da218c2bd,Domestic short-haired cat,Whiskers\ntest/756500fe239f62af,Antique car\ntest/75658e8646a6738a,Motorcycling\ntest/756678d2c7499b22,Floristry\ntest/75675fffa4feed4c,Military person\ntest/7567cfc81f7ab95a,Luxury vehicle\ntest/7567e7e69b5deb23,Amphibian\ntest/7568b16ca82621d6,Antique car\ntest/756ae5ca1f0d8090,Extreme sport\ntest/756cb25056699243,Close-up\ntest/756e73db3b8b0eab,Freight transport\ntest/7570e0dc22fb8ef3,Digital camera\ntest/7571f9c9b5c8005d,Luxury vehicle\ntest/75721998d3a0e51d,Close-up\ntest/75753af3e1691b39,Firearm\ntest/7577ab06aeb821b3,Black-and-white\ntest/7577b24c64899a63,Junk food\ntest/75780c3483760955,Luxury vehicle,Sport utility vehicle\ntest/757946c667ec0e8d,Wind wave\ntest/75796d8c182d441b,Luxury vehicle,Performance car\ntest/757ad9f0c68881c7,Compact car,Luxury vehicle\ntest/757de8a50b04ce74,Close-up\ntest/757e07d00225a5e4,Whiskers\ntest/7583344a534399b2,Close-up\ntest/7585e72c34132ed0,Performance car\ntest/7586d1002ca062bf,Aircraft engine\ntest/758a69003c44b9da,Close-up,Scaled reptile\ntest/758a7473c30f7715,Sport utility vehicle\ntest/759003f032d1b0b8,Childbirth\ntest/75907841ae4e806f,Flatbread,Junk food\ntest/759090ed86870302,Fried food\ntest/75939ec774d4dc35,Perching bird,Close-up\ntest/75947d8821388c5f,Luxury vehicle,Antique car\ntest/75955499f911589c,Rural area\ntest/7596fb960ed01b5c,Close-up\ntest/7597a57ae2f03670,Ball game\ntest/75988ae1d4f562b2,Scaled reptile\ntest/7598b97ac6a6cd88,Portrait photography,Close-up\ntest/75992afc0c741fb4,Luxury vehicle\ntest/75993a9d9a9315ff,Combat sport\ntest/7599d011c2426f43,Sport utility vehicle\ntest/759a526044a638ab,Calabaza\ntest/759b28953392325a,Ball game,Team sport\ntest/759e733594462bb4,Bonbon\ntest/759ec916f36ddc47,Jeans\ntest/759fb63d2ed33d2d,Athletic shoe\ntest/75a3556c95321ac2,Electronic instrument,Black-and-white,Close-up\ntest/75a42926dc30609c,Antique car\ntest/75a4de176241ea24,Outdoor shoe\ntest/75a4ebd26116c098,Electronic instrument,Drums,Headphones\ntest/75a56e0b21ed3bb5,Antique car\ntest/75a594316e795d2b,Plant stem,Close-up\ntest/75a688cfe4a9b959,Luxury vehicle\ntest/75a85917ee5fbbf2,Compact car\ntest/75a911a2a9c651f3,Jeans\ntest/75a9c3c196e298de,Performance car\ntest/75aa112303ba3d66,Sport utility vehicle\ntest/75ac4c6829864326,Close-up\ntest/75adc9a823be9588,Cookies and crackers\ntest/75aec4504b7c4a49,Black-and-white\ntest/75aeedaabc305bdd,Boating,Rowing\ntest/75afdcfc98cd0c70,Chordophone\ntest/75b0bc7958f2e542,Sport utility vehicle\ntest/75b571685b198ff3,Arthropod,Close-up\ntest/75b6883696e8777b,Auto racing,Luxury vehicle,Performance car\ntest/75b8a642ad35accb,Luxury vehicle\ntest/75b9b04e4d3481db,Hound\ntest/75baea5e3c72f8a2,Motorcycling\ntest/75bc5b4587c859f1,Sport utility vehicle\ntest/75c2c22d873d18f8,Close-up\ntest/75c35fdc8f7393c2,Rural area\ntest/75c37e760b2ffa45,Luxury vehicle,Sport utility vehicle\ntest/75c390623e67ef0c,Fried food\ntest/75c41dd7baa9bd1a,Domestic short-haired cat,Close-up,Whiskers\ntest/75c5ead08e6e30db,Ale\ntest/75c830c2b6adf3a4,Chordophone\ntest/75c851843ca2685f,Luxury vehicle,Sport utility vehicle\ntest/75cbae48c14f4539,Close-up\ntest/75cd38ac81c1df96,Luxury vehicle,Sport utility vehicle\ntest/75ce12f34a8746bb,Hair coloring\ntest/75d18963267f6063,Sport utility vehicle\ntest/75d283a649f0aaff,Close-up,Plant stem\ntest/75d2a3f752c5fa84,Compact car,Luxury vehicle\ntest/75d2baa7c0d65ea4,Dishware\ntest/75d336e08ec65d4e,Close-up\ntest/75d56f655f75da2e,Horse and buggy\ntest/75d6415f2985eed2,Junk food\ntest/75d6e3f8c07b55df,Extreme sport,Sledding\ntest/75d8c41b0287819d,Firearm\ntest/75d9000a6c3821b5,Luxury vehicle,Antique car\ntest/75d98387edbe3cc0,Digital camera\ntest/75d9fa1045a8ddb8,Monoplane\ntest/75dca5f1d272d057,Luxury vehicle\ntest/75e0cb0b5b5c43d6,Sport utility vehicle\ntest/75e116e77e2c81a7,Fried food\ntest/75e184de4b379f18,Siberian husky\ntest/75e4377414802a34,Extreme sport\ntest/75e4eed80ba295d0,Black-and-white\ntest/75e54c40e2d4ae61,Close-up\ntest/75e888c0f1f39a08,Middle ages\ntest/75eaa4c7379a0d9e,Antique car\ntest/75ec3a8b5e5f96f4,Extreme sport\ntest/75ee3538a05ce6e9,Headphones\ntest/75ef1a05b336912e,Portrait photography\ntest/75f13c54f721c85f,Sport utility vehicle\ntest/75f17f3961792986,Steamed rice\ntest/75f200ad755bd264,Electronic instrument,Electric piano\ntest/75f4a4edc11e476a,Perching bird\ntest/75f6cee67ba1c9d9,Close-up,Halter\ntest/75f744e48ca3cd68,Junk food\ntest/75fb079e0ce9a67d,Church bell\ntest/75fb4e39c837113f,Chordophone\ntest/75fe50ff5fd9c17c,Blond,Close-up\ntest/75febe8d08161943,Rural area,Canola\ntest/7600c15dafe88166,Compact car\ntest/7603429eee16e229,Close-up,Whiskers\ntest/7606418ee8e8ffa9,Jiaozi\ntest/76066ee743ff249c,Compact car,Luxury vehicle,Antique car\ntest/760931f98b3d9f5e,Whiskers\ntest/7609dd085d1c198c,Jeans\ntest/760b4441c9977577,Antique car\ntest/760b4d01101effe0,Compact car,Luxury vehicle\ntest/760f50d60267ad96,Flatbread\ntest/760fe576ed17f602,Melee weapon,Hunting knife\ntest/761268d00b88acbb,Horse and buggy\ntest/761380e00eee53e2,Ball game\ntest/761ab702525af9bc,Performance car\ntest/761bb5d0667b1e3f,Extreme sport,Boating\ntest/761d8ed4cb05a785,Jiaozi\ntest/761e247ec8c1a17f,Luxury vehicle,Performance car\ntest/76222be228f94141,Compact car,Luxury vehicle,Antique car\ntest/7624447e696a7c80,Machine tool\ntest/7624934958d6dcb5,Rural area\ntest/76249d14bbc07bba,Bottled water\ntest/76260f741eeec495,Luxury vehicle,Antique car\ntest/762b34e85d323175,Residential area\ntest/762cb3a2145429f4,Perching bird\ntest/762ee483118f5749,Pork chop\ntest/763341c81116f9ca,Close-up\ntest/7633438bfc3d0cee,Close-up\ntest/763498970f01f54f,Close-up\ntest/7634ed48dbe51123,Antique car,Performance car\ntest/76367a8aae3925ea,Close-up\ntest/763985d80fc068e7,Arthropod,Close-up\ntest/763ab262f759a7ab,Ball game,Team sport\ntest/7645bae264564eef,Flatbread\ntest/7648f381477b0db0,Middle ages\ntest/76490525d5b01b8e,Boating\ntest/7649ace3f3a0de90,Luxury vehicle,Antique car\ntest/764bab3424d4b2df,Luxury vehicle,Performance car\ntest/764e02e4f878857b,Siberian husky\ntest/7651b084a339895e,Performance car\ntest/76596e4a364fd50e,Whiskers\ntest/765ac256f05e4b98,Chordophone,Electric guitar\ntest/765b62f979202b66,Luxury vehicle,Performance car\ntest/765ccf6464a32ec3,Arthropod,Close-up\ntest/765d958679f3cc72,Floristry\ntest/765e50605cda79fe,Luxury vehicle,Performance car\ntest/76613bdc52e75d47,Road cycling\ntest/76622722d31682c2,Beef tenderloin\ntest/7663977fff9c2230,Cookies and crackers\ntest/766453b0992325ee,Boating\ntest/76649244fff19e20,Frying,Fried food\ntest/76684d7c0325d199,Aerial photography\ntest/7668920edda33743,Close-up\ntest/766d5fc55a4c7edd,Close-up\ntest/766e5e223e1e3103,Close-up\ntest/7670da9037a0d71e,Sport utility vehicle\ntest/767486c643ac2505,Luxury vehicle,Performance car\ntest/7675aded8b60c387,Luxury vehicle,Performance car\ntest/76789369bcea672e,Antique car\ntest/7678a54a84218df7,Close-up\ntest/767c631ce95f830f,Vegetarian food\ntest/767f86e0515f1500,Jeans\ntest/767fe4fc10b09079,Procyonidae,Whiskers\ntest/7680296393b54b18,Stemware\ntest/768048303c84a3a2,Billiards\ntest/76823d656db6f271,Black-and-white,Close-up,Whiskers\ntest/7682e65839335e34,Luxury vehicle,Performance car\ntest/76836a45b9b0263d,Luxury vehicle,Sport utility vehicle\ntest/7683f951de20e679,Floristry\ntest/76864ca4c4705249,Floristry\ntest/76868f5fc48f412e,Luxury vehicle,Performance car\ntest/76880c36f38c01df,Dishware\ntest/76888b369a797de6,Compact car,Antique car\ntest/7689c39ddf48ecfd,Black-and-white\ntest/768c1eb262776eeb,Luxury vehicle,Antique car\ntest/768f323aabb19dcc,Cookies and crackers\ntest/768f9f96d93f0431,Close-up\ntest/7691bf91d3f1e9d7,Aircraft engine\ntest/7692caa9e3404422,Athletic shoe,Outdoor shoe\ntest/7692eccf5cbb79ad,Sport utility vehicle\ntest/7693cd525d384ed9,Boating\ntest/7693e87d3c3f2a6e,Close-up\ntest/76943814327811ee,Whiskers\ntest/76944c9c911a2acd,Black-and-white\ntest/76950f255bc15e8a,Close-up\ntest/76951f4b050690c8,Compact car,Luxury vehicle\ntest/7695836a68e6ddd3,Luxury vehicle\ntest/7697820940aa07b8,Antique car\ntest/769aa6b124b77003,Galleon,Barquentine\ntest/769cc5db23b0ba0b,Black-and-white\ntest/769ea057051b16b8,Performance car,Luxury vehicle,Antique car\ntest/769faaf11d76c4b6,Roller skating\ntest/76a16e31ffacfda7,Domestic short-haired cat,Close-up,Whiskers\ntest/76a18565a08ad756,Chordophone\ntest/76a61c860c8b49db,Soba\ntest/76a71a8317e32ba5,Scaled reptile,Close-up,Anole\ntest/76a8f6529e73919a,Backlighting\ntest/76ac54f3f4156b75,Antique car\ntest/76ad1abdb9e0c0d8,Leaf vegetable,Vegetarian food\ntest/76b30545b2733486,Compact car\ntest/76b3ed7fe4812eec,Bird of prey\ntest/76b4c51281effff7,Ball game\ntest/76b536639cc36d0f,Vegetarian food\ntest/76b56e65e50162de,Luxury vehicle,Antique car\ntest/76b7ed9f47e368e5,Residential area\ntest/76b801c7187ab910,Hound,Close-up\ntest/76b82537dd5a8cad,Luxury vehicle,Performance car\ntest/76bc82be7ce1525c,Bird of prey,Close-up\ntest/76bfb38c880de50f,Brochette,Yakitori\ntest/76bfe83b59e9a3ab,Corn on the cob\ntest/76c1552233203cef,Extreme sport\ntest/76c1749c1ebd6a94,Wind wave\ntest/76c4418826b11413,Luxury vehicle,Antique car\ntest/76c7c26a6daad921,Arthropod,Close-up\ntest/76c82981da1bc210,Close-up\ntest/76c9b212e151c98c,Melee weapon,Hunting knife\ntest/76ccafb831ddb1df,Arcade game\ntest/76ce70eef13243d0,Horse racing,Equestrianism\ntest/76d1843e23111eda,Close-up\ntest/76d67fe26f7576aa,Whiskers\ntest/76d8b6c96664182c,Aerial photography\ntest/76db7b5a2baa774b,Perching bird\ntest/76dcc5d2586270c7,Black-and-white\ntest/76dce79ee6681e0f,Extreme sport,Sport climbing\ntest/76de57f8c0e8d4f3,Performance car\ntest/76deeb9578600a8e,Black-and-white\ntest/76e3036b647e831f,Sport utility vehicle\ntest/76e4efb021f0e19e,Luxury vehicle,Antique car\ntest/76e75ea23a0f9e77,Siberian husky\ntest/76e75ee1b02ea4db,Luxury vehicle,Antique car\ntest/76ec1da9032fe1ea,Extreme sport,Wind wave\ntest/76ee605651f57c35,Arthropod,Close-up\ntest/76ee819155aca107,Luxury vehicle,Antique car\ntest/76ee87af549040c4,Combat sport\ntest/76eee4ae86c82907,Extreme sport,Motorcycling\ntest/76f11b6cf83fe1e3,Close-up\ntest/76f355364a8d1c81,Domestic short-haired cat,Whiskers\ntest/76f536980989f5a8,Billiards\ntest/76faa5748f4fc096,Equestrianism\ntest/76fb692b2eef7f82,Full moon\ntest/76fc515a9729018d,Jeans\ntest/76fec68553c420a0,Close-up\ntest/76ff7dd120818228,Black-and-white\ntest/7700b39b4aada014,Vegetarian food\ntest/7703358364cefa76,Wind wave\ntest/7704b866f061e8da,Close-up\ntest/77055ca05c5d42cd,Luxury vehicle\ntest/7706319e1ff8b217,Luxury vehicle,Antique car\ntest/77075aed0d775688,Motorcycling\ntest/7708578dc7922ae6,Cosmetics\ntest/7708a285000247af,Ball game,Team sport\ntest/7709f027d003e49f,Arthropod\ntest/770a6af42ad7782f,Portrait photography,Close-up\ntest/770abb00934b28f1,Lawn game,Ball game\ntest/770afae6b466a8c9,Plant stem\ntest/770c4263911b84c3,Jeans\ntest/770eaf7b6c164177,Motorcycling\ntest/770f0d2c76d9b28e,Sport utility vehicle\ntest/77140422e6a53c47,Close-up\ntest/7714c386ad78c542,Firearm\ntest/77151236ef91117c,Black-and-white\ntest/771540cf9cea5e33,Barbecue chicken,Fried food\ntest/771649b9accc0219,Boating\ntest/771775b41d5660d3,Luxury vehicle,Performance car\ntest/771831b82bfae166,Compact car,Luxury vehicle\ntest/771a44e8d370d94a,Rear-view mirror\ntest/771d68a7ed1f44d7,Military vehicle,Gun turret\ntest/77220c9f19c6b0cb,Black-and-white\ntest/7722dbdb3d7dc762,Brisket\ntest/77279b55cccccc79,Luxury vehicle,Antique car\ntest/772a110041bec350,Sport utility vehicle\ntest/772c28e24a46f44c,Luxury vehicle,Sport utility vehicle\ntest/772c722f59898cfa,Luxury vehicle,Performance car\ntest/772cd0c8bb2c423c,Leaf vegetable\ntest/77307359074f7bfb,Close-up\ntest/7730e797f16ed4c1,Black-and-white,Close-up\ntest/7730f288b2c27903,Arthropod,Close-up\ntest/7731f4dac10915de,Ball game\ntest/7733495ec4c62c51,Sport utility vehicle\ntest/773356385115f116,Compact car\ntest/7734c4015a937e05,Luxury vehicle,Sport utility vehicle\ntest/7735919fccc60f53,Whiskers\ntest/77364a3b65b100d0,Luxury vehicle,Sport utility vehicle\ntest/773837919a1940e2,Performance car,Antique car\ntest/77396d2c3aba8b24,Chordophone\ntest/773b68032d0c6fa8,Black-and-white\ntest/773c0e5d10d2f3e2,Luxury vehicle\ntest/7740bf8bdf43cbc7,Glacial landform\ntest/7742a7758209c6de,Luxury vehicle,Sport utility vehicle\ntest/7744d843c473be59,Falafel,Fried food\ntest/774515fe5cdbc0cc,Close-up\ntest/7747f5bf7cdbe8ef,Lawn game,Ball game\ntest/7748a34926fead49,Luxury vehicle\ntest/774a20234749713f,Ball game,Team sport\ntest/774f6ad0cdd4d375,Close-up\ntest/775084a96bb5aaff,Chordophone\ntest/775727c704184dc1,Vegetarian food,Junk food\ntest/775886d75af70e2b,Luxury vehicle,Sport utility vehicle\ntest/7759bd65ed87d1ab,Antique car\ntest/7759c46042c05f81,Perching bird\ntest/7759f0b1b5fb382f,Luxury vehicle,Performance car\ntest/775ca838d901bd71,Boating\ntest/775cc2fb40b8172b,Chordophone\ntest/775d1d650466698d,Ball game,Team sport\ntest/775f21969ead240d,Luxury vehicle\ntest/775f8441b143f34f,Plant stem\ntest/776050cb1f300fe0,Vegetarian food\ntest/7760852908e9eef2,Frying,Junk food,Fried food\ntest/77610c816c6f34a7,Lechon\ntest/7763a12a66321626,Ball game,Team sport\ntest/7763b866a6127dca,Luxury vehicle\ntest/776449b04b9a14fb,Drums\ntest/7765b375594bce10,Full moon\ntest/7768514e1fd8d16c,Close-up\ntest/7769bf5df1405c7f,Luxury vehicle,Sport utility vehicle\ntest/776b6d273259cc51,Galleon\ntest/776ea6710d045663,Ball game,Team sport\ntest/776fe95fc23b17c7,Ball game\ntest/77708210a3df5ee8,Shinto shrine\ntest/7772dc10c892f2f1,Motorcycling\ntest/777321f8c85c14f3,Luxury vehicle,Performance car\ntest/7774792061599f4c,Luxury vehicle,Antique car\ntest/7775aca10a58f28c,Luxury vehicle,Antique car\ntest/7776cb8351c2cc5d,Compact car,Luxury vehicle\ntest/777af9a1923c2113,Antique car\ntest/777be71c1b7c978b,Sport utility vehicle\ntest/777c5a61d9427ff8,Extreme sport,Road cycling\ntest/778082c38b13f711,Zeppelin\ntest/77811591e46fefbb,Floristry\ntest/7781992110fc5e0f,Bratwurst\ntest/7781cee46393124a,Black-and-white\ntest/77851e03060f5c9e,Microcontroller\ntest/7787f4ce9a7403ef,Boating,Rowing\ntest/778a6f1a0040cfb7,Close-up\ntest/778e772cea702755,Close-up\ntest/778eb3a2f1fcbb58,Watercolor paint\ntest/778ff284dd7c9880,Freight transport\ntest/7790a7b6995f4a63,Vegetarian food\ntest/7793239e5bf4022d,Luxury vehicle\ntest/7793b423a2e5ccdf,Luxury vehicle,Antique car\ntest/7796986244462c56,Whiskers\ntest/77978e66332508e4,Black swan\ntest/779a23819c233802,Ball game,Team sport\ntest/779cb41b6d8292c6,Prosciutto\ntest/779d13ce0ef0af9d,Monoplane,Gliding\ntest/779d7e11230f4999,Team sport\ntest/779f7ed647586a52,Vegetarian food,Junk food\ntest/77a050013aa4ca2e,Perching bird\ntest/77a0fd39ebf9e92f,Close-up\ntest/77a1d554cb304bcd,Ball game\ntest/77a1f9a92783f9fd,Antique car\ntest/77a6489e7182a545,Boating\ntest/77a858b42642ee77,Antique car,Performance car\ntest/77ac27a9e6bda26c,Roller skating\ntest/77acdd4d72a544d8,Ball game,Team sport\ntest/77ad80892094bf90,Whiskers\ntest/77aede386c6035d6,Performance car,Antique car\ntest/77afddfee810103c,Drums\ntest/77b001e0e2c1721d,Close-up,Scaled reptile\ntest/77b03c83afbfca34,Aircraft engine\ntest/77b04159b2908717,Floristry\ntest/77b0e818e5f56d90,Whiskers\ntest/77b3f93357a5eed4,Fried food\ntest/77b468edfbe9f47f,Ball game,Team sport\ntest/77b4b2273494938b,Luxury vehicle\ntest/77b565f9e8cb4451,Aircraft engine\ntest/77b592644d4e78a4,Vegetarian food\ntest/77bbc99e7a9e1a79,Luxury vehicle\ntest/77bbdd2308cb2cad,Residential area\ntest/77bca489b4f3be18,Floristry\ntest/77be5aaf2dbe2a2c,Black-and-white,Close-up,Whiskers\ntest/77bf2d4e916f334e,Bagpipes\ntest/77bfcbef44776c4b,Digital camera\ntest/77c03069687f9299,Frigate\ntest/77c2413d919bdf18,Extreme sport\ntest/77c2e584231cabf4,Residential area\ntest/77c74d2d30cdb22e,Arcade game\ntest/77ca79e7578a250d,Close-up\ntest/77cb48a204f9d3f1,Cookies and crackers\ntest/77cd7ba841a82853,Brisket\ntest/77ce4a69f2341202,Luxury vehicle\ntest/77cf31981a39a05b,Glacial landform\ntest/77cf5fe7a0bd545f,Wind wave\ntest/77d266e22a9752a5,Luxury vehicle\ntest/77d4110c0e0b7713,Ball game\ntest/77d4350e191eb6fd,Junk food\ntest/77d8f3cb838fe5e0,Vegetarian food\ntest/77e32d1561fee28b,Nile crocodile,Scaled reptile\ntest/77e392dad514ce38,Performance car\ntest/77e57925ee37175a,Whiskers\ntest/77e8d8775449bfaa,Compact car\ntest/77ea693bab7e3eb4,Luxury vehicle,Performance car\ntest/77ed741a16419617,Antique car\ntest/77eda7e5706d8275,Miniature Poodle\ntest/77f4e457f641e1a9,Arthropod,Plant stem,Close-up\ntest/77f65889d721922d,Blond\ntest/77f6ac329fc23f9e,Luxury vehicle,Performance car\ntest/77f81ecb8104f8ec,Close-up\ntest/77fa2ec434105929,Boating,Sailboat racing\ntest/77fc9f419ee0f3fd,Combat sport\ntest/77fca0f5d5333485,Luxury vehicle\ntest/77ff2a3b0385039f,Arthropod,Close-up\ntest/78015aabea99c748,Combat sport,Barechested\ntest/780648d97a98faee,Luxury vehicle\ntest/78089fcf83aa3a33,Floristry\ntest/7808cefe7ede300e,Amphibian\ntest/78092ffa66bb6cff,Compact car\ntest/78095c2601bf38d3,Sport utility vehicle\ntest/78098ed9d6246d5a,Close-up\ntest/780a147cb88610b8,Close-up\ntest/780cdc225226de88,Black-and-white\ntest/780e3ba15cd4b6a4,Floristry,Plant stem\ntest/78105a93f25a7a4e,Digital camera\ntest/7817a54419096904,Divemaster,Scuba diving,Underwater diving\ntest/781a5841a341f2fc,Rural area,Bovine\ntest/781b109443f01a92,Sport utility vehicle\ntest/781b33abd4aa16cc,Luxury vehicle\ntest/781b344b1b1f1fa6,Close-up\ntest/781b60ce498b769b,Compact car,Antique car\ntest/781bd9cedac86e51,Compact car\ntest/781bf4f95432d8f2,Prosciutto\ntest/781d2c76422dde33,Bovine\ntest/781f66c7aab0bb1b,Middle ages\ntest/78237450c652ad1f,Calabaza\ntest/782659036399c8e4,Auto racing\ntest/782817b6b609fad2,Luxury vehicle\ntest/78291757df555c56,Blond,Portrait photography,Close-up\ntest/7829a7f2cc3ee78d,Domestic rabbit\ntest/782b771628a84dc4,Modern dance\ntest/782bacd6b9f80bdb,Arthropod,Close-up\ntest/783163d31a164a6c,Luxury vehicle,Antique car\ntest/7831e259aef71302,Compact car\ntest/7832908c62edca16,Brisket,Beef tenderloin\ntest/783295cef3d7264d,Electronic instrument\ntest/783450880b2a146b,Close-up,Whiskers\ntest/783482dfbccafe33,Newsagent's shop\ntest/7837c05650d1ab02,Aircraft engine\ntest/7839d7cf6abc8087,Brisket,Ribs\ntest/783bb8eb79f9f49e,Junk food\ntest/78403694dace353c,Team sport\ntest/784295e0bb821d03,Jeans,Extreme sport\ntest/78439cc80f384223,Combat sport\ntest/78443b8cf2eed297,Close-up\ntest/7846ac54d7b6f106,Military vehicle\ntest/78486e0893ad6d3b,Luxury vehicle,Performance car\ntest/7848db6e90277dad,Antique car\ntest/784e1808476e91b0,Extreme sport,Wind wave\ntest/784e38e8af3fc5dd,Auto racing,Performance car\ntest/784ef84e26874fdd,Compact car\ntest/784f2d858132ae5a,Lumpia\ntest/784f2e9717f0f7bd,Luxury vehicle\ntest/7850c3cbf6f2c884,Monoplane\ntest/78520dd01ee220f9,Ball game,Team sport\ntest/78528c05ffff9ffd,Luxury vehicle\ntest/7852ce66a5ef301c,Close-up\ntest/7855028ff9b4e29e,Compact car,Antique car\ntest/78550ab8b714d03c,Ball game,Team sport\ntest/7855152397be790c,Compact car,Luxury vehicle,Antique car\ntest/7858297facb75371,Close-up,Plant stem\ntest/785b67ba03ccf497,Performance car\ntest/785d7fc50b780b9a,Luxury vehicle\ntest/78625130183a3c3d,Firearm,Shooting range\ntest/78652a450b46a707,Luxury vehicle\ntest/78664bb65a5fca01,Khinkali\ntest/7866de2a5ae604d9,Auto racing\ntest/786986ad5735e719,Cookies and crackers\ntest/78699532b37f970b,Portrait photography\ntest/7869e1c856faf8c7,Domestic short-haired cat,Whiskers\ntest/786e0aef703f6d6d,Antique car\ntest/786e2c055f3cf147,Ball game,Team sport\ntest/7871ad04a2a85451,Compact car,Luxury vehicle,Antique car\ntest/7874e5c52d133217,Close-up\ntest/787730a301d7fe0c,Luxury vehicle,Performance car\ntest/78787e785d30266b,Bonbon\ntest/787949f2cd906411,Blond,Close-up\ntest/7879a27ea878e91b,Fried food\ntest/787a8b5a1ae49580,Machine tool\ntest/787cd4648f53de0f,Blond\ntest/787f1de0787719ca,Brisket\ntest/788325e805f4caa4,Vegetarian food\ntest/7884df4495450812,Chordophone\ntest/78852726350eb6d3,Fried food\ntest/78864a7ab51eec1b,Athletic shoe\ntest/788689b6cb4acdff,Arthropod,Locust,Close-up\ntest/7889ed0f372c5dc6,Extreme sport,Aerial photography\ntest/7889ee425ab60184,Machine tool\ntest/7889fbdd1d6bd868,Auto racing\ntest/788f6f582be2c9a4,Luxury vehicle\ntest/7891681f542f82e8,Auto racing\ntest/789188bb7fe08b43,Luxury vehicle,Antique car\ntest/7891af91b8eccc95,Luxury vehicle,Antique car\ntest/7892ad7113275c6b,Black-and-white\ntest/78939c0301b4ea29,Close-up\ntest/7894736b28194c51,Luxury vehicle\ntest/789ad4f989018ee1,Luxury vehicle\ntest/789b6ea093c2d71d,Luxury vehicle\ntest/789e24d11fb221cd,Luxury vehicle\ntest/789f94a97582717f,Close-up,Whiskers\ntest/78a6df7e84d7e269,Road cycling\ntest/78a8e54c31c5a0c7,Black-and-white\ntest/78abd60dc46b7873,Close-up\ntest/78ae4f171b576ef9,Digital camera\ntest/78ae58adc07feaad,Steamed rice\ntest/78b1a7f28a13da88,Senior citizen,Close-up\ntest/78b2edd98b6e679c,Floristry\ntest/78b3e28882b650b8,Residential area\ntest/78b3eb79b4c2bf77,Amphibian,Close-up\ntest/78b4a2456b8ba662,Fried food\ntest/78b4e79750ed67cd,Hound\ntest/78b54460bc4f6944,Kitesurfing,Surfing Equipment\ntest/78b5907b841c055b,Residential area\ntest/78b6377067244e2a,Jeans\ntest/78b6f4f09825f335,Equitation,Equestrianism\ntest/78b8edc800fab6e3,Rural area,Bovine\ntest/78ba0759fccd5678,Performance car\ntest/78ba745ac74a2321,Black-and-white,Close-up\ntest/78bc277af198725e,Bovine\ntest/78bd051d0c57e53e,Barechested\ntest/78bd227334bc6da5,Military person\ntest/78bd87a1a3e79191,Compact car,Luxury vehicle\ntest/78bea59f5b1bcb6c,Dishware\ntest/78c1bbc2f5dc01a0,Antique car\ntest/78c29d2e1966290b,Monoplane\ntest/78c2bb883436db75,Luxury vehicle\ntest/78c2f428333e5208,Machine tool\ntest/78c5d008323747ef,Auto racing,Luxury vehicle,Performance car\ntest/78c84f7b8da9498e,Luxury vehicle,Sport utility vehicle\ntest/78c923c50f86ac75,Hound\ntest/78cb0500cdd8b38d,Aircraft engine\ntest/78cb4766112506a3,Cookies and crackers\ntest/78cbb688cdd387f0,Galleon\ntest/78cc460b617a95ff,Luxury vehicle\ntest/78cca32bb8a44712,Black-and-white\ntest/78ce0c2e6da62e0f,Vegetarian food\ntest/78ce109ca9932e03,Close-up,Plant stem\ntest/78cf3a2344703fcc,Luxury vehicle\ntest/78cf6ef90ff6079f,Black-and-white\ntest/78cfc728e7c5ffce,Molluscs\ntest/78d03c12c935d6ab,Modern dance,Musical theatre\ntest/78d1001cd7562d84,Compact car,Luxury vehicle\ntest/78d104c81eb16c2d,Whiskers\ntest/78d33d12fe8ca10d,Backlighting\ntest/78d3ad697b357b55,Auto racing\ntest/78d5b3c7d8cc1a67,Freight transport\ntest/78d606fe0ef13f04,Jeans,Extreme sport\ntest/78db56a083cacd2e,Plant stem,Close-up\ntest/78dc61fdd3f595db,Frying,Junk food,Fried food\ntest/78dfd2627db8e0b6,Equitation,Equestrianism\ntest/78e1d763746e5d6d,Bird of prey\ntest/78e448943e50dd62,Rural area\ntest/78e51c2e3524befa,Yakitori\ntest/78e5506bae694afd,Microcontroller\ntest/78e966a7799da137,Floristry\ntest/78ead33a0d5c07fe,Arthropod,Close-up\ntest/78ec2e08430699e1,Combat sport\ntest/78ec6d49e9d2a500,Ball game\ntest/78ed492ed184a9de,Compact car,Luxury vehicle,Antique car\ntest/78eeb307bfe92920,Dishware\ntest/78efeeaaa1f0065a,Ball game,Team sport\ntest/78f4047f2d141727,Kitesurfing,Extreme sport,Wind wave\ntest/78f9f8dbab608154,Antique car,Luxury vehicle,Performance car\ntest/78ffca9e658fb3c0,Cosmetics\ntest/790180baf370ce86,Antique car\ntest/79043eb4b3f7e1e2,Hound\ntest/790578570ac3f5ba,Black-and-white\ntest/79074161a3d61884,Luxury vehicle\ntest/790974d67e658f37,Sport utility vehicle\ntest/790d577e86472468,Rural area\ntest/790d6aefc613949d,Close-up\ntest/790f3577fb822b1a,Rural area\ntest/790fbad48a1263b5,Microcontroller\ntest/790fbbb0a200f8e2,Performance car\ntest/7910a6a5f9d020c6,Herding\ntest/7910decdd5e92a8c,Bovine\ntest/7911a675b1974ff3,Performance car\ntest/79127465afc57559,Digital camera\ntest/791a337673d37ad1,Extreme sport,Bouldering,Sport climbing\ntest/791f9d539b3e1db7,Luxury vehicle,Performance car\ntest/792197580fd23eb1,Ball game,Team sport\ntest/7922a75b9b15d4ff,Dobermann\ntest/7924d91dd62861ba,Ball game,Team sport\ntest/7924dc4fa4b34475,Luxury vehicle,Performance car\ntest/7926b0ede206024d,Black-and-white\ntest/79272d960a87bdc4,Motorcycling\ntest/792b52774ab44d4d,Extreme sport,Sledding\ntest/792b91680b388cfb,Close-up,Whiskers\ntest/792bfef65abbf9d5,Nuclear power plant\ntest/792c006e38a82b20,Chordophone,Electric guitar\ntest/792d017baac03af9,Residential area\ntest/792fb38d38ff8811,Extreme sport\ntest/7931a958173065fd,Luxury vehicle\ntest/79325c50d79233f7,Goats\ntest/7933904e5681da41,Luxury vehicle,Antique car\ntest/7936076e23541980,Calabaza\ntest/7937a4d072fff3b2,Performance car\ntest/7938a967f8ce36a1,Black-and-white,Dishware\ntest/7938d0b56036152b,Aircraft engine\ntest/793a6e492fbf02c1,Arthropod\ntest/793de03f4ea4a7da,Auto racing,Compact car\ntest/793e85046a71cc26,Amphibian,Close-up\ntest/793ed7062e130016,Bonbon\ntest/793f6979b787991e,Close-up\ntest/79405dc26403f4a5,Close-up\ntest/7946f5169ff29715,Jeans\ntest/794801f8258c9d45,Black-and-white\ntest/7949328a4f4361b1,Vegetarian food,Pasta salad,Pelmeni\ntest/794a78489f27b336,Ale\ntest/794bafc51767b654,Aircraft engine\ntest/794cf9966fd9f9a3,Compact car\ntest/794f700dfd7c2f29,Combat sport\ntest/795050ae774bcd6c,Chordophone\ntest/7951e51e6656795b,Compact car\ntest/79535794b5ef363c,Close-up\ntest/7953c63060f650ca,Luxury vehicle\ntest/79547392605a0591,Compact car,Luxury vehicle\ntest/79552f367754dd99,Ale\ntest/7955fcb323bd7a5b,Luxury vehicle\ntest/795d8392a32195eb,Hair coloring,Close-up\ntest/795df546ff1922f7,Close-up\ntest/795f19bc8c8e0f37,Performance car\ntest/795f3a56344d0d6f,Antique car\ntest/795fcc806f8adbab,Modern dance\ntest/796004921deb13d2,Vegetarian food\ntest/796573398e83f830,Jeans\ntest/79658f8cf41c5a0c,Close-up\ntest/79678b21b96cb568,Close-up\ntest/7967ef6a4fcd89bc,Sport utility vehicle\ntest/7967f24f32f144f7,Bird of prey\ntest/796840b4d287d987,Jeans\ntest/796a7cc117e943f7,Whiskers\ntest/796bfd0d438dd38a,Close-up\ntest/796de94e0926191a,Black-and-white\ntest/796e4c1ae088754b,Luxury vehicle,Antique car\ntest/7970c420fa72ed60,Close-up\ntest/7971ed0dc346c92a,Luxury vehicle,Antique car\ntest/79730a5eead6c418,Vegetarian food,Fried food\ntest/797760fe9e48763c,Plant stem,Close-up\ntest/797780d5b761717b,Plant stem,Close-up\ntest/7977f07ba0cba1f9,Machine tool\ntest/797c1c804f70812b,Luxury vehicle\ntest/797c6ebc4697960f,Luxury vehicle\ntest/798037f896dca5f0,Sport utility vehicle\ntest/7981ab970b64bc9e,Coral reef fish\ntest/7984128064afae7f,Jeans,Rural area\ntest/798412f38107c244,Performance car\ntest/79862fb9ee2a8e1a,Jeans\ntest/79864dd5d3a104b2,Rural area\ntest/798665a0ccab36cb,Compact car\ntest/798953096bacd737,Firearm\ntest/7989e4cb9c14659d,Luxury vehicle,Performance car\ntest/798a26294567016e,Black-and-white\ntest/798a9fe812df20dd,Luxury vehicle\ntest/798ab561787c12e9,Bird of prey,Close-up\ntest/798acbc8a642ad0a,Glacial landform\ntest/798b4e014bfec86d,Arthropod,Locust,Close-up\ntest/798b727a4c5d529e,Fried food\ntest/798f6d25b452855d,Compact car,Luxury vehicle,Antique car\ntest/798fad65adfa31e2,Antique car\ntest/799243342ae8573b,Close-up\ntest/7992baa42e3368e9,Flatbread\ntest/7993a6ca27a7d5b5,Wind wave\ntest/7995cc73376787b2,Arthropod,Close-up\ntest/799613e771186405,Classical sculpture\ntest/7996189cf2506a6a,Lumpia\ntest/7998e710871cbba1,Compact car\ntest/799caf52ca2311f9,Chordophone\ntest/799d744bd2d12faa,Flatbread\ntest/799d8bf8a2711581,Close-up\ntest/799e2e9118c6ec75,Bovine\ntest/79a3c5e31a009037,Molluscs\ntest/79a5b1614a2dd857,Bovine\ntest/79a633f1b367125f,Bovine\ntest/79a6e1b0ab5fb8fd,Luxury vehicle,Sport utility vehicle\ntest/79a7975b3d8232b5,Shooting range\ntest/79a9a6d65d6d73b1,Black-and-white\ntest/79aacfda2f64535d,Extreme sport,Boating\ntest/79ac573024664928,Cookies and crackers\ntest/79ac57ab75beac63,Forklift truck\ntest/79ad76390c331bec,Boating\ntest/79ad931345696bee,Jeans\ntest/79ad9c02ca6d2cb1,Close-up,Firearm\ntest/79ae315a6befbe7e,Forklift truck\ntest/79b0a5bfef91cbb6,Rural area\ntest/79b0ea21be3baf24,Compact car,Luxury vehicle,Antique car\ntest/79b12487ccd10772,Luxury vehicle\ntest/79b23383a15bc835,Compact car,Luxury vehicle,Antique car\ntest/79b2cf62e0ab10d9,Black-and-white\ntest/79b3248a27e1f1cd,Boating\ntest/79ba090f8bf4b13b,Luxury vehicle\ntest/79bce72a3b5fcf7c,Close-up\ntest/79c09ede33d81760,Blond\ntest/79c1d7420a04e66d,Perching bird\ntest/79c23dc6e49190cf,Luxury vehicle\ntest/79c3e8a7a7cffae5,Outdoor shoe\ntest/79c4ae7e59fa3104,Military person\ntest/79c7758d2d8d373e,Vegetarian food\ntest/79c80b03d0445a3f,Luxury vehicle\ntest/79c92ac1400c050b,Aloe\ntest/79c93fb5d9a43f8b,Luxury vehicle\ntest/79ca863140473064,Close-up\ntest/79cb38b6bebf4b2a,Frying,Fried food\ntest/79cd12410ec32488,Firearm\ntest/79cd79d26df3ed06,Luxury vehicle,Performance car\ntest/79d020e799f6095c,Miniature Poodle\ntest/79d5279ab05f0cd5,Luxury vehicle,Performance car\ntest/79d5fc0ef3c55d56,Whiskers,Domestic rabbit\ntest/79d618387f667481,Performance car\ntest/79d7e96ac78fa020,Barechested\ntest/79d9b18a646a2d28,Compact car\ntest/79da079bff537a5b,Luxury vehicle\ntest/79db451beb427491,Luxury vehicle\ntest/79db4a134426e10e,Boating\ntest/79dd063c6079a236,Luxury vehicle,Performance car\ntest/79dfefb42f551443,Divemaster,Scuba diving,Underwater diving\ntest/79e03631ebb5b289,Luxury vehicle,Antique car\ntest/79e5308f54baa2b1,Divemaster,Scuba diving,Underwater diving\ntest/79e5af22bed56704,Luxury vehicle,Sport utility vehicle\ntest/79e5d5668729143f,Vegetarian food\ntest/79e81d0211035b2d,Close-up\ntest/79eb1f140b43f145,Performance car\ntest/79eeaeb33f733015,Vegetarian food\ntest/79f222e665f671d2,Vegetarian food\ntest/79f28b0cd2717979,Domestic short-haired cat,Whiskers\ntest/79f8f7a3e7187ab3,Freight transport\ntest/79f92a8cbd95dba3,Performance car\ntest/79f9f7a65c251b72,Luxury vehicle,Antique car\ntest/79ffd5268b40e8d4,Luxury vehicle,Performance car\ntest/7a0063c2c40f7bf2,Monoplane\ntest/7a00b1afd89d8d3b,Wind wave\ntest/7a010884b20284a4,Close-up\ntest/7a03f1b5553ecd4b,Close-up\ntest/7a05ae226d8e7842,Close-up\ntest/7a0a1e76783f5c6d,Frying,Fried food\ntest/7a0c5f6593bc7262,Scaled reptile\ntest/7a0ee34a8f270da7,Black-and-white\ntest/7a0f1644c372f281,Residential area\ntest/7a1090aa5303e88e,Boating\ntest/7a123a31fdd57415,Aerial photography\ntest/7a15191573bac7f2,Ball game\ntest/7a156b3b3831a142,Black-and-white\ntest/7a156bd2686141b8,Breadboard\ntest/7a15ba7701007f3f,Luxury vehicle,Antique car\ntest/7a15be4c586a3e51,Arthropod,Close-up\ntest/7a15c115a293d486,Drums\ntest/7a166d07c573ac8b,Sport utility vehicle\ntest/7a179fa3d74c1cb0,Electronic instrument,Electric piano\ntest/7a1a25e140c07dd7,Leaf vegetable\ntest/7a1dec83a4c5b024,Sport utility vehicle\ntest/7a1e23710269818c,Military person\ntest/7a1e6bd044091d41,Performance car\ntest/7a1ea6873eabc1df,Boating,Rowing\ntest/7a1ec116924562d1,Luxury vehicle\ntest/7a20fc5bc67df141,Whiskers\ntest/7a218c8e82e84d3d,Gun turret,Military vehicle\ntest/7a254903c98bf611,Blond\ntest/7a260b7ad5954ede,Monoplane\ntest/7a2b80713cc6d610,Musical theatre\ntest/7a2fa4a98ccb491b,Shooting range,Firearm\ntest/7a3011c16ae73924,Hair coloring\ntest/7a312d59f726a692,Auto racing\ntest/7a3131cbbdc2232e,Auto racing,Performance car\ntest/7a31a146672f2dec,Luxury vehicle\ntest/7a33339130a7d2e9,Full moon\ntest/7a333fab503a15d6,Luxury vehicle,Sport utility vehicle\ntest/7a338a711506b31f,Blond,Hair coloring,Close-up\ntest/7a367a6cd28e11f4,Close-up\ntest/7a38295dd3772bf9,Aircraft engine\ntest/7a38cdc345595686,Fried food\ntest/7a3a47baefe4dd8c,Boating\ntest/7a3b0cc780142eb8,Luxury vehicle,Performance car\ntest/7a3c5af5def7042a,Boating\ntest/7a3d6af7f97fa846,Road cycling\ntest/7a3e493b7d2676ac,Steamed rice\ntest/7a3f072d78b246d4,Close-up\ntest/7a43d69f71ab0dad,Black-and-white,Portrait photography,Close-up\ntest/7a452a7197f0d295,Sport utility vehicle\ntest/7a46c6eb1e5db052,Compact car,Luxury vehicle\ntest/7a476df79e9e9c3b,Luxury vehicle\ntest/7a482f5841950b03,Billiard room,Billiards\ntest/7a48ac6aaaa23e59,Briefs\ntest/7a4947680999ce76,Headphones\ntest/7a4af660063a8e6f,Close-up,Anole,Scaled reptile\ntest/7a4bbdef3482b6fd,Cosmetics\ntest/7a4dfb7bb7cd32ae,Musical theatre\ntest/7a4e1b285944b27f,Close-up\ntest/7a4e78194ac27f4c,Luxury vehicle,Sport utility vehicle\ntest/7a4e86f013496c44,Prosciutto\ntest/7a4e9f7971abbcba,Plant stem\ntest/7a4ebfe1d5be163c,Outdoor shoe\ntest/7a51578d02e7bf5a,Whiskers\ntest/7a52ddae7601c3ef,Rural area\ntest/7a5300f8db095d2d,Miniature Poodle\ntest/7a532558ed4cff07,Musical theatre\ntest/7a5475cd9f9bb68e,Machine tool\ntest/7a5529050cb5cffb,Close-up\ntest/7a62f7e9487a7424,Fire apparatus\ntest/7a63eb7270e3a0cf,Close-up\ntest/7a680e99d98a29da,Compact car,Antique car\ntest/7a68575266150667,Ball game,Team sport\ntest/7a68edc9d8dda4a1,Blond,Portrait photography\ntest/7a699acc3915ae9e,Jeans,Motorcycling\ntest/7a6b73d9f84c30f7,Vegetarian food\ntest/7a6bbc1a2ac4667a,Black-and-white,Portrait photography\ntest/7a6f91e39b9df09f,Arthropod,Locust,Close-up\ntest/7a726963ea6bb8d7,Close-up\ntest/7a72d3c76c89fa1e,Ball game,Team sport\ntest/7a753f9bfc5c7b7b,Compact car\ntest/7a758ef08c249d3a,Domestic short-haired cat,Whiskers\ntest/7a7acc1d5a60bcf2,Vegetarian food\ntest/7a7ad7084b691cd6,Zeppelin\ntest/7a7ec4085bcb9fd8,Rural area,Bovine,Herding\ntest/7a800dd12fc9897c,Boating\ntest/7a8183334384a923,Great white shark\ntest/7a81eafe20bddd72,Sport utility vehicle\ntest/7a8310880bf3e7d3,Dobermann\ntest/7a841c9a45f02ed4,Close-up\ntest/7a880d7a614eb53d,Chordophone\ntest/7a882bae1c4c6a1e,Ball game\ntest/7a89223842410530,Chordophone\ntest/7a8b1abec846eeb1,Compact car,Luxury vehicle\ntest/7a8b37aaf71b533c,Luxury vehicle,Sport utility vehicle\ntest/7a8d547d86093816,Bovine\ntest/7a8e0cd92e423d1c,Luxury vehicle,Antique car\ntest/7a8e5c40b693284d,Luxury vehicle\ntest/7a8f7ba54a2fade8,Equitation,Equestrianism\ntest/7a90f08663383481,Bovine\ntest/7a91dcea51ad22ce,Close-up\ntest/7a94eefde35db2d7,Bagpipes\ntest/7a97a032fa9434a0,Compact car,Antique car\ntest/7a9aa593eb3d1606,Close-up\ntest/7a9b92df6038cdd6,Ball game,Team sport\ntest/7a9ddbfbfa8c83f0,Close-up\ntest/7a9e9c05a1925e13,Boating\ntest/7a9fbb313c4f8eb2,Luxury vehicle\ntest/7aa08ac481c9ccc7,Junk food,Fried food\ntest/7aa10b963e63d267,Luxury vehicle\ntest/7aa2015a2088aad0,Antique car\ntest/7aa3a12ac99ed461,Jeans\ntest/7aa50ed00dca4959,Black-and-white,Close-up\ntest/7aa5d3c38820737b,Goats\ntest/7aa6fd72e9cb7f2c,Freight transport,Boating\ntest/7aa9298016f098a0,Close-up\ntest/7aa9dbdbe7347844,Luxury vehicle,Performance car\ntest/7aa9f132eba66f51,Black-and-white\ntest/7aaabf06d97444bb,Road cycling\ntest/7aaeaa2119e88666,Brochette\ntest/7aaedceb4fd47489,Antique car,Luxury vehicle,Performance car\ntest/7aaf2ad4bd23d9a8,Roller skating\ntest/7aaf5b4bfb257375,Compact car\ntest/7ab09901f87e9188,Luxury vehicle,Antique car\ntest/7ab11756254d1e42,Rural area\ntest/7ab27cdba72b2e86,Close-up\ntest/7ab3af0130b12bc0,Jeans,Barechested\ntest/7ab40842c2220357,Residential area\ntest/7ab42121bcc487cd,Chordophone\ntest/7ab45838e1dddfe1,Luxury vehicle\ntest/7ab52159971eb568,Compact car\ntest/7ab638a708b7590a,Luxury vehicle\ntest/7ab87069f5ff34d6,Luxury vehicle,Performance car\ntest/7aba2c72da7cd02a,Arthropod\ntest/7abbf6f332cec8ad,Black-and-white\ntest/7abc74b108634fef,Luxury vehicle,Antique car\ntest/7ac20c760c0aea6a,Residential area\ntest/7ac24111563f8399,Hair coloring\ntest/7ac31237867b3666,Extreme sport\ntest/7ac355b44ccedaae,Domestic short-haired cat,Close-up,Whiskers\ntest/7ac3b6a80a5d12fe,Leaf vegetable\ntest/7ac48329496abefe,Lawn game\ntest/7ac799eeb9f22f3c,Frying,Fried food\ntest/7aca23cbc1f36bc1,Vegetarian food\ntest/7aca766207b180f7,Close-up\ntest/7acaf6790af9eb36,Luxury vehicle,Antique car\ntest/7accec022c01914d,Close-up\ntest/7acd07c33b9b6788,Leaf vegetable,Fried food\ntest/7ace30de895d9518,Luxury vehicle,Performance car\ntest/7acecd618697effb,Ball game\ntest/7ad0c1176ffccd39,Black-and-white,Modern dance\ntest/7ad49c73c0e03792,Amphibian,Anole\ntest/7ad5379dbf3ffd47,Vegetarian food\ntest/7ad7139f46d61e98,Black-and-white\ntest/7ad92543beb4edc6,Musical theatre\ntest/7adb736159e254b0,Luxury vehicle\ntest/7ade13213876043d,Scaled reptile\ntest/7adf304ffb48ca08,Equestrianism\ntest/7adfa151e82d4b09,Whiskers\ntest/7ae0a96cd9c593ee,Aerial photography\ntest/7ae72c054eaccd13,Classical sculpture\ntest/7ae8296dbed3c913,Machine tool\ntest/7ae8935857f83c58,Motorcycling\ntest/7aeef7ea7f1e565a,Compact car,Antique car\ntest/7af28df09ed1700f,Compact car\ntest/7af3d210c7ba71ef,Performance car,Antique car\ntest/7af5dad145c8bcb3,Middle ages\ntest/7af661cd1361cc0b,Close-up\ntest/7af76cb3ebc39aff,Performance car\ntest/7af8f80ac971b0c8,Black-and-white\ntest/7afb845c4f5f1d43,Luxury vehicle,Performance car\ntest/7afc0eeba054827a,Whiskers\ntest/7afdf90c951393c1,Compact car,Luxury vehicle\ntest/7b00a9c8f6fd37f0,Inflatable,Boating\ntest/7b00c03d4db8e6aa,Compact car\ntest/7b0102d960a2ecc6,Compact car,Antique car\ntest/7b041082370b739f,Military vehicle,Luxury vehicle\ntest/7b0717afdfa2f59b,Whiskers,Domestic rabbit\ntest/7b095ff4b2c0c774,Luxury vehicle,Antique car\ntest/7b09e089162990f3,Luxury vehicle\ntest/7b0e11f9d0efe404,Vegetarian food,Corn on the cob\ntest/7b11c7057a90d1ce,Fried noodles\ntest/7b13ea4c95f8e1df,Boating,Rowing\ntest/7b1694e3a3a70ba3,Luxury vehicle,Antique car\ntest/7b1778bedfccaec1,Bovine\ntest/7b18e9fa6de09c23,Close-up\ntest/7b19120fc1071ce9,Close-up\ntest/7b1c5a7a2d34e222,Compact car\ntest/7b224d05ba0a79b3,Black-and-white\ntest/7b24104205250a1c,Compact car\ntest/7b2448d6b13f0139,Compact car,Luxury vehicle\ntest/7b2455f9ecd4d31e,Fried food\ntest/7b25d240fbd72a0a,Luxury vehicle,Antique car\ntest/7b26b9e3f9cbc863,Blond,Close-up\ntest/7b2748543aedcc01,Close-up\ntest/7b2756a678e0b804,Black-and-white\ntest/7b27921d5340e0f8,Gumbo\ntest/7b27fc9124a2b7a3,Chordophone,Electric guitar\ntest/7b282a92e95f6ec4,Close-up\ntest/7b2837cbc6b97b51,Auto racing,Compact car,Touring car\ntest/7b29ec39afea6463,Black-and-white\ntest/7b2d01e851b7db65,Close-up\ntest/7b2d2860dbb8f7d4,Luxury vehicle\ntest/7b2d5f33d3b6549a,Luxury vehicle,Antique car\ntest/7b2dafa96e239aed,Pork chop,Fried food\ntest/7b2e6ed4b8db49ae,Luxury vehicle,Sport utility vehicle\ntest/7b2f9297bd5c0f08,Steamed rice\ntest/7b300a676664e707,Domestic short-haired cat,Close-up,Whiskers\ntest/7b3069957dc238b9,Close-up\ntest/7b324fc2a82d8d96,Frying,Fried food\ntest/7b3473b920a8c150,Luxury vehicle,Performance car\ntest/7b36128659e82f79,Vegetarian food\ntest/7b365733079f5ff1,Jeans,Arthropod,Close-up\ntest/7b374da4c57aa17d,Military vehicle\ntest/7b3a52954981717e,Whiskers\ntest/7b3a9108f85ab0dd,Ball game,Team sport\ntest/7b3c13b540a204d3,Close-up\ntest/7b3dd9c35408d6b5,Microcontroller\ntest/7b3ecddfbafdc3d0,Luxury vehicle\ntest/7b3edd4f77292e50,Black-and-white,Portrait photography,Blond\ntest/7b3fcfbfe54d7943,Luxury vehicle\ntest/7b40a372e74b7abf,Black-and-white\ntest/7b458ba2f42a9696,Aircraft engine\ntest/7b45f8fd2e56fe83,Close-up\ntest/7b478830a3fee8b1,Fried food\ntest/7b4a89687f0bac51,Plant stem\ntest/7b4d8bac80d7fc1b,Luxury vehicle,Antique car\ntest/7b4e76912db1f304,Barquentine,Frigate\ntest/7b511830954ec287,Luxury vehicle\ntest/7b515617752c01bd,Close-up,Whiskers\ntest/7b51873a85057519,Luxury vehicle,Antique car\ntest/7b52df7deadc10ca,Luxury vehicle\ntest/7b535c075723b810,Domestic short-haired cat,Whiskers\ntest/7b55ebeaa9089a1b,Auto racing,Luxury vehicle,Performance car\ntest/7b5768cf17afbe6e,Electronic instrument\ntest/7b576f6b96186be3,Calabaza\ntest/7b596b41ddc891f7,Close-up\ntest/7b5b4c39c13e8923,Full moon\ntest/7b5bef9e06685673,Close-up,Plant stem\ntest/7b5c580c077b0cb3,Luxury vehicle\ntest/7b5eb3281d33f210,Miniature Poodle\ntest/7b5fedb751e85b51,Coral reef fish\ntest/7b60281b37fc9140,Black-and-white\ntest/7b61177677967007,Scaled reptile\ntest/7b634eeba6a9a7d4,Frigate\ntest/7b65bb42b5a5d13b,Black-and-white\ntest/7b68f23554a69b02,Aircraft engine\ntest/7b6a6d716ac44524,Bratwurst\ntest/7b6abe06f41536c8,Luxury vehicle\ntest/7b6ac9da1662ac51,Figure skating\ntest/7b706904d7e44b6f,Compact car,Luxury vehicle\ntest/7b71c9d26bc00228,Flatbread\ntest/7b7496e6ebd34965,Arthropod,Close-up\ntest/7b75d77d2092f20b,Antique car\ntest/7b7622af147a3124,Luxury vehicle,Performance car\ntest/7b77bde7e6f9e9ee,Angling\ntest/7b7852b8b303cd16,Coral reef fish\ntest/7b79db225477269d,Sport utility vehicle\ntest/7b79f11b0efef79c,Compact car\ntest/7b7d0fca2d53f0d6,Athletic shoe,Outdoor shoe\ntest/7b7e37e99fc1e3f2,Close-up\ntest/7b80f1a6337c0253,Touring car,Luxury vehicle,Antique car\ntest/7b81293259a3eb46,Whiskers\ntest/7b81e1a122767de2,Close-up\ntest/7b8631d84509324a,Antique car\ntest/7b89d6d2c62e9bdd,Close-up\ntest/7b8b83ce65bfa2d5,Whiskers\ntest/7b8c5496c929e26a,Extreme sport\ntest/7b8c61750337f348,Whiskers\ntest/7b8f3f8473ed8470,Black-and-white\ntest/7b909b380c47f7c8,Plant stem,Close-up\ntest/7b9215c0936eefc3,Luxury vehicle\ntest/7b93bf448afaff8d,Sailboat racing\ntest/7b98c8951c6ba884,Vegetarian food\ntest/7b99b3043db2115f,Close-up\ntest/7b9bc0193859f63a,Combat sport\ntest/7ba17629e658e376,Compact car,Luxury vehicle\ntest/7ba3e9394a44f387,Jeans\ntest/7ba47fad6f26b967,Blond\ntest/7ba5635cefa99957,Close-up,Scaled reptile\ntest/7ba7270f77b01c33,Junk food\ntest/7ba7d680e59822e5,Perching bird\ntest/7baa166f67a3a475,Bovine\ntest/7bab50865282f116,Chordophone\ntest/7bad96dc4292630a,Black-and-white,Portrait photography,Close-up\ntest/7baf87db06ba929e,Compact car,Luxury vehicle\ntest/7bafc4c5c65aff0c,Compact car,Luxury vehicle,Antique car\ntest/7bb16f3d88271fa8,Performance car,Antique car\ntest/7bb20b5dc43e4890,Compact car,Luxury vehicle\ntest/7bb5605445eb43ac,Frigate\ntest/7bb5869e69ae16c2,Portrait photography\ntest/7bb793688d28a988,Fried noodles\ntest/7bb8369211de93a3,Church bell\ntest/7bb869c5b2d128c1,Plant stem\ntest/7bbaafbb4b5710cd,Great white shark\ntest/7bbb7e3fcd31a4e5,Whiskers\ntest/7bbbdcc2afc50b4b,Close-up\ntest/7bbcb9a698001483,Fried food\ntest/7bbebeb3720ca511,Compact car,Luxury vehicle,Performance car\ntest/7bbf5af7638f0590,Hound\ntest/7bc1d32c120a2fd0,Close-up\ntest/7bc342d079ac1474,Ball game\ntest/7bc36f102aa499e5,Solar energy\ntest/7bc77dc1cdbb0cbf,Underwater diving\ntest/7bc8688fef277b29,Whiskers\ntest/7bca96d293149283,Antique car\ntest/7bcdf1f800e1fd8c,Luxury vehicle,Antique car\ntest/7bce5750acc2aae0,Jeans\ntest/7bcff96b1ab85874,Close-up\ntest/7bd08b7c809658e8,Performance car\ntest/7bd1011f5e84e8b8,Firearm\ntest/7bda02268a57cc0d,Extreme sport\ntest/7bda87d3c0929fef,Performance car,Luxury vehicle,Antique car\ntest/7bdbc21789d167d7,Luxury vehicle,Performance car\ntest/7bdc12c7fb0d03af,Close-up\ntest/7bdec557548366a4,Antique car\ntest/7bdfd75512ff310b,Equitation,Equestrianism\ntest/7be3f21d95bd6808,Plant stem\ntest/7be4d0c2c5753924,Ball game,Team sport\ntest/7be4e39796389b66,Luxury vehicle\ntest/7be634d29202f46c,Frozen yogurt\ntest/7be7ec20c31efdf4,Close-up\ntest/7be9361654bf2f68,Leaf vegetable\ntest/7be972a9fb54c909,Arthropod,Close-up\ntest/7be9d1c34f7db254,Military vehicle,Sport utility vehicle\ntest/7beef7f982fd8db7,Close-up\ntest/7bf2949dea1ff01d,Figure skating\ntest/7bf478b4a857a6a6,Coral reef fish\ntest/7bf57f78189c84e7,Fried noodles\ntest/7bf770f26ccee929,Extreme sport,Nordic skiing\ntest/7bf87a08d43b43e4,Boating\ntest/7bf9c82292fa5f93,Close-up\ntest/7bf9dd642155cf41,Compact car,Luxury vehicle,Antique car\ntest/7bfb5f072a905982,Performance car\ntest/7bfbc33187c4c514,Performance car\ntest/7bfd850c39f2c937,Whiskers\ntest/7bff80ede2a02f96,Close-up\ntest/7c003e3b9292fe82,Miniature Poodle\ntest/7c010715fbe7e9d5,Close-up\ntest/7c011a4a21ee0fbc,Jeans\ntest/7c016860a45dcb1e,Rural area\ntest/7c030b126773131e,Trampolining\ntest/7c04003e5e5e4bb7,Domestic short-haired cat,Whiskers\ntest/7c0424593af3d5b4,Musical theatre\ntest/7c044527c41ac5f8,Backlighting\ntest/7c05543d3799f788,Extreme sport,Wind wave\ntest/7c05762a55ce6d8f,Plant stem,Close-up\ntest/7c0635e747fd01f7,Amphibian,Close-up\ntest/7c08f32689c9e2e1,Residential area\ntest/7c0be7ba1345da9a,Performance car,Antique car\ntest/7c0e56355c5e9796,Miniature Poodle\ntest/7c0ef13e57799792,Domestic short-haired cat,Whiskers\ntest/7c101b9c0a376395,Arthropod,Close-up\ntest/7c139f8ecea59117,Close-up\ntest/7c147cc7d6f7786f,Peafowl\ntest/7c14aec763a6b0f9,Blond,Hair coloring\ntest/7c1671792df95418,Luxury vehicle,Antique car\ntest/7c17da3b0ba0e0e2,Luxury vehicle,Antique car\ntest/7c1807c32495b2be,Luxury vehicle,Performance car\ntest/7c1a196cd9acf80a,Bird of prey,Close-up\ntest/7c1a4eb736b3fc4d,Jeans\ntest/7c1c90cb28cd112e,Luxury vehicle\ntest/7c1d0a4cb3e91da6,Aircraft engine\ntest/7c1dad380219da05,Black-and-white\ntest/7c203c3f10e544ff,Bovine,Close-up\ntest/7c22b9b314f3f611,Digital camera\ntest/7c23c4187089e3c4,Compact car\ntest/7c256d1bc6f4ec46,Church bell\ntest/7c26c4f42e278f0f,Whiskers\ntest/7c2b9eaa751bfc1c,Vegetarian food\ntest/7c2d3e168d67fb4c,Rural area\ntest/7c2db392e0e8c1a1,Compact car\ntest/7c33c6e015d44d6b,Road cycling\ntest/7c350ef5d455a59e,Close-up\ntest/7c3ab0b1bdc4ada1,Fire apparatus\ntest/7c3c20856c175c7a,Ball game\ntest/7c3cb87929d36760,Arthropod,Close-up\ntest/7c3ed8c819f1c5e8,Close-up\ntest/7c40994e112afca3,Firearm\ntest/7c4291a8856abd2c,Ramen\ntest/7c470e6d2dff0458,Floristry\ntest/7c497ec55db32d75,Nile crocodile,Crocodilia\ntest/7c4abb7d18dd115b,Extreme sport,Sport climbing\ntest/7c4c990efe91df86,Close-up\ntest/7c517f19776b6ba3,Luxury vehicle,Performance car\ntest/7c55719f696c057e,Black-and-white,Luxury vehicle,Antique car\ntest/7c557d7b8d0b025d,Whiskers\ntest/7c57095dc5c406f3,Ball game,Team sport\ntest/7c57be8707db6842,Luxury vehicle\ntest/7c5a3d63a77a83fd,Senior citizen\ntest/7c5cbe8d4a4a7e01,Close-up\ntest/7c5e8d6a950bb261,Plant stem\ntest/7c619e10aa7f48c7,Close-up\ntest/7c624e7f4a5efbbb,Compact car,Antique car\ntest/7c652104d0d8ff43,Plant stem,Close-up\ntest/7c65692ebab010ad,Luxury vehicle\ntest/7c65d2fc6946e02c,Aircraft engine\ntest/7c6795384114a1ef,Black-and-white,Close-up,Whiskers\ntest/7c689f15ea42e7e8,Compact car,Antique car\ntest/7c6a3cea88eb3156,Close-up\ntest/7c6aea09df145b71,Close-up\ntest/7c6c8058d40624dd,Miniature Poodle\ntest/7c6deb494724965b,Headphones\ntest/7c70f645879e6952,Chordophone\ntest/7c718f850fba4d2f,Extreme sport\ntest/7c72299e58fb5bd5,Close-up\ntest/7c7248433db66c7a,Procyonidae\ntest/7c730baf77a25ca7,Church bell\ntest/7c743a55e84cf932,Ball game,Team sport\ntest/7c7543f7acc0888f,Peafowl\ntest/7c75c29ee3871c6a,Rear-view mirror\ntest/7c75f0ddb12be940,Jeans\ntest/7c796d50c06a169c,Ball game,Team sport\ntest/7c80a1c03f915a4a,Luxury vehicle\ntest/7c890fd02a7010b8,Luxury vehicle,Performance car\ntest/7c89270ca6a19842,Luxury vehicle\ntest/7c8c08736cca762e,Frying,Fried food\ntest/7c8cf10d3f377dc3,Coral reef fish\ntest/7c8d819535713e66,Compact car,Luxury vehicle\ntest/7c8dc83105dfd04a,Luxury vehicle,Sport utility vehicle\ntest/7c8e01cc75825fc7,Performance car\ntest/7c8e2e4d1b2dbdb4,Outdoor shoe\ntest/7c8eb90ee44cd24c,Stemware\ntest/7c8ee612dca41547,Whiskers\ntest/7c92c574215ec065,Halter\ntest/7c94d985ab3613e9,Luxury vehicle\ntest/7c95f830117b2cff,Equestrianism\ntest/7c96c67fd0e51758,Luxury vehicle\ntest/7c974b7bc7f82a60,Performance car\ntest/7c9a87c61e00f225,Jeans\ntest/7c9afdf2e62d1106,Horse and buggy\ntest/7c9c7d314498fc0e,Close-up\ntest/7c9d4af415f2c1f4,Luxury vehicle,Performance car\ntest/7c9d6e9cc03afbff,Equitation,Equestrianism\ntest/7c9deb6b0ebd5a0f,Wind wave\ntest/7c9ee0105ce4f0df,Nile crocodile,Crocodilia\ntest/7ca045d70922dff2,Angling\ntest/7ca0500d6cb8bc5a,Drums,Chordophone\ntest/7ca071a4caf84c3e,Close-up\ntest/7ca0d1b5f7df1b5b,Floristry\ntest/7ca0e6d2117faba8,Toy block\ntest/7ca4b878d1e9efc6,Ball game,Team sport\ntest/7ca4cea45914a82c,Headphones\ntest/7caa69f05b172036,Blond\ntest/7cab193aa193d1f2,Billiard room,Billiards\ntest/7cad20daa647f304,Horse and buggy\ntest/7cb12b82d4e5ebe2,Motorcycling\ntest/7cb3febaf73ef691,Auto racing\ntest/7cb437948674ddae,Coral reef fish,Close-up\ntest/7cb613718c6a1a4c,Equestrianism\ntest/7cb89e1e87386fd6,Combat sport\ntest/7cb9337bd4935673,Luxury vehicle\ntest/7cba957296851f2c,Black-and-white\ntest/7cbd3266e6670d28,Stemware\ntest/7cbda66283a4fa8f,Lawn game\ntest/7cbe0738c6a98509,Performance car\ntest/7cbedab6ba7b7f2c,Junk food\ntest/7cc0c0e6f631d128,Equestrianism\ntest/7cc0e8b1a1ab346b,Fire apparatus\ntest/7cc14c943bc6d26b,Galleon\ntest/7cc59958b7c5b5a3,Ball game,Team sport\ntest/7cc64650c252e581,Close-up\ntest/7ccacd9ce3caf7dd,Jeans\ntest/7cceae5cebc2462e,Black-and-white,Sport utility vehicle\ntest/7cd1bf239a3e3283,Barechested,Close-up\ntest/7cd450aeb42fc132,Equestrianism\ntest/7cd629e520702951,Black-and-white\ntest/7cd6db206a278bdc,Roller skating\ntest/7cd96e3777adb851,Close-up,Plant stem\ntest/7cda894b357d7140,Leaf vegetable\ntest/7cdb9683615b87e5,Close-up,Scaled reptile\ntest/7cdc180467b5125c,Arthropod,Close-up\ntest/7cdc8e4af682a5b3,Performance car,Antique car\ntest/7cdcb137fe34a95e,Team sport\ntest/7cdfd76e93426d44,Sport utility vehicle\ntest/7ce00e00cc53b85d,Pit bull\ntest/7ce07447bde6f5b1,Jeans\ntest/7ce0a38f7c7fc803,Extreme sport\ntest/7ce21353735d171e,Steamed rice,Fried food\ntest/7ce35b29105d2152,Blond,Close-up\ntest/7ce560e12da0fcca,Compact car,Antique car\ntest/7ce65e38154c341d,Vegetarian food\ntest/7ce6c5488967bc32,Dog walking\ntest/7ce6c85b9a05c1a0,Close-up\ntest/7ce8a2b666695627,Arthropod,Close-up\ntest/7cea3bdee61b1132,Luxury vehicle,Performance car\ntest/7cecb82db46f2b58,Close-up,Whiskers\ntest/7cee2c12442ac36b,Close-up,Erinaceidae\ntest/7ceeb04805056494,Close-up\ntest/7cef0b8150d098f2,Auto racing\ntest/7cf03067e39d1ed9,Close-up\ntest/7cf45fcb6b44f70a,Compact car,Luxury vehicle,Antique car\ntest/7cf4d7c48a6fa340,Combat sport,Sumo\ntest/7cf619b96a46c2b3,Coral reef fish\ntest/7cf6aae18d16392e,Black-and-white\ntest/7cf74a9cf24e6f28,Close-up\ntest/7cfb4c966356203d,Performance car\ntest/7cfb92a14b01427a,Leaf vegetable\ntest/7cfbded672392a27,Close-up\ntest/7cfdf829a735e9e2,Whiskers,Domestic rabbit\ntest/7cffd267b0a548c6,Crocodilia\ntest/7d00f9a1e62d91c6,Black-and-white\ntest/7d01dd1fb7b08acd,Compact car\ntest/7d0442d8590c09d5,Compact car,Luxury vehicle,Antique car\ntest/7d0bc6911b95b8c7,Arthropod,Close-up\ntest/7d0c8221ae913f3a,Domestic short-haired cat,Whiskers\ntest/7d0f2bc6b87a3097,Floristry\ntest/7d1022875efb9af9,Close-up\ntest/7d10422b77ca0da0,Monoplane,Aircraft engine\ntest/7d124e4418bed071,Black-and-white\ntest/7d13f9d93d62a459,Military vehicle\ntest/7d1949269499fc0c,Arthropod\ntest/7d1b24efe1084d65,Close-up\ntest/7d1bd29016801cb0,Whiskers\ntest/7d1d21f41c88d20a,Floristry\ntest/7d1ecaa360b6e480,Domestic short-haired cat,Whiskers\ntest/7d207a9afa57dac3,Junk food\ntest/7d20e86d93d4d361,Compact car,Antique car\ntest/7d22f4ca91c6efa1,Plant stem\ntest/7d29c2a421c4f9e2,Ball game\ntest/7d2abc5fca3ebbf3,Frying,Fried food\ntest/7d2ac6235b51a95f,Aircraft engine\ntest/7d2b37666792d13e,Primate\ntest/7d2b41c12b05028e,Boating\ntest/7d336fc5bfd3d0a9,Chordophone\ntest/7d33f3a1af56ccbc,Close-up\ntest/7d3706d3cc7c0619,Firearm\ntest/7d3804235a558926,Breadboard\ntest/7d38eaeac6d9bbdf,Ball game\ntest/7d3a9bd38e6db780,High-speed rail,Black-and-white\ntest/7d3b51fc58f97065,Perching bird,Close-up\ntest/7d3b8fee150a38d7,Microcontroller\ntest/7d3dd650d22bafeb,Fried food\ntest/7d4118e1eb9411c7,Arcade game\ntest/7d428a2cb9f17539,Ball game,Team sport\ntest/7d431d66f5a4cf34,Close-up\ntest/7d49fb8eaff819d8,Black-and-white,Close-up\ntest/7d4a9afd96862ad3,Coca-cola\ntest/7d4aa6396d40996b,Bovine,Close-up\ntest/7d4ae982e383fced,Performance car\ntest/7d4c4d8613d248d7,Compact car\ntest/7d4cd090e4b606ff,Luxury vehicle,Sport utility vehicle\ntest/7d4fbf0d58acb9a3,Domestic short-haired cat,Close-up,Whiskers\ntest/7d503fda335c1619,Close-up\ntest/7d507d69b6a321a7,Luxury vehicle,Performance car\ntest/7d509e4d36778096,Luxury vehicle\ntest/7d528a1c75e51f7d,Luxury vehicle,Performance car\ntest/7d53cb841e5d895e,Gumbo\ntest/7d53d3226df534b1,Close-up\ntest/7d56314ce950b1ba,Luxury vehicle\ntest/7d577c1dbeab97e8,Equitation,Equestrianism\ntest/7d58478ecb2bc3a5,Military person\ntest/7d584e07f42f4486,Divemaster,Scuba diving,Underwater diving\ntest/7d5b359f9a3cae3b,Compact car,Antique car\ntest/7d5ceaf37ead875a,Extreme sport\ntest/7d5df77aa81c9776,Canola\ntest/7d5e8e5493a67a88,Ball game,Team sport\ntest/7d6440b8e5c8cf0f,Scaled reptile\ntest/7d64c6f9f6289698,Whiskers\ntest/7d67428953dc9866,Bagpipes\ntest/7d6a2d3d88297edb,Black-and-white\ntest/7d6ba172979520e1,Whiskers\ntest/7d6ce0422bf68e23,Perching bird,Close-up\ntest/7d6d9b7eaa09959f,Extreme sport,Wind wave\ntest/7d71e33ed26e7b71,Sport utility vehicle\ntest/7d72d4c85be18a36,Compact car,Luxury vehicle\ntest/7d7580f7f2c94eb6,Siberian husky\ntest/7d791f5d7bdeb5cd,Aerial photography\ntest/7d797dd83efdecd7,Leaf vegetable\ntest/7d799fa8a13f9993,Middle ages\ntest/7d7a525a430133b7,Ball game,Team sport\ntest/7d7ac7a1bcd34bf5,Amphibian\ntest/7d7bec14a1c3547d,Amphibian\ntest/7d7c3d30d70902ac,Junk food\ntest/7d7d6a22dc2f061e,Close-up\ntest/7d7e388bebb741a1,Aerial photography\ntest/7d7f3f5af7129b3b,Arcade game\ntest/7d82af7c32177188,Frozen yogurt\ntest/7d834a2a1066755e,Luxury vehicle\ntest/7d84369a34e2b7e2,Outdoor shoe\ntest/7d84b9c9e9e1bc18,Plant stem\ntest/7d866de0d61288c6,Jeans,Briefs,Barechested\ntest/7d868b9753f5360f,Jeans\ntest/7d8a18ad4ca79e06,Scaled reptile\ntest/7d8b2323cca9e72f,Luxury vehicle,Antique car\ntest/7d95b2c72be9e771,Luxury vehicle,Performance car\ntest/7d979606b7f26976,Jeans\ntest/7d98c7ac25062f08,Jeans\ntest/7d9b99288fa972c0,Freight transport\ntest/7d9dc7901b6a60d8,Whiskers\ntest/7da12a56663eff08,Extreme sport,Motorcycling\ntest/7da158104bbb968a,Extreme sport\ntest/7da21dfeb0c6863c,Luxury vehicle\ntest/7da294e5e4459134,Luxury vehicle\ntest/7da62937038d9563,Luxury vehicle,Performance car\ntest/7da7553ad66894e4,Auto racing,Go-kart\ntest/7dab5c475d269e8b,Flatbread\ntest/7dabb6176ed6c479,Vegetarian food\ntest/7daddb49a2881e5f,Jeans\ntest/7dae3ea1fdc0b480,Siberian husky\ntest/7daf9925854e5bdf,Fire apparatus\ntest/7db06c1a2958c8c1,Bonbon,Cookies and crackers\ntest/7db631f4bc382dbe,Extreme sport\ntest/7db83fd760bf78ba,Arcade game\ntest/7dba2ebe709fec68,Plant stem\ntest/7dbc00ff054a7939,Scaled reptile\ntest/7dbc187c45e9c33b,Equestrianism\ntest/7dbf2812e56d9088,Ball game\ntest/7dbf64bf820bd2ef,Nile crocodile,Crocodilia\ntest/7dc210552d5023c7,Touring car,Luxury vehicle,Antique car\ntest/7dc43fc7219bc7ad,Barquentine\ntest/7dc4669cc69b57c3,Compact car,Antique car\ntest/7dc69fb497f770a1,Boating\ntest/7dc821c88a1253f5,Computer speaker\ntest/7dc853c1e4e8d8a1,Vegetarian food\ntest/7dc8d8a5d66a8b84,Luxury vehicle\ntest/7dc90c90edc23dad,Close-up\ntest/7dc93a9035332f58,Luxury vehicle,Antique car\ntest/7dcac0fe4c2dc835,Luxury vehicle\ntest/7dcd230f1ca99a56,Close-up,Whiskers\ntest/7dcd5249189d7617,Aircraft engine\ntest/7dce30e55c2fe9c8,Aircraft engine\ntest/7dcef60be0c85761,Blond,Portrait photography\ntest/7dcff0dd227a07a0,Luxury vehicle,Performance car\ntest/7dd0ba62de299a63,Ramen\ntest/7dd0be249685e9c0,Aircraft engine\ntest/7dd172cf64d32f21,Melee weapon,Hunting knife\ntest/7dd2c9e0dad7daec,Close-up\ntest/7dd32b3dc252c81a,Aircraft engine\ntest/7dd3d8099084d084,Performance car\ntest/7dd4ebb1d7fe920c,Luxury vehicle,Performance car\ntest/7dd51ec986517877,Chordophone\ntest/7dd522c308323aba,Luxury vehicle\ntest/7dd5d975e0cbe896,Lifebuoy\ntest/7dd6a5d1dc4ab0b8,Antique car,Sport utility vehicle\ntest/7dd6fc57ebbbec22,Freight transport\ntest/7dd767f0cba20e48,Residential area,Aerial photography\ntest/7dd7e21c92058449,Whiskers\ntest/7dd816000bf9d5a2,Compact car\ntest/7dd9b0e4f1aa463f,Compact car,Luxury vehicle,Antique car\ntest/7dd9f5200ba9414b,Frozen yogurt\ntest/7ddb063a55849518,Close-up\ntest/7ddb81000f14b1af,Plant stem\ntest/7ddc815e09a31a33,Luxury vehicle\ntest/7dde434931901d91,Compact car,Luxury vehicle\ntest/7de846b24c302ca5,Close-up\ntest/7de87096c236024b,Blond,Hair coloring\ntest/7de97adaf122ae5b,Monoplane\ntest/7dea30ee16a96d84,Luxury vehicle,Antique car\ntest/7deadd40c16d63bf,Whiskers,Erinaceidae\ntest/7dedcf040fc64121,Halter\ntest/7df05412281c951c,Close-up\ntest/7df350877afc3998,Extreme sport,Parachuting\ntest/7df50e606d062f7f,Pasta salad\ntest/7df625e247007d89,Electronic instrument\ntest/7df69baa01412889,Ball game,Team sport\ntest/7df7dbe0823d24c7,Roman temple\ntest/7dffb415ec83b38a,Erinaceidae\ntest/7e01b3324b0d8fff,Compact car,Luxury vehicle\ntest/7e036083daa888ba,Electronic instrument\ntest/7e03c4ec8575210e,Domestic short-haired cat,Whiskers\ntest/7e03fa4125f2c943,Luxury vehicle,Performance car\ntest/7e05bcea71f7278e,Performance car\ntest/7e071a743b0dccba,Extreme sport\ntest/7e0ccdfd18a023b9,Luxury vehicle\ntest/7e0e4e005e9d4476,Double-decker bus\ntest/7e0f021ef62071ed,Luxury vehicle\ntest/7e0f79745a3780f8,Molluscs,Close-up\ntest/7e102636e7abfc43,Domestic short-haired cat,Close-up,Whiskers\ntest/7e120fd678772903,Close-up\ntest/7e157efea59e59ee,Compact car\ntest/7e1ac169153ee26f,Extreme sport,Boating\ntest/7e1b733c1f6917f5,Compact car,Luxury vehicle\ntest/7e1ccc4ef7631d23,Hound\ntest/7e1d148e544e7ee7,Electronic instrument\ntest/7e216388b59a66d9,Microcontroller\ntest/7e24f9fd3b5496e7,Luxury vehicle,Performance car\ntest/7e25608f72e21e8f,Rural area\ntest/7e25e056fb32ed02,Military vehicle\ntest/7e2799434b85d290,Luxury vehicle\ntest/7e295288b9607043,Black-and-white\ntest/7e2b3c801488d275,Antique car\ntest/7e2b568efd3a15fa,Trampolining,Jeans\ntest/7e2b6d445e41cacc,Comics\ntest/7e2eecd78be9265e,Luxury vehicle\ntest/7e2f77fdef4ace55,Solar energy\ntest/7e3645072abc630a,Luxury vehicle\ntest/7e3b9826f570a0cd,Black-and-white\ntest/7e3f84fd2cda7d78,Classical sculpture\ntest/7e401cfcaddb5496,Vegetarian food\ntest/7e40af513ac532db,Luxury vehicle\ntest/7e414e3803ec40b0,Aerial photography\ntest/7e45aca90467c47c,Black-and-white\ntest/7e4afb5a37404abc,Luxury vehicle\ntest/7e4b93055d361fba,Orator\ntest/7e4ca50f2f309c9b,Fire apparatus\ntest/7e4d06b8bb6d30a7,Luxury vehicle\ntest/7e4db36489495aa5,Boating\ntest/7e4dfb83b5163a66,Close-up\ntest/7e4f3f30c4a81edd,Bovine\ntest/7e50eb948706f75b,Luxury vehicle\ntest/7e511237e6072dc0,Luxury vehicle,Antique car\ntest/7e54f27606fc0080,Computer speaker\ntest/7e583f5940278811,Compact car\ntest/7e5b0db679e83535,Gliding\ntest/7e5cb81a3e07c4ee,Ball game,Team sport\ntest/7e5d2988aa7e20fd,Antique car\ntest/7e5dcd477f4e6029,Luxury vehicle\ntest/7e5ef98303e53f7e,Close-up\ntest/7e5fea4c67c023ac,Cookies and crackers\ntest/7e618547179ec2b3,Vegetarian food\ntest/7e628627480abe16,Luxury vehicle\ntest/7e65a2d4bdd04779,Rural area\ntest/7e6798d55a1b5339,Equestrianism\ntest/7e69d58514d86cf2,Military person\ntest/7e6d3c174863db06,Plant stem\ntest/7e6e6df85ec448bb,Auto racing\ntest/7e6f59972865abfa,High-speed rail\ntest/7e707e89289aaac6,Residential area\ntest/7e70d2fde3f04936,Luxury vehicle,Performance car\ntest/7e71a3b3e5ce2735,Close-up\ntest/7e72d763471afe06,Combat sport\ntest/7e759ed4d9ab2a40,Prosciutto\ntest/7e762d89bccbcaa9,Fried noodles\ntest/7e7637994a6468e9,Compact car\ntest/7e7685ad2df3bf50,Close-up\ntest/7e7761c527efd1e8,Gumbo\ntest/7e7b49f6729cbc61,Performance car\ntest/7e7b6dab1a7dacd9,Luxury vehicle\ntest/7e7d9f79169da85a,Vegetarian food\ntest/7e7ddfd47f7174d5,Aircraft engine\ntest/7e7e13732ae2a035,Close-up\ntest/7e7e3f2e5f2d1499,Wind wave\ntest/7e7e8878865d56c4,Luxury vehicle\ntest/7e81cdb0c2f4923d,Hound,Close-up\ntest/7e82b0f562e3248e,Antique car\ntest/7e843028755ae78b,Black-and-white\ntest/7e8536334520888a,Ball game\ntest/7e85e28df1951c4b,Close-up\ntest/7e85e9fe070d05ad,Ball game,Team sport\ntest/7e871584d1024376,Close-up\ntest/7e8761a98af1c3be,Close-up\ntest/7e8877d93e579004,Performance car,Luxury vehicle,Antique car\ntest/7e8c78ee20df969f,Close-up\ntest/7e8cce00ad460513,Auto racing\ntest/7e8e5117efecf3df,Jeans\ntest/7e8ee1e7b69f795a,Compact car,Luxury vehicle,Antique car\ntest/7e8f00b4f9edab7d,Frozen yogurt\ntest/7e8f1095b4e20df9,Ball game,Team sport\ntest/7e9320f10a4f1d60,Ball game,Team sport\ntest/7e93b527286990e4,Microcontroller\ntest/7e9596503b795607,Extreme sport,Sport climbing,Bouldering\ntest/7e961e03378c9e02,Antique car\ntest/7e96eaf941429091,Auto racing,Extreme sport,Personal water craft,Boating\ntest/7e97709a871678a9,Luxury vehicle,Sport utility vehicle\ntest/7e97a8ae94472689,Compact car,Luxury vehicle\ntest/7e9b744775a82939,Close-up\ntest/7e9bda209af185d6,Road cycling\ntest/7e9c3df4a829ee37,Antique car\ntest/7e9cdf3a0097a268,Close-up\ntest/7e9ff6f71f4bf476,Black-and-white,Close-up\ntest/7ea1f06d1e7e806e,Bird of prey,Close-up\ntest/7ea593ac27c93552,Arthropod,Locust,Close-up\ntest/7ea5c925e38701e4,Compact car,Luxury vehicle,Antique car\ntest/7ea72796104eea81,Extreme sport,Wind wave\ntest/7ea79b41d75e6935,Residential area\ntest/7ea8e1fce63d4356,Chordophone\ntest/7ea9323ffde46c4d,Luxury vehicle,Antique car\ntest/7ea9cc41830d2c3f,Ball game\ntest/7eaa8268fd6fa5be,Sport utility vehicle\ntest/7ead8a05548b7dd8,Bird of prey\ntest/7eaf5a330d66b8b4,Medical imaging\ntest/7ebb37d216a264bb,Extreme sport\ntest/7ebc1f8a1473dacb,Close-up\ntest/7ebef5f4986f0b4c,Compact car\ntest/7ec0563820191541,Close-up\ntest/7ec145d26a83d11e,Compact car,Antique car\ntest/7ecaa9386625d181,Aircraft engine\ntest/7ecb0e5f87a293fe,Inflatable\ntest/7ecb88316375b278,Machine tool\ntest/7eccc7dff9449616,Whiskers\ntest/7ecd9afeb3ad0d7f,Compact car,Antique car\ntest/7ece678c0248c714,Extreme sport\ntest/7ed138299d60702f,Performance car\ntest/7ed27dc5cf7018ac,Backlighting\ntest/7ed310c2c77b61f3,Performance car\ntest/7ed585a3d7255a86,Performance car\ntest/7ed5919fef151c3b,Plant stem\ntest/7ed629e7b9833137,Close-up\ntest/7eda7315dd2d1ff7,Close-up\ntest/7edcaee4aee70177,Domestic rabbit\ntest/7ede6389fe7811d0,Close-up\ntest/7edefb3a23eaae63,Equestrianism\ntest/7edf1639853804b7,Ball game,Team sport\ntest/7edf7963e52fc54c,Domestic short-haired cat,Close-up,Whiskers\ntest/7edfa33856fca48a,Water polo,Ball game\ntest/7ee02a9caa4cb171,Hound\ntest/7ee05c4b5c79602f,Jeans\ntest/7ee3946200ec74b4,Auto racing,Touring car,Luxury vehicle\ntest/7ee3ecd8e512013c,Flatbread\ntest/7ee48908e5105de8,Sport utility vehicle\ntest/7ee83720462da82c,Close-up\ntest/7ee93c49f529e5c6,Close-up\ntest/7ee99fbf96d4864e,Plant stem,Close-up\ntest/7eead39eaeb8dc55,Luxury vehicle,Performance car\ntest/7eeaeb4ee8db780d,Team sport\ntest/7eeb98647c8628a9,Compact car,Luxury vehicle\ntest/7eebef492332b17e,Ale\ntest/7eef57e1cee3f75a,Chordophone\ntest/7ef00086e26bfdc1,Rural area\ntest/7ef0c314a1f73b75,Close-up\ntest/7ef20840c2714472,Bird of prey\ntest/7ef2f6aa628ad768,Antique car\ntest/7ef93b7b0f3c5c5f,Sport utility vehicle\ntest/7ef9a16c97692e4e,Blond\ntest/7efaaa73dc2deda7,Close-up\ntest/7efb6e7f32b67939,Scaled reptile\ntest/7efd9c3dba023ee9,Rural area,Bovine\ntest/7eff771658d79306,Close-up\ntest/7f00e660a2392055,Floristry\ntest/7f02b11dba077870,Coral reef fish\ntest/7f030a47fb811206,Luxury vehicle\ntest/7f03291274741677,Barechested,Close-up\ntest/7f05481e952b20d1,Frigate\ntest/7f06359bfe79c319,Scaled reptile\ntest/7f0960fd88d3ed23,Close-up\ntest/7f0b4aec14d2b253,Compact car,Antique car\ntest/7f0e24f93002d75e,Combat sport,Sumo\ntest/7f0f06d852522b6e,Vegetarian food\ntest/7f110b43962cc29d,Jeans,Luxury vehicle,Performance car\ntest/7f11fcb0be389d21,Firearm\ntest/7f13616780e4a7fa,Frying,Junk food,Fried food\ntest/7f14e9918c74889b,Sport utility vehicle\ntest/7f15b5dfae575e39,Close-up\ntest/7f17d07dcc75de3a,Whiskers\ntest/7f197f5acecc598e,Hound\ntest/7f1c6d5e108157cb,Black-and-white,Close-up\ntest/7f1cd208489410df,Luxury vehicle,Performance car\ntest/7f1f7b665acef629,Close-up\ntest/7f2138cce8d1589d,Close-up\ntest/7f23e2fd834802fa,Black-and-white,Horse and buggy\ntest/7f24fef00587fc49,Antique car,Performance car\ntest/7f25039ac646ca06,Machine tool\ntest/7f2765cb4131fa4b,Close-up\ntest/7f28c79a8a9f3bc0,Leaf vegetable\ntest/7f2933b67c1099cd,Auto racing,Touring car\ntest/7f2d38087576f254,Luxury vehicle\ntest/7f2ece04be3482c4,Microcontroller\ntest/7f31d678b090085f,Compact car,Luxury vehicle,Antique car\ntest/7f36bafae1131f42,Auto racing,Performance car\ntest/7f3847662c324790,Nordic skiing\ntest/7f3edee8f6a451b2,Boating\ntest/7f422e3df0b6adca,Luxury vehicle,Sport utility vehicle\ntest/7f433e556faa6d15,Plant stem\ntest/7f45a0fce2fd3a12,Vegetarian food\ntest/7f4755589f2f3907,Coral reef fish\ntest/7f4818069081d445,Perching bird\ntest/7f48590ca9106dce,Performance car,Luxury vehicle,Antique car\ntest/7f4a17527113fb27,Luxury vehicle,Antique car\ntest/7f4b97d2e7ca75e8,Chordophone\ntest/7f4c29cc086ce5fa,Luxury vehicle\ntest/7f4d187ae02ecd5f,Leaf vegetable\ntest/7f5122855681b1d0,Computer speaker\ntest/7f517084ed366d6a,Compact car\ntest/7f584b7e76827fb5,Close-up,Whiskers\ntest/7f588825b75e241c,Comics\ntest/7f5a7de7d8f4a26b,Compact car,Luxury vehicle\ntest/7f5c8757c2458bd7,Vegetarian food\ntest/7f5e5b7850e24cba,Close-up\ntest/7f5e9cb705001a95,Luxury vehicle,Antique car\ntest/7f5fb52baffdcf55,Motorcycling\ntest/7f609381d8a2813d,Close-up\ntest/7f62d9be9fcadfed,Luxury vehicle\ntest/7f67ab456f2bc721,Equitation,Equestrianism\ntest/7f67f6051d99b447,Bovine\ntest/7f6b5eb71a9a108f,Close-up\ntest/7f6bdcbd497c04fb,Residential area\ntest/7f6c7e7fe55a9d87,Aircraft engine\ntest/7f6cceef5a99cf42,Antique car\ntest/7f6d05a76f2705df,Residential area\ntest/7f6feb85352aa977,Extreme sport\ntest/7f72d1349bfcfde2,Compact car,Luxury vehicle\ntest/7f72fad99ffadfd3,Aircraft engine\ntest/7f73e6bb07218766,Close-up\ntest/7f744a32f385488c,Compact car,Antique car\ntest/7f748d650ac12191,Luxury vehicle\ntest/7f7574c482904f60,Fried food\ntest/7f75f2934a5dc748,Black-and-white\ntest/7f7862867c59a1f7,Plant stem\ntest/7f7a1ed6595e9f04,Close-up\ntest/7f7a430e3a120bcf,Drums\ntest/7f7a7569587ca5f7,Luxury vehicle\ntest/7f7da074397645ae,Extreme sport\ntest/7f7ecbfd86b66cfd,Close-up\ntest/7f7f8c74fe51a5ef,Performance car,Antique car\ntest/7f812c5ccec32382,Performance car\ntest/7f82910d19474f6e,Arcade game\ntest/7f83c854786f9bfd,Rural area\ntest/7f8495c28f94e376,Rural area\ntest/7f857adda22c844b,Arthropod,Close-up\ntest/7f8b2ac37ad32bfb,Molluscs,Close-up\ntest/7f8c4daded1d9129,Great white shark\ntest/7f8ca364ed6a9d16,Ball game\ntest/7f8ca713eb2a5055,Flatbread\ntest/7f8cf672b29567a8,Team sport\ntest/7f8e3132a62c9d10,Ball game,Team sport\ntest/7f8e3ae16f03a478,Extreme sport,Boating\ntest/7f8fd53be5c28bd4,Antique car\ntest/7f9303bd735cc866,Close-up\ntest/7f93f77d6a9b2f02,Black-and-white\ntest/7f9518a6ecb16811,Scaled reptile\ntest/7f97c87b2a637249,Auto racing,Luxury vehicle\ntest/7f9a04d4c4cff392,Performance car\ntest/7f9eda35650e4851,Luxury vehicle\ntest/7fa0e19cd7267276,Close-up\ntest/7fa2884b515fae6c,Luxury vehicle,Sport utility vehicle\ntest/7fa38ecc07cc3872,Chordophone\ntest/7fa48393005ff3e9,Boating\ntest/7fa5416e38eed0a3,Luxury vehicle,Performance car\ntest/7fa571a289f811f5,Ball game\ntest/7fa782668c95918d,Headphones\ntest/7fadcceb53e1c384,Hound\ntest/7faef110353d439f,Hair coloring\ntest/7faf6bfb7bc1487e,Rural area\ntest/7fafe616df045f85,Close-up\ntest/7fb09830e12a7044,Close-up\ntest/7fb1e9f1f183eb94,Vegetarian food\ntest/7fb440446e8fae64,Luxury vehicle,Performance car\ntest/7fb49fd6901ec135,Procyonidae,Whiskers\ntest/7fb4a9902ce44db3,Soba\ntest/7fb9a62ccb1f4af8,Close-up\ntest/7fbd1ba4178cda7a,Luxury vehicle,Antique car\ntest/7fbdfa7a631ab80a,Close-up,Whiskers\ntest/7fbe2038d4bf8056,Siberian husky\ntest/7fbf1f12a60d7b71,Horse racing,Equestrianism\ntest/7fc0230d52501fb8,Close-up\ntest/7fc067da7ef2e95b,Close-up\ntest/7fc0e27b7fdfe883,Close-up\ntest/7fc1e7d37804c3e5,Floristry\ntest/7fc243ac0ba6bd67,Extreme sport,Wind wave\ntest/7fc4788a7d24f198,Performance car\ntest/7fc5025e3a4f1e7a,Close-up\ntest/7fc57d7e27096c85,Jeans\ntest/7fca6a4eeb6c69e6,Auto racing,Compact car,Touring car\ntest/7fcc9f97aca4f58f,Sport utility vehicle\ntest/7fcd6799aa5e19c7,Rural area\ntest/7fce3e566a3e1148,Combat sport\ntest/7fce4e4f69e54d76,Miniature Poodle\ntest/7fcf747ba7569f82,Black-and-white,Medical imaging\ntest/7fcf80c14dcead5a,Luxury vehicle,Antique car\ntest/7fd2932dced26fdc,Solar energy\ntest/7fd9951f54562603,Ale\ntest/7fda111795aa90a3,Performance car\ntest/7fdc22d3cea14cb4,Ball game,Team sport\ntest/7fdd1e8321249e8c,Black-and-white\ntest/7fddc884a98b2c89,Luxury vehicle,Antique car\ntest/7fde1896961ceeae,Residential area\ntest/7fdf88d81044ba22,Close-up\ntest/7fe6d0c937cf2cee,Arthropod\ntest/7fe77278bc63ae58,Residential area\ntest/7fe994bae130b14b,Bird of prey\ntest/7fea60b7e0795984,Headphones\ntest/7fec7e55b5746417,Wind wave\ntest/7fed59ee9b4d0338,Luxury vehicle\ntest/7feea3bef1305f5f,Inflatable\ntest/7feef7970d08c3c5,Luxury vehicle,Performance car\ntest/7ff20c5725dc2354,Compact car,Antique car\ntest/7ff32196b43d197d,Luxury vehicle\ntest/7ff3b1de0575401f,Close-up\ntest/7ff4139775627c47,Ball game,Team sport\ntest/7ff4a646df3690ce,Residential area\ntest/7ff7f5c9e14ca7c6,Pit bull\ntest/7ff8582317aac4c4,Luxury vehicle\ntest/7ff867b551ded731,Monoplane,Gliding\ntest/7ff8c15d8267a9e8,Sport utility vehicle\ntest/7ff906b778c3dd87,Luxury vehicle,Performance car\ntest/7ff935bc49cbd527,Whiskers\ntest/7ff9399f9678ff55,Floristry\ntest/7ffaca9685d941e1,Close-up\ntest/7ffcfec928901e11,Black-and-white\ntest/7ffd0cb4f9f8f739,Luxury vehicle,Sport utility vehicle\ntest/7ffd35d3f7d3c952,Auto racing,Performance car\ntest/7ffd67d4b7d920b9,Monoplane,Gliding\ntest/8000ca9a1cbf9c22,Close-up\ntest/800367f9f99a5ded,Steamed rice,Fried food\ntest/800379a9efd74818,Extreme sport,Glacial landform\ntest/8004826f01ceaeca,Boating,Rowing\ntest/8008c0257899502e,Cookies and crackers\ntest/8008f0e47b1a7013,Bird of prey\ntest/800a5ee2275ebd7c,Cookies and crackers\ntest/800ab3ee7d8d0eec,Plant stem\ntest/800bf8ca172347aa,Arthropod\ntest/800d167daf39bd72,Ball game,Team sport\ntest/800e733113c365a3,Miniature Poodle\ntest/80143d22c7387d16,Sport utility vehicle\ntest/80163e7e6a59ffef,Hair coloring\ntest/801b30037ed6d4ac,Luxury vehicle,Antique car\ntest/801d3288fe412729,Junk food\ntest/801e10ed905908ee,Fried food\ntest/801f586154e1363c,Siberian husky\ntest/80202c83508a3aca,Compact car,Antique car\ntest/802539e8caa1611d,Compact car,Luxury vehicle\ntest/8027721fd8895ce0,Close-up\ntest/802827c0e73b7887,Luxury vehicle,Antique car\ntest/802ac2a00d99f11e,Jeans\ntest/802bc294b39b2854,Vegetarian food,Fried food\ntest/802c2b223ba4f2b4,Close-up\ntest/802cab065d5f5ead,Luxury vehicle,Performance car\ntest/802d29b0d40d202e,Extreme sport\ntest/803007f3d026cd16,Ball game,Team sport\ntest/80319931b45e3556,Vegetarian food,Junk food\ntest/8031bc0c4c1bbaf1,Luxury vehicle,Performance car\ntest/8031c3b41a1286cf,Auto racing\ntest/803274329a41469d,Domestic short-haired cat,Whiskers\ntest/8032804733572037,Drums\ntest/8033714176cfff11,Team sport\ntest/8033975cdab65987,Monoplane\ntest/80347539902af7b0,Plant stem\ntest/8035bd2eb597ea68,Ball game,Team sport\ntest/80364dc170a91a85,Fire apparatus\ntest/803732bc9ddc64c5,Compact car,Luxury vehicle\ntest/80380575dd37aaad,Luxury vehicle,Sport utility vehicle\ntest/803813250656db2e,Plant stem,Close-up\ntest/8038dcc7f0652c6e,Close-up\ntest/803ab0b35647c6fa,Sport utility vehicle\ntest/803ab9ba1e277983,Floristry\ntest/803be87c0eb3c6ff,Junk food,Fried food\ntest/803df0dee9e9e1d7,Combat sport\ntest/803f054a8399a88a,Floristry\ntest/803ff485d461b795,Luxury vehicle,Performance car\ntest/80417e3dff03c289,Close-up\n"
  },
  {
    "path": "model_cards/Tag2Text/datasets/openimages_rare_200/openimages_rare_200_ram_taglist.txt",
    "content": "Aerial photography\nAircraft engine\nAle\nAloe\nAmphibian\nAngling\nAnole\nAntique car\nArcade game\nArthropod\nAssault rifle\nAthletic shoe\nAuto racing\nBacklighting\nBagpipes\nBall game\nBarbecue chicken\nBarechested\nBarquentine\nBeef tenderloin\nBilliard room\nBilliards\nBird of prey\nBlack swan\nBlack-and-white\nBlond\nBoating\nBonbon\nBottled water\nBouldering\nBovine\nBratwurst\nBreadboard\nBriefs\nBrisket\nBrochette\nCalabaza\nCamera operator\nCanola\nChildbirth\nChordophone\nChurch bell\nClassical sculpture\nClose-up\nCobblestone\nCoca-cola\nCombat sport\nComics\nCompact car\nComputer speaker\nCookies and crackers\nCoral reef fish\nCorn on the cob\nCosmetics\nCrocodilia\nDigital camera\nDishware\nDivemaster\nDobermann\nDog walking\nDomestic rabbit\nDomestic short-haired cat\nDouble-decker bus\nDrums\nElectric guitar\nElectric piano\nElectronic instrument\nEquestrianism\nEquitation\nErinaceidae\nExtreme sport\nFalafel\nFigure skating\nFilling station\nFire apparatus\nFirearm\nFlatbread\nFloristry\nForklift truck\nFreight transport\nFried food\nFried noodles\nFrigate\nFrozen yogurt\nFrying\nFull moon\nGalleon\nGlacial landform\nGliding\nGo-kart\nGoats\nGrappling\nGreat white shark\nGumbo\nGun turret\nHair coloring\nHalter\nHeadphones\nHeavy cruiser\nHerding\nHigh-speed rail\nHolding hands\nHorse and buggy\nHorse racing\nHound\nHunting knife\nHurdling\nInflatable\nJackfruit\nJeans\nJiaozi\nJunk food\nKhinkali\nKitesurfing\nLawn game\nLeaf vegetable\nLechon\nLifebuoy\nLocust\nLumpia\nLuxury vehicle\nMachine tool\nMedical imaging\nMelee weapon\nMicrocontroller\nMiddle ages\nMilitary person\nMilitary vehicle\nMilky way\nMiniature Poodle\nModern dance\nMolluscs\nMonoplane\nMotorcycling\nMusical theatre\nNarcissus\nNest box\nNewsagent's shop\nNile crocodile\nNordic skiing\nNuclear power plant\nOrator\nOutdoor shoe\nParachuting\nPasta salad\nPeafowl\nPelmeni\nPerching bird\nPerformance car\nPersonal water craft\nPit bull\nPlant stem\nPork chop\nPortrait photography\nPrimate\nProcyonidae\nProsciutto\nPublic speaking\nRacewalking\nRamen\nRear-view mirror\nResidential area\nRibs\nRice ball\nRoad cycling\nRoller skating\nRoman temple\nRowing\nRural area\nSailboat racing\nScaled reptile\nScuba diving\nSenior citizen\nShallot\nShinto shrine\nShooting range\nSiberian husky\nSledding\nSoba\nSolar energy\nSport climbing\nSport utility vehicle\nSteamed rice\nStemware\nSumo\nSurfing Equipment\nTeam sport\nTouring car\nToy block\nTrampolining\nUnderwater diving\nVegetarian food\nWallaby\nWater polo\nWatercolor paint\nWhiskers\nWind wave\nWoodwind instrument\nYakitori\nZeppelin\n"
  },
  {
    "path": "model_cards/Tag2Text/inference_ram.py",
    "content": "'''\n * The Recognize Anything Model (RAM)\n * Written by Xinyu Huang\n'''\nimport argparse\nimport numpy as np\nimport random\n\nimport torch\n\nfrom PIL import Image\nfrom ram.models import ram\nfrom ram import inference_ram as inference\nfrom ram import get_transform\n\n\nparser = argparse.ArgumentParser(\n    description='Tag2Text inferece for tagging and captioning')\nparser.add_argument('--image',\n                    metavar='DIR',\n                    help='path to dataset',\n                    default='images/1641173_2291260800.jpg')\nparser.add_argument('--pretrained',\n                    metavar='DIR',\n                    help='path to pretrained model',\n                    default='pretrained/tag2text_swin_14m.pth')\nparser.add_argument('--image-size',\n                    default=384,\n                    type=int,\n                    metavar='N',\n                    help='input image size (default: 448)')\n\n\nif __name__ == \"__main__\":\n\n    args = parser.parse_args()\n\n    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n\n    transform = get_transform(image_size=args.image_size)\n\n    #######load model\n    model = ram(pretrained=args.pretrained,\n                             image_size=args.image_size,\n                             vit='swin_l')\n    model.eval()\n\n    model = model.to(device)\n\n    image = transform(Image.open(args.image)).unsqueeze(0).to(device)\n\n    res = inference(image, model)\n    print(\"Image Tags: \", res[0])\n    print(\"图像标签: \", res[1])\n"
  },
  {
    "path": "model_cards/Tag2Text/inference_ram_openset.py",
    "content": "'''\n * The Recognize Anything Model (RAM) inference on unseen classes\n * Written by Xinyu Huang\n'''\nimport argparse\nimport numpy as np\nimport random\n\nimport torch\n\nfrom PIL import Image\nfrom ram.models import ram\nfrom ram import inference_ram_openset as inference\nfrom ram import get_transform\n\nfrom ram.utils import build_openset_label_embedding\nfrom torch import nn\n\nparser = argparse.ArgumentParser(\n    description='Tag2Text inferece for tagging and captioning')\nparser.add_argument('--image',\n                    metavar='DIR',\n                    help='path to dataset',\n                    default='images/openset_example.jpg')\nparser.add_argument('--pretrained',\n                    metavar='DIR',\n                    help='path to pretrained model',\n                    default='pretrained/tag2text_swin_14m.pth')\nparser.add_argument('--image-size',\n                    default=384,\n                    type=int,\n                    metavar='N',\n                    help='input image size (default: 448)')\n\n\nif __name__ == \"__main__\":\n\n    args = parser.parse_args()\n\n    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n\n    transform = get_transform(image_size=args.image_size)\n\n    #######load model\n    model = ram(pretrained=args.pretrained,\n                             image_size=args.image_size,\n                             vit='swin_l')\n    \n    #######set openset interference\n    openset_label_embedding, openset_categories = build_openset_label_embedding()\n\n    model.tag_list = np.array(openset_categories)\n    \n    model.label_embed = nn.Parameter(openset_label_embedding.float())\n\n    model.num_class = len(openset_categories)\n    # the threshold for unseen categories is often lower\n    model.class_threshold = torch.ones(model.num_class) * 0.5\n    #######\n\n    model.eval()\n\n    model = model.to(device)\n\n    image = transform(Image.open(args.image)).unsqueeze(0).to(device)\n\n    res = inference(image, model)\n    print(\"Image Tags: \", res)\n"
  },
  {
    "path": "model_cards/Tag2Text/inference_tag2text.py",
    "content": "'''\n * The Tag2Text Model\n * Written by Xinyu Huang\n'''\nimport argparse\nimport numpy as np\nimport random\n\nimport torch\n\nfrom PIL import Image\nfrom ram.models import tag2text\nfrom ram import inference_tag2text as inference\nfrom ram import get_transform\n\n\nparser = argparse.ArgumentParser(\n    description='Tag2Text inferece for tagging and captioning')\nparser.add_argument('--image',\n                    metavar='DIR',\n                    help='path to dataset',\n                    default='images/1641173_2291260800.jpg')\nparser.add_argument('--pretrained',\n                    metavar='DIR',\n                    help='path to pretrained model',\n                    default='pretrained/tag2text_swin_14m.pth')\nparser.add_argument('--image-size',\n                    default=384,\n                    type=int,\n                    metavar='N',\n                    help='input image size (default: 448)')\nparser.add_argument('--thre',\n                    default=0.68,\n                    type=float,\n                    metavar='N',\n                    help='threshold value')\nparser.add_argument('--specified-tags',\n                    default='None',\n                    help='User input specified tags')\n\n\nif __name__ == \"__main__\":\n\n    args = parser.parse_args()\n\n    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n\n    transform = get_transform(image_size=args.image_size)\n\n    # delete some tags that may disturb captioning\n    # 127: \"quarter\"; 2961: \"back\", 3351: \"two\"; 3265: \"three\"; 3338: \"four\"; 3355: \"five\"; 3359: \"one\"\n    delete_tag_index = [127,2961, 3351, 3265, 3338, 3355, 3359]\n\n    #######load model\n    model = tag2text(pretrained=args.pretrained,\n                             image_size=args.image_size,\n                             vit='swin_b',\n                             delete_tag_index=delete_tag_index)\n    model.threshold = args.thre  # threshold for tagging\n    model.eval()\n\n    model = model.to(device)\n\n    image = transform(Image.open(args.image)).unsqueeze(0).to(device)\n\n    res = inference(image, model, args.specified_tags)\n    print(\"Model Identified Tags: \", res[0])\n    print(\"User Specified Tags: \", res[1])\n    print(\"Image Caption: \", res[2])\n"
  },
  {
    "path": "model_cards/Tag2Text/ram/__init__.py",
    "content": "from .inference import inference_tag2text, inference_ram, inference_ram_openset\nfrom .transform import get_transform\n"
  },
  {
    "path": "model_cards/Tag2Text/ram/configs/med_config.json",
    "content": "{\n    \"architectures\": [\n      \"BertModel\"\n    ],\n    \"attention_probs_dropout_prob\": 0.1,\n    \"hidden_act\": \"gelu\",\n    \"hidden_dropout_prob\": 0.1,\n    \"hidden_size\": 768,\n    \"initializer_range\": 0.02,\n    \"intermediate_size\": 3072,\n    \"layer_norm_eps\": 1e-12,\n    \"max_position_embeddings\": 512,\n    \"model_type\": \"bert\",\n    \"num_attention_heads\": 12,\n    \"num_hidden_layers\": 12,\n    \"pad_token_id\": 0,\n    \"type_vocab_size\": 2,\n    \"vocab_size\": 30524,\n    \"encoder_width\": 768,\n    \"add_cross_attention\": true   \n  }"
  },
  {
    "path": "model_cards/Tag2Text/ram/configs/q2l_config.json",
    "content": "{\n    \"architectures\": [\n      \"BertModel\"\n    ],\n    \"attention_probs_dropout_prob\": 0.1,\n    \"hidden_act\": \"gelu\",\n    \"hidden_dropout_prob\": 0.1,\n    \"hidden_size\": 768,\n    \"initializer_range\": 0.02,\n    \"intermediate_size\": 3072,\n    \"layer_norm_eps\": 1e-12,\n    \"max_position_embeddings\": 512,\n    \"model_type\": \"bert\",\n    \"num_attention_heads\": 4,\n    \"num_hidden_layers\": 2,\n    \"pad_token_id\": 0,\n    \"type_vocab_size\": 2,\n    \"vocab_size\": 30522,\n    \"encoder_width\": 768,\n    \"add_cross_attention\": true,\n    \"add_tag_cross_attention\": false\n  }"
  },
  {
    "path": "model_cards/Tag2Text/ram/configs/swin/config_swinB_384.json",
    "content": "{\n    \"ckpt\": \"pretrain_model/swin_base_patch4_window7_224_22k.pth\",\n    \"vision_width\": 1024,\n    \"image_res\": 384,\n    \"window_size\": 12,\n    \"embed_dim\": 128,\n    \"depths\": [ 2, 2, 18, 2 ],\n    \"num_heads\": [ 4, 8, 16, 32 ]\n  }"
  },
  {
    "path": "model_cards/Tag2Text/ram/configs/swin/config_swinL_384.json",
    "content": "{\n    \"ckpt\": \"pretrain_model/swin_large_patch4_window12_384_22k.pth\",\n    \"vision_width\": 1536,\n    \"image_res\": 384,\n    \"window_size\": 12,\n    \"embed_dim\": 192,\n    \"depths\": [ 2, 2, 18, 2 ],\n    \"num_heads\": [ 6, 12, 24, 48 ]\n  }"
  },
  {
    "path": "model_cards/Tag2Text/ram/data/ram_tag_list.txt",
    "content": "3D CG rendering\n3D glasses\nabacus\nabalone\nmonastery\nbelly\nacademy\naccessory\naccident\naccordion\nacorn\nacrylic paint\nact\naction\naction film\nactivity\nactor\nadaptation\nadd\nadhesive tape\nadjust\nadult\nadventure\nadvertisement\nantenna\naerobics\nspray can\nafro\nagriculture\naid\nair conditioner\nair conditioning\nair sock\naircraft cabin\naircraft model\nair field\nair line\nairliner\nairman\nplane\nairplane window\nairport\nairport runway\nairport terminal\nairship\nairshow\naisle\nalarm\nalarm clock\nmollymawk\nalbum\nalbum cover\nalcohol\nalcove\nalgae\nalley\nalmond\naloe vera\nalp\nalpaca\nalphabet\ngerman shepherd\naltar\namber\nambulance\nbald eagle\nAmerican shorthair\namethyst\namphitheater\namplifier\namusement park\namusement ride\nanchor\nancient\nanemone\nangel\nangle\nanimal\nanimal sculpture\nanimal shelter\nanimation\nanimation film\nanimator\nanime\nankle\nanklet\nanniversary\ntrench coat\nant\nantelope\nantique\nantler\nanvil\napartment\nape\napp\napp icon\nappear\nappearance\nappetizer\napplause\napple\napple juice\napple pie\napple tree\napplesauce\nappliance\nappointment\napproach\napricot\napron\naqua\naquarium\naquarium fish\naqueduct\narcade\narcade machine\narch\narch bridge\narchaelogical excavation\narchery\narchipelago\narchitect\narchitecture\narchive\narchway\narea\narena\nargument\narm\narmadillo\narmband\narmchair\narmoire\narmor\narmy\narmy base\narmy tank\narray\narrest\narrow\nart\nart exhibition\nart gallery\nart print\nart school\nart studio\nart vector illustration\nartichoke\narticle\nartifact\nartist\nartists loft\nash\nashtray\nasia temple\nasparagus\nasphalt road\nassemble\nassembly\nassembly line\nassociation\nastronaut\nastronomer\nathlete\nathletic\natlas\natm\natmosphere\natrium\nattach\nfighter jet\nattend\nattraction\natv\neggplant\nauction\naudi\naudio\nauditorium\naurora\nauthor\nauto factory\nauto mechanic\nauto part\nauto show\nauto showroom\ncar battery\nautomobile make\nautomobile model\nmotor vehicle\nautumn\nautumn forest\nautumn leave\nautumn park\nautumn tree\navatar\navenue\naviator sunglasses\navocado\naward\naward ceremony\naward winner\nshed\nax\nazalea\nbaboon\nbaby\nbaby bottle\nbaby carriage\nbaby clothe\nbaby elephant\nbaby food\nbaby seat\nbaby shower\nback\nbackdrop\nbacklight\nbackpack\nbackyard\nbacon\nbadge\nbadger\nbadlands\nbadminton\nbadminton racket\nbag\nbagel\nbagpipe\nbaguette\nbait\nbaked goods\nbaker\nbakery\nbaking\nbaking sheet\nbalance\nbalance car\nbalcony\nball\nball pit\nballerina\nballet\nballet dancer\nballet skirt\nballoon\nballoon arch\nbaseball player\nballroom\nbamboo\nbamboo forest\nbanana\nbanana bread\nbanana leaf\nbanana tree\nband\nband aid\nbandage\nheadscarf\nbandeau\nbangs\nbracelet\nbalustrade\nbanjo\nbank\nbank card\nbank vault\nbanknote\nbanner\nbanquet\nbanquet hall\nbanyan tree\nbaozi\nbaptism\nbar\nbar code\nbar stool\nbarbecue\nbarbecue grill\nbarbell\nbarber\nbarber shop\nbarbie\nbarge\nbarista\nbark\nbarley\nbarn\nbarn owl\nbarn door\nbarrel\nbarricade\nbarrier\nhandcart\nbartender\nbaseball\nbaseball base\nbaseball bat\nbaseball hat\nbaseball stadium\nbaseball game\nbaseball glove\nbaseball pitcher\nbaseball team\nbaseball uniform\nbasement\nbasil\nbasin\nbasket\nbasket container\nbasketball\nbasketball backboard\nbasketball coach\nbasketball court\nbasketball game\nbasketball hoop\nbasketball player\nbasketball stadium\nbasketball team\nbass\nbass guitar\nbass horn\nbassist\nbat\nbath\nbath heater\nbath mat\nbath towel\nswimwear\nbathrobe\nbathroom\nbathroom accessory\nbathroom cabinet\nbathroom door\nbathroom mirror\nbathroom sink\ntoilet paper\nbathroom window\nbatman\nwand\nbatter\nbattery\nbattle\nbattle rope\nbattleship\nbay\nbay bridge\nbay window\nbayberry\nbazaar\nbeach\nbeach ball\nbeach chair\nbeach house\nbeach hut\nbeach towel\nbeach volleyball\nlighthouse\nbead\nbeagle\nbeak\nbeaker\nbeam\nbean\nbean 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dog\nstreet food\nstreet light\nstreet market\nstreet photography\nstreet scene\nstreet sign\nstreet vendor\nstretch\nstretcher\nstrike\nstriker\nstring\nstring cheese\nstrip\nstripe\nstroll\nstructure\nstudio\nstudio shot\nstuff\nstuffed animal\nstuffed toy\nstuffing\nstump\nstunning\nstunt\nstupa\nstyle\nstylus\nsubmarine\nsubmarine sandwich\nsubmarine water\nsuburb\nsubway\nsubway station\nsubwoofer\nsucculent\nsuede\nsugar\nsugar bowl\nsugar cane\nsugar cube\nsuit\nsuite\nsummer\nsummer evening\nsummit\nsun\nsun hat\nsunbathe\nsunday\nsundial\nsunflower\nsunflower field\nsunflower seed\nsunglasses\nsunny\nsunrise\nsunset\nsunshade\nsunshine\nsuper bowl\nsports car\nsuperhero\nsupermarket\nsupermarket shelf\nsupermodel\nsupporter\nsurf\nsurface\nsurfboard\nsurfer\nsurgeon\nsurgery\nsurround\nsushi\nsushi bar\nsuspenders\nsuspension\nsuspension bridge\nsuv\nswallow\nswallowtail butterfly\nswamp\nswan\nswan boat\nsweat pant\nsweatband\nsweater\nsweatshirt\nsweet\nsweet potato\nswim\nswim cap\nswimmer\nswimming hole\nswimming pool\nswing\nswing bridge\nswinge\nswirl\nswitch\nswivel chair\nsword\nswordfish\nsymbol\nsymmetry\nsynagogue\nsyringe\nsyrup\nsystem\nt shirt\nt-shirt\ntabasco sauce\ntabby\ntable tennis racket\ntable top\ntablecloth\ntablet computer\ntableware\ntachometer\ntackle\ntaco\ntae kwon do\ntai chi\ntail\ntailor\ntake\ntakeoff\ntalk\ntambourine\ntan\ntangerine\ntape\ntapestry\ntarmac\ntaro\ntarp\ntart\ntassel\ntaste\ntatami\ntattoo\ntattoo artist\ntavern\ntea\ntea bag\ntea party\ntea plantation\ntea pot\ntea set\nteach\nteacher\nteacup\nteal\nteam photo\nteam presentation\ntear\ntechnician\ntechnology\nteddy\ntee\nteenager\ntelegraph pole\nzoom lens\ntelescope\ntelevision\ntelevision camera\ntelevision room\ntelevision studio\ntemperature\ntemple\ntempura\ntennis\ntennis court\ntennis match\ntennis net\ntennis player\ntennis racket\ntent\ntequila\nterminal\nterrace\nterrain\nterrarium\nterritory\ntest\ntest match\ntest tube\ntext\ntext message\ntextile\ntexture\nthanksgiving\nthanksgiving dinner\ntheater\ntheatre actor\ntherapy\nthermometer\nthermos\nthermos bottle\nthermostat\nthicket\nthimble\nthing\nthinking\nthistle\nthrone\nthrone room\nthrow\nthrow pillow\nthunder\nthunderstorm\nthyme\ntiara\ntick\nticket\nticket booth\ntide pool\ntie\ntiger\ntight\ntile\ntile flooring\ntile roof\ntile wall\ntin\ntinfoil\ntinsel\ntiramisu\ntire\ntissue\ntoast\ntoaster\ntobacco\ntobacco pipe\ntoddler\ntoe\ntofu\ntoilet bowl\ntoilet seat\ntoiletry\ntokyo tower\ntomato\ntomato sauce\ntomato soup\ntomb\ntong\ntongs\ntool\ntoolbox\ntoothbrush\ntoothpaste\ntoothpick\ntopiary garden\ntopping\ntorch\ntornado\ntortilla\ntortoise\ntote bag\ntotem pole\ntotoro\ntoucan\ntouch\ntouchdown\ntour\ntour bus\ntour guide\ntourist\ntourist attraction\ntournament\ntow truck\ntowel\ntowel bar\ntower block\ntower bridge\ntown\ntown square\ntoy\ntoy car\ntoy gun\ntoyshop\ntrack\ntractor\ntrade\ntradition\ntraditional\ntraffic\ntraffic cone\ntraffic congestion\ntraffic jam\ntraffic sign\ntrail\ntrailer\ntrailer truck\ntrain\ntrain bridge\ntrain car\ntrain interior\ntrain track\ntrain window\ntrainer\ntraining\ntraining bench\ntraining ground\ntrolley\ntrampoline\ntransformer\ntransparency\ntravel\ntray\ntreadmill\ntreat\ntree\ntree branch\ntree farm\ntree frog\ntree house\ntree root\ntree trunk\ntrial\ntriangle\ntriathlon\ntribe\ntributary\ntrick\ntricycle\ntrim\ntrio\ntripod\ntrombone\ntroop\ntrophy\ntrophy cup\ntropic\ntrout\ntruck\ntruck driver\ntub\ntube\ntugboat\ntulip\ntuna\ntundra\ntunnel\nturbine\nturkey\nturn\nturnip\nturquoise\nturret\nturtle\ntusk\ntv actor\ntv cabinet\ntv drama\ntv genre\ntv personality\ntv show\ntv sitcom\ntv tower\ntwig\ntwilight\ntwin\ntwine\ntwist\ntype\ntype on\ntypewriter\nukulele\nultraman\numbrella\nunderclothes\nunderwater\nunicorn\nuniform\nuniverse\nuniversity\nup\nurban\nurinal\nurn\nuse\nutensil\nutility room\nvacuum\nvalley\nvalve\nvampire\nvan\nvanilla\nvanity\nvariety\nvase\nvault\nvector cartoon illustration\nvector icon\nvegetable\nvegetable garden\nvegetable market\nvegetation\nvehicle\nveil\nvein\nvelvet\nvending machine\nvendor\nvent\nvespa\nvessel\nvest\nvet\nveteran\nveterinarians office\nviaduct\nvideo\nvideo camera\nvideo game\nvideotape\nview mirror\nvigil\nvilla\nvillage\nvine\nvinegar\nvineyard\nviolence\nviolet\nviolin\nviolinist\nviolist\nvision\nvisor\nvodka\nvolcano\nvolleyball\nvolleyball court\nvolleyball player\nvolunteer\nvoyage\nvulture\nwaffle\nwaffle iron\nwagon\nwagon wheel\nwaist\nwaiter\nwaiting hall\nwaiting room\nwalk\nwalking\nwalking cane\nwall clock\nwallpaper\nwalnut\nwalrus\nwar\nwarehouse\nwarm\nwarning sign\nwarrior\nwarship\nwarthog\nwash\nwasher\nwashing\nwashing machine\nwasp\nwaste\nwaste container\nwatch\nwater\nwater bird\nwater buffalo\nwater cooler\nwater drop\nwater feature\nwater heater\nwater level\nwater lily\nwater park\nwater pipe\nwater purifier\nwater ski\nwater sport\nwater surface\nwater tower\nwatercolor\nwatercolor illustration\nwatercolor painting\nwaterfall\nwatering can\nwatermark overlay stamp\nwatermelon\nwaterproof jacket\nwaterway\nwave\nwax\nweapon\nwear\nweather\nvane\nweb\nwebcam\nwedding\nwedding ring\nwedding bouquet\nwedding cake\nwedding couple\nwedding invitation\nwedding party\nwedding photo\nwedding photographer\nwedding photography\nwedding reception\nwedge\nweed\nweight\nweight scale\nwelder\nwell\nwestern food\nwestern restaurant\nwet\nwet bar\nwet suit\nwetland\nwetsuit\nwhale\nwhale shark\nwheat\nwheat field\nwheel\nwheelchair\nwheelie\nwhipped cream\nwhisk\nwhisker\nwhiskey\nwhistle\nwhite\nwhite house\nwhite wine\nwhiteboard\nwicket\nwide\nwield\nwig\nWii\nWii controller\nwild\nwildebeest\nwildfire\nwildflower\nwildlife\nwillow\nwind\nwind chime\nwind farm\nwind turbine\nwindmill\nwindow\nwindow box\nwindow display\nwindow frame\nwindow screen\nwindow seat\nwindow sill\nwiper\nwindshield\nwindy\nwine bottle\nwine cooler\nwine cabinet\nwine cellar\nwine glass\nwine rack\nwine tasting\nwinery\nwing\nwinter\nwinter melon\nwinter morning\nwinter scene\nwinter sport\nwinter storm\nwire\nwisteria\nwitch\nwitch hat\nwok\nwolf\nwoman\nwood\nwood duck\nwood floor\nwood wall\nwood-burning stove\nwooden spoon\nwoodland\nwoodpecker\nwoodworking plane\nwool\njob\nwork card\nworkbench\nworker\nworkplace\nworkshop\nworld\nworm\nworship\nwound\nwrap\nwrap dress\nwrapping paper\nwrestle\nwrestler\nwrinkle\nwristband\nwrite\nwriter\nwriting\nwriting brush\nwriting desk\nyacht\nyak\nyard\nyellow\nyoga\nyoga mat\nyoghurt\nyoke\nyolk\nyouth\nyouth hostel\nyurt\nzebra\nzebra crossing\nzen garden\nzip\nzipper\nzombie\nzongzi\nzoo"
  },
  {
    "path": "model_cards/Tag2Text/ram/data/ram_tag_list_chinese.txt",
    "content": "三维CG渲染 \n3d眼镜\n算盘 \n鲍鱼 \n修道院 \n肚子 \n学院 \n附件 \n事故 \n手风琴 \n橡子 \n丙烯颜料\n表演\n行动 \n动作电影 \n活动 \n演员 \n改编本\n添加 \n胶带 \n调整 \n成人 \n冒险 \n广告 \n天线 \n有氧运动 \n喷雾罐\n爆炸头\n农业 \n帮助\n空调 \n空调系统\n风向标\n飞机客舱 \n飞机模型 \n机场 \n航线\n客机 \n飞行员 \n飞机 \n飞机窗口 \n机场 \n机场跑道 \n航站楼 \n飞艇 \n航展\n过道 \n警报\n闹钟 \n信天翁 \n唱片\n唱片封面 \n酒精 \n壁龛\n水藻\n胡同/球道\n杏仁 \n芦荟\n高山 \n羊驼 \n字母表\n德国牧羊犬\n圣坛\n琥珀\n救护车 \n秃鹰\n美国短毛猫 \n紫水晶 \n圆形剧场 \n扩音器\n游乐园 \n游乐设施 \n锚 \n古老的 \n海葵 \n天使 \n角 \n动物 \n动物雕塑 \n动物收容所 \n动画片\n动画电影 \n动画师 \n动漫 \n脚踝 \n短袜 \n周年庆\n风衣 \n蚂蚁 \n羚羊 \n古董 \n鹿角 \n铁砧 \n公寓 \n猿 \n应用程序 \n应用图标 \n出现 \n外观 \n开胃菜 \n掌声 \n苹果 \n苹果汁 \n苹果派 \n苹果树 \n苹果酱 \n设备 \n约定\n通道\n杏子\n围裙 \n浅绿色\n水族馆 \n观赏鱼\n渡槽 \n游乐中心\n商场游戏机\n拱门\n拱桥 \n考古现场\n射箭 \n群岛 \n建筑师\n建筑设计\n档案\n拱门 \n地区\n竞技场 \n争论\n手臂 \n穿山甲\n臂章 \n扶手椅 \n衣柜\n盔甲\n军队 \n军事基地 \n坦克 \n阵列\n逮捕 \n箭头 \n艺术 \n艺术展\n美术馆\n艺术印刷品\n艺术学校 \n艺术工作室 \n艺术矢量插图 \n洋蓟 \n文章 \n手工艺品\n艺术家 \n艺术阁楼 \n灰 \n烟灰缸 \n亚洲寺庙 \n芦笋 \n沥青道路\n组装\n集会\n生产流水线\n协会 \n宇航员 \n天文学家 \n运动员 \n运动 \n地图集\n自助取款机 \n大气层\n中庭 \n连接\n战斗机 \n参加 \n吸引力 \n全地形车\n茄子 \n拍卖 \n奥迪汽车\n音频 \n礼堂 \n极光 \n作者 \n汽车厂 \n汽车修理工 \n汽车零件\n车展 \n汽车展厅 \n汽车电池 \n汽车制造\n汽车模型 \n汽车\n秋天 \n秋天的森林 \n秋天的叶子\n秋天的公园 \n秋天的树 \n阿凡达\n林荫大道\n飞行员太阳镜 \n牛油果\n奖品\n颁奖典礼 \n获奖者\n棚\n斧头 \n杜鹃花 \n狒狒 \n婴儿 \n奶瓶 \n婴儿车 \n婴儿衣服 \n小象 \n婴儿食品 \n婴儿座椅\n迎婴派对\n背后/后面\n背景 \n背光 \n背包 \n后院 \n培根 \n徽章 \n獾 \n荒地 \n羽毛球运动\n羽毛球拍 \n袋子\n面包圈\n风笛 \n法棍\n诱饵 \n焙烤食品 \n面包师\n面包店 \n烘焙 \n烤盘 \n平衡 \n平衡车 \n阳台 \n球 \n球池\n芭蕾舞女演员 \n芭蕾舞 \n芭蕾舞演员 \n芭蕾舞裙 \n气球 \n气球拱门 \n棒球手 \n舞厅 \n竹子 \n竹林 \n香蕉 \n香蕉面包 \n香蕉叶子 \n香蕉树 \n乐队 \n创可贴 \n绷带 \n头巾 \n束发带 \n刘海\n手镯 \n栏杆\n五弦琴\n银行 \n银行卡 \n银行金库\n纸币\n横幅/旗帜\n宴会 \n宴会厅 \n榕树\n包子 \n洗礼 \n酒吧 \n条形码 \n高脚凳\n烧烤 \n烧烤架 \n杠铃 \n理发师 \n理发店 \n芭比娃娃 \n驳船 \n咖啡师 \n树皮 \n大麦 \n谷仓 \n仓鸮\n挡光板\n桶 \n路障\n屏障\n手推车 \n酒保 \n棒球 \n棒球基地 \n棒球棒 \n棒球帽 \n棒球场\n棒球比赛 \n棒球手套 \n棒球投手 \n棒球队 \n棒球制服 \n地下室 \n罗勒 \n水盆\n篮子 \n篮子\n篮球 \n篮球篮板 \n篮球教练 \n篮球场 \n篮球比赛 \n篮球框\n篮球运动员 \n篮球馆 \n篮球队 \n贝斯\n低音吉他 \n低音喇叭 \n贝斯手 \n球棒/球拍\n浴室\n水浴加热器\n浴垫 \n浴巾 \n泳装 \n浴袍 \n浴室 \n浴室配件 \n浴室柜 \n浴室门 \n浴室镜子\n浴室水槽 \n卫生纸\n浴室窗户\n蝙蝠侠 \n棒子\n接连猛打/击球员\n电池 \n战斗 \n战绳\n战舰 \n海湾\n海湾大桥 \n凸窗 \n杨梅\n集市 \n海滩 \n沙滩球 \n沙滩椅 \n海滨别墅 \n海滩小屋 \n沙滩毛巾 \n沙滩排球 \n灯塔 \n珠子\n比格犬\n鸟嘴\n烧杯 \n横梁\n豆子\n豆袋椅 \n豆袋 \n熊 \n幼熊 \n胡子 \n野兽 \n击打/击败\n美丽的 \n美丽\n美容院 \n海狸 \n床\n床单\n床架\n卧室 \n床上用品 \n便盆 \n卧室窗户 \n床头灯 \n蜜蜂 \n山毛榉\n牛肉 \n养蜂人 \n蜂鸣器 \n啤酒 \n啤酒瓶 \n啤酒罐 \n啤酒花园 \n啤酒杯 \n啤酒馆\n甜菜 \n甲虫 \n米色 \n时钟 \n甜椒 \n钟楼 \n皮带\n皮带扣 \n长凳\n弯曲 \n孟加拉虎 \n盒饭 \n贝雷帽 \n浆果 \n停泊位 \n饮料 \n围嘴 \n拌饭 \n圣经 \n比熊\n自行车 \n自行车头盔 \n自行车车轮 \n自行车骑士\n坐浴盆 \n大本钟 \n自行车道 \n自行车道 \n自行车赛\n骑车\n比基尼 \n比基尼上衣\n账单\n台球\n广告牌 \n台球台\n垃圾箱\n活页夹\n双筒望远镜 \n生物学实验室 \n双翼飞机 \n桦木 \n桦树 \n鸟 \n鸟池\n喂鸟器 \n鸟舍\n鸟巢 \n鸟池\n鸟笼 \n出生 \n生日 \n生日蛋糕 \n生日蜡烛 \n生日贺卡 \n生日聚会 \n饼干 \n主教 \n野牛 \n钻头\n咬 \n黑色\n黑山羊\n黑莓 \n乌鸦\n黑板\n铁匠 \n叶片/刀片\n毯子/覆盖层\n运动外套 \n看台\n搅拌机 \n祝福 \n窗帘\n眼罩 \n闪光 \n暴风雪 \n块 \n博客 \n血 \n开花\n花\n女装衬衫\n吹\n吹风机\n河豚 \n蓝色\n蓝色艺术家\n蓝松鸦\n蓝天\n蓝莓 \n蓝知更鸟 \n猪 \n板子\n板擦\n棋盘游戏 \n木板路 \n船 \n船甲板\n船屋\n桨 \n乘船 \n浮标\n山猫 \n躯干\n身体冲浪板\n健美运动员 \n水煮鸡蛋 \n锅炉 \n饰扣式领带\n门闩\n炸弹 \n轰炸机 \n披肩榛鸡\n骨骼\n篝火 \n阀盖 \n盆景 \n书 \n书籍封面\n书柜 \n文件夹 \n书签 \n书架\n书店 \n远程拾音器\n推动\n靴子\n边界\n边境牧羊犬 \n植物园 \n瓶 \n瓶盖 \n开瓶器 \n螺旋开瓶器\n三角梅\n巨石\n花束 \n时装店\n精品酒店 \n鞠躬/蝴蝶结\n领结 \n弓形窗\n碗 \n保龄球运动\n保龄球馆 \n保龄球 \n保龄球设备 \n盒子 \n箱形梁桥\n箱龟 \n拳击手 \n内裤 \n拳击 \n拳击手套 \n拳击台\n男孩 \n支撑物\n支架 \n辫子\n大脑 \n刹车 \n刹车灯 \n树枝\n商标\n白兰地 \n黄铜 \n黄铜牌匾 \n面包 \n面包箱 \n休息\n早餐 \n防浪堤 \n胸部\n啤酒厂 \n砖块\n砖建筑物\n墙 \n砖块\n婚纱 \n新娘 \n新郎 \n伴娘 \n桥 \n缰绳 \n公文包 \n明亮的 \n边沿\n钻头\n广播 \n西兰花 \n青铜 \n铜牌 \n青铜雕塑 \n青铜雕像 \n胸针 \n小溪\n扫帚 \n肉汤 \n棕色\n棕熊 \n巧克力蛋糕 \n早午餐 \n浅黑肤色的女人 \n刷子\n郊狼\n包菜\n气泡\n泡泡糖 \n珍珠奶茶\n斗柜 \n盾牌\n芽 \n佛 \n水牛 \n自助餐 \n昆虫\n建造\n建造者\n建筑 \n积木\n建筑立面 \n建筑材料 \n灯 \n牛 \n斗牛犬 \n子弹 \n动车 \n公告栏 \n防弹背心 \n斗牛 \n扩音器 \n斗牛场 \n大黄蜂 \n保险杠 \n卷/地形起伏\n捆\n蹦极 \n双层床 \n地堡/击球\n兔子 \n浮标 \n书桌\n墓室 \n燃烧 \n玉米煎饼 \n公交车\n公交车司机\n公交车内部\n公交车站 \n公交车站 \n公交车窗户\n灌木\n商业\n名片 \n业务主管 \n商务西装\n业务团队 \n女商人\n商人 \n半身像\n屠夫 \n肉铺\n孤峰 \n黄油 \n奶油 \n蝴蝶 \n蝴蝶馆\n按钮 \n梧桐树\n购买\n出租车 \n小屋 \n卷心菜 \n小屋/机舱\n守车\n储藏柜\n橱柜\n电缆 \n缆车 \n仙人掌 \n咖啡馆 \n食堂 \n笼子\n蛋糕 \n蛋糕台\n计算器 \n大锅 \n日历 \n小腿 \n通话\n电话亭\n书法 \n平静的\n摄像机 \n骆驼 \n相机 \n相机镜头 \n迷彩\n露营\n露营者 \n篝火 \n露营\n营地 \n校园 \n罐\n开罐器 \n运河 \n金丝雀 \n癌症 \n蜡烛 \n烛台 \n糖果 \n块状糖\n柺杖糖 \n糖果店 \n拐杖\n罐子\n大炮 \n树冠/顶棚\n四柱床\n香瓜 \n悬臂桥 \n帆布 \n峡谷 \n帽子\n斗篷\n科德角 \n卡布奇诺\n胶囊 \n队长 \n捕获 \n车 \n汽车经销商 \n车门 \n汽车内饰\n车标\n后视镜\n停车场 \n汽车座椅\n车展\n洗车 \n车窗 \n焦糖 \n卡片\n纸牌游戏\n纸板 \n纸板盒 \n羊毛衫\n红衣凤头鸟\n货物 \n货运飞机 \n货船 \n加勒比 \n康乃馨 \n狂欢节 \n食肉动物 \n旋转木马 \n鲤鱼 \n木匠\n地毯 \n拖鞋 \n红雀\n长途客车\n斑点狗\n航空母舰\n胡萝卜 \n胡萝卜蛋糕 \n携带 \n手推车\n纸箱/纸盒\n卡通 \n卡通人物 \n卡通插图 \n卡通风格 \n雕刻 \n容器\n现金 \n腰果 \n赌场 \n砂锅 \n磁带 \n盒式录音机\n石膏绷带\n铸造 \n城堡 \n猫 \n猫窝\n猫粮 \n猫器具\n猫架\n地下墓穴 \n双体船 \n美洲狮\n握着/抓着\n捕手 \n毛毛虫 \n鲶鱼 \n教堂 \n牛 \n猫步 \n走秀 \n菜花 \n洞穴 \n鱼子酱 \n光盘\nCD播放器\n雪松 \n天花板 \n吊扇 \n庆祝 \n庆典\n名人 \n芹菜 \n大提琴 \n手机 \n水泥 \n墓地 \n中心装饰品\n蜈蚣 \n陶瓷 \n瓷砖 \n麦片 \n仪式 \n证书 \n链条\n链锯 \n椅子 \n升降椅 \n躺椅\n木屋\n圣杯\n粉笔 \n房间\n变色龙 \n香槟酒\n香槟杯\n冠军\n锦标赛\n吊灯\n婴儿换尿布台\n通道\n皴裂处\n小教堂\n人物雕塑\n木炭\n充电\n充电器\n战车\n慈善机构\n慈善活动\n魅力\n图表\n追逐\n底盘\n检查/支票\n支票簿\n棋盘\n检查表\n欢呼声\n鼓励/啦啦队\n奶酪\n奶酪汉堡\n奶酪蛋糕\n猎豹\n厨师\n化合物\n化学家\n化学\n化学实验室\n旗袍\n樱桃\n樱花\n樱桃番茄\n樱桃树\n国际象棋\n栗子\n鸡\n鸡胸肉\n鸡笼\n鸡肉沙拉\n鸡翅\n鹰嘴豆\n小衣橱\n吉娃娃\n孩子\n童星\n孩子的房间\n红番椒\n辣热狗\n烟囱\n黑猩猩\n瓷器\n白菜\n中国园林\n中国结\n月季\n中国塔\n炸薯条/炸薯条\n花栗鼠\n凿子\n巧克力\n巧克力棒\n巧克力蛋糕\n巧克力碎片\n巧克力饼干\n巧克力牛奶\n巧克力慕斯\n松露\n唱诗班\n厨房刀\n砧板\n筷子\n圣诞节\n圣诞球\n圣诞贺卡\n圣诞装饰\n圣诞晚宴\n平安夜\n圣诞帽\n圣诞灯\n圣诞市场\n圣诞装饰\n圣诞树\n菊花\n教堂\n教堂塔\n苹果酒\n雪茄\n雪茄盒\n香烟\n烟盒\n腰带\n电影院\n摄影师\n肉桂\n圆\n电路\n电路板\n马戏团\n水箱\n柑橘类水果\n城市\n城市公交\n市政厅\n城市夜景\n城市公园\n城市天际线\n城市广场\n城市街道\n城墙\n城市景观\n蛤蜊\n单簧管\n扣子\n班级\n经典\n教室\n锁骨\n爪子\n黏土\n陶器\n清洁\n洁净室\n清洁工人\n清洁用品\n清晰的\n栓\n克莱门氏小柑橘\n客户端\n悬崖\n爬\n爬山\n登山者\n诊所\n夹子\n剪贴画\n剪贴板\n快速帆船\n君子兰\n斗篷\n木底鞋\n特写\n壁橱\n布\n穿衣\n衣服\n晒衣夹\n晒衣绳\n服装店\n云\n云雾森林\n多云\n三叶草\n小丑\n小丑鱼\n俱乐部\n离合器\n手拿包\n煤炭\n海岸\n外套\n衣帽架\n玉米\n公鸡\n凤头鹦鹉\n可卡犬\n驾驶\n蟑螂\n鸡尾酒\n小礼服\n鸡尾酒调制器\n鸡尾酒桌\n可可\n椰子\n椰子树\n咖啡\n咖啡豆\n咖啡杯\n咖啡机\n咖啡店\n咖啡壶\n棺材\n法国白兰地\n螺旋\n硬币\n可口可乐\n滤器\n冷的\n卷心菜沙拉\n合作\n拼贴画\n收藏品\n大学生\n牧羊犬\n碰撞\n颜色\n涂色书\n染色材料\n矮种马\n柱子\n梳子\n密码锁\n喜剧演员\n喜剧\n喜剧电影\n彗星\n舒服\n安慰食物\n漫画书\n漫画人物\n连环画\n指挥官\n评论员\n社区\n通勤\n公司\n指南针\n比赛\n比赛\n竞争者\n作曲家\n作文\n堆肥\n电脑\n电脑机箱\n电脑椅\n电脑桌\n键盘\n计算机显示器\n计算机房\n电脑屏幕\n机箱\n概念车\n音乐会\n音乐厅\n贝壳\n混凝土\n调味品\n避孕套\n独立产权的公寓\n指挥\n锥形物\n会议\n会议中心\n会议厅\n会议室\n五彩纸屑\n冲突\n合流\n连接\n连接器\n温室\n星座\n建筑工地\n建筑工人\n包含\n容器\n集装箱船\n大陆\n轮廓\n合同\n控制\n控制塔\n便利店\n集会\n交谈\n转换器\n可转换的\n输送机\n厨师/烹饪\n烹饪\n烹饪喷雾剂\n炊具\n凉的\n冷却器\n铜\n一本/一册\n珊瑚\n珊瑚礁\n粗绳\n有线电话\n酒\n威尔士矮脚狗\n瓶塞\n软木板\n鸬鹚\n玉米\n玉米田\n玉米面包\n角落\n小号\n飞檐\n燕麦片\n围栏\n走廊\n紧身衣\n化妆品\n化妆刷\n化妆镜\n角色扮演\n服装\n服装电影设计师\n婴儿床\n小屋\n棉花\n棉花糖\n沙发\n倒计时\n柜台\n台面\n最佳乡村歌手\n乡村别墅\n乡村公路\n乡村流行歌手\n农村\n双门小轿车\n夫妇/两人/几个\n情侣写真\n小胡瓜\n课程\n球场\n法院\n院子\n堂兄弟\n工作服\n奶牛\n母牛的颈铃\n牛仔\n牛仔靴\n牛仔帽\n螃蟹\n蟹肉\n裂纹\n摇篮\n工艺\n工匠\n蔓越莓\n起重机\n黑纱\n厕所\n板条箱\n火山口湖\n龙虾\n蜡笔\n奶油乳酪\n奶油罐\n创建\n生物\n信用卡\n新月形\n新月形面包\n山顶\n全体船员\n蟋蟀\n板球用球\n板球队\n板球队员\n钩边\n克罗克电锅\n鳄鱼\n庄稼\n露脐上衣\n交叉\n横木\n十字路口\n相声\n人行横道\n油煎面包块\n乌鸦\n撬棍\n人群\n拥挤的\n皇冠\n阴极射线管屏幕\n耶稣受难像\n巡游\n游轮\n巡洋艇\n面包屑\n压坏\n拐杖\n水晶\n幼兽\n立方体\n黄瓜\n球杆\n袖口\n袖扣\n烹饪\n农田\n杯子\n纸杯蛋糕\n丘比特\n马路牙子\n旋度\n卷发器\n无籽葡萄干\n货币\n咖喱\n窗帘\n曲线\n软垫\n顾客\n切\n餐具\n自行车\n骑自行车\n龙卷风\n汽缸\n铙钹\n柏树\n柏树\n达克斯猎狗\n水仙花\n匕首\n大丽花\n萝卜\n乳制品\n雏菊\n大坝\n损害\n潮湿的\n跳舞\n舞池\n舞蹈室\n舞者\n蒲公英\n黑暗\n黑暗\n飞镖\n圆靶\n指示板\n日期\n女儿\n黎明\n天床上\n日光\n门栓\n死亡\n辩论\n碎片\n玻璃水瓶\n甲板\n双层巴士\n装饰\n装修/装饰\n装饰画\n鹿\n后卫\n神\n熟食\n投递\n拆迁\n怪兽\n演示\n兽窝/休闲室\n牛仔夹克\n牙医\n百货商店\n抑郁症\n德比\n皮肤病\n沙漠\n沙漠公路\n设计\n设计师\n桌子/表格\n台灯\n桌面\n台式电脑\n甜点\n破坏\n侦探\n洗涤剂\n露水\n仪表盘\n钻石\n尿布\n尿布包\n杂志\n死\n饮食\n挖掘机\n数字\n数字时钟\n莳萝\n晚餐\n小船\n餐厅\n晚宴\n餐桌\n恐龙\n浸\n文凭\n指引\n导演\n尘埃\n越野摩托车\n泥土地\n泥土路\n泥路/土路\n灾难\n信徒\n迪斯科舞厅\n迪斯科灯秋\n迪斯科舞厅\n疾病\n盘子\n碟形天线\n洗碗机\n抹布\n菜肴\n洗碗液\n迪斯尼乐园\n自动售货机\n展示\n陈列窗\n壕沟\n潜水\n潜水员\n跳水板\n纸杯\n流行音乐播音员\n杜宾犬\n码头\n医生\n文件\n纪录片\n狗\n狗窝\n犬种\n狗项圈\n狗粮\n狗窝\n洋娃娃\n美元\n玩偶之家\n洋娃娃\n海豚\n穹顶\n住宅\n多米诺骨牌\n驴\n甜甜圈\n涂鸦\n门\n门把手\n受气包\n门牌\n门口\n宿舍\n面团\n市中心\n推土机\n拖\n龙\n蜻蜓\n排水沟\n剧本\n戏剧电影\n画\n抽屉里\n图画/画画\n图钉\n辫子\n连衣裙/特定场合的服装\n礼帽\n正装衬衫\n皮鞋\n大礼服\n梳妆台\n更衣室\n运球\n漂移\n浮木\n钻\n饮品/喝\n饮用水\n开车\n司机\n车道\n无人机\n水滴/下降\n吊灯\n滴管\n干旱\n药物\n药店\n鼓\n鼓手\n鸡腿\n干的\n公爵夫人\n鸭子\n鸭嘴兽\n小鸭子\n布基胶带\n伙计\n二重唱\n粗呢\n独木舟\n哑铃\n饺子\n沙丘\n扣篮\n榴莲\n黄昏\n灰尘\n垃圾车\n簸箕\n羽绒被\nDVD\n染料\n鹰\n耳朵\n御寒耳罩\n耳机\n耳塞\n耳环\n地震\n画架\n复活节\n复活节兔子\n复活节彩蛋\n吃\n餐厅\n泡芙\n日食\n生态系统\n编辑\n教育\n教育家\n鳗鱼\n蛋\n蛋卷\n蛋挞\n打蛋器\n白鹭\n埃菲尔铁塔\n橡皮筋\n上级\n电椅\n电钻\n电工\n电\n电子\n电子器件\n大象\n高度图\n电梯\n电梯轿厢\n电梯门\n电梯大堂\n电梯井\n路堤\n大使馆\n装饰\n灰烬\n会徽\n刺绣\n翡翠\n紧急\n紧急服务\n紧急车辆\n情感\n帝国大厦\n搪瓷\n外壳/围墙\n茶几\n能源\n订婚\n订婚戒指\n引擎\n机舱\n工程师\n工程\n英国短毛猫\n乐团\n回车键\n演艺人员\n娱乐\n娱乐中心\n入口\n入口大厅\n信封\n马术\n设备\n橡皮擦\n二胡\n侵蚀\n自动扶梯\n食用蜗牛\n浓缩咖啡\n房地产\n河口\n桉树\n晚上\n晚礼服\n夜光\n傍晚天空\n晚上的太阳\n事件\n常绿的\n母羊\n挖掘\n运动\n排气罩\n展览\n出口\n探险者\n爆炸\n延长线\n灭火器\n排气扇\n挤压\n眼睛\n眼影\n眉\n眼线笔\n布料\n纺织品商店\n外观\n脸\n脸部特写\n蜜粉\n毛巾\n面巾纸架\n设施\n工厂\n工厂车间\n集市\n露天市场\n仙女\n猎鹰\n秋天\n家庭\n家庭轿车\n全家福\n家庭房\n风扇/扇子\n尖牙\n农场\n农民\n农民市场\n农舍\n时尚\n时尚配饰\n时装设计师\n时尚的女孩\n时装插图\n时装大片\n时装模特\n时装表演\n快餐\n西式快餐\n父亲\n水龙头\n故障\n动物\n小鹿\n传真\n宴会\n羽毛\n软呢帽\n饲料\n一餐\n饲养\n喂养的椅子\n猫科\n美洲狮\n栅栏\n芬达\n蕨类植物\n雪貂\n摩天轮\n渡船\n肥料\n节日\n纤维\n小说\n小说书\n田野/场地/野外\n田间道路\n无花果\n打架\n花样滑冰运动员\n小雕像\n文件\n档案照片\n文件柜\n填满\n胶片相机\n电影导演\n电影格式\n电影首映礼\n电影制片人\n拍摄\n过滤器\n鳍\n手\n终点线\n冷杉\n冷杉树\n火\n火灾报警\n消防部门\n消防车\n消防通道\n消防水带\n火坑\n消防站\n爆竹\n消防队员\n壁炉\n烟花\n烟花表演\n急救箱\n鱼\n鱼船\n海鲜市场\n鱼塘\n鱼缸\n渔夫\n钓鱼\n渔船\n渔网\n钓鱼\n渔村\n健身\n健身课程\n五个\n固定装置\n峡湾\n国旗\n旗杆\n小薄片\n火焰\n火烈鸟\n法兰绒\n拍打\n耀斑\n闪光\n烧瓶\n平\n比目鱼\n风味\n跳蚤\n跳蚤市场\n舰队\n飞行\n空中乘务员\n翻转\n触发器\n翻转图\n浮动\n群\n洪水\n地板/地面\n落地扇\n脚垫\n楼层平面图\n落地窗\n插花艺术\n花店\n牙线\n面粉\n流动\n花\n花篮\n花坛\n花箱\n花田\n花童\n花卉市场\n流体\n冲洗\n长笛\n飞\n飞行钓鱼\n传单\n马\n泡沫\n雾\n多雾的\n鹅肝酱\n箔纸\n折椅\n树叶\n民间艺术家\n民间舞蹈\n民间摇滚艺术家\n方旦糖\n火锅\n圣洗池\n食物\n食用色素\n美食广场\n食品加工机\n小吃摊\n快餐车\n桌上足球\n脚\n人行桥\n足球\n足球教练\n大学橄榄球赛\n足球比赛\n足球场\n足球比赛\n橄榄球头盔\n足球运动员\n足球场\n足球队\n小路\n脚印\n脚踏板\n台座\n鞋子\n故宫\n浅滩\n额头 \n森林 \n森林大火 \n森林地面 \n森林小路\n森林公路\n锻造\n餐叉\n叉车 \n表格\n园林\n队列/形成物\nF1方程式赛车\n堡垒\n碉堡\n追逐\n化石\n粉底\n喷泉\n钢笔\n狐狸 \n框架 \n雀斑 \n高速公路 \n卡车 \n法国\n法国斗牛犬 \n薯条 \n法式吐司 \n化妆水\n冰箱 \n炸鸡 \n煎蛋 \n炒饭 \n友谊\n飞盘 \n青蛙 \n霜 \n结霜 \n严寒\n结冰\n水果 \n水果蛋糕 \n水果盘 \n水果市场 \n水果沙拉 \n水果摊\n果树 \n水果商店 \n油炸食品\n煎锅 \n软糖\n燃料 \n吸烟罩\n有趣的 \n葬礼 \n真菌 \n漏斗 \n毛皮衣服\n毛皮大衣 \n家具 \n蒲团 \n小工具 \n枪口\n星云/星系\n美术馆\n游戏 \n游戏棋盘\n游戏手柄\n火腿 \n团伙\n车库 \n车库门 \n手工模型\n垃圾 \n花园 \n花园芦笋 \n橡胶软管 \n花园蜘蛛\n园丁 \n园艺 \n加菲猫\n滴水嘴 \n花环 \n大蒜 \n衣服\n气体 \n加油站 \n煤气炉 \n防毒面具\n收集 \n聚集\n测量仪器\n露台 \n齿轮 \n壁虎 \n艺妓\n凝胶 \n百货商店 \n发电机 \n天竺葵 \n幽灵\n礼物 \n礼品袋 \n礼品篮 \n礼物盒 \n礼品卡 \n礼品商店 \n礼物包装 \n演唱会\n杜松子酒\n姜 \n姜饼 \n姜饼屋 \n银杏树 \n长颈鹿 \n女孩 \n给 \n冰川 \n角斗士\n玻璃珠 \n玻璃瓶 \n玻璃碗 \n玻璃箱\n玻璃建筑 \n玻璃门 \n玻璃地板 \n玻璃屋\n玻璃罐 \n玻璃板 \n玻璃桌子 \n玻璃花瓶 \n玻璃墙\n玻璃窗 \n眼镜 \n光滑面\n滑翔机 \n地球 \n手套 \n发光 \n汤圆 \n去 \n袭击\n球门\n守门员 \n山羊 \n羊奶酪\n戈壁 \n护目镜/墨镜\n黄金 \n金牌 \n金门大桥 \n金毛猎犬 \n金鱼 \n高尔夫运动\n高尔夫球帽 \n高尔夫球车 \n高尔夫球杆\n高尔夫球场 \n高尔夫球手 \n鹅 \n大猩猩 \n哥特式\n葫芦 \n政府 \n政府机构 \n礼服\n毕业生\n毕业典礼\n谷物\n逆戟鲸 \n大奖赛 \n祖父 \n祖母 \n祖父母 \n花岗岩 \n格兰诺拉麦片 \n葡萄 \n西柚\n葡萄酒\n草 \n蚱蜢 \n草原 \n长满草的 \n擦菜器\n坟墓 \n碎石\n墓碑 \n肉汁 \n调味汁瓶\n灰色\n吃草 \n放牧 \n绿色 \n绿色植物 \n欢迎\n问候 \n贺卡 \n灰狗 \n网格 \n筛子\n烧烤架\n格栅 \n烤鳗鱼 \n磨 \n研磨机\n粗燕麦粉 \n杂货袋\n洞穴\n地松鼠 \n群体\n合影 \n小树林\n生长\n牛油果酱\n警卫 \n看门狗 \n宾馆 \n客房 \n指南 \n豚鼠 \n吉他 \n吉他手 \n海湾 \n海鸥 \n枪 \n高达\n谒师所\n古筝 \n健身房 \n体操运动员 \n栖息地 \n黑客 \n冰雹 \n头发 \n头发颜色 \n发胶 \n毛刷 \n发型 \n发夹 \n发网 \n发夹 \n发型 \n一半 \n礼堂\n万圣节 \n万圣节服装 \n万圣节南瓜 \n露背装 \n汉堡 \n汉堡包\n哈密瓜 \n锤子\n吊床 \n阻碍 \n仓鼠 \n烘手机\n放大镜\n擦手巾 \n手提包 \n手球 \n手铐\n手枪 \n手帕 \n把手\n手锯 \n握手 \n倒立 \n手写\n汉服 \n悬挂\n飞机库\n衣架\n幸福 \n海港\n斑海豹\n硬摇滚艺术家 \n精装书 \n建筑工人\n硬件 \n五金店 \n硬木 \n硬木地板 \n口琴 \n管风琴 \n羽管键琴\n收获 \n收割机 \n坐垫/搁脚凳/草丛\n帽子 \n帽盒 \n双簧管\n山楂 \n干草 \n干草地\n榛子\n头 \n主教练 \n大灯\n床头板 \n头饰 \n海岬 \n总部 \n听力 \n心脏\n心形\n热能\n加热器 \n帚石楠\n树篱\n刺猬 \n脚后跟\n直升机 \n直升机机场\n头盔 \n帮助 \n母鸡 \n指甲花 \n药草\n兽群\n寄居蟹 \n英雄 \n苍鹭\n芙蓉花\n芙蓉花 \n隐藏/隐蔽处\n高杠\n高跟鞋 \n高地 \n突出 \n徒步旅行 \n徒步旅行者 \n徒步靴\n登山设备 \n山丘\n丘陵地\n别墅\n山坡\n印度教寺庙 \n铰链 \n臀部 \n嘻哈艺人 \n河马 \n历史学家 \n历史遗迹\n历史 \n曲棍球 \n冰球馆\n曲棍球比赛 \n曲棍球运动员 \n曲棍球棒\n锄头 \n洞 \n假日\n冬青树\n海参 \n家/住宅\n家用电器\n基地 \n家居装饰 \n室内设计 \n内政部\n家庭影院 \n家庭作业 \n鹰嘴豆泥\n蜂蜜 \n蜂窝\n蜜月\n风帽\n连帽衫 \n挂钩/勾住\n跳 \n地平线 \n犀鸟\n长角牛 \n大黄蜂 \n震惊\n恐怖电影 \n马鞍褥\n马车 \n马场 \n骑马\n马背\n马蹄铁\n软管 \n医院 \n医院病床\n病房 \n主持人\n小旅馆\n热 \n热气球 \n热狗 \n辣椒酱 \n温泉 \n旅馆\n酒店大堂 \n酒店房间 \n电炉\n沙漏 \n房子 \n房子外部\n室内植物 \n悬滑板\n吼 \n蜷缩\n拥抱\n呼啦圈\n人\n增湿器\n蜂鸟\n座头鲸 \n打猎\n狩猎小屋 \n障碍\n飓风 \n哈士奇\n小屋 \n鬣狗 \n混合物\n绣球花 \n消火栓 \n水上飞机 \n冰 \n冰袋 \n北极熊 \n冰洞 \n冰淇淋 \n冰淇淋蛋卷\n冰淇淋商店\n冰块 \n浮冰\n冰球运动员 \n冰球队 \n棒棒糖 \n制冰机\n溜冰场 \n冰雕 \n冰架 \n溜冰鞋\n滑冰\n冰山 \n冰柱\n糖衣/酥皮\n图标 \n身份证照片 \n身份证 \n冰屋\n光/灯光/光线\n鬣蜥蜴\n照亮 \n插图\n形象\n黑斑羚\n熏香\n独立日 \n个人\n室内 \n划船器\n电磁炉 \n工业区 \n工业\n步兵 \n充气艇 \n服务台 \n基础设施 \n成分 \n吸入器 \n注射 \n受伤 \n墨水 \n印泥\n小湖湾\n题词\n昆虫\n安装 \n乐器/器械\n绝缘杯 \n互动\n室内设计\n网站 \n十字路口 \n面试 \n无脊椎动物 \n邀请 \n平板电脑\n苹果手机\n苹果音乐播放器\n虹膜 \n铁 \n熨衣板\n灌溉系统 \n岛 \n小岛\n等足类动物\n象牙 \n常青藤\n居酒屋\n千斤顶\n帝王蟹/蟹\n夹克衫\n按摩浴缸\n玉\n美洲虎\n监狱牢房\n果酱\n日式花园\n茉莉花\n下巴 \n松鸦 \n爵士乐 \n爵士乐艺术家\n爵士融合艺术家\n牛仔裤\n吉普车 \n果冻 \n果冻豆\n水母 \n喷气式飞机\n摩托艇 \n珠宝 \n珠宝 \n珠宝店\n拼图游戏\n人力车 \n赛马骑师\n赛马帽\n慢跑 \n联合的\n记者 \n操纵杆 \n法官 \n水壶\n玩杂耍\n果汁\n榨汁器\n枣子\n跳绳\n连身裤 \n丛林\n废品堆放场\n羽衣甘蓝\n万花筒\n袋鼠\n卡拉ok \n空手道 \n卡丁车运动 \n旧城区\n皮船\n烤肉串 \n按键/钥匙\n门卡\n卡其色\n踢\n苏格兰裙\n和服\n幼儿园教室 \n幼儿园 \n国王\n帝王蟹 \n亲吻\n工具包 \n厨房 \n厨房橱柜\n厨房台面\n厨房地板\n厨房抽油烟机\n厨房岛\n厨房水槽\n厨房桌子\n厨房用具\n厨房窗户\n厨房用具\n风筝 \n猕猴桃 \n护膝\n跪下\n餐刀\n骑手\n编织\n编织针\n球形把手\n门环\n结\n考拉 \n锦鲤\nktv\n实验室 \n实验室外套\n标签\n拉布拉多\n迷宫\n网眼织物\n蕾丝连衣裙 \n梯子\n长柄杓\n瓢虫\n环礁湖 \n湖泊\n湖区 \n湖边小屋\n湖岸 \n羊肉 \n羊排 \n灯柱 \n灯罩 \n矛 \n土地\n陆地车辆 \n废物填埋\n着陆\n降落甲板\n地标\n风景\n山崩\n挂带\n灯笼 \n腿/大腿\n笔记本电脑\n笔记本键盘\n幼体\n烤宽面条\n激光\n睫毛\n套索 \n门闩\n乳胶 \n拿铁咖啡 \n笑\n发射\n发布会\n举办会议\n自助洗衣店 \n洗衣房\n洗衣篮 \n洗衣房 \n熔岩 \n薰衣草 \n草坪\n草坪婚礼 \n律师 \n躺 \n引领 \n主唱 \n通向\n领袖 \n泄漏 \n倾斜/倚靠\n学习 \n皮带 \n皮革 \n皮夹克 \n皮鞋 \n演讲 \n演讲厅 \n教学室\n窗台 \n剩饭\n腿 \n传说 \n紧身裤/秋裤\n立法院 \n乐高 \n豆类 \n柠檬 \n柠檬汁 \n柠檬水 \n狐猴 \n镜头 \n眩光 \n扁豆 \n豹 \n紧身连衣裤 \n紧身裤袜\n小妖精 \n课程\n信函\n信箱 \n信的标志 \n刻字 \n生菜 \n水平 \n图书馆 \n许可证 \n车牌 \n地衣 \n舔 \n盖子 \n躺着\n安全带 \n救生衣 \n救生艇 \n救生员 \n提起\n灯具 \n灯光秀 \n电灯开关\n照明/照明设备\n闪电 \n避雷针 \n淡紫色 \n百合\n肢体 \n石灰 \n石灰石 \n豪华轿车 \n线条\n艺术线条\n排队 \n亚麻 \n邮轮\n狮子 \n润唇膏 \n口红 \n液体 \n酒类商店\n列表 \n荔枝 \n生活 \n家畜\n客厅 \n生活空间 \n蜥蜴 \n负载 \n装卸码头\n游手好闲的人 \n走廊\n定位 \n锁 \n闸室 \n储物柜 \n阁楼 \n原木\n小木屋\n标志 \n洛基 \n长头发 \n冲浪板\n隐约显现/织布机\n环状\n遗失\n彩票 \n莲花 \n爱 \n双人沙发\n行李 \n木材 \n伐木工人 \n午餐 \n午餐盒 \n郁郁葱葱的 \n奢侈品 \n豪华游艇 \n雨衣\n澳洲胡桃\n短尾猿 \n通心粉 \n金刚鹦鹉 \n弯刀 \n机器\n机枪 \n杂志 \n魔法 \n魔术师 \n磁铁 \n放大镜 \n木兰花\n喜鹊 \n麻将 \n象夫\n女仆 \n邮件 \n邮件槽 \n制作\n改造 \n化妆师 \n化妆工具 \n野鸭 \n野鸭 \n槌棒\n哺乳动物 \n猛犸象\n男人\n管理 \n经理 \n海牛 \n曼荼罗 \n橘子 \n普通话 \n鬃毛 \n漫画 \n食槽\n芒果 \n山竹果 \n红树林 \n曼哈顿 \n检修孔\n井盖\n修指甲 \n人体模型 \n庄园主宅\n大厦 \n螳螂 \n地幔 \n活动房层\n制造业 \n手稿 \n地图 \n枫木 \n枫叶 \n枫糖浆 \n沙球 \n马拉松 \n大理石 \n行进\n行进乐队\n母马 \n金盏花 \n水兵\n海洋无脊椎动物 \n海洋哺乳动物 \n木偶 \n标志\n集市\n市场广场\n市场摊位 \n结婚\n武术\n武术家 \n武术馆 \n马提尼\n马丁尼酒杯\n睫毛膏\n吉祥物 \n土豆泥 \n搅碎机 \n面具/口罩\n按摩 \n桅杆 \n地垫\n斗牛士 \n比赛\n火柴盒 \n衣料\n床垫 \n陵墓 \n长裙\n一餐\n量杯\n卷尺 \n肉类\n肉丸 \n机械师 \n机械风扇 \n奖牌\n媒体 \n医疗设备 \n医学图像 \n医务人员 \n医药箱 \n中世纪的\n麦地那市\n冥想 \n猫鼬 \n赛事\n香瓜\n纪念碑 \n菜单 \n美人鱼 \n网 \n肮脏\n信使袋 \n金属 \n金属艺术家\n金属探测器 \n计量器\n中层楼\n麦克风 \n显微镜 \n微波炉\n午夜 \n里程碑\n军装 \n牛奶\n牛奶罐\n奶茶 \n奶昔 \n磨坊\n矿井\n矿工\n矿物质\n矿泉水 \n迷你 \n微缩模型\n面包车\n部长 \n小型货车\n薄荷 \n薄荷糖 \n镜子 \n小姐 \n投掷物\n任务 \n槲寄生\n混合 \n搅拌机\n搅拌碗\n混合物 \n护城河 \n电动踏板车\n模型/模特\n汽车模型 \n现代 \n现代大厦 \n潮湿 \n模具 \n模具\n鼹鼠\n君主 \n钱 \n监控器\n和尚 \n猴子 \n活动扳手 \n黑白照片\n独轮脚踏车 \n怪物卡车 \n月亮 \n月饼 \n月光 \n沼泽\n驼鹿 \n拖把\n助力车\n早晨\n晨雾 \n晨光 \n朝阳\n砂浆 \n马赛克\n清真寺 \n蚊子 \n藓类植物\n汽车旅馆 \n蛾 \n母亲\n主板 \n主题 \n动作\n电动机 \n摩托车 \n摩托车 \n摩托车头盔 \n摩托车赛车手 \n骑摩托车的人 \n赛车运动 \n土堆\n山 \n山地自行车 \n山地自行车员\n山地自行车运动\n山地大猩猩 \n山湖 \n山景观 \n山口 \n山路 \n山脉 \n山区河流 \n山雪 \n山间溪流\n山景城 \n山村 \n登山者 \n登山包 \n鼠标/鼠\n鼠标垫 \n捕鼠器 \n嘴\n漱口水 \n移动 \n电影海报 \n电影票 \n割草机 \nmp3播放器 \n先生 \n泥 \n松饼 \n马克杯\n桑树\n覆盖物 \n骡子 \n直辖市 \n壁画 \n肌肉 \n肌肉车 \n博物馆 \n蘑菇 \n音乐 \n音乐节 \n音乐凳子 \n音乐工作室 \n音乐录影带表演者 \n音乐键盘 \n音乐家 \n贻贝 \n芥末 \n神话 \n烤干酪辣味玉米片 \n指甲油 \n指甲锉\n保姆 \n餐巾 \n狭窄的 \n国旗 \n基督诞生的场景 \n自然历史博物馆 \n自然 \n自然保护区 \n导航 \n九夜节\n海军 \n星云 \n脖子 \n围颈带/领口\n项链 \n领口 \n花蜜 \n油桃 \n针状物\n邻居\n与某处邻近的地区\n霓虹灯\n霓虹灯 \n神经 \n巢 \n新年 \n新生的\n纽芬兰 \n新婚 \n新闻 \n记者招待会\n报摊 \n晚上 \n夜市 \n夜空 \n夜景 \n夜总会 \n床头柜\n面条 \n鼻子 \n鼻羁 \n注解\n笔记本 \n记事本 \n信纸 \n公告\n数字图标\n修女 \n护士 \n托儿所 \n养老院 \n螺母 \n胡桃夹子 \n橡木 \n橡树 \n桨 \n绿洲 \n烘干室 \n燕麦片 \n燕麦 \n方尖塔 \n观察塔 \n天文台 \n超越障碍训练场 \n海洋 \n章鱼 \n提供 \n办公室 \n办公大楼 \n办公椅 \n办公室隔间 \n办公桌\n办公用品 \n办公室的窗户 \n军官\n行政官员\n石油 \n油灯 \n油画 \n石油钻台\n秋葵 \n老照片 \n橄榄 \n橄榄油 \n橄榄树 \n煎蛋卷 \n洋葱 \n洋葱圈 \n蛋白石 \n开阔的/张开\n开始\n开幕式 \n歌剧 \n歌剧院 \n操作 \n手术室 \n操作 \n眼镜店 \n猩猩 \n橙子/橙色\n橙汁 \n橙树 \n橘园 \n轨道 \n果园 \n乐池\n兰花 \n订单 \n组织 \n折纸 \n点缀 \n鱼鹰 \n鸵鸟 \n水獭 \n外面的\n露头 \n户外 \n厕所 \n电源插头\n大纲 \n椭圆形 \n烤箱 \n整体 \n大衣 \n天桥 \n猫头鹰 \n牡蛎 \n橡皮环 \n包裹\n包/包装/包裹\n围场 \n警车 \n挂锁 \n肉菜饭 \n宝塔 \n疼痛 \n油漆刷 \n画家 \n佩斯利印花大手帕 \n宫殿\n调色板 \n栅栏\n棺罩\n棕榈树 \n平底锅\n煎饼 \n熊猫 \n面板 \n全景 \n三色堇\n喘息\n储藏室 \n裤子 \n连裤袜 \n木瓜 \n纸 \n纸袋 \n切纸机 \n纸灯笼 \n纸盘子 \n纸巾 \n平装书 \n压纸器 \n降落伞 \n游行 \n天堂 \n鹦鹉 \n护理人员 \n长尾小鹦鹉\n滑翔伞 \n伞兵 \n羊皮纸 \n教区 \n公园 \n公园长椅\n停车 \n停车场 \n停车费 \n停车标志 \n议会 \n欧芹/香菜\n参与者 \n合作伙伴 \n帕特里奇 \n聚会\n派对帽 \n通过 \n通道 \n存折 \n乘客 \n客船 \n旅客列车 \n百香果 \n护照 \n面食\n粘贴 \n糕点 \n牧场 \n补丁 \n病人 \n图案/款式\n人行道/硬路面\n大帐篷\n爪子 \n支付 \n付费电话 \n豌豆 \n和平 \n桃子 \n孔雀 \n山峰/尖顶\n花生 \n花生酱 \n梨 \n珍珠 \n卵石 \n山核桃 \n行人\n人行天桥 \n步行街 \n果皮\n削皮器 \n小钉板 \n木质腿\n鹈鹕 \n笔/围栏\n点球 \n铅笔 \n铅笔盒\n卷笔刀 \n铅笔裙 \n吊坠 \n钟摆\n企鹅 \n半岛 \n锦标旗\n便士\n储蓄罐 \n牡丹 \n胡椒/辣椒\n胡椒研磨机 \n胡椒子\n意大利辣香肠 \n栖息/鲈鱼\n表演\n表演\n表演舞台 \n香水 \n绿廊 \n波斯猫 \n柿子 \n个人护理 \n个人漂浮装置 \n害虫 \n宠物 \n宠物店 \n宠物店 \n花瓣 \n佩妮 \n教堂的长椅\n野鸡 \n现象 \n哲学家 \n电话 \n电话簿 \n留声机 \n照片 \n照相亭 \n相框 \n摄影 \n物理学家 \n物理实验室 \n钢琴家 \n钢琴 \n选择 \n捡起 \n泡菜 \n野餐 \n野餐区 \n野餐篮 \n野餐桌\n图片 \n相框 \n馅饼\n鸽子 \n朝圣者 \n药片\n枕头 \n飞行员 \n领航艇 \n别针\n松树\n松果 \n松林 \n松子 \n菠萝 \n乒乓球桌 \n乒乓球 \n粉色\n一品脱的量 \n琵琶 \n管子\n管碗 \n海盗 \n海盗旗 \n海盗船 \n阿月浑子 \n滑雪场 \n口袋里的面包 \n火龙果 \n斗牛犬 \n球场 \n大水罐\n猪笼草 \n干草叉 \n披萨 \n披萨刀 \n比萨锅 \n披萨店 \n招牌 \n地方 \n餐具垫 \n格子 \n平原 \n示意图\n行星\n行星地球\n厚木板\n植物 \n种植园 \n种植 \n匾额\n石膏 \n塑料 \n橡皮泥 \n高原 \n平台 \n白金\n大浅盘\n玩/演奏/运动\n打羽毛球 \n打棒球 \n打篮球 \n玩台球 \n踢足球 \n玩乒乓球 \n打网球 \n打排球 \n选手/运动员\n操场\n剧场 \n扑克牌 \n下棋 \n打高尔夫球 \n打麻将 \n运动场\n护栏\n游戏室 \n广场 \n钳子 \n故事情节\n犁 \n插头 \n插头帽 \n李子 \n水管工 \n卫生洁具 \n羽毛\n夹板 \n口袋\n怀表 \n随身小折刀 \n圆荚体 \n乐队指挥台\n诗歌 \n一品红 \n指/朝向\n指针 \n扑克卡 \n筹码 \n扑克表 \n杆/柱\n臭猫 \n警察 \n警车 \n警犬 \n警察局 \n政治家 \n圆点 \n花粉 \n污染 \n马球 \n马球领 \n马球衬衫 \n石榴 \n波美拉尼亚的 \n雨披 \n池塘 \n马尾辫 \n贵宾犬 \n池 \n流行 \n流行艺术家 \n爆米花 \n教皇 \n罂粟 \n瓷 \n玄关 \n猪肉 \n粥 \n便携式电池 \n门户网站 \n投资组合 \n汽门 \n肖像 \n肖像会话 \n摆姿势拍照\n负鼠 \n帖子 \n邮局 \n邮票 \n明信片 \n海报 \n海报页 \n锅/罐/陶盆\n土豆 \n土豆片 \n土豆沙拉 \n布垫子 \n便壶\n袋 \n家禽 \n英镑 \n倾泻\n粉末\n电源线\n电源插头及插座 \n权力看 \n电站 \n练习\n布拉格城堡 \n祈祷 \n牧师 \n首映 \n处方 \n显示 \n演讲 \n总统 \n新闻发布室\n高压锅 \n椒盐卷饼 \n王子 \n公主 \n打印 \n打印页面 \n打印机 \n印刷 \n监狱 \n农产品/生产\n产品 \n职业 \n专业的\n教授 \n项目图片 \n投影屏幕 \n投影仪 \n毕业舞会 \n散步 \n螺旋桨 \n先知 \n建议 \n防护服 \n抗议 \n抗议者 \n出版 \n宣传画像\n冰上曲棍球 \n布丁 \n水坑 \n泡芙 \n角嘴海雀 \n哈巴狗 \n拉 \n讲坛 \n脉冲 \n泵 \n南瓜 \n南瓜饼 \n南瓜种子 \n拳击吊袋\n拳头猛击/穿孔\n学生 \n紫色\n推 \n轻轻一击\n谜题 \n塔 \n金字塔 \n大蟒\n二维码 \n鹌鹑 \n采石场 \n季度 \n石英 \n女王 \n油炸玉米粉饼 \n队列 \n乳蛋饼 \n被子 \n绗缝 \n引用\n兔子 \n浣熊 \n比赛 \n赛道 \n水沟/跑道\n赛车 \n球拍 \n雷达 \n散热器 \n广播 \n木筏/橡皮艇\n布娃娃 \n栏杆/铁轨\n轨道车 \n铁道\n铁路桥梁 \n轨道线\n火车站 \n雨 \n雨靴\n彩虹 \n虹鳟鱼 \n雨衣 \n热带雨林 \n多雨的 \n葡萄干 \n耙子\n公羊\n斜坡 \n油菜籽\n快速 \n说唱歌手 \n树莓 \n老鼠 \n棘轮 \n乌鸦 \n峡谷 \n雷 \n剃须刀 \n锋利的 \n阅读\n阅读材料\n钻孔器\n后面\n尾灯 \n后视图\n后视镜 \n收据 \n收到 \n接待 \n配方 \n记录 \n唱片制作人 \n记录器/竖笛\n录音室 \n娱乐室 \n休闲车 \n矩形 \n回收 \n回收站 \n红色\n红地毯 \n红旗 \n红熊猫 \n红酒 \n红木 \n芦苇\n礁石\n卷轴\n裁判 \n倒影\n倒影\n反射器 \n注册 \n控制 \n驯鹿 \n放松 \n释放 \n救援 \n宗教 \n宗教的\n享受 \n保持 \n改造 \n遥控器\n移除\n修复 \n维修店 \n爬行动物 \n救援 \n救助者 \n研究 \n研究员 \n储层 \n住宅 \n居民区 \n树脂 \n度假胜地 \n度假小镇\n餐厅的厨房 \n餐厅的露台 \n厕所 \n零售 \n寻回犬 \n制动火箭\n揭示 \n犀牛 \n杜鹃 \n肋骨 \n丝带 \n大米 \n电饭煲 \n稻田 \n骑/搭乘\n脊 \n骑马\n步枪 \n边缘\n环/戒指\n暴乱\n涟漪 \n上升 \n高层建筑\n河 \n河岸 \n河船 \n河谷 \n河床 \n路 \n路标 \n公路旅行 \n路边 \n烤鸡 \n长袍 \n罗宾 \n机器人 \n石头 \n岩石拱 \n摇滚艺术家 \n摇滚乐队 \n攀岩者 \n攀岩 \n摇滚音乐会 \n岩石表面 \n岩层 \n摇滚歌手 \n火箭 \n摇椅 \n岩石 \n啮齿动物 \n牛仔竞技表演 \n竞技舞台 \n罗伊 \n狍子 \n辊 \n过山车 \n轮式溜冰鞋 \n溜冰鞋 \n擀面杖 \n浪漫 \n浪漫的 \n屋顶 \n屋顶花园 \n房间 \n房间分频器 \n根 \n根啤酒 \n绳索桥 \n念珠 \n玫瑰 \n迷迭香 \n玫瑰色的云 \n罗特韦尔犬 \n圆桌 \n路由器 \n行 \n罗文 \n皇家 \n橡皮图章 \n废墟 \n魔方 \n红宝石\n莱夫 \n橄榄球 \n橄榄球 \n橄榄球运动员 \n毁坏\n尺\n朗姆酒 \n跑\n跑步者 \n跑步鞋 \n农村的\n锈 \n乡村的\n黑麦 \n袋 \n鞍 \n鞍囊\n旅行\n安全 \n安全背心 \n圣人 \n帆 \n帆船 \n航行 \n水手 \n松鼠猴 \n缘故\n沙拉 \n沙拉碗 \n火蜥蜴 \n意大利蒜味腊肠 \n出售 \n三文鱼\n沙龙 \n萨尔萨舞 \n盐 \n盐和胡椒瓶 \n盐湖 \n盐沼 \n盐瓶 \n敬礼 \n萨莫耶德人 \n武士 \n沙子 \n沙洲 \n砂箱 \n沙堡 \n沙雕 \n凉鞋 \n三明治 \n卫生巾 \n圣诞老人 \n蓝宝石 \n沙丁鱼 \n莎丽 \n生鱼片 \n沙爹 \n书包 \n卫星 \n缎 \n酱汁 \n碟子\n桑拿 \n香肠 \n稀树大草原 \n锯\n锯木架\n萨克斯管\n萨克斯手 \n脚手架 \n秤/标尺\n比例模型 \n扇贝 \n疤痕 \n稻草人 \n围巾 \n场景 \n风景 \n雪纳瑞犬 \n学校 \n校车 \n校服 \n校舍 \n纵帆船\n科学 \n科幻电影 \n科学博物馆 \n科学家 \n剪刀 \n壁灯 \n司康饼 \n勺子\n踏板车/摩托车\n分数 \n记分板 \n蝎子 \n童子军 \n炒蛋 \n废弃\n刮板 \n刮伤 \n屏幕 \n纱门 \n截图 \n螺杆 \n螺丝刀 \n长卷纸/卷轴\n擦洗 \n硬毛刷 \n雕塑家 \n雕塑 \n海洞穴 \n海冰 \n海狮 \n海龟 \n海胆 \n尖吻鲈 \n海底 \n海鸟 \n海鲜 \n海马 \n海豹\n海景 \n海贝 \n海滨度假胜地 \n季节 \n座位 \n安全带 \n海藻 \n秘书 \n安全 \n小轿车 \n看到 \n种子 \n跷跷板 \n赛格威 \n自拍\n出售 \n研讨会 \n感觉 \n传感器 \n服务器 \n服务器机房 \n服务 \n集 \n缝纫机 \n影子 \n摇 \n瓶 \n洗发水 \n形状 \n分享 \n鲨鱼 \n卷笔刀 \n记号笔\n剃须刀 \n剃须膏 \n披肩/围巾\n剪切 \n剪刀\n羊 \n床单 \n乐谱 \n架子\n贝壳\n贝类 \n避难所 \n搁置 \n牧羊人 \n果子露\n柴犬 \n发光 \n航运 \n集装箱 \n海难 \n船厂 \n衬衫 \n赤膊的\n浅滩\n鞋 \n鞋盒 \n鞋店 \n鞋楦 \n射击\n得分篮球后卫 \n商店橱窗 \n门面 \n购物者 \n购物 \n购物袋 \n购物篮 \n购物车 \n购物中心 \n购物街 \n海岸 \n海岸线 \n短的\n短发 \n短裤 \n小酒杯\n散弹枪 \n肩膀 \n单肩包\n铲 \n陈列柜\n淋浴 \n浴帽\n浴帘 \n淋浴门 \n淋浴头 \n碎纸机 \n泼妇 \n虾 \n神社 \n灌木 \n快门 \n暹罗猫\n西伯利亚 \n兄弟姐妹\n侧面\n边柜 \n配菜 \n边车\n边线\n壁板\n标志 \n指示牌\n信号 \n签名 \n丝绸 \n丝袜 \n筒仓 \n银 \n银牌 \n银器 \n唱歌\n烧焦 \n歌手 \n水槽 \n啜\n坐/放置/坐落\n坐着 \n滑板公园\n滑板 \n滑板者 \n溜冰者 \n溜冰场 \n骨架 \n草图 \n串串\n滑雪 \n滑雪靴 \n滑雪设备 \n滑雪服\n滑雪缆车 \n滑雪杖 \n滑雪胜地 \n滑雪板\n滑雪 \n滑雪鞋 \n皮肤 \n头骨 \n无边便帽 \n天空 \n天空塔 \n天窗 \n天际线 \n摩天大楼 \n激流回旋 \n石板\n雪橇 \n睡眠 \n睡袋 \n睡衣\n袖子\n片 \n滑动\n滑块 \n吊索 \n坡 \n投币口\n老虎机 \n树懒\n慢炖锅 \n鼻涕虫 \n贫民窟 \n气味 \n微笑 \n烟雾/抽烟\n零食 \n蜗牛 \n蛇 \n鲷鱼 \n快照 \n通气管 \n鼻子 \n雪 \n雪豹 \n雪山 \n雪球 \n单板滑雪者\n雪原 \n雪花 \n雪人 \n雪地摩托\n雪犁\n雪鞋 \n雪 \n肥皂 \n肥皂泡 \n给皂器 \n足球守门员 \n社会名流\n短袜\n插座\n苏打水 \n垒球 \n软件 \n太阳能电池阵列 \n士兵 \n独奏 \n解决方案 \n宽边帽 \n歌曲\n声音 \n汤 \n汤碗\n汤匙 \n酸奶油 \n纪念品 \n豆浆 \n水疗中心 \n空间 \n航天飞机 \n空间站 \n宇宙飞船 \n意大利面 \n横跨\n扳手 \n火花 \n闪耀 \n烟火\n起泡葡萄酒 \n麻雀 \n抹刀 \n扬声器\n观众 \n会话框\n速度限制 \n限速标志 \n快艇 \n车速表\n球 \n香料 \n调料架\n蜘蛛 \n蜘蛛网 \n扣球\n旋转\n菠菜 \n尖塔 \n飞溅 \n海绵 \n勺子 \n体育协会 \n运动器材\n运动团队\n体育球 \n体育器材\n运动会 \n运动服装 \n点 \n喷雾 \n伸展\n春天 \n春卷 \n撒\n洒水器\n发芽 \n云杉 \n云杉森林 \n队 \n广场 \n南瓜 \n蹲 \n挤\n鱿鱼 \n松鼠 \n水枪 \n刺 \n稳定的 \n（码放整齐的）一叠\n体育场 \n工作人员 \n舞台\n舞台灯 \n驿马车\n弄脏\n不锈钢 \n楼梯 \n楼梯 \n楼梯间\n摊位/小隔间\n种马 \n站/矗立/摊位\n站 \n主食 \n订书机 \n星星\n盯着\n海星 \n杨桃 \n燕八哥 \n州立公园 \n公立学校 \n车站\n固定自行车\n文具 \n雕像 \n牛排 \n牛排刀 \n蒸汽 \n蒸汽机 \n蒸汽机车 \n蒸汽火车 \n馒头 \n钢 \n方向盘 \n（花草的）茎\n模版\n梯凳\n立体声 \n听诊器 \n炖\n戳/条状物\n竹节虫 \n贴纸 \n静物画 \n高跷 \n黄貂鱼 \n搅拌 \n搅拌器 \n镫\n缝 \n股票 \n长筒袜\n腹部\n石头建筑 \n石雕 \n石屋\n石磨 \n凳子 \n停止 \n停在 \n红灯 \n停车标志 \n秒表 \n红绿灯 \n存储箱 \n储藏室 \n罐/蓄水池\n商店 \n店面 \n鹳 \n风暴 \n暴风云 \n狂风暴雨的 \n炉子 \n扑克 \n跨骑\n过滤器 \n海峡 \n带 \n稻草/吸管\n草帽 \n草莓 \n溪流\n街头艺术 \n街头艺术家 \n街角 \n流浪狗\n街头食品 \n路灯 \n街市场 \n街头摄影 \n街景 \n路标 \n街头小贩 \n拉伸 \n担架 \n罢工 \n前锋\n细绳\n芝士条\n带子\n条纹 \n漫步 \n结构 \n工作室 \n影棚拍摄 \n材料\n填充玩具动物\n毛绒玩具\n馅\n树桩 \n惊人的 \n特技 \n佛塔 \n风格 \n手写笔\n潜艇 \n潜艇形大三明治\n海底水\n郊区 \n地铁 \n地铁站 \n低音炮 \n多肉\n绒面革\n糖 \n糖碗 \n甘蔗 \n方糖 \n西装\n套房\n夏天 \n夏天傍晚\n峰顶\n太阳 \n太阳帽\n日光浴 \n周日 \n日晷 \n向日葵 \n向日葵田 \n葵花籽\n太阳镜 \n晴天\n日出 \n日落 \n遮阳伞 \n阳光 \n超级碗 \n跑车 \n超级英雄 \n超市 \n超市货架\n超模\n支持者 \n冲浪\n表面 \n冲浪板 \n冲浪者 \n外科医生 \n外科手术\n环绕\n寿司 \n寿司吧 \n背带裤 \n悬架 \n吊桥 \n越野车\n燕子\n燕尾蝶 \n沼泽 \n天鹅 \n天鹅游艇 \n运动裤\n防汗带 \n毛衣 \n运动衫 \n甜的\n红薯 \n游泳 \n泳帽 \n游泳者\n游泳洞 \n游泳池 \n摆动\n平转桥 \n秋千\n漩涡 \n开关 \n转椅 \n剑 \n旗鱼\n象征 \n对称 \n犹太教堂\n注射器 \n糖浆 \n系统 \nt恤 \nt恤 \n塔巴斯科辣椒酱\n虎斑\n乒乓球拍 \n桌面 \n桌布 \n平板电脑 \n餐具 \n转速表 \n拦截\n墨西哥煎玉米卷 \n跆拳道 \n太极 \n尾巴 \n裁缝 \n拍/拿\n起飞 \n说话/交谈/演讲\n手鼓 \n棕褐色 \n橘子 \n胶带/磁带/终点线\n挂毯 \n沥青碎石路面\n芋头 \n篷布\n果馅饼\n流苏 \n味道 \n榻榻米 \n纹身 \n纹身艺术家 \n酒馆 \n茶 \n茶包\n茶话会\n茶园 \n茶壶 \n茶具 \n教 \n老师 \n茶杯 \n水鸭\n团队合影\n团队介绍 \n眼泪/撕裂/划破\n技术员 \n技术 \n泰迪熊\nT字形物\n青少年 \n电线杆 \n变焦镜头 \n望远镜 \n电视 \n电视摄像机 \n电视室\n电视演播室 \n温度 \n寺庙 \n天妇罗 \n网球 \n网球场 \n网球比赛 \n网球网 \n网球运动员 \n网球拍 \n帐篷 \n龙舌兰酒 \n终端/航站楼\n阳台 \n地形 \n玻璃容器 \n领土 \n测试 \n测试赛 \n试管\n文本 \n短信 \n纺织 \n纹理 \n感恩节 \n感恩节晚餐 \n剧院 \n戏剧演员\n治疗 \n温度计 \n热水瓶 \n暖瓶\n恒温器 \n灌木丛 \n顶针 \n东西\n思考 \n蓟 \n宝座 \n金銮殿\n扔 \n抱枕 \n雷\n雷雨 \n百里香 \n皇冠\n记号\n票 \n售票亭 \n潮池 \n领带 \n老虎 \n紧 \n瓦\n瓷砖地板 \n瓦屋顶 \n瓷砖墙 \n锡 \n锡纸 \n箔\n提拉米苏 \n轮胎 \n纸巾\n烤面包 \n烤面包机 \n烟草 \n烟斗 \n学步的小孩\n脚趾 \n豆腐 \n马桶 \n马桶座圈 \n化妆包\n东京铁塔\n番茄 \n番茄酱 \n番茄汤 \n墓 \n钳子\n钳子\n工具 \n工具箱 \n牙刷 \n牙膏 \n牙签 \n修剪成形的花园 \n配料\n火炬/光源\n龙卷风 \n玉米粉圆饼 \n乌龟 \n大手提袋 \n图腾柱 \n龙猫 \n巨嘴鸟 \n触摸 \n触地\n旅行\n旅游巴士 \n导游 \n游客\n旅游景点 \n锦标赛\n拖车\n毛巾 \n毛巾杆 \n大厦 \n塔桥 \n小镇 \n城镇广场\n玩具 \n玩具车 \n玩具枪 \n玩具店 \n跑道\n拖拉机 \n贸易 \n传统 \n传统的 \n交通 \n锥形交通路标 \n交通拥堵 \n交通堵塞 \n交通标志 \n小道 \n预告片 \n拖车 \n火车 \n火车桥 \n火车车厢 \n火车内部 \n火车轨道 \n火车窗口 \n教练 \n训练\n训练长椅\n训练场\n电车/手推车\n蹦床 \n变形金刚\n透明度 \n旅行 \n托盘/碟子\n跑步机 \n美食\n树 \n树枝 \n林场 \n树蛙 \n树屋 \n树根\n树干 \n试验 \n三角形 \n铁人三项\n部落 \n支流 \n戏法/特技\n三轮车 \n修剪 \n三人组 \n三脚架 \n长号 \n部队 \n奖杯\n奖杯 \n热带 \n鳟鱼 \n卡车 \n卡车司机 \n浴缸 \n管子\n拖船 \n郁金香 \n金枪鱼 \n苔原 \n隧道 \n涡轮 \n火鸡 \n转动\n芜菁\n绿松石 \n炮塔 \n乌龟 \n獠牙\n电视演员 \n电视柜 \n电视剧 \n电视节目类型 \n电视名人 \n电视节目 \n情景喜剧 \n电视塔 \n枝条\n黄昏\n双胞胎\n麻线\n扭 \n类型 \n键入\n打字机 \n尤克里里\n奥特曼 \n伞 \n内衣 \n水下 \n独角兽 \n制服\n宇宙 \n大学 \n向上\n城市 \n尿壶 \n瓮\n使用 \n用具 \n杂物间 \n吸尘器/真空\n谷 \n阀门\n吸血鬼\n货车\n香草 \n虚荣 \n种类\n花瓶/瓶\n金库\n矢量卡通插图 \n矢量图标 \n蔬菜\n菜园\n蔬菜市场 \n植被 \n车辆 \n面纱 \n静脉 \n天鹅绒 \n自动售货机 \n小贩\n通风孔\n胡蜂属 \n船 \n背心 \n兽医 \n经验丰富的 \n兽医办公室 \n高架桥 \n视频 \n摄像机 \n电子游戏\n录像带 \n视镜 \n守夜 \n别墅 \n村庄\n藤蔓\n醋 \n葡萄园 \n暴力 \n紫罗兰色\n小提琴 \n小提琴家 \n中提琴演奏者 \n愿景 \n遮阳板 \n伏特加 \n火山 \n排球 \n排球场 \n排球运动员 \n志愿者 \n航行 \n秃鹰 \n华夫饼干\n华夫饼机\n货车\n马车车轮 \n腰 \n服务员 \n候机室 \n等候室 \n走 \n步行\n手杖 \n挂钟 \n壁纸 \n核桃\n海象 \n战争 \n仓库 \n温暖的 \n警告标志 \n战士 \n军舰 \n疣猪 \n洗 \n洗衣机/垫圈\n洗 \n洗衣机 \n黄蜂 \n浪费 \n废物容器 \n手表\n水 \n水鸟 \n水牛 \n水冷却器 \n水滴 \n水景\n热水器 \n水位 \n荷花\n水上乐园\n水管 \n净水器 \n滑水板\n水上运动 \n水面\n水塔 \n水彩 \n水彩插图 \n水彩画 \n瀑布 \n喷壶 \n水印叠加图章\n西瓜 \n防水外套\n水路\n波浪\n蜡 \n武器 \n穿着\n天气 \n叶片 \n网\n摄像头\n婚礼 \n结婚戒指 \n婚礼花束 \n结婚蛋糕 \n新婚夫妇\n婚礼请柬\n婚礼派对\n婚纱照 \n婚礼摄影师 \n婚纱摄影 \n婚宴 \n楔 \n杂草 \n重量 \n体重秤 \n焊接工\n井\n西餐 \n西餐厅 \n湿\n吧台\n潜水衣\n湿地\n潜水服 \n鲸鱼 \n鲸鲨 \n小麦 \n麦田 \n车轮\n轮椅 \n后轮支撑车技\n生奶油 \n搅拌器\n胡须\n威士忌 \n哨子\n白色\n白宫 \n白葡萄酒 \n白板 \n便门\n宽的\n挥动\n假发 \nWii \nWii手柄\n荒野\n角马\n野火\n野花 \n野生动物 \n柳树 \n风 \n风铃 \n风电场\n风力涡轮机 \n风车 \n窗户\n窗台花盆箱 \n橱窗展示 \n窗框 \n纱窗 \n靠窗的座位\n窗台\n雨刮器\n挡风玻璃 \n有风的\n酒瓶 \n冷酒器\n酒柜 \n酒窖 \n酒杯 \n酒架 \n品酒\n酒庄 \n翅膀\n冬天 \n冬瓜 \n冬天的早晨 \n冬季场景\n冬季运动 \n冬季风暴 \n电线\n紫藤 \n巫婆\n女巫帽子 \n炒锅\n狼 \n女人 \n木头\n林鸳鸯\n木地板 \n木墙 \n烧木炉\n木匙 \n林地 \n啄木鸟 \n木工刨\n羊毛 \n工作 \n练习卡 \n工作台 \n工人 \n工作场所 \n车间 \n世界 \n蠕虫 \n敬拜 \n伤口\n包\n裹身裙 \n包装纸\n搏斗\n摔跤手\n皱纹\n腕带\n写\n作家\n手写/字迹\n毛笔\n写字桌\n游艇\n牦牛 \n院子\n黄色\n瑜伽 \n瑜伽垫 \n酸奶 \n轭 \n蛋黄 \n青年 \n青年旅馆\n蒙古包\n斑马 \n斑马线 \n禅意花园 \n拉链\n拉链 \n僵尸 \n粽子\n动物园\n"
  },
  {
    "path": "model_cards/Tag2Text/ram/data/ram_tag_list_threshold.txt",
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door\nglass bottle\nrestaurant\nconductor\nlogo\nsleep\ntape\ntomato\nriver bank\nlilac\ntooth\ntraining\npottery\nshop\nsteam engine\nmason jar\nbase\nprocession\nborder\nshoot\nfootprint\nhotdog\nbull\nstocking\nrecreation\nautomobile model\ndesign\ncountry pop artist\nriver\nretriever\ndepartment store\nauditorium\nsport car\nsupermarket\nbelt\ncricket\nwindow box\ndress shirt\nletter\nresidence\nmegaphone\npant\nwildfire\nbird nest\ncrab\nswimsuit\ncandle\nfuneral\nmill\nnational park\nplant\ncop\npower line\nperch\nblue\nfinger\nferris wheel\nglobe\nskateboard\nhelmet\nmovie theater\nuniform\nhammer\nmaterial\nkid\nwell\nbutterfly\nsideline\nfashion fall show\nplanet earth\nlift\nmale\nsauna\ngray\nflour\nsand sculpture\nprogram\ncabinet\ninfant\nwheel\naircraft model\ndough\ngarlic\nskate\narrow\nwrapping paper\nripple\nlamp\niron\nbanknote\nbeaver\nferry\ncourtyard\nbassist\ncountryside\nsteak\ncomfort\nboxer\nlaundry room\ncampsite\nbrick building\ngolf\nsubway\nheadphone\nfort\nhandbag\ndrum\nflood\nsaddle\nbass\nlabyrinth\nneedle\nsun ray\napp\nmenu\npresident\ncardigan\ndandelion\nwetland\nice hockey player\nnumber\ncity hall\nfishing\nportrait session\npug\nkey\nart print\nminister\nhurdle\nemergency\npainting artist\nflag pole\nevening\npurse\nrecipe\ngolf ball\ncoloring book\nmountain peak\nsenior\nholiday\nbud\ncousin\npantry\nlap\nskin\nflag\ntissue paper\nridge\nwire fence\nsurfer\nclimber\nphotograph\nsewing machine\ncooler\nactress\napple tree\ncancer\nstarfish\nautomobile make\ndumbbell\nbrace\ntunnel\nwindow\npaint artist\ncomposition\nschool student\ncondo\nconvertible\ncushion\nselfie\nterritory\nguide\ntree\ncourt\nshrimp\nstone house\ndress\neyelash\njuice\nbroccoli\nchain\ntourism\nmountain top\nconcept car\nfilm premiere\nlight bulb\ncafeteria\nbadge\nflower bed\ntheater\nroot\nracecar driver\nbasketball boy game\nglove\nskyline\nwall\nglacier\nairport terminal\nbug\ntrim\nrailway station\nbriefcase\nflat\nfountain\nperson\nlane\nasparagus\nart\nlantern\ndishwasher\ndirector\nsnake\nlecture\ngame controller\ntree branch\npub\nbathing suit\nqueue\nbelly\npoppy\nbow\npitcher\nice cream cone\ncave\ncandy\nroad bridge\nhost\ntraffic jam\nearring\nfile\nfoot\nwatermark overlay stamp\nmailbox\nsupercar\nrailing\nbedroom\nseafood\nwaffle\nbronze statue\nplan\nflow\nmarble\nbasketball game\nautomobile\nscene\ncypress tree\nsoldier\nskateboarder\nglass building\ncherry tree\npump\ngrain\nwildebeest\nloop\nframe\nbathtub\nsaxophone\ndiver\nstalk\nlily\nbead\nalley\nflock\nfamily room\nmanufacturing\npointer\nworker\nnavy\npotato\nteacher\nphotography\ndolly\nboardwalk\nwater fountain\nathlete\nside dish\nbay\nice hockey\nphone\nhero\nface\ngold medal\nblind\nswamp\nresearcher\nswim\nmeatball\niguana\nleather jacket\njellyfish\nsite\nsmoke\ntraffic signal\nmelon\nbeetle\ncalculator\nskirt\nplantation\nsculptor\nbarrier\ncatcher\nsecurity guard\nsketch\nawning\nsteering wheel\nmountain view\nbus stop\npool\nleg\nspotlight\napron\nmineral\ninlet\nsleeve\ntorch\nemotion\nmarch\npolice officer\nperformance\nlamp post\nfishing boat\nsummer\npresentation\nsaucer\nsuitcase\nsupermodel\ngoalkeeper\nshrub\nrock artist\ndocument\nbeach house\nman\nblue artist\ncigar\nrailroad track\ngown\nmosaic\nbungalow\nalphabet\nbaseball field\nshed\npedestrian\nrail\nsoap\nkitchen counter\ndessert\ndunk\nblossom\nconversation\nfruit market\nglass jar\nmilitary\nbeer bottle\nphotographer\ntennis racket\ncompetition\nescalator\nbell tower\nstilt\nballerina\ntelevision\nfeather\nfence post\nrear\ndahlia\nred carpet\ntub\nhole\nfortress\npack\ntelephone\ncardboard\ncity park\nplatform\ncollege student\narch bridge\nwind\nblender\nbloom\nice rink\nbirthday\nraven\nfairy\nembankment\nhall\nflower shop\nsuburb\nbarrel\nbiker\nsteam\ndragonfly\nformation\nelectricity\nbusiness people\nsymmetry\nwalkway\nfisherman\ngas mask\nloch\nyouth\nhanger\ndot\nfish\nstreet market\nanimation film\ncrime fiction film\nboar\nemblem\nhalloween costume\nkangaroo\ncouple\nspoon\nsquirrel\nneon sign\nsky\noffice desk\nbeauty salon\nbreakwater\nfashion look\ntoaster\nauthor\nnews conference\noutdoor\ncanoe\ndragon\ntool\nshopping centre\nladybug\nswimming pool\nlandscaping\nski pole\nred\ntruck\nfly\ntemple\nlevel\nsunday\nrailroad bridge\ncar mirror\nlawn mower\nflute\naircraft carrier\nfashion menswear london week\nsunshine\ntile floor\nskull\nfossil\nflower arrangement\ndiaper\nsea turtle\ncherry blossom\nfireman\nshack\nlens\nwaiter\nanimal\nbasement\nsnow\nautumn park\nglass box\nkick\nhead\nanniversary\nvine\nback\npaper lantern\nfish tank\ncellphone\nsilk\ncoral\nnotebook\nphoto\ngazebo\nketchup\ndriver\nfarmer\nbonfire\nchestnut\nphotoshoot\nfootball field\nolive tree\npheasant\nsandal\ntoilet\nfireplace\nmusic\ndeity\nfish market\nfig\nbell\nneck\ngrave\nvilla\ncyclist\ncrate\ngrey\nasphalt road\nsoccer\nhostel\nmunicipality\ncourthouse\nroof\nend table\npot\nsedan\nstructure\nfolk artist\nsport\nsport team\nprotest\nsyringe\nfashion designer\njersey\nheart shape\nkayak\nstare\nsit with\ndirect\nread\nphotograph\nspin\nteach\nlaugh\ncarve\ngrow on\nwarm\nwatch\nstretch\nsmell\ndecorate\nshine\nlight\ndance\nsend\npark\nchase\ncollect\nlead\nkiss\nlead to\nlick\nsmile\ncheer\nsit\npoint\nblock\nrock\ndrop\ncut\nski\nwrap\nlose\nserve\nprovide\nsleep\ndress\nembrace\nburn\npack\nstir\ncreate\ntouch\nwash\nstick\nreveal\nshop\ntrain\npaint\ngroom\nhunt\nbloom\nplay\npay\nbrush\nshoot\nhold\npicture\ncarry\nsip\ncontain\nturn\npour\npitch\ngive\nadd\nblow\nlook in\nshow\nwalk\nilluminate\nkneel\ncover\ndrag\npost\npresent\nfit\noperate\nfish\nrace\nwrite\ndeliver\npeel\npush\nrun\nsit around\nbuy\njump\nwalk on\nattend\nclean\nsell\nride on\nmount\nhost\ndry\nplant\nsing\nrow\nshake\nperch\nride\nfight\nskateboard\nlive\ncall\nsurround\npractice\nplay on\nwork on\nstep\nrelax\nhit\nfall in\nflow\ngreet\nlaunch\nwear\nhang on\ndrive\nsit in\nbreak\nlearn\nfly\nconnect\ndisplay\nlocate\ncompete\ngo for\nsail\nlift\ntoast\nhelp\nrun on\nreflect\npose\nscratch\nframe\ndribble\nherd\nenter\nexit\nplace\ninspect\nbuild\npick\nfill\ngrind\nskate\noffer\nfloat\nsit by\nstand\nrelease\nrest\nsinge\nclimb\ntie\nmark\nlay\nstand around\ncapture\nset\nland\nswinge\nrun in\nkick\nlean\nhead\nsign\napproach\nswim\nclose\ncrash\ncontrol\nfall\nremove\nrepair\nopen\nappear\ntravel\nload\nmiss\ncheck\nsurf\nmoor\nsmoke\ndrink\nboard\nseat\nfeed\nrise\nsit on\nswing\ngrow\nstrike\ndate\nslide\nshare\ngraze\njump in\nlie\nextrude\nroll\nmove\ngather\neat\npull\nrun through\nsqueeze\nlay on\ndraw\nplay with\nwave\nassemble\nperform\nmarch\nscore\nattach\nadjust\nhang\nhug\nsleep on\nthrow\nlive in\ntalk\npet\nwork\nrun with\nsee\nflip\ncatch\ncook\nreceive\ncelebrate\nlook\nclassic\nbridal\nindoor\nindustrial\nteenage\nmini\ngrassy\naged\nlong\nwarm\nlight\nhandsome\nhappy\nthree\npregnant\ncircular\nurban\nsilver\nceramic\n3d\ngreen\nblonde\ngolden\ndark\ntropical\nripe\ndeep\nfat\nmusical\ngiant\nmedical\nmedieval\nbare\nstunning\nbold\ngeographical\nhuge\nplastic\nfoggy\nstormy\ngothic\nbiological\nempty\nclear\nantique\npink\nsteep\nbrown\nstriped\naerial\nrainy\ncool\nflying\ncommercial\npurple\ntrendy\nblank\nhaired\ndead\nwooden\nflat\nhigh\nbeige\npanoramic\nangry\ndozen\nrural\nsolar\nbig\nsmall\nstained\nthick\nmany\nfresh\nclean\nstrong\nabstract\ncrowded\nretro\ndry\ngorgeous\nmartial\nmodern\nblue\ncloudy\nlow\nfour\noutdoor\nsingle\nmuch\nbeautiful\nsnowy\npretty\nnew\nshort\nsunny\nclosed\nrocky\nred\ntwo\ndouble\nmale\ngray\nfive\ncolorful\nautomotive\nvarious\none\nold\nrusty\ntall\nwild\nnarrow\nnatural\nseveral\nfrozen\ntextured\nlush\nyoung\nhot\nmixed\nwhite\nfloat\nquiet\nround\nbright\nreligious\nfemale\nhistorical\nshiny\ntraditional\ntourist\nyellow\nbald\ncoastal\nlovely\nlittle\nbroken\nromantic\nwide\nroyal\nrich\nopen\ncute\nancient\ncold\npolitical\nelderly\ngold\nfull\nrustic\nmetallic\nfloral\nsad\nwet\nfancy\nsenior\ntiny\nstylish\nlarge\nfrosty\norange\ntransparent\nelectronic\nshallow\nscared\narmed\ndirty\nhistoric\nblack\nfew\nwindy\nsome\nsquare\nornamental\nsandy\nthin"
  },
  {
    "path": "model_cards/Tag2Text/ram/inference.py",
    "content": "'''\n * The Inference of RAM and Tag2Text Models\n * Written by Xinyu Huang\n'''\nimport torch\n\n\ndef inference_tag2text(image, model, input_tag=\"None\"):\n\n    with torch.no_grad():\n        caption, tag_predict = model.generate(image,\n                                              tag_input=None,\n                                              max_length=50,\n                                              return_tag_predict=True)\n\n    if input_tag == '' or input_tag == 'none' or input_tag == 'None':\n        return tag_predict[0], None, caption[0]\n\n    # If user input specified tags:\n    else:\n        input_tag_list = []\n        input_tag_list.append(input_tag.replace(',', ' | '))\n\n        with torch.no_grad():\n            caption, input_tag = model.generate(image,\n                                                tag_input=input_tag_list,\n                                                max_length=50,\n                                                return_tag_predict=True)\n\n        return tag_predict[0], input_tag[0], caption[0]\n\n\ndef inference_ram(image, model):\n\n    with torch.no_grad():\n        tags, tags_chinese = model.generate_tag(image)\n        print(\"Image Tags: \",tags[0])\n        print(\"图像标签: \", tags_chinese[0])\n    return tags[0],tags_chinese[0]\n\n\ndef inference_ram_openset(image, model):\n\n    with torch.no_grad():\n        tags = model.generate_tag_openset(image)\n\n    return tags[0]\n"
  },
  {
    "path": "model_cards/Tag2Text/ram/models/__init__.py",
    "content": "from .ram import ram\nfrom .tag2text import tag2text\n"
  },
  {
    "path": "model_cards/Tag2Text/ram/models/bert.py",
    "content": "'''\n * Copyright (c) 2022, salesforce.com, inc.\n * All rights reserved.\n * SPDX-License-Identifier: BSD-3-Clause\n * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause\n * By Junnan Li\n * Based on huggingface code base\n * https://github.com/huggingface/transformers/blob/v4.15.0/src/transformers/models/bert\n'''\n\nimport math\nimport os\nimport warnings\nfrom dataclasses import dataclass\nfrom typing import Optional, Tuple\n\nimport torch\nfrom torch import Tensor, device, dtype, nn\nimport torch.utils.checkpoint\nfrom torch import nn\nfrom torch.nn import CrossEntropyLoss\nimport torch.nn.functional as F\n\nfrom transformers.activations import ACT2FN\nfrom transformers.file_utils import (\n    ModelOutput,\n)\nfrom transformers.modeling_outputs import (\n    BaseModelOutputWithPastAndCrossAttentions,\n    BaseModelOutputWithPoolingAndCrossAttentions,\n    CausalLMOutputWithCrossAttentions,\n    MaskedLMOutput,\n    MultipleChoiceModelOutput,\n    NextSentencePredictorOutput,\n    QuestionAnsweringModelOutput,\n    SequenceClassifierOutput,\n    TokenClassifierOutput,\n)\nfrom transformers.modeling_utils import (\n    PreTrainedModel,\n    apply_chunking_to_forward,\n    find_pruneable_heads_and_indices,\n    prune_linear_layer,\n)\nfrom transformers.utils import logging\nfrom transformers.models.bert.configuration_bert import BertConfig\n\n\nlogger = logging.get_logger(__name__)\n\n\nclass BertEmbeddings_nopos(nn.Module):\n    \"\"\"Construct the embeddings from word and position embeddings.\"\"\"\n\n    def __init__(self, config):\n        super().__init__()\n        self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)\n        # self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)\n\n        # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load\n        # any TensorFlow checkpoint file\n        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)\n        self.dropout = nn.Dropout(config.hidden_dropout_prob)\n\n        # position_ids (1, len position emb) is contiguous in memory and exported when serialized\n        # self.register_buffer(\"position_ids\", torch.arange(config.max_position_embeddings).expand((1, -1)))\n        # self.position_embedding_type = getattr(config, \"position_embedding_type\", \"absolute\")\n        \n        self.config = config\n\n    def forward(\n        self, input_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0\n    ):\n        if input_ids is not None:\n            input_shape = input_ids.size()\n        else:\n            input_shape = inputs_embeds.size()[:-1]\n\n        seq_length = input_shape[1]\n\n        # if position_ids is None:\n            # position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length]\n\n        if inputs_embeds is None:\n            inputs_embeds = self.word_embeddings(input_ids)\n\n        embeddings = inputs_embeds\n\n        # if self.position_embedding_type == \"absolute\":\n        #     position_embeddings = self.position_embeddings(position_ids)\n        #     # print('add position_embeddings!!!!')\n        #     embeddings += position_embeddings\n        embeddings = self.LayerNorm(embeddings)\n        embeddings = self.dropout(embeddings)\n        return embeddings\n\n\n\n\nclass BertEmbeddings(nn.Module):\n    \"\"\"Construct the embeddings from word and position embeddings.\"\"\"\n\n    def __init__(self, config):\n        super().__init__()\n        self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)\n        self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)\n\n        # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load\n        # any TensorFlow checkpoint file\n        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)\n        self.dropout = nn.Dropout(config.hidden_dropout_prob)\n\n        # position_ids (1, len position emb) is contiguous in memory and exported when serialized\n        self.register_buffer(\"position_ids\", torch.arange(config.max_position_embeddings).expand((1, -1)))\n        self.position_embedding_type = getattr(config, \"position_embedding_type\", \"absolute\")\n        \n        self.config = config\n\n    def forward(\n        self, input_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0\n    ):\n        if input_ids is not None:\n            input_shape = input_ids.size()\n        else:\n            input_shape = inputs_embeds.size()[:-1]\n\n        seq_length = input_shape[1]\n\n        if position_ids is None:\n            position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length]\n\n        if inputs_embeds is None:\n            inputs_embeds = self.word_embeddings(input_ids)\n\n        embeddings = inputs_embeds\n\n        if self.position_embedding_type == \"absolute\":\n            position_embeddings = self.position_embeddings(position_ids)\n            # print('add position_embeddings!!!!')\n            embeddings += position_embeddings\n        embeddings = self.LayerNorm(embeddings)\n        embeddings = self.dropout(embeddings)\n        return embeddings\n\n\nclass BertSelfAttention(nn.Module):\n    def __init__(self, config, is_cross_attention):\n        super().__init__()\n        self.config = config\n        if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, \"embedding_size\"):\n            raise ValueError(\n                \"The hidden size (%d) is not a multiple of the number of attention \"\n                \"heads (%d)\" % (config.hidden_size, config.num_attention_heads)\n            )\n        \n        self.num_attention_heads = config.num_attention_heads\n        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)\n        self.all_head_size = self.num_attention_heads * self.attention_head_size\n\n        self.query = nn.Linear(config.hidden_size, self.all_head_size)\n        if is_cross_attention:\n            self.key = nn.Linear(config.encoder_width, self.all_head_size)\n            self.value = nn.Linear(config.encoder_width, self.all_head_size)\n        else:\n            self.key = nn.Linear(config.hidden_size, self.all_head_size)\n            self.value = nn.Linear(config.hidden_size, self.all_head_size)\n\n        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)\n        self.position_embedding_type = getattr(config, \"position_embedding_type\", \"absolute\")\n        if self.position_embedding_type == \"relative_key\" or self.position_embedding_type == \"relative_key_query\":\n            self.max_position_embeddings = config.max_position_embeddings\n            self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size)\n        self.save_attention = False   \n            \n    def save_attn_gradients(self, attn_gradients):\n        self.attn_gradients = attn_gradients\n        \n    def get_attn_gradients(self):\n        return self.attn_gradients\n    \n    def save_attention_map(self, attention_map):\n        self.attention_map = attention_map\n        \n    def get_attention_map(self):\n        return self.attention_map\n    \n    def transpose_for_scores(self, x):\n        new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)\n        x = x.view(*new_x_shape)\n        return x.permute(0, 2, 1, 3)\n\n    def forward(\n        self,\n        hidden_states,\n        attention_mask=None,\n        head_mask=None,\n        encoder_hidden_states=None,\n        encoder_attention_mask=None,\n        past_key_value=None,\n        output_attentions=False,\n    ):\n        mixed_query_layer = self.query(hidden_states)\n\n        # If this is instantiated as a cross-attention module, the keys\n        # and values come from an encoder; the attention mask needs to be\n        # such that the encoder's padding tokens are not attended to.\n        is_cross_attention = encoder_hidden_states is not None\n\n        if is_cross_attention:\n            # print(self.key.weight.shape)\n            key_layer = self.transpose_for_scores(self.key(encoder_hidden_states))\n            value_layer = self.transpose_for_scores(self.value(encoder_hidden_states))\n            attention_mask = encoder_attention_mask\n        elif past_key_value is not None:\n            key_layer = self.transpose_for_scores(self.key(hidden_states))\n            value_layer = self.transpose_for_scores(self.value(hidden_states))\n            key_layer = torch.cat([past_key_value[0], key_layer], dim=2)\n            value_layer = torch.cat([past_key_value[1], value_layer], dim=2)\n        else:\n            key_layer = self.transpose_for_scores(self.key(hidden_states))\n            value_layer = self.transpose_for_scores(self.value(hidden_states))\n\n        query_layer = self.transpose_for_scores(mixed_query_layer)\n\n        past_key_value = (key_layer, value_layer)\n\n        # compatible with higher versions of transformers \n        if key_layer.shape[0] > query_layer.shape[0]:\n            key_layer = key_layer[:query_layer.shape[0], :, :, :]\n            attention_mask = attention_mask[:query_layer.shape[0], :, :]\n            value_layer = value_layer[:query_layer.shape[0], :, :, :]\n\n        # Take the dot product between \"query\" and \"key\" to get the raw attention scores.\n        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))\n\n        if self.position_embedding_type == \"relative_key\" or self.position_embedding_type == \"relative_key_query\":\n            seq_length = hidden_states.size()[1]\n            position_ids_l = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)\n            position_ids_r = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(1, -1)\n            distance = position_ids_l - position_ids_r\n            positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)\n            positional_embedding = positional_embedding.to(dtype=query_layer.dtype)  # fp16 compatibility\n\n            if self.position_embedding_type == \"relative_key\":\n                relative_position_scores = torch.einsum(\"bhld,lrd->bhlr\", query_layer, positional_embedding)\n                attention_scores = attention_scores + relative_position_scores\n            elif self.position_embedding_type == \"relative_key_query\":\n                relative_position_scores_query = torch.einsum(\"bhld,lrd->bhlr\", query_layer, positional_embedding)\n                relative_position_scores_key = torch.einsum(\"bhrd,lrd->bhlr\", key_layer, positional_embedding)\n                attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key\n\n        attention_scores = attention_scores / math.sqrt(self.attention_head_size)\n        if attention_mask is not None:\n            # Apply the attention mask is (precomputed for all layers in BertModel forward() function)\n            attention_scores = attention_scores + attention_mask\n\n        # Normalize the attention scores to probabilities.\n        attention_probs = nn.Softmax(dim=-1)(attention_scores)\n        \n        if is_cross_attention and self.save_attention:\n            self.save_attention_map(attention_probs)\n            attention_probs.register_hook(self.save_attn_gradients)         \n\n        # This is actually dropping out entire tokens to attend to, which might\n        # seem a bit unusual, but is taken from the original Transformer paper.\n        attention_probs_dropped = self.dropout(attention_probs)\n\n        # Mask heads if we want to\n        if head_mask is not None:\n            attention_probs_dropped = attention_probs_dropped * head_mask\n\n        context_layer = torch.matmul(attention_probs_dropped, value_layer)\n\n        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()\n        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)\n        context_layer = context_layer.view(*new_context_layer_shape)\n\n        outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)\n\n        outputs = outputs + (past_key_value,)\n        return outputs\n\n\nclass BertSelfOutput(nn.Module):\n    def __init__(self, config):\n        super().__init__()\n        self.dense = nn.Linear(config.hidden_size, config.hidden_size)\n        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)\n        self.dropout = nn.Dropout(config.hidden_dropout_prob)\n\n    def forward(self, hidden_states, input_tensor):\n        hidden_states = self.dense(hidden_states)\n        hidden_states = self.dropout(hidden_states)\n        hidden_states = self.LayerNorm(hidden_states + input_tensor)\n        return hidden_states\n\n\nclass BertAttention(nn.Module):\n    def __init__(self, config, is_cross_attention=False):\n        super().__init__()\n        self.self = BertSelfAttention(config, is_cross_attention)\n        self.output = BertSelfOutput(config)\n        self.pruned_heads = set()\n\n    def prune_heads(self, heads):\n        if len(heads) == 0:\n            return\n        heads, index = find_pruneable_heads_and_indices(\n            heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads\n        )\n\n        # Prune linear layers\n        self.self.query = prune_linear_layer(self.self.query, index)\n        self.self.key = prune_linear_layer(self.self.key, index)\n        self.self.value = prune_linear_layer(self.self.value, index)\n        self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)\n\n        # Update hyper params and store pruned heads\n        self.self.num_attention_heads = self.self.num_attention_heads - len(heads)\n        self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads\n        self.pruned_heads = self.pruned_heads.union(heads)\n\n    def forward(\n        self,\n        hidden_states,\n        attention_mask=None,\n        head_mask=None,\n        encoder_hidden_states=None,\n        encoder_attention_mask=None,\n        past_key_value=None,\n        output_attentions=False,\n    ):\n        self_outputs = self.self(\n            hidden_states,\n            attention_mask,\n            head_mask,\n            encoder_hidden_states,\n            encoder_attention_mask,\n            past_key_value,\n            output_attentions,\n        )\n        attention_output = self.output(self_outputs[0], hidden_states)\n        outputs = (attention_output,) + self_outputs[1:]  # add attentions if we output them\n        return outputs\n\n\nclass BertIntermediate(nn.Module):\n    def __init__(self, config):\n        super().__init__()\n        self.dense = nn.Linear(config.hidden_size, config.intermediate_size)\n        if isinstance(config.hidden_act, str):\n            self.intermediate_act_fn = ACT2FN[config.hidden_act]\n        else:\n            self.intermediate_act_fn = config.hidden_act\n\n    def forward(self, hidden_states):\n        hidden_states = self.dense(hidden_states)\n        hidden_states = self.intermediate_act_fn(hidden_states)\n        return hidden_states\n\n\nclass BertOutput(nn.Module):\n    def __init__(self, config):\n        super().__init__()\n        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)\n        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)\n        self.dropout = nn.Dropout(config.hidden_dropout_prob)\n\n    def forward(self, hidden_states, input_tensor):\n        hidden_states = self.dense(hidden_states)\n        hidden_states = self.dropout(hidden_states)\n        hidden_states = self.LayerNorm(hidden_states + input_tensor)\n        return hidden_states\n\n\nclass BertLayer(nn.Module):\n    def __init__(self, config, layer_num):\n        super().__init__()\n        self.config = config\n        self.chunk_size_feed_forward = config.chunk_size_feed_forward\n        self.seq_len_dim = 1\n        self.attention = BertAttention(config)      \n        self.layer_num = layer_num          \n        if self.config.add_cross_attention:\n            self.crossattention = BertAttention(config, is_cross_attention=self.config.add_cross_attention)\n        self.intermediate = BertIntermediate(config)\n        self.output = BertOutput(config)\n\n    def forward(\n        self,\n        hidden_states,\n        attention_mask=None,\n        head_mask=None,\n        encoder_hidden_states=None,\n        encoder_attention_mask=None,\n        past_key_value=None,\n        output_attentions=False,\n        mode=None,\n    ):\n        \n        if mode == 'tagging':\n            \n            assert encoder_hidden_states is not None, \"encoder_hidden_states must be given for cross-attention layers\"\n\n            cross_attention_outputs = self.crossattention(\n                hidden_states,\n                attention_mask,\n                head_mask,\n                encoder_hidden_states,\n                encoder_attention_mask,\n                output_attentions=output_attentions,\n            )\n            attention_output = cross_attention_outputs[0]\n            outputs = cross_attention_outputs[1:-1]  # add cross attentions if we output attention weights  \n\n            present_key_value = cross_attention_outputs[-1]\n\n        else:\n            # decoder uni-directional self-attention cached key/values tuple is at positions 1,2\n            self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None\n            self_attention_outputs = self.attention(\n                hidden_states,\n                attention_mask,\n                head_mask,\n                output_attentions=output_attentions,\n                past_key_value=self_attn_past_key_value,\n            )\n            attention_output = self_attention_outputs[0]\n\n            outputs = self_attention_outputs[1:-1]\n            present_key_value = self_attention_outputs[-1]\n\n            if mode=='multimodal':\n                assert encoder_hidden_states is not None, \"encoder_hidden_states must be given for cross-attention layers\"\n\n                cross_attention_outputs = self.crossattention(\n                    attention_output,\n                    attention_mask,\n                    head_mask,\n                    encoder_hidden_states,\n                    encoder_attention_mask,\n                    output_attentions=output_attentions,\n                )\n                attention_output = cross_attention_outputs[0]\n                outputs = outputs + cross_attention_outputs[1:-1]  # add cross attentions if we output attention weights                               \n        layer_output = apply_chunking_to_forward(\n            self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output\n        )\n        outputs = (layer_output,) + outputs\n\n        outputs = outputs + (present_key_value,)\n\n        return outputs\n\n    def feed_forward_chunk(self, attention_output):\n        intermediate_output = self.intermediate(attention_output)\n        layer_output = self.output(intermediate_output, attention_output)\n        return layer_output\n\n\nclass BertEncoder(nn.Module):\n    def __init__(self, config):\n        super().__init__()\n        self.config = config\n        self.layer = nn.ModuleList([BertLayer(config,i) for i in range(config.num_hidden_layers)])\n        self.gradient_checkpointing = False\n\n    def forward(\n        self,\n        hidden_states,\n        attention_mask=None,\n        head_mask=None,\n        encoder_hidden_states=None,\n        encoder_attention_mask=None,\n        past_key_values=None,\n        use_cache=None,\n        output_attentions=False,\n        output_hidden_states=False,\n        return_dict=True,\n        mode='multimodal',\n    ):\n        all_hidden_states = () if output_hidden_states else None\n        all_self_attentions = () if output_attentions else None\n        all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None\n\n        next_decoder_cache = () if use_cache else None\n               \n        for i in range(self.config.num_hidden_layers):\n            layer_module = self.layer[i]\n            if output_hidden_states:\n                all_hidden_states = all_hidden_states + (hidden_states,)\n\n            layer_head_mask = head_mask[i] if head_mask is not None else None\n            past_key_value = past_key_values[i] if past_key_values is not None else None\n\n            if self.gradient_checkpointing and self.training:\n\n                if use_cache:\n                    logger.warn(\n                        \"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...\"\n                    )\n                    use_cache = False\n\n                def create_custom_forward(module):\n                    def custom_forward(*inputs):\n                        return module(*inputs, past_key_value, output_attentions)\n\n                    return custom_forward\n\n                layer_outputs = torch.utils.checkpoint.checkpoint(\n                    create_custom_forward(layer_module),\n                    hidden_states,\n                    attention_mask,\n                    layer_head_mask,\n                    encoder_hidden_states,\n                    encoder_attention_mask,\n                    mode=mode,\n                )\n            else:\n                layer_outputs = layer_module(\n                    hidden_states,\n                    attention_mask,\n                    layer_head_mask,\n                    encoder_hidden_states,\n                    encoder_attention_mask,\n                    past_key_value,\n                    output_attentions,\n                    mode=mode,\n                )\n\n            hidden_states = layer_outputs[0]\n            if use_cache:\n                next_decoder_cache += (layer_outputs[-1],)\n            if output_attentions:\n                all_self_attentions = all_self_attentions + (layer_outputs[1],)\n\n        if output_hidden_states:\n            all_hidden_states = all_hidden_states + (hidden_states,)\n\n        if not return_dict:\n            return tuple(\n                v\n                for v in [\n                    hidden_states,\n                    next_decoder_cache,\n                    all_hidden_states,\n                    all_self_attentions,\n                    all_cross_attentions,\n                ]\n                if v is not None\n            )\n        return BaseModelOutputWithPastAndCrossAttentions(\n            last_hidden_state=hidden_states,\n            past_key_values=next_decoder_cache,\n            hidden_states=all_hidden_states,\n            attentions=all_self_attentions,\n            cross_attentions=all_cross_attentions,\n        )\n\n\nclass BertPooler(nn.Module):\n    def __init__(self, config):\n        super().__init__()\n        self.dense = nn.Linear(config.hidden_size, config.hidden_size)\n        self.activation = nn.Tanh()\n\n    def forward(self, hidden_states):\n        # We \"pool\" the model by simply taking the hidden state corresponding\n        # to the first token.\n        first_token_tensor = hidden_states[:, 0]\n        pooled_output = self.dense(first_token_tensor)\n        pooled_output = self.activation(pooled_output)\n        return pooled_output\n\n\nclass BertPredictionHeadTransform(nn.Module):\n    def __init__(self, config):\n        super().__init__()\n        self.dense = nn.Linear(config.hidden_size, config.hidden_size)\n        if isinstance(config.hidden_act, str):\n            self.transform_act_fn = ACT2FN[config.hidden_act]\n        else:\n            self.transform_act_fn = config.hidden_act\n        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)\n\n    def forward(self, hidden_states):\n        hidden_states = self.dense(hidden_states)\n        hidden_states = self.transform_act_fn(hidden_states)\n        hidden_states = self.LayerNorm(hidden_states)\n        return hidden_states\n\n\nclass BertLMPredictionHead(nn.Module):\n    def __init__(self, config):\n        super().__init__()\n        self.transform = BertPredictionHeadTransform(config)\n\n        # The output weights are the same as the input embeddings, but there is\n        # an output-only bias for each token.\n        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)\n\n        self.bias = nn.Parameter(torch.zeros(config.vocab_size))\n\n        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`\n        self.decoder.bias = self.bias\n\n    def forward(self, hidden_states):\n        hidden_states = self.transform(hidden_states)\n        hidden_states = self.decoder(hidden_states)\n        return hidden_states\n\n\nclass BertOnlyMLMHead(nn.Module):\n    def __init__(self, config):\n        super().__init__()\n        self.predictions = BertLMPredictionHead(config)\n\n    def forward(self, sequence_output):\n        prediction_scores = self.predictions(sequence_output)\n        return prediction_scores\n\n\nclass BertPreTrainedModel(PreTrainedModel):\n    \"\"\"\n    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained\n    models.\n    \"\"\"\n\n    config_class = BertConfig\n    base_model_prefix = \"bert\"\n    _keys_to_ignore_on_load_missing = [r\"position_ids\"]\n\n    def _init_weights(self, module):\n        \"\"\" Initialize the weights \"\"\"\n        if isinstance(module, (nn.Linear, nn.Embedding)):\n            # Slightly different from the TF version which uses truncated_normal for initialization\n            # cf https://github.com/pytorch/pytorch/pull/5617\n            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)\n        elif isinstance(module, nn.LayerNorm):\n            module.bias.data.zero_()\n            module.weight.data.fill_(1.0)\n        if isinstance(module, nn.Linear) and module.bias is not None:\n            module.bias.data.zero_()\n\n\nclass BertModel(BertPreTrainedModel):\n    \"\"\"\n    The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of\n    cross-attention is added between the self-attention layers, following the architecture described in `Attention is\n    all you need <https://arxiv.org/abs/1706.03762>`__ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit,\n    Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.\n    argument and :obj:`add_cross_attention` set to :obj:`True`; an :obj:`encoder_hidden_states` is then expected as an\n    input to the forward pass.\n    \"\"\"\n\n    def __init__(self, config, add_pooling_layer=True):\n        super().__init__(config)\n        self.config = config\n\n        self.embeddings = BertEmbeddings(config)\n        \n        self.encoder = BertEncoder(config)\n\n        self.pooler = BertPooler(config) if add_pooling_layer else None\n\n        self.init_weights()\n \n\n    def get_input_embeddings(self):\n        return self.embeddings.word_embeddings\n\n    def set_input_embeddings(self, value):\n        self.embeddings.word_embeddings = value\n\n    def _prune_heads(self, heads_to_prune):\n        \"\"\"\n        Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base\n        class PreTrainedModel\n        \"\"\"\n        for layer, heads in heads_to_prune.items():\n            self.encoder.layer[layer].attention.prune_heads(heads)\n\n    \n    def get_extended_attention_mask(self, attention_mask: Tensor, input_shape: Tuple[int], device: device, is_decoder: bool) -> Tensor:\n        \"\"\"\n        Makes broadcastable attention and causal masks so that future and masked tokens are ignored.\n\n        Arguments:\n            attention_mask (:obj:`torch.Tensor`):\n                Mask with ones indicating tokens to attend to, zeros for tokens to ignore.\n            input_shape (:obj:`Tuple[int]`):\n                The shape of the input to the model.\n            device: (:obj:`torch.device`):\n                The device of the input to the model.\n\n        Returns:\n            :obj:`torch.Tensor` The extended attention mask, with a the same dtype as :obj:`attention_mask.dtype`.\n        \"\"\"\n        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]\n        # ourselves in which case we just need to make it broadcastable to all heads.\n        if attention_mask.dim() == 3:\n            extended_attention_mask = attention_mask[:, None, :, :]\n        elif attention_mask.dim() == 2:\n            # Provided a padding mask of dimensions [batch_size, seq_length]\n            # - if the model is a decoder, apply a causal mask in addition to the padding mask\n            # - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length]\n            if is_decoder:\n                batch_size, seq_length = input_shape\n\n                seq_ids = torch.arange(seq_length, device=device)\n                causal_mask = seq_ids[None, None, :].repeat(batch_size, seq_length, 1) <= seq_ids[None, :, None]\n                # in case past_key_values are used we need to add a prefix ones mask to the causal mask\n                # causal and attention masks must have same type with pytorch version < 1.3\n                causal_mask = causal_mask.to(attention_mask.dtype)\n   \n                if causal_mask.shape[1] < attention_mask.shape[1]:\n                    prefix_seq_len = attention_mask.shape[1] - causal_mask.shape[1]\n                    causal_mask = torch.cat(\n                        [\n                            torch.ones((batch_size, seq_length, prefix_seq_len), device=device, dtype=causal_mask.dtype),\n                            causal_mask,\n                        ],\n                        axis=-1,\n                    )                     \n\n                extended_attention_mask = causal_mask[:, None, :, :] * attention_mask[:, None, None, :]\n            else:\n                extended_attention_mask = attention_mask[:, None, None, :]\n        else:\n            raise ValueError(\n                \"Wrong shape for input_ids (shape {}) or attention_mask (shape {})\".format(\n                    input_shape, attention_mask.shape\n                )\n            )\n\n        # Since attention_mask is 1.0 for positions we want to attend and 0.0 for\n        # masked positions, this operation will create a tensor which is 0.0 for\n        # positions we want to attend and -10000.0 for masked positions.\n        # Since we are adding it to the raw scores before the softmax, this is\n        # effectively the same as removing these entirely.\n        extended_attention_mask = extended_attention_mask.to(dtype=self.dtype)  # fp16 compatibility\n        extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0\n        return extended_attention_mask\n    \n    def forward(\n        self,\n        input_ids=None,\n        attention_mask=None,\n        position_ids=None,\n        head_mask=None,\n        inputs_embeds=None,\n        encoder_embeds=None,\n        encoder_hidden_states=None,\n        encoder_attention_mask=None,\n        past_key_values=None,\n        use_cache=None,\n        output_attentions=None,\n        output_hidden_states=None,\n        return_dict=None,\n        is_decoder=False,\n        mode='multimodal',\n    ):\n        r\"\"\"\n        encoder_hidden_states  (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):\n            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if\n            the model is configured as a decoder.\n        encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):\n            Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in\n            the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``:\n            - 1 for tokens that are **not masked**,\n            - 0 for tokens that are **masked**.\n        past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):\n            Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.\n            If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`\n            (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`\n            instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`.\n        use_cache (:obj:`bool`, `optional`):\n            If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up\n            decoding (see :obj:`past_key_values`).\n        \"\"\"\n        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions\n        output_hidden_states = (\n            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states\n        )\n        return_dict = return_dict if return_dict is not None else self.config.use_return_dict\n\n        if is_decoder:\n            use_cache = use_cache if use_cache is not None else self.config.use_cache\n        else:\n            use_cache = False\n\n        if input_ids is not None and inputs_embeds is not None:\n            raise ValueError(\"You cannot specify both input_ids and inputs_embeds at the same time\")\n        elif input_ids is not None:\n            input_shape = input_ids.size()\n            batch_size, seq_length = input_shape\n            device = input_ids.device\n        elif inputs_embeds is not None:\n            input_shape = inputs_embeds.size()[:-1]\n            batch_size, seq_length = input_shape\n            device = inputs_embeds.device\n        elif encoder_embeds is not None:    \n            input_shape = encoder_embeds.size()[:-1]\n            batch_size, seq_length = input_shape \n            device = encoder_embeds.device\n        else:\n            raise ValueError(\"You have to specify either input_ids or inputs_embeds or encoder_embeds\")\n\n        # past_key_values_length\n        past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0\n\n        if attention_mask is None:\n            attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device)\n            \n        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]\n        # ourselves in which case we just need to make it broadcastable to all heads.\n        extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape, \n                                                                                 device, is_decoder)\n\n        # If a 2D or 3D attention mask is provided for the cross-attention\n        # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]\n        if encoder_hidden_states is not None:\n            if type(encoder_hidden_states) == list:\n                encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states[0].size()\n            else:\n                encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()\n            encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)\n            \n            if type(encoder_attention_mask) == list:\n                encoder_extended_attention_mask = [self.invert_attention_mask(mask) for mask in encoder_attention_mask]\n            elif encoder_attention_mask is None:\n                encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)\n                encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)\n            else:    \n                encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)\n        else:\n            encoder_extended_attention_mask = None\n\n        # Prepare head mask if needed\n        # 1.0 in head_mask indicate we keep the head\n        # attention_probs has shape bsz x n_heads x N x N\n        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]\n        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]\n        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)\n        \n        if encoder_embeds is None:\n            embedding_output = self.embeddings(\n                input_ids=input_ids,\n                position_ids=position_ids,\n                inputs_embeds=inputs_embeds,\n                past_key_values_length=past_key_values_length,\n            )\n        else:\n            embedding_output = encoder_embeds\n            \n        encoder_outputs = self.encoder(\n            embedding_output,\n            attention_mask=extended_attention_mask,\n            head_mask=head_mask,\n            encoder_hidden_states=encoder_hidden_states,\n            encoder_attention_mask=encoder_extended_attention_mask,\n            past_key_values=past_key_values,\n            use_cache=use_cache,\n            output_attentions=output_attentions,\n            output_hidden_states=output_hidden_states,\n            return_dict=return_dict,\n            mode=mode,\n        )\n        sequence_output = encoder_outputs[0]\n        pooled_output = self.pooler(sequence_output) if self.pooler is not None else None\n\n        if not return_dict:\n            return (sequence_output, pooled_output) + encoder_outputs[1:]\n\n        return BaseModelOutputWithPoolingAndCrossAttentions(\n            last_hidden_state=sequence_output,\n            pooler_output=pooled_output,\n            past_key_values=encoder_outputs.past_key_values,\n            hidden_states=encoder_outputs.hidden_states,\n            attentions=encoder_outputs.attentions,\n            cross_attentions=encoder_outputs.cross_attentions,\n        )\n\n\nclass BertLMHeadModel(BertPreTrainedModel):\n\n    _keys_to_ignore_on_load_unexpected = [r\"pooler\"]\n    _keys_to_ignore_on_load_missing = [r\"position_ids\", r\"predictions.decoder.bias\"]\n\n    def __init__(self, config):\n        super().__init__(config)\n\n        self.bert = BertModel(config, add_pooling_layer=False)\n        self.cls = BertOnlyMLMHead(config)\n\n        self.init_weights()\n\n    def get_output_embeddings(self):\n        return self.cls.predictions.decoder\n\n    def set_output_embeddings(self, new_embeddings):\n        self.cls.predictions.decoder = new_embeddings\n\n    def forward(\n        self,\n        input_ids=None,\n        attention_mask=None,\n        position_ids=None,\n        head_mask=None,\n        inputs_embeds=None,\n        encoder_hidden_states=None,\n        encoder_attention_mask=None,\n        labels=None,\n        past_key_values=None,\n        use_cache=None,\n        output_attentions=None,\n        output_hidden_states=None,\n        return_dict=None,\n        return_logits=False,            \n        is_decoder=True,\n        reduction='mean',\n        mode='multimodal', \n    ):\n        r\"\"\"\n        encoder_hidden_states  (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):\n            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if\n            the model is configured as a decoder.\n        encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):\n            Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in\n            the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``:\n            - 1 for tokens that are **not masked**,\n            - 0 for tokens that are **masked**.\n        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):\n            Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in\n            ``[-100, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are\n            ignored (masked), the loss is only computed for the tokens with labels n ``[0, ..., config.vocab_size]``\n        past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):\n            Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.\n            If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`\n            (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`\n            instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`.\n        use_cache (:obj:`bool`, `optional`):\n            If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up\n            decoding (see :obj:`past_key_values`).\n        Returns:\n        Example::\n            >>> from transformers import BertTokenizer, BertLMHeadModel, BertConfig\n            >>> import torch\n            >>> tokenizer = BertTokenizer.from_pretrained('bert-base-cased')\n            >>> config = BertConfig.from_pretrained(\"bert-base-cased\")\n            >>> model = BertLMHeadModel.from_pretrained('bert-base-cased', config=config)\n            >>> inputs = tokenizer(\"Hello, my dog is cute\", return_tensors=\"pt\")\n            >>> outputs = model(**inputs)\n            >>> prediction_logits = outputs.logits\n        \"\"\"\n        return_dict = return_dict if return_dict is not None else self.config.use_return_dict\n        if labels is not None:\n            use_cache = False\n\n        outputs = self.bert(\n            input_ids,\n            attention_mask=attention_mask,\n            position_ids=position_ids,\n            head_mask=head_mask,\n            inputs_embeds=inputs_embeds,\n            encoder_hidden_states=encoder_hidden_states,\n            encoder_attention_mask=encoder_attention_mask,\n            past_key_values=past_key_values,\n            use_cache=use_cache,\n            output_attentions=output_attentions,\n            output_hidden_states=output_hidden_states,\n            return_dict=return_dict,\n            is_decoder=is_decoder,\n            mode=mode,\n        )\n        \n        sequence_output = outputs[0]\n        prediction_scores = self.cls(sequence_output)\n        # sequence_output.shape torch.Size([85, 30, 768])\n        # prediction_scores.shape torch.Size([85, 30, 30524])\n        # labels.shape torch.Size([85, 30])\n\n\n        if return_logits:\n            return prediction_scores[:, :-1, :].contiguous()  \n\n        lm_loss = None\n        if labels is not None:\n            # we are doing next-token prediction; shift prediction scores and input ids by one\n            shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous()\n            labels = labels[:, 1:].contiguous()\n            loss_fct = CrossEntropyLoss(reduction=reduction, label_smoothing=0.1) \n            lm_loss = loss_fct(shifted_prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))\n            if reduction=='none':\n                lm_loss = lm_loss.view(prediction_scores.size(0),-1).sum(1)               \n\n        if not return_dict:\n            output = (prediction_scores,) + outputs[2:]\n            return ((lm_loss,) + output) if lm_loss is not None else output\n\n        return CausalLMOutputWithCrossAttentions(\n            loss=lm_loss,\n            logits=prediction_scores,\n            past_key_values=outputs.past_key_values,\n            hidden_states=outputs.hidden_states,\n            attentions=outputs.attentions,\n            cross_attentions=outputs.cross_attentions,\n        )\n\n    def prepare_inputs_for_generation(self, input_ids, past=None, attention_mask=None, **model_kwargs):\n        input_shape = input_ids.shape\n        # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly\n        if attention_mask is None:\n            attention_mask = input_ids.new_ones(input_shape)\n\n        # cut decoder_input_ids if past is used\n        if past is not None:\n            input_ids = input_ids[:, -1:]\n\n        return {\n            \"input_ids\": input_ids, \n            \"attention_mask\": attention_mask, \n            \"past_key_values\": past,\n            \"encoder_hidden_states\": model_kwargs.get(\"encoder_hidden_states\", None),\n            \"encoder_attention_mask\": model_kwargs.get(\"encoder_attention_mask\", None),\n            \"is_decoder\": True,\n        }\n\n    def _reorder_cache(self, past, beam_idx):\n        reordered_past = ()\n        for layer_past in past:\n            reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)\n        return reordered_past\n\n\n"
  },
  {
    "path": "model_cards/Tag2Text/ram/models/ram.py",
    "content": "'''\n * The Recognize Anything Model (RAM)\n * Written by Xinyu Huang\n'''\nimport json\nimport warnings\n\nimport numpy as np\nimport torch\nfrom torch import nn\n\nfrom .bert import BertConfig, BertLMHeadModel, BertModel\nfrom .swin_transformer import SwinTransformer\nfrom .utils import *\n\nwarnings.filterwarnings(\"ignore\")\n\n\n\nclass RAM(nn.Module):\n    def __init__(self,\n                 med_config=f'{CONFIG_PATH}/configs/med_config.json',\n                 image_size=384,\n                 vit='base',\n                 vit_grad_ckpt=False,\n                 vit_ckpt_layer=0,\n                 prompt='a picture of ',\n                 threshold=0.68,\n                 delete_tag_index=[],\n                 tag_list=f'{CONFIG_PATH}/data/ram_tag_list.txt',\n                 tag_list_chinese=f'{CONFIG_PATH}/data/ram_tag_list_chinese.txt'):\n        r\"\"\" The Recognize Anything Model (RAM) inference module.\n        RAM is a strong image tagging model, which can recognize any common category with high accuracy.\n        Described in the paper \" Recognize Anything: A Strong Image Tagging Model\" https://recognize-anything.github.io/\n        \n        Args:\n            med_config (str): path for the mixture of encoder-decoder model's configuration file\n            image_size (int): input image size\n            vit (str): model size of vision transformer\n            threshold (int): tagging threshold\n            delete_tag_index (list): delete some tags that may disturb captioning\n        \"\"\"\n        super().__init__()\n\n        # create image encoder\n        if vit == 'swin_b':\n            if image_size == 224:\n                vision_config_path = f'{CONFIG_PATH}/configs/swin/config_swinB_224.json'\n            elif image_size == 384:\n                vision_config_path = f'{CONFIG_PATH}/configs/swin/config_swinB_384.json'\n            vision_config = read_json(vision_config_path)\n            assert image_size == vision_config['image_res']\n            # assert config['patch_size'] == 32\n            vision_width = vision_config['vision_width']\n\n            self.visual_encoder = SwinTransformer(\n                img_size=vision_config['image_res'],\n                patch_size=4,\n                in_chans=3,\n                embed_dim=vision_config['embed_dim'],\n                depths=vision_config['depths'],\n                num_heads=vision_config['num_heads'],\n                window_size=vision_config['window_size'],\n                mlp_ratio=4.,\n                qkv_bias=True,\n                drop_rate=0.0,\n                drop_path_rate=0.1,\n                ape=False,\n                patch_norm=True,\n                use_checkpoint=False)\n\n        elif vit == 'swin_l':\n            if image_size == 224:\n                vision_config_path = f'{CONFIG_PATH}/configs/swin/config_swinL_224.json'\n            elif image_size == 384:\n                vision_config_path = f'{CONFIG_PATH}/configs/swin/config_swinL_384.json'\n            vision_config = read_json(vision_config_path)\n            assert image_size == vision_config['image_res']\n            # assert config['patch_size'] == 32\n            vision_width = vision_config['vision_width']\n\n            self.visual_encoder = SwinTransformer(\n                img_size=vision_config['image_res'],\n                patch_size=4,\n                in_chans=3,\n                embed_dim=vision_config['embed_dim'],\n                depths=vision_config['depths'],\n                num_heads=vision_config['num_heads'],\n                window_size=vision_config['window_size'],\n                mlp_ratio=4.,\n                qkv_bias=True,\n                drop_rate=0.0,\n                drop_path_rate=0.1,\n                ape=False,\n                patch_norm=True,\n                use_checkpoint=False)\n\n        else:\n            self.visual_encoder, vision_width = create_vit(\n                vit, image_size, vit_grad_ckpt, vit_ckpt_layer)\n\n        # create tokenzier\n        self.tokenizer = init_tokenizer()\n\n        # Tag2Text employ encoder-decoder architecture for image-tag-text generation: image-tag interaction encoder and image-tag-text decoder\n        # create image-tag interaction encoder\n        encoder_config = BertConfig.from_json_file(med_config)\n        encoder_config.encoder_width = 512\n        self.tag_encoder = BertModel(config=encoder_config,\n                                     add_pooling_layer=False)\n\n        # create image-tag-text decoder\n        decoder_config = BertConfig.from_json_file(med_config)\n        self.text_decoder = BertLMHeadModel(config=decoder_config)\n\n        self.delete_tag_index = delete_tag_index\n        self.prompt = prompt\n        self.prompt_length = len(self.tokenizer(self.prompt).input_ids) - 1\n\n        # load tag list\n        self.tag_list = self.load_tag_list(tag_list)\n        self.tag_list_chinese = self.load_tag_list(tag_list_chinese)\n\n        # create image-tag recognition decoder\n        self.threshold = threshold\n        self.num_class = len(self.tag_list)\n        q2l_config = BertConfig.from_json_file(f'{CONFIG_PATH}/configs/q2l_config.json')\n        q2l_config.encoder_width = 512\n        self.tagging_head = BertModel(config=q2l_config,\n                                      add_pooling_layer=False)\n        self.tagging_head.resize_token_embeddings(len(self.tokenizer))\n        # self.label_embed = nn.Embedding(self.num_class, q2l_config.hidden_size)\n        self.label_embed = nn.Parameter(torch.zeros(self.num_class, q2l_config.encoder_width))\n\n        if q2l_config.hidden_size != 512:\n            self.wordvec_proj = nn.Linear(512, q2l_config.hidden_size)\n        else:\n            self.wordvec_proj = nn.Identity()\n\n        self.fc = nn.Linear(q2l_config.hidden_size, 1)\n\n        self.del_selfattention()\n\n        # share weights of the lowest 2-layer of \"image-tag interaction encoder\" with the \"image-tag recogntion decoder\"\n        tie_encoder_decoder_weights(self.tag_encoder, self.tagging_head, '',\n                                    ' ')\n        self.image_proj = nn.Linear(vision_width, 512)\n        # self.label_embed = nn.Parameter(torch.load(f'{CONFIG_PATH}/data/textual_label_embedding.pth',map_location='cpu').float())\n\n        # adjust thresholds for some tags\n        self.class_threshold = torch.ones(self.num_class) * self.threshold\n        ram_class_threshold_path = f'{CONFIG_PATH}/data/ram_tag_list_threshold.txt'\n        with open(ram_class_threshold_path, 'r', encoding='utf-8') as f:\n            ram_class_threshold = [float(s.strip()) for s in f]\n        for key,value in enumerate(ram_class_threshold):\n            self.class_threshold[key] = value\n\n    def load_tag_list(self, tag_list_file):\n        with open(tag_list_file, 'r', encoding=\"utf-8\") as f:\n            tag_list = f.read().splitlines()\n        tag_list = np.array(tag_list)\n        return tag_list\n\n    # delete self-attention layer of image-tag recognition decoder to reduce computation, follower Query2Label\n    def del_selfattention(self):\n        del self.tagging_head.embeddings\n        for layer in self.tagging_head.encoder.layer:\n            del layer.attention\n\n    def generate_tag(self,\n                 image,\n                 threshold=0.68,\n                 tag_input=None,\n                 ):\n            \n        label_embed = torch.nn.functional.relu(self.wordvec_proj(self.label_embed))\n\n        image_embeds = self.image_proj(self.visual_encoder(image))\n        image_atts = torch.ones(image_embeds.size()[:-1],\n                                dtype=torch.long).to(image.device)\n\n        # recognized image tags using image-tag recogntiion decoder\n        image_cls_embeds = image_embeds[:, 0, :]\n        image_spatial_embeds = image_embeds[:, 1:, :]\n\n        bs = image_spatial_embeds.shape[0]\n        label_embed = label_embed.unsqueeze(0).repeat(bs, 1, 1)\n        tagging_embed = self.tagging_head(\n            encoder_embeds=label_embed,\n            encoder_hidden_states=image_embeds,\n            encoder_attention_mask=image_atts,\n            return_dict=False,\n            mode='tagging',\n        )\n\n        logits = self.fc(tagging_embed[0]).squeeze(-1)\n\n        targets = torch.where(\n            torch.sigmoid(logits) > self.class_threshold.to(image.device),\n            torch.tensor(1.0).to(image.device),\n            torch.zeros(self.num_class).to(image.device))\n\n        tag = targets.cpu().numpy()\n        tag[:,self.delete_tag_index] = 0\n        tag_output = []\n        tag_output_chinese = []\n        for b in range(bs):\n            index = np.argwhere(tag[b] == 1)\n            token = self.tag_list[index].squeeze(axis=1)\n            tag_output.append(' | '.join(token))\n            token_chinese = self.tag_list_chinese[index].squeeze(axis=1)\n            tag_output_chinese.append(' | '.join(token_chinese))\n\n\n        return tag_output, tag_output_chinese\n\n    def generate_tag_openset(self,\n                 image,\n                 threshold=0.68,\n                 tag_input=None,\n                 ):\n            \n        label_embed = torch.nn.functional.relu(self.wordvec_proj(self.label_embed))\n\n        image_embeds = self.image_proj(self.visual_encoder(image))\n        image_atts = torch.ones(image_embeds.size()[:-1],\n                                dtype=torch.long).to(image.device)\n\n        # recognized image tags using image-tag recogntiion decoder\n        image_cls_embeds = image_embeds[:, 0, :]\n        image_spatial_embeds = image_embeds[:, 1:, :]\n\n        bs = image_spatial_embeds.shape[0]\n        label_embed = label_embed.unsqueeze(0).repeat(bs, 1, 1)\n        tagging_embed = self.tagging_head(\n            encoder_embeds=label_embed,\n            encoder_hidden_states=image_embeds,\n            encoder_attention_mask=image_atts,\n            return_dict=False,\n            mode='tagging',\n        )\n\n        logits = self.fc(tagging_embed[0]).squeeze(-1)\n\n        targets = torch.where(\n            torch.sigmoid(logits) > self.class_threshold.to(image.device),\n            torch.tensor(1.0).to(image.device),\n            torch.zeros(self.num_class).to(image.device))\n\n        tag = targets.cpu().numpy()\n        tag[:,self.delete_tag_index] = 0\n        tag_output = []\n        for b in range(bs):\n            index = np.argwhere(tag[b] == 1)\n            token = self.tag_list[index].squeeze(axis=1)\n            tag_output.append(' | '.join(token))\n\n        return tag_output\n\n\n# load RAM pretrained model parameters\ndef ram(pretrained='', **kwargs):\n    model = RAM(**kwargs)\n    if pretrained:\n        if kwargs['vit'] == 'swin_b':\n            model, msg = load_checkpoint_swinbase(model, pretrained, kwargs)\n        elif kwargs['vit'] == 'swin_l':\n            model, msg = load_checkpoint_swinlarge(model, pretrained, kwargs)\n        else:\n            model, msg = load_checkpoint(model, pretrained)\n        print('vit:', kwargs['vit'])\n#         print('msg', msg)\n    return model\n"
  },
  {
    "path": "model_cards/Tag2Text/ram/models/swin_transformer.py",
    "content": "# --------------------------------------------------------\n# Swin Transformer\n# Copyright (c) 2021 Microsoft\n# Licensed under The MIT License [see LICENSE for details]\n# Written by Ze Liu\n# --------------------------------------------------------\n\nimport numpy as np\nfrom scipy import interpolate\n\nimport torch\nimport torch.nn as nn\nimport torch.utils.checkpoint as checkpoint\nfrom timm.models.layers import DropPath, to_2tuple, trunc_normal_\n\n\nclass Mlp(nn.Module):\n    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):\n        super().__init__()\n        out_features = out_features or in_features\n        hidden_features = hidden_features or in_features\n        self.fc1 = nn.Linear(in_features, hidden_features)\n        self.act = act_layer()\n        self.fc2 = nn.Linear(hidden_features, out_features)\n        self.drop = nn.Dropout(drop)\n\n    def forward(self, x):\n        x = self.fc1(x)\n        x = self.act(x)\n        x = self.drop(x)\n        x = self.fc2(x)\n        x = self.drop(x)\n        return x\n\n\ndef window_partition(x, window_size):\n    \"\"\"\n    Args:\n        x: (B, H, W, C)\n        window_size (int): window size\n\n    Returns:\n        windows: (num_windows*B, window_size, window_size, C)\n    \"\"\"\n    B, H, W, C = x.shape\n    x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)\n    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)\n    return windows\n\n\ndef window_reverse(windows, window_size, H, W):\n    \"\"\"\n    Args:\n        windows: (num_windows*B, window_size, window_size, C)\n        window_size (int): Window size\n        H (int): Height of image\n        W (int): Width of image\n\n    Returns:\n        x: (B, H, W, C)\n    \"\"\"\n    B = int(windows.shape[0] / (H * W / window_size / window_size))\n    x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)\n    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)\n    return x\n\n\nclass WindowAttention(nn.Module):\n    r\"\"\" Window based multi-head self attention (W-MSA) module with relative position bias.\n    It supports both of shifted and non-shifted window.\n\n    Args:\n        dim (int): Number of input channels.\n        window_size (tuple[int]): The height and width of the window.\n        num_heads (int): Number of attention heads.\n        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True\n        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set\n        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0\n        proj_drop (float, optional): Dropout ratio of output. Default: 0.0\n    \"\"\"\n\n    def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):\n\n        super().__init__()\n        self.dim = dim\n        self.window_size = window_size  # Wh, Ww\n        self.num_heads = num_heads\n        head_dim = dim // num_heads\n        self.scale = qk_scale or head_dim ** -0.5\n\n        # define a parameter table of relative position bias\n        self.relative_position_bias_table = nn.Parameter(\n            torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads))  # 2*Wh-1 * 2*Ww-1, nH\n\n        # get pair-wise relative position index for each token inside the window\n        coords_h = torch.arange(self.window_size[0])\n        coords_w = torch.arange(self.window_size[1])\n        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww\n        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww\n        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww\n        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2\n        relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0\n        relative_coords[:, :, 1] += self.window_size[1] - 1\n        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1\n        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww\n        self.register_buffer(\"relative_position_index\", relative_position_index)\n\n        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)\n        self.attn_drop = nn.Dropout(attn_drop)\n        self.proj = nn.Linear(dim, dim)\n        self.proj_drop = nn.Dropout(proj_drop)\n\n        trunc_normal_(self.relative_position_bias_table, std=.02)\n        self.softmax = nn.Softmax(dim=-1)\n\n    def forward(self, x, mask=None):\n        \"\"\"\n        Args:\n            x: input features with shape of (num_windows*B, N, C)\n            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None\n        \"\"\"\n        B_, N, C = x.shape\n        qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)\n        q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)\n\n        q = q * self.scale\n        attn = (q @ k.transpose(-2, -1))\n\n        relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(\n            self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1)  # Wh*Ww,Wh*Ww,nH\n        relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww\n        attn = attn + relative_position_bias.unsqueeze(0)\n\n        if mask is not None:\n            nW = mask.shape[0]\n            attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)\n            attn = attn.view(-1, self.num_heads, N, N)\n            attn = self.softmax(attn)\n        else:\n            attn = self.softmax(attn)\n\n        attn = self.attn_drop(attn)\n\n        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)\n        x = self.proj(x)\n        x = self.proj_drop(x)\n        return x\n\n    def extra_repr(self) -> str:\n        return f'dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}'\n\n    def flops(self, N):\n        # calculate flops for 1 window with token length of N\n        flops = 0\n        # qkv = self.qkv(x)\n        flops += N * self.dim * 3 * self.dim\n        # attn = (q @ k.transpose(-2, -1))\n        flops += self.num_heads * N * (self.dim // self.num_heads) * N\n        #  x = (attn @ v)\n        flops += self.num_heads * N * N * (self.dim // self.num_heads)\n        # x = self.proj(x)\n        flops += N * self.dim * self.dim\n        return flops\n\n\nclass SwinTransformerBlock(nn.Module):\n    r\"\"\" Swin Transformer Block.\n\n    Args:\n        dim (int): Number of input channels.\n        input_resolution (tuple[int]): Input resulotion.\n        num_heads (int): Number of attention heads.\n        window_size (int): Window size.\n        shift_size (int): Shift size for SW-MSA.\n        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.\n        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True\n        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.\n        drop (float, optional): Dropout rate. Default: 0.0\n        attn_drop (float, optional): Attention dropout rate. Default: 0.0\n        drop_path (float, optional): Stochastic depth rate. Default: 0.0\n        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU\n        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm\n    \"\"\"\n\n    def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0,\n                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,\n                 act_layer=nn.GELU, norm_layer=nn.LayerNorm):\n        super().__init__()\n        self.dim = dim\n        self.input_resolution = input_resolution\n        self.num_heads = num_heads\n        self.window_size = window_size\n        self.shift_size = shift_size\n        self.mlp_ratio = mlp_ratio\n        if min(self.input_resolution) <= self.window_size:\n            # if window size is larger than input resolution, we don't partition windows\n            self.shift_size = 0\n            self.window_size = min(self.input_resolution)\n        assert 0 <= self.shift_size < self.window_size, \"shift_size must in 0-window_size\"\n\n        self.norm1 = norm_layer(dim)\n        self.attn = WindowAttention(\n            dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,\n            qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)\n\n        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()\n        self.norm2 = norm_layer(dim)\n        mlp_hidden_dim = int(dim * mlp_ratio)\n        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)\n\n        if self.shift_size > 0:\n            # calculate attention mask for SW-MSA\n            H, W = self.input_resolution\n            img_mask = torch.zeros((1, H, W, 1))  # 1 H W 1\n            h_slices = (slice(0, -self.window_size),\n                        slice(-self.window_size, -self.shift_size),\n                        slice(-self.shift_size, None))\n            w_slices = (slice(0, -self.window_size),\n                        slice(-self.window_size, -self.shift_size),\n                        slice(-self.shift_size, None))\n            cnt = 0\n            for h in h_slices:\n                for w in w_slices:\n                    img_mask[:, h, w, :] = cnt\n                    cnt += 1\n\n            mask_windows = window_partition(img_mask, self.window_size)  # nW, window_size, window_size, 1\n            mask_windows = mask_windows.view(-1, self.window_size * self.window_size)\n            attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)\n            attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))\n        else:\n            attn_mask = None\n\n        self.register_buffer(\"attn_mask\", attn_mask)\n\n    def forward(self, x):\n        H, W = self.input_resolution\n        B, L, C = x.shape\n        assert L == H * W, \"input feature has wrong size\"\n\n        shortcut = x\n        x = self.norm1(x)\n        x = x.view(B, H, W, C)\n\n        # cyclic shift\n        if self.shift_size > 0:\n            shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))\n        else:\n            shifted_x = x\n\n        # partition windows\n        x_windows = window_partition(shifted_x, self.window_size)  # nW*B, window_size, window_size, C\n        x_windows = x_windows.view(-1, self.window_size * self.window_size, C)  # nW*B, window_size*window_size, C\n\n        # W-MSA/SW-MSA\n        attn_windows = self.attn(x_windows, mask=self.attn_mask)  # nW*B, window_size*window_size, C\n\n        # merge windows\n        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)\n        shifted_x = window_reverse(attn_windows, self.window_size, H, W)  # B H' W' C\n\n        # reverse cyclic shift\n        if self.shift_size > 0:\n            x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))\n        else:\n            x = shifted_x\n        x = x.view(B, H * W, C)\n\n        # FFN\n        x = shortcut + self.drop_path(x)\n        x = x + self.drop_path(self.mlp(self.norm2(x)))\n\n        return x\n\n    def extra_repr(self) -> str:\n        return f\"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, \" \\\n               f\"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}\"\n\n    def flops(self):\n        flops = 0\n        H, W = self.input_resolution\n        # norm1\n        flops += self.dim * H * W\n        # W-MSA/SW-MSA\n        nW = H * W / self.window_size / self.window_size\n        flops += nW * self.attn.flops(self.window_size * self.window_size)\n        # mlp\n        flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio\n        # norm2\n        flops += self.dim * H * W\n        return flops\n\n\nclass PatchMerging(nn.Module):\n    r\"\"\" Patch Merging Layer.\n\n    Args:\n        input_resolution (tuple[int]): Resolution of input feature.\n        dim (int): Number of input channels.\n        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm\n    \"\"\"\n\n    def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):\n        super().__init__()\n        self.input_resolution = input_resolution\n        self.dim = dim\n        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)\n        self.norm = norm_layer(4 * dim)\n\n    def forward(self, x):\n        \"\"\"\n        x: B, H*W, C\n        \"\"\"\n        H, W = self.input_resolution\n        B, L, C = x.shape\n        assert L == H * W, \"input feature has wrong size\"\n        assert H % 2 == 0 and W % 2 == 0, f\"x size ({H}*{W}) are not even.\"\n\n        x = x.view(B, H, W, C)\n\n        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C\n        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C\n        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C\n        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C\n        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C\n        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C\n\n        x = self.norm(x)\n        x = self.reduction(x)\n\n        return x\n\n    def extra_repr(self) -> str:\n        return f\"input_resolution={self.input_resolution}, dim={self.dim}\"\n\n    def flops(self):\n        H, W = self.input_resolution\n        flops = H * W * self.dim\n        flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim\n        return flops\n\n\nclass BasicLayer(nn.Module):\n    \"\"\" A basic Swin Transformer layer for one stage.\n\n    Args:\n        dim (int): Number of input channels.\n        input_resolution (tuple[int]): Input resolution.\n        depth (int): Number of blocks.\n        num_heads (int): Number of attention heads.\n        window_size (int): Local window size.\n        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.\n        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True\n        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.\n        drop (float, optional): Dropout rate. Default: 0.0\n        attn_drop (float, optional): Attention dropout rate. Default: 0.0\n        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0\n        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm\n        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None\n        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.\n    \"\"\"\n\n    def __init__(self, dim, input_resolution, depth, num_heads, window_size,\n                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,\n                 drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False):\n\n        super().__init__()\n        self.dim = dim\n        self.input_resolution = input_resolution\n        self.depth = depth\n        self.use_checkpoint = use_checkpoint\n\n        # build blocks\n        self.blocks = nn.ModuleList([\n            SwinTransformerBlock(dim=dim, input_resolution=input_resolution,\n                                 num_heads=num_heads, window_size=window_size,\n                                 shift_size=0 if (i % 2 == 0) else window_size // 2,\n                                 mlp_ratio=mlp_ratio,\n                                 qkv_bias=qkv_bias, qk_scale=qk_scale,\n                                 drop=drop, attn_drop=attn_drop,\n                                 drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,\n                                 norm_layer=norm_layer)\n            for i in range(depth)])\n\n        # patch merging layer\n        if downsample is not None:\n            self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer)\n        else:\n            self.downsample = None\n\n    def forward(self, x):\n        for blk in self.blocks:\n            if self.use_checkpoint:\n                x = checkpoint.checkpoint(blk, x)\n            else:\n                x = blk(x)\n        if self.downsample is not None:\n            x = self.downsample(x)\n        return x\n\n    def extra_repr(self) -> str:\n        return f\"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}\"\n\n    def flops(self):\n        flops = 0\n        for blk in self.blocks:\n            flops += blk.flops()\n        if self.downsample is not None:\n            flops += self.downsample.flops()\n        return flops\n\n\nclass PatchEmbed(nn.Module):\n    r\"\"\" Image to Patch Embedding\n\n    Args:\n        img_size (int): Image size.  Default: 224.\n        patch_size (int): Patch token size. Default: 4.\n        in_chans (int): Number of input image channels. Default: 3.\n        embed_dim (int): Number of linear projection output channels. Default: 96.\n        norm_layer (nn.Module, optional): Normalization layer. Default: None\n    \"\"\"\n\n    def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):\n        super().__init__()\n        img_size = to_2tuple(img_size)\n        patch_size = to_2tuple(patch_size)\n        patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]\n        self.img_size = img_size\n        self.patch_size = patch_size\n        self.patches_resolution = patches_resolution\n        self.num_patches = patches_resolution[0] * patches_resolution[1]\n\n        self.in_chans = in_chans\n        self.embed_dim = embed_dim\n\n        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)\n        if norm_layer is not None:\n            self.norm = norm_layer(embed_dim)\n        else:\n            self.norm = None\n\n    def forward(self, x):\n        B, C, H, W = x.shape\n        # FIXME look at relaxing size constraints\n        assert H == self.img_size[0] and W == self.img_size[1], \\\n            f\"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]}).\"\n        x = self.proj(x).flatten(2).transpose(1, 2)  # B Ph*Pw C\n        if self.norm is not None:\n            x = self.norm(x)\n        return x\n\n    def flops(self):\n        Ho, Wo = self.patches_resolution\n        flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1])\n        if self.norm is not None:\n            flops += Ho * Wo * self.embed_dim\n        return flops\n\n\nclass SwinTransformer(nn.Module):\n    r\"\"\" Swin Transformer\n        A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`  -\n          https://arxiv.org/pdf/2103.14030\n\n    Args:\n        img_size (int | tuple(int)): Input image size. Default 224\n        patch_size (int | tuple(int)): Patch size. Default: 4\n        in_chans (int): Number of input image channels. Default: 3\n        num_classes (int): Number of classes for classification head. Default: 1000\n        embed_dim (int): Patch embedding dimension. Default: 96\n        depths (tuple(int)): Depth of each Swin Transformer layer.\n        num_heads (tuple(int)): Number of attention heads in different layers.\n        window_size (int): Window size. Default: 7\n        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4\n        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True\n        qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None\n        drop_rate (float): Dropout rate. Default: 0\n        attn_drop_rate (float): Attention dropout rate. Default: 0\n        drop_path_rate (float): Stochastic depth rate. Default: 0.1\n        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.\n        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False\n        patch_norm (bool): If True, add normalization after patch embedding. Default: True\n        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False\n    \"\"\"\n\n    def __init__(self, img_size=224, patch_size=4, in_chans=3, num_classes=1000,\n                 embed_dim=96, depths=[2, 2, 6, 2], num_heads=[3, 6, 12, 24],\n                 window_size=7, mlp_ratio=4., qkv_bias=True, qk_scale=None,\n                 drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,\n                 norm_layer=nn.LayerNorm, ape=False, patch_norm=True,\n                 use_checkpoint=False, **kwargs):\n        super().__init__()\n\n        self.num_classes = num_classes\n        self.num_layers = len(depths)\n        self.embed_dim = embed_dim\n        self.ape = ape\n        self.patch_norm = patch_norm\n        self.num_features = int(embed_dim * 2 ** (self.num_layers - 1))\n        self.mlp_ratio = mlp_ratio\n\n        # split image into non-overlapping patches\n        self.patch_embed = PatchEmbed(\n            img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim,\n            norm_layer=norm_layer if self.patch_norm else None)\n        num_patches = self.patch_embed.num_patches\n        patches_resolution = self.patch_embed.patches_resolution\n        self.patches_resolution = patches_resolution\n\n        # absolute position embedding\n        if self.ape:\n            self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))\n            trunc_normal_(self.absolute_pos_embed, std=.02)\n\n        self.pos_drop = nn.Dropout(p=drop_rate)\n\n        # stochastic depth\n        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule\n\n        # build layers\n        self.layers = nn.ModuleList()\n        for i_layer in range(self.num_layers):\n            layer = BasicLayer(dim=int(embed_dim * 2 ** i_layer),\n                               input_resolution=(patches_resolution[0] // (2 ** i_layer),\n                                                 patches_resolution[1] // (2 ** i_layer)),\n                               depth=depths[i_layer],\n                               num_heads=num_heads[i_layer],\n                               window_size=window_size,\n                               mlp_ratio=self.mlp_ratio,\n                               qkv_bias=qkv_bias, qk_scale=qk_scale,\n                               drop=drop_rate, attn_drop=attn_drop_rate,\n                               drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],\n                               norm_layer=norm_layer,\n                               downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,\n                               use_checkpoint=use_checkpoint)\n            self.layers.append(layer)\n\n        self.norm = norm_layer(self.num_features)\n        self.avgpool = nn.AdaptiveAvgPool1d(1)\n        # self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()\n\n        self.apply(self._init_weights)\n\n    def _init_weights(self, m):\n        if isinstance(m, nn.Linear):\n            trunc_normal_(m.weight, std=.02)\n            if isinstance(m, nn.Linear) and m.bias is not None:\n                nn.init.constant_(m.bias, 0)\n        elif isinstance(m, nn.LayerNorm):\n            nn.init.constant_(m.bias, 0)\n            nn.init.constant_(m.weight, 1.0)\n\n    @torch.jit.ignore\n    def no_weight_decay(self):\n        return {'absolute_pos_embed'}\n\n    @torch.jit.ignore\n    def no_weight_decay_keywords(self):\n        return {'relative_position_bias_table'}\n\n    def forward(self, x, idx_to_group_img=None, image_atts=None, **kwargs):\n        x = self.patch_embed(x)\n        if self.ape:\n            x = x + self.absolute_pos_embed\n        x = self.pos_drop(x)\n\n        for layer in self.layers:\n            x = layer(x)\n\n        x = self.norm(x)  # B L C\n\n        x_cls = self.avgpool(x.transpose(1, 2))  # B C 1\n\n        if idx_to_group_img is None:\n            return torch.cat([x_cls.transpose(1, 2), x], dim=1)\n        else:\n            x_bs = torch.gather(x, dim=0, index=idx_to_group_img.view(-1, 1, 1).expand(-1, x.shape[1], x.shape[2]))\n            weights = image_atts[:, 1:].unsqueeze(2)  # B L 1\n            x_bs_cls = torch.sum((weights * x_bs).transpose(1, 2), dim=-1, keepdim=True)   # B C 1\n            x_bs_cls = x_bs_cls / torch.sum(weights.transpose(1, 2), dim=-1, keepdim=True)  # avgpool\n\n            return torch.cat([x_bs_cls.transpose(1, 2), x_bs], dim=1), \\\n                   torch.cat([x_cls.transpose(1, 2), x], dim=1)\n\n    def flops(self):\n        flops = 0\n        flops += self.patch_embed.flops()\n        for i, layer in enumerate(self.layers):\n            flops += layer.flops()\n        flops += self.num_features * self.patches_resolution[0] * self.patches_resolution[1] // (2 ** self.num_layers)\n        flops += self.num_features * self.num_classes\n        return flops\n\n\ndef interpolate_relative_pos_embed(rel_pos_bias, dst_num_pos, param_name=''):\n    # from: https://github.com/microsoft/unilm/blob/8a0a1c1f4e7326938ea7580a00d56d7f17d65612/beit/run_class_finetuning.py#L348\n\n    # rel_pos_bias: relative_position_bias_table\n    src_num_pos, num_attn_heads = rel_pos_bias.size()\n\n    num_extra_tokens = 0\n    src_size = int((src_num_pos - num_extra_tokens) ** 0.5)\n    dst_size = int((dst_num_pos - num_extra_tokens) ** 0.5)\n    if src_size != dst_size:\n        print(\"Position interpolate %s from %dx%d to %dx%d\" % (param_name, src_size, src_size, dst_size, dst_size))\n\n        # extra_tokens = rel_pos_bias[-num_extra_tokens:, :]\n        # rel_pos_bias = rel_pos_bias[:-num_extra_tokens, :]\n\n        def geometric_progression(a, r, n):\n            return a * (1.0 - r ** n) / (1.0 - r)\n\n        left, right = 1.01, 1.5\n        while right - left > 1e-6:\n            q = (left + right) / 2.0\n            gp = geometric_progression(1, q, src_size // 2)\n            if gp > dst_size // 2:\n                right = q\n            else:\n                left = q\n\n        # if q > 1.090307:\n        #     q = 1.090307\n\n        dis = []\n        cur = 1\n        for i in range(src_size // 2):\n            dis.append(cur)\n            cur += q ** (i + 1)\n\n        r_ids = [-_ for _ in reversed(dis)]\n\n        x = r_ids + [0] + dis\n        y = r_ids + [0] + dis\n\n        t = dst_size // 2.0\n        dx = np.arange(-t, t + 0.1, 1.0)\n        dy = np.arange(-t, t + 0.1, 1.0)\n\n        # print(\"Original positions = %s\" % str(x))\n        # print(\"Target positions = %s\" % str(dx))\n\n        all_rel_pos_bias = []\n\n        for i in range(num_attn_heads):\n            z = rel_pos_bias[:, i].view(src_size, src_size).float().numpy()\n            f = interpolate.interp2d(x, y, z, kind='cubic')\n            all_rel_pos_bias.append(\n                torch.Tensor(f(dx, dy)).contiguous().view(-1, 1).to(rel_pos_bias.device))\n\n        rel_pos_bias = torch.cat(all_rel_pos_bias, dim=-1)\n\n    return rel_pos_bias"
  },
  {
    "path": "model_cards/Tag2Text/ram/models/tag2text.py",
    "content": "'''\n * The Tag2Text Model\n * Written by Xinyu Huang\n'''\nimport numpy as np\nimport json\nimport torch\nimport warnings\n\nfrom torch import nn\nfrom .bert import BertConfig, BertModel, BertLMHeadModel\nfrom .swin_transformer import SwinTransformer\n\nfrom .utils import *\n\nwarnings.filterwarnings(\"ignore\")\n\n\nclass Tag2Text(nn.Module):\n\n    def __init__(self,\n                 med_config=f'{CONFIG_PATH}/configs/med_config.json',\n                 image_size=384,\n                 vit='base',\n                 vit_grad_ckpt=False,\n                 vit_ckpt_layer=0,\n                 prompt='a picture of ',\n                 threshold=0.68,\n                 delete_tag_index=[127,2961, 3351, 3265, 3338, 3355, 3359],\n                 tag_list=f'{CONFIG_PATH}/data/tag_list.txt'):\n        r\"\"\" Tag2Text inference module, both captioning and tagging are included.\n        Tag2Text is an efficient and controllable vision-language pre-training framework.\n        Described in the paper \"Tag2Text: Guiding Vision-Language Model via Image Tagging\" https://arxiv.org/abs/2303.05657\n\n        Args:\n            med_config (str): path for the mixture of encoder-decoder model's configuration file\n            image_size (int): input image size\n            vit (str): model size of vision transformer\n            threshold (int): tagging threshold\n            delete_tag_index (list): delete some tags that may disturb captioning\n        \"\"\"\n        super().__init__()\n\n        # create image encoder\n        if vit == 'swin_b':\n            if image_size == 224:\n                vision_config_path = f'{CONFIG_PATH}/configs/swin/config_swinB_224.json'\n            elif image_size == 384:\n                vision_config_path = f'{CONFIG_PATH}/configs/swin/config_swinB_384.json'\n            vision_config = read_json(vision_config_path)\n            assert image_size == vision_config['image_res']\n            # assert config['patch_size'] == 32\n            vision_width = vision_config['vision_width']\n\n            self.visual_encoder = SwinTransformer(\n                img_size=vision_config['image_res'],\n                patch_size=4,\n                in_chans=3,\n                embed_dim=vision_config['embed_dim'],\n                depths=vision_config['depths'],\n                num_heads=vision_config['num_heads'],\n                window_size=vision_config['window_size'],\n                mlp_ratio=4.,\n                qkv_bias=True,\n                drop_rate=0.0,\n                drop_path_rate=0.1,\n                ape=False,\n                patch_norm=True,\n                use_checkpoint=False)\n\n        else:\n            self.visual_encoder, vision_width = create_vit(\n                vit, image_size, vit_grad_ckpt, vit_ckpt_layer)\n\n        # create tokenzier\n        self.tokenizer = init_tokenizer()\n\n        # Tag2Text employ encoder-decoder architecture for image-tag-text generation: image-tag interaction encoder and image-tag-text decoder\n        # create image-tag interaction encoder\n        encoder_config = BertConfig.from_json_file(med_config)\n        encoder_config.encoder_width = vision_width\n        self.tag_encoder = BertModel(config=encoder_config,\n                                     add_pooling_layer=False)\n\n        # create image-tag-text decoder\n        decoder_config = BertConfig.from_json_file(med_config)\n        self.text_decoder = BertLMHeadModel(config=decoder_config)\n\n        # delete some tags that may disturb captioning\n        # 127: \"quarter\"; 2961: \"back\"; 3351: \"two\"; 3265: \"three\"; 3338: \"four\"; 3355: \"five\"; 3359: \"one\"\n        self.delete_tag_index = delete_tag_index\n        self.prompt = prompt\n        self.prompt_length = len(self.tokenizer(self.prompt).input_ids) - 1\n\n        # load tag list\n        self.tag_list = self.load_tag_list(tag_list)\n\n        # create image-tag recognition decoder\n        self.threshold = threshold\n        self.num_class = len(self.tag_list)\n        q2l_config = BertConfig.from_json_file(f'{CONFIG_PATH}/configs/q2l_config.json')\n        q2l_config.encoder_width = vision_width\n        self.tagging_head = BertModel(config=q2l_config,\n                                      add_pooling_layer=False)\n        self.tagging_head.resize_token_embeddings(len(self.tokenizer))\n        self.label_embed = nn.Embedding(self.num_class, q2l_config.hidden_size)\n        self.fc = GroupWiseLinear(self.num_class,\n                                  q2l_config.hidden_size,\n                                  bias=True)\n        self.del_selfattention()\n\n        self.tagging_loss_function = AsymmetricLoss(gamma_neg=7,\n                                                    gamma_pos=0,\n                                                    clip=0.05)\n\n        # share weights of the lowest 2-layer of \"image-tag interaction encoder\" with the \"image-tag recogntion decoder\"\n        tie_encoder_decoder_weights(self.tag_encoder, self.tagging_head, '',\n                                    ' ')\n\n        # adjust thresholds for some tags\n        # default threshold: 0.68\n        # 2701: \"person\"; 2828: \"man\"; 1167: \"woman\"; \n        tag_thrshold = {2701:0.7, 2828: 0.7, 1167: 0.7}\n        self.class_threshold = torch.ones(self.num_class) * self.threshold\n        for key,value in tag_thrshold.items():\n            self.class_threshold[key] = value\n\n    def load_tag_list(self, tag_list_file):\n        with open(tag_list_file, 'r') as f:\n            tag_list = f.read().splitlines()\n        tag_list = np.array(tag_list)\n        return tag_list\n\n    # delete self-attention layer of image-tag recognition decoder to reduce computation, follower Query2Label\n    def del_selfattention(self):\n        del self.tagging_head.embeddings\n        for layer in self.tagging_head.encoder.layer:\n            del layer.attention\n    \n\n    def forward(self, image, caption, tag):\n        \"\"\"\n        call function as forward\n\n        Args:\n            image: type: torch.Tensor  shape: batch_size * 3 * 384 * 384\n            caption: type: list[string]  len: batch_size\n            tag: type: torch.Tensor   shape: batch * class_num (e.g. 3429)   value: positive sample is 1.0, negative sample is 0.0\n\n        Returns:\n            loss: type: torch.Tensor\n        \"\"\"\n\n        image_embeds = self.visual_encoder(image)\n        image_atts = torch.ones(image_embeds.size()[:-1],\n                                dtype=torch.long).to(image.device)\n\n        ##================= Image Tagging ================##\n        bs = image_embeds.shape[0]\n        label_embed = self.label_embed.weight.unsqueeze(0).repeat(bs, 1, 1)\n\n        tagging_embed = self.tagging_head(\n            encoder_embeds=label_embed,\n            encoder_hidden_states=image_embeds,\n            encoder_attention_mask=image_atts,\n            return_dict=False,\n            mode='tagging',\n        )\n\n        logits = self.fc(tagging_embed[0])\n\n        loss_tag = self.tagging_loss_function(logits, tag)\n\n        ##================= Image-Tag-Text Generation ================##\n        tag = tag.cpu().numpy()\n        tag_input = []\n        for b in range(bs):\n            index = np.argwhere(tag[b] == 1)\n            token = self.tag_list[index].squeeze(axis=1)\n            tag_input.append(' | '.join(token))\n        \n        # tokenizer input tags\n        tag_input_tokenzier = self.tokenizer(tag_input,\n                                             padding='max_length',\n                                             truncation=True,\n                                             max_length=40,\n                                             return_tensors=\"pt\").to(\n                                                 image.device)\n        encoder_input_ids = tag_input_tokenzier.input_ids\n        encoder_input_ids[:, 0] = self.tokenizer.enc_token_id\n\n        # put input tag into image-tag interaction encoder to interact with image embeddings\n        output_tagembedding = self.tag_encoder(\n            encoder_input_ids,\n            attention_mask=tag_input_tokenzier.attention_mask,\n            encoder_hidden_states=image_embeds,\n            encoder_attention_mask=image_atts,\n            return_dict=True,\n        )\n\n        text = self.tokenizer(caption,\n                              padding='longest',\n                              truncation=True,\n                              max_length=40,\n                                return_tensors=\"pt\").to(\n                                    image.device)\n        \n        decoder_input_ids = text.input_ids\n        decoder_input_ids[:,0] = self.tokenizer.bos_token_id\n\n        decoder_targets = decoder_input_ids.masked_fill(\n            decoder_input_ids == self.tokenizer.pad_token_id, -100) \n        decoder_targets[:,:self.prompt_length] = -100\n        \n        decoder_output = self.text_decoder(decoder_input_ids, \n                                           attention_mask = text.attention_mask, \n                                           encoder_hidden_states = output_tagembedding.last_hidden_state,\n                                           encoder_attention_mask = None,                  \n                                           labels = decoder_targets,\n                                           return_dict = True,   \n                                          )   \n        \n        loss_t2t = decoder_output.loss\n\n        # balance loss scale\n        loss = loss_t2t + loss_tag/(loss_tag/loss_t2t).detach()\n\n        return loss\n\n\n    def generate(self,\n                 image,\n                 sample=False,\n                 num_beams=3,\n                 max_length=30,\n                 min_length=10,\n                 top_p=0.9,\n                 repetition_penalty=1.0,\n                 tag_input=None,\n                 return_tag_predict=False):\n\n        image_embeds = self.visual_encoder(image)\n        image_atts = torch.ones(image_embeds.size()[:-1],\n                                dtype=torch.long).to(image.device)\n\n        # if not user specified tags, recognized image tags using image-tag recogntiion decoder\n        if tag_input == None:\n\n            bs = image_embeds.shape[0]\n            label_embed = self.label_embed.weight.unsqueeze(0).repeat(bs, 1, 1)\n            tagging_embed = self.tagging_head(\n                encoder_embeds=label_embed,\n                encoder_hidden_states=image_embeds,\n                encoder_attention_mask=image_atts,\n                return_dict=False,\n                mode='tagging',\n            )\n\n            logits = self.fc(tagging_embed[0])\n\n            targets = torch.where(\n                torch.sigmoid(logits) > self.class_threshold.to(image.device),\n                torch.tensor(1.0).to(image.device),\n                torch.zeros(self.num_class).to(image.device))\n\n            tag = targets.cpu().numpy()\n\n            # delete some tags that may disturb captioning\n            tag[:, self.delete_tag_index] = 0\n\n            tag_input = []\n            for b in range(bs):\n                index = np.argwhere(tag[b] == 1)\n                token = self.tag_list[index].squeeze(axis=1)\n                tag_input.append(' | '.join(token))\n                \n        tag_output = tag_input\n\n        # beam search for text generation(default)\n        if not sample:\n            image_embeds = image_embeds.repeat_interleave(num_beams, dim=0)\n            tag_input_temp = []\n            for tag in tag_input:\n                for i in range(num_beams):\n                    tag_input_temp.append(tag)\n            tag_input = tag_input_temp\n\n        image_atts = torch.ones(image_embeds.size()[:-1],\n                                dtype=torch.long).to(image.device)\n\n        # tokenizer input tags\n        tag_input_tokenzier = self.tokenizer(tag_input,\n                                             padding='max_length',\n                                             truncation=True,\n                                             max_length=40,\n                                             return_tensors=\"pt\").to(\n                                                 image.device)\n        encoder_input_ids = tag_input_tokenzier.input_ids\n        encoder_input_ids[:, 0] = self.tokenizer.enc_token_id\n\n        # put input tag into image-tag interaction encoder to interact with image embeddings\n        output_tagembedding = self.tag_encoder(\n            encoder_input_ids,\n            attention_mask=tag_input_tokenzier.attention_mask,\n            encoder_hidden_states=image_embeds,\n            encoder_attention_mask=image_atts,\n            return_dict=True,\n        )\n\n        # prompt trick for better captioning, followed BLIP\n        prompt = [self.prompt] * image.size(0)\n        input_ids = self.tokenizer(prompt, return_tensors=\"pt\").input_ids.to(\n            image.device)\n        input_ids[:, 0] = self.tokenizer.bos_token_id\n        input_ids = input_ids[:, :-1]\n\n        if sample:\n            # nucleus sampling\n            model_kwargs = {\n                \"encoder_hidden_states\": output_tagembedding.last_hidden_state,\n                \"encoder_attention_mask\": None\n            }\n            outputs = self.text_decoder.generate(\n                input_ids=input_ids,\n                max_length=max_length,\n                min_length=min_length,\n                do_sample=True,\n                top_p=top_p,\n                num_return_sequences=1,\n                eos_token_id=self.tokenizer.sep_token_id,\n                pad_token_id=self.tokenizer.pad_token_id,\n                repetition_penalty=1.1,\n                **model_kwargs)\n        else:\n            # beam search (default)\n            model_kwargs = {\n                \"encoder_hidden_states\": output_tagembedding.last_hidden_state,\n                \"encoder_attention_mask\": None\n            }\n            outputs = self.text_decoder.generate(\n                input_ids=input_ids,\n                max_length=max_length,\n                min_length=min_length,\n                num_beams=num_beams,\n                eos_token_id=self.tokenizer.sep_token_id,\n                pad_token_id=self.tokenizer.pad_token_id,\n                repetition_penalty=repetition_penalty,\n                **model_kwargs)\n\n        captions = []\n        for output in outputs:\n            caption = self.tokenizer.decode(output, skip_special_tokens=True)\n            captions.append(caption[len(self.prompt):])\n        if return_tag_predict == True:\n            return  captions, tag_output\n        return captions\n\n\n# load Tag2Text pretrained model parameters\ndef tag2text(pretrained='', **kwargs):\n    model = Tag2Text(**kwargs)\n    if pretrained:\n        if kwargs['vit'] == 'swin_b':\n            model, msg = load_checkpoint_swinbase(model, pretrained, kwargs)\n        else:\n            model, msg = load_checkpoint(model, pretrained)\n        print('vit:', kwargs['vit'])\n#         print('msg', msg)\n    return model\n\n"
  },
  {
    "path": "model_cards/Tag2Text/ram/models/utils.py",
    "content": "import os\nimport json\nimport torch\nimport math\n\nfrom torch import nn\nfrom typing import List\nfrom transformers import BertTokenizer\nfrom urllib.parse import urlparse\nfrom timm.models.hub import download_cached_file\nfrom .vit import interpolate_pos_embed\nfrom .swin_transformer import interpolate_relative_pos_embed\nfrom pathlib import Path\nCONFIG_PATH=(Path(__file__).resolve().parents[1])\n\ndef read_json(rpath):\n    with open(rpath, 'r') as f:\n        return json.load(f)\n\n\ndef tie_encoder_decoder_weights(encoder: nn.Module, decoder: nn.Module,\n                                base_model_prefix: str, skip_key: str):\n    uninitialized_encoder_weights: List[str] = []\n    if decoder.__class__ != encoder.__class__:\n        logger.info(\n            f\"{decoder.__class__} and {encoder.__class__} are not equal. In this case make sure that all encoder weights are correctly initialized.\"\n        )\n\n    def tie_encoder_to_decoder_recursively(\n        decoder_pointer: nn.Module,\n        encoder_pointer: nn.Module,\n        module_name: str,\n        uninitialized_encoder_weights: List[str],\n        skip_key: str,\n        depth=0,\n    ):\n        assert isinstance(decoder_pointer, nn.Module) and isinstance(\n            encoder_pointer, nn.Module\n        ), f\"{decoder_pointer} and {encoder_pointer} have to be of type torch.nn.Module\"\n        if hasattr(decoder_pointer, \"weight\") and skip_key not in module_name:\n            assert hasattr(encoder_pointer, \"weight\")\n            encoder_pointer.weight = decoder_pointer.weight\n            if hasattr(decoder_pointer, \"bias\"):\n                assert hasattr(encoder_pointer, \"bias\")\n                encoder_pointer.bias = decoder_pointer.bias\n            print(module_name + ' is tied')\n            return\n\n        encoder_modules = encoder_pointer._modules\n        decoder_modules = decoder_pointer._modules\n        if len(decoder_modules) > 0:\n            assert (\n                len(encoder_modules) > 0\n            ), f\"Encoder module {encoder_pointer} does not match decoder module {decoder_pointer}\"\n\n            all_encoder_weights = set([\n                module_name + \"/\" + sub_name\n                for sub_name in encoder_modules.keys()\n            ])\n            encoder_layer_pos = 0\n            for name, module in decoder_modules.items():\n                if name.isdigit():\n                    encoder_name = str(int(name) + encoder_layer_pos)\n                    decoder_name = name\n                    if not isinstance(\n                            decoder_modules[decoder_name],\n                            type(encoder_modules[encoder_name])) and len(\n                                encoder_modules) != len(decoder_modules):\n                        # this can happen if the name corresponds to the position in a list module list of layers\n                        # in this case the decoder has added a cross-attention that the encoder does not have\n                        # thus skip this step and subtract one layer pos from encoder\n                        encoder_layer_pos -= 1\n                        continue\n                elif name not in encoder_modules:\n                    continue\n                elif depth > 500:\n                    raise ValueError(\n                        \"Max depth of recursive function `tie_encoder_to_decoder` reached. It seems that there is a circular dependency between two or more `nn.Modules` of your model.\"\n                    )\n                else:\n                    decoder_name = encoder_name = name\n                tie_encoder_to_decoder_recursively(\n                    decoder_modules[decoder_name],\n                    encoder_modules[encoder_name],\n                    module_name + \"/\" + name,\n                    uninitialized_encoder_weights,\n                    skip_key,\n                    depth=depth + 1,\n                )\n                all_encoder_weights.remove(module_name + \"/\" + encoder_name)\n\n            uninitialized_encoder_weights += list(all_encoder_weights)\n\n    # tie weights recursively\n    tie_encoder_to_decoder_recursively(decoder, encoder, base_model_prefix,\n                                       uninitialized_encoder_weights, skip_key)\n\n\nclass GroupWiseLinear(nn.Module):\n    # could be changed to:\n    # output = torch.einsum('ijk,zjk->ij', x, self.W)\n    # or output = torch.einsum('ijk,jk->ij', x, self.W[0])\n    def __init__(self, num_class, hidden_dim, bias=True):\n        super().__init__()\n        self.num_class = num_class\n        self.hidden_dim = hidden_dim\n        self.bias = bias\n\n        self.W = nn.Parameter(torch.Tensor(1, num_class, hidden_dim))\n        if bias:\n            self.b = nn.Parameter(torch.Tensor(1, num_class))\n        self.reset_parameters()\n\n    def reset_parameters(self):\n        stdv = 1. / math.sqrt(self.W.size(2))\n        for i in range(self.num_class):\n            self.W[0][i].data.uniform_(-stdv, stdv)\n        if self.bias:\n            for i in range(self.num_class):\n                self.b[0][i].data.uniform_(-stdv, stdv)\n\n    def forward(self, x):\n        # x: B,K,d\n        x = (self.W * x).sum(-1)\n        if self.bias:\n            x = x + self.b\n        return x\n\n\ndef init_tokenizer():\n    tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n    tokenizer.add_special_tokens({'bos_token': '[DEC]'})\n    tokenizer.add_special_tokens({'additional_special_tokens': ['[ENC]']})\n    tokenizer.enc_token_id = tokenizer.additional_special_tokens_ids[0]\n    return tokenizer\n\n\ndef create_vit(vit,\n               image_size,\n               use_grad_checkpointing=False,\n               ckpt_layer=0,\n               drop_path_rate=0):\n\n    assert vit in ['base', 'large'], \"vit parameter must be base or large\"\n    if vit == 'base':\n        vision_width = 768\n        visual_encoder = VisionTransformer(\n            img_size=image_size,\n            patch_size=16,\n            embed_dim=vision_width,\n            depth=12,\n            num_heads=12,\n            use_grad_checkpointing=use_grad_checkpointing,\n            ckpt_layer=ckpt_layer,\n            drop_path_rate=0 or drop_path_rate)\n    elif vit == 'large':\n        vision_width = 1024\n        visual_encoder = VisionTransformer(\n            img_size=image_size,\n            patch_size=16,\n            embed_dim=vision_width,\n            depth=24,\n            num_heads=16,\n            use_grad_checkpointing=use_grad_checkpointing,\n            ckpt_layer=ckpt_layer,\n            drop_path_rate=0.1 or drop_path_rate)\n    return visual_encoder, vision_width\n\n\ndef is_url(url_or_filename):\n    parsed = urlparse(url_or_filename)\n    return parsed.scheme in (\"http\", \"https\")\n\n\ndef load_checkpoint(model, url_or_filename):\n    if is_url(url_or_filename):\n        cached_file = download_cached_file(url_or_filename,\n                                           check_hash=False,\n                                           progress=True)\n        checkpoint = torch.load(cached_file, map_location='cpu')\n    elif os.path.isfile(url_or_filename):\n        checkpoint = torch.load(url_or_filename, map_location='cpu')\n    else:\n        raise RuntimeError('checkpoint url or path is invalid')\n\n    state_dict = checkpoint['model']\n\n    state_dict['visual_encoder.pos_embed'] = interpolate_pos_embed(\n        state_dict['visual_encoder.pos_embed'], model.visual_encoder)\n    if 'visual_encoder_m.pos_embed' in model.state_dict().keys():\n        state_dict['visual_encoder_m.pos_embed'] = interpolate_pos_embed(\n            state_dict['visual_encoder_m.pos_embed'], model.visual_encoder_m)\n    for key in model.state_dict().keys():\n        if key in state_dict.keys():\n            if state_dict[key].shape != model.state_dict()[key].shape:\n                del state_dict[key]\n\n    msg = model.load_state_dict(state_dict, strict=False)\n    print('load checkpoint from %s' % url_or_filename)\n    return model, msg\n\n\ndef load_checkpoint_swinbase(model, url_or_filename, kwargs):\n    if kwargs['image_size'] == 224:\n        vision_config_path = f'{CONFIG_PATH}/configs/swin/config_swinB_224.json'\n    elif kwargs['image_size'] == 384:\n        vision_config_path = f'{CONFIG_PATH}/configs/swin/config_swinB_384.json'\n    window_size = read_json(vision_config_path)['window_size']\n    print('--------------')\n    print(url_or_filename)\n    print('--------------')\n    if is_url(url_or_filename):\n        cached_file = download_cached_file(url_or_filename,\n                                           check_hash=False,\n                                           progress=True)\n        checkpoint = torch.load(cached_file, map_location='cpu')\n    elif os.path.isfile(url_or_filename):\n        checkpoint = torch.load(url_or_filename, map_location='cpu')\n    else:\n        raise RuntimeError('checkpoint url or path is invalid')\n\n    state_dict = checkpoint['model']\n\n    for k in list(state_dict.keys()):\n        if 'relative_position_bias_table' in k:\n            dst_num_pos = (2 * window_size - 1)**2\n            state_dict[k] = interpolate_relative_pos_embed(state_dict[k],\n                                                           dst_num_pos,\n                                                           param_name=k)\n        elif ('relative_position_index' in k) or ('attn_mask' in k):\n            del state_dict[k]\n        elif \"vision_multi\" in k:\n            state_dict[k.replace(\"vision_multi\",\n                                 \"tagging_head\")] = state_dict.pop(k)\n\n    msg = model.load_state_dict(state_dict, strict=False)\n    print('load checkpoint from %s' % url_or_filename)\n    return model, msg\n\n\ndef load_checkpoint_swinlarge(model, url_or_filename, kwargs):\n    if kwargs['image_size'] == 224:\n        vision_config_path = f'{CONFIG_PATH}/configs/swin/config_swinL_224.json'\n    elif kwargs['image_size'] == 384:\n        vision_config_path = f'{CONFIG_PATH}/configs/swin/config_swinL_384.json'\n    window_size = read_json(vision_config_path)['window_size']\n    print('--------------')\n    print(url_or_filename)\n    print('--------------')\n    if is_url(url_or_filename):\n        cached_file = download_cached_file(url_or_filename,\n                                           check_hash=False,\n                                           progress=True)\n        checkpoint = torch.load(cached_file, map_location='cpu')\n    elif os.path.isfile(url_or_filename):\n        checkpoint = torch.load(url_or_filename, map_location='cpu')\n    else:\n        raise RuntimeError('checkpoint url or path is invalid')\n\n    state_dict = checkpoint['model']\n\n    for k in list(state_dict.keys()):\n        if 'relative_position_bias_table' in k:\n            dst_num_pos = (2 * window_size - 1)**2\n            state_dict[k] = interpolate_relative_pos_embed(state_dict[k],\n                                                           dst_num_pos,\n                                                           param_name=k)\n        elif ('relative_position_index' in k) or ('attn_mask' in k):\n            del state_dict[k]\n        elif \"vision_multi\" in k:\n            state_dict[k.replace(\"vision_multi\",\n                                 \"tagging_head\")] = state_dict.pop(k)\n\n    msg = model.load_state_dict(state_dict, strict=False)\n    print('load checkpoint from %s' % url_or_filename)\n    return model, msg\n\n\n# Tagging loss function\n# copy from https://github.com/Alibaba-MIIL/ASL/blob/main/src/loss_functions/losses.py\nclass AsymmetricLoss(nn.Module):\n    def __init__(self, gamma_neg=4, gamma_pos=1, clip=0.05, eps=1e-8, disable_torch_grad_focal_loss=True):\n        super(AsymmetricLoss, self).__init__()\n\n        self.gamma_neg = gamma_neg\n        self.gamma_pos = gamma_pos\n        self.clip = clip\n        self.disable_torch_grad_focal_loss = disable_torch_grad_focal_loss\n        self.eps = eps\n\n    def forward(self, x, y):\n        \"\"\"\"\n        Parameters\n        ----------\n        x: input logits\n        y: targets (multi-label binarized vector)\n        \"\"\"\n\n        # Calculating Probabilities\n        x_sigmoid = torch.sigmoid(x)\n        xs_pos = x_sigmoid\n        xs_neg = 1 - x_sigmoid\n\n        # Asymmetric Clipping\n        if self.clip is not None and self.clip > 0:\n            xs_neg = (xs_neg + self.clip).clamp(max=1)\n\n        # Basic CE calculation\n        los_pos = y * torch.log(xs_pos.clamp(min=self.eps))\n        los_neg = (1 - y) * torch.log(xs_neg.clamp(min=self.eps))\n        loss = los_pos + los_neg\n\n        # Asymmetric Focusing\n        if self.gamma_neg > 0 or self.gamma_pos > 0:\n            if self.disable_torch_grad_focal_loss:\n                torch.set_grad_enabled(False)\n            pt0 = xs_pos * y\n            pt1 = xs_neg * (1 - y)  # pt = p if t > 0 else 1-p\n            pt = pt0 + pt1\n            one_sided_gamma = self.gamma_pos * y + self.gamma_neg * (1 - y)\n            one_sided_w = torch.pow(1 - pt, one_sided_gamma)\n            if self.disable_torch_grad_focal_loss:\n                torch.set_grad_enabled(True)\n            loss *= one_sided_w\n\n        return -loss.sum()"
  },
  {
    "path": "model_cards/Tag2Text/ram/models/vit.py",
    "content": "'''\n * Copyright (c) 2022, salesforce.com, inc.\n * All rights reserved.\n * SPDX-License-Identifier: BSD-3-Clause\n * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause\n * By Junnan Li\n * Based on timm code base\n * https://github.com/rwightman/pytorch-image-models/tree/master/timm\n'''\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom functools import partial\n\nfrom timm.models.vision_transformer import _cfg, PatchEmbed\nfrom timm.models.registry import register_model\nfrom timm.models.layers import trunc_normal_, DropPath\nfrom timm.models.helpers import named_apply, adapt_input_conv\n\nfrom fairscale.nn.checkpoint.checkpoint_activations import checkpoint_wrapper\n\nclass Mlp(nn.Module):\n    \"\"\" MLP as used in Vision Transformer, MLP-Mixer and related networks\n    \"\"\"\n    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):\n        super().__init__()\n        out_features = out_features or in_features\n        hidden_features = hidden_features or in_features\n        self.fc1 = nn.Linear(in_features, hidden_features)\n        self.act = act_layer()\n        self.fc2 = nn.Linear(hidden_features, out_features)\n        self.drop = nn.Dropout(drop)\n\n    def forward(self, x):\n        x = self.fc1(x)\n        x = self.act(x)\n        x = self.drop(x)\n        x = self.fc2(x)\n        x = self.drop(x)\n        return x\n\n\nclass Attention(nn.Module):\n    def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):\n        super().__init__()\n        self.num_heads = num_heads\n        head_dim = dim // num_heads\n        # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights\n        self.scale = qk_scale or head_dim ** -0.5\n        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)\n        self.attn_drop = nn.Dropout(attn_drop)\n        self.proj = nn.Linear(dim, dim)\n        self.proj_drop = nn.Dropout(proj_drop)\n        self.attn_gradients = None\n        self.attention_map = None\n        \n    def save_attn_gradients(self, attn_gradients):\n        self.attn_gradients = attn_gradients\n        \n    def get_attn_gradients(self):\n        return self.attn_gradients\n    \n    def save_attention_map(self, attention_map):\n        self.attention_map = attention_map\n        \n    def get_attention_map(self):\n        return self.attention_map\n    \n    def forward(self, x, register_hook=False):\n        B, N, C = x.shape\n        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)\n        q, k, v = qkv[0], qkv[1], qkv[2]   # make torchscript happy (cannot use tensor as tuple)\n\n        attn = (q @ k.transpose(-2, -1)) * self.scale\n        attn = attn.softmax(dim=-1)\n        attn = self.attn_drop(attn)\n                \n        if register_hook:\n            self.save_attention_map(attn)\n            attn.register_hook(self.save_attn_gradients)        \n\n        x = (attn @ v).transpose(1, 2).reshape(B, N, C)\n        x = self.proj(x)\n        x = self.proj_drop(x)\n        return x\n\n\nclass Block(nn.Module):\n\n    def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,\n                 drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, use_grad_checkpointing=False):\n        super().__init__()\n        self.norm1 = norm_layer(dim)\n        self.attn = Attention(\n            dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)\n        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here\n        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()\n        self.norm2 = norm_layer(dim)\n        mlp_hidden_dim = int(dim * mlp_ratio)\n        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)\n\n        if use_grad_checkpointing:\n            self.attn = checkpoint_wrapper(self.attn)\n            self.mlp = checkpoint_wrapper(self.mlp)\n\n    def forward(self, x, register_hook=False):\n        x = x + self.drop_path(self.attn(self.norm1(x), register_hook=register_hook))\n        x = x + self.drop_path(self.mlp(self.norm2(x)))\n        return x\n\n    \nclass VisionTransformer(nn.Module):\n    \"\"\" Vision Transformer\n    A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale`  -\n        https://arxiv.org/abs/2010.11929\n    \"\"\"\n    def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12,\n                 num_heads=12, mlp_ratio=4., qkv_bias=True, qk_scale=None, representation_size=None,\n                 drop_rate=0., attn_drop_rate=0., drop_path_rate=0., norm_layer=None, \n                 use_grad_checkpointing=False, ckpt_layer=0):\n        \"\"\"\n        Args:\n            img_size (int, tuple): input image size\n            patch_size (int, tuple): patch size\n            in_chans (int): number of input channels\n            num_classes (int): number of classes for classification head\n            embed_dim (int): embedding dimension\n            depth (int): depth of transformer\n            num_heads (int): number of attention heads\n            mlp_ratio (int): ratio of mlp hidden dim to embedding dim\n            qkv_bias (bool): enable bias for qkv if True\n            qk_scale (float): override default qk scale of head_dim ** -0.5 if set\n            representation_size (Optional[int]): enable and set representation layer (pre-logits) to this value if set\n            drop_rate (float): dropout rate\n            attn_drop_rate (float): attention dropout rate\n            drop_path_rate (float): stochastic depth rate\n            norm_layer: (nn.Module): normalization layer\n        \"\"\"\n        super().__init__()\n        self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models\n        norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)\n\n        self.patch_embed = PatchEmbed(\n            img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)\n\n        num_patches = self.patch_embed.num_patches\n\n        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))\n        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim))\n        self.pos_drop = nn.Dropout(p=drop_rate)\n\n        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule\n        self.blocks = nn.ModuleList([\n            Block(\n                dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,\n                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer,\n                use_grad_checkpointing=(use_grad_checkpointing and i>=depth-ckpt_layer)\n            )\n            for i in range(depth)])\n        self.norm = norm_layer(embed_dim)\n\n        trunc_normal_(self.pos_embed, std=.02)\n        trunc_normal_(self.cls_token, std=.02)\n        self.apply(self._init_weights)\n\n    def _init_weights(self, m):\n        if isinstance(m, nn.Linear):\n            trunc_normal_(m.weight, std=.02)\n            if isinstance(m, nn.Linear) and m.bias is not None:\n                nn.init.constant_(m.bias, 0)\n        elif isinstance(m, nn.LayerNorm):\n            nn.init.constant_(m.bias, 0)\n            nn.init.constant_(m.weight, 1.0)\n\n    @torch.jit.ignore\n    def no_weight_decay(self):\n        return {'pos_embed', 'cls_token'}\n\n    def forward(self, x, register_blk=-1):\n        B = x.shape[0]\n        x = self.patch_embed(x)\n\n        cls_tokens = self.cls_token.expand(B, -1, -1)  # stole cls_tokens impl from Phil Wang, thanks\n        x = torch.cat((cls_tokens, x), dim=1)\n  \n        x = x + self.pos_embed[:,:x.size(1),:]\n        x = self.pos_drop(x)\n\n        for i,blk in enumerate(self.blocks):\n            x = blk(x, register_blk==i)\n        x = self.norm(x)\n        \n        return x\n\n    @torch.jit.ignore()\n    def load_pretrained(self, checkpoint_path, prefix=''):\n        _load_weights(self, checkpoint_path, prefix)\n        \n\n@torch.no_grad()\ndef _load_weights(model: VisionTransformer, checkpoint_path: str, prefix: str = ''):\n    \"\"\" Load weights from .npz checkpoints for official Google Brain Flax implementation\n    \"\"\"\n    import numpy as np\n\n    def _n2p(w, t=True):\n        if w.ndim == 4 and w.shape[0] == w.shape[1] == w.shape[2] == 1:\n            w = w.flatten()\n        if t:\n            if w.ndim == 4:\n                w = w.transpose([3, 2, 0, 1])\n            elif w.ndim == 3:\n                w = w.transpose([2, 0, 1])\n            elif w.ndim == 2:\n                w = w.transpose([1, 0])\n        return torch.from_numpy(w)\n\n    w = np.load(checkpoint_path)\n    if not prefix and 'opt/target/embedding/kernel' in w:\n        prefix = 'opt/target/'\n\n    if hasattr(model.patch_embed, 'backbone'):\n        # hybrid\n        backbone = model.patch_embed.backbone\n        stem_only = not hasattr(backbone, 'stem')\n        stem = backbone if stem_only else backbone.stem\n        stem.conv.weight.copy_(adapt_input_conv(stem.conv.weight.shape[1], _n2p(w[f'{prefix}conv_root/kernel'])))\n        stem.norm.weight.copy_(_n2p(w[f'{prefix}gn_root/scale']))\n        stem.norm.bias.copy_(_n2p(w[f'{prefix}gn_root/bias']))\n        if not stem_only:\n            for i, stage in enumerate(backbone.stages):\n                for j, block in enumerate(stage.blocks):\n                    bp = f'{prefix}block{i + 1}/unit{j + 1}/'\n                    for r in range(3):\n                        getattr(block, f'conv{r + 1}').weight.copy_(_n2p(w[f'{bp}conv{r + 1}/kernel']))\n                        getattr(block, f'norm{r + 1}').weight.copy_(_n2p(w[f'{bp}gn{r + 1}/scale']))\n                        getattr(block, f'norm{r + 1}').bias.copy_(_n2p(w[f'{bp}gn{r + 1}/bias']))\n                    if block.downsample is not None:\n                        block.downsample.conv.weight.copy_(_n2p(w[f'{bp}conv_proj/kernel']))\n                        block.downsample.norm.weight.copy_(_n2p(w[f'{bp}gn_proj/scale']))\n                        block.downsample.norm.bias.copy_(_n2p(w[f'{bp}gn_proj/bias']))\n        embed_conv_w = _n2p(w[f'{prefix}embedding/kernel'])\n    else:\n        embed_conv_w = adapt_input_conv(\n            model.patch_embed.proj.weight.shape[1], _n2p(w[f'{prefix}embedding/kernel']))\n    model.patch_embed.proj.weight.copy_(embed_conv_w)\n    model.patch_embed.proj.bias.copy_(_n2p(w[f'{prefix}embedding/bias']))\n    model.cls_token.copy_(_n2p(w[f'{prefix}cls'], t=False))\n    pos_embed_w = _n2p(w[f'{prefix}Transformer/posembed_input/pos_embedding'], t=False)\n    if pos_embed_w.shape != model.pos_embed.shape:\n        pos_embed_w = resize_pos_embed(  # resize pos embedding when different size from pretrained weights\n            pos_embed_w, model.pos_embed, getattr(model, 'num_tokens', 1), model.patch_embed.grid_size)\n    model.pos_embed.copy_(pos_embed_w)\n    model.norm.weight.copy_(_n2p(w[f'{prefix}Transformer/encoder_norm/scale']))\n    model.norm.bias.copy_(_n2p(w[f'{prefix}Transformer/encoder_norm/bias']))\n#     if isinstance(model.head, nn.Linear) and model.head.bias.shape[0] == w[f'{prefix}head/bias'].shape[-1]:\n#         model.head.weight.copy_(_n2p(w[f'{prefix}head/kernel']))\n#         model.head.bias.copy_(_n2p(w[f'{prefix}head/bias']))\n#     if isinstance(getattr(model.pre_logits, 'fc', None), nn.Linear) and f'{prefix}pre_logits/bias' in w:\n#         model.pre_logits.fc.weight.copy_(_n2p(w[f'{prefix}pre_logits/kernel']))\n#         model.pre_logits.fc.bias.copy_(_n2p(w[f'{prefix}pre_logits/bias']))\n    for i, block in enumerate(model.blocks.children()):\n        block_prefix = f'{prefix}Transformer/encoderblock_{i}/'\n        mha_prefix = block_prefix + 'MultiHeadDotProductAttention_1/'\n        block.norm1.weight.copy_(_n2p(w[f'{block_prefix}LayerNorm_0/scale']))\n        block.norm1.bias.copy_(_n2p(w[f'{block_prefix}LayerNorm_0/bias']))\n        block.attn.qkv.weight.copy_(torch.cat([\n            _n2p(w[f'{mha_prefix}{n}/kernel'], t=False).flatten(1).T for n in ('query', 'key', 'value')]))\n        block.attn.qkv.bias.copy_(torch.cat([\n            _n2p(w[f'{mha_prefix}{n}/bias'], t=False).reshape(-1) for n in ('query', 'key', 'value')]))\n        block.attn.proj.weight.copy_(_n2p(w[f'{mha_prefix}out/kernel']).flatten(1))\n        block.attn.proj.bias.copy_(_n2p(w[f'{mha_prefix}out/bias']))\n        for r in range(2):\n            getattr(block.mlp, f'fc{r + 1}').weight.copy_(_n2p(w[f'{block_prefix}MlpBlock_3/Dense_{r}/kernel']))\n            getattr(block.mlp, f'fc{r + 1}').bias.copy_(_n2p(w[f'{block_prefix}MlpBlock_3/Dense_{r}/bias']))\n        block.norm2.weight.copy_(_n2p(w[f'{block_prefix}LayerNorm_2/scale']))\n        block.norm2.bias.copy_(_n2p(w[f'{block_prefix}LayerNorm_2/bias']))\n\n            \ndef interpolate_pos_embed(pos_embed_checkpoint, visual_encoder):        \n    # interpolate position embedding\n    embedding_size = pos_embed_checkpoint.shape[-1]\n    num_patches = visual_encoder.patch_embed.num_patches\n    num_extra_tokens = visual_encoder.pos_embed.shape[-2] - num_patches\n    # height (== width) for the checkpoint position embedding\n    orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5)\n    # height (== width) for the new position embedding\n    new_size = int(num_patches ** 0.5)\n\n    if orig_size!=new_size:\n        # class_token and dist_token are kept unchanged\n        extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens]\n        # only the position tokens are interpolated\n        pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:]\n        pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, embedding_size).permute(0, 3, 1, 2)\n        pos_tokens = torch.nn.functional.interpolate(\n            pos_tokens, size=(new_size, new_size), mode='bicubic', align_corners=False)\n        pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2)\n        new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1)\n        print('reshape position embedding from %d to %d'%(orig_size ** 2,new_size ** 2))\n        \n        return new_pos_embed    \n    else:\n        return pos_embed_checkpoint"
  },
  {
    "path": "model_cards/Tag2Text/ram/transform.py",
    "content": "from torchvision.transforms import Normalize, Compose, Resize, ToTensor\n\n\ndef get_transform(image_size=384):\n    return Compose([\n        lambda image: image.convert(\"RGB\"),\n        Resize((image_size, image_size)),\n        ToTensor(),\n        Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n    ])\n"
  },
  {
    "path": "model_cards/Tag2Text/ram/utils/__init__.py",
    "content": "from .metrics import get_mAP, get_PR\nfrom .openset_utils import build_openset_label_embedding\n"
  },
  {
    "path": "model_cards/Tag2Text/ram/utils/metrics.py",
    "content": "from typing import List, Tuple\n\nimport numpy as np\nfrom numpy import ndarray\n\n\ndef get_mAP(\n    preds: ndarray,\n    gt_file: str,\n    taglist: List[str]\n) -> Tuple[float, ndarray]:\n    assert preds.shape[1] == len(taglist)\n\n    # When mapping categories from test datasets to our system, there might be\n    # multiple vs one situation due to different semantic definitions of tags.\n    # So there can be duplicate tags in `taglist`. This special case is taken\n    # into account.\n    tag2idxs = {}\n    for idx, tag in enumerate(taglist):\n        if tag not in tag2idxs:\n            tag2idxs[tag] = []\n        tag2idxs[tag].append(idx)\n\n    # build targets\n    targets = np.zeros_like(preds)\n    with open(gt_file, \"r\") as f:\n        lines = [line.strip(\"\\n\").split(\",\") for line in f.readlines()]\n    assert len(lines) == targets.shape[0]\n    for i, line in enumerate(lines):\n        for tag in line[1:]:\n            targets[i, tag2idxs[tag]] = 1.0\n\n    # compute average precision for each class\n    APs = np.zeros(preds.shape[1])\n    for k in range(preds.shape[1]):\n        APs[k] = _average_precision(preds[:, k], targets[:, k])\n\n    return APs.mean(), APs\n\n\ndef _average_precision(output: ndarray, target: ndarray) -> float:\n    epsilon = 1e-8\n\n    # sort examples\n    indices = output.argsort()[::-1]\n    # Computes prec@i\n    total_count_ = np.cumsum(np.ones((len(output), 1)))\n\n    target_ = target[indices]\n    ind = target_ == 1\n    pos_count_ = np.cumsum(ind)\n    total = pos_count_[-1]\n    pos_count_[np.logical_not(ind)] = 0\n    pp = pos_count_ / total_count_\n    precision_at_i_ = np.sum(pp)\n    precision_at_i = precision_at_i_ / (total + epsilon)\n\n    return precision_at_i\n\n\ndef get_PR(\n    pred_file: str,\n    gt_file: str,\n    taglist: List[str]\n) -> Tuple[float, float, ndarray, ndarray]:\n    # When mapping categories from test datasets to our system, there might be\n    # multiple vs one situation due to different semantic definitions of tags.\n    # So there can be duplicate tags in `taglist`. This special case is taken\n    # into account.\n    tag2idxs = {}\n    for idx, tag in enumerate(taglist):\n        if tag not in tag2idxs:\n            tag2idxs[tag] = []\n        tag2idxs[tag].append(idx)\n\n    # build preds\n    with open(pred_file, \"r\", encoding=\"utf-8\") as f:\n        lines = [line.strip().split(\",\") for line in f.readlines()]\n    preds = np.zeros((len(lines), len(tag2idxs)), dtype=bool)\n    for i, line in enumerate(lines):\n        for tag in line[1:]:\n            preds[i, tag2idxs[tag]] = True\n\n    # build targets\n    with open(gt_file, \"r\", encoding=\"utf-8\") as f:\n        lines = [line.strip().split(\",\") for line in f.readlines()]\n    targets = np.zeros((len(lines), len(tag2idxs)), dtype=bool)\n    for i, line in enumerate(lines):\n        for tag in line[1:]:\n            targets[i, tag2idxs[tag]] = True\n\n    assert preds.shape == targets.shape\n\n    # calculate P and R\n    TPs = ( preds &  targets).sum(axis=0)  # noqa: E201, E222\n    FPs = ( preds & ~targets).sum(axis=0)  # noqa: E201, E222\n    FNs = (~preds &  targets).sum(axis=0)  # noqa: E201, E222\n    eps = 1.e-9\n    Ps = TPs / (TPs + FPs + eps)\n    Rs = TPs / (TPs + FNs + eps)\n\n    return Ps.mean(), Rs.mean(), Ps, Rs\n"
  },
  {
    "path": "model_cards/Tag2Text/ram/utils/openset_utils.py",
    "content": "\n\n\nimport torch\nimport torch.nn as nn\nfrom clip import clip\n\n\ndef article(name):\n    return \"an\" if name[0] in \"aeiou\" else \"a\"\n\n\ndef processed_name(name, rm_dot=False):\n    # _ for lvis\n    # / for obj365\n    res = name.replace(\"_\", \" \").replace(\"/\", \" or \").lower()\n    if rm_dot:\n        res = res.rstrip(\".\")\n    return res\n\n\nsingle_template = [\"a photo of a {}.\"]\n\nmultiple_templates = [\n    \"There is {article} {} in the scene.\",\n    \"There is the {} in the scene.\",\n    \"a photo of {article} {} in the scene.\",\n    \"a photo of the {} in the scene.\",\n    \"a photo of one {} in the scene.\",\n    \"itap of {article} {}.\",\n    \"itap of my {}.\",  # itap: I took a picture of\n    \"itap of the {}.\",\n    \"a photo of {article} {}.\",\n    \"a photo of my {}.\",\n    \"a photo of the {}.\",\n    \"a photo of one {}.\",\n    \"a photo of many {}.\",\n    \"a good photo of {article} {}.\",\n    \"a good photo of the {}.\",\n    \"a bad photo of {article} {}.\",\n    \"a bad photo of the {}.\",\n    \"a photo of a nice {}.\",\n    \"a photo of the nice {}.\",\n    \"a photo of a cool {}.\",\n    \"a photo of the cool {}.\",\n    \"a photo of a weird {}.\",\n    \"a photo of the weird {}.\",\n    \"a photo of a small {}.\",\n    \"a photo of the small {}.\",\n    \"a photo of a large {}.\",\n    \"a photo of the large {}.\",\n    \"a photo of a clean {}.\",\n    \"a photo of the clean {}.\",\n    \"a photo of a dirty {}.\",\n    \"a photo of the dirty {}.\",\n    \"a bright photo of {article} {}.\",\n    \"a bright photo of the {}.\",\n    \"a dark photo of {article} {}.\",\n    \"a dark photo of the {}.\",\n    \"a photo of a hard to see {}.\",\n    \"a photo of the hard to see {}.\",\n    \"a low resolution photo of {article} {}.\",\n    \"a low resolution photo of the {}.\",\n    \"a cropped photo of {article} {}.\",\n    \"a cropped photo of the {}.\",\n    \"a close-up photo of {article} {}.\",\n    \"a close-up photo of the {}.\",\n    \"a jpeg corrupted photo of {article} {}.\",\n    \"a jpeg corrupted photo of the {}.\",\n    \"a blurry photo of {article} {}.\",\n    \"a blurry photo of the {}.\",\n    \"a pixelated photo of {article} {}.\",\n    \"a pixelated photo of the {}.\",\n    \"a black and white photo of the {}.\",\n    \"a black and white photo of {article} {}.\",\n    \"a plastic {}.\",\n    \"the plastic {}.\",\n    \"a toy {}.\",\n    \"the toy {}.\",\n    \"a plushie {}.\",\n    \"the plushie {}.\",\n    \"a cartoon {}.\",\n    \"the cartoon {}.\",\n    \"an embroidered {}.\",\n    \"the embroidered {}.\",\n    \"a painting of the {}.\",\n    \"a painting of a {}.\",\n]\n\n\nopenimages_rare_unseen = ['Aerial photography',\n'Aircraft engine',\n'Ale',\n'Aloe',\n'Amphibian',\n'Angling',\n'Anole',\n'Antique car',\n'Arcade game',\n'Arthropod',\n'Assault rifle',\n'Athletic shoe',\n'Auto racing',\n'Backlighting',\n'Bagpipes',\n'Ball game',\n'Barbecue chicken',\n'Barechested',\n'Barquentine',\n'Beef tenderloin',\n'Billiard room',\n'Billiards',\n'Bird of prey',\n'Black swan',\n'Black-and-white',\n'Blond',\n'Boating',\n'Bonbon',\n'Bottled water',\n'Bouldering',\n'Bovine',\n'Bratwurst',\n'Breadboard',\n'Briefs',\n'Brisket',\n'Brochette',\n'Calabaza',\n'Camera operator',\n'Canola',\n'Childbirth',\n'Chordophone',\n'Church bell',\n'Classical sculpture',\n'Close-up',\n'Cobblestone',\n'Coca-cola',\n'Combat sport',\n'Comics',\n'Compact car',\n'Computer speaker',\n'Cookies and crackers',\n'Coral reef fish',\n'Corn on the cob',\n'Cosmetics',\n'Crocodilia',\n'Digital camera',\n'Dishware',\n'Divemaster',\n'Dobermann',\n'Dog walking',\n'Domestic rabbit',\n'Domestic short-haired cat',\n'Double-decker bus',\n'Drums',\n'Electric guitar',\n'Electric piano',\n'Electronic instrument',\n'Equestrianism',\n'Equitation',\n'Erinaceidae',\n'Extreme sport',\n'Falafel',\n'Figure skating',\n'Filling station',\n'Fire apparatus',\n'Firearm',\n'Flatbread',\n'Floristry',\n'Forklift truck',\n'Freight transport',\n'Fried food',\n'Fried noodles',\n'Frigate',\n'Frozen yogurt',\n'Frying',\n'Full moon',\n'Galleon',\n'Glacial landform',\n'Gliding',\n'Go-kart',\n'Goats',\n'Grappling',\n'Great white shark',\n'Gumbo',\n'Gun turret',\n'Hair coloring',\n'Halter',\n'Headphones',\n'Heavy cruiser',\n'Herding',\n'High-speed rail',\n'Holding hands',\n'Horse and buggy',\n'Horse racing',\n'Hound',\n'Hunting knife',\n'Hurdling',\n'Inflatable',\n'Jackfruit',\n'Jeans',\n'Jiaozi',\n'Junk food',\n'Khinkali',\n'Kitesurfing',\n'Lawn game',\n'Leaf vegetable',\n'Lechon',\n'Lifebuoy',\n'Locust',\n'Lumpia',\n'Luxury vehicle',\n'Machine tool',\n'Medical imaging',\n'Melee weapon',\n'Microcontroller',\n'Middle ages',\n'Military person',\n'Military vehicle',\n'Milky way',\n'Miniature Poodle',\n'Modern dance',\n'Molluscs',\n'Monoplane',\n'Motorcycling',\n'Musical theatre',\n'Narcissus',\n'Nest box',\n'Newsagent\\'s shop',\n'Nile crocodile',\n'Nordic skiing',\n'Nuclear power plant',\n'Orator',\n'Outdoor shoe',\n'Parachuting',\n'Pasta salad',\n'Peafowl',\n'Pelmeni',\n'Perching bird',\n'Performance car',\n'Personal water craft',\n'Pit bull',\n'Plant stem',\n'Pork chop',\n'Portrait photography',\n'Primate',\n'Procyonidae',\n'Prosciutto',\n'Public speaking',\n'Racewalking',\n'Ramen',\n'Rear-view mirror',\n'Residential area',\n'Ribs',\n'Rice ball',\n'Road cycling',\n'Roller skating',\n'Roman temple',\n'Rowing',\n'Rural area',\n'Sailboat racing',\n'Scaled reptile',\n'Scuba diving',\n'Senior citizen',\n'Shallot',\n'Shinto shrine',\n'Shooting range',\n'Siberian husky',\n'Sledding',\n'Soba',\n'Solar energy',\n'Sport climbing',\n'Sport utility vehicle',\n'Steamed rice',\n'Stemware',\n'Sumo',\n'Surfing Equipment',\n'Team sport',\n'Touring car',\n'Toy block',\n'Trampolining',\n'Underwater diving',\n'Vegetarian food',\n'Wallaby',\n'Water polo',\n'Watercolor paint',\n'Whiskers',\n'Wind wave',\n'Woodwind instrument',\n'Yakitori',\n'Zeppelin']\n\n\ndef build_openset_label_embedding(categories=None):\n    if categories is None:\n        categories = openimages_rare_unseen\n    model, _ = clip.load(\"ViT-B/16\")\n    templates = multiple_templates\n\n    run_on_gpu = torch.cuda.is_available()\n\n    with torch.no_grad():\n        openset_label_embedding = []\n        for category in categories:\n            texts = [\n                template.format(\n                    processed_name(category, rm_dot=True), article=article(category)\n                )\n                for template in templates\n            ]\n            texts = [\n                \"This is \" + text if text.startswith(\"a\") or text.startswith(\"the\") else text\n                for text in texts\n            ]\n            texts = clip.tokenize(texts)  # tokenize\n            if run_on_gpu:\n                texts = texts.cuda()\n                model = model.cuda()\n            text_embeddings = model.encode_text(texts)\n            text_embeddings /= text_embeddings.norm(dim=-1, keepdim=True)\n            text_embedding = text_embeddings.mean(dim=0)\n            text_embedding /= text_embedding.norm()\n            openset_label_embedding.append(text_embedding)\n        openset_label_embedding = torch.stack(openset_label_embedding, dim=1)\n        if run_on_gpu:\n            openset_label_embedding = openset_label_embedding.cuda()\n\n    openset_label_embedding = openset_label_embedding.t()\n    return openset_label_embedding, categories\n\n\n\n\n"
  },
  {
    "path": "model_cards/Tag2Text/requirements_groundingDINO.txt",
    "content": "timm==0.4.12\ntransformers==4.15.0\nfairscale==0.4.4\npycocoevalcap\ntorch\ntorchvision\nPillow\nscipy\ngit+https://github.com/openai/CLIP.git\n"
  },
  {
    "path": "model_cards/Tag2Text/setup.cfg",
    "content": "[metadata]\nname = recognize-anything\nversion = 0.0.1\ndescription = Recognize Anything Model and Tag2Text Model\n\n[options]\npackages = find:\ninclude_package_data = True\n\n[options.packages.find]\nexclude =\n    datasets\n    images\n    outputs\n    pretrained\n"
  },
  {
    "path": "model_cards/Tag2Text/setup.py",
    "content": "import setuptools\nsetuptools.setup()\n"
  },
  {
    "path": "model_cards/autoback.py",
    "content": "import torch\nimport json\nimport math\nimport platform\nimport warnings\nfrom collections import OrderedDict, namedtuple\nfrom copy import copy\nfrom pathlib import Path\nfrom urllib.parse import urlparse\nfrom multiprocessing import Pool\n\nimport cv2\nimport numpy as np\nimport pandas as pd\nimport requests\nimport torch\nimport torch.nn as nn\nimport yaml\nimport torch.nn.functional as F\nfrom PIL import Image\nimport sys\n\nsys.path.append(\"model_cards\")\nsys.path.append('model_cards/Tag2Text')\nsys.path.append('model_cards/lama')\nsys.path.append('model_cards/gligen')\n\nimport model_cards.groundingdino.datasets.transforms as T\nfrom model_cards.groundingdino.util.utils import clean_state_dict, get_phrases_from_posmap\nfrom model_cards.groundingdino.util import box_ops\nfrom model_cards.groundingdino.util.slconfig import SLConfig\n\nfrom model_cards.Tag2Text import inference_tag2text,inference_ram\nfrom Tag2Text.ram import get_transform\nfrom Tag2Text.ram.models import tag2text\nfrom utils.conf import LAMA_MODEL_PATH\n# segment anything\nfrom model_cards.segment_anything import build_sam, SamPredictor \n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom utils import check_requirements,check_suffix,IMAGENET_MEAN,IMAGENET_STD\nfrom utils.downloads import is_url,LOGGER,attempt_download\nfrom torchvision.ops import nms as NMS\nfrom utils.plot import *\n\nModel_CARDS=['lama','segment-anything','grounded-DINO','Tag2Text','gligen']\ndef preprocess_image(img):\n\n    # Convert image from BGR to RGB format using OpenCV library\n    #img_array = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n    img_array = Image.fromarray(img)\n    # Define image transformation pipeline using PyTorch library\n    transform = T.Compose([\n        \n        T.RandomResize([800], max_size=1333),\n        T.ToTensor(),\n        T.Normalize(IMAGENET_MEAN, IMAGENET_STD)\n    ])\n\n    # Apply image transformation pipeline to numpy array to get PyTorch tensor\n    img_tensor,_ = transform(img_array,None)\n\n    return  img_tensor\n               \n\nclass Ensemble(nn.ModuleList):\n    # Ensemble of models\n    def __init__(self):\n        super().__init__()\n\n    def forward(self, x, augment=False, profile=False, visualize=False):\n        y = [module(x, augment, profile, visualize)[0] for module in self]\n        y = torch.cat(y, 2)  # nms ensemble\n        return y, None  # inference, train output\n\ndef torch_safe_load(weight):\n    \"\"\"\n    This function attempts to load a PyTorch model with the torch.load() function. If a ModuleNotFoundError is raised, it\n    catches the error, logs a warning message, and attempts to install the missing module via the check_requirements()\n    function. After installation, the function again attempts to load the model using torch.load().\n    Args:\n        weight (str): The file path of the PyTorch model.\n    Returns:\n        The loaded PyTorch model.\n    \"\"\"\n    \n    check_suffix(file=weight,suffix='.pt')\n    file = attempt_download(weight)  # search online if missing locally\n    try:\n        return torch.load(file, map_location='cpu'),file  # load\n    except ModuleNotFoundError as e:\n        if e.name == 'models':  # e.name is missing module name\n            LOGGER.warning(f\"WARNING ⚠️ {weight} requires {e.name}, which is not in ultralytics requirements.\"\n                           f\"\\nAutoInstall will run now for {e.name} but this feature will be removed in the future.\"\n                           f\"\\nRecommend fixes are to train a new model using updated ultraltyics package or to \"\n                        )\n        check_requirements(e.name)  # install missing module ,select no\n        return torch.load(file, map_location='cpu'), file  # load\n\n\ndef is_similar_string(string):\n    for s in Model_CARDS:\n        if string.lower() in s.lower():\n            print('get your method')\n            return string.lower()\n    return None\n\ndef attempt_load(weights, device=None):\n    # Loads an ensemble of models weights=[a,b,c] or a single model weights=[a] or weights=a\n     model=[]\n    # model=Ensemble()\n     for w in weights if isinstance(weights, list) else [weights]:\n        ckpt = torch.load(attempt_download(w),map_location='cpu')# load ckpt\n         \n        #args = {**DEFAULT_CFG_DICT, **ckpt['train_args']}  # combine model and default args, preferring model args\n        # ckpt = (ckpt.get('ema') or ckpt['model']).to(device).float()  # FP32 model\n        # ckpt.args = {k: v for k, v in args.items() if k in DEFAULT_CFG_KEYS}  # attach args to model\n        ckpt.pt_path = weights  # attach *.pt file path to model\n        model.append(ckpt.eval())  # fused or un-fused model in eval mode\n\n     return model  # return ensemble\n\nclass AutoBackend(nn.Module):\n    # for python inference on various Models\n    def __init__(self, methods: str ,weights: None , device=torch.device('cpu'), args_config: str= 'model_cards/groundingdino/config/GroundingDINO_SwinT_OGC.py', fp16: bool=False,num_classes:int=1,tag2text_thres=0.6):\n         # Usage:\n        #   PyTorch:              weights = *.pt\n        #   TorchScript:                    *.torchscript\n        #   ONNX Runtime:                   *.onnx\n        #   Model methos Card \n        \n        from utils.downloads import attempt_download\n        super().__init__()\n        self.flag=is_similar_string(methods)\n       \n        self.device=device\n        self.nc=num_classes\n        \n        w = str(weights[0] if isinstance(weights, list) else weights)\n        nn_module=isinstance(weights,torch.nn.Module)\n        if  isinstance(weights, dict):\n            pt=weights \n        else:\n            pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle, triton = self._model_type(w)\n       # w = attempt_download(w)  # download if not local\n            fp16 &= pt or jit or onnx or engine  or nn_module # FP16\n            nhwc = coreml or saved_model or pb or tflite or edgetpu  # BHWC formats (vs torch BCWH)\n        self.model,metadata=None,None \n        cuda = torch.cuda.is_available() and self.device.type != 'cpu'  # use CUDA\n        \n        if pt or weights is None :  # PyTorch\n            if methods == \"grounded-DINO\":\n               #Grounding\n               from model_cards.groundingdino.models import build_model\n\n               config_args=SLConfig.fromfile(args_config)\n               config_args.device=self.device\n               model=build_model(config_args)\n               ckpt=torch.load(w)\n               load_res = model.load_state_dict(clean_state_dict(ckpt[\"model\"]), strict=False)\n               self.flag=methods\n               self.model = model.to(self.device).eval()  # explicitly assign for to(), cpu(), cuda(), half()\n              \n              \n            elif methods== \"segment-anything\":\n                print(\"-----init Sam------\")\n                self.model = SamPredictor(build_sam(checkpoint=w).to(self.device))\n                \n            elif methods == 'lama':\n                # lama  \n                from model_cards.lama.saicinpainting.training.trainers import load_checkpoint\n                \n                from omegaconf import OmegaConf\n                print('---init lama------') \n                self.predict_config = OmegaConf.load(args_config)\n                self.predict_config.model.path = LAMA_MODEL_PATH\n        \n                train_config_path = os.path.join(\n                self.predict_config .model.path, 'config.yaml')\n\n                with open(train_config_path, 'r') as f:\n                    train_config = OmegaConf.create(yaml.safe_load(f))\n\n                train_config.training_model.predict_only = True\n                train_config.visualizer.kind = 'noop'\n\n                checkpoint_path = os.path.join(\n                    self.predict_config .model.path, 'models',\n                    self.predict_config .model.checkpoint\n                )\n                self.model = load_checkpoint(\n                    train_config, checkpoint_path, strict=False, map_location='cpu')\n                self.model.freeze()\n                if not self.predict_config .get('refine', False):\n                    self.model.to(self.device)\n            elif methods== 'gligen':\n                # gligen\n                from model_cards.gligen.gligen_inference import run,load_ckpt\n                gligen_models={'model': None, 'autoencoder': None, 'text_encoder':None,\n               'diffusion':None, 'config': None}\n                # - - - - - prepare models - - - - - # \n                gligen_models['model'],gligen_models['autoencoder'],gligen_models['text_encoder'],\n                gligen_models['diffusion'],gligen_models['config']= load_ckpt(weights[\"ckpt\"])\n                self.model=gligen_models\n                print('load gligen done')\n            elif methods == 'tag2text':\n                from Tag2Text.ram.models import tag2text\n                print('----Init Tag2Text----')\n                self.size=384\n                self.transform = get_transform(image_size=self.size)\n            \n                # delete some tags that may disturb captioning\n                # 127: \"quarter\"; 2961: \"back\", 3351: \"two\"; 3265: \"three\"; 3338: \"four\"; 3355: \"five\"; 3359: \"one\"\n                delete_tag_index = [127,2961, 3351, 3265, 3338, 3355, 3359]\n                # load model\n                self.model = tag2text(pretrained=w,\n                                image_size=self.size,\n                                vit='swin_b',\n                                delete_tag_index=delete_tag_index).to(self.device).eval()    \n            elif methods =='ram':\n                from Tag2Text.ram.models import ram\n                print('----Init ram---')\n                self.size=384\n                self.transform = get_transform(image_size=self.size)\n                # load model\n                self.model = ram(pretrained=w,\n                             image_size=self.size,\n                             vit='swin_l').to(self.device).eval()    \n            else :\n                LOGGER('not find methods')\n                raise TypeError(f'model=:{methods} is not a  saved method')\n                \n            \n        elif onnx:  # ONNX Runtime\n            LOGGER.info(f'Loading {w} for ONNX Runtime inference...')\n            cuda = torch.cuda.is_available()\n            check_requirements(('onnx', 'onnxruntime-gpu' if cuda else 'onnxruntime'))\n            import onnxruntime\n            providers = ['CUDAExecutionProvider', 'CPUExecutionProvider'] if cuda else ['CPUExecutionProvider']\n            session = onnxruntime.InferenceSession(w, providers=providers)\n            meta = session.get_modelmeta().custom_metadata_map  # metadata\n            if 'stride' in meta:\n                stride, names = int(meta['stride']), eval(meta['names'])\n       \n        else:\n            raise TypeError(f'model=:{w} is not a supported format')\n\n        self.flag=methods\n        self.__dict__.update(locals())  # assign all variables to self\n\n    @torch.no_grad()\n    def forward(self, im, augment=False, visualize=False,prompt= None ,box_threshold=0.3,text_threshold=0.25, iou_threshold=0.5,with_logits=True):\n        #  inference\n            #H,W=im.shape[0],im.shape[1]\n            if self.fp16 and im.dtype != torch.float16:\n                im = im.half()  # to FP16     \n            if self.flag==\"grounded-DINO\":\n                return self.grounded_inference(im,prompt,box_threshold,text_threshold,iou_threshold)           \n            elif self.flag == \"segment-anything\":\n                return self.sam_inference(im,prompt)\n            elif self.flag==\"tag2text\":            \n                return self.tag2text_inference(im,prompt)\n            elif self.flag=='ram':\n                return self.ram_inference(im)\n            elif self.flag=='lama':\n                return self.lama_inference(im,prompt)\n            elif self.flga== 'gligen':\n                return self.gligen_inference(config=None, starting_noise=None,negative_prompt=None,\n                    batch_size=5,guidance_scale=7.5,no_plms=None)\n            elif self.onnx:  # ONNX Runtime\n                im = im.cpu().numpy()  # torch to numpy\n                y = self.session.run(self.output_names, {self.session.get_inputs()[0].name: im})\n                if isinstance(y, (list, tuple)):\n                    return self.from_numpy(y[0]) if len(y) == 1 else [self.from_numpy(x) for x in y]\n                else:\n                    return self.from_numpy(y)\n    def gligen_inference(config=None, starting_noise=None,negative_prompt=None,\n                batch_size=5,guidance_scale=None,no_plms=None)   :\n                print('wait update')\n    def grounded_inference(self,im,caption,box_threshold,text_threshold,iou_threshold):\n                    \n                    H,W=im.shape[0],im.shape[1]\n                    input_tensor=preprocess_image(im).to(self.device)\n                    caption = caption.lower()\n                    caption = caption.strip()\n                    if not caption.endswith(\".\"):\n                            caption = caption + \".\"\n                    with torch.no_grad():\n                            y = self.model(input_tensor[None], captions=[caption])\n                    logits = y[\"pred_logits\"].cpu().sigmoid()[0]  # (nq, 256)\n                    boxes = y[\"pred_boxes\"].cpu()[0]  # (nq, 4)\n        \n                    # filter output\n                    logits_filt = logits.clone()\n                    boxes_filt = boxes.clone()\n                    filt_mask = logits_filt.max(dim=1)[0] > box_threshold\n                    logits_filt = logits_filt[filt_mask]  # num_filt, 256\n                    boxes_filt = boxes_filt[filt_mask]  # num_filt, 4\n                    \n                    # get phrase\n                    tokenlizer = self.model.tokenizer\n                    tokenized = tokenlizer(caption)\n                    # build pred\n                    pred_phrases = []\n                    scores=[]\n        \n                    for logit, box in zip(logits_filt, boxes_filt):\n                            pred_phrase = get_phrases_from_posmap(logit > text_threshold, tokenized, tokenlizer)\n                            pred_phrases.append(pred_phrase)  \n                            scores.append(logit.max().item())\n                            box[:]= box * torch.Tensor([W,H,W,H])\n                            box[:2]-= box[2:]/2\n                            box[2:]+= box[:2]\n                    #NMS\n                    id_nms= NMS(boxes_filt,torch.tensor(scores),iou_threshold).cpu().numpy().tolist()\n                    boxes_filt=boxes_filt[id_nms]\n                    scores=[scores[i] for i in id_nms]\n                    pred_phrases=[pred_phrases[i] for i in id_nms]\n                    return [boxes_filt, torch.Tensor(scores), pred_phrases]\n                \n    def sam_inference(self,im,prompt):\n        \n                    H,W=im.shape[0],im.shape[1]   \n                    self.model.set_image(im)\n                    boxes = self.model.transform.apply_boxes_torch(prompt, (H,W)).to(self.device) \n                    masks, _, _ = self.model.predict_torch(\n                    point_coords = None,\n                    point_labels = None,\n                    boxes = boxes.to(self.device),\n                    multimask_output = False,\n                )\n                    return masks\n    def tag2text_inference(self,im,prompt):     \n              \n                    raw_image = cv2.resize(im, (self.size, self.size))\n                    raw_image =Image.fromarray(raw_image)\n                    raw_image = self.transform(raw_image).unsqueeze(0).to(self.device)\n                    return inference_tag2text.inference(raw_image , self.model, prompt) \n    \n    def ram_inference(self,im):     \n\n                    raw_image = cv2.resize(im, (self.size, self.size))\n                    raw_image =Image.fromarray(raw_image)\n                    raw_image = self.transform(raw_image).unsqueeze(0).to(self.device)\n                    return inference_ram.inference(raw_image,self.model) \n    def lama_inference(self,im,mask) :\n                from model_cards.lama.saicinpainting.evaluation.data import pad_tensor_to_modulo\n                from model_cards.lama.saicinpainting.evaluation.utils import move_to_device\n                \n                assert len(mask.shape) == 2\n                if np.max(mask) == 1:\n                        mask = mask * 255\n                img = torch.from_numpy(im).float().div(255.)\n                mask = torch.from_numpy(mask).float()\n                batch = {}\n                batch['image'] = img.permute(2, 0, 1).unsqueeze(0)\n                batch['mask'] = mask[None, None]\n                unpad_to_size = [batch['image'].shape[2], batch['image'].shape[3]]\n                batch['image'] = pad_tensor_to_modulo(batch['image'], 8)\n                batch['mask'] = pad_tensor_to_modulo(batch['mask'], 8)\n                batch = move_to_device(batch, self.device)\n                batch['mask'] = (batch['mask'] > 0) * 1\n\n                batch = self.model(batch)\n                cur_res = batch[self.predict_config.out_key][0].permute(1, 2, 0)\n                cur_res = cur_res.detach().cpu().numpy()\n\n                if unpad_to_size is not None:\n                    orig_height, orig_width = unpad_to_size\n                    cur_res = cur_res[:orig_height, :orig_width]\n\n                cur_res = np.clip(cur_res * 255, 0, 255).astype('uint8')\n                return cur_res       \n    @staticmethod\n    def _model_type(p='path/to/model.pt'):\n    # Return model type from model path, i.e. path='path/to/model.onnx' -> type=onnx\n    # types = [pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle]\n\n        def export_formats():\n            x = [\n                ['PyTorch', '-', '.pth' ,True, True],\n                ['TorchScript', 'torchscript', '.torchscript', True, True],\n                ['ONNX', 'onnx', '.onnx', True, True],\n                ['OpenVINO', 'openvino', '_openvino_model', True, False],\n                ['TensorRT', 'engine', '.engine', False, True],\n                ['CoreML', 'coreml', '.mlmodel', True, False],\n                ['CKPT', '.ckpt', 'ckpt', True, True],\n                ['TensorFlow GraphDef', 'pb', '.pb', True, True],\n                ['TensorFlow Lite', 'tflite', '.tflite', True, False],\n                ['TensorFlow Edge TPU', 'edgetpu', '_edgetpu.tflite', False, False],\n                ['TensorFlow.js', 'tfjs', '_web_model', False, False],\n                ['PaddlePaddle', 'paddle', '_paddle_model', True, True],\n            ]\n            return pd.DataFrame(x, columns=['Format', 'Argument', 'Suffix', 'CPU', 'GPU'])\n\n        sf = list(export_formats().Suffix)  # export suffixes\n\n        if not is_url(p, False):\n            check_suffix(Path(p).name, sf)  # check for suffix\n\n        url = urlparse(p)  # if url, check if Triton inference server\n        types = [suffix in Path(p).name for suffix in sf]\n        types[8] &= not types[9]  # If tflite, make sure not edgedpu\n        triton = not any(types) and all(scheme in url.scheme for scheme in ['http', 'grpc']) and url.netloc  # check if Triton inference server\n        return types + [triton]\n   \n   \n   \n\n"
  },
  {
    "path": "model_cards/groundingdino/__init__.py",
    "content": ""
  },
  {
    "path": "model_cards/groundingdino/config/GroundingDINO_SwinB.cfg.py",
    "content": "batch_size = 1\nmodelname = \"groundingdino\"\nbackbone = \"swin_B_384_22k\"\nposition_embedding = \"sine\"\npe_temperatureH = 20\npe_temperatureW = 20\nreturn_interm_indices = [1, 2, 3]\nbackbone_freeze_keywords = None\nenc_layers = 6\ndec_layers = 6\npre_norm = False\ndim_feedforward = 2048\nhidden_dim = 256\ndropout = 0.0\nnheads = 8\nnum_queries = 900\nquery_dim = 4\nnum_patterns = 0\nnum_feature_levels = 4\nenc_n_points = 4\ndec_n_points = 4\ntwo_stage_type = \"standard\"\ntwo_stage_bbox_embed_share = False\ntwo_stage_class_embed_share = False\ntransformer_activation = \"relu\"\ndec_pred_bbox_embed_share = True\ndn_box_noise_scale = 1.0\ndn_label_noise_ratio = 0.5\ndn_label_coef = 1.0\ndn_bbox_coef = 1.0\nembed_init_tgt = True\ndn_labelbook_size = 2000\nmax_text_len = 256\ntext_encoder_type = \"bert-base-uncased\"\nuse_text_enhancer = True\nuse_fusion_layer = True\nuse_checkpoint = True\nuse_transformer_ckpt = True\nuse_text_cross_attention = True\ntext_dropout = 0.0\nfusion_dropout = 0.0\nfusion_droppath = 0.1\nsub_sentence_present = True\n"
  },
  {
    "path": "model_cards/groundingdino/config/GroundingDINO_SwinT_OGC.py",
    "content": "batch_size = 1\nmodelname = \"groundingdino\"\nbackbone = \"swin_T_224_1k\"\nposition_embedding = \"sine\"\npe_temperatureH = 20\npe_temperatureW = 20\nreturn_interm_indices = [1, 2, 3]\nbackbone_freeze_keywords = None\nenc_layers = 6\ndec_layers = 6\npre_norm = False\ndim_feedforward = 2048\nhidden_dim = 256\ndropout = 0.0\nnheads = 8\nnum_queries = 900\nquery_dim = 4\nnum_patterns = 0\nnum_feature_levels = 4\nenc_n_points = 4\ndec_n_points = 4\ntwo_stage_type = \"standard\"\ntwo_stage_bbox_embed_share = False\ntwo_stage_class_embed_share = False\ntransformer_activation = \"relu\"\ndec_pred_bbox_embed_share = True\ndn_box_noise_scale = 1.0\ndn_label_noise_ratio = 0.5\ndn_label_coef = 1.0\ndn_bbox_coef = 1.0\nembed_init_tgt = True\ndn_labelbook_size = 2000\nmax_text_len = 256\ntext_encoder_type = \"bert-base-uncased\"\nuse_text_enhancer = True\nuse_fusion_layer = True\nuse_checkpoint = True\nuse_transformer_ckpt = True\nuse_text_cross_attention = True\ntext_dropout = 0.0\nfusion_dropout = 0.0\nfusion_droppath = 0.1\nsub_sentence_present = True\n"
  },
  {
    "path": "model_cards/groundingdino/datasets/__init__.py",
    "content": ""
  },
  {
    "path": "model_cards/groundingdino/datasets/transforms.py",
    "content": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n\"\"\"\nTransforms and data augmentation for both image + bbox.\n\"\"\"\nimport os\nimport random\n\nimport PIL\nimport torch\nimport torchvision.transforms as T\nimport torchvision.transforms.functional as F\n\nfrom groundingdino.util.box_ops import box_xyxy_to_cxcywh\nfrom groundingdino.util.misc import interpolate\n\n\ndef crop(image, target, region):\n    cropped_image = F.crop(image, *region)\n\n    target = target.copy()\n    i, j, h, w = region\n\n    # should we do something wrt the original size?\n    target[\"size\"] = torch.tensor([h, w])\n\n    fields = [\"labels\", \"area\", \"iscrowd\", \"positive_map\"]\n\n    if \"boxes\" in target:\n        boxes = target[\"boxes\"]\n        max_size = torch.as_tensor([w, h], dtype=torch.float32)\n        cropped_boxes = boxes - torch.as_tensor([j, i, j, i])\n        cropped_boxes = torch.min(cropped_boxes.reshape(-1, 2, 2), max_size)\n        cropped_boxes = cropped_boxes.clamp(min=0)\n        area = (cropped_boxes[:, 1, :] - cropped_boxes[:, 0, :]).prod(dim=1)\n        target[\"boxes\"] = cropped_boxes.reshape(-1, 4)\n        target[\"area\"] = area\n        fields.append(\"boxes\")\n\n    if \"masks\" in target:\n        # FIXME should we update the area here if there are no boxes?\n        target[\"masks\"] = target[\"masks\"][:, i : i + h, j : j + w]\n        fields.append(\"masks\")\n\n    # remove elements for which the boxes or masks that have zero area\n    if \"boxes\" in target or \"masks\" in target:\n        # favor boxes selection when defining which elements to keep\n        # this is compatible with previous implementation\n        if \"boxes\" in target:\n            cropped_boxes = target[\"boxes\"].reshape(-1, 2, 2)\n            keep = torch.all(cropped_boxes[:, 1, :] > cropped_boxes[:, 0, :], dim=1)\n        else:\n            keep = target[\"masks\"].flatten(1).any(1)\n\n        for field in fields:\n            if field in target:\n                target[field] = target[field][keep]\n\n    if os.environ.get(\"IPDB_SHILONG_DEBUG\", None) == \"INFO\":\n        # for debug and visualization only.\n        if \"strings_positive\" in target:\n            target[\"strings_positive\"] = [\n                _i for _i, _j in zip(target[\"strings_positive\"], keep) if _j\n            ]\n\n    return cropped_image, target\n\n\ndef hflip(image, target):\n    flipped_image = F.hflip(image)\n\n    w, h = image.size\n\n    target = target.copy()\n    if \"boxes\" in target:\n        boxes = target[\"boxes\"]\n        boxes = boxes[:, [2, 1, 0, 3]] * torch.as_tensor([-1, 1, -1, 1]) + torch.as_tensor(\n            [w, 0, w, 0]\n        )\n        target[\"boxes\"] = boxes\n\n    if \"masks\" in target:\n        target[\"masks\"] = target[\"masks\"].flip(-1)\n\n    return flipped_image, target\n\n\ndef resize(image, target, size, max_size=None):\n    # size can be min_size (scalar) or (w, h) tuple\n\n    def get_size_with_aspect_ratio(image_size, size, max_size=None):\n        w, h = image_size\n        if max_size is not None:\n            min_original_size = float(min((w, h)))\n            max_original_size = float(max((w, h)))\n            if max_original_size / min_original_size * size > max_size:\n                size = int(round(max_size * min_original_size / max_original_size))\n\n        if (w <= h and w == size) or (h <= w and h == size):\n            return (h, w)\n\n        if w < h:\n            ow = size\n            oh = int(size * h / w)\n        else:\n            oh = size\n            ow = int(size * w / h)\n\n        return (oh, ow)\n\n    def get_size(image_size, size, max_size=None):\n        if isinstance(size, (list, tuple)):\n            return size[::-1]\n        else:\n            return get_size_with_aspect_ratio(image_size, size, max_size)\n\n    size = get_size(image.size, size, max_size)\n    rescaled_image = F.resize(image, size)\n\n    if target is None:\n        return rescaled_image, None\n\n    ratios = tuple(float(s) / float(s_orig) for s, s_orig in zip(rescaled_image.size, image.size))\n    ratio_width, ratio_height = ratios\n\n    target = target.copy()\n    if \"boxes\" in target:\n        boxes = target[\"boxes\"]\n        scaled_boxes = boxes * torch.as_tensor(\n            [ratio_width, ratio_height, ratio_width, ratio_height]\n        )\n        target[\"boxes\"] = scaled_boxes\n\n    if \"area\" in target:\n        area = target[\"area\"]\n        scaled_area = area * (ratio_width * ratio_height)\n        target[\"area\"] = scaled_area\n\n    h, w = size\n    target[\"size\"] = torch.tensor([h, w])\n\n    if \"masks\" in target:\n        target[\"masks\"] = (\n            interpolate(target[\"masks\"][:, None].float(), size, mode=\"nearest\")[:, 0] > 0.5\n        )\n\n    return rescaled_image, target\n\n\ndef pad(image, target, padding):\n    # assumes that we only pad on the bottom right corners\n    padded_image = F.pad(image, (0, 0, padding[0], padding[1]))\n    if target is None:\n        return padded_image, None\n    target = target.copy()\n    # should we do something wrt the original size?\n    target[\"size\"] = torch.tensor(padded_image.size[::-1])\n    if \"masks\" in target:\n        target[\"masks\"] = torch.nn.functional.pad(target[\"masks\"], (0, padding[0], 0, padding[1]))\n    return padded_image, target\n\n\nclass ResizeDebug(object):\n    def __init__(self, size):\n        self.size = size\n\n    def __call__(self, img, target):\n        return resize(img, target, self.size)\n\n\nclass RandomCrop(object):\n    def __init__(self, size):\n        self.size = size\n\n    def __call__(self, img, target):\n        region = T.RandomCrop.get_params(img, self.size)\n        return crop(img, target, region)\n\n\nclass RandomSizeCrop(object):\n    def __init__(self, min_size: int, max_size: int, respect_boxes: bool = False):\n        # respect_boxes:    True to keep all boxes\n        #                   False to tolerence box filter\n        self.min_size = min_size\n        self.max_size = max_size\n        self.respect_boxes = respect_boxes\n\n    def __call__(self, img: PIL.Image.Image, target: dict):\n        init_boxes = len(target[\"boxes\"])\n        max_patience = 10\n        for i in range(max_patience):\n            w = random.randint(self.min_size, min(img.width, self.max_size))\n            h = random.randint(self.min_size, min(img.height, self.max_size))\n            region = T.RandomCrop.get_params(img, [h, w])\n            result_img, result_target = crop(img, target, region)\n            if (\n                not self.respect_boxes\n                or len(result_target[\"boxes\"]) == init_boxes\n                or i == max_patience - 1\n            ):\n                return result_img, result_target\n        return result_img, result_target\n\n\nclass CenterCrop(object):\n    def __init__(self, size):\n        self.size = size\n\n    def __call__(self, img, target):\n        image_width, image_height = img.size\n        crop_height, crop_width = self.size\n        crop_top = int(round((image_height - crop_height) / 2.0))\n        crop_left = int(round((image_width - crop_width) / 2.0))\n        return crop(img, target, (crop_top, crop_left, crop_height, crop_width))\n\n\nclass RandomHorizontalFlip(object):\n    def __init__(self, p=0.5):\n        self.p = p\n\n    def __call__(self, img, target):\n        if random.random() < self.p:\n            return hflip(img, target)\n        return img, target\n\n\nclass RandomResize(object):\n    def __init__(self, sizes, max_size=None):\n        assert isinstance(sizes, (list, tuple))\n        self.sizes = sizes\n        self.max_size = max_size\n\n    def __call__(self, img, target=None):\n        size = random.choice(self.sizes)\n        return resize(img, target, size, self.max_size)\n\n\nclass RandomPad(object):\n    def __init__(self, max_pad):\n        self.max_pad = max_pad\n\n    def __call__(self, img, target):\n        pad_x = random.randint(0, self.max_pad)\n        pad_y = random.randint(0, self.max_pad)\n        return pad(img, target, (pad_x, pad_y))\n\n\nclass RandomSelect(object):\n    \"\"\"\n    Randomly selects between transforms1 and transforms2,\n    with probability p for transforms1 and (1 - p) for transforms2\n    \"\"\"\n\n    def __init__(self, transforms1, transforms2, p=0.5):\n        self.transforms1 = transforms1\n        self.transforms2 = transforms2\n        self.p = p\n\n    def __call__(self, img, target):\n        if random.random() < self.p:\n            return self.transforms1(img, target)\n        return self.transforms2(img, target)\n\n\nclass ToTensor(object):\n    def __call__(self, img, target):\n        return F.to_tensor(img), target\n\n\nclass RandomErasing(object):\n    def __init__(self, *args, **kwargs):\n        self.eraser = T.RandomErasing(*args, **kwargs)\n\n    def __call__(self, img, target):\n        return self.eraser(img), target\n\n\nclass Normalize(object):\n    def __init__(self, mean, std):\n        self.mean = mean\n        self.std = std\n\n    def __call__(self, image, target=None):\n        image = F.normalize(image, mean=self.mean, std=self.std)\n        if target is None:\n            return image, None\n        target = target.copy()\n        h, w = image.shape[-2:]\n        if \"boxes\" in target:\n            boxes = target[\"boxes\"]\n            boxes = box_xyxy_to_cxcywh(boxes)\n            boxes = boxes / torch.tensor([w, h, w, h], dtype=torch.float32)\n            target[\"boxes\"] = boxes\n        return image, target\n\n\nclass Compose(object):\n    def __init__(self, transforms):\n        self.transforms = transforms\n\n    def __call__(self, image, target):\n        for t in self.transforms:\n            image, target = t(image, target)\n        return image, target\n\n    def __repr__(self):\n        format_string = self.__class__.__name__ + \"(\"\n        for t in self.transforms:\n            format_string += \"\\n\"\n            format_string += \"    {0}\".format(t)\n        format_string += \"\\n)\"\n        return format_string\n"
  },
  {
    "path": "model_cards/groundingdino/models/GroundingDINO/__init__.py",
    "content": "# ------------------------------------------------------------------------\n# Grounding DINO\n# url: https://github.com/IDEA-Research/GroundingDINO\n# Copyright (c) 2023 IDEA. All Rights Reserved.\n# Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n# ------------------------------------------------------------------------\n# Conditional DETR\n# Copyright (c) 2021 Microsoft. All Rights Reserved.\n# Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n# ------------------------------------------------------------------------\n# Copied from DETR (https://github.com/facebookresearch/detr)\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\n# ------------------------------------------------------------------------\n\nfrom .groundingdino import build_groundingdino\n"
  },
  {
    "path": "model_cards/groundingdino/models/GroundingDINO/backbone/__init__.py",
    "content": "from .backbone import build_backbone\n"
  },
  {
    "path": "model_cards/groundingdino/models/GroundingDINO/backbone/backbone.py",
    "content": "# ------------------------------------------------------------------------\n# Grounding DINO\n# url: https://github.com/IDEA-Research/GroundingDINO\n# Copyright (c) 2023 IDEA. All Rights Reserved.\n# Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n# ------------------------------------------------------------------------\n# Conditional DETR\n# Copyright (c) 2021 Microsoft. All Rights Reserved.\n# Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n# ------------------------------------------------------------------------\n# Copied from DETR (https://github.com/facebookresearch/detr)\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\n# ------------------------------------------------------------------------\n\n\"\"\"\nBackbone modules.\n\"\"\"\n\nfrom typing import Dict, List\n\nimport torch\nimport torch.nn.functional as F\nimport torchvision\nfrom torch import nn\nfrom torchvision.models._utils import IntermediateLayerGetter\n\nfrom groundingdino.util.misc import NestedTensor, clean_state_dict, is_main_process\n\nfrom .position_encoding import build_position_encoding\nfrom .swin_transformer import build_swin_transformer\n\n\nclass FrozenBatchNorm2d(torch.nn.Module):\n    \"\"\"\n    BatchNorm2d where the batch statistics and the affine parameters are fixed.\n\n    Copy-paste from torchvision.misc.ops with added eps before rqsrt,\n    without which any other models than torchvision.models.resnet[18,34,50,101]\n    produce nans.\n    \"\"\"\n\n    def __init__(self, n):\n        super(FrozenBatchNorm2d, self).__init__()\n        self.register_buffer(\"weight\", torch.ones(n))\n        self.register_buffer(\"bias\", torch.zeros(n))\n        self.register_buffer(\"running_mean\", torch.zeros(n))\n        self.register_buffer(\"running_var\", torch.ones(n))\n\n    def _load_from_state_dict(\n        self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs\n    ):\n        num_batches_tracked_key = prefix + \"num_batches_tracked\"\n        if num_batches_tracked_key in state_dict:\n            del state_dict[num_batches_tracked_key]\n\n        super(FrozenBatchNorm2d, self)._load_from_state_dict(\n            state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs\n        )\n\n    def forward(self, x):\n        # move reshapes to the beginning\n        # to make it fuser-friendly\n        w = self.weight.reshape(1, -1, 1, 1)\n        b = self.bias.reshape(1, -1, 1, 1)\n        rv = self.running_var.reshape(1, -1, 1, 1)\n        rm = self.running_mean.reshape(1, -1, 1, 1)\n        eps = 1e-5\n        scale = w * (rv + eps).rsqrt()\n        bias = b - rm * scale\n        return x * scale + bias\n\n\nclass BackboneBase(nn.Module):\n    def __init__(\n        self,\n        backbone: nn.Module,\n        train_backbone: bool,\n        num_channels: int,\n        return_interm_indices: list,\n    ):\n        super().__init__()\n        for name, parameter in backbone.named_parameters():\n            if (\n                not train_backbone\n                or \"layer2\" not in name\n                and \"layer3\" not in name\n                and \"layer4\" not in name\n            ):\n                parameter.requires_grad_(False)\n\n        return_layers = {}\n        for idx, layer_index in enumerate(return_interm_indices):\n            return_layers.update(\n                {\"layer{}\".format(5 - len(return_interm_indices) + idx): \"{}\".format(layer_index)}\n            )\n\n        # if len:\n        #     if use_stage1_feature:\n        #         return_layers = {\"layer1\": \"0\", \"layer2\": \"1\", \"layer3\": \"2\", \"layer4\": \"3\"}\n        #     else:\n        #         return_layers = {\"layer2\": \"0\", \"layer3\": \"1\", \"layer4\": \"2\"}\n        # else:\n        #     return_layers = {'layer4': \"0\"}\n        self.body = IntermediateLayerGetter(backbone, return_layers=return_layers)\n        self.num_channels = num_channels\n\n    def forward(self, tensor_list: NestedTensor):\n        xs = self.body(tensor_list.tensors)\n        out: Dict[str, NestedTensor] = {}\n        for name, x in xs.items():\n            m = tensor_list.mask\n            assert m is not None\n            mask = F.interpolate(m[None].float(), size=x.shape[-2:]).to(torch.bool)[0]\n            out[name] = NestedTensor(x, mask)\n        # import ipdb; ipdb.set_trace()\n        return out\n\n\nclass Backbone(BackboneBase):\n    \"\"\"ResNet backbone with frozen BatchNorm.\"\"\"\n\n    def __init__(\n        self,\n        name: str,\n        train_backbone: bool,\n        dilation: bool,\n        return_interm_indices: list,\n        batch_norm=FrozenBatchNorm2d,\n    ):\n        if name in [\"resnet18\", \"resnet34\", \"resnet50\", \"resnet101\"]:\n            backbone = getattr(torchvision.models, name)(\n                replace_stride_with_dilation=[False, False, dilation],\n                pretrained=is_main_process(),\n                norm_layer=batch_norm,\n            )\n        else:\n            raise NotImplementedError(\"Why you can get here with name {}\".format(name))\n        # num_channels = 512 if name in ('resnet18', 'resnet34') else 2048\n        assert name not in (\"resnet18\", \"resnet34\"), \"Only resnet50 and resnet101 are available.\"\n        assert return_interm_indices in [[0, 1, 2, 3], [1, 2, 3], [3]]\n        num_channels_all = [256, 512, 1024, 2048]\n        num_channels = num_channels_all[4 - len(return_interm_indices) :]\n        super().__init__(backbone, train_backbone, num_channels, return_interm_indices)\n\n\nclass Joiner(nn.Sequential):\n    def __init__(self, backbone, position_embedding):\n        super().__init__(backbone, position_embedding)\n\n    def forward(self, tensor_list: NestedTensor):\n        xs = self[0](tensor_list)\n        out: List[NestedTensor] = []\n        pos = []\n        for name, x in xs.items():\n            out.append(x)\n            # position encoding\n            pos.append(self[1](x).to(x.tensors.dtype))\n\n        return out, pos\n\n\ndef build_backbone(args):\n    \"\"\"\n    Useful args:\n        - backbone: backbone name\n        - lr_backbone:\n        - dilation\n        - return_interm_indices: available: [0,1,2,3], [1,2,3], [3]\n        - backbone_freeze_keywords:\n        - use_checkpoint: for swin only for now\n\n    \"\"\"\n    position_embedding = build_position_encoding(args)\n    train_backbone = True\n    if not train_backbone:\n        raise ValueError(\"Please set lr_backbone > 0\")\n    return_interm_indices = args.return_interm_indices\n    assert return_interm_indices in [[0, 1, 2, 3], [1, 2, 3], [3]]\n    args.backbone_freeze_keywords\n    use_checkpoint = getattr(args, \"use_checkpoint\", False)\n\n    if args.backbone in [\"resnet50\", \"resnet101\"]:\n        backbone = Backbone(\n            args.backbone,\n            train_backbone,\n            args.dilation,\n            return_interm_indices,\n            batch_norm=FrozenBatchNorm2d,\n        )\n        bb_num_channels = backbone.num_channels\n    elif args.backbone in [\n        \"swin_T_224_1k\",\n        \"swin_B_224_22k\",\n        \"swin_B_384_22k\",\n        \"swin_L_224_22k\",\n        \"swin_L_384_22k\",\n    ]:\n        pretrain_img_size = int(args.backbone.split(\"_\")[-2])\n        backbone = build_swin_transformer(\n            args.backbone,\n            pretrain_img_size=pretrain_img_size,\n            out_indices=tuple(return_interm_indices),\n            dilation=False,\n            use_checkpoint=use_checkpoint,\n        )\n\n        bb_num_channels = backbone.num_features[4 - len(return_interm_indices) :]\n    else:\n        raise NotImplementedError(\"Unknown backbone {}\".format(args.backbone))\n\n    assert len(bb_num_channels) == len(\n        return_interm_indices\n    ), f\"len(bb_num_channels) {len(bb_num_channels)} != len(return_interm_indices) {len(return_interm_indices)}\"\n\n    model = Joiner(backbone, position_embedding)\n    model.num_channels = bb_num_channels\n    assert isinstance(\n        bb_num_channels, List\n    ), \"bb_num_channels is expected to be a List but {}\".format(type(bb_num_channels))\n    # import ipdb; ipdb.set_trace()\n    return model\n"
  },
  {
    "path": "model_cards/groundingdino/models/GroundingDINO/backbone/position_encoding.py",
    "content": "# ------------------------------------------------------------------------\n# Grounding DINO\n# url: https://github.com/IDEA-Research/GroundingDINO\n# Copyright (c) 2023 IDEA. All Rights Reserved.\n# Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n# ------------------------------------------------------------------------\n# DINO\n# Copyright (c) 2022 IDEA. All Rights Reserved.\n# Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n# ------------------------------------------------------------------------\n# Conditional DETR\n# Copyright (c) 2021 Microsoft. All Rights Reserved.\n# Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n# ------------------------------------------------------------------------\n# Copied from DETR (https://github.com/facebookresearch/detr)\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\n# ------------------------------------------------------------------------\n\n\"\"\"\nVarious positional encodings for the transformer.\n\"\"\"\nimport math\n\nimport torch\nfrom torch import nn\n\nfrom groundingdino.util.misc import NestedTensor\n\n\nclass PositionEmbeddingSine(nn.Module):\n    \"\"\"\n    This is a more standard version of the position embedding, very similar to the one\n    used by the Attention is all you need paper, generalized to work on images.\n    \"\"\"\n\n    def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None):\n        super().__init__()\n        self.num_pos_feats = num_pos_feats\n        self.temperature = temperature\n        self.normalize = normalize\n        if scale is not None and normalize is False:\n            raise ValueError(\"normalize should be True if scale is passed\")\n        if scale is None:\n            scale = 2 * math.pi\n        self.scale = scale\n\n    def forward(self, tensor_list: NestedTensor):\n        x = tensor_list.tensors\n        mask = tensor_list.mask\n        assert mask is not None\n        not_mask = ~mask\n        y_embed = not_mask.cumsum(1, dtype=torch.float32)\n        x_embed = not_mask.cumsum(2, dtype=torch.float32)\n        if self.normalize:\n            eps = 1e-6\n            # if os.environ.get(\"SHILONG_AMP\", None) == '1':\n            #     eps = 1e-4\n            # else:\n            #     eps = 1e-6\n            y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale\n            x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale\n\n        dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)\n        dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)\n\n        pos_x = x_embed[:, :, :, None] / dim_t\n        pos_y = y_embed[:, :, :, None] / dim_t\n        pos_x = torch.stack(\n            (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4\n        ).flatten(3)\n        pos_y = torch.stack(\n            (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4\n        ).flatten(3)\n        pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)\n        return pos\n\n\nclass PositionEmbeddingSineHW(nn.Module):\n    \"\"\"\n    This is a more standard version of the position embedding, very similar to the one\n    used by the Attention is all you need paper, generalized to work on images.\n    \"\"\"\n\n    def __init__(\n        self, num_pos_feats=64, temperatureH=10000, temperatureW=10000, normalize=False, scale=None\n    ):\n        super().__init__()\n        self.num_pos_feats = num_pos_feats\n        self.temperatureH = temperatureH\n        self.temperatureW = temperatureW\n        self.normalize = normalize\n        if scale is not None and normalize is False:\n            raise ValueError(\"normalize should be True if scale is passed\")\n        if scale is None:\n            scale = 2 * math.pi\n        self.scale = scale\n\n    def forward(self, tensor_list: NestedTensor):\n        x = tensor_list.tensors\n        mask = tensor_list.mask\n        assert mask is not None\n        not_mask = ~mask\n        y_embed = not_mask.cumsum(1, dtype=torch.float32)\n        x_embed = not_mask.cumsum(2, dtype=torch.float32)\n\n        # import ipdb; ipdb.set_trace()\n\n        if self.normalize:\n            eps = 1e-6\n            y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale\n            x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale\n\n        dim_tx = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)\n        dim_tx = self.temperatureW ** (2 * (torch.div(dim_tx, 2, rounding_mode='floor')) / self.num_pos_feats)\n        pos_x = x_embed[:, :, :, None] / dim_tx\n\n        dim_ty = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)\n        dim_ty = self.temperatureH ** (2 * (torch.div(dim_ty, 2, rounding_mode='floor')) / self.num_pos_feats)\n        pos_y = y_embed[:, :, :, None] / dim_ty\n\n        pos_x = torch.stack(\n            (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4\n        ).flatten(3)\n        pos_y = torch.stack(\n            (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4\n        ).flatten(3)\n        pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)\n\n        # import ipdb; ipdb.set_trace()\n\n        return pos\n\n\nclass PositionEmbeddingLearned(nn.Module):\n    \"\"\"\n    Absolute pos embedding, learned.\n    \"\"\"\n\n    def __init__(self, num_pos_feats=256):\n        super().__init__()\n        self.row_embed = nn.Embedding(50, num_pos_feats)\n        self.col_embed = nn.Embedding(50, num_pos_feats)\n        self.reset_parameters()\n\n    def reset_parameters(self):\n        nn.init.uniform_(self.row_embed.weight)\n        nn.init.uniform_(self.col_embed.weight)\n\n    def forward(self, tensor_list: NestedTensor):\n        x = tensor_list.tensors\n        h, w = x.shape[-2:]\n        i = torch.arange(w, device=x.device)\n        j = torch.arange(h, device=x.device)\n        x_emb = self.col_embed(i)\n        y_emb = self.row_embed(j)\n        pos = (\n            torch.cat(\n                [\n                    x_emb.unsqueeze(0).repeat(h, 1, 1),\n                    y_emb.unsqueeze(1).repeat(1, w, 1),\n                ],\n                dim=-1,\n            )\n            .permute(2, 0, 1)\n            .unsqueeze(0)\n            .repeat(x.shape[0], 1, 1, 1)\n        )\n        return pos\n\n\ndef build_position_encoding(args):\n    N_steps = args.hidden_dim // 2\n    if args.position_embedding in (\"v2\", \"sine\"):\n        # TODO find a better way of exposing other arguments\n        position_embedding = PositionEmbeddingSineHW(\n            N_steps,\n            temperatureH=args.pe_temperatureH,\n            temperatureW=args.pe_temperatureW,\n            normalize=True,\n        )\n    elif args.position_embedding in (\"v3\", \"learned\"):\n        position_embedding = PositionEmbeddingLearned(N_steps)\n    else:\n        raise ValueError(f\"not supported {args.position_embedding}\")\n\n    return position_embedding\n"
  },
  {
    "path": "model_cards/groundingdino/models/GroundingDINO/backbone/swin_transformer.py",
    "content": "# ------------------------------------------------------------------------\n# Grounding DINO\n# url: https://github.com/IDEA-Research/GroundingDINO\n# Copyright (c) 2023 IDEA. All Rights Reserved.\n# Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n# ------------------------------------------------------------------------\n# DINO\n# Copyright (c) 2022 IDEA. All Rights Reserved.\n# Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n# --------------------------------------------------------\n# modified from https://github.com/SwinTransformer/Swin-Transformer-Object-Detection/blob/master/mmdet/models/backbones/swin_transformer.py\n# --------------------------------------------------------\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.utils.checkpoint as checkpoint\nfrom timm.models.layers import DropPath, to_2tuple, trunc_normal_\n\nfrom groundingdino.util.misc import NestedTensor\n\n\nclass Mlp(nn.Module):\n    \"\"\"Multilayer perceptron.\"\"\"\n\n    def __init__(\n        self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.0\n    ):\n        super().__init__()\n        out_features = out_features or in_features\n        hidden_features = hidden_features or in_features\n        self.fc1 = nn.Linear(in_features, hidden_features)\n        self.act = act_layer()\n        self.fc2 = nn.Linear(hidden_features, out_features)\n        self.drop = nn.Dropout(drop)\n\n    def forward(self, x):\n        x = self.fc1(x)\n        x = self.act(x)\n        x = self.drop(x)\n        x = self.fc2(x)\n        x = self.drop(x)\n        return x\n\n\ndef window_partition(x, window_size):\n    \"\"\"\n    Args:\n        x: (B, H, W, C)\n        window_size (int): window size\n    Returns:\n        windows: (num_windows*B, window_size, window_size, C)\n    \"\"\"\n    B, H, W, C = x.shape\n    x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)\n    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)\n    return windows\n\n\ndef window_reverse(windows, window_size, H, W):\n    \"\"\"\n    Args:\n        windows: (num_windows*B, window_size, window_size, C)\n        window_size (int): Window size\n        H (int): Height of image\n        W (int): Width of image\n    Returns:\n        x: (B, H, W, C)\n    \"\"\"\n    B = int(windows.shape[0] / (H * W / window_size / window_size))\n    x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)\n    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)\n    return x\n\n\nclass WindowAttention(nn.Module):\n    \"\"\"Window based multi-head self attention (W-MSA) module with relative position bias.\n    It supports both of shifted and non-shifted window.\n    Args:\n        dim (int): Number of input channels.\n        window_size (tuple[int]): The height and width of the window.\n        num_heads (int): Number of attention heads.\n        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True\n        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set\n        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0\n        proj_drop (float, optional): Dropout ratio of output. Default: 0.0\n    \"\"\"\n\n    def __init__(\n        self,\n        dim,\n        window_size,\n        num_heads,\n        qkv_bias=True,\n        qk_scale=None,\n        attn_drop=0.0,\n        proj_drop=0.0,\n    ):\n\n        super().__init__()\n        self.dim = dim\n        self.window_size = window_size  # Wh, Ww\n        self.num_heads = num_heads\n        head_dim = dim // num_heads\n        self.scale = qk_scale or head_dim**-0.5\n\n        # define a parameter table of relative position bias\n        self.relative_position_bias_table = nn.Parameter(\n            torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)\n        )  # 2*Wh-1 * 2*Ww-1, nH\n\n        # get pair-wise relative position index for each token inside the window\n        coords_h = torch.arange(self.window_size[0])\n        coords_w = torch.arange(self.window_size[1])\n        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww\n        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww\n        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww\n        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2\n        relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0\n        relative_coords[:, :, 1] += self.window_size[1] - 1\n        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1\n        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww\n        self.register_buffer(\"relative_position_index\", relative_position_index)\n\n        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)\n        self.attn_drop = nn.Dropout(attn_drop)\n        self.proj = nn.Linear(dim, dim)\n        self.proj_drop = nn.Dropout(proj_drop)\n\n        trunc_normal_(self.relative_position_bias_table, std=0.02)\n        self.softmax = nn.Softmax(dim=-1)\n\n    def forward(self, x, mask=None):\n        \"\"\"Forward function.\n        Args:\n            x: input features with shape of (num_windows*B, N, C)\n            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None\n        \"\"\"\n        B_, N, C = x.shape\n        qkv = (\n            self.qkv(x)\n            .reshape(B_, N, 3, self.num_heads, C // self.num_heads)\n            .permute(2, 0, 3, 1, 4)\n        )\n        q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)\n\n        q = q * self.scale\n        attn = q @ k.transpose(-2, -1)\n\n        relative_position_bias = self.relative_position_bias_table[\n            self.relative_position_index.view(-1)\n        ].view(\n            self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1\n        )  # Wh*Ww,Wh*Ww,nH\n        relative_position_bias = relative_position_bias.permute(\n            2, 0, 1\n        ).contiguous()  # nH, Wh*Ww, Wh*Ww\n        attn = attn + relative_position_bias.unsqueeze(0)\n\n        if mask is not None:\n            nW = mask.shape[0]\n            attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)\n            attn = attn.view(-1, self.num_heads, N, N)\n            attn = self.softmax(attn)\n        else:\n            attn = self.softmax(attn)\n\n        attn = self.attn_drop(attn)\n\n        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)\n        x = self.proj(x)\n        x = self.proj_drop(x)\n        return x\n\n\nclass SwinTransformerBlock(nn.Module):\n    \"\"\"Swin Transformer Block.\n    Args:\n        dim (int): Number of input channels.\n        num_heads (int): Number of attention heads.\n        window_size (int): Window size.\n        shift_size (int): Shift size for SW-MSA.\n        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.\n        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True\n        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.\n        drop (float, optional): Dropout rate. Default: 0.0\n        attn_drop (float, optional): Attention dropout rate. Default: 0.0\n        drop_path (float, optional): Stochastic depth rate. Default: 0.0\n        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU\n        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm\n    \"\"\"\n\n    def __init__(\n        self,\n        dim,\n        num_heads,\n        window_size=7,\n        shift_size=0,\n        mlp_ratio=4.0,\n        qkv_bias=True,\n        qk_scale=None,\n        drop=0.0,\n        attn_drop=0.0,\n        drop_path=0.0,\n        act_layer=nn.GELU,\n        norm_layer=nn.LayerNorm,\n    ):\n        super().__init__()\n        self.dim = dim\n        self.num_heads = num_heads\n        self.window_size = window_size\n        self.shift_size = shift_size\n        self.mlp_ratio = mlp_ratio\n        assert 0 <= self.shift_size < self.window_size, \"shift_size must in 0-window_size\"\n\n        self.norm1 = norm_layer(dim)\n        self.attn = WindowAttention(\n            dim,\n            window_size=to_2tuple(self.window_size),\n            num_heads=num_heads,\n            qkv_bias=qkv_bias,\n            qk_scale=qk_scale,\n            attn_drop=attn_drop,\n            proj_drop=drop,\n        )\n\n        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()\n        self.norm2 = norm_layer(dim)\n        mlp_hidden_dim = int(dim * mlp_ratio)\n        self.mlp = Mlp(\n            in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop\n        )\n\n        self.H = None\n        self.W = None\n\n    def forward(self, x, mask_matrix):\n        \"\"\"Forward function.\n        Args:\n            x: Input feature, tensor size (B, H*W, C).\n            H, W: Spatial resolution of the input feature.\n            mask_matrix: Attention mask for cyclic shift.\n        \"\"\"\n        B, L, C = x.shape\n        H, W = self.H, self.W\n        assert L == H * W, \"input feature has wrong size\"\n\n        shortcut = x\n        x = self.norm1(x)\n        x = x.view(B, H, W, C)\n\n        # pad feature maps to multiples of window size\n        pad_l = pad_t = 0\n        pad_r = (self.window_size - W % self.window_size) % self.window_size\n        pad_b = (self.window_size - H % self.window_size) % self.window_size\n        x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))\n        _, Hp, Wp, _ = x.shape\n\n        # cyclic shift\n        if self.shift_size > 0:\n            shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))\n            attn_mask = mask_matrix\n        else:\n            shifted_x = x\n            attn_mask = None\n\n        # partition windows\n        x_windows = window_partition(\n            shifted_x, self.window_size\n        )  # nW*B, window_size, window_size, C\n        x_windows = x_windows.view(\n            -1, self.window_size * self.window_size, C\n        )  # nW*B, window_size*window_size, C\n\n        # W-MSA/SW-MSA\n        attn_windows = self.attn(x_windows, mask=attn_mask)  # nW*B, window_size*window_size, C\n\n        # merge windows\n        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)\n        shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp)  # B H' W' C\n\n        # reverse cyclic shift\n        if self.shift_size > 0:\n            x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))\n        else:\n            x = shifted_x\n\n        if pad_r > 0 or pad_b > 0:\n            x = x[:, :H, :W, :].contiguous()\n\n        x = x.view(B, H * W, C)\n\n        # FFN\n        x = shortcut + self.drop_path(x)\n        x = x + self.drop_path(self.mlp(self.norm2(x)))\n\n        return x\n\n\nclass PatchMerging(nn.Module):\n    \"\"\"Patch Merging Layer\n    Args:\n        dim (int): Number of input channels.\n        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm\n    \"\"\"\n\n    def __init__(self, dim, norm_layer=nn.LayerNorm):\n        super().__init__()\n        self.dim = dim\n        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)\n        self.norm = norm_layer(4 * dim)\n\n    def forward(self, x, H, W):\n        \"\"\"Forward function.\n        Args:\n            x: Input feature, tensor size (B, H*W, C).\n            H, W: Spatial resolution of the input feature.\n        \"\"\"\n        B, L, C = x.shape\n        assert L == H * W, \"input feature has wrong size\"\n\n        x = x.view(B, H, W, C)\n\n        # padding\n        pad_input = (H % 2 == 1) or (W % 2 == 1)\n        if pad_input:\n            x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2))\n\n        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C\n        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C\n        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C\n        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C\n        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C\n        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C\n\n        x = self.norm(x)\n        x = self.reduction(x)\n\n        return x\n\n\nclass BasicLayer(nn.Module):\n    \"\"\"A basic Swin Transformer layer for one stage.\n    Args:\n        dim (int): Number of feature channels\n        depth (int): Depths of this stage.\n        num_heads (int): Number of attention head.\n        window_size (int): Local window size. Default: 7.\n        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.\n        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True\n        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.\n        drop (float, optional): Dropout rate. Default: 0.0\n        attn_drop (float, optional): Attention dropout rate. Default: 0.0\n        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0\n        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm\n        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None\n        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.\n    \"\"\"\n\n    def __init__(\n        self,\n        dim,\n        depth,\n        num_heads,\n        window_size=7,\n        mlp_ratio=4.0,\n        qkv_bias=True,\n        qk_scale=None,\n        drop=0.0,\n        attn_drop=0.0,\n        drop_path=0.0,\n        norm_layer=nn.LayerNorm,\n        downsample=None,\n        use_checkpoint=False,\n    ):\n        super().__init__()\n        self.window_size = window_size\n        self.shift_size = window_size // 2\n        self.depth = depth\n        self.use_checkpoint = use_checkpoint\n\n        # build blocks\n        self.blocks = nn.ModuleList(\n            [\n                SwinTransformerBlock(\n                    dim=dim,\n                    num_heads=num_heads,\n                    window_size=window_size,\n                    shift_size=0 if (i % 2 == 0) else window_size // 2,\n                    mlp_ratio=mlp_ratio,\n                    qkv_bias=qkv_bias,\n                    qk_scale=qk_scale,\n                    drop=drop,\n                    attn_drop=attn_drop,\n                    drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,\n                    norm_layer=norm_layer,\n                )\n                for i in range(depth)\n            ]\n        )\n\n        # patch merging layer\n        if downsample is not None:\n            self.downsample = downsample(dim=dim, norm_layer=norm_layer)\n        else:\n            self.downsample = None\n\n    def forward(self, x, H, W):\n        \"\"\"Forward function.\n        Args:\n            x: Input feature, tensor size (B, H*W, C).\n            H, W: Spatial resolution of the input feature.\n        \"\"\"\n\n        # calculate attention mask for SW-MSA\n        Hp = int(np.ceil(H / self.window_size)) * self.window_size\n        Wp = int(np.ceil(W / self.window_size)) * self.window_size\n        img_mask = torch.zeros((1, Hp, Wp, 1), device=x.device)  # 1 Hp Wp 1\n        h_slices = (\n            slice(0, -self.window_size),\n            slice(-self.window_size, -self.shift_size),\n            slice(-self.shift_size, None),\n        )\n        w_slices = (\n            slice(0, -self.window_size),\n            slice(-self.window_size, -self.shift_size),\n            slice(-self.shift_size, None),\n        )\n        cnt = 0\n        for h in h_slices:\n            for w in w_slices:\n                img_mask[:, h, w, :] = cnt\n                cnt += 1\n\n        mask_windows = window_partition(\n            img_mask, self.window_size\n        )  # nW, window_size, window_size, 1\n        mask_windows = mask_windows.view(-1, self.window_size * self.window_size)\n        attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)\n        attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(\n            attn_mask == 0, float(0.0)\n        )\n\n        for blk in self.blocks:\n            blk.H, blk.W = H, W\n            if self.use_checkpoint:\n                x = checkpoint.checkpoint(blk, x, attn_mask)\n            else:\n                x = blk(x, attn_mask)\n        if self.downsample is not None:\n            x_down = self.downsample(x, H, W)\n            Wh, Ww = (H + 1) // 2, (W + 1) // 2\n            return x, H, W, x_down, Wh, Ww\n        else:\n            return x, H, W, x, H, W\n\n\nclass PatchEmbed(nn.Module):\n    \"\"\"Image to Patch Embedding\n    Args:\n        patch_size (int): Patch token size. Default: 4.\n        in_chans (int): Number of input image channels. Default: 3.\n        embed_dim (int): Number of linear projection output channels. Default: 96.\n        norm_layer (nn.Module, optional): Normalization layer. Default: None\n    \"\"\"\n\n    def __init__(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):\n        super().__init__()\n        patch_size = to_2tuple(patch_size)\n        self.patch_size = patch_size\n\n        self.in_chans = in_chans\n        self.embed_dim = embed_dim\n\n        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)\n        if norm_layer is not None:\n            self.norm = norm_layer(embed_dim)\n        else:\n            self.norm = None\n\n    def forward(self, x):\n        \"\"\"Forward function.\"\"\"\n        # padding\n        _, _, H, W = x.size()\n        if W % self.patch_size[1] != 0:\n            x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1]))\n        if H % self.patch_size[0] != 0:\n            x = F.pad(x, (0, 0, 0, self.patch_size[0] - H % self.patch_size[0]))\n\n        x = self.proj(x)  # B C Wh Ww\n        if self.norm is not None:\n            Wh, Ww = x.size(2), x.size(3)\n            x = x.flatten(2).transpose(1, 2)\n            x = self.norm(x)\n            x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww)\n\n        return x\n\n\nclass SwinTransformer(nn.Module):\n    \"\"\"Swin Transformer backbone.\n        A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`  -\n          https://arxiv.org/pdf/2103.14030\n    Args:\n        pretrain_img_size (int): Input image size for training the pretrained model,\n            used in absolute postion embedding. Default 224.\n        patch_size (int | tuple(int)): Patch size. Default: 4.\n        in_chans (int): Number of input image channels. Default: 3.\n        embed_dim (int): Number of linear projection output channels. Default: 96.\n        depths (tuple[int]): Depths of each Swin Transformer stage.\n        num_heads (tuple[int]): Number of attention head of each stage.\n        window_size (int): Window size. Default: 7.\n        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.\n        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True\n        qk_scale (float): Override default qk scale of head_dim ** -0.5 if set.\n        drop_rate (float): Dropout rate.\n        attn_drop_rate (float): Attention dropout rate. Default: 0.\n        drop_path_rate (float): Stochastic depth rate. Default: 0.2.\n        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.\n        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False.\n        patch_norm (bool): If True, add normalization after patch embedding. Default: True.\n        out_indices (Sequence[int]): Output from which stages.\n        frozen_stages (int): Stages to be frozen (stop grad and set eval mode).\n            -1 means not freezing any parameters.\n        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.\n        dilation (bool): if True, the output size if 16x downsample, ow 32x downsample.\n    \"\"\"\n\n    def __init__(\n        self,\n        pretrain_img_size=224,\n        patch_size=4,\n        in_chans=3,\n        embed_dim=96,\n        depths=[2, 2, 6, 2],\n        num_heads=[3, 6, 12, 24],\n        window_size=7,\n        mlp_ratio=4.0,\n        qkv_bias=True,\n        qk_scale=None,\n        drop_rate=0.0,\n        attn_drop_rate=0.0,\n        drop_path_rate=0.2,\n        norm_layer=nn.LayerNorm,\n        ape=False,\n        patch_norm=True,\n        out_indices=(0, 1, 2, 3),\n        frozen_stages=-1,\n        dilation=False,\n        use_checkpoint=False,\n    ):\n        super().__init__()\n\n        self.pretrain_img_size = pretrain_img_size\n        self.num_layers = len(depths)\n        self.embed_dim = embed_dim\n        self.ape = ape\n        self.patch_norm = patch_norm\n        self.out_indices = out_indices\n        self.frozen_stages = frozen_stages\n        self.dilation = dilation\n\n        # if use_checkpoint:\n        #     print(\"use_checkpoint!!!!!!!!!!!!!!!!!!!!!!!!\")\n\n        # split image into non-overlapping patches\n        self.patch_embed = PatchEmbed(\n            patch_size=patch_size,\n            in_chans=in_chans,\n            embed_dim=embed_dim,\n            norm_layer=norm_layer if self.patch_norm else None,\n        )\n\n        # absolute position embedding\n        if self.ape:\n            pretrain_img_size = to_2tuple(pretrain_img_size)\n            patch_size = to_2tuple(patch_size)\n            patches_resolution = [\n                pretrain_img_size[0] // patch_size[0],\n                pretrain_img_size[1] // patch_size[1],\n            ]\n\n            self.absolute_pos_embed = nn.Parameter(\n                torch.zeros(1, embed_dim, patches_resolution[0], patches_resolution[1])\n            )\n            trunc_normal_(self.absolute_pos_embed, std=0.02)\n\n        self.pos_drop = nn.Dropout(p=drop_rate)\n\n        # stochastic depth\n        dpr = [\n            x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))\n        ]  # stochastic depth decay rule\n\n        # build layers\n        self.layers = nn.ModuleList()\n        # prepare downsample list\n        downsamplelist = [PatchMerging for i in range(self.num_layers)]\n        downsamplelist[-1] = None\n        num_features = [int(embed_dim * 2**i) for i in range(self.num_layers)]\n        if self.dilation:\n            downsamplelist[-2] = None\n            num_features[-1] = int(embed_dim * 2 ** (self.num_layers - 1)) // 2\n        for i_layer in range(self.num_layers):\n            layer = BasicLayer(\n                # dim=int(embed_dim * 2 ** i_layer),\n                dim=num_features[i_layer],\n                depth=depths[i_layer],\n                num_heads=num_heads[i_layer],\n                window_size=window_size,\n                mlp_ratio=mlp_ratio,\n                qkv_bias=qkv_bias,\n                qk_scale=qk_scale,\n                drop=drop_rate,\n                attn_drop=attn_drop_rate,\n                drop_path=dpr[sum(depths[:i_layer]) : sum(depths[: i_layer + 1])],\n                norm_layer=norm_layer,\n                # downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,\n                downsample=downsamplelist[i_layer],\n                use_checkpoint=use_checkpoint,\n            )\n            self.layers.append(layer)\n\n        # num_features = [int(embed_dim * 2 ** i) for i in range(self.num_layers)]\n        self.num_features = num_features\n\n        # add a norm layer for each output\n        for i_layer in out_indices:\n            layer = norm_layer(num_features[i_layer])\n            layer_name = f\"norm{i_layer}\"\n            self.add_module(layer_name, layer)\n\n        self._freeze_stages()\n\n    def _freeze_stages(self):\n        if self.frozen_stages >= 0:\n            self.patch_embed.eval()\n            for param in self.patch_embed.parameters():\n                param.requires_grad = False\n\n        if self.frozen_stages >= 1 and self.ape:\n            self.absolute_pos_embed.requires_grad = False\n\n        if self.frozen_stages >= 2:\n            self.pos_drop.eval()\n            for i in range(0, self.frozen_stages - 1):\n                m = self.layers[i]\n                m.eval()\n                for param in m.parameters():\n                    param.requires_grad = False\n\n    # def init_weights(self, pretrained=None):\n    #     \"\"\"Initialize the weights in backbone.\n    #     Args:\n    #         pretrained (str, optional): Path to pre-trained weights.\n    #             Defaults to None.\n    #     \"\"\"\n\n    #     def _init_weights(m):\n    #         if isinstance(m, nn.Linear):\n    #             trunc_normal_(m.weight, std=.02)\n    #             if isinstance(m, nn.Linear) and m.bias is not None:\n    #                 nn.init.constant_(m.bias, 0)\n    #         elif isinstance(m, nn.LayerNorm):\n    #             nn.init.constant_(m.bias, 0)\n    #             nn.init.constant_(m.weight, 1.0)\n\n    #     if isinstance(pretrained, str):\n    #         self.apply(_init_weights)\n    #         logger = get_root_logger()\n    #         load_checkpoint(self, pretrained, strict=False, logger=logger)\n    #     elif pretrained is None:\n    #         self.apply(_init_weights)\n    #     else:\n    #         raise TypeError('pretrained must be a str or None')\n\n    def forward_raw(self, x):\n        \"\"\"Forward function.\"\"\"\n        x = self.patch_embed(x)\n\n        Wh, Ww = x.size(2), x.size(3)\n        if self.ape:\n            # interpolate the position embedding to the corresponding size\n            absolute_pos_embed = F.interpolate(\n                self.absolute_pos_embed, size=(Wh, Ww), mode=\"bicubic\"\n            )\n            x = (x + absolute_pos_embed).flatten(2).transpose(1, 2)  # B Wh*Ww C\n        else:\n            x = x.flatten(2).transpose(1, 2)\n        x = self.pos_drop(x)\n\n        outs = []\n        for i in range(self.num_layers):\n            layer = self.layers[i]\n            x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww)\n            # import ipdb; ipdb.set_trace()\n\n            if i in self.out_indices:\n                norm_layer = getattr(self, f\"norm{i}\")\n                x_out = norm_layer(x_out)\n\n                out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous()\n                outs.append(out)\n        # in:\n        #   torch.Size([2, 3, 1024, 1024])\n        # outs:\n        #   [torch.Size([2, 192, 256, 256]), torch.Size([2, 384, 128, 128]), \\\n        #       torch.Size([2, 768, 64, 64]), torch.Size([2, 1536, 32, 32])]\n        return tuple(outs)\n\n    def forward(self, tensor_list: NestedTensor):\n        x = tensor_list.tensors\n\n        \"\"\"Forward function.\"\"\"\n        x = self.patch_embed(x)\n\n        Wh, Ww = x.size(2), x.size(3)\n        if self.ape:\n            # interpolate the position embedding to the corresponding size\n            absolute_pos_embed = F.interpolate(\n                self.absolute_pos_embed, size=(Wh, Ww), mode=\"bicubic\"\n            )\n            x = (x + absolute_pos_embed).flatten(2).transpose(1, 2)  # B Wh*Ww C\n        else:\n            x = x.flatten(2).transpose(1, 2)\n        x = self.pos_drop(x)\n\n        outs = []\n        for i in range(self.num_layers):\n            layer = self.layers[i]\n            x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww)\n\n            if i in self.out_indices:\n                norm_layer = getattr(self, f\"norm{i}\")\n                x_out = norm_layer(x_out)\n\n                out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous()\n                outs.append(out)\n        # in:\n        #   torch.Size([2, 3, 1024, 1024])\n        # out:\n        #   [torch.Size([2, 192, 256, 256]), torch.Size([2, 384, 128, 128]), \\\n        #       torch.Size([2, 768, 64, 64]), torch.Size([2, 1536, 32, 32])]\n\n        # collect for nesttensors\n        outs_dict = {}\n        for idx, out_i in enumerate(outs):\n            m = tensor_list.mask\n            assert m is not None\n            mask = F.interpolate(m[None].float(), size=out_i.shape[-2:]).to(torch.bool)[0]\n            outs_dict[idx] = NestedTensor(out_i, mask)\n\n        return outs_dict\n\n    def train(self, mode=True):\n        \"\"\"Convert the model into training mode while keep layers freezed.\"\"\"\n        super(SwinTransformer, self).train(mode)\n        self._freeze_stages()\n\n\ndef build_swin_transformer(modelname, pretrain_img_size, **kw):\n    assert modelname in [\n        \"swin_T_224_1k\",\n        \"swin_B_224_22k\",\n        \"swin_B_384_22k\",\n        \"swin_L_224_22k\",\n        \"swin_L_384_22k\",\n    ]\n\n    model_para_dict = {\n        \"swin_T_224_1k\": dict(\n            embed_dim=96, depths=[2, 2, 6, 2], num_heads=[3, 6, 12, 24], window_size=7\n        ),\n        \"swin_B_224_22k\": dict(\n            embed_dim=128, depths=[2, 2, 18, 2], num_heads=[4, 8, 16, 32], window_size=7\n        ),\n        \"swin_B_384_22k\": dict(\n            embed_dim=128, depths=[2, 2, 18, 2], num_heads=[4, 8, 16, 32], window_size=12\n        ),\n        \"swin_L_224_22k\": dict(\n            embed_dim=192, depths=[2, 2, 18, 2], num_heads=[6, 12, 24, 48], window_size=7\n        ),\n        \"swin_L_384_22k\": dict(\n            embed_dim=192, depths=[2, 2, 18, 2], num_heads=[6, 12, 24, 48], window_size=12\n        ),\n    }\n    kw_cgf = model_para_dict[modelname]\n    kw_cgf.update(kw)\n    model = SwinTransformer(pretrain_img_size=pretrain_img_size, **kw_cgf)\n    return model\n\n\nif __name__ == \"__main__\":\n    model = build_swin_transformer(\"swin_L_384_22k\", 384, dilation=True)\n    x = torch.rand(2, 3, 1024, 1024)\n    y = model.forward_raw(x)\n    import ipdb\n\n    ipdb.set_trace()\n    x = torch.rand(2, 3, 384, 384)\n    y = model.forward_raw(x)\n"
  },
  {
    "path": "model_cards/groundingdino/models/GroundingDINO/bertwarper.py",
    "content": "# ------------------------------------------------------------------------\n# Grounding DINO\n# url: https://github.com/IDEA-Research/GroundingDINO\n# Copyright (c) 2023 IDEA. All Rights Reserved.\n# Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n# ------------------------------------------------------------------------\n\nimport torch\nimport torch.nn.functional as F\nimport torch.utils.checkpoint as checkpoint\nfrom torch import Tensor, nn\nfrom torchvision.ops.boxes import nms\nfrom transformers import BertConfig, BertModel, BertPreTrainedModel\nfrom transformers.modeling_outputs import BaseModelOutputWithPoolingAndCrossAttentions\n\n\nclass BertModelWarper(nn.Module):\n    def __init__(self, bert_model):\n        super().__init__()\n        # self.bert = bert_modelc\n\n        self.config = bert_model.config\n        self.embeddings = bert_model.embeddings\n        self.encoder = bert_model.encoder\n        self.pooler = bert_model.pooler\n\n        self.get_extended_attention_mask = bert_model.get_extended_attention_mask\n        self.invert_attention_mask = bert_model.invert_attention_mask\n        self.get_head_mask = bert_model.get_head_mask\n\n    def forward(\n        self,\n        input_ids=None,\n        attention_mask=None,\n        token_type_ids=None,\n        position_ids=None,\n        head_mask=None,\n        inputs_embeds=None,\n        encoder_hidden_states=None,\n        encoder_attention_mask=None,\n        past_key_values=None,\n        use_cache=None,\n        output_attentions=None,\n        output_hidden_states=None,\n        return_dict=None,\n    ):\n        r\"\"\"\n        encoder_hidden_states  (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):\n            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if\n            the model is configured as a decoder.\n        encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):\n            Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in\n            the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``:\n\n            - 1 for tokens that are **not masked**,\n            - 0 for tokens that are **masked**.\n        past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):\n            Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.\n\n            If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`\n            (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`\n            instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`.\n        use_cache (:obj:`bool`, `optional`):\n            If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up\n            decoding (see :obj:`past_key_values`).\n        \"\"\"\n        output_attentions = (\n            output_attentions if output_attentions is not None else self.config.output_attentions\n        )\n        output_hidden_states = (\n            output_hidden_states\n            if output_hidden_states is not None\n            else self.config.output_hidden_states\n        )\n        return_dict = return_dict if return_dict is not None else self.config.use_return_dict\n\n        if self.config.is_decoder:\n            use_cache = use_cache if use_cache is not None else self.config.use_cache\n        else:\n            use_cache = False\n\n        if input_ids is not None and inputs_embeds is not None:\n            raise ValueError(\"You cannot specify both input_ids and inputs_embeds at the same time\")\n        elif input_ids is not None:\n            input_shape = input_ids.size()\n            batch_size, seq_length = input_shape\n        elif inputs_embeds is not None:\n            input_shape = inputs_embeds.size()[:-1]\n            batch_size, seq_length = input_shape\n        else:\n            raise ValueError(\"You have to specify either input_ids or inputs_embeds\")\n\n        device = input_ids.device if input_ids is not None else inputs_embeds.device\n\n        # past_key_values_length\n        past_key_values_length = (\n            past_key_values[0][0].shape[2] if past_key_values is not None else 0\n        )\n\n        if attention_mask is None:\n            attention_mask = torch.ones(\n                ((batch_size, seq_length + past_key_values_length)), device=device\n            )\n        if token_type_ids is None:\n            token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)\n\n        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]\n        # ourselves in which case we just need to make it broadcastable to all heads.\n        extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(\n            attention_mask, input_shape, device\n        )\n\n        # If a 2D or 3D attention mask is provided for the cross-attention\n        # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]\n        if self.config.is_decoder and encoder_hidden_states is not None:\n            encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()\n            encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)\n            if encoder_attention_mask is None:\n                encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)\n            encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)\n        else:\n            encoder_extended_attention_mask = None\n        # if os.environ.get('IPDB_SHILONG_DEBUG', None) == 'INFO':\n        #     import ipdb; ipdb.set_trace()\n\n        # Prepare head mask if needed\n        # 1.0 in head_mask indicate we keep the head\n        # attention_probs has shape bsz x n_heads x N x N\n        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]\n        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]\n        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)\n\n        embedding_output = self.embeddings(\n            input_ids=input_ids,\n            position_ids=position_ids,\n            token_type_ids=token_type_ids,\n            inputs_embeds=inputs_embeds,\n            past_key_values_length=past_key_values_length,\n        )\n\n        encoder_outputs = self.encoder(\n            embedding_output,\n            attention_mask=extended_attention_mask,\n            head_mask=head_mask,\n            encoder_hidden_states=encoder_hidden_states,\n            encoder_attention_mask=encoder_extended_attention_mask,\n            past_key_values=past_key_values,\n            use_cache=use_cache,\n            output_attentions=output_attentions,\n            output_hidden_states=output_hidden_states,\n            return_dict=return_dict,\n        )\n        sequence_output = encoder_outputs[0]\n        pooled_output = self.pooler(sequence_output) if self.pooler is not None else None\n\n        if not return_dict:\n            return (sequence_output, pooled_output) + encoder_outputs[1:]\n\n        return BaseModelOutputWithPoolingAndCrossAttentions(\n            last_hidden_state=sequence_output,\n            pooler_output=pooled_output,\n            past_key_values=encoder_outputs.past_key_values,\n            hidden_states=encoder_outputs.hidden_states,\n            attentions=encoder_outputs.attentions,\n            cross_attentions=encoder_outputs.cross_attentions,\n        )\n\n\nclass TextEncoderShell(nn.Module):\n    def __init__(self, text_encoder):\n        super().__init__()\n        self.text_encoder = text_encoder\n        self.config = self.text_encoder.config\n\n    def forward(self, **kw):\n        # feed into text encoder\n        return self.text_encoder(**kw)\n\n\ndef generate_masks_with_special_tokens(tokenized, special_tokens_list, tokenizer):\n    \"\"\"Generate attention mask between each pair of special tokens\n    Args:\n        input_ids (torch.Tensor): input ids. Shape: [bs, num_token]\n        special_tokens_mask (list): special tokens mask.\n    Returns:\n        torch.Tensor: attention mask between each special tokens.\n    \"\"\"\n    input_ids = tokenized[\"input_ids\"]\n    bs, num_token = input_ids.shape\n    # special_tokens_mask: bs, num_token. 1 for special tokens. 0 for normal tokens\n    special_tokens_mask = torch.zeros((bs, num_token), device=input_ids.device).bool()\n    for special_token in special_tokens_list:\n        special_tokens_mask |= input_ids == special_token\n\n    # idxs: each row is a list of indices of special tokens\n    idxs = torch.nonzero(special_tokens_mask)\n\n    # generate attention mask and positional ids\n    attention_mask = (\n        torch.eye(num_token, device=input_ids.device).bool().unsqueeze(0).repeat(bs, 1, 1)\n    )\n    position_ids = torch.zeros((bs, num_token), device=input_ids.device)\n    previous_col = 0\n    for i in range(idxs.shape[0]):\n        row, col = idxs[i]\n        if (col == 0) or (col == num_token - 1):\n            attention_mask[row, col, col] = True\n            position_ids[row, col] = 0\n        else:\n            attention_mask[row, previous_col + 1 : col + 1, previous_col + 1 : col + 1] = True\n            position_ids[row, previous_col + 1 : col + 1] = torch.arange(\n                0, col - previous_col, device=input_ids.device\n            )\n\n        previous_col = col\n\n    # # padding mask\n    # padding_mask = tokenized['attention_mask']\n    # attention_mask = attention_mask & padding_mask.unsqueeze(1).bool() & padding_mask.unsqueeze(2).bool()\n\n    return attention_mask, position_ids.to(torch.long)\n\n\ndef generate_masks_with_special_tokens_and_transfer_map(tokenized, special_tokens_list, tokenizer):\n    \"\"\"Generate attention mask between each pair of special tokens\n    Args:\n        input_ids (torch.Tensor): input ids. Shape: [bs, num_token]\n        special_tokens_mask (list): special tokens mask.\n    Returns:\n        torch.Tensor: attention mask between each special tokens.\n    \"\"\"\n    input_ids = tokenized[\"input_ids\"]\n    bs, num_token = input_ids.shape\n    # special_tokens_mask: bs, num_token. 1 for special tokens. 0 for normal tokens\n    special_tokens_mask = torch.zeros((bs, num_token), device=input_ids.device).bool()\n    for special_token in special_tokens_list:\n        special_tokens_mask |= input_ids == special_token\n\n    # idxs: each row is a list of indices of special tokens\n    idxs = torch.nonzero(special_tokens_mask)\n\n    # generate attention mask and positional ids\n    attention_mask = (\n        torch.eye(num_token, device=input_ids.device).bool().unsqueeze(0).repeat(bs, 1, 1)\n    )\n    position_ids = torch.zeros((bs, num_token), device=input_ids.device)\n    cate_to_token_mask_list = [[] for _ in range(bs)]\n    previous_col = 0\n    for i in range(idxs.shape[0]):\n        row, col = idxs[i]\n        if (col == 0) or (col == num_token - 1):\n            attention_mask[row, col, col] = True\n            position_ids[row, col] = 0\n        else:\n            attention_mask[row, previous_col + 1 : col + 1, previous_col + 1 : col + 1] = True\n            position_ids[row, previous_col + 1 : col + 1] = torch.arange(\n                0, col - previous_col, device=input_ids.device\n            )\n            c2t_maski = torch.zeros((num_token), device=input_ids.device).bool()\n            c2t_maski[previous_col + 1 : col] = True\n            cate_to_token_mask_list[row].append(c2t_maski)\n        previous_col = col\n\n    cate_to_token_mask_list = [\n        torch.stack(cate_to_token_mask_listi, dim=0)\n        for cate_to_token_mask_listi in cate_to_token_mask_list\n    ]\n\n    # # padding mask\n    # padding_mask = tokenized['attention_mask']\n    # attention_mask = attention_mask & padding_mask.unsqueeze(1).bool() & padding_mask.unsqueeze(2).bool()\n\n    return attention_mask, position_ids.to(torch.long), cate_to_token_mask_list\n"
  },
  {
    "path": "model_cards/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn.h",
    "content": "/*!\n**************************************************************************************************\n* Deformable DETR\n* Copyright (c) 2020 SenseTime. All Rights Reserved.\n* Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n**************************************************************************************************\n* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0\n**************************************************************************************************\n*/\n\n#pragma once\n\n#include \"ms_deform_attn_cpu.h\"\n\n#ifdef WITH_CUDA\n#include \"ms_deform_attn_cuda.h\"\n#endif\n\nnamespace groundingdino {\n\nat::Tensor\nms_deform_attn_forward(\n    const at::Tensor &value, \n    const at::Tensor &spatial_shapes,\n    const at::Tensor &level_start_index,\n    const at::Tensor &sampling_loc,\n    const at::Tensor &attn_weight,\n    const int im2col_step)\n{\n    if (value.type().is_cuda())\n    {\n#ifdef WITH_CUDA\n        return ms_deform_attn_cuda_forward(\n            value, spatial_shapes, level_start_index, sampling_loc, attn_weight, im2col_step);\n#else\n        AT_ERROR(\"Not compiled with GPU support\");\n#endif\n    }\n    AT_ERROR(\"Not implemented on the CPU\");\n}\n\nstd::vector<at::Tensor>\nms_deform_attn_backward(\n    const at::Tensor &value, \n    const at::Tensor &spatial_shapes,\n    const at::Tensor &level_start_index,\n    const at::Tensor &sampling_loc,\n    const at::Tensor &attn_weight,\n    const at::Tensor &grad_output,\n    const int im2col_step)\n{\n    if (value.type().is_cuda())\n    {\n#ifdef WITH_CUDA\n        return ms_deform_attn_cuda_backward(\n            value, spatial_shapes, level_start_index, sampling_loc, attn_weight, grad_output, im2col_step);\n#else\n        AT_ERROR(\"Not compiled with GPU support\");\n#endif\n    }\n    AT_ERROR(\"Not implemented on the CPU\");\n}\n\n} // namespace groundingdino"
  },
  {
    "path": "model_cards/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn_cpu.cpp",
    "content": "/*!\n**************************************************************************************************\n* Deformable DETR\n* Copyright (c) 2020 SenseTime. All Rights Reserved.\n* Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n**************************************************************************************************\n* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0\n**************************************************************************************************\n*/\n\n#include <vector>\n\n#include <ATen/ATen.h>\n#include <ATen/cuda/CUDAContext.h>\n\nnamespace groundingdino {\n\nat::Tensor\nms_deform_attn_cpu_forward(\n    const at::Tensor &value, \n    const at::Tensor &spatial_shapes,\n    const at::Tensor &level_start_index,\n    const at::Tensor &sampling_loc,\n    const at::Tensor &attn_weight,\n    const int im2col_step)\n{\n    AT_ERROR(\"Not implement on cpu\");\n}\n\nstd::vector<at::Tensor>\nms_deform_attn_cpu_backward(\n    const at::Tensor &value, \n    const at::Tensor &spatial_shapes,\n    const at::Tensor &level_start_index,\n    const at::Tensor &sampling_loc,\n    const at::Tensor &attn_weight,\n    const at::Tensor &grad_output,\n    const int im2col_step)\n{\n    AT_ERROR(\"Not implement on cpu\");\n}\n\n} // namespace groundingdino\n"
  },
  {
    "path": "model_cards/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn_cpu.h",
    "content": "/*!\n**************************************************************************************************\n* Deformable DETR\n* Copyright (c) 2020 SenseTime. All Rights Reserved.\n* Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n**************************************************************************************************\n* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0\n**************************************************************************************************\n*/\n\n#pragma once\n#include <torch/extension.h>\n\nnamespace groundingdino {\n\nat::Tensor\nms_deform_attn_cpu_forward(\n    const at::Tensor &value, \n    const at::Tensor &spatial_shapes,\n    const at::Tensor &level_start_index,\n    const at::Tensor &sampling_loc,\n    const at::Tensor &attn_weight,\n    const int im2col_step);\n\nstd::vector<at::Tensor>\nms_deform_attn_cpu_backward(\n    const at::Tensor &value, \n    const at::Tensor &spatial_shapes,\n    const at::Tensor &level_start_index,\n    const at::Tensor &sampling_loc,\n    const at::Tensor &attn_weight,\n    const at::Tensor &grad_output,\n    const int im2col_step);\n\n} // namespace groundingdino\n"
  },
  {
    "path": "model_cards/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn_cuda.cu",
    "content": "/*!\n**************************************************************************************************\n* Deformable DETR\n* Copyright (c) 2020 SenseTime. All Rights Reserved.\n* Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n**************************************************************************************************\n* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0\n**************************************************************************************************\n*/\n\n#include <vector>\n#include \"ms_deform_im2col_cuda.cuh\"\n\n#include <ATen/ATen.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\nnamespace groundingdino {\n\nat::Tensor ms_deform_attn_cuda_forward(\n    const at::Tensor &value, \n    const at::Tensor &spatial_shapes,\n    const at::Tensor &level_start_index,\n    const at::Tensor &sampling_loc,\n    const at::Tensor &attn_weight,\n    const int im2col_step)\n{\n    AT_ASSERTM(value.is_contiguous(), \"value tensor has to be contiguous\");\n    AT_ASSERTM(spatial_shapes.is_contiguous(), \"spatial_shapes tensor has to be contiguous\");\n    AT_ASSERTM(level_start_index.is_contiguous(), \"level_start_index tensor has to be contiguous\");\n    AT_ASSERTM(sampling_loc.is_contiguous(), \"sampling_loc tensor has to be contiguous\");\n    AT_ASSERTM(attn_weight.is_contiguous(), \"attn_weight tensor has to be contiguous\");\n\n    AT_ASSERTM(value.type().is_cuda(), \"value must be a CUDA tensor\");\n    AT_ASSERTM(spatial_shapes.type().is_cuda(), \"spatial_shapes must be a CUDA tensor\");\n    AT_ASSERTM(level_start_index.type().is_cuda(), \"level_start_index must be a CUDA tensor\");\n    AT_ASSERTM(sampling_loc.type().is_cuda(), \"sampling_loc must be a CUDA tensor\");\n    AT_ASSERTM(attn_weight.type().is_cuda(), \"attn_weight must be a CUDA tensor\");\n\n    const int batch = value.size(0);\n    const int spatial_size = value.size(1);\n    const int num_heads = value.size(2);\n    const int channels = value.size(3);\n\n    const int num_levels = spatial_shapes.size(0);\n\n    const int num_query = sampling_loc.size(1);\n    const int num_point = sampling_loc.size(4);\n\n    const int im2col_step_ = std::min(batch, im2col_step);\n\n    AT_ASSERTM(batch % im2col_step_ == 0, \"batch(%d) must divide im2col_step(%d)\", batch, im2col_step_);\n    \n    auto output = at::zeros({batch, num_query, num_heads, channels}, value.options());\n\n    const int batch_n = im2col_step_;\n    auto output_n = output.view({batch/im2col_step_, batch_n, num_query, num_heads, channels});\n    auto per_value_size = spatial_size * num_heads * channels;\n    auto per_sample_loc_size = num_query * num_heads * num_levels * num_point * 2;\n    auto per_attn_weight_size = num_query * num_heads * num_levels * num_point;\n    for (int n = 0; n < batch/im2col_step_; ++n)\n    {\n        auto columns = output_n.select(0, n);\n        AT_DISPATCH_FLOATING_TYPES(value.type(), \"ms_deform_attn_forward_cuda\", ([&] {\n            ms_deformable_im2col_cuda(at::cuda::getCurrentCUDAStream(),\n                value.data<scalar_t>() + n * im2col_step_ * per_value_size,\n                spatial_shapes.data<int64_t>(),\n                level_start_index.data<int64_t>(),\n                sampling_loc.data<scalar_t>() + n * im2col_step_ * per_sample_loc_size,\n                attn_weight.data<scalar_t>() + n * im2col_step_ * per_attn_weight_size,\n                batch_n, spatial_size, num_heads, channels, num_levels, num_query, num_point,\n                columns.data<scalar_t>());\n\n        }));\n    }\n\n    output = output.view({batch, num_query, num_heads*channels});\n\n    return output;\n}\n\n\nstd::vector<at::Tensor> ms_deform_attn_cuda_backward(\n    const at::Tensor &value, \n    const at::Tensor &spatial_shapes,\n    const at::Tensor &level_start_index,\n    const at::Tensor &sampling_loc,\n    const at::Tensor &attn_weight,\n    const at::Tensor &grad_output,\n    const int im2col_step)\n{\n\n    AT_ASSERTM(value.is_contiguous(), \"value tensor has to be contiguous\");\n    AT_ASSERTM(spatial_shapes.is_contiguous(), \"spatial_shapes tensor has to be contiguous\");\n    AT_ASSERTM(level_start_index.is_contiguous(), \"level_start_index tensor has to be contiguous\");\n    AT_ASSERTM(sampling_loc.is_contiguous(), \"sampling_loc tensor has to be contiguous\");\n    AT_ASSERTM(attn_weight.is_contiguous(), \"attn_weight tensor has to be contiguous\");\n    AT_ASSERTM(grad_output.is_contiguous(), \"grad_output tensor has to be contiguous\");\n\n    AT_ASSERTM(value.type().is_cuda(), \"value must be a CUDA tensor\");\n    AT_ASSERTM(spatial_shapes.type().is_cuda(), \"spatial_shapes must be a CUDA tensor\");\n    AT_ASSERTM(level_start_index.type().is_cuda(), \"level_start_index must be a CUDA tensor\");\n    AT_ASSERTM(sampling_loc.type().is_cuda(), \"sampling_loc must be a CUDA tensor\");\n    AT_ASSERTM(attn_weight.type().is_cuda(), \"attn_weight must be a CUDA tensor\");\n    AT_ASSERTM(grad_output.type().is_cuda(), \"grad_output must be a CUDA tensor\");\n\n    const int batch = value.size(0);\n    const int spatial_size = value.size(1);\n    const int num_heads = value.size(2);\n    const int channels = value.size(3);\n\n    const int num_levels = spatial_shapes.size(0);\n\n    const int num_query = sampling_loc.size(1);\n    const int num_point = sampling_loc.size(4);\n\n    const int im2col_step_ = std::min(batch, im2col_step);\n\n    AT_ASSERTM(batch % im2col_step_ == 0, \"batch(%d) must divide im2col_step(%d)\", batch, im2col_step_);\n\n    auto grad_value = at::zeros_like(value);\n    auto grad_sampling_loc = at::zeros_like(sampling_loc);\n    auto grad_attn_weight = at::zeros_like(attn_weight);\n\n    const int batch_n = im2col_step_;\n    auto per_value_size = spatial_size * num_heads * channels;\n    auto per_sample_loc_size = num_query * num_heads * num_levels * num_point * 2;\n    auto per_attn_weight_size = num_query * num_heads * num_levels * num_point;\n    auto grad_output_n = grad_output.view({batch/im2col_step_, batch_n, num_query, num_heads, channels});\n    \n    for (int n = 0; n < batch/im2col_step_; ++n)\n    {\n        auto grad_output_g = grad_output_n.select(0, n);\n        AT_DISPATCH_FLOATING_TYPES(value.type(), \"ms_deform_attn_backward_cuda\", ([&] {\n            ms_deformable_col2im_cuda(at::cuda::getCurrentCUDAStream(),\n                                    grad_output_g.data<scalar_t>(),\n                                    value.data<scalar_t>() + n * im2col_step_ * per_value_size,\n                                    spatial_shapes.data<int64_t>(),\n                                    level_start_index.data<int64_t>(),\n                                    sampling_loc.data<scalar_t>() + n * im2col_step_ * per_sample_loc_size,\n                                    attn_weight.data<scalar_t>() + n * im2col_step_ * per_attn_weight_size,\n                                    batch_n, spatial_size, num_heads, channels, num_levels, num_query, num_point,\n                                    grad_value.data<scalar_t>() +  n * im2col_step_ * per_value_size,\n                                    grad_sampling_loc.data<scalar_t>() + n * im2col_step_ * per_sample_loc_size,\n                                    grad_attn_weight.data<scalar_t>() + n * im2col_step_ * per_attn_weight_size);\n\n        }));\n    }\n\n    return {\n        grad_value, grad_sampling_loc, grad_attn_weight\n    };\n}\n\n} // namespace groundingdino"
  },
  {
    "path": "model_cards/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn_cuda.h",
    "content": "/*!\n**************************************************************************************************\n* Deformable DETR\n* Copyright (c) 2020 SenseTime. All Rights Reserved.\n* Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n**************************************************************************************************\n* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0\n**************************************************************************************************\n*/\n\n#pragma once\n#include <torch/extension.h>\n\nnamespace groundingdino {\n\nat::Tensor ms_deform_attn_cuda_forward(\n    const at::Tensor &value, \n    const at::Tensor &spatial_shapes,\n    const at::Tensor &level_start_index,\n    const at::Tensor &sampling_loc,\n    const at::Tensor &attn_weight,\n    const int im2col_step);\n\nstd::vector<at::Tensor> ms_deform_attn_cuda_backward(\n    const at::Tensor &value, \n    const at::Tensor &spatial_shapes,\n    const at::Tensor &level_start_index,\n    const at::Tensor &sampling_loc,\n    const at::Tensor &attn_weight,\n    const at::Tensor &grad_output,\n    const int im2col_step);\n\n} // namespace groundingdino"
  },
  {
    "path": "model_cards/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_im2col_cuda.cuh",
    "content": "/*!\n**************************************************************************\n* Deformable DETR\n* Copyright (c) 2020 SenseTime. All Rights Reserved.\n* Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n**************************************************************************\n* Modified from DCN (https://github.com/msracver/Deformable-ConvNets)\n* Copyright (c) 2018 Microsoft\n**************************************************************************\n*/\n\n#include <cstdio>\n#include <algorithm>\n#include <cstring>\n\n#include <ATen/ATen.h>\n#include <ATen/cuda/CUDAContext.h>\n\n#include <THC/THCAtomics.cuh>\n\n#define CUDA_KERNEL_LOOP(i, n)                          \\\n  for (int i = blockIdx.x * blockDim.x + threadIdx.x;   \\\n      i < (n);                                          \\\n      i += blockDim.x * gridDim.x)\n\nconst int CUDA_NUM_THREADS = 1024;\ninline int GET_BLOCKS(const int N, const int num_threads)\n{\n  return (N + num_threads - 1) / num_threads;\n}\n\n\ntemplate <typename scalar_t>\n__device__ scalar_t ms_deform_attn_im2col_bilinear(const scalar_t* &bottom_data, \n                                                   const int &height, const int &width, const int &nheads, const int &channels,\n                                                   const scalar_t &h, const scalar_t &w, const int &m, const int &c)\n{\n  const int h_low = floor(h);\n  const int w_low = floor(w);\n  const int h_high = h_low + 1;\n  const int w_high = w_low + 1;\n\n  const scalar_t lh = h - h_low;\n  const scalar_t lw = w - w_low;\n  const scalar_t hh = 1 - lh, hw = 1 - lw;\n\n  const int w_stride = nheads * channels;\n  const int h_stride = width * w_stride;\n  const int h_low_ptr_offset = h_low * h_stride;\n  const int h_high_ptr_offset = h_low_ptr_offset + h_stride;\n  const int w_low_ptr_offset = w_low * w_stride;\n  const int w_high_ptr_offset = w_low_ptr_offset + w_stride;\n  const int base_ptr = m * channels + c;\n\n  scalar_t v1 = 0;\n  if (h_low >= 0 && w_low >= 0)\n  {\n    const int ptr1 = h_low_ptr_offset + w_low_ptr_offset + base_ptr;\n    v1 = bottom_data[ptr1];\n  }\n  scalar_t v2 = 0;\n  if (h_low >= 0 && w_high <= width - 1)\n  {\n    const int ptr2 = h_low_ptr_offset + w_high_ptr_offset + base_ptr;\n    v2 = bottom_data[ptr2];\n  }\n  scalar_t v3 = 0;\n  if (h_high <= height - 1 && w_low >= 0)\n  {\n    const int ptr3 = h_high_ptr_offset + w_low_ptr_offset + base_ptr;\n    v3 = bottom_data[ptr3];\n  }\n  scalar_t v4 = 0;\n  if (h_high <= height - 1 && w_high <= width - 1)\n  {\n    const int ptr4 = h_high_ptr_offset + w_high_ptr_offset + base_ptr;\n    v4 = bottom_data[ptr4];\n  }\n\n  const scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw;\n\n  const scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4);\n  return val;\n}\n\n\ntemplate <typename scalar_t>\n__device__ void ms_deform_attn_col2im_bilinear(const scalar_t* &bottom_data, \n                                                   const int &height, const int &width, const int &nheads, const int &channels,\n                                                   const scalar_t &h, const scalar_t &w, const int &m, const int &c,\n                                                   const scalar_t &top_grad,\n                                                   const scalar_t &attn_weight,\n                                                   scalar_t* &grad_value, \n                                                   scalar_t* grad_sampling_loc,\n                                                   scalar_t* grad_attn_weight)\n{\n  const int h_low = floor(h);\n  const int w_low = floor(w);\n  const int h_high = h_low + 1;\n  const int w_high = w_low + 1;\n\n  const scalar_t lh = h - h_low;\n  const scalar_t lw = w - w_low;\n  const scalar_t hh = 1 - lh, hw = 1 - lw;\n\n  const int w_stride = nheads * channels;\n  const int h_stride = width * w_stride;\n  const int h_low_ptr_offset = h_low * h_stride;\n  const int h_high_ptr_offset = h_low_ptr_offset + h_stride;\n  const int w_low_ptr_offset = w_low * w_stride;\n  const int w_high_ptr_offset = w_low_ptr_offset + w_stride;\n  const int base_ptr = m * channels + c;\n\n  const scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw;\n  const scalar_t top_grad_value = top_grad * attn_weight;\n  scalar_t grad_h_weight = 0, grad_w_weight = 0;\n\n  scalar_t v1 = 0;\n  if (h_low >= 0 && w_low >= 0)\n  {\n    const int ptr1 = h_low_ptr_offset + w_low_ptr_offset + base_ptr;\n    v1 = bottom_data[ptr1];\n    grad_h_weight -= hw * v1;\n    grad_w_weight -= hh * v1;\n    atomicAdd(grad_value+ptr1, w1*top_grad_value);\n  }\n  scalar_t v2 = 0;\n  if (h_low >= 0 && w_high <= width - 1)\n  {\n    const int ptr2 = h_low_ptr_offset + w_high_ptr_offset + base_ptr;\n    v2 = bottom_data[ptr2];\n    grad_h_weight -= lw * v2;\n    grad_w_weight += hh * v2;\n    atomicAdd(grad_value+ptr2, w2*top_grad_value);\n  }\n  scalar_t v3 = 0;\n  if (h_high <= height - 1 && w_low >= 0)\n  {\n    const int ptr3 = h_high_ptr_offset + w_low_ptr_offset + base_ptr;\n    v3 = bottom_data[ptr3];\n    grad_h_weight += hw * v3;\n    grad_w_weight -= lh * v3;\n    atomicAdd(grad_value+ptr3, w3*top_grad_value); \n  }\n  scalar_t v4 = 0;\n  if (h_high <= height - 1 && w_high <= width - 1)\n  {\n    const int ptr4 = h_high_ptr_offset + w_high_ptr_offset + base_ptr;\n    v4 = bottom_data[ptr4];\n    grad_h_weight += lw * v4;\n    grad_w_weight += lh * v4;\n    atomicAdd(grad_value+ptr4, w4*top_grad_value);\n  }\n\n  const scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4);\n  *grad_attn_weight = top_grad * val;\n  *grad_sampling_loc = width * grad_w_weight * top_grad_value;\n  *(grad_sampling_loc + 1) = height * grad_h_weight * top_grad_value;\n}\n\n\ntemplate <typename scalar_t>\n__device__ void ms_deform_attn_col2im_bilinear_gm(const scalar_t* &bottom_data, \n                                                   const int &height, const int &width, const int &nheads, const int &channels,\n                                                   const scalar_t &h, const scalar_t &w, const int &m, const int &c,\n                                                   const scalar_t &top_grad,\n                                                   const scalar_t &attn_weight,\n                                                   scalar_t* &grad_value, \n                                                   scalar_t* grad_sampling_loc,\n                                                   scalar_t* grad_attn_weight)\n{\n  const int h_low = floor(h);\n  const int w_low = floor(w);\n  const int h_high = h_low + 1;\n  const int w_high = w_low + 1;\n\n  const scalar_t lh = h - h_low;\n  const scalar_t lw = w - w_low;\n  const scalar_t hh = 1 - lh, hw = 1 - lw;\n\n  const int w_stride = nheads * channels;\n  const int h_stride = width * w_stride;\n  const int h_low_ptr_offset = h_low * h_stride;\n  const int h_high_ptr_offset = h_low_ptr_offset + h_stride;\n  const int w_low_ptr_offset = w_low * w_stride;\n  const int w_high_ptr_offset = w_low_ptr_offset + w_stride;\n  const int base_ptr = m * channels + c;\n\n  const scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw;\n  const scalar_t top_grad_value = top_grad * attn_weight;\n  scalar_t grad_h_weight = 0, grad_w_weight = 0;\n\n  scalar_t v1 = 0;\n  if (h_low >= 0 && w_low >= 0)\n  {\n    const int ptr1 = h_low_ptr_offset + w_low_ptr_offset + base_ptr;\n    v1 = bottom_data[ptr1];\n    grad_h_weight -= hw * v1;\n    grad_w_weight -= hh * v1;\n    atomicAdd(grad_value+ptr1, w1*top_grad_value);\n  }\n  scalar_t v2 = 0;\n  if (h_low >= 0 && w_high <= width - 1)\n  {\n    const int ptr2 = h_low_ptr_offset + w_high_ptr_offset + base_ptr;\n    v2 = bottom_data[ptr2];\n    grad_h_weight -= lw * v2;\n    grad_w_weight += hh * v2;\n    atomicAdd(grad_value+ptr2, w2*top_grad_value);\n  }\n  scalar_t v3 = 0;\n  if (h_high <= height - 1 && w_low >= 0)\n  {\n    const int ptr3 = h_high_ptr_offset + w_low_ptr_offset + base_ptr;\n    v3 = bottom_data[ptr3];\n    grad_h_weight += hw * v3;\n    grad_w_weight -= lh * v3;\n    atomicAdd(grad_value+ptr3, w3*top_grad_value); \n  }\n  scalar_t v4 = 0;\n  if (h_high <= height - 1 && w_high <= width - 1)\n  {\n    const int ptr4 = h_high_ptr_offset + w_high_ptr_offset + base_ptr;\n    v4 = bottom_data[ptr4];\n    grad_h_weight += lw * v4;\n    grad_w_weight += lh * v4;\n    atomicAdd(grad_value+ptr4, w4*top_grad_value);\n  }\n\n  const scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4);\n  atomicAdd(grad_attn_weight, top_grad * val); \n  atomicAdd(grad_sampling_loc, width * grad_w_weight * top_grad_value);\n  atomicAdd(grad_sampling_loc + 1, height * grad_h_weight * top_grad_value);\n}\n\n\ntemplate <typename scalar_t>\n__global__ void ms_deformable_im2col_gpu_kernel(const int n,\n                                                const scalar_t *data_value, \n                                                const int64_t *data_spatial_shapes,\n                                                const int64_t *data_level_start_index, \n                                                const scalar_t *data_sampling_loc,\n                                                const scalar_t *data_attn_weight,\n                                                const int batch_size, \n                                                const int spatial_size, \n                                                const int num_heads,\n                                                const int channels, \n                                                const int num_levels,\n                                                const int num_query,\n                                                const int num_point,\n                                                scalar_t *data_col)\n{\n  CUDA_KERNEL_LOOP(index, n)\n  {\n    int _temp = index;\n    const int c_col = _temp % channels;\n    _temp /= channels;\n    const int sampling_index = _temp; \n    const int m_col = _temp % num_heads;\n    _temp /= num_heads;\n    const int q_col = _temp % num_query;\n    _temp /= num_query;\n    const int b_col = _temp;\n\n    scalar_t *data_col_ptr = data_col + index;\n    int data_weight_ptr = sampling_index * num_levels * num_point;\n    int data_loc_w_ptr = data_weight_ptr << 1;\n    const int qid_stride = num_heads * channels;\n    const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride;\n    scalar_t col = 0;\n    \n    for (int l_col=0; l_col < num_levels; ++l_col)\n    {\n      const int level_start_id = data_level_start_index[l_col];\n      const int spatial_h_ptr = l_col << 1;\n      const int spatial_h = data_spatial_shapes[spatial_h_ptr];\n      const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1];\n      const scalar_t *data_value_ptr = data_value + (data_value_ptr_init_offset + level_start_id * qid_stride);\n      for (int p_col=0; p_col < num_point; ++p_col)\n      {\n        const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr];\n        const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1];\n        const scalar_t weight = data_attn_weight[data_weight_ptr];\n\n        const scalar_t h_im = loc_h * spatial_h - 0.5;\n        const scalar_t w_im = loc_w * spatial_w - 0.5;\n\n        if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w)\n        {\n          col += ms_deform_attn_im2col_bilinear(data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col) * weight;\n        }\n\n        data_weight_ptr += 1;\n        data_loc_w_ptr += 2;\n      }\n    }\n    *data_col_ptr = col;\n  }\n}\n\ntemplate <typename scalar_t, unsigned int blockSize>\n__global__ void ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1(const int n,\n                                                const scalar_t *grad_col,\n                                                const scalar_t *data_value,\n                                                const int64_t *data_spatial_shapes,\n                                                const int64_t *data_level_start_index, \n                                                const scalar_t *data_sampling_loc,\n                                                const scalar_t *data_attn_weight,\n                                                const int batch_size, \n                                                const int spatial_size, \n                                                const int num_heads,\n                                                const int channels, \n                                                const int num_levels,\n                                                const int num_query,\n                                                const int num_point,\n                                                scalar_t *grad_value,\n                                                scalar_t *grad_sampling_loc,\n                                                scalar_t *grad_attn_weight)\n{\n  CUDA_KERNEL_LOOP(index, n)\n  {\n    __shared__ scalar_t cache_grad_sampling_loc[blockSize * 2];\n    __shared__ scalar_t cache_grad_attn_weight[blockSize];\n    unsigned int tid = threadIdx.x;\n    int _temp = index;\n    const int c_col = _temp % channels;\n    _temp /= channels;\n    const int sampling_index = _temp; \n    const int m_col = _temp % num_heads;\n    _temp /= num_heads;\n    const int q_col = _temp % num_query;\n    _temp /= num_query;\n    const int b_col = _temp;\n\n    const scalar_t top_grad = grad_col[index];\n\n    int data_weight_ptr = sampling_index * num_levels * num_point;\n    int data_loc_w_ptr = data_weight_ptr << 1;\n    const int grad_sampling_ptr = data_weight_ptr;\n    grad_sampling_loc += grad_sampling_ptr << 1;\n    grad_attn_weight += grad_sampling_ptr;\n    const int grad_weight_stride = 1;\n    const int grad_loc_stride = 2;\n    const int qid_stride = num_heads * channels;\n    const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride;\n\n    for (int l_col=0; l_col < num_levels; ++l_col)\n    {\n      const int level_start_id = data_level_start_index[l_col];\n      const int spatial_h_ptr = l_col << 1;\n      const int spatial_h = data_spatial_shapes[spatial_h_ptr];\n      const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1];\n      const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride;\n      const scalar_t *data_value_ptr = data_value + value_ptr_offset;\n      scalar_t *grad_value_ptr = grad_value + value_ptr_offset;\n\n      for (int p_col=0; p_col < num_point; ++p_col)\n      {\n        const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr];\n        const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1];\n        const scalar_t weight = data_attn_weight[data_weight_ptr];\n\n        const scalar_t h_im = loc_h * spatial_h - 0.5;\n        const scalar_t w_im = loc_w * spatial_w - 0.5;\n        *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0;\n        *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0;\n        *(cache_grad_attn_weight+threadIdx.x)=0;\n        if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w)\n        {\n          ms_deform_attn_col2im_bilinear(\n            data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col,\n            top_grad, weight, grad_value_ptr, \n            cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x);\n        }\n        \n        __syncthreads();\n        if (tid == 0)\n        {\n          scalar_t _grad_w=cache_grad_sampling_loc[0], _grad_h=cache_grad_sampling_loc[1], _grad_a=cache_grad_attn_weight[0];\n          int sid=2;\n          for (unsigned int tid = 1; tid < blockSize; ++tid)\n          {\n            _grad_w += cache_grad_sampling_loc[sid];\n            _grad_h += cache_grad_sampling_loc[sid + 1];\n            _grad_a += cache_grad_attn_weight[tid];\n            sid += 2;\n          }\n          \n          \n          *grad_sampling_loc = _grad_w;\n          *(grad_sampling_loc + 1) = _grad_h;\n          *grad_attn_weight = _grad_a;\n        }\n        __syncthreads();\n\n        data_weight_ptr += 1;\n        data_loc_w_ptr += 2;\n        grad_attn_weight += grad_weight_stride;\n        grad_sampling_loc += grad_loc_stride;\n      }\n    }\n  }\n}\n\n\ntemplate <typename scalar_t, unsigned int blockSize>\n__global__ void ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2(const int n,\n                                                const scalar_t *grad_col,\n                                                const scalar_t *data_value,\n                                                const int64_t *data_spatial_shapes,\n                                                const int64_t *data_level_start_index, \n                                                const scalar_t *data_sampling_loc,\n                                                const scalar_t *data_attn_weight,\n                                                const int batch_size, \n                                                const int spatial_size, \n                                                const int num_heads,\n                                                const int channels, \n                                                const int num_levels,\n                                                const int num_query,\n                                                const int num_point,\n                                                scalar_t *grad_value,\n                                                scalar_t *grad_sampling_loc,\n                                                scalar_t *grad_attn_weight)\n{\n  CUDA_KERNEL_LOOP(index, n)\n  {\n    __shared__ scalar_t cache_grad_sampling_loc[blockSize * 2];\n    __shared__ scalar_t cache_grad_attn_weight[blockSize];\n    unsigned int tid = threadIdx.x;\n    int _temp = index;\n    const int c_col = _temp % channels;\n    _temp /= channels;\n    const int sampling_index = _temp; \n    const int m_col = _temp % num_heads;\n    _temp /= num_heads;\n    const int q_col = _temp % num_query;\n    _temp /= num_query;\n    const int b_col = _temp;\n\n    const scalar_t top_grad = grad_col[index];\n\n    int data_weight_ptr = sampling_index * num_levels * num_point;\n    int data_loc_w_ptr = data_weight_ptr << 1;\n    const int grad_sampling_ptr = data_weight_ptr;\n    grad_sampling_loc += grad_sampling_ptr << 1;\n    grad_attn_weight += grad_sampling_ptr;\n    const int grad_weight_stride = 1;\n    const int grad_loc_stride = 2;\n    const int qid_stride = num_heads * channels;\n    const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride;\n\n    for (int l_col=0; l_col < num_levels; ++l_col)\n    {\n      const int level_start_id = data_level_start_index[l_col];\n      const int spatial_h_ptr = l_col << 1;\n      const int spatial_h = data_spatial_shapes[spatial_h_ptr];\n      const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1];\n      const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride;\n      const scalar_t *data_value_ptr = data_value + value_ptr_offset;\n      scalar_t *grad_value_ptr = grad_value + value_ptr_offset;\n\n      for (int p_col=0; p_col < num_point; ++p_col)\n      {\n        const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr];\n        const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1];\n        const scalar_t weight = data_attn_weight[data_weight_ptr];\n\n        const scalar_t h_im = loc_h * spatial_h - 0.5;\n        const scalar_t w_im = loc_w * spatial_w - 0.5;\n        *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0;\n        *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0;\n        *(cache_grad_attn_weight+threadIdx.x)=0;\n        if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w)\n        {\n          ms_deform_attn_col2im_bilinear(\n            data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col,\n            top_grad, weight, grad_value_ptr, \n            cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x);\n        }\n        \n        __syncthreads();\n\n        for (unsigned int s=blockSize/2; s>0; s>>=1)\n        {\n          if (tid < s) {\n            const unsigned int xid1 = tid << 1;\n            const unsigned int xid2 = (tid + s) << 1;\n            cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + s];\n            cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2];\n            cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1];\n          }\n          __syncthreads();\n        }\n\n        if (tid == 0)\n        { \n          *grad_sampling_loc = cache_grad_sampling_loc[0];\n          *(grad_sampling_loc + 1) = cache_grad_sampling_loc[1];\n          *grad_attn_weight = cache_grad_attn_weight[0];\n        }\n        __syncthreads();\n\n        data_weight_ptr += 1;\n        data_loc_w_ptr += 2;\n        grad_attn_weight += grad_weight_stride;\n        grad_sampling_loc += grad_loc_stride;\n      }\n    }\n  }\n}\n\n\ntemplate <typename scalar_t>\n__global__ void ms_deformable_col2im_gpu_kernel_shm_reduce_v1(const int n,\n                                                const scalar_t *grad_col,\n                                                const scalar_t *data_value,\n                                                const int64_t *data_spatial_shapes,\n                                                const int64_t *data_level_start_index, \n                                                const scalar_t *data_sampling_loc,\n                                                const scalar_t *data_attn_weight,\n                                                const int batch_size, \n                                                const int spatial_size, \n                                                const int num_heads,\n                                                const int channels, \n                                                const int num_levels,\n                                                const int num_query,\n                                                const int num_point,\n                                                scalar_t *grad_value,\n                                                scalar_t *grad_sampling_loc,\n                                                scalar_t *grad_attn_weight)\n{\n  CUDA_KERNEL_LOOP(index, n)\n  {\n    extern __shared__ int _s[];\n    scalar_t* cache_grad_sampling_loc = (scalar_t*)_s;\n    scalar_t* cache_grad_attn_weight = cache_grad_sampling_loc + 2 * blockDim.x;\n    unsigned int tid = threadIdx.x;\n    int _temp = index;\n    const int c_col = _temp % channels;\n    _temp /= channels;\n    const int sampling_index = _temp; \n    const int m_col = _temp % num_heads;\n    _temp /= num_heads;\n    const int q_col = _temp % num_query;\n    _temp /= num_query;\n    const int b_col = _temp;\n\n    const scalar_t top_grad = grad_col[index];\n\n    int data_weight_ptr = sampling_index * num_levels * num_point;\n    int data_loc_w_ptr = data_weight_ptr << 1;\n    const int grad_sampling_ptr = data_weight_ptr;\n    grad_sampling_loc += grad_sampling_ptr << 1;\n    grad_attn_weight += grad_sampling_ptr;\n    const int grad_weight_stride = 1;\n    const int grad_loc_stride = 2;\n    const int qid_stride = num_heads * channels;\n    const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride;\n\n    for (int l_col=0; l_col < num_levels; ++l_col)\n    {\n      const int level_start_id = data_level_start_index[l_col];\n      const int spatial_h_ptr = l_col << 1;\n      const int spatial_h = data_spatial_shapes[spatial_h_ptr];\n      const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1];\n      const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride;\n      const scalar_t *data_value_ptr = data_value + value_ptr_offset;\n      scalar_t *grad_value_ptr = grad_value + value_ptr_offset;\n\n      for (int p_col=0; p_col < num_point; ++p_col)\n      {\n        const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr];\n        const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1];\n        const scalar_t weight = data_attn_weight[data_weight_ptr];\n\n        const scalar_t h_im = loc_h * spatial_h - 0.5;\n        const scalar_t w_im = loc_w * spatial_w - 0.5;\n        *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0;\n        *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0;\n        *(cache_grad_attn_weight+threadIdx.x)=0;\n        if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w)\n        {\n          ms_deform_attn_col2im_bilinear(\n            data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col,\n            top_grad, weight, grad_value_ptr, \n            cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x);\n        }\n        \n        __syncthreads();\n        if (tid == 0)\n        {\n          scalar_t _grad_w=cache_grad_sampling_loc[0], _grad_h=cache_grad_sampling_loc[1], _grad_a=cache_grad_attn_weight[0];\n          int sid=2;\n          for (unsigned int tid = 1; tid < blockDim.x; ++tid)\n          {\n            _grad_w += cache_grad_sampling_loc[sid];\n            _grad_h += cache_grad_sampling_loc[sid + 1];\n            _grad_a += cache_grad_attn_weight[tid];\n            sid += 2;\n          }\n          \n          \n          *grad_sampling_loc = _grad_w;\n          *(grad_sampling_loc + 1) = _grad_h;\n          *grad_attn_weight = _grad_a;\n        }\n        __syncthreads();\n\n        data_weight_ptr += 1;\n        data_loc_w_ptr += 2;\n        grad_attn_weight += grad_weight_stride;\n        grad_sampling_loc += grad_loc_stride;\n      }\n    }\n  }\n}\n\ntemplate <typename scalar_t>\n__global__ void ms_deformable_col2im_gpu_kernel_shm_reduce_v2(const int n,\n                                                const scalar_t *grad_col,\n                                                const scalar_t *data_value,\n                                                const int64_t *data_spatial_shapes,\n                                                const int64_t *data_level_start_index, \n                                                const scalar_t *data_sampling_loc,\n                                                const scalar_t *data_attn_weight,\n                                                const int batch_size, \n                                                const int spatial_size, \n                                                const int num_heads,\n                                                const int channels, \n                                                const int num_levels,\n                                                const int num_query,\n                                                const int num_point,\n                                                scalar_t *grad_value,\n                                                scalar_t *grad_sampling_loc,\n                                                scalar_t *grad_attn_weight)\n{\n  CUDA_KERNEL_LOOP(index, n)\n  {\n    extern __shared__ int _s[];\n    scalar_t* cache_grad_sampling_loc = (scalar_t*)_s;\n    scalar_t* cache_grad_attn_weight = cache_grad_sampling_loc + 2 * blockDim.x;\n    unsigned int tid = threadIdx.x;\n    int _temp = index;\n    const int c_col = _temp % channels;\n    _temp /= channels;\n    const int sampling_index = _temp; \n    const int m_col = _temp % num_heads;\n    _temp /= num_heads;\n    const int q_col = _temp % num_query;\n    _temp /= num_query;\n    const int b_col = _temp;\n\n    const scalar_t top_grad = grad_col[index];\n\n    int data_weight_ptr = sampling_index * num_levels * num_point;\n    int data_loc_w_ptr = data_weight_ptr << 1;\n    const int grad_sampling_ptr = data_weight_ptr;\n    grad_sampling_loc += grad_sampling_ptr << 1;\n    grad_attn_weight += grad_sampling_ptr;\n    const int grad_weight_stride = 1;\n    const int grad_loc_stride = 2;\n    const int qid_stride = num_heads * channels;\n    const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride;\n\n    for (int l_col=0; l_col < num_levels; ++l_col)\n    {\n      const int level_start_id = data_level_start_index[l_col];\n      const int spatial_h_ptr = l_col << 1;\n      const int spatial_h = data_spatial_shapes[spatial_h_ptr];\n      const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1];\n      const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride;\n      const scalar_t *data_value_ptr = data_value + value_ptr_offset;\n      scalar_t *grad_value_ptr = grad_value + value_ptr_offset;\n\n      for (int p_col=0; p_col < num_point; ++p_col)\n      {\n        const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr];\n        const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1];\n        const scalar_t weight = data_attn_weight[data_weight_ptr];\n\n        const scalar_t h_im = loc_h * spatial_h - 0.5;\n        const scalar_t w_im = loc_w * spatial_w - 0.5;\n        *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0;\n        *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0;\n        *(cache_grad_attn_weight+threadIdx.x)=0;\n        if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w)\n        {\n          ms_deform_attn_col2im_bilinear(\n            data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col,\n            top_grad, weight, grad_value_ptr, \n            cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x);\n        }\n        \n        __syncthreads();\n\n        for (unsigned int s=blockDim.x/2, spre=blockDim.x; s>0; s>>=1, spre>>=1)\n        {\n          if (tid < s) {\n            const unsigned int xid1 = tid << 1;\n            const unsigned int xid2 = (tid + s) << 1;\n            cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + s];\n            cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2];\n            cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1];\n            if (tid + (s << 1) < spre)\n            {\n              cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + (s << 1)];\n              cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2 + (s << 1)];\n              cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1 + (s << 1)];\n            } \n          }\n          __syncthreads();\n        }\n\n        if (tid == 0)\n        {\n          *grad_sampling_loc = cache_grad_sampling_loc[0];\n          *(grad_sampling_loc + 1) = cache_grad_sampling_loc[1];\n          *grad_attn_weight = cache_grad_attn_weight[0];\n        }\n        __syncthreads();\n\n        data_weight_ptr += 1;\n        data_loc_w_ptr += 2;\n        grad_attn_weight += grad_weight_stride;\n        grad_sampling_loc += grad_loc_stride;\n      }\n    }\n  }\n}\n\ntemplate <typename scalar_t>\n__global__ void ms_deformable_col2im_gpu_kernel_shm_reduce_v2_multi_blocks(const int n,\n                                                const scalar_t *grad_col,\n                                                const scalar_t *data_value,\n                                                const int64_t *data_spatial_shapes,\n                                                const int64_t *data_level_start_index, \n                                                const scalar_t *data_sampling_loc,\n                                                const scalar_t *data_attn_weight,\n                                                const int batch_size, \n                                                const int spatial_size, \n                                                const int num_heads,\n                                                const int channels, \n                                                const int num_levels,\n                                                const int num_query,\n                                                const int num_point,\n                                                scalar_t *grad_value,\n                                                scalar_t *grad_sampling_loc,\n                                                scalar_t *grad_attn_weight)\n{\n  CUDA_KERNEL_LOOP(index, n)\n  {\n    extern __shared__ int _s[];\n    scalar_t* cache_grad_sampling_loc = (scalar_t*)_s;\n    scalar_t* cache_grad_attn_weight = cache_grad_sampling_loc + 2 * blockDim.x;\n    unsigned int tid = threadIdx.x;\n    int _temp = index;\n    const int c_col = _temp % channels;\n    _temp /= channels;\n    const int sampling_index = _temp; \n    const int m_col = _temp % num_heads;\n    _temp /= num_heads;\n    const int q_col = _temp % num_query;\n    _temp /= num_query;\n    const int b_col = _temp;\n\n    const scalar_t top_grad = grad_col[index];\n\n    int data_weight_ptr = sampling_index * num_levels * num_point;\n    int data_loc_w_ptr = data_weight_ptr << 1;\n    const int grad_sampling_ptr = data_weight_ptr;\n    grad_sampling_loc += grad_sampling_ptr << 1;\n    grad_attn_weight += grad_sampling_ptr;\n    const int grad_weight_stride = 1;\n    const int grad_loc_stride = 2;\n    const int qid_stride = num_heads * channels;\n    const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride;\n\n    for (int l_col=0; l_col < num_levels; ++l_col)\n    {\n      const int level_start_id = data_level_start_index[l_col];\n      const int spatial_h_ptr = l_col << 1;\n      const int spatial_h = data_spatial_shapes[spatial_h_ptr];\n      const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1];\n      const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride;\n      const scalar_t *data_value_ptr = data_value + value_ptr_offset;\n      scalar_t *grad_value_ptr = grad_value + value_ptr_offset;\n\n      for (int p_col=0; p_col < num_point; ++p_col)\n      {\n        const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr];\n        const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1];\n        const scalar_t weight = data_attn_weight[data_weight_ptr];\n\n        const scalar_t h_im = loc_h * spatial_h - 0.5;\n        const scalar_t w_im = loc_w * spatial_w - 0.5;\n        *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0;\n        *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0;\n        *(cache_grad_attn_weight+threadIdx.x)=0;\n        if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w)\n        {\n          ms_deform_attn_col2im_bilinear(\n            data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col,\n            top_grad, weight, grad_value_ptr, \n            cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x);\n        }\n        \n        __syncthreads();\n\n        for (unsigned int s=blockDim.x/2, spre=blockDim.x; s>0; s>>=1, spre>>=1)\n        {\n          if (tid < s) {\n            const unsigned int xid1 = tid << 1;\n            const unsigned int xid2 = (tid + s) << 1;\n            cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + s];\n            cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2];\n            cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1];\n            if (tid + (s << 1) < spre)\n            {\n              cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + (s << 1)];\n              cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2 + (s << 1)];\n              cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1 + (s << 1)];\n            }\n          }\n          __syncthreads();\n        }\n\n        if (tid == 0)\n        {\n          atomicAdd(grad_sampling_loc, cache_grad_sampling_loc[0]);\n          atomicAdd(grad_sampling_loc + 1, cache_grad_sampling_loc[1]);\n          atomicAdd(grad_attn_weight, cache_grad_attn_weight[0]);\n        }\n        __syncthreads();\n\n        data_weight_ptr += 1;\n        data_loc_w_ptr += 2;\n        grad_attn_weight += grad_weight_stride;\n        grad_sampling_loc += grad_loc_stride;\n      }\n    }\n  }\n}\n\n\ntemplate <typename scalar_t>\n__global__ void ms_deformable_col2im_gpu_kernel_gm(const int n,\n                                                const scalar_t *grad_col,\n                                                const scalar_t *data_value,\n                                                const int64_t *data_spatial_shapes,\n                                                const int64_t *data_level_start_index, \n                                                const scalar_t *data_sampling_loc,\n                                                const scalar_t *data_attn_weight,\n                                                const int batch_size, \n                                                const int spatial_size, \n                                                const int num_heads,\n                                                const int channels, \n                                                const int num_levels,\n                                                const int num_query,\n                                                const int num_point,\n                                                scalar_t *grad_value,\n                                                scalar_t *grad_sampling_loc,\n                                                scalar_t *grad_attn_weight)\n{\n  CUDA_KERNEL_LOOP(index, n)\n  {\n    int _temp = index;\n    const int c_col = _temp % channels;\n    _temp /= channels;\n    const int sampling_index = _temp; \n    const int m_col = _temp % num_heads;\n    _temp /= num_heads;\n    const int q_col = _temp % num_query;\n    _temp /= num_query;\n    const int b_col = _temp;\n\n    const scalar_t top_grad = grad_col[index];\n\n    int data_weight_ptr = sampling_index * num_levels * num_point;\n    int data_loc_w_ptr = data_weight_ptr << 1;\n    const int grad_sampling_ptr = data_weight_ptr;\n    grad_sampling_loc += grad_sampling_ptr << 1;\n    grad_attn_weight += grad_sampling_ptr;\n    const int grad_weight_stride = 1;\n    const int grad_loc_stride = 2;\n    const int qid_stride = num_heads * channels;\n    const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride;\n\n    for (int l_col=0; l_col < num_levels; ++l_col)\n    {\n      const int level_start_id = data_level_start_index[l_col];\n      const int spatial_h_ptr = l_col << 1;\n      const int spatial_h = data_spatial_shapes[spatial_h_ptr];\n      const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1];\n      const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride;\n      const scalar_t *data_value_ptr = data_value + value_ptr_offset;\n      scalar_t *grad_value_ptr = grad_value + value_ptr_offset;\n\n      for (int p_col=0; p_col < num_point; ++p_col)\n      {\n        const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr];\n        const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1];\n        const scalar_t weight = data_attn_weight[data_weight_ptr];\n\n        const scalar_t h_im = loc_h * spatial_h - 0.5;\n        const scalar_t w_im = loc_w * spatial_w - 0.5;\n        if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w)\n        {\n          ms_deform_attn_col2im_bilinear_gm(\n            data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col,\n            top_grad, weight, grad_value_ptr, \n            grad_sampling_loc, grad_attn_weight);\n        }\n        data_weight_ptr += 1;\n        data_loc_w_ptr += 2;\n        grad_attn_weight += grad_weight_stride;\n        grad_sampling_loc += grad_loc_stride;\n      }\n    }\n  }\n}\n\n\ntemplate <typename scalar_t>\nvoid ms_deformable_im2col_cuda(cudaStream_t stream,\n                              const scalar_t* data_value,\n                              const int64_t* data_spatial_shapes, \n                              const int64_t* data_level_start_index, \n                              const scalar_t* data_sampling_loc,\n                              const scalar_t* data_attn_weight,\n                              const int batch_size,\n                              const int spatial_size, \n                              const int num_heads, \n                              const int channels, \n                              const int num_levels, \n                              const int num_query,\n                              const int num_point,\n                              scalar_t* data_col)\n{\n  const int num_kernels = batch_size * num_query * num_heads * channels;\n  const int num_actual_kernels = batch_size * num_query * num_heads * channels;\n  const int num_threads = CUDA_NUM_THREADS;\n  ms_deformable_im2col_gpu_kernel<scalar_t>\n      <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,\n          0, stream>>>(\n      num_kernels, data_value, data_spatial_shapes, data_level_start_index, data_sampling_loc, data_attn_weight, \n      batch_size, spatial_size, num_heads, channels, num_levels, num_query, num_point, data_col);\n  \n  cudaError_t err = cudaGetLastError();\n  if (err != cudaSuccess)\n  {\n    printf(\"error in ms_deformable_im2col_cuda: %s\\n\", cudaGetErrorString(err));\n  }\n\n}\n\ntemplate <typename scalar_t>\nvoid ms_deformable_col2im_cuda(cudaStream_t stream,\n                              const scalar_t* grad_col,\n                              const scalar_t* data_value,\n                              const int64_t * data_spatial_shapes,\n                              const int64_t * data_level_start_index,\n                              const scalar_t * data_sampling_loc,\n                              const scalar_t * data_attn_weight,\n                              const int batch_size, \n                              const int spatial_size, \n                              const int num_heads,\n                              const int channels, \n                              const int num_levels,\n                              const int num_query,\n                              const int num_point, \n                              scalar_t* grad_value,\n                              scalar_t* grad_sampling_loc,\n                              scalar_t* grad_attn_weight)\n{\n  const int num_threads = (channels > CUDA_NUM_THREADS)?CUDA_NUM_THREADS:channels;\n  const int num_kernels = batch_size * num_query * num_heads * channels;\n  const int num_actual_kernels = batch_size * num_query * num_heads * channels;\n  if (channels > 1024)\n  {\n    if ((channels & 1023) == 0)\n    {\n      ms_deformable_col2im_gpu_kernel_shm_reduce_v2_multi_blocks<scalar_t>\n          <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,\n              num_threads*3*sizeof(scalar_t), stream>>>(\n                        num_kernels, \n                        grad_col,\n                        data_value,\n                        data_spatial_shapes,\n                        data_level_start_index, \n                        data_sampling_loc,\n                        data_attn_weight,\n                        batch_size, \n                        spatial_size, \n                        num_heads,\n                        channels, \n                        num_levels,\n                        num_query,\n                        num_point,\n                        grad_value,\n                        grad_sampling_loc,\n                        grad_attn_weight);\n    }\n    else\n    {\n      ms_deformable_col2im_gpu_kernel_gm<scalar_t>\n        <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,\n            0, stream>>>(\n                      num_kernels, \n                      grad_col,\n                      data_value,\n                      data_spatial_shapes,\n                      data_level_start_index, \n                      data_sampling_loc,\n                      data_attn_weight,\n                      batch_size, \n                      spatial_size, \n                      num_heads,\n                      channels, \n                      num_levels,\n                      num_query,\n                      num_point,\n                      grad_value,\n                      grad_sampling_loc,\n                      grad_attn_weight);\n    }\n  }\n  else{\n    switch(channels)\n    {\n      case 1:\n        ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1<scalar_t, 1>\n        <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,\n            0, stream>>>(\n                      num_kernels, \n                      grad_col,\n                      data_value,\n                      data_spatial_shapes,\n                      data_level_start_index, \n                      data_sampling_loc,\n                      data_attn_weight,\n                      batch_size, \n                      spatial_size, \n                      num_heads,\n                      channels, \n                      num_levels,\n                      num_query,\n                      num_point,\n                      grad_value,\n                      grad_sampling_loc,\n                      grad_attn_weight);\n        break;\n      case 2:\n        ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1<scalar_t, 2>\n        <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,\n            0, stream>>>(\n                      num_kernels, \n                      grad_col,\n                      data_value,\n                      data_spatial_shapes,\n                      data_level_start_index, \n                      data_sampling_loc,\n                      data_attn_weight,\n                      batch_size, \n                      spatial_size, \n                      num_heads,\n                      channels, \n                      num_levels,\n                      num_query,\n                      num_point,\n                      grad_value,\n                      grad_sampling_loc,\n                      grad_attn_weight);\n        break;\n      case 4:\n        ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1<scalar_t, 4>\n        <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,\n            0, stream>>>(\n                      num_kernels, \n                      grad_col,\n                      data_value,\n                      data_spatial_shapes,\n                      data_level_start_index, \n                      data_sampling_loc,\n                      data_attn_weight,\n                      batch_size, \n                      spatial_size, \n                      num_heads,\n                      channels, \n                      num_levels,\n                      num_query,\n                      num_point,\n                      grad_value,\n                      grad_sampling_loc,\n                      grad_attn_weight);\n        break;\n      case 8:\n        ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1<scalar_t, 8>\n        <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,\n            0, stream>>>(\n                      num_kernels, \n                      grad_col,\n                      data_value,\n                      data_spatial_shapes,\n                      data_level_start_index, \n                      data_sampling_loc,\n                      data_attn_weight,\n                      batch_size, \n                      spatial_size, \n                      num_heads,\n                      channels, \n                      num_levels,\n                      num_query,\n                      num_point,\n                      grad_value,\n                      grad_sampling_loc,\n                      grad_attn_weight);\n        break;\n      case 16:\n        ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1<scalar_t, 16>\n        <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,\n            0, stream>>>(\n                      num_kernels, \n                      grad_col,\n                      data_value,\n                      data_spatial_shapes,\n                      data_level_start_index, \n                      data_sampling_loc,\n                      data_attn_weight,\n                      batch_size, \n                      spatial_size, \n                      num_heads,\n                      channels, \n                      num_levels,\n                      num_query,\n                      num_point,\n                      grad_value,\n                      grad_sampling_loc,\n                      grad_attn_weight);\n        break;\n      case 32:\n        ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1<scalar_t, 32>\n        <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,\n            0, stream>>>(\n                      num_kernels, \n                      grad_col,\n                      data_value,\n                      data_spatial_shapes,\n                      data_level_start_index, \n                      data_sampling_loc,\n                      data_attn_weight,\n                      batch_size, \n                      spatial_size, \n                      num_heads,\n                      channels, \n                      num_levels,\n                      num_query,\n                      num_point,\n                      grad_value,\n                      grad_sampling_loc,\n                      grad_attn_weight);\n        break;\n      case 64:\n        ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2<scalar_t, 64>\n        <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,\n            0, stream>>>(\n                      num_kernels, \n                      grad_col,\n                      data_value,\n                      data_spatial_shapes,\n                      data_level_start_index, \n                      data_sampling_loc,\n                      data_attn_weight,\n                      batch_size, \n                      spatial_size, \n                      num_heads,\n                      channels, \n                      num_levels,\n                      num_query,\n                      num_point,\n                      grad_value,\n                      grad_sampling_loc,\n                      grad_attn_weight);\n        break;\n      case 128:\n        ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2<scalar_t, 128>\n        <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,\n            0, stream>>>(\n                      num_kernels, \n                      grad_col,\n                      data_value,\n                      data_spatial_shapes,\n                      data_level_start_index, \n                      data_sampling_loc,\n                      data_attn_weight,\n                      batch_size, \n                      spatial_size, \n                      num_heads,\n                      channels, \n                      num_levels,\n                      num_query,\n                      num_point,\n                      grad_value,\n                      grad_sampling_loc,\n                      grad_attn_weight);\n        break;\n      case 256:\n        ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2<scalar_t, 256>\n        <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,\n            0, stream>>>(\n                      num_kernels, \n                      grad_col,\n                      data_value,\n                      data_spatial_shapes,\n                      data_level_start_index, \n                      data_sampling_loc,\n                      data_attn_weight,\n                      batch_size, \n                      spatial_size, \n                      num_heads,\n                      channels, \n                      num_levels,\n                      num_query,\n                      num_point,\n                      grad_value,\n                      grad_sampling_loc,\n                      grad_attn_weight);\n        break;\n      case 512:\n        ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2<scalar_t, 512>\n        <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,\n            0, stream>>>(\n                      num_kernels, \n                      grad_col,\n                      data_value,\n                      data_spatial_shapes,\n                      data_level_start_index, \n                      data_sampling_loc,\n                      data_attn_weight,\n                      batch_size, \n                      spatial_size, \n                      num_heads,\n                      channels, \n                      num_levels,\n                      num_query,\n                      num_point,\n                      grad_value,\n                      grad_sampling_loc,\n                      grad_attn_weight);\n        break;\n      case 1024:\n        ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2<scalar_t, 1024>\n        <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,\n            0, stream>>>(\n                      num_kernels, \n                      grad_col,\n                      data_value,\n                      data_spatial_shapes,\n                      data_level_start_index, \n                      data_sampling_loc,\n                      data_attn_weight,\n                      batch_size, \n                      spatial_size, \n                      num_heads,\n                      channels, \n                      num_levels,\n                      num_query,\n                      num_point,\n                      grad_value,\n                      grad_sampling_loc,\n                      grad_attn_weight);\n        break;\n      default:\n        if (channels < 64)\n        {\n          ms_deformable_col2im_gpu_kernel_shm_reduce_v1<scalar_t>\n          <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,\n              num_threads*3*sizeof(scalar_t), stream>>>(\n                        num_kernels, \n                        grad_col,\n                        data_value,\n                        data_spatial_shapes,\n                        data_level_start_index, \n                        data_sampling_loc,\n                        data_attn_weight,\n                        batch_size, \n                        spatial_size, \n                        num_heads,\n                        channels, \n                        num_levels,\n                        num_query,\n                        num_point,\n                        grad_value,\n                        grad_sampling_loc,\n                        grad_attn_weight);\n        }\n        else\n        {\n          ms_deformable_col2im_gpu_kernel_shm_reduce_v2<scalar_t>\n          <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,\n              num_threads*3*sizeof(scalar_t), stream>>>(\n                        num_kernels, \n                        grad_col,\n                        data_value,\n                        data_spatial_shapes,\n                        data_level_start_index, \n                        data_sampling_loc,\n                        data_attn_weight,\n                        batch_size, \n                        spatial_size, \n                        num_heads,\n                        channels, \n                        num_levels,\n                        num_query,\n                        num_point,\n                        grad_value,\n                        grad_sampling_loc,\n                        grad_attn_weight);\n        }\n    }\n  }\n  cudaError_t err = cudaGetLastError();\n  if (err != cudaSuccess)\n  {\n    printf(\"error in ms_deformable_col2im_cuda: %s\\n\", cudaGetErrorString(err));\n  }\n\n}"
  },
  {
    "path": "model_cards/groundingdino/models/GroundingDINO/csrc/cuda_version.cu",
    "content": "#include <cuda_runtime_api.h>\n\nnamespace groundingdino {\nint get_cudart_version() {\n  return CUDART_VERSION;\n}\n} // namespace groundingdino\n"
  },
  {
    "path": "model_cards/groundingdino/models/GroundingDINO/csrc/vision.cpp",
    "content": "// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n\n#include \"MsDeformAttn/ms_deform_attn.h\"\n\nnamespace groundingdino {\n\n#ifdef WITH_CUDA\nextern int get_cudart_version();\n#endif\n\nstd::string get_cuda_version() {\n#ifdef WITH_CUDA\n  std::ostringstream oss;\n\n  // copied from\n  // https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/cuda/detail/CUDAHooks.cpp#L231\n  auto printCudaStyleVersion = [&](int v) {\n    oss << (v / 1000) << \".\" << (v / 10 % 100);\n    if (v % 10 != 0) {\n      oss << \".\" << (v % 10);\n    }\n  };\n  printCudaStyleVersion(get_cudart_version());\n  return oss.str();\n#else\n  return std::string(\"not available\");\n#endif\n}\n\n// similar to\n// https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/Version.cpp\nstd::string get_compiler_version() {\n  std::ostringstream ss;\n#if defined(__GNUC__)\n#ifndef __clang__\n  { ss << \"GCC \" << __GNUC__ << \".\" << __GNUC_MINOR__; }\n#endif\n#endif\n\n#if defined(__clang_major__)\n  {\n    ss << \"clang \" << __clang_major__ << \".\" << __clang_minor__ << \".\"\n       << __clang_patchlevel__;\n  }\n#endif\n\n#if defined(_MSC_VER)\n  { ss << \"MSVC \" << _MSC_FULL_VER; }\n#endif\n  return ss.str();\n}\n\nPYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {\n  m.def(\"ms_deform_attn_forward\", &ms_deform_attn_forward, \"ms_deform_attn_forward\");\n  m.def(\"ms_deform_attn_backward\", &ms_deform_attn_backward, \"ms_deform_attn_backward\");\n}\n\n} // namespace groundingdino"
  },
  {
    "path": "model_cards/groundingdino/models/GroundingDINO/fuse_modules.py",
    "content": "# ------------------------------------------------------------------------\n# Grounding DINO\n# url: https://github.com/IDEA-Research/GroundingDINO\n# Copyright (c) 2023 IDEA. All Rights Reserved.\n# Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n# ------------------------------------------------------------------------\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom timm.models.layers import DropPath\n\n\nclass FeatureResizer(nn.Module):\n    \"\"\"\n    This class takes as input a set of embeddings of dimension C1 and outputs a set of\n    embedding of dimension C2, after a linear transformation, dropout and normalization (LN).\n    \"\"\"\n\n    def __init__(self, input_feat_size, output_feat_size, dropout, do_ln=True):\n        super().__init__()\n        self.do_ln = do_ln\n        # Object feature encoding\n        self.fc = nn.Linear(input_feat_size, output_feat_size, bias=True)\n        self.layer_norm = nn.LayerNorm(output_feat_size, eps=1e-12)\n        self.dropout = nn.Dropout(dropout)\n\n    def forward(self, encoder_features):\n        x = self.fc(encoder_features)\n        if self.do_ln:\n            x = self.layer_norm(x)\n        output = self.dropout(x)\n        return output\n\n\ndef l1norm(X, dim, eps=1e-8):\n    \"\"\"L1-normalize columns of X\"\"\"\n    norm = torch.abs(X).sum(dim=dim, keepdim=True) + eps\n    X = torch.div(X, norm)\n    return X\n\n\ndef l2norm(X, dim, eps=1e-8):\n    \"\"\"L2-normalize columns of X\"\"\"\n    norm = torch.pow(X, 2).sum(dim=dim, keepdim=True).sqrt() + eps\n    X = torch.div(X, norm)\n    return X\n\n\ndef func_attention(query, context, smooth=1, raw_feature_norm=\"softmax\", eps=1e-8):\n    \"\"\"\n    query: (n_context, queryL, d)\n    context: (n_context, sourceL, d)\n    \"\"\"\n    batch_size_q, queryL = query.size(0), query.size(1)\n    batch_size, sourceL = context.size(0), context.size(1)\n\n    # Get attention\n    # --> (batch, d, queryL)\n    queryT = torch.transpose(query, 1, 2)\n\n    # (batch, sourceL, d)(batch, d, queryL)\n    # --> (batch, sourceL, queryL)\n    attn = torch.bmm(context, queryT)\n    if raw_feature_norm == \"softmax\":\n        # --> (batch*sourceL, queryL)\n        attn = attn.view(batch_size * sourceL, queryL)\n        attn = nn.Softmax()(attn)\n        # --> (batch, sourceL, queryL)\n        attn = attn.view(batch_size, sourceL, queryL)\n    elif raw_feature_norm == \"l2norm\":\n        attn = l2norm(attn, 2)\n    elif raw_feature_norm == \"clipped_l2norm\":\n        attn = nn.LeakyReLU(0.1)(attn)\n        attn = l2norm(attn, 2)\n    else:\n        raise ValueError(\"unknown first norm type:\", raw_feature_norm)\n    # --> (batch, queryL, sourceL)\n    attn = torch.transpose(attn, 1, 2).contiguous()\n    # --> (batch*queryL, sourceL)\n    attn = attn.view(batch_size * queryL, sourceL)\n    attn = nn.Softmax()(attn * smooth)\n    # --> (batch, queryL, sourceL)\n    attn = attn.view(batch_size, queryL, sourceL)\n    # --> (batch, sourceL, queryL)\n    attnT = torch.transpose(attn, 1, 2).contiguous()\n\n    # --> (batch, d, sourceL)\n    contextT = torch.transpose(context, 1, 2)\n    # (batch x d x sourceL)(batch x sourceL x queryL)\n    # --> (batch, d, queryL)\n    weightedContext = torch.bmm(contextT, attnT)\n    # --> (batch, queryL, d)\n    weightedContext = torch.transpose(weightedContext, 1, 2)\n\n    return weightedContext, attnT\n\n\nclass BiMultiHeadAttention(nn.Module):\n    def __init__(self, v_dim, l_dim, embed_dim, num_heads, dropout=0.1, cfg=None):\n        super(BiMultiHeadAttention, self).__init__()\n\n        self.embed_dim = embed_dim\n        self.num_heads = num_heads\n        self.head_dim = embed_dim // num_heads\n        self.v_dim = v_dim\n        self.l_dim = l_dim\n\n        assert (\n            self.head_dim * self.num_heads == self.embed_dim\n        ), f\"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`: {self.num_heads}).\"\n        self.scale = self.head_dim ** (-0.5)\n        self.dropout = dropout\n\n        self.v_proj = nn.Linear(self.v_dim, self.embed_dim)\n        self.l_proj = nn.Linear(self.l_dim, self.embed_dim)\n        self.values_v_proj = nn.Linear(self.v_dim, self.embed_dim)\n        self.values_l_proj = nn.Linear(self.l_dim, self.embed_dim)\n\n        self.out_v_proj = nn.Linear(self.embed_dim, self.v_dim)\n        self.out_l_proj = nn.Linear(self.embed_dim, self.l_dim)\n\n        self.stable_softmax_2d = True\n        self.clamp_min_for_underflow = True\n        self.clamp_max_for_overflow = True\n\n        self._reset_parameters()\n\n    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):\n        return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()\n\n    def _reset_parameters(self):\n        nn.init.xavier_uniform_(self.v_proj.weight)\n        self.v_proj.bias.data.fill_(0)\n        nn.init.xavier_uniform_(self.l_proj.weight)\n        self.l_proj.bias.data.fill_(0)\n        nn.init.xavier_uniform_(self.values_v_proj.weight)\n        self.values_v_proj.bias.data.fill_(0)\n        nn.init.xavier_uniform_(self.values_l_proj.weight)\n        self.values_l_proj.bias.data.fill_(0)\n        nn.init.xavier_uniform_(self.out_v_proj.weight)\n        self.out_v_proj.bias.data.fill_(0)\n        nn.init.xavier_uniform_(self.out_l_proj.weight)\n        self.out_l_proj.bias.data.fill_(0)\n\n    def forward(self, v, l, attention_mask_v=None, attention_mask_l=None):\n        \"\"\"_summary_\n\n        Args:\n            v (_type_): bs, n_img, dim\n            l (_type_): bs, n_text, dim\n            attention_mask_v (_type_, optional): _description_. bs, n_img\n            attention_mask_l (_type_, optional): _description_. bs, n_text\n\n        Returns:\n            _type_: _description_\n        \"\"\"\n        # if os.environ.get('IPDB_SHILONG_DEBUG', None) == 'INFO':\n        #     import ipdb; ipdb.set_trace()\n        bsz, tgt_len, _ = v.size()\n\n        query_states = self.v_proj(v) * self.scale\n        key_states = self._shape(self.l_proj(l), -1, bsz)\n        value_v_states = self._shape(self.values_v_proj(v), -1, bsz)\n        value_l_states = self._shape(self.values_l_proj(l), -1, bsz)\n\n        proj_shape = (bsz * self.num_heads, -1, self.head_dim)\n        query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)\n        key_states = key_states.view(*proj_shape)\n        value_v_states = value_v_states.view(*proj_shape)\n        value_l_states = value_l_states.view(*proj_shape)\n\n        src_len = key_states.size(1)\n        attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))  # bs*nhead, nimg, ntxt\n\n        if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):\n            raise ValueError(\n                f\"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is {attn_weights.size()}\"\n            )\n\n        if self.stable_softmax_2d:\n            attn_weights = attn_weights - attn_weights.max()\n\n        if self.clamp_min_for_underflow:\n            attn_weights = torch.clamp(\n                attn_weights, min=-50000\n            )  # Do not increase -50000, data type half has quite limited range\n        if self.clamp_max_for_overflow:\n            attn_weights = torch.clamp(\n                attn_weights, max=50000\n            )  # Do not increase 50000, data type half has quite limited range\n\n        attn_weights_T = attn_weights.transpose(1, 2)\n        attn_weights_l = attn_weights_T - torch.max(attn_weights_T, dim=-1, keepdim=True)[0]\n        if self.clamp_min_for_underflow:\n            attn_weights_l = torch.clamp(\n                attn_weights_l, min=-50000\n            )  # Do not increase -50000, data type half has quite limited range\n        if self.clamp_max_for_overflow:\n            attn_weights_l = torch.clamp(\n                attn_weights_l, max=50000\n            )  # Do not increase 50000, data type half has quite limited range\n\n        # mask vison for language\n        if attention_mask_v is not None:\n            attention_mask_v = (\n                attention_mask_v[:, None, None, :].repeat(1, self.num_heads, 1, 1).flatten(0, 1)\n            )\n            attn_weights_l.masked_fill_(attention_mask_v, float(\"-inf\"))\n\n        attn_weights_l = attn_weights_l.softmax(dim=-1)\n\n        # mask language for vision\n        if attention_mask_l is not None:\n            attention_mask_l = (\n                attention_mask_l[:, None, None, :].repeat(1, self.num_heads, 1, 1).flatten(0, 1)\n            )\n            attn_weights.masked_fill_(attention_mask_l, float(\"-inf\"))\n        attn_weights_v = attn_weights.softmax(dim=-1)\n\n        attn_probs_v = F.dropout(attn_weights_v, p=self.dropout, training=self.training)\n        attn_probs_l = F.dropout(attn_weights_l, p=self.dropout, training=self.training)\n\n        attn_output_v = torch.bmm(attn_probs_v, value_l_states)\n        attn_output_l = torch.bmm(attn_probs_l, value_v_states)\n\n        if attn_output_v.size() != (bsz * self.num_heads, tgt_len, self.head_dim):\n            raise ValueError(\n                f\"`attn_output_v` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is {attn_output_v.size()}\"\n            )\n\n        if attn_output_l.size() != (bsz * self.num_heads, src_len, self.head_dim):\n            raise ValueError(\n                f\"`attn_output_l` should be of size {(bsz, self.num_heads, src_len, self.head_dim)}, but is {attn_output_l.size()}\"\n            )\n\n        attn_output_v = attn_output_v.view(bsz, self.num_heads, tgt_len, self.head_dim)\n        attn_output_v = attn_output_v.transpose(1, 2)\n        attn_output_v = attn_output_v.reshape(bsz, tgt_len, self.embed_dim)\n\n        attn_output_l = attn_output_l.view(bsz, self.num_heads, src_len, self.head_dim)\n        attn_output_l = attn_output_l.transpose(1, 2)\n        attn_output_l = attn_output_l.reshape(bsz, src_len, self.embed_dim)\n\n        attn_output_v = self.out_v_proj(attn_output_v)\n        attn_output_l = self.out_l_proj(attn_output_l)\n\n        return attn_output_v, attn_output_l\n\n\n# Bi-Direction MHA (text->image, image->text)\nclass BiAttentionBlock(nn.Module):\n    def __init__(\n        self,\n        v_dim,\n        l_dim,\n        embed_dim,\n        num_heads,\n        dropout=0.1,\n        drop_path=0.0,\n        init_values=1e-4,\n        cfg=None,\n    ):\n        \"\"\"\n        Inputs:\n            embed_dim - Dimensionality of input and attention feature vectors\n            hidden_dim - Dimensionality of hidden layer in feed-forward network\n                         (usually 2-4x larger than embed_dim)\n            num_heads - Number of heads to use in the Multi-Head Attention block\n            dropout - Amount of dropout to apply in the feed-forward network\n        \"\"\"\n        super(BiAttentionBlock, self).__init__()\n\n        # pre layer norm\n        self.layer_norm_v = nn.LayerNorm(v_dim)\n        self.layer_norm_l = nn.LayerNorm(l_dim)\n        self.attn = BiMultiHeadAttention(\n            v_dim=v_dim, l_dim=l_dim, embed_dim=embed_dim, num_heads=num_heads, dropout=dropout\n        )\n\n        # add layer scale for training stability\n        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()\n        self.gamma_v = nn.Parameter(init_values * torch.ones((v_dim)), requires_grad=True)\n        self.gamma_l = nn.Parameter(init_values * torch.ones((l_dim)), requires_grad=True)\n\n    def forward(self, v, l, attention_mask_v=None, attention_mask_l=None):\n        v = self.layer_norm_v(v)\n        l = self.layer_norm_l(l)\n        delta_v, delta_l = self.attn(\n            v, l, attention_mask_v=attention_mask_v, attention_mask_l=attention_mask_l\n        )\n        # v, l = v + delta_v, l + delta_l\n        v = v + self.drop_path(self.gamma_v * delta_v)\n        l = l + self.drop_path(self.gamma_l * delta_l)\n        return v, l\n\n    # def forward(self, v:List[torch.Tensor], l, attention_mask_v=None, attention_mask_l=None)\n"
  },
  {
    "path": "model_cards/groundingdino/models/GroundingDINO/groundingdino.py",
    "content": "# ------------------------------------------------------------------------\n# Grounding DINO\n# url: https://github.com/IDEA-Research/GroundingDINO\n# Copyright (c) 2023 IDEA. All Rights Reserved.\n# Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n# ------------------------------------------------------------------------\n# Conditional DETR model and criterion classes.\n# Copyright (c) 2021 Microsoft. All Rights Reserved.\n# Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n# ------------------------------------------------------------------------\n# Modified from DETR (https://github.com/facebookresearch/detr)\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)\n# Copyright (c) 2020 SenseTime. All Rights Reserved.\n# ------------------------------------------------------------------------\nimport copy\nfrom typing import List\n\nimport torch\nimport torch.nn.functional as F\nfrom torch import nn\nfrom torchvision.ops.boxes import nms\nimport sys\nfrom transformers import AutoTokenizer, BertModel, BertTokenizer, RobertaModel, RobertaTokenizerFast\n\nfrom groundingdino.util import box_ops, get_tokenlizer\nfrom groundingdino.util.misc import (\n    NestedTensor,\n    accuracy,\n    get_world_size,\n    interpolate,\n    inverse_sigmoid,\n    is_dist_avail_and_initialized,\n    nested_tensor_from_tensor_list,\n)\nfrom groundingdino.util.utils import get_phrases_from_posmap\nfrom groundingdino.util.visualizer import COCOVisualizer\nfrom groundingdino.util.vl_utils import create_positive_map_from_span\n\nfrom ..registry import MODULE_BUILD_FUNCS\nfrom .backbone import build_backbone\nfrom .bertwarper import (\n    BertModelWarper,\n    generate_masks_with_special_tokens,\n    generate_masks_with_special_tokens_and_transfer_map,\n)\nfrom .transformer import build_transformer\nfrom .utils import MLP, ContrastiveEmbed, sigmoid_focal_loss\n\n\nclass GroundingDINO(nn.Module):\n    \"\"\"This is the Cross-Attention Detector module that performs object detection\"\"\"\n\n    def __init__(\n        self,\n        backbone,\n        transformer,\n        num_queries,\n        aux_loss=False,\n        iter_update=False,\n        query_dim=2,\n        num_feature_levels=1,\n        nheads=8,\n        # two stage\n        two_stage_type=\"no\",  # ['no', 'standard']\n        dec_pred_bbox_embed_share=True,\n        two_stage_class_embed_share=True,\n        two_stage_bbox_embed_share=True,\n        num_patterns=0,\n        dn_number=100,\n        dn_box_noise_scale=0.4,\n        dn_label_noise_ratio=0.5,\n        dn_labelbook_size=100,\n        text_encoder_type=\"bert-base-uncased\",\n        sub_sentence_present=True,\n        max_text_len=256,\n    ):\n        \"\"\"Initializes the model.\n        Parameters:\n            backbone: torch module of the backbone to be used. See backbone.py\n            transformer: torch module of the transformer architecture. See transformer.py\n            num_queries: number of object queries, ie detection slot. This is the maximal number of objects\n                         Conditional DETR can detect in a single image. For COCO, we recommend 100 queries.\n            aux_loss: True if auxiliary decoding losses (loss at each decoder layer) are to be used.\n        \"\"\"\n        super().__init__()\n        self.num_queries = num_queries\n        self.transformer = transformer\n        self.hidden_dim = hidden_dim = transformer.d_model\n        self.num_feature_levels = num_feature_levels\n        self.nheads = nheads\n        self.max_text_len = 256\n        self.sub_sentence_present = sub_sentence_present\n\n        # setting query dim\n        self.query_dim = query_dim\n        assert query_dim == 4\n\n        # for dn training\n        self.num_patterns = num_patterns\n        self.dn_number = dn_number\n        self.dn_box_noise_scale = dn_box_noise_scale\n        self.dn_label_noise_ratio = dn_label_noise_ratio\n        self.dn_labelbook_size = dn_labelbook_size\n\n        # bert\n        self.tokenizer = get_tokenlizer.get_tokenlizer(text_encoder_type)\n        self.bert = get_tokenlizer.get_pretrained_language_model(text_encoder_type)\n        self.bert.pooler.dense.weight.requires_grad_(False)\n        self.bert.pooler.dense.bias.requires_grad_(False)\n        self.bert = BertModelWarper(bert_model=self.bert)\n\n        self.feat_map = nn.Linear(self.bert.config.hidden_size, self.hidden_dim, bias=True)\n        nn.init.constant_(self.feat_map.bias.data, 0)\n        nn.init.xavier_uniform_(self.feat_map.weight.data)\n        # freeze\n\n        # special tokens\n        self.specical_tokens = self.tokenizer.convert_tokens_to_ids([\"[CLS]\", \"[SEP]\", \".\", \"?\"])\n\n        # prepare input projection layers\n        if num_feature_levels > 1:\n            num_backbone_outs = len(backbone.num_channels)\n            input_proj_list = []\n            for _ in range(num_backbone_outs):\n                in_channels = backbone.num_channels[_]\n                input_proj_list.append(\n                    nn.Sequential(\n                        nn.Conv2d(in_channels, hidden_dim, kernel_size=1),\n                        nn.GroupNorm(32, hidden_dim),\n                    )\n                )\n            for _ in range(num_feature_levels - num_backbone_outs):\n                input_proj_list.append(\n                    nn.Sequential(\n                        nn.Conv2d(in_channels, hidden_dim, kernel_size=3, stride=2, padding=1),\n                        nn.GroupNorm(32, hidden_dim),\n                    )\n                )\n                in_channels = hidden_dim\n            self.input_proj = nn.ModuleList(input_proj_list)\n        else:\n            assert two_stage_type == \"no\", \"two_stage_type should be no if num_feature_levels=1 !!!\"\n            self.input_proj = nn.ModuleList(\n                [\n                    nn.Sequential(\n                        nn.Conv2d(backbone.num_channels[-1], hidden_dim, kernel_size=1),\n                        nn.GroupNorm(32, hidden_dim),\n                    )\n                ]\n            )\n\n        self.backbone = backbone\n        self.aux_loss = aux_loss\n        self.box_pred_damping = box_pred_damping = None\n\n        self.iter_update = iter_update\n        assert iter_update, \"Why not iter_update?\"\n\n        # prepare pred layers\n        self.dec_pred_bbox_embed_share = dec_pred_bbox_embed_share\n        # prepare class & box embed\n        _class_embed = ContrastiveEmbed()\n\n        _bbox_embed = MLP(hidden_dim, hidden_dim, 4, 3)\n        nn.init.constant_(_bbox_embed.layers[-1].weight.data, 0)\n        nn.init.constant_(_bbox_embed.layers[-1].bias.data, 0)\n\n        if dec_pred_bbox_embed_share:\n            box_embed_layerlist = [_bbox_embed for i in range(transformer.num_decoder_layers)]\n        else:\n            box_embed_layerlist = [\n                copy.deepcopy(_bbox_embed) for i in range(transformer.num_decoder_layers)\n            ]\n        class_embed_layerlist = [_class_embed for i in range(transformer.num_decoder_layers)]\n        self.bbox_embed = nn.ModuleList(box_embed_layerlist)\n        self.class_embed = nn.ModuleList(class_embed_layerlist)\n        self.transformer.decoder.bbox_embed = self.bbox_embed\n        self.transformer.decoder.class_embed = self.class_embed\n\n        # two stage\n        self.two_stage_type = two_stage_type\n        assert two_stage_type in [\"no\", \"standard\"], \"unknown param {} of two_stage_type\".format(\n            two_stage_type\n        )\n        if two_stage_type != \"no\":\n            if two_stage_bbox_embed_share:\n                assert dec_pred_bbox_embed_share\n                self.transformer.enc_out_bbox_embed = _bbox_embed\n            else:\n                self.transformer.enc_out_bbox_embed = copy.deepcopy(_bbox_embed)\n\n            if two_stage_class_embed_share:\n                assert dec_pred_bbox_embed_share\n                self.transformer.enc_out_class_embed = _class_embed\n            else:\n                self.transformer.enc_out_class_embed = copy.deepcopy(_class_embed)\n\n            self.refpoint_embed = None\n\n        self._reset_parameters()\n\n    def _reset_parameters(self):\n        # init input_proj\n        for proj in self.input_proj:\n            nn.init.xavier_uniform_(proj[0].weight, gain=1)\n            nn.init.constant_(proj[0].bias, 0)\n\n    def init_ref_points(self, use_num_queries):\n        self.refpoint_embed = nn.Embedding(use_num_queries, self.query_dim)\n\n    def forward(self, samples: NestedTensor, targets: List = None, **kw):\n        \"\"\"The forward expects a NestedTensor, which consists of:\n           - samples.tensor: batched images, of shape [batch_size x 3 x H x W]\n           - samples.mask: a binary mask of shape [batch_size x H x W], containing 1 on padded pixels\n\n        It returns a dict with the following elements:\n           - \"pred_logits\": the classification logits (including no-object) for all queries.\n                            Shape= [batch_size x num_queries x num_classes]\n           - \"pred_boxes\": The normalized boxes coordinates for all queries, represented as\n                           (center_x, center_y, width, height). These values are normalized in [0, 1],\n                           relative to the size of each individual image (disregarding possible padding).\n                           See PostProcess for information on how to retrieve the unnormalized bounding box.\n           - \"aux_outputs\": Optional, only returned when auxilary losses are activated. It is a list of\n                            dictionnaries containing the two above keys for each decoder layer.\n        \"\"\"\n        if targets is None:\n            captions = kw[\"captions\"]\n        else:\n            captions = [t[\"caption\"] for t in targets]\n        len(captions)\n\n        # encoder texts\n        tokenized = self.tokenizer(captions, padding=\"longest\", return_tensors=\"pt\").to(\n            samples.device\n        )\n        (\n            text_self_attention_masks,\n            position_ids,\n            cate_to_token_mask_list,\n        ) = generate_masks_with_special_tokens_and_transfer_map(\n            tokenized, self.specical_tokens, self.tokenizer\n        )\n\n        if text_self_attention_masks.shape[1] > self.max_text_len:\n            text_self_attention_masks = text_self_attention_masks[\n                :, : self.max_text_len, : self.max_text_len\n            ]\n            position_ids = position_ids[:, : self.max_text_len]\n            tokenized[\"input_ids\"] = tokenized[\"input_ids\"][:, : self.max_text_len]\n            tokenized[\"attention_mask\"] = tokenized[\"attention_mask\"][:, : self.max_text_len]\n            tokenized[\"token_type_ids\"] = tokenized[\"token_type_ids\"][:, : self.max_text_len]\n\n        # extract text embeddings\n        if self.sub_sentence_present:\n            tokenized_for_encoder = {k: v for k, v in tokenized.items() if k != \"attention_mask\"}\n            tokenized_for_encoder[\"attention_mask\"] = text_self_attention_masks\n            tokenized_for_encoder[\"position_ids\"] = position_ids\n        else:\n            # import ipdb; ipdb.set_trace()\n            tokenized_for_encoder = tokenized\n\n        bert_output = self.bert(**tokenized_for_encoder)  # bs, 195, 768\n\n        encoded_text = self.feat_map(bert_output[\"last_hidden_state\"])  # bs, 195, d_model\n        text_token_mask = tokenized.attention_mask.bool()  # bs, 195\n        # text_token_mask: True for nomask, False for mask\n        # text_self_attention_masks: True for nomask, False for mask\n\n        if encoded_text.shape[1] > self.max_text_len:\n            encoded_text = encoded_text[:, : self.max_text_len, :]\n            text_token_mask = text_token_mask[:, : self.max_text_len]\n            position_ids = position_ids[:, : self.max_text_len]\n            text_self_attention_masks = text_self_attention_masks[\n                :, : self.max_text_len, : self.max_text_len\n            ]\n\n        text_dict = {\n            \"encoded_text\": encoded_text,  # bs, 195, d_model\n            \"text_token_mask\": text_token_mask,  # bs, 195\n            \"position_ids\": position_ids,  # bs, 195\n            \"text_self_attention_masks\": text_self_attention_masks,  # bs, 195,195\n        }\n\n        # import ipdb; ipdb.set_trace()\n\n        if isinstance(samples, (list, torch.Tensor)):\n            samples = nested_tensor_from_tensor_list(samples)\n        features, poss = self.backbone(samples)\n\n        srcs = []\n        masks = []\n        for l, feat in enumerate(features):\n            src, mask = feat.decompose()\n            srcs.append(self.input_proj[l](src))\n            masks.append(mask)\n            assert mask is not None\n        if self.num_feature_levels > len(srcs):\n            _len_srcs = len(srcs)\n            for l in range(_len_srcs, self.num_feature_levels):\n                if l == _len_srcs:\n                    src = self.input_proj[l](features[-1].tensors)\n                else:\n                    src = self.input_proj[l](srcs[-1])\n                m = samples.mask\n                mask = F.interpolate(m[None].float(), size=src.shape[-2:]).to(torch.bool)[0]\n                pos_l = self.backbone[1](NestedTensor(src, mask)).to(src.dtype)\n                srcs.append(src)\n                masks.append(mask)\n                poss.append(pos_l)\n\n        input_query_bbox = input_query_label = attn_mask = dn_meta = None\n        hs, reference, hs_enc, ref_enc, init_box_proposal = self.transformer(\n            srcs, masks, input_query_bbox, poss, input_query_label, attn_mask, text_dict\n        )\n\n        # deformable-detr-like anchor update\n        outputs_coord_list = []\n        for dec_lid, (layer_ref_sig, layer_bbox_embed, layer_hs) in enumerate(\n            zip(reference[:-1], self.bbox_embed, hs)\n        ):\n            layer_delta_unsig = layer_bbox_embed(layer_hs)\n            layer_outputs_unsig = layer_delta_unsig + inverse_sigmoid(layer_ref_sig)\n            layer_outputs_unsig = layer_outputs_unsig.sigmoid()\n            outputs_coord_list.append(layer_outputs_unsig)\n        outputs_coord_list = torch.stack(outputs_coord_list)\n\n        # output\n        outputs_class = torch.stack(\n            [\n                layer_cls_embed(layer_hs, text_dict)\n                for layer_cls_embed, layer_hs in zip(self.class_embed, hs)\n            ]\n        )\n        out = {\"pred_logits\": outputs_class[-1], \"pred_boxes\": outputs_coord_list[-1]}\n\n        # # for intermediate outputs\n        # if self.aux_loss:\n        #     out['aux_outputs'] = self._set_aux_loss(outputs_class, outputs_coord_list)\n\n        # # for encoder output\n        # if hs_enc is not None:\n        #     # prepare intermediate outputs\n        #     interm_coord = ref_enc[-1]\n        #     interm_class = self.transformer.enc_out_class_embed(hs_enc[-1], text_dict)\n        #     out['interm_outputs'] = {'pred_logits': interm_class, 'pred_boxes': interm_coord}\n        #     out['interm_outputs_for_matching_pre'] = {'pred_logits': interm_class, 'pred_boxes': init_box_proposal}\n\n        return out\n\n    @torch.jit.unused\n    def _set_aux_loss(self, outputs_class, outputs_coord):\n        # this is a workaround to make torchscript happy, as torchscript\n        # doesn't support dictionary with non-homogeneous values, such\n        # as a dict having both a Tensor and a list.\n        return [\n            {\"pred_logits\": a, \"pred_boxes\": b}\n            for a, b in zip(outputs_class[:-1], outputs_coord[:-1])\n        ]\n\n\n@MODULE_BUILD_FUNCS.registe_with_name(module_name=\"groundingdino\")\ndef build_groundingdino(args):\n\n    backbone = build_backbone(args)\n    transformer = build_transformer(args)\n\n    dn_labelbook_size = args.dn_labelbook_size\n    dec_pred_bbox_embed_share = args.dec_pred_bbox_embed_share\n    sub_sentence_present = args.sub_sentence_present\n\n    model = GroundingDINO(\n        backbone,\n        transformer,\n        num_queries=args.num_queries,\n        aux_loss=True,\n        iter_update=True,\n        query_dim=4,\n        num_feature_levels=args.num_feature_levels,\n        nheads=args.nheads,\n        dec_pred_bbox_embed_share=dec_pred_bbox_embed_share,\n        two_stage_type=args.two_stage_type,\n        two_stage_bbox_embed_share=args.two_stage_bbox_embed_share,\n        two_stage_class_embed_share=args.two_stage_class_embed_share,\n        num_patterns=args.num_patterns,\n        dn_number=0,\n        dn_box_noise_scale=args.dn_box_noise_scale,\n        dn_label_noise_ratio=args.dn_label_noise_ratio,\n        dn_labelbook_size=dn_labelbook_size,\n        text_encoder_type=args.text_encoder_type,\n        sub_sentence_present=sub_sentence_present,\n        max_text_len=args.max_text_len,\n    )\n\n    return model\n"
  },
  {
    "path": "model_cards/groundingdino/models/GroundingDINO/ms_deform_attn.py",
    "content": "# ------------------------------------------------------------------------\n# Grounding DINO\n# url: https://github.com/IDEA-Research/GroundingDINO\n# Copyright (c) 2023 IDEA. All Rights Reserved.\n# Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n# ------------------------------------------------------------------------\n# Deformable DETR\n# Copyright (c) 2020 SenseTime. All Rights Reserved.\n# Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n# ------------------------------------------------------------------------------------------------\n# Modified from:\n# https://github.com/fundamentalvision/Deformable-DETR/blob/main/models/ops/functions/ms_deform_attn_func.py\n# https://github.com/fundamentalvision/Deformable-DETR/blob/main/models/ops/modules/ms_deform_attn.py\n# https://github.com/open-mmlab/mmcv/blob/master/mmcv/ops/multi_scale_deform_attn.py\n# ------------------------------------------------------------------------------------------------\n\nimport math\nimport warnings\nfrom typing import Optional\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.autograd import Function\nfrom torch.autograd.function import once_differentiable\nfrom torch.nn.init import constant_, xavier_uniform_\n\ntry:\n    from groundingdino import _C\nexcept:\n    warnings.warn(\"Failed to load custom C++ ops. Running on CPU mode Only!\")\n\n\n# helpers\ndef _is_power_of_2(n):\n    if (not isinstance(n, int)) or (n < 0):\n        raise ValueError(\"invalid input for _is_power_of_2: {} (type: {})\".format(n, type(n)))\n    return (n & (n - 1) == 0) and n != 0\n\n\nclass MultiScaleDeformableAttnFunction(Function):\n    @staticmethod\n    def forward(\n        ctx,\n        value,\n        value_spatial_shapes,\n        value_level_start_index,\n        sampling_locations,\n        attention_weights,\n        im2col_step,\n    ):\n        ctx.im2col_step = im2col_step\n        output = _C.ms_deform_attn_forward(\n            value,\n            value_spatial_shapes,\n            value_level_start_index,\n            sampling_locations,\n            attention_weights,\n            ctx.im2col_step,\n        )\n        ctx.save_for_backward(\n            value,\n            value_spatial_shapes,\n            value_level_start_index,\n            sampling_locations,\n            attention_weights,\n        )\n        return output\n\n    @staticmethod\n    @once_differentiable\n    def backward(ctx, grad_output):\n        (\n            value,\n            value_spatial_shapes,\n            value_level_start_index,\n            sampling_locations,\n            attention_weights,\n        ) = ctx.saved_tensors\n        grad_value, grad_sampling_loc, grad_attn_weight = _C.ms_deform_attn_backward(\n            value,\n            value_spatial_shapes,\n            value_level_start_index,\n            sampling_locations,\n            attention_weights,\n            grad_output,\n            ctx.im2col_step,\n        )\n\n        return grad_value, None, None, grad_sampling_loc, grad_attn_weight, None\n\n\ndef multi_scale_deformable_attn_pytorch(\n    value: torch.Tensor,\n    value_spatial_shapes: torch.Tensor,\n    sampling_locations: torch.Tensor,\n    attention_weights: torch.Tensor,\n) -> torch.Tensor:\n\n    bs, _, num_heads, embed_dims = value.shape\n    _, num_queries, num_heads, num_levels, num_points, _ = sampling_locations.shape\n    value_list = value.split([H_ * W_ for H_, W_ in value_spatial_shapes], dim=1)\n    sampling_grids = 2 * sampling_locations - 1\n    sampling_value_list = []\n    for level, (H_, W_) in enumerate(value_spatial_shapes):\n        # bs, H_*W_, num_heads, embed_dims ->\n        # bs, H_*W_, num_heads*embed_dims ->\n        # bs, num_heads*embed_dims, H_*W_ ->\n        # bs*num_heads, embed_dims, H_, W_\n        value_l_ = (\n            value_list[level].flatten(2).transpose(1, 2).reshape(bs * num_heads, embed_dims, H_, W_)\n        )\n        # bs, num_queries, num_heads, num_points, 2 ->\n        # bs, num_heads, num_queries, num_points, 2 ->\n        # bs*num_heads, num_queries, num_points, 2\n        sampling_grid_l_ = sampling_grids[:, :, :, level].transpose(1, 2).flatten(0, 1)\n        # bs*num_heads, embed_dims, num_queries, num_points\n        sampling_value_l_ = F.grid_sample(\n            value_l_, sampling_grid_l_, mode=\"bilinear\", padding_mode=\"zeros\", align_corners=False\n        )\n        sampling_value_list.append(sampling_value_l_)\n    # (bs, num_queries, num_heads, num_levels, num_points) ->\n    # (bs, num_heads, num_queries, num_levels, num_points) ->\n    # (bs, num_heads, 1, num_queries, num_levels*num_points)\n    attention_weights = attention_weights.transpose(1, 2).reshape(\n        bs * num_heads, 1, num_queries, num_levels * num_points\n    )\n    output = (\n        (torch.stack(sampling_value_list, dim=-2).flatten(-2) * attention_weights)\n        .sum(-1)\n        .view(bs, num_heads * embed_dims, num_queries)\n    )\n    return output.transpose(1, 2).contiguous()\n\n\nclass MultiScaleDeformableAttention(nn.Module):\n    \"\"\"Multi-Scale Deformable Attention Module used in Deformable-DETR\n\n    `Deformable DETR: Deformable Transformers for End-to-End Object Detection.\n    <https://arxiv.org/pdf/2010.04159.pdf>`_.\n\n    Args:\n        embed_dim (int): The embedding dimension of Attention. Default: 256.\n        num_heads (int): The number of attention heads. Default: 8.\n        num_levels (int): The number of feature map used in Attention. Default: 4.\n        num_points (int): The number of sampling points for each query\n            in each head. Default: 4.\n        img2col_steps (int): The step used in image_to_column. Defualt: 64.\n            dropout (float): Dropout layer used in output. Default: 0.1.\n        batch_first (bool): if ``True``, then the input and output tensor will be\n            provided as `(bs, n, embed_dim)`. Default: False. `(n, bs, embed_dim)`\n    \"\"\"\n\n    def __init__(\n        self,\n        embed_dim: int = 256,\n        num_heads: int = 8,\n        num_levels: int = 4,\n        num_points: int = 4,\n        img2col_step: int = 64,\n        batch_first: bool = False,\n    ):\n        super().__init__()\n        if embed_dim % num_heads != 0:\n            raise ValueError(\n                \"embed_dim must be divisible by num_heads, but got {} and {}\".format(\n                    embed_dim, num_heads\n                )\n            )\n        head_dim = embed_dim // num_heads\n\n        self.batch_first = batch_first\n\n        if not _is_power_of_2(head_dim):\n            warnings.warn(\n                \"\"\"\n                You'd better set d_model in MSDeformAttn to make sure that\n                each dim of the attention head a power of 2, which is more efficient.\n                \"\"\"\n            )\n\n        self.im2col_step = img2col_step\n        self.embed_dim = embed_dim\n        self.num_heads = num_heads\n        self.num_levels = num_levels\n        self.num_points = num_points\n        self.sampling_offsets = nn.Linear(embed_dim, num_heads * num_levels * num_points * 2)\n        self.attention_weights = nn.Linear(embed_dim, num_heads * num_levels * num_points)\n        self.value_proj = nn.Linear(embed_dim, embed_dim)\n        self.output_proj = nn.Linear(embed_dim, embed_dim)\n\n        self.init_weights()\n\n    def _reset_parameters(self):\n        return self.init_weights()\n\n    def init_weights(self):\n        \"\"\"\n        Default initialization for Parameters of Module.\n        \"\"\"\n        constant_(self.sampling_offsets.weight.data, 0.0)\n        thetas = torch.arange(self.num_heads, dtype=torch.float32) * (\n            2.0 * math.pi / self.num_heads\n        )\n        grid_init = torch.stack([thetas.cos(), thetas.sin()], -1)\n        grid_init = (\n            (grid_init / grid_init.abs().max(-1, keepdim=True)[0])\n            .view(self.num_heads, 1, 1, 2)\n            .repeat(1, self.num_levels, self.num_points, 1)\n        )\n        for i in range(self.num_points):\n            grid_init[:, :, i, :] *= i + 1\n        with torch.no_grad():\n            self.sampling_offsets.bias = nn.Parameter(grid_init.view(-1))\n        constant_(self.attention_weights.weight.data, 0.0)\n        constant_(self.attention_weights.bias.data, 0.0)\n        xavier_uniform_(self.value_proj.weight.data)\n        constant_(self.value_proj.bias.data, 0.0)\n        xavier_uniform_(self.output_proj.weight.data)\n        constant_(self.output_proj.bias.data, 0.0)\n\n    def freeze_sampling_offsets(self):\n        print(\"Freeze sampling offsets\")\n        self.sampling_offsets.weight.requires_grad = False\n        self.sampling_offsets.bias.requires_grad = False\n\n    def freeze_attention_weights(self):\n        print(\"Freeze attention weights\")\n        self.attention_weights.weight.requires_grad = False\n        self.attention_weights.bias.requires_grad = False\n\n    def forward(\n        self,\n        query: torch.Tensor,\n        key: Optional[torch.Tensor] = None,\n        value: Optional[torch.Tensor] = None,\n        query_pos: Optional[torch.Tensor] = None,\n        key_padding_mask: Optional[torch.Tensor] = None,\n        reference_points: Optional[torch.Tensor] = None,\n        spatial_shapes: Optional[torch.Tensor] = None,\n        level_start_index: Optional[torch.Tensor] = None,\n        **kwargs\n    ) -> torch.Tensor:\n\n        \"\"\"Forward Function of MultiScaleDeformableAttention\n\n        Args:\n            query (torch.Tensor): Query embeddings with shape\n                `(num_query, bs, embed_dim)`\n            key (torch.Tensor): Key embeddings with shape\n                `(num_key, bs, embed_dim)`\n            value (torch.Tensor): Value embeddings with shape\n                `(num_key, bs, embed_dim)`\n            query_pos (torch.Tensor): The position embedding for `query`. Default: None.\n            key_padding_mask (torch.Tensor): ByteTensor for `query`, with shape `(bs, num_key)`,\n                indicating which elements within `key` to be ignored in attention.\n            reference_points (torch.Tensor): The normalized reference points\n                with shape `(bs, num_query, num_levels, 2)`,\n                all elements is range in [0, 1], top-left (0, 0),\n                bottom-right (1, 1), including padding are.\n                or `(N, Length_{query}, num_levels, 4)`, add additional\n                two dimensions `(h, w)` to form reference boxes.\n            spatial_shapes (torch.Tensor): Spatial shape of features in different levels.\n                With shape `(num_levels, 2)`, last dimension represents `(h, w)`.\n            level_start_index (torch.Tensor): The start index of each level. A tensor with\n                shape `(num_levels, )` which can be represented as\n                `[0, h_0 * w_0, h_0 * w_0 + h_1 * w_1, ...]`.\n\n        Returns:\n            torch.Tensor: forward results with shape `(num_query, bs, embed_dim)`\n        \"\"\"\n\n        if value is None:\n            value = query\n\n        if query_pos is not None:\n            query = query + query_pos\n\n        if not self.batch_first:\n            # change to (bs, num_query ,embed_dims)\n            query = query.permute(1, 0, 2)\n            value = value.permute(1, 0, 2)\n\n        bs, num_query, _ = query.shape\n        bs, num_value, _ = value.shape\n\n        assert (spatial_shapes[:, 0] * spatial_shapes[:, 1]).sum() == num_value\n\n        value = self.value_proj(value)\n        if key_padding_mask is not None:\n            value = value.masked_fill(key_padding_mask[..., None], float(0))\n        value = value.view(bs, num_value, self.num_heads, -1)\n        sampling_offsets = self.sampling_offsets(query).view(\n            bs, num_query, self.num_heads, self.num_levels, self.num_points, 2\n        )\n        attention_weights = self.attention_weights(query).view(\n            bs, num_query, self.num_heads, self.num_levels * self.num_points\n        )\n        attention_weights = attention_weights.softmax(-1)\n        attention_weights = attention_weights.view(\n            bs,\n            num_query,\n            self.num_heads,\n            self.num_levels,\n            self.num_points,\n        )\n\n        # bs, num_query, num_heads, num_levels, num_points, 2\n        if reference_points.shape[-1] == 2:\n            offset_normalizer = torch.stack([spatial_shapes[..., 1], spatial_shapes[..., 0]], -1)\n            sampling_locations = (\n                reference_points[:, :, None, :, None, :]\n                + sampling_offsets / offset_normalizer[None, None, None, :, None, :]\n            )\n        elif reference_points.shape[-1] == 4:\n            sampling_locations = (\n                reference_points[:, :, None, :, None, :2]\n                + sampling_offsets\n                / self.num_points\n                * reference_points[:, :, None, :, None, 2:]\n                * 0.5\n            )\n        else:\n            raise ValueError(\n                \"Last dim of reference_points must be 2 or 4, but get {} instead.\".format(\n                    reference_points.shape[-1]\n                )\n            )\n    \n        if torch.cuda.is_available() and value.is_cuda:\n            halffloat = False\n            if value.dtype == torch.float16:\n                halffloat = True\n                value = value.float()\n                sampling_locations = sampling_locations.float()\n                attention_weights = attention_weights.float()\n\n            output = MultiScaleDeformableAttnFunction.apply(\n                value,\n                spatial_shapes,\n                level_start_index,\n                sampling_locations,\n                attention_weights,\n                self.im2col_step,\n            )\n\n            if halffloat:\n                output = output.half()\n        else:\n            output = multi_scale_deformable_attn_pytorch(\n                value, spatial_shapes, sampling_locations, attention_weights\n            )\n\n        output = self.output_proj(output)\n\n        if not self.batch_first:\n            output = output.permute(1, 0, 2)\n\n        return output\n\n\ndef create_dummy_class(klass, dependency, message=\"\"):\n    \"\"\"\n    When a dependency of a class is not available, create a dummy class which throws ImportError\n    when used.\n\n    Args:\n        klass (str): name of the class.\n        dependency (str): name of the dependency.\n        message: extra message to print\n    Returns:\n        class: a class object\n    \"\"\"\n    err = \"Cannot import '{}', therefore '{}' is not available.\".format(dependency, klass)\n    if message:\n        err = err + \" \" + message\n\n    class _DummyMetaClass(type):\n        # throw error on class attribute access\n        def __getattr__(_, __):  # noqa: B902\n            raise ImportError(err)\n\n    class _Dummy(object, metaclass=_DummyMetaClass):\n        # throw error on constructor\n        def __init__(self, *args, **kwargs):\n            raise ImportError(err)\n\n    return _Dummy\n\n\ndef create_dummy_func(func, dependency, message=\"\"):\n    \"\"\"\n    When a dependency of a function is not available, create a dummy function which throws\n    ImportError when used.\n\n    Args:\n        func (str): name of the function.\n        dependency (str or list[str]): name(s) of the dependency.\n        message: extra message to print\n    Returns:\n        function: a function object\n    \"\"\"\n    err = \"Cannot import '{}', therefore '{}' is not available.\".format(dependency, func)\n    if message:\n        err = err + \" \" + message\n\n    if isinstance(dependency, (list, tuple)):\n        dependency = \",\".join(dependency)\n\n    def _dummy(*args, **kwargs):\n        raise ImportError(err)\n\n    return _dummy\n"
  },
  {
    "path": "model_cards/groundingdino/models/GroundingDINO/transformer.py",
    "content": "# ------------------------------------------------------------------------\n# Grounding DINO\n# url: https://github.com/IDEA-Research/GroundingDINO\n# Copyright (c) 2023 IDEA. All Rights Reserved.\n# Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n# ------------------------------------------------------------------------\n# DINO\n# Copyright (c) 2022 IDEA. All Rights Reserved.\n# Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n# ------------------------------------------------------------------------\n# Conditional DETR Transformer class.\n# Copyright (c) 2021 Microsoft. All Rights Reserved.\n# Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n# ------------------------------------------------------------------------\n# Modified from DETR (https://github.com/facebookresearch/detr)\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\n# ------------------------------------------------------------------------\n\nfrom typing import Optional\n\nimport torch\nimport torch.utils.checkpoint as checkpoint\nfrom torch import Tensor, nn\n\nfrom groundingdino.util.misc import inverse_sigmoid\n\nfrom .fuse_modules import BiAttentionBlock\nfrom .ms_deform_attn import MultiScaleDeformableAttention as MSDeformAttn\nfrom .transformer_vanilla import TransformerEncoderLayer\nfrom .utils import (\n    MLP,\n    _get_activation_fn,\n    _get_clones,\n    gen_encoder_output_proposals,\n    gen_sineembed_for_position,\n    get_sine_pos_embed,\n)\n\n\nclass Transformer(nn.Module):\n    def __init__(\n        self,\n        d_model=256,\n        nhead=8,\n        num_queries=300,\n        num_encoder_layers=6,\n        num_unicoder_layers=0,\n        num_decoder_layers=6,\n        dim_feedforward=2048,\n        dropout=0.0,\n        activation=\"relu\",\n        normalize_before=False,\n        return_intermediate_dec=False,\n        query_dim=4,\n        num_patterns=0,\n        # for deformable encoder\n        num_feature_levels=1,\n        enc_n_points=4,\n        dec_n_points=4,\n        # init query\n        learnable_tgt_init=False,\n        # two stage\n        two_stage_type=\"no\",  # ['no', 'standard', 'early', 'combine', 'enceachlayer', 'enclayer1']\n        embed_init_tgt=False,\n        # for text\n        use_text_enhancer=False,\n        use_fusion_layer=False,\n        use_checkpoint=False,\n        use_transformer_ckpt=False,\n        use_text_cross_attention=False,\n        text_dropout=0.1,\n        fusion_dropout=0.1,\n        fusion_droppath=0.0,\n    ):\n        super().__init__()\n        self.num_feature_levels = num_feature_levels\n        self.num_encoder_layers = num_encoder_layers\n        self.num_unicoder_layers = num_unicoder_layers\n        self.num_decoder_layers = num_decoder_layers\n        self.num_queries = num_queries\n        assert query_dim == 4\n\n        # choose encoder layer type\n        encoder_layer = DeformableTransformerEncoderLayer(\n            d_model, dim_feedforward, dropout, activation, num_feature_levels, nhead, enc_n_points\n        )\n\n        if use_text_enhancer:\n            text_enhance_layer = TransformerEncoderLayer(\n                d_model=d_model,\n                nhead=nhead // 2,\n                dim_feedforward=dim_feedforward // 2,\n                dropout=text_dropout,\n            )\n        else:\n            text_enhance_layer = None\n\n        if use_fusion_layer:\n            feature_fusion_layer = BiAttentionBlock(\n                v_dim=d_model,\n                l_dim=d_model,\n                embed_dim=dim_feedforward // 2,\n                num_heads=nhead // 2,\n                dropout=fusion_dropout,\n                drop_path=fusion_droppath,\n            )\n        else:\n            feature_fusion_layer = None\n\n        encoder_norm = nn.LayerNorm(d_model) if normalize_before else None\n        assert encoder_norm is None\n        self.encoder = TransformerEncoder(\n            encoder_layer,\n            num_encoder_layers,\n            d_model=d_model,\n            num_queries=num_queries,\n            text_enhance_layer=text_enhance_layer,\n            feature_fusion_layer=feature_fusion_layer,\n            use_checkpoint=use_checkpoint,\n            use_transformer_ckpt=use_transformer_ckpt,\n        )\n\n        # choose decoder layer type\n        decoder_layer = DeformableTransformerDecoderLayer(\n            d_model,\n            dim_feedforward,\n            dropout,\n            activation,\n            num_feature_levels,\n            nhead,\n            dec_n_points,\n            use_text_cross_attention=use_text_cross_attention,\n        )\n\n        decoder_norm = nn.LayerNorm(d_model)\n        self.decoder = TransformerDecoder(\n            decoder_layer,\n            num_decoder_layers,\n            decoder_norm,\n            return_intermediate=return_intermediate_dec,\n            d_model=d_model,\n            query_dim=query_dim,\n            num_feature_levels=num_feature_levels,\n        )\n\n        self.d_model = d_model\n        self.nhead = nhead\n        self.dec_layers = num_decoder_layers\n        self.num_queries = num_queries  # useful for single stage model only\n        self.num_patterns = num_patterns\n        if not isinstance(num_patterns, int):\n            Warning(\"num_patterns should be int but {}\".format(type(num_patterns)))\n            self.num_patterns = 0\n\n        if num_feature_levels > 1:\n            if self.num_encoder_layers > 0:\n                self.level_embed = nn.Parameter(torch.Tensor(num_feature_levels, d_model))\n            else:\n                self.level_embed = None\n\n        self.learnable_tgt_init = learnable_tgt_init\n        assert learnable_tgt_init, \"why not learnable_tgt_init\"\n        self.embed_init_tgt = embed_init_tgt\n        if (two_stage_type != \"no\" and embed_init_tgt) or (two_stage_type == \"no\"):\n            self.tgt_embed = nn.Embedding(self.num_queries, d_model)\n            nn.init.normal_(self.tgt_embed.weight.data)\n        else:\n            self.tgt_embed = None\n\n        # for two stage\n        self.two_stage_type = two_stage_type\n        assert two_stage_type in [\"no\", \"standard\"], \"unknown param {} of two_stage_type\".format(\n            two_stage_type\n        )\n        if two_stage_type == \"standard\":\n            # anchor selection at the output of encoder\n            self.enc_output = nn.Linear(d_model, d_model)\n            self.enc_output_norm = nn.LayerNorm(d_model)\n            self.two_stage_wh_embedding = None\n\n        if two_stage_type == \"no\":\n            self.init_ref_points(num_queries)  # init self.refpoint_embed\n\n        self.enc_out_class_embed = None\n        self.enc_out_bbox_embed = None\n\n        self._reset_parameters()\n\n    def _reset_parameters(self):\n        for p in self.parameters():\n            if p.dim() > 1:\n                nn.init.xavier_uniform_(p)\n        for m in self.modules():\n            if isinstance(m, MSDeformAttn):\n                m._reset_parameters()\n        if self.num_feature_levels > 1 and self.level_embed is not None:\n            nn.init.normal_(self.level_embed)\n\n    def get_valid_ratio(self, mask):\n        _, H, W = mask.shape\n        valid_H = torch.sum(~mask[:, :, 0], 1)\n        valid_W = torch.sum(~mask[:, 0, :], 1)\n        valid_ratio_h = valid_H.float() / H\n        valid_ratio_w = valid_W.float() / W\n        valid_ratio = torch.stack([valid_ratio_w, valid_ratio_h], -1)\n        return valid_ratio\n\n    def init_ref_points(self, use_num_queries):\n        self.refpoint_embed = nn.Embedding(use_num_queries, 4)\n\n    def forward(self, srcs, masks, refpoint_embed, pos_embeds, tgt, attn_mask=None, text_dict=None):\n        \"\"\"\n        Input:\n            - srcs: List of multi features [bs, ci, hi, wi]\n            - masks: List of multi masks [bs, hi, wi]\n            - refpoint_embed: [bs, num_dn, 4]. None in infer\n            - pos_embeds: List of multi pos embeds [bs, ci, hi, wi]\n            - tgt: [bs, num_dn, d_model]. None in infer\n\n        \"\"\"\n        # prepare input for encoder\n        src_flatten = []\n        mask_flatten = []\n        lvl_pos_embed_flatten = []\n        spatial_shapes = []\n        for lvl, (src, mask, pos_embed) in enumerate(zip(srcs, masks, pos_embeds)):\n            bs, c, h, w = src.shape\n            spatial_shape = (h, w)\n            spatial_shapes.append(spatial_shape)\n\n            src = src.flatten(2).transpose(1, 2)  # bs, hw, c\n            mask = mask.flatten(1)  # bs, hw\n            pos_embed = pos_embed.flatten(2).transpose(1, 2)  # bs, hw, c\n            if self.num_feature_levels > 1 and self.level_embed is not None:\n                lvl_pos_embed = pos_embed + self.level_embed[lvl].view(1, 1, -1)\n            else:\n                lvl_pos_embed = pos_embed\n            lvl_pos_embed_flatten.append(lvl_pos_embed)\n            src_flatten.append(src)\n            mask_flatten.append(mask)\n        src_flatten = torch.cat(src_flatten, 1)  # bs, \\sum{hxw}, c\n        mask_flatten = torch.cat(mask_flatten, 1)  # bs, \\sum{hxw}\n        lvl_pos_embed_flatten = torch.cat(lvl_pos_embed_flatten, 1)  # bs, \\sum{hxw}, c\n        spatial_shapes = torch.as_tensor(\n            spatial_shapes, dtype=torch.long, device=src_flatten.device\n        )\n        level_start_index = torch.cat(\n            (spatial_shapes.new_zeros((1,)), spatial_shapes.prod(1).cumsum(0)[:-1])\n        )\n        valid_ratios = torch.stack([self.get_valid_ratio(m) for m in masks], 1)\n\n        # two stage\n        enc_topk_proposals = enc_refpoint_embed = None\n\n        #########################################################\n        # Begin Encoder\n        #########################################################\n        memory, memory_text = self.encoder(\n            src_flatten,\n            pos=lvl_pos_embed_flatten,\n            level_start_index=level_start_index,\n            spatial_shapes=spatial_shapes,\n            valid_ratios=valid_ratios,\n            key_padding_mask=mask_flatten,\n            memory_text=text_dict[\"encoded_text\"],\n            text_attention_mask=~text_dict[\"text_token_mask\"],\n            # we ~ the mask . False means use the token; True means pad the token\n            position_ids=text_dict[\"position_ids\"],\n            text_self_attention_masks=text_dict[\"text_self_attention_masks\"],\n        )\n        #########################################################\n        # End Encoder\n        # - memory: bs, \\sum{hw}, c\n        # - mask_flatten: bs, \\sum{hw}\n        # - lvl_pos_embed_flatten: bs, \\sum{hw}, c\n        # - enc_intermediate_output: None or (nenc+1, bs, nq, c) or (nenc, bs, nq, c)\n        # - enc_intermediate_refpoints: None or (nenc+1, bs, nq, c) or (nenc, bs, nq, c)\n        #########################################################\n        text_dict[\"encoded_text\"] = memory_text\n        # if os.environ.get(\"SHILONG_AMP_INFNAN_DEBUG\") == '1':\n        #     if memory.isnan().any() | memory.isinf().any():\n        #         import ipdb; ipdb.set_trace()\n\n        if self.two_stage_type == \"standard\":\n            output_memory, output_proposals = gen_encoder_output_proposals(\n                memory, mask_flatten, spatial_shapes\n            )\n            output_memory = self.enc_output_norm(self.enc_output(output_memory))\n\n            if text_dict is not None:\n                enc_outputs_class_unselected = self.enc_out_class_embed(output_memory, text_dict)\n            else:\n                enc_outputs_class_unselected = self.enc_out_class_embed(output_memory)\n\n            topk_logits = enc_outputs_class_unselected.max(-1)[0]\n            enc_outputs_coord_unselected = (\n                self.enc_out_bbox_embed(output_memory) + output_proposals\n            )  # (bs, \\sum{hw}, 4) unsigmoid\n            topk = self.num_queries\n\n            topk_proposals = torch.topk(topk_logits, topk, dim=1)[1]  # bs, nq\n\n            # gather boxes\n            refpoint_embed_undetach = torch.gather(\n                enc_outputs_coord_unselected, 1, topk_proposals.unsqueeze(-1).repeat(1, 1, 4)\n            )  # unsigmoid\n            refpoint_embed_ = refpoint_embed_undetach.detach()\n            init_box_proposal = torch.gather(\n                output_proposals, 1, topk_proposals.unsqueeze(-1).repeat(1, 1, 4)\n            ).sigmoid()  # sigmoid\n\n            # gather tgt\n            tgt_undetach = torch.gather(\n                output_memory, 1, topk_proposals.unsqueeze(-1).repeat(1, 1, self.d_model)\n            )\n            if self.embed_init_tgt:\n                tgt_ = (\n                    self.tgt_embed.weight[:, None, :].repeat(1, bs, 1).transpose(0, 1)\n                )  # nq, bs, d_model\n            else:\n                tgt_ = tgt_undetach.detach()\n\n            if refpoint_embed is not None:\n                refpoint_embed = torch.cat([refpoint_embed, refpoint_embed_], dim=1)\n                tgt = torch.cat([tgt, tgt_], dim=1)\n            else:\n                refpoint_embed, tgt = refpoint_embed_, tgt_\n\n        elif self.two_stage_type == \"no\":\n            tgt_ = (\n                self.tgt_embed.weight[:, None, :].repeat(1, bs, 1).transpose(0, 1)\n            )  # nq, bs, d_model\n            refpoint_embed_ = (\n                self.refpoint_embed.weight[:, None, :].repeat(1, bs, 1).transpose(0, 1)\n            )  # nq, bs, 4\n\n            if refpoint_embed is not None:\n                refpoint_embed = torch.cat([refpoint_embed, refpoint_embed_], dim=1)\n                tgt = torch.cat([tgt, tgt_], dim=1)\n            else:\n                refpoint_embed, tgt = refpoint_embed_, tgt_\n\n            if self.num_patterns > 0:\n                tgt_embed = tgt.repeat(1, self.num_patterns, 1)\n                refpoint_embed = refpoint_embed.repeat(1, self.num_patterns, 1)\n                tgt_pat = self.patterns.weight[None, :, :].repeat_interleave(\n                    self.num_queries, 1\n                )  # 1, n_q*n_pat, d_model\n                tgt = tgt_embed + tgt_pat\n\n            init_box_proposal = refpoint_embed_.sigmoid()\n\n        else:\n            raise NotImplementedError(\"unknown two_stage_type {}\".format(self.two_stage_type))\n        #########################################################\n        # End preparing tgt\n        # - tgt: bs, NQ, d_model\n        # - refpoint_embed(unsigmoid): bs, NQ, d_model\n        #########################################################\n\n        #########################################################\n        # Begin Decoder\n        #########################################################\n        hs, references = self.decoder(\n            tgt=tgt.transpose(0, 1),\n            memory=memory.transpose(0, 1),\n            memory_key_padding_mask=mask_flatten,\n            pos=lvl_pos_embed_flatten.transpose(0, 1),\n            refpoints_unsigmoid=refpoint_embed.transpose(0, 1),\n            level_start_index=level_start_index,\n            spatial_shapes=spatial_shapes,\n            valid_ratios=valid_ratios,\n            tgt_mask=attn_mask,\n            memory_text=text_dict[\"encoded_text\"],\n            text_attention_mask=~text_dict[\"text_token_mask\"],\n            # we ~ the mask . False means use the token; True means pad the token\n        )\n        #########################################################\n        # End Decoder\n        # hs: n_dec, bs, nq, d_model\n        # references: n_dec+1, bs, nq, query_dim\n        #########################################################\n\n        #########################################################\n        # Begin postprocess\n        #########################################################\n        if self.two_stage_type == \"standard\":\n            hs_enc = tgt_undetach.unsqueeze(0)\n            ref_enc = refpoint_embed_undetach.sigmoid().unsqueeze(0)\n        else:\n            hs_enc = ref_enc = None\n        #########################################################\n        # End postprocess\n        # hs_enc: (n_enc+1, bs, nq, d_model) or (1, bs, nq, d_model) or (n_enc, bs, nq, d_model) or None\n        # ref_enc: (n_enc+1, bs, nq, query_dim) or (1, bs, nq, query_dim) or (n_enc, bs, nq, d_model) or None\n        #########################################################\n\n        return hs, references, hs_enc, ref_enc, init_box_proposal\n        # hs: (n_dec, bs, nq, d_model)\n        # references: sigmoid coordinates. (n_dec+1, bs, bq, 4)\n        # hs_enc: (n_enc+1, bs, nq, d_model) or (1, bs, nq, d_model) or None\n        # ref_enc: sigmoid coordinates. \\\n        #           (n_enc+1, bs, nq, query_dim) or (1, bs, nq, query_dim) or None\n\n\nclass TransformerEncoder(nn.Module):\n    def __init__(\n        self,\n        encoder_layer,\n        num_layers,\n        d_model=256,\n        num_queries=300,\n        enc_layer_share=False,\n        text_enhance_layer=None,\n        feature_fusion_layer=None,\n        use_checkpoint=False,\n        use_transformer_ckpt=False,\n    ):\n        \"\"\"_summary_\n\n        Args:\n            encoder_layer (_type_): _description_\n            num_layers (_type_): _description_\n            norm (_type_, optional): _description_. Defaults to None.\n            d_model (int, optional): _description_. Defaults to 256.\n            num_queries (int, optional): _description_. Defaults to 300.\n            enc_layer_share (bool, optional): _description_. Defaults to False.\n\n        \"\"\"\n        super().__init__()\n        # prepare layers\n        self.layers = []\n        self.text_layers = []\n        self.fusion_layers = []\n        if num_layers > 0:\n            self.layers = _get_clones(encoder_layer, num_layers, layer_share=enc_layer_share)\n\n            if text_enhance_layer is not None:\n                self.text_layers = _get_clones(\n                    text_enhance_layer, num_layers, layer_share=enc_layer_share\n                )\n            if feature_fusion_layer is not None:\n                self.fusion_layers = _get_clones(\n                    feature_fusion_layer, num_layers, layer_share=enc_layer_share\n                )\n        else:\n            self.layers = []\n            del encoder_layer\n\n            if text_enhance_layer is not None:\n                self.text_layers = []\n                del text_enhance_layer\n            if feature_fusion_layer is not None:\n                self.fusion_layers = []\n                del feature_fusion_layer\n\n        self.query_scale = None\n        self.num_queries = num_queries\n        self.num_layers = num_layers\n        self.d_model = d_model\n\n        self.use_checkpoint = use_checkpoint\n        self.use_transformer_ckpt = use_transformer_ckpt\n\n    @staticmethod\n    def get_reference_points(spatial_shapes, valid_ratios, device):\n        reference_points_list = []\n        for lvl, (H_, W_) in enumerate(spatial_shapes):\n\n            ref_y, ref_x = torch.meshgrid(\n                torch.linspace(0.5, H_ - 0.5, H_, dtype=torch.float32, device=device),\n                torch.linspace(0.5, W_ - 0.5, W_, dtype=torch.float32, device=device),\n            )\n            ref_y = ref_y.reshape(-1)[None] / (valid_ratios[:, None, lvl, 1] * H_)\n            ref_x = ref_x.reshape(-1)[None] / (valid_ratios[:, None, lvl, 0] * W_)\n            ref = torch.stack((ref_x, ref_y), -1)\n            reference_points_list.append(ref)\n        reference_points = torch.cat(reference_points_list, 1)\n        reference_points = reference_points[:, :, None] * valid_ratios[:, None]\n        return reference_points\n\n    def forward(\n        self,\n        # for images\n        src: Tensor,\n        pos: Tensor,\n        spatial_shapes: Tensor,\n        level_start_index: Tensor,\n        valid_ratios: Tensor,\n        key_padding_mask: Tensor,\n        # for texts\n        memory_text: Tensor = None,\n        text_attention_mask: Tensor = None,\n        pos_text: Tensor = None,\n        text_self_attention_masks: Tensor = None,\n        position_ids: Tensor = None,\n    ):\n        \"\"\"\n        Input:\n            - src: [bs, sum(hi*wi), 256]\n            - pos: pos embed for src. [bs, sum(hi*wi), 256]\n            - spatial_shapes: h,w of each level [num_level, 2]\n            - level_start_index: [num_level] start point of level in sum(hi*wi).\n            - valid_ratios: [bs, num_level, 2]\n            - key_padding_mask: [bs, sum(hi*wi)]\n\n            - memory_text: bs, n_text, 256\n            - text_attention_mask: bs, n_text\n                False for no padding; True for padding\n            - pos_text: bs, n_text, 256\n\n            - position_ids: bs, n_text\n        Intermedia:\n            - reference_points: [bs, sum(hi*wi), num_level, 2]\n        Outpus:\n            - output: [bs, sum(hi*wi), 256]\n        \"\"\"\n\n        output = src\n\n        # preparation and reshape\n        if self.num_layers > 0:\n            reference_points = self.get_reference_points(\n                spatial_shapes, valid_ratios, device=src.device\n            )\n\n        if self.text_layers:\n            # generate pos_text\n            bs, n_text, text_dim = memory_text.shape\n            if pos_text is None and position_ids is None:\n                pos_text = (\n                    torch.arange(n_text, device=memory_text.device)\n                    .float()\n                    .unsqueeze(0)\n                    .unsqueeze(-1)\n                    .repeat(bs, 1, 1)\n                )\n                pos_text = get_sine_pos_embed(pos_text, num_pos_feats=256, exchange_xy=False)\n            if position_ids is not None:\n                pos_text = get_sine_pos_embed(\n                    position_ids[..., None], num_pos_feats=256, exchange_xy=False\n                )\n\n        # main process\n        for layer_id, layer in enumerate(self.layers):\n            # if output.isnan().any() or memory_text.isnan().any():\n            #     if os.environ.get('IPDB_SHILONG_DEBUG', None) == 'INFO':\n            #         import ipdb; ipdb.set_trace()\n            if self.fusion_layers:\n                if self.use_checkpoint:\n                    output, memory_text = checkpoint.checkpoint(\n                        self.fusion_layers[layer_id],\n                        output,\n                        memory_text,\n                        key_padding_mask,\n                        text_attention_mask,\n                    )\n                else:\n                    output, memory_text = self.fusion_layers[layer_id](\n                        v=output,\n                        l=memory_text,\n                        attention_mask_v=key_padding_mask,\n                        attention_mask_l=text_attention_mask,\n                    )\n\n            if self.text_layers:\n                memory_text = self.text_layers[layer_id](\n                    src=memory_text.transpose(0, 1),\n                    src_mask=~text_self_attention_masks,  # note we use ~ for mask here\n                    src_key_padding_mask=text_attention_mask,\n                    pos=(pos_text.transpose(0, 1) if pos_text is not None else None),\n                ).transpose(0, 1)\n\n            # main process\n            if self.use_transformer_ckpt:\n                output = checkpoint.checkpoint(\n                    layer,\n                    output,\n                    pos,\n                    reference_points,\n                    spatial_shapes,\n                    level_start_index,\n                    key_padding_mask,\n                )\n            else:\n                output = layer(\n                    src=output,\n                    pos=pos,\n                    reference_points=reference_points,\n                    spatial_shapes=spatial_shapes,\n                    level_start_index=level_start_index,\n                    key_padding_mask=key_padding_mask,\n                )\n\n        return output, memory_text\n\n\nclass TransformerDecoder(nn.Module):\n    def __init__(\n        self,\n        decoder_layer,\n        num_layers,\n        norm=None,\n        return_intermediate=False,\n        d_model=256,\n        query_dim=4,\n        num_feature_levels=1,\n    ):\n        super().__init__()\n        if num_layers > 0:\n            self.layers = _get_clones(decoder_layer, num_layers)\n        else:\n            self.layers = []\n        self.num_layers = num_layers\n        self.norm = norm\n        self.return_intermediate = return_intermediate\n        assert return_intermediate, \"support return_intermediate only\"\n        self.query_dim = query_dim\n        assert query_dim in [2, 4], \"query_dim should be 2/4 but {}\".format(query_dim)\n        self.num_feature_levels = num_feature_levels\n\n        self.ref_point_head = MLP(query_dim // 2 * d_model, d_model, d_model, 2)\n        self.query_pos_sine_scale = None\n\n        self.query_scale = None\n        self.bbox_embed = None\n        self.class_embed = None\n\n        self.d_model = d_model\n\n        self.ref_anchor_head = None\n\n    def forward(\n        self,\n        tgt,\n        memory,\n        tgt_mask: Optional[Tensor] = None,\n        memory_mask: Optional[Tensor] = None,\n        tgt_key_padding_mask: Optional[Tensor] = None,\n        memory_key_padding_mask: Optional[Tensor] = None,\n        pos: Optional[Tensor] = None,\n        refpoints_unsigmoid: Optional[Tensor] = None,  # num_queries, bs, 2\n        # for memory\n        level_start_index: Optional[Tensor] = None,  # num_levels\n        spatial_shapes: Optional[Tensor] = None,  # bs, num_levels, 2\n        valid_ratios: Optional[Tensor] = None,\n        # for text\n        memory_text: Optional[Tensor] = None,\n        text_attention_mask: Optional[Tensor] = None,\n    ):\n        \"\"\"\n        Input:\n            - tgt: nq, bs, d_model\n            - memory: hw, bs, d_model\n            - pos: hw, bs, d_model\n            - refpoints_unsigmoid: nq, bs, 2/4\n            - valid_ratios/spatial_shapes: bs, nlevel, 2\n        \"\"\"\n        output = tgt\n\n        intermediate = []\n        reference_points = refpoints_unsigmoid.sigmoid()\n        ref_points = [reference_points]\n\n        for layer_id, layer in enumerate(self.layers):\n\n            if reference_points.shape[-1] == 4:\n                reference_points_input = (\n                    reference_points[:, :, None]\n                    * torch.cat([valid_ratios, valid_ratios], -1)[None, :]\n                )  # nq, bs, nlevel, 4\n            else:\n                assert reference_points.shape[-1] == 2\n                reference_points_input = reference_points[:, :, None] * valid_ratios[None, :]\n            query_sine_embed = gen_sineembed_for_position(\n                reference_points_input[:, :, 0, :]\n            )  # nq, bs, 256*2\n\n            # conditional query\n            raw_query_pos = self.ref_point_head(query_sine_embed)  # nq, bs, 256\n            pos_scale = self.query_scale(output) if self.query_scale is not None else 1\n            query_pos = pos_scale * raw_query_pos\n            # if os.environ.get(\"SHILONG_AMP_INFNAN_DEBUG\") == '1':\n            #     if query_pos.isnan().any() | query_pos.isinf().any():\n            #         import ipdb; ipdb.set_trace()\n\n            # main process\n            output = layer(\n                tgt=output,\n                tgt_query_pos=query_pos,\n                tgt_query_sine_embed=query_sine_embed,\n                tgt_key_padding_mask=tgt_key_padding_mask,\n                tgt_reference_points=reference_points_input,\n                memory_text=memory_text,\n                text_attention_mask=text_attention_mask,\n                memory=memory,\n                memory_key_padding_mask=memory_key_padding_mask,\n                memory_level_start_index=level_start_index,\n                memory_spatial_shapes=spatial_shapes,\n                memory_pos=pos,\n                self_attn_mask=tgt_mask,\n                cross_attn_mask=memory_mask,\n            )\n            if output.isnan().any() | output.isinf().any():\n                print(f\"output layer_id {layer_id} is nan\")\n                try:\n                    num_nan = output.isnan().sum().item()\n                    num_inf = output.isinf().sum().item()\n                    print(f\"num_nan {num_nan}, num_inf {num_inf}\")\n                except Exception as e:\n                    print(e)\n                    # if os.environ.get(\"SHILONG_AMP_INFNAN_DEBUG\") == '1':\n                    #     import ipdb; ipdb.set_trace()\n\n            # iter update\n            if self.bbox_embed is not None:\n                # box_holder = self.bbox_embed(output)\n                # box_holder[..., :self.query_dim] += inverse_sigmoid(reference_points)\n                # new_reference_points = box_holder[..., :self.query_dim].sigmoid()\n\n                reference_before_sigmoid = inverse_sigmoid(reference_points)\n                delta_unsig = self.bbox_embed[layer_id](output)\n                outputs_unsig = delta_unsig + reference_before_sigmoid\n                new_reference_points = outputs_unsig.sigmoid()\n\n                reference_points = new_reference_points.detach()\n                # if layer_id != self.num_layers - 1:\n                ref_points.append(new_reference_points)\n\n            intermediate.append(self.norm(output))\n\n        return [\n            [itm_out.transpose(0, 1) for itm_out in intermediate],\n            [itm_refpoint.transpose(0, 1) for itm_refpoint in ref_points],\n        ]\n\n\nclass DeformableTransformerEncoderLayer(nn.Module):\n    def __init__(\n        self,\n        d_model=256,\n        d_ffn=1024,\n        dropout=0.1,\n        activation=\"relu\",\n        n_levels=4,\n        n_heads=8,\n        n_points=4,\n    ):\n        super().__init__()\n\n        # self attention\n        self.self_attn = MSDeformAttn(\n            embed_dim=d_model,\n            num_levels=n_levels,\n            num_heads=n_heads,\n            num_points=n_points,\n            batch_first=True,\n        )\n        self.dropout1 = nn.Dropout(dropout)\n        self.norm1 = nn.LayerNorm(d_model)\n\n        # ffn\n        self.linear1 = nn.Linear(d_model, d_ffn)\n        self.activation = _get_activation_fn(activation, d_model=d_ffn)\n        self.dropout2 = nn.Dropout(dropout)\n        self.linear2 = nn.Linear(d_ffn, d_model)\n        self.dropout3 = nn.Dropout(dropout)\n        self.norm2 = nn.LayerNorm(d_model)\n\n    @staticmethod\n    def with_pos_embed(tensor, pos):\n        return tensor if pos is None else tensor + pos\n\n    def forward_ffn(self, src):\n        src2 = self.linear2(self.dropout2(self.activation(self.linear1(src))))\n        src = src + self.dropout3(src2)\n        src = self.norm2(src)\n        return src\n\n    def forward(\n        self, src, pos, reference_points, spatial_shapes, level_start_index, key_padding_mask=None\n    ):\n        # self attention\n        # import ipdb; ipdb.set_trace()\n        src2 = self.self_attn(\n            query=self.with_pos_embed(src, pos),\n            reference_points=reference_points,\n            value=src,\n            spatial_shapes=spatial_shapes,\n            level_start_index=level_start_index,\n            key_padding_mask=key_padding_mask,\n        )\n        src = src + self.dropout1(src2)\n        src = self.norm1(src)\n\n        # ffn\n        src = self.forward_ffn(src)\n\n        return src\n\n\nclass DeformableTransformerDecoderLayer(nn.Module):\n    def __init__(\n        self,\n        d_model=256,\n        d_ffn=1024,\n        dropout=0.1,\n        activation=\"relu\",\n        n_levels=4,\n        n_heads=8,\n        n_points=4,\n        use_text_feat_guide=False,\n        use_text_cross_attention=False,\n    ):\n        super().__init__()\n\n        # cross attention\n        self.cross_attn = MSDeformAttn(\n            embed_dim=d_model,\n            num_levels=n_levels,\n            num_heads=n_heads,\n            num_points=n_points,\n            batch_first=True,\n        )\n        self.dropout1 = nn.Dropout(dropout) if dropout > 0 else nn.Identity()\n        self.norm1 = nn.LayerNorm(d_model)\n\n        # cross attention text\n        if use_text_cross_attention:\n            self.ca_text = nn.MultiheadAttention(d_model, n_heads, dropout=dropout)\n            self.catext_dropout = nn.Dropout(dropout) if dropout > 0 else nn.Identity()\n            self.catext_norm = nn.LayerNorm(d_model)\n\n        # self attention\n        self.self_attn = nn.MultiheadAttention(d_model, n_heads, dropout=dropout)\n        self.dropout2 = nn.Dropout(dropout) if dropout > 0 else nn.Identity()\n        self.norm2 = nn.LayerNorm(d_model)\n\n        # ffn\n        self.linear1 = nn.Linear(d_model, d_ffn)\n        self.activation = _get_activation_fn(activation, d_model=d_ffn, batch_dim=1)\n        self.dropout3 = nn.Dropout(dropout) if dropout > 0 else nn.Identity()\n        self.linear2 = nn.Linear(d_ffn, d_model)\n        self.dropout4 = nn.Dropout(dropout) if dropout > 0 else nn.Identity()\n        self.norm3 = nn.LayerNorm(d_model)\n\n        self.key_aware_proj = None\n        self.use_text_feat_guide = use_text_feat_guide\n        assert not use_text_feat_guide\n        self.use_text_cross_attention = use_text_cross_attention\n\n    def rm_self_attn_modules(self):\n        self.self_attn = None\n        self.dropout2 = None\n        self.norm2 = None\n\n    @staticmethod\n    def with_pos_embed(tensor, pos):\n        return tensor if pos is None else tensor + pos\n\n    def forward_ffn(self, tgt):\n        with torch.cuda.amp.autocast(enabled=False):\n            tgt2 = self.linear2(self.dropout3(self.activation(self.linear1(tgt))))\n        tgt = tgt + self.dropout4(tgt2)\n        tgt = self.norm3(tgt)\n        return tgt\n\n    def forward(\n        self,\n        # for tgt\n        tgt: Optional[Tensor],  # nq, bs, d_model\n        tgt_query_pos: Optional[Tensor] = None,  # pos for query. MLP(Sine(pos))\n        tgt_query_sine_embed: Optional[Tensor] = None,  # pos for query. Sine(pos)\n        tgt_key_padding_mask: Optional[Tensor] = None,\n        tgt_reference_points: Optional[Tensor] = None,  # nq, bs, 4\n        memory_text: Optional[Tensor] = None,  # bs, num_token, d_model\n        text_attention_mask: Optional[Tensor] = None,  # bs, num_token\n        # for memory\n        memory: Optional[Tensor] = None,  # hw, bs, d_model\n        memory_key_padding_mask: Optional[Tensor] = None,\n        memory_level_start_index: Optional[Tensor] = None,  # num_levels\n        memory_spatial_shapes: Optional[Tensor] = None,  # bs, num_levels, 2\n        memory_pos: Optional[Tensor] = None,  # pos for memory\n        # sa\n        self_attn_mask: Optional[Tensor] = None,  # mask used for self-attention\n        cross_attn_mask: Optional[Tensor] = None,  # mask used for cross-attention\n    ):\n        \"\"\"\n        Input:\n            - tgt/tgt_query_pos: nq, bs, d_model\n            -\n        \"\"\"\n        assert cross_attn_mask is None\n\n        # self attention\n        if self.self_attn is not None:\n            # import ipdb; ipdb.set_trace()\n            q = k = self.with_pos_embed(tgt, tgt_query_pos)\n            tgt2 = self.self_attn(q, k, tgt, attn_mask=self_attn_mask)[0]\n            tgt = tgt + self.dropout2(tgt2)\n            tgt = self.norm2(tgt)\n\n        if self.use_text_cross_attention:\n            tgt2 = self.ca_text(\n                self.with_pos_embed(tgt, tgt_query_pos),\n                memory_text.transpose(0, 1),\n                memory_text.transpose(0, 1),\n                key_padding_mask=text_attention_mask,\n            )[0]\n            tgt = tgt + self.catext_dropout(tgt2)\n            tgt = self.catext_norm(tgt)\n\n        tgt2 = self.cross_attn(\n            query=self.with_pos_embed(tgt, tgt_query_pos).transpose(0, 1),\n            reference_points=tgt_reference_points.transpose(0, 1).contiguous(),\n            value=memory.transpose(0, 1),\n            spatial_shapes=memory_spatial_shapes,\n            level_start_index=memory_level_start_index,\n            key_padding_mask=memory_key_padding_mask,\n        ).transpose(0, 1)\n        tgt = tgt + self.dropout1(tgt2)\n        tgt = self.norm1(tgt)\n\n        # ffn\n        tgt = self.forward_ffn(tgt)\n\n        return tgt\n\n\ndef build_transformer(args):\n    return Transformer(\n        d_model=args.hidden_dim,\n        dropout=args.dropout,\n        nhead=args.nheads,\n        num_queries=args.num_queries,\n        dim_feedforward=args.dim_feedforward,\n        num_encoder_layers=args.enc_layers,\n        num_decoder_layers=args.dec_layers,\n        normalize_before=args.pre_norm,\n        return_intermediate_dec=True,\n        query_dim=args.query_dim,\n        activation=args.transformer_activation,\n        num_patterns=args.num_patterns,\n        num_feature_levels=args.num_feature_levels,\n        enc_n_points=args.enc_n_points,\n        dec_n_points=args.dec_n_points,\n        learnable_tgt_init=True,\n        # two stage\n        two_stage_type=args.two_stage_type,  # ['no', 'standard', 'early']\n        embed_init_tgt=args.embed_init_tgt,\n        use_text_enhancer=args.use_text_enhancer,\n        use_fusion_layer=args.use_fusion_layer,\n        use_checkpoint=args.use_checkpoint,\n        use_transformer_ckpt=args.use_transformer_ckpt,\n        use_text_cross_attention=args.use_text_cross_attention,\n        text_dropout=args.text_dropout,\n        fusion_dropout=args.fusion_dropout,\n        fusion_droppath=args.fusion_droppath,\n    )\n"
  },
  {
    "path": "model_cards/groundingdino/models/GroundingDINO/transformer_vanilla.py",
    "content": "# ------------------------------------------------------------------------\n# Grounding DINO\n# url: https://github.com/IDEA-Research/GroundingDINO\n# Copyright (c) 2023 IDEA. All Rights Reserved.\n# Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n# ------------------------------------------------------------------------\n# Copyright (c) Aishwarya Kamath & Nicolas Carion. Licensed under the Apache License 2.0. All Rights Reserved\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n\"\"\"\nDETR Transformer class.\n\nCopy-paste from torch.nn.Transformer with modifications:\n    * positional encodings are passed in MHattention\n    * extra LN at the end of encoder is removed\n    * decoder returns a stack of activations from all decoding layers\n\"\"\"\nfrom typing import Optional\n\nimport torch\nimport torch.nn.functional as F\nfrom torch import Tensor, nn\n\nfrom .utils import (\n    MLP,\n    _get_activation_fn,\n    _get_clones,\n    gen_encoder_output_proposals,\n    gen_sineembed_for_position,\n    sigmoid_focal_loss,\n)\n\n\nclass TextTransformer(nn.Module):\n    def __init__(self, num_layers, d_model=256, nheads=8, dim_feedforward=2048, dropout=0.1):\n        super().__init__()\n        self.num_layers = num_layers\n        self.d_model = d_model\n        self.nheads = nheads\n        self.dim_feedforward = dim_feedforward\n        self.norm = None\n\n        single_encoder_layer = TransformerEncoderLayer(\n            d_model=d_model, nhead=nheads, dim_feedforward=dim_feedforward, dropout=dropout\n        )\n        self.layers = _get_clones(single_encoder_layer, num_layers)\n\n    def forward(self, memory_text: torch.Tensor, text_attention_mask: torch.Tensor):\n        \"\"\"\n\n        Args:\n            text_attention_mask: bs, num_token\n            memory_text: bs, num_token, d_model\n\n        Raises:\n            RuntimeError: _description_\n\n        Returns:\n            output: bs, num_token, d_model\n        \"\"\"\n\n        output = memory_text.transpose(0, 1)\n\n        for layer in self.layers:\n            output = layer(output, src_key_padding_mask=text_attention_mask)\n\n        if self.norm is not None:\n            output = self.norm(output)\n\n        return output.transpose(0, 1)\n\n\nclass TransformerEncoderLayer(nn.Module):\n    def __init__(\n        self,\n        d_model,\n        nhead,\n        dim_feedforward=2048,\n        dropout=0.1,\n        activation=\"relu\",\n        normalize_before=False,\n    ):\n        super().__init__()\n        self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)\n        # Implementation of Feedforward model\n        self.linear1 = nn.Linear(d_model, dim_feedforward)\n        self.dropout = nn.Dropout(dropout)\n        self.linear2 = nn.Linear(dim_feedforward, d_model)\n\n        self.norm1 = nn.LayerNorm(d_model)\n        self.norm2 = nn.LayerNorm(d_model)\n        self.dropout1 = nn.Dropout(dropout)\n        self.dropout2 = nn.Dropout(dropout)\n\n        self.activation = _get_activation_fn(activation)\n        self.normalize_before = normalize_before\n        self.nhead = nhead\n\n    def with_pos_embed(self, tensor, pos: Optional[Tensor]):\n        return tensor if pos is None else tensor + pos\n\n    def forward(\n        self,\n        src,\n        src_mask: Optional[Tensor] = None,\n        src_key_padding_mask: Optional[Tensor] = None,\n        pos: Optional[Tensor] = None,\n    ):\n        # repeat attn mask\n        if src_mask.dim() == 3 and src_mask.shape[0] == src.shape[1]:\n            # bs, num_q, num_k\n            src_mask = src_mask.repeat(self.nhead, 1, 1)\n\n        q = k = self.with_pos_embed(src, pos)\n\n        src2 = self.self_attn(q, k, value=src, attn_mask=src_mask)[0]\n\n        # src2 = self.self_attn(q, k, value=src, attn_mask=src_mask, key_padding_mask=src_key_padding_mask)[0]\n        src = src + self.dropout1(src2)\n        src = self.norm1(src)\n        src2 = self.linear2(self.dropout(self.activation(self.linear1(src))))\n        src = src + self.dropout2(src2)\n        src = self.norm2(src)\n        return src\n"
  },
  {
    "path": "model_cards/groundingdino/models/GroundingDINO/utils.py",
    "content": "# ------------------------------------------------------------------------\n# Grounding DINO\n# url: https://github.com/IDEA-Research/GroundingDINO\n# Copyright (c) 2023 IDEA. All Rights Reserved.\n# Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n# ------------------------------------------------------------------------\n\nimport copy\nimport math\n\nimport torch\nimport torch.nn.functional as F\nfrom torch import Tensor, nn\n\n\ndef _get_clones(module, N, layer_share=False):\n    # import ipdb; ipdb.set_trace()\n    if layer_share:\n        return nn.ModuleList([module for i in range(N)])\n    else:\n        return nn.ModuleList([copy.deepcopy(module) for i in range(N)])\n\n\ndef get_sine_pos_embed(\n    pos_tensor: torch.Tensor,\n    num_pos_feats: int = 128,\n    temperature: int = 10000,\n    exchange_xy: bool = True,\n):\n    \"\"\"generate sine position embedding from a position tensor\n    Args:\n        pos_tensor (torch.Tensor): shape: [..., n].\n        num_pos_feats (int): projected shape for each float in the tensor.\n        temperature (int): temperature in the sine/cosine function.\n        exchange_xy (bool, optional): exchange pos x and pos y. \\\n            For example, input tensor is [x,y], the results will be [pos(y), pos(x)]. Defaults to True.\n    Returns:\n        pos_embed (torch.Tensor): shape: [..., n*num_pos_feats].\n    \"\"\"\n    scale = 2 * math.pi\n    dim_t = torch.arange(num_pos_feats, dtype=torch.float32, device=pos_tensor.device)\n    dim_t = temperature ** (2 * torch.div(dim_t, 2, rounding_mode=\"floor\") / num_pos_feats)\n\n    def sine_func(x: torch.Tensor):\n        sin_x = x * scale / dim_t\n        sin_x = torch.stack((sin_x[..., 0::2].sin(), sin_x[..., 1::2].cos()), dim=3).flatten(2)\n        return sin_x\n\n    pos_res = [sine_func(x) for x in pos_tensor.split([1] * pos_tensor.shape[-1], dim=-1)]\n    if exchange_xy:\n        pos_res[0], pos_res[1] = pos_res[1], pos_res[0]\n    pos_res = torch.cat(pos_res, dim=-1)\n    return pos_res\n\n\ndef gen_encoder_output_proposals(\n    memory: Tensor, memory_padding_mask: Tensor, spatial_shapes: Tensor, learnedwh=None\n):\n    \"\"\"\n    Input:\n        - memory: bs, \\sum{hw}, d_model\n        - memory_padding_mask: bs, \\sum{hw}\n        - spatial_shapes: nlevel, 2\n        - learnedwh: 2\n    Output:\n        - output_memory: bs, \\sum{hw}, d_model\n        - output_proposals: bs, \\sum{hw}, 4\n    \"\"\"\n    N_, S_, C_ = memory.shape\n    proposals = []\n    _cur = 0\n    for lvl, (H_, W_) in enumerate(spatial_shapes):\n        mask_flatten_ = memory_padding_mask[:, _cur : (_cur + H_ * W_)].view(N_, H_, W_, 1)\n        valid_H = torch.sum(~mask_flatten_[:, :, 0, 0], 1)\n        valid_W = torch.sum(~mask_flatten_[:, 0, :, 0], 1)\n\n        # import ipdb; ipdb.set_trace()\n\n        grid_y, grid_x = torch.meshgrid(\n            torch.linspace(0, H_ - 1, H_, dtype=torch.float32, device=memory.device),\n            torch.linspace(0, W_ - 1, W_, dtype=torch.float32, device=memory.device),\n        )\n        grid = torch.cat([grid_x.unsqueeze(-1), grid_y.unsqueeze(-1)], -1)  # H_, W_, 2\n\n        scale = torch.cat([valid_W.unsqueeze(-1), valid_H.unsqueeze(-1)], 1).view(N_, 1, 1, 2)\n        grid = (grid.unsqueeze(0).expand(N_, -1, -1, -1) + 0.5) / scale\n\n        if learnedwh is not None:\n            # import ipdb; ipdb.set_trace()\n            wh = torch.ones_like(grid) * learnedwh.sigmoid() * (2.0**lvl)\n        else:\n            wh = torch.ones_like(grid) * 0.05 * (2.0**lvl)\n\n        # scale = torch.cat([W_[None].unsqueeze(-1), H_[None].unsqueeze(-1)], 1).view(1, 1, 1, 2).repeat(N_, 1, 1, 1)\n        # grid = (grid.unsqueeze(0).expand(N_, -1, -1, -1) + 0.5) / scale\n        # wh = torch.ones_like(grid) / scale\n        proposal = torch.cat((grid, wh), -1).view(N_, -1, 4)\n        proposals.append(proposal)\n        _cur += H_ * W_\n    # import ipdb; ipdb.set_trace()\n    output_proposals = torch.cat(proposals, 1)\n    output_proposals_valid = ((output_proposals > 0.01) & (output_proposals < 0.99)).all(\n        -1, keepdim=True\n    )\n    output_proposals = torch.log(output_proposals / (1 - output_proposals))  # unsigmoid\n    output_proposals = output_proposals.masked_fill(memory_padding_mask.unsqueeze(-1), float(\"inf\"))\n    output_proposals = output_proposals.masked_fill(~output_proposals_valid, float(\"inf\"))\n\n    output_memory = memory\n    output_memory = output_memory.masked_fill(memory_padding_mask.unsqueeze(-1), float(0))\n    output_memory = output_memory.masked_fill(~output_proposals_valid, float(0))\n\n    # output_memory = output_memory.masked_fill(memory_padding_mask.unsqueeze(-1), float('inf'))\n    # output_memory = output_memory.masked_fill(~output_proposals_valid, float('inf'))\n\n    return output_memory, output_proposals\n\n\nclass RandomBoxPerturber:\n    def __init__(\n        self, x_noise_scale=0.2, y_noise_scale=0.2, w_noise_scale=0.2, h_noise_scale=0.2\n    ) -> None:\n        self.noise_scale = torch.Tensor(\n            [x_noise_scale, y_noise_scale, w_noise_scale, h_noise_scale]\n        )\n\n    def __call__(self, refanchors: Tensor) -> Tensor:\n        nq, bs, query_dim = refanchors.shape\n        device = refanchors.device\n\n        noise_raw = torch.rand_like(refanchors)\n        noise_scale = self.noise_scale.to(device)[:query_dim]\n\n        new_refanchors = refanchors * (1 + (noise_raw - 0.5) * noise_scale)\n        return new_refanchors.clamp_(0, 1)\n\n\ndef sigmoid_focal_loss(\n    inputs, targets, num_boxes, alpha: float = 0.25, gamma: float = 2, no_reduction=False\n):\n    \"\"\"\n    Loss used in RetinaNet for dense detection: https://arxiv.org/abs/1708.02002.\n    Args:\n        inputs: A float tensor of arbitrary shape.\n                The predictions for each example.\n        targets: A float tensor with the same shape as inputs. Stores the binary\n                 classification label for each element in inputs\n                (0 for the negative class and 1 for the positive class).\n        alpha: (optional) Weighting factor in range (0,1) to balance\n                positive vs negative examples. Default = -1 (no weighting).\n        gamma: Exponent of the modulating factor (1 - p_t) to\n               balance easy vs hard examples.\n    Returns:\n        Loss tensor\n    \"\"\"\n    prob = inputs.sigmoid()\n    ce_loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction=\"none\")\n    p_t = prob * targets + (1 - prob) * (1 - targets)\n    loss = ce_loss * ((1 - p_t) ** gamma)\n\n    if alpha >= 0:\n        alpha_t = alpha * targets + (1 - alpha) * (1 - targets)\n        loss = alpha_t * loss\n\n    if no_reduction:\n        return loss\n\n    return loss.mean(1).sum() / num_boxes\n\n\nclass MLP(nn.Module):\n    \"\"\"Very simple multi-layer perceptron (also called FFN)\"\"\"\n\n    def __init__(self, input_dim, hidden_dim, output_dim, num_layers):\n        super().__init__()\n        self.num_layers = num_layers\n        h = [hidden_dim] * (num_layers - 1)\n        self.layers = nn.ModuleList(\n            nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim])\n        )\n\n    def forward(self, x):\n        for i, layer in enumerate(self.layers):\n            x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)\n        return x\n\n\ndef _get_activation_fn(activation, d_model=256, batch_dim=0):\n    \"\"\"Return an activation function given a string\"\"\"\n    if activation == \"relu\":\n        return F.relu\n    if activation == \"gelu\":\n        return F.gelu\n    if activation == \"glu\":\n        return F.glu\n    if activation == \"prelu\":\n        return nn.PReLU()\n    if activation == \"selu\":\n        return F.selu\n\n    raise RuntimeError(f\"activation should be relu/gelu, not {activation}.\")\n\n\ndef gen_sineembed_for_position(pos_tensor):\n    # n_query, bs, _ = pos_tensor.size()\n    # sineembed_tensor = torch.zeros(n_query, bs, 256)\n    scale = 2 * math.pi\n    dim_t = torch.arange(128, dtype=torch.float32, device=pos_tensor.device)\n    dim_t = 10000 ** (2 * (torch.div(dim_t, 2, rounding_mode='floor')) / 128)\n    x_embed = pos_tensor[:, :, 0] * scale\n    y_embed = pos_tensor[:, :, 1] * scale\n    pos_x = x_embed[:, :, None] / dim_t\n    pos_y = y_embed[:, :, None] / dim_t\n    pos_x = torch.stack((pos_x[:, :, 0::2].sin(), pos_x[:, :, 1::2].cos()), dim=3).flatten(2)\n    pos_y = torch.stack((pos_y[:, :, 0::2].sin(), pos_y[:, :, 1::2].cos()), dim=3).flatten(2)\n    if pos_tensor.size(-1) == 2:\n        pos = torch.cat((pos_y, pos_x), dim=2)\n    elif pos_tensor.size(-1) == 4:\n        w_embed = pos_tensor[:, :, 2] * scale\n        pos_w = w_embed[:, :, None] / dim_t\n        pos_w = torch.stack((pos_w[:, :, 0::2].sin(), pos_w[:, :, 1::2].cos()), dim=3).flatten(2)\n\n        h_embed = pos_tensor[:, :, 3] * scale\n        pos_h = h_embed[:, :, None] / dim_t\n        pos_h = torch.stack((pos_h[:, :, 0::2].sin(), pos_h[:, :, 1::2].cos()), dim=3).flatten(2)\n\n        pos = torch.cat((pos_y, pos_x, pos_w, pos_h), dim=2)\n    else:\n        raise ValueError(\"Unknown pos_tensor shape(-1):{}\".format(pos_tensor.size(-1)))\n    return pos\n\n\nclass ContrastiveEmbed(nn.Module):\n    def __init__(self, max_text_len=256):\n        \"\"\"\n        Args:\n            max_text_len: max length of text.\n        \"\"\"\n        super().__init__()\n        self.max_text_len = max_text_len\n\n    def forward(self, x, text_dict):\n        \"\"\"_summary_\n\n        Args:\n            x (_type_): _description_\n            text_dict (_type_): _description_\n            {\n                'encoded_text': encoded_text, # bs, 195, d_model\n                'text_token_mask': text_token_mask, # bs, 195\n                        # True for used tokens. False for padding tokens\n            }\n        Returns:\n            _type_: _description_\n        \"\"\"\n        assert isinstance(text_dict, dict)\n\n        y = text_dict[\"encoded_text\"]\n        text_token_mask = text_dict[\"text_token_mask\"]\n\n        res = x @ y.transpose(-1, -2)\n        res.masked_fill_(~text_token_mask[:, None, :], float(\"-inf\"))\n\n        # padding to max_text_len\n        new_res = torch.full((*res.shape[:-1], self.max_text_len), float(\"-inf\"), device=res.device)\n        new_res[..., : res.shape[-1]] = res\n\n        return new_res\n"
  },
  {
    "path": "model_cards/groundingdino/models/__init__.py",
    "content": "# ------------------------------------------------------------------------\n# Grounding DINO\n# url: https://github.com/IDEA-Research/GroundingDINO\n# Copyright (c) 2023 IDEA. All Rights Reserved.\n# Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n# ------------------------------------------------------------------------\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\nfrom .GroundingDINO import build_groundingdino\n\n\ndef build_model(args):\n    # we use register to maintain models from catdet6 on.\n    from .registry import MODULE_BUILD_FUNCS\n\n    assert args.modelname in MODULE_BUILD_FUNCS._module_dict\n    build_func = MODULE_BUILD_FUNCS.get(args.modelname)\n    model = build_func(args)\n    return model\n"
  },
  {
    "path": "model_cards/groundingdino/models/registry.py",
    "content": "# ------------------------------------------------------------------------\n# Grounding DINO\n# url: https://github.com/IDEA-Research/GroundingDINO\n# Copyright (c) 2023 IDEA. All Rights Reserved.\n# Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n# ------------------------------------------------------------------------\n# -*- coding: utf-8 -*-\n# @Author: Yihao Chen\n# @Date:   2021-08-16 16:03:17\n# @Last Modified by:   Shilong Liu\n# @Last Modified time: 2022-01-23 15:26\n# modified from mmcv\n\nimport inspect\nfrom functools import partial\n\n\nclass Registry(object):\n    def __init__(self, name):\n        self._name = name\n        self._module_dict = dict()\n\n    def __repr__(self):\n        format_str = self.__class__.__name__ + \"(name={}, items={})\".format(\n            self._name, list(self._module_dict.keys())\n        )\n        return format_str\n\n    def __len__(self):\n        return len(self._module_dict)\n\n    @property\n    def name(self):\n        return self._name\n\n    @property\n    def module_dict(self):\n        return self._module_dict\n\n    def get(self, key):\n        return self._module_dict.get(key, None)\n\n    def registe_with_name(self, module_name=None, force=False):\n        return partial(self.register, module_name=module_name, force=force)\n\n    def register(self, module_build_function, module_name=None, force=False):\n        \"\"\"Register a module build function.\n        Args:\n            module (:obj:`nn.Module`): Module to be registered.\n        \"\"\"\n        if not inspect.isfunction(module_build_function):\n            raise TypeError(\n                \"module_build_function must be a function, but got {}\".format(\n                    type(module_build_function)\n                )\n            )\n        if module_name is None:\n            module_name = module_build_function.__name__\n        if not force and module_name in self._module_dict:\n            raise KeyError(\"{} is already registered in {}\".format(module_name, self.name))\n        self._module_dict[module_name] = module_build_function\n\n        return module_build_function\n\n\nMODULE_BUILD_FUNCS = Registry(\"model build functions\")\n"
  },
  {
    "path": "model_cards/groundingdino/util/__init__.py",
    "content": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n"
  },
  {
    "path": "model_cards/groundingdino/util/box_ops.py",
    "content": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n\"\"\"\nUtilities for bounding box manipulation and GIoU.\n\"\"\"\nimport torch\nfrom torchvision.ops.boxes import box_area\n\n\ndef box_cxcywh_to_xyxy(x):\n    x_c, y_c, w, h = x.unbind(-1)\n    b = [(x_c - 0.5 * w), (y_c - 0.5 * h), (x_c + 0.5 * w), (y_c + 0.5 * h)]\n    return torch.stack(b, dim=-1)\n\n\ndef box_xyxy_to_cxcywh(x):\n    x0, y0, x1, y1 = x.unbind(-1)\n    b = [(x0 + x1) / 2, (y0 + y1) / 2, (x1 - x0), (y1 - y0)]\n    return torch.stack(b, dim=-1)\n\n\n# modified from torchvision to also return the union\ndef box_iou(boxes1, boxes2):\n    area1 = box_area(boxes1)\n    area2 = box_area(boxes2)\n\n    # import ipdb; ipdb.set_trace()\n    lt = torch.max(boxes1[:, None, :2], boxes2[:, :2])  # [N,M,2]\n    rb = torch.min(boxes1[:, None, 2:], boxes2[:, 2:])  # [N,M,2]\n\n    wh = (rb - lt).clamp(min=0)  # [N,M,2]\n    inter = wh[:, :, 0] * wh[:, :, 1]  # [N,M]\n\n    union = area1[:, None] + area2 - inter\n\n    iou = inter / (union + 1e-6)\n    return iou, union\n\n\ndef generalized_box_iou(boxes1, boxes2):\n    \"\"\"\n    Generalized IoU from https://giou.stanford.edu/\n\n    The boxes should be in [x0, y0, x1, y1] format\n\n    Returns a [N, M] pairwise matrix, where N = len(boxes1)\n    and M = len(boxes2)\n    \"\"\"\n    # degenerate boxes gives inf / nan results\n    # so do an early check\n    assert (boxes1[:, 2:] >= boxes1[:, :2]).all()\n    assert (boxes2[:, 2:] >= boxes2[:, :2]).all()\n    # except:\n    #     import ipdb; ipdb.set_trace()\n    iou, union = box_iou(boxes1, boxes2)\n\n    lt = torch.min(boxes1[:, None, :2], boxes2[:, :2])\n    rb = torch.max(boxes1[:, None, 2:], boxes2[:, 2:])\n\n    wh = (rb - lt).clamp(min=0)  # [N,M,2]\n    area = wh[:, :, 0] * wh[:, :, 1]\n\n    return iou - (area - union) / (area + 1e-6)\n\n\n# modified from torchvision to also return the union\ndef box_iou_pairwise(boxes1, boxes2):\n    area1 = box_area(boxes1)\n    area2 = box_area(boxes2)\n\n    lt = torch.max(boxes1[:, :2], boxes2[:, :2])  # [N,2]\n    rb = torch.min(boxes1[:, 2:], boxes2[:, 2:])  # [N,2]\n\n    wh = (rb - lt).clamp(min=0)  # [N,2]\n    inter = wh[:, 0] * wh[:, 1]  # [N]\n\n    union = area1 + area2 - inter\n\n    iou = inter / union\n    return iou, union\n\n\ndef generalized_box_iou_pairwise(boxes1, boxes2):\n    \"\"\"\n    Generalized IoU from https://giou.stanford.edu/\n\n    Input:\n        - boxes1, boxes2: N,4\n    Output:\n        - giou: N, 4\n    \"\"\"\n    # degenerate boxes gives inf / nan results\n    # so do an early check\n    assert (boxes1[:, 2:] >= boxes1[:, :2]).all()\n    assert (boxes2[:, 2:] >= boxes2[:, :2]).all()\n    assert boxes1.shape == boxes2.shape\n    iou, union = box_iou_pairwise(boxes1, boxes2)  # N, 4\n\n    lt = torch.min(boxes1[:, :2], boxes2[:, :2])\n    rb = torch.max(boxes1[:, 2:], boxes2[:, 2:])\n\n    wh = (rb - lt).clamp(min=0)  # [N,2]\n    area = wh[:, 0] * wh[:, 1]\n\n    return iou - (area - union) / area\n\n\ndef masks_to_boxes(masks):\n    \"\"\"Compute the bounding boxes around the provided masks\n\n    The masks should be in format [N, H, W] where N is the number of masks, (H, W) are the spatial dimensions.\n\n    Returns a [N, 4] tensors, with the boxes in xyxy format\n    \"\"\"\n    if masks.numel() == 0:\n        return torch.zeros((0, 4), device=masks.device)\n\n    h, w = masks.shape[-2:]\n\n    y = torch.arange(0, h, dtype=torch.float)\n    x = torch.arange(0, w, dtype=torch.float)\n    y, x = torch.meshgrid(y, x)\n\n    x_mask = masks * x.unsqueeze(0)\n    x_max = x_mask.flatten(1).max(-1)[0]\n    x_min = x_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0]\n\n    y_mask = masks * y.unsqueeze(0)\n    y_max = y_mask.flatten(1).max(-1)[0]\n    y_min = y_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0]\n\n    return torch.stack([x_min, y_min, x_max, y_max], 1)\n\n\nif __name__ == \"__main__\":\n    x = torch.rand(5, 4)\n    y = torch.rand(3, 4)\n    iou, union = box_iou(x, y)\n    import ipdb\n\n    ipdb.set_trace()\n"
  },
  {
    "path": "model_cards/groundingdino/util/get_tokenlizer.py",
    "content": "from transformers import AutoTokenizer, BertModel, BertTokenizer, RobertaModel, RobertaTokenizerFast\n\n\ndef get_tokenlizer(text_encoder_type):\n    if not isinstance(text_encoder_type, str):\n        # print(\"text_encoder_type is not a str\")\n        if hasattr(text_encoder_type, \"text_encoder_type\"):\n            text_encoder_type = text_encoder_type.text_encoder_type\n        elif text_encoder_type.get(\"text_encoder_type\", False):\n            text_encoder_type = text_encoder_type.get(\"text_encoder_type\")\n        else:\n            raise ValueError(\n                \"Unknown type of text_encoder_type: {}\".format(type(text_encoder_type))\n            )\n    print(\"final text_encoder_type: {}\".format(text_encoder_type))\n\n    tokenizer = AutoTokenizer.from_pretrained(text_encoder_type)\n    return tokenizer\n\n\ndef get_pretrained_language_model(text_encoder_type):\n    if text_encoder_type == \"bert-base-uncased\":\n        return BertModel.from_pretrained(text_encoder_type)\n    if text_encoder_type == \"roberta-base\":\n        return RobertaModel.from_pretrained(text_encoder_type)\n    raise ValueError(\"Unknown text_encoder_type {}\".format(text_encoder_type))\n"
  },
  {
    "path": "model_cards/groundingdino/util/inference.py",
    "content": "from typing import Tuple, List\n\nimport cv2\nimport numpy as np\nimport supervision as sv\nimport torch\nfrom PIL import Image\nfrom torchvision.ops import box_convert\n\nimport groundingdino.datasets.transforms as T\nfrom groundingdino.models import build_model\nfrom groundingdino.util.misc import clean_state_dict\nfrom groundingdino.util.slconfig import SLConfig\nfrom groundingdino.util.utils import get_phrases_from_posmap\n\n# ----------------------------------------------------------------------------------------------------------------------\n# OLD API\n# ----------------------------------------------------------------------------------------------------------------------\n\n\ndef preprocess_caption(caption: str) -> str:\n    result = caption.lower().strip()\n    if result.endswith(\".\"):\n        return result\n    return result + \".\"\n\n\ndef load_model(model_config_path: str, model_checkpoint_path: str, device: str = \"cuda\"):\n    args = SLConfig.fromfile(model_config_path)\n    args.device = device\n    model = build_model(args)\n    checkpoint = torch.load(model_checkpoint_path, map_location=\"cpu\")\n    model.load_state_dict(clean_state_dict(checkpoint[\"model\"]), strict=False)\n    model.eval()\n    return model\n\n\ndef load_image(image_path: str) -> Tuple[np.array, torch.Tensor]:\n    transform = T.Compose(\n        [\n            T.RandomResize([800], max_size=1333),\n            T.ToTensor(),\n            T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n        ]\n    )\n    image_source = Image.open(image_path).convert(\"RGB\")\n    image = np.asarray(image_source)\n    image_transformed, _ = transform(image_source, None)\n    return image, image_transformed\n\n\ndef predict(\n        model,\n        image: torch.Tensor,\n        caption: str,\n        box_threshold: float,\n        text_threshold: float,\n        device: str = \"cuda\"\n) -> Tuple[torch.Tensor, torch.Tensor, List[str]]:\n    caption = preprocess_caption(caption=caption)\n\n    model = model.to(device)\n    image = image.to(device)\n\n    with torch.no_grad():\n        outputs = model(image[None], captions=[caption])\n\n    prediction_logits = outputs[\"pred_logits\"].cpu().sigmoid()[0]  # prediction_logits.shape = (nq, 256)\n    prediction_boxes = outputs[\"pred_boxes\"].cpu()[0]  # prediction_boxes.shape = (nq, 4)\n\n    mask = prediction_logits.max(dim=1)[0] > box_threshold\n    logits = prediction_logits[mask]  # logits.shape = (n, 256)\n    boxes = prediction_boxes[mask]  # boxes.shape = (n, 4)\n\n    tokenizer = model.tokenizer\n    tokenized = tokenizer(caption)\n\n    phrases = [\n        get_phrases_from_posmap(logit > text_threshold, tokenized, tokenizer).replace('.', '')\n        for logit\n        in logits\n    ]\n\n    return boxes, logits.max(dim=1)[0], phrases\n\n\ndef annotate(image_source: np.ndarray, boxes: torch.Tensor, logits: torch.Tensor, phrases: List[str]) -> np.ndarray:\n    h, w, _ = image_source.shape\n    boxes = boxes * torch.Tensor([w, h, w, h])\n    xyxy = box_convert(boxes=boxes, in_fmt=\"cxcywh\", out_fmt=\"xyxy\").numpy()\n    detections = sv.Detections(xyxy=xyxy)\n\n    labels = [\n        f\"{phrase} {logit:.2f}\"\n        for phrase, logit\n        in zip(phrases, logits)\n    ]\n\n    box_annotator = sv.BoxAnnotator()\n    annotated_frame = cv2.cvtColor(image_source, cv2.COLOR_RGB2BGR)\n    annotated_frame = box_annotator.annotate(scene=annotated_frame, detections=detections, labels=labels)\n    return annotated_frame\n\n\n# ----------------------------------------------------------------------------------------------------------------------\n# NEW API\n# ----------------------------------------------------------------------------------------------------------------------\n\n\nclass Model:\n\n    def __init__(\n        self,\n        model_config_path: str,\n        model_checkpoint_path: str,\n        device: str = \"cuda\"\n    ):\n        self.model = load_model(\n            model_config_path=model_config_path,\n            model_checkpoint_path=model_checkpoint_path,\n            device=device\n        ).to(device)\n        self.device = device\n\n    def predict_with_caption(\n        self,\n        image: np.ndarray,\n        caption: str,\n        box_threshold: float = 0.35,\n        text_threshold: float = 0.25\n    ) -> Tuple[sv.Detections, List[str]]:\n        \"\"\"\n        import cv2\n\n        image = cv2.imread(IMAGE_PATH)\n\n        model = Model(model_config_path=CONFIG_PATH, model_checkpoint_path=WEIGHTS_PATH)\n        detections, labels = model.predict_with_caption(\n            image=image,\n            caption=caption,\n            box_threshold=BOX_THRESHOLD,\n            text_threshold=TEXT_THRESHOLD\n        )\n\n        import supervision as sv\n\n        box_annotator = sv.BoxAnnotator()\n        annotated_image = box_annotator.annotate(scene=image, detections=detections, labels=labels)\n        \"\"\"\n        processed_image = Model.preprocess_image(image_bgr=image).to(self.device)\n        boxes, logits, phrases = predict(\n            model=self.model,\n            image=processed_image,\n            caption=caption,\n            box_threshold=box_threshold,\n            text_threshold=text_threshold)\n        source_h, source_w, _ = image.shape\n        detections = Model.post_process_result(\n            source_h=source_h,\n            source_w=source_w,\n            boxes=boxes,\n            logits=logits)\n        return detections, phrases\n\n    def predict_with_classes(\n        self,\n        image: np.ndarray,\n        classes: List[str],\n        box_threshold: float,\n        text_threshold: float\n    ) -> sv.Detections:\n        \"\"\"\n        import cv2\n\n        image = cv2.imread(IMAGE_PATH)\n\n        model = Model(model_config_path=CONFIG_PATH, model_checkpoint_path=WEIGHTS_PATH)\n        detections = model.predict_with_classes(\n            image=image,\n            classes=CLASSES,\n            box_threshold=BOX_THRESHOLD,\n            text_threshold=TEXT_THRESHOLD\n        )\n\n\n        import supervision as sv\n\n        box_annotator = sv.BoxAnnotator()\n        annotated_image = box_annotator.annotate(scene=image, detections=detections)\n        \"\"\"\n        caption = \", \".join(classes)\n        processed_image = Model.preprocess_image(image_bgr=image).to(self.device)\n        boxes, logits, phrases = predict(\n            model=self.model,\n            image=processed_image,\n            caption=caption,\n            box_threshold=box_threshold,\n            text_threshold=text_threshold)\n        source_h, source_w, _ = image.shape\n        detections = Model.post_process_result(\n            source_h=source_h,\n            source_w=source_w,\n            boxes=boxes,\n            logits=logits)\n        class_id = Model.phrases2classes(phrases=phrases, classes=classes)\n        detections.class_id = class_id\n        return detections\n\n    @staticmethod\n    def preprocess_image(image_bgr: np.ndarray) -> torch.Tensor:\n        transform = T.Compose(\n            [\n                T.RandomResize([800], max_size=1333),\n                T.ToTensor(),\n                T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n            ]\n        )\n        image_pillow = Image.fromarray(cv2.cvtColor(image_bgr, cv2.COLOR_BGR2RGB))\n        image_transformed, _ = transform(image_pillow, None)\n        return image_transformed\n\n    @staticmethod\n    def post_process_result(\n            source_h: int,\n            source_w: int,\n            boxes: torch.Tensor,\n            logits: torch.Tensor\n    ) -> sv.Detections:\n        boxes = boxes * torch.Tensor([source_w, source_h, source_w, source_h])\n        xyxy = box_convert(boxes=boxes, in_fmt=\"cxcywh\", out_fmt=\"xyxy\").numpy()\n        confidence = logits.numpy()\n        return sv.Detections(xyxy=xyxy, confidence=confidence)\n\n    @staticmethod\n    def phrases2classes(phrases: List[str], classes: List[str]) -> np.ndarray:\n        class_ids = []\n        for phrase in phrases:\n            try:\n                class_ids.append(classes.index(phrase))\n            except ValueError:\n                class_ids.append(None)\n        return np.array(class_ids)\n"
  },
  {
    "path": "model_cards/groundingdino/util/logger.py",
    "content": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\nimport functools\nimport logging\nimport os\nimport sys\n\nfrom termcolor import colored\n\n\nclass _ColorfulFormatter(logging.Formatter):\n    def __init__(self, *args, **kwargs):\n        self._root_name = kwargs.pop(\"root_name\") + \".\"\n        self._abbrev_name = kwargs.pop(\"abbrev_name\", \"\")\n        if len(self._abbrev_name):\n            self._abbrev_name = self._abbrev_name + \".\"\n        super(_ColorfulFormatter, self).__init__(*args, **kwargs)\n\n    def formatMessage(self, record):\n        record.name = record.name.replace(self._root_name, self._abbrev_name)\n        log = super(_ColorfulFormatter, self).formatMessage(record)\n        if record.levelno == logging.WARNING:\n            prefix = colored(\"WARNING\", \"red\", attrs=[\"blink\"])\n        elif record.levelno == logging.ERROR or record.levelno == logging.CRITICAL:\n            prefix = colored(\"ERROR\", \"red\", attrs=[\"blink\", \"underline\"])\n        else:\n            return log\n        return prefix + \" \" + log\n\n\n# so that calling setup_logger multiple times won't add many handlers\n@functools.lru_cache()\ndef setup_logger(output=None, distributed_rank=0, *, color=True, name=\"imagenet\", abbrev_name=None):\n    \"\"\"\n    Initialize the detectron2 logger and set its verbosity level to \"INFO\".\n\n    Args:\n        output (str): a file name or a directory to save log. If None, will not save log file.\n            If ends with \".txt\" or \".log\", assumed to be a file name.\n            Otherwise, logs will be saved to `output/log.txt`.\n        name (str): the root module name of this logger\n\n    Returns:\n        logging.Logger: a logger\n    \"\"\"\n    logger = logging.getLogger(name)\n    logger.setLevel(logging.DEBUG)\n    logger.propagate = False\n\n    if abbrev_name is None:\n        abbrev_name = name\n\n    plain_formatter = logging.Formatter(\n        \"[%(asctime)s.%(msecs)03d]: %(message)s\", datefmt=\"%m/%d %H:%M:%S\"\n    )\n    # stdout logging: master only\n    if distributed_rank == 0:\n        ch = logging.StreamHandler(stream=sys.stdout)\n        ch.setLevel(logging.DEBUG)\n        if color:\n            formatter = _ColorfulFormatter(\n                colored(\"[%(asctime)s.%(msecs)03d]: \", \"green\") + \"%(message)s\",\n                datefmt=\"%m/%d %H:%M:%S\",\n                root_name=name,\n                abbrev_name=str(abbrev_name),\n            )\n        else:\n            formatter = plain_formatter\n        ch.setFormatter(formatter)\n        logger.addHandler(ch)\n\n    # file logging: all workers\n    if output is not None:\n        if output.endswith(\".txt\") or output.endswith(\".log\"):\n            filename = output\n        else:\n            filename = os.path.join(output, \"log.txt\")\n        if distributed_rank > 0:\n            filename = filename + f\".rank{distributed_rank}\"\n        os.makedirs(os.path.dirname(filename), exist_ok=True)\n\n        fh = logging.StreamHandler(_cached_log_stream(filename))\n        fh.setLevel(logging.DEBUG)\n        fh.setFormatter(plain_formatter)\n        logger.addHandler(fh)\n\n    return logger\n\n\n# cache the opened file object, so that different calls to `setup_logger`\n# with the same file name can safely write to the same file.\n@functools.lru_cache(maxsize=None)\ndef _cached_log_stream(filename):\n    return open(filename, \"a\")\n"
  },
  {
    "path": "model_cards/groundingdino/util/misc.py",
    "content": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n\"\"\"\nMisc functions, including distributed helpers.\n\nMostly copy-paste from torchvision references.\n\"\"\"\nimport colorsys\nimport datetime\nimport functools\nimport io\nimport json\nimport os\nimport pickle\nimport subprocess\nimport time\nfrom collections import OrderedDict, defaultdict, deque\nfrom typing import List, Optional\n\nimport numpy as np\nimport torch\nimport torch.distributed as dist\n\n# needed due to empty tensor bug in pytorch and torchvision 0.5\nimport torchvision\nfrom torch import Tensor\n\n__torchvision_need_compat_flag = float(torchvision.__version__.split(\".\")[1]) < 7\nif __torchvision_need_compat_flag:\n    from torchvision.ops import _new_empty_tensor\n    from torchvision.ops.misc import _output_size\n\n\nclass SmoothedValue(object):\n    \"\"\"Track a series of values and provide access to smoothed values over a\n    window or the global series average.\n    \"\"\"\n\n    def __init__(self, window_size=20, fmt=None):\n        if fmt is None:\n            fmt = \"{median:.4f} ({global_avg:.4f})\"\n        self.deque = deque(maxlen=window_size)\n        self.total = 0.0\n        self.count = 0\n        self.fmt = fmt\n\n    def update(self, value, n=1):\n        self.deque.append(value)\n        self.count += n\n        self.total += value * n\n\n    def synchronize_between_processes(self):\n        \"\"\"\n        Warning: does not synchronize the deque!\n        \"\"\"\n        if not is_dist_avail_and_initialized():\n            return\n        t = torch.tensor([self.count, self.total], dtype=torch.float64, device=\"cuda\")\n        dist.barrier()\n        dist.all_reduce(t)\n        t = t.tolist()\n        self.count = int(t[0])\n        self.total = t[1]\n\n    @property\n    def median(self):\n        d = torch.tensor(list(self.deque))\n        if d.shape[0] == 0:\n            return 0\n        return d.median().item()\n\n    @property\n    def avg(self):\n        d = torch.tensor(list(self.deque), dtype=torch.float32)\n        return d.mean().item()\n\n    @property\n    def global_avg(self):\n        if os.environ.get(\"SHILONG_AMP\", None) == \"1\":\n            eps = 1e-4\n        else:\n            eps = 1e-6\n        return self.total / (self.count + eps)\n\n    @property\n    def max(self):\n        return max(self.deque)\n\n    @property\n    def value(self):\n        return self.deque[-1]\n\n    def __str__(self):\n        return self.fmt.format(\n            median=self.median,\n            avg=self.avg,\n            global_avg=self.global_avg,\n            max=self.max,\n            value=self.value,\n        )\n\n\n@functools.lru_cache()\ndef _get_global_gloo_group():\n    \"\"\"\n    Return a process group based on gloo backend, containing all the ranks\n    The result is cached.\n    \"\"\"\n\n    if dist.get_backend() == \"nccl\":\n        return dist.new_group(backend=\"gloo\")\n\n    return dist.group.WORLD\n\n\ndef all_gather_cpu(data):\n    \"\"\"\n    Run all_gather on arbitrary picklable data (not necessarily tensors)\n    Args:\n        data: any picklable object\n    Returns:\n        list[data]: list of data gathered from each rank\n    \"\"\"\n\n    world_size = get_world_size()\n    if world_size == 1:\n        return [data]\n\n    cpu_group = _get_global_gloo_group()\n\n    buffer = io.BytesIO()\n    torch.save(data, buffer)\n    data_view = buffer.getbuffer()\n    device = \"cuda\" if cpu_group is None else \"cpu\"\n    tensor = torch.ByteTensor(data_view).to(device)\n\n    # obtain Tensor size of each rank\n    local_size = torch.tensor([tensor.numel()], device=device, dtype=torch.long)\n    size_list = [torch.tensor([0], device=device, dtype=torch.long) for _ in range(world_size)]\n    if cpu_group is None:\n        dist.all_gather(size_list, local_size)\n    else:\n        print(\"gathering on cpu\")\n        dist.all_gather(size_list, local_size, group=cpu_group)\n    size_list = [int(size.item()) for size in size_list]\n    max_size = max(size_list)\n    assert isinstance(local_size.item(), int)\n    local_size = int(local_size.item())\n\n    # receiving Tensor from all ranks\n    # we pad the tensor because torch all_gather does not support\n    # gathering tensors of different shapes\n    tensor_list = []\n    for _ in size_list:\n        tensor_list.append(torch.empty((max_size,), dtype=torch.uint8, device=device))\n    if local_size != max_size:\n        padding = torch.empty(size=(max_size - local_size,), dtype=torch.uint8, device=device)\n        tensor = torch.cat((tensor, padding), dim=0)\n    if cpu_group is None:\n        dist.all_gather(tensor_list, tensor)\n    else:\n        dist.all_gather(tensor_list, tensor, group=cpu_group)\n\n    data_list = []\n    for size, tensor in zip(size_list, tensor_list):\n        tensor = torch.split(tensor, [size, max_size - size], dim=0)[0]\n        buffer = io.BytesIO(tensor.cpu().numpy())\n        obj = torch.load(buffer)\n        data_list.append(obj)\n\n    return data_list\n\n\ndef all_gather(data):\n    \"\"\"\n    Run all_gather on arbitrary picklable data (not necessarily tensors)\n    Args:\n        data: any picklable object\n    Returns:\n        list[data]: list of data gathered from each rank\n    \"\"\"\n\n    if os.getenv(\"CPU_REDUCE\") == \"1\":\n        return all_gather_cpu(data)\n\n    world_size = get_world_size()\n    if world_size == 1:\n        return [data]\n\n    # serialized to a Tensor\n    buffer = pickle.dumps(data)\n    storage = torch.ByteStorage.from_buffer(buffer)\n    tensor = torch.ByteTensor(storage).to(\"cuda\")\n\n    # obtain Tensor size of each rank\n    local_size = torch.tensor([tensor.numel()], device=\"cuda\")\n    size_list = [torch.tensor([0], device=\"cuda\") for _ in range(world_size)]\n    dist.all_gather(size_list, local_size)\n    size_list = [int(size.item()) for size in size_list]\n    max_size = max(size_list)\n\n    # receiving Tensor from all ranks\n    # we pad the tensor because torch all_gather does not support\n    # gathering tensors of different shapes\n    tensor_list = []\n    for _ in size_list:\n        tensor_list.append(torch.empty((max_size,), dtype=torch.uint8, device=\"cuda\"))\n    if local_size != max_size:\n        padding = torch.empty(size=(max_size - local_size,), dtype=torch.uint8, device=\"cuda\")\n        tensor = torch.cat((tensor, padding), dim=0)\n    dist.all_gather(tensor_list, tensor)\n\n    data_list = []\n    for size, tensor in zip(size_list, tensor_list):\n        buffer = tensor.cpu().numpy().tobytes()[:size]\n        data_list.append(pickle.loads(buffer))\n\n    return data_list\n\n\ndef reduce_dict(input_dict, average=True):\n    \"\"\"\n    Args:\n        input_dict (dict): all the values will be reduced\n        average (bool): whether to do average or sum\n    Reduce the values in the dictionary from all processes so that all processes\n    have the averaged results. Returns a dict with the same fields as\n    input_dict, after reduction.\n    \"\"\"\n    world_size = get_world_size()\n    if world_size < 2:\n        return input_dict\n    with torch.no_grad():\n        names = []\n        values = []\n        # sort the keys so that they are consistent across processes\n        for k in sorted(input_dict.keys()):\n            names.append(k)\n            values.append(input_dict[k])\n        values = torch.stack(values, dim=0)\n        dist.all_reduce(values)\n        if average:\n            values /= world_size\n        reduced_dict = {k: v for k, v in zip(names, values)}\n    return reduced_dict\n\n\nclass MetricLogger(object):\n    def __init__(self, delimiter=\"\\t\"):\n        self.meters = defaultdict(SmoothedValue)\n        self.delimiter = delimiter\n\n    def update(self, **kwargs):\n        for k, v in kwargs.items():\n            if isinstance(v, torch.Tensor):\n                v = v.item()\n            assert isinstance(v, (float, int))\n            self.meters[k].update(v)\n\n    def __getattr__(self, attr):\n        if attr in self.meters:\n            return self.meters[attr]\n        if attr in self.__dict__:\n            return self.__dict__[attr]\n        raise AttributeError(\"'{}' object has no attribute '{}'\".format(type(self).__name__, attr))\n\n    def __str__(self):\n        loss_str = []\n        for name, meter in self.meters.items():\n            # print(name, str(meter))\n            # import ipdb;ipdb.set_trace()\n            if meter.count > 0:\n                loss_str.append(\"{}: {}\".format(name, str(meter)))\n        return self.delimiter.join(loss_str)\n\n    def synchronize_between_processes(self):\n        for meter in self.meters.values():\n            meter.synchronize_between_processes()\n\n    def add_meter(self, name, meter):\n        self.meters[name] = meter\n\n    def log_every(self, iterable, print_freq, header=None, logger=None):\n        if logger is None:\n            print_func = print\n        else:\n            print_func = logger.info\n\n        i = 0\n        if not header:\n            header = \"\"\n        start_time = time.time()\n        end = time.time()\n        iter_time = SmoothedValue(fmt=\"{avg:.4f}\")\n        data_time = SmoothedValue(fmt=\"{avg:.4f}\")\n        space_fmt = \":\" + str(len(str(len(iterable)))) + \"d\"\n        if torch.cuda.is_available():\n            log_msg = self.delimiter.join(\n                [\n                    header,\n                    \"[{0\" + space_fmt + \"}/{1}]\",\n                    \"eta: {eta}\",\n                    \"{meters}\",\n                    \"time: {time}\",\n                    \"data: {data}\",\n                    \"max mem: {memory:.0f}\",\n                ]\n            )\n        else:\n            log_msg = self.delimiter.join(\n                [\n                    header,\n                    \"[{0\" + space_fmt + \"}/{1}]\",\n                    \"eta: {eta}\",\n                    \"{meters}\",\n                    \"time: {time}\",\n                    \"data: {data}\",\n                ]\n            )\n        MB = 1024.0 * 1024.0\n        for obj in iterable:\n            data_time.update(time.time() - end)\n            yield obj\n            # import ipdb; ipdb.set_trace()\n            iter_time.update(time.time() - end)\n            if i % print_freq == 0 or i == len(iterable) - 1:\n                eta_seconds = iter_time.global_avg * (len(iterable) - i)\n                eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))\n                if torch.cuda.is_available():\n                    print_func(\n                        log_msg.format(\n                            i,\n                            len(iterable),\n                            eta=eta_string,\n                            meters=str(self),\n                            time=str(iter_time),\n                            data=str(data_time),\n                            memory=torch.cuda.max_memory_allocated() / MB,\n                        )\n                    )\n                else:\n                    print_func(\n                        log_msg.format(\n                            i,\n                            len(iterable),\n                            eta=eta_string,\n                            meters=str(self),\n                            time=str(iter_time),\n                            data=str(data_time),\n                        )\n                    )\n            i += 1\n            end = time.time()\n        total_time = time.time() - start_time\n        total_time_str = str(datetime.timedelta(seconds=int(total_time)))\n        print_func(\n            \"{} Total time: {} ({:.4f} s / it)\".format(\n                header, total_time_str, total_time / len(iterable)\n            )\n        )\n\n\ndef get_sha():\n    cwd = os.path.dirname(os.path.abspath(__file__))\n\n    def _run(command):\n        return subprocess.check_output(command, cwd=cwd).decode(\"ascii\").strip()\n\n    sha = \"N/A\"\n    diff = \"clean\"\n    branch = \"N/A\"\n    try:\n        sha = _run([\"git\", \"rev-parse\", \"HEAD\"])\n        subprocess.check_output([\"git\", \"diff\"], cwd=cwd)\n        diff = _run([\"git\", \"diff-index\", \"HEAD\"])\n        diff = \"has uncommited changes\" if diff else \"clean\"\n        branch = _run([\"git\", \"rev-parse\", \"--abbrev-ref\", \"HEAD\"])\n    except Exception:\n        pass\n    message = f\"sha: {sha}, status: {diff}, branch: {branch}\"\n    return message\n\n\ndef collate_fn(batch):\n    # import ipdb; ipdb.set_trace()\n    batch = list(zip(*batch))\n    batch[0] = nested_tensor_from_tensor_list(batch[0])\n    return tuple(batch)\n\n\ndef _max_by_axis(the_list):\n    # type: (List[List[int]]) -> List[int]\n    maxes = the_list[0]\n    for sublist in the_list[1:]:\n        for index, item in enumerate(sublist):\n            maxes[index] = max(maxes[index], item)\n    return maxes\n\n\nclass NestedTensor(object):\n    def __init__(self, tensors, mask: Optional[Tensor]):\n        self.tensors = tensors\n        self.mask = mask\n        if mask == \"auto\":\n            self.mask = torch.zeros_like(tensors).to(tensors.device)\n            if self.mask.dim() == 3:\n                self.mask = self.mask.sum(0).to(bool)\n            elif self.mask.dim() == 4:\n                self.mask = self.mask.sum(1).to(bool)\n            else:\n                raise ValueError(\n                    \"tensors dim must be 3 or 4 but {}({})\".format(\n                        self.tensors.dim(), self.tensors.shape\n                    )\n                )\n\n    def imgsize(self):\n        res = []\n        for i in range(self.tensors.shape[0]):\n            mask = self.mask[i]\n            maxH = (~mask).sum(0).max()\n            maxW = (~mask).sum(1).max()\n            res.append(torch.Tensor([maxH, maxW]))\n        return res\n\n    def to(self, device):\n        # type: (Device) -> NestedTensor # noqa\n        cast_tensor = self.tensors.to(device)\n        mask = self.mask\n        if mask is not None:\n            assert mask is not None\n            cast_mask = mask.to(device)\n        else:\n            cast_mask = None\n        return NestedTensor(cast_tensor, cast_mask)\n\n    def to_img_list_single(self, tensor, mask):\n        assert tensor.dim() == 3, \"dim of tensor should be 3 but {}\".format(tensor.dim())\n        maxH = (~mask).sum(0).max()\n        maxW = (~mask).sum(1).max()\n        img = tensor[:, :maxH, :maxW]\n        return img\n\n    def to_img_list(self):\n        \"\"\"remove the padding and convert to img list\n\n        Returns:\n            [type]: [description]\n        \"\"\"\n        if self.tensors.dim() == 3:\n            return self.to_img_list_single(self.tensors, self.mask)\n        else:\n            res = []\n            for i in range(self.tensors.shape[0]):\n                tensor_i = self.tensors[i]\n                mask_i = self.mask[i]\n                res.append(self.to_img_list_single(tensor_i, mask_i))\n            return res\n\n    @property\n    def device(self):\n        return self.tensors.device\n\n    def decompose(self):\n        return self.tensors, self.mask\n\n    def __repr__(self):\n        return str(self.tensors)\n\n    @property\n    def shape(self):\n        return {\"tensors.shape\": self.tensors.shape, \"mask.shape\": self.mask.shape}\n\n\ndef nested_tensor_from_tensor_list(tensor_list: List[Tensor]):\n    # TODO make this more general\n    if tensor_list[0].ndim == 3:\n        if torchvision._is_tracing():\n            # nested_tensor_from_tensor_list() does not export well to ONNX\n            # call _onnx_nested_tensor_from_tensor_list() instead\n            return _onnx_nested_tensor_from_tensor_list(tensor_list)\n\n        # TODO make it support different-sized images\n        max_size = _max_by_axis([list(img.shape) for img in tensor_list])\n        # min_size = tuple(min(s) for s in zip(*[img.shape for img in tensor_list]))\n        batch_shape = [len(tensor_list)] + max_size\n        b, c, h, w = batch_shape\n        dtype = tensor_list[0].dtype\n        device = tensor_list[0].device\n        tensor = torch.zeros(batch_shape, dtype=dtype, device=device)\n        mask = torch.ones((b, h, w), dtype=torch.bool, device=device)\n        for img, pad_img, m in zip(tensor_list, tensor, mask):\n            pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img)\n            m[: img.shape[1], : img.shape[2]] = False\n    else:\n        raise ValueError(\"not supported\")\n    return NestedTensor(tensor, mask)\n\n\n# _onnx_nested_tensor_from_tensor_list() is an implementation of\n# nested_tensor_from_tensor_list() that is supported by ONNX tracing.\n@torch.jit.unused\ndef _onnx_nested_tensor_from_tensor_list(tensor_list: List[Tensor]) -> NestedTensor:\n    max_size = []\n    for i in range(tensor_list[0].dim()):\n        max_size_i = torch.max(\n            torch.stack([img.shape[i] for img in tensor_list]).to(torch.float32)\n        ).to(torch.int64)\n        max_size.append(max_size_i)\n    max_size = tuple(max_size)\n\n    # work around for\n    # pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img)\n    # m[: img.shape[1], :img.shape[2]] = False\n    # which is not yet supported in onnx\n    padded_imgs = []\n    padded_masks = []\n    for img in tensor_list:\n        padding = [(s1 - s2) for s1, s2 in zip(max_size, tuple(img.shape))]\n        padded_img = torch.nn.functional.pad(img, (0, padding[2], 0, padding[1], 0, padding[0]))\n        padded_imgs.append(padded_img)\n\n        m = torch.zeros_like(img[0], dtype=torch.int, device=img.device)\n        padded_mask = torch.nn.functional.pad(m, (0, padding[2], 0, padding[1]), \"constant\", 1)\n        padded_masks.append(padded_mask.to(torch.bool))\n\n    tensor = torch.stack(padded_imgs)\n    mask = torch.stack(padded_masks)\n\n    return NestedTensor(tensor, mask=mask)\n\n\ndef setup_for_distributed(is_master):\n    \"\"\"\n    This function disables printing when not in master process\n    \"\"\"\n    import builtins as __builtin__\n\n    builtin_print = __builtin__.print\n\n    def print(*args, **kwargs):\n        force = kwargs.pop(\"force\", False)\n        if is_master or force:\n            builtin_print(*args, **kwargs)\n\n    __builtin__.print = print\n\n\ndef is_dist_avail_and_initialized():\n    if not dist.is_available():\n        return False\n    if not dist.is_initialized():\n        return False\n    return True\n\n\ndef get_world_size():\n    if not is_dist_avail_and_initialized():\n        return 1\n    return dist.get_world_size()\n\n\ndef get_rank():\n    if not is_dist_avail_and_initialized():\n        return 0\n    return dist.get_rank()\n\n\ndef is_main_process():\n    return get_rank() == 0\n\n\ndef save_on_master(*args, **kwargs):\n    if is_main_process():\n        torch.save(*args, **kwargs)\n\n\ndef init_distributed_mode(args):\n    if \"WORLD_SIZE\" in os.environ and os.environ[\"WORLD_SIZE\"] != \"\":  # 'RANK' in os.environ and\n        args.rank = int(os.environ[\"RANK\"])\n        args.world_size = int(os.environ[\"WORLD_SIZE\"])\n        args.gpu = args.local_rank = int(os.environ[\"LOCAL_RANK\"])\n\n        # launch by torch.distributed.launch\n        # Single node\n        #   python -m torch.distributed.launch --nproc_per_node=8 main.py --world-size 1 --rank 0 ...\n        # Multi nodes\n        #   python -m torch.distributed.launch --nproc_per_node=8 main.py --world-size 2 --rank 0 --dist-url 'tcp://IP_OF_NODE0:FREEPORT' ...\n        #   python -m torch.distributed.launch --nproc_per_node=8 main.py --world-size 2 --rank 1 --dist-url 'tcp://IP_OF_NODE0:FREEPORT' ...\n        # args.rank = int(os.environ.get('OMPI_COMM_WORLD_RANK'))\n        # local_world_size = int(os.environ['GPU_PER_NODE_COUNT'])\n        # args.world_size = args.world_size * local_world_size\n        # args.gpu = args.local_rank = int(os.environ['LOCAL_RANK'])\n        # args.rank = args.rank * local_world_size + args.local_rank\n        print(\n            \"world size: {}, rank: {}, local rank: {}\".format(\n                args.world_size, args.rank, args.local_rank\n            )\n        )\n        print(json.dumps(dict(os.environ), indent=2))\n    elif \"SLURM_PROCID\" in os.environ:\n        args.rank = int(os.environ[\"SLURM_PROCID\"])\n        args.gpu = args.local_rank = int(os.environ[\"SLURM_LOCALID\"])\n        args.world_size = int(os.environ[\"SLURM_NPROCS\"])\n\n        print(\n            \"world size: {}, world rank: {}, local rank: {}, device_count: {}\".format(\n                args.world_size, args.rank, args.local_rank, torch.cuda.device_count()\n            )\n        )\n    else:\n        print(\"Not using distributed mode\")\n        args.distributed = False\n        args.world_size = 1\n        args.rank = 0\n        args.local_rank = 0\n        return\n\n    print(\"world_size:{} rank:{} local_rank:{}\".format(args.world_size, args.rank, args.local_rank))\n    args.distributed = True\n    torch.cuda.set_device(args.local_rank)\n    args.dist_backend = \"nccl\"\n    print(\"| distributed init (rank {}): {}\".format(args.rank, args.dist_url), flush=True)\n\n    torch.distributed.init_process_group(\n        backend=args.dist_backend,\n        world_size=args.world_size,\n        rank=args.rank,\n        init_method=args.dist_url,\n    )\n\n    print(\"Before torch.distributed.barrier()\")\n    torch.distributed.barrier()\n    print(\"End torch.distributed.barrier()\")\n    setup_for_distributed(args.rank == 0)\n\n\n@torch.no_grad()\ndef accuracy(output, target, topk=(1,)):\n    \"\"\"Computes the precision@k for the specified values of k\"\"\"\n    if target.numel() == 0:\n        return [torch.zeros([], device=output.device)]\n    maxk = max(topk)\n    batch_size = target.size(0)\n\n    _, pred = output.topk(maxk, 1, True, True)\n    pred = pred.t()\n    correct = pred.eq(target.view(1, -1).expand_as(pred))\n\n    res = []\n    for k in topk:\n        correct_k = correct[:k].view(-1).float().sum(0)\n        res.append(correct_k.mul_(100.0 / batch_size))\n    return res\n\n\n@torch.no_grad()\ndef accuracy_onehot(pred, gt):\n    \"\"\"_summary_\n\n    Args:\n        pred (_type_): n, c\n        gt (_type_): n, c\n    \"\"\"\n    tp = ((pred - gt).abs().sum(-1) < 1e-4).float().sum()\n    acc = tp / gt.shape[0] * 100\n    return acc\n\n\ndef interpolate(input, size=None, scale_factor=None, mode=\"nearest\", align_corners=None):\n    # type: (Tensor, Optional[List[int]], Optional[float], str, Optional[bool]) -> Tensor\n    \"\"\"\n    Equivalent to nn.functional.interpolate, but with support for empty batch sizes.\n    This will eventually be supported natively by PyTorch, and this\n    class can go away.\n    \"\"\"\n    if __torchvision_need_compat_flag < 0.7:\n        if input.numel() > 0:\n            return torch.nn.functional.interpolate(input, size, scale_factor, mode, align_corners)\n\n        output_shape = _output_size(2, input, size, scale_factor)\n        output_shape = list(input.shape[:-2]) + list(output_shape)\n        return _new_empty_tensor(input, output_shape)\n    else:\n        return torchvision.ops.misc.interpolate(input, size, scale_factor, mode, align_corners)\n\n\nclass color_sys:\n    def __init__(self, num_colors) -> None:\n        self.num_colors = num_colors\n        colors = []\n        for i in np.arange(0.0, 360.0, 360.0 / num_colors):\n            hue = i / 360.0\n            lightness = (50 + np.random.rand() * 10) / 100.0\n            saturation = (90 + np.random.rand() * 10) / 100.0\n            colors.append(\n                tuple([int(j * 255) for j in colorsys.hls_to_rgb(hue, lightness, saturation)])\n            )\n        self.colors = colors\n\n    def __call__(self, idx):\n        return self.colors[idx]\n\n\ndef inverse_sigmoid(x, eps=1e-3):\n    x = x.clamp(min=0, max=1)\n    x1 = x.clamp(min=eps)\n    x2 = (1 - x).clamp(min=eps)\n    return torch.log(x1 / x2)\n\n\ndef clean_state_dict(state_dict):\n    new_state_dict = OrderedDict()\n    for k, v in state_dict.items():\n        if k[:7] == \"module.\":\n            k = k[7:]  # remove `module.`\n        new_state_dict[k] = v\n    return new_state_dict\n"
  },
  {
    "path": "model_cards/groundingdino/util/slconfig.py",
    "content": "# ==========================================================\n# Modified from mmcv\n# ==========================================================\nimport ast\nimport os\nimport os.path as osp\nimport shutil\nimport sys\nimport tempfile\nfrom argparse import Action\nfrom importlib import import_module\n\nfrom addict import Dict\nfrom yapf.yapflib.yapf_api import FormatCode\n\nBASE_KEY = \"_base_\"\nDELETE_KEY = \"_delete_\"\nRESERVED_KEYS = [\"filename\", \"text\", \"pretty_text\", \"get\", \"dump\", \"merge_from_dict\"]\n\n\ndef check_file_exist(filename, msg_tmpl='file \"{}\" does not exist'):\n    if not osp.isfile(filename):\n        raise FileNotFoundError(msg_tmpl.format(filename))\n\n\nclass ConfigDict(Dict):\n    def __missing__(self, name):\n        raise KeyError(name)\n\n    def __getattr__(self, name):\n        try:\n            value = super(ConfigDict, self).__getattr__(name)\n        except KeyError:\n            ex = AttributeError(f\"'{self.__class__.__name__}' object has no \" f\"attribute '{name}'\")\n        except Exception as e:\n            ex = e\n        else:\n            return value\n        raise ex\n\n\nclass SLConfig(object):\n    \"\"\"\n    config files.\n    only support .py file as config now.\n\n    ref: mmcv.utils.config\n\n    Example:\n        >>> cfg = Config(dict(a=1, b=dict(b1=[0, 1])))\n        >>> cfg.a\n        1\n        >>> cfg.b\n        {'b1': [0, 1]}\n        >>> cfg.b.b1\n        [0, 1]\n        >>> cfg = Config.fromfile('tests/data/config/a.py')\n        >>> cfg.filename\n        \"/home/kchen/projects/mmcv/tests/data/config/a.py\"\n        >>> cfg.item4\n        'test'\n        >>> cfg\n        \"Config [path: /home/kchen/projects/mmcv/tests/data/config/a.py]: \"\n        \"{'item1': [1, 2], 'item2': {'a': 0}, 'item3': True, 'item4': 'test'}\"\n    \"\"\"\n\n    @staticmethod\n    def _validate_py_syntax(filename):\n        with open(filename) as f:\n            content = f.read()\n        try:\n            ast.parse(content)\n        except SyntaxError:\n            raise SyntaxError(\"There are syntax errors in config \" f\"file {filename}\")\n\n    @staticmethod\n    def _file2dict(filename):\n        filename = osp.abspath(osp.expanduser(filename))\n        check_file_exist(filename)\n        if filename.lower().endswith(\".py\"):\n            with tempfile.TemporaryDirectory() as temp_config_dir:\n                temp_config_file = tempfile.NamedTemporaryFile(dir=temp_config_dir, suffix=\".py\")\n                temp_config_name = osp.basename(temp_config_file.name)\n                if os.name == 'nt':\n                    temp_config_file.close()\n                shutil.copyfile(filename, osp.join(temp_config_dir, temp_config_name))\n                temp_module_name = osp.splitext(temp_config_name)[0]\n                sys.path.insert(0, temp_config_dir)\n                SLConfig._validate_py_syntax(filename)\n                mod = import_module(temp_module_name)\n                sys.path.pop(0)\n                cfg_dict = {\n                    name: value for name, value in mod.__dict__.items() if not name.startswith(\"__\")\n                }\n                # delete imported module\n                del sys.modules[temp_module_name]\n                # close temp file\n                temp_config_file.close()\n        elif filename.lower().endswith((\".yml\", \".yaml\", \".json\")):\n            from .slio import slload\n\n            cfg_dict = slload(filename)\n        else:\n            raise IOError(\"Only py/yml/yaml/json type are supported now!\")\n\n        cfg_text = filename + \"\\n\"\n        with open(filename, \"r\") as f:\n            cfg_text += f.read()\n\n        # parse the base file\n        if BASE_KEY in cfg_dict:\n            cfg_dir = osp.dirname(filename)\n            base_filename = cfg_dict.pop(BASE_KEY)\n            base_filename = base_filename if isinstance(base_filename, list) else [base_filename]\n\n            cfg_dict_list = list()\n            cfg_text_list = list()\n            for f in base_filename:\n                _cfg_dict, _cfg_text = SLConfig._file2dict(osp.join(cfg_dir, f))\n                cfg_dict_list.append(_cfg_dict)\n                cfg_text_list.append(_cfg_text)\n\n            base_cfg_dict = dict()\n            for c in cfg_dict_list:\n                if len(base_cfg_dict.keys() & c.keys()) > 0:\n                    raise KeyError(\"Duplicate key is not allowed among bases\")\n                    # TODO Allow the duplicate key while warnning user\n                base_cfg_dict.update(c)\n\n            base_cfg_dict = SLConfig._merge_a_into_b(cfg_dict, base_cfg_dict)\n            cfg_dict = base_cfg_dict\n\n            # merge cfg_text\n            cfg_text_list.append(cfg_text)\n            cfg_text = \"\\n\".join(cfg_text_list)\n\n        return cfg_dict, cfg_text\n\n    @staticmethod\n    def _merge_a_into_b(a, b):\n        \"\"\"merge dict `a` into dict `b` (non-inplace).\n            values in `a` will overwrite `b`.\n            copy first to avoid inplace modification\n\n        Args:\n            a ([type]): [description]\n            b ([type]): [description]\n\n        Returns:\n            [dict]: [description]\n        \"\"\"\n        # import ipdb; ipdb.set_trace()\n        if not isinstance(a, dict):\n            return a\n\n        b = b.copy()\n        for k, v in a.items():\n            if isinstance(v, dict) and k in b and not v.pop(DELETE_KEY, False):\n\n                if not isinstance(b[k], dict) and not isinstance(b[k], list):\n                    # if :\n                    # import ipdb; ipdb.set_trace()\n                    raise TypeError(\n                        f\"{k}={v} in child config cannot inherit from base \"\n                        f\"because {k} is a dict in the child config but is of \"\n                        f\"type {type(b[k])} in base config. You may set \"\n                        f\"`{DELETE_KEY}=True` to ignore the base config\"\n                    )\n                b[k] = SLConfig._merge_a_into_b(v, b[k])\n            elif isinstance(b, list):\n                try:\n                    _ = int(k)\n                except:\n                    raise TypeError(\n                        f\"b is a list, \" f\"index {k} should be an int when input but {type(k)}\"\n                    )\n                b[int(k)] = SLConfig._merge_a_into_b(v, b[int(k)])\n            else:\n                b[k] = v\n\n        return b\n\n    @staticmethod\n    def fromfile(filename):\n        cfg_dict, cfg_text = SLConfig._file2dict(filename)\n        return SLConfig(cfg_dict, cfg_text=cfg_text, filename=filename)\n\n    def __init__(self, cfg_dict=None, cfg_text=None, filename=None):\n        if cfg_dict is None:\n            cfg_dict = dict()\n        elif not isinstance(cfg_dict, dict):\n            raise TypeError(\"cfg_dict must be a dict, but \" f\"got {type(cfg_dict)}\")\n        for key in cfg_dict:\n            if key in RESERVED_KEYS:\n                raise KeyError(f\"{key} is reserved for config file\")\n\n        super(SLConfig, self).__setattr__(\"_cfg_dict\", ConfigDict(cfg_dict))\n        super(SLConfig, self).__setattr__(\"_filename\", filename)\n        if cfg_text:\n            text = cfg_text\n        elif filename:\n            with open(filename, \"r\") as f:\n                text = f.read()\n        else:\n            text = \"\"\n        super(SLConfig, self).__setattr__(\"_text\", text)\n\n    @property\n    def filename(self):\n        return self._filename\n\n    @property\n    def text(self):\n        return self._text\n\n    @property\n    def pretty_text(self):\n\n        indent = 4\n\n        def _indent(s_, num_spaces):\n            s = s_.split(\"\\n\")\n            if len(s) == 1:\n                return s_\n            first = s.pop(0)\n            s = [(num_spaces * \" \") + line for line in s]\n            s = \"\\n\".join(s)\n            s = first + \"\\n\" + s\n            return s\n\n        def _format_basic_types(k, v, use_mapping=False):\n            if isinstance(v, str):\n                v_str = f\"'{v}'\"\n            else:\n                v_str = str(v)\n\n            if use_mapping:\n                k_str = f\"'{k}'\" if isinstance(k, str) else str(k)\n                attr_str = f\"{k_str}: {v_str}\"\n            else:\n                attr_str = f\"{str(k)}={v_str}\"\n            attr_str = _indent(attr_str, indent)\n\n            return attr_str\n\n        def _format_list(k, v, use_mapping=False):\n            # check if all items in the list are dict\n            if all(isinstance(_, dict) for _ in v):\n                v_str = \"[\\n\"\n                v_str += \"\\n\".join(\n                    f\"dict({_indent(_format_dict(v_), indent)}),\" for v_ in v\n                ).rstrip(\",\")\n                if use_mapping:\n                    k_str = f\"'{k}'\" if isinstance(k, str) else str(k)\n                    attr_str = f\"{k_str}: {v_str}\"\n                else:\n                    attr_str = f\"{str(k)}={v_str}\"\n                attr_str = _indent(attr_str, indent) + \"]\"\n            else:\n                attr_str = _format_basic_types(k, v, use_mapping)\n            return attr_str\n\n        def _contain_invalid_identifier(dict_str):\n            contain_invalid_identifier = False\n            for key_name in dict_str:\n                contain_invalid_identifier |= not str(key_name).isidentifier()\n            return contain_invalid_identifier\n\n        def _format_dict(input_dict, outest_level=False):\n            r = \"\"\n            s = []\n\n            use_mapping = _contain_invalid_identifier(input_dict)\n            if use_mapping:\n                r += \"{\"\n            for idx, (k, v) in enumerate(input_dict.items()):\n                is_last = idx >= len(input_dict) - 1\n                end = \"\" if outest_level or is_last else \",\"\n                if isinstance(v, dict):\n                    v_str = \"\\n\" + _format_dict(v)\n                    if use_mapping:\n                        k_str = f\"'{k}'\" if isinstance(k, str) else str(k)\n                        attr_str = f\"{k_str}: dict({v_str}\"\n                    else:\n                        attr_str = f\"{str(k)}=dict({v_str}\"\n                    attr_str = _indent(attr_str, indent) + \")\" + end\n                elif isinstance(v, list):\n                    attr_str = _format_list(k, v, use_mapping) + end\n                else:\n                    attr_str = _format_basic_types(k, v, use_mapping) + end\n\n                s.append(attr_str)\n            r += \"\\n\".join(s)\n            if use_mapping:\n                r += \"}\"\n            return r\n\n        cfg_dict = self._cfg_dict.to_dict()\n        text = _format_dict(cfg_dict, outest_level=True)\n        # copied from setup.cfg\n        yapf_style = dict(\n            based_on_style=\"pep8\",\n            blank_line_before_nested_class_or_def=True,\n            split_before_expression_after_opening_paren=True,\n        )\n        text, _ = FormatCode(text, style_config=yapf_style, verify=True)\n\n        return text\n\n    def __repr__(self):\n        return f\"Config (path: {self.filename}): {self._cfg_dict.__repr__()}\"\n\n    def __len__(self):\n        return len(self._cfg_dict)\n\n    def __getattr__(self, name):\n        # # debug\n        # print('+'*15)\n        # print('name=%s' % name)\n        # print(\"addr:\", id(self))\n        # # print('type(self):', type(self))\n        # print(self.__dict__)\n        # print('+'*15)\n        # if self.__dict__ == {}:\n        #     raise ValueError\n\n        return getattr(self._cfg_dict, name)\n\n    def __getitem__(self, name):\n        return self._cfg_dict.__getitem__(name)\n\n    def __setattr__(self, name, value):\n        if isinstance(value, dict):\n            value = ConfigDict(value)\n        self._cfg_dict.__setattr__(name, value)\n\n    def __setitem__(self, name, value):\n        if isinstance(value, dict):\n            value = ConfigDict(value)\n        self._cfg_dict.__setitem__(name, value)\n\n    def __iter__(self):\n        return iter(self._cfg_dict)\n\n    def dump(self, file=None):\n        # import ipdb; ipdb.set_trace()\n        if file is None:\n            return self.pretty_text\n        else:\n            with open(file, \"w\") as f:\n                f.write(self.pretty_text)\n\n    def merge_from_dict(self, options):\n        \"\"\"Merge list into cfg_dict\n\n        Merge the dict parsed by MultipleKVAction into this cfg.\n\n        Examples:\n            >>> options = {'model.backbone.depth': 50,\n            ...            'model.backbone.with_cp':True}\n            >>> cfg = Config(dict(model=dict(backbone=dict(type='ResNet'))))\n            >>> cfg.merge_from_dict(options)\n            >>> cfg_dict = super(Config, self).__getattribute__('_cfg_dict')\n            >>> assert cfg_dict == dict(\n            ...     model=dict(backbone=dict(depth=50, with_cp=True)))\n\n        Args:\n            options (dict): dict of configs to merge from.\n        \"\"\"\n        option_cfg_dict = {}\n        for full_key, v in options.items():\n            d = option_cfg_dict\n            key_list = full_key.split(\".\")\n            for subkey in key_list[:-1]:\n                d.setdefault(subkey, ConfigDict())\n                d = d[subkey]\n            subkey = key_list[-1]\n            d[subkey] = v\n\n        cfg_dict = super(SLConfig, self).__getattribute__(\"_cfg_dict\")\n        super(SLConfig, self).__setattr__(\n            \"_cfg_dict\", SLConfig._merge_a_into_b(option_cfg_dict, cfg_dict)\n        )\n\n    # for multiprocess\n    def __setstate__(self, state):\n        self.__init__(state)\n\n    def copy(self):\n        return SLConfig(self._cfg_dict.copy())\n\n    def deepcopy(self):\n        return SLConfig(self._cfg_dict.deepcopy())\n\n\nclass DictAction(Action):\n    \"\"\"\n    argparse action to split an argument into KEY=VALUE form\n    on the first = and append to a dictionary. List options should\n    be passed as comma separated values, i.e KEY=V1,V2,V3\n    \"\"\"\n\n    @staticmethod\n    def _parse_int_float_bool(val):\n        try:\n            return int(val)\n        except ValueError:\n            pass\n        try:\n            return float(val)\n        except ValueError:\n            pass\n        if val.lower() in [\"true\", \"false\"]:\n            return True if val.lower() == \"true\" else False\n        if val.lower() in [\"none\", \"null\"]:\n            return None\n        return val\n\n    def __call__(self, parser, namespace, values, option_string=None):\n        options = {}\n        for kv in values:\n            key, val = kv.split(\"=\", maxsplit=1)\n            val = [self._parse_int_float_bool(v) for v in val.split(\",\")]\n            if len(val) == 1:\n                val = val[0]\n            options[key] = val\n        setattr(namespace, self.dest, options)\n"
  },
  {
    "path": "model_cards/groundingdino/util/slio.py",
    "content": "# ==========================================================\n# Modified from mmcv\n# ==========================================================\n\nimport json\nimport pickle\nfrom abc import ABCMeta, abstractmethod\nfrom pathlib import Path\n\nimport yaml\n\ntry:\n    from yaml import CLoader as Loader, CDumper as Dumper\nexcept ImportError:\n    from yaml import Loader, Dumper\n\n\n# ===========================\n# Rigister handler\n# ===========================\n\n\nclass BaseFileHandler(metaclass=ABCMeta):\n    @abstractmethod\n    def load_from_fileobj(self, file, **kwargs):\n        pass\n\n    @abstractmethod\n    def dump_to_fileobj(self, obj, file, **kwargs):\n        pass\n\n    @abstractmethod\n    def dump_to_str(self, obj, **kwargs):\n        pass\n\n    def load_from_path(self, filepath, mode=\"r\", **kwargs):\n        with open(filepath, mode) as f:\n            return self.load_from_fileobj(f, **kwargs)\n\n    def dump_to_path(self, obj, filepath, mode=\"w\", **kwargs):\n        with open(filepath, mode) as f:\n            self.dump_to_fileobj(obj, f, **kwargs)\n\n\nclass JsonHandler(BaseFileHandler):\n    def load_from_fileobj(self, file):\n        return json.load(file)\n\n    def dump_to_fileobj(self, obj, file, **kwargs):\n        json.dump(obj, file, **kwargs)\n\n    def dump_to_str(self, obj, **kwargs):\n        return json.dumps(obj, **kwargs)\n\n\nclass PickleHandler(BaseFileHandler):\n    def load_from_fileobj(self, file, **kwargs):\n        return pickle.load(file, **kwargs)\n\n    def load_from_path(self, filepath, **kwargs):\n        return super(PickleHandler, self).load_from_path(filepath, mode=\"rb\", **kwargs)\n\n    def dump_to_str(self, obj, **kwargs):\n        kwargs.setdefault(\"protocol\", 2)\n        return pickle.dumps(obj, **kwargs)\n\n    def dump_to_fileobj(self, obj, file, **kwargs):\n        kwargs.setdefault(\"protocol\", 2)\n        pickle.dump(obj, file, **kwargs)\n\n    def dump_to_path(self, obj, filepath, **kwargs):\n        super(PickleHandler, self).dump_to_path(obj, filepath, mode=\"wb\", **kwargs)\n\n\nclass YamlHandler(BaseFileHandler):\n    def load_from_fileobj(self, file, **kwargs):\n        kwargs.setdefault(\"Loader\", Loader)\n        return yaml.load(file, **kwargs)\n\n    def dump_to_fileobj(self, obj, file, **kwargs):\n        kwargs.setdefault(\"Dumper\", Dumper)\n        yaml.dump(obj, file, **kwargs)\n\n    def dump_to_str(self, obj, **kwargs):\n        kwargs.setdefault(\"Dumper\", Dumper)\n        return yaml.dump(obj, **kwargs)\n\n\nfile_handlers = {\n    \"json\": JsonHandler(),\n    \"yaml\": YamlHandler(),\n    \"yml\": YamlHandler(),\n    \"pickle\": PickleHandler(),\n    \"pkl\": PickleHandler(),\n}\n\n# ===========================\n# load and dump\n# ===========================\n\n\ndef is_str(x):\n    \"\"\"Whether the input is an string instance.\n\n    Note: This method is deprecated since python 2 is no longer supported.\n    \"\"\"\n    return isinstance(x, str)\n\n\ndef slload(file, file_format=None, **kwargs):\n    \"\"\"Load data from json/yaml/pickle files.\n\n    This method provides a unified api for loading data from serialized files.\n\n    Args:\n        file (str or :obj:`Path` or file-like object): Filename or a file-like\n            object.\n        file_format (str, optional): If not specified, the file format will be\n            inferred from the file extension, otherwise use the specified one.\n            Currently supported formats include \"json\", \"yaml/yml\" and\n            \"pickle/pkl\".\n\n    Returns:\n        The content from the file.\n    \"\"\"\n    if isinstance(file, Path):\n        file = str(file)\n    if file_format is None and is_str(file):\n        file_format = file.split(\".\")[-1]\n    if file_format not in file_handlers:\n        raise TypeError(f\"Unsupported format: {file_format}\")\n\n    handler = file_handlers[file_format]\n    if is_str(file):\n        obj = handler.load_from_path(file, **kwargs)\n    elif hasattr(file, \"read\"):\n        obj = handler.load_from_fileobj(file, **kwargs)\n    else:\n        raise TypeError('\"file\" must be a filepath str or a file-object')\n    return obj\n\n\ndef sldump(obj, file=None, file_format=None, **kwargs):\n    \"\"\"Dump data to json/yaml/pickle strings or files.\n\n    This method provides a unified api for dumping data as strings or to files,\n    and also supports custom arguments for each file format.\n\n    Args:\n        obj (any): The python object to be dumped.\n        file (str or :obj:`Path` or file-like object, optional): If not\n            specified, then the object is dump to a str, otherwise to a file\n            specified by the filename or file-like object.\n        file_format (str, optional): Same as :func:`load`.\n\n    Returns:\n        bool: True for success, False otherwise.\n    \"\"\"\n    if isinstance(file, Path):\n        file = str(file)\n    if file_format is None:\n        if is_str(file):\n            file_format = file.split(\".\")[-1]\n        elif file is None:\n            raise ValueError(\"file_format must be specified since file is None\")\n    if file_format not in file_handlers:\n        raise TypeError(f\"Unsupported format: {file_format}\")\n\n    handler = file_handlers[file_format]\n    if file is None:\n        return handler.dump_to_str(obj, **kwargs)\n    elif is_str(file):\n        handler.dump_to_path(obj, file, **kwargs)\n    elif hasattr(file, \"write\"):\n        handler.dump_to_fileobj(obj, file, **kwargs)\n    else:\n        raise TypeError('\"file\" must be a filename str or a file-object')\n"
  },
  {
    "path": "model_cards/groundingdino/util/time_counter.py",
    "content": "import json\nimport time\n\n\nclass TimeCounter:\n    def __init__(self) -> None:\n        pass\n\n    def clear(self):\n        self.timedict = {}\n        self.basetime = time.perf_counter()\n\n    def timeit(self, name):\n        nowtime = time.perf_counter() - self.basetime\n        self.timedict[name] = nowtime\n        self.basetime = time.perf_counter()\n\n\nclass TimeHolder:\n    def __init__(self) -> None:\n        self.timedict = {}\n\n    def update(self, _timedict: dict):\n        for k, v in _timedict.items():\n            if k not in self.timedict:\n                self.timedict[k] = AverageMeter(name=k, val_only=True)\n            self.timedict[k].update(val=v)\n\n    def final_res(self):\n        return {k: v.avg for k, v in self.timedict.items()}\n\n    def __str__(self):\n        return json.dumps(self.final_res(), indent=2)\n\n\nclass AverageMeter(object):\n    \"\"\"Computes and stores the average and current value\"\"\"\n\n    def __init__(self, name, fmt=\":f\", val_only=False):\n        self.name = name\n        self.fmt = fmt\n        self.val_only = val_only\n        self.reset()\n\n    def reset(self):\n        self.val = 0\n        self.avg = 0\n        self.sum = 0\n        self.count = 0\n\n    def update(self, val, n=1):\n        self.val = val\n        self.sum += val * n\n        self.count += n\n        self.avg = self.sum / self.count\n\n    def __str__(self):\n        if self.val_only:\n            fmtstr = \"{name} {val\" + self.fmt + \"}\"\n        else:\n            fmtstr = \"{name} {val\" + self.fmt + \"} ({avg\" + self.fmt + \"})\"\n        return fmtstr.format(**self.__dict__)\n"
  },
  {
    "path": "model_cards/groundingdino/util/utils.py",
    "content": "import argparse\nimport json\nimport warnings\nfrom collections import OrderedDict\nfrom copy import deepcopy\nfrom typing import Any, Dict, List\n\nimport numpy as np\nimport torch\nfrom transformers import AutoTokenizer\n\nfrom groundingdino.util.slconfig import SLConfig\n\n\ndef slprint(x, name=\"x\"):\n    if isinstance(x, (torch.Tensor, np.ndarray)):\n        print(f\"{name}.shape:\", x.shape)\n    elif isinstance(x, (tuple, list)):\n        print(\"type x:\", type(x))\n        for i in range(min(10, len(x))):\n            slprint(x[i], f\"{name}[{i}]\")\n    elif isinstance(x, dict):\n        for k, v in x.items():\n            slprint(v, f\"{name}[{k}]\")\n    else:\n        print(f\"{name}.type:\", type(x))\n\n\ndef clean_state_dict(state_dict):\n    new_state_dict = OrderedDict()\n    for k, v in state_dict.items():\n        if k[:7] == \"module.\":\n            k = k[7:]  # remove `module.`\n        new_state_dict[k] = v\n    return new_state_dict\n\n\ndef renorm(\n    img: torch.FloatTensor, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]\n) -> torch.FloatTensor:\n    # img: tensor(3,H,W) or tensor(B,3,H,W)\n    # return: same as img\n    assert img.dim() == 3 or img.dim() == 4, \"img.dim() should be 3 or 4 but %d\" % img.dim()\n    if img.dim() == 3:\n        assert img.size(0) == 3, 'img.size(0) shoule be 3 but \"%d\". (%s)' % (\n            img.size(0),\n            str(img.size()),\n        )\n        img_perm = img.permute(1, 2, 0)\n        mean = torch.Tensor(mean)\n        std = torch.Tensor(std)\n        img_res = img_perm * std + mean\n        return img_res.permute(2, 0, 1)\n    else:  # img.dim() == 4\n        assert img.size(1) == 3, 'img.size(1) shoule be 3 but \"%d\". (%s)' % (\n            img.size(1),\n            str(img.size()),\n        )\n        img_perm = img.permute(0, 2, 3, 1)\n        mean = torch.Tensor(mean)\n        std = torch.Tensor(std)\n        img_res = img_perm * std + mean\n        return img_res.permute(0, 3, 1, 2)\n\n\nclass CocoClassMapper:\n    def __init__(self) -> None:\n        self.category_map_str = {\n            \"1\": 1,\n            \"2\": 2,\n            \"3\": 3,\n            \"4\": 4,\n            \"5\": 5,\n            \"6\": 6,\n            \"7\": 7,\n            \"8\": 8,\n            \"9\": 9,\n            \"10\": 10,\n            \"11\": 11,\n            \"13\": 12,\n            \"14\": 13,\n            \"15\": 14,\n            \"16\": 15,\n            \"17\": 16,\n            \"18\": 17,\n            \"19\": 18,\n            \"20\": 19,\n            \"21\": 20,\n            \"22\": 21,\n            \"23\": 22,\n            \"24\": 23,\n            \"25\": 24,\n            \"27\": 25,\n            \"28\": 26,\n            \"31\": 27,\n            \"32\": 28,\n            \"33\": 29,\n            \"34\": 30,\n            \"35\": 31,\n            \"36\": 32,\n            \"37\": 33,\n            \"38\": 34,\n            \"39\": 35,\n            \"40\": 36,\n            \"41\": 37,\n            \"42\": 38,\n            \"43\": 39,\n            \"44\": 40,\n            \"46\": 41,\n            \"47\": 42,\n            \"48\": 43,\n            \"49\": 44,\n            \"50\": 45,\n            \"51\": 46,\n            \"52\": 47,\n            \"53\": 48,\n            \"54\": 49,\n            \"55\": 50,\n            \"56\": 51,\n            \"57\": 52,\n            \"58\": 53,\n            \"59\": 54,\n            \"60\": 55,\n            \"61\": 56,\n            \"62\": 57,\n            \"63\": 58,\n            \"64\": 59,\n            \"65\": 60,\n            \"67\": 61,\n            \"70\": 62,\n            \"72\": 63,\n            \"73\": 64,\n            \"74\": 65,\n            \"75\": 66,\n            \"76\": 67,\n            \"77\": 68,\n            \"78\": 69,\n            \"79\": 70,\n            \"80\": 71,\n            \"81\": 72,\n            \"82\": 73,\n            \"84\": 74,\n            \"85\": 75,\n            \"86\": 76,\n            \"87\": 77,\n            \"88\": 78,\n            \"89\": 79,\n            \"90\": 80,\n        }\n        self.origin2compact_mapper = {int(k): v - 1 for k, v in self.category_map_str.items()}\n        self.compact2origin_mapper = {int(v - 1): int(k) for k, v in self.category_map_str.items()}\n\n    def origin2compact(self, idx):\n        return self.origin2compact_mapper[int(idx)]\n\n    def compact2origin(self, idx):\n        return self.compact2origin_mapper[int(idx)]\n\n\ndef to_device(item, device):\n    if isinstance(item, torch.Tensor):\n        return item.to(device)\n    elif isinstance(item, list):\n        return [to_device(i, device) for i in item]\n    elif isinstance(item, dict):\n        return {k: to_device(v, device) for k, v in item.items()}\n    else:\n        raise NotImplementedError(\n            \"Call Shilong if you use other containers! type: {}\".format(type(item))\n        )\n\n\n#\ndef get_gaussian_mean(x, axis, other_axis, softmax=True):\n    \"\"\"\n\n    Args:\n        x (float): Input images(BxCxHxW)\n        axis (int): The index for weighted mean\n        other_axis (int): The other index\n\n    Returns: weighted index for axis, BxC\n\n    \"\"\"\n    mat2line = torch.sum(x, axis=other_axis)\n    # mat2line = mat2line / mat2line.mean() * 10\n    if softmax:\n        u = torch.softmax(mat2line, axis=2)\n    else:\n        u = mat2line / (mat2line.sum(2, keepdim=True) + 1e-6)\n    size = x.shape[axis]\n    ind = torch.linspace(0, 1, size).to(x.device)\n    batch = x.shape[0]\n    channel = x.shape[1]\n    index = ind.repeat([batch, channel, 1])\n    mean_position = torch.sum(index * u, dim=2)\n    return mean_position\n\n\ndef get_expected_points_from_map(hm, softmax=True):\n    \"\"\"get_gaussian_map_from_points\n        B,C,H,W -> B,N,2 float(0, 1) float(0, 1)\n        softargmax function\n\n    Args:\n        hm (float): Input images(BxCxHxW)\n\n    Returns:\n        weighted index for axis, BxCx2. float between 0 and 1.\n\n    \"\"\"\n    # hm = 10*hm\n    B, C, H, W = hm.shape\n    y_mean = get_gaussian_mean(hm, 2, 3, softmax=softmax)  # B,C\n    x_mean = get_gaussian_mean(hm, 3, 2, softmax=softmax)  # B,C\n    # return torch.cat((x_mean.unsqueeze(-1), y_mean.unsqueeze(-1)), 2)\n    return torch.stack([x_mean, y_mean], dim=2)\n\n\n# Positional encoding (section 5.1)\n# borrow from nerf\nclass Embedder:\n    def __init__(self, **kwargs):\n        self.kwargs = kwargs\n        self.create_embedding_fn()\n\n    def create_embedding_fn(self):\n        embed_fns = []\n        d = self.kwargs[\"input_dims\"]\n        out_dim = 0\n        if self.kwargs[\"include_input\"]:\n            embed_fns.append(lambda x: x)\n            out_dim += d\n\n        max_freq = self.kwargs[\"max_freq_log2\"]\n        N_freqs = self.kwargs[\"num_freqs\"]\n\n        if self.kwargs[\"log_sampling\"]:\n            freq_bands = 2.0 ** torch.linspace(0.0, max_freq, steps=N_freqs)\n        else:\n            freq_bands = torch.linspace(2.0**0.0, 2.0**max_freq, steps=N_freqs)\n\n        for freq in freq_bands:\n            for p_fn in self.kwargs[\"periodic_fns\"]:\n                embed_fns.append(lambda x, p_fn=p_fn, freq=freq: p_fn(x * freq))\n                out_dim += d\n\n        self.embed_fns = embed_fns\n        self.out_dim = out_dim\n\n    def embed(self, inputs):\n        return torch.cat([fn(inputs) for fn in self.embed_fns], -1)\n\n\ndef get_embedder(multires, i=0):\n    import torch.nn as nn\n\n    if i == -1:\n        return nn.Identity(), 3\n\n    embed_kwargs = {\n        \"include_input\": True,\n        \"input_dims\": 3,\n        \"max_freq_log2\": multires - 1,\n        \"num_freqs\": multires,\n        \"log_sampling\": True,\n        \"periodic_fns\": [torch.sin, torch.cos],\n    }\n\n    embedder_obj = Embedder(**embed_kwargs)\n    embed = lambda x, eo=embedder_obj: eo.embed(x)\n    return embed, embedder_obj.out_dim\n\n\nclass APOPMeter:\n    def __init__(self) -> None:\n        self.tp = 0\n        self.fp = 0\n        self.tn = 0\n        self.fn = 0\n\n    def update(self, pred, gt):\n        \"\"\"\n        Input:\n            pred, gt: Tensor()\n        \"\"\"\n        assert pred.shape == gt.shape\n        self.tp += torch.logical_and(pred == 1, gt == 1).sum().item()\n        self.fp += torch.logical_and(pred == 1, gt == 0).sum().item()\n        self.tn += torch.logical_and(pred == 0, gt == 0).sum().item()\n        self.tn += torch.logical_and(pred == 1, gt == 0).sum().item()\n\n    def update_cm(self, tp, fp, tn, fn):\n        self.tp += tp\n        self.fp += fp\n        self.tn += tn\n        self.tn += fn\n\n\ndef inverse_sigmoid(x, eps=1e-5):\n    x = x.clamp(min=0, max=1)\n    x1 = x.clamp(min=eps)\n    x2 = (1 - x).clamp(min=eps)\n    return torch.log(x1 / x2)\n\n\ndef get_raw_dict(args):\n    \"\"\"\n    return the dicf contained in args.\n\n    e.g:\n        >>> with open(path, 'w') as f:\n                json.dump(get_raw_dict(args), f, indent=2)\n    \"\"\"\n    if isinstance(args, argparse.Namespace):\n        return vars(args)\n    elif isinstance(args, dict):\n        return args\n    elif isinstance(args, SLConfig):\n        return args._cfg_dict\n    else:\n        raise NotImplementedError(\"Unknown type {}\".format(type(args)))\n\n\ndef stat_tensors(tensor):\n    assert tensor.dim() == 1\n    tensor_sm = tensor.softmax(0)\n    entropy = (tensor_sm * torch.log(tensor_sm + 1e-9)).sum()\n\n    return {\n        \"max\": tensor.max(),\n        \"min\": tensor.min(),\n        \"mean\": tensor.mean(),\n        \"var\": tensor.var(),\n        \"std\": tensor.var() ** 0.5,\n        \"entropy\": entropy,\n    }\n\n\nclass NiceRepr:\n    \"\"\"Inherit from this class and define ``__nice__`` to \"nicely\" print your\n    objects.\n\n    Defines ``__str__`` and ``__repr__`` in terms of ``__nice__`` function\n    Classes that inherit from :class:`NiceRepr` should redefine ``__nice__``.\n    If the inheriting class has a ``__len__``, method then the default\n    ``__nice__`` method will return its length.\n\n    Example:\n        >>> class Foo(NiceRepr):\n        ...    def __nice__(self):\n        ...        return 'info'\n        >>> foo = Foo()\n        >>> assert str(foo) == '<Foo(info)>'\n        >>> assert repr(foo).startswith('<Foo(info) at ')\n\n    Example:\n        >>> class Bar(NiceRepr):\n        ...    pass\n        >>> bar = Bar()\n        >>> import pytest\n        >>> with pytest.warns(None) as record:\n        >>>     assert 'object at' in str(bar)\n        >>>     assert 'object at' in repr(bar)\n\n    Example:\n        >>> class Baz(NiceRepr):\n        ...    def __len__(self):\n        ...        return 5\n        >>> baz = Baz()\n        >>> assert str(baz) == '<Baz(5)>'\n    \"\"\"\n\n    def __nice__(self):\n        \"\"\"str: a \"nice\" summary string describing this module\"\"\"\n        if hasattr(self, \"__len__\"):\n            # It is a common pattern for objects to use __len__ in __nice__\n            # As a convenience we define a default __nice__ for these objects\n            return str(len(self))\n        else:\n            # In all other cases force the subclass to overload __nice__\n            raise NotImplementedError(f\"Define the __nice__ method for {self.__class__!r}\")\n\n    def __repr__(self):\n        \"\"\"str: the string of the module\"\"\"\n        try:\n            nice = self.__nice__()\n            classname = self.__class__.__name__\n            return f\"<{classname}({nice}) at {hex(id(self))}>\"\n        except NotImplementedError as ex:\n            warnings.warn(str(ex), category=RuntimeWarning)\n            return object.__repr__(self)\n\n    def __str__(self):\n        \"\"\"str: the string of the module\"\"\"\n        try:\n            classname = self.__class__.__name__\n            nice = self.__nice__()\n            return f\"<{classname}({nice})>\"\n        except NotImplementedError as ex:\n            warnings.warn(str(ex), category=RuntimeWarning)\n            return object.__repr__(self)\n\n\ndef ensure_rng(rng=None):\n    \"\"\"Coerces input into a random number generator.\n\n    If the input is None, then a global random state is returned.\n\n    If the input is a numeric value, then that is used as a seed to construct a\n    random state. Otherwise the input is returned as-is.\n\n    Adapted from [1]_.\n\n    Args:\n        rng (int | numpy.random.RandomState | None):\n            if None, then defaults to the global rng. Otherwise this can be an\n            integer or a RandomState class\n    Returns:\n        (numpy.random.RandomState) : rng -\n            a numpy random number generator\n\n    References:\n        .. [1] https://gitlab.kitware.com/computer-vision/kwarray/blob/master/kwarray/util_random.py#L270  # noqa: E501\n    \"\"\"\n\n    if rng is None:\n        rng = np.random.mtrand._rand\n    elif isinstance(rng, int):\n        rng = np.random.RandomState(rng)\n    else:\n        rng = rng\n    return rng\n\n\ndef random_boxes(num=1, scale=1, rng=None):\n    \"\"\"Simple version of ``kwimage.Boxes.random``\n\n    Returns:\n        Tensor: shape (n, 4) in x1, y1, x2, y2 format.\n\n    References:\n        https://gitlab.kitware.com/computer-vision/kwimage/blob/master/kwimage/structs/boxes.py#L1390\n\n    Example:\n        >>> num = 3\n        >>> scale = 512\n        >>> rng = 0\n        >>> boxes = random_boxes(num, scale, rng)\n        >>> print(boxes)\n        tensor([[280.9925, 278.9802, 308.6148, 366.1769],\n                [216.9113, 330.6978, 224.0446, 456.5878],\n                [405.3632, 196.3221, 493.3953, 270.7942]])\n    \"\"\"\n    rng = ensure_rng(rng)\n\n    tlbr = rng.rand(num, 4).astype(np.float32)\n\n    tl_x = np.minimum(tlbr[:, 0], tlbr[:, 2])\n    tl_y = np.minimum(tlbr[:, 1], tlbr[:, 3])\n    br_x = np.maximum(tlbr[:, 0], tlbr[:, 2])\n    br_y = np.maximum(tlbr[:, 1], tlbr[:, 3])\n\n    tlbr[:, 0] = tl_x * scale\n    tlbr[:, 1] = tl_y * scale\n    tlbr[:, 2] = br_x * scale\n    tlbr[:, 3] = br_y * scale\n\n    boxes = torch.from_numpy(tlbr)\n    return boxes\n\n\nclass ModelEma(torch.nn.Module):\n    def __init__(self, model, decay=0.9997, device=None):\n        super(ModelEma, self).__init__()\n        # make a copy of the model for accumulating moving average of weights\n        self.module = deepcopy(model)\n        self.module.eval()\n\n        # import ipdb; ipdb.set_trace()\n\n        self.decay = decay\n        self.device = device  # perform ema on different device from model if set\n        if self.device is not None:\n            self.module.to(device=device)\n\n    def _update(self, model, update_fn):\n        with torch.no_grad():\n            for ema_v, model_v in zip(\n                self.module.state_dict().values(), model.state_dict().values()\n            ):\n                if self.device is not None:\n                    model_v = model_v.to(device=self.device)\n                ema_v.copy_(update_fn(ema_v, model_v))\n\n    def update(self, model):\n        self._update(model, update_fn=lambda e, m: self.decay * e + (1.0 - self.decay) * m)\n\n    def set(self, model):\n        self._update(model, update_fn=lambda e, m: m)\n\n\nclass BestMetricSingle:\n    def __init__(self, init_res=0.0, better=\"large\") -> None:\n        self.init_res = init_res\n        self.best_res = init_res\n        self.best_ep = -1\n\n        self.better = better\n        assert better in [\"large\", \"small\"]\n\n    def isbetter(self, new_res, old_res):\n        if self.better == \"large\":\n            return new_res > old_res\n        if self.better == \"small\":\n            return new_res < old_res\n\n    def update(self, new_res, ep):\n        if self.isbetter(new_res, self.best_res):\n            self.best_res = new_res\n            self.best_ep = ep\n            return True\n        return False\n\n    def __str__(self) -> str:\n        return \"best_res: {}\\t best_ep: {}\".format(self.best_res, self.best_ep)\n\n    def __repr__(self) -> str:\n        return self.__str__()\n\n    def summary(self) -> dict:\n        return {\n            \"best_res\": self.best_res,\n            \"best_ep\": self.best_ep,\n        }\n\n\nclass BestMetricHolder:\n    def __init__(self, init_res=0.0, better=\"large\", use_ema=False) -> None:\n        self.best_all = BestMetricSingle(init_res, better)\n        self.use_ema = use_ema\n        if use_ema:\n            self.best_ema = BestMetricSingle(init_res, better)\n            self.best_regular = BestMetricSingle(init_res, better)\n\n    def update(self, new_res, epoch, is_ema=False):\n        \"\"\"\n        return if the results is the best.\n        \"\"\"\n        if not self.use_ema:\n            return self.best_all.update(new_res, epoch)\n        else:\n            if is_ema:\n                self.best_ema.update(new_res, epoch)\n                return self.best_all.update(new_res, epoch)\n            else:\n                self.best_regular.update(new_res, epoch)\n                return self.best_all.update(new_res, epoch)\n\n    def summary(self):\n        if not self.use_ema:\n            return self.best_all.summary()\n\n        res = {}\n        res.update({f\"all_{k}\": v for k, v in self.best_all.summary().items()})\n        res.update({f\"regular_{k}\": v for k, v in self.best_regular.summary().items()})\n        res.update({f\"ema_{k}\": v for k, v in self.best_ema.summary().items()})\n        return res\n\n    def __repr__(self) -> str:\n        return json.dumps(self.summary(), indent=2)\n\n    def __str__(self) -> str:\n        return self.__repr__()\n\n\ndef targets_to(targets: List[Dict[str, Any]], device):\n    \"\"\"Moves the target dicts to the given device.\"\"\"\n    excluded_keys = [\n        \"questionId\",\n        \"tokens_positive\",\n        \"strings_positive\",\n        \"tokens\",\n        \"dataset_name\",\n        \"sentence_id\",\n        \"original_img_id\",\n        \"nb_eval\",\n        \"task_id\",\n        \"original_id\",\n        \"token_span\",\n        \"caption\",\n        \"dataset_type\",\n    ]\n    return [\n        {k: v.to(device) if k not in excluded_keys else v for k, v in t.items()} for t in targets\n    ]\n\n\ndef get_phrases_from_posmap(\n    posmap: torch.BoolTensor, tokenized: Dict, tokenizer: AutoTokenizer\n):\n    assert isinstance(posmap, torch.Tensor), \"posmap must be torch.Tensor\"\n    if posmap.dim() == 1:\n        non_zero_idx = posmap.nonzero(as_tuple=True)[0].tolist()\n        token_ids = [tokenized[\"input_ids\"][i] for i in non_zero_idx]\n        return tokenizer.decode(token_ids)\n    else:\n        raise NotImplementedError(\"posmap must be 1-dim\")\n"
  },
  {
    "path": "model_cards/groundingdino/util/visualizer.py",
    "content": "# -*- coding: utf-8 -*-\n\"\"\"\n@File    :   visualizer.py\n@Time    :   2022/04/05 11:39:33\n@Author  :   Shilong Liu \n@Contact :   slongliu86@gmail.com\n\"\"\"\n\nimport datetime\nimport os\n\nimport cv2\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport torch\nfrom matplotlib import transforms\nfrom matplotlib.collections import PatchCollection\nfrom matplotlib.patches import Polygon\nfrom pycocotools import mask as maskUtils\n\n\ndef renorm(\n    img: torch.FloatTensor, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]\n) -> torch.FloatTensor:\n    # img: tensor(3,H,W) or tensor(B,3,H,W)\n    # return: same as img\n    assert img.dim() == 3 or img.dim() == 4, \"img.dim() should be 3 or 4 but %d\" % img.dim()\n    if img.dim() == 3:\n        assert img.size(0) == 3, 'img.size(0) shoule be 3 but \"%d\". (%s)' % (\n            img.size(0),\n            str(img.size()),\n        )\n        img_perm = img.permute(1, 2, 0)\n        mean = torch.Tensor(mean)\n        std = torch.Tensor(std)\n        img_res = img_perm * std + mean\n        return img_res.permute(2, 0, 1)\n    else:  # img.dim() == 4\n        assert img.size(1) == 3, 'img.size(1) shoule be 3 but \"%d\". (%s)' % (\n            img.size(1),\n            str(img.size()),\n        )\n        img_perm = img.permute(0, 2, 3, 1)\n        mean = torch.Tensor(mean)\n        std = torch.Tensor(std)\n        img_res = img_perm * std + mean\n        return img_res.permute(0, 3, 1, 2)\n\n\nclass ColorMap:\n    def __init__(self, basergb=[255, 255, 0]):\n        self.basergb = np.array(basergb)\n\n    def __call__(self, attnmap):\n        # attnmap: h, w. np.uint8.\n        # return: h, w, 4. np.uint8.\n        assert attnmap.dtype == np.uint8\n        h, w = attnmap.shape\n        res = self.basergb.copy()\n        res = res[None][None].repeat(h, 0).repeat(w, 1)  # h, w, 3\n        attn1 = attnmap.copy()[..., None]  # h, w, 1\n        res = np.concatenate((res, attn1), axis=-1).astype(np.uint8)\n        return res\n\n\ndef rainbow_text(x, y, ls, lc, **kw):\n    \"\"\"\n    Take a list of strings ``ls`` and colors ``lc`` and place them next to each\n    other, with text ls[i] being shown in color lc[i].\n\n    This example shows how to do both vertical and horizontal text, and will\n    pass all keyword arguments to plt.text, so you can set the font size,\n    family, etc.\n    \"\"\"\n    t = plt.gca().transData\n    fig = plt.gcf()\n    plt.show()\n\n    # horizontal version\n    for s, c in zip(ls, lc):\n        text = plt.text(x, y, \" \" + s + \" \", color=c, transform=t, **kw)\n        text.draw(fig.canvas.get_renderer())\n        ex = text.get_window_extent()\n        t = transforms.offset_copy(text._transform, x=ex.width, units=\"dots\")\n\n    # #vertical version\n    # for s,c in zip(ls,lc):\n    #     text = plt.text(x,y,\" \"+s+\" \",color=c, transform=t,\n    #             rotation=90,va='bottom',ha='center',**kw)\n    #     text.draw(fig.canvas.get_renderer())\n    #     ex = text.get_window_extent()\n    #     t = transforms.offset_copy(text._transform, y=ex.height, units='dots')\n\n\nclass COCOVisualizer:\n    def __init__(self, coco=None, tokenlizer=None) -> None:\n        self.coco = coco\n\n    def visualize(self, img, tgt, caption=None, dpi=180, savedir=\"vis\"):\n        \"\"\"\n        img: tensor(3, H, W)\n        tgt: make sure they are all on cpu.\n            must have items: 'image_id', 'boxes', 'size'\n        \"\"\"\n        plt.figure(dpi=dpi)\n        plt.rcParams[\"font.size\"] = \"5\"\n        ax = plt.gca()\n        img = renorm(img).permute(1, 2, 0)\n        # if os.environ.get('IPDB_SHILONG_DEBUG', None) == 'INFO':\n        #     import ipdb; ipdb.set_trace()\n        ax.imshow(img)\n\n        self.addtgt(tgt)\n\n        if tgt is None:\n            image_id = 0\n        elif \"image_id\" not in tgt:\n            image_id = 0\n        else:\n            image_id = tgt[\"image_id\"]\n\n        if caption is None:\n            savename = \"{}/{}-{}.png\".format(\n                savedir, int(image_id), str(datetime.datetime.now()).replace(\" \", \"-\")\n            )\n        else:\n            savename = \"{}/{}-{}-{}.png\".format(\n                savedir, caption, int(image_id), str(datetime.datetime.now()).replace(\" \", \"-\")\n            )\n        print(\"savename: {}\".format(savename))\n        os.makedirs(os.path.dirname(savename), exist_ok=True)\n        plt.savefig(savename)\n        plt.close()\n\n    def addtgt(self, tgt):\n        \"\"\" \"\"\"\n        if tgt is None or not \"boxes\" in tgt:\n            ax = plt.gca()\n\n            if \"caption\" in tgt:\n                ax.set_title(tgt[\"caption\"], wrap=True)\n\n            ax.set_axis_off()\n            return\n\n        ax = plt.gca()\n        H, W = tgt[\"size\"]\n        numbox = tgt[\"boxes\"].shape[0]\n\n        color = []\n        polygons = []\n        boxes = []\n        for box in tgt[\"boxes\"].cpu():\n            unnormbbox = box * torch.Tensor([W, H, W, H])\n            unnormbbox[:2] -= unnormbbox[2:] / 2\n            [bbox_x, bbox_y, bbox_w, bbox_h] = unnormbbox.tolist()\n            boxes.append([bbox_x, bbox_y, bbox_w, bbox_h])\n            poly = [\n                [bbox_x, bbox_y],\n                [bbox_x, bbox_y + bbox_h],\n                [bbox_x + bbox_w, bbox_y + bbox_h],\n                [bbox_x + bbox_w, bbox_y],\n            ]\n            np_poly = np.array(poly).reshape((4, 2))\n            polygons.append(Polygon(np_poly))\n            c = (np.random.random((1, 3)) * 0.6 + 0.4).tolist()[0]\n            color.append(c)\n\n        p = PatchCollection(polygons, facecolor=color, linewidths=0, alpha=0.1)\n        ax.add_collection(p)\n        p = PatchCollection(polygons, facecolor=\"none\", edgecolors=color, linewidths=2)\n        ax.add_collection(p)\n\n        if \"strings_positive\" in tgt and len(tgt[\"strings_positive\"]) > 0:\n            assert (\n                len(tgt[\"strings_positive\"]) == numbox\n            ), f\"{len(tgt['strings_positive'])} = {numbox}, \"\n            for idx, strlist in enumerate(tgt[\"strings_positive\"]):\n                cate_id = int(tgt[\"labels\"][idx])\n                _string = str(cate_id) + \":\" + \" \".join(strlist)\n                bbox_x, bbox_y, bbox_w, bbox_h = boxes[idx]\n                # ax.text(bbox_x, bbox_y, _string, color='black', bbox={'facecolor': 'yellow', 'alpha': 1.0, 'pad': 1})\n                ax.text(\n                    bbox_x,\n                    bbox_y,\n                    _string,\n                    color=\"black\",\n                    bbox={\"facecolor\": color[idx], \"alpha\": 0.6, \"pad\": 1},\n                )\n\n        if \"box_label\" in tgt:\n            assert len(tgt[\"box_label\"]) == numbox, f\"{len(tgt['box_label'])} = {numbox}, \"\n            for idx, bl in enumerate(tgt[\"box_label\"]):\n                _string = str(bl)\n                bbox_x, bbox_y, bbox_w, bbox_h = boxes[idx]\n                # ax.text(bbox_x, bbox_y, _string, color='black', bbox={'facecolor': 'yellow', 'alpha': 1.0, 'pad': 1})\n                ax.text(\n                    bbox_x,\n                    bbox_y,\n                    _string,\n                    color=\"black\",\n                    bbox={\"facecolor\": color[idx], \"alpha\": 0.6, \"pad\": 1},\n                )\n\n        if \"caption\" in tgt:\n            ax.set_title(tgt[\"caption\"], wrap=True)\n            # plt.figure()\n            # rainbow_text(0.0,0.0,\"all unicorns poop rainbows ! ! !\".split(),\n            #         ['red', 'orange', 'brown', 'green', 'blue', 'purple', 'black'])\n\n        if \"attn\" in tgt:\n            # if os.environ.get('IPDB_SHILONG_DEBUG', None) == 'INFO':\n            #     import ipdb; ipdb.set_trace()\n            if isinstance(tgt[\"attn\"], tuple):\n                tgt[\"attn\"] = [tgt[\"attn\"]]\n            for item in tgt[\"attn\"]:\n                attn_map, basergb = item\n                attn_map = (attn_map - attn_map.min()) / (attn_map.max() - attn_map.min() + 1e-3)\n                attn_map = (attn_map * 255).astype(np.uint8)\n                cm = ColorMap(basergb)\n                heatmap = cm(attn_map)\n                ax.imshow(heatmap)\n        ax.set_axis_off()\n\n    def showAnns(self, anns, draw_bbox=False):\n        \"\"\"\n        Display the specified annotations.\n        :param anns (array of object): annotations to display\n        :return: None\n        \"\"\"\n        if len(anns) == 0:\n            return 0\n        if \"segmentation\" in anns[0] or \"keypoints\" in anns[0]:\n            datasetType = \"instances\"\n        elif \"caption\" in anns[0]:\n            datasetType = \"captions\"\n        else:\n            raise Exception(\"datasetType not supported\")\n        if datasetType == \"instances\":\n            ax = plt.gca()\n            ax.set_autoscale_on(False)\n            polygons = []\n            color = []\n            for ann in anns:\n                c = (np.random.random((1, 3)) * 0.6 + 0.4).tolist()[0]\n                if \"segmentation\" in ann:\n                    if type(ann[\"segmentation\"]) == list:\n                        # polygon\n                        for seg in ann[\"segmentation\"]:\n                            poly = np.array(seg).reshape((int(len(seg) / 2), 2))\n                            polygons.append(Polygon(poly))\n                            color.append(c)\n                    else:\n                        # mask\n                        t = self.imgs[ann[\"image_id\"]]\n                        if type(ann[\"segmentation\"][\"counts\"]) == list:\n                            rle = maskUtils.frPyObjects(\n                                [ann[\"segmentation\"]], t[\"height\"], t[\"width\"]\n                            )\n                        else:\n                            rle = [ann[\"segmentation\"]]\n                        m = maskUtils.decode(rle)\n                        img = np.ones((m.shape[0], m.shape[1], 3))\n                        if ann[\"iscrowd\"] == 1:\n                            color_mask = np.array([2.0, 166.0, 101.0]) / 255\n                        if ann[\"iscrowd\"] == 0:\n                            color_mask = np.random.random((1, 3)).tolist()[0]\n                        for i in range(3):\n                            img[:, :, i] = color_mask[i]\n                        ax.imshow(np.dstack((img, m * 0.5)))\n                if \"keypoints\" in ann and type(ann[\"keypoints\"]) == list:\n                    # turn skeleton into zero-based index\n                    sks = np.array(self.loadCats(ann[\"category_id\"])[0][\"skeleton\"]) - 1\n                    kp = np.array(ann[\"keypoints\"])\n                    x = kp[0::3]\n                    y = kp[1::3]\n                    v = kp[2::3]\n                    for sk in sks:\n                        if np.all(v[sk] > 0):\n                            plt.plot(x[sk], y[sk], linewidth=3, color=c)\n                    plt.plot(\n                        x[v > 0],\n                        y[v > 0],\n                        \"o\",\n                        markersize=8,\n                        markerfacecolor=c,\n                        markeredgecolor=\"k\",\n                        markeredgewidth=2,\n                    )\n                    plt.plot(\n                        x[v > 1],\n                        y[v > 1],\n                        \"o\",\n                        markersize=8,\n                        markerfacecolor=c,\n                        markeredgecolor=c,\n                        markeredgewidth=2,\n                    )\n\n                if draw_bbox:\n                    [bbox_x, bbox_y, bbox_w, bbox_h] = ann[\"bbox\"]\n                    poly = [\n                        [bbox_x, bbox_y],\n                        [bbox_x, bbox_y + bbox_h],\n                        [bbox_x + bbox_w, bbox_y + bbox_h],\n                        [bbox_x + bbox_w, bbox_y],\n                    ]\n                    np_poly = np.array(poly).reshape((4, 2))\n                    polygons.append(Polygon(np_poly))\n                    color.append(c)\n\n            # p = PatchCollection(polygons, facecolor=color, linewidths=0, alpha=0.4)\n            # ax.add_collection(p)\n            p = PatchCollection(polygons, facecolor=\"none\", edgecolors=color, linewidths=2)\n            ax.add_collection(p)\n        elif datasetType == \"captions\":\n            for ann in anns:\n                print(ann[\"caption\"])\n"
  },
  {
    "path": "model_cards/groundingdino/util/vl_utils.py",
    "content": "import os\nimport random\nfrom typing import List\n\nimport torch\n\n\ndef create_positive_map_from_span(tokenized, token_span, max_text_len=256):\n    \"\"\"construct a map such that positive_map[i,j] = True iff box i is associated to token j\n    Input:\n        - tokenized:\n            - input_ids: Tensor[1, ntokens]\n            - attention_mask: Tensor[1, ntokens]\n        - token_span: list with length num_boxes.\n            - each item: [start_idx, end_idx]\n    \"\"\"\n    positive_map = torch.zeros((len(token_span), max_text_len), dtype=torch.float)\n    for j, tok_list in enumerate(token_span):\n        for (beg, end) in tok_list:\n            beg_pos = tokenized.char_to_token(beg)\n            end_pos = tokenized.char_to_token(end - 1)\n            if beg_pos is None:\n                try:\n                    beg_pos = tokenized.char_to_token(beg + 1)\n                    if beg_pos is None:\n                        beg_pos = tokenized.char_to_token(beg + 2)\n                except:\n                    beg_pos = None\n            if end_pos is None:\n                try:\n                    end_pos = tokenized.char_to_token(end - 2)\n                    if end_pos is None:\n                        end_pos = tokenized.char_to_token(end - 3)\n                except:\n                    end_pos = None\n            if beg_pos is None or end_pos is None:\n                continue\n\n            assert beg_pos is not None and end_pos is not None\n            if os.environ.get(\"SHILONG_DEBUG_ONLY_ONE_POS\", None) == \"TRUE\":\n                positive_map[j, beg_pos] = 1\n                break\n            else:\n                positive_map[j, beg_pos : end_pos + 1].fill_(1)\n\n    return positive_map / (positive_map.sum(-1)[:, None] + 1e-6)\n\n\ndef build_captions_and_token_span(cat_list, force_lowercase):\n    \"\"\"\n    Return:\n        captions: str\n        cat2tokenspan: dict\n            {\n                'dog': [[0, 2]],\n                ...\n            }\n    \"\"\"\n\n    cat2tokenspan = {}\n    captions = \"\"\n    for catname in cat_list:\n        class_name = catname\n        if force_lowercase:\n            class_name = class_name.lower()\n        if \"/\" in class_name:\n            class_name_list: List = class_name.strip().split(\"/\")\n            class_name_list.append(class_name)\n            class_name: str = random.choice(class_name_list)\n\n        tokens_positive_i = []\n        subnamelist = [i.strip() for i in class_name.strip().split(\" \")]\n        for subname in subnamelist:\n            if len(subname) == 0:\n                continue\n            if len(captions) > 0:\n                captions = captions + \" \"\n            strat_idx = len(captions)\n            end_idx = strat_idx + len(subname)\n            tokens_positive_i.append([strat_idx, end_idx])\n            captions = captions + subname\n\n        if len(tokens_positive_i) > 0:\n            captions = captions + \" .\"\n            cat2tokenspan[class_name] = tokens_positive_i\n\n    return captions, cat2tokenspan\n\n\ndef build_id2posspan_and_caption(category_dict: dict):\n    \"\"\"Build id2pos_span and caption from category_dict\n\n    Args:\n        category_dict (dict): category_dict\n    \"\"\"\n    cat_list = [item[\"name\"].lower() for item in category_dict]\n    id2catname = {item[\"id\"]: item[\"name\"].lower() for item in category_dict}\n    caption, cat2posspan = build_captions_and_token_span(cat_list, force_lowercase=True)\n    id2posspan = {catid: cat2posspan[catname] for catid, catname in id2catname.items()}\n    return id2posspan, caption\n"
  },
  {
    "path": "model_cards/groundingdino/version.py",
    "content": "__version__ = '0.1.0'\n"
  },
  {
    "path": "model_cards/lama/.gitignore",
    "content": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\nwheels/\npip-wheel-metadata/\nshare/python-wheels/\n*.egg-info/\n.installed.cfg\n*.egg\nMANIFEST\n\n# PyInstaller\n#  Usually these files are written by a python script from a template\n#  before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.nox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*.cover\n*.py,cover\n.hypothesis/\n.pytest_cache/\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\n*.log\nlocal_settings.py\ndb.sqlite3\ndb.sqlite3-journal\n\n# Flask stuff:\ninstance/\n.webassets-cache\n\n# Scrapy stuff:\n.scrapy\n\n# Sphinx documentation\ndocs/_build/\n\n# PyBuilder\ntarget/\n\n# Jupyter Notebook\n.ipynb_checkpoints\n\n# IPython\nprofile_default/\nipython_config.py\n\n# pyenv\n.python-version\n\n# pipenv\n#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.\n#   However, in case of collaboration, if having platform-specific dependencies or dependencies\n#   having no cross-platform support, pipenv may install dependencies that don't work, or not\n#   install all needed dependencies.\n#Pipfile.lock\n\n# PEP 582; used by e.g. github.com/David-OConnor/pyflow\n__pypackages__/\n\n# Celery stuff\ncelerybeat-schedule\ncelerybeat.pid\n\n# SageMath parsed files\n*.sage.py\n\n# Environments\n.env\n.venv\nenv/\nvenv/\nENV/\nenv.bak/\nvenv.bak/\n\n# Spyder project settings\n.spyderproject\n.spyproject\n\n# Rope project settings\n.ropeproject\n\n# mkdocs documentation\n/site\n\n# mypy\n.mypy_cache/\n.dmypy.json\ndmypy.json\n\n# Pyre type checker\n.pyre/\n\n# temporary files\n## IDEA\n.idea/\n## vscode\n.vscode/\n## vim\n*.sw?\n"
  },
  {
    "path": "model_cards/lama/LICENSE",
    "content": "                                 Apache License\n                           Version 2.0, January 2004\n                        http://www.apache.org/licenses/\n\n   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION\n\n   1. Definitions.\n\n      \"License\" shall mean the terms and conditions for use, reproduction,\n      and distribution as defined by Sections 1 through 9 of this document.\n\n      \"Licensor\" shall mean the copyright owner or entity authorized by\n      the copyright owner that is granting the License.\n\n      \"Legal Entity\" shall mean the union of the acting entity and all\n      other entities that control, are controlled by, or are under common\n      control with that entity. For the purposes of this definition,\n      \"control\" means (i) the power, direct or indirect, to cause the\n      direction or management of such entity, whether by contract or\n      otherwise, or (ii) ownership of fifty percent (50%) or more of the\n      outstanding shares, or (iii) beneficial ownership of such entity.\n\n      \"You\" (or \"Your\") shall mean an individual or Legal Entity\n      exercising permissions granted by this License.\n\n      \"Source\" form shall mean the preferred form for making modifications,\n      including but not limited to software source code, documentation\n      source, and configuration files.\n\n      \"Object\" form shall mean any form resulting from mechanical\n      transformation or translation of a Source form, including but\n      not limited to compiled object code, generated documentation,\n      and conversions to other media types.\n\n      \"Work\" shall mean the work of authorship, whether in Source or\n      Object form, made available under the License, as indicated by a\n      copyright notice that is included in or attached to the work\n      (an example is provided in the Appendix below).\n\n      \"Derivative Works\" shall mean any work, whether in Source or Object\n      form, that is based on (or derived from) the Work and for which the\n      editorial revisions, annotations, elaborations, or other modifications\n      represent, as a whole, an original work of authorship. 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  },
  {
    "path": "model_cards/lama/README.md",
    "content": "# 🦙 LaMa: Resolution-robust Large Mask Inpainting with Fourier Convolutions\n\nby Roman Suvorov, Elizaveta Logacheva, Anton Mashikhin, \nAnastasia Remizova, Arsenii Ashukha, Aleksei Silvestrov, Naejin Kong, Harshith Goka, Kiwoong Park, Victor Lempitsky.\n\n<p align=\"center\" \"font-size:30px;\">\n  🔥🔥🔥\n  <br>\n  <b>\nLaMa generalizes surprisingly well to much higher resolutions (~2k❗️) than it saw during training (256x256), and achieves the excellent performance even in challenging scenarios, e.g. completion of periodic structures.</b>\n</p>\n\n[[Project page](https://advimman.github.io/lama-project/)] [[arXiv](https://arxiv.org/abs/2109.07161)] [[Supplementary](https://ashukha.com/projects/lama_21/lama_supmat_2021.pdf)] [[BibTeX](https://senya-ashukha.github.io/projects/lama_21/paper.txt)] [[Casual GAN Papers Summary](https://www.casualganpapers.com/large-masks-fourier-convolutions-inpainting/LaMa-explained.html)]\n \n<p align=\"center\">\n  <a href=\"https://colab.research.google.com/github/advimman/lama/blob/master//colab/LaMa_inpainting.ipynb\">\n  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\"/>\n  </a>\n      <br>\n   Try out in Google Colab\n</p>\n\n<p align=\"center\">\n  <img src=\"https://raw.githubusercontent.com/senya-ashukha/senya-ashukha.github.io/master/projects/lama_21/ezgif-4-0db51df695a8.gif\" />\n</p>\n\n\n<p align=\"center\">\n  <img src=\"https://raw.githubusercontent.com/senya-ashukha/senya-ashukha.github.io/master/projects/lama_21/gif_for_lightning_v1_white.gif\" />\n</p>\n\n# LaMa development\n(Feel free to share your paper by creating an issue)\n- Amazing results [paper](https://arxiv.org/abs/2206.13644) / [video](https://www.youtube.com/watch?v=gEukhOheWgE) / code https://github.com/advimman/lama/pull/112 / by Geomagical Labs ([geomagical.com](geomagical.com))\n<p align=\"center\">\n  <img src=\"https://raw.githubusercontent.com/senya-ashukha/senya-ashukha.github.io/master/images/FeatureRefinement.png\" />\n</p>\n\n# Non-official 3rd party apps:\n(Feel free to share your app/implementation/demo by creating an issue)\n- [https://cleanup.pictures](https://cleanup.pictures/) - a simple interactive object removal tool by [@cyrildiagne](https://twitter.com/cyrildiagne)\n    - [lama-cleaner](https://github.com/Sanster/lama-cleaner) by [@Sanster](https://github.com/Sanster/lama-cleaner) is a self-host version of [https://cleanup.pictures](https://cleanup.pictures/)\n- Integrated to [Huggingface Spaces](https://huggingface.co/spaces) with [Gradio](https://github.com/gradio-app/gradio). See demo: [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/akhaliq/lama) by [@AK391](https://github.com/AK391)\n- Telegram bot [@MagicEraserBot](https://t.me/MagicEraserBot) by [@Moldoteck](https://github.com/Moldoteck), [code](https://github.com/Moldoteck/MagicEraser)\n- [Auto-LaMa](https://github.com/andy971022/auto-lama) = DE:TR object detection + LaMa inpainting by [@andy971022](https://github.com/andy971022)\n- [LAMA-Magic-Eraser-Local](https://github.com/zhaoyun0071/LAMA-Magic-Eraser-Local) = a standalone inpainting application built with PyQt5 by [@zhaoyun0071](https://github.com/zhaoyun0071)\n- [Hama](https://www.hama.app/) - object removal with a smart brush which simplifies mask drawing.\n- [ModelScope](https://www.modelscope.cn/models/damo/cv_fft_inpainting_lama/summary) = the largest Model Community in Chinese by  [@chenbinghui1](https://github.com/chenbinghui1).\n- [LaMa with MaskDINO](https://github.com/qwopqwop200/lama-with-maskdino) = MaskDINO object detection + LaMa inpainting with refinement by [@qwopqwop200](https://github.com/qwopqwop200).\n\n# Environment setup\n\nClone the repo:\n`git clone https://github.com/advimman/lama.git`\n\nThere are three options of an environment:\n\n1. Python virtualenv:\n\n    ```\n    virtualenv inpenv --python=/usr/bin/python3\n    source inpenv/bin/activate\n    pip install torch==1.8.0 torchvision==0.9.0\n    \n    cd lama\n    pip install -r requirements.txt \n    ```\n\n2. Conda\n    \n    ```\n    % Install conda for Linux, for other OS download miniconda at https://docs.conda.io/en/latest/miniconda.html\n    wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh\n    bash Miniconda3-latest-Linux-x86_64.sh -b -p $HOME/miniconda\n    $HOME/miniconda/bin/conda init bash\n\n    cd lama\n    conda env create -f conda_env.yml\n    conda activate lama\n    conda install pytorch torchvision torchaudio cudatoolkit=10.2 -c pytorch -y\n    pip install pytorch-lightning==1.2.9\n    ```\n \n3. Docker: No actions are needed 🎉.\n\n# Inference <a name=\"prediction\"></a>\n\nRun\n```\ncd lama\nexport TORCH_HOME=$(pwd) && export PYTHONPATH=$(pwd)\n```\n\n**1. Download pre-trained models**\n\nInstall tool for yandex disk link extraction:\n\n```\npip3 install wldhx.yadisk-direct\n```\n\nThe best model (Places2, Places Challenge):\n    \n```    \ncurl -L $(yadisk-direct https://disk.yandex.ru/d/ouP6l8VJ0HpMZg) -o big-lama.zip\nunzip big-lama.zip\n```\n\nAll models (Places & CelebA-HQ):\n\n```\ncurl -L $(yadisk-direct https://disk.yandex.ru/d/EgqaSnLohjuzAg) -o lama-models.zip\nunzip lama-models.zip\n```\n\n**2. Prepare images and masks**\n\nDownload test images:\n\n```\ncurl -L $(yadisk-direct https://disk.yandex.ru/d/xKQJZeVRk5vLlQ) -o LaMa_test_images.zip\nunzip LaMa_test_images.zip\n```\n<details>\n <summary>OR prepare your data:</summary>\n1) Create masks named as `[images_name]_maskXXX[image_suffix]`, put images and masks in the same folder. \n\n- You can use the [script](https://github.com/advimman/lama/blob/main/bin/gen_mask_dataset.py) for random masks generation. \n- Check the format of the files:\n    ```    \n    image1_mask001.png\n    image1.png\n    image2_mask001.png\n    image2.png\n    ```\n\n2) Specify `image_suffix`, e.g. `.png` or `.jpg` or `_input.jpg` in `configs/prediction/default.yaml`.\n\n</details>\n\n\n**3. Predict**\n\nOn the host machine:\n\n    python3 bin/predict.py model.path=$(pwd)/big-lama indir=$(pwd)/LaMa_test_images outdir=$(pwd)/output\n\n**OR** in the docker\n  \nThe following command will pull the docker image from Docker Hub and execute the prediction script\n```\nbash docker/2_predict.sh $(pwd)/big-lama $(pwd)/LaMa_test_images $(pwd)/output device=cpu\n```\nDocker cuda: TODO\n\n**4. Predict with Refinement**\n\nOn the host machine:\n\n    python3 bin/predict.py refine=True model.path=$(pwd)/big-lama indir=$(pwd)/LaMa_test_images outdir=$(pwd)/output\n\n# Train and Eval\n\nMake sure you run:\n\n```\ncd lama\nexport TORCH_HOME=$(pwd) && export PYTHONPATH=$(pwd)\n```\n\nThen download models for _perceptual loss_:\n\n    mkdir -p ade20k/ade20k-resnet50dilated-ppm_deepsup/\n    wget -P ade20k/ade20k-resnet50dilated-ppm_deepsup/ http://sceneparsing.csail.mit.edu/model/pytorch/ade20k-resnet50dilated-ppm_deepsup/encoder_epoch_20.pth\n\n\n## Places\n\n⚠️ NB: FID/SSIM/LPIPS metric values for Places that we see in LaMa paper are computed on 30000 images that we produce in evaluation section below.\nFor more details on evaluation data check [[Section 3. Dataset splits in Supplementary](https://ashukha.com/projects/lama_21/lama_supmat_2021.pdf#subsection.3.1)]  ⚠️\n\nOn the host machine:\n\n    # Download data from http://places2.csail.mit.edu/download.html\n    # Places365-Standard: Train(105GB)/Test(19GB)/Val(2.1GB) from High-resolution images section\n    wget http://data.csail.mit.edu/places/places365/train_large_places365standard.tar\n    wget http://data.csail.mit.edu/places/places365/val_large.tar\n    wget http://data.csail.mit.edu/places/places365/test_large.tar\n\n    # Unpack train/test/val data and create .yaml config for it\n    bash fetch_data/places_standard_train_prepare.sh\n    bash fetch_data/places_standard_test_val_prepare.sh\n    \n    # Sample images for test and viz at the end of epoch\n    bash fetch_data/places_standard_test_val_sample.sh\n    bash fetch_data/places_standard_test_val_gen_masks.sh\n\n    # Run training\n    python3 bin/train.py -cn lama-fourier location=places_standard\n\n    # To evaluate trained model and report metrics as in our paper\n    # we need to sample previously unseen 30k images and generate masks for them\n    bash fetch_data/places_standard_evaluation_prepare_data.sh\n    \n    # Infer model on thick/thin/medium masks in 256 and 512 and run evaluation \n    # like this:\n    python3 bin/predict.py \\\n    model.path=$(pwd)/experiments/<user>_<date:time>_lama-fourier_/ \\\n    indir=$(pwd)/places_standard_dataset/evaluation/random_thick_512/ \\\n    outdir=$(pwd)/inference/random_thick_512 model.checkpoint=last.ckpt\n\n    python3 bin/evaluate_predicts.py \\\n    $(pwd)/configs/eval2_gpu.yaml \\\n    $(pwd)/places_standard_dataset/evaluation/random_thick_512/ \\\n    $(pwd)/inference/random_thick_512 \\\n    $(pwd)/inference/random_thick_512_metrics.csv\n\n    \n    \nDocker: TODO\n    \n## CelebA\nOn the host machine:\n\n    # Make shure you are in lama folder\n    cd lama\n    export TORCH_HOME=$(pwd) && export PYTHONPATH=$(pwd)\n\n    # Download CelebA-HQ dataset\n    # Download data256x256.zip from https://drive.google.com/drive/folders/11Vz0fqHS2rXDb5pprgTjpD7S2BAJhi1P\n    \n    # unzip & split into train/test/visualization & create config for it\n    bash fetch_data/celebahq_dataset_prepare.sh\n\n    # generate masks for test and visual_test at the end of epoch\n    bash fetch_data/celebahq_gen_masks.sh\n\n    # Run training\n    python3 bin/train.py -cn lama-fourier-celeba data.batch_size=10\n\n    # Infer model on thick/thin/medium masks in 256 and run evaluation \n    # like this:\n    python3 bin/predict.py \\\n    model.path=$(pwd)/experiments/<user>_<date:time>_lama-fourier-celeba_/ \\\n    indir=$(pwd)/celeba-hq-dataset/visual_test_256/random_thick_256/ \\\n    outdir=$(pwd)/inference/celeba_random_thick_256 model.checkpoint=last.ckpt\n    \n    \nDocker: TODO\n\n## Places Challenge \n\nOn the host machine:\n\n    # This script downloads multiple .tar files in parallel and unpacks them\n    # Places365-Challenge: Train(476GB) from High-resolution images (to train Big-Lama) \n    bash places_challenge_train_download.sh\n    \n    TODO: prepare\n    TODO: train \n    TODO: eval\n      \nDocker: TODO\n\n## Create your data\n\nPlease check bash scripts for data preparation and mask generation from CelebaHQ section,\nif you stuck at one of the following steps.\n\n\nOn the host machine:\n\n    # Make shure you are in lama folder\n    cd lama\n    export TORCH_HOME=$(pwd) && export PYTHONPATH=$(pwd)\n\n    # You need to prepare following image folders:\n    $ ls my_dataset\n    train\n    val_source # 2000 or more images\n    visual_test_source # 100 or more images\n    eval_source # 2000 or more images\n\n    # LaMa generates random masks for the train data on the flight,\n    # but needs fixed masks for test and visual_test for consistency of evaluation.\n\n    # Suppose, we want to evaluate and pick best models \n    # on 512x512 val dataset  with thick/thin/medium masks \n    # And your images have .jpg extention:\n\n    python3 bin/gen_mask_dataset.py \\\n    $(pwd)/configs/data_gen/random_<size>_512.yaml \\ # thick, thin, medium\n    my_dataset/val_source/ \\\n    my_dataset/val/random_<size>_512.yaml \\# thick, thin, medium\n    --ext jpg\n\n    # So the mask generator will: \n    # 1. resize and crop val images and save them as .png\n    # 2. generate masks\n    \n    ls my_dataset/val/random_medium_512/\n    image1_crop000_mask000.png\n    image1_crop000.png\n    image2_crop000_mask000.png\n    image2_crop000.png\n    ...\n\n    # Generate thick, thin, medium masks for visual_test folder:\n\n    python3 bin/gen_mask_dataset.py \\\n    $(pwd)/configs/data_gen/random_<size>_512.yaml \\  #thick, thin, medium\n    my_dataset/visual_test_source/ \\\n    my_dataset/visual_test/random_<size>_512/ \\ #thick, thin, medium\n    --ext jpg\n    \n\n    ls my_dataset/visual_test/random_thick_512/\n    image1_crop000_mask000.png\n    image1_crop000.png\n    image2_crop000_mask000.png\n    image2_crop000.png\n    ...\n\n    # Same process for eval_source image folder:\n    \n    python3 bin/gen_mask_dataset.py \\\n    $(pwd)/configs/data_gen/random_<size>_512.yaml \\  #thick, thin, medium\n    my_dataset/eval_source/ \\\n    my_dataset/eval/random_<size>_512/ \\ #thick, thin, medium\n    --ext jpg\n    \n\n\n    # Generate location config file which locate these folders:\n    \n    touch my_dataset.yaml\n    echo \"data_root_dir: $(pwd)/my_dataset/\" >> my_dataset.yaml\n    echo \"out_root_dir: $(pwd)/experiments/\" >> my_dataset.yaml\n    echo \"tb_dir: $(pwd)/tb_logs/\" >> my_dataset.yaml\n    mv my_dataset.yaml ${PWD}/configs/training/location/\n\n\n    # Check data config for consistency with my_dataset folder structure:\n    $ cat ${PWD}/configs/training/data/abl-04-256-mh-dist\n    ...\n    train:\n      indir: ${location.data_root_dir}/train\n      ...\n    val:\n      indir: ${location.data_root_dir}/val\n      img_suffix: .png\n    visual_test:\n      indir: ${location.data_root_dir}/visual_test\n      img_suffix: .png\n\n\n    # Run training\n    python3 bin/train.py -cn lama-fourier location=my_dataset data.batch_size=10\n\n    # Evaluation: LaMa training procedure picks best few models according to \n    # scores on my_dataset/val/ \n\n    # To evaluate one of your best models (i.e. at epoch=32) \n    # on previously unseen my_dataset/eval do the following \n    # for thin, thick and medium:\n\n    # infer:\n    python3 bin/predict.py \\\n    model.path=$(pwd)/experiments/<user>_<date:time>_lama-fourier_/ \\\n    indir=$(pwd)/my_dataset/eval/random_<size>_512/ \\\n    outdir=$(pwd)/inference/my_dataset/random_<size>_512 \\\n    model.checkpoint=epoch32.ckpt\n\n    # metrics calculation:\n    python3 bin/evaluate_predicts.py \\\n    $(pwd)/configs/eval2_gpu.yaml \\\n    $(pwd)/my_dataset/eval/random_<size>_512/ \\\n    $(pwd)/inference/my_dataset/random_<size>_512 \\\n    $(pwd)/inference/my_dataset/random_<size>_512_metrics.csv\n\n    \n**OR** in the docker:\n\n    TODO: train\n    TODO: eval\n    \n# Hints\n\n### Generate different kinds of masks\nThe following command will execute a script that generates random masks.\n\n    bash docker/1_generate_masks_from_raw_images.sh \\\n        configs/data_gen/random_medium_512.yaml \\\n        /directory_with_input_images \\\n        /directory_where_to_store_images_and_masks \\\n        --ext png\n\nThe test data generation command stores images in the format,\nwhich is suitable for [prediction](#prediction).\n\nThe table below describes which configs we used to generate different test sets from the paper.\nNote that we *do not fix a random seed*, so the results will be slightly different each time.\n\n|        | Places 512x512         | CelebA 256x256         |\n|--------|------------------------|------------------------|\n| Narrow | random_thin_512.yaml   | random_thin_256.yaml   |\n| Medium | random_medium_512.yaml | random_medium_256.yaml |\n| Wide   | random_thick_512.yaml  | random_thick_256.yaml  |\n\nFeel free to change the config path (argument #1) to any other config in `configs/data_gen` \nor adjust config files themselves.\n\n### Override parameters in configs\nAlso you can override parameters in config like this:\n\n    python3 bin/train.py -cn <config> data.batch_size=10 run_title=my-title\n\nWhere .yaml file extension is omitted\n\n### Models options \nConfig names for models from paper (substitude into the training command): \n\n    * big-lama\n    * big-lama-regular\n    * lama-fourier\n    * lama-regular\n    * lama_small_train_masks\n\nWhich are seated in configs/training/folder\n\n### Links\n- All the data (models, test images, etc.) https://disk.yandex.ru/d/AmdeG-bIjmvSug\n- Test images from the paper https://disk.yandex.ru/d/xKQJZeVRk5vLlQ\n- The pre-trained models https://disk.yandex.ru/d/EgqaSnLohjuzAg\n- The models for perceptual loss https://disk.yandex.ru/d/ncVmQlmT_kTemQ\n- Our training logs are available at https://disk.yandex.ru/d/9Bt1wNSDS4jDkQ\n\n\n### Training time & resources\n\nTODO\n\n## Acknowledgments\n\n* Segmentation code and models if form [CSAILVision](https://github.com/CSAILVision/semantic-segmentation-pytorch).\n* LPIPS metric is from [richzhang](https://github.com/richzhang/PerceptualSimilarity)\n* SSIM is from [Po-Hsun-Su](https://github.com/Po-Hsun-Su/pytorch-ssim)\n* FID is from [mseitzer](https://github.com/mseitzer/pytorch-fid)\n\n## Citation\nIf you found this code helpful, please consider citing: \n```\n@article{suvorov2021resolution,\n  title={Resolution-robust Large Mask Inpainting with Fourier Convolutions},\n  author={Suvorov, Roman and Logacheva, Elizaveta and Mashikhin, Anton and Remizova, Anastasia and Ashukha, Arsenii and Silvestrov, Aleksei and Kong, Naejin and Goka, Harshith and Park, Kiwoong and Lempitsky, Victor},\n  journal={arXiv preprint arXiv:2109.07161},\n  year={2021}\n}\n```\n"
  },
  {
    "path": "model_cards/lama/bin/analyze_errors.py",
    "content": "#!/usr/bin/env python3\nimport cv2\nimport numpy as np\nimport sklearn\nimport torch\nimport os\nimport pickle\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom joblib import Parallel, delayed\n\nfrom saicinpainting.evaluation.data import PrecomputedInpaintingResultsDataset, load_image\nfrom saicinpainting.evaluation.losses.fid.inception import InceptionV3\nfrom saicinpainting.evaluation.utils import load_yaml\nfrom saicinpainting.training.visualizers.base import visualize_mask_and_images\n\n\ndef draw_score(img, score):\n    img = np.transpose(img, (1, 2, 0))\n    cv2.putText(img, f'{score:.2f}',\n                (40, 40),\n                cv2.FONT_HERSHEY_SIMPLEX,\n                1,\n                (0, 1, 0),\n                thickness=3)\n    img = np.transpose(img, (2, 0, 1))\n    return img\n\n\ndef save_global_samples(global_mask_fnames, mask2real_fname, mask2fake_fname, out_dir, real_scores_by_fname, fake_scores_by_fname):\n    for cur_mask_fname in global_mask_fnames:\n        cur_real_fname = mask2real_fname[cur_mask_fname]\n        orig_img = load_image(cur_real_fname, mode='RGB')\n        fake_img = load_image(mask2fake_fname[cur_mask_fname], mode='RGB')[:, :orig_img.shape[1], :orig_img.shape[2]]\n        mask = load_image(cur_mask_fname, mode='L')[None, ...]\n\n        draw_score(orig_img, real_scores_by_fname.loc[cur_real_fname, 'real_score'])\n        draw_score(fake_img, fake_scores_by_fname.loc[cur_mask_fname, 'fake_score'])\n\n        cur_grid = visualize_mask_and_images(dict(image=orig_img, mask=mask, fake=fake_img),\n                                             keys=['image', 'fake'],\n                                             last_without_mask=True)\n        cur_grid = np.clip(cur_grid * 255, 0, 255).astype('uint8')\n        cur_grid = cv2.cvtColor(cur_grid, cv2.COLOR_RGB2BGR)\n        cv2.imwrite(os.path.join(out_dir, os.path.splitext(os.path.basename(cur_mask_fname))[0] + '.jpg'),\n                    cur_grid)\n\n\ndef save_samples_by_real(worst_best_by_real, mask2fake_fname, fake_info, out_dir):\n    for real_fname in worst_best_by_real.index:\n        worst_mask_path = worst_best_by_real.loc[real_fname, 'worst']\n        best_mask_path = worst_best_by_real.loc[real_fname, 'best']\n        orig_img = load_image(real_fname, mode='RGB')\n        worst_mask_img = load_image(worst_mask_path, mode='L')[None, ...]\n        worst_fake_img = load_image(mask2fake_fname[worst_mask_path], mode='RGB')[:, :orig_img.shape[1], :orig_img.shape[2]]\n        best_mask_img = load_image(best_mask_path, mode='L')[None, ...]\n        best_fake_img = load_image(mask2fake_fname[best_mask_path], mode='RGB')[:, :orig_img.shape[1], :orig_img.shape[2]]\n\n        draw_score(orig_img, worst_best_by_real.loc[real_fname, 'real_score'])\n        draw_score(worst_fake_img, worst_best_by_real.loc[real_fname, 'worst_score'])\n        draw_score(best_fake_img, worst_best_by_real.loc[real_fname, 'best_score'])\n\n        cur_grid = visualize_mask_and_images(dict(image=orig_img, mask=np.zeros_like(worst_mask_img),\n                                                  worst_mask=worst_mask_img, worst_img=worst_fake_img,\n                                                  best_mask=best_mask_img, best_img=best_fake_img),\n                                             keys=['image', 'worst_mask', 'worst_img', 'best_mask', 'best_img'],\n                                             rescale_keys=['worst_mask', 'best_mask'],\n                                             last_without_mask=True)\n        cur_grid = np.clip(cur_grid * 255, 0, 255).astype('uint8')\n        cur_grid = cv2.cvtColor(cur_grid, cv2.COLOR_RGB2BGR)\n        cv2.imwrite(os.path.join(out_dir,\n                                 os.path.splitext(os.path.basename(real_fname))[0] + '.jpg'),\n                    cur_grid)\n\n        fig, (ax1, ax2) = plt.subplots(1, 2)\n        cur_stat = fake_info[fake_info['real_fname'] == real_fname]\n        cur_stat['fake_score'].hist(ax=ax1)\n        cur_stat['real_score'].hist(ax=ax2)\n        fig.tight_layout()\n        fig.savefig(os.path.join(out_dir,\n                                 os.path.splitext(os.path.basename(real_fname))[0] + '_scores.png'))\n        plt.close(fig)\n\n\ndef extract_overlapping_masks(mask_fnames, cur_i, fake_scores_table, max_overlaps_n=2):\n    result_pairs = []\n    result_scores = []\n    mask_fname_a = mask_fnames[cur_i]\n    mask_a = load_image(mask_fname_a, mode='L')[None, ...] > 0.5\n    cur_score_a = fake_scores_table.loc[mask_fname_a, 'fake_score']\n    for mask_fname_b in mask_fnames[cur_i + 1:]:\n        mask_b = load_image(mask_fname_b, mode='L')[None, ...] > 0.5\n        if not np.any(mask_a & mask_b):\n            continue\n        cur_score_b = fake_scores_table.loc[mask_fname_b, 'fake_score']\n        result_pairs.append((mask_fname_a, mask_fname_b))\n        result_scores.append(cur_score_b - cur_score_a)\n        if len(result_pairs) >= max_overlaps_n:\n            break\n    return result_pairs, result_scores\n\n\ndef main(args):\n    config = load_yaml(args.config)\n\n    latents_dir = os.path.join(args.outpath, 'latents')\n    os.makedirs(latents_dir, exist_ok=True)\n    global_worst_dir = os.path.join(args.outpath, 'global_worst')\n    os.makedirs(global_worst_dir, exist_ok=True)\n    global_best_dir = os.path.join(args.outpath, 'global_best')\n    os.makedirs(global_best_dir, exist_ok=True)\n    worst_best_by_best_worst_score_diff_max_dir = os.path.join(args.outpath, 'worst_best_by_real', 'best_worst_score_diff_max')\n    os.makedirs(worst_best_by_best_worst_score_diff_max_dir, exist_ok=True)\n    worst_best_by_best_worst_score_diff_min_dir = os.path.join(args.outpath, 'worst_best_by_real', 'best_worst_score_diff_min')\n    os.makedirs(worst_best_by_best_worst_score_diff_min_dir, exist_ok=True)\n    worst_best_by_real_best_score_diff_max_dir = os.path.join(args.outpath, 'worst_best_by_real', 'real_best_score_diff_max')\n    os.makedirs(worst_best_by_real_best_score_diff_max_dir, exist_ok=True)\n    worst_best_by_real_best_score_diff_min_dir = os.path.join(args.outpath, 'worst_best_by_real', 'real_best_score_diff_min')\n    os.makedirs(worst_best_by_real_best_score_diff_min_dir, exist_ok=True)\n    worst_best_by_real_worst_score_diff_max_dir = os.path.join(args.outpath, 'worst_best_by_real', 'real_worst_score_diff_max')\n    os.makedirs(worst_best_by_real_worst_score_diff_max_dir, exist_ok=True)\n    worst_best_by_real_worst_score_diff_min_dir = os.path.join(args.outpath, 'worst_best_by_real', 'real_worst_score_diff_min')\n    os.makedirs(worst_best_by_real_worst_score_diff_min_dir, exist_ok=True)\n\n    if not args.only_report:\n        block_idx = InceptionV3.BLOCK_INDEX_BY_DIM[2048]\n        inception_model = InceptionV3([block_idx]).eval().cuda()\n\n        dataset = PrecomputedInpaintingResultsDataset(args.datadir, args.predictdir, **config.dataset_kwargs)\n\n        real2vector_cache = {}\n\n        real_features = []\n        fake_features = []\n\n        orig_fnames = []\n        mask_fnames = []\n        mask2real_fname = {}\n        mask2fake_fname = {}\n\n        for batch_i, batch in enumerate(dataset):\n            orig_img_fname = dataset.img_filenames[batch_i]\n            mask_fname = dataset.mask_filenames[batch_i]\n            fake_fname = dataset.pred_filenames[batch_i]\n            mask2real_fname[mask_fname] = orig_img_fname\n            mask2fake_fname[mask_fname] = fake_fname\n\n            cur_real_vector = real2vector_cache.get(orig_img_fname, None)\n            if cur_real_vector is None:\n                with torch.no_grad():\n                    in_img = torch.from_numpy(batch['image'][None, ...]).cuda()\n                    cur_real_vector = inception_model(in_img)[0].squeeze(-1).squeeze(-1).cpu().numpy()\n                real2vector_cache[orig_img_fname] = cur_real_vector\n\n            pred_img = torch.from_numpy(batch['inpainted'][None, ...]).cuda()\n            cur_fake_vector = inception_model(pred_img)[0].squeeze(-1).squeeze(-1).cpu().numpy()\n\n            real_features.append(cur_real_vector)\n            fake_features.append(cur_fake_vector)\n\n            orig_fnames.append(orig_img_fname)\n            mask_fnames.append(mask_fname)\n\n        ids_features = np.concatenate(real_features + fake_features, axis=0)\n        ids_labels = np.array(([1] * len(real_features)) + ([0] * len(fake_features)))\n\n        with open(os.path.join(latents_dir, 'featues.pkl'), 'wb') as f:\n            pickle.dump(ids_features, f, protocol=3)\n        with open(os.path.join(latents_dir, 'labels.pkl'), 'wb') as f:\n            pickle.dump(ids_labels, f, protocol=3)\n        with open(os.path.join(latents_dir, 'orig_fnames.pkl'), 'wb') as f:\n            pickle.dump(orig_fnames, f, protocol=3)\n        with open(os.path.join(latents_dir, 'mask_fnames.pkl'), 'wb') as f:\n            pickle.dump(mask_fnames, f, protocol=3)\n        with open(os.path.join(latents_dir, 'mask2real_fname.pkl'), 'wb') as f:\n            pickle.dump(mask2real_fname, f, protocol=3)\n        with open(os.path.join(latents_dir, 'mask2fake_fname.pkl'), 'wb') as f:\n            pickle.dump(mask2fake_fname, f, protocol=3)\n\n        svm = sklearn.svm.LinearSVC(dual=False)\n        svm.fit(ids_features, ids_labels)\n\n        pred_scores = svm.decision_function(ids_features)\n        real_scores = pred_scores[:len(real_features)]\n        fake_scores = pred_scores[len(real_features):]\n\n        with open(os.path.join(latents_dir, 'pred_scores.pkl'), 'wb') as f:\n            pickle.dump(pred_scores, f, protocol=3)\n        with open(os.path.join(latents_dir, 'real_scores.pkl'), 'wb') as f:\n            pickle.dump(real_scores, f, protocol=3)\n        with open(os.path.join(latents_dir, 'fake_scores.pkl'), 'wb') as f:\n            pickle.dump(fake_scores, f, protocol=3)\n    else:\n        with open(os.path.join(latents_dir, 'orig_fnames.pkl'), 'rb') as f:\n            orig_fnames = pickle.load(f)\n        with open(os.path.join(latents_dir, 'mask_fnames.pkl'), 'rb') as f:\n            mask_fnames = pickle.load(f)\n        with open(os.path.join(latents_dir, 'mask2real_fname.pkl'), 'rb') as f:\n            mask2real_fname = pickle.load(f)\n        with open(os.path.join(latents_dir, 'mask2fake_fname.pkl'), 'rb') as f:\n            mask2fake_fname = pickle.load(f)\n        with open(os.path.join(latents_dir, 'real_scores.pkl'), 'rb') as f:\n            real_scores = pickle.load(f)\n        with open(os.path.join(latents_dir, 'fake_scores.pkl'), 'rb') as f:\n            fake_scores = pickle.load(f)\n\n    real_info = pd.DataFrame(data=[dict(real_fname=fname,\n                                        real_score=score)\n                                   for fname, score\n                                   in zip(orig_fnames, real_scores)])\n    real_info.set_index('real_fname', drop=True, inplace=True)\n\n    fake_info = pd.DataFrame(data=[dict(mask_fname=fname,\n                                        fake_fname=mask2fake_fname[fname],\n                                        real_fname=mask2real_fname[fname],\n                                        fake_score=score)\n                                   for fname, score\n                                   in zip(mask_fnames, fake_scores)])\n    fake_info = fake_info.join(real_info, on='real_fname', how='left')\n    fake_info.drop_duplicates(['fake_fname', 'real_fname'], inplace=True)\n\n    fake_stats_by_real = fake_info.groupby('real_fname')['fake_score'].describe()[['mean', 'std']].rename(\n        {'mean': 'mean_fake_by_real', 'std': 'std_fake_by_real'}, axis=1)\n    fake_info = fake_info.join(fake_stats_by_real, on='real_fname', rsuffix='stat_by_real')\n    fake_info.drop_duplicates(['fake_fname', 'real_fname'], inplace=True)\n    fake_info.to_csv(os.path.join(latents_dir, 'join_scores_table.csv'), sep='\\t', index=False)\n\n    fake_scores_table = fake_info.set_index('mask_fname')['fake_score'].to_frame()\n    real_scores_table = fake_info.set_index('real_fname')['real_score'].drop_duplicates().to_frame()\n\n    fig, (ax1, ax2) = plt.subplots(1, 2)\n    ax1.hist(fake_scores)\n    ax2.hist(real_scores)\n    fig.tight_layout()\n    fig.savefig(os.path.join(args.outpath, 'global_scores_hist.png'))\n    plt.close(fig)\n\n    global_worst_masks = fake_info.sort_values('fake_score', ascending=True)['mask_fname'].iloc[:config.take_global_top].to_list()\n    global_best_masks = fake_info.sort_values('fake_score', ascending=False)['mask_fname'].iloc[:config.take_global_top].to_list()\n    save_global_samples(global_worst_masks, mask2real_fname, mask2fake_fname, global_worst_dir, real_scores_table, fake_scores_table)\n    save_global_samples(global_best_masks, mask2real_fname, mask2fake_fname, global_best_dir, real_scores_table, fake_scores_table)\n\n    # grouped by real\n    worst_samples_by_real = fake_info.groupby('real_fname').apply(\n        lambda d: d.set_index('mask_fname')['fake_score'].idxmin()).to_frame().rename({0: 'worst'}, axis=1)\n    best_samples_by_real = fake_info.groupby('real_fname').apply(\n        lambda d: d.set_index('mask_fname')['fake_score'].idxmax()).to_frame().rename({0: 'best'}, axis=1)\n    worst_best_by_real = pd.concat([worst_samples_by_real, best_samples_by_real], axis=1)\n\n    worst_best_by_real = worst_best_by_real.join(fake_scores_table.rename({'fake_score': 'worst_score'}, axis=1),\n                                                 on='worst')\n    worst_best_by_real = worst_best_by_real.join(fake_scores_table.rename({'fake_score': 'best_score'}, axis=1),\n                                                 on='best')\n    worst_best_by_real = worst_best_by_real.join(real_scores_table)\n\n    worst_best_by_real['best_worst_score_diff'] = worst_best_by_real['best_score'] - worst_best_by_real['worst_score']\n    worst_best_by_real['real_best_score_diff'] = worst_best_by_real['real_score'] - worst_best_by_real['best_score']\n    worst_best_by_real['real_worst_score_diff'] = worst_best_by_real['real_score'] - worst_best_by_real['worst_score']\n\n    worst_best_by_best_worst_score_diff_min = worst_best_by_real.sort_values('best_worst_score_diff', ascending=True).iloc[:config.take_worst_best_top]\n    worst_best_by_best_worst_score_diff_max = worst_best_by_real.sort_values('best_worst_score_diff', ascending=False).iloc[:config.take_worst_best_top]\n    save_samples_by_real(worst_best_by_best_worst_score_diff_min, mask2fake_fname, fake_info, worst_best_by_best_worst_score_diff_min_dir)\n    save_samples_by_real(worst_best_by_best_worst_score_diff_max, mask2fake_fname, fake_info, worst_best_by_best_worst_score_diff_max_dir)\n\n    worst_best_by_real_best_score_diff_min = worst_best_by_real.sort_values('real_best_score_diff', ascending=True).iloc[:config.take_worst_best_top]\n    worst_best_by_real_best_score_diff_max = worst_best_by_real.sort_values('real_best_score_diff', ascending=False).iloc[:config.take_worst_best_top]\n    save_samples_by_real(worst_best_by_real_best_score_diff_min, mask2fake_fname, fake_info, worst_best_by_real_best_score_diff_min_dir)\n    save_samples_by_real(worst_best_by_real_best_score_diff_max, mask2fake_fname, fake_info, worst_best_by_real_best_score_diff_max_dir)\n\n    worst_best_by_real_worst_score_diff_min = worst_best_by_real.sort_values('real_worst_score_diff', ascending=True).iloc[:config.take_worst_best_top]\n    worst_best_by_real_worst_score_diff_max = worst_best_by_real.sort_values('real_worst_score_diff', ascending=False).iloc[:config.take_worst_best_top]\n    save_samples_by_real(worst_best_by_real_worst_score_diff_min, mask2fake_fname, fake_info, worst_best_by_real_worst_score_diff_min_dir)\n    save_samples_by_real(worst_best_by_real_worst_score_diff_max, mask2fake_fname, fake_info, worst_best_by_real_worst_score_diff_max_dir)\n\n    # analyze what change of mask causes bigger change of score\n    overlapping_mask_fname_pairs = []\n    overlapping_mask_fname_score_diffs = []\n    for cur_real_fname in orig_fnames:\n        cur_fakes_info = fake_info[fake_info['real_fname'] == cur_real_fname]\n        cur_mask_fnames = sorted(cur_fakes_info['mask_fname'].unique())\n\n        cur_mask_pairs_and_scores = Parallel(args.n_jobs)(\n            delayed(extract_overlapping_masks)(cur_mask_fnames, i, fake_scores_table)\n            for i in range(len(cur_mask_fnames) - 1)\n        )\n        for cur_pairs, cur_scores in cur_mask_pairs_and_scores:\n            overlapping_mask_fname_pairs.extend(cur_pairs)\n            overlapping_mask_fname_score_diffs.extend(cur_scores)\n\n    overlapping_mask_fname_pairs = np.asarray(overlapping_mask_fname_pairs)\n    overlapping_mask_fname_score_diffs = np.asarray(overlapping_mask_fname_score_diffs)\n    overlapping_sort_idx = np.argsort(overlapping_mask_fname_score_diffs)\n    overlapping_mask_fname_pairs = overlapping_mask_fname_pairs[overlapping_sort_idx]\n    overlapping_mask_fname_score_diffs = overlapping_mask_fname_score_diffs[overlapping_sort_idx]\n\n\n\n\n\n\nif __name__ == '__main__':\n    import argparse\n\n    aparser = argparse.ArgumentParser()\n    aparser.add_argument('config', type=str, help='Path to config for dataset generation')\n    aparser.add_argument('datadir', type=str,\n                         help='Path to folder with images and masks (output of gen_mask_dataset.py)')\n    aparser.add_argument('predictdir', type=str,\n                         help='Path to folder with predicts (e.g. predict_hifill_baseline.py)')\n    aparser.add_argument('outpath', type=str, help='Where to put results')\n    aparser.add_argument('--only-report', action='store_true',\n                         help='Whether to skip prediction and feature extraction, '\n                              'load all the possible latents and proceed with report only')\n    aparser.add_argument('--n-jobs', type=int, default=8, help='how many processes to use for pair mask mining')\n\n    main(aparser.parse_args())\n"
  },
  {
    "path": "model_cards/lama/bin/blur_predicts.py",
    "content": "#!/usr/bin/env python3\n\nimport os\n\nimport cv2\nimport numpy as np\nimport tqdm\n\nfrom saicinpainting.evaluation.data import PrecomputedInpaintingResultsDataset\nfrom saicinpainting.evaluation.utils import load_yaml\n\n\ndef main(args):\n    config = load_yaml(args.config)\n\n    if not args.predictdir.endswith('/'):\n        args.predictdir += '/'\n\n    dataset = PrecomputedInpaintingResultsDataset(args.datadir, args.predictdir, **config.dataset_kwargs)\n\n    os.makedirs(os.path.dirname(args.outpath), exist_ok=True)\n\n    for img_i in tqdm.trange(len(dataset)):\n        pred_fname = dataset.pred_filenames[img_i]\n        cur_out_fname = os.path.join(args.outpath, pred_fname[len(args.predictdir):])\n        os.makedirs(os.path.dirname(cur_out_fname), exist_ok=True)\n\n        sample = dataset[img_i]\n        img = sample['image']\n        mask = sample['mask']\n        inpainted = sample['inpainted']\n\n        inpainted_blurred = cv2.GaussianBlur(np.transpose(inpainted, (1, 2, 0)),\n                                             ksize=(args.k, args.k),\n                                             sigmaX=args.s, sigmaY=args.s,\n                                             borderType=cv2.BORDER_REFLECT)\n\n        cur_res = (1 - mask) * np.transpose(img, (1, 2, 0)) + mask * inpainted_blurred\n        cur_res = np.clip(cur_res * 255, 0, 255).astype('uint8')\n        cur_res = cv2.cvtColor(cur_res, cv2.COLOR_RGB2BGR)\n        cv2.imwrite(cur_out_fname, cur_res)\n\n\nif __name__ == '__main__':\n    import argparse\n\n    aparser = argparse.ArgumentParser()\n    aparser.add_argument('config', type=str, help='Path to evaluation config')\n    aparser.add_argument('datadir', type=str,\n                         help='Path to folder with images and masks (output of gen_mask_dataset.py)')\n    aparser.add_argument('predictdir', type=str,\n                         help='Path to folder with predicts (e.g. predict_hifill_baseline.py)')\n    aparser.add_argument('outpath', type=str, help='Where to put results')\n    aparser.add_argument('-s', type=float, default=0.1, help='Gaussian blur sigma')\n    aparser.add_argument('-k', type=int, default=5, help='Kernel size in gaussian blur')\n\n    main(aparser.parse_args())\n"
  },
  {
    "path": "model_cards/lama/bin/calc_dataset_stats.py",
    "content": "#!/usr/bin/env python3\n\nimport os\n\nimport numpy as np\nimport tqdm\nfrom scipy.ndimage.morphology import distance_transform_edt\n\nfrom saicinpainting.evaluation.data import InpaintingDataset\nfrom saicinpainting.evaluation.vis import save_item_for_vis\n\n\ndef main(args):\n    dataset = InpaintingDataset(args.datadir, img_suffix='.png')\n\n    area_bins = np.linspace(0, 1, args.area_bins + 1)\n\n    heights = []\n    widths = []\n    image_areas = []\n    hole_areas = []\n    hole_area_percents = []\n    known_pixel_distances = []\n\n    area_bins_count = np.zeros(args.area_bins)\n    area_bin_titles = [f'{area_bins[i] * 100:.0f}-{area_bins[i + 1] * 100:.0f}' for i in range(args.area_bins)]\n\n    bin2i = [[] for _ in range(args.area_bins)]\n\n    for i, item in enumerate(tqdm.tqdm(dataset)):\n        h, w = item['image'].shape[1:]\n        heights.append(h)\n        widths.append(w)\n        full_area = h * w\n        image_areas.append(full_area)\n        bin_mask = item['mask'] > 0.5\n        hole_area = bin_mask.sum()\n        hole_areas.append(hole_area)\n        hole_percent = hole_area / full_area\n        hole_area_percents.append(hole_percent)\n        bin_i = np.clip(np.searchsorted(area_bins, hole_percent) - 1, 0, len(area_bins_count) - 1)\n        area_bins_count[bin_i] += 1\n        bin2i[bin_i].append(i)\n\n        cur_dist = distance_transform_edt(bin_mask)\n        cur_dist_inside_mask = cur_dist[bin_mask]\n        known_pixel_distances.append(cur_dist_inside_mask.mean())\n\n    os.makedirs(args.outdir, exist_ok=True)\n    with open(os.path.join(args.outdir, 'summary.txt'), 'w') as f:\n        f.write(f'''Location:          {args.datadir}\n\nNumber of samples: {len(dataset)}\n\nImage height: min {min(heights):5d} max {max(heights):5d} mean {np.mean(heights):.2f}\nImage width:  min {min(widths):5d} max {max(widths):5d} mean {np.mean(widths):.2f}\nImage area:   min {min(image_areas):7d} max {max(image_areas):7d} mean {np.mean(image_areas):.2f}\nHole area:    min {min(hole_areas):7d} max {max(hole_areas):7d} mean {np.mean(hole_areas):.2f}\nHole area %:  min {min(hole_area_percents) * 100:2.2f} max {max(hole_area_percents) * 100:2.2f} mean {np.mean(hole_area_percents) * 100:2.2f}\nDist 2known:  min {min(known_pixel_distances):2.2f} max {max(known_pixel_distances):2.2f} mean {np.mean(known_pixel_distances):2.2f} median {np.median(known_pixel_distances):2.2f}\n\nStats by hole area %:\n''')\n        for bin_i in range(args.area_bins):\n            f.write(f'{area_bin_titles[bin_i]}%: '\n                    f'samples number {area_bins_count[bin_i]}, '\n                    f'{area_bins_count[bin_i] / len(dataset) * 100:.1f}%\\n')\n\n    for bin_i in range(args.area_bins):\n        bindir = os.path.join(args.outdir, 'samples', area_bin_titles[bin_i])\n        os.makedirs(bindir, exist_ok=True)\n        bin_idx = bin2i[bin_i]\n        for sample_i in np.random.choice(bin_idx, size=min(len(bin_idx), args.samples_n), replace=False):\n            save_item_for_vis(dataset[sample_i], os.path.join(bindir, f'{sample_i}.png'))\n\n\nif __name__ == '__main__':\n    import argparse\n\n    aparser = argparse.ArgumentParser()\n    aparser.add_argument('datadir', type=str,\n                         help='Path to folder with images and masks (output of gen_mask_dataset.py)')\n    aparser.add_argument('outdir', type=str, help='Where to put results')\n    aparser.add_argument('--samples-n', type=int, default=10,\n                         help='Number of sample images with masks to copy for visualization for each area bin')\n    aparser.add_argument('--area-bins', type=int, default=10, help='How many area bins to have')\n\n    main(aparser.parse_args())\n"
  },
  {
    "path": "model_cards/lama/bin/debug/analyze_overlapping_masks.sh",
    "content": "#!/bin/bash\n\nBASEDIR=\"$(dirname $0)\"\n\n# paths are valid for mml7\n\n# select images\n#ls /data/inpainting/work/data/train | shuf | head -2000 | xargs -n1 -I{} cp {} /data/inpainting/mask_analysis/src\n\n# generate masks\n#\"$BASEDIR/../gen_debug_mask_dataset.py\" \\\n#    \"$BASEDIR/../../configs/debug_mask_gen.yaml\" \\\n#    \"/data/inpainting/mask_analysis/src\" \\\n#    \"/data/inpainting/mask_analysis/generated\"\n\n# predict\n#\"$BASEDIR/../predict.py\" \\\n#    model.path=\"simple_pix2pix2_gap_sdpl_novgg_large_b18_ffc075_batch8x15/saved_checkpoint/r.suvorov_2021-04-30_14-41-12_train_simple_pix2pix2_gap_sdpl_novgg_large_b18_ffc075_batch8x15_epoch22-step-574999\" \\\n#    indir=\"/data/inpainting/mask_analysis/generated\" \\\n#    outdir=\"/data/inpainting/mask_analysis/predicted\" \\\n#    dataset.img_suffix=.jpg \\\n#    +out_ext=.jpg\n\n# analyze good and bad samples\n\"$BASEDIR/../analyze_errors.py\" \\\n    --only-report \\\n    --n-jobs 8 \\\n    \"$BASEDIR/../../configs/analyze_mask_errors.yaml\" \\\n    \"/data/inpainting/mask_analysis/small/generated\" \\\n    \"/data/inpainting/mask_analysis/small/predicted\" \\\n    \"/data/inpainting/mask_analysis/small/report\"\n"
  },
  {
    "path": "model_cards/lama/bin/evaluate_predicts.py",
    "content": "#!/usr/bin/env python3\n\nimport os\n\nimport pandas as pd\n\nfrom saicinpainting.evaluation.data import PrecomputedInpaintingResultsDataset\nfrom saicinpainting.evaluation.evaluator import InpaintingEvaluator, lpips_fid100_f1\nfrom saicinpainting.evaluation.losses.base_loss import SegmentationAwareSSIM, \\\n    SegmentationClassStats, SSIMScore, LPIPSScore, FIDScore, SegmentationAwareLPIPS, SegmentationAwareFID\nfrom saicinpainting.evaluation.utils import load_yaml\n\n\ndef main(args):\n    config = load_yaml(args.config)\n\n    dataset = PrecomputedInpaintingResultsDataset(args.datadir, args.predictdir, **config.dataset_kwargs)\n\n    metrics = {\n        'ssim': SSIMScore(),\n        'lpips': LPIPSScore(),\n        'fid': FIDScore()\n    }\n    enable_segm = config.get('segmentation', dict(enable=False)).get('enable', False)\n    if enable_segm:\n        weights_path = os.path.expandvars(config.segmentation.weights_path)\n        metrics.update(dict(\n            segm_stats=SegmentationClassStats(weights_path=weights_path),\n            segm_ssim=SegmentationAwareSSIM(weights_path=weights_path),\n            segm_lpips=SegmentationAwareLPIPS(weights_path=weights_path),\n            segm_fid=SegmentationAwareFID(weights_path=weights_path)\n        ))\n    evaluator = InpaintingEvaluator(dataset, scores=metrics,\n                                    integral_title='lpips_fid100_f1', integral_func=lpips_fid100_f1,\n                                    **config.evaluator_kwargs)\n\n    os.makedirs(os.path.dirname(args.outpath), exist_ok=True)\n\n    results = evaluator.evaluate()\n\n    results = pd.DataFrame(results).stack(1).unstack(0)\n    results.dropna(axis=1, how='all', inplace=True)\n    results.to_csv(args.outpath, sep='\\t', float_format='%.4f')\n\n    if enable_segm:\n        only_short_results = results[[c for c in results.columns if not c[0].startswith('segm_')]].dropna(axis=1, how='all')\n        only_short_results.to_csv(args.outpath + '_short', sep='\\t', float_format='%.4f')\n\n        print(only_short_results)\n\n        segm_metrics_results = results[['segm_ssim', 'segm_lpips', 'segm_fid']].dropna(axis=1, how='all').transpose().unstack(0).reorder_levels([1, 0], axis=1)\n        segm_metrics_results.drop(['mean', 'std'], axis=0, inplace=True)\n\n        segm_stats_results = results['segm_stats'].dropna(axis=1, how='all').transpose()\n        segm_stats_results.index = pd.MultiIndex.from_tuples(n.split('/') for n in segm_stats_results.index)\n        segm_stats_results = segm_stats_results.unstack(0).reorder_levels([1, 0], axis=1)\n        segm_stats_results.sort_index(axis=1, inplace=True)\n        segm_stats_results.dropna(axis=0, how='all', inplace=True)\n\n        segm_results = pd.concat([segm_metrics_results, segm_stats_results], axis=1, sort=True)\n        segm_results.sort_values(('mask_freq', 'total'), ascending=False, inplace=True)\n\n        segm_results.to_csv(args.outpath + '_segm', sep='\\t', float_format='%.4f')\n    else:\n        print(results)\n\n\nif __name__ == '__main__':\n    import argparse\n\n    aparser = argparse.ArgumentParser()\n    aparser.add_argument('config', type=str, help='Path to evaluation config')\n    aparser.add_argument('datadir', type=str,\n                         help='Path to folder with images and masks (output of gen_mask_dataset.py)')\n    aparser.add_argument('predictdir', type=str,\n                         help='Path to folder with predicts (e.g. predict_hifill_baseline.py)')\n    aparser.add_argument('outpath', type=str, help='Where to put results')\n\n    main(aparser.parse_args())\n"
  },
  {
    "path": "model_cards/lama/bin/evaluator_example.py",
    "content": "import os\n\nimport cv2\nimport numpy as np\nimport torch\nfrom skimage import io\nfrom skimage.transform import resize\nfrom torch.utils.data import Dataset\n\nfrom saicinpainting.evaluation.evaluator import InpaintingEvaluator\nfrom saicinpainting.evaluation.losses.base_loss import SSIMScore, LPIPSScore, FIDScore\n\n\nclass SimpleImageDataset(Dataset):\n    def __init__(self, root_dir, image_size=(400, 600)):\n        self.root_dir = root_dir\n        self.files = sorted(os.listdir(root_dir))\n        self.image_size = image_size\n\n    def __getitem__(self, index):\n        img_name = os.path.join(self.root_dir, self.files[index])\n        image = io.imread(img_name)\n        image = resize(image, self.image_size, anti_aliasing=True)\n        image = torch.FloatTensor(image).permute(2, 0, 1)\n        return image\n\n    def __len__(self):\n        return len(self.files)\n\n\ndef create_rectangle_mask(height, width):\n    mask = np.ones((height, width))\n    up_left_corner = width // 4, height // 4\n    down_right_corner = (width - up_left_corner[0] - 1, height - up_left_corner[1] - 1)\n    cv2.rectangle(mask, up_left_corner, down_right_corner, (0, 0, 0), thickness=cv2.FILLED)\n    return mask\n\n\nclass Model():\n    def __call__(self, img_batch, mask_batch):\n        mean = (img_batch * mask_batch[:, None, :, :]).sum(dim=(2, 3)) / mask_batch.sum(dim=(1, 2))[:, None]\n        inpainted = mean[:, :, None, None] * (1 - mask_batch[:, None, :, :]) + img_batch * mask_batch[:, None, :, :]\n        return inpainted\n\n\nclass SimpleImageSquareMaskDataset(Dataset):\n    def __init__(self, dataset):\n        self.dataset = dataset\n        self.mask = torch.FloatTensor(create_rectangle_mask(*self.dataset.image_size))\n        self.model = Model()\n\n    def __getitem__(self, index):\n        img = self.dataset[index]\n        mask = self.mask.clone()\n        inpainted = self.model(img[None, ...], mask[None, ...])\n        return dict(image=img, mask=mask, inpainted=inpainted)\n\n    def __len__(self):\n        return len(self.dataset)\n\n\ndataset = SimpleImageDataset('imgs')\nmask_dataset = SimpleImageSquareMaskDataset(dataset)\nmodel = Model()\nmetrics = {\n    'ssim': SSIMScore(),\n    'lpips': LPIPSScore(),\n    'fid': FIDScore()\n}\n\nevaluator = InpaintingEvaluator(\n    mask_dataset, scores=metrics, batch_size=3, area_grouping=True\n)\n\nresults = evaluator.evaluate(model)\nprint(results)\n"
  },
  {
    "path": "model_cards/lama/bin/extract_masks.py",
    "content": "import PIL.Image as Image\nimport numpy as np\nimport os\n\n\ndef main(args):\n    if not args.indir.endswith('/'):\n        args.indir += '/'\n    os.makedirs(args.outdir, exist_ok=True)\n\n    src_images = [\n        args.indir+fname for fname in  os.listdir(args.indir)]\n\n    tgt_masks = [\n        args.outdir+fname[:-4] + f'_mask000.png' \n            for fname in  os.listdir(args.indir)]\n\n    for img_name, msk_name in zip(src_images, tgt_masks):\n        #print(img)\n        #print(msk)\n\n        image = Image.open(img_name).convert('RGB')\n        image = np.transpose(np.array(image), (2, 0, 1))\n\n        mask = (image == 255).astype(int)\n\n        print(mask.dtype, mask.shape)\n\n\n        Image.fromarray(\n            np.clip(mask[0,:,:] * 255, 0, 255).astype('uint8'),mode='L'\n        ).save(msk_name)\n\n\n\n\n    '''\n    for infile in src_images:\n        try:\n            file_relpath = infile[len(indir):]\n            img_outpath = os.path.join(outdir, file_relpath)\n            os.makedirs(os.path.dirname(img_outpath), exist_ok=True)\n\n            image = Image.open(infile).convert('RGB')\n\n            mask = \n\n            Image.fromarray(\n                np.clip(\n                    cur_mask * 255, 0, 255).astype('uint8'),\n                    mode='L'\n                ).save(cur_basename + f'_mask{i:03d}.png')\n    '''\n\n\n\nif __name__ == '__main__':\n    import argparse\n    aparser = argparse.ArgumentParser()\n    aparser.add_argument('--indir', type=str, help='Path to folder with images')\n    aparser.add_argument('--outdir', type=str, help='Path to folder to store aligned images and masks to')\n\n    main(aparser.parse_args())\n"
  },
  {
    "path": "model_cards/lama/bin/filter_sharded_dataset.py",
    "content": "#!/usr/bin/env python3\n\n\nimport math\nimport os\nimport random\n\nimport braceexpand\nimport webdataset as wds\n\nDEFAULT_CATS_FILE = os.path.join(os.path.dirname(__file__), '..', 'configs', 'places2-categories_157.txt')\n\ndef is_good_key(key, cats):\n    return any(c in key for c in cats)\n\n\ndef main(args):\n    if args.categories == 'nofilter':\n        good_categories = None\n    else:\n        with open(args.categories, 'r') as f:\n            good_categories = set(line.strip().split(' ')[0] for line in f if line.strip())\n\n    all_input_files = list(braceexpand.braceexpand(args.infile))\n    chunk_size = int(math.ceil(len(all_input_files) / args.n_read_streams))\n\n    input_iterators = [iter(wds.Dataset(all_input_files[start : start + chunk_size]).shuffle(args.shuffle_buffer))\n                       for start in range(0, len(all_input_files), chunk_size)]\n    output_datasets = [wds.ShardWriter(args.outpattern.format(i)) for i in range(args.n_write_streams)]\n\n    good_readers = list(range(len(input_iterators)))\n    step_i = 0\n    good_samples = 0\n    bad_samples = 0\n    while len(good_readers) > 0:\n        if step_i % args.print_freq == 0:\n            print(f'Iterations done {step_i}; readers alive {good_readers}; good samples {good_samples}; bad samples {bad_samples}')\n\n        step_i += 1\n\n        ri = random.choice(good_readers)\n        try:\n            sample = next(input_iterators[ri])\n        except StopIteration:\n            good_readers = list(set(good_readers) - {ri})\n            continue\n\n        if good_categories is not None and not is_good_key(sample['__key__'], good_categories):\n            bad_samples += 1\n            continue\n\n        wi = random.randint(0, args.n_write_streams - 1)\n        output_datasets[wi].write(sample)\n        good_samples += 1\n\n\nif __name__ == '__main__':\n    import argparse\n\n    aparser = argparse.ArgumentParser()\n    aparser.add_argument('--categories', type=str, default=DEFAULT_CATS_FILE)\n    aparser.add_argument('--shuffle-buffer', type=int, default=10000)\n    aparser.add_argument('--n-read-streams', type=int, default=10)\n    aparser.add_argument('--n-write-streams', type=int, default=10)\n    aparser.add_argument('--print-freq', type=int, default=1000)\n    aparser.add_argument('infile', type=str)\n    aparser.add_argument('outpattern', type=str)\n\n    main(aparser.parse_args())\n"
  },
  {
    "path": "model_cards/lama/bin/gen_debug_mask_dataset.py",
    "content": "#!/usr/bin/env python3\n\nimport glob\nimport os\n\nimport PIL.Image as Image\nimport cv2\nimport numpy as np\nimport tqdm\nimport shutil\n\n\nfrom saicinpainting.evaluation.utils import load_yaml\n\n\ndef generate_masks_for_img(infile, outmask_pattern, mask_size=200, step=0.5):\n    inimg = Image.open(infile)\n    width, height = inimg.size\n    step_abs = int(mask_size * step)\n\n    mask = np.zeros((height, width), dtype='uint8')\n    mask_i = 0\n\n    for start_vertical in range(0, height - step_abs, step_abs):\n        for start_horizontal in range(0, width - step_abs, step_abs):\n            mask[start_vertical:start_vertical + mask_size, start_horizontal:start_horizontal + mask_size] = 255\n\n            cv2.imwrite(outmask_pattern.format(mask_i), mask)\n\n            mask[start_vertical:start_vertical + mask_size, start_horizontal:start_horizontal + mask_size] = 0\n            mask_i += 1\n\n\ndef main(args):\n    if not args.indir.endswith('/'):\n        args.indir += '/'\n    if not args.outdir.endswith('/'):\n        args.outdir += '/'\n\n    config = load_yaml(args.config)\n\n    in_files = list(glob.glob(os.path.join(args.indir, '**', f'*{config.img_ext}'), recursive=True))\n    for infile in tqdm.tqdm(in_files):\n        outimg = args.outdir + infile[len(args.indir):]\n        outmask_pattern = outimg[:-len(config.img_ext)] + '_mask{:04d}.png'\n\n        os.makedirs(os.path.dirname(outimg), exist_ok=True)\n        shutil.copy2(infile, outimg)\n\n        generate_masks_for_img(infile, outmask_pattern, **config.gen_kwargs)\n\n\nif __name__ == '__main__':\n    import argparse\n\n    aparser = argparse.ArgumentParser()\n    aparser.add_argument('config', type=str, help='Path to config for dataset generation')\n    aparser.add_argument('indir', type=str, help='Path to folder with images')\n    aparser.add_argument('outdir', type=str, help='Path to folder to store aligned images and masks to')\n\n    main(aparser.parse_args())\n"
  },
  {
    "path": "model_cards/lama/bin/gen_mask_dataset.py",
    "content": "#!/usr/bin/env python3\n\nimport glob\nimport os\nimport shutil\nimport traceback\n\nimport PIL.Image as Image\nimport numpy as np\nfrom joblib import Parallel, delayed\n\nfrom saicinpainting.evaluation.masks.mask import SegmentationMask, propose_random_square_crop\nfrom saicinpainting.evaluation.utils import load_yaml, SmallMode\nfrom saicinpainting.training.data.masks import MixedMaskGenerator\n\n\nclass MakeManyMasksWrapper:\n    def __init__(self, impl, variants_n=2):\n        self.impl = impl\n        self.variants_n = variants_n\n\n    def get_masks(self, img):\n        img = np.transpose(np.array(img), (2, 0, 1))\n        return [self.impl(img)[0] for _ in range(self.variants_n)]\n\n\ndef process_images(src_images, indir, outdir, config):\n    if config.generator_kind == 'segmentation':\n        mask_generator = SegmentationMask(**config.mask_generator_kwargs)\n    elif config.generator_kind == 'random':\n        variants_n = config.mask_generator_kwargs.pop('variants_n', 2)\n        mask_generator = MakeManyMasksWrapper(MixedMaskGenerator(**config.mask_generator_kwargs),\n                                              variants_n=variants_n)\n    else:\n        raise ValueError(f'Unexpected generator kind: {config.generator_kind}')\n\n    max_tamper_area = config.get('max_tamper_area', 1)\n\n    for infile in src_images:\n        try:\n            file_relpath = infile[len(indir):]\n            img_outpath = os.path.join(outdir, file_relpath)\n            os.makedirs(os.path.dirname(img_outpath), exist_ok=True)\n\n            image = Image.open(infile).convert('RGB')\n\n            # scale input image to output resolution and filter smaller images\n            if min(image.size) < config.cropping.out_min_size:\n                handle_small_mode = SmallMode(config.cropping.handle_small_mode)\n                if handle_small_mode == SmallMode.DROP:\n                    continue\n                elif handle_small_mode == SmallMode.UPSCALE:\n                    factor = config.cropping.out_min_size / min(image.size)\n                    out_size = (np.array(image.size) * factor).round().astype('uint32')\n                    image = image.resize(out_size, resample=Image.BICUBIC)\n            else:\n                factor = config.cropping.out_min_size / min(image.size)\n                out_size = (np.array(image.size) * factor).round().astype('uint32')\n                image = image.resize(out_size, resample=Image.BICUBIC)\n\n            # generate and select masks\n            src_masks = mask_generator.get_masks(image)\n\n            filtered_image_mask_pairs = []\n            for cur_mask in src_masks:\n                if config.cropping.out_square_crop:\n                    (crop_left,\n                     crop_top,\n                     crop_right,\n                     crop_bottom) = propose_random_square_crop(cur_mask,\n                                                               min_overlap=config.cropping.crop_min_overlap)\n                    cur_mask = cur_mask[crop_top:crop_bottom, crop_left:crop_right]\n                    cur_image = image.copy().crop((crop_left, crop_top, crop_right, crop_bottom))\n                else:\n                    cur_image = image\n\n                if len(np.unique(cur_mask)) == 0 or cur_mask.mean() > max_tamper_area:\n                    continue\n\n                filtered_image_mask_pairs.append((cur_image, cur_mask))\n\n            mask_indices = np.random.choice(len(filtered_image_mask_pairs),\n                                            size=min(len(filtered_image_mask_pairs), config.max_masks_per_image),\n                                            replace=False)\n\n            # crop masks; save masks together with input image\n            mask_basename = os.path.join(outdir, os.path.splitext(file_relpath)[0])\n            for i, idx in enumerate(mask_indices):\n                cur_image, cur_mask = filtered_image_mask_pairs[idx]\n                cur_basename = mask_basename + f'_crop{i:03d}'\n                Image.fromarray(np.clip(cur_mask * 255, 0, 255).astype('uint8'),\n                                mode='L').save(cur_basename + f'_mask{i:03d}.png')\n                cur_image.save(cur_basename + '.png')\n        except KeyboardInterrupt:\n            return\n        except Exception as ex:\n            print(f'Could not make masks for {infile} due to {ex}:\\n{traceback.format_exc()}')\n\n\ndef main(args):\n    if not args.indir.endswith('/'):\n        args.indir += '/'\n\n    os.makedirs(args.outdir, exist_ok=True)\n\n    config = load_yaml(args.config)\n\n    in_files = list(glob.glob(os.path.join(args.indir, '**', f'*.{args.ext}'), recursive=True))\n    if args.n_jobs == 0:\n        process_images(in_files, args.indir, args.outdir, config)\n    else:\n        in_files_n = len(in_files)\n        chunk_size = in_files_n // args.n_jobs + (1 if in_files_n % args.n_jobs > 0 else 0)\n        Parallel(n_jobs=args.n_jobs)(\n            delayed(process_images)(in_files[start:start+chunk_size], args.indir, args.outdir, config)\n            for start in range(0, len(in_files), chunk_size)\n        )\n\n\nif __name__ == '__main__':\n    import argparse\n\n    aparser = argparse.ArgumentParser()\n    aparser.add_argument('config', type=str, help='Path to config for dataset generation')\n    aparser.add_argument('indir', type=str, help='Path to folder with images')\n    aparser.add_argument('outdir', type=str, help='Path to folder to store aligned images and masks to')\n    aparser.add_argument('--n-jobs', type=int, default=0, help='How many processes to use')\n    aparser.add_argument('--ext', type=str, default='jpg', help='Input image extension')\n\n    main(aparser.parse_args())\n"
  },
  {
    "path": "model_cards/lama/bin/gen_mask_dataset_hydra.py",
    "content": "#!/usr/bin/env python3\n\nimport glob\nimport os\nimport shutil\nimport traceback\nimport hydra\nfrom omegaconf import OmegaConf\n\nimport PIL.Image as Image\nimport numpy as np\nfrom joblib import Parallel, delayed\n\nfrom saicinpainting.evaluation.masks.mask import SegmentationMask, propose_random_square_crop\nfrom saicinpainting.evaluation.utils import load_yaml, SmallMode\nfrom saicinpainting.training.data.masks import MixedMaskGenerator\n\n\nclass MakeManyMasksWrapper:\n    def __init__(self, impl, variants_n=2):\n        self.impl = impl\n        self.variants_n = variants_n\n\n    def get_masks(self, img):\n        img = np.transpose(np.array(img), (2, 0, 1))\n        return [self.impl(img)[0] for _ in range(self.variants_n)]\n\n\ndef process_images(src_images, indir, outdir, config):\n    if config.generator_kind == 'segmentation':\n        mask_generator = SegmentationMask(**config.mask_generator_kwargs)\n    elif config.generator_kind == 'random':\n        mask_generator_kwargs = OmegaConf.to_container(config.mask_generator_kwargs, resolve=True)\n        variants_n = mask_generator_kwargs.pop('variants_n', 2)\n        mask_generator = MakeManyMasksWrapper(MixedMaskGenerator(**mask_generator_kwargs),\n                                              variants_n=variants_n)\n    else:\n        raise ValueError(f'Unexpected generator kind: {config.generator_kind}')\n\n    max_tamper_area = config.get('max_tamper_area', 1)\n\n    for infile in src_images:\n        try:\n            file_relpath = infile[len(indir):]\n            img_outpath = os.path.join(outdir, file_relpath)\n            os.makedirs(os.path.dirname(img_outpath), exist_ok=True)\n\n            image = Image.open(infile).convert('RGB')\n\n            # scale input image to output resolution and filter smaller images\n            if min(image.size) < config.cropping.out_min_size:\n                handle_small_mode = SmallMode(config.cropping.handle_small_mode)\n                if handle_small_mode == SmallMode.DROP:\n                    continue\n                elif handle_small_mode == SmallMode.UPSCALE:\n                    factor = config.cropping.out_min_size / min(image.size)\n                    out_size = (np.array(image.size) * factor).round().astype('uint32')\n                    image = image.resize(out_size, resample=Image.BICUBIC)\n            else:\n                factor = config.cropping.out_min_size / min(image.size)\n                out_size = (np.array(image.size) * factor).round().astype('uint32')\n                image = image.resize(out_size, resample=Image.BICUBIC)\n\n            # generate and select masks\n            src_masks = mask_generator.get_masks(image)\n\n            filtered_image_mask_pairs = []\n            for cur_mask in src_masks:\n                if config.cropping.out_square_crop:\n                    (crop_left,\n                     crop_top,\n                     crop_right,\n                     crop_bottom) = propose_random_square_crop(cur_mask,\n                                                               min_overlap=config.cropping.crop_min_overlap)\n                    cur_mask = cur_mask[crop_top:crop_bottom, crop_left:crop_right]\n                    cur_image = image.copy().crop((crop_left, crop_top, crop_right, crop_bottom))\n                else:\n                    cur_image = image\n\n                if len(np.unique(cur_mask)) == 0 or cur_mask.mean() > max_tamper_area:\n                    continue\n\n                filtered_image_mask_pairs.append((cur_image, cur_mask))\n\n            mask_indices = np.random.choice(len(filtered_image_mask_pairs),\n                                            size=min(len(filtered_image_mask_pairs), config.max_masks_per_image),\n                                            replace=False)\n\n            # crop masks; save masks together with input image\n            mask_basename = os.path.join(outdir, os.path.splitext(file_relpath)[0])\n            for i, idx in enumerate(mask_indices):\n                cur_image, cur_mask = filtered_image_mask_pairs[idx]\n                cur_basename = mask_basename + f'_crop{i:03d}'\n                Image.fromarray(np.clip(cur_mask * 255, 0, 255).astype('uint8'),\n                                mode='L').save(cur_basename + f'_mask{i:03d}.png')\n                cur_image.save(cur_basename + '.png')\n        except KeyboardInterrupt:\n            return\n        except Exception as ex:\n            print(f'Could not make masks for {infile} due to {ex}:\\n{traceback.format_exc()}')\n\n\n@hydra.main(config_path='../configs/data_gen/whydra', config_name='random_medium_256.yaml')\ndef main(config: OmegaConf):\n    if not config.indir.endswith('/'):\n        config.indir += '/'\n\n    os.makedirs(config.outdir, exist_ok=True)\n\n    in_files = list(glob.glob(os.path.join(config.indir, '**', f'*.{config.location.extension}'),\n                              recursive=True))\n    if config.n_jobs == 0:\n        process_images(in_files, config.indir, config.outdir, config)\n    else:\n        in_files_n = len(in_files)\n        chunk_size = in_files_n // config.n_jobs + (1 if in_files_n % config.n_jobs > 0 else 0)\n        Parallel(n_jobs=config.n_jobs)(\n            delayed(process_images)(in_files[start:start+chunk_size], config.indir, config.outdir, config)\n            for start in range(0, len(in_files), chunk_size)\n        )\n\n\nif __name__ == '__main__':\n    main()\n"
  },
  {
    "path": "model_cards/lama/bin/gen_outpainting_dataset.py",
    "content": "#!/usr/bin/env python3\nimport glob\nimport logging\nimport os\nimport shutil\nimport sys\nimport traceback\n\nfrom saicinpainting.evaluation.data import load_image\nfrom saicinpainting.evaluation.utils import move_to_device\n\nos.environ['OMP_NUM_THREADS'] = '1'\nos.environ['OPENBLAS_NUM_THREADS'] = '1'\nos.environ['MKL_NUM_THREADS'] = '1'\nos.environ['VECLIB_MAXIMUM_THREADS'] = '1'\nos.environ['NUMEXPR_NUM_THREADS'] = '1'\n\nimport cv2\nimport hydra\nimport numpy as np\nimport torch\nimport tqdm\nimport yaml\nfrom omegaconf import OmegaConf\nfrom torch.utils.data._utils.collate import default_collate\n\nfrom saicinpainting.training.data.datasets import make_default_val_dataset\nfrom saicinpainting.training.trainers import load_checkpoint\nfrom saicinpainting.utils import register_debug_signal_handlers\n\nLOGGER = logging.getLogger(__name__)\n\n\ndef main(args):\n    try:\n        if not args.indir.endswith('/'):\n            args.indir += '/'\n\n        for in_img in glob.glob(os.path.join(args.indir, '**', '*' + args.img_suffix), recursive=True):\n            if 'mask' in os.path.basename(in_img):\n                continue\n\n            out_img_path = os.path.join(args.outdir, os.path.splitext(in_img[len(args.indir):])[0] + '.png')\n            out_mask_path = f'{os.path.splitext(out_img_path)[0]}_mask.png'\n\n            os.makedirs(os.path.dirname(out_img_path), exist_ok=True)\n\n            img = load_image(in_img)\n            height, width = img.shape[1:]\n            pad_h, pad_w = int(height * args.coef / 2), int(width * args.coef / 2)\n\n            mask = np.zeros((height, width), dtype='uint8')\n\n            if args.expand:\n                img = np.pad(img, ((0, 0), (pad_h, pad_h), (pad_w, pad_w)))\n                mask = np.pad(mask, ((pad_h, pad_h), (pad_w, pad_w)), mode='constant', constant_values=255)\n            else:\n                mask[:pad_h] = 255\n                mask[-pad_h:] = 255\n                mask[:, :pad_w] = 255\n                mask[:, -pad_w:] = 255\n\n            # img = np.pad(img, ((0, 0), (pad_h * 2, pad_h * 2), (pad_w * 2, pad_w * 2)), mode='symmetric')\n            # mask = np.pad(mask, ((pad_h * 2, pad_h * 2), (pad_w * 2, pad_w * 2)), mode = 'symmetric')\n\n            img = np.clip(np.transpose(img, (1, 2, 0)) * 255, 0, 255).astype('uint8')\n            img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)\n            cv2.imwrite(out_img_path, img)\n\n            cv2.imwrite(out_mask_path, mask)\n    except KeyboardInterrupt:\n        LOGGER.warning('Interrupted by user')\n    except Exception as ex:\n        LOGGER.critical(f'Prediction failed due to {ex}:\\n{traceback.format_exc()}')\n        sys.exit(1)\n\n\nif __name__ == '__main__':\n    import argparse\n\n    aparser = argparse.ArgumentParser()\n    aparser.add_argument('indir', type=str, help='Root directory with images')\n    aparser.add_argument('outdir', type=str, help='Where to store results')\n    aparser.add_argument('--img-suffix', type=str, default='.png', help='Input image extension')\n    aparser.add_argument('--expand', action='store_true', help='Generate mask by padding (true) or by cropping (false)')\n    aparser.add_argument('--coef', type=float, default=0.2, help='How much to crop/expand in order to get masks')\n\n    main(aparser.parse_args())\n"
  },
  {
    "path": "model_cards/lama/bin/make_checkpoint.py",
    "content": "#!/usr/bin/env python3\n\nimport os\nimport shutil\n\nimport torch\n\n\ndef get_checkpoint_files(s):\n    s = s.strip()\n    if ',' in s:\n        return [get_checkpoint_files(chunk) for chunk in s.split(',')]\n    return 'last.ckpt' if s == 'last' else f'{s}.ckpt'\n\n\ndef main(args):\n    checkpoint_fnames = get_checkpoint_files(args.epochs)\n    if isinstance(checkpoint_fnames, str):\n        checkpoint_fnames = [checkpoint_fnames]\n    assert len(checkpoint_fnames) >= 1\n\n    checkpoint_path = os.path.join(args.indir, 'models', checkpoint_fnames[0])\n    checkpoint = torch.load(checkpoint_path, map_location='cpu')\n    del checkpoint['optimizer_states']\n\n    if len(checkpoint_fnames) > 1:\n        for fname in checkpoint_fnames[1:]:\n            print('sum', fname)\n            sum_tensors_cnt = 0\n            other_cp = torch.load(os.path.join(args.indir, 'models', fname), map_location='cpu')\n            for k in checkpoint['state_dict'].keys():\n                if checkpoint['state_dict'][k].dtype is torch.float:\n                    checkpoint['state_dict'][k].data.add_(other_cp['state_dict'][k].data)\n                    sum_tensors_cnt += 1\n            print('summed', sum_tensors_cnt, 'tensors')\n\n        for k in checkpoint['state_dict'].keys():\n            if checkpoint['state_dict'][k].dtype is torch.float:\n                checkpoint['state_dict'][k].data.mul_(1 / float(len(checkpoint_fnames)))\n\n    state_dict = checkpoint['state_dict']\n\n    if not args.leave_discriminators:\n        for k in list(state_dict.keys()):\n            if k.startswith('discriminator.'):\n                del state_dict[k]\n\n    if not args.leave_losses:\n        for k in list(state_dict.keys()):\n            if k.startswith('loss_'):\n                del state_dict[k]\n\n    out_checkpoint_path = os.path.join(args.outdir, 'models', 'best.ckpt')\n    os.makedirs(os.path.dirname(out_checkpoint_path), exist_ok=True)\n\n    torch.save(checkpoint, out_checkpoint_path)\n\n    shutil.copy2(os.path.join(args.indir, 'config.yaml'),\n                 os.path.join(args.outdir, 'config.yaml'))\n\n\nif __name__ == '__main__':\n    import argparse\n\n    aparser = argparse.ArgumentParser()\n    aparser.add_argument('indir',\n                         help='Path to directory with output of training '\n                              '(i.e. directory, which has samples, modules, config.yaml and train.log')\n    aparser.add_argument('outdir',\n                         help='Where to put minimal checkpoint, which can be consumed by \"bin/predict.py\"')\n    aparser.add_argument('--epochs', type=str, default='last',\n                         help='Which checkpoint to take. '\n                              'Can be \"last\" or integer - number of epoch')\n    aparser.add_argument('--leave-discriminators', action='store_true',\n                         help='If enabled, the state of discriminators will not be removed from the checkpoint')\n    aparser.add_argument('--leave-losses', action='store_true',\n                         help='If enabled, weights of nn-based losses (e.g. perceptual) will not be removed')\n\n    main(aparser.parse_args())\n"
  },
  {
    "path": "model_cards/lama/bin/mask_example.py",
    "content": "import matplotlib.pyplot as plt\nfrom skimage import io\nfrom skimage.transform import resize\n\nfrom saicinpainting.evaluation.masks.mask import SegmentationMask\n\nim = io.imread('imgs/ex4.jpg')\nim = resize(im, (512, 1024), anti_aliasing=True)\nmask_seg = SegmentationMask(num_variants_per_mask=10)\nmask_examples = mask_seg.get_masks(im)\nfor i, example in enumerate(mask_examples):\n    plt.imshow(example)\n    plt.show()\n    plt.imsave(f'tmp/img_masks/{i}.png', example)\n"
  },
  {
    "path": "model_cards/lama/bin/paper_runfiles/blur_tests.sh",
    "content": "##!/usr/bin/env bash\n#\n## !!! file set to make test_large_30k from the vanilla test_large: configs/test_large_30k.lst\n#\n## paths to data are valid for mml7\n#PLACES_ROOT=\"/data/inpainting/Places365\"\n#OUT_DIR=\"/data/inpainting/paper_data/Places365_val_test\"\n#\n#source \"$(dirname $0)/env.sh\"\n#\n#for datadir in test_large_30k  # val_large\n#do\n#    for conf in random_thin_256 random_medium_256 random_thick_256 random_thin_512 random_medium_512 random_thick_512\n#    do\n#        \"$BINDIR/gen_mask_dataset.py\" \"$CONFIGDIR/data_gen/${conf}.yaml\" \\\n#            \"$PLACES_ROOT/$datadir\" \"$OUT_DIR/$datadir/$conf\" --n-jobs 8\n#\n#        \"$BINDIR/calc_dataset_stats.py\" --samples-n 20 \"$OUT_DIR/$datadir/$conf\" \"$OUT_DIR/$datadir/${conf}_stats\"\n#    done\n#\n#    for conf in segm_256 segm_512\n#    do\n#        \"$BINDIR/gen_mask_dataset.py\" \"$CONFIGDIR/data_gen/${conf}.yaml\" \\\n#            \"$PLACES_ROOT/$datadir\" \"$OUT_DIR/$datadir/$conf\" --n-jobs 2\n#\n#        \"$BINDIR/calc_dataset_stats.py\" --samples-n 20 \"$OUT_DIR/$datadir/$conf\" \"$OUT_DIR/$datadir/${conf}_stats\"\n#    done\n#done\n#\n#IN_DIR=\"/data/inpainting/paper_data/Places365_val_test/test_large_30k/random_medium_512\"\n#PRED_DIR=\"/data/inpainting/predictions/final/images/r.suvorov_2021-03-05_17-08-35_train_ablv2_work_resume_epoch37/random_medium_512\"\n#BLUR_OUT_DIR=\"/data/inpainting/predictions/final/blur/images\"\n#\n#for b in 0.1\n#\n#\"$BINDIR/blur_predicts.py\" \"$BASEDIR/../../configs/eval2.yaml\" \"$CUR_IN_DIR\" \"$CUR_OUT_DIR\" \"$CUR_EVAL_DIR\"\n#\n"
  },
  {
    "path": "model_cards/lama/bin/paper_runfiles/env.sh",
    "content": "DIRNAME=\"$(dirname $0)\"\nDIRNAME=\"$(realpath \"\"$DIRNAME\"\")\"\n\nBINDIR=\"$DIRNAME/..\"\nSRCDIR=\"$BINDIR/..\"\nCONFIGDIR=\"$SRCDIR/configs\"\n\nexport PYTHONPATH=\"$SRCDIR:$PYTHONPATH\"\n"
  },
  {
    "path": "model_cards/lama/bin/paper_runfiles/find_best_checkpoint.py",
    "content": "#!/usr/bin/env python3\n\n\nimport os\nfrom argparse import ArgumentParser\n\n\ndef ssim_fid100_f1(metrics, fid_scale=100):\n    ssim = metrics.loc['total', 'ssim']['mean']\n    fid = metrics.loc['total', 'fid']['mean']\n    fid_rel = max(0, fid_scale - fid) / fid_scale\n    f1 = 2 * ssim * fid_rel / (ssim + fid_rel + 1e-3)\n    return f1\n\n\ndef find_best_checkpoint(model_list, models_dir):\n    with open(model_list) as f:\n        models = [m.strip() for m in f.readlines()]\n    with open(f'{model_list}_best', 'w') as f:\n        for model in models:\n            print(model)\n            best_f1 = 0\n            best_epoch = 0\n            best_step = 0\n            with open(os.path.join(models_dir, model, 'train.log')) as fm:\n                lines = fm.readlines()\n                for line_index in range(len(lines)):\n                    line = lines[line_index]\n                    if 'Validation metrics after epoch' in line:\n                        sharp_index = line.index('#')\n                        cur_ep = line[sharp_index + 1:]\n                        comma_index = cur_ep.index(',')\n                        cur_ep = int(cur_ep[:comma_index])\n                        total_index = line.index('total ')\n                        step = int(line[total_index:].split()[1].strip())\n                        total_line = lines[line_index + 5]\n                        if not total_line.startswith('total'):\n                            continue\n                        words = total_line.strip().split()\n                        f1 = float(words[-1])\n                        print(f'\\tEpoch: {cur_ep}, f1={f1}')\n                        if f1 > best_f1:\n                            best_f1 = f1\n                            best_epoch = cur_ep\n                            best_step = step\n            f.write(f'{model}\\t{best_epoch}\\t{best_step}\\t{best_f1}\\n')\n\n\nif __name__ == '__main__':\n    parser = ArgumentParser()\n    parser.add_argument('model_list')\n    parser.add_argument('models_dir')\n    args = parser.parse_args()\n    find_best_checkpoint(args.model_list, args.models_dir)\n"
  },
  {
    "path": "model_cards/lama/bin/paper_runfiles/generate_test_celeba-hq.sh",
    "content": "#!/usr/bin/env bash\n\n# paths to data are valid for mml-ws01\nOUT_DIR=\"/media/inpainting/paper_data/CelebA-HQ_val_test\"\n\nsource \"$(dirname $0)/env.sh\"\n\nfor datadir in \"val\" \"test\"\ndo\n    for conf in random_thin_256 random_medium_256 random_thick_256 random_thin_512 random_medium_512 random_thick_512\n    do\n        \"$BINDIR/gen_mask_dataset_hydra.py\" -cn $conf datadir=$datadir location=mml-ws01-celeba-hq \\\n         location.out_dir=$OUT_DIR cropping.out_square_crop=False\n\n        \"$BINDIR/calc_dataset_stats.py\" --samples-n 20 \"$OUT_DIR/$datadir/$conf\" \"$OUT_DIR/$datadir/${conf}_stats\"\n    done\ndone\n"
  },
  {
    "path": "model_cards/lama/bin/paper_runfiles/generate_test_ffhq.sh",
    "content": "#!/usr/bin/env bash\n\n# paths to data are valid for mml-ws01\nOUT_DIR=\"/media/inpainting/paper_data/FFHQ_val\"\n\nsource \"$(dirname $0)/env.sh\"\n\nfor datadir in test\ndo\n    for conf in random_thin_256 random_medium_256 random_thick_256 random_thin_512 random_medium_512 random_thick_512\n    do\n        \"$BINDIR/gen_mask_dataset_hydra.py\" -cn $conf datadir=$datadir location=mml-ws01-ffhq \\\n         location.out_dir=$OUT_DIR cropping.out_square_crop=False\n\n        \"$BINDIR/calc_dataset_stats.py\" --samples-n 20 \"$OUT_DIR/$datadir/$conf\" \"$OUT_DIR/$datadir/${conf}_stats\"\n    done\ndone\n"
  },
  {
    "path": "model_cards/lama/bin/paper_runfiles/generate_test_paris.sh",
    "content": "#!/usr/bin/env bash\n\n# paths to data are valid for mml-ws01\nOUT_DIR=\"/media/inpainting/paper_data/Paris_StreetView_Dataset_val\"\n\nsource \"$(dirname $0)/env.sh\"\n\nfor datadir in paris_eval_gt\ndo\n    for conf in random_thin_256 random_medium_256 random_thick_256 segm_256\n    do\n        \"$BINDIR/gen_mask_dataset_hydra.py\" -cn $conf datadir=$datadir location=mml-ws01-paris \\\n         location.out_dir=OUT_DIR cropping.out_square_crop=False cropping.out_min_size=227\n\n        \"$BINDIR/calc_dataset_stats.py\" --samples-n 20 \"$OUT_DIR/$datadir/$conf\" \"$OUT_DIR/$datadir/${conf}_stats\"\n    done\ndone\n"
  },
  {
    "path": "model_cards/lama/bin/paper_runfiles/generate_test_paris_256.sh",
    "content": "#!/usr/bin/env bash\n\n# paths to data are valid for mml-ws01\nOUT_DIR=\"/media/inpainting/paper_data/Paris_StreetView_Dataset_val_256\"\n\nsource \"$(dirname $0)/env.sh\"\n\nfor datadir in paris_eval_gt\ndo\n    for conf in random_thin_256 random_medium_256 random_thick_256 segm_256\n    do\n        \"$BINDIR/gen_mask_dataset_hydra.py\" -cn $conf datadir=$datadir location=mml-ws01-paris \\\n         location.out_dir=$OUT_DIR cropping.out_square_crop=False cropping.out_min_size=256\n\n        \"$BINDIR/calc_dataset_stats.py\" --samples-n 20 \"$OUT_DIR/$datadir/$conf\" \"$OUT_DIR/$datadir/${conf}_stats\"\n    done\ndone\n"
  },
  {
    "path": "model_cards/lama/bin/paper_runfiles/generate_val_test.sh",
    "content": "#!/usr/bin/env bash\n\n# !!! file set to make test_large_30k from the vanilla test_large: configs/test_large_30k.lst\n\n# paths to data are valid for mml7\nPLACES_ROOT=\"/data/inpainting/Places365\"\nOUT_DIR=\"/data/inpainting/paper_data/Places365_val_test\"\n\nsource \"$(dirname $0)/env.sh\"\n\nfor datadir in test_large_30k  # val_large\ndo\n    for conf in random_thin_256 random_medium_256 random_thick_256 random_thin_512 random_medium_512 random_thick_512\n    do\n        \"$BINDIR/gen_mask_dataset.py\" \"$CONFIGDIR/data_gen/${conf}.yaml\" \\\n            \"$PLACES_ROOT/$datadir\" \"$OUT_DIR/$datadir/$conf\" --n-jobs 8\n\n        \"$BINDIR/calc_dataset_stats.py\" --samples-n 20 \"$OUT_DIR/$datadir/$conf\" \"$OUT_DIR/$datadir/${conf}_stats\"\n    done\n\n    for conf in segm_256 segm_512\n    do\n        \"$BINDIR/gen_mask_dataset.py\" \"$CONFIGDIR/data_gen/${conf}.yaml\" \\\n            \"$PLACES_ROOT/$datadir\" \"$OUT_DIR/$datadir/$conf\" --n-jobs 2\n\n        \"$BINDIR/calc_dataset_stats.py\" --samples-n 20 \"$OUT_DIR/$datadir/$conf\" \"$OUT_DIR/$datadir/${conf}_stats\"\n    done\ndone\n"
  },
  {
    "path": "model_cards/lama/bin/paper_runfiles/predict_inner_features.sh",
    "content": "#!/usr/bin/env bash\n\n# paths to data are valid for mml7\n\nsource \"$(dirname $0)/env.sh\"\n\n\"$BINDIR/predict_inner_features.py\" \\\n    -cn default_inner_features_ffc \\\n    model.path=\"/data/inpainting/paper_data/final_models/ours/r.suvorov_2021-03-05_17-34-05_train_ablv2_work_ffc075_resume_epoch39\" \\\n    indir=\"/data/inpainting/paper_data/inner_features_vis/input/\" \\\n    outdir=\"/data/inpainting/paper_data/inner_features_vis/output/ffc\" \\\n    dataset.img_suffix=.png\n\n\n\"$BINDIR/predict_inner_features.py\" \\\n    -cn default_inner_features_work \\\n    model.path=\"/data/inpainting/paper_data/final_models/ours/r.suvorov_2021-03-05_17-08-35_train_ablv2_work_resume_epoch37\" \\\n    indir=\"/data/inpainting/paper_data/inner_features_vis/input/\" \\\n    outdir=\"/data/inpainting/paper_data/inner_features_vis/output/work\" \\\n    dataset.img_suffix=.png\n"
  },
  {
    "path": "model_cards/lama/bin/paper_runfiles/update_test_data_stats.sh",
    "content": "#!/usr/bin/env bash\n\n# paths to data are valid for mml7\n\nsource \"$(dirname $0)/env.sh\"\n\n#INDIR=\"/data/inpainting/paper_data/Places365_val_test/test_large_30k\"\n#\n#for dataset in random_medium_256 random_medium_512 random_thick_256 random_thick_512 random_thin_256 random_thin_512\n#do\n#    \"$BINDIR/calc_dataset_stats.py\" \"$INDIR/$dataset\" \"$INDIR/${dataset}_stats2\"\n#done\n#\n#\"$BINDIR/calc_dataset_stats.py\" \"/data/inpainting/evalset2\" \"/data/inpainting/evalset2_stats2\"\n\n\nINDIR=\"/data/inpainting/paper_data/CelebA-HQ_val_test/test\"\n\nfor dataset in random_medium_256 random_thick_256 random_thin_256\ndo\n    \"$BINDIR/calc_dataset_stats.py\" \"$INDIR/$dataset\" \"$INDIR/${dataset}_stats2\"\ndone\n\n\nINDIR=\"/data/inpainting/paper_data/Paris_StreetView_Dataset_val_256/paris_eval_gt\"\n\nfor dataset in random_medium_256 random_thick_256 random_thin_256\ndo\n    \"$BINDIR/calc_dataset_stats.py\" \"$INDIR/$dataset\" \"$INDIR/${dataset}_stats2\"\ndone"
  },
  {
    "path": "model_cards/lama/bin/predict.py",
    "content": "#!/usr/bin/env python3\n\n# Example command:\n# ./bin/predict.py \\\n#       model.path=<path to checkpoint, prepared by make_checkpoint.py> \\\n#       indir=<path to input data> \\\n#       outdir=<where to store predicts>\n\nimport logging\nimport os\nimport sys\nimport traceback\n\nfrom saicinpainting.evaluation.utils import move_to_device\nfrom saicinpainting.evaluation.refinement import refine_predict\nos.environ['OMP_NUM_THREADS'] = '1'\nos.environ['OPENBLAS_NUM_THREADS'] = '1'\nos.environ['MKL_NUM_THREADS'] = '1'\nos.environ['VECLIB_MAXIMUM_THREADS'] = '1'\nos.environ['NUMEXPR_NUM_THREADS'] = '1'\n\nimport cv2\nimport hydra\nimport numpy as np\nimport torch\nimport tqdm\nimport yaml\nfrom omegaconf import OmegaConf\nfrom torch.utils.data._utils.collate import default_collate\n\nfrom saicinpainting.training.data.datasets import make_default_val_dataset\nfrom saicinpainting.training.trainers import load_checkpoint\nfrom saicinpainting.utils import register_debug_signal_handlers\n\nLOGGER = logging.getLogger(__name__)\n\n\n@hydra.main(config_path='../configs/prediction', config_name='default.yaml')\ndef main(predict_config: OmegaConf):\n    try:\n        register_debug_signal_handlers()  # kill -10 <pid> will result in traceback dumped into log\n\n        device = torch.device(predict_config.device)\n\n        train_config_path = os.path.join(predict_config.model.path, 'config.yaml')\n        with open(train_config_path, 'r') as f:\n            train_config = OmegaConf.create(yaml.safe_load(f))\n        \n        train_config.training_model.predict_only = True\n        train_config.visualizer.kind = 'noop'\n\n        out_ext = predict_config.get('out_ext', '.png')\n\n        checkpoint_path = os.path.join(predict_config.model.path, \n                                       'models', \n                                       predict_config.model.checkpoint)\n        model = load_checkpoint(train_config, checkpoint_path, strict=False, map_location='cpu')\n        model.freeze()\n        if not predict_config.get('refine', False):\n            model.to(device)\n\n        if not predict_config.indir.endswith('/'):\n            predict_config.indir += '/'\n\n        dataset = make_default_val_dataset(predict_config.indir, **predict_config.dataset)\n        for img_i in tqdm.trange(len(dataset)):\n            mask_fname = dataset.mask_filenames[img_i]\n            cur_out_fname = os.path.join(\n                predict_config.outdir, \n                os.path.splitext(mask_fname[len(predict_config.indir):])[0] + out_ext\n            )\n            os.makedirs(os.path.dirname(cur_out_fname), exist_ok=True)\n            batch = default_collate([dataset[img_i]])\n            if predict_config.get('refine', False):\n                assert 'unpad_to_size' in batch, \"Unpadded size is required for the refinement\"\n                # image unpadding is taken care of in the refiner, so that output image\n                # is same size as the input image\n                cur_res = refine_predict(batch, model, **predict_config.refiner)\n                cur_res = cur_res[0].permute(1,2,0).detach().cpu().numpy()\n            else:\n                with torch.no_grad():\n                    batch = move_to_device(batch, device)\n                    batch['mask'] = (batch['mask'] > 0) * 1\n                    batch = model(batch)                    \n                    cur_res = batch[predict_config.out_key][0].permute(1, 2, 0).detach().cpu().numpy()\n                    unpad_to_size = batch.get('unpad_to_size', None)\n                    if unpad_to_size is not None:\n                        orig_height, orig_width = unpad_to_size\n                        cur_res = cur_res[:orig_height, :orig_width]\n\n            cur_res = np.clip(cur_res * 255, 0, 255).astype('uint8')\n            cur_res = cv2.cvtColor(cur_res, cv2.COLOR_RGB2BGR)\n            cv2.imwrite(cur_out_fname, cur_res)\n\n    except KeyboardInterrupt:\n        LOGGER.warning('Interrupted by user')\n    except Exception as ex:\n        LOGGER.critical(f'Prediction failed due to {ex}:\\n{traceback.format_exc()}')\n        sys.exit(1)\n\n\nif __name__ == '__main__':\n    main()\n"
  },
  {
    "path": "model_cards/lama/bin/predict_inner_features.py",
    "content": "#!/usr/bin/env python3\n\n# Example command:\n# ./bin/predict.py \\\n#       model.path=<path to checkpoint, prepared by make_checkpoint.py> \\\n#       indir=<path to input data> \\\n#       outdir=<where to store predicts>\n\nimport logging\nimport os\nimport sys\nimport traceback\n\nfrom saicinpainting.evaluation.utils import move_to_device\n\nos.environ['OMP_NUM_THREADS'] = '1'\nos.environ['OPENBLAS_NUM_THREADS'] = '1'\nos.environ['MKL_NUM_THREADS'] = '1'\nos.environ['VECLIB_MAXIMUM_THREADS'] = '1'\nos.environ['NUMEXPR_NUM_THREADS'] = '1'\n\nimport cv2\nimport hydra\nimport numpy as np\nimport torch\nimport tqdm\nimport yaml\nfrom omegaconf import OmegaConf\nfrom torch.utils.data._utils.collate import default_collate\n\nfrom saicinpainting.training.data.datasets import make_default_val_dataset\nfrom saicinpainting.training.trainers import load_checkpoint, DefaultInpaintingTrainingModule\nfrom saicinpainting.utils import register_debug_signal_handlers, get_shape\n\nLOGGER = logging.getLogger(__name__)\n\n\n@hydra.main(config_path='../configs/prediction', config_name='default_inner_features.yaml')\ndef main(predict_config: OmegaConf):\n    try:\n        register_debug_signal_handlers()  # kill -10 <pid> will result in traceback dumped into log\n\n        device = torch.device(predict_config.device)\n\n        train_config_path = os.path.join(predict_config.model.path, 'config.yaml')\n        with open(train_config_path, 'r') as f:\n            train_config = OmegaConf.create(yaml.safe_load(f))\n\n        checkpoint_path = os.path.join(predict_config.model.path, 'models', predict_config.model.checkpoint)\n        model = load_checkpoint(train_config, checkpoint_path, strict=False)\n        model.freeze()\n        model.to(device)\n\n        assert isinstance(model, DefaultInpaintingTrainingModule), 'Only DefaultInpaintingTrainingModule is supported'\n        assert isinstance(getattr(model.generator, 'model', None), torch.nn.Sequential)\n\n        if not predict_config.indir.endswith('/'):\n            predict_config.indir += '/'\n\n        dataset = make_default_val_dataset(predict_config.indir, **predict_config.dataset)\n\n        max_level = max(predict_config.levels)\n\n        with torch.no_grad():\n            for img_i in tqdm.trange(len(dataset)):\n                mask_fname = dataset.mask_filenames[img_i]\n                cur_out_fname = os.path.join(predict_config.outdir, os.path.splitext(mask_fname[len(predict_config.indir):])[0])\n                os.makedirs(os.path.dirname(cur_out_fname), exist_ok=True)\n\n                batch = move_to_device(default_collate([dataset[img_i]]), device)\n\n                img = batch['image']\n                mask = batch['mask']\n                mask[:] = 0\n                mask_h, mask_w = mask.shape[-2:]\n                mask[:, :,\n                    mask_h // 2 - predict_config.hole_radius : mask_h // 2 + predict_config.hole_radius,\n                    mask_w // 2 - predict_config.hole_radius : mask_w // 2 + predict_config.hole_radius] = 1\n\n                masked_img = torch.cat([img * (1 - mask), mask], dim=1)\n\n                feats = masked_img\n                for level_i, level in enumerate(model.generator.model):\n                    feats = level(feats)\n                    if level_i in predict_config.levels:\n                        cur_feats = torch.cat([f for f in feats if torch.is_tensor(f)], dim=1) \\\n                            if isinstance(feats, tuple) else feats\n\n                        if predict_config.slice_channels:\n                            cur_feats = cur_feats[:, slice(*predict_config.slice_channels)]\n\n                        cur_feat = cur_feats.pow(2).mean(1).pow(0.5).clone()\n                        cur_feat -= cur_feat.min()\n                        cur_feat /= cur_feat.std()\n                        cur_feat = cur_feat.clamp(0, 1) / 1\n                        cur_feat = cur_feat.cpu().numpy()[0]\n                        cur_feat *= 255\n                        cur_feat = np.clip(cur_feat, 0, 255).astype('uint8')\n                        cv2.imwrite(cur_out_fname + f'_lev{level_i:02d}_norm.png', cur_feat)\n\n                        # for channel_i in predict_config.channels:\n                        #\n                        #     cur_feat = cur_feats[0, channel_i].clone().detach().cpu().numpy()\n                        #     cur_feat -= cur_feat.min()\n                        #     cur_feat /= cur_feat.max()\n                        #     cur_feat *= 255\n                        #     cur_feat = np.clip(cur_feat, 0, 255).astype('uint8')\n                        #     cv2.imwrite(cur_out_fname + f'_lev{level_i}_ch{channel_i}.png', cur_feat)\n                    elif level_i >= max_level:\n                        break\n    except KeyboardInterrupt:\n        LOGGER.warning('Interrupted by user')\n    except Exception as ex:\n        LOGGER.critical(f'Prediction failed due to {ex}:\\n{traceback.format_exc()}')\n        sys.exit(1)\n\n\nif __name__ == '__main__':\n    main()\n"
  },
  {
    "path": "model_cards/lama/bin/report_from_tb.py",
    "content": "#!/usr/bin/env python3\n\nimport glob\nimport os\nimport re\n\nimport tensorflow as tf\nfrom torch.utils.tensorboard import SummaryWriter\n\n\nGROUPING_RULES = [\n    re.compile(r'^(?P<group>train|test|val|extra_val_.*?(256|512))_(?P<title>.*)', re.I)\n]\n\n\nDROP_RULES = [\n    re.compile(r'_std$', re.I)\n]\n\n\ndef need_drop(tag):\n    for rule in DROP_RULES:\n        if rule.search(tag):\n            return True\n    return False\n\n\ndef get_group_and_title(tag):\n    for rule in GROUPING_RULES:\n        match = rule.search(tag)\n        if match is None:\n            continue\n        return match.group('group'), match.group('title')\n    return None, None\n\n\ndef main(args):\n    os.makedirs(args.outdir, exist_ok=True)\n\n    ignored_events = set()\n\n    for orig_fname in glob.glob(args.inglob):\n        cur_dirpath = os.path.dirname(orig_fname)  # remove filename, this should point to \"version_0\" directory\n        subdirname = os.path.basename(cur_dirpath)  # == \"version_0\" most of time\n        exp_root_path = os.path.dirname(cur_dirpath)  # remove \"version_0\"\n        exp_name = os.path.basename(exp_root_path)\n\n        writers_by_group = {}\n\n        for e in tf.compat.v1.train.summary_iterator(orig_fname):\n            for v in e.summary.value:\n                if need_drop(v.tag):\n                    continue\n\n                cur_group, cur_title = get_group_and_title(v.tag)\n                if cur_group is None:\n                    if v.tag not in ignored_events:\n                        print(f'WARNING: Could not detect group for {v.tag}, ignoring it')\n                        ignored_events.add(v.tag)\n                    continue\n\n                cur_writer = writers_by_group.get(cur_group, None)\n                if cur_writer is None:\n                    if args.include_version:\n                        cur_outdir = os.path.join(args.outdir, exp_name, f'{subdirname}_{cur_group}')\n                    else:\n                        cur_outdir = os.path.join(args.outdir, exp_name, cur_group)\n                    cur_writer = SummaryWriter(cur_outdir)\n                    writers_by_group[cur_group] = cur_writer\n\n                cur_writer.add_scalar(cur_title, v.simple_value, global_step=e.step, walltime=e.wall_time)\n\n\nif __name__ == '__main__':\n    import argparse\n\n    aparser = argparse.ArgumentParser()\n    aparser.add_argument('inglob', type=str)\n    aparser.add_argument('outdir', type=str)\n    aparser.add_argument('--include-version', action='store_true',\n                         help='Include subdirectory name e.g. \"version_0\" into output path')\n\n    main(aparser.parse_args())\n"
  },
  {
    "path": "model_cards/lama/bin/sample_from_dataset.py",
    "content": "#!/usr/bin/env python3\n\nimport os\n\nimport numpy as np\nimport tqdm\nfrom skimage import io\nfrom skimage.segmentation import mark_boundaries\n\nfrom saicinpainting.evaluation.data import InpaintingDataset\nfrom saicinpainting.evaluation.vis import save_item_for_vis\n\ndef save_mask_for_sidebyside(item, out_file):\n    mask = item['mask']# > 0.5\n    if mask.ndim == 3:\n        mask = mask[0]\n    mask = np.clip(mask * 255, 0, 255).astype('uint8')\n    io.imsave(out_file, mask)\n\ndef save_img_for_sidebyside(item, out_file):\n    img = np.transpose(item['image'], (1, 2, 0))\n    img = np.clip(img * 255, 0, 255).astype('uint8')\n    io.imsave(out_file, img)\n\ndef save_masked_img_for_sidebyside(item, out_file):\n    mask = item['mask']\n    img  = item['image']\n\n    img = (1-mask) * img + mask\n    img = np.transpose(img, (1, 2, 0))\n\n    img = np.clip(img * 255, 0, 255).astype('uint8')\n    io.imsave(out_file, img)\n\ndef main(args):\n    dataset = InpaintingDataset(args.datadir, img_suffix='.png')\n\n    area_bins = np.linspace(0, 1, args.area_bins + 1)\n\n    heights = []\n    widths = []\n    image_areas = []\n    hole_areas = []\n    hole_area_percents = []\n    area_bins_count = np.zeros(args.area_bins)\n    area_bin_titles = [f'{area_bins[i] * 100:.0f}-{area_bins[i + 1] * 100:.0f}' for i in range(args.area_bins)]\n\n    bin2i = [[] for _ in range(args.area_bins)]\n\n    for i, item in enumerate(tqdm.tqdm(dataset)):\n        h, w = item['image'].shape[1:]\n        heights.append(h)\n        widths.append(w)\n        full_area = h * w\n        image_areas.append(full_area)\n        hole_area = (item['mask'] == 1).sum()\n        hole_areas.append(hole_area)\n        hole_percent = hole_area / full_area\n        hole_area_percents.append(hole_percent)\n        bin_i = np.clip(np.searchsorted(area_bins, hole_percent) - 1, 0, len(area_bins_count) - 1)\n        area_bins_count[bin_i] += 1\n        bin2i[bin_i].append(i)\n\n    os.makedirs(args.outdir, exist_ok=True)\n   \n    for bin_i in range(args.area_bins):\n        bindir = os.path.join(args.outdir, area_bin_titles[bin_i])\n        os.makedirs(bindir, exist_ok=True)\n        bin_idx = bin2i[bin_i]\n        for sample_i in np.random.choice(bin_idx, size=min(len(bin_idx), args.samples_n), replace=False):\n            item = dataset[sample_i]\n            path = os.path.join(bindir, dataset.img_filenames[sample_i].split('/')[-1])\n            save_masked_img_for_sidebyside(item, path)\n           \n\nif __name__ == '__main__':\n    import argparse\n\n    aparser = argparse.ArgumentParser()\n    aparser.add_argument('--datadir', type=str,\n                         help='Path to folder with images and masks (output of gen_mask_dataset.py)')\n    aparser.add_argument('--outdir', type=str, help='Where to put results')\n    aparser.add_argument('--samples-n', type=int, default=10,\n                         help='Number of sample images with masks to copy for visualization for each area bin')\n    aparser.add_argument('--area-bins', type=int, default=10, help='How many area bins to have')\n\n    main(aparser.parse_args())\n"
  },
  {
    "path": "model_cards/lama/bin/side_by_side.py",
    "content": "#!/usr/bin/env python3\nimport os\nimport random\n\nimport cv2\nimport numpy as np\n\nfrom saicinpainting.evaluation.data import PrecomputedInpaintingResultsDataset\nfrom saicinpainting.evaluation.utils import load_yaml\nfrom saicinpainting.training.visualizers.base import visualize_mask_and_images\n\n\ndef main(args):\n    config = load_yaml(args.config)\n\n    datasets = [PrecomputedInpaintingResultsDataset(args.datadir, cur_predictdir, **config.dataset_kwargs)\n                for cur_predictdir in args.predictdirs]\n    assert len({len(ds) for ds in datasets}) == 1\n    len_first = len(datasets[0])\n\n    indices = list(range(len_first))\n    if len_first > args.max_n:\n        indices = sorted(random.sample(indices, args.max_n))\n\n    os.makedirs(args.outpath, exist_ok=True)\n\n    filename2i = {}\n\n    keys = ['image'] + [i for i in range(len(datasets))]\n    for img_i in indices:\n        try:\n            mask_fname = os.path.basename(datasets[0].mask_filenames[img_i])\n            if mask_fname in filename2i:\n                filename2i[mask_fname] += 1\n                idx = filename2i[mask_fname]\n                mask_fname_only, ext = os.path.split(mask_fname)\n                mask_fname = f'{mask_fname_only}_{idx}{ext}'\n            else:\n                filename2i[mask_fname] = 1\n\n            cur_vis_dict = datasets[0][img_i]\n            for ds_i, ds in enumerate(datasets):\n                cur_vis_dict[ds_i] = ds[img_i]['inpainted']\n\n            vis_img = visualize_mask_and_images(cur_vis_dict, keys,\n                                                last_without_mask=False,\n                                                mask_only_first=True,\n                                                black_mask=args.black)\n            vis_img = np.clip(vis_img * 255, 0, 255).astype('uint8')\n\n            out_fname = os.path.join(args.outpath, mask_fname)\n\n\n\n            vis_img = cv2.cvtColor(vis_img, cv2.COLOR_RGB2BGR)\n            cv2.imwrite(out_fname, vis_img)\n        except Exception as ex:\n            print(f'Could not process {img_i} due to {ex}')\n\n\nif __name__ == '__main__':\n    import argparse\n\n    aparser = argparse.ArgumentParser()\n    aparser.add_argument('--max-n', type=int, default=100, help='Maximum number of images to print')\n    aparser.add_argument('--black', action='store_true', help='Whether to fill mask on GT with black')\n    aparser.add_argument('config', type=str, help='Path to evaluation config (e.g. configs/eval1.yaml)')\n    aparser.add_argument('outpath', type=str, help='Where to put results')\n    aparser.add_argument('datadir', type=str,\n                         help='Path to folder with images and masks')\n    aparser.add_argument('predictdirs', type=str,\n                         nargs='+',\n                         help='Path to folders with predicts')\n\n\n    main(aparser.parse_args())\n"
  },
  {
    "path": "model_cards/lama/bin/split_tar.py",
    "content": "#!/usr/bin/env python3\n\n\nimport tqdm\nimport webdataset as wds\n\n\ndef main(args):\n    input_dataset = wds.Dataset(args.infile)\n    output_dataset = wds.ShardWriter(args.outpattern)\n    for rec in tqdm.tqdm(input_dataset):\n        output_dataset.write(rec)\n\n\nif __name__ == '__main__':\n    import argparse\n\n    aparser = argparse.ArgumentParser()\n    aparser.add_argument('infile', type=str)\n    aparser.add_argument('outpattern', type=str)\n\n    main(aparser.parse_args())\n"
  },
  {
    "path": "model_cards/lama/bin/to_jit.py",
    "content": "import os\nfrom pathlib import Path\n\nimport hydra\nimport torch\nimport yaml\nfrom omegaconf import OmegaConf\nfrom torch import nn\n\nfrom saicinpainting.training.trainers import load_checkpoint\nfrom saicinpainting.utils import register_debug_signal_handlers\n\n\nclass JITWrapper(nn.Module):\n    def __init__(self, model):\n        super().__init__()\n        self.model = model\n\n    def forward(self, image, mask):\n        batch = {\n            \"image\": image,\n            \"mask\": mask\n        }\n        out = self.model(batch)\n        return out[\"inpainted\"]\n\n\n@hydra.main(config_path=\"../configs/prediction\", config_name=\"default.yaml\")\ndef main(predict_config: OmegaConf):\n    register_debug_signal_handlers()  # kill -10 <pid> will result in traceback dumped into log\n\n    train_config_path = os.path.join(predict_config.model.path, \"config.yaml\")\n    with open(train_config_path, \"r\") as f:\n        train_config = OmegaConf.create(yaml.safe_load(f))\n\n    train_config.training_model.predict_only = True\n    train_config.visualizer.kind = \"noop\"\n\n    checkpoint_path = os.path.join(\n        predict_config.model.path, \"models\", predict_config.model.checkpoint\n    )\n    model = load_checkpoint(\n        train_config, checkpoint_path, strict=False, map_location=\"cpu\"\n    )\n    model.eval()\n    jit_model_wrapper = JITWrapper(model)\n\n    image = torch.rand(1, 3, 120, 120)\n    mask = torch.rand(1, 1, 120, 120)\n    output = jit_model_wrapper(image, mask)\n\n    if torch.cuda.is_available():\n        device = torch.device(\"cuda\")\n    else:\n        device = torch.device(\"cpu\")\n\n    image = image.to(device)\n    mask = mask.to(device)\n    traced_model = torch.jit.trace(jit_model_wrapper, (image, mask), strict=False).to(device)\n\n    save_path = Path(predict_config.save_path)\n    save_path.parent.mkdir(parents=True, exist_ok=True)\n\n    print(f\"Saving big-lama.pt model to {save_path}\")\n    traced_model.save(save_path)\n\n    print(f\"Checking jit model output...\")\n    jit_model = torch.jit.load(str(save_path))\n    jit_output = jit_model(image, mask)\n    diff = (output - jit_output).abs().sum()\n    print(f\"diff: {diff}\")\n\n\nif __name__ == \"__main__\":\n    main()\n"
  },
  {
    "path": "model_cards/lama/bin/train.py",
    "content": "#!/usr/bin/env python3\n\nimport logging\nimport os\nimport sys\nimport traceback\n\nos.environ['OMP_NUM_THREADS'] = '1'\nos.environ['OPENBLAS_NUM_THREADS'] = '1'\nos.environ['MKL_NUM_THREADS'] = '1'\nos.environ['VECLIB_MAXIMUM_THREADS'] = '1'\nos.environ['NUMEXPR_NUM_THREADS'] = '1'\n\nimport hydra\nfrom omegaconf import OmegaConf\nfrom pytorch_lightning import Trainer\nfrom pytorch_lightning.callbacks import ModelCheckpoint\nfrom pytorch_lightning.loggers import TensorBoardLogger\nfrom pytorch_lightning.plugins import DDPPlugin\n\nfrom saicinpainting.training.trainers import make_training_model\nfrom saicinpainting.utils import register_debug_signal_handlers, handle_ddp_subprocess, handle_ddp_parent_process, \\\n    handle_deterministic_config\n\nLOGGER = logging.getLogger(__name__)\n\n\n@handle_ddp_subprocess()\n@hydra.main(config_path='../configs/training', config_name='tiny_test.yaml')\ndef main(config: OmegaConf):\n    try:\n        need_set_deterministic = handle_deterministic_config(config)\n\n        register_debug_signal_handlers()  # kill -10 <pid> will result in traceback dumped into log\n\n        is_in_ddp_subprocess = handle_ddp_parent_process()\n\n        config.visualizer.outdir = os.path.join(os.getcwd(), config.visualizer.outdir)\n        if not is_in_ddp_subprocess:\n            LOGGER.info(OmegaConf.to_yaml(config))\n            OmegaConf.save(config, os.path.join(os.getcwd(), 'config.yaml'))\n\n        checkpoints_dir = os.path.join(os.getcwd(), 'models')\n        os.makedirs(checkpoints_dir, exist_ok=True)\n\n        # there is no need to suppress this logger in ddp, because it handles rank on its own\n        metrics_logger = TensorBoardLogger(config.location.tb_dir, name=os.path.basename(os.getcwd()))\n        metrics_logger.log_hyperparams(config)\n\n        training_model = make_training_model(config)\n\n        trainer_kwargs = OmegaConf.to_container(config.trainer.kwargs, resolve=True)\n        if need_set_deterministic:\n            trainer_kwargs['deterministic'] = True\n\n        trainer = Trainer(\n            # there is no need to suppress checkpointing in ddp, because it handles rank on its own\n            callbacks=ModelCheckpoint(dirpath=checkpoints_dir, **config.trainer.checkpoint_kwargs),\n            logger=metrics_logger,\n            default_root_dir=os.getcwd(),\n            **trainer_kwargs\n        )\n        trainer.fit(training_model)\n    except KeyboardInterrupt:\n        LOGGER.warning('Interrupted by user')\n    except Exception as ex:\n        LOGGER.critical(f'Training failed due to {ex}:\\n{traceback.format_exc()}')\n        sys.exit(1)\n\n\nif __name__ == '__main__':\n    main()\n"
  },
  {
    "path": "model_cards/lama/colab/LaMa_inpainting.ipynb",
    "content": "{\n  \"cells\": [\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"id\": \"_pRpIwnaOnb3\"\n      },\n      \"source\": [\n        \"# 🦙 **LaMa: Resolution-robust Large Mask Inpainting with Fourier Convolutions**\\n\",\n        \"\\n\",\n        \"[[Project page](https://advimman.github.io/lama-project/)] [[GitHub](https://github.com/advimman/lama)] [[arXiv](https://arxiv.org/abs/2109.07161)] [[Supplementary](https://ashukha.com/projects/lama_21/lama_supmat_2021.pdf)] [[BibTeX](https://senya-ashukha.github.io/projects/lama_21/paper.txt)]\\n\",\n        \"\\n\",\n        \"<p align=\\\"center\\\" \\\"font-size:30px;\\\">\\n\",\n        \"Our model generalizes surprisingly well to much higher resolutions (~2k❗️) than it saw during training (256x256), and achieves the excellent performance even in challenging scenarios, e.g. completion of periodic structures.\\n\",\n        \"</p>\\n\",\n        \"\\n\",\n        \"# Try it yourself!👇\\n\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": null,\n      \"metadata\": {\n        \"cellView\": \"form\",\n        \"id\": \"RwXRMaNHW4r5\"\n      },\n      \"outputs\": [],\n      \"source\": [\n        \"#@title Run this sell to set everything up\\n\",\n        \"print('\\\\n> Cloning the repo')\\n\",\n        \"!git clone https://github.com/advimman/lama.git\\n\",\n        \"\\n\",\n        \"print('\\\\n> Install dependencies')\\n\",\n        \"!pip install torch==1.8.0 torchvision==0.9.0 torchaudio==0.8.0 torchtext==0.9\\n\",\n        \"!pip install -r lama/requirements.txt --quiet\\n\",\n        \"!pip install wget --quiet\\n\",\n        \"!pip install torch==1.8.0+cu111 torchvision==0.9.0+cu111 torchaudio==0.8.0 -f https://download.pytorch.org/whl/torch_stable.html --quiet\\n\",\n        \"\\n\",\n        \"\\n\",\n        \"print('\\\\n> Changing the dir to:')\\n\",\n        \"%cd /content/lama\\n\",\n        \"\\n\",\n        \"print('\\\\n> Download the model')\\n\",\n        \"!curl -L $(yadisk-direct https://disk.yandex.ru/d/ouP6l8VJ0HpMZg) -o big-lama.zip\\n\",\n        \"!unzip big-lama.zip\\n\",\n        \"\\n\",\n        \"print('>fixing opencv')\\n\",\n        \"!pip uninstall opencv-python-headless -y --quiet\\n\",\n        \"!pip install opencv-python-headless==4.1.2.30 --quiet\\n\",\n        \"\\n\",\n        \"\\n\",\n        \"print('\\\\n> Init mask-drawing code')\\n\",\n        \"import base64, os\\n\",\n        \"from IPython.display import HTML, Image\\n\",\n        \"from google.colab.output import eval_js\\n\",\n        \"from base64 import b64decode\\n\",\n        \"import matplotlib.pyplot as plt\\n\",\n        \"import numpy as np\\n\",\n        \"import wget\\n\",\n        \"from shutil import copyfile\\n\",\n        \"import shutil\\n\",\n        \"\\n\",\n        \"\\n\",\n        \"\\n\",\n        \"canvas_html = \\\"\\\"\\\"\\n\",\n        \"<style>\\n\",\n        \".button {\\n\",\n        \"  background-color: #4CAF50;\\n\",\n        \"  border: none;\\n\",\n        \"  color: white;\\n\",\n        \"  padding: 15px 32px;\\n\",\n        \"  text-align: center;\\n\",\n        \"  text-decoration: none;\\n\",\n        \"  display: inline-block;\\n\",\n        \"  font-size: 16px;\\n\",\n        \"  margin: 4px 2px;\\n\",\n        \"  cursor: pointer;\\n\",\n        \"}\\n\",\n        \"</style>\\n\",\n        \"<canvas1 width=%d height=%d>\\n\",\n        \"</canvas1>\\n\",\n        \"<canvas width=%d height=%d>\\n\",\n        \"</canvas>\\n\",\n        \"\\n\",\n        \"<button class=\\\"button\\\">Finish</button>\\n\",\n        \"<script>\\n\",\n        \"var canvas = document.querySelector('canvas')\\n\",\n        \"var ctx = canvas.getContext('2d')\\n\",\n        \"\\n\",\n        \"var canvas1 = document.querySelector('canvas1')\\n\",\n        \"var ctx1 = canvas.getContext('2d')\\n\",\n        \"\\n\",\n        \"\\n\",\n        \"ctx.strokeStyle = 'red';\\n\",\n        \"\\n\",\n        \"var img = new Image();\\n\",\n        \"img.src = \\\"data:image/%s;charset=utf-8;base64,%s\\\";\\n\",\n        \"console.log(img)\\n\",\n        \"img.onload = function() {\\n\",\n        \"  ctx1.drawImage(img, 0, 0);\\n\",\n        \"};\\n\",\n        \"img.crossOrigin = 'Anonymous';\\n\",\n        \"\\n\",\n        \"ctx.clearRect(0, 0, canvas.width, canvas.height);\\n\",\n        \"\\n\",\n        \"ctx.lineWidth = %d\\n\",\n        \"var button = document.querySelector('button')\\n\",\n        \"var mouse = {x: 0, y: 0}\\n\",\n        \"\\n\",\n        \"canvas.addEventListener('mousemove', function(e) {\\n\",\n        \"  mouse.x = e.pageX - this.offsetLeft\\n\",\n        \"  mouse.y = e.pageY - this.offsetTop\\n\",\n        \"})\\n\",\n        \"canvas.onmousedown = ()=>{\\n\",\n        \"  ctx.beginPath()\\n\",\n        \"  ctx.moveTo(mouse.x, mouse.y)\\n\",\n        \"  canvas.addEventListener('mousemove', onPaint)\\n\",\n        \"}\\n\",\n        \"canvas.onmouseup = ()=>{\\n\",\n        \"  canvas.removeEventListener('mousemove', onPaint)\\n\",\n        \"}\\n\",\n        \"var onPaint = ()=>{\\n\",\n        \"  ctx.lineTo(mouse.x, mouse.y)\\n\",\n        \"  ctx.stroke()\\n\",\n        \"}\\n\",\n        \"\\n\",\n        \"var data = new Promise(resolve=>{\\n\",\n        \"  button.onclick = ()=>{\\n\",\n        \"    resolve(canvas.toDataURL('image/png'))\\n\",\n        \"  }\\n\",\n        \"})\\n\",\n        \"</script>\\n\",\n        \"\\\"\\\"\\\"\\n\",\n        \"\\n\",\n        \"def draw(imgm, filename='drawing.png', w=400, h=200, line_width=1):\\n\",\n        \"  display(HTML(canvas_html % (w, h, w,h, filename.split('.')[-1], imgm, line_width)))\\n\",\n        \"  data = eval_js(\\\"data\\\")\\n\",\n        \"  binary = b64decode(data.split(',')[1])\\n\",\n        \"  with open(filename, 'wb') as f:\\n\",\n        \"    f.write(binary)\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"id\": \"23WaUHiJeyBO\"\n      },\n      \"source\": [\n        \"<center>\\n\",\n        \"<h1 style=\\\"font-size:10vw\\\"><b>Predefined photo</b>: uncomment any line\\n\",\n        \"<br>\\n\",\n        \"<b>Local file</b>: leave the <tt>fname = None</tt></h1>\\n\",\n        \"</center>\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": null,\n      \"metadata\": {\n        \"id\": \"IFIDDD4IhPXd\"\n      },\n      \"outputs\": [],\n      \"source\": [\n        \"fname = None\\n\",\n        \"# fname = 'https://ic.pics.livejournal.com/mostovoy/28566193/1224276/1224276_original.jpg' # <-in the example\\n\",\n        \"# fname = 'https://raw.githubusercontent.com/senya-ashukha/senya-ashukha.github.io/master/images/1010286.jpeg'\\n\",\n        \"# fname = 'https://raw.githubusercontent.com/senya-ashukha/senya-ashukha.github.io/master/images/1010287.jpeg'\\n\",\n        \"# fname = \\\"https://raw.githubusercontent.com/senya-ashukha/senya-ashukha.github.io/master/images/alex.jpg\\\"\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": null,\n      \"metadata\": {\n        \"cellView\": \"form\",\n        \"colab\": {\n          \"base_uri\": \"https://localhost:8080/\",\n          \"height\": 1000\n        },\n        \"id\": \"-VZWySTMeGDM\",\n        \"outputId\": \"c42a411e-e84a-415c-ea2c-182abf34324c\"\n      },\n      \"outputs\": [\n        {\n          \"name\": \"stdout\",\n          \"output_type\": \"stream\",\n          \"text\": [\n            \"Will use ./data_for_prediction/1224276_original.jpg for inpainting\\n\"\n          ]\n        },\n        {\n          \"data\": {\n            \"text/html\": [\n              \"\\n\",\n              \"<style>\\n\",\n              \".button {\\n\",\n              \"  background-color: #4CAF50;\\n\",\n              \"  border: none;\\n\",\n              \"  color: white;\\n\",\n              \"  padding: 15px 32px;\\n\",\n              \"  text-align: center;\\n\",\n              \"  text-decoration: none;\\n\",\n              \"  display: inline-block;\\n\",\n              \"  font-size: 16px;\\n\",\n              \"  margin: 4px 2px;\\n\",\n              \"  cursor: pointer;\\n\",\n              \"}\\n\",\n              \"</style>\\n\",\n              \"<canvas1 width=1200 height=800>\\n\",\n              \"</canvas1>\\n\",\n              \"<canvas width=1200 height=800>\\n\",\n              \"</canvas>\\n\",\n              \"\\n\",\n              \"<button class=\\\"button\\\">Finish</button>\\n\",\n              \"<script>\\n\",\n              \"var canvas = document.querySelector('canvas')\\n\",\n              \"var ctx = canvas.getContext('2d')\\n\",\n              \"\\n\",\n              \"var canvas1 = document.querySelector('canvas1')\\n\",\n              \"var ctx1 = canvas.getContext('2d')\\n\",\n              \"\\n\",\n              \"\\n\",\n              \"ctx.strokeStyle = 'red';\\n\",\n              \"\\n\",\n              \"var img = new Image();\\n\",\n              \"img.src = 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\\\";\\n\",\n              \"console.log(img)\\n\",\n              \"img.onload = function() {\\n\",\n              \"  ctx1.drawImage(img, 0, 0);\\n\",\n              \"};\\n\",\n              \"img.crossOrigin = 'Anonymous';\\n\",\n              \"\\n\",\n              \"ctx.clearRect(0, 0, canvas.width, canvas.height);\\n\",\n              \"\\n\",\n              \"ctx.lineWidth = 48\\n\",\n              \"var button = document.querySelector('button')\\n\",\n              \"var mouse = {x: 0, y: 0}\\n\",\n              \"\\n\",\n              \"canvas.addEventListener('mousemove', function(e) {\\n\",\n              \"  mouse.x = e.pageX - this.offsetLeft\\n\",\n              \"  mouse.y = e.pageY - this.offsetTop\\n\",\n              \"})\\n\",\n              \"canvas.onmousedown = ()=>{\\n\",\n              \"  ctx.beginPath()\\n\",\n              \"  ctx.moveTo(mouse.x, mouse.y)\\n\",\n              \"  canvas.addEventListener('mousemove', onPaint)\\n\",\n              \"}\\n\",\n              \"canvas.onmouseup = ()=>{\\n\",\n              \"  canvas.removeEventListener('mousemove', onPaint)\\n\",\n              \"}\\n\",\n              \"var onPaint = ()=>{\\n\",\n              \"  ctx.lineTo(mouse.x, mouse.y)\\n\",\n              \"  ctx.stroke()\\n\",\n              \"}\\n\",\n              \"\\n\",\n              \"var data = new Promise(resolve=>{\\n\",\n              \"  button.onclick = ()=>{\\n\",\n              \"    resolve(canvas.toDataURL('image/png'))\\n\",\n              \"  }\\n\",\n              \"})\\n\",\n              \"</script>\\n\"\n            ],\n            \"text/plain\": [\n              \"<IPython.core.display.HTML object>\"\n            ]\n          },\n          \"metadata\": {},\n          \"output_type\": \"display_data\"\n        },\n        {\n          \"data\": {\n            \"image/png\": 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\\n\",\n            \"text/plain\": [\n              \"<Figure size 3000x1000 with 3 Axes>\"\n            ]\n          },\n          \"metadata\": {\n            \"needs_background\": \"light\"\n          },\n          \"output_type\": \"display_data\"\n        },\n        {\n          \"name\": \"stdout\",\n          \"output_type\": \"stream\",\n          \"text\": [\n            \"Run inpainting\\n\",\n            \"100% 1/1 [00:01<00:00,  1.73s/it]\\n\"\n          ]\n        },\n        {\n          \"data\": {\n            \"image/png\": 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j2D1zPlG1af4XuUBoPoX5ZUdWnROTnA38G+K8pNuJNulPlXlwA/+Lgu5nidNnUv1/Bc2/rexvwWap6zyg5Vf16EfliejTTLeBXHiT7t8CvAj7mOe7zoHwl5XyrT6S03yfVfyN/AXCHkuM4jvMTFncoOY7jOM5jhqq+XUR+FiVy6NOBN1O2/PwoZWvS/6yqh292u1c+XyQif48SBfPxFOfGHeBHgG8C/rqqft9L8hAPGVX9O3UL1W+jPMsbKXVyz7OmHjWq+i31LW9fCPwKSkTOihKp84+Bv6yq7xCR8TDq2y9/STuqegH8FhH5cooz5ZMpkVSvpByT8AzlfK7vBP4B8E31IPIxj3eLyCvrtZ8AfDTwkymyt6bI3w9StrN9rap+yxXK9aUi8veB317zfA3F+fbvgf+dIsenIvLQHEqqOovIL6IcUv6rgI+kOMfuuQ3QcRzHcX4iIqofLG/idRzHcZz/MBGRrwR+ff3zN6jqVz660jgvJ/UsoW+uf36Tqn7qoyyP4ziO4zjOVfFDuR3HcRzHcR4dnzl8fscjK4XjOI7jOM4D4g4lx3Ecx3GcR4CI/Fz2z4L66kdVFsdxHMdxnAfFHUqO4ziO4zgPERF5o4h8vYh8vIjcdcaTiEQR+Rzg/6acqwTwVlX93pe1oI7jOI7jOC8CP5TbcRzHcRzn4RKAz6j/3isi/wL4Mcrb+V4L/DzKIdXGjwFf8HIX0nEcx3Ec58XgDiXHcRzHcZyXjtcAz3XQ9j8HPkNVf+xlKo/jOI7jOM5DwR1KjuM4juM4DxFVfVc9H+mXAR8HvAF4FfAEcAq8B/g24P9Q1b/9yArqOI7jOI7zIhBVfdRlcBzHcRzHcRzHcRzHcT6I8EO5HcdxHMdxHMdxHMdxnAfCHUqO4ziO4ziO4ziO4zjOA+EOJcdxHMdxHMdxHMdxHOeBcIeS4ziO4ziO4ziO4ziO80C4Q8lxHMdxHMdxHMdxHMd5INyh5DiO4ziO4ziO4ziO4zwQ7lByHMdxHMdxHMdxHMdxHgh3KDmO4ziO4ziO4ziO4zgPxPSwM/yqt/4jVVWiRDLavldVQgiQlSBCFsg5lx9FWrogJW39C1QQiWRVMrm4wFRAIUhGUCYJaA6oCEoGMhABARFEAmhGNSECWZUgASUTEBBQFAioylhqkABEBAXNIEpACVrupJSyhxBQUim7CDCRVVCBQC7Frs9TLlkod63lVEFEEdXyeKpMIaK1ZDr6/jQhQciAZBCRkk4oz5MVCYKqlU4QSrqkudVvjBHVjKKkmioiCIGcFZFS1qyJYG1klSO0nEXL9TlAyFLKUMuDlaneAy3pER3yyYhKkZlc6qzcKpeq1FpF7VkziJDQUloVBGGpVS+klj1dtEoLSwRVVErdqJb6Fal1hLCQy3MNl0t9fku/h2pvd5GSVvdT5Sq2UxV5E4MideW+1tbj/eye4zMYgUjWXMsPgrbrcr3G2i3ljNS/syq5tktCiSIs2q8VYFLrv0oQav9RUq2UKQQ0ZUSky7nW5ygViwgE1dKNCKj0ctkzqioSQ6nXoS3aw2p/Hgm0dgtIvUaG/KqMUXt/kCpzRU4QCKm0Uw6BrEosnZ8pBJIupY/k+lzWNiLknIv+UusrEAgECeRaazkvJQ25yISVC+snNJlSAQ0mASWVQksTEHLt8lkV0VK/GorWCCjoREZA7KlLR1FVgkxkcmlnNR0Cscq5IgRKnkiVT0obFPnNhKpXspb0JtfB9GrTo1VniZK11J3UG4oJe+1zCmQJqCgTEKqS6HIfq4zk1htCy6/o3Knef2miJMyUcSU2jaxdj4x9VssDh1bzSrJ2aTqtsNTxaYqRTAIVVCGGiGZLmOlZa+urgaoHQ8lt0Vz6vNY+qQLkUre14YPdver/0jcDgpBzqvJX+5LdJwTIS2m/UNsLCCgxC1nKs+b6rKKQkVZXi0Bq7R7LuFrlEBsra9vnnJEY2KWFVZDaLjK0ldZxORI1kEXImmq7Wj/JkHrdmxJUcv1yUNhUXVDHxfKT1BbufdqeS4f6K59KfTTpUtAqw9Lu02WjlUnpv/eMy7VNvurXVc9Z6+mgg208SPUescqd6DCelCzIZOIkpEUJYSp2gLVaex6rL6njXtcX5T4Jtfak6ujcOgECRXdJZFIrbbUVcmr3KikjUx37zXYoMlCyy7WtREzPlzrLsleTe/pcpPQJzeXXVMfZqdZoBkRyu58qJK0yfoDW0gcJ7fkAJAaWZUGI9bpSj6XP9XGeaoOM5VMEMf2d1cwOVGu7SWnbVOWtXVf1ZLGdMhqEkK0+pLbxIBeVgFadKWgo9RsTLCGjZkvV50u6EEOsY0PJO4WA5DIa5aIyi40bE0ixZTchkiXCAuli5vbFbc5uf4D3vP8DPHN6G5YLJKwJokyh1GGat6TdlvUkTGEhZyUvM3lRcrrgB596L6fbyGtfcRPCMScnr0BX15klcnp2wXpzkyduvoK4mggxkhNIjJgtEZIwi5JzQsmsUBa0PLMKIQXmeUsIiZUoSRMxB2BHDMI6wuX5zHZ3hqwnLpcVxzevlbZKKwjKHDKSEyEJzzx9mxQmbhxdI6KkEJk1s07KYnWpC2gi5YWz+YxwdMTxdI2QIRLL3EOVRQIiC8dxxZwyF2enzHNiy8JmdZ31dAwEtvmSoIkpCCfHkeP1MSFMpb1zIDFBmEEDQbTY5Sg5ZHQp86dJhZQWYu1Xc9ohu8TlvGU6WXMUj9mGwEaKbO5SZj1NiGZYRSCT5h3p8ozTZ24zn9/m1a95NacXSkiJzfoI0sL1GycwrXjm2VN0yjz55E2CQk7KvJvJqkxxIc2JuDqGKMRpxa2Tk9IWuwtuPfkqApkP+5API8ZIypEprpAIKZQxX1RQzVzmhShl/FhrkeglKGsJ5BTRVSKlSAhz1TORQAKhjPO52HUBiBmihmYqKoIGEE3FVsy52BRZoM4Xbd6WBaKA5jIPVBQNwjSXeZKIFNtYYFKYxXQ91RYo8iymfnLRiWUoKONG1mI3NvuRA3s+1zKL9esyFudqA7RhSBS1KShCIFSdn9t1pkOhR4pY+qJUq32StaVRtOmopiPb3FZBpY4ZxeYUGy81tLFnUUVUCNU+hzLfQCHEgKRyTRKQnAkaCCIsokQ1e6WOc1r0/yLlqXLKxFjmbFmVlRRbKqswEeq4NhddHSLowqJaayVUDZ5r/ZQ8im5MdS5UZLPUYEJUiVUabK5StFQdFVWsKksbSSlDlFDnYmUuq7VPJi1/B+p8qtqyxX/QZ3xZFElVLmIZ+yTXfFOqdkYdb0IfU0Ito1abZpxDSJWhHKTY5WaHlwuqLQ6f/1/9kv2B6UUgo3A/DL7qrf9YzcDZy/tgkn048TXnhxkuaoZzE2KlTSUl1PSK1JlLn/7XiaZZqmLGyZ4ZWK5TE7ZqRFQDWpqAFcMKFZSlOCNINKNS6j2tMauRhpU/RHRs3ObsKs8mzdjokxlraCmeLwjVkJdYhJWetpnF1fLR0CdeVsdiT2xpKOZ/3DeLm5kctEyUiy6pAh9kr1xanQQq1EkntcOVPCIBzdY2QHVuLdU4NwNynBRpnTSbEq1CVJW1TXKreqgdRWu+E6FOTKvSV2XaK1d7fMo0KrWyKkNd1eImysQ4SECyOcZ6n7PBK9a5RpDewe+F1CcZy4Jdl7vcq/ZJsJVnnOQ0pYwNGKOhXQweBgPcHJNjqewpzCFj/c0GUOuPRa5C7T9N7bUMtIpFypkg0+C83J8s2bOPkwzGNh2fffheWzlqHxNp8mgy3q6rn6PV8eD8Mz0U6sSU1k97OUpb9P5lDiMdazh0+Wi6qvaTnl4JZkxQ+1S9rTkLWh5S0nex2h8MrK/2WU1vO3P0oVK0V6iypNXMGnTt6JyTel8ZJMH6F9KNERnu0pzV1pLWpWvb7LVB6P3J7if0cgNkkTqg69739uxlmlXu32WtOjWGvJLV8uBQDM37rMP/d5lruqT1/q4Pra1NL6ZBL5Syld6bB7kTYne2jkom79d9c9YN7Tg++1iH7bpxUNDez8d6y9WZ0ebU9T65GjgmS7l6cIRqdOWe96GT15RvSUtZcNA6sRVbjAg0B9ieyiumodVz+0mkOUOLmtrXvVZKk9luGtHGWKl9s7kLBzmWu9pZbFhu1+7/Pjwr2sZtbb91zWW1auW6Zy5D23ftKUgsE6hU+3+oDpU+Jmt1UlUlYXnUOhYxvd+0ZpXHbtRaG4g5lOrtgwiSqbVWCID5l4JKW/ArctoduLkZ4lqdzrkYtrnYGInBkdPqqztETc6Lo1SqqPU+V2RRyFIXbkw3kWpZrBWk6aBDTNbDkH5si6rVCdLHh3s5dcqfZtuMzikd0gzjCTTHsGALiTUdtDGkOI4jSysJUBckGHJv5SoeqzK5DaE624qtpMTyt5aJR0aICmgiVqdZsRdCcU7OibTbcnmx5Zk7d7h49llu33k/T50+ze5sy+VywfFGuC4RjTCpsOSFs/M7XF6es15NrNZrsqzJeSLHwFYicbXiSJ4kHE2sT26ykZNqM2Qu5hmN5XOIa6ZQ2j2ESFpK3QrKNEUkm/2sbFMmEsgsRIWcEykt5JTJS2a3nHH72achZZ56348zbdbcuP5Kbj/1g9y4+QqOT57g+NoJ0+YGmS2ahDlmVCdiFtYaSTqzCEgUogRCOkZCRpYAMZLyFg0BkV1ti6IxVMtCstQFzDApZAhzZreduTMvrNdHHB2tidNJ7cIrRHNppzyzWy5Ylh2RHet4zNH6mLAKhLAiTcKKiFYHS5ZMJrCS0o5Jeh/MklBN5O1MUmW1WpN2O1AhrdasV1XzqsKyZV5mLuYtsizkJTOnBQnKar0mLRtWFAM2qnLjxnUu05ZpzsQbK5btDmJkE1ZoLvmdXZxysbvk1hO3WK/XZIrT82gJECDESMiJ3e6M68c3ucwZTTNvesObWK82HG/WyHpitVqVhXIRkkSQiRwW0IjmHSudMNtjQYixzN0EiKHYblmpk+pMyLksqiukEJvuySSiZgIRZMWiiSiZhUwOlAVsrU7dXB0QQh1nAlFpfbfYOdXhPXR20+FYD692aZ3IlESBNo8bacEUlLYeHUrUeUAedI4tCrV5UL2tjWyW/Z4NZ2Ub7fhagaONPto/aF9kLPMHaQseNirYdbaYlJtuL7K6AJpzmfflTAyrskCm2vKQahMkXZpjR6TUeaaMT5N2x6E5Y0JWFl1AJ1YxspCZmREVJnKdk3XLM421klORGwll3tJX2hAbK3JzpZUnGpotoXXhxGq4Nlwd80qgipa2rYt7ZQGvOD3bAnWuDqRQ56dDkAFmS5usqZghVu5aPIR1pLf6lOo41L32F4qzr8hPaLZEe7ZQ0n/+r/nFdw+wL5CXxqFUB74uZsPAaTeuxoIOK4DQI2vMMpJq4peVb7AJk6oN9AokQlubVlQzSHXYtKXk0RjETBWaq5FxQmQGhNSB0JJWT7h5PKshVsoZWpm1GqnFQLF6yM3asfoowjZMrLWUXSRaEBaHLS1D2ZvZU4tin6sdd3dUEb2DlEl2EVQFpDqNLK3d+14T/zGfshJfaiGZYWNlGgq9b6IdtEhT0tV5qFRDmxIBogem/CBL1vHMP7hUg1ywqY0lqvVdI3qiGY9DeotOKc6LMrks12gdbHoU0RiFdOgc3WuvuhJjk0xTPFbuLLSyBkxRyJ64mgPQHE6m/POQ5jAPbd8eRP3UNu0y2Mu8l64OiCXiqHvoA4PTqQT+7bfpINcMctMM6QPjXg/qUO+VpuZuKZoe2ZskmNOhOsfqqlCTx4O8+wP3su+1nslMvjtqLLdor1gNv+LUFHJzFFnEXxiy6//t7bI3G6n1y0HxBvVY/rZ6tV+rTORcVuJLdJU2+S+DP90o2itPTVP7gTmjet5mqejgWLRBu7e3VueG1gHP9POo24QyGS3RmHfftw2O2p8J2JORqMWZtNdFqhGkQz0q+4Nn6W9lXGgM+jcP9zD91VYX1ZzjZeDugWWhynYqUQrWlwRCjcLQIMXA0tqHhw5resvqYZTTfYdSb8dyV4rsl4ap960ta+NeLk8f62qZkqo8BEKuq1Zj+466S0rkUY86qlG+UsfWuoJ3XyukOgDzIGgKzXiz0WvvlnDQWj3l3tPL+Pvdqcjs5rkAACAASURBVDn4WcYf7lVgHWX6HkkOVbrs/2glbH216ROpMr3vyFDtjri9hYpkjgRtCzD7jk9tY7ZNmsIgc0EHm6D1KdPg0ozV9hijvkWxlWGkRmZKjbHT7iDpjlEZmsGiwPpzl5Lk7tCrTiVz3JkTKFeBsAjj5iof+ujdzqQqk20cDAeSWPpBREi1D4l0O0GH/Lq+6LbEiOmFHnFWo3o19+jD3MciSyNabIeltmXQWvcWaaVFL5hjRXvh6kJVqCvcguRSj0vIRKgRqkBWQlJ0NzOnme3FzJ2Lc07Pn+Xi9Dbv/9H38tQHniKuZoIk1lKiyWKMnG9nbl47YTvPxa4msJMVCxObeB2NkbA54uT4Sab1NfJmzSZGRI6IeSHvtrz79ru5vn4SlRJRN01VF4rJU6xtX2Wk7hDI8wJL4vziWdKSCcfHxCWTWJjTQq6LJKLKFAO6zCwXOy7zGetr17l18gq2GgjpkovdlpXOpByJmlkfrZjWJ+h6wuIMSLmOwVOZV2eQuCIzl/EoT7Xtl+LgjJElpeJE1QASEU1c7i5Il5dsl5nVdMxqc8TqaCKHTVO9k8KsJWpgViWGyEQiKuxQdnlHTjMsSoywiolpfcJ6mgjrdZmgh4AsoThEpmGcV0hSpv7L5QIC6xhIaUFziU5admek3cz5+SmboxUylSi9Ka6RuEakOF9UI9MUCas1UxR2l1syyjomzrfnBImsIhwfXeNyp6RlYb3ZsOwugQVCZJkXNqs1F6d3ELng5OgmgZldWMNu4c7lM9x84iYnxzdhmYmiHAUh7Wbm3TmrkzUf+to3cHR8kzApcXXEyXqCHIirFSEIOU6gaxaZi3ao43eqi03CVMYmikfJtNukEzudWceJlEpkk4oiWveKaBo6e9HHkwpLqH1TLa0yIXv29P4OmlHnlHql2tTa9HPC5hNFBXc7uOif0BeV0a4npTv8W7RRK/JgNJjCORyolLHUZZeJLdzbuKFthO86j72qqVNCIdcIJZvHUEtWY5uqw0n3xp8FZaWRlDOrOo9L0p1fZrMVJxp9ftMeSYYnKIvpoTqmYEI1EWKJWEJhapFIocyjqQuY1VrJpVGKHVhbLee6iCgW6FEdQrW9bIfDpFX31nVOW5jpNnl5PqpTCWr0mE3MtUa1UhxLOZQxxOYSxTauYQfV6WfjZGo1XSLurKH6QjmDbSU1qlzr1zVoocpUj4gq8pk08/mf9fAcSg99y5vIYIhKjwbYL3GonUabNMvwsLb6a4JQGtsih6BthWriH7sRhYLkwQlCXRGtU7xqjHU3iFTJtiiYHrreypXND7y0hymNNtUeobVMoRlmtjUrSDHAcxWQ+oT1Dj0Cx+5qgzLD32pCO5pOYp2dZlwJlIgm+hpvqL9LawcZDM1aQQyT9PqdVsdLsN6v/Z5SDanmRYfugGFwrAxlb601GNZ7EwDpUwcxr779TWd0JqnVXTXEUuyrnWHQuWV7F1aJTda6s6KUNefeOu152S+/GZh7SrepvbvnH0BTwuZoMUefMISdVkPdjO72/NKdDBZuX+qP2sqmYIqzM431SjXwxZ5Ja7RZVdq1zi0CJO2Z5iYf+/nZxN9sZKn9LoZQw1wPB7Z7TBPvMbGpFVX6pfSoor51r7ZF3c5pV+XaFmXrQNUG1Sg0R5Y0Qx5yoG11a7IautzUZKWtzCE1OqVqucyJpZRNq0rCQopFQl1ZHqphv1L6fQ5U+RjpN14n1om1Gxi5rloHLVsIQnVeqnaZt+0ZYSiP9b1gxn4f1vf0i9WnqhJCXQUJQ3ozjtDqkAaLpCxG1F7PrUZVoPrZ0fY/9p7YxuB25ZBPGhupXtd0mxzWc08DPfpv/GGIhRkcn11HNkdTHibSNjhrjTCxzlDryiauZUXTokG7vOVhXDQDdbxXEIEwRnHVhZY8jnu614+C9G2jxSFY3HO2wmdbCUukRGxbSMeIxOoS7e1glSrQImur3MtBRZs+tbqzccHaSnXfgbSvo8wIHX4U++3A6K36sY3v7dpBy+jhtVUzN6u+/w6maoffDxg12GHPHEfytjqo9+7bh/QFJSXEYiPEEEiDl96qYk9PDpXXVp1r/Vn/MKfy1J6z9EfbtpCllFqrd8oWlGxMslHJ8qWp0Go5VOND2l4Fiq6XVuKDRQzo6+dUfSsHlRRQGdLsXT+OGbDfFr1ZpfbfJP16qdt32yTA7AoZM9Rmq+yN49LTW5+zxag9ZxPVHab2mbY4qtWxuNRILwlhT+/ZoqcgaFJSVHJaUIF1Lqviqx3MKXG6bFkuLtlenLK7OCv/lkuWNEOe2Z5fcHkx884feTdrhDgpF/MZR9dvMcnEFAMyPclpPibcvE5YX+Pm+gRZHaPTRJyOWbanhJxZr49ZJJI0ofPCxe4OaV7QkLm2ucZqvUKILAmW+RIBLi4uWDQTYixBhwKaYU5zrZ+EqLDd7ZhCZL3bsdOyrXgKAqFEO+e0ZbvdQg6cXLvJzaPXcXQUiJuZzaJ8+EdcR1Lix37kmKduZ5aLHU89c5v1+pzN0XWmeIRMgRgDUVYEViyqhBhLO7AiaNntUKLJAoskQo5EiaAzu/mSOS1oSqQsxLDi+o1jwmrDcTwhibIjwzoQkyKaCAmmqcqhZiTXxbhQI35ihAgpb5nnzOXuApHEtF6z2mSuTbeQsEFi6acWbS4UB+KSlSCJi/Nzbp+fsuwuOT65CZqYRdkcrbg5PcE8b9ltLzg6vo6gSN6VaMOgzLqwm4U4X3CZdhDW5CmgOhHkhLTM5CQkduQcCHlhuZgJU0DiijhNJIqTQaaJZ26fslmd8PSi3Lq+YY5CmDboErlz55ycE6sp8kwCXTLohOwSt++8m1l/iO3FM1w72XD7mad48+vfhK4CtzbHbFZH7LY7dA2vfe3rWa2PmcKaECckRkQSKiWSM5ljV4U57BDN7JZU7Y06JwqK5FRtXgGpES0SiuMgFRe3Sqh913SbxXla/67RHqb/GOalyrD/QWyA31tg21u409znWFVTm21fZxx7Og7TTVpTj/Z5Wwy09PvGkjk82iLWoOf2wi6GoVTqc5XcIsoyzOFsalPHCSkRiqmODTEUZ1lYTexSKuWtwTZTM7OK49/mZLnWTVloL6NNqHoELQu1C1o2oYaE6Kqq6NQjgGq75VCOycl1YcRcLKgtSoYa0NIND2unsihcQkeklmeMFpNctnIGsXrPPVpWS7uUIIDBPgVKNHso8+dm62u15cu4YIsV2gak4hTKmtpcZaL/nkfRHJyL5vsbfQqx7niKtQFD7vL1MHjoDqUyWalPV5fiiodUsXidfYPIjJNutZStZTboW9SOGa017+7G7EYuDEZQ/S1UxS5mG0vrkAwB6EoJ7d8zGKuwifTVX/N+hiCIlr3qWbV+tz8paUoImjNsmL5QvNl9wtO3/vRVYCuLOYDUJsCYc+wg4oLu2DHFZQZpN80PVM04YaZM6NoExVKHVmWt3WyLSWsSq/P9Eg/zgyHiwzrKcE27vwoicXwk2q33JlHS8xAzJms+g7UZDxRkEGHJuZ8nIzJEs0irjzY5bPW3HzFlq58jY73376SvqtiIEQOac52kSiubRUGVCaXVSMZWTVbBJo6pnlMmNYKp0yRozxiuZalaZjUouzImlAlm2wkqZbTsE9RCHlayEcqAUeuvtdfQp7sU0H6XoX5p6RPjJHGMimv9gz4BOXSm2ndIdRjVejL5MKVqjp4yoS7nb7ULrf2G+zcPdC1BT2k/l9+DUFfFTQeWckcpbW/1Ym0q2GoXvYwHshOG760fYXqwDnSiioaytzub/SHKJLUf6aDlTB+0LaxdHzRJqf0RHdafpOeDjk482aurbiwdSN1goVjklFaLTCW3c36gRzsIMl7W6xvaassYglzykyG9Hl46FGeIgBtqQKoz0PRekx9LVMvTt66arirlDtrrxFam7FiFoufLCpr1CXuWsR5t/NDcF0WKEWiOzd4+1HoaZVYwJ5HVx7hFpkTTldVGaX151G8W0VKco72PHkaKjP1gjASzshVjt9SBDo76Md7Q8ugiP2qKu+9zOHbxnNceOMLvldugpu++85D8LjnshicHz9R/NydT7w9WT0U3l9STlK1qZSJTztwRHXNqzdjy7/Je9FmWct6XRCHlxCQl6qJaK5izq/Ude3hrZ3QIuy/fR7PJJBRjmjJYlK0i0hyGUr3ObUy/Rz0fVvAoDYOY1u4bWtr7yV53qfebtscy3Vnza4spNWWJdB/2iJq87o3XY+77W4hL1dVy2cKGdmkzu8kc7GHIrUU8qdatF/15rKcSBEkzst3xzJ0LdLlgvniaeTszX5xz5/IOkjISIJG5c+eCMK0I05pnz7fcOU+sV0/y2p/8RpJmEokj1bLFI5WzRm5cf6I0fTxCtzvmJSNBiWlmt90yX56ic+Lp9D7mlEgIR6sTVtMKFWGbtoQ5cHl2mzktJMoZc0EDeUnFaTYJMk3lvDkyUz1DSco+F1ar6ySZyXkpsUQSSfPC5cUd5u0p02bNenODo9URt47XvPFVyi/9+FexHC8sF2e86cM+mstn/h3/8G0/xvfGwDNnx1y/eZN5WUiLMi8zu+0ly3JZtqWtT4irDTFsmGo8MTWqIsvMnEtU6ZzOmOdLclrYpS2ro2OON9eIMRYHfCxnk5G3rOuZVqKZ9SqzywsSI8cxcqmZZcklqmZ02iclSyLGSJiOitNPlW2+YHuZ2c3vL5EeMnM0rXjlyTE5JM4uF+ZZ2S4LOWmJIpuEaTpmCTPHYeJk2qAaIc6s4hErXQOZdSx2CVKjv7K2kfaZi3NuHCXQyEIkrlfEWCLj2AkxJhZJnF+es1oiJ8fX0Bli2jKnzGa9Zt7uypiVFrbbczarI+AY1UDKie32grg5JobAHGFZit6QVdFSxzdezayRcO0Wz6Y173rXO3nVkyeswpMkLoHA+2/PoAsiiRsnx6w3R6w3R4Rp4jismVZryIm4mlivN+S042K7ZbPZsLl+E2SqHbtuFsplq16S0h8zikQIqWy1s4X8RQCNJY0oEa0OYJBADxLQYQxVW9DPwzi9v6CC9oXJvYgnEVRySxsPFVK7T9exNq6083SG5GYPhxDQnAgSa+Rmtx3aeK19Z4E55pstrkrWhRD7+Y1l7O77xso5x7V8dWAPUdAl1TllqZeELWaYbVuic7Rui402FtR/udpPdShlFSKaQ7VHU4n50b4QqWg5f7SaSVEEJRDEzsXUvSotO2NqPTYHnYCEMtblXCIxJZDKlhWEOq+s40au9S8S+rOZvWY2nQiEwJRBmVh0KT4Di8oUaMcBSJcnyEgQooa2C6gs7tYdU5pb1LxSxiUofazLViaE2P62he+7x9YXx0N3KJmglo99olE6WbAEzZHAkKZPIKoxQyDnNORdIhj6pF8ZPbTVROu2imozZsEG/Tqok5AaW9A8gXXSD+boKSYZzTQsU/XQHGU2EajnHex189KAUg83bk6poTTQDaA9Duyxu40S6fVsxmZNkpogt1uUctncZPx+uMe9Jt+t/Q4UYXGw1pD1UIzMsi2iT0K1/df+vwena9h3KAwOVMw8Fz2IbBHpK3nVYyvSVwnb4bFQV19rWapnum9hVmyfloWaUjtvN9dpk1bQvjVFhjlGHRCGL3ojcNDS0pX1FEopl1SMCguP7du7qlxpz6NtMAnSnFkhxDZJiGIGKZiT8m6Tu/42fGvRUYKgsZTBwnvFwj5F9q5rCkpH+e+r0Wk4aN/GBHOi0JyQpvjH9i2HSNpErPXD1gxVjszBI+1KuhPC6qv22D2HldVvue943k75FFoZUW1OBcMih0YHeC9nEYI0XGCRJ5kuFubsrmvRtS771sv9CSNlkrdXdu1n+NTy2JZJkXqmlwlr7mGtCrZOU16G0O9qWmhQDAcKxb4NQkq1/w3V3+ujPl+t+3038n60WdOEUp7FVpWGu3e5aDkMlaulj1te1kf3hOAuTB77Yf62Xa9MlMulbXtr7vKNlnq0laDRxWkh7TI8V++LXVe0UjTnWa+nvSLXuhy3fEKPlmsP2HSmUs45MNm1AlQHcU3Too9CdUI2p1I3Qi3ysNVWE8TDmqy07elStInWBR8oY0Jrn5r8rhaxOqHmoeODYe0+SlLr9mN7q5W91033Ava8rKu2b5vjdu/PphEsq7sibartYc7B/Xqvv9vNRt08FHy0SiDWG3WtaGNac00fPk7NrfXnUCKcVhbuX/Vb6ftmrdAnI1Wwg0p5OcCQf5NlKcZq284vpVqTLeRVHWlOUat2a3tzrAxDaS977Xv2xHaYq5p+bOO9lahfZxdpGH4Zvc+hRyPZ9gatsm/PPYqQYvYPVbfsO+BbM+15IKVFaZutYNUoMtg22mUmKNU5t992URVJSlpmLi7POHvmAzz1vvfwb37gB3nljSOOVonV+hohK+fbLRrXxDgxE9mtN8CG3TKR1mtObhyxXq3Lrp5YbI1Q9+nmNCP1HLRFFMnl/JHdLrOcXbJcnvPM+Qc4v3OKLAspZq6dPElcXYMbsJvXEKhbkZQYNgSZWJkTnQibauVVu6G0yELOS40/jlW/lsly1mIzXOwuSMuOHJRr12+xOdoQZc1GIkdx4SREfuh7n+ZNP/315Bj59n/8z3nvM2f8yx9WznNm3pUdAzFMTGtlYmK9TCy7enj25Zawm5nDKTCR88Lx5rg4xJYtkcC8JCRAXE+sN9c5lluEIGWiyFLG9wQ5zaRdceBGhHnO6CqWF9NkOJ0E0aVGbi1st5dICOX8qHJwCplcZV6LIy6U6MHds6dst6fsdqdcXpzxipPrzJSoVlltYLPh5vUnCCGwYsOiCdLCbrcgq8hOl3LsQ1q4uDhnvQrkaydkDYhWN1pKdTu38IqTJ9kul9w+fYZXnNwihImkC3PKTAF22ws0Z+bdTGIBDWxTcfxdXJxyLJHz7SkfeOp9XCxnzNeusQorjo+OkXCC5sw6lDOXAFbTGpHinAtT2UYXVTg+2kCa0bTwiiffwNnZj/D6V7+GRSJHmyNyXnE+n5FSYpsvkbMdyHnp12lhA1zkhc1qA7pjmXfM8yVPPvkEr3/V64ibE8J6TRRhkokwTQRRQogQp7LFLq2QkAk5QSo6tZ39V/twqv2+DT11xUiRMva1l+QMI6mZzMO4ZjrFUjUHhJaX5MDgcFHTi7TrrG+NtkWL0JF9e6m9eKVm1iLcq/62BUOVEu1u80CbU5k9Mjq/2jEBYlZV15+2WGXjXHmBjLbDyVUo8m4GczU+hkDXPXtMENr0OSsaIQezOWsEqJbNZLnOj+1gbNO3oer3uquxNZ7NFaTakBaLZeWAOs9EqG+Fqos+2mtS7TxdbYt0zUZvdnFf7Mv041Sao8y24yLNtrXqlZq3UJzeIpRt1xZ0UMdfGwPV7Nn+oHXY0zon0TqXhHCXVfbiePgOpSb25pEtD7q/rkg9r6ikG7en9W9LeFYyf5IwCHezmeutuilvRlAx0syMq93fDKY22bV7N9Mctf38ZjRh3t9Qog/23p6mUN9KsndWkxntmrrVY5ZwO2DVtvFV54FUQzD0cvbJpu6VPZgTwszEwdgZlU4LdWuV1T3ve81haYHqMm2GpJ0DZe2j2idO9hgiNMOqK5leqyI1cgPQMHpNi5NJVZrTZdw20TtIn0iY3WeiUvzjPeKgG9AW/qhtv5sOh9HtO2G6QrcJXSv9aGsf1JtanR44H4xWX/Z7CGgqkwRbyWwCbYZnVew6yKYd+oxqb2qlGetWTtPpQxfsVWllap/LfW0rpq2at2bdk8PxefvXNniNv/dtnU2N1TLYIFkEpuyUCC1NG5bM4FYZzH7THzbBovUJ66ulPoLtji1KWfpVTWAONJFIqPuy6wAgFl1S379YB+/udO76o2iQ1CdW1ugmoW3Zf6yDu/9rMhvb9aUMYRjgcm1Y0T5YHTojWsRRH9tLXkP/LgPnEI1S81Vb4WmTuMGxaY5Me35rFesUau7jkVEI+1c9T7DtNuMbovajRAf1NZSq+C6GCB4Tchmv60ZVy7HqzPFA4bYi1eqUpmSsTYszpj+PjUOt79o9W+cbdJz0ODBt1xbTL0g1emrkUslS2vPaW/b6Y2q/r0obK8Ztkqbyka6ni+1VJjDRZFUEsgxjYRsu9yfEQ73tTa5heMa9BkLR4YyBLjRqcqjDNXv/HfT+mOfYxoMBPV6rY16Hv9dEam3U0owK3RRonwDsCaJ9tVcFXeis+HuOrHpGoTmEtMq5WN01o7yMYkEtgqa+aTLU8asu1hyWtslWPdFUJBJqBLCNIYptge+RSmXyI22iMTwQEOrEJjUdrNodqq1K6hhZ+tkQ6abQe5IOR26UOujnuh065OsYLrS9tjLcTAblsyci48oLXYT6WCi93yHNlrDRRcQOYB4d9aPo5D1dUpo3k5ZEnhObo6O9Kuw1Xu9tEzSsDk0/l0XGZZ7ZbXecn57x7O07PH36DLu0Yx02vP5NP7WMMMsW1TWJhXwjcJkUvVwIcULDCpE1q/VUX0Qi5FS2+qSsBJlZNLFJlLdjaSbXt5mlvCCpRPOcn11wubtgpxMnt17DhJatRbJCRNld7sgyE6Ky2dxgFdYoub4pS5ibAykTmcqiXtX3ouUcn9RCaDNZt8zbC3bbU2RaEacjjq/dQiabbAkxKBoyZ3Pme39ceed7Mj83vZ9wdId/+t0lsun2RS5vhMqKaDm4uhyEn8riViiOLmVhuz3j9HLH7vKS07OneeWtV3L74pw1iWvXbiJx4sb1W6Q5sZ3P+sQPyLmOTxJht2PenrM+WjW7TacNmVzShxKZsuTMkhbmZVe29KWp2Bu5vLgmEMgxFV2ZErpAyiWabMkKqxPO4prNasPJ6pjVasUctdxFA3O1pWJcl+eTgMaSf5oim5NIlBXnS/GlThrJUrdkSWDShYsZZp2Q9U12uuJiW+z+LLACkq6AhKwnclYuZtjNC5eLsMiGO9szjo9v8N7zcy7Pzjm+VK6t1yw5sz4KrMMRsppYJKMJYq59MClZMucXO4IkbsRA1oXL7ZYbq2u8d3fJcTjlLCnn55es4zGaFiYyOU2QpDjopsDldsdM5uTmTVSFJa/Q9THr4yc43c18/7vfQwgQw4RmYbUKTJNALlsSj0+OuH7tOmG1YbUqbxbTBPHaEevVhiCxnk8ZibGcq0WIzfFcbKrcggPaYoAqUephzm3gqNpF9/U5SHfwUMfxQ93WhkXtY+mQR9Nb4xhZFE/Rb2bjDmcp2Zyi9FNsrRREij5Jua24tHmvDMeDSLf9RNv70KuTueh/23680z5ZtAULe4Jif1QNWstlZSrjVl0QDVXH1vl2koyQ6hveaiS2UMek0Me4oL3+bP5BKg4VKXanhY80u5kauTY8d9vh3c7gkLL4qBZxajfcG21bbtZQWbRMD9pxKKE6knrbiTV6M3J6s0q0l0FoN0NDH7/2BCCYvd6uuI/N/uJ5CRxK1mh9wq7DqqslaDZbHXSboUkeDFrtZ3OwPzGA+lrr6ogZuhN9z2s1hNU6Or3yqxPDOmgzEZt32MqqNW0ZDJtHcVQQdfJaDum1BMP9EbpjSdu8YHSgFaMpULb+1FUe1b3nzuZJgCJoNeStPEP9fzGDrXbYFsGlxTiqb+QqhpX21bNBFIuZWPcFa+6e0mo0mCHYFJkZ7QImpjbpsteK7/lBhSGsv/x3Uev3cth3uqzQ612C/VJqILenLB3YDsCVYIezK+bmbiGUtY6KxJjfyZSsTaiHSAUpyjEyKGIxdby/Gm9NVNqkXqx0J5BNAlpbySgUYwZNbm2iYGkVyiuK62DUV1DHSYz2NjNJt/ZXbeGR5n03edg39ntdFWo8gNUrB4MYXSbNW94sL2iDhOWRbaChTJza2T3VW2nRRFoN8UPJaI6Z3OKMSvvVAw/HWaR0td4mTCXE2VaDbGLRJ9o63mPPYWo9pp86Yu1W9uCXQUbyoC9aW9uAXK5s4twVV8vPXutKddjZSkpPW8rUVZG0LWq2bdMUn6idHtejb6R64UrTh70nshUdG1Atogf6NEuGMu89Yx1ILfIMQtPnLQJseOPYfiayVxdtsmkpuoqlR6kOho/0tmsVa+WvQ4K02x8MwVaP1vOl38/64Hj/PYkcFxvq/1skYK+bot/tTR1tcm11aXmPS1T2eOY5MUfFqEfr700vWz3paKRhx0I0J7KqORzAXoFuZek+I+lFaOUoiZQx3L73ye7ItbF0bLj+XC2020TGbiz9vkPllf+G/qfWvMZbabsHd9OKM4yV7QezC57b3JLDfO1+oT57UbSD87Hrrv1obHqbSW+7aGeV2crbIArNYrB2batzRe+kmrm9o2pPjUt/8r6Kvt9/2t/dk9m2QrS+JYG2xbyNc/uVYm/2ak+vtpgiTY50tGmgrap2B7fWvENzyHUnUtfOrU0HobVx0vqfRU+ZzJT/7l/fdMiQl7Q2K2e15DxzfnrGnWdu86FveBMhxj2576OXlNeSj+VVYM5sd5d84PQDzHNid7nlcl5YdEJOXsFRCDAHwu6CZ8+e5WhzE/KahR3zsmO3S8wXcHR8xHp9RI7FtgxaJ7Z5IaOk7cz5fMYqrMvK/VyibHIsK/MpLczLwi6DhhXr42OOphUrUdhdINMGYYWGBZ1nlt2W7eWWZZvKW70ENCibac20Pi7n6wRhCrncX4WyKbNsG8l5y5wuWZaZkGcmgaNrR0yrIwiRoOV8Kq2TcdVASpm0FD13zsLb//X7ICfed7kgwDOX57AIYZpQUZKm4hAgFd2uYG+jOrs4Y55TmSSvbnChK4gn5LSwzcIkgbPdUnYWxAgSWKTYBiGsEJQZZTo6Yb3awFSO/kYDMxORpRyFkcvbxKacmVaJjWzKIkabTwY2TCiJbdqS05b58pLLZSEtC8jM5uSIJ65NnJ0lRJTrm8BmPXGeFy52FyiRy3nLZr3m2nSdvDoirCaECdHydqe4Lg7tlcRmQSniMwAAIABJREFU1yQSIcNUv8uSOVZYSbEIgiYi0l5rvoprlmXHMl+SdUFEWUdhc+06IrDMt1h251xfCZv1MVkhR+VyK2zzJcdrYQoTqyhsVscQVmjIaIaUIa6PEMlsFdYxksKaVQy88dWv56e86jo/fnbGe88Tulyic+Zid8m02iAysaSFtC02XpTA6fkWSUvdlhhZtgvL2RkpKkkXrp3cQFhzeXoOOrPsMsv2ks1KmKbAeopsVdA0s9bI5sYJN06OCBLYLZkpBo43G65fv8Fx3UYnIqziis1qBdOqzsnKdjrJfQeFWuQopsul23jVLunLklWvmYPAzhCyi6s8t4Wkqr7aizRsvm1jSNPPXQfZ/BIbq8zKFesv5VyfdixD02F1KbM6hsqP9qb0ep+mzcv7G1OtC7PBWhBBjZIptVOdNc2+kWaL5JDLGVliuzVyyVBtW3c9RKEdzqnd8lQLCtHiQAxCSgGJtDlibu1Qn92KYHWCBUVot3fa2GMNGnpwQ609zPaWVmu0eViVC5uPBLWIRWkm0jC6FZvfotQPnYEi9Y2ufRzdi7it8xBzQpZIWT1oq4fDwz+U2yqq25vdMBkE0r7oW4uk2ZFqoV/mvbPMle4FZhDmYUYjZoRhjRK651DGDlamC7luiZLBcNdxcm2tIn2lzvpsIVZBGLbmNYMsNBEqJRH6C6lpHbnYlLUzM75ZROrv3eC2FeumSGq5zDlkRljfL2kdJNVJbnk2qQWwUjGUtE9StD5LKUsewjttz6ZNHOxsq27QZeygaFNmWmNHxR6mbZloFvdgfJdnGVdFhVhfR9lN4HGS3CZ6tV7bdsLBmC7RTFNvI1Maodb34I1uctY+00PckR6mP3RkS78/NFRvu6VRyjaDoXBti0v9nMc6qbI0/mnNaoNSj5IZlJJIM9rFbswQ+Q9YiJm9ccG6Y9+CVJ+gytyh4wgsf3vSgyo3Zy5afKraB1KTQXM6lnLUrZRtrwDD+eQy3rTUqchw+FzbzV31g7WjOU67PtFeU22CbJMfzRaVQo2G0S4Dre9QoltM5pvc94iScRvQXiis9nJIK0dzCdd6S+1sIR1kw7BziPYCIizHNpbUa9oBk9Yow3S5VVOf8MkwQe0vOAit5rQO5CW6x9b/zcmorZ9ltL7Bqevt5i2suRFClwM1Pad9YG93HRgGdpML0T6u7K/c7fdRcxKZGNtT5fqvOQEavRw65NV6b5WxdtZYFY6+/90cNP2aLP0sqaYLCd2PYg67oGgetjUOfU8ISO4rUkouod5DjTWnuOigq8r/WRRn6wNmjB5U8/4XtW3KI5e3WGnrNW0cb20wytxYo6afrXqbsTro1l7796X/NrbxXS6ovfK34oyKtDfw3g3386m9dE9p9ITdKLevTPrGRR67T/9uf2uarWVaVv0eXZ5Gx5npvuJIkrrN1QxVc2x1JwdtfOtLVQcPTW46zVbCxyrodlev9f5Wul5Ttkho+r84vNNQL+3rPqju5bG/Cj/q1T4km4zvi5k5VntbDOpsCD8zuWtiam//sYOaqVv7tbz5Jy87VCKaE7vtJdvdGWenp9x84gmaX8zsoGEF25w3u3nmbNkxn265OD/jvbefYrM5YZGJzIacYdnNpDSXtswZZMXl+ZaAcpFTOWQ6BjY3rzHFqdxPFiKJlGaW3cy87FDN5HnHvLskHJ2Qw8RKlDidsEtb0rKwTQtLVkSOiVN5WYKIEEVJ5dV0aCgHwYb1hpPpGM3KnLdkEttlZpkvmXcXrOdzVrJhWk3oFOr5SUfsdqek3QVzWphZCCETw5q4EmIu2/bn7UxkIZE5z4nLecc8Z9ZTjZDKxSZImnhfyGjKLCmxk8C8XCKJ9maw0skVicokkRAo976Y0SWw2myIqyP+f9re7Vf2JMvv+qyI+F0yc+99zqmq7qrunnvbHhtjS7bQABpheAAkHvxkhOQnxDP/DhJPvJm/AF6wZAkZW4axZLBHgG1mmO7prq7uqnPZt8z85e8XEYuHuOau036YaXLUU2dn/i5xWRFrre/6rhXDdEAVXmEwGlnVc/Ery3JCEEZnmcc9wzhh3AAxsVSdKk4M6gzBpnXkQtEhaU0FlcSYMAbLQKVNi6RUkxhZ14V1feDp6RFVj1pBSDWn9ruRv/EXPuH7Xxz4R//8p7y5mfHLhYenE2cPl5Ce5cOG0Y2n5UIcZ3bTPtVmkgHrBlTLWbMhgeQmscmKTCamhsMiWGOREIkSkLjmGlIB9RthOxH9lvNtQDGYsBGiZ/OSdLpqKqqea/RIjPjlzLocGTFYO3CYdwxuxE0Dzs0M1jDbEbVJx80xsIrDysDt8ClPF2Xxe5yNqFgmFzDDgKqAmhwUi5icO+m9h5jSES9qOF8WLudHEGH1F15tAWtmzsuCkbRzjHbHGpTjujAOliWCU8MFOMaFh+eFKELwMKKMk2PaP7GbPyCqGIkMMnO42THOO2RwDG5ADMzGYKY9VhzGuEY9qhtY8Z6Kr6KATaAwse4fiQGV762bWfLlevuvBq6K/x01nwqZ/TxNZVuIbX83HSUnVm9LKzu+bveiNbDbHK3S/oDEkH0aybrMtHSvDA4lK9Gg4rOdTwbBUhECzXafZp1C7rdqRHNplYAh1TZKJ+vGrJCbn9iC9JBqXpFamGsOpVzb5O9k7VFs89L7qpPTN8U/6bWkomg+zKsYbpXhJLT5KkZHzlYoPCVJM12Z3NWs6P7q7dlyEmqxJEuGSojlMLAGLhWdfHXIVLEDq+8h3fW/us//bylv1QGloKidwU+1TSvIkUAdc/WUZvxJM1CrhH/E5NQkDNXY0PZDMyi6yGNR+NlCjpraUAqBG2OJURDjKbTumCnQzc8obSmuSfepBn5mAKjtbW6K06vS1XSRhn7GvPE0BFWQkkaTh+FlfY6aslLACdKCrIJGfxQ2NEc29aU8oxWWlJRfXIy7EgUDREpRsG+b8en3xnyAMi/FFm/CXZhiIHmRSjO6s0Nex7iL4lLGpVuO5mpsC/OlXJ6X8wuARTKS3LOUCoLcgy8igs35076w0nL7JC/yFDW+3ktS33M7uhPfShRd81iYArBlObheBNn4fcl26domGq8VTzGsK8LQm93tvrIuyvyjOd+6yEgd4vSc3iEvTe38jHzNtZHfQ04FLTddOmW7LtQxT3tH3lwzpaRET1SlHoMK15u4KnksczvqmL1oldSKbE0W+rGp/WvqpC5fgZIeK3U+OxC6TmFqmKnRo/buBAQm0M92LzS113kcpABirfmVgSRFjugAB2nPl7IWpf2W39/2rrKCWl2tRt+lu66NXeG0CMW+kLy3dukvecWlDc7WOgoNeLke78ZKyG8o60O7/buOahqTsi/2gF76rs5sHptcS6VPG8vPaiy+PC6dTJcps1IiPW3PaiMkta0FDEpAqdT9sQdNCzUcyp5NXTNNuPqRpmtz+m/dQzvgStqv6b68Zgpft8JNJZAH9cTDura7xhTNoKUT5U3SZqEOkyilHkFK62nF118aY11D66ZRWT1S+txf1G7Tq8npP/qRf/GRp1x/IfVZ/Xva3/WdL2/UIirSi17XrAbMtjakce6LkeZA6xVwXdO+aoNaavBLdlxJMUjOqlIKwkIuOJv1UmPSSuuFlHX8sVHLMnG9XVLSMFqNuQbsp2sau6g8qQCi1Hfn62M/XC1NsuACve5++SlBtLLGrmS/TJG+7FmDdk2ewGqct+ahMQGwISohgxdRYzr9KwYkKlYco0snU929et30vZZmG2yM+Bj48U9+ChpxdmQJgfMaCWvAy4FtTUduq/ra5RQNT+yhy3nBCAyjMJiRSeASVp6OD8yDZRDDwSlvDpH7sPEcI04kHfphDuzmQwLtzci2Lly2hfcfvmEYHMN4w5gLZxs0lXMgHfCgMqQqo0bARNKR6A5DZDQWNTCMBuKEqBKiZ/We0/LM6o+oJuBoPZ8Z3MQSInaa0nH2mYUv24KqwRMZjEMxrAS2kMbYa0RMOqfSqiLWpRMQTcQyYlCmyVVWe7Ib8gmuBsK68bwuRNIBJvN8w7CfAUuUVJ9VBbAOKw6xA35b8H7leHrkcjqzG2em/Z5h2qcUQAyGzPgx+QSqxMcCCYi6XKw9p8iRmE8QCTGwLmfOy5HL8RFnNlBYfcD4gHMWZ5VXk+UVnnc//YANyhsnPEnkq+XM0xqIWKxJp9H5ZWUJJ5w/Y+MFEUNA2O9uMHZisAPGOBove8wsmuQTuJwPoxrY9MS2nDktDxyGQ2KwBY+EdCJfRNl8SLaqT6f2iUmlRaSst00xg7CFjRAjYdvACnYUwvEZK4IdDG4YCcBu2jFNexgsa4RghGfxWBF+/BRZdYCYSAFBfaphlPWJxIhTn9bK6gkxsqwnfARnHJ6AmwdEB4bxBiNjKjA/j6k+krWJERc9ohPTMDKrkmPWjOPIJW4QPNYCMRDFsm7CermQNrCA8SfunxzOJDB1GCyXdWFvLfv9HW6YGQaTwD6TTkS8u7thmA8YN+CMA3GpHIhoF9hyEA2RjXT6pQXN9XoVRE0FPwrUUPfX4kdhEDzVF9Vi20ei95zP9xhrMO6AOJcxT0UjqJhUj0uz5xRjYrao5PIhEULgePyAbEf2+zdEN2EGhxHqwQjl9OjawuIgZcUQs++Dln1ZK9ummnbV/081x6oGlRJMzO/J723M+qa9W+3MfHdM9YqMdH4FzW7XsqHn+bDZVst4Zs2yKbo/6VRTdZ7GWO0p0VbHV6To+itzIc2VNMJHT96PQmas5cuLnqVjCl/putSRMm4lW6HGwTT1+2VWzZ/38ysHlPrjWYvwFIOyFGYtDk6PERVc8yWlqxkVeWKEapT3wlIQ1zyKNOO+LLzOYEwSWpV3hKv6OgUcqtFiSQuoQDTaC4LE6nS3uL907wrNQcp0+Ooc1OcoYBJqWwGxFl3UTtDJxlhvtEEznhoDojiTMR1JrN3i6K1p7Zx2oKUcNhZEmddytzGl8n2s49i6XWfkyiSvBrrSmEn52xqBfzGfQYpxny3PUmOqGPBdtLHlueahrVHvdk2dl6IA83vL0FR57D95wyr39ql7lVHUdiGKsqsGef53KabdamSV+TIduyIZHqXXdb7qpi759IKESlfAJLOMpG98bpjVkjKVGmmK0HUFT7WcfKNgDFVe01ClnbIFmovMAFcyqC03+2r4urnODnmJmCRlkua9OKkhz012/2ly1G3CZa6qM9opAKhU4fLOKgd1N20y2zN1+nVblEkDopohU8WoG+yXcnPt5GQgorK1pDpVmh36mvpXtjCTCv2VI1ir9HWDK11DNFNmSysLU+sFspl69kJOi5NeZPl6l7iCZbKyK4wXk9dd4vckmSyKLmLE5BST5BykJxT5ftmuLFsZ0H4xE3U0O3149U3SKWRnsIBD+YfuNDPgShdcgc55PlKkKY+hZP5VRlUbs6e1vYf/6l5Po7s3hDTVHEnPTOutrUJqVLAUa23LPyNAlaUoqIaqX6+muJOPQrosxRcrI0PTnlvf1+/NGYDWLvUkrc0CGrR11YMxZX95qZfqMHdtrJqhArAf+XQT1q77+MWS21P/ePFjk8myYb6UGb7V5va35HZnHdbpOjFCPcTpatMrnX7ZlJ7hRN13q17NO0Op+5BAmz7g0DEH87N63mrMtkwqzi+1EynbPkd7y/M+MorXY1DmqPs317r+W2nO3QOKbPSnqpmuplQ7Va49v6TClWdVq+HFeJaTGEW+PdQ9GNg/p7xXRavNZzT97/HpgfPlzP5wYJrmpksiBL9hRBiMI0gpKivc3twxOsuHh/csy4lxnjPAYNjWjdNy4rgsHJ+PfPmLn3Ez3SBmZsnHRmspKmkDRgyDsUQf2daVbbs0XZCQbC7nU3Lwo7ByYd0u7MwOM1jEwhYdTibGQQkx6dFgBVWb2DynI8vyhLHCYXfAmQHnBrC2C2Sl+jpRHeocpfoWKvmEJo+agFFyalYC3TSshBBY1oX1vHI8P6b0Emu4XBam4QYZDtxMQyp8TCTEVBxZBcQagg4IDkvEWq2R/rRTupz5AFFiZT4Y2SCS1oUo1iSmdQie8/OJy7Kg08w83+LsBDKm2iUKUX3e7w1r0HTKnRUwO8Sm/hOV1a9sj/fE4YlhcMzjjtvpLh0ZHgdQYXMGwoaYBLiZmIPLYoi64bcjp+N7nk+nZOvFpBNVDOJgNCPqL2DT6cOnRfmn//o9i494azie7nEYHp6fEbGM444QQcUTwsYxKq+HBBju5hlnHevyyGqE2e7YjTucdRhxiIkEMVixqK5Ef2H1Hu9XQlgxePYGjD/iggCBza9EK2whpVl5POvpxORmpknSEe55b/QmAx5isFaw1qWDaGxirHgCmw/gL4TouSwL1j3jBsPoZubdHjUORdgyII4KXrdUZ0pBfSCsZy6Xx1Tn0oyoFvbeyBoDa7gQt8iwGxGJGDPgQy6TYhJ5wESfDF48VkxK19sS3SxKAiqNjvgQGYYBrHD2K26THMxLNvhyfORmc1iZidZih8CHxwfcOPDZYrjEe0abawLHyLacuTvMuN0O62CyI4MIxijTPHFzOODGPcbOXEJgmC3GzIgoMSrRKRoKqFZsIYMhEk32QjX9m8p0SqeYewIaIyFE1uOFDx/e8uruwLifMdGhErACEpVQ3JEYMVgq402yTRIjl3XldDxjw5np8BmiKZNFYpprI0o0gs0AVT6vPgWgJNedqgBRzFhT3sOjZiMmF0cXzc9JaWpGiw5JfXadDVRMPDVp/0yB18TSMlmvimRdmSnBjfFdgt6NGWbE1HQ2Udgk6+n8HJV2aq7EppdbXb5yup1mP79YiZr31/I+yfZXY7CrUNdXp22zF5UtOGOyzdrYVpp9wmKvSsfgtCadMl4KgP+qPr96hpKRqyOPe9pWiphfc1kaEERV+h+1SKvxoJ2h1X6rdZQq2JK9MpKBXB3bDtAqgA1k46SUkq9l/fP/YiLONYOu5StKbknyGfoErWysYuoCKbagZiGt6WYZkQgUWlxyJG1GL4rzWVJPtFpZsRr2PciUTpdTatqQpDkxmd9YnYTOQaifbIzWrJdq8DUjvCDfacGUDpWp6IDCMjnNNKRnepRBKsZuD2DVqGF5u1Y/IDdJmxMu6TkfrVVSLU3tkNw8UzFPEv0pcG0xktuV5iSh5jEv0nL8vBSPhJJjaxpXrbRZSNcYQwNy0ptqjnXMKUJdVLtnbFQ2gBb5Lw66XK+h7KAkoymn7+QCr2Wn0ZxDVgqu16OOy5qo67C1oc5pSVnK4HB/alsFw65ApNxeKafgZHmoEfNukvOcZbGglEJuDoE2L7BKUlpfMRYQQqmXdXJX+tPelf4up1FYbYos9A0iswejEk1xK4tcCKWYPhS2UweAZZkpOd7VwdM0LukUtixbmvYXDdSZLYCG1PEogEaZm9ylbsG1dfJtRXFVwLZb+gVzSw7Xy7tMbfPVq1SvHlCLEubvrXG5/ykPZxwMPuRELyly/XL/kSr76V1tT6vRIrH1t6uGUeajO7mzflsso+49eX7KyyqAmOepg7Zz6l469aSsZ+naXgGlXvd8q21pfkNhNrUtNRdgL8+LWX5zXa1yWIN0snUlxy36J3l91XilFv2UXUMjSNAGQMk1QF5krgU9unHu9oL++gp25DEr1HbJfaiMy7K+9CM6spOt9t8CfLY+N2ixrewqK6bsa+33pt+0ym3TO30v2j+ke8/VBVVUGqPnlwUhXvan3vsS3BVFTYm4ljku22vrYynUXQIPkuWvX8pCieqWTR4KK6lxiSosltt3raVr+7/1j/Lnx4CoF0OV10CSldLxgh5ds7jrWOd5Lv0OL9ZNW4n5b411bPv+5B+rbXMFeEmzoaqRranmzhY33n7zFV+YHzC5zOCoUSaDWGELyem0IgzWsq0nTg/vOL3/OevtgdF9l4enE/ePzyzrxmmNmaURmMYblAmvBkc6ZMNkRymKsG1nno/3bMuGkBjhIQTWGNB8AOBeLMMwYI1lYsDsb3E26W+vgferoNEQC1AlhvPlxPn0ARGDM8LoBtxgs582IAhe094wGIsTxyUoQRTjQD2cTs8YCxJX0MAaYnLMUdCAsQlsUnWoD2xB2U9vMG5gszDcgA2BdVs5H58Jw55hNAzTiESb2E95DRjVWmsomiQ3RmwCvo3U/S3VXjA4cQQbcKTafGELnC4nFu+x1jHdvoJhYjBjYvPEiEQYJDlTNuuKBHak2kExBkZjELtLTrsbUCJBAufjkYcP37DOt4yHA/P+M5xJKUpWikdgscbg/cJpPbJcnricj8R1S/uiS8V3nQzEGLDiQCLWpbkIorxf8qlVYtGoLCsMKJrLWAQfWeLGYJW7ece6+HSkezQ8Pj2ybRdEA/u7O8wcOT09sa4bfluZ54n94RXHpyOX7RkwGCcMw4iPgjNK2ALBbzi3S8XNjeLsiARF1wvDIOzuXuPU4nUhxIixDqdgZ4cdLOsqsG3E4BEDgxVitFg7pO00aGLGGSVi2C4bx9NC+PCe/e6AI+IGmKY9wW9cljOjG/FZvK0Y3DwBEVHLYEacS0XHXYDIxBAULxYryZmPIWJd0pgxbsRoOC8bz+dHbvcHRrPPvhN42dg04gAV5bytDEHZ2ZGgFjtADAkYjWbk/f07bm5eIdwyDgOHwycswXMO6RRC5yWt12BQnXhcQJYNwRP1iePpiIbA6XRiP1k++eJTxA9s23t+7btfsN+9Zt7PuGnCDg5jp1SM3boEDmRbNMlwKjcigHpP0BT8N96ymYjRyHZZuKwXztuF1/YW51wK5mqFB6stl1ySQKSkUbVg8HlLKaXGCepGwOY4Zqz2gKJ48oEAPttZErILoBgCJgbEONQNaN4LQk6/Vil2TFICtgQlhCubU4qvW2zzrHmKjlaSb6wBRBM7sZY6QcB0QLbmgF6r9ACSGYkZaHKYDHonu03ymJXboNncJejfacPOnmnvTKmYnbYreAVSU5JjTGNb0slNmRdJNft6EgqSfy+2KymwE/Jpe7/qz68cUCqGXM+YASg1I0p9gOLExDzTxjRadzHWyqcYD6Uo1ZWjUa3N4jAWx02hBzs6oy79moVPqlmMUOoOlYoagkiKQkihpGvpZY64SchtA6QY3KkvkUC1CKoHIZm+2MYnCVThPxVjuNxj2r+vLNViOF07ZlJOJIBqpKb0NVujXibTV9vQNKehlLMuC1gpUcb8XV2weR4phm12pOo4xLawKz7XR9OzAan11dmhy05+lgfT8fIqE0jzAsVWRytWNDt/atStOY8iZBxfK8Ot5uxKSzVq81zESmq7SnFbi6uIczXgtc0n5Rl5Q2lzmJl6MRnFqShubMU7SfOcIsxlnJtJ3acWVIClimRxOrWOb7It27hojPX+JLggxuQ5JiPkxQUtstbNUVlfv8SZapPUfdU/Q5t7Q45A1W+yLFflUfqcnys0FlasbcgKCq13ld6XrUHqnMbqWFwX8i+buRDV1Ovann7tSJVxEVWMxryWpHa/Koh8ddRUh6KkVookxqDpxlBRrOZjafNzyumFRf7KmJTdSTM7klhkro2xJO2T29/Ng9D6pkUkJSvi2MasIUfXPa806rQGVQyooCYSMxtSY3G2DRseiCzHE/vdoe05RWGXfaON7BVoUfevNpqdZBcJaamPvyy1sVBkSrcK2NonXbY7ijHR9rpSIc+UJSr93vcyuJH3SWlgZP3EAuBSJ1RyDmhx9yXPkeTfQv67TEc1OKSMSFvjffp45VMVhZLTddPf+RSUstK7QdfrHnWf1uhAY8L0lPamDdpBDPVJPciXFUEBZYQ2Hm0om5zWRdXtDS+loICv9V7SPa0vL/eyNld9O1804+qZdQT6Pa2YG9Kp+f4pOa1fOh3fF/iUrHtiNfIkO9cvAMp+XxRF1FQmYsOKEyCAagadchpBNwovE42vWLcf6ffL71u35FtjVxh+kQSE9mnJVHukH5P2lpLq0X8+ZvT2YFL7ru3j/X9fdkRLtDqmPSX6jcEYNHjishCmdCqVMw4xJGbGumC8ct5OPL3/wP27n/Pu3decHh9Y48qXX32DmT/lGCN2HJnGie0SmKYJFYdI4LKs3IwzIZ9yu60Xlm1hOR95fHzAYJl3N9webvGaWBQ7OzJYi7UWJ0kfBNUEjqBcIhgVQo7wK2DUsW5njssz23rBWWHnRoJ4rCagQq1lMAODFf7Crw18/X4lrLCtFx5OFxZ/YYsL2xo4PT0zziN2TFCYEcNgHYpjNBaxwmU78/z4BDIw7V9jS90jSQWlBydMO0/wyrIu3L99z24/cTu/ZhhnnMx4hIWIUYM1pQREDriaVAslSMSqJYpFJDBgCOJwbIg/87AcMWZiP98yGIuRQKBWbmGyEdQxYBGTnWwxOe0lBWEHa1PB6JAdYuuyXWSYDgPjuGMNnueHR4aHJ+Z5YDrsGccdKiN+W7k/vcevJ47rEVVh73awH9lC2t/3+5nROM7bMdUvAtbzijfCYZgZRsd5XZCoOBEu64aXyG6+xfuNp6dnpnnksLsFicxDxFmBLfD0fMQ5eHV7wyygy4mn52dOx2eW05ElBObDa5bjmdlZxsMeN00cbgckKA/GYkXx6phDJIhFBU5e2bxnWRamzbLfK0HAh5EYA5MavHj86jHBY3xSe0/nB3bjiGMGddhQykpAjBs2miQn6iEK22Xlmchlu6DnC1E3tvXIfveK16/fYN3MMI8gDo0O731mFGduRhDG6IkOwrLxdHngbjdxN98QnJJO3ha8JJvXGGGcb7BYwmVJgMZo0AhxDeiQACtRwxY31vOJ3TghbkbEYRzc3t7xdjtxiSemYLAbGDegOuE1Mk0j2+nCFj2jmdhC5OxX1tVjUcZpIpgJMxisPWDGked1QL3j4eGGh/U94j9w2Duss2zrys4Jr1+/Zpx3KMrNfgcIEgO3hwMXDaznE0Lg1Xd+HdRizITxgSA21ThzF+ZBmceUEMEfAAAgAElEQVQZoxFrHCBEEj0+1du1RN2SXZHohIgakJjYeETCeuTm5g6rK+L2iBhC9KgaNAjeRFKZq0S8MFKAEwcSWC4bzw9vmUbHzc0bzOBQGejZu0pxLaQLxgrqA9YYAmmvVi3ZNM0jaKfxmex32aSjTKj4Q7XZi49Gtu+SWZGeC/hskpismHz2tiq2UWS72NdZX1U2Vf7/xacpuEWpcYbUaqT5pVKzeiClHV4HSzvdK+V92QbOnYt0YFL2pY3YVKD/2+r1z/X51TOUimGf/yz+SzWFslFoKWkZyaGF4jx0Dm8FptKg9o5xYqd0xlaJelMkoDXgY4ZipZiJEGNSOcXIBluNuWqKKbTUq+KjhMz+MPmB2bwrbCDpnNpsxCBVTaJo3kyzU5GnN/UrRU/AZKHb8lgKEBO4oYUlBCVdqkTvere8yJ/tjDurGTzJBmxxw9JGH+q4CVKFuTiAKokCmZ7dM77SEi41qQSlB3Vq33MbilF9DUCm/6fSG8/F4crDryFF9SUDedoDkmmBtor3iRjYcmQzMNE7Jpqc/Zp4m/uThlprhFiN4PJdMVvlBmEj4nIhz8bggSu2VF3gpZOpbaFD2/v+Nvc5A1DFGb5yMNrQNp+wbKWFiZCdtiqEyXA3QKplYHPRa8l1IvJY5/lvKS/tPb33rEXATAM1VEPrY/lPpDrNQj5KVaGdNJfWjs0kzlKP5aqjSitWXvMVXzqA0hzR0t48n41f1BzoHojy39r0pSqyohDLkaMlEh7I8kNbjy+nBpGa4iFZtotRhZIJkXmtVpakVBksTlCtl9Iem8VdSjC988da2lj9b3G6YlPIpaB9YidlxtrLFxRZogPjNCmlFLUKfLj/muPxiXk+8ObTTxiM4uzMH/4ff4j3z/zNv/E32fwFN0zV+ZWr1jWRiXUSykzk67Jm7/PAy4z17MY6zt3v7e/Un5peU+Sn22PS3JTTP0qqTmtroxVf96JQoovDXlKYSqHkApir5Hz+avwofR25/PoqrWn7LXHDTLfPa45cH6yRTRooLpTCj4XtBl4itrIgtdsR+7Ho5KYHw+onscyK/ra5xk8C++LVWLUNQ67699JOIAOFFfx8edSx5v00T1D9vvS13xOvPlcbVmtDUxV0Eka1AusN/R+dLrvqU3d5tTe6vSIPrL58SqcHurgXZd+P3amVQks7ryMqsQVB6OZO29/Fdmk7b7m49UtzW16CMdfs076PBeTO15e10+vOSFun3TPzE/J17RS42P1Uln5bXa0Rv3Sav/UO6lork1J+j+rRIISY0j/mYWI/zTydnrl79YrZOha/cTo+sd6/5/HD1zw8feD48J5lWzhfIqfN83w68u7de+5erXzvh3fMN58gCueLYqJj8yYVtQY2LhwvkdN6ZjstvHv7c9xgcOPMunicE07LGYDDNOPGicFanHNoUBZRTPR4kp6ORNS4XIcwAUp+WzHhAVnPOITdYU9hP48MBLFsGomXhXfPX/PZYc/6sOfHP70nxIGLKmuMELfEhLKW8fVdMmmcTUrcCkYNg91zWY+cPtxzUWWa7xjsgY2I+myYGYMYixfSvaNnGkbG8bts28rD0zvEOIbphnk/sRtnnBh8tVUkpe3EiDOCxVXrbZYBjRsPj9/w4d03fPbpd7nZv0EkccfFKBjLkE+Kc3ZAJIG6qlteK5EEQpvK4FdNjKWUEjag4hEigcjoQmKFhJFpHFmWZz48vMe8/VkqeH7Yc3o+Eo0h4rGM7Pe3GCcEE0E9OwM2rkBMoJdGlsvC4/09w84Rd6/wwWJjQLbIxcP5dGSaLDKk+kgX2dLpcgQGER5Wz7YJzlqe1wtuEx4ff86r2z13N7eICBsWDp9wt7/DjTPj3YLb0kE94zQxyQ5zGPOpdYq3ipVc1Ngm3TvMynTYQxgQmwrs79jhg0fDBbUbhnT6H3bFbZHXn/4Ag00gd/AYERxpDowYRCx2mDFBCXElhHRynpsmDp98ys+//JrX+x13n3xOADa/sTwfudnfYKcDMkS2bUNxPC0eq5HZjKxnZUO4rCsPRrmEjSi5BpZGtrCm+oFisBK5+I2n08psB/avR6KPTBi2JbCJErYNF1KKp9kWNCyco3K5LPjtzBpXHpaF731HebocmW5uc1qosF3g6bRyM++JThArDGbPNNoU+DPCIA7RwKtJckF5xzJ4vjN/QjARE1xiM3rPFp95en7gw/Er5mHAjBPWDIiHU0jpsZMV3j5+zQ++8xmf/vwtr97csbu5YT8duNnd8exX1udnTIAQDX7zDGlmCDlAZwAbY0oRFJdz5AUj2QIygliDMQNqD6ybweJrrSFjITrLnG2U1QpDUI4oTj1Ej/oFf7pgvGKHyCU0385Yi4rB5ZS3QLOzXNrwUVNsVsFklk5N48/prcZAVKl1K1VztkGxKUzHqCZlK1RXMOuNCsgo6WRtNagGBqW2ISvDooyqzVXsRMEQJWJDBNMO1GiBJcnlCVI/Q7FXTcmQ6XRnb3mI5LS94gvEBHhp083J38vekaTUS/NvUqZ/xo+8VMJ/3s/f+x//UTYntXWotwa0B1QAaU6ik0SLK9HdRCpItQNedr0Hk0pNpOYs8i1HszgKVywUAcTmhsRqkWnOy45FKQo4AR/DleNRDZUu/aswsCA5hyoFu/SprTrUSU+KjyZYmoVKSNFkUWJh4UgqVlyktNHPW5Sw9jVH31Vi962lpad1IBDUKHEC2bJAdsZkNYTzdaX+Rol9VkeugiNt4SZnrksh1JxGixCqWKSj4UMtnNuZ65KeYwsDxECIIac4FGevRD3ThpDTUCmGeh0dAdQ0Qzs777Voc35vQamLga7kejamOW7aee+W5A4lmch9LnOa/qLMWhmfiGbsqcgwOZ2NRrmnFer+do2s0qf0BKuF3iltM8tyUgrkRWjIvmoWPJPzlptTpdr2xZb+1jsN0tolzaF72bZaBA/p0jXKbKT1Za0lqKcery2Sjtwt0pedZc3MKc1rpLQ17/hZ3iTX2NJ2xGYdJunosWm/KM5ecb4aDwa65J/MamtrKc9wMk7pog/S+lDdvPJAcw0UUMZCM7hGpqYWBVQYDHncjLT+FwaKlD2k1oTRqlj6T03b7GSjjUfn6ZZ0LEmRhpzYkNua+5jlYds23n94y+3NK1SVP/3x/8O//L//kN/6jd/md//aX2ccdszjzJc/+TH/8l/9c+4fPvB7/+7f4vs/+M2UYy/SgRXt+c2hLINKbdP1+JUfCxgi1dsseqUBDfmQAe2eD0hh7BQAVwq4llL1onZcTe0ZTbEaC9DWaro/59abLFmxjaNkwDZk+Wm6MKcr1c51DDsKq7OtsahKZXd8RH3X++rO15KhYp+bTzcWGEJp6b/BJBBpgYS6QaLZOC/sy26GXqT4Xv2zl7sOIE7bTVtLDVDKYyNtTgrgWvbU9hZpr/tlNk4H8tWLy9LrvrqSx7rrNNkp28rVdLTH1ZTiX/Yp+qqyuWu9sSbPPfO+bimUoAx1n0zYdJbX+rzcuCyrJdXxKojTt6eToDZUXXBPWkCmn91rQEnSKYVXe3WJUpucItuCNbVHAsUR+BhQqN2zyrvK86tIle9yAKDs+xoVjYEYNzRaMELQgDGObXni3U9/wn438f791xwf37Msz5yXI9u2sa4baGS//w7vTife//RPwVq++xs/ZN5/hrn5DDu/ZjAJRjYmpWasXNi2BXzA+wvffPgaXYV5vsHtdqmQrw8EjXjvCWElBA+a0oatGGYG7O2OnR3wJukjlVTMmgghKOtyYrArn+0CWzQ8XWBdPZsGAopfDReNiKwQJpY18Ho+IM4QrDCoRYkE3ZLuzWkzW/DphCVRnBuz3oG3X3/Nw/M9bz79LrN7jUeT41tLT9ic3pfS7QubXzdPtMooAyFuhBA4np9x68rkYH79GutuERzRRtQYrE+Of0RxZiDqxnJ8z/l0QaxlN+9xg8usANNKO6BMBqx1xKxAYgTFEXJQxRpFYgJWjJLTg3I6zqac/cLJb2z+QtwuOLFYgW2NLJcjkxt4fHxH8Bem3S3rtvDmdo+bZy6XFTdO7G/usESOxyOjc9we7lARHk4fmJjxohwfPmDjyPzJjtnteff0CCEyWUOMsOmKsbnWVoyE1WOnEaNwWRfstCecVwLKsJ9Ylwu7YWAQg4+Kublht3tVGamD2BQIXwNP24ZEzxaeuL3ZMw+v06lkClEDEU8wYMUSQyyikWzAaBLtIaRaSj5ERixeAjI4fPRY45CY/I/ABvGS5MQm8Cj4jefTgiVyc/Oai26Y7cJgDM+njfNx4Ts/+B7nZcEZg3EpeG7FphpgbmQ33KDiUL/ia0B0w4ZkMwUNiCaiQADI8+xNYiw5H4BUYNwDDsWpZgZKzPUsV6IoI8JglKM3PJ1W/Pqc90zLutxzcziwm/ZIjNy+fs28O2DGmbie0ol3zhGMJYFb2dKVyIBHBQYzsPqNv/pXbvnZe+Xt4wmzzgkZIUJYePf2T9iCYd7fMQ17nFjEpJMNJzuwSEBVCEvg/v7n3N6NDDIzHkZuhz1uTDbe8fkbfvDFF7x58x3MPDPakdUk28dGRfyFTSQxtMKGSMTGkMY4RJ5PD/j1wq/92l8kmhE1lk0N6k2q+YaBuKUyKlESkw6DI+BFCBK4nE88fPMVr+4+Zby5Qy21ZrGIMGAZst0RqmINCUCi14styBWr70ElOjhxxBiI5QTLEjgxJtWmgmojVrs/+qzjpe4pUcAGkxg+adqzLZe3zayTo5b7ko6LxhA1HThQLIjE0zJshKrLDAaJycpUKfq0+FrZz+nsCUWRqBiXayUX3yxvxbFYsT6NSRBw+VACMcJ/+V/8Jy8sgD/751fPUKqOR28xdj99xAYuDm7IRr9ku6JYaZXJRANSWg2gZI6bztKw2up3lK9La0qV+gzEY0qaB6Y2LDmKmj3FhIv6bNwW9lFqXz6GsxjjkiaxnCYmFLNeyec3Ql6sBXgorInESEhK04hJhTQFiL465bF5Mek6Y6ozmXrQRRklnY6RWhBR3Uj1S2ym9dDmJ6fgFKcnzVEaubQ2pNn7CmNMlOs0xZkJYSQdt0txIjJxT5rZqVqcc1q/TaZyU6elzld/2ldNkYwxKcliIwPk9LpSALgV0ZbcBovqloyNTiyjpmJr5iqdMTt5nTNRfRs6xzb/VsAAQXMELF0QtABpL52vZrAnNo7WsVHaIKd+ZIdHFSEZfGl+KyGz+LGZxp9StbQ+Q+oGVNLzSguMmFynQNIcaj7tAJMdXqr3olJandhESgJ9pMptqVvDlZNhaRHoyoOIJMCMNLAJKU91CJJTmFhgPXBQGBSa2YB9BP0qHS07VpojTw00bg7G1fM6NmSbo8IOTDChKjl7vH2qo1WEMgtI2VpSwdByOmN5f0lh6RRgnjdyND+xnZqESO1jmafyuswSkiTjsRwVdaUW0r5jyXOfZbjfS40qKiE5KCQHr0x71DJHGXTNjMYA3L99z/H5HrHwxeef473n13/713n/9if803/6D/jNH/5FdIDt8cQf/LM/4PufvuHTz3+b3/ydH3J+PuOcBZVuLQlKOgZWNEVeJct5rOBcTOs3kazT3+LyeKWC5Jr30MKSSdH5UJUyL4DiAiRpdbhz/awyOWQwAKqpEiUr9LKv0+Sv7PdIca6b9Je9qo2/5uuu0wwrW4y0t6D9uspgVXm2lgLO18y1ple62jxlq6fC2p3MpztsltW6f1wLVBWwon+rtGiW06Kr5bo1rXF5QykTcLWosnEoeR+kta2MlXTPkqJ/yk3SRRXLhf39tQUv7BJppl27of83XZqutL71663TFe2xkk7bogA6rUlFvGIMuZBwkaeaaJ+7lfey/H2degoLuGcRNfCl7I8pPUoyw7X1p5RAlRdTUMEhJRdh7k5m7MDOcm1veaT2Rkpku6yn7pGtT2hm+RUWptSrqh5+iXJ1Qy1dn7WbkigQo0d0ILKlk7uiR0nOUPTgBlCxBITBwM7uOT6+53Q+8e7xLV/++GfoFjhvF1Q9z08nnHMYM7My8+XDkW2559X3f52bTz5n2n3B5XiPWxd2B0MwFo2euG5s65l1PRGnDROE83Fldq8wuwkBXEw7bzQGi2V0EypTtp2FEFaW05FfPD/BYwIUZjfg3JhOqJomwnLm/cM7BIOzA+9ng9eBSArWWO9YnWVQmMwdZvC44LjZDylwaHMahPgkVwwJRLBAAGfTQQt2GHEop8dH3j2/xdiB73/x24wMLH7DDMpgDOUUqqiRVQFbKwgm294ZBqfIZhjySXnm1hDWjXBZefv+gd1wZre7wewnbJxQC4OBp+3C5fHM8fKBYZy4ubtDRkMIAUQTezwbkLej5Ytby3o68fVxYwnpOvUbyxa4kIPEfuOiC6IpLdvagbtpT0gVXNL+71IdJzONDAwMMjAdYDwPPLy7Zz7ccvf6ryAOns4L5/WZablAUGIIPPlnnE32+8O6cTzfM9okg89hgbCwG3eE1fJ8f+HRHDFDmuNL2AghYgdH2JRzuGCw6LayM4azjzifUgDtINxOr9jdTDzxwNNlwRxe8/rmEzA27RGqECOeVDPK2JH9ZNjCgGPmdHrk+fErhmnkMM9Mwy4BdN5nHWUYbbIZoyqRVLsp6Y0RazwaBly28WazAzEEuyGsDGIQN4FfuSzPHP2ZyY7cHu7YTZaA5XL0PD4+sxsHxmnHcjxyOT/z/Hzi9WHHfn6Fj54YkvWtlzNnHghuZppf0ULGhmgDIVgcA5jERnPqCLIRBfZmQmPEuwQ+x3jGuQkbLJtd0zpTsCFiuWGLC9GfWLxisLy+e4UbP2PIvtv/+8dHvvOd7zGNcyo+HuDp+Z6Ds/zswyP7yTJMB9QYZjtiGLGSDhv6sJ4Y/Mab1wd2Av/5X3/D3/uffsrjcuDkT4zWcgnHlA1xufDJqy+whzuMGfHZwPMaWbfA8+mJeZ4Ra7j99HtMNyO31mAVLudnvnn/Dl0XTjj+2f/1D9mzcthNqBXEWrZTxLjIsq6MZuR8OWHdyHk5spsmlnPAWcN+b/nhX/gd/tn//s+5Gy2vP3nN609f8+rmE25ubpjtHuYDUV1KkfSOgKA+chHQEDk9nRmNYdrvycn0OWsiKcookVPcmBRGYwliWJPyg+iwBEr4WvMhRw1STvrLiEkpCEMJwCurgFWbg/Gl4HiSHqM5sCMpyFuSijXkMizJymdCOGsiizgENVrB6+KPV4Z49AxIPiSp6TKvMflKmvCJkH1w0wemhVTEPIDPOt2QYrJqSoA/2b26QWGfqGpOczNIDgwIGQcRIcSP69k/6+dXzlD67/+H/0VrVKqLGBemhJSssWzwR6lmJqAMFAcqmXmNNnZtwymJzp+iySVa2AoSAzmFI2YrzFZDsxSyjtkZqSkNkiK/OcxXDborpkP+b2LXmcpOKKlJBQ1N+ZCRGL9tlkMPWnQFzDtnMv3c3i95cXW4B9X8zE5nLRquClJr0ifKX3/6UTYYoYF1kGsfdPJQlkWpcF+YMkYqeTC3ooBL1U+jb2X3ikQlrq2PdUCbHPYFsuWFcZnmUmtrG6dE83yU9gikKGkFFsrCzLKpnTwVhyFb2IUhUuah+UBS5yedHJgjrjSnp8xJcmwKoNRSJdNjsiGtfQHSNhM16poBixLwL8BXuSVf1eQr5U5mBkh3hXbJLXVuBBKhmVIMrjI4tEuLo/rWV0AOSopAA6ipspuuzw5MdcbiFSuCfn7LR4urV1xhSUZKCsO34es2gOKzFOc2aHOwaupN79dU4ZQMxDYSaewvqylOWvv07U9yoJt7RXXuDYrPDAGVqxnKzMMGpkDHmkSbL15ouB+L0ss1DamC11cOpdQxLRGLCu9VJyxmUkACv8QKGmLiKieEEkdKCS5FUjV6vv7657y/v2e/P/D9L34diHz5kz/hH/+v/zO//x/8Pl/+yR/zD/7+3+ff/nd+j7/zt/8u//gP/oCdm/nhX/pd3rx5lVO7U35+yo3JQCgGUQdaCi2UbTnWEzViWcdAiod4SvH9kuZVtUlZKH3/f4m+e7nWr2sy5XRiOuZQ9/svfeZHlNbHZKmxTUuaXHp/jR8UuS7rMF6DWKXW1vU7Cv8udvNd1nKS/5f1eSi6tEbKrvtWgU1Jilwq6Eh3fbjqcmXbXG9avByJ66+/PUptn5b2e216WT1c/da6VvaCNkblmt5eqHZG34F8fXl2CS68vOhjKXclVbhIrO0A8AqEyLUuKtf2T6v83rw3CyYzdOO33td/NANKNXxU0mg7iwtp8tZLsclRip6Z+bH+vfyUdxYwU7tr+4hwrTN2tc6u193H9r6P3VOvJaX1rz5S9uQQAiEku2VMhYgICsNo2U4nfvKjf8Uvvvpj3r17S1Th9uaG5fTEcn5GZMbcvOJyuvDkA3Z+w5ubOz579TkXMxGiJ/qNy+kbxnlif/s53gfCeeMSVoYJJjEczyeOJ080I8MwEc3GKMKoAxjDGreshy1qIjYmO6cywUXQwWEvG4/PD1y2la++/NeJSeMth9s7Dp98zjDtGNxIjCmYZYxN6X0CTsd0ipVz2BhyfaGBVH/PYsyWAyABchRfhOSIM3I+vuP+8WuihbubWwY7s6zpABJjDZMYBmsSTCiKj4ZV044y2FxTNMZUQJuYWAIx6VkfVkYEa2ALgWU9cQonJAT248zd7g3b8Ymvj++YhgP72z3eOKaoqbiyCN7HbBsJQR3DoMwO3j2+Z/MuFUaXiFOI4lHrcGZCgS2nX83OpROMFWIomRIpbc9kR2+0Fh9WPnz4QFyVTz75ATpECBEjI1494+RY1wvr+YR/ekDjigiYEdwwMtqBNawQUu2jcTpA8Gw+Jpa9MfhlS3uphMQ0MJb1vDFOczoxVVoKvJMJ8Mw3N0zG8O7dL7CDSelmbo9qxGUQFREIW/LHrGWQkagbwRie8cwSkS2wbBvL8sRlfeLVzWfcza8Yhjn7OYoVRzApZB80nbAVo00pjESMGVHxrOIRa5FcpmI7P/P89I7NB0Y7IFPk1fwpp+XC48MDu9lys7vh/fMRG+H16x0/+tmXfPHdzzk+PeOGCXEDy/mR3Tjy5u4zjpcTT6cTkzXsphusU6bpBnEj1u4SgBE3bEwsESOJAR9U2DRl9AVNtXg0RKKk9CixgiMVx78sFy7P93g9Mc0T03jLOM5YN3JcLiyXM4dh5P7pPc6euLn7PuvRMA4WuyMFtUlh4d3oCArBB/y6pWC3UYgDBGWTlU0vHLYNu3uDGaYE1iGscePWXvjy7Z/yZvcZUSPj7oC4MRMVDOO0wxmQacDEDXM683D8Bl2e+Pn7d6ynM371/Nr3v8uK5edffZ22y3DBazrVcgue2Q1clo1hGjhfAtNgWfzGIMK2RsRG9vuBeXfD6uH2za8TorItTzizsXdwd3Pg9jDwyes7Dnc3vHn1CYf9He7mNdHNqHE8PJxYLk8Mu8+wxjIOmmulCcRIpJzy5gGDySwgMzjUWJK/FZvu0aJ5lHzEaSIr5IhtELBqEgRVfBmEQCiw1DWpoNgummxRFTBR8MZgYq7paZOOSXuo1vSyoJ2eIjGJkEJESGxBawyEdOJaDIrY3l+gGgrJ17SZUUlm0qWLSjYIZH8vpgYXH6m519r4JJJsyf/q7/6nH3dx/gyfXzlDyUguInrlDHUpE3nCe6pys/WSg22QugBLdLrWcbky3pMrVWoV9L+kOGxJTUj/SvGg5HRjklCkK4t3KohJTJCXdWqS/9UsT5Opv6bUksjMpGKwFcCsPwVNcnpWeStKRVSLGVnnWpuj0JzP5pGIZLZPvVezUZWjhTljVImtMr+2SGLqTkrJq4AI1yBacc5Sel4Drq6YHdL89Maeac56typppmw3U1eORjPA0yaRr80MgnTyRTfRWZbQQgOX6vQlGYN01GVx2nMvJcGQ1PYUJ0Vpg908klb4uUVmy3cJ/MoF/kjGGV0fJbdTxCZHOU+jdvMmRfaKwxG709O0sFUkFxJNchQz5FMduFLoN4OlvWui2WPpgcvekSz1SspU1bSYNklX7UxOZKzzoC895Sw/pZxUbYm8cAi66xMomU+7kgxsqkLIfUczKt/lSOe+aQbaXFYs/UehK1hMXR8xy2NyfmLtX6z7FlkppDa/TKGjIP3EZHwouXiy4rUAAJKf09aCMe39mr3Jq/Ug/ah/26FKP3T8gLqv5rmkZ24257SmEFUGgKGZpHmfieCwEIVVYgb3E6hnMhPxcon8+I9+xNv7r/jhX/q3uGwr4gM//dlP+P1/7z8iXjz/2z/+J1i38fVXX4KzjJPh3fEX/N53/n3WywVnXWZDhbwfpig4KmB9cpS7oumZY5mBz/Rdcpp8k9IKJvVD2c+X1DXQgyS9M3o9DT1YkEdRC7REZXw11l/ZV64d82KXXH33sp01uz81ooAwffSgpC1dpXvmmxoPsDaTIgvkvbB7PBVcJxko5H6UE9o0r70eXCospxQFLIBvBnpzoOFq5ZX7jOTagdJ/TQKHij7SFwNUpPbFV3Q6KXe0aeS2X1UdAG1kRD46B9d7XLumQVftje1Nvc3SZKjXI/2+0V9zPUSWktJa32C6+cp9TuzdFnWlA8uL5ARtFP4+/biBSb1wdyLRNoiruUzspm/nz/4y4Ky8Q/K+V5l3H3n/9frLDcqfWmrgRT/695RaUp3WLt3iRz/5Kft5Yp4G7ODwahGboscSAgwj5nTmqx/9MV/+7F8SwoYJDr+tSNh4vpwxw8Cw/4xt2/jTn33J59/5HX7rN/8y1llWH3lYjhA+MM536ehlYDmf8OE9xgjOOfbjzPH8zP3jGdwOMzvGfDasi+kUo1U8qMGTg6NZ71prCSguGFQC27ZwPr7ldN4I54XjxePmO+4+e4PGjbhGLstCFMFZx2E3sxN4CgHVETJj34pH2XJkakzjZyKqK8YM7MfIZAwaLTtrEYl8OB756fsvOS8rb4QReuUAACAASURBVA63DJPjeFkx4cJkDMMwoGJwYnAupZstq4IYJLNf2SLWkIBwF1ENrNuGxTGacow3nLeAxkCIHrywLp63v/gjbFRCUF6//j6Pq+fp8og9zIwpOYQQPGZIIJq1E4MZMCIcY2D36jc4qMl2hUFlIaZcN0RTTZrZOTQqm1oGApqLNWMcajLbSxJn+f2Ht3C5cHd4w+47N1zWDfWl2PwprastMhvDfHfLabSc799zOT1xMANOI6NTbqcBmYXzZeOyPHA7H1jPnpWIaGRdPbv9nm21XEJABmE87PE+sXMNBjUGsanOjVjLejlzDpHXn33OPE6sCJHE0POaj06QlGYkCltUxDkCDo2RvQh2S3Wnbvczl92B0/aK87Lx/v494yDcHe6w803Ot7WICTgEYy3YgEEZ3JgzNoQ7OyNGeXx84P3DPZfzA/M4872719yvG+tx5cN6z353g3WOzZOKK6OsurFtO/bOEjaf9GSEyTrisCeGwHK5cD4Goje8ef0pu92Oh6d77h++Zr/f4xWcm5mnPW7cE2OyaS4xA6ZW64lXiuIMYAPBW/xy4mm5J/gVRovsRw7xU6YxAcFPz89YUaZhRxSLP5+5u/mUn3z1nsPhgveRNTo+me9Y48qyeu7GmRANp8sJQsh6K3PErcW5Eb/NxO0e794wzBNBBX++MA8CVtniAmJ58Bfm+TVRRowOWMAZhdMjv3j/Cy7LPU9PT2CE4/OF21lYfGAeLMfzE//iD79iWTbcOPN02QjGYtyewYwEZkaJ2LhwPgbYyqlsFpyvxf33XhmWwKev9+zcmXG3Rz/9nGnc8eWHZ37+zQfsLxaG8ci2rRgDN86znwxvbg7c3t7y/e9/l89/+3eRYcPIzBZhi4l17nKpFi+RIaYaRGoFEw1L9AkozL6BFQtis78j1SbWCGgkOosNqVREIDEygypGXGbMRgYVYq4lJar5xPqi22y2WQJe0sm/ajSVYNFUolwTfFnr3maXDJfrIYrARqQezCMQQ8hsZkmZPppT1LKGK67iIKkQhYqwScBEQ6xpp8336eswmeIjZbAr+ZnJbxTRxET9FX5+5YBSH5+02Qku9VaKv54ToGqkqjnPhW+kED1qTPe0Yl+lXOdvGfbZmK/XKa2YMuVYZqEeLh+bw1KdyRhTVCco1jk0+mTwJOrDtxzK4uSkvrj8LJ8bbBpTpDemswObfM/kHBejr7p42eArTJ2ankBxDLUW6pX84HJPeU8ZjeaCdwZ1TOMl0gAajSY5CCYJXEY9mnSWGjYFPCh90g46ymygVmshTYSURSGtRlbpF0JbeNk5TxZvIf83QE26uUrgQOqTNYZQTpbqilglJ0yxNgF5Zb5sBqZEIHRI7rXNLVlGmjOU5iiNQdRCfWz/6w3b5r9rPk2CCtJV8Oalh6MJ2S7roqQgSf2/Mp7ZYan1ClofrnqQN5J2xHknA5pYSbF40pLeE1Uz86+Abs2JJ6+hWJ/V2lc6LtloQVOqhkFy0V6qo5re356VgNZ04oCRnPolrceiLSkllufQnBEtxWtfRLfTvWWoc5s7IK/SYIrIyLX8KC3dSEr+5fUQZ1OA7Nzlp5Y9Tcmr7+O7trwYx14m6omWH72vtK59UZ1fbbJXgLgrmLY4v2VvzITMSoElQaI2RjSnWUKO1GpknB1/8a/+Ltu/eOYP/8k/5PTX7nl3/8xyfuL08MDj/dd88oMf8Lf/s/+a//a/+284no58evuK//Bv/cecHp/T8cQhHTethRaWhDm3W/Oxyz7XcTB1r7VOUrpuMAR/YZ4nQqSbU+re0I9UAZfrtx8D6T42znWstI5xnaYyNy+c3V/ypKuc92twoa3N9LhrUKB80n5gunti1acfeRtFOfRtzne9wJeSLkoG0FXnr4AC1U6ONAMuUiGrLDvtXeRT5jS3o+jktvZa47Toipe/d9JfjMMi9C+gmZZWxlXnmp6CthY+uqhe7pFXPJ7asbrjdSB1bZa0C8t+Jy/GMW9ZWQ+UtPSs0+oDuy50wHdiB6TvW+Dp2/L3MWDpOq2s61DZM3Jvtbsv9/Qjg/Wx4WsyXey0l30pYHqV/cIIJ9kpZX/+WF8g7YmF9dmsA6k1EC/bxvPTBx4fPX/5t/4SJijDkE4pMhjWqLz/0f/Jz3/yRxyPR8Q5jqcL5+cPOHeDHibW9czju7f/H23v/mxbktz1fbKq1mvv87jv7p7p6RkJBAqNkI1QgIOwDcZhGdvYQdgQ/Ob/0P7NvziCsMEIBktiRjKCeWi6NT3TfW/3fZ1z9ms9qir9Q1Wttfa53SJshh3Rfe45ez2qsqqyMr+V+U2iHOkuH/HRr/xVrh58wDB6pnCgdhvc1RYZXDrMjILUHcamMui165Bo+eTzn+KJPLp8P5W5jg6kQiUSxKf1oalCWzl4qKwQQuDoe3yc6Ic9NoyYKNRdR+2Ug6vZXLZs228Qx0glYC88nRkY+yOn2x37Q4XZPsDaDSIVffCIUUIE65RRRq5MzZUzSAXguKxhUyuNsRx6YehHPn3zko8/e8n2wSOePn7G6EdO00TjNkTjqcVhNR38qsn8QzESVBDjeSgGG4WxEaaQxtp7xfsR1cgwnThKIIwjJDoffJggQPSecThSX3xA03VMu7fs/YmLB4+42l5jjEv6xjhMtCnMJI5prxDHFBSRGo05EsEkQCVKhThhjImTVKyFmIvwZA7O6DzlwMWSfIfjzWtu9q/pqg1Xj56CbbjbHWiaDjWJgyoBdp5RRsDSBMdlc832/S39eGA6HhhCj/cHDqcJUzechojtDKf9CT9NdFVDMBXSVBz7EZxgq8TxMvqJ2lUEETrX8PjymuE0crO/RaPQPrzCuRqNgZNXaudAU+JqygzwKY0OGDWlD1mjOE0gi8FibUycYnkfbusNtgqpCuLg2e32yOGGqtvS1Q213dLWMFpPxLCJFc45fJwYRmU8vuUwvGGMkcurKy42W46nPYfhlAEAiBY6V/EWQ0Nk64Sdc/ShT1xMVnDhRHQNPvrEHWaEaUp7ok0YB6fpxL4/8vLwlguxXD96io3Q72/p+zvEVdS2pq5anEuppX4KGJOeUann1O/p+wFnDIMqddOBq6ido663nE4Dp6HH1qnE/TRNWI1ENZziyAOpqZsNh91rNpsPuDudOA0npGrBByYZEWM4HXusgbqqOY4nnDNsNh3GVmwbpdP3EAEbk160bYNKz8Yp026iay5ptw/w0wEd9/SnntNhx+FuRyWe3TTyYHNJCIr4E0wn3uxPvN73DL5H1HDx8BntN75B3W55+ugDumZLY5WHnaPaOJ5//pJx6DFdjYQW7AgR+mlPhWEYBqrYU9eOIQTuYsXWXLJptjhrePxsw8Prp2gYkDCxabf8/PWXuM7x+jCwP0bMfmDUPZ989gcMhzv+wl/4Jo/f/5DN1TOCdEQxGA9BItEa0IqgKfKtEkNUwxhGdm9f0dhAVXWEMFJvH4E3dBV4UyWqmeARsp9eqpCrS/OfQCiRQTFx2CEplSxl0hg0CphAooBPu2eKeLRMRpEMPkk8P+AzK0tCScDQFMPsIxROYZUV323h6BVFYvFUAp6IjQYrJtkgOYx1PkjXtBeWKsYplkRy4bPFX9NAzhv55X5+6YBScSgTbpLBJFgMpnxVAnWSwIqhN0ewZINGZkeppDEtJ1PFsdfsAMccBloAo5R/mOKSksGXhZfRS8kb+fpc1do0bMbkiITSWvsVpJrZaTRSop5i4kRgKeW7PjVdPJoCpzEjhwlFXIemKyUXfS0xkcQ5Q5Gn6lwhr1wDOfrCwFzKnAwWZaCnEGDLIo50kjwbhcUVTeHMMfc1qJ6XoM8vXU7IC4i0RHwsYe6ARKwmeYVyR3YiSt6zZMCwzAjNLylpbCHGFCJIOflOwN08DnofcCBHaGXgQtI7S6riPFezIy7zvCwG8NpQXjkcUhyUOOfMvvvRBCRh7tvW7y7k2flZnTTPgIDO4zGDI/nUvzjhi2OwAHlrw72sp4UIPANDyooQXeaxTGvMUKLZlAUUSvhSSSddKSUtUlrmfplzGY9MJxukKMYizQWNJwMdhjmihuVEeil/mZyV0p6v+sxOSfGBFUroaAF2JSvcWTb3HHvJG0pJMVpXRSi+ryy/geRKedbmCKSc+qbr9shZ+9arTXVJbynj/+d9zuW++sz++gJ6iCxrKG2UpHme15ywpDB5oMbl3O+ch20ENYb+NPLxTz7mX/xf/5jbu5dMJvA3/vrf5o/+6PepL7Zsrh/y3/3uf4P0yt//7/8Rj548YYzCD//0R3znmx9mkk6TKs0FwfuJymXHV9Jpc+UqfvCDf8WTZ8/4zrd/De89la0JYULEplO6QQn9iaZtgZSiN2uhM1Bp/fu5TvoqAHJ9z/+Xz7kzvR6jNZz3Vc8tbVnDObLoblm1Ma+rdQzU/d+LHku6sOiH5d1yr+9zGlUBO1atui8TlQJ8ZL2Rr5sPRGQVoTXvx7q6n7J0uS+GuZ3r78/evwZmFhGf9c0ohbtxjipj9aOAbLqo23QIUfRuXi9zO1f3y/LO9XhCVoV63iLN7Z0jG4ueyfpxedJq3Ff7VgG+CmddIMx8iZqN0EI4/efN1BmgWY3vTElQTuNyU0Rk4WsqtsXX6Neves/9d35dStz6s1wj50K9933SQUuqnNXSlwxUTJ6+H3jv6RN++vEPOQ4nMELXNPgBbr74M158+mNev/kF05g0XIgCtePq2Ufc7u94/eXn4Hs2l0+5fvodLi4/SFHgznLdddhwiZ88x2HCj4KXSG0M22qLtwJacXvY8fr2lrrectV2uEnwFiwRw0hlaqboUoSMKDYq0UemOHA4DoQwMYYe6xIBc7XZ4j283d0RvfDgakvlat57L6WUPOxqfuVhx+cvDZ883/DSeF6dTrz68ksa62jqDc32glYsnXO0DnwwPNkYgvc4U2FE2bjIVeto1PDl6xu+//lrhuh58PQxnXGEqcf4SG0NXicqb4gViFGqDNYQYYiR6ANCYBc9/dBz2x+Yxomu3RIUXCaWjiRCdGMt0SSeoI3U+HFiGG0is242GOuorx8ynkZuD7c8P37GprviYnuNs4AEfM42kMxVaoxL6eU229ZJoQGG6COuqtKaNIEKi4kx+xAONRDVQ446ud29YYo91xeP6LYXiSs0erbbNlNaBDQajK2pEYKxVJL4/rxOxBjp6itcfcEYRvxhxyAHwnAihgnpO9oKtpsGHwR8SnncdBs0jgTxmLahbTcQlLYCay2fv3pBHyLfevQEqSp8zKXARVJ6p+b0nSqlyFq1WDEEk9KGXEwpVJWxRLGM0VNJorRIae8BZ0y22SvowHct4zRwmDxvT2+4tgceb2pqa6k3G6wdicOO/eHA/nTENo6quqBScK7Cq2dSpVJH62pOTDgfsM6zaSfMqLhoMc5hegs+RePcHXdcPb5gOiZSayXix4EQT0gFw3CiGiLb7ZZuX+H1hAsDt7cn+uHERdfgrHC4u2U3vUBF2Gwf0jQdh37HNBypEKRybLcPcDgq56nUcRhgOKaKZJWBUxhxKjgMJx/ABWrXME0NYZh47/EHPP/Fj9huArUT4niiMZaogWFI5MvGVkyxp60cW/eIYeoJU0Rr0CFZ/aPxXNVdAtI10AfFqiGGSOUMb774GYebW7zvIUZcVeOniRHPcb/n7ZfPiZMyhglVh24v2D58wtPNYy4urvjo/Svc5XcwrmMSj/oAITKFwHDoudxsMJdbtm2TbG+/ZbKAv2JST1TP4XbP9aZGmoZKUoRURJiM4VogtjUTBiMbJMD7Dx/RMPHw8gIxDhM8t8PI0Hve3Ak/+79/wjeffclHHzzjydNHNF1KXRTXMIZE9O/KDmwN02QIQ48MI2FzyS+e3/LkwlNfXBGsYzAOLdQNKFMIKf0zGxmqgSF61CpxitRqUyBINu6jBDRzF4lYJCZ9HsaRNy/v2E4/4/Kj36DfjWzaDpUKzZhDyvQoniyIiTndTHHG4DXxMYeY+ZsoOEbx27MtYcrBg+JCwNiIaCKQj5L8VYtDNRGIhwxEFZ8rJcbIfFierkuWWymy8sv6/PJT3liM0bnKSDZAS9i8kcV4KOdTpSKZ0ZWRqoshbTJwUcijZwe1OMGxGJP59FCT0BOIl0Erye7rPYPVmBzNAWjKsWGJCZGZTb5wceSm5Z+L026sokHRmDkOKEw0+o4ROv9b7p0MA6WUZAq7SwZjyAahWQFIs4PPYsxDLn9KAepMkkUxirOdXeQ59yNz6OhqDOdqYMDMpSBJtpl8BeLKuF8Zh1LKimUwJRmGC5hWkm3SOJR2GIhLWtm6QmBqa5xBNSuOIEtFQGJxDOM9B25JKyhV8kr0z5lTUIbRgBS6rRjy880CgBUFYcq7ZAbl5nEp82puf7n33MBOEWLpOZp/v29UF8AtpZeUsV8AohmUKU5zcYbiamDWxv1ZtlRSYCn5pEA76Z0xz5nFJ1BmTqj5yjQHCnhSoKUk5phLlufX5QeX7s1l3kWImslpSci85rU7g1d54pbIizm0c47iSXJez9dlLGYRUFJ95lauQkS1HAgUkeVxRXXFLbVKAVqNdQkAK6mFUSOiMkcbriekFuVzL7Jg5f7m9fzvPj1Y7tWze8/1jK7Gueg1MudE0lGayztLriwpksoMG+OhRP5pco77wxv+9Q/+KS+/+DMqifzwj7/H688/56/99b/JhCeOkc/+9Kfc9ANP3n/GP/4//im/+RvfZbfb07iW/WnAuohRg9i0ke7e3vD65QumGHn24Te5unrI5dUFV5cXoBErmvkmDD4oMY6IesZ+RMRQV13u66JjFhDp3NEv8rkffbPc865Xm8iwF6CVGRjIM0WWNb9+m6x+PxuvdbTSvfbM41aA2LL+Wa9FVgDhOr0rjfccrRMXx/5cCvcceZM4Q+wK7Pw6oHbuozDrxHWbZ5HLcq2u7zv7/9kX85hJ0XR5mcxy15Kqev6E+R26GILr75bnrpTB2Xidv2hlGtx7C2eyFllx4BWbgkUnz/vewjCaU9cy8FZSD/UcAE3m0rwjUrpUIlbnvV6UdVGOr0vnLGDSIscSiZT6s9yiGfQnEYf+u8D6r/n7fX6k9Xdn984K17CeSl+XVleAbSmEwJoizdWnKAMxwma7pbGOcei5vNxy8/Mf8bNPP+bV2+fE4YjGxDXSXmypqkvudne8/NnHhHHHxfYRlx/+Ft2jD2iNyzw3Fg2R3dgjwQNKMAaphQYHakEEHY/sDm85+YHrdgOmSu2uoQJidBirqVqWWsI00Ps7jvsdwzDRdi3Ogq0rrrptSmGMhuPNkZ0fuagvebBtaNzEf/Zdy9/7b/8q3/9n/5rTccvDrubWaYp8qmquqgdsNwNh6jmdjrx+8QsaJ3z08ClcOfoo7AeHNZKrGwkyCkN/5Oev3vCLuxNiah5WG4iRPo5YqYji8RrZmoqAstvfYW2KiIkqTD4wIRgPSACJaHaW6u0l4io2UqV5ZiDGVCokkLhInBdOpwPRROruAbVp8fnAbpoirup49LDj1N/S7/fc7u5om4b24hptG6xYpPBCxhL7LxQ+RiRpgM7WxDDlynGOyR9x1iLREk2gNQ7EcXv3lv3hlrqpueqeAgaJAWcT76pLIV/0hag3cwtFHFFDApM0YE2FkwoR6EyDPrhmmA70pwP7u1sqGYle2E2eTVNR1Y7KWYxMGCe09QXbbottGvwYeXt3y/40cnn5gIebBg05pU0CNuv6RGWhYBIxuERhLPYdmf9TBBcFQ43kNDoUQvTYmCpBiZUEllowQXFB6SpLcI4+WE67O35+eMV495Ynzx4xeM/+7ki9eQg60vYbpBPuTjtsY2mqGucjfRiwVpDok3947Dm+3BEctG2LjdCoMo0j1jhCcIRxxAcY+wTE9ccd07Cnaa9w1hEmYTxOpLLyFS9u3jIMHqKh3w/4ac/pdKTrGgTP7uZTrHGMYaJ2lnrbQe85hRucdEzhRFc5nGkZxoFIoDaCDxHpJ4y1hOnAbthxsb3AqufQe66sJgL13StMc8W+HzEWnK05nHoqPHXTod4w9RPWAsGjGjBaM6ol+BPGRCZrmcKRWi1VgNruOPgbtnbLs6uGff2Ym13P61dfcvPqc/xwTPancXT1Je6i4mrbQVtxcfU+7ablavsejUKIt4ieGI8HbHuJrRxiU5pqDBacoHiGkAjQjTOMfsRGxcSRTd3QPX4IRhjHibf7E9YK27ZDnHAUk6JrXKquJuI4TZbD2HN1VRFCKgJQNzV1K1TtFucMlXN8fqt89vYlMn1CZQPGWpqmY9NuOB5TxNvDq0uePH4C0nE87tjYgNMBDQ6GgbpKQJAxEZ0Mag2VWoIJaU+NKWVNbLLVvVhGoxgL5MCHHL+NOEsMYKLiphNVd8kPf/JjfvujgUkdGi2qqa5lSj9PXKoxA7wBw+STnrQKQi4tUw7sJWlCwaAxkOocLXaLqhKGwOHmFZ15y8WTX2XsJ4yrMbYmMiUMw6RSSCWIIrlt2VdVBUnvNS7xNpmv3uL/f39++RFKZhXBUQxa1s6yLtcpcwhzsS2sQOHtWMoPy2wUGZJzXyoiKZpZ04vDfd81K85hJGfSU5jBUzvXRr7MxuZspguE+VR2MeiKfypoOimMhbuonAgKIpmw68/xDNdOh87tWECU5DSbdKp/1isWwt+V0VhAFZ07mHJDheV+shM8aUIoZxN55sRYl6SW2VAu7yn9zPlSzETxGhIYltNjZl4UsmMrK2BqNs3XzlWOIqIY1LIY3SwGsMnAZDGGS5TXOoonpbSZeQ6GOQIgjW3Isp5drdUYr3Cs3O1kQOicYqbz3CyyWQhsF+f0vit7nn4keVqXcpH5fsn8JGk2pHEskQFrMjXVJQ1Lzfy7ZMSae+O9GO8zFJR/ytmSmcnoJIENFKdolt0MO2VZLc6NFidWTQbiUgtiXic2r/857SZ7bmXllFODNCcX8LgIcV0WvIBxSwro/MUyn+6dzC+j8dXO0hxJQNYZ5Q5599qz++ax1FmvqSltziOR52yexEtTcrMLQd8crZmnwJ/n3J8Dl+80anEccxtNni/BT/THIy9ff0EgcHF5zeOHz6jEYk0qJ1rGBAVrLMFEiIqrOt7/5nf4N3/8L7nbv6UeTvR3d+x2Lzkcdzx9/AHHw46//bt/jz/4l9/j0ZOnNEBT1UyTR0xATJXmQBRuXr/hT/7oD/nn//x/5/MvP+Vv/+7/wH/1X/6PPH3yHl23paRMeU1VaYah52d/9mO+9y/+T37rP/odvvvd356jmu7zthQHOTnVifS1yO3roijWaULld7Mah3wWvzj585xd3VM0kRa9t7xj/e7VFFjm8Up/wBItufRI7/1cf11g4bwYzKKTCqcX9+dSWTusDja+Dki4913Z5+e235/aq/U7q9Tz7iRprefpPbxhfu/sCjGP6XqPNmvZnclF55QoXX9X5IuuOM5WbSfvR6vWlnWkshpHXd3wFfJKhw8sxS7WqYhyr59FX4nMUcHllrPGSbY9ip5WznTdOmp57s9qvt2PJopZFrboxvz39byV1c+zwh153q+B9qTPSpzyV3+WyKl7+pqzRGSgVEwtQLhP+zHJvvQ+OaTOGbqmJgb44Bvf5sWnP+b5eOTFFz+nD2Mqne62tO0VlTXcHvfcPv9RqpbVPsA++RWunn5E3T4gEjgNA7e3N2ybC2haxDi0AjSRopbKpNPUc3c84GPAucRfZGJKcccoLkyoOKIO9F6J4cQw9Gi2y+q6zg6YJYikCPcAp8OB3X5P1W25bB9hrVCJ4duPhUc85H/7X37EP/tpzyFMia+nBh8EawwmTgQnWNNxVbW0F1dM4cTnd3fUu4ipa9yF4Cqhqhx+Gnh++5rbw4A3jqqu0h6hiW9J1TDEY4oCEsdRJobhxMlPtLXBdVUCeq3FWotrKkQiUULi4crR1FFTNBkml3GXlE7m/cTxeMJFQ9W0tE1LVMekZJBDGEVwcUBN5LLr2LoW70/c3b6mfztQX15zdXlB7QSrFV6gsi7pIJNstyGktD9nPTF4HBXBKNG6ZGNbZRh77k49/dgTotBdXlNXDmImbialkWErgngqESpbEauUalfbhsZADOVQzqIxYiIoHq8poqmqLLV7xPWDx9wdd+jxDjMOhDgQYpobzx5UXGxafKzAVLy53bE/Hajclu3FBiMp+lldhXihTosiVViTVG0qaMwH+JES62hJFd9izDosjoxxIviJ6XSi6lLa2hgl285T4jCSgHMk7llV9v2OYVS077nb3RBsjRHwIRJPB6yxSVf6iW13TbTKJIrrKoxCCErXXhNtZB8m6sfPaGyF2pqt8diHDWhKpauur4nGctklcvcoNZumI3Hl5JQ+YzGqXDszU1nYbYWK5CirDZfhir7vGQ7HFFG9vUK9crN7CxNs6w4xjkigch2DKqMqWjd4FB/B1BeorXMabI01yugtXmEUx10fuHjyLU5RsYPFU3Fz8DhnGEblzf6Oy3akcg0+gLjAMUQqlDHccRySX9B1wu0wcHP7ki1gXU033eDHN3SPHrO9uObysuXxowuuH255efeU0+GIjBMV0HRXNO0lbnuN227YbLfUzkCw9LtXIMLUD9TNBRbw4wkRg/cTIhFrLGKUqI66MkyTxZoEmFjbchwDohGvgcYZHj24Iho7Jy9ElVTKnpEYEn9btzXE7SXTZCB4xHkiDYrFVKmCbzJHLJM43hwn3Nij4jmevsD6nru7V7QPrmmbDRfNl2y6hqpxyHNH0zRYv+F4OKToQ1vhgcvtJdY5BqvUzWXaNI1JBxLOEq0jaib4RlApBYsSPUPxn/b9ROUjnpHT7hdcPPl1YoC2SyC7U5P35rwX6+qgXszspyYbP9mIiXA87Z46t2uxt6JCjBNaVfzwk1f85jcHxhC4G2CrERMj1qaIJ4jpED9HIMVYdlQz2xwRso0f0Vyh7pf1+Q/DapFeoAAAIABJREFUoVSMF1McNJkjLQr5GTADIkaTqVhcvxT5YRYjUNNgFMYBsrOaUpeKgZad6XySHACbT/C0vEsSy0MxJZeomWIG5Qtntz8rYpWZvODcWE3PiaqIuGwQL7mMJqe/zUbYyoqc31gc2GyYaSmLLumE09l3Q9Li2hk6c/jTJz1L5n8bWVKulOT0Ri2OckQxaZOQ9De7Nr5no3dJl1kiR8ozFy4fJaO/OW1prjC3jO6KVHPtcCwxYUuqViFEy4LK7S/pLZUYxrxhLmOawRNNY1c887S44yz4NX0LyqpPsgBNM1CZ22vOT5LTo5bUxBlcWX80j1Oer1mEs8yWk9sC0pReFpmu2jGDFJINvdzXFJ6V5v48TuuXrRKpyniW8c3yLjJkHqd78ypHpQmZCHZu5zkAV8a1zKH52elIMoOPJfavcJ+YTIadxrx8X4jtChfaHAFV+EtgJu82VjJP+DJei69XHJI8S+a2r1pSAML7/S7zQ86d3SLb9Z/EmJRKakjRPsZgs7Nscr9L5GWRb54a94CBLCM5d/6KnpuBy68Cku63L+tWiOk9Gnn79jU//eG/4fbt53z6+c959uFH/K3//O9ydfEoOZgmX5/J/gJhTlVsu45Hj9/jyQff4tOf3jKNA8GMvHrxKY8ev8/zX/yEFy8+5bNffMyXz1/wd//B/8yPf/InPP7GB4gE3t7c8OzhUzSv7Zs3X/Diy08wLqDTnj/78ff5wfU1T97/Fo+ffIvqySZFa0nSIy++fMH3/+h7bK821E3DOI642uKqJJP7UYAr0S0jfgY6LkDHPNxZ5u+ATrMuWjnYXzEGac/KVlVRMGd36fm9Z4Bv/n7eLspcXoPfCzC9jmqUeX9N7y6PLHopxjgDuUZMjmLM89/kCFAW1bFuedHf6XQ7RRWaorHzPLWYTPx4Lre1TkynbSuZlTTUezKMev77suKZ9/Oz7+S+dFffyvJPXclrWfH533Lv3ncX5Rxtnew+YX3El7HFWR+WSmkCKRokg1DFuNQYZ26DdyJ3KJpYmdHyVUWKOX0uv34dBz1HTa67sgZI1+/KgcazfaB5FUkR3Wp+qa6ikPUr534RW9mHzsDU9X4/z48SIbgAU8sop3uj9/T9AWdrxOaKszHpMytC5RxRk8N/fPMl/d2eLz7/M079HjUVwbdEVepqw+72NVN/QCXSbTa4zbdot0+oqpaA5Xg6JNtLHO3lM6rKoDSIH/A5IdCJJUbPcTyyu92lZ3WbTKwaCToxDJmkQYUh7tFYoapUdYN1ba7451PEVT4qsWrwfmB/Sjwu7faCprpC1LNxFds68OpO+F9fvqSRmt53RJRTsJz6dFhX0lKtpmNEayca06SUpKaF08huf+CT/WdYCVzUFS/f3BAj1E2L2sgQZI5wiSoIFmMdOHDWIWqo60u2REQSKJMC0hPRbeHDSpVd01yMWR1a64godXSMOnI6HdkddnTNFfWmIYhhitnZkkJe6zECrmqIUenNhCXi6o6rJx8wjAOD7znc9lTtFqoucfSpyxWJk41eGUF8JIwFFB0pB3YxjEzTyO3uNcc48uD6Mdu6AxRLyJWXcgl5Y8BUGGOTrBGaGjYu8TOJWiSmI6KgE2FUeo0QhVTtjAQkmAkXLG1TQ/cY249MuyNhuKHfv+VtrAn+ksMY2PUTUrVsmg1iDJMfqMUlf0MSj4/6ZH9gDIOOHI8HosJFe5Gj0XxKyVfLfujxIVA3Fa2p8DFwPO0RtWyaS3QaOI0xR5EF+sOQlcSQQNDNQ7b2Gi9HRht5/OGvcXH5FBtHTuNEP440VYU1lq6qEhBl0sG4dTYBhQJGLSqBCuWxpLRBKylaytkUraGiVAZEK0KcEugQE3m/VzBGiTEQiPioTCHN/TpnJwRRfIwcjz39GLGu48GTB2CrlKIoI5ePnhBiZCCCj7Qq9Hgum27WOdHViEBta4IR6qqhqSuijlhTpwOrmAC3CsEzUWHpxKHiMVh0E2mYsDlNSTSlKbZ1sg6DCG1rZx+FoFy07xH9wBh6vnw1UYeBo79jc3fi4nLD1faCjx5f8O33H+Cj4a6fsMHRiyFMyqa6YGAk7N8yVBUaDbt+jxHh4qJCCJyGW5wYNEg+9IOhf0tjK2y1ZRp2OLnAWKHOKYfRVmiEcZyYvEd1xItS24baWcTGlOXhA62pmPBIBma9UYahJ4QILhCCIUiq4ttPShw9RixVa3HdNbWpaR5AGCLm8UjXpLUXomEn4NTjDPhJ2L0ZiAxI9DgHgYbanXAx0DVKDBadBqJLPvpF59hcdjR1R910iG0wzkIm57ZVWu8ijnZjcKHj337yho8ewubBE97ujnSuA+dYDvXywV62OaLKzGU7+7qaIvXTJkfuT5gDBTQD8OPoqZjoVZBhT3f1HlMUrh82xKnKnLfZxkByUE7iQC1ZIYlgPPlaxSKLkioN/jI/v3RAKZmzZjEdiiNVjAVJkQoimXU8h8UVI7kYHeUEzmTjRvLmVlKalpSGdAJpyykxuWSgpLAvI4vjfG4hZotpdR5WypuT3+s1sfAXouDzbqYnzkEjEkFlPu0zUtKhMvBVnNzcmOJQlwFXzVV1krU5R+CghRNpiW1Z0tveNcSTk5424tlx0LB0l0SemJS5LM+QBWIpXCtx9Yaz8HlJU7IYzItVukiyOL0ZL5hPlGewIFvf4czdmIVJyu9cDRVrxyTNkqglok3z9SVEMEtbirOTmhk1x7DluWbnZ5q5T0kCpXre2iBO15U5UsRZTqoXuo2Vg7FqvuZB1zyu63iqOYpgZkjPDocWR24B4xY55/BIoMzhGUyZnTrmlIjCFbR4mYsTYWb293tzetWROdJOFc3hoMU3S9w7YbmjeChS5JedsHzDMopxvmaOsMpzJsGcec5LuTbPRRJ4ln5VFnJXWQFZReYLmBk1AVszwe+s3EsqzeqZ65V15uutHLp1BEY+gS1uHbJca3La59yZebSW96z/v/7XDDTPclquWMbk/FrJg7h+mpqsi0Tp2hrRgZ9/8kNcU+PHA4Mfs3Fq8zQw89xPtSWSfnWu4jd+/Td48/JTXnz2MdEPOEkcIbubtwzjQFXBv/7jP+D9D77NdNwzeuW99z/idnfHzz79KZfbDW27RTXy5INv8nf+67/PD77/e9zdvOLTn/yY/uYtv/rd3+F3fvvv8PjhA3A1xlhO/Z4nDx9yten49l/8LhqFL178gvc/+BDaigU8KGO0rLW4itKcdZlAzu9k2Qv0bJzPPrqK6Ls3Puf/zvOgPF8XWSbgomzqS/XMBIzmRqOLztQ8G8TMX5e5J1ocPmZupFlX6iKHotORRZ8XoHeuElqek0HOeK//ZWZnrX6Wtm5JQMN6LZzdWfRRbt+6kFnRjUVvFVB/5g/U+89Zr5SVhs36Q1fPPG9/AeFWXGJrHXL2LuapVN4znyuulOLS9jJ0S9sKJ2J5m5y1OEszp8WXamrpZDbLUZe+JbWdDFKZp3jh02LWX4a1bivPkLM5uureWXtKlGyJBNKyd8sCVmlGy85jvc7HW3MfIqwqaObvROY2p+Yl/TznFK/uVw2gyWkc+xOvXr/g29/4VYYYCMFjESpXI6JoiPT9iRcv/oxffPwn9EePMRZfP8IZx+H2Jf10S71/S9O2bB69h+uuifUW61rA4WNExOGaCkSwRiDWpISsE2ITEesUIsN45HC6IxDoujrxQasn+FSd6HRKJ8hN184OWWXqZMtJwMdceVYyITcO9SPHYc9xOADCZnuJkxornsalRK4vhxKRXTFIlQ9nEtiybROQcZwiXhX1Ic2LMNGHiUkVicrxdEqpHlG5Ox7onWPygabdIPWGqmpwNnOmmJjIYU0G74zF4LJ1taaaTXa9is34qqKYfAiRTs2VXAjFGmSc6KeeMExMI2wvntA0GyKptLspJ/kGRBSjBmMirQmMIRKDgHUgBvFK27bUVMS+5/XdLTF+SdPUXG4f07VbNq6ic8IpRm5DAEnOpwahH5Vx6hn7A70fqLuWx90jKmcRTZWcnEmZDSFrBMWiMlFZQ2cSO9YUIz74FJ0GxDASdUJlgqBoyBxgIdLHNFcEZSLpABMChJEJTzAQQ+DuMLHvbxi9p95skKFnfzpRNy2uqTjJQBgGLLBpt1hT0/vIEE9McWIYe/DCpBZtHNM4EbxHRRjGkSkKPipUI5NP4xjUc3u3QyroTyMqacRVDZV1+GgI3uBH5aQjpr3kyfX7xEoYo6elpms7ukaZdEJD4HA8YYzHEdjUDaZrEdtkt8FTaT48VEOtU5K0iVhbga1QoCISowc8ktPgNesOLyniwgp4I5hYgUl6YRxG9qcDoxgmapruEZWrU3XE4AkqBAuqDrEJhHKVQj8i3rM/9cSpp960OVI5JlvapspgCIlwX1MUlBNB8IgTXKxSZWoVJjF4iThNHFVO8p4ugonl0FkwUmHUEnRI0ZhBkawLu4tHOFtjpy3RNtz1N/R3E8fDLW1zpOsqNtsN122DM0rwyqmOaDzw+vYt1gvV5cOUTeNaIspxHDEaGcYeCQPWQFO3eJ2S/OTElUTevvk5G/uQuq3AdintF4uxQttoKtgb0uH+5Hv640CMA9Y4/Dhw0SaC7YDA0GO7DV3lmEJa06dxwBhHVTXUdYW0TfLpLYhxEAMtClvLtXSzvQJKNJagAQ2JszRtppGgPVYtpmrxokhluPOe4ykisaYyNf3geXMKyJs9TvZsW4OxhqZ2iZubwMXW0XaXVHVDFMumafn8s5/z3Q86bm52YBxQo1FQE2HeQ8FoSb21GewRDGkOGZOjp1UwknXmbEyQgHoMtamJanj92Z7HW0998ZCjh+intIa0JgYFmw7rbPafVMzKl8v+Wg5sKIcY+hUBK/8+n/8gHEpCSncyxZgGShU2swJwkiGU/5VDAEokhmbnq5j5s5MKYJJhkohlWfiGisOcwatk2BQrdeUssDJfiuGoxW3P6B2pNpMsSEFuwuy9MT86P6qE/Kf78xulmI/LdcB5SPv8/MUbUE2IYyDm/ia7K6yM97VRuDbhZ0AqK9r71X+LI7E2A9cRRKtmrh86O7AyyyifRJdoqPK09VjlbxIIJmfvJPepnErOaYfAUiUoO34rozx7PqkNufILxcydZbqUwJa8kGeZ5NcsPr7mQ+AVUMTSLlNSJDWDSeUdGUwiO3ka44xPrtPC5imucJ9ofb63jEe+eCaXRVl5WrPsziITVpOwOGRlVEFXQ7GAQIuDsawBzv4uZ5N7OfHOxuMcFVVczbyG5+esBJlHL4s4rRVd4Mp0al7m4MpdmWVbJnCcZbTAccxrkfmqBVBdz8PSnXm2FlkV5zsD3sXZLJ8F0zf58rLCy1yQfJ1kfXfuoC4pc8VRlqwfYQEW3/3M4stC09WaXaJb7t2znic5VTMSIOu3GCOH3S3/9t/8gNu7t3z4nb/EX/y13+Lq8jFo4TIROJunsjiVakANx+PA46cfcNzfcLx7CwrH04EYA8PoUVNx9/aGH/z+7/Hixef8rdORL15/yXvf/AZfvnycqopst2Atp4Png2/+KtcP3uP27obnr57znXHg5s1z+sOHdBcGVzs++fhPqcVi3YZf+bW/zOsXL3j+/DMeP3220ml6T57na+VcTllprzTpfYBorWPnJ6q+W7Fq/dF747B6QgJTyo6wnhfr6zOh1wx4nD8nHZQswMP8JJPnZlGZpe+Qxnbue7ougYQsOm8VGbPWcbmF9ySx3kWXa95t79mlvPu1LDpupXrmzbREdipLdMx6D4avXkHvbopLG3T5Ne2Rf969urzyXurrov+X/W+tW/X+o+b5pOfPeEciZe9Z7paZ/60cAC3VJSU3cCZtX+2py6u/QvL3wKt3U9x0/Uu6bL1vfN1zc1/vvWy1X51tDfna1RokncoSIjFzGkZVbvY32KrFmsSz4f3I4e6ON69f8PL1c+5uXuGjIdqWMEb2p1v86UBXWR4+eUrdPMC6K5rtAyaxBGNzlLtBnMv0C3HhrWTMRW4jISrTNHIcTgzTAWLEYRKJtua0BDFEcUh3QVcl59RiMTHMtA4Yk0vQCyrgg2fsj0z9iSEEXNVibEo9dkYwxhJIJe29CsYm3SCVUMWYHFajXNaKVc8wek5jJIwjYxgJTIk3UjLxcvAIqdT5k6cPaGvDOPT0w8AUJ6Yp6xdbJYAIki1iFJUKi8scLemZZS8WUVxeI2Uk5wSPHP0uJnI63BEGzxQCTb3h6mJDLKW3rcEah5UEtnmNOUpqwEVLZZVohTEIzgieFOGXuDUtlWtx25rJnxh94NXdjq6feHy5ZXPZYoDaKbUTnCq7fuDmrgcZMTZydXGFs5IimXJEb9oTBU9KH4s+YCKIE4YpJroNSYcZJwJG02FkmDzTdEBMxIglqEGNIeQx82ogDCkGwjgkRrwB7/cpKqe+wLZXGGOpxgOCT4CJesbhlJxRA9OgeDwajnS1MvgJHwaC91Q4ghEOwwDjKYF9mjkvZdkHbm5uCDFyeXnJOA5EU0E0+CgJOLHCZltjreV4gjgpR400myts0xCCJ0wAkSkYDHmOWYc6h1aW3p84HHtOU6ALE001UDuHcTWjMRhjMFEZo2I0oJZU4dDUGDV4lKCBKQrgIKbggqDg8xwH8Gow6tmNR8IUGbxyGiKXV9dsqw5HxRQS8YaKErIiEolIyLrVgLGWqso8atJw8hHx+0RqXk3UrsG4xOCLmAQuxQwRakSoqawlBI83MekBMfg4pXEQlkJHM5hk0BA4+Z7j8Q6NE213TddssBcVpnL4/o5JhYv2gqZpEzjse27HkdvxRLU70VRCUznqqmbbOlQN37yqEFPh8Yyj4sRw8tmyyzpQbY0aGKYRIUWwjGPEuJFhnHBbg3jow0gUoTIGGxNI6vM9dVXjFXanAT8c6cRx9J6bmwPNpsWrZdwf6TZHqmaDrYSu23DRbLGSdHBlMj2DGELOXNKYQJgpHHESMKYiReAAMaSURzGkA2TJe23LJGDChBjh5APGQNMa0IrgA63x2GZDjInWYR9BfST0EDUQveLeHmiaMUdnCa2xiAx8cWN5O/6cx5eXnKqbBPY5gzF29o9sia6zNWIqDJp0OAbvI8bUoBaRAGIyZmDygZ9mPEWIpubt6yPfebbFSw2qTAHEJQDb2LRPFExB1CEoMe9nM2YQFTWCUyHg30kv//f9/NIBJWdMPjUN+acWX+3MyCmOvhSHdgZJZHZ+ZocTZmCokFRqSY2R8n3OWYZMtsfsFyXbVOd3lpSdAvYUv7I4l1aAqPmUMYWmJUPt3LFXmTMTi7uZn7cqY74yNsunGFyzAUoxslbyQck16mYDZz5ZZTG+F+NzMZRnl7YYoSpL6lppQwHfZmeA2XGdIyFW8ik0E4VoemUS3+vckhp3/2R0JeIZdBKg8HcvlN3M70+3nMuwpE+miLe1A30+PvGenEoaIWQZFaciaiKD5vzkfe773NMUwZGc+6Xvs/xlfXX6XrPXUkb2vqNWGpTCw1NLIyX8P8vrXntKf7QAJ/Pf7rVdFumVaIcy65aooOX6GYgF3uG2KHNi9a6za2evb2ld4S5JY1HeUZ5ZLsuOkNz7bm7pan7mSgdzjlhxknN0Qnp9sRCU4mwvz8mrdNWPwtE1R0eVBpx7Y8wLLHciVaJkrgC3HpclzXHtXJb4jnRlSYH7KoeyyHyJgJK5GbJ+2b/jk+ZGAuuiRk6nnrf712yalqvrR0QDH/2l7/KND7/Ny9df8uzxU2qpE6E+SqmepqQwesnrq+s2/Mqv/mUurrb88fe/h3M1u5tbvO5S+zKpWt22/PjHf4K1ju//4T/l+ec/49e/+x9zurvlvW/9On/lN3+Tw37PMPY8efYB/8l/+ncwvwc/++xH/OBf/ROG3Z5Pnr/gH/5P/4i3b17y+9/7Jzx89owP3v+QSmqePHqGHwPWunkfmbVfHqsSqXbfwV6NFvcd/CL/+ecKIFh/vooA+d10yUUPquZU0Axclvmr956XEw7vtXS1sPPPsgZnfcqiI86xl7yzyqIHlscLZKNj2b1W712Byme6tOxEWmCMd+V3rvvv6731ulzvweVLWf6wFsX8SDn/0xocMXKmv4pKKLKY50I5YRDOxnjd53fmzCzg9VCsgXrmyrZL4+Zd9yw6q8ilRC1DPpBb68w8bwq4uZ6xBUCF8702pQpzNheX/WS1R9zTPYtoddZtqZ8yD8nZ+phFV/RFigAteuss0nW9nwBrXbhuY6nY5v2YSiMjhAAPtg/Y3b3m/fc+ZOwnnj//OS+/fM7t7Su8V4Iqk7ccjyeGYc8YBrbdlodP3sduLvHGcXPoqTH4KaY5j2JtM9MZhBhTZNTcxsRHczi8ZX+3Y5gCapTK1SAu8+EIYizOODAWtXlWaySVqDaoSdKojYWohByhOPYn7u7eIAi1bWlalwAnk7iIEsFyck494GyKgHAWQugZxp6hH9Dg2d2BaqRXmGICrTARMZYL2+LHwJtxoHYVja1o6jbxcxpD0zps09IPR4ah5zQcIBiq9pJt16XoVjFU4vCkozqXcntSxSXvE+dpjtKvcoryvIcbmE53HE93TF5p6gua+oLKZkdck0MkGKxJDqUKWE1zMKWFpiiyKa+1GBMPZuL8UxCLZM6kylW4GPHjxNEP9K9PvL2dqKuWbvOAcXdif9oxekPEsNnUNLUyxgnxKQY9xioDzRMRIUpk8j4RGIuhqusEQkiDMuHE4WUgE54QNBX5ti6Bg6I2RzCkuVVFizM11gmYCh9G/GmiNi2mcVjpkKbGoHRtSwgjIYxYOaHjxLDbEwWsqZAo9HFI60YTMbhEg5pUFEVVsZr8mBgS+Ba8z/aBYYwGZyxTHzG2ATVIFKyVxAvWNogVTsNI8NC2HU23xTiXomjyGBYaYnCoBIIGnLSoczgjiHFIDIwxMpw8yICzI85VuLpKka42Jj53LxiJWJvAo5jpSpRUvj2xkFhiiER1iCbi6GO/Q3RiiOl9oanZtNc0pqJo1cRpaZJsstdlcgGetCOH+TvjDMZVuJjS3vDKME2M4x6nirWWtu2wdYOxEEIiVLYxEEWTjC2ZLiPb0CFkPQlOhCkGxqEnxpAzeSxN29JVD9i2G0QstZ3oZeRiI9yNHiqHkQRGmHhBE0ZUB8apZxhH4n5C4p7aRRpgs2nYbjqarmPbtYxYJm/RqPQ+0CN4UxN8IEafD+AVDRbRFM3lwwlrWuIY0KYmaiRMAQ0pugyUOHi8n1A/IaYj1FWqEieGutlQE3EXET+cUqSYKJMXTIwE8QgwSs44whFNWqMmpnl86j1OPG0VEGtRXOLXLDpHytxI+5UPSvADdWOI1GimxYg5Va+SADalUYqGFIFpDJXJHGTOYOWS6D0epfdwCJFmU3MXA3JS9ne3GBNztFAgpNAmXIwEAtbAg4sONS3OCqoTUYWmFrbbC0JwIBN1VaEqSAi4pmZSwUmgtha7eYIZXnN1ZQin12zbmjG2SBwhpuiuVPDDksgIDJaYUpcl7WWZSAgt1D9ivpp/9d/j80sHlOLsRGbjYrZCiwV57qhKMXTIZ/6zYirRTmtHFQoHk5l/T3xAMweMFPDJzAaTEZnDUgTm093FySzGbsyVokj3k05eZP73YkoX/3KxPXXu3txVZtszR/Mk+EmykzOnPBUOnuwOF2Ah2fq5pSI5Ra2AL0vI+xplnJsjUKrefV0EhK5uWEAbWRn7c9dKo0hTc/2uBUBaIwJrDoW1U5226zmMZ8YhkMUAXkdizKLQlLo3j7Musj1zgOY2FpkuoMM89coDkHncpDhVxemYuayWa9NXyUgrb51THoozqesolGWOJ6evjHhupWaurxkMWTokM3pQ/p3aWhyerxvTIhg5f9jsgJbUMpQ5vXJ2smZPU+bTq7mPy/DPa1hzFMX63TP2YtKpQnqFWdbHfCKzGsQz50LnZ5+7UIusyU6YzI5g4iMxklMV18A12WgQKKXR567I4gzO187XnCvaGdy7P9tKlQZkkdGqn2v+nVVr5jmwtGU1pqsfxfHNo3Lm1JX73v3o7FenNFphv9/x9tXnHOJEc/WU9771bT5s/zIffecvcbW95vPPf4QovP/0AypjzxRYkkuY9eju5oauaWnaCx49/YDf/mt/ky9ePOcHf/T7vHnxHKvK0/ee8fjJU+7efElskyxO/kR/6vn9P/5D/uFf+WsEhaquuK4vefjwKb/zN/4LPvnpT3h7+4qbNy/4t//P97m+O3Cc/gE3t695/PQp3/6VX+W9x+8TR09dN1xfXOFsVTTnWUTNEsiS0oBN3hPWY/T1Mlwc7nICXqoIflUK0TyGX/H3s3ee3QConM2VNE3O5+n9dyxflHavIlPmv9532O/9nvUcWWfNalhkwWtX8jlbN9yXg5zN43fbXF6pyzYhy3flHEDv/z2/LbVltd+UfSKrkSU9OV1XIoO/7vNVuvOrAMF1iu56GZ/Pm6zv5z2MbFiyAmVk3vfmg4UsQ5P79lWfr54Duvr/vX16LYT7z8oncWWvVn332UXXlM68k8rGMr7pZ66oOqcnrnTafLjzLpC77AGLzleNEGM6iSbS1hXjyfPZi495/5vfYXtxST8cmPqRjz/+U372yZ+wu9shtsbVHeM0EMcJH3qatuHyyQc01QWvj3sab1HrEOmIxjCMBySMxGiRqkXFIHFiigPW1IBFfGDs79AQeXXzOZv6GpqaTXuBM022UVOFt3lzyGCaZNtXc+oOTFRqEaYE9sXI/nDDOJ6wmJxmZhFnMnhiM+l32kdj9Ak0mYZEMK6R4Hs09Iwx4qJl1JraGqpKaI3LVe8sosJudwdjpLEtdbtFSACcR/FBM/2AwVUNxjhMZRlOR/y4Y9SRtm3Z1i2YkX0ETCqNU6tBo88Aj8kn75CIZNLcn7yn39+wP+7pmi1us8G6TY4QCYgolTiCGDrrsC4BqD6maNiqNqhOmCiEmKJUbLSM2SbAKAWwAAAgAElEQVQGwRiHNRaMEoInkgjTnSTaCj8O3OwHhvEFTdMR+gNqHNXVY2IU5DRhYsMUDaPEfFgUkWiBKhWk0IBHMW2HkKrAiSUBbFIRSABMCB5ioBJHbVzSBfOenQtDlGJFzqAqTKeJfjqhGJpuS13ViCbOWEQwVjBVg6MFv2Ecj4ThyHja4acR62oMFUiFU48EIapn8FN6eTQYZ/EhAX7iNEdzCHhls3lADBNk59M6R+2qtK5NZBonoliM67i8sLhc7EGDnyEkLUTCpTqtSaTGIUf3GzEpQkMd0aZy5yFMDN4zTD1ytyNYpb3Y0riOigofU6qYaqESNxjVZX/K/03BczrccjycsF1LXV1QNTmd25gVZ6ughMTtC9gM8FgRxKQIIaeF3sSgQVMkhyo1EKyFSnG4HLHoOY0jPu5pphMXTcW2alALJqY92ZuYI4MUZyO1kErAI6h6hn5kf7xhCifatqNtH2Bdja0X/q+6irx/Ab/5F7dMQXj9had+dE21veB7f/Sa21uDE0eUjqrKoGiteH/DMJ4Y+5G340izH+jsLd2moekats3/y9y79dqyXPd9v1FVfZtzrrX27VzJQ4q0LFIkQ+t+MyVZDmzAAfIQBM5LAH+KfIIgD/kQecpbkCBOjMSyA8iybFmKRIsSKepCUbwc8px99tlnr71u89bdVTXyUFXdPdc+8oNBA5nA3mutOXt213XUGP/xH2N0tM2aVV3Rt5YhGvohMriG3kc0CF0TiLGnV4MJipFAbTpiNIwcqTBogD4MxKAEn86EtqmpmjNoOzpToSYiWhElUouimw5iKgCDesYwoj5kh0JPU1nqugOxxOgozjOp1gQ8IzEX4rCEkCN3mB39hdE7DoEYR1qVCXARmxzxiaFmYAwEEsNqjJ5x6EH7vCagdcr29ppV1+LtCls1hOGIIOy9gFQgEI0yFse9gvYDOME2K457m9abaMpbpIKrPattJAyGIIGmSp/pMNK2wuAtNZEHG8vOXjHePeO99wwar3jwqIN4xn57xcV5jTZnHI8jrTW4roFgMDpiuxavDkvEVBUiDeiAWouYCsv/z0PeJsZEpp8V72XSZ7KROGmVc9hI0MicwlqyQpZUkUgBTkqq4pQIOSkzifpY1M3CZCrKGhOYNINOhTlkICP4qX0lvw46h3ggswI0Gdg6G4DTZ0VoafEQnqh5uW1xMiWnSmpMDrJZaS7Kq+Zkhso87cJkBJRhnJS97P4sxuf958YYcdbO4W4LJVqm28/9Wkzq9KtK9ijNzZy6ORnA5a1JqZzfd8xKugpzSGAx7kv3yneyhp6U+plKyDytlHwgM5pxCkPAVPcIldSGgM5BkLIc+9LSWB4zgQtTmAlxaeEwAZNa1v8SRJjHVbTkyVgo5ipTJbrEoNLF905VccnKKJAqD8S8jsrnizCcNKdJgJW1tjSYFBbheSyMEM25OorhModamQzeprV/au6chAqhs0EFede+auIuXydtK553OQVpjMz7sdywHCATAJT7Hack4lP68xPFrvw+Az4y5aFRZerzPJXLsMh5pad+FzA7778CIhYAcMl+mtZ/qW63MN6nB+piLOdrpl1xAir8B17GQPAElG9980/42ld/m7/1xS9z/oULPv+ln8Eay6pb473nwfkD2rrF2pziLxb7MD1TJHnwj7sdX/+Tr3F99ZSHb73J229/hqo74x/9o1+h6zr+9W/+UwB2h1uG97aM/R2r1RN++MG7SOXYDUd+4Rd/jfP1WapuJIJIwFpDvT7n7/zMLxL6HX94+QFBBuLdS/7f3/8d/t6v/QMePHrCWbshGhhDYPRj8mTXLg1L+m/a06XtRY7ff/1NABBwT/6dvn8SGiQz++gEQFzcYwIuKcykea/NeZKmG86y4X47PnaOFwfAUoZlYbdY3lMb0qUCGuf7l4IDaTG+2hfmjG3/oRYtx8eaBCTfZ6menBFxDh8005wtL5rH8JUpnGT9gp20GIfpWfcaPAFYsEyCeNLC8r1XnitZZi9lHwXgPj07ZVp35dTQaWqAGfRaImn3upeaNs9FLGzO8r7OjrPlWftKzqQiSKfHCNy7piRln79TGJRaejPLuUX7Jpgso2NFL7v3wOmeU9/zDWKMiS2Tx6auGw53Wyo1HLYv2b5co17QCF/72h/y7rMfEMeeQ3+kbR2uajg7f0xtK9QJbb2mPwYuX15i6nOa7gJvDF1jeG0z4AK83LcMKORKsN46qqjEIXDcX3F7c4lXxVWW0UeOZsSMHh0Dthup6zWNdVhxBAWVgDMhhW5rqq7jgEqEMaQcGgG43d5y3N+BE0xVU9kU5mGNRSQZ/cd+z/54oHJVYioZyWy1ODEnamuxrmUtyZjEpYTGhpT/ySFc3txw2+9RHF29oTMtUYXImJlSjhgixkRs3uHOGcRCXVWJ+TWObG+vGcXSdI56taFXwWhFH/sEBgUFm8AINYq1CVS43W057HcYq6zOLnBug2iV9pXxWKmSTmMC1ihtbanrVMXZj8mf3taOQwAblH0fMLEU8lBMSPlAVSJKJKgn+pD0Qg0QAhoDTWW52R3wXnFdR3CRwxg588pmvaFrOyIWqwlocyJgTWYoWax4otYYk0CiFBqVZLm1ik5pPSrUDYjGbNqUHFl5XMRgcQQbMRrZ719y2B5o7QrTtWzajsbBqjWoerxLPze1Qb1l30cOYlGzpqpbXFUz+iNhBB9G+tFTCbRiGEIPMY2ltVlvMyl8yEfAugRURcOokcpWKBFXV5yfnWFD5PbuBk+gbtc0rgVjs51kcrh0xNjMcMoaTl0lI9wZR1XBqNAHRbBZ78kgoqtw1nGMEROGFJLpI/tDT0BpmxZjHZVLiZpLgaUiJAzC6A/s91v68YA1hq5bYbo11ghqDEaUiiQj45ScJuX6TM72mNtaMQZwEhMQi0NDIJrEtoqa2iBGsQgxpPYHYwhOQQP96Bn7nsodWTWKNQ3GOVQ6XLAMwwgmVcmLcWR/PHAcjyA1Td3QujqFulrBGQNjxNYpZNGZSAieen/g/WcfoYPn3Fzx2U75cN3wnh/YHiN9TCGpVgSpLX2oqauB0AyMfstoPH4Y2N154u0NK3vHpmtp2pa2renqiofrCrEdt6Ow7wE13L7Ys1m9jus21JLDe33kOB45+Ejwim0q6q6jrjqsVCnc16RwwKAhhXSKpMTfGnOxmpxvSGqsqQhmIHjPEAM6eMa4xVY1tWnyvEDrUh43Xxy5qnlubbbDmc41jR5bgdGWIIJg8BHERCSm/RhIKRyCzmfsaJJuKaPSHwPhsOXm7o6m3rE+C1hu8eqpG8fm/DGJNedTGHTWn0VBu4bKGcTm5OvDDusEYyrEGKJajmNkjIA4Dtsjx+MRa4UWhxg4YNjeRa4P14QxEG4SELoftihHvI4cQ2TUke1dz8XaYCrLcR/orNI2jv0h0DWG1bpjNwr4AxcXLUFq9rs98F/xo3r96BlKOTCrKFe2aA6Z5jslEcVMCq7kxGVBNNPVZk9t8b7OuWdmD2oURbISZEQyS0HwMaY4Rs3s92xYlHC5eGLeJdRbzZKindubLMvESImpZ0mgzh7vbHKndhmZqkVNldgo4VDlyqLt5ip2CvcVu8JgCAWOKxuk/FM98baLzjk1ImAz5lLCeSgGch5Xu7jXiUf5YwysoszGpZI8jWh6r4Rr3fcKL9Vkgam/Cz/WBG4tGRvlOxPgUyydjOxPpKOFUb8YvOlZcTG0ko0bmzLK5PxKk901AYr3GS6ITGDeDDSka5ZFyie6rMYJXMwic7q3pMihvAZnJR00eyLLGOs0nvOqT79PFMWYIInEoCtGbVrBs9FSZrlsv3SXGOfEccsxX4aCLDkJ98O3CmNmuVTur5tTQFXmvSLzdC4NI1VdrIXymMIOZP584fFLj0xKxvQeBarJebu01I/UCQya9uDUZJMBlMIyM4u26cImFCZaxFJ+FBFHMqRSlbc0u7EsHFPWx2mwxykQtwT9FrMzjVWGRXNbXgHFplf6LGjKM7e9umZ/uOEzf/uL/OW3/oIQDV/64pd48OARViwHP/DGkzeomhrjksIw2Z95TRiTGIJihdv9LR+8eMrzlx9yOOwIGnj08AH77R5jUqUiPQ7EyvDa26/xD//Lf8K7736bP/mjP+BLX/o5fvZnf5knZw9BSZVyMGl9xMiPffIn+OCt71HVX0Wj8tO/+vf50k9+EVc5zjYPU6JfSedK3+/Z32558OgRy0qTwhLgMVMlwOUaNXk6432E495rBsV1ciQs521agjIDr9MtJ4C7wJ0F3FrIDQMSF4y6BQjPIqR1YnkuQCpDCV8ri7U0Jq+bxUor/5e9NOUKm567XD7pkyJr7hcomM4NmUOukNLehdDKG/0Ufjhd+/H0o4VcWp4Hmh9xuifK+M7yYnGbcg2zjJ8/m0fjdHRYaAbzo8qHshjfJTC07NPye6fyqciOslLn55isayiwTG4OmSGtU49OvmkmwTCvyam/i7+XfZ7lx6LtpsiRRV/iYvBY6GTLYTnR09K7ipyEoS+dBFOf0GTQqaZ8MRonlgAo/fFI1zTsd0eGXc+ffOdf0/cDd/s9H11d8sYnPkO7fsTZw4qLR+/gmotJoQ9WiceR3vd0Fw8xsqZd9XS18NZZzVtNxTc/GNGqxmoAlCgewkgMcBhHtrs9Ko6LzTnBCqvNQ0JUxHv2fU9/+wJnbuikpmk3tF3NWw8c57Wy6wNXgzAaEBVCqLFU3PZ3bHc3oAHXtslDjCHElPcm+pjyqSioBowRauuobEdlwbowFTIRo7Q5XNYbJRFKBdUaMSkR97Ob59zd7qnPHlJJi5oqB2+loB8xEZUxz4fFGiXicx4OhxiHsZHoGqJrOfR3XF0/Z73fs+rOkbXBmJQ7yNl0ZoQxYCtDDAM3uxv8EGnqBkRRl1MFGM05kizO1kQNVBW0DqoqJkAuKIYBQekPwnHY433gEAbG3QEJhmbVUjmH05RPyZkITnBS41C8VFix9MeRF3fXmLMznjSfxtUdiufge/TQsz9s8RpZNxvEWhpjU/lwEYxNGpzSZGA5OVOjGow4jHpsNATjscYyqMfgsCSHX8x735kig1IBIhtGtrs7dvtb2mZNs74gOgUdWVeWH3+j4b/45c+w2+0YjvDk9RX/9ms/5Pe/fcj5qFI+LK031O0aHwLueGR33GK9MpgRr4bK1jTGIZVhDIEQI1Ysde3wPoeW2ci67hiCYKvIg65jf9xzu7ulcSlE0FqHSK5op9nWEhKbTyyOAfVKZQWPz8m1K4xGOlMT1BPUJ/FnU1UqCIxiqERS4uactys5Ngy7ocf4nuow0nZVcqBamyrkDUcOxyOD78HYlGNIHEhmjE3JQXJVuRIDlfVQyQ7hkPUCox6MJ5CKAFhJ+6M1MGqEkG2xnAstJAUeQXECUQQxFRorRlXujiPxeMfW3/Ha+esMrUWx7AZlv7/Fh56mW1F3a2pXJ3AhxpzbyhMlraEKwVjH/jiwPwq3e89Hlz0ae84+qvn333/BYFZEVyFWcAI+pry5SERsTeVqKq8YNsCINCMmDvTDjiEMfHQM2OMNjUScsTQELtYrzi42nDnD1VATQ8+nHp9z6QO9t+yORyQoYluaVUNVt7RVSrIeJAE0VpUYPE4g5PPLqU8V1nIKBlWX9BijgEEkgZzV2qEaUSOp8t4QcDKmnD9GUD9S14qpL1CxWFw64USxOUFV9IkxqeIJZPATh1pBtCIQsnMcxDlsAE8g+B7vA0Fr6rrmrKvwZ49xjwJ1bbCQwtiiJeXLgmMU/KjULuWAC2qJocdYwfuK2Cve9+wPIxetUteKrRyjN+ykp7IGBhhHDxVULunDo4f+eMSZEXEdbrMBB4N33N15Np0QqTm83NLUjmAqbgeLDEIMpAINhyMiwhrHXei5GwKtiezDjhB2jMPAj/L1o2cokYz1kI1BJGntJXdASTJVQkWMMSlDOZKvV1RDMgSVnFG/FCINCHYy0h2GkFHmSEhCR1PJ1IXNR/G4Fo3IoniKfpcS6xFzxbDIFNpQehRLBpii1CqTgcyCV5VKtSbDYrLfi7Gp5DjarBQoifpqTP483T+BCOlzCylHi8lhffl2JR9N+j15liQmgK1YNIUVAgsQggUIo6m/yaDSBbKbldKMCJdxsOXjiUnBpDgWlsh8n49ZFwtjJEhyrBMlPUdmtkaqViOTEVCqEBWwIAGQpU1zjHj5npH7xlkG3pRcpaG0WZInSeaxLGO1ZDQUVlGxk05jThfWxklfZ0Nf0bnC2iIHj1BsgQze3ANkTA6F0axkL6suTUAkBVhJuVmK0TznVdLFz7JywNq5it0pgPHqxL0a1pKrCJqy0KbFUDqekvEjqe2qlLCeQg0QWd53MVfTUGZ66/08TpSQuvzsLCtMAZ3y4E+mT94zWrbqwhhf9k2KIaTp3gUgMuhkpMYsm6Y9UlgKOW/E1BVTrs8FCLI3tYBF6XlmAnfjNP5JFobwN5fxLN8rczGtoY+dObAaiBjWZw/45V//B4zHgU9++rP80R//Hr/97F3+1mc/x+PHb9Ntznj84AF9r4R9ZL15QAl9MaIYzQmvjaPpWn72l36JN95/kx9+79s8/f532F1f8nu/81t876++hRpHXRn2xz2OSNO+xnr9BhXvo33gD373t3n0xpvsX3ub1XrF5vwRddMRfWA49ry8/oi//s6fo/1A/eAxX/o7P4NXQ20MIY5o9tirScrl1f6a18M7RI1UlZlDlqfJ+LiRmaC5PH4FIJcTgGIaR1hI0/JK8zoxjzT9LDuvyOYZVMrvTtsgVyE1ZpIX9++/DKeFsl5DOjMWoLBSQCKQZZ8lg06xmPHzsBhhynVV7n3qUCiyhHl/nMIS0+8TaHp/4GZM5+Q8nmD0BVAiU0fyZbkbcnKv+QIF5sp285lw0gRZ3G/ZkJOQ5Pxh/Jj2l0No0V995YLT8+P0ynm0VHOekY+RfZL7UOYx/TnfJy2h0tdXW7Bs1/RE+XhGkcb5/jPgNINJy3vMfbs/MOUinab9VH3QbDTOa3MGu3TSfUJI1Wdt1jViSEyTtu548cFTfuf3fwsXDwyD8MH77xMk8hOf/1nc6pyz80fJ8DJtSoDs0z7abW/pfcCampVrqboj/91/+zn+79/8S657y2UMeGsxTtEghH7kMB4RCYzjkePuQNOssKaisg1OBqJpEOmp3RmNSxyHyMDoR7b9DVc3W/yt43NvP8KowcUWEUdAUB/44YffxzSJ/Yk4+iESY5/yxJgKdQ6xFZWtqJ2hMlAbS01iwIgIVeIeISJ447FRCcwOi5TRIXJ7d+Ty7ppgoTp7jDFJXyYG0IBzhlVVgVg6EghixYCPiNRUtcWP0I8BkUBjU0Lw1jrGZo33A8Ou5+b2jvrsgs26wxiDE2XwI3dXO3wYqeqarmsT2xlFJ2Z8kiujesawJ0TP9uWRtop0jaMx0FWOz7+1QYLy9DrQWEErIZoV5qLDAs7V2c7wE+AayQ5dFXr13O7u2G5HqmbDw02Xchblc2xTb+ibgUM4ctjdMmxfYLqG9eocUyXmTsrRYpNRIOCkRkoxW5EEtBgFWkSU1kKdK9QZItaQk6untTn0gZdX1+wOt+AqztcPEedQE7Ax4lzNyipvdDUfvnvDN957QfCGTz1Y0W8NF12dIjkCeAxq2hQ2XgW8adh055gcqlkNe8SPyXk6WIyz2NoiUfHjgLMpB9Tq7IJ2taK2K263L7i+eYnUKy7On2ClJpiU2Lc22bktgpd0HowRhJEQItZCpEqVEI0wGkFile0YwWAwdeKLh5By5RiNOU9WSNXRrMUbAdNgx4i1yvFwy+XtDY21bM4uYAwcDzu8EWxV0ziXHP4GfEhyMjEex3QWmhQqGTOYb60gpKpgqTJcwEdDP4yE8YgzjqatGa2hjRVHPPiUlD/icUbxMbFQLAqSQCaiSYUE+h2sOoQGkZrr7S3H58+oqjW+P1K5ltXZGQr0hx3HcEPTVjTVCkPK+aPiMa6jH29pakfvLSEeORC5vbzlaI700tKEFZ4BZITos0O0xpCKBcQQOR4HxBnO6o5BFCMVQRRbrYi2wmmA0DP4kZshcri6Iezfw4pnfdbwuc9/Cb99Rvdaw2v1Gt9UHGqlbR/gbUPEEqIgOc+V5GiIGAKYQCCieDQanJgUYiWCocpVui0xjkmvoTguIyF70x2Ca1JOtB7hOEC/3+OGyLq9pbYOsTWmcohUCIL32XmYEsmlPV9oAVFQ47B1i8ZD2i9Dz9APqHps5ajbNU7ACQgVKnDexpS4POsPBseoCkR0jPgqsZR0GBiOI2igXrU0dSqsUBtD1VqcaxkHGLXHG4vVBwx+hxrF47B94BgDw6gcYqQfhZWt6JylF9gdRjoXUVex62F/GHGxZqWCM8r2eGS7H2HsqZ3SdRbjhG1sCHEkbZXI4QjD4ZAKTv0IXz9yQClZ3ZlBE4tCkzd0Vn1KIj5b8hxlD15kJCUyTCq8mKKkF6U8qeuSFWdPUkCc5DCNYriWhN2meJBNiukuAEIK9k3UTWUCIoriNhuHmcpeQA4D4SQUJClIxYgIMU7xa6qCzfcH5jxJkzKZGVoLpTgPHUV9LMDZZNTm94uyFiileefQuKyCUvyYBvCaStJHFhESkplFk0lUnlF0wnIXJm9lAVYKUFJCCgtHqzDTToygaaRSP6IYyOhz6pvmWcxg0QSMzPdbrq2FPZCmaGrXnLAdWfRjskoyd05MBpaUmDGQkhR8ZtSW56f+xew5RJI3YurQhGKk9W4k2WhJjBVQde77NIaZyVYMxqSjFIU79SNkcFDKe9lgnCM0JK+V2agMgJik0KQ1J3Pfcl/QBROoMASmcZo9zqWCH8pUlUJJe9Xm+Z3YFqpYsakiYWYZQjZe5kU9GY1lbIvXfg57KfTxZI5LnrdpLdybU50OCs3vama6wNKEnkINP+ZV2pDW7MJ0KgDvtLbTGkitk2yLz5Mr2ficknSbkuR9hnbL+lYtwKnBmoXBq5pFXCRJj+zdkTntcaoqotO4IEx7ZjLt8q9BUjUbtQahot40/O3Pfh6/3fLP/+U/4+m7P+Ctdz7LV37jH6ZD1NW8+973OT9/iI8R58hghMWUfRojq27NO298iu988+vs717S9z3f+otvcnP3knA4sj7rePTkNa6uXnIcBs7POr7/7AcYZ7jbXfI7/+Kf8sl3PsWv/9o/4tntDY8ePma9OuevvvVn9MMddVVxdnZOJcrN1TVPHq/xaFrPAv2hx1UV7XrF60/e5HjYsV6tMabJc1d2UZYCEUQclGSELHIpLUHLU1t9Wh+Z9zetsyTCMo90MqwX+XEmoDKH6mZ5EjODx5a1arOBXYDHeUUBgk0qKyolPLq0Oa3uFHYwM13Sns5hbJlJpwKT72O6v2QDdCnIZDobinOhsCjnb3HCbIoFiCgVG7O8SGaDzPL0npyS5VMnmXuPG1QuLIfVyZv5hMyfn7KA5/EjvzefAfM4zHnJFpfrDNycsrHyBTo/Y/7edEpOfZwAYgp3eTn4mal9eqwRc9izsVm2amKnnTZfmMdRT9paVjuUqS+hZ+Sz3Jz0twxtWQZFrpyAl6W1WWeaNZ4cniZ2OldSVawk3MuZF1Cq7HCz5JLOCj6Xrbe5JPToU9iVM3B9s+ebX/tXfPjsXc5f/wTr88/wx//+9zGN8tqbn+Xs0dsc+yPRNiCG/rBLCZjHkevDFle1VKbDqlK5wFc+84S/+sYdf35VE4NH1OKDMgwHjv4AMdBWFdvdge1+R2VbMAZnHGrAUKcwDVMRIgTrMEYR02KbjvVqzRgfMhwO/Ml718RxTB5iO9B251zeXDFKxUYqjkGp6obKWhrnsI3F2YbKpJLl1trE2EBwolgJDFaxMSLRYDPbQ4Olp2fIFcVEklF3fXfF9thTuZpGGgqrX/L+1NrS1h1Wj2ArRu9pSZW1AspZ27JqamIVOY4j4xAZIkg1YDQl07Yx4NaGuL/luLvmePMhq7MLrHVcX15ibE3brghDz3HoEefwRMy+Ipg0504M3kQqa3HG8uh8w3nlWDloKmHdNGisiDLwcGMZNUURBCDqQIxJfvVGiYOk6IJAdlhb9v3AR7c3RKlozx7ixBGoCNHTOotiiOppjEFZUZ0LQz9ye33D/vaKzcOHPNhcULsajANT4WzaQbvBwyiEHO4FykqSfh3E0lSpZLuRkJhJRrG2Yr8/8tHVC4bB060v0KgEBWstDUplDa1ThmD5g+/d8offC/Qh2SU/fNkzhJFelBGItkqsPg9GUsl6YyOIQZynqx/ihxXDcUscBoQ0OHEAW9W0bU2MStW2nF2cMRwOfPDsQ4II5+ePUhiQV7yMKXTNOFwSbPioBE0hmsYaGENisxmoDWiuftgzUltLZQxGA8KIxkhVQXSwPcJ49GhM1QUrHJEBYwy932PsEe0D+92W6Bq2hx03uy11VWEIiKswJLaLHRPDCGpiBTKMIBYbDAfRiXlkjEJhqpgEMGiMiIZUHS8KtQhhsPQEDhxAPSIBYtLJhnFAbIWKS0ylAoTQUFWGwWxo247KwP7uhtvrO6hbjDtj9BUxHpHdS87sOU2zJjhL8D2X2yvGMdJUDU27RlrodwObleLajkErdts77rA0TYfYirvjnu3uSOUq2rYlhgEjA65Kif33+x03t3eYBuLDx9QxsXm8iymJc4C7YaSuGqr1GfZwQN0et4bzTcfdy5f8u9/9Q0x15OJsjWmPdN0Zj7sGNbeAZdSKYzAcomGMgo3J5g2a9rcaktEcA0NUsIEaharC2hqJHiuByLiITkoVLBHJLDJHrIQqKHVnoHqDXg+owBA9qkfEJ8akNTYVL3COKKRKnqR8S51xDDrQ+wPDnbIbdzTG4VxFVTusqdLJqpoAMqNYehrjiGozOB/xGhEZqaUiquIai8SOECIhBuoWjFUwMVclBA0REwIStrhmhQ/g9A7K8kUAACAASURBVJjC78yKPvS4RjiOnuN+R1s5mqZJYdUK3ifbZrQ1ffaERWs4a1usiQSj7K1k8s2O4dgyDgNXlz1dC3aV5OfYHzH9iCg4Ezm/2PCjfP0nyaHkUHwMk7EXZQqvB5OZB5pYQkVxUcBqSr5VDOAS0S+SFpfGkpVcs3GQVB6dgCFICpdLeWmsSbRjDamkaX6S5MYkarGd2C9TUslsSCbPcz6ojMGPHmvtpMotw0LKz8krnbRTlpW4CkGlxLlnp2XW8nLYT3m2yn19f1JbC2gxsVLys4TCnEqHbGGFGEkgVPlODDF11cAJG0eYwILJw5k9TJNmSwZYJgVd7xkr85iUkBArxQjXqU0FUHjFhlt4UEuVtinsLqYD+uRL5fP747QY00WjmECVoo4vbY1s1JfrJI/Fx71iTEaeMTPQoSfXZ4OqqP/3PfhSuAQLE2gCfJZ27hLuW5p3i+8w2Y/TmJRBKvmD0n6b2V8FfCvtM3kAiqO+3G/u1/z8wkxb2FmpPOX87rKFs9WSOaZycsk8LsXwLOzFKRwmr59XwjExLKesjL9oWX8zwDuJDdVFWEk2wyTBmSfsLpFT4Lg0WkvLyj5ffMYsR9LY3ftuAa/KnwgahUCghHGVAgDlfol9OQ928fKa0l/KPs+zJeU5mquOgEiNM4YQe0zjePyJt/nEpz7J+z/8Lu/82Du8+dqbOFcTNNBUVQphKLFgWSGI1hB9wAfP17/xDR49eMTN9Zabqxvq1YZf+Mqv8PQH7/GJ15/w/ffe5R//N/+E/+F//O/5yS//FO+/9z4f/NW3iW1kHD3f/+G38Drw/v/yP/HaW5/mp37+K5yvtjx555M8evyQy6fv8Y1v/DsebF7j3e98i8+88xPEfgRjiTFydXnD2598kxhGOltxd33Lxfn5JAtFLMWkVlWMLUDfQl7eX6s6h47ef6X1VfKQMVW8XEzL5OSQiaKZwiWkoMx5vmeYYvF3LgZxcs9pBeR1mvd0AsPmqwoIeipH8hmQDf94r79p/ycjcnGnxVUyre0E6i/CqzNMMjNm0pouSpiQDRtm1tArI7oAnuYxLuF7UwsmQT5BTZN8eFUOLlp+0pvpeFqeUcKCbLg8H5jkTMZhFns1na0lXI28lkQWTz1hM0kevflVRkO0sCCLjF6Ereb1MwOIJuk/izuUe84nzyTlP3ZUpjFddOjU8TO3NbK8//zYkqC8jFFy8sWpr4Uhp5PsTT+9CtYoISiqyagUHFQmebaDp7M1u7s7vv6XX+P73/0mm4sLfvILv8j7ly/4xp//EY9ef523Xv8l3PoN6toRueZ4fY2rOqy19MHz/OYlVbMCqVPbneHhpuWPvrfj6EYk1vio9Ps7wtATGeiaFeJarm4uOQ49bXWGSJWrM0FJz+tD3lO2ws7qAYqmMCLT0J6vEXnMcdixu7ni5QfXmHpkt7+kah7w4OGbdE0D1mE10lpDLZagQmWFJrMGIyaHu494lBjA64BIzWHYkcqEGUZNjte26djf7fjw5jliSblnYpUMaJv6YbBIY7lYdYTeg3FoSM7MvQ80xlLbluCFq3EAlLZymDrghkDEEiUlz17h2B8OdCaiTcfdMPD0B39BbZWq2lCtH3Po76ibhvPNBU27wjihFoe1BqtQ2RqrHrWRyli8hTYkZ1RyokR2vUeMI0ZSmffg6LNS0o8GlWEyNo8hIuLwfuTy5kP8ENicP6ZuNijgNXmBjTiOKc8CtRXGMKRy47ZitbJUDvy44jgEXlx+xOOLDQ8fnGMlotagDh43Dj9Y4hC5C0dUldq2oJEYlJFUgcvm+An6wEcvf8DVdoc0G+rNWapopT4lZM/7WwV6G+mqBhWL90lD7aPBx0BVOTSmUuNeDZWkek6DejS4LMcMIhajlpWrqNeOoRsYxh47DNgYGPoDJloeP0jMpA+ff0DvA936AeIqRgasS9X2rFQ4sUnLUqUPCTyxxuD7A8N4xBjF2JrYwwEwOEIIBFEONpBy7SvD7pa6bajbFomRIUaGHIlSUaFWMBoRPVDVFYaKdrWhXXU8/+iKaNZcPHmLSOR42CJ+oLYVVeWo64baVexGpbWB0bVI8NBW+OgRdTSS5J+XYpcFRA2uqpGoVE1ixoCkPak9lQYijhA9zuXwXLciWpciZEQTi0sjUQNqajbW8sgKT59/yOV2z8WDT1JvWlogrB9y9Hv8YQ/BclYLah3Xd5HObThvKrbHA50J6LhjP9wSbcvGCfvtlsOwp2paKitsD7ccPdTNCmcsx6NnDANt02Kj43p3R78/YrqO81VHCJ6X1zvWq4bzdkU/RC7vbjAWqnpFGGMCtM4eEMeR7XFPbzas33jMYfeC97c99sZz0d2xPj/nbH2WqlvayIUYNk4Y1NJrzc5biC1D9Ck5dKzRMBAxKaeWV8xxj5odJiRHv7MBgzIEz2HYMgwDq6ajXbUIXWJV5QPKmnQGa0zJrCupiaMy+j0hs9wqVaq6o+pqRgJ6GPjhB9+mWbVIu8ZUHQ/PnyQwK46Z3QjJiyoQMzNRB6IfiNah3iVZSmGH91hjCDGFCyYdW1IhcJvOByMWVSG6gI4whoHrlzvCcc+js4qqE4jKpnEcgqGSM2Jc44ctdRyhMRhrwdQcgwdS4YVRhcr4lN/GAqPFjQP7qzukMayaNbJuMPYcK6lac7SKrs8TWBaTS3L4G+zb/9jXjxxQcjJmo9GhahFVbE4maEzqRFGgGlLZx1TNIP8ns6IGJZQn/a7TZ5kXVARA4XgUJVglGQCakjg7Uwy5lNRYYQpNmfXJpBrlVF/AHI4nkgAEa+2kWJcEwel5i0nJbCDNh18x4CEra9lGI5YKL5kNo5rDiMgJ7maAYc5tk8dBCyvqlBFUFH9BIc65iiQqMbO9AlpsgFkxXrwK8HHikZzs4VMwYgK2KGyqNFeJEp7KcEoGKUw21J0m7/UEYOQxKUBiUViX+YmSd8HM3vh7xlfBmAIzq2oiICw8rvPMvmrkSGmHySDFhBZS8I0cgqkTwJW8QyHjJTrN0eSzXhhNJe781e2bmXFawjKLITE3VJnXQFmLkj2ZhVlVbP9ipJY7TGwlJCWbXPS/sLEiixCgDJBNleOXLV2Av+jMUCvzMTEcYAKHSh4vQeY8Qid9W4TBoqkqyP2JyQrNEohKJMMlqzCFWcw2UzzN6aXzSp8N14URDrm85vyFMu73mvzKHE55b8r3S5sWwMHHvYIGTJEQwlQVL41bNvezca5R0oGad4ZOY2tOb7qwntVLZsN54hgRiUQVzs5f5+e+/EuM+z2vv/42bdthAOMsdZ1KVNsMukeY8rXd7Xa8//67tI2gwzWH8ZYXdzeso/KdP/0zdv2Rm+uXbOqW3/vdf8uv/srf5cOnz2C0PHrnk3zhC1/gN/+v/40v/2c/xQ/f+wHvfOoxv/Eb/zmdW3MYRz7xxtt879s/4KPLW7745V/k8tkzatcw7AZ23cBmdZb60zi8eEIUsA125SEn5i7hh8Jiz2vMYWKnAPZ92VB+/7i8VGXvoWSG0uKz4oVC87mji3DgrAXJYk/kOSt7M4VNzrL4ft6Zef1MKzh/Xvbk6YqU6cOyv+aQ0FJSviRgXiZ0Lpkn/qYVX8JrCiOmhAgCU9LMBNTk/k7ArS5Ykxm+mOTk6b7OjZrBGZnfZnG/8uYk7T5mP0+3S4P9ymf3fy/fluVenq7KbZ0Aqvm8Pr1Xavt0fmf5mmZX5jkvZ/40luXfci7NJCPTUM+A5PzkGWyS/H2z+LTMuWiSh5oHP7VndnZN90j6bZKFBeSbep+vXeg25RUmfUhnEFNziGYgG3aCcS1GAsEHnLPEUfn6X3yV7/3ZHzNY+LGf+BJ3Y81X//ovaZqKL3z+5xH7ANu0aAxst1u87xn7G1zbcdsf2R0OdOcPkmtRU3n4ViwvjlBRcbza8uzmJZs2YqSlaytsdcaxH7h6+QJMQ9c9QdQgGlMejhizVmlTslwsxiaQftAAUtFEi1gYwsC2vwM/4L2nV+HsnU9R24bV8QFy13P5/nus1ytee/I6bdcgMeCzF99gEljiR4wGKgxjhN4KXkCkSxXOsNSmAlFqcQQfeO8H3+X2cMODx68lwyFEwGNtAtSrquGirlHA+zHt8egJw8hd6LHi8DaAOIhJX66MoqPHqKczcBj23G49SsCrJ2pPJxXDcGC7u+Ph65/DGeFw9SEx9Lzx5ifYrFPYYB0tmArrDBKU2kV89MSSsDeCjUqvqb0+JikUiEhIjr+DQggjQZRBPZUagkClkRAio1kzHLbsrt5jMIEnj3+M2nUprD+nlTDGTYm+03M80YAVj8mr29Y1btXRKBy3PZc3O+7ubnl0seL8/AJHQwiARGxnMEeD84a7GLCSwihrjQy7A01dsz/uef7yGdt+5MHmCaaqUroAsaCamBR1rrbkErA3FBZrk/KejMZg1UL01AoBw3EImeGUdUAzpjNEAlYlhc4rgKO2FV21Zqz3HHY36OEGV2243e/44MUljesQYLe7ZdXWRCOEYUyMjKiI8YhU+AiqITOVInHwaHTYtkFxRGLOv2WR2iAa8KSk2mqgWV2A+gyOGqwztKSqdylNSMRUARvW1DhsJWxvrrjaHuguHnFx9igxfI3l4uycOI70hx3Dfo/f94TOYrtNGo+Y0p94Eoss2UM6V3GTxPgXY3CkvGMDA6kGmEIYk7xNBgFWGnzoUevS6onp2lGFaEwmHLTge7Yvr/ioD7TNBZ94+x2OMbH7hgheR1zT4dqGfow8vY4E6QHPg5XjzLVsNufsxyNDv8O4Fa0NSBgY/YGu3bBqLR88f8FwuOXBw4eYKOwPI3XdsO46xhB5uX2Jc5aHjy4YjiN311eYqubRxYbBR773wQucWDbtOZHI7W6HixDVsx+HVHX3jbdw3Yrti6c0Vjk/e4D2PUdnuL7e0V7tE5h0tqbpVqh1VKbiTHo2rWO0DftQE8aaQMUoPS4YWhX6AGhI4Y51wEZPVJ/OPvVUTYNt4VELly8/wlUpcb9KxWgE513i8AvEWKUQsb6nUmg7gaqjJ3B59QG3331JY9eEEDl/uMauayoL0HP57Dm1M6y6FcFYjHOYMabUNXrEELLNViHR0jpLj0eCUqkyxmxki0XUpwglW89pU2ImU1gB45DGMAblfFXh/YoogV0ISFTqw4AVRUflYi2YixW77ZH97oaqbXC24jxXGu2HPRC4eblnGPbU647KdRhnqF5/DaOCkRErMVeWSzmpCB6xFtTToAxOsKbjR/n6T5CUu56UYyFMWlkBELyAisGWspjW5LCnmIQVTNp7ZA7PSZSbbPDOdCcmJdwIUUNSUkv+EopXMbMQFETmMpKTF7OUjC+vnDAohCHTZZP3zTIbHCd5Ae4ZJCc/jeTSwUx/p6FIY1QUT5UUAlio6mUYCluqMGaKppuYRUmh11iMZ51LwgPJtyQT8FNAkigz8DCzoHRS8kVKlazFK4NbhVkTF+1E5lC98jf5PnNRwgz2TAZC6U0xpHR6hMp0E5a/pcTVc9LrorwmY1EnL/+SXYO5b3AvGQrMgBxpnGMxsLKxhqYE78TU32U+lah+Yn4tww5mMGcxX0pmzsxNKSGNJZ9RAc7muc7jo3P7yvyU0Z6MuDKm5VqZRzZSkoPPDIwpTxWzt1xSp7IBtmDInSA5M/D1qnG+aLcUI4RsXJf1vjAJZ9tqHjW9N0gs5n+ap9lYK3b4HGYi823MglkxsU9yONRkgE5W1nzPk57NY4ws086ctrGMyzwzizC7wnhYMJmKwVoSqaOCStotUz0ATlkFUcqMlacXtgqYj5mPWiKjAR8ijTP84de+ygfPn3K8vuLrX/8jNhfnvP/0h6zPnvCJt96mtlWutukTk8A1CJ4oI04tz569z93tJR99+B5/+id/zDge2Zw94Bd//pd5/uH76JVnPx749Od+Es7W/OQnP8u/+jf/D4/f+gTNxYavfOXv81u/+c/4yS/8NLvdkU++/WM8fe8pP/6Zv00cB97/6+/x1d/95wSuuHzxnOfPn2EtjC7nJ1hF/Bjxxx2iD0g2SaBWRzz04BzWuZTcXmQaW5FMec/zXmzoeEoFfBVIygsuzWthGaY1l0r6lqmbGXWqmWGWAR3RU35QkeXTI5TpfEkgbQkhlnnBA/fBr+mnhun8QEmJe9MFlBNyBgWYwU+SbPaL1hW2TWG/iZbzp8jCvDt0XoMTe07mdbxodjmiJsD6FDTJ4Z+LdTvFiBeHz9TueRDTe7Osm/fi6b6FzEZUJgDwPng1g2J674ITqYQs52R6Z25XCg1LQiJVwywBzbmtWUhPYey52s2SDVxyckGaJ5ND9g3FQJwk/qIfr8qqIsuyGpXHXxeX5zDLCWWbZ6CAaeVucq+/89FcHHzpnrb8KkLE5ByJig+BMUaCtbjKEqJPiYkxvP/ue3zjT7/Kze0HvPPOj9Os3uDd5++BdHzyx75A49a5GpDluL3j2fMfcPHgnE23gXjOyxcf4ZoHdO05keTBtk1KzDxE8Icjt4ct290V551l3V2ksAhbc313zQcvnrFZndHULd4HogZsRTL6rVAZh9VUW1hVUpJXJ6ypGQbP7njD0R8xGDZ1y+Bg8ELVtBiniIdH528QN56z6Lm6uuLdZ9/GauTJm59gszrHA72PFK5XZYSDeAaUqmpxzuFiYoa20nA9jDSuZb99ybPLjzCV4fHjN9L6DJ5Ykg4Daitq69gNI03WAUYRohdGLzjTIj4xsPrxmsgINskg7z0pN2UySKSytM05Z3WSaTc3V4wY3vj055ITIno2mzP8Ycvz5x9yXK95/Og16maN18A4ejpxXPee4CzqE0gSNCJWaSLsfcQbkx0Ykpk7BrB47Rn9kXWzQcOQyAN1Q2Rgf/mUo+9ZXbzORbNKMiqMOedJAtOjRKQm78vIse8RVRrRxOQCjFRpbCy4TUe1bjluX/KDZ8+4uN3z1mtvsVl1XA8DJgYqSdXDRu+JcYTRIY0gJvLh1XOO+y1n6w1n6xVDSKwGpyAEKlGcS3l8althgiOoErXH2BoNyWHpbCojY0zaWxdVxbqKXN4dCTIk5m3QSf8IOhJU8THlKsIrfjgw+CNdvQbpuLre4qWniQ7beWgcbtUgWqFqMVWNsUJdshabmkpcTnwfiHiazmERxiwAjEZszn+DUVy01FQERhoMXsnOFoN3gFGqoKnisg2s6gYrykUl2Bj51vef4t2az376x3l0YXj3cuAwVtQKzuxpKmjNir6tGMaevr9jvH1OZR0XF28yUFPZlqAp6XAtMGY7UxCcVFhpUCP04ZAknBEiITHYqlQNL2jKVGZtCkBPvDGDE4uKJWgERnbXH9EPR5r2jNfefJOjGrbR06hDAFdZHJEwpvxA0RiqdUNtzhjGkcvdLVf+JTHsWLXnNF3H6w/X7Icj17uRdfeAKgz4QXmyfoi5uMCayN57Xr/YQFRu+it8VB53D4jDwNXlC3y/58GTx0hw3Nxc4ZzjtbMNo4/0wxG1HlHP3d0BsQ1PHr9Nu26JQ0C0Yoxw9uAxWr+OmC0melaNwcRI7z1PX9wB17R1y6ZraTtDXVVUbsVF1aL1loEN+2BQ4yCkXHESDowxoloRjGBzyhClptHAMB6QcURsizQtVgJGG5wqmICxDqNC5VoIhuYs2fgW5XjY8/L5Jb4fePDkUzTNGqMDwzDy/Pk143jHa6+9RWw2aFszmIQj5DRzIBB0SE5qMRCOjNJzwCC2wqvndoxUVhh3IzH27G4P1HVNs66opEq5R43DoqiJiGkSuYYEpPkYCRYwjkDkSEwVKkfl5rZnuPsI4oHzTYtF8OLp/YbxxmIqyxgNrbWsH57R1Cm0MErExjRv0dZgwMWIWkWdS8nZw8hoLEFNCgGNnh/lS/6mkJ7/2Nf/+i9/VxM7iaTwFUXYFKUqKTMhK6UGQUSxZAp+BjMEm727hVae84kYTTlw8mERcgxr0guLMhappJpYTiEu22EmRTtdm2IL7UIRjCT0ulD2o0n6cmEUveK9VkVzwm3R7BGUSRWenl0M9fylU09ifvYEwGRlXqSEmMnMgrkHSsFsYE/tybcpgELKZVCQB5lDaYrWyay8TzyJCahI9/eqqaRliqPJzUyKZTG6p1xEwpR7qDCuApnZpcWAWnI3dBqHScFfGl6xeGBTe6cQr6y429zhWPCB0xk6+XUZaldsoVicrx/DWiiMsDJKoYAPypTAefmUORfTwiDTMlrZaFPwklg1XmNORnfPaCwGXH6vMMNO1+CyJ692d7KXprGbv6+lb7Ko4jcBM0yhY6FQPsotdTZSSruWa2VqjhYjozxdJmCmsA2X95wMXoCcg6gwj8q4Lrum2Zotuf8lLsKW8jNOQhAzy6nkTSqjUnzvKSyT01fp+sQ0WYCi+bPynYktck+kzltTKDlUClpXMu8UsNsKWUlZDGKWFDKNY+EhyKRIlufHPIYp744lhEBrG7797T/n6dPv8mff/2t+/e/+KmfrDXfXLzEiXDx6k0ePH9G4lhcvr3h4doGzFUECYg0ExVjL0/fe4+xsw7/8F/8nZxcNf/aNb2B84PmHH3DY7vE+8trbn+S73/0LfvUrf4/tcaBuLDeXt/z8z/00N1db/vf/43/mtUdv4TZr/ut//E/40hd+it1xzzf/9A/5N7/9Wzx990/5xKc/x9PnPwQd+Pxnv8zP/MJv8Ppbn+JzX/4S4QBXd1c8fPAQjZHd9TXf/t5f4Jzhx3/8i6w2F5TFYozNq1TzPGZGZJFd8+LMf+vEeJuhiuyBzH/HkkMoLgFIpnCf5boqzoOYc4BIVkgLLlFA+DAdYGlWSzhu4fNplgPz3podGvP1Oq1VYQ4RlXzvks5sCmVecEDTmsnvm+V97u25vIZPVYZ5LWeodlrqJan+DCKXULYsBIRcmTUBfcV5dE98gWbWjHn1g1P237z3lidg1GWH8rsCEzNw8R73WlAAJ1VOZe7J3yeNmlCX6ZEFiJzeSespSjnXs37D3P/Tk3EhJ5ef3D8vmAH7mU1qFmOlrzCyT8bunuDSjCym80tysYVFqzSDSuXM0NyzbNDiQ0rEasEbGEMqoPDy+Uf82df/gMuPfkjVrHjjUz/B88s7Lm8ueeezX+Ssvkhgp3qOx4Hj7pb9cMnN1QsePXhIt3qL59cv6M4uaGyFoQUxuDoFcu53O3bjERMDjTGsGkNlDUqFcw0fXV5xt9tTtw2aWR4YUEnJpavKEsglpjHJo62OoCNjfyQeDozq0dpg64paWob9kf04Ym2XUgxUMSWP1sjOB4xN4EyMB/rbG4bDiHUVZ+fnnK0v8EZoaqGrqpTnyRis5OIxsSJGT9U1VMfId99/j914xbpaYZ0jaDKkFGEQMFLjBGpXobl6W+1HPMLuqNhYpaTYcZ/YSj4Qo1K3NW3XJYevOKwoxjYYacAEPJbD4YrdzTVN07Lq3kCdQX2kylV0cYIf9+xvLxn6PZtuw3qzRkxLJZbB1kRjiD2YRjDBY0xMTmBTocZiY6QSi5j/j7k3+ZUlydL7fsfM3D08Iu7whnwvKzNrrqyuoYvNbjapbkoCRHChlSBAfxP3WhCSVhIErQhIABcCBAgtDuLYZFez2NXdWaysqqzK+Y13jggfzOxoYWbuHjeruRBKgALIfHEjwt1ttnM++853Aj4GggrOOtrGMQ4D4zDiJbK/u+XlxSs2dcv27A1UkxizMTODXMSCsRhT4WNAK5AYuLvbs7ap/8WCMZE6p5t31qExEKInSCT6SH/oGfsdm9Wa85MzxBoO6lmZiqCWQXusF3aHA5e3z3Gu5uz0DEvEx2IrJlsmqDJ6j7iU1awCatfgbMBHpWksdU5oIhroNbP7fGAYB17c3NLvR05OHhKElOEsRjAGUwWijlg1mdFT5TUscHd1zTAGzh4+pHI1d3dXhN6zqhrqdkXdrlKYYTCJQWZcYuflU94+Z89GR6IfQMDkUB+vIyl1e0z+QTCEWqg8WFF6kki5RsWa5OPFhJJRIdSiBN9x+foZEnvq7Zu8+fApX30onLYWbSteX0V+8K0H/OD7D/iH//zn/OT9G8Y+0qkSvMePI30/4LTHVGvqZs2mrgkqjDkUG5Kjr1oTjCWEAUz2IX1EYkSNovlQzeqsFVhBYiSRDrYFGLuO2+sbhIrzB48xtRArUF+n8RcD69pxFwdsTOndVcHYpPcTcoRDYysISVdKOGCspzHKdrshGsftvkcU6nrNMFyjHsRm4CILjK8bC2LY7QdG9VRV0tq6ub2icg3NuqXvOvygKUNfCIw+gLW0Z+e0m03alzyEKNRVw91nH3D+yCH1UzwwiseMARt3aBwIY0+IY8oiOPgEONWw2TQ8OH/AerUFGxLzTVYETrgOljGmCKYYHSYOiT0ZPGoCcQy0rmUVnrEPB4J7xDBEKiOMxibiiVdsYxMoYA1oYNgf2N/uUePYnp5Tr9bJZHcwxhGDo4s1Yg8pY9/okWGPqxzONpjaYesm718phL8cxjupkGFAbcoeF4NHqBiyPplRjw/Zpo+ZIRqTxpeYERVL9EzAayK5DOBBTZUINhoxCIfBM0RPHD03L57R3bxgDB3rkzXt2UNa02CsYbV1nFYbtF0RR4Mx4PJhgrMpkcLKeX7wluM6Cj/9aEff7VHboLZllBGJwv/49//eFzzm/7ev37woNw4iRAmoKC6HZUTJG7dACkMrmaYiRE/MJ1uJgWHTAmyyAZQnncmTOb1MPh2dY/8hnWrEfCqsGdUqmhLAEU6hWfjLToZTyCE/guQFQ03SPChHfdMJXgYN0lo7O3R2YWQeObBMdl1+FbO/0MM1hwylAiaDMMdqLsEJBVNYJJOhuAAJspFZmEkOSafQ2WEVyRo995zeqWgFKJksb9LGlitcTv0jC0d/YlvJ5CwJIBMzI93K6uw0zK0wu27J3V4YxYt7L0Mbirs1gyCp9FETi6M4Velk8TisIA+ZSHnTgQAAIABJREFUVO7chzO6mB0uOW4fufdBOR03uV2jzGE2pR0LiDCBQotbmHxPQwIjSjhgeVYJW0xMmnvjiZmhtGzEybjXBHJNjmD23fIN0h8FUMtgUvlaFu8n2OLeOBEWbVbaMYtqT5mm8ucz0DLXXY/KMt+z+OKztHF59n9krSsIG7NrOY1hZiB3DmNaOIGTiMrM7knp5csYkEXb5TDSyQ0v85177/Pfec6UNag4zBOrQn5NrSSmka8JSE9gRJzGz9Rn5XnFAZzqA9MqaMovPJWzDKHjy9/4Kodxx3/65tu8+ehLDONIW488e/YZm9MnGHUIFmcrRg04k4Q7nRG6rmeljv/u7/896kb57d/+m3z567/HL37xSy5ffs6DN59w+6uf49qa7//O92jXwl/8xY948uRtBmP5ytvv8KMf/Vt++pOfYFvD9c1LagZuwx6zXiHdgd/9vT/g0dMn/IP/5TW1c8RBiE75+JMPWZ/8OQ9OH3H9+WvaesUv3/8x/+blS979re/yzpN3cNHSH0b2Xcfm5HzqbyUwaUshCWSbht8MOExjw8wtPM3nOcaJRIkvAME0a/JYy0Oy9L0ws2KIi9GZBsBiyZlChMp6N+sp6dEedzRc8m9CHh9l3Up1S6uHTADAIvhyATLNdZPFsF0+6x54JKA5w6MW2isLQJN5SJZZubx2Wi+mspV9bm6/xSTOdcl7uMl1ugeu/JrbLz6595kef69Hn8nUb0fhdottsLCUChBF+Vvul0anch6vQfP3ceornZ5h76/r98o6BbVNQ68ceslU/gVpiGljW9RhLoss2FKlLMvflRqXei4aL9sSWuaLMu0TIXq8prAqV1UYI4xjwJPCOj/46Xu892f/GtdUvPm173Nxc8fPfvYzmgfnfPPbvwOmIfqOLgz4fkziyWtL9eAtYrRcvb5EGdhszjFVRVAIjKCRcDfShRGxwrayKa28yWFcrqIbRn7x8S/wwKrZol6RSsFYRFwCgqzFB4M3grNCGDpud3sGHRh9YCspC1TjVjhbsx88l7sdqoKr1iCJJahR6OPIWNooH8St7Jb24QkxBPa7Wy5vLnh1+YzT7RmPHj9BVi1GFWdSUKpEi7WOXj2H61vef/YJopbzkxN0JDFcnMHnOd+YFBnQ7XfcDiOrumbMe52OESOWaCOxEmq7TY6Sc1inqISUIj1KOrgVmzIwmxHplauLz/HAycM3MGYLClVIh5NGIgNJeF1szfbhE7rDLbcXL9nf7Dl99Ji4PiOIofIgFfhwICI0pmZbW5pVCi9UTUwWwTFEMDZldCP0SKUcdre8fP6CaAwPHjyirdeM/ZhO5MUkMXVjsdQ4ZxFjGENinjlv0BBpqjWawR4DrLdbTleW/W7goGCcS5qu6gkSkqZUbNnfDVx//gnr2rI+2bJvHK3Z4AK8eP2MIR44PT3DWEf0HUhKrmDifAigwROJ1F4Z+p7DvmN9umFtLIN1hD7QmUBtV2g0HELPGD2a3bW2PWO1tTSuIkSX2RDKGH3SW4kVgsHZtOYcDik00bZrHp+dIDmL1nZ7jt0K3Rg4DCP+cIetKqrtCusaCJ4xxKR9JUIlkYgnRI+xESNJYVu9x7gqhSnGHJViDc6DiiYtHat4JYlCx7w+Zr8uxnRIdLG7AGl56+m3sdTsxo73nivffLLiD56e8fMPPuOXP1e+5A5896Tm41XFyz7itSNYg3Frtus1GhXfHbjZ33F7EDbVmrp2qMv2dIxEGYgh7csSFMmyLOmsUTHBgli8BCSzkSMGxSdJjxA53N4QQ+Tk4SOqqk42nLhMx41YAkEM++ATQcEYjCSWphqTMvWJUEtihokV6m2P0zXRDFz7HXdXe4ySsnU1Dmc81fqMfdczDoEgCcx1Bm4OCgRaMZmZOFA3DV978hbjcMBHw2bTQjtweXNHp46TB1/CtE0iGKgDMRiJBJf6Lbie60PEyZ6mqolU2LrGxhWjCtYdMMOexnh8NXKIA7txpHvd8+rmc063NacnLevVGbY2tO6SthJ8qDjIml5r+lhjnSWgxC4QzUBTVYQeRGoqsyJKDw7W0hJCT2gExCSG4sUVl7dXmGbFg0dPWK82SCSRVqylEkNla4RISySyIsqIbVtG0yBAHzvi7U0CCv3IqrXY9QZLQ20cXgJ9bVhREYJP7HY7IGpJW5tFKk1jypi0djuDsyZl8xVH3RTbz+QkHHUC4EXRwWNdhRVYuxG927EbdmwebmnPzxFRtic1trboCKbOhyTiiCNgIrWxgMkJogJd7DkM8Ce/UlojVLbBn2wZx8CgisTVUfbw38TrNw4oqYxpIRabwYd8MjepZiezLObTHxUlmoqcoHMBOBQjalJ3IWWAU5L5PebT2UKPhZLNwpoqhWyInWj7JotUa74eJH2GEqNijUVzWJ2FKYSgiJCaiRa+yBDE0igun8ynpiIJzLHcN29l+m3MzkksRnMGBFKsO5Njakq7FBYEc7Y3KKfMM8hT2AtTJp7pxHLh0OoMEMwARIYSCtujXJPrWk5qTfG4NBnCyr1wNs10wVzHcpqez9zTpaLTyXi5V9IHynXKmjKmAAL3oteYwKNls+Y+WQAKpQ0XQYPl46kbS66wJeuL/HcCWubPSh3Ky5JYS0vQ6rincztPrKDcnmaBtJTvNW1yJQPi0lWS7KxOoqfMjLDkmGbQY4kxFGdt0eflrLyM1IVcFPeF1DkqAVNfierkAKlZtlee4XmQieb7H82R+V5mcXeFI4ZQYeiVfig/mrRLlm23QPT0uFlnpsbRZ3FiJaWuuMdOKo7g8ppy39wHie1CZkym30z3kDLWl5n0SllT5ZOWGiTc0OTyxSScT9KhiZqdbyFrRCWrJ0osE3YCI2yeX3Hq7zSerE1acO+881U+/PhjqqZmvV2DNWy7Az56gij74UBVO3w/EOo6tf0IiuXTjz7k619+hx/96F/wwWpNZWs++/RDfvv73+fp03do6ppPP/mQ1xfX/PLDD/n+97/HV7/6bX74z/8xl23Dz376F/TjgNoACk8fPuHx2ROsWmKAbhhpbUpXfPniGV/7xjcQPM8/+5w/+Nv/CX/2p3/OO1/7CjFUfOWdr/Mf/vJP+aNf/Dm/9Z3f5/z0AU+ePuX8/CESc0bLMm7QaV6l9ese7GCKcctRSGru/TxfmLTnCvhfxmUsIESZSyRDR4gpU+N0tzx4psUtfRSW3x+tMulVxtBUbpm5RROjSud16tjvnwEYa+Y5oRyD9imDpFlcN69byyU3HjcdpdbCHLabbjsD1sDUPnO73l+XOar3cRvM87oAYH8l7rIsn871+8Jzkbm9yicy/z4xbsvON22MsGjP8lWW2ZgaZ2Jj5n2yrPnLNaCs40XPqqzNywOhUu1k/9gJ7J9ApTIwF/Ut2WunkEl0OoxattkE0mFyG8w6THC8L6QaC2piTtiQD+jKIY2mfTbEODOSRbDNiuBHxgEchotXr3nvvT/l+YtPePqlL+NWD3j27HMOfccbX36XzfaUMI7gRwatGaxS12vECmotAU+1OWddbanrFSrpmeoDGhRjI5WFqq5wCpWRPOhTHe9ur/nli+e0q21iAxhwNoenZr0662pCNMQIfXfHIex4ffGc7arlwelDYttSmwqfR8rNbs9uP+CcxdSWqEOyUNUQgyNIw+BHHBHxULsE0puQWBwn2xOqdkXf37HvOobnF+x3PY/OHmAqRxCDU4/v9zy/eE1/GFnXLRghHjo8SWNpt4tpGzFMgPHYC7gVxtXUYog2YlcWJw1R2iTmygDYNP6CENQhJoX2JX0/wUjH3cUNQ9fTbtbY9SlDTE6UZUSkJhrwJPamM3l/i7CpN5y/dULYjVweLrm623F29pDKNWgjVCr0wbLzmrRFEGwdaW065PWq1M5hVBnjQNSBz14/51effM752UOenj9h9J4hjFiXnThrqKqKytops1dUTyRpCxkRmnaF85H90NGuG4zC+baiVg8bh/EBHQPRB1y+p0EZrCWcCr4+4frqmqsPf8mqtTTbB3Q3N3i1bM8ec3e1wxnBrUp6+ZD29KybihGMqYgiGFvRntWsnCW4xFx3piUlb3NUzmCo05yzgIbEFAxgrMNGZZDAGDyNtTijSIwYcYxh5PL6mts4Um82rOwm+TpSscJy8B3iLLZ2NHVNiCPDoWN/tceKsqpXVOtTPDZlONRAwCehYcnCFzKmEEWTtIucSGKZidBWYBhZOaE2BhGTEvNEx80B9oOn6z0X+wtM9Dx64xs01ZqQlOgZNHLaWlxU/rc//ZjLzvKq3/Px9R2nrWFtHa0dibFlNAFRi/jEUqvqDVVtiHEgjB033R1iLStTUzc1KxFUDJEExposZmEdDLGsbbCqkkizF0PUQBw8+7vXBB+pmxOaVQs27cnWOFBD1IiV5PAHP5MUVEhsOZIWU9JoqiEz6cR7cJZRB1Bla7bI2uI10g8jdxc3OHvNo/NTnDS0m3S//ejpBsFYwUblNirORFZVA8ZytdtjreGkEsbR8/pmpF6d8+DBY3qtQJK+aorESPeJPrAbO/a959Ebb7GKZzin1AI+Ct5m/9M5THMOwaNhTzvuETdiNwcYeu72I5c3l6yrC9arhtOzc043G5yNbK3n1DqirRlszV2oGTc1fqyojeEiptihMPa0bYviEjhrPHS33O47DmM6LHjnza/S1i2jRPyYvG5rbdaSSwcdKElvuUgdCFTWQhipmoZoK/ohMNBDjLjdQGND0m2qVlRIChEUx6pqiAoDI+IMNpIIKlYmAooiWBSRKmEV2fAPKfUdYwC1MYV164i/3fPq+jplfrMNq1XSDFVTgTF43zGMgW3tsDSoHwlCkr+Llpuhw0okRIP1I6t1halqRkCtSRtEVFxVZ59FMb9hCOg3DihV2JSSUcupbCbCG0EkoBpxJIcplItECQswJH+IqKBqJmMvaSSl2H8xKRZaIqRcu+QT5mz4SQZmtJwMlLCRzFYq+hoI1qbMZ7OBmWSKEzUyZ0jL90KKcbZwZsudZOGAFudBYWKdTw7poo75kbYYrpMBmJ0ivRcGMAEc918Lk1nJg5cjJklhfEziq1Kci+X9c7lMCU84BhQKMGCOP50cCCWHteX461iaQJfO3XzfJeNjIhOUZ4oUlGMOFUQX16WyJkN61p8qjrrJlvocRjUXefpdLvzceotTXk31LUDLETMoI45maShMxV06V+RxkfqygG6RrOECR2yYIoC8BMOWbXzs9t1/N/fVNAxzJ5qyoYkkRlEpb8ygSK5b+d0Cn1m8OfYoEwBMBoLSBUdJ+PTYOS1tUZhWpXyFXbXo/rkeC8bEBHwdjaU5kKRAz7PTmZ+hy5YroabmqJxHmmD3XsJxWNv0b/Lkc4HlqK4LvPXoPip6NB4SprgIQJrWluTwFQB32fpLUGTJ7JjAeFlCE0KISm0sYb3me9/7bSpX4fuO7aplOD3Dugab0kLS+XxierJlHBVcYhv803/2j/mzH/07nj7+Ep/86pf86pe/wLqKyjR8593fpjEVzlrOHj7k7/zd/5LLFy948clnfPzsU168+CldLxBMjpU3GGe4fPWa12cv+Mv3/oy73SV/83f/Ft/8+vf4i9s7wm6gbUdO33jAw4dv8br7l5w9esqLly/xceRsfca33v0O+37g9GzL0ydPaZual89fcnJ+jquq48E0DbbUklMIKSX0d9GQizY+nvN5sszcDhJ7aFLYyevEwqUXmKWYF0FJk5efry/lvD9o8vdRNQOpOu0BZaEyeVNKl5dnL1e0fBlJR6oAaMkAMhn01UlrrwD4X9AfKwCJFtZvnks6Z4+cwKMptnW5jpVJNM/X45qWuT0V+AiDK2HDpaEKMFOemQ5B7t1zuZgcLUR/xfv8bP117/PNpjWnzPkpdnxZ/sWtC5iemyWXfjqYmeCcfP8jFlYWVCthvGUc6eJhZUfOJZh29LRGmmlfnnfQPC7zfZchv9PaX9p0ptkxmSiaZQdQ1IesS5PHh7NIVMI4IhF2+2s+ev8/8P6H77Natbz9zm9xeXXHs4/e42RzxptvfQuMsLu9o64arKsR66itSwK6KnS7gc7vQBMY4AMMnedud8GqXnGyPqVxgiNijEesYDVlvro59Lx8/Yrr8Yb19imN+oT9xZT63U59AWF3oBtHhthjJGnbfOntr+C0xllLIBBtJPjI9e2BflRWVYsTJWpIxrSAmMRUD6GjtiAmZfNC0j4XiFRVw+gDRhrWmw3tCYzjnsvdFZcXz1m1LaePHtN3B5598il1s8Up7Ps9UgtRHKIOUzmqyoJ1YC3WGKxUbDaGYCIh9jgP0SgqDs2apWneOKw4lJiY3SZp2qgKoobYDdzevCQMkQePvpQYaR5WImASmytRJC1GPVYMVhMYJNmG9GpwJyu2ZxsOhxt2N5fcjTuakzXr7UOMafAq+BC4vgNrlYNT2qbGVZZIB+q52++4urhiCJGvPv0GzapmCB2VVYgOEGzlOFu3rGoDYvBe2R1GgheMSmYvRUzoaVBCZfHdgZOTU8R7AgFrHK2zeFUOPtL1AxI8qgOIsr/Z040dbbPGNxtud1eM4RoNgaqyeL+jbh2VbajqNodIB8RAjcMEEBtR9Tgcul5hZKCWmsoJMfrUvlaoJM1iZy0iafwpNmejEsTAGDpqtahrGINgiIhGbm53vN7dofWKk5PznEEthRenY/kBcQYx4ILiUKKBZr2i1hQOdzfsqVWp6oY6Z79yRnI22mRv+BwGN4yRuhJaUxOxrEzgpFJa19BYxbm0llkx9IPH70de3V1x3Q+smi2bky0B6H2PVYNxDnGO1jb85HVPoCHakV7hdWe5HRRxEddUWBkJagk+6ZJUpkmhkobkPFcrJAaG7pbr/RXtWHOybqnciqhu2iOsOFQDVqCpKmoHlVEGr+x9h+9uubi6wVDx8PwJtmqIpEMBaywp8yBYsVQh4kNmNwUlZH/eCgTJkQHi0pwxNvEtpmR9DSFGQoGuxbFqV9hVi3UN++GOcRgwfce6MTRVQ+2EYZ8yH1YYwhjpCDhxbKs1e3/g0/2eptly9vQxxib/ozE+VT5axCRNqMPYYcVz2jZoLax05MFJz0o8Vd1wNTagwsFHTF3hY4N0I5UIrtqSbJY90R3YtgeIPcEP7Iaey2evOKlu2KwNm3ZDtVpTNTVba1g3DV2suI4GJy1BPNuztxh0hUrA9z3jcMfFxQWHcc+D00c8erBCKgsBeh0RTMrcTppLQsSS1iVIyso+H//HGHFRiNagwWOMZb2usayIYeQweg6jYmTE93eMVnDOoOOANDW4Gmvr+cAakxhL6rM/Y9KBsCnaXRYDjESMiTQSCH5kf7dnfzggorjtKbVLwviifQ5ZdcQYqJomZR70kf3djlVlMFUkdMo4prqPVcqkZ7HQKTIMdBqpLbjGJdsbg8HinZ01N39Dr/8PGEoG1ZTnC5gBhvQlkqXNMCQnRgrNPLGZJDOAxGT3cMFsSBFgYWG+FwsrTGyPpdsYF45ncW6TgRRn1okyZXJLNlRhMRXB22xEJkt7Mr6yGUIS+y6G2+wRpKuSwVKYV6lghQuT7n2fCl/CeO738/2QtvK+hOvM0QsRY7JroanNAtkhFqZQviAplK4kgZYcllNM09kdnQ19m9tHZNbAKqEIqYzpgkjM7TmXNeZ7BJ31iITCDJqt8KX+UckaN7fOfL9jvkvJYiQLsGFxnZEJ6Fi2YRGqng/2FyDa5PAV32hpji+4O5OlPhe0ZCbUo5ulBwqKFoOLHFMfAlJA0cXdlyyCyc8UYRa+LSFmx0yt+WpZOKv5mhjz6Ui6QzRze8yO4nyPUv8jnys1zjSmy2lO6nudnmVyGywVgczUz/MTQmaVTE5NcYBk9q5LRruYneUjdkH+/zGBrdSrcCjmSkw6Nrq4waJ7l8+dHMj8ZwHc0jiT7Kzp5IxNv1s4ZnN/FId07uPCUCjhjzPIkBz9FG9dOCUzUCHFQ6X8PnXExAbUud1TYgJlVdXJ+YseY1MC1Mpa+vGQjKLasTYbXr+64PbyCqkrqmrDtm7pfM/Xvvstvvr2V/mX//T/ZrOpOexu+flP3+Ob3/oebrXmt77z1wi24dHDR6y+C//T//DfUtWGfW8xTYUGZdVWdH1MhsnLT3j94CEm7vnw/R/z5PEDbq8u6fa3+NATdc2jp++yHzq+993f5fXnn1MZx8cffQbO8s7X3+XtL32Njz78FYjl9vKajz98n2+sfsDGptTkRwBnWRfKdqQ644ElTrS0mZkzeqb+cbn/NR1gwMRyO1qkJK93eQyX/8k0HtNvwzTGFmNNdRJen26X19mJzVJ6PRUaYQbEpFQhz/fC7i2gz/JZ98M6tcwPzfda1CXkp87rZCnHAmyRmSVrUOwi3jWvLrk0hfNydEYwtcTUPQXA0HnNlmntuxf4VuaZMAFd07elblMvHO8xy32iPHcJYpV6Hy3xuSGOykWxMRZMVS1hr+maVL4Z3IkxhbRasRRh95lllOuSdRXKen1fH1AWFRJjUwg1jhSgkde6zBqZ1sxiQ0z3EySH0Rc7h5xWeNqCy1qayxY0JKaN5nBnk85jvQLBE7qOX334Ab/4xY8ZAzx58mWGaPjgg58z+JE33/4WZ2dv4VXY7a8xrsLUa8Q6JDv/ISoXF58x9iPbzQk+Kn7wqE9j9dH5E9rK4SwYUtrppJtj6EPPvhv48NNPqEzN5vQx1sdkCxhPjJEYK0YFPwyg4EyFcY5NncIjDI4oBgwp+6g69vuO3d0OKzUra8COKBYjDqMeDRGVlCmurSw+KtbBuVEqK1wNDm8sqE2pyjFJpFcjzlSs23P6eMHl5UteXVzQDbec1qdUxhHxtNtzmtoS1KVsVXmfCGKIxiZxdAUfFacG1ZrKOaIJec1JTpWKxTrBxazpk0elMQajgZvdFeNuj2nWtA/PicFAHGlMRRBScp0Y0cogMQGJa+uoXVpvkg5IYj6OClaF7foE3zZ0l5H+7oAfr2mbE2y9QiubRJBDCo06+IG1g8qNvL58ze1+T7vZcrZuGceRGCWFkscABowzbJqakzYpdo4+2fj1yqKHnt4HVC1rgf1hhw8BouFufwvxwHCwjCEwqsGHERMVCoM+4e5EI9jtORuFw80Vg8LTd75HtVrhux3d7hJCoK7XKWROhNoJIoZKBEvEubS3Y9M61pgKawzOpvlpTMvK5CQqMe0Hick/EJHEFMoZCK060BqPIt6iAkPvubh6yW03sNk8xKwapk0uJot7ICnDGI0oNTYZ9lRSpQQepqE5axlRDv2OvtsxqFJZi7gKreqkjZaZSV5jYo54YW+UTQ0Yy61XujDm5wsbJ2ybwPOLCz548ZroNjw8fQzW5dA9ks6UMdS1oTINrw8jvdq8X7jsS1gOgPcRFzxOLEGU6ATrwRMJ1lBlke0QEtOs2pyxaU/xvue275D9FdbV1PUaFSEahzGCxQGGEEf6YWB36DkcdjhreXz+BGOaVHfAmpwUA8WJw1qDIdLUBu+FISZbV6ISsSTN8mQHJBMlpXT3+GQbC0RGlJRkQPPaIzFQiUUZcHXNerUm+DXd0HHX3WGNx2lOPOIqmnqLHK453O15HkfakzXr7QMao8CYwmVtwKhJ+4WxSWMMqOsVaCR6T2Vh64QHteOTlx1ffbriXCK9Ct//Ws3Tr234tPoa/+aP/oRGIiFCFENlTohujYsWCTe0ccew6mnDyNgPXO9HLm5vaOs9GEXHkUcna9abFbW0BFqa2LO2l1TxhNt+z+5uxBnh8RtvYpxD40A0iTEnxkJMfV0OyI1JNpP3fY6ikLTPRoeYgFNHlJDlYRIRJlpBoqBUNNakjO22QjTZkt0Y6O5GDocd55uBaCuoV8lmsg4xNdYmLCCoZM3msueHZCOFka47sLt8gTVCvT6hPV3jbIW1Bh8iwyGiMa1pYlJiMBd8Eu7H0NbCoJ6hG9EAUUdqK+hYpcONyjJiCWNAomcQxfkkq1GZKmUw1ZG4+v85Q0klMqEbxTArmkakxRE0ZWBASdm5oPCTlHISy2SMF2vOmJz2U6GEfqRrk1ZGVmBKHSnFeF96isnYXfiWFJFWEZ1c1vSdYUr4K5bitB27rfmTTPeYkrlNJ7qzozm7lWFhBC4BokVdF8ZqeRWNmskRYDYQi2Gf7NzkzJhi/GUWRHE0kWTUm4wCZNf/yFFI+kilP6FofhTjOE3YBVAwGajpR7MGSPpgAsNkhkomp2dRx9lRPnZcli9Z/HvEKdGZNTWVSyQJHBeDWxOTJhnaRT8inWyXtj8SoUU5BikK6MJUl18XBjJ7EUzOmsCkbQOFLCVTpsNym0lDpYA8y6lAIX6l9ovFGWAeXZDZLjrrYxSHYBlyN/1a5n+XHbzswRn0POY+LHzz/F4mAKncczH7kk1Trlw8x5qZwcHifkeoqty73wJYjXmDNgtwNlE6ZXEzjsZFKtORIlJ+CPfmJFM/LDMZxkVZ58BemZCkyTm7N59LO6VpNTMNJjf53rM1/zGDqQuHe3JwF80k091xKox5LLlpvgohKBoiw9izbTfcXb8i6IiqsG4buv7AZ5/ecLO75W/94R8yjJH/6r/+bxiGgReffcKTN7/Ki88/4v/43/9XPv70A3747/8lxtX8te//Dc7O38Q1jqdnD/md3/tD/vjf/CNUHETl5OQRJ6dbXr76nA9+9h4fffhz3vvJX1K7Clc7Hr/9NqtffsC3v//XefDgMauq4qNPPuaNswf8+8/+mH+/27M5e8zg9zx8+23eeuvr3N3esru7YXjwkKuLC9anZwQ/JCAm6BRmbcvYXDA9NK+Rc3p0PWKEzSG/aZ+a1uj74+iL6D9FLDt379SZUq7LA2/e4nIfL9fDaZ5qplHP4yG9iZSRU8bIzOKbA97KM8NibKmWcTI9LbVIHi/TnpVvILkMZa5MdZvqXH66XIkWXwJm5hAerbFzmVgM/LmfZtTOgWRAAAAgAElEQVTvuLTLTWLZBUe9sfxD5jfT2pWfKYtvJgaXMr9f3qYsln8l/en48bLc2JjbaNZz+/VXFvAplFVXjg9GlnZMVtwhkenzgZnOpNxle4ku+irv5xMCvhiX8zlIct5FlRhSNrRIAgvSQYAhhoCnp9sfeP3pZ/zsFz9h3+04P3tCdDXPPn/F3f4Vpw/OefPRt2nrRwSfyrDdniftF4EiKTB0PVf7CwxK2645+B4TDFhDs6pwLoXVOPFYq5ioDDGCT2EdF3c33O5uON0mPZvRe5wVxqCMmYVpq5a6aanqLc7U+dTeoyStIURydivo4sB4e8euO1A3Fc6mDcGIJGAl27iNtaxrpV0lx3rQmm98uWI1WF7deFZ3husu4qywGyL7YcRHhThw2N8SfGD0St0+otpu2Qx39LdXxNjRnjxgXa2obDqUDerwPqW2tqZOgFHOZowFqykMJ2TA0xlDKwJi8TGNFEFIVLAkPh1Dx76/JWig3ZymrEb0OFMjpma0HtRg1IGtAaWqHJs6wZjGCljLqra4MdB3cZovGi1Ga6rzt5BTw/X+itv9NXZ/SbU6wdQtrlqhQL+/Y/B7xvFANMLZ9hRjEpMp2VGelNwHVAPbqka85+ZqYOgP9KHHmBovgveK94oaOOwVH0aIIzEIgwZ2fqSPac1zBmq3xmhiAKVlNyCqeCJxP3III+32hIerL+GlQiWybVdsN+/Q7XfsDwe077G1pVk5VnVyFGtjqcqegyEYg5hIgwXClBgokEAjIhmwCYltoUnfahADOEKIiCghGm4Pe/r+QD90VK7l4cPHRDFpLSD7BcU+1gRqGVISkIgh5ixbTR0hKKpJ76ddnaA6EsaO0B+I/Q5jLaZdI85RuzVBEkksjmn+jkEwYnEuWXyq4P3IZXfg+cUtr3Yddv2YtmnBkIS6JWXmNsYgVnAGRq8MMdvskoEeaxk0rTdCwBvBx6yTJgaqfGAdRlQDSsTZlBRDs/ZJ1TSYaInjyDh0hPE6HbIaw3bVYmxDUE+3u+Zm2CNSs1qf4KoUW0McgCR4TbbXg0l9eFqndPYiyhgDGmDnFVtXSBxySvq0d7gYER2THiFJkH0MAxIVZ2qCszg1SIj0pENX6xWNyihJN662lhhW3NxcoOMtrYOmabi7NuzvLjk9PaNyLYf9jtWhQ9sNnhE3je+UlV2NSaBKDCksFEvX3RAxvL66QMdIj+X56zv6CMYod896fu9bX+bTj/4ct7+iU2E49MQ4UK+3iG1TlkiNONMwioPYUTkHDAxhZES5u+24vLjgV+FTKqs8fnzO6ckZ/WFPbEba6hZnDI/eWGOrDd4LUUeGaAjRITlbKhIxVfLnggaGkAAmEwaMtUTrMAomeDRKBqMcSo+k4FZUQ462EIwp0jgmgfdRaI2wbh6gfiSihOAJ+4EoISUXMG7yQ+t6nTxIjdD3jHT4ITIMSWLCbU9ZVXXSjSMmTaSgOKl5cF5jbWKPaTCMGjDiksj/OGKNpTUrwujpwx1dp9gGagcuKthIIOI1JhFzLN2Y9vW6ztk71bD+zSZ5+80DSiEmhDmdeJbQmskS5sggL9+R4kkFCxqyJVcMjDCZf7OfPXEZ8h1TpymCGIshC3MbQHO82XTqqgunsYScGI7joRQ0IpJOkI5FZshlKXBGuS7OaevzIEyi2rOZrQUMQAEzGYWaTwIXrZLuvMj8lZpqNnyPDfIlY2MylWdTd3KWFk7AxObSyajU4n8t+mlyO/L/pJR38Z2gk6NmSpgOX3xNjKW/8vtljaaCzh9Ozt89p7vY9zLfv7wcC4RY5rNtyfWJBWaQRZ11fv7SVThiiS3AgOT4FON8edEMIBSQYfp7UeeiATZXWadvp57MQFZxIdPYNSwz0M3tnjM/5TH8BVFxWTCXNJ2EiM7ADNmwmYCn6dLsuGqcwMniERdm07LB0ihPfxZ2EcxA58S+yM8oGbR+vWuWHWuZ63nMVGByoo/adi7iXK57AIAu++v4q6m9Enskjy2dAdWZbZZfcW7DMn5Kaeb5shzfi6adhuJ8oTCDIDMAu5z9XwQOpmx3xCkFPAghBvr9nosXz1httxhJFPbu7o7DYc9JW3F59ZKfv/dnfP7sGU1r+MM/+NuMGnnj6ZsQDG+/9WW6w4E/+j//ISePHnN7dcVnH3/If/6f/V2cOlarmtdXr3h6/hBX1Zxt11ij7Hc9dZP0MNSP+AB+N/Lzn/2Yr37jt/j9H/xttPecnj7ge3/993ny5Akf/fKX/Kt/8Y/4B//zf8/z16948vhNVucP2bQtj996E4KmWP+7O0YfODk55f2f/4R9CHx3e8rKtsnZEyVoUsMTSW0UYixk0MV6JNPeUE4di04WxKOx9etey+UKWOwHRUcu/eqLDr6klMVlnS6f5d7N0y6DBYsVXvPdZOZJFfB6KtNiLi7n5a8r+1G4lt77bvHh/eung4t7N5xAiaML5rVt+cm89uv0bmL9HV1d1oB5jyPPy7hYEYR7Fy4Kcf9I6N4iP73NUw79QoMt9pB8+j7PMv7Kf4/Wprx+yMQQlWlslvVyApeVJBnAcT8eVS+PkXmfm4GxX3dAYCBrKJSWLmzJDFoZA5psrzJPQggUFqs1cWJJxhB59vlzuu6ODz/+gNvXz8BtePjwbS5eX3Bx/Yy63fLky++y3T4EqpRG3NVYY5O0gyRQJowdd/2B8dAlG4wUvmJsxWqV2EhJJFiz9mPEe08MCSzq9gPXuytCHKhWNSZaur5HxbAfPFV0RNPSbE5YrdrMEEj1iiHkNM+FqSMEH+mGgZvuBgmRVdsgNmWuKiNJTcQZS41lZQznbeSbj9a8+yXBm0jXV/zJR5eIbWnsSPCB/eHAwQdCVIJJWY7rpmZ0AYmGarXGOcG0jrfeeMCajhev7zjc3XB+/gArkWt1BCskbVGfk9E4ajHUdWJ0RJPs0so5GrE01uEVeq8QK4YQGLrAcLhl1x1QBVdXVFWLkyoBU1SUQCk1JrGxfDq4jVERl8auD5FxTHpWYiEEIarNYywQjCCaADonhu3pY/x6R7y7o99f0t2+wq02OHEMtxdEI9TtBjHC6NPpvIjFhJBDMxNjZRRLN+5xETSk9lURXJUyKRrrsJVDKoMEodKAqkdMxVqEaEHxk6aIFE1WI4hEYlD84cA49Kg4VpsTamcz32CgUsOgKd39ulnT1A390OH7PdfXN3S1st1sMNUak7NPBypCACSyy6yJSpSmIqVXN0pTCZvGcRhHOu8YozACAZPCk2KP9YGrmxuuDwN13dBsHmDUECUzW5A8vmWa98kWVaJYrLjkrAI1CawoGTUrEQKCRotrGqKzjH3PGEYO3mNVcbHDmZjE3auGPgr7EPHq2WLARPq+53Z3Sz90oBXN9gnOmDTfNV2LaRCbwBOcso8hAWiS1nxrkq0SJIeY5cyGMQoYj1GDlTAB6UjWloygISZOls02ox+TXYBijGUcevAdYRwId1CvTuiHA/v9js3ZQ8RYhm5ANKQseCiqfXItQ9Ydk5TF7hA6EEvwQj907A4HqFY4bSEolSbtNyQyjokQISEmORdRdoc9qkKzOYXR0JoVgzEpS6TPIbr51NuaDF4RsdUKr8rdcEdnK4zbsDpv6f2eYb/DIJi2YutqLq4PGJJgv0ZhOOypKoupKsaxI6hh3dTcdSPWKzFG7pqBF7eenXqkskQMVy8u+fzqwPuvDlSmxtQVlzc9oe95FAN93DH6HiFwutlydTeC96zriK0r+rsRa5WgFavNAxhbYui4uNrz4sUVdeWIVKyrkXazYhtHnIxUGVg0EbzCGJVhGPE+IFLhqRjGHjTgx8DY92xqQ9VYgmkJQwAJmKYmdJ7K7lG3ZmKEiGEcAioB12wwOFRHVAzOOHzfpZ/ZiqAeYsQLRPXcHm4xIVDJyIPtiiCO/ah0N9fYyib9v5DWxHbrEGcYVRj8SAiJsWaMIYSkm5VYiwY/9hy6O+q6ZtNu8hiLiI+sV2uaxhJCzzh6amdo6sSuDYdIGD1NY5KekodhDIlhZ4V9PwkP/UZev3FAycjsNi1stIVgc3atir2ngkrAqMWQGRtS7pWE0KAYrdmsXbCPiiIKUjQsAiWEawpVWjjzM2d8du/LPZSwMJxnQ17nOIJcFhYOuWR7PmZH2xaMZr5HeaJo/r4ASDMQtEzxDPIFp/f+a6mpkFojsmRPaW748n3RxTDZ6pw5MbOXsWwZMvhSAKbCuMoJQFL/lIfljWrZtMW5KX03/XRZrXvspOnZ9z5I7Z1ORCcAg4VjDtnZn5CG3LzHRv7y+UZzLHMem5r7tLCt0piRSRj62HiXua5HpWB2SPL9yKcRx05TuVLm30/ATBkPTODFMkxiEsLO/81SJUvdj+UYXYCNkzeix47Jsn/y747vdvwqjnIpSdGNSVtbpnRz3LcT9FNOwWGREWtuz9khKj8/Dm8j32sCcfTYgV6+CtusAI1H7JP8/7J2TM/X4/EyhQOWX8uiBbPxUsbikj5U2CKqTIzL6Xel3Zjbe14z0gcT0yOD2yzGSjqBiTg7L9/TKMzGYhrXJsOpQt91vH79khfPP+H9D97n6998l03d8sYbb7BarRj7A69u7njvL37MD3/0LyAa3nzrLT78+CO+8s473N3d0tYniEkx+3/4h3+H9ckJ/+6H/4rgI1YrxnHg5LTlVx9d8enHH3B9+Yz9/honlso6xm7PzfVLKmcxJmlB1E0LUfjkw1/x4uNf8fnzT3n46AHvPf+cEAO31zf8h/0F3aj84Ae/x2eff8Ll656zLz2malv05hpVz+7umsdP3uRb3/luOiUyad7EGPEjVLVlUu3TxIc9ZkPOYLHk+bTMxGVmZO54jB01/vw6+mgaF3PPH7OPIMbc12VMlXVHj8dKyYJYWIpp3Y4TKFpCoMtYWK4rpqwH88TPS+S8Ms3zYl47l+DMlE1ybrgv7FXL6fof28f0eNCXC+YGnH63fFcgs7z/Z3ZfCR0tv51CuY7W6WNQ8HhXpywQi4W1FOdoZZ32yiUQfVToewt9AnbSIVRZg8q1y0Oeueq5r5iZSeV+SXNJyPlDpj24sNSW6+ERu1Xmos6agpK3J532OatlL0wheQZJhw2laiU0WRMf/NAdePnsE/7k3/4rggbOH76FcSt2uz1Xl39JFGV79ojtwy/TtCdEHJVdJcEQDFEyy8J7rl6/5NAfoGpZTaFAFZVL+iZORioDmBSykxgmgtfk6O4Pt/R3O4IkgGccRvqYDzOMpapXNK6hcjURk7JBxZHC3ahKm0o6YOz9wG6/ZwxJV2XdrtKcNAlMgcSqaGyFMwYngojhoofDZztq09CNd/yTn1zStKcEejzCMHpUEqPC1BaHpoxNEdarDW1t+do3TtlUe9as+c4bNc+ed/wxjovbjk9fP2PoD8T2IavVmrZaY2rBxIgzgZOVo2ksMabwiNpZMBYbEoNIQgq/q2pB9gde3zxj13XYumW13qRxYKAWWLWJzS4Ij89XfPaiw4tj0JAypKlhGCP9mDV6mIFRzWMVC6IRH8ek26JwpwPVYPHDFcRIU1u6qyv6u2usUSRE6tPHCUgXxYpNTG41WFPjjcVGxTlDyqOWM3GZQCMVGnNGY/p0UCqrpK9gQOJIiBacxUQ72d3WGIxW+TzbgB/oDzf03R4rjrreJPFeSZpMZV6F4LMcBvgU04Rb1djK4McaHzx3Hey7nsp4aucI1hFyOPuoPoUJG8NKoakdK+vAGOra4VXoYhL1jWjKljweuHn1nL6PBGfZrk+xrmZUj2qAPD9NXuHCYq0T0sE7mBSimg8mQ8pclJLsqCbBHzGIBCwOMY7arRLLLwaiH+iHA147zFBhK4dWKexHA+wGRfyOu6HHa6RpTmiqlmg1Sy+kfjO2QanSwY9NzGKNkTh6CDHrvghRPCFq1vBIWjRpLVZUA30cCGNgtVoTQgCT5vgw+hTy5IvfmRgtUZUQkm1lXU0YB+6GnsrWaARbrYh+ABNxdoWtNggVSCTGxH7SaNGQ7tmL4G0DYsBHAjVVW2FtlcgJjhQ+mRScCSiISRH0Goh4NtvzBMLaOh2AmbS+pDmUcswlDoyhlhTa7Lu7pG21OmHz4E1oGiyG0UcYO1bRp98ajxGlrWoq27BuWqJxXAZNIVPOEKUl9B4FHp495ubmmjF6elXadsPN7Q1GI+v1istDy3ANwZwRY89GLM26xdsKj6GqG7pxyId6EbeqOdylsVOZFaGpsCbQrgyrCPvbW/Ad1jpCP2LNgb2vGYLnLtxx6Fuaw0BVO1xT09Ytxhm8GLrKcRgsEYtEoRsTi61pVlC37LoDrVfqRugx+HFgzcgwKrvbntWJ4GPSuqubCtTTDQMthkBN7z1iYFU1DGNk8B5bpTV8GEfQQGUthgoxhiHA81cdpmrxtkGaB2hIWR1FHQZoFCR4yNp2US0jhhQ3CEIKZw0+0vWGECoqaRhChQ8eEbCuInplNMIgK6TJEV6qeB8Zo0MC9ENARVP5JCEeIUDvf7MUpd88oDQ5PksjtSxtmU2h2SXMwEqchLKVYjInECpfu6BvFI2BcqYmWk58TaZPFlQeCpvCmqwooDAH5JSwlIVjWvQXstEXY+kcsrG29DpLmSbXnklLASaGwAQaCBlZltlim9po+W9e8k25ThbC4jMjo/xbDOmlzVv+zHtFus9xLBepsbIDw72vmCHBI1M6W7BLoGQqS+m+e+Z/Mb6nz4R7jtzx+9nAnktl8k00zo7/kjRWrp+o1Yvrk9BsKlwycEjSaDIb67IAORA5usPyGUvNDF38vWzzowotKj4b+TK1wa8Vgl6wk8pNlWNQZXpmKbcs+mAqe2H0zYyupUlxPGbTcwvYyP2hclzAyeEtZSlMutkhjgvHbFFeWfAASzgoqczLlliOkfuC1Gks50+WfrrMmk4i5BClMrdlrnZpgem3qa3SHOO4TZjrWepd2G7FA0wgZFmrmB9CqoToXJcJPJtuPEPZqZgytVcqY+7Pe4CmHwMhBlzrjso6hZpODmuO2o6BTz/5kF99+DO6wx1f+9a7vHH+mM8++5izB2esm4b3/+JH3N3e8PHHH/O97/wWo1eeP3/BZ88/5sGm4Y//9T/hb/z+f8HDR0/BWh6cPeC73/4+Vy9e8OMf/5Af/vCf8e3f/pv8TvW7/PQv/5J/8tH7PP/kF9wd9mzqFatVy747JBHQZsubD5/y7NnnBBG22xMO+xuevXrO68uX/Nt//n8xiOXdd9/FB0+sAsYaPvjF+3zlm9/k7a98jS+9/W1ur2+omlUy6vD0Y0dTtazrFicujQeTacwhYCrILHUK/2ceHvNcnvzt3OoJXJrB9S++5nWHMna/SM2Zf320VpcL5nVkGnHTPjoDlELR8ZoXwDlGv3xQ1uUydo4zDU5r0TTHdF52yndztebxmMt7FEb1hdf8za8Hk2Ri3KR6zcGjEwg+PxrheN87fk75hc7t/mt/Vebi0ipZ7jNTjVn2w1STBQh9tB/KF1vgqF2O1tFEMwfmfsyNPKWrXrTXkSj80fpe1rQyzvJvJmYkmCxYGGSK+EzlWmwtKMeh8eWZ8v/Q9iZPtmXXed9v7eacc7vMfF01DwVUASgCRInoTAZJIEhRtARZlhyhsGQPbIXt8MwDzf1H2FMPHeHwwFbYpmyaIg2TlEkzKEoQCILomwJQ/Wuzv91pduPB3qe5+R6ksETeiKp8efM0u99rfftb3xKCc9SuwypNzMxVU9jMCkmMl+A8T54+4Z133uTi4gnlfMbp6SnFbsfm7BSRSFWuWNx+kXJ+G12sMMaglUmAeB/C41rqbs/F9RW762tMUTIvb6GUorQKZQ2KQCHZgSNpXvoQ8UFonGezuWS7vWTf7ih0mZKpqJJCK8QUKVOYGKIWgoIQAxLSjFCS0pyr5LFhdWJmrXdbrnZ7jFJUVjIonsdTcCiVNHKQiJEI0ROCsHMtta95Gmuuf2xo25odJpVXSKnDrcFqAEXXebrGEaLBFpaZLXjjJeE/+rVXeP/RAy4fw6vLim//4IIGCLYgLI/AlMkh2FxD0bAIC6wtWBQKrVvaVtF1YLRhFx0Ejw4KiQ11SPtCdFu26yu60FIuVthiRlRgicy1ptSBv/2FlyjtnhAjH17M+R/+4IJNV7IBKpLwtZFIGzy4Dhc9UafMy0lLNeI6D8ETfTrsVSJ00iLRILElBkPXOWxRsTpeohTsmxonipkpWM0XoGf47CZIVDQxZGYnFKJymnBGoF2leRWkRPW2DTKIk4vOmjzkaE+pQAI2plTxbbuj2awJ7SZlQZsdUeoq65FlUD1C8C75DaF/Tt7jIwRt8zhRSUOn69i1Ndu2w4UaXS4whU0JM2LKahZdGl/k8e2jx3lF7SLoQCWe7W7Hen3NxX6PLRbMq1lmOiatHSTg6dMvOARBKz3sScNxXIQYPUprxGSfbJAfsIl9HR1RFCFGtE4sLEWaJ16VhJgOyJt6T7PbowiUR0t8MFxd7VDRQbVEdHLwG1rEQ+YJIMqjQiDEDi2JE+xdCivb1FtKFGVZ4aLBh6Q9RVRITGBYymzo805i8SYnAsomn9ER0YqoVG6DxHZKJzhkhk5N09RIMWNxcodqNie04No9EZ+YU1k4uzSRQhsCVWJOkkIDiYFOZU0kIkiLCYqgAKVQykDMERwhIKEj5n7piEkmOnaUInRBiEpRJjQVL5oya275mNLDW/Fs6z3bXUsU4Wh1wmpZEUKktRa1dxwfLQjdHLyjazS7sGUfOrSKRHwCJSJYA65LGlLazojdmrppmJcttizpttDUHYuZZWctvu2IwVMsljTtjqIo2NcK5wIzU7Bvha5tWZWRqijYN8Juu2d2fExTJ0aO8y13bh8TOs92dw0BzPyYorzHrJizu3xMpVaEagm+RqRjG2G9a1FNR7XeM6tqCmOZzSwrY1kuE9jbelgWhn0rBA8zO+NSDJ2rKfAslxXXu0jjPfO5pYsndO2GsprhHUTfAAFRhjY4SltipMA7h/eB+awktprOOawV5osZ1qSxpXY19b5DjKaYn1DMboFJWTO7usVHj1UWozwdLVYMRhTolKNZdAK4tYvZv0hJzMpiDhxl806wxiLRo02J70JKYqFUtuUChEihI6ZIbSD4xFaNikgCDG0+cP6L/PwlAEqjczj66H3wQNauGSyzTLiO+ZxtcBCzMxxiRqfzQp0daGG012PvjiqhZ/5ExoxxMYunKUI2XfszvZhFQUddpx5k6E/t+hCpnEyRwZKL2UyVMZyqt/hUpnLmWjA1hUVU1uuZOJGZijI6L5PGnBh6U6ZGf+AdmfrKE4+57wuV1IKEUStq8vCh3NOQqPFUXPLmHXurmj67y8C+GK6PB1pCMi3XBBiYsmhuDuObxvkhsyaXZeJ+SN92uUEOwhgiB7hd0vQf65Qemcrd9+7UwZyCWT19ljy8cuGGemdcgQQOTAMGx7L1Y+SQxXXQ0ZM+ueGq3XA0hltzto04nNIcOi8HJ/GT/u2f6SOTGjP08fSa54Z/5bbjxr2De9e/6wAYk2HmjZhrBmwn5e5XjUH76Jl3Z0NoYA9Mx0g8cPymNIl+2g7dP22LONHc+ikucv/UkWU5jpDDoo6G5lASiQfv6538g/cdjP2xM2NeF/o1oD9J7XyH846ZzIbRMixNjF+I9G0Zubo65Qfv/ojPfurneOHefQqxfOjDH6Warej2W77x1a9g5pau9czMi2x3p3ziZz7Jow/e41Ovv87Dxw9YrFY0rkuGeFGyXK44OTlmuVrShpbHDz/Atx0f/vCH+ed//DsURtE0jnkB231DAHzjKebC6z/zKfad48OvvsqdW7f5wTe/zvnFU0Lo6FzDX/l3fpmqLFke3eLhxXucHJ3Qdjsi8OCt91hfdTw8PeP46BjX7NEIp48e8Ob3vsVqecwnPvVZXn71NQJgbWS72yL7lvVmzyuvfISuc0lwMbf6AEbSj6nDtbKnlNzUthum82SOZXk/BoZg7r9hvezB1jxrEtYvyHOG3/CmHgzJTn1im+bQ8gEtSH09riYyTosp+NAvTXkOP59ENGE63airRBnW12E3fWaNet6d/a9jG6aT/vjMJfQr8gF1kREkvrlfTN4Ub3w7WiNxaKFn5vpkzg7rEM9ccnh93yGT5fVgfen3UpmWI98u/Tr6vFo/+844LfVzlo1+bKXzo0mn5wcM+m/910qGcTccLOWF8OGTh9y+dTsDYCqF/mQNMSFydXHOT37yQ06fPgBtUcUR7dWGdrfjKrxLxBCKGSdH9ymXL6TsVKHF1Y7gA75rKKylqWvONxcYUxB8oKhWqOhpdxdctxtuL+8QVQIs1vsdelGgxBKjwodAcJHr6wvq/RofA3Y2oyiWFLpAFQU6KqJJ4vq6D7/ybjzsk5iyM2V7LmWr6tjt1uy7BqsViyKL7KJxBKIkQWodOkJsaV3MTrWAKDqJIB1eFBdeI3pBYQxaaxSOSKTQBcFHNvuG0Hm0GEQnh+SkCKy05etfe8z3H13x4MLxtapgszcpBbmxKD1jYRXBO7quoal37M4epsyWt17AVBVNcJiQgIjgIxJSPysfaLqObb0GAmKEcjbPoYcps3GhFZU2vHIEL2uhKiqOZg7femZhjzYz1tsGPNSuw0RH7TzeNdSuwZSJBdZrQfYZg5VOQI4FSj3LIIoiBoOd32VmC7zWiI7c0iGl7N7V7LdXzCrHCydHSUS9gaAiLlqUSsl3vEppwVOcg0ZJABUQbBZ7TmFwVU5+ktLbqgRIBAhR6AiEJtDuzmmbOglPL4/QpkRUkYAjlfRFuxhQUadnSBLDDzExefKJRTrIDr2+i0KZgkKnrKu77Ya422I6S1tYCjMDsUTReBfZ0aGDou00WgJVYWm7PU+vzlOYiijKxTJpZEWHCgHBYBWJAWJT5r1IEv0XpYkxSZJ4LRCS3INI0hcsMlgXlMIScziRo3WJJZSycnfomJJCxfoAACAASURBVLSeYnoYMQTqtklZurWlW1+yd3u0VDTbBlMULGISifdZ6Dxzb5KYiaSMXIkTmMI/jbUQNQtjEDxGa6xYMAVYmxg62a6J/cFwEKIEQlSouCeEpFVbisLj6CRilMmZvwSrNHXbsN3V+CCYakFpC2xhKJTGiULNZvjgaEMK4fMtrJsmhbfZNKdFF6BKiA4ktQf4LO5ts52d92x08md1b0sqRBRKks6Niknryau8fsdkG2jxRK8xBgqE0G653JzSRkNR3WFWVdw/thzNWz7zs7c5XQvt9Y7P/fIr/JP/9wGnT6BrGwqBaEu8aFzo2Dc1dXuJREdZlFS2wJYlrm5o2z1X7TWL+TFur6gbjy0ci7Lk0jt2zrGcFewahQ57ojLUzrOwKbmLd0kraD5fUHc7vI+EtuXW8ZIY5ijx1NtrrtZrtC6YL44xtiLoNJ90DJjqGFcsMSxQdITowDmCd7Te0W0c3tcoFSk0LBcFVVViioJbpeWksnROgUrlWm9SuTTCclay3TmCa5ivDLttCc2WIIomWKxWWGvoWmhdw7wsaKOldS1aeZa2ZCsp69usEJqm4cnZOUZrZtUR1XyBsRVKJ0Ft7QQ7q3A5vFRjcBhEWXzn8aHDe4d2LSYGxKdxbUubMr0plekpOmugdSgUWlsIATEm+ZkxEpUmSkwaeghRp+yPPib2YchoiBKNhF4t+S/m85cgyt2bPsmwGaW2e02EbOT2zpfk5UQgRpeNzWz2SdqkY84AR16Ye6AlMR56wnKK9485DjKdZkXIiWGh97Oy0Z+NrUFQdzD+1HhaKJLS/uUFtHfqDnlCecXIhnqvmyTSZ4lLqhch7V8TL10yINUzpsYSktvuwNmesGOgF5YeXYcbF2cGxjQv0PicnhnzvPTQ/WNSBogeBpl8Qm47ldsrt5Pq27F3vAarl6E+4yDJzkSvETUAd4yMlJtMnfHmod9Gun9yCGPvrPWvzdf1t4fsAIX8h0PB60OjPznmh0De6NGNdTgMh4uDDd/7bTedst4x6u17pXpmTS63qIP+6G8YHVk5eM94Z/p4+vCWflhOnZDxSiXTgkzbQIavlcikLOM7AgOMmk5/+gsOyp37pe9Xekc0x+gjuQ/H7G6B/sQ8l2nScH1/CqMjNvGN03g+aJCbLcMYzjj5PM+Xvhlmd3hNdrRlLA8y1ofYz5p0fwgBLToL/R2+WCCFrvR/OvRK0/2HLTD8y2h9+JdhQE1fIvgIRhlWRyd86df/FvfuvgSiqJs9JqTTkbp2aGN59MEHzOyM7n7H+cWaB2//hNfe+AwPHjzhs7/0q1RHxzRth/OB4DrK+TH3P/I6Z+fn3Lv7Ij/5yY+5vLrijU9/hr99+Xf52p99hfPLCy43G5aLW9iywLmGrtnyoze/x62TO5Rlybe/+RUuT5/S+o6yqMBWrDdrjo9u8Zkv/AqvXTxlsTzire9/h+99+8/ZrTcsjo75+V//95OWS6G53tacHN/m4eMP+PGbP+Bqt+PvvPwyUTSlsRwtLV/+rd/gjZ/7DL7LTuWUeTI44XLY4QM7Rw765ZmPMOwTg3ZN/4jJ2jzuSnLj3sNxMd4yWRv6KZvfJUNmAQaAI+bnhTwU1KQcod+FVZqLwxqYB/EARuRn9OyWAUfP/4sT8Co12+H+clCx8fEHbZquHedt4jLc5Av1YV+pzD0A3bfJlJE43S3HPWXytL5hZNyCx6aZtHR/6BGn5R6f/SzHiXxINJZrrP0zxwOTO2XEAIe268sqB/vgACpP9quxOnndmZx39QuozmvpeNgzWbRuFEoEdvWO+bzCecfl9QUvvfAy+7rBGAsi+GbPO2+/yVtvvUkUmFdHXG/XnJ09RLzj7mrJjx+8g1R3+OgrPweRlDmrvcZWx1gtaJ0c3P1+x9Pzp5TFnMXsNkYljSbvGpQoqrJEaZtCgoKjMxobCiREXLPjbP2Y/XpLU7fMZktWJ3eZLZZYW6W1UTx46OhwWUcz4hMzKQqi+mQwAih88Ox2azbNFhGhVJplCVZ5vGg6F9m5SNfVbDcXVMagi4Kg5hhjc6Y7weRdzYogfVIEEmNHYwhWs21q6l1DkKQFEiRiVcfKGDoX+PbDmviooW4dVyFwsfdYCkJOh20pkcxSEAq0KmgKja/3nJ2dURZzquWSzhhijFgviBS0zY7r9RlRWZTV6EJTqJSxqxQFUeGJtL4lxo4Hl57f/7M9brch6kAbFW+f1XT6jK3LWWljCsNBBFuVmDhLB5k6DFagVgpRiiBCqZMaU73fs3Ytcz1jcTynnC25fTvw4osat4u8crukvmr58YOK9y62bNuGdx++Te09t1cv44sSiDifADmlNUJkpgJGPNpojILgEzNlZgxBAjMpBuZvKzmdd4wED08vNlyvL3ExUNgKZXXSahGVgCoJRHzerxPjTbRQiqIL6WgyRDBK4WLPfJ/YapKcOA2slvfwIYF9bbcj1C3aFki1AFviEHAeJzFpELaXnG23tN4kUEWB8SGzbtJct0ooSsEFg1Iao9PapKKi8zlznILQhgR0hSSwH5Wn2bY0rkEXJYWuSHLeMftTad3QIjgVIQYMCpRCtGVZzPAxcHV1hleacnnCTM8orGPfbnAhYMuSopxjtUVLxMeU2txok6RrVCCoFmsqVCzSOicu2YQ+Jm0ZEVA6hZ8PWTE1Wim8b7LeU7Yzo+A8WJVFmyULaKMRH7jcrNl0LdrOmc8WqExfTlnmSiKezke0GLQJmFhgNElE33fs9jvadsesmrNY3qEwKoEE0SEhZZuMCX9MYFlMGZWN5OzjKuYWhoijDw/1MRAkoMXgYhJpltAg1tJ4y/ryMb5rEFMxXxyjTQVRs953vHKrZNl53nyypdk3HJ+d8bnbnj88deykQYsC7zAqjeluphOL06cMkLumw3aXRF9TaIsAxUyhjiriOrDd1yxnBaWZ4fyerqux1tB2DUUBbRNpvcdUC9yuY9c5VlWkqApiK7ggrOYVu+tLtpcb1GzO7Og21toUZhgTUBhDRCtofIfyDlVUxDDHSCToGsRjdEMXHEo3eO/Y+sjuqkYu9lglLOaa1XxOMauwYqlK4dgaOm/wATpRRNHs94rYBspixvVlzapsmc+T2LXRASlmdLWjk46qsLioqQPMCsWLRzMuLi45e7ql05ZyfptlWaDtDG3KbEClEFWUypI3FgkeGZJcJNs7olHRo4LDBcfaOegcVehQKmCNR6zBSAJDnWhUSAafMoYokjLWhQTWKpXZcDEBxzqL7vsoSJAk1K1Axxv+/b/l5y8eUIrppCWffY6Gz7AwZSNRekcqWXhJJqTPptZfk1zSMLkeRt91OG+UJKAYBmdeGEj6cVjN8+snjITeWcjlGsEWmTxjNB31oDucTcWYM7b1xDEJQ31Hc1PTwx89cykpxU0Ft/N7Qg7nUxkYy6akSC9onK6N+SVjCaE/QU8VCvm63qnNGiACKfivf9Y01DDVLOAR0UnEDpgkjaYPCUyGd39Hfrf0J9qZK9V/Nz3N7wGS57CRejv4ZpsM10jfZwzjY/rpRXSTkZMuTL2qBmHikXET6UXVnwHNDpzJ3qjvGTYjy2rELnIYp4y6GL0T1oMEPTOpD72AHJ4kZLS5f3XMcdV9PXPLT9plNIIz80p6sCkMY1/FsWbSNxqjo5yuU5O3Zlcjn9SS9cf6KMmbDlGOohz+EGPKDJEWsBtuWrzhHMfeUY0D9tHPsqEdBqZHz6KbhoZNALweZGV8zmS0/dTxdAjYTT8xi9EOwVAHDLsDZzc/o3fc+3KNQOfYz9NI2Z6llU4ShCnONM7lSf0YmW1JayeJ7ClrDtxcpSSnIY+DnkOMPlGmI3z0Y58gik7iz96htWJ9fc2T80uOi4r791/h0YP3mR8t2Nctm9On3H/1QxRauHPvRWazkma34/T8kpfvv8Surbk8fcK3v/4VHj18nzc+9Wm+882v8Z3v/ymf/fQXefMnH3BydJd28w3e+LnPcbl5yvpqjQ8d++vIg/AetjpnvTnl4dMPcF3HcnnEcnbEB++8xcc/+hr377/G7OQedxYLztdXhKbhG3/2VYJv+ewv/A3e+NlP89K9l3j4/ltcnJ/z5tOHvPqRjyHW8von3kCs4vzJKd/51jeoCPzZn/xT3nv0E/6rf/hf0+0c2ppEW5+CBxH6oKuY+6CfC2k8jHPrcOQwLL/9Gt2z9SQ/p79SATkjfBo3PaI4jGVhHOf9SIx0+RE9DzLkvWxYf/LV02yC4+CaZEGMQAwHkaAhpBN8lQ90JMQR8JBhCx3WQSZtMnykF5Me58PQPjGvVbHPWpYuTuFe0xWwv3MMfesLoGRs02SjjeFz8SZFdmg7ufHd2BcHAPPh4jGyuoZFbgTmB2YSYwOqfjfuDzYmLKFU5MO1ZqoyN9W1ema9Hcbk5JUyFnfURexXnTE8s+//Mcx4uj8zhOqihHq/J/hA1zhuHZ/w4OkDqtmSxWxGqTTvffA2b33/G5xfXXJ0dJeuC7zz/jv40DCfr9h3kUvXMr/7Icr5LY6O7+GbGlVYdHkbrUsUiuA6HEIUx9HxC1hTYpQQ8fnEf57AkqAISqPwFCpiw5zOd+x2F7iuxdg5q9sLjryn8x1Nc4GrtxS24N6tGXWwtFojIRvuMTnnRhLA4MlAm0DTNmx3G1zoKIxQac1MAgTHrvXsWgg+MypUgZnfQVUWI1ViJ0gkSTQHogooP4KZRUzZwayyRAyXF5e40FFpi+R7jFJoLWw7R+MV1mi0gNKWhUrp0WscliKvPwErBo9O6ee1whYKXa0InWO337M9e4qLgZOT26hywW7zmM3j9yhOXkJZhYsd4lNIXheFfUygkI+9QyJsY8Hp2uO9RZVJ0yeUFU4ksUhwaK+TjIBE0BBCYk4rnQ5KBZ1SqWeNqdjtudpt6KJhPrtLqRRFaXj1hZJ/8LdepQuOD374mA/dW/H1zVNqp1DFDKUrPAaaSx5dnjObL7h7vECrGV3oQGkKq1KmtNCl7IM6cQFESOCAijQqZIYR7FthZQ2hveStD97FSUVhC4xWiFZJU0mrnDHWp2xmMc0xq8DHpK0V8SgVQemUPUpHlEsp1CMJVVBZFyWQGVtBo61CWyiiTdmi6i3761NarZnPl5iqYr255PH2CmVnmKLEiqDxRJ+SSvjc3k4c2juua8e23VKVJQU2zTWyKDeamPtXtCRbXwESiKVCdMXcVvlQUlMoMwmtlhSuikc5l8TZjUVipN5t6ZrAbH4LbpXMi4q5tuzLhgUrfOdofUfT7AhKMSsM86Kg0CWgid4RjGCLJTpAJxErmhgNPqbM3FpC0ihTySNsYwtECjGEkNh/Oihc0PhMQjAScDrgo0WHdJi531xzdv4UjGW2eikJ0BsLUeF8RxuFSApTNDnMrMqJE6Lz6EJhpKSoVrS+RTnHfndNLQ6jLYVdoPJ89jkMuc9Qp1RB8F0C8WhBFFFStjoJghKLiwojyUdr8z5upMDt1lxsNogxVPPb6LJK0TdKaCUiRvH0quP3Lh1P9pG28ez/2SN0UMwxLIym9clbCShCDvktdUVUgaA1iCY4hzJJ4Dm0De3lmqqcIcsZbSgItqBUmvqqxruOwhZ4N8PEQNSKtm0orUWXGrqUlfGFW8fQNpxdn/L0/WvQltmde1RFRc8IdyGxRo2KKGkwBqI2+JjaUcVIKwk8Ud7iVYnyLSHMKa3HB4fv9oToaIJjf1VzedVRlRvKQnE0V1SzGaqYUSlDdAoqj7eG1gu1V6BexK3PKLyjU5bghNVM442hDgpjFB86qhAfuTg/4+nZNjHwbt0j2iqtdXl3jyr5ownETYcLEYVET9AKQtpPTI5gsGSNOK3QUlCWkkNeHYSO2jvEdzSxTSG+OmCsIUjSagtB05Nn+mO4IdRf0uGbhBRi6UxMDMUAyty0ZP/tPn8Jotx9GFgYDJrBMKdnH/RU+96dT3TjFHOt8r2GGHN8suRTpV6n5KbtGBPLSegzrWWra7DpEoAQh8xxOdZ4sM5iFniXxMyRHiaY1mviZGTTfQQxsiB2//twTV9CGQz+/LTsDIfBUIzZMCa3S7IYEyU1hDBqOY21HpyDHtQa36sZ+Os9iBBzO6jeZB376eB5qOFRyfDUDGEVuYw3+2C4ORv8/anG4MtMT7AnTk585jETF3nqZEwM+5vgw6QAkyYf/z24JfFGxsGEqjw/vEpGhl0PTBBvAhEZMGAELsYeH52evszqYOyMNenf1/9p0Cfqn9lnVMtexTiXYEgN/oy21Jgq9pmG6l8Vp2UZ65ayEzK56NnyCtCHe/aeZg/CkhlvMaaFdGiveLOnp6wrnmEFhed0S6p/qnOI4wnoUNxxMjAIzd4o+1Cen/KROAbnqmEcPFuOvo8PuhFGRy6XaHBe839DKG2ez0OKb5Kx1L+rX21UBiYGwCJnIPLEpD2X+7Gvb678wGDsww6izid73tHs92z2W+7cu8vJasGff/Vf8pMf/ZAPfehlPvHGZ/jt3/nH1NsLnpy9xd/7T/9LTp88Yn15SrE64vtv/pC/8Vf/Jg/e/gm/9Vv/iLOLB7jo2P7vZ1yeP+bhez/iD37/n3J+ec7tVUU1r4gRCr1iPosUs9vsdjVRW45OTliuVvgPEri4LGa8/urr/Pjtt/nxj37MZh/5mVdf56xec3J8D9fA44unnKyWdJ0nBBI7Ybbi7Uff5r0nH/CLv/zrvPDyfearE0SEF158iYvH79I0W+r9DmUjSjSOOhl5MVHlx5ESh/6KTMdlHBz/m4B2zG0u/JRxNZlrYxfFcRxN1r1xkudDmX6xzV8P4ys+B3xgnFcyGbhxKHv/XZ45k3WsH4P0+8TBOJy+QAbWzLDkxh6czyHs/aXTKskIsg8aaeMGwZih8XCdichk/vV9NG0jkHAYrCzjzbkOk71o2iUH68C0P+LBPkVf5httNr11YKH1a0a/z0zL3PdlPNwvxsqOzx5v7dnYfckO3x3zmtjv5jK0QQSVAe+YxLaBrC/SM57yuzpHYW3SCREo5zPuro5w25pYFfzxH/0RHzz6McHB8vbLPDo/R6Ln1kuvsZid8OTynFC0vHz0It5G3vnht+iajsLO0EoRTGKARN+CCPsu6bIURiNacIDSBYhGYkgpxDXoCE4pxHUE1yDB4XyLk5QYwEXBa3jxCO4vFR+ctVzt9rz7aEvTOVoTmMWKuydHxLKkiIooKctZjPn5bcPl/ooYWipAdx7nI9fO0BqhlBIpLTYa5lrwaEoliAaDxcd8+BYTPCTR41REx3QKXBnFsqo4317z+OKKqBJg5FxykEuT1vDQReaF5WRRUGiNROG6DXRtB6IogsJri3NJfjoGn9tKiJKEqrVEpDDMjMHtFFfbpzx85xE6glVCuVjReoe/aqmWMwpliUojEjMTR2PEIGhUDIlBoxJI08SA0imsX0dBE0BMstSVRnB4cRgVkKgh6sxGSw6sdy2Xpw/Y7vcc332Z2eIY1aVjopnVfHRl+cE3HvCdx1e88zTy0qLGRFBSJg0a3zGrVpTFjK5q2O73nD5cM69K5qsSbRdYU6FDOkRRNmuSoIe9MRpNIzoxtnTHfrvn6nLN+uIJYgrmR0sIBq0jMdc1olFaJzFzBNf2to/DaIXzbdKhUSXe54NbAo4WlMKSNHZVBmcETxM8hR43e6sAp4kYlER2my3d9RnbUuHqDj1fEaKjvlyjC4sqCxCLCQVaKxocGmEvGu9A2xVKabyyibWj+1VBYUhhVUqlIDanCmLoMDFiCoUN0Oevs0XKHEZIwr0qH7Kb3La7bEcURUW1XGJ1QasVpVKgDdYmoASBJTaDeDXNdstiu2E+0ywWK0JRYSkovBB1xABCh/Meq03abRV0gKIFnzTYglL46BJYHNO+HnyX21Vj7JLO1aAUrm25Xl/Qdh3F6i6mmKOkyna5TTpRVhN9SwNIUBQ5dMjnED2TdY08Di1QmDmqMjSuoW02NK5l3z5GG2EhJfPZjGAq8Kk9VAh0RELwoDQhDFYaEehixCpDC/i2RSlgu+X9qzNMZVmt7lKWC/AOJBEcSjqMtmy6xGRUCNGnOfijM08lCrEthRhKY9kFofGOgORQ20AUjcphhNGUFEVB4yO7esfDq0uWVc2yMNyeV+hFhQ8zmn1N8FtCgGJeEuodykDXRrq6Ybk4YXlUoQTq/QXvvPc+mIoX777AcnGbLjok+vQzpEzTVidNtrbzmNAxKwJBWwImh7JCFxVeAjY6lLJEDY4Mk+kZ4lrEtfiiQBNwEa5rx8W+pQo7CjOjWhqsAVsVaAylDbx8vGTrDBflCo3DOU/roPSB2ycr5rM5LsDZkwe8/d4ppio4vnsn9YeqEJLiejqMyzu+AMoMGUwlCIJNVpIKKK0SAOUiNjNEO9LaooR0WBAMREUpKWOxRKF1DuUdp5triIHVSlFWRxixeGOzLaEgpHBtn3o7R4fUQEpaE2i4Pj/nL/LzFw4o9Z+kizQx1KeOfPpmMLDSupqZMCqgggIcRJN/6pRWUU8c7/4Z+WecGHP9NdA7BBMzLSPeYeL6pVConvycvnOT0stoCk9ryEH69LFWw+vTpO01NHoVuiEg8NCg/ak+bh8S9+wFopLhKAMzJLkcMbr8SIGoJg60EEM2mId4iZulluGdyXHyicIcDk+MD9kf/VP6vk7PGk7p+6c/hxnyrwIVDh5/g+n0jICp9MtyhJDBx4lncfMUfPByOHzuzbLefGfv3KuhHxOnrc+xl36LQ6yJTMb8EAqT2+5Z9lY/3sZ2jH1d+zYdvJdUxylTSN1ol0npU52G6ZPAVDUJr5uORcm/T9k/z/0M9eoZGdO6pAxbU/bfeFMuz+A19d/182Is8/C8yXxOfp08M148ccDoxyH/7Hgaw0k4GMcyqbP0Ti+D2zphqqRiDjMopmsG4OxG+cc6jI3Ug5VK1HDpxP8c65XLo0POOpZPvEPoIeHeqezD7BIg1o9HFTQxdATteedHb9P5lsfvvMP7T97li1/8EuenF/zJn/wRt1+6y259yT/6n/5H1utzTo7mEBUXjy/4l//Pf8/H33iDvRPuvnCLV175ENE5/uP/5L9gs7/md3/nH3Nx9pg6RPYXZzj/FF1ozNFHODpZolG8+rHXePGl+/zpN7/BvcpzcfmY9ZMHbC4LSluyrx3ruqYoFgiaN9/5Lu+8+0O+9/UjREXuvPBRXrz/IrdWRxzNKr7+1X/GK6++xs998g3Qig998pN87q/+Gndvv0TddMQY0FJQ1zWf+fwX+e/+2/+GOrT49Zbf/fL/yb/7pf+ArmnTKU9I6+Q4eydsuX7oJb7wgISoKANrdDpnx/7ux8S4rj1/bh7cNe4yz5l4ergq/efpteMmYFW/toRn53U/rgSG0PDpm/Pwmn45zEwlCcQdXtcDGQf1SotdGpO9CzOpWXbkIwyC0VOoZ5w2Mr7mRqFk8rPfoZ5trnG9ePZv0/t/ymdEooe7ZViXxrr2oPBzzyRuvmRK9+rBqzj+OdCvOTLYKwOElIsjMTlWKiQfZWpOpbbqGc0yNLRksB81YYZDYkk4RxdTdqngHNYUnJ9fMF/Nuf/iK3zve9+impW8+/AtYlS4zvH06fu89MrPcO+Fj+Aax/X6ivnqiGNlCVGoVMGHX/oE50/e5qOvfxbftgSXMoB5POvNnuhBaYXonC1Lj2xqpQyi0oHfLuxxTUep0ji72u+JYpgbg9E1J7eE1+4uef14xvfe2RCsxVRzTpaR6DVNu2G3vuB0fYU/T9mb7GyOKQvKcsbl9Zrr9Zq23qN1wCyPiXaGMXO6Qih0SYSkLwSQ9WOUUniVlgMVyMzyiFGC0ck5MlpT2YoC4fTpKWe7NXMzo4kpM1AsBZPDEYwWbi8q7iyLpKHSabYdeFFo7ahDAhskGqIOSd9GaSoMXUzMlKZLgq/Ndo1rGmZa4ZtAOVtSlTO260tc3XLnhRmz+RFaFF4iohSND9hYoknlCZIPhiVp6igpWeHpSHali/0eGzFooniCDxhlQDxRxZR1TRTBBPabM/x2i7IVd04+nHRSvCKoSFkUFAq+/uMrajR7H9g6x3mTGEU6CkYrZsZSh4jvOqJSrJbHuKpmV+84e/8cXWpe/dCrUBSgFCYKGoVWkjMiptqJRKwIT05POb24wJea1VEWgVeaoEBI2aVDTOt9YTSl1VzVOXV49PgY6JymUyrHUHjK0qKCIDEdgIcQcgYvoXU1wbsENIVI40FJBhMUdJs9XjxGKXzsaKOlUke0ak3c7ZmvCo7vvIzYtHaEqIjKoKOml84Qr6gKSWFX0aONBQJeEsuqD1LwwUEUtFhAk2IDEzve6azPFxLbbHg2EQlgrKVr9mzXFwiG2eIW2s4xCDUOHSB4jxGNj4KRYrBlCmMpbUnnOurNmvX5lvnOMVukVOjBHBE6RfABtBC1IXYBrSu8OAJZAFsnwMiqBMI4pYlB0XQeQdNFUmY0AWULdpfnnF1eMSvnzI5voUQTJdVflMmeZ5t02JTCRUeIKXuYVmm+RYl48flAPu09BYlFWbBAqRIiNP4atV9zcXZBXVyhjyrU/BbHOoWCGg8tCWhURuiiovMBiyRWEwHloVSGq8vH7NYbVsfHVEerDDh0KCvoGOh8S2ULBINzScfN+aR4rkQjuqSOHa5zzMSiVMTYpMXTISiJxKiJKAgJyBcBFyNRRRarI6rlDNXVuBh4snGUu0vmxZpKO6r5ClMoQiy43guViZRFygZ4e1Wx2Wx5cvqIqAteeO2jFBiIli4mRpRRmsIVROUJvsY1WwpjiFVB0xVsghDaHZ1vOV4coaXEFgV1DASfsitKSBZvQwp9Lco50S7AtygaCA0LY3BtAmuuux32fEZodyitKExgubLo3Zrbt4954aWCzR4urh23jpZEbTheFDx9fM67jx6hZite+fgbBNUQXU+xdgndi4lJJSrbSiIQ22wg9IQZPfhHaVwp4JHtCwAAIABJREFUtIDH50PybNeIRsXs0yrBSwRJQL/WEMVQFLcgCvuwo941CG3S7lIqhwFLYhIqRQwdMWqsgeguWW+2XFzXzMvqp1lB/0afvzxAqT/9IiSWUYyDbZvMwHxKTxiMX1FZiyR2iJSAI0ZDlCRCF2EIEelttdi/K29yvUF96HAOqX2yTZgonclMdIM114cK+fwXlRf7VJUbRvv4MCCDNjHRSPtaqqkxL5AinXvDcgy5Sm2VXYOpRT/4oLnuMjkb75GKCODpw56Imc0UDl994CeIpNCpHo2YGs8S6enzA4x2Q4pJJs+56Yzf/HffByO4MH5u1vUQbEh/U0oN9/4rHbI49k8aV9lBjI6e9jy8YXjWTwepnnm29ILro5ZWen5PL+zFTHqXXsYWvMHqGnwMOazrzXaZ6oKMruPoKEr+CT3XLOaUx70TND6vfyJA6IXuYzKzPGO/DfdMvjss3OAyTt5wWO6+1H1Y3U3gp3cFJ7U5/Hsc143kSDGO+8nnZtlsfl+YssPyu5NDnPoxkqdHHxpy4zlaqYM+Gdxb6deu9KW+0Z+HZYqHd8c4Mp6eU9+bn36dAghZQFcjKT7fp/h7RQLRRldVIKS5HaSHhT0Wod21fP973+Lp6fs8fPdtutChF0te+8hH+MKvfoHvfe8HnJ9f89nPfZrjOy/y5S//JqtywR/+wW/zoQ+/jtLC+2/9kA9/+K/x51//Gq9+9GPYyrD50Yaf/dRnuXPrhN/9vX+CFsWnP/l5mui42OzQmwt+8Qtf5LWPf4zNumFWzdFO+PKX/xea2OFjS9M2fOyjn0BLyZ9+46vU2yuqSlHXey73Wz7xuc9z9uQhyiju373PwyfvUh1bHj14n6995U+42l9zfHzCwiy53OxYLZdsrq44OTG4VpJobHfKx17/JN4H/vQbf8QvfvHXWNoZEkIGhvJclQkLSab9cAiKDjP0OeD2FAyMk++f94k9u5VxNvXM1yFkLj8vDOtCPBhDPX4wPEMkjfMQBgzz5hgfIl55dvwObLd+vZfp38bfDx94k5Eqk1aKGXgZ95ocWHPQNtLvq3lEx9yGfSGnQJ8Ms3IChMnNOTv5TJar5/ZE/5rn1W1SR/oyycicSr9mflB8zvNvgEoT2HAI2RvrRb/UHByIQBxYm1GNmfYGLbex8fMrE+LUZ+4MMaWhCsTEGnEeHxOHQ3kwtqB1jnsnJ0Qj/Mb/9Zt0Vxec/v7vMF8dEVygnFe8+onPY6vbbLdbmnqDz1puXqWUyjHCnRfvc37xlPX1BWWlqcqS9XrP9a7FaI3u06Upk5kbSdC4z0sVO5fE/5XnaFaxv97wZHONEUOFcGsZ+Lt//T53FxVf/eolj88MLTNK3dIU2dnWwmx+n+X8ZRwNyjvc/oKzq6fsH2xoJeC8YnXrLrdeuE+hKyoBr5KQt0GxiykUIUhIGk9BYxB8CKgIXXAYHFoJM6tQIlg7xwoUuuD6/Iw/e+8tlse3sbM5tFBoPUgORA3GFKysZWYUF2tP6zVdABcchRFmdoWNhib4xK7Q4J0nhIardgcScSGAFWbWcOv2Cdt9y+nZOYtbL7Gcn+BtYHnnRepdy9XFBeu65f7xXWxR0gWPSJFSxiMZ4Ehz18ZAVIlZ4YAcJJVCwnJmNaMEFzqM1QQx+GhxPmK0olAtm6tzuqbB2CUUc5wLaCdgPZXRtKHm/EqjjKWODVFF0JZWOgSNNQXWO3AdhD6jrEaQpI9zdIypCuqm4d1HD1mVBbdP7qJWFq99YkmRdGsqDKe7LT9+9IDWtRSrFQUKLx1LI8QWxKRdU5uI0hVQUFhNYQPLELm+3uC9oosBr1zK8B2EzrfUuw58JDrHZrvFWMtsWYEy6YBdJZaCtYay1CnEUwkqOtTyiHpT82T7FL044qWTOxS6wnW3aLo9rWto2j0VOoXhGZvGuUREeSQqtC0IMVDEFLIXiOiQMvwJ6ZAhxDgImWsxoMB5T6VSJjohOcJBRbzOkSEEtDIQhfOLM9aXF6xWJ5TzJcouiGVKD08bCSqykYht01z3aKIkAY0YQmaPzVmeHGGPA7tmw+V+w/X2muWiY1bO01oUPHvfUIrGeo2oJIwtJLAw7XkKoxPDrhVF5z0qJIZH5xxht+HJ6WNEC9WdFyhVScCnTI6i836TsuJpTQod9FCoiqK0eNcyswpRkdp1NG2K/JDos7Ov0cEnO0G3tEowrkSL4eX5Mc7BpVsTr88x1lHMDYWdg1gCns4l4NYqCBJYmYqua1lvL7m6PKdaVdx76T4dPktkGCTreVnJDH8f2YcOR8D5gFKSNMrCnpUp2AcNKDw2hTKGkBmSHT54fFRo3YdPRkIMKFGUqkhsG7FIEbEeMMlKutjtwAe8u2YWhPligbY1LgTu3rpHvd3y7tvv46Ph5N7LyGwGroYoKfRXpfkVvEaUT6GPwdIqg/cdvmvwCkpSfZfzBVFUOkhtNxTW4pRCYbBGUva/2EtVpBBTo0sKNSeKoo0tWrZoOpxvMaHDLm4TlWe37zh/umd9+gEr61jNDcd37uL1jKa+5ngxp4sNjez52M+8ggO6LtCwIGqPAIVSxKDQyhGsEPxotyV/LEBsUVrjY5PAWtFITIxgo3OClRggapxEiC0alzKSB4AkFB8koEwKTayMoJ0nWMGHEk1LiBHnGpzbIATapqbUHYvFMUrPOH10RvP0CcW9e7zw0ouY5xpz/+Yf+defmv7/+/zG7/2LqFR2u2IPz4zOZW+xScxGL30WhJwRMvR6Qyn+OuQFLlla+Qw+9mwm0jt6y2rqtU+MVZUzAfiYVfVJC2sgMU0iedNh4kgjB8665HC83hIf6jXAW9kJSapwTPK2ZdM6vQU8g7Uu4wlrHJ4zdbOf49D3m+r0fDxGBo2o/BmN9F7faer9qhvAVK5npurFrIE16vaoyUl06pcDJ+AZTz9fNwFMEkoahz4cytlbz/14ORAk9VlParzjp4EwyeHLjmHM75CYQYQMBGQkYBBXz2Nq6rSN5YIBlRMOygCMJxWxdyQmYUkH7T1xfYSh74fST94db9Qr9m2bwYRnGEPZ4ZiGLqpnnLSbQNnoKMsEUJpe0WsCje0wKbFA6JvlOYvR6FDLwXfp+htFn9534/eQyyBMwm9u9NPNZwQZxcj7NWXqaPexxGFys2IckYcC7BNG2qTd4+T3EV6Y1Gu47zBkdnAge3ArpJPg53rn2UHs2ZJJ2FulcJUY2Tc1PgYWi+VQCsl1GUImSVTgKFAirK/XvPfgXb79ra+wa6/p9i3WVsznx2w3NZ//+V/grR/9gC984Vd5cnrFV/74/+C7P/w+rr7gH/zn/5Dv/vBHfPYzv8D7D37Cr/zqv8c777/HX/nYJ/Gd57vf+zr/92//rzx9/AEf/fjr7Bq4dfsOj69Pefrue/jQcjy/yy998Ys0uy1/9Ie/z353SesDUcOdu3f5+//hf8Z8seRP/8Wf8PjhB7z74Ed86df/Jr/3u7/P53/5F/nmN7/D7mrNcqEpTImsltw+fon7L77M/Y98BIxmv9nw8dc/yfe/8V3my2N+8Vd/hR9+95v84Ftf47133+Xv/L2/z5PzK770179EWZ2knhc/ihb3fRcTTOHz/Fd5UveHGf14Dv8a5tFP+34AbSZg58G18eaYYnwfI8TwvBF4M/R2gDAkgVf9+w9A/wGWOSx3zPcdrtf5E8Ytd4Q8JsN5SKyR6wv4GIdrJI670DBHhxcdrvFjfadlme4JI6A0NmPfVuO6PLzjoH3jwe/PrC8TEOmnhcpOsKrJvTJZfw7X3/5wi6F/0iUpG9aNfSjGZHbEsd1yL6aU1HF8t84JHkQEN5wCpV1B91URoW27tF5ESGmuoPYt86Jic3nF//yb/xub86dcPv2Aey+/zOd+6a+xvmr40MsfoekUbbfBt47aJYvJlCWIQQsIlt3+is36nHp9wWuf+DQfPD3HRIs2uTTRYLTCGoOI4HXKatM2Na1rkwZJIShlOD19wn7fYnSFomVVBX7+tRM+eXfF19675s2nDi2aVixBSQoBi8mo15mL3vnA9fqM3fYpygqhaSlswb5raVqYlyuOFsfIzGKUzYlHBKMN+xjQkbS/BsGKsI8pq5XVipWxaY9SmuB9AtBEeHB6xpPLM44XC5wGiQGJhl6lN7HXFYUumJmSiKC0QWlDcB4VfLrWR56uH3N+uWa+rKisTeCIsdhCMxONaIWyQtd0PL24pOkMy+VtKptO8jsitqjookKouT57SrvfsTArqlvHSXuJ3glSOYw/HaganbStZqZIA81C7R0nWjB5P92HQNM5XFB4HwjRU19fU9d7dFkk8MNLzjaWgENtEzNCKcmMLoMyQlkWNLXHxxZyWmxCZNPscD4xYFAaS4VRIaWhF8E7RQwt++0lneu4M19x5/YJL91Z4oPjel9z+v4jzmqPms+pqjkh1hTiAEuhIHpFGxJjz6pI2zmaNmU729VrdFTYoiKBEZJAmwzEWZVkLEwATEgsKVWAKHwMqJCANx8dWlt0TLpTVgTfbVlvrxBlWK5eYTFfsN/tQQLBB5QOFFZDCKw3F+zrlqP5AjuboaTMoZaKTml8SMLzwYOXfDivJPstuY8JKVV59IhXBJv3gugHnVgiOEl2bWVL6v01V9eXgGFWLbFFCuMKqkSZiNvtQQkmGgIGpQOKAtGa1m8xBApVkChbCoKm9S4dwIsntC1NfY0La6zvUKbi+OQ2Rs9QakYkIFYjOumZRa+R4AmqS6xGNNE4ur2nbfbUl0+52u6ZreasVi8kcfwoaJ8EnxPOkuafVYrWO5x0FGaGokK8gOrQOtJ5j9YlbevREhOhK3MUFCn8rKMlSGaO+5RxsCEd+IWoifWept1hQqTQlmoxJ9oKgqbzLYoOmobTyycUtmR+cgvjPCIGyhJ0QSmaEFuCRMpinvbcEBHv8CrQBUVoPUYccz2n1gEtlhB8Zhx6CgUuQBsUAYfgmWGIUWgyIIUGq2zeW4S2vabzHaIrClXSZbHsfbtlvzmnrTeUhUnJEGJgvXPcuXePan5MHVsq1xHsjMpWiOlQDhBFUCUiOpU/JM0xEUezX7O9uiRKTTGbY3SFmVUoMyMGRYgtXZvsNKNrCqloupCzfXrKcoZI0pfTMa3LIlBKys4YpUuHAgS8g+vLS+rN/0fbm/1almTnfb8VEXs4w51yqiGzxu7quWk2u2mxOZik0CZh0rAEyzYsywLhBz/4f/Cr4XcD8oMNG/BA07ZEmJRACaDIJsXm0BTVA3us7q6qrq6qrBzvfMa9I2L5IWIP52Y2BYnkAfLem+fss3fMsdYX3/rWCaKeQoX9iWKcYqXg4Po16nnNpJqndaIuobJc+JpVI2zWUKjBh+R/JVmeSBuVENN+vro4Q9uWvf0JrnaoGFo1uCiIVUIEj8HFiIZEDgmqbFYNcbuhnjukqiltmfZ9L3gTMZLCVpNe2JYGKDaw3EY2TcTZQGmTHt52cUpoYNEabtzcp5g6TOvYNkt++9f/p6cbNv8Wr79yhpLNgwrIm9JIO0YZTNdstIskSmokGbzjEJlOILtn0uyYlcrIZhoBI91rOJ1NJ2DSO3mRbKTle6c4dBlukwoPMTvqIvQBByoZE+sAslweGZHvOwe0c6wlPTVdP7Bldp2DH87WyIeM/eln5zR0p7kdk2Ns2kpnffbG61CtnYYbCVoM+kpd2WzPXklZGczOfTow6Kqd3evE5BIZUlt2oUs9p0IzWNdZ1HTOR9w5VRfZ7fcOSETHrCild41G4QUisS9gCruQLGHV6YoM4Yxjn86M9Gz6RzNqzkGNua+XMAoNyWN2SCPfAXiy418kIvYI0JErIz23jcrgTO60dd9fQ98kMKUDsBj1kfbtlupo+gxzHXCRnM3u/fFzdHB8RoDRjgPXj8PBYc1+02gOXy0/O/XqbtcPxVExOkd4p2NGzAiTC6QDWtg7xIPDm13MHYc6Pt2RGz+3b4PBye21ja601VXH0yDEfg7rTtsaduujMjwP6LMXepvCKwpjUwYZknMT4xVQQ6TvYiFpp0SjHBwdMK0/BL7l69/7Ki+++Azf+NKf0q6WRFcijaekYNuueP+d7/DdN9+hLid86rM/zac+84vceeEjbGJk/+CIP/r87/D9+9+naoWTx8fsX58x35tx992GVz74SU7XG24dHvJTt/8m/+B/+O/woeHh5n2+8C9+h4PDWzTbFY0PmCIJvU7n+3znu9/j5rPPc+fZFyms4fpzz/LGuz9gfzblq1/8Y1rvcdbRNJFqOiP6Da4U3njn+zx/5w7bs0u+8sU/4st//HlOz044uvUMzz1/g8uzh3znW1/j1Y9+mHfv/YBPfuqzLJvAdJpAvQ742IlyGo1EY0zGx/Wp46L7PZ4H3Xum64sReJTA/wGcSD8jYzZfmozdQ4Y50jFes44y4SnjrgNN+/fyDTowfcBHtH/IwKga7tON6y4USfJ4ildAKa7Wu2+qYd/p1+j823Zhnk8Bmnfqw2hXzB2kT1ygaR/orhnbCFfm52ibGdYb3WWDXS1BX7SdvZydgoyOB/q+0vE1fTsMbUlft9FBhKaT6u4rXTj9WB2xO9cwmrSRhvIN2nV9ffLiK/l9FNq27deL1kEVA8EHDmZTvvXt7/Cb/+y3KAg8//zzrBZLDg6fZ+oDn/vpT/KFb7/HMmtc+CbgnMMUJdY4oiTx/9XFfRq/4fbt23zrG/f4869/hRvPv4whMTSds0lbxho0Rloil4+PadoVk709irKgrqYsL5a8+/At6sJRuZJWAqUruTV1vH/c8Pq9Y6KpaY1DjaEkOfiiwkZTzsBtWLP2F5ho2C8ELWq2Ypkd3gQxTG3Svbi8POXk4h6cNkwme5Tz6xT1lM22xVlPIYYLEebGsQ3pkKu2lkpS5qlON8eUwnKz4vvHDxCU2UFJ4zdJgBmbQ8ksXhVnLfNqgrWWqJEQPdps0dAS8CmcYgtiI0Y8z908op7VWNI6UIpBNaAuZc07Ob3g8ckF1WzO4fwAVdiYFNJWSwmxoRJDQ8nh/Drr2rBcnnH+7j0Oj+4wPTrClRXzylAXFg0xEdWdY+YKVF1it4hQagvWsVHFGYNDKOuSdRs4X5yzOD9OIa/TGZrdAWsNIm0/lVFPiCXWlsxnU4zAtm1oNy0h5MzIxiX5HjUYpkzFIyaJ2oagrE1mMYcaS4PEmmJ+mzYuOLm8z73vvc/J45uUZcGjs4e0rcG5Ce3pKXG2oTQFa7NFQsuSlLwlknSXrBWcE2wBhoKqejadPeLAGpwmDSnNWbkMNtWrDahJ4rcOSxsUVU+QZNM6U1KZAleCthsuzh6y3SjXrl1jvn/IUaVQnPPSx/e4f3fJS8/WGIXvvdPyeAnz2U2MXXO2ecj6wTvU5T6Tm8/hXKKPhNCApsyyhRGss3ivWTctR00ERWOLGIPaCApODErKFhVCoDIZIDaR7eUpq8WSaVFhqgkYobYVrZSJZbFdUrmCNtvuzhbJc1CwTiiqKSEETHAQU3t58VjrcNHRti1lJbhiQow1i9Uav7pEjo+p5jP2JgYpysTyCoZtDARtKRCMOFofKMVAaFmfHLNYPiZM5jx75xXEWFotKFA0tFBGkieTDug2YcvGe6wpKTFUwYNZs42ZgBCSlmbrE2CWGCopI6Wqx7kUhidSYNXjZIu1AjGFtYkaVnaLPagpwx7GR5r1mocPH2DxzPcOsa7k8vQu67Zh/+g6ZV1DDCljXlGxkRYXPGuBWgqsJBDKYvDbgDGwXV3Q+k2au2pYtg2YisYsiRooo6M2EPAsFVpKbNuwOn/MuQmU0yOsOiIetUqQlsKnTIbrtsUaR20b1iKoVxanZ4iD6d4+VX3I4uE7nDVbqnqPsq5ZLi9Sxkws3s4po3C8WVIYmwB6qyibxL7PTNnkbyutL2D2LJPSgvoszj1h03jqAN7NCKxTuqs4YxWh1ZJQlKgopjIEVYIkUNUWglfFi8WGgEQllAFbTHAauFVfx7d3iHFJsz7n4cP7tFvLpJ6wjhvKixV1veTatRmRObQwLVomhSNUBU0LW1XWoST6CRoT65aQsveV12dprbaKN50dlDLIE7NemxECDheSJpgzwrwy+NxnKRgugf3GQEEkaoN3FrxBTE1NIEwNk6qgkkBpAtoGHj98iLU1+7evsyc1eGG92OB9w7X9A/4qX3/lgJJo6P3Wzg3rhLI1a7ZIapve0B7CDAaneGwsDw5aT42gAyR2Px/ZhJqav3OEBUti9WSDTZNjmUJDNMU97xj57DgZY2ZM0lLJwEV2oo12YTQCWSx3MBhHhdqBkAZwqHdalQQxqILJIXI6OCedDT/YqoP2h/SeSC4DKSPCTj+oZJAsV7B35ztFihwvLcnRHpKTk30EGfXByJDODW8k0fw6/pQZ11+VpJVl+nt0Dg89rBL7S0WkB/hSzQTNGRH6nuo7KLWKaM/r6EGMYXgojLWjlNR/ua5jb2HHt9Du/l21B/UOzVlXEMmOUhJS1uxk9mm7JbHXOuZechg6kGlowx0tJZWeoXTV5+rHTH+DURt31/dtd9XxTL/jQDXaYTSFGEfXyhN/D9jF+HtmVIokTtc7wF1bXSnEE/pNOxcou52Q6/kU1kc3j+TK/L3yzf667gKT+6q7YgAud+d7V8bOwe2c6/G1T2pusQOoma7OXZlVewZSBwLshhymn1Fyivk8ZTebNdvY0DZbqspSFBUDwyR1fOeSqgRQQ4iC+sBiccbDu29z++ZzPHvzDouXj7n/4EFaeSthuV3yD/+fX+U73/sWn/zkJ9isWu49POErX/o9nrv5DLN6yq/96v/O8uKYYjrjf/mf/3t+9Mc/y4f2fozNastnf+bn+MxnP0vTwje+8mWm0yliK7RZU01Ljh++w1tvfgfjCmrnqJ1jOp0Rt2tuPnvIax/4IL/72/+E+8fv84P37vIrv/Ir/Prb/yduv2R9ESgroZYJh7Nb7D93ix/91Gf4jd/4v/jBm28ync249swNHt+7y+0XP8gHX/kAv/6P/g9CgF/6z/4LPvjyqzxanvKpT3yGxWqdQZ0UwpGc7mE0df1rGObv7sp9Zaj2/T2MtGHJyevgKIxyzEjcudd4z+vYt+OHj186uk9Mb4x5hrG7Bt2Zw+Nyd6pzaYyOgBvSeNW8X/TMLNG+HglcGgXlasbXNR29dG2hsvtUJYfuPYXxMxw47Ja2+9mV9Sr427HKnwx50/F/xjO2v2e/jV2dv/0zx6F8Ovru7v36/aFrkdxG/famu5ZA//3ue6N6Xx0fPSre17/7kQ7V0lDpyhnpsm2Otz1VpY0hST5oCnuTEPEi2LLid37/X/C1r3yVV194gUcP7/L2d7/DnVfv8POf/Qkuzi/52je/yfmmguDwmrIdGQFrBU8gtA1qIocHcyb1LY5PFkz37nBrdkCrgjGeoihS+mJj8a3S+A2hXVO4yHx2SLAVlorH9x9yfHbCfDrFmIgxSRNlryo5a5UmOPaLpBlSOoP3iZ1RxMiyWbNu1th2gzjD3mwKAR6enlC6I6a1walDgRCTJtJ8dkhRTtguT9msLtls1pST6+zN5syqgtZvqbzDk5xgm5Mi+JhCcCpJWeNOTk+5XJxSlzmMzysVZQpONhFcWlOmxuCCsjg/BkmyCkjSSCqspS4nmEowe6BGuWYtNm4Rp9ggKVudCtbOWWxb7j16wNYL+9duE60jxIglUsSKQpSgARMLos22alVwvXqGvb2bhBuBi/NzTh7dZW9+wPzGIYTEdDFRaKLjctFQGsFWEyoT8VFpVy0uKufeY02BaRvOL09YbxfYukZdgUZPQUpRnsxRS/ApTb2RImXbc4pnjYQM5ojF5YylGrrD3ojUJgESlNQUtEVg1bSItqx1jQ0etGBzdsKqOWdSV8xqw7sP3yPESGUL5vMDKCzFfJ9JPSEEhy0OEBw2r0khxpR91XW6RImhn9a1gMfRagCxCfiyad65rJkUrFBKgcHSqieaZPMl0NFRY4lty+N7D9g2DbPZIdee26cyhhiUg6nh7//sK2CEf3qy5iMHE547Mpyfn/B4FfChBatMJ9fZK/bZAuenj6hMCqeq96Y4MUmjzoy1edJ6YlSQzBLyAoJHJOWn8kYQ68BENHpOTx6xWF8w37tOdXBEmfW+QuFoxSKqBBpqo0yKMmfVU5xYNhqYlRU+Jn7kRkICnb0n+pbED0ngUlm6tHo5B0Gp5jX7k0PauOGyXbJa3adwM8rZlEk5o7IuJzNpiSGJgK9Xxzw6foiRiv1rL1AUEwyBZQhUtgsXTqGqpXO04sAqBYK0OTzYtkRtMGIxtqCyJcQ2h0EmLbXoBS0CRGHrY5IOEYi+QaLSqgcreB8RIjbrWa3bmDKQLRuiFJTTCWG55vy9t2lpwBTs7x+hwXD8+JxohL29Q2CD+oAvs/h/DKgzJKS2SiC1V1pvMTohqqMoU7CUJeBsQRtKVIRl2Ca9KZK4eKwKqmu3EovN1lhTYQWiNlQ27c+NbziYz4mqOHFJbkE9R3u3qYwjXF7y8O67mOmUlz7wIxgsl4tTLlbnHKihmh+gk4p5MWEmFY0GDIGoaVwa8pzTEqslhVF8zjhpY0MksiKJunupKWzJWjeU0VHh2GoSmJ8goAGn4NfbxMorCnwkiXjbLU5NShggBRIakBZjU8bGYlpzctqw2Bj2j24z3b9GMS2gWbHdrjn3nsXDBaU9ZX9/zrVZApyLoqa2NsVS1dBqYO1r1qEmxIoQIIqiWiHRAwGxUGkClKzJpAciQSPWFMToUni6QInQakFLosUJEXEJII5aEGObQgZVseJREyiMEkNkcXFGu/Jcv3GDejrBGsFEYWMM88M5KPh2zMr5y7/+6rO85Sxp0nOTBkhCyABDd9rb0xBiNgg76slggPegQHZJtb/T2NmV4TNJ2hFGOogqCQ2SNUfA7Bi5VpIAnyDspBHWgWGTzMqO0pYYRlEzAyrpFmt5AAAgAElEQVQDQrEvy+AIiw7Mp6HUluH4eeT896+OWSWdpzD8P5e/u65XUhiFVXX3TyyfLoYTYsyQSaZtDQDKyNUXyUegaZVMcFyi73bAX6rbSPMjW+RjIG+sddFjVr21rN1F7NY8a79oTI5L9yUxWduqM5zH57XdV+PonmNQoAOOQk9g61wCjUN4wViX6KkvAd0JIchtEOMoq6FCNyauVHn4Xn5m1u4ZQqpGZchNJNI5Y11/xG7UD+2W6zRofMnQBDshV7v93NUyhXzmu+aTAsgsQ0aAE5DCUEf171htY6+lL/ygjzJ8MgAy9HUnEzY65/JJV223/a62KP290u+RaDD0INDOPbr5prEfD2kZ0iHEMI+ldMo/yiY3Ar6uBB9ecY6HGXBVU+fq9arpFDkxq4awpq6fXR4DCbdUrDNoqwQCTUxwiPrEnBKbaO0G7XU6BJImRgy88+4P+N0/+UMKK3zkwx/jtdc+zJrIyy+9xP7hET/2I5/i9W/+GT/56Z/g8OYzrNdrXn31Zb7xzT+n+chHeeWFF6lmJYul5bXXPsZi23DvwX2ODn6AWMfPfu6XeOvth7zwzC3a5RpXV+xXcx4sH7A8X2ImNXeu3eD88SmtVyIeauE/+k/+Hq++9EneffP7xHXL4nzBp3/8J/nun7+BmAhNgNJQ2SmGyOPzh9x69SX+6A9+l/PjY74Tv8kv/OJ/yGsf/jAXy2Ou37xNoekk86d++udYryKvfOxTvLgNrNabZETiSemBIwFD7D36DtQYz8gBeMi7FR04OOwZ0oPNo4VmAGwyRWQHqFTdmYMDyJHnTxp+HXbQh2p24wSEPvf56GECvaG7ywLKIdm7UEi/ZvUXjvZcHVVE88c7GnQ7LKVhPUjjuAM8klPYaYiN6zwuRz8/Rm2XW2RohPFPHX1XRn//Ra9xcUfP+ote3SX92vDUh43W2PwrjmyBJwo6vnn3S1K7DIy3q+vdk+ujNWmsxFF7JHaq9mNDdVjLhcQWiT4JAPvFin/8hd/j3sOHfPS1D/P6d7/LYnnJsx94lf/qb/0iD+6/w+uPGhyKsSmzVeUc6gocDvWKxi2FNdjJFB8sb917BOtINamRuKUqHGKSlpnGgtVmjQ8NVqAqaoKdEEWwEd679xZBPYezmkDA2qRhY5yj1cieE26VCsawjpbgU1jPtl1xuVkhGEwZqPb2mJiKB2ePWC2E/ckRwdqUflwlG++SbL8YqJylPnwGPUri44vVGfcfPKRwnqP9G1STa0QjYAK1KWhDcuyMWJqm4dHlY5ZtSzWZplkTlUJgG1qcGBofMY0So9IYoIlo4djbO0wZxoqCwqT5Y10ECUCg0DLNHdKa4VWZILQq3L37iPuPHrF3/Rqza9cIweFUsZI1qqzB53jeFrAoGi2FqViJp6GhMBP2j2raZsNqueTNt9/kcD7h+rXr+Czya21JA7htQ0TwEokeGiJeHSeXpzQXJ5ROsNMyH4LQqQnktdVjRNhqSxUtVtLBYilZp0otpSR2dJvFa/Gejb9ATBIKjkpOo+0RrwSTDr+iS46XdRF7WDHVWymV/WbDjWdepJrss7q4YLtdMRWLLYoUNl0I26BZbrMBKoJN902HuGXyHjqbVGwOP7fJ4XUGT5KA8Bl8T3PPstWISki6sFFSaJIIl4sLNpdneFFmh3coqylrv4ZCeaasOHDw23/6Pu+crnjvDL5195hb04LoDHsT2KghNpbQRFxlKVD2yxl+s0paS8cXFKVQuQpX1mjpiGKxajAUybm1yVYtBYypiaI0IWXHswp+ueJyeUrrNxxef4ZpOceSsvwlcX+HGIO0galCXVX9OPMx0hgQqVhGsF5RiQQ8ZK2egqTX5PMBa0Sz/enT+A8+7T7VnFk5Q+OWsI4szk5ZyAm2tMxnM2pXENvIuw/fJcaWw6M7uHKCaMtG1yBQIBRkJ12SZlVqj4hpI9E3xLhFRGhXga0N7LkJPmxYmwu8QiGKjckGqKwQvef8csmqWVNMKpwrsj/h8CbgJNCKUESHNeDFp7BUM6HWitqVtB5OwgXsH3Cwf5NmG9i2Syam5NmjfVxdY4uapvWYSUWgATxGKlpxWBFKKQf5E91LLDMTaQnYxmOwuMpBLDCxwWhNiOkwQUUI0lKpEkPAmET4EBWMztG4Ra1BKDFGc2iVUNoCr4KJLSePHrJeLbl2+w57B9cgpn1ouned2d41DMKmWaOLLae6wjrLrJxBYalE2HhLUE2ZByVDjFEhNHhNkSgqSq2WYCISlXVOOOVMAs6DthSBvIaDisfMKkKb9kNnLU7KbLsYjIYULuZqnC+IcUOzXXP8+DFSOJ5/5TX8ZpspwBWYSF1YqiogccrWN5wu4ezslEoCk2lkMp1QVROMcRRlxZ6smRWOIAXrWLKlIkQLQYh+yKKtRmglMUzx4DUhFeqSx6GS9NlUhVICuOQDJuzBUFAxcwXbkATJYzTUBrabM85PjjFFyfXbN6lcRYykbJwxUgloTM8q3A5l5C/9+ivXUPonv/MnqqJESai/kFKqZvmcZOxmhyyIRyiAtIAkdklnxCZjKIXCZUBBdtzuLLjbaaIMp3KD9sJYSyeZsk/oIOykbroKA+z+fwB2xtckxktK0Nf9vwu/MSCR0OkCPQ1lSHd+4oMnhIx7VtZQsp7hwsBkSXfrwCYZ3tOdlutv8kN1PpDsaMesQ5QnQkx6AWjsmT19GTC9gHJfj64KvdMSMyhkcsul/jZZKEKf0kD9CXluB9XUDlG7rAtpcHWOf+8GZeOcqH03aw+oyQAaqAJJzLx/v+8RGbXzKLxIB3bJADYlyKtLx5x7LIdVdP0zhI51oEzUgIjNPqfkdg2j/ldQO3Rw30c5vGsENmUOFLtwUqRPjZ5FFtnlnmXgczwXnnyNnc6unQeHSfrP6Fl32S3VDmIZzuh3wxi76+IIqOk6QUY481UGT/67R7pSWs3UZ3m0ZVDamHFY6C4zLP3ZtccQ1tbfny40s9MhGMJwd6eoENWTdMtM3wedUsoAM6UeiKIp/bKabIx6IOmK9Dy6LuGAJMBVFLbec3LymOl0yvzgkOV6TWELaldmgKSbU9KvjaaNHD++xx9+5Y+5fec5ju/e46UXX+Fieck7b73JtKq5f+99vv/26/gI1288w7XDGV/4/Of5pf/4P+U/+Nv/JW9993tYbfj13/iH/MLnfpHlYsXnP/9PKUrLZ3/mc3z8458m+kAMW774h1/gxz7zN/jV/+0f8O69NwmrSKyFw9mc88fntMFjCkt9eMAv/63/nI+8/DH+/Ktf5PzBQ+ys5uDoGR7fv8v333md8/sPkcKxWa2ZTSzV7Bp//7/+b/j6177JwXzG5ckJk+keH/13Ps21Wzc4fXzMN77+r/iZn/4crtzn+vUDRNwOMN/rFkm3QafVo9Ps68N2o/bMFJSerqwjsCjpcmlep2RnlKaw4NE8yafd/UjUmCNnzYiNOFzQz5KnsOC6e8joEd15QNr3ugQRneAuaD5d7wCLMbAT+31zaKe01uXnmFEY6zDqRxBLbyqlOTAqb3fNOBxZGebuOORsp5ZX3pPRz36FNqN98anLV7eaD4dE41dUHX1P+vvsPuvqvYbidd/t1sRdGFuf+F7fyh2S1y2WkAz7OHqGgJUuLLpjPSVPPSoZcM5geP+1jrKVgSTfPT6ixkIMaIi89+ARn//TL1DEyHw+441vf5sCx8sf+QSvfuBVFosFx2eP2T+4QbMJKA7rkiiwxJwlLGfrcdawXm84Oz0DDMYZTBEpATElIRhC8EkvpEjhOIU6orG0RmiWK05O7xMl3csiFK5CjMOYQCTwTD3hqLJsIyy9smgCzbZhG9aEwjApp5SmwFrLcrHl4fljTF1S2Rk2Z9kRDFZTYisfA4GYQlTUEoKmFOuiBBziV1xePmS53FJay3S2nzL/iCHiMAjLxTmPTx6xNz/AimHtl8kmUYcXKCzgHM7NqFxBRU10BWVRUBqPk5SO3FgoJDGLjEQI6ZReg+Al7RWyTaKrp6sN98+PCVGZ13uYaoJXg8WlMIcM9IgUGIl4tahGapuyqNWuQDObxsWImCSYHLXlYnPJarOk0Mj+ZE5RzZiXNZSWiVQpHEah9Z7F8pLLk8dEEcqyJhoh9qFdBmNSiJ9mkXSJim+blN2pUqIXaucS4ykURA0ZR2oIIbJeLAh4yklNZaaYUlEXmBjBaIl1RUpWoVknVQsWywVnizPqcspsMidgEHE4cXhj8OsF2/WSqEpdWaqqQmyVwLa0yOFJulgxywx0MQBprUoHykkHMTGPIiFpaqlLGdUMWTNLsc5SOGW9POfs/ASCUE/2cKXDuAKJChbmpbCnsFEILoVbxZCAgwSoJWBLNIH0GgJbTWVMMg8WomLayPnyPtv1ksP9I/ZmR1A6rK0S8yam9SVITICzjSmjWQw0vmFxdkIkUJcF5XRGYQyl5OgOAz4KIYJzBU4Momn+hAjGGmazGf2hvfeIGpqcFc9qSGF4FKxb5bLZEENK+Y5GWjQJlNuU4SqYCG2EGClFaENguV6yXJ5iTaSuKtaLFdFY9mZ7bILF+w31pKQo6sQg06T1JJKAVpHE1nGiEAOL9ZIQPZOyxLg0t62JiX3owKrFRiXYgkI16SRJm1knBpfXk+iSB9KaFpeF/G1MIbUVjjXrFBq38SxXS1bblnJ6xHR6gLMVxllC09Bs1myaJWikcsK0muLqCU1oQRNzqigsqEE0jX8x2tsORpNIiVHAmkSjUMV7j1GP1xaHoKIUJtIoKbRPU0RHpEVVcRTJvzYQC4NEwQfFGKW5OGN1+ZhoHYc3XqCUiiCgYpGYjjdCJiIYkfRshFWzAoloaClRJoVQlvvgkni2ovigiLSYXCHnKjQKUVKoq0TT+wQGxeIIUXC2xemKELesvUWkQm3S6VKBwiYWnMSQshdGRcKC85P3WVxsmd+8kbUABbYrXFVCNScGQ6mBEFoKv0FlnZhbbQCNrNsNReNBtkxrYW86pZpUWLE4V+KKgmhLghZsxdL6gnWskOiIIRBMipKIJvkuKWFo9guF1FfZz2i0wRKQkBKVmejxJIA7BjAxcnp6wuXFJdeODti7dkBMuS7TAbMYNPokW2ArhIBB+bX/8b/94U7fv+HrryHLm8t6LyEFTHVoAGB04PoAdGnwotq0iUrHMOigmc646xyyBAp1Bn/nNHUU/F7fJAMGY8NxMGTzI3Rw0IbzfPprBs2LBJpcfQ2ATxdatvtKp7DpflYGpZ7OjuwM3ARGdPfq/gF9trjugV09OuN5rMXSGctdSKEZ7qO9+9wbur3dufv1nRYQSKKfPQuq+7DTnOje6phofdBN7yj0Ze2rJqOG6rhGidbcF6dzqnrgZgd7GfqpY8GNwJvBBh/VPTMBxmLLSJ60o7c6MGXsTMmo8AnsGI+S7BzsOHm5HbQLn+o0WtK4HYNR6fL0/uD4Dy6P6dodQTWDHTLcL42lzEwZzYexO7vbo4YBZpLsCHX10wySDNpRPRg0ruNAKWKHspEaIrVPdmgSk0FTpinye6ZjZnXzM7MxdChjYj8NDuN4vF+tUe8Aj53tkS82dsaH/3ee4tgpVFJ0vH3Sf+zvMYRJ6pV5N351jLUMLdKHoPTMvKH/Ovg8GaAJKA0kB9J04DhJONqI9CGQrnBURYXftDTVBjRSFg6i0hqTxApJaYATvyqiVpkfHfLv/siPc/LoAYvTCx6V99mfzXn8/vu8f+9d9q8f8tyNZ3l0dsK7b36Le5oM53/+z/8Zx48XvPrqB7AIr7z4Ki9/4COgirHC/Ufv8/FPfJrtYs21o33u3j9lOt9jWpasvefg8DprLln6BdvVBeKgcCV7Bwf85M/9+/x7n/15YhA+/skf50/Ofodbt27x8P5D/uAPv8BkEtlsl9ysb9JIiw+CXy35V3/8pzx68B6PipIH771JMZnxwqsfwK/X/PnXvsQbP3iDev86B4fP8Nnrn6USMLagCS1lPgW3pEQBRk0GiGIy4Mfj3nSHFHm8jgiSYw6P6cdVNz4G9qHkta8Lc9zh2coV+FJjf59hKHf74NNBJTowIY+s8VqAdqzI3bVqPJWvzoXuWeP7d5+NgR+BPix8DDR1c2UnJDb/1A71uFKe8fVXoZen1LgvVwLTOh7VU9qp3zxGden/O653t0WNbISh6qPXDylNPnSRnWvGbTjaQ0dXdc/sDqr69sqns6o51FtHZcvXpJPm2K8ZycjI944JMAHFGofEgIoheM/52Slf++a3ePPdt5nN99DQ8vrr3+RwNuPVD/4o1249x+lyQ+FKZrPrrFYNtnDYzBxOgrWGoizS+PWey+WCy8UFZVmhQOnStSYIq2aLtwYrhrqa0bHlvIFI4OzkjOX6gsK6FN5rLGVRokEQDbQhMC1KFMPjVaDxgXUMtBowhaWa7mFMgY1JgPXx+SmbZcP+/AZeY5rfmpgkzqTMZU0MqFiMlB1yT20giiXSoEYpzR6yXzE7XNCu1yzPz7i8OGFvcogra5brBY3fMpnvEcWw3lymjFLVIWVV48oSZ8BISSVFFqBuqcQltoY4SpEUqiA5c6cmp7olsg1KbE0KVWw3+BA4u1yx3K4x0wmToiKGSPQtxpY4k5g9xhbJqVMQKXAKAcMmi+5qzCH6MR1IGRzWpr+vlbc42rc06xXri0esFsf4Wc1kMqesBGILIXC+WLBaXCClw2TdVOPzXmYgaMxsiCxUrIk1dXGxghjYP5insI3oMKbAGYNYC1mY20Z4Zm8fNUk6WNRhXMDQYCQ590STZCFC4Gyz4GRxTINhb+8mZVGTd0+iKAGPBENZVdR1xba5pN2uOXl0ihXL/sEhrq4xOIxkRpJJqqGanVJrYnJNxKWDB81Z//JBWLCKWDDqUB9wRMQ3LM8vuFxdImVBvTfD4tAYMJrEto0x+EZYFko5rZKWjQpePOlgUamkgOjwMbKNDYhSO4eqybpElsY0+CJydPQ8cT+wbTYcLy6hiOyVB9TVJFsjghGbNIF8cvCb9SPONmdMy0OKyYw6u7I220oBCD7ZJ5hksaY8Rem3E6FwhnazIEalMBYibIhIiMSY6qIKTVQWjaeJSm0cIQaQkm2bNMtCNBTqacQjCgUFi80S1ZbC1czqfc4Xj9kGsK5GY8tis2A232c6OWBSJTF0TIWzFZE2MZ+oaEWyllmKJrg2P0y2WUzsrSkl27jJIXUJdCuMsCLg1VOYAqcJcPQh5j0xpXRSCRShQKxDczisUSH4llosm82Gi8UCyhnXbjybQJQIqpHoU8hkPbFUk30wEy4Xp5xuFtj1gmlhccUUdRB9QEwBkpK1dHt4jInho0ZwGGKIqE2WrRFFQ0gArxFEAk7XCAUNLoXdRovRnLDKCNZajIL1kUZb/HrF44fvU6lh79nnKCaHKJGISSGj0SPGJuDSuESkUKiLEjRSWEdLRH3DcrXgYrli4rbM92ZIOQVbEI3BpICidIYeEruriRFp0vqIJvDbujmthag+MWjFEKNQlXN8TH3kXAkYtjSJERdhQmS1vOTx47uY2nLtudspghAlbjZoaCgnUwSHGotXS2UrKOYY8YTQIGyxpmVeOGylbOKGFZ7lmUeOl0wLZVJaiklFVU+YlDWGgqkVZs4Rqdh4hzcTfJtYc9pFRWnMrFSbfNesLV3iiMYQnEeiw2cfLvqGuF7x6HJBVc+58+qLiKQEZA6HhwTcioLaVK/QYgyEv4BA8G/z+msAlCJWcw40ib0RZUIOwzJFColTsDEbloTsNCvImEWUT++B3uGXzniVHeMeGYzC8Xu9ooh0NHLoDTrtnvIUIxqyA5Cvv9LuTzCdnmJoBu1Ot4dndqF7vepRhzIh7KZZz2DcCPXpw4h6mz4ZoWOR5CdDb0iLvzK6/1Ck3av7ymWdi2Ski6YnB7pY5LHRD+PwpsxTSvcYlSE9t1MsyU/rsIyxo9MXZjDAB/BqXMpB6+SprKb+Nrlv8+36e+0MlKFNpP/OMNbo4A99ss2Gh6UbK10fp/dSFTNVMWbwsO9vhgxTuSQqWbeqK2TnEJLTrpJi4PsnaERlAEIGEfwr4V9X2m7HWermiXZhO6lBdtv8SQdzp6aSQZFRew7OUm5B7ZhiMbel5jbLzlQ/R0Yl7h3c3Xn0NMdXkjuyW9ShQsMHvThaF4vcQcpDCvfhWblHdbffnnjtzIuR0y1mx9nvbildsyLZqE8wlJN0ymasozvpSxtlOl3HpNOlejLh4uyUzXrJbO+AXotOc6CqWBSfgcmAKQyF1NT1lLffeZuggZVveOG5O/z8L/wyv/Wbv8Z2u+Th5Yo37r7DbDalRHCzCXfuvILRyBf+8A94/s5tfubnP8f1689w7727XF6uKeyEN7/zbX7w5nf5xKc+Tds2nDx6yMn5BZ/+sZ/g1//f/5Ubt45Qb/BBwMPsaJ9bzz7Pqy+/xmyyx/H5KUVV84u//Hd46603WJwuOdirWW8X1FXFNgaeee453n73LWZ7e3zzW1/m5sEBr7/xdepJTYg1l4sF63jJtaND4usbfv+3/hEvf+gT/OxP/hTqY4ovz+2UBFgzrTpTjYUETnTHC132w8HxH8YxSkcUGfq0By61BwA6AyGNnPHYomdIjd7Jo+QKaMOT1+z+vx+AO2OzP2KRJKbdA1xjkKdbZ2QA3YdRvwtiDHN9mMu54nltzWDWCMC/qrv2xFIy+k6vMzUCrUY1Gxbx8UoxPlAYFv2d38NRUxeaPbAFd5/ypMbR7pqku+XXKyvWDmhEv5bq6KvjV79t5PW2f6Nby+NoqTIj2ycfzGk+2bci/YWiSoiasj2RQx9piWLw3nPv3l2+9OUvc3pxwcHenLPTC45PH3P9xi1effHD7F+/RRs9GiMX2wCqFHVNaD1NCNSTGnElxjpEIs2q4f6jx9jSMqlqCgk0bcM2OJwpkp1X1xQ2pZkPMdkQIrBpG84uTmg3W0pXYrGUJrFsxQdakzRPnBUKEzlfrZMCo3PYaorNJ4JCAoUvF+c8evyQanbAwf4+ofXZsUlAhyMSrMGKYKOl1cQ4MPlwqs32pzWWEkHZJKdfZ5R1SV1O2WzWXJ6esTm9hzUFs8PrlNWMqq45uH6ElQKLxZhAgcmi28LEJjFVxFGoxUvH+Gi77TazPwxRheDB+0CjnnazYblecbFeURcFs719GjoNjgJHQSEWtQZjC5w4nBHWbcDYpMNpQsRIyZYEKqU1Li1gQSNRUhs6ipR6vi4oiltsl2uW6wuOL37AUT3Huch6cUErNWKFsGgpqgltKaiFkpRxC5MyuBWuwJgacemQbD65mTSSsgNXS4G14EwKE0x6UgGioqGlEIuIyeUTMHUKNfSg6mgWF5yen3IZI0U9Y1pOMVIg+RBHg0/MGAcqSoMhBnBFiRNDbUu2zYblagnNitJNKcsaVxZYJJVVDI0mJo81ZXL0jKSQbUmhZI6IGId1graRVbthuTrBby8x5YTJ3iHiElPGaGL2WWuJCG2AiTVIZfHaUBhHgQUNGHFABwokoWe1jnWIqLgkTxATM2UiJW3ilrCNGybVBDctaX3LcrNgtbzAEJnVEyazPbzC4uSMi9U51/ZqZvUR87JGTNKVUZLQfhtSZuqoSXNQTSBG8NsNhJayqtJB5KpjpHkQi2BpQ8z7TSTEiIjSNp42BqqqImhiZYpE6solAKNyCI7aKErAimVvb0oILavlgkXbcnD9DvPZIQKslguC35BkxQ0FSrCJlWFFSVEwliAeMjs/MdLTGmokCR+XamnEY41BpQBtCLqlDeDE4wQ0Zms+JhZQlJSN0agganuWqFMDxtD6htg0rC8u8SYw3b+OncwIITGvnCmTlieKGkNESJZfQ1UWGFNBjPh2y3q9QNcthTFU5QRXz1O0iC2S7Z733aRhm1jUxgplTIxHtRYjjmgiagyhTf5H1AargpUZwRRgA6jHxZZoAqbdcn5+gd9s2du/wcHBNbxL/o7RlPEwkHX5NDG+Q0ghd6oBrE2C2JJYNMY5Dvevs94/oAjCNmwJmxWIYk1B7SrEpjA1ayyIwboSmzdRsRYbAyoJtCokAc3Q4IPHxwZbGrBFPlzzlKLEJrL1S9599ACHZf/WDWxRQgCva0ITkMZDJQncJofsW6EhJP9CDeImODuF2GLMFpEGGyqciWAa1ssLHpycYrTFOuHWzessJgqywSJUzlCVlqkYEIcvSrxO8FLQmsQ2TUBx2tOjpMyNEHExI/akrJHrzQXbkzNiKcxvPUtdTDAxzRlVweuWIYIqgX9GOukh84RN8pd9/ZUDSmokpcakzEZsNnLCJZvNhnJ6hCsKehNNFJuZGtqzR9K9BkMwG3cjQ9R0DqEmuueToEKniz6YiZJv0gEL6Q5jRscICMjGW3IxewvyX9MBVz8duE2d8b7DFuoN5+RuDE7y8KyngT9PPUV+alnGBm/nGtE/KwFaoxs/xaAe3u9YFWOwRHavVTIOtOtQ/NCwyh10tDMOu3swtEUcGFGDXlAnRUrn0/Sd+sNCQ9LGMQYWMouge6837sfjY2hDQdMKPGLQMBqjg9vxpEM0Ojwe1b1zIjp55h33dQSQ5jBKY1MI1w4frgMZpW8b6dugD5zqgabORTZmYLR1E6Jnau2UY2d49MBOcpr7idq3n/RjvKtDHOZc11ZjoLVzhujuPbh50n92BQBjcNY75sdVILUr2O7MHV2T9Q4SCc9AHDMCxy51dsoUuox9XbHp2rlzxrXrETPMc5OdwG7m6LhtM0isSuO3eO+pyhnff+MNXvzgyygFlSkhaNJZymuaxVBVBdtmRT2fYKzNIrttzv4lBA1gTDaCUj8nmn46eXv0+AGTvX0m0xkH8z1+9DM/ze//8/+P9WqJFaEwjjZE/sZP/ASlOeDe8V1+6W//HV64/QKnywX1vMJUwisf/ACPHjzgN//x/83q/JTXv/t1XrzzMg8evc+1m9dZnF1iguHZ5x4r+E4AACAASURBVF8gvP8elSmpDmteePk19g+v8S+/9GX2rz/PfDbn5o0bXJ6f49cbFudnTG1BGyBYpbAWowGH48bRDSKRu/feoShmbFul3bacnD7iEx/5BM0Ptty8cZM3v3dMWQiXqzP2p4es12skKvWkxmZjOSiI2mToSzdDUvagnjlIWveijEaW5DnwxMjMQHcf8pUcqZ258pR1e5hCebwLdIGPu4Gew/N058BicBCvzpPdVd3QL6bjzZVhHg2gxbDHdH+P1wKgDyUW6LNEPv01bicZ/TV6xpV5PK5Jt+z3pemB4r4C/Zrbf298r/Gy/ZRdrjvYuYJljZ4/2pSutPETr1Gbjdt1pzJ9l0rf9/0mr6N9BEb4d+q0KOPdt6t70vPz3iOd2Gf+SozKanXB3Qf3eP0732XTbJnvzXl4/Ih2s+X2ndu89OIH2ZtfZ9FsICrbrcO6dNQWYspsVE/mlPUk1S8Gmu2G08szXBGpCoOopwmRxxcXHO0/gy1qClPSSnIqhYBYiw8t69WK08tzYgyULoURpdCeFErQRkNUKIzBikPclNJK1nDZtWt8CJxenrBYLtjbO0DKGRvfUJgSUaG0INZQWMGRMrN5Ak4UaxNDOqoQgtJEj2rEtND4BtEtSoUnsQiQksMbz1EVJaHdsNysCNs12+gxdU1ZFpQOSpsACTGCqMnCDgFVw4aAb9N+F1BSFrPE6e9CUAyKD56Ts8dsludU1QHz2SHWpYaqEUKZ7l0ZR11aClcQxeSU5gYfk08yt4I4yzYI4lOWpTapTScWg3ok+swYiggBE1q268uUXtsazKph0ZywITFaptMZ1jiK/ZrZ7BBnShLOErNLL1ijOJHEIsqMNatJp0ckiT87BWMVKylszJNZtUbAuawnqUlrRbLIbgisV57F8oyHp4/Ynx5yOL+G2JKgiY3iSaEdNoc6heAxOcRcs/SGGtCqoJokbRnfNIR2zWazpmgtha2ReprCjCQ5ckaKbPsErDE5HXtKDY9EdLtlu1qwXF8gGqj3D7BFSvHere9GuiiCzm4lhS1tJbnFtkU1Ze+yJoVnJsspYgtLIZatlojooEUUE6tBYugPSkIMSFAKTWX2bFmvFmzOHmLqPbx62tWWev86m2hoNpfQtLhpyQab9khsSmuuCibpa4oOYyyKQ6VCu4wMIihFYmtpgRaWIODUU2KwAtNZskVCZnWiCSzzIeSwtICaAidKS8oCt2mWtE1DWU147vqzSacrQiFwMD/AxwN8s2W12rDYNtA2OFtQuIAxLoeCp73Zdo6EJrsqHcIllqAPQk3EmpZEH4q02lCLMjMNK6mAIu2FmsTMNaZ2ynQuxMB221K0LZvLE07Xl8yrOYfXbqOmIAZF1OaQyoATSSBCBg5KVxM0UBiHqk+goZvhtKRtt2wXa5rtBZOwTaBtOcW6KYLN7Sn46LECG69UEjM3w+JjS4yeqnJZ8N6h7ZaYfXLVmG0jT2xbpnbLvZNjsBU3b7+CRkuwMa3J2MR+7bpRYkoA4JUi67gZU6A2ZYKMGjDWYYi0mhIL4KB2FYYKHwKb0LDaLBFZYrwniKGeTbDFXgpHFAtBiWJA017XxhTGb0WQqsa6WXe0TCQSQmS1OKG9fMzF1jPfu8VkXhMbj/qWTYzENmCMEqwkjU1jc5RRCi0XTfZhzAGwJbAVQ2mnqJ3gYkNoVjTLFZeXa+azGc/cOuLk7BxXzyjmN9h6iGFNbFtWAVQ31BaMMThzQWkNuAlqJjRa0UhBo7lPKTDaJv8vRJrtkuXFGZv1gupozrWjQ0RniaUmg9yGJa1LnUsaNeRDlpzs51+DaPybvv4aGEpgJcdD9+Fgge1myYO77+Lqx9y4eYvJZA9xRULOxFyBXjr3s2NMDE4sdNmjcvYyoWcS7Zj20jmi+Tv57Y41A4MBa/rruzCaAdhJ61DqDRV2AJKxIdwBLTuhb5Kf3PfbYIgqkvVeBudzuOcgoNn/7p3uoay7lKzh1YmxXnUshr+HDa0rf19eEZImT5ZV7+3o1C6WLhhvxypOdRgDLYOVPCqH7NQqOSmj7Ec7p+DDVdrFCI/uM3AHxhDd08ErERldzSCk27OPQLswwR2HaOT4jJyu8Us1MhbA7rLxDWN2VPVRvYfvjlopC+YK9LpVY89P+4sHwKZ/Qv8g3XVQRg/f7YdxvzIARXFc/67uHbuvEx0f3feKk5keG5/ilGlfd3miXrk+O+tb8lR19H0dbjOeUk+pVbd+DOUf+4NXddBMX6aBxTgW/NccXz4GuHfbefz81E6Do0f2CLOEvoycW8kjOaaT/vfvv0eILV/7xleQynDzxnPUrsztmsPmsrNvnGF2sI8rCoymjEkCVDluPUiXbasLK0xl3z844OWXXuP733+T47NTWu+5+cwtXvvwx/nyn32By9UFdeGYTie4yQGH127yxX/5Jf7u3/17SHnIXr3H8fEZoQkczPaIm4b1dMFHP/YJ2vWar3z1izy69wPKWcnv/f5jwmbFZH/Gqy/9CHt7z+DblsP5Hq989GOcnRzz/LWX+MiHPsQ3v/Vtvv/GN3n7e9/m+P59vvWtr9LEQGxa6tkMEwMPH92lKErKasZzz99ms11x/+7bnJ6fQtjy6P57vF1W1PWUF158mXfvvsOzt19MaatN5Gtf+jOuPfcsH/rAa7QhplN9EmvQkBg3UUB1GEnddBuHk6oIGodVZ2Cvjfaw0UlAx3js9qSrByX5qn5gd1+NozE8ni99mO8OcAHD4UQ3hvPKrNqvs1dWjZ1CjCPDujvFK3vd+N5pyR/YSU/c8ImXJkdx/M5VEGnMbup+7FaLfq0f7ae5YxgWod2SjGSWdt7vnjEAScPhxROPHa1XPduwGxPDx7uvfk8dVaXbwzsQX6/aLlmjBegYOOO26YF/ZZCA1JQBqBtDnYZGs11xenrG9956i3ffv0vwKQvvgwf3qMuCOx/4MM899yJFNeXs4ozWByozpTQplMfYAmMt09lBCksCovdcrhcs10uMiVnsExpVxJTcuvkSpatQDVlLQ1BN4S7bZsNycU6z2eJEMU7okmaEoDgjCI7Cgbg5VTVh6hKbKLENclYqEXyIrJZrzhaXNBqZTPYJWRvF2pRdzJoizXGXHAEriZFgTZLl16j4tqVRTwwB0UgUj0SLsqYsqqS3oo4NEVvOmJgKYzyTyjKpK9bbDcvtmtWypV1fMq8nmHoPXIlRl0SPNWJipNWQx5fkkAaHAIWTLDDukwO0WrBaXbJqLqhnc6Z1yTbrV1oniU1kC6ZFQV1m0XOTtDvWTcO6TemxSzEULrIJW9brgI9J3+j/Z+7Nfm1JrjO/X0yZuYcz3KFujeStKs5FFmVRotjsbo3sloxuCTDcaLQf/OI3/zl+s/ViwPCLAcMtWIIESWhJFClLFMVJxaFYZM3DrTuecQ+ZGZMfVmTu3KdKahhiA06Ct845O3cOkZERa33xfd+KFK+mJMqBnAPkKOytMb4MqBjwXWC2vM7y8Dp9jnTbC0iWGi0SMRKVA2UUZnD9UAqnDVYpWQwpDDsNmCyr5UYbdBBZZiqSb1lsGt4/kcaTRKrTh8h6s+FyvabtPFYbDh97ilrNCggnfT/mQFJxXHL0CYzWYBQ6pWKfIH5HxhhJrnTAVDW5rokh0rZrtttTar/FVQvxQ7EVxol0NGWFMg5KAaDYtbTthnV7gQ4ti9pRzw5I2onRcHnPNYiPmZakr7YGHQX4SjlhBjmZ2jHwY4LoJXmOCIgpzAlHp6woPUjE3AnIQ5BYWgVy1ETviX2P0QpnHL0WZhDO0cwN0fdgG6rmEFvPsLXBo0uxEIVVYqKsBvuSrPAxYWYNlbJjFe+kBDRNMWGUR2pficQGpCqj9LdABrSyBKTPSRhtQEOlLMYpku/wmw39doN1hscPjrhxuOD6dSs+U95gcuDZ567z/t0z3r3Tk6jo05w2bPChpV9tR6bGYrGgsg1JSbU+M0wK2VFRPF1zJqlAGzsWZoapFig9w+UeYifjrNLCQjQlXtACVButSdmz2q5Yr84xfUc0FbduPY81BqOMjJFpxyQyRpecKo3jfxdDkRUadBm3UDKWNLXFVUtyDMTQ04VAFzrxEDKVgLyuErBOa1zWZNWAc5A9SvWYoAh9j8qRoMQTzeoKssKkSAwtFxcP0aHjYTQsDm5ysDhEbCvEl01n0aIMJBBZuACUGcu8aitgnUZLhUQlfSDqjNWQQi4CTJnnhFTU4HMi+ETbr+lSR2hbTBL2m7EVxjgBcAF0ojYGnXWxGhF/KU8kq4Dqt5yePMRvznFVxc3HnxT2U4xgIrVSaG3pU0CljspmrDPo3KPxRKWFtJIjSmmcyuSccDjEm9lLGawcCP6SmHueeOwmRwuHM4GZk/ZqVI8yGvHzt3Qh4WPEZwixo7LigVZVLXW1JieN9xFlFxh7k6hqYsp06wva7ZbOr2jqGU9cfwbdOIjCuQtQVBclMlGFcJMlsJD5NY9+wrmYnP+stp85oKRK5CQvRzEWzApTHxK7jp/+5D0WR3dZLo94/tMvcLiYSenNlCcgixxpF2DtkvRd4DVMPmXqyeZKujdczSC5+WB4uMsZ91dMpzvI5LMDHqZLnR8wHS2MiiHgGwGSSaC5V9FnSFT+ofh7sv/Aps+TleMhmB+u/KrHxf/nbcQmilfCOA+qURZYiB1MW0pWb0sSPybrE7hCfQD+mHy6a9u9J1QADJgE4Hn30ZDYMQHgrlYwku/uH3nn+JSHBf19sG38Zwpi7QMkO5+qq4nU0OvGCyTv/U0N9mCUbIzhfznvqomprIoB/ZDMDd8okzqDdGUqy7uaYKrhLmE88sAgUHsGvP/YtmcMPl7FtEUn2dCYCO7eEXklhracyDOH5G/6giS1f0FDknjFA2Uwgv+wt3a/++/aam88GfqdvFC7a2J3zXv77bXDlfZhksfvqEwjm2PXJ6c9fziPIicxLHzrjZ9y9/47bLuOx2/d4Mcv/4D55xtMhmvXrpcqKkOwKc+zcrWwP0NLyhnnGgZh7MBnkrsSbw2lFLaqeOojz/JL/+xfMjs8YNE0WOWwUXN+/hDnFOTEv/gXv8LpyrPZ9nz8U5/kc5/9Ai+/9iYXFyeYnFltNqSkaL3H1jN+/df+a376yg+4f+993nrrVW4sjzk9P2dmE8oqHp2e8PjNW2y3W04uTrl54yYPHjzg4PAGOhsuHj7i//nqf+Ls7AHd5YXQzlOkJ9PEVAJby+3nP8Zjt5/j0594gXfffYfKQPvjc6wx3HnrDe6+8xZf+a3f5uHD+2idOD+95G//9hu8+OkXuH//PW5/+uMEhfgXqKEfDpPv0G/3gePhHRtxABSDKfcoib6yjWPUfoeSsXUCMEAhPU767s5LaDK/FMnaByTNeTjqPmOUMqbs2C0Jrc3u5VClp+zowOP1XbnxyemmrKVhXt3fPjCWTo/1IfuO1eI+ZNwe2ZPjp2q/XYfvDP9Rk1+GSWeHEO8tWExuand09UHm0fTN3b+v/WueSnuvjph7v5Um1R/4fDjc7hkrdm2dUxoXvtI4jhaXtii825SkZPcQI0TvuXf/Hi//+BXuPXqEspau91yuTjk+us7tj36c4+uPQ1a0XU+3aUnaUtdaVvRdReUaAQJ0lvex71mvN2z6FlUABTHONjTWom0tq+Epkwtgm5JITTbbDevNBalv0doIeJPk+WttsbrGVZbKNVht0boCDEonrAo4JUCQColVu+Z0taZte5FJVA0oi8WQtcj9YmkjraRkt8x7ihx7MaXOQA6k7Eu5ZU2tK7SbY40lhDm9T2x9ABzaJDHnriJzm0kRtsmiZodUswXBe9r2gvPNJZuux1iDcQuUM1jtOG40ldX0KdJY8W+rsqWNwuQPwbO5XNG2a3xqqZzl+Pga4MSg1SoqpUmVZqYr5nWNM5IgdCGzDT0pQdt19N6TM/TR47tASC2ry55kxUNI2DqarMMI/hgtIFxOmdiXUuJWcX1ZoaoZOStmOlPVS0xWdNsVod0SVmf0naGxM5azBcZZtDUSQ+oJly5LpJZUkmVUpQk6EZMSE2o0fQ5YawvrVmKcGLZ07QVnqxWbdUfSYJsFtWvwGEJKOK3J2aN1otLi4YKRZ5yTADcqK7wvi0NZkUqV6Rrp70nLOGqsxi7n5NiRQ0ff9/RdhzGGuqqpZw3NYknMIkU6OblHCiI1rR242VKkakqjsjBQYnmvdWH3xBiI3tMnkaNbVxFih9OWpEVCpFWmTVmkmFHknykl2m6LVQZTa+wgB8QXv67iYZsQQChBtJpoLX0UxsdifoSdzdDKokIUE/SckSIOGTpPXTmsbshGyswLYN0LIFTmqBJJQhafqkQScCxmlE7o1DIz4LMt4a6MkT77YsatsaZU2tbCiFKASZHu9JL15pKMtHlVOazVHDSaX3xmSU/Pg4uMb+Hzt+YcdRdszxwXHSQv4CrGoOpE224Ky9ITQ5nrTYWqRGabTUJlQ6MNppYF85ASWldY7ahUQoUMylDZGTZVGLRUHtSALnLy2HF58oBHmwvmbkEzv45aHBBTxgNJO0wMoIUU4YxGa1P8lqTal7LSF7NSKE2Zow1iz5wAKxXqjIVqRpWlYmcfWrpuS+ovaJoKWzUkU2OtlTwBTdJWjNSNJkZPij0ZizUKpStIsOk2bDeP6PtEszzkaH6EVVakZ1oYkDYLqCpHjWOcWVFkvQWczwVMNsqSY8CpPBCoZGywmRhbFKWYUAarEy5FsJp4WEDH3mP0mu5yTW9gNp8hcHYimUTogoxbzpLtgsgGEy6xpmezuqAJl9y8saSuavp0iTGWbdtjjKcyRmScJuOUpu9ajAmywKiEPdQJtQhrxWRdaahVxbr3pNjhnMTW83mkOpgRQiaFlqgUR0czrFL07Yrt2rOYiy9bow11Y1FJ0217TFWRgIii7UQ23nYtVD3YhDMNlz1cnD1CGcvy6BrzqiHliPE9oNEmo5K4pMVc4v6sShH0XPrPoBpLomiI/moU8k/afuaAkpR+3wVdIh8w1E3N9VtP8ahbouqGB+uea51hPpcBZJx21CTBHj1/ZBWZIZgHKMm1SDn0GFKP216SnQT/HRLLAhgMQ+IYgqrCIhiBmcJsUWXfSRIugfWYSTMckeHaStAXd/GsBOSTwXjnITNcshxZ7nvf5nuXa+8CScbfYTB8HRP0/xxSMNn2JAcMkp0diDSuzDOYi38wZFal5GRpmFHumMv95CS0VnkWjM9yAAX30pCBOTQkVsVHawD3lJokyxl2ZtNp0kbTqys0aiUpyWDkviMq7dprBD6GO7+SU+0DLMMBhp40HqB8Okki5PIm97lDx6RaXVmNK11KTMOLV8YIssby+FUZzPcTpB3RbGBSwNTHbHdVAxsrTwCboS8XAGSSbE/ve2irXWo3PfyQwE7YYmq/LYaeNHxnrM03JEoMQNrYEJPG37XZeD2pPK/yAHZsuzx0o0kinsf/wfRZ7H7bMQQ/mOR+oK3LOymlW2UTmr3ZI4sMfUNNDJdzlqSLmPGh5Uc/+g6JlsefuI0zM2Y284d/8B/5V7/5W1JRxTnqSmj3CY0l0zjLtmvJJOazBVaXaiwM/MISvJf3J+ZM9B7fbvjUJz7DtRs3OD09pw+ed997kwen91k2DfPZgoPZMW274p27r0OGt95+nSdv3eL89ISmqtlcXlDXDQfXDnHWMasa5vNjnv3Ix3jq6We4d/8u/+xXfpM//b3/i3mlefO1l3nv3Tk3rl/j1tNPC6PhYsO9t9/lzpPv0m8v+OTHPs53XjrjtN1S1wueuXXMe3feIUaFczXb7YbP/9wXOW3XnD864a1Xf4L35yJVIOH9GcfXj3j9tZ/y6hs/5vziIT/63jd55Yff5HvfeYZPPfcJ6DpO793n+o1bnJw+Ynl0IEn4+FB3z3nAJ4Yxb8+cXu3Gn2Gcu4rjD+CLDP2TcSEPALUElnpgB45MTXl2OYlflrweVwf0YX4zEzB6N+sOd6B2g0IJKoallQJYlblw6n2k1K4/75tzTxd5KPLbsaV2QMh0SBk2PRk/xnbIJQCd7Kd2I8vUk2p3x9Nx5D+zXbl3yn1/4AgTH6aJAHrcf/ciT+b5yZh6JfLYu529axjOOtk5jUDlLo4gT0TNGZk3xrhovDDx5EtlLCFj9K6P9n3Pu+++y/d+/EPa9QZrZ5yePwKVeP75T/P4Y08zmx8QYqD3PX0bmNcHRK1Q1uDcTIooaGmfFGGzXrPermWB0FQYXYlnkBJ5V1Yy+ghYABmRr8W+Y7O+oO0DofgoxhTRWlOZGc5ZtKuwxmK1LW2uMQacDihTmBtJ7uvR/bvi72RqrFugtCTRQzM7Kx4UGYNKgZQDPnYQE9l7+tgzb2ZSrc6CMZXcs3FUVHQhsF6L+XdGoXSDNbIUZVRmZjULp2h1ovWgkxEpXVVRVwtiCsTgCX3HxeUjUug4WByxtDeY1zOUDgLxa8VFH9FK0283PDo7pe22GAP13KKUEWCFkvSk0jdzJoeOh5dnoC2Vm+FLeelhrIkqEwsbKypDto7FoUPbBqcVyihsMkCShFpZCJl1t8b7Ho1UpqsqK1XgSv9yGBKWbDLVfEkzn9P1G9rthk17xrrfsGiWzJoFdV1Jj1caYzVDZCByQ0PWGm0Nfd8TkyLEiMeQs6HOFUpntpfnPHr4Lh0BbIOu5zirAUtSDnLCGGGggsJqMRjXQNRGxtYkUi2DBcFY6LfbEo8Or7bFkQWQQeJttCNXc5k3+y2+W9FdXqIu4fD4GjEFVutT+tDh3IIcISpL1glFQGUr83DKxTdSQDyy1MGKnVSErZuaDkT2ViJUbaRokLMZlwFX2F0pczifYVQmKqi0LsbzoDRSKQ2RyYcY2PqWro2gHa5ZMjO2SF4k8dS1o2nmUrVKe/re46PHbC7IphX2TbPEqBpdzM9yeceVUqTkJe7XiUorGKR/ACrh6OmTIWUxXk9kMWvGMPg7yvwlTO1135H6DTkosm2Y10uUNSiT0EYRU+TV9y45XXvOttDFxKPLd+hDZNNrZsqJl41StNmgsTRzS20U5EgMHgKk0Mn/jUY5R1XNQRmSFsbUoj5ARahUghTJNgkLxM7ok4wFOgsZxwfP+eVD+m5F9orm4BpH8+uEHCQaM0pymKyxpmFcVBoIAUqkk5ID6JLfiN8UWWM05CSVoCOGClH9oBIma7AGlzVkSww1KktltRjOIShms2totqAtDRFjoc1SHU0bqFSPTpG2bek3j5jZyPHykNo4tO3w7Yq6ApMcPYEKMe3u+4S1Y7SP08KGU1ny+aQkDnXWsdqsqGqHMpJ/WTF4YtOtqKtKJrpSIbFtO6w2aKtQKqKdxbjEidrKAqhuUXQ4ldjGSNiuScaS00zG6LiiTmsaBaiO6niJdRpHpFJQmUjnNyxqi9Ylbx5sCTQ4o8jZE4PHmYq+D1J5NAurMucAKdBenGONQqsKpTU6Q79tabseY8QwPydFSyalTOwzm5yJKWCsBRIpaLyH9WUvElUNOUe6zrParLA5Y+1dtLFc9pZqcYhbzoUV5VsuNwmdeua1g9oVk+2ykDPGk2CTKsyljqg0XZfJfkPl9nGGf+r2s5e85WkwpicleQ2Ht57kKZbM5jOWTYXSjsYEQjIFnDFF6rMLrobbnYrApsGbKpS0IcD9sMB7WK0vWMf42wgoKTUG1HLMEjhOY2muBIqyIxMsYjxfzgPowR7odJXtkaHQSqdB59AJJCqfprfDarPIV6Ysjasr17LynBMj6LZjj5Qr+JC2El2w/HdITMvFMABwOQ+srQF22Zcd7HKuAlrsKDll8FQjsLa71+HCdhn4uKo1LoNnGaB0aVim0ojhxHp3nOH4athjqLA0OW+5Lin5Xdp7AloM96hKu+c0gJH7cN+uts8k2i9ShQEMUuyziSaXwdg/hh8zI5WUMWGVD6fVlIa0QxUzvN0hd0nRDtjIwwMYE5hcjjEwzsZ9p4njlW3qySJC1SEJGu9SVqrUpHdkCY5KSYvhduWSch77tSaLX5ZWpc9Pn9MQ/OWxn8opd317j+ox9s68b9UlNzEyI8YkfAJ+TZ4Gu7vaySan/SozEa2OPug7BtswNg3Sqp2MLyO22ZGXf/RD3nj1ByyPjnj62edZzBbEEHn3ndfwQbyBjo6v0XZiDDg/OCCjsK5mc3rG0fGxSA9yMSIdnMLUDthOOXD24ISv/sWfUNcVL/7ci5ycP4SsWK9Puf/wXV743Is8d/tT/NVX/5K7d+7yyRde5K+++Wf83Od/nu98/3vcfuJpDpfHrM4vSLnn4PiY4xs3+NYPfsSdt98gkWnbFb/xW7/DH/3x7/PRp57j0cUZT956jNX5I5qDxE9fe8jzzz3PN77+57zxxmuopiZmz0eefpZmPuPVt14l5cDFgwuuHx1y927Et4FN34KCb33nGxjj4LmPcXR0wBMf+TivL2bcfedtbN2gTMN7b7zO2clDbF1zcf4I8Dx4eJcXX/gswQfayxPe+PFPeP/0Hr/2lX/N4eyApBM6CUVbqWJSqzSr7RZ8ZBs9t64f0/so1OUcx4FrmBsG76xc3jdVeu1ONgaqeN0ktQMCZFwoY0bevdf7Oq2hT+exq8u7MJEvDwCPGnoh4yQm39uZg6fpfnvz3nTcnYwj5dyqMGDU+K6ovflFjrd/xaPkdDpUDC13BUxi8paNRtPD3J137MhxYPzHtj0wLO+fa/jlylg3be4dE3cKMe1PVR9yxA+5jg9+YWyDYa5Tu340ThHThY4yx2kZTvG+ZzRZR8t4mxRt7NmerXn13Tf58Ss/IqHZrjp8XDNfznnu+ec4PryFsQ7ve7ZtRx8VWmuUMzjt0JUDUikBDcF73nv3Dm234eDggHq2QOt6b/5NZHLqIQljwuSEIXO53XB6cUIKPYGE1pra1tSuwTqHM5WAVlqLpC6DMQpHxhmZmzGanA0X6xXnpyesug7MHGNnIh5PdAAAIABJREFUIjHXSqriqCzV7GIiRknK+hhwToxbjTNga2o9w5la5AfFKNsaS9v3nLYrgs/oXJG0mATr8hwMCp0VXa+pMLQpieePKlFmYRZTWaxzWFuj6pqu79Bk7j16xHbjMNYxX9Y09Ywzv+Xi5D7bticph3XiIZO8J5dKyTlHKWiGLrIRW0y5G3S2kBup3IQq5bTBFDmbsmIZEKnRKIxR2CTePEmJQWtMkdXFJdvtGuVm2PoIYyu0shgFOfdjme6YyjyYE1YpjLakZoGp5kQfSV1b5HorDpYLjo8OMfUMpcxQvJpKl+pXJHLKGAepmBRrhI23bs9Zrx9xuVoRc6SZL7C6IVoxhrK2QsXCgMrSRyqtC9ui9EoNWSsqZai1AW0J0ZOSYmlg6z0xZtqUMFkhBU6EWRFShDh4sATazYqYeiqr2Vxe0K0uwDli7tCmImtPNjXaaJxxoCusqjDI3Ku1yIqU0qQYUET0QgsbSymM1jgrwINWsYy1jGNwVlkYJUmRpSwsKUoJeKe0SH4U2AzeR7rNhvO+pQdstaSpFlRaAMSIMCRAEbIMM85ZUOKJo1PDdpPY9ht812K7DfX8kMrUKCVG4iYHco6FM62Kh2MQQ29ryWmOJlLFluTm9NlijbDWKmWISmGzJscebTLr81NW2zVeG2ZuRl03GKvRqiKYSFaR1sOds557F5GYFB5FTIozH4ofjCJjiblHKYuxoJMiRY9Dk42jMjN0ZQkp4mNP261I2w1+lskzmM0qaleLX6nOVFaRsiYHQ0qKXFhNMYp0rd2uOD19SMqB5ewG7qgi1BaSRmlwJQqwSTxQtY7S51WWYixZobNIn5RKRaKm8LETYIOMSVKR1qdATBEjQUWRnmVCkv5itCaZhLOWy4sNtZHqhrPUEVNkbhZcnp9Bbcn6kNi3aN1h0yWVFSnzY3PNfObQymNLPnQaNxy5OTl55jpjdSKEzLlvOaibUtRA5v1Nvy2m4RZfckzfRmK3wZiaFCUGShG66Nm2Hl05AgFyIsfMtvUM1XXRoGLGx8Bm27NtKvpSYMVExcZ7uuA5qGsWyxnRNHRdRKssDMakMNaVPFgWZLOxbDZrNq0pZAYxq04o8DJvtV1PiB21s1DYbqAEpM+B3ge2rbAWZ64GJQzF4AUYcs6UKmqleltO2GxIKhFzxhhNzF6IFjEXRq8syOskbXm5WZFjxs2OMc5hZguUm+O7QNqeczTXGHeE0hZVWZSRxZiELUoviWNjlqChQNVEwFkDucKYny0E9LMHlNQYkkKeeMoAtW14/Dhz+eB17j9w3H5C8d2XLrj9hS+xyAFn9ehmPqyS7kKWCXyyyybHPa56KA0Wu+PZCxtjZAgNCe2YaH/YJoO5LsH1vsRsd107cGYnX5NJ90NAm8m/DPtcOR+Tex1dObRmVACMAfwk+B5aSE2/NwAOhf0yHrsEsmNiffX6BGCRkqtMvjfI+PQHvjFcvdJTRs0gxBiS6uHad4nICMWMviW7IHpMhZTcizwrwz/4wEbEf9e2ME1EhtbdXxYfAYpc2Csjo0Wud5cI5REg2L/CVFpnv8dOGoZ/KAEazH53PUtPm3Sv/0zhFaBQndXus738bNcvym3sX9Owjxr+s0sbFUO/3O+nO/ZAAVhKwKM+kDDtnsMuacy7TyblsYY2VajC4Br2Gy5syEQnME8BjpRk8ZPTaqbV4KZy0l0iOnkHJ+/qQMhSSFI/HnZI3AeQdrISPiR/gd17rkfB2e6EOVPeizwyDbpuy/277+I0/Ojvv03XeT7x0ef4yQ9+xCc/83kOjg/58le+QlYG71uuHR2y2W7pfeLy/IJmVrOoa/G80CWRUZQS4gYxLLSgDSl6vO/5kz/6j7z88ne48cST1DPDanWBVYaTh3e4uDjl2ec/we2nnuWl2bd57dUf8OjkPhcPH/LlL/0ym9bz11//c7JvuX37eSLPM5vPqU3Fen3B333r64QU+KV/9i+5fu2Y7dmKP/6D/5PGwfnlGUornNask+fb3/obVpenbPotVVVx9+47fPIzL3J89Az//e3/kYv1Gf/b7/7PLOYzvvKbv8k3/upvIQaMddy/f4+qWfDU7UB9eMAvfulXuf/eA1J+h1W7wdVLtps1124ec/LoEfP5MZvLLfWs4c/+4g957+47qN5wdO2Ixz/6HIv5YgglChgn7CGjNPfv3uPtO6/zrb/5Br/8W/+GG9cOpS9kWR2ULizjssqirWd4FyYdZACSh6E2jb5r0oF3sq8r+NH+D/Jb2UGXfwc2n8qlfPDY/yeozjAXj5/kSf/fzaF7bMy86+dTIGvga6U0sJmG2SZPjjWZicfLn0LwA3i+G5XVKPEd2MDDBL2bMceR4UPAnOGOd8fdb7ndeDQZ+/OkCUZ8acL7VJN9Jtf+oZsa7+rKOXd//fBZa7coNZ27lEIWMJA5IEQx6fUxcrZaU9WOmAKailljSmLoOXv4iG+/9H3uPrrHtYNjTk4vOLs84dnnnucjtz/FYjbHKsN6e0nbdmTjMLXDUAngksDHDqMsYdvTh55777+DVpqjxYLGzcQrI3uMFZFKKp4vg1eSBgKKs7t3Od+eonRFXc2pZzW1dVhjpdKYNgxgmPi7ACScETtgKXUskoAHZw9ZbbbiFTQ7AMR7RGdNyJF+u8HHjqaq0EaSd2cNVd0Ic0LlAqAIG0fGcwVaQPiLi1NOT89pmiVVNRMpMQFjxOdTGFgKHwNbND2ZiCS08tiKEbpSkHoBfI3B2oa6mVNrSx9b2vWK9vQB7z/0HDRztust29CStUNlR2Nm2KoGpYpsyqK1IxiBgytl0VqMjJUTn5s0GrAXyVIucYEukhxlqLNDKTFuTloYOFpbNt2a1cUpIWRMdUg1W4r0DbBGPDODknLfKpVqQcWQOmsBXgy1VFGzYNyMlDy+27C+WHN28oCja8fcuv44ppkTlJZy8wpUFr+dYXwy2tH5LafnD4ibLUprXD1jVrkyZioUrsglAZVQWTFraqwRkGqo5iZyO2F4gcLESEiBFHsuz4ucqq7KWG8AJ8swHrIV4FNbhcrCkGtMQ+cNm8sN86rmYHFEcorOR1arNTEpmqpmMZszq2Yo61BJQQooYzHW4nBlEWlLTJFKC/tM54iyAlxaFwihheRx1YIcC5uJQJc9KYHBQfHfIZTCK9mU92DLo4cPWQXP8uAms9kcZxsBI6PE0LJOYQhKYs+kMhYBeKK2KA2mWeCSxTYHkBXBR+LqjOzKO2UsESWMDiXsL60TrmnE6yyIIbsiM28aGiWl4+UairwtZS7XZ1xuzkgomqrCqgbjHFFljM7E3KOVJoZEm6Avnm4i+xc2VF8AYW0gRY2r5nQhFU/tQKUtymosFSmJekKTaGiY1420bfBsNpekPmHmM+aLI2oyGgsuklWm85loIjoEdNrQdWv86iEHVrGYz7GqQ9uI77cCGHixbjM6kzHE1lNXmZQE2M0Z2hCoVWZuLSlFauPofEJ3HYtFU8DRjDEVp6tzDquK2tkhcsB3AZ8jde3EJ0zLPbd5xaKakbLGqg7oies1frMm9TW6CtgEbXcmkuLZjHa9hn5FbxM5iw+PDx1tlzh7ICyilPsiHc346LFa4WMk5SSG0LHEwSqVsTHQR4g+jpLaQCDHohXIO6eIqCQXGCONnGS8TxqMJqhMkwxRJ5HuAdpoep9Yn0fmG0WczYmqRvkVOl2icsJYyBoMFdkkGfP7xJahiEAWaaeyZJ+K3UiJaWIkhxUJS0TOmwogqLNF5UzXd0SdUdpirRYvvGHulsYgJYWPWSSORgA1ZVyR/0KoRLLucsLNDNtti62WLG/cwh1ex9ULtHJENClk1smzVVuMCpic8L2nqmfUlUVGbYu44yWpGlisN3TWzJSRwj1oVJomUP/07b+IKfe4qUlSryApRT0z3Ll4yJ13Xufvv3aX9jP/nk9rTUboq0rlPW/NVJJKkwdgoYBL5WfJc6fVZeR8etxXjjLxk0PvdpsEyh8MFPdC4wK+DACZGhPiD/1quefhg0ng/I801+B9ABT6+t6nCBRiBo9fRrPXSfC+A4im+qqrAFAamVrTe1dj4j84sTBO3MMn+7hBOf/wwBBH/BINl2B4cvzJ16fm6NNtB0Beba3y3CeNsldSXg1g1HA95ce9c+gJiDZJUUoiNlzlLpUqzy0XEOTKwzOT3yWQvHo/H0x+rsoLyfug6/S7ZcfxeyMboNybmramKveWUsGk8tjn08RkWP6bGbMYBiCFXY45XOeVi8pX/pA/0CGkLVVhVuydtSTNuQQSI6tij0Uw/DPWRCPn0lcG2enQpurqFSVQRsaLcu4P+lvlEVibppzjPQ959odkflkxgqt77TBczJXEc2DgqRLAiQXSTkrkrGM2m/F7v/d/8NZPf4iaOZ564hkO53PW6xUvfOEX+MynPsvp2SmmclxeXrBetzgnJWJXp6fY5ZJKG3zb0lS1BP0YSAGMJacsWmmjMSHz2c99hvsP3qSpHD/8++/y1rtvknNHjh0qJY5vPMYrq1d4+iO3+fpf/ymzk7t88Re/zI3rN+nvvMezt5/lcnXCX/zlH/Pi574g+7/yMt/93re4/bGPc//BHd5+8zVWl+cslnNe+v4PhbGQerou8+RTh3TtGqUy1lV86uMfx2THpz/xGW4//TSLazelChCRmzee5ImnnuV3fvvf8cr3X6X3G/ptR8Dym//mt/niF77Maz99mT/9/d8nKfjIc5/kwcOH+K6ji55bzU3iDcMLH/8M3/7bb9BYx/b8hG9+46s8e/uz3P7YcxwfHFA7R+oCWmspfZtlBSfHzJ333uBrf/b7vPbyD3jy6ad48dOfIYYOnbV4YhRlsh776Dj0sANE2QMsRhBoBElHCGXcf7/y4m5ekz5lyonKwsv4TRnvB8n2bt4ajrvro3k83pXBqaxiDW/q8IXhEJRjC+OtHCvvlbmQYww+gkpNPhtPMhm38uT6hh/zMNKz/zLujrW76nI3HzaFlybesWmnH5T2GI42zAfTGx1/3P2+42ZePdlwvqvit/1tGqHsgXfDuFTGdKWy+KykiLYOH6TyWAqZTbvl4uScg+MDFnPxuAmdpw+R+/ce8s1v/y1d6Hn8sSe48/brrFcdL7zweR5/6qPYeg4Z3nnvbVbrlps3b0oAGBI5t6wut8TYMWtmPLy45MHd97j2+NNSerqakSJcXJ4Rc1skbw1ttxFZjK6I3QZrIiFY7p8+4KBxHC6PqedHWDeTRFJJED8s0FmtxLBWZwSaD8SQiFETc+B8vebkcoXRmqZuSGiy8iLnCb2kpgogsjg4oLFLjFOoLHIG8RELpCyOT0Zl0KYwWwzbtmO9OSOEnuV8KSyC7Iupdwn4c8YqCFnjbI1Xmk2QRMQYzaI2ZAx98ZdxqXhQpYTJGa8CZ9sL/NkFzgW2/YZVSLR9JK3OMUZzcPM688U1jKnElURDzFYYLgaR1Gk7MoNyVGQzuKoMS1oys2nETyOVLq5VJiaRyxsUVjs633Ny+R46Jlwzo7I1KItWAadLyl+kjlGLzHppEpdxkN1pap1obMXGQ45RmFXaAlbYY3NH9o7NOvH26j3sgeXw6CbWOAzyfAc5sVOa87MT7p8/RDvLbL4svi3C7LBZgUoENFpUSDgtdsYxdOSoIXiRxmmZd2NhYiY0+HL/CTbdFozDVgusMTilSNlJzKF6YXIphVglZ3m/uhawHF67SaU1zgiwpmrNbHZMTmKOvN14cp9ZzC2zqhKQVYsIxaiA1QqTnCSgZIwSRh7lvVAZaizruEXFhA9S/U6sDxwxRYJO5JRQQRPINCi6sOZsdc5q7bHVnMPDBm2qMrz0JGWIWmGyQaVMrzVBDVVgIxSjcMjEGKjdDEdNxKKIqNSLVMb3bFfnbFNgZh16vkRVBo9CJYvqA9aJMXNGo23FUePokqGNDquzSKX7nkdnD1itHmHdktniWEzdc4KByaUiZLHfqKsZKYu9gDZynUPhAYumMoaEJ+eAVopaR+YmEn0rVd2iVLfVWRjcIXsUCBOHTNYKb3p0ypjtBSE9wjrHxWnAKY+ZHRFjQLUbdNjg0jn0PU2TqaqaymaMSkRaLrZnLGeHZAXWGGyEVbuhJ7GsmsIy0nSdx4cWazVVikQiObRsLjeQNatwibaa2PfklDm7XHFRpLcqZtroaXuPl0r3pBSksmMKdEG8OWMKxGwgenJOhGRwJnM4m+OVZd0H6uUS7Rxn5+eEboOLUapAK1OUBSIdzVrmfaPFXDvnjDKDlDWjtBh8awwWkcUZ44hOg8kYKqJRWIrhO5qYulFOrJQiaQ1RPPuC0SgqkQtqQ8yZxipUikQFrqrx6zVx65kd3mS2OEAfXCMFimxSgP2u9+jck1PPbKYwbgE9BFeTUsDmRLZG2Ncxo0kimyxS6oBC0aB1os+BCnlnm+CoSfREgknYrHFa4Uvum1KQtFEbQlYQMpVyeC19rkeAu4Wq6FUUv7jYs71cYZfH3Lh2g6pe4IyWxZ6gaLModY6OFxhqkZD2GR8C/brDbjYiz6tqKqWwVhG1IeXBokAKExhldkHrz3D7LwsolW1Q76icMc5Rmcz3v/82J9XT3Hr3XVabT3CtURS79JK8ibxjCLAkOByS++HIu9VfOc801JwCMiW8L2jgEEiOhpfFo2mg8++uW5zTgf2/M8SaecwX9tZfJ+wWkeHtDJevtAx7AWuJMo3W4+86FwFESTIycQcucBVd3B1vYFUNJuFXhGdXSjznyb/lZtTALrr6zeFaPwwOGna8qsscwKYCyGU5ydB2k+baSbfyABBNn+sO8RiOIc8vTy6qyNcoCcWElj9tpr1uNFzisG/OxcNo+FsxoLuSBOylKaU9pyDg1b70oU11NYnZS2g+pMcURsA0uRnAi+khhh4yJGAD9lPUgmg9ycJGNoLay6EGQGrIduQ4CfWBfrfru9MuvYc3jbK34Ry7d3f6hIZKG7KL2m+CLH5QZU2OtNemkxbZf60me+wDfPvJqfyux34xuUc1SSTVMLYMz7+wlgaW1ZCoKilPKysvEU21S1C1+AFcu/4YN6/f5KfJc/PgBid9z5d/+Su8+tM3eOza4/jNmluPP875asPLr/yIs5M7vPjiz2PqA3RVcbI+AwzBaw4plH0jfTcQMWNSBdlVNPObvPDCL3BxfsYb93/EjYMD3rt/St+veeGzv8jJ3Xucn1/y3CdeoE+ZX/jUz3Pt2mP8+EcvsZwf8dj1x3nrjZ9ijOb6409gG5G9/Ot/+ztcv36Dy/UFf/fV/8Sf/NEf8Mabr3C52bCY14Q2U89mnD+4T1Uv+JVf+S3un5zwz3/113nlxz/kwckpDx6ecnTtBvWs5u4799luWjCWr/3VX3J68pBnP/Ycl2dn3Ll/l5P7d/nf/9f/hZwCN289jjaR9bnHqop61tCHNXce3OWpW8/yd3/916TaE3rF9Vs3WVTHnJ28x09efYlf+41/SxcizlIq04h0hgzZap756DOcnD4ik/jqX/wh1289yec/9wWOFyLNjllhdZa66gOwuHv8DIy+LFndBKAYZoMdS3PPo2zskAWAHdmvhbWbKWONgFPF5mY0Cp+8yXs9fDpiTQGN4d3dzQZ5chP7L5TBjKuK494TJq5c6nCmYny+dx0fBGJUWYAYBoyxzSZ775jEeWzBf3AGmrzYO7Lslf3zP/Bd+EcP+49u5RlcnVf2xsErpx+ety7jCjkTYioBfVlNN0Kbj7Fns16x7beoC6HG21nF3bv3OD274Lvf/S6PHR9wMH+MH/zgJaq64otf+BLLG49jnSH0njvvv83J6pRry1us15ckIovZAkyFtY7Desbb997l4eVDbn/kk+i64mh+bSe1y56sBNRUKbNcSvnxSM/mYsXpySk61xwtlvSh5fT0AdfbnvW8QtmahZ2xmC3QtsKYhCGgUyTHiM89fY7oZAghc+/snMveM3MWnRWtD+QUSDZTaceicaiccMZim0PIhqCaIsXWaAM5iImzGSK/AixFn1hfPKTvNzinsRYUCWUNWou0waLpEY+lpDQ6WbzWuJzQTqr8zBrDvLIkIjYY+m3E9xta3xFyJOSOmck4PMpFur4n+MTTTzwFKuMPl3StZ71ek0PH8vgmBwdHWA0xVbJApGqCL9WzrKWPCawlx4RJ8kZolUfi71jQIwtg4bQhpIwyNd12xWr1CO97jhc1ua7EY0lJzCjVqEqCl8QnqrGKG41ikTMnXjGvG7oER8Zw3ie2ZHIUNpRCqvlZZ8lo5rUYq2/ihnV/xsM3X8dUFR+9foCbHVKZGX675c13fkLSNYcHRygNISdS8uSQiz+K+A/FUs0UbekIaBLe65HJYY0AJkarcUzUSqGsVJzDVRwvjpAWUuhsSCZRITLJiAEMTkHoA2ebC2II1PWCWV3TBanSF3KUdsoaawzZaslfnKNPHd1mw3q7pakcB80SjEi0+pgwsUcp8ERCSiQl8tKExhiLy4kuwrrtJe7UVoCx7MiqJuZMyBUKj8ktDy4fcn65pbEN7nCB0walNVYbYkIq9+qEU5X43Ggp765SljjQGlTO+CSV2VQEZYunZS4m0lpjmhm6qsXove+48BvsakVTR5qZpSn3aYzCa3A5g69p1RyvdobNq/UJp2f3sVpx48Y1bFVJPpIyCo/TnlyAj9BHbO7lOWYDMdH5SFUWTo2KmErTngWsjRhtRVIZe1w1Y3W5YtmIJNQU4GLbBiyZWW2F4W0tl5uOZCJHi1qYLHisVVx0azCZyApjFSqd0HcdmsRmtcH3KyonAEvoA5vQ4vuA5g66sHZihrbdEor0KcaAj4GctPhzpViUBhnK55qaTQrC2swGazSdcEpEOmp2aoEBqHcASozO+6RFsuZmmKxRzYLKaC4vt3idYPk0OlcsDzVpe0G76pmZOdVhQ60V2jSghbnWxoSeLUkhCrhma1KyAtg4YXMbJV5RKYqXU7IiLzNGo7NhowSatMYRktyPTpakhUSic5GIKXkva+PwpOIZVRVYMxMN4u2nAqfvv0+uK5544nEwGt+uxcO0amjqGqfAp8wBFpUiberwKmB8ZDFrULXCaINKhqBlElZJiTeh1mSVsCmgc020RqopkzFZwDmXNVoZUJ5aCQjto8eEiM5SwdSkHmUsEYfKQrjISea9mXWorLA6UWfF+vSM89U5hzee4GB+QNaGqB0KK1UVgRqRSkalUMFhbSTWUdiWSVhhfU6kNnCxuoS4Yb6Y0cznaCNeTyipxhk0pP7/7x5KV7YpaKGUhtSR6pv84r//H9B2QV0txHRNFznVUF60BGKJLHpROVhJSvcTcaWK1/w0OVWMn42QT55+ZwjqBlgjl/OBGrwsshjZ6ilt/UNAgiEpuBpwDolCSR3G7+yOkcfPy4H2gId9Odr+6mcaWTZXzjf+vIMTZNWqAElXrv3qAcZgPYOUipjsvwfcTRk9A/g0CtOupA9D0LwDDYa/7pKcQVYxuYsp2kShA4wJjObqWWACSI1/n4A+w/OfBP1Xn+H0+oZN590+47Emx9jzqFK7vym1n7jt/j6kTGr/OeSrz6/0SqX2krZp28kF6jGJ2hnZFtipJHeDNE4Km1xBVXIJSsdeIww1NdllNA4ermk8+xCo7z6HyRP5QA638326CudEdrLS8Q0ZE3R5MtP729/KtZcEdfocphcyMqOunJ2hWfIEaGZ8SgwsvaGthm3w5TKo0aYllbEpaTm/QY8yp1woEzlDUy148b/6Mh/5xMe4cf0GbRc5Pr7JC88viTmCtiyc4/76lPlswddefpmnn3qWT37moygMbTfjwft3SGy4dniMk3rY+JxoVEXrAnWWwNspzfWDJcvP/jx/991v8Ikv/hLd5YrupZ7moCHGzE9+/BJf+uVf5VOf+Rx/9bWv8eQTT3Pv/ff51ve+xsc/+iKvvvMT1udnPHbrCQ5nxyxNQ9KG+vFnQGk+/9HbvPXDl/nzP/2/ufQrFo3j+rUb3L//Pk9ev8bnv/DPeerpp/nmN76Jm1lO37/DPBneuvMuXXeBM8ISOl+d8/O/9EW+/vW/4NpBQ4g9b7z2Osv5EhUCP/z239D5wPnFOR+5/AiumfHw4T3m8wUHs+tYW1FXhptH13h0dMD56V1a3WFPFYdPHnGx2jKfL6Tkd+jQOHnKOYMSk9c+RM7Xnv/2v/kP/O7v/k/8u//w3/HSS3/Nb/zyr7NZt9hGKpeQDUkN4c7Qp4qMY0Q1ZOxLO7O4EenY+fftRp39OaKA5sOYXLgIukhjRXqaxz5Z6gB+SO+WP43HzfugsBwh7QLb3Qfj78NizA6slnvcLyKRySWxVWM7lE8yYj6aB4nwMINAztNjfMi7OV7AMKDm3WVeGRcnocHkyx8CB6n9H9QwNu5PIvu7T8HlD2tjpoAX0/Bib5/xnJPPUpFMDnIIZTTaGEIQpoRPiYvVhhgzISnWXUc8O+ft197kh6+/wmee+zh3HzzgzZf/hmeeeZZPfO5LHBlH5zveu/uQk805N5eP8cxTNzDWYSuDzo5slFSdyoHTixXV7IjbNx6HIBOYNImWQFjXpFQ8l3TC91tSjviupwtwfPMpmrrGRMg5iueND+iu5/LyEec+sXANSosZ9Xw2Z9Y0OFvLnKotj+7d5e2798iVYz5fot0BisxiXpFVg7Yig4gp4mNHyi3RJ2x2aCtBftZ6Ny8nJf5MKZG15tH5I9ZtR6UcjVugVURbW8yeMxK6Z3JS1LoiKyWl3kvSZJKR6li+pe09vU547wmpl3eorAw31YzaHBH7jvNVIAWo5jXL5REpVQJ02Tm3bjXUrmN1dsL29C71+oLq+BapaohJsek7FErGpSSsHh2KXEwphKNRZBaDkk8pwKKtAkRq8s47r7A0PVV9SLO8QTQBg6XRClmw1ihlUBhyYWrWtQBKjTI8aju22ZE78XyBSDSGg4XBeyEBpZQIeCBjlYOk8WRLzPBuAAAgAElEQVQOqkOW9ZJcd5yf3eH1n7zC9dmcxfyQ91fn6HqG0poHD97hcH7AbD4j4kBrac9KYW2NyY6gFCmIRLI4+JAJqBzEc0cZyEnARpWJShgsOSu81zgLTml8SmiTyBGUsmS/xmpHTFvO1g/Zbte4esnB/BitHSSFUgIQSKn1mowXs1ulsaYiZs1BdYDC4EPL5faM1Xo7Vt7SWlNVRvytfKT1iU4pcq7RjcOEwKGRBKhu5qQiJSNGfO/pYiZng9OeRw/vcLk5Y1HNuTE/JltLVAafQwEzAimDNa54j3ZEGlCOxsocknTG2YrsI3Vl8CGRa4f2kUYZqRalLVkVb6foqbMm1xA1+NjSb8+5e/mA+abh5uGStLgOdPQ5SuwWE9pvwN9H9S12veaJSkADtEdpw+XFmqYSaVqVNLZu8L7lovcsDuegxUzZ+x6zDTTVDFe5cUro4pq6nmGMAZ1YakPbbum2ayojC0aKTN9v2Wx7Yb7nQPKB1ge874TBkcUeIMRE17dAhVGK60fXUMay7iPeZ3TlWK3P6bcrdFYkI6w+DNisJX5Wid5o5tkQSCRjMFqjdYW2tXheEnAF/ANT/OIyVdQcWMQXRxmSDZAUJiqy08JU1IqcDRGo1FC1UaGsI3qY64pLE1iYBp/WrC/Omd98hsWt56jQZdyr0VHAvE0qbL/1GmygmVfUdk5dz0iqoSouaCklrLb46NHWECmLHiljy2gU0GQtldNSNhxqTaZFZS1m+mSpkJYrkcfp7bgwm5IwsKzSVMYRkozIPnlsrfFnl9y/9yaHBze4dnyTy+RRfYctPkwpdyTt6CPFtyuVan2aNnZEC6e+w24UOkW0ycxmS5yqBVQqEumcE9pUJMr8YWrmMRDMnJg8gzBuhiViCKkt80iDlWULTBAQyRuNB1TKLFyNxpOVgHHb1HP3zbv0PvLEk09jZksiTkTdMRHxxAxOV0X8mjAojHP02aOywehEV2wYnHLESpMPb+DDklXc0K9atOrAKnStmWlHMDXKDpSZn832MweU9lcr1R4TRmkN2w39+pybN27yzFPXWRr+X+reLNaS7DrT+9beO4Yz3SFv3qwcqrLmkWSRxalkUurBpEhJZAtSqyW34X4w3LINw096tdEwDPjBMAyjDTQEtR/slrvVDVlDtyiJkijJoloSVeLMKrJY85yVeTNv3umMEbH3Xn7YEeeceytpww092AfIqsxz4sSJ2LGHtf79//+i0ZjKudLGX2s6SsPpJFA7YKhL7KQDbUzLIFlLSo0k2cc6bNGZeLeBsrSRapIZLCGs1a5tiwoIdwaT1sGSzti7C2SWYNL/7W7oCjA49fban/bCfyDLZXlNCl0om/T0tImwPQUwdUDWktl16k5I+uQ1ROD0765fa2KPLR2adAU2rY4+DdqE9ffX/t7e4hpg0UE7Z0AmuoRr5Q+V7l2Xz2n5WutHa2+uAXer61qBWrK85rPgiMJqx5uOY6UYscR1GErj6vx6+iyn2l2691sG2ynjWG1390+DSdJeRFLMdODP2gW2LWhEWqnbCv5J5UM5lYutgJZVctfx0jrMUqQFCtvrWIK6tONx7fkkVpOCtMy6pWFwC8zJ8iEnad46oCdrfVw7v6I0xk2LAnYAWdpdPMNS0rM9pX27TbwTe23Vo+KZA9PPtfOAsW17rPrzqr/KqUcb23OmdlnnVqzuN4GIYZk8i1hCVO65ejePlg+hXjhp5oQAo1GfGBuMscwVLl26TC83XL50GXWW3PaoQ01R9ukNtzjYv8bLrzzL/Q8+TOlH+AgH1YzBqAeSQIJGhctX72MyOebv/ew/YFbP+bM/+ROefOpHePSB+/nlX/kl7nvkMV787gu89N2XuevcOb78p1/i8uVL3Lh5k1s3/xibl2xtbfHpz3yOSxeucnB4xGhzi96oZGtjk+9/93ucv+sCT//7f5uHrj7CL/7Tf8zf/dzP8o//yf/I9mMX+Nuf+gzX3rlBEyaMsk1ee/0V3njrFW7t73H3Kw9x9z0PUzegITIabeCrCSc6YbIYc240oopzvBXEROp6zsOPPcZPfP6nefG73yPGwO7uBZ795tfJ8j5PPPAo796+ST2fQ2YwPuB9pNzcZmse+PiP/GiSYEuGV8C0RqgqqUSzKBfOb/P69w8gwM2bN/jP/+EvUM0rnBUIrfdGG3ikFSgxSk71qXVQRTuBRzcHnJYcn/reGcZSJ9PtpoyVn5smfxfWwcyVU5C2/VrP/MwKmI1rM8RqXjDLz9fn8nXgJkDH312iPWs9X2w7R8Tl5lCq6NN5eKxwnpUc/azMew3bkfWR+AObbXUPsjp/e8O85+TLo2UJ+ujad7r5ef1a1z8/+/vvOf3awnH2M7OcqFKMoq4NBZI3MS7LUIFF7WliICYtOXffd4Xx0ZSDgwNeevlN3n3jbbRe8NijD/Hay69y43CPq1ce5JFHn2JxNOZ4OOTNd98AEe65+ihGMsQEtksLUZiFwNx7xNcs5g02ZNgiR5uGonCtx0qG1zp595Cqrfl6zvHRLQqXWBXqYTTcxkprbm8tQkEQxWVgS+ifc5hgqJuGqp7SVHPGsxmHk0Nm0ym5ZGjdcFhN2b1wkRAiO9u7STYhLeBmDV0VG3GGPCg6neLIIS+xklZiI9Im+kmO50yBX0x4/fobGAlsn99JY8qEVILbKl49Lhqy2MaczpFlPYL6JLmtPfMwRxqPX0ypqikbWwMGvR6myOmbPkYjqoZB1mdW1dw4SiW4+xsDBqOC0ipDmXLusmLnOfdf7dF3kedfsrwbzyN2yI2DfeY3XyQbDNndvhvbH4EEFE3SL0gMNpMMkpFURcjg0t/V48RgpSBGZXJyg6OTt7in36Po71BLj0nw5C1rJ4HQqV2FrE3KQWOSmB3XQoXBZg4iaZdeHE0rw8gV1ChdsZgYI3VoIEyJ3uPUMqnnHM8nbGYFkilRMvYXEw59oK5qRs6yMRxxZffBdiwJzpQgSXgWjAVxEGqyqIRMMFbQWBM0tGbVBUikbA2jgyRpjbNCtfAk64tAbIS5QG4sIaa+W4vHOWUyO2B8cpvgGwbDS2TlYIm9NwhRMqpYYWxJrTUlOaKe6ANRbIrlo0OtwWYDepnDNwumsyl+doRxhmFvQL+09MqMBnCSZD7eB2rvOdSaYWmxMYIRfEhgaOlyrDXsj084uH0TVU9/636GWWLqeevx0SDGtdUZLYW1ZMaiRlM8KDl5VpBbizFJUlcLGCtUvsHkDkfyevF1QF0qQCHqQXU5B1gFDZ5e5rDuHKUIYea5sX/IxuyQQQlEYWA88+DJUHrG4yWwMXBkeYYleZnNG48JsTUtb/CxIUwWHE3mxKbmYDGmqmZUvqGpPcEr/TwSQsNkoTRNQ7WIyXw+pHW8aSLRBGgaVNNcGpxSxBSrx3b71KUqAGntAXIr5FmOYNkYllRRqWYzjmIP8h5u5zx2ekI9vkGvLBkOBvTzvGWrKd4VmBBw1tBEi6IUzjEXTSw7TUB8YsA4Ov8rFUd0MTFJsZSxaBeKSEmGzxLYV7qMECPRCpmQzKKlLRmPIUSDOEsec8TCVg8Ob+wxPaoYXX2CYe8cwRTo+IR+P8PaPtrKaUfaEGLNeHOLRV1RL+Ys5g0jYxn0C8gcIUBmDNFHyizHiycagwlJ2umjB2OwNq0ZqpagTZIca8PAFMz9Ai9KpsJQZqiMOamTbDJ6RUNI5vqSMWs81iiFOmL0vPz69yAKO5fvp+wNmDWeAkdjmwSziGDEpAqk1rZLcKQJoOrJXWL7GZdhglLNF2hUqqMx1k3JnKHsmdbHLx1LaPChJiOAOKRO/l5N9FgRfIgQa6BKjD8J1FHIrRI7OalaChcRlxE1gDoysRzeusH1o9vsnL+Lnc1z0CiNQuE0geTWYmyZNqV9YrM5k/zerBjyYPAEjNj2eYQEwEeDc5ZMLeKztNmMIj7iFzVTPDHW9N3/DzyU7mj0rIlVUzdTXnx7j82ww8Df4q06556H34eVQNYagdGZDYsQ6EzUpI0JZeVnCnRSA2kX1i6G6yp3nQI03gMvnE7WVRNtuOuGSPr7WVbP2WC/Y5wA7YR7Bgq5AxDUMZ+QdVHZeiC/fosrsOpMS7efrwv52rPFTi6kRA1pRxtSaVJN4X/rjdiyT0wHRdCZU3c+VOle2w4pp5+vaufHBLSpTaLqrxKUZUDetcepNkkCprAOsrRfWCYGZ277rMSik6d1KZqseYsswRfSczqdoLXXsXzcywNb1/2VVDLJTLprOn03YSmZPN3fuiSlu6V45vl1LCbWwaQ27+kYQrr8uU6umfpM98RSJcW1RLD9lmgruVq2y/qTOCOJ6ZqKFdNh1fBr75xhc60DJ+stksZV26+0A8ZYPoelObCms69xCBPw1J4j0EFHp5qTdcld2tRoj1lnN630PHTMpY5NlM7TtafpsK3Vc0FRDa3f2mrsnkoql/faVfJo+7qhfSodkCbd7NSCjq3evDUDHPVKYjSQCaOsh9MIrXdAFEseAK3YOXeBn/6pf4B1ebtDlDwGLu7s8Myf/j5/9md/wA//0Cd4/MkfYX8+5mNPfoj9W/vsjAa4Xo/cKJWvKTeG+MZjxPKJH/okX//an/PFL/wbPvaRp7lw7gq/9uv/iot3X2Z6cAtrYOvcZeL8W5h+n2oyxw+3ePa5Z8k+vMnVhx5kfPsW83rAld27OJ5VfPgjT3Phngd4+mMf5/f+8Pf59X/9mwx7Qy7e/yjn+lv89jd/k/e9/4PEWjk4OmRvf48sc7zwwvN85MP/HmoM715/nSc/8Bi/93slu9sDTiYTjpsTzHiGmozKK9n2Lk987G+wt3fIV//iz3j0ySeYjGcohulsyvMvPUc+7BP8hM3NDY4PDzFZzle//hWe/tgnuHrxfpq6xjmT0tMA4FATcKbg5NYtbh+MuX37kL/5+Z/i5t41/ud/8j/xX/+j/4bDgxMy51IFKXWYzgyXOwuxun6ynNu1m4uWXfTOWAfdmFttlnS9dwlbKi0TjqX0DVby+O7fsj4kWPE91y9yNc5Or2/dK5xeldZYgPEMs1BaoEhb1rC2njDdlcnqmK59lnOrtmTA0+NubVk406p3arT1z9OA7Xz21rcY9A5fOfXV9XMsP187wxmWcLd2dfex9qVT97m62fZjy3JTQzUlOk0d8RqZNhXWCXlumB7PePPdd2jmC9558y3eeuedRPPPcv783/4hWWHZvetu7n3oMWzfsdHb4sbNPbbOX2J78zy195wr4P33pEoxL+41RB+R6FlMA6jgRXA2YLNeazMQUfVk1mFUmS9m1HFG4Qzb2yPGM48PgikFEwGbI7nDWoeJkQxo1CBkoA01NZJbhuUW6Cbz+YLJ5IigyWMEE8g0MK8WDPrDZH4q0lbssvjG43wFpgBXohG8JNmSc7EVhRjEe4IJqfx0jOzt7THzMzbPj8gchKbBGkdpDc4mw+4kRAgESUayxntm8zHeB1CLGM/ACW4wxI42yFwyiDWAREMTIeYZBuHarVvM5xNG/RHFwCEScaHhnnPCf/kzH6QcDPirL3+fcxvbbA8t19+5wbVxQLI+O5fvJzQVk5NDru+/hJUBVy6cYzgc0WCpQmsfYDJEDNpK1FCIQZfvnczGnEyukeO5/9xlKiLzqNhMGZjkvyOkqnxZltM3eVrbrcVjWoP2QG4FiZ48ZMzinH6eMRlP2Du5zXZvE+s8ldc2noxE8URJIsPMGNR5NrdytquLjKdT9g4OKTY3uHL+CqGJNNUx0/mM4/Gc2hvKYZ8sz5MpewhYpzhSoYkqpvnOmYBiEVsQg4e2pDwhUocJMSi23Ej+K03yaFKtaAQyl3yYAkqINUNxjMOYW7cP8Y2n19tisFXgxZHq6YGqxWHxIqjrJ3BFUxl3jQEnOSoGZ4QgEKIHDcRYY9UwGGzj1TOpj9m7/S6hWbB17jy9chdjHZpbrBQ4A2IjJh5gsz7iciKKCbCYjXln/10mTcVga4uiSDShKirGWbCBnggmpup7/d4Q64TGe6ZVAk7FCFVYoJIlIZEVBiYZQvckEiQZ5hMjFBl1EzBB8ao0eEQDto2NyyJDiK1EL9IrSwqTMTl8l3om7GxusZhOaOrb3K4bPJbZ5JBb+9ewUUEDtQcNFUEdAaXWORkOEwWVMsVJJrFxjI3keUbpci5t9ZjXkaqpCdFQDhwiGYUDHzNya5BMCN6TuYxoBafg2n5vSIb/QZLjTwyKWDAmAAXeKZP9Y4JscO7KJYYbF5ku9hlu3cN8MSE291DXkWo2Yd6c0Csdg94Og6xHjaSqgeIpTYEBBi5ZlBgcIDTWJFirXRgsJTZGrImpj6knWsHEjNyU1HHMpgo+NtROsAEgkEnLfiYVDhBJRuDbgwGmPuavvv8drMm4fOURRoMSS+RwNkddxVEV6ZkFJs/YKvqYEFmoUuLQPBKznEWoGc8XHN56mxhnbF7YQU2fXrGBVSUapfENVszSKDsaQwwpC6slgV+glBRUUVsT94boZ/SGwkkTyNyQTCySC1ECSc4PTj2CYTI54cbe22xsnGdrOEBdjmjA5Bb1gm9ILC/nUASvidXrxKV5A8GSEWODRZEYEnvPtdJyVxBbFuBi3oBGrFP6/RFlkaqJEpVGa6wzhCj0cpvkpOJw4iErCCqYkKS4GuaoUWzPJGDJZvimBhMIi5pX3r2GwXLl7nvIyoLYKEHAEAlNwJgkf+05pRGhxoA2yehfJDFB27gh+hpUcKRxpFbIRdAoBCy1BqwowRgo+kiMBB84qWY/OI76d3j9tQNKS5ZBl+W2gZMxFomBrNjmkz/0CW4vDM9/7V8T7/oR7n5gATZnnWGSGBK6AiaWkXraBVmxO9YS2tbotBMJdNeTTtCCDynSTcmsJKnQ0sfAtElnt5N8hoW0Hl/fCSRK+NDK7HRZelnTtSXsS0/d56mLg4RednIG6a5Hl8kIrAX6y13bFcOm62DaVoUTaP2kVwBP16CpJGw4leAsE3g5a0Ha7XprC+it2p4z37VdcqG6rASmsPQg6hL+DsLqKnPJspG7ZHxlxHy6ndf+vRaYn0p/tDN3PS0VXD6TM82PmOW+BbA0CVm1R/t2966yBAr8e1LI1TWsEqdVu3bfh9YvqNNXdhcjS2EYsObXI6nvryCXrm/HJajYMaVM28/Tgcn3f51eFdcabgXQrafDK4nXerIlkhaKzn98/RuiLJ+XENsxtsyw6CaEpaCu6w8tsJYSXVmeN5kdd/e1Yo+ldpPVj66Bg0vBybKPdG3VnWl13bE9s7TzwHrimf5hVj/RfW81nSyB3nZPfPmMXft7sX0eHWDQ+c+oJMAKSYbpyfhPyVCS2SFkpMpJ0mroU/hscKqIpPLBFstsfsTe4bt88KkP86tf+A0+b3Meuuc+/vm/+Gd85jOf5hgP0xlbmxvkZYZGkCJHqwVvv/sO1998jSrMePY7z3Lx0glXHnyUz/zop/kf/rt/xH/09/8TvvS7X2R7c4Oj2RzrhMO9fZ5ffJ3NQY/HH30YNjaZNZ6D6RQj8JVn/hyD5e677kKjZzo7YOprXn/xBX6390W+9+1n8O/7KI89+hg/+fd+lhdfeom9vRt842t/xe998Tf54JMf4rsvPM+P/c1PcfXCPXz4Ix/m+OC3mO57op/hm5rD+ohLd5/j1t41nvv21ylHBTbvczK/QRUn+CqQ55vEaoHmAzY2rhAryLOSfm/M9s4m3/jGV3j66R/h4GDC7vnzKII1MSU4fs7vfPHXubK7y2/81q/w5Ec+yfsf+xCPP/oAzSLJKoihBRLTfB2TwLQFalJPkA6AiCl49WcACNP2127NjPFOevaOkhzafrs2H7YyzKUyeW0wGlZ/73ps12c7Jp60B3bz8fK8euary76/WtdijO14icvxIiT2b5pK4nIYrV4p6O3YttpKy6Vdc9eB/zsBRquiEd3/1wHiO7+Ws/jaBsfaB6sjdH1dWVtrz5zt1FR25oj02RoLVKEzS+9YjbK6yeS7Z1o5hrWAaStYKePFAqfCRlmysdnj+s3rPP/8d1hMxmgdODq4hcSa+eyERVPTHw65+sgHePDxp+lby+HNmxyPx2xu72LLPr0i8OknN2nmFa/fmDOuPLMYmFQ1i4VLsk3rsMZgXfJ1MdEimpiUi2pG9DXWRUb9DZppw2w2T4xvl/pknjtMW/1JJHm/1FHAKJk0eImUYvHVgmldUfuG2fSYxeSErcGI7fM7+GAhc7CYcnJywo0bb6BhxpWL95Lnm5R5SePAh0i9mCZfTGdwKsyrCVEaRtmIXumAyI2DY47GR4zKDc4PNlnEgFUY9hJ7QENgUXnqRlh4n+IAYylchrEFvXzQSjdSbKaEVOpZIxrT80qTvqMvjsnxmNfeeo3+9haDwU5iJQRPhnD1Qp+P3rPDt/5ij+duzvjGnmPQu815B+AoXUEgMvOeYCz9zcsMNs5zPDngtbdeInOWrZ0rbG9cRLMMbyJowMQMaecvl5HAnr19qjhnZzTC5hknxtAzFuvWKvuIwxlDv8wIPlDXE3Jnk/l77akaj/ceaWpELHNtqMKEk8wwqzy5lNTUFLYgLxIzIG3QGUoFpCZkjiYo06NjxuMxrp9z/90PMiwGVHUCgtxwh3O7FwjVhOODCeObRxRZzmDQw/VKxPTxoQIr9LKCKighunZMhXbtjvjGk2cGNKeuxrhmgrEWQ4E1QoNSmF6qVGWScbAl8u7RLSazE4qiYGNzB2tSFa1cLDGmNTz57wgSAzk5qk3rORWTh54R0IAnS/mDARGLif001xmw9Njojyg2LpOpMp8cMJkestUbMMy3CVoRszEh9hCExSJgXGIH3Tq8wfHkCFeM2Do3QoloSD5fNovJC0ZKgjH0Bhk9Y7B5YvaZzFDaiDZt7iFCDFA3c/q5AU0+ZkVUpr6i0UBdzbEKwSpNkxLoGCJePbmmKDRopBYhx9DTA+o446RKLNRzgx5vvf0W33/lBTLXpywKnBUKoJASyRVnDBvWYLMsyaWcSWCf7YFpMCYH9WQ2b8dZqvolEdRBLzf0hgZrMuqgyezdkK7RFSAZtfdplRaDsRm5zWjU4IlJFkjauHPt/Gd84HA+4eDogI1L93HX9lWMBKrpMQOX0VdFsxLNewyGCjvnWFQN89mCmZ9RSCQr+/SKDCuGWV2jziXgh1RQpSFiDZQYvEacWLyvEjPLK7VNFiXiDY0NxLDAqlBpoCEQvSCa4k0vKY9zJhk8WwtOG9587TVu39ijOLfLlYv3YXPHuZ7gouXjD/V54p5dvvrcMee2CraGOa8eBZ579QDUgMtwXpL8C0vR36QuN6ibKb6K+DAn+Ip+McCajFJyAtCoJxptja4TAIsNRFWiDzQI1lqm3iO6oMwzqrjg+MjTMMVmyfsLo8tNaxuV/b0bqEQuXLgHsS4Z0YeMxiuNAYMnk0CgIQvJJD20LJ5khWPJSGPJeEeQhjxLXmSqStWu3mWMSJZYnVFBmobJeMLJ0U0KC73RkKy/gyg4UuXJnCwx44zBasDEiHpl7puUB6hAbWjUInUkELn+9tvMbu+z+9CD9Ho7hBBZVAFDwEmZ1kCJxBgQZ7HiiBppNGLaxCtaS6YGcUk2KaaXJNCtdUBXDD0zBc4lkCsQKGyqBqkasFmOlv8fl7w5HF4rjLg2wDJ4AaseK5FqdotXXr3J4x9+iv2NXV6I20wbYcu5tBvWgT4EwJGZzqMlnII3lmBKB9501WY6jIO0zkTt/JcAIl3Zl5UPRJIMrNgNK3AnvXQJIiQGiiNqaE3DU6YoXWTOadCpk0J08qR1eGrlQxRbhDlNCEYsS9ld2xIdU2Z1+13qveKdrD6SZaK7/qUu8Dbt+ZalVcWtALU2Eu7AgvXzalcmewkadNwOoXP8t+ZOTKrOLDItQnbN+HxdyrgqgN0+p1OvH2wc1kFRBlmZObPysVmP/ju5xVmfHiPmVKLWsbC0BU7C8lfi8lhprzieqeyzfMbdPXadcXkOXXv+60+665odbNKCXprAWNWYjl0HpHQFLHWsslXX7fqIcsokXdszL0sNr3Pp1l6Svp2SOEMqQZlouytZ46rVljDUUu6nyz4vAi31sO0u7bc03XRKLKVtYdbaa23MdOOcNmFfYY7L0byUzqy1cWeyLKuf6576aZ/krr1XPLAlKJlOmszIO7ZYd/0JZLVLcGzZs7omwOC1ZZN11Y10rQ+2wKVVQDqItZv9VgmslWT6l8aRacHrQDnc4Of/4S/wK//sf+G+e69y9a6L/PL/+ovce/9VnvvOLp/97E9weDLn6PiExc2K3mDEzvY2aGKAve+pH2Lv1m2ee/k5Pv9zn+DJR56kv3WO//A//i/w0xmRhs99/me4dXzA7//2b3Fhd5cn3vcIw9EmN4/2ObdzAVNFJocHvPPmizz71Wf43E/9FL/0T3+R+WTG+ctXmF9/m5P9Pf7gt36Vc/fez4/9xE/y6qtvcP3GbZzbJFQ3GbiMl174Nu9/8kOMhhu8dus2Dz74KC++/RoXLl7k7Rvv4GvBW0OvcHzgox/j73z+p7mwscUf/dHv0Ct7fPbTf4evfXWXk5Mx+zdeZ96a3L70yvNs9oZU1QEbmwO+9hdf4Rn9M/7ky1/iqSc/xud+7O9irOKKEpcbrl9/FWLkjTfe5OrVe/nud57BSeD8pYucVBXOJv+JUyN6bYybtm217VdiQEPAIsmWxghWFYjtce24AjBnGTCB1Whr58mEfrR9vIXMdTXvGV2x8Zbo0Dqaamxb/CKNjbTinKY+W9ZZtt0cxnLNS/9IHg4qLUOzBWc7n7hl0YgWsLUdeEQLEJt21GkCo6ye8Vxaux5ZojRrc2zb+sJqTjkLLiUW8PraclbKvHKDWnvzFMFoOVfBckOCte+eapu185za9NKuvXS5DmNSGxgxhK66oCTpS5E5+rkyr6Z8+1vP861vfRNfz9gcbuAXTar2djLGZv4rgc8AACAASURBVI77rt7P3fc9yM7uA4SovPLKs5hBwc6lB1CgKODezT7NNPLqzQU3TjyTesZsERBynLOoJDPqzESMsYSQgK3oF/h6Ru6U/qhH45WD22Pq4BO7T8C0Hi3RdIU3cnxM5RsihsREqPHVhEWYU9qMIsuwxmFlyO72OazrM13UlEap44Sn7+tzbb/gxmwTH445ms5YnExR48ilpOwVFFlG5gqiKHU1Z3tng+0yUlWe2XTM8eGEOpbsbm8TQkR1nhKPxnA8T34jgYCVHJsNGA36WKmJxmJI3j2JldGOQ02sG28U5zKkrf/jxTBfLNg72MfEwMV7LhKkoGmUvBA2s5KtEg4rwxe+fYDJkql3ngm1N9wIqfpWjDXBKIUtcLYk+FRifGfzEndt3cdxfcjs8CbV5HXK4Ra753epENR4CgFfLbh+8zazekp/MGSzt4ExlgxLadKYq7wnU2EeFvimQp1w49YJ44Njhr0Bea9M7CTnsHlGmRXgcrAGMUqhAyJwYdTDSYbEZJQb29mii7sqo4jPOT46pprN6NkhO3dtUFqlZ4XRxgH9csD4cM6lSyVx7plOSox3ZOWA4+NbHFy/RX+wweb2BYreAKOGSfCpyqYNUNc0MVkOGBGspM8wisssZV4Cgu3YITpIsW9QjDfcPrzGdHpAMdxhc+MyJkvzkTMdO79GjMGYHqKWRhy9LFUZq7xBnSLR4oqijWNSDpMUDinfidqgMZJTg9bEENh1jlBNcX1BaaiqI0p/m7ryzJsFJtullkgTKy4WJQU1VR6R3SvUCJlGLEUat6FCSayiHKDMiQSaGurFnOnigLqpMGqp1LQrjiRzbuBWUKwqEpWFCUQCpo4sZguyvEfZK7CuQMXhbEEuOVYCTsBYxyDWVNURJ5MJpYvcvbnF9Ru3eOalZ5ktGi7fcy9Xrn6M6WLC5PZbWO/JjGfUy+j1CrKsB6SE2bpUgTAzBcaC2OTlJUZbD6ckg3Likkm+pGfjCWyZSKRJki/VtOHoPK4WoqZrNq0cltbbCXEIaaOPEJlND7h1+wBVuHjxYcgLZvUhwdfYusaYOU0MBN9gjFLFkOhyOIpBn5yCxXzKYnrMbCK4DMqyxGmS9tVx0W40CGqFRoSotvVWtBhrUxVKGqJARjIN9yQQWKjpJWgElwlOkt9QpCFog5AxPj5hf/8aNs/Yuv8JYhT254f0ZwWTecamrflU7xL/xx98h5MwoP9O5On7zrHb75E5x/FkQRTBqqNyPm0K4AAlywpsz2BUaaqKo6NjQmjIe5ZePqAschBLg0VMKlzvvWBEcepwElk0NaAUJsM3c2xhKUZ9SjMixlShMfiQfJAmY27s75Nv7LC5tU0hrdxPodKAWMVpineaCFmWkWUWUTDatBJ614LOSZqbNsAsYi0WqFXJMWiTJLFRFJW09kmZk3OOqu4zqz3NIuIWY7YLSzbIKbIhUUpQSdUSNWCNEjOLxEhsZqhGZhTJC6qasHd9D++G3PuRB9LcGdN8oiSQCjHkThBNEZhHmSwa1OSUYjBFlvxVETK1RJG2gqlFTMRrQ9LNp7zEmkAgxc1WY9rIxqRcIzYUpuCv8/XXDihF9ckRvaU0GsCJJFMygUE/5+LlbWI9ZuP+p/n8ww9yTmu6Cm7asVXUgUAImkzp6ILRs54+bRDaZa7thH6a1dMGlSqsszSWu5btW8ugsw0Eux1RWf5XUMJatKlrp1uTWdGxfzqIagVWtHHlWg7fypd0dUw4E9yvB7FLLtLSI2ndkydZw5o2+e3QhVNlpNtflI4qt3Z9ZnkHdwAYuutoX2eVl+vmsiJJ4x8T/YRT/k962r/p9LlPpRB3PO4919T++cHHrYNKKxvYdaZSBwKsmG3SlliEzp8rXZ6ckq2s3/fp31pvq+7ZrKSUupQjrj/PFkhaPy+6fFad/DC0CYmYtWOXTK7lI2+vP6z1vK7BVkBVd3An1VNYqw7YDidZl2RKW0EmrrGaWpprap6WdbHmQ9a1wpmMrZPPaIxJJippHlxeP2f7B8uk2aCtfKZLsle/0yW/Rt/7jJZpYPuX5O9mltVglmyN1VNhLS1e9hWlA6qFKCuwav1iFRJoK6TKEQJBWDJElr09rsbuupTTmMRUirQy2hiXXmzSPpMAhPYZN9pwbneX3/2df8Ng0OeFV17l5HjK3s3r/Kf/2S/gK8+smnD75Jid7RGZszz68KPs7d3k6U9+gk//5Oe4eOV+hr1tTKM8/vAH+Pa3/5LRsMcjjz7Bi3/wR0S1YIV8c4tHnvwgN994jeuvvkSwPa5cvYd7L93Ni8M+r7/0KrdvXWe4tcEnP/YJ/vf/7ZeoZcxP/9zPcRAcITYA7N+6gTOOrz/zZd7//icIxvDu669xdHiLa6+/yMsvP8f3vvdNPvuZH+d71lGcL1AiJ8c1w9zy3/+3/xWvvf0aZZHxpT/8fUa9PqKBPHNEHwnWcXyyR7+XsbW1xWMPPc4Xv/xb5KbAZo4mvsp3fMXR3i1+/Kd+hosXLnF8dMhv/vqv8srL32VjY8Sot0EIyu3jE374E5+kqgPWJNbGaqNCU5lpbXuOdOzI1B9jC7CIpPLfsesj2rofdQi+6Wantn8KmHh6bHcAanfujgnVMY5M++fU7NPOXd05Wn0fHRx6dqx101DHAlyXoi6vQbpfJ4HB7Xe0vRcrLQNkfa1uB15ElqzUJRM2xtVkvmwFXQ2sO8zv3bzdgVwdYCRrIE9agrtEdzW/LBtnCfqsN9iZhlg/RNf+sdaubaaw/GwFYHW/m9ra2I4VHTEqeJ+qlC004LIc1JCLQbxydLzPtetv8+abb3LX+V02RkP2Dw55451XuPbWWxTlgIcefIKLV65Sboy4cfMmi+mMrZ1LFBsjNAgmy7DS8Mq127xsBZuVzCrP4eGMMh+CzVAMmWja/ScDL8R6BjpHTGS0kSNkHB3NGE+n2MxhnMOLaQ1lTdsTLNEklkTmKzQGqmaCVjWqFa4UzvWG1D4ynsxBDEWWIWo4P5ryuU8UfO/5mk995H5effUm34+gtsDJZYbnYYShrmbMjvc5uvk2W6MSm9sEAHmQytD4GtvUjOcNtTiCzpidVCAZjc3J8xJjs2To6xIjS9qKbsYfUavBxF6SwIWIyVIpcxMdkEyOxUCMAYOjjg0nJwcsqillWVDaUQI5tGaUCb0sI8bAramlsp7M5Eg0aJVMfIdFTtGDMgOvBfO5Y1oFQki7/dY6JHgWuqCfbVDuDlnUU2azfV5+eY/ClVy+cBczX3Fj720QS6/Xx9aBpvX0qDAcoGQxwxmY4lFjl9YR+WCDixsXsLZYMs2NSbLFzgNNYiQYIDbkKjTasi5tgxGLJUvJlBVMgOnJPifjQ6zLGIx2KG1Gpkle9b4rjp//Dz7Bs9/4Nm/vDfgbT93LV57/Hl/4VqASRbKc7Z27GZ27i3k14+joEDk+pD/cZNDfpAmK4MHUlE6YVjPAUtoszbOhwWqqLiXW4ENNExrybIBIxsFsn/nRhOAjF3bvR7M+aqAkSR8xBrERFwURi49pmJdGKUwkhDlGPCGckJPhQsRageBZ+AU9DVhpWdokPx/rDLl12LZa9+FsTL6xgTOGPCspscwXM6KP9MxJAkt7hkFeQIg8dGmDYAtujefcngQOphMkpgSujpG8tbKIJ+0MFCMheupqgbOOoiyT/MY5Mikw4kANtiDlKjFSSsRLes7btkB8in0DcbkpLG3JcRM8i8U+i/FNhnmGGYzQ8YRnXvsGtw6PGW3v8P5Hn6DXM/T6PbJyi+3Ny1QaqOZT6uoAsYodbLDd70NmyULOvKkZZpYmL8k1sRzVz2hEEhsyRKJGfAskika8ClXTAizBgwohAFHwwaJSYXygIkIUmjagdS7N376ZcnR4k9uHB5y/6yJbWxep23M7yXEWKq1wpkSyJCsCcCYDY8kkJ0XbniIvU25Ve2bTY04ODiiKKTYvyIo+zuREXLuxHZOHmTFEp0SNhKg446g03UejKQfwcYESCBiMMTSNglhijBiJ1KHm9v5b+GbO5sYOg8EmdQxAxGqOtuPg4nbJbz97jevzDJv3mdcL/vKtGbtDz4jAfgzJIwwHkqHGpOpxoaFBWnmbw7oedqPENzU+eI5Pxhw2C8pewWA4QPIBgT7WKiEIagJ1SItmQaSua/pFRgieqvKozMFAQaBZTDnev06Mkc0Ll7DFBj4oJkseQkjKHzJSf1ATcVkf5zJUDMEKVnIKa1GE2ETUeUxmUugjKdkwklg+qkJja5zNEWk3xJuQQBcRirJHNCk/W9SBSR3xB3NUjsgyoe9sGk9FYts1DVRRsEExTcBIzcHxTcYnY8rdEcPhNos60heQqNTWUhiHBEM0sfVHsvgo7QYdyfvN+OT7pKmq50yS359pKx4qBisZuRpEfCKpYFMBDYWgprW3ieCEIlok/GCyxr/L66/flFvAeVLVCN+CHMG3CfCC1579Y/7tn3+fj37m57n/6nmsn6YAy7mWKp8MpLtKMOIUCa0HyTJIXPfQ6QK4li1xymCJJdYU1wI/bRPws7K1FCueDuqXgarASs62OtdKVtX++z3ACGvZI8sgvI1zW/+XtehXOr+VLtxfXcN6rL3isZwGBCCuGCCsYvT1O03V9tLANG1y2u3h6pIicjaS7lrorFzvzK2uEI0EdLTXEvW0P897JYNnQ/AueeqeS8TQ+gWtPf+zr/eeJb0rgDUtHVBbo3JJMr5OVtm1WCfb6Bhs0lZ9OX2jbZvp6jdSwtXJ/9Kvrkvk1r/cqTqUtKB397uS6GnarTiT8Kz8h9buVODs87pDL1yNGVjKa87KwJaAHyxBtdVZdcmWWwFpse2z5gyoJ+1YWZ+wEhy0Ak/Wz9wlq20vVk1aftX3/LaqLEHjtYs7fZ+6Ps7a59tdXjuwOl20GLMCspecsVX7rUSGesexp10/aTXkXRvKsq+2198l4msA8emxu/6cVtfcGc6v32pXMD4x8xou717g+PiA7fO7fOTpj/PSq+/wzusvYHOYVBNKWzAcbaGScXh4xPbGBovZnP29dzk8uE2eWZpzNboh5CbjZLrABOhtj7j+7tv0y5xy4GjqBbev3+KLv/6bvO+Jx5kuZtheyTvPvM7uznnObZxnPJsRmwXPff8V9t5+m+FwRG9nm8989qf5zsuvcM+Dj3B0PGNn+xwHN28z2BjwF197hrywlH3HdDrhm9/5S1544wWKjW3O330P48WccztXODw+ZOPcDlL0+PBTT/Hya99jeG6Tuy7cxXAwIs8K3nnzNcRmzCZHzCtPbzRA1HLvQ48x+NPfI1rL/HhBqAx75h2y3HHr9rsYX/ONb3yVx9/3OAcH13jkAx/lg098gJdee5nxfE5VNSTujmANxKjLjYuVBdpKKtyODpzpZHAtRKmkOVoT04l257Sb7zqcIrFbW5hbVyMIs8IupO0j2nVPgdiCzevTQgK013CZU+tnquC15EuuLTSn1p21saTaMVxlJW+X1RhYsagSAL1cg6STsi5/YinZU6GVHZvlh2cLFbDWTmuL9Fq7dw11CgE6NRd0rGGlW4/fu5bo2n/fM8Hc6ej1a2xvppvfYlu5pitmYI0uZeHWJGNeVZN2TMOcejajni249sYr7B/dpu9yiMrrr7zG29fe4eDohJ3dqzz82PvY2TmPKUtmdYP0Buzu7GIlQwnkLke0YjyZoqGmn40YHx9Th8BwOEreO8ZgnMMh+EXFLMyBQJ5BXoywEmnmNYdHh1QxYvKsffYGa9I8nGS7ghVLiAtsHVn4GfNmTmGFrJdR5psYlPHJnLmPZCbHGME6y9VNx9//7ANMq5pvhFf5g69MyPKcoU1+RHP1oA2umVH4MZv9BlNY6uYEXYDLe0wXnnhSM3fKYFBwZSMQfWQRDTM5xyJkKQGzYHAEaVGCaPDBozTgBVuPsb2CShe4Ik9BuUQ8grUOYvKYCnVgPD5hsTiinxcUwxEBqGPyvnCS44xhFiTF77ZNBDUlC3luGLgMi8WKpVcIRiMlytZAuHUcmFaBSJKJZBqpvMf5mjpUqGT0en2Obr/FyeFbePH0ewNGGxdQybH5iH6vAGMIGunnBVkUgnpEShJ3LOLFEDUQQmiB78TCskhb1c/RSbiUVHpeJFKQNtdsa1wr1qBqWUznTA9u0PgZveEWruwjKoSgDArH+dJjG8u//Fff5Ls3Z5zMhWfffIkyd2wVNQcYfG2XTLd+vwdlZNbMOTw54ujWTfq9jNHGNi7PaOYV1jiKbEClEa8ktoCrIUuV4/KsTw/HfHrC/u13Ceq5dO4i1vXBLDBmgYuewgp1XeOsgeBx7Yb4IlSYds4XhTxz3BrvM+qXuNwlo3Q1VHWNqyPlIMO3LIHFPLCompbROmVRNdSLObPZglrfItaGxeKQRWgAR1PVbG5sUvb7FHnJ3ZcvMxoVnM8dpllwsVCujkbcnHmOa8u4EhqvWGewUkLQxFqIFRjBUOBi2qj32mA1sTbUpKpcNgvEaMhMC07YNOf64HEoah2ihqgeoUEk4ucHMD8gAkVe4uOCl77/CkfHh5TDTe5/+Cm2ti+nqon+hFzAUINGnESK0lHbbbxfsHdwxPHBIc4pG70NsrLPsS+RekqVucTAaWZEyZLPkgZSBb82RjVpQ8ThsLFJIAwBMY64SKBAMOn5JBNsXQa9HmU+GTM5OUD6jnvufRwvSrOoacSQi4PYoCRGVF176tkcHxNzp9MqqGmw1mCNhZC8vKJERoMN4rDHYjFjVi2Q+RwjQq/IycqcIBbFokGS34pYcgpCDGTawkcmMa4cQh0NVgJWMpykMSsKBye3ODncY9gbMdy5BFnOVD02CupT3jOvKnY3cl69MWYcIKijMEodLHXluFVD3xkGuSPUkQbBERB1KUfNHFYt/awELCEKXiPWOUQEX4yo5hOaUDMZezI3RrOKsugnv2AEtRmQ/DvFpSpxhcnTvGocdfQczitm0wXZxmU2RqPE0hdN/kTi0SwnBsHFZEptCotRjzUC0vrCiaXMTFJJqaFSj/eREIWgASsOlZza122OZXDWJR2GRGIdMOpwuUEJEBwaU35TWIMpA8Vwk9DArFnQzGZQzwCPzQN5v48xPfA148kRB4dHZOWIC3ffT3RK0AbnChZRMJngWtsZ2wJgvlVSWZP8eRtJzyJVKkzzMCGRd9RaqugTW0kbJE5JULYQjUlVBbUjd0AMASehjdksja//H2Kb/3evv34PJVoDzvkxjQfqCQvN2dzaopneYna8x8tvvs78j/45bzzwMf7Wpz+FdS4tXNFjCcQgdKWIU/LfWQ3fGUhYBoiakvoUyxqSYcjSTYku8DzFWl87i5UunYRTybqunRNo+WRtBrzaqV1/nWYntadaAj3Kuu8N2iWbLCGMLn3s2A9LdkIbvEZW3InTYFGHToSU5LMKpF1XSncZkbMmJWo9LUTX7seswQFK51cja6DAKXPr7grbbKcLqGXZTm3b/YDXaeArZUudRGApsxNpqYwr4MF2SToJKOkS/bPnPgXC6Nn6ZKucoAPyuhbudtPXwYk7wVbrgMjyrLLyGzLvOU6WANQSdCBNGCug58x9yMprJbaA2B2ZXEvwRU+dZZ2p032X9eNkPXlt+8YqRzp1Lln7DZFVKXBZ++5ZaEsgmbe2Y9ZIMqhvEbzluDJiVyw8zoA4Z653ef3SJupRz/zqqk1WPIb2DlsDSrMmJZYz/08gWCczXLVMByDcAW5MtNs1ZlSC3GjnkrgEx8yy/Tomy+rXRdIu8AoDkCUDUNs5xCB85S++wpf/5Ev86Oc/x7R2fPSpT/LIAzNGw5/h//yTL/Hyc8/xvg9+CG0CEha89dJLHGyNOL59i29/5+s8/oEPcv/9jxLFEmtPzJWrD1zld37jl8mt8q2vP8PbN/fpFz0mkwnvvvMWH336aT708R/imb/6K156/jn2rr3Ngw89yvHJhKqeQYiMRgPG9RidB7xVfu1f/gs+8akf5/j2mDoIZX/IrH4HWziQmr/1wz/BW6+/yWJ8wi3vMb5hUU34wm/8S/LccTI+ZLGYM5s3fP0rX2HYL8ispej1cfkIpWD30n1YKXnx+W+h2pAbwS+mvD2Z8IXf/jXUGUITyIoilS5HGZ/c5lvffIbFyREvvvgdtrcu8eGP/TA799zNox/8OA9/4ClcYdGFEtNaTqRjwJ7paWsASLdqhbUjlkb6cflU0+5XBxx2QJG2MkjTOtfpCiRagSLt2tYycKzeYWFbUorOAi2rURrjyvC/k6zo+qHdba31T9utyGvzXcdUWs7da+NnxYOVZaXPJblY2kxtOWso6yN4/b/L21iDupaf6/o1Lw9s5+3ut5ZD+M6v9Q2h5f2sf746+/Ja10jSrD2CGFfzUldkwQAaIliDtZYYFWstucJ0MWPv3XfIrOfo9j5GlJ2dC4zHJ1y7/i7X3n2HalFzz9WHuO+hx+n3h6mk/NyjLqdf9hFpk0KTE6sZxhr6/SFNaLh5cIiV1shYTCqpLIamntN4DxiKXjK9DRhCjByPp0zHC4yxuMxiCFjT+eW0cQCGEDx1Nce33jKljWSbedq9V8NiUTGbzgmqOFckX0dn2SoyNkrH7/zxK8x8zs1xD0MgLED8ITbMKPwMEyoWizHWgC17RGuZzRyTyYx5fcLJbII0Ndub29x792UwPknCjm/z8Lkt9mcTottkWhtmGJxagtilibeXiM16+OqYOi7oFSMiGSIVEYdkIJlFamEyPuRg7yZFb8CgP0QQ6hgxRnBZkrdYhejS7rGKS15PRjBekCDMmojGhtIkMGaygFwV9QG1HisVoamZ1QugwVNT+ByvFqkn5FoxmVZsn78PGfRoFicsZmMaLKPNLbK8D5oiQCMlUYUgimqZxmCEYFvxvlhMZlYbYaKErqCNrqSpede/AQjt2mlRAvV8ymR8QhMWDPoDNt25lBhFQz8TcpNkIe/OlDfHc0on1DEjuII3Jh4fFaGXfI1MTKBIOxw1KmVeUO7cRVXNiLMZR4fHRAvbo3MUrpeYBC5HncUEsLVSmIDImLCoCI3HT/e5VJYMezmL6haVb+g5i9aGMld87dHFgsz0U1luC/PKU9ee0maYzOJDw2QWmI9nxLYCI1Gom5rpbM5iNsb72OYjgaoJ+JBkOpYUS2Y2TRaSFZTW0hsO6YulxhA9bF18mCiwqCu+/8Z1+rbm7ksX2BiO2NzaoFdG8jjhid1tJsGwf+SpTcG00lSxDYO1g+TZJ9LKeBRR14L5KVYzkiR7qJBU+1ky8jWeqDZthKjBSI1qQ1OPCYsxVT3BaY0Brl+7ybWD65hsyOWrj7C1c/X/Yu69mm5JrjO9Z2Vmme0+d8x3Tp92aIOGaYAEBHEIkhpHzDAIhhgiNRGj0I1+hP6DQhf6J7pQSKMRYyRihiApmgFJEB7daH+6+7jPbVsujS4yq3Z95zQVIQUupiKO2bt2VWVmpVnrzXe9CzOZ46wH12CDo9lVUdNVG2KGRocWyIopk3zCtl6ybbasLlfM8o7SbMhmc/B9mGnU4ep9s0x0ZMUpwamoJdVaUOSIbwlayPICbIeojImGzjqc92lRDVRdzdXVEnGW2dEJqsgIXsiDRlQMz1IEvNKEIAQskmWUpsB7jXMeFwJaqQQ4K5SPIID3Ua7ABRv/P5mTTRTWe2xt2dY7TNOSFTllUYLWMUNg8BDqaAtaizIZLniM1zQqSpwghkI5CsnZNVuulo/xtubmzbvk+RRvMoJEcATd0WagRHNIgQ0d9WSOcZ6cedSGKyZRj1NBm/roPHe0gPeKkPqAaIOJswk2sYlzrdGUEAJ1bqMwfGjBKVrX4buKrlmSZxmSxQyNE1VgZRvZsxNFF3Z0XnDOUa1XWNdxcLigzMukMShEHKBFo8DFEGMxHVhPsB6vevDbodHJX/Mo7SlUsrtjxggyMdg2ENqGIJYgGmc9onvApicUBNouboZpAZEMF0Al9qFr4zMmRkEoabTnal3h1y3l1ZYsc2DXrDY1+dEdFofHWFeB02BicrI8ZRj2KHAdmaoJovAorE+REN5FwE7FTb/gLUqghhj6mzYzO4niCDZYDAq8IOJiOF2I4JNGI0GoxRLVnDQm/HIhoF8+Q6m1ZNOOd37wXbKTz7N+8BafLg2/9q3fxT1+jz/78+/xZCdc/uSnvL+ZcfrCKXdPX+be7SOc14j0VLOoAqHQBB3RxaePvR5K2LN54LrF2Bt0vSUZ0v/3XvGeadE7fVy3y8e7n9E+T08LIE8BJGNGQrz2uu16ndXBYAGHPhSJQE8gD+m8GpztMBjGfRY8RkyPwSRPDmcIHiTt9w7AWqQL9sb9PlwgpHvEsKa+eVRvHD/T+s+CNs9+3zv7Q+1HbcDA1nia2fKsFR/27ZoAh/H1MlyXyv0ZYNIeRVTP1Cc68/swkH1LjEowBmKSYz8u52cy00gsn94RG8raw0Z+9PkpGKN3XHoA46k69aEkcZoct9T1f4fMc+zbvWelMfqOa59lKNJAApL+r717Ny5RfEY/RtQoVTjD+9gL4+7/HddaA06Ny5Sel8bunpXxrKMXf9K3XT82ZRjfA4DU10ul86lL9Oyha2Dr+P7XminxGkI6If0j91LqcRyrwaAjsBelV71zGReuMfAaYNBKGtc/glFjrZfIqlLO8/nPv8bJ6S12O8vzLz7P8cktJmXDrlpz6+Q2P37rR/z6N3+T87Mr1mfn/PEf/xsuzj/FFBnf+I1/ggR47+23+cKXf4Xd6ory9ikHOuMrX/oKV1ef8PjTJ+xWZ1jvyHMBsYgodFbwxS9+mbfe/gHTm3N+57/6A0SVfPf/+iO+9rWv8Pff/z5///Pv41c76vWGxjZ857v/J//8t77F5195Cdc5nju9y0fHt7h96wZt63j//gecnt7l+edfpF6e8/j8E4wOTGdTbt48pdxccXZxySSLu/3B56zOLlk9OefwxilfePNNYUKDIAAAIABJREFUrp4EZosZ22qJNTGzhg4ZbbXBWsGUOWk44lzHS6+8yq3jE3743lsUueHm7RPOL8756jd+g7ptyFG0rk07WynNdsp0M8B9aWyMe+7Qi8bAhA+Q9Ogis+kzxlPfr/o7jhhq/S0G9p4kYDaNPnka9R1PCP06SNR+02lzoQ9zjeM2PVf2l6qhUqO5VtKcKDHsW3rAK4xbgUGnrF9rvO8ZkGEYA+MmjI/v55lnmbnjJhrWWNnXeT/XMdRDnrp2NDs+3bTXxv9nNSN9/UYFk2cQ5QA+YHQEZRCJoSKo4b2TjHOtoXOW5XrLoycfs1tdcu/0hNIU1M2GTx8+4tMHj3j0+CGZyXnplde4ceseSmlWu5o8N4jJmMynWBsoRON8zLxELqigqHYbVlWH5DMQcOKR4Ok6G8PjjWa2WBAzfjmctzT1lvWyoXMtuREUPq0lCYQgxLCaECIbIjhKI5i8pFMpfD4I27qhWl/hURidYbI4z2U6o8wyGhd451F0ApWrUW4JbYd2FW1zhS5LRAnBZHR6ys6D3QbW9Y71akm73QKB6Y1TnPdsA/zdT99hcXDA+fmWm8eHfHJ+Tl54CtcyLydUMmFnMyqraZN9pUyGCVCHlmlmsN4RVOyrynsypWg3W85X51S7FZPDBUU5o3UWBWQqZtBSxPCnLNPkJqNro8PgvEcFhVfQ2Y5gwXlFFTraNqDXFmvbyG7wnjpEdpAuPDkl6DnKxlBvZRxtl3F85wSdT7DApJhweHiHpt6yXV4g6oKinJNnEzItKF3EtVWIc3dazyXZlL32YUj2p1F9wL1Hp02NzChUUBjRUb8ntFSVZXN1ydbuKExBWUwjQEeD0hojHueEKihqBz4zGKPZWIfD4G1A64CIQdBU3iMSRWlViJomkunoMHqYTY8J5THONbT1jqtthdrtmEwzytyA06jgcfUaZ5fkqqaqduRac2MiOFdjraXarZGQYYuov9JWgabrWG0rMr3BO0ttLdvdBts6PFEk17WOznd4Zwkm6oChEytCouREYfIILCYwQ5Qmy7IUGgqTosR1gihNZ5soB2KOmB7fowyB6fyQ3foJp4sjfPcC1dV9LlfnbLdXrNdTLmYLTFFwuJjj24ZF1nJ0NOVs27FqNdYG6sYS0lZXQCWfISTJAoX4mInWi0cHj/dpfg4a5RUSXAIE17h2SWh37JolpRa09zx+8pCz80ucTDi99RoHx8+jJjlmvqDwEIwiuEC3i2FbwdkICKRJPstSbtygKIsZZVlivWDrml1boVqHKTOU0RQqi4wLSenk0VFrDnDOYa2nc5rM5NjtJyiVk+tp1Olzlp1LfpqzBNdRVUtWbUteLJgvjiLw5jy50liJUs74gM4ybN0M/hKqiIlRRNMAXXCId5iUsU1UZC/n2qCUxgYVBQm84LHozFDoAsoJ1nU0tqPdbVFYlJ5QZDlKGURnhKyIulFGERwR+HQWcTaCmptHrLYbTGlYnNxFRKdI9hjd43yLCGQChYIQLA2QoXHao3yM9iDEDVHv46aUC5FYISHO161Er1hJ1FHtcATlEe+xvo3ae0FQKkZaaB/7nJgCihLfWbzrcNUG52p21lKWOcXsgOACm90Vq63FNoGyXHB0cDv6v97ikhaFCgqlW2JymkSKEIs4g5eIEfikMatDDcrSOAUmaZ91UfA9BIf1Ft9ZWudAWTovBG9pqh0KKIsSkZiNzksUljAieDKQEMXQQyDoJFXjk55m0MyKAxrZ0WzXLJdrvK8o5ydAyWq3Zl4uUEoRLCgygtIoFUOKndJYUYPMgFKJ3W4yggiZj1ILljyBXlHHz3uPVgEjKmbkVHm6h8YHlzyVkDZHon6cSIaoJHBj/xMPeWu7LbJZ8sOffMTBGzdQF+e89zDni02HanasnMZKYJJPUK7i3bffIlQ1zdmEG5/7EotsMuzsJ0JhRPD6rDHp6A3jQSR4ZAfGhdHvwYqnvV9g/OV1AORZUCBuoMro/Ge9hP11YawZhNpnpwlhz0ih1xJ66ll9mYei9Ob4yNYd1WevwZNOJEN/z3DZf9ezhJSMNDhC1GgZzPfeEb/WzqMwhZF1PgYjPlvvaN9OCV9nFMjH9bbu2/d6e/bNuGfNjCGUEfiRbqHU0+pO4+NphkovW5IAvIT2Dk5Ej2WMwKS9mxOZUX20R++cXKuN+FGoZbxh4On+Jvt3OpSwd3RkqD/j96z2/WF4Xg8wjZ7xTJcflePpc9cANhLYMWy9h4R7hmcd1uGW43aN7bOvZfo29Ts/fM8AoPRv/7pbzmiM7AXGx5nu4n0GT3aYMwYWj1f7niVJQ5F9Txj6Q1CDMzskfRux0eLClaTjh8G4N8gDpN2Up65LDnvMHhfr0gOMMqrnXoPt+jiK1XJDiFTqqNFYAkRrJvMjvv1f/mvW2x2vvvIGgYDOFQ8/fAjlhH/22/+CuumYzEqMus23f+8P+JM/+besNhd8+t47/OTir5kdHCLFhK5t+frikJ+99zYSLI8+fYjSmtdef52L8yfYIHzhS29STmb4qmZ9dUnwjuPDEyamJJ8esZgf85//2m/xuRe+xJe+8jX+/f/xv/LuW+/w4otv8MavvNmjeeyqinq745P7H3Hz5g0effSI5+7c4eVXX+ev/+r/ZtdsYkYTo7l9+4jp5BC8Z1e1nD7/Ob7w5a+wWm/oqhVlXrK4dUpRlNw9fZ6zhw+QPOf28RG79ZbDG3fp2orGOnQG9557keVyResajo5PMUpTNw2nd1/mldff5N4Lr3B6927Ui1CAU3GR95GRotU+vHEMb/RgZ888C+yTHCjVnxCkF/mSMLRHGPXqQKTzj/t4P8KU7OeMPpnBeA4cuqcazTdjhhN7Fmo8H5l6cS3d1yaCmD2EdH0+CX3Z+7KIDKFvfQa3NMD21/VOLOMjDBUP/W+GuZ7RGHl63hktDGEPHPVj8drx1Fgbz1TXcfpn15Px0565bxid8IzCZkladz3jt9/59AQUXhmMzrHWsq62XKwu2W22dM2O05u3WF0uubrc8PY7v+Dx+RlN15IVc27fusPRyR2sU0iWtHKyCBDgHdo7tMnRxpOLcLWsqNvYIFqVBNvinY06TiIYU1CUWXRiRGNdQ1N37DYVTbMmk4xSG5RYQqgJLjZ08B6nDKKKmNq40IiKIQhOSWJmeJpdzc7GMI88y2MKZxUwBrJgCU2N9RWKmm65Res2ZtDRmq0LtDLB+im2qakrR9vuaJodLR6vMnR5yOHsBkUxJ2Qlbb1lcXwLf7Om2l3wZHnO+eqM3Fju3jxBdM3pCQS35vbBnA0Bq3NaCkLIUXpCqwuCs/jgUUVBW3fgLFVTsdkt0XnB8dENggpAi1FgUpsIgjEZpTFMMoNzgbWNeiSt8wQf6LqKuqopJ/OYzS7NAwpBaR1389HMREMAJwHlA9uuxu4qnFfkRU45PxiY2kY0gkKCUJaKspjTtFuqestmeUmeZ8ymNzH5lEzneJMYsfi4iZOAYVEhshxF0CbqbSgURmkynaF15Kx769hVW5abK6q6BmOYzGaxn7sYoqe1RnB4DxqFqAy0ItOKzvsY3hESqK50kjIglcsRQmRNqhAd30wUKI+Ix3kwpqRcTLGupd4+pFk+RGWGvJxT1TvaekltDHlWsF4uaZqG4OuYbSp42s5h2xaD0IQQZTmIyQqi2LMgmSDKoJVGKUOeZ2QzjckyvHKIisLYojPQASMTOu/JjY65P8WgsugQilZoVcRwuiB0XUvbxtTlB4tjjic3kUJRFg03b2WcfRRQBczKE45fXbC6WvPpo0suLy95fPEItGKzrPHGcPv4iCxYVPOIlxeHWCl4vOyo3YS1C7TEbI0iPoInIYZfBol6bVYsSjIC3SADYFSHdxWuuqDZLCmNwjjH+dklTy7O6ILi6MYLlCd3mBcHmGKK0EXgNCtxwWJdIKBjqnLvMQLOd0iQqFUlMczaaIN1jkwMkk+QfIJGaJoK325pfWRVmbKEcookIWEfwKPJsgLBorViWV1xNL1JGzwZGQ0JMO062mZNvVsiBA4ObmEmsxjREGzUNVMxHFVEUWhB6whKOAK4Ms452iBeR42tEIOLjJASAktMiKE1gidIZIogcY5oQtwEFCNkWUZOibMTmram7WroalymIhNIl2TFASHpixoXqNua7eaCpq0w2nB4fAMxGU5SBjACJsQ+JS4mjjKiKcVQ+4Z//Jtv8ON3zshqzabusJ1QeZd05MCgaBNQVxCZMoSoYWxdG5c633M6BYLHSQxoD6JiaLeLYFOQLm7aqQgmKu+RTtE4x6aqI6hjW7bLT2nUhOnhTaZFjt1tsG0TpW5MRkhsWKWialJvTxgUbbBpjbYE72maGm93ZIXGGMNWeUQiwOLxESx0kR0XEjDlXdwe8apAdAwFRTRI1OgyUpArjZcsoSVRKiZIwKgM5RyZFhrnqKqa3bah9R1Ht++SG0UockI2wVuDc3GOK0N8RNAeowStMiyCEUPwyWYcuYg+6S0qScL7QCZZnDMNWAEjBUFVQIgMJcB6jQ82bgYlEowx0XZzyQ7TxT/oJf7/On7pgJLTnvN6ygtvfo33l55Z9jzlzYzdtsK4Cag5ZVaRZyYulE7xw7/5U+qDN/nWzS8yO9DgXETQfJwM1NiU6w24MAYZwrWTMaxtDxaN066PmSfX9z/HgNHeQe+zk0XL0+/PBRLaz3XPvj8SeBNdcn/tKf1j+kxiTz+PvhZPvev9b/xwk2FzVhITZmQTDwLSKQRoAGeGpyYnd3Cq+/t9BiiTdidje4R9a4URcPc0ytDrWSUHut/93rN5wui6pzt2D6CEgU0QCIPwc/qUHO5nQ/8+8xAYXIsRa6V/f+Nwir69Iunxet182IekDTUJgzt5jU3VpwZHer2VpzS+kjcoo2Z42nm73lZ9n+4dPa59P2zz96wo+n52vY+pp/rb9af17AtGzLl9ffvrRKnhOX0GvWvNPXzet8340CJY7xMjJ7VDoqj2GixCBGoGphS9vlMcA5I8axk9Yej7fu/o92WPsfcpYDS1tw9JmLJvq/5Z+8oSRj0hLmoyvCs/nkmu9XGG8gX246vnQQ5ixz2ALnGR7sdNBBQHFa5BiD1iA3tGymwx5xu/9pv84Ic/4OjomCdPHjNbzDhcHHJ4dMLz915gfblmMV9gpoec3n2exfwGjx5+yuOPf0RV7dA64/2P3ufzX/oqTbtlt17z9js/IzMZXWj5nW//t1xeXXD/4UNuLA65uFjx/odvM53NeeONN+m6FqULBM+9l+7RBc90MeNX3vw6P/zef+RnP/4ZIXR8/tXXeevnb3O5W7Hb7JgWJWVZ8vO3fsLR8TGff/lLvPTyq6yunvCjv39IIFLIr84ecxmWVK7h4OCEu8/d49XXv8hvf+t3sa7j1ZdeoXIdNw6OeFA+5sP33uf12ZyXX3mZd996iy999RsEH/jJz/6excGcb/yj3+KjX7zNhw8+YpIVfPc7f8TjJ495+XNv8O7bv+Dg6BbOBUwWCC62swseiTlhktD6CFEYQMJ9h4vda6/PRcr6GXe6+pExBjtCmrvD0Pn6uWC8ZkUsJ4HfEgZh+B7x3xdrdG+BPbKV7hs70H7+GvX3Zw65fqqHThRD5Np+k0f242G8ZvkeoZcR6ykkICZdq8KevQSjZ8WJ7vo62n8Shjrvcdfrd+jv0V+Xqr6vlIx/8OzsO6x3+xcx3GNgc8lek09riTokKgJJcb41MYREGS7XGy4uL6jbim21oqtabiwKzh4/4sP33+eTjx+wXK0IpiQvDzi5eZfbp3cpygKlM7QpUFmGsy0mj/pLKovaPJ2PotfnyzVFOceIwlpL02zRxjCdLtBFnkS4AW+pmi3VrqKrI/uiEAhiUV7obMWuXpGXBdpMyPKSzBSIzlOkYtooE01jW9qmZrerojZQVqZ5zaGlwvgGqSpCV5OpAGJx3lNXHShFlxWoRrGtalzX0tolmg6cx3ctThmKxW3M7BidzyAoJOl/Ne2SthG0mnFwPEcfvcC229Gtn/DB4yUiLVeXK6bTkqZpyXJDmRsWWYENmloOcUaYSEsnJevVirpuEJ8ExCfTGO7gPeIiiuisBR1DcBQK5wKbnaXetTQh0HlBDCA5ZWbQWUaxOCZTBYhDxI3WjD4tiEDQNLsNXVMTM+xqsuKYTGdkEsOGPFE7JBAF9OM4yggEytkR06nCOUdtK6q6graOaaQngqgCraOwesKQojMs0TbIdQTQDSm7ltG07Ya23rFZb9jUkYE0KSeI6CgeLKCMSg5qHCCiY2ikJwoia/FRN0rFZ9kQgewo/Ry1Q1QgaahKSr4RsL5DCGgfBY2xO5xvaKslzm7I6ejqikePPmW1XqOICUyKrKBttuBalFJoHbMq6QSmZqYkzzVG6Zi5Lyi0iSCr0gZvDIigUSiTx3AyZbCqRUlJCB1GZwRjUJIzIbICbGIoijZ4JWivIFh8U7PerHHeM5kcMpufYLSmzB33juAf/+oht+7O+f5f3mdpc164c8hxYfnLnwurdoIzNzD1muXynI+eLLGhY3V1ydnFJaICs3xCkSvmasULJwVXVWDVQO01jTMxIUzkckS7J8igLRTJsxWh29A1l3TtmlIbpOt4fHnF2fKKpgtMD065cfQc0/kxujAUOmqBeXQES7RhEhxdyOimU/R0ksYpuLbGuZCyugWcRP0jpRQKgxeH1irpN2V4X9PVDbbd4psa7xXOeExmyE2BEwEUmc5w2pFlCpWXGDMjuBrlG6rtjrqp0OKZH9xMKeM9zjaJ7atSlkBBaZglFp0FyOJOh3iT6LYO18XoD5f6qRVFqQ19gqNAzNCniOyVaJcrMlEDkyYAOoVPlUVBlhuc7WLo5GZJJ1dM5jUTk5NlJVXbcLW9ZNfumC1uMp0doL3Cty1CBDUsntbZuOnrhUzpGHddCK8fW/7V736Fv/uf/g3zVlhuV1hv8EonRimRbRcgeE8dGiSYaFtkYNsaXKCuajKjKcoJLii8qLjxHMB5GwXck62hiWO5a3bYrmORFeSzY3a2prq6wjc1VYDZdIrratbLh4jKCSYnNxNMVhKUwmMxMonAjxAZfz5KuLjgUWgsAVWWKDlASLlxBXLJos6fRP2uzjpKpfEIDhvnE0DNYv2jVtjeviaRDLKUfZ0UJhxlNTxkHuUDdrdme3lJZgxH914mNwa6HabIoJigyGhsiDpgTYtzFcFFT9voFjGGkBXRfkl+nARJfQhQgkv2lgkxG2IdYlZGHQQrbQpBVsk/d8lvUoSUWb33XxTpneHiO/8lHr90QKnID5gfZtTrK8rPv8oLuWVnW3Ltae0Jtw4XPD7bYndX1Cff5Fe/+U/53h873vgnv8fztwMSfAQegh8ABEkG9t4I743u69/HczIImvZG6N44738z+k4g+D1AEb/bAwjXNGV6Rw+uE22eMkD3YEsYwlz2P0mgQ6pF7wLH36jBufS9xZrKc83Elz1w8fTjn2WmjIzzZDjvz/fBfCPHY2xpJ1ZWSCEafUjO3iiP7Th22K8fcq2NrmnAhGev2u/BR0e6B6uG8+l9RirfGPbqlXv2oMDYmRm3RYIbBgem70uqn6xSu/u+DyXK5d7RY/BaQirUGNQggRxDOu/+mT16MISyxF+r5KX0TudYpyRce1NDkZMH1zuwMoiK9y06gEXPvI/rLdG30UCUSDomQ9uMPvZvZg+2CaGXEkv12vfzHtAdhQX2Hl3viMmeuREYPeMaMNePzz6Usf9rDF7tPdPo2CV9qRBGDbZvxV6Cfv+9oIQ9SDoeS/2dR1UYDzlJ4E/aj6J/Wp8vcSxMPBjYqX8MrKbeAU7A0SAo3rOdJAziz01TMZ/No+Enqc9LiMwB5ynFsNttefToAV89/SruUBAjLJcrMkVcYGxgs93y4PEDPvr4fbT3tG2HMppmdUWmc378/b/lv/jmb6FMxt27z9FUNZPygNtvvkwx/4Af/d1fUa3WfPLxL/jCG1/mxuEt3r//IdY5pkXOdL7g/gcf8vzdlzC6ZH54RNu2tLbl3bfeosxzPvrognq7Rd88ZnE44zTc48tf/CrPP/cCWmX8+jf/KZsnH/DlL9ziBz/9KaumJs9jj3r5c6/x2//8X3K53fLyy69STma8/MLnuLo859MP3yG4hs1mzezogPsffMr79z/iznMvs7xY8q1/+Xusri559513mOQF/9nXfoP33vp52sDwvP3TH6Mmc359cQgp3MMHF+nsYZ9pD+KY8Z/RX/a9MjlZ/dyUgMk4d8nA+uunhf0QkeHafQbHp+aHEAZURkHEIcdPlp5tGXWRolEyAkvDML0PLLqh7AMIRMI2R7xOGT0zjTnl0+bJ0DRh+O0wow87H/vG6hmy/QbNWKg7jOa0Z9eVp54Trn9DKltvGI5Pjpfsfqn7h9evfk7azwEwart+jKdf9gxGSXXTaQ3TSuGdYEPAhYCrax6cneN9jbUN1m6wrefs0Yr3PniX7XbL+cUFOjdkWc7p6fPcuHWX+eIAbYSiKFEmo+sseZajVRQx9t5TbSrWXUdoYTJd4ILFiUMXhvnkBFMUaJVSKwNtU1Ht1mzbGmzMEBuaijZUmOyQLgg2CMX0hHI6i1oaEp16HwSlo7O97Wq6tmXXVBCiqHfpWrQNWLsjl5guXImlsZbGKqwu6Jym6VrWqy27ukaVmqxzECyBDD3JEV8QckWY3KSc3WRSLBCJAK8XjRcLYnCi8K7BhQzRMWRkOjmCySH+xLGrV+xWD9lcbTi7vGRalhzOJxwezpgUOdAxyQ3HRUfRWcRXtCHgVZyfW9dSSB4tCFG0IYLdOr0PCSnLrhKCnpCZnIloHDlKRYPeBAi4yAxitBWWmCMIWGdp2ppmvQKE6WJBpuZ0ygAdNggmiWeLV4ntERBl4oaVOESBdlEHKJ8sCBPHrt1gm4ZqucJ6z+HsCFPOyLIS0VEVzaiYLStTyX8WaNqWzWrDbrehdi250ZTTaUw3LzFLkk6bXFrpNOel8CpxuOCwLgIxzitihjaNUx3BKVrboAHr/GDvG8LApPchRPCM2Md919C2a/LQUmaK1XbNk6srNtUaryfMD+6Q5SaGqFVbiiwnM4FJMScrJhS5QbKC4DQiM2zuEwMqhoUolROCiUxlFVAYgrioWeni+p2JS9pzbeJOR4F60Tmta3HBoVzKquQD22qD7SpcF+icZ3FwQqknSHBo2zCbZpxMZlQXjr/56BE/fcfSqhXbleH0xGDMhIPS4LyJwsRmjgmOTbWh3l5w//EF2lm26y2L6YzFwYLDeUvpayaFQeewrDx1yNh1GXUnkZkTor3ifIfvlux2jyjFItbhdi1n9SWPz59QtZZidsRzp88xObyFKWZokwMBbQTphOBbpnlGIQbtHFk+JRQl3uSEoJOgfYZ1NiYp0J7WuQjS4dHJ+XUSmZyaDBM0mZ4j82OstbjO0XUtnW2xqkJneczepzO0gdxEjT5pNtT1JU1X450ny0vKckrQGW3SoYmswJjYRCsh0/1GcAoHQ1CuX4c7+nXMqbiq6LSxbILHu5osMzgf9X4Iltp5TEgsHxOzDobQEQK4lLSoCxB8h2stPjTopAOmrafaLtnUGzJVYG2DQ3F4fIoyBU1V01lH11YorcnKAq8SABti3WJIu6ZpPdt2wv/+v/0tV2cbrCjWVUswkJssClrbuGJ6Fdn0joAPlgwhdA4jHpSiLEpMFjNAKkwKi4x+iKg4f6AVWIt4RyEGyQVEkQfFZnVFvVujs5zJ4U0yu6aY3qBuLF1omeg8aipnGZlWKQTQJFBIUGJwQciTZqsEjU0uHXGkRgZc8u2MxPcblML7NoaJ4SlURhvSXKezyGILDk/AqAQgiabDY61DiUOJT/aYJ9eaTAda2/Lo0RnbznKymPPC3ZvMD6eosmR5foYPgpUS6Tq2EqhcAxOFsxkhKKqmo2tbjO0ITRvn7SxHtIpzJCkbY5oPffA4FYgi95KWDhsZkUGjJOwzgQfQ6BhdJC7ZKzHbtFZqbwD+Eo9fOqCklEZ3junhDW4dQOE8izKn62rOupq6tYTJKe36MXc+93m+cO8ut37v97lx1zChRSQpwkuIQE+v15kq7kPPQIrf7Q3xwf1L/1x3snvfMtriyfXvvbngB4GT3sYfo1SS7hcGu37v9A9/9+eHI4zs7+svLaTv9qEHkoz4vTO9Z0Ds60LPZGDPaBh2rGUcZgd7uKXHzZPTK6N6jn7ZM0RiG8eYWEHwPlYilkkj2CgUHHq3vG/L5BjJyMjuEQlIrKoEtDyDdOydDQmf1cGTsHUPoiTPXkaX7iGlEe4wchVSS+0dvZHr0bdj/JT6X+ivSy8xgTcusQx6/SPpDZ+B3dWHNTGARPtiyoCSSLrtHkCSa4BF337XQIf9CfZqXwz9Ys+SCMkQ2/eLp1s1jETheyDFyf6Njtv2mqvaO7Psn7Nv/x7k6H+jGF0YHTzpWzneVY3K1gtTR2ZFfw+/Z3mNxsY4YK0XKk/kh1j3VFR1rSuE4Tf9expYFaMyPJNFMOyN277cfchq77qOrwtJjHfQ8B/6U3JyR+/4Wt29j1k9rpcIpYVHnz5gV29YvPYFgvMxCw/RWI1x3UI+Kdlst9y7e4cin+DdmtLkbJuag6MjAp7GNTx+8pjVcknTttC5KKBpo1N2dvaQGzdu89N3fsaNoxPmB4eIzrm8WvJwueTxpx+zvDqn29Y8fPQJZ08e8blXP8+tW3fBdtz/6AM+ePvntM/f47nTF8kXE27cuIXtLMe3bvLH3/l3/KNf+3U+fPdtDg4XKHUCLvDCi6/w+htfYVbkfPjBe/zkk/dxPuNXf/WbvHv/EZfnK4pyyo3Fc9y4fQdjcgrTUmYZn370EdJ68kJzsVoj3nJ19Rhrd7x//wM6V/P3f/PnOGW49fNbfPGN1/ijf/s/c+PGc3zlK9/gb77317R2Q6EU682G3/iNf8btG7d4VuK1AAAgAElEQVTIdMxwFBSoEBAfQaC9vlDfB3RCl559sU/PAP13khwnP/Tq65cO/axfosL1cLgerFH9F+O+PDCg9hk8h9HTh2UJ19YOP8wdo9LKeFkN1+fUIKP1cHQ6jQM1DP0wnO9rNszx47kuDYxhTPZLQtif7v/aj49nJsbPbMvr66+M6jFaY+nrOprrhiPNSf081NsSqRxxA6HXKVHpvQiiFN47QhC8GOrOkuWGuq6ZL6aslhvuf/Q+4jskQLVe8fDxY3bVDqsyFvNjbt99gRs37zCdL4asaJJYFrnKMBp224pdVeFDzPjUBTBBISaKGWNyjDJoHTVCbHAY59jtNmyqNbaNosiKQOc7JnnJtrpimmlcPmGS5YDG2ZSOXcVdd20Eax1VtaVq1mBb5nR412B0TemjMOq63RHyOdZrap+zbQ1N4/Dtmq6p8LamczW5ytDklIsFMjsgy46omi277Y75fIGUC5QqI3vFezrfxTAPJWBMZF2FhqI4wJicXIOzHYhClRPyoqRcHNO2a9r6it16xeZsxaPzS+6d3saHLZP5Ad46bs2nTMVzW+Usa2hcQYVGMCg0ymSUSoaQLVGa4FzMDNasybSmxmN0nsK+Ag0BIxJF2VWIcwpxXvEB2q6la7uU3UkznR+h8iIKEAdF8A7RQuejE47SoKJeoUJFoXeJOi4uxLC9IIK4uGNflguCyWiMxGxg1rPZbNC6piwjs0WhEYmaJJvdhqZr2NqYWKAgY1oUMXQpWGySNPfYGE1A1IVsmgbbtlETRgQRgwWCsigROhffGyZm0grO4tNmqqRwIYWgJaC0QEoW4eotdbVkVhi0slxdrniwveLqaoWIUMyOuPHcF5gtDunaLUpnOBczaDrbxDIqTcgLTF6S6xxtJtg+pD9Exo4XwYUYummSTTMwpaIaGEab/RzqI+MnJLaVoDGiCD6w21a0tsP6Dq1zzHSGSSF/KIcBitCxqhw//MTx4/tRl+zxeUdROs7ainfX0amMoAMYZcgzh1AwM1Pc9Cat29Csn3DRWS4ePWHy5IwnT844mE+4c3obLZCHmqP5IdvGU7UtldVsdw7XrGjbFd5vca4hKM3F5QUPz87obIsqF5zcvsv84BbzyZwsKwii8WlOVBrEBYzSBG9pxWCCQRtFUHE90CpmgQxKaJXQWheBnxRapBJ6qX2SOEmhTt46go6gZW6inm6bRLGtb7D1jnq9xGQZh7dusZgv2HWBiyfvUNuWg8Up03lBMAaPxXpLIGAACQHto66fV4HaWgyatW/RztLamkLniCkQDVrF7HKVF5ou4L1FCHQSUOLQ2lDXDa7ZUkxntN6QEzcVouh5hJFdiJayD4EOAEtoOzwdJssosoKsFDbrLV5ypFiQlVNyH2ibGuNqdGZQWU6WzTCmIDMGiMCJhJ6DRgyHVcInTcbDv31MVhzTeMfkYE7QMXmAEGKWxiR2LqHDOUtQhizE5D/QgleoqcIpDdYhCLlA8CGOoZChJGr3eJEUKkoSwm65uLyALjCZn1DM5kjwlC6gZydkkziOJASCd/jg6ZoOwjaGiZoclWmMUSCG4KMkQGRVKrLB0ohkBIMkgMiBROaVEb33V0MM0TYKAh7rXNKDDNjQ+7Me77rEwIoaeCIQXE2e52wuLnh8eUGjJsxv3ODe4ZSv3zF8/UslK1/w5FM4uTHjXM35yx9WbM8bxLUMXLbMkEvA+RKCUNkuRmfZBoVDKVBZQZ5PYoiwKGLWvZgLF+Km1dAOyuBDIFMJP8ETE0SoxHqKSWW01okdpXDyn3iWN62F4AIHZUG3uaQ2JeCiEekCi6M7/NavfJ2Ld35MNZ9jfeDenWNy1cXsLz666tL7pdHthOAH8c+94ZcQInrwZuzkwch+3rNWkgUZGUB7UKI3hntz8rojEEa/ScZzGLpvuiI9S/X4Q7puZJl69mDYnvXEsJMskBbmBC8Mu7p74EzGF/ef++OaP+PHH1P5JVVl7wn0u+bj+0VdmKRZo8OQoSKwDw0aoKRUz+j87AGkzywUxAa6ptgzAn1SiMY+7Ci2wUARHYvv9MZ9z2Lqd/IHNOS6txLYh4z1567hb1x/X/0O/J4JoNL3MuxeyFhw6Rrc0L+XVC659oShREi815j3pYaO34ONT7WfEA2XHixiX2WfyuGH+vV7Z9fbOZU+XdH3MzXstIVr72fvku1D50BwSahv3yYS1H6MjO4QhGtsunGZnznCEMw16l/7Ju6d6etHD+7tQ1KHMfTUiB4HEO2H17jA+xDO67pg/fsZOaD9ZDDCzQLE3ejR+O6BhzD8aOy8x74VAbF4vz1TEpyzrFYrfvSTH/HqG6+z3m5QHjabNfPFgoP5IRaHtR2TsmC7XXJy7wUaaylLQ7drKSdlFDYNcTEpywLrO/CKtutiNirvKXLNneee57/+V/8N/+HP/ozQbXnw6ac8eviAF176HN/98z9FieXxkwc0TY1tGuzqknw65c7pc2gVeOetn3H37h2uzq4oZ4YgcHB4TJErtpsNJ8cnfHj/A1arC+7ff5em9VycX/DGl98kF+H8wQO+91d/ykcfv0VXByaH/5H7730AeLrO8cJLL3H24DE/+dH3eeHeSxR5TtPuePDpfT736mvcPLlDtb2isy3r3RXTmcG1JbtmQz6Z8OmHP2dRxIxYr77yOpPSsDicsl3XtE3Nm59/ncV8RlvX7FY7CJ6Dk0NC54bMVkN/Cv3ofAZ+uNZnXEQYE+DQq4Ht+4iX/drUAzMxS2kY3YX9+tSDNj0ImYyoEcqxn2skzVPjPgtDZrJ+PKpRvdToWdfG7Wjp8YHBeBRhH+YWAr3QdkhiZDEUZxgdyVgLhJ5mOGYl7is7qsh4Hb22yA2NI6O//8FDxv+V/bPSNDGAvqP5oJ8jwtAWvS0QiPNOSIX2qT/EP0qZ+G5E2FaO2sZkI2ICq80VH7//AY8/fR9jFKc3jvnk/sd88MEv2NQdk/kBx7fucPv2KeVsjmQaZQx5blLoWo1JAPrqak1rHd7FzFxKDFmeRS0eUclBlxgS5SwoR2grtldLmnqHcw4njjI7QJHhSsu8PKG2FSoz6HKCEoXzAW00mTIgHgktrt7QrJeotmIWWkwGdA2t9cyzkp1VVBZW7RRpwLuKrmmwbYV1DZpAnudkB4eY6cuofEoxm5PLgotmQ111BCmZHR5gjMabXkw4ag8VRsUd3QBBhDyBZgZQWMosx+ss7tw7iAzwDCkWFDrHhIJt17Ban1Hf/ySGfYji6GDOiy/exQbH7aM54fGSm3cPudg6LDPWnaILUWMkzuuCkDRhlMarFnFLlJ7jXSDDx937NOi0TnYsGq0CXdew3K7pvCczJVlWYrTE/pKcoIBFiGxJoyBTmsJkaB3QCryKToN4jzhPZX3cuU8ASKcsykcWXZnPYzaqEDDe0zUty+VjunbFolwwmR5QbZesNyvUZIL3Qm5yOhO1moITOvGIcoBDfBzzWseWB41RBbmeDCFvxlu876LejDEYZWhDFJqd5iZqFqGj/axiyKjyFtc1rK7OyHzLRMWNvMuLcx49ecKmqpBMMzu6xXx2g+niJrOD22Q4OjZkZkJR5LgQ6HwNtsW2Lbvthm55zqScM5stMPkUpRVdL4gpOjHH+8QaAUShQ9TNDAlY1AQIOU4coGNKciUo56m2G7a7HWI0WVFQlgeoLI9ZHcURaHFe4YKhAYxTKPFkHqz1ZIUhK3JUnuHI8SHQBosLJN2XAqM1utA0LmBsRpHP8cFimzW71UN26x2PHj3h/oOHnBwfcXr7hNl0QlddcXM6J0wm/OLyId16haJmXhZ8/GjFB5cPqduOcjrn5PYLzA5uUUwPyHRGrqJUgReFeIfoqDuldQSMnHcgARsCNnjERdvGAZlyEGLOU60i88MAyjtQMTxwogVtLc7FkBzv2sGw8kDnLZ23+C46+z400HUYV6FWDm0rzi/PqKqaYnJEW+3Ybq/QWY6ZzTCe2IaExDRN4V4amgCtAuM9212NszVMYjipsoo2BKyAT+LbWhk8HicepTSBqDcmJsNkefxMaicT2SMhxMxdEJlYRkWGJ0XMaJYpja1a1s0alRecHt+jmJVAjWs8q+2WptnQ1TsmkxnFNGOSFyhthuyhSbIqsRU9ksLfgskRG4Ei8TZazSnMFp1FHS0ffSqlfJyDyAGPDw4LZEqRBU/tHYWKvlynQpyD8TEcOMQkBUaibX15dUldb1H5hNnhIbooIuO72SWbJ/aLrA+NNYKhBKVpLTRNS1VvKSWw9ueYvGReTiHLB//TO2JdRfBhb/+H4GlDi1YGIW6ai/f4tN6HIDg6Iieu90GhczGMmnSPEDzWhThPWMuTx+dsrGU6O+JkdsSiKCg1tNbz4OMdH2/WnJ9XvLYJ3D6u+eKNCdvGYFeervUpyMSBeLTOCUFQ2mD6UEofcMHRWkfXbcmDIy8KdGHQugCETPqMuYos7ZwLGlEk7V4XJfpSuJwEAaPQXhAV6+sl+3+3mf4/Hr90QKnzgakO+HrFpoKsFMrplEJDU11xdXHJcvc3PPfK13n5a1+I8ariCUFSjG2LTCZ4b9EYVHCDYQo85cD33yUjOQzQAYOVKCE54GPWgVy7fv/9047q6OiNyGRs7lMkQ8/guW6wpnlQyUBtvgY5CIPhLWNgJE0C0Th4GvTY1/fpr3sHo2cvJcxj5BD3nxmhD3uneQxMjbVcIjU+EMREqCEBTtfbLDkI9Db+/l7Ptuhnt3EfDhd6nZGhFdk7GtcxoqGu8WP6t7fvrz2rB4Tc6FlPA07JSRvBGX1I397xj2DgoAXVe2NcBx+GthmBSf9gzcftnq71IQEkIwcu3q6/v05lGvEbpNeo2kOJA3Bz7T6Syjv+3N8L0t76ZxQzQTEjwKMPHuv/7dlJYciY1/elkPpenwXt6R4SS7oPc/Fxp79nK/bAWo+q9siM7JGcKMaeQiqHWvXzwgiguvbMvl3HjbSPZX1aX2rolSPn86lGuv7f5ISrp9p+AGGl/8bvQfKeLZnKZQxsqhWvvv46z919ESOwPDvnu3/6HV5+7TUO5jc5vXubSZ4zyTI+OnvMiy+9xm5XMZ3O+Pf/7n/h23/4hzgXxS/RhudfeIkvf/FrPPrkE6wNBOuZlQYngSIrOTm5y7/+g/+O//F/+O/JBLwVQttw7+4d3vr5DynygsPZEZvNBdvdluXFOd/7q+/w5JMHvP6VNzm6eYtd0+K6DuVzjg8PKKcF3/vrP+d3vvVtPnnwkKaumRQl7737NlW1ZXd1xV+9/x/46MN3QQWq7YbdtubB2WM27YabixmXV4+4e3qLD975kL/727+MmXPykldfeY0bxyfUVcvlkwf8xV/8Cc63rC+3zKcLGrF4LJvVhupwyZ//xZ8h4jm5dZcvfekNVqtL/uJPv8O8nPP8cy/y9g++x4+//3f8/u//Ie9/8BHf+u1vE7BRL0SBdw5JmY+uzZ29Jk3S/9jraCUdBva0430/GWS19/0j/W/PGoodrt946EffNZZi36/6zj8Myh6o3GexHERTerbpqK8PgvEhPN27rx1KRfCp123bC0+nxw7jdMy4Hc3HMho/A2jTZ3Bh0AmTHmEb/bQfvYP+3nh++4cwpeuNfu1nIZ2/BvrCAAaHVKGBfUlcpxQ+0UDT2qsLOhuNOufjrOW8ovWBsiwxufDOR+9ydfmA7eYx8yKjnM54662f8en9D8mM4uaNU+Yntzg4eo5JWVJMC4w25FlOZjIypai7lu1mS1XviGGZMaNUmRfkWY41MSRNFCl7E4AluIb15SUXTx5D8MzmB+jpgkJliIvskEkxZdvs0KbANpbFwQwbmjgPeIvfnRPaNd6tka6BZkdR5ojStF6oGk3VKZ40jrproGvpmhpnK7SAyTTFZM58eko5PSAvZihToHUOTNi1Gy7W57Q4JrrE6SRyqzIMydEXSQBS1AgJriZ4i1GGaTbDKI+3TWwLNMFasBW7uo5iukaRZYa6LFCTQwo9Y7q4iTKA85yffcjZT3/BfDKjqxzzeYm4BqorTm9mlLsWKU94UkHTRTFajaBUhgsCZNTtljA7QitPcALaMSc6OzYYdMiomi2Pzx9inaWYzimLGZk2iDL7ISoB8Q6f1nwtUeBc65i9t9AZmQGvfGTGpveulKJWgnUOvCcPDteH+isfw8u8i5sCtqHIFF3lOT9/glpe4rqaopwxzSbYYGO4y2SBTaFnGo+WjODjQ42AVjlgKMwEsTaKaGtBfIcqCnzIQTRBOzKEMoXKWRWBOIUn1xEk39UbdttLuq7F2RaF5/JizYPHn1A1NWW24ODwDuXJKbPpMZOiQBcFHmiaDc5aXOYIJo7PTHIypWkRJCux4vGdY7fdEjY7ijxHlVG/RYngvIshfUGDjkF4QzZdceDiOq1RKKVjCJdEIelqs6SqdmSTkmK2QFQPtIGomBUMUSgXxYN1qZjqMmmadbgQMDJFS4FTcf7PFOR6QhcCzoPtHNbFJBHKR1FeUTkiHU5lTPMS5Sz16oJtvWT54BEfP3jIjeNjbp4cMzU5Xmqq+pzZfMbZoyXvvPsLql1HOZ9zevoSxckJs+kBhcmjOLkkBUFlEAQdAO9xztLaBlyLZIbGbzDaEFqHDoo2RHuu9g7nO5zYFPLloe2odjXZrCTPpmjKmJYeh3UtQRIbWyIz0oUI5ykdNyy0nuJNw+X5Qy5WZ6i6olWKoA10NbNJyWKyQJsFmAKDRwWX2EGRyWES23ahc0LoCC6Qm5gcqlMBJTkKF9k8wSMqhjJ2eFzk9JH9P8y9Waxtx3nn96tpDXs64z333EtekpekKFGiTJESacuaY1uOu9OGu5EBCJCHOHEQIE9JHvolaDhIEAR5aATJQzeCRsdtpxMJPTjtjjqttjVYAylqoChxnu88nHHPew015KHW2nufS/VLoADZgqRzz9mrVq1a9VV937/+3/8jxkqJFPjMIXQs966EQft46GK9Q4RAquKxUiVDfIYgQccUxNnkhKKsUUnOIO+SGslmGgDNXIIl0MkybF2iPNiyYlafoowmSTJS1Y2ABBIb4tpohAKvofU/hGs0hTQ+RHAskiAiwGVDPKBqSkg0645CaXB1ASGQ6iwytATIlm4g4rvBRvJHMZ9zdHwDlaR0enukvU2cc40WqMM5hycCeN57LLGITVAKowRKZRgFaAOujsylEhbFAl85VKIwaUIwSdwfhEaEqKukZJMAJ2Ns3boAirhAChHZw4gIBAsZWf4yBBBR4NyFRv04SKz1SAnT4Zij02M6gwE7G3sYo2Pat/dM5oE3g+DaqGS6sEwKz/WJpX/Lg67JZcYij3LnpXfUECu9Nb5hCBIlwQiBFRIVFAkS5y2FdUyqGmYTumKISg1JmiJNB6VTQqBVoSNFNb6MXqYtpzJBi8hcjUytELXufrlF3v4/SHkzKeXwfd49dFy+tMt4OMGLGUJ5nOzwwEcf58qbr3DtaMhnkxHFdEa6uYWvHJ0849bRVdJwkazTgBleEEKDXHIPKMPSv2tuzjLYlA07w7UOrWjTUoib9Hqg/gtc5/aaGGOu6fksg8/VtetgUgvktOlly5S8wPL69e+fYa+secWKcPa0VrSB7NJTXxuFFYgRWsZEOPv7tnOy8cjPwCn3oFYtk8nLBggLcbOPp+pn4YD2p3VH/Exz6/1i7Utn7hcI3q6CobYPtO860vGDkk2+Lk2AEFYn86zG/oOfNrBbzhpWwuD3fO3e7jUva5Wy55p7tp1tGVcCIdqgrWUftM/eirKLM/27l4F0L/ARlgFM/JdkFUzeM/Jnnk/eMwjhnoEJog1LVzBnCC16ud72OuiyBurc8/4FRN0I7yG0ldCaO6wBfkvzOTPI60ExLNPeaHSEwhqQtHZf6xxaNTa6BhyKpZ3wwc8yiP2g/bS/DT5qnkCIlVDuBQlp5x0x3Uyo5dgt9dKWi0GTVnhPh84woyJFbQkchCDBE0uRA8JLLuztA/GE5c7dWwyPDvFlyWsv/YTLj3yI/fvOcfXaVfa2t7lx94hntGB8+5SRLfjQEx9GS/BNeTslBLlO2exu8tHHfwWtDa+++nOyxHI6GeOqkq//3/+ETz37OfbPX+Tqey+zObjAT378PL3+FqbRYHno8uP86MXvI7yjms/QecJgd4tHHvkQqUl5+423eemlF3ny8acZHh1iOgmnw0P6W1tszmum4ymdbheUI0k03gWuXX+H6zfe5b6Ll/jQR5/i5R89h5ZwbmuTYjYjhJrheMxweMLh8BZ5lnP5kQ+zf9/9+FrQ6fV59tO/ysLNmZenvP3Om1gZ54oravKsS9Awng7ZGmzxwvPPIY3kpy+9GKsVAd9//jvMixkX7nuIyaIg0Zqfv/EyF/b22L9wAS0EKBkdn7DSK4sfv5ymovk3xJSU0Mx1EVp7W02Ee+31zDxZgqyACCvbXe5zNHvLWkJvaJmwYs3mRHPfsARq2so06xa0mvMtUL3qzxmANUT7jKWHG9sOfgl2B/xyPY1trux+ZX3rm19zmirOWuh6BvTqAGbZwHJNbOUAxKpx2pv96/eFtbZVTFWNGioC5/xqqWv+27oZAZoKWIJW/BICRemQJqGuI+joQyDRikE34e7xKe/fvMLNa69zfjMnV4GT8SG3b13n4PZNvIfBxja9zR3SfAuEQpmMfm+A0RJjDM57hsMRw+GQorJIrTFaxXQLCT7I6FzbkqqaI00CTuJsQTGfMB4dUVcVeWdAv7+N6XSx3mOIbCrvA95JtFHknT2mR9dQboao5pTD27h6gq8L8twAihLFNCSczqAuA85XhLKiLKco6WKAmSSk6SZm5xxZ9xydpINK8lj5KThEk8Y+nRSMZgcgBJnWaC+xwSFVZNh5F5DOg5axYpaQkS2VCKTMMCqwZXeYF1MQltQHxsMjFh46WkEi6XVzcq0p6inTyYTSQmfjvlgZzyegBF4aRpMx/XwXX8947b33MVKzsz3g0oUHWMzHGCzGevZRJINNDmYwcwbvJZmGID1BJeg0Q8o6MgUc4MGTENyC8fSIWbFAEhh0N5EmI6go0r0KnAM0hWniOUMUV/VNGpc2GidU3AcbZo+3IgZIKuDqmrp0OGxcA53FKAHeUvkyCi6LpkrRfAFesn3+PpTWFJNj6qLC+5osy0jTTtwXhaH2AoJFEtOkpdMELITIPHEhatMopZCUSOOxriY1GVJDN83BOlwNSEFmRCP+XTEvJkxGQ6bzKUpYMm24cTrk4O5t6ioekuyeu0x3cD+6NyDtJkgvcCJqlJlgMVLhdYatLVpUEegXOmqLmC5BCLQMhMTFEtt1jbUl1XhMXRcknQ263a3owwgfQSXpcTTAofNYCcIpgkjQUrGYnXI6OSEoT2Y0g2yAMMlqfQ11TFmJUAzCOaTRGC3RMqbHQawMJqSIDkBzsBuzKVTj2wu0EnSUpnKC0ntqHEIovK/BZwjhSE0PqQJqq0O33qGej5iM7nDj4JBrt27y6CNz9rZ26SWa0WLC29eug1GkaUqns03W2UDKhKKuCTb6NwKJ95KAwBBTqgo7JyCYzcZI4cizDkrFtaGtHua8xIvQlJWPfq0SCi1qnFGkWz0CBi8VQmlS4RFeIUUSq1Qp1bzD9lCTqAfoHLP5jEll0YMt+v0ucniHrHeJhTTMq0XcM0NACk+uo78GMvpZxD1JE4XXIyfFARKpFEprQqvXGgwIj/FRIF83WmEt0ySmhjm0tPHQXSbIEO+tZLRJqRTeN7ugsDEFMIAIgaqYM5mOUFlONthESElTP43aCRyaMlgwBqk8WslGI8lRO8+iKiiLOTM5opPFtceLBCkii0q4EiFlZBBJiRTNvGsjAW8RPs5Ro6Lb4UMEVZ0r2coUw3KKMRvIOj6jTwQ4HWE1FUEKvEcoz3A45ODokLzXId/cwchelGoIgbIsyYRE4qP7a22THt7urREF8SruR14GhEhwwUbgGIX3CutLZkWNL2YoFUi1wegUqTVBSqwPOBt98Na/rludtkb/ygUfWatttfgGdENE/194hxMB5x3H125ipWL3wj5p1iERBikllV1QaYFTGuehqANz56lEQuEyTgsHXmODR6sKIyP4U4YI0ColED4hEQErIuM8aSpfRqW8BJ0FUiGw9SZFOSVUC8KiQjPHpIF+J0Vm/QgSIrAu+mUagZBRKyvTGh80rScTwbxfFCj9v//80gGlH3z/2zz7qU9y6QFBx9TM8j7bPcXo6AraTjCzY3xRMb7xJi+9tM/2Q4/z8YFClTd5+73rbF54FBWOmdw4IGw8xnY/OlJLt3jdm2wMdt3D9PgoDOhczDEPFpZOLmtATsOIEGIdf4m/9yvYLsZ8bRpMRNohLHMSlwF4WAuTm3bj4rcSEI5dbZ38JrBorl3dsDkJDU2qQhM4hNaZXgOz4MyPq38vaUirQAI4i0ZKgbjnWpoxifovClyFFRKBg6DiQrQOOKylALRMkn/dZ5Xmse7xt6HUKpBYPd+a1pJgOSbLwJ8VFCGa47wP3r9lX609ePBrV66HNmFtrEKDvLfvaZWo0s47ln/za0FYCzS1bayBRaxhQ9AKHJ353QdFxNtxaloQ0JapP/vyzkZRZ9/sL46kQmhAoCVQJu+5Qqxs5EyI1wheN2PWClq3onktn2gdfhHtT62pLrskl8ycsyK8zUi3bKiwPq9jI1rJRscqaj64ZoNYga5roNcZ8LCNEM8CPD6s5mYrUnwvQ6nVyVqyUsRa4Lz21FJGmnc70GE5z8OZcV6x61af1ZjHDdCLgBQpSgSK2YxXX3mJH//keep5we7Fi5zf3+fO1eu8+uar7OxtMxsOsdWCP/n7f4//5D/7fTpip5lnkQUafEx7mS+G3Dy4yu/93r/H57/wBe4c3eZHz30PnRtOhye88Nw3SZIOvoQHHniAOwen3Lj1c7r9Hp949lkeu/wxisWYfjdBhMDuzgXGU0e3u8HPfiM/lroAACAASURBVPATXnnxu7zw/ZrH/tbHeeLpZ+h+dcDxyQHvvfk2Tz/9LFeuvMnVq+/z9LOf4o1XX+b6rVsondLf2KAMgf3NLbyzfPxXPs7lRz/KV//4j9je6fLaKz9lMa+oXE3/4C5WajbP77MoZpzbHGA6KU888RSLyYjRcAo6rhu3Tm6gVMLbb7xPCJLRcIxOb3F0epPFdE45m7Gx2WVvf5dF2aNYTOh1Mvb2HuXGe9c4Ai5fvkxZxWpBhCb9cU1MnzPTrp07fskjXK4vobVRsfrmB5bOhrUm1tLgWssQTZKnWE69M/OorYrYpg+3q6ZoTqZa81hqvxFTfRA0e+16O+EDdiClJDi/3D8je0q20z1Sw2VcL1stJSmjVsU6fHzGCsVKWymWIV7V1mwLBviw2jHulWG659zkDPC0TD9uxt6vDbiQ9zQUwLXPBivtNB9TExFEx7xp37uAr6PPoZOE2gXmdcl2p4sMnm43YTSfcjq6wc3rL5OqCiMUd46PuH14yPB0RK+3RWYMJu2QZznJxoDBxg6Z7mIyjfCOonK4umQ2m2HriqJesN3dJhUJtRMIqWIlrNmY+WJMmqZoK5hPT7l18yrdbk7lLFoI6mLOnUWBC5KdwS7WGJTO0NqAFqhFgfKWXHu20ylBFtx0YxyB4cwxspK5t7ja44oSV87B1oRgMVqTZwNkb8Cgfx7V7YLu0iXFUcf9JkRwSKqE4OHwzg2G9YztwSZSRSddugDGNMKiApEkCBVQyjSyBxHId3WBrxaMiiGj0SGL6Yj773uQkCXkeQ/lQCuBMRmh9hycTqgXC/Jsg+4gw3mFl5Fd56yFzFNhqfSAQf8y+e6M8fENbp7c4u7BCdsbm1y4f59uOiNLDXYx41yas6c6zG1GhaGSm8xP79JVFVW9QJgOMsRqSqPhAePZEVnDIiGNfpWQZUMaj/6nEBIpNFYG8iTBh7rRXIyAkPQGW9bUck5ZBEIomc2GjCYjVJrSTbLovwiHFCmp9pAJhFPIRNKVOxTFDFsV+KDIu102tvYIWuFCRW9nHxVqqnLOohhSLMYkOiPv9NEmpsEpkggSmHiSL4NHekeqdQSWBcigoJqSp320kRiVUJYTyrIgyTLSrAPCMFvMOD2+w3h2SG4MudbcOT7m2t271IsFvbzD+b1HyPpbdDd3QGWRhCkCVliMzglB4NyCmkDpbAQAZKxGqKQmkR1ssA1zwyM0SKei9pbKEDoDMcbXgen4BKUFnaxPmvYxOqa7uADOSrKkAZKmMw5vH1BVU5J+nyRLSJr10REiQNH47c43DHmtyHRGmmoIDmcFC1+SapCYWF1OhqhRFQLBeSpfUgcLXiJcYFpXOBlTOCtXUgpNXS5wXgElFS3j3+G8wzlBnW7SHezhZke88+77XElusZjOsEKTi8Dm+V02di8jRA+ZaJQ3eC2j7lfDygxOoJVGehfZQ0FT1568M0A3hxReeLxXEewMAqEiIw4BipjeFGOxvNmXLCaYZrSa4jgK8AGtu+BFU1E7MmKEhMV8yGIxRamUC3t7GJXjcST1lLC5Q6Y77ALORRucFQtOxkNyE5l2RicoBHWINhcT1gLepjgsQcfeJA2DxUtQImrUyEa40AiFDI1+kLANg9IQAavIkonpbQLQ8RBHR+BECI0SEmsXjIdj6sqSdPtkWUZQCo9EeY+Qirr0OC0RToJz+EY0OzTtJgq0bmLc+YK7d69hhKe7s0ue9qIfR6xyFlMTJTJIJI4gHUE1QGkjc6JEBAALGwEJbTI6nYKyNni3QJsUZaCuJcKYyJzzUVOtKuYcHR2wqAu298+jpUanWUxvLyqCkmQ6QfkIegQhqEO0EYVBCREFwrXFO4HUGqVTnHMkATQKHRyVCCTBoLTGA0VdMC0qEjcHLDqV6DzHJAOkiym1DkGiNIgqamcRlgfYihgDRd8gpjFGBpZgPhtz59pVuv0u2xd2yU1KRaOB5SqUBOElznmKEFMAC1fjUSATXACJgyBxaDwaZEFPa4LTVFrgQzxgUaHNXFBIqZDR6UEBXhLT/Do5wVUUlcDXCxZlRT0ao0+OSEVFdzNDZFsElSBD2vh6MQrRUkZNLCGonI8pq7/Ezy8dUHru+Z9xcOUtnnhwi4P8AoPLn8T6MdOjd3nuG/+KmxND0smZHR5yY1Hx4Z0Om2bOmy/8U/7FK0Oy4v8g3/8oJr2Pj33mfrZ7m1GlfB31aT5nWDnt71oQQaqI7C9L5t17mtx+HxoLXwEqTdtLh7Z1/6VaBYqE6Ejf258QmnLqMdgMrUPagCIt02oZcDf4SRTZbcAJu4wg1vCJX4AiiVU7ZzGFmFzR5oELiGKBzcbQgjNnGjzj3ceSmtpIitExncEFCA6Lj6dmbYTfxhMNOLJsre1rix6sOeviDOsjjmGLtqyfvMcNZwWQBGJOqWq1fpqgLIi2It49QsrLy9s2lq2yHvyvT6nINVoLEJt7RzHw9j00YeEa6yQE1oCZ1f3OBHofiHjW+/SLQZ/Vp2XnraKk1etaf4Jwz7/bb55919GU3PJ54osJTVAXQbEVCOM/0FqrtxKjrNBWuadRDzn7vK09NeMY/ySggSZ9iBv5sqcN02KdvRP7s/52YiAog2iCXo+grQi4DCM/MC5nwab2GePzSaL9e5r0uQ+ASa59nOb/f0GaKC1ItzLGaPONsspyTBtQefmM7TWrVEcCWG+RIsHo2E/T7/DFL/wW57Z3ePX1l/jkU88gXeBHP32eG++9zeu24sadW3QzhTMVDz76GG+89i5SbjYVmeLr9gQeePhDvH/zPa7fvMYXv/CbHJweI3PF6eSQJKQc37pNMZ+QpprJZBQ1WpzFisBDlx7h8sMf4dEPP874dMSdm7eRwvIX3/pz/vQffYUr77/D4fAORmhefvVnfPbXPouzjm5i8Eog84TB9jkuBtjauMCl+2f85V/8c5T2ZN1tOpmlKkt2di+xf/EBKBMkBUF08b5mvqgZj4cY+T43rl3jynvvcvmRD/Nv/bW/zn43p98ZMD0esZgM+fizz/DME7/G//i3/zYOx+bmBgd3j3FC4b1kcjphMZnhlKMnDInusrN5PzozdJI+7775Jp986hkOj46iiG6I88MGh5SxCkycYQ3AGFbM0iUosSwC0fytAc2jbGILpMR1eX3NatdCESI1fv2jIAafje2KJl1MtgykdoUQ7arX2mwLlDT6Z+18bvaKVlI6QNRkECuQM65pDUtUtNAxzbPEeS5FwNmCgMIk6XIfXD5/u961Y9I+5vKpW+hNEZZ241f2E1jqHCxXzuZ/mm2EtZ6t9tn2X2KZfHLvltnsZ6HZV87uGDGtLQqUOg/OR+lgJSVpopkuyiiiiSI3YKQFW/HGq6/zg5d+xN07V9EysLM94N1bV5jNKpTO2No5RyBhY+sczpUsKslWbxslNcHPObw5obI1SbfD6OSIJM3Z3b/EntE4q0glqETjXdTR0Qg2tvY4PT3mys23cR72L32ILI8iqNZXeF+xmI/xFoKoWSwK/OImSszJQsC5CtnNqaXn5pUjbp7MmAVNOZ1FhpIxhLrEEVM5dJ6Tb1+AjR162QZKb8W0DiepZYUPimmoUSJWPTImvoHRZMRkeorOM85vbEQ/rHmreZ7ifACRkggdy23bOa4scQ4cklpE4MsET5JrzucXKSc5ppcynQ6ZTMfsXXyAgObWrUPmrqLb2WVjexOvmkOIhj3kRaCoS4Sr6Sc5rppRyRloycbux+juPoydHTE7vM1Pf/gSna0+F89f4PzeFlIWVMWYnf4GVhgOvWCMxUlDkhqkSDg6ucvt0SFS5mTZJqkSaBW1LqTQjUlI2kqrMkA1n+NCidKGygeccDjnUSJppqtDKYFRGV5UqESxvbuNFAlaaJyoozaN91iitooyYBee8WKKrWuyrB+1vb1qNGECoEFIlNDknQ5Z5rD1gqqYMTk5wCaaQb5FnkV9leAqtBaIINFCLrWsUBIhJbWYkac5Ve1Z1BUmyegnfXwCw+kp4+NDJrMhG1mHTZ1x5+SIq3duUCwKNrsbbJ9/gP7WDibt0+02e1nSoaqqWO5dSSrvKRYLEmHJ8g7Sx/TITtqj8p7KeVw1j6CmjOtVDxMPwZQhUYqkDtRmG6kElXNU5ZzRdMR4NqXb6ZFkHVKtkMJCUXP74AbeS2SW0t3YR0eEKtoFguBWPkIM6KMIsg6BRTVnNC5JsmZd9Ip58Aiho/4aEudsDGqbNdSKyOiw1mLrmk7WpZdvYoPBoJBZlzIsMHQwy4Nvi5SShFgSfTgZs5gv6F+6QDfpMpoN2cwGTKqShfPMCk8/F3SVQSUpdfDgPE41a7YPBFcBLmroIdFaRWCmYbwaobEioLWKa78XGBMracngCNjlvhj57IaaANJgREAjMaYTfXsfK8AZ6TFa4uuKW8enWOvp9XbpdjO2s5zN3YDKFbff9dgMfJiypTrMK0eZ9EnTLlU9Y1bOGI2HZEGQGI1Mc6SKFdJ0kHgRSQO1r7EejIi2EfWEbFPNMPpQ1hVQlbjg6GQaIwOF141NSwiKuq7izi5BmqSpGC2QAeaTUxazISLp0N3cbirKeoLTeA+19PjKR6AWhbMSG6KQuNQaF9NHYkpbUBGQyHvsmQ4uWGbljHp2TJmM6PY79LIUVE6qFaWtIktrSZSIxdmctQTnCCh62lCFAiMVOtQ4GdjKBsxKS11ZlNIkKrKcpJLMT084unuAyBO6OzsIqdFOgwvUxYygFKmTzKsZUlgyY3EqxSsTmWMugoYhbrQsSQwh6kvhBFY6aldGdrDwBJFQB0eW5yRJF2stk8WI8WhKOqvI8wqVpRjTJUhDwOKtbeaeilVNZRTUr0NFBAobMYHgOLp9xGIx5fz9l8m6eQTYZIpHUEFkKQVNLTRCShQOrxQmBFxQuKBAuOgrNHCLFwJPl9pDN5HkSlPUcT0XqolzBJG8ogLCE5mQShKFtD1B58hsgRCavpXM/YDptGJ6WnB694hUHZF2BGm3j8k3sD6l8D7G3EoghUMrheD/54DSXnad7/3FT/mzRcr9n/0D/qONG3QHgro+4XDmKOQ+N4o+n/jM46jyhINr7yAO7vA//+9fp07vY3p8FbOz4G/8B59BKs8sCHIRHV0XPfblyWoL/LQfIVrwYRUnN39ZZve0NHbfXCCIlTrc2jMsYYc2+GzAC0dkJsml/hDRENaBrdbpJTQgTgywfQv+LMEEwfJ0twUJQixDGyRN+swqAG/Yr8tPFEK+Fzxg6WW3QWmARgdpDZwRUajx3tLMIXiU1kgXnZPnv/EV/vEPr/A3/4v/ko7KSIXE2kinbsd7NfisQJp7+xVWsElYpog1p+cBWv2n1v1fOf8t4yeeaqrG4V8yRZo2GvRp9XLbRtbYa6EVcxdtMBNn1HrEsYI1Vu8dKZZ9XsNI2hssmWvrnzYUWfF0xPK9r1d1u/elreZ0q2P0QUBn/Yq27VXp8kbc9wNjEts8o21EnFCtDktoA9vGefkFM2v1DKJJ4Wte5Sp1RdCyiGIXmtBtGXzGv8cxWGOgrd9iidhw5lW0VauWr8v5ZQAb549czot7Abp2KJa21xjikgm09nUlgOAbFsdaUPoL2WMf7HfLVGvfscc1mp5xAWpneAuItoC3WFsfhBA458g7OTeu3CDtanRQFFVFplNs5fjCl36LJOmAFzz88GPIYLGLGdPJmK/92f/Jx558gjffeJd+kjKbLciTDt5XECSJyWIQUzme/+53sLXl5Z+9yMHJNbppQmYyNvo73D65yU53wPvvXaOiwCQ9yqLm3M5FJqMJL/78Rfb2z/HP/uwr1JNTvA+EixfxrubXP/N5yFKefPpJvvbP/gndfJOj0V0euP9B8J5iOMLNZjz11FO88dbLZP0+vqrY2dri85/9LZ7/wQu4pMM3/9U3ePmln7F7cZ/HPvwpfvev/C7/7f/w3/HY44/w6s9+xkd/5RM8+/SneO3NV/j289/m87/6BbaynFkx5vx959FO8NwPv08tK7KgGZeWSw9c4sp716hDYDqqAUmaKJSDd6+8TX9zk63egHfeeZVPPPUJup0B5z9+gbquY/Ue62PqW6Cp8BL3hNXcFaybRDz5cmeZhc3Llu3X1wD+uHbH9cA3kkeyTUn1LTx7DyMzrPiRIoTVmtAuNb5te2XxhNUqFXwUDg7N/F/N4dYEm3WzWa+98A3wGvsV8Ni65s03X+N73/kmmZH8+me+xEMPP0aaZqwA32gF6/uzCKvUOZoVya+l44kzBrwG2LaHNcv1uB281Viu/s5yz2gHRjTvyofGaQthWahPrL2/9jGdj8wrozUERbBg6wIrPGma4aQABxv9lPl0zMsv/ohXXvsJ08mYwUYfJTQnBycoAt3uJpNgCELxwKXLzMuKjWSPVCcUk7vcGg3pZF3yPEMlCcPhMZubu+R5H2fBOcGlHY3wgmEdKFFRL8cWTKZjjk9OOLe9j0n61HjK4OPBXDAEZcg3NkiFxBdjnDtAihEqqxlPSnR/GysS3n//daYnQ8i2MCoFN4kaJEmHbPccae8caXcXoXqQpAjnMaHGe0/la4LwZORNuefoCAstmE2mjE6PcdrRyXtLtpHWCo3EyVhC3teWIix4++77bCVdtja2kEmPrJNFFpPIGjw+xMqGBEy3T1Ca/f4FhqMTbl+/xcnBERsPPsy5jftxHioX4sGUaOwkxOAk6/aQDmadQHl0g85WTqWyZvvOyDcu0tu4QD49YH58jdffeJNrVzMuXDzPpYsXGE2mOF8wSDY48CUXupajozk/e+11/NZ5djcukErD3M5xwUEtCMpTuHlk0/vIaBBSor2gKOYIAVnSJUkyvHJ0kARrcaHCa93Yh8QJE1MefUB4Qy1CrOrnIBE5mQpUteBofErtKnqdPhtZH+vLeJKexKBKhXi4WTtJkB5HiUSR6pRkkCFqx6KcMDy+zkxLOp0+WxvnSVMDIbJyygDBldTWIbzDljVzW9Ht7iAzTeVnVOWUo5s3WJRzEgkdKRmPhrx17SrzYspGPuDc/n10Nnfp9raiOLhQeExj3wqpNLa2OCq8iyXrpTLUlWdeFhijyHxN4gMES5CxMqJVBhk8TsQ0pRASlFIkMosVlFxJIiVZtgteYucTju/cQEnF7mZOsAVHJzNKJeme26OYLZgv5uhOhgoRjHY+YBrHJaY9KZRQlL7GIZEWZq6IWlJJhnMOo3sgYqlvpQ1agw5RVD9Ig5cOJSKLVCCjPpUMCGuRIseJBbkX6KDx0oEHKVKC8EwXM8bTU4TRXHjgI+gQUzT76Q5CJPQGJduiR1XNKOclk+kB/W6KyjokKgOiLpkUCoVGoIhV8BKCAJMafF3GKpQuYDRUzpE0DFDnC5QS1MEjgsRgCK7ChhJkq22kcSFW2yKAq2q8lBBKnJ1yPDyiKGt0f5vd7U36meKRc10+9ZDj6d/5Elfe+hGXvvAJvv7DU373dz/Ni998ka+/Erg9HFGXkiiFnSGMZD4fMp7WpEkada6MoVAKE8CJGhUShNI4rwgNmBT3Jo3QsRodMsFLEat0yoAPc6TcQIUIgBNi+q5HgM7RImoNWVdxcHKHwi7Y7WySJhmF8HincEFjRYkhnv5ZFUiJrMUgQ6zOJSXBghYBJ8CqqDqaCIUSlhKLEYY0GeDTCOK60wXzMGWjA2WvT5oZgs8QvolWRKxgjZRRL0sKhBdokYL2+KBQLuHueIpMNH2V40JgUZe4quD2wW1mZclgcxuTZngRq8ZZ4bGuQmhQUuPqilQLjBkwLw9RXhK8RQgLQeIavbaOyaIenXAkKiEQcApsAC1TjPBRY1UKEplRO4vDopVkI93AD/oEJ3AuMJvPUQwxWqNSgUi6eK9BGIKvECJqTAh0dBYkVOWUu7dvYJIu5y9fQskcNGQiQNAoSZz/UuIU4OOYBxHT7QIK73VkXxHTzIKK2kZCgaxjwQUvaqrgMFqBUNgI7TXp9DH9VaGxwRIApwXCGwQeI3NsUGgTSMjZyBThHJR2n+l8znwyZzKckU8O6HZB5RnB5ASRY0OCWoA0Bb/Mj/rDP/zDX2qD/9vf+2/+8NX3TtA7z/Clv/pluvN3ePOn/5Lnv/k8k+xhfvW3/ipfeOYxnvjoZbj6NZ5/6RaPfvRJwsVPsi1vc+X6DY4PSsr7nuHf/jefIcznkdbWCPjdG4xLKRscoXGnRYQKzuBuomEniQiaNOgJEWduq8qtO/RrgetaKZi2elsMEppgU4AIMWh0IUTnRLYOTpts4GPOL6E5jYq4/JKxIIiGLVqllJUOTbwzZ8Gz9rImAAhy9ctl35rMLtVc4kSTPyxiGBOnazN2shWGDdx44wVef+0Frr72fX5yveQ//oO/iZaO3DR0TyWXEXo8lW7Hq3XVxSqIaZz25Qm3lGtPs/zKMgiI3W7+FhpwYpmiJpd6ISvMSIAQMbC5B0hoq6UJEQGX9t2F5qX9QkyHeKv1stVxE4nvrpXCXo7/2n/O3L19poY5EJsPawBWw6z5AEbRLCItsrDevSW4yHI8zsJKq4DyDGh5dliWz9ocbrQR6No1LdCx+nJoxwuWC24QYikSfPY2cq2dVbrNctgEzcRdA2ra9xJEU/mpncuisZXV75Y/tnNhmR7Wthfb9o0Ne/wSYPsgg0mcnTZrFSSXZKJmsNrUu5Xg+lpHVNPW8plW/IhY/nhtbWE1b6OdNpWiWnaUaOaVkrz37jv8w6/8CZcfuJ9iNOPPv/MXFMWMWzevYOuaoii5df0aP3j+m/TzAd/93l+C9izKKQ8+/BhPfvjjvH/tfTqdLt1uhhWRRfLzF1/kT//0q7x75XW8L7lz6wbF7BiJROsuFoUPFddu3+Zv/Vf/PQ7F3vY2jz78EeZ1yaKccv3mFRaLEV/87OfZ29plVhRMx6dcvfkWJuty7twFjm7e5fToLge33uPmzeugNI9+7Aku7l3grddf5eGPf5ROPuAbX/8ao+ERUnl2d87x53/xDQa9hNH0kHfeeQ0nLU7AbDilmBS8/cZLDKfjeLKuDXXteOJXnuSlF77HN7755xTVgvmsQErJU7/2LDdu3+LuwU1mwzEPP/YIz37qc1y99hZFGSnfWnq29vb46JNP8uFHP8zD9z/EW6+/iun0+dSnfpXO5ibWN2f3TdDVzmqJX1Y3FCIQpGoOD8RSC4DgVmDP2lqxXI+I69V60mlcr9q0sYZ9hGjSfVacvla4nmZOIUSD+7Q8pUaTa2WGBBEFGfGh2SaiY7acvxCp1zQB95n1MDbW4GkopXF1AGH5zve/ww+e/wYv/+xb3L51nSvvv8v+3nn29vZBKhwa0aQ7tHuBlxLrXXOg0K5xDZBEPAiRsHZWsHrOdZtqf7VkE4u1bsdl5IzVn7latPeMe4lart1ijZ0lGgdQUNawmFukjA5ihcA6RTfPMNry9ntXee573+TFH/8QfGQuGQnWukhPdwFvOniZ0B9ss3vuIv3ugP72DvP5gjTJ2Tx3nu6gF0/8pGSwtYMxKR0Dly8mfOS84VMPbnN0MmVcOBaLOaEeMZvNKepAp7dNSE0MQIxCilhSXkuJcguY3saevsP89DqZ8iTdHNfd4c7QMR4VHN6+w/b5+2Fjm8uPPUU32yTrb2N650g2LtEZXKLTPYc2nfgebc2yYqxISEyCRhKEQwWBSgzVouT49C6LYkavt0kn32oOthyEGluW1HWNrQKIQKUq+iZhY/Mcu/v3YXp9OtkgpgI0b1FJjWqeT2hQpoMImruHR0yGQ4QynHvgEbTqYq2PgV8DpiJiOXIlVdRlkoIQHEprRqc3yDd2MdKAaYNogcOT5jtkG3v0ts5Th8DdOze5ee0W03nB7s45prMFiRJQntDLax57ZI+NXp+716/z3sENklQhlUHogJQZ2nQwaU5ickyaRwH3vEvS7bOVGZAlynTImrTROiwoPSiRNqL1K2aL0gnapGgtSUyKUR2oHHeObjGbjRh0enSzLgpFXRW4UMa+ErCNc6Xa40cpEBgQTclqJCiNzFIGvT5CaCblgtnsFLeYg1AopahsiMABKSHNcBZUp0/tBOPpCadH1zg8uolfDDk36DAdz3jrnbe5cvM6/Szl3N4+m/uX6O2dp7+xh04SHA7vY6n64MHZ5uCKEFMAtcIoEzVTqOllOb18QCfL6fVzOnkOQiKlWVZaUyECs8JWUA0pqwWhrCiqKVVdMBsdMTq9hfUF/SRjMj3iYDRhPK8QAfKNPkqmeDRJZkiSPGqrZB3StENqUrTRZFmfTtqPdqgzZJZhugO2O/sYlZOlm2iTkyYpmeyQJilamciSlAohm4I43hJkiPo5IlZbjEtYZJp4EfWAos6eIRUG50qGw6MoZN7boZNvErM4oMIhcVShQHsd9zLp0HmPTtpnWJywmAyZz0+x9YxELWdGs/fICIyKgPK+EZwPTYWqKKxNiBW1tLYkzKirgJcpCEFtI/vU4bAypl0pFGgRWV4Salsyn404OjlECsX5vQvsbW2xnRl2OoKeAVUZdhdDvvODNzm9XvHSTUt+VHD3eIFTMatEE+0+oGL1SpOTdHo4KViUU8piDj5WKUdEEfUolhyaFNTIeKt8SVEvcHWBciUEiw/QURoZauZeIvAIFXVyWjaLBZSvmQwPOTq6TZb36PXPRYF4wIe1kvZCEkSzL4aoSa0a1rC3ETyyIsTqcqpl7wMhUMsA0sTy8SqgdEqSZEiVU+uEcVFyPBlzfHSA8lUEQISm9oLKBSoPziuq0lNYmC8WGOHAl0xshulsolSgcBIfLKNiyjs33mPhLf2984ikgwom6g7ZkqAiQ04R08m0NAhvcW7BoNcH3SWoyI40yqCVxhiD1HG9yXRCHSCR4EWNUQaDJygZ9YIa2ZlUCBIVmZkyRP/BSEE30eRpB6UTaidZVI7FZIKtFihfoKVF6BiTCw/Ol4yHhwzv3GJjc5fB7haZ6sUiCFLivMAoEYrC7AAAIABJREFUSSUkyiR4IVEhkk0wOQ4VwTTfxMYykidSJdBNdUBDAgJq4UiUxBDwUkfmpBRIvcI6Iuu58V+UAQFGxTRuIcWy4qeXGi0Vmoo0S8m6HdJBl3ywhVUd5gvNYlQyGY0xxQytPYkJ2KD561/+9H/NL+nzS2co7W+eZ3/7mOPNh3ik8xY//eY3sN7xxklGf3Cbt77+d3hdDPjE0x/ixz9+mxdPNsn+ZcUnP3yJ14v30btP8sV//2+wv/0w9bRES4UUNa1XKHzjEDQsjiWrY+Vis86HaePddhFtja+tbhNJFeEXPssKwFlLIYAl/X/F8ojetWlESkMLcLW6EELFrtwTtLduc2Qyxd/5JkhvK8ysf391viqW6IFs70dzzyZ4btP72sBCNe21rrQPPm5AUsVF30EaSr79ra/y3M/e4/hoyu/8u/85O7mFoCFYdFA02c/LFMQWZFkXcl2dCDdMDBED5Gj6DVuqRTOa4Kc9Gm57vYwEgmhApUBYAkfxJqsYZxXgtJcv08NCQx0Uq/YDjR4GtJlejSbWWgPtl30gNJU2XIOItPhdPNFuNLKWaMn6XIpssOUnqOgEiFg/YQVKtOO1jpS0jxnW/rYOKonV2K59r73vOmwSh3otRatlJjSlgNp0q3Z+tXNv2caSyRMBH7E+9qvuL9/dkql2Lyi2tMWwmsmhOXFZe+crhgbxxBCI8Ofq6e4ZuuVYtEhrk7TTpPSs3m1rq81Vzfcb8EkAwTcYgMOoNKaohrA2z9v7NqmkiLNCwGtBuWjE2CMjI96nxagEbZAslnieBEJTnruoCi4/fJn9Xo//5e/+TwitSJXm3dd+jPM1++cv0O0PODk9Yv/BS/zKx57m8qMP8id//HeZzwoee/wj7F7cp7u1wWtvvYkj0E17XL12FRdqvvClz/GVr75HmqdomaKCoJKBS5ceIu9sMhne4fNP7/Pd577PK6+9yW9+6YuoLEX3Ul5/+ec8/pGPUVvLH/3RP2A4POb9q28QRM1Gf4PR8RE/ee77zNyMH/78u3zuqc9gKSmLAlvO+drX/ilvvfkyV2+8y4+T7/D0M5+krJ7g+3/5De4eHiK05XQ2xBUT8n7O8fGYvU6P/fsu8tu/81f4wSvPMz45pNvb4Pf/w/+UX//cb/LWW2/x0ssvcfX1nyFCxeHhHba3Nzn5vw74/Od+m+9/61v8G1/+PF/+nX+Hr/zxPyDJU3rWoTOB0h2++Btf5su//deQXvIP/9e/w/FoyqSYIVTCX37723z61z+NChKnVrpicY2V+CiFEbEW76JYMOComxfdMAcapfUQ2tCtBY7W5kyzrrSALbBcGwNhmeLZpmSJsFrj2xm9BMxbJm0zt5CR0h/THn0TMMY9qt0LV2v42dTv2GzT2Wa2xls4pAgMT4dcu/IG77z2Irvb51gsPBfu3+fBRx5C6rQpnx7wSq3ZXZv4uux504e1Sp9n0kdbI1u/YmV4a7Dy2nVLc13bTdeuX/MfQqBJL2yfWzRlkMUyPTcIgVGGrGuwOLyFTBvOn9/i5sEpP/npTxke3mA6HTHoDwiuJMjAdFFTFgVlZVFpD2sD57e3ufShD5PkA4IPWOfYv3A/UkpKV1LOCxxddJojAvQyzW88vUPma154e8KL79+gth7jA97OmC0klQKVdmI1M1JEEpnS1DNkMaSY3kUyJ9USn6UknftwSZ/xbMzodIotHcoo8osPkmzchx7fxM4s/Z37CHgWdRkrEc2HlIsJlbMoGeh1NkiSDjJRCF9iKwfaoGVKSAR3b7zHZHzM1vn7CKHLqCjRiwIhbdQkCZI07aFzTUf28ZqY2hUMITjKUKMDWBENTSqztCXnAkZlWFtxfHqH0XhEN+mQDzZwSjGvaowBJQ3SBwwxcCmDRSqJbioY1cHjpKScTGPxArNBELHOkZc1RsRTbV+DlCmmc56dZJvtjR2mJ9cYj0956813mc5m7F28gNwasHNui5s3b7K7M+CzT20xmt/P3GUMSwUigWAQAlywzSFTw2p1jk6wJMIhQgkk6FCzMIokGcSUitoiREpkwcSiJrEysEchGY4mDGcjhC1J0i47mz1KQUwt9BKtFIEcUAQhUEEt/et4UCibvd5GbRjhsBZwllpqZGeTre4G1luK2ZRyeBIZA/kmJh9QCxA24KXATU6YzofMTm6QdTbZyjc5mc147qcvMRkO2e52eOj+B0i29un0NkmzLlKaKLjtoLQBsIhG70WomE6lZFu9ylPbGi01tU9Y1I6yHjFZVMwncxCCrGtQMqUqwQaLCAEVakSQBGkRUtHJuiRK4Z2jl/ZRcoNiMuPo+C5Jt8e57QepvWA6usF4dMrGZkKvd47UGLwUSG8hKJySeBxZUFHvSQiCc8jgyG1KEIJCLEiTFBcCPlh8rTAqEIJuhM1TwNEWO0hFDgJcqOOcgSZjQqEA4zUBgZKeqpwxOrnNfDGjs7HD9sYOSsXiF8J7XIhi7UoprLdk0uBDTe1Lgq/QwdPPOjijWcxHjEcjivmYjf4OJtmI4x8EQiqEsCgRhfJdk5ERQqMfJAxGQmYCSe1YBI8026ANSlRIr6iFQxOQQbEIgbxlexcVh6c3MdJy7tw2W50NFIbgJUVV4mrLvJCczhNmBKwYsEg8vY7i6+8cczCxOGtwzmOkiWOkA7WNFQ5d8OgkQWkTtYlqOJ0cob2k1+8w6PSoaKo4ComznlwZEhHZgrphRaZSokSNF4ZUdUFkEEoINd5X6FDiy4rbJ0e4oDi3dwGjEtrD1ipI6rjbRMIvHtdIPGijCQ6qSC/FiYCWUUtJEm3ZyzYNzyOliPYgPaBRQREk1MqjVYLWO9TOsphOuDMrUNNjsiQn7W0gjSHEugd4KQnO4XVCISATFpUCXlH5qI92MDrm8OiAzY1zpN0+HtBC4EVFsBalVfQ56oqgdDwEsBXOQ3/Qx/p46KZV9Dfq4KIthoDxkakanCchYL1tQJYKiNXYouJWTDOLa6BDSY0UGToYFBYvHQmeTpKym2UEYyi8ZTydcDQ8xd0d0t/o0x8MsB4Oj0+oRyPOXbpIf2OLVCXNAaGK/UsFBZKMDBcsMpUo60ClSG8wQi4P52wrI4Ii4CJTtyGdSAkdBFIoKhHnl5Ueg0R6kFLjhMTRaJ9KFddiFQkOVghU8AgMTmq8LFBINAaNRIuaVBukVHRTTbW5wcw67GLB8WSIPpjg6hskvZxf5ueXDij92YtvU9tzPPt44Htf/fv8/LZnZ/sCj3zkIuO7V3nu5SM2t0veev8GIc35tU9+nKuvvcC771zB1zO6nTGXepLf+43H0OUMoSTex51yJZQp18Lu5nW1vq6IxuVoXel7AvbGgfTRe1xlRTVAU1gLlmNaWeN4hyaNR8QAOAI5jeBue/wbIiPBLyOBCC/GeH3FSGr7HkI8MYuXt4hT62w36UhrQXMbJt8bqq8H9+uJVjTNnmW3NEyrKKPcAFiBzEjefePHvHf9Dh995jdQ1nPfgw+gpKGqFygdRdSkZ1V9qxmTqOe6xvZYgj6NQDGrNLX1E44V4CMiOiviZkdz/rjO0ooAylnNKpbA4fpYrFLKVnomy5BhLdhvwZam52JtTINgqfuxLIvX5tWuui8bAMmvN3qmb01gEoA2qTKswSItVtZOiOb9n3milsnCKkUsjoNb/rzep8YEGsBr+evVOLaRY2jbC6v3ttbUWXAkhqThzB/hbDre6tn9mRuvAjoRWrDKr3UupnnGH8Pac7aAU5vl2zDVWoDoHrAK0Y5LtBkRIojzi4XO2/bD6kfatLnGymSbitaOfVjOk7ZEMIgGww0NLrUaxPXbhnYNaJkOrMLj5olX81KCD44sy/j2t77Jn/6Lf8wf/P7v88JLr5Jpzfs379Df7NLp99Aq5VO/+gVuHZ7y3eeeg4UF1eH3fu/LPPnEk7z16qu8fe0qN957h+TTn2HRG/DExz7EtVspJ4e3GeQdzl94ACU0k/Epd4/u8sDFh/AeLuzuI7zjtbdeQYaCN994lSTpMNjoMjw9wYUarQ1vvf0TDu/eQpjAZm+byWjKhx7+MNfv3CKTCUVRc/H+R3nhJz+gn/X52j//M4aTu3ziY0+QZ5v8/JWf8+DjH+H/Ye3NniU77ju/Ty5nqVPLXbv79oLuxkKCJAQCJMFtKEoUN22WPKvkYYzDilB4Ihx+97P/AL/4wXaElweNHeNlPLZnNPJIlEYUJXEVQRDEDjSARu/LXWs5ay5+yDxVdZvy07AQ0Re3btU5medk5snf9/f9fn9/57O/xN6ZC/zP//x/oDM112/eJNUCHGwPN3FC8pnPfYb/6X//Z7SHJ4w3tnns4mUEKffvHfDw+vs8uHkdJQUP7t2myCUP9m8zyRL+4A/+O5588ipf+dVvcHh4yLys+MIv/hLf/KN/i7AJSsPs5JhEp2wON/knv/dPKbb+OW+++Tav/+SHFMow3d9ncu4csl+viEs2EkyQwwqVBJCl64KZugqJhKV/nfP01WqkXwGJ6/PuFC7cr18irq8RNRdxfOPD2OqTJTyClcrlyAp0dhFp88vnAEtnpX6RWDbAS/HI/I3PLdH3O4AvWE+qBFsbG/z6r/8GqfZ4K3jjzVcpihEnJwuSwZw0ySO+JUPUu/ac6nGq3ilu2YHTy8hyrqy/26/sYYl+ZE1YfWDtuOLU39Zw+tV54k0Jkuy450BEIA462wWLBwcbw4TOOq5fv86//sNvUZ98wPZmQXX4EC0TrBDMypLadHg0xWiHrb09Lj72NBuTXQyOpmpIpKIoBpTlAuttZNPkyEShBEwyzdZA8crrB9w/mbPoIEkz6vKQ/bKGJARGhSiQKIzvEF2JrY+gPkE1M6BBqZxsdBbjE1znqaZTThb7qGxMsbGH3h6gkHTeYVyDRLOYT0kGo1hFK2EyOU+SapxpWVRzqm7GbHFMO31AqjMkGZtnzyE8TI8eMD16QJIP2d19kqqpmc7vMcgzJpNzJIlCaoF2eTRet6HUs5J0rg3MAu9Adhghkc6ggc61CCFIZNjQHx3c4mD6kCTJGQ6HZCpDJINQCU+I6Gu53OxhvCPXKd55dKJQcV0XDCg2z7JRDBmklgcnh+yMtmks2LbBovCiwziD84ZBmuIH27hBSaYn1CIYtL/x7lt4C/cuX+X8uV3SrODunVvsnpFMRMPZbEBpBQ/mlrIT6HQQ/DcI1Z+c8KRJMG1NbcWhOAHhSUijj6VGygQlCHuVWCHLe6jqBdPpMU4YJuMJhZIY39G1HWmeIVC0ogUfGBmdtHgnSASAocWixQClBR0K7Qs0BoNGpRIYIYWLZesbsiQj3RziOseinDKbLfDzKZvjCSINsrPu5D6tLRkPJhyWM65de4vjk0NGwwFPXn2abLxBsX02sHWEp8XRuWBKbXyQ41rrwFqMFXhfxvXV4kwXTImlpBNdkKJ4jZUe62CQj2LZ+xSlJHkR9hhap1g3R8XqVypN4gowwFtDXVYc1SdYNJMLTzPJxsEXxXac3blCdw7qasHR7CaJcST5NuPRGCUSpDFYwEhQIsgYlRdoLfHO4UTCMMlDdUVv0DJFJBKpBKZr2FCekzZ6XwiP7drgqahCST8nFBZBLoNHCzZcI0NLeXLEbDpFCcnG1hlkXlA5g/KCRKVY34J3JCLFe0UuAn9HiQQpNFY5Mh9ANZmP0DpjPNyhaVumiwq1aBhkCfkgJ5Up3ikckCiJoEMnsGhbvEnR2pBoUNLRdC2NsQxo8U2DNzWNd1ifoLSgdg1aKar5jOl0ipOerfEW4zxBKknbAtKipEeLBK80wlq88RzNHFujEReGKfOmpW5rJllOrWBhJa2RGAzOB9BlkAbmkhASZwVeC0ziSF1C3cw5Ojzk4MFdzkwmDEYbqGSEUQJjHR6DsS0OTyKTAP5pS2osmVYY0eBw+K5GGcnB/Ij68Ag5GrOzc5EkymgtFmstrTd42wWZmQ7tSWQwZu+6IBXXTtAJiZc+7i2C8kKqNCxrLtjCCAKTRqgg2RNeYFFYbUhFADaU6RhvZMFQWmjqpmZ6dITWisEgY5DkGB+qh3orGWwKtHeYaYNKG1zbcef2TVo/59zuHgiFNQ1KKryLwJAOHpWudegkW1qbqCQlzzSLxtAKgteeINwLJEKq6D/lg+JHhnhCEMAVJX3I/Kq4/6DFuZDuVR6sMBhvwEPmBTgXQBs/D+tkbZhXFe1syijNYGMHU59w7+F9lMjx3jEZF8i6pWwfcq9uwMJomCAHQzwCoxyIJB6/I5MJBkVLSyISBBoTY/EET1s2CFejMwXDAuXDvTNOk0sJdKACw0j44D0XKsuFfaaOvgjOdDjf4bMhWg8ROLxTkENCqE7tfWCCCQkyeN2QiYRcKwrlMHmGGG7QtinWbLC/mP/snunf4/VzB5Ryhpx/4iqzt99kpj5Opl9lerJPXZU0FtRowHDvEoktuX/nLgub8/nPXOJPvvkDWplS7d/kL777fb7y+S8wyTx5pA4G3BZWAfAKuTBAQg/SsKyqttwce7f0M0KEwG9VgcsFozTvoiniKvD2EE3BYkUpETevRE8kH6sPeBFLulqWkIAXIILuVsQAtZcAuSUKEz8X5Uset2JOETO5a6wSfDBjtf1GX/SiunBt1gPx8J0eLOk7FHfXMkxG4XwocRuPkiTnePxTf5dnPvRRbL1g9NhZOhqUDNkWRVjQQgWDaFAuVhDRsiJQvMYhPnHxeq9EcUsgBlZt6/soBKH0/PpNYJlxX0oQInKyBgssRWDLSnVCBCAvyvTWuWt9O3wP5PXn6YEnsYJS1oO05e/9538mhlkPf9Zf6hTIEP4eQtNHA8u+jb0kbsVC6O87yxLXp84Vf6yblEdYZhkYrTdTLM/klpvtdWnMWjcfaXf4eboKHKc+ubx6YtWm4AUjVkBV/KcX3qxHdn1z+4C3Z1n1YN/Kr7sHcSIQJRwsQbrQod4QH8RyiK3LAT2hH4pAXRUuBNTLO+SXZlFLRlhPifNLQ/+fhblE/LwlAFCnhvra3F4dk+XvVVmys7XNR579BFc/9CyWAdJbvvTlL/PySz/k9Zd/ysb2OSZbu9x95y0+9uyHqCtLsft5fu3X/j7f/s53eO6Z5yjnx3z5S7/MtXfehCTh6oc+THVSUQyH/O43fo93r11jUZV85JmPMJs17G7vYpzDYFBlQ1t15FnCg3s32Zyc5ZVXXqTzNR7PV778VerplJeOj0B5NkYjPv/VX+P//r/+T6S2zKctepTzb/70f0NlWfCF8DOeevIpyrbDuob9/SP+/E/+mJd/+COSLEXIlHN7u5y/sMeNt99mdvyQlooxKccHM/LOMt7c4OPPvcBituDNV17i1o1r3Ll9k7NndjDeMDu+R1e3SC+Z1xYjBDfv3eF//O//W86dOYPTmt/+D36Xv/6Lb6OEIB+O0FpjTNjcyXzEx59+gcXhjNY7yk4wnZdY94Dx1gZZknB0NGV7YxtkBBedx9LR+TA2bd2SZ2mUJsQ1RYUs4hKwiGOoryoIIWPn+8EtVgC8W863+BSMYKnojxVH62qOrjFvZMzoR8bccj0J6GWcBuGEPRDL2vHE2qgWBDmpxIHUWNfRyeCXUBSbjCcX+Ktv/wldOeX1n76MVgl7Zy/z/Kc/z+bG1lLSGYb/imXpXFyH11cbsZz4y2f72uKymnA9yPXokrvaIqzj12u/L1fauGldndu5kBlUIlRvcXiQEqUVWgpU4oO/imn54cs/5Tvf/jPSfMCHrp7n5rV30dkQoTWm63AWilHGZLzFuQtX2N7eQ+sEK8PeY5AlJCrlzr27dNawu3EmjBkl0VKQCkvXtlyfd1jvyFJBoiUnixNmJ3OK4RZOxXLjtqVtT6B5SDk/JpeeLM8gG2DTbZrOUM8q6sU+VbPAiRGD3cfJ8nEove7CPke5UBxkkBSU9TEy0TErrMA46nqBEIKimDCWEwQe0zXM5icsZnNuvHUzsH2M5+K5S6SDEUpoNodjzu9s42SGiWb0DjBCBEYKwXvJW9AeHA34UOLa+RBMdNEQGODB/l0e3LtDNhyyvXU5BDK2wesEJRMSH4qzCEA6AVLHiaUwODLlSH1Lax1tW6GERhrwSOaLKUk75/jEo5IcqQQJoAcpUhQ4oCpPmJ0cYMSQrb0nGKQhMz7YusD+/Wvc3j/m3Zt32HvvLOfO77BpBSdHD9nd3UHUD3AnJ4zzLaQ6TyMKhAUvOhItqLA40ZALy2g0whpHiqC2oRy2l6EohvaBPV3Xc8pyijEtk+EQrVOMqTAuePD4QmKtx3iD0gK8wklBLkO1pvAoteRS4ZXCO0EWc5qqk0gSam8JiUiDFKBUinDQ+RYvDKNJytYkp54f08xu0QrDaHML4xvOjoa8/t7bXL91j61ixM7uJTY3J+TjIJ1s247WHofA0nd4q5BSoHSCQIbKrt7jRGAiCBUq4aksR2kV/ubdUtqphQqsHNczmMI+x4kWLQXOWrxMMbG4h3GeFEnb1MwWxzivGA3OorIM62SoHEeLElB7EEaTF2MGucK2nrZcsH9wwiAdkWcFKh3gvSKYrHicl7SEsSxxWNsSOJoWLRKEtdT1DKUX6ESj1BaoUDE2STO0KHAEnxktBZkU2K5DqARnDWU1ZVoekQjF1uZ5bKwQLqVGO4dyHmGaYL6sckCjhacxFdZaVCLpvEFZQYkLJtESvM+Q0pMWBclgCK2lrU5o633yJCEdFKTFECcViVbkeYExNZ1UeOewDhwGb4N0K4XAPklynDEo74Kk3DTs33vAfDbn3N4eGxu7CG/CdbcSrSXS9cQCiTMWo6HzAQRtrcKYjodlx3zRsegynBUo6fHShMqkLvjkOXpPXgKjJFZs80qSj8aMBgWlh5Oy5vioIksNw+EIrTTeS5QahWeScCilSOgQuUalmqZ1JELSdiV3Du9jhGD70hNkaYoWCc5bOjzOORpv8HgSqcOzXUikjpXNBXillmsyUiCswCsV9tHOB8ag6FPwOoCPUpKglsxjsMF8OsrqAsQUzKi1VAyGG9ikoO4qqqphVi7ItSJLNU5ohMk4qRsOywpx/ID9w2M2h1vsjvZocMhoam06E+drYMoJ7xgMUpwXWDqG+QDlJK01OByF6xDeYrzA2T5mlNHPMTCVauNppMU7x4CEyne0xjOf7iOVIh9tBFBTEivVCToV9jfSgEwCkOuUBhTCCRa6oNjYoCoPqY8eAoaNvSdQyZjWOcrpEfOjIyaDhNH2GWSxSa5FYKrpnNY4dOKRTuJ9MKA3mIgFJCgHKT7I3rzFDAXWduRJgo6FbawX0IJRklqBNwJtPV60kashUFJg6MJmxXrKEjQGNfAIXaK9QciExkgSgiywoyWJez8tQpEWi6eToZJv27TUR0f45phB6kjzET/P188dUHr+c5+laTvai7/Fb3y44Ec/fZa7H7zD+aee4/wW/PTFl3DFLht2Trp5lfLm9/mzFz+gFZq68gwGBSfvvciffuuP+Ltf+02sCpttE6PuVaUXv6SnK8DFgNT1QMIyQgsbZ6lWu80e5Aj0kHWYysUgNGwspQiBuPN26YNiWe1q14NCSwCJPCJmkGPsKCw+bpz8cucbzrgegkbSSASuxKkYfT1QXf8Zqvr0TfCngJB11MOvnQPAu8Cs6k8qhMQ4uPj4k/xKArPDGWeuPs72zhbSAD6YWPbXv2+flLEqQY98xGx5z1BZ+Vb151+BCaF5ftm/cIXi/61riPrqXn4l41vDElev5fUOH1j35PDWrgCi/kILvwzDYN0nYwllxbESrqETQV6IWF3aFVBxOuB6JFZhBR49ck96xpVf4hPhd9nDMT4+AP2KKbX2+VMn5XTQ1Es7T5lc932KJ41CryXO2AMbcfTj1/7r58mp3p6K8NZOsBy+0Ucs9lP6HkgJjRAET5/wckDIOIZDiNV1wq+YQDHolGvn8/D/w0QKYO96mLoKGt2qC0LghcL6kB0OfooC6U14iKAiYLsa16cMveOQ6uffutk9oVf0Ifqj8fJqTsUVyAevMaU1zz73HP/F5f+Soki58vgTvPbGa9x8723efe99Ktvx4IM3GO1ucvdonzMH2xwdnPDCpz/H4f4Rly9e4off+x7H5TGvvvYyL7/8fXZ2L3Dn45/ENAvKWcXHn/8UX/jCr/DyT15je3eTza1zLOpj7t3f58a7b/KpX/4l3nrvOrdvvkHZNjz7/Oc5d/USne84c+4Cf/gv/w/u3buOGmpKU5NMhiTFBClhPNqgcwu8cGyNd1BywMO7d7lw9SmaxZyyLmnre1jXcfv9d7l84SLbO2e4dfcGX/vab3Lvxm3eLF/BItFZwnBrwv6Dh4xGG2iZ8+KLP8bbkpt33ubq00+jSXn/+geU1QKFIC1yZAdWSo6OjxmPt9jfv8OXv/plvvGxZ3j33WuhSlCaMFIC4yyvvf4GT1x9gsVixr0H+3g5AA9//G/+BRujM/zD3/nH/OSVH/Gp557n+u17mL2aixcvhEIFQuKMw7UN3/yjf01WjPnc3/ki40mBJNC9rXNB+rsO9srwvPHWr42rONbkGsThOcVGlBFM8n75ZKE3gGU5H9ZGfkRklsKxfuAu/7x65vRMQrGcIKeRmqVLmjUkQuNN8Pf4/ne/y5/+8b8iSWKZZSW5ef1dHty7y5Mf+QgbGxssnZ3kGiO4Zz/BKcbQ6lnnl6BtP4n6nkUYbLl+ri1Oaz9W3/Xrf49ztgfFfVzUnQ+S5ERprLHLL1rnorzZ4TpLVRneePU17t25weXzWzS14r23XmdxcsLZixcprcCg2Dl3hTNn9tjYPoNOB0jvaOoSJyRJmuKso2lO0FpTDDcDa0YJjHRYY1mYLjBXUkkuFW1j6RYN3ktGk3N4V5KYivnsIblboF1Dmg4gHdBJxd1pSTc7ZlAkVPOSeTvHphO29j5MMd4FH+Q4yuc4CV1Tkg5ypB6gRcGinLGYHiCFIhsU6LTAkAcQtasgfPrSAAAgAElEQVRouxYlJJ0QiHzASOVMJpvkgwRjOg7uH9E0hmIyRIic1muMbCEGNAqJUx7ru1DVTGgEoWS2wuOkp7EW7YIHpfeWcnbEyewIj+PchYuIJA9AhAvJriApl1gfsvlYiycUphDOgfW0XY1xlhbLtJmxqBtGRYHKxqESTprj2orRcIjWA6Tqq2x1lOURZR18g4bjMyRpjsNRtk0ApxmQFmd54kMf5ej4DicHd3n1zWu8f+09dvd2QBV0TcnmRspw4CiKGXcOHvBgbkgnezg9xIoc4Tq00NRNhSOL1sCOhJB5V0oyrypm0ylWtBSjARvpODBaPEg5wHlHIjM6wAiHSjQDpVBJKCGtPRClRzKa09q41nhjybXCSlA4BjgaG4JzpxzCWBrXIrwnoQXTMqumuHrBMFMUUnP39g3u3P2AlIxF2fD4pcdJJzvk2YDRaAsrJNJZBqpAaI13FlxwvDHOxurKBM8ypRHSkyQ5znpCxfF+3+sQNsiwnFIYBMYTfOecD6bIwuHVgBYTy9s7hukE4YIP2bQ6osVTpNuM0gyhNZ33OExgScXV3NsWpcKar0WOynJGyYiB8wHYmR6RJHP0YAIJCCdBOrTSYZziwdsg4XQe4ypyEmSmGWdbSHeCFBVa5mglQScByIhrp7Wh7ciWRXnCyfExTjmGxS6JKgILyoUi5aIL8igpPEpJOh8Se1K4EGjqAUZYUqHpBMHXUQuwwUvPSIvwCuU1HZ4kS8gyjTUl1XzG4f4+qTpiZ2OEL4rgUyWTwJiyFlqB0IGNU3ce3bUAWBOS1F3bUDXH1PWctNjksTOXUFIHY34lEQSws3EOLRXGe5wIYL9EgnMYa6k6z8MmMP5an2OFItUBwEFHAM5ZjAtgjvKAl0s/U6WGCNeEZGICQwtpOkQbw7Sp2T+uyLQgzzMSKbEiAJGFSLCipnMVvhKYznM8O2Q+3Wdr+yzFYIxUwXOoNjY+sy14gfbBzMF7AuNQJiA6OmfxQiEcSBQdHu0JRVZ9ANxRBLCMIBe0zqB8SARYJXBegQrJGuk1nuAZGR2GQkVWOpy3COUY6ozMKZq2ZFoeM5uW4DOe3rrArFmwf+uIrrOcu3ABIRMqY3BCgmuJKRecBWklmDBH63mJcaESY1kvsNZjnaGsKjAVSZGQJQWQhj2DDIWhEArlBCJxNNKSKg1OItQOqTfsjLdJ0pRUSIRIWSpMhMIIH9iW3oZWJbGCn7X4psLPFpS1hbxga+MseTFCJSmpSrFCMDmzx8liTrdYIKoFhTsgnUzIxmOQOUKkGC3QSiKsBaVRPniXOamRwoILvsoohxcJwkmsAOEsXgEueilFRq4ljA8hHMKFuEd5EVh1yzhTIqXDW0HjDIVXkeZRY2gRVoHqMMaB63Bdh287umpO2y0wXYftHNYbpm3NvG3Jyoqf5+vnDijtNO/wgMt8bnefg5Nn+P3f/Sov/c1VsjM7NLOHXP6N30YNt/DtDJznpW8bvnN4iJQFk7FE6h3SySZv36y5dtLx7DmFt2HhEN7GvWEMymUvr4lyKr/m3tLjNstQTiw3lCtwIUrnooSlr/DiesBJBGGRFqHELN6jhMD6MHhVz1IQAnyoFhSAA0twjQ+X2BMCjgCsS3qd0wrzigK9PshYj437vbTvwRlWO+cesIlfkUJGjGgF1ISr5SKQ08u+fECsRdh4iRjkWy+ZbJ2nmR3y3hsv8nY64tMvfA4l02AmLu2yzT0gIoVcA9nCqy+7/rdVe4PT/kV9H/2akfF633tmSLyrYfFcAw9EHyStAwqCFWhID5SETYWIzfAieuZE/OqUrEwsD3MKFFi9twak9Kddi2Z63thyHIoVIBPeFo+wleK3lrK7PnA8zVxZe4ceBFu+L8Tq3vv1Np4eTium2FpQFt9b+WA9AhCtGh6PEY+6wndYl92IR67S6ttrMFNs65qfIHF3uHauGGz33xSr/i/BMx69Yf6R/p5ufu/rFYLX06wEJSRNW5P4ULmx6Uo8kkFaPHoJwveWwEC843GcnRr3YvX5n70i8RUZKb0a0jqH1hrnHJvjDZRWdLLlYx/9BW7evMHmmW3KWzOEEuzubDFMR3z/h38DyvHlr/8Wd+7eRiQDVOL46te+yvmLV3nqI8+wMdqgtYbtrR0cYG3HdFrTVDWpTEm0okgGTNIRG5vbdAtLZRpQsHf+Im1nePaZT3D99nX+9I/+FcbWPPXUU2xsn+HWvTs0Vc33vvuXiCTnS1//bcpZyb/75h/xpS99ldfeeI+D+7e5du1NvvKVr/LC57/Ij3/8Y1783l+xf3ifG7c/oO4qnn3mF7h9/Rb/7s//XxLZ4DUsZsc8/dQvYJyhWswYjSf4xQFdJzieH/CD732fyXDMaDSkairqOmTideJwXrI9HlOMxjzxsY/y9V/9DcrDircO30ZIjbeeh/v3+f63v8VL3/8Ru+cusJgd8nD/LtZL7t95H1OXNOkxx8e3+Jf/y3/DretfxxvFM//J7+GMQwpBWZX85OUf89bbr/L6Ky+ikwFlU/IrX/46G+MxXsgIGEVQopfBeXDWRhaoX8qJA1OI5bIhiV57a2P99Hz72QG/DrpAYEIqwYrNGkFdH9sTPrUmhfNuBWL1tF8fAVVEeB4rgiEznrffeY3OztBZTmcMQmguXXmce3du8fYrP+bC2fMcLWo2JxvBqDP6KXkfA51TBS1FP2FjGwW9NvZR8GgdOls+UtZwMBGzO8vqoLEPfeIjJoYjizl6DkloWrs6ZjCsYD47wXYNwyzl/v073LjzJvt3jtk7u0NWaI5vzBgMRpSNIRttcv7MBbZ3zwYfJDzedtRdy2J2yNbGWdqqpW4rhE7ROsW5DqsU2A7TWfpqTpnQ2M4yXRxju0hndzVuPod2inEdtBa1VWBMzrTz2K6jmh8gXIuShqpuMFnBcHyebLyHHgyxxiCEROkMIUEZg8kkMhHQhtA3QaKVJi22UL6jaU9wxuI7x6yeM5xskpLw4P4DxufOkhUFIZz3FNIzeGxC7VtMVXE8v4/rOrJhQVZMENkYKRVYhxSxIqYTIC3SQestzpiQ61OOsjxgOj/E28AsTNNQ6S0k/cBqiUChjKGqZ3jpybUM0g/nQkAsJFKnQeomNVLAznjEOZEENrzUZEhwhmx3CC4kFduuxdQNs2qOVJJivEuigmzJhnJz6DTF2eA51FlB4wsGw8cYbV+lqo8pH9zg3v273LvzgO2tCXtnd1BCY+0x0ix4crdApjOcUpx0CXKwSWPvk2UZXmahSpmxoBuquuLgaI51JdkwYZANwAps22KwqL7ym1AYb8HLsMarlESHfZ+TBJ8VEZI0FhBIsIa6rbC2wydZKETgoKXGGI/0gsa2OGsRvg1MxXZGJh2ZUlghmB+d8MH9B5RtRao1w41tBmNPnu+wOdkhzSUGTyY9So9wXmGcJ9Eag8eaNtx3BzpJA4PDhepl1rdYG2Xq0SsupJTDBJeSYF5tHJ03oMJaq5SitTFB7BVJmtM2NfXsgIOjQ0bjCePxNp6UBon0CrxBCo3wEifC/lXpPIJLCQ6L0RLfKrySFMMJNskxtqatpnT1gizPyTJNLnN0kgESYwWLukEBUnl0ItAqpZAG6RImgwFKhz5bKegZ15KQMD06OuLu3ffRSc5gtIPKB7TBdCP46WCRSmK9iKwohbMerUDoUFUr9RLjYShBScMQRSsCYCOURrkgJQp+q5CIQeivE/hkxHBrwsCDMQ1H5RzKBcOhROgUoQqECJLoRGVhnZECleZ0xmIIEsmqKkm1YrS5h5AJRrY4NNoJkAItoBMOoQSoUEjI2OAr5T3MrcE2Epxk0UHVelorMEKhhIzWpi5KJ2WUt9ogcRWBARa8oMCQhFJiSQq+QYgGp2AynOAGksZUzOoZwhyD8BT5EOMHSE7wtuXg+Ih5UzEYjLh4/iJCKYQS1JFKZLxdJdwJ63gARIOhfbDvtnjXBsN4meKdRUmDt2EdNi4yjDwI4xA23C8rLMZ6vGmxQiFkDqoDERhAzodnm+7jMxyeFo/GO0HjO+q6xLQNaaaRg03sfMZbb73P7cO7iGzC1uZ5TGvprMXSomVClhconYMzYb+hEmSa4hOBljqY/HtIhCCVAYQbb4GXHiVBoxEiwQTPalQEkwyhAMgQj1KBcYTQwZLYi3ABjcUpkEIHMNyDIahovBckGJq6ZFEumM+Dz5AYJuzuXCLJdGCmqSA1dUi09wyEYLxRYK2kqxqq6ZS7x4fo8iGbuWJUbJMMRngpSZIskA0gyNVkiFelzIP3ojSBbeUU2sfEY6KRmJDkUBowCB8rxzqLFLHSoJAon+J9h6PDW3C2Q3UtmZFY0+C6lpPpA2w7R4mwApZNhzMteIs1Lb5tMdYjXPBLM1Iw8h5kHtRZP8fXzx1QeuNGyd37L3LjgeUb/9lXqW6/RicKPvHYNm+/+DccVCOeOr9DYR2LquP55x/j/fcuYD/yD/ho+jo31TNcPb/DCy98ir2sozfcE8jljUP08re4XY7ylCU7aRXOL0GK9YBvGfD3gaX0BI+YHpiKnYkBs8VCRMQD9rKWUV2ew+Fti4iUaoUOLjcehFABvBIrregS3Vo/EWLpqXOKmu9Cv/v9du/hQQ9A+ZVb1Mrgtw/qg7ZWRv1sb6PtBHhihSnvAmXSOrYHGS+/91Neer/kK7/x95i3nt2RwlobwLC45ehZJ54e0GF5R7wLjV5SGB8BllZgX/xdEMqt0vsRrR873r8lm+pnkJg1tsgKyFhnioT7vW5WtDYmTqM1P/NaZvR9rPYWcbyelHW6ZzEyWR47AntC9NW1w0lk7FkPpPVt9eEGrkCTHsBZAynEMjyMKjH/aCOW5+8DzvDAWgc1VtdlBYqsX8v+NP24Whe9RDQOv0apjRflbwNMeubXo3BKH+CtyWrWiXk9YBUOfbp9PzsG/M/8WAbZy7HmWX1tNQb6X2UELhdlSTubM96a8P77b7O1vcv5C5fAqaBRZiWnXFa/W967cDApewlrP8JCH6MUejkKl1dErNYjEZmA3scHf3Td10mKairG+RBhBUmacnnnKs8++2mc9czncza2N+i6jh+8+D1++Utf5/yFi+xdeJKHd25hrOf8Y1dYLOa8+P3v8HD/HlmSs332LJvjTYT2LBYzbn9wh7t3r7O/f5v7d+6TiZCl+/Snf5FyUfPFL/4KL/7X/xVn9y6we/YMj19+klu3b+O84Mrjl/nVr/2H/MU3/xRrBTubu+w9dpWzu5d53byNExbtWoy1nN3aYyMfIaNPhm1bPvXJT3P79n32jx/yzMc/zvvXX8N3Haghr73xKpfOP8mdu9f53Kf+Dm++vY9SGRrQhcBLwcl8hhIOITytcZRdx0YxIMlG/L1v/Md89lOfZz6tuHH7Bjt7Z/jQE0/yyhuvkgxSGtsxyoKhaVMuME1LnmVMTw4w3mOt56+/802sn1EtpkiRcjSbMx5MaEzDjQ/e5c6Ntzm7M+Hu1hZ3b90B32EJ0jYnArW+R3NErPS5WJRMDw/ZPbdLkuVrz4S4kV+OpHX/ueUgj1PRR3BofeXxK0ao6D0DV+t1bxD5KIy/nM/rwKmMY3StbdKHynDGO1DQGYNKJPPZSZAa6ZTj42P27x9w5sx5Ll5+nMOjE/JiQNu2zGZTzp8/H9e9WNwgzgW8X62Va0v0qap4fXv/lrXgZ97qf+/lfUJEoVV/3DCn15dS722knoMXhqZp48awo2sXvPbOm/zoBz9AOMvm2cvcf3jCrZu3GBQp2Xib3d09Lly6TDHcoDGWuq6wzQIhOnQ+YbK5TV3WlF2DzvMomxRRYhLMOZNEo/DU9ZyDowNsNcU2NXma0bgKQQNKgxPIZExnYX9a01XHCFPhY4ChkxynxyTZBqNihyQdRNm8R+gEFUsdawSkafRWCUxJaxZIJanqafiMAJ1IkiJDyZyB2aAxHWVnOXPpfKhe5oLpvPRgdHj2aTLyYUY2Kujajs5B3TaYusEag5KCfDRGqQQlBdIKOuliRa+Wsi4pFyc435ClBVk+wEiH6Wq8VOAExrV0vadYB01bkg8zdJKDF+RJgdGagUjx6DBLfBM9N4IkQoou7FmQOC2joXnD9PiA+aJktDFhsnEJkaYB4HQ2FN7wgc3urWBRztgYTjh8+AG2LalMyzDZQOU77FzdYevc07TTu5wc3uCdax8wLDK2dzbZ2phQLzoK5mxNBInvGAx2uTP1DAaWhbO0teV4ekxbHWFMSzbI2MgK8BZjugAgeYEkgDoeE9i31mJsw8CnOCytFQglaW0c9HGN8y4kPKumpKpLdJaQpymJHIALZr1KeZRTKG8xtsK7Ek9g21hjWdQ1s+kseG/lIz72+DN0zjHefgyEwtfH1FVN1TRIJZkUGySyxQlFmgwwzgZD4AjsCi8i8CmW/irShfEpBDjrkdL1m/m49nmEcsE7zvrl1sxKwHp82+LajrpsMF2L1ord8xcgVr2yvgtLhjF470lUhkeHtcVakEEuVssysH2MxQgXkwaCJE3QQiNSQ9cuqKpjTAltmpANxugkQSjBKMnorEDahqmdMcpznHJoDbkObCaLpbMdbVchnKOpO6bllLJrSSa7bBVjrNRY4Ulc8BgSUmDReCFJvCIRoSIl0qF1YLYZ7ykbh5QGrRRJIlHORRBD4E3wgHVxWUbIAPoJg9OKxGdYb3HOkGpNNtmmtS6atB+jzBF5llCmCbpIwh6wc1TzGWU9Z1EtkCplPNwIniUyMEk6IUI7vAQbQE+dapQLYLFxJiSHrcNnKvi8erAeZCeYGYeyGkMbPYckxjVxDGicA2NDdTYR91wOhxct1tsQZLctWniUDT5YztQ4L9DKkyc5dd2wKE8op4eUWUJBzeFiQUPBaGuXoiioSkPnSpJUY5zCR/AiVPYNiRzjgkev9yC7EuEdbV3RNEGCqJMcqSQdFuUdthN0wpPaYEovIsMNoeiLMjnTUllJXgSAItVB6qaVDqbOUtL4YB6fCk0ig9ejc5phlgTGbGs5LkvarkYXBRu7l0iG56jmJyjTMiw2GOWbyKRA6lXSxyFAhHsS8ECJkorOO3Tgk8XiRgEskkIEv1IpUbECu/QCKT3KB3aWk0HKJlMZgCIRYlovFCpJcJI43kO8q6yja1vmJ0dU82O8TCmGYzbPnWFQjBGJI5cpBhUrzFk8FuU1yovoYRT2X2meMZgM2ezOcTxdcDw/4nhxyCQ/Ji8yRD5ApgOkzoP3n5BLYgew9HsmCeNM4PDShiSiAutqJC6uoxbflNSLCuPawBJsW0zXYNoK03V0bQldR9c5jOnAOsqmRUavZ611YC0JHfAKlVGMJlgZzOxVqlF6gNKQaU2n/5ag99/j9XMHlD71i7/GG2+8w1HteOvb/wLz+GXGu7vMDu7yzuuv80G5RTZU1Lde436zRdccsHHp4/zSr79A/b2XaLfP8Ttffo7aNBQiJiVjdlD6tWC63/zFTfMqhGMJHoQgNVSqCIh9NLnzfVgdto+qr861fm1FyHr1QKjH47yIxxPLc8UnGl1bcXQyZWsyxsmUIksIEp7e7DeCC/ErvVwv7Lyittv3sq6VFEvEjMQaJYM+RO2ZNv178YIswaTTQXwfba8FBbEPVXlM3ZRooTDW8+CoYfPMBX7hyScxtkV6iyUG3T7IlOTa0df9kfAsfaCWQFcPAMYduxCreyXW7uVSErj0q1k/Sw8higgc9p4762yfVX8DWLEejcTb0V+KNSbLUobVB/t+DXoSrHxKZH9c1sCa2Iclw2oF2KxhDIheTtmfZNnadXCjB19OQzM9ILQas3J1fLEGXnmx9p3Ypr77y46vt6wPMt3qWj1y8r/N53b9M6fYEesd9mvXnZ5jtAJwoAfKYh/WUEa/dg36dokeyVsGl/1xVo3p5+Pqusa/rQXwYbz042zFIJPeIZRmNp3y8O5t2ps13/v2n/PZL36RwSBnMNiIJU3DkunihrZvVmi4j143S8h3taxExuOyrREA8GK9qatrFjzZXHgAeQnWMZ/NOb+3xz/9/f+cN6+9xe17N/EIPv7x57hx7X0+/MyHeeeNa5w7c47333mDDz54j3nTcvfG+/zjf/L7qCRBJzkHR0c8eHiHD3/4WR577Aqm6Xjw4IC2rvi3f/j/cOXxK7z44x/y2ONPkecZg7Tg8OE+V556gpv3bvPCC5+iMb/AY5ev8O5bb1FOTzg8POIzX/gCo8kGzz77PH/+l9/i/f19fufv/yN+8vIrPLx/G2MtUhtuvf8eP/7hX/PyT77Hg4M7DIcbfP3Xf5tLV57iJy+9zP70PlcuPMb5ncsMRzmv/uQlEus5engfKRQPj/bR+ZCPP/dJTNtx7d03uXfnFk3TkqWKjc0NFosZvvXoRKOLEc89/2nqynN2d5u7d27zB//sf2WzSBEClJQMhwOeeOJJ7ty+z3x2AjjOnb+IsYLjO7c5v7fJrQ+uI3TOw+Mj/tF/9A2mh/sc6AykY7o44MHBXR6ffBjvLWcv7OGc4tbtG4yGHyXVSRyH8WY7Rzmf84PvfJeDh3f5rX/wD0mSOF+Xw3O1fvbjwkcj0X6FWQGRfjm+xCNotwe8DBs1tyJ8Ltcr38+n1bRYTrJTy0H8XXjw0mG9DPi5l2Qq4+knn+LOjQ8xLMaMiwk3P7jJe2+/ySde+Cx16zg4eMhZdRbTtrz66k+4cvkyZVXiETgTsulLqatn1a7VbFr1Z7m0+CiVXnZo9d3lh0SUgAS/BylFlH3H5WH1AAqb+5iVUQrarqVpKrSW4Cw3bnzA9evvMj04QKsBV65c4faDfe49uI/OJ1y5+gST7V3OnzmLSnKqugn7BhtAE9saajvHigQpE7JBjnM2MG2lQiWD6LfmsaamnB+xf/d9fL1gPEgZFAqpKrq2paqhaXyoYuROqLoSnGGQp+hihBpM8GqI9SleJCidIYUI5aZFYOcIKaIfUZCvu66l7UokMlQP6iqyTNJUh+TjglQleJnRNI7WVYFNqRR5NsB7g7Rd8F2P8n+kQskw7gJoqBjkQwqdYK3BVAsaOw1ymHKBUgnSe7QeYkWHLU+4e3Af5RQiTRjmeZC9uBLrQas8FElJE5RU5EojSVCREQEWg0fZAAZIPE6oEIR5GwIAqeMzSNBnicHQtR11XSGNoZaSzTMXyPJQGrozwcdGShn8pnwQfXiZUAw3475PUdVzis2zSDTCWYTQJIMMnQ/JNnY5ufsO5eKA9v4BBw+PKbKMM2d3mM5LNgYpnU8Q3rNJg7BzDuYNtq4plGNQDDBC0Joa50QEkxz4wLbwzmBMkAzKpmJWVjAcYtIUSWCcIxVCBGNnKZMYDDlGySYbk22QCmeDya11lkSDa0ucmeLaBb6r6UxDqjPqRcNiekJrK5Is58LFC5y/dIlMJ8yPK3SRkVGjBzl+e8T9WcVxOeP45JBCAsmAJDc4KZEyCSxdH5gd3vogeYvrovMeazr6hK6xAXCXMjzPhRdgw55RJRKpg4yys5a2LmmaEqwjyVLywRgvgqRViFiZCoPDhyBYhsp7YS2RARSIS0sSwUyJwwofJC0eOsGyCEOaF6g0w9QdZVPTdscoqRkUOTKJZeaRKJFjWk/lKmSmaHysshcyAtR1xaKcs6ha8nxMPhqB0lgHHaBEAsJghcC5wARRQpGqUAXO+WDNYazB+ljWpbfz8B5NgqEjFx6fCFCCtgOndACRYkLEOxVjJhnYVcIjZYgQUjVARwZgMz1iMZthfMdgJ0OZBc2iZTafU1pLNtokTbJQYS1s+OmURRmBoUFZGaREeDAEqaUDb01gXjiH6QLryUqJbsLzo7QGax1aOISo8UoivQUr8b6jsR1dU4N3pGkWJXoyyMOsj2MqEBgsFmeDBw7eIY2krTs6aymKMXUzY//oIb6ZkxVb6LygbRdMjQGZxOqISZCG63BPhEzQ0UPXEqJV5x3CdoGtkyfkeWCJSiGQUtHZaLosQtIhEyFJLpREyQSBQPoAhITJr1fxLwYngkOojJXkEm/RCFJhQLhgPeoSnG1YVBVV15BJzfb5q0yGCtOVoDdphwVtXeKtpW4qpA2V2JMkw2kFOvgBaiGwQqBlBLNkkOL5yATzvZpIqCjpCkogL4LCRRASTFqIAAo6H2TIsW9eWZTTCBRaBLa9aRvKeoFpKmzbYb1gvHOe4WiDJM1C0RSVIUSL6isbx+pMiuARJ73HB0drpFCByawdpJJ0OMR0OyyaBW5acrKYcXhwl5yO0fYENZigdQFKI0USn+EdieiiL5bBuwAm0XW0rYGuoTVz6qal6xqa+YJqsUAqgUwCwCUiAzbsC2S4Zokm0QNIFZubGUJohNZk8R5opUiUCj5zSIQMrMVUCnACKxXeetJ1zOPn8Pq5A0rHpeTsuT30wzvcOpgxN0e8sH2Z47JlfPYSi5++zV/91UMOb9/i7Bf+U7781AHvfusDipN3udacJ7NTjmcVm4ULumnb4VQWsv7LDOWK4SNi0BwLx7D0fInBpkcsSyz3IJPrN+Jy5UOx2lCvMrvh1ed0V4HqivFBzLAa2sZRVQvu3b3Jlac/Q4aJutWIRvuQa+613ojACgoexWFxFiJUj8MSMpVA7/XTgwbrHhh9r1bgTQSjvIileCNwFRdH7+UqcBWB8qg83P7gTX7y8o/ZmIzZO7vHJBPMju9w6/o7XLpwGVQAxRwhAyPiw8q7VRCzDGrofXHEKqO8/Ees2D2+/xSsZ57DccIDVIq+1loP+MRFFLXsw/LIPdCzXinP9+CVWx3D9Wh6j1SuCbR8D0j1flG+v4hLyMJFAIS1cdKzedb9t3q47NS5vV8+2PtPxEbF+x/ZZ8so8dQdXjLclmy25Yg+DaisI0LL4HEZXAVie9/kFR4kV+/1QdbyeOsBqugvyfLAp4LQNYCpZ0At4711RtPy9H4pZeklOMsrKB5pR4ww+4qLq3EVztGPo1UL/PqlWH26K54AACAASURBVF3XRz4jvAtm99axs7HFaJDwZ9/6E+48uMMPfvAdnnzqaZKk4Wh6hKkX4AXZoGBn9yyJ0ksgqM86+djXQCvsL5aM/VtrU/TIEb1xuA9l3fs5EtYtDaajrGq00nzkmWcp8iGTyQ7zaobUikznPPfJF8iGCdK/xe2bd3jzrZcZbYxQWjLZ2Wa8uUE5q2irBRuTCVpdYWN7i+OjQ3703e9wUpf86td/lQ999CM8/4lP8s771/nwUx/ijbffYTwa86Mf/RXFeMCbb7zJxfMXkYmm7Vp2tnf4zGe/wN6tmzS1Yzadc/6JK8i/8FS+Ym/vEuP3rqOTJN6mhFGRcXT8gAcPbqF0yKzWXcdiXvLCpz7DS6//gHevXePC3kWef/YzvPXqm2iV4FxLKuH555/jaF7xyU98mpd/8mOuXL0K3vLB9RtUjaF1C4ajEWnn0aRsbG7x7htv8rWv/iaHhw/Z2dpmfrTP9H6NVgpjDOPhBs9+7BMk/hX2711HCUk+GHByckTrWmbzE7w0WJfzmRc+y0c+/EluvPcuf/O9v+TM3h62czzz7CcwxvLkhz/KtWvvcu2dN7hz7wbDwYAnH/9QHMaCqqw5PnjAq6+8xGK+4NKVc0jpVj5pbiVOjvvsVR5gDUzq1xRxepqtrSvx+cRqk9x/R55i+q2Qp8CiE5E5yXKurzCm1fMHLyJII5Ai5dOf+kUuX36cRVVx9PA++w/32TkzYXt3h3PnzjObz6mrCiUFH3v6SY6PThACkiQLDNkIBvf+TqdWi7VESMSI+q7QPwj7fvcswH7jio8y7H6ZcaeP5X1f4UtG/7SwzjoTfD+0gq6refvaNW7d+ABrLUU+Zmdri9sP7nPjxk2GGxs89fQnGY1zhuMxKsmwiFApzWtwjs54OusRpkWlkpN5xSjTSJ2RFkUI7jtLaxa09RyaGd3igKFo6NKEjiBj6LxlUVVMpzOSNAlqRK2YTLYYpJsYCbUV6GwHZ4NBsdMyBq0BUVRCIX0SKso5i/c1OIP3ls6UjPMJcjBAM0D4De7fr5hOF0iRovM0lAdPFP8fa28SZNl1n/n9znCHN+VUWVVZc2EkRmLgABAgIEpNURS7aSkU3bbsdsjqTUcoeuWNV170zgt743C3w+FQd7jbtuwOS62R1kiRIiGMxEAQBYCoOVFDzvnmd4czeHHOfS8Lai8c5osoJPLle/eee++Z/t//+75/onUs+OBQKrCzhQj+IqlUQTrjBDpWDQxVbeMaIlOSdkK3vYzFYqoJo/4+/cEeoVSyp+rvk/TatJePUyPI0pQ0a+NUYOHoNA9G3ELPE3+4UGChNkEm42nsDBxeCJQ3i0ElwnMXwmFtkBAU5RRvq+C1kmQkvWVWVYp3wb/CS4MSfs5kF1KB1EGGgsfXCic9VkicCuNH6iBpDUVMBMgEn/ZIl88juydppTDZ22T7cI+dw3063TYPXbyP8vA2XmuqzhRRDnnw1Ao7A4nwiuGsYlInWKcIDIUwhoQI4JoRnjQm0dIsJ+0tB+aAC8mThhUnlMRTg1DBgyXuVcOuRCOlCyCEm1HOptjpAGUmtLIWg3LGeDijLA+oTEWe5Rw7dpbja6s8fKrNiy89wO3Na+ze7lC1c750IecH7++zMw5l27N2F5+WlIXBGoecjgNwFBk8QgqkUigdmFNCKITUgWkiq7C0OhekdxCq+QmPdzVCarRO8B6cqTg43MLWnlpK8iSn2wpyFefdHKgPRTw8iQjgmox7Th+lg9KHynjWGcCjEdE/VSBdTPQJh5ch8SydjwCYDJX8khbGTKiMxY3GkJRorUhlCkohsgyXJEghmFUWRU05GzCeFkytR4iUrNdCiyz4WnmFURblBcpB7QjMZmHJlEYKj5ahUMk83e4M2oU5wPrAoLXOUvkiAKPGYZ3FOZhNi+ABkyUkXmOdw1obExM24LJCIKRFOYPxBcoHCXjtPFmnh64KDvYOmI63GEwKZNYh755AWg8YjFDziqi1t2gbKpUF4DsQwhqvVunC+ugJMs3ShqcgvabyilRJhGxY4hrna4LULglAoQedOVRuUT5U35MKhEhAqlCyHYcTMXITIRhzPrAuFYK6Y6htD1cWFHVB2l4lW1qjt7zOeFphXYUUKa2shco7qFySkiwKLnkZjKwJBacsgXEnCcwrS6heGYR/YT5NfGAbKqHxqFjTKbgnCpEE9h4OiaPGBHkVmpCLDPqWRTLLz02ccwGpBu9r9odTxpMppZUcX1llpdcOiSgzxnVy6lqy1M0RtkVdT5lWUJSGynpm5QxBTavbRuYJVgfwxwkdLGXi3sUCWsXElggMOBVjKi8ETRVczSJib4ImIR2SBFT0fzIOU5aYckxZTKlrR41Et7osrbTIWi2UDlJVKRReKpQQCBIEKngY4nBSI7wNSiUf2ORIFX3GiMfwpEJgE0VLJ1Q6pb+nOJxZJuMp0+mYNNslS3NcEqv7ljXFZIzyBUorjA2VMa2zSOuoiAA0IGVgE2qZ0+omSK1BKXSSkkbvuCSRJCSIJMUKyCPY6LXFujhP+qjUIqzJIVYKC1XtBdbJwMKLDLfkSCT0s3j9zAGl3WGXL3/+FH92fZ+vPP8027sz1lZyzN4W+eppehuCtY3jTArBmWMpqdasnb5INdwnaS/xhSdOY8e36A8dxXifTz75mPu//KtcPN7FuCO7R7g3eJ7/rwiAQ8NImPv1LILNhhEhGqaSYM5kasABTyPrChvnhdH30ejZxyBS0eqtssGU19/7kAuPh4emlUNawAd/AIfGi0ZmFqix83ZHgEg2RBYaqCNukmJgIXwMPGM7FrrxRUDrGgPzCGiEvX+sKNYEC4SMlhOWvLWCc4pLP36DN0f7lEXFsEwZjCd88+vf4PSFh1lZWYmLEjQytvkCTAAGGpbJZz165tBJxE7CvCYXWIAQ8/veXNciRI9f8g31NoJhDcOoARgaRDE+G9m0KyLO4b41IM9R2MHP7/28P8wDGb+4Z1H/IJpnFcFICAFJY0DeXNdCcnbPHZi3Hy+OyBjlPAJyMb0evtq0I97L2OxwrEgJDnmm+PkFACVjv51fYzz/nBFEzMQ3L9/chyYgC/fJxxPPMdYYwc1NezlyKxctuff2zj93L8AzH07NJ/xnPiOaa/YLMMgt/rwQ7DTHmMOtR57hvcefE4Wab8Tn1phuL68sM556nn36OV5/43WuXrvMBx/+mC8+9Rw/fvt1rl79ACFSnvnil1haWQ5mgTAPUJrjzYNjKeftEVEG6r1nPApZ+e5SD3Qy9wVT8zZHGY7wbN25Q5ZnKKW5c/cO589dgETRa62SJylSSY5vnARXU81K2r02hpANnVUVv/zCz/PTy1d58Px5Rs7y8CNPIBXc2tzklVf+mo8+fA/RaXPr7l0uXLyfq1evMxsfUE0KPvn4PVa7XQ7HB3zwwXucPXOeyazPeHdKp9vi/vMPsn94wDNPf5GD0Yj9QZ+Ly+d58pmnKd6p+OEPv8fzL77Amz96JTCkshbf/PavMRiN6Y9HbG9vcen99/nz7/wR3/rWr/D4Y0+Sdtv83vX/jV/+9q/ynd//fZIkZTqd8MgTj3NwOOL0+fsQd3cZH/TJW5pHH3+R2aRisDdiMDnAmApvWnzuoae4/MlH/Kf/+Dc4c+IMiVT86NU3mdqCM6fPc+PGx6HyjFJ0eyskOmE6maCUIk8VW1ubOF+RtyS91TaHB5bnnnuJ5557iY8/uMRsckjWShiN+jil+MLzP8fh3h7tzk0+/fQOJzY26B/u8dH77/LIQ49Q1TWplNTTCe+++RqXLv2YJ599Fkgx1jMbDui2W7GSDCAkEj8fQ0cwFRYz64KhuRjOIWh3DULEguUqCGxRKRcsoPkBjoyWo8UdjgzHI+M2HieuJx7L0so63eVVirLgUmVY6vU4f/E+7n/oMdIkQ8ohKytdRv0hQga/sjxthwovSs7b16x1RCbw/MLjz6Y63mLtPNrCI+ibbAzajt63I0UOmoIHBMYh3mOEJUvUnFErnGc4HHLtxlVu396kneak3R6TyYzxbMLtu7dZPX6Khz73OCdPn2cwG4SgN8kQrgYUB/0DRJKC0CEgjea43bxNq5WHgLgcYcoZrjjAmhkH+0PyVOG9x6qMWW1wpUVO+zhTgIdep0fWXaLVWUW3lkFlmMoyHo+ovSOVCVomCCxNLjz4aFjwCkcdvSENQjpSLVEyI2vlJCJBZxnOeaqyBNXDWIfKWqg0Cb5HBM8MpdKwpsggSZFe4V2N1wQZnVSh3wnijiRU7bG2jPccrDUU00Mm40PyrA1ZEuwm8xSDC8bSWYbIM9AtlNJoqRFKBU8b9Hz9huCbIyKIJT1BjuAhjAo37/eB/O6YDgeU9YzQLSxZmpHpFKfTkEBzJvhOSoXwwUtzwQ6P40uoGJwGyVR3ZYVOq42tChIpgzTEgbMVQtgQWLR7pL6DNSUmXSLtCoQr6E/6XN28HYAtKRj3hxxbW6fX82ArTqyvofojVlSXSZ0wrQWFDTOC1MFA2ds6lNeOVcXSpmiMjYEGDiETpFBzCahwduEpJiTYAlePqcsJxfSAdp4jpGFS1gz7Q3YHA6yBpU6PtWOn6HZ7LHeXOLHcYn0JGFt2d2t2JgJvRki/TqY8lTMYYwKYmnQhyzAusK1MVVBXM7Am3GuVknbaJEmOVAmmMoEtn0Sw2IUA21YFxliSJCFNUqQSTMYjBoMhs9mYshizurbBUmcFqRQWAwSGmnfBcD1RKYgEgQtFZ8I0jLMAGm9NU6IjyMmkQEsLIsEJE/eJocS5dXU0xl7s5EGikzYqC/OPqQpmsxmlPUDLHLW6TJpqnDX0+yV1MaY0JVL30J0VEgTOzTBVCVpjvSNJBcpU1AisAxljD+VcWFeqGlPXqEQjpcY1I1eAMYEd5YWlqoJpfe0NEoEtCgpTo9MWqWhhfBhrTngC28UHZoRUCFwIxFVgk3XynNIJbB2kk+OiZFYYnMrRrVW8TEAo8rxD1m4jkhizORuS1jLAI4kMgIBvKkUgo+dtGMsIF6r0ISPQaEnn8VFkIkbzbhmPkcZCP3gTGX2hurYTAezw1uCwKKFDQRZtEaTBI8hbEhkYYJNiik469FZPY4pDstYSopXgkGAcdT3DFAWyrHE6yDl12opLlJ8zc4I1VPCDEtKFim8irNTOC5IozQpG3qFAUmBPiQiEOlSMHzzh+Utc8DYLA5kUi/HxO96AsxghqbyhHI2ZFmOGtSPRPXq9NmdWWjx6WnDmsfv55IN3eOSpr/H2++/xxSc/z80rt/n0bsp2v0Sngsp66rqmrkoKk1AVJcgpba1QaRaN6BVW6VAgQRL6jAyADV4iGqaSdIQyDTHOEzpcn1Zo6YIcr55RjCdMy1lAG4WANKHVbbGUthGpJBGhr4d9eTie9eBk6F/KC7x0SB8q00kfoD3X4AB1iTEW52sUBmcMoq6o6gJfzNjr7zEeDEGV4CSzwjAbBPKIkALrBbX1KO/RWuETg5bB+DuJ/lJdrUhUipCSJElRSkZjdhFYkUrEtoVYTot6vi+C0AfDMha8wSx1qMpH8LjzcVw3iiIZE/YSQeD9RdXWz/D1MweUvvzC46y7bX7527/EdOcm9z38POx+wnvv/pQzjzzJ115+iPXVnJNpxZ3bl9hJP8fzzx5jafoGb7/yfezoGqq3weMXLtAfjflwkHNWt4NZ4rz0erPZjbvKJticv8Rn/v8et8//4KeCWaq459NSSAKzwB9h0TTck2jsCUDQk9u0y9OPPklRFqykobpGyIw2Uhjmk2KQ4R1tS9gIOyGiaXXTOjcP8BsGB0R2Uxx0MgaeTR56wfJYtDHsp5qg4ojcTAhanRxjJswmU7Z2Dqisp71yH63U850//7/51d+4n9XGTNZLhAjlWZtApLk3TasboMQvLo6GuhHHRpD8NKCPaOJxP2/vwtn+yLP6TGAwjyhEk3tp3o3yQZibTYZ3Y2mE5imLBhRqJJKLoy5CrCNAZINwRPCleS4NwNN8awF0uCNHa95ZIMaLvivm5tTzt+45a4PKHLkj3tP0vvnN8XNcY96GOXgWAbeFVDBCkSKy5+YLUhO8LeLMObBEZBEcvfUsPtM0Zv73hp3XXMUcyDk68uJ7/shRmtt8zwNfPOPm7nDkeA0TYxGNck8Xmd+nIw+38UwLJokaQdDhJ2mbixce4AtffIGPP34bD3znD3+PSx++zWQ6oNVeQihJr7vMo597giQNMhXn5jTJebtE1KY2tPXZdMaVyz/lzTdeobvU4eWXf4HltQ3yPEV4SRI3CojIZLMmLMDas7l5k4PDHc5sbDAaT+ksLeM0QZPvPMV0xokz5/Cp4OzGGUoLL738NU6vn2F3MuTGlcu89earPPzI48yKgss//YjLlz+gxtLVCW/96HVOrR3nnXdf59TxFd5++1WK4QFbxYRW1qW/v823//6vUTnHlcs/ZevGTe4/fR/OeDZOnaLfH1EMh/z1976PK8Z8evU6/YNtzt13H/3+Pk44lBDc3t7j7MnTnN64yLg/IktSJpMR1hqm0ymtpMUDDzxA3m7z3gev8+QTz3D5ysf80rf+Pv/uf/9dvvPHv0+3u0x98UE2Tp1nubtOIjR1XWCqGoRmNBqQpwlZnnLx7AX+5nuvcObMaQ4P9/nw2k+YFGMKa0jTFKU1J0+eRumE4WRCe/kY4+mIshzRznKyVpvja+tcuXKN5aUVNk6fYTCccnJjjd29XZIk58TJk2Qq5VhvlS1xG60Ff+9rv0hZlmHD7hrZJbS7bZ59/kuQCpzx7O/u4rzno598QLfd4rEnn0Sr5F4ASSx+NHPmQiLcgOr3zBhHyIULJi6CwC4lzMMhg84iOD46Xo6ctzlO1GXHjGH0YfKEyjjSMx6NuX7lYz78yXucPnOWBx54mKVeD2srTpw4wfLace5s75CrlI3lZbxXYU+oQhWWeWVV/D0A2ZFpiTlY3zQsXtvcSy+yA7xrwKIQeCA8FosM1hwh4y0ELkpjvAhJnjRLqKuKspqyu3WHW9cv0x8OWF9appW32dk/ZDqZ0R+OOXv+Ac4/8CitpR6zuqalUzKdoIVlNBpQWY/MU1SaBeDFSKimJEkLfE012aGc9KEaoxBMZiMcmtIpnBFYU1ObKVVdAYJeu8vS0inSzhq6vYLIc7zVzGYlpiiofE2St0iVwNsKay1VXVLMBvS6y6RpG60TlJQgdcjeCwHSzftO4oMn1mw0ZGJCBa886wUzVB2AngBSBU8bofQ8KSQJtzqRgfnnfPQqcS74UriaUKREoZRHWJgUBVVVob2gu7aB0ik6zQKgZOogrygKyrpk0j/AuQM6vXW63bWgS7yH4erncmEpwEmHtWEdUoL5eu8jc6ooRlTVmOlsikxz8laHrGFMIAJzVZjQzZzDWYMWCUrrwL4ScYs+Z/VJkBZXW3S2EgzDywEzM4a0hXYeYT1eB8eP4N1nUYmi3VmjVh1MPaPdWkPnHSQl48mIW3e32Tvoc2unxYm1Yyx3OwhfstqxJNOa5VbKqFL0Zx7jwj/vXWBRSUddVsgkC8/BW6QLZu9eugCm4oHATJFe4OsaU02o6z5ZzGSXtmTaH7MzOsAVoWpekndYbvVodbt02ktkSYbVkmFR8/Ftz6d72xyMPPtFicgH/N7bNXUZ9mFLrS5VnHhqZ0PwrDWpamOtoi6D6XeQHh4gdRoq1GXdIOfwgAjV17w1pJmKc5jBVIbJZMxwOAKRkuctessr5K0lhFRY53BCIZWOaJFHyuCTpKRGCYeSIFRgfhkRKqWJRv4mQZjgC6O0ItES64KsyBhPVUMtQnU8b8OzCDLbkAC0dWDYKJkilMH4Gd5YBnvbuIOCqi7Zmyp01kO32kihqIsJRTHF1SVSKHSaBJDS5FjlKL1FioTKeLyxlN7gkIEN5SFzPjq76uCv5S1O+lg9UeK8ABRahHGdtBLSRhIbDbURoTIgso4EvyQGtDYEuEqjrWM6nmKqCu89S8sbsLxCcgi6fZLuyllq57DWUNU1xaiPThI67S7dvIcRwZBZuZDsCXFBGLsOhVfENSJI+XQEZ5wITHNEnPM9aCGD6FWA9KFIB17NvQedrRHWYqQMnmJIpKvDGFChcFFg4wYvX29raucY9PcRTrC8so5QCbYc4IRGK4VHoRMFqcaamqoeUxQzqskMpTU6lWidI/IcIROwAuOrMF6RaCfxSuGDCzMVMgDZEvAuuAr7RmsQ4h+Pw/gAHklqamEQ3uF9ghQa50qk1Mi5OsFSlxV3hn0m0ylp3qazvEYv77CSK86uZDx9JuHC2R7DTyqeOiO58ZMpT68J6Hj08R5SJQxmgnFpqF1O7ZewPqFyNXU1ZljMsMMDEjzd5VWSvIfOW4jIslJehueqVNy2q0XuKLJvlNTgC8pZyWTWZzgZYZxF+wQyTd5dIklaiCwllVHhoQhAoAt9p7k/wlcoL3HG42xJbSuq4hBTzzCjKYkWOK2DN5GpqKoSYypEXVMYh7IurFNCMDEViMAiQqWkaUbSSkmS8GylzFBKoDOQKgUlYoJHAglegxIujrnGiSfY6kjRuC3Ze6xA8IFhJ2MxbCFMYCb5EOc3jFlHiLykiLqT6MMq4j4xme+xwh7oZ/n6mQNKD5zWXH1jm6VjXTZnCS9tKA4uV1RmwNt/+5c8+eyLdJL7ePKRx9HZARv3fY725DX++I/+EpPkfP+vvsfShYsclsf5pa+/yMlHa84sJ3hv5gyMQMlt4sajQfcRudDcWSGAQg07oOmxCwceoKnARgj4wxFjrstHul4Muhu5GzQASESLZyVCCs6dPsZMBPd65xReeJxU8+A+ZIodPvomiVAINKIhYaNDlGM1PklxCz1n8RB/3CsKiECFaIqShjOGrVRksngfqbgNBuFCJruacLC7yeFggiPBOzh37iw7+33y5VOspDm1lyTe42QDvhzpiPMAGhpIa862ie/7Oegh5udvWgYgfMNC8vcAGfPcjmjaLCIAFSMfHwGBo9QTorwj/gxI7PwuhTM3gJX3kQUgaMwH5/e0Gcw+itLiNTQV+yKHp2nhHDhZtH0RxIVfQhYMIRcAZfz7AhCBBrgRR0C45pgNMysEpwFIwAf68qInNH3mCLgVx4ZonoiwsXERYDsaRMJ8g/zZ95u/NWiS+8wHmj6weNcv2IELNOwz3zjyzjwwjsyy5m+Rmr14HWUnLUDe2EkWd7Jhl83P7+cRuV98IfZDi3CaRCis1vzS17/FM888Q57m3Lp2g2Onz2DuOAajPpc+eIet7Tv8wtd/ma9++WtonaKVnvPFGsac84srrK3j+pVrvPHK99jv73Lz1oRHn3iM/rjg8YcfxksR6b3hO8oLQlnqFu+//2Pe/dFbTIoJvW6Ps2fOk0lBWVTs7N7ljR/8gNXjy/hK8Nbrr/ALv/gN/vQv/5rTZ8+ztnaMrNdj99anHBzu8emt66RJztlz57ly5TiDu5u0sxQzGfHalY8QrmB3v2TQLxEyxdhY/aUo2dnZ5eyZC6wf2+BgdxvvHZtXP+H9d9+krEu+/ou/zLt/8sf4esTB4TZOrfM7/+u/YjTYQ6CoZlPeeu2HfKBztnZvoYSg026DlGx+uskDDz3Khz9+h7u3bvKvf/tfkKaa/mDAxsZpNm/c5M6tm8gDh1IJB3u36XaWWVs/x87ONr3VHlZMmI1qhNBc/ulPEGnCD777XX7n//q3PPnFx9ndv8u1qx9jq4qXXvwa1y5fwTlY7i5x9coVOr0uw2JMXZRUpuLCxUeoxjO+8OUXSbMuL7/8VZz1PPDgg4Cj1W5TGc/q2nFsUSKzjFarzUsv/QLH106SpAleKLzzJF4GqYROOXH6Ai92l6hMzadXr5HlOfddvIgzwag2jIGwDszXtKa/en/PxqsZ3413iIj93s3XxPhTHBkz8+B6sdb5WP1lzryM3z8qJRUN40nGjLQLfjzWh8D9ow8/4I//8HewdcGzn/8ia6urYROmc7xNcB7eeectfunnv0FVF+Ay0ixdlHe/Fzk6OqHNwdnPrN4cxZK99wiv4tojFnOUs0AdGDoiZPe0TKmrwJZJ0iBBEM5zcLDDpfd/gnc101GfRHgunN6gNp5Pb22yvXdAb3mFE2dP8tCDjyNVTo2jlWRkKsV7y2w2wtmaRAQZhvIVmALnKspyRI1mNryNsCV3t++St3tYIxkPS1pdSV1MmNmSLM1IWzm9tRNkvXWydIk0yZE6obKG8bTAFKP55tPhEMZgjUdIRaoSpG6RtVJarWB6rZt1UfgoQQteKChBaQx2PGVSzCLL14BWaJ2GACwQLpFSoHww//RC4VwoP22cwRcluwdD8qWcbpJCEkA+rR2pVCiVYg2U0yGmNHiZkLc66MieUE6QyhQnPFZqhAYhE5TJwfeYzQYU5ZByNiJJc/KsRZLkCJXG/VYw2A7rY1NFiFAtSadYU1DPZhTFlNoXSKVo9ZYRWgcJM0GS0sjqVeMjFMvOB/NtQ5M2s97hXI13IYiTPlSj9MIg1RpZLvFeo3Rgh4kkjFctBMgEW5eUZUWFR+YtsrxNO81QTqKw9NIpVjqk7DGbHXDl2k227m5xfH0VpTJA0M4dwhmWV7pMKziYzBhXNkBBTod76UqEreL8kAcJn6kQhOw9ziIwSFfgqynWlMH7K9HsDYbs7W9hSoNIFe28Td47js7adNIWIs+QKsMISeENlZWkVjIwjlktKFFkTrN1WJIlGagUpEJbi/ElKgY7QkikytEiD0bdLvjlzMoxs2LGdFKTHFMkLsdUFWCRGrI0DTI/7xgN+lSzgtpYWp0OSdYFKZFKYxEBuFZhThBOhwpuuCA5umcHR2SUOWTs383E65zB14ZKhoSuqwxWQJpJamNxPrILbY21NhgG22BF4VwJViB1itApUqborEMtLMVkwMxX2LJEqBWoqyCFU9HrK9WQpEiVkSuFVRIpUoRwZN5GYDf5TgAAIABJREFU5k6QiCVS46UijetBnOGCBEoKNBYjHcqF4N15H0FXj8UdYfgF/z8nDDJ68ZlojBwg1yCnTZViVkw5HA6wLqfV6pJlbYTKseUBOgnebknWIYvttMZS1QVVOaE/2GMyHtFtL5O08tAvTSwCFPf8IsoTvXDRE4qYiAhmz011VOktXkis8HgXokErPMZanEjCvOcMztTNlhHjRJBBeR9KsDsHSWCpKi+oTY0pC/b6QZLb6xxD4KnqIXiDLStQBgsBALJ1BG0VIm2D9ZR1Sb8/JHOO7lIP3Y6eO15GMEFSS4+0OlyDr2KFP4kwi7jFEeRyMsrZvAj/cAohFcGj2WE9rOSh8l3w6jOkAgbTEQejIWjNytpxllo9kiylrTwtCeOy5p0bjmv9K/hqlb3N6xTVKn/x2k3u9isqm1DaFKE8SRqkoZkPFQWNV5hWijElvjRMy4JB7VHVgPZsTLvTJm11kGmOJ1ZPlCHu9UiECJI2Zz3jwTbjwS6mBpkqkiSnk6fkSRedBV896UOcLbxDmBo7HFKaKiAvVmPcDNwUXxf4CsbVDF07htMJ1WyAUGBKQxrHi4/G5VJqpE7IdEK7nYDM6CpJpgXHdQJSg5ShSqdywatMhv1QGDMugJIIQo1FGdYIGWr5heqJNlaqDB5HWkiMl/OiKcEaZpGW93FsSx38jlMduILIOA6cQSOxzmLieu3xCCfxKiS1nErwOkWlEpUcqWD9M3j9zAGlT7ducfXqJY67xzDDQ/bujugeX2Vt85AffP9dtnanfOWF52glKRcf+wLnTmR8/Ndv8crlhK+8/EXe3foLPr68y8bLD3HmeJe6NLSkDQteM8mLRYweXgvgYh5QNtKcZqcpFgyUORgTD6CQkQjdZLriokH0wInHlMw5IfNlp/F2zNo51z96A2sSbPc4a51zAYFEhsWyaX0TKHioqpraGbI8C9S2IN6coxCCkNG3UfIj/dyhvNkrx0+FjY8UAZtsNt2OUBI3aApEyCT4KL0joqLWcOX6TS5vHjAeTChKQ5ZkvPeT9/mN3/qvEcUO1679lBe+/BWqakLq0jDhRXaPbzbsDdsnZgs+gyDQRMmN11EAzBo2FjFbHHgijiNGq0cecRNGhP+4eQb+KPA2z9w3z9yHJ+wiw23RX8T8PnpPAE8aEEeEQC0Au+HE0oXsh3NBSdEwsOT8OkPgNhfNiSN98kgf/bvnXwBSDfDWyOv8glN/L2gnACxy3gujSWTUsAgWUpTFaSMjzMVzxj5y1PA3pm3m/5q3xP8HFNvjw1beN5uA+cNZnOfos/VHLoymDUeacuTa5/LO/xDKxZHg+jOnETJK+Y5Myv7IeRfMMxt91SQIw/Fj66weWwbr+LV/9J+zuXmdP/qD/4PBYIgpSna27vLX3/tzHrz4IGc2zjGtqlDdRav5E14AZYElMZoOuLN7h4PDQ46tr3H16g3StEvqYWl9neWVJSTBM2Jurm8lly5dYnfnNhbH//I7/4qvvPA1fuVb3+btN3/ED//mu9y6dZ0vvPgc9TCU3r7v3EUG+7ssdTsIDPV0yp/+yXfY7W+zvNxDCcWsqMjzHLxhNpuys78PvqasTKhQUlUkeY/lYz3stMZR8v2/+TO++ff+AQ899DB3eh22dndxombz7nUeefIZJuMx1WzIhfPn+PDjS/zmP/mv+O//2/+OPFGUlYckp9Pt8dprf0MqHUom5GmHJ596kvvue5B2nnF4sMdkPOBwNASVcTDos378OJcufcT6meNUrmT79ia4AusV6eYN8kRxauMUp04cZzKrGfUHHO4fYr3hT//sd/HO8Cd/+AfcvXMjsKB0yp3N2yyvrNM5tsra+jHefP1Vtg+2Gewd4q3FS82V65usr3Q5sXGef/riz9PKe0HG6AV4Rae1hBuOcKbG2YqyqDi5cYpOr02ahepdxi2kXFokpFphrKGYGV79wQ95+JH7sFXJiZMbERdXERiKfknzDMCRvt2shx6QjeF8mMeaanABMF98Sxz5dX44Of9kYB35BkzmHjCb+fGbgCVWPCQExj5+L88yHn7oc4Chu9Tj8HDAtes3OHbyBKdOniLNNM996atMZxWdZUmaNsE+0XBzIePz+OjRFPzlrHN4H5I1Pp4zFslZsGTjnO3xmBigomQY2t4jhKWsgr+EyjKyJEUrj7WWsi749M5tdrY30dKztNQjFRZrSg4GBxzsHzCbTLlw5iSnzp6GvINjjCsm+OmUoRBkHqp6QlnVeOfCmuxDVtj74J9gas+43GFr51OES5jWFSfz49hyjBZTKAq67R66e5L2sRNkuoWQKUqnKN2iLKfM+ruUxTSuEQEkE0qjE0lLd0HlOC3IREZNBI5iWWURmZQO0DJQ440p+PTqTYxK6LYyVAPuKB0rwgWJdTJPQIS1xTlLVYxwtkJJT5aClzPWllKypS4tHUzANRqRZVR1RX8woi5DcJpkLXKdB18Mb8N6mqjIwQatJSUOdEqm0/Be2sLaClcbytmY/cMthILe8gny3hpR4IJ1FuFFMCjVmoqKybBPUQ7xQpAkOS2dBjN0meAUeCcR1mNrQ21rpHH0B/s4Yei2V4JhtFekUiJEPWdqS6lIVdxhyIROuoz3YTeDDoFTIgS1r9EovIaqnDEbDSlMSSpSMpnhCP4pIEJFQcBXlkSn+KV1cCu0Z/vMijFXb97h9p0t1o+fYGPjBGmqaKc1zs2471iHSakZThy3dm9jhUfLBGVrKhxpmmOrAu0sy+0lagSzckppC6Qt6bVyLIKt/T1Gk1FkWFYs9Y4hWh06Sz2SpIVK0pgZT6icDObZQmCVZOJKvLVB2iaDtEpKjdVtLBZv6mDWDiQehJAhGJYaL4M8UHuJ1pqslbMsHFVRBvmaKRBYVtaWg0SpqhkWY/rDEdYZ8rwVAtesjdca68JcaX1grzsvSZzDVCVCwGRygKsNaTv0/TBdBxDIxc2yjXs1KSyJ99SBcof3FjtzyCwBnYZgUAgEikwrKlfjvUApiVCgXIJW7cAe0Smj2YzBwQ5lbTi2chzhFZkHWktMp3uMRn1KapaTNTrdNaxSJIQqTrUIckYpE4w1KOlCHWcr8LopGqIC2NIk4aI6QEZwQSDwLjD4fPSikjLshZwLHj9CBvZmsLgHTUqQkgU2YDUZsD88YFobWt1VWvkyuUoQEipfY8wU4xy5E7i6xuORrsZ7h5YSm6YktkNtLXsHu2Q42nlCkrRRaY4TCQY/lycFG3JBjcN6QerAyiABayyWJQYj61BlUiSUwiKcD35IkiCrE8F7KkSAButBiQwEWF8hTE2Q8Qaw73B/C5m0aPeWUSr4emENQV4XZIAIiSEwWaRU6DQkGRQS6TMS0yEpS6rKMKoGpImk1VkmT9ooAjurtBVYi8aTpWHeK43H+uC7U/nALNReIQXBQl4EJpdzgspblPLkQnGmAx+OC7TMqMuCu8MBhfWsLq3R6fRIk8DoUQDeUjhQVrEzham0vHSySwfLiV6Xu9OKocnwTlMAlQSnJTKmKbT0aAnWK7y1qK4g8wZrFL6sKMohtw9HqP0DNI6V1R5p6wQii0UUnKWcjhj196nLGUoJUq1o9dqQpnSlotIO4cZUE4efGHxZBJNrM2M2mTAajhCuCu0wAmPqwEj0DiVSjFBkqSZROWn3BO1MY5WgleboRCOVRgsVWIgqCZWDVZgHdJStay+pZI2QEmUDqQQBSrroLRxM6oUIznpy7jkmovQgeARb7+PeJ+qOhCARch6Peq9CwaAYs3nrESZUr6wAJ1QYS6RYnSFbGUp3kWmLLMuReUqaKrIsRactdJKhpKTSgkyKaMnxs3v9zAGlK+98n08PKp7/+XXefeN1vvwf/Tqj91/n7Z9sM1WrzEjZ3d9iZ2efu+OaE994mRvVY/zqb/5Dzpxc4itffYnNWxNOPnSODBu8Hmi2P4tAsvEcWbwWTARoNpj3ZhsWLz8HmACMt/d8LyD1ItDjvIsGaME3KIAXi6NpHFW1z6g/5JPL17l2YPjyS7+IlxLjK5RIcbZCKoXH4H0oK6jwpKLGG4ewCiF0bJmdgwIibuTgqH10zCfHDHSjjmtC5UYeFQjpNob3IdPlAISiuTMuanQf+fzzfH005buvvsXFtRNM9zbZvHWHajrkuYfP8G9+7w/4uRe+RlkcQNoGW+PVgs0lGwCkGQQcZSFFhBSiBGGRYXZiAcqJxtPDhffncjLR6EFdNMSWC5lFcygiYOBdBAAjo8s3QNKCobSIscL5ZHzmMUo6gkQErXhTitPLkMULpo/ELImc34P5Q2vgrKMgyBHMppl4xBHQc46lHOlbnrg5bQKl+Ek//63B7BZ3uXlfzquMucXlzIG/8L6K5Gcf+/4c8mwCSRZx7FE8aV7l7v8F1JmDYkffaySJiybeCzYevQGLOzH/zD3eMPE97/3igYrFFdzTljljkTlw17T7XjlNQL5kZAo6TKS3exIfvBhUW7LU6XLh4sPs7e1Smwmnz16g01nho48+5MfvvI1SCb/yq79GUdZkOgvziAoZBSEUri65c3uTJNG89NLL3NnZZXiwz9Ubb3NqY5VltUpdVwjn8bWi121z/ZOrrJ88zldf+CrFs08zOjjEZwmnzpxHCslybxkpBb3VJaajKRsnz/HUU5/n/XcvoZXh3/z2b/PlF77K2ZPncdLw6afX6K30ePbJLzAdT+kf7NFLO3zl+a/y4ccf8fFP3sWZGtluYwrHr//jX+fK5et8+NrfQqI43OmzvrHBiWMbVFXFp7ducPr8fXiRcP7sBbZ2tvmPf+M3+O5ffQ8lUp56+lkeevgJLr33Q6TQFJMxb772Cq6usUqRppJEK770hS/yyCPP8sarr7KzvY0pCgQiaMyVQrcyequr3Lh9E8wULTOKokaImmJWMdUpWuVUsxIpJa08Y3V1mdWTJ6nKMVvbW5i65uTKCpuf3qSsS65vXuf06bOcefAB3n7nXa5fu0ZRTqnq4O/S7rQZDAf809/6L6nrgl77WGAmeBFMHL3AKoHSodKU0yoYngqJdTKa36bBz0DAZDKhv7/H3/7ghywv9xiNBuzubHH+/nNkWY5KktBDvY+b+djHZQMO/V1gVkYUpWHrBq+46HkmjiRUjjCP5vOUJ0jVmnHgFkPxKI50lJV7bwNEqJzpfJRFOD7/xFM8+uDncN6yvbfDX/3Fn3DtymWe/8pLYGtu393kcxcfZH9/G5FIzm6cwxiLUMm8Tc2hvfVzUCzEdGIxfcxXkka2G2XYCKSSGBvkO425s3SO2hqEcHTbLazxTIYDcqGocFzdvMne4ADpHCfXVznc2eLGJx9TlAWz6ZiyKmlnOedOHmd1fYWWMjhm2KJmeFhRlCM8YJSa30/vBaUPQJirHbO6RHvJwe4+WzvblHXN6uoxMik43N7EO8Ox9Q2OnbqIly2czIIpqMpIVIqpLVt3rnF35xbWO44dP02v1SFNA0PH6yQYVHsQsdyydwFIkNjI2vIgw31NpQwysvGE2WSMThLyvIWXwW8p+ARJpPHUMShugE6pJUoGL6hOplGxqo2WAvIemZcopUhUQpImGFOxtX2Hne09VlbW6XRXQ+UhGf0ycCil5sF7kHOGAEPLBOcb+2OLFG2EzvC6Jk8yljrLTMyY2XjIbNJH6xzVapN3V9GkKAx7u7fYPdxhdWk9mHpH5l9dOZyvMa7EI9HWQLQ/kEpTSmh1eqRpQifvBrYhGlyNsRMSrfCxNLZycdPv3dwDRKkEowyJFaAEyreg8uxu3aYsCzpLS2TJMlom+NogfahcZi04JxmbMUu5pjCKNEuRvodMu+ilGb4zwkwOuXV3i83bWxw7vsb5Uxt085yWEoyKXS6ur9GRitppSt8i0wlGC9qqjcfiXKjMNhwe4p2hl+fYmWNv54CdvX1mpqTdbpN0u3S7S2TtJVAt2irBK0fjEOQEJMKhtaax1K11hregtSZXipkN0kFpZqHSkVQoqUIJeKFQSQZa4b3E2wqPDckZD856ymlBqsMepZWkjGYH3L15QFVOKQpLZSFtr3L85AlWV1copxOKqgbjwxqikvisksjoEIG14D2kCS6RyEQhXYZOEnSighk0AiEzwCKEQEqLcp6y9jhR44Uh6bXBWZy3JE11KG/DfkkHNqRUodKfEh7tNLV1DIcDqnJEp3ucldYKWSJRtsDZgqS9RtZq01k6TVmXTKcDpsUOre4SorOMSEIRn45IKK1A6zQoIwg+ZQ6H8i5KUZsZM4DyuPA56R1ohRIqyAtdjbCGRIZ9eI0Fb9FEG4oIzKM9UqS4akL/8A6jvV1Uu0NvdR2pBYkvcWZG6WRA/euSBPCmxBRjvLCBkSocWE+GQqUdpLekSuFmE/r9XYq6IOv0WFo5QZq2ABXkPoAXNqxLLqwB0gkUCaa5dqGQaFBuPh8rnYRUtmr8KsP+tzYg6iCTLX1Nkoiwhpg6fL4s2d66jlSSXrqKtTUCFwsuWJTSQbZmBOgk7kbDftK4OpgfqzCv+DSDtEXLClTDzjrcJ1cDOu1lZKtFSRUBBIGe1dhEBF9eDxCelxee2oXiAF5IrHR4H/yUVEyoTEzBYJyQ2pqd3T2GRUV3ZZWTK2torVHCg1DY5oYIaKkA7DoHxloG4wqbrVEIhfMpJFBLqMtQHEBFA/Po+BiAV6lRZLHyZkw4tWo6rLBUV5jJkMO9Lba3DtByTJ4mOBk8z1xdkWUZeSIYjEdIN2O53aMWiqKuKewMYyzWVDjr8LWNZAyNSRROaRK9hEgyWj2NTjWJzMnzNKyDaUKGR4s87P21w0XwVMc9lZeRAeZVrFoa90tKYo3E4smlYmY9KSKMIy+oCT7IIYSJ0aEOz6dhe+NtqIyHAhHM/aWNQFOsRmtxOK8wInov6gyRpsisjWrlLKUdknaCa3dppxrXSsiVJksUSIVTgXUshECLWODKg0CibeiH1hQYW352Y/f/6/UzB5Q+eftv2Zn2+M7vX+PT2wk/ff0vefSxp3n6JcXOX72CaLW5s7vPyQe/iJkM+fjGLl//xreZjQZUuseJpVM8daEmkQFZ9EqCizqQ+BJ/d3c7B1Ka3ejctPmeDy3kAIvAmIi0E37zoUKA8S6YZ8rIPPFh0moyphCC2tlsyF/+4BUO3QrPPfEc0zf/FKoBqdig9hlgghYUh/HhdgtvmPS3+e7f/ClLxx/jueefj5i6i5rmxu/HR5lSNHp24IWbg2VeSLxwYVPdyOWa/HQEPBomj5AJyllsFCnZsC1CJYK1bpcXn/8aX3z+G3gzo799h//pX/8LhLV88Naf8aMfvcbHn7zPA+cv4P0MRdgESA9OhvKQjXztqFmzcBYvGhVP2MyqiCyE63IxmxM+H5L+4ZeorIgBE3FRbDgbJvpkqHk2WkRfax8DoEBP9ws0xEWZxhyUcYTuL8LCFJ+/i6ygps2N30Noy2cBSzfvi/cGYqHRgoV3iRDNZ0PEJP28/tv8v+G4kUV1NHg8AimFPwWgjXiWBihqvEUaz6jGUHIO5swnfQ/UQQzoG0logyI17IRF8IY70s7m+c6bsxC4CU8cG27OahL4CIyFMeTmrQvjZ+4PFl+KSL+fD87mNHGiF/Oz0YB383t0pE3QgHN+fs8WBztyvxERtAvi0OCPIGmM72X8XeuUC+cuMv1SwZVrH/Ps0z/H+bOP8u/+z3/Lxvo61y9f5vb2Fuvrqzz80JPI1XUSJTHeRMNYgbCWM2fPcu7+c/R6K0zfeh1Kw6efblKbipXlZZCaTprzw1dfY9zf54ff+3POP3COonDoRJNIyfLqGp9/8hkmwyl5p4PzhoPtHTqdJXpJmx9v73Jl82P+2W/9M/6H//G3ub17m//k1/8JxjlavTWmRcmlyx/R0prNm9fIWzmbN27wX/xnv8nv/vsW7/3kddJ2By803/r6P+DdtR9z6c03yPOEX/zmN9na3mYynqCVYvOTywgp+MrPvcz99z+M0JLtzTtsb97miSee5PbNHZ587Aneev1Vur02ZjRE9TIq6zHGMREVLzz/FV544Re48cl1BvuHnL94lt3tTXSe4GzNbDrkmY1n6LSXONzdRWYaqUKJcCUcaydPI5Tk7p3baNXCuJoTp59gfbnL9u4Wk9mYf/7P/xv+5f/8L8lcxsrKKqLVYzVN6e8foirLU48+ytpKm8PhkB+/8y4ITaubM64ndJc6dNIVBoMDsnZOJttII+feI3u7d7l2+SD4IH3wPvc/cB9Ly8d58OGHQVqUkBhvKeqCvJfxzW9/nTdee5VzF85x+txZZqMxed6mqmqkboLpOG+KBeF5MRcsXg2e7JsBKDzeLT7fGOw2y6M/8u/IIOAoOLSQREcgRx6VjTbzQwP+KAJbMoA5SiYknYTpbMj2nVus9FZYXlulLEs+/OgSj33+CS7efz/FJyWtvE2eaTyaaVWHrL6S2Dp4figpwYlgHB3P7SLDEnysmhMTCc6hdLjCopoihSBNJLXzmDqURu7kOV56ppMZw/4hayurTIcj+oM9rCk4d/YUpTX89NL7TA4PwFiKsgDv0VKztn6C7vIy03JMUc8oxgUWyZVrN/HOsLp2kloKEpdSOEtVFSG09g6DoNVpoaXBYzhz7hzrFx6kv7dFr7uC7J5AxYy/9TArRywthcqvBwfb7G7tYGpHbUpObGzQWlqj1V5D6eApQh2k88JFY1CvwBus1oAlWAY0z1Vg6prJeERVz8iVoN3JqJxCOINH4p2g9I6qnDDuD+itHKPXXUNpSSaD4amXkfnkLEqEQDAkaGpaOkMnKdWsYvvuLabTCU4ojp06T67y2Fd0rFrkED4JjDIs3kcmFQLrDaK2CC0CywwJmNAfpERlKfgMoXK6vXWK8SH1pM/hnVuAprt0kmo8ovRThJYc7N1EyhbdlQ2SNJRtT2TwikmURlmBV7pZzPAItBUYFQxikyhDqIUm00sQ9wjWGWoHHocWkCgV1zGDNJJEKuqqpj8ZUgz2Mc6xtLQey767IO2RJgTpzoXr1imdtIvwoCTUkwGGFon2KKXQ7WWSbIVs5STleJe9vW0Otrc4trLEmbPnaLUz0jRHYNhYX6abrjCYFcxERl1K+qNDRv0dnLNIH7w+D3d22dk5pLIVaZ6z0uuQr5yg016hlUmE7wQgSoAWHRwWmzi0F2hkMJP1MhjNOo/TZTCVrQXSa4Qo8VXYXVlT45MEbyw1YFwFRYWoLViPMR5EkHKmeahclyiNyls4YTm5cZYbN69ihSTNcmxR8f+w9qZBlp3nfd/v3c45d7+9zvTsgxnMDEDsAAmCBECBokhK0WLZsharVHbZ5cQl20qUlOyS40pJKSepiipOFCelkhQpkrVFjEiLFCmJpECCIgkSAoiN2AbAbJitp/e733POu+TDe+6dAUufUryF6cZMd9++5553e/7Pf5n2e1wZ7WEPHWZxcYnFZhsbAkVeYCmRwaCMwgeFTAwhTZE+0BCtah0L0cxXKoSUc7mUlDMfvMjWcbYkTQTWJwQCWhtcafFFjhQ+nkNVGgs6LEIpgtCooPFuSq/fZzidUK+ldNpLkDQQZAjhQJYoF9PCSkDrOmnSpJE2GI17jAY75L1dWs02ZBlOJygiU2wmwcuLAoWjlFFmGhd9RxCxsiiquiLRAaYeFQQ2SEo8SQgxMUs6UKBnDHgkIkTg1xWW3nCH6XiINAmN1cMIE9nZSkTzcpxHSYGSgTIorIh7dpA2mv07UMGD9FjpCIVDBotwDpUk1PUiKp9grae3uY7OEtJ6lPxWiBLSC4yIflBSQImPAB+CwoOSOgL7IjJnEB6jSgSGCkvB2RKNoLCglMRLT0DHk7IMqGnB5o3L1Fsdalm38sSRGBUwGPI8SptkUkMKg0RQOov1EudsBClMBLIqAR8Q0/m0TJBGYHxGyHP6u1uMtyYkWqMaTaSpM9Ee6QyyagSEEHA+VM8VjeVnYUCeaASN9+AEWmneubrLm9evkXabHDl0Ct3QeCErqUUlrNeqKhYCTmsmBHwoaPmMS6XCDBTbuWLPmxiSoCTGBPAKq+PGEn24QPt4jrAi1j0KD6HA+wnj3g6DwYiaDKSmImiUQ6aDMcFZgrfgPYNxznYZTeNTFRj2xxgFIiTIWoJOmqRZhknqpEmK1hqjIyimFJjK/1gKV0ngI7nCBIeVIvqnBYEVOVKCxkQWYojt9gqeqqqbyILDRWYzocSK2KwxGHwlrUTEM4vW8cAlPRUg7XE+x1lfNRoVTgT2hEbLemyqZA3StI1vZNSabRrNjFqjQZLVSFNDLUmQWpEYSVBRsWWIFayr8AghQpW+WBnNu5wwHcK0jx3uoSa7CLtNGObs9XrsTXts74zg47/Ld+vxXQeUlF5he9RDXhozFRnfeOpPOPvsInfecQ8Pfs9PcuyOM4TeFUyScP9+xW/88Re4/cx/SVZPqJs4+bWJ3S48KF8V/+JmIXoroeE7yQ0h3DS7VrcATxHIvlmMytkEFGHO/rEiIJ1CUIIUWHSk6wEoibQCL2KcMAik9KRpk4987w+RiD6jrUv86YUrPD94jgfuvp+yGNJIqsQYH5FjW0mZxmjEwglcrc5wsEO70UEqg/LRPyOSKKN8TQJF8ASjEG7G9phBIxIrRHWwq8AnERfKgAcHQgZ86apDjozcFCGw+ZS33nwWIQ2HVtbQypJLzaEDB7jjtiNcfevzvPn885iQ8WdfeYp//U//OYPxNnWlsdXhyfnIIIqU+riRzqyYg/SAQYRYmDui+73CVZptHyl9AlyoTEGrdD7vY6E/Iw3JikVS8azitVVTf04w4iazKXbfono1+kXcHKMizMCpWWJF1SOdodAwUx0CVBDMzMeq4gjdAvjMUvPmr5ObOBZz4CceooSIIFmUKVZsKi+rcW2JPkkR0BIVQheEmNNB5+7dIS7+gXgtEaCKlODZpjOrNGfXNwNMhSRycG6pKkN1GBRhNnuqCOQKQRLcY1JHAAAgAElEQVRxx4jF2wy7uQU9nMkXZ1Ba/A1q/h5Hxb2fPWP1tjhmPhVR7BgI4lbj9Nl7KCIISbVRCllJg8R8EQgBlBS3MMZunfm3VNOzT/PqejZu4xOFCmymml8z03LvJMIkHDl4nA+8/wluO36Yl154kfd/4HEuvP0m6+tXOH7kOH/wiT/ge574CD/6gz+KIwNiN0MIjw2BNK1Rb9XY2tql0+6yb3EVUavzpa99lcXlRU6dvIuisBTFAEfOD/69v0tva4ckzfjD//d3ePC++3j9jRe58s6bPP6hj3H08HGajSbf++HvZWnfYf7qC5/lwUc+QLreYjSB/rDHfXed4hN/+NvcdvIMq/vW+NAj7+f/+u3/E49jeXkNLTJEInn7nUtcPHeRNGvQH/ZJkwYEwdEjx2ksLXFkbY2gDH/0+7/J4cOr/L0f+xkWF5bZ3t2k2V5C1lIuv/E2nUaD5YNrrC0v8dbZ13nn2nXSTOKsR6UCoQKnTpxhd2ePG1vXeevcm/z6r/8a/e0t2suL7G3vIhDk/TH3vvd9nHvlda5dvEhv61Va9RRdzygnI4xOOHjsCI89/lHefPMtvKqxcWOdwbjH6uo+ytEUJQKLSy0Wlg/y3//b/4Gf/dl/wU//zE8xKAUP3nE7F965SGbqTPKcU3c9wAvP/w0mVbSyDCkka50D2FFJb9Tn3gfv4dKlq6yZhCBjpGuZF3QaCVm2zLfffJXuWpdDRw9z4ODxGN9SzSXvPIcOH+KvPvtnvPT8s2xfvcaD730vj/3ID3Hl3Lm49lRzd7YGxUKm6lzNBq74jvH7LiQ7jtU4ZSrAVc6XoDmLaZ5eOsNYKyaUr5R8USYdv6ziy8dVTNCb9OwIJCGIaSTeIZCVD5Kn1Vzg+PFTfONrX2G506G/tw224NiBw9zY3uCtt9+k8c4VxN0PcuDAGr7MSWoZk0mJVDL6kVUSt5mMrdqomUHQs3jcGavJztklkiAs1lWgtoiF4mQyZjyZkhlBM0ugyLl48W2ur1/hxo0t6u02pfMEO6WVJrGzqDT1ViNKQ6XmyvYuAagnTdZ3B7jBDgRHvbXEaDolVYZCODIDaSoZT6eIJGVp4RD7Dp9EGgllwXjQR6g6h+68A6kiQJP3N/Eu0Fqq0y0XuHb1KnvbO6RZxtqRE9F7weZMx2MG/R57O7sgBIvNVdJGF5kEMBphHVpM4z0sSmw1DpUUuMIzGo8Y58NYLAnJwJaEchwbX5W/iMSQJNBsdljZfwQdMqT00YclxMJLiCICZMKjQjx3ZDpFo+lPLBevXaYcjbGpIGt1kV7jpcSpiskkXOUpqJFSYghoo2O0svMU3sY0HuIcEEkFXvrIlJo1m6ahZNrv0dveIoQJiYqyGCkShsMNRr0NZFpnbeU0tfoCiUorY+GY4iSCwFZnIi8DuNj8EcEQcBHNUbHzXFLMdssKfKsO7yGy6bWWqBAbfTE1LyEfj9nd3mU4nqAST6ddw8k6BIV0LrI1pAYSfGWam6iUoHRsoCuBCzXStIU2KYSAsxIoKEWO1F1UK6FbbyHtkP7WBlsvvEJ7cYHx8UCqNToxrO9eINENlhptvvXWJXr5iFR7OtkiF69fY2N7k6IoqJmM1sIK9YVlOs0mJmmipMHZAmWiiT4yxUiB0TFSXbgIqlkXm2VOxAJKEuaSt0mR40kY52MmwxEmFXSbC4RgwKhYsElBahQilSCjn6EgKgbqaUpiDFtbm1x8+20a7Sat9jLLy2vgUpTyuMKx29+ht7tFb2+LtNZkYWWVZr2Fso7SVUWs9CAMAY8XGk+BDPG86W2JUFFG5udrp2fWBBMSqHzxfFAIEXAehFCkSQ3vSxAGIVTlpahJjGYyHtHr7WKLMSpJaXYaaGkohUI5idLx7k9tgcmnGAaUIWClxvuA8yX1JEWaDmXRZzy8Tv9qn0ZjmdbqCqlOEaXHBYlwjqD9XD2gpMJ5jQyeRGpSURJkZNEaYbAyFsAaopeZ8ggURkhkZSYeBEilmY6n7A16oCDtdIHIgijKAh8yynwaZcpKI72d2ywk9QVIW/GcF/IINpHGJqTw0avMW0QoiTZPEpMqZCaw3lPmE8Zb62gtqTVbNGsdlDDvkgGYAJaUAkdSFfhFBTohQgR3pK4CQKN3hVKCwk9BxfasETIGExCQ1rF+/W3S1iK1xmLc94JF+ZLSBlQiSFLQSTyTKhXBRiVjyldpwVZ7cUlA4RAYAhItK1ttGc+/waSoWpPaZMh4sIsY9HDJlGboILIk/pwUKK1oColUkqktcSUx3U8KVHAECdpLyvGA6zeuk+o6h848SLddR0mBRZLI2OxkJu/UAYRA6GihYoRCpXXG0uFChh4YekFQaI0xBhc8qQPrJhTTvKqdo3S8zKdQjPHOUpZjinKKKxz5ZECvtxd9n6RCKJhOpkRVuiJJakhjIG1Q6+xnJa2RGINKDdqk1LwkUQErZCR9UEbSRClRogQfpZ2IWBd6IjAohERWrDEXovddDLLyqCrVLfgSzyyJNHbQlRAQJEFqhA4kpgbeYrTBioAKEUj31iO8x1tLHqAvABReZEiVkTYbiPoiaqHFYrZAs9YkWzDU6g3qNYOqKVKdUtMerUCFyC5zVQVGkKigQHmE82RKokKOzS12tEe/v0uxt8t4sEeYDsgHQ65f2+L1V19j1LtBPpkyno5IvKUhPYsJtNstDq6kLDRTvpuP7zqg9OAHP073/Bd5+hvvkHYanL/wNq/7DpcmXX76Zz7C7QsJ3bbkU3/4u1xtLJBvbnP2W1/hyNoRVo/WKScjmhlcubLD2tHbcLb4jp7szcetheOthI7vVAXOAIA4VVTFUol0SREUtlpEdIwPwAWFDoEgSlyInirBQ5AlNaGQKuBcwWDqETpDTq/wH3/tl3j6rRE/8RP/BHXoToTI0Soaw8VYAlEV8xHcqSnFR+86we7uFq+//Desnbyf2/avRqohkIQQ+3CiSp5SmuB8VUNIgoxpRoR4E33Q8cAYYAbA4SxCxK6hzAx5OSVROhrTofBKsdEb8qW//CTedPn7P/VfcMfhQ2xtnufggaN8/Rtf5sKOpFEvOPvtZxmUORJDrkq8t/jRCBcsaXspmuFVzBnto0SsFLGjMQMmpDCVVERDkHHT8JG5oqkiSavOpKgoiFG64XFzhGfWPo8gy9yweqaBE3OleDwUEiC4CkupwKdQdbsJc+NwhfhbxtktrBgR39cgPcLHERa9O0yU1VWFnKuqm3mhw2yvq2JCw82CMBCQQSJEhA8jZ+xm2k4g4kYRPqs8qIi+IKHyxQrVV4WIeu4ZoDO7zhBm+urq/ajM65j/llldOvNVmhWzFbVpTk+qwDY/SxiMvWJfgUozIGeWqjCXCgbmYBwhxPdZ+NgRReAqMEiG+Bp95JfeBHXUjFEV/0QC2U2/rvncrwpt73wVX31z7sc1YXaV3Pzm6jtmbEUfqXzIKh0qmoxVn6oxkGUpH3rsCXyAU7ffw2c/91nuuf8urAsEbzm1/zD/zyd+lxNHjvHQ/R+kqAoEX1guXTjHC898HSsEt504QbfV5d777mZUjOnvbfPVrz7NPXc+RG845vt/4Id5+q+/ylef/AuW1/Zz4eIFRpMJ65cvs7G9xfHHv4d8VPLKyy9y/PBxTt59kslA0lpY5ad+4h/x+b/8GX7vP/4O+w+uUjrJaFSgXGC8s8dv/fZvYvMpy2trdLuLbG/sYJTmT/7gd/iej32cy1eusbl9jc3tLRYW61zf3gHvmBY5470ddMMwKKe88NJL9Lb2aLUyLr59llqWYuop+9f2M+wP+OzzX2OxucJP//hP8sw3niKEknIsue89D/DY93yUfJrzmc98ks1rVwhlzonbT/POxYuUkyFCe3xDs7W5SR7G7G5ukChJqQK7/R6dZpdue5n7732YclJw9Z2L2HEfIx3d5gKnT5ziytV30Drw4ktf52tf+QKnTt7BfXec4b0PPsQ0BC5evMSRg8d47lt/g6kZPvnHn8WWE7RWTPMJU5uzr72P//B//Af+8T//OfYGQ2QQbA97NOpNLr/xOrffcZrzV9Z5+eVnaS0scvr4cdJaM8Y5CxOLL6PxZU4oS7755S/w/NNf4T//b36Be97/OMXOiP37DyMR2DAb0pG7KIScs/Vm/n1qPp6jAGg2nGeDPTYU4lyZyRxm3n1zX7oKS1KVJM5LMWeciqrJwnx1qLDrmXy0wqGDKBAhsktCcMigY8MmeLTSlM7SXupyzwPvZePGBqiA7+3w5JOfJ61lNOttHnnoYfatrWB9ZVxbWJRWKKWioWR1wBU+4Gw8TM0udNYIUELEQtZalBboRFOU8bClKu+UfDpBGE27Wcfbgp2dTUb9IRfOXUClNa5ducxtt99DEXKuXrzA8uICqU5BQt3U2Ov3GU5zpnZAQNBuNZG6yfHTS6gsRfkMZ0uuXXiVQyffQ5FbivEu169ep7G6xuLaMZK0g5cq+l6YOvXVDr2tGzTTDO8Co8Euw909VlZWme5NGA1GuMKyvG+NrN0lCI0vLamsUU/b+BBw3lHi8V6ws3OF0pXkxYj2wgK1tEGt1iUkkTk8LQsuXLzIaDDBqJR6oxZTsQikWZ1gMuqhhjYxqQoNJgic0ZhgQUZqfipUdYIqq9TXyMSpJzUCgnFvj+sbV9kZBBrdRZoLq5QhekliRDQoF7FY81JG4EpGBo9SAWE8IY9QphTR69GJCVIKXDHFFQWlLxDeUrgCa0uUgUxltLsNJoOS3iBndfkwiyv7KF3BdHyA3k6PwU6P6STQ7C5RyxoIAkXIK+ZMZHEoSZRdiYAoJlVQgkHa+PtirLjHyYCSnkTI6LEhYgpT9KWKRuBuWnCjf43tXo92fZHFzhKWCc7b2BcJcZ9UsgrUiOY+CAEmMVUUoaTs75GqEuWn+FKSuxKVJAjrMCYDo0h8HSXqCKDRPkCY9Ll+/RLffO5rtLMWJ46fpt3O0C3B5t42wZesNDpcuPIOZ7cvU3hPK81YXFnFtFrUGgsIU0MIyMuSYPOYOihzrC9QOmW3v8NwNKbVaWKMQvkEr0KUQ2qNRpIaHaWPRtFu1bFOUvo6anmBoCvAXEikMpWhucbMzgVzfxCBC4G9vT6b69fwrmTt8G00Wi2ytMFoMqUoJxTB0ajVWa3vZzWs0hv2GI5G7N1Yp6wPaS0s0m12mVrLeDqKEdvKxJNLEPH8JSTaiKrZKeLJIczqB4HScc1zvmr2KgPBxTODr4IFvJ6btpvEEFxga/0q21vXqDfqtFsL1LI21josDiMChRsirEALT+JzMq1wWpLOFmAVAUfrAk4YVCJpZ21Ma5lQFgy2rjFUKfV6F5PWUCY2GRFEP5gg8SLOXHxMPfM2BxQOyF0JIUEr8NZRhgKkJngfGR0hIGzBqL/DuCxImwuoNI3sSCFwDpTMCM6R1SJryyvAKhICykvwSfS18aCUQXqB9TGuXUqNxaOFxsp4/nLegogeaIlOMWkT57uMpkMG/RGTwZRGvUGt3owmyvE2ErDgBVZKrC0pg8dojfHgvMA5F8+I87O1IC89NigUDusmCJ8xHPTY2jhHd/EgaatNUAppJYpKKmYEMktJTA0ZIBUZgSlepdE02TvyIgLDDoW3Pv7uqgMuBEihkSGJYAclKEfIUtrNJlMryft7bPW20aM9urUFmo02RsVxKyV4aSiCRfqYZme9h7JgY2edSV6yb/8BllYOIFEoVXkeiQiGxysxaC3RRiNkZGXNvJTyssAjGBcO68Y4C0VhmZQlg/GU3b1N+oNd6olGasW0zJGFjfuvczgnCZQIlWFFwKR16t1VsqyBSQ2trE6QmnqSEJEUgxazk36UrKdeUYa4BljlmYYCjab0vjrDQEiiHYqqqntXNSFUECgfsL6EEOu9qMwxUamjFd5JTBKZvYmORuwyBKSXhFDifI53YEvB2BYEFIVUoA1W1ZBpk1a3Ba1FGp0G+xeadBcWaDVTOs2MRt3QqXsSFQen8AHlLc7NpCPEAAgR47lyWfkaWoufjsh7PSa9bYa9DWxvB1eMeerZb3Pt0jm2B3usWk/nQJ23buSU01jvt5uG3jTwzuVt0sSjlcRjGKPZzD1XSwETz1pI2L0g+GW+e4/vPqD04D6e/ty3GYrjfOADj3L2+TpF9z6O3nk/9aWDLKxkvPPM53mtPMBBm/P8qy/yxjDln/38f83k0hWWF1eZbl7k8l6LA7cJvHUYqXAuypX+Nrkb3FI4/q1yOKIcrIq8j4liujqoW5CCEovyHu01Xkc2hXIupoxIV7F/YNDb5KUXnmHXJdx974Psa1veeuULPH9uk6Pv/bt8/Zm/5kNpE1EeYebHFMSMbl5JnZSmLHOefPLTfPVrz6D23c0/uf19BBknkqwKeGUDIVFgozu/lDoyNHTABwPW4ooJUyEjtTk670UmlAuYICilZ9K/wbNPPcmxk6fIlo+ytLwPIaIZ3r133MONG7vUmgtcO/ccdvcKX37yL1hcO84Tj30/A/clVppLZCvLXL96jtWlNVKrkNbxmb/8DJujgu/7yMc5cfhIZHJ4UXXCXVywXSzlvZQoHx2NpACHRRDNF2WYoclhfv9CJT0MIbJywhzwiMwkUaW4RJCo+jMHCqrJO9PNVf45c2lV9ekm7DDHom6OHxE140FE/40KcoGQzsHICEbYGFM858XN3JrEnM00A0Zw3HT0ViC8rkzKI8Ap0Xgkfta5DbfI6vzcBjw+p5xBYKH6z81lmfF1VX4iEKmQs++u3k9dvVe3khxCdRU3k9FihTsvLMNMAxzN4yIA6BAzlL9aISvlzXfMQTG/jxGImhmXV0VthbTN0kYiA6nyoZFy/iJVZdA0k/nFMRLmV6BUdVcrAHd+M2fI2a2LAjPp5XwUVNcREErO2VpORFAMD8YY0iyjtFGCdvb1b1Pvau688yQLC2u88OJz3HvmHv7m+a/z8PsewU09iTaxiAiekCi0UJw6eTuLS6tkjRYnjt3OIw9/kLNvvR6HhoPJaMpz33yGOx64kwfue5yrl85T/0LKyy99k7K0bN/Y4urlJ7nw1puA4seX/gEPPvA+Gov/gAP7Vnn04Q/w1FN/TmNhjYcf/iCvvPoau5s32NheZ5yPaDQaPP7YR+m2l/nUp3+fp558kjsefIj9a0d4+/w7LDZbnH/zLZ597hmwKUcPHuLy9WucOXMHxw+foNfb5VvPf4N9Kwe4cu0SZRCcPnWaU6fv5OxrrzDe2+XYwcO8/MKLXFq/TKuesdcfo02NgwePcur0PdzYuMbJO06z88wN+v1dnv76V1hodJlMommuKGF98zJIQ+5LJjYuwolRjCYD8jznuRdepFOvc+3KJRrNJrffdpJh7lk7dpy9Xo/ecMCP/+N/wef/7Is8/pGP8t/+u1/hs5/8FF/86l+QJZpBf8DBA4fJGhk6CyAcuSuQpUJ7Q1JPSVSBmg7JVJ1XLnyL82+8yQefeILDRw+ws7vNYHeL7/+BH+T8pXOMxyOSWp3UZHEPUQG8RQVFCIKi3+eRhx/i5HvuxhWglaIoHc6VaBkp43GeyWp/evccmrUU/C0Tdz7Kb8HbRbXuzFbMm2P+5tTwsyniq2evpslMRueZAcS37KnzaaWZyWylULEINtGwsqw8kdpmgbXl/Vx86yyPPvohXnn1ZbrLi7RaNba3hqysreGLnOEoR9eaOCLDsiirzmO1NkgRz5sRY44yhJnNfl6WpKlGKUNhp0zGOdNxjpLxtbnS0kxS8nzC8y++yRsvvUimFY12k8l0jNuZsLS0n0vnzlG4CQfWDnH6xAka7Q6T0nL56gYNWaO5uIRM0hhnXwoCljxESZQPHp8WSKcZbV5lrzcgqy1y4vQjKNOgxFXXYJBS48OU0WgbPy3Jt7aYFCOyTpN9+9ew1lOUgrS2RH1xBRDVz0sSnSEdsRNMTPasaYWVgnarjbB5JZdS7E4GDHdfZ3N9yOLCAr3BNvVWgwOrS3gE9Swl1Q2ESmPqmJfRFwVHgohnFgTKOwwBEeJrUKKyemAmS9QYUm5s7/L2q6/hk4TuvoN0D2QRSA/Rb6nKrCU4KEMApdCV50OiPTp4bGkprMNZx97WFoPJhGa7zsQWZN6ilEYrgVYpQmvqaYLWCaWzDAY9ptOcWqtDd1+XmlSUBQhlqDcXqDW6uBJ6/W12N8/RFym15hKNdrsCJy06eNzU44QjYOP7oDXeT0lCQBGjzBMVWSlBxn1dYHDWYUuP9Y71vR7be3sYFahldVZW90NuyG1kgWlMbIzIeB4leMoQQzaMjJ5TWkl04gBDI1uizCVWaowwCBJwCpE4gvNQSIRWuIqJopIMlXQ43l5BlyM2rr/Jy688T1Kr830f/l5297YwdcHb589z/toVurUataRGktRIatEjaZiPEa6kLhTBCYQ2aB0wSYLzGhEUteVVFvfHlCVENI6VUpGqBKVlxSD0kfksBN5P0KIkcYbSK9IkiRIhJcE6HAU2QBGiB0sqNd4WbO3ssL2zTT6dYHPLysEDNOurDCY98lyT1A3NrIYt+riyRARLliUcWltlMpmws7FHf28rdunNBu3FVdrNGtPCYW0MX/BBoEwW76OTMQWuOku6mVxUqGheraNcRqgIxgQfz2hKSYKLzdbIhBGM+3v0BnskSnD0wDGkSSubh4TgS4zWeFfgnMOoGGkihWKUW6yfUBBItSEREitlNd+izcSIgMlSGrUFhlmHohwzGPcI/V0a9YwsNZg0RajYfDYhAHoOKCSFxstoyJwYHeVZMhD9XGsAeOnxPmdvZ51ef4duZ5nO8n6sVPiZQbdQgMWKgA8JUtVxNidYi/IgtCDLUnCeiZ+gpUZYKMoSlKBR76K1ZDR1CJ+gzYTSTTEmNqAdUAgfjeWVoNFcIK21GI97DEZ79Aab1JI6zWYbm0jqokmQCqciQGSEItUChaZ0M2/UyJl3Vd2RSUmBI7iAkSnDwYDLl15i9cBxap0FnJDIIDAi2pJbAVpIjICkSvq2MkcKgSF62BIMaRa9mHQIuMQwmbpohVCBG1IITIiJklLICER6S6kUhJLaQovUNyjHjr3BLjujXZqNlAML+2jWG2QoekEwLKLx9LS/yaC/S7ezzPGT+9FJh6AcWtdASTKqZGirKEVMUiuKgulgi/FgC+0t0nmGLqaqi3LK1JeUpUNai889ufdYW0Jluj50ZZRCJ3VUp0UzbWJMNNIXWpPKuE4rHZsiQgSU8BTuXacTgo/uV0KouRdUKeOaYiqiqAk1CgI6OGRIKH3ABVvtwxZBlaAtBEgV0zGruSuEIHEBJwQZDlfkOAHTkcejcJUPk5OSkNVQ9Q5Zs0N9oU2n3aSx0GGxtUCtY1isGZp1TbcBTSVIqlpPOYdwN7OvA3GPlFoglMMKifIJtoAynzLciUzKYX+LaQlb1zZ4+5WX2bh6kWG/z854iJuOyZSkkSk67Q5Pv7FOcAXohOWDi6wuZ7QbHQYTx84gZ3s4ZjAuCbLEWYcrRQy5JaClpMxLamnGzl6P2w7v57v5+K4DSncs7bG5I+l0C9584zK3Pfx3uPuRR/ngmRbKQoqnue8+zrzvNPsHz/JMPcVi6Db20XVTakuLPP2JT1Cc/gl0yHEhgWAjxVjcLHZvjQOH6t+Jw3N+oK4et/opuSCQlR1bqAzrQlmilULYPlcvvE37yAPUjSAoNad4ex1whcfaKavH72I1bZKalL2dy/zBZ57mnRtj7lrax0/+8CP8L7/xe4TQ595T99NZWUWp6GPgQyWZ8yB1jYPHH+Dx1u20D51iZblbScMCHh0ZKdrgXSCVklxMCc4jbM6N9S2axuDthI3egG53gc3JkKWVI9QaKTYIghZMS0er3ublLz3N5555nsm3zvELP/+LjMdD8BZbFly9fpXDRw5y8fVn+PMvf4Mf/+mf5b1P/AgvPfcMB1eP8g9/6h/R0o5XXnuVxBgyHL507PQHPPTo99NdXcOV44iVhOiBYYOrfJaoPJ4qSZuo+uo+RiiKIEDauWdP9GFyc12vCAKvqu8jEIJDiRjwGv9FEk3GqQCZm4vTrGrykeJUMWjCu0CkyMOJkqQZAuKqsSPDDHCKrQQ/A7Cq75jxoKLsq+rWzrD16rodIjKQ0EhfsZd8xVqbReEJkEFWJurxOkSIQFDc6ojMHhFZW7jIJHCi8huqXpWuunnuluqywqQq1EhUgJkEG24ylm6+YczN0kMAUUln5t8RwS1EFC9G4+/oNXQzVjwykBCRTxFCxcoidmPnbDLiGzwLxBQigkiSCHhVfLb4WyVzcOzdpuQzdlKYs9NmtW8QYi4TmvtWvavqhhmS9LfB01pEZsOtkKOoZHYhxChqE2KR+9Ef/D4WFxb40pNf5vjty6zfuMThY8dIkpS01mA43QMJyhjuOPMeOp0u1uVkrQ6lt3hvOXrkNkIQPPTgB7Blgcni3f9nP/cvMUrR39vh8KFjCFNj+dBBuour3HbmLnY3t7h86TybuxuomkElNRazJX713/9vZKnm4z/8Y3zqk7/PYLLHhz/8Ea5evcrhI8c5efIYiwtLDIcTnn/2GXo7O2gjGfW2+YPf/S3yYpfxdMq+5Rr//lf+HcuLR7ixd4N9iwug4NXXX+HMqTuwNnD9+nWcy/nWc1/nvnse5tjJ0zz3zW9y6j2naNY7vPDcC/zVF/8cNxwAUFpPWm/we7/zm7z67WfxMgfpGBaWfWuHCNNAv1fisSy2F6nV67xz5TKykeFcjklTXF5GI8aGZGPnKhcuDqJkKtQ5d/k8t504xWi4x4XzF+gP+ggvWDuwzMULl1ldPoD3Uz76xIf54uc+zTCfMiqn9Ne36Q9GSBkLRaMVgoTNG1ucPHOaty5d4v2jXb7xlS/yr/71v+HVs+eROkHmJe977DFGgyH5sM/R247T6rbxpcWrgPLR0wIjmE6n/Mtf+h9pt5uR+Yrg4htvsHbsKEE4vPUgNUJSSVlOlwEAACAASURBVFjlu8dutd6IW/7/OyWet87p8C5Q9ZahfwvYLSoZsZyD8TdXAyAyNG75OzMPJSJLUlRzzVvPcNijv7vH0WPHKa3DBsGpu+/l5dde4TN/8Wd02inLSyssdFZp1Fex0xHeKRrNBl5qJqMJJpUYExPGHOB9BJ5tWVbru8Q5R5IYnI0v2BYliGhm6YOg223ig+Xs22/jypxD+w+ws9PnypWLHD1xG8GWnD37FoUdoWTGoYWDLN62xKGDR3HScO7yVdZfjuDg7affw6kzp/FCkHtHaQN5PsbZEh8szkpqjQbFzgaNTsKoTFi5/b0kOqXwFi88icniumZ9JVXPmI5HOFHgyh4rBw8xHJUMJx4rPEmtQShVNCXXnlRohFQxHQyBUxFUk1VghfZx77U6pdfvR88EN6LIJ6wd3Yf0jglpPB95QZo1yJK0KohLhA/URYrwoFQEB4L3VbpbiEwRqZAoysoHIlSSn8Fwj53tDSahpHvsCErXKmacirKXIOZ7o/fRjDSewSJgXEzHjMoJpS3Aewo7JYSc8WjKQnsBKQ21Rh2kIBEK4X2U6gGTYshmlQZUT2q0Gm0QCaKAQhSRKeA0obSxoSME9c4y9azG3s4mOzcuUI4XqLcXUUmK87Eo04lAC/DCI31MZ8OHmNqEjOzpqsHnXcC7Eu8F+XTC5u4Wg0GPVrNJt7PKJEyZjB1SlSgZzZpRJpr3Og9KVr4XlfGCil1sMdv9lSdIicpapDIWWolXlJL5/uqDw1ZMb19OKCsj+mIyYG93C5O0OHhmhf7VGzz5xSeZTgcMxjlTHziy7zAr+48i602kykhEgguV7DxJ0PMwC4kSJrIRpY0NzmAIWJQ2CGJjKVOaRMRGpg+WRET5all6pk4gpEHrhEwEnPKxqeYlpQRvDQRBpiS+LNnb2aQ3HDCdjijzgm6nS3fpECEECuNRSTueP2oZtjfFCY0xkugE5BmNc4zWHD16hNF0hc2tDYaDPuvjPgtLy9TbSyipKIocoQTTyQClUoKzVdx8ZJCJeHDC+xKpNKW10bRdBKwrUUEgtYpnHa1QaKbjIXvjG/gQaNRbVbJniS8LhE7BCHTNUEynOAfGKHwezbSVmCJNQmJSdNBoiKBb1cSLMs0U4QSpbDD0U4ISJKKOTFLKcsRkMGR7d0yWJRzct4oxjZuNOSFxEnIfo+qdnVA6hRKSwgcIOQiDDSX5uM94NEbKhH37TpAkKSIEMgRe1yi9i0bdMiMohzKCNBXgEmxp8KHApIZESShKQuExRN8dYWJ6WfCWQELDRMnbtLQIYiJmbEp7ssokOqgqHElJOs1FXKPJcDJgb3uHyXRAvdHBtDRp0qD0BV4amlpG5pOwVZhOtJWYJTFbVwUWiIBSKb3RLts7lzh4/B5qRqKUBwsCAzKgwowJJSidR6oo2fJBoIOKnrLC4ILDWR8l4xU7TOg0ylgJ0QNWCoIKSK8RwsVrVgleahLv457nSur1OlnoUIz7DPd2OLf1BvWGYN++NYRuUQ5G3Fg/jxeB20/cR625gHcF2F2ElYwGm7E5WxT4yQhvCybFFF+UFN5R5GPCdBKj7LXEaUkSFOgI3sssoW7aqG6KTuukaYJMBVqkGJUhFQhZWXY4WUnl/bweL22J80UEn/ERyFRyXgsiKssOIaNnYgjzdSiU0fcouOj8KoXBivieIy2pSCtMoArXCg7pPNjA1Alyr7AEvFZIZbBpHZW1MK0utc4CSwsNuu02S4tNuq0a3WZCqwatTNCRkBCBEuk90lsQgWCjhYWWEQCzslI6aEBJJpOcnb0+k94uu1tX2F6/wTsXrpGPr7O1N2FjY5dz336D3O9gXcbpkx3uuvd9TNUab1wfkImUQtVAloiaxgrBwAu2N8YEG+spjef6zhbZW44kFRhjWK4pGkbjisBmgFRpCAoh7M3gK5kwDpYwlezt9v+WCuj//+O7Dij9/q/+FtO0iR9OEEf288EnPsSZ9jrDc+dZWD2Ba7RZPniMH1raZuPcSVb3H+Hy9Vd45eXn+Nj77yIpxpx59MdorCwyLcbxBrkw75pCmB+Mb2UrzQrvqH0P7/r6TTZJ9KYJVUyArApEKXP2djZw/ev8r//3J/i5X3yAlvYUwpM4gfUCaT35dJenn/o0G3mLxx//CLYMtDoHeeJjP4E88g6JLvmjP/8SH/n4j/GfPv9prrDMjy6tUNe2YnpE9pAseiQyZ2WxwcbGdfabCdl0hGw3iGZxJVH7GYGZojKGVhqEC7QNvPbGyzz30vPccef7cUXO5Rtb3J4ucCBLUUohbQzPHN84x1+88ir/6pd/nenoOodWuoR8Qu5zRjsThEzZ2LjGKDnCoz90J0sry5w6fJjVZsYLzz1Ja+1xXn/jZV44+ybdxa9SyzLe99Cj2GLMSncZIwpkPYsMGhllP7K6WaK6Mz7Ebiohgj9OEF3nCUgvorG3rJxzvCSIGGHqmLGDZAU83MR9K+gjJvmI2dcioiBmLCUhUJUDUgQbbh0vcfFxzFLO4kc5/wgEjxMxvFtQABofoitOhF9U7OQGwQzKnAv8xMxBqBqLFb8AEQ/bfu4dFJl3M7twKarvDdHAXMyAnOBwFqQ0lYzMz0E4dQu4qsTN652b50KUHob4fFLOWDfcNODlJqTkiVI2ccscmv2sIKCFqZLLQvRV8jamTlR6GBdcJc2Ziw/jx4p5F2R8bl0ZXxMgQcYubWWQ7308oDJ//24xOZ+tA9wsmGfsipkDcRyC74bLZh/ndfMMbPqOr3pmPl1VlC5Uh8uqAA8BrSWT8Zj3P/QY//uv/s+oWsGf/vHvcuncG3zgsQ/w2Ac/znjYQwWBd7bS+gtqaQOtWywsLeOcmxcoUgisLRBSoJVhOhwjUsknP/3nXLxylscefJxjx4/y8KPv5/aTt3Pj2jpfu3CBI4eOUWs0+dSnPsHayhFW2wtcvn6Z/+6Xf5k//eNP4azi/vc/ymQw5I1XX+OHPvaDnL90ga98+YscPXaQ64lABXAu4AvH8duP8dor27QXU3JfkBjDte1zYA2b69s89YUvkGnDW2dfx/qCw0cPk5hFrq9fpxATiiLnttN38NabL/HlL36WDz7+Abau32B9rx/pxAQ+++lPkRcTpLBk9RqZrrHS7TAal/T7e7gyBhLsTfoMxgOarYTJZMJya4mxK0gU+GDwHnZ3t8BHZppSiswssH59g8/8pz8BG9MyVBDceecJfvu3foW6+QU+9/nPcPvJM6xvbmKaDc6++TbNeg0tFcFFA8xSeHRN4EYTFtodtq9d4o/+6I/4+X/7S4wnnuWVVTauXkUow9JyjcSkPPKh70W5gLREIJyAChILaBE1++1WOzIZpMCOJqxv7vDlr/01Dz70Xu6550E8vqLuRzBTem6O2QpbCjOcO0TGXpgHR/hbxvVs0t4KGs/mcTUDRKg8w2Y/JiqG3s1ZMZ8Zs3nmK685YavXJEEJrLNMi4Jv/s03uLGzzr13348RCTt7YzY31jn/5nM8/qGPMi1LvvSFL/DY409Qz+o4L9nc2sHUm3SaTXSi2djuxZRUYcFDq15HaBnZA3YajXKNZDwtyFJTHSYDk/EoAiJZxptvvEY+GHP6zBkm0xHvXHyDFE+qDWffeoPrVy9x5PghDuw/zOrCEtq0cVZwbWePnfEUWetw73seQEjBtc1t8jKndCVKSFRiqKmEWlqnWUvpb/UY9XJqB++kmyxiQ2QEtJWhsDH8YDydUkzGpMaQpQkJms6+A7x19mUGziDTFllSI1XVGpkIkCkKXzVlJEF6ggqIUCXQYin9FFeU4CJ4NRn3UCH6mSzuP4kxCVKUNBZXCNYyHg4Z9jbZ3RizVF+is7SErNUqRlKC85O5X5UMMeY9eEFRlOQ+jsuAZdjvsdvfwRNodbp0dEoZPAhPouoEJD44XJkzzkeMJkMaSqFclNEJKSKbRhq81qh6Ay0TUu+Q2rK0FM8KXkik1wQcXjoKJZn0egwHe6gQqKUZ9U4bVxUlZeFBpTHpxsc9xWAQSuBcji1zQvAsLu6HZcVg3GN39wqpUGS1GrVmilIZrtRMyUkE2CDRujKdVrPghkDwkZE9nozpD3YoSkstSeiuHaSQgpEvwCsSFQiksZOuoseVJBBUbGxqLXBBVqxBSZAiSg+FIJFxvym9JNiAL0smYkxRllgfsBaEs0ABEjKp4pyWCpXWWD54CsuE9cvXCXKZ9olFGuNtusOc0qQURfRIyUQtpqyJEBPppIlm/FLF6w7xrJSouO9qXUOXPibwhgIhqzZQ8AQnUMEjKUHA1EPpXBxTSlTAncOX0fg9plJKRLBY69jeG9LrbZOPRtjS0mp2WF5dRWZ1hA5Mg0QqQCps6QmTAlT0vHJSIaVCCEdwJaWF3UkfpQQH9+1n1G6yvddje+sGWzsbLC4doLW0P0quvIigbyLxZRFlj9X9CES/sSpVhuAdpStITI0sjeDBsNdjNBwQ7AQE1JsdsqRDCAXW5wiRYlT0wXG2IC9HCCFIlYGiJPc5tTRDhTpF3gPjcWEKSEJe+Y0qTS5LasJQWMmUACqgQ8CGEikUWdogpIZa3iQfWW5s9GnXSzrNDipLsD5QOrC+SkLTdRIpSBNNfzoAbyjzCb3BFsJBq9VGGROlnr6MTBM8VLI+r3RMYxUe7y2hCLjSR2auCngHuQ8IYailmry0qFBGRlvQ1KTElVOMNjgpkBiC9pRlZXshTdxrRMXcDdFTiaqxkdWatA828dYyGk7Y2lzHZE3apkHWFjgvsGGK1LoKXLnpTyODj0Ww0lhf0utvsLN9ndWFo2RpQk5O6RxBOgwOF0zVjA1oETBCoePJPnroCIdQtcqqxaFkgpeRdYZ3UWAoImNGqahkFF7jnIOg0UZhhCcRFtCEUOJDh7KckKFRaQ3RquOEYzK1XH7nBkpvkOcFC6lB1xL21t/g8miIoCTVkXUXgos+X7464QsBqSHRCampkTU7ZElCmjWQaYpRilRmaFUiVVo1AuKZ3IUoC7PVYURQEGxkXUWnDIdDo6Ss7legJSCgGYtpTLrzHmttfL6qTpABbAV4ekAoHesEPXe8jXu9r0A9X1LmMFaSUhmEThFpiqq1kc0m9Xab1YU27W6L5U6LbidjoSbpNhTNBJpAiiC2rwqCBe8CiAnCC0IhQUU3JuXjmPEBcu8ZTEum/ev0d3YYDTfY216nv3mVjY0NXvr2O5w7f568yJnmgXJSYG1OomuoVLGw0MTUFui5QLe5D4HGhg6D3i7dg0usNBTNkHKlLFBlSsjH2DKyGEsnImAmQQTJwGe807NRsqcmpJkkkDDNpyTBERmJERw3geit7E2lLBH0Jrd0Eb8Lj+86oPTl9Ee46+4XeO7Fc4jlMyxNX+Gpz/0eb14dsXriI3zsR36Eo8ue1//q9zk/OcwP/2ffx6/9xh/y3KWSv//RJpPhhP2rdXI3QAgTKe5E8zxxKwOletzKUoJKIiBuJrNVf71F2jLz5YlymkQFXnjhadY3hjRbS/zA3/mH+O2LvHCx4MEH7qKwI8j7WFUjjPps9yaoheNceeMZWksHkYstVGeZ/+qffhgtFIP1y4ySJv/Tv/lFZK2NsRMIVAAJjHt7fOlLn+b5185y/wOP0KjVGZYFV/a26Licdr1DLUtjARsCHosUEusE1pf/H2tvFmTJdd75/c6S293q1tZdvW9odANgg9gJgSBFUpRFSqLFkWhJI8uyYsLyox8c4ddx+MXhiXCE13CMww+eGHsUMZYocUYaaUiNKJEEsQPdABp7oxf0Vl3rvXW3XM7ih5N5q8Hxk4MXgUaj6tatzJMnT57v//0XPnnnJT6+O+LYcoaSKXuTXbY2PqSgx5NJRiR16ERUFSpKefeTGxTRSVZagsomUEzBODSSxfYCRa/k0aeeYzlL6HZa3Ll1LUQ+336fi5c/YvX813j+F77Ev/o33+fK8dOM797k1r2bLPSPce7cQxw9eiREyHpVd1ckwoERAQSShC6axEFteh3VaQACH0ww8SjX+BXVMqNa9hG0sfU1rK9ggAzqjt59IFP4TwOLCOqVH7DBgBAftO41MNBclwDQhH8sDYAU5osi+Bw5ZJ2YFrhEjWVTkH7cZ5bd/GQdI+kJ9MsGtnI4Ql8vSC+DJ5IKzB4fCigvQ0HtGsAFgqyE2udECGQDcMDcm6mRWDYSP0Ut4asBKFEnHzhfESSD98MpDb+P+87Df+bvtcAj/N03EsNgtO5kzfiTAciklsw1Y7VvKBVAJy/2Aa8wqkG+FhhjNjyM6h9xIoy59BIa3yzfmAs33KhwnPtG2vseVsj9onn/9JpxEfMx2j/XegaJ/fGRItCjhVdoT0g20prh3ojnf+Er/PGf/C+U45xsucvNm7cp8pzZrEDHWQAHpcA66HTbpO0ssJyUDrKJAA+ihMYJA97inePG1ev89b/7HkvtlNejlD/4w/+MvPJ0OhmbG5ucOfsgH3/guXX7DseOH+e1117mwRNnWTuyDCjOnDrLP/yHv8erL7zAow9fwAjB7a0N/u5vf8D6+qccOnSQ3e1dhJBUueFXf+PbbN/b5fX3XyWzEaVxFNUMLVIOrh3hkyufYK1FtzIKVxDFKRs7u6RRSVVZrl+5zntv/I888czT/OjvfsTCgUVu3vmU8eYOnd4C4709cIFGb6xDeZjulaRtye/+zreZlZbvfvdfMBrskSYJOolp93r4SYEAZoXlzIMP8tbF11F1waOVwgtLFEcsHehz69oGv//7f8AXv/A8/8f/+k959eILnDp1iv/9f/ufOXzkIH/8z/8p4/E9Pn5/xtqxwzzzxS+xvrXNvY1tNj6tcC7HWIdE0c1abI5HHD/9IOt3bnP5g9fpxxk3d9c5uLrCyz99haeeeYqqMmStlCRKIa5BWx+6SE6EiALTgN1SITPNaGOLyWAXZMVCu80H772DcYannn42dDVlkEeHuygwK70MAJIlbKrmnviO/e6T32fu/fvy8H3g1LvAIRfuZ1ibYv8/+0DT/e2BsPJiBULW1tyV496dW/zJd/85470thJxy4shJVg8c5N0P32Br51PSLOPu3U0eOP05PihK3rr8BocOHaDd7SOTiG7aRscRt+5uksQRWZLgiAKQUZZEcfA3lD5IFra3tonSBCEinHOMR0OyWHN7/Q43r83I8xkHVpfZuHeHV197ifF4zJFDa1z98B2m+ZBHPneeY0cO0+2tIYVknDtu3LzGztSQtnp0ep2QOmYFXkbEqaSlW1RViCpeWViisjnb2ztMRoa1k5/HEpi47TjBGBd8grwhqUoiV6DbEXGSBgNuK9jY2qG1fIKs30USIa0EdBhv6XCiCs8cFUAFJcAbS1lWeGORwiA1qCihRFCVe3S7XaRs0Y998Ez3IEkwvkQIRavbJel28NZji5K7g3uIHY0SljRr0++2QyCJ0zhX1exdRSTDJnQ03GU2mYCAbm8RqUMqmTEG7z3GGoTdxXhL4UqoPLPZGN3KiHo9Ep8ioiBF0FIjfARCYZxDuEY+HdhUXgagMHQVIZ+O2Nm6x95wk+X+AXr9ZdAxlbM0vjZRFuGshhqQCWspzMqCBEEUJQjZqiVYnoXuGp1skclkwL2de4ihZXlhFZ30kVFGFaowtJdYX+JNkN3hBEU5ZZqPmM6CPKfXauO9oHIO7yWJrkNQpAy+TxK8NUGmXT//vRAUtgoJdg4MY3JfMUaBEyRaUrqK0itiJ5HK4KnwMkajSeLAVJZCzf2AnFAomVCYgtloF7zl0MFDtJJFcm0QrUV83+ClxruK8WzI1t49WnGLbnuBLM4Cg9iHY8UZEhVT2SKEoHiBrSZg61htbzGuNrJGoFUH54M/jifMCYsLYJoPCZ1ex/PCHmOwzjCdjplOp8RS0FlcZWH5IEkUo4UMDhVWMPU5TsbkUc1UlBoVaXQlAgvEBEYC0qNUHPZ7kcN6yyA3xGmX1bUFovEeg+0tPr12hWxzg1a3T6uzSKvdQetghm4rh/MVWIGzoc2kRYSUDpSnlbYpipLd3QGj4S5VkaOkpp21SZIWoDBVUbNBI7QOLEBTTgPwqkUwgPcOJzUdHeNdziwfoaKIRCnaWRvlPNPKYZGBESSD/6nXEKsYZzVejoP1gopR3iO8RmSSVktgjSGfTZgOd4mnkizLEEKjhcbVTVMlwOQTbJkzHA6wpSXttImyDEtIHQxWLyEZGRlqKFX7v8SxwjuFKcMzSkgonEF7hTEWpQJL3wuDluBdaPUKLzDWEkUWrTTauDqNT+GVxNTMN9N0U2jqgLAfUwISL3HWoVVMuizJ3ALVtGJ3tIcvRvQ6KZ12C+mi2kRdBmBPuLpID0/ZyXDIYLjN6toR4qQNzpHKNlOT01K6lnaHvXyjHjDeh72gDOCDRFN5V/sRRaExJGRgqSOxwqB8kA5H3iGNx1KG+9bOkNYwsxZ8gS1KjC2RzmNsgbUVs8mU0WQPrTTdOIx9WQZPUlMabL6H1xGtOCaOOqA0Io6IozSAn2mKk9BN2mGfLgVC1vOdsM4FqXyIP9I+sMxyEzYbHpC+9hpTgLcYL4MPEBUQGJeybvTbOsVj5gAsQoXrbpUiRuxLSan38r6u/RyYSlHFCqE0VncRiUa1U5KsR9bt0l/osbDQptdrs9xLWWzF9NvQVdASEIngS6SExZkKZS2VM8GPd6rqUk1grUE4i7WG0jpya9mb5ezu3GN3a5PN7W1urt+F0TY7Y8Xg7scUMuHKtZsciyrOrkpEpBFKMTGeG7eG7E0NrWyBxXaKUgmj2ZiqHKOcYDAqsYO7eOsYT0u0ThhNFDduQ6u/i+z2aImIX3rwEBevDbh74xaumoG3jKuKwle0g5yDclJwc+RpJx4dx+gZGGbMqpJAS/EksaaoAuMvlqBFAPJwil4n5uf5+rkDSifWUiaXBth0la99IeHyn/x3/PFPByT9DmviAz536xHKDy7xl//2Fa5WV/n2P/gtvv0H/zlrDx6nmE5QCqzTQYOMAFfVoEPgPOxvkj9bJP6sd9I+s2L/TY2xXjAp9UjpqcqK7vIRBsWAsWrxzPkHmEwrWnbAlStXWe15fvTjv+fw6SfJik954cXLPPTwlBc+vEzv8CP8yje/xbHTZ2g5D25CV1iqyQ7bg6tU7YOcPHIEX3sxKTxZu8PTT3wJna2wt3uP1y6/w4Unn2Kpt8znLzyFU2NIkwCASYf3GhUoEpT3rnHl0xscXFrj//wX/4yFlTOYPGdrZ5v+ifO8e+MTnsjO0slaIB2VtZx7/Dn+68e/jKkmJFmKLy2zYpfNjevgYpI4w092eOWlNxkMBny6vsEXv/4dbg0mHHv0q5w4vMR7l37C2We+ytpCRi6P8f7Ht/iP/tPfQAmHa9K5cNiy5KMrH9DpL3Hw0HHiOq1L+RBbjAzASSVsAA1QIRGFmoYqfW3u3Rg/u7Ch8cF0yHtf0+ehKbWkqG1nmwKqxkH8feBIAMlDSWTx6PvrpwZoFGKe6LFv7hd+R4WopU4ygJw1qBTmWA35+EB9bEAuUTOQJDV1nqDC0si5RM0RHP2pZW4gkfKzxtqq9umYCzqFw/h95kwDKjXzWwpZMxV8AI3mUjNZy8qC/xT3MZM++6pdWhpj6wYYEvUvq3+pEI080Na2UH4O+jVsCVUDd5590+5wvBKHxdfSh/l5+H1G175MtZbR1cCcb97RIHo/w8C4n1UxP+bmQvv7B4v7/tL4P+3Phf23NM41tgbRggTH1RMnloLFxQWKmSVKU37zl7/FwsIq25sbXL9yk5nZ4+knnydrpZiyYjYZ0+pkcykRLjCyAh7pwDh8FPPdP/uX/OSlvyefDPGtVWhFzIoZWdoikTHFpGBlcZUr+iq//q1vsTMao7zge3/1Xb7+9a9y7cOPOPvoAxw6uso//m/+MdV0yt3NDRbTlHffvchwtMO9rXXG0wl5lbOw3OLP//T/IVIJRxYPsbu9iXCaqix55rkvMMtL0ts3OffIw1y7dRM3dpR5gTeeim36ix1++Ld/xTe+8eu8+NrLrK0e4Pmv/iLvv/cOL+3+hMlsEkBk5dDYUAjJiEle0Wt1ECJhud9FRylRkiGkIELze9/+Q/7qe3/KZDzDiBmnT57l7bcukrZTkjgljVN2d7YRDoZbQw6urvDg6XPcuX6Dra0NVg6s8d//D/8tw+EOi70eW9sbiKTF5556igMHVhkMhuTDMdPBFnGWECddClNx9NBJfvc7v8Vf/uWf443gsYcf54FTD6KFIpIpeDh6eBUlLdbMaC0u4E0w/w/svVrWI2xI3amnlXEOZSVvv/JTfviDv+bpZ5/j3INn2RpukcYSa+rklQZIZj9dsvGJm8tH5wmS+40TIeR8Hdj/TrMJb5DnGnoVoSv4WW+5OT9zH62CIJ+jacyIOsHTY0WFU5JDx0/wa9/+NndvXKPXX2U0HrC0ssIjD10AM+anL/w9ZZ5z88Z1kkQz3B1x984mp8/0SKKYylTko5LxZEQrWcLYACJKb7E2UMxTrRiMJqhYEmmJcJbRcBtnDfl0wshWJFrSX1lER0u8+85lPv74Pawp6S8s40xFWRUsLPTwOMrSMcunbG1NuLM7ZjIbc/j4w7S7C7R6wSzbOxH8VAgb7naSkUjFYK8gL0uiZIEDhzpYb1A+zOmiKlGExJ92OyX2irjdDab205xJsUfc6qFUQiRVkDsoj9CBeeGdxykPwlEVBaUxRCqqGZwhuUilGik002JKPh3jjEdFKUpHSKGCWbUSCCcxSiBdAlLOJQciEqSZJrUFvpyyMxjg8zHDWU6SpWQymLWKOAYMm3fW2d7cQMUZWbtNEscU+RgnPdprIq9whDh7E0mkTOnrBbSM8Sp4ijihkMaDtEShIsU6gxUlQoaUUSVF3SUOhqqlrbCzHYazMdaUJFnEsYXTKBWFQtO5OjBFICKNV7oeAw9ECBvi6EUcBRaQqJteUgXA1hiEh07WPLtoegAAIABJREFUo9VpU1Uls+mYaX6PKOoQpzEB/W9TWoEwhmE+ZlZMQnNMBwDBeoGpPzuSEaUTAZBwJZPKIi2UwhNJTYnF2cAYDr4wwVdPEBLilIzDWMYhdCWKNN42co6IRMdhr+LCHsS7AFhLF1LFrCmDP5irSNIeWZQG/zY83gq01PgmalrF9DvLmGhKnk/Y3bzFyFo63S5Jr0tpQrJSaYOE0Nf7ciVTqrKEKEPKAG5GvsQWU6ZmTEVIRXU+Ap0ihSLSMUIHSDqWCisqVOnYzWdsj/aI4oR+by0weqo8SNtMSYnAVbVkP4nCM9/5Oka8xNnAgvK2Zo3Xvl1EAq9CE8h7S4AnLA5Bb2mFpaXwnJ5OxuyNtxnPxugtRZokdHtdIqUQIgrph5GqQdMAAHnj2NrdoiorZrMpKopot9ukrS6xiii9qntlNoCaeGamAhM8dKwLUqZIRTTpxM5XVM4TtxZI04REJ2ilkVKQRiH5UBuoahuHWER4KqwHK6LamFqHz/MlzlbEIjCgs3YP5yxVWbK7N8YWI6SEVucAOkoZlznT4U1296YsLq7QXV7FyQjnK7QQtU0D4B0qDp51iQoR6Q14MrUFKgIXYmDq9LZgGyK8DQxGT71nlzgngrQQgXAaV9ap0QQpXlUFE/xEB0sE58HasO9EqBrUMUGaq4MPqfJhPxW1W7TSjHE1YTrKmU3HtNopWauLVhneBw9X4x1GFQx31xmNBqwdOkIWR5TOIJWioiRJowB+o6isqVsqtSlFnWyNU6AFpSlR3mLrMKZaYINyjtIV2EnJYDrEK0tbxbWUK8eZEEyhjGHmCqRVOGdxwhKThAAj5ZEiYWFhGa01kVT4SJNpRRa3SHWKkII4joNZvNQkUuFkVbNURK0I8eDqlHDv8bYMKo/ac1aKEBCiUQjhiESC81VtSh4sAayXVMbP967Om1oV4dAixutQ68jGx9aHlrQQQZPhhMbKCC1jbJygk4g4TYnaXVoLXdoLXdqdNotLGQe6Ge1WwlLqSYRnQSjCXSMJtZPHVwZDjnciNEtomJ0WVxZURUnlPXlZMDOW4XDE7u42m5vr3Luzzr2NW2ysr7N+bxPvSsqZZf32OtLOaLU7JL1F+u0uS6tHuHNrk+eeeYiDvae4cfsWqjVlVBjKaclk5phNKmxVUAiBMxWRKsPYWE3pCpT3xDqmcDkqSiEK92ieGzZv3WFxEe7eXed8BL907jjvdY6zszXirfffY3swoqpseN5JiLsJadamnI6ZTCdkQmEjRVFWRDXJQWPwOgCO/VaK8ZrKVCz2uxgf8fN8/dwBpTffvso3nv8tdl/4N3z47/6Ui68NUN2MoijYun2N7//5n3DoyBl6T/8uF6oJH7z/EV95/lne/fG/5sy3/xMWkgIlElpxFGxmZIQQjia6nYZN4ms/mDn7wO+DCfd1Z+eeEp5giFzH09MktyHYunMX0z7Gs2ePMRsOOLi6ys7mTRZOXSDf+4jpdMLW+i3i/C4HTpxhYhXdxaMsHT5Db/kgrXKbwfYuy8uLXHr/dSZlhwLL4ZNdMn2DTu8g3TTGYpjNJtz49BPG967w4Sd36KydZVIo7tzZod39gO2tbb75q7+Nd7OA2COoqgItKv7vf/Y/cXlDcPKBz/HlX/8j7t29ii0t/cWjfO0rX2NtdSmkg/iApCsvwVWUrggb0mrGbDzi737wL/jbl17l2JEHWF7ocfvT9/nw6i0qE7F85hHOj3c5urBMNjPcfvc1Xr++yR/99h/yT/7Jf8XS0hquGFPs3uGRC48jZDCb3Rtu8unNT9jZLSFb5ADgKJFCoXww1Au+GKEjHhLfoOZ/zBPJ/LxwCnyeAI64GpzZBxOb8r8BT6QQdSIfCLEfTx/eWzOAaNgszfxoFL7N9KmTk2pGjazBFFX7jTSgVMOEQYReRcPUaWRqTZkX5ty+sa5qjsD7sLGtj8m7+yR79Z+NKW0oRGt6rgjlpW6O3e8f0/1AyBx09TV3x7vQvWqYO403S/NnfZyNAa+sr1SoPWUjxgvHQDASDaoIMQeIvLehk0jwfPCu8ZmqpWlzNkXQrovGR6OR7gnmflDeu7lscv+m3mcfBU+s8K3Psg9rPtGcpfGz3/+Z17yOrnlO+2hU/Qpj7GR4j0TWnxX8Q4SSvPLaC7x16RWGky2effI5WjIjcZ476/e4ffNTHnroHK1WSj7L2bq3zrsfvsfzz3+Zhd5iTaar53BN5RYITGV45gvPcu3a+1x67y4XnniMk6cvUBnHYpQipOOhRx7GI5jMCg4fPYz0iu3dO5w7/whXPv6Y3e1NDh0/xN72CG8KonbC5xYv8OMf/ZDFlSUG410OHznKwkKfG1c/4fr6Nbb31nnskac4c+w0t+5u8s7lV1k7cIDf/Ae/x5//6Z+xtLzE5x57kmPHTvL6G29w+/YN0kiwvLLGyRPHuXVjnVdefo2nn/4inSTmgw+usb0zoqzKEI0eSbCGhc4ijgohY9z2iNHOgH/9vT8l0pp8NCDWCiElX/7yV/nx3/xbrn/6KU6WqDjlL//iL2hnWb1GSA4ePMwDZ84hJWyu32V3uMvbb11kOh6xdKDPsYVjIOG113/M+tZNIKHXzhgMdjCzKe+9f5nW4hJJpnni6edY7C2xfuc2Fy48xfHTD/GPfn+N29ubHD9xin6vz+7ehDiNsc6ysLjI3nDImXNnsVXj71b7nc3vryBVwtWeOFLihWTt6BEOra2QT8b0FjocPXOWty9dZDQe0u0vBv85wCtReyWBcMFjo7mfAlgs5kDpvlhtfz2rV5gaJK5vCgLbaX67UAP587VpjsrP15TmfpmvgK5e2Lyq10fLA6ce4eyR8+zu7ZK0WkzKGR7Hk49/kRufrvPhe2/T6Xd49vlfZDqZ4BEM9iYcObbIdGqwtmJlaREvPbu7O3RbbRYWu+wMh8wGBYsLXQpbIEqJsyG+VyvY2t0mjVK6rTbj2Yj1zXt8/P7bFLMJnXabvNKgBTfX19mb5CxziCeffJpTp0/w05++ydbOLlHW48IDD5EsLpHPLNY37YcQCe4JEqfKCqZVQaRikjTFCcirHLwMjC1vyOKEWDaNDkPuDKNpzniWAwoVt4lrxmwiZCiwQrxeABosuKLCkzMZ7BB1MuJEB38XFMPpiHw2QQkV1iUdEcWh0ytl+JqSKVIGrwohAwvWhUgmNBqPAVOFNT5psbrWwytDUZZMJ2N2N28yHuyysLDIbr6HLHPa7R5GKiIlQjdUd4hljJZRncgkcVLghUJ6iwSqmlEXuKfBg0QISQjs87VnWdO3EPjS4bCUZkoxmTLLRwhhiVotMnoh0pzAGLYIUBKtwj2FEiiliIlQwjMqKwQeFcU4WbN4ggvH/JkVxi/G40A6tIwDg2O6Rz6dsLdxl9JZxMoa1kBlZgyHm6RJizjpYJWnNAbngleUE47KVlgZGg8Cgi+R9MRRYLc6ZEg4I8ZKgcLW0tVa5udCkaQ8eO9CMI0Ixqoq0lgMVgV5u/PhOaR8aOjNqhnGGjppn3bSozEuDBIlh1cOU1OavbVEOqTWoTStVkYZSYrpjN1pToYiSVPQkCQppQksZ2MqEumpqhCdrSJFL9EsZYIRBYvtHjuFZmKiEFUvdS3jS0CVaClw1Yyt9W3y0lBJyVL3ID6SOFfhnUSquJYFCvAaJ0ARmmmYsIfRKIwJTTZhVJjndW0QxjH8K6QHL1EqwVqL92EVLYqSrN2tmTqCyWyKsQW5KXFljlICrSPSdgsZ6zA/rSAvivDcxpAXU6RSJEkbrRXWQiFcWLdRQSFnPcbZWmkksTbI55RKQ4PVVRjvQeqQlKpkSLuyLiRg0nBULVJaMpXivcQ5ifQuJIo6gxEh99M7g5cmrF8+AGyFC/e7tBZVr2XlJMeMrxK1uwxnI/CWfn+FWGUU01GYOlGQ/CmCsqB53kQysEjKUhDHYdyVL0N8urO1JNOghULrAN7KWlzknUD4MOeFEJTeBXDUOiocWhiUSNHC1Q3mpqkIyGBcHe4tgRCBIYk3GN8AFoFJH2lFqjrIuEVlZkzHY4Y7Q6Ioor+wRCvrEeHYXL/BaDzk0JHTgcEoFFHt4aN1MKW3zmKpMFUFzmOtxVQ5whuiWNf1jMf7Au9sMHgWEleZOtXRIm0ZZKsuAFETJXAiqn3FNInqICNPksTEUYwWESSSVCUoNCKCWGqcUAhVKwFETbXwhH1G5WorDomXYFyFo0ShaHyIHR6hFLgQgtGYiuDDmmXrZG6LpfKgvQUd461FymD+LwREMmJeXAsZ6ut6/Xda4XWCiBQySpGtjCjp0mtlpJ2MtNej38tYbGf0WwndlqAbeTIJKSG9tVnpvfWBLWvAeomlYGYclSuxRU5RFJR5ialKcmcYFyWT0S57O1tsbGzw0bXr3Lxzl2qac/v2HYrhmPG0oChnVN4TUYc9RApJxIGlFmmnS6vdJxILeAf5cMa9yYzZaETSSbn42jv0FroY47meTxhVJXc3Z1RlSV4Etp0SCqUEzpZIZ4gTSVmEoAFXhgawMRWR0piyZOrgk5vbtHdyJqXkL99VnNy6S6wyUiqET1CZwOcOY2dI6ZmOcsbbAyIhObC2CKLD7vY2s0lOK1IIJamsJ2tHyHaLWDu6qWSpvcSptZiJ/PlCQD93QOn8yRU6ow8od2+ybo7T7bfYmVgiFVPlY27cvIHsn+Y3fv0brIk7/F8/Wufko89ikj5Rr080ucnGYMrRgwuUVpG2E6x1dYHd9FtDie6bYhlV+57UZs1+32dpDkDdt8mfd14lQMzpC19CJxF+vMmtW9cZ5J4T5x5lQXvGbokbN+/yzt2KBQYUVcFSN+LdzU3OPLPG0X6bmx99wMW33+GxL3wV017l2JFDDHZucP7kCd567W+QBw3PP3Q+GMKZgnasuHbzFt2D5zn/8EM8ePoEt65fJvMV7924xxeLkq4SIBTCGVCKrdvXEYvn+NJDpzhycIVjx47DudNYleCLMcPtG8RLGamI8LVHjnQWKRKUMCgpmcxmWCTbRcS5Z34DMbrDa5cuMprlrJ76PFnW51e+/nV27nzIG1evsFkovvONb3E+O8GFc6f4tV/9HfYMfPLuK3ywvs3j5wvG4xHtLOPypde5Ny1YO3ictZU+WtiaYUbwN0AivZ3LlaART9zHJBLsXyP2u+7UhTyEB4yYM1UatkrY6DRyx/0rHcAmX6cAeES9EWje4ecbriCFVIENVgNNQTpVM358Y2jo6p+T+z/XFGnN+dSF2mcm4fyYRE15B48LcrXGIHf+zsaIvPmKnx+vr5H58L79Yq8+KqTfB11CteDqB0VgBIUC0tbgRX0v+XAsDYAz9yCbj7Gor1bzN8KD3dVjKMJmwdfH3BiW15SJfVCJmuJODfxJX1/+ML5zuFCofbirni/NcAYobZ8eSwNKiv3PnYNXzUeI/dEVdZHcfKUB0+bpgvd/9rzYZn88m0adBFzF6uoKRnmeePLLZL1FfvDD77O6uswzTzzH8soB2p0u1hguv/MOn3z8Dhs7Gzz6+GP0+ks4b+cMkeBx5dEqYjYrOXHyNFm3z0p/mdWDRzl2YA1fFkjpcV6QtjuYsuLJp54MBU1ecfToE3zy8RV63YRDR46iheL2nducefgs1698wpG1Y6SthFMnT7F2cI040jx8/lHGexPubq2TSs1TTz1DJ17g/EOf55WXfsh3fvt3uXv3Do889AjvvP0KZ06fxhw5yTgvuLt+h5NnzvLw+Ye4ffMmm1sDzj50ljt319nauMFkWnL99ie0koiqcuhI4SoRAgt1MF+OY4V1mhMnTlAVObu766RJTG4mvPHKy2xtboVgyyRGa0AGD4dW0iGNIh4+fw7vEw4dPsKrL/+U3kKfZ558kldfe4lPrl6hKEbMZnvELUWcZJw7/3k2rt3i2KGjeAnHTpygLCwPX7jA448+zfqddR7+ygVklCClZu3wCT5d3+Diqy/x/Jd+iagV1SkeClMZFpf7WBtk1K4BHkTDXqxNJV2IFq9MRVWFlLr+gUP8o//iv+T9t97n6AMPsHFnk0ceeYQkzlA1GGu9uO8eC8Cm9PuMPllLehvzbVGDTCEIQMwnuPBy7hfYMAZD4qLA+ip0tUW9dtR3+D4XMADbc3JffRsE5Wq4S6UQNMl0QmtUlPHe2+8QpzE7g20Wux2OHztKmY+DBCBJWeq1uXXzDnY4Ym3NY4zFOVtH2cNgZ0iRz1hc6tKKU4rRhLyYUc0mpFmHvdGAzHbQ3iC8BFtx9cZtrl/9kOHOgLWVRVbXDrCxs0U+mTAejylLWDt0mqee/RKWmDffv8qogKPHH2Tl0FGklgzGBZWvUD4KYyXDJlxLD2ic88RCY6uKwdYmWWuBdCFBSElLaRCCSHhsVTItc5wXTGcVBonW3eAlSGAtIMI5Cxe62Nb4ECMtBZEW6Cij1zuGExJrPGVVMZ2NGY1GRColyjTQXFuP0k27IyA0zgVpsjXBrNxTJ/zgsL6qWaS6JkYWCGepygI/mlJVFa1+j0IJEh1RWpBRxmJnkaTdwqvAwFDNM7CuJwRRPfdCF1sowjO1LvogACNChIh1ITR4DbbEuimz8ZS8mGK8RauIzkIfLSUGF66zrItwEXxAlI8ABUoG5pZ2OFtQVR7tRSicZEiisjZILaQTOBvO34tQiDlrEHXhUs1m+Ok0FK1xghxP2NvZYjaboJQmyzpEOkXiiaVARhohYoQIwdXeC2xtfiZs8HbyqMBG8QblbSgq62eqrPclSFE3WnxIekXUfpJhDkqhkMLhhEcTz/cGpqwYjYc4U5J22ix0VpEqQXmDF2G9td5hvCXWEaUpkEQoIowxgW3rwBOjo4R4cQlnLZNiymw6w3lDKzLISKNlSCosVYZOAxillEZqG7yTpKIVJYxNxLh0WGuC3xAWWwwohECUM/b2NpiNS1ZX1sh0jPAVtrComrlr68LVESRDnhD2ImxoPqpKIaxD+SagpWFSBm8368O6h/c4UwPmNrxTKo2pKryKiIQg6iaoOKZbVkzGA4p8SlXleKfwWNykIDUpURSHuUbFdDSjqkq81HS7fYTSAZhzoSbRtjYH8DY0Jm0Ab30drWlVA/xrgh+PDjJk7+q1UIIoQhqYq7dy3iFsFSRaOiLWGZXNMQTwMVzLOhAF8BRYEQAlg8SWOaacYasCaS06FnipMbYkFiBEwmS6h2zVXjJKkYkE5324FrWNQ2UcrjB1Y08g1BTtBAaJEAEQHs1GSBvSPqO0hRbBXBxvqernRtywjqSnIDT6rajASSpyZkWOddDK+iSRRgowPgBwom6Wmua54x2WCrwmQJEFuYNpkRPZiigOSXY4yyyf4sucKhtgjMGO9uh3lqDMsfmEQV7gXEWSpAhMvYUO6GSRT+rjFDgTWPg21gihazZOYCcpEaEUSBURRRloSaZSRKyIo5RIaYTWgTQggmTMS4icoJKh0drsT5Vvsl3D3s9KGQy/vUV5H/xQhUDZ0AKx9V4hDkKEAJjOawmJcjXDnnq6sl9/CASuYaR5gdTh90kVhYaxEqg4JlIpsY5RWUSURKgsI85atDttOp0O3U5Kr92i045pRYJeJIgUtBAkQhCLupnsPNY5nPNUBox37FWGqioxZY61hqqosCYnt55xUZCPJ2xt7fLJjY/Z3dpkY2vA3vYW5XSCN2O08NiaDXx3Y4/xLGeht4ippauJUERJSpJklMIT24Yw4PDWsb07RY2mlMYSRykCSaShnbYRacxwUuGqGU5rnDNUkSR2Ao/CiyjwVnzwEysKy7gIAHyLhEhKRpXF25I0Cqw34QRSKlZ7GYMSSlNxcKWPjloMfcJ4d0KeF6TtDseWNLvjAumCHN4Yj5WQOIuvSrYn95hMZuiabOB9SKZux8EjblKEJEfZmiAryb2dIT/P188dULrz5vf5eG/CsHT4nasIFYwXo3aPVhox2M65J1aQcUqrc4I//M6DLKqKAxfOACWlkkyspij2WB9EnO21sMbVtDqJlL5WrzS3WP2Q8DXCSjBonceeN53bGsQI/1+/REg1We5IhLeU3QUOLq8xidusZJq8KEg1fO0rX2YwMwwnkoOTHd67coUzT3ydU6dO0e12OXb6HB/cHSO0RO99yu29AZfefotuZ5Vu/yAHj5/FO0NZTnn/w7d599IbvPfhFc6cbSOna8i9iHcuvozSXXorZ7AuPLwFYKwjjiTvf/Qxz331G+ze+ZSr166SdXus9tos9jL2BmM+uX0b2VrkwtkOuvaiCabSJbI+4VbWQSrFL3zlP+TI2lF212+QLR1iJy946PRZlrRg49ZH+HSJX/3W7/J3b77NF599hhdfe4PB3pCDh4/yy+cf5vrD5+gvH+CVn/yAzcEOjz36Ba7cXOfYg4/w8PmHaSVJAEVqDySoC/PPmCw3mwA//z5zRsk+w8zPpWS1ceM+ijAHoBrmmth3bK4p7w2Dqclra363uE8KWRdRc6S9/uBmi3L/sVAvgvcdAzBPMptPKtGc3/579k+6loDVm9mmGNgHh/z+R88Rj2aue6i9j2RzzL5OSxMyMPaah0/NahK+Fs3cJ10L7KF9sCqcc93xQeyDt3XlKmt2WcOZasQ0bj42oUhBCLxrOiQNu6zpGIr6GJu5UJ+TgOD95OanLBouV0Nbm/+O5n4XNUvK3Tdu++yx/bH2+79/Dir9+6/7QegGqPtZtkf4nPvBx3A+Z86c4bcX/2OEELzy2st881d/g+9+71/yyCNTZpMJD59/hCIPReP2YMATTz7DgbUjCKHYGwypqhm9di+YaabB8FEqwUK3j6gcJ86cIZYZL778As9+4TmMs0wmE7TW9Fpd4qhDKj0797bY3Nzk0sVXubl+jUOHjiOU4POPfY6//vFfsHPrJndv3eL02dM89PDD3FvfZnOwwXsff4DCcfjAYbwTXLz0FpFURGlKFEneefsSWr3PN/+Db+OcZHf9Hm+/8zadhS5SVIyGu1x+6yJlXvLM009x4+5N7o7ucPfOLTwh4aecVngZ/MMQis8/9SQbW1t8ePkySbvFSneJdy9fxhlDp9vn0OFDXH7nEkZZklbGdG8a5p32iNJSGUG8kLC40GVt7RC3b68Hk95I8svf/E36Kys8+fTT/OjHP8SZil1XsLh0nLhy/Nqv/RYv/vUP+Oa3fpvJeMCLP/0Jb77xCoPBHndu3+XVF1/i3N6QU+ceZLy9w5U77zEaDHjz4ss8+fhT6DgKiaCVYZKPkcMQOJBkKbs7Q6wzKKVot9p02u3AWpFh5u4N99jZXCdN21x+902+/NVfYXt7kw8/+Jg3XnyZC49dYHElZ3F5lVaWoGSQwYh6zjWgjfXzFWC+ZggaubCs7xe3Dw2JfQFw8LGo70eoPR5qz6f6vp67wYkG+G7YoeF+FM3C1dyNvmYLe09lKra3N5iOB1y/tsHucIcvPPM0D5w4jbRw+94mo9EeUrTIixynBDuDAUmaEcehuIl0zMpqn5deeYmqnHLyxGmkFty4fp12O2M6GbG7u8Viv0OVGybDIffyEZcuXqS/1GGp3yXJFDt7u+xN9tCRYjQYsbRyjOee/xrjSrC1NcQLzQMPPESnnTKtPJNZkDOkSTAhNcaFQtEH1kiAsiusqVBCsHxghThuobWojXElxhjKsmRrc4e9oqC90A9FlQBjSoqqIork/BnQFCJKR8RJ8E+SSs2fCYUpKauCKg8yIPD0Op35dfPC1XNEhvXJS6h9Aitng0kyoRAFh/M2VBFC4qXFhRoLaQ1VMaOoKpzzLC4dJe30MBiEFRgzZjIdMMm3GU22iKI4JColHZQMhYbzMpiZ1hJ1aj9AQWAhiTqtU+ow+5QgFGnljHy8RT4bM5lM6S706fVXiKSicp7KmdD8RmGED+AUCoQC0aT/hs93ZWC6BQ2RwlQznIRIpwgbEqlyUyKpsC5ImLw3OOsIsFWQHYMgLw35NGd58SBxEtMuJkzHI6rK0MpSWq0gSXJEOKEwvuYry3r/af2cyUotUxXWk2oPPserhMI4GpZz8Mp0OAyI8IzBBS8TBYSYc49Co5HMqhlFVWI96KxLomN0FB5ilSvrvZfFGhNkW0jyMgdr0VIG7xYf5pD0IeY+ZOVGSJXQ6XZwQlAWM8bTPZjskOiYTmcBLzQ+isLxCE9ZeD4tIROS4Z6ldMFXE2mpTEFVTXHlHrmVSGnotPp0O2G+GlfW+4zQOPRSY4XD+drKwHmkcPW+ZZ9NEdDAgLZ4FwDU+RNduMAydGEPtO984SlKE0B+FQIGtBAIoeh0MoSETqfDZG+byd4AF0fEnTatJKUqC3Z3N6jKgqTVpp0m6LgbgCRfBaDIW6wJZgDSRyAcwVg5SMds6ZiZkjiSpHGMUzaEfEgTmBC5QMlmXxMYaI7aO5N6XnkCK0dUiEQRO4sWUQDjRUgSdM7jfAB3K+cRBohBa0VZxQjh6actSGIi7ylsSTktGOczVBzTjjKQkiSKg/+ob4yTa6aLdCGRSwTWkfa1+kCE2Zy1FoKHoAepowC2+uBZFOFrQCiAqgoTgBsrQMQ4H9jwLd1CeBd+vmbvCWeRIuz/lFdoF5on1gdQwtsCT4T3htIbXDFiNBsjjUXEKXGUkADe5AxHs9DU0+DyIbNyhPOG3BbEQmBdhEXt+9IqQbvXJZYaGWkiFRGpCLRCqphIBamX1AolNAkKKTRW1XK8WgZvfS3bo071btboekSEB+uCfNjVLVhVe/5a2fgy1uzUJlijbk57Ua/n9foTAP/AqHe1BCoY/4d6wYsIrzVSS5SOiKIEHcfIJEGnCa0sQqcJSZrQSjLSdka7ndLOUnppQpYqurEkVZACsRBEUtZgkavvyQpvFTmOsrJMK0NezJhOJsHbazrFGENuLFvbA27fW2c42Kacjuh32ty4douOMhBlTGcV+XDIzXu7fPzJx2TSg4hptVIOrCySRhGJgpH1DIYF+IzFfptJaXHlcZlgAAAgAElEQVR1eqZ1DlUQ7i9JkGLWTXqHCw0P59EShAwJjP1ukIwv9/tsjEu6KiNu9ZiOR0RRgnIzHjqRMSkdN+8MGc9mc/JCHGu8qajKkILnKg9e4yJFZAVR5IiVoCUq+ispV9ZzBlsbIDOEUjhr6bYzskQxK3NiytDAkBIlffCP8xXbOwOE0PQzjXNizoTTSlCZwBr0zjKaeiw523tT9nL1/1kT/f99/fwBpe09hFc4lYKrkCIOKJmOaWUt1IOP8LVffJZTSwkt5ejUK304eVBpj+NrIOyMqBvX9LoaNKo3zeHlP2OJMq+958VnAzqEbbe8D0hq3hH2x0Fu4jzEaYoVFf1Ol8oatLD88Id/xgtvXWXh2GN85elnWcwMn3v0cZL2MssLPdppQnLgML/z7SNc/Mmf8Od//Tc88/Xf47lnv8qrr/wEtXKGP3q6RTndQ6qEhYNnOXlmSvHjn/DxtauMRnu82W+THTjBvZ0BX/naE/Rbui6qXWCCAA987nGyWDHavM7DFz6PVoJef5UkEkSrh/nyL66EzoGqU9IIyWnS73+G1AqP4uzx41hvOHz0GN85dJi8KCh2bnL5zZeZFo5TJw+wnMAKMyZ7uxSjLba3dzl5/ATWlRw7fgyKkomxvPnOWxw9eZ5feP6rxFmLbitDy/vBikby5Bt6Cc3TvYFpPPd59HAfW8Tvv6eZAw38dP/rsy46+x47jecI3tc+SvdPgH2PngbG2P9WwwQSc4BJIGpgzgWc477Z5OfF3T7bqjmuBpgQdRef+vMbT6F5Z+2+8Qjz9rNnKva/EwrHxgSbQL+Norg2+dwf0/2frBf4hj21j3iFP6QnSEFVvTGvZaT1gyqASPWiO5fDNKBQPbC1mbqQ9SbuvvG8/+rMgb0aSJt/vZbjSaHmElb/mXmyDzrOx0Xcd83m43M/OHcfxDQHmGvwsYGO5jLB/Ws2f/nPHvt8zWiuVq2RO7h0gPF4yqEDhzh8+BCd7/e59N4lFClXrl1hMhhy5dr7PHjuPI8++hSxisF73r50ke2tWzz66BOsrR0lSZP5RDDO8vknnuH4yUNs3tvh6JHDOOeZjaa88PJLrCwv8MxTXwA02xubjIa7/P3f/5DL777Oxu4tkkTz9V/8NTbW73Hq8BmOHjjIxTcvsbu7w+V3L5NXFcdOnuSdN9/gmed+gXv3drj46ut89NFHZJ2YcjJBx5qtzQ1u3Vzn4OphjPP8q+/9GQvLCzhpSWLFdLIb6LdO8PiTnyN6N+ZvfvgDDhxc5fbtdYQMAIWxDikN+IhOd4ntnSnrG5ssHlxCJ5Jef4FWnNFa6PPY44/x3uXLwR/Fe4LkWdDKUsqixBBkFnHawjnB7mDAK6/9lE63w9ZgwHCaE6U9kihlZCt0kuAN9LorvPrCS7T6KywsLtNb6OGMZXu4jb/6ETeuX2c0GmHe94zzGbc+uoLXirMPnuOxzz9JXuT0GlNUAVpptrc2sUVFZSveuvQWW5vr5HnOwQOHeeoLzxCnGadPn2Jne5vRcMRosMu2WefTTz7hR/77vPnTF7m7cZPZbMLFN15FKs2p0+c4d+48y2trKOlq1pGqNyj3rRXC1SDC/m1d35Gfvd/q9c81DEIfPC0cQc7gvA+sRNGA4fetPfXXfS0VD8thzWf0BJmJ83VKUsVoNOTSpTc4ffworXbKgckSR46cQKsEJT8F4L13LrHQ7bCydIAXX/wJ3V/ucOrMacbjPfIyZ7w3oSxz7t2+TTWbkEQRWiuqIqe3tsT6+j2wFbub2wx2trh+/SaDwRZpkrDQ6WHLgq3BNsOdAQ7oLx1ktbXCeOq5dnOd9sIavYVVWlkEROS2xAlPFCJ3whjZ2kTV1rUrCikkkdIkLY8SIelTCYWUkFdF2OxWBlNZKh3TzXr1cuPwLkgkjLWkWRcZxYFBocKa62v2oxBg8hLvQ+FbVmFuSSGQKiRugcILX3fEmRfS0oWoduMNZVUyHY/DBjuOQ26EAKFrA1EZg6uoqhll7hB1SmvSapP1s1qCZYiASDt6/RbSpczKnI3tEdPJmJkpKfSYWCWIVgulWyA8FoNyCVJ5It0wkuoC0ofxsFXBrMgpipyyzMFMiNOMztIBIhVjjaFwBSEXyc+JwcEtyCJkgrdgXEiaCuyN2s/QVcHFxcN0NMQqRdrukTnA2yBHC6GzQXokYxSqTt6aYaMgb9ZScGhhkSzJKMqctNsibnWCcX9RMJoE6WMWt0K3XikqH8A6gcbrUECL2kvRmynSOSJvcH4P49p44vBE9B5vAyihlcDLCH/f00fKkCUVwJsZs8qBV2iliKOsPm+Ltybcu17MvR89nkQLIhFANusJLAQn5tsxKcMeWNaJow27XEqPylJUpHFFSlnkDCYjhMyJ05Qk7VCpCGSC9zNKU+JFjIpicDn5bIItpuQmJ5aKVhwjZRzKZOdCkS3DxA+kTh+8Y6hBk5oVY1zY+zvLfN/uvcDaAIC6+nrXngeBzSUFvpYP4mTtU+Rx3pLE7Zr54zECIq0o7AQlBO1Oh3aiGGrBeLjDZDdnuL2BdZY0zWi12yysrCCIqQpHaSxCB8b2nG0sVGimo4KHEzGRkNhEkWpFGq46RjfceR2MqEUNAtSJWvjgNxksHWwIPRHBI8x7aoahQatgrmutxZgirEPWAhbn/1/W3uxJ0us88/ud5dtyqaylq7p634BGg2gABEBwAwiQFDfRHo6l8VjjTfLc2FY4Yi4nwhH2X2D7asI3vvCER2GFNJYmrHUkcQFJiAQIECCxNdZG70ttWbnnt51zfHHOl1Wg5s7MQFREobIzv/287/M+i0KhiUSMSiOSdtszIQGhvMm2pGapB+0lgTElpjSUNqcsK1SckERRAIYSD0JKh5ChLiakTpo6SG5BRr7ulkYiVRLWixoltR8+WO++6pl8td8H6S0TpFQYa6hjQWRtkMwCtqSsphR5ThxJnxBpfC1amxpTVUhXMaPGOrsAwWLhqIVB2Mq/X/rt00qjVIrQCoEm1j6BU0lBLGOIFVbGaK1RwvtyaSG95UTTEwhBzQFL1woX5HL+XnKVr/S9bN0u1u+mJSLgok2dvlCSS3God/J/boY2Ukg/jHB+YOSZfAIhBVWo26XSGKUhiomUQqcRBH/KKElI05g0jUmymCRLaScJ7SSllaVk7ZR2okkSRTuyJEKQ4MMFEiFDwpjxzywLNZbaeYZcvyyZzqZMhyP2Rn3moxGzyZyEkv4858bOmGI2YX+8z6Q/YjIZMR8N0BjiKGbQn7G9u4MzNcvdNo898ggvvf4eT1w4wsbmSTIi3vz4Lq6a0Y1baO2Pb5JKepkgi2K29kdcv7fFvIJUR8zKyid1huMphZds1s55U3knEab2bDnw/mY0gY4haEBrSutIFRw7eoTElCwd2eD6zXtsrrRR1T7r7TGDmWBrxyLmvmNSQqJ0jDGW0tQ+/U4pIq2oncNogZCO46sxe2PDdz7VZhItcfv6LlmUUxf+9O8Vc4SQzApD7DzHz9YCoRU9JEK0kNJSB0Z7Ga4DIXz8khLCJ2wCpZWUkxpcGF78Gl+/dkDJCk0dn+Tipy8xufo6Y6NI0i6JklSizRPP/gaPnVohczasZMIvLChqZ5BSk7UEzmiOC0ld+YPhJzxy0eAKJw/wIwAOvHF+1UOpYeg0raAzgR0iJQeCHB/tmLXbpKmkFo5ICPLuKU4//TAXj69y//3v8ca+5djFJ3js6EmyKDQ7SqKNYe3kZR7+1BbXP3yLjS/8Bo88/nl2XQYhLUNHMRdPn8YN7rK+fpKst85GN2WniHj6qS+z1FJ86sEzgF1EKyqpAcPxjXXy0R67w4KHzq5iCx85KoVEa0273fINvpUHyV4CaAyYm17a+smXDn5SqVCIPOfFl17gzfc/ppzXFGi2UsEv3nyVTDvGc8OxUw9x4czGQj42M1M6y0c4dvIBbm7v8PRTz3iPjsNeQM4uvlc0bBHRyLXCGTmEDTXnrmEZNT+tO3SeD3f4/pSykLg1CFTzoa75pgZIgca0tvlTw4g6DFvIQx+xwCcPXSmNP9MC4MGbw33yHx1m+TTvg0ZOIsXhHQ9N34Iic7A9h5lLC8BpcTz9e01dkego9H7NveIW13vjVsTCNyLQFBAQUjSc9bK4RrPuAuNocdzCVx8cfxsKZzwrogGsEL4pC2DLYfbVAlzjMLtJ0KSdI6SfIoT/v/iq5uEYTpi/lNyhI/NJYGrBbKKRLTYNOItzcPiMLA7rJ77wEHK1AJsOfrjFO72fR9rJePTRT5NPpwgpsEXOZDbknbd+iasLzpw7TVE4OlkLUxVs3b/P1v1b7O3e5fbaGsdPnaaxuNdKUJmCZ5//CkkcURXvEKeSWKcgJQrH9vYWg2GfYlbx3e/9BcurPbZ3b1PZGUtZxK1rV/jBvGBvOOJzX/5NHjz/DK+//gZxlHDv7l3Onj3HaHuX5U6bztIySrTYPbPNY08/xpW3r3BzfBUjJYO9CbV1fO+7fw3GMRrP2Dx9mqKoaC2tIF2NlIL+cJ833nqTE8fPI0VEEkWAwdZhguYjEhHC8drrrzAdzVFaMRoN6S33OHH8NKmOGZcl/f1BSOBUrCytcmN/SJLAbDwHpxHSS4Q2j56i2+kxm0+Yz8Z0Ox1+/ML3GezucnTtCHkxx6kI4Qz7+0NMIXnx4+9x6fKn+eH3fsDJsycoijmtVsr9e3cp85I4zShry9adG6RxxLFzD/Otb32D+WTC6tFN+v0h0hlOnD3N8uoqy2srKBxXP/qIN9/6ObPZlCRtkbUy3n33DWxt6WQpL3z3+5x74AJCS5SSvPP6K+RlgUzg0sOXGOyOPA3dGbJWQpR4M2AnhGf8ubDmuQpFhBAlWoBUBiU9Lb42lrI01JUjryvKylDX/rkklEYIv8bJSCGVn0gq7aVCMkhfDYdA+/DwXoD9i3vOr6lKerNU72fh2S+j6ZB3r/yS9V6Lhx/5dKiTFePxiKTVYqmTcef+LbbvFBz54vNYU9JJY5R0VGVOVcwZDfYYTYZo5ZhNRty7e5OV5R5rK8uU+Zy6mrOxusJwNOPdD99DS00rUZw+d5F8PubG9j0Ggz7zeUm316N2EStHTrC62WP1yAZJ2sMS0coi8tJQ1iWR1l66KCOffmcseZ6DkMRJ7Js5DJEIUdPS80Xq+ZRRWTHLc08/x3v6+GjtBrBQaBUTZRkyNFTWWry7kAEVoSRYUzPaHzIcDmh3O94fBr/GK9EEUnhA0BmLMb6GMtZLI7Xw9Yi1pWehCG81oLSAKPKSHSEoi5JiNqaqax/UoCLSJEVLQEd0E8MXn1inPxxgipS6nPLQ0Yy3b0y4ZxOyTkzSWqWoSib5mHy2TzHaQidtWu0VojhGSoi0IFLeqxJhkdL6yfRswjxMpXUkyNoRsezghE+WK3NHbZX3rBJzhDHMizkV0NYxAoURBTTsMULRL0QYaIT6yTnSrIVQmiSKgnTMEgmBUF4GJ1GUVU4598mATvh0sChWLC91wRriyG97KiNWlySzqQMSdkc58zxnOhwjdETaWkbFrQXr1jNojDfHlt6YXMUSg4/dzudjVLqMFd4YVQbgVyhfaRgrETJCKE0+HTObjWnFKbV0OB2T6laA28A5g2c1EY6JDwCIZfCVCs8dVXm5jgNKZxd+UggVmtlQTUlfSwrnqGuLkoq4vYJKS0xVYfKcsigp8x1crtjotmhrg1Almin7g10m85wyNLpp7Btxh296IiEQUnufI8IDHa/9NFgPpBZzHM4DB6EuCKu6l5IRosyl99xbrN7Wp7pZiWeHKolUvvLSSpKkHerKpxJq4U3uYyXRcYp0jjKfeGmTg5WVVaz1701aGVHaZjidcP/+fQ9Kd3re60p470MlVcDABEiBdl56SbAqkCpGqBgXJIhSOiJb45nnGkTtazDrIxp9vdxwRENNFybpQoggYVXkpaGuZsymY4q69DIzlRBFKan2qX3OenDTs8p06B+8jFcJhxHSe7vphDqqcHWKLQuKvKQqKiKdEGfSM1hE8Okxvj/zcn0V1ivfrGNDpdRI+UwRajuFdCXzPPd+VRoyrSiMfwYqB1VdUNoKhaGoDMrWGFMwLucYY0mUT8GDwAAUGukkWgsipZHKy7SU0sSrGqW9LC1S2sfQ6wgpI2QIQqitQEeaJKxwCoGTwqdPChXAKecZU4ATHh6ywbfVty3+TNWwAC+9RYJYqClESGtWYV2V4b73aoggd7XBcNq40PtIKrwMVGiF0xFWRUQ6RqcRUZoQxTFZkqEijdKC5W6LLItJ4phOpEmziCTKSNKUNFakkSDRmkT5Z0QkBIkIsmEhAmBkqRDePN3CtCy5PxszGg3p7w+YTCZURUFd10xGQ3bu3WIwGjIvKvb7I+7evUkxGVGVjshV1LVlXCicyX1qqfFp1caWtFsRrU4XZzUKidOJvx9VxNnTp2kvp9ThPhzlJbo2mLoCAe1WQjdVzIuc8WTO3f6EnBipoTLe2087F54/BM9HDdS004R5VXlpLsGbTSt/XqRb9K5VZVBKsr23x8lTLea1IDIFRzc3vNSuBJf4Z9hiIG5h9egRpqrLbLDHejfC6S6urOjvDTGzPYRzbC6l/LNvnOKmepiT7j7/5JkN/vS7Wwxu3cbNZ3QzRRT5Z9VoYtjv+z7OChGe7xZHjFWSOPOhHKaoqadjpIOysiD9vS9kAHpVijWGo0uHx4///1+/dkBJr5zm0hd+l299Gn5R32dgW9TZGpcfeZgPrrzLxc01zixnPjkl7Jx/TFpP6/NjT0+LriuUKBjv3KNwFivbrK4fW0xN/U3a+E2EJrFp+IJfRFMEhx7U18XWeuZT1DSp3vRNWGhlTWqFRsiU5774DWpbc/317/HDd2+Qbj7Mg+0UZQ2RjBFhymKk5MJDl6nyGT+6couTZy/QkyVPr5/A1CVSyxD1LDly7Dxf//p3uHHrfd7/4H0+/cxvcenkcY4e6RArD8otsBFhUU2CStrh8ceeZuP4Bs38qihLBN6R3wVkdcHGcF5SJPFTkyZTy9QFxkIcRwgpKStLunKOz3/lMm+9/QZ1knHqgYe4PJ/xwsuv8KXnv00cJ1CNuXP/Fr2lI8yrmoceeJClNMHFbZTwFP4QFeBf4gC3cLYBCdyi6PHT7gVMsujjD3CZw8BS868PATGLvx1y/liwWhpWTgAhBBzS3wXJUnO4mjKl+Wk/8bvlYBvcwv+peTmUsMHrpNmXAwRiAcS4T7KwGg34YbDsk9K3Bvw4hOE0HxYOlveaFDhjAojaeIsdbL0LVteExD1vou7CxzTUahdYYU1CnFwAMc1J+eSxdwswqTmW7mADFxN3z+Rq7PQP/q2DQ5/PYrsXYBkNw+oQcLfA5tzBcT18LRy+LA6BYIdBIBE27vDWHt6zxYTTHTrfB3t1CFgSi5PWnC8tFaYyZGnCM194lhtXr7C6rnn9tZc5ceoEmyfPcfL4MfK8oC5nmLLkkcuXMfVDtNtdL1MNB9QPbBXGlNSzOWdOnyHrZGzdusf9u3cY7+9gnOHvf/gCSmkSrWl3l+h1l9nvJxR2zmw+4e13fs7lx55muD9gZalLr7dMXubs7m4RaUlZlFy+/BhHjx7HrjqWuj32p3127u1x7er7OAWT8QQphE+YSzKkkty/fxekZjotkKZAK4urDa/+7BXsUzGxjtjf2+fSI5+inFVc/eh91o6sMdwfcOToKoIa53LPYHUR0/GUa9euE0ea/mCfOzevUVYlcZmyP+wT6YiqKBG1BO1boPXVVbpLPXa2tohQdNtd2u0OOPjZT35ImqZMpiNa3S6792cIm3Nivc10PORzn32CH3/37/ic+QJHjh7Fvg6uqHG1I5/NsWVFrn21t3HiNO9deZ8qL3nr9dd46aWXefjSQ6yuryKEII1jup02H9gPWFtf54jaZDQYk7Uzbl6/yhc+/yxFPuf02eOcPneGe3fuIHWb0WjIhQvncUJw/tzDzI/NWF5b841x2iKNIi/hQXo2YpjEYh2uGBO5CVVZYM2YophTzudMpnMGk4LJrGRa1szzkrws0FqTl0GS5PD+VSpDxSlpltFpt1hZ7rKytsrKSo80bvxZwuTVP6mC7AhMVaOjmDu3bnLk2PGDJBegrkrKuuC1X7zK7Tu3ieOE8XRMFEm+9Nw3eeutt7h78xYbR9cpqhmT8ZjJdMqt69cZjHfpZF2oCzA51kw5sn6CpXaKMyXGSOzc0E7aTCdTtrbvYuqCIxtHSaNVtu7dZmv3PtPxlNWVJY5udIjbXWobM5vNidstylpiyhpnHWU+RcYxQkBR5MRRRJwlvinLa5J2CxmnWOEQtUPU3suBuuLO3g7TsgQjiNIuSoOOW0TKS06k9owBFUzYhYRF8EIY6qjAdCuKkjIvKMuCYj5FKYm1hkiwCIaoypmfegvtwX80aO2Nt4WXyUoRpuIyRlvotrpABJGXTuazCcNRH1vVxEkKcYtEp0jlTXOVtpxczbi0JvnMqR6vzSZ8vA95pbgzlMxd4uO7Ywc2ItI13TjBtmqi+YiyLpkOdhDCEccxotfBmpRYpZi6pj8YMhwNSBOFFpY0U0ilMFYwLS21y6EWvoG3ktqTcFDWYkrjZRpxglIJsfYAiHPSDwal8vsPwdA6rCEhNc4JwjnwAJ1wjmI2pyxyTF3inEVqSRK30EKilAKb8+yneix1DddvxrgSvvn0Ki+8eoPrI4tUEXESUYiSoq6oJkO0mJJmHlCWwlKKCucMSkUooYki5ZlplSOJFCqLqK0M/poWhMY4n3YXSck8r8jrCeVshBYaVIbSkkQLnKkIq7YHdmmcVzxAkCUx0mkiLVCxX6ciKbyXlBMYa3HWAws21Mw13pRXYhGBESmdt5SQpkY4R5KkKOXVA8V8yHS8y73BFmkiEbaisCmzGoyMCY5G6MTLFa2LQUsqJ9DCe9FZ4dA2MDhCva6d9dIcPINJSi/h8NKnpjEXKOG9zqQOzBgVgkuUN8T1ycGh3glsnyhRBwwfHLGKibTEuor5fMp8PqEqZkRRTJJlaBWuU2dB+gG56Y8Z7vcZTyYoFdPtrXkJWChqIgfSygWzXgRQwadB+s+q8Z5QXqZscdJvp3VmURMa58VRnk/qTQeM9Wlnhcm96bOx1CIkREaSLO2SJC1inS7CTww1NgzXtYiQMsFDHX7YWkmIdYS0ikp425BUOJSOyaucoiyZzEaY4TZZlpK0loizlg9nQFKWJRgP+qooQZg83IMBHLEGY0ucNVhncFXBZDZlXuXEQjOPBHXtFqAoSJRTqMjLgtExcdLiaJAQxzomkRolhU9D1BEIn2pGkAKB9PJh0VwvEhUYzx7AUcGuwvpUTgITUklMXQQfPe/xI/ESPy+MDf5cAp+W2EhlhMdhEdY7nPmJggefbPDXssaDj85Lz4z0sl2rPFAURzE6jVFxSpylxElEmmQkmWdPdVoxWaJJVEQaR6SRIoljokj5obIUaOnoaE2kQWKInQ9SiIREabzKx1kP5jpJAcyMYT8fM52OGQ0HDAf7zAZDBv0+e/1ttvr7DKYziuBHlugYZy2pkKSRZr+/x51b18iyFt3uCh9/9CGT/g4ujElLK0ijxAvLhAexhQQtKtJUstJNsVLTn86oqwKkRpgSVxWcW19GJ96vSAjH45cvMB0XvPfO2yy3Y3rdlEgr+uMZ23sTeqtHMNIymYwWPYsWAZIN973DkeqIVhxhkEzzGos3e5eAFgfgngzspExB7Ry729scPXaM+/sjNjePcGdosDXMqwRE4b0vcSQRnD6+jGttMBp1EfWUcl5SYfHpvzXSRczmhmww49zqHi+/eZ0vpzP++TM9RvJJklIyvHmdq7f3uD0YsV/VXg4pPObhnATdgSih08lY7rRQQnN3d0w+nWAFWOlHV426K44Vx86e4fatMZvtAb/O168dULrw8Df4r3/rKfZ+9L9x58YdsnOPo92cRx55jLNH13GxpKgrUu3TDpoLq2lqpbVYqSj2blHne7z37i/58NoutTF0TjzKf/Qfn0a50oMkARxagEsLikGQOzVU/cBKaUCEhuEgReMZ47t6K5T3g6gFIlEYDO0kYTbY49rWlAtPfYuN1YSrH73HyvI6nV6XhjkhEMRa0ep0+Opzz3Fhc5m93R3aEeQ1KCV9TK2TdJaWeeLJJzl59jh65Rhnzp5ipZsSBUNCGRa+A48dg1UCHSf0ljI6Sex120BRerTXBt8beagVP/DC8Kbmzjrqcs7+pE8nbaNUFyklS70Vnn3mebSWPP7IZaa7dynyKZeO93jhxTnnHzjHe2/8hBf6t7m2tcPv/JPf5eTqEq+98lPee/8Dzl58nOKhi2jlLydvgO6PsXAcWlTloil3CzBlgTixSCA7wFT89nMAMhEAwMMv5xrgqNlf/w8OPG8OfaRr4jIPMIjD100DdjSzsIOfhyGk5gPDxIjA6afhMHEIRDn02QuApgElWByfhfwrICgLPlRA3pz/0Rw5ZND8NslvZZGTZm1v8ioCRBKmpH5TzcExWmy7ZEEPcv4+kA34FlhHByCb12sLmmnKIRAItzC7xjXnNjCNFqhYs2PiYCcXJ+cQg4wDz6uDcycW39Owtw7L3/7BOT782+HjutjeA87UYUBycYgPb97hZ0bzQQvQK5w7YzDW8cd/8oekytBdPcaZM+cQacrLL/2Y0syYzgu+9pXf5J0rA7bv3UUIy2NPPEWns0RvuedjXvH+G9Nxwf5giyvvvs1yO+LhRz+DrQxpGnP75g4379xkOh5w5sw5isJw88bH2HdeZT4dUhvL6lIX61a4vX+VvcE+k/k7vNZtsbrSYWt3i6yT0eutgLFsrB9jqbdMPq9Zk5o//bM/ZHf3PsbNEIUgSjLKwpJXOToyFPmUpErY2ruPMSVZGpFbPykaz4Zc+fBNZFyzvz3hUm+VWhdsJymXHrzAK6/8nDiNGfaHTKdD0iQBIV3hk6kAACAASURBVJmNR0xGE6RSCGOYDvfRkY85raoKpTyt3zmHqwpUprl59yo79++SqZi8KCix0O8znUyYTsaMpxMeefTTLPda3Lx7j/WN0zz59BfZ39vm0oOPcP29D3nmua9y/dpVytx49kCkcNbghKU0Eitqbm9d4ycv/ojlXofXXvs5X3juGa7fvcGNm7eJkghXCyKtWF9b5emnP8dkNqXX6jKfjzn65BOce+gycZyyeeIESio6K8sUwxFHNje58suf0e6t8vlnn+fk6fMoKRf3psOG52hgFUqoa8Pe1jV6wpElM65/eI1ZMaM/nDCd5IwmM8Z5wawy1MbLJuu6QuuIWe4oakdZ5dTGIYymwN/+kVKkWUK73WZ9Y52LFy9y+eGHWVtbPlhNwjPVOIeONJUpMM4zF5wwSOnvoaKu6Pf3mI36bO1sM5tM6R1Z4vnnvkyvt8zmyRO8+cardJd6CKEZ7A+4d2eLrKWwFJSzMdIJlpdSNtY7lLMhO7t3OXbsOPPZiLquETLh+p3bXHnrLVaXl2ilMcPhDv39Paqi4MzxE6StiGlhwEmSNGOWW/b2+9zb+yUrKxv0ui2MKTh7+gxZK8JUEotlNh0ym88QUoG0jGdDiqrwMefFlHK0Tz4cUDqH1RGt7hqt7rKvJaRACO1rGhUGBM5LgwUieP94oMNaw3xekucFxpT+/xtLEnvJhcVf73VtKEsfZZ9mbbKsRZQokAojFZEznnHiHLHSWFd7sCpSJDrBVJbRfMR8PF0AG2ncCsln2jdtDpJEkWUaVxtu7sD2S9tsTQS7E4EjYXtiESQYaxGiBmoEjkhojJZE7SOkwmBNQTHaZ55P6O/fopP2SFsdZpMxReUwxjKfCTqdlFTHOKuppUaoyB9DJYiV8xIl6VmO2jlcrRGq41ktOjBnrPV+ZogwiAwNoQmefzqsud7EDKUE0gmqfMZkuEsxn3kmRxTjpB+uOSMx0qBiwcPrGQ9vtPlwa8D2oKQ2ljevTrFkKFWCwjcPWUbsLKIqsWXBeLxLnQvQmrjdQUf+WrK2QNoSa0oiJcikhDgOrCFD5QwOQaQi8jxnPh3iQkx71l1FKY0WXtoubR3WWFiEWgjl23chyCLvYVlVAmRj6i3QQpJjqUqACKm0t6OyDmm80a8RAmElwtWUpkRbQBgk2jfHlWMwGVNXc7K0i44yJvNd9gdTsAVGpKj2Eu2ljo82x5IkMUJEOKU9gwXQUiIqARiUUJ7QhR+MSQDhAls6AGah1nfCp6SZkDoVrLuBRnUQglIUB9eE8NUxSFzt0EiElkQ6oi5K9vtDBsMdtFZEcUycLtFut/E+oF7CVFaVZ71EMZunzjGdzphN54wnUyoDSZqQpr6OFzIKYFxTr3qWC8JhTeW3FfuJZ721hd+fxfBXBuDL11eVqamqmsqW3lZEOFLpPXrSSHljb+1rCCkdwvi4eF/F2VATKnwsQIWPUvGslMhYdJDc4QSuNj4hzFmqYkpVzj3QJxz1ZAx1iShTHILSlIz6u34AorwfnLGGGOt93Cw+AVX68yOVQklNK0todTN0nBFJgdZBXiY1IooQQqK19B5GQnrWkfSiVyG8l1OEB5Er4TzTs7JeRuxqn+xo8D5wAkQA8kN2mAdKgzmo7z0dOEllvazQSQVCEIW6T0qBDD1r491FYPAZ4eVmUniwNFcKQwxRgogFIklIo4i41UK3UtqtFp0sJW0ldLOEViLJIkVba1paE0tvIq60QmmFUIIkDHS09PwaIaRP+gxUJy2gwqEdKOuohaByggmCwaxgPBuyvb9Pf3+f3a0txvu7DPb32d/foxgN6SqLmY8ZjGYMxwXj8YRZXiGl5MTRY+hWm067S5bEpMoF1luFKyqW0pTW2QfI0haTvKAu57RTTYVklJvAtJNUzjPwtLVY4eG5pUjyhfNHqGWb16/vcme3QGDJ85K7W/eIk4xub4lWkqEkrC31cOWIdrvNak9hEAzGOXe3BwgUs/GEeVn7tZDgDykERnjGcGkrluIEpSVRkkA9Q2cZRT7DOYPhABgRQqK0Io4TTGmIUsVwltMaDnBJxmhScPHCeeZ5xfD+B6Su8mQVLJGESX+L6daA4bwiz2eIyuCkpa5DIriz7BUlf/nLHbbnu+z0p3z00Q6bPc3aaoe9GewOc/Zyy9QJRnODNRKUN4SvhfOMaukoipKBs2g0ZTkPsk/Pbvc4nkTbmgfPnWFlfZOpWeL+bsWv8/VrB5Q2Nk/w8Lrk/7iVYC99m4sPH+Ojn/0t773/Ed/84iNkWYpkxnDiiFXqtZ0ClPPSJoTG1Zb93ev8wf/5r5jqs6yv97hz6xZ6T/PYk5/j/PHVBesDgnb5QK+yYD94xoEMtPzQNLpmQggQqJpIjLQoam9oh/VpGkqBq5HJEp//0jdpZwnzYkKWtmlnqV9sms+1jth5I77l5S6xcF6EKSxCSXTYXt+4Gnq9HseObXL+zIPMSh89acNC6ZxPZaudny0gxSIpRQuJCB4KwlmUawAPb8DlSTgKERZp42qM8IVfaedYKv7+7/5v5vR47rlvc/r4CRCaTuaL0TMb63zvlb/kz//995lWhs1zj6PNjDevvMmXvvZtzp8v2Oi02Lpzk93+hNzGyNrw81de5JHLT9JbXqMhejTASoMjWBe00IfkSA1IIg5Qmv/Ayzf+fq07AKD+4St8YzgmjUd3YwbugQF36J3N9RLWB1gg2AeeR/57ZAAS3CFMpCGCEab4zfYvzMYPGcF/cjNluPbs4hgcmFsf3heCdA3fWIbPEg6EktRFRRLHvPLaKzx6+XGSNKVJa/ODEhGYR+E6XAAjXqLnKdPNLngqfnMbCdGATc1+SWRIjPOqggaoOdjucEsdAgoPgWqNjLEB3lzwehHheC02TgYZrJ/sHJa2HRzb/xB0FLbnAPkJ4NA/OPiH3t2AS7+CTB3+dcGgPARshV1qZEIIx3Qy4+7dazz22CM8/qlP8a/+9/+FpW4XZw2D3T5atBiNh5w8dpJ8PmRleYmN9aOYsiaSGislTkFlK6bzGX/3g7/lo2tv8MWnvsjq1l2OHztJu9fh7MULxK2EF77/N0RRxK0bd7h1+yo6AacMSZTw7e/8Hi/+6FXetR9w4849oqhPVVbc3rqBkYL11XWmkyEPPnCJ2zdvErdadNs96qqiLnL2x3u4CPLcT1m67ZSVXovpaEwcJ3z26S/wF3/914jaBs8MiZYQJ5Z8soep/OT8ww/exxQ5n/3C54njjFQ7lMlJ0iXiVAERG+tHubdz1wPjdUmsNfPJlOee/Soby8f46d//kMFgnyiKvQY9pGcMBwNqPWKuBCLKKHPBbOpBjrooSbViPp0yHe2itGZ1dZ2X//4nrBxd57t/85ccP3YMW9Yc2zjGt771bd58+w3efe9dpFLUhQ9FkDpir7/PvaUbfPjxNt/5zn/F2QsXGQz2ePHH32c2nfKf/s4/Q8qEE2fOs7q2wd7eLq2sw5H1VZIsQeoEgfK+NULSUhGyazl3+iz7d2+AMbQ6sW+mxMFzsHnO+dtGIlEUxQQnavrDAR/cv86rr7/PaDZmOJpQVRW1MT7i3noJiMVQ1RXOeg8Si7+ljZQIpzDC4IxvKNI8Yzgacf/efT547yP2dvb41je/Srvd9s8356jqmm6nw89+9jOe/sJnaLczojiiqoyXUkvF2uoRLlx4kA+v/AJnx1RlxaNPfpoHHniU0f6ARhqnEmh1U6I04s//6v/hv/v9/5b5bMz23bucu3iBu/duM52MeeiBBxFRi/3hnN3hHrN8zvbWDrdv3OT8mQd46onHuPLuO9y+c4tud4m03WFa1kzLGhkliNwgTElhBPt7Q0o0UaRpZQ5HyVtvvualQInG5kCacOTIBsvLGfuDLSbTAoegUBFVMaWYDqjKilZrhU7WwjpBmU9otZa9Gav29YsVDoHxTVYwBdZSUFU182LOPM89jd55r8TG88OZmlrJII0T6FgQJUtkvXW0ToL80WIpvS+HlI24mgqHimNaUUxRFmwPdigmEwyORLeIdMvXRdI/07SQJMpLopSFfObI8ewNMTbMKo2Rnv1qbLRYixoLUylc8Cry8eblZEZ/MqQTCR/t5qCc5xSzITv9PqvrZ1k7egKlExIVoyVYYanCNS5EkO5JgTDe4H1W58RxTCQqZCuhMp5hZI3x6UfWU/898wjPYBEWayzGCC8H1Rpja6rZiOl0DlWFEpB1ugipcUKipE9CclhaseZoJ2Gel3zvlwP2y5L9aUUqM165OgEEuYqQ0rPTbWA8qVhhpEboiMrMmU1nyMLQ7bRRaYdODEiH1i0oHa40SBuDcERRilQpw8mY2WiMdAqlEtAWKWMi5RDEXvqIB4yF8sx1Zz3wICREWpNKnxxWGV9rFpW/NytbM64N1njqV14UFGVOWcxJopgsS8JyLrBOYaUhwqJUhJSCSjgSpTxbaWkJJZfAwP2tLbCOpc3TUI2pa8XMGmZlwXLvCK1OC60EtpYY4dn0HqCx6ET4SHV8DW8a4AMZCglfSymJlzg2lZYUXlba+GaF9dkrq0M8urP+uYQITgzeb0UqQEjysmKwv0dZFNRF4aWRaUYSZ+goorYcsK+FRCqNUzrIRBOydkyrvcp4NmE82qMcTplPJSudDrq1jFLJgvHtN7C5czzgJaXCYDHSs2Okk2EAGGwUJNRlQV6WPiAAQEp0uGc9IOwNsaX21gG1cNS2Jg7grlQanDcolw6f7ohAG0nloPZmI1TOIqwkr3Kwhrqq0HWBc4Z6PqGqZh5QCeBelbvgvqLQqcZSkooIEUXIWKN0G50kxJEm0YpIJSgV4yLPKFRKo6UKETM+idF3Md53yimBFKWXlAkZ+hzvARMJG/yE8MN2QsXr6lC3eimxsw4rlZdROuelVEgPVApwUhKF0aAWMiQuGw8OW4UxZuHtW1qBlYpKghMeKNJJQpymyCxBdRKW2y067SXarYReO6adpnQTSVtDqjSplN7PSEsiJResdCHw10Oog6PAJIill+4dlLAukJQdOTAPa7K2FWVVcGenz3u3brHb32HW7zMaT7j68TVGkyGzwYTZdEI+L5DWEDkQpvbXtCnIsGRaM53OmBQFRmikTpAqptvNkDJirdWmlcVIrT3TSfj70dU+nU9Fnp1upnPqWmCMIWt1OX7mFB9cvebXKeeflSaQJ7J2FyLFnaGhLfq0RIGWAmskzgoGownOTtkdjFjOUqIsY6XbpZjusd5LEM6wN5xxd2tIpCUowbjIF95wTYtpG4aSE/5+CetOFEecWznFsICrH71PJBufP7FgvDpTkCUp48oia0sUReyN5lw402F9cwOT5zCfMJnOacU1Qvg1yQImnzOYzsgrMGWNcAakQxiDT9rVVLXg3e2CXdPCTBw7oxlX7hmWl2agFXWlsNKLm11pAzgawBvn7wlqb29QlCUKmOdlYDc3va3A2JqzF06zfvQEOop54Lhki1V+na9fO6C0mtzl3//VlH/6L/4lZpiTiRF/8NHrXNva4+UXf0K3FXH03Kc4fuY0yaGY2wM5jUWWQ+bjO8yjTd65scexSclkb0acxOzMJeetL0SQIE3TAQcT6yYeN7R/RvjFRwbfJIQMS5YIQI1fbLwtmwebKkDjH7jSQZS0WY9ThBK0Ew2mZj68T56AaXVI4sS3zhZqDF3lfU78fxJpS1BeWy2do6hKdGuJWGtWOi3scIQW/qHmkXCJM8EUrCF5OG8m6CeE3qzwAO7AAwY03lIGJ5SnWSqBNN6gWzhHPZvyw7evcvbhL3Hz7i1GwxGXP/UIQnhTsvGoz8e397i1N+Sf//7/yHjrDq005Y13r3Dp0kNcefttSvtVTh3dJItTlBnzyqs/YPXYBZaPn2Opt0YzhRUNaAQgG4aWPwc0CWDhChDhQd8kngABFDxIeVt4FwUkp3nfAUhx+DsdyAOwwP0qYCUOsAbngndBQIk8KyBsTthWJ+oAfjRb3GwDEICbBnrw7zjMtHKHvvvgOj98nA6/DgNtzZ9sYBg1dwxB2y0cZDriyofv8/UvHWM6n6OUpqwLTFWTpKnXuwNNAlx4XPqDIuVimxsjTn99mQVCtIDJGrmfO2BSLOCkMElbADROcoB+uQWw6BB+Yi8WJ9gfb2EXWybcYXlds8di8Z2Hz+HBuQ04lPuV9yzeJxrsB2922PyhATcPv78BJZvtb45C8/6GbWYwITGn0834vd/7fTrdmJ+/8gvu79zl1v0SnSbEaY92r8Obb7zOqy+/xP2dLdrdDv9YxEQy5tKnPoUSglhH5K5mY22Zs6dO8fRTj3Hr9j1arTZ5VdBudRBOcfzEaR585HHeevMX7A93SZKUeT7kzLmTfOsf/edceOhxjmxe4qtf+zq7+3v82z/8N9y4cYPLTzyKIuKdd99AK8Xp8w/Sard48Yc/oN1pceX9D9jeuUlR5JjCkGYJ3U6Lxx9/gnfefNt7n5QFt2/dYHNjnY+vXSVLIoSEynjNuRRemy9WJEpLvvLl3+SBMxf54z/6Y9IoY7DX5+Ijp9nd3+XIsU1++7f/M1596SV++vKLFFVOpBXtbptnP/scP3npJ0wmhT/ySiCtQEVBoigkIvLpNHZeU1cROMGDlx6iv92H2jDYuYkKLKitrVvk85K96T22d5d4/su/SWe1x/172zz7/Fe4cP4821t3GI1GRFGMU4BURAL629dYX7/AM8/8BiqLcO9XPPrIo/SnY3Z29jh29KSXD7dbLC9dQChFEiVYKUKB29z/hkQriBP+6X/53/D6qy+TdlKW2iuUlTfFBwLbQgWfe4mw3jyxlUT81Q9e5hc/f5nxZEgSKYpQ9ApHEDR7zwPPODTUxlJVvvQ3tgrpMQlFbtFaUClHLCLm0yFWK9KoTeYspSsRwWPQWEscKX724ktUxvDTn/wQKwxLrS4rR46Hgs3fiFkUs3lkk/Lcg2gk0/GURx56jCRr86d/9AcM97dYX19FSvjDP/g3PPnkU3z3b3/Albff4eTpVVbW13j//Q+5dv1jTmws8eBDF3j1jY/4+NY2Okm5e+smtZU88fjnWF3boKhK+vv7LC8vI7Muw90dHIa11VU6vTWSdpe020OKmKMnSmqnUTriyOoRxpNdhv0hcavL+pGjJLHi/vY+N6+9x1aSMsvHRFGbI0dPsLy6jMkz3NIKCO39jVzNdNwnr4JvSmUYzaYsdTu0sg5Cx8QRYEuMgZ2dbe7vblNYRzv1JvRaeUNdG8co0cZp4cEDFyOkz/QSCIwIvngOqDRSeTmDCKxnL8tJGY32GeU3KSaWOErJopaPelYRWIGW+MQv6WVOtYDCVMHsO8JhSTCgY7QG4WJQNbaqvNm1qbDWUNVj6nJKbYxPOJIaqR2tGKrKINKElY2zJCrB2Yqou0ZVGCaTMb2lGBJN1YAFCHxL7ddsKROUkNQxLFuBsDOsuY8rC5xTPpVIeDaAlSL4tvgGxNnSmzoL/D47wWw0ZDzsY6hoRS1k4v+/FAfSbmNqlJJ04pgkUtwflCidUFVTzx6qNTmVb0Cll4b5ybdGhbWxdiCiGK1AuYS01WNelczzgslkh7Vel+V2m1ppNCV5VfqUKmuYDD1zQOiEKE1oZS1q56goiKQjktKfb2ExxsuirfXeOtbWlHVJXuZoDWms/XZaR45fFKtK4kxj2uqZIpFyqBRaUQcdaXTkJWqR8D5+AZVBKkXkPBBthSSWgtgYRv0dxvMxS91l2u1VpJTUc9Bxl2WZMZpPmc52cUVMq90laXfpqARbeeYL2tewUtTY2oV6uxnb2GBsTBjYsfDhtICtQv3kHEJ6Nrp0IVVMyFDrKx+gEJ6PSmmyNGU6nTLc7zMrcx8qICBpt2m3OkgVrgchfUS7CdckjZ2AZ5s5ofwAqK5ptdu0Oimz0QBjDLPKMOvvEiVdslaLGIUQhtJZDIJU+F7EGN/kSuOwIqJWNc5VFPMaihplSi+Di2IvIVV+2CasxSmFEB4kjGuHKSuctlRVgXKS0ihiCa4qFhWmNA7pakrrNXPW1BhryU2OrGFmc4ytPIM1MOMVAi0VSZwQRRE68gwupVN0HBOLGN1JiJQmFQlOKZxW+LG3r9mqMCiRToaexR9TLRrgz2CFRSEXkl6MRSrt/YiARi7sWwTnGVbCMw4lIbnMeRaiXzVjnHREQuFE7X0XvS2Vfz7UBiskUyeokdRaYZUE3UbGCa1Oi6gd0+p26LS6tDsZ7XZCr5WylMZ0U0lbKTIpSKX3ntLK925KCJQzocdRIB2RsR78l5pIqYUqZ/HTeUPoCpjj6M8LxpM+e8Mh88mMvdGQYjbhxrUb5OM+yhVM8hmnemucWElZSWEydfxff/I3jAYjlJScOv0gP3/9F1jtawDlAOcHw504QqJQkaaoCobFHBnunUhLWp0UpVp+KFDn3L+/w/39XY4t9VhZW6PbaaEjhdDaEyaUN56u6ppISR68eJFIOI6srbI/cdT2I9oqwgjwvoECV1tGwwmray3qbJmruyPu7Q+pau8lCoJ5YbxPF5BXFWo8ZX9vD0mJBrb6U7aGY7pry4ymUy85tOCkPQAihe/DrfHPjEjHFLYmImOwv482FqdTst4K1aiPwB60a1ZgaklRlKRJRDmb+w7HOW7f2eLOXX/P4yyJNEylCbJSD650Eoma18ymM5yBWEmmOHpxi3w6xTo/BChll9Obx/ho9gFZkjEc5chZhY49uC+kwMQd5nVBzZAYSW0cWtcg4tBH+eGVtQ5R+2GH76slBsf6kTXWlpYoplN28j6myondnF/n69cOKP0Xv/Nt/vUf/pSvZj2iuiaKVvnGP/pPGNoYNd3j5lDxwPoZugoUNdu3P0Rmm3SWuz520Rn6wwFvvLfH8aV1qrPr/MZv/S6T6T5LR0/x2Lk1BDXaCVzQxjvlpzY29N9OugDeSJStvCdRmFIICMCGB5PALyh1oANb5/zDwFgf8ykkOvjOCAk6bdFVlpde/T6qu8kzX/oqrSzx+l9tKfOcRGfUovQXdwBS6oCQCudjA9tRirK+GHLShqmjQFKRz2dkURcpJIULDx/pPQScAhq0PTQecoG+WhBVWJD9hMM5FcAOg9QxSXedf/Hf/08c6WRIW9EfDrl/5xrXrr5Ba+UkFx96jC99+Wv83U9/zPbNj/jaZz/Dv/3LP+Vf/s//K3//1/+aSbbCV774PB998C6F07xx9RpSRTz6hYs8ePa8X4SkDAkdLKSG3gi7aawOgBIIk6UGQ+HQWwLABs2k6BCIQyPNOgB3Dv5xAzIE8OcTwM4B5OO/o3l/ADcaMOqQXO7gA8Unfj8AJZr9PAQdLWR5h8ENccBwWgAUDbx58NkN404EALR5gDUJbv4lqUVBUdbc3trioU8/5g0ElaauHVt3thmPBzz0yGWaIHAnvI5bSB0MGsXBdgY5m5ACZw78x/zfDuRti3MofuV4LPb8k0K/T5wWEfb30HHnV97n+1L7yY/m4Mw1gNUnIKNGYinkQqMuP3mq/O+icVz7B1/IryBK4Vd3ACw219gChfRFucV6HwcJWZwQ25R3XnsNp0piV/Ps89/iRy98l5u3ck4eP4OOWjx46RInT59lZXWFfJZTU6Okp4nrKKKqa778ta9z7epHLHXGrK+vMy8qyqqkthWbm5s8/6Xnqeucnw7vIeaCybxifeMcly48RiJizp8/gj1+nO1793nv8c9Q5TlZa4nR3pAzZ86hlORHP/wh//g7v82zzz7PD1/4Hne3buBsTTVzaK1BQF7lvPqzn+BMQZqmJEmbRx+7zNGtXcq6YHvvDqrGmwTrmJGd+0hqUfLEQ0/y6OUn+X//7E/YG9zBYqid4r3336Eocy48eJGjm+scObLO6TMX2Lp7B0fJdDLl/fev8MvXf46pK0wt0LFGZ0DlqCrH0lJKlsVEScS4PyB3NRa4deMqaauHqQ1VXjKZ52AV4919XGzpyIiq2OcnP32RTz/xee68+yHLaytc//gmRVFTVjVlOeVzn3+Gzzz9GH/2F3/OylIHKVLi1Gv1z5y/QLvV4b2PP2Rvb88X2VaCiklaGc5ZZIgVrZ1BOxmiff3vaZqyefwUX/vmBldvXUUnKcbkKCkxljB9tTgRjHaDXHqws8uPf/xdTp08xnC0j6n9tE8gF14bUgqQJpiI+jjlKE48DdtKZG0o6zlW1FibgXVYYagsuMIydjMm0xGTySAwUixaSWbTOatHN/nJj/6GVibBlhzd3KAopmQ6xjjBtCh598pbrPQSvv3t/4Hd3T7vf/AeFsf1G1d56olP86OXf0o+GrKxeYx33niXzePHeeKzT3Hh4QvMhnvs9bfoD6Y897Wv8MabL/FH/+4v+Pxz32AwL7h38x4KhVYR5y6coioc9/o3OXH8DIPxDqPxiGylR7fT5cTxE2RJm263R29pBaljRtMZVekojKHV0cRimY3LqxzbOMq8Ntzeuo2Z9jnWyxBYNnvLpK0l0qWMzlLKDoZZIRHO0mlFpDql182Y54JaGGyd8/8x9+ZBklz3fefnHXnV2Xf33AfmwDEAcRAgeIAXJFKURFG3tNThtSxpw1p7Q471htcbDq33cMhhOyStV3Y4HOG11lLsWjItHhIpixRJkCB4E+cMBjPADKZ7eqZn+q67MvMd+8fL6h6A0oZjg39sRQDRU5WVlfkq6+X7fX/fA+WIdcxg0MUWY25vrJEmKaN8jE4i6q0mDR/RiJvoJEXFGuXAR5Lchvt5SJ7RoEpkxdQM9iqhx44ObG4rJLHKwHuGwy6rKy9jBgUzx5aYaiXgbQD/CVKmkOQVXAELH2KjFaCVCsbBwmG8YWTG9HpbFNuGKDWM3JBIaJx3JHGEFjFeWlQaU48zBDAajhj3+1ivaU/PI+MIby1ShGOYmTmA947BYMDO1k3wmrRZp9ZsEOksGEALRaYlg2KIcjEoGPqcRDusCaVlpHW4n6ngv+N9kHZRsVykG0knAgAAIABJREFUUIhIYsYj8l6Xbr8XJD1akaXtKtnMBJZKdb+aJOhJpcjxDIclUkYU45JEJQhXUCpNrCIsnsKXaBsYMKKSGwjr0F7jrSdHYqRHOkXanEUpje1tsd7ZYmV1hcUDSxxoxHifo3zJ9tYG3X6HLKtTy5oUtqTMhyHIxRd4HIUAJilnwodUJS+QKvgvOWMwpgwsEGKs1KA0mQTvDdJLjBIkVfEeCYVQGicDcO1kiRXB70shUUKCdThdggsG3sYa6lFGb7DDaGeLWrPNdHMRrCRLa+SmQEQafEESxRxKIozJ6I/GdDdXqHUjZDZL2mpReIkoCqy0wUvIR0ingzxp0hj2AusD3IicrBPDuiuKFdYYhApsNVH1I6WQExQqgOveIbxCRxrrHOsbm3R2t8JcqYKnVS1tIL3DS41HI5QKzUMPQlf7craS2AmUiAPw5DxxGgeAVgSpnlKSoshhVFLkI7qdDmmUkCQpRki8VRgtETIkRnrhUCphVAwZj3Jc5e+mRWC6qZiQxOkstgyFtcRSjgbkxTgwHWppMKQfG7QvKL1FFA7jbLjnugJjLNYY5F6jEJAaFQX/HVGLaOkZ4ighTuo4rcPfOsxHauIrVlWMQWQYfLAsLiQlWnAqGFmjZAXaiondONI7pJd7El0nPLjAkcKpaklpKWTlS+rVHtPEETyiwgpOBWYgHuU8kSD4W7ng74cLjKSxCkC8kpoy0ogsQ2VN0lqNRqNJo9Wk0aoz1UhpNlKmk5hmXTAtJbEISWBKQCQEUlY1QaAI4X2QLGlZqUVUMDifNJYFmv2KDgrv6VvP1nDAVr9Dv99jPBjR3e2y29nhxq0brNy4QW/tFp3dLbZur+OdIPFw74mj6Cg0iUb9kvGoz4N3zXGwkbD2+g7R8DBFu02/O2LcG2FyS+7HDLdWqSvJRn8QiAgAhjD3OUc9SlBaB7BUxBgZZMSxExTdPs3UECUZmjD3+0KwemudGxs7TDVqTE01mJuZIatn6CiwqaxxZFnEocV58I5Go8n5y6+QyahK5AyXkLE2JKvFMVljju3dPjpSNJpNdnYMpbMonzNfSzgwPU137NgaGbwt8NZjfEGvMKxudWnU07CqtwTTaREIIcEKRuwFNEgZggoSQBAhpacoLOvbO0xN1Tk222JlPMKOe5X1hwAtcd6QDz06MoyNxZcFWjo2XQ1V9oNfl/CMvEOUgREuvEK40GBZbHnmF86xfPsWsZScOTDL7q6m++qzRLUaWMvcdJMbaxvEoqBRbxHXmxS5wYw64AtSDQ+fO8jFzSa9F55GyrySwkbgC6SPET7CCotCUrqQ4oZzeGeIVIwpLVeurQWGsY9YmMnoD8Z8Lx/fc0Dp6mu3+Vu/8AN4O0TWM5yznDl5inyUs70dsXCowUJbYo3Fln2e/+Y3aR65l/vuf4AkkhgDC0fPcPbMTdbzy+xe/xZusMyptKBRmyFVU5UBMpXcDLwPSFxYdAUDwQmqLUTwCLACbHVRWS3weylxVeyj87hKq+qR+EBEChK4yjDPFGNWr77I1avXGKk58jzi689d4J2PPUotrSGsZ+RCdKEd9VBC4bBYJYmQOG8xosBFnjiSYILJWOQVeI/0mtGgy43ukKMLdRI8Wiqct+GcrERXCyAnK6NJb6vOdiWzqnTiAFRIuYeQ/qYkTjhqvss//ie/wVSrRbM1x8zUNF965mvc88BbUb5kanaRH/i+j1DWmugk4dJry/zKkcNcP/4QR9ozvHrpJUaDMbVM8is/98sMx5Zjp8+g1OT7qEr2PZAoTMZ7iVsTxoifJNdUr1NtE0h81SlMwKg3SdzeABj4vW0nhf8+UDQBe+7gvIg7PIuqjxd7759sU20v2GMM7ZOrwwt77Jw7PnuyB3+nNu7NjJm9PVQAyd44ib1/h0dgN+ydnydMcsLjsCQ6wpqSj3z4J9npdYKxp/coBYtLi8wdmA9YkLfVbyR8hK805WHc/5KxFPtAIJVXUxgN9yacZ8+cIABfkz+FYB8TEntjtT/cd5xTRV3eG82Jmfcb8cHqjRMATu4BQXsvT8Zfijd8l/7Obapz+64UuT2gruI1VvveBwcnL4v9dwmPtZY4jsEKlFZMN1r80Sf+kPnDBzi8cxeHDh/mh973g6xeXuG+Bx/g/vsfZvn1a7ztscdwFq6tLHP82HHKcUmWNrDWEiHD/uKI1Ws3eNvbH0HriKaOGQ4GwTNESmqtJu9995Pce/fdfOJTf8zI5nzgBz9MUVqyxHFgep6P/8dP8NrVl7j/vge48Px5/NjwEz/1UyyvXOflly9w7sHHOHL8Li6+dJ7RaEDiPYNS4EWBimo0GnU2treYnp6hJqfpdHocOX6A977/Azz1xadR7ivEkcAaUYX8WWSmSZIaUTRNWUb8q9/9XQq66EgwHHjiLMZagxQRO7u7/P6/+7fcWl3h5OlTjIZ1dnZLjp44iZJ1Go0WI9lj2B+CNOwOdsFDXdVQPuOBc2/DlZav33qKLKrRGQ6ZnZ9jnI/pmQFCSRJVo3QWm5dIrzHe0h+UFKM1/tXv/DNmp+d46C3386nPfBxT5rz3yQ9w/OQpThw7wSsXL3Du7ge594F7ub68gpMOKROarRY4x8HZeeppDWMdUkrK0RCtJVmaUrpKXgGV0eO+pNQagYoTxv0hxoYC31mHrYqlYLDrmZjVB85tMAcVRtCotxgXOVEWOElB9uGRUUQsNdYaSmOrO0KQTofOr0JHMDZBemGdB29wUmJMiHxO4oT5+Vm6W1tsbm7QPHIimKdrOHniBDMzH0U7y+vXb1KvN/j4H/0x7//g95M1Mz79yT/g4oXn+LVf+7s0pw9yY22Hrc0dBoNdThw5g67P8NjjT3Dz2qtMN2c4sHCAv/hPT2FNzlcNPPTQvSzMzbG5tcufffJPmVqcYlh6PvOJP0GgOXvXSTpxh5s311m7eZvFA3NMt9roRoyKNWu3zhOZkiNLR1mYm2dhfoE0SvBOUvjQSJquJUhgnHvaszW0SRj0dvjiU89QS3Le8fBjnDw6i/USy4gsaTKzuEApYl5+7Sa313c4ebBJ0S9Z61jyQqBUDLrAiDo1oVm/sY4jxwwHwShegc9zytyRpS2azRa1tAU+eFwYDFhFUq1XXCVJtrbq7AuLEYEt7aqkJi1AOU1/Z52N3TWEE7Rm6qjDi6g8eL4IPEo5IEKo/ak0cDxKsAVFEZgu1gyRosAag3WWftGjmdRJ4zZZPIMSClSwA1BeIFRE6Qr6/W3GoyFaSGqtedKkReEsQkREUqIUlD6nNOFqrNc0caQZ9Lbp3L7CcLdOa3qROGvhhEC5KDBNdIiDj63B2JJYRAhRomSEkBbjPMaGFDtvgz1VKQTKFWxtrrF7e5201aQ2PUtdplgcY0zoHssoeAUpjRSKyHu8s+TGI5UO8tKiACHIrSOKI1J8VaATwGPrA/vFTyTe4V7qpUB7WaWLpRTW4Md9CnKmm1OYVo1mpMAM6Pd22dp9idxq4qyOF46d/gZ2mKO0ol1vI+MA7gti8BKfBhZRKqumiSV4JBEkS75iJlrhUEQkQgfZq/BIb/FSEesYMCBsOHhFYJ1UaU7YwDRVkUQQY0RJHNeRznBzZZnCjTm2cAhZazKd5NR0wfaoS30mwo8Exw41sdZwe72kMxT4UhLVawzLktfXrhBt10mas9RThbUCvMaKEilsKF5F8K4JxscCX6X2OeNAhiLdlkUFOrmQdCZDI85MfEOErDzNQCtNvz9ie+s2xWjA1PQcaa2G0h4nQKPDb00onA/rESWDjND5yZoqpDTLiZqiim3HCbwLYJwWEa40ZC5CZBG1rM5wNKa7uUanXGF6dg6VtpE+oRyP6Zsg4jKjYWDbloZGVieKEry3Ye1qHcpWfkg+R3mLNWOKUY7Lc7SUjEdQFCWRhMJZpIrIvKJMI9ACVWvQimtESUymU2RUI9IKlcTBm08IpBbENgE8SlhMZSUinayYkq5iSYqw1nOh5lCVAbbwHqnDCjiJFGYSIuFDupZyYAnG7HLClK8sFbxQoYaRDqEg9galIpwD7RyI4LdrbfidFl5gUXgpEFpjsowyq9FKGiTtJo1mylyrTdys0W7WaNcz6qlkNoaGFKRIEgiG3qJq5ltXrS9DCrIWOvhx6TtUFpO6qvrZDb1nxznyoiDvjdgZDegPBox2u7yyfourr72CGHYYDbokec7qeocLly5T04rji4ucOHoCY8N8ESnBAjAT11jJu+zsDEPDQSvKzhatpTrzUzPog3Vu7JZsDrr8zEMnuOgV33r+GmpVsrndpdfrhSMV0IojmrWErdEQp0BbhYzAWBOCChIQSJwxaGk5ddcpbnYL+lvrRBKGuaWuDVrHjIoRsZbEUUohBNuDLrv9HmsbWyzMTDM3PUOr1URFGhFJhsMcTwlDxc2tdSJRhTYhyUVIPuzkIw5P11mYnmZ3lJPnY0xhgkzVB4csJ+tYl3N8tk5+a4AVHmNKyuGY5swCCyPJZqcLhUEJWTEcPd6JyjkjrKViBCHhWjL2hpZMsFWKnnWe2xsdlBogsVgpiSb1A45+YegNLQrLVLOGkoJOrwBhMWVgiFtvSGKNzWMKE9iIUkoSHPcebvLSZp+mFMT1hCOHjvH85a8RZxpjCprNhNwLrq1c5eTRJZywHDm4yPqO4drWDtqN6AzHzK9cYP7gA1yRKdIOUVHGqD+k3pohH+0QpxECQT62OJMjlUAoh041UapxeJwJnkkz7QivSkbj/Vr7e/H4ngNK//Tv/9e86yf+Lr/4sx8gcpYoiNhRdceol3Pp5W8Rm7dy4MBRVGuW6eaQj338j7DDDqfP3sf0gcOY4Ygzjz3B0uFFrIv49oUbPPTIg8QbOYeOKEI4ryT2HuMC+m2FQ6kAVAkVuhpeCCwa5XMiFFYR6KKmDEkKSQI+JII44VBOY7xHCYV2klKGG5kEjHV4J7nZ89weeg4vHeTk8WOs3rqOLYfIekruJRExUpWUGJBQV5KRC4W/l4LRqERGKc55nPNIZQFTJVh47HCL+w4eYawUMhhihJuq1FifYytwqzAG5aqOto8CUknF6QwIUqj1XSjgdXVzFEnEwtIJfvjDH+Vf/pt/zZnjCcNSc+Lhd3Pt6mV+v/dlfvxDH+KBs2e579yDbG3dxDrYXl/j8becZbtv6fS32dxYYXun4IWXzjNz6AQPP/Y2Il9FnN4BAuxNxm8ieOyDCPv1+kTK9ual73d53MAeYSgACHuIzB4TagJa7bOBqJgtExDnDvBoXyv1xofYB6vuBJ3eyLgKL/6VrBshwO9Hvu59NxVwISaGadwJO1VsJ+H/0uMSQgRaI1V3VliweQAlK5C1rJI5siTIMYWUlanl/ujuA2vyDZ8bxmdC8Z5AXZWnlJwc/wSHeaNcLGAx7o5vttpL5Udw5+ffeT6T8asEm3vXRlhz7O9nYrA/SdXbY225yZGGbVyVePhd38neEE8Aw/1rR4jv+iImH7s3Bne8PXTDq7dHOiKeUvzUT/40tzd2ecfbn2Bhbp7f+Re/xbDX4erF87zr8Sd4/LF30mi3GIzHnL33HPUsY3dnF1cURFFYpAkpcL7gQx/6PgbDIVJJjLPoWDMajsFBGmmimXkWFpbo9Qq2Oh1atTa3l1cp56Z55umv8cKLL+JEwerWLu96+EFevHCREyfvYmtrm8cefxTjYNDpIiWs3LzJ8eOnWL21jHclH/6Jn+JPPvUx2nHCYGeXXKZEUmLGJYOtAYNel+1xHxkptFQY6UlqMUeWjnP46HE+9+k/452PzvP8s9+gXksoXYl1lsUDB+mNO5hijC8L+rvbZFmNC6+8wtkzZ3n74+/k9D33cmttndGoj3MhrvkHf/hH2O5ucfnSRfrdLrvdLp/9i8+SRRFp6omVo5HWOHX8NM+9+Dz5qAgJWG4Uqk0dc3DhCNe3Xkd5zdvf/W6+8IU/x2k4f/ECUaSotw+Ql0Gusji/wNe+8hQf+ZlfYG5qloce7DPojWgtTIWkLy8RXpIPBmgpyL0LDQI3if0NvyFb/YaUdxWLFIT0lM6TZClSCIyRaJ0gqjlaYZAI7F6iZJg9lFQI7bl+Y5lYRxjnKQ14LIKQsGZFCYRUD49DOEtejvf8QMQIrLVYARGO0ofFs0KCg9JYbm6so1t1yjIsmpSKkd7TrtV56eWXODI/x2//0/+Jv/P3/j7nL36bH//oT9Lp7nL82F2URcnTX3qKne4Aa0p2N9e4595zzM7Nkw9zajqmMTPPy5cvsXBgkd5uh6XjJ2lOtxh2+1y7tUazPUO7KMhHJZfOX+a+tzzAOx99jFu3byPGCR/84A8xMEM6uxZnFaIeQ1Rj4dAJbq4uI6OMOG6j4wa5LcAYnLU0JChhEFoxlWWcPH6Irz7zLXZ2N/jw972TpYUWkQQph0y3p1Fyilq9Tb3dJo4LNtZKtlTEbleSlyLEvWvIxz2kA+8Vu51tLAOGeZ+pqWniegPvBY3WItLBaDRka3OT64Nlmq2M2ZkDxLU2SihyM8RaA06gcFhhEdV8iJ/Iuxw+z+l1Nujt9JBJRr0xTRJLjPP4okBFWTXXa6wtyPtdnBmhtWdydSlfRQ7HwRBbxxqtNAqBEppploL0WFqsDb12RUQcS7wp2N5ep9vvoOKUdvsIUdLAeBuac14jcShfMYukRTmFlBrvx0iZ0po5RmP2OMP+NsPuDoNBh6mZg5AkiNKRFyNK46nZMUmswuJfGbK0hlCm8qkKsfNCCcbjIcNej9KO8bFm7tQp0ihlbEqGvkBZS2QDgFAIcNaT+jFK6GAP4BWI4O/lEVV0tgvR4R6EDOwUGVKoA7gjPcI5tBUULqwpvbUYHJFwbO+sUoyGLE7PkyQRxgwxZcGN/jbD7gaDQReVztCYP05Wr1OvZUgVk5HghWSsFKZKchM2yN4Ka/bXV9aAm3Cc/V6DEumJoxhvDIU3gKN0gR0iIw22xHiHiKskNCS2sGHt60IDcuAKolxW6wnL6vIlRnmXQ/NLNJp34XRJLEfUVMpsM+ZXfvoMN6+vsPK6YGlpmlMHEz77rVW+eWWMiZqgBG0XU28dZjwc0u1t0u8Z4lqLRm0qAOGVbNB6jxUujK3USCEx1oQbrQvhIOFGHZKNQyMNnHFoFRHpFFRgJEnpGPS7dHe2SNKU2bkFpPQ4S0i58wTppQ8spJD37nDWgPQVsCTwJg/AvRDgDKhgNK21wtkAyjlrA5sLhXGWlGD83ZiZxuWKQadPPC7JhaDf2SVLM0zkaMqU8WBMjEUwwgw81o4CCFsahLOMZYlAooUK0rc4pjk9QxLFJFkMOkZmGW2dEOuIVNUCqy0K4xcahRLlDd5YnNCVDUZIJLPOUEqL9sFORKOwSuCUCOscr/ZWiYLgn2a9xTqNs4ZUCsqKnSODw0cl3Q7SWqeCJDHI3DyeMvSgvED6Kj0Thc0VRkm0kJSRpqzFRLUGUdYmbdSot9s02g2ajSatZp1mPWI6FWSRp6klkYdUgvY+sPFM9bsVlWFGRSoQqmL+Vw1To8M1NcbTBwbjku1+l+5ul/Xdbba3t7i1scb27Q1Wblyns75GJuAth4+RNWoURUGEJ0aGUCXjuH3xIt1BF4knUxHjUU6jFNSijDSuE8Up0gusLRmVBdaFwAu0YzQco60lVTGracLK7TWOTG/y/nsO87d/+G18/to2V3d3+K/+2gf4kcevUxRtli+t8Q//5Qq7nQLvFc8vXyfNmjQbdbrdbpjPbFhbl9ZRlCGww3mLcyWb2+vcdegU53sdMCU60qg04f5TJ+iWmhsba+x0OsF/Lo5ROqYoHMs3b3Pz9jZTrSZLCzNMz85Rr9UpihFXVm5gi5xYeJyUiIqBbYsc5x3D7oBuf4fZ2SUGo4RefxTWRM5Ta6acuuseNjY7nL9+lSTyFNJya6NHPx9yut6mNxoHnykvsELgCOQLPVEjeB9k3tW8pgXhWvYh7ZAaIRBGKExZ4G0wvC+8BS2ItIAyXLOmtBxuxFjviOM6xgWj/tmpJp0ChqWgPxrjbD/w06QgSxRu3GfYLyFJue/0fTz/4gVqkaAYCHQmEDLi1YtXmG23UTImjSJs6dne2WKIIDIhuW9ldciSXca7sO4e5CMeeeRutos2l154jqmsRm/Qh3EXpUVgDXrw1jAeSEZ2yNg5FqZTalmNjZ0Ro9H/zyVviDHndxRxM0Ls9hG6hS1yuttX2bh+kYsXvsWzr97iBz/84xxtlTzzlSvc/ZFf5+t/8j/yJ889zm/+d3+HvOiSUNCePcJ/8fPHqSvB5q0Vlo6fRZkhAoWdWFMogZOWxIcua6ojSjdhXniUMyCCPt87CyKqImEDA0N6B1LiLPgEDJI0CtGZMYISwBu0Dq7/b33oIXq3X+YTn/44P/YTv8jObp/i6haPnpsl1QWRAtu9ycbyKq41xbXlHkncpNVqBd+CsqBWnwLhQFWIvwuTWyYdH/vUf2BHH+ZXf+FnKQXVDT8U6d6XRDJCO43UApxFKQ+i6jbdAVR4QjJSVAEg1ofugXcSFTW47+zD/Opf/1t8+nOf4uyZB/nQ+97L66srLC0ssnVzhc88/RQvr97m137uo9xz1xL/5rf+e8oo4zd+43fZfv4pPvGZT/Pg/Y/yCz//N2i02qRpHExBuQPTesN1MfH4mNTsVfnvJ85I+7jBBARwlXm6UKHYuZNVtE+wmfjd7L/3u0EE/wZgaF/atr8zLyeMlv19TY53/wPZO+Y7j/PO5yYb72Xsec8dTlcEP6hwIKLiHuy/ZzIO1Qg5d8fz4bkJZ0HKCkzyiiRT9Nb6LPgFvBP0ezs8/aU/Y2Fpidm59+C9xmKDnNsJBDpcM/AG5hJMzOz30wLvPG858Vvax9He9HABdMLf4Sk1Oe/J2P7lAM/kdYv/q0Eg7hz7N+9g8py84ymxb9c1AfOEmDxBhRjuX3eT61OEDseENRUAR/YuDjcZABlAYYRnXA7J0BxYnCVRCcs3cpySPPn+D/HS89/mhRe+yfPPfZsTJ05zJD5OPapR5Dku8tSzBuPhmKTdwFhPlmW8ePEl7GjMfffci5ZU9FtNq9WkMAUqDrHU3sJbHniAQdnhD/7g93novvtZWprn1F0n+dPPfIrr1y7yN/+bv82J02f4whe+zAvfeZal2UV2Bz2a9Yw0rXH00EGW5uY4/8oLHDlyhC99+Uvce/xu/nRUUJ9t8Mj97+Hue87xqY99gn63x253m3vO3s3zF+6i29tkdmaBm6urKB9MiH/5l/4GG5vbvHblEk7m9K0gERFow2uvXybSCls6as0GmXc4X3Dw8GFuXl+llSW8ejHmxee+zdbmNg8/+jg7Oz1+9Id/nKe+9jWe+sJTTE01uefUGe6+5x7+7D/9GV4mFNYh8bz0/AuUZR4kEDrMRcXY8uT3PYnUiltPr1JaWL16laX5Q7gip9nMmJqew0lJPY1ZWJrlua9/g8tXn2Xtn1/nne//IA+euw+dpAhM6HxJgldEFPxYrHeYSEKsQQS2LN7veW/s/WAqBFJWKHJv0OPKxYvcurHGO554G4KoApMDIOBd+M1bASqp4ZxgPBqhhWOqXqc1pciSFCE948LSHVg8EqkitNIYa0LR42AwLOgOCsrSkI9Hwe+gDDR1KxTGGqQt0SLGVTIXLyQ3V27irec/PvNHlLZHeu4xxq7D//47/4ixGbG9s067vchd99zPcy+d5+Rdh3n8xFGGw5w//dQnWVl+nfe8+0le27zMy+e/TppN8/orl3nve96Ny8fs3F7nPe98F5u7t1l/YZ3u1i63tzYpnOXkmbs4duQ4l69epZnWOLJwlPmDB9l46QKDgeWDP/Q+Vm+vs/rVb1EYaE3NM714iLERbO+OqCcRGsh0jMs0o6JAScdrl67wzW9+hZ2dXX7o+5/kwMIi+biDKz2DogOFJanX2O4Nkdu7dHqeF5cLOmNY6fTwfkiERasMESlsXjIc9kFEtKeXmNJQ2pDmhleh0NeKWmuaZmuG4biLGeWsr9/AuzVKaVhaOkgsEqwK7DGPCY0v54lRuGJMt9uhNxhBpJmanUdHisJ5huMchEOWQaKmncaaHOdyBnmfJE5Is1kSnaClRkQKL0RopsngzSNsMPiUwiO1R3hBJFKSJMVGjrzIWb1+nRtrK7TazQCGJVN4CaYcEynNuCxIIh18v6xExpJIxYxHAVoVOkYgcNYhjKXVnMZkDfr9bW7ceIVRv8vxI/eRtKeJooyoYnDFUuAcGCcorad0UQgwyTts7m6QG0uaNUhqUwTOuWVc5EgdYcoca0qSKML7sK4TkQqggvAIGZp5Ch1uE1bivQuJgsKh8iFFv4tzCtuIK/N8EZZwVbGqVWCPGe8RkUBEKQcPHcFLQb65zvW1NWZn55ibO4RYPILLt+l3NnDZNLe3tunurJPFR4jjDCcEY2dw1iAdOF/inMH7OFxTEFhLOkGowCx01oAI7DEnfGhgekPhgzuVQjDGkppgPa6FZ5znOK0pjUO6EETjhQyJWQQW2mjcoXvrBqOxYebAAUrRYjPvIgvLOHcILA8cnedrn7/EV17dZmMwZjrd5uR0DR1HNHVCf1QyMJ6BM3hnKZOIdrTEuMgZ9zvsbi+TS0VzpoVIEiKRkLo4MDWNDaCDjoM0bm+dEjRuzoXrQsmIJJUwMfzORwwGXcpiAMJSn8qIohjIKY3BlCE6Pqrm72DVFKSUoREbgLjggiPQwiOUp5al2AK8s2gVWExGGExpsS7ITUblCDfI2e11UalExhHOltRjgrm1K6iLAjse44aGXW/xKgqNwmJIltRI45haKyOp1ciSJjrOSGJNFoc0RBtLEhsYU04ZIhfR9Yak6lsWzuN0iFqPhMWLCGtDsyrI+Fy43plUDTGxkGigJIQNWWvQBLNqL4IixFfSKSUgIqHKFKDwPvwWvMNFlUTKG5zX4C3KAEJRSI9XEqUblFGETJr4WkraCHLPWrPGdX9EAAAgAElEQVRFltYwrYz5Zo0sVTQTTSOBSEHqPZG3aIKxND6EgiQEia3SGgh1VS40LtZ08HgjGJWOYdFne9Cj1+3R2d1huNvllUsXuXD1NXq7HUy3iytypHMhEdeENL62ivZ850b5GOE90fQM46RFi4h6GuRjpTd4YtAljzz8Vp56+qtsbN4kizNGwxGDfg/hwY5yfFkiECjnwICqEskTHxFHGq0ipqabvPPtj/G5LzzDdy6vcXF5h8+ff4UkSTBW8vzXz/P4fUdptFN+7/MXWNvqUpMZXsOj97+NuflZvn35ErsvvRyO34SGq3AOjMeWVSqZl2xvdTk43eHkkcNcuvwKjSyjs5Nza2uNM2fvY+HAAfI8Z331Jtdu3SIfdaglMUmsMQLWO7tsdDdorl5jYXoWryKu37yBVo7S+MAoEyCtw3pHJmK80AFAMSW93U209sFfyVpacYqxA3Z3r1FTmrI0XL2xy3Yv5/jBeV6+voaovPcmMkPhQv3rqjpCI9CVHNp5ApsOiZEO4S2+8Cidkhf9IGVPExCKcWHRzofEUO8ZGkMiMl683sOXBTLSzLcVUkv0YJez7QaFjtjoxqxsVFWakBTO0IgVM1mOi9pcvvQynVEPXRhK4bDOsN0dUbiSU4sHGI8cC805NrdHzKQJM6fPcunSZYq8Sw/HeOU2uszxQlKPE2ra8fqNVU6dnqfROMA3v/FVGolFiJDu5rzGFB4nxyAFdR1xZG6aXum4fnMbKYq/stb6//L4ngNK/dGYuajgxtMf53z3LD/2A7Ps3r7FxWc+x3eef4XlrQHi4AFeW+tTj2d5x0//OkeOLpKc+J/5x7/1D/n6N77BOx68H6wkqmnqRc5wa5nXVrc4ePI+vK+0vBUQVAx7lP1tlm9epzY1TVZfYq45S64dzpsQpTkpAoXCmRHdzRuUxiMXJGmagXSBT+4tPs+hEW7CxgfDLKylGJcoUZKPhrSmT3Ls3jrp1BLf/+hjSOGQwjMuBwzyPldeWOaf/N7/zc/80q9zUPUZNY7yjqWDlOMBprC0W4HO6UVIPCiFI1VwZflVGodOc+DgiT3pUgBOgo9FQYiCHAy2uPzqBQCyRpulw8fDIkPoqsPmUCJ02qryYg+1Nc4jYs/cwjxvqz/C3MIcEgVlwcN3n8MVI2bvPkuaxHzuhcukUYooSl5e2aZfa/DZz32SA1N1tBNcuHieq5ef5+SZ+/jwj/wsBxcOVLfg7wZbvPf7YIGfAEp30JXuAE0m/kd7sfN3sHTe4HnEBEAKu7gT1HkjwLP/mLz/zbK3fSbV/nOuihn9z3m8cbu/CjR54/n9Ze+7U9oXWEJy75i8D/IEpWQ1Wg68pywNRb8Ik6u3bO2s88VnPs/MwgFml45y6sSZKr6WPZnkBCySsupe7eFtPoBVfvKvN40bE1DIV3jM5LubfO/Bz2vyeAPMVmnZ/Hedt99/veoq7H9Pd2y153km9rZ749i9ecDZY7LtHfwbrrv9T98jJvnJmVTHgqiAxUr054JMM9Kq0mVXg4qn2+3z/IUX2Ly1zvFDx5Cl4e6772a6VSPWjiPHjjM/fwBBhNQaVxYgPFEi6eeV94GSdDodLly4wPFjh3nltUucOX03INBSkkRhPpIEjbcQmo3b6xydP8p7/9v3c+GVFzl61zGEULz3fU/y9OdzVleXeeTBJ2hNtxkVJS+8/BL3nLsXldTwaUqB4+bNdVaX13j4rQ/zwR9ssHT0GB/58V+kO9hmevYA95w8y87j7+Hjn/4Yf/jH/55URQy6XYSK2NncJR8bBt0urbkDDMYFp0/fw+f+/JO0223Wux1UkiGdJY0VubPISJLVErTQnHvwYd7+9nfzxS98lrnFJYrumKzd5ud/5Zf5kz/8GEUJHTPm/rvP8eQTH2B94wYbG2t86YtfAFsyM7dEfzgK/lnOYi3kJiRi5KWhFPDlLz/F3WfPEamEUX/AxsYmI2c4efwUSkZs3V6nvXSAtNHAjEYMhpscPXiGx972Lp54z5O8dvEyU7NpkEQIjfehS20q2v64KBFOEOsEi0WL4PGAN9VPRQfJkrch+ty5MCd7zVNf+HOOnjxAHNcoipA25LyoIudDklMgURpmZ9ucO3uUVFnwJTrNMLlhNB6xMxgxGPWreOUQiZv4lGajQWE9OjGIeIwxhqIcUxiDtzAoRiHNJjcUJscYgzOWyGuK/pCnv/w5vvilz/P62qv8r//on/HFz32GIwdarN8qaLVm2L29zVR9AVcWvHz+Ocg7zLQWefbFb3Pt6kXuPXOOzrjH9MIsx08/TBYbit4DnH/hWRpTbcbWcuGVZ5ldnOLM8aM8/cwz3HX6FM1Wi7vP3Y+TMVdevcLC0jEGec7yyk0WDh5mVBq+9vyL3L55g9evX2XxwGHmFg8xGuYkaRMZx9TbLcy4ZH1nk+Gww9qtdepZTK2uWZiaZXppitdvLaMzgSenntTAxKzcuEUyPY0tY252VukMHYWPGHlIopg4aeELybDfZTwaYERELcrwzuKVrIxEqxu4jKo+jwgsau/IGnNQL2lZT3/YZ6ezxu3bNxFeM1WfIo0jdC0UhOPhmJs31uh3d2k0Gog0QxtLb7gTkoxUTCIilDQkUpNphYpjpKxhvWW6bOORRFETlKySgxzSCVIlgu+TioK/kLLgCjw6dHd1zGDQYXe9S7ffofCGQ0ePUMvaIJIqbSsUw8b6sB8rEMLilMSaEkxgd0vp9hpLSmtknOBtiRCKRvsgWWsWlxd0tjeIR32iWp2sPoOQEaUDjcMbh7ASVwzZ2F1jlI9o1GdpNlIkjrws0cIhYk1RWmxpwRmSNGFcWGIHeTnCyuAdBSC9qiLFCf5IUiKcr5LVPGNXYHSBiuo00gytFVIJSjtCSUkiU5wrMdYQ6ToqqWGspd/fYjTO8TLl6Mn7SZKEwpRI0w9JmFLRSFLiY4cphiUbmzeI9AaN+hRp0g73G+vxIieKJKUpkFIDOhQLzhJMlWzVmHOYCavRFUycbjzgpCSuGi62Ah20CsB3pGKEDiCJs4YEjaFga+s2Rd4nbc0wdaCF9walcoyIkL4kQ5GZkk99Z5koifAqxXvD5ljS3YnQOkH5GJWMSLSBwlEQob1FCEk9q+FjMOOCwU6XnZ0urWaLJE0Qkag8RgOrzlVSRGdL8sKgoxjnKwaViPDAeFwiKBnlQ6SEqXYNHddxzlDmI5w1GGNBK3SsAwBQlhVjqTLu9hEqUgEwURECR5nnuNLgTclg1Mf7MvgpOUM+MuRmjE6SIGo2Bl8OGZdBmqdGnrxn8B7ySBDplDTJyKbmiGsZupaRxHUaKkbr8J+PQqiP9jI0AoUOsk4KhA1MD1kYjPAoYXBGMBQmyOXxlAqkDrJZBYFNh0HrymfIA14jCYCGl5LSOyLpyL1DOhW8ZLUO0rtqvSSFQEgdJIbe4nyQTlkbPMe8jRgpTR3NKBUo0URHQBqj601kWkenNaJaRpLFJHGMqsckkSaJJCKJkDgiqYhwxCKAEAbLqPQYAyMR4t+dlZjSMyo9vVGPXnebfDhga2uLW9vb9Ldu4YdddjtbzOmUuSQjwVDYnFG3TzEuUF6glebipYusrdxAxxH1rE4S1zhx6i6iY6fY6vXo9IZ0u1vYKgHPySDp65clN9c36Ax6RFFCnEbU04x2s4nSGmNzHr7nDH/xzDq2LHAS4ihGSo2tmsGxFnTHA771wkvsbm7x4N2nGYgY40tCZGuNleXXeeXK68xkMSQDZmbv5tL1DdqNjA07xe9/bYtIOn70ve9no/Mler0RwllGow69rufwzBzLOsaUphL0hSbVwDhsURAjMMbhteTV1Ws8fP8DrMwsUvZ2iOKY1dVNZmZuMj97nFpNsnjuBMdOHGft1i1urW8wGO2QCkumY5CS8dDzWv8m1gnG/TywpoUEG9LUhbOU1uCcxXvL8vUVptpNsnqbJMvodDs4b+iPR1y9fImalBQ+5/LyDrl0vOvRR3juwgWkiMAFwC8QrD1OBiZcqGMmDXQRkmV1incGj0U5iYwTpJSkNQUuQtoC4S1DIxjhyYULjDPhUIBRRUhVTFPmDhxma32AH26xqyIudwvquo9OUgwW7QXWytCEKA2x8ly4toYtwSqNdWVV3ySUZcHxg4uYwjLTTsmyOrvLqxxeaDEyPUQcMTaS2EtGxpAoRelLIqV59doG0knqqs6z3/gaNQHexChd1SzCoaIMhKdwBYdnawyd4MbaJs4FafX38vG9B5Rci9rGV/mDLxziw3/zFyFfQ2YNbuYxL66PKVzE/ceOcfzEIaZnGxxemmP5tQuMTYe3nWrwW//bb1H/B3+Pg60a7aVjKKlI0wzJkOHuOokOSLEQDmc8cRzx0rOf4dlXrrK2I3n3E08wO3eUBx58CGM8RvoqLYcAGHnY3d7g2ede4Nyj7+Oes6dJiMldTqyg7Czja022t3vU549QjrogI0ab11jZ7rMwt8js7BzvPSfQvcv01sZMzx9CJwnCa4bbr3Lh8jWOHDvN6ZmE3/29f83/8Ju/T3/7Jnk+Ih+HibqsYgPxoXvkrePA7BKfW/kYX/7qlzh+4B9wZHYKdKCnexukFHEUUwxvcX35Gjtdw7kHHgoRs0IEY1YlkC6gtpMCvjp5rBAYCigFUgpqtQb3nb6Hje4uMklwlKgkxpeG06fvZ3pukUvXXiVJHPece4B3nbuXf/5v/wU/+mMfRUYJ80tLbHTH6PmTlJNkJPvGIv7NbJ47FEZMnhD+zQBBxcwR+4c/AYDeDChNpE4e9oxw/98ed5qE7z9JxQZ6M9DxV7//L39MQLHJflx1/KEj5CYePpNNvwvcCD4TzlWgjgjMtElXzlVymkmSBSJIPRMVsTg/z25/h1ZrmtbsEu/5vo/Q6w9oNKarSVdgjcPLcCyeKiXH2TtYU6Eb5/2bj2sySPtpbd8tl9sfAue/+3uvBu8N4xhenwzG5LWKEu33vw9fyT4nH+CqY9nnEN3x8XsdTH/HK+KO/4dz2f97cqFWf04S4Pz+dkKEhe2kIMRX34OUgeqsY2xpeO7Z73Dh8it855mnkbHg8Xe8k15nh1hFbGxu8vrV/4t3PfE+3vr4E3R7lm53h+kZaNQaZETkgxFJs0kSJfzIhz7MaDSgXa/jHQyLPEQZe4+3drLEY3tng9sbNxjnORdeeZ5WfYqb124gophjBxeJ3/8hLr76HUSkSZM6B48dYnZmhm8+81XOPXg/b33kMYphyeUrF6nXI7751a8grOTQ3HG0rnFr9RWe+86zXLt0iUuvvIyWlouXz3Ps8FGOHz1ObzDgyuuvYimIGymHDxzlxvINGBk6Wz3q7QxtJeO8CABY5UehhGCuPcNgMGRsSlZXrvOOR9/FX3zhz/npn/sofC3lg09+mP/wf/w7au0av/m//CZTjRqPPfIQn/3iJ5lu1zFF8GxaWV1FqYjC5CgsUZwwNoYkVhArEicozJDvvPhl4jghbdTY6m/Qrs1w7MgxFmenWDq4xImzJ5mZbrN4+Ajvfs/7+OxffIZme5Y8LxgM+wjp8OTBfNMGr75iXLKzu02332OqPRXAf4JJqJThWrGAqiZGJUKEubOW5771LV69com3vPUh3vGO9zCq6Ns4VxUHITU0yE4kOo745V/6a9y8coH+oMe4P8aMg4eIdBHSjdHY4IFCRQN3IjRPZLQPQLsC4y2TCOskSkKEeyIo8ZRlSZzV2d1Z5+Pf+jIvv/YKD77rER6L38duXvBf/vVf5bd/e5PN7mUaMzO89PLzLBxY4sLLFxBa8tq1a9z69/8nDz78IE+86/1srN3gwgvf5vr129x99iQSzQMPPMDlq1eQ3TG1Zo3hMKfRG/Psi88yNTeNMSVLB5e4tb7O9ZUbzE8tMcyHTNWmKZ3E2TwwBG1Jq93gxImTqKSJMY656YOkWcTm+i7XX19l0O9z5eprjPIu8/Mz1Bt1rl0+T6uhOXP2NHfd9yC+HGGlY3triytXVtja2mHxyCla00s41cZFFoGgqWK2tjbobW2T1KZJ4wyZZSTeUpjKjFYoRCzRPgIfWJuB/WDxvsCVBc6Yio0maWR1suYppBD0+h2KboeN5esYJLVmnWG/i5KKWjulNIZEK7KsTpJqnNRIkWCKnDSJGG+8RjZ7FBc1UMbhym3SrI6IkmCuK8N9SeCIpUI5QYnG+BBpbE24v0VaMB4abt94nZ3BEF1vkU4vkrpghp0LQaI0QkQhsrhKkhE+pDsKr7HSUOQOrTQ6iQMAL0zw08BTEICFSCmsKohsSpnENA5Lik4vJJ9t3KaetZmea6FcQb+zxc3ryxg7pjU3T6M9ixKassixVaFfCAulxzqLJHhojvMSEQl2+12ktWSNFnEUARFKSaQOvlRCBIaWchIRKYw3TMmU3BVVaEtYZzihSEUNbw2m6kJoGTPOS8a9DUpTkkQxU41pclsS0rtKXGTR3iGVQovQmXcuIdaaqdlpyiJnt3cL3VsnSZtESQuhCXOdFwFAEnkwPgb8HqgS7l2+kuNJpcPd0YVmqRCK2GsKb4MXnNZIFyMJzKbSeoTJEcIzGHboDnZRUUSzNYf1Lsi6ZIJ1mlQ66nGEKf8f1t48xrLrvu/8nOUub6tXa1d1d/XCZrO72VybFLWREiVZimzL8tiOPbADTJztHwMDZGJkMgj83wwwgCcBBhNg/hmMnVkSIcGM43gLrM2SLIkUF5Fsrt3svatrX996t7PMH+e+6iaT+St6AJtVr9677757zz33d76/7wJ3+w6hEoociqqiLA2RiiiMBuuwpo80ICNFpJuk2iJ8Cb5FjqUZdyibns58hB0X5P1dege7yCSl1eoGSU5pESJYSOikLgq9wVuL1yGExFuHtwUqcrRij/eW0d42xmREOgIpUTJCahUASSkpRkOE0MS6Jh/7wP4sBhnWlJRZjqsKIh0yoIt8VAOBobklMSS6iUoTOo0m3hlEAiqdDT6CjYQ4kkjdQMWBGaiI0V5ifKjJhDSU0gVpigfhbJ14F2o1Kzz4qma0Coh0LacJ5unBuNwQi+iwOoqB6gF2eyiv69QxHxER5J3BizIcT41CVIS1hgwercI6PLr2ovIoIUBIvNJUSuG1QOlWUD2kCbbVIFYJ4zQhlS2iGISKUJHAK1C1r5VXQc49rgwclAyxYdvOk7kS6UpcZRhnI9x4hKnG5MM98t6Avd09dna3KIY9ttfvcbCzyWg8oCoKzi2eoN/rYYuSOE157NELPP/sJRpJTCEExms6UYzVbUZ5QV6WSCBptfFJjDWecW7pVUN6H7zLY+fOcGRuhoXpGUbZHEWWsd/vU2ysgymIhCCKVLAq8CWjQc5omDE6OEAkMVpqmkmTJx9/htdefxVviiA7w5BEDikqKgNrW5vsb+2w18vYOthlqj2PtYIkkQzGB6yuaJam2lhXkZWCH16+TVka1mWPVmOX2bkZutNTvPrmFcb9ksoEIHxta4O1rW2m2l2iKLBzg1wXnJDgPKXxyDrR0RvDMIObd2/w+OmLvHp5wLQwVE5w5doNptsLKN3A2Jxmqnn45CInjy+wszfg3tYWg4NdlCmRkaQpI3b2Rnhr8BKEDUxQ5z2tSKFas6xur9N0hlHm6XTbnD15jNG4ZHV1ExEbKjOEWJGJhPdv7xKnMZ9+7Ayvvfk2QsRYW4LUMGn61g1eLUOggPKeSCuqmrnmbKjElK9AhOZ7N44YjwtGriKKGjQ7syxOTaN7++xsr4JzoWnoAyBmhEMawaXlJnePLPPW5beZUiEcQKjgfxbFFl+YEHghAoS30FSkTU9ZRETWM3ATZpBhqpnSarfZOxgzPTXD6sYGM9NNRNJhtLHDw4vz3NlrY3qraGeovCWRCikUw8LSUoL337uJ0i4wlosKJTRCeCIpaLYajEvoSIkxltV7O+zs9jHOBvXFz/DxMweUnv78V7h5+WWOf/JJzugNxpWn05ji+Rd/AZHOcn19yJnzT7LQbpJgKSvLwtHj/Ns//D/47k/Wefhzv8Er3/kWx578FF+dPwW2osgH7KyvcO3oBc4vLxLVHAKLQ5Zb/PCVy7z8wTaf+OxX+Df/9x/yhd/8PS5d8sSups8hEN6S50N8fsDqtVf43ve/w1vvvcfXf/23+fSzzyB9zHhvg2/80Z/wS792glu33+GzXzxBN9/mg3d+xBvv3ubkQ48x3rxNZhVvvvU69zZ2mJvp8synX+SFT7+I6a/wrb/4E3bKFlv7+/xoeZHm1Ek6VU5hSkoUshEhhSTG1HeQkIaA1PRGA5ZOPMK3b4zYrCyn65SCohoTJS1MUeI1vPTya6zuDBn29pnfXOTUmdMkIhhyhXC1AEwcqrx8SCQQtkR6i7AemaRhYpFwZGYmJAx5EbxnVHBumO9MMRofsNEfsbmyxjeuvMbszBGOzc1QPHyah558nguPPsbDR5cwVQEu2In6eiHlH7ihiZql9BEZFBwyZQ5/EKGzMlnhHwJI+ENw4VBxNGHX8CCT5eNgSM2pOYy1n7ys3h73twWBySbFRPpU/+Pu86mEuA9i3Ae3Pg5s+EMQREkOCZmTzzx8/wN7PNlTxH2Ppo9+h9pQvp4UQxdSoAndmqysaETB4LedNnn6wpPs7G+FxC1vQUnysiQiSGEC+as2sfYTiGmyZ/Wxn4B6UMfI/sesrv8UEczXzIr72wrdLVmnWN0H2h5M/uMjr///A+0mZsUfTfarz2d9rCYywtqr8yNj7v52aqZVLV843MbkOz0AiIZY0Fr65gFfe1J5RxxpKmPIq4JTZx7hrbfeolCOyFj+8j/8MZEUHDtxGhk3mFmY5s76Csvra9xZWcW6jM9++kXurtxlfnqWbDwmmWohFWxubLGwtIh3oKKYRAjGoxGmCouw0I/25HnOT994g9u3btBoSKZmjrC5vc6wN+Rv/4PfZnmjx6c/90m6U22anRabm5s0dEqcRAwOerz52mtc/eAD9vY2OXVymXsb9zjY2+R/+Rev8tQzT3F3c5tIGnZ3t1CJpyHaFHmGlJ61rXW8KfAUnFo+yXhQcrC3z/e++00+9cLnaX+rwde+9sv86Z/+BeNxDyw4rTm2uMT+zjbrm2tY43n9lVfYX9un2YjZ2t7m23/xLaqq5Pf/h/+eVqfJaFzxUDthf3eVb/zbH9GeSuhlBVJ6lJGksWZYjEK6JAbhJJEVmCJQq1WsEdpwZO4ItrCceORR7t25QbPZwVhLI57i0jOf4Klnnw1yZAnZeESRGdZXNrClYH9ni2efuhTkFUxYFkGy9oO//j4rN27ypS9/hej0KRpJiqznPKF0KCLrCcU7j5Sam3c+5I//+N/wwude4LlnPkP/YFib4Hq8iDEEP7FDGaaD7tQMWaeNMWEMWDxaSUzl8N6CN2HYKvBK153pkBRnXRVSvUSExmKErRcptQebkHgUEQIVxZx66GG8EFy/8gF37t7gxNIy8zMtPv/kcxgj+LX/8u/zxmvfJc8NRxYW2Tvo88ZPX+HE4lGclCwtLfD+1XcZ9npIn5O0FT/3pa+zfHKZP/p//4jTZ09z7uIF1u+sc/fuCjdu3uHrv/pLzC4e59jxJWzhyXNLlKRcfOISW1sHfPjhXY4uVyyfOEX/YMBwNMIjGGcFODB5RtpsU1QFr37vVZJYMujtkI9GzC0d49KzT+Nj6B1s88Szn0W6HOljVq6vMXd8jqaMcdOznP/EMYyNuLG6xY21ASWStNEJEdujA/rDMensAo24GSRYtdTERyE5JpExVgRJQcUkgt2F9E4f0p0QOniMWI9xhnJUsrN+ExVpsBJaCQ2hyYd97HhMa3aW5swcaWMGJ6I6tTKACVp4mrqFEzC1cIR8bwXfWsITZKCRDnM+ApTzgVGtwn3ZCBsa4UCkBFSeg9GQ/f0dxrmj2eoyN9fB1qCYiGOcrH+uGbRe6hr+DOIqlKeTWIaFQ8UxzlmqaoCSEWmSkGrN2FgiH7wr0Q7lJF4JRCRIbQvZSmm1Z9kfbLO7skK+N8b7CpPOErcbTEXTZKMhNitotJuoSKGVQkYxXikSKYNU2IHyMVJpnLAsdOfxXuJFYHkK7nu9KBHAYFxY9OIsQsaULkd7h5KgRASI0P32Bi8M4/6Ysgj+FEhNkraYajdJdcJo3CfC1zLCBlVVBM8yWRGlEqsVXqpgx2BAqZh2uwaW+nuM81UW5udpt7oImeDRKBX8Qg0hJlrWXXSsrGsDh63rr5Lgi6OtoxAmAH5S4XKL0YGRhLeh5eVKhsMDbGWZandJ4ga5MwiCZF55AqguLcNcYUXwkTNVgfdJAC5kREmK9wrpI1ScEEcal2iEDNwn4RylcESEe22KAG8RrZRu+xjjIqd3cMD27g5aBd+gWIdENpU4IinxSNIooT/uIaQiTjRaSqpijDU5Ak+zocC3SKIIJTVCKKwzeCnIi4qyHKKUYjQuqYqCssiDkW0d/Z5qRZxOobVGRxGxmiVOO6iGot3oILwhTVs4BGVpcbai2WwhRRRCFLCBYRW0/EgrML4KhuF4nFMgLZGRoUZWHiVDoyIkU9dVmQuMQg9hEUjwb7R4ShxaKoSzKCGDM5EPax6PC2wUF46hRGFFaFJgFV46Jg5c2ktKBFbGdapkCHmQQlEJjVEaoTRIjZARTsr65yD1JG2Cg4yCZGwYiyHVOIw7a0tsZajyjCobUOQ9qqLAFhXVuIc0I2w2ZH8wZmd/hyrr0esfUJaWY50ppiIVwgAUFFlFXrpgDp2NkFVG10PS6GCqCh0ltBstrJQsH18kqzLGFMgqmIPvj/oY5/A6ZjwOSdzlaIyoKoTUSAVNLSitZX19l1ZzGilLuk2NS9q0mgmD3i57+RDjLHmRHTZ4y8KQaEOv9Ay21pGoEHSRJrSTmO3xiFhrhANTGZyHqnSUg5JESNqJpt8rqfJdYgUGjRICX1qEdFhb4DKBE5KlxQUKA3mesbbVI8tK7kjFOM+QKsiMl6bajLng4e8AACAASURBVIuK4ahH3EqQVRVY8c4FaZgUga0sgr8dtiIC1jZ6LM3tcWpxka31m7Q7DcYjy43bN3n0/DmESg7rEqUFS4uzzM/PsD8csrG5y/7uNr3RGGPrOrwOl9CANRbVjmrmpcJ7mJ5q462kyAb0hwXSV3QbilhCUTneX1tjutnhxMmjvPTqe8g0wdcVkuK+aYiUmkQKNJ5RUdJoJWgZriVHuOc5J7BOE6HBaQyO9sIRTs5OM91ug5JYa+mPe0ERoQXCSGQUtDfBn1Xw1rU7PPf0LHdOPMxo7SZTOqQUjsuyDjhxCO2xAsYIEgnTScReDpX3WKHBV2Fd4Q17u9t0Om3yImM4HHB8aYHcVHgqTp1YJvf7rPQjvK/ASRwKa8ZceOQsb7xxHe8KnDeUJGFNI1RttyAps4yZZhMlUgajnLXNPqUtiSOJtR8jBPxnPn7mgNLv/9N/yP/6P1esn76ElYassKQNSawE58+cY/6hac49cpJuIg9N94x3XHz8C1zJdvmd3/oVPnjzZXIBG/euMd1tsL25Bt3jzHSmmI4VuQ1RoJEQ7I00X/7132H9//pDusef4vNHu6xkOVESU+YFsZTkVUXev8cP//pbtNpHuPz2DXrDmNXdmxy/docnH3+SqVSR+RwpNN/93p/yha/9Gs18nX/2L/4nVvuK9Xt3UT98h6jV5KlPfYFnP/kCXH6L3mDES2++zanjJym3rqDmHufJhx7m5p0V1sYl/bzgzTdeYXGmSafVot1eAG/CvZ/gRK+kBUqckHzy+S9x/lN/g2MLs5TWEMucm3evoBpHmEo1WljOnFjGmxu8fGePN95/n/ljyzz56IWgYXY1eCKoY6hdjYl4xsMNvvfXf05eNfnVX/2v8FRkw33GwyFTM0tEUYyUIUpY1EVWZ3qBv/lLv8Y//2e/z4kLz9Hfusq/+sY3OH7xKX7+whOcnp+lKjOUDEPJh6i92hsqUNyDDj0M70ny5sQ8+n5ymmcS7yUmMq9a8uY+AmJMnr4Pbhz+1R86Fz2IMH0EhJqAAofgkJ/AbxOw4vBN9WZE3fWrf6/fex90Cv5UUHN1xAPgjKjpxffhKIS7D2T5B/YFguoS7ut/Q7FbGx3WGvUSF3aCkJiAU3jlmOlOUVahWBmOerzz7k8pTEEUJ3Snu4cHyXkT9LUimHQHn+37MrvJ3niCVt478eDTNZDyoG+Vq0GYCRDmahnifZjM+1BUOTc5LhMW1/3z82AC4IPna3IOJkdRhp164BxNQK96cfzA6Rd+8t56Ye7v/+3wK/n6wB8im/XY9/UYE/WYeIDBNGFrIQTWmfAZwnPkyAJ/5+/8Pc6//DB/9qd/xHA8ppSC5dMPU1aOuyvXaTe7jMYZ6/fu0O60WLt7m0baorQF436f6dlpvIo4sbzM5r01svGYh84/Qj7IuHr1fVbv3uX5Fz+PMQaF4KUf/ZjXX/8R2XifX/jFr+PKlKeeeIo3Lr9NI+myfGIKrSXD/pinLj7Ov/v3/45Oq4lwhvfefpPu7Ayfff4LPPHM0xxsbvL6T16lFBVRovjd/+afsLJ5wO//j/+UnXyNf/y7v8cf/Mt/za3b73Nv9Q7W5Mg4AWEZjscUmeGRc2f4pf/i15mfOcK/nvtX/OIv/jJvvnGFd975KVI4FrozfPrSZ/jWt7/JYP+AI4tHSeIGg+yAE6cusre3R1kNaM8s8NTSAteuX6bV7HDr5k3mjnZI8wRrBLH2VEaAd2RFBtJjrEEph0gEMpYopWlGktJY4gSqcsioEly//h7COdbzjM7Nmxzs9fjFX/4Vji0sUlkTtqcU/f1ttncP+PHltznainjmuU/QaXXotDsYEXxgNjfvcuvDq8zOzvDyT17i6fGYp565hETgvA/sIBE8J/AeoSRlWXH81Emefe4S8/MLbG5uMD01zfb6FnMLC8QNjfSqHoMKKRxWerQ3RDJFugjhSqwzRC5GKhcYUwRjZgAtPJGSOGNxzqJVjKQgigTea5yIyU1WNyGCxEMoV1/vGi1iTh0/xcLiEW5v3uTCo+d58tGnGQ4y0jhmOBjxyee+yLuX3+L73/s2v/DVFn/rt/42eTbim3/1bU6deIi1lXvs7a5z8sRJ5mfOkCZttI64cPECd2/dYtA7YHd3g2baYHp+gctvvcmwX3Lv9gonlk9SlY6jx9t0Gh0GDct6vseNazeZ7s7QaTUxVcXtm9fYvLfO0pElRoUhT1p443n4oWUW5mbJxgO8daSdBgf9HuOiYHa6w1Q6y7h/wHjoaHZnWThyAmMMm/s9ru7e4+7KDoNhSZw0aTSbDAuLUMFzZXb2SAB0nUB6Q6QVSEksLMoFKZKQwb8trhkAzgdfH4HDeosxQUIjhUd5cGZM2o4p+yOcUswtLBIpjfHzVNmI8ajP/u4uaVLR7s6iGw1UHRkvvEUIgaojxyLlGfQ2GRYVp5/8FC7LcMIfenRULjSOvA/AA86TlRXbG/cY7/VozR9BdGaYnZ3CSuq5RiNUXLMZwAsJMhihSqfwzmAFVN7RkJJRUSFqVq53nihqoHSIph55RxLDjHLsDirGVS1VNYbMWIa7mxiT04haDIsBnfkuskwY723gjGE66dKeWWTuiAjGqnIig7EgAispNNE8EaEJI3WoQYStEMqAV2gBUgRrBKkUum48ipou4qNQoSjAeGozbItwUBWGosjY7+0zGA1ptjq0Gi0QEVVV4gRkpsB7E2LovaCqxnjnwrUpoBIK6TyRB1+nSrmaiRI3ExrNJtYZxtmAwcEmDd1ENqYDYOAFUmk8tR9Kzeq1ztT1h0d6SeJB6CBLNDWYgnchKc+CEA6sYe9gh2w8YLo7T3tmLgDMwhMJRSSCJ04sFU4onJMYSZCcIBE08XgiIVFpjJcRDoFUCoPAWInyksqVVNRsGBGhpMVUJcgoNKwIsrNUx0Rzc5TtBrYosVWBHQ+IhaLRiFGxYDzIGeNpNVpUNqfX62OyYZBMaaiqKlxjPoDx2BCwM2m0xq0WaZIQCUHaahNPT9NIU4QUxElKnMQoIcFLhJIoFc6NdSEeHATOaUalqa0xEpRKyC1oZYI3aw36ekTNIpNo57HKIr0M+e0+MDgcIJzA1M3CAAnVDTFhcT4AuJJgQh7q5OBZ6moWpPc2uEN4UeOKoXlsRRU82XyQaFsXjoOpTc2NclhitPc4EWGlQIkgA1QiwosCIXS9L/X14Eqs9RR5hsKA8ZQ2Z5D1OegNiFwJVUmZldxbv0MxznH5OKTz2ZJ2IyFNm6Rxk3YrxpqcSCXILGPUDyEHJ+dnmG21wqzpPcI7dFSgvQGlqKqKsjB4B0U5wPZ6pK0ppE4QUnPt6jXuNgWdtEPpHDhHXlZorYmiiDzPGQyH7O7uUVlLLBVSQlGUaB2xu7NDrCTzR+ZI4hjhoCgKnLVoFFpGoZZ2lsFgQJZlRFJhjMWJkAKZxCmjQUaz1aJTlIyLAucMe7sHKH+b7d09Dg56WOfQQmJKQykDAz7VEmMcZeSxXlFVnkRLimzMwf4ej104R3d2jtXtPTbXN5BFiKxHhETZzV5GGikaacyS9OTDnIEJ9wonAuMWX8+PMsw7VVXR0jE3r9/hIBszzka0mkGKubG5y8zMBktLJ9CRRtRqB3xIkJxrpyx0TtBbXuLD2yv0btxCAhoVQHsf1rtZXlGZnGYUfNKW25KhLXnjyk00mkRLIgXjynF3a59ms8nRIwu888ENVLMR2L0iSNusBOo1byOOiaVk2B+gdGBfqygm8rUvJZbSgLMVThgaSYPlhS4bvQxfZBRCEMVRCAYwAWTHh4al1E0iJRHSobyidILRsMeLF5d5KwYx2EYrzaAosMMhUgqEc1RWQ1USCUk71RxkzZDmPK4opAMRU1VghiVZccDmdp9Oq8lwPKQ/yFiY7bK1dYDL9ml2psgOKrQM114St7n54RWKbBBk2ATPRi01KtK1vM8H1rstcTh6ByMEnpmpafZHI6KfreLtZw8oJXHC13/9t9Bzp3D3XuPDfpdzZ09T5h6s4+xikw4mxFEKiRAGayyNbpvnzsaM9g/44gsvUuzc5i+/81eksWF9a5Onn/86UdEjqxrhAAmPMwWNVDJnrjMzM8fzLz7D2eZFdk2KKbLgmG/DwDvoD9geSm7eepMbWyO6Rx9iKYUb77zJzudepLu8gDEpZ89f4o2NfW5tDXjq5MN84df+W04stfkP/8//yZ5fIFIls90Fysoye/QMyydge2+PV1/7ASu3bnHsoUt85sIZHn/kHJeeeIwfvvZT2mnMj1/9CSdOneOJp4/U2IkIQIsUdZqXYzTOWDx2nBlRa55jzc7KNS5/cJeHz0fcu7nL2Uce5c0P3uONG6s8+vTnmE0FjSgOgE0NwASmRSgUlZKYyhJp+MkrP2ZYdtk52KXyIMqMN994mRt3VvnUM5/l2PJJOtPToRh2AlebEc+2G+h2l7/xC1/lx98aI6Y1v/tf/2OOdlrhoq3BJA69eAJTxtfre1UzhKwCXU94ztl6sS5rwOgBZooIrJ/J4yOMHl8jBUy8k/zhR/+n0uAOtzHRz012Ucp6V+tob8I/H5fmuRrskPUT93PiwuM+SEI4j5PvUe+rO/xeHH724b4AaiKJmYAm4vDdAeCagCCEgkQRTHS1ro95HQccaUVWjtAI4jil1Z4hH+zRmZ5BSoFCUFSG3JRE3ThUWYcFlj88yIcSMzkBYCbUeg4ZFhOw6xAkE7KWqH2UVTRhH4W/12BQDdpNCueP+iWJGjC8zzz76OPBI3//t7Brsh76DwCBE+Cn3tXAOJsczUkPj8NjfLjVB0HAB/ePjwKYE4qtkhHGO8qyoj03zVtvvU2z2yFfX8MJw8bWGmVR0YxTGq02P/3pa9y+cZUnLl3i9r27VFnO0tFlRsMD5hcXidoN8jyndJbu0hy7Wzu89vrL3Fq5yXPPfpKV23fZ3NqlM9Vmb38LqQqituD67bs8fvYpPrzyARceeZgf/vD7fO5zXwQjuXnrNkp6ejvb7O87oijmwtlznD1/gWazjVYJG5u7WJ9j8URpRFVU4ATPffaLZOMhaWuKU8vHuX39MtIlCJ0iBJRWsHuwT0MlGBsS74RSxEnClfevMCr2kUpgS8fe3h7vvf8+/cGIRrtN0mjSPzhgv7fL+toKo2yI0+dZspInnngKrz0+9hTVkN4YKhvkOZIY6QLQYoVHComOPHFAw9GRprCWbtpGlllgLSQK63OQniIvidMmZVVw7uJFkiSmqArm52a5fPkO9+7c4elLTzMe5nxw9zZHpmZpNpoonbA/yEgigbGW7e0tnLc8+9yn2draIU6iwPizYUGRZwMQklazGeLFnSWONSY3/Obf+nv89Xe+z8L8PMY6sqqiNxhwrNUM76/nAFnPW2EKkhgpsM4GaYJwSBUjVLhmRb04sc4Hg2BZJyf6kABnQw4KplJIoXEigMpCBgq8q2l9Dkucxpw9e45rtz9kce4o43HGlasfcvXKu5w89xAzU4+wtrbGhcfO8drrL/GbZ/8uw96Ai2cvcOzYUY4uHWd3exvrWmSlZ3VtlVt3ryNEiTeG/t4+Wml293aYPbJAs9khy/aRheD48WPMH1lg/uhR9nsZ771/mThpcfL0MQ52t9lzOXGsmW3HLJ5/hNmZLkUkOXbsNF4oeoMh99ZukShJo5GiVIOHzp5FKMn+wS6u9MjGFAmC/YFh/8N7jAYjDvKSKqsQJMwtziBkhPeOSAdvFe8Do0jLAEjgBc4GyYmpwj3TYTFVgSnD64R3WFMEOdVkvPogOKxKQz8fY11Fqls0F6fwWqOkDjOQhaiRMNPoYoqc8XDE5uYNkjhmunOEJE1wUVjYKDxeNlFJQhp1GOxdw2cF+4MBaE2kI6y3IQ7dBpZEYQzjYZ9+NsBKycKZR1Fpk6BfDwtaHSfgw31a40FYnPR4V4+ZSRKtdWgJZVmCcHhTMhxlJCpCa08xGh2C8EpV7HtHUXdrYxmB8DSShGj+CMJU9Ad9FhYWaUzNQFUhIx0YaHnBcGeD5tQ0MgnUfpRDWIO2JjQ9gFSGFM5YqXBvcQ4VibCOtwZNaB7YGkjChxSvwAxxNYNJUNoaGBOWqvJkhQnyNQQ6nWa2sxAABO+xXhBrHdZZBE8pIURtCAvWK6wKLBthA1BnfVhweaXrJk5IidQiQijoRE28LRlnY0R/n0h6iGJ8FBGpCC90uM15BUKiJk0eFeF8YCZa5xFO4VRgBgjvES74DZV5HyUMcwtLJOlUfQyqIJ2RYcwmIqQDQgA+pBCgUoQM0h5HAFGdUEgfmpJWBpDLyQnIF5iUBZbYWayrsM4RyYzKjPHek5eG8XiMLyqSOGxHWYuixGWGUQlpK6bMclCCUZFRFoGRFMmQ8qaVII3TsJiMUhpJi0Yck6YNklihdYTSAqkiXBmkjNZ6fBQjhMe5mgkuQljPJNAGBFYKhA21q8cE4FLJuo4l+G/VMmUnRTAUx9cyuWB6jhN46TGuIJUyqAqkf6CJF+YHJvWKC1wj5z3CWXAOR/AwstZS2uAlXllL5YJnmfMW40tkJaiEp8QjvAIhcCi8l2HOF+CVo/SeyILF4qzFYzDGoyZsJWGIIouoKvJxRj4aUJQllXHYyoS6zlms9+wPBojS0kxjhBNk29t1HVbLSqUi0glpFIzShRM4Ixm7imYcMVYROolo6oiiyA7303mBtb5WDJj6OAkMNfuT4AkmVJAD9fsjEqMY9Isa0AiSc6lkzTx3FEWFMcFTdMImK8ucrMqJ0Ny4dZN7q3fpTE0R6xTrS4o8RwhPkefs722Tpw2cdVhbsb/fpyodQgb5UVOnWCnodjqkiSIvAnNwe7fH7bVNTGXBeSpTITwU+z3anRSpFGVhOXnqFFNpzLU7d1FCURpJ5Sw7vRHXb9zkmU6bU8ePk+Ul2+sbobHrBTqOWTp6lMEwo8hCMnCzGTEoCgJwG6SN0oXEQyPrgAEcWjuyYsxwXIUQkKJiqqMoneT6nRW6nQ6d9hzosD5x3h02mivjmEo1yltUODOHRAcHKGORQhJJh7WW6WbCsLR84sw0Vw4sd1YHxAoGWcn6bh+hYxKpuHLnLiJKKI0NjQAkjnDsQupcRKeZUDlH2u3QSjWNVBNpgXMSUXspbQ9yxnmBB6qq4NrKDgYYDEuSNKHZiEiTCFfmof6SkCSKZhqSU50KgFTqJXc3Nrm00OXi6eNcvVHiygERYY2rESEtXkRI7ylMwZGmoT07R79q8O6NTdhbxUhBhCKKNDiJ9YbBYERvOKSRagYDyEvoJI7Z2XneP9hHKo+wHm8qeqMMLWTwVVURQnqSOKqJEqJuwluU9owyyyjLme4m5GVBQ4mPL9n+sx8/ew+l/QHzM12kG/DuNiQnHmKqk6CZYliO2Vi/xmi0wMnlZVqJor+zA27EvZ2cU6dn2Vm7yWIbBqvvoBWIuM3VD7/F3ljy7JOPYapLLJ9cRktJb+cWr7/9Fh+89Tar9jzn5zqIAo5PxZTGBvMqUxFpiVQpDx/v8pO9Ps9+4WtUNiKt7vHtN66xkVlOGsf88ikuPnyMC5/5EueOLiGE5bMXT7F69a/Y2bzJoy+8wBOnZxmWgtPHFukPDtjY3eehh0a89J1/z6BQnGzEvPLmazS1YjaGT1w8TyOOuHn3Fkm7y3SnEyZ9IYMUwoWo2WzUp9NsEtUSPQV4aTF6ihe/9FWuvPVDPlw94MTJk1y4+Ck++XOniKqcze0NrFChK6oUXoSLWxDMvlWwNCQbDKgaxzg/q3jvrVdYuXuXsyeOMrv0MCu7Q1559x0+056h050Jk4wgGItGku31OzQbXUR5wMz0AnRgJpF4U6F0iHn1IuixRQ3sBCJt3Q2rwRhVk2u8Dzdc73zQ9z84qieA2MdHek1PmUivDhlDh/9wSGEPQNDHmCUExhYP/H0CZgS2UnCWOPTeqZ8UUGunaqbLoczNH6ae1Xf9+yDIfeTikAl1uBsPyOy8D5RIV3/fBwGysF8hsWXyrSafWVWhy2K9QykVDH7jmPzA4YUibTb55HOfwVhHq9MJDCFCN2n3YJfmTDdMsEKAdZh6uxMhlRTBr+njsrNDsGaCtbgJAHVfjjjZ94+euPCoFahAbTJcv9Y5dyhf/Agg9LHfJ7LEyVYnnyIOn3twTHz05E+gIOHFIcMNcT9t68HH/d0PwKw/HFWT/9X7BiihkELQiCKKyuBKQ9xKaduUOFIgBau3byGSJuceOc/zz3+Bt9+8TJIqTi+f4v0PPsCUY0ZZH+crxtmTxGkaDLjjGFMV3Lx+k5t3rvPiiz/HvZV7rK3eoTt3hMXFY/SG+0RJxNL8cfJRyfe+/10uPnqexaUlCmPp9Q+IdcrsTJeV/g5RnNJqJ8wtLFA5w+b6Bqsrm5w8fZLNrXXSpIG0JcdOL3NvbZsLFx9lnH+JH3z3T3n33ffY292iO91BSMlwNERqH+j3tsILxfe/+5d89tOfZLozwwufeYFrV67yu//ov+Of/KN/SIlBa83q6l0EnmbSYPnYca6Ph2xuboOQJFHMe1fepdnq8M1v/gmx0tiqQODp9XZpqRZlZQ4NfrEgZeg+URdTeIeUnkhE5NmQ0hounHmUvYM9dg/GtNLgYaKEpsgLnAnxvHGaMD0zC8bw1ps/4Stf/SpTMmVvbZXZtMn65gZPP/4koyxnMBgwHI85eeIMj/381zm6dJyLFwlyBS/Z2t1DK7hx/Sp5ZXnuU59i/d4aJ04cw0iBKQ2bBz1G+YitvU3m547QiCJsWdTjrx53wmPExIXNE9WmqE5MFp33wWcPQc5DSJ4S3oSFifPoKEZ6j/B1QYlBoWqGog0BBF4cNiU8niRp0W5O02nPIOOYhcUlrn14lZ+8+l0ePv873Lhxnb29LT75mefYXtvnzdde5qFz5xiNx9y7e4s4VjhRgbDcu3uLa1ffYW5uhumpFmVZIhGkjZRmmXL9ww85evwYjXYbawzvvfM+x06dYutgxNrqBlVRcfKhY1x55zJPPnqBqhqyeWuT8WCfJ849weLRLnc2NllZeZftzQOOLp9gei4lUhHdzjSViNkdDikLhzXQSVr093cZjzy2NAzKfYqyotnpIkVMEiuUiqh8nYxEuB8671FyIkVx4dbgPabMgpzXlBhfkY2GeJfTmuqidIKOIwRgcGBK8jxnlBckMkIkEalu1bedkLwmfaCrKyGoOUHoNEFFbVSrhc2HDAfbVCZFSoWJw0Jey4RiOCadnWe3GNOvHEYqhIpC9DoCTEWRFxR5Tl5VqCSlO3eEWHZAQOEtqXBID8Z7yipI5Zx0uJoJZa0IrCRjsYRmofQOLyzWG8bW4rIho/GQTqdLM2niY4WSIEUCoonSmhYBvHTGkxUVtgYH0maH2elZNBpXGryvUErRilukWlDmQ7L+OkO7RiNJabWbREmMlgonfS2+C3OCF6r2g3FUwiNNAH/MRNvsbb1u97iaZuycR3lPWWRkzoOB3JQUXhInLURDh7PiPN6GCHQvaumSuH8PMs6DqwM+lA3mxShK6ykqh9Ie6w1CS1xYN4dYc0KSmRThP4ei2W0iCofJDyiyPsN+Rac9Qxq3kSr44ggv6uQvha+5x94FOYaWofbCVVhbMcr7eFPSaXeJtA5whjfheAlJpH1Nuw5eU8EnSuClQeoEVCP4oyAQXtfSfx+uC1NhTVi4CSWwlcVgKMwIMRyRS4UVBusdwpcIVwaGjbGYKg+G3FWEjlLiKEZFCXlhMGVOJXK6rTYi1nihgyxNJoBAotFKksRJYKqIBCkU3prQrfMWa8NJKsvAFPLIkIxXgfGBWSVVXLecQlVkxYRlHcB3ISwCFSSAPjB38IElq5Ug2BSZwHIigE3Ch0arwSIdUFlKKTDe1mmeHutKhHN468BZrA0M07KqMK7AVRXWeKwNn1X6wIay3mO9x9Tpg7qux7wNc3kpgq5VOIetfWGE8ygCiLqxu02VZ9gqpzLgTIEQkulWQruZsHx2jvOXLpI6x4evrrLSH2Kq4AdLFSSYFoFSgrlOi/GgRCpFIGKpIHuzrraCkKQ2zJ3eeYajjMpZesOMZiNmttNExmlYIwgOVQX+8C4n6mYkGOfxISQNQbDcEN4ghcQ4R2TDuZo0j6232Pp4T8DATqdDNs5h4knlBKU3OFuBlAz3Rmz3xyjliQXoKCLSOoClNWvJGkeeFxRlgbM+zGd5xYHPaTYikoZmXIyhBixHeckwD6BF8NOr63FvKK0jFRqFZ39vh31b0mg3KUY5pRd4r/DO0xsX7PcHLEQJS7MzDHo9RlVRs0IrWo2IhfkFxsWY/l6PwuxyMCgorT8cM34ShOM8TkgiqbHWopWmGcWMETx89hGu37zNkW7MQVZyd3WFR840wEcUWQZC0Gy0QlNXeQb9AdsbGzjvcH7SqQbrKxpaYx1Y64mk5QtPzGLjGX747lWOL8yihWM4zlnfH5GmDbSUQQYnI4yrQr3jwVKhRKivG3HE3OwMUdqi0WoyPT1NLCXrq7dwRR5MuiUYG0yqJ7ukgUYSM7KOynlsnjEqMrRQlOMC5SzKB+aqo0Ai0C7CCoUVnsHYsb6+wYlTU5Qnlrlx8xbWFDBpVONIpEeiscYgowrNmLnWPEZItBYIZxFaoxpNTO275H2o9fLSUez28UJQSXA7PVqJoijCnAoW6QQVFbEWxJFCSXm4phIiFI5aQGU8+/0RSgvGWQVe1qy0ny0E9DMHlOIU9u5tc+TUBZ7+9KdIpQZTEAtDw+5wa/Uad27doLezwEIr5Y3Ll2kd/xQPn3+K7Rtv4dpHuXHnNts3N/jeq2/zi1/8DP2DPqqxzQ9+/CPs9MMsnw5Ub+MjZPMEn/vlZzi5ssb+5h1m54/UiDp1N05SuTGDrQ/44ff/HJGeQQx2eOTis+zf22RmepE0jhBaYKsKLTW33/8JxxvP053u0t+6xQ9eeoUtucTnpyU3332JRvcY276gOzPDKX4lpAAAIABJREFUXEuxe2+D6/e2yWSHc49dwu1E/G//+7/k9vaIr331K8y2E84sdVnpHZBGmiwLqR3OekaDPe5cvUx/VHLioYukkSZpdxBS443n6NJxinzEuQtPMhi+xPf+6pvMnXmS556cYmV1j6OLixxZWApeoHUHW/mw4FVa0d/4kI2dPc6cvcQnzp/hg7dfZfnhxzEqRkdNzpw8TW9vldEwox3DuL9LUZXMzh5BOoEvevzg5e+Qjfq8/vKPuXp7lU987st8eON9Hj/7KHlWIHRElDQCDVvUiPWEvUKIHQ30QVH/HEAhp8LN+j5DxNcUnTrG1dfQlHjACecQQQrAT2C0iLq3w305lqjhhBrzCRNmDUSJsJ/3HUrqbU5YLBM4S4jaUyns2wOkKeRH5FUBSAmQzOSb+MkmwfMR9lh4T70x4Q8T2Cb+TfVbDo+g83Vh5z0y0riqCjcCP3lF0M/bIsPZEilEAJpq2rVzHhVpdvf3kAK0VzxodG2tRalAf/44L2iyP3BfejgxqD78cofH4fAEfYwpVL9uAiJxn4E0SZU7fK3/j4GsBx8fB+3E5Fjz4PP1j9z3ozqE9w4ZcPdH0cdgs4993v2xdjgu6o8RtWywchVKBkmncILtjU1W7r2PjiTeCeYWFonjJpUTvP/uZTbXVlg6dpRXX32FbqfDW++9x0PnljnY3OZLX/5lDvZ3ObV8ivXVe8SNmP29Pb7y5Z9nf6/Pm2+8wvPPv8DC0RNoqTj3yGMcWZzl1Imz/MEf/AFFdsCrr+zz7ntv0W1NURQlL7zweaqs4k/+7M9Y215jWR9l1O8zzsZ0OzNY47h18xrXb17Be0eVOZ58/Bl02uDty+/w0us/ZdDb5eqVd7h5/UPSJIY4YqkzRW80oD8c0YhSEKFz++d/8RdMT8+TDft8eP0qpx86g5KORhJR2ZKirJDek8ZNTi6fot/vsbG5EeZsa5hqNljfuE2/N8A6jy1s8H2INZUI/mrOGjCCOGnSbiRU1Qjrw3WhkUSRxBeW7vwiOMcXPvdzvPyTV1i9t07UDgWZcJZYSjburWCfeprFuXlcZdjd26U9PcvM9DwHe/v8ym/8BtlgzLHjx6msIVKS6e4UW9sbHD9+nJPLJ9jbPaDdalPgUdayu73JrZXb2CInTlJ+/IPvsbO9z8kTf5NxnvPeu++wu7fJe+++wd5gg9/6zd9m4dhRtJQh2rcGk2wNgkoEE/mEEwIlBUqHhQtWBMal0AgZ/CYsYb6TiGAqO5l+BFhfIup5dXI5hTlb1oB3+CxrDXNHjvDE409xd+UuM1MznD33CGfPXMR7S6+/y+7eFm++dZkvffWrbK9uYL2nOzNDu9kgK4pQIBcladuzfbCGNRn7e4q52WmOHj/CvdV1Tpw6yq1ba7SaDVbXtui0u/QGY+Rmn/XdDCEks3OL4ERIwcPjypxWs8XSbBMdee7cuk4hJEePH6HRbGBxoD1GlCSthE48TVZK9quMrZ0NruyuMOz1abZnEF6ikybNbpe01UITmLPGliElCY/Nx3gJSmg8NjDujEPqFOtBy9ARlJFEkTCbNFBRgO6sBVtWjIZDqiqYcUodkTbbxFJhvUN7jVPR/bl1Mv8IUS+EXfCMVJ52M8Y3ZzDFiDIfsL+7RRI3iJIWzc4MaWOKSFV478jzPqXzeFNSmSKYSJfBO0frmE6nEyRHVmCqYZDhAAaJ8B7jDVmZU45GqFgRpw20VXgZUiZdoLAQKYH0Ipjw1uyqRrtDV0Is4wCKyHo56MFUDlsarMkoy1FYBMmEKA4eRZES5MWAwnp0LYeKpcaWOb5mMKhIUmRDirKHGOTEZYtEp0itsCok/ljnUUqFQt3awLoWHqyow1ombnShTrEmgD/GFhRlxmgwpFKKqTgAYI14Cik01hXBO4bacF3cv99Q3xuEEEGdrQRKOhDBL8l6sNZRekUsIqSKoAZiQoJGqFFCuWIREyasC8zLJJ6mMdUmyQp8YbDFEKM1WiVo3UTUTQ6PQXqJ8wF1N85RmjG2HEAliWJNo9kMCzTn6oZkkDIKoDY/wHiF9BZnckrnkFFE5DVVOaqBCVdLoAzKW2RVUYz6FFnwKNKxxAhTA9qeajRCNVuI2pMq0hFRDErHJDpBRxFaxST1AimOGoAOc2/k8TYLi1WlqVxIfJMiDfLHuvCTQoGI8T6awAxI/QCTWsoQ7uMEUoexoUWdbisn8vvA4vHChXtb7bnvjEN4V7PTa5DOO5x3AcjwHiqDdrY2NBc4E6Su1pQ4V+GtpSwrjDPgKqrS4LChMWMtpjRAqM2sC++FAAIFxVtgveWuIlGC4KZpGY1ynDFYVwbgyDqssVS2wjkTQDEbkg/TJKWZJCgZs7O5Hpg33gIhATDSEaQR8wtN/sHf/Q2+9uW/z/5wjX++93usb3yAtxO305CiFRqUHu3B1ovx0Cy0eBeuL5BIpRBKIaTHuop+nlMUhrIKvjNHG41wLVoTmF82gHXe++D/U88h+LoenNTNPjSVpVBh3UHtKetBOB9YYD54kcJ9r01dp6I662vGIpS2tsKwFiOCL5X04ISq748hwdMZhwWysmQwzgN7zDuMMVgvSJpN2q0OZWkxVfD9cS6wc1xla3BDHN6bJ754nhBzPxqP6TYj5rsdel7QG2cBKMSTJBF5PuLOSo/u7DztZsy4H+S/RWVYX1vn+ImIRhRTpAlHZmcYOsn+zkaQmhp3uGabMCqdDwm5InKkkSTLDDOdmLnFEwz27jCVdtja3qfZWCFpdNjc2KTbaTDTnUZpRaszzUF/SJbn/x9zbxYcWXbe+f3Ocu/NDYnEUgAKtXd1Va/V1dVkr+ymSDalEcVFomhJIWk0Ho8VDnnCS/jNEV4mHPbLPClsh0OeGFn2hMTZFNQyEkWaa3Prblbv1dXVVY3aq4ACCkgggVzvvWfxwzkJVDdtP/FhkhHsQgKZefMu537f//svcUgV7mHeWbRziAwqWZXewCFsyc072zg5YrtIGa0N6A1y2ttDECoAO3lJIhW5M3FwOC7AA/iXKcXi7DQPPHgSKTMqlZSsktHeaOMdlIjgixYZ32OLExlrey2Dh5uR4YRyXuGto4jXrvLRAdep3SG1cBKkw6FYXu0wM7HKwtQivYU5Lve6wT9TCKQI0SqJhJGQWAuy2MYlQxJvCTlvkn0zFeYOHuNOp4/rD9jpD5EuDwMa5yhLR7vMmUhVqNucQCRhQG+kQHuPSoJXoZSKiPLHC8RRGEdvZBhZH+7NQsdhuo299c/v8XMHlNpLP+an7/X43JFH0DYPpn1myOr1N/nJj39CRy0y3VJ8+xv/FpPMMr1wP7N6gkNTnvV0gocfvB8/3GKiOsEviITX33wVpya4cfsmrflj9DbX6W6sohixfPsSvbUNJjLNbL7C1761we//3u/gyxyRyl1U29mC8x9c5ep6SlEu8/7VVZ6pL/DA0Uc4JTfIEo+MqQaTExNceeVVtl2D3/78Z7jwwUUGcpZHj6TcvfAq3/r+96lOHeKJT/w9fuHJJu+9+RKXrq1w4uGn6eQjkDmZ6zGyDqMTbt+6wm2Ts7q6TLL4aJiQxRoRCdubq/zo5e+ztplz3/U1Hnzofk48+DBzU/tQQtHrrbK1tU0mS3qdNbKJOVoVyRsvf5e7nR3mDxxn/775XcmPJCS4Faak6h1f/dt/TfPYszx4AlZvfsD3X3+TRz72IscPHsA4Q5oIyn6bH7/0fZbeP8/RQ0fIjePEQ49y/7EHKQdrrK13KYqCNy9eYf7ICb7yy5/nO9/5G+ygy1anw/T8Ic6cPhMQ+91iOFAdBzs9jBkx2NnEK8n0zEEqlUrwCWJsiBwmER6xa2ezt3DEi/gezZgYg0FjoIfdX30IHNjDFkSkHEemkfexxPB7GNYYYWAMg42fEnvNGLs2ifd8xh5QJMa/EYGeu2sGLXb5Brv/3tuy8Y0kGpjjw0pB8E/aA0HCqwI+FtlQ4+LVe9rrd3jr9e8xPfMVJhqt0GOK8B0FDimg09tmONzmvvtO4B33LND3gDH3gEKwByKJOC0aPwK4t2f8PgbgPgqYfQgEDKO80CTtgkf8/z7uBZc+ZAj+M9DXR84T7jmeu8+N3ye8/p5Dvvv6+Jvd4/7hw30P/Lj7HcK56OO+9taR6JRKtUHFCUrrqE9Nc/rhU6ystXn/0vv40lLvNlhvr5KXPSZbTa7euMTRxWNU04ydXp9hXnDt2lW2d7a47/6THDt+gmr1DgcOH6E1O8vRI0fQMmFubj9SWN547c0IMiQcOX4EZyz9UcHRI0dIlWKr0yataOrVCs6WNJoTHDp6nAOLB7l9/RpXLr0PBMqyF5AlGZONOudef4v3z7/O6sotjh4+SGt2lu5Oh5P3n+DZjz/D91/6AecuvIUQoRirViq8+fbLSAwX37/IYFTw4x9WaDTqjIod8CGKOHUJs/vmaDZbHDp4mPcvngfpEBak1/S2e5iyjOayEplI8mGBrjqs8SgD6JCwIoTCQmgYhGdYCqSVCBwPP3iK1nSLM48/yY+++xKpkiiC2ei+1iyPPHqaFz75SU4+cBLhJf1en/ZWm5vXb3BneY2HH3yEffvneO3sWVQ8+gKCEafzdHf6XFm6TJakTNTrpCIArg+cPM7y3dv8i6/+S77yG7/Nmz8+yy9+5nOklZSdzoD19ioLC/s4+3KXAwcO0+v1adQnPny+C4/yEUi6p3CWYzDW+eCjIgITQ0mFUkFmMW6GQvXkITK6fGRM7E5sXWCYlc4GHyWiH5oPINPC4kHefedtvvXNv0FJzX1Hj/D4qaewec61a5c5cHg/q+urbLY3aG+1qbVaKJ2wdHmJW9euUK/NUKs0eejh01hhWLt2ndmZCaaaNYb5kK32FomeIUkS5ubnWb27zc5OF5018CTUqpPMzy+wsLhAr7vJmdOnmZ2a5Ob2BrpSQyWetFpjem6S0icsr17D+RSrJanz1GsNNjc6VDJHp1Nwc2WTO2t3sVIyPbOParWFdYasUg8sBAG5NZFJG1LDTD7E9HfIKhkqnQhFmxLBt0AnOAFKSZLob+6kw1vPqBxRFGbXb6fIh2RphbQ6EePfXfA2imWYiKmvEhWKcGGR8V5gHCAVUskAvOIR1YQsbbKvPkeRjyjKLp2tdVp6hyR1zDXq9NdvY3Sd/rBPaU0wOU4q0The4PIhQgxxvoLFBImfBCEU4zuSxJNkGTqrkCVVRKKRKjBGfZyICh8I9j7eq6SQaAsIF5N44lDIGQbdDp3OFsJ7kkwilUfrBHxBYQzaBfaOc0OQitLGCHY0zkVPIueCgfXEPgIzZESRjxj2++QmZ2Jigmq9hvSK3IXQDekFtjQ4knC9BLEbjiRIhGyBMQ5XWkpRULiCpDZBJamiRWSLOQcUSGyQ2okgH/roYzzYCreiOLgSoZlURlBajw3vEsDPKFAQsSCRKjCGMqVwLsh6EgRShBh3JVOa1SomM9giJ7c5pelTFGGarysZWgWZiZCCPO8y2BkgNOgUavUUoQLLwrogFfMQWS0meDFFcx9jPV5Z+tubFMMhMlNkOsG6wFLDu2CujidTkkRIKtrTaKXoJA1+JokkSRLqsoJJFAkapRPQKVprZBIGTRqJlGENwhm8D7JAPCSmDACbluSmRDAGCxOESIK8TIRaT8hwDWkEUki89KhEYWIKcW7LCBKIkEZHSCN0+MhEtAhDBPEtMtLrw/S/xBsTkuU8FKYEbzGmxJQGU/YDG7CM8ejG4W2JdQbjg6TMYcAGcIjI0Av/s+ChLMoAbBqLdcEbSekkeM6OBvQHPXJfYkvDRL1BojIsjrsbGzgTotHDuR2KPBFBCLHLqvdUdAjyUUKgUZRKI5zG+REeibEWJT2HFqZ56vQzWCv4YOl1lm/fZjQymNJjhcWUgfkxZtgba/EuAF7WhVAT7/eOq4jbhAtrZVkaytKQVjJmWs0ga0rC8YuoEfeOOn2899kICXkZrCMcYTvCYuqCfNHa3dLOR3nubuomez2Cdy7WEQYrwnE1CBQhnUzKAE5ZEfzAHBLnJaUxDHNDf5RTFo7AgCspraNWrzFdryKkw1iJFAFgC/bOgR0kXGC5j9cMMQ6J8YGlokXYX4nwTE3W8VLQ7Q7wOJoTE1STjI3OANHZBmfCPT4C2ptbOyTpKklSYX1rk5lmk4XFKaBge2MzsOF82LPWBS/VwnmENThj8d5hSsvb71zgxAMP8doKKNXDILl87TqNSp3uTpf+VsINd5NKTXP44AHu3OljTGAEegIQOx7OD4YDZlJJoyLBaHxa5/bGgERKNnsDOt0RAo1WikGeo4WkcDawLr2LNqlB8KaFIJGCJJGkiUIqqFQU1lk62z1wwYh+nNwsVBxojwE86ahONKH0yHwYAdfoRyejSsc70iz4ZRlnAXtPyiOMjOT67Q1OVmvsn26x3JwOUn7hccJTeEumBVI7nFHIYoSu7pD6wFb23lOvKuamGmS1Gq4wdPoFvf4OO1tthv0eAJkOrKiBKfHSYR0IpXAeUplQFB4to0+giN5WsY8ZGU/pBbpSIUVHAoQMLPZ/31Pe/uj//BpT9z3F8p1VTh5eiCOfgjsrK3xwe4vG/AFOTE5wfpBzo7vJ33tohmPVLmvX+9QnWnSXL5CPQut+YnGa114vOXD8aaz0HDh4hNU7Nzj7Ro+K7XG93WFrfZ0LSzc5/MBjzB8/FmJxJREScFgBSlY59thn+MqBZ9npbrO92UFPznLy0Sc4dnSHrFIJJ7GWlCLhuV/4PAMkeWk4fPw0Ny9f4OxrF1FZlVpzEVefZebocep1xe3lFV4/d5Hnn9/Pkckq27evc+G991GVSWozBzCm4Ic/eon7jj7Ko/c/AMaCDGwQVxZcu/YBt1fustUruLt9Fj3RQDcmKH3CbLPKu++c5aU3zvPIkYNcubnOIyenWbrwBtudHkZVeCibZrHfY9/kZEhSI3gU6KzCude/TTdb5DMPP0p79QqXblznC7/2D6gkmkF3i4lGnXzYp725wfrmFheWljl6YptqvUo6e4SDizk6a/LYI89xd3iOL//O77K8usHc9BRLS5fpFoBOyPbFxZmxjEnibIjtfvvNl3ntnbPsn1/k8soqv/8f/QFppUpcagKSew9bJDzGbKQxwLEHXOzKycQeFDL+21D8CnYtpsXe+41NvMM9z+/dScZNVESVx5jTvZK63c/we/I4P34fwuuBeKMY/52IVPV70+XEPdsUHgqCrC3eQDwfBlHGW+BsmFIKBIkKFMokzQgQnqDdaXP91lVWVm/z0IlpnA0MBFMWAZ2XitnZedbXg29BDNokGE3aCHSJn9k/4+0Z83N2PaHuPRyeD+3P3ReO/3GvH5b4MKtIxueMG5t575mt/39J7uBD+N9H9tW9R+3ec0r87KYRv/NHQKN7waXxIfP3/noMIjHG0EJjrpXgztod2pttHn7kSW5ev8qjpx7joccep+wPmJ9eYLvbQQrBzVu3WL2zQmmmOHH8FGn9QaZbU1y8eI6HH/0Yly5dZGZ2ksLlPPjww3hrqVXrPPTgKWZn5qlkVZwJKXO3bi9z9qevMDc3w6Onf4mnnnyKb//tNxn4kscePsWbb71Ne3udjz/xMSqZoj4xzZnHHmen2+ON186ysX6H/qDD7NwBXHuToiiYXzxAszbBlWtLzE23cLZgdmYf3YGh3d7A5ZbNzS1G/S6VLKGkQFjI+128cJx942WyrMLOVpeVtRtIbyMAZ0lrKVpmHDt+jOl9s9TqVX569hU63TYCKEuDGTq8Cr4IaZpSlH2UdFgLlAKhNNJDOcrx1qGyhGqlQm8wQqkEU5ZUaxXuLN9kVBomJ1us3b1LllQoSsNEdZI0yXj2hU9x+rEz8dx2VCsV5ufmOXHyfrx3XLt6mfpkk0a1RllatFKUpcVLyWA4YG7fNEWeMz01RSVLozF2oIy3726QJAnz+/bx7AsvMHIOlSik95w4dhysYWHuAIcPHOVHL/2Ajz/5BPP7D4ZJN6EBHoPMNq5tXhBMPYxH+ZDMFvw1IrvBW3AgVGiuXCz0rY2pok5EH00ZwLqoHJRIQqUUzmlFaJ59lHy1mhMsfXAeb4Ycvv8ovc4mWiuGox63lle4duUDBiPD1MwCt1eWOffOq1SSlNbUIpNTkxTG4PKQelerVTGu4Nw7F5htzVCv1xF3OwghmWxNMhiMqDcmSVLN7GyLuYVZmvUqytc5eugo/Z0thrmlcEO2bU5hUzZ6fYSUXFy6xERtgseeform9D6qusLm+jY3126wsdEFUWX/3DRDZzC2hzUpSicIEfaPjSwRKUIjnkiNkhVqWYVEhyZcKwlqbIQriYtY8KuyHlMYRt1t+uUo3hEEKlHUsxYq+vpoou+QkKA0wf/D43RojJVQEbwwIXVJB+kTcbqoCKahXnmUCya0UoOVA8rC02+vYfMeW5tbjHyFbHKStNJESkWiU5TS6Dhl1yrBoXFSopVG6iCrFEohHFSFDGy2OAnzblxjuRj37UAnoamMkpsQXitjkpfAmoJhv0NhBpSDHtKXJGmFVCcIGRgykmCijfDBCsB7tEwDyOEdXmlGdhSPiURIFUIpPChdRcoMa4b0uyO6+YiRKalV66i0QmkM0gW2rotreu5tiLV2NhhW+8DI1UqhRQWd1sI+QiKFDmuEd0gZvZZ2h2HxzunDthNlah4Z8yMEqJC8g3WgJVplYbtlYAUJksDUjSEQQurAzIoBFUKyy54RIjQDGkGiEoROyKhR2CGjQY9+bwvZ1zTqdWyaUo6GjIZdpE/RKsUUJaUxJFkN8FhjKEYDcCXOOApX4p1BOhtqIevRafAyqmUamUqqqUZpRSqrSJ2gUo3UCYlOSWWCVBqhAwAqlIoUdYdG42OT501IMVbCBfaKFbFeijd4L8Hr4G8UzwchQ9KYVClCEEztdQo+2DxIBc6Ga8ZTRt+h8NnehqhwZyNbqDSRqeoocSRJANWsDY2lsD6AiN7gXIGxJd4GXxiTF5giMJNyU+CtC8+ZglE+DEliLgBVQQ0XmFw6SymHBYUpKJxBolEYiqIIy7N3OOfo9XoYYyhLG2ojqUiyGllSQdmc7cFWGE5bT6aT2MkpXDEK8i8vgscbftfwXEblQqh1JMHjM8AyIawlrEnCKXAemQi0lhw8uEizdRxLztnXXub8e9fpbIbhk4syMaLEWghB6aBZbQTwN95dfOSJBdZsVCk4gRCKsgiJi81qFvZnCcYGtz8hAhN0PMh1JgQLOHax2gBMxVCYmEuwG/qza00RJBK7YTrhecW4yvU+rN84ScRGw4BchPd2zqFEdBSN16W1jpEx5JFp5pynNEUwRq5UaNYnwsfakLTqfGCvBUxEBla7d1FU6e9hF5vgn+bDGutFAJ8SrZmcqOKFIC9LWq0JEIK8MHj6uNJFM3gZ6xlYW1vDWYlMIK9mpLUqE/Ua25ubSB281Yg1sLcWIz3GOXJjYyWsuHG3Q3d0jsLAnbxPllUY5SWbvkNFpwyGI7Z7QyqZorM9YGQ0LvpbhVZC4IxllA+AElOkFNZSr2SsbQ2xRrDTG9DuDhAqIdUJgfvpMZGkEO7HQRGgE0WqFEUehmFaKdJK8EvTiaYcFuRlGcHDyOrD471ijDdags/WdKtB6avk5YjucIAZDbGlCcbXQiCEJ000JFVcv08iwv3PS4e34T640R3SXFtnfqHKTGuSValxvgzHVAVAksSDk1CUyKIb1FMEHysVAbKagmqrwkyryuaW4OZgB18otM7Ic0NvtLMbxhP88oJ3KyVYnyNShXEFWgamsxKhrhMeGpnGi+ClKxB4pcDbcE/6OT5+7oDStbs7LPfPMXviKY7NtyiLEmsdlUYLqSXbG9e5prbpuxqYAa/+8LuIFz7LG5ub/OIvfYpv/dXXmD96gvffeYtqXTEoLGceewJtezQWjjE3WWV9bZlL17c4cvLj1GtLrKxvMzl/mM888xjalggdCx8hUcKCrnDqwdN4HMWgy1p7C12fYn6ygZ+ooRCM+lsM+iFi/fCx+5lIFApBw2+RZk3WuwMG622mZuY5dd8Jpm2XnXaJSieoKMXZsz+mPncfv3v6eR44pahPP8zp5z/DyYkB3//ed7i1vsmnpAMTqHtKOPCGUX9Iv58zyh1KJ1y+epOZxeMcOZrQqNdoNqc5cPAIulIhHwx59Z1zVKYWeOCRp5idmuLooYPUKhUkQd8+GBUoLalpRc9W+ZXP/jq3z3+bt8+/xtmLa/xPn/0NWhlsdzYoBz0EJSurm2xsD5m//zHOfOJTPHz0ALdvXKbb65PqnH3z+zm4uMWXXvwcK8s38QI+/enPUZ+b58B0i2qlSmDojA2uo6eOc0xNTfP2hQt89rNfoja9yeRkKyw2sTiNRA/GnfqYvSOjXMP5gOg7KcK0ftz5+z0cw4/1ooxZKvcADn68LaCVCokGIqTfSRWLX8LnjwsZz14jN2aljLduF2v6EJoRwYtdfV2EJSKr4EPAyL2vjViLZI/5Ij17HkUC8IE2WlobDAUFpEozKEckaQY4hNDsP3CYT3/616gmFbyzlKagLEuuLF1le2uDufmDzMzu453rH/DwiQeoVCchSlzi1w/b4vY2UbCXZjd+yJ8BlfwYVdkFnj60e/ZwmV0waQzH7B37n901u95W7AF6PyOJ8/du2Ucf4v/9pzFA5PeO6b1A1N6hEbvn2O6xGm/XGGCLz4XiCITWDAYDnnzmKT71qV9i+c4tjh46jEDyN3/9NRYXF9AqRSeaazev4mxBWTiazQlqE026W9us3jnHsUMPcP3aZZ5+7hkWDhwlSTOWb95ie6vN0QNHyE2Js47RoMedtTX+8i+/xrDYQUjNwcVFnjj1OD/41g+pNyT/2z/7IyyWZ554EuEFn//Cr3Lx0lU21tdZW13hg6XzHDx8iCeeeoI3XzuHlIpGo879x4+DEHzxN77Cd775dX79y7/Bq2ff4NpXpw4cAAAgAElEQVRrPyHLFMu3bzE9OxOmutYxMgWZ1mgFTmnyIkcMPfVaM5p2ClwBPvV4I6k0ahw9dIhyFLTps9NzdLa3mJ6Zol5vcvXK1SDdFY5Ma6xxkCi8hSSR0UAyxHE7Z9E2oVWbRZOzf/8B3rtwkVp1kqWrS8x0tvm//uT/YL29TlavUNUpM/umefCBR3j89Gmc88G7znuSNGGy0WRh/gBCat584xWm51qkWcooz4lmGCy9f4kf/+RlXvzsZ5mYbKHTlLI0JEkailopmGxO8ptf+U3mZuZ44vEnWVvfwjtDrVLl8IkTXD1/nu3NNoNeh/W7txkMTiCFxrhit7EM/mp+z7RfgcFjjAAbr00hkWiEC4CdcAqhHEpEV7Ro7FI6gxMuFq1B5rO7fka5brjmHcu3rvLyT37EsSNH6A9zrJU0WhMs7j/IzP79DBqTnDyxzssv/5BaIlm6+B61qX0sr6yQZlWq1SYzszNsrm9QObCfYelYWb5DnufsDAzHmlPBs2p6koNHD3Dr9t0gbZCQVlKcL6nWNN6N2O6uIbSh29mm3b6NKQ1321sMRhalHatrHbrdDo+eeYJ9i8eoVGssXbsF11ZoVmtgEzY2e/QGIyZaUzhjSCsVJusTJDoYyVuKOLlU8d4UJ8YIsqxGpjNwYIXFixAR7rxCOYfWEl8YuqMdpHEMc4PyHhJFojRKhvhoZGADCCdRQuGkwkmJ8AIlZTR+vsc3x4PTCXiHkh68xbgiNMZYsDnWlGgl6e20GQ628UUXkw+o2j6rGxuh/qrOMpHuZ37uICpNA9cnmtdbLxEyRRAbMRWbERd8J4KZaTifFcGLxysfV87gAylkaHi8d6GZJyGxgpHpYwd9RkXwvpHSgPQkaZB6BRaUjQyfcF8OLCeHNwKUCud0ZBN4qXFGIoRGyjTK1qNwJdYGKqnSmsrChNyVDIYDXL+P9VCtVlE6w/scO8rpd7eDjK1eRenAdEETDauJ8j8f2ElKg7OhWY++OILAvgiDJBWuUe/D1RWbzlBXhGYSU+DQeMpYIwjyYZ/SGNK0gkyyADJKCS4PKUY2gMtCeQygXUhyExF0LoZ9hv0+layOzDTeOrJEwbDEDLaxI8AaEg+JKhnubDPMByAlAylJZGhqUIJUKnSSUNcJSVIj0RV0poKBeKKoaI1XOprGZ2gRPNy8DPHdIgKk0gX0ywsbayoZGKTCY0LnGiosCUJ6hA/JSTGWNbII9m7xNtCAcV6i4zFJkxAYXjqHMT6AQ7hokO1QSqCURMpgYo0tAqPKh4GF88HAOk3SwCoWQWoYGEYGY3KK0YhylFMM+5RmRFlEOZk1lMMcYwP4U5oCLSXeBgGYAYyTGA/OlJSlwboSZ0sOHz5Mu91mq9fBmJJ6pUHmLd3RKFwPUgZ2xrCHNTaADF6QFyE1TSHJVJBMChnlU8IGxmPcZ2HfBcDTxejgsGsd0uswaNhb7XEurPnCB4YvQiJEAFxAYwaSi2e/h9E9Xn3lbVZXh5QleBdSHgVEnzmD0AIhUry3KBGYf2P7h1Bej7UJIGTw8TGERnTU69HtD8JxEy6EH6CwXoQprxQkWlLPKru1Pc7vqQkiy1+Mazsf6ztvd88jKffqfGejET9hrfcWlFaxEgypa6WPAxnvYoFsg6VKlK6ZssRERmtpC4yzJFlGqzkBIpidSx8GAdaWiCA0Ag/SibgGh2FyRcTExghk6ViPOh/gOOktiVJMNioURjMqCqy1NBoT1GtV2psdvAjsTSs9RVmihKU3KKnXEnZ2urTSlIoMxuQ1rxnYMKyOer5w7GUY8HoB2nmM86xu7SClINOS4SgPgysUuh7WHIFnMDKUfkiaVsN5K2AsxXTOMRiWpEqw1S9p1DOU1gxKS6c7ZLtXokUWQixCuY0VYS3WUmII6ZCp0szOTNHt9SnzAT7CMUlcf6UA41w8tjGAItxdQhCW95GB5sBLettdpvdVqVVr1Bo1jHEMhzm9wgazfw+ZgkpDI21KMRrhMHjvUHHY4ZxleX0bzyqYMDAK55nDGolxIMrAvrTKkSpDteKwO4FhlClNpkFKTSIFOzsDuu27SJ/TqKUgE7rDnGFhSV0gBwipwj0nsWAceizzNB6jgiR/zApWIqiWoig/kgAi645/zwGlAVMce+E/YLpV5856h+kKlINV8uGIVk1x8fIHFOX9HDp0kLK4wHqnj5k6zsceOsX0wiKdnU2W7qQ8cPRBvvfSNyhLuHH9XZ44cZjFmRr75xY4urjIiRMPkRUrvH5ryCMHJ3n5O3/NqeOHOTTbQiqNxAStoQwFUC2mr5BKqllGq1EjkxLrIdWSla02Ip2ms7HMci54/sEH0Qz44z/+Q9KHv8w/+L3f44//7E/Jkzpz+w8zzAcsdyytmWkQJc3WIfbtX2SunlHP68yfPsijR2ahe5Nnz5zh/Ts93jt3lmNHjjJZU2xvrvLTH32Tl159A1FtMd1IefjRM5Q64bGHTzJbT7jx/ll+cvZVyCZZ3brF0sY2f/8f/adUh1scPf4IQngOzs8Dgeo4TqdIlaYYFTx08hT1RoOXrl7hfFvz6JMvstHdZrG1QLvjOH/hXc6c+RiPPPEJtpMZznzsOU4f2c9kqnnznde4c/cy+xqTTM8foFK9iPCG/TNN+t0t9jWrtBoZr37v72jsW+BTL7wYtNGEKYFWitWbV9lYvYooCr793b/jH/3D/5wEiY9U7HAjCOTcAASFO0EcJuDiYhC6Hgsk0Qg6ovzjoaCQcQoTXuPHv4s3GeJnOed2AQUp4yRC7DGExO57jmGGeyAHsQc2uPGSN95WsQeywPgzxa5cDIiSqPhd3NgrKZSiu+DVHpqyC8z5XXQpmECCR2mJGYXiRTqBUIp6bYJjh45x/sLb3Li2RFbNmJ87xLm3z3L56kVa0/s5eeJhzr93jsnJFp/+zJeCJ4HUvPnaT3nu2efwOkHIEGV8bxLdR0EdFW/OYnzDixKHIMUQu5K98b7YTVxj7z13D6snHnuxe9x2WWv3AG8ffYz35R5T6cPbeu9/P/SITwYW2r2A5i4Uuvf+H/nwXTYWe95S96bVeQf79y/w+c99kclmi9nJSaxz/Kt/+1XqExlHjt7PS9/9LleuX+T6ynWmmi2ef/4F5mYPMNmY4m9f/0tOf/xxfvCDl/iFX3iejbtbHLvvEFeXLvPOO+d48cVP0el02NnpMj01hU4zVtfuMCz6vPDJT7C6vM7lq0s4C88+9wzvnX+bD25/wDDv8sufepH1rQ6u8NxdXuadC+8wHHTo97vMzj/BU09/kjJXvPSdb/DUM0/yjb/9NrP7JvnFz/06yzducHtlnW6/x3avTaNeYXt7g0uXzlORKUf2H+TO1jrGFuAdw5HBlRZFTpE7Th84RZ4b3jl3lplWBeMNlUaFuYV51u6ss3ZnmVazSaoSHnzgQbo9g+BqmB4rQaOeMihByIxiWJBVVKA8pwrjPPWJKvcdPIL3CYfvm8fljmpjkgMHD7O8fJ2trQ2++a2v45LQpE5NznJw8TC/9bv/YWDiSBG8QKQgtwW5MXzve/83i4dPsLq6TKs5RafTDalQSnB3bZNXXn6FK0uXGA67JCrh6ec+wZNPPhmacC9w1vP8Cy9QTRO2t/qkKuXgwTls4UmUJksq3Lx5k/Xlq3zj3/0Z1cYUjYka1ltE9EoK4A5j6Ac8pCI2DkpgvCERQfMf3ZUCpC89UsW0qXh9SamxlLvn/phaX4rx6it3DaaFMAwGbW7cvEw+6nP9xhLN1iSffP5FtIHECYTK2Oz0caUh04oyL9FCcevObbK0QpEXKF2hNTdPmk2RVOucefZFbt24gh3tkBeambkZrl6/wcL+BTzQbm+gZPDW6w17LNYPc+jYIa7fvEVhPB979BTn3jvHG2+8wdz+BY4c2c/WZpvuVpczz7zAoPTcvHYBnaXkxpBkVSZrDaYaU3iv2De/n0Zrgq2tNirVZElCb9jFW8iqDVQSmhfhDd7HFLS8xBQlIilICPIkKyFRCQpFUfTYXrnDVqdLZXKKiXoLnVWD/FT6wEgKZVyYVPoIjEiFlYrgFRHAGOliFLLt0zcGFZtr7wRCWLwvcMKgnA4sFeXIKgLhHIlWmFoTkwgmpuYRnass1O6j29lm7uQzDLeH9Dod0sk6jeYMQiR4Y0BGgEaMjYYlJsolhTegQq8RmjeDB0pvAROllwFAEVYFCZxQ2NGI4WiH3mAL5Sw6qZLowK6TMkiiVLxOvAhggxAS5xRIHVLKKioYWFuC0bYp8dggz5EpuMAawJu4WOtIughJqIlMsd6FZMNhn8HOFq63jaxUAyitJIkIDX9ve4SuBAYaJgAXiRCUHrRQoUAvh2gLuQxBGMFZ1tEvBlhjqFbrJEmKlIIUR7AFT0CrEF9vxnWBwRPADyx4WyJF8DkLzazEBg1PAMy1j1PlYHYepFKGwpbYckS/3abMC0yjj85llGJZyjJ45ngVotB1kpAmDeREjanZFplSJNWMis6QiUbpwPqSSiNlmN5LqWPYisMLh/YCJ8dDFL078PJxoBJKGI+TkV8QSwEdawbGLCshg9xQBIN348OdNEjGwQkZQDQRvOJ2aZTeIXy4JnVicQKSVKFlBS8dWikQlrwYMOx1GA77FHmBzQtSDVIritJR5gXdYkBe5gHsKS1ZVeMdFKM8eA6VA/JhTjHK8T7EqpsiDBSct5iixDqPs2EwmaggtSmtxcsEldSwIsTAl6YIa4orwR/G2JxiMKJwBY2sSmEMpbGkmcaLmCKpEpROSFVCvyiDn5ANPkolIoCekQHivUQpGJYGhwprBQEYkiKGz4zZdAGKxhCj7Z3A63B8TZQXqkjBF0KSJFV6o4Q/+bO/4M76Hc5duEZZhtM/mL4HGVIiRAARIkCIUOAD8C0iC8YRpLHjk8WjQmMbphoMC0IQkSlCYqXzWGmwPonncIiqdz68myOCIBK8jFJjISKkpxgOe2H7Ym0W6gkd9o21u8Cb1Arn4qBKSZSARHgSEQDjwJYi1O3eheRZHwYxYwmbMY7CCqpZhenJyZAuFifPLhTJMQlNxto6yJ38uIYkyjCdJ/EB5HPKBgam9YEViQMX/OUqWYV6GrySEpWGStUT/NJwSBt8rZQGoQIA5qWGIqdRqZCpwJ7MUk1/lOMRSBlF/Rq8TyAO3bUKflTKBxlXJdEB3FNB7m1dkDiGfiumk8bvZr0jBQZ5TkEA9CZrVbI0obCObndEb2TIKpXIyI1SMGCsbNi1AUk9lSwLiXGekHCLYpCPWF9fo1LJaDSa2NyEQQVEFqkLA18RQH4tJFJYLI7b7Ta1RkZWmyIRwVdOokCljOGRarVKo9rAl5J2UYaURy+xwkbLO8VwVHLrzjojL/FhsWdQOLZ7eWTOaRLtSLRjInV408cD2nu6o5K765tBImosvW6P4WhAogRZmrDRHdIr+hhsSL0UgLBoUpqpovSSRCu8C7K4NMlimm9kHMp4j40DIRf7SieS3X3+83r83AGlT/7yb/DlX/80dbtDvXuOl15aQlSarKx12Gzv0N7M8dUew8E6mx048OQvcfJgk4eOH2Eqg8/8+h9Qzhzhieo2P/rB3+FnH+LyuZe5fvFd7n9gmS99/gs8cN8RMtfhJ6+/zdn3lnjg0CFu37jMv/zWy/yTf/hlBqYkESASgTIBsRwONrl69SLN1gITtQlwOd6EgsKJKosHDlOrNFi9leDTFgjJYDCinHuaX/nFF1nsvc+bxw9w4U6PfNBh8fhJynybq1Yxc/gkw06HXnuVdy+9T7M1y6ToY7prbG9sIRsNHjlRY4uM0pkgi7CC967doZNLFvdNcfHyFSozs8j+Fq4saNaa/JuXvs7dTsrphxb5wY9f49Nf+h0mRuu8de0WCwcOM9Wsk6lgDhZM+mxMDEkZbq/gypK8O+LJp3+Rj794gMX5SWppikAxP3MA+Vg9NLVPfZIzTzxL3l/l3Kt/h68s8Jtf+gr/+k/+O0qf8sXP/X10uUN7dYUbS29w4P4z3NnYoN3r0tp/CKUkg0GXWmUCVFhIdZpy6dIF/vyv/4LW9BzvX77IxnabWlZBVxTeuOC/QJhafPghdpv0sVeNBKQd4wEi4jJuz39oTFr5CGgxfvg9tCj8vR/rR328CRGmBhE18LuLGbsJZCpOVsbUXTUuqCMDQIiwbXEoFDyQxO434t4UOh8+bPdn4CPSuPA9AtYRTfpiEyiVChGwNrAQHODKgrW1Nc6+9gMOHzyCk5ITR0/S3mozMIYXP/4UrjTs9Prc7XQC3dd52hsbvP3eBZ5//pOx8GC3meVeCdp42+I2KfYAtl2sKBY64aUf/m57ssBdhUj42UUzp7iP9vCbscxxjyF2z0eFxmy8Tbv7WOw20IG+vOdWJSJQRaR978n79n7Pvd9pDHKOn5Pinmc//I/gzRXev1apYHwaGx9FaQyt5iTPvfAMmCqt1hSHDhxiZn4etKRabTDdnGTp8iWcLzBesLm9yb65WXpXl8n7A3Y6bQ4f3c9ks0W322OiXqderQUD6qTK9MQM+2YWeOKxZxm5MCVcuX6b13/6Gr4qubNyHScMOMef/qt/Qbe/wzjiOEs1+WDIO6+/wYXzbyCkZ6vdYeXmGmeeOIUtLQ+ceJCv/vmfcuGd17DeMxzk1GoVRsMOhYeJxgyTzRa9fodhb4QrShrVBhVdoVNuI4RgcX6RixWFNZahhbRfstre4N3z79HvbXPt2jVm5ubodDqMeiOq1ZS8MIBlbaNDpVal6OY8++STvH/xXfrFEGFSJhtNKrrC3XaXwTBnbX2Dna0dmlMNLi2dp1Grk/sRqEB7HvT7lC3LVHOSZn0CU1q0TnEyGH1roVlf3+TqrSVubyyztrZGd5QzHPQoJLS3Grz8wx/yre/8O2amp7izco2TDzzC17/+dTbbbZ5+6inm5+bBe5oTEwgFDQ+DUZ/J6UnKwpCkCT/5wQ/58Q9/hJWCrc1VHj39ONNTC4GKH4HjXZ85IUJajwCkj34lwUtGiECpHtmCXHhKH69NQTBBliC1Bh/Ox3E0shTBcyk3YwPHvZXJGcPOzoitrW3aa3e5vXKFx848g7dw89YKw2LIqChQaY35/YdY31pFqpS11RWSNCOv1Jg5eIjpQ8d4/52zDAZLHD56nJnpFu27EywcOsDUzCTnznWp11JkBeb2z6MSFbx9DHQ6O/R7HaRwHDt6hHxkGRYFBw8fo9maY+naRW7dXiHRCZvtLabnFphAMrtvjivXrlDxliRVNBstlMwoXU5/lLN6+S4ey6SQ1Cdq7JubpbvTCw2QczHd0odmwdpIG3d4LC7VpKqCE4rBTp/+9ibWDlGyRmuxhVYVtA5MC4kPscGxsbaRdi9FSPVy1uCL0IjnZoiRJdoJisGIQb9DWp+gVquTKIlIdLgHCoFQKUpkIKDIg4mpMIIkqaNqCi0aJLrK9vZdpmbn6e9cYaE1jZwpKE3Jxkab9aU7TE/PMbdvDi8FuTUUhCyv3IziGgnGCYQI0iehQiS8IJoZI8FFw1MR0nz63R027tyizPtMTrXQWlLRGWUg6ZMBWrjw98gY4BG+V5B/hZSoMP23WC+wkQHiSoPWEimS3UGLwIAYAxEWYyObwFiK7R5eSXQlxdqSVFeCCW6Rk4+6uLRGszWDFwpvLSpLQQU5qoXQXIgErwyegKIoJBk+AFwEI9/E1ULDp7IAgmAQGDwCJSqBsaMTwOCkJonyK6KBs6u6yMARmLxLkRdUsypKBr8pYxxFGQCk1JQMXYGWITFJeotKDGklIckkqVIBINIpKq2j04ykkpGmmixNwCsKIUgJa4H1FiXHzk2hllJeInwApYngj4s3Si+CObfw4ZqwIpYqiHDOi2A6YNnzYBPCh1StELMUiyeDkGqXEagTHcGiAByNk94GoyFFWVIMDcPBMDCaVGAEW+8RwjIY5ZjC0OsNGQ672HKEKQcUxZAiz3fBXKWDXCYf5eChOxqEGjMCFzIhsvF8lFgZEqVJkjSsia5ESTCloYygnYvSOE9CKcM1XnpPmimUJrCZygJhDQKHEYJatUKq1S5DyDtHbh2VSp1aNQ0sGg8+E4xGOaa0Qb5GDIiRRCmdDwboQoYhrSsiQ8zifQBJpNSkSoD1eGnR2qGphMGksiQqeLYpJTGlDwEBpcPJIMFRQjE5OUmrNcGlK2ss3dpmu1vixkmi4zpPEA2MwZuQPueqodZyLqSeMvYUDQV1MOn2gBO7aV3GS5Q3VKsKZyy2lHHwET1fiD8T2UIxCj6cZxLlIInAmZSSfGAZFH0SHbzAhBcIVQRIzQqsDaBbliiUTrAkjMV0hvCdynEaXLxGgmI81IQWx6i0Ib3OOmrVlJmpBqnS8brY83FyPofo0wRBXudiq+Jjze0IeZ7SBZaQJ3jlFAS5roqDa+klWiRkWqKiR88w7zMa9HAmWAu4yLbzTiCsoihKaj6cs3mehwFT4sgSjRB61y9IE2RewQB9PGJVkUVlSXQwshZSk6QZgnC9ep3gnCNNAhBqvENYh0JR+hJrSjKdsDg7QTVVWOdC0t2wpN6YYFjkWDmu7gO7Z9wz4WF6aoaT9x1hqzdkOBqSd3t4oQJ7yxtGRc4gL+gOhowKE9hFxJF99PcMViQOYS1KZBjv6A89m+0OhyqT2F3vIRtrocCknJpsMjU7R3NKYJJ1Nu5cQxPez3nwMqT9jcqSUWHDIAZFaRU7Q0+jGjzqhLbUhMSYkkFuMAKElOSlJS2HdJ1lMDBYGxisUgpGRUFRhuFSIqNkF0+KRwpLWXis08ynQLVONzdUE0lv5HBOgAihH9KHaz30dHJ3HSnvGf7/PB4/d0Cp1tA06GMu/Cn//K++y1J/keeeWKDX6dOcnqLZ8whZUBaWyfljnL7/EJkpcCikF5w5dQpnNYkR/N7v/7ccPnKYf/PP/huubWpG/TX+2R//L3zl136Tk4tNjh4+zubmNt997W1MMsGhfQ3efP8KHzv1IKPVt3n/To9Dc/PUJ6ZBpKwu3+Grf/FNPvbUczzzxOPU5YjVu+shCrXS4tD+KgcWD1GbncFjEJUq/8lXfhW//Rr/8x/9c652NPOHH2IkK+isRlGMOPP4k5w4vMBLr55nSErrwP08/dAR3n35O/wP/+R/5ZFTH2dnZ4cLt9v8/n/8j9HCgdBMTu3jsy9+nt43v861G7exM4d45tkX2PrgVZYuXyAbNOnaBqvXz3P72gXWc/itg1PMN5ucOPUMO3dXuHt3ne32KomTzC4cRFU1k/U6S299i7966SWuX1vi2c/8Hr/9pS+yvrpMS5VkSTXQB7WmUQ/GnEmakSjP98+e50dvvMf9JwQ3Pvgpa2sbrK522dn639nY9nzv23/OxTtt/uvnPscDZZ/t3pD33niFS7dWuP/4fUw1GmGxdWHyltWb6OkD3H/oGH/9V1/j9bde5cSXfhtnx3H3NlzEIpojxmlC1GFEIMfd08GHG4cAcD9r6BzAkI9cIPHvxswTZKDzB1DCR9JMvGGIMXwRPs7FQndcW0UCe6CCcg96zhgs8rtTj11YRohghEsENXapSmEb7Bhhgd2kilC/+bg9AcxyNhi9IoNmXSUJRVGSZhlaQe4s05MNtC+p6IzK5CxXrnyAlJqHH3qCicYUH1x8j+c/8SlOP/4xRNRwT7VafOlXvhBkF1ruSgDHS3swF/V728fe/nJxX4xJkx52DefFPYfho0uWi0+OteS7x0+IKL8Z76ywl3a9ryCaSu69q/jwxkbQCO55C3aP6t7/7X3mz7zJ3ut3gap7JDCeD38/EX8mnntCiDDJdQ6vJNVajS9+7gtIpemNchYOLrL/4BwPPfQwr7z8ClcvL/HMx5+j3e7wj/+L/4pKtc73vvNdOt0datUa3/jGt/jSl78IWlLaEq0l9UYzbKkXPHjyJI1GhQPzs7SaLYZFibHQ7u0wc2g/b73+MqefeoL+sKDSqPPc089z7fY13jn3DrOz8wyGmywtvcOld99mOx9STypcv3mDz/3yF3jy6af46U9+wFNPP4sqFYPeAK1SCu9otiqUfoC0gvtO3IdM6vzklR9S2gJkSHja2dzCWstGe539s/NkwuGKlNyU9MUO3/vuN3DGkcmUSjVldX2NMh/iTE5zqkpvZ8jWVo/j9x3gg0vX+dXf+lWeOfN8AACqhqnmAqOdHVbX7iLdAFPmrLS74Cw2LekNBySupNmawA4FolbluSefYuXmCh9cvcJo2KdRmwhSBevRKuHCu+/y1luvQGI4/eCj8Mgp3nvnHN/9xtf5p3/4h7z1xrtMNGtMNBoMRwP2Ty/w/sX3mWxN0x32yLJKlLoG+VDQ/Gu21ttMz85Q+BIrPPv27+MP/sv/jH/6P/73yHSCpaVbbHz1j/mVz/8aM/sOxhMqNHsyRrx6aZAkZDrFFJphsU3et+QWVrbWGZUlxgq8ykhUIMwLKchUinWh3U2lIpehGK9XBYUXlOVYaivCMM9ZFuZnybTkg+tXyIucc++8TbfdJlF1Tn3saaYXF5mcmefqpXM4M2Dl7m2SShPSjNPHTjI9fwShqkxNzlH0u7z39mvopMpme5PttQRRHKNaqXFt6SoTV24zt/8gjck6P/3p22y3O8ztm6e32ea9184yMTlNkjQYbne5u7FCc2aB5as3WF+5w+NPPcvkxBSajIvvX+LEI4/QmjxAYQbMz+9jamqKfm/AxuYWy8u3WLl5HWtzZntdRnnBkaMnmGnNUBSGra0+1hoGw5DMV6mkYR8qia5kOGPotXfo9TpY78mqDbJqC6VDeopFkRGaObynGPUZmCFCeoyTlMZGf48ApKS6SiIUaaJIo69iNlllcrqFSFKkDQ2JlwKpKohEUJaG/nDIaDRCOk0lbZJUQtNslSfxVWYmDY+/0ESn0+TH1zjz6AQ3ru5wdd1RNmcQckSn36V7dYdao0VWb4CS5MoG8IhgZupUaNqCYWIAACAASURBVOB0KtFaYWy4D8XLBSEgH5YUoz7D/oDC/D/MvWeYZNlZ5/k75pqw6TMrs7ztrvbeV7fU3VIjBAh2BhBmGMxKg50FRmMYhkW7A/vMDsvsM+wssGgHxIAkhEbIjrxQt9qrbbWp7i7vszKr0oS99pyzH86NrGo9+2kffdioD5mVkXEzbsSNc973//5Nn6iuqU/MUVd+hyhNTqgqn5CRZMz5REzrLMZ5yZREoauvFuONoCmxpcAUOVGsqwQbi3MphS3Q1qebWgFah96kVQc+Sa8Vcam7hu3AxOQkcWPCR4g7S1LrkA375MMeQaNJ2Brz/lTW12ZKSS8rMJJAOjJjCKRvjIIqDUprL08TSuAUjMyPNRGGEO8DJTEYhMgoS4MwPvlJuhLl4+QoKmleL+0yGAyoS3+tFcLhpEKLiEhBramIZY25YBIbB4Q6RGhNqJVnFWkvO/NQgPdDE85UMovRhiar9tDvdRrPPqIC9Ua1x8jE1o7kQeCluDgCFFZYymr/NqbECYFwgrLa3W0FNyilPdtZCVBVAq4x2MJRlH1ya8kGQ9J0SJL2GQyGDId9ymxImuWeGVJCkRakWUpucowDV2bex86UlCOmSdUMWleALUA4lJQEKiRQATpQDAcJWTr0qYy1iDiOSYZJxWxydLvDihHqy5JMaEJdEGiFb6c9OyqOQ6RU9LOcrHRIkSN0SGFHyZlVgrFzlTTRUDjHls0LzE1P0oxCeklKkuXUajXCCCSKAkOtHvlUPyHoD7qkeUkURmityItig2kEHpBUzgMXzngQ1BhHGEomWm1qtdBLWxFYMSCKM4RtYk2AQJEkQ5z1gJoCD/7gvZAcftAwPT2NEoaVtS7dtSFZajwDSjms9ElcqoqYlsLLQ/364QfctvJ/MqXxKVSislGQDlRlT229dFHakompiOltGRfPOHodQbMVkCSGopTkJkfZyF/HVViFvUKq4IACi/azxA2PQFsaRrJLrNsAO1113eelpaa9dAoHhSm9jFL54k46D3Ib8JLyCnTBWMqixBjvn1kLNGWeU9iMzJQorX0d6PxaIKwlCPywU0nl/cxMCZQ+mc5ZzzDaAD/8OiSd8OmizoPP4gowbaZdR5T4hHIlCaLQg1OVDYh1jiCOEdLR6Q9J8wLhvKwSLM46gsrDDucZTkJ6QCvD+7SFThIHmsIV1fkIYh3ihCQKAsJGQFyrkec5xvg1NNSKPM0xpiDPc5SCLVNjBFphypKkNNSCiEFgSbOsGhKwkbyJEFUyHygVsG2mRaseMt5uogh4PCnIV4aUWGzpGKY5QRjgCkOa5lWSXgmi6p2sD4nya1kEpUGLkBLHpU5CLV5mamYWUzV6yuI9oPCsNR2GNAPYtjDP2toalJ1KjqxxWHCeaZkUuR8wBALjBOtJySanQBQ4o8mEwZUSY/x1KpxnfK8OUvpFTiAdRV7QT70cszQG5QQNHdA3YKTAFgbhtP9cS029VqfnSnZFQ1rtNv3EQloinAFbeL/RqjeRI0m18zYw3mj8e3f7ngNKvZe/wDN6jec++xmeXm8zHhzlk58+xMTMJlpz25mbaVIMulzMNdNtRSswTCxspaG99ClWmnywyOrZw1w100Z2XqIxuY9mscbTL7xG3Gjy6U9/grp2OKdJCsvd972Dp598jDNvHOThu+6lRp9//e/+Ew/+1G/wwl//70zc8Y95753XUpvbxf0/fD169RB//Wf/llatgdRTBLO7uOWeB9gmBGEQo0oQNUFdxxx562v8p7/+O3be+oPoV58l0oq7r7mGrbOTrDLgsW9/g9cv5txz4F10Vy+xa2qMlYtLTG/ZQ3PbPr7y6mne9cgP8LPv2sLypXNcc/VujLAopdm1aw8/+Mj7OHL6FLPTc0yka5jWFC88/xKzjVtoj00RTy9w7uhbbN+8h7/72J9z66238s4DDyLyHutnz5CVkOka++JxtgYx33n8G/zZJ/6Gn/mpDxBM7OOed7ybE2++wMqFs3z+yGs8+MiPsWvr1SglqOsAW1p0LUTqGjdccxNHDp/i+NFXeeKxz9HpDXHSMZY6Lq0Oef7Vw4zN7UGm67zw9BPcduD7sPUZRNThox/7c3Zv28OW+e1cc+31tMda3HrrbTRnFiiSPocXL7B+aZEnnvwq97/jPRRJjpByQ7o74ozYkSabUYFzOWL+shH3ZXM9LeSGYfXGtAyqSf8GdWYj5U1UbKKNm7MVYFSBS+KyEM0bPV7pC+SqD81Ihw0bbpmCCrzyYwdbHc8DDn6q6ewGWnS5gKvApdFNSA9AiSv+lcaAqMx+tQYcsVZkWYqKAoo042tf/Asunn0LkQ3Q0S1oDM+//CS79+1nfHwzTz37FEeOvYUKNFdfdT0nTp9g68IWlJZMTE9RYAmFvgKEuXz77qS10bMdGSMqx9sS4L6b53Pl8SwVKFSNZpQQfkKL52TIkXyMEXvIbSDr/r+VZr46unOj97iaJG1o2N5+FuKKb0beR1X17Iuqt5332x87agIvY42XmVujH+vKr8ZUz2HEQHPWIXSAc5Z+Z53zp8+xeP4U+TCh3WzyA+99H0EYsX33LuqtcWoiojMYMjM1xcsnD/GNp77JOx++D6nqKJeSJyUTE1OeFmws7WaDJx57nPXhOr/xq/+UdrONEpaHHnyQP/2TV1jYspPDB1/ik/2EbJgTRnWmp2ao1xtcXL3E5GSL1dVV9u25lvjSMt3OEncdeDfHTx5jfmELCzu30B5vcP1NN/HNJ7/GWBQitWLHjm2cPnuC/mAd4ST5sMDrYzT50LC6ukqe5MhQcOLsGZwtKIoQiaMWKFLjSJOcdlxjmA2IayETzTGSNKffXafZbDLsJvzLf/7bnFta5sjJ/8z6pWU+8qd/xOTMLGPtWfprPRbPnmTz1u10ugkXV1ZJhj10LSAoBlVzoJBG0EsGvPc9jxDLJi+df5HCpXzqb/6WG265jZ07tjPebnPyxAk++elPcPTkm2zfvI3Pf+5L/Jvf+h1vGiwtSM31N17HpeVZTp05zfPPv0AQNNi1dQqpFc0w8vKoSiZiyhKEYqI9yYWzi5iiIBCC0MGe7bu4tL7G1fuv484Dd3HxwiJnTx8j7ffRcxJzhdeEo8Ra4WnoEpKsxEjDmXMXOH36BI3GBM3xFspVEm9TIm3NS0ekQGgNhUVLjS0d0oBwBcZIJhsBixf7CO0n8UhHKWJq7QW0yhEW4jBAYji7dJ7tu65hbvtuls+e5Av/9c9J8i4L8wtQSJY7l4j0OIunT7KweTdOS8Yn5lhcX+fmm++g2ayxvrbO8WNHWR30GZuZQ5+5wPLqOnNb99LvZ0zPzNBdW0OKglpUY9hdY2lxEWTkwWstOXvmNO3WGOHWLUyM19m+5S4mp6fZt7+SLRFi8pwzJy7Q6+VEccTC/GZaYxOAIkkTwPHmyy/z1ksHadXbNKdnuHr/TbQmx5lSirX1LslgiFCCQIf019cZDAcQaOqtNmHln4CAAAfWQFlSWEhMgrMZg/UOgarRGJ9CBZowDpA6hED45CqrKqZtCkbiZOmL+YpZ6QIIVYwoDWu9NZYvXEDLgLhVJ4oayCAAC0WpCJRkXOXctw/eefd2lldC0h6cOzXGjJW83utzsScwNGm0Jqm1DMlghbXuGmbtAlEtYnJyGhWGOOG9kIyxREGEM5ZhXvkvKE2oNVme011fpcgSSuEItaJVa3hPCAriimYfKI2QllD5+Pa0NGCdp+S7ygTdOS+jNiXWpQhyz5xKhmSdPrU4QtSaWCRRLUDKGOUUUloPiqAwVmLTIf0spTSOmqozO90iVJrSFljhG0QpAuJwmrg1SZ4O6A372DSnXm8T11toFQGuAr28J1ldB5S2oK7rlKZAx6GXgwgonfZNYJFRmoLEFFhTMux1cElG2Aipa7BFgRWKEEmpDCUGoXxKYL3dZGp6AWEFWgfIOCSQESKMPRNLS7DeZFXjmRwIH2ceOt8Y2moP9D5YvvmUcmTs6z01HILA4tcJ4SgQOBGgqcxrq9pHWIcQPoVQWkOJI5cgSs9eKfHsuwKHDmt+/zeVEYAzZGVGnqfkSUoxSBkMBvSSLmm/T5qkrJU5Kk0YlgXCWKR1FJXfFNJ5BpsQlFmOzQts6SiKkrzMsdIzYqRQEIAKAwLAFpY0zTzsY0u/cgYByhnKUpCmKUmWUOaFP8dQEU00/HBQBySDrveerGqH0gnCQNBotAiDgLRIKLKcMFTUavXK6FlDP/HNuw4ICHBSEkaRl8YZ768jJcT1Grdeu5fNcxOI+QXKvOSZl1/zXmUOuoMOpTBMtrdSlKlPO1QhhSuItaSpY4YWcAaDl2IGgaYsPIBnhNsAEbCKQT8ljKEWCpzVSGlRQmOF8vLWSr7qqscZA2Xlq2IMiECighrzC1tY6yVkSUG9JhGyhaxMkQOlkEAQBL6u84sZAljv9kFUUmxr/WdJyo2KUFrv14lz5K5kcrZJ0k2pxZLOygAra7RmA2ZmYk4eGRAEvhbWlReOt7nwgxBTTTItFue8Gb4RJaXNvVE2EukUpXCUzoM3WkpEleZnnPZS82otyZ3AGjCSiivsbR0AhFAUlIROV6wdsK4kN4619QKpYZiX6CCo/FqVH2A7yXgzJggUzkpK63Aj4Bqq8wlRwhDEAUoGRFVsqHOS3OUoqcD49SAtCy6uW1a6OViLMQbjJEqF3ndRlARRRF5Yn3hufYDEMLfUY+XTIosq4bXMKaxfQwLlQViUo+GEB9GM3fCHwjpKCVqWBFKSZynSersAAlnBydaHP9QD7NDhlGCh2fJBCkJwfm2Aco5mQ1FYi1ASTGVj4vwARQBOeZlqMw5ZXVllarJNUTqiuge8qFjWYa1JFLUZDAdIqqTOan22FaFAqgpGV16q5mSwUd8nuWVxtUPcbNBstggqTykjBYEQBCjvbSUs7XrA5oV5jp3ooaVEWYNw0rPBq+gUiyR3Xq7bGUKSVIN3A2AodUaEpBAWKaGXJIiaRmBJC0hMEx05kkGn6gWl986qS9KsxBUJQnrgb3amzXhtkhPLfU6tZsxNW3+NOIN1AoMgIPT1qJSA9oMc4xOEa+r/5x5KQsd8+bFn2HvD/SwcPMRw0KfebhLFEVvn2/SXVnjj7CVmphucPXuax772JXZt38WuqZ2+ybYlJ86c5C8/+XWuuffHESsdrnv4Z/jhccGbp47x2vOP8sbBQwglGSYpUSG4cP4UD77n5/jOy9/k2TdOsmVsM+//pV/hzW/8n3ztlSU+8APzjLdCHr79WvpLR/jkU0/x7PNvkkTz3PXQbdx27R3s3ToPxlCXlhPHXmP3/qs5e/Dv+C+f+xZLeZstwC/94q/xxuGjDMsuaS/l6KHvsJ4U/KMf/odsn23zwsEOcaAJGxPUJjv80j/+Rc4sr7J90wyTjRpnTyV89etf4Sd/6EdITUI9brBzfpwjb77Eq88fR91xN3fccifdlVO89MJXmZq/mnv3zfJ/PP0YIojITcGRtZRXXj9E0bnE6eVLbNt7Hf/9z3+QhakJPvbJv8A0tvDOd97Hn3/8T/nf/uATSDPgTG75gz/+I3RtkrOrln/+y7+OCgKscxTpKmdPnGZibhtBWOfOex/gqe8EHD9zEWP8Ipv1UqZ3X8s/fN8jrOeSL37hi3zmW49z88M/yPe96yEWl2/k9dMnmWtG9C4tk6ZdWs0AVMTcWJtwcpo/+p3f43/4nd8maS5wQIVYlxJIVZm/jdIWKoswd5l9NGra4QriiPDTjxF4cxnvcBsA0OX7LoNKbgO8uWzc5x88cuaWjLx+JBtiJ0Yyr2qA7zcAWYEWVzClrgReLn/vNhKTLnN/RvcYuNJl313+W6Oi1qc/4KF7W5HTrSMOQ4b9PtbA6vIqf//Y18nKIWNByKXh1zHhFPXmGNs276bebHPv3QfoJj1cVnLk2GtMTy2wdcsWSgc6kGBKnA4rYOVKAGkkRxSXz6tiT8nRhlq9jCNTRDbkhJWkrbrfYz/V/RvjUP9Fju7n7SbmDi85HD3mSvLQiFg0+unl9+HtX99++y4mGm4DRLt87BFcVXljVcynKxPo3KgA5rL70uh78JMxVU161vo9nnjsm7z62ktcuHSJskw5s3yKn/jRn2Z9tUu7NUk66NOu1TGF5f0/8g+wQrHv6r3MTszy6U9/iuWLa2zduoUH73+YIvWNvrWGQ6+9Tl6UbFuYZZDlfPZznyAk5ZVXX+fnf/7n+chf/CWnzww4ffY4eZLRHp/kxptu4LprruJr3/w6WjpuuP4mjh8/jQ7BqZB2PM7Wq7dz8OWDzE7PkQ5y7rrrTvZ+aQ/nz50hT3K+/ezz1CJJTUoee/JRn7AkDWWR04hrmKzwjJhaCGXGWqdPhkBKhzWQm4STZ48RxRpTVJMYobnlllsZ9lOOvnkYFbb4kR9+P7/7O7/NRL3NSy+9SFHC1JYFJsfmeO6F59iyeZ4iNURCEamAVIWENkAZ5+NalQRnUHXJa2++ypkT57BJDoHgS1/6FI8/+U3e8cCD3H33AcYnpmiPtTDO0Esy2tMtji4e4Sff99P8X3/yR6xcuECt3mDfvj2UX4RzF8+zvLrMNXt3c989B7jjrgPEYQ2qz2igFeePn+I/f+HzbNu+k61bt2CrKTdKs3Jhmbvvf4AdW7dz620HKIrcF+fWVozNUcCArNY8iw5ChC4wqeHsmSNMzM3ScDXOLi4hBIRhk0Br4nqTEIVTXgJX4qUnRjhcKElW1lhfW2N8ahoVjyNNQVYugZxAUSNJu7RnJhmbbHNx5TxxEJAnA6anphisrdFojHPjbQ/z4rNf5brr7+X04hJTsuDMqeOcPnOYwjjmt+9lZnKcKFKcOnMKWxZEYUBRFhQ9y6bd80zML4CzLK5eYqIW0uuseePPYYf+2iXGxqYZH59ChSGbNm2i17nIueVLhHEONmR9aYnZma2cOXacWnueYXfIloUFkJa0KD1zwuT0+gM662toqZicmGJyahMTN9+NdoJekvH6odd45tnHiaKY2bkFao0maW/AMB1Smpw4jGm3JlEyIu/1vYmxjkEqSicwGrQSGAWNaBxUyMTUXh/tK7ycZ2SLaYQDO5J8eRDJKc0GY1b5hq3IUwZra6z3O+RZl6gZUq+N+cQxe9l8uBY5ZhqSvdMtwnLIc0+vsL58lDfWJzl2fJW916wigxnq4xKbWrIs9+mfQZ2pqTr97jLra4skSZfG1CZqUZMwCBDCUuINbZXWhDIgTRMudZYYDHtobamHMYIALz1L0WGAQhDKnNJKbzpuBYuLF0iLkloYI8IAVxmLKqnRFSvIOYvUoKxCBzUacRMxOUdpSwpXVFJOhZIhUkjKsqSXZOT5EOk0OlAEkU//ciogKi1JWRAoD8QqoQGJFQaQRPUxGvVJOsN11lcvUZw7w+zMJsYn53DCkcsS4RTDpMcg7RAISU1pBi7HlBkqN5TWR9kHwlEKg9KSmg5ptjVyvAlhTBRIojBEyQAZxGgdQKCoqRCEInceQNHVrlPYKjiEwoMlRhAq7/dpACn9sCLA+/ZoJEKoihk02vuqAUe1EdqqNpJVIp2wfl8trAehfH00shf3VAEpJFY7IumPbbXA5SXaWDpJB9IB3f6QYW/AYNBnOOgyTBOSJMfkqW9Uy9KzVSgJFDgrKHRA7GTlAVx4UGTUWFfME2ML8jwhGSRoHZGmg6p2kEglycvSpzcVJVkV7SWlTzYzYsQi9swEK6x/nfzFRem8Af5wvY/U3qBYRxENoRhWZtvOOowRZIUhzws//JSe9VlaS55m3gDXeBCnVmuQmwKpI2QUUzpQRERBgHQlrXaL6Xab2eYEudEYa0nzDNWKMWmBNTk4wUrnInlnnVZUIwo0sXJAiVIRQJX45oesrihRKsASUgJOFIQSakHNKwDKlCQZYAmwtiCO6kjpfXo8W+OKgalWiLKSMyqNE4p2u8ncVJ23ji5htKPVqtOoR6AEcqSKFHjGXlXXaa0pTInDM8QsI+uKihXhfO3koGp4HTrQREB9rECFkrSIcbJgbKyFlnUWFsZ8JVY6hlnmJWZOVCwjhzB+GGmd9T5gtiAUDbJSYFAIZ6rf8b5sFi8LFDoAJfAhyAqlBI7CJ5OXruJpSuyGTYLzRtGwIQnOjAfEpQOUN38Oaw2Mc4RBXKVGes+xelRDa3+AZqNGbH2anRQFsdLooE6SpQhZ9QzSS9XK3BJrS1p6I2hTGlSgiMOAeq1JEAUM+31KOwQNRVkQRTFRoLFGMCgsKEE9ismyrFJoKJwrvSLAOh/2E2h0oHFoMB6cK2yJsYYQD6YJhR9qC+mldxUTTwlBKGQFOI/cXi1hI0TpBs4ZSiznlrtkeUYtiOkkKVTG8hLh/W+FH4pJB8I6ogqs75eKxaVlFjbvwOQJpTV+GCEsc1Mtdu/ZwVqnz2q3R2dtHZdmGDvyYhr1Ap49Vwh/LRhrqIWS3BqGQ8f66hq1MKQsNK603iNQOJBlRTJwYAzz0zUWl8fJBx0K67z8TXhgXFrvnqckxKFAiDqDLKMeSS+fLiVDazzQ5/y6LcOQUHlG2rbt2xDxBEffOsagX9UDLkcKRxgGFKmmRFUiHoEWjoKEqbmAIBkjKy39fkJZFBBo//oKi8J/Zoyo/K6ApEhoNib/35qk/8+37zmgtG+uztMvP0dcTCFEm+lN45w+8Sbd9YwLKwWtqEA7x5GTl3jfT/4i68uLnLQxN0tHbhySkrqU3PHQj/G+d9/G0UNNUiRb52aZqxtWXsl5dTDA2ZDuICGIa6yvd7iwusgP3XcPRdlnbHKWM3/26/zHTxxDzLR57NMf5/SjA9ZWlljtlhy/0GVYKrbvnObAVTNM1xIiUZA6EGHIS0dOMbPrOv7+pUvMf9+vcvPKS9Rnd5D1ujxw7QLnzr3BM2cUhw+d5NDZJWzzSd574HbeOnOe/bccYDKUlHmbo4efoD23my9/9iNcWDV88IO/xkQqONfrMl2rU+Q9zpw5xNTCTmqTcPjoCW7ct5dTi+sc7mo4/SzHDz3LRH2WtdWLFBIeuO4GyizjQmfAILMUhePVF5+nv2mau267n727r+Ls+WNM7zjAVCNCqzrJzDI/+iv/luTCCQ6dOMdzR47ywE3X4/I+X/5vH+Px147yUz/xAbZMt9k2vYlbfvafcueNN/D7f/gfuP+uA3zuc5/iZ9/909x+/fUIoXi9FvI/vuMHWJieoixy9u3ezXW7d/Plr36WT3z+b0lKw23X3sS23bsYDHuEExNk5ZDf/de/RRhH5MO+33isd6eX1i/UMNInj9p7DyGMPIyEqwCjqsG/nPg1opy4ash1BSQgLjOchB2ZP1/BCBrJuEb/p1rMAEZaYv+tp0JXu4lzbDxnKgmYhxUquqaomkBrNjCr0TkBFRVeVCa4fmNVQvipxuh8pQeTROmq51SBXVJWcjNLkizy+U/9MY14nHKlS64lsYx58L6HWO+u8/H/+jF+8JH3Mj01xT/5Rx/g1NnzfOUbn+FXfuFdGOOnc9rmOGsQNTxyLUa9jbviKV9+hTbYY9XE1FYUeXHFGV72g7oCTOLt1tcb4N8I5BtpxKv3ZYOpVFFKhfJTKa449uj4/m0Qo7cDeWVMzIgRxRWgonv7m+7BrivOsWI6yQo885ryy9fPxjROiI2/K2Rlolsx0KSDoih4441DvHDwee6++z6eePzbLC/2WF9d46nnvs077nuYQb/DoTcPsXXrZpSMaI+Ps7R4gYsrSwQIJmemePXYEQ7suIdv/P1XaDQa9PtD+v118jwhrrX4+Ke/wN6rr2Xfzi38xr/8EL/xoV/hK1/8Mj/z/p/gf/53R9mxaYHzq5dwpeXVl17koYfezY4t23nx5aco3SSpSSkuJWyanmNufpp9V9+MrLX40D/7LX7rt3+T6264HlWGCCuJA8nY1CRaQq+zAqbEqBJRKlwG4WTMIE+RgaYsM2pEZP0hQaQYmMRLVwqLiCR5VqLRbGpPcsP1N/ILH/xlDr95hN///d+j3Qrpddd57fVXIQyICfjgB36Boih5/eVDaCHJOn1Sk9Bo1UE62qEkCqF0AiszGs1xVjtdlLSsnj6MJiYLLVoLwiikPT3BVdfvR2jFM49/m8PHDtNqjnP6/EnqjRppP2dp6QIPv/s9TMxNI43giW8/hSv61AMNznB28RxZCfMLC6RJirWWKIx58u+/wRc+8yn2XXs1QVvz5BNP8s5H3oUZDrDAxPQUZ186xeYHdjHoJ0RRAyVGmY/Os6uwoHzRroKAc+fP8jef/Cbbdo6z96qraNZDzh5f5JZrd7LeG3D2Yg8Z10FarCiRTmJt6c1G8c1iLBU2rqFlissMIYZhPgQXIExJo+Uos4Tl5WWkHKcZJigbUwQF/X5COujz1vFjXDj6BtaWNCcmyY+f5OLpo0gM7XYIdogsS5bOLVFrjUEpWRmusri4yKZN0zTHpjj+5nHOnzpHfbxN0j/Gwr230K436Zw/jymlr04CS9RSnDlzmnzYZWJmjM3btvHi888xPjXLzPQc2io6pqQZGiKlGKYDkrRPs9FGBzG1VoNkOGByKiSMGmR5jisMp06eweUWJwqU0nTOLxE3x3DW+qTXiRb1ssag38FZkJGg3qyj1VgV+SwQUlNq5ZlhhSVw2qfIKIWoYtD9Yqc848zhTbhVtaaUksJalDEQKlzhMGmfzqDHMOkTakUYRzSbczg0zikQEAXaAxPK+zKtDgzPdASBsuiGoHvRMFi/wEq/RJzpkBYXqLXGkSoEKQmVJtSO0mS0p6Zpjo3T63dI11cY2osEtRaT7SauEFil6PTWWTl9BhUqmmMtGlEIQlKWGUqkvrmQElJHKaAnFVJrihKkCojqUzR15KfPShLooJJl+qbTUlIUOUJYhK5hDUhKL+2wmjiIMAhyM2Tl4nlkkkE9QgR1alGLQAfkQuLQaGehLCmxBFp6NotQQAAm9RISpSlFybDMwzDbAQAAIABJREFUicKYaGaaotvDpF3WV3O6w4wwsERlSV70yAYJJo7RrQZRoAjCGOoBkWpQD+uoMEbEgTcc1wEoiVWegR1KRS4cYSkp8Ixs7QylM14SVU3kR3J4qTw7StkINdJfGAehQFnvZeVc4f1WVFBJcXzDI5z/HYHAicoEfiSVGdUuUqKV94D0qWjex8YZS1lmFEXGIEvo9xKSfp9hv89qd41iMKCX9CmKgmGWoguLdY7ceVBFSoES3nTd4+GGQPoaShsv8zCixJUFfVcirSMvh56VJbSP/w7rSKkpCi+nTJOUMPSm9V6eJTAFoPw6KYT38ZJ+EllJW7xXnKvAH1HRyYNAEyjpEy6VpDQGLSUDm4AUGLxJUxBGGGvRSnuvJ8RGDD0IitJhrCOuxaR55uu5CgTUQUAkNaXzkfK2LMmKgsys8pXHn6QWxCS5pcxyZieaPnhGCXLnBYPDosAqQYZFO0mkAkrjyMvcS+Hk5frVOShNjnY5ykRIERKqBkIIcpNQC0tKl5OnAUI10UYTCvyQVWhwBcIplAywla+Vxvu0lBZ2bNtKrCXdbg/nHEVZIpQXVVJWNXtVfAlXve7O+3oKazZqa+NAK0kkgsrg2mIlSGWxxl+rvV5Juxl6g+3cUiDRgfcLiiJFkftzD4TCCln5z+Blf0pS4I2QrXMoEWKtY258HCd9YIwSqmJgAVQG7lJ6L1TpSPtDnCsoiVBCkskCZR1GCYwShFYRhJoolERBiJCaWiQI9DjG2o3h4Ygxr6l8WpXCUHrwTDg/DBDe60oJg1dbKoTSCCnQcYTTCrIM8EwSW2ZkJkAoQWYNARatNTmCUFhCCUEUoDONzQ3KKYbDFNWWjLdb5GsJwyyjm2UU1tKuN1BOkoscJywiFNRk4NlNVjCswhoCJ5Ba46wgFNrLFKs62FhDWRYEugLwK3P1EAmlRYUCIQJfu9QaDPsp3fV18jQjimrI0K+FV9bvVcFeNRGeUbnW8TLUphZcChxRIJiZ30pcFpRCEqIRKkApR3s8ZmysyUkHvX7XA9ZO+PW/2oYlEmMMTkkmG5Kbtozz6JELSBewstajVasj4xYGz1CSSmKcYtDrMUhy+t0BRVEQuoJcuoqdBHJk0SI8gC1LLz0trWF5aGg3S6QIESJC5wYpva9RWZbUpPUsytJQnr1Ar3ucfpqjKhV2PW7QmJilXxjK3jKpLAlchMPSW1mjbJQgFU4aOnnOerdHLQ6RVqI2+ktdfT58Ol82HHD1/h00xjfxvbypD3/4w9/TA549/8aHz2RtnNGk9RZ3X3s1aa9PURvjwEMPsWVccalosW/3bk4vrvBD730vO8KM8ckWK0eeQ+oQIYasn32VM2e6XHPVNvrDjK2bp0iLhPPrA452oFuWZNkAU5Ts2L6b7zz9NDc+9P0svvgVaoHk4199hXt+4V9x855t7LnrQTaN1Xll0bL5+gPcf+NWXn31Vc6dWWXqqjtQGFpTk4yFIVGjycTkLLdsn2L3Vdfx0J5x/vwjf8Shtw7T0n2+8qXP8oVvv8b2bTtY2L6Tuatu46Zbb2b/QoOTR95g3/wCLz/3BFsXpnn0uYPcfuedtGZ2c2S9x9bt+7jn6l1EKkAjydMB33j6eVR7ju9/xwFazRorScKDDz3Eg7fcRi8bUmvO8OrSGj/xTz6EWV3ijbPLDNYWKXSTnbv2ImxGs6Y5cfIELzz7bRKhueO2u9m9ZdNGh2/DFjfv2YlKVyiSLvffcR+HDx/k0cceQ8/sYWZuM0JrFi8tYXVEI4Q3jh9j/9XXszA1w+Ztm1nYsZebrrqK5c4aO7ZsYaymUU4SVsZozgkatQZHjx5lz85dnD97mL379rN8aYWJ6c3UwoBQamKl0MInLSjl2QpK6g0zPC08zFAxGqtTqIol4XXdAoERtooqHYFCGzwVRhwbn6xTGT5fyR4CP1WQEueML7LwUz3/u7AhsfIzFqTWKLw8SwpvkK2q48rRXNBVQMSIYzQCo7jMbtFKYZ3ZiG6V1d+s7MmRlbmzrCZ21hoPaFjrdbc6RErfHA6HCb/74V9mMDjPemeVsKHIckUvtfT6ayyeP09uLIM0oV5vMj8/z+rFZW689lbmFzb7pkh4hD4pM2pxzW+C4rLJubzyddv4eYUmvY3lVb2ylU73ysdsUJG4fMwNwKhiNr0NIvLjq433cWS8Prrr8ut95RvKxrUwArDEFY/bYFhd+UfEFdfFiMm2AWhefrwcwWUbcrrqmb3tvK94Dlz+sRMleZ6zaXaOm2+4mcmxKYbDjKzMWVtbpz8Y8MUvf5ZjR9/k+KlTzM0vMD87x+rKKucXT9GeaHPLXbd7H4n+AB0INm3ZyolTJzh85CDHTx9m19VXMd4e46WDz7Bj61Zuvukm3vOu7+fxp5+hNTvBtx97lH7vEq3WJI889DCB1CwvLrF88QITEzMoIVm6eBYRRkyPzTDWbLF//352bt/GS68dZNO2ecabbT7/9c8TSIOOYrZt2c62LTtZW1vHWkNhHfUoJi0KkuGQOAgwRY6SgjAISZIMqcEKRTnMcaVBKc3uXbsYa08xOb/ArbfdwXhrnNdee42XXnyOhYWt/PTP/TTT45s4/uZrdPsJnU6fpQtLdJMeDz74IAeff4nMFvR66xhrmR1PaAcDFvuW9uQ0NWLypCSUDUwhUTqkLHKioI5Gs7B5KyDZNL+Jp595knd933vBOa7dv58P/dq/4PrrbuTNQ0fYunMbu3bs5a8++lHOnj3B2nqPNOmxffcObrruZtJBysTEBO1GEykVeZ4xt3me8XaboB7zlS98kUfe814e+9rXuHjmLHuvuYbhYIjTkrmFzVAYwioiHj+I91HOjJpNb9L4ysGXSNIlduxcQEgIVQ1bWEyZEbfa5LkjzbxcrBbX0EAoBYWBwnoAIUn7FKZEqACpJLv2XE2apcShZnZ2ijiMcbljvNkiTXp0kwFzCzu57s53s2f/jZw8+gbrnWWUTOh2BtiyYGxynmHR450Pfz+XllZwLqLR3oTQMSIIWV05x8W1FWQc0m60GJ8ap95q0u90ufraq+hcXOGqXbuwZUE2WKfIUmr1mGYz5tzZRW69bj/tVoNOr0uRFTgEM5t2YFzBeLNFLW6AcoRhzNhkg9XViyyeO01ZlsRxjCkNae5TCEMVEAYhzXrE1Nw8Ak17ss3Upnm2bd1D3GpWYLeg2WwzM7OFydl56vUxtI5QQQ0jQpQKkUKTkRKgAYWVvrC0QnpDZiTSWMhLTGlRGJwrMSbD5t6wN5IOW6YM11dZXbvIxe4SCojjyDdYDspq6GKlT3kRlGTpkGFnjWGSs9JZwtghmYUkG7DeyegP+8ztvonM+eSderOJUDUPsCvp91AnETmgQAcCXYvBFKwsnaK72kcEmt7yIr3uOjL0QMFgOEBqL42QSiMCjdMxJghwShHEdXStTqjqiLhOWG9Qa7RQUYiSGhXEWBkiRODNb6Xf46X0w5KAEKG8VFMIgVYeoB4M10l6a5RZTnNsmkajRT2uo0SAQxBbiZN+wIHyzYQVVajGyCeGAuEyRN5hsH6B7vIZXNJFlAnInFgalEsZi2B2qs34xDhTc5uY37mHLdt2M7tpjonpzYxNztOemqbVniVujhPXQnSgvCm7qlLNKhCntAZtvTeUT6gVnmEkvAG2sJ6p5dPVvHRHGkWkFI7Sd87K+8aMGn4rReUTU7E1jJ+oS2GrQZRECInU0hvvlhYVaEyekw6HdNfWWF1dZfHMGU4ee4M3Dx3k4Ksv8PrLL3Lwxed5+eVXOPrm65w89hbnzp9ibXWF9c4lsjz13iG2qscwSAzWlVhbkpuEQdGhyFLydEhZJtgspeh2KYqcgUvITYK0BussVloK5zxDRARIESGso8xyBv0+xlTG4SNvxRE+W/ndjBIIPfPFjSZGVW0gwEJZlr6JrOSVnq4mPaCAxRQlYVzDFB7AVCokEN43ZlQnikpq5w1tFUp7WaG1liiuIaSkyHOMtWRlgXKSwXCAoyRQwvuv9TxI1x10adbqxEoRSOHXkOyyvNhWNgxKS2xpUChKKTGm9KCEgkCAwRFIQb1eq6Q8in5/DScshcxRwoOGDqAyGA9khBIaoSx5VpmjY1nr9LxszjhCHaIlPHDvvYRCcPjEOfKipMgTtBSVP5JnfyghCZT2nzEcmTM4KUmSFK00ubF0B33/ujuJUj7NMA4DakGNMDBYDbEqyZ1hkKcM04KGhHY9xmTaA0eBN+gvi3KjRiuyHFX9XSv9Z6MWaoSSKK1pt+rU44g4Cgm0pN6IkFIQaYlWmkgpQumFboUROCKE0CihqcURjSgkjAJqShGHIbUwoF6LCaIY8OAp1lZDUkta5lX6MXjdIOTWYqq+IdAhKgyQMgCpcSIAGXo/Mu0Zms16k3bcwFqDUgGmtJTW+J7BlkilsCiCIKARhkhbgMk9ecZarCv9S42lWY8Za8YIB8NBjnPO7x+BwtmSPPODZCskIgzRYQ1bgS9SOlTgh7GjYAnrfKKd8MUITlZm9xKQourPPIgrlSBUQcVSg8IJpNRYKVHae67ZUd1sL/cTsireI6m8dK0yZE+so92MWe9ltIIanc6AZJBQq0dsmhtjenIajMDagtX1Dv1h3zPTq8G+qz73gywnyYfUwpDppmDXzBivnF5BOEGeQ5oOKIuUXr+PzTMEil7aZ72X0BsMcbbEOmjECkeIydLKQ05SYCgLS1l4CWxhLWv9hLKEmVpATQTkrqAILKcvesWFlo56vUatJgmjOmvdhCIvqjrBgpCMjTWZmWxRixqsrK2ByQicRAgorCDLCtoNh7Oa5Us9hPSehyDRKvA+e9L/vcJBPxkyOznG1oWtFEXJj/6D/+5/4nt0+54zlD7+pTf48R/7SeJAc/z0YQbLxwhVTjuMyVaXGGvMc8ft1xHYLmtrPQaLZ1jdcQOXDn6G//VPPkNrpoESdcp+QjF2AgFs2bWP0liicIw773ov9z7w46xcPMmXv/k1nnzzErdet4VuZ5HDT32J+vbb+PZzz1Hfsodff+hGli/NsXj6IEsrXT7w4z/EM1/5S7723HeIxud49yMP8o7b97J78zxhEHtS8OAMj3/m3/NXF5vs2Xc92zeN88D7fp7XDx/lY998AlHAvr17eObZx9i0/QZ+7qfejyxSvvX5z/JfPvck2/bdzkf/5iO8cX6ZwSDj0FvHuf36a7hm5/sxOqAoPX3N4aedd9x0K43xGYTL2T3b5MK5N/j7Lz7NTbe+g++76y5emWzxnvf+ABPiPBP338fWuc388Uf+mE0zbWbqhmHq+MKXv8j1193KYiq4f/NeSlsQqhomy3GRZCzSrHe7TE3PUUSL5LrOxOxmJoeOm/bvZ3HxFNY66sEUk9OTDPKSW264naR7kadffpne6jrtZg0tLKunXidpTrFp8w5kNWVVAoQwSAnb99/Mnn038Orrz7G0eIbXX3uRrfObSGzEWK3FIM9JBpdYW75AY3yCyZk5bFkglQeRRv5BVNPcy2wiT9+WV/7EXW7g3XddhxtElBHtEa5gNPm6wzhbgU4VaFAVoLZCyUcTMPDSLeHw9Eaq6MbqwG9LQhNi4zEj4GoDOFGS0hbVfaIys76iGPJLrI9Gtd43yQmFFD7RJ9AKY3yD9PJLz2NtyTXXPcTRE6+wsMVw6dIxZNjAlBkXL15gZmqaqLTYPGfXlu0Mun3yoqDRrCEqU8eiyAmlpLAC5fzmZ/GGnhvncMXrN5rAVOv/xi84N6L8eCWxcKPXpbpPjNJ83AbzyDvwVX5UIyaZGPGWRnDSFaCVqApIHJfNzUfktO++Aq4Ala64Rr77t+wV79vootmQPbrLD347JHZl8lz1fTVV8e/nKEnL+3tNjE9Qr7eI4iZ33nMP/SLnyIUj9LorPPXMt6iFEf00o7nc5p/9m1/mbz/6t3z1619l6855Hv3Wo1x91dU8dN+7ePLJR3nokUd49FuPcfb0ES4uXQAEzz75BI1azLDX4ZWDr2JdwLcff5pjx4/TXz+HVIYslawurbJpdhNpamjU67xy8AVWOkuUpSWsRZBkHD1+mEiVHDhwH2ObtvKrH/xFnjv4En/4H/6Af/GbH+IP/+O/B1viVM56b4UsyXBOEjmLLQsCCWUJAkWkIoI6FAhykWMTQPtUlWuuvpmpqTmktrzwwgucOHqMpVOnKH/kx8kST2vfvHkTY/UxHnv8MQY2I3U5F5dPc2lpjXvuvYt9O/YThHWGZohGY4qSflKncHXa9Rr91T4du0o9bPrUlyJjmCTEOqC73mFy+3YOH36L+dlZxuIGv/mbHyIdJtSCiFtuux2TliihefqJJ/hXH/4d/uB/+T3+21c+Q1Rro7VAhorx9jRPP/U4d995L3EUMoIfozBkvTNky6693LJtCzfdfid/9X9/lHc8/ACbtmwmNQXGGrDeB6bEgSsr3xZ/LYkrmiMhJJmz3PPAAS4uHUNJy5G3TtGqS5aXOmyaHcOWjk63i3GK7qBLY6yNRXsWhIpJ0g5nL56iP+gy2ZpC2JjCrrJ26SzjrSamCBkmGevra9TrNUJT57pb7maQJMxv3szJo2+xdvo1FhZ2sLSScf0d9xI23+D+e9/BU08+xURtjGcff5LVzgW2bLuGuF2jN+gRhQ0yDIUpCVSdtW6Hnfv30B0MaY2PMeh2CFC89eob7Nm9h7eylMZYzHp3jaVLy9z70LuY23ENj33jUVpz0yyePM/YxDhKwbU33MzF06sQF1w8tcapk0fJ84T5+a1smt/CWmeZc4sn2bR5M7VaA2RAGMSVNlWTlglRuwZSETdbCAdFvyTLUoaDlM7KOkEQ0pyaYrI9RSAiHIJAWi87FSWRDH0hrgSi9OmZVYQsaZlgi2KDJVFS4JxG6xpWOAZZj5Wzp0EqGq0Wdd0gqtV9Oo9ShNob3zrlryxX7RfGWaSKqLXrqACabhyhIpS1SB1QFh0a9WmCeJxQ5+DqJGmCsd4s1vtuFLiypHSWztIqSdoljiMCFVCfXECUGalJCcOYmpLkyZA4jGhNzWCVl60pFWIkCKEIVIBUAa4ocMaQ46MJi6RP6QxGQCwjcNZP5qmm9VTxxs6hnKRwJS7NEC6nNxxgKVBaogmotafQWld1gsYYCdLvWoUUSJuR9bsMOusIJZibmSDNh0hjyI3FSIPDm9OPNTRzE1sIg4ggCnChJoqaqEASxzUINHZosBIyY6oBTyWZFuCM9O+r8hHUgipFES/jH+0lSniplBXOs3esl/+UwnisCLCuYGTsrRAgc/JyBNqUHhirmpaRtEtpz1SW1ksp87JkkGYUSU4+GFIMB3SSSoq23qEz7JMNE9IiJf9/WHvvIMuu+87vc8INL3fu6ckBM8DMAAMCEEASzKQYLJFi0IqkZK3iSpa1XsveLbtU5XWV7HJtSbtreXdtWZa8q1VcilQkaYqkmEmAJDIwGMxgcuyZDtPxpRtO8B/nvjcDUX/ZvKhC9XT3e/3uveee8zvf3zcUwRTcYivQJEJKGZi1ErQM0e9OBWlI5CwyUZWUxVLYnFatjXOevPJRiaMa1kcUJqT8Si8wNg+MrDQBD604QktJYSxOqeChpCVKCZwVGG/wOIwISQFS3KkDpJRV83IEKAWG1KjB5Krmn5IV48oHL0ulVJCGWRskcFXqUVkalBU4KSgGObawgaFdAZsjCb5zwbg8+FeGxV6I6vVREtjizqKiiDhJSOIYYUG3GkgVmojWCqx3OGcZ5kMyG6R6qdKoWFFL6/QH2+TOIly4LiiHkUEapqrml0ITqyBfjmQE3gZzZBXRL0viWpPC5CiRUIsVUaQwA4tyFm8tXgUvI+clQhRj8raKZGCJ6QiHY6ozwcL8NNcWr1A4T5llJFohtUSiwxxjgw+T9Q5bhkRMBZgygBdW2FBD++AJiApegBER1ngcBSryxMqQDfNQSZWellC43NFdGxLXJZ1aAxXVyJTFWUl/GO5JgafwFuE9kdKkOpivC60QQlPmFhF5vA3zC2XwNLQ+PG854JFoERhFKINV4COLKjxeepyTaK+xItTFhohIRCSNCGPKcP2dQyJp6BQhJVFUCxt4IfBS4LFoB84Eby/vXQVEVWAOEmM9iRD0ul227CZxkhBFChKFMlWamLf4sgQhKYxgo2tIYkUca5zJMc4FCW4lRxNISiGCVDbPieopCIOpkjyND8Cz8mrsSxVFIREvmGSHOS/WisIZFBXzkNA0oQIWI3vH8yiAjIHZ4wQ4ayjKkEhbr9dwWpJnwwCCVWyhkJpJNXiCRYbFM9Wps75tGOQlqZfkQ4eUhtWtmxig1OFZNgS2lHUSQRQYX5UM1t2lcggAjEH7MDds9jWnLq3TiFIGWQaiYLMfc3tQUtiSVCiUF7hCEqXh2jhfYq1nkIG3DpskSFMEn1wbh5rOC4T01Js15mc7bPYyeuWARmqIRCWhVCakoesaxilk1GA47FNkgzvUCCVJ4oQstywurlBYQWlyhLEEqmHEoLTMtjV4ye3tDUxRkNZjLGGPaoRBOomWGmMKTA6pkhzavw9jIFLfu2/6/3N83wGlw488wuf+9lv883/2s9SyV/nOxXW23BRSay6cv8yhg/uYSZsc+4F3cPPcc1wsp/nlAy1++/eHHH3HT/LUFz5BfXae3vpNvJ1nMXNMDrdRzKN1ys7ZFIY3sfklPvquN/Ced8/SzM7z/LdaLK6t06lf47/6+Fv5hX/23/HTv/Q8v/rPf4NzFy7wxPkBH5yc5fraFudWLUnSZ+vSd/nDV57gbT/0D3jXWx+nXq9z/dIG58v7OfqDb+WhBc2//91/xwc/8lP82I/8EB/74Af4m7/5JC+/eo1YGJ498zJvXHoXDx3cQ2PXMd75oV3sEAM2XZv5HXtRusGug0eII0HuSjAClKqkMo6iP2Bra4tGZ45YeL781T/n9z7xF9TaC5y9eAs/XOXs1RX6vTVu3B7ywP2PsdA+Q6PdYG39BpeubNAQ4f1W1m5y7ND9lLevIe0+vCjAlvQ2urRabRK6/MFf/R6PPv4R9sx22NKG2n5Bd/kSUZmxsrLIigvpdfv27kYiuLZ1i0O795NtLBMVOcPBNv/P17/N697wJmzS4MCePRhjQ21uNXOzC/zDH/0Qm8trvPPxd/Inf/oHzO67h688/RwfeNe76A17COVxWZ/f/+Qf8qEPf5xWcwKpNQIV5BncYSSBGAUeAK9NE9MiJCiM/AG8GCWsjUCEEQpgq0m86jCNACFG4IIcb+BGsaYjRsodyVqV6iBGm707QIWk8iAgTAB3PJ/8+D/EiGUQCpHw3RFE4e8ivtz9OI4kfgHFNjYwsqQzODugMzHB5MwsE7MLiOzd/NEn/2+mJvez0TP08y2KjXV6210sihs3b+JcyYc/+FHqccILLzzFwf0fp9vPibTnq1/8LOuDHh/70E/g0ahIcucsxF1Yzd3+RmFhHLG6ZMUEcx68ECCpNsfhbo4M90bgUChkR55Do/tSXZcK3HG2Spzwo2tXKQf/znE34MRrxsBdP2cEVP59tCZ4rfn6GCULPx6zrO6AmyMgTIg75zQGrXx4jxG7rNVsobIhEokpCppxRI2I29uDoMF3imbSYHHxOod37mFlaZl7ju7l7JlLqNhy9sxZ7j/+IFEtYs+O3dx/9DhYy+nJOXAFl25cpjMzw6/+D7/GZG2GL3zx8+zbN8dHPvBBfvPf/Tr7Dh5gx+wCL77yClev36I92UI6QZRqGmWTfpZhRZCYRLHmyuJV/s1v/2t+7Ed+jqefeYqi6LG6ssz/+i9/g7hWY3Zuht1zO7h2/TIf+9hHGfYKnnnuKVbXbtGZmMFtraM0yAjStEFNNoitZGBLRGYovePylYvcvHmTow+8jvbMPL1Bj9urK/zHP/xdJjttEOGzDAcZly5d4rE3vIUXn3mKta1V/sk//RX++s/+mt/7D7/FdraFisKzG0eSwikymWKLHkJLbO4pIkGn1caUliNHDrF49RLtuMl2d4s3vPnt/NTP/jy2sJUvjabVnmBzZY3JmWk+/7nP8aa3v5ntjU0+8vF/wDe+8yX2L+zhytIt8m6Xv/3833DixAluLq1wc3GR2Zm54IvgoF5vMjE5RbffZcfCHlQSJKVPfv07HDiwxPTkNEkaj8HjAKoGkFX4YAgcdq4Sr8EPM0w5ZPN2j4vnXqG3tcXEkQUylzO5YzdXrl9HEvx4tvs9poucWquOw7Kxuszy8mW2tjaYaDWZak2zsrHMdrFBtlJSj1q0W1PEUcTMVAurPPceP8aO+XtYW1tGRwmri5dZv73EO9/7ETozO5HCoouUl06f4dLZ0+zaO0drdoYD9z+KsZaiv0WxfpvrVzeZ23eIvYfuY6o1SV4MWVne4NbiVXCOW5dukA0GnLjvXvqDAUrXWOv2IaqxsHsfN1YHLG2dojm/wI0r14iTOvvue4CpmSmmZybJSs368g0mZqZoTDxIo1ZnujVBKTx6Yx21qdjcXGd1dQWcCmOy1kTFglq9EYr5pE6ahMKy3mzQaHcCgGAN2WCD9eXz3F6+zPzcflrNaRCWyIPBgZJoK8htCcKC0GBNMIN2wZNE6KqjLRKcM/T7m3S729h8QK3eJJmYQRLMaq2zqFgQyQipFHiNHiX9CYezFlSQ2WkP6AbSF1AW5GXG4PY6RZ5h8oK2C8mJVgnKbADeE+saqp4QRzEqTijzjDRJyIxgq9dn5+7DdJptIhnjGYbGjC8QLkNYcEVIKjTW4Ms+pfFokbK6cQslS9pTk2gZAylemLDYKkkiJMEKOlwf64ZY73EqQimJLTI2VleICZ5U9ThGRxqnBAlxSI0yBdhhmGtF8KoLfnIFSIvyUIugNdsArYgSR7vRQWuNSFOUivE6qbq2brzWh7lfoirPQGsswkEpPcpIYglGyCr0waOEA1dJDXy1MlShabEIG1AhBNYHRoUVZZX65kKggg/gjReZS2FaAAAgAElEQVQhGny8+sggAvSIIKVwnsJafFFSlENMbhh0B/SyLv1Bl3zQZdjPGAyGDIsewyzDVnH10lWxJCM5mgorrPUKr1SQ2csYCThnyPIBzlgiqcOmUAfJdlYYIh3Tjhq02g36/QGpioi1pChqKOGwZBTWVEuqxNsAIBtbomSMV4FZNcxzlI5xxiGrzSNeU+QFSseAwBhHUZSMvXcklReaGS/Y3t9JJ3LeVR2dwBjyQWMVzKeFwFqHUL5KZRJY5xG6ak4Jjy8tXgWGnJZgTRb8dUSQIXkRZIgIxs0wKSCKg+zTVclfspJ9ScIUEFcVayhTJbHWKKmoJSlSBrN4haddb6JkQpJEwVzaubCpl5BYO25eWmPIS4N1ATBzlTTJOYcVFnB02jHOJ2xvbZLnAXT1RmAFZPmQIoZWs0GkowD6uMDSj5SmxOFcBt6xb/cx6olgZW0L7wXWWdIoGjPrbRUwE7yRfCUNCvXhsNtDCl8F7kh0pFCqFnw6Q6eWSEmkiMEYIqXxiUYaGRL6sJQCShkT+RZrXYMUGViPNQTZmIKpdpsK0cIKsGWOrsz/dSPFGU8kQMaG4J0m0EoQdFhuXKuBpHSglCKOEoQV2Mjigdg5vHRI58J64kN6mMntOMFN+6A6UMqRpglxEtQFlpDUKKxCWkdRpbYGjNhW8CcIrQNTjnAdldbkzhK7kCFmlQ48QKsIluMBzAjgtcLaMLZDPR3OM/c2jNXCY6zFUKLLID0sbNgPxTrGSUlpQsCLKA2JjhFShPS0ytbBV6ELxphg5zAyxkdWjQ3QVZFfVhOy957CFZg8rFVKgBOWVqJJZEo2EDhXjCWcwoPJQxhEGikSqahFMgQ2ElHi6GU5Whq6m9AvVMUCtigvsc6SZX02NzfY2FjFlH2MdeDDNfPCBVanzdFKBVmjcdzczokjzyCvGJ3VnlGKsN+wMig8jCtotBp0Wm0mOm3SZsTC1ATXVwecOXcRjaEUJU55nLBIr+j3BiTa0p6aprttmHRDpBAYIjCglMWajAMLu8jiSS5eXyPVAmwIbEhUTBKn431KqgReSJwLrF48dOoJaazoDh3b2wUi0hgHSbW2KVKE0AxNSW4czlhOHN5LFCfgdRWG8P07vu+A0j/64Hv45uf/gjMXzyE3rrCYNfiRD7yfRmeajbVbfPeJbxDvPMLePTt43YF3k5NgXck//m/+Cb2zn+H013pcW+1x//F38PLJl9nRTHnxuRfYuWc/nSjHDrb5xmf/E3/0F1+kR5uf+flfZspeZ6JZY/rwwyw04b/91V/FqhluLGVc2vZMzR3how8/zO1L30EmcxzYtc7S5janzl/FiBqTl6+x0PIIFXHkxOP82i/u5ktf+ktqnQVuXjrPJ//8j3n/29/LwQM7aTUmOX58ktOnX+bwwRmmdUEzckynnl3NiKUy4hf/i1/h+otPM3HkAe6ZSOmXliSKKzaErRZ5ydb2bVQkaKWKCy98jTMvn2aQp2QbW7z43DPcWttkev+9vP2jP8t8VHDxwmm21m6zVUDdKxSazCqsdVy7fI39ew6T9busrt5Cy4LWxCzdrS1SmfHMM8/RmD3ON5/5Do+cOMZwY5Gnn32BZ069SpwkbG6tcv/Db+GIkAy6Pdxgk89/+g8R0QRXLl1m18ETLK9v8V/+0n+NxKJ0SH8R3lcLiScvM7RX3Lq9xJ69ezn+wMM8/NDrqacJRb/L9WtnwGk+/9nfxdmE5c1N3PPfoBCSh3/gzZUeV489acbO2XdFpYKoDJwD9XIkwbrDZXktUOO9vOvrCvkdFXtubCk9/h0YMZkYe+FUdBuoklDuyKMqZRZuzFIaASyj9wnQwgi1COfjx0bed4MUd143/rwVS8HjaSQJ/WEXaxSxkizsmCPPS4Q3zM3tIorb9CPPVu8SwgUztl17djE9Ocf5Cxe4dmORT/7ZJzi4dz/3P/gg/awgSSL+7b/57/EF9EzCxmCTRm0yJOe50XW685n8Xawj8GMQ6e4jFMl3/I+EEMGwkbuYQIBC3UHi7wJ/AkupApGkHF+dCpGrIl/vgD7jK1jhSa6ivUv5WjaUHAGGoxf9Pcfove423x6BYeOf3fW77u5/jyWAFXApPc7KsJB6Rz1t4KwnjSN27tpFrBOEF8xMdsiLsPH0zrO5ucqffuqPmJnawzPPfYe5mQ6WgiSRxFKzdGuJvNdnbm6eHTtniKOIr3zlK3zowx/liW88zYn7jvDSSye5cOk8G7dX+aH3vIeFvYc4e/pVpic6nDpzmhMnTrB0/Tq94ZCsyPFAM21ihkNKY4jjlLOnLvM/vvxPiYRCx206jQ6vf8PDNFozfP6Ln6XIerzlLW+j1ZwB32VicoqtrU2yPMMLTy2JyPIBxfYmaVMECryAqKEZOMv0jine+uZ3gNcsL98iSZt0t7fxtmDQ3ybLC2QU85Uvf4Eyz/nID3+IqVqLv/nSl/nuE9/m+q0r1NKQ6qVqCbYY4ktPYQxT83X2zB9kazjk1uoStijwRcHkZJ298wvcPH8KmUK9UefyxVf5nf/ztzhx4iHe8dZ3hmfTO5597llSHbN0c5HH3/Y43hZYA72soF9mLMzN88qZZVqTE1y+cZ3+8FW2e1vU6i3mF3YRK0EUx/SGffJhj0995q/47leeJEay79799AcbxJEkradoLyi5w/5DUEUsj4SwIRrXOfjqV7/Fd574Kg+97nUMp2qcP3MNlcQs3V4iiTVzO/dQmBKX92mqlMhLpHacv/EKRWmRMjAONrbXqaWSen0WYxXT7TlkUmPfPQ9w4+oFsmJA5iS5Mxy+736uXTnH+uYyeFi+dSv4DcztYHLPQVJtWdh/BGUHdLc2MTMlve4mG6tX2FhapNFssHr7BuXNG6A0h44cYpgNKX1B1h2yc/dOisyy1N3EOs/+w0e5tHidrf42fZUwMTvL8pWrCAv97jbveu/7SNoas2W4cOMGU405Br0hu/fuohQFqzdX8NaTFUOuX7nM1tomOkmZW9hDZ3qWzuQESVqnLLMwj2UhvWbQ64fiV2uEr2TZWtCamKHVmmY46DPc3qa3fptau0W9XkfoGOVBKz1OMotUTG4E2OChI3xYr0yekQ26DIbbKKlJkxTVrONUjLLBSUiqFLRAS4/3wfBZOEfhAasQ2ND9Nj6YA3tw2YCiKCl9webKLdxwi+bUFBaLi8LGIY7ruFozRFarCOMceZ4xtAUlmnRqlt3Ts/g0QlsoC4Mpc2ItsSikrKGjBi5WxA1ZNX1KpDN4L3G2pNFOcEWO1AneGaTPyYzCmpB8llmHL4c460iTCJ1GIV2wLCl7Bb3uOr4YIA3kGKLZSfLbAxoTTbwYkGgZjLxV8GFSSpFGMUJrpIqCjEUq0Dr4Z1RAnjAVkFKtGtYFM1rvoBCh8y5Hz533SC1Cc8tLtHdhk4An8j5s53xohvgqUMP5ytvRhahySUhdQgR1oXElSkQoG8xxhQPhDJkpg4QiLyjyjEHRJx9kDPsD+v0+vUGffLhNnuUUeU5RZjjrcNZhvQl+k9JVqVVqDHaJCsBwUoArMcZU4RgibIYrSybnPco7tFREOmyKrTF4GT57TcZEcTAyL4qSYT9ne7PH/I45Nje2GA4MCIvzRQAuRGCKhYh2jzFhw1iYgloU4yswoOoDYqytWNcWqTV4yLM+ZRmYQkJIlA5Mi5DcJinLMphjq7uXcV8txhX7o6ojRmWj0gHkMXiUVuCoQDSHVjJIqlzl3ijAmBInApCAUKEelWHV94JxWpV3Fo8PZt/eV8w7Na4vR4CDlOEcPeGcw9gxRFFENhyw3d/G+xSlqpRdHYffwSKSqGpAS+r1JsZYHBZry3FNpmQwq9fCMtlsEyWaepKy3d+mP3BEKkJIibE5oizIhxpRC0CUlmCEquoPixAKqSQHDh5gmOVsdocgLKUZQlqn+viVmWi4BggR/ImAYZYzKApiHWFFhJaOei1Fyjg8E9ISK0ekNVJplI6p6xR8jPO90P61gqI01Gqa3qCHU+FBEggilVZBG4aiLDClQ6vAGvVxgkhT6nGMKS1JPQ1mxlEcwg8I0klX2lGxGQAeIdBaIUQ1bp2lKEIKtWDEFA5sxLIwEAUgaCRNREuEDSmVg14foQQ6CnLoYDYdPPXKokSpIGsVKsgZw3xSmX8LGyTqSpN7T24dsY6IGzFOClTVvDTGolQAP1Tlh+ZVRDbMw9zkFArJdr+HkIqtXh9ckMR5BzKKKa1DVPK2oMiQWGNwEnSkQmgpHj3upVYASwX+CEbhNhrhg2+Q8g4NFIR0P+sEpRQkcuR3BkZ4dBITS8Ww55CuxANlWeBzixQaG4FXgqWNLXJfYpwilhZXglGGrWFBXvrKIkDS6/a5cvE8K+u36Q/ywAAVdwDDQFYyOKkpXJCBBlahYzDsVhJyHVLytEIai7AeqSMiGfHQPZPsnp+BOMKIiMJFYEpM1qcWWdq1BsPBZjDjdg6ExiMprWfQH3JoTrDYrVEwQNuIwmdQQYMKxTDLuHbrPImyWC9QOqLVajAxMUNZOPqDAaXJsSIkxVkB0oVrlCgwxrGxuYlTgbkKHutHjXBHWQZWVekcO2fbdDqTlK5AW0v597bo/78f33dA6cmTz/DKiy+zIFNufvfb6B1vgcEqZdzgxL0HuL34Kjcuv8zW4iFuLZ2ms/N+YmXZpQqe+uarNB76JX7xXY+zP97mxaPHWV69Rmdqgabrc/3iSYxL6dUOMXnwQdYv3ODlF5+h5odM7rmXY3tnmUg9+Q/9HJfOnaU9eZhdOzrMzD3MxI6dlHvew9zcLKs3L5BsgUhjGjM7iPJVvvTV0whVY5s69+7azdlr6/zupz7Hrtl9rK1s8+kv/A0//N634YdrRL7GR9//Xr7+ra/zG//qX/CjH/pxTtyzjyiBxx97A1/70p9ws9djVkguXLzEwv49lEYEz5rKFMEUfZYWL3PtyhKyv8mn//pTvHz2BgMT6IK1qM1jb3wXszsn2aHWWJjczWqcsKw0v/CPfoZP/8nvMig6FHkBIkfXaiyvrTJ84RkmJ6e4fOks7333ezh3+TTPnRzSnt/Lf/7Gd/DsU9/ik//p/+LWrSXue+w9/PhP/BxXrlzkdjbk4YcepS6h3+/ywrPf4uvPXiGNYXU758MLB2k12ygJ68u3mJxewKhAd+5udml1JkmSlLwYcmDvgZBAkCQ0E8Ww7HPz2mV+/bd+m9/8X36Ta+uK+Qce5KGjR/lXv/4/s+e+B7jvoTfRiQKNGe5krY3+H8rBOyBEwGdGoIIHZGVCOQI+JKOmX3jF91L75N/x7rnbSBpGIEH1PV9VQSNYYfS3xUiGUP2Vu1g74YGWd/CoMSRxN6LxvaDMGK6oCglnQWjFs88/wfzcLOUw4+jxR3j++efYu28vTz39Eu3ZSd727vfzuS98mnNnT1OPU7yQ3F5dIi9yDh4+wv7d+3Gm5MWTL3H02P3cunGTtLGL9s4OhxozbG12uXjhIo++/q24Mg9JIc6NQbtwfuHzhXO8K6r27mtWyRe4+7qPQCA/ui1+XHTdAePChCTGI4CxTO577qO4AymNf388id65iqOvvvfu/x1AqvqXrJhOd8uNRgDa+NZ5xkw0X50TVVCgEBIlgmnhHYAxAK5pGiEEtNodFnbvYXljicn2JNdu3cDJgn53SNc6tk6+zFTnGm94/VvYOTfJpauLNJKTZPmQ/nCLC1fP8/kvfI5de/eyY36eY8ePo7Ds3TXNRnebyxdfZseePZw7f4rH3/qPWV5cZNeenbz/wx/hq1/+El/43Kd53/veyz3ZUV584XlwJf1uL+wyhCM3eQCF4lrYNpYZWz3D6TNn2blrwNTkLHM7drK1MeT0qe9y9dpF1rdXqcURuS3xwpEXOV7XcCanLIYhFUZGICXlsGCru8XJl15ke6vHlWtXgrmpVjTrKaYwCKm5fvkS//HsVT74oR/mC1/4AmfOnGV1ZZFby1eJYo1xls70JPVag9WlPj6S9DYy3nr0GKlu8IMPHufzf/UZZhfmqdUi1taX6W9vYl2Ctp44rnN18QqXr11mo9/l4KEjLOzYgXeWmR1T5MOMtF2n3Z5ASc3y0k1+7Ec+xvWV66Rpk5OvnCTLhhg8UxN17jm8j1a7zp9/6k+Jkohjx46TpjX27NuDFTmNluOpb3+Jr32t5H/7t/+Bbl4SKYetgOrwmIU5J89ypI5IEo2zgbHwO//7/8HN5fO85/3vZrDV5/rly7QbLQbKMN1OyYeOrWyAyw1Tk7OknRqR1txeXacsHdY4arrOYHtAZyGqmFQCrZoYISn726xfv8J0Z5JGex+pECgKLl9+lVPPfJs4rVP4ksvL11hfXWfKlkymMVdefYW99xzl7KkXGGTbmHzA/v2HacQ1VhdvYYloN6eJ6gmbGxucfekk8/Pz1KMa28Umi6srLMzv5t77H+TS1StsFEPiqI432wzXNnnp8jeh9LzuxAnu2b+X088+yeTcDLrRIkpqXL2+zoMPPohKYr79nadwWU6z2WTXwgL5YEAjaYKKSdIU70qG/WEwgxXBk06rwBxzzoekHxNi1J0PoQgSjZeSeqdGZypiONjg9vJ1uhuG1vQ8tc5UAA88ICSZKchtiTMWawrssB+AViRxVKfZ3o2IgtZptKFJRFTJcSwxA4abW2R5Qb1RC0baVuAJCVdSKaTUSFUPlHxtSJph07h7fi85BusFZtCl3dmBLYc4E4xZ11dWKb2nMzVDPe0QRQlehkK4LAtUKfAyIU1k2DhX7CuvPM6bEHjhHSUWb0N3PrTGI6SYIFISoYIXoHMlKQJvhuTlgN7WbZwdUFMxrjQIOwARGL6J1tQmE2SUIorA/ojTBCaniGoJUioSqfFaVuamQUZmcZXxLkgbJmvng2zLWj8O/CgxKCED+CdVNcd7FLoK2iBUF9V9LBzBWFgKoACvCVt5h0DivEO4Mmys8LhK/uNwOGPGm9KizHGZwQwG9IoCOxzSH/ToZgOGwz7ZcEBZlpRlgTVl1eQKDAIpPJLAIhKhxMH6gFA64cPvlg5vcoqKgW2MxRgLUmOdJQR/u8qrMRjDj8ARRQiOcNZSVN5tSgVgxzvLIB8CgjK3JElKlCg8JZub65QGlAwm2F44jM3RMkJJ8K4Mn7M6VJVgKCqZjK+S5YRUGOdxrsSWOc6F8AFjPNbaAO7JAPZ5NwLbw/hzpQvvy2gtDhtE5xxSBc+suxt/1gUQwY2aWj4kHwnrq6sSZGS+AnZjJSogJki6nLFIIqRUY+uEwIC6w3AX8rUR3HcaUyFGnSpe3XuPMyWl9xVTwOG9QQiPMRYvbNgniJD26d2osWmx1iBGqZEVkCTQGGNQWgUwqZ5QS+q0mw02t7oARFoSxXWiNDSunPMI7Sl9AP0azTplWQCOuFln754Fbq6tYI0mHw6Ch6evmHMuMAOlkJVRemgqW+8xpaOeBlaFlSEVrJbGQDDGjutlsBdyCSoSlD4Al60U+uI2bujxUUwUKUoEqBStItI0Rlf1oXNgnKJLhpcCicLoCmBRIeHVe48pwnZZieDJFsbHqIr0Yd2VKgDIRlbgQzBsD5pcEZhAo6bkqPyTAqVDlLyxJgC5MgDKprQM+hlpXaKlQ1QySY+vfIaq9nKV2EyVVIcLCX5Ij0+CvHJgCmoqIZGeUoyYQQH88j6MAe8tzkmsCbIvOWJMOku/n6N0jdJ6vBSUBBmUBFKdUI6eEVvdS+uwpgzzjiL4wI0K4MrbVbmqkT5SIAiJUmHPZawbb2ckgkjrwNIKsBmjWl14SHSEbDYZDrrYLA/+ZQR/2khWiaJeo7xByDAPClyYG2Sos0U1GFbWu2z2uuR5RiTEeF9nEVWKaCUWlxZpq8RGAaW1bBUhOCBglpKyCGNcAV5YtLDE1lLPc7Qq8brGlu2DrDHIPXmW0eoo+plAuAjrhiFV0AVweKNbsrayinExvYGgleZYE1WbDocViqs3t/BKBSmpFDSbdfbs20stTRFesLzew5Q52TBjJcqQZWCWReFCs9rNKK1HRqoCq8FLhRQhUKDwAaiKhaKeRGxsbIZ10ogAkH4fj++7KffNV7/9axvJboY3vs1nvnGV5vwEa5dPM7XrGHt2zmJ6a7x87jprKxd56oXTbPQzrlw4R9en7L7vUd79jsd43UzEVLNOp93m0O4d9IY5Ltvk1EsvsHy7z4nj95KILZZX1kiAK7fWaLY6vPj8UzxzZYuf/8l/SHt6B7/y0z9JrdymHkU06jG1WNKKNTduLLK4WdKZXuDA7t34rRXOvHqBcxcvU4iY1917hGOHdnHm5NOsbvTZsXM3vfVFTp16hZdPn+bchcusLt1AaoXWMdeuX6EeRxhV48F772FxbY0D9z1CKj3tmSlSBHbYIzclCEEx3OLcy0/wxa98ja3tPk8+8Q0uLm4wMAGM0EqzsbXBgUN7EXmPpdvbbKws0tteYnF5BTFY5/zl89TSTtCjO8/8waM8ePwE9bldPPrIozz26BtZX73NzkNHOLT/HppxTKOm2Nja4KmXnuXMxevs33eAnVPTTE9PcGT/QfYtLLB49WWefOKLtKf2c/j4MdZWVtkY9HjTYw/TTBKmOnX+5FN/yK49B1m+fpkbize4ubrKPYcOIxTkeYZSkmG3R7/MmZudJBtkRFqzcGA/x+85TJl0+MC73ovp9eg5xY/88IeYaDReAwCMDK5HcZ1CKIKcwIMMRVJoKo4KBx+gp/HXVOyeESAyfufxMYY8RswAXmuqfTd7aAwSjf/jzmtew1UJr7ub7QSB5hrqGIeUdxVC4g4w9dpDjA26AfIs55kXX+DGlbN89euf4eiRY/zlX/wx6+srJBTs3XsvB/fuYd/CLi5fOsuO+R3snN/NZq9PqzNBrBXNdou1jQ0efegxrC2JlGbH3j1YKXnr69/EU089ya6FHUzNzI1NgMcpenCXNO9uAOnuc5Cjk77r2vGa360IRONzvvv8w6b6LoYXAeAYfT2+RELCXfdg5CvCXayo0cd4TSrb+Gd/55qPa1V5575QGbrfBTqOgbXxQnsXqFV9diHD2LVVR3iUmIcQJEnMyMhza2MDheANr38jyITORAvpJYNBn8OHj3Hs6P2cP/Mqj7/xcS5evMaVq5d43wc+wK3FVf74E39Ar9hma2uLpZVbnDz5PBcuXEDphKw34NzFVzl49F4G/S5ZP+NjP/EzTM3sYn5mmlajwczsHG956zspuhlLi9fRSgStuzFEWhHFirK0GA+1OMVqT5HnbHY36A+6KKk5d+4Mt1dvEsWw0d3Ee0ckFWVRkKQh9jzrF+SZoVlv4bxAR4paGkyWrMtYvHmd7qAX9PW1BkkcgxNI5xn2Sw7s38/Hf+qnOX78frrbGzzy2EM8+d1vEjdS8iyjlmrwVUrc3BzeC/K8INscsrS0zCMPP8ZnPvs5PvzxH2d+ap4r569x+J57eebZp0nbNbJhH+c99UYLpTWtZpsD+/Zze22DEw88SE0nnL50gUN7D5Emms5UhyP3HaO/PWBheoaXnn+a++45GIBfm7O2vEKz1SYvc5544ls8//wzzM/t4Oj9D3LyqSd5zwfeTxRL1m4tsffQUXCeWrPORHtqDEobY8iLgpXVFUyZk9RqaB2xtrpMrRbz4R/9UW5cX8SbgnYtwlpoNxOK/pBWe4Isy8mKIXFcQ8oaxgVDUGck671tJI7ZyRDFPuwPqDU6JFGMlhIVxeTZAKU1tUabRnOSjdvrXD5/hka7zv4DxxFFn057ljMnv02jVqfemUYJzVBJrp5/lUgInM+5du0cUS0lGwzQUYJSkrzfJ0k11y+cZ2KiTWeiQy1tIuME4jqT07sovGeQ5dSbNdZuXSff3qChYGF6iu3tLdJaTK3dZn19gyRu02m12HPgCLHSXF+8QZFlPPi617G0vMba+jpSCDrtCSamZ0jrdQrjqNcaxGktpIoRzIBREUlaQ0cxAhVMgivz3ShKkDoNE0Dl+dDqTJLEdYpiwEb3NllvmzIrSNA4rymHfbZuL7O1cgvnLDptkDQmUXGtmr8kxnuwHmcsGIfxBJ8DW2AGOVFcp9mcIU4bJEmTtNGk1miT1lvEjSZRnJLESTAFjhJ0rCi9wBUlWiickuSDHlm3T7/fpSiHxM02E7MLJLUWSmm8N0FK5m3YkEVpSP4aN0hU+NqXuNJi86wygS1R3uJdjrMF3hm8HUK2icu2KYt1inwLmfWg3MSUQ7R0TDYbNFtN6q06zXaTRqtNZ3qK9vQUrYlJ2u0pWhOz1DqTpGmDtD2BjhMiqQk6WgVSVJvbwAYKgElYXxyhc+sFKB/YJ05ULA4gkuEb0od121Xzd+hyCITzeGsCyGILbOkojaHIDSbPGQwGDLa7dNc3WN+4zdryTVYWr3Pj2hUuXjzHufNnOHvmZS6+eopzZ05y/sxJzp87zdUr57l49SK3Fq+xsnqTjY3b9HpdimyIKUM0t6vk4cIHWWNpSqwpyfKcQZbRH2Zkg2F4TZ5RZkNsUWAKE4ynnamMc30FRlXnNF7bZJCjjywFRusYIanUjZduUW2uR+u+I1KaSEfoSFeuDSUChXEGY21Y131gCDnrxhtlKQMI6qwLciQXgG1rPUI6uoMheWkrQ2tRxYEHpkzwPlJEOqIsQ+0cDLJDPSakHJ9DWP7DmFA6PL+IMBbEiIWkBFJXaYQi1I9jhpKEqIpNj+IAPkutqr8VwCKtA6A0aqQpOapDAkg1CpKRUiJluM5iNLSqGnLEnIbAXBrZKngffJgcFcuJIIF2PoAgdzPDR7WWd6GxJ4SrkukUzUZIKRM+AG5xpAMjD4cSCiEdaRShBRiyYDBOzESrRavRpNVqsW/Pfh48coBz5y+TGUvW76JkADmEiO5ItcJFDYxOKSmsDcxBNEhNohXGlQyyAQKBFI5auwAtkTLCUlBrdNBxDYwlNxmRbKIakwgZ46xAiij4G3nGxt/gUcsezVEAACAASURBVN4zzIchxU9V98mBjiWxklgpsCbUNImOSbTCG1M1p0dlZADnhZCYsgLMhMSUdsw6HIET40apICTEKYmqkOgAQobDVTW9cVWAzkgmSyWjrVj3dgQkQfWsgJOy8uIKzCakwjtLHKvxTBWA1QpckuGZM6UlG+ZY50MCpBAYW2CdJ0lqmNJiK1N7IRRRGszJrbPY0XjCV/ulqs5XoaGgqnlgBIR6EYgRqhqfUnii0XisxrV3HmcdKlJVDT1+SsYJ3JKQzKikCsBWafBVUlocaZIkpSgt3pZhbZIKpMN5QRpH4XmQAZAtCkeWFzgHhRXkBjLryY0jN46iNBjjGJah0aIEJDrBes+wqNK2xZ3n0hOkmlIKYqXZGghWegXGOhoadjQSFqZimkkgQCil6OXgvKSflwhj0ErjPRTWkOeWRCtcaWimgl4es9o3aOGJ4ggdV/tBASqN2LtrnkYSY0qDKUqGwy18meNKwzDLsWVBpCqVjYFeZlCRQmoV5hQZ5k09BpWC314SSUxR0hv06G0N6PaHbA8LfvmXfuF/4vt0fN8ZSqvlAu98U8of/Mt/T7T7IR556CG++fllXr2+wtH7jrD/3kf58R2HOfX8N5m99010GjlnFofUl5fZt/ceUpexvr3N1Ow8k60hWWk5cvggynaJ6zWeeOkUK4MMkTlqzSZZc5J2Y56H/rMP8pEGnLq1wczkFCmWJN8kbtRZ2Vij41tBctKc4M3vfj8HHtpECFg8/V2+8cI5NrqWLHd898knYdDnsft3snhjkVLMkcaK7a0eJYqiEDiT8cq5C8zP7SaSnrzW4ZsnX2XP0SbODOhnlvc+cpwbq9dptSYpt5f4iz/7IybmjnDPwd2s3rzMl7/8t5y+skJcq5HnQwrnsQ4a9XroluDZ3u6yNtikzApezQtur67ikgY7d+wiKwVb3VUSHdFpT/DwAw9w7/7dzEzN0NaKmpIcvOcQmJLV1VsoIVldu83+XQd46+PvxuvvcOnqFWr1SQ7u28nq9iILCzu4fO0sT585x+viOR49sp8X2ylT7RrPPvccD9z/IP2sZO7wceqtNleuXqAsLZl1FLZEeyojYkeeF0xMTpP1cwaDIa12k0ePPUivu8Ub7j3M2tI1du/axwfe8z6mOx0KU1YLlKsKGndH+gYwBo5GIE1YGu6QR0Z8kQoFH9G/vb8DFlSgw523vBv0+DsgxGsYNiO/nDH2MGafeB9QeTF+/WuBlJHGfHTISvMeCiL/mo/wvaBSBZcIqNVSDu0/yM2rjkG/y+/8zm9SGMfLL77AlekGHznwAJ/45O/TSZpob7n/2HFur20wMTHFvj17iYTn4Ydez7efepILZ1/lxrUUpeHIvUc5uu9erDX0h306zTZUvlEj+vj3HqOiZsQsEncxdu4AR3euVgUYETwjBKJKb7j7vEdU9cqMfNTZuAvQGfU5RvTb0XcFoSCsmqyMxsWIbTQCfEZvNMalRn/7ez6/GANUYgQy+uq+Vx2bEVA1eu/xuKj+iNaaoixJk5iiNySKo2r4eRr1Bgs7Fpibn+Le+44zNT3H5MwEL7/4MmfOvcRPfOwn+Re//q/J8y0+8Zd/xj0HDmFcjkbzpS9/kcIO0V6QpkF7733wZFldXyHf7KJTw3B7A6ElKknobW8hpKBWq5PELR44foJvfPnrnHr5RcpyGNKdKiNQLyOcEyipsM7jNZS9qhMtJYPBgDw36Dii2x/Q61+jnw1DslAcIXSINC+GFudL0DA5PUmxtBqMsbMh3kIcp6hGDakU3c0BswvzlIVhY3U1LHymYGphhkFvQLPT4eWXzvD2d74ZW3hUZJBK0C8sqjQoUfDIkeNcOneevJch64p2e4rl1VWiFM6dPsXW+jpH77+P5ZUlknaNtBbRanTwQnLg4BE21jY4eeokDzzwIDjHxbMX+MxnPwuR54lvfZMf+7EPsbHc5er1G5x68UUGmxvsXphi38EjDIanUKpFlGhWVlY4f+k6690N5udnee6lp/mBRx/lB9/3fqxQNBvTzC0coNVp8/S3vs3r3/Ymev0eaa3OoN+jKHKiKCFNUtbX12i2WhALJudmeGzqTdgiI5WaUkc0pzu41Q0manUG+Ta6rinXPMOspO5AlyXKQa1ep92aorO1gZaSje4aO3fuYphnSBmTFznNziTZMGM46AOa0kqMsdy8dolGq8m+g/dx6+ZNllZvsLy2wcRkmzLrcu7ks8zNzyMLha7FlMNtLl+4SVqr4/ICawp63Q3WVq9Qq7epNdvEWrB+e5m5hTmuXrlBe3qeOK6zubVGpzPB+VPPs7F8A/IeC3PTHDt8mCSuc+P2BpurazSmFYlMmJyaJI5TlpaWyXt9kILpySluLi5jjaXWaJEmUfDHGnUrtadfZJQIEh2Rxil5WWKMDT4UgkrqHOIXnPG4oiSO4sC6CQIBbOmAiEZ7gmZZsL52jeWlW2RTMwhjyfIe3SynUUspbY4yBUmZoyJPpJKKracopA9msi5ITYQrKbxDNXfghEASOp0h1EGAl3hjQ8x3Nfd5V5kNW8F2b43t9S6dRg20QMYRMtVoXyfxTaK0jvUWW+RhIyvtqAeDEh6GBc4LHDnSWzwK6TzeFgyHfYbDLrW6pp6G9CCUDUwXIVEK4iRI6kRUq2QZEUkUzGGJdCUuCwbjlopNNF7nfJXa5e5Il62j9CGUS1TMV4dDqqoTP+r4i1EwqBxN+TgRWABVKRCkh85gvcQbsK7E2hJrApBrTBaK+Cwny0oGWY8sK8iznDIfYPIB/TwPSWdFQVEWYY7zFus81odtk8QThcQSvPR4Jyh88FFytko4cibUN86F++cDY8L5sGm0+CDBwAWplhSExpYjEhUm6UD40ERzvgqWkmFj6GzF/CV4AzlrAxhD5fnoLaO0LokK8vWK3W2qmsPjEM4jkZTOkuU5cRI2KkmcBuWf13hnMd4FxqMardIVu9qDlsG3SEea0pTkRYmSEaUYpcMFGaBCBMN4EdKZtNIVWBNkax5AiuCvhB/fZ0ZMclExdmQAhkZ+PaFeGzUDfWA8+PBiqaqHC4dSEm8FcRwhtcLawAgbN7OER8rKpLti+Uilx40xARWjzWMrxtoIyGMkmxI+mPvaKmZe+BBrHobCmC3nq7oVISt2b6hanAueUEpViYhRVN3rEKRijMU7iKIaxpZYD3EcI71nmGcYZxnkBdb2KKVB+BYx0C9KlK9ResfszBxlkbG+PQix995Tq9eIIirz9lqohxQESSAUeUEc13DOYktLWZYoJSGpoZRHYLAmx1uwoiA3BaKMkQwxqiBNUurRDuI4phQCYYaYyuzeC4H1Fpc7kkgHA3fC2A+130gFEBh3VggiGdKntZL0swGIEE4hPONxoZQGIbEmKBvC+1Vjyd0BB8cV5WiMyQASaar7EoWaPnMWrTWRiiidZ5BltNKQCKej6A64KILJtrPuzvuLwIz0QiFkOL8YjXEF1geWrHNm3G4Nj394rgpjMD6MRy3C3Bi8Rz3GB1+jwISVFVCksM4RR4phno8QzzBMERTWEkuJ1hIrAzg5ruFt+Pw+uMQjpcRWSdlQ1eBCYhEY54mUqhJKTfBjciHRETzSBXCuXk/peyiGOdI7IilAEhJA8Qit8E6AilAYfGVEr9FILxgUOaULXr5SCKy4w6CDkABYyLB2YmFsKu59JaUVFK5AaY0RgkiA8g7pFaUQ5F4ylAnXtmG936cZD9gzUUMnCuFKajailgiKDLQQGB0MyYUXJKOxUBSUQtLINAZC0p2KiVSMtyLIa8NdZu32GivLq4AIjd2yDD5XzqO9pd5OcXlgUnoBU+16uCeAFArrHaraOwkqrz4ceIsxClFJLaUKzZbv5/F9B5SOHt/PyqlvsOPgg8xen+Dtb3w7++dmyEWTyA+otyZp65LBrp1s0CDWLd64sIObl25ybWmFY/vmePGVVwiE3CEmL4lSQRol7DxwH4+097NvbgfDrdsk7Rn23XeC/totHr1vHztTzYP3RQjTo1ehjjevn+bGWhdV9Mgyz/T8PFOtNjtm5gLaPLhJbWqOvR3P2fMXUN6wdOM63xquUv6/zL3nk17Xnef3OeHe+8TOQKMbuQECYACTKFEMoihK1EgrT1hr1qnsXY+95apdu2xX+S/ZKq/Lrlnvjr21OzOeVDPSzChrRhTFDIIgQBAZaACd4xNvOuf4xTn36aZGfrXzwg9FiOh+nvvceM7vfH/f4Gp0+13urG0zMX+EwWCA3dykl5dInbC8vo6JW/ynv/Ff89SRcdbTkqWle0zPnqBMd8idYqxRZ3ElZTUzXHzzb7j66UH0eItd1eLAyUlq9RrD7ibWWnrr6+Rl6k21tEaWJTubm6xsDGg2Y6+fdgPWNjep11sMh7tkxqBlxMKx45S9Df7248ucO/sEX37hJa7fvcSRQ8e4cvkizzz3CnfuPaBVn+Kpx5+mNT7Bw80tDs0dY25ujtJabD4kKxTPPvsKZ0+e4Odv/ICJsTFWuEtfxExMjpENevzWa1+jyCzPPfMci/cfoJImCkVRphRZidaaja0NDh09RneQMTMzTZ4VDPs7vPnWm4y3p9m2OadOnSFWfjAOjz7CBmpqmJDTLKPRaMKogAj0X8TIDLtaxo+gh31gQ7XM30dD2QN9qiKgeseoKxtQ+up77B7DpUKyRSgO/AfEHsCwn9HDnpTLf53AWRGM3/b2fD8LaN/Oh5+40cLh6NxhzLALpmRze4csF0Ta0ZyeoN8fcvfeHQrn/KBmvDFns15ja3OdrY0V1lfXuL90n/WNNSYPzHNkfp7z558hThKUiJiZHOeTTy9zaP4kxpk9xlG1V3uVWTieClSr9t2OPJ9GgE8AZVx1jYQYdXH2s51GbC0xsjLfdwV9VWiD9G3v8lbAzi+/9l3zz8QIf+Y/RkXm6O2fvYv29snt+6TbB1nJPXaUQFA6g5Y+LrhWj6nXEx8HLHzhIYWn3kexpF5rsrndRzqNlhFH54/RUDW+9vpXGW+O8fpXv8Jbb/2Ufr/Phx+8S1yrceH999ntbDE1Nc6g0+X8E09QlobLn1zEYXiwuMRLzz/DyXPHmBw/xPGFExyeOw3S8dTjT9Lv9RifbLOztY7UloWzC4y3Nffu34Rajbz0nd3SpAhRgNQM8wJblsRaI5AUecmwTNFCYkq/SjMueFFoB8IS6Yio1SYvN0kSxdrWOv1hyli7ibECHdV47NHHuHH7Jr3eAGcsKw8e+FWS8N06pSXLq6u8+/67fPzhRa5cv8ITT52l2+1ysD1JLyuJQqxzv9vl8qWPGXa7zM/N056YoNfP+P4Pv8PU1DiffPo+sUx4/MlnOD01xvU7t3niidPMHTzCxcsfESnFw4f3sFJx9+4dTJpx6fJl7q7c5H/4Z/+c7m6PXmeAzTL+3f/1u+zsbtPvbyJlwd/+5G84fHgWpOLEiUe4e+82i3c/xZiMyelH+NKLL5AN+whTYIm4eeseSWOKDz+4yGDYYXpyihvXPmViaopWq0lSS7DGYK2hVkvY3FxnbnaOMgNjLGVmGAwzBlnO1u4uwyLn2ORhJutH2e0O6aRDuv1t7Jrm+NGEqObodPxYemhmns5gg8gphFBMjU2QZxlYw/j0LFM6Znd3g0QmxHGdLM9Z31hma1eRW99IEUpjTcEXXv5Ndte3WVm5z80rH5DbnDztU683wDrSQYd8OCSuNYiSCFUYkkjS39kEV1CanPfeegsVNZgoCk6cPsKgu8vyvVuMj9WJhk2a9TqzszOsba7xYHmDOGlQ4mhOzjJ7ZJ5Pr17h7ONPsrW2jbU5loTZA4fopylJYwwV+9hq5yQaH82e1GPSLCPPcyKp0XHNJ+1khY84lhZrHN7rUASGBQhhwuI/p19kmCxl2ElRuqAZ12g2p4iaITU0z3FCo7Si0WqjdIyMNFqCFuCspbQpOmmgomgUMCGFr3eljnxsuAgMB7cnq7auxFFiCoMzmZ9PnO/kpkVBf9hHNSKSZtMzB4TD2JIy9wVl1t2kyAbe50MrrC0Q1gYfDINwAiMFQpRoabFCEkmJloKWdtjJNlESEWmFUj7tSaK9N4iE4KCMCwsRYb3Zb+S8WMziF07G+ZFWOetjp9kLRWC08HJIYdF4U2EZwJOAdiADEiadl4h5KUwJDmyZYQpD6XzKUJmnXlqWZqR5QZEOydKcYdony1LyzDP7yjzH5IbSlBQ2x5b+vElhQ9HumafW+K69CZJtGwyhsf765gG+MNaQl55fUDqDdj6qXgs3SipVENgGfqZ3oqofqkCLAE5RMQkc4aR5UG3U7ZCVmg1cVZ/4laI3sXbBayjAXs4npIkAHrlqXgvzsApghnEW4wzDNEWpBj5v13utaK0ojcCW1pteO4FWjOZ/ay156c2YB8OhN8gWUBQZ1ngQTOLZu416A4DdXg8hBEqG82EtWmtkMC8uytInd1VgIWGR67wwRjrpk56CgZlwLhxrOB+hgSUDq8ifVw8mOGtHTBIZmpCeTWKxJSF4xTPepCAkNvtaSAYwwlj/fVprhNTBD8qNGFQePAoyuOr8u5AGV/lwBVmPrQCmUbvKeRAMSRRpavU6aVoGRpehLAxoqNUjhnkKCKI4QbgMk2cUJvdm80Xpmf6uS6kTjEyoxePESnPi6CHWt3Y9UwcXPIoy0rxLkowjZYL0lDScdWR5QWk8MFmWNoBq3ptH64hmo44te/SGlmJYYGxOZiyJnvYsPFtQa9dIVEwtSahZTaRjBv2UvEgxAWiTolIe+P/X0oOMyru64vBgkhSCOPx3bnIslqyw1LQONX9gtwV4RlJJJMXoeRAqZAkEppvxBmm+HpT++jvnUMqzyJI4xjlBURoPkoc6t5+nJHHk1zVhVRtWD34d4/bWHiJIKKULQCOWWGjSvKSV1Px4YCsPLjybufDMGROOZsRMFDLMFf46ed8mFQyypcdWXCUPLcN3e1DfOUlpgk9T2JZxXuqrpN9PKTROVOnYQdgmwmqlLNE68YISIFaKvi28bE3iU2eFpAgNARVpmq0mUsfYPIXgBRRp4Q3JXYkgsLRKEJHFoUmC7Flrtdc0Co1pKb0znsPRaNaQkQYLg/4QQREYrqB0FEZVByJCCX9fKRn8lJTGYsBYSiHoIVnfTbm11kNHDi0UhXMYKbCloJFoXDyGNQbjDNLDjpjge7KZ++GnXU/QkQx+en7sF86R9nMGgyI0Tvy85p0TjW8m4XCl9y0zWKSKEM6DdD4nVCCk3hc65RtpSO9l6x3pJDpRni35SzLd/9DX3zugtH3/Jp/e2mTywDHE4hbpYMh0o46xlpXVTSYmx+htrHDn5qfsRnO89soXuf/J29TGj7GyeJPbkxkfXvwQ+fEtpg4f5fDRY8w2IDOKhYVHmZ3eojCS1pEnefyJ8/Q2H7BqxsmW7vKg0aLRHqdZA2XB2JzO7ia/+Nsf8S4xvUzw3PMv0h6bYrLVJKlHnD55koXD03xy5RZxlFCr1bi/ucXcoy8jb9wjmjvMr33j62yv3MeWOe/+4g1E0uTs089hsg4kbc6fPMKU6NDv9Ll5a8j8kaN89PGHPPbUF+hub9CaOszv/Bf/hH/zr/9Xbiyv8vmjx5k+OGR8+hCT7Qb9jSVu3L5LEcd0O11UrQ5ScO/eXW+QbR2dTk6j1WSYFohkjGc+d5ILly+R9VNmJ2dIN1e4efUjuiUeGW8l7PQGnDxxjgdrGzyrEu5fv8TM0fMgYXLyINOHjnJgYoIkisiHOXluePqZF7h2+x6nD8/xv1/8gPn5BbY6Kf/9N77BYGuLvMwZ4h/Eosw4fGieOIrxGnBI6gnDfoe1nU0On1hgbKyJjnzx8u77l7m7tsXxOOHcY+cR1vd706xPnqVIFRFHMcI5VpaXSOoJD1eWWTh5mnZ7jMqDp3p9BpipPI5GgIPvPIlRGTYqVz/DBKogp6oTth8Aqn7pv8eFVIXww6qBJPZDUr/E6PEtNapu2h6bZ/TLvW1/5oPVEewVL0urdwHL4fmjaBVhLGRZiZBQ5AVXPrnEkWOnOHHiJO9deJel5Ydsrq0xN3+YRMd0e12WllfpDXaJI0VjrE77wDS1ehutNDu9Dg+WVhgba5KVaaA3+/0f7VHVravO5giXq/hZhIJ/35HsA5aq4nYExFVFUrWK+Mz5rLbri0I/VXlA0HsIfOYkUxGehNj7WVWC7e3P/v3ctwe/zHgSeyDXyHo7FP9idPS+iHTh2IR0lHmOEIpBv0e9MYmW0M9T752m8MV3ESFQtFpj3Lp1k3Q4JIp95H0tbnJgeo4rl64gjCWSMZHW1KbGMLnk+vVrHJmf596DDNG0HJie4fiR42xtbLK5tUFZFiw8co7jRxcYDAe8NPEKExOHuHHjCnGcMFQZJ06cgOPHOXrsGDdv3OLD7Q1ipXj6mS9w4dJltnc2KIvcyxryAkRCs15HC8nBQ9N0djPW1leI6gqLQQhoJAk69hLNyCZkhaDIhyilMKXxnhqlAQRlIRibaHmGQl5Q0wojDaWxTM2Mk9uMrDckqcd8ev0TttbXuXvrLqfOPcJf/eB7nFg4xmDQw0tgXaDfC7a21sE6+mmP9bsb1JI6veGOf15VhIzH+c5f/D5PPvEUc3PzbK53yYZL1OM6N29e80VjnvLO2z8n0RFTB6d48eiLNJIJDp05xg9++GOmD8xw5/51rNLIMsO4HOU0xxdOsrGxyeraChsbK5xYWKA3TJmbmURYy8zMJINeD61rfP9732Vrc4nZuSOcPPEIn1y9yofv/4LDR4/z/AsvcWjuCFmWkdS8nGltfYX+oE+90eDSBx+ys7PJrbv32NheR2qYOHCI6WNnyUXMzvAmhRkwMR6RZl26/XWMi+h0HUkySbM9Tnu8zvr2JsLB/JGjrK5uo7Xksc+/hrVw99pFNpfvoHTC7voaVjqG/R696xeItabVnmCiOc2wN2BzfRUhBGtrd1BaUm9McfDIAubhQ5xL6Xd20aYA56g3Jmi2arTbcPvmTTJjKIqS1phgONjmygfvcOrEKdLdbfLOLmMTbcbbiqTe5O79DaIootEeIxlrYZB0BhmN9gQfXfyIznYP5QRxfYLOTs65x58hMwVCOOLIm7+WeUG32yFuNBmbbFMUGbtbm3Q6Qxr1OpHSCO0Xkz49zBdpEoVyElyBsYbSWhQSLSOiyQQhLYmMcUrS0AIpNdZB25QIJ3EYjIPSZjhTUpQ5trAMi5RGcxJZ1knxz0BURihP6UA43wW30stHBJbCFKT9HtnAA0IKCVIjnQHpu9NTzZZftKQdclOAy30aHHgz37Tv2VKNmEgkEPmCM1ESpWOEjBCRRjmHjJWXv0ntjZ/xQISXTrnRIkIEl2VhQ8fTeUmadiIYNUvPwrDeoJYAiihXdeoDkwd80SzCwkuEGdWviXxaUVlirKXM/QK5zHPyLGeYZ+RpyjDPsFlOlvUZ5CllkZFnJXmZYvPSJ5GVBUW4L50TWOGQrsSEecg5uUd6cZ7BZZz3+zO28I0sA855Zo6tQB3rUPjtGeeTrIwJQNo+Jq8VwasJ3yRxI9DAM7Z8MmqQpwUQQgjnfVcEfrXrGIEU++elqhmG8ClQlVm4FMov/Fwly6neJ0ZNoAqgCZN96HBXUJ9nZjgHWVF4CVvpZWAqkt743ASvkrAPzkcckueFlx46fFy8K3EIbOkQOqJZb9JqaLTU9Po+VVBI6aVMLiRYAXFgHRU6J7eGUUgKLrCtRuiQ30bwoxOjf4M0z3q5UFWuVKx2F4AWY+xoAV7J9/YaZ9L/L7BUfGqvCMAeI0ACEeQmsmpAqsD4dlg/Ffp7P9zgovLRkwIdgCw38uIMXqLSecZiYLV4c3CotZoIITCFJql5uZ5QgnoAIYT0aX2lKEiLjEQmRLKOYUisgXiIKSWFsEy2x5kba/HenXtIJenv7JCmA2RZksQ+TCVWmlocI4UjzXOyPMdaR2EzrLPUoiRccwEU5GaXvOhSGocp/DMXiziAMhbQ2LygUa+jZYQTjqauEamILNcMU+/tJOzogiGlIIkTlC09I8dpz0gS/pyUWKIoIjc5wlpcLr33UeRZSR6wDnWecCOGh3WlB3m1QoU63wTgbGRwEJg5WDNKNRZC0qzHdAep9w9z/roVpsRmDqN95ayE9+epfIosYtQw8Ifmr72urr0UCCu8EXesguQN8tID5caE+54g6VReSiYKv7aItSNT0gN+0t8zMtKIPAe8zNOFZEQvs2QELJWlQUVe3idMBcaGdY+sGqtmdC7942ZxtgSnUUr6lEehSaT3vKxA1UpibQUoNFpboibkEaPnOpaSTAqEVR5ckhq0Z5ZJ5RsavjES+eeBkNkpXUDo/Tlo1Wu+eWIc0hoQEcYI4kgw1mp7mT7epFwIDyIqgfdEkxJcySBN/bFL3+QQTtHLjAeShcRK8KEaHrBBSZzQAdDW4Cylc4S8QRItQcY4l3vPwZEPiAd6/Fyyz6vKKawURCjCg4BTAkUM0lEXESby6edChZAJh5f46tinTzqHDmExkfRgdaT/fw4oXXnvPdLmApNylVdeeYVIZAxFzPR4nSLtY/Iu9bpmeWsD16jz0TtvkFs4+rnjHGKV//sP/4ADMwf59Mr7HMkKNtaXOHnqWR4/f4a8u8ZwmNMcm2Ky3fSJDfkWwxwuf/wRY8ef5IlmHZtCva7ZfXCbtbUlPr50kW7ZwtRaNOdP8eTpGht5j91Bn2F3nc2NdVbXtyiF4sDCozx6/jG+9Ng5VH+Lnh5j++4l7jxY4bEz55hq1yiGmjNHZki3cu5u7rJ29xrvXXufeyu71MenOHf8EBc++piXlrZoTE7wjVdeof/gIhP1Sc6cnCGJI84/80UOz0zT31jkwr0BJ46foiEdG1u7HD11jnR3ifX1HTKbY2UEheULX36d21c+pN5o8vpr3+SpJ5/m0oV3ePhgmZ/87Q9ZW91AJTWeeOJJbi4t8c2vrutXtQAAIABJREFU/BpJHLNw9gkaseBHP/85X/vNRzCrqyycPkcSKd811ZqMjCiKODY3j1YR9+/fIjNgpaJZb9MUkjRNcUrjyowb168wN3uYeDKh192lKDPaY+PUkoTFuxtMz8wyNT4WugkOK2D80Dy//fhzrK8scnBmBpMV9Pu7rKyvcOvuXR5ZWGDQ2aG0lgsXP6R96ABPnH7cewL4aLeRDK5it7hRBbX/5TtQgdPkuzqigm18t0+MJhAzmrCrd1fgUoWa7AFQoWoQMnQRfUE8+nWoQkfyugoQYa8DsY8ctQ+YECEJKHxL2J4MC2aAoixJ85SV5fs0Gw3KbopSlomJA8Q65s0Pfs4zn/siY3GdtNNhW0qUsKyvr+GEJG7Vcf0+SZxQlDlHDs7yH339m4zVx5BSstvdZWtriyefOk+eFSRx4inYo0k/HD9iNKnsl+tVbC9cRa/87Hnbf732vKNCoSoYFduVKbGopAsjcGiPBSb2fecIigs75PZO7ghH2n95+Du3yme359dD+wrzUHxXxSKC0aRurW8vSKnxanTvR9Jo1YnjmK3tTSYnxul1e+zupmysrjMzfZDhMMWWJQ+Xl7lx6xonTy6ws9Ol0WigEPzl9/+K7c0H3Lh/j9e/8hrPfu55vveXP2TxwT3aEy2yQQZWMTl+gKmZOXTc4sUvPsrOTpf337/Agcl5Zg4ewhkfZeysYjhM2dre5eiRw2ysb7G2sk6k4NwzTzNz4ihSNLD2MlmZIpxBCcXB6Qk2tneRShEpSyQkw0GfibEGhSto1OpIAQenZul1u55Zg6PMQTpDs9Vg6uABlm4/HBmYIksKm7K2tsLj559g8dZdGrWCflEQaYmSCWaYUuSGQafDThQRNRVJU3Lv7gOmJsYpbImKfeFI6ZcDReGTjJrjbQaDHbqdAY4SEUckcROwpINNuoM+9+4vsrG9xelTp/hHv/kPWd/e5N7iIg8f3OPjy+9TjxrMbMwiEHQ3M77+ta9x4cN3uL/0ECUFw3zAs0+e5+bNqzhjebi8SjroM+jvUGu2ePKJZ1BSc/z4CS+rs4osdzxcvMNYS1NrTNEfDvn48gW2t5Z55dUvsbm5w7XrV5icPEBRlpQ46kmNqekDbK5vcXAu4sHSQ5rNmNXdbbZ2tzk2f4QTp5+kMXmSpetXKI3gxNln6a4vkW1sst0bst3v4izMTM+j6m0ccPjMs2ytrVNv1piZfwwDNOozLC3eZenuHfLeOu3jU8weOkKeDlju3GSYdmgfmmd6ZpZyaPjo3Z/R722TG0djfIw4amJKSWPsIGP9Aqksg94AAmCQDjMEhlpI4Il1QVIXpP0OebPG5tIiM2NjTE+OE9khOhas727RGRZeApHEDHLL7kaPWr1GlneYmTvCzvYuE1PzFMM+jfoEpYHUCJJ627PorJd9oCQqaiB1jSz393cJ5P0eAokeq6FqGoqC3BaUxmAzL4WLdEQtaeCERkYREQ5i6xelxqKFxlgRfC18DLsMjpkKL2WPhAFKv2otLXWTBXm4N9qVrsSUPUpnKXoFaS9lbGwcldQQpaNIh6RZhyLr0uv1ETKi2R7zstfQgXaiQCO9MbZ0xHWNVIIk8gWlkhIt20RSIiLftXRShZQoiIUK8LnG4oKc1iKk9MwJfKNuJJsKc1fl02ADq8VhsU7hXIFnjIRx2fpFMTJ05wFhHZQGU1qcMWSlIc8zsjwjTwvSNKPIUvJ06KUNaUaRp2SZB4vSIsfkPp7elZk3tnUOZ4uQYBNWLbIy6Ba+xeP8at6zpvwRG2u9Ka/1NUFZ+kaBK62ve5wNi0iLDP8IWcnz9+T1/gz4uUwJ7wmXO0GsFNZ45lskvLeZwLO19rNhnfMMggq+Kyvmr/OSJ0Z+Jh7ksBaUqton/txWflJCOA/shFlOehTLz93VolCGOTrUOzYAhdKJYK4dagApSLMM8BK60hji2OKkZ1Rq7U1ghdwrn8pwvMI4nCixTnhpoPAL4FazRbvRQAovOc3yfDSXW2v9YjfM+Sp4OsZaUubOn2VX4V8hkSvUcs47J4zY7AIPADkZpCFKgFIBtPPSP0/+8N4wxvlnu/JTlMozDRweJBLB/6hCBpWU2MBE00G3JoSX/HifpAB6eTTKL0RVZaLriEU5kvc7KyiNJdKxv2ChbpQyABGVt6N1pEVBM/ZR8c4qhA4eR0F6JKwHYa2znnGmQGqBNg5yR6vmKKRAxhM445g/OIN0sLKxwTDtk+Y9v0B3lUTM0K5HTI9PUNqS3jDDCUXZ7VNklmYtQWhB0bPURYRzhqwsGRYWUwriqEZRZN4MXQqcLSgLSz6wFAJkElhG0pvS1+IYgCLPgyTQGygbHCgPIuhII6wf81RYbxhjaMQJ9aRGr9cFLTFO0YjrAcyz2AAaO2HD+sJX/7pKzXNQOOMZUMID+N4g2t9zSiuM8c+SMRYVS1rNBGMkvbwIFaPCWkduvBxOyuC5KkfubQjnx0jhHDow4wQEtrghiiMGeU4iYwSePVSU1t9LgRmn4wrgdB5QMIpaHDPWiCnynG5uPPjgnE/JC3RYJRVae58tKfaB+NZRuhKUN9eWQviUvlGpbQKY6kbrHCkkSEeplQfmVOTBI1MS6xihFWmaYoVnP4nwj5IiSKEViY4oyxxjDJGWtBoNHI4ISSkEsuH90v0haB+wAMRxQiSUX5eJAMZW42vpxyrhvGeaC4mYWiuSuAwgjfdNqhQjwoW9E4zGAD8nCoSLKBVIF/nUVOcHVhXmRRnGUg/0BS83gicu/qZTwcfNaO0DNnAIFfl5SmmU0mjlGaAqeCNZDJGMvM9jWaAbdYRTOOlIkEgtKbMcJzwY5d1DJFEt8vNBaYi1JkoS0mEaPJb2L+L+w19/74DS9978lG//j79B7cEOU8cO0zA7bBUF/d0+D258zK2VTaJI0B2W1Ow6V7c3mJw/wqHODscfnaPf3WHpwV3W+hGHI8WlSx+T6UNMjQnyLKM+Nc/4RNtr6q1jrD5OTw84OneQnZ1P+dEffw+h6tRjxVauSZKSrqmz0+ly/Pgj1MsOF957m7mDsxxfOEOkYas7YJi0Of/0F3jly6+SdG5w+8IvEE7y6PwMP/r+n9JPDjDXK5DjhzmxMMulD99lq1+gJya48/AeD5ZXuHFnhZMLksW1BgfOPEV7Zord5Qe88cM/5Tt/+adsDiRxFDMzNsHh+UPYzYfcuX0TKxRRmbGxuUFrfJzjBw+y6Xo4Y1ldz5g/9TiPLCzw+quvcW2qDuMHINthKhHMzR/B6pj7Dx8iM8Pq2gonckNiBvzoB/8Pv/7Nb3P2xAKxECw8+RKPPPIIg/UlOpvbTB2YwLoYJcHaEmt8ROrBqWnS/i5PP/kcC4+c5Y3dH7Oz2+X0kUcwomBlcZkf/Pg7HDv5OK+/+mvcufEJK1sbfOmlL5NEdYrCMjt7EOsMSsbB1M1x/sxZMI6aPEJNGD764E0+unqdY6fPIJIan1z7lLfe/gWnz5zj4LGTNKYmOX7iFLUo8gXrZ0AK9gEdIpghQ4XMVAZr+42X9yCj6lNV9y0AJWEA2vvILz1sjj32iqhMK0OHT3iwq2JRfVZMR0DM9+37vn1zlZBV7Pn1+D/8oJ2mQxr1BrWozk+v/Dl5DkILokSAKdjd3uKp88+ycPQ4P/rJXzPobJL1t2i1xslKTXOsTVxLMGXpJ6xGky88+zxrD1fo1HY5efIUM5MzPPXkeeYPHQ2dyVCgVtRmqq5mNVD7osZWAFz1EnzmOH/55RnDVc/zV2GBoRj7//p8+NPbHti9DVSglN0Dg6ousxN7YNBe63l0Sf05r/4+6i7tTSKhTPffawOlNkwSQgSqdWloNhtIKSnLHsNhSq+zy+TEON1Ol0tXrnDlk4scPXaMetLGFo5ed5eV1RU+99zn2N7YYXNrk6X7iwyHHXKb8vLLL/LR5ct8/nPPs7r8kN3uOp1ejWE+oFlvMT93mDiqcerUCU4cWaD56Djf+/F3uPjJBVZ/vM4//Z1/SqezS6teZ31lmX5/l+52m4sX3ufCxXc4c/YkL778Kr3eEIlgcfEu3Q/WyG1OrZYwMzHL9nYPAZw+fY7795aZnBijMF1UCe2xWYaDITub2zSbDVoT42zvbmHLHv1Bn6eeOs/5p5/jX374L2hPtkFYT7818KVXvsaBmVlqybtkeUqaZqysLdEfbIfJXuKEoru7yz/+r/5LThw9we17i/zirZ+Ru5Jao4XJcj9fhwVxrZEwyDIcDuNKClPSbDToD3voPGNyYoKd7TW2t5dxIuLcubOcPH6ak8fPsLK4xuLifXa6Wyi7yVpniVrU5P7SfTY37mKKgvsP7tBqxbz20gv85//Zf8t3v//ntOpNdnf7fPjeB3zh+ZeZOTBHJCKyQcoHb33I+PQUVy7f4Oxj5zh64ji/9o1fZ/LQDO++9XM++eBdVlduMD75HzM9fYztrS3iOME6iKIYpST1iTaDXpc/+Ld/yMrGfRpJnfZYm+WVO+SF5erVT+HaNYaDXY4/8iitpIYqSnY6fQZ5n7hWZ9AZMihKTpx5BNWcYHXxFot3r3D69BO0Z8axusGw16PX26Kzs8z01ASzh48x7A/p9ztkWcrk9GHi5iQTU3Ms3bvLyvIiRpScf/5Vlm/eZ+7YYTbWV1m8dpk4SYjHJ5mZnSeOawz7Bd2dDZQw9Ha2GatHRDXNTr9Psz6OMwXzc3P0ejv0N1Lm5mdJTcb9h2uARcUNpK4zPt7g8OHjnDxxhrsPHqAUHDt+mCKNyNI6UTzOoSMLtKem6Q1SjHWA72Za4XzCkYwY9DrU69obUGsonSXPM6zz8fZOWrSOcEiU8JH0TiVhbgkMS+9WCiL2sqRQICoV+cVmKC6dKzzIIBMkYRvSgknAFEg7BF2Qdnt0tjdQiTclNVmP/nYXJUHZnKwIyYu1hOZMhFYxtYYmTiJi7T1flBRIpahJ7WOqwzMUC4ik8l/tvLeECdOMDYawFYMEfDce6ZkXPjrbF/C+eeNCYe3/bsMiQ4VFkAiegjGA0JTBt8LYDJOX5HlBng5J+0O6/R79fo/BcECWpWR5Tpb5VEhbFN6gvDDkJuSrWS89UDgPHIUZ2hfxgDN+MSS8hD4M1R4osJ4FUmXa+EaQN+IFSSwqgRk+OEOBNaVnBxgf5+09mjzoVJniWmNGXySkCjIWhxKVzAqkdOiweh01aCpaEmGO2mt7hOnTb9MKn20bvnzEhBlJ5IQLHu/BdDdIsZAi+EZ502eLxCK8WatwPr4eFcol6xsrgT1dQVOltSPvHsDLMJwB53PRSiEoC4MMMhglBHHwismy4MVlvVSmut8IzZp6Q1OkBY2ohkNg0WRlRmFCwpkFUxY4a1HSgzhlkXuPnrzwiWkisKaMCT5UXqJH8KCRomJc+NrLAsb45D7rBArtgaPKC1MJdOKZRIVxaK2I4njEhvCLOn/BoijyNVIAUKVUoN0ee6UqLqSi3vQyZpP7/bci7B8CHaQwe85ijsL6wBStIs+SqppYwu2xoqvUOySDdODT0qJkBGya4CljnCU3ffK8i7PesNyJHOcitGqwO9iEZIwkEhgURw5OsdHfYX1rlX6+hi0lisTHgw08g/Pg5EHAYQzkBeAUtaSFkyn1SJAXpZeoWQ/iCNcKwQc2PHgWXRdIVSCV83KiIqfbz9jdzWlPzpDosQCSCupxhHOQW++fYwNo5JO6vMG7f69vI0tT+tpcQSwjdBZhnBvJgGLtQXg/djiKYAJfgbqRUCCVZ4uG56lUPuRdYr3JdgDYtJTYLPOgoYEoiYkRyCQhTUsMEhEM3OPA9ndSglQI50EqjcAoMQI8Kh8tgb+XCmsRWpGXJXFINvTeXaClJI48qyXPfTiCNQ4tNUooPMc2JMMJP04nUUxZBmYXeFNx5fxcwJ4RuQyJMtYGo3hReZ/66yKlQDoVmFV+PJZC4aT2zKzSorTCFgXOeiuUKJIUhQUXDNGdB9C03Bt3pBSURYG0pQ+7sl4ipwBnSyLp0CoOIKs32ZZCopEYYQLADN7kC6qAAM+U9ew0pRRiBM6HMViGGcXiWbLB0iPEsXrWsr9rEFJjA6lgtMYMZuU2yJn9nOw954gkUsX+uZYSa0p0olCRZ0fH4J+1BGqNFkJoNG7kHWhsiSlLIgWFE6i8oD4xTl6WCGeQ+ECcIkibvW+eB6e0DkECwhHHsbfh8EPTaI78+3r9vQNKs8+9zitPHObybpvZqRq795bolDNIbTFxQhIVLC4+pNfZZqm/hh1f4Dde+ioXP3yP5WPf5CvPP8O//L3fp3XyRc4cmuDhlT5TU20uvP8OR46fRfCQzXyVNac5ePQsY1pw5/pHvHP1Js2kxYW3f8xOJ6ckYeFL3+IfffkF/mEc86dvvAcu5+1LV6i3xri3usKVW3f5zX/wOk889SJPvXaIhcMn+NzRSf63/+Pf8WBzwM5Q8PlXXkW1p3n5qecp8pRvfPO3mJmd5NMrF+nqCU6NO773p39OXjicKXm4ss4LX/0WJ5qaR04e5F/8xe/xkRznk7UBLzx2nvcufcjP37/AW++9g9Mx7SQhloYrDx6y3R1Qb9S5ceMqOzsbWGMprELlJY/MTPD+z75LEU/yzEKbC+/+lIFVPPfsKxydPczjjzxGfazJ6sYKSw8ecv/OVT66/glHZ4/z/Itfocj7fPtb30IJOHj8BJ98cpW8HLBw8hRSCuq1mDwr0LGiLAqOHprl+JGjPHv2MS6++SZpWtBq1ens5Aw7W0gzIC0yyjLlnfd+gWy2GQxSEt2n0+ty+OQxiqxAN7yPQ2wN967fZGJ2lmatxr/5t/+KSzev89znXmRqZoYD05Msr67yTJzw5COPMnfwIFGkSeLIm4L+CohhBMiEPyqw4Zff+tm/7rFjRmle1VZ+BVr7q4yyvf5ehomt+n65fyfCZ33RV2EYf/clRuCLL3Lc6Me+C+DN27a21rl56xp5t8f1Tz/yRnFljgRy6xdCv/Xrv8XO8hadQRcdKaCkP+hgaPD4sSfZ3Nyg1RpHyZJma4wsz9nd2CCOY86cOYeQknPnniLLCuLpiCqKt0oq8VI1CSO1dnW2fVcEKjAmIDmjg9g3YlWHJ0Kh+SvO7Z4cLlzxEXMJgvPiCBCqgKC9U+tGX7u36Wo7n33PHtgXivJ9l0Ts32Yl4XN23yQvRtJHH6FrUVKQD3OyvKRWSximKaB4cH+Jj69e4o033mB1+T4bq0u0xiZotcbZ3t6kyOb8IisvGA66/ODH30fHjv/5f/pf+ODdy1y+eIkPL37Ays4asZa06g0Ozc0wMTVDYUrmDs3yra//OivL6yw9vMULn3+ObrfP08+e56PL15hst2nUa/zkRz/m+Ze+wHe/+x2uXH2Xzd0NemmXYSmoxw2KLKPfSb3UBYEzcPvePU+7LSw3bi4y7PdpjiVkhU+HWV9bR5QCZEq9EbOzu4FJCxSOLM2p18ZZvruMjiRKOIwtKHKHIeedd99irD3F1IEDOKcZDDImx8YZdLcwVjLWhhxNUcacOn6aL730MnMzt/nrP/oukxOTnH/icSbbE3S6Q/qDPvfvLiKso7O9Bc4yGAzJS4lUGQNnaOuErZUVarX14M0hSQcZd+884MHyA3JXeHq8ACO90WVhetTKlEufDlClpJHUODRzhN/5x/+ca5dvce2ja2ghqTeb1GJJmhZceO8im2srmHyHRr0OOiFpTzA+0Wa83cYYjUkVX/nyN7l75Rpf/OKLPHiwycmFBXYHQ3rdDrVagyTySSYPFpd4+623eP/Cm3zl61/nhz/4IbOzMyw88gwHxqeJ6w3GpqdIs4Kl2/eZPTjJRLtFlGjayTTGSc4+cZ7Obp/mxCTCJWyu3+WlV77GG3/7M9qTUzhVYmsRu9v32O2uMTc3jygFW5sbpGmHSGsefeIFev0dFm9dI441vSKl3WozObPAnY9usXr/AUrDoLsFosVU8wgT0wdYXXpIr7eGoPTpfnnKWCOm08tIVMSphSOsr23R73YpixRVb7K4/JCtnR2GpZetJc5yeHaWg3PHGZuYYXJyHKcsu7tbmMLSbmsmJw+QZdAfdJBJggtSY2cdRZaT56Xv3DqJiiKE8MBEs1nDlQIRFqqRVhgM0ikaSQJ4mYEVZaDEV2zLCJTyLAphEdZLIPJh30eMh06ztUOsKbBB+onJsc7gbI50OcqWWCXRZc5kvURHETq2TNWbxCrGCUlUj9GRT0bSOsZJhxKamkgwFXiAB7WE9YwiXyt7Kr0c8XAC4FFB+sJLy6rRrgyeRRYvC7DB74YgwxIuSEMCw0gJ4RPJcBTGYgvDsO/l671un17WZbfTY9AbMBj2KHopZT4ky/oUZRlYLjllkAIZb6yEHLGg9phCYTWBCDOQcw5jQzoSXibkbLUdL7+3zkvIbFgw4DxjxUo/emtJ8NeBkuBxFzrLCkfhHNIKwEsfpLOYYITtkGQmSGECA0iMOvz+y1yglTjjZQ6FsehIgwnAzT72T5Um5vc5SNVCElmsAvvFr2+DXEuEBZVERH5GVtW8FhZ9ynnGjFBypB30HjBipM63ThBF3kPGWC+dHAEWMtRdocEklWdkSIK0UVTXQiKdCR4eXs/lpThBNif89VQ6obQFQhmyIkPJCKUVWVEiI0VW5uHZ8exBFcynlZQBXHKYokQria7VUHGMkJqyzLCF8ewt4ReqTuDleOFeFdL7quAcKvLfq+MoNKAceZ5jnT+HzroAzIW0tlDPRFFCHEcj4KFih1UBI1VaVPV+FxLaysKnrDkpUDIKC97wbEpfC1khiYSXyColfby9C3JRJ7AiYDFShusYpFFaB2acIysKhPB2Gcr458dRkps+WTYEJLUoIo6biEJT5Bn9/hCVDpHxLhP1CQ4dGOf6rRt0Bus4OSBWNbRokhtLuzXDkSNHmGiNYzEM85w0K8jTjNJ4ZowVZu9hc14D61xMJBWxBlOWSB1hKXClI4r9/TfoDujlJVYIWtKQpsvU6gewpkRKQS1JUAjKogxjUmDyiKq+9h50WIMz+IQxa3HS0mwk9IY5xlkGWY7WVcqy344pixFA55/2YEsRDNul9FJW/7tKK+mfo1jHFM54FowDaQX1KCFOYmKVBymUbzqX2dAzyYTAS5s8mOSlrDI0Qv39Y4XDCM8gcsY3ggtnMM57c1V5d1r6scUZ752K0uS2GN0Xw0GGlAn1JLDcgH6vT4kNzc8SJb0ZfmZKL8kLTQQnZWBselaYlf6+FpXEDkZzIhWTLyTDmXC+hJDoKMFYS4QjivQocMED6w7KkoJ9KhT8mC5CUpkQHjT0Y6ofq6tEZRvG+SjU8SoAg074xkC1jrHgxzPngTPrPKDirMMJ64MBnNm3cPBjp7V+zkOCimOQdRoKiDQuqpEISVxXHgBSkQeQGjW0ipDOUVpDXUUIBVooVKzQTpJnQ+JmgkWQD1KksQzSnPHJNknSxJQgXImz3nSpLAxaaaJY0+t0mBob88mXYTYvioKyLADv6wUV2ObPlTEFURxjQ5iA1sqP99X9/Pf0+nsHlFq2w3tv/Jx2f5ULP/oT7i8+5NnXf5ta5Ch37/DRhx+z2U1Z202JkyZm2OPB8gavfPk1xhJBc2YcPX2cl1/9Mn/zxh+wuKtpj0WIXcXW9jqr9x7w7oVrnPr8V/lPDi3Quf0u1z65yRPPfJFjesDf/PUfMzAt4qNH+Sff/DLOWF5++Wuc+dyrdLYWuXt/nQll2N3e4M233+ff//4f8uprX0H077N8vce79/rkcoJvf+trfOenP+fodIujY3WajTa1Y0c5NNPmwPgYB156hWZjkrKzytUzV7l19z71RpOejPnkvR/x53fv8cqXvszi2ga56fEbr73O22/+kLNPfp4njs7y0f0V/sGrr/KT7/8Jl6/eZqebe6+bzCCNYKfT8TGlznH77lX+eGeJKEl4+pnP87Mf/AUDk5ALgahfZaZdY2N9kyfPP81rTz3DH1y/yJXbdzjQnuG73/sLFs48Sk1LbD4kjieII8WBgzMsL69w8MABxsZaRLGiPxzQ3+yzvbbC5U8vs7nR4ac//QmdrW0W79zgo7ZhZbPDVFPR2VgiiRpcv3QA1Rhj/vAJ7t25zWBynAJLq970BopFjjQ5v//vf5cPL1/mq1/5Fs88dp5zC49y+vxznD/7GO12nURrDk3P8Ni5R4kj7aniQlA58o/MIn/F61f/9O++XMUoCh8Q+5CGXwX6/DLg8dnoeU/vr7olrpIdUAEaYVL9DDjBqBDxhYTvEhKiRH0z0BfJ1UFJfGfu9u1rXL9+ma18wIHmFMN+j5e/9DqbWxt8dOUDGkmNy/du02y26fctZ049xidXPgGZc/3TTzDFEFxOLgaMNZr81Q/+iC9+/jWKXJJ2O2xtbjDsbTF/5CRaJf4YQvHi8ZuKYbXHRxKj3fSspf3nr0p6E/tPbDgm9yvO7d55rYrzPbbQ/s9W7wvrnP0X109U1e8qlLH6NXsQEqOj2EPuXLgXxP7tiYrpFpqNIzDSd3pKY5HaGzxmRR4opIZ2q81ud4f1zW2mxhsMs5S19TV2dnc5c+YU09Oz3Lv/gIdrKzxankUA/bTD7MEDSK359m//FsuLW/zs5z9hbGKKi1cv06w3GQw6FE7w3DNfJImb3Ll9j87OkAcP7nF/8TYnFo7xrW99mxs3PmVm8gh/9kd/yJlHT/P8519hZek+P/nJgIuX3qY/2Caut9jaWmZ1aYIsl9xdvMXKyj1qQqAkGFOSpwZEydkz51he2yS3BSo1pJlBaYilw5YFUwdnkELS7Wx7YKYo0PWYt99+j7FWg/nDh9jcWMcpQdLSFKkljiMerCxyZ/EmtbhGa6wBZe6jVLX3LZiZmcXkmiPzc3z4/g3+6q/+HJVojhw/zgvPvcSgm3Lp0hVqqkZvd5eZAwcp+kPS0vvdNGoJeVqgEs1WZ4cvvfISS0tLbK0vIVTEO++9y5U/eDaJAAAgAElEQVRLl0jzjCwbkg17n+kOBpcaNrY3OXnyNO0yYXltjf/zX/0uly69j3MZWkacPPsYu50dPvjFL3jk3DmOH58jHTS5fesaA2Ow64v8s//uvyE1fVYf3mJze5NTCyc489hjvPD81/nOn/wZZxaOMjk5Ti2JaTVb9NOSu7fvsfjwDqvrq7z09a9x7MxTvK4aHDxynD/7vX+NEppWZjn+yKMYpXjzp3/JwyXLk8+8yNyxR7l+9WNOnzvPk8+9xsryOmMTcwhnefzRzxM3W8zMzlAOh8zMTGOGGaKA6YMnKcqYGzeuEjdiWuNjlFmfmbl5Ni9vkQ6Wqel5DkwfY6u7xu0bHzNz5BCdzgadjTWkhPGxaeYPHiVuT5ENSsrBAOdyXLaLjTVOSBrNmMmJSSSKRqOJFN6Lo5kkLN1bZrfbIWooau0GR+aOcfrEaQ7MHmaY5uys+dj1ybk5rn96j1j3kERMHJgFXTLob6F1jK5p0mGOK6RnxglvXh05fHR2LcEVJVIrSpPRatap1WKkSMiDFM3gsAo0Pl3NGB/xLpwHlmyZ4WyBy0siVdLrbCGlpd5u4+IaShpqUqLqGhVJtFIoFaNEC6U8E08JjQ4LeaHqwc/B+7bkhcVpLwlQ4b40YUDy4eJeTi6QiJD2ZAKi7jPVqkWK78i6AMw7LKXdx8ENYE4JaKl99PBowSYwJsdmhv4wo9fvM+j36O126O/u0Bn06PT7pIMBWTrEFnlgfOU4CIa9Lsi1vGzMVX4dwQPIVvNHAG0cjgJQFbPA+E65DYuJKhGuCON+1Vk3FkrriJWkdAbrQqoRviHghDdL1UGyBowWJc4SAEIwQULlpxGLLS053ttKOBfkUBUu42O+C+sCA8x7G1XzYyn9wlACpfF1Q4lFC+//JtSezMU5759ihCES3jNICpCRj4n3YIbnt3iJih19LsBTIRXKv6SUFMabtVpnkM7LF4XwJrfV4lGI4BUTjMH9Nl2Y98I8ab2HjqNKcgtAU/AucviobusqK4GwIMchlI+XL7PgIlJaGq2GB2+l81ImLO1WLazVPaCjowiBJB2mKAWtVhstNKUNokwhiKIxQJBnhW9UhQRApXVIx/PXUEjfqRfCa2akEujYP0VxXPPbMwaCf5pzDlNajCkpbelZo1L7pF7n3ysqcG5fbSMDS8kzH0qKIsPgfLBFkDdV26/S3EZpjtYCCmN8xhVWeflOdU6Qe4AWzptGm8CaMSU4RyQEhStx1gNZsa6j6450uOu9kqxDE5FlJc4VGAxFnjMzP8lEPeHhw/tolaGSFq1ag3p8kGYUEUeaWiwxNmWQFvR6fdJ+TlaUpIVFxREoDcJ6I2F/+VFOADFW+NQ1If31iyJLrBXW5WRlHyMltfoE/UEX6CBdkyJPiZIaSkY0GzFZainyAlONg0qFGtxiwvNX4tAhnVArhdAJ1ungOWQY5iW1JAoAhiBOYrJsiHPGx9gLPHMMiaHE7bNfsAGUHtl1SUUcxaQmRaHJhhkKSSQEka7Mv/24q2TDS5pNuZfO5yCSgghCml1gwBPAbm/Q5lmTUpLlGSifQOqc90kzuD3gw4GU3ifKOktpgkw0yM9c6Q3xjQRh9rx2qLx/CIBzYM05C0IJcmeInEbjx9IqJQ1XhNHCNxZKY6jSNq2xmMJLyi2OQWpoxXUoc0wh/l/m3jvYsuO+7/x09wk3vxzmhUmYgMEEzAAzyCAIQBKjSIoUaXotaWWVdyWrrJVrtSu55N0taa2lbKlkS6ZL0ZKDJHIZIJAgCTGDAAgCBDAABpNzejnefM8953T3/tHnvnkA6X9c/GNP1bx58+a+c+9J3f37/r6BxCaZbYRwAG9mHk1WVykh0cYZhwvInm0nM+tlM5gsGCZW2vlnZWmKtnfuZE8Gi6uzsmfMEwG+5yxdhASrQkJf4ksPEXgIL4fnB+TzRTwvIO8LfN+Z7Le7HbZUSnSETyAUQqSAT7fVwJOQKxRItcXEXazVTpKJA56DwEcnmkDk8FVAN04QKJpJFy+Q5PMFFwKFYx71GGKu/NV0E0GgcOEoaYSUmjS12Axwkxlb3xjX1kBKjEkcOC4UcerSF10durki+tFsP3JAaeroI2zpX+WpJxcI+wqcu3iJ1sA5Hthuee3CAqXpA6Sry4weO8SO4SLL1XUaqc/WnTupXvgu33zuefonbucf/eS7+fzMs5y+Pscrz3wZL03Jl8q028ukOcn63GlOvZQjac/wjRe+z+ily/hxRMuUiNOYcrXK5//6v3LXgw+zfXILu0tF9HAO2Wrw8vFXqXUt5bzgwuUzXL52AWNgeHAMKS0NFKWTr7A8e45PfvIPubm4wr5jj3PnWJnV9XUmBkbZPTWBsQmqNM59B/bSjVJGxrbz6GOPcObUcc41LI+86+PcvmuS3/u3f8CVc+e4NrvIevsEsxcV/ug2olaLqaFBzvsBj9x3F6urdQ7ccw/7du/i/JnX+fSnP+WiOLsdmrUWkaix9t1nKBVKPP7u9zM0MEChVEHWFjg5c5WTJ7/Prsky56/epNowXF9YJUXzyb/6Cw7sP8xPPPIo+ZxgaX4WXwrq62usrjbZMuYm1EC1uLFwnU9/9jOkxVEevPMOnnv+RQgVr7/2MpN7JghMnStvniRfGGK9usKLZ1/nnsMP0Ww1eeLLT9BfCNmxay/1vbvp6xtCpwlpHPPC6yeo1et86vNP8NXBb7Jjz+386i/+c2wa4fkexliUJwl6AzWuC2hw0cUOqXUUxg2/nWzb/P3b/++tBstvZa68/TWbN2tvdR3fvs/e/oToORzdoj32Jp6N9+hhKRvv4R580zPYRGf78jIURGc0czcZtVpt0lSzsDjLsbseYnenxdzcVZqRYd/23ZxNDMVCwKf++o+5en2GNpZiWCJuJ+y9bQ+nL51nvbaKxNJNWgwPDpPGTVYWZ/n6Spvx6TEmJyb43N99mve95334ykcITS8O1jF85AYwluU6vHUcstk5gFvGfNZmi6qejFBsLFA3p+xt3hxzKVucZvvMePsOULRyY+G6+Wz3gDuRLXzdwsz+wDnvfcrNMFOPEOWwKLvxPb1ltegBhGxMsBv3RzYeS6XotDuMDg9jtKa6tkJfIc/lWpWdRw5w9fpVbGoYGBig1miwtLJI4OfIF3OsrK24Y9aK+uo6P/3BjzI1PIZM5vgXv/4v+Ve/+9v4vsfCzQWSVDMzO8fXv/ENtoyPU2t30GlMJ24yOTbNe9/9AWauz3P03nv5+le+Q6tbo9vp8Jd/9cf0l8ucOvcGq5FLeJMypd5JeO3M6xTyA6yuLTPU30+rsU7cMfiBWzDoFNZWq1RXaoT5ECMSlO+Rakc/DwJBO21hYoGWhiRO0dInkD5+rsBadZ2+vhypFhTzPt2ki5/vJ0pjFlZnUR4MqGHaSw1aUY1AeTSTLnumd3Plxg3uO/Ygc3Or/Jv/+xPsPryTVrtObbXKF598mvn1Oarr6wQZG2Rmfp6hkX5yfkBsXPR0ErWhJekf6MeKPH2FIea610k9aLS7RDlnFjw5PQ1ymGvXr5HqBBBYqQkqfRzZcxv/w4f/Mb/3rz5BUJacvnCKdz7+IF988gkGSv0s3rxBkrawNuDwkbu58+4jHP/+K1y5dg1Jh0ZtnTdfP83cwhIf/eiH+eaXv8ldR45y6vQp+kfGOHTsbparTSQB1+aWKRS6LC2vsLS6TIrg7nf8BPlCmZWVKuWxccZGRnjvh34SK3xiqcgPjdOoNTn26Pt586UvsrA0T7E4gtUxC/M38F56kdlrl1i4OkS7kTI07lLwFmeu0q1VabYStmy7nXxpmPHJkNW5GXRcxWuHVBfnKZf7sAQEuTxDwzuprtUoFyqMjW3B6+8jSASN6gpDw0MI6RKDhJdH44wkRRBCnFBvtvCEZmWlhhA+d995kCuzawxUilxvtrLhPWZqyxDtqOmKwTjBMzGB7eLFMbQbrpjodOi0Irbv2o3yioRSUugbyOAGSbXewIqY/pLnGEgC2lELYaGQyyGFpkObKO7i5/N04yZrKytILL6vXCEiFNq6RbeTLkHRc+kzvqfAEwhPovyQQJWQKsH3xrB42Ewi41ucdEI45huQxVM702WDK4Is2pm7Zl4sPRakh5OQiazQxwp86/yKTJbo4zxIhSs8pURm3i8YMMK8xUPHzSwSKRVhkLEfcAvSbtwlabSodROarTqNep319Rr1VpNWo0bUqNONIrpp28nRTIoxzly15w1xC8yQWTqWyArmxNUfadbpVy4VzHVMDSZjHEkgEY5BaLNjC6SLYxbGpZWlwsl5yKKxsRaTJV9JIZBeZqSMm197EgYvszbM6EHoNAXpfIIMoLJrjYA0NVmnN/PvQSGz4+ntWXkOHNCZjk64dyO1DmIKPPf7gQBQpNaBFT3PIqQD73v+P6lJ3ecUznxVCpenJqzJPHEyOYp1848QmTmtMCSZt5MnpGNlASKTaBirnWQrdWwgLzsuEKAsBu38tXxBukEoVoB2jK6MTayFdamAmXGs2DCXTt1nIwtmkKBUgJcBMALXLff9HGkxzO4Oge/72CzYwcRdfKXQwslF87kchXwOm/mMkTU6/CB0bCwr6MZRBhIKpO87eY91Mo+sdZh5hLjmnSvqevO+xWjrQLTs3pUqm+9VT44jwKaZPLC3suvJ/pwPSk/WR6+pqJwZt80KZSeFBWwPqNQurKIn2bMQ9M6tFSA8DAka8IUHVmcyPpeG7NKg3D3s/LsyLog1+J7zOCQNnHeVNdk9k8+K6w7adLJ1jIfEkMs5c27fU4yOjtGKOrQ6CZ7ooxB6zg6j26WVJHSFM1wWUtHuxHSihE63SxxDYgQFLDYfZudcoZVLekwy4ETirpVAkPcV0ncelCZ1ZtmxSRCeIdIWzy+iReRkhNZH4hK0TCzoZs88GTtMWuHoJphMCruZ7adIeyBjmqJ1SjvqgHBpXlI6ZqMf+O4apu7+QSqkgLwMs3HuFvOjp5FKk5QodfeC1K7gt0rQ7HYIM+BYW4GV1oH9mfTaWovS7h7WWCKrN6SdNgsqImMsygw8NNb5qHmeR5KBLT1bCmPBCJUx8EzWHHX3tSaThSpn1yAsDhKyKcIqekwpIXveYRY/A14To0EKPOPA5jjpuvFAOEDd+XZpJ4cGJx8jAyqsC10ROqaY84m9kG6jS95LCKSiS0KSpqAgJ50sywixwbzbaAAL0MaQaJ0lLDo2UiqcabcWKZ7n43u+Y3lKByQaQCpJkqT4fg7phy7kwmoKhRKRlYwWC/j5AiKXI01jBksFVFjAC3LkcsWMQWoy5rLFUz7VahWruxTzJQLrxjujfbRJaXdblAYqWDQSS5R2yYU+oa+wicmOT5CkCXnPd41skyKlReuE8aExx361GmM1RieuEZB5++WUz2qtyeBgiTROkfbWvdh7JKS8VSODxfMCB3zj2H1aO/Z0z2vu//eA0qM7A+au1fnYz36UZ//m96hVI7aiaee3cu8Hf4FcVOf509d452Pv5vD0ENXlOZomR9GXFEdzPPudN5kf6vAf/8Ofsuvuj/Hzgy/wd098ga4q4+f7GBwfxUsk129c48TrJzm0axqB5tTVefbs2U6ntcJdH/w48y9/hTfOXKApCxw4cIR+1WJl/ixLi6vs3HcPZV/z9JeuExvLwOAYVnl4xT4KgWYkhIvnztJpp9zs1NDa8v/+57/i6xND7Nk6wVNPfJpd+w5z54E9vHn2FJXR7dz/0EO8euokt01t5cD27bz/xzv0DRbZ0hhF+ZLilim2r60xPHEbhfEhDh48xJaRAVZmyyjf443Tb7J/736aSzcYPrCLaKjI3n27uHj+Kvl8SEcn7DlwhNW5eXbs2s/B23awdXiEleWb/OVXnmC5VscPJOcvLzK70mHqzgd4970PcWNxgQ//xPsYLgcMyC5PPfm39I3cTrl/ABXkUb6iE0eUvDy+p2jHKXvue4zHHnqEr37mz/B8yVyjykhhnH/wyI/xq//Lz7LaXGNo/A72ToxAmGet2WF8dJzJrTs4d/IEM7U2dx19kMAPSdodVlaX2L3/EOPj03z6058iqRrGk5hyIaTW7GSIs0HiZdiE3ug6SOFlHaDND8omgGIDKPpBUGjzz36YQfTm7e2AEYgNQOSHg0m3Psfb99V7n94Du/m1WrsFhenR5DP5nDtuN6Bq40wFw1zA1atneOG7X+fUhbPsO3IX9x84RKH4OH/yJ/+eJ7/xFO951wd59fXvcfPmDUZGR5lbvEEhHCJOu2wZGUMKQTtO6BvoI1RFUgT1yJKmAXPLF/ipD/8ErU6T6cmtaE9glcV6mdeGlBuTg6HXcc0IpNnC4FYSGiCclEJkXQHnn/C262HtRkpJj7m0mULeM0vfPNjdel9xC4SyG19uvXoDPHz7lXire9YPu1qbd7th9d27zj3K2MagLTMZgUFKQ2pSPBkQxynKV5w5d4nH3/kwX/jSFzl78RJaGB546EFWVhcxaYxOmvzYo+/g5dcvEyUdfAXNZptLF1/hIx/9GV56+XWefe4b7N19O4szs/zu732CP/3j/8jc4g28IOTuw4eJOxHzM5dRXsrKSodiWOSzn/usk1/l8rz00vPUquusbVnn/gce5MTxV+grlwiLW6nVG6yvrrFtaop/+ou/ws2ZZb7y9Be4OXeddrvrOuiRQClBHGtmF1acka9r2mF0FzzJUP8YW7duY2B4mHMnT6KjLvmpMldnLyOkROU8Brx+du7czsLqc6S+h7QCX6Rsn57iIx/6Kb745S8xOjzC4tIS567U6eguvu9x7coCu+/YzyuvH+fkyZPkRxUnz7yJCgosLy0xNTmFkjA9PUrcTFhdr1Ioh/hBSKe+SqWYw6QpGIU0FkWXs2++xJadd7Blxz6u3TjvmFAocoU8aduyvLJIN43Jhz6tdsTeXftZWljhZ37qn7C+VmV6+zbaSYvBwSG2TBxg923nGBwsE8WWrmnTrK/x6svPM3NjgerKMtZotFb0V3J85ot/Rqea8pGPfwy/4vMHv/+7DPaVCL2AvXvu5ObcEovLyyyurTGxbQe5fInJbSVkEOCpMr5nmCzmCJSg200Y27afl77zInc8fAybSAqlApVKA2FDrl58DT8sc8f+Qwz0DXHx3DnqzVlWT5xlfHwXMzerXLt2jW3Tk6iwj/LwMCtL87z+8jdZWp9HKcHt+w5RzBXR8TSNdpt2tEazvsjwyBSlcpmZm9e5fuEmo/tuJ15bo9tZRSvQiUS16rST/Xgqh9U++VyR9cYKcSeiETWRCCYnRmh1Ys6eOc9Afz8Ww/jUOLV6HXzB4PggUdyEbkK3XmVt7ga+gSDIoUWBvsF+lpZWmV+5ikkAnTA0PEy70wLtvFzibhffC6iUBwlzJSAlCHOkSpEYQymfw/cEIm0yXKmgpKZULLjXeSGe5wpjpWzmAqoyxkMv9cmBGM5TJkUqZ+zrDGJvjUPSE1i0+13bkzu5QUdZgRT6lhkqktDzcMHzltS45BYrHGvJdayzbrLOACMrIHWohkxSjOcAVuUJx6DEsVS6SUoSR3SaLZr1Bq1andr6Oqvr67SaVRrNGlEropN00UnkDKmtMyJVPU8ecEyOjNXZk3OAwfOd/5PVllarCQgXC6885+mS+VUoJUhxBY5JIXZlujNNxs09CSA0GGlJrdkwxgVneuypTIqIzfwu/Ixd5Hw8jHUy5J7swBcKJa3zLtEu2l1KhdcrtIyTjgjP4kmXliSkdQWTdcdsE+dx4itngO4XfJLYOCmcFaRZ8yRUHiJLP5JCZqChwbM9tpLIZCRio0OPFG6/PQm8TbOkP8fJ0pCZm9sNQMcY4xLyjMl8vJTzNLSZp5WXSbyUS0GyQm9YOarMe0hrBxDkCgFJmiBSQ5wmWKQ7DwL8XOiYMNnsmcuFCKVIU5xxrBJoY915EoB0Epw07T0zWYmnTQYEOuDKs4Y46QJOeidkzwtMIrwAI33XnMIgVAZA2SzCXThZmLEWJT3SJHXPhfSR0tuYx3vscYdqOtmi6B2LcYCmMw42+NJDkhn79nqBoverWYGvBFYbtNEEXugYeZm/kRQCnTqQRCnlPlMmkfWkK2bJjH+VUiSxM0LWQri5KpPZbTQwM38ska2RrLXZ9RMbxSjaOHNvrUmsJkksrShGZiCq73lIaTKgoeCEPEbieQVErk0uqCBtDisMk5NDrNdbtGKJ5w2hO7Hzw5GK1MZ4KiFqaIRxEpt2NyZKDWnswLjA+Pi+x/j4IMtrVXLCgahC+QisM7k2KUYmxCai24w2gPt8rkLR12gtXKMkbpFoRYqmQwff+nSjClHiBE8O4td4yvmCWTewoFOT3Y+GRrdLoJ2sSloQWXqftpqo03HyqEwP2wNh3FrQQcNCeNg0dU0DaRGZjFBYgTYaDfQM7EXvHkdhjHYAb7YOtgiXTobz7nL+aw4ElNagMuN6x4q/BXr7Qm6w8B22mTqQxGg3PmX35+aAhJ4EzQKJTVAy2LSDTGJpLYEMcJZ0DtgXGbCvJcTaQKrdvSKgaywpFt+4edUoizDOI8hkXnsWm3lCZSttKRBKONDKCiqFIu0oolXv0NdXJp/3ibSBNCXyDDl6clE3L/rKR+MMxD0hyWXeUZ4VKOEhgszbT0h0Nq/4nk8QBvi5AtL3sUIhPQ+/4NIDVWooV8pUSn0kxhB4hlBBlEjqq0sMDoyQIsgXCwjrYdOENOm64dkKkiilUa8zNtSPFhJPeVibIj1BrdoiDHz8MIfWlrjVJsBDWEWq3WDi+T46NS7xLgPKtLXUWw38IMDzfOI0xaLRJsVikZ7aaFhE2hDmfKQMQCSugWEVRiekOnb+eJ4iTRO6SezmR+P8+mSWfiiEwlMexiYIIYi7+odUQ//9248cULp0bZm9uw+x8vKTvPDmEtX8GHEaUQkMe8e2UCjfxt79R+jPFQlEghoeZBSLlIbVSGJGhxjJl1hqGx7oL5KsFElLU7zzsXdRUl3qi5cRusj6SpdGDZ49cZHJAw9yaGyEmTefQ03czif/j1/nd37lGVojR9k20Ed1YZ6+3ZPMdwNEuZ/V5RsMTm/lziNHqUvJ4s15hvqnuGf/dj7/lScp+wG1Wg3hFenEdQQKVcjRaXd57oVXKQ1NMRpW6Ioij77zA0idIE2dk8qn1ljl+twMsbXsn67yh3/+pwSmSLq6xHqzwTvvOsz7Hn6QgSDmpee/yhvHX0brkIfuf5Czp97g3MUa1y+dZa7W4K7b97G80GJtfY2hsS2MFyRznTaqW+XP/vwPqJRGGRkeIcyVoVqjWu/Q8BWq3M97fvxDfOQd95NYC2kHlM/fP/0E3zhxhpK6xs//3D9lXKbMzFxlYnIMY308z2dkbILdtx8gsC0uXL5OZcsO2tfOcfP6Jf7DX/9XHn/fz9CyMFgs8tlP/SWFgQH+13/2AdJ2hwHfZ8fWbRy9+25GBgaora7z7Lef554HH+Tn/sHP01cMefZbT+OVBti//wCNqO6SNkwW04szcHSoOY6GT5aIYLNqNts2A0k9dBwAcwuc2Az8bLz2bQDRW4Ght+53898/9H1tj/myeX9q43Wb2U1v9w3qARVuPenoiVK5iSAIfLpxzMryIr7MsVhd5e7Dx9g1dRv1ao2JLdu5+677yBUlszdnacYJ/cNbuG3bfq7PLbLSalApFjl98RRBEBIGigO79pKalJmFm3Tq69x3933MLszRVx7nj/74DxHAwUN3srK8xMTkVqJuh27UpL9/JDO4zJZoFndNkG9lGFkyKn5vBtxkM/p2sO8HwLtb57anib91XjfziXpmppvf8wcBog288BaGuPE/PwxMgh++j41FhiDz2nDFSrfrUiiCwCcRUKuvUS5U0NZQqzd55qtfY/HmIq8fP8nCzBIHDx/kyJ2HmVteYH5mnsXFFaJYUsgVmJ2bZ2Z+gb/74ud59J1HKVaGaXaqPPDAA9ycucpjjz9Gvdnlx37sxzl08HZefOkFvvGVL9LtrkCQR/h5hvvzTI5tQcsYVIF/+Zu/QWUwZK3eZHi8RLsVMb+6xn333cvhI4f55jNPo9sRu/fsYWx8GwvzTT760Y/z53/253QbMalxshAlFCrnEQiIo5jEE0ht0AJ836fRanD90g1OvXaGuFvnPR/4AFu3b+Mzn/0btu3ewfkTF0iiiKRTJV/w6LYNgSeJ4zavn3qDWiOmWBzg/MWrtFpVAivotDVBeQAbQlptk5g2Om2juzFBqYBOJd1uBykDQhEQiJB6u440gkDkaFVXqQwMkMY1onabiaER5hfmmNq2h4XZC3STJiYI6BpBOVT0F4sMDU2zY/cOZq4VWZibpdmq4YmA6xeuo6XkX//hH/Brv/wr5HM+py4tsL7c4fQbf8XUzilGt95J3ofRwVGu3zjL+csnyeVzzMzPUiyVELUav/mv/h1/+Vd/gjeUUG/UuHlllvNXjjM+Msm3n3uGka37OX/lKstzswxOjlJttamUhihXSuSCkE7UwUqf+dlZCv39RE0IcgFeKSRtt/D8Inlfsn3rduS7Ps4L33qKcrnAvt2Hef3468RGEsUtpEjpdOrsu/O9XLl6iX377yWKNPM3LzEyPMbk1AjdZJWh4S0MDG1F+AWm+6a5dOpFXn3ma5i4zfUrF5nauQ/dabP19u2szs1i/YB6dYVCPqRQ6EPblJMvf4/p2w7RqC0S1ZcgTrPnVRIElsCXnDt/nsD36SsXaXc7rC4vkhrIlUP6+3OE0sdGEbodU19ZZm2xRmIFMihTa7ZppQmQMDExgZUBY31DxOUCA4UyU1NThL5H6PvkwwKxdqyDIFA0Oh2kl8PYrvMr0RaB7+KDrQGR4vsh1jgT58x22rFeLEjhAzpLAxNYUox0QiChnKeczFgpBmf8LISXTWE9iVdmwirA2MDJLawDN7RNnH+FMYg0JcpcI0RWtEoUnrJ4viQVgTPZthbTjYnSLlGzzbowomwAACAASURBVHqjTrTeZK22xsrqCo3qOtV6lajdoBu3IU02UsaUlCTS4BlXlAlA5SRJHDtZkJVI6WKgQ9/DC5yXQxgESCSdTkK+UCBfKhEGHlG7xflLF4i6ToIjjMUagU5TAj9ASGcKCxLraXJSoWOTJT0bdx60JRUevkwIZCa9sKljDgA6dV3bJDu3SapdHLdyA7fyfbppF4NE+ArpZ9OOSZHCzcM94MjLurlKSMikcZAlqwnnU6NTTagEKnDm60IqrLb4SuDnFd2uRTicA6kcs6eXzCaQGJuiRGYLbDXS6yW+iux9nU+LIQvi9rJUMi1Rnue8qgzY9NZ8KAXobF5W2VrE9P7WOkv5FMjQd91sL5PGSCeByedDpFAEviIXhiRpipIeyvMzBrWbi7VJsNa6a2YM+TBkYGCQhcVVom6XrnDFqtfrfPdYBWninvcwj5XO9NpXHhhBPpfHmBgfiOPYgQ7CAyEwwoFzKquUte7JwDLvj9Q4o2ApXXS4lCirMl8Yb2Oed6CDwZg0k11m2F3mkbSpdeSM+w0b/lso5SSpwnPHnbEJMbeY181WfaMxqFTGqNvM7hCQGV452ZdxPkruHLii20jhPNSkOx8O8JAuFUsIEC6JznnRuGPTOht3jGPm2J4Br9YIL0Rn4FLo5bFSYRRIAnwTIP0cnhCM9w+xVluirbsIGZLL9bOlr8TxU2ec1CfIUautEwQe1mpk5vvWanVIu4LUaGeYn4HbSniknS6pSZmdnyVUHspotHRmyo78ZfDx0Wh06pF0LVZacoUQJSGOodGpEsgCrfYaVd+jEg6ifEiEpdtZzoD5MibxHKNRSrA6A+QkXiY1lEJCaknRvSWcSwU3xhnmawf4WEsG5mTWGlmKnNRgSQnzBZIsIUtYk4FQZgMYdJfIzRs6cXdc6PkYCZXhARqrDSfrxaI9BalxhvNWZ4x4kTGJ7MYitMei11h6aZiOfZRJZ6VCW0PgO2ZMzxT/llecwJgUiSJJEvccSMcm6s0rsXbzsbSWVAgHqGIp5ELaWhJZja8z1hwQZLLhCE0ofEdghI24+54HnLQu2THRKTZNiZOYMOhDypTB/jxJXMQLQ4Tn0Z93KaKhUuTDvANEfM/JJS3IfIANPDwvhyd9QinptiK8fAGb8wlVSFdYhoYGyYUhOeXjS5+c55OYOGMPBwipSLTGN3ZD6ZJGHVJrCJTH4vISfYUSFksQ+Cjlk6Ypmtg1iqTzzmq1moS5AJUroU02NlpnjdBpthgbrZCkCTYxTtIcuPFMG41UEutJklaXwHPX0qSaNI7RccLU6ASxdh5I1mhINJ50Xn7aGnzPY2VtjTCQ1NbXEIHEE4qoE6M8Vzt5KnAsJOMk2oV80QGMABLSNHGNGIvzwpOCZrv936iI/vs29Vu/9Vs/0h1W8rXfWrhwlsA3nFwwPHzvJIs3l6hUAl569QS79x6hvxjg+5CkMJQ3vPi9Fxgc381gJaAo20Sjhzm2TXPy5BUCmxL2jbFteACvMcPnnnqWU9dXmG37DA6OsH3HVn75F/4JP/34Ea7P3uDI3ffRXrjOxdklrl2d4crl85y4cpP7jz3Go/ccoeQnnD/9GjdX6tx59FF++l2PcfzM6wxOTHHm9GnuOPgOtk1PoGzC8noVpQKMthQHt9Co12hry44dt/HhRx6gsTJDPe0SWMvNy2d47dQpTLPJjaUmW3bu4vj3v87ZqzdZnltgpZawc3qKq3NzjPb18ffPfI0vvXoWrzKKTmLa1XmE0bSbdar1FkmUsLReJx94bN2+A5VaTp05gydDFpZmQUoqfYPoNGJ28SatWBPFlsMH7ufIXcd46Og9lIpFUqtdp9gKdu/Zzd7pYb77nS+xZcd+xgdHSKMWl974PrGGcv8A7VaHNJGEQQ6R8/mln/nHfOPpv2Ni51H2H9yF7XY4dnA/lVKOq/M3ee3UaV5++UXePH2CZqfFkTsOUQhD6p023XaD05cv8NDD97F08wYvvvgChbEt/O+//M/pK1Xor/SjDM4sTZiNARYhM0f9jLgpM937xlrqbawk8VYA6BZskLFcxC0o4S2v+yFgUu/nm4EgIFs4uNdudETY2H22QOl9treDTj8IUNXrdfzAI8kG3ZmZ61QqZTCWCxcv8F/+9j/TajedubOQfOg9P0WtVicQDmdTHtjU46XvfJ2B4QEkPi+/edypwowmarYxKEbHpkm0Zm5+hsWlBVZXFynkQsYnd3Lnwfv58teeYtu2MYwss2PbFK1mmy0jWzhz5jTGRvT3DWULKOM6oyJjJ20Cfd56LjefA7vp3+56CNQm0Mhtve7fD4JIm4EntwB8C0CYfbGb74nsq938GvtW7GvzxL3x3a1bic2kM7ExqQtElvJiLFQqLoI9TiOGB0YxwrK6toaUcPv+fVy4ct5FnxZCGs11ZhbmOH/6AjPXrrC4vEi9uc673/0ulmbmee3McdrNGkNDQzz5hSe5ePoNSpUS99z3ECa2nDrzJnv2HmLPrn1gPF478SKdpEukPZT0iJKYKI7IhWW+/fffwM9JWqZLs9Hg7kNHufvoPTz11S9Sr69QXa5x7epNRoa3cOze+2k1I0y37caVxQXm5+YQPpjI4BUkA5UK1fUGfuCRywWu4DGuI6qTlGajifQtQnqsVddJOoaJ0XEmRrdz8dJJcrmARqtJasINdlvgK5I0YXZ2gWatRqSbGAxSKyweWhqibkSrUSNOLB2bksvlUFYTtTpIo6g1VqkUSiwsLoJviLsJCEuxWKIdd4naMbv2H+GP/vDPOfXGJYyVzC7Ns7Y2T7O+Tjk/yHBxEGUNyICd2/dw9do15pcXAU2+WKJYKRPHdXbs2E3aiTh81zFePf4qVqWsrq/Saaxz7eplPOFx7OgxqitrCJsyMDjGw488SqebEHc17bYkDCXdFFaWluhGDaprC3S6Db7/4qvMXrvJ/I3L7NpzO8dfepHzZ46zPHuNobFxFpbXeP21N1haWOC73/kyKtV84yt/TU76nP7uFzh47zH6BkYJg4A4ttTX6yzOXKZUDKi3NH5hgGKliB+EjE3toH/LHjCaoFhiaHya1fkFJrdMYMMKlQB8Jdh94B6iWh3dXCdfKaO1ZWnpCrFpkhjN0GA/lYERCv0DtNeW8XKKNIrJF4oEpX5qrTqBJ9h12+14QY7VxRskSYJOY3zPafkJHQgipMIoy8pKlUKlQCGfJ047tJpNOp0OhWIAiaHTsaQ6oNVJ6HQSaq0m+TDHbbv3cHDPQQ4ePMqOXbvZs3sfO27bTS5fJMwX8FTgJBmJIbWCFImRHn4gXVKOVPh+Dt9XBLmQMJfDC1xX1SWBeQijNo15OhtQXFyvkYAK6fltSXvLs8FABihkY5vR9IzCtbFIY7Cp3Sh4s4wxNz5K1wG2wpLz8yjP+fSksaXearO4vMTNazc4f/YiJ46/xisvfo/nnn+Gl57/Dt/73jO89sr3OHPyONcun2Zp/gr12jxpVMeYLhZX7Epl8JUl9OWGX1KaujSeYi7PQF8fw0PDDA8OMzg4wtDIKANDI1T6hylXBiiGRaT06HS6pFYS5gqQCtbX1oi6CUmSYBEkJsGTklS4+RzjDHT9QDHUXyEX+kRJnDG3MmejjKokrCH0AkzqWGHKIwMtXAqPDOTGvCM9i+8LUpvgS0GSsYE9lQEHqcZXAuUJfJy8zaBRviIIAyfnsJksWwCeRGTpRjqbxw0aK5xExUluBLF2ZtYyYx+JzODaBXJk3kUKx/CSOIm/NShPZpK1zDg6m4CEkgjfgVgaSLrOuNz0PLKszWSGID1FGPrkAp9CLqRULFIs5OgvFqmUSvT19VGqFOmvVOjLF8kXC46pGgb4fmYsL5ykxBjnodSJUqJY0+4mRLEmcfUNFoGUHn6YRyqf/v5h/FwerSEMQqTnOeBAOM8yq42LvVYKKXykCNw8mvkq2TR2xupCInppiq6NDsIxr7R2bHWdpdLZrIPkSEeOweGp0MncsphsrJNMCumKuUQnrkjbvEbpLd8y0EpmCwUnw7y1PpRSECcxnu8jAU95xHHsvGwy8M4aZ16/wbDOGoQuM0tk0eaOVZLqTGZpDEJl4Jd144qwbg2shHUAh/IRnvcWq4A08+7aWH8qB5x4AoxxwQNaCsLApT/6SpPPR0gsSaowNkAjiVNNFKf4QQWBx5bJafZuH+XEiTMQVqjXaiRZPLtCOK8aq9FGMzA0iq+gEyUOoEUCPkHOQ3uCNM68aqRESN8FBVjQmY+LwMeKAKM75HLOqLyYt3RNnSBXIukkGKWwErpJQiCc75KLiQ+xWiGti1aXZP5j0sskTxKVy2egqecYXlK4Z1neSnIkG0NQ0kmXlczyZBww43sKPwt3kNKlpRnp5gST3TNKKedrZ10KpFCO6WYy6W4UO9aIUgpfuWREpRRSWXwlN557g+yRFDMmSSbdBeeNY012jwmSbuQYSNaxW+hJN417bzfpuONzh+d82BzY6u5XX/l4QmV+fg7QllJhhcGTgmKQd88BDrg0UriGqpIbfj4qCNy6PZB4gY8XBPi5HPgKlQvpGxxgYGgL/WNjDI+NUS4P45f6GJ+apm90isEt2xkeHWV66zZGJreyZdtOhia3MrlzNyNbdzI6tZWJbTvZun034xPb2TK5jcHRcaZv28WO/QeYntpF//AwUxMTjPcNEvg+MmOSITRR18lh0zRBWgfCah2jlEcSayQpOSlJ0oRmc53+vn53fYIAISVpEiMSjU0NQgoSo1mqLjEyNICTu4akJOTDIitLywRBQiEsY1LnmWaRhEHgmJ0Z+BelCUHWmElSTdKOHANKWrwgQMcJWgsazSZx0sUPckjhAN04idFpStSNCHJ5gsAB4Fg3pwaem6d65txB4NhaIDYCRT3lgzBOpi4lUTchiiI+9rGf/m1+RNuPnKH0x7/zO9z3oV/i0vEXuO+DH2X1C5/g6s1J7nmgj6X16yy2Ukb6c8RRh3Kuy3Pf+jrdvsN40QzfefU8E/sf438aHeL5v/k9rp5c40VyvPs972VwqMDxc1fJb7udj3/4I9yz7zbOvvB5/vYzX+FP/n2Tn/9H/5BG7HP39AgJMXu3lKAwzv1HD+BJxXde+Bqjox+mv1hCJhHfeu77jI1uZUIMs6vUh0aT9g0wPWg4efx1lpZahELR6ESoMEch79EfVrhwZYHzp07wG2feYGxsnIfvupPZfIGgUKZRa/DChRl+9qP/kIcOTfOtJ36fpbUWLW0JZUzLpNx/3+OsrK3xjkfexQc/1M+FU9/nL//Lp1lJJaEvaXdiUpOAX0LrBN3xSZeWWF9dJReEaFsjNYrUSLo6ZXVtlW7sdKO+bdBX9JgaGsAaQ+Ab0hR8AZ00ZXH2Ar/7r/8vVL7I6TOvsH38g7z60gtEwjK5/y4qlQqtVotWlJCYlG9/5UvUlubxZMihg/t57wOP8YlP/j+stRPuvH0n7/uJn2Tr9tup12pYE3H8pRf49nPf5Zd/6Z/x+EMP8+xzz3LPocP8n//i17hRW2Hr4DS/8Zu/Tej5bB+fdH400rnzO1lbRp3EbsTVOr242gTuCLB6wzAacF1Qi6OLy7eDOG8HJXosIbnxvc06CD0jxd7Wo+iDyD6P+2OMdotf0ZM22A3mjBBm4/9cVoFjVlkESiiipENf3yDffPppDh7eS6E8zKWLV1laX2BsbIRGvUmqYw4ePsSdBw7zxSe/wPzyDa7evIoxlq1T27l44TzveORRnnrqsyytXef+O9/Pt779DMZ3VNVODHXTYbBcplQoMjw4zNVrF6n05Rmq3EGr2+bm/Ay5Qh/vfc8H0FqTKw6wbXIL1ZU6l6+dY2BwAM8z1GpVhoaH6Uk1sD1w6RaA1vNJ6hn89bp+t6JYYQMoEpvNu7N0C9Nr5IlN++69KIv05dbPNwOJtxo7vYWme5+NBfoPuQMy2HLja/arbO5Y9v7Vi1W22vkxZC4brK6tY0yK70ukZ6mt1AkCSRJH9JfKVAZKROt5xqcnmVtc4LVXXqdWXWdsbJBtO7cyNDjKG6+eZmFumR17ttNYq/HSK6+xMH+Nu+89Qm21wfnz5+kr9/HoOx7m8pUb/MUrzzM7O0u9mdCMPKwSRK2IJI3Yv+0wzUab2/ZOMD87y2Cxj758P3ccvJ2tE9OM5AeZm5/l+sw8D95zDw8+fJRPfeozBCpkenKS77/yAtXaOhZLORey3kkQUjE0OsbsygrKl/QP9TE/u4iWAul7mam8JYpirLTMzs2yZ+cO5ldqPPvyMwSFspOu+D62ozECcgVFp5u62HbpIpd9G4DRdHSTtBvjdUPAECmLVik53yM1Cd1EYo2Hlpb7jx2lutZiYXmJ8aFx7KDCpm7SjzoJaWy4evYm/+YT/5aF+jo3zl1EhzF+USJtStpYoxwa9u3bx67dh4g7LXZuG2V1bY5qo8NwpQ9JQMsuMDt/lWZ1nee++zz33v8QyzNXmN4+zdlTJxks9NFsVyn293Hk3vsoeDmGxiYIS2WS1NCs1Tj16nNM3TaNnxNcvXSOWr1NThYo9fXTNyawSZUHHn0PK0sNxkeG2Hv0GKnw6aYRUvqcePmbtDt1RidG+dzn/xP3HTvE+ZPfpDJcpt1cpzSyjebqKi98+xtcOv8Ktx99gJ27jmAQXLvwOrMXz1Bt1Hjo0Ed56dvf4vEPfJDGWgPRSTC+z3oUMdhXQlQmOLTrMGvLa8igQd/EBPVajZXZGwz2D1BtWBQJc5cvcs/DH2R+5iq5viHW5i8itSGv+hjs20Kx3M/87EVmZy6xsl5jvbWG6FqidpNiIaSQz7Oy1KA4mKOTCpJGhDGCSrGfvoEhzp87RZxEhHmfRhA7ZofywQYUSgX8XIFDW7eyd89hysWAibFxrJJY4dGKOqwsRURJhCcMofAJvBx+0Uf5HkYIEgwIi7AJiXWdYCUkaeqefylkFtfrZZKY1CV5SeFMPK1F2BSDj9WJk/hgEKlBZ3wmKRzbRlgJeCCd7MUVnK7Q0kY7Kr0xyMQSx22ibkqnnZAmLTqNJiura6zWGyyvrtCu1Wk3anS6DZJuG3SKlU5Wo6SHEC4VxxMSL3D+HQaFNYKUFG0N5VBRKpUplMt40iP0c2itqdWqrFZr4PkM9g1SzhdRuQBPCOJuhyRpI7WPVIErMpVAeSFShQ6gsqBIWVxdY2FuFmM7SAKUJ8iXyiRJgml28FTgYBwtCJVPt6tpNpsu6SibHxCQ9wO0cdHzQUkRVZON+k8Kx/pSGVtF29QlO+E5qbYVpFrjea4YirShGCpnCGyd3MpKJ38RxsV9J1ZD6phFiTWQJatJBYl1xDIjLL7yEBhEoFwKb2xQmaRHCifR0DpFWYnwpAMMkRtx6cb3UTYFYUjSzAA682sRUiF9HykhHwbYvI/AI++7rrkQcpP0zM1LLs3JFa++sJQrFbqpoVqtolNNt+sSl1Ax1hhCK9HExNYSbIA3Lg0oUCEgUb4l2TAud35cgfQx0jGqkxiMLyCAIPQ3Yu0dy0KijHsGrFQkNpPfCbc2cusDRdpjZxvH+sXb5I+ZAUax0RkoYzeOV8gAbIIRljRNsNZDmy6+n3OgL86fSCkHJhntZKIWnEQMsFZl83xv7QdGOhaHye5BbVOEAazAV4pAKUQG2ihls/WjdUpTmUHBWpMa7QIAPJsBBm7BYW3mwyYEOjEZI8oxxnzPMQCli+lzIJrIPMW0zcJtbXadZAZwOmmXL0B7kBiJ8nLopEspXyG2iQM4hSbtNkiSPIgAZUBYhc7APokglpKt4yNErTbz9YTRgYCZbkwuVA7o1ilWW7QNHDsyrODlB1irX0EYiVKQC0N8P0/SdQCgNBohPeK061IKTeKAXakwVhIYS3F0CA9NnHqkIqavL6TTBJ3PEcoi7eY60vfwc33ojkYFIUIGGZPPycRE5hmjpEWTgAgRxhJKZ+js7ivnIySsJfADlyrXamdgojPQ1hkLDVJ8QozK7gsckCbIwB7lGqAOyHL+YcZmXldKOtarUXgZLSTVFogdk9IKMBphIRWAMSTW0km7DgxMBRjnQyeERXoeRjrgysNihEYZTSAk5XyFdtTFYDMQRTqgSRustFibgVLGse884557hE8GiztPHyvxFfhZI18qiV/IUw58PE/RAUI/IPR8pCcw+YBQFinkC3heiBKWnJSIXB4RhOREiJEJuUIJDIi0TS4s0ZJQUIpiWELmQ2KdAT1pTD4XYKyH1wNkjSZJYjw/wEhJGscIX7C6WmV8cpK43SQ2Pt1ui2IY0okdW5WseaONxg/ySE+AcXOu5ym3lk8Mvo1BSroWmmtrlELfmZQLx9xEOyC714gWQtJqNcjlcvhBkVR3ETbGE4Ko0yTq1BgeqWz8nk5TdGpoZ3JXEWuiZtMltRZKpBYSnaJ8Sa0eky+EpGmCksoldWpLGCgCX2Tjg0+z1ULTBSy5wK2PU+MYxCaVSD+HSVIXGJBYPM/HaI2UvkstVa5WTRM3nisl6HYcoPWj3H7kgNJfPPFNFjsJUVrm1z8+wrP3fIT/8f17GQ4SRj6wg8kBHx13UF7Kc1/9DC/MB9y/a4lnT/vs27MbZQ2XT59gTvdzM23y0DseJZ67wvkZzXo1YvvQJHeUQ9pXT3PmzBxSVTh/+RL/6VOf4icfuZcL33+ab774LIt1xfjgAHNXL/Orv/brTIxPUpCCuZsXmJ+bpVGv8eVvfY0Tb/SxMLuCsfPMzMxz6cpVGu0uu/fupn7hIqPjfTQbLWqLc9Q6kevSpykon6Vag69//zVMrsxPPv4g1s/zc7/wP3PX9ChvvvQVXjs7z7Y77iaUiqXVJqVGk4Nbp6gtXuBP/t3vc/udd7O2cJl6o05sLDqJ8VQAtsi9DzzEjz1wN3//7ed57IHD/NEnP0lpoEx9PXbaaF+wdXyKViHk8vVLdLsp5XIJIS2f+9KXuffBdxFHFj/06RoXv3xldo5EFui2UxZvXKVaW0OGZQ7vuZMd0zudD0mxxHp1lpGhcUamtvOtl4/TV66QtKtImbB/+w76hgrYVDA/e51j++/A8wNu3LzB3I0ZWFkh6jY5f/EslWLIU3/3OfYfO0L06mnOzs9SLGV6fCEhA3IEDpW3m9guPyAPA8gKgd5k7nwsdBb/eIsd0wN/NnyKRI96zsZCwsU/y/9GfH2PJbOZESM3SdxuMXSMyRgsG3H3vUSIrDOd0dwFAm1jioWQ4yeOc3nuIlfmr/DRD/w0gyP9TE6PsLyySKpjjt55lB21db7znW9z4+ZVSqUcc7MLLMzN8fTTX2J4dJD773+QU6+/ysHDh2k0q4R5S4qHJxQ2NRRyOSqVQSa3buX6jeuMbxmjUMox1r+F7z77LZarDYT1uXL5MvML53nnA+9jfWGJxdV53v/+DxCEeQr5EGvdpCiFG5C1dH4Tb99sBiS9/VS+laG0+dy6M9TzBXD72CxRvEUT+uHXR2zezaa/biXtuZf29rGZUXYLTrKbdndr1z3KvPuhFAKUyLwSnNzL8xWddptKX5k0tnSiFocO7iONE/pHh5hbXeX6tZucO3+esekptu3cRtwdp5TPU2ussbS0xIlTbxK1alyaPUu5VMAPitx111FOvPoyv/jL/xuzN+d58eXnOHb4Xq5cvsb0VB8TE1u5sXgdHWtsYikUS6RGc+rkG/zcx36WBx5+J0998ctMTY/yxBNfYGZ2jc9+5vfpehGBEkwMTXHgwJ186clnOHnuTVKZsrS2lVa3wfL6Crtu242JodW8TKA8qqvrTA4Ns2fvfl743rMI3yefyyGlpWMTB5cKt/AcqJRpJymXrlxmqH+Atfo65Hwkmnx/kbSd0okMNvQpFvvxvDzzi7NUCnnqK3V+7md+lrMXLvPyG6+5jm+a4HuS4v/H2nsFW3bd552/FXY48ebU4XZENxrdAJFEMFMSTeVA01LJGtszdrlm/KIH+8E1VX6Zl6myPDX22OMw8oyGLnssW1S2NCIkiiYlAiRiAw000Ogcb3ffHE7aaYV5WPvcbtJ+muKpQqPuifvss/dea33/7//7IknuFFNNRc94dCNmd6dgZe0Bz338aQ4dOM2FC+8hUoHY2SWSnqMnFrm3co/X3/5jJucXkbEFNKY0RJHmwHyDZizp7w146qmzHD16lq9/7Wu8+85lYhWzub1GZkomW5O0kw69fJPPf/6LXDz/Lk4r1m8/4PiBRZbmphiOMl77xrc4++wLNCeXSBtNTJ7zyc9/gaXDpyiKgldf/RbX3vljRoMhkzOHGWaGH/qxn+fOrbvcvfpdvvXy7zDKDaPhgPc/fI+JyS4/+rO/BKLFsZPH+O7r30auWhrS4ArBYLDBT/z1v0cyfZz7N9a5v3KLe+s3aS4tIBodqgpufXiBotii1Zpmcuow7cYUkzPzbFy/QprGNJKU5aWD7O32aCQaIdvkmwOGDx/S216hKEbsbK4yGG3QdDFL03PsbN2j0ZjkvW//v7z46U9y5/Y2h5eOMTnd5MHqHvPTXYpcM3X8SZqpYLYzhzx1iItvn+fO9jpxkjA7PU1RefZ2ctJmi2MnTodW3uWjHDu6zOziIpcufkhR9NFas7m+g3UljQZMT81ibcFOtkHcsXS6CWU2ZFSa0MKAYmKySaMIvKNEBfBoZUNFV6hatCbENzcTRWEMsdKUZYWQNWTY126VylHZMlS57dgaoTDCo7AoFQQOaT0uVkQ2xrqK0lVUxlOWGVVRMBqVVKMRo9GI3UGfUT5i0BtQ9PuM8h5ZllFkGUVpyasKicGMtweLlDoIUrJ2DaQK7zW+TjdyNUfHmpL9BBcRKvIChxKSVqfN8eVjdLodvJDBfVIaenub7PRH2MrRnZogiUJSkzcVhQ1OI+ssESBRKB0FKDmBCSOCbYiqLDFlTrud4o0mjhtUpqLV7bK6thqSrVwNcJaSbNTDe1c7ZSSVMTgRYMfGVYBDKElRjFAanBN4r6l8gOab+/UBagAAIABJREFU0uIdREJBRFh04dGR3ufMBFhxYJOUhcV6UaerObxQWOcRyoWYZhHSyZQIQocj8F2qygTgs/KouuBhTGi108LgXQUypMrV+l5IyfMelXg6QBS3QCs8gTsURWng2yiBUhEqkJcDJNoFZ5bHYJ1nlJVYF1wJo6wiz8tgePMmCJ8E4SuKE3TURGlNqzXBcDAk8i4IRwpkHVlvXBjZtFII6+rvrKj9c3gXwLdWeEwwQeC8RXuBVpqiqkidwYz6lNkotNMJGdqBcITevCCwjf3i3odI77KGcAtjqawjEuM0X/C1g8Q5h6jZVxBaMvE+tOnIkigaQ9tlnbLmKbMhugbhCxlYN+FY84FZVbs5ggskLLypwdsej6ssY7qwq88XLQMTzNUiYRxFeGcCCHo8B1Qhel3IkBrnvQ+O+5qPAvUileAkUToiryyRlCE2ft9t5PA2iL6VtYGX5QnnvQgsNGMDrN8jkFrjnQmtOU6EKbECIRN85ANbKSrBWwbbAqENOrIgNHi33wLovCNOI+ZnWmxsB1FjZEOKq9CgnUNIh1AWLROsiVhffUDc6hKnbVqdIFhIIqTS9fMVXho8unbieGw5ok4fQAmFETbwX0pHFKVY7xhlDYRuEtmK1YcbpKmg25lHiQgTjef1KmyL0rVg7sNc3kEkYpwMnDOsDzHp1EB/AqRZeE8+HAU4vhDESuC9IVG1+1qEa5RWEZ7QYhYcR7VgSuCeCu/wNfjcW4sXHlOE9ckoG6GlII1SjBNU0gRuGYLIe5QMIpcVhFbg0tWBBDXYXksiQEUxxhYkUqK9QihwoqrZZyWR1pRVFcIYfBC/pNR1QiI1XF5jlCLVCi8USIVG0O02wzVSJUgdowS0O23arXZwfsWaWGp0EkEZWhPjZhctU4gjogh8aRkVBYnWqCSikWjyYY5zOY1ml+Fen2Y0wa51TCmFtwVxGiOAyoH3FmcKtItCW78OwnZZGoyzwfnkPVZrzKggbnfQuoWzlsLktJotlJNUogohMXWIgClDS7EmRUZBkjbWBHeZyVAOvBWURc5gMGR+YQ5TlkRJhLUFrnI4V+G9RcsIZwqywR6T3TaD7U1QAuUdkUrp7ewwmUZEQmFciTOG0lriVoM0bWHyPJxywqPTNCSrOo/yPvCSlCNtBKC81glZVgCWJG3j62CLqjKBZVh5JrrdIBq52kGGRuqaKiYlZZYTRdH+etq6MEZaWxJFIdFOeo2pHHkxotlM+UHefuCC0uziBF//1qscfvoz/LN/9mu8+PwnWXB7zMweY6ob2h6qfMRuv8/E8if53IIjnpznLz11lG/+p68wsfxpnn3xMxw99ix/+ZclN94/T944QtOOWLn9Ebc/Os+/Xr3D0dPnePG5Z9ncWGN1N+fmzdv8o/fOE0UdXDyBlrA6GNE0m/zub/wrWskMX/pv/ltW1re5/WADrGSy0SDVETMTEQ/3HAdOP4PKC+S84q/97V9B7d3iX/7av+Oln/l5WqNNTp55lpU7H/Gn3/wGyweOk7YatCcneOlTn+XZowfY2l3jmRPLqOFD/uBPvsXcgdMsdCbYluFgfnjvDv/oX/1Tzh1Z4trqDmLqISmKVqNF0zn6XlCUFUkj4dVv/iknF6bYuXONX795FesUVgYrvi8Ne9sDLl66QDM2uGpI2p7jJ774JTqJ4Prth1x4/21+6gs/hjUj4ihhVOb8yKc+x7/5t/+KuYUlHq73ub6yyk/+9E/y+hvf5dadWQ4eWGB7b49ECT669AEvfuwZvvC5L/Kd7/xn3vzOK0x1Yt58/bvMLB7g+XNn+OYr3+TZJ5/izDMvcujQQQ4dOMQHly7xyhtvMN3s8Adf+0MuXL7Ezx44xJd/6mco45SFuWl2t3eJkyamcggF3ptwKPqxMFO3nflafniMVeQdgazvA4MhtMI9LlKE5z9qowq3cZUr2NrDfc67OpmAGiwKjxwwtQi1L4g8EiZ8rULIuro35goFLlLtnKqdTePtUsrjbOgT336wQhQn3F25yx++/DLnzpzkyImTDHp79Ha2+Pr9NXYHO2zu7dKvdlhf2eWJEye4+NG79LIBM3PT3L79gLzcZbgB7QloJw2GWYV3jjRJmJ2Z5eDSAlmR8fEXXuDc6TP87u//B95661UmF2bZNX0uXP6A//kf/E+cv3CBty+/w9//lb/Pa9/d49Kli7z4/GeoKlA6AA2dDxVWaevvJ75P6BHsA+D2vfvfd9tPfNvXgx4JdABSPu5eqt9W/JcJe/uPyUcupDFcczyBHacOfc9x8Vjy2/ds3b42NRadxsefr48Tj7AWXFiECq3rBYVga3Ob0WDEW+ffZG52JiQqCMnywYMo65icmmV6ssvcc+eI4phLH15hOBJ88lMv8cqrr9KLJVVW0B9s89d+8UvcvvSQjjZ8/lMvcfmjuxw5cIjrly+ycGCemQOH+fpv/DZFXiFMSA+ZmugQa8nOzjbvfHiJkpQbd27yYOMmcbPNV77yfzK/OEvuDApPRMKrf/EWG1v3kMrisz3u3v4AU0UIDxffu8jJoycYGUsnjun3tmjGTT786BJ4RaQViZZk+ShUT+sWRi9hWOZcvX6ZpCXpZ31UFJONKlIdYbwjisKvIxwMhkOKag8lBYM8J5nsoFsTbO4MiISkspa88LRjR0NPkCjLv/jff51/8o//Ob1ejw8+eJfp6Wmaepoqr7h59zqtuMFUp8mLz306tBr2HyIjRdYfErfa2Kqg0WxQVRmzs7MM97YZjfZ46/V3+OM/+DO+8c0/BW0gqq3rwGiUkQ8rdGz48298jb/1N/8HfuO3fovpySZf/oW/xsT0NL/7//w7+sU6/d27PHXuDAjFRxdv8O7FWxBrzp09x8knTrO+cZONe3foDXp4I/nzb/wp+d5DTj31FC/8yM9y6fy7XH3vPDKStJIUjSDWKWt3V0i1IY476OYut+6fpxNPsDuUYHpEwK3Lb7O+8gFT3S56/iDJ/FEmJ5uUVZeP3rvMwSOnUFXJXKvN3uYGiweOsbd+Dx1rsp11dlyf/qhPt53Sbnm0aHDl6kXiRLF8aJGirNjd2cahGJmMhSPLrG/ssDvo0T58jJnF46ysvMbs3By7exlznUao4DrBzTu3yEvL5OQko2HGvftbIBXtZpfKOFZu3yGJYxoqZePhFmVpmOzMs12Fal+cRHgvqOyQjdWME6eOc+zEEksHJ3i4skq1O2S6PU08OUNVebIsw5QFozzDFiVKBkdv0mzRaLWwPozHQnpiKYitR7gKbEh/0loQeRXs8bLCDEtMERZ61hjyUU5e5uRlQa83wJYFg16fflEy7A8o8yGjwZC8KMhNhq1yfFnV7Tshycv5xxbTUtSLjDAexVG47uk4wlmD8DoENRAW/75yteNFsp+ASX09lfViV4ZxcVww0XHEwtw8cRqBd+SFwVkY9nr0h0NsbpiYnqIZR+hYURUjPFBWBmNytArx7d4NESJF+BSpAnPGOijKjO2tB+R5TpK0kcpTCkNejMjuj4JbSAceThifA/R/bA81JghNSoZ2d6UUxnmsDYtF52rQsTUBXk3NEPIWLUPrYlgg25oDFVrPgjjk8EXgC4nxuFUnximCS0lKWQsYAq8lURSRDzIobYhkx6BEFMIykuCu1FoSNVNUFBElMUJJYqmCi0RK4lhhjEE5iJsxjUaTtY09qjLs03HKXV72cc4G0UgEDom3Bqnrlq86vU74MN8JbdcuMItqfg5CUnpHaQoaWqN0ilcV+CIMay6ERwgfWncEgZtiBUgf0v6EC+LKuEVCOYJroeY9jWczSmtGVcl00qDd7bA9yLBSURkb2puERAiHsp7KB4C7lIFxpIXE1qKi92BwpCoJm0hwGvhaSELUc7Sxy8hDWQWhVklFVAtxrjJYY0h0jLBlcF4gUPWcRDhbVxzDPM+aIHiNQ7uEF3ViWi1q4lGRJFYhNU5JEbg3tgyi1GNQZaXC/ld1Mq/zAULvvcOZCuMsUvjanRM8X4mWGBuEtcIYlFQhDc1bIqnQXmB9gO6qOuVWSokez1pqEUvVorhUDiFDu1GEw9kSRYYxe9jS11HpzQBpRyBcCMBRwqNQtFttpptNLty9SpxqSluivEA5R15uooVB+IhIeqwTVOUuUmu0iomjJgiQLlyThIoobEXDlkhfYFUD50EnLbTOKKuMqgyib5gHJlgRgPCVzWkn4bgUkSF3jqLYo9VeAFthhSbSYycmYGoXmaidHcLjZRAARaRqAVHW11dZp8w5YhKqsgzdgtUolIBrB4vwHocjz0JrPb4Mbc2mFpHqeSf1/FKOXaeE65n0hP0iPO1mg8w4ZCVqEH49p6yLlkqAty6Ii2PXXNB+cZGoXWuOOG6ADKm1SjdQQJykqLiBqQxxmpI0YqIkCaKxVhS5YXp+mkg3SJtttBTEUTuMD0Izykc0GyEdDR3RbDYDstYLrKvAWaQzpDphdXOPAwvTmBp6n1uLFpZ+MSLFEUlApVSVZzDYpTvRoawMQjsyEVqOjbXEOkZrKEpLM4oYZBmtRpvKieAwlrJ2L5boOLRHmiqkzvWynLnZOXI7hKoWyNFYW4ErwZVIGcYXLRRxHNXhAKEFDUcQYW0AigshyXo94lQFN5dziEpQlnkQhmvWUmlKyjwjiSSNqMFIZIHTJhyZLciFo93uBBdwFUwMkda0kwbWgRdBFELpIAQriSsLvIdhltFuNBFeIaTCWMuoyGk049D+60MS4agYhuMkioijwHeq8z9DYSCK8c7XYQqBA+XHi1fvKMsKrWuTBQ4VpfS3Bwjhabeb/9W11f/f2w9cUBpWBaWOOXD0BJ94+gDffec1XvjEF1nSgv7uLs5anM3JdrfJM8uxU0/TbQjMcIebg5ifO3MG0VtnaXEa7IAN3aNhQI56SG8Y5CXxKCdbu8cf3b7KoWNnGKwN+Tu/+PN8+89+n++8cYmf+8kvsXXtO3z74lUaKuX8hXeZXTjG87eu0FRlbUVV3L55j52JDoOiz6knzvATn/0c1o04f/UBu+vrfPGl5/mxLw34hS//EnurV9nc6vGp534OU2zx9gd3+PhnP8PR2Q7Xbl7luRNLdBsRZa8H+S7PPP85Dm7e5Kt/8HWkiBBK0mxPcej4cY4cPcKtjW3Ofuxp2L7HnZtXGIwKSuvxKqbZ6uCN4Td/+zcpSPjEx85y3QzZXd/B+ZCc4RHkoxETcTiAtvcGrNy/x3NnnwRTcenqbb74+QrlFV4YpPJs72zTGwzANyjLEVevXGQyMdy6cQlTet5xObozw7mTJ1hbW+Xyldv8jV9+jlde3iTqdnnp+Y9z8eJFzpw7wwf3bvPicy/y1ttv8OSzP0TWHzDc3SVqppw7d47NvV3+xi//dcqv/BqvvPYKSafBX/nyL9Lf2yVN07rfu/YLiTrDS1ALNI8BnQFqG7Kv3Ufem1pIGrdfyX1nU4Bu11Uuwf7k1RlCpUXIOj4yiEwhptHXvczfz/x53LpSR7TWcYvheYErtD+d94+nidUJDyKo08Z5jK1oxII8H/HGO6/RabWYnO6wvtvnSGm5feMq5997nc985ic5e+5ZlhaXOL58gN/7/a+ytbXNmSfPMjczS5o2SOKK4WjAwtJRnI9Zmp2ltDHbvR67ezsoU/DMk2e5u7rO+x9cZKo9RT70DAZ7LB87SV5aMldwaOkIeeboTk7x4cX3mei06XQm2etv02k2yXoV1nm6UxO1s+wx8PZjt8c5So/23WN/+cckm//y5ftVOsE4g/R7JJ7vcy+FS+m+MPX9P9e44lk70fBBTGJ/HuD3Dy4/fh/q14nHf8tHTI0x16QyFdgKBwwGfQ4cWmLQbnLuY+eIk5jNjQ1eee0mWT7i6PGDLC8uM7IjVh4+YLC1hVIwv7jI0eWjLP/SEa5cvcpHl9/F2YN86oXP046vQblGZeHEyZPcf7BKe3KeN95+i7945bs4lyMjaCQtPJ6Haw+YaHcQSvPmG2/w5muvszA/z9/9B/8jv/tvf5uDy0sMshGiEhTliBt3rjIzu0DaSJiamWDt3ojIF+wWFdJqpqem8MLSTBLKylE6z/zEJL1sL1j2paKoClwFwip0VPPOrCVGhcE3TZBJg6L0uKKiUgoKG3gkSYKxJVVW1W0Jjso4KgX/+t/8GsIZEnRdxQ897k8cPcr6dp9f/Yf/Ei0MVy5/GOz6keLWg5tM7LTBe/qjIUp7zn/wId1mG+lKoMXeaID0ik4zxToDWrHdE7SjNlHcZVBkCF0xOddlUIyIdOBLeCuxHhqTLYa9df7Gf/ff8x9+83co8j5PPPkx/uiPv8YvfvmXOHbqKWbnZzBZxdvvfMD00kFEu8Wpo8dQaUI+KJieP8yxJ57l5ofvMntwmfX1NbADlo+dYmbhMGnnMEdOCcreiIOHl4nbbfZ2B7z31svoqGRmcobRaIsITStKca6DyXdpCEnZ30b6HZqpYKKTkIoCt3eHYwtdLn50jdKOuHrhTc4dP8TRY3NMtk5wd22VJ04fZ2dvi+7EMtevXOaJJ09z8cNLDEYjpFBMzM5w9PAhpEzY2NqiPTVDWQ7ptGa4c/s2u5s3SBsx733wOndufkij2WZvmLGxvY7bdMzPz9Nupox6a0zOtjhy8CQPH2yQ5SXraxvMTk7RGw6Z6HZQSnPzzk2arS5pI2Vp+SDNiYTNjRUQhlGWM5G2KMqc+/fvsL2zydnnnmHmwBTx/Bw7Oz1ssYOQTTY2h3S7HY6eOkRve5esP0THEXgY9ns04oS4oYm0oL/XoyqzMO4YG1w7xYCdnW36vT22tnbZ2uqT5UOqIifPM4wtqKoyAHxdaM1BeZSrHRp164qQkkjUrpYEIiEwLiw2PTosvgGLw/gSvMIZiRc1zFoIjA+LX1FHyzvvQmCFCM6lce3CizGbRoNzWAJ3QylNFCkOHzzI1NQUrbRJXoT458Gwx+7eNtmwT2eiQzOOiKKwSO/nBVVZECVxiKquqsDj8CAqcM5SVhVbu3uULsO5EqwDqShMhpSBr+IECC3qmHbHGFzu8SFOnJByJ5TAuTolSIaWAD++lHuJdwInQpuaVAIRSbxxSB0Req48XgkwYUEq0Ug3drmo8N6RJFIeJwJ3RQuPDpAVKiypjkhUVDu0IJqaQoiIOGkg67YXJ8PcMdh1DdY68spijcNUFZkr8GUQTEpZYYwhEpqF+Tnwmv5oyKA3QBC+c3CRERanqhaLLEQ6wboSqWNwVXBUeI+1Jsx5dGgn986DEpTGoZ3EJAHYW5kB1objiNoppLQO7CfCsRgWGCCEI5hcPNSuDynE/gLXA15LVD1OWuFxZUVWRuhGQpxGFDak2zkDzleAxCjAyXrOFrZBahmEI2NAWKTQ7DOqPHhX84e8xznLPgSh/q5lWeC9JY1T4rjm00iJV0GQzcsiCDrj1rnQtISvv4jbn7vVlIXHJxmidpYBRVlhbWi31rWzLPwj63a1ceKif0y41Y8SbBGhvVOEc7Qsg4AURRKlFVUV/C6x0rWTSaKpW2w9wasiJT6o8vX8JzxmTbUvyCEUsU7x0uGkRAtNaQdU5VrdUh6EU1uBiALnStdzYIkKPC4qekXF/Y1trBsx3N5AxTEVFU4UlCYUE4WUVKJEpy2E0Mgooqgy0maKCCF7xDK4g5JyBKIiiTRDIYjqVjdTZQxHAxppm1hIpJ4IDj01icskwjeIkoROKyPLezTbE3iviZM4LPYjgfQKrEdpVTuF6rZK+ch9KIQnqud9hQlwcGdtSNZyhqLMEb4IrUEiYWSHAWcwjkF0oa0VIdHCQeB5I2q3mRyvLfx4NhqODytD+x+1uzNqNDDW7hcNpFSgQuqWEhqEpGFMaGQIeUR0W53Az9ENVCyIhcJ5S7vRROkGcaKJGilpq40iQMArY9HNNolKqeyAONIk7QlMBUmrAcYEELOOKIdD1I6h05nAodBpTBIpyqLAGgs2OCKV0BQmp9NK6+NQgrAkWlKVoesFJRCxJpWC4WgEwoJOMEVJIjWZNQgRIfwISUplQviQc8FFF0UxgzxDRwolI0xlkdKjpcKKIAJXRYaWjkgFAX5gKlpRgi8znDNYVwRXII6qqCAPlXuhwjV5b7jL5PQ0JpPEQiGUJBsNGVUjFmbnw/o8UbjShB/RWBDheHUupz/o0223yI2tf7PgshrsrtNKwvkU3HKGonZWOT8ubYfxO02T/VZVL2E0Cvs40grvFbGKGNRzi0h36nUs+4ltzlRMTU5grURKGGUDms124BS60D1hTHDfy3qCYK0N1ylvsU4yGmUopcirIaPRiHa79Vg/zQ/m9gMXlBKjiKOIS6/9BUe7P8Wplz7DsSMHQrKG9GgKLr3zLS7c7jE1d5gobtM5dRKjND//Yz9Nt1hjffsOW4WnO5Hw0Ttv8taNhwz6FZV3pJFkb2+bi3vbyM4cJ45XFNv3GG3e43MvnGP+4Gnc1mVev/A+aTxFf3cb1YgQG+v8xn/89zif82C9j+5McebcOU4eXqQbGX7vD/+Qr9y4TBo3+cxnf5iW22N9s89TBzqs3blKsXmLQSl4/+5VJpI2nU6b+akZ3Ggd4SyyGrFx9w5vvvoyb77yDRqzx3j3wjvMHn+KyA7p9TO6E5OMdnd4cN8x1WkxeHCdazev49MWkQt2NR1Fwe5rKqJ2k3biWNt6wGwrZbiXh4mUk6hIUaoGzJ7ks889hzQZt25expQ5P/ZjXyRutLh+/SYnnziAdCnSCSam5njiidNcvbUCznHv7i3K0tDpzHHo+AnuP1xheuEgeVVxYPk4Tkasba3z0Z2HzCwe5x//81/lxOkXmW43WX7x48xNLzAxs0izPQG25MHmKktLB2jqmAMHDnH/4QMWDx9jc6/P737tT0gnZ/mrP/slyrJEj3vm65Ha1ewJXL3qZ7y4D48H636d9iLHXJ3HXTJ1fDLUJxT1yVQLCOM+d28J5Pu6vQ5RJxm4+j945EaqP72ubI7fV8qA8wwtd+GzQxUpFML2t9k7nPA1lNCzsb5OWWQ8+9wn2ShzvvDSJ/jw8hV62+v852/9CVpHTE8vsHz4ENOdKe7duAmlYG56ibXNDTZWH9CJE7Ii53/7F/+YtDnJ1bv3sKVjcW6eJJFYa/HSs7m3x4WLF8nyispU7O5tUpiCRmeCbnea+VHJmWee5NLlD5mdn+NkeoIHKzfQSYPJiS5vvv7nHFg8xMUPP+Bjzz7P09MfY8wUEeNS5WPXorEIOL47THYeiUrBvVVX0et2hP+yle17b77ui/bf97QweI9LprUjqZ6YP8522q9Gs/+R+4+N3+nRa319nIXtlI995vgtpRBEUahCJpHG+zYSwYOV+5R5TpmPuH/7JvdX1nBRxSeef57p6Ule/e5fcP711+jvbfOpz32KhYXj7G7v8vyLn+Da5RvMtKbZ2N7hgw8vUBVw6KlPcH9li7mpWbQXfOPPXiYzI0aVoZm2UU6yMD0HzrHBJlt7e8RaodoxWzt91od7/C//8H+l20nJs4xnz57l0vtXaUxOsbSwSFUVZMM+rSim0Zhkc2ub6blpTh47xd07D2hPdhkNR1hboYjY2O5RmRKdSrwzSBUx0WqR5SO0grIKFnREaM0pc09rpslwsEPcCO0xNjOUpcO5HJFETE1PkKqY9a0NhAjW8ShWSA+NVJNXmjQKNdlGa5aVD2+S3b5FGiWkzQaDwZDc5PTXt7ln7H4UdmUKsmyPwWCXMjdMJRFN6TDSB1dst8PBxQPcvnmLREdEOufa9RWarSZFWeKMpcKha2t96UqK4RC85qMrt9javkeaSl5741sszB7j9vWrbGxscevmHfr9bdrtaT724ks0O9Ncu3KNuYV5th9u8Od/9jKbO5dJO10OLB1h/cFt8nyb1dWCZqvBaHOFSGjmDx3BScf6w1UunP8uTz9zim77OJWvWLl3h+tXP6Ddmmd6YoG4GjA10yH34E2JosHxJ57nzJPP02lPhUrvR++RxgmNyYjCSnTUYuHISe6ubtMb5aysrBMnKcfOnOXiB5e4v7FClQ9wpeXEyae4c2eVhQOH6DYmWV5+gqsfXeD61aukyrN4eJEHG7eZbnUYVjnVUHDh/dfAaSItefjwDoPhNt7kxGmHj67skSYJ3kGz26CX7bG+vs3M3ARrqw8Z9Ic8+fRZWu0Wed7HO8v29jZClDQ7KdNTHXZ3IGmmjMqcf/5P/iknT53ghadfpNXoMjs1zfbOgGFvg3t3b/DuhRGpdmgka2tb7Gz1KXNPHCcMhgNa7YTJqQlKYdlc22B9fZWyyAkcTYuzJlwjlNwvREjha7dM7YD1KrRNOHDG4ZyqXRoWU1ZhVKvbhKUQNcg5xMwrERbJXj0CeSMM3lcI9L6T1grCgq6+5I2TvIQI6UDCyzoRNfwdWnJC60e72eDwwYM0W10ipRkOc0AxyjJ2d/r0BxnNRps0aRJFYdJrrCeK0gB59Q5TQZGXVKbEIaj2bF39F4yKITKyOBs21FlIW5pq5BAxddKX2h8ZfC2M+LArkDXfZrzdkY7DVdgJvHSkjZgiq0iiANGVIvCbPA6lwr7wPrhowu6VJEqiZBREIdVCxWlwnWlBolOMd+jIk0YR0xNdHDE7vWEALdcL+sLkOBuSrIq8FxxhrqAiiEdl5RFe4bBYYUiiIPiVNiQCKQk68sRKYY0lH2QoqWnHMarbYTjshQTHogy/lxDh8+rFamktCI+rHHI8VroxGyNso8OFNh7BvmPH2MCu6XSaOD9kMBrV7V+ullZACLUPGVd1Ste+OAV122coxBkfWEDBKSVAeBQB/JxXhlYjJY1SvC0RWkOkEIlF4mmZlLyq0HEaHHgiFGg6ArIipy0SnCNEbdcMTedD8lMQlsau4zB22xqsLbwL7X1CEOkYjaOqYDQYkjYbSBVSnbwMjgFTmXpeVjtkBTWg+ZFoth/MMnZySVAqxsvglBNS1vMbgfHBCaFqF7wSMa52WOTQAAAgAElEQVS+T1C7YYTCOrtfsHQWQtNUTiQjoljvT2GE8xgVGDjCg0SGFDDvQ+ubeIRscG7MdgjuJCXHE6KoBit7lNIkyQQWh5YNMAOqckBVgiIiitrByeIdWkmMK3j/nfOsrW/Qnkwx2w/xehJrKsrCILRGaeiPMpRMSbUOzjcXhFlrXYBvxwpbVkRxTGaSkKrnA6+nKjao8nCOlaVByggjBA3dRdjQ4tmIOuHgk47Z7jwDKYlFMyTFjd171gUhH1VPu8OxYgHvJcLbcD0QFX6cfmbAqzEooxYHhUR6aKUpRsSUzqBkcGFaU6Fd7VpTUJQDhHDE0QSIcBzIWgyWBK6VVhFEKiAnVIDOa2dJ2138ggqhCp4ggEuI4og4ClHxURyhVVSLq46JqRnyYU7U6eJqB5CQIaY+iZsMRn0arTZpElK9TJFROkezMQFCEGUFutlGeEEUh/0UIPChGtAfbJE00yD2SxHi6isT+Gi+wnqLRGCRDEZDZiYWMJ79cB4lPDuDXmihUlFwQ3rPKC9J0jY4gakMkRZEOqFXjGhoTZKkVDZchco8RytJWZkQ0OBC0aGyAaatpMZYhxKa3d4e7W6TvcFOAP1HMXt1YFYSB3eOQAbh1VZIZSGK0XFMVTk67Uk8CukdThiEgd6oR6vRRPmQVGoqi8HgTEGkovDbm5JsmCOFQqkU521dyBCMRjtoDK20jZMSb8P1SQhJq9FCCoUzFlcZVBShI401gDMU3lMUBa1Y44FIB+NBXgSelNQRrnblDkcjnK1I0wALVypiOOgDnqrKgxgmgqvMmJJWq8HjSeZSyNCqC3hCKma/3yeKBJHWeD8WwH8wtx+4oPT0x18ijRtcu3qJ7b1djtqK2SSjv36bu7fucP/hGhcuXmS3iFjYdeyJFkePH0cLRyO2bNy9yFd+/d+zE09zenmJh9s7bG3t4XWHZnOCA+1J9nY26feHjHqb/Kc/+RaTE9N85f/6NTqtFqdPn6bXv8/QKCYbniiNQUlE3ODQE8/wzKlFvva7v8nawJHt3ufrH75FrCT9UtBNLVVR8PDBDb79yp/y/HMvcPPmdQ4vP4ECnjz3NN996y1Ue4Zuoli7e5MXnzvL+Ysv83/831d5/72PeOP199nJK/7u3/sS23nGL/+tX6FlNtgYeiZS+M63v8Hqxh4nlo9x7foVTjz1PMuDHS6+exETC5566dM8d2qZr3/ja+xs7SCdYFCOyKyj2W5inac00GhN8IWf/hI/8ukfYnlhkbK/ytvnY75z/n0+++m/xMH5ebyt2NvpMTWdgJZEQvKFH/0pzm6uc+XCeY498UN87MxZekWfU0+c5vmnn0ZGMZvr91Fac+Gdt/mZn/p5nn/6NA/KhBNLRzl06Ci93U3+/JWXOfPEszz//CdZWFjk+o2PWD51mt7OkG9+57v8zV86xvKRUzz/sef51X/xT2hlI5YW5sFrpLT7zpAxFFuIeupQqxLf70gZV2OEsEFYqh8f/98FX+/+cx8liTmC9B/eZ5+fM3YzEappYZAZCwxBKBoDwsfumUc3t++EYuzMETWzybv9TwkxmqHiurW6ye//8R/wMz/+kzTSiJ/6zBd4uLbC8UOH+Ge//xt85jOf4tqN68xNdcn7Pa5srPPeRxdQukk/y7i7co8qz9je2+HjL77EwaWDTDRb7GUfMjM3y+zMIsLD/fv3SLRmefEw3c4EG1s30VJx+eoVbt6+xsc//kPcvHMbYQsuX7zEX/3lZ7l+7RYvPv8CRw4d5cr1jxjs7PHqK9/gxZc+R6PTYm5xAaWTmknwaB8ydv3waN8+9ot9n2wT7nvkHhoLT+NJbrCcjx1M3/Ne+2XE8Kr9j/WPqo2Pw7QfKzjWzqSxSOkfbc/4OHusRDlulhtXSMd/h4MhMCXGHX0BzCjY2dlhotNgaXGO4e6Auyv3aHQ6/NzP/BX2ehusb6wSNxIOLS0y9dQJzp5+mhMnnsFYw4MHa+zubLG+vcvERJcb168xGo1YOnISb0quXr/Bpz77SR6s3eP1t17DD/Y4fvQIt27cYHNznampaSbb0wjjcUrQ2xui6p7+7b01BgNPkjYYDSxHlo/hZcXpkyd4/Y1XmZ+f58lTZ8mrkt29Xbzz/KUv/jjZXg8fRVw4f5433z2PcwWFKTFlhdaShdk5VNyk1WqRRJq7d++R9XZq8U6TD0uWlxfpZSN8BWm3iSsypBZkVYWu21qmp+bY2doOTkXrUFFwbigFRWHoDXPaEw20T/n2d1+jP+ojPDz7/ItcfOdtlBSM+j1aaYKXIONwrllXsbc3DNyASOPIMNZjjeX4seNMTc4wHBbBFu4V1lUoKdjc2UBJRTNOUHHM0sI8eT7k/uYaw36PKIl59dU/Ax3RL4ckaZOyGPKdt97k4f0H6MgRxZKN3Yf0hmucOPUcDx6s8IWf+QXW1u9z6PgcOxcuk49KXnvrWxxcXMLmfQ4fO8ITT3+ClRvXaWrPiWNH2dreZv3BHVrtCClTVh+u8vRLL3Dg0BE2N9boTs4x2V3g8LEjbG5vgvf09nb3Fxxxo4lDUFYVBw8codfbZOnAQVZX13jm2Wd59+032Rvucuv2h9jS0Z6YZungPAcPL9Hr36cSTeaPLCOBG/evkNkRx448yTDPKKohUUNjqorFiRlcmZHnBRGSvKxYe3iXWKfYIBGE9BxryHtbNNOUIs/IhgVKRxTGML84TXeixYOV++hYsLb6kNWHD7HOEicKJQxCWKQxbD1cJ2m2WDywwPbeDqu9DV7/5uvc+vA6SSvBZZ7hqCQrDaPRMEQnC0MzjcEKFIqq8FgLxjqE9kRxAGYnSQPpPe1WArICK0jTBrGOUFKQ5zm+gjwvGBUFkGKMRcaGiVhTlDk7WYmQmkiFa4oUAeQcqp4hDl0I6lSkUH31WtRMhGBht9bsu2U9wYkbIccDYHD6eAI3w4QxzNYXTV+L/QFWKxAq4vSJExgT4pGHwxHeh7jxnZ0d9vp7KKnQUai6V1VZQ4cVSnqKqqAc5mExb01ItSMsTrzzaClRWoITAWAtJFIEsQU03ngiobHWoyKFcYElYl0QQGKl8N4CEi3D9seRDi0SXhEAM464lQQAcRTTbIVqPU7jvULW0fFSa7SMAJAqcN1w1GBgg3NVaPEKHVSUZUklFY2motFqIKRjMNyjGA0pqxInfUhB8sGVqaXCyggtBLF26BicF/VwHzDBzgtiIbHG1u3YgsI6tEgY5SNandb+QlRLHaLevdyfO3gfEugQLlTxa1eKqBexvuZ/Iep2e6kec/IEh4qxJVleEOmERqLJchFa10NOVe3Oqo8bghPAOxd4KjIILOM4do8P/BkXKixShd9QxxHCBzaQ9SFNK9IGVEgwwlYIb8MC3nkSHSPjKDDMCEW9hKh2owlUnNYDcRiUAz/IBiGmFpXGA701BmMscRoWgniJqcJiKi9zdBKTxnE49xBESqOUwtog8I7nd/v1yvqcFKK+JgiP0vX5qCRShwWbdw5XO02s83iCm0z4AD0fdzIF19KjEBElVfgOQgaB2jtsWZKkirJyeBkCX4T3GO+Rrm7bV0EwcvWpr3wtrsk6lr0KiVVWSqR0QQS0ddFeaBQz6CgIWcYWpGmXOO1gvcPZCitztEzQOuLc6SdZufk+w3yXjpkO7aplj1E2Cq50K6l8EJ5aKaRRE+cqKpcTRxOY0tFoSeIkJjeW0lqyCqan5xgOh1SyYDjs4aoC74OLyuYDVKMJMjhiqsqhVURBhrTht5toLVAUGejAYitNBm5Qc9wSBBoIsGF8aAlVdVeAVDUHzod0PVmLhiF9zhN7ibehdbXZajOwBu2D8D0YDlCRQqjgBBwVI7qdSWanDhElCYnSwSUqZXAdCUnSaBElKXEUUxRZ4DdJaKYNJAoZS0aDPt3JaVrNZpjvIAnc8AhTVXgkUSRJdMIDv8bc3AKjfp+0nWDyDJQKEGUJaaRDPL3xVM4RR3Fgz5kSFQf2aeUdSTPG5gXhCgBlZvCVJ04jhNTEaQNPaDn2ptpn8ElCumEcBYHDuBLnSqIopb+zjXMlQjaJdYzynryosBhS1cJXDiUERhKEJTxR0gxjgijwaLLRHt12zGhUoLRkZHOsqQIfMEnDb+hCirLUYJwB62gnTXCK0g1ptdMgSNUV58qEwqSMQwBHWXjKMidtptjK0hAK7zymsGAdrXan7pQJ464QYUwbLyWNtRRFRquZgHBBYBZQFX1MlaOSCBknGANVnuOtpxUlyPpcL43BAY04xgMVDmEM2TBDCEekg4gYac2gKELYkxNkWYbwCmMspixRQpIkSXAImpLSVDQbCXEcIWUDj2NUZMRxjJSK8ZLH12NDXWlHKU2elThraTVT4jihKEp+kLcfuKD045//EbAZZucaN29fZ362xfW3fp+N3ZLG5EEmO22izixFvoloRCy2NXevXWJmaZbRvfd45+I1enqCJ0+d5A9/77fIc0Pc7NKUjny4i8dTFgVOCJaffIZOrMgHm6yv9HG6yZ+/+QYHlk8xM9mhqByLR06x29vm9MdeoNuUrNy4TJ5lZP2Kh2uWUVYwEgqtJQ/XN2jolL94dR1jHLdXXqbZbHDx0nWS7gRSQRKnTHdTyh3Ly9/8OhPdNlEsODh7hNXVO9y9v0Oz1aUjK7qTczy9NM/DlS3OHZxgd3eT+akJGnGbrNdjamKGuW6XfjGgqiwTCwf4q1/6Ms+dOIQdbHH+6gqUfUo3ZPX+Gs1GI1DaXcVEM+HIXJsWBuUqHq6uM3IRnc4Ud+/cZmF2mumJBqvrG2itmJqY4s7dKyzPLbJ19x5Ot4jSlG475fa96+x0J3nqzFmiJAKTYZwlTmIqUyIwPPHEc3z5C5/itQ8+4IVnPse3XnsDIzU37twIcfcoPvfSp9ja3kUnKROdJpNTk6w9fMjf+Zt/m69+9T+yvr6NINiwpRc4W1EWBVVlSBtNZBTgnr62Fj9SWYOl1deZH/hxpWnM1/GIfVJ0rRTUZS0pazcMQYn141IPBMum4LH3gDFAIDwltM0pJYM6zaOElfD6mnsBwR3lPMhHWG4hwgmtpCIrMjozU0gr2N7Z5NVXv83U/DTF9ja7/R5nTz3D+ffe58HKNa5e+5DlIydpNrvcuHOL3JQ0mm1GoxHZKOe9Dy+hleDQgQOcOfkk9++v0NvaYntnm4WFJe4/vEfpDN2JSc4+eZZbN25w89Y1dKS4cvkKp8+eZXd3wOHDy6ytbnP/zjWW5ieZmDpEmjZQsWLu4BGePHOGA4tHQySw92ztbtFodkjj9HvS9Pb36/h7j/fPvmD46Hnf42waa0TjRL/vdyz5/8prYB/iObYcef/4kx4TIn1diazdUo/dvb+B4y3e3+b6H+/Gj4tH2/fYd7XOEqcJyimkFsxOT1F0Uj7/w19gfXON1QcroWWr8Ex2Z5ianuGpJ5/k8KFTFIVBR4q5uRlOPXmaWyu3mVmaZ3dnky/95Z8mihdweUG706DVbPHDP/yjdCcnOP/WW/z8T/8cH12+jLEVN67f5cPLF5mZm+TQwWXeePsNWmlCr9dn6eAczVaTY4vHeO38m3z2pU/Q6k5y8onjrG+sY6rQvvfSSy+RiAa/9Ttf5eaNmywvH8dUlso5vINsmFHZqm6xECwfPsKD1XXmpufotNrcvn07nClWkRtD3ExY31qnrILINxqOArNBCqSCNI7pdrr86Oc+z3/87a/iHFgr0UmorloEVWnCYGsci0tL2FwwvD9gujvJcKdHVXdySAT9sgiTkDjCSbBO4WSM9gJvDK4yVMZy6sQppqbmeP5jz/JHf/x7tBuarAgL4NzkpHHC/MwCzjo+8+nPMhwNKYqc6zfuoonRGNKWZnvXQKRwlSWOcor+OrplwJaMcouWMBiuc+PyGxSV5+sv/yYLi0dopC1mpua429ulFTeQMkEkgmOHzzARx5SxotGMGQxz3n3rdawZsbx8CIvn7u1LqNjwQy/9CF/8iZ8m0k2217YY9HN2dnocO3aCp59+gcuX32OyPUMkYqQLTpyTZ55mZfUG584+ywcXryAlrNy5zpVbl1icX8Jawf37N1lcWiTLLe3GDNMHpkk7XT66dIGiGJH199jaesCgP2B3Z5XRsM/0wgGOHDuDc5rbt67QaU8TZSWZK4EKZy1axyHmOYJWs0WkHJWtQptQaRBS0J3skOUFUgnSRkJZjVB1NHc2yJHaBfeVFFRlWHTs7e0glESrlDhStJoa5WF9bxdTgZARURwmwsJlKAmNNKERRfR6OcO8DAtf5UlSzcLCHJMTM1S2oiwzCjvEZCWtRkqShIXrrinIigxTlUFUoUJID96TlwYjHGmqHpvAhaLEvqOTusJe570LVzO+haeyptaXHP7/Y+3NgiTLzvu+31nukktlLV1LV+/TyyyYnhlgZkAMhoBAQABEiqJoSbS10NRmKvyiCIfkB7/Z4QiH/aQHhyMcIdshO6wgKVGkKEIgDVgEAXCwzopZuqe36b2ruvbKyu0uZ/HDd7O6h6LCD0JGAFNdlZWVee6553zn//2XaRJPQExYxRpa5BhBZG1KKSpBlQRgeJzdoSIxNNI3ZyjKWpJifI1rvF72D3Y5GOwSo3R7NZ66DoyqkklV0MrFpNX7iqqsJD+jAUxCEKDDaLAWvJvKsBrAw8nhVhuR7FirhPEZI12bExtZSXJo7C2yNWOE1ZFlKd12wtyRZQZjx8Fgnywx0vIJwjzxU68+wDsvkffeo4KkUbkGuAjBycgZYdjQGH5Lp11R+chwVJF3ElaOn6bWsPPwPrltQj+iZirFV01KV0Dj/dRfcUplVaiYYJTUIToxwkILCqsEnAvRMxqNWTjSI6aWunQQHNpKqpFqAJDmdNzMH2F8mmndo8R/Lvp4+JlC0/QwRpgXPjRAoCslKSjPKF0FmENfLa0sSgdcIy+bNtKCEqKbMsK28lEkHhqFiTLNEmNJtKGVtkitFSDMgEkME1eTm4yP9byVsM9VEANsT8AEmQ9yI5hDMCnGeMg4ccE/SmWDw1RfHQImBKKL2CwReaMxlEH8xcq6xCYGGolT42qPUQaVyFjTpB6BIgR32Bw0RsaRxk8rxshkXIj82trmflPyXvT0PcaGUWEO+5a6oUNHRMon/pMiuZRUOzlwJtZQVmUjFbOo2LxfrYleUsGil8XCR98kR0Lwjsl4LIzCxqDXGNtI5CIWQ6IhiQ2oggFtiYhXVfQVzle0co8m8HDzAbfu38RFWHuwTac9w6SuiFZix8fFBKwiMVKDh6AwuovCo3ykm1lcMWFSVlRlQSxLXHSMRgmjUUFMJPZcRWkyaGPwDsq6RlUjUgIhZIRYEXzN1MtgXAnLpdWCSVHiqj4BhTVtjEowyhFJRGpohZWo5bCAURalNLUbY4zC6gyjFJOyTzUZCShrPTNHZpntLTCvDIlJqcsh126tEXRNjIZYRjKjee7ip1ldfYpogMpRFGNsljE/tyBJiyYRcFBp/GhMTBOyVguLogqBcjKknXXozM7SbuV47ygmhazX8ZHlBlFxcDAgnxHGtDUKgkJp2c+Go11a7Y4weFwtXmvEBlwUcFOke4E0sSgvDM5EBaxO2B/tkWYZPnrSRKOMxpU1IThCdJJ4GhBPn2JArzdDWY1BaYIPuOKAyaSg1clJkhRlDS7ApCpQoSKErEmVhOAjvizJ8hRj0sY8PqUuJiTNGptYTWIzxuWI4IV5KM/V6Bgpq4o0zUmAoBRaG0aTEe2sK0QCpv5lImW0SmOV2Js7V5BmqTBAo7gOBWspijFZkggLS9f4qKh9TWITrEmlFRY8VeVAg01TUGCMlaZIVBR1YKHbEYC78vgoSX7tLGkSLAPBVSTaQtDUoakagmYyHDA72xWD9MTiI9RlRWLEfy+1KUoZKjeRuijLSNMMH4Koe5o9TJEQm8ZhWZfMdbsNGSMcrula26bZLkSHsiwFtMxT6rpsGlk/vcdPHVBqdWZx+w8pi5LST7jx4VvcvnGVYZzl6PGM5RXF0vwMg/4e169epRhPuDPb5blPXuTtb/wW33p7j+5sj0olPP/yz7K59pAkT/jo6rsMq5TMKmyS0koTPv3ss5xbbPHNb/0B1wrH+SdX2B9u88Tx0zws97h0b59XPvdFEsY89dRTJNWA77z2GqY9Awe7oCwKhatq6aYFRV07kjTBGE9VB04cP871a9cY7+3wO7//b2nNdMlbCZnNqWPkW699l1aq8LducLC7y7AoeOVzr/C9H77G8nN/jrtX3+KH777DpKzRJmFYOnQ95oN332G7PyEox/7mGlVdsdjJiaMDRnvbHJtfIHupx/69NTYHW9y5sYbVYHRKXQ8ZDfq8/r3vsH73FqdPHKfT6vDMuSdptWa4ce0aaw8fkhpFYg3Xr13nxWee4Q+/8Vvs7FbcXC/4+Z//Mvs7G2xvbbB2/y5KGU6cPMVCskgIgbIssTFSTQo2d/d46cUFinLI/OwseZbwuc99iQunT3Nv7R6D4QHReR7cvk13Zo6Ty0vMz82yubXB/MIC0VUsrp6gNzvL2v27LK2sALC3t8cff+ubFOWEk6fOceaJc6weOy6yPxWZcv4P2SJqevAPDVOGw2JdNcySRyBB09k67HzJAjb1XIrRc1j5TM145B9T2wGm3k5Nb/iRbOoQIHlkhzr12JFiUIoMwUoEGDt6bJW/8Qt/hT/85r/l5U/9DH/wR7/JSy9+mv7GHktLR7h9+xauLBi7Gk0Lnc9w5tSTXL7xEU8/dZ5L71xhaFNSrVlaXuHGzSu0bEbRHbO3v0/IPfiSPJ+HCPc3N4iX3mOuO8tuf4ft3Q16s7Ps7O+TWs3nX/ksMTg+uHSJZy48xbWr73J0tWJt8z5f+NyXeOG5l2m1e4AmtTl17VlfX+P06bOEJJUOcDNeh2AcDbPsELH7OHPo8Ol/esh5DOh7nHmGOhx7GeX/v8dhGHDz/49MvqeQUDxMe4uHv6Gm/2zelDDXOHyNRx5Lj/6SpCdI8ZslKZOyYDQes7S8xMrKUcpqggoR7y2b1/pcunKTdmeOU2efJjihVxtr+Mwrn+WDS+/z+c99jtvX73Jq9Rn29g+4dv8uZ8+f5juvfZdPPvccT154hhvXPmJcjHnh+U/x+utvsrWzIZ1T58isZmVxnrosGBcVX/r8n6M7c4REKW4+uI+2cGxlhU88c5HJuOLtt9/m1MlTFCPHzmidr/z8X6AYjRmOC37rX/xzhoM9DvqDw7hobQxpnvLh1WuMigGtdsZo0MNVpXSxM0tRO44tr9DqtLh18z5pK2e3v4/NE6LyRB2a2N2EXmeO0ydP896lPVQinXZrIjZPCNGTRMvc7AJ/+2/9bf7H/+Gf8MzZJzhz9inef+cSxqRMypH4w8UonfngMEbjtdz3vbke5UGfhdlj1OGAsxee5o0f/AmDnVsMB3fIkxaOjHanBYMxvU7CE2fOMNft8YlnnuY7f/Ia7VYu3fbUYExgVAiYkCKJW7V31FVJbwZyqymGFrxHe89ouEvhIuN7B/TX77Mwu8xzL7zI3u5DXDWg00moh55Ou0u3O8ee3iUqxZUrl7l96zLeHeDUWb7683+DC0+cYHN7i/nFZWoPb//4DZ574XmqcsLszCwqKJ586iLdmRnm5o7I3NQWo6Gqap55+lM4Ehbmj7B+b4Nnn3+Bzb1tFpeP89GHVzB5wq1b1+nNLdLqisfO9s4t9vpbBDyTyQE3rr5L5UopmF1gb2eXy5feY3d7k3E9IYwD0WmUSpoDciQ0BrghBHZ39gi1JKkU40rWeGvZ3NwhtX0GgwmdXkY5KVAxCmPIGJLEYFJDdB4VauqqYH9rD5vnKF0SY2BrY4+ZdpdUp5T1BKcsylqCq/ClpK6ojrAMJmXBpCiJRHQiHemtnV0ORhOKYkxdVpTVRAxUiY0UzTQpMR7np1JpL958PjBp4sWJXjxJmsNvVPGwY37ImG3WlsfXJR2axdAobBBfDW00nnDYfSVMdzkBlSwBH5HGTJOWGjUN/V+69aCZVDVaK8pa0tp2hwf0+/u42jUR9yWTwuFrT1FXuFDhnUintZLcNMU0VVVhjTBcEmvIs1QO2QhbRithL9k0PUxa1VpjbYbysbGWkkOB7JcybBrxJPJ+ysppsbx4lGwS8Vj29zbxk5IQI7WTKHmjTOOFOJW+60Z2GxsGg8E5ea/WGKA+7DWFoKkbJslwXJANB8RJISw0ZdHWYEzDzAn+0d/wIrmKSthnaI0yRoxep1n3zbVVSiReOgqIZaJiUle4KtLO2ozzimpcN0wji0JAsoAwyIzWTdrd1NRaHza/VHNggoDRwsbQSuR3IZaUlWc8tmTJDO0sZzQeE0MzD9F02h1hhE8m4qkkfTCpp5oGTAwip9NTBrZAgWRJSpLK4XlSVRirMd6TJIY6+MPDTEARtcbFgGm2VqWjsGm9F7BVgUU3Cb1yPWJoJG8N2DQFvKY14NTMXRthp/gYMCahDhpsQtSGoq5Rxhzu38TG9+qxegDTGI03c1EYLA0zScmhPAQITqSAIT4qAkKD4T6yZ4CgplHDNH5mj5pTILijatg0ChlfYwWsK6qKJG1AtQZsDlGkVFNmYmhY/No06XBW6ocYA6kVT1VtHq01KC1p0DahlffwsSZrdairkok7IMaS2qcQInv7+5SqRdCO2fYKVT3C40mTHIuhrgbCfDIRH0uG433wjQQHSG3GpHRs7g5ZmIuE0lHZhNJZqnJMUCOwHhXAOWnG+drQ1RrlKrTOGtmn+KI5VRJNTe1gqTuHsSOc2yGGGpPmhFBQuRLTSpmfP0ea9Gh1cvK0RZ7nRKPFIDzC1s49+v37KOUgagajDSoXmWl1UFFCErb7G3Q6PerJhM2tDYiBwWAbnbQJDhYXT/DcxZdBZcQQGB4MQRta87PknRmpU4PCJoZyNMDkCTHNiEo8IFEwGA7pZC0xp/dB0vmck3s7sXIPR5G0bkJc33kAACAASURBVJd7rC6eYDwc0+m0CbUnb3coigqjha3iY5BxjMJYUo2kVP5niEq872IpHD+tDWVZN0BiW0IAfMSPJ5RlgdKirrDCG2wkvo6qrNA2oG2GDzXj/i4myUjTHGMzquCIXjXgV4Y2qdzzOgNfk2YpNhMwJERZt8pJQSe1wvBNLDF4Yi3JbNomKB6B4yE4Ei1M0CTNqV1FjGCzhMK55r6NeOdQeEyiJTHUe7yrSJMEH5z4iUVhElV+RHumh4/iC+a8JGoqjEgIQyVhCZMRrTzBGovSCYFArEsB1rMMQqSoJ4B42GWp+BdFL6mRRmlMYkEbUqTRtb63TZYmGJtgk4zEGiaTCpxDZwaTpJgm4MD5GqMUeZIe4hQEYVDGpjYJBAajMe08a84zzXkpNrJcY5oaRlMWpTB+sxSFoapHGPVxksR/7OOnDii9+uJT/Jvf/j4hW2G53cNnlpXzn6RXwJkLFzhzfIHN+7c5cvppTiaR3swcqyurjDbvMUh6lGGLotJMbq/xn/z1X+NnP/ERX/vWa6BTFo+uEEc7DIclSQpv/+A7rC90uffgISoErl6+jI+BN17/HsdXj3H8qOba+2/z53/mWX70za/RXjnJydNPkWY5dbhE8I5h9DgvHgAaMGlKkiWUoxFGG/I0Zf7ILPv7Q5KsxeKJ02TBsXT0ODqMef/Sh+R5m9F4gK8URIOaTJg/9zR/+StfQG3dxpuc7tIqcy25CdRgg3+3ucG4Mvzknfela6IMcTzka1/7HY60c/YGO2zt7lAOSzYPRjif0JpbYrXT4if9HeoIW5u7gObO/Tvs7O6SZBm//Et/nc8+d54//PYPeObXfo2D3T1icHz9G39A1LOs9x/wD37912knJbuzshFkMz2cTQlKU5YTBoN9isKxv7cHKqGoSnozM9x9sMbSyhlanZxXX/oZHty7Q6gDWxtbDPv77OztiilYqNlaf8DNu3f4/Bf+PN5V/N2//jfZ393jN/7Vv+A//9W/TawK7ty+xWvf/xZl7Thy6w7PbG/yS7/wl+gl9hC1l4dokh8nrHD4EzFejpFHANHjoMb0eQ1IYYx0kaeWFfL0pkBswCd1CDxM5W6PFVWPagh5H4eyqymo0oAfuvk6yqFic/+AD97/Cbfu3sImGReefJLrH77LaBw5cfw03/3+dxgPxzx57llOnznP7OwC125dZzzZ5/ZH1+gf7JPkKV/+uS/R6c5x8/plNJHheB9fF+xUjm63zfPPvcCNmx9hgmew32fUH1A1VOPRuKR2jq2dPsePDWlnbfI059xTF0nbHd5+83X2+mukWnH61FlUEOQ+EBgPhqwur9LKW2htBVw79FyQTrlgQjLycYr4NSP1GAbXFFqP5IpTSubH09xU04RWcmgJU5Tv0QSYShunlZv86FEK3/RqPPqKR6woHn970xdUh7NMq+mT1aPC8HA+PnqfxmistZR1SWITofgmmqKsSbShO9MjTTv0Zo5Q156D/kiSjpp7/tqt29y9e4/f//0/5Jknn8YmCUuLR3jvvUt8dP0Kv/Fb/4zWf/kP+cnbl1hfv8effGfI6VOn+f73v8WkGjLTSlhdWmF+bp4Xnn+eu7duE0NkZnaRi09dJMstN2/do/JDtnd22Hi4Q6fd4+WXXuIHP/gxdfD4GDh94hT9vQE7gz32DjYJrpJC3CgSEjywsniErNXi1t0D7t65TQzC3CsKh7ZRDGutJdEtTq0u85WvfJVv/+CHXLn6IdFJFDPO44qSD69cZ6G3QG4MQUPtSqxJmYzGPH3+adbXHrJ6dIUXPvkZjh9d4vzZC+ztj1lZWWZ+ocOb776LzqSI8jGIAW/tMLmVYltZEpMwt7TKE08+z2t/8h267ZSi3CTPMmyisWmXdmsGqgMWF2bZ39uh253jg8vXuHP3Hlvbm6gElAkUJdQe0jTFWo3CoXWOMiM5gKgWeZ6xunoUHTx72xsMdzZhonBhzEzeot2bY2V5md3dCSdXT7N2b5NLl6+y35+Qt3K2t/o4V5K1c5yrGfT3Mcpy6omLLB8dkyc5+EBvpkPpChYWjvPmW6/TfbGHTjKOrp5hNByxtyeeV0U5oaodw70d+oMPcIXjoD9gYXERXyu2Ht6nqiYoV/KwKNne2CZS4Z3EEpfFhBgDw+FAQHgVCLWj8AHnIx/1P8CkKcpYisqRIEEJtpExEyNpKglj45GjCI5y7LBYjBLeRV1WmAhZnkinXWs0EVfVOCexvSZNUIklMSlp6sFk1FEO4K08x1eeYlwSA6RpRgy6MZEWGa3zQjN3ZYXzDpto6fCqSO0cu3t7WHsgRqlGk6SZxIX7KXshkplUvEwOmxeNEa+WJK/EiOxLFgXhqAbEr08jBz1JRJN10k8bFI3ZbQwKrUKThqXwTa95qpgTvPvRwjVteMhaJWugjTQpYeIzk6UZEdnv9vp9+gcHjIZDvPcCDMVIjA7nNCEoWmlKq919TGal8UGYhWmWMj87gw+a0WQEiJzHKosLkaBEllN6R/AKg6QkhggEc5jCCQEVBGScknqJNVNmssTAFxwcjMm6C5x84iLD4m3K8ZZIElJhukzXei2pF4D4MBEAE6FJzHLKN30pCVowSgxhrVIEBa6uKcuSmV5CZjWlSQVMi4qomveFHNBDUKjHgjhijE0yoHxfzKx18ztTgAZADsshwKQsWWjldLsd9svikIWD1lgENAzaSMITCqUEpFQNiBeClxQrpUQepSKaR3HyIIy3ybig3crIkhYtmzIcFJBqgtJikh60jI8PH2uUHDbn9Me9CUGRaE07zYS5U4yJQdEyuVy3ALkxVN4TjRWvLGSuuyjjzVS2RWyag021EKem1vFQ+qmMfQzcEj8hYxo5oU1AWWon7BulGwbOFNxUBu+ECSPMPzEkx2i8ko4+XuS4ukF4ZArpxltJJJ1aiYk4sfGUag7sU8YaWvNITq9oHJrlomua2g+5WQMNeCd1jHgPCct5XJWUvpa0wdRgVWwAvqYBigBU2oAxCalR+KCxh3WnsFMkRQwiAa0CMRjqSmSzKnhCXaNDpHYTal/hXM1yYtjv7zEuR8zMdjn/5LM8uPITbFJSR0uWz2CUp6iHUgsYgwqgrLBDrAHnSjqdHt3JhNmeZbcc0u5kFJMRxpRYa0iSBcrSEajRytBJ2ywuLOPqGm2gO7/EXG+e/e111rduUSeOcjxCpz2MTYhYstSjdM2kCjgNuYp0u5az5z5Bb26O1KRkrTYhesbDPbyLxLDHjevXILSoKjGlnp8/xrnzz1BOBhwMDrh1/xatlkVVA+YXj3OkPcvBh3eJnZRed5GTTzxJfzSW1NTRiOgrsjSj1emiE0nUUkERo8XHQNZqC6DnvSQCj8eYqEjzvGESakJUTdS9sOIEbIGiqEgSS8CQphaMIdQVZTlhOB7STlKs1tSVgxDxtawfVRxK0qU2jOqKVtfgSfBBWNMRzWDYJ2930CrFx5LaeXxd411BkthGMi3y4/F4xDQUom0kbKKuSiKKvN3GJLk0i2KkLAsInrzdbkp5hcJIUmGSCMe2AQwn5RithP0YtaRWVrXDqUiWJOJ914DXZVWSKtljjcmwNuNgtEeWJFTRNSCkgM9lUZMYhdIGryyuLgjeEbSMt3OSQjjs72GtJL75uhYQyVUNWUCJ31wUUDm3hjxN0NGiTIqvxgRXkWpD0umg8BACmdZUKLIsOzQbV8FjrW6ut9iu1N5TFUOO9HpEY0iSlBg9ztUoq8gSK98LgUlZ4n1NlkqKaD31gRMTYZHf4alqhasc8zM9qkrka0qBqwNBwuUa6ZvI9XXDwnRBFDfT1f2n9fipA0plOeSlz32Z9fu/yXa+ype+/EVmlWJt4yEOxZG5DgtpSp7lXL38LjfubvH5L3yFd7/9LlEtM5ffQJ17nr/3F16iHDxk5+Edrt24i+0codPK2dgaMywcbQ8Py5L76w9wyjI/32N/d5tsZo6l5SUGBwckVvHu229wsHmXje0DPvuFRVZbLcb9A44fO8XgYJP9gwOMgyxJKCYFnU6XThK5PzjAWMW9tTWs1vTm5jl67CQvvPg8D25eZ3ZxgSPtRdZ2hpxeXuDyR9eJtacoN7ly/z7/3a//A07M5mTzF1HdHidPnGZ/Z521u3f4ne98DZX3ML5gMnFCa4yO9fv3eHD3Lh4wqSX4wJEjyywtzdI7Mku3pbl/e5NnXniJ5W7O2vo6e/0DVk+cZDKpuHbzBjeuXyIZr/Pjt97iK1/9CmsPtzl3ZpU/fuMt/pt/9I+Ze/1HrM51uXVvg/m5I/ioOLK0wqR0tLOcqixJkpyHd64zqGpmWuL+f+Xye/zMyy9ydH6WWAX62xs4J2kxu/0+e1ubDAYjdjbusLt2gzsPt0jmFvi5L34Zk+RAZGFhgdNPnGNvOMRWE+Znurz83HO8cekyJ0+d5tlnL5I0RryPgID42Nd/+hEenfGbQoh42NT5mIQqxqZwQ338Z4+DD80fEvQ3MjXjnrJY5HUe926amo3GKbpx+PdUw3iR7oBmb3+X2tVsbT7ER89f+urf4H/9Z/8EpRW7WzsUxZj2zCItqzi+fIThuKac9MnTlI31dXrdWfZHFefOPsXXv/kHzPcW2O7vMX9kjuPHj5N35lhaWub86XN08xnGkyEER5omTMYTTKKpq5Lu3AIPNzYozxVsbezR39/lSO8IP7r/Y97/4E2UFVPA1eOnYDyi0+2y399lZ2uTM2fOYJQ5ZGlNvRhoJAaPe08dHrw+ThH62IU8NMKcAksKQeJifPTzpkBqMl15xEQ77D8+9oKPvf4UZ4p/auZMX3t6wf8sAtUhE06+c4h5SbPzsMg2RhgyXvJBscaKkWldi+dG8IQQ6c3O8tKLLzMa7vHuu2+z3x/xxBMnIRh+9OPv8nDnIQ921jh1bJlxOSHUAuzeunWThbkOMzMdNjbXGE/6LNlF9vb26Q92SBLpyh47dpJy4tne36IO4vPw9W98E6MUv/AXfpGzp88zqQ+AhPW1Nc6dP8P2lqWsxzx4uEa/3+f69Q85trLC/YdrjZ+DQyVWwB4PViuKckiepcy0OuzsDcSbovY4FzBEbKbZ3N6kr/v8F3//7/DpFz/Ly69+nn/8X/0jdve2sG3xHCirgte+90ckRowxQ9MNd1ERfSTVGaH0XHzmGV7/8VssLC7y/vuX+eVf/kUmhefrf/j79GZyaq/RiaEuS4KXzrdyFqMtRxfmuTuaMByXDMsH7I12carHbHee+cThYs3G/pCiKMlbHcaTSLG3xiQEyqpma29L5pfSuNgcHvGkJsFomPiaWFVolZKmbeZnl7FJm9Xjp9lfv0eabNLONSoqWt1Fnrhwjv3+gPGgonQp/Z0h7U6X9fVbDPY3SPKMwcDRm20TgDooWjpjNBqizQDnAjtbdyjKgvGk4OaN62xub3P/7i3G4zH9/iZKBVwZiTGhqgtqPyJqTStR7O9tU1UW7z23b3+E0oHxKFA7uafq8QilJTJZTGUVoPFKgJFEGXQ0VARc1I0HjHiCjAcF0Xs6ed6AbZLYYoyhlaWgPVYJ20YpOWvVZQVGk9mU1Iony2g8IEsTQKRxxIivIDSSU6MldQ+vKAtPlmk5yRKYFGNqF9BoTNoVs9oYsMaQJS1iFIAjbecYmwBSVBmtD2PXrTEkqSVv5SKRUXIIjI1cqskIODxEaiJRK+q6QuPl0KyMeF14Jwe8abJPDAQXqJynKCvGZSFCphiIJgqDJwImorxqpGsiAxVTBI2Nwk4KiKzMNKwFaYCoxi7QYg0oDDPtDKsl6Sb4ik63RbuVCRDUeMtoLUBLCAZjNFF5SbGqg9xTURGio9NqcWL1JEFn3H6wxmjcx1U1ZQjS6W7O03V0Mkg+Nv4vAYU53C8SrZt0M92ss7FZS8WYXCtN5WBzb5dFo9nf3sGVtUTOo7GNzNw3rKwGB5DdRk1NY0XaJAcDQ1CPQCCPP9zoVdPFLSYlvblZOjM9RsNJA/o1h3Y0Wke8UgTjGwYej/al6CRwRlk5QE63GBUf7U4RHAL8lWWJ9zDT7jAcDKgnAZSkmRkMqjHC9o23lGmS0qSZpZq0Kak9rGnSbAEay+3GFhoXHUU5Jk0srXbKYDAiFDXRGob9/qHJbogeo8QnSjVMpemgKqQB513AJpa81QKt0U3UuTIGE3UDDhqMthADWTtHBc9kNMAoI0CZAhUNkaZz60HrKNBp8I0sjEOmmaS2CYg6bRbG5lMmNm+uUWwYaJY0SZgo8X6xJpH5YhKRmGmRICqtxDi8loM8ITZ+O7o5TKsG2JLX0c297pp1LhqJ+jLWHj4XpQ5lrY83PBvnhGbNk/tZadWY6Mq10sqSJinddluudxApo1Ea3SDJWiFSnagOg8JdHQ5Z04fMNN/IeoKkQFahJEER0FgdiA7GtSNqYSISPTp4hpMJkGMBPymYyQ3zrZSF1LJe1GgbmJufI/oZghaw3qoMZVOUEs8faxR5q0PtK8b9HXwsSVzCkaVl5hYWWZibZ3d7i5t3r5G1MpE2mZSgPcposiTHuQMWV55iNNlAbZcE53BuwmZ/nTRYrDJ02hmVqzHGUlclrowo7VhaWSJNRBakkhQ3qUhtSmwn9I6scGTxHEpl5HmP4d4uxEBZVei0DXqCDpaD/T5ZYpibXaU/6oOxaGCu2yVWBSqRtMVQVygTSTsZaZbT6IExScJ4MBSvoCY5cmoKfjA4QFmDTsXjLYZAXVbEqEQupxFmszaMJwOyVDMZ9aXZNvaMBvuUtUPnGTbLiUoTNVS+pK4KWmkOXrzviEhKZJZQl8JwUVoxmRSEuqbbm2dYlA3vNOB8idUKi5JAAE2TGFZjEkuaZgQtn7GuS2wrx2YtAbZjs4ZWJYnVWCN+Tkpp2ddQGJs2XmMBowPVeEK3lYqcK7HSdPCRLM3JklTW3SihD76uRQZmRe5X16XIq/O8AYMU0QeqqsD5iizrSfPAy+qljQUjzW/qGh9Eejm7sEhwDq0N3jlMDCQmQxuLosaYlKpyIpvXlmhkX/FeQil0Awi7WmRqRVnRyZMmBTXiGvWLsCgFFI4aJnVBq90iTSQFTqtIXTlhUBlLlrYgaiauoqwrLIq0Me72XmSvSotfFmi0Nhz0d8gzQ1VVIuuOEoRhjMFaw9SjWHy9LEmqSbNcZMUmFUn6T/HxUweU1q5eYlKNGYyG9AcbuDrh2eee4MzJo7gA5XhItb/Dbqn41Od+gfP9A2Zn5/jcq5/lj9/9bZ58/tOs2w5H/Cb/82/8K5KshVUZCZpeuwUnz3JcRwbbO+z3h8QQmV9c4OziDO/s7WJ0zpde/Sxf/91/yU5sQYzcXNvAErn87ptsXE84cIqf+/IvogYPqUvP+vqmJPwAdagYjirZuIxhPOrjnGV5aQ4/GTKbJIy7loODXY53lzk616WTKJ44egxvDNt7+7zwwidZX7vPc6dPMi4KTh8/TmJhfLDBhx+8wX5o8cqnX+LdN9/iYBLQBIrJgCpAYi1WKZyT7tekGFGFSGEVuztDDoYVT8/1mMkTTn3mc6g0Z2Ghw6V3Jty6mfDd177HqZUlXvnZV3nj8jW+/MUvs9SxvPrZV8ms4Yuf+QxbO3ucOPEE3XYbQ+TBvY8Y7OxRFeeZ6bSpyw531+8wt7DCYOMOa1vbtNa2+JXVJR7cusyHH93kzvo6586d5+yJJ7h0+QM++OAnbO33aeUZH1y5yvrBgE9/5s+xub3NytKyFAMx8PILLzA/v8Br3/oGH1x/j2o0YreYML+4xMULz2DNI+rQ4+hpnNLuH5tr8U+DBdPfexzY+TNYJUo/DhfAYcIONKhB/Dg+NJVj/Qdef5o49/HvSVtZKYVzjmNHFrl9/Qbnn3ySmaVZXvnkKzz8y7/G+z/5Ce9degciHGm3WD1+hixp883Xfpvdg20OikBRSwxw1p7hxvWrfHj1fV59+VXev3SZTz73InVZs7K6yhOnz/Dg/n1mZrts7m/gtWZSaHxdY03GhSfPo9McExzvXvqAC2fP0Zrp8MPXf8TVa1foH4yIiUGbFt/+7nf51AsvMDs/S2JSjh87ToiB4XhIp9OTLpjWDe2bKXpzCCIpVAM4TUc//nvgzePG61M5iDQsHwPwmAJJj12wKdTYgEpMu59T7OoQnOLwv4fF/eG/eTyE7t+7tlMwk/gYnPjYa8l8boAlrTEqwbuA0r7p+iryJGNYTui026wePcb163s4P2Fz6wGXr7zP8ZWjXLhwnvWNDYblLp1ul1ae82B7g2OrR5mUB2xszXLi2ClOnTxFtBVrG+tYAiEGzl24QJ7MsLb+kJu3bpJmNU8+dR4VEz64eo0/+cF32e2PeGL1OPev3GZ+aYk8T5mbXUJFy5lTZ9ne2cTHiugDZVmR2YSxmzT3W0AbRWpSFBX90ZB2d5YkzTHJQDqpQVE1MbAxSJTvzsYey0tLXPvwFp1Oho2GxGrytsHXDqM0RTVm6Lz4Axga+n4kTdoMRn0qV7C2vskf/sEfg3IcjCcsnzzL1//177Bz8BCViFQmePGYiSoQGplKNzX8pZ//RX7vX/8eNoMbt2/Q681QOcgWTtCzhnu3b5AmKYODIUp3GFYT0izjqU+8wN2b11k9usTW2g7jeiBRrdHRSix5p0V0FVXQGCWeKq10lrNnn2Fnb5vxaJeyGDAcj8nSHBciM3PL9GaX+Ojah+zs7FEGz507dzl67CTzs0uoVLO7eY9iXODqNjE4EhMhjHj9B/+O0UQzmUiErIk1tanEnFkp6tKxuX2fOo7FT8Gl+CCGoiFUaGtwrYzxpMDVhjSx6MRhojjzWB3wqCbqtsLXXg4bOiUhJfgKYkCnEv9tVaRtDGBxBHwITKqS6ByaQLuVY9KGbaMUIWp8GZo44ibqGS0yzSQhb7cJzmNtIoVWCDhXNZKQpLn3hN0h3H6RnCwszKI0DAcDAYuiPzSstSZgtcjtVCP9MGkm9HgjAI/SCrFFFd2VtZYkSYg+YpWhqlwDfgmIM2UneS8HQ2NMk8YqPj0hqqbjKmuiVhaUwZoMCCjTsFtcSZ5Z0qx1mBIVGop9cJ7S16QmitRJBUpXkyUZ7W4boxPSxErKWfR02xloYSG46cFZgVVIsyPIAVZrg0lyotYNY6i570Ci2L3D+RLtjDRetBicEgxRGSFilI694YSl5SMsHz/DvXsfMZzsNAflJp5cgVJW1sypcXT0RN+kIkUaY/LGmwjZe5UWXxqtIDoxHh4cDFlYWKCdFBhTS4pZAwz4GMR4OwqDy39s31ANCKEOD/Ax+sMIeR9o7JRlrfdRMSpKJuOSzkyXPG/hXcA7R0SYV1Ebom8M1hvfqkP2dCMnCEoTMUTvIHoavOLQT5GGPVJVNZOiZHY2Jet0qIqSqeAsNPNNIBjXAAUapyK2YWBqIwy+hvQiY96YOhumfoGyd5VVSfCOrJXSbmeMRiNs0NTlmBqFso09fFQkFpGPNYdhrQRQUTFiraY905L54gU8k/QoT1RRJC7BYBNJQfVVhZ6OkVLC3mjgoBA95tCGQOGdbz6HyNK0tqBlDtGwA7U2ci2agBNBdoM0tyQOSTzblEGjCM6T2FQkVI3PVTkpCEHWGWMsNrHUZSlx6CpIY4O6kaY1puW+MVfXsvbq0ADEDVPaTFN+YzMNlHjoHNaTupnjUbzQUPqwfxkQsGlSjMUY3QVq5xopYBB2xWOy/xjlPJJoRaxLYfo2THGlZGwPyWY6QqwwqaIsSnwIpHYeawzjcoD3Fd7B8uKCJCvmHepY0M0q1h9cp9uzmIGlmuxAZVg6soSdz+nMz7A0v4D2PYLKSNMWxmo55OqU1763zf7BgLzXZnHxGDPzS/S6swRfcvT4Mep6zNb2OqN6yLDYo3QZ5888z87eiMneDm9/8DoXn3qa29c+oDuTURrPuHBMqoqFmQ44jasDpRNfoBPHz7I0fwxfFE1qXSS4GqMCRVWztLJC0uoyv7jK+toDbl7+gEkxxoeKfn+LM2eepNueYWnxGHUxxuaG4yfPcePKJQaLJ4Caja17GGN4ePsqnfPPIL1Dg0kyomvSqYPHU+HcmHa7K+BD0wSt65JyPKK3MCcpY5WkOgZfooFEGwE9p6zNWGN0Lmm6OsO7knFVYzWkaUKaGVyQRtCkLhvmoiUgCWDDoiDPc1kHfUAnQr+bHPTJOzml87hYYZWFukLFGmNyXJOUp5V4DCklgEXU0vCrSwFB2522+DX5moA0Wbxz5J0MF4TxaknwoSBPErTW1EGkz3VRYlRzbkBjVSIeROUErRV1jEQtf7eqaxTT9ySSwtF4SG5TAZNUFDDbOcrRiO5sByUrjOxXVmG1sKdxAa0to8mA1kzOVA0RVST6mtQmwlhqbmQVxcjeWtmltFZUTnyKTNQonRKDE0ZymlG4Md2ZnjD2m7RyYwxBT5McRcZbFyXtLG8aJ8J6LMYTFJ487cj+XHsmZYGva7JcJOd1VVNMCoylCcwwoDST0uHrkqzVA8DatPEkrkkSK9K4SJOsCq08FZavUfgpWPUxw7v/+MdPHVB69yfvcmW9TzKzzN7Nu3x0+zavXjhCalLyXBIYLr7wEoVDDL/6uyLROPYkf/Nv/ioXTizzx99/g1sPb7GbLLI622LSrwhR0a80Lzz/ST669Ca3d/t42yJrGwY7O1zu74IyTA52+aM/+jZFgBBKKVIaHfzDjTXW0SytniG2K/b37lIVA7yXyMTYFAVFsLQ6PZSBwf4eiU4ZDQcc9Pv8zu/9Lnne4ty5J3mwvUf/YIerVx+ycPoi//Xf+TWGW/89GTVxeMB4VLHQtUzGQ3YO9li/dZP/59tv8PIrP8v2gyuMJmNOnXiC8XCP9WpEq5Uy7I9YWl2hODjApTkuzfjUc5/i6NIcYbzGB29/gNu/w306rB47zcWnLlBWY14fjmjNH+cXvvolXn/zh/y9v/arzOQ5x1cWcGj+4lf/PPdvXOXO7VucXlc/6AAAIABJREFUOf+s0LvHIzY3bvGN//cb9BZO8alPfoYQHStLS7Q6GU8fW+X//Of/O/tDR27gzbe+x/d/+Cc8/9RFth7c4cTJM6hQ8/Z7b7Ez3Gdzc4sXX/4MKMWTy8t8/rM/y7de+zZ/8ee+QKItk3FJnqXcvXGN3/367zF7ZIVyMuJXfuVX+eyLr5BlqRQsTSHXaALQjQmle5y58mc8Hgco/kOG0YIYy1dTH5+mX3n486mEbepXoPU0iU6eHBqgQTUv8AgU+VOMGwRES7Rlb3DAsRNH+cyrrxKVI293yE0boy3WWs6fPceTZz7BqKi4dOUS99fv4ZVHJVlzMIvUTZTxE2fO8upnv8A7b7/F5SsfMH9kmRMnTrPYO8J3bnybmXab809c4Oad2zilKcuK8+dPceH8U0wmFft7Dzh28gxbOzuMRiPefPNHtFs9ojKkJuedn7xHFTzLR4+ysnySuw/u0m3nZK2UTrfH8tJRlpeWG8PxpNkIG7ZY01mTIZy6bzSAnHo09sRH4yYbTNP25XGiV8PwajrKH2c7qY/5MKnGfP3Rzzn8+k/Plmn3sHkVpp5Xj/3Kx8lOj7304W9FGuNueXaMkmRU1o4QHPhIe6aHqSzdmRnyVgsfSh48uM1geMB+f5v5XkKSX2A0HJJ0I08//TQffvA+Z88/zc3rV7hz+zbb6xt857Xv8IUvfonf/M0H6LDJC5/6FMdPnuHic89STEreeec9jh5f4fqlK3zil17kzq3vsrI0T4xw8/YlTPAU1Yjnn/0ivW6X0XDI1vYO6+sPKCYjbGJRWKJyVCGgdcqgPyLVirzdQ3nHZOyYb3UphjXLR5YZHOxINypRtJOEqoLZzgyrywtcPHsBUHzt61/jb/3ar1BUJZ1Wm8FwgEk1BCfGow0W6B2cP3NGGDyjCf3+PllL88br3+PJTzzH5fevo5Tn//7f/ikb/TWwRkwmlacqFDEavNIkWrpoL3zqFS48dYFf+av/Ge9fvc6D+w+o6xEmrVjfWuP481/gCdvltR/8gBAUdRzLodgpioOSUyfOc+LkWeLTY/7lv9lndvYIG2vXZc66gtlZ8PuKNM2pS8dse566DIyGIzrLy8ytnGF/OAY8E+fp74+4fWed+w/XITcwKml3W2w+vEmwLSYuEEOBjY5JcEQTaFtFCAX7/XX2B7A3qFBoUl+zenqF7Z0drG2Dlph3FTQ6GLTNydKEshyQaoW2CpuIdNEEj0kUNomYyjGqEFmUq3E1wvBJFElmqAsnbB+0gFJB5FvGSJpWfzzAKPFDUs06mbe7VL6SZDYMMSiqykm6llconTRdO6GzmCSlKGui9yRZRpLl1FUFykuKmRZPlDoIwyXJ22TKkLfazPZaHEyGOF/ias9oKB1VpeQze+fxQdZpm1hs1uaQHtWA/UZFEsQ8GqWoi4KyKhvTZtBoYevEiG/8XYKPoLVEmDuHj+IJkmUZRutmXxFWT2wKYTHyFnZGDJ5er0en3SbRliQxeHIBRJzGNw0NMUAWH5g00aAMWtmGpSVpPJ1eh9WjJ+n3D1hbX5fDu5OFynnXcFYcIehGHmAJCNsmKo9tDvbaJCLLiwF0JmuubpJXtZJroCL7gxFpp8944oiucQ1v2BaHPoexadAo1wy1Rk9tGrT4PIm/VhCwrgFBYpRiWYLaRMoxHhQsHOnSm62pJ7UsuI0Xk2oWDx9jMzenY4QYoDaAmTEiiRU2yRR80Q07pfHOiYGqGDI302FmpsuoGBMa4LB2gamALcUIIy3IZ4+xiY5v3r9SGmVTYc/oBkwLAd14a02la+OipNtt0+10GB70wT1qkIQgJrNWCXgmZC+h6FjBDVDKCKuzYdFNg0FMnMrEAxGNrx1VWdJtZczMtDgYHQjQ1tQ4ytdNQ0ZYSHV0RG1R0UjsvRapSqc7I/MjNEw9oyCGJk1INaBoFKNsA1UpPmghNEa4cumZBrJMN9NAxDdAqEHWFrT4gjxW/qHUoZ2mMFl9jdJW6sQoDABXl1RVKelZzgl7pFlnJOlLajHX+MDF2gsI3UgmtZ7OXak36lCLPFZbAQO1jJkxcmgWM/BErrkSU27vhZkQfZMI19Qtrp6aiwec8yQNeyUqS6Ci153Fpi2K/TEqCLssNnIi8XKSQ3JQksYV8GBkLKxK5F61ibCEuylWWWKs2d65j8nb+KIgyxIWj56m9GMePLwHXtPrtNg9OMCHAhcK8szixjt0e7NsjRzjyjHTrjl1vMVf/Ft/jbMnLtLKc/749bfZ3460sja+qsjzlNd/+BrZzAxJbwFUxf7eNovHTuJ9oL+zxdzyAk9/4nnSGxnr67coosOm8wz6I2Z6OXvrFZ988lnme4v8zMtf4PK110mSLlVdMdM29GYzYlWjfE5wIt1Ze7jO1sYec4snaXVm5J6Pnjfe+T4zM7Oc/cQnaGc1V97+Ibv9beaPrvBg7TaeyObGQ9rtHmmec+roEyyvrlL7iizNuZc/4JUv/RIfffguw4+uMAmK9958jWPHTkCw+LqiGg/RyL5A8FRFQd6ekTuxSaxMVEL/YJtuJ6eV5SiEeeibYAFjDDq14sEWAgfjMVneatYtsCYKYz0GkqQl8idZEIi1GDR3MksVIiZN8EpRR2i3cnnNhp3jJjXBebIkY1BWwqTUCZVzUvcoxANKSxakzPsEqw02SILwpCqw2qCVwrma0MzPYjLBJIbEJvi6kV+HSFmWwsTznqphzhaDPvMzMzjnMDanipG6mlCWY/J2C5XqxjxeE6pKjLu1pJPVpSPGIEzEqiTJDa6I1FVNmliSLKf0AlYZZfBONR57si7VrqIsByzPr1KVNdZmFHVBZi06msN9xJKIRQjCojMqIQaRzmsyoqrRStLWrNEcjAtmOh2UyYhRkkRRDfDcrHJaafbHBbGqMdZS+UCSWZH61TVZx5Lnufg+eY/zjnaeYVODNpaqkM+u0RibSaiCtezu7jPTa0tAibWS6OiEJTutRWIQdlOWJI1C16BQeC+Mzaqs+Wk+fuqA0vq9D9k//Vf5h199jjfeeoMb19/kn95+i5e++J/ymefPYGKg15mhGyLWKlQWOZgMMd0WZ4+usDDb4bMXn+bO1V3+/t/9VS6udvng+m3eef3fMXGKc8eXGT2c52Znh7NnzrK3fpO1saC+Cg0xsLN7QCtrsbd/QJponBdDuDoErAmk8xkvvvIqF5de5r+99z+x1x+hlBz6v/DqX2F87z3c7CpffOlFXvvRt7n07jXGlaOqCwbFBFTCaFTx7LPPcHz1JBw5yS///C9w48pr9HcPWP/xD7h09SNeeOFn2Lj+E65sVRxNx/wv/8f/xeZezRs/+j6j4YDhuOCjj66K0aBSuKD4/5h7zyjJzvvM7/eGm6qqqzpN9/T05BmEATCDTASCJChmkaIoSqSCaXG98sre3WN5dVY+Pl77HB9L611Ju2tLWlvJklYrWaJEikuKUQRBgACInAYY5Mm5p6djxRve4A/v7Z4BxeMv5gf3OcD0TFdXV9+qeu97n//z/J5b7r2XQ/Nb+NJ3HuTWGw+gG03+6c9+in//u/8Ty6s9ilGf3mqTVTvi0e8+yHce+Bq33HYPd95+Lwdlwo+973727JpjYrxFO4lBhEl6e6xNZZb43jNf5bbb7sFaT3t8gvMXz7Bt+35W1gYsLa8w2RlH6Yjx5gRPv/gS19xxPzev9njphaf46Y9/mGe+910eeuwhqkpwyDgeePRRjp85w45tO7j+/beRRRHNTocPvf9DUBXEZh+/9fv/G3u37+beuz8Aa+v8+m//OulYm/ktcxx56zUO7t1PO9a1hTBic4xD/acIKjnwNnXgauHm+/9twyX0/S4mIQJI0PurLOlcuf3VH6HO+YqItclT2nx0fvOz7//+jc/D+mqZm5tnbtt2kjio8oUbMj25hTdPHGX3zv3cc+fdvP7a61gML7/6EtZ5qsqxZWyMndfs5+zZs+zevZvW+CS//F//t1xeXOXue+7krTeP8wuf/SznTl/m0acfYXV9gYVLC2gdI+v4ShLFrK+u8uDDD7Jt6y5mZtpMj0/y15//Mnv3b0FIw4WF00xNT7N7516eP/wkhw7dykp3lTeOv06kY3TSYs+uPUihaDQbmwKbq63n1JOytyf/Nlpk2NxEbnwtxAU2nxU24oveX3UsPQjkVcdSXPXSqNWkTauR2LyvtxnKNkRDf/WztfH1jRteucHG6yJM0cWmmiQ23VZXmuE27sWaIEYrofAYYp3QHEsoRgVpmoaceZ4j0Vw4f5nV1ctkqWLLzAxHjrxId7hGVAnKwuK9YPHCMn/79S+yf/9uTl88z/lzZ1i+1OW1145w4PpruP32u5FErPXW8LbHtm3z3Lf3Po7v2ctbx06xa36GH/2xf8Dp46doTE7wn/768/xnn/lJmnGbRrOBjiKeeuYJFi6ew/oS6z2+dJSFY2p6isuLy9x6034GfcPl9XUQUdhgiJj5rXNcf8N1LK1conA5a/0BcZzifEUWWw5ecx333vtu9l9/gMvr/4Hf/+M/osz7qFhjqxDtGVYjhIqCUColpa248453cHlxiYcefgxEhVeCRtbi4C23cOrEOfr5kMu9RcrckjQ0pS2RIrBgrAvTGIRlotPhHXe9i0HX8OhTz/LGsVdq5suQ9lhEI43JopSFYR4qxasKartwOhkxyruMdXYRRTGtTpvUZRy88RAf+uB7+d4jT2DMKayLSFSXypbhvXPhOJcvncF5w+Xzp1A6xQkYrq9R2JyhFVy4fAEpHbGSmCjEuKyvYESYsAuJlSmmsmgcWRKcG2UlGOQlkfBEqWLPlibDUqMJ39+Ixxj0LqGlJNUNZByhM0NednFxg8p4yl6fOIJR5SgrR1nBmIJYxuT5gCjOMFVOWRpirxHRRuTCgAoXSa4yIDTIBFE5yrxgvBlum+sEay0VNghQNqZyAuMF0oQNWFByJGUVXAJZswlKY4oRAkFR5WywC+I0xZjgfJFS10049du1tvX3hgNGgyHhCttiayeGl4LQ+GlREryVeAO22GD2WExZ4K1DCh+YLPW6sOE8QHrKokKJCKRARnUkxtauCa0wpaUqKpz0pI0ETYzzOY24QeVgbGwcUwRbvow6yBqOKZUkS2LiKMZUJkTK8FRlGS5WrceYAoeiKlzg/SQRWlFf9IZmHyUVRQGlEbSn53GtaZbOnIbIIiqLizyqvuBWhKfROR9ic1iE1LWz1hMrFYQADN55tJAQCby0ddl0HeFwkrLM0d4RC5BpQuUtDrlZVSycRSOpohCHMjYwooRz+Jp3IWrXWRgShIjPRgsrIhwPqyS93jpTk23GG2N0G11G3QFxHbXC12ICjlhHQWgTItyPkzhZi0fCE2mNdxtuoeCqQ8rayRFg7b3BiE57QJKlSJ0gK4vFIpUObW242o0hMCZEgEGDDw1t9csmiD9SXxmoiBDnFSLEy3BBUMqLkihrkjRa9NfXqLuZABv2Ol5AHWfSSKwDKz2qzlZV3hGhgkvSixC3qJtXJZ7KlwgrGI5yklaLtNUkzVJGhUHYELOMvMUKhdqI5/nQ5CS8C5xJpZBaUpQ50mgiKVCxDDGVzdbUIOp5t/Fa0gglccZuRl+sD8UR2hvqYkQ2+Eqo4EiIVRQEwvoMvSFQCrHBHgtRbik0xlnSJMPZMgg90mFsGWq3PXjv0Dq4gYz1YC1JoomTKIBpCW7BONahqpxwDJwPcN+kkYb3h4CqqsLvJoMLTUlJZQKvTVQVxgR2ltJRvWYKhAuNSqoWhNnYIxFYUkoHkc2aEiEdUliarTajoocpDFmaBlZSpIijJDhQbIlQmlhqVKRJmmPEcQLWs7q6yNTULGmjQ+lCDOfoW88wKnvMdmaYmt2Pd5L919/ExNQUxbBPGsc889RDLPcGIdwoSgoRERV9zp/tsdDPkarB7Mwc77/vR3j/u3+eeNjn+OEHset90mgyxNYIDc15r2B6aiu33HI7Tz/xEOvLJ9kxN8PstusY9tdBO8Zb44w1WgzWlpka30q7M4GOFSdOHeGjH/pxMjroZoaxijhtkRY5CCiKnDwXuKLA2gTlJXlR0OsNaHUikmYDU69jw+4ajz/0bT7woY9w4dRJ1teXeey7X2N66zymKFDK0og1nfY45069xi133sfa2jKdzhhZZxwlUnZvm2fLznmkSJie3M6egwdZOnWCNB1j1BvUQ06J8hJvbPi7ilBpiKvjCMKtF5SjEdPtsTAgcT6IJc5SlIZmKwUVBFCcw1Q5adoKDuWoRVENGOZDEiVQWqFjFdZaaxiU/RA51BloTRKnDIYDkjglVjHG+7pYQrLeX0YmCb40YGyIq7uS0hU0ZBziVDYMTEajPpUpiNMmEFx4VJZy1KPValKORmjlcUJjKssoHzE1ORnez8rVpVEVSZaFVjQ8AoerhmEdlrLe+/kg1DiJTlKiJEFFoSHOFGU4N0iJjiKUV+TliCSOyauSWGt8BdIZKlMyNtnCeAE+MBw3jAdKaDAGjyMfDmmOtTEOUBrnXUDNiMBYwhmEDY6zqirRUqBEhJAReId1xeY1OvVakSjFaNhlsjVJMRribYVxIYIfaYWQ4Xd0HvLhiImxJlIqpJTISGJzg5SCNMnCtYnzlHXjZwB3K6SIqMwo8NO0DqK7kIxGIwSOJGrivUermEE+oKry4EKXsnY2hgbUsD+WKB2Ftj1EzRjjh/rxQxeUpm/+OB//wEeYlAPe98530j37Ao8cWWD/bV1cYUA4nFBoGexhKtaIkSNLE6SKSISle+IZ/uqB53nne2d46twpbrv1Vp797ld5+eR5Bt4yNznBzPxOts3PsbJ4HucL+kVF5i0oQXe4zqDQOASN8WmmM83R0+dJ4igAOpcus3r0KXbtvZ09k03OjHdY73XJWtu5af8+Xjj7LP2VN1i5vJ0P3nmAV148wvz8Ds6cPYMhsDpwlouLl7nnzjvZ1WhycLrJ3zx8nH179vLo84dpT89w+vWnWDp7mqldN/DaM4+Rq0luu30rL7/yGlU5xFQFOLV58oKIwcoSD59+kzhSXLxwml7e5Yt/12a9cCwXnqw5zfGlLtu27yXLJG++dAqbvsqhW29n547tRKnknttuIxIgjENaT+UM3hYcfvoJTlxY4c/+6v9i59Q2PvCjn2DL5DSDnUPW+m+xtHKRquxz6MZDzM7Oc5NucdehAzz30JcpXMWjzz9LKdrsu2YHJy+cY2bbVm7Yfw0vvfwczWbGR+//ER793neYmZ7AVRXX7tzNk488yPJql5PnnuKWO+5jJm3i0g733nMfDz/8CKWU/Pnnv8DP/9SnmZudwScZOk7DBssBeIywV8WTrog/bxeT3i4G/aA4nKjVcynDm3zDSvwDLCh/7z7+/pf9VY/hBz2e8OE2Ns2xqu3QCiksSqUsnjtHJBRGWh5+7BF277oOUw7YMjXNhcULbN26lbKwLC1cwgzWyRLN3NQsy6vr7N+7l9Lez7A74LWXX+Pk6VO86/77Q0xz+AxnFs4QZ01aScag32M46DM5O8WP3H8PR4+f4PDLz/DTn/oYh24+xK//xr8O/A/jWTh/kfe8835+4qOfRArNqTMniXTEnr3X0sgaRHGCVxvchrB5Cw7vDbfWVQesvlh6OyD9qmN2xSb0tufoihOpjptwRYjavPu3PxVX/uUqR9LbbltfNQrefluuvq242pn0fZalq5xVQXmiZoTXUxAEpQk5dkmAvXpvkCLUSWexoiyDbfXnP/NZtm6dY329y5f+9is4U9EtHP/+934f7wxaeiwVbx59lZmtU3T7y3R2bGF1bY1Li+tcutzl+muvp9cbkPf73HT9Nbx14hSf/pmf56FvP8z66iU641O86927+c3f+E1aUxGm0kSxYHVlhZ3b93D77XcxHPV46KEHcCaAMaXzaC/pd/vceMPtfOPr36LSJWYUyBzdtRFmK8zO7uKavQe5cOkky2tdrHI4HDqapj0+x/7rbkD4ID69+OZzkIASJVEN9XTGEskAj60qaDabaJXxox/6GMIbzp05ya49e3n+5Vd49smnGW83KctVhsbiZcp4s83S6kWUboS8ehIib9fu3c+HPvJTHDt6jv6gZDBar1kq0BpLUU5yz50f4PDTz3Bh9RRZQ1P4EiEi0rjB9p3XopOUx5/6Hru27WR9aZH1vMtbr7/F64f7dIcDGh3F2tIIUzmsqyiLChuDQjLemaCyFcKXRFELpQVpJKlGBqsFsRvSTDvYyjOWaLprCZFSaNvHIcjiCTyW0na5vB7cfxPTE1R2iVRF7J3bxkya8PLZ8+RVERgY1RqRNLgqbMb6aytElUWIklEJ3mQk0pE0I3zcZH1ljXZzDC8VZa+Hc5ZKG6z2+JEniQPQUqsYKwUahbGewnqaTU0sNN2qIGumaKmJsozIDKlsANJLqUKkwoLWEd7DcBTa2LQKEcX+YMj45AR5PiKONLZu2xERtQgT2FgBPO3q5rKNjWlOKQzVYBguMnGBJeQMWllAEYlQT21NgTUObyUmL4Pr1Yf7CpGWqv57EAqcK/EEh5P3Ahk5oiim2UzqqFxMEkXoJIBYtdRYL1FJggKwFfM75knTDhcuLrK+uhLiAsZQFqE9pnS2hjgLpA8toFZY0ijGugC0FpFDo6lsiDXmlSPFo219bLwKnCcZYYuKZnuMTjLBWnQRZ4dEMq0vxEvshgvHuhDLE47Yhyii8oTqeCmIhSBxmijSWFsgVFQfk3ptdA6hFBEQNZuUlSEf9Inq36eC+nkKQoHaPGHaUKetVRBhfHB3bggHwZIfoNIb7XgA2mkK61npjxhrt0iazXDRYx0eh0KivSeqf4pjQ+AIFxUCi9AhThLacDyyrsNOiILII69McpXUCO9JtWBiImPVBbZGAJhDM20wngWH3dnzl4J3tnYLhfNT/ft6g7oC2wOhQ8zPe1A6xBW8pTKOVEiazTHy/mATPO3qrYgSAZ0k6+a3SGlCKWEttEi9GZEL7tLAJHLWYfHoGvQ+KnLaRXBPNNtjjC6toADrBE5IpJdhRCLqVjHhsJXFI1CE2FWVOxAppZbEWIT2deTE41RdlyI8hXMoF1rIghPDhPVXBR6KksFB5fDBsSRDm12IMvr69/cbBsLgLqoh9qJ2GxgqrDOYMieKFF6Gi/FUawa2wlZBoPamqBvTgug4GhYgJNa6IJhqQVkMapaKwGjJsKwo85x+T4MgRHhMGHwZeyUmGUWKsqw2R0pJWsdXpdwctKmoZpQJhY5iVByidnGkEUiSmo3SbDZIs4RG0mHbth0MRwVT09OMRiO0ljRbrQAGBqSOGI0GeBytia2ktYNs+fICU9NbGQyGqFhSecsbR7o0I0eqGxhj6HVX8OUA8iblqCBrJiycP0vCCFxMLD35sA+qzVp/jXWvmGq0GbeedKVNI24z8D3+7shhllYSts1P4U2JjjUrK5eYmZvh0qVLzG3bzr5rbmIpVYxPbCXNMpQ3DLoLXB72Ofz80yytLCKlZmHhPJ4CIzyPPvooUmo+9NGPc8e972Lbmd187ZtfpKwGSFOxdKlH5SqSpEGzM8GN192FqErGx8dpNztooVAKVteXKazjyace48UjL9JbOIvxFYsL50kSjfIFxicUdkRV9Dj+xkvcfvcHSNpt0riF9Y717irjZo5dO/ex55priJ1nbNc+KlPWQrUO7WQ4hK8Cs6bZqmH39XlFRXTXVxGyZgkR1gosmNKGd62K8MbjnWVYFaDDADvENROKaogzdcOuCq+fsjRUVY6xBVmSEekErzTWhKa0ZiIp8x54T4XHlJZi2GVyyzQrwy4CiZIpRTGkHIwQypBkaYiLCsWorGhEMWBRURiMrff7qEjRaLWorCONErzXXFrv0kgbKBS5s1Az8HAWoURI/BhLLAXd4ZDJzjjWS5yrsLZCRTHWeaI4IorT0CpqQyGIUqLmAWY44ynyIUkjxlpQKiZ3BTiPEw6pI0x5FfC/qlDaI0SENZbKFlgEzWaHqiqIVWAOKRxEgUlo63bH0uTkxYBMK0qhKOwAUxYoqYl0FMQsAtx9OBrSaqRh0EZwsioBRKHgStrAphsMh2Q6rHPWO9IopjCWQb9HK41BxlTe4wjx80acoKOISMfkeYF3lijSRFEa4n9SsN7rMdluUVWGLAspkVGRI6QkioM476wPsX8FSofrlkhp8rwMlzTOEUUbZ9AfzscPXVDaPruFy8eeZ2L7Xhqp5tCt93H42Bd4/rmnuOOGnUy0w+JrrQ1tH6MhvdU+p3snGK32efDBJ/mtz/0d193xPjqZYnrbXpQp+KV/+o9wjQE3HHgvx194lqdfvcCumYz85At0q0k+/bH38vnP/xllEeF0hEcyM92hzPucXBuEqUAVVMbu4ir/+7/+I77++W9y8813cvuhFd44dZqDB+7kxv3b2R+9hz/+wuf43Be/yHiiMDri0sWLZFEDFcXEsWJlrUtjZo7mxDjnzhzn8RfgcmU5t3SBsWyM7uJFfvU3/i1JkuHU18icRGQtzpw9SWN8gt5qSeRgY/TqnUeUI04cP0XWTDHFEKc9SST4+nf/jmbaoco101vG6PW7HDx4E0vn3mR++zw3XbOHqt+FMufi2Qu0Oh0m2x0ef+LbPPXcU1x77c1I0+XNCz0++KGfwQyHlHHEM8+/SHtmnD37rkVYRRZlvPTKESyCc8feZOATJpstGtkEt9wwzfV7dvOeO27n2Buvs7S0RDNJcVXOZFNhh2s8/NhDLJw7TllZbrrmWv78L/+Up994k5PHLnHgpr386V/+BT/94x/nX/3Kf893nnyMf/4rv0w7S1lcWuGFV45w8ptn+MxP/Qxbt0zjgO7aGmVZMTM/dyVy9v/ysTExu/L5FcfL1bfZiGddcZpcfR8b6kEQM0S9Aao9MlfF2+QV1xT84Me2wSHAEYlwEYKzOAHHjr7BY09/gQ9++H0k0SR/9Kd/wLlzF5hpLub8AAAgAElEQVSZnSYf9JmZnmVqYo5Tp09yZu0sWsALLz3HG28dJ5VNbr3jEK8fP8OZk29x+vwZ4kgy/lyHrDPBTQcOsb7eJR8WDHojKmOJYkU+Knjuxedpj7W5vLTE/ff9CHdcdxutuEFnvMlb5xd47713Mz7e5otf+RzXXXuIbm+dYb9H1sy49vobCBXPkgA5929ry6uPeC221NPSeiO4kTa5un737cdsI/a24VTaEOzU21hMG0+ZqH8UUDfy1d4x72sA6Ns8bpuuJX+VW+pqgclf/dy+XXba/LvY+FkeRIAugAjimo41wkq0ljUTbUQzS6l8AHSLLGbr3CRCHmTnzl1MT83w1PnnOHXqBFWZE6cpSysX+NTP/ATPfvdJ8sEaXjbYs/sG/sk/+WV+8zd+m+uv30c+yvny1/+GHy0+RhJF7Ny5l/GJDhcvLWFdRXtiksULpxisDXjwgYe4tHqWgR2QNAxJqlleKxiM+owG63zogx8FAQ899hCj0ZCiWmN4uY/0gr/96peJtUDbUE/upERknonZJsdOH+fS8mVWuitYV+EKhclLzpw7ysjcy5e+8g3m5+b59Kc+xdnfOcmqXcRWhlanxdrqCOFjklRD7pARzG2boygKjh89ycK5FZYWV2m21/De89M/+zP8j7/yP/PZX/gpLlxa4Vvf+AZrcYhfVc7hUmimTVIds+e6WxmWirVRwS07d7Jn4TquP3AjL7/wGNXwLKsDyeHnXyMvK6RPGZoukQpullhZzhx/nZMnX8cLx8rCKXxpkRGcOPkKsY5RSUw8UCid0VtZR7UyqnKI0JICj11zlKYgij2dVkXhHWkkmRoXjIzH5p6yWKUxtoVeNyevHFkrwtiIRFcIXWJ8RTGy2EowrEbMXzeDP3851AmvXeCue/Zz+BS0GimjYkiiod83jHdmkEjGGhk6jrh8eYDUgRFgrWTYr/CRJZaK3qBLuxEj44hiUJL4Aq0kPpOUpgKviZRGOo/zlixJKIsKiUIrgS8MWZJhq5JgYNEkSpEKAtNKhsm8qnl4lasCQ4kYIQRJnJCkCRCA9tYEm7f04cITQb3Rkfi6/UZFEmtKBnmBKiRKQukqsjTCFNAYy8IGqighAluVDPMB3gJeIXyE2Jh4ahU+92GTKIQPAE48zUZGkkUkjYwkismtxxlHYUJrinCOsqpwBvKqwnio7AApHKV1EDW57rpZSCN6piQf9QOzSQgQwZoehHLQUiJcmC56U+KJIQotWa5ySCKsNSgToNRWCZQIDAUvQIsAKr208CYXFi8zlrZwKJwMAq8XMojEWoGWdU12wD8LGy76NxwvAEmi0CpCpQlKWnID3imsM1gZ1r28LEnTjEaWUuZ5GMyIDZizxxuoaieGwwc+hQhClhaixjf5wI2BzaY8WcOyHZ6IGCMcFQWDfo92q0Ena1IO+kin8D64ZGQdX7Q+/K5BPDZoESKeEo0QFi8EY82YWAbeSDj+jkYWGnWGo4ruIGe9VyKThCxtsuLXyWsnG1JSlQN6XbEJkJY4JDVw9erZgxIYZ5E+OI6MDwMshEBGoVUt1oHJ1UgzUJpRv0cxzPE28LiMtxiCuG982D/o+vRYyeD8kV4RCfAbMUtsDQv3aKEpXXAaCTyD4Yg4btEaa7O2PsBWRTjjurBv0TKwhxDBSRN4QEFotRhMFSJ7zjq0kSQ6QyQabTw+MiRKBXdAsE4RxTGVCLwkvMQbE96vtXglXeAlKYLAaIwNFecQaqzlxmAqOCeMCU19OlYBPyACi7Ux0Wa13wtMEOsxRUlJcNEJb7D4+oIMyqpERVEtIMlQVjIYBheHqPcFwoOQpMmV83+SpSRxilSKoihoZk2iOLwHm60GWaOJUrVDD89Ye5xIaZIo3nRdKKXBeXQUkcQRSdYg1hodR8QqoihyvJAsLi8wu2Ur1jmK0YiLF84yt3Urnc443oGpcvCOM2dOgvKMNdPgKJuZZTTMcVoyljWpcNx523s4fvJJFpfWGKoVpqc6xGnMqbNHuXTuHHnp0XHEai8nTRSJVPQGFSrx9IUn1QlZLOlM7OKD/+CT5Gcf4cmTJ8nNODNzsygFtixRcUq7McHuGxscf+0w051JUp2xe/chWq0O5868zsvPP8TKwltUOmZQenCSyBRB+IwlQ1tSdpdopW0y3ebEsTd57cizZFnCDTe9gyPPPs4oD41+pfGI3joNIZiY2cG2XbsZDnqcePN5Dhy8i5Vej1bU4Nbb7qU37HNRSE4de4W4kTJaHVIZyw23XM+Ba2/kxSe/i/QwOTlF2mjhrCVptXAqIlYxSimSNKXoDwJrRjgKV5GqhEio4P5wQS5KdBocSDacs7RSDNbWA4RcqFBEoCJsMcI7QxxrokiGiLd3lHlOFkV1m1+MdRXFsIuzOUZliDiiqizFqE+Z58gS0iwL60msQllHUVCWQ6JaIKqMwQgRBh8ugNuzLMM5T1kGKLhIUlSSEamIvBhhhCeWEl1X2xdFQb/I6Yw3wQbOkJWSfDCiMkM6UzOUNjSGaR0i68ZWZHEW1hMM3khSkRDrjF6Z46RAiZQQsBNheEHgF7rSIJ0ijWKElmjp6RdDZBxTVoKs2cBUOUpHrK8t0xkfw5vAoFIe+oNVdBwTyQxrKqzNwVgKk+NsjCg0JgvRXlyFclA6h/X1edBahLPEaQupkuDylZI4kjhfBq5VZZFa0R0MmZ2ZwasYnAnGByVRWmBd4JwmXoaIXRSKOqQKQ45BPyeJJGmaBeaUMBirkMYQZymSwI8sqhFKSeIoDvw8KRiUBaoe7ngpSOKIbr/AOUtrrBVa9Or9ivWOSElwECeKylaMRv3AKvYVP2wJ6IcuKN1wYD+nLl7iudeOkOTLjLVgqlFx5vQRnn9xH9dva7O4uMy+G29nYssETllOnHkNFo7yb/7jV2mPtVHbbubmu9/J17/0u4w1Z4j9EOdStm+RXLr1CN/+5mO8vh4xGg3p+5Rctzn8xkmkSHHKYkuLUo611S74Aq91sLdJDUgMkHvFkaOXWRo8y2Cwiqs0z699h6NvPc/CmeOcXqxIspiFMrA5ciHIkow4DkJC2mqyY8dObrn+Gu47uJ8nX3mL//JTn+Kf/dqvYSQU+RDrIpyXpDJibdhD6ghpPDu3TOBHKWUiwVT0+v06JC4QsaMclJRU2IFmbHKWT/7UTzIq15C6wclXn2dlbci3vvlFpI7wJTz11HOcv3COPcf3cNO1+7n14DsYFgVjk22+8O2H+bFoihMvPsCP/dwv8Yn3vJ9vPfYQe3bt5vEXD3Pf+99Hf+UyhTO4fp8Pf+BHGR/vMJ5q/sMXvsCrN+ynHFV0prawsLzMcGWVv33w6/zsJz/LqZNn2D8/y8mLq9x0yz4O3HiIpdVlvvXE9xgMS7bs2M6/+19/g9/+3d9j685tjC6d59lnn2FlNuPmQ/dxy74DXFpY5MTrT3H7vXdz1z33UA0HPPbYYbZvm+e1t45x/Y03MllNEcUJG/n7qyNQm4KQv+J5CRPDEJXSoq6CrF+f4vvEiU0RwW+IULWQUdtSQuGjACk2xZCNYeTVPz84dWTgiYSvYvGb9bLWBhhaPsqZm5vhD/7w97j2ukO0G9uwruTgTYd4/c1XUTWTY211jTPnzlMYwvQlLM2M8mVsvspLZ18j0RHNJGa9u0wjynj6pSdoNCbQSjM7tY3jvaNUrgIEsVJIDN1hl0uLizhj+asv/kdefuVV1ns9PvD+H2HH/gHvvPd2Ll9Y5ND113DdNXdy7PhbnD53mk6nw7A/JE1dWOi1AuuuCG2+hrIGiECY4NbHUwpg47gLuclD+H4JLoBkLQ6xySbYjC3Wx95fsUBtfp+8+p42xKYruuDb/9y4jb/y8/3feyRX390Vt9Tm3W7G8TY24DK0hmjNKC9wriLaZD2EE4szgtkts4yNjbOyusZrrz/OV/72S3T7ywGqV/WRqeLIc0dYXltlz9werjkwy+VBRG+tz7vfeRcnTh3jpSMvQTXkoYceZjjs8+777uE97/ogp46d5fLSF3jHrXeyuHWC9nibr33jr2k2Y4yzLK73OXhgnOe//i2ef/4pbr/zTmamp/nMT3+GSMd87cGv4SpJFGluvGaOheVVZFQy7BWUvkdEA2s0Fy/0eP3oGRYXL7Bv9zy7tu9i5/YddDrjfO/xZ/jLz/8FSRKxe/s+xlop1+7dzQMPnKUzkVAWoHyoky2LUPecNAIXZteuebZMb2H4zS7Ts1O0m23ufcfd/Pa/+T/Yv2eOz/zn/wWf/bl/RHs6ZTTISZsp3lW0m2PMzO1gdX2dk6fPcPSVN7GVY/HAtdx66x1cXl/CPNQjVylaCo4dfbGua85Io/AatWKANAWliEMbixM4Z0kmAhhz0AWtMqQr2XVgL8dfvYSKM5IkwcSKVMngmlAlsTYIYYjciHbUIE4dsTAgCoYuIxJw7bU3sL4wYn5PypuvvcCWyRlWu2uUZkDlS3wUGAwN6Tj1yndpj0X0e4a9M3vp2Aw5FpGJiCJ3lPkIVIPG2BS99TVSIVldWWPUF5SmQMkiOAOkYmxMo1REhSJKFINBQWUt2qlwkS2ht1YRx2H1iyMFwpAPKqIoorIVrlKkUQQYjHAh6qgCr8SVoeHJiYjIVaRJxHBQIHwQipFQVgYpBIPRiKoqiaMErRRCS6zLg0jgQsOVdSGapbQmzZJwYS4dtnLkVU5/MMS7Jr7emJrCMOjnTHQ0UsXMTDeZGp9ifGyCLEnxQlG5sL7KSNYNZxKpAjDXWYOxlqocUVWWlfV18jJA5KVSwWlSu0WcFvjYI70kqtd6VVX0K8PIVey77hCTW3bxwnNP4vEhslKvXxuhL1MFgGfkBSA3K5CV96BFHdmK8TZAt60DVIg9eSC3BjsaMr1lgrmt8/SX1/CidhVtDEaEqunQQfhWbIgUwVXig6oDGJyIKUyFLerYOApjRoDE1JEji6PRLImzGNGT9WZeoSVYEaDRkVSBL4KvrfeBbQIGtED5CO9luCAToc4ZH6bbUmgiJVF4YhX2XqWztFstymE3QJx9+F1Go4qqshhvMM6EAYZ3WCS2jgdaYZEeiiK4PELddBAglJRggyDlhCDWikZW0uyMk8QJVV5iRHDyeFfg6mPpvQ9tccBG86jbGEJZUfsRwrlO4MC64PZzwRHlLUxs2YpMU6SxpGNj5MbhKhkuNmtujvHhosAIgbOOWFHD9BVCGKwAawWRCu4YJQXShR2LcmF4o7yinxeMVUMa6QRxu8NwZQWxMdT1Fi1FzbYK5zglCK5BPMrFoC3OWaQILK5BsUqUJwwlqJFgRIC3O2FppBFbOh3iRLHWWyOWDeIkotvrk+gklGKp4L7SqgbpSk/pqnAhXlmMLbDOYp2p4bWCwSDAlL23eB+EosVLUBUWCXXDlcUWOUU+YsVaoii4OpQOjavCixCvSyK0lkxvmSJOY1rtMaI4Cs2OKjRbVUWJVoo4TdAqotVs02w22Tq7lWLUrwHfkihNKfKSJEvxHuIkRceKTGW1C8yhVAx4fGURaUwzS8F6iGMYjCAS5NWAyakZGnHGqCqxvmAsawRHlxLYKscaR9ZoUo0c40ZghcdVLvBThqvErQ5p2sAVBXN7ruP8wjnmthRce8NBLi2cpdHqsHd8C1um5rh08SRpc45HHvgenfG9NETBgR37OPy9b6H0GJ20hXMDbrpugm9963d46vmXmNxxP2k8RaLjIJjGmtIahJCMel067WmGwwG+rGjNjmN8hStz0iijNTFPt7vMVKKJM8dar89oOKI7bJJ2WlSlp1etomTOK68+w+njL3HXXR/h8toikJFmgsGo2oxJXTx9jLXFC7xx5Ajv/MBH6PUdX/3K32B0TGtihv7aGs5Y9uzez8Uz5ymGOf2qoD3RZNv8LvbsuZFYtzh1/jjGCgQRQhqkM4gyB+VwscIKQWEKlNIUZYGwkDRaARxd9nHGQpTgaw6lkg4lNGV/hGFAQ00h00bd/FliqHCRopGmlHmFLcqwpjmDlAEW7yX4sqA0Ek0a6KJe4IYGUXksgiiKwnksjlEqoix6CCDNGrSSLMTljaU/zGk0UvK8JIoSlFcYG7iFOglxcgN4ZxgNBqQqApVghCaWim5hSJIGSZQxKgqSZlo3Y/ZoNjLiuMGoKolcYJUVpghORw/ChPdvPhyRNsYY1DFY6w2REvT6XaRyNHQQZDZSIyryyMghnKAsRhSjHnEW4ayueYcKW1QMixHtMmUwHBHJiLIqqawhbmSh0MCUaCFZGKzSiGK0USSTLQyeqgRvLFZUCFM7a4VnUJp6T6Ip7QhbFaF0x1O3lQNK010fkjVT4ig4uIWowAcHfr0bQWhNPy+x0tHQEdZ6olSSO0NVjsiSEF81vsI7S7/oEisZmIZRMAC4ShAnunbUBp/2sNul2cworAmvAysZ9gc00wzhHYUxYa024TwphQptmyqhO+pRmhItM5TMqH5AMuf/y8cPXVAa9Hrsnkx46oXHePT1i5xazNmzNWP5/Bt88T/9Ne+7+xAnT5/n9MV1brx+L1tnZ1g6eYRvP3GUu9/7MV5/5Wni/ioHJ1NOj4/xjaeOkDUzvJM8+Ybn8w8fQYiEsYkJdNLg9msO8ROf/CR75yd46tHv8Ft/+EdM755nbWEZKSNKIor+GlGcoZKUm9/xLnZsHePwc8+xuHiZxeVVpM/pdkcUfpozl88TiZSt8y2S5gzeFghjOX3+LLPbtrNnbobL3T7bt0whhsv86q/+C/Zffxu7Jjv82299ic984uf40hf+lMvrHtXI2Lt3Hy8dfo6k2WG2M053bZmjJ07ia3CiVgotEpwssYhwovFV+Dcj6VnJzoMH2NPKeOWZB3j6/AI7t81y4sQ601nJQm/AgC3gBd97+BFefOYwT+5/lp/8sY8T5ZZ/+S/+HfvmY/6XI3/H+dNv0h3dzfa5WcanJvjUx3+CGEEyOcmN11/DN779LdqdJqPRDNunZjmwaydi1OfsmZMc2r6HW/ft5Y0TLzM1PcvC0iXaWYM33nqLnbv3Mb9rnp27dvDze/8hO594nPWlBZyosIMRv/QLv8iR117mDx74JqV1vHS4z6/d9VGOHXuVM2fP4bVnstlEOcuzx97gsSce4fLSGvtvvI3rbjgU6mhdLV78ADFn80OEGIGHTVCmF3XE6vssLsEif0UkuNosE6CsGy6WIFLViLP6/7WdvRahNh6LqBlLG4+xJimEFpT6+1QccfzYCZ54/jt84sMfZnZ2lvOnjnHm+DG2zGzhrTPH0cRoKVE6I1WWcpjjpEQaTRw12LVvltMXL5FGmosrS2yfnUHphN7qOv3eEs1WzOLSRUobgGtaK6yOMcazc3oXj771CGkimN+xjf6wRKsGL77yKqbyLO/fw7Fj50mTlDOnH+DCxTNs3bqVE6dOsW93RKxjUq03L1rCYbgi0gm3sbW+ouGE5GKoDd8Q+zbcXYrQbhN4TCaARuHvRQjFBh9i47nZ+CH84AXxatJVEJk2HuvmDX5Q8u377qO+p/q2m6+/jQgDIbKCF6RRcGWIJK7bkQRVVTAxPkG/GARuBaGOVWtJZzxjekuHg7d8nEcffRhpY7Zu38XRN1/i5tsOgUt41/2f4LFHn2Z19TK7d+1nZb2Pla/SbE/w+iuvMywL1oYD/uTP/px33n2IV56+wDvuvItUj/EXf/U5ZJwx8jm6dOyZm+OlF17i8HNPEaeO2dkpds7vYnZ2hrvfcS9PP/M9FpYXSSLLpZVlVnt9JtoJwllSmTLWaiGk4PzlI1SVoKkTDt5wK7fdejNZlmK94rEnn2N8apy8yrnx4D7+q1/8xzzx4At8+SvfYcI2qPoVKMgaTaJIsXPbdirvmJ3bRpJkvPnKMZJGxG/99u/wJ3/yp1Q5rCwu8Jl//o85f/I0d7/rJj735aO0xzIGozWytEF/2GVw/HUQEZcuXOJjH/4oSbNDWZZkk1Ocevxxtt1wE08+9hQylTgEOtZo6RhvjDBGUjpB1IgonKQ7LAOINaloTkYMlw3NVgc3LOn7Pq+++CqxzFCxwxYu8FKUxdkBUkRokaKEridJfazyWFlgyojYaqJ0nMtLS4gq5r3v+nG2TM+SD0b033iB4XAhxG5EglEgtQ3/CcfYWIfDb53k0pJjot1kWPbxylFUFYKIi2ePkzVa9AaePAelMqTLccKghCaKLBPtNnkuyLslZlhhiwrrBIXxjNYHyCgmiiVCyVAPLhUVllIJImHI85JOJnC6gXOWsrSkqQdb4U2KkIELJISsGUGhZS2OI3zlidMoOB2MY9gfhGiPVMGObYNLUKkYi6EcVcGJEYF1kPuCKNJIBwbPcBSmvMlYgrIB0JuNJyQ7EtrtyeCScp5G1kDLhMLY4L4SodbXFgGQLQTBfRXr0GwlFUXpSOKY6UYL61W4uLaGkbGUVWjYscbW7SmGSoAtwSuH7ffoLndZWuyx2u3jTBncQvW6GElVl4QAShIhsSKI4s5aoigBggujsm6zDcr5IIBoH+JptqzolyV2AHGa0Ol0WF62jNYHVDiE8TgcqgZ2CiExlakfc4ArB7B1YC0ooDKmduRK8BJsgBErFRxWVWmQaMpRQaudBNZVZZHCY2wQ3nAuiEeJR1i1uc77OloYVnyPVhtteSlRFAWxEF9H7GrotA/xrcF6l+mJSRqNNvlggPMlw9GAYVGBZbN0ZaNJzCu5WelOFdp8clsEYe+KHQW8C89HPbwonWNt2Kc53mJ8YoLVXjc0xRmD9BqzIbIESedtmwZF7b71PpznVD1Q2mgaE9RcofB7Hj9+nCxrMDczQ5pl0O0hXeBshbiIQzlPhMc5C64CLGIDnu5BisD9kt6H84+AwdBC7ea21qOjML0f5DlJljM/OcHJtW6Axuu6GU74ekgWNjRO1AFTF87byuvA2bECpEXIFCkksZCgI4RWm0DfSIVzeaQitoxP450ityU6inEEd5/NbWgpM8tY47GuFgq8w3mDcwH4622IAWqlQyxSS8DghWIwGNHpdGg2YgBarXCR38oysixDxwlSa3Qco2RElKS0JydotmLKvGKs0USnMbYqydIErMbHmkaWkkYJ1M103lnKYUEUJ7Q7HYbFiCptoLOYjSZbfDiGZWWJ4ggRcjlI7YlkCgQ2WlU3ZHoH1lliBHlVkNsKLTTNVoOyLIkiTakVY1u2gtWUgzqKG0U4rYibDSozIJFb6bFIY2yCC+cuMtvOKJXDCShGfXbtvpats/P0RxXTUxFUEhFntNqep594jev378MMh1xz4x3cefMtPP3YV+l1l9k6P8+oPyAdE/zxV/6GWMHOGw5gsovs2DaBForzp17llTdfZue115PFMb5KkKnkLz/3hyxeOMvc/DytToeqVzA3N8eWLbOcOHWMtbXLLC93AzfQCe5+97u57sDNLJ1fpD01xdGjbzE5Ps5RY3nx1eeCgCodRW5RSiMiQdYcp5/nDErDPXd/hDNvHeXALbey8O0Fzr3xOlvm5nn5rVcZDfuY3JAlkihyjDczrtl7MwevuZU4idm2YzcHbroN4x1CaqoK8tGAOGthjCORClMYEp8w9DY0MkpJnGicMwjnURuNnpXB+hJjBFpKVlcuk+iYWEMsytC8agRYi6wKSlPitUDWEbssa4RIrI6QImFU9TDWI2NBp9kKMHpvQEW4QUGaZCgVxFHjSryzpEqQJDFWeEpbIQFLiYuaSA9ISaKD+CCsJ00iVByFwqOywjqI4zisPVpSlRaqIgzOrENFgshDWYxY7ffYMjMTBimA9GG4qrwLAq6SyIrQdOgrGmnEoCpBOBKhQxumrUiTuB6+hgbVYb8P5BRREtARxpKkKcKBiAWYApyl21tnujVGJGOqSCOTBgz6ZFEa4vquCqBtY4jwZHGIdpW2xJrQvO2cIZKAcigvqUqDLS1xu4nwGmHD8Et5AUqi45hqVAaRrBixc3wL1oFUHmMIwG6pgRA5Fl6RlwMiJbHGBHcsEWVvgBKOZtYOOJrK450J8fXOOFJpjLUURR6EMaHDMKyyjEYjvK8bjr0jjlK6/T4AaSNhNBqSRFlYY1QFeKSEKA4RelN4kihBxzq4v83/z6HcIhqjNzjL//3N7zJ+86f58UMD/uVv/RXNVsLFy6/wypHXiJstrj29wAo/zkcnZhG6wdiOA9x63TxHXljjwsIS//Cf/Q/87Kc/wY5ti1xcLnF2iIo1zfYY+ahicfESabPN4SMvs76+zKGdY5w4e4axdod+d5W8yHHj0/x3/80v8tjX/5xnDx8lVinNGPaONSiuvZ53v+/DHH3jBY4fPUq/lMxMTbMuF+l2u4x6Ge96x61cunSC08ePIZXn3OlTrC5cYP81+xgO1zl/8QKjQvLasaNM3XkHCz3P8fMLyDhDNiPe9a73Up15g6MTM9x/y01887uPML99G+PjE5SlY3JijPXlBZbWepRlUEi10lRlRdqexJZr9EYjxoVjz0TG7z3+Hc5dPk+8FpNljkpA1zomWzEXLy2yPCy56b47mZlI+T//6Hf+H+beK0iz8z7v/L3hpC91mumenAMCARAgAgGQBEXLoi1KFiXRllY2ZVuWyutd2Xth11Z5tSVvWauyS1uWy7v0WrYkJ1klCaJMUgxgEANIAgSIOAiDybEn9PR0+uI550178Z6eGXD3bnXh72qm+0v9hXPe9/k/z+/h+o1r/JP/9df5X/7n/4G5hQX+9Jk/5YknP8ahvfs5ff4Cj753gYk14CTtIqfur/P9F77HT/+Vn+R3/uhTvPjiy7z86kskLc333niN3YcO8uaJS3SmtzA31eHazZvk7YKP/PBH6bXa7Ngyz2htjY996EdY7d+kCo6VjVWm56bYsWsXTzz6FP/q336K/TsW+Po3v8ji4g2sj3Dzz335C8x3u1y8dpWL15fodrfw6MMPs3371riQ2JyqNpc7+Ua32tdCLDvf3PlvRuDC5nXYbKONbJwAACAASURBVEyJDph4P3GhL0VcHHgfF6ah4eKIaLtpYJfNY4tbugIQF3SBuNwTMuaoNxdocaEb65xFAOUhzwp2b9vNan+D4XjI7zz9X9g61ebJRz7IF770WZABYxypErgQVfkkkzg8tatYvnGdYPt4n7J9dpbBygZDU6ETSZHpCPyUikTFuJhQEmsMpZ3wnRefZeAMRw/ewyd/7pN8/nNfZWbrDOfPnycIx7eehW57lj98+vfpTc+QZS3uufcuRuMJS8tXmZudvlWZvOk02oy2uRAbDW734tyxyL5DvIvNBzSizGYsLoIs/R3v6633dvMtvVMYvKXyNIv6qEI176245Qbg1q3vaJh7l9vo1pNqfnab+PSuZ7K56L7lYou1yEIKjKmbz2fjiktSbG0osoJRVWNMiCdNHycHg7V1Lp5b5My5S7z86osQakSQqJsZSTbL9m2H+YW//Yt86l/+G4Qq8bWgmEp558xxzl04TV0Z5rfMkuRdrly+xHvu28cnP/l3+Gf/7F/zO7/9W+zavo83T75IlghwCX/vl3+J7Qv7ScIySbvDpJ6wY9ce9uzbS3+jT6/b48jeu8izhMnGOivjDYQUlOPorNI6JVEJOglM6hprA2VtOfb260xNT7N48SrPvfQdNuoVuq0OzgQWr6zxxU9/A5lbMpUgsy6JWccYT11VdGe28rGf+FHqkeFLX/oyN64t8dqrL/LwAw9z7eo65bDmD55+ml/9lX/IvsP3sLze59KFVbKgmest0O+vMK4mzMzNs3PbXq5evETKhE53GlNavvKFL/Dss99i9doVbgxukMuE2jo6OtBKDZnyaKByjrqeYjgKIKN7YWgnzPTy2BbiA5O1PsbW0MtJrad2G8zNL7B4fRWVJFhnwKWMKkeeVmgZUDLHmDpufpVDjMDaCZWXeHIeuGsfJ069w7BUbKz1SYoWsixQspnCewEenM1j9bm1JJmmyFKuLF+irQuCryLPBE+S5zgZcPWE2ekeEytQNiHUFcEZinZOf7yKFAWEQN5psbJhwEuKtiC4BFsapNC08hStA0hD6gXWeYyQJFrR6rVYXV0lkRl5KzC2ll5ewMiQJFkTAxCMx5PIQSDGmoILcSJobGyxUgJjHdWkJEk1zkYYbbuTIYNnXFZMyhIlBanWZJnGWE9Z1RStjETJKNqODDLTZKliUhqqypCmBb3OFGVtWF5eIU1zdJphnbvFckq0xrlApjVpkoGUTCpDVVtUGtkOa4MxVR0ZhARP1cQ/RXOM0dxm8QllECoKIePRiO1bZ1haXscJjzfmlmPWuKZWWCiE95Qhws4VnkLFGIUUmtra6IERCoibm1RKkiTC552zUfiB2JQ0NUWrFaOYrom2CaJIX7sI5pRSIoOIjjUivwYRtSMPpFJHkQ1BFTwoEYWEEDlkUoA1ltFoRLvTpsgyBut9Slc3i3+B1JpEBISLQrsiMvYEGm8tNlRYPKgUEaLIVRmLMx5vImDdBI8RgsQ6aiXppi163SmSNGU0GFBXNc5EqLrBxYpu4sI/uok2RSzbiEwRTrsJjvaEhvXhMT42A3kR/87haMJoNKbb6lLkCaaKbtsSH2vbiZvCzfPanc2udw4rXJOx3nQ+Cw8i+FuteUorfHCUpiZPc1p5wdgM4nUQt5hSkbsYELpxuYSAdTUNYBLro0hvXRQEA7E+PISAUmA9KBQbk5pux9DO2nR6XdaWryOlpAoOLwOp1nHDIyVJHgVILRRJplEk8TPn43bBhsgjE05Q1zW+9oxNifOO4AKtPGO6V+Cc48rVJUpT4huxUCJwwYJKaRDDcY2lY5xDak2mU5IkI0GQKkGe5RR5C5kInLO0ux1mp+fYMjdLq9XCVWVEUSSasrRoJcmLgjTLUM1mTKUJTjYbNyvI8oxUJxhbUqQtbAiUNjoFjKnJ04xEJjirSFsJXsY4r5AJvelujDI5h7FRwEPGVi4vIUkdzsSI2rBaxluDUC063Q6DwQrt3hxCSJSzDIdDkjSlDJYcSXCeWgVKN6I3NUchBOsrN8lbBRNrqMYTpue3YIYjpIasM8filfN4WbJ46Q26s3uZbvXIihnmeltYWV2j1Wqz7pepygnd7gxrw5IPf+DHWFtbApHQ66Zo5zj+5su0p2eZmCEORbWesW3HEZypOH78Kll6iP0HU6wSLK5e4+r1E5xZPMkD9z9OS23BhhRvKvYd3s36ygZLqze49657EMLy5tuvsjrcQHqHUgLVytChwJWOXHXZNp9RFBm7t+/j5ef/jFbRxQmHMQN63R4TZ5jqtem05iirMaONEq0E58+f4sqZc5w/fZp+OYQ0cPH8GbJCo2QgTzscufs+zp85SbdbMN3LcSo2umZ5To2PLksMMjhsZWh12sjgSSR4bxnZMb72GOdot9qxRdQ0HZreEqoSZ0qUhETllK5mfX2J3dv34lRBjUZKhVUixoHTBJXm6CTFjMaxwS3vopKEICQ21AxHY6QMZGlBK5vCB4NxHislzjdtaCoewyejMVU5plNkIAW1FwSvqa2LDlypECJDa8FwtMHYGJQWCC0RMiF4KKsaLxxSS2SakeiCcbWOkB6dSXzlUFkEOpejCZ1M02u1sDaA9TgfqF2JD4ZUtRAuoJRguL6BEDUb/Zt4kYGMjYy+NORN7N6LFBMm1KbGhejCy/NWNNY6sGGC8QYls7gO8yEWEeiE0nuKNKUqq1jq1G01HMd4XK/qikynKKWROgKtTYjf2SxRTdmIJwTJuBqjtUNpwcRNCK5GBIHToNKicTMrNoZjinYKqUSS4KsxwjqyJI1piXg6ojIG6Rw6quwkTdlIVU3QrQ6VNVhvSGXBsIoN6EiB9ZaqjuykVpGTFgVKxbjuaDSiyDJCCBRF0RRHebq9yDXMizZ4SWUrrHORM0eMWFdlBSGQpDGpFV2W/41H3qY7BbMLe/nJR+/jyxcvcermNZI8gAwUMzu5/+4jDPpLnLt2k6P9AZ25ef7Ckw9ydvnbXDj7Cjv2HMXJM6wsB06eOM102mYtCbhU44Snrm1s6UAyOzXDfDvl7dde4/vfDwThaXc6JFJx16NPcHBa42+cplUO8QJ0knL65AnK5Uugch573yOMrqS8YyWp1iwuXsSUFa2pHr0tW3n/Ew9w+YTl5ddfQhI/kNbB2++c4dCRw8zMzVOFNfCBlfURB/fuZSovUEKwc89RWhtX+f3nj7F1fhtfe+57zE1tpdfuMrUwx/xUm2OvvsJ4bFFOkcoY6VJBY3C8755DfPfYiyxowa/86j/kwK5dDFdHpHmLg/fcw2D5Gssry8zv2MPCwi7ec+QuLi+e46HDe/jIIw9jblwlbbf41V//FURrnqtrsG/7Pvrry0zffxglPIvXrjDd7jCpDInQTHdbfPqrz3Du8nnGG2tcvjrm6N27uL54hR/+xM/yyZ/6WS4+8ijzMzP8q3/xL1jYtxOvNe+/7yjf/tYzfHu4xtXLi9x//708/tSH+PTv/x7fXjrPz3z8k2S6x/seeYhf5pf45ne+wbMvvBCjiVnCxEu6rYyTZ0/w0Hvex949hzh8z1EO7dtDK28jfYR43ulcEZsmobA5/WyiAM1C7lafjCDyqW6xj97tbBKN28SHwG1tId65ajYOm4weXCSURkEp3v/mY7oQRangGohswzZAgg4aQYQ/DkzN9FQP5+Hu99zP/pkddDsFD9zzXo6//TrbduxksL5OOlfwoQ8+xZkzF7h+bZHJeMRwNMGYgE0VNmj6ZYUaGwKBmalOFCKzFsPBOE4daZgP3iGJYkbIEqrxhJ2795PKDsPJBO8MlanZs3s7q/0J+AGzM1vYs/8gly9fIGm1UXZEp90hlWkzwWyazxphSPg4QfdBxPYYGcUkLWMjT2hEv4ggasS+5g18t86zuUB3BOQPONI2L/FvuwOFdMsx9K7LHVe4LQA2zrLN95nmOYnQONXip+AHdCY2n3yM4EXQnWuiPDLRm8+CRCkEEtv8fXVVx4rOADeu3+TTn/40x08dYzjYQChP3spZ2yjRUjDTm+fv//InWLs55o1jb/Lamy/wl3/0x/j05/+Iv/sLf49WmnHX0UPcf/goe3bvZ2Jqlm/cpNNuo3WLVhsefeQhrlwzOFcwMqskScHi0gb33l3wlS9/jZWbl+gbyxe/+iVaWYv33PcA1gU+9JGP4MVH+M3f+OdYKZjpaeoqTo1UEBw5cpjFq1ew3sRNoPSs9Df49vPPsbR0kayjmXFTMYLlYPH6Ik+f+j3+xt/8JCKXZEmNyOFG5clVwo7ZefIk4Y+f+TT9YZ9nn3uWxcsXufe+ewg4Tr5zhk9+8icZ1JKn/+hPuHFzjeNvfZ+P/pWPcurtU2yZ3k4poK4NixcuUrsSnSm+/pXPUg4mcaAQPGkq2dLSlEEiq0AiAlLUeJHRH4GQKS4oBA6lUoSosE7QH0xw3jIqCyofheHCWmSeILIWY2OpzRhXeTozBfu27aXXaeO8Y2PjOtXEUCiBcwKle8gkkLc7eGUZDC5y+XrG1ILnwolzLMxvQVSBIrSxvoP1k7jA1UnzsdOQCuYyyXBYIkOCEintVpuybCGgiQYZpPRRlPCennboNKW0iqJQSDWm3emBrxmPa5JU0G4ntGWLUd1HNzwhrTNSJSH1uLqKLiEhKCvBRn9AK+lilEP6gCwDw7rPTN4hTQJladCqQKtWrGz3LgpSNsbKtmyZYjKpqGpD0cqpjSXLcipKAoK81cYaSxpkjHmZODk1NpCmGZOyJghF0W5TmwpjIdUBbwWT8Zi6rCkrQ7s9oNXq0CnatNpdysowHI8jaDqIeHz2jpv1kNA4YRBxkepGFkSINbwBrADhA3hxi4fjCdSNIC5CFE+sBx0Ua6t9dm9fYH52C2XlKe0oxusawnW4dTrxSBWhoAhBHWL1uwkG09RKEyxKStImZuy9jZK3oIlyB8rxGGygU7Tot1sMqnGs2BYS4SFsgohDwxbaHK7429Xt8T4tMkSHA40AI7VGIKK7KU4BGFdlbB8q2qRZRhjbWwMY7ywuCGxoWvWaSXEUpkIcNghBqCsSGdAyBsakEuisWehaQSYkQQWmigJjLKYaEbIUK2FcV9TGUFUViAh/3eTsba4RfHyJcBJ041ZWjfsmaRrXVMMAj3BrCFpQGUs5rJiZ7tLqtdlYHaMCpNZvjiWggS+HEKfziAhKbUA6TUy+GabI5nlJgfDxOgKPLy0+TSnLCZ12i3arRTmZEAN78fxjceiGCaiJsXIpBFo3spiXJCLFBxPZMvGMD8E28QuNB7Te3Ewp0lRzYNcO1vIscnm8xyOQITqhItQbbFVT2iGjcWwAHE6G1DVYZ2Kbnfd4HMFLhHTNQkyhlCCEgpnpDjt3bMN4w2g4RihFlicoBGlaEFJBLmOUpt1OUakmTdJYQS4VWmra3R6tVptWEcX9sq4BC1LQyVu08xbWWVxwaDSIgPEOKRXtVoGWCc7FNYoNm85pDypGQKRUdPUs3jtS7yk0KNW0tBlPluTRj6YVOk1wAdR4jKlrnJUQAlpJbO0jv0oIgnMgJVW1zsnjr1BVG0yqIfe/9wOcP30GIyR3dXskqo11jrSI7oupdgvjDFIqJuNJdJjqlImt8BpurNzgxtIS++++v3m8wKQcUU9KPv+Z32Jm+wKDyQ0eOvoRZu+6H1/3qZmj1SpwwZG3C7JOQdrNaNXT+PI6w9UVWt02u2d3cWPlBloq8jylqvtopdH5NA8//mFe/s5XObj/ILv27gMBxtYcuesRbt44xeWrVzn3ztscOvwQOw7u5NyZl1kthwwrR+Uc5y6cIVQldT26BeXXMkOJlJIKLQ3jjSW+841n2LBr1JVmYWYn73/io7x97AUmGnxImJvdyt33PgQ+xYYJ3c4MK9evcuL4MUxtWL8+YOInqJCwbUvBoD/BOMPP/LW/y/e/+ywffuovsmP3YVZu3kQLgcORCIVyFoMgKIWtDJWIou/6jWWS1hRmMsK4irIsyYWiyDsRZG+jg1SlKXmrGz9ZNjaYLl5fpjPVAxXQeYoJglxplBmTBEeiM6RMkCjG1uHTKKxInSKUpByXWFujtKRbdAGNEBrn+jhTkSc6IiyyjMrBoKyRSkGWIINAWINKMkaDEapIsEKRydiCPqotqVSoVCN1ikLiQsCYkkTLeLxSYKsx46pGiAxlJSHUSNViUk9YHfRpZ5qVtdU4CA4hMqVEQGmFkhpnHBZP7Su67Wmk1Ig0wzpJsOuENBZeSN2icg4nBMZ5dAOfdkLigokMP+tQWRulwJgQ+aSpwilBksTSA5wnSzLypMCHhlxrPcE58qJFkIqgJRKNd0MSraKzEAjN+dsZx3SnTSYTSmcxAVIl8UqiITomhaY/GbJ96zROpJhqhC9jLNyUFUZrtNQIpRlOJiRSopSkled4IZiUJb3ZLl5nhLqmyFICCtP3zE61CSLy5bwbo4RAJ4rNoqLxeBwHYokm0fF4OuiPkECaFFhrkEiMi812UgmkBK0U1lqsq0lSSaYzjA+oJDrT/jwvf+6CkmkvcPob/5zfftHzj//xL3Lha7+FCAHjBK00Zfn6JXSrx09++EG+/cLXeeqhe7j44le5cHGAD+tUrs301Hb6w2XeOXOCbjdj6+49TDZusLZeEYRCq0CeGK4tXuaKdwQJC/c+zCc+/B7+r0/9LlZKDlUl7xw/zme//HUSocmShJZWzLUzELC2sczxE2fx6TYefGya0699l6tLNe1exrgcs6Ua8LXP/xeuXhtx4MBhjp84Rael46ZcSAbjEYf2HuHooXsRCrbu2MkDe7fzT/6P36C/NiQfXeBzr19jy8I88zt3Mt3rcvnCedYHAz7w5JOE/g2multo5TWXL12J0FIF3blp1Hidc+fP8eDRB1ivByQBzl65TK4Stszt5Eee+EucO/4S3xi8zCPvfQ8zU1v5kaee4A+efod/8x8/xRe+soeZ7gxXry/R2n2URx96Py0duHT5Cju2zWOrCbPtlC989o9538OPM9PJOHbsFa4t3+T9T3yEvQcO8tyzX6c3nfDcd7+OCQptPMIbplodPvOnf8jLZ97gqW2z1LXntVfe5itf/z4PPFSRZJrjly7w3vHjrI7HCNnm+dff4KkP/BD7Z/bSX7rGiemteBlYHVXs3DLP6tpNlq4OeO9734cwjrmpFmmAVpaCaOqSnSAoh/B3uF984xJpuBS3OTsN8b9plNlk7GxG1wK3E1NRYxC3fu6J4oQSkhCikCUbVWNTQNm8MyFvSw76TpeLVnewfyIzRm7ayY3hlVef5+jBu3jtteO8NHwL70Zs37mdw3ffTTtr8c1vP8v+/XsYrY1ZvH6dBx94GOqS7zz/AvcdOcK26S1s2IrV1WU2NoYsXr+KVDm9VoeP/8RP8O3vPs/i1UXWhhu0Ck23mKKqLMPRKnt27aaddvj+917iue+8yGQ8ItOQkvLow09wafE61nkm5ZgtszOU4zFJSBgPJxRJwuKNK+zYtQvvPEmW4htbeqwIJsY7ZNyMCBlV8Nsi3uYrf8elEZg2L5E92sDON8e7PjRsKrGpB8Xp8Lvu6I7WuFuMo/jeindf8Y5bcEvYepfAdOtfm0/q3cJX/FA2AGIhQQSs9+gkoaprlNIYU+NMIOvm2HLCiXdO88UvfpGXX3sB5+Nkc/v2BYytGZc1U8UUb75+jMknPkFZGd558y3mt8ywtrzMz/7Vn+b69Rvce+RuLl44y5G77mX7tv20cs1bb5zgv37uabbt2M2TH/ggdVnx1FNH+eIXP8NDj9zL6soKf/zZP+G733qBtO34m3/rb/PVLz/DoZ072L5jnu88+x1effUlZJJy/txpOr0p9mzdzztvnyB4QyJjg8Rjj3+QA9fX+NaznwPhqN2Q4foqo8EqBw7u4N67HuP5l59naTgk0wlXr1zjoQfvYmFhO9r2aRU7GA1iXbF1nsHEcOLMVXwi2Riv4mXNxE+wyvOf/uMfsG2mxSd++r/j0tU+y+trvPDK98i7bd549U3md+7kxLG3aM/NMtzoI4LDOgclBBnoJBrloMgyhArI3JHVliAzulNtMFPUoqKqK2RQ0WHoQ2wDkQ5TeZSA4UjHib8MeBfbVoytKbIO3lk6iWfbrl3s2buHnfN7uXjxLKdPv8FUL0FKT2UCwcFo1EdnU6RZRhom9NqBtWtncWbEYLCKs+s4U1FZS1YU5CrDGh/ZKSaArEhx+DCN9UNSlaKTlOGwRiJjO5icAEMS4fG1wViFzD1ZS+HGGuc0hgxt2gzH6+SZppskWJEwNrF2fDQZkeQp41BS2oCrAkWWoJJAKlK0h0wXSCfIyJmb38q1mxcoTUWe9kgSjbUKQYoQrvlueaRU5HneuAyjdTtuchW+siRZSlUbamsZjSfgIsi43W6hFOAiRFSqOBRKspys1+L64o0oYkvNcFzhPdE5QGBYjqmtp6pM5LsgGI1LBCqyaWLai4AH1VTsOg++xvsQxWPrEV7ePvbHPSQ+xMiakpvnlejqSQC0Y2yGDCYl3W6bbC3FmkkUOTajuiHg8Eil0A2XZhMsTdOEmBBjQ3jZOD9V/Fsa1s1mu6TWCuMd64M+c7PT5EXBeFIhRIxneRFdVK5h/BGatrNNkZ/NIc2ms3RTmCACRJujoVKKzbps6QWmshSFIityqsmE4F1kcwhP7SFFgYjCuya6yZQgRqO0JNECJVVkQwgIYVMQcvjRGC0FTqYRLOoVN1fWWdi5jSIvWFlbpbax1p4QmtPEbRHJE/82f+scERrXEFEMCdEl50N0rUofRRvlY2PZYDJipuyybXYbw41zVDZGYvCuGS7FNYcPHktAhTj4uBXTDuH2sMSHd51PIlRaQPN62qqG2tIuWvTTAb25Lv21daqqIjiLkwKHxXtNImO0M8ZiIi5BCh3j4KqJhDdObo/H+uierqzFWke/XyFEnyJLGQ7HXLl2nSBsFEo2hzTe31pXSSlQKiNNiAzSTNBJOxRJ5LZInZDlEWyf6ZQ0TUjSyCDKsoIiy5mZ2Rojlj5ubtKs2DydkhcttE5IVIT/a6lRSYrSmw1onbgiaDBbhc4oilg5niaSUMcq7V7eIygZ68obyLYgcqe0ThFpjq0rpAK8x3liFCaVUWhVAudqvCOyv6y95SrUaYfBqCJ3zevjAnmWMikndPOc0tRopRFa4p2gtgFcSfAe6x0XLp9GaMlzL3yDVt7m8Q//OC4oilSztrqGqQ3SWUQqGFUlqcwZbfTJE83JV57l2o2LTGqDKaHd6XH2wim0Srjr8N1Mb5lhaeUSSarYuLlB0ctZWj3P+MVrJJxneusjdGcO0OnM4b0Ar9BCszG4wXQOq6MRRaZ47eVnmJQVk3pAmqRokeKsZ+u2OUpT052dZ3ZhK3m7TX+wQbvVo56MmQwdzhp0XrF49RhvHF9BJWPW1wGfQXCsLA1jw7G10a1IYOJGeANaS06eeokrS9fobpvF3bTUSfxuHTx8Fy+/+DW2LCwwGq6T5inj4Rq7dh3l2tmrXDx3irIyaOEZ1RUIja88Ux2LnZQMrWf7zq1YM2F+9z7mFnbR6XUoul2C9dRVSZpkWBc/K8GCMVVkWOkWLg2kSYGoKiofsCGQFO0InLd1hNULH6PdbDoUA9bUVFXFfG8GtEYJIsDfxkZYLQtkkqIbx4mf1BRpGo/LQSJdoCxr0AmtPEVJhcFg6sgdGpuaTqsLiSIEj6lKpKkoEkWmcpxXeBFV8jpYujrDexsj1KMKYSqscGRpF5lkcQBQO7yJrLYQJK5yOBypSontZYKgNEEkVJMRSgqKbo+gYtwvNK5dYyq0LjBIEi2pRiNauiBJUwKghMc6gzEGoSRJ0onHKCw0MnqiEnTznccLnLDEFt4MZyJg3wlJmgukyFBSUtfRMV/0isiGM9FpWNclaRrF9CTN0KnC1x6FRgaDMR4fJCIIympAoixp0muivhLnA6WJjuWKgA6CUTWmyDTdrMPEOGpToZWiqmrSRJElKUiJDRKlYrNpWmRIIalrC6FGpV187eOQQGoGQ0M3T6NrVgiqqsJaSyvPUVIjhcLUlvF4TCvPY3OkTnE2MBqN6PWmcCEOoKy1BBFwzpKnKVJFN2w1noCIbW/xXOFIdIJrHNN/Xpc/d0GpXrvMjc7D/Nr//lMcShZ56co5UAkz8/v5wFMfQS+f4fk3zvDN1VXqesz/+e/+Ax968n46U29i+l0uDw0f+uBTPF6v8PXnvke/ErQSzeqopNPukSnHcFQRnERqgQgKqRLKm9cQaztQQdAqOpw4f4Z79x2gfeMUlbUYa1lfXWd9bZ3uTJe5bsGknvDxj32MIg/86+tvsbZRsmMLnLrQ5/KS5OylJXbvP4DoL5FkGUmWMxgN6XanUFLSH08YlYYdu/dw8sQJ6v4G//3P/w3+t3/+m0xMgtBtyrJiNs8YrCyyd8cOri0t8vQfP83c1h30OhkbK8vRsq0keZGTSpie38EDDz3Ox//iX6Ks1/nMlz7HybMnuHzxMt3OAmfOvMlbp95ifXXCN775PEUr58bKEsMy0G1v4ZEPf4z37NvJO2dO8fj7P8ChrVNUFsz6DX73936PD37oQ3z/2Iu88vY5kt5Wdmyd4a3zl7l5c52nPvQQLanZNdvj4mmHylqUwz5SaRCapEjZv/cg/+B/+kdcOX0OlbfYs3MH9z/2fh59+GF2zs2wMhkj0Hz8x/8aWSL5xnf+jMUTb3Ps9DtIr6k9/NJf/wUG4wEnT51gsdAsyO0c3H+A1ZvLOJnQ6hWkWsaFPNF+H0JTy9hEi1SzIG7KXoBm4b+5OIq3vPWzzd9vTjE3/w/cEgw2F4SbfiW5Ga269UCN4CBlzCUTo1XONe1kBLwzjXspQjZNbbCmouh0+ZPP/zHWDllbm9DuJEwtzDJ8FT7zzJfptno8fN8DvPfe+zm/eJ58qs3/+It/i/0L+/n3v/f7PPjAQxx78xivTl7m53/ucyyZDwAAIABJREFU5zkwP8dzr70C1Zix8LSm5xkMhmyZmWF9/QbDEpRIsXUEs2VpxrDfZ1yVdPM2ZtAnSVL2H9zHxnDAm2+/yYee/CAfePQJTp+/SG0dhw8e5cihI+zauQfVtBLYSUWSplhrkS7ECIV3iAYE6okOLeUBIWMVMnHTsOkk2owGwh0Rs8bxE9/iTfdQc30ayWgzb8gPiD531LrdcjxtupjEbTnr1tVvXfP2bzY/ObevdIeD6QduK4j1yMG7OOlwAZEKRBL5UmmWIoKgqitOnj7DF7/wGU6eOk67M8WN5ZtA4Nr1GyAkicrZuWMreaL5l7/xm/z4j32U7z53jErc5MbLy8zO7yBTKYKYez554gyXL94gOMPS9euorOY//OffZn5uG6+/9ga/+Mm/zkc+/EOs9K/xwSfez+e/+hV+5mf/Ms98/Xm+9o1n6c3MsT4wfPe5lxHeMTunWVrpc2XxJFvnd7N46RJCQJKmMT8eLL/927/L7NQUWue0M8mgPyTNE1QGV69f5frVz8dplUooWgUHDuxn7/btHHvpbZzIWVvbwAeJCZYgDItL5+l/awlTD9g63cI5w465Oc6eOMPSxct86nf+b956Y5HWTJfZqSnuvesQrxx/lbWBof9OH2E945VlvIuwetVqNgcuEGQgJAl5oilDzfrAs2N+gV6rjXQjlodjRJJT2hFaB7SQSKmpxg60o8gFNYEgBUUIuDQjqWpy4amBXCfYEPBesXvXbs6ePsXZE2+RqISZXpvp6TbXyjVQCdYJDJ4UD94i0oIi13S276DXnmdl9U1C3Y/TfiqcLSMvLiQkIo+bcm1JgiVMBgQ5hVYC7xOQbZwdoNO4qc51IEk0Y+PxXqJVSpbFYvHl1YogM6rxKlpBPXGQ5hRZQmUMrU6H7txUVFmEw9UlKlPkWUpta1pZh7EfR6incGRpxnp/CWEcHTJGwz46b2FdTbDmVu08jaPHNTEk7+OETYg4iRXAZBJh3MFLrHWxBVYETF2RJpK6qplMSjKbkeUF3V4XpwTtbkk5nuAqi3eO2pjYPColxhq88wTrSHRKmhfRCu9jyxgyxtMcYOu6OUA1jksZ/y1CI6c0fB8hNocSm9aWeJwLOJAJEIVHj2N5fZV9e3bR6RRMxgO8dVGUcZ6gIrtGKtXcTXQCBR9IhCBvWqO8cxH2r1QjmgiE2mxHi6JbrIFWDPtD5qenyLoFot8HJ/DGRfdRE81FhtsDFBHB0VJEZmAEgEtCUM3mJr4GUka2jZAepROEEEghscYjnaeVpJRpiitrZMotYUDIQJa0kNKCj84vpUTcfDfu1rquKcua2jpqH0UPEFTW4fyEVEqMdygrkXnG1LiiaHUoWm3qykReXzNlvuVClVGA06FpjBMKG9ytUUEQoISGpumVcHvt4F0ACWU5ZjScMLdlil6vy9LaGtILMBY2tSIiz09E7RFCw9MjOpW9iM6geDwSCBtuLVSEByV1bEIVilZvBq0VtXfMzs6wsGMbVy5doq4NjkAqE3CgRMCFGGOJDBqDNWVsM/IW62u8MZHnYi1GiLhhEAFFQMmM6dY+5ncsoJRiY7RCmnZjm1USm8myNCdNU5SWSJmRpZpMa5JUE3TAOU2rlYBTcWMsIdVx06PTBI9FC4UQGqGTGNlQCaPRmHY3J8vb6ETgjKHd7hA5RZLRaIj3NjJcgMmkBidIkig6Kq2btjdHUeRkWcrITUh0SpIkkY9GrF3v9rqUZQUiILTGWkOiE0xwkQkmBVXtIz8kzxHWoxKFEgFvLWU5IUtTjPfYeoKQCq0TRqN+3IRWNZKADUkUngk4Y6MAISTra6tcu3KJ1cGQtcEApRJSITlw6CiZjccTVwYQ8XHztiKRGpW3CA6UkBhTcenSG9ShYmpmFzP79pNmHUaDPq0sws3fPPYyW7btpsi3sza8TscWnL94gyLN2bvvXlYvLrHPxzr4shyjsoR3Tr7EyeOvksuapesjyqrm1LHXKHo97rrrLs5cOINMFHmYYsv0DPVonS1zHc6+8zLXLp2lLkv27N7H9La9uABFbxpjKzbWlkEogozDjJmZgrr0DIcDiryLL0umez2sGTOsSopeh3LQ58HHPsrWhb0oqzh//iSnTrzOT/7cz/GNZz6L1p7+xlWE6NEuesxtnSJrFawsL1IPr+PUFCFo9u45wNUrl9k2m2LqMXpmO62wymS0wuKV8+zd915mZrZQtLuYqsILj2+4YEIonJaY/hqmmtBudRm7mtm8i/eG2tcEFzfrRd6OrDThmyGCRInoNHIhYOyE0lRkRUamC3zSjq5E56i8o/YVQiTIECBYqnKIdzU6yUmTApXmjEcDUgWuduR5hiEOt1QiKCeGVKR4l+CDx9iSyXCArUeQ9UDE81uWFAw3+mjvcKVFZlD7DFX0qMcVqZbkKm3W3zCuY1y705kiy1s4FzB+gvMGERSjYMh0BrZmMh5QtHKQaYOtiPFq4xxCJwSVEls4A7Wt6PRm8QS0juuMuh6jZEC3sggqtyXSO6yJwpJUCmRsFkUInI0FGt458AFbW7QEKTUKjRIB60pUJpGZpgoWnMXVFXVdkqVJHNwkCQHPpJxQVyO8aoYEIbb0jUYj5rpdAlD7OvKqpKQo2rR7PWofHa5LS3229qYJaKiHJKQYO0EKSNNsc15EZW2MC6YJCoUPgbIy0W0pFWgPPkatq2pIt92KQyYZz/1ZljUO5TjoqqvY9pakCiklKtEMhkPSNCVJdHSMWosQURBXzd8sRGRDee9JdIISEmsrsixnPK5/YFf0///y5y4onbm8zP33HmHl1Lf43d/9z3zujWvIosPc3Cy785JXr18ln9nK/JYpytXLDIs5tuSCwY3rDNVWfvXX/ilv/+E/5XvHl+m0CnY/+CTbww1OnjjBTOpZWx+Q5DlSgag9zmt6c7NYM+LfffozyFyRFwVlNWHx+g3KusInOZ1OgQgV2dw0Dz55gANqhZuriyxeuchj99/LdCsj76RMMWSqyFmZ1BR5h3L9JpWv6c1McWTPAZyHufkFslbG1p27ePx972OmSEiE5zNf/K988+uvxBysr8AHiqzDyQsX6bRbXL52Mtbo+sCObVt57/3v4a23jvHy66/hxzXeWK5fX+Kxx57AjzeoJxt89c++wMFdh7ly4SqZKjh39hyLly9RGUNaFMxs2cLclhkeePhJZgvBG2cu8Fd/5C8wWVtiqriPtdNv8KVXaqQd8vVvfYnLi1d56cTb7NuzmwfufYiDu3exsrxINVnlzMUzrD4zQRNYWr7EzNwsG2VJZlKuryxjbEXwgUOH76GsJ1w7f4k8S2lnmsMH9rB1eopWljO/sAMVAqa/gREG7RS/80f/iY2NcQTNJSmvvXGMo/v38Pbp4ywsbOPAvv0o79k2t4XDe/cyqsZcW15i7+423kuUCHdKD8BtMehdX4lwW6K4k29w+/eb4kHkJSE3F4i3+tzuiFc1ThexOfeMPxPI2xGrZp0olcQaR1VV8cubaHSWcv3mMq8dO05/OOC+A0c4e+4i9913hLNXz7KtM8/KWp/3P/Ik1jnqYc1bZ07x+HQH7wMHtu3jrj138/qxNxl7z3A8pKqjA+TUiROcPXeBjdEqKhOEYBFK8OIrr9DOM9b76/RaBcGn1LUjTTVaJEx1p+jMbOXqlUtsW9jO7OxWvJI88sAhOq0ORdGjCoKjd93DcGOD6V6PLIuTxOFoyPdffIVOpyDLM3bv3cvKjZsc2r+f0aRkPK5YWVsnzxO63Q7tLEdp2UwcN51koQHQ+uZ1lcjGkeR8wKvm3dkUjTaP0HEnRLPX+38bnTavcgevKd6C/89/3/64vFsqijf9AaFp0wHXbEh8UxUdn0tkXcQ2pTjZtc4QakdapEyGIy5fvMigP2FSjWkVgoX5LrU17N+3n42NCePRkHbeYeJKVGb40p99lQcfeg9Xr9Z8/Kd+gv277+XCxct84QufZ1xO+Oa3v8NgMGZ2dprpqR6oNo89ei/9tTH3vecw+47swakOb375Nd6XPs72+f185vPPUI89/dEaG5MxwkkOHzrCvp0HESguL94A3WJ1Y52N/gqPP/YEV6/e4PzFi7SzjGAMBw7u5+y589xcuUbebqNUgCQyc5SQ2DJ+L0ZjmJuZ4eM/+jF+7df/LZ1ul345RiaCRHiEV7SLjO07tjEad/HecfPmCp0k48TJk/yjf/D38T6hPxpgtODy6UWWV1boqhSlMmwNIpHoJJ4sp2amkcIxGKxRmZqimGE8svydTz7Gv3/6G5QqcHNjhXK8wZaZCDmsxlE5iHMxASpBpgGVgRVjEgRKpBSyG5tQpKQyE6Y6U5STEeNyiMo0L7zyAloGdszP8oHHHufoPY9w7swF1jeeJ8kq+qM+eRiyc34fVxbPkeexpndlaY11VWKJk7BEBlI8wQsq38ShQg0yoB3IVAEJpiyBnF6eYKoS1zgDBS3GVYUroaolOlFUdSCIrZCUaD1AqwwpMrLOVtI0oSrHJElC4WFcGlrdaSajtXgskVGEGI3GmMrgi0BZVXRaPRKVMOyvkrYlImnj64BMopPLeof38VVVUuO9w7kIwE6S9Fb7SF7E6ViSaLyz5HlOWZWxkjcVt9q0jDFRsAkRUtnpddBKYqwlSxOcN2AD1plo4fYxHB18hCJbHKPhiIDGI6idiUMGFSLbqXEoBjYfszku+OisEjI27m0y8DaPGVLeFqtjp7SPDW4BJJrRcIT3nulWh420T2VHt04WAhkXx9Y2N/c4Z1FCUdcGTYrxhiAlQjccHRnHI95H0LlSsnGRyNi6Mx4zKEs6RU7eyhnVBqFEbNATAVS8TaJkw+ZRJEqiN4cySmB9wNhAXVUE2XB8VBRJVONuijXzUVCrXU3eKsgmE2o8GbHCSwqB0AItNEkGbZ2Q6IS0FQH44zLgrEcHzVSesN4fsLo2xHhAaqR1BBkZW14IvAJlataH62xvb6PdbjEZjgjG4APYTZ4R3HJz0Qg9XgQQ+tYQadPZrBoBTcR0xB2nmIBznsFwxPS0Ycv0HGtrG2z60OSmOKrE7eGSAN84j7SILicliO+TjmKdFBF2n8kYDXFNRE9rycb6TVKlSKzl3IkTjKuKYCzGGpwzWMAZiwgW3zTzyQBaCrJERhCtTsiLAt1ro1Vsc0zyFK10dPSo2Him0Vgv2TK/jff3uuRZi0TrGDXTCXmSo5ME6yw6SRvBMTJSvAePY2FhgbqMzsI0TWIkpXHMGVtHdpqQpHmbNMmoxkOElPSmp5BSU5thFEMl1GOLUAFn62aDJwlBoJQmT1OEjCUd3sGmW0nrAlNH73iRtalMzWRSo5KUIi8INjDsx3WSLhKctTjhUKlGy5S6mpAnOp63nYlQeRNdT+OqatZ88TPufWSPOe8YjsdkaYaSCUWWYn2NlDq6lvLYfiVUYH1jhW89+wxroz5COTpFQlFM0x9NuHjxJHuPPoC1IzItcTK6OY1zCJVQT0qE0kzPTnPf+36YjY01jr3+IkVrjoOH7qHd6rCyvMRr3/8uZxdfp9fexnAywFcTlkZj8mKW7bsPcM9dD7NlbpZRf40LZ45z/vTLOCGYmDW0gnRuHq0kRaeLTg292Q5btx7g6s1llJrhnqMfIIwGbN9zhOtXroA4yY2bZ/AisH7yOurEG0zqCTMLC6xtbOCsIZEBY9oU3ehwmtQG6y399XW2b9/L+x56P2dOHmO4dIai2yVPJGmSU8g2up3wwD0Pcc/dDxC85L2P/hCT/iouMew9+AjejtB0UKpgenaOZXctftbSlOEwcqJ2HzjCqZMvgLfs2nGYbTvmOHz3gxTFVGz5NHHwYL2PjZUytk8K4Nxbr9LZMkXRaRHGI9L5Wawx4GyMjaaKvJXGoadoPqNSN0NrG934eOrJhF4xRZp3sVrjqMHHKC3WI7N4DLXWMqnKuI7RqjmOO4x3DId9iiJFqhznLHUQZFnGZP0GRVbgpcH5CHrWWpHIAqUzbAiY4El1xsSMKLptUqlIihZeaFwVwAlUOyU0PD3nPFVdR4FFR0HK+BpjHKoRzxMURVpQjodYb0nbPaw3JCTx+Ezcf6RJDrgoEDmLSCLgW4oIoveuwtsJqUpJZRZbY/Ex5m0mpElKkmY4QRThg4sOc5VR1RbvA4PJGjMzM7GVM88x5YDalGRZji/r6GCqDeNhHwhIlaBSTZAC78D42Drbak3jQhSch8M+WivyIif2VUQXb4TAywgkB/p1jfOGPJ1lXFV4IZAinhulVkgUiBjrdsb+P6y9WYxl133u91vT3vuMNVd1dfU8sdkUJ3GSZcvSlTxosB3Dw8W1jWvEF0iQhyRAHvIY5D0JkIcAQYaLmyCGL3yTi1i2LMuyTMoUKYoUyWaTzSbZ89xd83SGPa0hD2tXsWn5KVf10I3uOlWnu87Za6/1/b/v92FkRKIoGQVH30w+ROMHlcaxOxiSCQ9aYaTBuioK69rgfUA1QO+yYSwJqVAmRpmLqqLf6cQYvIycJikVvijROvKjkiRFa03wJg7ARYhMTh8gOJI0+SdORf//P37ugtLLf/0d2l97nr/9f7/LNXGCrLWF1R221lf4wY93qUn55V/+IsX6dS6seZ44OMm9B7eYmzA8HMGTRxZZn5nl0p2PeOKFX+G5sydo7xR0uhmWmiAgSzN6nQ5bwTKykscfO00rDPnwWhudKXa2t+hNTdJtp2SjNgunnuG5k3N88vFbLD35Of7zf/Un2Pe+w1++8gFitM07r36Pq3fWOT4/yXBjmyMHUlavjenNpmxvVzzx+BPcvX+Hj65e48ihg9TDHaYmD3H20BLTYcTdqzcQsseTS/P82z/9K4xMqeuCbn+CdrfLH/3xnxDWr/HKj89z//Yd8tJy+949nv38Mzxz9gi3rn/CammpvWX+0BG6bcn3Xn+d3/vnf8DhpSNcubfCH/+n/wnf/Xf/BxeuLtNvZSyvrNJKE04sLXDg0BKHZic5ODfNRKfN+bf+gYn+JBvLq2zWnhPHjvOv/81/z8b6QyohGeUFH1+5yngEZ0+e5P7KBllnnv7MBq1WytG5A/HNlnU5eWaBN1/7EVurD7hy+QpHjx1CSUWwMDcxyaDKqYuSI0uH2d5c4+IHdzh5/DSdTo8r1y+z8vA2Fy/d4Ojx04wGOW9feJd21uYnb/+EqZkWR4+d5PrNm9x5cI+lA4c4MDfHhYvvcnPlIX/4u3/AoaUTJFo0RYxun5UgxaeI7j0odGh2hnvxtb1fPey7mfZI0kLsG1Aa7lKsa9776ka/+KwYtWdnx0NQjzxLwPvAcDTkyvWbbG1vcvTwEiePHqPIS1ZWV7l04wZvvXOB4WiV51uf57/9r/5r/ud//b/zja//GpMT09TBcuXiFS589D5KaFZWNpjs3ePe4QfsDMf0O30Gwx1M1qYcjXnzpz/FS8i0wIo4CV3bXCPLDHkrxZgUIRKStENdD3HNNPfY4RNoEzeOi7OLTM/0mJ6dY3t7k42tASsrW9gq5/nPf4HBeIzWmiuXr5K1MtZ2Nsg6insrDwjBs7a1yScfX2b8i1/i/v0HrGwO2R7ltDtdet0WczPTzE9PMDvVp99uYxKNUXHR3RN99jxBPjzS4MbetPdT1tJnRcGfXXf2J9CP+JP2H/yIyLjXuPMZVSo0j98Xkz79Dp/9U/x1rz7ZO0fAI0RsuQlCkCiFShSFy/EIlpdXuHv3FuNim5mZGZYWFxFOMsoHHDlyhAsXLrK9s8mxY4f54MInHJiaZmV5DVB4l1AVniRr8fi5xyirgr/4q29z/dZ1bCh5uHyflYd3mZiZ4ua1u9x/8ID/4j/7Y37hC19jY/Vv+Re/+wdcvHAF4QNVGcjaLYbFLkYr+r0eS3PTHF6a4Z3z73Ll5sdR6nOSxQOLOC/JBznOWUaVIDWwurzKaDxkcWmRoigZDWN9cu0CSaLxzpMZzcbODlmmUdkcN29foTfVx41KlFKMCk+ComNahHFNNS5Y2dmKbrKq5re+9etYYTh//iJXrl7lk08ucendnyJmJKnQMTpqa6SWMQIrAvUoJ9EB42OFaz4Y0M4EL795mbyUtDPFqPJsNHbvRAbG48gXQpp4wBeAilBelCRLEiYnFzg4v8TB+WneeusdnICttW26/X48vEZwGqX1GDnFs8/8CkdPnWZiYoHBMAIi33ztZeyg5sD8QUJwPLx9i2NHn+H62iUqOcY3Gw4tRYSFClA6Q4sO3uaEMMLohDoEWrqDwlHUNffWH5Iq8MFSWU+atAm6RVV5ZBqvg9LXDIa7JKrCCIHz4IBydwcpFVJ4nKtQJmM8HDHKK4wOeCJgtJVkBJ9w7tQ00o+5fHsVoTzOxQO10TPYekSEWsZINKFGygRBFSdlRsUJmrMgGnBw8DjvydIUk2hs7cmLEqkUHqhrG2MyeGyIj0cqhFLkRYXJbOTq1NGuXZZlZAuovUhy87uIE8A6BHzjlqqdQ+qEQCwRIIjIxGGPoRbXeCGjYBO/nfyUo8ej9w0ITf181JTC/nMXeclwWNBqtWglKbYomra32IgWGqE8zskVSkqEiGDiGo9QGqRAeNk4iGK0Kmrw0XWmkLFlRyu0lozHOXPzs3TabZQDFWpQCt9EuoQQaCEiO4hH75N7TZwx3h2nuqBFQEiF93HNRtDUD8fpp9aaxGjanTZaOLA1QoCW0EoUSZqgM01HJ7R1ijSwtjumLKooUDiHLSzDvCTKcJH3F0LAu4gc9x6Cj8yr4WBMPWXpdjoMW0N8YnDO4xqnm5fEqXuIzrNY1iGboo6mOELGKHy8TzT3ISGJyMMYyxIhMM5LXFXRa7eZ6k8wGAwwTSSBxqkmmri9Eg0XSjQ/0+bfH8Urh7eOuqpxIjB2ruGK1TGDJzzbWcahxYOxqUyUqFCglIjQ2BCn0S2TkRhNmqax9U0o0iSh1+uQZV0SEyfxiMbhiyJN2ghinFLLhCAcmZKgNO1em8H2Np3eBFmrTZIlgCA1KUaZyEORKoqLwlPUNcW4Ymqqi0CQmoAQCpSiKAaYNKUYjz892CiFFPGQnBdVPAipBiNgo5OyrqOQ70O136IrlEJ6H/eYe2McpanrMh6WiAdUS1y/yzryolwQjTNKkY/HRC4XECwiOIxSkXdUV9RV5E6mJiEEsC7G8rXWFFXdxDvj/8HagEaQ5zlaG5JWSpGPCSIyyKSmWf8CzkdhuNOZ4MSpM9y7f5vN3TU8lrsPbrK9u8yRo2c5ee4p6nyAkhlpasAJSiwGSVmP6bQzWmmPpUOnGO2+R7fb4/Cho2RJgiJQ5GPG5S7tTg8nC4p6gJAttPbMTC1wcHGR6Yl+jOdKzfziUa598jYbuyt0JtosHThKrz/Hzs4KQVha3R5zcyf5+NKHvPCL36Aajzl25Bxr926STszQGuR0sjnyYpfCDiidoZ0o2r0247wAr2NLlhPY2uND5O+gYi19p9Vm6dBxkqxNkvQ4duwc3X4H6R1T3QlkXdLt9bClR/kqCom+YjSqefyFzxGE57EnfoHthw9ppW2m5pdYGdxGlJH5Nhjs8tyLv4KUAq0lwdf80pd/ianJRQpf0+92kVJgnW0YZiC1RhLngCDJ+pNc+uBHtKdm4zBYKkJRRKeM87TTdownCxAuNniFENs7RbAxmlw7gqvRWmNFgAZGX1tPWY5paYMiivlFZcnzGt02BK1inNpX2LomeE+3NxljZi4ynUpnESKl3ZnEE2HS1ltqJzAmRZoUS6CVppRFgdeiaaA12BAFn8F4C50GMpUiRWSClbbG2pputx9bXetxbId2xEixJ4pYPkShq50262TcV4fG7ZkmLbTUkRkpBHlRoTJDUAIhIgKiqmukUvtnk8pXEYdTlcjg0UkHhCH4ukl9gJQmpgAkjHcGQIxQS5MhLFRlICWhlbSpQo0Qfr/1MzEaZUyMVUtBNSzx1pJqDVpD5UAYtrZ3mOp2CcoQXN2sr5JEJ2iTxRZQovA/0+2CiE7MoBXYePYzqaGxWWOdB+9IjN4/x5RlRdY0riIk1o0xIiZGFvvtGEH3nrq0JEo3xRkKlGzWHk2n04vRyuZgMtHrRzGLCPIWSlFVJUbHEgWjNUliYjur0mgV3buJaTEajWi3sj24yM/t4+cuKF159w3+ZmKeZ/6j/5Jvtbf47/7HByQzB9HViIVTz3Ls8BIH5maZOnuMbrvNR5/c5PyDK2xsDZk5c4D7t+5y6tRpTj2d8y9//5tkdoebm4HJXpfhcARCULnARC+jyBJCFbh19RK9qRm+9rVf5c61t/hwVPLcs09z7/ZdTh87ydGTJ/j6l56mK3fZGOxy7dolqg3Lu1d3eLj+Qy5du87qbkWi1sgri0xCVO6U4JkXX+BrLz7L1RvX+On772N94NLNW9zdGbK58oAPZ6dR5Fz8+Br5YJu0nZKXBcE7tG7TShU9Sr7zDy9z5e4AXxdkrS5aBB7cuMLqg5usb26DNGilOXfyOGv3bjM7M829u/c5tTjDhQ8/Zry6xtzMHM9mU9TFiCLfZTQecvvefTZ3dnGl4+Thg9xZucv1ew/ptA2qrPnyr/0OTxxaYHZimqr0rG2WZCYj1YHVwQbr25sszszTMi36T2X86MdvsLp8F6MTfvtXf5MvPH2WqxfO88mV9zl//QpHDi9ibU1VV2yurfLG+XeYnphkcqbHcHeTDz/+hFNnbvHPf/8POXH4KN/+m++wvL7C5196lpmjGW++/QYkhuXl21y+fpVTJ86xvrbGrXt3+NxjU+TjERvbO0zPHMCkKcFbEAYXKmI5b3QL7R3392p494WEZrMvQgPFjLpxtLjLuHDD3oZQPsI92PvbT78eiJXOkmh5b6zte4DpR/8NAkGaKMq8YHllhcOHDmCMQYkIJu2lLbaGQ2obm7I6aYdffPFFPnfmCQaDIc5VSG8Z7m7x2p0b4BX376/wF9/5DitrW9i6Iog6cjtkoA4KcNgQgZpWCKqUGlOKAAAgAElEQVS6xhJt+anpUBYFhRsBNQSNFI73L13ESMNjZ06ws7PJO+ff4Ju//g221zepXEldOXqdz3Hhw0tYGzi/+QEff/IBLz73PEcOH8NWNXVec+XGFebmFtjaWuP/+Ys/Z6Y/y1i0aLcnKccFdVGxub7NJzgm+h0OzswyNzPNzFSfbrdNmhl0Y8GMk+94GBN7kKpm+h9fluZzfPpj3/v7T7lKe5/fa/Xj0+/TfM1+3BE+8/rx6UN+5mPvYeLRbxr2YiSyib3QTMSj3bQoSpBRBH3qiSc4d/Yxrl/7mJW1dbY2t3j1H17FpIKiqKldQZLBrVu3aAmJCIbZhQWuXbnFvZU7vPmTPnfvrfKb3/wmR5YOcejgEa5dvxI5X0aQmIx8PGJquoXQh7h+d5nB377CL3/li7x34SIvfL7N//p/vkW31yUfjXji9GOsbm9z7uxpvvFrXyczfS59cpU0zahrSztJme5M8+H773Ho8CHWBgaZCMphxcP1WwSpKSoY5uNoC7YCvKTb7rKdbyGkJ0sNb79znqNHXmVisktRFmhlCLVAOoFKFP2pLk899QSfXL7Jw9VVnjh3iueeep6DB4+ytb3L3/3d91lbX2M02GTyQJ9KVAgPOgRqYhWqDzbyqvICH1viI8Sx9lRGcPHeHYKRHJ/vcfveiDoIdtdAGYk2nplei9pJEJLaepTwSCUp64L+9EHanXkyk7Cxtstzz7/IzdvXuPLgY5zMaXU1eekJdQRFbmzs8P7598i6PXZ2BoxG23x04XXGw112VtY5+RunWVw6xN/cXqEqK4ZFTquXIqoyWuqNwAuNFCkhGFwoIz9IthAKhE0IzoCy2KrEeYGtPEYJsjRFBEeiU1INSkHpHT7IyO9xHplME4JgezggUy2EMg2EW+FlBgxI0njBiGaRFCFBK4EtBXVZoVSMeFRVARhM2ma4u4tWEiE8DkUg23f/uBAr1COLRcXDm7UoraitRSaSNM1QylHWNc5a0m6LYD3W10gZp3/OB5SWjQgkeHh/Be8cOtFkmSHpKsoyAiul1lRVnAYapRFSxc2USQkyQZSW2juStIXfq8z1j1hN9teFR1pDia6QPTQOIrYx7Q01fAPbViJOrIOIgsbO9oADB2dptRLGY02oKlwdW8mCEPFuJgRKycb1Kj6N18nGFbmnYTRxqhgXUygZK+Y1Eq0kE/02nSxBI2J1utLo4ClsEd1QzuOCi/EJ2WhfIQr11nqs9SRakyR7zycQwhHQsQmtAYDvjVHSVkbajnyOzBicMuRlia2q2DxUSYqtYYzzCU83S5FCUOSW7bLE+4Cva6zzeOEjuqCJqQXR+FabIY9rgMohOEbjIf1+j4mpCXwQjHYGlOM8rvpiT3DbEyJUXDNk/H8KreJeoGERSULDwJMoEZ1OgsaJIwOb2ztMElicnsYXY8qqBhd/DpF7Fd8UFS6+BxqbkwwB50GL6PCSKpBksRI8zSJPQ2uJaEQhow29Xp80aTE5PckehNUgcURHbK87SWPBoq4LknaGFJp+r4+zjixLEEqgtMbb0LSh9UiTLP7fQ6B2FqEkSspGwBT0un2kMtEZYGu0jq2CAkFVV/H/2TCFEi1RMqEqC6RWKKUJwaNCaPYlxEm9VJHFoxS2ttR1TbvT3m/Zqx0k7XidIwN1UUe+FY3g5D1C07gk42uqtdrfoimtUEFR1XWMAXpH1s5ITBLXIVsjRCCEKNxppdCpwQtHPh7FuGjTgOt9I1gj4/6pspi2iQ5IpRv3ehwcJYmkqgqUjmUDIS4FCCGpXAUhAnQ73R6Li0e4d/82QhqmJucpxlsI1eX4medQJmVc7zB2FZPtVnQzKIUnsgB7nTSuI07Q7k5w5vSTTHRnkEIjjWFja4Wy3CZJEmZnFimHBXMnTnH50kUe3L+NECWDrRWyNOPo6SfpTc6RZX3ylVXm5g6TiD737twlrwJaeza31mh1Z9lcfcCDm9eZWThOu99DmAxb5rSyjIWFI5RuF5+D8RKta9J2i4OLn+PO9csMt8bktYJQ08kMVtVMzh6C0EEhObi4RF1VHFg6yNbmKqOtXZQa8tHFdcDw0ku/SZmP0UIgNDy4c4fFY8e5fu0SRiVM9SeYnD5EXZYgDP3Jw4x3BtS2oD3dZnFpkVs3b8USAaEw0jHKR3R7M3Htb7LMQbrocotKICDwZcnYBooiZ3f9AWlrnp3NFaTOmjibxyQxZlZ5j62reCYQ0ZUknIpOXFvTbnWQRkfsgy+QIjpvtYxitdESayuG+RCpNRP9KZJWGyUMVT7CupJOp9OcfaBWik6rw9r6Mu1WJ7KwRKCuS6qqBCw6acdYZoiO3+2dHTpZggwOYbqxdAhPUVT0uu3mTRvFsaIqAEuWRGC+9QLVPEcQsQ1RycBoWFNVjt7sJM4GVJLGJtrmtql1GmHQweNcwAZHqloIL/Ea6ionOEuStgmY6GBGURU5Rb5LtzeBVjoyWYWMbKYIoEJL8GUU6ya6E01UPOBcibU1WaeFE/HsYGtHZUuEUhidYZKUICS+dtiqRAlJlrbxHkyasjsakClBJ2tDUIQQwdY6bcVmuGbsMixGKGo6aR8rXGQJCqhdRWo0WiWN2CMJwWKMoplS4KzDO0uaaJTSeFfuI3PwDqFiZNh5i0SiVLz/Sq2xPg7rJno9TJo08P+4ZxGmGX75eB/zIpYsJCrepZMkRYjYHKu1RioZB5cu8h+l1nj785WUfu6C0vURPL14hm+9dIJLr3+PX/31b7G59gk/vXCfZw8f5dd/+Xm6qYT8IR++tkF7cpbHez3eunCBxYPHWV++xfqq5U/+4PfY+Oh1hq7NaH2T9bU1rFcIpfGuoqyjhZ5ixKhMEGaSp59/gemex/RXSUXGV7/0ZTY21lFac//BGpmZ4tShRW6ff5+tlbtMTE5w+8EDNscOL8GRgbKEoMhaiqWFRR4/fIDNtYdMTU7zz778VXYHuyStFFfl3Lh2jRu3bzEeDRnlFaPBiMc/9xTLtz7hYVFx5OQZliYcy8v3GIc2J4/PsLLykENHD7O1usK7753HOosNiiRt0e0YNjc36S8eoli+x+Xb15ErN/jo8ke0exNMTU6xc+sDHq5tEUJF1hJUdcmDlZyN9W1u313kmWef4Xd+65cI5Rr/y//2P9F5a5477zlMMsWBAzOcPdvjJ2+8SkimeP6Z50hNvHC9FmgULdNiu7BQVty/dY0bPU05HvP481/ixIEZ7t27y/TEFILAcLTDyupDPvzwEtPzfbyLroXljTWsrbAh8OS5J0hvtvjhj16lEyCVgXE5xtaed965iPMKaWvK8ZDxYJfSVZw9+zhHjx1naXERpRSimSQLEV02++BLwj5QdG/Hvw/N5hFxAbEvQX3mI+wBM8P+J0WIXvm9zYts1Is9gKkQ8tPHNr8GEW2bwgc21la5evMGjz12Kh6GnGOUF2zsDHBBM7VwnN//ld/g3fMX8ZXlBy+/itKacT7mr77316xvraCTFO0Vq6vLeBFod6ax1Qhnh7S6E2TJAutbG0hhqKoREO30UkSprbA1iQEpAlWdR8VcSpROGOc5wQ14/8MRpa3BO779nW+TGYUNcOrkOV5+7TXm5heQJNy7f4e7D+9jPkgo8orBcMS169cpqjGbm1vM9xwHZyZZ2d1iVG6R724BkKQZiTRU3rK+arl78zbtrE1/osv01CQTE11mpyeZ6HaYmZ6i3ek8mjvgMzakfSGncR40r1sQj76iYd+hts8+2v9a8ZkX/1E3076AtedOCuJnXt9//CEeOQx6GXkWgr3DL7H2OEmoypI0a+HHBanqINwWuztjinJIb2KatdV18qLA1p6Hmw/IspQkUfQ6M9y4epkDS7PMTbdopSmb62ssLCzyxNnHuHHjIzY216idBSOpxmN2hxt8/tkv8v77F/nuvVeo65yVtQ2uX/kYrWry4S79+UWypM/v/uZXeeLcObzzfHjpCleuX6Pf6dLvdHji7Fle//4PefH5p5ieP8CZx8/w8quvINKa2ldoEdgZrFO7GiUkqTR0+l2++pWv8s7b57l57wYuOEbVkL//wfcoyhGyuc0URbSPq0Rx7uw5jh0/xw9eeYt+t8evfPnrjEcVf/Zv/5RBXjIe7VK76GJxoSJpso6+jqBhFyyu8nhJhBkHMCq+BiaRWCERXpCYhNHQ413k1hS1p6RipqfpZwm1l5TWU9QVSSKRiSSpFF2jGG2tc2PjId5VHBWnsK6K15V0uLKZSiqNE7A9HHHp8kfINGN5ZZnz77/NaLzN5OQkkxNdPrx0id3dAUI67t+5gafEyAxtNKiAkGm0bZPiCo8QFcZIhNZULsfIJL7vjEZbQ6YlXnl0CBAMdWkhcaQqTsimppcQLiEfbFK5LaCgriMPJ7gaKVoNxLHCjmK0TgpLWY8RAUzSIfjYKnbrwQpCVHiTkJkUJyzWK0bb23HimBhcHVvavIggUoRFN06a0Fwfe+JFXBctwUSotHMuCnlFjbe+YUQJvLXQtKOJECOxhAi39Ht1vN6RJJErIZSmrGPjUtgTiUKE/FdFjA4JoaKQFB6hpe05kSTx57O/TojPriX7S81e0+geUDq6KyHuIb3XSDzj0ZAQZml32+zsDqiqCgBFnJgqEaOiYn/KEfYP0F5ELlb8viKaWZo1RkuFkgbLp47aiX6HLDUQLJ12RiE8iRSEXGNdhEnriAFtfjYxrBEadV1L0Yj6cQgTQuPTFQElogtLC4VoNvnYgnroUCq28kgUg+GYcV0TnMWLBo4qAioEVsUw3kulJCiBEY2kI0QDRo0bYyFiJMAJj0IRQnRVBaA/M4VOE4QX9FpdltfWqKuKrNOK67hQURAicou0ivXROoDFRpaHj7G4iPTyBGpCiFET75r7RRB44dglMNlLmJmYYVk6gqtQMkSmjzIorUh0Enl5UjRRs+gWA0krzZBKk6aagMMkEVRthMQFiw1gtGn4IA4hE9rdFj7Ea8QoHWHEecnU5AwIyXA4iE4VLbBO0GpljEcjOu12jFBKSVVU5GWBSdP4tvLNvcpD8B6VGfK8aPZS8b0EUbzZc/AKJMFFMLuzNUpqjNEQGlcuCikFZWkj/4t4zUkhY1RE7AnINVJIEh15Ht47dDzPUlSRC1LbOh7EhMC52GJomohenBZFJ4hv3MYAdR2/r2yEMeujO841MOzIYQmEYElbhtq5GBskxg8Tk0Sem9AR3i6bfV5oovjeg3Px+mzci57oZjJZsv84Z+PO0pg0DqOVR3jHcHfAuCg4deZpDswtceXCTxitL4Mr2Fpe5sGdy4yLFc51v4ERGq0VxXCMljo6xHxc4xYOHMKYo+Bj/butHcdPnGH99lXmDh1hmFeYrMP29iZBOLrdHibNWN9cp9VKma9KJmdmmZg5wNTuLq1WH1fBaLDDsCw4fOQcw/VtTp55nDvXznP+/Gv80lemKWyNSTpUo5Jeu0271SExfaZTTVFusDMcUG5Z6tGHuKaARIYYiTRBkKWSpYUFppae5JPz51lbeQgu4IVnZXWVvNghuHVEq42rHDdufIQQgsOHjrK1ucH8oaOgJTKzKJmQFwVh/SF5nlPUgePHn+futQ9YHT6gP3EASJiZOYQtNDpT/OjHf8f84gm++IvfRDSxIec8la0jV04mBFGjlaYuSg4eOUZZvcDu5g6zB+d45/Xv8NQXvokyXRKVotKMoA24msbAgtRib7lEeI8Nnn5/GpVlaBHj2F5oyjpn0mT7LlRrawpb0mm3MUmK8AKPoyoLvPW0piYIPrpOdNNG6oqcJGvFoYmI67IQnkRpjNYxOmZMFFlDQGIQOo2PR5CPitjcZxKkMHgRsL5iVAz24c/WNe7QEIfS7TSLzDbr2BlskLQNBIXUYr+oyNU2DkSUiHc2IRkOdkmMQQuNkAZniyiopArV6oF3KKnBOYZlhTIpKml9us+XKrr9gov3AA/jwaBxxsbXISCwRYFWkc/nm/uYq2oERDdc1m4KkiRVmQOe1CRRSPEebxSjwQ79dlyTK+fjoEsodJIgtG5i0pLdwQ69VsNIchYZXLz2hcUkJjIZ6xg9C7YgS+MwC6XJx1GAFFrhUThfIzEMBhv0O21MqwVBxxIQGe+7ewOjsozOfmX21uj4M3JExInwzf1baUajIUZG95iUBqVjAY42OkaMRcBow6guSVsJIHGV/ZnzzX/Ix89dUPqP/9UfMtfN+fjCm7zz/gfsiBmSoJhfOoEtRtF+ZQQ7W+uE1gynZ6exRc7k9ATd2QVm+j3CdJ/1e5e5ffM6r71/g7ooqFyESCpi7nP6wBJhbCjqku7sAb7whS/y4qlFkvIUzz73Rd5+90OeOneatdUFpicncMUWt24K1ChnsLvMrZt3GZYpeZUjvaOTGYrCcez4IuvLWxw92KXtci5fuca5p57mxImjLM4tsL58l9fefJXt7QFGabrdFqY/w2MzM2xsDXjm8VO8cutj5o+f449/7zd48wf/juV7dzly5DChLkHBmZMnuDTaZXc7HpqMScA7dgcjZhaPcXxpESVhpgU/un2HTq/LnVt3OPHSU1y+eY+52TnscBsH5HVOkqQQLJu7W6AVT54+xSvfewujDe+/9xrTLY3oH+Xs2XO43S3GecnS4VkWJyeYm5jg8MnjuLrglR++TJoqRptDDs5N88Gl85w5dZKithyeP8Dff/e7fP23f5cFo7n50TXWNjZYXDxMrzPJ7Nw09+7fZTDIefpzzzI/MYNtTzDR7vKLzzzH//V//zkHpucAiasDCMXW1i6XP/oYmw8o84JLlz/m0OFD7Ay26HfO0U4MNHlclWbEqFmseIxRI7GnKz0iBPzT0YQIaH40KxU31SLEA8G+hb35+j2doUkHNM1jMeq0LzLtuZNCpAAFQBuN8oE7d+4y2+0zGucYqffzuhLJRx9f5t/82Z+T6EDS7WGyCYrxiOXVVaanZwgo8uE2pS0RSlIWA4KzdFoddIDJuSNoacjHO4xDjH8IIaiDR3kZuRdCUHuL9xYlo0of1f6UOoyp6pK8LmiZFkHZ2B7kDXfv32NxYZaymmA03GZ7ZxuB4Nbtm2ytr+CcY1wULC4ssLUz5NiZJc4dPcbrV+7CboWnjjeDumLkQrSXB0clNeVIMxgYHj7UKK3ptFMme11+61u/AQ2o1IZ6f0K793vzasbX8x/zsD7jRPLNI9nXhvZPlY9qVXvfMTzybtkzuO1rWY8+z6dP9RmRqvm3hGZDuhd1UDLCd5M0ZTAcsbayzsWLl/jJWz9iMBoxKoaEDc/mxg6Wmtp5XICJyQms8Dx76gj5cEy/C1/60lcZjyVV5dnZyXn8zFlC+E3efuddrt78iCBrUtFh+cE60/9smpMnTrNTbPPO+TdZWDjIvZUHLC7O8fiJJ2m1e2xsDklbHdqtPrtbW2RpQmISVja2mZuZR5sOz7/wNL/xu7/PMLcEp/j+375Ct9fBKouSmjIfxny3jDbrgwcPsjAxz+L0AdY2V6hcQVF6Nra38EKigsIHQcCiVKCi5q2LF7h4+RqjasjSwjxXr91ktDNAiJpBvo7wChEM7fYEw/EWs1M9dnZzxnmFMAKhAnkRCLLGKBlP8kJidIKS0VFSVIKsk3F3ZSNO89stPCMKF+hMTdGfXOTO7ds44XDekoh44KvTMcVwAxHa0TkiHFcvf4jDgVMEpwm2RiMIIk6fChf4+MpHbGyuRK6GtUy2UiYzTWdmlnu3PsI6T5Ae6wZoKgwV0niESrF1XN+EVCSZYHdco7SODjB6EDylKxv7NaRGYFJNWcXmHu8qOm3wMqWsBUJ0aKVt6lFNXu3iGSJEm26njfYB/Jg06WFdjbOugeznCBm5FniNtRXB1ygVCCJC+L0ySJGCjAecypYkRKE9WtRV43CJrgUlm9hZ7dizHQoZ41reO4bDAaAwyuC0p6ptjDg1B78QHK5p2BNCUZY2OlGkoyxzXO2xSUa302Jyao6791biBFNFV4YKAWzcZHvvkSEeel1dN+7Rxgq5v7Z82gr5KIvvM2uREPvtonuf31t4hFBIEWL7nq0pRjm9dofNLCMvCoRXzbRaNHDW0FQvRxdGbJ2CIGIrXGgWMy1Uw2qLrBoZPFoI6uBIs4wkMTEiFuIho2qiR6phMRgVD/y+GY6I0AxjQpxWxo1s47oIIXIviLykIKPLRwRJ6SqcDVTOkXqHt4HehMFkKSrVCO9iRC6EGFEKsVI+NKNV39yH7SPivSA62KQySNmI9DI2/hACRmmsDygSiqJCVrElSOHwvqK2McoSCLGJjSgMxBYbT6UEUkYHFiHG26SWaKVJlIEAVVmilaTTbqFMikkSslaLnslAwqnTp2OzqZIgBVnaRklNonXDtGhckz4QnKe2jsmJbiOiBAgWqQ1ZltLKMqytm8ZiTV3W8f2YJKStNLZPColRBh8smVMoncTGVBn5aHVZIE0S4fMhcp+AJu7hUDoe7mjmK1IphPQQYn2484E0bQ6oTRRCqU+jf85FsTIuq7GAxJgkCj5i7z0fG4kSYyjyIsbAQ2SoVWWML3rv0drEtjWimy9JDOVoxM72DlPTU2RJ1sBoA9a6+D7QUVBy1sZDuLMIGQ/PtrbYuqbd7gBEscBaSKGsyn2EAULG2I6UjzgfFK7ZO+7hE0Tj2PI+Ni9CXMekjE5KY3QEu0tQKorAka0kwEcWpxNRdCqritRkHDt2hrTb5dDhEyRJC4qKu9d+SrCW5ZU7jAZbpK1mPZIRyO+CxaSGqqqafWkU0mWIznxJFEN7nT4nTzzJzNIh/vKv/xwbSqq8YHH+EB2Z8Pkvfo1qd5tUpyzMLoCzQCDRGb6uWd1YJrhdgqsZbm9w8uSTHJhdxFUDQuK5c/t9zj39IlpLhjvrtNuL6FQzMznP5MIkD5cvs7tzmcHOkG2/Q7fbo6g8Tla0dIxEK2HYXruL7CyytrmCxTHRalO7Gusco3HBgYXDnH78ST587x3eu/AGp48/STlrOXTiFDeuXufg4ZNUN0qefunLFPmYne11sk6P+QOHGWytUuUDUmNYPHyMO5evMLl4iFS1WTx6kpXVuxR5gTFxKOJthUdQu4BJUjAGSYgRpBAIVnLuqZdYW17jwa1PGI+3GWytYXrQnz+CaffiGmM9FQGpY+mEJgKii7LCJAlaJ/usvuACZT3a31eGJiVhXYEW0MpajSNFUhUF4/GQNNEkWRpFbR/odWKrZZaleB8iqlOA8w6PI1MZUkY+oNCa0fYuUkkCikRqqkYkGo2GpK2kaRr1UdQa7SKcpduehiCadrTogNZJigySus5x3jMuRixOzlN7yIyJ7XDEkgBjEpAK5cE5T/AW3e5GLIFU+CquzWnaicNtEYW1shjhXU2710EoHd03UgORibjHcnQWBuMB01N9tEkQQmNtSV3XZGkS1x0JdemwdY0goHWCVCa6s7zH1tEVZHSCCzHauzUYIoPDmA42EMUzIdGpQWrN3o6lyAsSPGlTuCOQSOWpyzqWF6iU2saW57yoUSoK1kIaSh/vlT0To36xdESwOxyhgqPXmwIZ45IuxGGFFDEeHDFHjnarHUHej+w5CHG9FE1LZe0tpa3pJmncL5iGjRQ8RseSDILCu8gDM1qTj8pGkPr5ffzcBaWvv/AU5XCZV19/lQcbu3x85yHPPPkURw4aTh5ZIvgKXxRcff9ddndKTna6fPfNn/DEqZO89/oP6VZjkJYbd69x6dp1hlUd7YrEm7UCgpRkWpK7GiEN2oMZr/Hdv/i3dI9/gV996nF2Hl5n68EttrZGpIx44623mZ5fYHPtNvdXh1R1h3vL6xS1i7WpVjAqK+bbMPAlqs5Y3V6mvTjDzNwBjh89gihHCOWYOXCIBw/OMxgLzpw+imh3eOnZ5xBSc3wi5Xvf/jNOHj2DHj7g42t32HKahdlFisEaujvDeHdAu5UyGkaLYaIltYeJiXkWZ6cZbm6ytDBPVdb89u/8C159+a955SfvE3zO4oFFRjsb2BLyugRhyVo1WaLpii5H5md5cOcG71+9wemzz/D6q6/w0rd+m8fOPs1oZ5O/fP0f6Pdnca7k9Xff4qu//DWOS8Pqyi3u3LnBTj7EWUjaHUJtuX71BhNT01z+5CKjYsBXipJuq8PVTy7zyfU7TExMYp1jXFYIJem2O2hXcenS+6Qy4cq1q7FqUkBRjul1p9Aujw0mvmZtbQXdQCpnZmaYmphCpRG++/Lf/y3dXo+XXvglZmYz9rNFj0ak4JGDPY8qAnz6x/CP3CyfvYr2g1B7GsIjgkLcnPimGSbWhwohQPrIYvCxxSZIAT5Q1TVbW2vsbCzz/kVLrz9FWZbI4OJhYbTOn//Fv2dj4wGJyZhLEpSyDAeDhvhfE1xBXYzxrkYGzagekGhFXjhaacZosIG3FUpojEpipIGAFgEtBFmSNTZscF6gtYiQORNdSkm7Rz7eIm1s7QiPSlJEiNOTjY111rc2SUx04vUTRVHXjEYDnK1otTt02l1Go5Jh4Sk9BAyddkrAIYWi8p7RuGB5eTU6DIRgZnqK/kQLpRK8A1/X5KMR7U4bF1xU9P+JFssoLu2/WPti3qOvYJNJe+RBfFZwevTvH/2y/SfZe7P8zJM3zI39B8VNnoiHM9VMVvfeU0bHiUoQAaU0VTmm006ZOzCHVimeTZJUMRyNyfMhJlPRwSMtWbtDVZR85csvcvP+NgcXuxw+8jgP7y+TpPFn2++3efKJJ7h/b5nbd65TVCU4SVVVrK2tIjx8/cu/Cg5++tOfUowsX/7SF1mYPc5Pfvwata/4wcsvE2yMYk70e7z0wnP81ff/kq3xNm/99Cf8/je+Ds7w41d/QqefcfDgEllLc3/5Kl47hHdolVIXAZVE8/Jff/f77I62sK5CIbFBsFvndLQg3y2amJncF3E31lZ5UN2n2+2gjGZtbZkXnn8RK2rWt7cYDUu0zrCuYlzkHD38HBcuvAAySOMAACAASURBVM/TT53l3spddsa7kb/jqnhTdYpaCNLUcOTQEuvb2xTbO4xtHoGSecH8zAyf3N6m3WrTTnqcOvs0Fy99RNo2dPszGJNQjYcMC0BCO4HBzg4zc9McWzrKhQ8/wNYBI6LdPZWBbqKZavcZFRbnC3YGD+lmPXppSqfVJVgbD1RygFCesvJkaYvJbspUd5qyHEOrz2gwZDwe4nQbi8dTk9eeJGmhQqB0FpOmlHWBpURpTS+L8T4hNN7npFnAiQRjE2xdIZIuVYAQElyIcZvKVZFBYiuKUmBrG6fhzQYlydokuoO3DmcrnI8tUVknpWU0torRIV+D9Q6VSGpbIn10FxAcQuv9SI0QTbOXqPDeoXQU5UUzGNra2o3sGJXEiJKMjgSlFSqNhwGERJsUm1us9dGXoyUCj/eBuq5I0z4ET6LjNawankGWJBAU3bYhL2tqK3AIvHMReNm4pz6NPX+65rB/cOazw4jmc5/qS3F9UDI6e6SMUaXSW7YHAw725mm1W+wMB9EuEr8oHhAa67z3HrTEyTg0080G0oUI5jRCoYSi9g7XwJkTpQjYGEusahwe7xVCxaaf8WhIWUWGTxyr77GS9layRoCPp9amij6yRbTSjPKcUR2ZPxCwHmxzkIgtpiBc5N60+xOYdhsxKhuniEVqifIyNuyIWHOvZXTTxkNFnAZFR4iI9vsAha2oSkvhC4Sz8QDkA1sbK0gJ0xNdlg4uMdVrUea7KBkwTROXFBKjDaFpHwsh0MqiAJolSRTXTIrUijTJyFKFq21kNgnoZG28FnRaXbSUjHdH1BrmOx20aqKOSjVxghiJ8k2kACTWWop8jPWWXr9HVVZ4D6lMqa2Lzhgh8MTXEx8Bvx7QzSBon+koDcV4RJa2YjGAq9AqRhi8D6Qm8jGUkiA8UsZrynlHkqRxQGPtPvAX4bFVhVGC4BxSy+bw6ZAQDy2hAeH7gAuCuqoQCMrK0u11GY/LGFWXGlvX2LomS1Osd6RJgpBQ1zl1VUeRVCXx561EI4ipKGhVOVonpEkreoW9jU1Fomn0VaJ538Vro65rer0WIQSqutxvgwsNx9D5KPhYW6N0FIXi/TmKMdbFNcUR4ntcyvia7znfm7u7lCpycpprz4XI37TBx8auEKOtLsToVCDCe62tYwRVJ4Smie7Q0jFSbRhsreCVRXUyHt65i1AtzjzxC7QygzQ9PBaHj+602iK8iM11ooouMSmjQ1HGCKh1gblDRzDtLpMT01ROcujEOQ7NH+e9H7/ORJJhZ+cZbW9z79Y1auvIy4CvBZtrt2hlQ5J2oC3auPGIVrfPjZtXULoHiaOsSlpJxk49YGP9NlPzC1hn2di8Q6m2GRU5UoI2AlwS3bpGIHzjpEwEzrdY2drk/vYbzB1c4tjBY5TjAXduX6atHb3FeWZnD1APcoR3WO+YnJ3nxvX3CC3F5uYarX4PRWyHxAemZhbomAzrPfloh2OnPsftK9cpRgO219c58/mXQBpOn36cI8ceY2HxIIlu4a2jGQOT6oRWt4fSyf6+1NcFSZagjGb2cJc7Vy7SmTpGUY6ZOzqL6WZxyFE76ryAJlq6ty+VwLgROL2QKCEY1yV1VeLqml7So6KinbTwSOq6piUUCYpgHT5APh5jnWVqYqaJh0tCHV1yW7s7zM9MUtWWlszi3jnq9ARhqBvHVFXUVLbCKIOQ0TUofSAvcpJEkyYJUsYYlQueYR45OmmSYpv9b3AO56GbZpQhemCLcUGaJgiVoJVhLxQbXI1pWHnxWvXU5Tiyi6QmiFiSUfvQFPNkCF8jjMR7QT0eY1KNNhmS6OJENLcq52P7boC8HJO0DabVipgJPFVRxv1Fc60H53F1jXU1Rkf+ofUWjcBVJTKE2HqmTbzPSEWRD5iemyf46LDSe6aVNCVISbBRhB4XIybbXRCx7MDZEKOTAbIkRviCsLFd1o/ppgkoDUJhixxCjVJZjHH7Eq0Uu4MtJrs9TJJEFqu3EAJaqijgS0VZFGijSFvRQYaIqQrnI/NNNkMhqSTlMCfTpnFsqrhfcnHtj05kj24E+iwxBAfW5iDrnz3z/Ad8/NwFpV5bcaA3x2h3yPzBE4SO58UvfpG8LDl7/CgtA/lwjdfev8xbVzZ5ePoJzOJJHlaSqYWDfP+Nd/iXf/Tb/N3ffJv13RLrQQWispe2SFopWkhu3brBwvQU1NsoV/LxB2/ww62Cr3ypy9utitGDu/zp3/wDlZcYo7i/usGxw4ewox3WdkqMlIyrQGUDmdFYaZjo97hyfZ3BuKa4N6BQnj/61vM8c/YxpkzNq2+8xupuzYGpSX68tcbDAXzuc2d59umn+eVnn2Ht4W3G22t46+m3Ja+88kN2S4uamOYLX3iBteVlThw/xu72BmnqWV5dQ0gTq14FCALL92/xMG3z4vQEr7z5Lv/Df/NbvP3qd5BKcvXOXdpJhrdjvJc4pxHB41VNXtdUxQoPHqywMDVDkna4ce8+qjVNuzNJJkdcvPohWcvQ6miC8Jw998T/x9qbBdt13Wd+vzXs6Ux3wh0AXAwECYIACXAWaYmUZImybNlWLHnocrnb5a6Uq12pzktX3lKVVCWpykM6rmonndidVNvVtmR5aCmy1B4kihIlcSZBggQJggQBYrwXdz7jntaQh7VxSTru6kqi84Q7X9xz9tprff/v+31MZRmTrS3eO/82N1fX6HW6OKfRwrKTb7Nv7wGS1iv0hxMOHz7IKy+9xGDlGqfPvMj1tR1ubm+AFby/toHxEzzw7aefIk1+ROwk11Y2QIdNoyPC2aqx9FuckDid0G51gsU2i+lNd1jef5DF+T18+2/e49L193n44U9gTEWkE6w1u1O0hryKaKpbgxHzA2HoA45qEAXC7/D3JNlGoLgVm7vljBEiRAM8nnF/RFXX3FhZo6prlvcuMR5NqKqS4XgUfi8J5aTg7Xff5sbNK3R6LUYTQ/neRXb6E/obVxEavKnDBNJn1MJSVIYoMhSmYu/SISSeyXiHSR5iH7auydodrDOM8wlKaqr+FqNijDU+5HG1INYxo6qg027TavfI8wlKSaanF6jKiqqYII3BuoooWSCOOkRpRl6NsUVJUZZo4UmTLjuDbYyvSXUSKkbb03TbHUbFiHanRbvVwZQVRb7BuEzY7E9C+5BsULFCEQHCh0mtisLUzdowTVVN641WwR4vRWOg3XUI/H03WfMsej44xX3ofUJ84CYI7wufs+sg+LCQ9GEn0q4fjb/ndLrlWvjg64QPhyjROAZ8IyaFrLy/9VLEYZvvEaI1WRyBq1FSkaaK1ES0dcZgp2Ck+oTCEYFSsLa9QUe3+NpffJPjd97F5atXqCvH3J5per0uZVkTR4per8vyvn14J6hLj7UFx4/fyUMPPMTrp8+yf98BRpt9Hn7kEa5cXeP++x9i5coGkor1tXVurm+QakG73WJ+bp66rEi1xhQ5Li84eOggZ86+xfrmCt99+jU+/9lPc3Njiyvv1yRZTK/dJe702Nwc0s1arF1f4caNNXQs6E6nGOOY6sSU1CRJm8FgiFIpMQKVCWSkuPvYPbz62hnuPXqc6dkFVtdXubFyk3NvX2Juep5UFdxx9Bjn3jlPu52xvTMgihW/8Ru/xZPf/Q+cfvsNUm0xvqa0FY898jhrK2vcd+8xep1ZvvV330KIisSGic/8/kX+2T/7bb7//e8xvPk+yhe89MrzpJ1wuKicQyvP1FSHUTmiNIZuKyHLKpJE0Yq67F3ex9rqCtIV4EoqC9J3OXn/KZLOFOfPv0F/6wY1MClytscjOlGCiixFVVK5kkRpeq0pur0epoJJblDOY4oKKyy1z6msR8YKV0t8ZXCuojczh5Zt8q0beFFTSkchYryI8VZgjKaYOJBDEpnhraY2EYUZgAwV8lJMiESJkFPYwlHXwSEhpKGcVCgdI20SvJhCUjlwQgces5P4qnGHCo+kxDiP81XY/PrQxmRdhXAOKcOGSnykjSxcO9bYxnHELnumKHOUjoIga8MmJ0R4CE6ZKEbXgsqYsM775meoMPktygl4Txo1zUwqXK/B/OuJI884r3C+DmwqG658JcKG64P1wt+6+j+YBjYP2bB5PiwkAeA8Uqtm8BUmsA5H7R2TfIK10Gl3SeIBpfOhahnftKgplITahGCNp6lJl6EhzOJCtMeHRplESWoT3EpCK4QX1MaxsT1EEETuVrtLpzeFEaG5LUDnHU6Ehk0ZEll4pxDoxu3rgjNJanSkkEpQD2q2R2Xj7q1xUqJVhEM0TXESHWkq75mOYqbbU5jKEAvFpCqbSJBB+KZdrzZUtgxQaufA2914hWzE/UiHuFuiY3SiUCIh0jFaS+JIkuqUtJ0xNTWH9zDdmwr3lAYMlSUZUZJgfEUrSSmril6vR2Us7TQliqJGDwxtOYIwUY+UpqhKosb1k0WB+VRbQytLyNImkmFCM2CcJLjGLW1MtStQCOcoipIsCwKS96HR1DX+Lw/hftHEzUDgfHARSBGm9BIRXG4iNAFOT7cxIrT6dNJW4FRFEUhNWY1pt8LvJqTC1cEh0okTvLFBfGrcDt6Fa2MyHjXQeUmksyD0CMALirygKArSJAHnsXUQoG9dJ765VxpvqasADwdPHEcBtG1N0EwbJ6D3Lji4vIdb4HofuFidbhDpjAkMpdp4rPPEIjyXzgSwbF5UwXEWR8HFkcZoHVHVVTNQdIFN4hxCSiId+Eq32GauEQ7qukb48NoVSoWooRDgZRNfDdcxHiIdB5aXEjgvggMlikiTFN84uJSOmr9v2DNaaxEKqrrCC0NdVaxcfYPVG+coqxqtJO+89iqz0/OcfOAh4riNdaHRrnYGjcQikVEMKqyDSmkMvnFdSlwtgQA31knKxz/+OS5dfIuH7v8UxWjCx376Cd6/cJ641SKOErY21+lv9dneGlDVju5Uj7k90/QnG+xduJ3lvcepRpblgwc49dDnibsxWZYirAttUEYxyUf0pvcgdMTb50+HqvYqxKQTmWIKg/UeU3lUJqm8Y1yMQUr2LE5x7OgJymGf0XCT2hR4N0ZLz5UrfVpZhI5T7jx4F1mWcvryWa6tX2JuzwFmhzNoFQXXbpwSzIGCSCoW5vfT605z7cIKqzducvJjjzI1M83+Q4eYnZ3HKs38/AJaSgpXI1WEtYFtI6VE+nCdWG9DfDqJQSg6WcK1S++y9+BBRoM+ShF+B++oyjFlXRJlWVMgE1ILpQkRXy0jat9w97zE2hDLTFSM0cGhMsgLqromiyKCPumxdU1ZFMHh7jxVXoff2UtWb66SxcFtEukwZKhrg8GTxi3iNAuMQaUZjYfhLCQEUmks4d5qjKeVtdE6gsbdFJxEkrTdxTVnHeU9pbGoNEHoODTKOsG4KJifn6OyniSNqesC6yTCO5KoDUJjXYW3gX3YanWDSNS8Tpw3ZDoJZz88wmsm+ZhxOWJqthvKOKwJEXcRnLRIDyoCIRmMBnTamiqfEEcJtakphhPavXZznlCBT1hOwloQpUjdNLRKhamrUFrQamGNQ0SC7XyHbrtFHLep6gqpPc4SkAI6wrpQ2JCXBYn2EAfH061IjDE1Og4CU20cWgvyKifRCqUjBJqqNlhraaUxIo6oTYlWmv5oTCQlnakAQrd1cGEKH4Znge0WGl2zVrYb6dttr24GQ0qppnkTJA33GUcUBaevFhpc4HtFcRwGSUogdIQxNUkSk4t/YIL//+PxExeU+u9+n6//+B1mD9xDNFrjpjEkruLokf1E5XXWrm6QTu/ny//4n9N59kdMH36Qz9xzGO8tjDZ4+Y3zbF19j3LqMMeOtLn83nk21/rEcUq71cKakkleUkYxGIsVnss3Vrj7vrvZvnGBgTH8q3/7B9SF5+rNbWqpQ65SKd6/toKzDpRGpm1m9x1g//wU5145w5E7bmdz5V1WNx1SpaA1sRa88NpzPHL7In/+N3/BixdvUg9zVJxSe4v2jpeef47+zgb5yhXeeudt5ufmcCjeu3iJTz14DHP+Ih9/5HE+e/IY57OYnbLPofk5xGCVXpZRlY5RbdizvI924ri6us4jjz7KYFSx5+BhbJ2jo4xer8c4n0CssTZMZgWigY5a5hYXObj/GKvrm8zOThMBZQEff+A4zz/3V7zwQsLa1hAhI7ppC6EiGJZ88xtfR8UJk8mQ4aSkKPvEcRdpxoyKEp9oEhFzub/O6N1t3r96hae+Nw7TawnTWUQSdVha3s/C0gx1XnL6zEvsbG4gncJLwanjx7ly5Qr3nbiPCxff5dC+fbx49kUiJ3B1iTEJe2bnue/ue7njwGF6nQ7LC0vsXz7CTpXT31ljbrpHXVdNFlTuTpAhNKx8IAY1F55vIgPc0gqaaejuaeADR5MngDQ9tzKqAuEd29s7PPX001y+cQ0hE4q8xCLptrIwpfGCvMipjEVECjPJuXrzPSo82/2S0egq48EWeMjzEUhHKwkLaJXnCAXDUZ84SpnuzpC2Erw3DMbbFLVpbuqBN1KaEilhUo5QOqasC/bMLLO9HapErTVIJTFItE5IUxlcBq6kqnJqUyKUpqpLtDGhnasuiNM2NTHlZIek12laMEI19aQak7X2YEWoBtYquJjG45qV4SV6bUVeWzYHBQERDv5WY1azaZQNJwQZkxeWurRoZVFKwS13TyCoI/2to91HrGK7B71bj4++BR96kpuPi4+4C3ZFw+aNDxnQdoWljzjUms/ZjcR99NfZPVHeArsKGSagIlIhKuBBeY+QgdWhpOLwoQP8/M//PBevXOKll19kMl6j1UlZXDzAyvWbtLKY4XiCb1ueff40X/i5L7DRz/nhj3/A3cdO8tyzz3Pi7hO0Wyn79i2zvLzMA/c/zGtvnObYnUe458Rd7F1aor7LcvjoIfLhgG//9ffZ3tnk7/72B3zmU5/mic99nj//xl+xsDhDmqW8+MrL7Fvax83NdYbjEiMlP/34YyzsPcjVjSHvXrpIv7/DoD+k1Wlx7PjdrF6/QVnlmPGQOJUYV2NNwT0nDxPHGRfee480y+hliu3hGJ21iDLVGMgkVe546O4T3HnkTl59+UW2+jtcu7HJ6tYar732Bj/9+E9x++E7Of36GUztyU1OrGB1/TLOV5x57TQ7O4aHTz2E9zFXr17j7LnXmOp0mL9zlvfOXafff5MbN9Z47NGPsW9xmdFgm+trO2xulfzyl3+D914/zY+f/iYrqxexMnAAbLUN9NBJhhaedi8jSmvuv/9RhLS89OIZOtMp3lisEGgVwMfzexa4fOESMo3Z2u4jVUpRGqZ7U+yd30MatTEY+ttdzr97lmymy2g8ApUwmhisUERViJYhA5ekk7aoK8f2oI/VglgqkmiayjoSqdFk+BrKcga8JE41lYkY54as5YnjmqKcsLNumYwHjSNDIqPA3SsmjizT5FVOmrapbYXSmpmZBUbDAY4A4vWOJjojGseYIorSANqtJrSmu+R5TaSTXUdCbQ0yUjgfBASIGg6QJ4ojPDStI8EdlcZxWNOdx7vAJWgMLAHgLQRKiYZZ0qztUmOtxUuaj8NkPCYWniyCqg6uESEkpnJ4r9jYWmVcGGqnEFoTRTHOWLSMwoEbdhl04eL+UONkwzXaFboFzYGC3Y+H5SEAswW+ibOBrWuKyZisldFuZXhbh2igI0xFVagkj3VoSczi4HoJ3zJwkLSSjYCjkBJiG+zxOlYkIrAV4lgFN4prDs4CkiTC1nVwhNQOid8VK4RiN45lrUeo4HiJVIRXCotFZm16uhXii4gmtirBB3hwXRmKuqRYHePrmlgo8p0+K9ubeARGhZ+VNA1bSRQHMUBF6FiTREEYiHQUgNJKksQRUZJSljVaKrRWtNKUSGmUCnB4ryTdXje0Lrmw3uokQjhP1ECoXW0h0sEt0m6xtblNb2o67BNs03onwXuFEgE2L5voXIREehlcKEqSplkYOLkmFhWphmdjuHWjCFsOi3M13lmSJAEBSgYgrpAaIZoQhbMoL1GyEaNEuHdIKbC1RYX+qV2miZSSyWiIrQ0FRRAupKMcGJw14UAlJM6FwWtYapsBWhlq71UzvCkLh3OWWEUkSSu8ancd3mHKHzhJBqU11oGpLa1OO8TOhESrGOcNdR3cSnZ3Ek4jzATRRjciq45CI5aSIbqmlMZaiNMsiJ3+VlQmFAJEUgcuoQzXfVlMSJOUuqoatyOYqgrPn3VUZYWKFHVVobTCN/+nKArcud37vgvv981+Q4lwzdwSxeqqwpkQZXPeNyUboVVOeIGWoXWrtmFAVNd1uO5qE+rJJdjQwYV3ijiOmZ3bT6vdZdjfYjJeZW7mCInuYmqP1h4tIeCJHcJ5nBdorTCNa0crHfYyOqwxjgA5VypFS8mehb2sr65x7uzL3Lj6Lu2ZWa5fusFMd5bezB7ipmVTyeA+2bdwO0v7upy9cI5jJx4ncoKNcpvBep8HHv44XnqkjDGT4LiqjOf9d19n7/LR0LhlLGVdkCYpM1Nt6qJiYGA4qAkgZ0lhPAUeaTSJV4y3J6QtySTfZn7vQfpbV+lvr6NiRX8U4b1HSctUq82Jk49RFxO6s8tsr28zMzuPsA5E4O44gqNU6yBCjPIRWTthamaOdpqyf/k2ZNImUTFxlGLKCn+LL2RNYBYhcD6465yQqDhGeY1OM7ZXV9h721GOHL+LZ7/zV5x6hOCcMS64jrynpXQQHGRwEuVlEVi02CAQ4ECEe6iM2xSuIJEJVVURKYV0BLZmWE2p6wrjfEgbCImKI7SVKGfZGewwvzjL+uoNpFTsWVrGVMNQ+pR2gmgrw9nFNrwyIQReC7STVKYg0QEajZBU3oLSFOWANMvIkla4//maylZUwtJL2ngXWhPHRQ5aoeIYXUMoRgrwatckP6wLjNu6NiQ6ohWnoFTgVZkKrZLmnj1GyxiJYjwaoKMUoTPqKsRFrb9VrBMEYCEFpi6bQUoWxFYVUU6qwI6KVGh/dQJfBSi6jjRSx809NDT7OSHQWYbTGlflxFFMPtpiamaBqqrRCjAeay1xEoe/hw22L1PVZEkQdUAircNrjzeCrNXCNhsHpRRuVNNuZwilET48H0msmqbU0D6Hkgz6AxZmZxq3U9inOGtD82DTjimQtNsKHYdWOA9NyUFFFMV4DdaE+PBgOKDVbofhtgxcPgjDsvD9VYCmVwWtVgvQWFeQV45W2uYn+fiJC0r/4n/4Cu0Tn+TzR6cQacrt0yWvnH6OnZ1D3HzjBb77Zp9f/c1f5959GZ/7qU+y79BhemmClpZRJ+LxLOMr//5b/PN/8V+x/sLX+Ndv9lHSMRyPGeU5Sgp63S7OWTZ2Bsz0Uia+ywN3HeHdt84wmwquiYJLaxMG4xyiGCUVc+0pWlHNMA8TKxe1+OTjn+WJ+w/zv+ysMRqMyOuIWDp8nDA908Naw+ULF/hvf/f3OHTPg/zqLz7Ki2+9TbuVcePK+9xY36LvDM+9dpq3rl7j46ce4sDd99D67t+QLS3x5Z95gvNrG3zpE6f4zlP/F29f7fPgyVOce+tlnnn1LPiEfFJQG8dMp4v0W0xPt9k7O8f9n/gkx/dO89Jzz9Ht7SeNL+Lo8su/+PN848//mL4JXII4TZHSc+r4gxzcu8CFS+/zzA+/z2Bzm1YsuXL5dGghGo6Z5JaoFdHyNUV/wNPP/JiyLHEGWq0YJx33nLyX69evMZoY7th/gmPLizwfR6jaYXzN6mAzuORkoMyPxkMKVXBILdE2hvdvvE8+HFOWYcMxtzDP+XPv8dBDDzPs7yASzbg/YiqbpixyjK2ZFEPiPMWbivcuXWCmM8WNq1e4/+Q9PHzsOCsrN5BeMi5yHn74Y4zHk12oYpCM5IemyR/gt4UM4saHGp9RjZX5ltskOFzEbkWxcY3DxFhMZXj97FnyoiBOO3gRYHDj/hAvQwXkpBGUlBCksaCsPK1kjlhETPI+42rUbDwVadxBpxnChKrM+aVlMI619essLR3EFmM8lrnuNDs7mxhbI4WlNjlCCDrthMFOSRJbLJJJMaCTCIQ3KA1VLajzgqHYZs+eWUwuGIxGAfxuA3fDA2UxxhiLsCWtOCGNE1rxMpW35JOb1DikirGmxhqBVYIkaVFWBWXtmORjVNSidIqbmzWeNRwagQpgYSnxNsA32+2IoshxeIwPG3jvwoFFeIdM9G5kxAvwzuF9E238SETtA3HoIyLPRzJrt973oazxP/C5H/UdfCgmd+uF0vjfb21MgzgVgKW7EWY+cCo450NcBUAGDor3rplOh+aGmakpelOnaHdmOP3qq0gNB5YP42zK0p5Z7rrzGN97+vtAsJJ3ewk/+8QT/O7v/kteP/ISUzNdVjYu89hDnyLRKYsLi/Q6s8xMTXHP3XdzcN8hWnGb3lSXVtbh9MvneO3VZ/nYI/dx7uxpynLIgw89yvRMj//sl75IMbGsra3z7oULHLztNlZX15mbWeTg8u10Wm3qSnD58vvML3Z57szz7FtY4su/8Iv8+EfP8dIrrzAzl1CanAkVvrJ84shxbCW4cOEqyg3RUQvlW5S5xeUeozzGhYz61nrO6e0zCKFYWb1BmmQc2b/I4489xomT93Lu1TMcWN7HUz/4G7SCSVExKSxHjxymsJ7z77zJ5+/4Am+fP8fNtaucPHEXn/3Up9nY6HP+nbe5tnKD0bCkzmO+9OVf5+K7b/KHf/QH/PG/+zf8zu/8Nk8/9woXru5grSMvHQaBcpba9ynrMS1tiETMZDDm/PkzdHuLTPJtRoWk156iO7PEtevX6HUT+pNg9fZ5xWC8Q6/bod1O6fU6GCN5f+0Gh247wrjcoDc1TXtqntiHCfN4vEKataiMZZL3ETomztp4M0JYQSdLEdJSG0t/+wZKp2F6XWpc4SCNwFeUuQFX4WOJpKauglPTyoqs3Q5ChHbkRYHqtgPzozZoGSJzsUpw6pYjswAhqZ3BUoD+4AAAIABJREFUCIEmaSZoE9JWi+3BEFMa4kQiIigGlixJsEUFziFljPcSi8Xb8LcRQoX1Rt26LhzGmDAVTWRgbsjQQmLrmlhHGGcoypookljrsVaAiEKDk4wCR6GuMK5GJdDrtRFAUZZIJeh1E6rKMRrlOBvcVlZoZNLB+BAPdgKk/xB3jVutkWFoEZaS4AzyPthoRGPR8M2a0BgW0U3MLbx5a9gT3CfjyYTFXo921qLM8103QyI1kfQYJ0IcWn4gBHhhWJydoZOlSOWpnQibVx/iH97WIKE0wUUbyj0sUoSGqqIYk2YxdW2oiglaa5RUKK1RWoZNtBOYBkrvCByxsqjJq5yiLhiNJzhrcVVFWVVQO6w0KOl3Ra9IJ8RxTKx7LMzvRUY1C3s6ZJ1pnHYkweZBqjOk0kQ6iH1SyUYcUCS6AZhK6LYypEhY31oja2XEStFKk7Apt4KdnT5ZrMiSVmDYKDBljYhjrHWBbeIsVkrKqiZrtfAW4iRGRRJTGTwhZkgTuZNKU5mKgCkP02EpJc7UtFtTaATO+lAVLcProHlpg7dI4RuxIgiYshGGQwnGToDf40iTFsKHuJRxFluWoUHNu+b+AtYbpBckOqEoa3SSMilG2DKAXsMhQmO9Q9hwUPaE16s1gYESRwqlAxA/iCUCrTVVVYVIptDEaYaOYqwpg2BKuA7LOlw/OorxnhAPFTIMo0yopJYi1IvXxpFlaYiN3jLrNZP14FCU4Tm6dS/3gHdU5QThRSMshThbWCcgTgPg3DpDHMUUkxykIE4T8ryg2+kEUVGAFpqyKsMkP2mT5wVxFDPJw/6m2w1tada6IEB7GmegbJreJMZYjPVgDFVpdmOoNG4R7x3OhlipjhSRUhRFEVrxCA4yYw1ZFIV9nhDN4RrSTpfu9By2zBl31xhN9qDiLnMLyyRRJ8i7SlHbEDmsrENojZYSY8ogTEuJcZ5Q3usCON55VJaBcBgr2H/oAD/42z9jzIQr1y/y2c98mffeucjK9ct0Oi2QHp1ExCoKXB/fQskF9iweZPXCO1x4+zz3PvIIFYZL504jVYdOu0VvahZv++wM1pnds5+qrkn1NMN8HdnOKGqo6+CoHFUjeu2EsvR4BbWVpMqxubPO4kKPwc4WM/NLHLr9Xt56IzR8lqMJI+8QlWGnv8mlH/8di0v76U3NkSZjerMzTM/PU45zVCRD41bjRKuN5f3LVzh4xxGuXb7EyrVLDHY22Fy7wOKhA/TmppACCluHvawLZR5SRzjCa7TG4b2nFSVIAVmc8vblS9z7wKMMxyMeefzzpDoNg5I6rI2xDs423/DsrK2xtqQTZcHF5z3eKrAWEbWI0zZKOVQU4rba1UgpiZOE0lm0F7jSoCWkcRJA+irixuXLaFvy8vM/5t57jvPCS89x8OhtzO2Z4a2Xf8jtJ+5BTu/BeYHUClOGMgSimChO0B5AktcF3fY0iHDU18JRmsAfnJ1uGtNsEAKNC+6WNEopygrtPOVwxNLeBWoriKMI60IMrvI1Og7sJFEbvIW6rHBaMqwrpDGYusTXNpRE2ArQSB2aAysK9u09SGUgkkF4lrbC1mUo3wBSEdHf2aSdtPBCkqUtjKup65pOq4lR6yj8XBtYQTpOiWUYgnnhqYsSHUVB8GkcasM8b4QUj2qE66pumlqVwliLEJ6iKhGiGaQLgvtLSYpiQkvHwXlbhwKk4TgnTZJwRvBQmxpBjRQxrraUdUm3nbE+GNJOFJ12F4EODl3hEUqQRIHdJIUKJTIyCHnOB6akyUsSHVPjQYkQXzRhABFHIcqo45hIqiaNE+LoIeptkCLBqRZSxSweO8YXTh3iqZsZP8nHT1xQWpmMebCjObqgGfVH5OMB28Lx3HPP88abb7Htpli5cpHzr2/z6GOf52iiUKLGeUizDu20za998WdY27qK6x3g5z77BG+cv8L04n7mpqZ5/83TnH3rQgM4dmxsBcXuT/7sG2RZxle/8XUOLc5TxyWPPP4ZrB3x7htvYesRlQ/Vo1Io9u5bZH62TVSPaPmI9UmfVjLN/vkY61Nmlxd467Wn8blkYgqqwTqDrTYfv/dByp3rbG3N8l9+6ddpZzHDsmCuo/neUz/k3jsPgXfcsXyEtY1VfvkLX+CdM9+lO7WPB1v78eMRL7z2FmubOc6PkUiyLObixbcpjSGOUp579hlOHbuDrz7z13zu0SfwtiZ51jG3fDvXLr1O4QV7988xHG/hraN0NefOvcpt859j3F/hO089y80NcFHwtmup8T7Hqgg5KRnYCq0FtS3DhYRnVBbEWcqebsyq22FYaK6vXOFf/pv/HaqKVDgef+wJdCfjG3/1Leamppnfv8S1i+fJy4pnTr9CJ4uJlOHI4cOsbWyw0t+mk7Voac1Tz/yIz3zycT5z8iR/8qd/RNZOAUGUpUgVYWyOE4K7j53gwtvneP61l/ncJz7Dk8/9EOscP/f5n+PeEyfZ3BjQyZp65l3byYenbCrEj6TDWYsFIi+wIgAQa6nC51ofqsytw7uayoQcPt7S74/Z3Olz6b0LbG/3wx5Bp0RRihAOkoSZrMv11ZsgQ82wjEIbyNzsEq0sI1WC7cEWSkQsLXRZ3x4Tpwl1OWZj2Of43mUm+YjZdpfOvlkSNeHilVUAJmVBN4mY2TPN+uY2Qkqsq9jTalMXlkldYZ2jraEY5+hkCmtL5nsRG9s1rs7Z3lrHOksrTagrQUUdmnWsw/uK0jpaWiGdbYC5liSxjPwQ62OscUy1p6lrw3a1hpxI6mpCohO0zih9RZruxUjP2oYlySxp1MGIChV8teAE090pXLcLQiCRRJFAaYlqNrtOCoQzoQ61ObSF9fIW4aPJtPkPbUr/o49/UDH6f378I49/IFr3YTfUbvKtOWze+ppbL72mfazRoMJC7sUuX4HGqSBEyEdL4ellLZII7jx8mNFEkA87vHj6NO00Zm1zi85cm31LB+jNLnH86FGK+grGdHn2hR+ztT7gV7/8RWbn9jEabnPbwUW6vRmMdTz73Cs88+MnqZ3g9Jsvc3h5D1eu3GB1/SaLB/bx1b/8Cr1umzRqsXx4gftOnOLu48d5692LLC0usHxwiSf/9jv42vLy6TfYu7fLb/7GP+ab3/prjh06wMceeZQnv/M0B5YPcH11hawdUVUl+5cWeOWF5zl2/E727Fvg6pUhG/0NqlohqhAls83mTQrFxYvv0u1kTPUW+OxnP8k9J09href61XXmpua47/6HMZVhY3OL7zz9PeJYE6uYzf6Ev/vOd6iqnK9/+y9QQuE8LLDEm2+8y/333c/nn/hZfvDM99l6sR9iFWnKPSfu5vCBJc5cOMc3vvl1Lrz9HsZr8lySthKsh30H9vOxU/exdv0Kly9fZG7fMhcvnCGvLfn1bXqZDy4uE1OMd+imPYbjAYKabq+L9C44PJJuOGw4jYthafkwVa049tBjXH3zBdZ21tBRj4vXzoNy0OoQaYfQ4cYfCUdVF2jlkVaBiDDe4agCb6V2RNaR+5LBaJ1UpODGeG/xVU3csRS1pJjUqGYDWHuBKKFyCkSNndR0ujO4ssTHoW3lFk8kjttUvsBiMDY4CZ0vEE4g/Bgdd8nznHxikXWINzlbBxA6QXQ1zgQ4sg8HbUmw2WepBq/CBtB40laMtZ4iL0iyLEzmTU2el4AljjQ6VtR5yTifIGQUrPwNtwcnmZrpkSjH4kKbMq+pC0PlHUVVYKzEK0Ljl4yDsGQMtfXoNEVFClv5plEF8BYQTatysLAIggXdetM4Fj9oWvHeBTdJs0gIH7hCyGAzlyKsXbUJsYpWK2U4TlC1RChPqkSw6FsLKggiKIlSAunDpljHCqUCYLWuKuo6FCgYD17FSCVQKDIVBHHjZGBQOIE3ofVKiYh8MqEox4zzHGcMCo8xJWVd4rwhsGnDpFkpSSvRZCIi6kREqkUUTdPSCVGsUHGKUAotNQKJ1nI3MrF/70Eioai9YHZ+ibWV92llaSMmSNIsQ8hwsNfq1kQ2NAFKKUnjlMp42pEikpJYBZ6Eryw2ZBXRcYbSOsSWmvYm7UIzmI5iEAY7zkNDrFBYGWrZlReUxu3eTmTwl+IQmNo1/65BeuJIUfggvNlQk4ZA7rbwiYZZZExY440t8A7G+Yi0lQWRVAUnTBRrjA9DpXBrCNwfHamGs+dQOkJJhRWhPUppiZ1UpGloalI6IY4UNTWR1vjKUDtDlKQI6RGYECnHBLGNCFPX3HIUCKlxfoRwNXGrHRxdnsA1kyEe4lwQdiKdIYWmrHKEDxXekYa6lAgtMDanNgF8G1geNIDr0Mjom0GfFwqtUxwhZpTnOSrV1NUQEYdGPOMdxlUIgrgTRQl1XVObCmsMRVHS6/UadsotTG5wlBtTBVeCjsI9W4IxVXAqRfqDLYN3SM8uLw0HQnlqE4aHt5hU3tvd+LxvorjSe3xdhaZD4ajLijhSGFs1FuYQd/He7gpoE2NwIkDMpZc4NO2pJdrdBVauXiba3+IWNF44wNWABuGIlMJJEYaUkcbzQQGAMwZX23C4VyLwy4RhanqKo8cf5PRr32VmaorVGysk0x1urlxDqxAnroynEI7ra5uotIdUKb3WFOdW15jfu8DS3r1Q5Pzgu3+JjFParTluO3wHo/6A6dl9DIbblFVFO51Gqy5xlrHT36CuRtSmoJO1GNum8asK7m3b8Ag2t1YZD/ocufMISdZl375lrl7aZiLCc9Wb2sfm5mWslGwOB1TFkP76VX7pV34HGUXBSZ9No0Rwtmxub/HOW+dY2r8f7xM6vRnG4wlHTz7A2TOvsLm1yW23HwsDyrIiihT5pEQmcXjKRNN4aC2lsQg8WmrGK9fZ2Nhk8dAROkLRWdiPEZB4R1UUeGdI4hQdRcRJjJeS8TC49qROIPjtQjOp97TiCCEcSZJSlQaZSIYbQ9Isxnuze1+snQkuy1aKjDW2NCTS0t/awmM589ZpUiW5eeMaP3zy61y/ch7V9ty/9zYqU5LoNkU1RmiJioLAb62hrixxlOKEQ+omneEjxjvrtHVMErVwVlCbmroqMWVJ0mlR2sDzqWzBxIyo3ExwITV8NilBS42OWngkTgqcKbGuQMkWEWEAE0mJyBReKXAxcRysx+PJgE63AzIBqhDD8/6DljnnUFpS1jUOj0qCU0c0DvdIgoyDu8njsabG+AqQDYYi/BxXBai9jiMcEmNrkqTFYGOT2xcWGYwmxFHYQxpTEccRvrY440Lku8jJ0iQMwmRopB1NJihf4YyjtLfcnJqiGNCZDvtwqcFKh0wyIhVRY0iUwhNRVTl7ZmcCk1dqIEJgiRONlhHoCOlBYFBxEBcTL8mLEULJsG66YCiI4iiYCtoZeEscR4g4RTpJmkQYV2ErixEJ9GY4fNsBjt1xO7cvzpHomDiVvPlW9R87LP1/evzEBaUo7vLq09/j7Zd/hPOKfbffyZ65Hred+hgPZ4pnTl/jrfOX+dLPfJq1905zIRWcPHYwgNmERirBgUOHWbn4N0x5gdi5Ri9N+diROa5evkwUWfbvm2M4LCmqGisM3gtMpVg4eS+Ldky+scPnPvYYF25c4Y59C7znRkzqDB9Lpmc61DhOHDvGlMv54Q+eC2waJRgOh0il8GiOJvuRQocsa+04f+kKWmjuzmaIhWJpfo62gqNzbZ55/iWuiZS9U9P8/u//ATsTi1hf5dzWHP/o0w/xPz/5Na6sXyYuczatpD2zh2J1ByU1aStGOU9ZCrSKiaOIy6vX+Z9+71+hdMzZN9/hgVMnOP7gw/zql36NJ5/898wd2KLjLMNcsG/5diLv8OUVzr37IvedeIBXzoxY2VxDeknuKmrjwaXIqqaqPNYI0jhUJLY6LUwVJjOJFGwOSg4dOsH1F16nKCv27F1CIpifK8HlfPqnPs1bb55FyIjPfeKn+MqVdzAlZMIR+wpfW1bXrjIqDd4ILl++weOfeowHHlrg6JEjvPzyjxFxsDfW3pA5TafTY3V9nVdfPs3Khfd55+IFbmyvs7axwd23H+U7z7/Cxx9aZTJY5sVXX+Ohe0+xuHwwTIF9sAMGK6xEyDDx0y40Bzl8uMDwGFWjnMB7yaQuKCcTiqpgPB7jreLS1aus3LzJ5RsrbA6GjIYTSmvYMzNN0glAs8gVwXa8s0JR2Ual9ig8No5JhCQTBuvGCD3k6IFFulmE8JadfMio30c6uL56g267x8z+Hr2sx+nz5+nnBb0oQviCSEVsD/rUJieL2kgRMZXNMH24x8vn32XvzBJ13qfVmmVUlEgqxmMZmAO2oCrKsPF0Lgg5MqE0JappV5DaY6WkqEpMrYh0Sl1OyNIe2ubMt+MwkZQ95uamWOlvUqkYV5UkKgmCXDXBCo/1kvEQxpgQTVEGKXQDkmssw9A4HyYkeaiDFxEcmpkjlSlahA2hdrKZINrG/kpICfChqNvfj7fdirjBhz7rQw4n8ZEv+U8+dlkqu1/0gePpQ0nLwIoIBM/Q8PNBmjK08tgPmBNCqlCDKiN++rEn+OIXv8js7ByXLqzw1b/8CnmVM9VO+K1f+Ces3txhsNVn0pecfvUsrY6lKK/Q7WQcWl5kZ1SwuDfhwQcf4MgdBylrePK73+XK+xd4/d2z3H3XnfzUQw9y5uxLfPoTn+S+U3fxzsWrtNIui7OLvH76LD/3s09wx7HbWdq3l337D/GjH/2Ik/ec4k9e/ypf+cuv4eoxh+bn6fX2UgxK1vsFTz35AvN7Z1lYXuLq6tWmESyivz3hV3/5V7jz7pP8H7//v9LuTrEzXqedxUhbU8WaUQnae5SoaGUxRWVZWJonTbv86Vf+krXVm3zmM59gYeFzPP/DV7hx431MUdDqhmavtOPZ6a+jnaV0hlhl5LVBGM/b5y8Q6w7zS4scuO023I+eZs/MFIcO7afV6fKtv36Wdy6sMNNd4uyZNxEyxZQTWt2YPXsWmerN4KkpRyP6gzEoxXtvnw7OkDyiLse05zWzCaytDRBxQl04lLIYN2YwGGFsYNrl/R0SOYvqTfGpB2/j737wHJNBydnzZygmWwg5YH6PYnZqiUrnjEdbwVVhPc7klCEdT2lEgMp6SxZLyjJH+rAJdiqwOiIRXABCtpnqzTM7HTEav0pnqgsipa4hixXWapCWMs9RJGTthCovAjx/VIaWUO3YGq6RpW0SleFkmJzFsUTHU5i6pBg7Wu0udVSwM6pJlSDNWtR1iHVaaYmilMFoTKT8rpsnxFAc4/GEdtamNzXFJM/J84qslTXuJcFkOGFmdop2N+PG1etM93p4HMPhOFxLDnqdDpNJhRPQbndotUFbS1EahoMJQjny2jDOczrtFkInaJ+ATrAVGAn40E4mpUZ4i4x1cFO5W+6gcEeRzaDC+9B2FmDiBCurDK6kGIXxoeFL+mAgChE4QaQ0QsnAvKhKUh2T6phRWSOcD9N277EIkliQiIhINfZ0a/FEWBtRGosRBuEU1gWHkrMNPJngxLH1CB3BjRtbWOfIi20OHjyIsp4rK9eZVBOyOCGJUtI4ZrqjUXELL3po3SKJI9JEIrVA+IhIZcRInHKkaZeiHKMTjY4itIqRQgfukRDESRRiKTJwQiajLdAR3hqW5heJtGYwGhILHWJKIhzgtI5wJogrUjQxAxUm1VILhAwcCyU1RjqcN+Hwp3XDxKmoijK0k2lBbQypVuR5tStO4D11HqJDZheITmhQxDf3F78bLXPWkbXaiEhSTWpasQ4ROXwDsg5xN4RjMhmBlyhBcMrpCK01OtJorYMTSyuQAukjlJZEcUqe21B3LXVoKHKOWKlm+NBEwEyJdzVR1CUvRiRJqxHdwDVxfgggbymCgEnTQBvHScP2sU10RCFlRF0JvJAoFTWxnwqpAkxcAFUR4nRRpLG2wtYG70KLU4DNg/TByeAAHYUWRqUkQhLq2G+FVlzDplERwjnqqmA46LM8tUx/Y53u9N4ARfahMcvVjnySI6UiSVPAk+dF839WjCejJvoZorJaxyHG5QuSJAmuJefxMojGiVQBSivC36MyRdNmGYWWRCGpTUEr6zAej7jV76t1+NsYG65/IQROeOqyIop1s42RDRW5gTo3cV1vHdZ6TJkTxxGxDuuLkRDJBIMl7fSQzoMODX4e2US+wi5JSEU+yanKilTEbI+2ESLEFWmigbutdDa8rrWAvcvLFM9OcJHgxuYr3HbgIRCOui6xZcFoXILXbK9v4q1n6bajpDpms7/Osbvupzczw3/403+LF4phXlIWN8knfbIkYt/ybQxHAz7xic9SF46t7XXG+Yj+dp+Zuf1s3HyXllakCHIPUUsjKoezkmIwxmQ9Tt33aeb2LFKPBpw79yqrO9so1eKekw+xubrBKFJk7TZf+Nlf59xzzyD8AKk8adpmXNRkaQeMIUnbDLfexVhDrGPKvGZ2aZlWJIlUxsEDd3H07o9RjA1WAjLCeEFVQbcVYX2NRmHr8PxlWUIUJ7TjlCuX3+PwibvZHk9YnJ7BORda9+qG4avD8xPuDsG1OCkK4iQNfUHOYtFI4cmNoZW1kN4hvML7kAQpy5I9Mz2MtWEwW9UYV6MThVDhHvX6mZdxxQBTVWxurLHvwCG8m/DOhTNk3TmW9t1Oe3Y5rD0qCXGt0hHggI66MkRKMDRlgI+jw4DWKcqqYmfYpzfTZjsfEPsIqR1VE+FLdIZzAq9h0J/Q685gRRTEDixaNuts2g4NkEJSN9B8rzVRFgo2nPNUxtJJM+raEyuNUGCLmnFZcnDxNqrKEwkd2GvVGEsYJAkBiVAMJ0PSKNx7w/e0mHqMTAIIPBIyiF8ytH5HcYzQGrQMWJEiNOXGUlFXhlRoBpsbzLdajGoTHGXOU9sS62psLbAqICyqqkZJSJRGIfEywpYmDMKybohSW48XKaMiJ01TlIgQsoH3O0GcJERKYytPnCT0t/p0o4RO1EFFMaVx1MKSKUkkY1Qc4YXG1RVKShIZ3KcWQCWoWIAUREKQCB32HcIS6RgZxbTSFIDaQkFM3JvjjiMHuPvYIZZmp0ljjRYquK9MaKxMUf8vTkb/6cdPXFD6s//zf+S/+70/pOzdye1T4K3i13/ll1i98jbPX2whW20WDx1hdWdI0jtIYUd857vf5sbKNvc/8gmOHLydthpj+tf4yrdfZ2b5AFPdmj/+2jc5cPQUh/ffjrZXuV5tNnZVhxYONzvHb//WP+VOdZP/+l//EbeduJvHPv8zvPD0N3AiIktiprpthoN1jGgz2Fjl5Uuv8eY7F6hqR92AQfvriuPLUzz1vW9T+vAkG2N4+OQpDvQSDs7tYXhzyM7qDb76tT9lY+sag2FFaQUff/ReBlsraJ3xrWee4iv/xX9OURp+4Uv/iP/tD/+EDdHmZ3/6Z7j2xrO8984ljJdUeY3zJd4LYtVFyYRYGLa2+wghWFm9SU7M7/43/z3FeIPf/LXf5N7jrzEpKyrjOXxgH88+87c8/ewlLl+/ibNnGQ4cxkUIX1JZx//N3HsGWZrd93nPSW+4qXNPntndmdkcsNiASAIEiUBEAky2RJUsuyiVbZkquqxy2ZKrXJTKtEou218sQxJNEiLBDBoLkAgkAIILYHOY3Z3dnQ2zk6dnOnff8IaT/OHcngUpV7ns4gffDztb3benw9w+73v+5/d7HtlGikKlTjgtOIM3hkG/QEfYdi2Ni4yripOh5EPvfZinn30FbTKytkEKxRtXh9z17iWsl/zi3/6P+Mp3/4xbDx2lU3bxjBFSMKo9MVaUSPqDeW66+QBlx7B2bZOOKnjwHffx3JPfQzFlYoRU59je3SVaOPPqa5zGg3BEb9neGPP1S+eZ33+ML3/nO0SjePWtN/nwj38AFxq0TqYRpXS6mccjfSS4yNrGdV5//XXqYBEigTib0OBby/rakHFTE/CExuNsTd207AwbhBbkhaFb5vR6Mxi1SBkbOqahk0t6eY+B6bCyPWKrvobRGda5ZNNpR+nmQCvuvuUg2fGDjKuGa+vr1O2Eychig6cwJR7NldUVxrZhX6/D7nAHGRzOGTQe23pGjcUTkb5GK8ObK5dpfbJ6UW8xMzPD5esb9JRKlSIESkW0N4jo8Nah9gwhIhmNfAARJEFG+rqkbR2ogsZVLC7sZzy5Dj6yOawosk46lXOWjjJIaREFGB1ovCDP0kl5pjXeRoJMHfIYCqJIGwCJmE5aAkJZqknNJOoEBQ2wvrHBxs6IxeUF7jp2nPn5AYO5GTq91O1NQ8Ppjb+I/x4oF1L65+3HX0sc/ZWn//Ua3d5z/npK6a8/J/7V2tyeUU5OB0khEH2cMlbSU0JIaYf07UfGOxNCtBgNvYFh/9IBnnv2BR75k0dYWbuKFC0f+OhHefihd/Orv/q/8IH3vIs3Xn+B7kzgwoUr9Ody9i/cRFQ5j/zJV/nkT7ZE4dkZDlla2MeLp59ge2eN5YP7ub5+jX/w3/x3HHr0ID/zMz/Pb/zr3+Hy9cvsDEdYGl4+e5YfPP00txy+iQ//xHsJdc2Vyxd58pknsFjuuuMWttZ3uLy2xu//wRfZqYY89vgzrFy9zIc+8BAHD9/Jk49/B2M0bQyM6jGnX3qebq/H1midnckuIgTysmW0I9JmWAm0AeNSRTXTine96wEGg1l0Jjlx6yHuPHkbO6tD7n7gDt71I/dw4OARfvZzP8vDP3I/L7x0irJXsr69yb133sdLL54mTk9zm+h55fSLFEZw5113s3JlhX3L+/nKV7/J0QMn+MafP8J9Dz3In/7Bl/FCYuOEsmuIJlC7GjXeIbTrXPLnqXY9dhIR0tDJBxSLy6xevUAYjhlGjzQDttY26PZ7tNZRlJI2WMZWI4Kg18kJoWJnuMZv/vGrTKo1tOiyMd5mcXYWJQe0oSAIR7/cTyEqXLuLzi1SW5K1ax43HhNCQxscpijTpi0oVFaSZ12aakiMgaLoMpqMaIVnY1ghwjwIaqGpAAAgAElEQVTKKTIzS6/ss7u9ym67BSanyLrYNjKsxvSLjCgjzjqy3KCEpPVjAjlS9IEWHyLeJ+tJUZSMtjbx4zEmK8hkjRYqmd1ocNGC7IBN4GSpA1qla05M/luU1IwnFbMzA4o8Vem0VjitUErglWQ8roDEBnBuyogREnSgrlqGo12UKvB1Q9HvgGtorKVpFZUP6SbSaPJOH6UNnW6PycgzmTS4IJAqo1vk6GnUX5qCaBMEXKoEPU3pFQUi3lDMezeF8MeQEkzsMZcCSkBCLgj2xAEqgJIBLQXgaWrH/FyfhdlZlpfmGczNs3XtWkrHxEisaoKeJo98SiUMJxOayRgtUrpDiIDWJOObFBQmJUYwkWAERXcGHzxNU9PrzjM/Mw9aMbO0MB2WJIadbdPabJRgcSHZDWdm+jS2QWeCTt4hkxmTtkZE6PZSZSVTEkxKvGiTJW4WyYyVTrAjnobo4xTA3OKjI89yyqKgqSaomCGmdSji3vAtDWyUNkitaCuPIBnbgk4JvShEYgoqTVQSFxOjZC9NFqxL3KIwnfepxO7KtaKuHWWZBjJ7S3iMESEl0TvsVDuvCEihsa2lmkzQOqm6k+hDgJhWM6QkhsQESpBzAUGjtMKYVCvUxlA39RSenUyeYqqmh1StSDKKiJYCoxMrUWsxHZQIYlRTblb6PMkEp7AusMerUdPaYAge56YMr2ml0lqLlIosn4KvbUBnZQLWIghRTsHg6RrVNC1KabTOadsarTWj2qEzwXgyItMZ1vqUAqorjNHILJsOXbmRLkhpofQzYspI1FLQ7XaxTYv3Ea0lTVtTGJ305NOanJmaisqiSKbDELFNQ/CeXrebBAEx2Y6EjxTGYNsWT9K3V5Mq8RiVmF6HI8EFxNtfSnq9hQR+b+s6MUsCuBAoimSk02paEWRafRPpVar2KmqCVMWNPnHHfErg1m2FDyKp1YWHENEiDRPbtqLIClY31llcWKANiV1TmBLvmzSAliBi4jvJICmyMn1N09qOjxHrwzQpOa01CkUUOZ2iR14WbG2+haifosUg/RArHCoaDh49jNSeC29eoNftMRxvEJqUsFJFxm33P8xLf/QcN9/5DkZrq7ShYjKx7FSWu+59H4PegAtnz/Dm68+ysbWOCQXNaJ3gA4NeAA1FleEyg7WWmprgJSvrq/jTz1IaTfSS3e0hUmR84Ec/zPJgH3/w/Bc4fPwO7jp2F/ML+7n58AnqGJmdO0w1bhInqd6FIFm/do3Gwbs/8EHeOH2KV19+hXc88BCdfp/aT8jzHCMDPmg63TLVq5sRWTkdBMZ0oIxMa7zJC6RROKO5eP4it9xxB6EekWU5dlSR9QqausERybMOqGSpVApCaxF4cl0kw6fUGAnWpoSLFxGjO/jQpErxZILUAmRGWko1la/S77HpkmUS2zY88I4HefKx7/Dk099jYWEWZyeoIuOuex/mwfd8BNNbYjDTo5lMEEoxHFdUVUXZzdOeCGiqlhCh1IqMZCmUyjDcWuHgvv30yh6Va6f3sBo/HCOzJOmwtkWGiKtrDh46TOVC4nlJku1OGpScsqhci3AOZ1tyYyiEQkVJCKl1lA75p8P66GjbCYNBmVK/vsXkmtpabGOn+0IPQuEReBfo5HmqbqHS75YIzHUXEr8pOmwzoakr5FTMIIRGK0WwluiSDdVaIEp2hw1bmyvcevIkO0NHphVWBETjkswhK9La4ixjW5NnOcYYPKCFoLGOItNEZ5E6x4YKUxRUuzX75ueRSHSWyhmZzjCKKddJECVMqFkadEEbxq5FeiiFQhuF0AWIZFCW0/StzwtUDMTp36F0quIqZVDKENoJC3MDjFRUXlCpLqbocPLWm7nzxDEOLs6SKZXWqpCO5KNKVxIzbdQQ/t8ctf8/P/7GB0qHjt/Fr/zj/xq5cIiNVx7l3z1xnqJruO/WgyzMfZhPfraLRiHqitYGtq9f5t53f5R9l17Ddxfozhh8JbhYD1i6+WZmBks8dFjw9HNPMdy6xLpeQkrLcLQJKkvAPDymGvPdR77I7186y3joOLx/P8thyAN33cPKxVVefvUsu8NddhuH0BV//LU/wWiNUTkzgx5lHDOO6RT0tn0Dzp67Rsx6BNfQNZLHv/8Y5/bNc/7aFkePHGGwfJT9B3N4q2RcnwNhOfXqRYIPKCGZnZ1ldryDHBTccctRPvGJz/DAPXfx8qlTPDseE3SB8B6CY1I7Mq0ZhzF33HmS7eGErJCcOnWKT/zUp/n+9x/lhVee4sjcHONrK7TbO4TWsX92kd3rl3jy1HNUraf2Q5469TRVk9MpS6JV5GiiaSFGCiOJsYAoyPMSlMc6S1Zojh46jJGaffsX6fa7FEXOuHZcWd3kwNICNnTZPxhgRjt878knYFJz6sUnGY1qjM7pdhS2qWmiIMsK7j1+N0eOHWSm6CAjPHrqRXoSrm9cp/EK7QNSCyLpNFFmBpFF8gDOJltgXU84dOxu+rOzfOThhzj/+gWeffExRtXPMpMdIrpkIyImqCEieVTOXTrHH//xH3J9bQtMgc4HyRgkoA5xCraFxrukpY5Jb1vk6ear19HMakW/bClz6Jg+ghm2doZcv77Lme03WBtalAhcW3eEoHBI5rodlJFcWl1nazjGREe/E1hY6HBgoeTS9R06WZfKTiiLjKzoYNuKrbHFR5fqi9GRGUNUkSyLCGHwTlK1DS44FBnee+6+6y5WVrc5urzAxqTm4EKfDEFdWy6sbULaZ+CRRKESgBZSskZFOjpNyGsPzu8mpsD2DlpqqtZi8hIRMnaGm+yOPd1CMT9YYFRn7FQNO6Mx19ZWkmZcasqsh9AK5SWZTjc/UoHQKhlJtEbLHCPTXV2uJSEm6OeVC+e5cPZ1vvPd73Pf3cf5B//xf4II8e1OuuTGCeJffeyRT/76W3/oufHtP27Mom6AOvfKbT/0EX+FvTS9UZ4mnMT0jjROTQ9xCoRlerNJmG40JWnDpCS+tUgi3lacu3CO4e4Ot912G4OZPk5UOCagU5//jdffYHFmiaOHl6it4DM/8ymsyPh3X/w8jfe88cpLXD1/nvvve4BIZH4wi6/HyAjz/QVWrq5w5NAMn/q5n2c8ctxx8n5+6Rf/S7ypEVKSm5y7T97O62deZ1JvsLZVsDUacuXKNZT2LCz0uH5ti1/8hf+Ux558jN/63S8SzQZlJyNXPcrBLA+//yN8/t98nkmIUAcyIXnfww/zqU99kitXdoitpp93WN1Y5Z/9t/+Er/yfj/L0s9+n353eWJU5dVtDDHz/e3/J/NwC73r4QT732U/jradtWl59/FXue+gd/N5vf4mVzXU2xg2eSKeQnLz1Vl585QxLC0sUnQ7nr1whiEglHc+9fIonXniO+Zl5Ll56i7Kr+Zf/66/Sm8m5un6dA4eOccf9t/Poo48je4F+t6Qru5S6ZGHxNspsl0ujdaSATNVgSrZXt8mzebQJOFczMztIrxeVODDdfk7cFhRTUK2mRglLbCpmB3NkZoluNuDQsRN4VTLcvMzuZMJod4eN1avMzCxPNecaiyO6iPVXGfT6VLbLzmREXTWUJp0s+dCgpGA8HrJvcR9V02KkZTRaZbY7T1t1qJuIdUOK0lH5BqG7VLWl1+kT2opct/gAMubkpcC5kFIPIVW7MqPI/QwhputjjOnUvHY1mQ+EKJidnQUvCaEmy2AyqREqR2mJpMI3gqzoIBDTuiMpDSEDwmiCtQgJdV1TliV1Y4ki2aPG4zGZ0UhtqOuGEANN7RBSo6YWp0wbyqIgeouLgp1RIKqSeuyJPjI7P4vpl9S1J4S06VdEWtsmnk/UeGdBTs0yMaZqgDBpnTSkG2OfBqA6BsI0SeNikiUomWo7EejnBdIYhBDJ/iKmaOVpjc85y/rqGpO6QkTP1uoKxkiq0YTdzV06WYbpGoRS5Jkhl5CVBUXWQetIjB2ClDhSOqrQyeBinSNgMbpkPLEsLx0gNzkiWrROiuq6FngpyDsFs4NUzajqhgMHFhn0B9Rti5KgG4FWedq4Em5YsIbDISFaGqcodXZjmOZDSCpmpfBNS5ZPWT4xUJYlk+GEPDNEJNoYvDMgVOLrBE8UyTqWhiLTzbp1GFLdzFmHydOBhJCSYNNiq6aDPhdcGubIxJSIAqJLwPjcaCbNBE9M8GjVTxwoYjqAkJHg0kbGWktWFmiRNkzeWtqqYWa+TPwKIWlbR1FkiOmAp25b0rwk2ViZHthIrRPLQ2tkm8x9IQSyPCdVtlIlTJksJWHaVHcSQhOjnSalFKG1GK2o6jF13ZL3O2ipaH1i80XSsFJMa3t71yGlFTGSUltSJI29F0lh7huUnKa72maajknpdKXidEDdJwbBnvnMKINRGY7Ensy0QOUdijKfDpLSCUoIe5yi9O+4Z3YzRYY2yYSlc8VwPErpNRyZFIzr8fTzJRucmjJEYnRTiHGOEAmCLJShti3aGKTYs0Vq4vQ+wTubmEek12YIMaW1eLu2KE3SvVvryDJDmu/5qYEvpveHad5dJ2h3utwLlNr7nkM6IxMqDRL2Bk5imhqMDiVARpP+ZpESU7np0lma59rKebSK6CyjrSSlUVROJMNga7FNhZ4O5GR8O0UXfKCxKQmXoOcZBM9otMOVq28yv/8gm8Oag4few86VM1g5ZlxNKFWfVhtiiNx28lZGW2O63R4QqUYT+gsDtq9tEZ3g/T/2aSbXz3K93sbXEWFKiI56PCQ3OcooOj0YNg2uqSnNQmK1SUk1inS1RMYB25MrzMwWbI9rtPRcubaCUZoiFwQ8S3P7eO3Fx3hyY507brufh97/QZ7+8+9w4qbbePO1C7z3Ex+mahvqakJbObQxVKMJ1WRMmRm+9Fv/B8N6wtFbTiK1odmpEAc8RadDt9dN1SUR0USGkxbyBFDWQuGiJwpQKkNHhfGSnZ1N5nKNiRFT9HCtRZQ5MoLzDh8CmSqSyl5q2iCoqnTYrmU6TEKm9kdtWzKVYYQiM5HJTosqNNtb68zN9albi8wz2ujSNVVHyjyB30EyaUZknR6f+MzfxlYjHv/eN1g+fJDl5VvIdIGMgbZu8UGmFJyfUPZL5mYXUiK2cljhyMsiAaZ1RiTghCcKQacc0PiIJ8lSfJM4YEWnIIgkUmrrhn5/HqVzQjMiy8s0ZIotddOQZz2iEFgizkdaD91eF6kEdVtPgfUZrXXk0kDUNNEyHG5z9OAhtusJWVbgnCUEh8JNmXQZQinquiVqQYOga8oba6J2guFoQmYCNqT6ohQKP60ia53aBd56ciWTuINIFIaN3UssLS4wbkNiUyLwbZ0GelLjgkOgsCGAzlEqw0+vVVZ4ZJZSacIlGU+RFWxXwxTWEGmNy6ROFtBMp/q/b8jyjHFdkcmYbKESjJdoE7BRI6fp5CYohJEIMU3XBnDW0ThPkSV0TZYl5pFCUO0METMzNDNz3HPrSW49vp9blhdRgAjpUDvECEKmAzT2PJ0SF0BLQeb+f255i6OatdWz7HMti/MH+Kc/fStrF5/njx99jHPrQ4Rz7G5tk80vocsuB/ct8vjrv8UvfPbDuI3LnHvhEpMw5qc++Wk+Jz1f+9IX+Fe/9S18KDl74Ro7OxNcNaSKEKoakDiZIWLgG999lPn5OWw94te+8Nv89AeP8hc/eIXDR47y5sVL7A49uSpQSESnYHZ+nrm85MrKNcZOEEyHDoE/+N6rlFlJVIqDi8tcX73Gxz7785zYP0dvfpl33nWCne1NOsUsw90tLm9epw2Ch06e5NnvfYvffeTrbO3u8B/8vZ/jnvd9kA/dcxc33XEvfQ+Lc4v87Ic/TLWzw/Yk4EPklhPH2bx8mVrkfPrjP8nJwzfx0kvf45VXXuLOQ0u83M/5F//qf+Nj77yP7z71HQ7uO0lwLaNJzS2HbiL3XWJV09gx1pfMlDlLcw3rQ01TCSpvQRsMOcFN0g2j9+gQscEjpEIFz5GDRzh37jXuP36EwrQomeoKNtaUGn7ji79NmRk6gw7Sb3D9WsC6pPOMXqNNTmVbNsfb/ODZH1C8mIH0HF0+wfbkGv/kX/xzNja3iTIgZE6ZG5p2AgKWF2fQQrCytkbXJE3rTgvvuvkY+/fN8ehTj7Gxsc1Gq2m8QflAUBEZBTFYKtvSVhbbNNSjCceO3YyNmu2xS5sELWljnN40Rrxv6GSaoHoszAyo6x1yWXNsqaCfKXy0TKrItStDNreHNHYEWaDf1Rw7Msud/VlklHzrmau0bY3zLdZuszOZRq6D56aD+zlxbB8HFufZ2qi571b49tOnuXzdMbETsiyjqVv6/T59MqwM1MERg2Dctsz0BhxanOXitU22Jy0uSFx07Ns3R6foIfU2x266hbWXT3PsyAEOzu/nldcucmVjh0xNAZKNTbpoDUw3jABNY2lbQU2LyQXaQQxjJh6UMajoCDaQKVBK0+v1OXTgINd3G8qmYq7TZWVjC10YCpM2BFLBjMlpgoDgccHh6pASUhFkdIDCowgikstkYgnecOjYAT700AO8+10Pp0m88ymRNE0H7YFH37b78TZe6Yff8O8NnqZFuL1U0Y1h0l5d7oc6bAmo8PZH7j1X/JU3T4NH6ZsSSpI2CWmTI6aqVhCIoKfWDVheWmYwM8M3vvlNPv9r/5Zjx07y4sunWdvZxUaHJOPa2lXe+dCDbI8qrq+scP3qQb7xja/y6c99gicefYp77rqZx595njcvvMkDm3ezdGIfFy9vMjO3w0c/9hFeeuNVrly7zte+9W3ePPMWNgzp9+e55dgt7NYVZ159i2eefJFOL+M9D9zHweXDnHr+JXZ3J/yjX/plXn7xFX7/y1/iV37ln9P6yIH5JaoYmZmfZXd1m7fOvMrvfeF3ueP2Ozj92st0jSBThtfPvMr/fnGVi9cuU3QFk50Jd9x+L1/70l/y+tnXcMLgiZisQztJa7ZU4Gi46eQR2jbw/DOnuffOe5iMxvzlY9/nttuO8Ru/9uv8rc9+glOvnWVle4uO6xLXRhRFxlo1wtTbZFnETQJRAUbx4z/2blZX1tmdrLNVVRT9Eqda4vYOH/rUBzh+/D7ePHuJtZ1NRFAs7l8iixkraxvoPGMSB4wZo2SHrJyj7Je4ZpP1bY/qdKYDCc1wOKHTLRA+R8VAKSFk0MZAaCODgcIUA/JYIJWik0fOXXyGOI4sL+9nWGuW9y3gbcRWm2Q5TOoarWBhpk9WZPihRLscJQMZFikmuDiDkB0GsxpLZNRMGHT6KAKhcTTWY0KBUSEZgrRkMhyiZQE+JSxCaNOAqVsko1x0bIzX6RQ53f4MSkhqVyG1ofURESzBRlQ+SHWOPEs1oGjxwrK6MyLPysRuCBGdFcTpTWqc/h6pzFBPJuRlwWh3hJKCrDRsru/S7XegjTR1PeXPeHQ3ZzDTJcsU2dgg5BihJE0VyLWi2+tQtQ3dbk5bW2xtKQcziAKCd3idMa4CmcwIxiXWjRIpFestucqQRtPpzCIVeBGxbUUIkOeafr/LngUty3KyvEzjAJt4EzEGpI9E52itpW0ddlgRvaX1Fu9bZPRoBVlmmBvMsrQ0T7dTEH3E5DllZvDLguwmgTCaJGwTrKxco5gqygtVJp5VcIzGNb1uyXy/wEhJbQOmyLBtQ55rMjlhcXkf1rZorchMjqtaEJvkRRchBdqkIVNelAhpqOqGaB06T9UnSBtZLSP4Fml6iOiIiKl2WtzgFaopPso5R1QxgWe9T7YeJbG2odctcS6po31wSK2nQ4cEaJdiryQl0MJMEwSR1lYMOjkh+vQ8bWiaGikNShpC9OlrjQkVYK3DuRYUZCojOEeZ5QQ8RZEDESUFwYVpXSglSrRSZJlB5xoXkh0uRglaTuvK6RRXK4GIaUgSg8LZVNVLPw+f1NdMr51apdpIiFPTmcS5lk6ng7MVUjIFUidxidL5jeubnKrRa+8wxjAcDqeA7DR0kcIAqbouVUoaeW+xtsXahhg1ZRmng5CIEMm+WE12iXh6gzkm45Y8y1L1S4LzdTL4xXSdTP++Adum2nye58hGIGTAa0lmSsJkyF5VLk45gTFA9JEQA1KmwZhGJZ24tfS7cwx31ul2+xA9TZD4JlUYGzsmU1mydwVPNakIzoMiVR1FYnvRJtJVAvJqRpMRnbKLi/5GzS7VJKeKdWdT8of08c5Z8iwnymmKeNqYD96jM53W9z34cPA3Pm+CGiRsu1RyarUMycI0fXibuJzBQWYypErDP6nzlKjLBEWZIUVOU1U0fo28M0fjwMcp0Nyl10Sv16dtXargxcRnI0YUCWIshZyyuCJrKxd48cVH2Vh9jaXZRa7vGrqDHO0yaDXVOJLrjO21dVZWFqAsufTmGRYW59gd7XLx7CUWllre+b6HefPUy3z7+b8khjFBZRw6eph77nsn4+0xKsLyvsOczw+D22IwO8/+/TdTvbxK6OTU69ucvPN+Lq9e5ic+8mG++51vI4TE2YC1AYclWonSktXVq0gZ+dzP/xfcevQOdneH3H7vPUyipL+8TDmzhBYS0dHkSrK5conTTz7GiXe+j2Jmjvf+xMfodge0owmZMazvXsK7Fmn2rGOSiKGxFdZZ5ns9mtZPod4QhE5WvujJsoIrly7S3bePiW1YnDlA5Rs6WUqnWOcS205EolbJYhpI9SfTASReBkTUNKEh2Jb+oEeM4Os0yHGVpXIN82qOECTGFBBCSg17C0ITpgw17zW5DCgZiEWXuX3HWJxbSjzgt05z8633oESOiZbN6yvUSGbn5pNZrU2vWz81SWptkm1ZprpZUWRoLamaikxrINAGm4YdSiYzmw80bcXi0gFGk3E6SJ3yHNq6oVPkKWHYOGQA24zJRHqtNk6m1Iv05EVG1bY41ULrGI43IRM4dAKbq4gOgvFoTBQWabqookDEQDPcpSgEJispTZ+d8RY2OHSI5DrV2xob6RUZ26MapbNkQtQimV29x+8ZFKVJBmVrmZ9fYHfXoouM6D3KRkRWMCXk4fCE4NBGk+UmrWmIlFgOFtFGlMpx0VHqnNGk4sDiEkRBJ8+JUmBbi8gU0SVTpkAyGk7oFwWNEwjpbpQiTJb2aVIEjE4XVBlAhkjj06HQTF6SmQwlDLULhCwjn13gnQ8+yH23H2N/t0zoA8D5iBWgZLKI7vlnPZF09rK3H0r/aZz9v53j/H99/I0PlCZuzJG+5Dd//0u8+0ffw3Pf+U1ev3AVNTgIKufC9XWs7vKBO+5l5cXv8bLT3HZ0gd/9nV/jhTde4f53fJJzZ54idr/Fz3384/Q7fYQ02NqTmYzRMBH1y8yiZ0sm22Oct2gB+47exkffexd/+Hu/z/m3XuN/PvsSnZk5Rtkst588wbMvvkwMmrzIOHboAEpqlvodNjfXaDPF8SM3k4WKN86/xbCp2bewj66J9BaPcdfyMhfPv5r66OEmRGjxvuKWg/s5MD9HP1d8+xtf5gvf+EuGo23WR5Y7bruPp59+hieefIq//3f/LvsG76IdrvDrv/nrrLU5BxaWKArNrB6zHSdMtnf5869/ma+MambnOlTjFiXneM/t7+Ar3/wWf/jn3+fmE/uZjNZY2dglL3JePvciiwPB3EKHzd0+1o7R2iG1pTezxNJcxrlLY5ZmF6nshJYCSCp5SeIfRODSlcv0OgPKGPjN3/kiY9tgvcDWkHUzfEgg77rxhN0Ji/MyGXQCeCyjqkaoQAyOtgmgK7xNHdY3zp9hrV5jPp+n35ujIxWNtVRCY32alPq6YuwqxrahcpBriSo0r73+EmcvFbzvgR9jaFt+tARjG77/xOOcOHyQbtmh9SlCUrUVINjcGmJdYGFhge3JNZxvCB6yUlPokl6ZJxB8qDF4SlNzsJQsdpaoqorn33iLiRvSKxSz/RmO39Kj219iNptPJ4A6R0ZFUzd0uyO6nT71aIz3FUp4Qm5Y7GfMz3fpdrqcfuMCr62sMqksG9u76eazTsM6HyPbU1V3VnSSxlTCg8dvoVcahsMKT8CJiDSK6CL1OPDo08/x8P23UGaaowvzhKFjs1mF0JJlkswoIon3EKJLmwIRiR5USCeIZJ5ClCgNQabKgoiONkbQgjiN5ZNJMpMTXcConKodsTXcJgrB4vwijW3ZWl9n0J9l225z4ujNxCBTbDUIfAw3AL0+JIiqEpJxU9PJctZ2tvk7f+tznDh6O3UzhmkvOsTpLavgRuR/r1b2wx20t8dJN0ZE6T173ZS9ygJvf9zbYaW9QVJ6695Iam+AlC4mqRIQb4yu9qJITEG2ESGnaacYp4aaHLHX/IiRyWTM8y+d5ts/+AsuXr/EC6+d4vCBgyACc7NLVDsjjt98kq99889488w5xvWIy9dW2N3Z5tWX3yRDsn5tlYXBHPW45urlVW455rl07irdouToTSf55f/slzn7xvO8dv4sUVgoDeu7GxyKJyhEyfGjh/ncT/80jzzyFRb3H+R973sf/ruP8/ypr/LF35mQd2ZYmB/wY+/5IF/4w99FqBIbKi5cuIKznqLQvPjWi9R+l8JkGK2IvsF0Z9iuN1L1Vc8QRUSieerV52iqCcFKfCGwu5Ys17TW8o57bucTn/gUW5s1165epRo3vPz8y9z6jpN89nOf4Pf+6KvsPzDDL/1X/5jP/czf4Uff8z6KfJav/dmXKXv9dCpsEsgUPL2yRKrIaFhz5dIG1SiANMwvLrC1u0pvdo43z53nwvlrDHqDxIExknNnL9K2DU0zYjCzyNzsMkprBoMum6tbrO6+yeycZHlhie3JBlLW7A49ndwQnWBtYyvx5wP0Oz36ZUFZKFrraWLD2tpFMl1zbbVORiSdszMaolWOyecwsqaqtsG3dLKkUzd5Rm4k3Y5lZXOUhubG0JWOTp4hsww7nmB6c+TBMxmv0++WhKjIjSHQ0DQ7dDrLzPcXKE1JPRpSaEsjFVF2U4pAZ7iQpA4SQ1sFmiKZ1RCp3UeWe7QAACAASURBVNQ0DY0dY4xBBIExkuh8SgtpSVtFekUfQURHj3WOLMsxWtH6ZJ5RUiZWkHOpapIlVuJoMqbTyfA2JUV1lhJI3qdh1Hg8weSarFNQEmjqFussUkHbWvIsYzKup+upxbaRsjeH7GTYtsZXNTKXGJ1TRYeUim6vhzYa3ybguG0nqeolFSqLDHp9JDltLfCuxjXVtBIViN4hSDfnSidVcGESi6dbFhT9EqO6oDVl2aUsC0SM5LlByPQ1t7WnDQnMH2yDJCAyQacsCbYiywo2hGJmMKAsDUbl1HWFipF+OUDqjCJPG2RtLTJqnG3IsxxHPY1HRqRPqayRs+S5TjYvpaag5PQzyaVBEGkQRKERIiSDWwQXEiYm6xZU1S6aVOMjOoTRqULlU+VMSpU20iFST2qyIkNJTZ4l1lBmDMPhBO8tRiXjTFSJoURIaa8oIirTNK1lXFeUnQ6FKbBCJq32pKKaVAzmCgIpYZKkHJKIxDYtnU4XYcCIHBMMrbfT1VoRg8T5NEwSpJPfBJiVUwNQQHmIIcGuhdRTq2eyryXIsyfLslTn2Bs2aJ2uTSLVqIRMgzfb7g280nXEmFRFc85NLXCSum5TDUtGvHd4HwnR41ygtS1CGtrW0++XU9uXJU6ZF+PxiBg8mWnICzOtlQaKPEupueiT2U8b6maM9x6lOnQ7fZpmnbI0tM6mf2uhkcRp3U0jtQfvcbGlm5UQ05BUT4d+obG0NpKZVFl0LkF8hUh1QELEtpayk/TVNypxIeC8p2/ylEqLnqhSekJCguR7n+y/zmFyAyrSOoeeMq+I6SocXGBYT+gURYLmukQ9a52jyAukSAMlNVW8B0Kq0JQdBAmGrKcb6mjTDYEI0+TdtLonSNVTG9w0+ZVeqyHEVHcU6kaFUZIseyGkOonKsnQ/4luyImN7e5WxrdhF4ILj5VN/Qdntcts9H8c1NSrLp0KDNlUxibgYMCLVEiUyDR4gpeqlJAaBjJEnHv9TNsYrzO87wHA4ZHFWYkyLtTmNSzzH2eUF/Mgx3BmiVcZoNOHJ736b7d1tZhf3c/y2k9jW89arrzA7OEKvvDlxH4Pl3BtvMugvEbzj8oVzzC3fxB2dEiGgqRuyTo8qBu596P1cO/0yN937Dl587iWkDHQyBSKnaT0uOpyUBOuRwtPLF5kpeqxtbDK3sMjrp9/ko7/wMNa2LMz2uHr2PL35RUSW88arpzEzixy5+Tjj0YQD+/YRomXdX0mvN+dThVOmLW0Qgiw4dsdjolF43xKFJIQGoyXOJQEBQaYq93CDmcN3MJzU6ExgGp8GqG2kamqyzECM6CgQIdI2LXU7JmqNDzkIRRBpTcrzHOkjXkAdarQx7Gxv05tfJitm6SiNzDSNbZjEkDg/eY4NASECq5urjMZbjJuWYC1zvT7XrpzDZ4qf/Pj7GfRm2dwZ8tJrLyKN4shNd1KWXaLw6bDItck4ptLQzDaRvC/ZWdthMNNj3NaQJblEEyRNhNJkbK9eJOvkSFGwduktxsNNZvcdRklD9J4owQZL13QTkyx4gmtp24Z+twc6pbliaPFNy3azhu4U7NqGLJYMh7scPnSQYduSmQxiqpl668mLVBmOEkajIVqD1jlZUdB6l3hl0acWRJHjnUDKyLhucdFjtEoyDBfA+5SC1WDyHB9ge2fE8r45mgZEJ133bNsijMSoJG1CZbgm1VxVpkCnNUurxGAKLvGVglIUUrA1mdA1kugjLjqCVITWo7VBa0XrLLkxtK1leXGRLMuILokPurMzyTzra/IpW1CagmY0oejN4KNnNmoiklopXLdHsbTIncePcNeR/czlGXuH4CFAiBYtTEpoTRsUN6oVpETTdHvy9uE6qQ78N/n4Gx8o/Y//9B9xbVOyePu76ZuSE7cd56k3znD19W2OHDrEe9/zY9xx/Bg9PeasfC/l8q28+1Dgf/rCs7RuwOkLl/nMx36a7zz9AktH9vPII/+a9VHiJCUagaeODusk997+IEo5Vi6e49yFiyzMDVgqInMLs1zbrOgMZsi7C3zsQx/Fbp1nY3vEqLE0TcXOaJdm0nL+Yps0p2XJ7u6I99x1N6ubW8TxhBPHT2Dqbdbeep0//fa3uP2+u5lb2EfP5GSZZuXaRR575nEurG5w9OghLu00vPf+d/LkY49Rzi7xs5/5DK89/zQvXDzHtx97guNHDvHVL/8J57Ylh5ZnOX/hIloHnnt+E6KmNzPPs6+9hYgaddnT7fb5vUe+xD233sTi8j526xHr17eZWdjPu9//blYuXaBuK3qDlrwzhzIVu7WgHe4ydl3uv+cBzpw5w8z8MkFJcAKFZW52gbLfY3X9Oq715GWGl4FTp08x1ymSYUIbWg9al+BrShMRccIwQKa61K1iZ6zodiJNqLBWI73EBkupNcSArxu89hRaM8g7VLbmUAmuLnjonQ/QKfs89fQPUArGkxHee7IY6XbnCb7FBcfV62tkeZfvPvUMc70BtRzxje/8OUb3efDu+1mYX0ArRb9MSuuLKytsD0fsX55HeMFsb47caDKTYbQnxAlFHNPJImWm6aiCxjrGVeT8ynlWdyZUtuEdtx3m8NI+lC6ISIzS0yF9io3qINmMDi9bpFeETNA2Ai/stKZlKLOMqmk4c/EiZy9v8uA7b4VoqIhkoyGbOw3WB2bynNqPWTSC4cQiTM76zoQr15NWWARPjsQHgZKRYbVDr2NYXWk5NNhlMmlp84Bzlq3hhBjBKMX2cETrdOJKCAdZ6jJ7BFoIMinoDLps7WyS62TAKIoek7bBqBwjM7yvUcJwdWuXifMsLxwkMwPKfovxLUYqFpaPsNjvJo1wd5Ympkl7iKkipoRKEXwiUeTEkAY3nW6XMtMUnZJON6e2zQ29qoi8retOKc0fGgJN/+fGYEn80J9vP489COv0/Xv4o7dbbWnxjTcYSmLKWbgBW7jxafYGR8i3B1iQlNPRu+lCPX1rjFjbEtqIx6GyDptbW1y9usK1tas0zQgZFJcurrG4b5Zbb7qNs2++yvbOhGee+wof/fjH+bOvP8Yzz/8BRbfk2rVrlKVk2BTkgwGTtQ2efO4pHnzXgwxmZtBKsn//Ap285PYTJ3n/aMJf/ODbPPbED4hmg8efeJTZ2UUkgc9//tcpejlHDh+jmXg2N7ZpmwnPnn4ah+STH/xxbrvrDj5d/xRf/sM/4j//h3+PZ06d4Rtf/zY20wwry+mzb6X0ho7kuaaxkfGOZaG3jx/5kQ/y9W/+CW+cf5NC7nXoA1lUVEhqL7ANXLq8yte++g0uX7yc0hRK8q6HHmR9bYPr1zd5+snH+NCH3s+TP/g+//Dv/yL//T/7H3jf+x9gYXEfk2qCQGAEhKiIueT+Bx5gZ3eH7z7xfQKasuxQjyp2drcJtWDELq+8OmJp8QCXrpyj7Bisa8g0RG/omC65ydgebjHoDdidWOrJCG0kKM9ovMXOVkXPZMzMR8bOEDxoVWJDS4FhttOnzDUnjp+kdY6d3RH9gWF7eJHhSNPJDzFuxwzHI0KY4DeGDEpD2bMIZXFekudLhNBnMtpgt0q8tegd43FLXubEuI21Iwqd4Sa7lFkBLODbXZCBqNOGB5kl7bhqcU6gVMa4Hk1fzA2D3kEmjWfSNhRlByU01biiHo+JHZdU4lIhpCfEht1qF4SmzDJyUeDsEFDT+5YMERKzTcRI29b4GLB1mxIoShFipChynLUE5+h0S0QUdDudxMETEikiIbjp75GgmtRU44D1lrpu0g23slhfpXU4pIGFbZOlzruAHe2ifUFmoCwNYnpirGVKEVa723hrEy9BizSPFBLd66GEZ7y5js40RabpZBpTZpTlbOJGGYMCpE4VXuscM/0+eIu1kTzL0qAlBoqsQClJW08wRqd1MDToLNDrlMz0C5pJJAaHMgXa5AgRaaxF5okhU2qVqmpZzmTYEJRAawhS4NqaIAJ5NBgRENHRLYqpAl6DUjRuDMIhlaEoS6xryfIsgZCVQGZJsqAEEBzBR4JKi2Tb1iihE+TUOYTSxFwTRUCTUiSJ45I22pAGCyG2aJVjXYsxOc4Hgm/RUoLIcDb9fJTppwqkCNODhsT4kCpgspK8m6MCiBCIUmJJw5oyN2gRUnhfJ5ZSxOOjTwrvxtPpDGhtkzbm1pLlJda7G1VFnRbpxL1BEOL0a/Ap4dLYlhgESmgKY2ibJlXNSWkl59LGMcn+Ul1UKkPTpppH8B6tFcLraeVpymuKHu8iRWlwPqVjfIRqXKNt2jAxrTTmRYFWgrmZmQSQtg3IiHN1Sj44izEJaNspS2zbEqNOKR7AWpvA4OHtg6Ki7NPWbXrt+mm9wzYIEVItVOgpkLaltSmVKEV6TWspkCrBeZt2QlEUhJCqQFKmNFcUIh2y+JAOZfBEkeQvMtNUdY0wCpObqcxkglTJqGZ9oFQqQcpjSpMRIIqpqckoqvEkvd5CSMM9nVHkOZNJNR3uuAQcl4ljpaS6kUQjJuC+nq5FiV9F4itJkEHivCMvisSBCqnatmch1EpP7xXSEZbY+w5dIJuuBc6lwVOuczSa1tVIlYxTq1dPs3L5NLnKubAx5u6HPsLNN92fdjQiHQS0TU3b2lRlA7QEoZgeXKXUicxMsiNKSYieajLhngd+kvmLz3Hl+usIDTO9Ptu7QyCj150n63Q5ees9iGrM2vV1pC555/0P8/if/Sl53mHp8AEIgRcef4xjd97Fbd2H6JgcjKKeTHjrzBnoeV44dYqF5X0s9RSvXD7F7tYQyow6Bn7ig/8hNIHV11/llkP72b10iroRiTGVS4rWMmkdlRAsDHIkLYdvOUzZGRCFQWaKIyduJi8yRGNBCDbWN5hfXmRj9RqjpuHDn/ocdtyAr4kypQRl3sG2E9rGsbV6nc5gFokG4QlSMKnGzMwvJKyGkvjgibYF7zBCozLFmRde4OCJW9idNHRMxqQaJZ6ejWyPh1NQfZdoCgI5/xdzb/Zj23Xf+X3WtKcz1XSr7kRejhJJUaQkapan7sTdHeQpbQRBggB5y2OQdP6TAB10PyRxdzttpd0NtW25bcuWbA20KMoUxXm4l3esO9d0hn32sKY8rH0vqbYNBGgHcAEkLot1q06dc/baa31/3+/nG5ynb1vyrGA23cBHldqflaTvOqQi3XuHPbWRiqZZc2ZrB0dAGvDR0TUd0cN4NE7uxuhR3tMcHXDx4tt8/vMvYZXh5tUjPvXpz2EKwat/9i2i6MirTfYe+Qwbs9OYokhtkU1LdBYVErPMSk8fNEEFsAIfQeuCdt1icpOu7+CJruP6nau8+dr3KIsxgZzxaMIXnngi8ZWiwGQZ9WpJFJLG9hhjkBLq3iKkJisngCK6nhASp8+MMsqsQrXpminyCiVzonD4EMmEYtU1oANiKAqI3oPzjMoxKEN0AQbAdxQCWRZYhkGFyljMTzDaYGQClPvgCQiEEmTGDMDyHuEbRuMz+OAxSuFaC9ajqyKtCyQXqEvgJ7SQ0Dsigd6lr40xJBZgcEiZMW8O2JuMCAzNd0YjtMIYA4NAn5skNJdVRrQgTBoYaiTeObTKQUp0nmO9pBxFVJ6KSuKoYPPMLo8/foGnz26xYXIG8hFuWLeUTNFmyccswgfO0YdnmPjxOhrEEJJOUDg6/3c88vbaNYcuKg7e+kv++eFl4vyE24crvJVcunyVpvXY47u0XceZs6dZX/4pLx9OODcyvP3ufR6RBW/87GVUU/P6qz/l7LmneEnNuXzpI24fzslzTYiC1lqu377KP/rKFyhXd9m/obnz3jt869Y+G+WM+8rSLpb0sUJ3Lc38Hq7t2BxvUT36LM89dYaf/PgH3Lt7wLqJ7G5ssTw+4WB+j/N7exx8eJmTO9c5vLdPUzvkuRE7SqLXK17+zu/ye9/7Lss24oPEZIr5D17mM89+np3tAmc0h0dLPvvFL5Jznxe/9EWOD+9z0vZ841f+ATfvfpNb+1dp+hTHEmKMkJ5Te+d4ZrNkvV5xeHTIp7ae5f78Lrt7j/CPtrf499/7D5w98xSPPHKBMxszfuVLX8WUBTduX+fu/duMpituXX8frz2PPP11Hnv8LF/5+lf51re+yRde+gY/+uEf8+Fxzf3jmi2h6DuHaz312oFUKD1GGU1va5SPVCJj0dZEoHea3ChyoQhe0Hea3mpEJuiCSooyoENBrgO9Sy0tSImUjtxnFFXH3fk+Y7PB3fuHNO1ttCoQokMKg9MjtIKmbRPIVyaYp/WWo8P7rI6OwAimo1PobMqHV29hbt4bFnGJEIKu69gcVzSLE8rcsDVSTLKGQrSUmUL6tIlbtR37Bwvqbo3EM84zJuOKx6Z7tD6ys7mFLiYpex8jfQhARJuMUmQIKZjFiklWYr3FSYUQBSF4etujTUHTwE/f+ZAP9ueEGHBN5OyZDZaLlr+8egVpKsrSEGKgFDmLdUvXWmLfc3R0wubmFufPbWHWBWPbUS86eutou0jTSC7fO0ThmG5POKqPOb63wEdNLnOclZTVlHa1TJBJNLrIqFcduICSFpCsVit8EHQxopEYYTHCk+sELF27no1ywvmtLdZtx439W7RNsgBHIotYc2b3UTaqioNlTfCKKFIzixBpQyRFap9RInEZnBQp2xs8SibrumssajtLAtqw+QshZd2FkMPq8onc2ccWoocKkiDysTT0scvowWcefF38xX99/P+H7/vgfz1Ynh/YQ1PEgAF2Din03afHJ3kYBTFZwWq1oF6u0ApGquDSlcu89c5rqBjIgqJtLNFI7t29z+efgRde+hrf/4sf8fnnP81Ln/0Cv/PNb7O1s4nUmjLT/MNf+ft85zt/wnTnFDvbu3zqycfZmG5w9rxivT6B3jGqCuR4gz/4w3/Jan7McrGisYFqVOFDi+sjn//ys+xsn+aDt9/jrfod/uDP/gO9SOye//F/+G8Yj7f58Y9+wiNPXeDC3jZf/NI3+KM/fYXt3THHizmqK2ijI+LZESW5ENTdCnCczHv++A//GEWGCQ2bu09xq7vF5qTE+Z7YNzgkRZZzZneTFz/3GRbLE5RWfHj5Eo8//hjleMb3f/AjvFFsntrli195ifu3lvyv//P/xOHJPS5e+oDoFV456nVH9IIqL7l+5RbL9QllPiIvClzncLlisawpzIit0+e5d+sOd+7dQOYgM4OUqZpZ9pZ129AdDYdQOlzXI9SK3rfEVlMvHVKWnKwjZbTovGVVCyaTAt03FEWGF5E2aq7dWdH1xzi7YFTmCLvBaHqeEDJav2CFRWioSkluLCH0KAPCTOlizroOrBuBQ6HUBOsXmEIhy60ktPoG3zcok+O6Hj0cWK3PkTrDxgahC+Z1wyObu+QK7t5dIZVER4/3GabIkf2C4Hu6xhKURJlk+hbkRAK2S1y3KARBSlTUiJjRC4uKSYwpsgIfFQ9U33W3xhR5uq6LHO9TZXs/ALa9Cyn+1nUDdyXBIm1vyas8gXBDYKPMBzhyTbte42yPGoCYOk88QIFjnJcc3DpiMpuQF4aTk2Oa9QnjWUm5McI2LX3tyFRO2znKomBj7xRZXoIW9G3PbDZjc3ObybSkysYsF4vkOPAWoxQM64+3PaOipMhzjldLkIpSaUKUyMyRZSoV7UhBptO9ICsKjMnwweNFRxSOBywDJyIqpGgZPm0oOxvJRJYOPyK1nEkVUxtdlGQiogj4IJHKJFGpLOmsJysNAYeLHo2mcxEZEo9P59kg6kMCvICIdojLpQYdIR/klgTBQ9BgQ00UjihNipRJQcTRtW1yuYrE20kCZCRGyWxri6KYMj+8g3U9IkZGVUVrW7q1RQk9xHbAkQ7pUmnarqNvWxyOHI3zMcVSQqpnNjpLTiiSG0VAul6DRKtUvGGkImAJMiBl2nwbNUTcEBhjEm8nJoZNYu0kh0wcuDuht2RZQQwCS6CPAS1EAqwLEgTaOkyeI6JI4kQIuK5LU/e+Q2oNPtK5DhcC585u4lyXmgWFJsQULQlRMBpX5HlOsD1CJAGtKMZ0vUOqFOHyoR/aojzep4p7ayOjkaJtW/q+T8KI0mnuIcUAmU9QWufA5BL78DEk521jLXluqFcN2uTpUOUDMSikLjBZRtuvQGic7VNDVmfJximipxgq2INPLqDgsdal/RikRkLnMYWhWTWURUkceEttW5NplfhryCTGxdT8lrhHkWiT4yiVnejEr+k6uq5hMt1I/BbryIuM4JNQZHQSXUJITmJrHcF7qrKkd0kASyrqYFIJJPCvGCInWhOcx7v0LovDvT9tWZITS2hFDOkQ6ULEuogNgirL0vMoBoeATjE6U+zx6BMTPrz0OqfPP04WJKvlMaemU7yAGHoI6dodmxzfD63FQjxsQOxtTwq69RidhE7vPI898zxPPP0M7779Ctc/eg3MKbr+Q5pVQ4ySc3uPs7uxjSgNt29cQ1RTZltbPP/lr/DKd/+UXAfee+MtprunqEYVhc7wnSU2HZk24D23b95g+8xpnnj2U7z/ysssD4/o/Jrt2Rlmp1/kqUef4bd+858RTclrP/4xXRNQIkPlFi9r9DijaARPPvYMu+Mdbl1+HRUybAzkShAaS+h7ju/cQ2aKk/mc2cYG84O7XLt2hb/3n/8q8/u3qcbbaGModZ5cpkbjQ4YQ0NdLxtunCSK1Djb1Mrn6QqTvO6RW2K6m73pG4wmxyFk1S1RhyMYzTFMzmWzTiUimSwgWpRVlMSIzOUHLgS0X8V6SVwUYlQpZpEgO3BgRQQ9yqkArQ92tGY3HKK2G950A2xFcOzRCGrq24dJbr3Jj/31WiyWzyZSP3n2XfLqDNIYb1z/i+v7bSL1gNqpSjNIr9FMbnN4qE1Op7fF+cNE9MN57R6ElB4cHlJOCvgscHNzGrY/Z23uEk5MVH771CnZ9l0ILNrf28A6+9Eu/RufTdZREfciygkkxQ+cKJVQCf7s5ZTVKjLzgU6W9T/ubST7GWo/UGYujA7anMxpnydWwZjoLscNkCqlyEIauWyNEco5GkdxBdduhRLr+dVEl4VhoXOextksto1oNg98kUBuj8TKt68eLE0ajAtF5tFK4LplIlElNjC4EEImpKIfom0w3JZQWuL4fmFQZMSTW2cmqocolk2qTLnp0odOgPEYQCu+SA9LHSFXkBB+JUqB0jtYFzntEqdAmJxcSnZf0TctkZ5fpqV2efvQMT+5tU2XJjRzjEOWUaf3RWg7nj4ej8mFg/olx9+AaTUeftIeRw+BbxABC0vJ33KFUr+aEukGXBnlccWq6w8xK6nqOjXD/4C4vr1sePXuaO++8x6JesLe1y/zohNlkwv2jO9y6f4/puOSDj36buutTxSMpdx+8oHcp0fzR++/zm1cuQoSARpnIrYP7A3xRIVQkLO/zL7/5r9idldy9fZ82W/BsMeFUnnNmMubW/n18lExKxfJozo9+8irZqOD03ine//A9lC6oNrbYm1T83re/xXTnNN7VrF3G9nTEwf37LGvHbDLmtbde57/+L/8LHt0ac7dt2Ksy7puMXgZGO9s88/zzqO6If/1bDavWkuskJhRZsjEfHt7luce/wHGEuW7IhOfwzhFvv/4mzp2glcF1NW+98Rq3Tm0zX57w/PMv8qXnnqNfn+e7P/wh2hiOjgMvbZ9mWuQ8srfBr33tl9HjMc8+/RQfXrqN95Gbt+6QZVmyqxtFnmVEEVBaJr5EFIToyUyBUhCFx0aZbtgmAwlVVeBdj4oSHyMtAREFi3UcaiojtmkxI4GNOZUUKJFzuFxzWF/FBUGVBSQ9Qhh2NmaEEDlcpA2x9Z6qGCNkYLk4wYZIJSas6iWTqMj0iByBziK7u5vYruPW7QVZtJyZ7DLKFZlUONuzXrfcO6qxdgU2sLm1RYgdn3niHJPxmJFJDQ4WOKnXCGPIpEKKjOPlikXdcHKyZDQteP7px7BegHVE7dBRkwuF0hZfSFyoaazgJx9c4/bxnNGowHrJT975iPE453i+RpkRkkClNW3vKYsiVaFmBut7pFHUbYNSirM7Y77+ua/xb37/Bxwsl0gFEUlwkf2TE7a3dvng6u0US1EOKeNw34ookZYbqcGESKUULkRElDjpaTpJpQ2CBIRrbYPtBut19BgCzaKhznvm9ZxAy2g6I3jJdDzh7v2bNO2CLq/oWo8LfdpUu8Q0MCZPP196tjemlEVJMUy1e+/J8hxHg8nL1HLzQJgZxBn50H0UHi6KMCTYfsFtFH8h7oYQDzemw3/yEJX0iY+PBai//kMMP0QpOQB4I25wRCgR0qZaK/qQYK/OBUJwrOsmuZky0Drw9a98mdF0xP/5f/2z1IwlMoQy2L7j9sERj50f85kLn+LO/kWuXLlG26z57/77f8zRQaDrTjiar8DkXLx8kQvnzjOZjHj73XcxeszxyR26vicGjWPFP/j1b/DO2x/xJ9/7DtOdHdrOMp1tMJ8f8aWXXuL5Z77A4dEx+zdu8NT1x7l+e5/gLFk24sz2WX733/whr73zFvfnc377m7/NW2/+lHJSITAoFVOlagbBthTFjGprk8WqYd3XtF2HCgIZclZHJwhrefqzz/D2ux8i88TTUKXm61/5Ok89/Qw/+fFrPPb4eeaLOT/7+cs4G7hx7TovvfgiVz+6zrXLB/zT/+1/Z3rqFFcvv4dUhqwMtH16D4+mJad3zlIVFakUSLGxucF6saZe3iSIQB8b6nqFyWCUT1g3DV3dIY0gUxo9krR9R4gCLWC1bhhXI3CWSgbWXUdRGPoehM5pO4ECtAHvHFmeI4zF6IjSJXV9wrrZR2c9xmX01nD69GNEkWG7O4gltEJgJPQuUuQjRNR4KlarBVIWZEVFqQpUFKxpkTpPUGlAypyiGtFHTXA9QafGpsTrieA8re/IR5KmPSZajclThbG3S4TOOJ4fY0NNpvMUS4sZSiiyrGBz+wzL+ZJ1e4/eJii2FqliOZOG1jZQk/CZ5gAAIABJREFUlWifoZTGtSviMKEt8gTTFkbg2xaiQBmFjpG2twliGyNdnVxM6ZCvEkB0UIcTBNmnKKGXKGEwhRp4Fxrf9ml63naMik0m0wlCwnqxYjQe0bo1UnvqeokaAJplJZiORxTVmMlkxGiygXMdQUw4e2oHI0sgVX4XekC4AVoAyqemOaXJC0VuFEpGyixHSIs0Bh1Mij0RkTFNoF1wgExg1AARnWrM8wytNUZogvJkqiCIiDAKu/bkeYq1QSQIhe3WRJkOughNiAqkRwaGpifoXctYj0hHGQcMAkCWowb4avCWIBNANLXHQG978qokWI8SGh98WketA6MRPlCUY3xnU0sSSVhztkvRyJiqy4WQOG9Tc19Wpn2gSAu1kCJFtaIihmZ4DB+D6eIDNpCUiBgpTI4hw4p0TSqV0XQr8jxP7aBKgkwwaSGGjbZITToohX9o4xc4DyYvaOsVmUi1971P7BklHppfIYImcZ0QAqFFchI4hxKC6DtWvaUoy3QYkDKBwod0gbc2TahlhBDJlEEoklCaFwmw2jsiCaIuBheMGHg/3vnUZioGY+3QAh6FxlkHiodxvWC79Psi0Vrhuw68Q6kMpMb5QGrJM/R9TcQhFWRG0647TFHiXWqBSv8IrO/IywxBRAlDlJ4sU0gZIKSGIR9SbKkLgTxKvA1EqQCP9z2+bxPLKgTKPBuYh2kQJ6NksVhQFGOMkBBSnMu7NJyQSYkl2J4QQ1pXQhgOT4p+cFxBek2k0mglfsGJJeUQ1c2SuChkeoGVTFBaMQiMSktiSPyc1BGS3odZlqXXInxMZUSk6b/zATUMlBI3K0XvI/6h+8VkGVrnCJW+PkQxCJyBvfOfpigKop5RjCbIoLB1Q19URA8+KHxM8U2dGfpuPUD+U+QyxTAl7WJJUZYokvOzzDLyvMC6ngtPfQ6pc+7uX+XM2ed54/UfUuSBm1ffwS1PeOzpJ3Ehohz09YpHn/wUb778Q15/5RVGm6c4s7lJv64px5ss3ZIrF99FqIyT+QEXnnySyeaM65ff4vbxVdZxTZYFbt3b57MvPM1f/Oi7rOojiJ7D1V2e/PRnUQcnNO4YR0Rpj4kCIxTTjVMcVLuJX+chqwrmB3cpR9MUE8o0y8WCECzvv32JX/57/xmr1RJrV6iiRElFZzu89/i+o1+33Ll9A5Vpnjt/Ftc15GVFs1wiQky8UqPRStI3DHHbEmLkw5+9wmhzk8M7N9jdPY+TEmM0Sgqa9ZpoG4TMCDLFO6UQWOdB+PQ+Q6S2zxAIwaONJPQ9iNToqAU0Tc14vJEg9VKlAbX32ODJ8owgYP/qFd78+fdZrA945LEXObl3jxAcu+MppVKsmjWb26fJxCbt+j75+BTIMZPNGVIJ+vWa5ck9lM7IjEBoECZHZppgHX3fsrWxwWq1xuQV7776A+7evEiRb3KyPKRbH7KxfZY6aMbTGavGo8oCL8CojL53qGENUiJFDOumJohIUVZJZI0gfBLlTZUTtYa+I0bovScvxti+RWaJGdf2HTJI0ApUhohicPllCCHIdIZzkeh7RHSookhlSn2HKTPuHR2SSzWUMXh6H8DbNOwVAonGdo66XrGzPcPKFIGVSpBJMwhaASXNw2sPKRiPpknIVWqIy8bkUCTilUeT0zct29ubRAm5ShHeru/TPSAmQVNrhXcWIdM51AtJXo3RIsXro8mQmWJjc5Nqc4Mvntnk8d1dRukXIJCeSykEQkS0+o9PKfLjM8pfd3b5K43YcSioTiJ5BOzfLkLp/wcot5GoqHBdxESR+Ag+PakGxXg2SxuUCBvjCfViwXKxhFChZIftInqySVUULMMJvvcElRpa8iIQfcDbpMdVeXpjBhUTlb9PtXpSyaHOWOCC587xMfcPT0gbxpb333uT9uQO2vdMJxv45ZL3P7w+3Cw0eEXf9pzaOU3dWXSIvPXOGyw6x+L2HUxu2NnIOLx3h0XdYrTheNEyGW3wo5f/nP392yg54vd/59/y7W//K8499SJbs00uHxzy3N6Mdb2mrEryTJEpIFh8kLTrNR9dvcqN6zcZb+yxqFc0Xc/F6zfpQs0oVyyblj42mLjm9s0rnD21zc9uXeTDazc4tfcoexvnuX7jgFdfe4Xq67/E8rjnhWef5tvf/QG/+pVv8Hvf+XOqYkKJYd00VGXF1iwHYVmuLdYGohe0LqKyyObWBoKeZbvEYBBS0PQ1WuiURQ7gXEQWD5pHSJR5ItIk+J2LFidbWpvhLIigCLJPE/boEyTdW1aLg6R8BM+onJHlFVoZ1v0CGRPkebWeQ17y2PlHmZaS7emI3kJpWuZdjYxrZMiZH51wEi1CCpS0FHnOzqZhPDrLuJgyLqZcvrPP6b29ZG9HpU2ACFQmpyoyVJaho+G1d1/n7uGCtrE8/9xjqWrTBjIUIUiCc4jgCNGTZwptZhAcUiv2Zpv40FN3oJRg2bac2psh+yS6Hc1XOO+xIUFEyyKjUDIBsfMciUZJzQ/+4i2EEIzyHImjd5IYPOfOnif2aZEvqjzFI9qGVd1gtBgWlcQ9cSRQpik0JgqqomBVW7rYkxGZmDFN2yC1RJt0+JEhUhUJyDkal7TzOavlIRvTHVy0ZBnU9THGS8qqxEuNUYpmvUYJSZ7nCVrb1cmtptUQZZNkKokxibmSP7SRP3AFif9I5vlk0k18wnH0yS94sGACD+NpAx7pofPoF/7uL6zFn4jPDT//4c9CPLTBa22Q0SNi4iwc3r+HV5K93dM4u8bbnslkSgyReX1CLOD+4QGXLr7P3pkdnvrUBW7ePuLSR1d58rHzPP3kp/nz732PxarlzPkNEJGNzSkvffYL/PtvfZ+vfvVFfvt3fpe6PqYwhv1bN/nRyz9AaPilr/0aIWYs64ayqPijb/8xjz91lp+/8TN2Tu+ilEEGzcn9E5579gnO7Z3n1Z/8lJ++9hOevHCeT51/hAund3nj7fd4+Uc/of9ioPEdN65dx/WB3/vD75DnBu8FyoDMBMJKtNDMprs0bQthyd7ONlduNGzOxpw/c5Y33nwXbTv2Tm/zl2++g1aAEuggCH3g4GDBZz5b8fWvfJUXXnye45MFN2/f5Hvf+S5lDk89/Th/9O0/5f/4F/83y37Jm6++yflz5+jqnrzKmM/XyEyggk9RyhCZzbawVqB1QV6l1zMKQfSW+ck+WxunkN5yZnOXWwd38cHT1paoIujBNhwC1sPKr5Gxg2iRAvIRbOqK3htWbXLOhRgIDO9jD42vWdfJkWCInJmOyPKALUecPnWOg8Mlk2pC7A4Z5ynipKSm6QyCEucdOgSkiiitmE5nnBzcQ2vP2BQYAZePFpzbPc+srLh+9x6i0KybOZkQaO0QscHQDNc6tM0xkSk+aowZ46wjxBVCG6qQIaSi6ZObz+QGFyKutbi+TY1TWhGdpTQZuRRYuyKEFux0sNBLvBcEemKAIq9ApriO9T3eebxOF5nSOtm7VWLL+D5F4jKTkxc5R/PF0E6laNYW53wCIusCIQMiODa2JqxXPa7zNKuOWmrGkxGr5QqTFzxy4QzXbl6jrZdUuSTLM8bTkkxmtK2nazuklBTliLbv8S5wrW04e+os08kG+EBUCrxNoHeZKscdHqJ8uMG0Lh24kwnCpVYVLUGkanslXYpsiVQf7WMgRJlEfqUHESU5SaJSDGVyWNuSZTn4dEDumo6+6RFKEaUiyqRgGJ3YDN4nhoJ3HmMKnF+jFTjXJTi0SzGgpqlTU1OeU1YlwTta7/A2iWU+Jn5PYj87YnRoVWGUGvgSHqMTDNvjUCqJGUKKJMCQXE3aGJZHC3y0iCGmJpQCkVhywNAiFx+ut1KmmEBWFLSLRaqt93FwSvnhsBYYlVUaKsYHfy9x9aIPqDQS+cT4IeJsjzQa5xx+cL746IiuR5IYNVIm56yLkQfmfyXTDqZtWrQA7z1922FjTHGGQaQAHpZE+OATp2ho+Ey8woB1ls3ZNglxFFDqQcTO4ZxFCJ0a0bwjisQcFMokJ5hL0WkRFSLKoUUt1VcHD2VVEGPAhgdNbYlB1js3OHxtOnR7PxxwgeFAK4Sg7VpMFgnBEqNIVdQhDlyg1L5nbWrGCz6kqFxnU4SnblIUTmmkShyRBwcYKdMUX+kc73u0NvR9itoVeYYxgwCkEx/LNg2TKk9HqJCYMkJJcmEIw3MoRRpmWufobJc4VMrgmi65hFxI7qYHjkKRHMZpHiTx0ZMLlUo0HtzzQ0TrLLklo8foEc4nMdKHNEgKIe1HlEjAeu8tymR4nxAcSmlc9HTWYr2nIAkJIsYUI3UCSNe6E4rTFz6TBAffc+Xih+xsb6GAzCQ3Z5kXaX9EQInENxYxEiIYk9HRpPenksgIOssesjHNwFz69NPP8+rP/owgQClDkWXY9ZKmniO0xBM5uHeXJ7e2KSYTlosFF554lsNbN6iqEYtmxer4hCuXLzHZ3OT044/ipWIxX3D5ypvsX30XEaDFsewVP339+xgE67ah9WsKGTnz+KdYNx8gOslifYSKqe3r7LlH2Dt3nigEz372hVQDoAS3969y6tEnWK5PWMyPcdZy6d13+fqv/0MOb99gtnOGdW2QLiCMog8h3Vt8asLLJyNMUSBMBr3DOsui61LzslDJJdp39C45OhGBi2//jGtXL/Kp6gXu3bjC3ulzuGgpdUbXNjR1T+8VVZYhlRm8KxHf2yE+aSAGokjv2ygUDA2MQoq0JiiFCwGdG6IxyCxL65EFRGoRXC5uc/POFTqh2Nx8gma1ROjIhQsv8N0/+tfsbZ7DechGllmlWXWOUHuefnobozS26+jqmvd+/hMQLUErPvfSLzOanqIwY27d3SeXgfW85Wh+Gy0V89UxLghWi0tsbp/mzJnP8exnv4bMKpQEKUusSG4fLx3CO2I2XFs+4n1IDa1FgdCKzjm87el9WtNKM4KYWGN1UzOaTuhDctZJUpLEu4ZMS5SukEIll2UgsTlDaiBvG0/XtRS5wqgRzvVIDF3XgO8piyrBHgOp4EBGjM6QMvHbFss5o1HOeDxBFWMIPTIrCV2f7nUD3iJGT3CpbV3LJG4nIbnDyORqzYwhykBTt0wqQ5GN8DGVbgiZBmdKqXT+lan0wGRp6IbKKQpNOZmByZjOxkx2Zjy1t8OZjTEaMfBhU6kBw3onhnPLJ4IY/2kfw0xepNvWw/Xub+vjb11QmpaS3gaQkjtH1/F2k+PjI6TOKKuK7UmFVoredjQiklU5x0eHCKkoyi2MGSNMmVwWWUHuery1SVEUqYK3iZbgBS6EVM+rBfh0AxE8GHxFBApNmvgYZR7aoL1rOTg6RIuIzKrktPGpKaTta6SSFOWE3OSU7Zobt66TzNmSfDRBYVksjtAqY3NSsOp6Wgfd4QH3Awl8GSK/9W9/h3kdWctrjGfHrHvLu6/cwasM4QMipCar3qbH7ENk2Vk2d05x7txZmsUxm5tTSj3CBYULHdZ7+r7n4o19MlPSdg0hJLFmtHGWIDWT8ZRpobm7f5lbVz7CujUfXLzOwf0DJlWO0ZLT589j+55u3aJFx3Ld4EKLECVKRWyM2BCYTUr6VYfwkt29U8xPDrGDUOfbVNMqZaCQKaPpQ07EpkrHQFLgg6dQBSbT9H1LpgPWp3a5KBQEhw2RrnUgHJIEIpxNKjZmJculZb9dEyJsjkeoXHB6FnDdioyI85bFoqFpWppuxcXlglOzhufP7/Ho3jZVlYPUBBnJVQ4Dj2htLRFJ13tWNjUfrdqezvU8enaHsiixAjphKPIKmXlMkSfb99D2cf9kQVcvcd6SZxNmszHjoqDIBJNRxu1bx9hocSrjM08/Qh9btrd28F3Le5eucnCcruzowIlAJ3s8FjPUTp/eKTg8rvn5hx/x3KefQB+vWK3XKAnBgQyKOwcH+Jhs/c56hBhAsERkTIt/ICbrvoJMjwihoW0Sl8NoRbQuHT5khjEChEwbNxFZ9Z5JDOxs7LBcrWnCkrpeEfoTRibZifvYYLsFTfBsTXboux6jkzVWxIhGoB42rMWBAZCWyjhsuERgODD9DR8fD7U//sMnVPiHn/rEX4l/5Q9/5Vv+DZ99EKmL6YAwLOsPWuUiaToqpWS+WKAykw7AJDdTXowQAkyZ065WZGXOr/7Kr/L0059CKtjfv4OMP2Rdr3nzjbe5c3DEZDrizv05r7/2U/7xb/xX/NN//i94+onHefWNtwmxZ2d3h+WiTRwSGzh3eosLj57nxtVDlosTRPAcLw55+f/5M5wQfONrX+WtNz+ib+4zKiRf/uIXuXXzDvVqxdHJHb78lc9z/dI+JtPMNre5dfcGOv8qR8sDRqVh0XcEZ4lGUJhkp5VegE0HZCUVRZVz/+iAu/dr6lXDeHqG0EmeePwpBKn1Zv/WffTEpGrxLLkgbNtzcOeQ0XiD6zeOEEJy68YhH3x0EVVK3vzgAzrhefm1l8nQ+Exxef8KCsW8EbgYKUREyAYZ6zQBch2bGyX1uqOSYAKYTKPoycoC19dslBNMljOuRqzbVapW7+1Dq7ALPLQXV8qQ5YpMCmTUZOWUdr5ilI8JWuNCB7Gj6yFEz7iQ9L4lRKjykt4pZFaxXrXcu3ubxjbYZp3ctFHgvCPEHNtLfKgJoUOZnNV6ieqS2BJcIB/PCI1g5TyVqsB61CQBZYNv0LIHAkJUBDxIQ+x7KiNw0eIJxG5NT8AFS6E1UaXKbOcsXri0MSOCC8Rg0+tUJ1h2JiTjfETTrAlRkFEQvWcy26Lv2zSNEzlN16PGE7quS4fCB1FRZBocaE3bpfiS857cZJRlRVYYyqpCnMxT/CS41HalDa7tCCqxbGLwjIoCFTuCd1jlqeuGIBxaZVSjEbf2bxN7lzbaraUNPadmm0wnMw7vLxLnxznadk3XdLRtjwywO93BZ+lG3FqX7s9ysIjHiA8iCdRS4qJAmjw5CHDgkjAIPAC/IVAP2WyS5GhLZQQCJTUhegKRiBnuf4k341xHMS5TpDgEXLdGyjQUcDHAIB6l4FlEh0iWGYw1SKmITmKEZt315GXJuq0RIiPVqUeyokBpPfCOAuKBQBMTVyHGQByqnOVQNy0jSKFSnCrExIIQGo8CTwK1xzhAuiNBBrQXw6E8iWpCSIJPDaNap5hHjGFoe0t1Cs16nZ4+kZgUCZybANVxEExiCEPTWqpuFwPjBpWi02n/l6IOzvkEr/YeQnKjeB/SvlHJFMFW8uPgwABjLsr0HBmlUUaxXqdmXC31wxhBEj6TUOEHjpKQafCQGZMehxie8ywnxPS+07khRIm16TEJnZ5TMTBxIpCpiI+e4FIkNB8OLG3bIEgHJyEzsswkV5hPDhkhVXIIWTs8ryL9vi5gsjzdr9QALQ4B5xylyei6jixLkNcQExg4OXI1fd8n0cQF8jyj7pMo6VwS2lP0gqGGXSOEH6KfKW4XSVGyFG9Nz4VzySEWfRIEEDK5uIbXTkmFVioJWtEOjKrkZurajszkKC1xtoc4iKuuxXvPdDRJGTYhUDIxfLTWg+M+oh7ADkUcANsepKA0FUiJ7zukTteSc0nIkTIJBSHEJFgNuwYhwIeAD8lbqbVGm8SLdH2LHBwF1jl0niNCRMmY4OMyRytNa3tynSOFoFk3TMYj1vUyibQyIQK8D4OTTWAyk17vaBIPzJjU3utafvwn36RbtXz6i19mtT5mPNvh9M45drfOEp2nc0lcrGYVEkF9fEjftWzMZsgQee/Vn1GUimpjl+V6Rdu1qLbmow/f4+lnXiDPckbVlKKs6PoVTR+IdHQuENSYydY26ztr5GiD5bpP54i+xtvUIDYbbzEd7VBVU7Y3HSJonE6vdV5NWNUn/Pxn36OPPZfee4cXvvp1YrDcvH6RfFJxcnKX3eIx6nu3CASizDFGM18tuPDYYzgn0robPevlCiHjQ4dmDBbXJ/d8ZjTXL77LX3z3D5jt7HH75k1m2xMObl5i99HnwHpc09H3LZXOyIxCibT+e5diVloP7wNpCKHH+pDuc8GD1g+3j/PlgslkghjaJ9NBT6C0oShMalQ7rFkcH3Fq9xFUEPz85z9id3vGh+/9jMlkTN2tKPMp+egU1i9wLuPOzdt8kP+UfGsDI0vm925TL4+4d+8qeT7itXbF2QufZu/88xze2afUjnp9gw/f/0sQkVv7l6jKjK3ZKZCa7dOPE0XGrf1r5Fpw6swFlM5SfCtYwGNIjL4A2HZFiJZRMUm/k0ustuAdJkuCToo7C+qmZmM2xXs7fD7d5yISIQ1KpXtBCD15phAyOTPxkhgShyzLKjQKaz1FWTI/uM+sKpGZRilD2w/3liwjKImWkrZLUeCtjRkqS2uyURmeJK5HIZHCJ5nQB4LrqUaT9LIqjQueKBRSP2hiE3gZ6Lo5G7MJQerETlIa7z15lqeoXkwFAy4GsrIgr0aMphO2t2ZsbW9yenuTU6MiiakC4jCkEiJx+ZJ4LT6hIj2oG/pP/3jwLR/glXz4O85QItgUNfMR6x2LZg4qopVlMT/g0npFpg3WC8bTDUZVhpDJnt27Hg0YH8nGOZWUdNbTdAJVJMU+y5KKp2NG8JLeOuIAD0wZ6rTxSHnyRPfXQgEBLVOLTtCKpmnT5MHDdDRGiDRV0QUoL2nbNSFYRkWBERIXk1WcmKrn67bHaIGRMsXFbEjTST9MoKKjcwEhFNanN+j66ISPbtwFoWnbNb1LU24hDblKv8utuwfsbE7pupbWe2bjEutajBDY2rJatcgsVRFm2YTOCWwX6J1ntWrYmY05VUi65X1ev3ef43rOurYoNLdP5hilsL7nyUfPkxH4+bvvc3A8x4dA03mCdKnZS3h0lnPl2k1KrZEeXOwAS64M1icOgQsRlSXg8gMrZ4xxmPJFghdECibjDYJvkiE/RrTU5FolmGjWcrQS2KiQypFpg+sXaAqe2NnkSE+o6xVHS8uokhADy+NDOu8JviNXip3piOLUDuPRhPeu30XlGTvbu5TVJMXIYvJ3L+uWXKeWs8Y5CmV499ptbh3OWfcOiyDLBNtbG5waDgkiGrxbpalxkPiYNqIn9Zw79+5BaOmd4NzpGW3v8XFNWSToqBcpBqNiw5OP7rE1nXH13pyb9w6oyhE7W5J1t2LVpKhaxA+bU4V1jus37nC0qGmdY16vqOsaHzQm16z6mvc/2qe3LVWZgxfM2xWTUZ788iTx3nqbGCZDBMFEhxApQqaFRBLIMk1RVNRty/bGmHXj0wFSwrxucf6IOwfzISdusEAuBaPRKbJik1FZsX//mEIq6qbGW0fbBJoubYKNjokVlmBDSfoVw3RbJbDcA4h2fLjpS0vKX7uU/kK+7ZOf/P/w8Tfm24Zv+iBx9/BnDCllOeA4Y7KOrus1RVWk+JFzNG3zkPPxYPKuYpeEF22QeUXx6IjleoEShr3Te7zy01f5yktf5sOPLlJ3NcvlGp57igvnHuXb9/8d50/vcLRa8exzn2Y8rjg5mnNm7zQ/e+M9nrzwNBfOPcF7b1zj+tWrvPjCizzz7DP89LXv8xv/7W+Aq/iTP/5zslwxnmS8/fa7nN07gxIZzzz3PNvbu7z58zfx9PTW8cgj51itaspcsbG1R7O6Qh8jZTFBK08wgnVrMVlqhDo8OGRzY0xVaZzzVKOM23cOWB/P+V/+yT9htax5+ZUfI4MjlwU2hHSQioLb9w7Zu3fA0fEx8/mKU7t7fHDpQ4KRTGcbHNw5RBrJeFTQNj0iBBQSHJSjnGVXMxpNMCqQZSlTLlVFkIZbd67g6hVCKU7vbdMuj5ltn8eEJJxnOr3fhBSMxxv065p106CLIjlbZaRrO5ASoxWFMlgbOJrPcUGghhiXyaFrAn3fpkYSVaKFxeQ5k/EORVZxfHTAej3n5PBugimjWa6hEhXeL1EEtBYYVdC3HUoLcinwocX3GUJloCsaluRlhnQdq3qJJQmdowy6GNCkhhvr3OCEEUThKcsxQW/RdPfxdkVuTHIHmpLD9THSdQnOTIfwIERGZy3OdVjfEm1kOt2mLCus6xIvqY8gPaNxyfreMXkOBEPT1qzXxwnCK8sENB+4AjFGsiKjGlfUqxWhd1SjDJ0ZrLW0bQtAUeZ475NzI9P0bZNQJEVJWWRIE5mYnOVyjtQKoURiC2lFOVKc3FxiXU8+UjgCbu3Yv3XIxiyJTEYrhIgcH8/RItWae285PjrCti2mLLE2MJpUBJvu9WLY1EmlEdIkUU0btEzOjCAEWguid2ipccPB3RGGzVscJoIB6wLLxRypSBDkGIb7qAORHBZKCBAaT3JOJLdJQIphYzsUCRSZQUYwOiMzqclKCInrA3pwQT0QYHywSJXi7cFZCJ4YInKolSbGNLwMYRA6k9NExEDwDqXEMNGF4JL7W0ByjiiJs4kfIpUkOD+09fkEMpcK4QdBXilMloYdISRJ7cFE1jo3CBNqYFMLemeJNg0Vw+Co8kHibAL35sYQH8SkBUNMILE3XN/TNWumkzEqz5IbzAmEcGRZMTyeQeyIif/jgyc3ZXpOhgNkgkAnN0uIIAcxKbWapWayQBKUpJBomQ4fjbUYY9C5xNp0YBBCEV16jMELUCmiqGWKm3lvUSoNTq1NB2AfeqJP0HPXp4Y5o1MULEY/OGUSlNXZngdNqDFGlEqNctkw8MhyQxjcVIn5p/A+UhYVxuTEaHHBI8XAgQmpVl2IMAheA4Q7pn12esuEoZTiwXMyPD+k/YaUgrZt0+cViQE1HGT63pGbJAwEb9NzK9Jh3fskqGmtk8DjA1EktlKZF4nZohXWpWsIMTTnWoeSYhD/hus3PIiADCOtZIBj2BaiTTo/oNSD0VH6numOz8dimxgOnMm9obVhZWtscIyq0cBhkhDFw4MmDLyzkKJ/6XkNmFzTrhYUu6fp+wZnW4yasHIuObYfOKxFHOD3ieHp+ySw6nzYn8r0vimnY2RUXLn2Nj7MObV9gQt8iLG/AAAgAElEQVSPfprgJXkmaNdzhDQYYaimU+7evsFyccRiteTypXfIignXLr3G0y9M+Pqv/zpv/eUr1M2am1cvAqBNznSyRV7McDInhJoqF/guMN3Y5oXPfpXf/3e/ye6ZJ9ja3EW7jsO7V6mynNNbezzy+PNMpztUownOefquoxiNUmEQivfe/An7+xeRYsxnvnYGo2G1OuH6/kWW7oTTexeoJjMO9m/g2xonNNV0xq39K4lhKdLQwQdBvV4yLgoQQwNvBGct6/Ux9+9e5oM3Xme9XtPfuUmW5+il593bl5nuPYpUmq7rcN4STZaE7cwT257GOpquZqPcGho1JT4kMVdGUMaAlmntqdfYpmdzvAXD2m19Au/nVQHa4LyjGo3Iqyk7szHXr33AdLaBDYHNzR0OD++xdWqXstzEGLh/d54aJJWmDYKu98wX93jv9Vc4PriJwtPWDcv1guN6Sbl1lq5r/1/m3uTHsvQ88/t90xnuFDciMiJyzsqsiaNIShw0kJRbU7fVbblbDRhe2PCiF17YK/lf6J0XvTRgeGigDQ+CF17YoqiJaqolUuJYpWKxhszKyszIIeYbdzrnfKMX34msKoktowEa6AtUJSor7o2Ie8/5vu993+f5PSxmcx49usf++/dQIlCofPbXZQEpsP/eOxwfPWXdrPjExz9NIieMKXJTViiFLAoCEZRmueqoyoooJTLk+9z2dlVTZIsgQtI2K3ShiEKRtMZoQwoRGSQiCHRR5TXUekgBU+TEPCU10VpS6OHmqiCGjkJJgg+QAlKXoDSRzGnTsues6XyPr5dr6rKiKIueYyjRSvdJmwKfyHbmKAjBU9UGXZSkfuAtoscoEEKhhEQpw3I9x+hIURkCkmFRQYwok9XeIUFRVwymYzY2hmxvTdnc3GB3OmazqnvxZF47kkgkVM9JA3HBXbsoO/oS6KfSTPoJEqfUr90/zcdPvaEkhcD7hIgSESRJ5O5w1zlChGXTkWgxqqb02a4QXEdrfabnd2u0KjhfRJarXoItclc0+pweITEYoQlaZaWA7SiLLP9NySGCRKKoimyxcj5vXoJE8jFHS4qAiJHKDNnY2OR8dkZRjYjrjtliTtFZGtdx88oLTDY2WawW2b+PwEWFNDURWHQ2y8iSotB50yuMpmlW+WYTihgSQyU4PT97Pl3LVqy8uaeUN1mCpFt3zMUcKQQb4zG2XbPozillZhCMqwGdt7RNYBWX+DYxqhV1Kbn//lu8a+es5ytawOgtoGY4qFivfT4sRo+Wkh++9joxWJqugxTpOo+3kiR89plqyc7WBouzJdF5tAqczs+z2kgXeVpSVUgCnjWJSIjkg1yS/UYuEUlR6pxSEn2LjCIfwJWkVAaCJ3mHd5ogAkpnv/v2pALbcnpyxnxmado1m+MhG2XF5nSLo9kJL+xt07gVZT1gb2eX4WBIXQywneCoaWitY922nK/XzJuOxbJj/+iEa5c3+Zk718ArtDS8f3zG4XmTrRd1SWEkSkmEBhVFHwWsMEojVZ46tjbwxrtvY+0CLRXb2zsYJVmv13mh9XmxyshZhRYaksSICs0ZP3z3PUaDktvXb/BwP+DiMh/wlETGyGQ0QEnB8WzF6WKJloJ7j56gRU5D2ChqVm1EG8nKBUamyJP1QuSYU5WnC0oVOB/RMvXwOUmUEWcThMCwHNJ2c6rBIB/og0UmSdtky5oymqbpsF1LS6RQJUToXFYWnaYF14Y71FXJYFCjRD7otU1DiBFd5GQkrfNkN/U41cwfiHkS8RH/2d9ePD/S23n+EB+Ik/pG2cVTn1vbxIcW4w9J4J/D6p5rGi8GAb3d7kM/x8XXXcijksh2ChJEqTHVCNV0jKthVin0UtL8wef3SJuC88WC1WLB3t4us3PP4eEJz54+gwT33n8AZUAJx8a05As//3m+8ad/SlnWnJzN+NxnP8PHXv0k9+/f5VOvfpxXX/k4G9M93v7xW7Qry61bt/jB66/xiU9/ik994pP8w3/02/zKV/4+3/mLb/P5n/ssD/b3WbUL3r57lxdu3eGlO69wdPCMx/tPGA4qXNScnj5iOr5NVVX85q/+KmZ8iZMn/xsvffwOP/jh20xHBU0UOcVJRIyOhNRhQ8HGZASpxoYzQrti5/JlFmdzmtUa7yLagOu6PmVJYC6gprpkbSGokoOjI04ODxgNBihR0TaHbE93kUpjXcfp7JDZbMbuZo3zkkIqUlQEYXh2sqYoYXdrRFKJpmkyP8cotFcU1Fza2iOuZjw+OGa5eoBPDiEMzma+AClQ6irH1rYLYsipHiFAWW/T2S5zULxlvj5hHEeEtM5K2qQxqkKLgo16wmAypCpr5mdnlNqQygK7XBAL0xcWBYPRlNP5CaUeQA+mBkjRUoiOpDMUtnUl5XDApas3WBw/BQHFoMRZCykQvSIlQwqZAxCjRGpJVRhiMnTWEHxLSBYRPAIDaNbLFZU0eCLZdW1JXlKaguV8hi4EpdE0NisErHVsb+/y7PQE1zNgzucHVBUoVeEcFNYQoqUqMmdIpFw8A5mnZz0bkwnr1SqD+IOnmc0YTQYsV0ukyly+bBeItNbl6Obk8KFDqZLoIz55zhcrUooMhzWgCcGzWpwTvSeJhIsJ5yKxc6xmxyzna6aTDabTDUQULOctSkmG1RhdFBwfnmAnDVIXDOsBcjrEWY8QZc/YyaDQ/Bnls0PwGYLqYkKlACnmUALviWUuZvL5JRfjMYXc2PeeejBgOB4gkkMZhXWht2Z1KJ1TYoJ1WQspMwBd9vtyPjNkqxZC4GJEm6wIieTf3ZQFPnQ50UWAbfK+pEjEGJAiEVJWbkMeOiiR01mdc6Dq3JQEXApoXRB6W7ePHiFzUlehM5g7N3QSPub729PL9nvVjo8BHxOds4imxYSQ1WtCYL1nc7xBWC5IveUxpD4FzWc1m1IqD6lEXr9jv7dk5lG2cveuwf515XPljTLZxhCCx4fcbJD9+1aIrMbN7b6cghM/tNPElO/N2DfffHDPAyekyj9nQvRnnXymCwiUlDStZbq5TXCWFPxzVVBOUWtxPjIZjZ6nxaWUkCKDp4NtCdFTXKSeiqyL7bocHKC1Rqkcky2F6BMZ8xqilCL0Q8wUPVqbi3em/+zB97a4iyAJpbPiMYRISpIkBT6EXklGBge7BqkUMWWlV1lmxVYiIZXCO5/5XEohRLaDhhAJKjeU6nqUd9de9RtTwHvHxsYWWiicCMj++TF61usGekahEOJ5wppWqmdngZAZb3EBwc1XbG56hZAnQ/k6KDEmN88ulM7OBZTOn53UGufanruUcvx5yqeDmGJfeKbMdBTZ3inJwSGd7SiqzEWTiNxw7YdmzmfFSuzf63TxGcTEYDBiOVuwe+0G87NTjBZEZ/uGXL7ehSBDwUVuGFZVxdLarCYfDPrCFIp6xKs/92u89e0/4NnJj6iqIT/7+S8jQs3Bo32ULOicICDZmI559N5dSPk93t7e4/jwMZ/+xS+zXp2wd+UGo+EGN194tbcYCbQS1MMR5qRk5/KLfOzKFe6+9SNuvvQx7r/+Ay7fuMNkuMHm+Cqf/9zPU5SSY7ugLAvGW1N2rt1g98Ztmvk5icR0OuXxg/fZm07pmoaH+3d5eviE6ASUDQLLj177FufLc+7v3+dwecClnRuoomKwdZnQtdiuZb5YML18DSkVR+/fI9o1PihCcpTFiOgjSStc1/H+vbc4ONzn0fvvZvD15jarxYyjpw/ZvfYFdq7eYViNaFzbw+tBlAatDFIkWp9tqsYU1PWwb/TnJDClc3pkvg/z929XLePBGCkUQhfZfp9SFh70a4lIkcFoxJXrd8A2NN2KF198FVUPWS0D48kmX/nq3+fhg8e8+dd/SogeRMlwY5Pdyy9gKLDaUdRT6uGCYjjk5PE+V27eZrJ9i/bslO0rlzl86JhOtzkg38+D4RDvOs5OnnF6fAgoJts7XL76Ik3rqO0aqUfomDlxlR6SRI4ddKsOrSWjjY3cdFIGVCDYBl1k5WQIWd3btg3D6QgpTG6G9ut18pHKlL01LatblRRIqXPTNQasaxGC7LYQEhsz1uH49Iy6yta5QMqKsuioCo02Ei01bWMJ3jEe1eiiIqKyuhiB7M92JuWBhksxi0fGEyDzklLPLhNEUghEVaClxHeWre1NlMpg+CQLOhEYTsaMN0dsTSfsbU7Z2BizNR4yNuY55/Ci4rhwaHzYUiEuFEnp4r8v9qCfjjIpv8zffq1of7oQpZ96Q0mpATokKqOwfkX0Hh8iPuabTRnZxyrk5pG3EWdXdG22o1nnKOoRnsTW9oCz+Wme0oSEd5GqyHLXkCIGRVnUBGPQRhOSJ0aNawPWRmznMhcHgSL0EtacZmKUJCKz9F5rCinxrmFQTygGU2y7AlcQgWuXr/P48DFN12BMkb2NTtJ1DS4EpDCoJGg7m+nzwRE8FOMa4TO07/TkmMW6oewtH0bmWNN8OEoEoTLLIziadaKxLdZHpoMRa7vup4CRjcmIs9kS79asXIcrBbtDy4gZj5YFC+uohMJagzGR6DN8MqbcSEoRus5y/8EzhFRoA6VJCDLk0MeIysM5XHOOsx6jDUIEXJuL9E44BkZTmwIl8l82XhJFlhoSAyqRJ+UJXLC4VdfLxE2vwok415FCoOkUxkhiSHifWBO5MxqwNx0RY2TZLVDCs7c15valy9y8do3vv5nY3drjW299n7mF7a1tWic4PD3neD7jeDHnL9oZG8UIF+FkvkCKgnVnGW5t0nZQ1wNi/zmUSmIkpM7iRPYKC7KHvTCCelwSkmBQZE5BwHL7xlWuPDils5ZSaU5nc8qyZLZcMKjyxMI6ByKQOs+6a2g7x6XxmFt7l9naHfPClRscHM0YpwwzTSHQuZZl27I5HtC1Htfle6MqCiDStYHOtmhZsVgsePGFy8xmc+q6QnpLdB4lFatli+98TlEQUKp8MPQhYW1gOhzkpk4SLK3HhY4QHSezGaUpGFZTlm2TmSPVAOtyk6gqSnRVUBQjiAlB4O1330WYmsnmEElOpkFKVD+tVTJL3y8aPFKKXq2ULUZS5BQVycVU8G8LkMTzv/jg/154jIEPLcYf/fNDytGPfu3feKSPfM3feDHy9EAqgbWBZt0AuaFdD0fI7PckioT1kboeYbTGd5ooAqN6wPnZKe/eu883vvHH7B/uc35+jhKSuw/fA98xHpXcuX2Lr3zxy/zrb/wlv/WPfpPdnV2atedP/vCP2dqZ8KlPfop6OGHv8h5f/8Pf51/+r/+Kf/of/2Ps65E/+dff5Iuf+Tyf/NinmR0t+eTHX+WlVz/Jf/8//g/8+O5rCBJGC87OTnnn7jssFkt2trf53Kc+QV1MeP2Nd/nhd77PP/mt30BWW/zSF7/Ef/bP/nP+23/x3/HjN17PSVApkLxHFIaE4uR0QduWlCpw88Z1qnrO/Lyh7Tq+9b1v8/DRQ4pC0qYMqpRGYIykrErWbYsyBcuTUw7377JaLdjZmEASbG0OqIYThM7AYq3zNTgwntl5hzEKZ9fIyuCCxq5AmxlGKuyqycVuMWRlPRuTXdarJcuz8zw89payLulspGvPae0aJUqCz1wG7yJaFfiQCNETKLDJkQgZfG8cNrisTkl54BGSoqpLJvUlUgGzs0ekENncfYVLly/zYP+Ayc1bHJ8dQTimWXf4oJGFydYU72i8wKhArSyFSrhoaYNmvTplMt1ibS2yj3Xv7BqfHJ1zjOpeeRtCntD6wLBWJPLU3bcLYvAIIj5EyrIihYbdvas8ffoIHztKMwSd1QzRZnByXVSIpFnbFS6sKarLdG1ODkrRE4Pl5vXLPHlyjHchM11kge1agg1ZlZPIqSs91PLC1qSUwHaZceed61k2GW48nowQSrI+OMYUKtudCNimIYTAum1xNlDVmbVgytzILUvDrRevsuo6zk7P0ZUGHbDCIoXm/HyN0jVJdHjXq0RiiUwBhaBwgW49Zzgq6Nb5GtZSU2ideUcCIGRor8xDEykEWkp0ykdAmRI56TiB6IGhJEieEDNLUusis1KkQmmV1w7nsK5DIihkVvz6nsMn+maHkpLEhfIiD7eQAhd9TqCUEmtdH3WuwCfoE7WC91QbVd/8yWtuBsoKord53680MiVoyGBUBKQMIBZJIlPMtuqYzxTee0TKiZ4xemzXEIVhPFYEa/GQQeUpYrsW7x2+b+rIkJVKSmSYfF0Pmc/OclOgtydlBZTIChJtMCqrZlJyzxWGsZeTXqS8qotje0x0nWU8HqMuUtisI4mE1EVuwvQ7jRSityWKfFZMgqQyoDX6lDlOAhKesuyTSGUipswW8t5llVBvQZI6q1tChKKsaJsGYiD0E3EhE4XO1iVtiueKlZhy8Ayij7jXBVVV4gOkFHBWkGJAFwVlmX8HHzMfSSiN7JPNQujx9iJfh1IqYoiUdW9VMwbv/XPrWraqCVLypJQVNSll5VZOXsu/m7U2pyf1Kir4QE0sRVYU5WvaZK5Syuoi7zzOBTang2wH0SXWNljb9SwiSdd2OG+zoqfITaL1umE0nvQNrEDqXQ+j4ZAU80ZteoC1QPQw7vRc0SdIWOf61yyykiykPqlNomRW0xXmwsqYf89I6tcs1auQVB5Gx4DQ+Tmpt9U551D9+cEo1Te3OiAjLJKIef2IiUBWOCWfhwGD4ZDZyQkCybpp2JpusFgskVo+bw7GHnJMD4VGZE5ZCDlJz7bZYi1Lg+8S0+2KB08XoEtsu2QyvYQ2EuFbcB1CCA6e7tMsV4wnY5QsGW9MqQcDTFHw0md/jhsvvYrrOi5t73J2PuPOyx9jfnJGdJ5m3XB55yptswLbsTWeID/+OcaTTV77y+/w6me+gF013L/3NqdHdxHFAE1EhIQSqoehJ4rBgFXTcrD/AJUS1XjE9tZlzs9OiCrx6K0f8+DeG6x85vApXVBNNnNTL0WOjx6zXJ/S2MjujdvsXbnG+f4THt97j8nutcy1uTjXicwi61zg+OiQ46NjpptbWZG8cKzWHU/fv8+tr36Cv/jjr/OZX/wFFmcnbO7toeoBdTnAuS4HLfjAaLKR77WUWXMq0SMcchOXBJ2ztK5jY7BDkFAbQ4yeEDNWIiZJ0zQsTw6IMTEsDcO9Pb4wHvBo/z1uvvxxFqctdaE5PHzM/OQpiQ5JPlPvXtrl6vWbFKag3Kp48ZVP8f47gY2dHWZHh9y+8wLV8GO8/u3/m1/5B7/FSejolmdEYdE6nyG8D6SYYdIxOW7eeYXbr36WqhwSw5LJeAfbRuLcY+OKQTVFIFiu52xsTtGmJJIVmcGuUaQcjiIMLgWs7UgpUZohUWZunkgQXW5ql6bM6qKY8DFQmDLfgzL1fMCAKTTGZByNMgXWRWLsMOUwH89TonGORFbvyQgpJtq2RZeSclCiVYFLibLQdI1Fp0TyAaJFVBXedZSFAZnPP1ppgk/IlBu6QUaq4QjvAhuDIbIqsbJgsLPLZDrh6uaEvY0xg1HFxqBmoHVWqybyXtYPdaBPj/tIcXMx1M481otC5YPy5Kdnd8uPiwF7HpRIZ3+Kr/3/Q0PJWtjc3GJ1/ihHd3qXGQsKUsq+2SyxNTjfcXjSoRQIEXGpYGtvFxMtKydRBIwSdNZnTkUAgUfmQBA0Kke5Kui6ZU53Eybb4JDImDeOuixIIbN2IjkdJSqNVooULGezU5quYbFes7m9yydeuMXjg8fs1ENWy3PO5yfcunmH+fyYg6OzbOnzDqUM080hLkZGheH8/JTgHc7laYmWidGoom3XeIYYIwmqI1pHFwTqAr6lJBjBuBxAcAwHQ5zvOD05hc0aJXPHtOsaDg5O86RSJELIcNDzlcVUkhQEUlRsGcds7qiMpbMOgkRpgXMtVTEgpYQLeVP0ToBPlEZQKoXS+X0lShaLhihLFOC8YDQeomSgS/ki9MGhix4QnQRCBazLUaMpZCYDKdKFQCUVQmi0ir23PR8uuxAxRa/sSFme6n2Wbd+4fInV0vPO/kPqouTF69eypFwlxpVhNj/DdZKGyDvvHnK+bIkyoY0hxcTjozNOdWBQD2m9YtmcI4Rk3cwpxfUsGVeRlBTRRbyIWTKd/ZKIlCXKEklRaLzMcL8UIqYw7F7a5drODYJ0NIsFIcDT00NcTCzbJTJkuWQi5jjkrmPtV5zOZ7x09QWsCPzgtR/TdA1t6zHBIqJDisS6XeOdox5ork02eXx4nrkIIRf1rXWIlLh+bZcvfOpFfvfr32ZnWtB1Hi1yo1IVsm/eKVxMDKsKicB3jkJB2zpQFusDSWhUXRDbHLNb6gJdFAykxCefo1IdSJkoiwJdFiQnMYVhNj8nEFguzphu7RFTlr1H58FHhNZ5wkc+4OoLuwD0UZjkf56LjCKkPt/tb9jfPtrk+dBDfLhblB/p+b8+aP5fPC4AsRcpbunDX0wviU+9ZaXnPfUuTpSUGGXofGS9WlFWFVGE51NF530GoyqJdQ6pDFrDxsaEg5P3OJmfsFqfMxgIfuYzP8/X//CbWJ2o1Qa2q/nh91/jv/xn/wWutdx+6SXef3+f+w/e40uf/wJVOeTg6SHv3X2HoujY2DD8wdd/j61LV/ja177Gg4fvMTtp+OqXf4m/95Vf5MqlHepqwN7mlK9+9Zf44ue/zKMH+2xvb/LGm2+ybpZ89cu/xG/9h/8Ryf8+9++/x3e+/waXr71A2zR891vf5ea1G3z3O3/JaDDEtoqirJmOxiy7jmUzxy0bhqLjzu2XkG7BarnivG1pIjRugTKGjXKDxWLOeLLB4mzNe+/d5fHTZxRVxcnBPvPTOcVoEzPYoDIlhR5Q1ANiVCgB1fZlludnBJ2lz6N6jNIdIkU6FMHB7OSAZHMiaFlWSKVZpoBOgcXpDN+tiDHSeseEId6twfisIkWw6taEmC0fW1t7+K7lvDlluqmQKoHvMKpkazpltlii0RgV8P2VE4SksQ0iWGy3ylZZt2Tz0g2eFKdE61jOTtAqQmwIy0Q1neDaNT4pBpMBMTbooqSqLHGVFaMuRg4PHmIKS729Q3u8hLhmtVgzHBpKOQBt6FwHqkYkz3rlMLVkd2fKydExLuYIWy0Ego7re9usbMe4HrB2OeWuKgyd73AkYoBSKqqqYNU0hCR5vP+UpDyDcsxy3VGWNV3TonRkfjqnHtdU5RiiBJd5YlIqgsuWKiFhOV/k5pFSuZiUktQnOColCc7jnWcy3iCGQHDZgm2KkgRY2zEcjrJ6LFlWa8tIarZ3N5HScHR4TD0eMx5uIYB21VCobAmyrSVKxWJ+DgK8CzgP0/EkByDUA8ZDqIdT1qs1Dx/f4/qtVxE5oo0NpTGFJKWs+tDaEKLLP3/KKWEh2F6Z5ImAkQYRyTZpUYAISCRaZiZKQtAtW6SIiOTQUmB0gUD1jJ6IkkUfMiKfF7IAgYiWBc521IUh+UjwjrqqcuEV8qJqrUNLlSPAU1a6hBiwyaFFAucRvWXS25YYAiJElEoEG/MAsOcExT6dDOFJLhKihKLOCYvRYwbjXIj3DUKNpIsu2yGCR0mN4sIylscHRZELBlLCKIP1DiHABY82BjtzVHXZ8/UkMVwoWXLSWm6qiP5Yns983ju8d5RlkT/rkO1sUhuEzCobpfRzJYuUfVNJ6QzE7kHgkFk/RilECjib7V0K1XM/cuFUaJOVUAqEkjhrM3dJa0Q9IDQtGI3SmloNsa3D4ZDaQEyEaHvljHi+l2ld9JDqiqY5J/iUWR2qoijzuQKR9ydtCnje2gh9Q6dPwvtQypnsk9cugPvRxl6llJtYot+b6Ys/IdJzxVCKAaNK5usVg7ruAdeZY+qd/0D11L93PjjoVQpaZx6VkOR1qj+HVEWR1WdakwBlElpr2kULKmMmsnUsv9cxRApTYJ1H6dx49MHn5l9vw/POg8y/p7UWUxR01mL0gNgDtMuiQqrc+Ct0gbc2A8alIHnfJ8NpnHcoqfIwhYtzQlaFhZTxBFFC8Vy96DMbtQe7K5FZXdFnhaISkuhSryqqCcLTrJa4AKUZME9raqWIAoLI58+YcnJkSmSFndbZAtr/TDEGyh7OjEnowZDNyRWePvwRl3ZfYTwes5odEZOnGo8Jy5adnSs8fvIQoQVLZ7lx5SrdqqEu69zIpkASkUmwuXGJ5cmCo6ePmS3OsXbO0dlj1t2MN773b3j5M1/h8o1r/P7v/u/8+uc/j8JhZMXp6ZzJpGS1VDzdP2Tv+ioXzyErQ8bb2xAj8+UJvm2hbSiMxnUt9978IctuQYiCjUuX2du+zeXLL7GcnfHondd5+uQtVs2CneufoF11jLembF+9xv6bf82nrl9FYbJ6xuSkrmADi/MZxyf5fVgszliuz3BuRYiCg8On/N7/87tc3t7hRz/8FvfeeJ3rL77CV/7hf0KIic562vUCqQuqeohLoa9vesYWIE1WAkbrWJ3PqeoKoQSq0s/3A8j3nQuW9XLFcnbG4/tvomiY7r6IKkcMd69x7cYd7FZgd/cKf/UXv8fZfAa2YLy9xXx+zuzkMevZM0bXJlTDEYvufc5sgz14Qlkl5scnlNWKnb1b/Mnv/Z/MFsck6xEiYL3DdpZCFZjCYLShaSLr+QnCrXn9Rz+gOdvnS1/+deat5MdvfZtf/c1/SkoR5y0pRapqlK1r/UCoa5t+/TT4Xsm/XC8YjQeUZoAXkRQskaz0M1qjyhKRfB4siZiHJynveV10eb3vbfwhWSpZMTs9YlyVpACykqSQ96PxYIBQpj9LCITMKixlyjzMShEtBI6MmAk0lEUBpoKmZbKxQURlFRWCQEdZakg9u1kWLFXDzsu3uHrtMje3N9geTqhrw7gsMfLC0SCfp0ZeyJLkh2sV8VEPREriebPpg9G3+GiJ8xOG3/+ujw++38UQPldEhVT/H8/8d3v81BtKUQpm58dZDRRyYk7eFPvZkcgbuVGKmLKtTAWBUiXJJbulUc4AACAASURBVC5vXyK1S84PZ5y6jkIq6lqx6tNZrPMoIkJBFxzWtxipGA420DQsmlXej/uo4hgCo8GI5Bw2KVqXp5OJ7H9OItJ0HefLFSFGTk+PeCtm3363PGY9z6krqqrRwiNSh5ZDUlEghGE4GND6hjtXrnJYGZ4dHOKlIMUCkRJ3blxDB8e9Z4e01iJCltmOJiXdOhczQQhECNlHHRJV7SikZnNUsPJzotdMdE2pDW3nWXeOJDNgmSCY24ooS5AVJjqOug6pFavW5kmUyjGYjjx9USrL5r3Ikxi0RJmEiyEryVJWckmlKEw+SBmp6ZzPgDqZOQ5KW4Rcs/QFIkGlcrKb6w+ehUwgDdJbUFApj9Brlp0kSU2FoI2eqh6gk2LVeqQMrL3n7v4Zxgjee/+EaCKudSAqfLdisVoiKoHWNc9OllSl5OikpSgM0+mQzemELiYm0w2Oj+ecLE5QQqG1oFk5ms5SjcasDw56ybDIEY5aowqD0QpCtgeqfgP0GgIJqfO2UFLQek1Z6ewrHpQcn8+oBhWXqjHL+Yp1t6QqDOvWsjka8uTwhDeePKM0NeeHS56eHSOjI3lFGzq0DiR8vimlYDoZUxjJ6fmSj92+xvl6iZSKx08PIAhab5mOxzx8csqw1FSlouvygSoKjynyhLX1HXVRZKZXgCCyfaMsKjY2Nzk+PmNzY4tqaDg6PEMLTzkY0KwXmCIzzmRMjKqCpbPM24badsz9mlqXbE03efXOy9x7dEDWK2W4a9stsNYgRCINJVuyxvSe50S+RsqyzHacGPs5PNCXBiA/kOVfwBB+UtOIDz3tJ//H33qIXvae+0gXUwDRv+YHE4IkLr51r5qKESHAFEUuhnQuaO7du8eN61fxPh+Ij48f8fIrr+JDbkRIY6irEdPJFNsFZrNzlIx87OXcWKuk5urlS5yet9x/9A5bk22WrqWuK26/8AI727/N/sPHdM2K+48f82ff+lN2dgxf/NKX+LNvfo9v/eW3OZuf88abP2RY79Clhv/ra3/Cjb1tfuPXfpU3frTJL/zsVzk5OefWzZs8fPSYdj2n6Qzfee37zE/XLJYrHhw8Yh2XXDt5xt3793h29oTZAoJPtE2LNAU3b97iN37tl/mr732f1998LafZuMQPf/DX+ACq7Hj3/j3adpWl1azBr9HaMxxsMXu65vjkBNIhVWlYLZZIU9P4lvniCCaX0OWIS9sTUhBsTsfMZjMODyoWzYLt7S0mgwFHp49pXcQGSW0qmsUZTdOgjMALR62HrLo1z1b7DOoK4SPzdcfu5Uv4GOlCw6CQoPNEPimB7w+Ju1evcPjwAcNywku3XuDxk4c8eRIZDbeYtZnTo6RmPByztB3D0YSQAuvlGUY6hvVGnlY7uH8wR5cDfNsifENRJlK3oixK7GpJSB7bM0Wc61iFinVfuKbgkEkjURSqws8tA1PRNCWvvnyJFAKz4xlqQAbgyxKhEiJahIbz5Tk2BoS3SGFoQpsTu+aGvd097p8es3lpwny+oO06uuAJSWGiRPnI9nQDEXuLrA4IUxCd5+rOJYTtaFs4X5yxvbPJbLEiLBe44HuuQkCh0Tpbc0iBrrVsbo5YNd1zxYOXWZVRDwzeBWan5yRAK4mSRbaEp0jwkcFwhC5qTmcznIuYQnJytOCVj99hubY83T9i96qiHo/pWkfbBi7t7pLtQof46IgyUg9LaFLmO5hIPR2xcWmKKQSt05yuZqhS8vDRe3Stxeial4qS8XhEtJGiyFaYpAVY6PBU5EGNUll9KVPK+3vI0esiZcuMLlRuJEiVbYsxgtGELlCXdU46lZnXJFN6DkI1PcMtZSNbv0/3hb+6YLuJDIXB4XxL0grhMww/qx7ytNSQaAMQEylJSiOIyVFWA1LrUXUN3meeVW8rkiknj2VVUW4+hNQfmIWkc4nxIBcaMdgcMa0hrfNkXpCotUHL3Ggyva3KmIrWrnJxnLKiKatM+qFX9JTVpCeYguqB3lKobCnq+U+SvBdrIbBdl5XnhUEriYiS0KuOEQIXHKUxOWHVuQx8tm0G0SKzIktIQshvZ6EktSpoo0Nq3fOGskrKuS6rdLRCKI0SkrW1FIXp2T1ZPV+aKoPVU7b+aWWy2663VIWUGVcQSCkglSAiUckjY8T5hqIukLJX18RIjLmREUJuoBVFSUq638fysEYJqEqdFWmFoW0blNZoXWb+h5KZCZQyiiEl2ds9oXMux4R3FsEHVhYhxfPmkdS56VEPh5mpJRVC5fO+SIGmbSjKGkFAyV7hhCQSGY02UEb3g8Kc7obIKoOqqHqFcerVT3mAlRVxXU6dC1k5qk2Vlf4hWyQLJXGub6iJC5WAoOvyMLTrGmKC0bDOzW3v++vqA+j6xUApXTDGVG5mCiEIKSdOu+iRFyyv6NEkHCBSzA3emFVVkmyJiiEgZCRGKOsRo9GYoydPqbe30IViMt7ABQcRTFkRXEKSC+4kBMSIMAWFhvV8gS4rQPXWFc/u9U9T1lfYvfwq1q4YDkacKcV63dL5jmo6ZTDa5O3XX+fai7fZf3CXqiqZbl7iuD1ECcHB/hNufOwlFot1r4YrMVXBg3vv4aPn8ZNHRJHZZyend9k+vs3LL75IaTR7e3ucHj7m4OiQ8cYOpakxepPjgyfM5qdc2r7MycEh4uQY267Q5YDdqzf4o2/+AV20dOtFHt4Mdrn9wkvsvvAKt669yMDUjIYbHB3f5eTpOxSVZNZULM4XXL81pipqrJ2z/+gRHwse6aGaDohBQmjYf/qIyfYeX/jFX+Ovv/MNTo6eIssSU40JbcPZYomYtwS75u79H+Fty7xZENyaL/7Gf4r1ERs8083tHLYgZFbDhEQUMbtvhMK3Hd26oemWTDevZEUR9I36PtFTSJLvwFoCsGo6hkqxPDvmZ778Cc7PG7qTExKC0fYGN268zNZoyjvvfJtBrTifC5yLvP/+PSa7NxmZHV589ZNcvXqDk+MHvPPGmkt716jqEftP3ufw8V3GZYGSJUlqCl2QosC3K5ILJJ/v06Nnj/jmH/0fJFOwOdnl8cERa+f5zOd/AaNqhDKcn8yYjCY5QEfl9bQLli402bmgM6st2gy7L4cDggjoJLE+kqTH2ZZyUGdVZoCubakKnXPVpMI5lwc0RvcK2Qzeb9tsL0w6W2qFqqBdUZsCU+WERiGzTVoKxaCqMUWFj1CaiuBaKi1pXV7ndTVk3q4pqhqpKoSWlIUhdB6bBFZpbFWyc2WXF6/tcXM6YWNQUVcltVG9YEV8YIV/Xlt8uPb4SV4LnjeRfnKVcuGI+LsqmJ/8uGga/a15+/Pvm57/aVGU4t93hZJb0baeycaUrl1iVIHDg+7fJAdC9jI1ld9Y14Kosrf86PiMymimkw1iaJidryj0mE+/fAfXLHhy9IxFm+1gQkqkTnSto/FLChWJKfvXkVCZinW35mw+p1KJxsUesEjeuFMg+kDrApevXGa1WJFEZLFeYKQmuog0gc47zs6OqIzOccFllvB3LtL5QDmYUJiC27s7qCRZt/scnOSpwaODIzaHAsoGFh4fNJQJ4QNSC5QGXCLFbGmLKdE0kSQCV3emVG3k6X7L0/OznDpGnvRKMohUKYE2gSZk8DYCghBEIdkYRLo2ApZgE1Ai48W0LoOzBX0ogUhoncDlxWo4MoSY5fAN/RSsqNnZGnK+OCL4BLFktuiweXAIKaFIeC8gKrxIaCkoZRaT+2jZHHecrSbUdUHbJnwILG1iIKA0hqosmE40y3XL/f0jztYL6lrRecPXv/0dKm146eoltramjOsRIRlc9AiVI2kXqwZTSiaTIUYF9g+OWNkWiWJ7Y4pUkcGgRBQ51ni2WlJWIJt8IFQqU/2TJNvzUobeGu8zBC6oLI8ToGQgyRYdFaiCq7t7bKeIbRuWq0W2IwRLXZdgBC/cuMptVfNvXn+Txq1QpWQ26xhojRKZXTEeV9jOUleaolQcHy84X7fcvFFxY1rxlZ/9BP/yd/+I40UHSXH3wROiSnjbcj5LBJ8ZGaoHZ8YQkSScbXF4CnK6SESwtXmZajDmiT2gcwtOn2XmzcZokiHEsxl1PYSkSDLmpARv8dEzGF9Ce8V4tEFdVwzGQza3L9RJkAJU1ZikJC46MIqUZE5EyYNNTA9rlQhSzPdsShdch48uxB9mIT2XHn2kjf9vbyBdqExFEh9Zo3NTKT1vGJFSX0D1300KYog5Ohz1geIpCpSWSKdISFrf8cJLLwGB5fkZvpXcv/+A4DNbZXt7jLI1MQYe3H/E48ePGNRDtrc2+ewnP01MmvuP7vI7//V/xT//5/+Cm9duMd3Z4f3vv8a9H7/HumnRpeC7r73Gw/0H7O3t0to5ZXGF+cmc9eocHztGgwHr9YJVe8j/9K/+F5AFAwn/ze/8Du+8/YB37v7P3Ll2k2tXb3Dj8i6//Mt/j7/63nf44z/7Gjs7Nzg7adCm4Pj4hNAuuHHlFkezGaouKOvMIREqT4pfe+Ntnhw8ptCa4AU2uizNF44Xbt6hXeUkzhigsRaCoVl5njx7j6QFy5WjrGvsfE7ns7Q5NGtiWDGbnfIP/oNf5qXrVxCqpm0bktdMN28w3UwEazE6koRh5daZTxNbrGswg4AZDPBBYtOS6Fp8FxhtTWmbJdYFAoZRXWGrOboQpFRAEKyblhBjTuF79x26ZUMxGvD02QrvS9rGsr1bY7oVlzd3cjpNUdCFGcFbFssW2y0IMhDTgrIck5KnpeceDEcMN6+yOn4PlyIbwxKJZO0lc7tE4NASXB/bLlVmvO1OJly/8gLloOPh/pLCGKywNE3EuhWJiAtrRpNLtEuPNkVWnKgFpaoQ2vDq517mz//qLYwuWC5XpLSgWgy4evlKVk62lnVrQQhs8CijqauacTWgaRxNnBFkpJYlM28Z+YQRueG0PanZPzzHOovsWTkEQSaaJLTKxWPwnrLQXLl8iWdHpzRdm2+oFCnLGmcDG9MRg8GQzjqGwyHBZXiwXbf4kNgYbWSuU8xWHm0krrO88+YD7rx8k89/6Wc4PVlxdnLO4nzBcDRhYzpFy4Rza9r1CqUzvFkpyc7VHeazc47PjjldHGK7ltIoJoOazfEWpycr9MjQNVmpkM95+RApUgCR4Zoy9lHjMiG1RsoiL1FSkUg4Ut4ne66QUBIpBClkS7RKCmcTm+MBRsm+YE0koemsz8/TfyP9JWaLsG1bBIllWKKTwKsc8CCVROgyT6ML3StqMgdEJdB9mlbsFzYjdYaWxogxhpQswXu0yfaObDHzXJDmQoxoY7ItIESU0hTGIFOAIHuOUx5qiADJxaxSUQphek6PTQx0QfA2r7kxZNtdDChyMZ5iysV+AJ0gKNnbNSLBuWzJR2R7qvcIrZkvFwyGQ2QPWe68AyUQIu+PbdMSfZH/jtw0cC431CCrDgRktQsJYwqaHhodXaB3xRNDypZtJfpUrwygd84zGo+IZMV1brzlxkRC4J1nWNeZsyj6NDEkpR7gfZtZaDFbaVJ0RATBBcbTDZp1gxYGT1Zj5uIk9qwgk59Dtip6nwsG1aeFIRNtmy1ZSglszBYqROackcC2HUKqbO/t7XfJSOp6wPn5jKIq834Zc6qTlAopFfVwgHMX7CGRP3MiNkbGdY2QCut8ViY7B8g8sFK5OevJ9jDbWUKM1IMhz8Hn0ZLooboxgMjXUXQWqXWGp9OrHfvGY0qJsip7VVYeSnnvKYuC+XKJVJLdnZ28v0iJMTpzlfpGp/e+50FJgncokZmoiWypTD5SlSXr+Ryjp8SeCwZZAR2zvw9hDDoJRAx4b0kJxqNRr4S9xI9ff5PPvXAL13ooNNhItB3dYoUQ6rm90wXfNxMVRVHw+GSfrbJEakHbrCmLiu3t2wzrqwzrMV03RIoCVQ4JPrCezbhy4xU2R1N08TY3XvkYd3/wfaQyoBXDrQkxOkxRcvTgCVSGzfEUbTRaSrpVw871qyxXBwTrQCjaVeDN177Dz372i6SkaFzD1tYee3vXuXtvxubmlOs3XyF4T2gjpqp5eO9ddq7eoCiHgOXBWz9k2c5pUmazRVVx5/YneXb0iGu7N1idnVFeHudDW2nYfOFV7PkxA9vy8MlDvvQrv85s/z4/+tE3GRSXkM6hlODo8AlKGP78G1+jtY4vfuXXOHn2FNd5isGQrZ09tq++wHtvv856MaNza05OLAhL4yJtnPPWO9/jEz//G0Q9Aq0ZDAbZnWIkITqCyCrBQhtEytfEYr3CxogxChs8scvXU9et2dy9QhdittMD1XDMZ37ltzh97z7nT97ju3/4++w/fMD5+oRXP/4JPvPLv81ousvJ/j6pXdMgKQtFu1oRuo5g19kOKTR/+ae/x+2Xr3Prxiu8+/aP+epv/jwCQ8IRbY2TCUSkqGpOZnNqDMnlYYX3DucbXMg/9+0rL6IEbFYFlzZ2CYuGTjb4tGRcb5KSQF80Vq2FCJUpc8MH6Oya4SA3aqTok3SLAreeoxQkmW2s0TtSjCgzIvj8XkbvKExGAfQSH5SAxfqEejTOKhuTk0t98FSjAS6mvnkn8b5jUFaU5ZAoJSpJKl2z9B6ZPBpJEjKrIl1g98ouREmShjYJwsaAzb0RL9+4zK3NLSZlRWl0DuhIqV8TxAeC0L+zAvmbXaG/a9D9U5Qk/du+Q392kELQAvLf95S3W7fu8NZ775JSxeXdDebzI2xUjMsJPqxJKiekdL7D+zxlQuRoWK0lNjgODg4oBxXaGK7duMnsdI7r1hitKHQJqe2nchrQlANFsonS1Fi9wPuAjppgPUZoiqJPhVGGJCVaGS5tbiJdy9myybC8dctosMGVvS2+/d0/ZzSaZj95MshCEFzL6dJhO997vT0pKoZxwtbWJWyE1WpFExqiHKL7OM1xCSu3BtmgSoltFb4NjEeGgcnMIC8g+n6alxJlKRiPDG0zR6ua0bZEWsF4uoECzk7POV8tkEqidMKnQHAJhccnj+1ihqYuIlUNV6YdyxWczQts8JRC4UQk9dHFWii6ECkLldVkRYaktcFTyQqXIj5FVGw5W6xR4f9l7s1+NM3u+77P2Z7t3Wqv6qpeZ+Es5MxwuAypkUSKpK0kTixHkRPHgAMLcRDkJlf5B5SbXCVAEMSOZdhwYMuQBFu0FQlSKFILKYuLOMPZ9+l9q679XZ79LLk4bw8pQ5ZviNgFNLqrurr67er3Pc9zfuf7/XwWeJ/RWEHrA2gQXtEFzcikGG9ZNB1Bq3gzawTBRROfsw0CB21LH6J2dn0wJhWSqukYJBlSehaio/cOpQN1EzkL5WxOsr5J1dbsiA2uHxxCsCjvCaKlqcEGz6KEo+MTNlfGaBcYJRN0mrO1scXB0T20HBI6z/TkDLu9EyfY2pAoFTWweJzwWCyJFOgQbT9NH6hlBM5bWrzoEEiC0OhEkiLxtscrF5lMgxznHEoleKeYjMbk6YBnLl8gFYp/9a3vsLu1ztnhjHNb60xrycVza9zev08AyrKjtpZESW5fe8BPfO4Zvve9D9k/nbG+sUM7OyVPJfOmZZDkhCBoraXrPaFxyFRG/TbLzZfXS3OPYDQYoqTg5HifNElp6w7bd7hO0JmWtqvIBwV5OsB2loCjbzsUPcN8lca1jIqMsiwZDtc4PlnEtE7v6AloFbXWUkuUzyNrg3hDj4g3yafTipXVVTrrkSpuyliaFJFLDoL4CxbZj44E/uI16eEi+nAY9fBLxpPweNIbN3Z8xGkKy1NSuUxFRftXVIQKEW8otVIILXG1Z5DH5BVOMxplJFnCG6+8zng15cpjP0O56Lh37z7nzq3xl3/2Z3jpldcZDRIeuXyF3km++OLn+cbXvk0IPf/3r/wz/pu/qblw6Tx7F7awXnDv9j4v/+Blbh/ewL2uSITnbOJ55c03GQyH/NTnn+fJ5z7FL/+DX6ZvA3XfMBllGKX43/73/xUnW1bHE3zw3Lp7n7XNVcqmiupxkbF/6zZJOsLS4b1lPgv8rb/9JTrn+O53X+Pa1Q8ZjQsIJS40vPnuyzRdA1aSmgKZtTR1T5IlLDrHtZuvoo0lCI+UGVpC7TUy6LipEpp8MMEMAsXqKvcP9imrdglnbDg5O+XeYIW2nLGoF9w/OWFzPEFpSeca2kXNcLBO7wxZmkGwHHYHJLonlYJF09O0Fu8hSQ3TasYkH6DSOX3f0+uEQTGhdy1VH40u3kOaFRRphrCCuZyyc26P6fwewyTycG7eusraqGBlbZP7D+5QtZK6axgmI5COoC3WCQozAZ3R2pbUtwRvMfkK5bV7cTSp+hjn7h2dljStxwiBEhotND74WKkTCU8/9TgXds/zh9/4JnPb0DcLzDhhYDrOnXuad958j3lzQhAwSfcIvqF3Hc89/QzTwzPyrSE7O2usfCnjrffvsv/ALdmBBXsb2yzqY+btjN5byqZFBIWS0HvB3cOzmKIgyhVAIGxgNp+SSU9XN2xvrHJanjHyGU0VSFNDV1YEYnKC5UmmJFC3LQ/uHxGEZGdznSAFdV3j+lgjmZ9WGJlQzquotvfxNepcwDnY3z+M6uzeIhBooRmNRkyPpnzQX4uwZetp6xaFg77ldP8A6y11tYjpCANt2SAFTI+PQAV631NPS4TwCBF5UkIn7O2Omc8XHNanLKqaNR9rVVLHQUlsHflleihdLksCB8glSBlhUfEQE2d9TIeoCEH2y+FhCIK6a5GLBV3fE5SkrVuSNAER7VVSKkQAKeKfk0qB7yE4uqrF5BmDLMeraP7qmu6jyL1SMgJd/ZIhI02sRAUf4dzGoERk7QSp8HgaG01wBon1gaA8ve8QKlb1lU4xJkVpRVM18f/KxVqExWNkZAhaG2uA3guCiNU6JWQcOPiASXKqpkUYjRcR8Bxc/D7OqxqVGKTWcbOilpa0EFBSkyzvmbyPBwEmMfTWxvvDh0wdG0hNStlU8TDIeRJjyJIERzSj4T1aaszSEKyVxvcdLIdAQkh615PIHw4ngmfJJgsYoyPvycWTeesdJklwrse56PuzzqGVous6fHAkD417wccfQi7rchahY3JVS0XT1NHstQRDZyZyjbwPBGdRSRI5T+FHrpdLBpUQgtRk1HXNcFiACAQHfd/jbYf3Dq3zZRosJtaVEpgkQqadcwSWsSQCXdeR54NY7e5bEGoJTU9iUi08bK/H2klTNaQ6wehY/ysXHQHiUEjGTLKWKtrpZGQs2q6L67Y28d9lPUpGNIQ2evnaj0M7rWL9y/plbVEoCC3e2ViNXKYfUxPZpWo59NNSIDB453FOkJpkaeAKPKy0xUBQtP895Jz4EPXw0ei4hMfjSZSmtQ63/D+IZruWNE9joqqPUpxoajP0IdZoR8Mxp7MpxjfUDaSTFYKDdFxAECzqCtr2o6GejDoovA+kSmPblmKYx0PzwZCqacizAQKJUQlVVdLVc8aTlNPjmjyR5HnKzvoGK8MhbVWhhEaphDT13HrwgNW9c9S2IuuKpVVSUAyHsfroPFKlON9gO8P6lc/y6MUrlLM5QsLmxha3rl3lzvX3efq5z7O2sorWCYuq5PT0mB1/GaENeZ6zsr7DzTe/z92rHxLahrXJLl4aTmdHHN69TkfPb331l1nZ2ORn//J/xemBp+8sTzz3IidHDxgcTyOS5OCQb/zx13BecHjvPvu3PuD6+z/gxa/8At/5+td49vM/zdHREbbr2bx4kec/85Mk4zEqy7h45XGMhR98/zssyimDJEMpyE1K35Scf/JTTHZ2Obh+h6wYQmPxBFyw4KPJD+FRKITvcQSqas6584+gkSR5jtSSclEifOD46ruEJGe8topJDZCwsrGOaDse3L3GyfEZddWysZLxiRe/wGC8wmPFBic39unNgEsXHuPOtdskpuPw4AFX33kTvAY9QJqM2x9cZzo9YnN9xPpKxuWnnkM0M6r6jGArVOeppjNU3+LQoBKCkAxXVxlt7nK6f59UB+4/uMmDRYusD5E68Mrrb/Of/NzfQCcJXSIQrif0gq7rkEqxurYeraVa4lyFpWcyXImvPx+wIlYEg5OkWY6U8T66qpto3JQKtMC5ftkQiMBtqRQKYsoaixE5VegpdErTVIg0idB7H5ZcK4/Eo1ODNwqBwoh4SCIRtFKgTcK4GDJvW0bDIZXShEnBud0tPnZ+m0dWRgy0iZzlAIilSObfsc/4D/vt4THQsosRoCImKX+cb+qXfumXfqxf8P/97X/+S0GOePzxp2kXd6hrh0lylAaDRIqoi5c4mrJBehkXdSvQJiPNEuaLktH6mMd3LzHJE2azQ+4fPuDgZEaQilRHfpCzAd+DD448LyiSlBCaj0w3JhUIoRnlmrLu0NmA1Y0tUhcIVYnva2xvUWgub23T9CUHxwfUTY9E0zWWEDRJolmbrNJ0DZtbO6gQXwgIKLKcPI03nvfuXOd4tiDRjr6Jp3dO9EzGawhb4r1jdQK4GCuW0mCSyFpqg6O2ntVihFENi6pmMFwnVQmDDIQLDAcrBGVZzA+QQpEXKU1X0/Ue4QxaS9JkwGS8AUSoZ55BkTsS5Wj7lC4sDRlB4MLydAmFUSnBCazzaBPAClwfT7EQCqkDXdvRh2X6pY0njAJBpjPyZIAWUUnqfY+SAR0EWMfa2gpVXeOCpm46rJBolVBkA3JjSKRkUZWsFhVr2REniw5TrNJ2NYNBQVEUnNvd4/D4mOEgpe97suGIR/fWeev99yJk0gta5xA6mqjKuqRpAzYExsWYrilJssDx4X1EgPtHD7i2f8CzT1zh/sGCsrE4ASoxpEZxcWuFtcEA5wNvXz+gbFtMMGjdc2ljm+PFEcN0yNWbD1CJiswcGbva9/cfcDafkWdDtJa4rmNjOODc5pDhYMz6Ss6Ht+5w885dds/tIpWmyAtOp1OOz+ZoGTck08UCGQR10zIYZty9e4uTxYKydPRlw97WBokJHBxMydIBTRc3F0JGgbd4fwAAIABJREFUPXBQHiE8eHAWpJc4IDEZQlgWiwXT+TFSe1xwOOcpsgGDYRqfX4MJRidUzRzrmji49ALbdsznM5Ks4Oz0lOl0zmyxYDQa0TQPq5M+vr4lTOe3kKJmdTxByhQXAg8eHCEDFIMC7zs+/dzzKK1/JIkklj+Jj9Kf4mHtTTz8/R9JLf3I25/9vOUn/JsBp4/eWWpRlzfAAYEnfs+UiqklH1wEjcoI55RBgJTYeO+JVIr5fMrZ2ZSu62ibkld/8BJHh4foPmVjc51yUfHKqy/x8svf5+lHHufjTz7J5z77GVbXz4Hw/OqvfZV//eq3ePKxR/iJn/opbty6zpOPP0KeF/zxH7/EP/mn/4Dj2SEQb7THown3HtxnbWPExuYOw+GQPFljYCQPjqbUZYlQDudaimHBIBtiRcd03nB2csjW5hjhBNPZgrpp8CIONYrJKivjERsbKf/d3/kfGY52+Pt//+9xbmcVo8GLmCIlJKASJFAvqsg907CoLKdnJwjjyUcaj8N2oBIIWuK6uC5mOtpwirxga2MT29XYpbkseAchRSXx5Onq9Q+4fvNdTqb7zBcLptMzjErpWoeQmtPjY+b1lJWVdaSAcjHFS4dJBEp6lIwSiFzHNKPCQ9NQNzO8d7Gj3/kI3R8UDJKcvq34G//Zi0wP7vHxjz3KB+9dJRlqTqo5AkUvpgwGGV3ToqVgbRxtMM636ESRmZzt7UfZ295jZ2eX8Tjn5HSfrjwmMzXDoceoAmEKimHOdDZlkEWNsFSxQpLkOS989vPcuX+Xi+cv8v0ffB+jLaUvGSUwGlr6pqVpJTuXt7HBkqZr1GXJzsY653a2uXLlAnbeMxqM2NtZRwnF9Wv3OL+9gxKQrww4OjpkXpUEFVlSw2yAWWp+hdYMiiFlOYfQo7QihI7hIKezLSKVhN7zyc98ktffvoHWirZtGRYZSkVOoEkULoSYTjACkxmch3PntpnN5wxGA+QylZPmOcVwiLMuCiFMSj4YxfqXNnRtg9SKREvyPI/6eGA0KhAS5rM5bdthloDxtfU1BoNhtLH10Zp2+dE9ijSjrCLQdjAoODs5I2DJRymDJMUEwXC4waAo2BhP2N3eY319A6M1wfr4/RFg+3jAEAVkAuc6kiylt3HRsn1HVU6p6pKmswipKLIMk6ZRgb4cfrShx3tBlmWMxhMGxYAkz8iKnCxNQCzj9Ut4ciy+RQZTubRhFcNBTPp5R9M0gF4mZFqSpMAsLVhKapAGGyzJcuiS5xkQQdtVU6MSjXINHovQxLpNkPguIILC6ASTZDEFRVQ0qyxlmBf0XYOUGq1jzaq1Eb7svCfPMowxRMtXZO2sbWxxenQQK4Q4fO9p6gYlFbbrkUIwyAuSJMULERM0QpCkKYPhKHIrlFjWuBRV09B0LalOKPIi1gKFoK8bkqKIhjhp4tDIx+ueWw7PskGxBKebONhwMaGk9EPOo0AkCmUSrHW0dUkIgSTJlja1gLU9znuGwyECQddYEPHwLJGSuqxBiqUsQ3z0PHEhguVt38QLCxGO3tU11nW0S57PoMjpbIcUalnlT5b1UUOSpEA0l/W9JUtThJAkS/uSIIKkzRLsnKYFQsUkKcTEkbWRQ6KWoPfgQQqF7Sxt15Nl8bkStddxuCbVEoLsPUZHmHXfN1TVgtSkUQTgPWU5I0kl3vUkWY5JcgD6LgoPFFDX84iFGA7j2ZKUtDYe3pkkgWXy0bs4UFosnysPh2naRPGItQ6tFdkyHRVcPEAVStG2feSmhECSLsHoNgp74gGSX1ZHxBK8bknTHIJA68hQ9ASmsylrk9Vo7bKO3vUx9ec9bW8ZDUbLAVQc+Hk8wjmQEqkUi/mMLM/53v/zGygjmWzvxn+dSpA+pjdVunwutj1aa4oip+87nAp0VUVSZCzOKtLcYIJBakndVpTzM27eeJPD++8TQs98Pmc0WWNj9yKH9+7y5HPP8e7rr7F3+Qo6yfngpd8nXRvz3ve/R993nB4/QNEwmKxwsr/P0f4+O1ce5+Llp7hy8TEOb93ncz/9ZT7x3Cf51u/8FpP1FQbpgL7vWV9d4+nP/AS6l5we3aNsKrRWDIYT8rxgc2sPk+Tcv3cD10J5fMQX/trPc3z/HnV5wrw7o247FJqumXHnxg2srTh/5WP4psc5z50b13F1w/blK5ydHlM2M7J0hEw6Llza5c7VNzk6mZGlGecvf4yzg0N8XTJe3+LSU89itOblb/0eb7z6J0ybBasbm9B7HnnyOW5df59idczZ0ZRJOiIpRiSDIfl4jDKR+/Vw0Bj3cpqAYjEr6dqalZV1BD7C0vue6ekpX/8X/5S33/guV995matvfJuD/Zus7l5ic/Mct95+g489+xyPv/Aiuc6pD/dxwwnBw2R9k7Yu8aHn/XfewjUls/kh45UV8mLEadOxc/4il648wcnxCScHN9C0nB4f89xnf4a6dVx/63VkqgjCx2uUMmiTxgqe8+RJyspgTFlVNPMZK1vnGE3O8e7bb7L/4Aa/8F/+PE0Fmzs79F6SmIS6q7GuIS+S5TUlVr3nZ1PSLKHIxyghaTqLVgbbR0NlnqaRveQcXdcxnozxPlZsu75BoPHKRNGEEAjhKRdTUiOQKqI6EpNRtXOSLFbEU20iRsdaAlAUw2W91mNyE5nLRsXwgUyok4TD1PDYpz7BT3zqSX722cf55N4We8OCfMmbU0KiH5pKxcM6WUD8e5gsBfhztzv/5pn6R7/3b3mIgh8Oxo4QvPfGHT7/zN7//ON6nD/2hNK0kpTVgtc/fBXdNEz7jkQ5XDBkKsZtrVb4IJdx7hgbj7tGi3cNUkqmRyd8b3/K5mRA0zVYJ/FKoLPAOB1jEktrA2VZxmqFtZw28WOCgHSBWVXHzrpeZWN9k0VVMpCOk65msr5GmjqO7xzx6eeeJa9PuDcr8cmYRy+scevgLtkox9Y93knSZMjKisO7no6AMSlaKoz3UNeEsqUnLjJllZOIjg7JWDr2D+7ihUYHjXCORHt6qSmyglRJysUC2XZoL2maM8AxGm7x+PmLHJRzkm6dQdqSpDmn1RSdtYQuRcvAuY1tprOWsqnxOC5ffJS+nXNn/w6DxFBXljulZ22c4j1kRmA0qAZEouNti/AROt0JjOwotKUJATCxQugd1ncIGdBFirNL6GDwOG/pGo9SJS09SiXkJHH4khmSXDKdnRKkRknPOEl50Ci8CnS2IXhB7wPDfEwwZ5y0FS7x+LDAi8AgW6Nra0TvyU3BveNTHtvZIQH6k0CmNNBhMSgEqTY8dmmHctExGY+4euMoLma2x6Gx3jCdnnLz3pSkyHnj3fcIvYlT6BAipFIA/fJn9HLolnL/7ITvvLXP0b2SfOj45FOfQArJ9GzOSprTucDB8TF3Dh6wsjJkd3NAmmlu3D1EZJ53bt/ly5sX+Jdf+wOuPjhEFyla9DjbcnRcs5qnzKoOLQVVPcf3jsrGG8a+jya+uq4RwbK1vUEIlsnKhLU1x3Q6RSiDFI5skKERGBWHb2eLiixLSFRH3Qt6JzEmQ8geudwAGi3ZWFtHWIUMhnObI4RMeHB4gPewNR5z8eIudw9rTspTmsUJJ7MTdjY3yfI1EI5r198nBE+ic4ZZgvSWpmppmwj8rRtLkgluXL3BxsYaIniOju9jjMYMouVPyyWoO8ST4KhSFj8cEv3o25+3aIo/W2t7OEIK4c8uug+n9QEwOt5cCwTCKGwfIv9DCIKwCKE/2ryBjWprpZGBaHKrO7QWrE7G2Kam6XuO9me8+sarPPrYBf7q5hdRcoMX1GfZ2V3DA8N0gg5jrl27gSdwOD2lmVVc2trkhY8/y4O9K/zOb/0BoxXJY489gTLROPb8J57ic595gZ2tPd567yqvvP4Kn/nk89y6cYff+8bXWd0oeOTSHs6eMptWbO1t8+iTj/PtP/wOexc32D+6z8rqJsfTltdee42d7R12Vs9z7/g+q9ubXLr0NHdvvkdXtbx/7Q6/8c9/m8/+5Oe4d+cuo8xx7eaU1EQlufL6h6fcxiztVwFjBYlQKKWZLRYokeNsNFo1vo7ffR/rxzM55+z9t0m0ZJgL6lbQtJJeKqbTklEOZ4sD0J6ejtuHN7hy4WM4HM510a7VN6xvTLh04Tx3736IdxleBoJtETIy+4IUlHXDUGi0igamNE3p+yamzRINNtBUNa6tuLS3x+mhY1EG3rl1xqyDzWGKDy0b5x4j02W0LskORYOzLTJ0rAxyTGIicNX2XL92m3EhqNpDjmf7SB84q0sSn7AyaTG24Oj0PrsbAwQFZdvQ9i1tcLim5Ft/8h22NzJ+87d+mw5DqkcYu6DvGvLiPPcO5nz605/hw1tXaU5rnN7HumgezFTB2nibwaNr/N63vsnHPnYRRcF4vM7G9nmmR/ucHZ2ASuhstMOtpUNWRyNOl4OZbKDY3t2iKSvSQuD6irovl0DlyLKZtg2/9hvfwGsD3pKpaKPUUtAsB0kQhy5SC06Pz9jd3WV3d4u7d+5xfned+0dnTKcLZGqYT0t2dva4f+8enoDJE8qmwWQJYeEpZ2dsbK3QVDWDYcZ8VqKHGcpk+K6n7jqkhK61TOdnHB2dsLKygonhFLZWRxw6z87OOn1I6NqYpvLO05UJat2QjdYRWpAslcVKGJSIFaI49hD0NorUlCCmUH1ko1z78Brrm6sR8BziTbLShlGW0jbuo4XICEEXFEbAzLYkmWE8zAk+1sliKMLivIAglpXdEEUPIcKWrXOEEDf/eZphfUuwPYpYkfFaY3tITEycdG1L8D02WEBEuCkx+RLCw3pWhKbGTX6KcyIOWPoeG1ysDFhPL3pSHc1xTd8xHo3pbRtrakIQGovrLKpzNFWNMgleRs5mXIEDOo1ylqZcMByNYuLERwh2mibMjm1UsgtJ8IG6KuldZPzJxNA6R+djgsU7hxUC10V2Tj4c4FhydfoeYXRcp/pI8Xs40GJZTzJpspRoLGvbMo7tpIpJJikE1rXkchjr0baBZa0qSZMI5w3yo6pUTL56nI8DHLmsXPV9Tx88bdMSvFje5EtUukwEBYnwILVemtDi/ZYSkjRNUUYj/BKibju0NhT5MLYhwxJMvqzCKbVkkgiB95oQBM45siRlPm/QOvvhpTKwfA5bQKO0wPY/FFMs2jg8W5Ql4+EQvWxfOh8+uoYGEa18wVuE62MqWZtlTSQwGOR410U9eMx1gI81kgC4YOn7jiyL62fwgSDEstYYltYzh0kMSEPdtTG9ZBJSs4TXh4CSMYGsjUYrSW8dyiikU2glKYoMIyUhQN91ID3aqJiIYskWXd5JRDuxiqwoF616fehp6obEGNIkpawalBIMihF4R9mVZHkOSkWYOj6+LkWkfcVqqKCpa85fvsL8+BkOzs54Yjihn09JRKz5gQUvSbIU11vaukErhVOCRGlmtmMcAlW1YDBIqEKJUVFIUVdzPnz7LSYrCpWmJJnl8MFdLj71PE1bUpYleZHhfIt0Nbfuf8jZB9+mmXme3thFG887r71EFQTYwGBQgGvZ3nuM4/sP8KLH+56qqqnLCpWNWN3bRh9NaRYzhBc0oeON115h9/IjrBZjFqenSDx3mipu7Gcln/qpL7K6vcOFT3ySr//mV2GQUFan/MJ//Yvce+86t2+8xcVLF+ibhnde+jbXb99ksLqK6g2bl/ZoyhOeeeY5bt1Z4d4HN5mfnPHm0S3m01NOTyxr5y/ySJpx5WNPcOPqe2zuXcQ6z6LukKM19i48hrx/HYVkZecca5urPP/pz3D73rt85ot/lfG583ghyVcnyEQjg6Xr7NJw7NFp5Jq1bUNdzhkMRsgQ8EohlaKeNbSnC3xXMi3n5FnCfO7oQo4Unt4FxhubnB0fsrj6Lh9+8BqbaxnbW48wGG/iZxUfvPldposDJuN1qtkZ0s05ONonyJRPPvoUN956menBPapZBVZy77Tl8Y1t3n3pj3nvle+iC0PvAiE4hNMEKbHBxV2TlDRNw62bH5AOCtAFly4/wXdeeomVUUExKPgX//j/Ii1W+cJ//Nc499in4totY2VYBv0RT863Fmd7iuGY8NBwJiPOpO5qssQgiay8RVPjjYuSAB/AeaSNgx/rXWQohphmDDjIx4gQyLNV5uWULMlJTIJHYpbVcicsIlFYKVA+kBsNdUOjBAsRSHe3ePTxC3z63DZ7w4LkYRLRLW2mMpYkFB+9/H/YcIA/M0z6/3O4JB4+nvDnfPzP/OIv/ApAPKoQIVAHwP8HzlC6tLONUR0PDufoYoDTx/hgyI3mZP9BPIUwmrZpSVJJMhzSdRV9E59UpetJBwnKaMYqR4qealrjACEFc2Gpqx4j0nhSoCWeeBrWuSpyWTyUrgUhSVVCkRU8cekcP3j7PW4eHWD7nubkiIlWGASLg7vMu5JOr/Dk40/iZg8wZpdbd+8ShKCra+4d3efC+T0WsxlBBhItSHXB2sYmw+GQw7pje/Mi1iWcnO0TlMBoTdM6hjrntAzI3FD1Hq9ahPMcHBzjnESpGKn2LjBbgDTQ9ce8ce09Pv3xZ3AlNKHhbLagkIo0KcBB1VlMqtFGkRJV7A8O7rK+tsHmxjaLxQJMAnZEqzNqNyXNe4LIQZVx+GA0Rkuk0bg043ha0dYgtWS4qulmMSpoOwU6oJyj7B3S9qTKkOiA7S1GpQzTjMZ2SK8JwVG3p2SFYlAIul7Q9S3DQhGUomwDLvTokKKVRBtL7TIWjeXx8zsoK7l274h7+7eQQTJbNLhgubJ1heAX3D884unPXKK3Pt6gSkWWDjmZldy6d8RgsMbECmxbU4saT4cWcZJddz1GaRKZcOe055HLV7j3zns0XWQoiGKITjJkUMwXJcPCMlld4c7+bSw9r1x/n7/98z9DKRtu79/hw1s32dncIQjF0dkpXkqOZjM8lq3JCl3Xc/eg4fi0ZXH2Le6enWJMTAp96dPP8c9+9w9QSUJZdlRNSy9TgtAIHVMy1joa22CCQ6aCgUp49tHH+aM//QGzKt6ADYpkaYiRjAY5XdXStVUcfJpk+fxS+OBjssMvYlQ9zQiuJS0M65MVjvcPaPqGk2lgUkyiCtqVJFnKuY01QoibhNJNmQzHtFWJs5LJuEAQOWhdb/HBILwiLwqKwRrew437+8j7B1w+v0uaZqSJ4eXXXsL5nu999yW++PkvfGSK8cHFTesynST/ghXzoyn9jw6Twg+vBmH5WbGp8MPThoiBiLwH1zuyLItwziAJQtH3EUwvl4BRRHw/1hei3SkEKOuSeXnCKM3py4aVzU22zm/yCfkJPvfCkxR6xKJ1FFnO6mSPd959mwNxjBCSyWDMT3/5C2RZgsrhrevvYkkIUnF0co9Hn36BP/qTP+V0Nufc9hb//X/7d7h48WlOjk4YT3Y4eHDCS995let3r3Fyus/lx17gb/7if8Gv/JOv8nt/8luMRgNe/OwXGKUj3nn9TQbZkEt7u9y+cx2VB6p+werKhOAt5dmC+/Iah7dvs7qxzv/5j/4uDz68xda5DcqyIlUCZGBRlaiQILXDJPEEHG/RJsQkim0RNTFmLAqcFRH8PD9C6wikdKkkCRlBEhNtvSdRCVpDEXpOju6yOhly/dpt2nKKkY5qVjIY5JTzU5JCYZ2P6letGORrLMqO47MFg+EQlRkGpmD/3gNynVE3M8qyJkkiG68Yjqn6Km7g1AjnLLbrccrhUse90zOOpw0qwP61NyhGq6TasFKsUddTSgt5mhNEhaQDp/HOc1a2pEWKWMw4nt3iycuP8td//ueYnp3wO3/4Nfq2Jpw6Xvz8C9y6cYuThabpU5JsTFk9WFZ+NNIplDJoabhzdoBsW0bFNoM8ReePk+nA+++f0PWB+WnAVxmpXqOzPVoJbt66SW8D+XAFZy0yMxwujlmf7FHN51zc3eOODdzev402kmwwxJcVUsKirWi7Gh8EvZXYzuG6nmJ1jXunB6wNx0yPGjCWs6nAiZJkDEUY0HvLcJgzKAzzWUnbWLRW1HUc8kTYpkFIwYdXb7CyNea9qzepqhZkwHYdxWhA09UsqpI8z2jrGoSjb2uKYYptBSdHJySJpusF+TDDiYBtOoYba4T5nN530Pc0db0E71osHWmmmM7n1E1LbVuc6/AykOYJ1gqsXXB81iNtxvmtLSAOuJXyUTUcAnLJC4z3GgGhbKw4pZK+6slSw3iQIHWK6yPDQShB7wVKiGhtdR1+aXJr6hoZJEIpmkVDsZojJJggsT6mgzwxdu+W6QahNB5Jb2skPlaupIxgWGeRCKSCzjm0jgp656KJMghwdY3WSdzMax2j/dbhOocxCUpI2qAISsdTX+vxbR8330mKXlaYHsKhwzLpcXz0gKLICX2Hz1J0ppFJxryuKJI0Wt6kisO5zjFYMkmE0UijI+hbOISOmwqvJT7RSGPovKO3McniQ6xUixD5O3iBCkDw9F1HkWbxfR+o+pZUaqT3y0RSj04yfJRWE0S03unE4IMlMTnOdsvhimdzc5NFvcC2EcSvRWT19D4akwBaIoPROcdisSAvhhEC3bUo4QkhVqtcFxlURZ6TJBlSarwDJQVJZghYvAxIoVHKEPoycp86jyCmCpQyKK3ouybKU3QcMPoQkzRaR8NYWF4DY4IqAWLVLYR4ELtYVOjEUJgREQ7vsDbWxKSKw0qIWm/10P4mBNrEHZcIOjL1nItV06XFTxAHrS5IrA3kUtLUFVkWD7h6G5AyZZBleLGsnqslENx7siJFK0VZN8vBU6y/Sy2WlTVB13cRvq0TetHiXQ+piYZbH7lrOtGxLuuJ1yhChIE7j9YqYhweVsiC/0gkZG0fzWpSRegygcQsmWhBEbxAy4SyOWY0XMELiQ+WQT7EKEPfRpPcKCtAxWujcHHQZ70H5zC9o/UeJ+PByxOf/Rxvf/cVXv7D3+f5n/w8rm+obY31Pj7ntMYoQ+Mcs8UCIxQqS0hCHPwqenoMg2FKnmfs37/DV3/jHyLoOTsj7pCTDUauwwZHN19EppuKw9ur73+fm/feQSUZoRaMihFrF7b43u0bHN69z9rKJtZCPa8BSVOVdG3HyGTMjk84eXDA3/qffhHblOTDAUkxIc/jobvQCSJo2mDpphXVbMb2pct8/me/xEu///U4+Cbw7h99i7qZoVXGUA75w9/4dbTydK7mjZc+BDVE6SG7jz3F7u5FUm/YffwZMqP42r/8u1gh6GVgPj1hY3eTs7Mpznhuffg6ufKsrOzR2Z7BaMDR/gPWz+3x5Kc+zVf/0f/BUxubrK7v8f0/+B1SMUDIAlcWCC9JixGt8+T5gOAdddngrCdWXROETvEEattT246VlVVkiNUq6QLOl9y+9ToHp/dJhxlGp4yk5mNPfJxze5eQnac8mXF3/yofXv3XbG/v8YM3X2Vw7gIXL7/Ib7/yMgc3b+Bzxwuf+QofvPs24UFAJxJF4NVvfoO6niJCR1n2YARPPv8im+sX+Oa3f510nNA2mkRV9CGHNNC1DcoJar/ApClIwXC8ijEJTz73An/yR18nSRVpmjE7vs+VRz+GM6u4tiELUDvI0gS/rAC70KFkSlmdoUwEajvhcb0lVYKmr3DWRztlCLgAdDXrW+uEPpoWO1sTJDTBYVSGUzEh5KqG8SBHCskwVfS+YTwuMEHieo+VEqGjZTRLUwY6IVjBgkA3zhhsr/DcpT2ePrfJbpJFaVfwOJb7DOLgPWqAAgQRwwQiTnBE/OhSvhk+2mv8+0gq/bsGR/82+tKPNjMEgIzrIvLH9cDi24+98vbLf+9/+aUPbtxlOptRliVG6GhG8j0uOCyKrBgyGg/pu5bhcAwikKQpRnukc/Q+kOrAymRAV9UsOhtfuEEAUQPZNv3yIh0nxcFDWbY4G2j7loACEfvwAsWinnFuPKZpbATMNTV1E2PDvrM8uncRkWZsb23z4OAuEsHZ6dnyxCcWcZwPrK5tMJmsYhpLVztkiHfJZbXg1v5dssGAzHjqNmCtpxeOndVtmnKOMoLxcMDqeI1EGGZly9raKlILehsI6AgtdjFWPhkO6KsZi6akms64c3gDj6WxPXUHPhiMSVhUc9q6ITiYLUouXXqcTKQIodjd3CUbj5lkGRuTIacnpwgEaeKxHjrnl6dw+fLiHU+RU+PINJzNu7gBIND3IZ4MWU8rYt+9MNGypNMEgif0ApUMEFogZENTWzKV0vSWWR8YpwO8EKRZihDQdDFSHpZAQ0NOWfecNlU0DgnFIF9ZXvig7mvKusdjubCyyffe/ZBgDAJJ7y1dsIxHY7Ik5+6dA+Z1S+cqTtuGYZrTO1gbDzi/u07fwZULe0yGG1y9dSeyUEYpG6sT9jZHDIsU7x1tVZGnI+6cnPHpT1ygdpa//sWvcOPWMd/83utUznJuZxshBYumBgxPXrrA5XOb2GbGycmcRih21saczc4QytA7wYWdDdbHOcNhwc07ZwzGKYg+RmmlJoTIdgohxshXx2M2hin7Z3OapuaRvct0bRshnijqsmVldYQQlrqu0UmGJdB2HoFGKdAqRQgH0uMEeBwiaLwLpGnOol5EE6Jr6foKGyyNdZyWHafTBmk0Acn69hZl1bF38TL7+/cpa0cI3TIOHPWpRhuCtUzygtWVVZq2ZV5OKcsFVbNgbTJhfWWNUb7CZCXnmWeeoW/7eFq5PBV4uH5KKZdKY/mRce2Ha+syxhkeNt1+tO/8IxeAH/0Ty6+vlrpPDZgkxS/h9tZarJQoKWKcPoCW8VTaO4eWGmt7TJKgtCJVKaPhmKqruXXzNk888QSffv4ZPv7Ek3ivSbRkUS74lV//57z9/iuMVye8+/517t26zs7uHt/61u9z8cIu+XiFH7z6MscHt/jw9i2uXX+P0+kJjzxymQ+vPcC3LVc/uMVrb73L917+U67depOzxQEvfOZFnv/403zqmU/SNwn/+Fd/hU9+4knef+f3aPc6AAAgAElEQVQdTFLw+ss/oGoXbJ+/SF3PWFtbZ2W0xvb2Dl/68l8hy8aU8zMOD+6jpaXp5yxmB8xcySjfwqiMrm9pqjltsBEcLTVOPGRZpHShJx14EIHO+ah4T1KEFQRvcbWnbRQy1VgR6B30rsMoRVYMSJKUJElZ9CUIz/GDWzgCZVWjpcXoFK0ypM4YFgWBwHR2iu17xuMhgyIlU5pyOmc8HlHXc1pXMZhMQAacq1AaViY7zOdzvPRkpFEwEARBxeqF8JqLFx5ByMB8eoxIFMXKCr5dkBdJhPtTcP/ODZSs0QgGxQaJWccLjRIZa+NNtrfGPHZhg9XhBmsb5/nw3Rt85St/idsf3OHFz3+B2bRm/7ihq+aM11eoFwckxmEShQkebVoEDdq3jNMJvvXsXXqck5Nj5qd3aZoZg+GYqzffp6pAJvHUMQRB1deczqcsyprgWu4e3mZzc0LuHA8OS4qh4fDBXQ5ODiiyNdqywzui1coHggtkeUoxHLM+GaKMobMVna0pkhQbHEIZrKuRIeBsR9s6etfGzbgTS2DxQyRa3NA1Tc9wVLCzvcO1qzdoqiby05TA2TYewsnI0yvnFcUgBxkZOTIEjJIMhxlN3ZElKV3TY1Ss4+gkQWoYTYb4vsdbyyCR5Jmibmqapom1QtvSNBXjUbTdzI8XbG5tkKSa1fGEtvG4EPB9IC8GjCYrBKXQS3iw0klcS0TkHxmpEMLQ9g3z6RkIyJIMjwTvI9BXavoQh6xpphEKQFK3MUXXdXZZfR4shRAx6UJ46LsIqIeLmog1W6UlbdPRNA3j8RjPUmNf1QBYYpWKAIM8i+ukjMMj28X6X2JUfHzaEIRgMZtRjFdwXQMIsnQJm0WwaBvqriMzmkTFAZBUCu8sfe/QSUrwljRNEEFGwLXStG1PXTcURRENaTrWLLrgWVlfp17UdG1MXkgR602pkPjes2gbhoMBidH4Plp+lNaYLCXLcvwS2i6XdUDfW8rFgiRN4xBFCGzXkyYJQcDq6gblvCTN0phaCrF22Pc9xWiINtHyGpyntxbreiQS20WQtVSSNM8J3tHU9XKYEjk9EbIe9deDwRAQ9F20uQXnyNKMeVXGIdMS6K90NOghIseq7xuCjfBl7y1VU2GdpW86ZBK/brTr9TRVGU28aUbfdR/ZlKUUOOfjIGiZlIoJIej7Dms7qqrCaB3B5UJ+ZNQLPqBNtNgtG+FLsxm0fYtQErOs/3nPsqJrYs1raUCTMhrjut4yLEYYbUhSQ9e1eB9ousBoZYyQajmcBSEity1YG4m9IgLwtTHxL5IxPadEZIEJoZAiMp2UVBTFAOfiJl9I4oBQa4zReBcxDU1VLZkv8eKfqgTnA1E1TuRwwfLxB/SSW6aVjL8GbB8rmE1fYesenRqkDGRZEVNrPuID6rplNJosQecBFzxNWcXEhjbxPsp5suEYkycEG1jZ3eXw3m2SoiDJMpwPpFkROZSJIfg4uFtfXaMsa+r5AqPifcjZ4oSuaVgZrzCbzfjNX/uHzHyPThIqJyBbBTPg/JUn8dMFH3zwOleeeJqrr72Msz1HZ3dZVFOC84zXznHu3GMMB0PGwwlNU3Fudzdez0cj1na2WJwesDg7pZGaxaKknU95/gtfoK1b8JayOuPczh7WR4zCnZvXKVaG7F04z+bWLtXRIZ1tuHvtfS4++yxXX3+bxlqGK2s8+eKXee+1l1ldlQxGE/YefY56UWLSLQaDjCee/hR5sY61no1LFzg6fMDq5jbvvv0Wzz33Aneu3cKnGTc+vINJAov5Ebf3r3L13fciOzApkGQkaYY0hsnaLjtbu7z5p9/k4OgOwc74z3/xf+DJz3+J8+cfY3Z0yurmOUZJRnCxshhNh5IkTeMgtHPMT88I3jJeW8VLj9QpvfN88Mqf8sq3v8ZoskroOza2z/Hsl/9TssGYtZ0LlM2CosgpywUq0exsPs7162+w+9gn2Lv8FLtrq7z55nd5ZPcRvv3Hv0vTTfG9Z2W8SlmWzM6OCNbhHXil6Lue9ck4Wk6FIMsKHn/uL3E2O8E2M/pgUUEhTYLJR3R9tK1nScHm5g6D4YSzs0Pqpc02KMmLX/k5Pv7Mf8Tv/u6vsro+ZnX9XOStyRDXSGnonWPRLBhkY4wp4r7RB1JlmJYL8iTFKBW5iV1L71tyU1DVVUz+eYeUAiUjv0grgdSaul4wGY4QTkXrZJZglKbrPTKRJFm8HvkgqQLYIiO5tM7Tn32Kv/LZZ/mZK5d4dGXE+P9j7r2aLLvuLL/fdsddlz6zHFCwBAgaEASbZA/bsL0ZdUdLEzMaKWYipNCTnvQR9A2kF71MzAeQFKGRRjPSaNS+m2SDTQcSvgCifGZW2uuP2U4P+1QB3dETGkVQId23Am5m3br3nrP3Xv+1fktqQghYIdAy3a8l8sne5BPC0CdhCIFM96f+0PD/mZD07/v4d3Blxaf+e5q7BI4tfPzObb7+hZs/s8jbz1xQ+m/+2//uv97d2adpW0CjZIaRqt8gtTRdzappid4iXMdyOWcw3CB2c3zd0YWWLiiCDqy7htxs8uIzz2LocD6QF4ZMJQeMi6SWDilxNjlQPJJqWHJ1exsfOoIIPPPUDbZHY84vZ3gLG3nGxnhICAkKOSmHrJfn3Dk65GR+AbZjOl1jcoOVrrfdATpiypyJKcmDpBhUzNdTfLB0tqOpmwTy8o7lukFlGQcHN6iqitl8Qds61nWLs57FagUyUmXguwT59j4wGk4YDiQ7u/tsVUPuHB8xX825rBc41rQu/XujVGxv7GOkJMSW0bgAkeztq/mMy/kZ63rBfD6nWdbMZpfM5xc0NYzHmt1hwAaJ1AUqHzEsRmkq7TuMsOwNa7ayNecdFLnA2mTVxaTKV6nS1Mr1i3vXdDhrUapgZDS2bWhFQEqFkiNWTUjVkcGhomZzNKKrLc4FogjY6FPta7SE4Am0CCKFkqyaGa3w1MsGFbq0gFcVKs+YLS2IyGw9o2ks1/Z2eP76FWazGeezKcE7ZrYm04pROWQxm7MzLvntn/8aP7z1EZ956ipHx4dUxvHiM3u8dHWHrbGhGpZkRc70YsX7H96lblpaKfnmV77Ov/6Tb+HbwLfe+D6X3jMebvDy0zcYFYYqKxkNKp5/6hqv3LxOEwKHZ4s0oalXLOslOtNY1/HM/g3+zbe/x3zVsfaea5tbTFfLBDGcNylSqUhtcQ42RkOuHezRrQLWC84WM9bLC77++peYzRvqpqHf26KEorMtq1WLsySlPziE8EiVxE6hJNYGMiUwmeDy4hKlI41rUVJzsLuPlAVr17A1KVAhcnxxytn5OSp4tiYVq2XNYnZOyHNESLZyJVKdelnmDIc5UXiaZsWgqEALtjZGVFXFqJpgTEmRFXz5S1/gyvZeP03kCTvpEwxScl+JXgB6DEZ9/IwYe6O/gEgPTu2Dx/FxpAP62FovUPXgUqUNLkYW6yUCgdE5rbUgUmtiaH1qP8lNap/wguBTp0TXtnz80cdIA21n+eC9Wzx4eIeXXvgsk9EG0vdslZCqKqQQfP5znyVExfd/9AMOT09479b7zLsVQniWs4boLEtXsznZYFTsYW3k+Wef4+e+/mVeeeUVPv/5LzIoC+4+uMdyueb88pimbhhONsiLisnGFtPZEf/Jf/pPWMwDf/rHf8pgUrJ/dY+2bjg7PaOznnXbUo2GEAqKckhm8hRDu7wkRAulwq9qqnJMbS0ySjpn8SJi8gFgaOpUK42I7F+9hm9SA4/MFOMiJ1iRGE2+Y1UHXP/Z6UyDDWkCWFY0tsbZBms7IhFFYHdvlywbM7+YMxlVGOH56pc/x8VlC7ZlNrugqWteeOE5nr15k/ViycPDO1gRCEoyGIwIQXN5fkG77mito6p2GEy2mGxssJ4uGA/GKKW4urfNc8/c5NqVqwgR+fwXP089WxMJXMymdOsl4yJSFJrlfMHl5RoXHINhIEOgZMV02dLajpCleutKD9k/2GXvyk18dJyfnbJetdx7OOeZq9eZrQLn81OktMzmU0TWkAnJzmSXIh/jO4t0Nd4ZxhsHKJ3hQ85iecbV608l105jiXQEDBuTDVbLBQhwzieWw2jEzu4m08sZ3jq2t27w5jvvkmUZ83bKvJ6T9+wVby2ZNuR5gYuedd3RNR6VZXjf4EgxPJMXtF2HFjJt8FSkQ+BcoJCG8WCSQLsu0jW25w0Lus714POcwXDI4dEJVZWTFZrFoklcCgRd48nKvG9dsiync8rMMBoNmU+ntI3lmeee4vXXX6W2LY8OTzE6YzgZcvHogvNHU1zr2NgY4azj9HSJDwJjDJubGzRtx+XFgtlshRYKozWT4RC6QAywbi4RwiHwtF3DbDWlMKlFtC/9wvQFDjJCpjTed6zbFV3nMHlBWQx67k5IAGql6HxExECVJ4ePbyNd3UCWyism4xFSZ6Ai0pHcalLR23D6iE4AKfpqdknXdARIjTomHXqt9xAV0uR468m0wvTMH+s8zjuCdUgFRkF0AaEzIBV8DIZjcC0mk2ip0qBJKjrbEPHkJkNLje4FJe8tTdOhswwRkiPK24Y8S7D81WKFd55yUKKVRvWEckmKbc0uLgkEiixDovC+RWcZtm7pvGVzOO7hy47Qx5+GwyFKSrq65bG6kYZ+nuV6zXA8RJokfMQQntTcN60jU7ofWqRI2mPRMxBTxKztaLsWbz1Nt+7jVsmpJnTih/jWYus6tc1JyaCq0Cq5YyBienZR03bkWZngzRHqtsW6ljLLepErIkWqu5Yqo7Wuj1gmgcNZS4wK7x1ZrhkMh4nPUy8JMZDngxSBcqmtR2vV85PSsCM5cZMwEmOgaevE0PQeZQRa5alFsU3RdCllOmjrNNTxLrlzTk5OQKXI3aBMTDLTC0lKpoGj9x6lZYqMYVFSJFFRKKTSOOvx0VNmJVmRkcm0/xak15dp07O/YorOZ0m4DSGm72yMVIMRtrP4GDAqSxywPE+x5pj4hl1n0zAu6yH5/drf9WBzY1KEUukEBibGpFnJxy7ntHdQKjm6k0NZ4HxEaIEyinXv7npck57laUAaY4rLZU9EIw/RY71DAMVwgM4LVJY+s2I0TmB3KTBGsbW7y70P32d7Z4/gEsw/dF0aYNVtchfKtHfTWtHVa1bzS7p1lxxaUqIGJUeHd/nlX/s9VpeXnJyfJVZfXvL0U89wcv8+ZxdTJjubCAR377/HxfScotKMBhM0A77+K7/D5ckx1caY2FjK0ZjFaklWFGxubPHBmz9mOluxv7uDb9eMN3e5cvN5vLcsz+dEbRhsTJBaIYuC04cPmF9O0TojKwref+ctsmrEYrHk+vUbXD48IR/lmGLMC6++xigfMjud8dQzL2DrjpPpDOuW7F/dpxrsMdq5ymSyQaYk+wfXyaoRX/zqN1lfTBFKcfToPWzjCM5x47nPEWzA2whZxtPPvYQZjhiMxgyLMePhiO/84b/gRz/5DvmwYrG8ZHb6iM98/hdZT+dIY9jcHafIolJ03qd9tk6RLmsblssFs/kl480xWucIZch1xmK6YHlxjzsf/ABPgzSKX/z7/5iNnWeRxjCYbNKuamy9pKpyXnn1qzx8cMzF+SN+4Xf/EcPJVf7sX/0LTh58yGw1xxPZ3tpnPptTL6d0bU2M4KLDo9BZzs6VA/LRNp/7wleYDLYY7OzwS7/2m1ST6zz4+IeomMD8QUW07MsahGTdrmibFXmWc3rxCBkEmc7Ihztc2RpzeHTCj995g9/93X9AjAVepOGVQJEJg183NO0akfX3oSh680aDsJ7huCLE1P64aFZM8grXWCCQZYYsU3ihEMpgMplcS22H0RKRlal4ypRkgxxhJTrPU3FAkdFORgxu7PD66y/xG1/5Ar/43E1enEwYCIlNIDYQAiVTOx3ExDMTjy06fdsnMWXeiE/EpfiEtvF3qTXx08/81P/7tFfob8OPYn8u4e/4uU/96dO/6vEE/N8hGH369fyNSfzffrpIr5cYOXGC2+99zNe/+Oz/fxlKdj3n7vkRjYuUgwmjjU02BiX4JXePa7oYcDbV2WcyJ7qW1XxKXS+p8gJJAXjG5SbRS3bHWwykYKUiUjpkjChd0FmLQFJUI3IjaBdL1p3HRI+KgeOTB1gnQRmWs4b9G9uUwwGr5gIncnaKitg0BCVwtuVEBMRgg67zNM2ajshmWTCUGcFI8mpMU695cO8OR0KypQtiDKxtxyx4NIasGmC9I4GR03RkbDQX5w9xwrG5tUXTNXgfycsK2S5Z1ivwEaLGmIigZWNUcX1rwMnJJbOlJ4jI1vaI3OVkusAM0gRwYzhi0cxx6xbjHUoVBN9xOq1RvWKssHi5pBxKDBJTBLrG8WCdIKgmaxHRIqscYySdS+6qLi+4MnDkWJQXCCeoqry/MEH6SO0CWgtkDH17S6QqUrzKKYH0GhEcNtQMDTiVOBQ+Gk7O6wSBVQ5hErMhWonAk2WGddMipcUKQ8wUy2VHmSmCLGi8ZUvlrJrAc888zwe33mV7sMV8teZiOmO9v0WIjjwzXHZLGteyocfYzuFQ3Do84p/9r/8Hq9mcW7fvUuSCL7x4k81qTJYVBBEZ5wV23fLO+7eY10s2yoKb13b5N9/+AdNmzf/2/TcJwbI73iY4uLxcsm5rbh8/oMhydoYVF5nhpaef5cHRlEfnl2hvcUgWjUc5x5/98McYqVitO25e3eHjh8dPmnekJtXERp02bUjOZlNmi0u2qjH3Zme8/NxNPvpozsnJBTc2Nzg/vYCYOB5CGpSpkG2DwqfaZyTWRXKTMRzlrNsZuRJkucS1HaNhkf7umEMUrJuazOS8fP0Gr774LLOLGW/decCpv+T+yREPTxSDTDMYjZEqp+46xuUGw9EY5yKZEYjYsV0F2m7JfN1R6pJCFZTFKDkRjcIHS9O1xD5KIOLjMEK6EUqh0TpL008ESEnUPeegz2mDJIS0ODxuu4nh8WihDx8HUnMjJJBilEgRwEdOTw/5yZt32DqomOQTdq9fQaDJy4JlPSOEBlPkqDw5LlfLJoFrJUzGY+58eJvnX3qey4tTXn35tX4TK6id5e69u5wcnrKxXaKM5t6DQ+7d/bg/RCRI6lAVnD06I4YOlY2IQbI1GaNNxc3tHX7r136dze0rOBs4OjnhO298lze+91cMhxXeKa7deIYfvPkumcx5+81b/P4f/Mf4NrC7vc/+3lVs1/D5F7/M5u4ef/bnf8Ld+7fwIqIyxVNXnufs7JzFbE6z7hCmoMyGjMfbPGrvs7mxQ1Nbdva3uPXeikp4slGJswIpOhSgAiwvpgitaOOavN+MxyiTwCk1WgsyqROvqktTZ6UFWitUVKzaFVGCFlCUQxbLNd4tOdjcohwPyExA+YAWhqPLI5ztyExJVgxw0TDe2WO8vMQJ6NaRanATFy45P5/RtZbc5Gxu7LK/f53RZMj80QXPPv8iTd0ipWBnZ5/L80uGuuKjH79FlcHp2RmDasTeRHJ0vOQpU3F4eJ/x9gFKrcjLQLOqyFRFjCvyvKCsStDwwd2PGI6H/Pidf8vNa5ucz9aownB+csr981MOH95CRMkwr7h+bZfZ8hBrA4PBgONHF1y58jzPX91ha2+T92/d4fDBA7p6jURzfn6CFDnzVUNmBkyGQw52Dzg5O04Q2KiQKkNGy95wxOVoDHnOH/3F/87e/i6nFzMODvYQGJrG0rgFwTu8Vni3guBICP+O1fwCrxy1XbE13mBcDKBz1G1kd2OI9QLtJGIgccuGTBdUwyFxMWe5bhJwM0RMrmmtY7WoeXDvkExrrlw5wNEwm9fojMS3iZ7lbJHEkMygtKbtWuIiQTyVMpyfz3g/3ibTmjzP2T/Y5Ox8QZZleB8YbwyQusDFhqzMUDqj7SwPD88RwuMcjMclq2VD3rdkjQcV5XCMInC8WCD9itW0pqgLNoYjdiY73D9+wPb+LjqnZx0lFlL0Ak2G76bkmUGZxH8QWcY6tsl50otBQQJGEW2DyRTC+xSfyHOsWyO9xCEIQRJUckfGEJ44nIJNjTdKwnq9QOsSUGnqJRVaZljbkVcZapzcmj5GohRkWYYCFiGtB96lQZFW4IJF9oVjSNWDUtN17IJHR40hMaXQIrFrhKBtHSbTROvQQhO7kO7FUUBMbBGtVXKaAI8HAFL2zjbXkpvkoHEhudtElHTRk2cF0WSEvnnVSEMQMk3/2zXed4DoGTziCS/ocVsYMRJw1OuWMq/ApLahEFMjFzEQvCMr8ifOLy2T66TtOnI5SJ9ZBzGKvvnM0TqLizEBbY1ABZfWrxiS80aRBBptkEpg5ADb1UTboYVCmYLYu1wBlJbE0CKiA6lSzKJvxrVhjYuRYVYiRZrSex9RSiNVaiCNMSJUcijSu/NUNcTZFqPSNr9tPDEq2nZNUeZIoQnRQwg4b3HBkfWtfYQEUY8yAfWLsqKqhhSDIrmHfWpmrX2gzCpsU4NURJE4UIIMIQLWtRiV0XWWTgZs+0lZQySJXKovIWm6Dtd1qZSASC4VnbXpwCslhTJJfBHpc+hch/cpzmedT7pr8HhnyfIMQUoufMIeSZPhqANCpejl4yZX2UfjhQB8/9V9HJunZznKXixznoBDSYNUkhhSg5vQaXjlfUT2rv4YQz/kCr3zUKNjaivzMjVB2hiJUiIDlNUILSTT8zPGk03aeo11nqyoaAkUKkM6QbSeIiuIE0ExLvn+//w/8Yt/8Puc3z/iMzde53f+4J8ipOTRnQ85OrzLpBwgg+PuBz9m3Vq6uOCD97/Lcj5DFZoidhgfqFeWYgQRj8pyTu89IC8qjh/eR/at2sfHR9w7vMdLX/kqhY98+JN3+KV/9I9plnOKrOT25QlXbj7TO8ZgMsyZbGluvfcReSXRecnm7gGDKoPtLc5PLxjujPnBd9/gi195ldK2HN/7GK0i3/3Ot+liy3g8JKrAw6MHdLbg5udfpz67ZHpyRplXmKxkvD3hQ+8Z7+6yfqtme/8auZS89trf41/9D/8cNdhhPB5TViOqokjiZ3B07Rqd9+Um2ze4cfMGNzaewtk1TddijOKD7/4Fi/kF1599ie2rL1A7jxIiuVa1oRiM0KslRT7Ey0CVZzQW6mbF6bxBDQfEMuPm069x9fpnOT08pigLtFBkWjNvHOfv/oT64dv4Dv6Df/BP2BoecHl0yEc/fZuvffO3+PjtH7O7u0053OXo4f0+UpdKEwhJDhFRsFxM+flf/x2Obj/kT/78f+E3fv8fMl0suHrlgBuf+QYffv+PU1qoCzS+JfQDj+gDjYWffvxTBoMBKzslr3LGWzv8n3/4p+iq4Le/+TscHl4w3oRqOKQNFi0VIUYaOsabGxiVIYVJwhXJqT4cjxEhEnTE2YZAiy6GWLvC6CwVGkTQMonSqhdpmq5lvDmEzlJkhrwqiVrQjgR6XLJ1fYfPHOzywmTEsF8rUwQ6pAiblGR/Q115knn4O9Nej2Wlv5l/ePx4/BMeUodsauz9W8/6ux6RdFYR/zfP++T5sY/Y/l3P/5uvDx5n15Kd+TEvNkbSwAKIT1xYPUJERrogqFIbw8/s8TMXlHQ5oipKdsqSiKQsQMk1dx/cpV5HMm3Q2hNdWuh9TCA+nZWJMaBSxnmxaimMoXUtqzaQKYmI6efWdp3y6g4yqchzyXyaWDJGVjRNQ1lssLc/4vjkhOOLIy7nJ0yGQ5xvmXeOtlkhbJsaRKTi524+y6MiYzAa887b30OrAo+nyEdMSsN0FchMhlwnlO9FfUEUEaOHbI93eOrGFc5ODlkuUt36en6ERnJ89hDhLcIJsrxgPMpZL9aUZc7ldArR4jwYnZPpnEIrKpGnVrv5mt2dimExYbZeUJVDVt2cjWyXQilWnaVzDu8cbUw59RAjVTlgvDlhej5FSoWPK8osEIOlXncYXaClRsVUtTrMWhaLu6ybHISitZJ5q3jrYQA5wDrFaCjYKAserebYANFnCJLd3HqJjIJMVVTFBGsbdje2mS6maUPUebxQSFUQacikoA0CIz1GpI1z7OihmhG77jjY2Ga2muIiFEgGW1Wa0qmctlUoPKWQ1MHTuEAhMgpl2JmMONjaxnVQrzoMUKHxwVGaQAgrhtUgAWi3xqhMUI3GCFVhihJpEuCts5H3PviY+ycnfObp62R5QZ5ntJXkuau7PH3tKqN8izff+5DluubDhw9p2y61CW4U/Pj2bb777vtURY7zNavGUSmJD4GcCNawMTHM5isijrPzS9auI4uCzKQp2rpt6WyLQlGYjDIrQUsCnq+89CI392/w1k9+yq2P7zMe5cRM4AU0rQccIgqkjKAlVSWwrcQ5zyBXTMbbdOcrVAkDZbAyp3NpkouwWB+4uLxARIkdlrynI1IYRuNNpqs1pfFI5Yg4ijLZ+zOzzWw5Z5BnFOWAzk6ROKYrz7JuERiybMByuWI+b9janSAnWzgVKEyydAuRJryPQaV9LzePa5Ahta49jsRJpcAHRIxIQh99Sz8f+spnnrie6E9LIH1IjQ8mTRxHuxt86fXP0TULimJAMShYTGvmsaVdtTw6OWRVtwyHFWVZ0tiW2DYYAyB4/jPPMiwrXnn5ZaQ2LBZLXBcYDDLKzHDv0X1MdUCmRsynCzbHm3z2hZf56M6HzBZp0lTpjMHWhK6DydYOq+WCZWwpzQ2+9dc/4sXn5gg0f/XGd3jrne9TFYqyKIkhsJxPMVnkR2+9TTWsMFLz6PiIX/61n2fjYJs/+cM/4i+/821e+9KXyZRmc+OA+XrBcjrn3XfeRsQCLSPrxZIre09x+OA2esvw9JWX0NkAuzihtZEYBba1sJ7jkahMIGzAxYDvlrhOYGOH9VCpApRla0RqUpMGVSpEG9EmAY9t8ORa0fm01OooCUi87bAuHSIv7SmqK5h3DWdvf0TnwLImekEXPacXC1ZNoLMr0CVd3TIabtDWNU/t7XN6eHvwu3YAACAASURBVA+pHKM8R8qAaJecPbwgEDg7O2FxuUDnmkcnDwjRU69qfuHvfYPjh/co0FgbOTr3LGvHeHtMflyydg2DSoHUSAmZFCwirJYrmugwMrXxvf32rcTNEx3jXHHzynUmmz/i9sf3uZxPCWqD0bCArmV3ssH7P/2Qi4tHaJXh3JDzhWcy2ebe3UMm401OLmZYf4FrJEpUdCqwNRwhCLzz3o+p8hRbRqlUYy0cj84vOTo65XNfeJWNwYSNzR1WnWNxseZifsoo20DlOboqsW2NzhXLNlWmR9GxWJ6nZr58wPRizUzPiB6qfIB1SWRSxRYPDx/goqMNLRWDJ7EbIdJB3zmL1gmIu1rV7OxuoKXk6HhGphXRt4goKIsSFxRaaUwfsVmt1oTg2N7dJYpUHz69mCJVQErJ2Wlqlrn69A3efesW61WLaSOuDalVVYA2ORLBaDAg1wqTG44fXSKE4nK6wijFRgwYOcCYhuDWxKgQuaR1C1btCpOr1CCHwgabKsGVx9MSY8BFgVAaH0GIiHOOpmlQfdRMaZkEcRtZrzs0krpr0msTCtGDzmN/25Mh0rYt69WCrfEmWgo675ACbOsIzpMVOjVuCVJbJ+lzFzKSqbKvYu7jvTFxXLwPmEwDES0SMDnGiAu+Z9aAMvmThjThUwGHkZIiM2SZSYfw6Om6Fl0UuL7hynqHCwEbLM5biDE5k2TiAoXQ38tFWo/UkzizIEae8HqstZRV4hlFmWDPKqYBQ1YUrBYzELJ3cgls5whRcO3qFWxnEVIilSDPxlzMpmTjivWqJisKIr1rt49jKqX6NjOZ3EQBgnVkRZVAtjKQmyw5+bpAdBJnU0RM6RTFRkhCSPiGEGISvLPUeqVNajl6LExIlSC2QmoIkUwX+OD62vs0aHC2heDprEdpUoQvRFzXIkWKZYcYWC3XZHmBCAFrHSIEBAGpJNKlNdQ5j/MdMQSGgyFVVdK23RNot5TpmsqyNExK9fCJXaS1JssypEnxxuhjf+ySSFKMJHiPNgpBAPEJ7FxISRSeGC3RpUa91jlKUxF7KHZMM0qC9wmgTkSZDCE1QniElGghUPpxs1boSzKSo0gbTdcml4ULgbZre8dQYh0ak2Gt6x1UfclKZpBaEzsPyD7OQn8a61vkQmKO0e87Hh/IlDHQCwFSCqJKLkLbJpFRyfR9Cj72fMEkQhljEqw5ClwXKAYDtIcuBmTUqeDDeXb2r3N2/IiN7V2cbSmriug9ygeUoYdBJ0edjDAYb7O3vcVPvvsGIVj2r99gPjuDqNjdOuDrr3+Vy+WKh4eHPDi9T1unIfLRw4/RRnDl6gt0S8NifcrLr/wcg80ttMnYvXLAxYP7DDfHnHx4zP716yitmZ2eMa5GXNnc4Z3v/wCqiqvXbrA8O0cpw2q+IB9WdI2lKg2HH93m9odv4XXHnQdvMdnaYXtvC992jHf3eXj7Np7A9PwR7/31Dzi++z5Xn36Rd350zqLt2NreomlqsjInL4Zce+4zbO/scraqiSKw7ixbu5vgBbjAlWdeZmT2GJVDNq8/x4OP77B55WVe+4VfY3trwnhnD193SGTfQttw9amrPP+5/xJdlAwrw/TRBYs7t1hGQbee84M3/pjR5j67T72EVJpMCvK8QGiD0Dnz2SXZYEKmFSIvUDKj7eYUZc6rX/sF7t95j9/7p/8FZb4N1uNsTTUpqJuOux98QKYCcrTFR+/8OZddh5OC8+MZp6fn/PJv/QFFZljM/gJ7Mad1dxFSpvimSFFj7yXBtoisYzTYZXrvAd/9q7/gy1/7Ob701V9hvVxzfvIRe7tXebRxwHT+CNF1iJiuJSUkOi/Y2dnh5me+wLs/eRMZI4tFy8ZeRErNlavXuH3vNuPdK+wOnyb0IpRUiqazKbaq8sQFExKkwDc1ZAqhPC4qFDCrO8bZAN+m6yvdCyMyJvabkBKlJI11jMYVA1ViTY6Y5Ax3ttjaHvLclS2eHo4pRH8vioLEy09rwb+vcPP/+BGhz6v34O6YXI4kAVoQk7kC9cnz4VNOKHjsNBIiCdfEJAQ+dg+JPllB7/yPvWAkSLqhEB6i/pS7SfK4iuGJJBYDCPW3fE9AFKh+yC5c2l/8LB8/c0HJOI9DMBxMKLOSplvTdA3DrauMN1MLQ9Osaep5v9ECXEyTlpwUH4ipvna6WuI7DxtD2mZNZxVlMWAjSwJBKx3RWtoFRB+SGEVHCDFZ25sZZZFRdy0OxeVsgXOpZcVHTyQykIpOeN47ukdx5WkODgbs713h+t4e55dn3D96xLlrma9bhsUInRVk2tAJgbOWrdEmvq2x9RolBaaqGFfwSEk0gqKUGDegI7AxHjOdn9L5Jc1sigstmUjKpSZtwmDE2fyCZQzILEMKRWMX5FnOyeV9WpGj1Dk3dvfYHI8wjWS+mBO9YjjU6Kamadc0KxhUOeVwk8Ui0PVVrOk61wQvUVmOIDJfRZz32CgRIdHuL6cWk2dEWgySsorM7CEegXc68Ry8R4iIFBZihpCRur2kblr2BzsEGkQUOAdZlWObBdCxFCVVIVHRYumQCKxNA8woJSJIFsuOphW0PjAeKowqGRSQCcUi1oxyybpZMV92FEYyLA3VRs7uzpDbt9/n/umcdRuoTEGI8OKN61SV4eHpGde2U53l7v6Ib7z6JbQuiCJtIDKV2kdOZnPq0LG5sUFWDBEq0HrHlasH/OpGxe72Jrka8+5HH1M3SzbKEX7dsDUesarXXNnfpdjSzJdzpgvItE7utQAxeLa2d7i2v837P73PeDPjfLZCa8EkT9b5rmupW/BBsXYe62pq2xGioguWK9t7vPneO8RM0kU4X68JQWKbdGtRuk8ES0mUSQwSwlOUqVVnVZ+RCcHe5ApRembLBcOiZLmcpxphAk2TMs0rpzk+P6fMM9pOUa+XBBEQIZAZQ6YlO9tXOD47wUhHlsFoUDBbSPJszGzeMlvW5HnGcFKipaa1j5sCH29aM9q2hRCxPG51S8BUqUAIg1LJ7SL7dp7gQ5pGuLSYSRLnS0pHJEP1wpPAp/aeEIg+cUmETKA95zvW65a1bWiRqMLT+jXGlzgE3nm8lDw8fMRfvfHX7O1s8dJLLzMeTVBSIKWkrtdkTtCtV4xHE5b1ip9+eJfnnn2Ws7nl3v37rDvH4aMZXX1GNR7x4jM3sSHgYuTh4V0uLo5xQbC3cw1TDDg5uWC5WKAU3L7bcnj6AOcd+1sHdCEw3Nzm8vIR+wd7KJnhYseHt26jc8XV/X3W1jLYu8b5tGFr6ym+8Uu/yZ/823/JX337Wzz70iscZBXy9D4XF2coBUrD5eyCaCy17Zjs7HF+esxnXnqFzkVat8DZgrIwOCeQ5ARnCQRaHzFCoqNBi4jq1b7ORkQQdBh8TIdL2WaE6Akxo8rLJDp0NdY2aCHIlaJ2lt/82ku8/8F9PjxZgBmw7JYQa9ogcV0CT4eQ7Oa3b99KwqJUZLpgPBiwvZ8zqEoGpSG2Ndeub3OwXXL79oz12RSKiM4DmQ60fs3swvaxoIDSkb98469Z1Wu2diYcHZ4ThaTcyBlujsgGGSjFpNogKwJOByaDIcuuZTlvqOs129tDunWkYZUYIjgmGxXLekVTdyzXC1AleVkwLHOCk1yeLjBSE3yNUpG6PiNsV/z1m9/DNlMGBzu4+SVKBHyb08SQYjZ4dK7R1iPFiuAkUg7ItODq3jV+ev8+q87z5rtvI8sRl9Mpn3/1S7zz1nvsbl4hzzMuLmcMihHNusFaS22TcyKGCEoRGo8SlugDXXDI6FFVRdMFTs9X/N7vfZN/fXZBFzzz+SWzyzkxJCfKY7YP0QPpek+tU47L6QUyws7uLqfn57RtSxcs2qSI0bpOsHxjElS6bjra1rG1NSIKwXrdUA0qbNdycT5LjVPKUK8b1NBQFAXrVYdSGUj6liSBzyJDbZDS4L2gXSf2kxUwmYxhHhgWJdIUjMcjJsMJInoG42EShYJNB2Ught5x0izRSmJ0lqLoztOu1mhE2tz1QknTdjgRUCZHyYjvAkX+CTQ7+CTsS5nGjdYnF4cyySivklqFCwIbEvNFxYAQmsVsnoZvQvYNdCnSE0NE6FRT70LoN8YyrUkmRaxiDAkIL9J1nZrEUgLAuhYISCFQQpFKeXy61l0SKRbTc6IEfCDLCpRUOOsgunTQCiC8S827QqSpuHPE4FEmQaOVTCzEEBJguioLUoudQeUSJRXZYERhsvS7H0+UIwRnQYFRGi88SI3H09ouDSCiIPr0mQXvkzPch7QWxORS0UYnZpK3yZljMtbrJYXJycscHzw+BKyrcb5NLWJaE4ToW38kqufaaJkElhgDwVlc54gRyrJ8wvoL/vE1kcRHZ22vrji874ghYruGwXCEUiZ9diIJL0Jqgk8Rr9FwgtIK7TzWpX216Pk98fF66SwmL8iyVKct+tccY4qUaaORvcuKmMQKIWSKG0aXvve9mJQieWkdhfS7pPokap6M7CnS+KRaPQgUCYAthX4C9vDeo7VMDYQxooSi0DkiJKcBISK1ShJSJH0HZWp2LouSrrF4F5HSU6/rtO8h9oenJOy4zqWYptb4GKiUeTLnF7p3apEczFIkror3of9zckYnmSkSvEUDmdF01j+JOYbgUdokvEcI9JCI3nWdYqohkg7xUmCMTAIgGkjgft9a8mFJfBSZTy8pigoZRCq66NmS1lmiEGlIIyJN2zDtGg52dzi9OOT2Rx9y4/nnMUGQicCoEPijI6bDVLpwUh8TIyniLhw6drgYQQxZtg0//+rX0FnJ4vicwfYes8s53nrq+ZrN3SGH9+6wbhr0oGS6OOO1b/w6tl4AjmY5wzeOLC/p7AJpDKOtPZ575ee589Gb2Nkx09NHzPwlShe88IV9tvd3mJ2f8tkvvY4Okb2rO5xdXjKfnaFLjTIZtlnT1Wvy7StcvXGTXGua9ZK6qdkwif+WlUN8cFy5dhUXIk89/TKv/epv8Oaff4s/+M/+QyaTTZp6ibMeYyqUNugYuXfrbW6/8x2+9M3fRxQFFw/v0fnAH/33/4ybn/0Cj86nfO23/yM2hvtMRhOss0jVXzvBIVykWS6YjEYInZPpjOA6mtaRFwWz5Zx/+J//V5SDMb71zNdTunWNlLt45xiON7g4ecDo+k1e3NtnMT9he/8pfvjGX/LSy6/y7o++zRe/8U2kLuncKt3rbBJWlRZpLRaRqFNBQik1P/zed1BKcee9H7P6xiMuThv+5f/4z3nt67/CqBrQuhKcJ0rwvYsleMFsfslP332Ttl5g8oLtvae4PDsnuI5iNMQU25zN5mx3DZkeoKSGKLC2TeKQ1KDTXjuJ6p4yK4jSIKWmXqeSgtwUtK1PTL08Q4qIiBFj0uetjKazLZu7W4yGA3Z2x7y0v8V+XqQzJ+n9d9GjhEIKUAoeCyr/7z0+5UiKgehnT5Yg0cflpFAgSyKP73GP0Ry9kygGiInJJwKP83QQVf+r1Kf+FfJTYlPf3hlJeykh0r3lMeepH8ikXywQT4qJPvXyHzuYBDiXhLyf5eNnLihdzM9YkzGYbFKIVK8dheL5p54heE/btJycHUHoIX1EhNBkOpJJTesCg+GESTni4vQYoeBiOU8XyqDgyvYOG4MBy3rG+aKhWVny3KDXAqEKnG2QUvLo9AQlNZubW4TpCU2b+BBpN5EI7zFGvPDMfUtWFAyC4879+zz/7HOUrkOPSw5P0mJ47WCfrq5BVuxt73L48A7exnRTloHp/JKtrQ3c5QJra3yATkp2iwGPjk9QZsR8MSNTGjOosM6S9YLYbK3JZKRxYMYjmtZxc2eHel0z2RpyenaCd2tcDATnWEwXXJicwaBj1S0RJBGE2KF0cl40TY33HSYfoKTEy+TmKscu1fKWJUWZM5sf07hI2ygyk1RULfvoURBYZzCloO0CZigYV5JZzFI8LajUUJJFpJDY2LCsI5KC2XxJ13mEjAhZYFSOVDU+FJg8I9cd9bomlwaiRxqNMgXOQdtaZvUqbVpkwPoO3yhEUEQjWC5bFgNL9A3Lbs71/REmCMpKMipyQjMgsiB6j5MWJQ27GxV3T87ZGE44PJ+TlzlioSnyAZ4estwl8QvhGVYV1/avMC4WjMcTmtAkJovruDwXnM1qBjodaLc3Rzjv2NvZZzlbk6uCZ/avoIXlR+fHNK0lKA3BQzR01vHSyzc5enjMa6/cwMbI4emHqKCYuzrJ1iLl5JVM10cUgsY7FAEtJR/dv81oMKGQihgdnkjUCayZKUmIMTUguL7s3hmEDChVphuRS9XAURiWyzW2i3iZpnxSgu8rAPKyZDgYsbc9ZrGqcb7BaEOpNIGAUY5BOaDzjkGR4Z2k8Q69nlLoEkFOXV+wWq9QWiSumXdYbymKqwigynI+unPM9HLBMM/QOsdkAm1Mun9EQVTppqmkIi8MRmuyHliutGIynlAvlgwGQ+rOgWsweZ42x8qADwQb0SaBEn0URBPpooRCgTBUWmGqER/++F128hLbdUgp0BiEjrzw7E3KMk+cHwm1bVk2Hefn52ztjjk/PUUKhTSA9MzmU1btCmUERVby9I0bHD06o7YNHZGT0wte++JXGAxKjh9VjAYbvPXWj9i/coPF4hKhJCFAUUgijjv3PmZjMGRza8ILL/0y333jDXyITGeX3Lv3HoNBQdN1XFye8t6tj+i6yMZkk9Viwdn5BXtXr3H48Yrj+/e4fvMmVbXJ/HKGlIqmnhHcgkVdMxjvMx5vMT2/QMrI7qTi1k9nBJuTlQqWyaXUOZe4UCEiRcT7Dl1olFfY6NKARRja1hKkRuv+cNM70LwXrOuW0XhEkCnCaDuHkYIYRwzMNsMK5m2LVIK6SwBIESETGZ0IRBnTNSIjIXTE4OhaR7QjKhEZKEHT1Hzjq78K7YqfvvcDqu0JMTpOL5a4jdT6JLXE0dK0Fh0lXkBRFT3TDIRS4OF7P3ybUkssinZt2d09oFstE1jZtWlSLwKz2QVVMWRjtMX1q1f58muf5+M7d8mMYTzKGQ4U87mjzIdML8/xsWO2PMPHVaqM95LV2vHx3XuEriObbHJ0cU5mGpTJWHYQfY0kZ9U1KJkxqjLW9RqhQAuPDhItM7JMErTn0fyEMisZV0MePjjimaev8sGtjzicP8JosCEnSGhbDwSUNnQulWRoJdjfP+DR0SldY3FR0rSOQCqPoI+bROtwPiaQvYx461EStFK4YPHO0raSAEQleHh8xpdef4XJZJu6s+S54+JimtYhowm9M2VjlFqzVqsVtmvpuoLZdE6ea0whWDctLjqigPHWBi5cYkxGVZUI1bCzf4D3jof37+NEgoXbpsMiyLKcbrkmxshqPSeGZXIhxZyBmbA7OWA82k8b8GiTSwORXI4CurUjNxntukZLSa4F+IDr4zlKKiyR0WhMZlJkSguZVNzgUeu6BztHiJ6Y6mSA9D6F4DBGAyHVyccENkXZJMbonpkQAtF1mKxKh9vYR8uI+P6aoxcQtNIpnhVTVF4GSdcGhNY41yCiw8fYH+qhsw48CKPxISZ3pxCJNSdFag3qGTZRJqe5EhLbtkQkUSm8cEQLyigQgs66xCDqHUZSCRASYxQ+BobjSWo6Q2J9SCKyAJNpurbp3T7pCCFjSH+/VnTR4+hbmFRyBqm+sUuJ9NwU43N97C0Nx3RmkuPaWaIEFwOZ0ayiR2Ya6xwxJtdk513vODNPoNaJa5W4e826h5oriRAxXSsJaITJc7TK+khVOhCIXtBT8lPCDPTCqyTPS2I/IEkDbEGWGbx3GJND7yqD1FT2hNskJMgkzvkYKHViWFlHH9eKSeAlAbyFTO6k5NCBvMjS++GTKJK4U+mr2zmLMjne2X6KTu9ITPFDeoCtjyE53XqBSklDtI4udImLFTxlaXC2Q0pJkffw3hjxLoG+Vf+eRERaY2WKtJssx9oUevfBJ65UlgFJcDEm60HZaT+FSoddlMRbh5AqvUe9uyD0EpAQKg1AlEqV5CH9vPABoiTvHXpVoVkuGkJMTq4oP+GcRCESByt4ohDoKJ58hirPkSHt6UKMKDRt0yFIbuvJ1iYnD+5z/bnnCfTxcSKrziVhTil0bhBt5OTomOFkwNnhfebNBWXIuXrzJoPBiNlph4ySdd2yvXdAu4Dl4hLv0j3FxsiDe4cMB1t4L3n+lS+wWq0obWB6cc7B0zc4enCXSAK6C+L/xdyb/ViW3Vd6357OdMeYcqzMyirWwCoWRVES5W52w4Zlw09+sg3/XX404Bc/GLBfDTdagNztbjS6JVFNiqJYxZorK8fIzJjueIY9+mGfyKJgGTAECvBNJFAVeSPuFOecvddvrW/RtjtciJw+esqws9y4dTPDuJWm7TaIKpfk6CIXAeh6yve//wdMteAL9zOOTu6AqIn9QLdaYWTi/Ntv+Wf/7X/P2dcPcW7Dxx//gmaSOJlO6ftIlJGIR2pF3SzwzrE+P+fg1m20qRi6HcVsjkgC1w9MphMg8cmf/yVvvP8Djm/cZug6UpJoo8ANKJW4vHzFw2++4KsvPueND59y5/6Mz3/1V3z59af0MfD1V99yfPc2b77xgM1uoNu2lIclGjX+HiqGtkUoganzPsfogquuy22QInFydIiUZea1kRiSp5jPUKagaubsJguObiVu33uAjYnl0X/O//o//g+0m+d8/XjCrXffJvQDPvWkmLAuD2ii8LkUynlsjGhpAMHFxRlVWXK13WOtZr16zsvzNfffeIfd5QWrzTnOW6SWVFrTti26KtC6RirN4HuqyjCd1Pz0P/0v+Jd/+i8o6oZ/8p/9CYeLd9i5lklZY4eBSip6a4neUVRVLmPKVAqiHSgKhSkMSufG0uAH6iYPd8rSoIxBF5qkwBQFZVMxm0+YL6ccH0x4+2DJYmxZFiKr2HngnJ2z5rWEMZ4v/5HlpGwuGsUh15O6n4NskJjxmI8gNZRvgTokjTYkMQ5wEiB8B8MnRJXF4hyJVSMmVoG5OwpSrzX3717f8AqRbB5gvE5xjG5NVYFakgNzcuTD/R0Q02teoBAw+LxO/F3efueCUhcDs2lFqRLt9gyLRonE5vyCaVUh/J4Sz7TS5AZNkXPfaBaTKaerKxamZlYpLslCRVFq3OCI0XJ2dYFAMiknLCcFL7pXrDZtXnjhKLREmwoXcxvLrt0hoqApSo4PD7i6PGOwLu/tEYSU6JNgLg2d9RwuFmxXF5xeXrBpd7mGVWlOjpcM/ZyutfTDjn7osTGwcx2TsmS32WJdrpx1Q46AFFrgXKDtPbWR7NotR4sFQtQorTAisHctSM0QIiEplDHcuX2Lk+Uhp6ePUbFHyYpCtZRaknzEupbHp4+ZTGuUSmx3AySB0WSQmlFErUk+T3W0VgijwTomhWDdZoj5xrsMG0+ZWSCFADVOgKIeNwF5odB1Ep9KiiKghCSR8AgKJfLFMCR8iCgJtYbZTLFtI1FoZlWFUAPCeGQoOJwI9t2AEwYjs4gTCWg8k1pRFoltP05MkYiQq9ptbDFiQlXXQKIsJNNU4UNAGY1PcLndc77d4TwopXHJczSbIrTm+dkVd46OeXJxxj/76EeE4HExEEMkJMlmu2Pf71kslmhTYooKVQ5Y79jtBi6uLC/OXvDpV18zPzhiXlas1htundxk13XMZ0vsYGl0yfl6w9XmivXgEFogk0eaglprNrueQgnmVcPgHK9WuY2qoCLGHEGIPtveSZHKlHmxmBLICNGjVMnO7pFSUFcN+7alMNn5k6Nj17ExgUgCISJVVeC9ozAN1g3oosTFxDBEOuvR2hGTZTKdUeiCzWqDBPqh4+IKnAu4kUqbBZ8agNmsGds9tkzqhEiW1eqU+fQGptZjJXCg69c8Pd3RFAVRRG7fvocNUKmC//1f/1t+7727aBJIjRjbfFIUWXj2eaFVKE1R5t/Nqq4R4+/i2/fe5OLsnDfu3+PFq+dgPTfv3GG2mOdYUhSU1YT58oj1rs8r4kqiRIEPjifPX7I9e0mxmHF1dkp1cEz0uflCKc18PuPy9CVXF6+YTRcsl0t2u1UWr1885dmp5/nzx7gQ+OC9d5k0M3y0LKcNQx8ZXMej0xfEKNnsO6b7lgdv3uPWrdsUVYkxExbTml//+hdsNhe0doNRBb3tOTw8Yr3vWF2u+Nkvf8HxzWM+/PBDPvjwDygnJV99+iWn5jFDt0Fqgy4Nf/Gzf0O72/K9+x+QUm76qcqS4xu3ODt7QbvZcLBcsL1siL4nCosQHpUcQgYO5lNeGsNmc8Xl6hkxBl6+fJUXUEGATtl1gMSLQIxgjKaZTCmjZN+1tK6lEHHcXMIQHKiYoYtlSUJhvWc39Ay+53C2oCoLut2af/tXXxGjR9Wagp7QD8Q0XiDH5j1tyC4AmQXYwQ1IIikOID3vv/MWj16+IOAxwvPx58/Zux3z8piJadBasu96+hipy7xIV0pQGsnh4QEH00Oudiu6zrLZ9azaPUUh6DpP1Vhmk4KrV8/RVQPJ5VinAh8FO6cYguUP334fieST3zzian3KT378EzobietzEA1GBnoJl+srpA4QskvD+7yp6a1HKYnf52ayZh6YTmG33qPILgWFp24abh3f5pPf5PgeUiCTpLMDi8UBm36gDz2F1iyWRzx69pSqusfxjZs8/ewlB9OG1eqKmBTGqMwW9A4lNALFbDpnt+uwPk/twzhpH9xAED3fPP6WbbdGS4Eib/iklKg4NrECZTPBWpeZZ1KiKsNsuWBxfMh2vc+V5mN7qJQyR0fKvE5IIZc3BOeom2rc4DgOT+bsdi39MNBMq1z3rRXNvEEh0YXBhz1VWWDKCednL9l1W3zf5cjCWORhmgkpagpjCLGjKCNVMUGHzPfr9z2FKtBS5TiLjwitSC6iSRiTF3BSiZE1AtZfb+gFZHwSMuUBZVQZ7hJiJMVAVeTWJyFVjpylNE4bs+Vd6xLGxJqPyAAAIABJREFUsoE08uFSjBitM9slZeC/GGNoud0rg6C1VFkYGsWIlGK+X0yYoiaJXK5hnUOXGmuH/BmQF7gpgvcpb/6JCJXP2SkKBu8omirH2kQW/U1domT2c+w7C0plYDcQfaJQBh99dsOMMbksZuRIhB8dUEVZZJkiZdFDm9zKZYxht9tkFw05OpZSwgZHU+drqJASScLogq5rkSaDrbXSo2AiiCJHxK+X5EprEgGhBa7vaepJnrgnQd+3qHHi7KPLK3Kp0KbMolcMeQ6kFC64XFBiMkdTSvB+QClBMipvaIUcgdTXTaOZNyOkGktmHFKoLNoohdYQk82ijs/XRqUL2t2Wppzmtr2UxmNNIFUJKeQNR8pRuJQyLDe+nlwLApHgPSJJtMoD15wSF8iUxb/daoOSOk/TESOMPjfYpSQIzqG1yo8Rs1NAqu8iHrnpLPOqtJav2VkQcG4gxuycBkFZVShtCNFnB3LMolBOgCSkyoJsBJTWWJtjp85nZqbWGbiOlEip82sVuS0KIiEE6iqvH30IWbQeX/N1yUd2bIXRtaaAHMGJMUdTQor4EKlQEFPGR0iJtz67zlJEiBzPC94TYna/yhEM7FOiNgXBWqy3EMbVtMzHUPCR2XzO9vKSq1dnHN+5DWTWk5G53VGXBYUxrPZddswNjkePv2RStcRpw4snz5m+/16O4JHohz1Dew5+w2JaMFhNFJLVrmXoArqy3F0eMEuRl998g1SK41u3kUJw485dEoLzZ89p9y27/Q5VVHzx67/hzptvs70848aNN9m3+ywma5EbOAO4weaBTRxg/4I37t3lxp0HWOv45jcfc7Cc8/CTv8VvXyCixzSGX/75X+HjwKwRTLXHJpfP68WM5fEd5kfHRJWHjUJKps2MzdWW+W1Bu2/x/Z5uu8NVDe999BG3H7zD0Oc9Uo7NBi6efsPqTDF4uPf+h2wvXnL5/Dlf/vV/5PT0OW5oSUWVY5NR8PTLL1ncvM92fcXsxnH2q6m8Ftn1LcWkyWK1FPhgCdYiJGg0KclcJiA0u26F89klKXVB8JbgeoQAFxORRBhaEJ6D++/x0Uc/xWjDx//x3xBwWNsz+DReZ3OiJIzunuvhQGEq+i6LT7jI+cUF2+2GoDRffvYr3vn+h1yttrTbc4yZ0rWPKUzB/GhJTJJ+v837D1Px6Zdfsrxxg/tv/zGH8zs421IpRfAerSTIjDNAa1RRZCetyCkTm3qEMpDS6DKzxGRp6iXeWdSkYrKYMplMmE0qDg6m3DqccaepqEY3Lym7pJMaZRIpMNeWoN++pTHqNeon/3iyUhZmfvsZyOJ9kBWJBGFH8ueI0Y0tRgUnXYvWQpB8h3AvQH2YrwMpAh7hLkjKg3njO9VHXEdtI0QJ7adEkUDOkDKQYhayRBxIhUY0P8nXxdEBm0T6O889o0Dyf3UO5tXfR5L6h99+9wwlXQASby3OtRws7qCSJwTPq6tLEAJlalK/wQ5hBPIlvEhYZ9Ekgm9ZrTc09QQXPdg4CkDQ+oGXl5dMm3ywS5HtwjFEpFAURYlSkrLU9LYlhJxn1krhxmw1MeZ4lo/ZTi0006phenDEcrFEuR0rIXAIXPSY0iCiZDGfINnw8vEjBFAbQ0yeGBVGF/R9z9HBAa+ePkch6dwOd5FFM6MNIrUEBoauxSe4eXSXwq0w/oo2BqQqqLVirg39bsvZ5Sm1nVDIOVJEai0ZVCKQ60odFhGnkPJGP6PMJHYIJHITxlwIbPRM5wekKrDrN5STmn7d4iMkkds3CiOY1R6fBNbni74gW7g1OrMxvELpEoXHkiGfWmUIdF70J2IUCJNww4a6lLiQWMxKXq5fUpuCugiEcJVbxlKND4mAyNb/lChqhUmJzmcb7lRoKqnY9B2pKFgeLpCbjt4ObG3LgzfusNu1NJMpDx8/YTJpEFLR9QNVmS3cJ8cLgpRMmhqvMkj2zXv36LY7+s6x6y1u6Flvt3z15Fse3L7HG7fu4GKgD4mPHz7i2bNTkjRcra4QAlabLTuxBWV4db6mrCqMNAzOs97tuNqu84k0qddZ7VIaDuYT3ODxrUNpxZPTZ+yHjkpmDoYpNI0u2e8HmsaQoqXQCuciPiqcT0ih0LpkM8bIqqrKbQ8hC44+BYoixy3bfSSkEVQtDWWpWSyOOH1+yqw2WNtTNQ2dG1Ay80bqomJWT+h3PWVZ0e63rHdbjFZ4a5lPpsQUqeua1fqS88tnuMEzX5QEBzE56mpO0cypyhIilIWibnKzmNI1xCIDMTWk5EDmZjqh8rTU+3HBGix9O9B3NrOQUkKIPG/3wUORWSrffv2Q1ndMPv8NLy8vOF4uuHH2nLqesNkPTCZTbt2+hzKnPHn6ghBAL2dMmxJZaf783/2f9Fc7/ugnP+L7H76LkpIoBTZFXpw+48tvvqBbb2h3G/r9wNX2ksm04ni5pJkqdnsL0mGHnn17xZOnz1lvNgx9ZL3tqOYzjo6Pmc8mpOS4cXxMdPDk0SPWqw2np6/46xe/wRQl2+2Ksqjp+o4YJN3QIZTmxdkFUbwiFZInz18iTMnp01e0+w5GjLmRCmstp88fIXHsd7epm0N86CmU4fD4GJRnc/WSpnmL45NbXFw9R1UCEUtKnTBKMKsqTm4u+eKzzyhngqpQo807gMqV8fmcK9BK0Q0B33vqaYEOUCSNahbYtmO+bNisV5kXIgUx5rhwlxyUkv3Q4VzHBsG9m/eZ1hXr9RYpNUaVtKkjSkkpJD5I7t495vTbl+NkGSbVhPmiIoSe58+vWLcdnz96TF3M+ebZM7zKom1nB2bHc95/73tMUsB7y8dfP8Jay6SZcnK4pOsHVAykoaVFUMSAV4LoLd+7d5OziysceeH/1v23UYXnNw8fsbmyGDO6AVwc7c4FnROcPn3CzrYcHEz5s//r3xOjAD8gqgZpAro27M9gYSog4VVu1DNGo73ADh4rBsqqRtGy7wbAUxUVh4c3uLxcs1rv8Q7qsaENJNbDN89OEQLON2f4YcCIgoePHxKj5vHzZxwsbjCpZjSTzKQZvEWpkqZqWLUt82mDjIr5dMb52TNSyhO3SZ0h+EY1zCclX3/7DUkGEHlyXxpB9HkxlBtePVoXGKNoO0tR5ljadDHh8ZOn9JuWzb7FKENRVWij8c6jx4jrfrvJTJfgmUxnpBQ5Plpy4+SQzeopSiomk5p21+NdoKxNBj73ecBydX6GKTWmUPjNQBQQvKM0hqHrKOqGJA1G1ShTUlUCZSoKU+AGS102hBDGRV52pGiZ6L3PwoeKeZNcZndEjGJ0i+ZYr5IapcXY+pT318bI7HwYuTohxuw0jtdxobx5V4ns0IiZayM8RB+xvaMsMq8jt2gNKJEnwR5B6AeaskRoNTpgspgUQsA6R1mUme0UM7cmSUlRaHD5/KpFdhqElBuqfIoQwADCGJKNaKGomgneOgIJWWpUYRAhN4la76mqLGjl8dYICQ8hC0g6x9mM1AgkLjiU1HhrMVWRnS2I15ylsZoX2/dolWGyUkC0Of5jpMSHiFGjmCCz42hSTQguYnTmdKSYOVUpZS6FvJbQUnbUBeso5xNsn8UANUbiY8pOnkk9IfqA0Tq/Kimw3iFUCaPrpzYZWeD9gCC+bl6TchQGY2bSXYsvMeZGwBQTzvnc1Goz2+u66t65DHcGUNIQfMTMMgsrxYj3Lm+aR5FFjZyqLDJmd5IdG+GuiytiTKMYlTLrKOXX42P+DFMSFKOok1IWi0L02XkaBVVZok12cGktX8f58v3z80rEXJRSNXnTKSXBerTWkFQWrLTMAib5+3KV+MjbilmEDtHneJ7IG04tc6OdFDFzxZxj0lQjpyqLQJDFXOcDdd3AyOe6fj3XW8M0chrj6OK7bo4jRqIUdPvc7CaEQBYlPgmi8wihiVERgh0/v+z0U1Lhk0UkMLpAGsNmdYlpGiSJIXhKZQgpIEyOCyU3EGV+Xc10SrtZw+3bhPEzlSm/twIFLrFtW0xVU08mLI9vIdvnvP+jn/Dk4RW79QrvA0YJpJ4wnR7S6JtciM/ZXe052+ZW225IfPD2m9jzS+y+ZTFdsmlbZvM5/b5lNl8wHHSk3jJbLFFK8/zZY37/j/857/7wh2xeveLw5hvE4AgiUs9nKKNJuza7zoREKoONDV6WlNM5T3/5C7r1FV/+4hkxdBzeuY2oKi6efYOZNehL6LyjvYz0VhCC4ebyhHv3385sPVNgmhqCY786p54dIYKnH1pWL59QTKf8l//Nf0e73uFdbuy0/R4RI6dffsYnf/4v6NKeGw9+yL377yOV4Nd//Rf4GHj3x/8Jj/7mV3z/Jz/laHGAFgUndx4gpKGNKaM+igKkyu2DRCZFhY8J6SOBzAtLApSuSEWZhwgxsNvvs3AcPaYociIiBKqyzAPkkOj2Wya3T/jg/Y+4e3SP569ecXH2LSL0Y840IYRBvDbDJUqtM5tvdDC5kDBKk/DcfesdjNry8Itf0dRT7r31DubFmi/OXoIYqKcT+mHgzZObmGqJKQq6neX47h2Obt5lOp1zcLgkUSJVj9SS4L9zq4YUKcuKQpVImbmmKSSMNkhhMBKkNHRxT318QDNfMJ/XHC2nHC1n3JhPOSwMhZDIkSmU/1zr9te0oHy+FOJaGrl+9YwRr39MIenvRspSkojoibqE4uS7BxYKETvSa36R+K1/u46/RZK5j6zfAeKoGTnYWdAaZJ2//tuiWRofW3hoPgR1I6dY0iht2R0ifcPf/w78XQEuACJJtiGybP5/LijNmxnSGPpux6ZrKeqBUkV65xlcTzf0VCbbVF0S+CAwhWSwkV3q0KpgPq3HDXXB+eUFIUqSFhgpcM7SCggEppWhH3ILQgyJqtYkFC56QmfxLqLLfGEVUrDdW4KPGfCnJCJGDhaHbPY7ynoCvkOrE+gG2tbx4N4DmvNLZFHR9h0+wrazlPUSFVq8HQjOMwgDKhJdz9nLUy62e0KUHEwOOTs7R1cGomU2rwh2m1to5jeZNzP80FIWAZJjsJH15pJ9NBSlpHUB33ZICYfTkgA01QQXeowRpDBeEENAAkaqPI3y0NRZ/Gm7LZvdivn0Di4MmEKg3BpdjGBRD1FEDg8Sx43jfCsQNHkK6hVK5NiUkoIos4IuR8shMjJ4gdGSUkRckuMJxrPvEjEVGOW4XF+RBzGCWDg2Y0NfCJ4gE7WR2Q0VJLvtQBKKaTNlWhXsty2Dtzy4veTWnTc5mC959uyUV+sV82mDiNB1A8aU7PqefW/zcwiBro00TcOtGzdZzmY8LJ7z7OyCD+/dZNk0nJ9dYm3k4bNXPHr2LfNqypffnnKx7VF1xdHhEcaUbHeO1gpS2uNS5KN33qKpGz7+4mtu37oDPqKNZL1Zcb5aoZREKzFOIAVCjpqwELTbASkkry5XPLh7ixTf4Msnj2mMJsjA0fKA49khXz58iJEQUHSDxTvPwXJJ2w9suh3TScVq01KXNc5ZfJKElCDmCWR0CWHGFrHAyJsZ0IVmt92hlM5NOsNASmCMoSwltne4YcfaDnjnSexz/GA6gxiI0ZOkYFKbzN0IsOtauj6Q1BUTI3GxwA/QDpdMqg5NYN5UCBkQ5ZabR4eg5hS6REuJSCCTzxXkPjsCQgzEGHG9Z7vZ03X53BB8AJkntEJKXOdBGcxiTv+yIwyOYtbgpeJy2+Kvtuw6iynXrG0kJsXXnz/i4uySanFIqQM3bt7gxVePOTo54fzVBV+hkeYRd+68QVJbPvv4C37xs79kMakpjGR1sWK9ueSP/uBHXKWBvd8ynR2hy8j6+Rm/2p+zXUcQEoemnh5ST+ZYD3GzwXU76kJycHLMt4+eMplUpGTRpsqA1aIAYSC1aKlwNjd57Ps1y+kcLRUPH37Jq5dnue1rZHYUqsCokqgMOjqGwXG52nK3PqQwBYfz/Bkez0sei4fsL18xmS156QRdP1CXFTgY2p6z8wtCAlTezE4nC0J0bHdbQCBldtIodJ4cqYjzgdXFJVoZqqpmcbjg+e4FRbUgvLrAGE302bESRKBPjoQgBYmMudHncntGXUzyol56QhdwaeS5OMHNwwn/9Mc/4H/74inT+SyXGZRZAIlRE1hhI1gPf/PJp7TOok3BX/7tl9DvmC2XzCcNx6Xk/OyK/WbP0cEh3vc4H7C9xQ57qrLh/PIRdVmhyzkpCZqi4ubBMUTPbr/l/GrHD77/NsPHXxOD5nBxjI97nEscLKZsVwNffPl1FhqUpOsFn3/2JZHE3g1MJ5Gu2/LWm2/x8OFzUipIQiPFgNQQYo9OAmJLPT1EN4pGV1xdlQzDinJejo4aR9vvUBIOl0dcXK5RoqL1e7p2z+2bN3lx/oKEQglFXZTsO8fFxSVuEBTa4LzD+oGynHGwOKYsS4anj5jNJrjWsVutaZqa7baHJBiCZNX3HE8XnK96Lrab3M6i8vUoOIuiyBsglRegfTcwXzSwj0xnNeurHSJJikrTtj0iSUxR0bYdEolSZvz+vBw6PFrS9j1CKXablrrKrVdVUWJUdm4YPQJxnSP4QFSSsirYtztoE8poRBI5hifyhjDFRCrzhtQPPYNLCFUQ/AYrZoRuTz2bE73NjhPGGvoESIHQBjdCQZuqQhZ5s6dkBos7n8WTJPLiO8dssis4Opdf4DWDZowfIQQhpjzNTjKLJzKLENEnQvQ466iKihgGonD0bY6Jh5goqorddoOox2p6mSNB1zydlEBqgdSC6HJ9cl1PkEJm11aM2UEUM3jaeQfXzjJlQEhCyF/LhJm8gUeIDLdG4WyLIeSWsRH4rXQxwsOzcyLrc1kcyNFuSyJlvqZWFHWBSCqLvOPz3my3OO/y55jBizjrR2KpJAaHUdnp4+yAiAmtDG23Q6oyO5pGxoQIkZgE2oixHSiNbuDrwgcoypLkHFElRPAUumTo+szWUVkEAUjDMAqme2KMlMZAjHRdT6kNfgTXxphQSuQoWQpIacYoVP69iikRUxaXfHBZBEmZyOO9w2iNNsVYKy9QWhK8wwefRUchsW7AWYsxhsH1CJFyJItI8AFtDDke54hxjJXp/DqM1EglR8Eou4REWRFGjocUMsc5lUIZRT3J7caSLISk8dp9DZxNKYuoIcbsHvIJTBZFYwrUVYW1NsfjvEVrQ4gpt/QRSTHHb53PThVnHSCymCcSITqKwuC9Q5AoigyTzxvQDO3PcUXQI3vrOpkmYhhh3OMGZhRspMwuDB8yp04gGdyAxVOpmqIoESmSXI5hyiToB0+RcvOXKguUVNmpohVKGgbnWF1dcXu5wIcc30pAFAI9Ig5sn4fsWmuqacN+fQUxosnOjxA8ymSXkmsHgvcsD094/6M/ZFIvMOaHHN/8HmevfsPp02fZ+SgiKSpuvvEeNw8XbFYv6YdVfj9QlCZxfrWh3TvedpFnl09494MfsDo7Zd9ukOYtirqmmtV0/Y6hG6jKKR/9/h9RTmrOv/0WkTxSJpKLTCYLXBAEmRmeRVFiqiX1wT1effst0Tl22zWby1fUYs9kPuPowXvsLlasB8udN97l0Zefgi7pfcJFgYySoff4wbPf7nJbX0z0uw2bs2e8/9M/oXAeqQL/4V//K/7wT/4rvM0uSGLKsV3vKZRmu14z2MB2+4J6ccAnV5e8ePmE2eEhD97/EW235vbt27zz9vvcvPeAn//pv+T4+Iij+29T7rYMtmfSlIgYGNqeupmiTZPPT6oYo8QKaSQosiuSSN/2eOuw+w3r82+4+eY9UqywbUvqO0IMXD7+nOb4iFvHtymEhhT4+tc/49XLh/lcJwSlLqimU5TUtOsLjMzuaoRiCJ7ptKLdWVQ5YX1xgd2vCdLwvfd+xMc/+/d8/uu/wad83MYUOL51G1XW3H37+xwc3WOyOKLbD9w8OSFETxIRo0qszUxVIF8HpCSEkI0FZYWUhsJk4T/FgDcVQQnK+ZLZsuG2WXLn4IDJpOB4PmWuNWZ00bxOecNrwWj8v9fawm83nv22bHJ9+Cbx98spv6tbjiGPsbWUwD5CaD1ajsdCoGBJsQOa71xGrx1NI/NIz8C8Nf7UkYEUA6QVovgR1xDu7+RCxqTJuBgyR1lQFmaU3SDpGmnN33lXcrPc9fsiXj8eZMZi6yx3f8cK0O9cUGomMyoZWe/3VPWMEDPEc7tdse9bEBB8bhkZBk/CUEhNoQTF2CahUuZxiLKkaQr6AaqqQMYB7/MF9xpYGaWgqmc4N/5sPDHkZpWyOeRkueTZq2ckXSC8JcRMxR9CQCrDYj6lDZGDwznr9TnPT7/F73bsXaBRNYfNnMthi4+Se/fvcfd+yebiCc8fPubqyjMozW7osXYHMbLbd4QoMpixd4QI01nFcrakKaBtA1olRHIMQwY77oZAINJ3iapIHC4WXF68JAZDFwA3sJidoAvHvK6wu469DxiZHSxVKbA+EuOAFhnSKqSkLGC1bnHWcbE5I6VAVURE8IQk8ChCChgDx7M8+Ru8pB/ygsOohI4RmSSqyifH3nnKYoQYJoENmTavRaIqNNaRs+MyoFIgOIOPiUmRnRm9l4RoxpNDRGtFU5eEKOj6XPNrqoEbB4Zu12G9x4ge5zpmlaZRhtu3TlCFYbPb8PWTT0Eb7pLY7HMrT12WGD3BekdhNEeLBWcv1sQkmCrNq6stH3/1LYUuEKLgxcWKJ6+ukOGMy80GUZUMSRGSxLo8zatMyeVux+HJjB987wHBCTbbFlBMy8yo+vSLR8QERua4gI0RmRKFylrzpNC8+eZ9vv7mCajIixen9AmMKZk1JVEG7t2+Q7trEQSW0xLnFS8v91gbuX085WKdWO8SlsjBsuRyvUJrhVYSiaL3OTMvlcD7lJ0/hcpMgBhwg6PnCoOg61yumLYu8wakxkgQaWDTdmNDoGC6aLh36zar9QbvoSrmkBzb7TnNZM7BwSGnp2c01YxZXXCx7jlbnzFtahZNYtJU+L3A+ZbF/ID58jaDdWidxV1rA4VIJNdxuW9zdFU1JKG5urii69sx6jBuHESkrDUpBoYgsEPgcuMQZsrgPGVVIlVNCnmye//+HcqywSdBQDJfLNludjTaIJ1lgubHH3yA0jVvv/Umvvds2i0///Of88GPfsB+s2OzXlMbQWUqmjpzJZzv2fWWvtsxm884P3vFkyfPODqe0PuCsql549Z9btx+wNnpS54+e8KrV6fcPJmz37/LdveCoqoAzWADQ58Y7IDUYoyMKJTSGFMSwsCNk1s0ZsaLp0/YTaYIchZ91+0hKvq+42ByhC4KwhARKDo34GLPvGwojGA5rbl3OOf3Pyz56otv+OLrM4QfqIuag8Wc7VWHMQWLgymtv6RcGnbbliFsENHje493Aa3IzCMC1WRGiUakjmBzKcN8Puf2rZu8ePwcPwScTRT4zMSQkqrMrZEXV3sEFUiFUrBpdxAUb79xxPlqy8W+JQpFrasM7jYls/qQd773Fn3fs1gu6TrLyxcXdK6j63qSTgzeErzE20AMihevzjhazrF9zy9/+THv3b/FVEsW84qr3tEP8OT5OcEFiIFbtyoGLxkiTAjUdUnwlh9/9Dbr8w2/+myDkIIvvzql3Qtu3TjCVBN2raaqIDrLdrNjsZhi7Z6IYLNdY4SjLGvaASai5uJqT0pnFFoz9B6pZI4oS4iDIwoLpSPKLW2X3bL7TuLChM5KVtuWuqnpXI8UmhBGVoFzIDzedmyuVhiR4bgiSO7fu8tvvnoEIg8cSALns0UfAq7v8F3HtG7wg8daS987qlRCVCQh6V3AR4fDc3XRYUOiMAKlEyIZjm7eRAL+bEU/9ISQAfS7fU/fd+z3JUmCrmuGIdIOiaqqSUIx9A7Q1FVBsAOSvAEPITAMFh8iUeTrRbu3nBwfgoh89eVDpvMZxydHnL16iZDQdR1Secq6zBX3NjeTBpsoDQzDkJlwk4rCSIbO0vXnlJygiwJPYrVZcev4BoTsfJZS4kRu0RRKZXfGYEEKyjI/jtQK4fKwJo5tLcSxmYnrSaUYRfu8HpJEvLfZ6Ts6cnxIeSMvBZGAtd1YRhAoqpKqrnK9vcjOKGnK7EQxGiWvXTejQ5Vrx0hCCcaY2Qj0VPkaEq1HaUW0ITPcnMuPB2O9el7byBF4jJAkKUhx3KQnjxAFKXl8DASfUCOINCRBaUxeZydIUQCSUhsUkiQ8yQdEJDttYuS66TMJ8DEhlGDoOoZhQCs9rpYlITmUEK/dJWG8rxsckbFNDZV5Q2JMYLksJiiRYd8hhfw8QnbMaikzI4qRRagEUhmUMji/yS1qUoNSr5voriMgQqg8DHAOEdMIEIfg0zhtvh4ykZ0OLgtpSeTNhQBidISQ0IUhBvJEWqRcclJphr6jLMox0pajY3F0A/V9R2FMbj1SOq+xTTVCoGMWGH12NJGgahqqsh7B8Jm6pbVg6LsxxqazO4IcMYw2ZOHzeh3uPFEkohDX+iggs1tSKEJwZCQ3o0s1t8F57ylKzX63B7JuJkVuyGv7NnNVlCLJ3KJWFDUkl6H1KruUlc41cTGlXD/+2o6X9yQxRbx1GGPGo47XUcr8OYzbLXG9B8yu2zRGJ0OKCB9omobVdsV0MsFZR1Mq+vH7UAJjNEJAP/T0mzXOWa7OXxFS5PjkNrPlkuPbb2CqhtgPkCTXREEjJdvVFWiVBT1tUDrzwwZrqavsXhVa54Y6Idh0O1ShKYxiPr9HsB6pCyin3Lp3n8eff4YOkjgtCUmgTcnm6oIUa3on0FrgRKCaFHz89WN++Ht/yMe/+Dkf/ORHrK7O+faTv6KcHnBy6y0EiW674+LqktV2zYP3vs/B4QKUZnAtvhvQhWHY7BFjPPk1m6sosmOrqmm3LY+/+IKyzHB0byPYwGa1Qpoz3v2Df85XP/tX+DhArNBywlsf/oBCCJKNxMFhJOyuzlE6YUzB+nxLtHv+3Z/+Ga/OvmJ1ccH3/+gn7DvL1JSBIeiPAAAgAElEQVTEELC2R8jsWLv53vdJKvDZz9dcnL+i85K233FwcsSTrz9jff6cZn6D80dfY3d7nnzzK/rhkn966w5VPadfrdAiUTRzrLXURyckpdBCIIyk3+VSA2Uq0ji4FQI2VxeEdsOLrz/FpjNWLx+DnPL80Reszx5ydO9dnn39K47vHDO98SGvXjzjq08/42//8v+gmQmcV0zqCoTm3lvfY79zHEyWDLszBtdjPQihsLannBf8+Kf/Nc++esTLpy948ME/4fLp5xRNwdOnjzGmJgqoyoK33v89br/5LpP5IcvlzRxL1lt0YQh9wgiTnazklm0QCJ1dty56mqrK3DMMSeYUjT6oODk44d5yzsmkoakly8pQSzNSC76TOMToWv1tuej6OP1/3AT/b//y//mWxujZP/QmSRAsxGek4ifjF0cBSNUQGrAbUnWAoPzuqad8n2RqoPzOwUQk2XPQBiFnpN+S0/L3jfdLHq7PoeSHE+P7kcWugtc/EsZrY3rtdBppfeNdInufKFX8B78Pf9/tdy4oTSrN2asvkPV97pzMEEHh/e51leZm1yK1IIUIqaAsS5QEUxkODw5xoeNyc8E+eGg1y/khRhVoIdh2F2z67Kjo7YAfwKiSbPCGqi7YbHZordFNw3wyJeGYT+YgCxAK6wP1ZEZpNNv1itPzczwFGEUfQKApVEFKW3btOavdnhdXZ5TVkrciJLfi8uxTpAg0kxItJwwuUk8n2H4HKRBsR28Tq+0epQyL+QGTxqAkuF2i6y29jVgX6OyekBJdz3hC8HR2R+8C1aRhMl2wG/ZMZycIrQnukvOdR5UlqBGerQXeZvBfEJLDxZzppGZwuSJ+SC1S5ZJXmSybtiBJleNpSGZNJNGx6RR9yCwi5RKYRLKAHrPHWIpCoGSFEBFjBM5GehfQKlIKixCaUpcYmfP8IRkQkeDBRptz4loRoyKKyHIywxQS6Yfx6FB5WpM6fPBMpyecX24ZUuAvPv6U+7fu8oO37uDdhMH2zKcHdN4Rkcymcyb1BCUMkh0JS0gDn3z+iK+ePqKuFywmU55erljtt7xx8yQfurKAqGiHjsl8xmLS0EiN8AKiJMS88SAobp/cZHEw59O//YZd37K5vOSD995jt+vQeuSPiAwjVzrn3gut2XcDgcjx4ZxvHnoevXiCdZGbN4754O23KU3JZrPjs8+/xQVH2znW644sMecq2mZa8ME7P6T+9ecE22OHjjvHJ5xdnrFYzKn0hPOLS5yPaAP7tkNJQ6nyhC5EgTQaEMSUORm7fYvWBhE9NmRxUckIhaAuZsgUeePGTR7cvM1qMoVYcufkmMvtmovTFcbtiFGwHwaiuEE5OWTqL5l1nkJafOjYd4G2j1SlYTq9RZIFvd1xrNdMKs+zl9/i95Kz51usrNi1e4yqcCHXj/ZDdmoNCaQoqOoSoQoSCQUkKTm73DGbzjk/e8ZsMac6qpmfHGOkQpca5yz7tqWupxzMF2znK44P5hxNb3Dn9g2KMhCGwM3DOUMfORQzPv7lrzFGUk9K3nrre8ymhuV8gus7zs8vubi45Gx1yXxmkPOGy+2G+UHJZF4Td5JCGdZXG6R6yfn5Cw4Ojggp8cU3D7FBMVvcYDmdsm97dDNnsvQMbsWde2/z+Se/xuiaYfCUrsX6gSIxTuBBqAptCkTrOZhPkZRsN2e4fsvELGntQDOd5hp4Fca4j+ToQPPHP/0ht9/9p5x+/gn/y//8P/H84pRtFJzc/pD9LjLsLKvdjourU8IIFVXWZihpgqYquXkwwdpISAldTljbjgf37lKVmq+/fsx6dcUbd2+TQo82HlVkVmG0eWp5cnSTybyg9y+pzYTLqxUiCrwP3Lt/xI8/+ohf/u0nXO4uqKSgHwYOpjOsDfzNJ19y//49nj15zMnNQ2zXY7uW5UHDvTuHrHZbnrw8R8iCQHY1NHWDbR2X+46Xmx2d2/LGwSKfewbPYrng4nKdI6UysdvvsVHgQ2Lwe+pacNk6vvr2MRenV2hdsV61bNozVKnyhtYPGQ4sBFfrPaiCqinZD7vsphAN9bTBdp7oJjhb8vLsjM22Q8m8cRXJQAgkLMlbohTZ5hwHZFDEYkEUG5pmgnc99fIGD7/6lEUzQ4kCpKQwhtpM2SPwsWPfXTKfGlxfoKOkLgpiCNy5e5uu3eN8Yrd9hS4LfBhY9yus9dT1nNA5kvNEAt3QZ6u3SEgfmNUltm/x42bS2tzKmMHCI1NAFegiCynWWZyLlHXNdt0hpGB2pLC9RyuDdwkZI7P5Ic5aur4nWUtdFQzWwbbNIot3LJZTBJKqKZlP5rw6P8sxCFPgY2KwHmPyhjM5jzGZ46O1oG4qrPOEmN131bQamXCKFDX7wRAGz+VuxXS24MHd+8ynC1aXK5LWeaOZ8oZcG5WB9C7H6AqjURQjw24EX4+UHp3GzSkJoTJs23lLURSjayhvvpPwKCVgtNUXKse0rPUjW0cSXaCqS6TO8TIlJEbmmCS6IIbMurtegGcBJy8ec9totjcnn3k4mREusE7A6OCOYyV6bsZTxBTyOm7kXKWxHahQEkuGPldKAhk8bGOOXxmTeUtFUaC0wrvcpAUQgycETYclDJ7kIm6w+NFVnkYhTIg0pp4kMkkKbcZWMoEWMFhBaYrrDNPoQkqZA6ZVjgMrhS4UdsjtaSGFzBob/2Y1QRJ8yGUQ+XRL8Fn0kzK376QU6AdLYczrOKe3liQzhDc4T1FVhBRzDE7kRrJEjhRKYi7B8AGlxvawkD+blN89kODtGI9MESVronCYoiSEvJHeb3c0zSS7iK75HDFifcjXBjFGJQc5soXka+aQSPm9TzEhpcIYMzrAsogSnUdK2O9bClO/Bo9noPW1+zr/zoUwZHdfiAQZUTK/j0JohMiNZjE6iqLKx4LMLrIkctwxjWKQEBKpFIO17Ps9vXPosgalcD58Bw0nYnTePKWQo0Nt3+fPTStCTKQIIX7XAtgPA2VVjYIbZJ5JfselVPgQXutQ17tZlacm2U0I9L0lJpg0E7rO4qJDaWi7HbpKTKeGQhc8e77iL/7Dn+VBiyjohz261CyODjJDirHVbwxaKgFBKK5enlIsDzhcHBBjpCkbVErs1ysmzV1SgqLOcb7gPNu+o5lM+L+Ze7MnybLDvO93lrvmUllrd3V1z/Q2mAUYECBWEuAWICWSEikrrAj7QRF+8D9C/xV+0ouDD7Ip27JNS5RNmKQpECBAYIBZu2emZ3qr6q6qrNzzbmfzw8kagBApMiQ8+Eb0lp1581Yu957zne/7fZuMLEVvyPTslL1rN+gNB+gsZ7UakxU7G3ibRakEnZWgBTIE9MaeVYWObtVg6zPuv/3H7O68xnD7gDuv/RzloM98MmUym3H89DFNW7Gze4XgJUrHRdcP773L9vUb1JMLevv7kadlLCIodF5gQ4Qp6yKjNS1X73yKyXiKq5d0yrOar3nlC9cJKuHDe+8i8y0StcWv/u5/xXCww3o6pij7TI+fsJ4tcF3Laj6jWs9YraZ8///9U+6/+y2E6Njdl9TVlGL7CBMCqU5p2hn9MmexXHFw40WyMqeenvD4o3eomjVaSU6entDZDhkcL3/u15iNx5j1mtniKa2oGD96wPbV29z/9reRWvHKL34D1dsi0UVsPVYaLwK2aWL0VyqkiN9X7zvOxqfMTj7k+fEH3H3t03z89tsk/R2OH33I2ekDgtbM6gozvuBXP/8iP3jrR4x2Rmwf3UYzZ9XNEc7QmI6u7ej1tqixJF6T+pT50uEdpEnJYLvk06//PKP+FVbNguX4nLfe+A67uz2q1ZJ8NOTWZ79AkWTc+dSrlL0tdFqQZgWzak5elnil8Uk87xljSJSMzcxCkaoEFQKNFyT5AKMS0lHJlb0e17f67OWaXpYyTDM0Hi8DOkQH5KXg8R+TdP52MenyLPkfu3/4D275a/f9zxCTLoHcIjiQDqlHm5ZHHa8NKgFVIHzEUPxE3i1qQj99lJeUbPMY0bu5ifL99M8oiBWpYXO7Jwi3GVdsIr2XkMZPfvbwE/eP+4i3hriQo6BxIb6nP8PtZy4opTpDqCGj4XYEd2lDKhSDMqXzFVqBNQ3GefKy5NruDvP5Ga1JY82pWcRGtiSL9j6VUuQJ55MZi6pDywyRpLH203tq01DXNYlUrJerWKcoNPtXDkgkTM4vNjnxhiRV0d7fdtES7z0mBEhSbh5dIc+22Bn1+cN/93+gtebtDz+iPxxEir/MePfeG8jg6Rob7X29bbQMhHXDVn+XdaKRtuNZtUL6gBAZt2++QLCGdb1g3SxoG0gThdaeqltGu65OaKXAKcdiPQcv0DpCTLNcU5Q7iGBp1gtmyzmtS8lcBFk7HygSQZJpVm3AeMditSZNEoJ1JFqRZpBqz4tbBW295LzRLLpLm32CFJ66g67WaJLorvYBExQORSI2C6zKk0rFalWhkoCWHnKBcAERYlVv2LTFGBNjXzZEXHqhWpZtTZ73qGyDlj2MDIBlZ7jNeGzx3uKcpyHFmBzHivn6gkWw9LG89sKrpDrw5rsf8nw953D7AFO3rJoF3boiURmTesaqrZHE3P+6sdz76BFCN/T0NsZ03Lp+k8++fJfjszkdHmO6OGhPC9K8INElVddQhpJ1XeFMS5Im6K6jn/Z4880P+Oa3v8v23jZNXVG3lovJjDzP2d/ZZrK4wEnIdIKQAqVStIGzZc0P37nHsqupvUcLibWancEW88mMs+mY89UUuYkaTeqG4DxlViAJLCvDmx99zPl6Tb2a8Jvf+AYP7j/l9s1Dil4f2WrOzqZEu2SMEmgF3kbwuJaCLCtwxhJQ9IcJ5JK2bbEiCkw4RUg9O4MBIeR453ny5ITVdEaW5QQheXD8iH6vx7Vrt9jf3eHZ6YRrZcFgsMPZxYTp7Bl5VlJkPXRa4NYXVM0UIQYsVo5lc8z+YEgqSqbjE6brC6ZrTb59N/Kb+gPG0wWms/QG2xyPn2OtphgWJImiSIegFe0qTjDTYogTAR8SBjvXKHop1mvmszqKuN6QJZr9vSuMtrZYFEsuxscMi5ReJkmwFFLRCcvTB+/Q6+/ybDzm8GAPZzom8ynL9YLWRLFwb7vPjtgGL3n89JzarEgePMF0S0bDPstloHMwHT8nySuuHL7IoD9iOIiw93v3x3z04XsMh6dcu3GXw+s3uJMXZIkA+VX+6N/8a9I0jQNx57Bth1QB782GUVFinMe4FVIEtodbtEZwdf913nvvu8zG5zSuo0hT+r0Rq/mKrV6f/UGPL//cVT71+pdR7Yi3336D8eQJMg3YRcXJyQmHN67zl9/5LntX99ge7HHx8CM+/akrJLbHx8en9LZKpE+4c7DH+axlZS3IFNNNSJSgTAt0mjBdjHn/wX2k9DjTkfcExgtEKnDW8+x0hprHiHSv7GO6lt2dHqP+iGs7B4xGQ1bVNMKE8XSuwyKQnWA+O6epMp6entP4lptH+9x4YZ/Da0d4Y7g4nyEcHI/nhGBBaCQWJTr2Dw9o2hXT6SntckyeaFSQSBVIQ4fFkucpd2/d4vj5GStbM7lYoIqC9XTF63dus5gsWFU11aLBEUi0oW0d3WpM6xpEomlsQCWSrlmQa03wcUXKNS3ebMC1iaehRUuHMxWCKPiORkNMc8HSCpwSFFsjrOs2LSw5+zspVdOR9nLmF8ccXd3naPcKVdOQlXlcjZcptVXgPEni8KLCqz7GD1g1FQhHU7UMB316vYIPPzynKAeRg5KWSF0xX85AaIZlD91JnO2wgVjVmyo0nsHOEHs24bW7L/P48TNqG6u/J5PppqEqVmo75xCbgZfeNC1JJVhPl5jOovMSiWK9rCnKHs4H8IHgAqPhFkmqqY0hAE1jWC0bdra3yFLNcr3m5PiMNM9Zr2paa2nqFtOK+DoLsG0XXRQBtoZ9OuNoG0vWLxASbNfSNisSmVHmBW2ziOwUG9t16mpFIiSp8ARj0TLBiAhMBk9jDfIyqtNWKElsJETQOU+mMpASayJQVQtBa1q0UsgsRaca7xRKBJzYTGI3jJs0UajgsR6UyiLDxhhSmWIBkSq8FAidIdE4ITbNW1FI824jNhAdNM57lIjxRylj25zpDEmabOqYIUk3kagQBWUtJUFplI5xKEeMraUqwsSNCQiZ4DfQaIyPLXiKGGEOkiyLTUfeuRiPSmKkQ0i5cYZIvAx0rqMoy8gECbF++rIIRCeaqqsimF8liHDJNoI0jWPC+J5sODwhig/BWbTcfA6cjQssLsagJBK1WSKXIrKM0izfsFeik+uvCVvOEIInTRO8N5jOYK0lyTKssTjvSbLImAsbhk9nDNYZlO7RtZEPGeOcehNjjMctg9iUm0gq06LShKIsyfMU49VGeIrAV+ssWkeO5+U0JLpC2DCFIsfMO4PQUdQETZJsQK5E8LYXxBamYPEEVIjg8hil8+hSx9Ibx+az5CNvybvYxuYEzpkY1xTRCSYgimjexUSCtQx6Bd5HdxTEJlmtFXXV4jecphACrTXoJKOnM5IkxXuHMY4s0xhjSNMsuqh8hM/GMKDECb8RL6OgKUV0NYYQgetpEoV07yJLKzJRNhOujfvOeMtl/iYWfmxgulIwnU4IMjq7rQfvHHkmcLMl2VbC+PQJwUjeeOsNqvWC1z73ZXp5jycPPuC9t97g8cf3Obx6xGtf/BrCtIhkEAG5m+/RfD4jk4pB0YcEglKY4Dk7eUo56NPrb8UFpU0cMAjFoOhBmiETSVZkLBdTtAykKme4s8vJ5DnzVYsQ0Cv67Gxv09vaoalqTp8/oK0WDEY7XLsyol08QquMO5/5ZdrnZ7xw91WKrZ3Iw20b+lsj0ucpSiWoVKK0xjnPtRdv8s6bb/Dq17/GveenHO5uY+oGJyxeKHqJInWSXCgGZY/haIetnavs7+wiskNqY9ne3ua9t77N2fMpIuvjFmO+8tu/ztGVa0ihWE+esXd0jRACTz98n+n5KZ1ZM5k8Yz455zNf26O3fcj8/B6LuuH80SNeObhF27QY55FJikPihCItC3jWMZ+tOL9YQJJiW8sv//Y/5sEPvsX1V1/lhde+wsm7b5L1EvZvvUrZ32a1qDi4ldLZwM7BHjLPKYo8isJBIYXD1BGcr3QaXTxK4KxlvV4w3N3l/P0fsVrMMJ3jvTffYGtnl4vZM+p2xYMP3+a1z32V3Vuv0t895Ku/tM1qvmCUpHznj/8VznrWVYXKM8Znz+iXbTQRWCjyAuOW7O9dp14sUTJjNptx+vwJt1+9y4dvf0RnO4wbkvd2uPP6Z/ncl75GcPGxCBUbE61DakmWljgXF8eEiIVNKpGRK+YdTnhWQHZth1tHe9wc9dkvc4pEkct4HRMybHpKJZdnH0Q8e6m/p55w6Sb6u11Fn9hyfuLvYnNd+Q8FqP/U7ceaVsAJhQyXTKso1hA8wq8jEymoH+fxRNhEzy6dk/EkGISC7iJiUdTVjdB9KfKEn3hSEfUR1Ga/+ieOSOBCFxnKm4hb+OQZftrrJT95XGsM6v/vglKRJ0g0dbMECUErtnolbWoIpFjTUHeOaH3pY00XWTuJ4/T4UeT0BIE3HQ0tbWs43N/jytFVsqlmfDbGdA1Be7TSDIoBZd6jrWs660hyhzOOF/e2EW2L7dY8fz4lS1KEiAOAQGxXiXl/h2lW/Ns//TMO91/k/DzQ29lBdhJnA22zpm4MdTPmYnyKVhlZnjIc9LBNTd3MCUFiO8Ph4XWWizOQKU5YXrr5EvvDjOAdz8fPaBqBzlJ6WXRtGVsjRILpBN5AlkoIlsViims6riTXOV+dc/vGdQZCM9UdiVZkhafIMvI842I2psgCW6Vh0vS4WIJSCV29wjlPRyAXGd3K8LSdYn20+qdorIkrVl2nWNc9MPECrVMdwceii4O+xKO0w5WgOsgSScgCGY5gAlIHSu3wXlFtwNFFGVjVLcFJeqWmthHS1nkHLsFph8RRVx2LWcV82USLr+lAxMaD0WBAUy0Zpn2saXj+/ITKVnQNnK/mtE3Hemm4OsyZLDsqYrve4eG1yAzqWlobkDJlmGUY23I2r7g5HJEmKdbFFXO74S4FKekVJUKl1J2h7WJ1shISqVPS3PHk+BkPnz7lxs1rfOnnXuPJownDcsisVwFxULhu1vFC4z3WRCijkA5az+l8xk5vyHqypD9MWU9b/urdt3n9zkusn6x44XCfycWEdevpD3v0hyXVomG5rHn0ZEySSRazlu3hASfHJxxdu8Zb77xDXqyZLzsat0KpjDwrENJHzhcCnWYkWnHn9i3mFwtmyxlF2sN4w3yxoN8fYKyJUbREkZSO84vn2M6RqoSl6nB+iJA5UnjOz58xGm5zaixXtg94+vwBxx+/TdXNEKpESc1osBtbEdeGva0B2zs7IDWT5YxKOh42Da5ruJi3vHiww+FuyXSdsqgFVZvQeHAyJSt3ED5DZn2MbWisJnQdAr1p2PEkacZkOWF3dJXBcItutaa/u8uw7OGDjYyVAE9OnpBowe3bR7x49CJv/egvqdsZwdeUSc7e1W162wOev/sGWpd0bcvXfvnr/OCN73Fxfs66tZx9+AgpFEXWY7C9RzMPzFctvXzIzsFthJLkHaB7HD874d7736efbXM2PubohVt86pXPMX7+hGo15/DKPvs7+5hBx+mzZ5i2xdiaNMs3LTMxYiCcw4SWulrHSUc1wYcaJTV1Z/BO4USKSnbYv7pP09YMC0W1qugagVQpVQN/9b1nfPz2v6BZnvHWg/ucVEuqxiCs5vT5MT4IkhSsPaN2NYt1yz//p7/O9/7ilEdPniC1oq1qfvjeMa216DwjTxP2t/sMeoZMGTpTkeaSyrZUtoPZApkmdIt47kVKrh+MePmVF9kd7jJfLXlfdnz1S18gkyWPPzphPgukRZ9eMUPoHRbPLijzEuEd5SBnfLrg+guHvH73OiIo3nj7Hg8eHrOuK/a3t9gZ7PDw+ZwkUQjruHl0CLbjK7/4dbZyyx/+u28ynY1JlEClGeu2IUiLpCMRmocP3+P2zTsE9nln+S5ZpukVBdiC2llIHGXaY1kbhCpQweF9FYsohrsQAq1pWFZrpEyQ2pJLRddN6ZUlxgiMWTAaqE8g81qnZCpQLyc4Z8j6O6xWK5zJoqCdbKO0iK16Cq7svcDDe++Q7B8SVMIL13d55/49pvMLUtWjC2vyXiDTnqYNCLmmdYL5csTBzlasgZbphlVS0nSGtlmzU/RRIaVCoNOETBcokTJZ1XQ2tnK5IFj5hrzwdN5RV4Y8LWJDk7W4ro0cNOfQiSTREi91FG9MjJ/oTDKbLaKbwzqSrMCHirZr0FJy9fAaWaoQeC4uJvgQGG716Pf7GGt4/uycxXyBc1CvK3b391ivL2hNQ6Jj85IPDueJHBMlCcazu7dLXuQ8fnzCYDBguVhFsL0whDTgQ01Z9Eh0StGD+fQxR8MhN49u451luZ7ROovwCSEohNQINIdXDiCAlS1SphRFgQ2BzlYIBQ4f3R+bdjHvLDYEykTGOJzzJFogvSBRkqAE3hpUFofjlw1gPgis06AzhL1cQY7CXXCevOgxncwis8x7hNRYG4Hi3m3arYoMofUnol/AxftgEWgSHaPqEeJto2tJK2Ln3GYir2Ob1mq1xlhDWZZxsqk0NrRInZCXFisUUl/GkOIvKTdOtw2LLYoDEiccCOgP+ohNdfyla8d7F4H3PpAlKWmi8S6yh2KzEFhryTbOoRAcXdexPdqhbiq0kEjnY/W7sXgbWwVVomLDLVEk8D6OLQNxQpNl0UEmN5woa2J7VyyNcBt4uiNB4Lo4IVOXUTDn0DoCqxOdU5YlMigyKeiaejNRUpuoV2RWxZaTgDMerfP4muoU0zUY60n1pg1WpZF11JgNLDyKNGzEN+ccwZoo7nhH11VIlSGFjmMfG9vqtEo+iaVJFbk+EVzdkShNonXkJwYLaiPgGZBJGrlHztOZFiH1J+1SqY6uYOc8flPWopTcMD8iV8n5jn6+jTGGooiV5Z4AxrBZ09xA46OLyPvIiSzyfCMWuuju27i4QvCbGGSEd2sVHYJRVNTRYcvGvQYEH+OEIXiCC1wWaHu3We338ZYQIpcpNjensT3YtuAtbesoewnBON764Rs8eviQkMD1F19ie+8AbwyDvSsUywl5UdDf2qVQihZJvbigP9rHKEfXWHb2rpHu7tDUNVvlPmSB/Vu3OPn4Q86eH3PzpQHOxvekWq0ospyQatK8jO+dTOmMxVqHShNUIhGJxzQLhFBkaUIvzRht73Dtxl3Gxydo6XF1S6PHBOG5efc19g9v84M33ubFVywhaIKztG3L9Vu3OH3yhDRJePLR22xt93nlM1/A2dhat7W9h7eObDDAztdR6FQpFkE1OePs+XN6wyHWWarlkvHkgmL/gIOrRxx//Ban48fMFkukyhEycHTrDvPphKtHN9jbu0q/N0RfV2gUzgY+euc7CAGDvSscv/d9vvZP/1v++Pef8Plv/C7Xf+7LBCfQScJ8dhHdPOs1/X5Jt25570ff5Z0Hb7F99QjpJXK1oJ/HggbZCCaP7rM+fcRr/+C3uP2ZL7GYTGlXU/p7O3zpH/0XzGdTnAvkeRm/i0FBltCtJuAlUuUkQtEFg20Nq8USs1jx0dkTEil466/+lMV8hScaLFyQKJkwvxhz844ksS0P3vkRX/rlX+f//u4PqNYNnfMInZLK2A43PTtBpClaB6xUHN39PKHuOF095fMv/QrTx8946y//H+Znj2hXNQcHR5S5YP/adb741V9DyRxva1zXkeRlTEms1mT9DIHAYMiUpEwSjA10QbJUMDrc5+7eFre2+hxsIpfJxukmRKyGQvwYKf2JnCHi1UMQ3beBvzt69vd3E/1N0a1LF+JP7+Pyvn/bvv/254y8p1jeIK2E+V8gwuX+XHQcUUN27TKP9smxRKic+EXbb9cAACAASURBVET0uWzhDPUxZHt4oZCXAvdPOo02xxSCJfga0T0EWRJINsKVRjXvQnG4eehlVPzy8T9hu/xk34BxsXH0Z7j9zAWlRetwac5qOWZS1yRZH5VcYTgY4lWGtxCWK7oWTOeYzWeIEAgqxWYCRYarO/ICgtQRuLp9wHaZINqKZVGSiYSD0S7T6ZR53aK1JksLImfQoMucDx89wroVtdHkRU5wLnIcXAAXB0TOWRTxAjKfLmjW73Dn1me4++IdRoMdFvMJjx5/QNbr4a1nOBqxnK9Ikh6pKlhMx6zrFSpNsG5Fb2ubtl1vSPyQKMH5+Cl5KpktFqQ6QcmANQ3BWCClaS1t55E6oZ97Bqlnsejo7x+RaU1mHQ+P3ydzJZ2L0MtUKoz1tKsaQUpXd7RlbC3CaGzXsk40etP4JHJNnigMCYlusN5g8QRZECz4IMBIEiFj3K1zCBUHKMgAuUJISzAa6zOUblk7gZYJUjcE57BWRgutDpR5i3cKoWKN5LrusFKRix4iGBrryULMg6dJyqyq8F6hpQAt2d3KsNYwXjd4EQiuIRWak/EJWdLDWkEicrouDk5Pl0us1SgNKokNdcF2bJU5TVNhXcei82ibkSWaPO/RWEXiEuarVRRXyl4EfRLNi6HzzGdLRLhsrYlsldOLOVme8g9/+Vfw1rIcBoRs2Bn1ePz0hMlixnA4opcnnJ89i8wPAkmakMnY9tZ0nr1BSaEDPhjSpOB0NqaXFggNX3790zwbV6ik41e/8jrvvP+ARGd86/vv8uLhNebzp4wnC37+1Vt8/0dvMZktGWyl5GlJonKs8RjnscbRVrEhsRiCSAPPnz8mmBTTNFzYCBE1ocP5PqgM6RtwnotJi/MRcu+lIiszsrLPdNZy4+gWj58+ZFXVNNMLOtOwag1aWI5uvMbp2QWL5TxCFL3Dhoa2szTnLb08ZTxZ4ba2cWFJphRZscf+lSs06xn7g33mK8eoLFmjWTlNf/cA7zPSYkjTVvQGBVmR065XNE0D2hJUoChKdvb36ZUZaqvP7tUr+M7SredYa1jNlyQJ6ERRVXM+ePgOjVshXA9r1sgy5eHTj3n2xtssFktu3b7F4w8+pvszx2w9Z7S3hVYKNJRZnw/ufczuwXW2ZYY9Pefw6BZJuctiMmZ8fk6aWAbDLWaTC2bhjDTL+fzuF5nOlpydnWCaGcKvmJw+Y76qeH5yjFmvuXblOufjJyA70mJI3cwQ2uCcpLGS0IYYEd4Zkag+o9E2pnEc7vQRd+5wMTlGB8l8uaa2KzoMz6dndHaXZxNLL0+Zz85ZVJrJ1LM73IPtguPTKc+fPsUKw9nkGbtXb5FmT/lf/+Q+8+mSRhpoc5yH0aAkC57KOC6Wc7Jc8cUbr/Leu49RIjYazbsZ2SCnM4ZeyDcZcIEUgTQt6fX6bJU5i4sLlrNz7r//gLuHdzm6us/04oIPfvSI0dEIvEQoz8n5BbupJn/hKut6xWh/xNHRC4jOc7o/5WT8NmvjaE8XeHOO1IqsSOlWS67s7HNyfMob3/8hh3tDmiXYLsGQInVCvVrRtRGY33Qt/YEmU4KjG9e4/+HbDPKcF164xXf+6juILMGqQNNNqbtAYQf0ej2EqJE0rOqWxqjNhChlZ3uPrnOY9hyPo+oEdJJ8WBDEnFxobBa5KV3bEHB4ndJWHUW5Q56MYuwmF6w6z7peo7Xm+ON7pGnGle0htqmZzhWrtkJnW0hZ0i4adO4octBNQln0mDaxCev2p+7w3vv3mCzOqFYNgpyuNqRFn+H2LsoF1uuGqqoY7R3ivWRvZ8TT4xNWdYX3LdtlTlcvSUTK+bNnDHf36ZaGMk+o68g0jC5aEZl7xIhPva6AbON4ERxc36VZC2YXU4pewXDYJziLsTWJSlFS0+uXKK2YTxd4POWgYHw2ZjgsqKqa3rBH21XkuaZpO0Z7I/plQbNuaLuWgyu7rKuKdrak6tacnD5Hq4Rnz0+RSjDY6lEvW5QE5TXOC66OdtnpZWhhGKQlOkmonMOJ6PySoiGRmuAVztUEmSN8wKE2kbZYGNG0lqKI/IIApDKK4IiA0gqlFc74CN7cxGFkIuguxQ2d4BF4GRecvAuxEUo4pA+oNMG0jpCmG76SoTYd/ayHkmrDT4rRu+j8ii1biOhyMcYgkriAZr3Bmo6mAZWkEDx5nke2TvDIAMnlKuemlUs6R5posjRBSIXxBm8N3jrSJItwaRFpMUgicylWvZGIGKNFCZx1hI0Ak2cFbdfE2nMVh6lyA+92zhDShM65CAzvOoy15KXAObG5j+Ny5B6CiNy3NME4Eyc9UqGFwktQSbSxOWtx1m5cNJLWdFFECxIhFIiA1inWGV544Ra2jbE8pQVN15AkGfNmFqHZKnbasWkoMqajV27FNjMCdRuBylmWb6JZfCLWhU2bWUBQZj3SNDbxOWdQWiCUYLVakOUbMWTTPOe9JRDI0sg8iYKYi0JOtOyQpGrDDQKtJZ0Dleg433FsPovxeAiRG+Vw+E3bWldXVHWDVjmpjhgJY9ooyAWPbTuSNCdJFM4LEp2yXC7p9yKIWkixcRwZEqURRNaOUmojGrlNA6CM7reN0IiK7qtEa6wPxLI+gfER2m29I82iq1eIy4ar6AhQUpJmcZLsnEMlcSU/8jk1WsWFZikFyA0QV2waFnUUCQWR3dV1He1uw2Q8YbRTolPN5Lxh78YVHjw+pjOeva1tdvYOKXSGM4LdrW2KT73GYLTP/v4+9975Nh+8d5/WSH7rv/znKBvoxAqdZWSJxHaCbrVgvhzz8TvfZevgBUCwms0ZjrYQzlE1Hbv7W5FFKmKbsgsBrSXVYkbS76O8INNlHAMHiUwSRJKSphl7+7v0t/YIUwHZkmAWqHwIUvH4w4+Yz86YX0zo7VxlejFB5zlaRVeSUornJ894+fWKk48esXv1CmcfPYifIRkB2lqpGK1VHcuzFZOzCQc3jnj/zW+zc/0293/0fT5++ICv3HqRtq14/vghnTesjSdzhrxQvP2Db/HFX/wtZDFgdLUkBEFSxtj41sFV0o967N+5hfKCi4/uc/PmHUox5Iu/8JvgA1a2KK8QwZFnmtl0yZXdW9R1R9Lf5eu/+pvYDh6+/wGf/uIv8+Yf/xG93HP27ASePOXZ40ecTU74td/5r5FS063mJCqjN9ynqTuCiSKedwEjHaGq8VKwfeUQqQR1VZMXfR68/y5nx/c4fO11/sHv/jf8+R/8C+ZmwtJVyNUYFSx5LumNBuxfO+Lj++/y6MM3+fxXvsH/+Pv/PctnT1lVS9JEkJYFWZLh6hqSgJctQaQcvfAqSSh5+OQtImtN8Zfv/RWv/+Kv8e73vsXW3oA7N17h44/f5ude+SyZ7lHPzvnh9/6E17/0NQY7OxgREJmil+SIusF6gUlTFr2c3k7O56/tcXc4ZKRkvE6KS4E4iuFyo1v8tJD0Y39QLKK41DZ+TFH6u7e/TVj6scvwb/7/T7jYf237T3flCE8UxrIBpL+6icTKT2K0m5NuNBGJWFjD5rouNry0T1oAhAAXYktcsh35Rp8c7E/9GUCQINgm1OPo9paOEDpCiAsSQpQbF+1POpwuo9w/3tvl6yWMJ/kx4ulnsqnf+73f+5nu8H/+l7//e4hoj76YnbMynsYE8qzPqChZzCcE4bA+oBBxhRdHlvcp8pxVs0AoTS/PCG2HDwn9LGfQK+mhsK6j85KtPKVtpqw7g/eGQmeRlxA8Xiq8a9m7eoPDrT4q6ZPkCfPZLNpyZUJZ5ojgSXSCCxbrY66+Xi0YegGLOcFXJE7hvEXplK2yx1aSErxn2UxYVBXGxzYJb6GVgp28x2QxJ08zTianPJvMGU8r6lYwGJVYY+j3S6wP1MsIXeyEZVm3qCxyCiwdLw6ucvzslFXbMSg65l08Dh1krAktNLZZ0tae2gaUKhFKkPQSEq2oTbRVB8BbuH79JkJ6lqsGExRWxYGm3DSAaRVtxDqJwk6iM9atJS9ilbsUEkGCTlJaOqwTFEoSWCNCivcpXkgKDX3dRsVd5GihMM6TIeIFTwTQccUxkwrnW2zX0SuhTBtaX+NUQKicxgYgVuMGp8BJGiExPoJKB0WBcZEzUtWRGVEUmqatWbUV62pF1TZ418a0uorMhd08IUtyTk7PqWxDZ6Kt3nUtqVJYF+Hji+WC7dFWnMR0jnXbcOXogN/8pV8izaIYt16vESLyP+arNUJprO2irdW3aKHxDrrO4n2sJ+71+nSm4fDKFSazBaum5WQ8pl7VPDtfsqxWtE3D1jDl+elzlquGJ8enXCwafFfHRp5McXI2oV8W9IYFL9+9yfnFDCECxkCZp6RKojR0zjPa2SFN+hAC88UMqT3WtBzuXqduI2C6zAvKrB+Bpk4hZcx+Kx8wJrBY1ZwvZxgh+fRLL7N/5SoHO1c5GV+wv3MVLQtWqznYml4vBwdVs6b1TRykC4HzCVmaU1VL8lRSJnDzhbvsXP0sSbqN8QnBNgzLZ3T1iqP9Q4q0xKsc5wIIjdIZAknZG5CkGeVgRLWYkyYpg1HJYFhQrVaML0549uwx69kF2NhCIoRjNj3F0tKplmmzYHw+YWHWpCphvFwgNQy3B/SKgqdPT3l+eh7jgkUeV6sRDEZ7jM9nKFVirEWSMtzdp1k0zFZLDvaP6Bc9RHAoETh64S7Xr73E9WtHBCGYryNMfTwZMxufsFwumE9POB0/Zlmf4nzNcHTAZDaP7UBJsrkwWbaGA5K0x+7g6sbVkMXoTBJrWxsjmC7PCcLhXIvralrr8SaWBiQqx3rJdL7myrXbfPqVL7C1dYWL6QV1NaNqmrja2bZk2jNfn7BYHNN0CXYTqVCJQmrJel2RFCnIQLWqePp8TJGnrJuOol+wqhpuvnCNs6fjTbuVJnjJ2WzCfPqYuplx/8lTEPDSS7e4enBIr6d5970POD4/I0iPs542WFIveeWVl+inI4xf8fpLL3L/3fe5du0qz2djPnp6jmkDznaY4EkLTdsa7tx5kffu3UOnPRaLKYNeSXCGxdrROI9ShqpeozyUiac/0Ny9+SqHeze4enVIs56xXM54cvwYryUuaGztkEFTLxtSLRmWmv39q+gkRJeAGoBIabuW0WCLpusjGeK8Idg+IDm6fsTk4iwCjLsOXMD5Jk74RKzpFkohrUHkCa5uKLZ3cLKgm52jNOzv7pNlOTrPcNZxdnFBEALTttRdhbGCcqDwvmQ293SdwrnAwf4BHz68hwRmqybGyzxRqPCeEFoa0yBUoK4qxvMLer0eTbuO/IAgybZKuqrGS4Wx0XbdNhVpnmG9xTqH9Ta+D3WDyiRbZRE5JTIuWGRlympeIZWIbUTGUOQpiRZxrAAs1xVVXbN/sM9yvWI2X9DUFYRA1sux1pEW6Y8BzEp+0sJmXSwc8N7StR1N1zGbLVhMF5RbxaahRsbJePAM0hivy9IBW70U6aFMc3plD90vqbo2Mn5ag5CKJC0QUtCuKnp5EVcrQ4J1seI40ZK6aSh6/Rgz2wg5SgRsWyOkjPBWD0JJTLiEP8fvibGWtChwQuCNQQdAepxz9LMEJxxeKkSIrS5SKdqNKFKkCQGBMx2ubfHOUzctznr6vR5pmtB1HUqAUEls7VIKazsSnZAmKYqAVBLTGYR3aJ18wkIyziOVpq0qVKLJ0hSUoFmukUphuo48L1Ayxot0opFObCaaAiVi+QBSRhaRszGarZMNWDsCoi8FJSHVhvvkSJMkxg6cxXUdWsQ2qRACOkmwbSxgMdaSFX2ss+Rpvmmrk3Te0ZiOuon8Hy4bc+OIHJ2mtMaQ5+lmniBxWFJVbFrFUuquRSUa62LDmlKS9XpNURZkOkYBo7NJUNUdg2ERWUZSUjd1hIHrH7u31Cbi53wU+drOMRxtUxRlZLsQPw9FXlJXFcNhfwMbB6U1TVdjjCXPctJsM1PwcTKhk4SoIymsaWNbctXinKfXG+BtFDSjyBOZUsmGw5SmeXRdEPlGbdsglKQ/HCICGHPZFBcXafM0R0iFczYC7LuGsjfYtN2KGJfbvNgx1h0FUgDrHN66jSsridENpeisRUq1eb39JtcRxUbvPFVdkRf5RpSKrr1Up/gQ6LqWrCjiAvLGpeQ2zrIsyz8RsMDj7cZtICIoOFE6AuAzzfRixu1bdxj0e9TNir29XWprefutN0nLAZ0P/Npv/Davvv5FDg5uYJ3ZMNYUOsk5/vBd3vnu/8KP7n2P5/M1Itd88Qu/wPziDJmUBOGR1lFujViPzzl/+JcQWpLyKtPJAl3mDLa3qG0HXtLr9yJPLFUE5wimJgmR3bp3cIPp2ROsXTE+nyB1rIsvipIk0/xf//sfcHDlEOsNi+UCpQqyfISpLJ96/eeZPnyISjP+8N/+IcX2Lrc/9TKz6YTpdMrp2Tk7ewcMBkNuvHiXtMx59vgYnacsp1OGg5LTp08ixuHkOdZ6Xvr8Z3l2/DFvvfXneGlYjhc0bYdrKs6mZ6yWY6wxdFLgRUDJjrPnJ3ivuPvpz1NoRZCKJMmp1yusiufV0WgfQcbLX/w6o90D3v7zbzLY6XH40mdJkBgDyAR0wnwyjY6sbkVnat783nf44P47SC/4/Nd+hfXFBefLjmxnn4PrV7l6eJs33/o+H73/A5588A6j3evc+fkvoESClQntqqXIc9JBQVbm+KqmqSd879/8bwz298n7WxRpyfMPfsj3//3/yatf+gqiC8zOTrBmzY2ju7SrFutbTGi4c/M2D96+h8Qwm0+Z1/Dwzb9gPDtBCcne0V2+8LXfoHUF69UsOpW9Y7S3j+5v8+H771HVFySZxjdr/tHv/DMunox57/6/54tf+DpnswmElq/++j/m2ZPHfPdb32S2OkcEgw6BPEvRWUHlJHaQsHP7gF/4zE2+8dINvnxljxv9guEnzDmJFJv0xiciSNh8dX5aKvIbaUN8Iq78525iEwnf/OuvPf+lC1Jcfo//9r38Db/+rife/CY2j9hcz4WI16cg5cYJqYkcubhPET552OWREes5BCHtIeRlUVUgyEvu4uXmQTjQCeRXIb+BKK4TihcRxR1EfguKmwi1tRG3RBQVL4NvP6GoCcB50FLw5w/WfK53wejw5n/3d//gf7/tZ+5QQsSK2Ot7R8xnF5AUFCLFrE95PF4wXbb0h7sIabiYzdGZYtV0uG4GladeC8ohzFc1OutT5ildveLx4zW9XDJfLmgMXISO1sYWLe8dXQgIaTHOo4Nhd7DFcF3jCNy9fptJ1yB8YDqfk+QpwXV0TY3MoxW2CAWDLGHLScLJBVPvWCcGsxlw1N6xWi7oSc3aBKTwbOsUr4irVjJw89oRT548oJfmrJsamZWUueblF27y5nv3sY1ECkVVS1ynCTpBY9lNJf00p2s9NZZcFrx/8RCXJGiZ0FaS3Z0RdXVGZxVt7RgmilHPYUx0Hk2Wln6ZsbM1Yi1mZFWKU7EBL4QlibacLBeYLlp5y0IjpEaPQKs165Wl7VJUGgghwzmLCJKqchSFZu078rwmuBaFpxQpxniCkiAiUFb5hLqTLLohUkNKbI4blAneWpIgkKlCtjVBZ3El0UqkkCgcQRrKnmSrn7Ba1bSN4PBKyelpwIeWYHK2khRUwvnFjLP1BYkSDHOFTCVGJAgKEBJJi/OWREQrda9X0rVrOpZ0+gZ5b0Taq3j59nVmizlV7XEBWudJVCAvcraHA9ZVw6qtsUJgvOPK1g69JMMZR+cNWkLnLCqHne0hx8+fMV/NUMQaSYfHCAgitq7tbY0wXYuSGfeefIyvA4NBSS/RNMGSqIQ87zHcKtgeDNE+0NMVw/6A0/MPuFh5ikygJQTXMiwO6ILlnfc/oqoWpEkRq6ylQDgRwbMprNc1o60+9bpBSliZhus716jrhs614ANd3dKFDqUd+1sjVKp5/OwhSEfVdQgE/aygqS8Yn+ekac5wq+ALr3+ek2fPuLoHy5llrRXjyRN0MmDY62N8gZAxilA3NVoqdgcZeZ5yZf8GRTpkfPaci7Nz9g9vUJmWIk8o8ykX8zFW7ZGVJb2tAdYEEJ7FbEzbSLTQXIyfcu3aNXZ3R2hrWE3PKRIBpqHr5jgL49UMFyIrIk0iPHY+noN3DLZzAlCrliANicpJVWA6u2AwLMlaz9VrV1jXa+49+gjvBXu7S0bDARfTCU3TcuXqES8e3uHKjSu88/YbHB5eJxOON77/Z7QovvLlX+DPvvlNlrMx+3vXI9RXSMbjKdtbA7p2zcXkKUJ1HNy8g1kH5rMxeSIiJ8U7bOvIkpwi2SbLBlRrw3K1wDpDYx1V26J1wezilDyNGXrTdRSDAabuCGWF8RmNdQTjqNqG9emYDz58QuUmrCdreklC5hOauqEoE0gEmgGLlac3KFit17igaGcWKQRJogghtilaJ0lVj3/4G7/Ev/xX/xoVIJEZ4+MJhDhBjvlPR1kkHOztMl9XXDvY4ctf/BI723vUi4azswWT+QyRZGTZgMEgYT8d8P57H9N28Hj8Ma2VvPneR2wVgvHpOePTOcLBoJfQVFEQ7tYtupdwcbYiCE0INZDw3kfHvHj9iBfykrKfs7o4oWvj96JfZpT9hNl8zCu3P0Xwhn/2O/+EP/qTb3Lr5Zf4H/6nP0BnikQFeuk2oZTcOOrx6du3uH//ESJz7FyR1NYyn7YkJiN4wdpWZCohhASdKKxdUtcVqQgYp0hkj7SX0jULdICi7NF0Bms9okwpsj6LpuLJ4w8oQgKqT1PXTOWayboGr6irJcF7yqIfrdkIvAmMn2UoBVVjkCpgbIUzntHggHUzjTFG0zEot/CtQ/kYc/KmQynNYDBCVi2ziyllmpNIyWQxZjkx7O/toAElt5iux/hUY7voTkEL8BtWSpJQaE2aJvT6jro2pKnCCVDBsb+T0dkU3woG/RQXDE8eTTFdG2NlQXL/3ftY7/DBUa06BqMtmnVN2SsY9PvMbMAYs+HEeeb1kmG/QAFdY5CJIMsTzMqxc2Ub05mNSC2Zz5ZI1dFVHf188P8x92axll13et9vDXs6851rLlaRVZwpijYlWbKE7pba3elBcQbDCfKQh8AxkDhADAfIg1+clzjIUwIEcBIbaHTacbtb6YZ7RA+GutVqtSiRFFkUSZGsYrHGW1X33nPPvKc15WGdKlItIwOih2yAhWKde8989lnr+3/f76M/0MjQBSURSYz9aCFJpaJuLd4JrNYY32JWJsKAnSc4IsjZO4KP1famtRRJBoQomGTrmvfgIttFaBoREb02gBIab6NwopREiHU/i2Tt/AkIknhDIbYLNb5BBEGWpZiyRnuBrRxJFnlVeV7QNC0hBJJURzdwWBd3IR9NR63xJEmGEArhA61dc3psdIAIpRFpglAS2hqto5Mj0wohAz5IlLQIkVC2DUWWg/JYBwlgZBSKhAWnFFIrlIjxI9a8pDRN1u1r6hGw3IYIDzfORCi0CI9a4twasDxdzkmSFFcaUpVEkVip6K5SIrYLKUVj2zhAFI4sS8mTlE6eI7WmrFekMkFKHV1ORFaQINZ/BxEisJzYuBZCHGgiYrubWrfghRBr3oMLoCBJkshUFETxKUDAIRS0TbsWNGIMMLqNQGUJMs3WLiNF3S4QYl3VvX6jWWNpWwuC+No9hJVLiTENCI8zgbJaxqjo2klVV3FdG9ZCX+ObyNMiMq7STo4zDVIrvBBIrQlthWmX0eGjFVLG2KZUnuDl2i0gUDo6ovRaVELE5kqzrm+PcbVAp9PB2Xb9eCNY3FqHVKBVbFMK65imEhItFU3dkCTpx69LkLi2JtNxWGhtFLG8czgBzkbOmQghuouJ4pt3DinEOhIXSLTEWKL7IKy3fA8n+1IQHGxsDFksoiihhGA2HeO0YnPvBLN5yfPPvkR/MEQFQdU2aBHb8pqmpJMX6KLgxsGM5Urx2JPP8sWf/DJ/8K++xpnLT7CjU07sncOUFSJYZCapJg2jE3ucufwU9Ztvc+/Dm+zu7uJaR9bp4ATx/GTCOq4YePPKNxhsnGHv9Fmsc/S6HZIsx6iA1oJOt8ubr/4FX/jpX+RPfvdfUM4n1HPH5//9n+fuBx/RVDXDXp8Xf+6r/NY//2U6ezu8/PLLHNy5xRuv/BkygV4WmBzeYOtLX2Qx3md8fETjpszHD7h/9R3ef/07nP3U8ww3ttm78ARnL56nnC1IZcpodArX1JTNiouXLnL/5g0miyO6mxskSuOXk7WIOaC/eZZ+r0unk9IsSoRW1E2NQKKd5+SZs6RJSrbXY3x4wL0P32Vpr3P1O39IMy+pVEa+s8szz7yEqS29bp/jgzv8xq/8L8zLKc4blJPkiefbf/JbdAY7fPXv/H1uf/8NQtlw9dZbdAtoTIkWA8r5McJGJ2NSFOTbAx58+D2kazmerZhMJqR5gcsTpFIgFR/deJ8//oOvsTy4ia0XJGmPs88+w+Lb9zF1SWtWjAY9bAM/ePMKTkhWD47Z7Xa4dOk0V16xDIc7lPMFJzb3EDrlhU99mtfG15mWDUYK6vmEjf4JTu6eYnYcaNqSw4Mxv/+rv8K8mfPY+ct0t05xoX+OlVvS6e+hwj6uXtFp4ebVW9x1KS89dpHPPXeRJ3eGXChyUiQIHyPWQZAS46IKHglEH4s3j2xHHzuUHv3loTf3R48fDXb96OEfClWfkKIesc/Wf4/GoIfXJH7ot+UPiU+fuORhpHV9ww9/Rv4b78UP32OxzrKEj6WyR/f/0fUR1rx88bEQxceta7El8xNCloDwyejeOhr38LYgnq8FD5spBeCjZhT02j61vja/doJ94pl9dB/Xj9NaR5Yl/xeP9f/98WMXlMZHD5BZwWQ149TeCcbjA1blMYfTlsZ7JAVdNHmm2N7KSaTjQXOIqTVCOpJhQttUnDjzNM8/fpabH37A/ngfbzUWj/Oe07sn2OgV3Lh3nzRJqNvIWsp0j8tPPcH9NowDTQAAIABJREFUWx8gTMO92tAkilsfvEO3N2J39wTGWqyxLKsqRq8aT5YqklTjdUGlBTYonLRgJVk/xVlH4Vqs9ngjGfRSUg+Jg1SARbLSREAXlgZL3it48uKzdPsZ9fQAlUgmyxVnT23FBQgpu/0BKtRM5wfo4EkzTVV5jMyYzw1bo4KmXpD2NpgvFlhXoZMevg3MJg7jonU8TyUiyfG2ZTF7QJAJFRblFXUzpdtpOTi6ztmdS0wmE+rVhKbxyDRuLNtSErxGJQLTOrZHFbYWOJMTdFwUZEi2hyn37s0hSahdQy+1ZFrjWoFB4GWK9IGONug0pawMSeYJuqVuVQSz1QEjBFkA7+Kka6MX6GaW/VkgqAJEhGZu7W5gKoMUOYOsS9Lv01IyncfJ7iDrUfuGSSkIUtDv5LgAo9EQWsfh+BAtFUVnQBCetOhQL2rSvE+9WCGC5OrVfYxTeCIA1doWTcBVDa7XoWlqEi1oK8vj5x6Lk0rvWSwXgKL1AbRCG+gXXYaDDdIiZzY7pm0cHoMmkHUK2spwMJ4y7A+pq2VkJ+CpakOjLE1ryFRCVc9xtuT4wT2mS4O1zRrQWpAlLkrMMqd1gv3JIdNqyagY0Ot0KStH2zqWi5okdSgVEMZi2xWLhUWKGJ/opwU2NMymKxIiMG64vUUSBLPJPWyIleOZinWzwToQCc7WnD19jvm0gnRJkp9HVSvu79+j6YfIjBEmCrVJwnKxpJNrhr0BnhjNsK6mrhvSvMPdSYO5+R5tsOTFJrN797F08O4Uw+2EJO+SqT4uxHYYgSIvOuzsDkiVJE0EhNPU80MW0/tsDjtU8ynFoIuwFRqPyhRlCLTGRuCls7QthKRHEJ6GQN0sScaBum7op5IybZk8mNHtdMmylMl0wnQ6Q3nBY2dOcTieEdDgGpRwDDYGvHHlG7wQ/iovv/Qim5sbVMsaoQyvf/tbfO9b38Fay+HxA27cvoYUHiccTbNiWS4psiza671nIHJarThu7pFmEZSYdTt08hQczBc1fXKCtdjgWCyXNO0KhWSxvM72zhBrOigmzJ3HlSsWx0tm8wVZEvB2wagoOLWzSbfX5aNbN2gPHUGDywWpkywri9AJxhi6whOsRARB28QK7kwKtI6x1STJqMoKZxac2h6xt7vNKJP8zFe+zNd+9Tch7WG9RevIipFKszna4Rd//m/iqhpnPdvbO2Rac/XwDh/cuM6tu3fI0y5nzmzScSk//XOf5w/yf02mM46ajINywuLA86kvf4mDw0OOxwec3u3x3Esv8q1vvokdH2MdrFY1n//Ms3x06zaXn3ycK29cpdMrmC6OOf/YBY4f7DM+XqCyHrVpkC5hedBy9txZDpc1nzp/icbVtGWPc6dO8oVPf5FvvvotLj9xEVNJBhuWz77815gfjlmZVWwLaxK6qWAVPKPtU4yGKVfvvE8hO+RpB5EJiqSHsZ4kG+DKBb3RiCAagokuocZWNCY6TbzoULuauZ8zGiU0s4BXgcHWCaYHt7CZop21jxg3edajKg1VHd2gVhhSr5ACOmlGU1eM58ecu3QBaR7jyltvs7GzzeNPXubtK1e4dXCXre1tTp08gxSC6WSOcwaE42hyRLfYQJBgkVTGspV2ma5mnNg9yYc3buKlY2EaihCFBx8iTDnJM7RO6eeayWSMsX4NeYY7Nw7Y2d1ie2ub+w8eUFY1WVHQG3bRKqNcNZTLEmsNaZ5R9DoEJ2iaGh8iB8Zaz2K2pOh0KLodmrLm8GhC3knZ2hgRXMDX0X3DwyhEkj5qg7IuISSWfqagldQri09bQlcSrAEXgfKttdSNoZcqXCtwto0DhqZE+IAJntZ7cCBlGp0WCoLzsQEvEBs3hcS6gPEP22ACiYztcCIVNG2NShKsE9iqRAaPUAqvYtuaIw5isBaRaBRgvUAn0bUl1sKCSCJ/JwovnjTNUevLomDjQEVnmpY6NhYqQWsdXsZijTTVWGMjwFVKnPNxM+8ikSJLO5G2Kj+OIQQHWkmEs8gQwdvSOfDR3SUC0Y1E5DoFwLeGVqkoFoi4eYMY4R4MhhH+vV4Qh+AikDlEnpNQiizvkArQQtI6g05SjGvJspQQIqssEfE96bxYw8EzvNa0bYttDVle4Fyg1+lGQSpYlIxNe84ZtNIxouglwsfnIPKeQKg4UJVC4F10jK3mi/XjdHiX43wLRIC1lClCNCipcS4KkZFJZEiSglQqJIqqrcELpJYkSUJRFJFl5CFJMqRiDcBu12DxKIZJKQnKkYjY5Oa9IwRDCCaKhkoTpKC1hixL1s13UUuxVqJ1uo67Caz1602bIssKdALWhjXjSCKIIO8gBFIo2qakrmu0zvCsG91wBOmQ6/df6wxSRied95GLpFCEINZtbbGpL0njALKparI0Ax/dSW0T6+DxFqVifNQYG0tpAlgfXwPnA3rtHnDrprckjcwhKWLjcfDQ2shiSpNYqALxM48AnWYY0yKVJsni50HKhM2NXZTUFFkXrAShSEJknug05cH+u4wP7zFbHrMqay5efp7N3SH/+//8P+BVy3vff42//Xf/AcIaHJ400aRFn43zz9EfFXghefz5Z7j61hXmxxOSrEvWEUjnwTc4IWKKxyfc/PB9SvsuJx+/ROVahkqQpwmmid8l9XLO3u42i8kBk8UEFyoscPbCC3TTTd5789v8+q/8Uy48doYz587ypV/8WwidUxvDYjnh1p1rpEVOknS49u4b3Lr6Dk88+2lW4zlnLlzgd37plxhd3GC0qdnePsH2yV2a5QotJYnQjHp73Ln7Nlmmma8OWZgFZWN4/snnufrO65TO0Cwdulb0dzM++9e/zOp4RpplMalgG5J+h74UHNz6AQut2cnOMxr2ef2dV9Gyx5Vr12HrBJ/+wr9HNuzFz0O74lu//atc+/BNWu/JdQ/bNlhb0S4b7lYfcGJnxT/9R/+AF17+DKd3T2HtAcgerpri25bj6YTJeEzaG6CUheURf/p7v8RiMePcpc8yvn2DJ1/+IkJolosJwxNn+OBP/4T6+AFIxwff/BZGOPZOnmOynJEIQRIs5bymsR7hQeeC3YFEZwm/+y//V/rDguVkjM4y7u6/y/nLz3D44B5Na5AhngMm8xl5eg+ZFbS+QQkV+XTDAXvFLqd3LzIYjLh78waf+Rs/y/zokFe+9afUmWb0qRf54qdf5D/+2S9zNu+uBQGBCx5LQAuJEqAexadY/wRr0eQvCyI/fPmPyhk/evhAjGITMTE/dNlaYonfTet78ZBBBFF0YX078odvSXziz4fRt09G4KSU/48Erb98hId3AvGxq0g8DNhGV9DD+N/DK3z4vxaP8HGQJogJ24DgYRJQC/VDityj5/fhgxSSID7GbbNueovC08PHEz4h9vEjd+KhO7RxFp38/xzKfbiY0peaUdEhlFOmxweUbYJUgrw7ZNjpUa4mCKkY9GMNdKfIqWwd3Qd+QaIzOqmjX0hO7mywMo6d7S3atkZ5uL1/C+lHDLM+WbfPZHaMlJ4Hh3c5a3bQrmJw9gyDqsbpDO0DrcwY6JR7AlrbIIAiU2gpUUDWHXDq5B6z4ynj8SGlqWmcAxdI85RMBBKdk/X7j6ZEKtGY4KmMI5EpB8djgotg6TzPWJYPmCwUuZ2Q5xalIVhHJxEsW0NabJCrgul8ggmGtmowxtLPNMOOoNA5IbMIb0hloNAFdbsCkVG1kqKT0tOCurbYdSOMFR2qpiFJU2Qw5FJAnZCPRhzPHqBlQtYrcGUV6w6dxukYZUmVJtgU/AobUlCgVFQ908yRuxotBK2VdFNPNxPgNW0QsfY1eBCeXtrijSfBY4wmVC2STrQ6K8nuYA/TlFStR0goEodrA3Ud4uLeZTSN4Xh+QKoKcB6XdBHtAuMagqoZ9guytIM2EpevyHROYwMntrbp9rocj6cUSYesSMmTnIBgYzRCyxTjDa+//QPOPHaB2/dukqYC19Z44XBGUntHkeV004JeXjCZL8i84PFzj3NweIvVqqI1ljzViOAQNmCsoywrsiTleHqEqZrY9pJoQrrezPVzLu+c5cHhPVwqaRpD4w2SFOsBlXLmzAlWqxV10yBUh+4goVukFBru3LlHEDESkBaavDOkLRueOLdDLhR3HjygdjFmtzvsM+z1mJVzTDtD6ITtrb1YBx4qGmvwTgIJWRY3CJPpA5z1JKLleLGPxNAbZCyqlsYIrCnJC839g9so3aGaNeTpmP5gk83tIR9ef5dunpNIwWC0yaA3ZCzBNyu8jRb+xoVY+yvhaFqyqm/y5MVL1CIjSwry4QYq1figER56OiXoBOHBrxsDq3KKbSoa7whmyeagoFnNSHyFFgm9QtFWK0aDPvcfHJFoSPMOpmwJ1pMNRiAcs9t38dKjE0m1qOj1u5w+cZKiSLl+7SOa1rCzk7O/P8EFz/bOBo+dOcPZx87w6qtvsbW5w/mLF3j/gw+igDoa8Pb3XyURDUdHHSZHK+4e3CXP+1y/8xE729ssVyu8XZJkEYiuswwdUh5/7Bx3928h5ACpUt778FtcfvYFPrp+l9auyHSM9i2nYwI1bbOgrRo2Nkes6jnOtRxPb2PaiiC3ubv/EbtbG/RHHe7cOsYGh7ee5558nOeevsxmf5ODo2MODo8okh5arygSwHkaGShGBT4otgcdXn7pBf7wj18loLBekijwCkyAREksAudgfz6lDpqvf/01/ou/95/y3VfeQwmNUSLCVV2cwORCsdXpkSYF9aqh0Bk4QUgEH167wbtvv43WiqAsTz/zFK/8+Vs0K89zTz3Fb/2rP4Z+yuULu5ze3iYh1mF/5ct/ndWkYmu0g1KSJFf4pmWnHyvU/8ZP/jS3b+xz4uQO9WJJZ7DJdOm4ffM2RTfFBUeSSprWsLe5x/Xb97jyzlWM8awmh7xz6xafPn6BV1+7ws7oFPfu3Gfj1GnGB2N++3d/J3IQOjnNvGYRLF54WpewswXPXn6aunJcv/0AUy8osg28M8yn9yjLlqdf/BTNvGRxdBXbOho8WlmyPGCtoG6PSFnSUSt6W6cJ1YogckR5TO0CWdBkOfS7PR47tcuDwwXzpqKlpp9kBBqGeYfKaGTTMExylsf3OXfmBHcn96Ojx3puXbtNtSpRQRAaixYZCkWW1LTraPBwOOLixad57bVXybVmdbxk52yf1jmMjQwTrWHU6YGMy0HvPM7FKM+qrpBIik5GU7d0ujmCgG0bhv0c4w2ubchSyakzu5jas5wbOt0UIQJto0BpymWNFBGmvJhFlprS8hGLZXY8I5EqRtpbR71qAKhWdXTIAFme0NoYWe90M9q2IREQqkA6SskTUMLTSwWNixHyOD4Cj4n/uQgoFs7inIrR6eAxdY1GcurcSXSvw7IssU3LytgopoiW1jQxJiajwV0I8EYiEsA7rINOmqCDw0uJtXHBprREB4UKsW5dSUVqLF5JvJK0pn0k7Gghab3F4wkqIlERKlrshYhxs/WG1Ls4sAty/eF2hiTJaI0lCInQgjRJkD7QmjYugr1HCY0lEh4U0BpNpgWZTLBSR5EjOIJ11MZgEOTrzT0hRAyBd0gPXnh0omidi+2oWqz5ihapNb6syLRGIbEiELyN0X2h0QhykdNSg9bYpl3HEKJLywlFqhWLtvl4VyE8rlkhdcBaSwjR3ds0DUWWAQobWpIgIFFUyxX9wQbGVCQywr2DsUgZRQ6BQuvYkNd6hw4W6yxZ0kH4BJklhFUZ2/CUorWG1WpJr+jF2vEQIlfDa7pFhxAM1gsSGSidoZN3aOoFsQK+gzEGuY5Seh8F2ocxuMicUmRJgW2bKKgJgbMR6N22lk43xztLqhISGYtKgoiuU+/aOCSUEIJdA9PjtiXRKb4F6w0gUFJhTEDrDAk0dU0IcbPT6RRAIIQYiQtrB6TuaCDyoYRM4uYwQN00YFekaXSNSRkdfSpJUEqQJZqqWkUMQ4DgDHmeEVzkTj1kiLSmRQoRN6LBg9RxEycl7hN8Eectic7wwRNcbIIkXTv4QiDNM7wPZGlKUWSU1ZLx+Jhhv0/VlPTSnG63j2kia8mHFoRFJSmmrTg4us3333kVlRcEmXDtylu0ypF0ClI9ZGNvl+V0wvXlknNnL7I4PiYtOgxPncKsSpbTOYOtTU4/dpHJ4TGjUwOU0Kg0pfUteI8SnsP960wXM0Ka8r3vfJ3TO2co+hlCaZblgsMHBxTdgrfefIU33vwOIYAKkl5fcXj9Pe7v32dy8y5nn3+Wra0uB99/jePxXRKdkiYpvX6fLO9iHJhqyWL8IWdP9rn29nd54a/+LNXKcunJF/jw4D3+7Jt/xMUzD/hs/hPcu7fPE5eeR0qBUopeNuD+g1uUTpCqWPqwmpRImeBM4NIzL/C5n/y3OXPyLHXZkuWS1fgAlfeoTc2D29d59Ru/z/79e+ztnCHPcq699TrHqxlBC3b3TnL/xi2Ozr3Lk2d/IUL7Oymnn3mWo9l9FnXD6fOXqeZzrr31Bhc+/Ve489H73Lx1C5M4bt27wd1rb7KSPS48eZmP3n6TvEj57Ff+LcYffcDJF1+mnrT84PW3sSalUAUfvPltQprxVJoxPbzN1/7H3+Pv/Pf/DLGxyeDM0wQ75S++8UcknXie0EmC7nWxQtDWS6wxaCXp5128ttRlS7/XY1VWtEZhTcvW1gne/N43uXb9Pba6I556+a9x6vzzvPbNf81yeh97fEQny0j7fQyWU6cv4Ms5h/dvYk3J+PAe++9+j/Sxs/z83/+7/NRTj/PS7hlieUBsbjRyLSAJse5q/Euun/8bxeUvXywfMY7+zZwjQcAi+E4Fcyv4yiDE2xbQrt09//ie56sjyac6xDjk+jctsPTwLyaBu0uBCQG5LlFSBLoahirwH+5Kuio6ZB8eIXjGXvA7Y8mQgBGeXEgyCcX6YT9ReE5mURwKgBAOj0QKuNYKTijoqYcRtrXrx8fzXLIWg4SIU5aHMpdArsWkqIp5wcfiFIGPw26fUMDWv+nEQ9eXg0fCk488wjXLSYSHz+onXpH1U+/X9+mTglKmf7xQ7h87Q+kf/3f/7T+q2hqahlW1oDQekg7buyfYG21HWJlZxkmMtfgg2N7cQumcIu/irCdXCYfzMYPOJnubG+A1OxubXDx1iq1+gbeGO+MjyraOTV91hWk93U6PjWEf72vKWnD58cc4e3Kb01s9jpYVMoH5fEVd1WRZEe3eWiJCQKgsTmhoWawWtG20Vg+6BZ74RVEUXXY3RtjWYL1na3ODQbeL1prNYZ+N0Yj5asKiKtna2qYtSxpfMl/N8brD1s4eiT3COceiruj1B9RVze3DY6ZVCV5Q5BmWCiUzXnzqOcaL+1grMKHB2hKCZasb6BWaPOnRyTdwTVw42ABeOrTyZLmgChVfvJhwOF+xaB1l2eJNQ5opvJQUeYYQgVRChkaQojRkCowTaJ+wMi1buxnYGeNF5Cx5IE0jYH0yl5hWg7BIJfBesDSRUTDqWryV+NaRiCSKTsrSOkfVCrwSJEhaGxhojfAZjdPkWY4QAilSUl0gQyApEobFiEU9XVeVC544c5bGWAapR4oMF+Lm0piAVBlbo2GsWEVB8Az6feq6Js8LpErIugUH4ylaSap105uxDUmSsDEakRc51nqOpzOSJGEym3Hq5BY6Tej1hoxnc4xzNMZw7eZN9g9usVpOadoWQWRUORyJEGxsbPO5T79E3RqCN8ymJc4Jtja3gej0evHpy/SSAtM6JqZCBEhIOH3qJDsbG6QdTd02WOsY9br08i7PPH2J2XGJ0jE6amyNN5ElNRyMuD+eUjYteZazvbNFmiQ09RIpKzKZsFiu2Ns7RXAasT7h9AZDlquGtpUYZwkixwaF1pApt+YaBDIFMumwu3OSPO0wnhyxtXMCqTr0u7tMliuOplO86NAYSWMNG7t7DPqbXLzwLI9feJ4L5y6xd+I8uhiQD3fAC1xtUaqAoHDGxKpiPCI4gq3oFAmp8CShQdiSRHpksCjtUNJSFBlV3dJ6gyIyHkxr6eRdEJKdrT0QmhPnH2Owu0XdVHzhS1/ClxVXrryFtJbTp3c5c/osOstZlRWD4YCLly7SHXQRwZOmBd28h8ozvvfmW2glGWz02Bpuc3S0zw/eeZvbd77P8XSCD4ZlNWOxOGZeriib2MQSXECJhM2NHQ6ODhgU0RF0fHyLr/70F3hwb8a8XEQ3gerQVYo0kVRuiZEV6EDVtlhnsdZgjKWX5Wz2eow2d+kXXabLJfPVjLSbsNkfcOncCTaHI46mU6xZMRh2OHPqNN3BJg+OjijLNi54spR6VXNia8TzTz3P/PiYXmfAZHYcoyYiIKXA+hYXLM5HoL0MloPxIfOyZTxfcDQ+JivWTJMQnQGowLkzJ3jpxU9RllWcCBcFiS549fUrHK2WzKqSrNPh6vsfMZ4d0HiPUoJ3Pnqfva0N/t2f+0Uunn+aPOtSNTO63S537h/zre++xmq1iNBC4dnZ3uLegzG379zhg2vXCBjOntrj/sEhx0eHSJGgkoREJezubDHsKC4/eYmD2QGNc8zGc86d3qapLddv30BozaSasbmTMZsdkYYomiQix1oIMsFbgQoClbTYZsXGcIfLl54k7fYYj4+ZTGpGG32quiLpKJyUKC/odvtYFy39SsaNXidXyNZibUUIBbaUvPDcpxgOhgx7fQ4OjunlKYXSbI82SNMCvGNVl2xtjTC+wXpNwNHtFVgjkMEhVULjPbfu3CXROVubm3S7GatqhXF2zQJqOX3mJEIKRv1NjGuYraYcHBySKsWsLOl0uhwfT2NktClRSeQKeR/w1uCMJdFx8bNazKjKGmMDSqWsyhqdKISQDEcDvJcEuxbmrGE2m7OalxRFF2MimypNM1yIDVZJFlum8k5KXTUoKegP+pg1Y6XT66wrxP2a3yWiKK0CpjG0bUuaJxAEaZ7hjMd7AUrS7XYodEYx7JFkOQLFsnXUxuGaBmcdWdrBm0BjqriRVwJrLNbGz6NWlp29bRrraOs6bnR9jAIpCXiPzqNoJwAe1tMnsWXKWkeaSGzwsc5ZhEfV5zFqLta6iIyxIKWRSYKpG6qqotfrIqJtCWcNxgesE+RZHuHOBBRrYLOI/B58QGtFbVqUSkArrDUoBFIpsjwDLK61JDqlaS0BR5KmCOnxgkdQ67ZtqZ2l6BVkefbIzF/keWy3CYCSeATWe4KPk+oszxEIUp1A8NQmOrLSNMO2LXmaIYlDLGsMzjqEUmitCcHEKJZpscbRHw4J1tDNhwhvCBKCMYR1xNu0FXVVxedDRJ5klhU0xpJmMaIWfFjDiBPquomxwrbGteZRLE6nKU1b01oXG9qspWkMQSgmiyVFNyfJExAgpaAxLTov4nUrRdbJcTY8+q5z1pH3+ySZjmKjElRVQ683oCwr0jSNjmHn1+1uNrqpk4Qse8gSWjNFgo/10OtYhzEGa2JMLu/E9VUIAqliDCLVybqFKMQ1gZB4Z6mbFcGDkhn9fo8QPNZaBAKtI2uoKDp4F9v1fAgYY+N7JniMsaztBvQ6XUwb3WchxKIM9zAyKAVZlsXJ/Tp+poSM66kQv8fdOiKnVGSVBOT6Mx75aSEEzDquGXx0FWZ5ZGx5F+G4QkabgJbxuo21cV9iDFmeAwG/fl8GfHQy4akrQ6/bo9fvI4KMjzFJY2ucjgJZ1RiKVNPWJdfefxNdDNnZOc/yeELjLEmRUJeWL3/lq/Q6ff7iG39Kb9DDLSr621soYuxZ9zu0VYtQGVs7exzevQFFwmA4xAuLDNEZ513LH/3G1zCrBUtnOH3uMQZ5TpZ3OJ7PCSpjczBkMjnkw+sfoLsFWGjrFVpn3L72PikNwR+SdLrcvvkm94+OKXSPU+cfZ7FaMJsccHR8D4Vgd5TQEUuGgz4+dHjuxc9zNB2zWs0wdYMIli/97N/i67/3q1jjOPf4kxzdv8u9e+8znt1EpF2G2ydoK4MmQQXJ7pkznNjeYefUSV78zE8wGc/YPHWCxe33uPad36b2JXn3BMYLZuMD7h/c5nh2yHvvXGGxKvnc53+SB7dvEFTgcDGjLQ2XPvUZpBQYA2/9xbe4dfN9Llx+hq3NHcrK0JQTjo4OUHTAeTKhOHHhPMvVks/+5M9z7c3vgRY8fukFvv/anyOTnNOPP81gMOLUucdo5jNu37hK6R2L5YL9Wz/gwytXeOkrXyXUFZvnL3Dm1C7H+/eYTadoKRmMNjj9+JN401JOD+NnTAqEUDTO0njBchWbS5d1jAjnScpyPmM5n9LJC3o7Jzj/zEuM+lskQjI6uYvOuxSDTZyQ5DJlPj7iaDnn0tMv8Nobr/DCU09zML7B//Rf/0O+cO4Ce90urbUEEYU+JaOQIx5Zfh5GtX5cxw9f2cPglydw0wT+bAEDFdiN2jJOwD8bC27Ugs/1BLsJCBFRLDH45amD4HsN9ETgF3Yknx7CU73A5a7gSMDUSv5KF3IV2UriE66fQ+f59SNB4+NtHfnAXSO42gbeWsLJVHA6i5D/hw6gNsQk/3+1L3gcOJnHKgWJ4MjB16aCu6XgcvehSytG08z6Z/6PZeD3bwlencF35oJXpvDGMbw7h8NacKkXzzVCPPQZxefoEPjzY7izghsVHJSCB7XnuIKjWjAzku18/ayKQBAPpan1i7lu3ZMBXBBoKfjjdyZ8+XRFMjr93/y4XuEfu0PJBMhCoEay1x0wL+fsbGxz/uQJptMJx7MZq7YGoejkfQaDAcNMY+oFRsKoW0SVb2n48KOPOHXi8xTZlGp5zFFbk2rQ0pMIT2MVC7OitYaN0RZPnz7Lzs6A/dUR+03Cyb1zXHn/LbaHIzpFh7Z2dDp9qsbS6w+pqxWr5SwudExFW+V46+jkXaQPoDMIgl4emUJJVqBVQicvwBmm0wnOOkbDIbPplOFohJJxAeB8IC96zKcPSNMRbVvR645o3ILFfEqa5Mym10TjAAAgAElEQVSmxywXK4KAbtqLzo1MUy4D/dGAqlywbGo0Kcs6gMoYpQIVLErVzJvIMLHaUXR2kc2EqolNVoGWUSG4O65oXUqqUkpjkKlmURr63Q55pihXNZlssSJQmYatzgjhDK1RaG3JfIJsHYtKY4UkVTl6bSO21pAIsNKh0hThY1NLImMTybSEXK/t0gRkaEBkiMYikoAIDiccCocOAkmCx0Vgq47xuUTnKJ0w2txBOMWg3SR1JZ2kh/Mpy9UC0noNzo9V671+j7IyHE5nEFgzGjyLxQxjDK5u2d3b5dadm3RknPRKCa61aC1IC0FSJOSdLovlknNnzpInGpUohoMOYV3xuVrVBOm5tX+He4f36WSCqjWUVUBIidSKhMir8LbllddexVkfYY3K0e91mNc1uUq5cPY0O6OzvPLG6yxWU5JUEkSgCQ5rG4LqUpoYcfLScDA5YtkeUAw6salGB07vbmP3W0pbsqhqxOQBIVhM6wndjOOjBWW5pPRTdkebBJuiNUxnh2t2BiglmUyjI0cnEqUKBt0RRdZhPjugsZaApqpqhr0h2/0dqsWS8fGcjZ09ytUKgSRNE8zUsNnfoNvp0e0M0JlmtLXFxmATnfWwdQXEDUyh4LBeEqyKr30IqNgRRbAGKTRCeKQAHVrqpowZ8ESTyEBAs5iPyUY9RB4jMME5+p2Cw4NDbJDsbPfi51gpytoy3j9ke2+bdtZy/e33Ca6lWVYcZzN6/Q7HTc1oNOT02dN8dP0WV9//kE6uuHj+PLvbe7zz3lX8wT0WsxkP7h9w+eknkQGu/eA9vPB0egmD4QbLxQFSuAjyFZ69rQ3OnLxA0xhQHmgxRlC2M8Zlw7BX0Mxqnn36PKksuL5/g0J3kCRUtiUlYVXPybMCbxrm5QqdaPJCcv70KZ689BTfe+sD9GDIajlnY9TBOclnX3iOy+fPU9U1t/avU7YNexs7aCXYfxCFbhJJYxt8rXBNS0KX5azmJz7/U7z29lt0ewVVXSGFWld5QyjjhnOz6PLY+dMUac6Nmw+o2xInBa5tETpBBhMZGkiM94wPxhwfTqjKkvtX3keJ2KDX2BaA2ta0ixap4LtvvMvT53YQUnN6a4fJgwcssiXBS7RIeP21d3j32kc0NlCkmnOP7XE4H9Pt5kwWM0Rl2DvR4/Hz5/BOUS0OKVROd7hB61pSIUkITOua199+A2/n5CojSTyf/dxzvPPeNfqdDgezfVItWC0hoY9WBltZyCRpqmkbE6NTMqVXFIgk4btXvs8Lz2ScP32GyXifd9++ifI12AqZ5HRlhnKKe4fXadsaLXJc8NSNwxm3ZokoZA4Sy0c37vNTX/gc7773Pv1On0E/4fLjT7CY1SyrGpI8gvTLBT2ZUCow1jCZ1wjnqNDUZc3i7j65UmiluXv3LufOnCYQKAY5uUgpK8+tO0cE35LojOBSQJEOcnbzHQ6vvMvo9Baln5MWOf2NEffv7uNcjWlqIPJKlJA0deR7mcbjbBTutVbY2uGEJ0kCIsCsWlFWJca2SOXQQqJTyXLZsFpV6ETFCJ2K7iTrPNt7O9gqbu7rpkGIlKxIH8VolEqpVi2tbTHOIFWMa2mpUP2EEBxpWjA426NduAjGr1MWixnbYYODaU0iUmal48TJs/R1QEqPde36sTnSXkJbVUSrT6CtWrZOxPVAIjSNr2O8zTYkGmxTQZsRMoGTLUiBIrr/YitZnLamknUcwuOFA5HhgyRZO4uUzGicIVlb14UUCNOi0vWm3Dqs97ElzsSKeicEkVAjccFHMLmMDJkAEALpuiVMC0FjLK01pJ2MulwghMZZh04zbNOQpQnSgkoTnI2f8batSRKN15I8LdYT2RjRUkIRiBGlh9NYD4/4N85HSLIjIGyEVWdZRruuuXfBg1yLcH69WF5Phj0+NjK1DY6ADQkojREWG0T8LlECHHjnyNKc0c4uwXuaeoVSaXSY2QbnFHUbSwicCzhTofW6lt4FpASlIxxcSkm3KEhST6ZTpBbopkGrFC2OKdbxORECtrVoEctPmqYhSZMIJG5bECEOcZRAa0miNXjBcrkkyzKcW7egiTxygKRAKRkbfgNoqddD7Xhu1mtkAVKunTtRBEqSlNa0pDqP/LhY6QsiutuCsyDWrh4p8NYSnMfagFaOxXxGCAIfLFmS471/BLY2pl4PG6K7XwiJdS0EgZQagSbRKdauolNLrAsB1s7/KDauXUlSrKO8nqqq6Pf7WNvQNg29Xhf7iVY+JSIny7oYlyR4TBudiXkW1wMQ4uvvIbh1r56KzCW/bhkEUMSNl0CgUoGUETIvHEBLkqbkWrHyHoFFa4UIHu9sLHcBpMwpUsezz3+Ow8kh46MJTz3/Eu+89W3GkxWdosMb332Fu7fe4/TFJxl0enT3Nkk7XexiQZakKJkiNoaUy4rxgwXz6RijgBMnIcRq8Ac3r/O7v/9rhOqYvKM5UwyY3P4BJh/QK3aZLEt2n3ga17b0hl1+4W/+B/z2r/9v+G5Os4zsr8HWaYrdHXrbO7z1va+zdWqLfr7J6YvP0LSGbqfLaOsEeZZTV3Mqa0mV4N74iPEDx7W3vksjPL3hFqc5R+1W/N4//ydkgyF7e3t88/d/A2Mb7h1c52iyxAuHOC7Z2dihZU5/o8fd+7cw9YSd1Zy7tz9ib/cM41s3eXD3DqGTMRruInHs7J5i7/xTHE/nHN27SWljkcUr3/gjUi0wxiKEJuttkyb5GjAfePlnfoanP/Myq9kRf/57v8nmybNYoTh1/jzPffYnuPHG97hx/R3uvv8ReS/jzo2rpHlGvxfLc7q9HdK8R72c4cua1/7kD7n25ndpGkNrA6iEi088z7vz19jd3sB1O3Sk449+89fYv3udtFPw1Iuf4/DeEb1iA/qO47s3EYoohuuE2hjsArAGKRKSTNDv5qQyo3YVmoTR1h6XLn2Kdjbnux9cRWk4f/oUmZd42XDxM/8O249dYnr1Ks89c5H51PCf/Zf/Ob/8T36Zv/0f/SeEIGi9I1WSLInfBQ8PT/hLwOv/74dYt5p9AnsU//0Tt2mdpDLwZ1NBLuB8Hvi1ieBeBVii6I2AEMVusc7UOSB4wQsZfDp/eM1RSDkMgndqQMT2zBQROZ7yoV4m6YrA3zsTGK6F84BngeC3J5Jcex7KXg9B2mtbLlpAR7HO3gnqILjdeI4qyZe3ojNLrN1d4VH4zDMLkqkN/MR2FLkaEahM4D0jaY0niNi6/jCyFlscA2MPX18ICkM8f4n41eedpBGWYdD8w0Fk9koEIqxJV4LoLn5kdBKI9aChaT2sz48/ruPHLijlnQ4nT5wio2UxO0bLhOPJmNVisp5irAgm0M1zmmrFDEiHPVpX0tQJlhpnJYO8R90a7ty9gwyW1WzO/vgGeEPTVJi1yqhkTq9T0Cl67E/GDAc5TsN8f5/vXHmb5XLOjf0DLpx5jLYpCcahhKatK0KwcRqDxRjLYLTHYJgzn89RIokLtzShXI5p6prNrT3Onj2HbWvKquTo4AgrNac2h9y6+gMSKehmKVprdndPkSqPJ0MpS9YWdJOUo9JT5NscjQ+xtHQyye7WJrYJjOczlnMTW0Cqku+89x55lpBmOTtSUxtPvxiyWu1jBThlceY+zuaYuoDW09GObh6obMKqTnj30Kwb3Dyp1mihGI5GODuL4HPhWTUC6+PEJpUuOomQ1LZGOM14HBCiIE8CiIQmNGBbEqXobvXjZF5oyuUSqSzWe7JEAx6pWqxft3kIibMKgiKXjoDDBUltAtcN1K0jKIv1hkRltLbE+RrjW2ZziRQ5QiakoaDxNUfjewgPq6XCS7MWemKUsvFzttJNjo4OaI2NbSAytthUPtDrxhrUrFMwn8/wVpBnBUJYWtNS1yVCCP5P5t7rWbLsvtL7tjku/b15bbmuqu7qBhpo2AZAEgANZsgx1FAzE9LLKKiYkEIRMiE96e/Rg0IaGUqaUQxJjUgiSA4NAAJomDZoV11VXfb69OecbfWwzy20RhF6ERQx+XSjbt7KzJOZ5+y9fmt9azIcoURqpknukTXWWRbrDfP5EolEqz657mPaDV4qgrAMcg0ykClNmRcolbNY1KAiwXmcd4x0ZEvknCzmPHx2xPlyTVZI8jZjazikKHK8s7T1Be8ePeR0vsRIj5YKGtga9XnnZx8QMtgdTrh2sEOmI3khKXLPsFLI0GOxMBjTMvdrtNSM+1tk2RCZaVR9gS56xOho2hUiRvpFTsg0WZ4TY6Ru1xiTFtvD3jgtfPMe+MgHj39GX05oQsRQI6whz0tOZ6cordndOYAgCcGymC85Oj3m9u077GwrmvUc7xyHV2+x2jRooVn7FZksyWSavhpj0Qqc8VjXUFU5Sii8b2lMTV17iJ7t6ZTBcAshoW08jtSsp3NNVva4tncAKkfrirIa8uzkiKbe8KPvfcBifsHR48Dh/g6TnUliJmjN1miUpqoyGXOJgcFgCirHRMGmcUyn20ymW6wePuH47IJcaarRiFxp8lKwPz3kY9dy0TylyBWlyrhx80VGo0OePnnEfHbEtavXWLYt0Rp2xopHz1b8eXuPq1dfZLMxDPI+i8URyyhSVCDLkMUQHGQISmkIAnTeJ8qc+x8/5vbL19kfTRHARx9/RH88ohqO2QTHWx+9w70HdxNcMkbmC8/pbEZjavpZiRYgMknMKp4dHSH1VzDWsr9/wP7pCSenR2yMQauMXCqyTLPcLLh+uMvJ0TGvvPQy880pINhYz2BY4FwDWqKCQuWKUheUVYHzcHoyZzDq4aXAzC3eB3YnE0bDCffqp6hMUCpQeeTG9dt87Ru/QtOCyDKm/RKE4cWXrnO8mPPo8TOELhmNdjhdnpEXGdeuvMjx00dcv/USD58e40xLWRT82ld/ibw3xtmax88e8tZ77yZbcBEpCoX1a8rhgD/8wz/lfDFjb29CP1coaQnGEIVjMJhgW4NEgddYF4gyw8UGv1a0tWM41Hz/vR9y82wPomU6lQTnEapEZjmbukF6y6Ju8BEKaSh1IC9ybBRpYygcfenolQ4fjqjrhuOzY0TW0Ku2GUymfHD3LZCa2eIMXWhEFDgPw0GPtrbYYOj3Ms7nc8gzJnpKL++zaRMoX+icXjFg3awoJ1vUsWa1mqOlQlc53mzIhWJ2fs6nPrWPippRT9HPRxwdr+i5QJ73mU5HPDl6gm0NQklMG3A2Mu73IRrqeonOSnIpaDYNL7xwyLwJtG1LUJqoFNEpolBkg4psMEQuHdu7FU1Ts1nXZIWmaRpECOm8n2cUWUVrLNYlwTO6VAqRF5LBcERfBebzRccbCZRFQVFUxGhYnG9YzWOKTWU9VqsFzjlMXNLvVfTyEeOtLUy7YtMI+tUQ7yEqi3ABEZPTQsTYTd09eQeEFqogErF2Q3SOEBXegvMtOlR4J1M9u9uQZzlEjQ8JuuyFSBvfEIg6EkRMkRwhkN3CWfiIqHJUt9J2wVOWGh8M4rItrVtFay3RWhCjJ/iI61xJKpdI9dxsnwQupQgyYlxNmVdU/T4CmeLHOiCyAhMWKDRadG2MzRItNU3wBOeoij4KgbGOtm1TNAs6nlPazFzOYoOMIJJTJStSBEkqgUYilU4tvpLOBeXxPrEWpUiNcTrTSUzoomRaSURoUQpM2/D2T99gsr3D1f2rGFtDpqiG/cR7ihEhk8vJ2sSQQgoykXctRhGJRGUK7w1KCbQo0iYkJGi2samVDpXWC0EK1m1NVlVoLfHR09ZNer1RUMok4kiZINEipla4pm1TFFqBM4bNJrUgap2g4HmuuzTEzwGvvhNCLhuWQgzE6BEiOY+lTE407x1KKIy1AIlB5j10DCsEXRtr7IIaIgHkrSF4yfZ0C2eTeKWUJkpNr6xYr9colR4jEcEk1tQUvQLnA8GB1LHbCKZKeecjIV4yjAIhQozy+XsrpeyErnTdEyK1spm2Te6qouR4foRWiv5wSN22IGXii8XUTBw7yLZUWRK9hEitfSHiZUDI1D7sQ8CFiIsyHVtkwi9ISTQRqQNZliNlpMxHqTGybXCmZjIaprgkoDKNcBqtBUqmQpYQoNk0fP5Lv8xbP/gL8rJHrmpiMKz8mt5owrOjj/ln/9N/w5df+wqf//qvsV31ma8WFP0ex0+esHd4hSzPKUZDnt67y8GNG/SqHsPBDvPVBafnT9gdbzMaSo5P76F0n6rqM9jusTIGjUBlOevlhpO3f8L84im//g/+fb79P/53kAsujp5wcOUQIRRKb3HzzjfRouLqzReYn5wzOz3mvbd+zNlsgRKB1npmGw3BI0TL2+9/F+sbZFT0Ry+ghCU4RX+0y5/90b9i2SxReXLztia1STsRGewfMntouHv/A/SooBrudqkWRTEcE5oNNz/7JT74Uc3b77zDS58bMh4M+eqvf4tXPv95/vh//m+5+9E7hNjifIPzJdE4Xnz5s/yj//A/S8J0iDx78D7HR4+Rus/dd37IvJ5z/O45wguOHtzjU1/+Ja5++rPsHl7how9+xONHj3nw9nvcfOU1bn/2C1x98WUGk30+/N530c5wul6Q7e7y6jf+Lj/9839JlSXe23C8w3hrh9//vd/j81/+DP/6wSPOTu5DkdpHHz74GNXf4tG9D3HrGeVgh6Y+RwRPCAJjW5wVCC/RyjHQFbmQqZ1TRLKe4NUvfZHVxYrVxVNe/dSXuH77JiHPaVYt+7cO+PUvvMaLgxHy9c/zL3/yHrdvC754eMDBf/2fcmU8JuIoVIrlpvRTB3mOnYwhApfUpF8EYef/LfKW/l1hiLzQi9yp4AdN4P9Ypv39b+45/ocjRU8lQUl2Qkr6y3T2CyLQEzIJN1F0ohjYECg6xHZqVLt0GnkEChNjcjULQYtEx0hEUgK/M47kpPidQJCuWpJLMlGIYGKgCYIQJScBvj0X7MrIlVLgY4fsvoyZdc+iDZHbA/g7E/HcwWQJFHXgvbN0Ag6I5yyr9EKTSwrgd69EbvZAikiM6bX9vtW88cQThCAETSJpfqIprkvkifCJdyFCYz2Z/recoXRjd5/Qzrn37CGq6CNlydZgRNOmVhvjHApNLhMA2rSGKAObNhCiRZU9oMVi2Brs0s80p6uaw2s3GE0mnM6WQMCZhvP5ksZ68mzI3vYBwa358ft3cbYlhIZHjz5iPB6zPRgjhWDQq5gtZrTeoAMQHKKrwfbR0dYLZu2SXCgODrb5+NFjXj68w/ce3Wd7MsFvNqxOT1MLhZBkWc5oOGR7MuCxjLT1knVrUCim2/sU0XFw+AL1eoaxjrPzU8pCP/+gNa1FiAylHYNehY192rbhlRfvcHbyhM26Jev12RkOOTk6wztom8hFU5CXFX0NpbDMXGDl1oyHA0RwWGepDWw2gTxPlb3exvRlxNPaGSKmyb/wmhg1QqbJzd5Oy/qiYZYJVivISbXBSkPrIsKn+mEVJA6H0qmedTZfE7EIGVAoiJIoVGLvJJ4yhSwQwiMjOBPQhcZYlyaMUSNjWiRvj3coVMlqvUDIHpGIsSu0jshcszs55Hh+CkFQFBVVpVg2hiAUIfoUGUEhosP7iIueKqsQPlKWFZkqmM9ngKDIS7ydsTfdojErFssNLjienTxBSE0MHVvCJBdU8IKL1YoQPEorRExTR6KlNQaZSUqt6RUlaZwlEg/GJ/6PQuIjCJGhi9Rysr01om5a2nZD9OBc4GwWqErdTTEjTd2SoZIybTTeNGyWFqkt/bKHNRvqdZvU/G4RqDubIypi7IbMg8gswRWs1xc0zYY8z+hlJYvNghAc/V6fYFKkQtjE/VrZljLLKXs5q02DtTVSpAuBzIZ4nXHnhVs8eHaPvck2p8enzOZHKKE5l5GDg1tsmsj5as5kvE1ZbuGdIKCROme+XNO2lixALhVFqo9JNcMpR0XoHFOCSNtsWK9Xqemms6UvVxsQJlUQZw4TBTZ6tEhtdNZHvHUQN7jmCbksmOxtc3F8xku3b/Lw43uUusCUfRaLFY+enXJxdpdmtWK8NWG6PaUsChCC0/mai6VnNNriytUXOJufs6lbin7F7HjOsD9Krqthn9n8LNl3fQtCobOcpq7J9Yb5xTGL2Sn3zAYTHc6eM+kN2R0P6fcKTo4eMN3uk2UjesOcrZ19Pvzow2400YFsdeJQFBKijdx/8DEuwD/50j9mIMcslm/z0osv0hhLs1kxEymeUvV73Ng7YHu0y+n5+7S+JrSOvd0+n33tK/zgjTeZtWuWswv+6E/+hGs3rnLv4WPOTs662nKBzB1FLtmfbqPnEJTHRk9VFdy88QL33r9HrgTDYsRivkaiUiQ3CprY8OT4KDm7XnmBK7du8OjRMW/+9MdkDv7O13+JwbjP3Udn/PXffA9VCnSWMZ0e0jSR73zvDearOb/6y19mOhlQVj1E5hlMKvIq4+5HH6ZN05alkH0mkwl3P3zAxcUpvTJjd7zH3uF1rIh4P8A/PaJX9VmvV2AjHsWwyohiwbOL5Db94PFTYvSYNlDokDbmwpLl0LSBIks5dWhQWSBIQCl8sKyWjnfOf8agyKhkhTGG2hralWW+XFHmKbqgECgiPoDzEWM9RX+EUhuk8lSlBNXy0f23GfU1z45aLmbnnB+fUOSS4/kc4zdMxwNmsxVeROxyxeHeAev1OfPFikwXKKlQGewfbvHB3Q0ewfnZGf1+n7Y1VEoz2dvi2bMnzNdLVKaYbI/YViP65xd4KSlLgfGW61f2+PD9h4gsY2s6Yrwz5Xy+IMYl1rm0oVWS4bAEPJuVRxeQl5r1uqU/ynl2fo6zka29HYJ15KOCuqkxJnB2OsM6S9u2NHVNFKBipKwKskxzdnqW4itSptiMVhRFTjCp4cW0lq3tLXb2tvj440csNyuWswWtdJxdzMlUDkJjmyYxW3SOAmQucQECGq0KlMrYbM4JQlMUJTFWmNbhYosXOa2zZFKz2qzY3SlBkmK32uG9wTUtznuscF2ExxO8IXYuHBsaou8TArTGUuYlwQWc90gJWmZoBDaGtEiNAYJKG2fvCZ24EYIjywYIkSUngLDJdVRkFMMhMsTkBhSCXFwCIhJcWUiRBCMlkwvLQQiSLC9ASGSUhOCeO0ESuyH9nTN14iGpjovUQb+lEFhrgG4KLH6Ock2mlhSfjcEjlEwubyERweOBXAikVkRj0rI8xq4VjnT+j5FMXS71BTiPd5FentHUG1ShOHn2lPPTp6w2C/Z2drt2L99tZ9ITuWR2xODJlE4g7qg7YQZCcOn67UMCvKtIahlIUd4YBVpJYvR4YymrktViQVXlSXBGEHxybsHPt1dK53hbp8KfmCbIuSqIHoxriNFBVM+FrzzPgdSoltw8KVoWYkjtgm1quHXeJZ5H1sUakc8dPcTIptmQlwV5nifnmndIlSXwdpSXw/fUmOaT4Ka0ZFOb9NmMCZQrpKQ1hn5/gA8pXhdcei+0yvHOIYRESoH3kbJX4KNPTrOYImqXBJEoUvxSS5nibJdz/pBa4aw1hBAoyoLWtGzqDVcPruC8S2KVFMmxJMB2TrckpgWkkinuFrsIW7fhEiJ9hpTWtMaQaUWUqTxCdhtVgUhojAhloVlenOFMTb8ckmUaYz1EcM4SQqDfHxGjp/UWIeCFF28zGE+YHt5gdHDIw/ff5uOnj9jfPaR3cIPv/M2fM97a4fHjI17ZbPjxz95C9yr2dg94/82/4dmTbYiKZ8+ecH7yELTiK9/8Fksxx4eMz9z+dMrtxDlIjfWex0+PWWxgMr1Otb3Hnc9/jjf/6i95/PgBVw5vU+oR27s3eHryCCUiDz76Get2zXRrn7y3y61Pv8ZwMMU3luXFMYv1Ba3zDIuSXBdsnEFnabVdhwaEoFQ5i9OPuXrzJlm+w1e+/rdQIWAVuHbJ0eO71DEiQvqcP/jgHQZFxuHhDV7/zd9meXFBFm1iu0RLb9Tnr/74n/P2W99lPJmiqwG38pL+YMioP6IoKqbTPWZnH4NSuKZF5Z7jo8csljMG1RbWt/zlH/wLjo/vUfWHKRXiJSI6JuNtYnB89JMf8pmv/xbXrt/inTe/g9QZFsFv/+5/jBU5sW4Z72wRhOGd73+P8uA6r375a/jGc3zvfbZvvcD9n36PD376Iz73tb/F+WbG4uQuy/k9dqZ9js8WbNYrDg5hNKhYbk7R/YymSdcrJQM2GJTIcBGiUrjokXkqv3DGUvZK9g+vMl/WbFYzhjtTxns79HaHfOsrr3NzvEXWCfUxREIR+ZXP32I7T2VFV0cjfEjMvUvXTCr7FJ17KLBuLUpCmSVW0C848/Z/v8U0HLiMtV7PBL81hO/WAr8S/L0xHOaa/01A3gkyl8/oufsnCmzoCmLi5e+6qJeADqeGDD//OZ3xQ/q7mO6dd8fi8iqSC5Ib6bkCk9yXSkosggz44SLyzKY91iMbeLoSjPrw3jryYk+gZeSSHi46TpIjMpSBunMXqhhpguTIpudgg0h7q6RqP+ceeR8phWCSQ+sEhQ7YECmRaB9xXnUopiSaxdg1KiN+7k7qXsrleV0GjyyLX+hb+gsXlKZbA+59+JCo+wQ0RVVS5DmuFViVNuRaaZSUrH2qin/w5IimNkStyJzHNC1ZnuCDR0eRjY+UeUaZa/a2kzjULGdcnJ8gVTq5CQFZodnd28Yvau5varxpMKtUC9u0nnqR+Ea9qkeVpWpo06b6VKkkT548YBhh4DVv3btPkI4fnzxBeYkxFm8N/uFDGtuwkakBQ2vJYy1Yr9bIsmbR1Oi84HTxkCvb1xgNBxw9uYu1lnW9wLsVy1VDlecQI72qz2g0YFRmhOhZRY+rWw5396gbT68oePjkGGSO95b5qqXXH5FXOZvVksbl2FBQ9grKsqRtLdbpVKeZbXCiRWqPlJGcDIKnqQ3IDBcFZUwKvguCHMFHTx2uybCduBNFSNW+ErwEZxKLQboAMrJsajQRF1xnQU82xCaOpR8AACAASURBVLat0SqCzvAqolSalO6MwTSB47mnn5dkebL6ByuTy0LlNJsLBpOr5LrfPQ/F7vQ6pq6RZcatF26iHylidJydnnJrO+e9pzO8qABomjq5vJqWEBNc82B/isg1w6LCO8H9px/R1A22NXhvWNee+XKGMZvU6qLg6PgJwXu0yHHWEqXHtoHGuWRrtxEVAw6BzgXCkJpfJPhgqPJkM7emxeDxwaCyDCkTr+HibM7GJZW4bR1llhZazkWisITWoEQSAquiggxKVXJysmQyGrBabhjnClrD0hhsY0BB7QxlptkYi3WOshKEIBF4qlJh2jXTgxFzUtvZZr0h4FFZ2qT4YImyxARQQZGpHlp6nG3Ish4+gHUrBoPdtAFWktXilFHVo16c4+ySfn+Mda6Dji6pehNeuP4So+EYZzxru04TBymYzeYJnmoMfSmIWFw0gCDLxKWxNVUJR8d8Pme5nDEc9vEu1Y+XebKo15s1laqIkGzxxYBeWWE2G6pywGJ5wdlyRVZorh5cZbO+QIkab2v6Pc16oxBSs5qv6VcZm0WLxGPalmdPzhiMB4y3p7xw4w6D0YTHT57StC3DcZ/WGmZnxyzOIqPtPsXgCjEYrkx3aRZnKNmwNcqQvuX46T2kCFzZmdIbFDw+uyArSvq9MavVCXUM1PWKxsyZDMfsTHcBm6puhaRQisFwm4ijzQt6/RLnI81mxcnZnDd/+hMmg0OEW/DkaM1LN6/z3ntvcvXqIbXZ0C97iCAo83QO25mMyaeKr3zxi5yeX/Dqp27zwx+8SyhL5ufnGFezaFpEpohSIKTHy3TemM2XWGO79IlkOJqin52Qa4lUIjVDxUiWSVoXsEQePjrmZ727vHTzGlVVEh2cnc5ZzVd86dU73Lp1lbP1El2WtKZmExTvfvSEUbkALzHtmk+9chvrHG3rufv+XbzZMJ2OMNbhYkYIhvnpkpPFOWWlcSExc4KPPDk65vd+/4/Y3pvQrNecnR5TqgAh4FtP3Dh0L6J7B4TMkGWB09mSfiZRMkcXPbarnM994cu8/977LFaG1aZByCY5K9B46+mXFVm/5Gre4/jpPaLK6fVHNBenjMdjjF8jZGKFVUVOXyceUO1dggFnkjy3SN2QVxlReibjCqUg2IJJf0iuc67s79HrjTAfvovWmn5/gLXQthtciCgZ+OynbvHWj++ybD3CBaxpMM4yGBasNhZjLdpnKa6jkhCeFRLRtpiwYH/7RfKsIlIgDUynQ46PZmzv7JP1suRyrDecH5+xmi2x3hDwaC0pCo3KJWWpqXqaa1emGGMxtefB3XT9H29tc/XKNc5OjlgsF3hrkUCz2iRHgQ8p4pNpmnXD1taEwWjAw3uPqfoV1rRYayiLHkWpidKT5xmNEpwfH9NuVlibhJ0s08QOno2OlIM+VTGgKDLmqzPSCrsgesNys8QYT1ycMu6lCMRstQGVQ2iQucZbCz5Q2xotI7nWRB+T01IIrPM4H5KDIziQCkskOIfzARM8BI8JC3r9goPdbbyTNKYBBDEBH7v2s87pIWJqTNOKYD1e+k4ASOJB8EkAERKst+ispJdXWNumCvWu7SzEQLIZR4RWiJBGmZIMb1LMS+cZSmiIadOvdZ5a+qQmk0nUaoxByAzrA8F6pE518cEFlJAQ00Y/DWBEV1OfuEUxRoKLFHmCqmupCC4NZC5dOM4khxM+CSLRO0LwSKWex8mUIr0XHZ8Ja8lFxt7uLsHf5ujsmKPzJwyKPjhLrjICYI0hhktekKcs01pCSAg2EGViZeciRfKCJEXZg0OmirUkOsjkRIpCIqSkaVqm0yFKppZMSYph5lmOaVu07gShENBCJXB2iGghcNYkhywSFwL9Xo4PaYAC4H1CHoSQYmxad0KVShvKhA1LMccYYyeK0bl0Ou6VypAyucmtc5Q6bbYCSXSMweOcxRPJexWNMTyvJIqQZVkqqFEKlWmCTe1xIXaRRiFSdFFKjPMomaE0uKZzzYmIFAko3vkNkgikVBehg+hDej0deNx7j+yYTVprogTT2gRtj/658BaIZM8bkURij0W49BwodemGSjBumeUJPu4vN10xMc1IbBktkshkrUFojY0wqHrYEHA+Pm83VFoRMvAOfAhcHD2iNxqxvXONz3z562SZYHu8xfJP/5hhPuDm7dv88Id/Sb9f8cvf/A36VclHH73PZ7/yFZxtsXbDo4+PybKK1WpNXpQ8uv82e9evU+iS/qjHa69/g/fe+D4711/j6fHHOJUc/9o6yib1QUbvGe1MuWobdF6yWS3ob42ITwSh5zmbHTOdTpHBs5qdsbU1pVmmwoMHD+6yXq8oc02eaySKXh6RylLkOZkYMVtZ1m3LuD/E1xHfrHn43o9RSnL19stMRgO+/b8/4+DqPpPRlLvvvQ3e0usNGFYjvvS1X2Uzn/GdP/7nvPGX/4rbL71GaFve+tF3EL0S4zVRF4wmU5TSPL7/gNV6RWNaGhu4cvgS548f4IxnZ+86W9s7OBcp+2O++rd/hz//g3+G8Z6qGjIclczOnlENR7SbhqePHrD37CGP3v4JZ7NTiv6If/gf/Occ3rjDw48ecHH/Pj603H3zB1TDIV//6jeodIHayrj5xV/i8IWbHH/4Fr/6n/wTYhzz1nf+T0ZXDnHrx/gYqVRGURS07ZLzx+9SNy2oAtea5H2JqWW7JO0XdJHhnGbn5suc3n/CzsEWw+mE3/x3/z3sKvDWT/+ar33tdV7/0ut8/qU7lEIhY2IAKtk1mEXHSKbop5byOf7j8ssrOwECOrYg8Gi+YFxk7I9Ux//5xUKb/82bJIBIApckFYV+pZR8oYI8QktER4EWgSAu3UZJ+AmXAlKMz3lo8XLOQEzrns4bdBmRE594ZCMiQabvuoufEENiOtdLAeqTakzndjIkEezMSYIGJyAT8OkxHBnBn13A9QJ6kufXrkttKQtwbBQrFxESdIQ6Qs9FTC54cwlfGqcygOepwwhWRKyIPG4EYwmZ6WJvIvBwJRjTKWadOMcnhjaRnwtbl/9hRKC8g+yTqPL/77dfuKDkvCWoCmUhyIBtLZtYpzx1VuC8om1aNnUkdiAw4x2qUCAL+iqnpYPpKcGjo2OizFmv14xHfQoJ57MZ1qTsYcxBZhlg0TqjX2WsTMtIZbjgGfcqiDXLxRzhHI2xFLnGdE0nWlpc8Ag0IUSskGnRP9ki70synYNxbJpUfUlrwYHAohtHAFoZ8DFincVFR5ZpHtx7j+VszUv+Bk+PHuNDmiY6FzEmZzTqMxgVDPsjqiJj2MswbcumMcw3S+rG0YQW7RVZUXHzxi2OT59h2wahJKNehfJrCDnSSoiG6D3eSyaDAY21qP6I+WaG1o7GOYRWaCKNUUQSqNXpQBSqq24MmKbEe0+QLlW9kqyDwQpQoLXoausjZIJMRWxTp+mlzrG2wbmIJBBVaskpZUQH0sRSB9beUZYqXbDhuYvI+EiJJs8HnJyfsW42FOWAUg0IQRJI9VJPnh4RgsS2nq1JwcF25LgeEdQEHxyn58d4FCIInGsZlAlSeXZxThyOadqGzbpmvjrDFi2RyHy5AQRKp7ypMZ7lIrnhBIoYM4osLeCGVU6ed/EK3xKjo8pyZH+MbR1KC6pMkEmNDR6tM2rjyPKcQb/HRbtOC8CqpMoie1tDVsuGxXqTILKkGMelDXvQK9jb2adX9Dmbz1lcLFGyZTBIjT/WW6RIx1ZmGes6Q4oCREndzAgepExQYx8TFHa9WZIpQe0sxA1t21JWmraxCCHIM0UQGl0UXN/eTXXzQvH42VFiHUSJaTdkWcGkP+Hp0RGmXaGjR5YZUuQM+pME0Q2eKEFnPVoT0KLtBgiXlZdpQa2QWOewwSBCRGdpqim1RilFu9kgCWwPK7AVgsBqNYcix2lABERwZJr0PgSPXS1xtaWqSkZ9Ta8YUVc5i8Wc0yf3Wc9mZPSBQGssxydn1HXL3v6E8daA5fkCLXKstezsT9BZwez8nPXyTRCaxXxGxLK7v8OgVzKZDCkLiQiB5eyIoc6Yn57SLzRRFqiipN/PENFgnWS6O6YqK1Yu0ragdR/rT5BiTcwVWuWIqDi7WGL8EoD5as246jOuNFV/wGgk6ed9RAbrdkGvP0JLyfHxMaPxhEcfvMf9aNBK0++PIGb0dAKjOivZGoy4euWA8fYUgLv3P+AzL73C177wZb793e8w3R+nc9t8DkKT5RmqEKw2DbVPjY5FBUfPjnjh8DrDsuQrn3+ZQa756KNH7O/v8NGDDyjyVMuskHiThIzZ8pzhoMBfzPjxGz9ECMVrn/8MWVnx5nf/gqOFxUWPiBlr54nNhnc+fI/PfvplXrx1k2CTwPbC9SvMmwvO5uvk6jMR72qsUFjfQFNSlYKsEJg6p/GGenPB4smSTOVY79HSJ5eLafEuooxip9rn4GCLj+/+lGHVo7UN/X6JaQ3V9jamFkxG22ya4wRv9KZr9tBolXHt6gFZnjPd3mdxfsRq0zApHErnFGUP19SEaHCuRQpFbVoIHpV1bSsqQlzjTWTZWIqhJFrByfk9ghnyyou3qFeW+UXN1nSX116+w/2H92k2KSYaHUyGQ7x3vHznVY4eW+qTZwRalsuG45Njold4a7DW0OtXFHnOYrZgurvFdGuHquohlODBvfs09YaD/WvsXT+kNRve/PAx77z3IS5AU9eMhhWIQF5phIm0NglK29sjticj1lJycXbOCzf2WC9rzMbTWEOvzNEKnn78kMV6znq1IsZAmaXZpAvJbSSztPjUSuI67klV9RgOe5yfX6RFSAy0mw25lOQ6RxQapTRZpqgbsI0nKxM8N6ZkE661qWFMwbDq07ZzpOrRGIMWunPc5FS9KaIsKITGBcd6vWAy2UmLUiKr+Yp+P8eESE6gNg5ChvcR4ywyJs5LWmE6jPO4tk2LZO9omprd6Qv0+gNWa4vZbNImWYBQkkBEZgqdpeYyZKTQOY1vyLROUGidot+iE3HwHh98xxL0+OC6IVpypcRLkYfO/UNMG/roaU2dotEdKwiZOBRSaXzbILPEl4khOV+0kp2LxKFU0UX5XMe3ScMR6xP8WAWPtT5Bw4NPxssQKbLOjSOSkBaFILqQJvmqe21AG0MSn1QStIIPZLmmbQwpKSVRRZmEPOc4f/wmi+Upwa3QB6+ipAYpsTa19YkuKqW1Rus8rQuFRRDwUYBIQxUlBVJphEiioZBJCErtSJIYIMs13pgEuwaMM3hjIIYUxS4rQttSlmUnqAWiEvjoQYo0OPAkbpILGNtQFhWzxTxdx30XwZASby1KScqyTGUpwXcunTTsueShJEevxHuDEJDrnEwl5pLtzjsCgfMpDidkgsyn9yaVrjgbOudQRCiBVALTpoiiEIoQbHI3xXQMiD8HzIZO8INAW9dIITtuUmrYu2RXFUUJqJ/H9DrnnHMOpbLEZHKOYJPzqWkMSqvk2O22jCGmiGJquosomTRTKeiaIDtRMyaIfIz+eWw1hADBp4aqkJxMIiYhEJmanJZ1w/ZkJwHOYxI+lZCpsVFnBCTeW7y1DLausHPlMEXdipJoHVduvMjnvjin1x9TliXjQZ9mveDNN77D1vSAi8U5P/7eX3HrpZc5uH6b+eych4/v4oVgurXHvfffZTafsz3M+Mn3/5g7L7/IcHePV770q7z31l9hpUerkr3dPZQsUSrDucBwNOHxu2/R39mlXpwzv3hCUSkau0GrxPUyG8PhlRu4pqZtEhduvTgDPApJkaX1dFnl1ItTXn3tl5gfbdD5ktOzU3b293nt63+bR2/9iDd/+G2WjaAhcOdTnyWQMxxNGE2SMWA83Sfv99F5ibeWxdk5RTlm/v5b/MGP/4ZBb4ILnp3RhJuvfJUXX3kttZJaRzEccOezn+P02x8TQ6QsS7QqKfMJ/WpAdB6lM7JMc3DrZX7tH/5TrGm5/5M3MDYJ9bde/QLnj55wfPSUkwcfg63plQVf/I3f4gu//GvYjaEaT3h0/x6r93+IiJFMROJmidzax3tPf7pDzAruvP4bXH3xVS6enuDrc8qiIJoMbxPzrcgEkg0E8DZiTU3wFhs9ZTEgWkNUJHh80Bzcuc7f/6f/Ed//7/8XxEDwd//RP+bf+fVv8cf/4k/4m/MLnt19j/1vfINCpJimFCo5PjuXTUSgVcEnKNtdLCz9dCkYGdOyaltyqSgzQZmlKG6M/FzU+P/tJvGA7QQQ1T1mToquBdI1Q0nRPZ/L1xafh9lCFGxcany75DSFKDi1sQN4p3NORCQ3LRCFoMMrAYmpSPeYMx95v4abBRxknQDfBewuj8naw9/bEbwy9MiYCjKaGPnBRvDOEjIuuUmd2ajjIq184IUsspN3sWIpGAC/vRX5dgN/fQZfGkGIMjmcSAqZ8RIbBX9yHimkoPLd8ZGCuw1c05fC+Sdvn2x7+8QtplcqcSD/LXcomSDZnkw4Ol2mad/8HBc8uQRnI1pnmMxgvE/Z5eiJndhBCOT9fpriK8lkZ4dYzTmfrVBCsVm3zGxDaxx5XjLQmjaki6bxlnbVsoiRqiwTnM0JdC/DWsdicYYxiWFgoqQsKsqioG6aNAERyeq9kYJrd17m8NoVvF2wXm5Ybdas3Zoq76FGmuYi5S2zpiEYm2CaMuBEZBO7Rh2GDPs9fvbuO6zWJtnqMoU3guFozPbOhO3pNjJE7KbBh8CmWYMKbDYLVgj6vQIiXDvcYXcyJtcCbw2nZyfJ+k8gywLKpUXifGVYtTAdV+wx56K8jtKSeX2MFiotdNuGXt5HxJLWLjDBdRbzTsnWSVhCgsgFOksqeiYjQnuyzBECDEqBiRnGBZwPSFV2Vm6D94kfUeUZ2/0lj89LotQMSkH0DqE00TraJlmyvQ+gJbIElMNFTW1rpM7wLrA052zv7aKzEf1exbOTZ5RZzsn5nN/51g1mRw/IS8WiXtMaw2pdI1BoWSJiTtPUvHX/bdrW8kQqIgbhFUp6Wr/C2YCzDiVSa5BSGVVWUJUFWZahs4xC9ymyDCUjpVKgsxTVcgYXHFq3OAdNm0DTTbNkVjdEIlv9MXmRMxltcT6bUzfJKux8y/Z4zNZoRPSSTVPTtglKKYQg+OTZNEZwPt9ge5pmExj0MrTYoLMsiXIhLS5XLhCswQZHFiW1tTTGd5tlhcgUbRDYYDDLyLjKupprQ/CeXrXN6eqcLNdEl2JvEkeRp2NyMVtwMT/DdQ1aAockspg/pcj7tKbGI3BOQ3TcPLhCvxwCyV0gBCjVOd8AuGQ1SIxpUtRCQK6L5DxxNUoJyl4PHxy23TDqlwx7OaYucM7SyzIIAdManK0ZDXvY1iJCmiATA6NBLwGATc2g3+fa/g71asK9+/d55fZ1hpMJf/7d73I1QG+Qmnea1ZqTozNEDKwWK5yPbO8IjLQE6zifLVLD4GjI1s44LVjbDf1+zt7+mHq24mx+hBwPGY+2me4ecDabc//pUzb1CbvjKcE7Ts7O2dmZUOqMi3PD+dljjLUpIy01tQnUZk6vyOn1+gTnWG0sk1HJZHsnxQii5+Mnz9jb32I43sWac6Y7h5yc3UNJS56LVFPfBB4dzzk/OQFvmG5NeeFqzpW9Pe4/fMCoXtLWhuVyg1Y5X/ji6/zwrffoVQUvvfgSf/qv/4L1pkkTSpnmH5lW3Lp2wGBU8tN33uXa3gHXr+4RpOXJkzN8/JDJsE+hNZmSZDIjuoiQia1RZCXGOt5988c8e/aI17/8Bbb3pjx48pR167hYzqFj0EQDLjgWfsn9hx8T/IYrezvcvnqTrcku1kpsu0QXGoxlurWFNzXbE0XdSHLdS01UMm1Ws0wzGgzIyPn4eEY+rdCZR0WHl4F147D1ip2btzkfTPBonpy1yCygbODBk1OeHX+fne0xZS5Z4vBWovKcGDXRRfqDCbkQ5FlO27WICZFE9NlsnmKxMRB8SC4YoVA6I0aZuDS1hVxhXSRXCdp59GxG3dZUA8lg1KeXR4TUVL2MxTzQGMt6s8E6R9Xr0+9XDIcZm/M5s9Wc1hlaWzPd6qfWSZtguy4aNus126MRMYftrR3mixk+rlMMz1lMuyZYjzEtea/CB896VWNMQ6409aZFDjK2pmNmFzNMgIjHWIsQkdY6msYTvOf8YoUPjl6luVharHfMFsd4n0Tt7ek2w1Gf89MZ3geyrvXLtAapBfVmw2qxYjAcobKctnGoLAMhEUgGo2EasgSH1II8z8iLVChhaofOBMEHZA7OGpypsa6m18tTnCf4xCX2mkzlHOzuE2Lk+OQpo9E2mc659/Qx1XLB7cOrVConyojWYC4Xw0p1UZvU/KaEQoiI7Xg7zqR69hgizrZIFckzRYyK4Fb4tukWiWnz7YJBSPncBh9FSAMQH9C5wjmD6pg0MSb+UfCBGARKJahzDBC7Oh/bsXvkJa7Bdz/HkBgy1pPnZTf97GCjMSG9iYlvFztXrewi594aTPD0iwwBHXA72T58jNSbmkzr9N44h+hcN0J2m3ZiigN2MFWkRITEs9BKk4rrL0WoRJwQsqt3donLE4VIM+/gQWSgFDdffJXDx2+gpgesnUINKoRUCJvcWUrnyQHXtZxFH9JGoosfKJVEPKmzJIjpjlUhdBclSRuLy7avzaamKnv46Ak2bSpAdODpJLBkeUbbdtHtKHARijKjqAoKnaN1xsqtKcuCQOJOhiCTE6FjXtHFzJTSeO8TKzR4VFEiRef0ieC8A5LTxzpDVVZdA1bbuaVSvVKMyeEgSS16qSnRJweYvGwWFOnYIajrDSor0n19EmdC9GQy76Ie6dgEH8kzjW1Mcs9lOTpPDidTu7S7i5dssG5dbx1CSoJNa0XZcZqsc3hn03dByY7HFJFSpaa2LnJ52bwtpCR27BQhVSfICRAKFwMR1Tm5kkgXfERrmcQiQUckEcQAbWvJ84q86HXNcCTHI6JjPoGzlouzp6zXS/J+n/Vmgx5Y8sIhYiT6wK07rzAcb/PGX30bKyTeGp4+usfTxw+JSmC8Qecl603Lo0dPmM9rPvf6L1PPzghC88E7b/KF17/J1Vu3uHh2zO6NFwnGcefVb3J29gjXSqbbU2Zrj1QxDSqUpCwLzk8eM5/PGY93OT+aIVRJOSzxmw2mkTy8e5edw2vUzZr14hznHbmUIHVqdhSOcjRiPofD65/l8Uffocp7aC/ZO7jFzv41VkcfU3+4ojUVx0dPeOHOZ9je2efehz/joZDU7Zpbe69y/eYdNs9OUZmicZbPfOXrPHz/HWK4z2q5oswLynzKiy+/xuzZQ3yzYTg9ZHp4lQc/+yneOrwSfPj+O0x6I8pccePlVwhaJ0i99eiqz0uf/gwPP3ifshqzmM2wzqBFYPvwgP7WhKMHH2CbDYoetz71hY7RJxlOtxGl5uLBPfYPbvDww3f56I2/YLi1Q5AF2gd6wz6f/uqvgw+cP3mIDEvuv/MTjJEs1i1CK5QAGQIWTZCOKpfEqPAxpxztEpzH1OcUkyGxEfwX/9V/yW/86rd42RX8r3/2h/zuP/j7YDyv/8rn+LO7P+Wr3/w6N67dRndtn7GLkHYZ0fSJFXTtmgHnU8NkOlFeulc8G2N4drEg15pzU2Pzhn5RpfNLF7uSiEtNim7pzifyYP+PW+zu83ODj3x+/0/+1SVhaKxTRCx255ZLOSYi6eeRLFlb03csRhAKGSNCBCKSn9Tw6DIGLSLWRz4ycF1filDi50+7Ox6bIFgF+KOzdB/ZHccjJ/hgLfid7chu9m/wjNIZH4AbeezO6en3GQJNJHTlAJfR7gjP79dEwdXi8jjE5wfLR7pY26WZKHbXzvR/OCCPkTsDGP1fzL1JjGVZft73O8Od3hjzkBmZWZmVNVeRpepuUl1mk3RTTVqGTQuEDViiAY+ANjLgreGFV7Y3huGN4UEb2YZkErYBQQQoWZYpcZA4dJPd1TXmUDlGZkTG9OY7ncmLcyOr2vDK5kK5KSDzVcS797537znf//t+X+KR3bM0BMmsgLC8vCQ/cdK/ulgvs25fE7h8C6r//3r9/r/++XMXlOaLBcpVOLOkQYNScWKlFNa0CJWSFwVtWRNMvKkHKxiN+tRlze7WHsHVBAT9foHKNFVjWB9vQfDMlzN04kl1oFxWjEZrpFrSViWz+YL+2gbf+wvf4refPiXkI2aN4Rfe/4AffPYZhy9KhJSMR+tcObhCNTvn2WxBCIHGeq5ducLJ2Qvy0RDTLDh9dIezswlWpNi2ZaVjpfr6KzfZHo1oTp4xf/qcqqyQXdxDqhCbLKxHWk8qMxLdopVi1B9xblqKQUFbr2jKjDzRVM2CXOWkiYa6RCrFeJRj6zkq6zMrz8lmfdI8IVjo9zQvzqYomaPTuFj3Dqq2wqA4vjhhJ1mQjA9YSf8SiN0fbLCsnjNME1QQBBtjZlJ6nIEgNDYYvIxf8xhZsWRKolJPktX0E5jXGiWgqgxNECQiI4gQF8bBdw98Sa/fp21rgshACsqmpa4NQWY4L8gSwar03YJNkAiBdJ5VWeOcZNAbMOgPqc2c/a01jEtBONJEM5/NIHhenE2ZTedMJ55pJbr6V48IBqc9EsWqbml8CULQ1AGRCJT3COGpfEWwmjxJ4gNRC3QC66MxedZDJrLbCKcEooUbGS3ojnitY5wnaupa9midZ76co6QkSSR722tdvW3KclmhE4HSsb53fVDQWNBpQp4prI2LI60FkQetcR5m8zlNbfHBYF3oFleq28AkmO79GBuPyxhDVa1iC6C1mNaxsbbB1sY6R+cnLMsT+lmOVAmJAJdIgvekWULRy3EOEq3wruXp4ZcooZkta0xb45VAiwQvYiyrV/SRKiXNC3QIOJGwt7fD2toWWEvTtFjTsKzmBNOik4L19Q2KfIQVcQKwsi7G+wIoGQjOsSwbxqMBRZ5jrSHPM5I8pW5qskRjmpI00RRFyvl0hjU1RZGRFwVXs6ZCnAAAIABJREFUruyxKudMTmcsVjPG62N6eU6SSrJcUZaW4SDn2ivbnC8aDg72qRarKOR4i/SCQaZZlRXrwzXGo3EUCkSI1v4gefTkGdbWrG9eYXI6ZzjuoURgMZmwKmuKvOB8OiXd6XP1yjvk/SWPnh8TQnQ21m3N6XROaWtaY7hYlYi2oZfleO+ifVUEtjfHrMqG6WLOolzFaWkQ5EVBv8gIUvLk2XO++PI+QWcUhcLeW3Lrldc5fPYUpfqUxlG2Fe2zYxrTxAIBYyjrkmtX9zDP73H3wV3qlaOXFazt7IK0vHrtgB99/BHv//S77O3t8uTpU4wx1CaAV3gBm1s7pJlmd2cjssOC5PB4wo8+/TFpochSGWMxaGRosd5FZ5+AwWiN+4+e8sknn1AUBQdXd8lFzr27n3N194DBcAPpHkJQXJydc2Vnl0dPD1ksFhy/cKyPClZlw/37R5wfr9C5QImSfm5ZG99gev4M00ra2mJMTa8Q7I43WKQ103LJeDDi6MlJN39KcE5SpCPadkELpNry4P7nLJcr2qZGC4f0Am+jPVsiODqbsrXRJ1OKLE0xQqFkgpcta2ubfPLxx/Qnp1S2JM0Spqs5eE3dNgwGGUnSx5gJShqklgSRAEUURsQyTtO1Qiae3niXxaRE4GK0iMDG5gbPjldU5ZwnTx+xKuc0JhCIzpS6LTkYXCfNEsp6RlO1tMKS90YUqs/x8xOKwRBXzWhMxaJKkQjOL6acTU84X5xSJBopNL31Eauq5E8/+py8iCwXvCXRkkxHJl6BY7laUNdlFxmJcOrFfBlbzpIUYxUPH79AAPtX15GVwxhDXmgkKVLq2F5lovNFKUWSxo12mC0xto2waRUrv5vaxFhWovHOs7G7wfVr+zx58CRWpBOYL5e0RpDlPUxboxOBMSXWGLKiR57GEg4vAkEPo/jnI3tt1N/gyt5VVuWC6WzCbD7HO0u5qqjtlNVoQNBjdJphnEUHT3DQ7w8iA8NGwdtJiQrQ+kCvN+jYQgbZOmxrGA6LKHLpGDcybR2dRlLhXYJpLGme4Y2Ljg0paUNLIA4OqqpE6yQ6kESMAYMkUSlKSVywCNWBS4NHBEdwASdj9byS6uU5dc5hnWOYRoGNAMFdbgskWZoBGmdjs1qaK5xvX7pKk0RHRmWIDhzZMf2cNfSy+H6CjzEx27jIGZQGQhYjbzo6XpSOMXHvAiJVsdGtm9zKrk01xsA6qO0leLo2IA1FL0OEhGJ8g55XmGyTdmFIlURqgTUQXAAlO0ht3HTJWLmDEhIbbKTuCPEy4hUIMWqHikzFJApKLniEcxjrGA6HSOXRKqFxHoTvQN/Ea9wJS1J81XindYJWKUhJ3TTYtqEYj2maGrqIiJB0TJAorMku19E2dWzaIw6BlI4uLEJXLCFixNGHrhHO+7gu7wZp3kegt5QiiiVdTFPJ6PRomhbZReq01LSNpa5rhl3T22WMTKkYW4vrwTjYkYASgrqOUTWZaKSOG03nI+Mo0VGERMq4mRIRuxCAJE0QQrz83DspCT6gkgRTNfFYhcDbGF0UMgLqLyHjnhjRC4huo9aJYiIOUC+ZSUJIjHXxcyDidyUeb+Qp+SAYDsdY57p1XRQKQ4iCEzhMXTI9Oybt50iZUi5LNq8knYjaRiE965MIzfHhIYiACoKmMahUELygWTY8+vIe88WUcrVk2N/g9qtvcf/Tj1AYXFvz5M7nfPsXv0O9v2Jjc49mueT2299g8GSd+dkx5bLCBf0yEpSmGaOtLWaHJVVZsb5zkyDuMNoYkEiwdY3Ke0ymp5w9f0aSK+5+8iccHT+N8ciQkqU9bDAkaZ9eb527n99huZyzf22fbbPF4d2PefXdd0jSgiJbx4sUDAyKAXu713h49wuk9qSZom4Nb3/jL/L7v/m3Wa0WCJWjpWRycQKi4K33P0Aaz2q55M73/4DZ7Iy3v/VzDHSKD4K6MmzvX0PMc+r5BGsMs7amXq7iZ0Zr6saSIDl7eJfj+5+hlGT34AazxRkff/+f8M43v8NyeoGQkbW3e/td7v/RHzHorzNY30fPz3j64M9Yrqa4I0ttK548vMPu/U8Y71xn8vwpvbUxo/0Bq6oC1TFMM8XkosQoS6Y0JAlK5bhgSTNNkQWKvMDYwM/+q7+MfVbz6PgztvbfYr8+4d/67vcAR/btbzFJDAmaSgXsMOXf/Wv/Ju/duEEifjLS1GnflOWKNgTWBwOOzubkhWYt70VxwlqOlyXjQY+BTuhlOYoFT58/Z2N7i/XhGh8fvmA0TlnrD9lMs9ieKS4HwZdi1NcjZPHe9RMCU/j6330lngj51b8JIBPwU1l81dcR0UIEegj+yhrkomPdEbqWtjj4GAK/MoLWBWyXd7MiQrG3C8GWhkwIFF2kNkRQPwQOlOQbo8A8CBoEHgheYELgShEYJXTku69EpAjgCOxkkMo4YLlEdQsESgoy3fGY/FcOLyF5+aofNXB0Eo9dEQsaTBDcMXAgL5lH4auzKaAEekLwc2MYqNi+aojcpScVnJeXbXLya9fg/3Huv37thCAJlqD/fCONf+6C0uHRE4QJtK1D5posiWJMKxxKSoZ5Tq4DiyZOE3JVIPKA8OCcwQtDngTq1pImOT5Ihr2cIpNIFEL0WCyX2NZinGekM5xtaNuKYBtmFyc8Oz5i1Ri297eoqhXTquXWq29TNpbz8ylZktBWS+pViYbYBqISkl5OoTRf3vso1tU2JbUxJIkmQbKqaoxaMdKK7Z09/HhIMJ76yROUU1R4CBYVIFEOY0oyLZiaWCvaliVBKrSWVPOa5eo5g2EPnWiq1QpkjQ4tadpSL+fMy4qNdc3R5IIk22FLLrB+RZJq8iJFuATbLkHE6YjzsSVkOm/pbR+wpjP6Q0FZntO4hrZ1pErjTUliDbr7UroQQAmkgqbpqp5lIFiB8A6VRjVUOEUbCrJkRNUusK5EidiuFjA4IfBOkSQiAkTVkHnrkSIupAwJufAdJyEq0nVjX9oBvRUU+ZgsLTAq3hy0EGyOtqFacTKbRIuxiRXRoyJhOm/5N375A/67//Uj3LKJcExHFI+aBuECQQYSEU2PSmqCFJjGgALTWrIsIUs1Koks/9YamqbFe0FoJb1eH6Eh0RKPx0uBQJNKjSMCOhGeICPMNxGKca+HdTUIRWMD89U8LqQE7G1t8/zFc4rhiF6acFE2nJ6eYmyFlx5cwJq4sOv1oqBkTEOqUrY2tnh++IKQRCt27QwqRMCrFhISgXcRcB58rNcWIrrm0rzHlau3OJstsQaS/jbYM7xyeAJVVaN1CkqihQYRF7SmWTBIFUoE8kRRmhhhq6qWNEmZ14HbN3aZXMyomipm5q/ciFXjMiGgqZua6eSM2fSUIh9QN0uu7N1EZz1EpkBrrDGkHYcgKzJ0kjIYDSiKnOVyQdO21G3JoJewtTamqUsuVhe0jQbv2d7doK5almWDC9C2VXQAACG0rFY1L16UHBcZWRKBwo8PV5wvVhxc20WEwJVr1/jBH39CrgU7u7t88cV9ev0eN67v0TaRwXN6cYEDRj3NvKw4P37BfF6xtdPn9duvo6Xg7OiELx8+5truVdbG2xRa423Lwd4OeaYQKsMTGBSDGOeqKoKEYb8gVSlV22C9oTfso5OU5eqimyoJUq3ZXh/hg+H0YoEXiuGo4PzihHQYkGScnE/YXDuPLYFt11ATUs7OpjjvGK+vMxyuoZXn4vwJeRLjda0vub47xhjJYrZgY3eD9vstzw4P2dvcwNuWL588RWaaIhVgLLNFxbW1fa7u3yAfj/BScu/uYxpvKGcVVV0hHZc9pxHa7xueP3/M08ePmC9KpJK8+86bnJ1NONgzvPbqa2xtraOyEb6RDEdDjp8dUvTXefr8iBs3bnD64hgXUj6//5hPvrjLaDhEyJYgDNmgx+Zajys73+KHnz2mWp3xxuuvUk7P+N4v/jyHh4d89Pkdzo9PmK9WiEThTXSOiEyAzJDCcTFZMC9fdDXalkERkB5s0LQBXGuxxqDnLjK+MoG0nv2dPebLJYNeQqoqLs6ekCYGTGBhagqGpJlCFQUqSVCLJYmGJBHREZJl9PubnL94ivXRjVrOG177S7/EH/zOP+CtV9/jYjbj+PQR9dJgm5S6XLIoVwRvsd4SlGS5mmPahI8+v09Ztexv7zIcNRyePGExWaAGkqxIqZsZWsoIuZ6ck+qUsi6ZltNYNJAOGPUGPDk9IuiKqixZNCXeO7TUOC8YDteoqprVakFZLrDWd6KQp64MJy9m1E2LsZZ7Xx4RkCgd3Zc6yQhBkCaS8ajPdLJkPpmjksj6Cd5TrhxSR3HKNA15vxddFkHQK1KMGcSBVldzvpov0DLeT0xrKKsp1kl6/QFeaZw1jIZDgg+sjcYoBcIFpos5IpO0pgU0QklQcVCxPtqi2m6oTc3dO5+xt3cdq2aMxjvY0pKlGukdynjI4kQ4OGhMwBMHMdZZrHUoGWjaBp0qqroiS1OyJKUyLSOlEErhmqaDtyS4NkE4A17RGo9tWoJKaUwJNmCQcZCVxSGIx8TYl46gafB4H0WQyKwREZiNe9lgJhxRwAmRySDVApXIbmkqcN6htUIqRUJsIQ1YJJFtFVqL9wIpQeiOeeN56dyAaBmRKk7SpRAoFLWPzIhgPc7E9l2pFCY4VJLQ2jaKNd7FBb7znTO3+9kdN8cB3hkGwwHTeYVOYkxPoMD38Ou3aKsaj40b0rrFmjoKEzisj8OS4ANCSZzxpDrFWktWFNFF5mMsTgC+c1VF0SXCn4XwRH0ruuKiQ07imoYQQnQWB0+aJh3bKMbR8RFWrGWKFIq2MVjb4kMUsKq6JknSyAZR+lLv6Frp1Eu+EES3UmxhU93ezUUcRQgd5F2ikviM956Xr790RsdzGyKMncg5io6kGDm0zkIQnViryNKsg6VbWufi66H7XRprTcftEgQhYgGLBB9c5JN2ovElvFxqjZQqrrc6ftLlNY4w8CjkFEXRncPI5DJde50QvPycyDTF2uj8BiKPiXjyJNF1RdcCFSOVohPCfBTiQsA5R5LFY5Rak2YZxsSYplQxnmeNI0pLoHXCzv4NdKqZnU/o9XtkWhN8ZGxJBDrt4a3nysFtVmbBdFLyyhu7PPz0006ISrg4PWZta4f9/Wu88db7bG/vUR2UnD17wI1bbxF8yumTJ1x7803W1rZ4enEPEQKD/hpmtcRbw2TW4oXEB8H8YsJ0OuXk+IjZ+YSybij6Cqkc1apE4giupa6XzE5PuZg+4/nRA5q25dr1N0iCxnvLzpU9xpvbPLB3ef7lva4Dy7G5vc3JUcNoYwOZFHwzSXj05QNc2zLoDzgOjiTRFH3FZNZydWcfmiWT8xMe37vPxv5tlLK88cGHvFX0kOWSJ4dfUq6mfPrDQwab+6isoOj1kUnKjbfeJS0U26+/xh/8nb/N0eEjnJB8/Ed/yM/80q+yXC04n07Z3t7j4cc/5Mn9z1nfu046HJBnKfVKMDl6xuH9e2T9BNM07BrD7s4mWdojTwT/59/5W0wvXuBEYG/ngMV0zmQ+4+nDuzy6+ynVaklvZ0w+GHJ29JDnjz5nMj0heMfNv/Ahx48+xbdLGuNZuQV5b4Of+uDbiNWC40efMdzq891v/ix6z/NnP7D8yr/961z8k99FC0lrPVvbAz64eZ3WGH5w54e888abXNUFD+5/SrK9xTAdcH19k7KqaJxj1OtTNS5yBEOgX2Tcu/eQf/A7/wd/+Re+yfvvf0iiEzRR3E+VJvWB7//R7/Er/+IvMr66w+5wgBGeQl52PRIHnLKTgcJXbLsOTNbd26NY8XUBCS6NMyIaGEL8tFxqHVLAWPFSnhJf+x+1h/dygfKXkk14+QwRQpALwQe9ONwT3T4yiueXzqoocghxiddwiCDxQnIr8eytx3uCiZJZx1+Lv30sL9vVBJdofiliRO0/2IREXwo2HWcNeC0N7GpBJjsxqcvI6fgO+SsjwdJcNnESOy0IBA/rOezo7ndeinHdMW2JwDvDEEUsBJdtngC7MpDldM/zr0t+XxeTvnq9EAEDcdij/zmPvDWrWBUcBBRpwta4R7mCxrrY6lY3OAzeB/K8x1Y+pDQVIstBaYJdcTqboLIx1/IUn0Uny4vTSYRx10su5jPyfIBUgrKK9e5ZktLv9ZiuFtx9+pRlcKSzOX0sn9z5ktu3X2d7NGY6K/GICMxzDqEEyglGa2PStqVwNbPSoESGVpqgEppgCa0j7fXpacXZ4wf0dMrOjVfYfv1NmqZmcnqKeznUkORZwqg35Gw+Ic0ygnVMmhVFb0SmJKKXU9aGLCvoZwVn50dI5oTQELxFiT77V26zWp1hvKRuKxZiTiYDq1oyGm4wXy6iU0BGsKQQDkKLkpJUtEznM2qzJHiJFkmsQPSCvqoItiZojfMpPmIWokYbJAOlsSGKBvioq5omYBqFGKQx3uMNiXIUuaBtFVkyxDQljgadxk1EYyw6GSFEQ1KklOUUHxxSwTDrs1jNSDJDIgf005y6dTgTM8VCBJwQTC+WIDJOjloap/FeoJWlaizOaG69tsb7715j+Nuf8+SsJBUJWZbQhAasQCiIX1kNNtDvr1PXK1ziyRMdGRsKTLBU7QrnfISMW0OiJUU+ACcw2uCsZXdvhyTNOv5EnO6BRCOogmFeXdBLcnb7OYtlw1lp+fLxc3RPsTses7054vHRBTrTHGxtc3h8waIxrFYlSR4XcV4FrBXgJbb20EFHrbVUqyV125JLic4kbenINNTeU0hFluSoLKGtWtbWxtR1Q2ssUmpm8yl37t+hqVfgNTY0BBU5T611YBoG/QF7m7tMpitOzp4ThEUpSWlsnCzIhDztoJsyQkKNqylXU+ryPILk8x7lakHRl7R1zWq5wGMZrW0xmV0wWV4wq+Y0teHmzTcY5GsIZ0h1QiIjmk8rDQlI4TFtg3eGtilZzKdMsZweKZxxBCdZNBVJmnIxmaGFpnWBZ89e0Oul9Hs5WkkupnNs16RzPj3DWsf21hZSZ7z73rskheRPv/9DNq+MeeOd2yznE3Z2tpjPZ1zZvUrbGo5Ozrl9+wZSCB4+PiRNNINeivOGjc0+prVo4JXr13CNpf30U9KtdbbW1jg8OiLVkndu3eLLx0/xwXBle5vnRyfUpUUnGYk3EDQyyRinKdtbW6xKy8X5jLaOgGYlA7t761y7ssfR0Tl1s2RtfRvnPNcOrtA0NZaW73z489z98Ue0pkIr3UWuLHmWoLKCtqk4fH7MxvptGhOoKtNNuRsONrf4+McfUaSWWWtQueLF2RTT1IyKIX0Vwc15mtA6x9Gz54wGQ6YXK360+ILPv3xAXa9I0z7Hqws++vRzkiQKpMZbtJQUeY/BeJ2PP/kCRGBna4vHx2csJ1PSosf6xhrLeYUTsmvq7KN2D3jw7CmZzrhxZZeeVrw4OkL6BOdqXrn+JucTw4tpzcHOPj/1+k9RlgJTBpb7fW68usNiodnZHtPvCaYXU3589w47+1v4NrJSZrbBJ51NXioW8znDYZ/5ckWSKqQz0W5NIFUC4RxKRweKkGBsIHjN+fmEZbvkT7//+yyXJ1hTYawmo41tWtqCkZTLRVdBYsgyhRbxPq7VnLYuCWEVN2NSgxd89Md/jCnnPDs6Z1WVnOmG5aDHIBlwNd+irS1VU5H3emR5wWQyxxpDVdU8Pz5hNBwzShI2Vpus6pKL82PyoodzltaBUJ5RP6NpBPOypGotG/kYJRJOzmfMFzVZ0aARZHmCbSB2ZUlE6pDGUrceIQukNhA8TRPB9jJI6tqjkowXp1PyVGO9Z9xf4+r1V9AaApb55BSpbATkWofS6VfLIhFZhUJKekXBaDji4mxCU1UkOro8dCKpypoTe06eJ/iuBUt0LunxcMRwNODFyTFFL2cxnZMkCd61oAIhGFzToIKOrBypWVYrXpw+Z1D0MW3L5mjE26+9Rm+wyXTWxxqLbVeoTJJ07hXvYhwrBIclYGV0pFjrqCvDoG/xbXSl1a1hNO5HV1mwHTNIE6wlBI1QFmOalwBt7z3BtKAM7fSCjfVNkkEO1qKCx7k25oe7Bi0tNFooXHAv4dDBRZcTQKK6qnUlY6trUDg8UmconUbRpJuaah3dqaJTNISIi2CpBLY11G0bmTpSR+aOVFgXBSqhFUmaxXgVMdLghQAlEKITQUTceIQu+iuUwNUmnlcfP2vOWpyxHetJ4LzFOcNyNiXRKY8Pv+D05JxXb78bm3mcQHZOr9aZ2E4lAgpBYyMofb0oqOsmCpjE+FwQSXfccePw9Y2T75AN3rvufcUlu1QyuraERipwXnRrBGi8p5enyCA6YQmCjy1+xvnI9JJxw+RMjRKRFaWTBNEahEoI1nQJEt9FJh1ZVhCs7zZasSmvqiqs9xRFDy2jO0hphTMSKTVa69jehiDRaYR6d44pZyMzSQmJ8Q6VJngbh8KX8cQgBJVpkTpBSYWxkcPmnesExID3seI6ioga114KPrKLTsbI3VfurMjE0t05NtZhjSXNU1wQiMuWNgG2tWyM1yirKgpqnZtKyK9cZEonL0HmWusI9/ceoSJiIxIrY3uTt7G5lxAh4N65l+BfhIjCZl1FVlSHyZAitsoJEYUlhSR4S1lXzJcLkiQK4L3hsIsP+c6tJ9FZSrVY8OEvfpfV7AXXX91lNMhRLqVfJFx96z2kkGxu7xCMpdfro4Tk4PYbXD3YxzlwxvHpj/6Q2/1v4LwgKQpOz44p8pxESfJ+Tv1iSpezJmjN7o3bPH/ygL133+PLu1/itWI1ryhXCwZZTtXUmDDlyZdfsHl9n6qsufnaT/P2u9/iyRef4Z1jbeMK+WDMW99c4x/+xv9MyARnh46qbjh46316a5ssTx9x/e0PGG5c5fGnP+TOD/+MixdPKXoaleSkyvHk3idUF59ig+X80QNuvfs+mQn8/K/8GmcnT/nN//6/RsiG/jDlYP82r7/3F9m9eh3nbIzI4piezXj9OzcYjDfpnZ8gs4K9q9f4J//b/8hq9oSybdm9epvF3GKC4vDJPbywrMqa/voWZy9O2drdJckFpy+O2N5c5/Vv/wKrWjKfXPDoxz/kmx9+l88/+oSt7aucPn5IVa/44uM/xLcB1zaEfo/eeIc/+Pu/xcHNm7z69i9w8eKYgzfe4ezJl7hEkA1y3v7m9yhe3OWXvvOX+e3f/DvUbeBKrrg+7nHzvXd572dvs3fjKqvtLSSeNM1QUjBeX+e3vnjE3/wv/yb/w3/xN7i4UPzm7/xf/PKv/itsFWOcdyRJitbRCToeDjs+mePZs5L/5D/7b/nuv/Qt/tZv/AYfHl3wxacf8x/99X+HdLiHC/D4+Bmf37/Hz/3MN5jOZwyzHkKkFEK9HCbEr2gHuX75NOiEixBAXoo+McIbuFRVur2lkB33SL68h9K1mP1kef3XRJFOd4rPsa/+7Stp6dInFaNeohOaXt6nX77PS/dpbBlVIbqPhgpkCF0sMEbBQpzcEks8u/gaIjbiBY8SgVup7NhKgA84GQWlNSlYk53TKnQRvo4jB4L3MvAZ3cDh5VOlg3bHo/r6MXQpYF7XgldGgZ66FLAkuvvvh5nAbsQqBSUuvWOXx/81Uam7hBJHFeV+UP+cQ7mliA8SAri2YTaNm6VY6ye6vL1F6rgQCErxyu4+VVmyKlIKBE/nc67vjzh+8pTRxiYjnZAgmJQ1w96YV6/vsjbocXJ8zHnZkqiE7XHBolwxHAx4+613kNPnnFuD8ZbzxRllVaKFxHvDyswZqyJmw4XHhIB1ksXJMbu71zGzIxZtzWAwZrla0Zg6LixxrIC2Njx7+oh0PGZje5PNg33mywuU8chaElIQwVGtFmiZEPwSgac36LOxtk4vT0mkJNeeXtajl2jO0cybjBaHpk/R20D5lLOLktFwC2+WLP2IZJySFpZct8xmpyhSclmz8LHWVArF+tjjxYT5akXIirgoVzCdLcAHaiNpDRgVv8iX9mDZWamttwgZIxPea7SwOBHwPsF6QWOmBGsiMFj1SLIEKT2hsQjhcU5isPhmSl+MECqBoJAkKFkT6KOzDaSZx5p7JTAhuo60zlFKY0wdGVu+IklWpEGiSFmsHHkqaIwl0Yr9vTVkkGiZM+wNKNIMpTJO51MGPWhsRT/rsygNWsfPXl70WMt6JNLTeIU1ULYrnGtojess6HEh2NRLzu0CIWON8N7+PmmaslytaK2hLJdkWcrJ+QuaxjCdTWE4YntzDZXuYXseuappzIJenrG/dY26trz3+nXuf/EQmWrscoWVkn6SYqyNHSvGsz4sSJSibh27u2MmswqdeG7e2uX5s2OCKgjaE6SgNSbGIb1HFwlt27AqS6w3EV7rBW3b4FzNcDiMXAWbsrO2wbPn96CLHsgsw3vBqlqSJBmBBJ0oqnZFqnpI53C2jO04zmFcrPMtq4ok7yHKOSSS2fk5Zy9Oma2m5HlOkfcYjzZ47dY7PHjwMVYEZuU5xTBHSxgVPVaLJbUKTC9OCd7GCaGIE24RPEWmGRUpVVWxnE9JVULeX2M1NdTWwsqRZDnD/oheP0clgtlqSVOVICHTkjyJNuuDawfcvv06WEvrA/fvP6A2ljTPGa8PuPnKNtUiTl/393Z48OARWZ6RpAn94SByw5IIhfXOsT4ckg1SfnznDheTc46P5+h8wOlkjtInfHrnE64dXGVv+yoX85KyXnDz+gHrw3XuPn6EqxqKOCNla22d9999B98a/tmPPsaYKG4KJHmqGA8KDp8d8vzknIBntlyhVMLWqBfju6s5G0PNqpqzu7ePKix7e7t88dld3nv7TQhw595DTk4v+PEnn/Hqq1donGQ2q/ngvZ/hfFmzXD3FmDGnL07Z3BpwdnLMwcEeL05PEVqCB+MEQUvm9Zx7D+/TGsesXNK2Db1+wnxqeP/9n+bq9gG/94//EYmOJl8ZFGuvAAAgAElEQVQvPNYanj8/YdUYpBY8OztFCYUWns/v38N5zS/8/Lc52N+LD+RgaE0UF6/v7nD71Xdo60+oU8mjw+ds7q5jheP0rGJ7d5d+yBgO1hiuJTw+esYbt97iy89/zMI02HcCeVaQZIorV7fY3T3ANQ3Pz18wbxdIIVlfW6ctLxC5wtmK7Z3rzCdHZKMe5apCJSl4TaIiuFcpGPXGhDqwqhwtio3xBheTZzhXgRwzKgYMeoZnRyfk0sWSLusIMsIVjTPkaY9ekeNVRVW3EUzrQcpAkSVcnDxDpRmL8hSSdZxzVNYxmTwmzQX9IufgYIPxcMz25g6///0/42wyjfMxnbIyLeVyiQ6SvkxxSjAtSwaDDZYXF+hBjkoUtqzpZQmgUUjaumK5miGFp20DSuQoFMKXiDQlSQRXD67y/PAZZ5MlbePw3bpOaR25MwI8kqZpCU6Q93KWFwuu3upRtjVPP3vAYFhQLRdIHN5bkiSl6GWkSUHdxCmflpqsX1Cu6uhiTROKXg/nA9OL6HRJelHQGIzXqGoXgd1SIjSsmprqvCUvesxmC5RUeAnVssEHB0qQyQG+ia4zrRXGGo5ODxn2RowHa1gv6A+2SZQky3oIKVG6jxeGWT1jrEf0lAIV3RBFr4/UGi0Esj+G4FmuZvgkYdmsUMojpCGYBOE9Pgi89bS2QSpi0yjRTezbhmANMgRq09DaFt1LI+MmS/DeYJwhyXJEB432IQ7QQrcHkDJQtS0iiOgcQYAngq8D2BAjeGmWExAEa0FFsUPI6OQl+BiJIvKNovPKdlXtsa1NdG1doQMoexch7bJbI15uDNrWMhwNo3tDKYx3JEiMNfSkILgQ3Wkhbg+8c+A8jTWAJy8STk+P8OUZRZ4yyPs8rabkKgKcQwAnOjFBSPKiT17kpHlCW1b44Hj29AH3HnzBeH2DN9/4Bt4p8kJjvOmaW10nqHVBiG6jYG0bh0suJUk0KmgaW8f2N0Br+VLs8B7SNI3xQi1xjniegsNeAtJfCpKOpHPDWGuxziGsjZE/EQDf/RxNmmQ0TUUUvQRplrEql2iVRpcyHoKPcGZfMij6BATeG6SKHCLZHaP3DiEFSsoOjC1QUmNsA0oQbNzlSBE30/3BEOtMBLz7CNFWSYyZBeKG6NIFVTerGDuRgNI4azuXk8MLTVACkcTz5a3FORvh3t1mTna8TykEWZZG3qBU0ZnefTYIEdpPB9P1oRPpRDw+2TkPvLdfvRaBl5cx0dC5oWILrQ8eJePnO4RAkipa62O8OU5ioePMCCWpqorDRw9woUa0hsn5jO2bb0DaZ9jPwXiKLMeZmiRNWCzmKJ3xwbe+wfxsytWDW/zoH/8DNnd3GfTHSBdwsiaTUVDNih5eK8qqpj9OWdu5xqMff8qbH3wYh50+wuETHfBk0Q0VokOx1x/S29mj/yu/xmK5oL++wz/73b+HSCRNIILyQ0C1Ja23vPrOBwwHG1x74118Y3loPqZsDQdvvEeaZUwmFxRFn5VvmcwuWC0rvrl/QKIz0IqkN+LW6+sM8oQvP/sR8+kxrlkiZYoMUOSCanVGb/M658fPorCK4ejJfX7vt36DZT3j1Zuv0Lbn3HjtHSbHzzHB8+p7HyI95PmYWz/9syyPzjuYvWO1POPhFzPa1pBlBpX3mZyfce3GBywXS1bLc5xwFGsblKuSxWTKeDzEr2a0dcPDTz7i4Sef8M43/iLXv/M9tm++yY9/8E/Je9ucT+YElUI5Z7ixz/iVq3z22Z8RvKSpG77zL/8ae6/c4k/+3m/xl/79v8bv/N1/FIeelefm1j4bt36BH/ze7/Lsf/m7nJ/PCSHl2H/IR49SXuSK0WiPs0XCeGObRgX++OMnfPD6Abt7e9zSS/71v/rv8R//p38LNWr43i//a/zM1VcpdIIUgqXwOKsYa8HhbM7eWp+yqdkd9fjrf+M/5O/+7/8T9+/cY9L02Bjc5D//r/4bXn3jdZQuWDUNR2czThYr1iZLdFLyyv4+k1XF5qgfBxGxMxRwBK86Tl104MjObRO/f/IlysddRoiJzCMvQEkHXoEMeB9VDnXJORIBGwTipTjikQgsIHyMwMZ1RVdKEJ9OgOjEl68YTZdijHgJGP9KxKL72YTOEXmptoivBcS6zJrgqxha1GskaSeEie74JKL7XXSupMv31NlIPRHYT2yeexmTvRTE4kY83se4dChdeoogE4FUf9XcBnQFH4GRCDgtO/bcT0puX405wlexSAI1gr73BPmTUt7/3z9/7oJSbBWJN2zXOkrjGY4EWklcN60rG0+OJvgKEwZM5lMWZcvm9g7L2TN0PuL49AWLeY1+cc7GYMCtq/uoPKGfFgwHY3pZhijnPD8/ZhVgMjX0e30an/LKK7eY/yDn3Ho2tvd488Z1vnz4kJOLc4IALRKcdTgLxnpccNSrCYmUWC1JScGWJN1DYthbQ0qHDArhAkbCyWpG8vQJ2zvbrO1ukz0ZIuanGOXodS0PSdGPdcVtCSSMsx7SNTSVQziBsy2mKZnWgqZVbPR3eFYeUvsEVS0xbsbB9nUOrl1jqCXPJhUqUWhvyOQ548wxF5DnmqyfM5/HBqjWQusrsqzH1c2cZ+6c44nAJIFxntAIQRjm5Jkg2ATvUpwLaGJzjnGeQigUYL1D5xpfGUQq8W5JaSxFoiiNRNfREtz4Bc7EVhOdBFwwCOGxTUWSDbH1Cp063lhreT6ZMpl5lAwo0ce2CUgXY1be0JiG1kSAMzJjMAi8vp5xOGlpRE6epKTGMShydjbWeHR0yuH5CwIJAYUICQMtGPcCsybH+cjZKcs5/b5mvpphjUUoh6BAyCJORF1shguXFZVe0lYNRa8PSPCSsjWsmnOOjw6p25pFvaSfF1hjKcs5UilK27DwCXu711CrmtHIcnzyiP29a9SNZWPjgHpVcfPVt/nBJ9+nJXSxgQItwAtLOlK8en2dQTHkzsMLXr1+k+OTZ/zar36Xo5PAP/ynPyDPC9rwlLWepnneYBpL5QV1O8d7T9MEtHJI4VHZgP6wx2tv/jQP7t5lOFBoXXD6/BHegTEwXEtYlBesqim+tgQhyYcD0iShaQ29tE+5mCPQSJWiE4kro+Ca2R7ewXBtg7qZc/jsHo2LYvLa+nW2NrY5fnHBfHnBztYuaEEx3gQRgbeHT+8xX76g3+vhnOF8cU6eDmJDVxMItmVzY8S1g6s07RLrDFJKZvMpKkm4vn+FZbuiKmMNdxZg0C+YXVzQyzNGoz7lcglecuOVm7zyxmt4GyM+hXT0hop3t26xsTvg2aNjfvz0iOvbO/zst99DhYSqrTk5O8eJ2F443BhRtTWL6TK2F+Y5jU9ZzSuq1YrVdEHR79EfDXjw5B5vvXGLi2Xg97//py/birRKGY3W2VsuKQp4+PQZJkQnwtH0iMnpC1pfc+3KHtPpjJ3tMQ+fPqexdJGSgGsN81VDqnKubO/wyrWrPH80ZnYxR+djjo6fk/W3cU3JrZtXefet1/noR3dp6obxMOedt94kz3JOXpyRJ5LVZMGqbuj1+9y8/Q6L+k/Yu/IKP/jDP+LweY2xMVaihcbbyBDzGGbLCa0NrNoIjsU4UJLZomarV2MaQ5bnZClYB95ULGeOIilog2W1qikShdAJj5+eUbUtZ+czfv3X/yq0lv2re7SLmlFvCIni/sN7zGfnPDo+ZDafcWVvm8cPHpGkPX7u536JvfGQcpVwfvSCu3c+4f4jxXzygrd+6qf5g+//gFf290hSQyIt0ER3mYhsuZs3bvGLH/4MP/ro9/j480dUy4pB0kJIEfRjw7p3ZHmOa1b0i5zgWqazJSIZxEiVM+zs7VLOSnb3r3B+PufCGGbn5wgdN7K2AZm6WD3rA94EkiTHW0FVOxqnCSFHCIFxsRrcU9FPNN4qlG4Y5Vt476jMksOTp1SLGq0Vr93aYntzh17aI1EzCIqT6QkitOyOtymrFVLnDIuCZmoZjUZcv3qFBw/uIIWIriidkKUDWtNQ2YZl3SAzibCSRdOSKtje3WQya0gTzd7OdZ48fYFWGbP2AjxIIckyHeNQIrpurLUkWdo5BQQnx6dkyYS2rdle2+SoXNAv+iAD1sdK8IBisSgJPrC5u01dVeRSdzFdjbE5UknyIqdtGxazBeubG8gsY7ixzqpuWM5WqCRjMV/gvWM47NPUFWtrY7SSqETSSwtW8xbvAk1rkTpFJQojHVVTkWWGJCsQPqHf6wOOLZ1inMFKR7AO7TXCxmYo4SXCW6pySVV3QG0RYkW8zkh0wWpakwhNopLIV7EpTVtRtyXWOmRwpENNsAbhiA1vQSKUZ7Ws6Q8KcLGmXXhHWxlEL0d5j288Mo2iNyKyIC6FvhAcUicE7/FCRwakVrEoQSmcdWRZGpvWAjgbkFrEWvquyt1Z24FeFbaJ5SJJkpAkWXyeq9j4KkV07DhjUSpGqhKpcdi4Bmtb2rJCpRqLRQlN7SxZlnXOjvAToFPvLxf78d4rQmBra4+jO09pLz6lXb/FeLTFcGMtuh57PZAaJ3wnlHgm58fMJxNSc8akaZiUjoePD/n2h68gg0Il0REXgkSnGu8CwV+6qUIEcytFfCuXE3yJEBprHUWWxKhcltFWsZhFdY1080WJD57gicBp67CtRaURxB1CjGh5AkWWs1qtyLKcJEkhuQR7Q9PWcTgku81JF/0SCpq2YTAsiFHHeP+QUuNDjIqhFL6JUTPVweNb0xJcQOkY/yB0mxwfBRYtJFZ0hA8Zr4NQEtf9Dtc1J6Jiexudk1ulEUjirEWIOICUHloXweoeSaYzkAoTAt7F9k4fPFpHQa2jxcdmO+tIiyyuT5V6GWlzNjZEiw6QrmTXHGsduYybu8uYiXPRPedcdCTIztXgrCfJLlli8e+SNOuaHkUXbxTYzg0WnEUlGtdxl7J+n5uvvcHhwzv86Q9/wGpZsf/Gm8zOj7n3xRG3b7/NejbAWEPRKyjLBe//Cz+P8pLRcI3p8oLTyQuefvk5b7z9Ab71sWmyc1/JRNBay//N2psGWZrl5X2/857zrnfNPbP2rqWrqtfpZhZmYRgx0hgCIctgjCSMAissbGSFFZYtEVocHoWwQCaMw8gyy0QYgbBMhBAIwcAwMMz07N2z9TJdXVVde1buy93f9Sz+cG7VEOGPnvySERmZN+963nOe//P8niRSCCt57t3v44u//e8pLg1oHCRRCnPwuxQBURxhG8+ci9KEOGvREpJWq8urX/4solE4YQiMwrgQ6zRpu4NQIWmrSzmrGO7t0Wp1mE7GLJ9YZ/PGm5y98CTnnjjP4tI6o92baGtIuy2efv6Fx0JpK1TEMkBXBQ+uf52mybGEzPIpUeIbB+sm5Du/7/vZe+0t73CsvDNxMt5haWWR/aMDMmm5+caXOdrf5eyVZ7n8/LtI0ozF9SUWT/T543/769x7cB0RpjhTkjcFAKno8T1/6a/yzVde4YnLT7G8cYo792+xff8G1XREox1xS1HqKUunzqHv3eHCe97P4fW3GB9uMx2NaG+cRd6/ThgIzp69yOqZ82xdf5ViNuHe/bs0jSY5cRFz9ixmZ8RbX3mVRl1hp3gno+htjgdbiGiR/Z199v7g45Q6wy79AKPhTRLxCcr2RX536zRqN0UtAXsBk9Etivufophd52O//DOcTDOeWe1TP/ssv/Krr9N+8Dv81tH/w2h2yMHAIhav8gPf/TzPLiiODfzsr3yCtQ/+INd/75u8+ye+k7f2nuCtW10ilbJ5f5PZSsbs4C5vPRSE/csc7H2BC88+z6Unn2at3+fLt0bcaynORyFLCEAhhONaAadTQXsOw9Y4psbjNyQCKeZrkHN+j4pA4h1Ac7CJl1Dmrh/PFrKPPTXgnT5CzNcfEWAx3l07dytJ7Hxt/BZz6f8Tl3Pgi4O+9RugsY9ZdWDdPIY9d1A+kqnsYzHHO6+ssL4NMhCPRZlHkT8PRA8ei1ePnE7ysWvKO1lF4B/745jgY+eWf/SBe+Qscv4+f+vpmP/8W44s5tG8R1FEh0AKM68cmAto82f8z4pS/u544St3ENmGPyOffVu+5Ec/+tFv6w3+4i/9/Ee1NtRuPhkQlqLQQMLqwgpJHBKEgkY3WCmpypKiLBhNpgyHR0wbyeUL57FljQ0UsUopm4qqrihmBdOiZnmhRSeJODzeY2daEKk2KpD0egt0F1b4jsuXuHbrdWYiYXlpgY2NdXqtiNF4TKRSyionEILB+JggDB/XjVZ5ybiqyBt/YS8aTZp2SWPPO3mkIAolyZRkJWwjleTu1ts0ZUNe5dRYDx4XvmViVHg7bxJ3iWNFGljqpkIFkko3VGXO/uAQoQT5bMSsdjhbE8dtimpClLZoyYiqgcFoB2c8ub9udtE6R6ouiVpBygylEi8eiBCwSCxVXTAzCd00JcsEC10AR5ZF5HntRQEZESqJDHzlrMHDgBunESpAWIE2AdZ5i30cJigVUc4a0jij31nwFZjOk/Kt0wjpEBJ8sUjOdDJA0ODqhlFl2BtYbKNwKOrGEs6nn1porLBgQ8Iw9I4pGqwNmOSGJGtR14Z2oojSjO//7icRecO0EewPBoyKGY3VWFHhTMWkaCjqBpyhrEtMbRHCoQNv/S9qg3ENha7RjSWOYqQKsFYjsPN6WT+xnRYzptMJu/u7ADSmQsiAsvTuqkhFLPX6rPRWiOOEotH0en2qqgYsx4NjkJLbt3eQYcS1+7fQZeXtnNJvTpaXT4AIKfOS0TRnUlo63QVOrCygS4mMBd2l0xzsDzFNOTePCvJiNLd2zxdj7dlg/bYkCv3EW2Gpyprjw010PaNoCgwQSwtKECpBKCTKaayLabValFVJgOXZq5cIcIwm3sYdBIDwE+RWq01VGo7HQ5wDJWOCMGI8nvkpqLEoFdLtdnBOk5cTtBWsLiyzsrCBNg1RkrB3uMdsMvRwRhuysX6OcjqmKXIPWW+a+dTQ14hiHYv9Pk+cPMnZJ86QtVpsLK5y6sRJuv0OKyt92nFCEkd+I45i48QJVlY3SMKETrdLt9thsddHiQZDwdHePtsPDxkejYhkQL+dcnSwz4PNLToLKZNihrWaLI2ZjgtWV3t0+ilhlHJ0MCRUAm0c+UxT1SVLy2tMiwGXzlyg3V0G09Bve+6bcAHdzgKB0yyvr3H3zl2iJGQ6GTM83KMUDbNJQb/bwYqUg+ERQaApJjlF3iCUQMmGMIqpreXUiZOcOn0CI+Da9bfotlvkecFwOmQ2m9Fv9zm9fpbxeEReTOi0FEEc8nBni/F0SBRnnDl3iVMbp9l8sMXWzi6RlRwPRlQmZ3l1nZ2jAcZ5QG5ZeBdKIyTTWU1pK4JIEIoQrYFG0Epi0qzD4GgbcPi9e0AYCupakGQhUejF5JXFVR9AMDUqDTncO2JlY41WK2Cpu0xpDVXdMJtMePrZZzh1ap3bdx+wOxpQFpqjoylRGnN8dIRKu/zhn/wpdXXIYHDAsJxQWkVZwv3NO5w+9QRvffNlpmXOw71d2p1FhIgYjsZYQoQU3Lx5m8G4JFAhdWWYzWaYwhEFCVjjq9KdxpkQaQJknOFkymq/xzNPXaDKcwpdUuYzrjx5mdngAWmU0F/fYDYdUOEIaFDSEsoAiBCiw3A2AhxWhwzKEisc0jrCKEVR08oCZJBQHjvavS55fpO4FTCdalxjCSPJYDxkWtSYWnP14iUSFXI4GHL+7BXWlld5uL2LbqC3sEBR1eRliRG+6a3RGmu8C8RojTWatJWQRDFoTWMM/YUl3vPcBe7e28IKQV3D9tZ9Do+HVGU5PwjOWXlSopSvgu/2WpRFhcMDhJUKiSJFtxOyuNRCOV9lPxrNwDlCpZjNRTKrfVtZksWMB2P63Z6PiVkx59tB2ornTBOJrmvCQJC1MgSCqqhpd7rUdYVwgrrKSdMYgaDbbeOspdvqErVSjHGUGLIso9deJI3iebOhRtcanOe7VGVFVZe4usA0FUYLMimJEkkSxvSW14GA6WyKUAFh5NeiSAl/+BUaqw1pFKAC5d3ccUDa6VEXOfl0hEURhn7Yo50hCCzW1UzLikZremlCmKbYJKOeTdHaEkrftGlr7QMIxuLMnKjtLE2dU0wmNHVDGEVzl5IHE3vXhmQ6nJB22o+ntASSKI6Qwby23RjQczeLlOiyIS9902oYRQhj0cZ/ZpvG+Kic1qgoREmJM5q6qTC68UULUUKr1UZKSRSGICDNUnTjXbaBDLDC+evA/L3pBCRZShgoRCgJBYjJhNnBjItPP4sOFMoKwjpHRin1rCCwhma6i3UliysnMFqyeOICSbtHmiY898I7kDJEBSFCeNErQGB9S8ZcRPIjeRn6JtpH7B0sNFVNUc7otFoglBfnmtqz1qIIrGEymdHttLyQpBsfT3GWOIlJkhSjG4wFg6HXW2A6ns3B2fP3tvbx+KoqiZMEFYZY4w8IYRRicVRlQ9pK54cVi9WOuqkRStFpd3E4mtoDsoWQCOmLQhzzOIebN6UpNX8/ecaQL1lSYDRVWdHutBDO3xbz9d1ZH8fyzCFJWZaEsaKcFd5lpBSRCj08uDG0O11aWYtwDomdn68eaUg8ai6SUlIWhRf4tCGUat4M6H9Fz8Udh9+LehFMUlcFgVTEUeQPqgiqqvRQ9wBk4F1hxmissURRBPgG3TRJvMnA+MifExahvGgYPGLMiACQaNMgrWFyeMAXv/Qp9g4egnJUs4KV5UXSSNJeXEJmXQKNb12LE/KqIIkUYZyye/8W97feJM56nDn9JLaqCUNPYZFh5jlXlUbNm7OElHRXV/n6l77EwtmT2KKg1hWhcJTacnA4whpIe33a3TYK6c8tt98ijUOKwZC8mBCqNkU548ozLxImbShrbBzx9ZdfonGafr/PvZvXacqKy888x6uf+hTFbMrR8IDxYIesE1GXFcPDfdJWjFAJcbtNIAOy7gJZnLJ5+zpGJGS9BazTVJVlYfk0L37oBzi4/5D+xirlYJ+XP/lvGM9GTIsC6RxnLryDw70DkqxHb3kDpWJG4zEv/4ff4KXf+w3eeOOrpN0uiZRMRkOECwhJSLIUGWSsLJ3AhhF3X/0yo/EeRT1ABHMwsglwgUCFGcXokOe+88M89/1/hbNXn+fwaMxyv81XP/VJVJIweHifdqtNFLVYXlsnziKaKOUn//ZP8KPveQFlJb/5G/8X1fIz3E4WOfzsz1GXY0xd4tI+qhkT9E+x8M6fYLazR7n/JazbQ+cnGE4GXPt3f5+gucvdP/7n9NlmeLDH+59Z4zffuMW/+MVf4N994k8YlDfR/SWeffd38dmPf5Fvvvo2TTDhQ8+cY3fa8M9/9V8zDE5Qt8/TtE9y8FbNaBhy92sfx5bXKIsSJpsI/BnHVAe8/4PP8bd/7Ee5vLrKNIr52O2MC8uKF/vQOIEK4OcfwJtjwff0BY0QVM7yR/vw69cCvrAFf7ot+OxD+Nym4/NbAS9tWz6/J/jMjuDlI/j0Psxix5NJQCGgsIbfPQq4O4EnW14oyXH80RHMnGA58s3rkoBPDBxbR3CnEDzI4eEM9nLYLgUHleCgshw1gsPaMTKCsfWunnB+TrCBY9jM2UaPHE3CzZvmA+TcISTmwnKAP2d4N6NAPooMC++mksJHeR8xlvx65f4M30jMQd7eyemFnMc0Kpg7vWA+eHokoMzXLDcXtzwO2DuhBD5mq+eup0AYcP6a2Fgv6Pl12w9ejPXXI+vmgyTrh8GBs2xZyRsvv8mH3n2GIGj9k/9fos+f+fq2O5SkaNHrtKl1RZYlHBzs41SKlZJpMaY2DVEYksWGaVUxtWAmtW8gMQGxsNy6+SbjmUajWVk4xcm1JabjId1OHyvheDIjSVosLq2yMhzTaIkmpJiNWV1co56NccbR5GMidZKH23vEaczy4hJ3bt+n0SA7CQvdZZJIsbe/R2fxBEupZJIXxJliZ3cTFbSIlSAKBO1ORm3V/E2hSaVgUBzz4Noe3f4SvcU++8cHSKcwTUONJg1Xee7p83z1y59HyIqyEcREFHVNLhvSOMUYRSja9OIYEypkpCnrKZUrMCYnFtq3AzQzTLmFcjOU3GBWaY4KRQIYWRNHCWmgKVzNaFrTWexhyxGNhKCuaXdXiOOaaTFiWoS0gpRup09RjOhkDld5AJx2IFXibdMqpCpnaBGRJDGNaTBUFIWl31smTR2N1YzyMUIIIgVF3VA1ljRTRAHI0JBkIyYjhasDDmiBhlakUKG396VJSqgEjhrpnM/jW0cgE5wuqUvBkTMkYYZuJsSyQ0BIfymmjeFjn/wi0zqhNhaDwFY5WYiHTrqSWgu0bYgUBLJCBgLTeJFFCIm2/nUJowYZGkKZkJcBeVFibUMkIYpDQmkIgprxdMxwBjSalZUVtKmYFTPCKCOKIE4cC+0O1sFkNGA2HnpOTpyyu3dAeyHj4cNblPWUOAyJpKBuAlAxcRzSzlaIggAXlHzfh97B3n5NICKSJOL+vQNOn3kX7bTFa9e/wTufeQ8Pd+8hkxBjLKLw4DkrLEVlOZrMF0mr6Xegzm8Txt5OOptXVxOBagRBoKjKhqbRaFMRhQ3RHH555842ZVkTCIetLUYaCCTCCLKky87xHmvLy2S9HrFMmI4Oef7ZFyiNYzI+9jDVQOLQdLOQ9ROXeLi1g2KT/tIqh8fHnDp1gt2HW0iVsbpyiiiKMfUKB2WNDCJOn1zj8uVLHI8G7O7sURY5R8dTZrkm14579x7wgfe+n6WVZbZ3dqimNafOnUVrTSRDjo9HZK0WnXYXEwgO9464ef01Nne2efLMCtlamzu393nq2UukzwioU7781VepipKDwz26VczxuKCuLVIJLCHrp5ZJ+yknltf48ssDjnYHGAfT2YjO0ipv3n6dDzz3FOurJ7Ei4uRKn7Ie09Rw+84u9zYfMBodM7nxdbq9HtOywNmS0UQSVA2JjGm3u5zYWOPsWQIAACAASURBVONTL91mbXWRvJj4+nMgihRVZRFOc//hA0LVcPPaHR9z0TNefPoF3n7wNp24w+raCp2llHe+51nOnT/B/t4hD3bv4czM11fXNYm0rLZaXDh3kfMXL3Hz7bdQSnDjRspg+5h2mPhYqPEjGWctEoMKNMIFtESMFt7h0Rg4sb7CrdvXIPKfTymg1fIskzjLOHfqFKdPrXE0OAYZsdDtEASKt+7coaw021s3UNUaF0+dZXGxj9gMGZc1n/nUS6iwYW9vi1YcMZvkRFHC8fCQxeUWn/nMS1SV5a4u0QQ0laDWoHVBq71CUeReOMfRThSD4YDxJMeZmqPDAz7z+W2y1KIrg8wUrXbKcHDAuSsXOTqYULuGRGlaKqYoDKHShDZiNp6wn1ccHR8zHRxjXE2oLDduFISkHB0PEHWNMTXCBhgiKAOyOGZ9ZYNpHtA4AxiiALJqTG0EmhpVW0hinAnJx1OWTm/w4rNneXg/Z290QJKllC5iOKi5/XAfcesBvVaXzkLGM1fOc7Czz3Qy42B7h/ZCh8HBAGNrLjxxjtfevI41FdZWaAdOC+IoppqVrC2vsLjQ48HuJqaB5eVFepFi/2jgXRsBTPWU4cwQhpIoU4gaDxkGtG7IZ5Zev8U73/cu/uB3/oimqR83ihlnORzMWN1YIEtj6rFjY2ORldUFDg+nWI6oK02aZbjAMRlNSdOUSjeoSKKbhlYrxVjL4f4xWZZw7sJpRoMRVV5SzgqcDVhe7KMtdLstDw+f5IRxj1AqJpMps+mMWT1DNzW2cnSSFioImU6PSZOQLO2D9MDhlfVlpAhRkUNXDutbFLB6RlBNUUrgJS5LbQxlUVAUpWcJOUtgDU4E5FVB2ZREWYfGgdYzWqqNEDEqbDOpFa6ZkmQRRhuccNQWRKPRkwmdfgctBFo7WkDhNHU+I0tidO0rynWjCTzswXMW0eT5MU1jOXf2EnrOldBYIjfnRThQoSJKUpra8/N8ax/UovFRIOsP2I+myf6gDifWV2mMQGiLC3wTW7vbRaqYQXFMpBTWOV+aAdR1Q5K1iFqpd/84vyk2jUGkAbYxpEGIth4O3WiH0QbdVIgonhef+YPD9nCMjk4SXVqhHtzkZLbEzbuWznKLKFvGWAu6YLx9h/7SOqqfsXDiDGGWEqUtNk6cJlQJRjusMAhhKWcN/YUYPb+/xuj5Y4aqKrGmIQ4j6qICV6GtIZSSQAqs8w4m4yyNs7SjmOkjrqbAM0JU4OOCTpPKwIPs52w2hI9ulnVFkmWPY3Q45uxLL7pa0+Ccefw6lLN83pjmjypVVSGlIi8rWr2OP6C4OQg7CDzTys0jkS7wIGwlKWpf1GFqf/u60FgLWZbRlA1C+OZb7bwoU5uGQIVEYeTPA2GIc565aYz2tEkhPHDeWYqqwoUKoSQIM0dleMFQRZGnhAQKbbQ/ABpLVTWkrbaHlStFUxb+e60xxpAIiVTKu63m8b1KN/TThXksx793H4HhjbGIwHsLnPMsEhEIcBal/LTfNP54WBvtxSbrgfIIHwF1zItRoohiOuHatVeYzbbp9Fq4JqTf7uEsDHd2CYD+04tUGEJC6qIhlcoDr+W8NKbImQ1zdne26C8vIYVBEvqSAa39ax2EOAkWzdqJDWInGN57wMqJVfav77DSS7BNQ9TqEHe6mKqkms4IuyHGOS5eusrs9Fm2br/F3qRBRhmqEiwvrbPz4C5hu8va8iqJCjl/9hIPbrxFYzSJCnj5cx9n7/5d6qBiYXGZvDzPrD6gGVXsbl9j+IlNPvKD/w1REBDJBGcqRpMRKukgG+3LZbbHJK2IpN2jchVnn3mBrddfp8kWWVt7gtHmTdrtPkl/iUBJLn3H+znc2aMYWSIpuf/WV7h9/zr5rGJt/SxZt8Nga4s06/C3fvrnef2LX+Z4+z75cMKV97yfnYMDbj3YIm/GRJFniKYqJg0FadrhyjPvZbZyhsXzlz3z9WhGb6HDJD/g6ns/yIkTJ/nkv/2/GRRjgijl9KVnmMbn+et/48f40JOX6AnoLXUQ/b9AM33Aj55e4X8xklPP/gi7b/w+S0/9JOOv/wrdM08z3b4D+Su+CGn/HqX4A/LxAxJzwNa1G0gpuLVzj2g25r/9rU9gv/aQVFVMz17m//jffob9W/f493/6GUojWT5/gv/qP/0Bnjtzip/+2Je48ZlXGFff4In0AwRhC1VOcRdjFD2EdGSBRgtJ1dQk1RgdOgYiQJiKN3ZG/PqbihcvZnxowTemZQJ+bQ8eHsPPPeddOMI5vj4RfPKBYMFZShvMm86hIqC0IKwkbEBLS20ES8vwvf2A0nj30ksDybVjx9+95AUUg+X39uCgFryr6zAWVCD42Lbh9XsSayBsHBUg3dzlZPGOSSEIHcShoArhO085/uJGgDSgpOOfvC04GQv+5mkoHESB4zf3BfePHT91BbQT5Bb+9EDwjS1H5ByhdUjLfB32biQpHco5wthz9t51VvB0BhrLVAteGzlOJ3Cu5ZXwI215OBZc6EFHeW18hmNz6GhJ6Y1KASTCEQKBBCkgdF5NF8KjED0vLEC6bxVMPXJASTdvxv6W0QqHRfmpP9/yXvH4b+raIPIZwulvq/7zbReULly6ROBgOjxke3ebrN2hlWbk0xmjWtNOE7T2AR+kxNU+Ay2jAGdARI4sWSZpecuWkhHdTofldpu61rQ6KQcHe7z9YIdLp5YRosKQYkzN3viIw7xgpQtTlREqhVFtvuPZi7z0uU9w7+GAvCqJoowkiQlcw3BwzEw3JOWQnUHNTOfowLLUW2E6PmZvOKMTJaQ2oZX1UNIQRSG9/hJOg5zOaC8uoqbHlFjyXFO7gom0tLo73LlxCxfHWF2jhWSiC8IgxNXQOEmtzXw6qFjq9hnvbXPxzJMcDB/SzWLKQvBgd5vJ5IB3X1hmOukw1ZLxOCGJU4TzYGTTjAhtiZIBVgY0NmdxIWVrWBIKS62HrLXXmZb7xFGE1rDSb7OYxWztbWIcaKMwJqSdRnTafYbDbRDKT+mMn1wpE1PhmJY5EZI4ykhTQTErMUbihJ9cKgJ07WjHMZOxopIBceDhkyhB1ooJGFDXkkhJJCmVDTBOzvH3BkmNjEJcOcZUGbmaooKQVFpCJVjqBHzmK2+Q5xXaKVa6XcxhhVWOdmxAW5JIUTSOdtR+nPOvdYWwGiljJGpuodQEwhEEEf3uMkpMOC5qhEwwc8U6ikPKuqLbWkBrw5VnL/Jg6yHSCfq9FQIcgfJA8bdu3eNwMqCdpVy+/AJt4P6dOxAJLpy6wLU7r7Pa6XBclrR1QpjGrC+tsrl5b16PaXFSMBhOsVaye7RNvxXzypv3eeq5I772jW/gjOOLr7/EQphRlxZpNTqSSCdQTlLXFislSeYoXc3RzGAqKAPB6RMbjHcPEVZxdDzkxefeST7JCRYD9ga+2j5EUNWG86fP8tTlpxiMp3z166/QSTps7++hlKbVW6DTX+HwuKC/fgoxqxhPx4Qq9JP4MKCaKoq8IFIFu7sHrKwukLUW2Rt+k5MnnyCfjlHG8PaNa8g0JtaCg4MDJuNjZOBIooirF89z+uQas7JgOh4TBxHH4yELC0v0F3pgHCdW17Dace3aW0gkgYJ+v0dIwJ0795kVBZcuPIlSLfa3N5mVE05tnOLocEDjAg73Ryyvd5g1mle+dBNr4OTGEmGcsXd4zKSAqrGEsW+FjBJFe6VDFiccj4ZUZQEhnFzv0YmvoIspz1y6wBMbTzAsCq7dvsnx5JjZZMDZjVP0u13evv82eZ1TVzVh0qAwnDh9hdu3bzAtCoJWxNbDI0bZjI986D/i3s4Oy6s1VVmwvbtLUTcU2tJLMyIqLp09x5nVU5gAPvPJzxO123z4g3+OorI82NkhRDGa1WxvDrl+4y6XnjrH5UvnuX93m7fv3GNra4/l5XWee/EqMlA8de4qw8kO5hzcKGqCxpK1FFJCPqmRMuTK5QsMRkNu332AIGB5ZZWHd3eRTvD5L3+DLInIEkFd1gTEqMhRlBpV1b6+Wgp63SUabbl76y6jWU5lGha6MBkO2QsDth4eIBLFuXNnuH3rLmfOnuT2/XuU2vqWEmlJUoWybe7d30EIyKcNrXaCs4alhXV293dZbK8St2LSOMXqBKdnEMYMRlMmsylpIlD4GInOjT+YO8FoOGF5sc/5i2e59+ALtHspVTGkKRwqbFHoABFlxB0FJmc0GpC1U4rKgBIYIzgYHtHUkthW9Jc7TErvcmzqmn63iw0EtZ4gIkmVl4RpixPLZxns71LWEUqAcTMgJu0ssdg/y/kzz3DhxAUqW3Hn7jGvfOGrEFiiyDdxLff62AKUMjz1zHm+/PVbLLRi8rrCKtje32Hv+JhONyPPK1qtLs5phsOaqjKoEGpTUuoWnWQBrQ9ZX1jieG+fcTnBBCFWgsMQpwqURoUGKaDWfpMjrHc7aK3ZfXiXLAsRQcJ4NEWFKVb4pq+NM2fY3dljMq6wtaPWFqyglbaIlEEEIaPxhFlR0Ol3aHRDXSsW+gskLYWSiv29fabTnK2Hu8hAEoa+zEBKi0VwdDhgOp3S6rZJOhHHx0ckSUxvqU+YRLhKM5sUZP0Ove4ix4MDHDW+L2aGSkKKyYTZ4TfoLy0yy3N/oNlYQzlNIkPaCpwBK2I0DoFCiBAZWOLYVws7vOASdntMZ2Ok0xBAJGOkS4ijkFw5essLTMcxWgfEifDAYaspm4bSOHpCEQlHqCKstTTCUFFgQ0c1dqjIg4SV8sB35hyh2ayil6VzwLKi0ZpYKJyBIIJSN1TWUE6nTKuaaO7CN7UjSRIEDmsdOgClHFBjbIUTkiBpI6sKJ71oIayf0GpjcMKgnKBpNMaCFBInFKGKfDxC+ICEcQYCkHFGNRqRxLGP2DnhixqcpjQ1mUw8oNlZyllJJ3Z8+mtfR0m4Q8N/9v7nWb/yHqrSoDA02qLCDktPf5h2L+boYJf9gyPWV0+ighCMJs9z78wxhul4SquVIebAa4lEqghDhRSKpmyI44g48k2qQjia3BCFCTiBFL6NLnAO0XgxIp/V9JdTD3OdHwKM9gOXMIxwThO4eewMQdXUpK3ET9EfwVyDeYOZ8GxH3dSeUxkIlIrQuiBNY4zz8RBjHIFngBMricM7o7SpcIQopdCNnjuc/d5bBtCUJUlbQhDQOEGWZVgN7V6PB0d3WFz0HLdAKjTV43OAm8fLhAgoqsILdDhUEnmGlQqZNhWNcGQEJASIeUT2cVER82ZeYamainaWYYx3DAHzJjbvRAoc/kMnBGEU+scrPYTeaN8KF1gv3KkwoKrd3J0k5nFFh3eFzveB4lG0xD6y2ftoSSD9c+88b9THbr4F/rXWUTQNKIkMIxZXT3Hx0newsnKChZV1xNVnCcIQ4QRJJGlsw9TmBMbhOgnGWRYX13nn+/4SJ5+4xOHWHtHqBlJAFMYIFVAUFVVjyGKB0IooSyjyMSfecZXbX36FlZNr3gm23MEQEMYB6xtr7N15nVvfHPKeD/5FOr1V3n7jFTY3b7B7vM+skGSyot1KeOuNr9BoS7/TsH3nTXq9HuPBAfcf3qG/vkIn6zA+2KXd7rJyeoPReMjy6im+94N/lV/86b+PXDvDuUtXSNI2LvBM2aPjXXb2tgk6berDXab5iKaq0Q08yG/x3sGAsBXx8h99nB/9qf+RL1z/E9bPn+VwAjLd4N6N17n6HX+e05efZmVhkZc//XHu7zzAlDW1NpiqRAxBOIlSiq/80e8xmZV0+8s8PLzG7beuceWd7+fSxSvcuX+DyuQIO3fcobl89SlWzl+g0aBmDWu9ZV67dpezT17g93/hX/Ij//gf8snPvkx3YYkyMNQN7O7P+Mjf/BH+kw+/l7YUICX/0//5+3SeL2jfq/kH//lf433f8xH0hXcQfPM/cPjFnyJMlpmNDpCD36GT/zFNpGiahvHWJ6k1OCJCoZhWhywvL3MkBLf+15/nh//ZH5DG8M/+++/H7h7z2bsV+/sb1OOHDGavcjD8XrbHOTuddzEt/5gnfuCH2X7jgHz7Z1laX+Dkk3+HWSPpLV+h0mtURy/R72ScXF/mv/hb/zUvXn0HqQz4p39YIlzJX14D3Wg6geJ375d88rrjF783BQ0EjgcV/Ovr0NWOQeMbGM1jNpCPvYUCZgJ6AspI8HfPQ20tYSD4WgF/sgV/76qPz2kn+NwA7s7gB9egrwRR4PjDkeH6w4DEGRoESG86eVQ+EAAtCzrw/9MpeG7N8efXIJEOheBfPIDEOn78lKC2kErBH48df7pp+YUXQTmoga8MLX9wOyAzAmN4HG9zwhG4wIvd85KnWMGpJS8mFc5RGvi1XYfO4Z0XPSPw0Dp+6bbgVCK40IXaQCjhH1wLsLsOrSHCY2WckHPciSOcc6OUgjiFH3nScaUPKoAv5PA7Nxx/5ynL6VjiHDzE8a++AT90VnB1SdBYeDANeGlTkDY+waICiAMILbRagn/z2znPjF7HHLyHYOPbp/982yNv/+pjv/zR0WDE8WCIUxmRkighyOuCdqdFK0toTM10NKUpNVmnSxJL8kmFJkTXjjhS9Ns9zpxb5+T6GsOjA5baGZPxgId7+1RNw/FgSJqE5LOCxgj6ix26nQWWltd433s/xMmk5uWb1+nGLTbvvMFX37qBcyGhTCirmjhLiELYPzykqWvG4wl5WWFNwPraWd5x9Qw7uzsYIRnPSrq9xTn3xNu+hal4+PABIkk5d+osR3ubbB8cUwlvYw0DRxrPGI3HJFmKNZYwCYGQQMUIG1DUBiv9BVQFgoAGJyz9xR57B4foyvDMpRdYa3dZ6PcZFhGNUDzcvU+tfUbeNDArBmBnHJcllRBEiSAIGpIIqtpxYqFLpxMhki6TyYS89tGoWCnyWjMpa5IYaCxOxAQqpZUGDCdTQuRc27SkrR4g6WY9WmnGJC9xrmRWTsAJtDMY55viHBIXKvKmRruEtZU2kQqIIk3T+Iu9FL7uNwwTGqtxpvE/Q6BticNHLxrj24YCEdJO+shQEnVitra3uP5giG1SJrmlqR3WOALr1d52GDAtIHeOwFqMFkzr0gPMjMXoAK0dVgqkiJFhGx1EGF0yns0wTpLECYuLS7SzNkWZ02m1KGc5LmgIlaTfW2H7YJfF/hJPXrqCbuB4UKBUSFNMCUJJ1lrk+rVXqcOG7/rghwhRlGXN2to6aMNkWiGDkMOjQ3RtsBo/IURwZ3OPvcMR6+tdNndHZHHKa9dvsnO4i3IObEOrm9BL+5jGswecnm9whG+566QthA3JC0sQZThbsbDQpqkzWlmbdnuV557/TmbTMXk5YzzYQ4oEgSBNY9aWT7J/cMT+wUNmecHyyhrT6RSBodNKOdjfYm1tjU6nx972JlmS4BzkxZjpbMp0OqSVpmysr3LqxCl63UWm4xH9XouDg33ub95gNBpw5uwaSRBwMLpNVcx457NXeeeVJ3liDmbO65w0y5hOc7rdDpGKqLUhihNms5yFhR7aGs6dPs/GxjqT6ZibN25xeHTI8tICnaz1uDEvbWeMj6eEAlq9DhunVtk/HPBwc4+dh0csry5xuDti92DA4soSl5++hFNzVoRTdBYWmE18Y1fSyRgcjBjnU0Rg2dseYmzBB973AXZ3jllcW+TV197g5u03OR4OqauGJ86d49TpswyPp5w/fZJzF84znYbkpmBhocNwdISoM1QcgDBoJLNyyu7WbXYP9siLMYFytOOMIIox1rKxvIJtEtYXV4mDgG5nmS++8jXOnrvKGzfe4HB/n4VeCycs9+5vEiSSpZVFWlmHhzuHbO/e4/y5MxzubhPKjIVej7Kq6XRiIpVR1ZoiH5NIxdUnL7K0vsLW5i7nz53kfe96niov2dw6YFxUWBkQthIiZVDSUFchIgixgcPZAAKFdpqD42NmxiAjxd7+XWb5kH6vS14OMUZwPN0iiBZ5+umrtNOQ5aU+9+/t8vLLX2M4GtJqpRSzGU+cPUGaBYyGYz7wgfdSjAuksiQthbUhxjTkZc47nnua9733RdrtHq1khfVT5ykr2NndQ4V+7Q6EwYkG4/xaZXTN+oqjHbbpdRcZTQ7RdYmwJWkYIVxIqS2BCNEmRwWWK5evEHf6aFuDlUzGE5RQaGdZXFogkoq6EJS6QklJmjQYkyOlpdZTqqZkMpkRBGNCOSOIFNiU81dfQAU1tXbsbN+jLCqOjvbIZ2N29g4o9QwVB4SiQNkpTz11lXw25fz5kxgt2Due0pgSYwVLvS6tJKVsCt9S5RrSVkYQxGhdo0JH2kppakNT5kzKEVGWkE9noALKvKKuLEr4YYMOLML5uFVZ1VjjD6adbkqWpcRJzP7OMaHwNu98WqHimIWlRbpry0ynBU3RsLy6QhanTGa5j4lkGYPRlNoa2v0OjTE0dYMQgnYro9VtkeclcZpQNQ2tTosojsinOdb4ZtnJZEZvsUfaSqmbxosXSoLxfJg0SZFKgVCkccrC4jq2brDUdDttuu0+jbHkZU67lSGDkNXldSyCOAqZzSZ0u4skkUIFliSMiaOU3vIGVhgm0+H8OXbYxtA0FabxoPM8r4hlQBzFREFG2u0Q9rtMRmN2H+4iQt+bY2xAowXjSclgNCMOI9+MKUNUEqKSlDovKfP5IMSUSBVQlDkI0K5Bm5K6nCJcQ5ymJK1lLHLePGUQwiCFoJjlSCGJsoRACFKpiKIYFag5yNO7PJxzBHiIstY1QkK/28bWhqooSEKFMZokS2kaizENUegdJ57dE1DriiiKSNJkLgpIP0h1FiEVTVWhVOSdXeDFKGOwjaWVZB5yrb2DJqDNcOceop0yrAzZ+XdBlFLUOVpXzIocJRUqVty9fp3X3/gq2wcPWF89Qa+/SBq1kS7w/KN5BX2r0/FOO2eRKsQ5R11XfmDZNMRhSBAIAhdgtMNgSVopBCFCSIwpMNZRGUO702EyGvroURD61jNnqLSHa7eyFs56xpZFoJQXmXSjkYFEhhJjvbNoNpuRZgkqVNSVftzEJwJHXTekSYZ8JNA4fOugCEjimLIsvYilFEmUEgQBVVX5yLpSIOzjqHqcpHPhRs5dOd6VNp3OaLUyrLWU+QxjjN/PRRFCKpzDl4qUlWebVRUEvoEujiKm4wkq8AKrDCM0nqnmHBAIwijCzkXpAEGSpFRNhcGRRDFa+1ZCa71Y7AQYC0ma+SbNwMdnEd7R1mq3KesagsB/FoVv5LN6zjhxjrKqsM4RhuqxU8nfhiAgwOBFWhAYBAof5Q2kb8MKhcBMc/I8JxKwvPoEy/0NpApJOl1EoBBBSK1LzwlrDNNyQqYipEoInMICi0snUMZRTcYoJQizFkWZU1cVgTG+5VcIXBAQJ4rJeEaDQwWWrVt3McbSb0dsbz9gNBjTikMOd26zfOYKOIkpc95+4yXu379FXTYcj2vaaUBnsUeSOlqtPtPxmNl4SqAi3r7xJkVZel7fdMI0n2JFxeb9B2xt3UFYQT6eoGvDU+9+D+vrZ+mtnsYRMDo45HMf/22u33yNyTjHasfhwQFNbSjqEtMU3H79TW6+8imm04IHm/cYTmY8890/zMVnv4PZ9gOGk2Oy3jJPv/v91JXm5le/xNJaj4uXryAbiFXE6slzCKEYHBxz+OAWOw+vsb99g3E+JXRdTl+4zNe/+GmsKzCmIVDeLUcQUOYl3azPu77vh9i5u0ly5gTjTsbqUpvzH/gwG6dO8V9eWePgi6/z2uSYjQ//OO//8X/Ij75wmnMtxUzCD/3PP48e3uGv/+B/x/rsiF29zSA4x/jNr5LXhyT9Z5F6H5lCPb5OnPgoltUORAzGsXT2gzT1HpaAunGEDj51Z5PPff1VPvqzP8EVIdlSCdfuH3L9079GFo85/8Iz/Mcf+Qgff1Xz2jXJ+MFbXPyu78K1FmlFTwOOhecukaTvwLa+C7P1Bfq9ijI9xwsfepG/9hc+QoLjY5++zY1RzUfe3We9H7AQOm4Bf+9ffpNf/rFV1giQSjDQhp+5FdAqvHhB4LlmUgqkglB4JIULHIuhYBgLfu5536qmhGDTwP9+U/A3LlguxYLGwfUc/uQQPrTgeKrjb+NrpeB33hYIDXkjcDagFtBYQWM9dqFCUOAjXS4QrPQEHzkpWI8ESsBvHRluDCT/wyWQFpIAXq0tv/Qm/KNnA9ZDqK3gdq751TsBaQON9s4n67yYJGyAdr6BE+Fjc3EG/+h5h7YC6+D3BwFbh4KfvCLoCpg5+M2Hglg6/vJJSJUXk/7pHUGz45nkoQCFLxhQkW+RU4EAJZChgAg+8gS8awWUMNxsBL/0muCvPCF4qivQRlBi+cdvCJ7tBPy5U4JCw2Zl+KXbgvu7sDlxPJjAvaHgzkhw7RDeGAruvz6gW3+a00+cY/nkmW9b5O3bLij99E9/9KN1EHPq3AWeOHeZldU+2tSsLJ9kPDqgbkp6rR4LK306/QX6acrh8YgoyUjbfRaWlxiNJ9TWoCcDnBB0k5T94YijYsasqDkejzE4hsMxB8MhzgVkWZ+l1WWW+32a6Q6hSzkaHLK1e8iUiFMb51FRCxXHrKydIJGSg709dG1orCOWklLXXLryDAv9lKqckUYppmxwc/tyohRpBHHgGM922TzcZ5ZrOq0Wd7c2yWyEmsO7W60uzz31Lu5sH5ClAbY26NpvvHCOSAXUxnqGkW2QkSJREaUTnFs9zWh0yP/L3JsFW3bd532/NezpzHcee26ggQZAgCAJzhRJUaZlTRYt2Uws2ZZjqWyrUokSK06cxLJdjsuV+EGOn+wkTkqWIkdlSbEqsiRTjCiSIsEBAjEQQAM9oLtv953vmfe4hjysA0rv0YNP1X25t+49e5+z7z5rff/v+33b21fY2dwiiyBrL5FXhof7exir6Pd7rC1tkLQjGnIar4h1BFKQSE9lagqjsbUgabc4LSpi3SXPLe00wpkmQIXnecT5NwAAIABJREFUNQJN4xRSR1R1gXc1hZlQFA7nDXEscZVHCEdj5+hIYqxHKkgTgXAKJQUSRazCBEtLQt5cKvA1rThCqRmNMQwnoZWrqkqSJCHWKePZnLLKQWmyuMXUzCldifQGi6dC4WoCR8JJ/HzGtKzptTWFbdA6tCQF1pnEWEVpDZVtwEPdWCrZ0IsyOmkHvEKHg6QwJVnUIYm6xHHK8mDAUq/Phd0dtre3WF0K1+TpbEwxn1M3hPNykGVtRuM5587tcDw64ej4gMevX0fpmOF0ytrGDqPRCaejI376r/0NYgcn+8dsbO3wxNUrIdZz/JBnn3yane0NNldXORuPkVrwiY88y2d/4OMY43nysUfoZZrl5T4vvfoGTz52nbv37/DB93+Axx57F7LR3Hxw/532TgSBhxSa+sqwiEIjaWjFNbPpnFba5fj4iN3tTc6GB3SXVmmM4fDoCC9KvK2Z53MOT/aZlSNm+YRIKfCaIi/p9wdsrK5xNJwynx9x5cKj1M7RShJ2z1/hZHqGtw3F9ISmrjgZjjg5OGY6HXJ4eo/h6RF1MydKLBc2N4l0RmMKBv0WS90+j1++SFM5TFNwcHLG/b0DvDH0Wi2qqqHOK0rryZKEpbVVtBRkaUqWpgxHZ2gJs3xEFEVsbW+QJAllUZLEGuEchwcPEcKxsjTgi1/4Mk3ZMDydcHw8pdvO6PcH7OxuczaasrW1QaQ09+4eIr3iyuULLK30iJUiUpbbbz4gTeDsZE7alnSSZe7sH/HmgxusD7rs7J7n4HjEvJiyNOjzyKXLJGmLw+Ep62tLqKjFS6+9golKjCnY2rjKaHRGFiUoErz0HI0OQHuiNNRRR0rTyXpY48jilKuXH2Njvc9yqwUiIUvh7fuHvHX7Jvcf3iSSKXlZ8cgjl7l4/gKb/S5HB4d888VvMhyd8OxT11lf2uY973k3b7x1G2tDU1GZV5yNprRWB7z5+msYJ+i3OnTbHR4eHoD16FaLt+7expqKZl7SXV0l8o6myFGZDq14UoBokELhpURkmrqq2d87DK1pkWE4HjKczRDKI+MuG7u7pHHKxYsXqacNVsXcfOtt7h8dcfHcDmtryyg8n/rox9jZ2iZylkuXrrCyscLde3dRTuGoKEtP4QzbgyXe8+6nEa5mc3sJaxyvvXGLk/EIISVJJGhnAuUtcdIKU24Xs7bWECnH7b1jJpNjui1H2lKsrW2yunqBqi5wJkeny/S3rlKWJVWZk7X6SKWoihmCiCxNMHVO2m4xnReIuAnCTSvFVhLoMK8KrAvx0qV+TG8lwZgSDIznBZnsUxZjrl5ZYe/BPvcO3uR4/JBpPqWaT+kvrSyAvXNOjg6oasOD4zGDpMPx6ZjOoMVwOOXaxUepcktlK6xy5PMKU4ZNunOCSZ6T1w1KZjgTmptaSQvpCow1REIyb8I1IoVGaY3wjtqAjiRRHBFpSUTgAyk8+azA2zCBk8DS2jIf+vj7KGclpig4OThBKsFoPAmeoFkRmqcQxElKq53RlIE7ZJ1lNp0HdkpjmEymTCezEGEB0jSm127T7XZomia082lFp91GCkm33SLLMiIdLzg3hqaqKE2BloL5/AxjLEmchZa2OGZ9bQtTNmxtbXF6tEfjK4TWbG2th1iMCCbzVpQE593SJo2z7O/tU+QF1rrQ1CZ8iHepiFhEZKlHaI3QkixLaA9WqYuCYpajk2TBdwkMR5WEGE4Wg9QOESnipEeWtZjMhuTzgjSJqOuGWLUYT4YI5/CuQfhQoR6lCWmckLZ64BvwDmscSI0HpsWIJM2wTYNrLEparG1wwiOiCLEANaMj1KJRazYv0XGKTjOaeWDtKaWxXqDjlLwo0VqgFtwbJSXOe8qqBgLrIU1SEBLrDFGcBFi9NQsnySJi1jRUVYVznla3QxT9EbDca017eYXXb3wLg+T+w9vce3sPoRVvvfkydempq4ZXX/0Ww9kBG2sp53e3uXj+CYSMAh8Qj9YJdR0YaK3OIDTFubBm8N5ijSWKAnT7nZY8j8fYJgCg0zYyDNRx1tEsxBucp6kNrXYaYm5Vg5cKayFLOyRJim0ajGuCayxNqMsarYKLyOFw3mGdZz6b4ZzBmAbvDXVdB6HNCaTQJEmC955mcYxFVdPr94EFl0OH4Uqko+A2a5oQ+fIuuHBcEJSEC/wNFtFG3Q6MJ2MNSZxQ5PMg9kmJF5Jocb0KEdw/WodohzU1OoqIs9BIWJV14JIoHThxKjiyTGNQSqOlWgDhg+DjrcVah/SOKIoDx0SqP4pwKEUcx8EvtIh+OGMQXuCcI0kSnDX8EeA2QMWdd0RaU9cNTV2FanAl0TJ84fwibhf8hlEc413goXjvUSoAs11jKOuCuqnpr21w/soTbJ67hJaa2jZ0esuoOEEpiTAWJyW6Cc3DrTTGLUpMimIWnjPynJ0d88brr5BkGXGWhs9OG9ruGqnQcYoyjnld45xg+/wl9m7fZnT6kHO7m+R5ziwvKaYjvIiZTebouM3Jwxvcu/stCmspi5o6N2xfvkqWSqSVIcadZDz73Ac5O3yAVjlWVMRJisIxnw4pq8AHXNt5lPHRhPs3X+HqU0/x5isvc/76M4wf7rO6vcvp4QGvvPhVCmvwOExV4tGhCdoLNOCEoJ22KE3N9s4lnvuRv8T5q49TFzU3vvFlWv0e7/nY99LUhtVLlxBOcee1Fzjcu41tcrrdHksra4zPjjFNycrWgKfe9xxH9+7x/k//eXqdAXNT8eYffhljG5wTi5bSIMrFKiPtbvLe//jPcnFzje9+/AKf3l3l+kqXZ7d6XO1q4iTm+uNP8egju7xvPeV6cY9Xv/Y8lVb8b//q/2bv93+HT37v9/OffPJJfvHXf403XnuVp97/g9z99u/xM//930GXFVU7ZePcOpeWu+zduYn1kqqsKfIcKWBychPlFZFsSIQj6fVwkxH/4//5L/i+Xo8H85xb44pvDEf0V1vkoxHSWV7JE1556Zj25hKz/Tvs/pl3odYMq0+vEq9cxe0VrFyPiWZzmL/N+id/ks/81F/hP/2e95ApzZdKya99o+Bycszj57ts9yI+twf/+F9PeLx9g8++/xozG6DW/8NNQT0SmHeEXCu+g4CWC2cSApIIxkLwXzxlWdeBKVR7+LtveH5oEz46kNQOjozg3x0LnkwFH18JzsgT6/nXb3viQtLY8Lc0EAtBrHzAkCiIJLSUQGtB1nZ8ahee6ofj+PpM8PkHkp++7FmPPZGUHGD4B9/S/NQ1wbs7nsJ6Dhv4Z7cUcgqlBRfKZDESjJOB+6kENYKODu3o/+gD4Z6ikbxUW/79fclfvQwbkaf0nt89EdwZC354Q7CSBIfQv9yHe/cW6IXKkHuJNYLaCWrrafChIT0Ys3l2y/OpHU8sJCdO8s9ueD66JvjEKlROoBT8d2/AtvX89HVBbmDqHL98V3K8J4i9wDiBEkH08sphPcgETl8b8mT7Kzz6xAdY2978D1dQ+oVf+sW/t7m6ijE57U5MU42ZTCYU5Qxrgqo4K0u67Taz6Yh5blhfP8fKoE8Uax69eIWmnNLKEo7PRqBbbK6uo2PBcqtNPp1SNYZ5mdPp9ul2OpTWsby6SbfTo91e5pGdHfYf7HFwckJpJR/52Hdx/bEr9Nsxk/GMwfI6Vy9e5vT0kLIuAlg4Skg6GU8//QTF8JiD4ZzLm9sU5ZhpA1fP7TKbT1jp95C+YTg9pazrhdsmZW3QpiobrIDSNsSZZDgZ0umt40qD1Io0rpFeE8ngrIjiYFFWKiZWijRpURqDFpKVTodupplORpRNyWQy4ezsmNF8SmMqBm1JJ0sRNJT1CKkUqcoQSKyXFJWnk3VIkxbWTYi0YCnbZL3Tw2OYFwVR3CNNOygZkcQZUkQ0paUxllgG4LNCopymsiCURKsEsRBoBoMu3gRJ1xiLFJ4kanBNhcJhjKTxNYlWCGtpzBxrBTLqkUQBeOYQzOoK55tgXVZQNRXeWTIdoeKMSEY0lUDKiLIuWB9oOt0287xhXgSwq3U13nvyuqSsGyrb4HA0QKJAaYEUEcgwIfXOUbnQ1hGLiF53lXa7xdIgwCGVIFhRx3PmVY2IWyz3l6jrirKpwTniSHPt8SeJZISOBUcPD2mlKcPJKZPZlHbWo6orptNj+u0u999+yBf/4Hc5PnlA0m4zG59ycLBHXVUomaG1wDQB8t3vJXzkuSc5ejjixq2HzCYnnD+3zkvfeoujScE8n7C2skm3t8Ta0jpv3niNs3wcAJxCoCNFkgWe0+qSDBW/vgnxQhlx6fyTGKswruTi408jqorzFx7j9u2bzMYjnPe0ukvs7l6g1elSFAVNU9A0lvF0zO75ZXa2r/DyK9/G+gqVpPQ6GUtLmxjrePDgDsfHD5CuZNBts7a+zmw25uK5dS6f3+K9zz6JLUtOR2esLG9zc+/bxJFGOcvTV69T5UM67QGlNaE6utMmbbdI2xleavYPzyiMY2trG69ksN57QaudEUWayXRIY2skniReuBEc9DpdRuMZ4/EELWNsXRHFKXfu3uPi5jrPPPUY3W4XpOL8uYtoIcnznLv37zMajzl3boO1zQEqCtGQqqqY5TOGp2PiTOGEoaostatZHgzY7PbpZQmtwSpv3XyL7bVlJkWBFY7IKlZWlnnzzT3msylZO2E2OyZSbY4fPKDxFueg1++ytbnClfPnqGtDUea4pqaqGkbTErCB11Xm/OEfvszq+jK3D/aJ0jYP9+/ifY1UFmgTZxlZ3Ka7iJDUpmR9fYsPvPfdXLpwjSjK2D894bd/67c4mQzR8wq8Z2l9lZWlVb792pvMqylHZ4c8ePAA4R2jyZC3791nVuagPVIJkl6fyXFBf9CjrEt07FFxRlU0YVGearaXV2iaEqEUTT1lNpkG4Kn1NF7RzHLWl9Z4+GAP7btEkcKWnq8+/1Uq29DtZBzcfcijFy+xtbWNjlKuP/k4g16XL//+HzIan7F1cZezk33agwFrvS7f9z3fjbQp+ewULwWuhKKoOJ6cURhD0hJ0Uo2QDUutBuscxkpk3OLhyZwirxEqXkRPIiaFYP/gBERNHEmEismLCfn4iOH0DKEVppjQSEO7k2IbS6oVESHm2l1qg0gxtaKxCXiF9+FeluoI5SVm2kABKEc7TlhfXebCxcfZ2bzI/sFDtGyoK0GdV8hIkSVRiFBFLrSp2YbT4yHWnVCZnOOTKcZVlLXjwu4FJrNpiE07i3eSVrdDq91iMg48sNKVaAVJlHH50hXGoxOybod5Pg2RayFQMkJ5R6vVQitQcRCYtNILUHHDfFYhkaRpjBKKsgqMk/WVDiLSvPcDz5CmMcdHx8zHsxBPbhqMsVRluG6kVjjrUbGgWTRjRUlMv9thPs9hAdaVEvJ5Hv7vlVy4WhzGWNbXVjm3u0WcJYyHU4qixFpLUzZoL9FJQqI0vV4P48F6g3UVZZ4zL3OqekZRlPR7XYo6R2KDw1hEJCLEZOJIEseKweomVqrvHIuOY6IkQipBpDTaK4wpiCTEkQpg6lZEb2mNw5NT9o4OA5OxrDB1TdXMmRUFGEEiIE1TlPekcYu03WM6zoMrxlmUlVjhKKsCqQ1ChLY33ziSd1xNWQdnPXVZB8HAGZSA0dkZrbSFbQoi7ynzGVVjqIs5TRUcuAsaDbEEaxuqqqDVTmnFEVVZobSmahqE1qg4YjKb0srSUPO+aGkzJtTUp2mKkuo77J2qqYl1jDOWwpQYF0ScegFdhgWbKWstINkWvCCOYlq9AWvrazhbMZuNGY4nYObUxRnL6+fZ2tpEi5qlXpdHr72XjfWL6LizWMM4vJIYC0WeY52n3e0vRI7AG/I+cMGUUjRNQ5QkoaCiqmDBRmu3eyADX6ypPZUN5RVlXiAVxHFChMLXBi/DgC6UFjjqOsTRnHe0Oy3KoibLWov4GoFH6IOY0e/3Fo4iFSJzcYRz9jtCjlKhav6dWFoURVgTuBl+IZTEcYptTHDiQGhsUqFJTccJ1oYGIbngHiVZyvT4jCQKbWrGWpw1If6l4yDqvCO0QGB6NBbhBFGSIqOYqixpqoBHyLJWALvzRy1EOgo9SXXTIIREaYXzi2Y2pdFxcEAppWlsgNU661AquMLcgsNlrENIgpAVxVjvF0mWALQObnpCg7iz1HWDt4YkiknjOADQrQ1/Z3E+UZyE3/OB6RXYU4KiLJjOZwhn6SYLhpSK8NZR1BVx1kLjMWVNWZUIa5kXcwSSNNYLFpiiKnLqesrdO7d46cWvM5rOEE4wPT0ir3Ja/QHGVGRpiyxNaJqSUT6j02qHe+FgiftvvM7SyhKTvOR0NMRUFc9++OOcO7eL94bbt1/kZDJCKEUkJbOpJckGxLrhZHSMahI+8xd/gqOD+9y48VUql1M3VWDXeBPuF8TIOOEnfuZnefUbXwVpKIanOCd49nu/lzRqUeU525euhIF6mTMfndDYOsCQHRjn8FGMkoJpOaYRloP9farjfR5734dIen3W1nbYeeRpNi5dR1pFlLRYu3QFkZeI6gyR56S9Fd5863XmozHGGNrZKnmTc/XZj/PBH/wsb7/2bXwx5/bNN0mSCAX0VlZpDwZB+B8s87f/q/+aP3XpHNfXB7SEp/bBpeJcSA0YU7N/+w3+r9/4db74pS9RiISdSzu8cuMGn/u3v84TH/wkf/37P01n0ObDzz3HX/jRv8AvfuFX+MHv+ghf/doXGe3f57/5n/8Jf/ETn+aNu4e8/uqL1HUdrjkvuPj4Yzxx/Tnm44dc/fCniNY26Oye4+/8/b/H922f44V7d7kxMTy73eMnnrjA+64+glg/Rx3HJKdzpnvf5PTbn+fCIzn/5Ic+yg+fT3g2m/LkZsk//bmfol3N2f+Dn+fKUz3+8X/+GT6xYYl8GE6sKnj3Y10+9cQmTwxatKUCLfnuZ1Le/9Qm60lKrCRDBJ3Y8diaYNCCa2uCzbbj0fOC7RXYXBasLcPKkqDXEXzPOXhXS2KACPjlM8HF1PGZdUHjBY3w/P4sDJe+bxMSASWCXzpwTEtJ7gRSW6xQeCGolcfFgBPk0YIHhEDFng9vez6xJpECbpbwb972/MA5z/VWcCtNEfzdNySfXhV8et1SOcncef6Pe4L6hBCX9SIMLxb3IyU9kQwOySUNI+n5b98nWJKhee5O5fhf3lR8ZtvzTN/ReME3Jp6vnsIPbAge6Ydist+deZ6/JbENwUYpdRDEVRhEoIJTKdKB+XR1zfP9lyT9KLCd/sXbnlUJf25XgPRoBT9/H6qx4+eeFdSNp1Ge/2df8MJb4djLxmO8wDiPweGcwjWCBjh7fcL51jc5f/4Rdi6e+w8Xyn35ylXGwyHCSu7fvU0US6o85+h0SO0E7SRCS8nJ4SkqikmThK3tVVJnuHHvLc5OHoBznB5NWF5aZ2d9gBczymLM2XBGu9sjavWYlXOyNEMKw3Q2I9Ye6Q1VMaI0oUVpPJuytPkI0lpiIiKhyFoZQjo6bc1KO2N0BpHWlLbh/Mo5Zvv3uPvgAa3OMm/u7dFrt9DDY8qy4eL6Ck1TcXw6ZpZn9OMU3++SpAk3b75FK+5hjaeuLFoXjMoRG8tt2llK2UzRUUNlGoqiodXps9RrcTacIqQg7Wf0uynjsqDT6ZBEEZEUuJYPYomdUzWO2kriOGE2n9EYgY4TnItomhqd9cP0w0GrUWjhWGolWCkpJp6xOWMsDGmWLaY/hqydEMkeQhjOhicI5UIcQEl05InxzOqQi63Lmnbbo/CkUY9YCAweJRTVYmoWU9PuWObTJPBRIkEURcyLKXUtUW1FJzLUtUf4mKIOVmMlM1QkyOIWAtDCLD7IQ34/VhNqO0e0JHcPTzm3KVleSjCNJK9rSiMQIkxAvAwLKUyDwWKcJiailbRQcUIrjtk4t8Le6T7d1hJnR8f0+n32Tw+YNTWp1kSJZmNtjc2sS2ewxDSf8/pLLzCaTILjoCpYWj9Pv9Vlj5pqZsDVjGc5xtrwXJFhXgewd54b8mJGu9OiyisOD+7z0vEBnSimLkvuPngNvZcG54ZUGKv45V/7AlIojHN898c/zm9/4ff4j/78jzL8hd9Fase1q4/y2puvs5LFrK31eTB8gK2bkM9PIGtr0jil26qJI4cQmnaa0ZicSAebdJTGFHnF4d4Ro9HXGJ2c8ujjTzCdTFhZXiXSnr2DPdK0g3PgG89gpcfJ8Zjjo29SN3NWBuvMmjFHRwesdXNiqdhc6rCx9gjdJKXXblPUFdbC67fucXI6ZF41mELSjaHbyWjHPdK0z7179ynzb3NWjHht74ALO7t0koxW1sE6RzEvWV9bQ2lNr9Pm0pUrTKdTsjQJmxIcUSSRCobHJ/S7XZIowpuGUZ6TxBmz6Zys1WJ5dRWakuXlZb7no8GePy4nzMcF7XaXOzfvUlQVy2t9pvmE9a0+Gxc2eHB/n9dfuUmcCbbP9Ym84tpTlxjOTxnPc/o7Hdxcc3h4QKwky/0u5Z37NGXOfl4wLaYsL/V5fXiD69ceZ3dnm8PRkNnwmDz3tGNIuwOa6RgdSaazORLPysomVy89ygsvnKBjhbURG50u1lZMS8P49IS1tQFf+vo38E7jrlqsNSwt95gMHdMyRyvL6OwUhSBSgtFoSGEaRvMZ1y7DV776PEfDh7T7MSfjMx56R5zGmNM5X/13XwibuUJz4cIOw/GIs/EMITW+KUjaCVKFD+/IVqSRCZytgcVZQSy6uNzSVBWR18SZJo4cSku21wb0+h0enu0hfcpklPORD72f0+Mp2sX87pd+k+ee+QCXLp4LYrxKuHf/iMev7HDlygVanRQVJQihKGYNVT5l4/wGUnucU5iyYvvqFWrgxRc+R9ad02lfo6o8WScG68miiF6nDxQoDEpLVlYS+psrHJ+MqKxn0OnQbq1wfHwHKxsQM7JWB0FJFGXMizNs1ZAlijSKmU3OwM+xtWVaNwhr0D6hFg14xXqyxH5e0NiCSDvO766Rlxn3Du7iRUzlCPXSKgYkWdzj/r0DdnWffDTh2qPXeOXV50mihChVxKlmWpwhjKOyNc7WoflNR4jEQ1nR7nSwNiFKJK+8/iqrm0tsdnc5PjllPBmRz2a0WgnLvQFnpyNcpJFaMrdzjo6PiIRmdanN8fEpZdGQtRNwjrqxpN2Ilo6Yzuc0WGxjWR50qX2DbTztTptyXtDrdOj3PUJpet2U/Rt73GgqGtdQ5XOSLALnMdbhhURoQdoOUZbGGoQOzWWD5R5SS6b5DGMs3X6HqmmoyoJiViClZC2L2d7dZHg2YjqZkRezEFHr9ymrEryhaRqEA6sdbZ1SlAWzeeDn4Dy2rlGRompmmKpie+s8GqjyiriToIFOu4MrK6QKkUlPhsViLZTzKfMiDxtfZwJY04aWF9PUqCQK3BwcQqQI3SZLE1YH/eBMsQGYbL2lsQZTVETOoVyI7UhpcK7GSA9xRCwklaixZh4is1bimgZnKhKtUDINwyLhsbYJTCRbBWHR1jRWkWiJqSSVt9RVHVpsIkWvMwhVOAswNV7ijUN6RaxTTGlwQi+EkposBbDEWiIRmKbBeosSgTvUamdoET7npQjveaoi0ArnLUvtLsa60ElU5mi9EHLi0EbmrAlTWBpElGDmJefXL7K+vMlXnv8NUj1HuhnoFLeIdz7+rvdSjs6IZUqk0hCh9A5jPCqSOFvinSOJYgQmsHSERAqFJbiTrA1V9R5PmedgLd6BjhWNDR2O1pjgbjKWSETkjUFGCqk0zgXxyktPp5cRJy2cD9GHSAucC9NyFcffcQe90zTmPMGZ5YMoJIQLDWUOmibEwKWKA+RVB4dUaCkMtCC1iPpEesE/sWEsLxdNxqGwJGzY3nEqYUzYXElJaSqyVoi2g0RF4TVUkfxONFEicN4FcbcoAh+pqkMzrVRkWYt5OQtx3sYEoUwFUD94qrpeNNv90blHUVjrShWEHgdY7wnmumiBZwgQJudcaIlDEulo8V0Zmpkk1JX9DrhcSBUo594GwUHK7zgdHaH90Np3XFpyAYhXgWeFXLxnmnaWEuCqCilkEOQag2gabDFjPnN4HQXBLIrpRDHS1lgDXgkkIaI7n80pC0urO2A6m/Haay9hTEkr6/Cnf3CZbz//Ra5/+GOs6QtUxhCrmCyLMFXN+tYuWb/Pvb23KZwnanW49viTLK9tcvRgj1svfY3x6R6mceyeu8rbN94i6kWMzx6idYeWTqnrnM//zq8yPLxHlHUpixzlJPmsRmmHdeF10Yng87/6KyyvLXN4b5/W0hKzouKF3/hVrr37gyTZKvdffZnLT16n2044uv82ta1w3hHHmlhoqroJe4VY45zBRnDrzm1+7Z//I55878dYPn+NrZ3LgeNVzamPC1YunCcdrNGYjJXzT5L12jx4eB/VCbHfs7MDGBta7TW+8tu/Qba0zCuf/02EEDQmuO9knNBKOhwNJ/z4X/sszzxyIcRJpQI8sRTYuqC8e4zaWeXo/kO+9Ntf4OHpkEZ6Hp7cYv//vc28NJy7sslf/uHvQvYSfuu3fouV3U2unbvIP/jZ/4zNRrO+ts2NV7/JBwZrvHpnj/65bbROMXjSNGI8GfI3/vKP0dUrfGWpy1/6qz/O9rVH+eqdW7x/exdrDfdvvMiPfvoz2KaiKCtmwxOKO9/ixz74Xn77d77Kl298k/l4xIc//KPEWvOLv/15WtsX+P3f/H1a7HPvq/8r/X6P2f4b+NkZOau008Ah6kuPK+aM8pJOb4COIh7rhJYwQQ/vLQLBOoLn2o7YWZJBDN7jhVqg///4I5S2CBEK7GMRxOIfWwWJwntBIjwx8Of6Hjl453ckGvjJbYXZCaJ5JYIobkRw3NRA7VlcgwLvIBWC87FcCBoei+AzlyRPpCAWTd3P5/DBdc+PrgpqL9ECbtSQINjahNqAdT40VC6+PEHsj4GJ8fzU47CTOAySwsM/vQOfWIHFvB9pAAAgAElEQVSPrApqJ9lr4Cunkg904V2D0Ab3YglfvK3QNkTphA4uJCXA4ohkiCWrRcR2pS34nh3BeuIpvePXDwSiknz2EqTS44XgV048+ween3tW4S2gPc+PBV+87VnSjqIJraNC+GAycJJQROlppKdxJVpIGqL/35rPH3/8iQtKTT3Hl3NGo0OG04p2v4NDEiVpEA4iTRIn1HUVpoK6pmxyjMlx3vPw8ITVtRUGyzvoxBLHlj989QZNY5nmNb3uEk9cvYpvOuRlgXSaUwlnpyMirZiM59iy4OjwIVGSYas5rThiOh0xn87Ip3OqwvBKPuLuwR61tSEbHimUdRRe0ltaY225RzEuaUlFK1Xcv3+Dh0bQHSyj44yV1QG+mCHilHo6YpwXnIwLdCywtSOPNJ10ie31XSYnb+Nci7qBMp9RFjWlHWFtQawyrPMkSuOMYTafcuvefS5vb2DjhDQSnJ2ecXx2wIODU+Jei821FaZnR1SVQ2pHJ1bUzoIvMT6jKKYoHWFNgdIps6lhXNY4N+Tc1jm6ccKJOsJay2R+RqJzfHmKK0GJBC88ZQMbHcAYpt4gFXjjaBrIjWRrvUtjczQpMzNGC4tDkSgL1tMYj0gNprbktkQ1mpaWpNKiVcnM6bDAcBFCetJIA471lYiiaKirMkA8vUYhaHUkH3vsab7+6hvMpCKJYGMpZVpLypMKg6EbZ2yvL6F0DESMZ3Map4lVSrvVQUUytP+VBa205trFTYZDy/LqKjoSbK7tsLt9nnY7ReLI0ozGe0Sk+OaXniefT9i9eIFOkmGamqPhKfce3qGpQGqorKEVtwOQUgoKU4UqaQtOWQadAYnKkM0p46MTLm/vcDwaYrxEWUmUpgitGAyWQ1NLPkEkiqXWEsYJPvjsBznYP2V3e5vXvv0a9oKj3e7QWV7h6698C2EcrTjCOk/tLKo2eJtijcOLCCNCxlmpNXyakk/O2Ll0geJsiIw0a+urZFmL3Z0typUpcdxi//Auy50gyuwVFbmZcXx4QKRjkkiysbJMljZ86KkPsLl1iVSnHD14gNaeveMzqrLgbDTm7QcPKcsCKWBee1559XXWlvo88653sb52iUG/w8baBVKd8urrrxBlKf12EmIGSof9i3CcjEYUTcPa6irLa5vM8zndfh9F2NBmUUJZVix1e0zHo+B0y7p0Ol2a5pRpPmf3wnl6gxWwElPn5FXOpUtXOXx4l4O9A3a2tlhZWcVKgcHQHgx48eWv0x206HcyjqSglQmUrnnrpXvoKOXDn3wveTOhKR3DhzNsI7i4vovUnq3NXV598y1anS4HJ6d4qdh7cMzySos7D+8wyyse7B0xrqZkUYfT4YwPvf893Llxj1lzxtJyyuHxkOz2Ha5cuMy5jXMMlno4K3DG4pTn9t69UM8edfELa/rNW3eIZYWxFZfPP8at+7cRXnD+0i510bB3/z41kla3z607NxmOhvQHPS5f3WF8OMOlCc18zuNPP4nxmhdfTkg7fYSAdz15neFozBdeeIG8LolQRFh8DdIn5LOaleU2WeaZlZ7xtCBeTlFpSlNVNE3FyfEZUZyx3u+xs72LtQ3L7RntzhYRJ8ymM97/3PsYjidU0YiDk0PyWYXHEacRm2t9Pv7RZ9FOYuoA8U1ijY4bPvHpj/LGrbfZ27tDt6+oChMYWXv3uX/0Nu254sLFksHqFmlvwFs336KWgiROSUSGEm3iWHA8bZjOThmXObKJcLFj0F/n6OSEqnF0khTpQ5ZeOEkvHdDomqzdJi893k1oqpIsjdFWM3dTGqHQtaExlkG/y+rOBXSkKMYTLl48hzENWM94NsF7g7ExabZEHPW5f3RMK9Kcno5IIsVk5njq+nu59WCP4XyONBYpokUcOcLWJUXtkVHK6UjgmoTuoEc1y7l06RLlSs7de/c4PTwl6/bRKkJoQ1HMsSbEYqSzpDJFxwmtVkp/5SoyqtBeI1Vwx2mtUNbQb/WJkoTJ7Da2stimoZhV2NqSJDHOgbGexnrSLKXb7/Dyqzc4eHjG6anCNODqBqUjjHUoqVEqsC68DYwYZywSyLIUD1RlaJRFhdrwrJPRGWTUg4pyXnB8eBICSc6SJhGnxyc45ynrIsRMUkWZl+DA2vB8s/mUwaDL+toKhw8eMp5NAVBpQpK0GI/GCO+pG0s/7oHqcTYa040gTfSikDkBK5HOobOMZOFwkd4hXLCfN86itKO2hkwItIwD48TXNM5SzAp0pCmNCREzFwQl35QkaYTzIEWMl4vyCKUZDSckWYJWASDurEfIEPVxpkGnCqU8SkR4F/IJjQs/R3pqW6ClozI1xjqqsqKpGpyrSVEIJELE2LrG4cmbEovF2AapXBAnJCgVWlSTKEUJTbfVDjXrQiC9Qi/q3dWCaSOlCJBqwElQ1iGtoxFBQDGNQQqBcB7b1EFokWHT4y0E8JJGShdiLLrDs+/5fo4ObnL3/g2K0vL2nTtkaZetnR2irI9QCi8Vpp4jnMIK0BE4E84jylJME9aqSiuMa6jrkkhrmibU1ntj8daA99SNQwkLpiaKgmM8jmJUpGi1Mw73K7rpAO8czpnQVCYS4ihbYKFD9M84MFUJ+ACu9wtRy4cBnnENWi8a3xYw6FA6FoZ/UaQRwmIXAhNCoJRaCEfh4Z1fROE9zju8FyA8ztsAZ5chXiVCZzXWO9I4oZnlKA/GeZDBHSQWjWd6wQQNDUUOTIMUGussOo1J0iRE4aKYSTUjWrjNjPPBaUbYeFprvuMKs86ilEJIGeKvziGQoV57UcVdNxWtqA2EiCQisJWEkCHgJgTWOZwP61OlFxspJfCVxSof0F1ShQ1yFOFEeF29UPzxZrjGBHYYOsKb4DIQApIkIl64JkxZEqVhbSvi8BqbqiZtt0iyDJHECK9pyvAeex2HenElKKs5vZUN0pVV6npKp92jd+06adZCExhU1sPbN16i111h/+iQna1dvBUkrTY4QdztUU4PKec1abfH6sY2cZyx++gTbJy/yB/8+1+k3ruFm4/xytFOFFUJz7zvE9x95evc33/A8dFD8sLgclAyoS4txjVEcWj0i3SEySdcvv4kL375KwiRcnp0ysVnPoT1hluvfYuP/tCPcfMrX+TBvdfYu3+X6XyGdSE2mCodROA4QeAJb5tCWIfQkqPDQ17++lf4xLlHqWtDq9tBZmkQ65Rm+fx5HqmeY//ODV5+5UWEiGiMIY5ivHfUPubW7deZxQmPP/E0gySl6LbJqwLlEyaHxxTJmKVOnw996IM0eYkqHWKlC0i8gPm05Mbbx9T37vPCq99m87l38T/9yA/z7Rdf4cTM+ZVf/xUeHB0yGAz42b/1t+hsbfOtr32dWKd84GPvJnrqA/yXf/qTiP0zfvxv/k1arZTHr+7ywisvEC8tcW7nCY5vvY2zhl/4V7/MI+cv89mf+hn+4U//JH/vf/95vuvcNR7Ox9Rlwe+9cZM/+ynHwWjEyfiUr795i6K7RHd1l0Jakn6fMs/ptGKEhEff/xzDV19m971X+StP/32+8PnP8eCt59leu8w//6V/w4/80J/i3Rev4BzgJf/y336OVl/w2U9+N4NYh88LKagaQ96ULGcdGhxfG51yNYrYkoNwPxaADOBqT4gvShf4Q9Ei2v7Ow7rAutbvkPcX4Gvr4R1ZKhKhfCFyHoQj80GwfcfJI3wQdkOtWXhuv7hvvHM/fCpjQVZafE9IPtX2+LbE4YlFiL1+pA0fesQvQrDwDnzfL87FYrFCBrck0AO8l8TAzHv+9lXJZuwR3hELSU/DZ3Yd5xNBJIL7KPbwZy55KiMWTa4GgabwAlzgUPmFkCUFXOw6ri15hJfcrgSFlXzmPCwnIdVzo/C8ceD4608q+jrEe48a+Nw9y7lUUVhBFAVdO4huHu8E0nuaxOMTj5INKnaI+E9WAvoTF5Tm84LhfMxoMmEyL6ltg44kS/1l0jgDIWi1U2Jl2dt7gFIp5XTGbDJmaXAeZI0TimuPXKXMD2hLR6wFZSXIkpRYCo4O7yOVwCIxDXivOJlMWV1dpfGOK+cvcnqyz/Unr/Dl57/G8XBEFmd0l5ZJz0aUlaPTW6bXXyaSBePZjFjHHE9HXN1aIy1KtFDEkeRwOqPT63A8PEb6Ftpa1jotlPScTQq6cZvxfIilDs0OUpK0LNYIuukSEsvhyRlRK6PKS4xQRJ2IWAsm8zNWuiusRJJiPGYqLGfDM/aPznC24JFzFzFScjIe4eMMqcAWBb4csT7wlHWG1ZKyqtBpzbw8YzRLwQrW1laZTgs6dUaSbWDLQ5oGpvmMxFdgcpTs0e2s020lDE9zGmOoSoeXDmckwyiiygviONQxOtliNs9ZXzYoOWc8HiOlJIoClJXIUznNbCbxkUIJGxYTPtivjahpJQuqvQQhG2IZbiNChvDq2dlJmODGBtMIklgTtxZtG67GGkHWihjO5zyy22E69/T7a8iyYnN9hUd2tynmDU4pVpY8+ydDytoyyYeYxpO227S7CYPlZTa31kG0ePPOXYrK0W4vs7K0hHcND/fucnZ2RGewwtJgha2VdZ786Cco5jmTyYibt27SiTrc27/PZDRG4CnyerHA0KStDvO8xuLIkojGNZiqpNXt0sr6FM1JiPiZiiiSGCfI65LEa7I0xRjLuLFUtkDpmF/73PMs9daIY0s7XaF2jvv7+9SN4ebtPbROGSxFCG+YTuY4o2icQ7Ys1nnKoiZKYoyJKEpHNC8oXE1tKsaTCRur69x889s8du1JbrzxdaSypEmXeT6lk8RURUUaCVwS0ZaS8bRma+MCnUGH/b09RsMZaysN33jpVZSzzKqck+GEnZ11Bt0Bvekc4zzeNBTlHC0FqxubFIUniWPe/cQzHJ4V9Ho9Ll8+xxt3H/Cx9zxDEidMJyOqqmQynmGbiqXeOZ57z/tovGc6m+N82HhqnbG5ts7Z6IRiOqPTauFNg7WWoiyQUiwiF57pZEgUtXFlgXMG7xqqogDTcPXSBU5OT2n3BlQIvM9515OPoLWjznOWs5hpr8PZ8IxES+JYMjo4o54apBUI5fFIppMTkjTl9bducnC2h6v9ghuWEWeK2hQcDU9oZZ0wFa0ltTNsrK1SFTk7G6scjedkumZ3o0+vpSmrnNXBBpeunMNbyeuvv0XZlDjjSdptlDKsriwzmcyChbbbQ6uE49GQZ599lsP9fe7euU+73yN3NW/dusG8nBMpxd6DB1y9dJHrj14lqRNEr8/zX/sSRIrbb93BO8Py2jJHh6e8/tZtrIdYKvw78RcZMRqOWFvpY4o56yvncT7n7VunRHHG2fF+EH4UOBUYaluDZZ5+7DGMDxPicysbkMQ08wkvv/ItRrOc0ubgPJtrA+7cCnyxSAiefeYJer1lJsMJnTTBA/8fdW8Wa2t63nn93uGb1rz2fOa5XOUql122k9iOk9hxxzamSSe0OkgdRSIkEmohEKgRdy1BBFIjIaAvUN80ETRNC4jo0EDHTmzHcdsZHNvlqnK5hlNVZx722dOav+mduHjXOWU7CQHki/Dd7HXWWd88vc//+Q9l03Jy9Ij9/TvceucuQrVsbGzx6M4RTdNw4+YD0mKLaxevMFuVnD7doz+SvPD+93L/4JDpZMG5M2fZ3T3FnbvvcFQeorIWUWukNLiqxYWWXpGxWK1wzpKolOAkrRMoYbASOt0x5fyQcX+HfDRmazjiwf4dypMFo81TTA8isLg/OaY5OKaqS05tbTGbbKCk4tTOOcbjktViyWzZEIQlz/vkyQHbO7u8fese3Vyws7PD3QdTJgf7tN6S5rGbJ/MtBBIbFCr11BUoG0jyDkFmzOb36Xd7rKZTdKLx3jAaDJguDwFIVIq3hv6oS1NVNK1hVVWsVje5euESOQLbVCiVx2GbVDHC1wRKM8dZiwwisgasR0pFmidMjhdkaULeyZjPZwQRaFz0a6tWAiE0SkvaxuBc9KBRUuGsZ9m05EUWE1CkIsk1OpW0jSdJNVXpKasSEkE3ydcSaoGW0ZdIa8VysaKuKvw69lvrlLaqKToFIUCqs+j1kxUIFPPZnKAEIlEMekM6eZ/ZbMlysaDf6zPe2qU3GlGoBJ1A8BZjW0DSyBKPwxEw1mCNjRoKHML7SEW3Ld4aklwDGh8cXkTJkxKCVCcIrSh0LGoJnsI7XBuNhj0x5lwLQfoEmFHgPBbIBl3MmhXSthUyFaSFjEZ7eJSQNPhokh0ESkdavpYpbdlEMCLUYKpoqiozAgIRPE2zQuCwFpJOhyxNEC4QWlDeowhoGQfmQYgnEiEXHhf4gcYYMp2gVYx6D+v/I0T5T2g9BP+kVNBKgbdRUiVjUwodDbN9UGSJpNUJCEmSK2TdsnXqMv3emJODE+bNnE6vS3RuiWay89WMxck9MKA7Y7L8dGQq6YSmqjFNS5oV2KaO/jnOIxKNMRVFlq7ZTcQGu4csK8jzDITGCbDGkWc5tm4j2yeRa6BDIZQA6RHKE4IE56O8y7kncqtMSnyw6zMd1pHRHiUTxJq15r1DyscgCmtfH7GG/yxSKXSSRIYTIl6HIsrfmyZKPr0La5aTxTpPfzB4kqImQjxWQQjapqE7HKFUglo3GpFrryQp8AJQCufie7BuTEx0lhJk3HfnA03T0Osm4FyUxa0LMAI4G021pVxn3nqPszZKztbeVRCwbRvBKiUfB0w9+b1f+/YpJZ8UjD7EyPLgfGRXPU6Dcw4Zoi+MlmlkJhi3NoaJCU+IyLiqyhVKJ2gVow+9i6mEwjuEEpGRJiVSR3A40QXeBeyakYjQqKCx3q99mdax4N4RnKSpakbbCe285PjogFRnnDr1HP2NMaa23L7+MtPFEa00fPl3f5tL155jcnLMeLRFlubgW4pUMdrb5rvfvc5wsIUkJev2sGsZ11Pv/yTlwuNoGI1GNIsFadJh9uA+IlmBVDQVWBvTciUgg0eqhJDEcIEQDFp3+N1//tuYumJns2B+NOd9H/woe1eusf/yt7n93T/izp1XeHjvLk0TSDNNMJ7d7V1su2JWRhN+T4tExXQ9LxlvjOhIeOFnP8Pu2cvUZU0ILRZPtzdEK0mnGKAvv4/DgwlPf2CDg4d3mB0cYlqPcQ6dwfmL5/mNv/f3uNrf4Ms247df/mNu3b2FSDJcW2OlYzzsIkSgNxqidYIXHhniNf9wMeOmXqDnFZeeeZpnr14j8RU/8wv/Ct/9xncYdkccZVNOJlOkEBwen5AW0e/sxa99m3/jc7/A//yPfotf/7d/nbrI8MHz8MYtXvq93yNRnn5niN/aQynPB37iE+ylmmunu6inP8g/+u1/yn/47/4GX7j+Orlt+fd/9e+wrEr+p9/5It98/dtcefZ9/PKnP8udd+4yOH2eq5ffz6snU4q8z6Ksea6b87n/8h9yZrfD0x/6cU4eXOfSB17g3/mVX+c//s/+Cz7z0x/GX7hKCB4RAomvSVoPzuG8ie9iIXm4WHJsWzY6PfCBT23uEohy1Ahcr02TntCU4jPt3elxFiKka6VpbIREICc+7h/fYxHQkSK+B9ZLju8DIZ4s3vMYQPeEdc5ilGCL9X3Oev7w5FtPxKC0EN+3TfF9JPjBdElYX/NCkfj4ORciJr7F1xMbyhOUepI4JxDs6MCuVuuXQfSRe67zg/ytd7fy8dEJ63VH4CoAhMjWfb6A58+tj956/69m8J++T8S9DlGivJ0E/pPnZPRhCuCDwLr41xAB++AFrYFDKfn7f7iiq3M6ow4/yulHDijNS8dgsM3h5ICk040pMnXJql7S6/Wx3oIU5HlKkiqO58esylmMRh2d5uLpPe7cvc/+/i0yZTAouvkAREBrjRJRYz1bLUnzHoPBgKoxVI2hP97GKLh26QLTo32KfgelMra2T1MknuVsQkpA9fpcuXwVmpK3Zm8SbItTCVmvS5HmpKMNNrp9pm2L0ZqT+YKqTUl0pO8hBBujPiznKBHQnZS9rQGhShhuOkQ45P5+gpOG733ve9SNpZ9Z0AbrPN20xzjXLMOSTC6oDByuprRe4r0Eb7h7MOXpaymJUlw6c57vXH+V3qBHoSVnhi3LZUUlupRtS9UYermiSDuYTk69bDg+jnHIpgWrl5zePE1HD1iVJcu2ZTzapa011ipsSOkP9zBuhrTRBNV5kGqAFpa9UUnVpJShQ1k5njsXuHN8jJYJNkjyJKFpA14mOK9xgFABJSRBOpzQsQMlFFOTYI0kKEdrA56GIo30w7qJQFaSFqxqS21aApaiO8D6Ea/ePGQ8HrBaGjw1jR2hM88wSShyj0TTOM94rDlzahOtc/7kNUXTSvqdApl1GBYFOivIMkm3k9Pv9GlN4Pe/9nXOnk1YJSlHRw94/bWXqesV73t+QL834Pnn3kev02H/4T7WBzb3TnN29zTf/M63uHDuGpPpMaY5ouj00WrtD+JahsMxSiqkUdRNzQiD7Ga4lePho/toEc3LkYFUJmAdJ4cH7J4+w/b2LvPphMVsgQiOLAhMpjk+WpAXKYfHj0hUBsGvmTiK2awkzXJoPc62GBuwRiCcpPYGLaM05/BwHyEiwKIDPNi/C8pxcnKTEBpSnZLqBtlL6eSaNOnT7nvmyzmNsSjgPVfPcevBIaVpufvgAZNlyc13rjPo9bAqQREIwtHYmssXzuODYFUvUBL2xruMBwMePHhA2wZUY0gT2D21Q1nOOb/Z0lrP0cEjrG1JkgyhMjrdjCBgvlqis5S8yKJfgm0xwbEsV2RpgeoJVuWccrmiqVvKsqGsaza2dukUOau6xXpDmiq0SNnfP+DV629x+dwp8jzl0aNH9KqK3TPbBOHZ2dyhqlbcO5hy/GDO5GjJctnS7Sg2xz2aVUu7auh1FDLVzKaWVeXodWBve0h/c8Arr72MV9D6iraNnc3twZhcd1hlFXmVEBI4vbuJ8IHZYsKlC5fQomX/cMlr199iuHHAqL9JuG3ZGe7S7/YYFSMWi5KApCmXbO3usH//Pt3+MGr0S0O/n9MddBm32/zxH/0xKk9JEoEPUS6yLFucFxzNZ9y5fYtqYZjdv4dUijfeeAPjUna3dji/c4o37Os01QKk4szOLg+OHrGoVphFCzIl7+Z4YXj71i3GwxwvNFaCac36BahIspS9zQ3O7u1y7uxZ8rxDW1syrbj3aB+oSXPBnXtvofKMF579MG4RQwk2NwYMhgPec+UitmlQSqOCQBcZ79y6wXde/AMsKZN5Q5A1m2gSDblOWTYrXnjqvZw6dZrubEGRR5DctgK8p8gT7j/cJ+91qJqKVChKr0m8BOvReYeDg7t4O6FXJFhP7P6I6GPiBHS6PWzdkAnJJz7yYba2BixKT+lLjtsJjoZunqCKgsOTWUxds1FylWdjdra2mM9K8l5K3tlkWj5ksVyi9AkXL1zm7XfuIZWgalc8Otwn0RqUxtsWYz0KiXAeYyHTPbJhxuHxCUmimc4XpP0OqD73Hx5w5+5dOt0Ol05fZVU2yJAy6HU5ffo0h0fHHByfoBIFeEzr8dIyXy25P1thpEYlAmc8KIsU4ExJkgg6RY+mXheqNiA1tMbTtpaik1HWDfPFikVV0+1qrIM0z/FOUJVNjAQWsRsfXCD42Nps64ai04msoqohKwp6vZTeoMt8NsMZS7MqacoKfEBpgU4j2wmiPCzKZhxpnqBVRtPUFN0uddVgZSCEljQpaNsaGRTG1CQ65dzZSwQrWK5ahuMey+WSxXwKOiA6faw16AB1u2JRthT5irPvcdhgEevUTtZFtffRXDhLJKaNCakxOc3gvI0DSx/T1JwTBL+W93iDsy20hjzREYQIj1PaNMIHjG0JKFzjcdIjAghnYqpuloBIY0NHCISMLIyY2OaRIWG1quh0epi6xgWHrefUbcuwN4iFu5QY165ZMzHu3rsWEQKmNrStRUiHqaOhcmNiMlWiIlASzVtj7H2UXoFOdAREgqd1nkynEfxQGofAreUASsbjIRBRNiYBAj5YpNa0psUDs9kxiyomCQ/GG3QHWziXsNU5Q3ASU5d4E8G7k/17lAffopwu2XvmU1iziQ9tZBE5Q7fbJU0V3scBvyIeO+95wnyJXkHx2HjnI2OIx8fVkWY9lpNZ3E+VxOva2zXzRgPqSWCL99FbSGm5Bj4iKyfEFeIfR2ZLGQ1k1+WJFILWPvbKWv8NEcDxa08j5xxBsDZFj/MaY0iSFC99DF5ZR02LtWl6WCeYISRSqbhfIgI4rjWYtsYRSJIkgklEtoH18VxbH9ldUkV2QMDRmAiNJUpjnUMi12yF6GsZ1pI+QgRIWxMbQ0mWolQSgaH1/jw2AxZrXy4fncVRKtZg3jmkXv82eIJby/5UvG8iO8AThMY7kFIjhASxPq/h8b0Sz/WqXNLv91Eqo2larGmRicJbhxYSnEMkKawZVRGQE5iqxg9GuBDZFT54nDGoRCPWceRVU+IBlaV0hmOe+9BHKOcz2qpkfuhJ0oKD+7dpbY1dai6/53nu3LjOyy/+CZ/93C+Q9Xrx3HnHaDgk73Zo2hIrHASJN5GJ+J73/xjlZMm3/vj/JMlzTJLS7w85fHSbNqywDoS3DIYjbDVBS0ErYyFurY/yNOfwQaO1IM8yjG/wwrJanTDY3uWVR7d55cWv4JQkeIXzkKSKRGdcuvoM92++wcoEdJ5ilnOMayhkhsVQzueMzuxy6Znnoz+d92A8wjjyJKGqaoyHCx/8MBtnznP/tVfoDbd5+egrBNkgVEAoRbt07BrFaHPMX//1X6H71dN84St/wI/99Cf45u9+nusHDylrzyvfeIUzp8+T6ASCIAgf/ZOOZvzv//0/5m/+2q9x8trrLDaHvPnSa3zkMz/L6Z94gfOv/Bi3vnCAtn5tDh+DgapmxWZ/xB/+zpf5+eeeYefSHqac4Vzg6OCEm7duk+SC+f49ilxTnNrjU5/9DH/wv36B3//ql1iGlvnSMxSBX7OMYxEAACAASURBVPvwRxDBI12g9I4PPvMcT73vGZ49tcNmv0f3qat88Nln+EKSc/edl7h++wH/3Re/hrE1tm25t19x+4tf5mOf/Gl+5W//KtdffYtT57f5yPs/vA5ikLTOceHS09y4e53v3rjJeHeD05un6SvNVr/HYM1k1CImHQsUeZI8ATlYP4ZCeBxPsGYuBQFrjtIaL1o/q6JcTSIe3/ZxfuBdy3yezPvuFOEYFSIADQoZnhCdnvxGrkGu7wdv5Hqb4tLlk++Txxslvm9Dnmzxmg0V2zlxaU+wnTWnSrw717trE0+OjV9vSWSAgkOiwrv76YV87Oj2ZG/l4wWvGwhCrI+dAK0Ezguk8E8YqQXxeHZ4zMoEVGSJvbuBUTn0Xi34r04N6CwL+sO/4oDSajHB6NiVy/MYaRvS2C2s6pJur8/GeEDiSrQIJELiPPT6mwTnSHTBuXPnOLh3nbcPZ4wGAxovuHjuTASTkNi2oSi67OxuoxBM50vEasWqLMkySVpkbAx69McjtAhxYFE55osljXUkacC2DW3TUBuLlSqmvFUVxwePkEoxzBRv3rqDygpWVYNQsXO6WJSMhgYtM4ok57icsrF3hs0NWB22FLsVB4/2CcKxMSqwpgIE3azHajnD1pJGWCbWIrxktmqZ1xLrFcNugRA2+jC1hof7+5zeHPPOzRu0JrC1uUMuW45nt0DvUruSYFYkUmKtpF9skyQZR+0JKu+yO+5zuL/P1s4mmc7I8wKJoHWafn/EbLricLWk0+uB6GJCRZFrXKjwvqUsZ2z3NYOsZFZDTy84AeZLWFUgQoIWOUnSo2rnKJ/jWgve4qVEyoTgaox3ZCGJHWeb451BoNf3aaCnLCNleViCIUciSXSOEIK29TSNZzAaUqRDfFvRtlOUlLx27xF5XjCQ3fhS6xQU3T6722PG/Q69fodnTI/Zql3LCMCZFmEDNkl5eNzy2muvcP3669y7f5ei10N4wc2bb7GsZmxt7NLtDvHWMC+X6ERRVjW9ImE0HtEpCl543wv0Oh2+/dK32N4RnDl7hapaYZqa+wcerSXdzghteszNI4RULMslQoDFo0QSb34bsMKhRMC2MT2jKHLmBFKtaWvD/vFDut2cugqsqirG7aI5ObZ0OtGIzRj3JAHFA+UiGp0XRfQEKOuaVFqWpacoNNZUPDjcx1rDeJzhmgk7m3tM5gu6nQ5p1ufmvZvU5RHWxrSmze1tslTSGfRh/4C9rQ2cyHn06ICd3S1OJnMGo2i0Wa5q+nmf8XCLUzuWvHOOXEbD+F5vwGI6ZbaYcTw95qmnnuLecoEzlivn90jShG6vQzAJ3d4ImehoOBvgwcOHbG1uUnR6JJnGIakqw3w2ZXtzm3G/R6IDk5NjmqpCpynd1lBXNYgl6JS2aVG5YHJyxK2330JKxdVL1yh6KdeuXqQoulx5z2Veu/4G0+MZr772OvPphMl0SrmocMYS8oy9vRGjjV3mr04J1tDr98jGCeg+y2XJxsYO7fEU4SDTKjIeVIZOcna3LlAulgxHXaaLOZ6ao+MDVssW5y0z0/D8pQvMTx4ilaC2FfvTh/THA4oiJe9sc/vePrXxiGAoVyX5fI4AqnLGqqmxJmW+hDR/k7asaf0Sv1KIRKCTEOm9KsfTsCiXHExOSOhy6cpp9g9GfPfNd1hMLD/zkQ+wXEwxzrAxGtHv9blxbx9rfdSdOxEBYZ0x3uhx++gmrbc4H1BEY3RnHD4Icpmzs7mD94qbN+/w7Hvfy9bmLraqGXYH7GydQuou0/mcIATVEl558Q36vZyAYbDuKuJDLLicAzTlfI5XOQ+OFtQh4BpDOzlinEratmaxmlGkBcEGgpWsFkuOjk44PDngwf59Or0hg9EOxydHlKbF2RgOUDY1P/ahD/DmnRvUZkqgJk0gDQlSaKRLCaal6A/JsyHT4yM6iSZRKW1pGfQ3SFVBNxmibIXO+7RK0e9sMej3uHvzDZLM0x8mhOC5/fAOi3bOIO9FDx1pKVfT6CMRPFt728wmsWt/evc0ebHD9Xe+i9QSKTStqUFkFGmfqm7WhWmCqZbMD45RssOrr7+O1jF6e14b0lzT63XJU0WuEwbdPtOTOT6Fsl4Q6yDJ/QcPyYroM5emGi8DRS9j6ipEotne3kWoY47dCY6WIGLSlMoUKtHIRLMqa1AxpUioWKgaQ4x013EQZI0lTdW6mIu0cKk0zlvK6YoQBOONPkmaYq2h08mpQsDa6G2T5QnBWWwb74terxdlU1KRZhopFScnE0Lw1FW9Ltijsa93liDBixh7nqQpR0cHWCOwwdK2htFgSOOWKCVYlguqVYmWGm9rlnWNEZ66ruIAT0hiDJmLRbqPbCbnHBiH1wEnWlqbQGXotxZnLa03a6+FOD4VEqQMsDYtFiIQFFFiJxVKBtK8QEiL9gpPTG9zeJIkQUiHDSB9wBJABqyxVFUTPXakoKxX9Ho9TF3hBZRlSUAhVGR4CO8JpiWYaFqvUoVpK1SAxsVirlquaKVkUZUMN7bJ8w6y6GKJBa0SgtbbKG+R8Xw7H8EviUCoyErWMpoxCwnCxuhm6yxShMgQ8T56SllHVhS0bYOQgTdf/xbH8wlboz3yPEe6hG6/R1WWbG9v0bTxvNjgGW/u0nFXOS5fi+EV1mJDbB46QOkEISQ6ifJKLfW7vkIh4PFIpSMwsQaRhIhG4ULGYkCLlKatSbIkSgG9j+Ca1EhklC6v5WUCgXUWIRJCiPIvY6OpPFKihIhCEiFBBKyNfpN67fuTJMmTYi76IYU1PyeWPI+/S9asGKRcM3HCE/mITtM4jw9rwNCRJlGy6ryJbCUfPSshgl5KZ0+S3STxnk2kol0ZhF4npq2BLdO2SK2wDlrvSNegVTQ9j8BarKne9XgSMsr2HsskEQLr3BoYiowlEWLh5ddAkRCCVGkEHkRscnrvn3hyuxCvuUA07pdSxZCXEOVXjw+bkCLeh95jTAsErIuAalyAx1hDIIJgqV6DUsESREB3c5bTCQMvEH59bpoWfEuQWZSmlDW33/4unhQfFP1hn043xxjDYjrl8M4dhF4RpKZqLc3siNa9wqO79/nxj/4MQibkSQdbLQhBonVCXnQo6xbfNvE6amryXoeA5PL7nuP1F7/EtK2RMsPjufb0C7zzzoukcoWTktF4k6mtsabChyih0UqTqPxJEI6papwLSBVwWrJ/+x18W1EulniZ0liHtBZrY7BOkhYMtnYo8oy9tqUuFxwfPqToDTi8dYcGj6waLr/wM5y5/AyTO7fxtqWtNUhJb2OEk4JLVy4w7BbIfsb/8Tv/DGzFarEg7XWA6EH7s//6L7MK0FZLXGgZjcZ86lM/x6c/9UlOtw13WkPtDHeu36Be1aRphlBgmxaRJNx96bt0e3t88fOfZ6Akdw8PeP755zlupyxaxUc//nHe+d53uX/zHRpr2drYYDI/hsSS9noMrOe1u7d5a3ZMcv8B7XDBvCxJshS9MaCdr3hw/w5PXbtKJ1SYvuK//c3f5GRV8tnP/V2wASeif9HKG7p5wcc+9CzX377Bb3/h8/zSX/8c5zdPRRN+Jbj03g8wnS4ZlSVFKvjoxz7GLPF84mMf4a995Mc5VXTgwhnuPfs8bdOQpzkuBP701VfRSckHP/gCX/rDr/LRH3+Os5t7zNuGYZJhzApkD4iMmMjS+UGmDeFdKOYxq+gJZZA1WPJ9Mzz+9/czg35oid//H08+hB9cwQ+BST+4EvHnfPtn1yD/nF89/vznzPVnFiD/oq0GYgPi+xlc6snndSPg+w/cn1myXINL765XBNDih7f7MQj1Lnj3/TOF9fcueDQwKgp6vqDT/SsOKNl2zmpVUluHdiU6SdFJhpaS1ngKB81yydHkgOkyxplnukOSpEhvOD6ekCYJddVStha/rFFKkZNwrjuktA13yxV5WjDqdCjLOVJCN02Z7N/BZQnWC4I39LoFG0VCkmR0Erh1+xaL5Zyuyqjqdh192q4flIbQOFbVgqquqZaH7M/mjEeaXn+ALgJtu8J7ifcCY9bdz0xhmgWl8WjRRes+86UgpJ62nuO9JctzMh1Aw0JAXTasgifXkjRRdLOUbtFlkOccrQyDnqbIE1rveTiZ8nA64+mnn2MyOaalZmUFG70RPWqa4GmDwIQuae80afBUjac/3maYNTz0DiU64BxNW8aOmNMEJDI6g8bBvgiRRq4VSnaol47gDMbAG/c0+SBnUa2wMuX1QyAkSCHp9ToIGVAqIEKJ9yJK5lzA6gQpBLlokSLDOGh8Gzs5Mo00RJEgvaejJVpDkOtQV5EgMCSpwtuaqjlksozIctOsOH9ql2UJnX6f/mCTXkcxHo7p9IaULuXhxJEs5vg2+iskSYIKgaUJrErDqNjg5t03Odq/w6KqOH/uEmkWi+rG1ggBeZ6xnM+ZHB9StTWzxTFtWzM+u0vdOCSB6fSAVdlh//AB165d4tTOHieTCYmCo5NDTNswPrPJYj7npKkJtNimod/pM53M0amnbWJ3TwJJIpCJYLWcM5mc0NiWVGmapiHrdAgyoXXL9YDOg/JIBVLEQUY06rSx2y8cGIv3MbXGWoFznpALsiyghMG4FQTIi5SqqcgTjZtMmCyjr5lK5swWJYOsS12u6Pf6bAw32dvd5OBgzr2H9+l1upzMThj1ckbDUxR5l8lsRussO5vbjIdDqnKJ1glF1qWXJrR2hRKBM6d2eOn119jeGnH4aJMHD+6TJAmDTp/x1g7HHFPO51jnyLMMrTVb4w3SJEOg1j6aj2PCc+pqRWtq8lyjdIqxlqo19LOMRCdUdQNyRd6XeONwOkoolosZH3r/c3R7Ocv5lMsXz1HWjqPjCS+9/BpVXWPamnNn9ljVNfbYowQUnYJHj0ru3b/D5s6Yva0RFy+cZTFtefvOlEIl3L7zkNnyBAigYNjrkScdvIXDg0OsMxxOj2LkdOMQw4SqnVNZi6ha3pYPWJkmJgA1Fpkk1JVjsajJOxmdPKPXyTieHtPvDlmWK6yMMhgvAl4pThYT/I2KLI3AuAgCGwyZUox3dmlNyv7JPs47bu8/4sPPfpCLF59iufgel8+f5dX5TS5eO8f+wQm9bo+zZ86zms6olzN6RYISgUUVpVlHkxN8E3jPtSvc2X9AtajAO2SIqVOBQG0bFssFnTzn+KSlLkuKvIdXgvHGkPPhabZ3HN/89rd5dLjP7be/zXhzk0Ev4/DRI05OjnjxlVd46sJFQEUPo7bh8lPvIekP2f/KlyDUpImi01H0i4rlcsr29gUIhoPjY4KVJP0+RT/HiRYhNXmRE0TLo4MD2ha8CySpZGNrxN7pXd648yYq1RAKvBYIayjShN7GmMVySioEJ4ePsLaM3cjplG6WsdPts721wWQyRdbLyOaUkrzQXLv6FO1qSmun5LlEAUma0JE5eRpo2hLnWkoraRdzjFuwWHk8bTS99DAoBvS7XVpb0vgolwlBUtuA94FumpGmKVqBtYGsKxFWsFyVtLalNoKtnQ2E9Kzqitevv0LeG1L0cqyrWCw9wq0LWQTBRUmJMwaEx9gUi6M2LdPFitbYx/29KOsJUNcNQQTaNgIASZqRZhlaS7QU1GVN1UTDXaV1pKbL6JVEkKzKkiRNaNsG7xw6S6nrCu89rTFk63SpalXjPYgQfVaqsiHROTb31FVMjQLBatWQpUlMy+p2SbMEnGReVpjgYpfax+eKc479g4cokZEUBd4HmqYiTTosZktca6hWFb1uSr+TUYiUIsvXkewxgcqvn1NCRONlJ0AoiakDxtTMRCDVjm5m8S4CE1JrhNIIayMT1YFzHm8cSoKzisa0lK2hF6AxjsZapHDoINYMjOgd44lGoBBi4u66eeklmBBYTpb0Bh2cEzhnoi+jd7S1YzQqCN7ifYLDEIEJQWMcRS6wtUUlCYJAtVrSlqt4jExN5+w5sjRd+/RE4ZYX0ezUhRBjw+2ahSPAeUun08G2PoIc3hHLlzULSyr6/SGdosBajwkxBlxKhTUWnQZOn7nA3tkLFMWQ+eERrijYPXWeelnT6XRomprpbMJkNiHLcop8G9M7T+slwcVt8SJKBtIkJagIaFpj6Xa7lDaaN0P0iZIqmo2jZDSWFpKAgcCTVLumaen0uxD82s8n+hYJJeL5CR6JwLlYqonA2hx6fWxCIJEK5zx6nWwXiM8oQkxpFEKSZ+namyim6LWmpuh03zURDy6aSCc6gnMh+jlFQ3aP9zHYIoI6kbkp1mND0zZ4Asq72FkXoHSMX9c6JY7QItDHmqnlQ4im66yZT0hcU5OkGuOiHO8xkUEKgW0jQ0+JtY+Jjef/sf+WQKz9n2KxpITEhsclVJQDhvXxVTIm1nkfx7daa5qqIlVqzTaIbC/n2ghoKbVOs4NIG3q3NPOPPaxkNB43pkWsq9ngIrAXQS+NUkn0APVh7dOW4qwjKHjMdjBNRVb0aACN4s5br3B8+IDO+CxZlkfAlAzvPUWn4PTFi7z1ykvUTY2UOc6vWMxmnD1/iY9/4tMgI/BpmhWPuRZaJbimZvLwAds7Z1BJQq4TQOKMxQiBFBLbNNQC5osj9s6doZzeYV7VTE4OUd0uYeGQokGtWRHBCUSIQQIiUSDimFnJhNtvvMY3v/QvKBtD3umjvMWtSmSAtg0Mt3bYvXSFzfEeSZFz46UXcc8YJkfHPLpzlyTRpGmCb1uWywVNkCycYW884OK4xzMXT5Onis08YaQ09BLSn/wp/vPf/IcMB0NaAnmRkuaaz/zUT5DNVrz2pdd57tOf5IWf/AjWexZ37tM0LR//6IcZnznD8f0Din4H4R1f/Gf/grlW1MsFL738MvrcZdy9d5jYmhdvvAb9hLfKEz72wR9nWbXo8RaDakVva5OtYsSf/MvfR5HihOKd773GT/3df4++0Ny7M+e33vwyw6ygd+kyH/7ETyNPVixPjrn/5uv80Te+yas3b9LmGZ/77C/yt3/2U0zLJaNej9/5+le4fPUSV3Yv4K0lyJTucJet0WYUb4nAxXO7/JuXfwnjKj72zHPIRHP/eMFSOp7d3kJLgQue91/cZWP4SUDgCLz26Ijf+t2vcnzvbS5fvcTrB1M2en1++uoH+d6dQ15645tcvHqRn33vhyjbin6arTlIP8we+v/L9FiO98Pb//iu+cum72cx/d9991dneiz8E0Gs5aqOtJBkSf4jXc+PHFDa3d3h5OgARGBQ5KRpl0QpbHBokaBFYFnWONVlOFZoqVAqZbmcs1zUNHVDr9AsqkCvNyJVgrquqCdzptMVU+kQmUD6mrJcMl9OmS0mWGM4eFTS2zoDLhpEdjoFu6Mhx0cnnHr6ClIEmrqi2/esVhNmyyOcNzHCL8uxoUaqguDnPFpIiqJDnibsbmyS5B3evnGd2lqsNQgZk1LO7u0iul3m+7cinc1Ft3gtFGUddaidLMeuHtG4gMoUrgkIJ2gMSBUYdS3DLL4MhPTsjDpkWcK0WnL3/gSVZSSupW6W9POWcc/j2zn4mtqkyKRAJykOS787JpmtSEXAmoBOEuaLEwbdFBM0496YPO2RdgqkUCxXFaa1dPtDUj1HJyllvVizCRoWTcK8yugrRVlrBgWkWYe6ibIunSzRKiKu3huCEmTCYR0U2rM1bMDD/syRFFC7hjyPHWB8gvCKshXMlCTJHFoEHBqddihSz3izz2w6wXjBzqnzbI13eOmlr3F672kOjx6RFilF3iGIQJb10OsY2cl8HrteQoNMKMs5OumRpwVeKhpSRts77J09z4PbbyGbit54gJKK0XgzgjJpwo2b19FacfrUGd56523apubKpQsMutFDJ9U55WqOwtErCgSBJMtZlRXd/gZFljAejFAC2p1djmYTuumATr/DamUwOHZ2Nzg+OcR7se7+GpqqRogEhGfVNmxsjNkYbbA/OWbQ67FYztEi0uSl8nhb0zhJ0cnIdLZOQbGI0OAdtDWRLpnGpB0VWpCS5XJGliXoTLEoaw5OFgz6ga3NbYxtOT6ZYk1gYRuGwwEETTfP2N3Y4O6jR1y99BS37tzE46nKitnJlCsXz7O9uc1bN66TJYKj4ylN0+KFoF5M2Xr6KVxd4YXj7sMT7t6/y9UrZ3nrxg0uXzpL07YMe12U6CDClMbDuOjQ6/aYzKZsSIUU0cDTe0fwAmMsgUAn62CtZblY4K2Nw1upaNooI5BIjGnxyyVaZ9RNRa/fo9NLGfW71MtF9PUw0dj16HhC0R1SmcBwc0g+GDPcKCHJSZNA0e1wdDLHO8d7nn6a6aMZRycN00nL4cEx1y7vEjBMJkc4JzDOMuz3cW1MA0qzFLtqIqMEj3Oe6eKEtJthykAw/kmB60WM/bTO8fadu4TW8+yVy5TlkmU5o6lWnNra4GC+jPHDziCVptdNqRJBmiXoJKN1Fk2NN45yJZCqIUkb8sQzm0aZ6Zs330ZlXTIdzTrruuatd27z6NGEIktJU4na6PPCB96LTHvcuXeft95+izpY2rKlXTme+clzjLc3+MqX/4DgM5wxsRhJJUp6VuWC8WjEtfPnyJNs/aLzKBTjbp+tUc4XJguWiwYnNLt725THx1jvmMxPePtmTInZ2dyKxsWlIYSANYZMKoxq0ElCMI5WB4KQpFnBa2/c4NmrV9nYGTIYD0lWiueuPcu1KwYLNE3D5OCQza0d7ty5jw0O42v+9OU/xQeJt4BWbHa32d7cZn7ScObsLu/cmLNazDFli8wCPnFcv/EyO+OzWAy2atjeOs3+3RsEG7Cypqzh4PABMgkkIiNVOVmSs7vZJ8+3mM0WLKvbyGDpdTSXLp7l3v19DmYTgmvWSVwVpu3jSwOaddJnC1LhhWEwUPSyESodc//RPnmaMJ0dsbV5miUCnUusCfSKPr5csjCGyhhMtSTXXRKR0E26zKoIEqgkJo1Ka8B7SmtZtguEVEwnU+aLeRxbuQDeP5GbPY4AN9biQyCVEhGi9MmaEAEJFZCKdSCDo2ksmxsbZFmOVJr+qMvR4QGtbWJ65iqs494lQusYQBACIgjaJm6ftZ6yqtfpby1ZpnHe41pHd9CjqhoSmaBlgkdizZQ0zwihpTVlHNgEiVYJaRpDNJarFdgKqyStb9E6Je3mWGco25JRf8zmYJM07dC0JjZqvFsXsRHgkUS2jbEGQosNKUJ4Ov0ORd5BCbnmy0dfHIRChIDWOU1jMMZghKZqDd1BLLSVTkkShxIKFXwEWoVc+zcJtBDoNZtFeAhB0NiYsiSkwllIdMrB8SGdNKFazlFKoTMdpTtaEJTCixgzH7xDI6idwxcJoVxRTo9JtKCuS7qd/hMCjLUNEBlHygWcadBSRQbemh0GxMJfCFrv8TIOhqUPMeyCaBRfdLoopbG2hiDiMoKLjBRdcOnSs/gQOLz1JuXRTeTeebxvEUqwWi14/Y3vRYAwTbhy4SpZlnDqwjPknQHCOxQhgslKR7NwHKZuCc6DSgltTZJm8TyuZQfW++j5tfbWceuSS2tJ05T4te9RCCLKGteeV0LG517APwHFsjWbnLUXUASuAlpLgvForfB+3aEPDrX2W4rR0xF0a5smLjd4Eq1pzfo6WnfelU7itfl4e318/wgh0InGPU62837tfSRobbMGq328v6UGJM46TN2AVEgVnkj22taAIMq6ZARrmrbGe0c37+OtQanolSSUIjiHW7OhhIgpedFwXD2RnnkfrwnWjDbnfWyUfl89+JglpbUihMgcC0HgQgSWQMbzGi/8mNqmJDJJcEE+Ebg89pd5UiqGQJqkaClRa+ZSlDwqdJqgk5gaZ2y99mnTWBtIkwypoxTRK0FwFoNj0OtilhVSwXjvNKI3QuouSR4DB2QQSNEiUPQ3ttg8dY7e7im+8ydf59Spi2yeOUVH91jMTtg6fRFoWZYLvIjbHITES8mjBw/Y2LvP6YtXSdMUL8A6g9AJhdIYUqqqZjY94GOf+0Xuv/m/YJYLfFWiSaMh+hp8DghUkuCwCCxaCIJOcBq8bSirBd/44ueRWpF1EiQSlaYMN4csly3BNHQ6BZunz6CTjMmpQ/bvfY9XX/kGIZEUQuOSlO/90b/k7Hue5YOf+CRPndvmwplt+lKx2SviOQ7RR8bnCc9/9uf4zB/9MZ9/dI8HD+8yqVt0Krj5rW9x5Sd+CnPrHqpToJwjU5KHt+7x5a98jRcfPeCv/Y1f5D1nTqHThNXLr/JP/of/kQPlWc3mnH7maa52DC/8/Kf46he/yqwx/Mnr1/nXLl3l0s4exsDf/Ft/C+9bXvr2t/jq177OfFnyYz/3cww6I159+Rv86i/8PDoE/LUrVN/5Op/++Me5dOEab8wWfOR97+Vjp87yj//Bf8O3vvUiswc3GW50+OVf+hu8eusev/W//VN++mc+wd//r/8Bv/53/i3G49NsZikXzp1ie3ebIs1jchiB8bDgzKkzfP3bX8X7ZxBIrp7axHgfm/RBxmRPJbiysQFC0jhLohSXL13mzltvU3nNv/pzn2avk6Ok4ORoyj//nd/jN/6j/yCGH8BaQvuDare/bAprP6E/f/qLgJw/D7RhzYT6i1ceftgM6S9d7uPJ/wXf//C8j7f38XL+34BJf9Hv/p8AWf/fp9hHimA7CJpVSTqSTzzqflTTj17ytlyhREYvE4x6fVKVYX1LKjXKe0xbIZKMQbePDrGDLhX0UsWiqTmZHNDUitZBIQsSmdJ4z+17d5mhkIMu2+e2AU21LElkFjsUSrIoVyS25f7Dh+SDbQiBcxev8Oqd25w7c448U7Sm4eT4gHJ5wHxRobICbTxXrr6X/QfvMJmXVJXBeIkOAutaHh0fI8SCVRUHDZPVkuNqxbnT5xhtbNMZdJkPBtg6cOPuS5RtitAJja3Q6ZhulvNgCml3gPRLulrTNAFnPEUBKmkxYk5rExoH+9NDrI2JGlVdMshSbty6SS0qmrbldD/B8Lr7ewAAIABJREFUmwrjA6URZCJhWS6QSY/RYINUO8r5MclwRK+Ts5iXbPT6USvbWLQ2hGWDbQ2KlqppafMchCDN8hiLqzMW0xYtNVpYqtqhVEGWGUCtO9MBYyps6xDSIpVEK0GWtljrybSjn2ruHLY4YRhnBmz01FBCkaSwagSN9RzWPqZs0OICjIZbBNPBucDe5adoW8GZ85fQKqXoZuzt7dG0C2wI7G5vQxDINSXbe0GWdVkspiwbgbU1qZYMRgWb27vkXiHzAbsbPeqgybIOd9/6Lr3eGN96Noc71KuSxWLJyeyQD33gA+R6gHd3SVNNnqWkSQdjDqmrku3tbW7d7dDtDjHWYFpDaz1nzl5GEUhVglAZRdZH1TU7e7scPHxElqTYaBWHx8SuZgBnBc46hPToJA5YbGOYHR3S7XRJRaCplygZB/I6URT/F3NvHixZdtf5fc5yl9wz317vVb3auqrX6m51i5bQglgGGLFNCHAEhGbABhkz9jAMGDyDBYHCDo/DweIIG4E9hIcZezCeQcJIIATWhpaW1Fp6r+6urr3qVdXbc8+7nHvO8R8nqxFjYAZGOLgRFRXxql7mze3mOd/f9/v5xhozytnoJaSNIxyMJgyG4yBCzDkPtZpGSUtZluAcvaXAo7rn+Cp3treJEJQOppmj11EUVpKVFd5VNJoJjzz8EDs7fc7ec4KrN26wvLrAWrRMNZvwhQuXOH7mBOPhkLI0tJodVnurrHQWuXprF6HCVHWaF+wM9qEqMEVFu9Xk5MkTTIc5Z06epNFqUhYlSRLjvWQ4m5GXFVGUhlplN8SUFleVKBVRWkMSB6u9KQzNRhtjSopZaNZKkxTvPM1Wk7woKMqSvDJUVhI3Q813bguWVheJm3UkCgrDZFag0iaDwymPP/o4l2/eZKHXI6kltNrLZGVBrZEicHz56acp8ymN+hLP3LxKmU/YOezjSkezmWJzAEctqTMdjdjZPQxurTgh1RHj8QhXeRppLVz5HVhjEMJRUTLNclQMo7xE2IgodRRVwWg6YZobdkcDMjMjq2Zc394iyzN0XMMaSb2Wkk0mdFqdAMivoF7v4cgoBrtYJylnOSazJLWIymf06inWGeIkotvq8soLLxHHgpdeuYywnrIw3NreZmmhx+rqOvVmi40j64yHh9y5XTDNSqbjgmavyZmlZT5VBbihcSUIT62p59cLQT2thzp4EabsWgqsk7TSOsNyynQ8wjrH5vFNDg4OyKdjRByztLzMsbUNhuMMV+2zdfs2SatDktR5/qXzZMWYZs3Tbsbs7htmJsIZwZ07W6wtLxKnKZ3FhfCFKhRJ0qEZS8qypEodjz38BoqqYPv2NmiB1HXGxQgtGngVxOOvffwtbN/ZYSJ22N25zWS4h45TGo2UwkxQ2nFwcIMiGzEe9ugttihszMwYiqxEJBF2knP+1RdxlaHbanN9awtbThnOhtx39ms52Okw7G+T2wLvptS04KEHH+cjH/8YrU6PPO9j/ZTOYovmzgLD7HBeXiEpjMWKCUnsSWsa6aPgTDMWJWMGwwGdVpM4VkyswWQ5ucmx1uBshckL6p0GSqTU6oJJloESFIWh3oiIIkEr7TC4eYf2Qh2QjAcTrHchpmTDRlVrHZZLpQcdoLPCS1Q9AlxgliiBtxYda8o8D6KDFlB5jClRSlGvp0ggTmKimmSW5aSNhFhFZHmODJYSpBCoOFQTV3NXBl5g8sBhiZMaWVayuNAN9eiUTPOcqKwoTIl0jjKb0GhErC4uMM1KkAnOSybTMbYygKc0BQ5H2mtRTTJsLaKuNN4FWGmqg2PK2oLSlBhbAgI5d/FKK0Ijny6RXhDJAP6szSHCQkniNEEEikIYOOBwTqKERCuF1CE6U6/VUIAtK8pZho4kZdh547EBZG3nZRiIObBcUms2qI9icpWj2s3QuNpoYF0TLUoKUxLNHcNyzl2SUuExWF8hhcSUVQAYO8FoPMBZQ+VAK0mkQ7U0zmKdnfNrQpWxq0riNHmtIQjEPNKlsCI4biKnkAiM94EpNDem3I1RVpXBlSWDYR87bw9r1FsoqZn294n6r7C+vgTNRYx3OBwXL7/My688h9CSU6dP0et0qaU1ammKKSusybCVx1tP2kowEqxxVKVBKomXkso4SlmGoY2zr0UylZIoHThK3nusd9TrdUbDCY12M0RSpcAKP/++D8JViGOpEH+7C9i21XxIGdrFgsARmEKBoXQXoh1gz955tI6QWgVguwuuJx3FQHjtPARXmI7mTK5yHmULLYg+QJnwNkDjHaHrWkhNks7ZRFESwNM+xOy985SmJEoT4kjPnYmCKI4oshncfc94jwXywhDFMbHSlNbNo3thi1i5wHiSWoGcv/PntdpSBAGUu+8FwJRuHn8Lbi433+kKGVxDiLkDzPrXgNnyK0QmRIhiB8NTYK7cBa14P2+Jei2qonBSEs+bkbSSmNfENkWsIqL5bVlbvdZQ6azFq4goTckmU+rtLmY2JYnqVMaiVQRYWkurTLMbNBdCMYP3NmAarEDrBBCsn7oXD9SaHYa723gkZx9+PTdfepr1zbNUxZQsL5ESlA9tdCpOiGot9rZu0e4tkSYxRZYzHQ45cfocWxeeJzceZ2F1/SQrpx8m0h/AWolUglazTZVn5Jmh0+sFAcMZChMa6vDBUavTBFNYwDGbjXBOEM0S6o0a9XaDOKkTzUJ72q3Ll2gvHUNUU1SV8dIXnyTLxiyevp/s2mW+56d+jr0Pf4KXP/JH/PA73s5iZ41Yhc2vnAct74qjUgp8K+Xbf+xdrLzv/fzMrWt897d/Jx//449y7NhpGkcWOHbqvnnESuKdYGFzkwfe8AS61+DIYpd6mqAR7O4cMkyb9NoNxrt3qCrLUx/6AFfOH6PfL1jeWOfvfe/3sNBMWW7WWL7/LNnZeyhdycXRPv/pgw9y+Znn+P7/6B38zgc/QcRD9NIaeVmy0qrzk+/8u5w6tsH1gyGnJxNOLrZZHDuOftM3cvHDH6a/u0Nz4SH+j3/zmzz7hZd46fkv8KmnnmJw54AP/O4HedMjX8NKskEkBeNixvQw42ijjZx/tvZHGe/7nQ9z4swjbPaW8XFw1Ekf4soH4yFCRyzWG3g8kVKcXujyfd/8ddTShNc9cJpzJzeJgTgO0dSo3uSBzVMh9TN3AYs/K/L2FxxiXsrwp0Ul/+cITXcFmj9HZBFzQfnPv7d/z7P6q/7evy0g/dsC01/0e38d5/3vOu4GngVCOkBRFRVpnPwlXsF/v+OrLigd9g9J45g0XaDTrDPp9xlNZ9QaXToL3WBbVhKponnloCeJBLtVQSzqNLxnNjNEKgAHV9st/EGfptVoByK3FKMJspEgZExZQZTU5qwby+qRDVyiabaX6c8yVo4dRV26ws7uHVwRrOSHw0Na7TZrR0+Rjw64eWuHtFYjSRQHe31mpUXj8F4xmk5JYkdWTqgK8NLji4K8DF8AjVYHHQk6C4uUs4K9Z/pMbRQAlJWlmzRZXVlhWg7xlOAjIhkxE5oqdjifY32EwaAiiG0D6QVZOSKJYgpjWex0yId7WFMSkVJUTaQWTCaWaQUyDiDByjimkxnOVNjSMBnuo2XI3huhSHTEwXCfKJbExHjhaHXazA4OGY4O8HjKMqfeaOBMQTaTwcosFYX1NDo1vIzmpRyGONKAw85bV6SX6FiRmwTrDFHc5fqgZC83JA1PmRfoWCKUwfgKpSzWJlgLuXAkKsY7g/cle6M7pFGb/nhGY22ToydP02o2uXD5Co8+/s1orxmODkjSLmkzwRQFVRWagYwxSKnY2x+TdBfBBavzIK9IfUy9XicvLZX2aBypCKBAhcYpw8aRDZSUXL32Ms1mneWlVSLV5HR1gs3NU7TagUuyvLRAo1VDCsHGsWOkSY12u4PUMdNbd9jb3ifPZjTqTdaPHGX51Cke6byOK5cv4VREGkWU2YiysOAV9VqIBZjKY5UPoh0KhaQ/HCCFpOvh9JlN0jRilhuuDW/SaDSpNxrkk9sIL8lmhr29XabTAokkTRRe2MDX0IKysFhCc1tVOsaDMRElnVbCcOLJqoLD8SFaaqS3CCmJhOTq9TskqebKzTvc2NrBakkiYW84ptduYK1nOp0htWJpaQEVafYODqmnNertOtJ5GkspOwd7rC0vcuHyDe49vkFL1dg8djTE2KzHUlHmAhlp4rRBXhRkWRYgoJVlNBojETQaXeI0nk+qg51TKEEkIrQKjgelFTqKSdMUJ6AoS3Sk0bUULz1Ru0VTKxw5e6MDVjorNLsdjFeM8pILly7RXlxibXmNOE5xwlGvd0nqnna7QVZkHDmywUF/n4qUVr2Hryf0xzlp4pE64qA4JEpiXG5QXjCaTUF6GmnEzIyJZYqOG/OIYQJeYs0Q5SVlWRG3I7IyI4oURoKpKiItqEzJ9v4eeZFROUva7DKcTPGlZZyN6bUaVM6AleTTGZ3OIt3uMoWbYSoBZRchNL3uApWpODzcRSJwVqMbdQ7u3KTdOMPunQFrq+vsj/bZXN/gSv+QyWjMYneBsrLY0RhvBGkU4YRFx54kkXzpqWeQOiWOIpK4JC8UKoE0jSiLjIP+AGteZTqdkNQFy0s91rpHsBYaaY1LV67gyynd5RbeTcnzKQZHd6HNkaWj9BZWuH71chAsfIUwlhu3r7DfP2DichY7C7jSYb0mm1XYvGRppUs9jdBScePqLSosg/6YrRs36HVrtOpdGu06tTTixsVrJKkmtxZshbMBAhzpBpGKef6F8/QHwyBgVhPSWBGnkqKwtNM2rWbC3t4Bs2nBdLTFuFhgaW2Tzc1Nrl+4Q9LtMexfZ2YM+dRiihnTyU06bUeW51y8ch1XNmk2lji5epQbN7bZ2rpOu50TySl2VpBIyIs+Ozu3WTpynIPLBxiboWkQCUdV5QxHJVWlibxFUuGtp1FvMzMlFZJiHOIeg+EOxh8iYkUkE6qyQitBkRfM8jGVC5s7Koe0lsJXJFHYkColSNM2k/GISCu8lSHWM3cUBDCuIo4isllJGs+5AyJECsGTVwZnKrxz1OeipvOC2XRKWZSBySfC5tpaRxRHRIkmjWKG82tCOmfUlGUV3JtCEkUSZy1lUYaFsA/tSpWzTMYZeM90MiONI/K5kNto1ohFiiKllkTEaZ3xOEPpCOlCNKb0GZQWW4JSEWuLq9SsIxFQa6TzOA0ILYjSCMrATwkxIovTDudLIiEQVqMIayKFRUpPmc8oZjOsDE1n0st5DbzBVlWoKFfzjTShoUrGiiQNiE6Un8f7QsmG8szrb9IwrJQKqRWmgqw0r4GcJzZHC/DOUOUVtUaMEDpUtHuJ8gJMcOxInZAVRdhEl57JcEAtSZnOKrq9hMqYcE3xkqqwoa1MOrybx6+tx5sSpKYq8+AwLCqyvKDTbQM+XAeND5ts4+Y17gS3R1XhqpJ2u82l69e4eesG9oGHWT+2ga430EcfociDC2Y6GnP16gWu37jM0vIKZ07fQ7e7iq8gLyugxLkquGBE4E6kUYRzFXiJE6FZTklPEoGzBj13gAQOFPOIW4AxKwlVFd4beZ6T1hKUUkGwdQ6t9NytFmDSWgf3sPdiHg0UlMYEoVUFMc3PI3R3Y3SBSRTo084JdBS99vnwPghbWiuqqsLNYdTGFAFu7irw4f7u3pYQUJYVUlbBJTP/vCgVAaFxERc4WkKE+7WE9ZW+6xIitPGFWKxHxX8C7xXeY8qStFF7LSIm59sa4UP0UekIpMA4GwQeCba06Llrx8M8nmcpS0OtnmJLRxKH5/bucyqUCq47Ny/TsQ7rAqtKpxFaRxRFEcQJFWD5c2UpiFHz1yKcePgjlQJUcOwgsH4eeZQhYujmMVMhFMgI6yvKcopOEpqtLtPRkMqUmKKk3mrfhRLhcoMHyqyYt+uBcAr83NWrBVJqkkTjKsPaxiYLy0tce/FFtq+/iu62uHbhRbobR8nycg6v96HoIUo4duYss/4+l84/B+4ct66+ynNPP8X6xia2EhSzKT6uMxjmyLIijmMaacKJBx7i3BNvZOul81y99Corm6fwruLmlVeCWDkX1WKdMhoOKY0j1TGZmyFkjCS021VlxTDbp1bvcuqhx1haPcpgZ5e0s8Qrr7xApAVLi0d473/73/PhX/wF/rN3fBeDs/fzv/30e/jVX/01/vN3/SjrR5a4uymXLgz7vRJEMqKKBUu9RT7wmSc59+j9HD9+lPVOh0defw6BJF2szd01QYro3nOcdxz5XrwQpLWUKImopgWH7QY/9U/+EctS8Q/e9YNcfPYL/MB//CN89IP/F8MyA+/40B/8LqfOPoJrLHLpxkV8GvHypz7FfQ/cy9ueeANPHN/kn/+v/wtT1eZ6/xA8uKKi1qrRMWOG2zsMz1/jxOP3c6zdxUUl3/L1X0vj+nUeeOQ+LpclLz97nqc//Um+7Ud/hM++/0OsnbqXl7/wDJdefZZilHF1sENndZPjrTZIRZ5NycoJrc4q/ZkmUgm1JGWY57SjFBSMpxOUFnziuad5xxu/FotEC4HUkvWFNt/3t95MkkTUomjuZoFTZ07z9rd/O32nWY3uOgXnUtJfMuX1ZzmU/vTPxFf8/ecJL1/pCvqrHn+RS0j8Bf/+lf/nr+pQ+us97j6f3v/J8xPqIeazah8eX5nEQciWX93Y4lddUPK+ClPe3DJKJC6fMBlnVCT01jaoJSFbbq1Fp1Gw5wIrR47jhAObc23rDqmTTK1Bj3PeahfZKgdcdWMgQe8YosU6nU6Dw4MRdmZZPrlMW1q0s/QaNZQSbPUPUXGdpAaXLr1MM7KgDKK01JMGsRCM8zIAffMJWTZFaYHILCQR0ltmpkIohVaaSlhcBTYrQ/WwEEgpmY0HnH/5C/THOZmVJDJCS02zUQMF62tH2N27gfcxqgq1jCrxFC6n8gWRSshNhaDAlBE6aXNm7SRXd66zuNhB2CmFLxE+Iop6SJ2SlVMGWYExEmsL6lENVxpG4z0KM0ZLg/ITvIiIlKY/7bMaLVJvhKlvpDIyB8PJGFsakkbCxnKdrZ0+Jhd4V+JtWESFKto59NmWWCMovKOVhOlLnGgqoyiNw5SCspDU4hZ5RmiWIiNJNIMBYCVLDU09aTMqKha7PfqTfbrdDkU+JbcKnaZkpiIvhmidcmz9BK3OAkrWMDZCNTf49FMf4tbeLe4/0QLn6E/GYMGbiqIwtNsdOis9ZqXB6Yjd0Yz28hIqimklMSgoZYWbDDDTHWLtMa4gUjEIRWZKvJKsr68T6ZQ8H5PGMcPBIYJgV283O+hGqMKO4wYLvUVMBZUx9Pd32d3dwroKs7REt1en1uuyuXGM/v6Qm3e26XU62DhiUoxJdeAuCB9iaVlRoaOYu20EOo0QFZw6usajj5zj9z/yGfKspNttMp06WnUHaG7tTXGiII4UcZRQuVAfLVyY5JUGrNOkaYR2Ei8ytgeHWFvR6baJaxKfjymLHC/msaw4otGMEVRUFdzavcPy+jLb27e4fbhNI21Rq9XZ2b6BKeHlixfwrmRn/5DpdMITrztHr7fOpes3+NKrz9Oo1XBCsLW9S1bmPP7gfYioxnQ8YUSF8wahQj219Iaa1mgpcLai22qChESn4AzeKbwNtdhxpEPzjq+IdIQSzDewGUVREquINEnJjcFXFukckQ3V1c1ancnBAQPXZ3FxBWEzanEdZwwXX36Jzc3jyLYjNzmTWUZeGiK1xiTPOLF5ktW1ZSKvectb3wJUHFm7zLVLFzi6vkqvd5Qnn/oElcuINOS5hQiyomI4NrSSgkQmOARJUg9tKWiKsUEndWauIKsKtJIoCYmIWel12FhdxZSW9aWjGG+ZZjM67S4my9DGUJUVsW4gJAzLnCaWvd1bEMPpk+vopSVu3LzJnb09TGl59P6z7BwOyLMZB9t3yA92uX1nn/Fswtv+1uvJSofzgkuXbrKyukQ9SejvHjCeTDhz5gyPP/Y17B0MKIQnKw7YurNNWYFTlkhHIdIhoPAefOCgdDsdNo4so2PFeLyFWtugqWpMpyVPPvkcPtLcd+okaax45dLzNNUK3/i2t/Ds519kcaFLlCRYrZHEfPmZZxmXU3JniFQKJAyGI4wXZIUliiNSNSHSKzx7/kXWjmxw4dLVwGkwM1aPPUhnaY1Op8bVy1e4tbWFrNfJphmVmRLHEiNhPJnQbKyxvzdlPB0SK0G7nZLGiqzIOLGxzkP3v45Iw43rWzzz4otEuk233eLIkSMU45KL8hZRVWLzEmUNzUhjvWdSGOTMIVTENBtTixT3njnN2RNnifQzFNmU5bV1Ll27QWUyKm+Zjiqy2SV0HFPlOSUCLyxpIoEK5xX1pMHeYIKIBJEWFLYgUoJ8OiTSMY16hCknJKmiKAOnp6ocg8GEPDPM8lmoK6/CAnQ8KhE1zyQfoRTMpgbrB6hYUW80aCQtRqMpeVGG5hspUc6TRBHUElKtkYCtHCYvcd6/xiRxCCaTCd6aUOohQl15XgaukU40k+mYJIoxRcFEKrJJhk8dS0s9ZCapSkscK6QQNGt1KufY3e/TatSYjaeURQXNGrFWxEmERVLaijiJUVqRT2fIRDNSGe12irOW5eUFOIB81mdWZZSmCvFhmaC9YDaYkdZrNJIakZVU82YrPzPYbEphLM7JOX8kRPpCE4+lHeu5OyTBe42Sikir+UYdIHCYpA0lyUWp5mDhMGWWwqMJLq/KFSES7ANDD+tDk2BpEammIohTSkYoGSOUQGlNoiOkduQmuMYwGik9lbfEUsyZLsFhJaRC6AgVKYrZDBVpynxCLCSVq1A6sHWsk+CCp8B7g7Ue5QTGVVRmilWCSNep8hxTZOSTKWmtRqu2iMBSITG2ItYKJSSls0gFOBGg1hpkHIGKabdajAd9Pvn5P+bY1mne9IY3UegWtw8vcbj7KjLtcOa+x9GJ5PTm/bRbPaRSzGZTdJqQ1upopZFKMRwOMFmBUmGkk2V5gG4ngZ8D1RwSL0OzmQgbeBVJpNRzUWHuehGB6QkJUgiM80gVB0yALREqODiVFDhjQhwRgXcirGn83Ql/iAVqHQcHkwvOISnmjCzH3PXkKE2O9YHDZW0AZbvqbiRQhaag0gQA/TxW56zH2RCRTJI4ONIklJUnihSuKufwfIOVEDFnqZlq3uIWYmnWOXQcY4oQOddazzeoIgg43pJEcWgcU2F9I+etdcjg4PJz8VpoQnnGvJFNcLdxKTjIrLN4B1EceFDWh9iKEDKIRPh5PCfEzaWU5EVOXTf/VMBF67sCdxC9AjU/bMLkfBXmfOA4eebNg1VgYCmlcXddd4B3oRHOiyCkl9kMZES3t8j+zasU2TJSCsaDO+SDEFULBj8ZuHrNdO5Sc3hrwBpkJOdSShCha2kNaTUn7ruX5z71GVZObJLZKfFCOzTbeYeXUPmKNGnRaLToLHR5/tOf4qJ/lquXX+ZgsEu30yYWMZUoSGQTKTQXnvwsTosQb0azcvQ0ZjTh+pWrbN5zH1o6skGffJYB81gkELUW2Vxa5cr5F1FxTCpjVjZO0Fta4Nr5p2l2ajibs/XqedYfeR1Hzp3j1MIaX980vPu/+wz3PfIAX/6DP+DS6JB0bNk4c4IHTh7lk5MJau5gDi9u+MxJHVHMZownUxyWX/upd+PPPUrz6gX2SXj4gXup1WqUs5y8EVMX4fXCwf6FLawQrJw6wmw8YVZZammN/dvXuOeNb+bJP/wEY+9RvuL8i5/n1s4t4npCaRwXX77A6j2P0mn3eMujT7B7c4tPXrjKrb09PvpHn0Yoh9+ecL1/h3/8P/wifVdwuH+H3/2t3+TGznV+5mf+G04/cg+5Mfza//4bbN24gUwibu0c8F3f907e/3M/j/M5reUa3/K2J/j4k1/mv/zHP887v/d7+Ve/+duQtDh+/xl+4Hvu50irQSQlz1+/xjgvONtKeOzMvfzrLz3JtWcv8uA9S/zE972Ll2/eZG15if/xN/41t/tbfMNjD5FGLXZMSVfHSAG1enA2Q4BBG2s5u94Cdy+LkeTStUusLSzS6fbm4tx/qFrwF93Av+vG/8Pu/K5j6v97/HVGz/76Rac/6zHNpfzXOEoAylmSWvpVp2B91QWltBbzSLfDpes3uXil5EgaM5nNiOttfDHB6hjpJVUVsuN4R14E4KSNFLGXrC4sMBzNWIhrlDd3eXlgWUwTzsoOt8uCXT8LsNLKkBtP6TyDw0P6e7v4/piPP6l46xueYKnbJvPQrcfc6k9CxKCsmGYjbu1a+ocNfFUSJxHjfp/ZtCAvSlrdReq1GnnhSAl1t0JpkkbFNMuJpSebjSnGGaLRo1FvIpynn804fewEzzzzJWq1Bnu3D1lc1nz6i5+nFWua9VBR7JwhETm1JKcood6Q9PdLfAWGnEdOPMT+nZvEqaIZG2RkyfyQeu0YWT4lSRRaG+pJ+LKrxUkAIhpLOZuQe4t0OY1EISpLXKuDnXJzb0AzKqnpBDPNmbgGpE1KWaLdhNV2ys5uzrgQ5MUQryyq0sE2LCRVBWnaYjqbsdBdIFIaIYZk2QTrU7SOWOrUmExKap0mvbrjiXP38PwXdzCmYM8JfC6YyAQzb0nyTFnqLtFpOiYSHnroPh596AE+/YXz7G7v0Vvo0YhiXJmDEBzfXCefjmiunOJMu8f6xhL5tEJayWgyYX93h9FowD2nT7F59BQNK5iRkizVKbIxt6+9Qufhh1EuIcZSekF9eYNaZhGuCq04ruTY+gaJhsqUDIYjZtMhVTnCujWee+ZZljeWePi+RyFKiYSk16hTlgU3trbYunWdvd0d4qRBq7bAbDbmqWc/x/G142xv3QoRtDLDFjHNRoLQjsrk5GVOpARaehSWRrOJ0ppeq0FZCQb9CZdvbLG5fpI3PPo6Xnj5Ja5uT3G+4vrtQ3p1QZYbolTQrsfkmUFqWF6IUcJRGDjsC9Kp2rC0AAAgAElEQVS0QZZnJE1Lo2ZANmm3lxhPMqQv6bQTJuMhQsYkMkGi2T+cUboxRVWRKE1W5awsLGFu7WDTisloSKQUiyuLdNptBuOMzsIC9UbCrZ0drty4zdGNDU6ePMOFi68yyCfEsWLz6BG0rnHz+nWsHaNUm9P3bKCpIYFiOmM2GhNHEVIF/ga2otYJmf9EaLCOosjQUiKVR/g4bNRsmGKXxqBNSTNu0Gw1MaMBznkiKWmmNfZ3g7C1sXIMoWBUTojTJpGX3HvvWZ5+/jka7QZLaz3sRIa4XjZhsCdoLnZpt9psNI4wyyc0oyZxDexawUKrgzQFzzzzNI1GArJJoy6IJiOMK4MzrzRYJbC6INKKJFHM+jOMk8xsyXKtgbeSrCpYX1pjOBzT63ZY7q0wmmQMJgNmtwtm2QwvBcZCojzaWx459yB7/Qmusoy2bzKbZsRAJ+ogcolVnqnJmZkJxzeOsri6zvlL15GxwyqPTiJ2d3f49r/9jSz0OkRRi/d98PcwtqDfn3F04yR5JZiZiqu3rrGysk6kUkQkqLUGDMsKaR291YRi4lDSY3KH281oNDUL7SZPvP4xOmkdb6HVXCCbwOWtqzz74gtM7ZR6o87R5RVevfglfOagAavdReppyoljayy2G7xy4RIvXb5Ibh1SSyISerU6zkoK4jkjSrGYTInxHOzdZmGpx8JKh9ufvkNarzHOMvb3DonLkheeuoGstXn0iddxZOU4H/nYh9k7GHE4ykmloxKWSze30UbQbtXJbZ92tMCxzRMMh1NOnjxFPanjbMXiwhLddoNzj76JS+df4mMf+UPiWpusnFIezljdWMFVkv7+Lg5JZkpGE0ESKW7P9jixHtNsd3j11jUO+lOWm21WlhdBJpCU+KIgEYArKfKMOIqJ8FivmM0s9ZbGVoLJZEy9u8QyTQ73dqgngvFsQlk4IgmVUcjYk+hVNteOMRlnDIb7FLMZy70Fdg/B6op8WpDUmsTCkZkxKxtHkZXm1au3ObK6wE5RIESErQhOu9KCDa1meEGqE5wweC/ptVrc2RsGt5D3eCdwQlAZh5KCOE6IpcYYS57PkCrCWEPqU06cPkESa27dvk0xmSEUGFuxt3uIkoper0MUacbDMdNsRhRFNOsJOpIs1nsURQXAaDyhJRoYG1g0kfc0koSNjQ3G0ymNdhPnCuK0yZG1dfZ2b+NsYCuVDtqtlLjuUWVCe2kBNctAGGylSaLgdHBOomWdWFdzkeFPmqtSEZPNxlCUCFGj8uCkovRQVJ7clEgjsD44qqz1oUm1KkikRCMDA4kAZsUJKuNJNMgKkBFSerwK9epeWKx0WBeiT9Z7vJMoHxrhytJD4TGiQDjDbDajk0pyU1GTKd4rLDAzhtJGWOnpTyY0G20wU0ojqExOs9mcrwgdOpKYyuEqj6sEUnlMUeIJsSbrDLMix+YZo8Eh7d4xLB5p5+EpFxhAxhqKqqQepfjKhOggGochjiJWllc4deoUd/b2qKZ9IiX54nNPsz3cJo4KmknK+Re+yMriIlGkUVEEwqPjmJpuomQC0gZgvqnQWob2PGdxIlRmx2kdh583iMkgsnqP85IkjkniCIkjMxVKSaIoDtwl6+YDDwdIhNBYa5A6Qqjg/JFKUJbByeedQ8oQ3RJO4L1FaxVq6itQUmOdoSgKlFDcnawLKfAOVBxh84LCOrrNOt5WCB2cQ9Ya0iRlOsuRWiFcEErcXLJQUoIKDpxEhpKAREVMjZnvIgPLSwgwlcF5R6xipBDzdjSJkJKqKtE6APYFgijW2NwSRWmIZ3qPFWETIpUMsdCiQAJO68Cosh5bWaI0RG383BhhqhDniyONjnRoZfSgpZ43v4VhLyJE8qQIIl2elyRpHaU0ZWlBBaabFHOXlhBI4XG+mgt5cq5jCEI9m8BaiHUYAEg1v795PA/vg9FJhgGhKQpefek8cT1leXmZva0X2Nt9mbXTD3P91VeJhOTLn/1D3vRtf5dYKFKpKKYzKN08omfxQiFk8hoDChmBkAhSmp06px84R0nFpWe+xGYkiZN2aLOzwekm0oQoTui1Wjz6xjfzpU//Mf2DA5zSPPiWbyGaTvj4H36Ag8khq5vHSLptrl+5wSNf983cvnie/Tu32L19OzR0L6yytLrIcHuX2XjEzv4O7XadmY1550/8FJ//7d/h9W/+Fq6cf5bHvu27ObLW5bMf+iBaBxf5ZDpFzgr6T3+Jv/fmr+PU5hL7X4x49P7H+dwrX2Jnbxczdbx44WnufeBRpifOsl6OEaWBaY6oJ3jneeVjn+ELLz7Dk09/kfX7H+DCMy9y885V3vfLH+Tnf+g/4dLFLxLtTkIiz5bI61uw2ENUDooSbQuSxQ6yHlNTTVSsuPj+/4dGY4kPv/df8EcvP0nfzKgnDb785acgUZgqOCu7K0f4hq//Os6tLaOl4jMf/SO2Dw9590/8OJlo8t6f+xme+O7vZ/Jrv8L7fv2X+L//RYKwbW4PD/m2v/12RFxn55UbvO9zH+PFL32efDqm12rzTe/8fqJmQiwgzwqK/Yz3/vo/52d+8ifQvYSf/Yc/xi+895fYPLbKG7/mYU71OnhvcV5y88JtLk+2uf/0/RzkO/zDx97B5/I2//L3foOj938T7/7xd7G5ssTIOo4v9/hn//K9/KMf+ifUHSQiyApaydcEB4RHo5hUBTvFhPuF55c/dZknFp/jh7/je3A2iMh/U5w5X53jL/NY/ixn1d/kw85FtHD9dPkYL1wo3vgqykBfdUHpsdUjxFlBp1ZjWG+R6IqGr9Om4uIrL9FZPsL66hp2HkPKyxIrIBGKNKlRZGNUnLCwlJAdDpgYx31Ji30zZA/DwFpmQEpKrZ7iC4sf73P91oDKFKSp5PkXX2Dz+HHsbI88m/Dq5YtUYoHd2T7OK3rdDZrNDlJ5yumIvHR0Yk+V1hC1JZYXl0l0YD+kyjOdlcRRSmlmocVukjGdTDn/yksMpkPi2KN0i06imIwPwiLBRlhnGY76zKaG++65B5MbTOVopzVQFdYLaK4h7m2TPXsZNclQztAf7rI93CNNIyJtQpzHSSQO4yccHExJawWxLHA6CY4bC9vjAxrdHkklSevLFLbCzBc2RemppRXD3FEYz3L3OCtxShIbtu7k2Eozm8YkqslhVlDv9BClY3YwRAhBVYVxTVlWZKZEZA5hHZGchCiVgI2VJsfXurx0/Tb1NGe52+azXz7PJDPUBXQSyaSIKQtAlyRJgReGssy5fVCQ1trEtQ7NSLC6uIBOOjQaEdnokNv9fU6duI/1lWX6e3vYeoeDSZ9OWmd3bxeUo91pUK+dYPv2Nnd2D8gqxeLyEVaPHEFLxYXta4hmwsvPPs36+hlq7YjJcMR43Gd/5ya+LKisJ58NWV5aIZ9MuX57C+sKhBX0FtrcOniJ/fGIOxf2iVWNh889TLS4QG+xw872FudffoHC5KHZQ3ukK5FlQSIFuJwXrtzGWkdCglrqUpaG2axgMrakdQnSMCsscZJSS4OLw1UOX2Q0aykLiwtMTcmR1QXe9sbX03pBs3cw5vzFywx8RLtRI26kbB/0qUpPrCWzrASlGc1yVJTSiATGCOIEzt3zCE9fu8NsNGSWldTSmLII7gQhPEpleB+xurpMu9Ol11vm4o3rNFJBsxFR5lOE8fRqmlprla1b+wz7GQ+cPsXZY6eIk4RrW7cozCGtVo/l5Ro3bt6kqCrS1PHyS+e5VrtGnEiETukfXmNjc5l6GmONR1SSSCi89SSNlKIoGE3GdFudsDiuHF4F5kR/cIhzsLTQIzcS4Q1SSlrNOt6HymdLYH1EccT+/iHJ6jLdhWWe+9QfMxzOeODee0jjhHKWI+KUtY0e92WnePD++4jrMVGkaNQTdg/2iHRMq94iFppilqG9xKscP43QUtJQMSJJ+dZv+EaGswmf+dKnyIsJkVa04jb5rMKqEXGsKF2GNYZ82J8DWj1KxuzuDanJNAC084JYa4aDA8bDEXmR4xRIGVNL6uR2jMIzLAvqOuL29h1Gs0NsJmjEdVzlWTu2wbA/5blXXyVNY7wUJGnE7sEhh4dP0lnsMurvooSk0egFobDWBVnDIXjk/tM8NR3QPzzEC0+lBLd2txlMdjhdBHF+OtlB1yQOSFoRo2GGllGoh51XmUutyV3O55/6AtXEMRiMGMzGeCeQkWVpqY33JceOnuWzn/88Sntc2eadP/J9FOMRtWadwXBKGsVMsileS6gMWe7QEQz6+zASZEYRRwopSrrtJs5YpNR0mgu4ouLes/dwbesKS0urnDp1FlWMWD+6yT0PPUCzsYQ1nu/61m/mt3/rX5H2FigLiy/zUN2dhJa208fuoxjlkJXY0YxXnrlEdgYEGddv36bZXmLUz4gbDd76hjcwnM144flLbJ5cw5mK0XQvQE4rS72e4JyhciVeWKbFhJtbV8gqwWAyQSrYfepldD2hJTOMLsgLwWScUE800uU4oUKNs/AUM09lDc4maIZ0e6sM+x5jHBWetKGRTuJlgRJ1XAk7N29Sa7SQvgqOAy9YXlqkP9xB65hsltFeahHpFtvXBzz8+ENs3T6glnSoxTmKhG6nh8kNmVBYF0C7SRy4Qp21FWzlmGUTILTUIUJcOmk0MUVFmWfoKKJRT8F68rygXq/TH40Bye7tA+rNlEatRn93SK0hiNMYXwU3QWUt9VpKmsbs7x9SS1OEEPQHE5rNGt12B0uFsYdY4eYtXZbpZIqtQizQliWNWkYzjekuLFIax2SWMa3G1GstluodpuaA8WAXXA1sydLCCtYa4kYNqRTCO3ScYlVg43jvEJUIjY62xBpDWWTIqB5+T6TBHeQqKhcEIqUligitYlAeqx1F4RDeYo3HCQ1V8FKouqRma2gp8b6ichUYiyRwiYS3SO+RwiGERiGpzIzMjsFFOG9QIjBnkCFCLFQcNvmeEHcTEElNpEFFEYtrR7EWcJrKg5+FhslIBHYHlUEKFTb23gURzDssob0smxWU2Yx8OqDTipDKkaaK0WSGROHKEq8NXjl8ZfEkaCfnYHCHEoFV06y3eOR1T/BQ6RCVYzrd5/rVZ5m5hN5Ck9pKwtHuAueffZJGqkjic1ReEMUxWlvAIGSEtAVVMaPeaKOtBBymKkl0hI4FZhYYX1oKvAsb/7vX7DD0EGgVIXCoJGE2nhLX6+ADZN07UMpgXIkxQcaJVEXmXOAPtjt4IbBVhTEFsU5wDqIoZjA4oNsO7YtZVoQhitRUNqAktA7nUhUl3jkiodEeSgt4gZMerwJTyTsfYmbzyNvdIIdU4X2B8BR3XUQCqipE5Kiq0ApbhRa+yjt0nOAJ7zkpFNo5hHO4eYxNKEmWZUwmU5pzfotQAuXAacJznGdY55BRRBzH4dpgbQBuy8AVuzuBr6UJg+EYKQVKSTwBGm6rwA3SKgqOsvCwQQbhSEhJvd6grIKgjBcoBFVVBjehd4GJ5e28ESk4u8NVJcz4tQ7cPRkMUOGcZIgr6ijB2wqFR1Q5l57/Ii8892lKV7DUWaDVLmFgkPUmaxsn2Dxzjlc+9zGm/QOihTWE9URJAyeqEDW05TzSGOGoEL5CRwprLUpZysmMhaMbjPp9Nh98kCvnX+GxJ95I3xmU0JRFhhj0MZM+eqVDa2GRJ77l24g+FTMZHdJbW2Olt8yNG1cZPv0ZXn3mad70rd/J2soq9z54junOTT76f/4GhRecfvBRWss9ZpMRDzzyGM9/7nO89bu+m/H2Lp31TRZbi5x78DFOvuXN/Mrf/yHKgxt88qmPcGv7NvU45i1v+gYePH2K3/v93+HyqxcYHQxg07D6XW/j506s8tPv+UleHE/IDh2/8j//OpEv+YEf+6/4wcfvJ/KaalyAthxu3+LTT32KO3nJ9njAZ97/fpaWFnFC8p1/5zsoTEUnm/LEo6/HiyAKZgd9Eumw2uNlTOeRs/ProAhcOSmJi0OeePvf4fqTnyFunuK7v+N+PvjR30dFEa5yRFKS1uosqJj70iZ14BM3L6O7a6wvdDm9uMrR7jK/un+bH/3+d7D6ysv85tN/yNn7HsYUOe/9pX/K6bVN7uzv8uHPfpIvf/FJ9rdu0T3zEK/s7fKFn/1Z0rTJYP+QerfDxtu/iR/84R/nqS98jp/YXOIXPv4B7jlzirPnvoYPffD3+fZvfjvHl44Anq1pn9P33ofyimsvvsq/+cBv8dKVITU7ZjEteeDsYxSzi/z0P3g37/65/5o3vOUt7O5e5NjGfXM5xDOdZewORqwuLqBEgM//F//Tr/GLf/9d2Kril7//G9HSBl6Z+qpLB/+/Hl/p5Pnz3Up/84675/mV0bZ/O0r4Z3OpRLjWCYEVUAz7VKYIa6+v4qHe8573fFVv8Pd//Z+959rwFi8NclqNFvfcc5ZvfevXsrTU5v+l7s1jZMvu+77Puefcvfbqvd97/fZt3gw5M5zhkBRFilQkWaQkitqcIHYEWQplMYCNKDHiWAFkw4rt2IIQ2DEsyUFEy7IciRItkaK4iMNlSHGZ4Qw5+5u3dr/eq7u6trvfc27+uD1DRSEFwVAS+QIPD2jcrqou3Kp7zvf3/X6+0ygiK6Ez13ttSoW0EKbgaJqS5xllprEtm2bDp930SSwQRUazsigKTWYgsxVh1+f08hyd3jyDWcqZTojRMTMNK/N9RlHEtz38CH/8hT/i1s4MSxiUDGk2+vQ6c9x35T66DZejg110VbHiCZJZTKe/yoUzp+h0eqwuN9hffxllBGtnz7HadYj37jGL6gWFH4b0FpYIfB9LWFx8e4PRwU1GuxZpGVEiaPoNZtEMx3GpZMne4IjQVQRySpEnjBLBoZVRqRw5K1DCY388o+EHDIYDGv4CtmrTbK9SlRbapOTExAlkST0VUdKQlgmjWYrjtPCw8dyAZHaI8kN8LyRJIrrdJZQFYGj7HWwRY/Qhh1GCEQ4HwzFGSOI0p9Xq1jGCUqOLOrMurIokSVnseyx2AuJpjnRcorQkN4pZrBlOs1ogySuGo4yl+TZvvXaRrXtbhK058sKgq5jKNLCl4mTTYFAURkKlaDf7dDs9jmYFtqVoNzt8/olPETYCivEEr9FkmqaIyuB7LnO9HpWom4Q2NzcRlSaNEsKghRCCLItxrApdxKyeOk1cuOB4OI7LZPcWH/jAv+TGnZfY3t2uLcJFyfb2PaaTIcPpGOkaFBLPcVlZWOHu9jrdTpezq6eRUrG/u8XBwS4HO9scHYxqC7ipnW5pkuB6HraroNK87sIlCp2ztLRCv9ui22lyd2OD+y+dZjw+wg1sVueXyZKaoTKZpUwmKQv9RWZJTKfVZW844vrNGzhuwfr6Nnt7O2zs7CKli9CGB17/eg4PxuRRgj4GoLq+S2VsqrKeeI6nCSsnlmmFPQCGg318x2HyamtGliIdi7QoyIoKbQRpmuE6Dp12l2a3wdVzV5ns7NOfX2L3YJPF3hJ7g0MuLp9gZXWR/dERnWaTbqtJgeHkyTXmunMo22E2nbE03+P2xgbLJxbYOxjQbrTYH4wQVcrKwjyOaOAqhzieovMSaSv8wK+jE8bQanVwfZu8yKi0QWtNp90lSWOU5+I6ikoKDg+PSJOETq9Lu90mSVOKquavlGUOuqQ/12dzc5OlpSVWl1eQliQ1Bc1GQDKLKUrN4vJCXWVu1TFdWylCz6tbtoocWwkank1VluiyIEljoAazBr0mt9fXuX79BZSwcVTAudNn0HmJVeX0Oj0qcmzbpyhKqBRZUdTMFyAp6+x6Eo2phMbxJMaUSCmxHRvLKjE6Q9mSsoSlbgdjOSx22uTakGb1dGJh8QRlUtLpNLBdmzSZIaVDVVrEaUySFmS6ZBynqNLi3MlzzOKYjVu3iPMpG5s72BJeuXud1BQMhvuMD3bJTUY8S7E0BEHAwXRIVWiyWUZlDOkMLMsmLwpA4zsOvU5Akdts3dtnOBqR6oLKltgNSbNjYVkZbhBwauUktlTcvr3LyfPn2Nnb59b1G9zd2mR/eMBCt0OeJwzHY5aWVri0tkboKKbRjLzMQNgkJuXSqTUeuPogdu4SKzizdomw2eL5559jcDCgEhW6LJhbXOTk+Us89czX8ZRLp9disL3FzZu3wbY5sdiinEWEQcl3fMe7uXzxMg8/8ijCpLU4IAxJnrA33OTe3iZZekRRRBwMRyzO9znRP8XtF2+TVwVHekqZW0zjlDyvIfyiKrF0gcTGFpK8cOj2FnnDww/z4vO36bR8tNAMj8a87qFHGO6NSeMM4Uk8TyBlXV+t8+MJvuewtLSM68EsSml4cyS5RJua0eE59WbS9RWObxDC52hvjNYCxw0ps5IzZ84hZEWz1eBgeERlG+bbPkvtOV5Z38ZzK3JTsrm5S1VqpuMJR0cjppMpWuvjqE69uQsbTXJdsLc/IC9KbNeugb1CkqUFtuvWcabSUGQJ+rjBDVEzglzH5dzlc1QSpsMpvh8QRzOyNEIphe+6SGmzMD9Ht9uuHaajMdKRLC2voJRCSoszZ0/hhi55UZKmGUVRYEpDGATMzXVpeQESi7l2iBKwtXvA7sEGRuR0Oj0cy0chmOstUOQgywJlh8TxlP3JIUpZ2NKmN79AScXezg55Vke+Kl0dQ4YFynIQQuNIiSMk2IJWp4MXNJiOpzWA264jS0LUIkxZ5pg0RylF4CsoDO2FBdymw+7OHpNRSpzm5FlJlWYUaU6WF5RZilSSMGigEHh+g0a7w3Q6whiJbzt4Th35k45DhUAKjWcMjquwZO08CcImURwxGo+J44hoNiZJJqT5hCwy+FLiOjaOtCnICTt9tIEojqmqotYkMo3QNVDXmJw4i+sBlSNRTkjgN5mOJgisOs5ngeXUG3mb49Y0XRwD0g3IGlStZO3asn2FpSz27m1x8uR5rl6+Qq+7CLLi4GALqRTNVpvB/jqVqGg02zXPS1iYvCCexHhND0cokjxHWmALhd9okcY5RVlhjmvFlFI4UuF6DgJDlhcoIcmzkiAIyOIZnh8iTEWFPmaPyzrKpSxcVx2rHhAEDXzfoygydJkiqFDSodQltq2Yzaa0O506EldBJQTWccTJsuq2uziKaqdaXiLtegiS5gWOI6k02MrCVja6NAhpUVnU3CGjKbXGdpx6XS4EpgLXdaHS6DwHXWG7DoXRaG0wRVHHLh33Nci4Uja6LOv1vNF185nRZEmGqQxu6B//vXW7neXYVBWUWY6lFFLZdRSvNLW72LaP3UYCx7ZrLpOySfIM23bxAx+da6RSdRmAqAWCb4CP6ta2vNA1n8gcM0UsSWXKWngv6s+TPo7FlVoDdZyt5jhJymMm3PETUDvCJKY0tUvKgFSKiooSuH3jBZ78/EfIdI5teSRZSaEd0ljit5rcevFl0iLj/H0P0OnME8UZlmsTtFoIXbcoGgs4BrhLYVHpgsD32dm4SU6FH/i4fgPXc9m8c5t2p8XzX/sqYSNk/e7LjJOM5uIq7XabvfU7FCan0+tx/uJFnn/qWRqezfzpMxSTGXvbe0jfZeXMJW6/+DLX3vQYNorb11/Ech2uvfEtNIIAXcALT34Bz/cIG21e//b/jP0vf5lb04KHfuz7Uc0e1vYNXnr5KZI4B2WhpKIThBwdHrG9f8D8fJ93vv07WFhYQDoO6fptfu33P0rrzBkefvgxfukX/xHTHfi+97ydbHMPb6mLCBS6LKg8j/lGh6ee/Cq398b83H/7s3zljz7JzFWYUjKNY6SyyaOYH33Xe8GUDJ5+lqzbpdga4lqC8cvXMa7CbQRYlsJYgm5vnmc+8yUuvfu7+Ym/9kM896v/hmcmw5rRVNRNikmacvGBh3nrt307H779IhNhEYwmPP6xj7J2+gy/8A//CV+9eYv3/8RP8z/903+AFhbrN28g/D7v/N738OnnXuQjX/oKn/3wb7O7u8n58+f5u+9/P+eN4pW7tzFVzpWr5/mBn/0f+Nvf/7284+JF3nH/Nf7ev/wAT1+/S360R9GyOXniHI+94QH2yooXX7zNyzde4bH7LmGVkt/88Mfon7ufW1/6AhvrW3zog7/F9tYdxuOET3/i47z+/Bo7exM+84VP8MZvewvPbI84io7oBg624+I6Dra0SUzF3PwiVxf6GCFQoo6QW1YNVv9PwZfz5zm+dfvc/3fHnxaKvtU59TrgG+d8A3b+DY7TN3+MumCg0obCsvjtTz7Pex8SdJbux7Wtv/8X9Xf8hQtK/9uv/NOfn7t8mbYloNHm2x+8jDQ53U6DtVPLICuyoiL0Q3TlonOIZgmOZx/D/zS27WOVGZUFRay5Nz1iUE7QVt3cUiA4yItj0cEmz1MoMnaGE4yReMpiFpUsipz1jU2i1CAsnzMnTzGcjbHdgKW5LjdvPMvWzj7KDlkKu9wbj9FFSjLcY2d3kyvLbXRyxNbuIUURYYYDQipKU5CjSfKMTqONU+Zkecpb3nrAk5++wyzyyNICx/dpBi3iyYTKhlLnDCcxiJJxkjOIITMS6QpKYdF0AvQswVIuJxdO4AcBuqroNBbwbI+jyT5ZnpDnmkpXFJZPv9GjyGZEaUmSVLTbTeSx7Ogon6qSeJ5PpiuStKLdnaMZNEjLnOFswDT2gCaOJRGyYJrmZHnJ+OigzpIjyNKknmxVFs0wYL7nsLGzj9dskmQRmBKMS6ErijKrW8qyCqkCFhoNNu5NSYTN6RM9PN/me97yALN4hp3ss9YSKK9JaipSXdDrdVjsdBkNx9x4+VlWVk7Q7y9x9uJl5rtdrt/bpNNsk0Qpe4NN1lZX8F0XdMXh0ZASTdAIcF0PWzncvbtBnpUMh1OsToe5+S6h7WFbhg99+HfxPZfvec9/Tiuco99sgrFIdcbC0iJXLl7m2uUHGY1HHIxH7A4OcI6re6dZysHOJgeH+2ztbjOZRiwttTh37jS+F3A0GdfOCAeKsmKWRHh+k+949E287tJFzl/+bugAACAASURBVJ85x4c++nEunD/LLIo5nI45sXyKNz38bWzc2aDR8Lh26SqrqyfZPdzHFDlzcx0C12E8m7GytMLte7sMJ/uUgLIc3MBnZ3cXz5G84eEH2NjYo7Ikvu/j2h7CUhwdTbAdi27P49sfewtf+uITTMuYaWpQAhzbwnMrKuPR7/cpdYawKi6cv8DB4RBb5azMLRMdDVlbWealG5vM4hnKs0iPYh5+4Bznzlzm7IUztFsN0qzAsipcv45OfvErX2eh02ESR2itObN6isWFFZaW1mh3m1w8c41nXniB0uQUBYxGI4TR2LaD77kAdZWwzonGU2zbQdoWh4ODegNbaPI8w5ga7iuEqAGaZUFeFEynEdFkRuAF+K5HVRSMDg5YXlrEFBrHU3WIIs2xnQDbkWytbzPX79f10FWBRT3pt6XAURbKqjC6xHclJsupCk1cZAhLUlS1xf72rVsMZ0OCwGVlrs/83CqjyYw0S7hy5kzdqJelVFIxzTOqHGSgEMqi0pIy00ip0KLAUPNhbCXx3XpBWcPfQ4qk4Lvf+TYO9/ZYPXWB3a09JmVC4FsoR7E72Maz7RpYbimElKRxRJ5UFIUAaWg25xknQ+ZbXRpNlzs7d7m3P2Bnf52725v0u526YnhyQJpFSBwunr/KLM24u3kLWznk4wwv9CiyHC9w0GikYx+3Ghpms4TxeIqlNCdWFzlzbg0hajDulYtn2di7R+CFSDPj3vo+WvqcuXye2zeeRXgWwzhiOBozGk3YOxwxizPWTq5xZu0Ui8vL3NtcJ9cWptI0HRtpO3z/j/wo9zZ3GBwc0ms0efxTj7M32ifoeBgTs9jvUyYFn338C9y7e5tXtjdIipynn/w8GS73XznPWx59lNs3v0Sj43Du9BXmm3N4XoBdSsJWl53DXcajI+J0Rl5ECK9FWRp8W7A7HvPss08TpTNmucYOJFVWN2S6ljludK0I3HpTaVmvjo8yksiQZxlJdADSYXF+hazQuIFiMhsThjal1uSZQSkPKW3C0KHZCHDDkCKZoVRFUtTXf5JOkLIugLBVjq1SvIYCC4RokaQFw8MDtCjZ273H1u4We/sTsryAysFYsNzxmY4Txqak23IRuERRiiVrkaoSrzIfK2xPUuQZhS4IGx5ZnBKGLsqxKcu6ia3b65Cn9QbVEmBL8FxJp9Wk1e4wnUYMRyOKsmBxfo6lxQWagcd0WvOBKl1vGou0pg+nSUZZ1IKU5zdJy4Kj4Ygsy3F9VX+PjyPSOEPaFo6tjj/jhiLPQQhCP0DrjMPRkHEaowtw7BBL2VhKk6U5w/0hjfYyC/NzJNEO+5MJQlp0uwvMLa4eR8lypLJxjgGwrmtjhEFbGlkZPAc8W+FJSaezhNdusbe3zd7hgLzUZHntaCqKjLLIqLTGcxyErBCVoTO3hLBcsrSgpMLxbALPx/dspOehHBflSJSo8KSDkBZ22MRvtRgMdhmNYvIiI85ysjwnSxLKPGYcZYSBh2fXjiSo8Jo9pmlJktcuMqkchFJYwsWyFJoCJQRCAmiavUV0aUiTFFSFqSqSNEVbGtuugcmzaVQ7nasKx+0QNDrMZhGCCl2mdWTOQFXWDJmsiDFGk0YzirygM98njqO6Zc1zQQqCMEQpid3qcOnC63j6qY/w/EtfwRhDnuZs7t5jY/s2u7vb7O8f0QjbuL7HrTsvsz8esbywWJeSSInWOa7vMpqMmU0nlCan6dXV2lg1KN1U1FFGU8eylavw3bBuwENjdA1EerV1TWuN53l1HEvUDWW242JZFkWR1q6hSmDZ9jFJURMnMY1Wo66qP34+YRmSJMXz6oKQsihRSpHneb2GLOqG1aoCYWk8L0SX5XFbnzi+p9agd13W/CNlqxq+bQkc12U6HtcOpWMXUJFn6LxkMhnhhgG24wL1RtNWCqMNhalww5DA9dBak5oSLIknHV5tWFOqdh+VRUmRFUirZppVVUWW5lSVwVHq2KFErQ+J2k0ohKivY1VH3oAaYG5Zx26jV5khFnlZgCVxHY8i18cth9RxRFNzL19lPVnCql1LeVE7z8xx1O748YRloWwbY6pj0a3mPdVw89oBqHUNi7ctSZymtLodWj7YYUqJ4ehwQOlJ3vKOv0LotVHSY3I0xG/18X2PvCywJEgM0nGQVo2vEI7H7t3n+cQHf5mVs1fZuvUcX//K5+gtnMD2XEQhubd+h0k2wg4lunBYmD8F6YS7L3+JuYUVuiunMFqzdvUKT3/yMwgpaDZ8mu0GX//C53nzu9/N9vPP0lla5fT997O7cYesMpw8e5bVE2tk4wHr11/hMJ5xWFpcec8PM93d5Gf+xl/j3fdd5MF+wMoTT/Kh3XVMoTECpC3YHexx6+4Wpy5cY840WDtxlhNnVpDKYesjn+DDswnT3SlveuRRPvhP/jk/+8/+Hs987Gu8srfNXKeL1/Dq9/5wwr/5lV/n1//wo/zM+/8mV7Tmh9/30zSeeYUnRnf5W3/jp/if//7/wuTTn+ehtz2GFha//YEP8q/+8OMcrW9z73DAUCrO3HcZSf35tQSIdsjqfRdpdkO4vsEv/eP/lbuuwopKCgSh3+K73/WD9IFH3/o2zlTwxX/77/kPX36Bt7/9XVS7OwyOBrzj+9/Lr3zpD9h+7iaeA61eyO/++9/lN3/lV/nD3/1NXn7uS8yO9slzw9lL53nnO9/B9mef5IkbXyctCkazEUv3X6IUPsuNgNBv8563vZkf+8HvZxCV/Mj3fRdr97+dVzZv88bLF2hbks9/7nHOnjjJ/Vcvs3r+BI63zO988N/RnA9wLYmnHGwh0ZVmMEwYHe3wgz/+PkbaZq7l8Ucf+hhPXn+RB69e5cUbt2i1XJRlcX5hHmkMllS1G9CyvkEZ+qZOmP8UjnrA9R8viZlv8rvf7Gd//uNPC0Xf6hz4f4pP9f9/AhD+WpncsedUHJPghMBUhlxa/M7jL/JdlyqaC9fwnL/EgtIzTz7x84vdDlfXTnPp5BI2JWWVEscRRZYwGR6wsX9E0AiwZck0nuD4HooK120dgx4hnu3zwis32B3NmGQ5R1nOji5IPRfZ9HB8j3bTZxZHTOKIo6hkpiWxLpHYiGLGYH/MJB0zTWvrba5NXddbZsSHA7IoYZIXSFth6ZxxXiCEotNfoec0WOqG7O+vM0sNzWaLfjtEJTMs1aTbWWAaJ4wOhkSb9xjv79JacHjhuRGjKSysrPHYA/cBGmWHXL5wHzraxwgotGCSWpRlDdosRwnFGCgcHCpONCVJkUJlo4yoEYAm587WHeKi1hodWbfZ6EpT4eB5LXIhsJWHNIZYlyAUYbeL43q0Wj0O9u8RKBfLcjka7zOaHDKNSyaTBCkFj15YJY5rgHqea6y6p7W2PVs1+8CSgizKCHohuohwXJ9K1HZ8yxEkZUmlJZWEvmfj+ga/6ZKXAmVZICVWbnjpRv1l67gKu9VnqdskLzJW5nqEnuTpl17gKDckRT1ZbnstcmHTbIYksylulZOnKStLbSpjkemc0tT2as/x8b2Q0eiQ+cV5XNfmYLTD/PwqfddFlBmD0ZjJeMLZS5e5dnqNUEmyvGYnrSyvsry4TJ5mHBwcsre7x+BgF5PXUbAkz5hNZhR5hZE18Bpp0Wu38P2AaTRjrtdDKckkjmg3moSNAJ2mvHT7BufXzrC9s8UsGvNtjzzI73zicV5/5QInl0/yyc9+hul4gNGCUTJDVBA4LrnRvO7SBVrNLl9/4RkKCpSqWSuOHSJ0iS1tgsDlvd/3vUSTmLv3tpC2ZGm+z2g6ZXg45sLZNZLccPHcGl03wMgGWZ5TVAYhNKYoaLe79BshzaCJ7zR47NHHmOv0efnWDba2d9m4c5tKWzz+5JMYS5IVJfPdLiQu9z1wkcBxUK7CFBaz2Ywyz1mcX+X6CzdYWOqzMN+n329SpBF5HvHxx7/AtQurXDl7nmbYZDyLkKLAsx2EhEoKwmaIF/rEScJkPGF7Zw+/4bK0tExVVdiOQ5IndDttPC9E2RIhJJ7jYUnJZDJjNB7TbrXwfR/l2MfwT0FSZly4cOW4sU8SZwW5LmpQrWMjbItOv1vXD5tXb6fimF1h1cBQWaF1zcdQSpIXGbosEWikcrFtj7UTy3TDFo4dEiU5k3hMlkbMd3scHO4zi2MmcUolDUKJWlBIc/qdFtoSKLfCdupqY086FGlCmWnSrMSqKgphqKyK23fv8ZbHvp2dvV2G4wFFZZFlBpMfEgQhg8MxWZrjhyFCmnozXgl0mWNKgW8LTi2cxLLg9u42SZGRaoNUCquqUG4TXRo0ICrB8sIip06cp9fvo6RksL9NVoLwXXRWgSspK4NJC6xK4nguJq+wJLi+C5Zgf3+H6XRE0HIpsjFZMcV3ehwNIybjghNrfZJkipQWR7Oi3pDqksk0ptQFQeAyGR+xubXFnTs3OTqaYHAoC0MhLBzhc/7cCUb7Ey6cWUOUBXuTA2wnRAjJpQsXeOSBh/CVoDPXIS9SJBmHh5uUaczV+y4z3zlJGDq4QYvJYMxCv8sXPv8UlWVQvsMLL3+VzfWbKNtjmk5pej5ZHCFsD8sY4qxkksXM9wMmUUQ8nZIUOZaumTpGSmzHx9gWc/N9mo0+ZZliy5KXXr5Jp9Pm4tnLFDricDhgNk0YDIf4oQO6jmWHrgIUyAq/UdDpdpAiZ3iUIiWYIscUU8LmKfIkRespmAjHbaALiSfalIUkDD2aoUelK9KswFiSCkklIDc5niOZm2uxtBxw794+7c4SaTbDUFCWFaWuKE1JxTFkV0CeF/VAqKxbhKQjieIUZStsx+PkmVO4rktZFhRZgqkKsqwWgeM0pbfQptSaLE3pdFo4to8lIPACsjwDBJ7rYoxhNosZj2coKZGOy/zqAo5vMzwYIqhhtaNJwvgoohKCMq/jOzV010LaitLEYAxSegRBkxMra0yjKSiF4ygGgwOa7TZ5POH+R97Czvp1Sp2TlyVB06Hf6dOfP4EuNIPdXbI4qd1USUoSJXWLWxLVQratcLBwXIHf6hA22syGQ7Qp8GwP13HxbA/f9fEcG8tolKiwKkmJoTW3iOd5TCZjoiSrRbEyIi1j8rQkTWPyZIZlGYLARQrw/SZ+o8HB0QFFUdUOF9tFSAvftRGujYvEtTSh16KyJVWl8FsNsiwnzzKqokCYAsoEaQRJMsPCwrdr1AuWwGn2KMuKg4NDSlOh84pCZ7hK1S68OMMkGaFnIyzwm02chkMUR0BJVsRYlo0QElNm5CbHmAqdFUwPj3Bdn0a3w/7+Hk89+RX2Dw+Ijga0Wg3+6JMf5szZK0ijGY0yfN9mazAiKwy+XXHffa+n1T4BJZxcXaEZetx85Tk29nY52NzHCRrkZUmuC45GY9wgIJsmOJ6DsWpENAaEsHG9EEtJpKwFIkvZ2LYiiZK6Ac0CY9Xf30JIJuMpCINlydoJYypc168b3rTGHPMtKlM3uUVJhG3beK4LxqAsSZ6l5EWBMeD7PrZSaK1BGMoyx/U8jjnVKLsWrSypjllMup5YlznaaNI0Ayo8x0UKWXOspMS2JFmSE4Qh4hjoLTDkxyJUu9lGHdeJIwSWY1PkBRKw7bqMIS8KKlOLQ9Kyjl2HdWuaJUTdTKzr4gRbKUxR86FehSbZjlM75LBq4DQVWld1e2upqeNx5ti1Vb9/HIs/ujIYY7BeFdWOW9Pi6FiwNBVQvzfCOt6kWQJpK7QxaGPq5jlZR4mdY0Hw1do9cbyxNrqsOXASlJIE7R6255NNJqyevUSj1+Vgf5es1MjKxbYczl97A1ZpIZse+/du0jt9FoVBoerorqlI44jhwTbCUziez/atF7mzdR2B4vnnnuf29ZeoKsHFa/cTzM0z3riHouLE2mlG44Tu0jLnzl9m+dQ5Fk9exHVc0lzjBV0uPPwgX3/8k0xHY64+8m187Yk/otNfYmd3AwuHo90t1m/cQOcliyfXCJtNPvWRj7F9sEs4t8B//3f/Dn/1wav8wNvexkrTpSoUubKY3tzmo09+GTdwSLMcy7JwpM2ZtXP8nb/1t/mJH/0Rzl4+R14UCAmfevwp3vu+/5r/8X0/wWNveoS3ZjO+slXx4JvPcuP/+A3ulAln1k6hXJfP//4f8NXr11m7cpEfeu+7OXv+HN0zp3jDylk+9R8+w19502NcfeQ+Pv65j/OJj32ahVOr/MGnP8W//sAv01xaQfkej55Z5auf+xSlbdNsNcmiko/+0q+xeHoVk4B/eoHbT13nkXd/F9F0QlnO+OH3/Bf82Du+k8H1e1x+6ArPbe0h187ysz/+Y7zrbY/xhsvX+OBv/Du++sJTvPH+R/njr/wxXemRlyX/4t/+Gp9/4gmieEQ2mWGEIM9zzpxb5uKlq/zOlz7P1uE9fumXP8Bv/96HuGj5fPCTv8vP/NWf4MvXv8an9vbY33qeq0tX+Ot/86dwQ4v3/9CPsuA1OJrO2Drc4MqF85is5HM3X8E1BtVySW6t4188x9Re4mCwwYMPXOOt734Xt155ge/8gXfz+lNneaC/wL528LtL/J8f+Rgv3HqB1XPXuLm3x1qvjbYspBDfGNACoqIWa///0JP+POm0P/N1iW9xwp/3gV8NB/8JEecvHG/9Z7yCbyk+1a+/es1FCabmTLzmYJKlIRKS3/rE1/i+BxxU/wKhq/7yCkr/+n//5z/f8R2k41AUMcYI8iwjzyKiJKPIS/b3hiQlVJaNVC6O69Pqtgj8JkHo40ufjY3r3NsdUxjQRpNjoawaopgXCY7tsdDvYCvJeJqgrYLAgYZbc10C2+coj8kzQXbcLjTX6dLp9CmmCadLj/NWm3ExJbckh3GEclz2RxMOJlPa/T4rLcnG7jaN0KesXGIdsJdKjC1xLRiNR4yylNRUBL7HLK0Y7M5ICwspJL7JiUYjVk+s4ofzZOmIorRo+A08pUgyjZI2tnIRWuJUJSc9g6sFg4nBcQOEKplmCWWVE+sYN3TwZEVogytsjMgJ/B6tRpvQqx1RQRBSVeB5IW2vRanrzVc8mjCaxeQ6Iy8jKuGS5AKBjed4XF2dQxvDYFahKwslwGhDVhR4boArJVkZc+HsPL6TsjMqaIZNkqTC9z36/RahF2Dyuqb3MJqxNOdweXmeYTRhOD3ElDl3NwaMY81EN9nLYTAuGEwTzq+tcnKuQ9gIeeD+B3HsLr25OUZRTik9LMsi0QKTZ9gSGk2fhmeTZQX7O/tUpiRLYw6Hh0hL0uv2aYYNuq2AlcUV7qxvURZlvTh3Qs6eXmMhbBNNxxyOD8mSmKPBHgeDLQ73t7GKHGNybm2tI0xdBWw5EqOps6iWhWXVsD4tNJPpiJ3tLdJMs9DuY6FQ0sNVNpWumFtYJJpF3LxzE892ObHSZ2v7gJX5FqdXl2l5TdY3dknKnNTkdDtdlGVzeDTkgauXWV44ye7uDsJKGRwkWOQ89tgbKdOMIk8pkISez1y3w3gasbFxD9d1aDQCxsMJ/U6LRx96PTfu3OHyyUWSWc7C2knW13dxfQdXNWg1enQ7PbZ3tim1xPc81k6uMJmOsG2XOMpYPbGMYymOBocUxvDGBy9RGcWb3/wYg51dlOexvbPN/t4uz924DRRsbe0Sdlu0mj7SksSziPn5RQ4Ptjlz4QoWJYvdZVQlaPo+cZESxwnKtrClYnl5FbBIophWK2Q2i5jr93E9jyiKaTZrt0UjDLEdSaU1ti0pjaYqDcpROI6D5/uvcR1sW+G6LkHQZDKdkmQJlq4rkh1HUZZQZAmOY2NBzZV61UnPNxbnNcjTIKU8hptCnKT1FNlUTJKcl6+/fNzeY7FzOGV/OCSOIxxL0Gp3GcUTzp45S2UkWWaI0qQG5VYCWxk8RyAl2BY4ykEqF6PrphmparaKqAwYySzOube1wfBgQJrFFFqjsOm1+nTbi6Rxgs5hOplwNDrg2tVLnD9zgYceej37ezvM97scjWbc3r9HktcsjDItcW2PwG+h8UijCY6qkFKwuNjnwsWLtPwmX/vqM0i3ju6IymJ6MMO2Lcq8AlTdPKYFgWdz6fJZZtmEJK6jd8urK0wOxzz88JsZjqco6XG4N6AzP8fS8jK3b++g/IAkjoknEULaSGnhuR6u45HndXtonKS4bkgSxYStJlWW8ta3PES/08cSBq18Frs9orQEARfOnePa6SuYNOfZF57lma8/wzgekpc5qdRURjO4t85wEPPOd303n/v9T3Pt4TeQJHD23BqLKyc5HBzw8vXnSdKMvBTkpcF2Pfpzp2vyhk6ZTGJMVXEYHeK4PnOLC9x/+RrdRocoMpRlhOfX0ZE8g9m4II1TGmGH5ZWzSAmWhNXlM0ymEeOjAzAZs6MRjVYP1/YRZYKyPXyvgQoddvcm5LFNkhR0Oi6W0IzHgmZvnmI2xXEtpOVw4dKbUJaP67TJdO0mO3n6NBcuXCBLUybjqBYcpcCxFJWAi2sL3LyzzXy/y617mxhhKAsDlXUcKeG4ur6uM7eVTW+uSx5nSMvC8SRJUjC/2MN2XHq9Nr25OZJ4Sp7FGFPQbHp4vlevYOF4kw1h0z0Wfyp6vT5HR0d1PXVR4AcuYcOvN4pSUFbQn+9jdMVkPMFyBIPBhO5Cm8kkptlpIqTE9z2SJGdhZRFlO7iuVcN2dYXtN+h358nijOHhESYvcV2PJI6wXBupNUkyxQ4Kzq6cot9oEjoN5hZW0GXCZBodCzW1OGQ7Pp7nYtt18YEtdO3EwCJo9nHCgKPhkNEspqoMWZETpxmzJK4bx4oCJRWOIygqaM8tEAQeB4ND4iiuhwNGUOq6QUxJWQsLRf39JixJo9kgDJtMhlMqY2FLiRGGSoMoNGVpMHmO4xhs18HSBmU5+GGb2SQ6blCsKA2URlCaAqNLmpbC9myEKHF8nzDsIYVNXmoqy6KoDGmaAJIKxXA8AFngShtLCRwvpNkKmY2nYOr2Pd9zKYsCIQymrAcfyWxEOpvQ7rToLS6zs3GPnb1tDgd7PPfi03RFhN3s0esu1QJdlHDq1GmO9jdZXexw8fw15ufPMddfYfXESYIwRGIRBB2uXbmPwKk42Nll7fxFZvGMldVTVCbH821sS2KpGo5sKgVC1mt4KZEV5EYT+DUXqxY3DFgSIVTtQCpz8jytHbbKRlgVVaXxXJey0BRFdsxlAo3GtV3SKCZstEDUwkeeJVjH7hhTGVzXoyhy8jwjyzKEEHieT1kalFJ1658Q2LZid/8A1/dwbIV81TFV1nEv13WxhERrg+O5UAnyPKvb9Kr6fqALjU5zhLKxfa8+3xgsW2GJ2rGlbBfbshGWIEqz+r1BYkt57F6UIOrPWB4ntQtLWgglSfOMPMtxXQfHc17br9mOpCzKGvQtqmNekqwFJlPzkyxRR/trPlv1mlvJkvVGUB7zzLI0rcs+lFWzuBz7NaC4ZdXcqFfv50h5LASCbUuOv4yOm+RqEcsSAqEUylIgbCqtaTbazC2s0p1bYP3lFxgMR6TTGCkknf4J7nv968minCqJGe4fcvrMVco0qq8rk1OZgoPtOzz9pU8wGh8i3YAbz3+d0WzE7v6Ag/1d3vpd38fqqVU2Xr7BlYcfI43HbK6vI7Ug0wVzCyssLy5Tmoqg1cGqIC9Lwl6TyeEhXrfJ/p07nHv0Daw/9zzru1tMJ1Pue8MjdFfWsAtNmk45ceo8585e5id/6se50F7gJ/+rv84b77+CkIJillDZLpbSdRv01m1+7yufY1JlWJak4fu4gSKdFdx33/30106CEgRK8vFf/S3W3vpmHnrgPEmW4jgOz3/yq1z98e9FHoxR3QW+/IUvUcQTPvuxT/I7n/g41slThEnGtavXmDt9EmUJlGV410+/j87hmP/uh36Ur5QDjKf4wtPPsrx6gmQa8fC1izSk4PpTT/L1m/f44lPPsLK4gMBi+ewpPvyP/gVveO93MT4a8Y4fezdP/eN/xRNH91h73Rv5qZ95P0889VUeffh1LHbaLAchj73uMh51o+MTv/6bfPTpL2LZNnuDe+g4oShzvEaTN7/t3ZxfW+XGi8/T63QwhcZVNsUs5vc/+ge8dOM6rdDj9x7/Qx567B28mMB9y5f5B7/4cwzcgLlM8OIzT/Ohz36OtdVlfvEX/iHLjsfudMY/++VfJTo64C1vfIxf/dxn+C+/4zv55Kde5AtP/AH+1XP83Pt+kl/4mZ/ki1/+KjduXGdjY5ul+R6P3HeV+1aWqCyLyyeXeO6lG9xLxvw3P/LDnO/3OL8wX0durfp6f80hc3y9/0WYk7457+fVZ/kWh+A1weRb/vszj2/lUHr1l/9EXPabPrDh/y4g/WmB6f/d45vG445r9wy8JsKLijpNcexQqqioMGwZwcc/+hm+57KL0z9D0/f+8gpKv/kbv/7zWZLVQF8FQlfEScRoHFEet3Zs7g44HE3J8wILQbs3R7PVww+aNAJF4Pmsb6wznmoCz0XritD18AJFVtTtTcJSLM33aAUhs0xzcvEUrpQMjg4wpaknILYhTioQNo4SVFIRNnv0koprqUJPJtyrUrSj6FoulXRJk5Kg4TGLpihRsNQP2Nof8fzdQ4ZFyZkLVwg7c9hhk/3xmEIb8jLFoNgf5oynSb04swM688uUpcAO2+wOthiOYwLPQ8+GxLMY6YT4zbm6VrcsULLEsSoEDcYaysqQFRGmBFHN6vy7yOn6hjLPibQGMvKyZJwYlHLRJmc8HTGeTDgYjvB8iV3VG8+94SGGeuHQaHSwKkFWFDS7J2n1zjOcThgnObOsotQZvm1hSk1WFPiuT5FnlFZOoyM4u3iSo8mE0G9Sloam57Daq2t3leMyjibYtmE8KcnyFF/A9sEAVZUcpA6FqRDUk7nzp07y4MUFlto+860OoFgKIB0NyHSTpbkWTUcRqBK7ytjcvEM2PqDfbGEJl/3dPdY3Nii0IcoTlFMvMF7N21vSwVQVTbe+yc/iCYP9/4VzRgAAIABJREFULXY3brG9d4PkaIjIUipSmoHNhZOLrJ1aYaE/z8u37nB0dEglBZZUnFyYY77bIgw9ZrOE+y+cwQ9chCnxbAdbBZxdO8Xy/DwL8z1OnTrJ6ZV5bNvl5u1bDKdTDocx67s77A4OmEYzFLB/OGQW5Zw6tUJeGcbTMb7n4gYBRZZzar7PNE744jNf5vylM4jSZRaPCR2fweEOmckxGiphmEZTOt0e97Z3KMqS6WxGXhSEzZDN3XskacTewYT94QGrSwtEszHdbptknPHA666ytbHJwnyXo+gIbQoWuoso4aCTmKLI2dmLmWZHnD11lgvn1tg9OKDTCChKzfBoilIWm3tbLC8uMzc3T+D6QMXq8gLNRgvf8XE9xXBztxaFlMOVE6fRSLQl6msNi739Q3YPD0lnMUJAFM+IZjG2a9Pv1mKj0Ro/DHGOuQ2v2jqVtOppuC7RxuB5HsISpFmG4zg4jouuDEVZ0Gy2yLO0dng1AwpTIKREVeCFAZvbW3S6HYqytsobXdXA3GNUpxD1VEAgKfMCISQ6z+oqYDRziwt0Wi129vbZ3N1l/2iPZhDU7gklKIqK/eGANBtzYvkCnu0xjfYRqobcOo7Cs22KokRYCiUUealJ06J2XAqBMhaWLbBrli6VraiKillRQ9PzXFOgkZWm050nSVMqy8IYm1Mn1piOp/T6Deb6i2ysb5NPDwhDn0A5GMugy5w3PfwwnVafzZ1dhBQ0mi6e5+N5DRb6S7jCIWiGzKIZo60jdJZjWRKt6wYRL6hvgGVhuHzhHI1mi8H+DovzC7SbHYaHQ+bn5xG5zfatdYoixnJhdXWR3e1DJsmYPMtZ7Cwwm0YUWYHrKOI0pzKQFzmpzikrwOQsrC3jVhWdtkKYMb3eIreevcHdjVsczvZptjv0uotoU1AWBXfv3ODezgbCddDCUEmFKRWeCMiMhXSbDHYG+AF059scHR1y6aFHKZKYV26/xCu3r6M8D0s4gCaOI06cmCeOZjjSI5pGTESMUoZ+r8diZ443PPxWZrOYPElod7u0Oy6u7bLQWyTNxzV/wwmYRhHtdoMgbLK3s8v6zjpFFSHEGNuVCAp0niGp2TdpmRNHOclMkRYZDT9lfi5kcDghyiqy5AjPVehckenaYaHTjGk0pek7jCcRw8EOpsyphGEWjyl1QRgGoGveVLsZ8uD9V+j05skjQ1RmGFPRbCqkA6XRmKqi0hoqwdxy3QA5m0VQGcKwdkIsLPSwhKHd9JFKs7+zS5FnVFXNmynKmnmmi5I0zXEcG6PrJjhRWcxmE3Z29snStI4BWYLpLKbICoqybseqMAyHY7AElajI0gzbdUijnCAMsCzJdDrFbzRYOrFEv/t/sffmwZamd33f51ne7az33LX73l6ne/ZNGqGREFoBGQgQyxibYIxTZSc2IUDgjyRFJS67TJGEcsqBQGyIXRVUMVsMkkVkIwmBQAtiGEkz0qhn6em9+97uu5x79nd9lvzxnB4wZVKVGCpUxaeqq293VZ97+tzzvs/zfH/f7+e7jnAwGw9RWjAcTxhNjjgcjUA74ligOhlFPsFXBYejIVvbp1nvZGSo5fWdMVjbwDaG2SzwpKyxgS9jQnStLiuqZkGnk+G9J4kieoN1onaPo4MDFot5aI1aHoRjLUP8xXkiIMoCT21ta5soSSmXwOgkCcB9FSehRctZalfhTYVUHq00nU6frNdn7+49pvM5pizx1tLUNZYg3BhXkySQJBHSRQitSNttGmPwApJIEmu9hHRHCCzSNESJIo5TvE+IeytUpubwaJ/aODySOImRQlLlC5wpSKNoOf0WxK1BcHYsCpypA7tOR5RFYMoZ0+Camvl0TiRjVjfXkVmbXrfLmVNn2T55io5qcfHik4wWBZvrJzCNZT6bsrJxglQn7DzwFEp0qcuSVqdNlERoHXNv/4jd3V1eefUFqqbg/IWH6PUGTI6OyPMx3nlc1XDz2lVu3bxB0uvipccsOUs61m8IGlnWYjGd4bwhyTo4b0AGxo+pLaZxJKlGReqNinqto+B09Sw5PizjWYqyNsRJgpaKpizA29AstmQGKbV0ywKLvCRrtVFKhmsgUtSNIUmCg6moKjqdzhIi7qjLCmcdUaRDnAuBlmHgUlQ51vsAo/YO2wQ4d2lqsrRFHMfBpUNgCDlrEc4TxyF66IzBNwZpPSrSqDi8BiEUQiuwljIvUHop0hEa34RSwb0lFSyj63ioqmoZObNIqYii+3y+8Bru85OkCp8p5zx2iYHQSob1EY+SCh2Fwg+0/MNzqgxteQF2HpoQQxRRIJFL9/OSIyN1EK10EArvtwcK4RBSYKwh6fRYjEbcuPoyhwdDXBOeb/PUQ4z3b2LKMV996UusnXuA/mA9QOitQdgG5xw3b13l6rUr7O/dxNY5i2JKPpsEl3UUceHBx/n4r3+Yt77zPexeu8xrr15i58wWk8N9ai/Y2jlFvz+gKkuUiLDeE6cp+6+8xGc+9iGe/Zbv4OalS9zdvcNjz76L17/wXIDIxynv+evfS3V3l+vXr/GmRx/nu779mzm3s8aTb3mS1Y3V8J54T5QmNM99iec+/zxTm/PjP/VT5CsneM+z7+TWK5dpD7rUJbz7vR/gyZM7tGKJNw50wmPv/Bp2Tm1hy4a004LZlJNnzpOsdKly6F3cpD1o8U/+6c/yqcsvE61t8J3f9s3cu3aZn//VX+P5Tz9POZ1z+rHHaXUd8fYWt778ZV6YHDG5e8ijTzzEK6+9wtNvfZbXP/s5fuwf/hSfuX6VF668zu17Q8a3dvna97yVtVMn+Zpv/UbmR0c4rbny2ZepTp1Erbf4X//ej/GVT3yaa7uv885nnmL99BlUloHWRHGM3Zvy+qU9DldPcHTjBsNiTF0XaKUpi4L3v/M9/PZv/J9UjSEvK4RvEMJSVwbTOLy1VNYwPzrmA3/5A/x3P/Jf8PijT/PCRz9Fv2P55Q/+MpdeucKj3/g+vv0b3s3XXniU519+mQ9++MO8+Adf4LEnHudXPv07/MX3vo+HT2wTb0RsPfMW/u5f/iv88H/9o5x74AE++vo1nv3Af8Tp7irRoMPV67v8zu99nNvH95jieexNT/O15y9wZ+86vV6HuW/oxhkW8YfyiVheY178qXCU/uSY1/+dWvTv/F3/hOf4t4tYwRH0RwW0Py4g/XGB6c/m8W+PuoFzjrASuPAqvMM5g3OWxnoWpaEsS2bzOc1iyMs3h7z8u/+SZx89TXvlBL3eyp9fQekjH/6Vv3/u/DaurNk/GuG9xpmavKqwOixS+0c581oQtzt0OwOyOKExNcJZbFNxfHiTF166jI7aSB8qRXUUIzyUZYnHkkSK1U4LKyXTqqKVas5srDI8npCXDWXjiBJNolOqpkQISRJnJO2MdJEzWDj2q5xbMtTRRxZKCVVZYLylNjXnTp3ElVOee/2QxkJZ1rSTjNXBIIAXV9Z59LGnycuC4eiQvC5RSqKFIElbtHtdnLeMxmM6WYr0NbPFMYs8J05aOCFC9aJ1RAqiRDKtJfOmzcnVFYyvGaxuYp3hcFqxtnkGZ2d4oKgFWRbjZBsvIhZljTANvl6AK9AiJq9rUBWumeK9RutQUd9UNgAbtUJLjc66OBmzmB8ymY9BxSivUDisNZR1E4C/1NTe0MtStCjpZy2aWuCtZXOlxbxowAcmVJ0foSV4JIV1HE0WbK4qhtOanZ2zxAimZUOaaL7uqfMoY7h7b463DuUtLRVx58hSRy3SJOXkapd8PMPWNZHQiMqQeMN4/5jhZMy0XNAoT7/XZ31lnX67T6TDpmM+mzMeHjObHXJv7y7tuGEljTixscK5rVW21ntsrK/RztpEUtPOOnhPgE7SUNQNVWEw3vH0ow9ijGE0m5BEkvF0Sq+TMp9NyFIZDiml5Ssvv0x/pcfpk1tIqbh06TJHoymRsqwPuqz2V9g/PKYoK5xzRFKztraCaBqm84KiNmilkEvo56UrN3jl6ivUoubNjz3B5RvXEHWFFYrBygqj4wkIiVaBO5SXOYt8ToTDWYP1mqopsHVNXXraWYvGWa7fvkJRG5qFYX19QKQkt3Z3WRusUpUVeTlhURZMFjk39w7otNt0V1pcPHUaVzTc2bvL4f4hhbVsn97hqYce59LVy3RX+qyvnmA+L5lMxqyvdknTFlXjaJqKuiqYLQpm0zGD1Q0iLxGRDNBtKUlaKRfPbrE5GJAmGe12AGTrOKauKvrtLloovJRIPEprmrqm3W7jXQAoOmuQSqKkDJZ3IQMnYXlwUWrJTBEQR3GYvHiDaxymqnBS0Gq3uHntJpurG2Ei6kL9tnU2xHaUwi8PzkrpNyB5VVXTODgaDbF1w/BwyPraCnfv7oFv6LV7GOMZrAyYLQrmiyndrIeWKcfDu2gNm6srgTtDqEHWMsbWDY0zVLZBaEUSa1pRhlIepw3OOwyKsjJkWcqsKIljMFYQJwotQ5yu02vTTRNaacqTTz7G6Z1tFtMRR/tH3Lt3hyQNEY6sFdOYAi8tVVEynx8zmk+IpOLiA49wYrCJa6Df77F/cMTxdBxchftjVJQyKwqyLEIqT7cd4xR4Izm1vYnzhihWXDx/jjc99RRp2mZ9MODg7iGHR4c88shjvPlNb+ErLzxPnIa2r7WVAavtLtPFhKauWcxqpIzpdduoSFA1wUllXEUrgqJuWO2tUS8KZqNjrBFk/SzwmqxkfLjPrVuvcf32FQ6mE2pjkcognQlRNOGJUMSRJHdzdvdukw4GrPYG3Ns74t7eAXt7N7l7cA+hdIC8SoGzNZGOSdKIyWSPwcoai6JibQ1EbXnskWc5tXGK47tHXLtxiziCOE6ZzUe045hea4V8kVPXOVVdUpqK4eKA0fCIO3evEqeGLNZ4ZHAwSIijcPisTE1ucqwzSGlxVYn1lqosqKuE0sRLzp7FOsWiqomjGMcC3Va0sx7OW5Q2rKyssihKbFNTGsPDF84zG81QSvDow4+gvETRYm4MCklTF6ysdpEaqqaCgI8J7YhZFOJdcYAxN1VDWTXgG4RzVM2cw/1Djo+OqJsC70KEJNJJOFQikEoiVURd1DRFRVUVDIdDqrwka6VILanKBu88cRIxGPTodtuhVW6+oKkqqqLEW2jKhtW1AVIolIgYj6acOLVDq9UljRPKpiYSHisslffUrqSqDDKOWBQTlJesdtYYL0pkpMl0m0wKWioODW9RxGB1i7KuOTg4XAL3HU4ABNA0AqQTxN4jVYNMErJun85gldFwSGNtcE2I0N6Fc7hl+1caKaJIUDeO3upJsk6LgzvXWeSBg1hWFb5saOo6xJOaBi1CdbxSkk5/hU63z3w6xQvCfiBJkJFcRtwjIuHBW5TyeOeDgNzuMZvPQ5y5MdSNpW4WmMZQmpoEETAGKgC1024fvGY+K0ArHDYwdKyl9g3GOYT3xEs3apR2yHo9pqMp3pYoFWPxFIsFSgqMcVhTkZcLslZC1u3Q3zhBU1l8bYjjiK2zZ1FxxGK6YOPkNpiCWV7Q77a5e+0yyAgtFWU5Znp8m/5gQJ2X3Lt7G1sX3Lz1OmcuPkF/4ywA5WzGZHKdlY0NQJG2MlZXV2inLXAeqQVJmoSWOCForCGKUxaTMVnWAgnWhkOIQISGHdPQylooGRrwlIxwDhaL+XI3LQLoGke1FOezLMOYkqqul4NoSRQnRFFCkiQoJSmL6o0hyn0YfqRi6qYmS9oUxRTnJe12B2csTdPgjV0KSjE6jjFuuXZGinwxI1E6zLhdgHfXTWAU6iRFxdES3eGRUYRpmjdA+loq6iY4R+vGIrSmNBWusQGcXDeYukIqBSpa1ssb6toQaRXWa4IjS0lFVQfnVbjHusAbqhuWlxTWBefgfXHSLYWwssyJ4phY/5HXKmVwLcmQKHD8UejtHx7WhNZBlF46lpAhrud8uCZYXp9ShlOnd34ZJ/Q4KVDCkaYxd2/eZDIekiSCfD4FYViM95kVQ8qFZdDvAJKs3cI7Q20cVVmzt3uH4dERTWUpC8NsPCON2yipmZcNV6+8QpK2eP3lS7zy1UsYBw+eOYWScDAcs33mAv3+CqPDQzrrA4TWLBY51698hdFixkOPvpkrl77I7atXeejt72bj8YukNw6oNLz33Hl+70MfQax0+Pr3vps3PfPUUtgLEVkhBMJaUJL6lVu8Vs759Z/+J/zW0S3+mx/6YS50IwqRIXTMm594gh/6O3+La5/6OB/8pV/kuRe/yuOPPkKSasgNUa+FsxaZZtStiGq44Bd/5n/kQ7/1MT74v3+QvcWC1cEaSkh+/7O/y8u3bmJlynf/p/8J737bM6ydXEe+eJWf/smf5Vde/CxlZdBace3ll8FrEtnwkU9+ioNFiROet7/trfy1v/JdbDWGnQsXcNZy595dfvof/Qz/4l98mKfe8izf/Je+gbPb58iE4EM/8/OUa6s8+cRFdCvDWkssJNQVthFcfPfTfNtfeAcrnR5f/NznaGRDlRsE8NJXfp+yySkrSyuGQTfwHWsctW2WJQ2Sfn+FT3/yEzz4zFv46Ic+x4tf+BgvffUS50+f5LF3vIv/6vu+j+mNXW6VBT/5wY/w3O9+nGZ8zM0rr7P32hU2NxPagzP8Z3/ze3j2icf5z3/0R0mM4A9e+T0+8o9+mjed3CRbP8F7nnqcL1+9wUJm7O6V/NbHP0ZejPiFD/06zz33efaziPNnz7GmE5QI6BOW14XFh87D/48Sb392j/8nYpX4E77+s3vcvzeFohOPcyHKW1UV1lQ0xjKdFxxPZuwPh4xmMxZ12D9UDl5+6RLPf+6jvPCZT1AOhzx4YZv+YJvB+uafX0Hpw7/0i39/o9fC2QWzaYGpDZM8R3rQXlA0jlv7BYcTS6vTpdfthFaTKNyoJ/du8vvPf5HaKRAeJQVeCCIRbLeNcWExbHVYXVuj224zmkwQXhALh7OKu8eHKC3C3+mIqi5xDuJUoVVKVsFaJch9xV1Vo7wilpKZqTDOk9c5EQJTLrh275i8EtRNOMQ1VYmOFVZGPPXEMzx0/jQqijg6uIutaqxxRJFAi4TD4QTranZOn2Nzrcflq9doZx1ObA4QQpIvyhCNiIJdOEkT7KxAJi1Ob62HjV+2gtKrHI52iXQLrMGpjJo2Sqas9k6QpT3SOCPNunQiQ9aqcKqPtQZPTqqgqmKiWLDaHuARzPIwMe0mmkgnyLhHp9UQa0NlM7AOLR1VXWNsyPDrKEZoydMPniGfj5lOhgznDXVjaHxJpxM2/lVVUNYzFJoHHzjL11zc5vrdIatZholSnnn4YYS3zKuGB06tYo3jtVuH4AXXD4aknXXWOyfJS8/+dEpZN5xaWw8LPwInoNVr0zhP1Erpr60RxRErnR5aqmAxNxZTFkS+YaWtObF9gp3NVQbdHo9evEC30yGNgx3b4VBeMJpOePnKZaJY0Vvp0O5mLIoSYx3TxSyASROF854zJ7YwzhEnba7f2SNOYpoGIqUoqpwkiTmzc5IsjlkUNa9cu8ZwOmd7a504ipjOA2vCVg7rPM4Jbuzd4creLlVT0e30EFKw2utSVjVCe6TQPHThIlev7KKiGK09Rkju7h1y6vRprFNM5wsa2zAej8JGRwd13XqH1pK6MjQ2RFOkEDz9yEWu395jXhTkZc7tvRtkWUyn3WNjdZVuN0MqyNIueZGzOegjG4dpDJXJORztk3VanD65Qz6rOXP6JEkS2s6ms5zxZBSs/O0e4zxH2Ia6zNm9fY+VlTZKRHSiGOMNXkkSJZEitMJIIWm3uyRRhPeC2SxAVyWKTq+HjhVKyDA9VYr5Ykar2wYXIjYBlBkgsPiwyY+TwFRyOJz3SwdbsLNLrcJG2Gtq16DjiLoKh7EoVqg4TCODtTRUCAvPG3BQROBk+OW6VDU1GE+336PVa3FwOOTGnVt4D0eTOXVRIIRnPB8TZSlb/U2OJ8dMqwlaedIooshLnIHFfEFlQ5TUidCapj0kMqKxjsYY0jihqqtwyIiCjd97TysKzTOtVhYYL42l3+2zMRhwcHTI/uE+127e4JXXXmf36B5SKwSG7Z0dRpNDYi1Y1DWlKSlMBU5Rl45ytmBrZZXTJ7fZWN9ga2eb2zdvMZtP8TQQKYpqQrurEUpga0u1bJvQUrG+vglesjbYZNBb5czpU4yPxlz6yiV2ds7xzNveyvHRhHk1IkoUo+GYd7/3nWytnmQ0G1PVNVorkiQiiTMa4xBakrbaRK7AVVPasWNlZZ35aITXM6SPmU4PwBtOnTpD42vyRc5ompNXjqY2CFmhCBPiWDYkKaFiXVhk5JkfHdLOEqoSSpNz4+Z1lLBYSkxdoVFYYdE6oTvoMZsdo3WCdBanPLYx+LrkmWe+lhtXbzEZDkmyDqPZMXkxo6gWHE8LjHEYD1JFlPkcgeThczukccqpcw8zGc8xpgmRWmHxzgCS2rnQypgJ4jR8v0hFtDNH7dosSomOWixyg5QJUiv62YJ+t0/dKFqtVeq6CnXLixpTQ6udYpqS0WSCw9CUjtlkwcbWJnhL7S0XHnqQm9duUNU1eVniXHAH+KUdO59VKK3JYsXwaEIUQ7vXQUaCe/vHeOmYjOfB0aUlzglwgnaWLVsOS7TUdDptjDFBtG3Czw8RNuRSSoQME1RTG5zzZGmCcJ7J8TiAdh0kUUKWJRwfzcjSlPXNDYr5gm63R7VYMB0PiURDuViwPx7R76+TpRlNWZFEKaCJkg6mqqiqmk6rRT9NieOMSEsSFaOkZG1jncaHGKZWgc0kZQSARIa1VTiUBxFJIp2Qtjp01tY4vHuP4XBEYzxmeSi3TYh7YSuiCNJY0zjByvoWrSxlNp1TLJ0mURyjdIzSEhUtZ6rOk8UxSkO3t0K7M+Bw/4BFUVDbOriSqgpnbEAV1IZEClpRhFYRrXabpNVhsSiCM1AFN4fWoeUKGaNcRaQFaZTiBXR6q1jrmcwWgEd4gbAGYxqq0uCqhlgZpHYIFGnaod3tcHRwSGMcKulQVQXWheZCZxvyRY5H0G13abe7tPoD6rygLmuEDDy40WKfxXjKYGWF8fiA3Ts38b7AScvqyhpVuWTTKMv6iXMc7t4CVbK5fZ7NtRM8+tibgoDnalSkOXPxKUxpkVrT6vTQcYrUGmMtSkqiJEGqED8TQiNkTLGYE8Waqq6WjD2BaWry+RStwgBKLtepSEdMZhOss7RaKVEUoXXYzzQmFFPEsaYqCwCEVIil0CoI8XtnHYvFDCUlaZqFFrXlhN0jiaOI+WxOmmZkSUJTN1hTv9HoFqUpCIl3liiKgpuqqol0gvUgVBi6WmOWa2aEUKH0wtkQqfN/pGlNSEFdFDRNg4g0SZaQxTFR1iaWiqYJrjOEJIpTIq0wpsHY5UBtGbdRQgKepgrtpgBKqSDOGYPzPjRCSoXWcfg/3D8Ae0ee53TanQBOv99GF4V2Oe/EG6+bpTAkAec8ZvkzCz+j8G+tscGVJEVwKvGHjgG/3Bd4EeJ2SsXhpyMFTWXp9Puh9ENqMJb5YkKi20RZyuVXvsDWyR1avTWEkBgn6HR7tAcrAbGA5KE3vZlyPmUymxInbc6cOUdvsIppFEJJOq0uxjes9NoIFTPPK3rrG3S7Kwgp6a2v4uKYfDZnd2+Xx975fh77unehDvZ5/fJX+cB/+O38yHd/F5uvX+XXvvQiLzz/JfTWJm2VcHbrJI89/ihxkiBleO/viwoegW238Knmc7/+G9yl4Yuf+Qz/6nc+zfHkiHyRs9Xf4rUvfZFf+tSnePV4yHQxJ64cjz/zNKNPfJL2ow9jFwU6TcB5nCn46P/y03z89o1Q0tDKaHfWOByOaWrD1uZJ/uZf/aucbg04cfok0kHz8uvMVzp84jd/AzpdptMJTsfU1mOyNeK4hVOa97z/G3n7qbMU1y5zaHKSQZ9//j//Y/63D/8SdbvHe7/xPXzgO74FUzecOLEBacyFZ9/Cwc1r/OtPfYTdcsHN3UNcWSK9prPR42g0DpB+7/mlX/0/aHc6FN4jC0vaUjjjwUGUaJQO935nwTuFQJJ0VxFJihKeh976dnbOPc2rf/ApVKfD6PCQra1VxsdDfuInfoLCzHj7N7yXb3r/tzA/GHLj1k10GnP+be/jdz/2SfLjfT7zmd/hiSef5fjoiGo+Z1hM+eRXXuAdjzzMf/tjP057JeJb3/dOHlg/wdHRMb7fodtb48zFM/ydb/oPONtZWcbK+TdaxGT4sIfP/J+qevD/v8cfdWj9cSrTfdHo/u/WGqqqYjabMh6P2b2zx43r13jxxS9y7+5rlFVF40BqTbfTYWt9jbV+l3YaU3jJpctXuPbyi1y/dZsIxQNnNukNTrJx8tSfmqCk/7Se6P6jchXWC6SMWd/YpKlmyDxiOpmSI6kRVMZiXcP+4QGumhFFESc31+j2Olx+YZ/DWc2gv7LMk1cIRwAHG4vH4Sxk7RbS1hweTiirmrqokZXCGo9Ektce6wxRVKIjxSI3GOtp6hJEYKJY4TDOIqOIWkekVVCLrQGZSG4ejsFrjHNhiiagmU+5d/V1Vk+dw0zvcq/cxxYlmyvrlLMpjRFATNU0VN7TsgmuLhkf5vQ7G+yc3ITyiIWvSdI27U7GfDHECE+aDNCZR6UxkSjZv3OXeL2DXjkBvoNpnUJMC5oGvExIOh2cPUKqPmkiGQxWKBaWyWhKZQuQFflcknlP0czo6wYrQkvdWi/GVFU4LHtLYxuefWCb2SThuTsa42Zou8A4i4oEEsdGt8OiiclzTztuk7sh0gUL8+mTa5xYXWc8bmhlmvHxGI/igZVVoOb0WotequhEGcejAmEU2xsDVnoDXrkzY2elj80LvJLobsLtRlLFCXk5wwvLcDRGIlhUBePRBBl58rIIJkVraSUgvKFpDOuDARvrW2EjjcO7hjjrUTpHZizKKoQ3ZFFCYYK7xTSe0XTB1dt3GKz32VYnsc7Peow2AAAgAElEQVTjLQz6fVZXVjk8OuBgPOH97/g6hKmZ5QHa/fCFcxxPJksbd4hcWWu4fmsPX4VNcK+XYfUmUaRotVoM+qHR6NLr13AxGBfEgjRWJElMlsZ4ZziejolVRCIVjz72ACur22z0G4igXsxJEsVzw6+y2u0TmYjh0ZDNzRN4Z9m9d4Bt/DJ+ELLQXoO1Dd41mNpxffcutlGBk6E9/X7gXXjvWBv0+eIrt4KYUR2wtdEHBdvba5zeOs1LV14nnc/oJC2Ejjm3c4bJNOfwYMRsVuCcIU4Vp05t0047TBYls/mYVpxQiZK1jTWe/9JXyfMpJ7a3Obh3yNlTJ1EyTEAkoWlGCkE7zdjZ3uZ4NGKR58znM1qtLYxxAda5tKovpkF0arUyXB1EKR3pYOs3NU3TEEehNaU2BqEkdd0QRxGNtSgEUZIiraIuG7QOz1UUFcY64iRFheqqYARW4RB7X2iSMkZo0ChEVbPS69Ht92mahrXVVSId020JdroDDg9HTIsRs3rBTv8sVsB0NiZRmsViTlFYtIpJhCWvamKhkF4S6dDciHGkOqYyFVVjQSi809TWoV1DomM6SUK7pdGmQmhDUwZ+1OHBkDu3dqmamvH8mHbaYlEZiA3tJCJyhkE/Ye+eoKossQqiXqJaTFzgQ41nc6bFjFU7oK5rFkcjrl2/jiGnMhbvBHEiUFFo3qqMwjuB8TWHkxGHXx7RbXdY6Q8w1rB35x5ffuFFkiTi2Xc+y2c/8zvI2NLprbK7e4teb41XL73K17/j/Txy4RGqquFweMTaxjqnT53nYH+Xw+EeopgimhqBo5jn3Mgv8fCDT6Jjwb07xyyKBXk1p375RWb5nEVd4oVBCQ06RBqLpkF6QTeJeejhx7hxZQ/XzJmZBq0iZoXhcDrF2gaLZTyf4IXDI4i9RKKx0nL75u3QGChnKBFjC4u3CXWT8tnPfwFdVzz48APc2R9ivacsofGWXrtmbbCBLitmeY6QEUmS0B2coq5yDveGNEbQaXc4vbPDzeuvgY6DiCM8XoXWMxVpohh8bWlqifEG5xyNlcwLQRI52pmg3bVYN6Eou+jZgkgmWOEZTodYK+mqHiu9Ve7e28dqEBryYsZXX72ER7NYlFy/fhsLNI3FGh+uDykQKkROYq2I02XTWTplPJnTRhGZiKSdkbTSIDaqEJepK0NTORaLiigOrUqtVhamcmWIvSSRwjpPHGvKoqIug7MCIUKduDFMJ1O0VHS73XBolLAyGDCbF8xkSZxo6qZmsNrDNSVNVdDOElQDXho6nR6ZSgL4P2qQcYvcjpagfM0gXePszknMrERbg4jC+yx9igW8bSiLHFyodw9xJgfe4BqD8w1ppPFCgbcY75bInYjW0t0C4T10xoLWNKVFInBWonzY60g8hQm8HmcCXsCaAGe2BIh2JgRIh5Q6cFoI/D8pBcKq4L7Qkkha0ApqCYShipQe6xSICJRE6XDIdt5iHQghwZV4GWJXSxwodunkqOoKrSXOhn0cy9p7JwTOAgaEVkHQ9orKe7SMmM1nNE1BGic0TeDK1KYiS1soJRAyODhrU2NcAz5C4Hn5q1+iXkzZ2txi9+4t9vZu0VRDtncuMFhbQ0pPq3URi6XMZ8SRpyhzZJTw0BNPsZjkKB2zmE2J2x2OhwckKqFoGnQkETIw+lAh6iSFAi+wxhMnCmvqUAEvLdItHeI48qbCexGiUUou424ej8Uthx5KK3Cacimk1XVNt91dNrUFJ1dVN4EfZA2OIIhYG+DWSRoD4V4Wxyrs83RKYw3GeLpREpxGdRCT6qbCCRFEOxfiE03TYO39Nrhw2GlKg7MChCSOE7y1eGPAhcGYdhbpPA6B0mGa3jhL4xz9NEW4ACbXaIzN8c6gdWgdlNLjlhwkpMAsIxwB/ht+CQVRpJfNahoZabwxKBkGKGLpEpJCBgcRQfyJkxSzhHbHcYwjRNnuQ7zvu27wwclsXbhPumVrnFi+v/cPeHp5j1mmUILTyS8dTEKC9SGOp3XYRyLZOX+R0xcvcOUrmmw6xHnN9de+SlFUTBdziBOuv3aZ3upZuu0WsdYBWm4Nti5QCurplKauqJ0lrgu6/T5PP/Q0Omlz78ol5rvXmKuUk1vrHA2naBWzf2uXTm+N9c0NTJyxurPN2cee4B3f9s08vb1FP435tm/5Fg5fusLXXHyc5uCYV+8esLG1yZseeYwf+JEfZPTqZfZeu8bRvQO2HziLFOH+7r3HG89o7w6XnnuRf/A//UP2Rdh/zDwIoTDG0u9ndAcDPvf85zmsF0RxwjQv2Z3lzOYTPvKhj/LuRx4hf2mXt37ne3Ea9j7zPC95RyttU85mrK+scnplje//vh+kvHmDJ59+hHhvwq/91m9TNsc8cv5R8lTz3AvPU0iQVY4xFge00ojv+Yvfxuc/+Qm+Mh7y+M4J/tIHPsDP/d2/xyeuvMIvfOxfM5wueMc3/AUGZ87i7h1w390HjshDMj7i1uc+y8vTXeaV4JFHnuHTv/mb/PXv+WuM8gm/8Csf5s1PPcRv/PZvIhNJu9/j5KNv4YWP/Qba9eivrTEeHrGxfZ7x0QFleRScrDoUVzz67Fupiobj61/lP37X+/jxn/hZnvimb+LG9T0u/d5vk3QG/Mo//1VOrK1z57WrfMP7GsRwxM07t8m2TvLs17yZxWhGWVcsFnO++Xv/Bi/9wSWOh3s88q738snfe4W//f3fw7/69OcppiOeeeQbefnFV3j+xS+ye2+f7zqzQX9ljZmvKcoS03FELHlhXv6hI+n+F2J5bfKHcax///h/9/ij7573nrKqyfMZs+mcfJFTlCVFOefo4JDbd25zsH/E4eEIrQyDQcb7vvFruXDhQbLW6htrKwS3pF+uA6ap0bLBNgaZJDQlLIr8T/X/8acuKE3GY46OW7hmQdUovG9I4pgsiRlOFxTxCVobXVb1CJOPOdjfY15X9FbfTc8aLJJOb5VBb4X5YoQQoULVWv8GJE9qgZSW/emYeVXTFIZWrDgeFVTOB+jksorYIcjSmLqRrHS6lNUMqVbDod85UCBUBFqiS0dlKiKhqSoLUWAVSAFaihCPwWKainKR86UvPk+cZUzyAmHN8vWJ0LokJWknIc3alFWFkoJ+v8Vkcsh8PgTdYm19QF7PUJHEECZK0UaLewf7mEIwmk7ZXLMUZcPWg1+L7q4hkozJ8WWsE2GjWhesDNZx9YhbeyM0Fi0aFrMRKlKkkQyvR8WsZZbjyYgoW2G91WExdQzLCu0XeDvFNoLIGpxrMK6kNnUAM8YRStX0uxJVZezePaYdV3QTgRMWsjbnTmyhhWLic4bH96gaQ+Mibh3OOJjfJFMldSMZzyv6/T4Nioun1hnlglY3pRVNsS3FSpoS24Sj4zGzeUEcCZSw3LhzF7zFYxiNhpw6cZL2ep9IaxItSJMIWzvyIqfd6izhyxZrcqSzgemlNEKoAHR0Dc46pIjweIwpuX14hBOQLfkOUsLqoENmutzYvUOUJXzLu99HP8tYzAuu3rnHt73r7fR7K1y9dZNLV1/HNoY47jDo9ji/cxJsgGcO+l3iqCCf51x8eIe88Fy9foUsa+N8ybwqyToZxlRUNkQja2ODiCEinK05L9aJhOehc6eI4rCBaqoZV6/d4ebt22wO1lCR4vy5U8RRxp3dIxwOX4VpmvWhtlgqGQCYQnL3eBQm+tIxWRhkFCGcoaxyZtMRx6PJG5/rfDFlz1ZEnMSaK7TbLQYrq7R0zImtTY7GIzK94Hg6o2wqprMJZ7a3ME2NagsG/R6Smlarx/luG1uB8Q0iblEZw+HxkNW1Pv1O4H94SRDBhKdextmSNGEynVLXFaapie63y3hHu9UO7V+lpd/voZSiWTrshBA0pgmulq4KDS5VHez5SpK1WpR5EQ5zWpIkKWXZ0HhLmrUp8pzxdE6r5ei022itw2bK+WU00qOlxovlIQuJ9DGWHOsAJ0h0ysZgDaEsG2vbgWdTLMjLKsRmDw9wJgc0i8KhY0enlyDQzPN6WV8NvnJYZYi0pGrywBhxKfNFiUTSiiKUbsJ9sBacPLHNvcNb1MZjpQqCexmYdlKKsAxEGc7N8ZWHtuHi+Uc4HpVYG96fTtpHVmA8tJMWuS/BC4bzKTvOklcNN27voiNJt3+Ca7eugvdEMZimxogImWoab1BC0jhLr9fBS8O9ozt0Oy0uv3yV2XzOs297K9p7tBA4CYfTGZtrp9kcbJDXc3Si2dk5ye8//zym8RwfHOErQ6SgpRTWlGjdppWGz85wMmZzfYPV9S2Gdz/PQ489xZ0br1HbCXkxQWpJHGvuD+GE9zgDRClCt1nMxRvA9ZaOGU8TXr92FakD+Ln2Du81XjoUkshJTA3eOJr7kFiCkyNyKY31zCeWYnqTt77lCWqjSFttsiZHSMt8McY1NYvZnMp78rzk7OmznDpxknv7Q67u7uOrEdsntyjLCWUZYiBIjZApSRxRVpLhUU63J8CmYC2Tw4Zy2VpknUFFAp1FxHGLRXmMksE1UpYlTR0cI4uyom5KKltw/vQDHI+OaYRlpdPm5GaPo8Nj9keTcMCKE+JWF7+YY43FOAKoV0iUiMALxsczykURotE6ReuYJA1ijcdQlZ7eyoCmMtRNjVSKTqeFFCowhtIEYy2dbvsNB55cNmp1+zFNY8gXRdhQRRDFQYjL0pQTWxtMJlMOh0OODodY61hf63F8NCSajjm9s4UXnsJ5wODQ4DVlkTNXM0QZ4iiLPKcsJ+TllPV2h1baw9U1Td0gpaCqHVoH12BdBdd0HMVL0RlAIK3DuiYcipsAzIxIUR4whsV4RFnmGFuBETTewZLB5E2NMgYnFPY+D8aBEhppXXBGvHEAF2ihkU5CJJC2wrvgaNEibP+8UqAkmnBAtsLhrccbgzMVVjpKY0An2PsHCOswTROEI2fwNrhgjXFEUgAOrT2N8ygBVgmiNEEriTUhmouxKAlCVkgkwkc4a2gEIBRxu01d1jjT4NA0VQ3eUcwXy3t2AsLjhWI+mXD91usY59BRSic2TCZHOKuYTBaY0rG1s01L5LQjTTGf0epu4BuLlA7ReOKsz+nNM6RZj7qowkAkiiimE7JunxtXXqXVblE4xYUHLpAkcSjuaAxS6GWbYWDpKRkGFHEc400RHN4KTGVxTXA5RXGyjE8bEMFpY60jjoKrpa5LnHEYFwYfOtawPCwoFSGERSmwhjfG22Vd4RxEURwEES+xVmIbg5Ah5iqVII4iqqUAp5UMLh8hME1DpJYuIV8Tx0loSiM0mpnGhOtNCKIkDiBvtXQJR3q55142rIllW12zdBUJEe4HSPAOY8IL11ripAreVRmcxEKEhEGAad8/1AYxCII7SUVRgPtHMSEWp9BxjBBLiLAM664UEqk0XgQnHUvxyBi7dDaGCLuQAuHVkrm0FHCX7CWPD87j++1WCLwLLXXWWu4zVLy/L0OIJZ8qxJBN07zRbjcdz6iqiqPxHYgTsu6A6uiIVnfAnXt7nLh3jUXcYufcRfLpmBef+yyXL3+FweYWxpRMFlNarYR5McM5S5K1WdveISmOePXqK7TWBiA0URqjWx2izQ2yzU12Hn2YCxfOc/HEGhtRHEShxmDx5DblB37ohzj30DbFl15m8MhjfE/nzZTznFNnTpJVJfneEKqlEOx9AMlbhxKeT/2zX+bF1FAJSLKYZlaEw6wPg77h8SEf/1RgCLVbbVb6a8TtTVa3Nti9vstNUfHf/+TPoOqC79Vz3v8d38qHf+7nuOkdbec4deECP/K3vp/eouLc1gbphTNE/S7y0ZR3HdzhH/wPP8HPfvgjfOIf/xyHvYQnTp3h927cwFlBTENZ5vzLD/0qL7z4Jdr9HuUipxnOePKZd/DcjT3m3Qg3mrK5fYKzFx8lv3YTZwJI37uwh+mmXb79B3+A3V/4eQ5HU77wwX/GhQfO8+pXv8jPf+bzvPzaZT764Rk6iUFbuv0OzXBEFEesnz3L13/TB/jYP/1ZjkYH5NMpIDHeI7wlbWdc/sLzmLxi59Q2H/rQr/IHf/Ap3v7dH2DFKt4i3oEfj9k6tcnm5hkEDQfDCVcu71GUC/72f/nDKBNz684uB2hyY3nhtz/OaGIw1vDdf+M7aSfbVNM7jI6P6A3WeOm1Gzgb4WvDs1/3Pl68fUjn4JAnv+ZtgFi6Av9N18wbfhp/35n274Wkf5fHG1yk8CcccDQr+cpLr3Ln8pfZu3eX48mUqqqQhPVoY3WNtY01trY3GQw6XHzgAR5++EnStPfG8EZJHRiT4VZLXRmmo2OGxxOm85qVdkbTNJjqz7mgVFqBsZ7FbMG8lvQ6KXVpoIFIdlDJKmI+omNy8rpg4RzDo0PuHR6wmm2T9XusFA2LKqcsC7wIEGmUWt7EIMsSTG3oDAas9hNu7t9DqWDXTnRCFFfkTVgw8hJ6nZQ0jUgzQV6UFLZk7iSNB7ykwoGEoqq5vzYbHzZpTrCEXYkQnV62zUxHI27emrB1agfrLMqBcY4kEtS1RbYEcdImbbdAauaLOZKKIl8gfEymMqaLBZNiQpq0iKTACYnKgkX9cNzQG/SYzu5iRMS5s2+jqubQ6iFnKXGyzmJxQCp7KJ1iqg6mmTEpGnpJTK/doIWkjWbcaOIY6soiVIdBp0VRTKmMwlpF2dQIMaUsFJGUIWJUTlE+VEU23uEbwwOn2rx2fUzZ5CwstPodFnVJJ5NIoRBxSgVIqZdOHcO0KpjPc9JEcJQ7Vk9ssb1zgsODCUejMYtKoq3EiwwiRWkFN/dGSKWJlGClFZO0NFKmtJKYSAla0nL21Glc5MAGxgMCarNA4kNTjfAB+ig0SgmUDy4rgMo1VK4iElGYxJsaXGgM2VpdY6XbRQqJ8II0Tui0MnZOnuCRi+fYWukwngYGAT4Imeu9HuLMKaw3eDRZp4upcpqq5HA84sLpM5ixJ+1mbK1soHXGK699gfMXzlJZz2zkKV3DSrfDolR0uxnK1PS6K+SF4fDoGB1F3N495GCY86bHg2NptJhgqppT22s898KXWVQVWbvN3t17nDl5EiWhbhwQNnr4MK3zDmQckbVT7GKM0hIhDK0oDQfZsqasGlo6WPiNM4H3ISP6gz4ra1ss8jFvfvARYqk4e/okImpxaXKVSTkmlQnt1QxHw/H0mG63TZK0ERKipI2MBFnWwlU1F7d3WFsbYETE6VOCu/f2Sc9GpHGG9/aNliK1rPROlKbTbnMwX1AVJSqKwqTesQTehvrjyXhCmmUIIVnMF5RVGQCmCCbM6Pf6ZGnGfFknXNbh8Gq9oCwrfBJAmwhNWZZUtUHI0PD3f7H3XrGWZud55rPCn3Y8qc6pXF2pq6sDm1SzGUW2RCUrWpIlWTJGJiwLmAQIGAgTAF8M4MFcDXwxg8GMjZGsgSRLkCwqmJZIm5JFcshmajZTt7qru3I6eZ8d/7TSXKxdJQsTAA14MYC8gQLqosJO5/+/9X7v+7zeBZwMaCkJy6ph//hwG7eyzhkIIbq9vCX45VYz0WiVMK9qamej40hAW00JosEQ0NKxutKhMQZjKzQZ64MByMB0OkVoResMad5BaUWvO6BczHFtTiIDSmY0jcU4mM0bDvcnmCbgg0MQaL0DpdAIBp0i8tdQ9Ac9FosFs4ljUSW44JDC8/SVp/DJgMnuHvOFZTydomSHpnHMpzPefvsas+Nz3rrxNlLCw919rFXxBtgReCMQWR4Px8HjnEJLhfWWqqnpdAtu3rzFzu4OJ04eR2eKosi4cP4SSaG4vbPNdHcE/Zrnn32BvFNw/cYthsMBxzbXmc8WtHXLySdO0fohk8kI33ryYUGwAqZzqtazaBt6gx5bx08y2h/hwgylaqxrI7tBJshE4a0gz3tYl9DrrlNXLdP5mE5P05qKqnbk3S4iGFwI2ODJdQHKYtqWxjqEd2ysruCcZN4EuoMuvrUYrxFBorXk6tWnGfRWORxNSbRC6YSUgiL1FDqlaiyGeF144twZ1ooVJpMFKhGkshO5XTrjzoM79FKFt4YkheGwoK17TCaGyTyygoq8oGwDBMizHCkSnG6xTmO8Znzo6RQdqspTFJIQHEfjCQvTAIJMRWGlV3Qp65KTW8fYPDZgtVMwXdxHpQmnTp2irVp2ncEt/56SGr9sjwoBqrLFNQaAIk/pdTtknRStE9IUXONYXe+zfe8QZz060yiVEpZC2GJRsra2wtr6gMl4znw2o6oadKJZXV9jMpmipKLodDBtCyE6Gqw1jEYjgvec2Frn3v0dgjP0e0O2jg/ZPzxiMhthnSVVAm+jy6hqPIvWYmZHFGmBoKEsZ0jnKAZDUp0x6A9IZIrIPK6qsVJijCEXDmtbrG+wzmCNxwdBwEUR2ts42xiD0wrfxmuGrGrSZaV5EPGQrbyKv1cSJ6NYZ1xAGYu1LU0dw6RS6ujSCAKCwwVP8J7gokgkRRSPpFDx4C1lZBKZGA+Mh/0YYQw8iv1YtBSELCzvoKClJklSbPCIoAgqEJwhcXIpkCTLPxvdesHX8fOQEhfi6w/eYoxBWlC5RiUycqJCABmYz0qMrVFBLsUIh3TR7dLtpKQyir8hwMH+Dvce3GM8mVEUGU+cXGN1dR1bS4yd0rqW9c3jqGZGsJY7t6/TWd1A+kAv65FkBVmnG7lpdUkTAs45GhEj4k1Tsrv9gLOXzzPsrJBlBVJpjGmR4ZFLNrL6EMuK+rYmCIkP0UHrnMOHyB5SKhaSeB/wgeiwsRatdYSuE2votVbMZ4a8013++44Qomj4yA3t8SghET5g22UhjkqW7nK5BLRarImzbJYnRI9YQCCWDpr4HJI0IRFyGZVbsgelWjoTwuNVeowV2tgw5wLCP0rBRCFIKomWctl2GsjSBBt8ZHUJSWsqWtOSyChGSQCpHjuURACllvEz+Sg6G0iS2IQYiKKS90vgeNsiJSRJgjNxHgwhtkJa50i0iiUtIYqp0aUdCMITzcVy2WYR35NERne0CTYujpZxuEB4lEdZfh6RofT4qL3kzXghkTq+N0oKrLEkeZfFbEJvbZM7X7vJtJ7T7Q0oq5KVteNkaUrQGbdefy0KdnnBcHWTrDckzXvUdcXdG29TNwZrLXmhWV1dpbsyRFpDCCmDK8+zt3OAkmlcpq8O+d4f+kG+/0MfYF0r+lkeQ3nex7hqohE20L16kafWu3hnyZ88xwdHz/ClnYeMdvbxziOyHJFm+LqKItEj71hQBN/S6eZ8x9WzNJ/9Cn9U79HTmqZtSZflAC5IjBegNcEZZkf7hPGYl+tDvvWFf8fNB9s8/51X2dgccuf2A771mS+wd/IkP/X0Zb7255/iysULvOfFd+IOx1z/089Tb21y8flnWF3TbO+NWaQJv/s7v4FbGfLf/de/xH/7H/09rAC1hNsvFgu+/MqXkXlGaxy7u3vU/ZT3/cTfIl1b5/r1t/i1j/023/uOF3niuWcoPvwego/XIrto+fwf/ymdM6e5/vYbXLx0mXu7I/Kkj1YNv/Wb/5zFvKX1oLt98icuUR3eZXDhCvnunJveMdCKb375M0xDRV22SB3TDzFOJvA24GRMihxO5vyP//Sf8ex3vJcnzp7mF3/6uyi3d/jZn/4ZPvQzP0hwG1hzSEBx+dwZ9s6cZ+fObd79nu9iNK85/uTTFF9/hbevvc0P/Z1f4Auf+yTPHDvB1RPn+C//ySf4xsuf5+SpLd7/gQ/zmT/7Iv2NPi999/vIkj4bnZTvOH+efpEvl41/9REefcWJ196/btXb/21D2d+wxyPO6v/1EQDJ3HhuHzVURrJ15gnOXClQQKYc62sDVlfWCFLh7YJuoeh3V/HeYNoalaQooVgaKBHLy9XhbMK9e3fY3plS1QaBp7Yuiurfxse3XVA6e3yTyeGI+0cHIBJ6+QbeBRblDDNtCXf3EbMZRTVFtpZUJFgNt69foyM9VVWBrzG2frzREyqJUTcR0Cpl0OmR6gQZIElT1vo57WSOF5J+pjBecDCxhETj2sCq0Az7BatrG7Rtw3Q0ZywKPJJUKioRsN5Teo+UKc47VtYGrK2toKRkdDhiNq3xvgad0EgJtkJKj7U11nsa6zBSMkygtZ48L8i04mh6REdp2moWt3VpSqICTkRbcJKvkKYZUsbmDIvFAF5oesWQRTJAZX3q+T7NYkEw87jJMw68Ju0ep3GdyDbRXerguHBsnZX0iLIs6QrDbE+xueYwi8DK5hYr/S7X9u+jRYoMAVu3mHqP+SzQ6w5AdIkoxGXVoPdARoHmcHKIlpAkBSFZp9stMSZQW8ugK0hzSafTQ6ga31ru7+xjjWM1X2VuU053t/C1ZftwjnEzJJK14Rp51outNMtWkpVBj06W0B+s4VVKwCFDjK/pXGJcQ5Jm8Vkut7FWRAdSrEsUS3RcrLgOHiACam3b4JxFp9lyYIpK/KmtY2R6k0FvAEhC0CQyJc9Snr/yNFW5oG2h0+my1844feo406bklAykacGFMyfpr2wxnsy5c+82zgdq4zhazGlaQwiCp86d4e7DffbGB2zMjzOdHmKtRylBp9PjzInj1E3JaH8bWzW4Ng6OZWUpq0POnsmYzirms0OmiynOefq9DjIYykVDb5CxvbcfoxHEoavo5DgfByOsR0iJ8YbWyOVACXmiSZWgaixN5QBF1UaibqdIUFqRpB2effIdbKxu0LQ1rZUMuz188Fx78za7R4esdrv0+jlpt2DQ69G2JWura+R5wcFoTNHp0s37OFNhm5a6rHl191usrx3HO8+te3fZPDakm/WWhyFLojVS6gioxVOkOUVeEJDoIJdbaUWSpBHyEKCsatK8AKXRWUb2KAsuBU3bULWx5caHQLI87EqtSJOc+XiGS+J755yjKDpkWR7bbESE0FvvlqwE4jCrImfBWYM1Ft82UcAUDgnLVifD2qDP0ewoOgiFQElBnoBxNTKR9LunKUF9NlMAACAASURBVKe79JNAkut4PZSW4XCN1bVjXL91jYCkrgxVWFB0CgiWRAsKr5mXJW1VI6RFqRaVSO4/2CfvKQbDDonO2T8akWnFyqDP6eNbBKGoGkeS5tx/cB+dakwQTGcz2qbl7Ikz9NfP8pndz1A3YyAgfEB4i/eKnf1Ddg4PqFxDisQLcMbglaSpIU9zVBCUrUOhGPb7rPb7LNoJIQjK+YJFOWNl2Kfb6XDj5nVMCNy8dYsX3/teVsZzdurb1G1GXdXY1pBmive890UW0yn37z/AO8/Vq0+xs39A2TQ0vmRvOkY0At3pcf3efU4ay/ETp8G0eCSHk4bWJ2iZkGiFVAneW+ogabzENC1HoxFTPad2gBNMSo/Go3FkqabIcoTM2Frb4mhyj/3DBokg6+c8cekCh7sTyr0pQfWoykP63QGLcckzzz3LmVPHqecVh0cjjHRLN69GyQGu8rTGIKWkO8g4Otinu5XR7ea888p59nYPmVcz9PL6ZaxDhBQlDZoxWeI5vtlBhcCi6TGdlORZStVUJKqHzgqqcUtvo09eFNTVgKZ61D7VREaYFPTyIjYXhQTrLL1el0XV4L0gOPBecOLUSe7f3aaeT9g4dgK8YT6dR7GVyDMKS8G1yHIyHdsWO2lKU9UkSjJcXWE6G9MpUvQSehsEuCAICFrrSdOU2WxOCIGzF06RZynW5nRai9YJs9mcxaKMDqZBQTmHtmk4Gk9IEknb1gx6PVZWOuzuSk6dOMaisZw6vYZMAg8ebC9dQ4FyZknzjNDGAa3TXYnuH9sglKCbryCDxnuFlgXOWJq2JUsEWZKglUbpBJUqglOEJV8tMt8gkQrnYtRh0dSIoEilx3hIig5plqKVQPjo1CQs21y8jdEu00ZotnWY1qCS6LxwPvJ2dAh4Yf+yITl4jGsx1uBlBAZP53PSXj+KEVrHYdB6NBJsbJl1QSBDgvcC2xq8tTGCZQ2tbZd8Ogk+Qp29iVDssBT5VZAEJUEJEhU5L4rokvV+WcJAC0oAEilSZIjcm0RJpExihNJb5uWEQqURjozEOBdTeW3D2qlzrG/fZzw6YG1li/0HuxjvyDtrtH7GYn5I4xo2uyukSSDNe/i2YrHYww83Wc9P4OqKID2tk9G9rBVHe0eMJ/vojiAIzebJS2gf32chYguuTOL90eOWUSwZZyYR7xFRmNcEH7/TxrfkaS8KJSEueIQUWOvQWiNkBD5772J6ykWYe9vUyx22wJgGxBL0vTzXWR/ZQ0pH9pIklr54Z2nbGi0FVW3IBwOs949FGr+ESCcytpe55XOSiVo2qcmlKyU6la33aJ3Eggq5FMNciFFwHz/bGMULWGsj4F3H2JwMkAhJYyq0ivyn4AMuxO9wdFXF64aUMd4dn5N7lOIAIaLbR0qcs8vyjRijEyzfr+Wh1yxddEmqY4GG81HoaTzOGZSO/FKdPBLNiJxGEeeFxEeH1CMgrvV+KVbFOcqH+LrFo/ibX8bmpARnESEuoAgGlXRJiw5X3vU+JqMR6WSX6dEBOwcHPPuOc7hyzKWnn6HXX6cc7aK1JC86PHn1ebIk4fqbX+VwNsFZjxSKrY3jHDt1Ad1bRfRSFl8b8z0/93f509/9FyyqGhE0g17GO09vcX7QJ3i3jOgJhFySn5ZuxerGXZQ9htraQJQtdamYP7jPg8ke8+mC3toaG2tdRF3GBW5QS/cRIBI+/NG/C3sjPjWveP7dL/Dw61/CB0VWZAjhSdIeyjnm7QLnHdY4gq+5U8/pZgVbp07yn//iL3D6+CrN3bvs7k34mV/8hxxce42vWkt9NOPhrbucu3CWNvNMJyN2rt/gywf3+dTtNxlVNb/2v/8Gq70+o/+h4s/euk6iFN60IJMoCiJoWoNAMC0ntKamPxzwru/7IE9fPsNAZ3z4O1/gm6/fpBz2eLhzRLeT0q9nvHbtOmsbQ37n138FvdrHyi7v/NBLuGrE7s238KFFBsczL7zID/z0z9Ic7PKRv/V9+Lfv8hef/CTX37pOtZgiC0kIS9FVRFkueLCtwZgWpTS2rTj71FV+6Zf+My6fOU9wnuH6OsdOn+Hma29w9Tt/hHJvStKWnD15nvr974dewe3dXcbtgp/80R/g5T/8lwzXV7l46Tzz6h08d+4ytq75yLtfZP/22zx8eJ2TKwWJ9AzWNnhifYOnzzzBaqe3/G4HDmdT+kWHRMefq+Vt6C8f/x9Eof83ISmy1/5mCU1/VVyK3sfTax1++LveSVNeppvGa/neZMz2g1t00hAdcEJRN1OqssFbGSdS7ylCl7zoRhOmj2LqrK64c/MN9rfvxLSSiEse78A6+219Pd92QamTZdzenvHwoOTM8U2qyiJsxf50THW0wMwcwXlSYnuVsJZ1kfLm6BCZPBftzCLBtpMlvNJjlzRAoVJ01qHIO2RKkiQJvUEPuzhgt6xYNJKFqNFpglcepVOCb1jMZ2xsFPS7Q8p+xd7oAUfSkCuBktHeKlwcfgCUFHTzjNV+j2AaTJFS1y1Vm7CxcQyCYzI9AuWZTce4AEXeiY4GGRBYom/CYJuW0sctV3dlg06qsM0CKTxFpkll/pg14L0gURohNcY7hIWsN8Tamtn+HYwVaGEJQWHKKSJaTXAiIagMJSy5FJxY8zTTmtq1BGmxEowPzK3h0vENQluTdRJcE3lKxkYWzO3dMd1ug3QF0lsq4/AigLdYIXnl1gFJp0snLPAyUHQ03U6fewcl26MJLtQUieTQRJaAtYbZwmC9YlTnrK6sYw1UwnHi+CZFfpzDwwNOn9hiY7Cy3LZBWRp6vW4cjFQWBxxhAYEFUpkipSMTikaY2Joj/PImGQc6KTXCeWKrjseKAEFhmoZgDFrGTaAkzgA6VZw+fgyCIEkKxPKiGvkQliRJKInAszTpsDEUfPe7X0RrifOByXSOsQ1rq5rpdMydB/c4trZKr9/h7ZvXGc9rnjl/HpHA7t4OUiXcuHmD8dGYJMnxUjIZzzi9tcn9BzuUtaU2U46fOI5IBA+2Y9PgbNZy5/4262s5t+7e4/3vfYEHd/fZWN9gVllm0yltY7jf7i+HWxFjEsKRZYI2xFpgRx1BoQRwgdYsGUMuQQj5mBFQZAXPPnmR7qBL3USIqmsaRGjReYfKtXzjSzeYzi3vfNeTDIoezpcIIh+irnPauUeYBQ8ebqNVSjNrgZamWrA/PuT1G29w+sQEbxVnzpxEipTJfBajZcst7mI2QaexyWY2m6GUYjye0O12I8vCmThsSoHKEoqsg0gTcG7ZthhYzBfoVJN2MkIIGO9omhatNZ28iIy2AHleIFWsL1ZKkqgIN0fEQ52xlhA8tTW0y8jD2vo6TVuTpssonAg4aymbedwChsCsLMFZNIrKNti2JM8U6xun2D0cIUk4vnWKkZQ00z1IBJnKcT7Q1IbpZEaWRddVKiQEw2K+oDWRlyNkYFE3eNOyMsijawuHzjOaypMf61KVNYVSnDl5lmOra5i2Zjqfsqhannv+XZw+uUWWJnzhi19hPBtRN469gwM2Ni8igiZJNJ1uBy01Ow/32dzcjND6aoZZROdJToAsY+5ayhqME3jTEoKO7gRjGU0mWLvAo2nbGRmap595htnRhO29Q+7tHqClZjSasra+zsnjJ9g6eYadB9soIVlbGXLj5kO8q2iaGH08PDjk1s1bPBzto4RHZAHfRBZMLjM21k6wsTLgjddeR0jFfF4htGRtY5XhsM/RZBpjO97R1DWNs7iyQaqcPMlp6rjXf+7pp9g7nCBEzfrmJheeeJayPGI8epucgBcBbx279+/SVC1ZkVFPSp66/CxH4zFa1ygtmE92WNQOg2Vn94AgLEWekquc2lqM9+S5IvgZTTlmMROsDo/TGfR48/Wv45SmlwQ6RUpTNuRZl8ZMEYuGC09cptddYbp3jUUz4K0WrDaUTUVdN+gQ6BUFmU7odbqMZMrKyoDR4RFt2eCEp9fpc+rEcfr9gsnRnOHaCovJEffv7ZKnkumo4tTpTXqrGW3VsLOzTZZ1MI1BeEmwBo+NLVZEF0KWJPEgt4Rn26alVpDoeJ+ZTeYUmaLbyxlPYotYXdcRpJ2lWGuXfJcGHwLGWHSiSTLN4f5RhAULwcH+KLoZpAAfWF9bW/J2JOPZnCACxrccjeeMF0f0BwWDaUbVeJyr0VqQJhrbCnQQuLbGe4OSml4+YHBsk3Y+Z+vYJlIkcUmEQNlY5y5IISzDMUvHRawwj9cO5yJnr20bVAio0FAkCcon5KlG62TJtFk26oQY/5UChEpJEkOhU1Kt8SGh0++h8xSpE/I0J/gYBRQWvIxtap54H9AiIfjookizDG9jLCkQkCE6eJWLImBrTIwFhwRCQ92WmGBwEpRK48+Zj05NJQQhyyjbmgRBnhTgHNZYrPMIH93D3nvEUhxr2oZEekg8Qfhl85ZGCUVoDcG56LQyNaaqSDsJVd3QLdIo0BAdWFJI6vmMRHmcWTA62qXTWwGvWBke42GyH91PSuDJOHfxSSYHD7hx/ev02xYznaGSBGTGzuGYp688Q9ZRzKYjfNvS7fQZDAYkUnGwc492p+Hk2SdxLraViUdNSN5F2LO3JEoTgid4B0ojiG6Y1jiKLNA2bXTA++WSx9p4CA4uMksA70z8TKqGbt7FBcjSjNY0SJVgjEFJgVYyCnzekxcaIR9FbRXW1NGlhsA4j0TFRWJbI5XAtZEXpBP9WFQRShJkdJtLFRcuwfkY63PRkWWsRfpl/IulAOxifM9HtSveC51DK4UNMTZmCcv4nkDLBIjimRIsHUQxqiFlhHLHM2YUg4J3yEQvo29+ect/JPAJgrcIEWVKv5z/YulGfGECjxDxl1QCncjlnAS4eKRTy+WTVAq1ZAcGiM4j7yI7TEStFgEuBFTwCCHj+xais0ukmkRr2sUCqWMMsttPsbbmXe9/iW+++jkmh3t4KTgY72LKOXJ3h0vPvoDtd9h9eIuqrhFCsXnmDNvbN9mfjFHJsp0uJJx56lmuPnmZzfUhf/H1N3jpnc/y+udWuX/jAbWBopfSK+J7HKSAgwmvf+YV2vUBxze2OHnlNF4I9MaQV//1Z3jxH/wEzd27nP7g81zZuc5Xb74N3qPzlFFVM93fZ/XZJ+kNBvHAJ0FYT+/kOrf+4BN8i4bLpqWT9jl3ap2dg21OX3mOv/0DP8yDr36BP/z0n1FK4rIhjSJd6QIuzXnl5c8xP3sW3TQ8/30v8fIf/AH/4l99jP22IdnZY3/3gONpynhUcbs+5LNf+RxvPrjDzTv3+PCP/RSvfvyPuD/a5Xd+92PUGrCxXTQEhyZGKUFireGb3/oGf/ixj/Fd3/O9nNw6Tn7mJB/52R8jzXMOyzHnTq7TusDn//RPqGYl7/mJH+Y3P/Z7zAKEwzFFWrLVUdwfVcznDa21aJEgrGe1rvnw930vo+mUT/72b1EVGaJ1SJ0QlrYkKQRy2fjpQ8DYyILrdbqMj8b8/Ec/yItXr7KoDWm3w1u3Djn7wgtsnT3FyZVV/t2116gHG7hMMFhLOXPhAq9+5RqnL53h1bv3UEWXpM34o9/6TX7yl/5jauvJdcJzz1/g458cUjUNn/n0n7G9fY/nnjrD6V6XtU4vfu+FwDnYW5R0Ox2WNPqlK4klO+nb//ibIib9++6kx79/rJZHUWY1V7SyS7CGuo3fr7TTp8hiw6hGkSebaCRqOStMJlNmswlZmkVmXOuoqhmj8REP3voWTTlb/jfRVSywyw/02/f4tgtKSEdnmPF0cY5yUVJbSxY8roUWiZCWwnsaEQhKIY0lNwGhM7a2TnCwfQtbNRgvGHZ7JAlMJ3NE2sVqjUw1QiVI4YgNnpLGOaraURtLGwLHB+u89L7z5BlcXOkym4zZncy5/+BhbFRTgcNQkgVofUDLyA7qKUHtLc4H9nYPOTgYoYQn+EDtAiJJUUJjXY21Ftt62npB0RtybP0Yi3KCa2qQktZ58iAQribIgv7KGkmesJ55JpVhUZbYoFEp6DSyCUxbE/KCTHsWtcH7QFIvIM8x1uIDWBetkkR9BVFXkCRIKdBKsbUqebC7Q7AWmWTkWQG6Zm8GwTiOtt/GuQg9LxdTZgZMo8m7q4TMc1BWiHSBwKCke3yDNdZx9viAO7tgqwYvNGkxpN8bUjKhqQ65PT8CvYIL0U6fJIphf4ALCVcuX2Z9ZUCaFxSJpqlbskKx3h+S9/toIcAanDekWiCFj5ewsBwUlhuiJCRIlS5ZDwEviQObiBq6kDKKRUIThMdFnEN0MxM3pM47Et1ZtnJY7PIimcjoapIi8lQURIdJCKg0I000yPh8Ui0o0h7eChqruHHnASoNnH3iSdI0YaXf5YmTp5hXNcJbjh9TnD11itFoyrycc/rkGVYGXR7s9TFlzWwxYTqb8Nat25RVyaC/Qq/bwzQNvUKxuTbAoNBJRqIVm5ubvOPpq5SLhrvbu6RasLI64MHDPYz12OCQRIt8rhRFp8d8NifN0sjt8CkoixYyMlh0QVXWaKER0pGkcfvb76R0CsG5E5vsHM2wtsEEz3x/j4NkxjfffJvzp8/y/DNbTMopWkKKxssEGwISRWtbim7K1uYGSZpT6Izt/UO2t+/RG3boD1bZ2zvi7NkTXLpwCd96Rkcj7t25z3DQpz/oU1UVx7o9EhmBnMEFysWc6XxGf9CPkdQl68kHh87042FTK0m73EY+jhoEjwwgZYgDk5Sg4qCT6DjsCiRCRmHoEQNCK8l0WiKFoNPp0FQ1Dx9uU5uGvd19ijylrlpOnj7GpGqpJod0+31SkZAnaawZDoJB0WXz/CVaX3M4nVKkXUTQHOzcpd/LyEXG0WKB9ZIs6eKD4Gh8SKfbRcmCyh3F24+E0pgYs1XQ7Q0pFyNUFiMb9RyUik0/o4MZK2tdnG/xHmaTQ0bTXY4WU6TIGB0dsdLv8db1u7TOgBCkus9XvvYms1Kxe3CICDXHVjtcunCVl8cla90+T1y8hC4SHt6/x7Vrb7O+0ceFnG+8/loUA6uSopORpwqpFCuraxxNRzF24T3ewGC1j0w1aaERQjM+2uOpq+/gzv4uGx2HJ/Dq66/jq5qD6RFXr1xmdDSibebM5yWLsmFne4cmNLTGstLLcMLhhQIvSTsZe/u7jA53uXHnDqW1S7ZeFCJHo0OMEXR6XVrZ0PqaLJGo4AihohCasp1TaIdpJuSFo65rFpNd9vdXOTi4R11WKJEitMa4mp3dfVIl6A4GXLl0mX5nwGI6B63Y2XuLg1Bx+dIHCUEyL6dMqzlJLvFNS92WeEw80OkFAcdsdocTZy7wxrU3CKKh3y04c/ISd++/SaJ7JGlGVWq06tPWHj1QrG0dp6g0+wctZTCMRmNc6ghVw5nLT2Dqmtl8gg9zPAmdjqW1hiLro5IC44AgKbKMelrSlAaJompLBp01xuOSlWNdBNHddHS4dLAJgUJEsUfr2HAlYDjoMT46otfpohT0s4TGxBjccNhjNikj9FbIx7OOcza2ttll5ETA7vYB1nqaKjqZvfdRbNHRVVHNS+gWS8a0jKIUcW4rckWv32V/f0zwnnpeIUJLJ0+x1tFUDUURa+XRiiQ4vFkevr0jKzRJlpMiUCLHtgtKGSLnQAlSIRDa46RbpmMcwZvlvUVEHpRUeBXQMqNxDuXjd1wFS7J04WglIx9t6QrxS4vLX7oM4mvLtY619G1DWZZUbbN0+VgizijQusg+6WYZWZrig4iNW0VBlqURqhzifaNwEq/je52lmjyFbhEbzZIiJet2kIdjQnB45xDGYoPHWoera8pqRtrrUtYtwoNKE5SPm1DxmEETP4tUpfjaUAeDTCXeL2h9i/UWpXW8ZntBmiYUSY5xnrmosEu2pQg2ipYBukWPVGfM5yUra+t08hVM1TA7GnM0GtPt9TmUR6TdTfJOhyMUqAKle5TOsba6TjCCw8NdqvoJvFdMDiesbW5R9Pu86ztewDpHwDCbHMXvTtPQS7JY5ewCzpooKCFRQsTmSaGBCO82tYllM9aQaYVWOrLPlgc2qR61tYUYbXMOZz2dbo7WGi3iou5R5EiKGFMTIuCWRQJ5muGcR6axIc4tuUd+GTnzfumGNzVZXlCVFiUFSRJdR8FG0YQQYvwuRMZQYw3emsdRCo8nqOjwEUsxyTiP1hoXAkmSRFxCouMhGjDBo6xFCoX3niRJIlxca4IE4+JSIlPxtcrlEie2B7cIEb/vwbulYLtsa13GAKWILqewPKgrHV+3FAFwhGDxFqTwqCS20CkV/x8XYgTdE6H5zjkc0Q3E8t8TMkbjpI4/y8ZE9hIyzow+xOcjhSBNU5SA0jvQOSQa4QNZ3mcaRuzt7GJtTF6Mx/sR/q0F3lS88dXPs3d0CMR5VirNweHekv9JjEerhI+86xlObW4RXr/PsZ/+MTLrMW/cZWwrkqQHSqB0Qlhef2W3YKWnqDaGMOzgvaBqaw6CYOXFd3H3tz/JF2+/yvd99D9lVnm293f45J/8MVcuX+RbD+4zncy4Ml3QGQziTO2iW5mgaKdTKAru37zHRz/6D3n2qUt85RP/lkpn/OAL7+az12+wMhjS1hUBi0oUGyfO88zJC7x17SYXLj3F6dPH0ELwF//br/DPPv1vuL+YUFrH3f1tvvjqy6j5Vc49/yzn1vv80W/scnh4xKDX4yd/6Md5tm35X37/15kpyIInUwHjJVqwfG81NgSyTsa0bvmX//rjvPzKy6x1N/n7P/8PuPL8k3gBT50+znqnz/gJT/fhOT7567/O9WbKtddep1/0mJmS2XTCp/7w95nNS2yzwAvP6mCF4bsu0908xj/51V8h6JytM0/yy//VL/Pf/KN/RL42xPo2OuWaWGQgVUJn2OMjP/i3ufbFz1IGww/93H/CFz/+x3zku7+T5y49z63du/yrT32C5979bt763Bd46+Gn+N4f/X5+9P3v4eHtGzxYHLFRaPo9eO+Fs4h8wKevvpN7f/IXrJw4TXN0QKoEZVkTELz+xpsY4/jyl76KTHuQ9amFwoUIoId4rj69uoJGcDSbsjYYEggYEVA+oJY/E3/dx3+IvP0/PJZKvie6Q1vraJoa7y1CSDZ7PdY7BQKLUglaaowpqKuSxtTUVcXR4T7B16SJptPr4J2gXFSMjsbMxiO0TugM+hjnaINnYUsWs/+fM5S6RZ8zm6fxpub67TlCOprKkKUFaetYa2uS2nOIpwmO2ksWznLp/AkSIVlfP8bRneu4EMiHPT70/Dt4+ctfZFo7tE5wxnI4OsDUJSvrG6ysrGPQ2ACNE9igca3lwa23SJSgGq5wbNDBu5a6NDz13BXOrmW88s0b1EHQugaEZrqYIYhDKULgvad2gVRE2F/wHmFgb3TEmZWCIs95UC+YWqiqhrqtWB322NubIaSiMjWpVDQmcPrcBZy36ASUL9Gtxdc1TkuKIgobBkfd1tTNMmaT5kzrBaltSX1C3baEELDOLQ/qEWq5aOb0U4WQHi8M3QK292qevXiCvb190mGfJ08X3N4raU1gfzSh319h2Fllb/8hWil0EvCu4dzmKodTx7id0oiSbj9ZcmwkuUhIVZf729dZW1sl0132DhcgugyyDN8IDiuPCC3vunwFHwT3d+9S1g39tZSLF04SQk6iJYJoAy4I5GkR25UItELipcY4i6xtZJIYR5ImKJkC0RESgiEYh7UeIT3OtaQix6gUj6JpDbQLnPGEZoHxLS44WqtogkfouPUKLkJ0IW44gwvUTY0SMQITXTsGqR1uMsbVC0S/IFGSqo6VsQiPEI4Tpzao2wrrohBoPUymhwz7qwwuXgQnWO33kDJjY7jBrCw5HO9ycn2TWwdvUUiY24a9gxG9TkprW1rTUi0W9Lodnn7yCdY2TjAazXnt2l/grGG8OKCazVm0JcEIEu3JRcAIh3Hy8Zbu/NNnmM0s3aLHopkwnS7o9QqKImV0OOLYsXXG4wk6SUFF67c1lvXuKoLArXu3WR2ssDHcYNDbYPdwn69fe4jXiuOb63Q7gpVeh/F8n0U5JwmKLM4yKC3JdQepE7Y2ezjjuHPzBlW7YG11jUGvx4XTF/ny175Blg1oZzVVU+K8YfPkOlmaMhlP6HX7cbgjsLG5wc6DbRprOJgcojsJnTRG4JqmJk0LvHd4F7kWIXhkEMtNropW/ZgcQASBs9FuLJWKW8gAUsRtqhQCnerlEL3c0kiwxjCfzym6XfJ+zng8Ynt7n+n4iHylYH19gLOWweo6nSzHGYsLhlk9pzfsc/niVcpxxRs3v8XB3kOKdI3h2nH2RgdYY2ibBU9feYFXvvpVMmmpncM4i59VdHuabm9AXY6ROJQXoCPslcyT5h1MMGgt8DIggyHv5Ji6pZoloBST8T56uMbKYAsvNLNqxN0Hr/H6NELlrYBEy7g9DIGdg21mswlKW3b3PEV+SNsYFuUiwqbRnD9ziclRDaKhrR2ucpAkuNAglKObZSADW8dW8K6lmgYa3+KkIM0z3nzjGnVdYoxFKo2tp0ymh0zaBemgy2QyJs8yGi84mk4oegnOK2rbsrCLuC0mthoJpfGlRxGbdprWcvfum3R7mjTzVMHThhKdJczKEb41aJUzGdc4bxGJJtEZTVnS6ziCCGjrsAHG0z3IIqB2bg1v3/4W3jg8+bJC22Nly3DYpxMKnn7yOfr5Cr0Vxd2bHmRgNj2irWecPDGjyApOrW9RzCyNW6B0FmNCXuCcwgpFbRTdpOFzn/1Ttg+OELlH1Atu3bse7z3FJs7UVNUc71sODg7Y2btNngu8Tzka1zRSceb4ORyB6fiAyegQnUoypkhlSVPD+grM5hWBjLIWHI0butkmayvHmU4m9AY9Ap66hauXB2zfHVHWFZVpCJmirEt6/R7OOdJU4YG2NY8rtWezGWVdcfH8WVQKu7v79Hs9JrMFK2td1taGkb+4hLr2ejnBw3Qa69ezXCMI9rLi0wAAIABJREFUpGlCXc2XgOeAtZI0TamqioCgN+jS6RSMj45YtC2NMXQ6OSEIFguHTqNroTvMkTouKJAJYdZiLcymdeS2qYLh6johOGbzGSFaEmgmE3rdnDTTKJkjpcNrga0bEArrBdprvI0uEanzJacvukEi08hjjCNHkylN8JIg48JIBEFTtbRNjVMqumuI16YYtRWPnZBKKnzbYBYLup0OXkSGTcBHZo8NCKtovCPxLdo7TIgwYx/AtSZWDluLwOFDZEAFGxlPSaEIQiJVgpYZCRq5bJwLMrJiQBKEIwkJHRKKXBBCS57nFKnChzS6UoTAeoGzxMavYHDeAcmS12cYrqxSdIfUbfM4ci8ItMYQ2gZha+oS6kSSFzFuhHdsbJ7gcDxlY32Fg523KauS2WSfMii07DJc6ZJ4S5F3CR4GqxtsbZ5jPKtogiVJKpI046mrz9Lv9zjYOWDJZMaXNddv3mRcNlw+f56nTpwFFxDe0VQldQ1Ka1KdkCZ5nANCZOwovfz8QnyeqVbRFZJotEzwQWFCFNzkkr9lGoMg4EzkYKV5AsrTtg1tY9AyjYyTJdcziioBoaIgLxDIoLC+xdoYjzSNoZtnS8ZWIFEJzlpscGRJSrLklC63bzRVjXOeotvBhch/ct6QpzmmrtFJilZLwWnZXqiWsblEarSQmKahPxxGcLWITr0gPEInCBmwuPidljJG80yDUhIl4y+x5Bv5wLKYY4hWehkpjFgGb2NznRAx4lG3JrrUBWito1NLRKe2dTGWGEJsO30E2I6OwQBK4hAEGX9uxb8XR4lOshiJ00rirY88omVsz/rohpdIVJ7GxXJVEwIkaf6YSaXyGOmtFzM6q6us6xOMp0f0+n2GgxX6uoMJCYsgsMLij0Zcfc8HuXT+PN/65qvsjY9obMt0XPLKxz/F1+qEH3jvBykuDHnr9z7NvfGY4dYxDqZTOqxEZ+dozJuffRW1XvC5V77I0auOv/NTf49//Mv/PT/yCx/l6StXyYrjfPk3fo+v25Kf7He5+NKLbPz5H/Brv/8xNlfWMN7z3R94H0WneDzDCwlYDyJwZ/sh+aDDVpbx4z/+IyACJ9OCu2/epVgfcPHFF3jP3h5fu3sDYyvqpuUnfuAH+Z7v/E6mB2MuPnsF51rKt97mtz7x59yuxjhvGQzWeOl9H2b/1kNu9I/xYx/8IDJLeP6pZ+idP09vseC5s5vMuh3yTFO3HqXiRlgGQXABrWNk1wVPUqzSWzuGaOeMfMLJU0/QO75JmmRIBKdOn0EKyZ9d2+frX3qFYvMYr3/jazxx4Ule+8pnCGkCXrK9vQveE3AoNPNmQfX1r/LxuyPevvYaH/iu7+bq1Q3+p1/9VVSeYdqGRCc4swTT6xRQrK92+P4PvcjrX/oiG+cvsrLSpfveD3F66xz/+H/+X/mjV/8P1vo9/ovnn+dXPv9p3vGBd2NnB3z51l1+7Z/+cxa7d7l+7T63H9zg/U+d5eLF5/n7P/9THLz5FT7woZd47eVPkf7cL2C15uWvvsK5Z65w46sTpi5wamudl97zbs4e24yufRM5bnVrOJrM6K6vc2P7Hv1uF2cdtxeHXFk7EeNpiL+2QPQfhKS/+njUHOl9rDJAEEXmtkXiEIqYXZOCIsuAlLZumM12kViqyjKeTDjYuYOzNWtrfbJ0FUJ04+kkYWVtjQ++9BIvfPB7uH/jbT7xb/5tbPsNGXl/8G19Pd9+h1JQSBFbD/qpxhhHZeLgkQRJzwcq50hIIHgWEhZBcCJVPHh4l3I2Z1bXeFQUeqqSZy9d4IuvvYYJGW3bMpuNaRtDSLt446krGPs+BkurAzuTkrYp8Ynizs4YLSQ6FRxbX+X1a7fR0uC1ZGN1yMH+Lk5Ltp68RFuWlPfvE5yPoECv8RJU8MthKu6FuiJg8CRyabd1gfFRSZElZEmKNTXNdM6ssKTdlZg9V9DRCu0jzFAGcFWLSSt6g4JaKjq9Fbr9lGZRkKicrhAE4UlCS+ssIRiMd4SgYouUEHGoqxbITNGaOQ/LCULmBBpGsxovBJdObtLPBU3SozaeuXdQ7pPlgfGkQQmNb+fsHRgunXuC6w8PaYInBEuvIxjNBaqf8nAmuHjxKTaOrdNNOwjv2VpfRycpzp9jPp3x1sMbJEVBN0uXh9kUnfQp0j42qGVMzy9tyUTewKMhRkTwciDanUk1EkFjW1SwS26NJ/iYqVeiASeRj/zWy81e4xtEmiBcrCz2IgokQWcoCTpRLBZTekUPJ2OrmW8sQWswcWumtKCpLSFoyrrBNIYQHJ2gqEysZPfOoYXAKcmZ41tMxlN29h7y9o1raJUymizYXDvGcLjOfD4hER6VaVZThTFz/k/y3jTasvSuz3veYY9nuvNQt+bqobpb3VKrWx21RpBQxIxkpGBjM9kEWxBWCCshMYGFEZMXIVYwJDYCY4OAgIwEkhAYSUZIQt1Sq9VzV3dVdc1Vd57OuKd3yIf3dGOvlXzJ4kNWOF9r1aq6556z97v//9/veb74xBO0dcJ4YiCWOClZXVwEGpIkYW62y2Zds7W3j6ksTeMpxp6qMLx46RJxJCjGI1Se4ERQDtdYFpYX2NjYwzlLHEVMRhUH+wPuuusEm9sNu1t9xkISCYvwnroA4yOSSFJ7TztNkU3JQm8WLyVWCcqmYby5wZO7L1LXAdr6tjc8zDPPPMW4PEAkt7O6vIapDePRIc3EMNObQSvB4HDA/mjC2topXrh2DecMnW6Xdp6jtSTPUh685z62x4dcu3mD5cVFtre20VJgk3Bw7vV6jIsSYxz4lJXFVYoqsItMU+GMRqUJzjiiKEaqeGpuAekCE8O7UO8TfjoMBJTS6EhPgaMBnN00AZjrXYOT4hVTibWOoihJIo1OE25cu8Htd97BbSdvw1lLpDM8J1jqzjGsDki14qtPfJXbTx/l9Ik7ySJJlkQ8/fyz1LZhqbdMlra56857iVSOt5LB8JDJcMyZM3exvbuHo6EWlto4yibYktx4RJ5kVI1Fa0Mnd8yoNsOhZzzpo7SmEdCvHZGMMI1hYmsiFQDjSnpWj65w4tgxBhPLsGqQ5YDhaA/vEvqHY1q9LkkSMy77lD5mODhkbj6lKoY4GfHC+Reoioa4P+bihfOsHTvKTHcO6Sznr93kVffeCQqiWJDYYPGajIf4NGGu3Wb5riWefPppKhMYF1vbOyBBasHa8hrJYBfvHXedvp1uarm2c8DBaEASS6yr0LFifr5NFkn2DwesLi5yOBpS+WCma4qCw9GY1933WsaTCTvbB7hYMK6GtFWCckNUmpBEmrIYo5VHKUNdhySJUApTN0RaTAHrFagYLypqUSOtwurA99GuROuYbnsZ3wgO+iPmFxfpzi3Q9YY7zt7N7uaITiemHo1w3jKTSybRCjOzPazR9OZXqFyJLBI67SVU1Gd9a59yXKJ1xmFZsH9gWFheZby+Q5ZHWAxHji4xPMiZDA3zvVl8M6G2Fq8Eja0oDypGE0vUWqCbdOm2ZwPrxg4Dy0UJxpMSJTvs7o6Qy448ldSVxJmKyjXAKqPRIU0zYTI4CPa30SHnLlzCNA03djZZXVji9rvv46knnqK/fzhN83is9VPgeYBwR5GglWfMzfWQyjHX6UAquHpjg831PaRSDA6LYAuLEjySuqlJs4ROr40Unv29w1AFQRHFmvGkRJgSrxOa2pBkMWXZoOMEEWmU8KhEQUD9MCkbhDFICaPdMePKhOSQVuE73kCUpeg0ZzwwxGVJt5WT6AypNVZAWZVEeUVZZjjrMNZSVRXdJCRYtUyRMB32hCSV8aHW419On4QncdBh0C29x/qIWImQLIg17W43DBamaQnnHNJ4PHFI0QpNhaOYjEmTlOFkjK0bqmn6T+LDd8w6XFUhIvnKfVdLjXMglArGMR0qWoG943HE6HEYgiE8InII5VDeEilFkiZ47zAWmJ7prK3w3hNLBWKajJLhXDgYjYmkojH1NC0TvlveeyIRbHAGSZy1AhxaKholoDYI5Rgd7jHf6yB1D1cU3FpfJ0tj7u6tEkcJzggWFo/gmgqvBUXZhwSGw4Yoa3N0pc3B5gGtVsrc/AIYqIZjmrqmnQq2t65B3Oa246eYjCe4siDJIpSARKXgGoTrk+qEmIiyqkiVxmExXqCEwDUNo/4hUZYCbrp51jhXIXyo/ykVTLOgpsp6j6tCCk+gaMoqmCade8WyGUUJOImt/fS8Y6fnJkCEtdjLRrSXByjTSc800eYoqzHtqIsz4fPnX44YWFB5AkKipaA2VRg6CY+KZBjKTEGUXnisC5WYrBUFy5z3CKGDOVLKUCuTnqYuEEKRqITG1ngpA4ZASYSDSEbTBFdgTNW2RniIZEAwyCikiYX1GOvI8yzcr6XCTgeqSiiE8EgZLL7eO5DmFaahjPR/cv+2AZ/gPfW0/qScR015rAI/hXMrvGXagJhy4F5J1U0fot3Lhtcw0AuA9CCC8VISqRRlw7kJqVFaABahJEgNUcRr3/Et3PGqe3nuz/+M5y+cQ/qIhTvupzl5mm/60f+JI67BXLrE7/2fv0trNGJm8STf9g9+mE98+De4tbPNYTHhV3/3t/ngB/4PWvesBYvjyQWGWnNw6UVGzhDlMeu3Nri5d4U//tSfcnX3BpOyxMqGLz39LLc2rvGG3W/FHC/JWilnXnOGH7rjFDLx1Nevcn17wqmvuZ8fft/7kBs3+Mp/+EsuPXmR13zN61C24dp/fIzFNz9Euxrw5MYmtZPsTxr+6A/+hO/4znfRVCn3fcOb8FJy8tX38AMnj/P8pz/HJ//qc+zPdrnz1FnmFhdYPLJEhEXphEc/8nG+aA5RTuJVizc8+BA/8sM/QPHCS3AwJC6Dne4t972aN6708EIRpzHbVjAoHDqWRMrSAE0NWa7ppjP8g7//A5R7Ay7duMS16+fZsxPe+5738to3vpGLN67whS99lvZMh1THLN57D5tfeYoL57/C2HuGB4dcfOFR4jzC+IR+/xDhHEJLjK154zu/jstPP8etF89z8utu5+D68zz9whyf+O3foGhAKoFzEosmVlCWNaZuyLuKE696kN/6vQ/zmnd/M3eu3s5Xnv8yP/Ff/yMefeYqhYbf/OlfIPPwUz/3r/m2d7+T1z30ML//73+T9/+d7+N/+bEf4/EvP85HP/pRbhxcJdIZuYh42x3H+a0k55E/+Ti7sWVgLT/zgf+dQ3b5u9/8bfzTLz/O//BPvo8H736QlXaLPIoJEbZwFE4jzYWtIW5+lt/6k//Aa3/0HvrjIfO6GwZJwuE9DMcjmrpmbnbulRHA36ah0X/OfZpWXYCXr64vXzf+05exnto0FGUR4P7TAb+Sf/1c4l5eBMiQWG1qw9b6dYb9TZz1U7ZdeD4djUsclna3R7vTQUnANsF+6R15K2NubpWi1mysX8cJQZIktFoRWZ7/jb4ff+MDpU9++i9oRQ7fFIwahZehxxojoazYE5JSBuirFRLR7uBjQW1DZPDypWdpRZqRq7nvta/hWCei6mtmZubZHrvpIdXhpWRcN5y7eIPtYUEpUozyxB5q0SASicJS1CWR8MRRRqczT7+/T9E0TMqGuj6gNpBoz0ynTTqX44VlbfUMVXHA9f0h4/4BphjTn4wDWNHE3LANEsvARRTCgG3Y3NsjzjQdqREubD7STg8VeRpfk3kZNpJCo9KUW3t9Ghkxk9QIOcAREad5+LBYSR1piimwsqk9VqipQj0cAC0S6SCyNa4+QGBpKcHBxLKy1KGTxvRyjbGWi5sHjExMY3wwkZmaqLI0aHqdHpNqAi5iWCkOR55UaCQSLwwj21A38ODd9/Dg/XdhTEOcZCgn6A9CckRJjXEJ7bRLf1IyGNeBP9FEvOPNr2e5N4dSEmEhvDMakMj45eGaQjgZKmbOkUhLL4vR7Zy6cQgZ0TSezFuQhv26QMQSKRMa6/EYXk4ZJUlCoqCd5niVUJkU68qwAdYx5aHBmgZrDd40RDrCe4kVBmEtWZrSynOEEkQqwolg2jFRybg05EIjYsl4NAksEDzORoyGI3Y3tuj2WqwtLNCfGK7eWifNtrm3u0JpLLpu6MQS4SX1pKGZNPhuSm0N2mm6WcbbHnqA/dGI/qDP0tw8i50Znjn/ItuDA/bOjac63wRjPE4KHDGuqGgQtLOImZlZrPT0OjmH/SEIwdUbW9x9x0m2tvbY2Nglb+esLR/hYHBIt2UQzpJk0Okssbm1Ti+JmJ87go4Sspk2cdLi/KV1PJ48TpjpdTh1391ESrAys0RvOWEyGdPpzjPpH7CwvEBjDU3ZEMc5eTuhGRXs7W5wZG2Z+dkOVy+/RKQALZmb6+KsYtZWXNp9ju2dHYbjTc7cdpzZ3hyDwZCinCCnlcWqrlhYWWLZTKiriqIu8HhiEVOZA1xhabXzcFgFGmNomsArkUqgX2ZeEOaQSiq0jqjqEohQ0wO5UorAHghVShVFZHkKCPrDQ5CSqqk5HEzCA1asmZRjtg/2OHJ0gcHhIf3JiGcvPMuJ48epTcWkmbC40GG+18VRMy4nrG9tcO+dZzl92510ZrsMR2OyFL7y3F8GWLRXaOFJhUQhaWrDYXmAMRUri7PMz81y59lXsbG5z+VLFyjqgqFtiEV4aK5qTyeOSYjotHvsHxxw48YmiUyYnV2mHeXslgrVzhDaszZ3grW142zu9hkMJrS6PVZ7M0hp2R0EFfbs7ApXL19l5/CQvh2ztXuDY8dvo7vYo7vf4vjR4+TpV5EBo0IQbkUwMpQVXLl2hcPd/WC9EpJWO8e4AucblPa8/vVvYaYV0+vN4UZj1neeRMiK2nfIXMRjjz3BbHcGXzd0spSF5VmqmzXjw4JIRgzKBp20uXL9GqPJIe1shmJwiNYw28sQtHBFw6SoMV5CAihPaUJVK5JVEEhikSpGKGicJI67OGsobEEsQeKoqpqZbpflxSPkeY9Ll15i9cgSprLc2lzntv19XBzxwrMvMqlLWqoDuiBmjjjKuHFzg0hX1JPreC/odtdI0px2nnD+wg2iaJa6OmRcTKi3tkiyFt1WGzvZAeNZXTrO0VefoL+/z9L8IjqWjIZ9bmy9xMbeCGMiohhmFmeYTLZwjUDKmiSSSF+BCgYwV3lGhy12qoq6rPDC4kTN9RtXOXXyFJVxGBVhveFwNKTXnWVrZ4PFhWWOnLiNCxcvUQ5HeMKABQ8q1ogp99E4WFic4+xdZ7h69SbrG1s88Or7sCJUtHZ3+swvdOh0WgyGBcV4grU+LBeEJG+1MKYO2xkxBee6wNCpKkcrTml1WzTG4bwNVS5hqeupfMB6msbhvUCrhDgVVFXNcFChEompHDqO8FGCIaY/sriqodINTSKYn1lgUo04LIZ4LUirBXQmQq0i8uBivKlQicLaMDiQwhHFik6nFWpaBHNQY6Zgf2nAFjTWEfuQ8mmsw9YGa2E4HKGmAyXnAyOmrkqEdSR5jHGBxaSjnLgdKl+TKTPEOYfyEh3pwDFyBqECwNwSoaRG2nAfMcIGi5qHytdgoKgnmLoiShIkEmEE0gociklTMxmNUSiMcBhXExlD0xRIoSmtJdYRwkl01EKqA7rdNkIqrLHgPNaGa4GpHRZB4YPaPoqikAyJA3vOKwkYqmbIYb+ibCJ67ZyW7tJOw4DMK4V3DbgGh2R+8TauD54jzjugRJCvTCL2hgWTa5forRylqcZUpqTdyanKPeIM8jYY02d8IHFlyVYxoDc7z9BUHDt2inwvJU1jyqqmrMuQ8poO7CoTkl693iyxifGxCrBq0+DxVHWDlGFIImVIEHtriFSEtA4txCsWYedCksYIT2ta3ZQSjA32XofHVKE+o6WjcR6QxLHC44hkkFMYU00HHpI8z+n02ngrMNah45imCcMjHYWUuHMhdWNdMGO12u1wnzRhgCIEOOExrsTTnkJlLfjAxQo2RwEy2FJVHGNVSKMJD0JrYqUpjSXN25TFCB1lQRrSGLw1WCXDw7oMFWnvHQqP1ilCQG3D4ApCSkYpiVARQoaau3F2ytRUCB3OzdaGFFOcJAghqa0JJ1EdvYJ0CK9p8m+aGBBTvYsQTIdshO+NUoGbo6fcJARehLptEmlEpKbVTYuOYiIV0TiHTDSRguW1kywsnmAiPIfLa7zm1FEuP/IV3vXub+T+pSUiIZHeMl5d5cK55/jd//gp3vMdb+CbbruDp+I2+3Gfoip455vewm2vOs3mjQ0+9cEP8+8+/WF6J5dRIqYeD9ncuMVvfui3aOk2Y0rGdsKoGjIqLXJUUVfwzNNP8va3vRXp4dEnP8etK1f5/ttvY3hrxPt+/L/jS3/652Tes3b2LrafuUC3HcyBxeVrXLx4gZW3PkDxyJM8srHDN7/nPXzfOx7m3J99gTiL0K8+jtQa5wwyiuksL/K6934LJx96HclMl9nFGaIoCsmM0qBSQbk94Bvf/d1c+fQfcrl21OMRWgjmTh4lXnOIIzOAIM4jVB7TeNBRxOVHvkRneQZ3OKQWiv5hxcRJOnnOwuo8f/8734XQgouf+iwf+NVnuFWO+dCv/yqH+9u86V3fxUc/+GvsbG6QxJ6ZKGF9/QBFzfxcjpppYcyI2fkTyGiG9qu6nD/3OHe96l72d7e48eI53v0TP8PnfvVf8pGPfIhjR07xre/4Vn7hiefRuoGJ5MjSEm9673v58O/8Drc/+AAL+TzXX3yK1z/4Kv70k3/ON95zD/fccS/vftPrmG136XVneMtr70B7Q+MUP/8//yA/9c9/hg+d+yBf+7a3carXZdNZRDuBRFANG/7w4x/lx37oDN2ozf1/9z188g//iF95/4/yoY/8FZdvXOMb/963012c5wO/8r9xcqnF2dXjNHXDpKx45LmLfP7Jr/B93/xOVhbmeOPZY/z+n/4lvahCCmi381DfDEAxXg7vxXHyyvP/36ZhEvw//7zh6hSG2Ma6V4RCfpqOLOuKLIkBgu3UGCQRTVNw0D/koH+IMYZYR+CDQMPbik47od3qEkcJ1tfURQF+j0i3qMqCly7vMR6NaeVdejMLxFmbKFcw8pTWULkEJyMiJUhyRayT/9v////b19/4QOny9R3mlmZYyCJGZU1lDUnWIok8jZGUKqaJLV6GDmmUOe5YWGB9c4P25gLtrMXWwT5CCHpK4Io+Td2gkxZqUiC8DTydKMIYycbuHqU3WBd+nNq/DN5TZDKnVhLvaqSMac/P4N0Q0/csdruU5QAEFKNDnnzuWbyIUAiK8UscX13g7LEl/NFFjKnoV57JsGRv/5D9coJroDQ1ChV05FJSVQ2tVOKFJOvmKA2T8YRI7lM7y0xvgWFt2BiUDHxErHIGY0cxGZC2O+Rdi0cTJyklDd5JJsUBWd5Bpd0py0WQRwJvS6p6hMOi9AFx0sKZWbJuRr8c8xfP3CAXCVoJVDqDc5ZepxMUpMZSmwndOKUq94m1wsuUzc1NDtpDWlKjnKIclRSNR0vNyYUOvnS0W0GX6yJJGschWi+CdcXjObF6nAvXLnN4OCKPM+Y6Xcq6YVZKminYUSBROibOEsbjAQ6LFB7rHMorhI8ClHyqGcV7hB1RWwGiwguJEinSaSJVUzZTm5ZUeCVphKWUDj0FgCoviKTCeU8i06AGRmNVMKoYEaDsiZbUjcHW9bQWF1hOzkFqBaVVPPHsM+wf7rO8doR777yL8eiQg8MBH/vzT5FnOQ/ffx+nj51A6oTObIcsEfR6Gc7PBDNhLEm1otftceb0KYb9Ea9/4Dg31vdQukaoktlWzkuXLjE47OO8YXc0YlJOD9uuodeOMFHEpKqxypJFmijKOBw3nD6eUVvPaG8Q7EzSk8Yx3SxjsHdAYz3HVuboxrBwbI297Suo+oDXv+XruXxtn631q4xGhk6UMioNB9dvIJVmZXmeJIo5ceoISwtHEaLm+o3LdNptlOyxvrHN8PotivEu0lte/5qHcGnEYFTRafUoy4bR6JCTi0uApyoNnbTLwbDPjeIWUZqysnKMLO/wxa8+irEwGQ3IkozJZIJAMtubBxS1Kdne2SJCQhQjZNhuC+nJ4oSyqSnGE5TWGOtxOOoq8EUQgjQNxiEpNWkaB56Dd68ARJUQQTrkA3VFOIsXmtGgnD68adI4YXVxkWo0phpPkO0MnGV5doVrGxtcuXyDY6sL3H/3Xbx44TlUY3HGUQ13mMtzTi6vcdivOHVEMeqPuXr5RV599x20dcbK8UUG/QNOLi5xUFYUkzFJ1MI2BWCx1pAkoRqTxF28zOl2Ojz95PNIqVHakSmJihMq5ymNBGeQMsHhsKKkP6p49oUBt50ynD59G0VTsbF9k9n5GR5+6M28+OIVtjevE8UNCzOznL3zLK12zh994mMsL8KRxRkWFh7kq08+jWuGjAxcvPgETrQY1fBHf/wJCm/JGk9pHYnUoQbgLGmWMtw/JNUanaSIJEIqjy09fmIZDDc5svY1tHXGZFDgjOD2U6fY2LnIkeMnuXH1CuOyz+GkT2wVqytH6PXmSbcPiBhiypq6ahDCsTOp6c102eoPyFOBkyP6pSWOZynrioNhEA/IxtHJJK1Y4ZvwsFFajY4yEm3xETSVJZUtrG+oRg1EjtnODHkrJom7jOqaw/41qtGQSy8OKSa7NLbmzz/zUZbmT7F2coW6ttioYutwyGy7x3PnL+DqGp1qTJQjpzygY0dv5+bmHl6/xPbOdRIVkiOT8ZBOHCPdECEFt66/yExniYNbl1g9epaZlTVm5hfY397i1v4tZBJDnFLUhpu3zoeqSyPQqWVt+TRVdZFZHJ3ZHkKe5sa1y2wdDvCiJtcpM7Or5GlC1sqZWVhmf2+D+WKRjZ2bXL91neX5JXrZHIkSZNqTaUVVOSobmGQaSZKEwezsbIeb69s417C5tU+3naGl4PJLm0yqGuc8e3tDJIKyqKYVE4FEYZ1nZ3MfRNCYp3GKsQYhNIv19vG8AAAgAElEQVQLiwxHQ0aDgnY7pZMlmMbhGktdNFR1DS7wT5KsBd5hjafXmWeiK/Ymu3gj8Q7KsgmQ5cZjm5osjzHKMi5HzM/MIpWiPzrA1zuMRcM4Tel1uwgrGbup7UxqUkBaiUPihGI4HGGcR3iLcw1uWrWtq4pOGgEC7UDGEuslmY6RUhHr6JXBlHEO4TVpGlFNxripUl0iiWKJsp44EVhilAhLusYZvHGEUpqE6fJGhEI0RlqK8Zi6rLEEbozGYZwntTCkQagINU3bSBUMqq0kx/Usxnkaa1Eio7E16BjZTKDxyEQhZYyUYUlWTIZIMeX02BrXeMo6XFNbMoPGYb3Guxg0TIYFXtggyPBjtHcoH8QbSSyITYRUKTprc+vaJa5dOE9vaZXZVgfjDfPzZ9jf3Qrpxf6AXr7MpXqLxDTkccR2XYFsMR6OqMqC2fkFdtavk51o09iInd1dOisLrK/fojO3hJjs0WvN0TQNxofBg5QCW9Y0ZUlVlwgl0fPzOByi8XhjqaSd8nU8XhLYQHGo+OEdzlkaU4WalwDrQjXHGIOz0wEbblpDbCBKcV4QRQlpEgQTUodEmzEGL19u15mwOHMCpSKUigBCDdIHNlFdhy144P0obN2AkNR1gfUgoxg7HT5V4xpjwr1WJzkWjyIoqr21gWVmLYhQBzPGEudpMANaO/2Me2rqwB8yBukdAoNWUJrAzZJR4BVKEVJfXhDSU5FGa0ld2+n+3+ONJ40jGmtxvGxqjaibCufD5xkvsC6kq4SUVFVN04T0tpza4nBT9pULaRLv1NSU60LKwE3B+FOu18vyRKk0xjuiOMHUBill4DYxfT98+F3jBQkSaTz7PizSF073ePDoEt/9DW9GPvcC/+Pj57hzZMmWBUQSgaKdpTy0dobBba/j6+5/NScWWsxT055rIYfwmc99jtlf+GWOnj7DN//k+7jz3W/l537xZ5hdXWR7cEhVGS5dvUYiMtYWj3KitcSWVxhzSD3FeGyNBH/6/n/O7z/xODdHO9x7T8mvfeBf890//P24T3+CQT1A+Br6h2zcvE5+9jQqiemcPMLTzz/Dw+bv8Fcf+zPm7r6H//Z934nCsiqTAFiv6zAJLQyqFa5lSkrWzp4KKQulcN4ghUZF4eH2rT/yQ3ztsUWeuXGJf3buGa5fW+dwd5+1I0fwlZkmP0BWhuF6n+zUIhuPPs6NqGQhi7mx7ygijcosomooyxIzmlBu77K+tc3P/st/xbnDPpESdOKcT/7BH+Aaw/7eNplUjHb30EsL/Jff+z08dHKZn/3xn6Q9v8DO3hgZD/muH3wfVzav8Mjjn+OxLz6C8Y6F5UUuf/ZTXNu5hY8lg3HBxz76Cd73L36Jjc98mX/767+ELUu++NEP8/Xv+AYWbztLuTvkmS9/gV/+uV9k5exZklwyP9PG1QapJAmWdqsNeIqi4Kd/6Rc5/8Rz6J7k+NoJPvH4M3z+0ce4+NhjvPT8OV79rm9h0jrBf/+z72ez2uenf/z9/PA7306e5bzmSMUf/8Xvc8fRk5xIJGu3nwkpRSGxQrLRWL568SJve+19rC6sEGuJwXBtZDivF3n0yks8fOp2Xh4/CK/wwpJl2bTm+rdlkPTXFVim16CA559edD001jCpJlRVgRIQTauNUoc/xzZgxhz0t0CGKrdxISwzHo9BeFp5B6VylNIkScLqkVWcKaiGfUaDEYOmz87BIaNhaCS0Oouks0eQs7DSarO6vMxsp0OWxOG+gsR4KFSXT/3V46R6j5l4hij9/3hCiUhx+vQZ/OAi40JRO0m33aGbeHbd6BUNcNkYhNLYUpDNLLOYdsliiOcXuLW+jtBdIm1ozJDCgpMK62us98StFtaFqpnAogmR+nDTCKA+aSVjDEpGodcsBVJEJDpHuoqoFVM2CleE4ZOKU1KpKMYF64eblCi4tM2kKsjijLw7w8rCAkuzp9g92OPClesg49BVV2HoIqYWDJBkcU630yLCcLi3Tp63GBYy8BFISKTH157SW8ZAK645EgukUuhUkXhHHnWYdCVV2RDFNWmeYpwhiRxVZRn1B1R1zSSuaZuCrNOmE80zKkpmOieZmZmjneY4r8JQzUpMHQw3vTxjvjuD8LO0Ztv0JwVKSBQRyIjGeIzPEc4yN5uSyn2MaXHh0hXuv+frKG0TIJS2CVUfCc44YqU4c3SZGzcu423NsJowl7eonAm9/ylc1HuP8+HAqXAoBMYLGu+x0mIJOvtIBaiijjKsr/EmQolQK1CuQUYQJTFESaifKEXkISZwkmKt8U3o/zs0jTCUdYGMPI2VaEXYvIqIxo+mPXePNIYojkAkVLZEKnjy4nleuHqNk6uLvOPsWYqipJXk1D3De771WzBVQ5bGiOnB5+7Tp8NFRkd4YbHSkfucxnlanQ5Lc/NoLbjr9Cl2dvpsHOzx1LnLbKxv0h+MKOsKtELHGXkcUZuGdruNBZyTxDpjbm6OW+vXyXOFFopbmxu081aAh3pHXVtSrbm6dZPd/QO+5uEHuHLzOn1X0mtJDre2eOvXv4P1LcP2cI8kD0a0iUjJc83Rzgpz83PUteH2U6dYXFihsgZjCs6cuoP1a9dYWujx9HOXmZm1PPDQG3j2hSeROkEKiJ2nPx6QxilLp28jjlNsVXH/vfdy7cYNbl65xc7BZb7p678tbAGF586Tpzh/+SVu7QxYWj2DFyVFbemqkDRozXS5cPEySwtz4UAaKRrrqY2l2+uhi5rhOFji4igJDBEhMVMFshJqysawCKGpTR22tDIwJKy3OCyR1AGqK4MpJMk0poGyqDDTf3fUH2AaR9Hfp9fusrgyT5bHvHTpOo999TytlmVt5Qhj63nsqS8yKraR8gjjSYlXKWUd4LJZr4txis5M2NqqNMabFO8aetkMtfW0WjPEsaCphpRFnzSJWN9Yx29rvG0YjCfsjYb0ZjPiokS3WxQ720gvmTQOa8cQNSwszKCMpz/oc+7559i4tUmjoGompOkKn//sZynsmGpySJLVzM/N8NzFq9y4ep4sE9x7+91cvr5FfzQGX+GNxUoFImbka8rDMW9+/cN86StfJsoSqkEd1MmuJELyhc9/Hu0VJ0+foDM7w7kXnqcoxpjG0mrNkOo2udfkSY7vSK5v3OLUyRO00nmef+ar1HVMZSboOMWjGFcll65epr+/QxSBcBFV4Sl9SaxajCcNSgviWNFMFP2JZG3ZQqRQTY2vY7xTTKoGWzXgHE4KxsUE0WRUJmGxJ4hqz5kHXsPm1WuMBhNmc02ezCFjx97hTczBDpHu4KPwMHdkaYWoFVMUI9bX1+lPhhgpML7BjDTbg3Vm50qaRjOz2COLcgaHu1y5/ALXb95iq7+PV4EdGCcNZeNISWnlcRA4eI/CMRhv0O0sUtSHjG8ZqkaxuXGL/qhPnHu0bQKIlig8rErQtWBr9xrzs4FTMtzepCQK+vjIE8caM6kZHO4huh32dg7Jk5LJcEInydiXMUm7S2d+lv1Bw97zz7O6uIhPHK7xYSPtBUmaEseBrTQej2h1EyocnW6LWGteun4DKzwnjh/j4GCfeprikFIhdYDm6mkVWWtNniVUdYMxhkF/zMmTa5w6cYovPPIlsiTGG8dhMWJhaTbUS8bhoVdoSZImGBdU6uPBiGvjMd25ObqdHvNry9y4dhVZhyFVp9MljnTg4VRjep0WTngGkyFxlNBky+g0YlKNkIcRwjuaoqGbK7S3GKkhFsgowpcTkjRCOg8iRok8ALiFwFYm8BK8wQkbKtw6QmdJMDTGUyObDUBwZw1VWSGsAdEESDdgrQIr6A/GGGuoiwqnwgO5aYItzZmKJE/xmlD9lYERl7QyZJTSeIsWBms9mTHQGJpJFexXsUchcV6/Uu8bF0XgGfqaSeVxtadoJnQiQRQnWOdReQj/G2NpnCaLBV7EOClAWVLhqf3053fBvBdribWWvNfCG0uUaobXLjG3cBKdZcj+iP6ta2gp6CwvcfnFr9CaX2Fm9Qg7Bwfh4G0so2GNFzGDyYh2K2Fzf4Njy0usHT9Gt9dlf2eXWMfMdo/Qnb0X6w1NaTkY1LQST9Zqs9CbI8ty7HiHjUt/ydEzb8fRwdcNrmkohKUpx5STgrqZ0O528c6E1LQLKVdjamIUtQumJTEtQhgbbLPGBaB0Y2uKukYqHc6kWjAuK1QU0BF6KjVwwqOjmDTJpqmlYDasqxocaKURUmLqmuArUVhjpuY0SeMb/HRA2FQ1URIH2HdjqKoapSVNXRFHGulDnayuJghpQQmSrIUjnPWCDENQTu1unmmy1zucMZTDMaWSdDod6togtMdWFZ1Wl1grmipwDq0FI1WoO4ZtDsa6V5hjUgTTXKST6UDMo6Uk7waQvhcCFWkcAp8KVBQhtAywcOcwlcU5Qd0YyrLCOULdExneIzlNjXmPUBLlQ+XP2vD59Uog3JRdJjT+ZVnMFOvghcYLT5rE0yqcQkro5G3ipMPAg2tFrB5Z5KETR7ijnREJhTA1ZRQjtw5onTrLb3zw1/nx//X9GNOEIeDiAm/8x9/Dw9//3Rgt0Zcv05AQWcND972Z2dTw8U9+mAf/i3u59idf4Ad/9Z+y8dJl5H23T/lUilayxPd9x3fy7nd9HVy5wo//7M8zsAZlHGsz8/yzH/sBWps7/PLH/5Db3/lOnn7iEcal4L8aV5z/wle5NWq4+PizzLzxDQxVwuZun2v/6t/w6HOPs+PBCsUfPfoVvvYnfoQk0lgvmTuyFD4PeYaYOGSWTutUAWuBBxnJVxhUENiUHkd+Zo0KSI6scfDFz9E7dZzP/NVjvP21r+bYHbeH6zpAopls7ZGudfjk73yYF/cKWsZx9Pj9vOrOBT7yhx/lnrMPYvducuX6Db79H34vpZlw5MRt/Nr7f4qnPvNx/u3HPoZPEv79b/8OnXSerSjje3/i5zk+3uXvfft7efoTn2Z2cY2TZ29nYfUkph7x+7/zb3jsq48xk2R4rZCRZHN7nT/72O+hXah3PnDPCjuph+eukM63UdIzkILVlVM89+wTfM87306eHfDo0RMkrYyDnXW+8NRlJjcPePPbvhY7HtFqt/nZf/EBnrn6DLu7eywdPcvJe1/DhWcf4dLNqzT7V3n2s59BAt/93/xj/sl3fS+rrS7/6Lv+IW96y9u5eWuXf/crH+Tq7lVGjeUb3v71PP3olzhYbHH8yDFA0tiKJ194gQ995BPcvHKe3L+F/brgHfc/SCvO+MnveCcOj5YABuH1dKRikUIh1F///v7/+nq51vaf19sAPMbUQdBlAojfOkPT1NOUaDgHGWPxSKQNMoGNrXX2+9s0VUWv26Pb6ZLnLZTKmOnO4W1FXRY09RjnDIPDmuGkDvKOpEXjJE63mD9zmjO9FrOdLm0dobREqSCTEoJQUQbE9FqvgJnuDEqHyptudUlarb/R9+pvfKCUnbqdB07Ocv5FxZ6sKJ1ERB5E0NLiwXqBlCHu1ThD2ZQsd3NG1YCUhE4SoboLSDPm6vUtvO1SVwLnNN3eEkszHW7d2mAw6FO7ikipYAmzjoZgYailRVBinaSlEhakJ9Ee1esiBvuUZjzttboAxo4S6sYi4wwKx8LiDHZcYg4SKluwu77Dzt6QVpYAllgHwF4wKoYbeOMdjfAgPFopynFNkuTMzmbUEowx1AbGxlNagqVCaLyUzHV6ZDpCKyAJWznvBbnqkiQVdT3Emhq0ZlzWaJmwtHwCLwWRb1B+CApWV1dpscZT519if6dPpQ3L85reXI9WJ2N3y7CwssZcN0M4yWQyIFIx3eV5pPG8cO48lfFQBz2raDwn187Q6fS4sv4SaabwMrxvTjicBKscWsgwGPJwa39I3mqzs3vA5o095s5mYEOsHusCMFc4nHTINKKpDFZKUBHaSoRsaDRoFTZLzgm8MRhXI2WwhiEljZdEPsKbMb6aBJNDXWJl2MJpVOjbEKbwOkrwJsJ7h7ASISo0CosFF2wsToaDtpIaQTT9+1B4wwN33sNrX/VaZtsx3ngSGeGcZy6bo5YVfbtLHk2/0N6FrZaOUdLjhUZVhrEbk2hFFMXML82ztNJhYXWVlSM38HHDMxevMpvkZHmbVrfDoBiBMyRRjJaCNO0yGO1RGYuvHWfOrGHNAFsbxqXF1IKmniC1opk4kjhBZ5p6UoOC4eGAjfVtzt51mssvPoNIcp56+hyDosEZhUpSJqMRKysJCzOztIXD1GPuOHM7rayDa2pSpfC6RZR3SNOc3c1b3Hl2iVMrJ9jrb3LfXffi64YGODzcx1bBoFMVTdg6Ks3V69dJpEJLqMm5eXOXV52dp8pj4lTy7m9/D+X4ZZtam25vdgowdWiVglF0WrNEsWc8Goahovd4II4UUnbQSmBsg5SSJIlJq4RI68B3MeGAGQkJ0+GjbQxKgdOCSMpwmLUvt6IF3lmMqajqCV4p8khx9OhRPve5z3Ps+BGEM+zvbiO94fTtR9grJgzHW7z21ffigVc/8Dr29tZpJ23itIUvHL12i6oZooqEyahBJJpH/uLzHDtzhoNqgBaSu87exXjSMC5KmqZkMAjDs3FZkiURlYVnzl1kvtvlxMo8L1x9CZlqlrRHyQhdOQxQIvGDBukTynKCkoLeUpdJWXOwN0IQMTioGfbHqLgmjiPyVHLz1hU29vsI58m9ZrYzy7G1jPboEONrDve3sM7TuIZyVKMy2O33qQuHUwbvLDoSxHh87UizlDvvvpPXPfxGXnjxHGmsWFk8yckTxzHA3tYmw7qg7nsyrWllMSPrWJg7zbj0VDhG+5LF5Xm2trfY2t1hOMmQDvKZlIkYE5UJ7dY8W9sbWB+jM0mWNEROkCQS/JCZdo/DYUUlFco7iqGnRNPKPVoalPJ4X5F2O4yKijxK2b50DoumG8fccfpB5k8e5zOf/ANQjqqxHL/tTmKVcuHiBXQ2x+xihzlXsDjrWFxY5bOf/ix5Z5Y3P/x6nj/3FLUdkSY5eeqYTCIWVu4mzhP2tzbJiw6TcoiKLdZoECVSeyrrAEUxKElFhEoNE/pcr54kjpfY3NnHVEParTZJ1qGm4WB3gJhIOr0UWxuQETRwsBcTtdpsr++i8kNme4ucWj2DVwmbGxs0TYlSMeVwwNLcDIWtuffYCfY2d5nNZ7jn+CpbBw0q6jA4GIWtZ+KgJphgJIzqEVVV4XxM3Eo52B1wx6mTKKV49vkLHFldojGG0bik1W3T67QoizGj0YTZ2Q5zswuUE8toVHCw3wchSbMIaz2jYcHNzW2SOKHb6zAeDpGJo6wLBoMShyeSmqXFJVp5xsZ62ArGqSJvZSAEQssQSXeKKI5JkoiqrPDGBNOb9extHzBI9vGpxAhBK1sgTiW2GNBdPI63FtsBXx9inUSKGu1ShGuoyhIX5HTT644NFTZrqcqamJA6FtqRRTGRCjaoZlIy7B8ip6IEj0EKjxIOsGitADsdKjlcBN00Y2Ir2nmOwYIVwfKFw1UTlDA4HI2XCC8C/6mpGRUTqB2FqPHGYw1gGrxvEJkieMVcSKMoj3WBBaVFBE6DbnBK0ihH5SekkUAphzcKYxxaKXrtDCEUjXGh3m49pnZECETtUXkMQlBrR0ZKPR5hjKOuPGtHjnPuWh8hC3zZp7e0QK87QzkYk88us3z0DtZvbbK1fZ2JsXTTHkOG5LFmPBpSRIJJuce9Zx8IlSwl6A/32dm7ho+OEdVdirqgqAV56rGjEaUFU43pLi9RM6LM56mkxhUFjbNIBFQ1tWmo6hKtJHgJVtBUNcY4GmNROMbGIeOISMahAqpdSIYT6jppJ38lLRSOkGFAlOgoVIKcwDmLdw4dK5y3SCWxzoRhPeAbh1Y6QL8d01SNJ02TacWiFQzBDiQK7wTOeeIpjNu6BsS0amccSZ6DUhgTlMLOWKpJRZbmeB+GisaFxYtzNhjIRLDFlWUZPuNK0ml3aOcdClGgY8mkKlGpYlyOXqmf100dINpTTqmOY7AWKUIySMURSRIRpzHGTuHYQhDriERGjCYlWmqQKiQadQRaIKQPghOnp8bOKcNJBKtiFEXUdQ3S4RAggtXYaYtzEOv/i7o3j7b1rOs8P8/wDns+8zn3nDvlZrqZCAmEGQQRBVEUtRoaAQcssctWrNK2SkGtUrBYyy5Fu6wuqxxQFMFSqxgEEUmCEgiQkOHm3iQ3w53vmac9vcMz9R/PToAW2u5erNXV+6971nnP3vvu/U7P9/f9fr5pjGhOuIseQVVFQU7p2MIo0zTy1ZIcYwydVOGlom8k2ew884fmuG1pmqt7bRoqLsitia7Xynsy7/md9/4Fr/gXb6J/5+cjX62qGK+vcvqOz+CWDnPFzdfTlQW//cv/hlP9LWanD/Bvf/mnmZnu8sG3/hS/ePcdvOiaZ/LB9/4B+dwMO5u7HD56JQuzS7zxe/5HXvL8Z8Uo4/QUP/PTP80f/If38Pe7lxjv9Xn/b/02H/jQR6gXl3joi/cwdnB0bp5gd7j38YdZD55Pfv5urrv1FhLn+eI9d7NRewZjy5HlFRKpaErNjcdvAmLscepVz0E6i1/t46dbhERFTtXk+0Q+5fEQxDt0ni4+8VqS7lfMLD6Dd7ypyXs+8BeMkwZ33H4n165e4rZveSnCOkKnSVsU/NrPvYvPndzjfX/6Z/DpL7L0mpeRu12+cOftfPD9/5kf/YHX8bqXfj/Pf+FL+NmffhuLvQ5nH32AOx48gWu1MYWh1Z7G1Z4/+U+/ybOvP87aww/xq//TWzl6/DnMHDrApQtnuLy5x8L8PI/edy+z09MUowJVWxIXoks+V/jCkSaSz372XpRy/N4X78EawfT0NM+89Raedd1LGIyf4GXXHOdP7/kzfuJNb+bC/phP3/0JirXL3Hn5DDtJQeOa23jP972Otf5OTOM3IKkEqxcvEyrHH/3ab9NcnOeNP/kvWT97iWceWmFKN1hfX2fv4CG+9OBj/Nav/QoHjh3ix37l3XziD9/P3XffzwOfvp23/tjrWTi8wqOPnOY7vumVfOmzJ/nC399F3s74yMc+zm/80gvJpECKuE4dlH1mmz2C0FQ4WlIyOYPD1xCT/qHw8v/d4+tBw/+x9xhLMGIhwFduV9cldV09vR+XRUFVjbCuIE0Vmc4npRyB/UH/aRC7FpqsGV3Hc3PzzMzMUhRFXNMTUzu7/X3KqqSoYqxfa02SdOhNT3Og3aXRymNkWgYU8fXlVyQpmMizEZj+5XVNrK+GjZHj85/+W9zOJuJgi0arTbPR+UZ+3N94Qem6IytUwz1GJWwVEjdR/Y2xOGeofKASRHBugNqUFMUAnwnyEDC6YHZmAVFXfOGeE1zeK+j2EoYhUOHoTXVJEqhNjUHhg6QOE+CuiAtEEyI7JY4hJF4lNFVKGiTeSVIR66I1gV67hdcpWd6kEhX1uMD7lF6vy26xxTVXXUF/+zKn9s7SbU4RhCbNUkbjMTJAYWsIRbyoJk1MrQkoKuO58oqryLBsFyU+VEgCF85tUwyHhNhRHg0sKJSoY1abFEHCsBiTaE0WAsIrgkxRaUar0aWZ5EhBrGgNAVOVWCPIdWAxVxRloJdkbI3HVCHl0HKH0e5FNsezXF6/QGkqkuWDhBAYlWOarSalqRAqwUjN1u52PE/4gFOCdqdLc+Eqrmh1scZT+4AImsQnZC5FiASCRQiBRbJycIVhZTl3YYNuJyMLDaRUESoqABzOWrAChQLvI7dGxsmkItBSbVRIsM4iEVglwUisLJEYJAHnCjQePy4wdYVVknoy0QwiOqGMcDhq2rKFCBbhJTYEvEtJMwkyQeqAnnAqAhHaHILAOjdh1yTUicUUJb32NDKX0c6OBxWbpZSON9q1N+ggyNJ48AshCUrijAFfU5cjQtbG1X20d2xt7vLA+he4uHqBgOJZ11/LxQubqExinEDXBuMsg6pABYWq9smkw+eBUWm56/MnmJ9pgbPoNEKuO60uG5vbKJEAgaIuGY9rrj1ylE67SavVYuPS+ch9EJbTF/bwwTDd6XL82JUsH5hjeWGJB+6/nx0x5uorr8I5wajok7VypOyiVYq0Aang8s4GW7sjtrf2OLqyRFl79od9NjZ3OHHqBIdWllk6uICQKQ+cfJQ8TWm0Mqa7Cb3ZGV55w9Uszs4zGNds7W1x/ZXXsN8fMShHCAONdov+eEA5Kllb3aCuKg4dPIhXFj9pVAmICDIVk2NKy6ei3vgJ/yDL86ch8IF4QUkaOTv7Oyx2FiisQYRA4kO0qxMmMYGAMwUejXce42sUGZfX1lhdX6XbzbF1xeOb6xw8tEI765JUnmuPHOHsecnFM2scPrrE3tYOs70FpmcP4aVmf7TBiQfvI80kz33+y/jSg19kXGxycf8cq/dtYmtHI21xx12fZ2VpAVMbZCLY2esz1etS1IbFhVmESFnf3sYhWN3YoZk2efnLXs6n77wD6SVZrkl9Ejk5wbDb30NLiSU2byWZ4tjVC6yt7nLp0nmCUzTbOc2mZLAXeOW3P5e//8LnENazv7PP9mAXK3JEkPT3xwyLijzVJDql3c5o6ya5SPFCQSkiCH8S9RB5wvLKIi954fMpSkun2aTVbjA9M824tJTFAFNWqMIwvTKDNZ6k3SF1jkPzc5TlPieffIzZhVl0CuOqIGs28bKm1YwV41OtGa695hrue+hBGllK2miwvDxNpi03PudGHj3xGUZlxebQMjQQfDwvJVlKmilS7RG0UHpMniukLbGixLkRW7sWwTSlLTh1+hH0xTM4E1CywezMPFu725ja4lzB6cdO0b7s0NaydPgW2v0xSR7Zd7fe9iwurp/n7Jlz5HNzDIoaLaaZ6vZYXbvMcNBHagXBIJUFa8kSi0hS6vGYoBJ0ojl45fWsX7qAkBqkhSQjSXOuOHaYqtpB6TYPPnAv1VDQ7Wi0aDI1o1m9WCAbGYszU0wvXkmSXeSJ048zHr6y7VQAACAASURBVA5i5bXS8dxVC/o7A4oMRo9bWknKly5uMn9gmotnL2NvuJr5hRbLh67izIXLnDqdoFwUMNJUoomV5LKR0J1uI7ViNCpZ39imM9Ui+MDu3j6b2/sIkdBpdSiLEWVR0ek1yHLF7u4OWEkzSakk9LotvBB0O22MsYz6A1rdJqNxSR08aTvHmhrdShBBotAYU1PVkdU1HPWxEkxdQfCoVLF9aZXRoM/8yhLj4RBXGXS7we72KEa0IrwFsR+YXpqjv3WRpQPHWFw+PokCDamNo6EE5AKCiq0tOqX2nqIskWFCPA2xZl1Jjc4cwptY/Z5EJ6QjguGbrQ5eagIy7gc4grWY1GBGgogiio7aPM+piprS15jSYrAEEQi1wwtPbSpCVdLJs+hiERHsiYBgPU00VeJIZQMnLSFTiJDiRxoVLMpHmLGSOlatZ5Kk2cCHgPYx0uasJRMB5VKU9XghybIYK7JVTX9vPzJxfIwJWO+oqhrpA41EoJ3FOEiDBmshaFqZRNqa1b2UZquBTEasFxfx+3NoJChNaS0yWPrDXTJpeeL8o1xx6DhCOOq6Ju9Osbu3gSDjxEP3MNNb4dKly2yubyFlCyEkO7uX2NneI9EJSZaxuzdAd9pc3jlLd77Lgdmj6AMpziqU8ITaUwuPEw5Xeco6ivvBTVzyQiG8QYeAtQYhFVqANzXWQaOWGFOjdYJBImQgJYp8JkQ3XjkoUVrjvcfWjqoax9aqEJAqlrJYFwFlsS0oEDQEEbk/LniEklgf0DpFTu6vhIilJ2VdY01NlvSwxkQn2yT2LaQiSxIUEbjtiY2EHoFQkjzVmGGB1mmMSeoIGs/SDCEElalwKjrg8jSj8jVBE7ENSKwXCDK0VNFVJCoSlUwWJIJg4zFirZ0cMo4Qksj/lLHQRSdJhO+rBJU3cFLEBjatUcLhJu13Ckh0mETv4mfrvaSZx9isx6OkmhybfNl9JCTWCYRMEMLjnECrhJAElmam2R70qWtLK9NYV8dIayIo5mY4urLEixenONpp0BQCQhzCGh+diUkSmaoNEv7L//J23MufyYuvu4r/9N4/J3hw/YLPf/DP+IP7vsi3fftruWXu+ex96lFe/O3fw5f++HcZpppP//5H+c6ffD2JdcwtrvDa730TL7z6CHd89u+oWhmLM0tcffVNPOe2W2JBhlbo+Tnm5md4w3e8hc/++1/gBa96LQ+dX+OH3/Z2fuc9/5pDx57Jz/3Ln6F84FE+8YGP84S3pGnK/MocnV6HzlyP2z/wYd72sz/DnX/5cZaWjyNX1ygkLHQm98IIVKPF8LFLJAtzZGnkcxHAW8vWidPkc7M0D8zFOK98CngeOVtSgM8Ey996LfV/Oc0fv+/32bp4jv/2wYe57duvphwWNDtNQjD85Z/9BVVniT/60DvoegPf/RJ0Q/Keb309g5bmu7/5VpzWSF9y/xfuoDU1TT2GD911Pxu1RpSOXMd44403HOf44YN4BLf/7K9z/upp0r0znHngc3zf23+R9Q/fzofv+hRZs01VGdqdHnU5xnpDhiaxApN4XAVBJlQuQUjDwaNX8cSjJ7nnzrt49Q+8lT/5Xz/G5a1f58QDJxi/6NkYrXn0/gc4c2ED1y/47Gfv4rZbT7G2v0kqBVVdUmz32d3YotFoIHstnvv9P0JeG5YXpplLAiuHDtJuJnzoE3fw7372J3jnL7yTTqvLP/kfXsmVnYw3v+Pn+PNf/g2a7SZ/+icf548++Fe89vtew2te3eTQVQe58dbbWFu7yMzhZYzQaJ0ipKQhA41kKroEpaAVFJWHRH59Z9J/L2ISfP338o8JTELIaACpy4kLqaYox4zHeyRa0+3NkugMKSV1HQcK1khM6kkmolKeNVFKPS1YJyi0TCFIEp0jW02CFBEFIyULvYw8zdBaxcIvAvJp0PeXG/WidmQnDdaTAoanx94T0ZanbzUid1kENoqaSkmUzpBZRtJqkjb+O4+8zcqK3e0+Z3YcSXsKWQ7ICBFIqRso41HOg0wnFbmGYCXL8zPUziFDhejO8NkHHuChnTG9zizNdof+5ireWLY3ttCdJjoRMI4NQcIzqUoN2BAt64RAcJGtY6gpZIPKe0oPQjZo5hVVOcTgkDJWS0oXJ+qiKchVTUMr+v0LVHZIc1ohMkPAYUqBcBOHEgAKF+LF0tmobipVsbd9ganeLEudaYJwXNpYY317hwRJ4gW1BBECxlQMigoDBGtQQdHO27FyN0tQQuP8FK40BBswdYlOYaqZ00lTvMzJ8gU67TY5mke/9CDKluwOdzlwYJrPPvAAzVaHteE2r37pa+jv7CJ0hg0lswtLsZ0uzZlHsrmxydbmTmz40AmitjSamqo2pI0erU4zuj5CoPAl0ioyIfATcKJzFicDShsEAq8ERpUIG5k1nglY3VuCC+B9rOnWkzYtPNZDLQK5DKR5Slm7GG/0JcoHZBAIM3l+WaNSiwkCpTPSYGOVpwsoJdAh4GWcqEqtMWFMU3TxokTIDqWvJkehj9Bnb2Nriwso0thIh6SysRmnwJCYGKOTwiG9QAmJMx5qiZQaVMCHGiFTvA/oUlAPah567GEGps+Vy4d54PTjLB06wA3HruOJs4+ys18x1+1R7hVkGYyqitpNppi1pa6gNDXOW3IN3U6bAbt0exlVXeK8oqktzjpqk5LlKeOiAqEjeFJ4BsMt7rz4RORUqQStEjQanSi8MLS6KWvbF6Da4cH7HuSKo7NcceQ4mW7ig6OqLf21bYQaMzU7jfIj+uMBy0srNLJdkixFCU+SSQ4051GywX0nHqY2UIxhZ3eNcTFiVPbZvbDDM6+4lk6aYY1kZDTnHj/F7NIsYwtKK6gs43GJs5ZGs8Xc9BStLOeJx5+k12jSkCpWzAviTUmIVujgPXISj4s26ygwSxHbFLTUk8m8o9FQWOMwlUGrDBnKuMgEKmdJkgxjPNu7A7JGg+AD3c4Ua2ubGOO4fGETrQXLK7OU1ZjzZx8nzRpcd/UtyFAy1WljfcWDJx+h205pZC2EtZSmxlYlN998M5XznH7iSbZ3RqyvbUCjyfzsMjdfcxX7Q8fJ06fpDwYszncZlRWzc1PM9mZoNZrR4ZB6rj12BY88dpbxuGR+bpqpzhTBSkpn0FoQKGh0BMYQQbFKkydNqrpmOBzRbXRppopm3mB2boVLlzcoCosKmla7yVWHrmRr40m63UN86cRJ+uMBgoSd/b248E8lV19xPWeefIKbrr+ZAyvHuP+BE8hEkbpAisCqhKIw9MuC7d196tqxtbNJohPytIHSTa4/fh2Xzz/O1nCHTr2AF7HYQTQS+oMR1x2/jt5MC5G3uO+++0hbTVKdUFVjDsyusHlphxe/8NkMigppImPO+ZoL65c4NHOIjY0B3fkjjC5uoIzj6MIRLq9voNOEVpaRZhJrChp5i04zoaj2yISnmXiKYY1TCseQZkMzHG7gqxzjA2JcU9TbKCVJZAW+YH7hEFoF9nZ22dvZI2gFTpE0O+zv79JM2rRbORKPq2u2t9c5e26DYd2PCzcB3bamHJfk+Qyj4RoqDQQjSVRC0ppmdb0g1TP0plI2ty+wv7/BsNrEVmPmul3OXHiIuipRskW7M83WZp9yaPBK06JBVScEFGZsyNOEyliyPMFLSd7o0mgJtrY2uenKG3A2MLc4z5nTj5E3YnTvgdNnufUZ1yKNZmHuALPzS2xvbmAsJIkEAqM6MlSG+wV5UzI7P01torPh2uNXIQn0ByPGoz6mKhgPC5ytaDYUvTxB5pLp3jRlYSnLfY4dXWJkas6eKWk0FLs72ySZYlia2PA2KhkPRzSbTebmp5mfmcUZx+b6JgCpkgQtaTc13d4058+tggCtoB6MsGWJ1gJTFbGxLMtotDrgPP1il9KMqRHgHW48xkmFTBpob9DKYWqPTsKEXaPpdbsMBv0INZ6wBk0VP5MsESgjMLaimbWQQeGcw9Ql46JkNOqDtzgvkCGe28qyJA2WtJnEBtOnbjqDY1TFena8QEmP1NF9kqbRTeusx1mPl/G6iBA0Gx1GoiRxFV5MBp4+EIyFUEcXQYiuaScswlvKUcmgPyAVgtK7+HvjsdaTirhYDkmC8YYgA3Vw5O1WlLB9wIYIU88mMF6fOHSekxcSEyxSZYwGlxlsn6Y7e5R9F5iaWYFgWZydY32rz5n1IcYEpnoNyuEG6d6YlaxiVbRIVJOtcocsbSNtoBwPIAtYrxmsXeTi+nkaSYN2o0urOQXCUjnPQm+RetTHh4TUW+anZtGyzW5/i8fPPoIUklajR5o0I7Dae0bVAKEDSaInN/cBvGW4v0eax2ZiqRLSJKEqC6TS1DYykoz3NNpTZEmO8Z4k0aTO42uDt5a8nSEClGVJImWMESpFmuZY56iNiQsLH9tzdaoJIUT+UoisocllLwp7LsbrlHAE4ckaWWzA9eB8oNHIKIoCnSRIJUB4rDc4E8tMWlmDRAiCqXG2RqiMp9YzOonuX2tNdBchUFmO1YLceqSHkTWotBndiyrg5YRdiCPIgMWhVWyAVlIhZIxslkWJkCIOBF1N0CmttM1oVKC0JkkUqEguF1ohdDJppPPUlaAyEZBtnYcASZKQKBWHmDa60yWBoGLELxBb4pwNZDrG4bBjEilot9qMxzUbaxtMzc6TJRmHDi0x3e1xaL7L/HQnuu7CJMooJm9NqckCESBglUQ9dI5PP3GBzf0tTtxxD29/zzt58Df/EP3SF/C5omBQWVrNjIc/9HEKITl+8AALSYsvnTrF7Nt+mureu/nIhYc5dvR6Xv3dr2S8u8Ozb7yNTVGxtT2gKPuIEAXFuFsarDPsH1IszM0ytdDjaN7jDa9/Jc277+Ll/+pf0JrtoG48xjWXnsnG1pD7185x7rELXFrd4HlXXsVtV17NL7/7N3nRc1/J617/KsRjTzDMJJ3uXJzeYai3tnEbfVrLC7gvPol4wbGn12T3PHiSzvIBbkmgOTOF0BlCPbWTTpp4E8GF/3on9+zs8uzdPh2d8V0vvInjVy5j9sesnjjPYDSmWlzirW/8HlrDiuqxszRffAtrj9zPWkegVx3J9AyHlxbYa+Z86aEL3PasW3jrD72R6ZVD7GwN+MB7/5CPfPJvUAuHecMP/jCNPEP7mtf81m/wzDMP8lP/6qfoNFo89P4/47EnzpK2WwiV86xvfgX9i2s8cuJe8kyhqPDBRPdhKgiVQTlB1Rc8tnuC73zdD+Azyaf/2/u47SXP4Jarn83JnZKP/dUdDMotbrz5Gbz7l97JE+fXePev/iof/uuP4eoROmtSFiU3v/r7eOwTf0NlAocPtPi5V30TI2twRcr5cszhpRms9Tz+0CmuPX4DZy6dp9fr8fEPfY677v087/yZd/HpE3/Pm3/2nfz1r/9bDi7P49CUdcW1Vx6ikSqmF+b5ybf8EDdecWRSSAN+ApKWQkFw2Al/zzuFVBN20P8PHyGESUQ3PK28+Am/sq4rRuMxdRUFf6ViK/h+fxfnK7IsYVSMUYmm0WhSmIJiXJGkCWmAtsriv5Mc7VtM9RKyLCOgSZQk0RqhxFd8ck+xmEJUfyCaTYKf3AfKWDIg5Je3Fwngn9LAv0rae0p4kiKWCHiiE/DgVM6V117FXZ/p0Eshb+dkjfQb+rl+wwWlS48/hrOGfi2Zn1lgHErGdUkmBGnSxNkaY8d4nqo9DVzc3eXsehclLRBQekCzpZHbAZod6spRuajIVeOCjaLAWhsreCftZ064CHV2avIhxryzULHCb987wCKlpxSQhkhXDyEggsW6fbz2tBKHthVVtUeeKk6fPUszT2k1ElAFOQ2sBCE9SEPqLMYpAhqNpKk0Q1eDhMpbNvc2KPWIksDq+hpJsBAUQQa0YFJr7zGkJHmX1FbkSUJOA+UdrnBIZajKMUsLS2R5Qq/RpZHnKB014v6oJFWKTCeUu3sMRmOe2NyhNJpBf59bbn4ejTzluA60tKC5MMOdd32SPM9Y7HY5emAF2ZlGoFian+d04xzGWETwKFTM9U9yz95aUDkRrBCQfJleb4UghATpDKmM76+yFukFQinEZEIrpEQKhQoxHugDKC+x0WWIChIVZGylAaSwaB+wqSax0JceqwPKCIJIcF6TJh6VJnG/CLFO2Qni1MqA1n4SO4j2P+8lzsaDTsoYL/TeovE4b7AI8BKhNLmUJImkKh2iGkebukqxwiJlQoKK0+AgEMSaRyU0zgo2tmIc41N3383O/jZDP2Zta43SSAZnai6fv8TW/pi6NhR5wtxUC6Ez2s0GDz9xHi8DnVbKbKvN5cv79FpNrj12Lfc8eC9JpsmkpNOeI8kku/tjxmPDYLQLUsRpofeMB2O0TClsYHZ6FiEDo4FHyQRChQmG6Uab9e0NMqXY2FAsL84wLgx7u9scPNBBywaNxDFWioEtuHj5cXIVp6Grl1c5ds0NNJSiPxixu9qnOdVB4zmysoh1juFgn8W5LotLHTa2NhiPdtkfbtFtLbK7M2Bnd5e1rQt4WZKkKdPdaebn5xkNSy6vr9FodVCJYnp2msXxAjKLWNk4WFRPN07ICczTT6askcsUI24ROJvE84KUaCXI8wab29tIKVg4sIgiox4VkEQnU2U8tfU0Oj16vR6bWxvU1jE7N4fWiv5gyPbWBtWEP7F6aY25uRlGV4zYK8fkzYRm2sEg2Fq7yNHlJmmaMxj12RsOeezUo3TaCTdc/3wunHuImZUWqZqloZuceuQso+E+KI9Ulq3BGtZDnqbs7DmcMwyHI/CB8WCPmekuK8vznD19jns/fw+v/rZv488/+gEOHzzG2XPnaGVdTFUBAmuiMwDnOXzgAM1mg539KMKVdcXcwgyDvTWCtJx48CGWF4+wunaJ9YuXkamg3e0hMUyZCPC//tgzWV5Y4fgVN9DrdNjZG6EkpDl4F+Op46JGyMDG9i4fvf2T7Ozv0G3kHFyYZW5mmjxvRYhh2uTiuUusHDjG3v46jz16imuvvo7egRlCqFhYWGFrfZNhv0+WClppgpEtBsMxIZU88NDD7O9v02goShOwpuLwkcOUVcGpU4/Q6XQYFIGGVDSzGGttJx0WF2YoqxE7RZ/BYJdG3iEROWW5h8SiVQuEI+gGzW6PjD425NjM4d0IScW4jtcihcCO+2w7j/EDctlj7fIZrLa0uznCO5YPrXB56zTWeuy4pqwlFmg0Y649TQNmvIdWINsJadUhuEDpK7LQxLoE68e0pw5gTMnywZu4fPkSThSsXn6EyxdMhECTYn3O5s4OxoxZmD2C9RZbj9nZW6e/cwHZnGJ+aZnLly7gAkw1e6i0wdR0GxNqTp56iLnpBfCe2akpFheazPWafO7uM8y2e6wsH8dXJaY2k9p4SSsVGGvQJEil0TLDVpA1EgajIXqnz+z8HFIqrB8yMzNN8J48T5idnuLY0QWyTLO6usPu7g5CKopyzKDqk2Ydpma67Pf3GI7HhDFRzCeQ5DkHVpaZm1+AynDhyctYU1ObyDvQqcQ5w9ZmnzxNabZS6soyOzvFYFgig0d6iRDRoTjVnmZ2boa9/jaVsEgZaHc6TPWmyJOc8bhCNzPAY8sxeZYQ0HESiMLXjqJfYIMhTADB0gHBU5UjmnlsJhVBIrXAC4HSTFg5SXyOIImxo0AjUfhiP4o4VhAcpEFgA3SaLWoTHRbaVRgV8FbGGmLpSUMg+BqCRngJXjEaDqjL8unFQ+Qnxsr6pA4kqZzEyT1K6Mi6SWMrqQtEkLTzqIbBFA5tDUiBCjHqJnyYiBAeoRS1nQDXncWXBcEbhNRYIfGKKPgHy7jsk+eSjfoCSuYMx12CSBnvp/T7jqGp6bQbdPKcVnsqNlD2DrPSMAhf40OFDwnWeRYXFxiMKgwVVx65ksXlRXb39ti4NOLC+XN0ZnrMdKYYF32qccFoNGJh7iC5kOR5kyfO3sdjF88gnGJcCNqtnKXZadrtLqNywEy3RQgeFzwIQVmMUDLgbEWaJ1QmREi0t6gkoS7GlGWB1Cmzh6apvKOqa4LzeOeorYnHQpoSMegKgUcpNYlhS4xxCA9KBayPQklZF0hRRZe1D2RpRlWVdLutyfcLIcTIivUFWd5AqhhN1VKADJPiiRwlc5QSeDvG1g5T17Q7XYDoLEsTvAMlY9MZUhLEhPU1aZ3L0pRMaWxdIJFkWY7MmgRXxTipSgjWIoxAJZFvqJSK5S1SooSIrBZhaOQ53tf0B0OWDh5i7AravQZSJggp8SK6p1QiUWkUi2prKV2JER5UPFaEDCSJonaOPEmRWlMUdQSbqwRbe3QiSbIswu49CG9ptptICbv9faa603z7t76MTCdMT3dYme6htZygNyKvU0qBUlEIBp5Saierv1hjIw8s8C233sbnheGl1xxnWno++Km/4bMP3MnR2UVe/T3fx/TiAc6cPsvy0RVsMWa9HNGZ6jA30+J3f+v3uOmV38/bfvJHsK5EaI1xmiSNAPs0j0w872KD4yN/+xn+7t7Pccc9D+B7TT7z4U/x/vf+R2pv+e4feyO/9vPv4MaXvoCXvurV9L/wOG99+z9n79yTfPDf/w6PPH6aV119JSu9WX75l36QV3zzC6CRsvdX59gVsPrYJa669SrcmW2S+Sb6edfEhtybD2FHFbqREVzgOS94CY3ZNl9833/l0K03cfT5N8V9MAZ2cEJBf8CJR1c5Ndrlubri9g99jN31PQ4957k004TFqxbIzl/myfNnuf0Tf0u7L3nVT7yB6sJFfv77/yn3pzYO7qohUjj+5i//kp//l/+GWW/YWd3j7+/6Ox67dJG7vnCShcNHePe7fpFDK8uISQFE99gUjftLVuZm2Kz2CQcOMVXWjFfXyHozLC8d5uXPexl3ZiknT36BylSxcVomKBFoNXLGpaXTmefIldew0sr52Cc+Sb00Q3CSzfJBrkxSPlePSPIGrePXc3FzhwfuuANlS1Jrcd5DqOnN9Xjbj/wI7xg6blma4WMf+XPe91cf5cYrVviWZ30zJ8+MOLWzS6M5y8nHt7hpVCK9wkvJ9sYGzD+D733TD/He//1/I4RpPuoMnfl5jl5/K//5T/6QrfXL+CRjZeEK9tfPYw+vRO6gjOslKWBvMKQ2Fd1uD01kz0a4x1M79/+bx/9ZCvlav+drbiP42q/81JbhH9kOIajqitFoiLOGJE1RQlJWI8bFACEk3c4Uadp6mkOX5U0Ggz0QktJYghMUlUJnHWY7c7RbHfI8J1EqOh6FjMPur/F//ar3FJ76eSL+AxDZcl/+C0lcwAYQT/mWBF/LgPVlp1UUqWSIwpIWhqtWDpD3lkjzPVqdRdJvsAT0DReU1vcGBCfQ2RSZEogsQ1FiXMD6UZwQSAFSELwmD5ZxUbKx00elAWEF7ZZkfzSmFgIlPJUdElycWBgEw7JCeIvzMX0bQiARcToUtCALlgJJmigyaRnVisJ6jJm0sNR9ylCgZUA8xQOSJYEaSKhChTVDpNNPfzkOg3Y5VrkY5VAB7aPtVuEj6weHE5EnZGrDVNog1QqEQgvNzNQU/a0hJWBchQpR9JJCI52gkyQIGeOBnSSj3WqSNxLyJGF/p8+BlVm8jRdapCIISRAWG2qwmuFgwNlz5/jS2i5jAx7FsCiZ67ToTXdwTqLTnPW1VdY3d7CV5ZJK2DsXobfp/AGcFzQTzX5pCJp4gbXg8CgnCcmE1+AjmX5YG5IabIjRhhA8IniqssJ7QzWqCTLGAIWQEyB+oDY1e+UAW8dpVpI0ImMiWOpgaUuBtwIvPC7E11JkkVVVjqj2h1gpwCSYukTgSZ3AVBV5pnDeIZxChoBDUHmHshLjPaPSE4TFS4W2HuNKvHFgHTqVaJGjRTrJ7wu8VEiVUdSO2g5BShJdgXWIJKFSDZwtGNXDSa18QBE4f2md2+++i6o2lFWBwdNQCaNCIZoKX3s2ii18UMwvzLC/O6Aoz+KDJ80z8lYCLjqu+v0xSsJgt+ChR08TQqyTl1KTNTTHjqywtrnPE6cv0mqneGEph9XEFg8+OIxJqE0Z26rSBs1Wl3I0RIUxg7ImkTlSqcgqSJvMzExT1Jatfp9m3qEcl9x78l4ubK6jnWFuLmOxd4j1/i6Lc0tcHuwzGjsS6Ti/eoFet8f81BSJEuhgKKoSKslsd4HVbBWvE0Se4EpD3mxw0423orRla30TQk53KlYWN/Mm3XaEehbFgICnriLsTmmFiBbF6HhjouaHuLgMk+YZY0xsfPEeEYgESO9ItMZbx8MPP8zW1jbXXX0sVnJiiZ47RdAJiRT0BwOytIFWmnFVMegPue05t/Hg/fdhXEWr02B/f0xtJSdPnuLhx8/x8uc9k1TNMNVqsJulnHz0NIdWjlBWBqzl1htu5uChK3jg/lN0Wm3a0z0eO72Kmgn0R9t0Z+axVcXq8DLSJCAMIzNi6PYIKgGR4ILA7O/TdIK6KCmNQ2vNcFwiyVhb3SVrpWATwCClQ6k4oXEW+sMhrU6MryQiYdDv401J7T1T020efvgRLl/eYn+8R6fbRCcpnWbO+uYOpq7xCIqiojfTpZPlCB+PqaQpwYE1gTAB3jZbDa644hjOVozHQ7KmwuLoD3dZXliiHJd4oRk4+PTdn6bXznniyTNkzR6bW7ukqaDZarCzt49XAd2Q6AZUQ8vW/ohgAsWopNFMufVZz+fhkycZVyVmuMXebvzuq7pGJIqVg4diG6JOSBsp/dGQshhEPkgda8LbraM88vBdEbrqI4Mj78a2QR8UWXsGjaUYxtYkoUpEcOA047pCScXC4hK7m3uAx9uELM9ZX91l7dw5ipHF1RJTS8YuVninXtPtdqndkKzVpvaBRtCUKIK3tDspqayoql2me8uYag/nHPuDPcblHjY48jzF1I52nmO9wKEo+2MypdjdOx+vISo6egZ7ffxQk2d+AoGuqKs9Ul8z3PNkac4oSyhcATrFtwB18AAAIABJREFUi4JRMWKhNc/S4jqPPPI4B684TqOZ02w32N12JEpgKkdRxUVqohOSpEGrM0V3pkO3N6Yux3R6U5iipKw8w3GFEoJWs0WqMjqNNrUtMcbGxkqt6XS71Eaztb/F7vYAYyos0Go34sDK1SQqIU1Sdrf2KfoDhA902h3G4xGjMJ7EPFMEls2tfYL3HDy4xPOe90w++rG7MMHRbecIkdBodTm4skyn22ZudorzlwMy65LrDJxibXOD5sIs9XCEr2oaQuJEIMkm6GVbEwioVEemH1HM9s7jg0NnKbYaEhA4X+JFIJiasjSxppzoSvE+svy8cxhr8F6QEoUFgaXyFpUllMMBtY3g27Efx+e1cdrpyhqfalyIt9kxOiXIGhkOgSK2waU+RmOtUZTY2PZFjBf7EGJbHLFa3gG1DejgqKzB1w4lPCmCNInNYUIoauuoTIX0Gmfj2VUEMEKSTKLZSkFFjFaURcH83NWsb44haKZ789QofBCM64pBMcZLgRaavDnFkatuAXme2lR0mk3Onj8BKmFcDhjVfaRQtBs9rr/+WuamZjhy9Q2cfOAhzpYnGJghSVPRyQXCO4bVEEPNqNpna7hDgSKQcvWxZ7C6epnaDamMY3Vnh+W0QTNvoFExcoaLzhsfCFJhaksjkWAcRhlqE5CipihLyrKi0dLUtaE2JVU1RqQ50kXXuhCRWxRcDcLiSEGGOChREmobv1cRSz+0kgg0UmoEkGiNsYEkSaOQaS0iqBgNNwbvAn4yoBYEtJATxlcgVwqtI+jGe4ObtP2lWQYEpEgRHqRw+OBIdQ4InPNUtcULUErRSFOkByc1xjlcWSE8WBmLMiQBjEcl8bWETMDHoZ+tTLy/SXJqY+h1u+zubuCDpzfd4tLGGu3uDCkZBhFb9BSoXKOzBKFEdI47gSoFOkThy4wrTG3JstiuGN3NIUbuEo2exARNXaFkQjNrMj89z9z0DFdduYJKNKmW5LmiqmsSJUiVmtxXCBCKCdN7MuiaMK6EmDgU49JSugBzKd/xvd/Ja44cYHziAqpqcOPzv4n7T9xBs5OxMNdDecPqzjbjuuCPb/8kD26v4ZziDW98Ez/5wz/Om3/sBzDVmKSRMxptsjfcQ/cirBwfEQ6uqDC1YW9vnzMXN1g8eAU/+M9+lLlWRqORQYDdJ7c5u7HB2iMPoWWbb33tK6ifuMTc4jLXHbyC4cYW+oXP50U3PIMjxw4hraG8tMOd5x+g7wXTB5cYPnoSdjWdQ9NUuwO8B7O1w7mHz3D8215M3smZu3qBsDfi2a97NUm7FVvzXBxO+yBRIlDtl3zrj7+e0S/8Kv/8599Jf2Ob4zfdyPrmFscOHyEMC9Ye2+HY0iJ33n4H73j3r7C9uckXf++PebKXMy1gUA8J4wYymWJvr+Jd7/5FXvOyb+EVL3wRf/eJ2/m7E6f43rf8KDMzi+R5i9pGNq4IHldajr32Fbx1cIm3/4f30N/eomp0efl3PY96Y413/c9vQSvFdc+4io/8x9/ijrtuZzAKkAmkg07eotUWfP8bf4y3vOWf8MHf/wO2djf5+X/6Zu46d5qrOk3e85mP00gVz3rxN/PPXvVd3PnHf8An7/s8/f6YECS1t2gnqHcrfvf9v8O/e+sbedf7PsB0b4rP/c1fc80b30Ajz/iWm25hZnqWJMv58V94B6fu/RKrF57gVT/1Mzz50P387i/9Ar/2vg/zhS/ezXd90/fSmJ7irT/yZs6fWeX2j3+SY8++mSRJuffTdzJcPcENR4/R7Ewz0+5MolOWXqdNZTK+eHGdm2Y7dFvduCeLryf7fKWA8lQLGrFp8Wv8RXhaIpm4dP6BUCImz/PU2cpDkP9g0/CVL/t0QuyrOUohwLiuOf34IwhfcWjlIHmaU5QFRVFCiOKR0jlKJQTh0SIjk016vdn4PEo87aDUgq8b//vq9/91fhLhq37+us8kvzLa9n9HyJtsP3nCBpLFqRYmX2CqV9Nu9ZDq//IJ/h8/vvEtb0IythaRWWpXk6kMiaFyhqKqsAgSrQlSUclYZqtdzf5wgNZxQbhXGAZFTZanNHKBNQOErJAh0GxYjPCUhY9fhBB4KSfa9lMMgoQgHUFIkjSg6hpvavb3+mhtqF0N2pOIBGWjYOCFQymBIqC9RCcpprLIHJycWNVFtIybyhECsYXCW4RSSOKESCqNoYQg2BuNufrAIRwGWY2oBzuUrsQFED4QgiRIiVKBohihQ83SXJeg4k2fFAnWWGrjsEoyrmpCECSJQod4kAqfkCZtEq24/9TjnDx9lsoIHBaEZL8u+NQ9p3nRLTdw6OAKPjh63R7LnS4b4y2sc6xu7KAdHOvO0en1Ys372FAHcMFRlhWJlFRORKty8AiVkCUNOp02goTaV0jh4zTGe3rdLrluoIXHGoNITLwR9THm12x26PaaFEVFqgJSJOg0xzvPoL/PeDhifX0L7Q2dmWl6vXaszNUpeUOSNWMbiJYJLtUgINEthkIhMbhgSXwSuTdCIkMUHpqtKTrtdqz3TZvxRt3nuOAjm8TXZDpFqHzCFDCEIEl0hhaSmWaLkCUkWZPxaIgOASE1TrVJyprYOigpS8ejTz7Bzv4AU1uUcrRbTapgOHpgkcOLhxmVnlOnHyJRkrnpaZp5hveCoqxYmp/G1obVzV1G4xIlFFkKi3MzeNFgc3OTqakuU902B5ZnsUXJzTfeQDEybGxvIydcJznJtLfbDRqNyBlJWpp2M2FzaxfrHSEIpBdMzXQZTKyc/UHNQ4+vYuoRB5dHHFk+yLC/z95wh5XFLj602ds+B9UGspnyyJOPcPniGaamDuClJtMZ43qTK684jLCG0SgutFOpWF/bRycJ3WYXYwtuvvFa6ujR49FT99OensV5C0ja7S61czGbn2RYF0h0BiJWBCOioKyUxLnIzUKEaKH+qtOSJE103F4EpAg4H6MBywcP4ELNzv4eZVkzNdVGuAAiobQBK3xsgRGKNI921v3hkI21ddI0pdHu0kslrWaKt5qt9cdoNGe58tgVccqgPAlwaGmFS5cusru/x/LCEh2d0luYAlKuP34VF1YjNFWpdVqNFqaqme11mZ9dwj7sMNYinGFc9Smp8FbA/8Hdm8dKlp7nfb9vOUvtVXfv23tP9wxnKHKGi8ghxUUUpSiiZUmJHMuWFziQ40QCLASwgSyAYQFOpCCKHcoGFBmRZSaKHClWtJCypEiiTFLkkJyNHM7W09PTPb3dvnvtdZZvyx9f3RlSkWLAYGA7B2jgdt2+t6qrznfO+73v8/weElQSC1jjDEE50obg5u1XOTjcR2tF5SrW+31m4wrvA41mB4PFeItRlmm9YHbrLlYU8d8XU5TMIuQ0SB64/BDPfuVpmu02/a1TVNZxNJlgrMfJnEVh8D42lA+ORqRKMByPY/qQCTgUWaJJVHz/Ewnnzpxh7+AeihZrg3OgUopyDjrwla8+ydzWYAKn3vUu3vn4+3niic/hnQAh2NzeQngXVYBOsCjBWxHXrQwY41hZbdNOM9b7m3hvuXvwGqlWpM0ml85dRAZDJ2tRDOdkmWYyHxIw0QpiHTY4TFUzqY5IkxRvLOiMYr6gn/fYWF2j6PSYLyyrvS736ynOO1rpgNlkiJUOYwPr6+t0Gl0WecpiOkRLxWI2p92N9sVWq8OwmlCGGtXIaWYNgvNMFos4Ke11wZUsigUb65tMjm+RtlOK6QJ8hZnvIGWHWVnQag7Ikpy13jp5s8Gd26+wqGqkkMznPoL9E0FvdR2lM8ryLhqD6CkOjytk1qKhm9Q+YbqoaDY9o+ke585eQArF3Xu36HcmKO1RSWChCi5duMy8vMHzzz3DpQcf5PjoaBmBDpMyWqAG3SZp2qBYVCBm1FXB9ulTiH47pi6mks2tTSaTIdY51lYG+GAZTuZo5el3WkxnJU7A+sYaq2fPcv0zTzCfLej2mnGAUxgaqaDZyjHGY8sCV3tkiI3kReFiQmyaU9UlCkGz0yZVmoO9Y9rtnMPRhI3VLqbfjBYgF0iEIlQ1qdQ0ej3y/RFKJbTyBrlOOTAFYjFkpb2CSzSuKOKm2Choxtr4BPAZVbAOXzucd8jgKcsFiYjKneAlHkOSpGAtVVFRlVUcgkWSEsFHq5TwgYCKgG/vl2phFRUnOAgK7yLA07vI8al8JDI7dGw0uRBtDK6KyVkWjK8RAWpXgfUEW5HoBtJLnBC4JYPOmprgI6xZhBii7kVkOkWQqUX4GhnbUGilaDSbEWzso0nPWRO/XwYSp0gNVF4uFW45Xjk2Tz2CCQpXVrEhgiDL+6wODFU9i2tAeOZVRVlPIGgWxZRFaQkmsq8WC0sjT5gupuzt36Iud9A5jEb7qFSQqoS9vT3wJaa2HI0moBTXdu7T6zRZwfHWt72DF1+4zuUrl7l99xW8hdootErJdILWOg4HlUIGH1npPkLTramRMsFUdYS0G0dZLiLI+kTdU3hSGyAYahc/X60VVV2gZUq31cFbj1MKpZNoW1zGfgu/3JBIEdlKUoD3FIsCUFhnyPPYOQqcwFujEjsRCd7VKKVRMsXUliAkaB0ZlLXBeUFZOVSSEoJEaIlzFVmSYn3AhRDPJREHq8ZYbG3jc8q4oTHOxJq5qgku0Gg3yPIWQioQjtrGDaPUcsmD8ujlhlWqWI+38jYjNaSz2iPJEgqzINGbNPMWVW0xzuFltMurTCASkFqS+oSWa5ImmpBp5mGOVpI0S6lKhzUSnTTo9JqkSUZpKhpJQjNvsLa+zrm1Nc6srdBovJnQx3Jd6yzhBIobjImKK+URqMhdY7nZXG6m4w5nuR1UkjCdM71/TP+Ri6T9Pm5F861/+bv53P/wIr/z6f+LLz/zFFne5r0f/vdYVJaVK5e4UC64fXTIsDYsyjllbbnxmSc5/e5H+Ref+CVuHe5wunkGpwROKoIzHD39IovS8OhHv52Hv+ND3P7cszywsUpr0EKIQLCe/sfez0+WY37sl/8Zr104wteGxaKmv7nKmUffyksvXeVwMmN/OGStWLAhBJ/6+D/kf375JVodzb0b+xz+4Rf4ipjzEfMeDl+9Q9Js8q3f9R2sdbpkncZJaw2EobXaQugkNi3rAj+16PUuYVSgtzcw9+8zlooPfdu3kVeGcw+/jXNnzoCvGV17hdaW4oG1jDtHLZ743Je4+tST9C9f4uMf/1l+83/8RzTf+gC//gs/z8VzDzIJd3jofR/CzEp++md+hufv3KbRa/DqzZd5W3+AVHoZfuBxkym3r73KqUffypdfeYF84xR3rr/Eez/yHWSJYGdRcH93j62NDd57/hzjiw/y5NNPYoSmMjXdMxfZbg24cnqFH/1PfxhjLHUdePRbHudn/5t/wI16ilIxCVxJyTvOX+CVZ5/nsy8/y6xeUCwqlI6sOW0hP3OB46/d5of+zx/iyre8DVMvcKpF7QJaJ+hEEmTABcP7Lm7y0hc87V6Xs97z1Msv8k9+6Z9Q91f41kc+gO5l/Lm/9tf56b//M0znFR9+/F386I/9Tf7Xf/77eKV5+Mplbu4ecarZo4fH+sC0LFlttsALLq2s8dRLL/GBd70NDRHEH2eTf7wR8HVfyyWjaPnZxziDZfNn2UiKvJM31taJTevNVtPyB954Irl0h/mlxevNNYbnDZi2C4HK1IQQyJIUJWNaZFVVKCVYXd0gzzt4JEIq2u0uOkljmFES+X8Z4uRlvtmkOnGmnciLTr73r+or/Zs4Tl6XiInpQeWoNCNNs6io/CYe3/SGkpKaPHHMiin0WmSpQJFSVjVOCBqtPCqNaoeUikQ1IocmseRJTm1LQrB02g1QKdKNUdLTUDW194hlspgHtFQoBC6ccASI6V8uJsx445EtRaIstam4vXufZstgfIGyEpVBUOCFjaBmG8ut2ntMCAgZN8CgESxTxkhxRIWNcy5anIipGtIvQYgskyk8ZNJSFQvMomD/eBRvoMbEyYEQsem4XEF5kpBnYmkP0AStKBca7+YIZ6jKCI0u62jnkdRoEVPKClNy/+iI2oNxdfSG4xFCcWdnl88aw+N1wfmz50gVnO92mdy7Tyk9LjiGoyFH926x/fDb6a62GM+HmMrERosx4DQiRBYRIixj15fnqoJUZ3hXxbQbHz3oqITK1lHajMPg8DLa6KSQaFK08Ei5TARanvneC8bjCQeHR+zs3KO7usK3v//dUeItDUpopEreuJBJHVNQ5FIeLZVEqZSgZVQe4TGuIk9SfKhRyuM9KKo4zRMK4RVWKAzgBKRKIFwEnkXeeyCRMQY6SRrRD6tTqCIjAhHe+Nx8iJ1hrSQrvQ7HoykuhAhi9Ckf/tZ3085XuH885oknn+DC+U3qqqSZ5QSV4oNBColINJ1OBx8czSxnPJ6hM8U7Hr7CF5+ucb7m9KkVXDnn/PY2QSseOHuOW3fvkiU62vuEotkSPPzQBUZHJUeHEz767Y+wub7OnXuHPPX0c1QWVFMxHM3QWpPKjM21rXgOWM+sWDApZjSbORdOn+fsubPcvHOPlDUmY4OoPd5nCHo0Gg1SAhcuXiRp5AipODo+YlFbJuMxdTFHa0Gj02fn4JBvefAStZuBSAk6cO7Kw8ynI3rdLjLExL1Go4UzHk9FCJ52p4MzhuDAC4dUJ0rQ8OYF38dNjiAqEO2SNSEi5OwNu7L3jnazTa/bpd+VpJmmni9QSYInIIQn1Rpw7O7tMZmOWe0PkDJhc+MUs0nJZDynv9JBCsX62iZ37lzl/OktTGgxO9onEMjyjGbe5nDvLsPJPkme09IaZwOtPKMsFtSl5WsvP4MPJeORpN1qokQgT+DRh9/OeDbl/s5tinJB8AGlMqyDlcEqwQeOjg8QTqGTCNw2piCEjPV+n3JuaDW7SLOg0+ygNJRFxXE1oqgNaSqRIqGRNqjKmnZrlVk15Wg0ZzJ7mXYzxQXLtddvgoxR9s0kZ9DpsFsfs6hLbrx+nWJRsihrJNHqaoJBiJxyYckaKT5YdnZ3uXX3HqnK0SFFohj01tg53Gdv/x4zMyFLM7wTPPf886z1unR6axyPj9BZynQxYzYe0u8OQAhmiwKtNMaVaK2xxgANXr72Km9961s4Ppiwu3+bCxcv4yg5tbnOxVPrPP/cNRp5Sj/tMZ4M0ULTbvXj++E9B0d7BD+PcdEeEhVtVeOjHXZv36LVzvDBUBUNTD1ic/MUebZOUTisLUmk4ODgmDRtITVI6SlKx3g2pz3o897Hv41bd1/ly098CSE8whpImzQaLbqig9CS48luVMw5w+T4MMYRzCLvJVEpQiVYP6PbbpHnijwdsLV1id29A0wtcEKSoHBW4RNIUs/KygNkOmP3/hHO1XQ7Kzga5HmHYTGi2xpQ6CnT6YR2u4sxJXUxo9/rcv/eXZq9Fonu004MaZagW5p79+8gkgzva7IG2DogT9ghSUKSJ1jvSbOE1dUek8mIMxfWGY7GvPbKjTc2oJNpjHzXIrC/P2Q2nZClCbUJHI0P8MLywkuv4YMnzzOwjn67E1WzZpn+WVakcpnSEky0HjkIUiC1RlkVQzmMpj/o46zn5q097t095C0PXaHba3J0NGU0mmCM53A0JOu0cPMF1llaWQe8ZzwaYm1BVWrKpMBWJdoHgk9AL3PJWSZKWvvGbFFJ8CHGq+tMoK1AiBShFF6aOOxIMrIsXj+tj2EBIcTfKYyMgwAXrf2WpVrZO5yzGG8QIdr6jBB4Y3DWUFsbJ/DSo0SCTpahEUGBr+LVUkaVoieqbawBG2pkSFHORTs5gjRrYDzYEJA+2rQSAS7RS6ByhkWQ6shLtL6mriqCiHWS9x7rLLUpSYNfFrsOobJlHWAJSIJIY+Kt1EuVl6TnHP00RSpDbRb0O2somZLowMIsKE1BO28zrQ7w6AghVxmNvCbPLEpKDnavcnhUUNYlSsUQh92jY+oyDpB6/Zx+c4WD0YgKuHN7H6k0+JRTa5ewtqB2RAW6MwTnkXppTwsSaysWi5Isi7wkLWLamA81KmhMWZC3c7x3WG9xpiY4h3HREpVrxbwqccYihKUwRRwstXusrazhnQEiP9BaExPJ0pRIYQXrYiJcbeulSka+YesXQmCMQUlFgowKtCXfMqatxbrH2hpTlks3QUxDllLgXTwfpRLYOjYVI70gkCpNJSQIgciSqI7yLsKWXUA1coSQ6DQjyRpYa3G+xjpPnmmUiM/llhzENMlZW11nO0lpdpokRzlJM0UITZ5kNJstmnmTPIeirrAhkDQSZKZwOhBUgpQZjcQRSkviG5QdSyuVzBaGZA2aukXpF+RJTpqmrG2sc+nsGpmKG191srU9SVM6aRSdKJ1DHDBXwwmVF7TabVQzY5m1vqxCAvKP7TYF8Tow+Oi78MKTXFmD4YTnP/Vpnr/5GlI3ORhPSBczjl97mR/+0R/jwmCdV194kb/7E3+H51zNHz71PN994xZP/cFnubp7zP/xm5/Eba3RHE+wQkQbaWmZz+eElQEq1wxaDXoffi+q14qbbkAmitpYuu94hA986S181/d8H7LXo//uNuA5c+4ML3zpaV65/TqHynL9xi1a66vcnsxRSlKMJ4S24txml//qF/53nnn+Sd729nfzPe/5IHI04fTlM/HaJSNqmCpyR4ICnKcejfC7Je2NDj4DYWqy7XX+yo/8JdIzZyluvM5o7TTVeMwrTz/Pk3/0JV4c3uaFG9cYLebcKASPf9u7+Ng7PsLFtz/A2t/623zl2lcYDDb4Rz/5U3zl536e1f/wB/jVX/plXrh1G5UERsMhblHzvR96nEG3hfCAUsyeeY6hFeT372Ga6/TbA27cuMETn/0sp89e4c/+4J9HElPQvPfcHo2pnaPZH9CRKe/5Mx8jffE1HnvsrSyIKZaPveNd/OB/8AN8/Ed+nJ2DV2inDVbPnMGMpxweHjEMx8zLisXS0VF6Q6PTZjaZ8Hd/4u/wT3/qZ2l3e1RVxWBtlQcffIz5aIxzlun4kDRRqPYA4eDxxy7ziyrnH//szyBEyi/8s1/lv/+5f8zvf+lJfuDcNluDdd73sT/H2e3TZPWMTprzg9/7ERaq4pmv3OJXPvNZfvzsKaDJ127dYreqOS8lD1+6wKlOzuZ7HuXefE5fJSTO00zTN3s9J8C25fEm7HoZYrQ888NSwheWqwMheUPJx5uKnaiQOlHkyJPinrCs64OzGFNjbRXrdZWgdUqiE5RUVNZw92AXISVbK+s0dQoSms0Gp7cvkCeaLE3xCNIsiwEif0pvQyz3p1+vihJ8w1/+jR3/r6l14uR9FLSkROZ9Ws0hSdZYqqC/ecc3vaEkU8hShbeCVFkEDusdWZajc7Ek/gtsFhA69io9CmEcjgqpPAKNdR4nLZU3CB+nc6kI0ScdAB/5KQIRJZ2RmoT0iiA9LsSPv3YSpQVlZZgUM4KOxUOuBMJbhE/iRS1IPHHKIpEEJwleodAIrZDoaINCUs/nSCEJCqwLGOeQUscbc4hud6klpzfaDHoZZag4qjTDWpKmCdY6hCIWTMSbd1nXFFWJdA2kig2KRCrIPN4l5FrQa7Vwvsb7QFlZ5sUCoaIVYDqdUhR1tBYStbZRbxVZMwdHR3z+2a+xubaBVtEGZgME5zHeM/KGg8MR55Rm0Oty1EypjENiqJfgY+Ed3hhEEn3GLO1EQmq0SKI02pjo1z65QMgE0jymbaiArz1KafzJdBNLCBovljdcETBlzfH+HmWxAKmZLIp4sRcyelOJrALpBFIF6hAT47RKIrNBg1QpEoGxESqphMB6T7AO4aKM0wuJDykQE0WElAin3+huKxVtUz54cJbaO4KUeK8IOIRzCClI0py6tqRLP/4JS6HRzLDOsLa+iqfi/PYa127uUZmA0hWlK9lYHTBbRLDyxYsX2Oz3ubVzl72DIYPVFdbX1jjaV7jKkSiYlyWf+/JTCJmQpJp/+cWn2eymbGxuMtw/YDwbIQRU1qKVpplJHrx4mu2tDW7deYVBv83WSlQyKCQXL1zg4OCYVrfJ7d37aCXpdTpMFnOKxYRUCjIk3UYDrRNWeqsIp/DOM5rHSOhG1mI0nrN9aoONjTYvXn2B2hYMBgOajUGEgxNQWlGY+L6dP9vnGFgsCtJGF4LHeosUlkG3F22CwaIT0EmKUorJbELwjtWVAZPhGOMdiY43sBDiVTOuYoETURYriBu3mIojUUsWGMRprjEWpQTtdotGkpEoRVkU5EpFKKmzZM2UG6/f4pWrr1IUBa8nGSur66ysrjIeTSnKguPxkGamMNWCra0z7OweYc0uw/GI+0eStdUtNtY26KwOyOo2k9GUkCcMVlYJ3nLt+jUOhnfpdDOCUxRVgVBgbcHF85eYFGP27u9wf3cXVOSWKKDdbjE6HCOSFOM8rUYTX1dolZB3E0RlsdagpOTC+dMEodnZuU+r0aKRwuFwjELR1DlbawN6/VWOn/sata+oMSSJJBUa3UmYLBxaS6yrEEKjhGK1v4KrPLP5jGde/AqNLCNPu0yGQxKh8NJTe4fQkspWNFsJ49kUqQU67dHudPFScu36De7sXGNrrcf5U9u88MoNLp+/wGuv36fTblMbQ6uXEnxCkuRUeAYqoVrUaA9Sxyah0Ia0Hc+F+7v3ed97HueVq9d54MolHrz0KEIZfG0ZHo+ZLGYkeUYY13SbXdbXBvQ6fY4mE46OdpjPh5FcEhxJmpAnLVTSRCULbFpSVxVCJ8wWM/CwfzCisHOK0izvV5ZQe44Pjjg8HjJYaVGZCi8ct+/dop918FYSREKaeRbFFFc6lOqSpB2kzMhEl0Grg2saDvfv4nzG5sY29w/uoXRGXc3w0tFMK2bzId50OD7uYGpBp7POcHqMFXHzZ2qLyXOmkwUmg3Z+gfniBRLVxTqPThStXnO5PlRMoKocZWnjZs46AlNmoyGbq9u8ev06WbvFZD5lXlTsH+3T6nSYjxY4KQjCIqQvUgN7AAAgAElEQVSiLBa0Wxn91Q55u03S1OwfDikWOdPhmEG/zd7eEUg4dXqDvJFRjGcMj8dI5aJa2DrazYyictRE66Qxcd23Wm0SLRhN5vF6kipCMBSVIVESrRPyrIUXkrIyqFRHRo1Q2CAxziGUZDSrmM4qsryJtZGfNB7PyVs5eatHqhOCcfgARVkxn01pdDvIrIkWAq8STF3gTB2bCj6qJoRcgjhF5CeFAGrJ0/EOnAOPwfuAqCXFYoFKcuqypK4MNkRrEyHEJkFdg7PkKka/SwHWmHgtBGLEeZxeBwJJACsCeAMyoGQc6EgZQcxSxEZbOGHzWAMQ03KFigV+8FjAC4kLAVMb6qLASYF3PjYNnI8g0yXuISCwTuCDQCsVi3WtY90QXGzUBouiiEEYb+JlcAQ0KVJLpPPUlHgDVjpcsGgJjbRFCIL5fMaqqZiWU+4f7NDOt+i1GhwfVGxsb7GopyTSII0j0ynd/lmqypHoY7SI4RohSShmFSzZihe2z7LaarJ/uMer117mZnaTtz3yzqhUlinO1+TOYOs6NjNDfI8k0UJlQmAyn9OmCUJRhQXe1mgNi3IeN9c2ILIY5mKdwTkDMpbkdW2pFnO0FBgf1UjT2YwzKxuxBgws0+M0zsbmm3M2ck9ktN9JLZE23j+dc8vENIdSCmMqEhWTluOtMwKslYzBIsE5ameRSWTMKRnvnbFOUnFrlSj83KPTLNaxKjIynYtqprzRXA6P4/3HGkOj1Y6cS53GphQCG+JQUSYpUiUEb+JgMkiSlmZja41mo8V0PiXTGUkiyXTOem+VRjMneCJMORV4p9CNBBLwmaKrE4KMakNdg3ApduHZ2mhxfLzgwtkVGiqqibSIn90fb/z8yUeI6qsASAXO441gcjgmtyoOOVUEoXvr8JVFZBqhYm15oniQ7Wg1l0FgjGX03FV+/dOfIV/dwBy9jEsVWguydic24NY7rK1v0s0S+kmLR95yhTvX7/DuD38bh9bz2LvewQu796lcwHhLmjXQrSYbD1zk7t1DJpOCrNVEbLQJXkRVG1GpqHVCXRrW1wdcPrUSVWKpJvhA/8wWrX6bq197iTIEvvwbv8bN23dQDz1Ce+82spXSVpbf+P3f5vSDb6GV5Xzn+z7EQ+sbzA+P6T5wZjnHi/93sd4HIkojuMBkMmc+XNAOAa8E1Z0prctrZG95EKoSX3hy5Tl47Qaf/uxn+cKta9ze3yFJAlILgpnz8OWHeP61a7Qu9PmNX/kEv/PMk7R7LV69cZ3ndm/y7M/8fZ783B+hO3lkvqkG5XgOVUw41Ahmt27zys4eD37/d/Kbn/glfu93f5sDU9PuDti+9AA/8h//Df7shz9I8J69o0MORhNGicYrhZ3P2To14PYXnuT8xhovWM0HTWBVCN71vndzvHdAmXfIpOTU2XU+/F0f4Qt/8Fn+8LOfZnywB1Jgg6Y2hnZDo5TkO77vh7h+74Aaz4f+wg/zkXe8i//2v/7bvPzUF1jsnWJr8zSH9+/xyCMP88jb34UrLJ/65CepfYWrSi4+epntiw/zSLdJ+sGP8t7+BhuPBL63/yipTnjx2lWuXf0aly5dRqU9Fltdzm9/lKpcUJsut/eOOBqN+fTzT/Pf/fh/jlQeBxSF4aW7r/PAmQ0eXNvkG2U68fDBY1yNs0ulITGpzNkFeZ7Ga8Abqym8KfsRJ4/E9SVCwIWIrbCuxtcVxtXYRUm1mDJZjEFLOv1VWq0VBHGfGP9Iuq0OSZKQJUkEVSPINKTd9tK+/Y2v+0+UGol47v5bqUCCP72ZxLLZtGyHKQGy0aHZ7kQFlvB/6s/96xzf9IZSs5ViUTQDCDej5gSUFy8kPjgIghAMOIMTOk4ziEXHSVKTD44ldRtBIEhLCAZnJNaIpT85YEPAhRid6omTEBGRyrH75gU6E4io4MY4jU6i/L32Ae/ixCYs0yZkAILEeoUQmkF7ExccSZqSJxnTyRRnJ0vZtyOouPDlciojTyLJpeD8uXNsdLp88dotXr29F/lC1qCFXA45JF4ovPB475jNFni/gggR1q28Q4QIZwvCRPSUjsVgpmIks8wzsjRlNh1jbISMx+6vX7Z2NQSHBw6PRty+v8vlM9sYlWFlTqvbxwqHDwLR3wKdkGQ5emnxEcuNuhMC6xxZomJB6F20+4UTGb1HygRczcHhkJ29w8i4UZog4gQS55eTT4hQb48IDhFU/JyXIEkzn3Fw+3WOioKFlahGxt7+If1+N0ohQxKLIOHwUWIWm0FSAA4RNCHE2EfvLdKHuPkyJsqOZSBYB16SCLComD4nIjD8xO5gvcfURYR1KxFtAdHcGONmg0EFgEAQnhAc3sVGWaJTzm6fZWd/n4sXz3Pn7i3ytEmSasaLOQHYWluj0+5xPDvm/PYZFvMZZarY3thgPi9ppxmbq6u4xYTQTKgP7/HIuYu8fn+EUhotHLPFgtl0yvHxiK21TdK0yQtXr2NMPF+yPKcsLYe7I0xZEZTntev3qHwgySQfeO87+bVPfYp+d5Wdg4QHzm/zLQ+/hXkRuHX7JtP5lDpIrt28zXgyw1tPt9NlPp1wNJmx3muzNmhTW0NtZxyOEqalZFEfcW90wKDZp9Na5fT2aVaVopO3mU5HTCYzEiEQVoET1N4gnaUqFzQbXbyP7BalFMaZWHhYS11XmKogSxRCWLwLaC3QQr3B5Yj8WrWEGoaohlMx7lpK+eYUJSyHKScA8zca1W/G3IbgCXiqsuTO7btxyp9lNFptyqoEGcgyTVU62p0mzfUBjUbO9ZvX2Tt4jU7e4+79OffuH9LpdUjTqKabTcc0E0V30Gf38Iidw5vUbo4QGWnWglAync/IVM4rr77OrJwzLypa7TVm5fEyVhQunDrF/tGE6WROb2WN0lRvMjQaXWp3xGIyRpKQCljf2GJ8OKbf7bN/sEOSCvK8g6mjQuHunZukmcMUh0jksuWrSXQT1bAkiSQxOb1Gn2I8ZrZ/gMYxHU2pqTBVRfPUgPFwDCikCkSxiECqlKr2JAqEUBhb4r0jSTSj8THHowVJkvLY26+gdZ/Ll85TVpJ2I0X4HhtrFxlOJ9zeuU+wivFwxvrKOllTM5pOlxHZM1ywDIdDNjbWmM9mnDtzhssXzzGaGKQMWF/z7DNf5WhRsFjMWWmt8NDlizz44CXK2tNZlHhfI6WlLsa0O+sMum3a+SpFWHB0tIMNgnq2iDaOIGmoFmVtmJc13kVpsciigqGcV+RpzurGOuPxHRCBnXu3aZ+9TDNrc3brLC6zvHrtJRqpYGOliaLFaB45YUVdI3TASUG7mbIopnjnSZKM0hXoVLGoShKVghAcHd+lNjEB0JuowFXRL0XwgtnkHs2NBxisnUaP5xyPF8wmRQQ315Ed5r2g3ekyHU2ZL0q2T29SVzWtRouDcJ/ZbEbtBA3d5PKFNW66e3gso/EM6YhKURl5MlYFFuWcTFjMxDIaWYbHx7Q7Kfv3jzl34RSTaYZOE7bPnmbv1h3KyZzxdMZg0EYlKdIt7821ptnO0HlKNSxABPYOj2k3c6RQJHlGkgiKukQlim6nTbfdo9XpM5zMKA+OETpBpg6VZRwcjTk+nLK2MaDZajKbLji9vc10todzlrI29AYD1gZrZHnGfDxiUZTUxjCZDxm0N8iVwjsTWUIS8iwnKI/Kc5IsQ0pNbWLgSPAh1s3BLe+hgcpUEBKkE6Q+gEowwZLpOBBSIQ5RQog/j5LU84ra1+Q6J0iPcY4chZSRGwgWvCKEGuOJoGRrsVIsmymCujakpsabqLBxNiqujYtJs8bFa6BHEFxUu8UjqqiFTlE+xrA7XyOEIklqVPBLtbDELrkoeBEbZj6mvIbgsdZhjY2PITCAdzY+dzAx/dXGaXYMvBBYX1OWU4yfsagUedqm19+iNCVVDTpp0mg0ECqQpR2UbiCkZlLM6eQN5jMw9T1EY4PaCwgG50qqWtBpDdAiYKyk1eoQcJw9tcG8rphWC4bDI1YH69RlhbUlzs7IZCvC1FVU+ljvMMGDkqSNxrJxGRvuiqWS2VtaWRoV8Ut1Fz5yroJ3CBlT3ZwzuEWN0JLFrEA3UqTwBGMwzmNsjZQOa11MJHPR9uiMIMTuIFrHe1lVVUBscLjleYeKyoCABe8gQJY1yNIUG2PhSNPI5VJK0siSJTNEIpMkDg+9XwoHoiKxNjGdLq7D5aBQKiSxGeiDQOkI6XdLGHsQnkRrtErxIaqdvA+kSrDS67K+vkojb1IUc1QjJ2+ltFsdzmysYqRguDfH+kAjjZbzNEkRDcjbDXKdI5QikZpUaLwFZwONVLDaapMlEiX/1QQU+MZNW1g6DHAOVEyYyzdX2d5YIQTP5GhCMTR0VlbIpQTjkQksM2He2JiK5WeCc7BwzEZzPvCD389i7x4/d/0VyDN6TU2vlbOYz3jxs0/wL3/tt7hRz+m0+6z1Wjz4yCU+/VP/gL/y8x9H373Ll2//c2YHu3RW19BSoLQGGS2njVYeMZPL4S3EGkcIga8Mtr/K7v0htqjR3QYnigw56LOxfYpXnn+BdHPAK3u3eUC9kw+98zE+/3u/waQo+PQffZ4XnOc/+Ys/zFZ/hTODAePxkOeefYbvfvQtEQ2iFSfJWnY4xmc5STNjcTCi3OhFK6fxqG4eazk8KEXroYu0+g0Il3j0/e/lhb1beGspCYxGUz56fptRMeHp49vc+pVdXri5QwgSvdrnn/7i/8bBaMaLLz9Pe2MFaxxr22cIOuMDH3g/q2ur1K/d46XbN3nm85/jyes3OH3nKr/9yd8lpDGYp93tceVbHuWRRx8GCePZgi+/coPJZMatvQOCh/lswu3yOkejCd/5X/4Ek5UuTzz3Nc5tDVjXHXp5zsrDD6NvPM1kf8xnfvNXOZqVrPXbKDNjOF2w9chb2Oxu88znfofv/dhH+YH/7EdxQXCx0eZDH/kQt16+RrAJo8N9LJ5PffFLHN/do8Lx2DvezbV7R3zh5m36K33293cZrPWQBG68fpXH3vEBCIGiqrk/X3C21eLLX36K/eNb/I2/9tcZpOvs7I546e5VBv0enzs6oH/6Ml+9fotGf42Xrl3lnW97LN5rpAY0rWZjyQY7WRi80Y8pjaWoS7yLykUtBc45DvZ30MqzeeoCedZ8s9ZeDu+dqWMSqK1YTCdM5zOKYk49n1CZgmA9RlToSiGkhlaDrbPnWRnE/Y+WMtoXhSAVko3e4P/RcBG8qYh680V/3cL8/9UR/0+xNxAIOqHV6cTG9hv39G/O8f8BQwl0UCRKQjAYH6VpMngCCXLplfInDf7g4w1Ge4SI0z9YRnf6ZZRfnF/hsRivMT5u6nEnk6F4o1Y+ILwl4JEiIPOEdp4ipabQhtorrAmQglAKLSR1iJ5LaUGlCbU1JKJNI2mB8GRKkWlH2spIVUK/mTAZDhnPpgShyJRAyxh/qqXGhviarLXLdBvFeF5FHkSSEpwjEeoNRYwPgsoHgnccz8ooXXcepTVuORU8iW8NAJ5l88JHBkHIydKE4KINTQAqRG5CkAERHAqHd1HFdePODmur6yxCQqWbXL70MCubG9TWsdLro7I2iTgiMnslSsllMRknSZlYJnC4WIB4IZdFb7yKHI/nPPXcKxwMhwhlscbHxCPr4jDHS4KDEHS05aDQLuCqktE0NqHK5WagOKo4qhxyXvMHTzzFx77rg+hU4zB4AUJFbpYIkUex1DuRComra2wwWGdJls0x4y2o2FgUKsH72GSITaDlzVVAWMK466rGlHMESyaPjVyfE/mmCx7vIFlKMr0XeBt/U5JCp9Xk4SuXObW5yUtXr7Iodzm3dQZszWJas7V1htJZyrqm325QVoHbd+/y0JUrbKz2WV/ZoN9qc9Bokqc5aXaas6fPUZrAyqDP3u4haZoyLAQv3LyD9wk6y6Jdjph0t7IyIATBC1dfZ25rrLc8++oN8izj7KkNvvbiVRbWM5pOUAJS3eDOziHSKxIRE08WxYx2q8GsmLKxsopzc4Ly1KYiz/o8eOkSi9rgTYmSKXna4HAyxIeCVtZkrd/h1GYfgSZJNGDxvqYuZrjegERHbzMOxtMpnVaHNElgOcknOLzz4Ay+rpiNRjSyBI1EEpuCwjsSPOFkekpsJomlFtcSltOJ+L0TD7cQsTjUUi6bTfExZz1Cx0avrRxpmpHnGeudVbZObXLlypXI2bj2Kq1Oi1NbK6ytdpnNavbu38L5EpWmnFo/y/7wBjvHBzAcMug0SaSidgsOrYFXU6wTTIsFs2rK9noX7zTtRpNzp87w4rXX2dnfxdiaK5cuk6Rtpotj7t29jatqTO3JdMrYT8h0ikozpnZMsVjg6wprKrQMNFtdrt24zWRu2d7eYmN1FV9PcX5BZWBew+HokE4jI6krZgUEoVFakuqUZtZAZh5jHKtrG6Qyp15MmdUTLMSUTRNB98fDEaUzpDJGUmutsS422Y31tBotWq0mOpFMZ3PsnbssyhJQNBodVjorrPU2efmVV6N1Gkmn2We4f4zKm7g56JBy/uxZLj9wGS0lB6Mhr927yWRqUCFBkVCXnpt3bpEmmiTJOdy/yt37d5lOFwzHRUyNbGe0Om1Weh1arSYidezs7pBmOTrvEzycPnMJ7aCq5gwnh7hKkcgWhZ+TakmiFM0kR9SBXm9AI28xnI4xztGUmtHBlCRRNNpdEp1QFAW99gohSNrNHlvr28x9zanVEUHNyXUjriMTmGWC0hUUsyk6lXjpmIzv0UgyWq0WRTnHOocwWTRVBBWZAV5jfEGSOtrtLqkKTCfHEApEKEnT89GiYwfU1pHlHolCC6hcoNloxHtqpplMJ7TGLc6eOkVdz6n7ffZ37iDTlIEVPHD+DIIGzzz7ZExHkgHnIyRXJgKVBWpX4ucBmcS13GjkODyNVoO7d/doNJrkrRRbR17UbDrFO4sxhtm8INEwnVcUxYIkazA7rmk0mnR6bcYHU3SS0Gm3CSwYjyYxATPP6LYabPT6qDRjd3c/DrUcdHsDLlw8xeG9QxaTOWurm5TFAp1kvHrzNvfvH9JoZlHdlOfUtWGxKJjOS/CORpbQarU4Hh7QaTVR3RVSJKlT5KnCKUGiNFJlCJ2g0wyEW9Y0gFdIYQna4X1GmkdFWCoC7XYXmXhCsNTWLBWY0QYUlkoTQiDTCc1WRuI87VaHrNmIk1xnkSI2b3wQeBHtAt7LaDMQHs8J18kiM4UycQwnEdGibj1SKerSYY1CakGaJiQiQQiJ8zEFDuchCEyw4DxFXSECKCGoCKRpBoBZAoL9182khQjLwl4hnFgiwUOEKwcVLUNSYOuaREiMLzBuyvFsPzYoc8npvqaczqhtwBoPIaqne+0BwsX1ECpPrQLjeQn2Hqttw/5in8qkaO1IkgrnJGtrW5SLijzPCLXBCcNGZ52k26SwjmJRczzcZ3R8hJKGNJO0VtpgY4PcE+2DAo13UFsfB3LeRJuZdUwWUza6HZSKapVAhGFborXeOocQRHaRicmkwdZL5VAkqJbFgkUZm+M+BJTWeJEgbKwjgzyxocX3vygK0jwjhECz1WA6m0XLtI7sPSkTgnPxawXImJIaCEveiEGnWaxfJXFdS4E1NuJ2l8pfQdwweuIGUi0ft0LE2lQnhDRByxiIEc/F2FRTSRLZSwFSrVFZTreV8MDFTdZWelRG4FVgOve0u5ozW+v0Wh0KU2NsQjFa0G4mKJ8idUA2FO08J5dNTraPAoHQwFLJl2b/+kTaN7adMUY2DrLTWHuYsmZxPGXv+JBJv+TCW86QdmKgzIn95Bt+HghCorRi9Z2P8D2Z5Hf+p5+nv7nF8dE+Ng28ev1rfOJ/GTLZPebqzdcYViXSzfjaV58hlZ7f+8qX+csBdodHjBYTWg0wRYG0DqSidekM7csnEeRLvYJ4s5kEMZWxearPn/nuj7LYG9HrNqK6IQRUknDx0nk+//STfP6ZaxzNx7SaCbe/+hWOJnNCM+VffOb3ePyx9/K9//5HmBcl4c4dfukP/4AXb7yO+fUuH3z/+1i9cHrJzhFce/o5XhsOWVsfoLMGajKKwq80J23FBvRiMuHGtddIgqRz6SzT0ZDPPPlF7h7sEft0jizN+aMnvshXrr5C//TDvP19H+T7/8JfpK4tQlX89N/7SS6+872cOfMAlavJG11+4D/681w4d47+8ZikduhGRn004qnnXuBw0OC53/pd9KBNb2OblbTPd3z7+1l74AztNKWuS4pqykMPnKed5Nx86vNoqZByyfVxjidefY5sd8AjFx+I57iALEu59MA2wUGFojgaUTvDfjGhlcb97Dve9lZ67Q2++Ee/RShqPvmrP8dP/K2fYtFu8ouf+HlWVvp0zpxj9+aCs4MVfuhj38fTn/8Sdw+P+NU//H26zVX+0l/9q3zi4z9Nt9Pm1kuv0hoc8cnU8rlnv8rf+5v/BVvrPQobeOnmdZL1bcyi4Lf+4PPU5ph3P/4R8t6j7Owc8amXn0W8fMi3v+cilVrl5375V/iHb32YTOScX+lwevAQSnyjuic2AeOhhaKZNt5QCsU9UkBKzWR6yOamY7lzYlEZDvbvU452cYspznsmiwnFfMGkiMEb3UYDkoRmZ4V2c4XGRo9Go02n36Pb7ZKq5MQf9+YKW379xpr7ui/EG4/HhlIIJ6lpf1JT6d/thlNYNo+FECgB3ZVBdBn+u8BQEsKjpSRYjdPEyQyAj2k/iNiVlwiElzgX4c94i/cxajN204grEUAKRIgqIdVSuFxQFVPK+kRZFKN+CRaBwgVBo9UkTQOlcaS5JLGCZqqRRNuYkiCkx3tJI2uhJOQyZaWzQo4la6U0dE6nIUhabUSQzBYj7ncaHE4WKJURBTsVMgi09EtQpcQ7ibUR5ls4j1AaQYqQMVY5IFBKYu1JkSUiG0lEtZMMahm/ahHCROghnqX3K6phiB5e421UgSGQwWNDjAz1Qi79puAJqCCZzOYoJdgZjjiqDAfTKeevPBTT6qSgrC3eKRAaL2V8TnzkRQWJE5rga4y1y2lSVDG5EAjOM68KRpMJtq7ImtFmhhMIR4QQSheXpCcqkoRiNpnx0suvcGPnHtY4zp7ehEabsR1TeUcwNdPdA7781Zf48OOPRaaTUEvGjsDWBuEdVBJbGbRKkRKCcTgb0LkCAc64WEiLBKliIRu5XMtJk/fIIBA+TnhF8IgQi/egYpqWX8bdxmJdsb9zn7y1QEiN0B6TJrE48I5Os8FDFy9HSLbQbK5tsJjMUNtdjg/HvEXDxsYaw+ERrbzJ/fs7ZA2Jq2rWBj1Wel1SpUhlQlkVnN3aZNDvcvbMeXyoOHXqFNfv3uLUxgoPX3mEl19+lbzdpN1tsbc7RSiFc5ZsmWCwsT5ga3uNojLcun2X127dYmN9jUwlbK2tcPnSFVpZTmkds9GI/5u8N422NLvL+357eKcznztPdWvs7uqqHtStodWSGkFLIJBQGAxhinGMDbGXvRQCtiFZXsmyQ+JkxWZYBhsCDrEAYyRIIiNQJAUQoBG1uqXu6q7uquoabt2683DG97zT3jsf9qmSBMbLy2m+xOdDrV7VVffeOsP77v/zf57f02wE7O5npKMB7U7CTCshlIaTKwtc2RmyYr34Fseaer1JnmfUdUC71UCHDkSbySgnCZW3tCNpJjHNtRMURc6tOzeZVCmlrSjynGZSZzAqWFSCTqtDmmbYqiKU3uWYSUFmKybjknrUIdIBoQ6obImtLFppKg9CQ0nrhSOpsH7liVBqeqhT3h0nBVL5IaooCpoNzxRQWsM0UiCFFxbT8ZjXP/4wC0sLKKCsJggjadcTxmkf21DowDEaHrNx8wapKGk35ymM8qBWoZA6xrmIRqNGlofk2YTt7U3mF1ao12fIsoytrT3a9VlKpSmrY6I4oh5rwrjL/efOk44nxInnwx0eH3B7e98zdqRAS2i2WhwebBIoRf94n2IqYBuXoqQiTce4qmJpZobZzjIHx8c0aoo8TUnHY4QzFA5AeiiqC+mlGVFYI1AJk3zMyI4ZDPeo3IRIaZz0z5cONCJQbO0cogmwftLyW2yU575Yi5O+vCAdjShtQUv4YoMkjsDCxq0bHBz3ORwMOO4fkY4FLAgO7hx59oaUPPzIw5xeO4l2AlsVnDlxmsNej1iHtJImWMGtm9e5eWtErdbi1vImW3euIKRiNB6Rowi1JBIR3U4LpSRZVrC7s8O1a1fJTMogHTHbbbG7fYMyg9xNCLQiEgmNZkISxahAo6VlprtCrz/gxPo6g0GPNEtZml9lcX6eKy9e4eDwiMloglSQVyVVWhAsKG7e2mJSTDCmQCpD6So2traQMiGIOggcJ0+c5fqrl3BI0mHPN+MVFVGYUFqFNi2cjgiVb4FLJ32CyCC1QztHFJTcf/Y+RsM1Nm5f9gv1QJFmXgxsNLpIHaKEpigsQZURRRGDwZBABRSiYHd7i7nODJiCTtxCLVomk5T97X32lxZYXVlle2uF7Z1tolhTWjt97SHE0W62aLWXqddCdnf3CWsBzlXo0EM95+Y6LCwssLOzQ70RMOo5wihECEFRZKRjQ5aXRJEijgLAsLg4y+LKIge1Iz94WzcdaC2tVoPxOOP46JBIBTQaswgrCIOIST7EWUu30yUgpswqTq6f5sUXX0SFMTMzXfrDjCBQxGGNMEroD4ckcUS9mVCWFdZZ5sQCw71bHB0dE+g6iYqQ1rtXlYoQwvn7b1Vgiszfh43xgonx99WiyJH4bWplLDmCQAiMxcfZ1V0mo/EdcdZR6QqDI440oQ6RriAOA4T0wObgLgRcWN+yVpYYASoIqAWCKNZYIQjjyLuLSrwzzVTTa6V39vqPryTSIbUkJAxjkiQhCCMfDZK+odY5i7ABJSVhEDKZDGnHM4AhqdeI4tjHloMIKQFhsVaBMzhRkZUlyklU4HA6pKpK0vEIU1aM8h7F4Jik1mO9W1gAACAASURBVOXw8A5Gp1RlhpIaWwkGoxQhtqh356gcNJMWoQgwxQCBJVKOQGk67Q6U0Go46lHJQtxlY+uYoBaTRAGBytDkuLIgadYJsGztvkSztcapmVP0bl5jMj7kqD8hyyYEgaIbLzCpLDEgXIlUIYGWaAEBYF2Ftf6/lVC+KGR6/1FSoZR31gopPcMT4d1Dxng3deWLGIwxtJKYypYgJVmWQ1VgnPU15RJc5SgtSOmHojwvCeIEnETpabzRGKRSTCYTLyy46ZJFenQDQtyLs+GmqzNbYUxFo9bAAoFUpHmKEpJxXiCV9C5z6RsMbeXvs1ppXGUYlYX/bxw6iYlrNQ+vtt7Z47UF4Qs3KksQBWgdgBAsLCyxvLRCnISUJidKuiwvS86f7TLbaCGQTJSmPdckdpbZboeAAISPWGupp5GWv4CHnH5d9ZWilJ9zdKCYPTlPMlvHFv5c7t1gburE514LHOKu2wlsWtE8McfNT32ez27cot1qc+HcBVx5xBeef56gvsyF+86zsbNNWlX88I/+CB/6Zz/JBw82GJqcj/3OR/nYxmWqSUaGptGcRUlJdjhAWwjmm3/mnyG+3FMOkUa5kjNPvo7J5j73mqSEwEmYX1+jXY8ZvTpAhwkf+fjHcWnOUAqwmpWVk8Q6YOu5S3TvO83NP3wWUavRbsZc2dyie/kyX3Nq1b/mxrK9t8sfXbpEJSyhavE3/9r34AR4g7hv8D7q9XnuYMCdZz7HzKk1jm9t8sL2JlUc4QaGVqNFIUrGWclKc5Ef/N7v4E0Pn2fc6/OR9/8qX9i+Tq8oOHvuDP3lWZ7/5B/x+JNv4z1f/zSzzRaT7V3Ceo1wbpbXdd/Ju7/wLP9i6xpKaL7hW7+Np9/8Vto65syZNUbWC8o3jobkpeWpxx7n8isvMzCFX3Bbz/wUGBZOn8KMDfHhFo8++A5s5hnCK1pxcm2Rb/srf4Xf/+hH+PyXnsEBWQmlivjSJz8FBrqdOfbGhtuv7vDh3/sE29du8Mv/+69w30PneO9f/QF6r15h69WX6Y2OyW3JXGsWcsnMTEB8co2Hzj3KH2/fIpuMOf/kkzz46JP843/w37HW7ZLmOe96+t387Pt/lQtPvY0L95/nw//Hh9g7uM75869jbzgiakb86F//ATa2R/z+xz/At33/DxDkub+OWTdd5d41ud3dzH6l+875ayEBd6lEQliEkMzMLPhUgY6mriTBOC+5dvMWey9/AbIMpwTN1SXas0usNzo0OjN0kyZo5Ze7QYgOQqQUqCmi5O579asU2ylb6J4XSfDl9/X0jzM97/67tZU/HYt77R9CiCmn7TX+und/9qlYprSiWW/4S9JrfH18zQWler3jN0uVF45sWSDxLSNOOISLfB27wLtWnPCOkGm9ZSnlFMrnOT8aQEiE8+pjEIQsLswQSsHmTsH+MKesrGcRSsDgt3ONFm9581uIbcnx8Ji93h7FMKM0OTiDFNPGJwytVofTJ+c57o/oDUY04oil2S6j8Zj5ThOtLWHsD++4koXlWfZHBeNRAU5gjPYRPSqk00RCUVgwlaGcZBz2egQ6miqg/od0flWCdblP30wt8NYWOB2DcygHlTM4fCa/rDL/5rCWssim+f3MHyzD6Cts9BYrPTlfWI+3FE5ihSCvLFpJ0nGKsZbjfo9B75h6ow5S4aqCtEixSsN0j1lWjjwr/deqLFVRYI3P/QuHFwWF5yLESrDejDgUBWUoUK7ybVrWIDFUrvS27MoLa8pZDg8OePHlq/QnGdYJJoXxVcPGc7Eq6XBCcOmlqzRiydn1ZUBhJN7FVVqczcnzHGMgjBOsjHAmn24IHQaLKSYEU2C7U8qn7oS3jluMr1O2xgMtnUNS4mxFbiHRNazzm1YdgBYRlTCYUcrG7iETHTHXrRHHCqVCIPSRQSloNRJWlpapiorJZIBSZ/niK1/kDa9/hEfW1pmpxaiwxtLSHDfvbHFt4xYPX3iQKIzY2TtkY2cHpODw6JD5uRW6zRkmZUpvf4OZWkgpLbOthNXZLlc2NhilGQjfEmEqx86gx9kz65w/t06rHlFaR5n16R/3CEXFw/ed4qELFwh1DScL+uMxt9JDZjtNJpMZukWL/nDE6aV54lqTpeVlru9fpt1ssrSYkNRjBv0RtSgiHfQIGjVmWl3KouT4YMjOUY+k1aZT6xAqL7DoULO2skppLEEosS7AOVhbWwXrt7BRGJJVBThLIDXNeox1JUdHxwyGKUmSYGPBKB0RCA/UC5X0zX1S33Mq3o3HlqUhUAHWSu9Sc0xZX5b9vX1CpYm1B+zbaQOcjiNKa5jttJmf61AZn7ePIk2ajlhbbXHr1iGj8SEvXd1mfJTRz8dUVcV8a57JcMLFc/dTb7UYjifc2dpiPD5kkk5IopjMwDAdIXVAFCWcWltjf3+PmdlZrly/SulKmt11zp08S+94QGU86yBJamR7Ewo3IdSaBMXO/g7LKqDeaJBNMm8rLwXjrGLiBrQbdeouR6km125tEMaavDA4U1KrxSgdMBwMCOKYMBbEOkEQQpjQ6x8jVUhl4XA88HQ26Qdfays/tCDIsgzrfINeEPrXYzS0yMhvtYWGoqxQSt3bWCFC6u0GMoCj4z2OjrfJS+fZdVqytLTE0f6QUWYIipyVlRVOrZ8kUCFxIMgnJd1Og+WZOQrTQhrFwsoC+3ubuCDi5EOPey6HbvE1b3uKP/n8l7i5e4h2FatzC5w7eYJynCJRbNy6w/b+PklDsDA3w3hwTGZSGvECFx58iNm5ee5c2wEtSZKYQEOzFeNsDeH2sFbRiOvUdcC5k2uUacXJc6c57h/6uLAocJRsXH+JPBswyS1Hh0c055oM85xmp+kBvKMJQS5wLmVwtEskHLo+Q2glpSgoqoLhYIi1Bilh1O/h6gHGTnCmwBS+ZUspQV6MGI172LxBkQJVk6PdAbn1g+tsd5akVqff6/vYuJBkecbc3Bz942NM7Af8m9dvcP7iGRTQqjfBjbmz2+eF569w+n7D+toqo0EPgSV3vnjCWUtYawIBWXpMPokocr8QqFzJZFKwsrZAFPh4xWg48U1TgJy6OvJJgUDQakbMzzTojy21WkyrFiNsRdLw98siLxiPvfs4LyustQzTCVHQJwz9AOpchTElYRghRUCjFbByYoVOd4bOTJdarcHM7Dw7uwc4WxDHMSfWTrC0skg6HnB0tDd1CFlUqKirmH5VUqYljVZEicMagXZuChgucEAYRIgpW8hYQ2V8w5nQEluUiApU5BdxQkm0VFjrzyfG+vOBM+CspSxzpCm522rr3S8OpSsmk9S3zzqHlNO/awyumsblUEiLj6JOnQk6UJRGEWm/BJNSU1UFsQZNgZYGLT170jdKKr+sEgIrpnXt1mBshURSjyKiSFBWvh01jCKssxR5htaeWQU+Nm+NI9QhOtQkcYBRIXEc0+/1UKFmtD8in2R0FppkuyXtVodGJKjSDKSmN55w1N8gPDpktrtMd2aJYW+HND0icBXD4R2SeockiVhY6CJVTlFVtOMu6s6BH/ZdQpoO0DIlLyRlXjIeD3FWcX1rk51R4Vs/7ZCqlHRnlojjmFZjxvurXDG9mfiIYlEZrDOESlNJh8emS7RWyMILds4mvllNK5yz9EdDBoOUsixp1mpTBlVFaSoaUey/hpgyR6xFWEte5kSJ9lFF6UVKh6DKcybFhE6SUNmKMJCYKkepmKosKMvCN55Z7wkzjnuLF+scVF5c9MU2pY+S6tC3dpqSalIilaKsCpJafTr7C3/G0xEznTkvVFYFwkqSRpO0f0SS1NAoHCXmXqtcRhDGhEGAEBYVhAjro3rzJ5aptdoIIQllwfJ8mxMrszTr4T18USgls2GMWAwIg+jL/KN78+WX4x5/5vFVrVH/YY97MNyvmNmEUoStOkEz8fPIV4BvHV488kPetKl5+vO6msaMC3a/+Conn3iM6MZ1vvu97+F3P/RbBLUZnn7qaZ44/wBi8yafGfS52JjjVw6POf+ObyGNbvD5Z19ke++AsnK0kjpmMuFge5tJllMP4z/jrTAV3iWo5NS17ZessqWJZxtwOIKZpneqCQhWljh34izNSy8yCgz7Rz2cFRS24KmH38KP/vD7MJOU3v/5O/zm70m++fVv5VvPLbHxueexi7OcaDdx06SCVYoLr3sdy48/ytWP/z7H7QXW1ld92ZKQuMqnTob9lHE94s7mFpcPd5lfXEE1O7zxjU8y2NmmtXaKz37031ClKT/yt/4mj73hUdLPvMTwVIO6Fnz2+ZeojOH5F77A7OwszTDgTQ8/woyV2OGYZGmerNejt7PD7Kl1DvIcOzdLvVB8z7d8Gydnuz42KiS2FDzTn3Br75CnT51icHzAKxuvkoQx2vg0TYAAUfK2r/t6/u9f+9fc3ttEC8lYVkyu3OKjH/ggc2dPUKsnHBwc0AxDGuun+Np3fzvZ3g4f+Y1fxVSW5vIsa1/zFv7WE2/n537yn/Dq1atIKowo+P6vfzuvLHX5pRsvksiKxbkap9cWeeTim5gYS7vdQqYjJkHE6uOP8NTjj7I/6vEN7/kufvmXf50gkHz+mS/x4Ouf4Fwt5nc//GFe3duiHrT5w2de4PR9p7mwNsvjZ0/zprOwFFe88ew6b1tdJVIRRlh/DQmDr9JYvhJW7a3+03f31JEGEusqojihZpo44XH3EmjXYsI44c7RGDs64uz5Ezz62Jvozp0g1BFB6Jlrd6nL98g/wt37pnc/418VYL2XSvjTj7t/6d/+f//tj79Yd9JfhJgEd/W1u34sRxBYwjBh2pABr6Go9JoLSvedfWhK8vfbOQPgwJQVFVPOjAOFH/zKKchQUPka6uk/3N7NJluDcBbjHNZYFFAPFYEraLdCNu7scXPzGOO89dpMZdKluRm6sSAQIQuzJznjTvpWo94Rx71DxuMRlDnWlcRJncXWLIvdOoNhm6IUtOIGo2GGdSWVkygrEcIihaZbq9NpJvSGOZQl1lgqIXDGAw/RFTZ3jI8O+PylF8jSCYhwug3yjUGCylf+mhIhpwwgHUxt5w7pydFgrI9nGbzd3p8qETiKssKUY4IwwFbFl5tgmL5X/FPv8/1lhZSSLB9xcLBFmuconxvE2IIy0ygdMUxHtMIAbS2q8pu0Ik/pDQa+6ngSkecjAu1jblFlCMWUWTWFjFdCU8kERMXW7iH1m6+yONf1bXjOUUYVhfH/Dq1CdJIQxhGkE6rK+MFZ+g2Th456vpETgs9/6WU69RYLyx46LlWAjg3VxAPgxnmOGfSQWnvRrapI8ggpFVWWoQONChWlM1R2av13FVIJjM2ZjMcUVY4U/uJXlmN00CAtPKvLOqiQBJGid+OASW9AmmeMbR9VNOg2FTpMKKqKQGcoIbBSEmpQOqEjl5mbW+LpJ98IhaPdjInq6xz2jmk1Gxz1RjSSDmVectxPuXL7NqWpSMIa28db3Nre4OBwRKe7yJUbt5BSMRiM+MyffI6iNOhQIid+2I9rNc9McI6syLm1eZXz5x7gygsvEQKnTixwav0s6XhCmudMJjmh9sDVdrPD5u4246JAGMFcq8aJ5RXipEVSi1mc7/D8S5c4sfYA/aMUrTRFlnPpxhVOLS3RqC8wrnIQ/nW0RYVNDJUwiKnVPYpC0uGQ8WiMsZaiSpFKcntjk6rIWV9bJYliyqpEaS+ahlFJGMfUGg1QmtI4RoOUZtMLUkEYfPnQdteZJCRxGFFY32bjD1CVtykLgbN+YGq3Gl6YFRXWlkgdUVl/AVMaz5QQGlflDAcjDg53mV2YITMTRv1jer1DqhTG42OiKGLn+JDXP3ARJSMCHTDMUzSK+0+eoDszw60729y4lXM0OCSs1cmHIy6crXH9+jFSQxBIhIVseEwgK+KgwdZgn9JVXL9xAyUEzVqI0pL7z17gpZde4uB4H0uByVOkBmO84D2ZVAhSVhdWmJ+f5/nnnqFyltRkVFQ0axGu8PGDOGxSGahFTbQOKftHFE5QlSlKhYRSIFVAOilRsSLSisJUFJXAWU0SGEw1bb7JPD9PaYUUDmMlZVbhypyF+QV2D3aJw4BOfZYEQTnuMczGNLunGI4nxMKwdbvPeDQhCjVaK86dP8fVl68w321z4uQSWVGQjca0opjD3phAQDmpMDYniprY8Zit/jGUPlbUOzhkYWaGo71dFhZnadabbO0f0ev3CKOY1RNLVMWYhe4cIxVTmQFlIbh9/TI1LQiiCCc1aytLxFogVEB/OEGjyfsjWs0G59bPUNMxeVQhwghlKk6tr3Hj1lUCHdBodJhfO8nNVzeZm19mmB9j8pJINOgkDeY7M2zv7VOWlq3tDTqNBrWgRW94xERWJEGEDjRxqMjHI6SqyAsfQdFopPXOyrx06ECyu3tIMRpy5oHHGfUKjvYPWT5zkmvXrhFFNcaDYwyGUXpMFCWE0xhMnMTkZYmKA45HffrHA5xQnFxcph41SNotXr62zYvPP8/62hqNMCGbjBDW+WHdZ2goc2jUa7TbXU6dDBmnE67fuEmn1aLbnmFzY5fbG3c8dLP0LaVFXhJISMKQWhIQx5owUDQbAb1eymynQa0W4wpLFEXUT3TY29nidrbDaDghlIIwSKhKmEwynDNk4zHSwtrqCnEtxhVQJglZPiEdTeh0F9i4tUlVltRrviAjiJS/fkw0taRGFBiEc1gMJ06cZqEo0UiMmfhzinaUzi8lbGUoSj/g47ygZJ0D4xvZirwgURJVi5GVIWkGHmosFMKa6VkBcN5lYvFMGjcFdAtnkTrwbpWipFELKSr/eXOmpHIOYSoqYyjzAidyrIgIVYQS0zNYkXuHFxWuhMJWuAJymxFVFaoWY6eNcm56vTRFQZkXnr1iDdZZXz9vK5TwDEwprReulEQIRZxEoKSHAFcGa3wDW1BlBFJiRUUQxggZ+LNCJei0u8iZeayT1BpdoqiJyjOSdoPCQm84IgoVZZUyHB/Rbc0ySocw8Ry0/lEJtQ6BiJjrxgzSMQfHE+woJa5BvVGjO7tCWZVeuFQxWdojl4qyHLO7XyKPxjx2/jFq9S4mbLC/vYMWmiSMyScjJlmOCcFUAicjZms18n0oiwKMoxDOg7NNjjQGTTWlTgZklSXNc4qymrK0HEe9I/qDHkmoCZSi3fCOS+e807cymXcTTePZhbFI66Yu65LRqIcU2jcsAqYoqaQgDCXFcIwSEOi78cIpw8l5AdcZ38QlrC/NsdYQRiGj8ZgwiTBVRafRYZiN/dlNaaTWaB2SFSUiCEmSxDOcrHfF+cifJIhjqqJCyhChfCyyNJY4rqFVgBPOA8TzAgRESYQMvAMoDOpoKdGJ+CoRKJ7GAEUY3Pu9eyPkV86VU8Xnq0a21yDu8VVsFvHlIdW/NnIaNfFcqLu/73lod8/LfqkJEtIeot3izDuf5PqHP4pbPEV3boH7zl6k+8BjvOWtb2Z5vsu3PvV1/Juf/zk+t7PH0//Fj/Fd3/JOJoeHRPUGdz71u9yq1ag3G0z6I/Zu36Z33KNz/oyXr6zFSokVFqUkAokpSlSopjxU37YsOw3M7T3kbMtf65zD9lJ6O8cUhZmmJ0AriZABVX/IydVlijJn9Pq3cv9owPJD90NkeeSdb2Nnc4urL1+FdoPF1WUQjsX7T7MUaJbGJVVzxv88zvjnUDqMNQxLxXsvnufshYf5jasv0Ww3OO0W6HQiZu57A+987E08vTLLJ//4Mzz04FmUMTQuniKpB+SdhHe98xuIVhb54m9+kGeuXeb+lRXazSZ/+PHfY3Zphoefeiu/8PO/yFu/9ikGZcnp7/4O/snpVT7y0jXOLC3458I3KBGrgPOr67TqLfb2bvL+3/5V9oaGo6uvMspHWGHI8zEzrQ6f/pVf5Q8/+vsc3jrB+IEH+OKv/QpPnHiSK8WE2y/ssHFtk/1xStGa5zve9U28++l38L/+5M/QfPhBgsOC0dFtZlzAb33sQ1y5fp3hoI/Wljc+dpHoqEfpJFnm6E8qDrb2WezEPHvpCzhp+ablb2R8vMe3f8+30rUdfuof/U+snz1Bd/0ih/uHzC/MsXRylde/8c185EMfYmPjJt/1bd/M8djQWllhxwgu1jsoLdFS8I43PY7WjkpHFEVBZS1Da0kQDIqMlUbzK0Nlft7HIaYOTaYFRw4P/UeDMmOkqwM1rDEoIVhaWCSoNxhmx2yNRuwcbFNvdBlVByS1WeIkmcbyhTenTNnLdwWkfxeY+t5H9Ct+/Y/rIZhmTVHC+uvsX8Dz8JoLSkEUof3dwnN1pk4ZcF70QWCkRFmvNQo1FZCsj3ABUxu1ByTj/AHKOIuwXlE31ZBi1KMWR4zHGRt3jjHSYgoP4yqd4Nz6MqNBDykgHToIYpI4YXVphfWVNdJszM2rLzJOJwxGA164co3+cIQtS0ZVxfzXzDBxkrg2S+ZKKqmQDoK4gQ5TJBprHGVVQeVz7DqOiaM2/bRHoGOuXL3C5u6Rr653XhARsSKpRUyGY4z17hnwlb9aWJzwzQfSWb/ZcA5pDYkOadTriLsVrNOwqkETaElZFpSupMJ5+yXKx9DuNUoI7/dyjhAvMIHD5Rm2LCFyWFuSpkOiVoAVFqkN2jowgkZdkaYlWguUi8lMQWkMh4MhURB5QaYsuLaxxZ2+36DXNfSHYz7/zGW+5V1fS72ZIKyG0GJs6lvXhKNeTzg53yWwGa65zMzyWQ6PR9y8/iWM91chrKJwFlcart3a4NGL57FCIWWEkBPuLoHCQNOsNwiDhEkxxlWlV9KVIFO+plUq39ajhMQKM23kcVgCwlqNWpKg8LD1qqgRRAlWhFT7KVIHKGswIiHrDcFmlHlGFCgaiabbbiNVQhh5OJybvn+XZru8cP0Gb3n8CSIRsLwwj62kbyBzBXGsaDdaPHT//SzOxnRbLYSOOX/yNNdFSRAGjCaz/N4nP0mnu8BhltPLUzQpi7Nz7PdHCGc4eeIkt802eVp4W/H2IXMzTbLJkCu9AVvbu1w8dY5HTp7CSXjm0gtcvP8B4oYicAkSiYoqAhVyZ2eT4bDP8uIysciII0kcR1gneejcWeYabUJhmZtJfMTDwtJKgztbL3PfuRqEkjsH26wutciqto9fKeGHBSTGZLTDiEYckJWWJAkwlSFZmkUpQaQjtFLT2mGLsQIpFXEcE08rPrEQRRFSOpi6kJywHkzrKqY6MwbLJCsInCJyEluViAiyPOPm9Q1CIUlHA3Kp0UFEVRVY9L1IZaQiqiJHOIMOArTWzC7MYqVjNMlptRXjUUoqNMopHnv0UY73S0ajAxYXzvD8K9cwZsLi/Bw6itnfO6LXP6SShkBG5MMhC7Oz9PoDhpMhuSwIhYeX6qjO51/4Ak888gZmOx22DvdodeYw5QAhS8ZjS6Bhpt3h1u1bqEhS2tBz4YKKRFnKXGEmliiqUdqME6fXGI0zbm/dpkwrDtOKKNJ0u3OMsiFVaZmdWWQwGtBszjAejjFK+2ZMrSicobQQo6fxicIfGqRG2pSqLHEqJKxpolhR5iVYsFKgYsn6iSXqtTaHB9vMtCLOnztN7yBi2NuhN+4TN2MW1x7ihU9/BCliwgSSUFE5w8H+HVrNGkWaoSqLsAIVSEbZhHRScuLCWZRQRGGNfDLg6pWXOLO+zNVL14k+VWd1fZUrr9zi3MnTnLvvLJNhDkrTqDd47I0Pc3i4ypUXnyeUGuWsdw5IKMoJOwd7FBPB0sJpkiRGOh9LefXaSwxHOc3WDPMrDTq1JQJlqMWOuNPlytwyW6/cQqNxmeX+R95AGCjKUcr6xfspqjlOrizQbDVQKvKMFBWzvXeHdLKLFSHrJ05x58Z1krgDRcWJhVXWTqyzuXmFwmRU1ZgiK3AmJC9LD2KN21hjGE8UqIDu7BK7O9cY5yWbm3d8/MgZRCA42N1HhTDf7BAFMTOdDofHB96FVhXktuD27W0effwhVFh6l5YKqEUaaeGBiw9y9dIlyCcclf7+oFRAPWkSxQFlVpHKIS6XFMYRTIsaxr0eg+NjSlNQFBkY37YVKgiVIooiBI5eb4wwhjBKuO/0PLGG8WDApJ9i6o40P6J/lFJMCoQ16KRGFGmaUUI+mZAkAXokcSiatZh2lFBgSZUkHU7IJxlpf0BhLM1mi/n5LnmaoqSPQFtbMRqlFGVxr3m2tCW28rb9zKTUkmDaIBTgD2/eRaGmZRp3rQxewNEEgYLS85KcE6TGO/mkUogoIBL+a4Pn6WAE2lYIFXpGEAIxfS6z0jOX8txgc+++tKV3BVMZbGUIE7+NNB55hBbK92IK72Ym0CgjEFoSlIIJBhsIUNbHiIUHP0eRj2DdbZ+rjAMXUKZDBBVCax/rm8711hiq3DfEltPa+cp4l02A8FERIVFBPBVNHMZmtJIWZZVTWUstahIFDZRRJKGGvOTE8hlmlubY2b5JJWIG4xGDUUo7MaS2hDCkFoZIWzBI97F2Qq0Wk1ewtLzA7OwiWtcx88tsb+2ig5BheYibhDSiGo9dOEGe7jM720ArzWA4oRE2aTbrZHmKKQpCIYijmFhp2t0288tL9A+OSLMcaT0wPQwCciMxVYUOYqSpkJUhqoW0Wh0OD45AjVDCuzIbaobjwwFz7Yi8ctQCMZ0JfMlEVRmCaZubnULOXVVRlhmuqnCBPxBVZU4QBBRliQxC9o8OCcOEbJziXEo6FkgdorVnJPnSCAvGTbEAmkCBrLwAEeqEIAkxgx6NRgMlBUHil6WVE6gkRAUCUwmQkiBQ/nvHEUL497VUwp/pnEWo0I+iUqBlMF3AgVLhdPEzLQyfRmPvPr68V/9TI+JrxAT5c2u4/xyUyj3gLRVK6ukf8A3OtjKYUY5KQoSWoKSPw3I3fqNwgzHbX7jK2rvfQvfMKt/8g9+Lk5JGPeGbVr4dJyVxUsdpRWf5JCuNJpubV/nOd7+XxbkOutPkn/6Nv8Hzy/8qUwAAIABJREFUTpAIxfbtTRr1Nrd3t/n19/8Lvvs7/1POvP51OCFgGm9/5Usvs1hrsfbERcyoQtVDfJW7hTBANBJEP4VOjESh5pqcfedbmHnlObIyZGFhkVdfuoSIQz738iX6g5Rup070NY/ydWXJ/uWXeP8HfwedxJQWcpPSQ/D2Rpv2TMOLgELQfd15PycKgWJaiCK9mPro686hyoytdsz+zi7bk4LF1RPEpeYdzVUebHcplk9hl3YIlIe/u0igpebGpat89uY1/up3fRd/sLlFphxXbmzwv/zcT6GSJkElkP/sZ3nm8suYwPDQmUd56pufJgkj3m2hPymZrXsemlSedbbcaLCQxHzi1WtcevYGaTagGOwhMAhrKEtBmk/49LPPEJYlCw+8nkdkjd96+Q4/9gNP8fKnPsvO8QaVrvjOb/pG3vvub6fdSOjImP7BDv/jf/mjXH/1mJ/76X/AB/75zxDMLqLjOUbpLVxecbij6I/GLNYjXv/AaVRecfXKq7xy40XW3/w0Dz72Bu4MhmytnkRe7fPbn/u/EEpysLnD3Pxp8qLk+HjIfCDpVoYvfvYZ2t2Ix86eZTg5ZnF1ibPrD9BJ6jjrucCDLGUz7XNhYRmCgAAILEysIdGBNwndTYT5tz0KgRPaFwzkEwaHu+RFxe7ODn/0Bx/n0s2XefObHue7v/X7qNfnAMvwaIfRoM/SQoO3v/1JnGgy6KX88gf+JT/2w/81rvLoGmscSqjpveX/++f9z/2s///scZcPFQiHnL5ur7W49poLSs3Eu5OE8cO7EH6rhvCxKyEUoXToUHIvtygdttS+xQxLNWUMCSzW+o1JIPBDI5ZJWVJmBcZAlNQJAkmZSW8fN4bW/CwrnRlKM0AKgS1z7CSlNx5TOUsQxgRSQGnRTlFrdFg9uUZyOOSgf4w4HnLjaEBCwXCUETUCFBYhI/IsZ+9gyPZef9oYJlGhxBjD8soi3bpmNAkwSjIcOorCQxaNtHTm53j84gUageMP/uiTjPIKiWNpaZH7z55mbqaNsBpbWcCLQa4qccof2owzvkFlihh0zm8ycZY891WyzoGwgkB5+3MpBepuHSO+YriYjBlnKcppxlnOKE2J44TSjckmA4LuEgES50q00ORVjnURoZjQbtQppxHGwWCA1hZjMjY3D7h6fYN+P6coLUJAbByg6Y1G3Li9xcMXzpMXI0JXx1WeNSGlJqnVWVg+QdSeo7nyIKMiprT7LK3mbG2+4rkr+C2aCRU7hyMya1Ha4lzhW2OkIpQKyhJTFsgwQitJZT14PdEhMrK4ytcVCxeDqICAynnIuf8WEmEVQkmM81FDja99tlIjhCOzE0KjqAK4ub+PQZCIOp3W3fpoD5wX+JroQCvqUcwTjzxMpxWBFUhZ83wsKYlUQJY6Oo0uj1+sU2ZDtA4JoxDZTChWl+l0WqS5oTvT5szqaVaXFrhya56D/S2kDbi1s0cShmztbjPJPOvFYqnXY1YXm2xsDxHOMakqXtzcJOnWWOyukdmQGxu3WF1eRFQCqRJcVaClZWV+jm67TrM5S6IFcdTAIIlkQGFhYXmO3vYRkQ5wRAhZUhY1NnYPmOncImqepMhzbA5VbgmE9iIb082ddDgjUChCaQmsQqoIGwjiUIOT3s2oIxwaJwQqCIni2r3Nn5JTV5JzCKkwzvjPPAKcwOHuNeAYa1G2wrm7MHbfQNRoN2jWajTaLfKsIC/964JSlFWJDANiHWNNgRaRHySzispWuEjQbteRqiCQIUopsmLCbGeW2VaTQf+YS6+8yJ2tOywvL9JsxBwcjXnhxWcJwoCK0m/2RcLu/pC9/R6PPvAgt7bvUG8r7j97P8eDglu3t7m1fYwUR6yfOMVCu8PGnZeYmVmiyCTNKOHChQvsH+9SWIOoQMYWk0/bFyWsrqxyfDRgZXGNTqPOzt5t7uwqCuOvYTLW3NreoREmiESR2ZRGXKfUhW+ZJGRU+tiDJvQsJ2soSoGpQOkK6woa9RZCRIxGfYSswAVkaUHcjNDKt+5s7x1x/swcKyvrLC+vcbC7xY1Xb3Lcywhlg/2bt6id9lyu5fU2Wjh27oyozSdsHhzyUKtOKRU3b99hbnEZcNgs59zpVYJAsL11wMLaCVqtgMHBhMsvXuX0ffdhcAyGA7Ksx+mzZ5FO+Rurs4RRSJwk2JYlqftGnK29ynsJRERGg+1DaMocY3KoNJevXOVo2KffP+b8hfMMByVbW/uUsxMWZueIo4gkjlk9t86ffPrTGOuXJa1Gje07O+xt7/H42x73In0aEcUhyJB6omhcaBIEkt0tx30P3MflFz9H0hTIQNNZWOSgd8zjj70RZRyipnjxhWcIdEhWFuhAg6pwwlCmBTrOCeMJ21ubrJ1YoNntcP3GFebmZtnZ2WRmaZHVk0tUmWN75zbNWpNIaSIdIeqSsjJMsop+b8zlFy5z4aE1Jk7TbrfZub3PyTPrJPUGg0nFWDgf1XYgJMRxCJSkxRhnC2QSMUoznnzsInd2B7xy7RqTLEdrh6284zcQ2lduO0m93qDTqhEngrJIKYuS7TuHVAgCIp544glevnyDQZbTH4wRTtJtz6KVpp7ELM7PcDzsYwXUGwnjccrlyy9hbEEtqGOykriRMNPtMJmkrC6vcdA/5vjgwLtotZ4yA6FWS9Clt+ojBbHDX8+xhJOAIktxoZqKrHfnT0kkQxw+FmGmziJrrW/jtJaqFMhQE0m/ZLBVxXg4oChK72pCeOeksX6B4QRJKKZcHE1WVlhKhJOEoV8mSWshDAmcplKFbyW14ISjuutktpYkSphI3/QmrMNKca+6PRBA4VCJh6cKKRDCkJcZWZr5ljJnsbbEVAZbGHQUUBQGDQgZTq/Hlso677qaOp2QIFXghX98dboz3sUulSWpJV7MT2pgK0ITE0URZVWQW9BK04okp8+eoT07y80btzk62CautQnbCrufMRwWrK112Nx8mZmFeZxt0W7OIpRi77BHWYZ+ECwskY6xSjATLxAimZlt0qrXSFoPMtuY4fL1z1JrCmqteRwSU0GJxuVjcHdb7DSh8826la2gLHFISlNhKoMS2rMnwwgjPBtQ4qvlTWlwzmJdhSlymrUQrS3WlZSFwwbS8ypL7wBFaco0525rqXUlVVFOYyZegDLWICq/VKwqC9Y3TxZZiiTAOQGUGOto1OtoNIUpvSDonGdV4aPiOIeKIsrSIHVALUl8s6rwMGLj/MLIOTtlfwqUVFRlTqPbRTlw0oKK0dIijCBIIqwQlA4irQgChbACqSKcu0df9cPjV8w+XxnS+PcaBu8OvP+ej/+QAdNNWVI+Mm+mfiiBkhIXakxhkVpRVV9+bu5GCT75/3yKTzzzWf7+e96CDjXNuElV+TN83Gp5MU74z01zsU3NSf7VB/81b7v4OBcefoA/+Ls/wq+9+iprJ9Z57vJzNOI2VkHz1Cku9wo+c+km6w9dZHTpFb505Rq/9+yzfO2738NZHXDlQ5/j7JsfQTVCsBXIwD/XtZjqyg3EYw+w+ekv8Mwff4azX/8Ubzh3jjsCHn74dfzzF68gheSgnPChD/wm//kP/WVKBMoKnv2dT7P2lid4sDNDVxt+4/3/ks+/8gIPP3KB9kyLu2kodMCXeU3+FyO8w1UqONgb8YHnXiCcWeAb3/UNfO973oFWkt5eD3C033iRtc89C4CpKoK4xuZvf4hf/MKfMJKSn/iff8IvIyvLyAk2to55+zc8RVSO+PgnPsIP/fDf4RP/6n/jzA/eTxwGKB1w8sQqehoJlsrHhYtJTv/omJXFBRYnhnPn7uPFvV0CJzCTCYgKTUImHKsrCzy/vccf//qv8Tu/9Au878f/LiaUvPW/+iG2/8FP0GtKlmc6nF9fZ1SkhKrGd7zvb3Pu7H286dEmtdEu/8Mv/VNGuxv8tz/7D/mZ9/1tvu8f/jjf/653UotrvHDtCp96/nk2fvODSCR5lfOX/7P7WXYRP/XT/z0//b6/xyc//Qx/+MmPIYShl5W88uIlls+u8vf+/j/iF375F9jWn2B/Z5cqmufnP/ZhNp+9yjve/gbe99fWSVot7n7KLt+5w0Pn7uO5lzb5x7/2i9y/NM/KqVX++jf/JaycfrDuKbzePDLKMj79uU/xpWdeYOvOFvv9IVZIlEn5+v/k3Tx97hFeeuaP+Ds//t9QT+o44RgOMmxZMegKpPk4e+OKJ7/2vXzdu76Pj3zuk3zLW5/CGf3V2LLX4PEfg5jkwN87cL7EKAj/Qr7Pay4oPffcl1AaQumbspScgo2ERIvQR76kP2nGShEARiqsloRCEThHgXcxSOFwUnjLoXJIBQGKPBuQ5RnWTUUP6R01CkeGYyaJGI97aGlA+kpX5yxxILEEICxpmjIpU4wE6RyBVKyuLHNm/RT98Yi5bp3nnnmOncMhRji6zRbtWoIWcPnGLUZTDoRwd11FEqVDgsARKCioMMIgpMaUhocfeZDlpVlmmg1EmdOK6mSTMUiYa3c4s7pAqByR9nZBX+Pqq+qliiidRckIV45BSSpj8WHBCqQirNWQKkCIHAKBEiHF1D4qpm4nh8Waiv29I6rCokOB8fRPQNDvDxmOxkyyI0pXeSEM/zMkAYxS/PZKArYgVFBLAqrKcnxwxNHhyDvJnEQ4TRRohqMUay1ffOkqzVaLxZlZ8jyjyivKrCAIfCPN2ulTLEcL3NyecHy0j0wCvu6df4nPfvJjXLn6aeQ0LoAV9MYjBsM+ndllL4hL50UuMiosgXOUpgTjcBZsZbCBIkwSJoMR1ua+ltL6GxcixJjM/x1RIkTiD8HWIIxDA7Yokc5QlRnE/y977x1s6XmQef7e8KWTbu6co9RKVrYky7KMZSNjHBZjMMksAwO1tQvM1AJm2d2Z3YGBYShghjCsvePBYExyBDlKtmVZlhVbsYM63U63++Z7T/7CG/aP97RkQrE1tYI/pvbrqu66VafPuXW+c97wvM/zexr4yjC5dQo9P8Pq3CVWh4bq1DmmZzaTaoXAYhCgJVZKZJRSS2MiNJXwr3AtdBQce1ESqo5x4LUKoDnAS08sJc6V7Nu5k/UhPP7i0/zAlnexrTXG5kaNrz/1EmlWQ2vYuXUrTy4cxytJHCmiVHHqwnn6/T7TUxP0+z3K0nLm/DkO7DjAnVddw/MvvwROhoFaeJSS6CQiH1jmFxYoN1qu2rUXKXRoZCPYtB2KSI2+3lHYQB7YvhutFWcvnaYxuMRgMKSyILSk21unMdZCK4l2jqEz2NgR6dCQZ5VH2hIhg3vRIkatex4nPMZDaQzeW4ocEhXhlQii6is+82BsD6cOEDhpBBu/INyXqgwQew/lsKQ11iKSwWGUJgnWKqyriLTHEBp3sI5GmlJWFQJPltUojaQ5Xqff67Le9/S6GdONhLd891upj9Xo9D2ra6cZ2AFal0y0UsZak8wvrSG0orA5kY5wMsNWgqLM8Vpz5Mx58CXTG/bw8suXObT3IM3dEySNBnmvS1ENuHDpDNZ66ukEmzdN0Wn3OHL0KEUOvcpQKMd01ERVPYrcoLRCySDyfOuJb7Jn1x6UbKBlHedyfOVG+X9N0mqRxhE3XnUdp2fnyXsnEVFFWYGWOsD1SwdOQjYar5BIoSmKHlv27uHCxQt0252Q0JWSSGcIKdFRSj6smF/o0l09QpH3efnYefK8oHKGWHvQkEYpJ48fY9v2LUxvrHHu+AL7Du7g0uU17nzDHfhhwXw+hzQxVXdID0FzrMXE9DTzi+s88fTT7Du4C9OxrCz32L1nB2sLS9z3vvfx1c9/kRtufR1CDFhdqejnOUuri5QnDP1+h7Pnz9PpLNCspzQmt2NtiDAN8h5vfMv9XD56hFZzAxfmZ3nqyGEmx6a47tA1tOpNBu1LdIeWdNtm0iRhbXWZx575CssryzQmY1YXHFkcYcqcp594mp17dpJEdWxh0Tql6A8x5TqT0xuJG3VuPHg1SzPbyG0bqQw7dl2LjiwLpxfYffUhvv71b9Acr7F6ZoFeJ8cCkUqxlUMYiaGHpiRSCbgSYxa4vDAkyRT1RotLFy+TTUQ4o0Iz3nCNOEkhjRjYAXWVkkQRUaxo1iNsOeDSfJukLlEWbrrmKjq9NrftvBWpI8bHJ1jqLmIINepppLC+YH19HlxONrmZ1kST5fUOZy6c5+ixC9SyBO8txorQ1OY8zgcXhvGe5W6P0pbMqDHyvCJViqzhuXh2GSc1Z84/zMTkNE4oxlqT2JpBCcsgHzIx1aI1XiOtK87NLdDvd3F48qpiZWmVDj2aWRpEbaUpi5xaIyXN08DXqwzOSnAKW1iqwlCWwdXlhUXY0KpG6fC2opFGRMIHSDNQGUdeFbT7XZSQwTHpPQIHWKqiQmpDK26MFn1XijQMY80GZeXAh2i29QrnDL5eo+r3Ri0tKsyLwoa6dBUgza4yAdKNYFg5wITmXAR4iYpV2ChhGPRziqrCVCWRjChMHyU0eVEgjaWRxGF89T7MqUrgvAx17DpsOrzXgWUoDYKCSOgQJfcVlbMoEYUWRTxGENYdziBtjigLlAzMQWvkiPtkaK8uEMcJF84fp9Uax1ZjiMrjyoKsniCFZzBc59yZ4+ikDpWl2RyjqkJEJ45TZuqTWC9pNBtkUY12X6NkgfSaLIlojo1T5TlOaLyyTM1MUav6HD99lKy+l0g3aJ8+Q3rTDGnWZNif59L58+y95hasqTBFgXIWISXWO2SsMcKFlrahAQ3eGbT3GFdibYG1ESYwh0mFBq1QkSKJg/hWWYWOEwa9tYBRcFmAo4twPwdFRWkMjbEYYwsSEVOagqochM9kJOAKZcR5LAYrwBV9hDcIYyiGJXEkMcUQJwWTm3YQJUnY0FtI4oR8GOD8XkqCmT7E04rhgCSrYUX4/Kk0wZQVKk5J6q1Rc1gQTVASrRRaK4qiHzhixiFE4E1FiUKp0NgqkpiuleRJwuTmaaJm42/sLf7Gtu/bYdL/lNc/sPeUAsTIOSFHddzOO2ZnT3PsyEmK/oD9V+3nqkMHwxgLdO2QxnyXL/zRRzjaaPCJj36G7/3Au3HWEsVREK59kKY6p86xdP48X/yzT/HsygV+6P3vJ0sUL33jq/z7x17kM5/9FL/wy7+MdKFJVzk4tG0/73v397Fv9xjKeY6+8BIff+QhrrntJm659RDCS/bfdBAhNSFAorEmCIIqiagSxfoXvkFy3S7KyZTZoy9ydmWdp44c4SsPPkSlwQ4EzSTm5QurCKHIl1fwLua7/pf/AaTEtbs89anPcnxljbHmFP1+jyIv0JFE6eBiCfVofoS1EiiCCBoJTVQ5NoqYfXfexn//nreF+B2SDVumiHTE8twFvnjqJJMXF8EYjpw6zL/51V+nH2lMdxVd05SDAqlilFdMjddJBsucO38aY0q++cXPcn51mZkdWxBCMVifJ2pMIqXGK0FZFiRRgq4lTCYbWM9Lxmrj/Mhdb6a6agfPf/1bfOL/+k2MT+gYQzNp8NjDj5DVUqxUtKZq/MFv/SZ//H//HjMTW5k9c5HxTVM0xjaSZjUMoHXEO26+OTQ/Dh1/9uUHyCvDvgMbkCeO0V1eZPapb/KrjzxIPDbOU089xnre48Ybr+PypSVuef3dnNeG3//oh9i0qc6zJ4/wJ5/8BJOTMywuzZJFig37d6Fm9vH4gx/nI7/yyzzwyS/wrasO8p7vey/XTE/x6O6TdM5f4mf/42/z1u96N9dtmeC6zfv43PMvceuBgzx34jle/13v5uLjX+Wm199Pb9CjVRsxtuQrwTPwguXOkJPHznHk6Gk6VRXWtkKwa2uNe2/azf/xH/+Ua2+8nt6Tz7M0v451Ai8qUmVZXBXMr1wmn4z53h17abbGOLY0yfn1Ifum0m8nj/1/vr6dW/TfqrB0xQ3JiEUcKYsQr7n0A/wjCErtdhs5im7hQ97ej5rJhFChIpQRAs77QN0RYuRKgkSF5i28CwsjAYzUebxEOwWiGo3eCi8MRAoKifMeIRTG5Fyav8jYxBT1SCE0KBFYMgHCLEjSNPBSbKhnXVpbJvWeOI7Jso0sry0xsXUrWVxnUPTotfssrC6TD9q0ezm5t+HkQQa+khSe3OSsdz2lMWidIiJLLDwm0gza6/jpGlJnRErRaNRZavcDl0WGDXFYGBq89Vgp8VWBk4LC5CFn7csRaNsHRd9feZ8NY2nCjukxUuVZ6/RwskITRD2HwHmHJAgZ55cWuUJYks5gq5K03mQirZPVx6hTjPLtAqMd2ib4ShJFGXhP5BUVAu8cZSVYWesyt7BEVQ0AkF6Gxaqtg5IoIcgHQx578kXuuvN1SCmw3pK3BY0sJdYaHdVIaxPMzLRAeNIsQVcD3nDjHSxcOkG3v4y3JkyWpeXMuYvctXELeRmEt0jGeFnDqjxM6DrwmqwxwcIrPNInVMM1ilZFleeIJEWg8bZEGB+cTVKN3i8xOmlyFJUJwpCx2NLwzYe/RrvTY/vWcXZt2cj5S8tIDcvrXY6dPM511x4iTppIp4hHIKtExEQ+gkqACjX1TlpKEe6RsQaHIVKKstJY6UHYsEj1AuViJtIQO3jjjTegIsFCZ8gnH/o0461pGkmNy/NLbN1YUq9pBoMSqSN6/YJaGrNtxwzlsEczaxLFdV4+PcfZix/n3tvewO7du3n0ice49643UpQFOtJkOmL37j00JjPG6mMkcR2JQBqP0GLE9iJEHWU4X0qkxkpD5AW9tQ47D2zhLffcjTcVx06cYWp6nBvqV6Nl2DRkPqG0FU4oKgGJ1zgZxCDjRzGMkY1WKQXCQ+FQQpLGGusMGvXKCSm8SknwBLA23iNVOGlSMpwKhucNyH/n3aiZMghSxnmUFmgRY90oZuIJkFyhsFR4F1rKTO4pSoOSEVs3buZ1B2/EGYW3CuM8WhQ0Wylbtl/FwvIU/V7FWnuV1fVFBIZUZZQO2oOSVMU4G053o1qN/TsPYHHs2D3D/r3bOHl0lry/RL3eYGVpnaWVLlFcML90kaXFy6isyfTMDI3WDC8efRatwPdKUiHZsHGSsoCLly7jxBAVa7qDNmNZC5Va9u/fwt7NO7HG8vzRl+i2u2QT0zz/wnFuvu4avvXMAkoY4iSw7GwuME7Qt+BdhYpKGjWF9ob6WJ1Ll8/RXl8lSRKK3IILEcOiYylHEdZuYSmzQbg3XpNkCQd37WT7rm14p1g4v4DKMrZun+Txxx8nSTzveec7OfrsMbZt20h/vcPMxgbzl5dp9/v0qwKhJC+fusCTTx+m0xvy1FNP8r53vZMdm7exsLrGZKtB3m4zaBvuuPE2et0eXsUsnpplud3mpTOzNMZStmzbQdaWNJKE1912B089/iiLl5bYvWMntrdOf3WdF1dfQuqK2265nlrWDFYcDdfcdD3nTp6lrhWzLx/jhZPfYnLzAfZefR3LCyssrxyjWykee+I5WpN1Dlx/NVmjRulzXOFJmw3a7QLjK1IRIbUka8LLL8ziq4zFpVNMNZtMTAyxXEDFjl6/w7DwZLUJHJLeoENVGlpjGVlUp56k9AcLCKWZX1ni0uI5Gnor0+MpzcYYSSrAhHaiycmZ8OVRDmzFeq+NThOmJyfI65LcFHTKPhcurtHKImaXl4lTS2tsnKLTYW3tcmjfdBYtw/zf6XXRSUQa16jVJlhd7yIjxezcInlZMTndoF/k1Os1tm7Zwrnzl4hUaIutbBls+q6i011DK0urFtqKjINYR6y120RxjNQZ+IpNGzbRG7RBei4vLXJ5/hLbt22m3ooQ8wZrA3tlrDXFlulJpNIUeTWqUBesrC5TGkNZhDYs6y2hlhSqEXcvUgrnBZHWxCrDJQZTDUN0X3msAKEVTliiRNIaHxsVcrzqDLLGEacDuoMuOnGUpaQSPiyVVUJvMAhcSVNikTgbNnpVmRN7R12nWDyxUiAsReUZDAZgHTb4QJEIKlNhjcdVDl3LkDIiEiE+LKUmqWW4ITRqGaYyZDLGmBKVJfQ7HbwEw4h76QOIO4oUVRScBc4EVqZXFl8OMd5hXWgEkqMmMVvkdNptUh1T2tBSZ50jL0rGoji8t9JgXR8pwAlHvTZJWRbkZRsxFDRb49SbTcquxZnQmietp7fcZnymyXr3Mu1uh7IcIoRmx86DzM+eZeFim21bp4lkg1SF9ZWsZ9h+Tr/bIe8PKPpdlA8RQeciNk9v59LiChcuLxD5FI5HKOXpl2PIusLZEIdVQiI8WFPgcHjnQ7NRFFFrZBhb4VAIX5KoGlRixDAKsGslFLjADHXGYlwoNCgLA1bixSgiGIMiwyvJsCxYXFqmmr+IKSrGxlqM1Rpo5/DSI3Roc8OM3E4VRFFMkedESjIcDME5hsMO/TxHaZgQFfgKgQ4FZiqInjrS9HtdojgmNJS54ESKQlNuYCgpXGHI4owkjsFbKlOgtSYRMUZ5fOUwBlCauF5DG4OQCcJbBj6mH9fYvHUzV29qsXesQSvSyBEnyv+taEYQXl/dCPrR5P/qftC/8s9rozn97Wf5u21Mzjr6ec56e5726irnzl9k9uJFlpYWkKYkFZ52v+Br33yMXQd3ce1VB9ixcztX7z/ES088zPP9Nu97/w/w8nOHWVi9l5mJJliHMRUijhAoTLfP1772Nf567jiViHn4S1/kLz/yEeJkit/4td9gZtM45fI8Os1IpCTWkpmpGvv2TOCtQemYPds3MdXKmH3xOZbOvom9112NmVsl3r6BURVzaKvWGoFAbt/Gi48f4w9++4MsmTWiTPETP/bjJM7w2LlZuv0uWRazOL/Gj//YO+ldWGVs+zR4SWkMYIizmH0H9vLzW36aTn+dBz78Ed76/vdw4x13vHpfR86kV+6hDyNY5SqU9uweH2Pf7deTJsnI9+X51Kc/zY03vo6P//mf8cDhb3HidwueffpxevkajVqNojdACMmwk9NIUnrDAu8cF2dnmbswS3PDJAgFca1+AAAgAElEQVTH5ZUF+rbkwqkTuFtv548PP8dPvvltPHP4a1x7zZ2kSYrHXsmF0Iwi6nfcAFoFwUxJTt99H9985AtoBlTeMz7WpKhK8sEQO7Q0WlPgHWvddaYmx7nn7ru49847AE+5uMaTLzzB9bffycPf/BZfmZ3j0vmLVK7g0a++xMWFjzDIpvmTj/0l9TTBSktS1yjhOfLMc9RqdZ76ypcYf/4wy3Pz/Pb//CH+xQd/kff9/E/z1Q9/Cj0OcVFjqtXn1376J/jZX/8Vjqy2eTQv+be/8q+4c9ceDr/wBD/wxtv5yy8/wU+85R6uP7CHxbUBZ+bn+Pn3fCfDYY8ffed3s7K4xBeWt3DzdC2sjQN14krSbXTvPLtmprjnDXfy9DNPU+ShbKCXF8Qb6vzpX32Zf/HBX2BrnPKXw4pHH/4azUyOuLqSUsBcf4nf/OC/48jROb7jDRt5w/YEqVJChuS1u/5bFZH+7hXWFQ5BLAWMHKf+NQkNvnr9IzCUwtOGjLRAEoCRUoRKVKUi8IHU7p3DeU8kxQjWLigYTQxKvrKI8lIgLYDDCodEY6vRRtELGlnMoB+2Kj6SGCMpnYc81GcKG6IxAo8WCi0UxobseyQFUknGaikaz7CsWGtfxIkqVJlaTy2RTO2cocinWVi6zNzSKjiLdBZkcFa40uNdkMqsVOAMG2amaC8uoaVifrlNa3qM+kSLZhST1WooFazgUjlKk4NOsKiRmyhcDoGzFcaUQIVVAmMrlFJ4K/Be4iqoxQmxjigtOJEg3BURKUQOhRTYwlGbHGdlMKQ50aQuJe3eOoNhH6kUU1kL3WxQtOeQOkaKCO9z8H2GxRAlDaWvgNA4Y63A5hXnzl5ifb0TgKGjDDxCIrWFvKJSFkHEytoqaSxoNcdZWFxEpYJhv0u79ER1z1TdhoYCIemuLbPUuUxNa67adxNPPvUlVKyxlcNEioWlNZBgjUN6TUGJlQLjLRaB8TFWBBiiUCMEpvBUlafb7ZHKBKlGTSfS47RA6QjpRydr0lNWQ/LKYFwPHUNRtkmSSTpr65xbWeHE3AWmazWiKMIUAis1x09dIo5rXH/9tUjp6FcldQRVMSSkqARKRAgvUMDAupFrT+Ns4I0pLxHEeCRKxvhIUyhPSsSGZnCzLC2u87mvPsgt11/H5rEp4mSSPzvzWZ4/dgpvPNYLpKmYmagzUZfs2r2Tl06epNHQTExuIVYJZWn40qOP8IPvfCtuww46xYBGOkZpKhSGmvJMtFo4IcNn0oMXgtKDVgIvJCZgznC+wskYpSOSJEVGLZKkyaaJCbzz7Ni2NVTMO0b8ERPEYB8hiBCyCNsfESIAglBAcKWZwNmSyplXuGxahEd5N9qw+NAcJEaQTpy7skYJIiwWbyqc1kQy8M+ccEgZXHtpkmAIzVBX4ixCCvAWOTI/OW8RSlIUOdY4GvUmOEcrGyfL6mGsskHgEj7EU44eeYmp6WkmZjbQSGKOvfgM4+NTNHdcRZUbxseaVCS8fPpZWjMTLMx3Ec6xcXqC/tCRO4HWKSuDDt3+eabHp9i2+xpcvJ2zsyfplRW7d29k49QuvvnVx/A1iXElihgvDQiPM57SGlTk2L9zL5iKLRvGaNRnmJmZZmysifOSVn2cLKvx3LGjrLQ7nD3TYcuGGQaDAWmUsTToYkrD2HgdqiFKwLBnydI6Wmm2TG+k0+uTpRm1JGN1bSHcB2nxKLwJrhWPRytJmkiE0KiaZnpmjF27dpBFMVMbppkYH0PIGGdyli4s8s9+7APYruXaQ1fhvKM7sLQmWmyccLy4cJJOZ8DW3Tt56cgLzGycojVe0O4P2LxtJydeeomV+WXuuvd2PvShj/L+H/lBqtIyGJacOjvLxMQYWifU0yaxSpm7cJkbbtjHdLaBjdNb2LJhB1rXqNfG+dajT3Lj9Qc5dfIc1xzYSyQlkR5HOMv02ARKxWzYMMPl5cukkwkzU1s4dOOtHH/6ORpRiD5NTm3lnvvv5tFPP8COrZvodQtKn1NJS7O5AecFy4vzjM3MsLR0iRePPk5/UBHFdVJnWW130MkyZtWjZI1NM5u5YdMWnnniCbrdNr70pHFKIjMmJye49eabmDs7z6XLT7GeW3Scs2/XFC8+9zKTMw3EasLtd91Ea2ycyhp6nXXOnDpHr+yzZetWdmzdxcULl4lkwkQ9R5KxutZjfrFH2lhAqZizp04xvmkjUihcGeYbgQjxDmlRUjAxsQXlPBcXlqiMozKGqekWeV4RRQovBMOyQCnJ+MQ4eT7EkhBpTzEMLXxS5hSmZDgomGiOsWFjnVbWol9ahHLgFXlZ0e30cD64EW2Rc/bMWeKaop5FZF4iRER7ZR5VDJAqNKpliSDSGfVaTF4WeGdp1DKkdSGq5R31rEZVmeDC0OBshRvFmdwVjooP8X6MJRYSW1T0253wfrgqnLA7haksVZnTyhQIibAGXAkqDuK9tVgXYkdKCFAhNqeVR1QGJwTKuVfcEN46mrU6RnikAO1H85+vKCpLOVQoKqQQKJGA15TWU1UFQkG/H8ScyjucKahKS+IqsAIVRaPfQ+KdoRwW5P1BWAsYjwVMVSFtRZqFx2on8KoRIl2xJKnVkEqQECNdECakcnhX4UbVz1lSG0UC61inmRirMVG/FydLFpa6mEHFcJjTrtZopg3q4xMcvPFWzpx8iXZvlfHJjfS7a5jhgMHKKnGmGeYlw6LPwtyQfqFJW012NnbR7g8obM5qexUhBuzcPgSRUOUNskYN05lDRBF5aVhcPoGKc/bvexOm3IxAU3hPZCvkCGgtnURJgZQSXxn6eYHDolyJd5ayrDAmHHkYX5LIaAQiBm8chTUhyu0dSIvzBq8FThBi/ZHASUuWpYxPbQFRUlUFRZ6zuLpKqjVTrTr4wCqKtGaYG/AKjMWWFQiHMYF3WVYF1aCgNjmOljE4gfMWhUS58BzOGnQUIRVIEaMihVkrkDUZmE3KUos1cRpjhUJJP3IgCaSKyashcTMijiPSNMaPylaWnUA3xpncMMkN2zeyd7JBQ4bTN+9HXCh5haB0ZSLn27Si0Q9/X5TtNROSrohZo9/gyqGV99jS8OLxWU68cBwl2nR7FavtFYaFIa1rmpNTbNi2hRtuu5lt01NU66scPfwsFxfmuDx7hgdnzzOdFJx55/dTO7ibf/VLv8x1Nx/kQ598gKefPMH9999GVRmUk3RenuP8i0c52b7EXz/7GIurq+R4lvslb3jTO/jgz/0Mu/Zuoio90dYdJGdPU0iJUjEKSaQEToQY5pb77uGfrS7z61/8NP/h936LXezlPf/jjzATr5NsaOFmT3HsM4/Tboyxd/t2drz9dbzln7+be374rTz2Xz7D+ZUOb3rjPdycC0597MP0UFgrmJ4Yp5GOoSdSIKBHYqUwnSHnDh+n1y65+l1v5LlPP8DYwRv45GMnOLWc8/a33kWjliJQrygSdtQ4JwmFPMJ6MqOYnmwCnrnVdf7wscdpX17nE0c/wX3vfi8/2trCZ//oo9TTBnfc/1a6Cxf4+oNfI9MxqU7IK7jm2hu48dDV/Oj99/PZz3yOp1YXOH38RQ5efYjiBcczRx/nwOU38hNvfgveWG6+6R6ePHKcc/1V3nfrG6icZbi6Rr9wbNw8HSKhznDNvfdyeW2Zrw1y9NgEelDS7a/iLYyPTZAmdQZVh7d/19uoup7Pf/5BHnvxBW574hHe+o7vo74+4KGPfYaPfeEBDh9+hv/zN/4zn//936PVTBnolMlWjFlZI5qepN9fJtIZNi9wOrh12oNVpILVfoe7vuN+/vOffJiF7mnuv2ovC3e/g51nn+GLX/407/6Xv8wfPvB5vudtt/PGA1dx8649zCQJa0XB5t2H2JAm/OJPHiLyJd4WHD53iZO9Lr/7Az/Kv/tff4p33/denruwwHV7X88VUTVgV15FqgAgwWGZaDWYSGIWLZhiQKYbzC4t8pu//W+hGme1u8bi8jqx8jQjQSE1WZbQzw17br2RjVPTvO7qrcyurbFjfHz0fXeM6j7+/+u/6gqSnwESKfFo1GsqzYXrtfc9VRYlFZUtsTi0kAivsB6kBOtNWDxJQIXFSDWCzgjvkITFlR85gIRywYwkFdKr4EQQwW3gsSg0cV3R7XVZLzzKCWwU4nLrwyFxpVA6GrU9iRBbGgWkiqrAiFA5Xw6HeB3UeZVGGKHAauxwyMCWdNYEOIevDImKyUWFlyNnhAsuq3KYI2uamk7om5LmxCStVoOlTg9rFZ2uIdE1kjRlw+ZpLlyco5cbBv2KsijRSuEwoRixMog4QJ21j5DGQR4WsEprnDPkviISCVIKsixh285tnFhYwesE40oMAuUlWIuSoKTjmr27OXL8ZQ4dPMj5s6dZaTtWlpdYuHyeeq1FVeVoVyAcOF8Q6fDhG/qShovCWke+apfvdPpcmFvG+mDvlSqIajhHWemw2C0lQnuKMmdhaZ0NE9M0soSpqdbopKygUhneSDwGrROyWpNIS4r+Goeu2c/RU4fJOwsYHaBii2vL5FWJ9Y4Yh3YCVzqUi8LCTlg8NkSifIIUYaLzvkJWEpvFaK9GG5sQV3C+Gum1GofHVI5ICio7pBo4RCGpaONdiak8kZZ0TEENBWgiaxlgOHPuLJu2TDM9s5miLMA6CtsnRjMAGirC2YpKSWzlSbxCj+q6K1eM/Huh8cXaCuE8kVEIHRoClYOoFvGOt76ZdrvLxblLXHNoE9devQ8RaWbPnUWjSSPFeCvh2v37aI1PcWmph2fA1du3cNs1V/P8sRPc8YbbcFXBvt1bKMoBCIUUBiESvBdI65Da40RoAXHSILAMSsiSBGcFUlm0jbEEtsNYo8mhgwfYuHEKZVUQ0VBY41BaYY0BJ7BO4nQ4sZdW4ERJJJPAL8LhZMj9eu/RUpEgELGjKgtU9OqkIhF4E1xloYw+tMj5KzZqRmwxJYmTKDA8fBBbpNSUVUWapoHZ4sCJUGXq/RU5K/xBeKRSxEmGj2KwULmSNGrQqI8jPOQ+tCYlzTprK2vsP3gVL589z5n5w7z5tts5eOAd5MOcsxcuc271EkIMmV98GWMGxGqcW153DWfPL/LwN55CKs3ePZuA/cxMb2bf3r2srq5S9HO2z2ympVOOnXyBo70hJ/VZdENxYnaWOI5wJZSpYSxTbJic5vTFUygtGa8lvO7au1hvr3L2whzOe7qdVVpjEywst1mcX2K1P2SsNc3Ve/bywtGj9Is+WkFNx4imRkpLPQ7gRaklLi+pKpg9t8DubVvZvH0LLz57mKIoqMohUo+j4ooq9wG8KxVjY01qacbq2jJJPaPT6/HIo99kst5i/8FdHLrmFvLc8ORTx5ia2UykNMNOm2E9o93uML1hEklBnCh27thGpz9kvd9mbEOLHZs2MT29gZMnTlL0Cl58/gS3v/UWTs/OoetNzs+eZDlpcmH+CEdPX6BWHyeTGVIJ+oM2zuacOn6KDXduZjCoWF/u0m6vUR/fwNJ6j288/hgzrZhjx+fZvKHFrt23IEULW3miRLBhciN5p40dlrzu1nu4PDfHiROH2bp1C6Wt2LJtmsc++wC33H0v+dCgVYIwQ3wliIQiijKmJmdY7/Y4d/Eiq+0B3kI9E1y17yaef+lFdDJFo76V+972Lo4eOUq3vY6Wih17DjAcDvDO0O/k7N6ym43jUyS7YNCxeFfRicfoD3q8593fydpqh5dPnuTrX3qYelZj9/49XH3zIbLmFC8deZaL504zPTHJju2bqfKKyYkaR559nhvvu5MXnznM3MVVxuuK544e5ZCrGJYDisoGmL4NnJskqjM1PYmrLBfPz9FuryGlJk1TkiSm0x0SJxmxjpneMMP4+Di79+zi9IkztDttojiIL8Z4BkNLIiWbNozT7RuOvHye17/+JvJco5KU9nqPfNjBOYP1FdaaIFgpS2UlupaQJimTE5Ns3jjDwvwCrXSM1XaXqhxSeUG+VLGyvMrUxDhxHLO4tIL1jnwwoLO+SlmW4UCHwEKKhHxFHMpqoT1NRSHu7HSMTDKSLEWpsNxybsRIdAZTKXxpwEKcJSBjCm8RzpE2W6jKUFgT5lEnMGaAYNTCZT1EgLdoofDK0x0MQ1TXQugpsGBL8tIgbEmaZmgEDol3jlgJykER1mXOh7FPgFcxqbaUVRVcowSXNyIcctSzLDAvhQ/Ndq7CGKgGCdicygiUivAukK6EVaH5zSmMC3OIdQXC5CgVHFvaR0RCE0capUqKqsNqL0Ga0BhWVZZK9RHCUa/VqMUZIviwQdaZbGxhemorbZmQp8ssXlrFiIostnSHXRZziTQJU7LixPNPIOo1VkyJVgnSxnQGgqyeMeyuMPA9slZGlZcYCcNqwObpXRS5x1QFzhmMqxhWBZERJF5iqorKC6ypkKZEOIcWCkmJ9zIgB7TByAhKQSxlcN9LQRRpsiQJQonz5CrHVS60cnmJEpqqMCivKSpDUQ1HNlyLlpJceEqbU5iITKng1MfjqnAg6bTD2JIkSRh2u3hl6Xb7RDoUtXjnMDbE1uM0wXhDnMR450mThNJVpFkWoOJaU6tl5JVBoKnVx3DWvBItd4BTEbqWYnNHs9akqjwDoZBpk2zjBG/ZuZldEy3S0RzunMdZRowu8Tc4KeKVv/g2Xenboyr+2x7w2l1XxCQ3ctldeY1hlfO5zz9AXyQ4+iASprZMcs93vpnNmzahkoT4SiRPEPAKG7chKFFH2kxv8iycH1DGTd546600VQQHHXOf/yozt9zN/W+9BdvvYy4u8MiffJYHTrzMy3OnWCgCt9V6z2Sjzne88a380P1vIWmv4s1mLv3F52g4w+7t29Bj46RJzI49u19dx4y4WPvf8d2898x5fvupb/Bzv/OrbNk4xsKxi2yeqrPyyNPYe2/lrj0b6J+4gPcOWxqUirjn7W8OjZETDTb+8Hv4/mee4XeOPIEtHSbSPHP4Jd7y5jcG56AWYBxeKcZ2bGUSxyf/1W/x6Re/xdu/53v4hXe/F2er8Jn3I8bu6P1VhO8KeLSPyRoZe/dvhW6PzmqbxBh+7m1v4b986hO88973cMv+PaS25HP/6Q9wjYgn/vqvGAxLpNCUBeg0YryecXn2BHTnOfz4IzQ37ERlirzq8cjXH2LT9Ca+8NDX+efv/yk+t/Qo33HNbSQ+4vZrr+G6ssJ4WO10+NLTz3LvrbejlAzMOR0x++QTfPrTf8Gm7Xv5rg/8FH/wv/0MSa1Jb3WVAT1M2kbEkkvnjvGmO/87Ls1e4Cd+9n/iuae/wdL8LKeOPMXp9gonj19m8tDV/Juf+5cIX7LY6SAzzYUzl1nvLpOmwV1aloZYCZQrqYajMhQHVVHw4AOfwhYFVJ4P/vt/za/+77/DLXt+kvf/4vfx43fcy0PFs/yH3//X3HP3uxgUBWQZE0lCphi1HvvAL3Oet9+4H9fvsulDH+b4V7+IuA/uu/UaQOE9aK3Dt9B/u9MnqLlSKGxV0S0KCmeDU9Z5mlnMenedZq1FmiXs2b+fS6eO0BtarPcMKwvCcvWOrcExawqqIg+pJ6/wI8wM4h8WQ4T4uy7Cf+ix4fv+j5+h/ad8rb/n1REelBYjDvNrP2q+5oJSoiMsgBUj1TlUnXoRgJSCK24hKEz48EVSYkaVds6XI2vh6I1HjjLZDj/6f7gQvUCIAJ/0nlYtpVsMKVDERmILBzoAdisigpkpDFLShyYD74LF03tJaU1oVxEK60L0yAlDFAWru1MKU1oSa8hiSXsYfssAnAwRYKkURD6ASHXEwvIKMktxnT7gqHxFZYZYH1Gv1Wm0xuiVa6yvdekNQgtHaoPQVZOKqhKYwmJE4A5UwoUGN+8QeGIRIYwBW4yaZiqcV+AqKueRMjSl4ME6zWQt4eLli+zbu4OZiQmee6kANBfnljh/eYlICQprObR/F2MRaCkpKovAoG3I4SuRhQEDj/UV8wvLtLttrHSvKp7e450kVqENyuNxvkBKyeWFOa7euw3nLPgQP4giHdrahELHQUzzUpI0x8mSGsOqw1UHb+Wpb30GlaW4Imfj1EZ8KbC2ovKesigw1oRGFeupqnAC7guLEy7wDoZBSIzr43hvqaoSRBAarSdwBvwIPoomSus4O0CVLsTtIsFgUFLXEqmv4KRCY4mhwgiPspLVzpCFlVVaEzNUpsQMh7hBiao5vHQ4X+Ccw2pACJzwOBeF+KePsD6wtxSSwofNBs4gSXHeYURFqmM2T0yRxRGtKGF+eYl9u3ex1mkzC0AQbzbNbCBJE4SX3HzzjbhqiMhzXFFy3f6rKYWit7qIsCYsLq1DWolwFkeJpAKvkDbARZ0bCTTCUiJABKs+yqFETEMkFJFn14aZwGHoVwjhSVUcFicusJCED5tbr6AiVJw7LbFCoK2AUWDDeRdOQqylqCrKqqIyBbGKkSpk8JHhZBUxOrkUbgTFda/Y45314T23Bin0aIQJfl0lVbiZQoKwYdxidMqv9KsVu0K84nwSSFAQqQSV1MLJs/OUWESsmDs7z/GTJ7n5+v2cOnuG/Xu2sWnrDiKRoXXCnh2KwaDH5fk5lPbUdYrWkt2bNpLGTXpbtnPp4hxbJqfoDUq8De6p1tgUW7ZspN8dcmm+oF+WYEpKU5BmMdNT4ywtdqg1EoyytFfatAenGY81WXOSQQ7GSXpDxYVLK0yM1ygLwemzZ2mOTaB0jZlWgxtuvoXMCxqNKZ588jFuvP0aLpx4mb6tcA4SmTEzMYMZEywvL9HudrBWMr+U0BifAgS+FIioAaYgnYjwlcd6SRynKKlYb3dQkWS9vUZpKmSkmGzBjp3bEMpy/uIczx9+iR/6wNspipJOt8c408QqwRYVDkdpIM+HdHsdevk6WaJ57tjLZP44k1MbeOjBL6IaGUdePMnF0xeJapLHnn0c7z1T4+NMTW+mVW9hh8GF1RifYny6RdnucfTFk8TyHIO8w113387Z2ctsnBkHmzOsVslaMZWzlMM2aZKFxkYUpiyZ2LiZ+blZ2otz2MKx+8BBykFOEteYPXaCN33nfUT1OlZ4BBWVsXgvsZXFlAVzS5fodHpInTExsYH+2gpaexbnz3HDra/n61/6K5qNjcydP0tvfZldew+wurjK3r076KytMb15E+tr66wur/KNh5/A+wHrqwN8ktLKFIOi4vDh57j1lptZXZ5kLc4Zm5zg+JmzrKx32Xv1VTQaLZYXLrO2skJvfcDF+fMcPHCING2i8dxx64186eFHGZYO1JCzZ4/THYTmU6UiPJaiyKkqqArP8sJFSjukPt5EixCpKI0giSPiNMV7WFtuMzkxwcrCKt67wHBRgub4GFMTWyiKDnMXlimloPQWlWiWVvrUJsY5fWqW6ekphqYiTiVVpRBO05gaI9KKQWkQUcKwsFRW0ZycorSOifEmu686wMLcHC7S1LKEfrdC4GjUx2g0m6S1Gp32OvWxiVFdscMRWD0rC4vUBfT7bTr9DnIokZWk0CX97gAzdCgVmlCdDQUbxcgBNegPySKBUwnKgRaa2AuMjJhfWQmjlPcoAnRHeI+phsTSo5RGKM2QwEizpRmtkzQKiLQaiQSaRJe4YZibkRKhLFY6hFe0xjIq7zEmOMYBrLU4UQawNwbl4zCv+wpjS4ampKgKpDcU1uOMwZgcVxgiqVD4sPZDIVTYhPd7OcmoOcxKj7MCU1lSrcJBggAf6cBhlIrmqB2wMCEmZt2AtFVH9MPax1UOa/o8+vUH6A4MRW4oLlX01s+wZ2PCeuKI9Tg68tTrLWw1xMuKoXPMTEzQnJzi8sIFBt11+kUvtPvqlPVhh7HmJFP1SS6vzNHMJpicblGWYJzCSoFSnriUCJnidRkA21GMUhrrKyoVEWcZ3huUqGOtQUuDx2JNST1NAw/Lg/GeQVmSFwVaKqqyojAl3pbEMiKKLVZoskRjvQ/MrtF44a3FuhJTGYQIh3ZSaERUR6mYlbW1cOCYZEip6Bd9htUAYRymzBlrTo4OAn04ZCFCENxHURpTCYOIInTpSeKITrdNlNWJohghIoglSkJVOHQSgfDESpNJDWgq3aBsTNCYnuLmrRvZM9EgemWNGNbxUgYH9N97fVsk5du8Sv/w9WpO7jW5gpD66s+1OOV73/NejHXkgwLrCprNJlKqVxyKdnSALQhOZ6UV05t2saO3Tqe/xPZtMUeeP0Ox2ieZahFduMDK7Dzf/3M/SDXIibMMt3mKu3/yh7jFOFxl+LM//AM+/vBDDI1ldXWVL3/hE3z1wU9w64Hr+LEf/jE233831af+nEPXX8fhp5+ltXcPvYVFEAJhLFQuNPc1M647dIDNR57ghYe/wdcuLfKNbz7C3Xddw8c++mk+9Jm/5PmHHmPfrbdinUPHceCpbhqj/+J5kjjF70i59zvfzF8cf4G5pKSztsywKEkyNSoKUHgNUS2hXhM89J8+wscOfwsTSb70uc9wavYEh26+m/vuuJXxsRbOVMTxq6DgUOggKd2AQW/AsZdPse26A1h5meMvH+cLzzzOem/ILYeu5tOPPcg9u6/nVG+Nn/65n+Ern/8yL7z0BLGK6Zgev/ZLH+SvHvoKLzx2GDWM+cAHfpK67fLRP/8oQiTUM8FgUBDXJ3jg4kXu2rOTB7/4Zb77nd/NcFiQZRFt59g0OcEH3n4fIFlebzMsSsZrGccW5+iris1T02ymCnuDKrhyrTeUAjKhmdm8m0O79nCkrinLgs9+6Ut8+E//mKJXUs+aLHWX+PH3/RLf+MQDdKKKiaGj27lEowXtng34DCCt18I84CzOhsZQ6wRFlRPHQZD1WcKTf/5FfnfHH/Gmt9zPo598iI/M7CXvrnPXu94BThCPChXyIqcsBmgdI9OIyCgKY4mU5uEzJ3nvtTfy8SNbOX/hFDt2HKQyFd45Tpw9T5Jq9m3fiXch+OYJRhBjA9uvMiW6suTWg0cis3wAACAASURBVOpR2owkUmgJKo15/Y03cOzxR1haXAIh2bx5jOuu38Udd72JLK0TRQmzC2vs3bAxMG2l4m8TlK60tH17W9t/jWDzTynuvGav9f+ioQvPKxiQK/9BCEuOxoeNLlb6wE19Da/XXFBy/w91bx5sW3bX933WsIczn3vufN889Xs9d6vVHRBCCIMchILBEEjZccUUValyBexKANsYBbtiEsc4FKlKKiZFGQyyiRHIBkQgAiGpUUODRE/qp+7Xw3v95nfn6Yx77zXlj7Vfd5uAQKihKuePHuqde945556z9lrf3/f7+UJsNtMKHWJDW5ASFQRSx4lWqLN7udZxWhcEidSIECc63lqklAglSLwkeIkXAi1rkLyuc+chVuxqAXY8wfpAq9Gm1c7xwpImCpwgrWFMEUgZBSIpdDzYSocLjmlRxPhTXXVozAxvXczEN5pADQFOA4lWiLqOXOsEgYsTq2KGtK0ociBopRmhPUBs7+AImJnn+vUdGs1DEqnI8gwlPJPJiN3tIYlWpCEgZOBQztBSRzaBE9gyMJvMECogg0MqsN6jvMBWDh8KhqMpNtg4bQyeYEGGGDVsNxLOnTnB4nyDQa/H4X6sRyZJoiNMgJExglIZgRMeZ6LlOfZm5kjho3dHiNg8MorV05W3LC7OkSLZ3TvEERBK1OwrTS4cVagIWrB+Z5dyVmCrCaNhgrAa7wMTW9LowNxgBeVBYCinU7SWjIdDTh85ynNJEyUcVigOpiMuX7uOSnIyDcI5ynKGqxxZo03pHbassJVDZykyOLwMlLZkcrjP1DgS1UAJTRUsEOHLWZoQgovMCQ/QBC+xdoojZb8csz2t4tTHyxibC/HzSpBoBKao2Ns9oDpeooLCmMiVmNkKOVX4rIJK4WYWIQ2VkDgzBbmC9zOM8Civ4uIpZJw0FlOs82SiW0cLFLby9JMm29UOlamw1nF0fonnLXVtM8zKEhMCTalJvMSHFOuL+Nn3lpktSBKNEKBVSggSpMcjaiC+jIwM6fEixCx5EEjvEczA1tNzkUTMbHAgBA2d453ES48IhmBis1AQ1NEwi7MGITUIQ5AB7WTcUIuADnf3krESWaFQguhMlBqdRqdL8JETEkIUfe5OE+8OMIKvOWwy2nMFIh7sfIwp1Fnb2kIf/60UYANBxPUFoWNW3hMjeSrGSa3zNYwVgguEINDR0kS7lfH4ux9mPDzE4ZHBk8hGdM+FGK90QjCY65C3TnLl6hVu3txgkPc5euIs/XZgOpnwwsvXuLaxw2h8m4fuexCp5rj40hfQSjMpK4oqUJUz0lyw1Fvi9vod0tRhrWFaONqdDsY6Tpy4l4NRwYsvX2T/8ACJYjjaQ0hHM81ifNF5pqN93vPuJ+j2egjnqEZjvuqJR3juD1/g2PEeWanYPdxl6ipuFus0W80oBmUZS/1F7r/vEbY2rjCZHGKFQCUCoQKilATp0FmGMRXT0uK8i86zJKfTbjHf73N4uM0fPvt5jhw5xrOfvcgTj99PM+3S6zaZW+yQ6S5lUVLZKaYqqIoSW1gmh1OMDYzDlHJmmJmCg9kI5xRKKbJSElLHYHWeg1FCqqHZ7jI5MBxfXmVlZZnxaMKt27fpNpa5cm2P7f09PvjX/yqX/vAizlhef+kF7nv8A9y59jLTYsrZkw9gzIQ87+IxhDq2I6RAecnK6nFGowNSDQvLa7zwuSexpePUI4+wtrjGaDSCLMEHSFOFmQW8nSF8BUXBkeUBcyvHef2115kbtLhx8w0GaYuzx1fIP/TX2Lx5k6KacuTESQ53dnnwwQfYvX2bJMs52NxHKzh6fMCN65u88cYNPIKF1NFqeO5/4GvIdYunP/t7NPs9lAocPbpEv9vm1o0bPP3ZT9EZzLO4eoSF5TV2bm/QbjYwRUmz32f7zgbnLpzk6554Ny++epnDwwOcTJkWMfrs65pxpQU7m5vsbG7RygU6bZK3Enqt6P4JQiCcZ3NnHxCMhhOMcbTbTdqNJoPBgIPRAdPgyXtdempAMAk7eztYJ1laGjCsLDdeu0qr3WE0LRnvj1ic75E3UhpzPTrNJlJIxmXJqDCsb2yRNZvML6zRbs1hTEkj79BdWAHhSXRKuwVlUaFUGvceWcr84iLO2bixrcdeeMd4axelAj7LUKJJs9EiRRBEFQ+Sy4usHT0aY/J1JPz6tSvs7+4i2y3sZIwpK0LQOO3Z2dtBN3Puuec8iW5EmHbwTKcTdrY2kYWmnI4ZVQ4nDCpPmYymBCHotjqoLMW7WErgnKOcTiPkHIkRYJ1AWQ0WvPSMx2Ud47vr/HDYqqIw0UnaauialeHxKjaNCV+39Ko01hBLSaUkpR+hgkGqNArxOsUHQyvP6c11IguP6GYOzhEKhfBV7fYWBOMRXmJdwJkZhbDMqoAUgd3dbYrNK3RbHVqNjFxnVKXBGkGz0WZ96w1aRkGAncIwnjg6cwrhLLPZjMqNSXSX8/e8i73tLapixmhvj+FwRN5JSJoZkjbHlnpUlcFQIUXG6XtOUh4egMzJswzvHEJYTHBgPcL7yEPSEqXi9NcZQ1nNkCFQOhfxf9MS5y0pEqNqq42KXM9ExL2tDYGQKPCgvYTgSEQU24RQiBBopBllYpBKIH1OYRSNAMKUCBcIFlwApCEEz63NfbJcs7a8SDEek+qcnb1dmnkD73y9R3EIEdCK6F+ruYNCalKdEIQmTRNwgryZkyYJVTmjkTfjflpp0jRFSUklBbS79BcXeezIIqd6bRIh6jifA68QSnwF/JK3uZPe9s8veRNxRP3lHOj+tKenlaTdyYH8rWci/vjXJaWkO7fA8bMPYqoZtixZXXsQkWgSpXCtDqdPPQAIkiDZ29mnGo9pN5ok3QaXfvkTvHjxixgqjCuRiccozdLxAR9/5ve5Mx7RqeDabILe7SB0gkiaiPn5+NR8wE8topnGkqF7L+CGJRd3bjFRBc/deZn27hn+1g/8A374H3yY7nKHI099hr/7j/8R3W4vruVZTuueVcS4wBWG/gfexwd/8WP83J2bqBIePH8CN66QrTgcDQG8t4zGU67s7mKagvsuPAojz29/+inO3Xsvv/fsU9z/4LvopSkLc/PR7VK71JyPLXdf/OKLfOS5p3mor3n8iQe5df0Sk1s3WN/e5bv/0ffzz//hh+mvzPMT3/mdfObaFTa3LxOUx1tLt9Xif/7xf8G4qDh5+gLf9L7389hj5/mXP/m/sz+eUM5mhEQSqPibf+u7+a6HH2W0u8Oxr/4arl16hYVjJ0mzjKwoGCPIG/G8N9fKkTsb/PT/9JP89gt/wM7hLn/n+/8xp+YGvP8D346RgX//Cx+hmSuEC3jr6M0tsb+1yeZwyq8/9TvcuHaVVqfP4mAOayztvMXP/viPMStnPPaex7n+zBe5U5RM7+xEkcYYunmLs8fXeOX1KyAj11XK2JosfUTDyCSnsFPagwG/8n/8BEiFc5af+smPcOK97+Hnf+T7uLy9weneArNpwf7WOvv723xx8zb53CJpaXjigYfZnpWc6PeRAr7xr34AFRwCcFXJtCi5ffMNHnv0sbjHdnEYIVXkwgUcTsQUghI28uZkQqhgVhY0chl5gz5ghaC3usCFc0d54l2PsnLkDN3BClJlAJw7eTSetZM4Zg7/kWXxLdfPl7uefDkupq/09pf5d32p52B8TBb8RaGj3nFBqQoWpECFyGL3vnYiIPAh2rDjJau+SVsf7hS2VtW8sTHWVZXxABkiLNfI+LMhrlS1AuypRLwg+mDpNzWNPLC1fcBAdGPjmLcIH0UTEaJlVMhQHyYt1jiGk5JiUk/kdO2gsBYjDE3bp9lsI9IUKwRCqTeHII1EU1mLcYGimuFcA2sdQeckSU7egUSADQ4lLDYYtvdHJCICLfNEU5WW0fCAvCWZzBKMlzF6p8SbdtBqJpiYA4RwKJnQaGiM9QyHs8g2ULC+O8YHhReGNMT3xodozb/v9Cne866HqGYjxpM99vZ3mFYmNrb4yESSNRB5aaGHHe2C9NGOHhRVVZHm0TIrbOQnzSrDZFqS5opzp1aYjT27BzMIBhc8s7JCqgRbTxwDngqPcQbnElKZ4FPwxiOsxZkZu3u3Gc1KlGiTpil7wz2msyGNNE69hYkXmV4rY3muxbgssBWMplOK0kT3zHBEOiupJQ7SRKCtwXlHQCNkhhSGIGsHVTCIADrETZ0LgiAl1ts6QiVAKmbWcH1rl9sHQ4RsRKdRABc8CbGqOU6tJcP9CbPxhFYjjZGvVKJUgqnGmJCCyKCsEN6AClhv8c7grEFrUcdDReQoBXDWUQaHT2K0wPsSW1pKX5ElcGTpOEp7NncndVxW0Mgb9Jo9gglMJoeopIH3DjsbxgphoSlCiRZQ4RHeopWOGfZgMN5Tuni4KCuDl7Judws4K0iThOmkjFDsEBCa2KroIhAdAc5bJmaCCxrrfLQy1xDT6XhCr2Y+qBod4bKALOuctBcQ4sHFO0dwPlb9eY8rDTqNEcAgYpNfxIwoIN5PUItGsVoJEWKdr/QyKvQiOv3uNi9J4vokrHtzPZM1vJ7awBQ81DAppIgNkrYyb7bGORSeiu5cn4O9fbw1nD56imaacLA3QSeSfqdHJ+3y0IUHqKYzZsGzsbXJ+XOnOH/mHEI2IRiWzSr7wx02Nm6jteSVS1cZLJVcu3WDJFHoTJFkBToBEXLOnb2X4WGJCdvMJlOUywkqkKuEyWzGcP+AdqfJ7mSKAPI84+iRRcYHuwShkc5x/z3nWFiYxzlHs9FhmO7THRzh9vUt7tzaRkhBd67H3sEhlXeEYkSS5qQyw3vHK69eZHPjDoWLh8REOnSlmZYGT4J3M1qtFJ1IOnM9rBN4J7j3wlmWl+d5+aWX2D3YYH7Zkjfg3e95nNtXNsAKvDUEPUHUfI2qMsyKku3RIc1ei0fOv4sr164zGb2KCdGNaPAE62jmLdq9LkcXVlnqVmxsbSJCxomTJ8jzNlXlCEIzm1Vcuvgq5y/cz8PzjxAqxdXrt3nl9iucPX+a5dVF1q9J1lbO0m2fxgdDYRy2sizMx+uG8HHjI6RkMD/HzsYe27c3UEk8TM+1mmzeuolsddjf2UOqhKKcIkRApSlhIuh1+3TafXKpOHHyFNPhPmGq6S/2OFzfptfoIBaP8eKzz9MZLJMpaA6b9DpdBseW2dnc4sprr7G+fYeVYyfQjRxjMqazknazxdVXr/Loux/n3V/1AJdevExlC+7cusF4XJB1utx3+gLVZMJoPOTK61cJPk6Q17c2mI33KCYF7cEcRxbneFA/zDMvfJ7xzIAPcQ1xLg6OhCSRgiRNaxcXkVWlU5YGA4aTKbZ0pDqlqkp8CGRJSr/TiZZ65/HWU84KRrtT0rkGrXaHoiwpisBkZGh24rVa1Qf0PM/rzZugkeYszc+TJCmjaYHb3iWRKUfXTtLr9Tn0gkazFUsApMDZuIY4F0iTjLzVIkkT0iSJAH8hcD4WjIQArnS0GzlKRudzZUuytEG3HevQ8zRBhOgWFlrinEMQ6HeaJKFiNB1j0zbtVk5KRrPZoNvukDbbdBqxMYsQPbNpIjDTCZVS2EySJppUKrJGgyyP0NLz9/XxIcTyAaCwBa9dvIRzZRzwQVy7gmMyHdJodbnwwAMolWKNxboCZyquX3mFRmgxPdzHzyS2IZFWURxOSHJYXFjABot3Fk9gsj8iTPcxXmKkobQWLQ3WWpzzNVw8utRD8AQXHTJ2NqWVK3SQOKFjLDB4rK2wJGQypZGUMb7nDc6VDMczTCmZb69SlIETZ8+zu78XWR41IXZ7y6GSLnZWMpEz3KwgyxJEkNy+c500y8labbyUtHotCJJBa41MNTg43CXXgsPhDoSEts5oLixiqgbjybgeiAaki7H41MeoqifU10KBEBFYrZxHpgJhBc1mk6oYQQy/YePpuR4cKVKV4qXAeodUGTYIPIGZt2RCgwzoRGFsdLd5G8srrKniENZ5ggtY55HexmFqmjDXn8M5w9b2AcEbiskhOtXoTNaMrYDEEbwFnSGVQkpJksTXEIJBN+J7ZQX0el2sMzTabdqNXoxJpoF2f0CnP8fRpT5H5tpk9WcwBI8lDjclCr6c2u+3TeL/yMD9Sx6KRB19f/v9/6P/f8duf9aTWRRYkqxJolKKcIhKU+bn2gjnoduh8dX3Iwgcbu/ws//sf+WZ0Q7f9U3fzOrM8ZFf+xivh1FscpbgXEFZzFi/vUvaaHL51m06g2W8mTEbz0jzBtVkyNHF+VhskmpIVRzkDce88PQzzN97L//3L32U/eEQmQa0PGB7fMATX/9VnL7va3jql36Wn/hnP87f/6cfppPHobrodvC7M9ThBLfY4zu/7dv5xE/8L+xlGb/16U9x7L8+iZjNyFuxwMd6z52tO/zuq5ewTc2sMuwdbCEV/MzP/hyq2eV7/26f55/9A/7h3/leUqXRUjI1JZPpjOFkj8vX32BrfMjnP/95fvep3+b02dPoTpfq1k2eeOBh5potLq3fZHj+Pn7tR36YfHke4QUqS3DekiUtHrr/Eb7lW7+Dp5/8DMPPfpbnnn+e8XgfJRQLg1X2D3b52E9/hM/9xif5ge/9Qc7cP2B3fZO8Ve9rNjc5ubxImxTjLHubW3zyox/l91/6PL7p6dNidWVAsz3ggQtn+M1P/Dof+MA38YUvPIv3ltlkjJaKf/WLv8DSYp/hzSsIHZ3w/UGfna1tdKY52NlFNDOe/JVfxUiBlg0IAg0UxoGc8cKly6R5ihIRFSEIJMKzsDxga/eQWTlhudWhmzdRy6sU05LhZJ8f/ef/hAsLx3np6hdRWYtf/rVfZW6Qs9Q/yv/2r/5PhA4UM4dzU6598JsZjw45e+oI54+cZLnVJAQYTSa8/volTAhMJmOajZzR5ICqKqlMRbfdJWn3kEi0lGS5oNHMUVridUarG92o3kc8xrSa8MTXPs79993LXH+eVnMuMkhVgpTxy7/WzNk9PGB5blC3AN7FgH9lt/9fOpP+3Lc4ICg9qOCg3r+807d3XFAKriLRLYS0uBAvrkoogivwwYNKEUK9Cd71KKQMqBqy6/GkSYYzBtIsghuDQsuAextTJUiLlpF3E+qDt9aaPA1sbGyCVFG8CtENgQ84f9cNG0BHG6cMApzDWY8NAek1hfHI4FDeUQLICqlKsuAIziHr1rAQoJlptNAUxkZXT9xqkOgEO5swGu4RXJxcT8sp8z4nVQEtA0EHtBLMpKQwgem4QqoCKRRFELEPyQek8ngk02pGcJ48bzGtYsuaCTWcuwrMqsgiEEHiRO3gQuNFoNvKOTjcQ5iCYlZyc/MQF0B7gVQ61iAjSPMWvW6X3fE2SBknV6rC2BIhE5AxChSqEuNKSmPo9JvMd1pc2dmMzIYAKkCiU6ypgddBkErFPWfXyJIGZVqRZRKkRkhH5QWjyYSi3MYE6PebCKEYtOcZbW9y5Y0XCbZEp5qGFcx1OuRpA60UoiXpdzKqsmA8K0FaiumMkXUoBKpImRYgiIfGIDSJAlXnt5VX0T5a/05dHWWTxPx8wOOs5fr1G3zx8lWCznGlJU0kwTuci1GFqDvEGOfGzh4bm5ucPLqK9wGpM5qdFlUpkbqBUBkqLTGmBBUwQ4cLFoeJVdQyRaskQrlVIG90UFlGo9HEmHiwKdMC4Ru0izHNZhIBtC7UNuuATiU6EeR5QqvVJsmbGONw2tPMWxgvadFgOBkSRGSB1CExAqLeVCY084xUaiKZM9pKjXMonVNMIt9F6BhhiJJRZD0oqclVQiPvkKYtgodEeiofsCEn1SG+bx6qEDflOAFBEYhtexBjrx4QqobT1gcs42vBR2hsiJ/jaDcScaNex+ViNFXWkaS3L1ZxQYimoqhM3RWKBVFvlnEmUgtSogaQg/MxguLd3fBciGuVkAQnEMKTakVrcYm1Y8cJLgq8SgkqHLPKQZLQ7KeMtnZYWpjn4YffjS0cs8qws7/H5SvX2Nq6zdrReUwp2R0esHEwpNHMGXSXaDRSRuPApJgwnQVurm/QaDRIC4XVMJpammTkWcb2/g5pknL/yXOMC8+0GpMIwfG143TP388LL77A/u42R46tQnDkMscZyBoJ3V6bb/grf4Vnn3+eqtrDMaGVz7O+u0+v0+bBex/EWsHFSxdZ37gV68OdZ7C0xv7OJkJqbIiCvxaKI0srLM13Y5xJJLigSQRQVDx47h7Wd5t4o0izDh5Fp9Og3W0QTFY7LCxbe3vcuXOHdpIxK0YUVnPp9Ze5euUNAtG5Vnlo5Rnjgxlf903v4cq1TXrza2xdu83yyhpbdzZR84rCGCYbkbsz6M/Ftry5DqmC/b0tDoeHPPa+d7HYWcCVI06sHOOhx+6jnFiklkynBYWYRAEfRZCG4GLLmQuC+ZVlrl27irUG72eoTNNsakQamwsTBU7J2JyZaNJmk76EJFMcDPfY393H2djKVYUoYgZb0p3vcvT4MTbvbLEzHnPs2FGOHF0l0xohBWM747A85GwnoZUd48jSCp/73BeRjS7VdMrVK9cQmSUbdChvH7K+vcPCXJ+jq0dYWD5Bq93k2huv8crFl0HB6uIy7V4jsljSFttX11nstVhaGaB0hvcFaSax1tcQ/1Bz4ATHV48wP99HOo/WAYFCo1BIdKZj7EUpWu0my6srpIliPBxSmSI2YFVRaKpmnp3NPc6ePIo3t7i9sUXWbZGmKZ2FOaqJQ7cVk2JE6iV91SFvteh1epAMCdu7JGlC1kgxRcA4SZZopHcoobHBoqSiKgrymnsU2z7rFE0ISGRt64/T4dI4lIrRA++jUG+cI290CbjoLJGR/SCEjPE0iDElH5vKhBOkuSbRcc0VSqFqVyTeI3wg0zn9zhw2zZjMPN5XaJWQpQmJSkkaGmchSeM6qJREWs3aygrVbMRkfIBKBO1mizzJ6XU65J0uUiRoneBcoLIVs8kuvWaHgkDim8z1+kipaHWapM2EZqdFp9etr42CNNPcePUVNmcjlISkmaPyFlpB5cZMpiNWj66yemwtRreloChLtu7cYri3RzWdYF2o13XH5HBEO29y5p4LaCUwtqQqphwcbiHGmma7QaYkMiQM9+6ws7/NeDii257j+KmT7NePeTAaUs4KgpKoROHLwCxUHIz2SU2T/YMdjCnIM4EzjsnokKmcUZQ7bOyNcJUmoHj54jN0Fo8TjCEEiVCavNGJjA9vCKZu7qN2cYXovhTEYVMgcqNsiC4wbz2FK2mEOl4WAtZWGGNwIl5HrCmjO14GSBIEFkIsu3HW451H39XPZBSwlFIE+VbbsQgK6wLBWVKtqGoOiVKaZp7HyJyUsXwCBQi0jqJelmYQHCrRGCdptzuMDg/odQfoLCfLegQC/cEKzWbO2tIcC90WWX2N9YTYNFg3NCvuArbj/vidGJCHLykqvYnt/hN+NnzZjoav7FY7l4RAyCgi5mkjvicCRKbhsMLYitvXXuXKzg3kYJH28gIXzp7mbyx2+Z0/+CzPvfE6O3u7bNuSCk9HNnngzBqLaYMT7/1aPvavf5Ir1y7jdU6eKzpZHIiJAOXOPlNrufj/PMl4bsDxUyd45rnP0aBFJjyfevJTLPUucevqdT7woWsM7YT1129z8aWXedf5+2i0W0BAnl6muniFycEevSce4niScDkXfOw//DILq2ucOXWUcxfOoZxhMp1wY/0GtHNOr65Q7qwzG+/QbEiGwyFnj57gd37r13jPN36Qg3LGfJbz2tUbrK2usru9ybWXL/H5Z59FZi1OnTxNFgwPPf44g0EP5QPPPf8M/+3LX+D8Y1/DsT2D73UIiebUsfsZ3rnMoROYcsrkYJcf+x8+zLkzF1g9dYFOfyky84xhf28XrwRozeoj7+L0g+fJksDaw+cJQWK9Z/dgj7VeF5/njCcFr9zeYtgd0FtbZO/ODXb3LL/8Kx9ncO4MH/03P8dwZ4dH3/Mo2AKURicpP/PTP0UWGly6EvBZQKEIzrC1fpVyZkh1RiUEoqpodbtMigpXlTRUAys92IiCkLXoDBoVZ50oleDNDCk9J9ZWOL24xKSyfPA7votPfuITvOf81/H4qYf529/9PbSWNT/8w/8jWW+VzY3b7O9c5s7tOwgdh73GOl59+XWe+vRn+Jqve4hv+4bvwHlPVXlubW2AUMyKQ5rdHGss6xvrvPbaZYwd8ehDj9BstPBBMjOGx77qCZpZTqZydCY5fmSN4DTFbIS1FYtL86ytvJ+N9TsMh0O6/eU4OH4LYEYQiv1ZwfJcbMDU6suHSX8l3/cv92f/oteWPyXt9iV/ZuZCTBzBXwh77p2PvPmYpRdSEFyJ8wqvNMJ7KjNDSE+qU1ywBA9a6vrAEd0lSEEVbKyoddGFFNlAvCnMIOKEsK5ewktBt9/CqCiyjEYFi8uLqNrqJ5AILUnq+IsPoLRC4SkUBBWjWYg4+VYoCCIyn4LCW8FwPCWVGg04L950KKSJQvh4wZciTg8iTymQqMCNgyFWCmQIWKCZN6LtVyikb6HUEIInyTKyZotU10JbiI0f3kY3lVBgrMc7QWEmSCuRaNACqSBNFHkCSgRKbE14qCW7RLK5c8i4mNJNo0384HCCFLFxo99tMp3OGJdVzZCJskII8X2IjeqSIAPGBxxVZCAJhdKauW6XRKSMJlOE8IgQ6s13xP9EKcexurTAY/eex5oSpGfmLEkNoJoWloPDgmae0O8v0GjNoWRCnmmkULx2/TIy1RgCx5bnOXtqlVk1qR0kHuEVChXzuc0WecNG+3zwGBddLZNyQlWWVJUDLUitIxECJUJ0YvsIE/TWIlXAORvb16RiPJ5x7fodQpAY70hVqLsoonAXnCMIgRWx43BclOwNhxx3SygRsCFC6awXJD5a+IOQmGDIiFW7QUiQkmA9CAMqIyiJFwrjLNLVTSzWI6WOXKcgCTIKXhAhmkiHVhItMgIaZpfp+gAAIABJREFUG2S0OtpAsAJLXOy1TnAWpA3RmSfjVs+HuMFUuIhv8pH9FM+HEu9lLcSBDB4ZYgMapFH5FiE212ERdcQC7+uoXNxk6hCZRsgo5MVwpwOX1jDuKCJJanFIhAiKd3GzHozD10006ASVpjioY3BR1lXcXTTj4ygZo3Dh7nizts6KeucZ6vtHAO5bC26QUd0Pd3ewIa4/dzfIoobCKRHwweGFwxCrtKVwZCpCThUReOu8ZXQwxgdPojWdbpdO9wL7+wdIpVlf32dr+zbdjuKw0yJv9EGMaImE2SSQakG7kbO4dALrE/b2X8OFwOHoEPBYU2KBkIEKFYJAlrRQGhYHHY7lfa7cvIqL2V9ESOi3F1icWyHVUfhDO0LNqNEqo9Fv8PVf/16q6pDd/T2uXrvK9uGURtZABUmSNTi6eoxZUTCZlJjplHarzc7tbZoDhRs7ZIjr98riEg89+kBkhYxnhKDY2NhkZ2OL4yeP8uiDj/HpTz1F0IqtjQ1CafFqgFIpqY1R3yuXr3Ht5g2WFwZMphOsdQixhfeWMpT1+iSoKk+n12Rzax3pPFdff43icMpMGoaHO4zGM44fPcnKXIc0zxlPx0xnE2brMwa9Lldeu8hg0OTcsfMcHg5pN1NmicQKSZqmCCXJMos3sdXU14d/4UQEzztLb3GOdrfD1uFtpFBkeUKn3UckAqdU3AxKj5lWhOCprGU0GbJ3a5fXX3+N0pf0u4sYA+Xtkjxpg4d77jnNrcvXEImi0W5y+dZl0kbgXPM+ep0e3XaXbneOLEk5c+YMIQgWlhZQOlAYya29Ta7fuIxD0O/0SFWO9JrZeIRdqJhNBceOnWQwWOSlLzzLwXCP+XyJRDSYW+ux+cYt9ncPydoLBCHI05RpXRrhataYkhIpJAuDAZ1Wi8nuHt1mi06vx3QywVUG7yLjLrhAK28znU4phIyORSQeibGOg90hc50my/05sjShrCxZmjEbzyhKG2NfFawdOcLeznrkwGRZFIRCqGHaEWB6eDDEHxG1SK7QMroxRJq+KR6LIJAhrilKRJHdRXsAhDjQKoOjMiUKSWVMdE7gCUYg81pqjpkAnI/rqQiKUAVkiG21wnqCLRGiRRA6xsXx0Z0ZokSvlKydnRXWWbyNd5GJqiG2LkZv63ZKJaNI54zBWhMjbc6jZYp04BOPTBMQGqVSsrxZ1zZnKAVZ0iBVjvFUoZXEB0eeZeRpTpJE8SkOkOMgpdlssrQ4YJdAo5PT0ClpJmj2e7SbPSpVYYxFhiQ6YFRCK8sIeY5LErJU02glKNWg1W7TaDeYE5qqsigt2blzk7UjJ+HGdZYW5rh5+wYb4028n+LKId4F+r0u/WabwfwyDan5zd/6DTKVkrdaCD2mLC1l5ZmNxyilmIxHeF8xnUWhxR4eMNfuoiqL9wIhHVkjo/IzLl+5Tp5DIjM6vS6BlEGvz9geooOLwo2UeOMoigKdZjRCwKcBj8KJium4gLtiq/MIV7vgjUXrlCTP0RKE8xQSXBkZj9qlcbAoHSZ4hEpIMoHwjiTEqJozBuEVoR6mSCnwwsfW22DxNvJGfe2+VTLuXpAglAKZxNcQXL12emQtLoUkI89zKjdHd36RXr/NYK7P0qDDoN1+y3AUavdZXWoh3wRTx+/OXXfc289af+rh64/9oy8lFf1xd/n/3v8vQ0z6o68ttjWXaJ2Q5A2kTOMQUsT9iVjQuNGUP/iNT/Pi3jYDH/d17TPHudBKeeXS83zDf/ohDvYOePKTv8HVndv8Z+//ED/w/d/Lx/+7D3N7uEliS0ol0b7Cmyl3btzk/Ml7UFoidg+58swz/MKTv0HRTLj6yitsDw/wQuKFR2cpu3u7NBb6fOqzn8QFWOoN+NWPfZSF7/4ezpw7R/ABJSVT7XnyFz7G8Q+8l8apI4gXt9kfzfjn/+LHeM/jD/PhH/l7yMow3B3xuU98mjNnT3Ps2BLrO9usrPU53Nnl9ct3WL/5Bq++MuGh++5l5b3v5d/8/L/lt5/5HN/yTR/k8qVL3Hz5ClMHjz/4IP/N9/09nvudp3h54w5PP/8MpigjsuKg4uXf+gwvVQapFe993/v4+9//g/zSj/4o//7is0x8ye1bN8ikYDo+QGrFN3/7t/K7v/4fuLF+AxcsSmUkSL7nb/5X6ERHM4QtSdOEQODRU8e5ducaS4NH6HVafO3jj3J6aUDSSLjy0X/H1G7xcz//76icYaGVoRLFs0//ATrVOFPFNI1XlKaiPddiUo5x3mGMpyqq6J6vUzw+gKlsZOdpQdbR2GkVTRWmgKDJZUqiwdQmibludMW2eh3e9eDDXLr4Cv2jR2l1m3z4n/xTqsMhOzv7OGM4dfIBzhw/wbHFNW5cX+LjH/94PQS0SDxLCwPue+BeQLOz+RrDyR7tvE+73eBIWGSUN/jcb/4ejz/+OEoqmo0WvV6P2UwzKwqmxYSD4YTBwoC//Tf+S3SSx1624GNCwnusKSInVlj2D0c8+fRT3HfvGQbzKwgpSJwmz1sIIWkmkntWlxHEduk/q6Ty9u/fV/J9//NG6f5yBes/+Xb3HQsECi/xrozvYH1Gfyc1pXdcUCpDCbZC+lhfrWWLBIvA19MPRSo8hR3hAijZQ6rAzNQTBesQUmBitgTvYzYzSBkPyzUcxb8J3BUIHV1O7SylmpU0m43YPuJNHY+Liq4KkUcTuIv1CjgbEMLGg66Sbx2gcdHiHlJmpkAIMFIhuWtJhuADxlRoLSNHIITYGFavCjsHB0zLGTqNgpqSijRtYl1JUAI7LSLgTARUotBJhD2nMiF4SUBgZeTWaC2RwuET0DaKcbgI+fRuipUCbKynFchYpS4D3nl6jRZpmjCeFMwmFfujEePSAJI8zziytMjm7i5TYwk+xgsCIKVHShWdZaGC0IgAOCEIXpOnCe1MkWBIUhh02hwejChcPExrpTC+iiwHG2jmGYn3OJkhRRLdU8RsvZLQzJvMzQ9ImgNMkNhgEDJFqgaV9TQyRVkYFud7NNMmZWFQOolTP3zNPqrwZRJjOHmMOGUBlMrptFKGh/ukSTwYWO+oPEghKcu46GsUFDOk1HjnUNohpGRzcxdTH36ErXBSxnhU3XgQ2T2RgeCILYZ3eREAIdTT6ABaCAgWoWITSwaIoKGuiI6NFyHeR3iEiL9TIQzO17k8ESLwVFhccPVBFqwtkUqjhI7ROSEQPrbQSSSSMg4jpYzcnHqSGITGeoFWURCMFdgiCki1wAgCJTRBxfYYKeLmIoQ4EZd4NFEokyFGTD0SKSJ4HySEyIHQgchLC5HJEUSs5lY6isJe+nimwscYkSROaYlgyTSNDTne13YiBeCQMsX4+P45Qowe3T3U1a9BCupookArgXOON4WjUBvjg68PhyIKS/VhrVZauWugF6GmLwlBEDK6l1BofPz+iAjSjQJ0wBtPZQwKh7WW0lVxndEKZ0FIQ6YFqQjs7q5zbPUIQSi2DvZpNBs08xaD+XmKCtbXbzGZHJBkCcEadvY2MGVF5SOY17uKosiQSqK1IajAi698kZWlYxxZOEpZVRSTkmK0RaIzTp44Fts4RED4+DuJFegOj0fYgPAKERJGoyFSJYxmM0bDfRYXG5w7dQJwrG9tM9w7ZGdjHSE9rrDRpRdie2en04rvnvdIIWi2WzS7Z3nx2WeZTQuSccHGxh4hcTz9u7/HwvwcSytzpGkTGTTBC1ZWV7m1ucnVOzfJEkkzyaMLRCqkswRhSYRCZylKB7546TLLg0XOXbiHVy9dYnRwgNeCZqfBwtKA/lybG1cvs7ezR1k6Tp09zfUbu+zs7XDhgXejvWOuO2A23uX2xk1Ur8Xq3CKNRspwMmQ8HJJmKVpFGdbXhythA95ZptMxs8IgE8380iLGBRIfP2amKnFlhTclhwf7vPH6FXb31hlP9jmcTGm2coajbYJV5K0lFlbnqWzgjdffYGltmWavz40bV0mbCZt7O5SXvkCiGgQz49jyMus37qBIMVXF1uYtTp4+jRKGw+11KpmSBFidX2Aydug0Z/dwD3v9DY4fO0anvYBK+pw4eYo3rr/G9TdexTiHUGv0V+bYXN+kO1hDelg7epTXLr2KTuO1WtQikyfCOUdjx3g6xggLWmOtQSsR1x2tEEoyN9fDy0A1MyR5hgqKcupQBA73d1C2RSIUuwf7VKWl121ipCQoRTUrMTNLIgXLS0sU4zEiwGQypKxmjGezeJgXIKSK64MSNYsmfh7x0QECHu8i80jUVe4i1MMSokMk+BhF9s5jgsVWBu1BBoN3sQHH+8gXimjruKYE77DOMqsKnLWo2v5UmYK0mQH6zXVSCQgqrt0EH906tsK7evDlQQmFkgnee7RK4ndAxDiFcw5XFTFCFlzc/9QxpwhGhpAQiyFqod0HKL1HBosx8boqg4tCm5S1wB9jB0JEJoNzhsoHjDE0XB75PU7hvcI6GQUaQb0WxVh1bItVtQgR3VipBKlTvK+AyJ7xzpPlDdqtLvPzy0hfMB4dUBqD9I6rb7yGDYpuaxF17DSj3dtcvnUFZwzdo6skypOkkv2DA1ZXjoBImRmLD57RuECIjJXVYwRTMugusLPp0F5hvGWwNMBM9/nge7+F7c1bnD7zAFoF8JK93U2mo5QkWJI8IUkycBbvPGfOniM4gw0erXPMbMxLly5FOK03JCohSxKsLXHesbC0SmeuRAiBryru3LnOtKiQSmJcIGiQlUGiaHd6tJE4F9vmJqMxs/E0CjUyOhi1isJSo9FGJQkiiOhAq2YMzSFexjh5QNcOOoG3jtF0zLSsmJUl3XYXkScsnzrBUr9P3uqwvDCg37h7vX/roCKonVK1bnNXSwr13EZy9/TCmyJTvN+f7zQj/pj/+pNvfwlxk7t/xduezttfWyBgnY17OmScZ9WN13dzmtILVJKycPo8yeefxkrBtJgRjOe5T36GT//+0/TPnuHe/+SrOX18jWub1zixukTaafGh//6HeO7Tv8zPjCdolUMoGE9mXHzpdb72XV+L6DdIz53Ef+5Jbm/cYtd7QprgXSBvCrypMCah0+zR6vdq7locFu5sbHDp5S9weLiDlJJjx9aQcwYWNb/4r3+KwZkzdF9/hfViDy0ku1tbZEmLp3/713n+2ZcwjRauHPGxX3ma+SOr3H/hHL35ATubezz6vveTm4JXXnyBX+20+KmP/iLvefQhPvIvf4KT5x+hf+YEK2XJXN6nsJbm/ICHT64wt7DC5Wc+T6JaVGJGYQua7S55u88Pfu/3sdxp8f6//u08efMaB7evEYLEWU+7PceFhTlODlo87cr4HXQCExwnjq4xKoY89ezrnDyzSuZSHj53HwFHMRlx6eJTnFpbQadNNJqrd26xUVVkWTNuQbWm326SCkM1nYGUVHaKowXG43BIJGmSsLdXxrNekuCCwlqHKS3WRWejlAIpNc5LhPck0mPlW+d/awwhRDNBliqOH13hgXvOM7d6BOUF69tb/NAP/hA/93/9NA88cB8q04xm+8z8mP/iP/9WOrrBNAmIJEHlDZp5Rt7r4Zzn+InjPHL+Xv7aN36IX/i3P8+zF3+fb/iqb6NyJVpriqLg7Ml7OLF2AusMZWlYXVlmbWWN4Wgf6ww7W5usrS3jXIJWFltHnZ21IAST8QGj2YxpGY0aZ06dZq7XYzjaIISEwjuOr54kS7L43UDxlgryZ1sz3ilB588jDv2Fi0lf4uH/6Gr35tIkJcYRhxbEs807bFB65wUlaw3WOqyNjgwlh6RaImUgISHRFkMguArroKIi0/WBL9TV8/UhzFHVB0oBIUEKHTc2tbtA3J12OIe3JQ2dIFpNsuDI0iRWDcroCQxC1g1p4s0LnA8x6qZkHWsiPrZz0Z0g3mTiBLSQ6CR/k/OgtY7W4wBKywgMlz5OeygQBGZlbH+Iooclz1Mm07vRCJhOZxhrkTKJQk5ZYi04YeMBG0VkT1mMU3FjJ6KQFYKPQEwpkSIBAVmjgVIa7Vxd/ysxfsZ8v0u/16GsmlhjubO7jw0CrUElmsNpSWHrzaSD0WRMEAEtPVoJCgPOmMipSVT8gApPnmmOLnSRiSRNNfecOUplDFfWt6PLhkiRd97hnSPPFJAgtEZVCiUSnKvwHlqtHq3+HCrVEUzpSoSF8fiAZreDShsIUbLQadHIJNa8xb6JESSL9R7nIBPU7AqwVYwpIisSlRGsQ+cp0guyNCUIcJVlaqs4KRIZQUd4t7GOxFR4YDgeEfD1dFrWfKO6DyyA8YFERIYCCI4fXeXU0WMkKsPbCEmPLh6izCHjoiO8xAbw0hDw6Fo2AcndFhdvXaxnD3FbFmFk0YHmAOkyQlD4IKiqMm716vckMswiXN57D87XQZMIxqSMMS0ZJB6BFQGBf8sWSVyQ3qQKhYCMX1VssJF7phxC6NqlKmqXj8cKgZcqxg7vPpKg5mDHeJ0K8SDhXfxz48ta0IlOgmgVim6HUAP1tVZxiuTi95bgKGcTGnke3X+RLlsvmrJ2EvG2HLOoW93iJtea2FoRW1piNO1uce1b1wXP2+EPd1soZf2yvI8XOik0Qri6o06SBEmKJ4i3nmsIHpUmBGPw3sZGBqtwNpBqT6qg3WlQmDbOzGi25wH9/1L3prGSZud93+8s71JvVd2qu/ft7tvT2/SsnBnOkCJpUeJIlCVTFB3JTgTHjmEnTgIECGIjCxIDTqAogRB9SADDyCLAkmOLkijQkiWRIikuISlSHGqGHA5n6Zneu2/37b77rf3dzpIP5709w0VLIkpR3k8X3VWFWt5zzvP8n/9Cp7XAXG+VJMmItWJnZ5vReISjREqHr2uEj9BRipmW4MAYycFBzuJSD+yM4WBIbRzLC8vgYWNjgxMrq5xZfyAA2cKG94hGopD+qG1woSCoawSWpaVF4qzL1vY9jLSk3Q5J0qaX7XPsyRN84w9fZLK1FWQfpqEwyyYBM0kQXqFkTOVrqqqkO99j8dgKG5t7VLfvMqmmAYiuDWavYjjeZ6GfkZsZ48kwGHI3DbkRCpFCu9NFRQnD0SGFDfuP9SV1XjM3t8gjjz/G4nyP5782wJuSrNPlxNoaaZxy984ddvf3cQhKU3H7znWSVoTAEckIW1W0soz98Yh8WjM4OGB/a4tON+FgPGQ83GMyW+Pc+SfIkhQZycZHTAY/nlaEBPq9OeY7PfLxGOcktSkxzd7gbM3m5h0u33iDshrxwMnzrKwl9Ds9hDCMt3d5+OmnmM1qtu7tcfL0SWTaYXYwpNfp0lEdBoN97s3u0kq7tJKYubk+8x24vXGL/tISRT2mO9fmzPoFzPPPc2dvyIPnTrHYXSJRNQejQ3Ssub1xE60kaZZhjaGoCrJ2ilBLHOxscvP6NU4/cA6hFFt720Ras7S8zOU33kBqwIS15rzDGMdwOEQKiwfmWwuoWNPutpjMZlR5iQTSTkaaJQgZpKbOW8pZwWgwoipCSMZ+XZBEMVGckEQqnMEiACRxFBGLmMHhISsri8Rzc5STAfksNLu1scEnkABkCu9QMqS9Oh/Al0CIDAbRR77bIf2VAOYT9iBzJF9Tkk67jZeG3EE5DelOUiqEDMAJhAZf+jeNOYVSxHGKb2RmaIm1dWDHuiCLEgJ0FDduzWEPN8ZQVzXWBJmd8y68TeFROgrJb0Lcb+iRnqIuqU2NqRwuss0AQdyvn7zzGFsjvCdSGiM8lauQvomStwZrLMaUhDl6kFjhLcJZPIrKWKqqwpoS7ztYIUh0ADKkDKCUxyGEDjunDeEtVR3sCJRyRCJFyzDAkCIASVpH3Lv2ElY4tErxzqJUQq+9SGYrymrKmClFCTWKcVnwxqvfQEYxjpK0Pcdo9xIqE1R2zNrKE4wGOUUFK4urKFESJ10evPAofjbF1jU7O4JOK+NgeMj0cMixtZOcfeABFucXWFk5QZ5PQwLuZEDd7WGKGXEaB/8XIYmjNIQ12AJRW+I0QpMx10qI6wQpUpJ2QhIndLpzdNtdWlEWEtq0RgpPPh1Q5wWJUuG804o0SWh35zh9LkE3QzCEZ3Cwh61LXJ0Ta02kNK20Q6vd48yZDEeQ1CkZsb9zF1MUOAM+VkQ6+CVppYnjhODwJamtwFjQWYcnH3+YxSxrLCbeWhU0Z2zD9H2zcxH3//wOXdr3qoH5Exu2P4HF9Mfr5f7E69ubTP8mB+uPeDfivk8jzmDrirouac8v3e+RhVZI73ni6ad595d+nxumwKuUyXhKu7dA2u1y7/YtFhbaLB5bYqHT5eO/+zEef+ZxvvLcF/jDT34Gk8ZQ5jihqZ3gzOOPIBPJ7u4Wm1ubPLdxjYkpGQ6HFM4RxRpjTFCUoEmzNu94+9MoPKnWdHttxGzClz/zSfaLGcfWVvixv/pe1lYXeeeP/xBVbvnDV14h6SbY3YLUJ9y8cYtf+Ve/xif/9W/x7F/9ID/xo+/ll/7p/8Tq+kkeOnOGsydWme92idsZP/U3/i38dMJnf+/j/Pc/97P8lQ98iO60RuK48dorZEs9Ni/f4R//43/Cw+dO02knbOzucnLtBFee+wrOh4HASn+Fdz37LE+8552sP3CK/MYNPvLRX+XudI/ZrCJJUt72g+/jzMoae5s3GQ67GJ8S6TZlOSZJI7Z373KQ5/zOR3+bB545w1LS4/zps+SjAcPBJtPNG3z0o/+KE6fOY6Zjvvrii5y+8BinFubZ0DeCz2k5Q8Wa0rtgcRCFfbM2kqgZ4t7b2qYuK9rtBGcsRVHjBHgXaiztQ28EoDQoHImOcZUN5ugywgl3P8krzlJanQ5PPvMM5x58jMtXLvOMe4wHz57hfe/9ADEpRk4ZDKccHuwyGR5im377+NIi737ySV547vOcPnWaCxce56m3PcYj5y/Q6/b4iQ9+kK2dm8gmqCaOU9aOLSFEzv7uFr3uPFma4I1kNhlRFxWzfEIcaQ4P9uhkXYp8QqSjRoobUksHgwH5bEiappT5EJuP+eKXv8GFh88jyFheO8X9NDffDNib3uov+vrLwjT6U13fjcDV3Cd4j6k9SjQDgO+Ggv8Zr+85oCS8QIsEFUUhVUoQmhQXJF+2NmHRiAhnJbZ26FQQNYlsDgO2xMqgJRc+sFu0jsN00VTU3qKjNBQdziNUjVIWJQTOKaSoca6mFoF2673DWR2kPDJ4E3hC7G6SADLoZK2rUFJinWpufIujRKAwztHrtIlMiohjej0YjyZ4U+FtjRQOiSBWYQonJCjhwkSimex5W7N/cEjoPRXegBAR0ntm+RRL8BeQSJTyeB8RaQnChTh7Z9GSgPbiSWNPrGPiKGiy57ptVhbn2dodkFuPFi28klTGB7q7lmiVUJQFUgk6nTZL/T5VHUypFZLaeQ4OJvQ7HudCoSFtTVVWFHlOywNHBqVKcuaBdXxT1Pb6Gb35Ln57F+FCfLRvPAG0Upw+tkBpapI0C+akzlOXY6zLMEriXU2el9SNDA4n8WWYuuqkzYnlDhdWlzBR8JbyjUTRWxuAPycwBlRpsDow3VwdaABOepw3FNWUuBRYF+QYOoqa38sFnyclQ/oJnqIyOGux5SwYgjasFo8KFFXvcSJIq5w/KjJAKsHxY8v0e318XYEIk0AvDEKEOGZNHMwuCclo4bOAq2qMtWgZBRDIerBNcplVBEwqFG/CW0wDTnmpqeuKwWCMlDGikZLaxidICBl+D2OCXFQqBCGhzjrZAKc0xp4G5xs5nXcILFKoYJaHC+CTtwiiwEBxATCLoyjI10RgaQgRo0RwQBJHEo7QjhCElaI5QCGvS4T3FC5IApSKESgsrmkiNNaF7z8Az42OXHisLcknszDtaVh2UqqQPhG+rea5YY/yRwmRNH5gzmHqmkgHdphxR4xIe3+vPZrCehpfrcbk28vGo4Cm1RLBM04RgDuUwkqPdwYPqDgijSTVtETqKEgfXIhQNqaiKia0OgssrZ5kWsy4fes1sllOr9dm9dg8c91j3L17j2R+jqilQTps6Rs+FugoComaWBbnO6StLnU+I1EZWVczl3TptZeIdMrm3g2Q0J2LESJIa5wnJFhKi5BRKDSFQFofWJXOYG1NO0k5efwUtoSd7THd7gBnJ3Tn5llZW+Vl9QpZS1NNaqSWYB2i2dt0HFEWRfNbWHwN0hnW19e4/MYVNrdv0+pG1L7g9MlzHOwPUA271RSG69evcfPWTbJWhBAtrK1Jk4y5XockzTCmxJgchEUIiZGSlaUV2v1jjIbbjPOCSGuk9Ji6YDbYR1pLuzNHrz/PeH/IweAO1iqEjtjf22Zhrk1XO+bm5zl77kEqXfDNF7/OXK9NbUtabUXt5nG+STtpDGlVpPACWlmHRGpUMofWCiQYa1HCobTEy5RiWpFIzbHVeWyVcvLYGv2FY+SjMf21Ve4ZyXBnxN7ONv21NY4dP8Wd25tU+ZB2W7C0fIo7kWJ59TjTSc7mzRvU5QGn1tc5tb7OYDyh1+8RKcn8wjInT55nf3zIqRMnmGv1eeBkmxe+9jxlben3+mzf2yIvS6JEMz4cUpZjpEzwThFp2NzYoN3tMty8Q9xKcHi0CiCkRISIahHkp1ES0l+msxGtVkycRo0PmqNqQHshBNY6IqmQSmBrjzGWPK/ur8G8KDl36hQq0RgnGYyHDMYFygsSGaHbCXll2d07ZL7fpawNnTQhy9rUkzAoiaMoBCU0oRzeWyKVUFUVURRMoaHxS3NBDtWUtCF99sjHrdmzhbChP3Uhucw5QAV6v/BBA+8bVz4f9ONorYnTNOxducHi0MZC7YNc2BuQCUILcILa16AUSZrhXZDZIXxgTcngyRTJ8P1pEby5PALvBFl7jlrFWGPvF5K6YW9KKcMeWofI+gjNzMkwGXZBhiykQkUB4MH7IIlqfpFwFAUmo0eC1hTFBBn18UoTSQFKoKM41FUN39U4S1EH8+5IBt9HJRRehvWC81jrULFnb2uDtN0mWziDs0T8AAAgAElEQVRNnRcINEmaMJ8tMsunLKQJdze3GA9zyrpief0sVy7fIM1ixuMDfKyoqjxIDW34POsnH0DJmv2d6yyvHWO+t8iorNnauYyxE6qZRzjH4e4eT7/rWe7cvkVnbhFjw8DC+WCs5U3g+FofBmNCChCh8bNWIYVFK01NjtYqjAiFDOww71EyCr8BJVIGYDCO4pD4qxRagZIQa00aJyRpGs6sJmFUSEGapnSyFmXuiJQkkjoYyrdSSGOs9cxmgt78IlU+IhaCyhhEBFKGgaiQQd4mhUCoCCEl3d4cuhXT1erNafa34zBvzab+Ln3JtzZjf9GN2R8DKv0Zm8RvbzL/NE2nUgongjekd4693Rv0jp9CBspgGNYpxYmHH+Bv/tRP8pWvfZXH3/EkSZaweu44WSti6gre+MaL9JdXwUnujQ/43//Xf8aly69TyABwg8JUlvHBIc/9/qfJ6gGRs7zwwnN84+VXmMxGKAFaBDN2vKMwod7Z3tvlxrWrPPv+93Hr1ZdYXT2Nk4K7W7e5tL3L3b0tji20yd75DhaWTvO+H/8xPvHJj7PrKuI4xpmCyit+7cMf5Qd+8If5O3//73L91a8xv7JOL4votBTlcMj6k09wbHmBbiJpL51irhkO3H7t67CwwsFozGgw5mTyIIVxbN69g1SStaUl8sGQT336U1y/s8F4cMiDJx/k3/lbP80P/MizLC7MIYRnPCjY3RvhyxmRhjjSJGmLO1df4/XBPtHcIirpImcFD144TjGbcOPOLa4+/xw3Nq7yn/xH/4Cv/sHnubZ5k7V2F2lKTpw8y5XbW7z0tefYvLmBkI5Bt8PlOzdRbU3LKBwGi6X0hjRKcJFFmKAecEicCHuq1mCMw9kSFSW0kpjptEIqgRaCWoQBpnCeNE4pjUOIvGHQO5SKaGUtZsMxdWHprayRLa7QaneY5SVRmiKQPPsD7yGWCd+4+ALtOCbWUfDMbfaxajZgd+8uOlG87z3v5v3P/hidrA2EBLaTp05w6sQah4M74TiQAlNNGQ13KQpPlnbpzc0zOjjEGUPSShnt7KCiiNlkwu5OGPbUZSCahLZOMZ7N8MYymh7i8bS681x46BlWlhfJWj1WV06QRHEzoHffus/8qUhK3/qgoIKgAZL/qBd4617xJkA+qyt2R4ecXFgKzN/7xJ6A1Pjvti/+f3X5t/7RvCER1CZCeKpaoGRzfgvH9xoC+p4DStZ5Eq1JohglUmovcK5CYqlt3bBrBEoFLb5UzbRLSJRwWOux3iGIw+EvJGWVYxEkTVFkBUTCY3zZGPF6pITKlGjdCp4u1Cgpgt+AD/G5UgYpkNCqiQR3aOlxUgZTUKHAB3Pi0J5FRAicC7KsyWiAlBLVWA8nkW+SOgKQ4KVC6yAT8j54RzghQwNtAwPKNkPOqiqorQtsLeko67qhbgi8UERKgDdUKjSsASxz5PgQlS5kQ3l3GGuQjRHrYj9DacHd3QGmLImUYjgq2Gkf0JvLKGclRVkTRYoTywsszfcpq5peN0VsWw4GJZPpBCVDkpsDZBSxuX3AeFKwsrREnEUBeHCeJItDop8xoGK0lAgPFZbKBS8f72ueeexxslZCbXKkiSlNTVUVeOsp64rcjlBJjYojNJqqtggR4b0njhNOrs7zzEPLSDNjWFShmHeNjEAIpHdoaZHS4XDYBr73PkgYlAWLpzSCug47gKgD0FQ7Q11VTTF71CRodCwwlUO6YL5a1aaZNxNADQxeaBwygI9OBO8f5ynKksq4xog6wooK7zTC+iBxEOE5kZIYpQKDzlpsVYeF5I+YPARKug+yDxpQy0kRUmBcYBU5EQp9S/AWkzJIDpUKnkLO1nilqaqSJFKAQvoY6yRWBnN8hG203MHoOoAUjbQhkE7DGj+au/vgBSEtgT4tPQ4Z7HZlaGiacOlmykBjexbuc+EVvpmWeykx1ESNaaWzNcgw7Q8MgiCj0UrflxMGOzXRAFCSVhJjjA8RzlIHQJrg7+R8mMIbG6N18GSSTYpiXuRESpImcSjy1ZvsEiElSIGz5s29+uiMO9qzfYP1N7493psg/vPgG1A8MC9Vw3povknvQCimswPyYoAQbab5hMU4QzjJcLRFq92hnWYsLy8y11kgTVtY57l1ewPjZrSyjLl2l+k4p6xr5js9poWlNBXL3S4PPvo2Lr9xifHhLsfX1lk8vsTgcAZS0u728K4O6VMK8EEG65xHNbF7UgWGjRaOa5v32N+9x+rqEqsrJ4njFufOnWVnZ5+Lr75OtzvHI+cfZHhwyCyvcF425rlHjWe4my5du0SWJPTnF5nvdIiSlP3DATdv3GI43OLC2XV0lrG7u8Xbn/o+Jnu79Hp9RpMxz7/wB2xv79HpB/Bnf/cezkS0sjaj0YD6cIfZtEIpR9KG2TDw+MbFgO2dLYY7d+lm80xnA9ppxtryMRIraGV9Fk1NkrZxy8e5+MqUezsHKJ1w49oGnXYKziBUYMZ05ua5cP5hdKqoq4L+0jzHl0+QRgmmCnHzqAClxg60jlGxxBmLsYF160QRWCUixjmJqx3tOOKBtXVaWcriwnH2twb0F/tsXt9kc3Ob9VOKE6fXyTqL2NKw0O8zl0hu3RkilWJtaY3FhePUnYJMw60b17lxc4Ok1WJzZ5N+p8OJ9dM4BCfW15mWu8x3F1jq9fBErB9f4dbdQ86cP0ukY15//RLLnWWSJMW5Eh1p+nN94jhiOB5jrWEym9JfXKEsirAWJagonHtpKyGJa6pqQrvdIYlidve38HaBvDKUtWFa5FQOEiGI0oQo0kymJVJGqCi6D4g7T0jnylpMqxIVxfR780xnezhT0+8vIrxmZ/+AYTmh3cpoZW1kFID1qrYMD8dopZlfmCdKNIUN7KE00QEMEiDiiEgrjmjh930YEM36DgwnL5pBggx/eG9COqwIZsxg77+GdwFUOmJQhtdS4AIbWwqFd4baVcFD7ojl5AOgK5RsvBkCeOOcC+5SIgrMWhmAK2MMkQq+H3iwNkjybSQR0gewqwHLrBdo4fHOBs8dT8OerhFCEKmoqZ0crVarkQtKjiogLwJLy/uwLydJCjKirEpazhIp3YDIoX7yTehFMGoOHj1RmqJM8BCxhJrMGR+GFBKsrXng0XegZMzwcMDhYBszKmlFLSIv0V4iSehkMZKEY8sr3JuNWDuxzuHBPUaD/SB9rw1R1MLriKzb4cT6SYwtmUwvsLiyRhxrtvf2mI5yyllFURlkKpnvL7Ewv4YpCoaDHVZWV6lqg09jiqqkqvIGUAt1aDBe9/cDIYQLMlxTW8qyIq9K4iQhcrqJbw6/c5CGhZoRoG48Ga1rUnUjh7OElEGhMDikJJxRSLyTOOtxAlBBZi7FkUF3wzB1Hi0jjLEgQwKzaKTwoqkvww0bJHBxFBFJdX8NHBGR4C1/fEcT9Zehq2quIwzMf8c//3Hcpe/pdZ/JFEo3RJyCj8h6bfIrlxgOByz05gNAKCTjwZAvfOK3yPOSF69fZPLr/yfnzq4TS8ep8w+wcLjLSy+/ws1bBRWW4WCXwWsH6CRCOUckYorKIoHRYMDvf+EL2NE+D62v4KoRTz59gUkx4fbOiFYSN48rqFW4H7Sw3Lh+k24345tfe54rG9d56PxZ5heXmbx+lYXePHdu3+PTdz/JT6+e4qsf/wwbecHiQp/bd0cYaYkTze7eLh/86x/k5S99jkk+oSimXL16k85cwvH+Ek9//7PsXr5Jq7dI5K/y6S9+Hh1Jbt68TT6ckE+Dwf/u1j2890ynY2pn2TzY4nOf+yQf/53fZn8woddd5B/8h3+fd7/3++h0u+SjQ6rhhHR9Ddlvw70QTlObgle+/EWkb3qH7V2ybsJ4OmI9WWU0nAKCX/kX/5LO/BxnTy7ykVs3uXnpEv7sGdqiw2NP/yCduYt86etf4ZGnnubKG6/yuc9/ga3dfaJMU1uHVsEeottNkVKSl43nqW+SzSiIROhZShtMpueTiFhFFCJHxoqGKEtZhWTVVjujZSx1KZDK8EM//GPk0xGPPPIkv/bhD+PxDPYPeP3Ka5w5+yC3bt7msWceCSE0tqYsC37j1z7M4uJJkl6Hdz71bibjIdPJjM989tPsD4f80Hu/n0cffIhYw8HhLpu3r3L+wkNcvXaNa5de4aEzx5iOc/pLXTrtDsf6XbxKmY532dy/SV0LvBIkWZur14/A/AmrZYnWMZ2sG1Jr4xYy0ixJiZJBFq5URBInaK2CDLrpx521WCxaqm8Fa8R3WV/fse54EzhqhuoQTuRQfTcLEmg6Or51ZxB4HLUxfPWbr/Dl55/nb//kj3D2+PlvixRoLC+OAKijbfGt7MVv34D+jNcfv8MehR989ydVtSeS/n7v9L2+vueAUlHXKKkRtSfRjlhKJmVObSxI0CrYKCvhUcpS2RpRxUgdgXPhB9cxOE9RVhitqK3DTEt81Px/FGN8RVWXRLpFokLkqcNR2SlaaCpTYWobTApl1DCGfND+28bMRjWpXC4YGIbmU4SiT4AXChovCIHAVCXBW8ngnMO6IDvyPjBCtA4HuG0YECqOw7QHT9TI9KQKRaLSjth5fDskjcU6QUuBk+F1lVbgAtX+/sko5f21IaXEWI9zNUJZRF0HUEt4WjEcm0u5tzOkRuLrNre3R8gophiPsd7RiRN6qUL7AhFBEic4t8Cs2ANpMRaM8VQCYt1hdzBlezjj3nBKq6WZSzLaacpcV+GlD/43pibV+r43jtYa7TznT51keanHcJaDd+jJGG8VvtMmzy0vXrrCYFLSarVJWi20l+RlHcpx76ilYCEVJF4zM2ECXlnbKJYc3nikJWwVDvJijKEOxW4V5Ha1r5FIvDHYugStsMbgIhUmy15RVBViOsN4jRMCX1cURY4tS7YPxkF26EyziUlkI/OyLrDTKieInMMY2DsYs713QJK0SCOJcR5TC6wViBqcEAjnUVEwYq/qQ2bTCWZWIaWmNAKZeLyxVKUhbiX3vS88Fit0kG4Q7rkin+HLmtqa4N2AwNRVkKDWFc4aNArnLLU1KKfAhcKxrgqKYhqS4VSCMxWiSaqxxhDFMSYIFlBIcC74ZniBEwFEDpIJB5XB4kNqj/RY40gbQ1mvGvNuESYGUnhq77BSo5M2zpWhIEeimwbKNfIzGegCWG+pK4NWcWB2ONBSoaUIKVDlNHwmVzfR5QH5MdZSlRU6joKktoE3amOoyxrdDQVVXZtQiAPW+4YaHJ4fxwla6/sJVjaYdzXrs9nGm8mSkS40nM7S2BNj7RHdNBxcUsBgMmFnd4P5hQWUTJnsb8PeJnUhONg/oNPu0l/o4yz0O0sYp1k9tsb+oWY08kgZ005aODdkLoqJfMWwOGRxfp7u/AJbd+6GxKFEcXdrG2Et44mh8gGM8N5SVDV9G5iVEBKCnCBM040LYHhiGE32WF8/TitNSHSMsY4sTTl75iRJHLOxcZOvv/Q8QsYUeU5ROZxqGhUERzGb1nk2d7a5s7XLQrfFmQdOc3dzm8O9XR578DznHnoUqWOMrdBJim63iXTCxYsvsD/aojQlJ+aOs9DvMjq8Q7s9TxTHDAa7WAlpu001LZAuoirHGF+yd3DA7vYXeOKRJ3jo7EmubARQZ/X4OsVwRKwVc+0WQkAUtUje/i4esQ6BZzDc59aVq7RUyuLqcbyD42snyaIUlSSUZcGNG5cY6X363QWUDLHs1ji8hDhK0AhUlDAdV8zqEvB4U+HQxHFEXRtMbdCJpNdZIOt1KKua0lTs3Nwma89x/MwZVtdXSIVGxy2chCyKMLpL0uoiHKRpho40WmUYU7N07BjXb9yg0+8x18kYTQtu3bzOHalY7Mxz5/Z1lIhRUpDFbVpZRpIOOdg94PG3PUl9xnH9+hXm+l3anRV0JDnwAbwFg3MwySdMxgMO9/dBquD5AuhIk0QtUJI4zmjHXao8p8orbo03mc1y0BG1Cc2z1JLBeIzwnvFgFAxKnaE3N4eNNLv7Byil2D88ZGllES+SICsQitlszOhwRJq2SZIEJROiKCZRAmdrppNZMJQVkna7TSfNGtVL4+7SMOqDF14TS+F9MLjWslnzzbonDHQk4fVCeEcARGjYiHHSRnpBsLLxCBnYS55QG4hwcAS2korASqJEBw85H6GUAxX2jcBkiUOx3UBU1ntiGQrhoM4L760yFXEUBz+lxhD9vg+aMwGsByy6YT3LhhkhEahGelkiCUw7rEM6H0zLpUbJBhxyb45khTxinmq0jIm0boCV4EXlraU2NVIJsKCiBC1qtBBEMsgH7ZHbnfMoobBeogXU1tPprYCX7GzdYzLZx0eKYwvHqasJOwe7ZL0a6SZ0Wn2k96ytn6K9ZHlpsM+knFHVhjSJ6XVjRsM9vOvRz3MirTm2ts7lSxfZ3t3i9uYGy/1Fjq8ucuXGZbI45dz6wyzML+Jqw9VLFxtvorCdxVlCK08p8gol0vB5NdijwVTTtAgpcK7E1Tb4bMUp4ZsxDYPeh9+5GSg676lKQ1nWpFGEiMJwVWpx/7sOczQR0gZNRZ7PcNYTRY0sXoahkm9qHq2C1NDamtrUoTYVMniR+gA+iYa5f8TclToEBryJyvwlAouAtzZ/3/r3Wy/xlv/13/KsP9d3diRtbdaI9x5T1cyKKe1unzjNOPe2d1M7y8HePgtLi9y7u8Evf/gX+ebLLzLLDXvjCbt/8EVe/lrGj/zY+/ngT/5NRD7l4Pb/yIs721hnWZ7rsT3cJ1aW+c48dlggqe/XRtNRzstvXOXajetAkNrvDfJQV7oZpoY6IJUIZ3BWcngw4o1LN2l1u9y+eZflhWXSJOFHf/hZYue49NpFnnzHU0Sp4sbN61RC8D/8tz/LR/7n/4Xfe+5rCKlBK3QsOPPUE9z4+ue5fvkbjIgobEHWmmPj5ga//4U/5O+941385i/8H7xxdw8MFLWhNCVxonn8ibdTbg944bWrfPYzn+bWnRvsDQ7ZuHozJDhWFefOn8PHBaPpiANTs3P1Ip/5td/kh/7jf58rV79JrCV5ETb3vMjDGhUefAnjkkTD1cuXGI5HXHj4cR48eYpXLr3BR37j13FC8Fsf+xhPP/MU/95f/2luX32DOztDvvr8N1hY2WZ3a4ft7S1caTG6hTEOHUkm0ylPPHKBy69cx4gIW1ucNdQuR+gwoBRCYZu9b35hIQwKxgdBHh93yPOStz/1DNeuvIE1lnyS08papF3Jf/D3/g4Xv/4CvWOn0UozLcbc2bjD448/gRIRZx44T1VOqa0hTTK+8IXf4+bGXVRrng9+4EMc7O5y6dJFzp49x9z8HB7D+soqs8NdNv2Mve1dXrn4BtcuP89zX3+DbktxYvHdzIYFxlWMshKdTFFRyng8wwlBK50jSzO6/QWeePsycRJ6+CxrIaUkiWPihnF0BJrfX5eNF6qQ3P8/j2/O3O8EZ966pv4oUKQhETc9cVCkbA+GLHVTlE4bsEk2j3nr3nH0emEAklc1onSoUrK/c48za2caX9jmsc3Dg0XNm8//XoNIf7ZLNNt38ItN5ZEByf8PACVjPEYYSl9h05BsNCvrQLVSmizWWFvjqalcGW4kFFEkg1uUA+/TAJR4HaYpQuCUQCUZ0gt0HGHdCOsM0tVMGnNP54MpsEJiXaCrC6tRcaNfbQ7OI4NeZ8A6GSi/OLwIko9YR2AcXjUpK42ciSY2PERjSCKtaLd7VJWncppuOw2fRSq0UpjCNUy9gF6+uVgcStGwkI48XhpTTIJpszQBPHPeN+bGrokxF4ExgadqJt2YgEpKKRAE6YxSGqk8dVkglKSqBNc3DxE2RyDopAlJFjWvK6E2zMUxWRziY9NWijNVE4sePk9uDLvDMfFEciALEi2Z6yYs9FJ6nRZR5PDaNxNWTyQdaSvjoQcv0E4kaXeRcjwO3gJxiPa+fXeXy1duYURovrTURAjq2uKEI5aa3AvOPP0QcQKFkdhZTeQVtasRJkxmrfcgNVES460jdhYRRYgkDpNYpbG2JNIK70qEyPDOYEyEbjYe2fgiaRW8iSofJrMbe/vs7B0GCZX3oBXeyybNzuKtD1NID9qBEw5rLToSpC3FLJ9SFznWWnLraacxqXa0swyfSWId46zFywQRBWS+qiZIV4dGIm5xFEccbIU82nmM8Ghhkc5QW4urLM7UjZksIY3NSZyMcDJ4Ux0h/8IF6akjyAzb0QShUgomCGXBOCpT452hG2lcoG01QEyQmwkJzgkqa6iNDUBumaPiIDX0OjQzLd3GOI9upqZGOIxwWCUZDvYgilEywpQ5ghALn8YpWkeN6XW4D7yQmNoyy0ukioj1EQvIkRdFcDlSASR7kz0UWIce0bAZG0e65jDRSpNmKUm7Fcxsm0ZJELTe0oXVKZxHybA2TG2wNjRctvmtrTfkVRkAYGuDeb+1zM93Mc4EQK15UyFFMoCSeT5lmheISc5kuEVRjtnaDj5RSdbi2LEHuH7jBpFKSLNFjh1bQwvLXLdNFmv2D3YZTAYU5Yy11TWQEfcOD3EEmUR/oc9sMmNaCypf8sbGFeoqplu06bcVC3OLFLOaQ/YblqPCuwgramKpUVVF3NZUE8Mj58+hBEgLNVBXjtkkJ4kU/c4cN2zFwlJG1llmZ2ePurSIJPjVKTw0/inrqysUy0vs7h8ynhwwnkzZOzhk9cQJVtYfYDgJ9Olef5nR7jY6innj1dcpyxlpS2NKw2QwZmV5Fe8S0jSjqAXGabTUzKXzzKoJJ48tsjBfcXv7FnG2yGq/S5JFjAcH+Moj0oQ7d+9wb+MeD517iLIaIGNNJAuk0rSjJFC8y5IT6yeoy5LufJvhvU2kCLKgOGtz/dplLl37JlW1QqudkcZzCKeQUiOUh7Jimk8RQjCdjqmmefAuiVKkSsLeYQums32ipM+0sBSmYndrl+FgzNmHzhJ1u1x//TKbuzvMdbq4ckBvcYmiqiimYxYW5tk9vENpDKdbCYd7I4qqZml5nv3DA7rdDp0s5ivP/yGvvHIRm49pL3TIOktcvPQKH/zhDzIcjNnZvoeMYWtvi9a1NutnTrNx5yrj6ZDl9jGSOJzjEsV8v83u1ozxKKeVOWqjKYojzzOPUjFKJ5jCkCVd6srhZjXtpT5mOiacjJYokcyKmsODA8qyppVm5PkMIaHbzXjo7HnKyQR9+SqzfML+4YA4ici6CwCcOnWKsqoZDobYOUGr06XbbWNdiVCgZBRqBQ1pu4UxgsoZqsKADcWhMRYZx3gTmMy1nRFFURjsNKxTTyhOXSN50gQjY4XG+holQyKdwGGFCGcaIRhDiCYMRCqUkqHZlzUoCzLIYJSMSVspWgAiRkodatYGtD5id/rwQoGdIghNkiQMa1RIm1NKgpIoHSG9AB88nDSCyDcFuwoDKyFFc77UuNLjbEiQ9bJC2cDg9uHYaZJ5AtvJOot1NIxP2TAaw9BQEDz2JOp+jWdtCN+QDWDtZdi7hfOIyDcVkMSLupHigfJhL7QuyCm6C0tEkaaVRhR+hJYFZ889ijcVr198nZUz57l5+XUOD6bYBizszC1gixE60hyODtm6t8OoqphrtbC2Ii8qtg9usNjt0Eo1BwdTnJLEquLE8iJ3N6+zsrLOI0+9ndp4ZKTAGCIVo3SKlKYBZoKRrvKSKEoxxSFK0wSyCNJUM5bBuwrC9yBkkAILgh+fVqEG7mQdJuoQrZrRhxCIKAyBpFQgHdJJlJDNenMNG0kHabrSiPv9jkBFEhVpqgbEFDIwCPFvOTNpZMnN/81mM4RuBfDw/nWfo/2X//JHDSvfSkgQb/7Tn9f1HY2uCL6rZjzld/7Nb7JT5Hzfu9/FwsIiX/jM57jw5AV+8Z/+c4pMk08L8iowDRd7PZbme5x99DTduYy7uzepopJydsjT7/phemg+9n99ljRR+LomSRVLx44xGgypI4Exjp29QwKzPdQqvjJo7Wh1Wkzykl6aYUrLxDuMrVjoL/PM2x7hnd/3ODdeeo3uYp92L+PWtVvkZcXKqXVeef0lfvXDv8DnX3iRR595J9VsxD/82Z9h42/827xaVkjgM5/6FP/u3/prfP3lr3PtIMdFFjWRtLo1v/BL/5Jep8tnP/05ruyMKZzGO8uFx5/k+88s8Ouf+DT/xT/6T/nFn/kZ4gS2tu+ytbeLs8GTz9iadruDljn/2z/7eR599DF2R4KHzqyxeG6Nb3zx8xwOhqi4TWVtAPZ9SB4O/kOKsrTI2NFqCd7x5OM8/PiTJL7m4pWaP/jyl9jfn/ETP/UhfuUX/zlvf+wC1bTg3o3rHAzG7B9cYjwdUhYzpFSksk2rk3FwsMuT73wH5/t9XnnhMmmaMqsLaMzvYxS5r9ESNBE4y807N0FBlsX4wmDKEGC10GtxubbsDYYoK5GyotdeoLAF/d4Sd7du0ZufY7I9wFOwvNDn4qsvYk3Jx3/jo3z/9/0AN+/cYWdrhx/9wAfY3dzgJz/0oxxuX8MUewz3JYvdmEuv3QIzoMw32Ht1xssv36Df6+DI+IkPfYAknWdpeQklNHHSZq7dCccPKthISBmSUKVEaR1GA1IwmeVkraypdcO+8lZY+ohpW1ZVM6AKi/Kt3j5/HIvmrWlqRwCOb3qct6ZJCgFV7RgdbLPUPfktLMuj3hsfyCTWh3TjMGgKFg2PPnSKMyfX8PYw9ImieaPehlRW4E1w6v/tjvHnfYVvvrSEM+PPafP7ngNKSQRIR142xZo1lJUFEYyUR2VI4ImtDjeU8sRRRdsnjTmpJk3aWNeY/tLE9uoEgaCuplgnyU0eohWpQMUoBa1U4FwRrFpo5BrGob0jz2tknKKEozIVKhL0kgzZyHqSKMZ5g9ICYcEJiRYRIg5afk+QKTmvA1Algs9Lu9WinXqiyJOlGudKvAy+GGiFbjYyIZqo2FABcpS7hXcgHaaZPTrvwxSsocI7GQoK6UIR6rx/Ux/xqrEAACAASURBVD50v2tuCkIb6M3GWqRq0uZCaY/2UBaGWKZksWGh1w5GabaJUhc12sNCt8V0ltPSglLLYByMY3l1hbow5GXJtCyZ1iXj2nFQ5GwcQhpJjnU7HBzMKOrGKFUInIRIVNTGEJUKRI1CYGrHtCp59eoNKmsDbT7WPHpmneFoxN2tA7zx1InDOk93LgsSMBUmpDjTTJRDgSOFQEYC5SSxC34rwlu89LSiFnVlwTmEdSDjwPrJBM7VVCZQzMEhI4WINQmSTEdMRlOuXt9o3qMDFQLuj6RcTf4a1pQIFQeAyQmUsAgsifKoNKL0NXNzCYxzalMwOhhhohgdx8QCXG3JrKMVt2gBtQ0TjEhHCC8R3tyf4HKfTk8AdnRM0urgbR42RqEwwqEjaKUtIkA5i9IaVAzGQu3wwWqDdpqSdPoB8JTBx0wiaNclZVmERD4hUXisp1mn4V62UqBTSapiEq8Z+QqZalQsUArKMjCObMMA0AJkI/+SSHpzfVbnF7GmDoaSKiQXBpmUakzPLUIp/JEBr5ABiJVBuoaAKNLUpg4m+Q3LJvBtAiCsVACAQyK3axhFYTKSJklIbHImNH7iTa8npcO9pZUi0mHPEkhk87eSqmFCabIoNE+VCV5sVVESWY8yAXgMMkIQGLwKZvUtrYikJ5/uMKsKxtOSUVnSpcdaf5VIdTh2/CxpGtGb72C9YTwbMZ1OSCKFji0nl48zGhUc7O+yuraGKXNUmrK/N+Lexi6H4yFPPP0o+wd73Nl8g7qsGewX3EsT0iRDJy1euvgSa6t9jq09Spw4WjrB2dA4xVGL2le0kzbYmsa/H5Fa6FiSNEHGEednj7A0P0fWybj+xg2qahb8uJy9P43TSlHXjpOn1inKnHaygFCBhnv33jajyYDZeMC5Bx8lbeV0+x2uXLnFvZ1NrD2gnkmOHzvJqdMPc/HVl5hNZmzOpiwsL7G82OfkyTVuXL3LgxceRMSOOAt78WA8w9U1X3/xOZSQZFGLtJXywte/SStuMZyNObbcJ4k00gviKAUdIZOUWTHlZH+O17/5GqPBgFhq7DSnHWmmZcXBeB/poRiPMPmYcVkwHg6onSRpZaytPUgcZ3SzNl7vcuXaJc4cX+HylSusrqyytn6a4d4+UmmqYoZ1nmI6YzyYcfahC8H3wDh2BgdM85zrN6/h/QSlIiof7vO5LCXVhvnFE4z3hqjaIJ1iNszJojbjwyG3711nYa7PD7zvvYz39zmYjtm4cwcvu4xnBZaS29t3MSrm1PoFtrfu4bzlxNpxXn7lJSKZcGxlCVsYJszIWi3a7ZSHH3oIZyV3795DZJKiyEFJqrpkNp3ibM7W7m1SGaGEpBjn6KhFlkmKqsJaQaeTsbC8RFHUTKcj4jTCe8fe7i6jvUPm0pQ4TgKLMQJU8G1rt9ssrRxnVtVs3jukrCq6cRyGTnVFnKZEwqGsph4XpKmiqj1bmztEp1Kc9rjKkvQ6hMY7CjJqqVE6DiC0lMFzzh8xSkKohveNnMv6Zo8LwwYZtmicDOxR3Zh6SynwTjXpZwJB8Hkz1oTnEMILahEi5YPHUyN0PkrI4SiYwWGdvO8jqSKNMRCpAIAFiaLDerC4IJt2oY023hMTJG1SBRa2dRYhzP3hmSBI97zy9wFyHYV6xTX+GjTTY994wCkpwNaNIXnDyvQahERrj3Wh1hBa4YQNgR0ojAjsCOkNsdINMyx40knlsVUBUjE9PKQYjvBZi127R38hwxrLc899gbjVYTrY4cuf+W2ybp9ubwFjgwQ4aaeMiykZC3RP9Llz+2WwBa9ffIWkPc+5hx7jgSzF1yXj4Q4Hg32cNXTmz7M/OOSVL32KC0++n+9/9v24yiOUxBQFQii8V+HzNclqSkUI4XHOh0GOL0lkFAZQkiaJz6N8I0+TTfqqDFYJUio8BuPrwKBvQMNIiSCL9EdnoApnn9QoGaFUI2XzwRtUSoEQEQKD1oElceS5JJVsHidDvdtIxkNmR0g4DsPSNt12JzSCfykZSv9PLv8X9vbfyk44aoid99TOMzk85BOf+CQbxvKpz38RIRyRivnYpz/NtKh416Nv56kL7+d3//W/wbcyfuTZZ9HOcPv1N/jdX/51Xn7jMuPqEGM8t65dYjSrUK0E6zxSBhbicFBTlTXGG2rhWewvkijJzr0dpPUUlQ2122QX4eD8Iw8wOii5d3ebWmh6C22efvQ0T77tYd79jqf42uc+wcd/72Nc2R4gVcJ4lrO4sMhzX3mV3to6kXI8/cQ7EQl86Mf/Ghd/9SPozhyf+dTnMdWQL3/lEiqZAyyrK0tIGVjUN2/d4MwjD+GTBFkrZqMh/+Qf/UPuXvwqP/9z72Hr8jd54bUN0lbMYDTD2hpcCAOKo5gP/Oj7OHe6zdbmXaq9bW5cn3D16kXms5Sdrc2Q4usqEhU1bMcSIRy2bjw+o+C5mrXnuLpxm9GkZrS7z/xij6Tl+CvvfJQffM97uPn8F/kv//P/jLOPP0m5e8Do8JA403Q6GRgoi5rZNMczw1lDfrjP57/5NbJWQm6qAIxHmjRuUcwmpFHo9SLteOLxMyyurqKM5aVXL2FFSPg2XuL9jHE+4em3Pcq1axs4Y3j7o2e59M0vsb1V8Luf/yzD4QxjYGfvkE9+6lNMxxO0VJw8f4b/5r/+r/jvfu7nyXzCL/3yv2B3f4MHXzoOxYTBeMJHPv5J7t7b48zJEywsLnLjwPDoY+/i9S3LoDXP8PZV/vbf/SGE1rSSGCHCQFUQ5N1V7THOYKqSsqxZnJ/HOcu49Dx36XUeXO3xQOs4sUruAxgNfyecZ0hmZUgBP0pvP3rUW5fqW5lI/zdz7x1kaXaWef6O+ey1aauqy1eXae9bviUhEJIYQBLCB0uMNhhWyw4wwwbMBjALiIndHSYCYgaGwbPDsBhhZoDByGFk2qm9U1dVlzeZlZWZN+/Naz5zzP5xvuxuCYkgdsWMvoiOyi6Ted13znve93l+zxdjl71atbTDYwxOI4m3Hq1h7epllpYT5mULL4Jq2FjT7GGGqg6pmaKx/tbWMZuEtXh7POL66jnWr5/hhr3H6LQX6c7t4hWfm0GIiJdVkJ+nUPxyuDyCsQHd2KDFP8Bw4EveUGopjfEOb6EqLMICfidJwGKdDUleNH7+ptjyrqAyjmEJqXFETTaUscGCoqzHuTHT2QwnI0obJGuRCMlJwoGPNM4rytognAQp0dJgbUmBJvI1g/EMKT2JDMBqoQRC6RBB60MMupOWjdmAbtpGepAqDjeA3+mYhgLKm4KNqol6dRWKFJWEg6Y1Fu8k1tfEBHWH9RbleLnRY71tNv4wHbLOE8mQPmNFwxSgmSQJgZc06TQNNJydg31TeDY3bRpF1C6wo4QQGFERC4/2nt2Li4xGEulnFNM4SPMJh2ghJP1WwnRakcoMBEjVRFZWFXEkSNOMto0py4KyLtAiZieifXtWsjEa4Y0lWHwMvpiwuTHBSUjG17FVGfQKUjK9PmJzaxz8+lqyf98u5hbm2BiOwgQTgXACJRUXr1zH1BWJDlNbI2qk04FtYwOrRycJ0gpUHCLEa1ugpERKjZaWoi4Aj2sWL+sEQjW3lfVIYZBYnA3WrKsba3z2hZcYDLdJ4whrbYBGN0ox52zTvGxi5wmNBd9YCWaTKe0kIlIxlQKhE7IsTF8X8oykNRdsLXXN+StD3OoGs9IgBKRJRLuV02klGO9JpQgR1anF4AKQGhAygjpC1hYfR1S+xnqD9hE0NgIjFMYFlY2QAZQfA9gahMTKwOgKz0MHxRrNguNNYJlZBypYLb1ySAdKWPDB0uOsxAgDWgUAs9AUtcUIj2o4TNK7wBZConFgAsi7cDWmLtBeoGXcWE5Ay2AZ8U6Eg5xzKCFJs9AMUA0DSWmNVCHhqPIG74N0f0eJFH51jaRfvLwFeO/whOLEN++hIExvfWN9AYG1BiV3vk/T0KKxNHjfcMIEIBHCEDlLrIOK0BuDTyqUinAmNK+dV0gbuFR5p8s+eZzKGsaXTjI/rzDrEWZWcfnKJZQXLC8u027HKB1xfbDO5dWz1NWEPO2zsX2dbncOqSXXN9cYbg+wRtBpd5iVU07ccRSiFk995knSFNqtRZYPLbPY28W5M2d46OEnIFKYasLhoyfI2xF5HFMVDhlJxlVoiCdpRm0Nk2KIdoIk6QbJAiH5w2vF4lyPvNMlzdvkWZtWuklVSpyNKGzgslhXs7a2zrGbTnDXLXdw6fI5Xjp/kbXhiFaWs7Y2IYol586c49raeSYzx33H70ToA+h4H5cubLC0NBeSQoYbxHHG4tISk2JMURYUZy1pnJPFkvn5JTrzbZ5+6hlUXuBUjG2SCYtRwcpgHacydi9qBsMNalty7PARYh0UmSKKUFrQyhOwjqPHjnPy6VMsL/TwXuKinEx4bjx4K3kSMZ2usD4ZMZnWbA3X8R7uv/O15KlmxU45f/0KIvecvvQ8z7/wMKPCs3drBdXKyRb7uI2Kuf4ck+GQ8daUY7fcRKs/T9ZKefbpZ2nnKePxFUw9I8okk2lFlGqshevbJREVeTvGiWXyOGeut4h1kq3BBqvrZ9kcbHLvPW+kl6f0omUOyGVuPn4js4nn7OmL1OWQJMsRRCzML9BKMq5cucLeA/s5dOgw506+SBRZRCZo6S5pHOFaESd27SdZWObBj/0Vl9dXSEVEVbrG+jclzTzLvT6t9jxuNiNKY4wrKArF3FyfdjuDOCGOY6rKcPL0aSajAoGjriqK0pDNLyAiTe0dysO4KHByRFFXeJmwZ88eoqzL5ctX6HTbWFvhnWc0nhAJmJvroyvHdDLDGE9hKranU1qtBOMtOKirmiiKmE6naBkhhcI0KbBShb3WOktVlHjXFKK2xmFwOlg7a2upEPhqRst1cFVNoSBB4p3BegNxBCLYzIwLzQGlBVpGOBfAqaWt8FWE1zYMS+SOhjmoq4UHrcMaL7RCKk3UsOe0iJAqwlNhCc176yoMoa6QwqBFAErXdREUM3JnzQwqIuMsWoRJs9ZRGCI4i2gGKEKEAzLeY9hJ4QxKXYckpH76oNAhjHWVJKQXybDOOiQ2dFVe4c9JhWzKUutDWm2S5ZRFhc403Zbm8tYmSdzm8vUVpsWY0kr8bIopDC+dPsmJO+7l6ukXSZBsj8fUxQjijLiXU1Se5b37uO+1b8AZQxSlCFPzxKNPMKmm9LMeAkEU5/TylCyJeOc3vJ/ewhJU4J0NYO4oeZk35X0IYBA+2DY9zfOWEu/i4L1oPh9KSrSU7FR3xjbpoY3qy3mHd00anpQopYmRoQGFDZwbqTFCYYVDiTDwFCIMGHdqyTCcbNAOjvCuufDeOlfTdDgbJEQInQliekEzYWyUB59/YPtymsKLL/L1K7/1ioHlc68vZJb7kj2qz3uBguVUoOotrlx+kfkjh9m8ssJkOkUJyfpgi5bWqCTEzr/+ja9ldz/jV37l/+HDH/ko+w8sM6tmfPLRp0iTnCReIF7oce36GlYm1I2lcTwe450BH4HU4T23nulowsRZvKhpJRqcpipqvvZbvouO89x735381X/9PR5zBZfXJrz369/CI3/xcQ7fdIDNyZS0v0waK1raMpiUGOM5dPQYD7zuDaxdW+Hkc8/yu7/xi7z7G7+ZW7/qAZLf+Q1KalYGA/7iY4+C0OhIgKvZu2sRtkacWd1ERTFPPf4sg80N9tywi3vvu5eoNLzxbV/PY3/0IX7s3/1HtrRAFCVJkiKtZ8/eBfbvX+bc6cs88rGPEr3udm678xiPrYxY6iakIuOuN9/Kn/z6aSzQW5yn2JqyubXNXDdjWtc4KUmFItWSsrScfPEC7/6Gr+F7vv1b+dBv/ieePnue86ee5/FHHyaNJWcun8HVnlMnT7HYn+O+2+/gmVOnGG8X1FWNs+G8ab0hiXMuX7zKtHSkUTjjVbUj9TW1r1BR4Hl6NKayrJy9zK4s4zVveoBrF0dcWl9nUm+jpeazL56h32tx9NAuTp++iFaSBx98mheefBpLxMbmGGMMR288wHd+29czGY75/f/8Ya6srbF77wJHD+3h8Uc/wqcfPcWzz57C+Ak3HLmVfm8RYxW3f6XkD//gw5x86uO86c038N6v/WZQisPH7mKx0+HF02fJWp2AlVCqGVZbJApjPNc3rvLnf/03dHbvw/iCb3rLV+CNp51GQSBSgvT6FRXRy/dcsPxpD508CwOEz78J/z+sMTs/Z7i9jReGbqvz8lk0kpJDR+/gzOlnmL9nP3jPcLjFZHuMtabh4zqMd8xshdIxSdIiS3KSKKOdtFkZjHjx9GlOnvwkrUhz481HuP2utzQqyMDQ+2JrwH/va8fetl1Zdkmg2YG/1NeXvKFU+3DQyjNJNaswtSSOYnCKsq6xHrwJ9q0wzbekUYpxlmkFdV3jzYxaS7JEYaoZCs20moFQ1K4p4pwJ8OpI4azHWoEvDLFWCGepixqtYpxQIAN80tU1URxjsUR52lhgKgQWHUco77C1bVIQHNYOCSVUlyRJ0EKHz0zTwXSNDS4oJTTC1myPxoR48aBVT5UOll1AN0UW4pVEKIND1aGR5EWY4ssm0tHj8LaR88mgtlI7gD8CFBMRCrwA1Gyk+MLirMdbkCIoWdp5znw7ZfXaVfI8w1Y11Ww7WIl0BDpMqNIsIU62MdLiqLGmDIlIzmKdp6oLbG2RwpLFGnyNcKC8ABeKGSMDd0FiEcRsDYfU1AhUsA0Ki/GWSWGCrRBPv9NmvtNlNq0pLUElIh2Vk2gNJy8NWd2ecHiuQxIr1ocj5ltdkEE5ZIREmQhrDCrWYAMgVfow1RMywlYW6SwOhxeCGkHiwmtqXIXAE5cVRk/IWj1WV9e5cm2VWEu63Rzn4NrmJqKhMku5I5cMzUsayb4QTcofIU0tyVrUvkZHipZqoWVEWc5w1pDHEWksObxrjqXde9geTtiejahnMBlNGQzG1MbQSjTtPKMtFCqKguXAepypMFiSSOKIkU6hm4ejIoiUQ7oKvA4KN+eR3lE5i/BVwHigsMIQtrnAwNIeLFlIKxOSgJQPk3i1E2tgHTJywQ4nCRZMaRvLqcEbEYCwGLRXSOsbaX34LEsdJhWJC5g8qcK0FiXRDTtJ+ABZl8I31jIfnj8CfID4xxFh4tpIb4UUr6oYdyxsYY8KQFgVim1P4EZJESDzKkDChQvrU+hm+cCMUkEJYp17+f4XngZeHdROSIG1YQrshMBLy4wapbLAnpJhbRTCh8YyHi80rW6famuM9Ipy4mlpzVQOieKo4bbUKKXBGDY2r1FUY7p5m3arjTElW4MNZjPLwcMHuXRpldJ71ra2iIGXzrxE3prj4JF9bF5bYTrxXL16DektnbkYI/Lw2fcZj37yIXqdDsdPHOXY4RMIPFVZkkhBgcOUY4rZmMFgnQNHbmJzI9iOe1EXV9XkSZs0ipEyQHqVDAfiKJMUQ43yAiUEBw8dImr3mM1GrI82Gc8G1M6wMRoy1+nRbrWZzCZsXB2xtLiPjckWy8u7Q3KJaDNa3+TBpx5CEqHiCCsrjPQYGzHemnLjbfvZd+gA0kXY0rI43+X8pU1On30ap2LG4xlZrEl7bWIpqKstJmXegNstWZqFQ7MHLyWamKquWdq1yKUL57iyssbx22/HGYeKIhIFmxszVoaGc6uX6GQdSifxZspff+JvuPOu+zl3/ipboyky9pTbloSYJFXcdPx2jHeYOhyeh+MBF85dZO++vbT7HSIpGQ/HZJni3kN38NQTA+ryEsIKjh4/wmB7ndH2BtZ6krxDpGFr6wJqbjfadsmzBQ4e2E+n36LVWmWwdZ0ouglbSGSkaAnJ0u4WexYXuXrxDGcuwnhmibVi6dAhyqLi5FPPMrd7kf7SApfOnydNY2ZJTDtPOXTwRrJ2TpZ2SXs9igufhTTBEpJopoUna83T37UHUTtmpibPYkgSlqVi9fqA0y+dx1SGWWXodNuMRyOiKCJJInbNd1FoUqUxwe9LXdtgx/UC/Iy5Xh8pcqJIkbdyvK3odXOkyMjjUOTYJpI5jROsliwv7KbTbgUrSgOklXgirVFaEekoBBtIj9KigVYrlA4cI1OXRFFEIR1eCaT31HVFMauJxpOgWMlyfJJjyopKzxqlB2gf9lLrQTjLaDDAVQWzvEPbOOqVa0HFHCmk0AgsUkmqchaGZ3jKqsDXFTaJcDrCbwzwTgCONMmJrMPbElV5fO2DSslalLEIa6mtxQqPJtQd1tQoJRrmRLDwVtZghcMnEdbVCJGz0//xjcLJuRB4Ylz4T6oIoTwWRaJivKtCY8RJjKiwLuzB0kMkJBMfLNkaiZYJEGGVDwe02pDGGSc/+yKtThftJZ0sI52W1HXBcDymnSUU9QwvBdPZjPd807fwzMOfhrjHrBQkaQfrJ1TTISsb11lfW8ci+fCf/wFatzl4aC/9hXlOaMG5C8/SSxPqMmdvv4+UG5y/eo32rgNEIsJTBtWWNcQ6Ck0eC9ZC5Zp6DAVWURUVGoFxDq0VvtoJgQBrPZFKwYJ1FY6QhGmrBrKOAKfCHutKhEoQaGxlKYuKpJuj6qA8901zz3nTTMXDnuqdxdoKFWVgq6D0snUIA7ASHUuMKajljtIsWENlU9eGnBxBrFUYYr58QHpFCfDldWj6Am2hv0en6B/C+fFqdUJwM8DqygV+93f+E06AK0coW1OXFVm/h9se8c9+5F/y8Id+h+3tir/4L/+V9fUNTl9YQWYtNjY2uLR6lbd99Ts4tHsvn37w07x49hzHjhzm7OkzDGuDcJbKhZRXLQVYQ10bhBJUrmQ6Kbjl1rtYynMee+o5sizi8IEb+ZZv/jY+8ae/wYc/9TjTOGbf8jJ3nDhCeWmNVMEMy8ULL3H54hU2Ny33fNU7GKxfZrS+yqc/8mFmXvCpTzzI+asb/NKv/QqV87j+Er6YBUVEVdLKc4wR7N+1l7e+8T7ue83buHL2Av/+F36OjdGAe+65l24S8cM/+a9ZW71EmnR57KGnuGYMkdR0eh2OHznIxRc+S2onjFZXmAwHRNGUxx//G3b3M/7Xf/kj/OJP/ite995v5djenP/7p36edGkvs+2Kd7/nG1maS/nVX/1FonaKLUrKasLupf2kUZfy1Bl6aYs9B05w38238aefeowPfNd38OG/+hs6rYjBYJttn9KrJb12m5XrG2AjhIM8F5iiBpFhbEi1rmtDJ+1QTGYkUYep26Aoa8DRiVtEzqCSDKEkJZaDJ27HDmdsbGywvTWjVo44VZy7tMp73vmVbK1PuO22vVw8f4Xv/I6v4/f+4+/gki6plxQexsWE5555lstXrjb1FgyrmmefPc/8a97OT/3Y/8g/+yffS9nb4nX3PoCxNVpGFGXJuVsP8tcfXeXZF57mxqPHOH70Zg7tWsTZmtfedWtgqynYSVoTQuGMpSxKHn7wcQaDLb7u699NURRc3x6zp99HCXj97cdxIqR779xooWH9ytfhGPt5d2DTBHZ/hyrp7773LJHUXB9uUZQ1yjvm+j2MT9i/Z4m//OSYhx75Ud7+Vd+IUG1UFJOkbebn2iRxFLi/TSjGzqnBCo/ygoW772Nlvsvq2iqz6ZALa+vc7EHTsJmBL1c7sPPBFTWtSlJpgljkH0Cy+aVvKJmQdqV0iBrP2gkIRzWpsSLAn8MhvLFyyWBxqaRFSoWzGrTEWphOaryPqb1rhuEGpYICxLkIVHDvIEFhUSKA6LyzqEZQ4JzHGo8mgJCtrXDOEottTF+HSYxTTMsZSWSp6hFCQCY8pjQBjhzPiOI0HCTdTuJMw0eSzZmVADIUCmwZ4ubjSIYPGzKomwhP3dLAPv1O2kv4/SiYioK1znkSmeA0GBOKyFjIV24yGaLepYjCDeoddWNDEt5TSUOSSKqyYC5P6XY1a6tXMV7Q73WQQlA5SVEapHOIyiKEprah4x4JjXIOnYRDbdIO6XmxEph6gkDjhMYJg7AC3xyIerOSaVFTAsZLWlJDmhDmbICPsH6KdjFaQ7c1Ybg9pTaWyXRKHBuUDQWnFwQLHzHS10wngu22YVbC6asXmO9l9Dst2pmi307BeaamIhEeLTzWmFCoRlDWRWMRDGoxubOwOUHhwg3mnGwK5WCN2r18A6PhmPn5LpPRNpuD7TChbRLWMA7lJYZG3eQVxtvAYnI1+JpIp8gkR1bhPUx1gi8DLgzVKNGcxdWGspwhNXTaGXE3QqgYi+ba5nXMrGZlbRO5uUma7oBuY7LYU3nLdAZJSwcug48D5NQqaquCUgCwRoDTWFfiVIgkBY8UHuU0tTOIVAaoPC4c1JUBFFrIwLG3YLynxgUFETlWOLTPgqXQaxwlQmkipbA+wPDBY1WjanIKr2TgUSgRJr3WomWMb+wCoYku8E3SoJIKoUJDVygZ7AA+TFwBZrMZUmtqu00ko5crRe8bea0LEO1gtwsFt/cgfYSXEi9ANUlHQRD4yniztoYobtIkRWgW7WyEtpnSB4+4x2mDr1VQkwmF9B7hQiqfE75J9wrKSOmDagsToqG7rd1s+ymD8Vm8U9SDGRdnq2ysr3H08D6iG3r08z7d/X16vQ6lqdjanLJ6dZVuJ2LvvpupJp7t7SlZknPDDbt57rnHueWmBV73mvs599JLrA82eOHUKU6euYASljTNiRXs27PMidvuphiWPP/cU5w8eY5bbrmNhfkORmnK7QnPnDnD2ZNPcODAIZCXWV9fI89TFheXKQqPtxXeqjDBqgsEhlg4pAi5gF4IpJOsXLrC9asrDIohw/EqzkGkPUo4ptWQ2CTk7Ta1Demd61tD5pZ2sZjP0T+xxPPlc4i64t7778YWBc4VnFmfwkzwwNveRBxrJttj8naOlhGamDe84U14kdCdm+PZpx9jaC1LeYdyMiOKHOVkSC/rYuuK2pSgo9BUdCV1PQkHPWu46cSNPPXkaYazKYmS2MmUgH7QrQAAIABJREFUjY2NwKKra/btP0C3lZPEkgsrq0zKGY888wiamERLXJA5kKaL2HLGgUOHeOn0Kp987GNY4zlx7Cg333YPSSpJZBz4OrYgyxJm22PGm1OwltFkSren2Hfwtbz0wscZTKZ0RMKBPcdJdEwcSZw1VG5KliR0tCDyjvldh1F6jolfoxhvkOmIVjaH8xN63UXuuK3H8y+d5dTzLzC/tMzBI4cQqua5Z19gZgtuOXqMa9cuMx4OWFg8FhR4DWtwrj+PtBHKxxhXUhUB7B+4XI611U1Wr1wNr5W0DcRUE2nFtCjJWhm9Toc8SVhaWmB7OqaczLDeIBOFUiKkfypJlmcIkRDpiHOXVoFNdu/dTyvOKGczFuf7JJEkVgGMH2cJ8bikGM8ASavXIk0zvLekcRhCWecJo54oNJBlsKUKJ1BaNBAhgdSCPM7ROg0NZhkhnCNSkC/mtPMWrbxF1F2ktzCPMxYZpUQqpEjWrmQ2HCNdmBJ2Mk2sI9pzXeI8Y2lpjmBz25Hth8JWuoLp0OJdjTEVWknKWiJmBXMqIkpSjC2RkcKZCu8NViqcDhbnoqooywkz3yEjxq6u4mWE1JI0jbCVIc1yvLdIESFQTErB6rUN0kgzqwRWj5BJiZAeqcB5jW5qMu89ZT1lPJkx1++ztT1hTuRMZ1NcWYfEUREsfqY2GAxGVnhXU1obFGBeIn1E6Qtwgu3JmKyTsnf/Ls5sX2Z7PCJJI4ws6IiEG284TGkc1zfX2XfDEU698DTXr6+AnOHqAp1GHDh2mNHWFc5fuEiet1EotosJvXabPMm5+cTNnHpmStFeDsxB5+m3OjhlmUzO8eE//W3uft1b6WS7OH7zzSCDWrYuC1xdIqjxVAh0KACVbkJDbBPeEqyP0jZYA0JSaqQESZwGVbxVQQ0MhOzgJvxDZzgZUgpVkqKjYFHUOsWaCcH+Ll8e5gkaNYEQGAtZGlOIoKYTOgDepQpwViXTAK9XUWCGiLBfKhHsmcITrPY7jSpeUSd9eTWTvsDlv+CX/01+7KtfGykls9mUMy+9yE0338FNJ27GO8Hzzz/Or/7Wh/C5xl4R2OEWu3btY/7IPr7iXV/Nb//sv2f/ocPMLSwzP99n8vCnUAJOn7rMhfOb/Iff/BBP/f6vc/b0SVIrUHF4z52RiAhUHOorhQjoDmF58vFnKGYzFpZb2LHnxc8+zz//J9/J+QsrRO0e/Uizf6HDb/3S73DT7bfywtPP05nr8vAnnuLOt34D+qmn2LhykWP7D/JXn/gET04mxK2EvYcOc+bMS/hMELkIKWZoH/TxdlZQ+oi8lSKEop2mLC/2eOL3Ps325lXueMNXEk0Nu265jX/3o9/L13z3P+Xkn3yUP/jIx1HzCwyHY+6442buvvkQ/voqp8+fZ1wreguLtDPBwvIyqjfPeHCNtc2rlOML/NPv+WlMbx6PYHerx3vf8Xb+z//9B5lZzWyrpjI1SEUnzUgV4ATnz1zg3MUn2CivceKmQ2ysrHH6xRe4dvkigykgJlyfThmOBpRFRRonKA2T6Yw07RD7GIMjiSWzaYWR0MtyfuaXfpl3fuVb6B7ci7QlWntwEmsKRBnRX+zx+GceY3lxCSk1hQrcutF0m6W5Fu98+5vJVcL3/4sPsrirx4E9e5gRowHjK3wkufu+u/i2938/v/nLv4rIBwy2n0Zv1/zE//Bt3Pn6B5AJ7NqzxMXBdQSSwXCKwFJXgicefY7O/DLrnQP86YV1rsWXeNuxo/TSFoZwrm00ELhGrUhTi0fdLvOuz2Ka4+KEbVczNZZenCBVRaQ+r73wqp70q9VKL983X+Qegs+3kL1aX/jKn0+nE2bFhDhN2bf7BrRUjCdTRrOKXqoRSvLi2Rf42je9jluO3Rv+nXCNQKARcOxoBDwEeKFo0jhhNC0pnWd+YYFSa5YOL6OlCns08nOfxH+36wutdj6k0YqgwtbYcBZWQQTBy/nd//+vL3lDSWiwZQ1eEadgqoLKaHSSkwmDFxDpsHkG20xEZQVpEiO0IzUaHXnqssISIoOVCKwB30Sz48Lk0IkdFmXY7KqZI07CQc1aRSVAa0GmoDQeawMnxXqLMIou4QNa1zXr02skMbSEoKo0tQBnBbX2RLpiUmyBaw623jcSQIUSitobnJBkqQavcI2NrSp90HW4AE2sgIjGxoOlQDQxwxZJmA7VviAmNL+8TwHbxKYHqSQybmTJFuk0QoSiXQpQMjSunLNs14KiKOm127RzzdbGBviIXh6TaDA12BLKeoQkWBEdEmtVYMoQoq7NZIJMgr3KWIuSEZDgtEB6jRQap3xIUVOSTrvL2uYY7Wqkj6msIcejlCCWEuMsMQneC5R3LPa6jMYzqgYmrVTj8ReykX6H99vYcOhYWDzI9asbSGVZ2dxmbTgmk5pWrul1UtI4Yqk/h9MSW5c4XSJsjlciWCbqgpZ0VM6jZQCBxkJQC49xNaaOqR0oW7N7oYOv9yHjmKIcgwxpObV3JGgM4KVB+VDMealoZSn33nMzR/bdQDEtQrpLVVBV27i6hUsTal+gVIZwCiNqnK9DalCUoKkxxmIIKhahoJMK4labTqbJOn0sM4pZxXg8YzIpGZeOjckV8lGP7em44XxApCJiGVLsrHMQhSQ2LyQiapKJrEIQh5hmG9gOQVwnwccIZwMXJMh5QpKb9KhSIhNNbQqUWAZXkgjFVHjwGtCkqk1RFiBDaqESCuFDM9b5kH4WS08sLIXUyGZCLqQKzVtC+qLzwYJRVAUO1bDRQoMOQnGPB1NVBC6Ja1RI/mXwX2gOhHQlEf56SG4ToKQGXGO1MK/Icz2AI26sd9LvgGOD2kGIV+YRzjmc1CREVFTEOqEuKnTsKJ0hFjpMBIQLBw0ftmvhJAZLp91ioTfHbFZz8XKXbi8HB2fPn6fdirlwbY0Dh29mMdvLaDzEeo/WCbeeuBXnbqIyI/AJSZqjfU0nz4iTjFtuu5PLays8+cQj5EkOwpNlCe1uxnS7ZDqbMKNmfjmmFefM78k5sPuruTy4ztOPP4Gpal7zwP1MtmtOnXqIG48fJc3mWB9ep9fN0EoxHm+BS9BSoURo3s+oQStMpRE2BlEEhZgynL54kroyoDxpmtHtZVSxo6igKivWNtbxtmJ+uc/pcy/R78fUzOjPd9i6NOXRTz7MTbffxE233cL6yhVOn7pArtt8xbteS6ffR/mIjfVrSG+QWuJksIAeP3CEA8fuYGvtOmL9IvVkzN233EGsItJORqvVpdNtk0RpsFl6iZkUOBsSCaXxdJZ2sf/GLV545EHuf8MDqHbOvqPH2Zxuc/jQIW48cjPG1ihfUkwnXNkqmNVhSpzEhrm8xaVrU2679TizasJ4ClcuXELYkvtefx+33nETxfYYXFCcSuG4ePJprg9O0+vNc88bv4IzF88Qi4gb9h6k312kGt7F4TxjtDmknFnyfoqUMZEMypLJrEC6jD179zIYTPjkH/0Wuw8cYUTN0sIiL517hDhxLM8t8MyLT+HjOfYeOsT11Ws88ehDtPs9+nN9GG8wLivSvEu9MeXq5QGdfJ5FNFJ6pHY4bVE+KJyqIEdmMBjw0NVr4BxzvRZz8x3mO3sQomD3wm7G5YTRpODAvr1UNYwnIzY2B2yuDzFl2N8W5gS9bovdS0vccGg/SZJQ1ZpiWjKderanE4rC4AT0e328MBgHpq7BC5Lmvk90TFFWiDpwY8pyhiDFi5Ca5rxBqxRBCO9w1gWOkJABoAz4hoOId6jGjuRlCHIQtaCqJbryxN42PCLAGYwIQEwlBVo04RsyIVExcZIhrURZAImwJkwdfPh/8M3AIzCdojglzxOSuE3ayenN9Yl0irUGsEgpqWtHXVZ4GxplcRLRyrvknTZ53mN+aRkfKaR0RDrDGojTjMlkE4nHSUeeKtqJZKE/T5LlzC/1kVGKtQ7jKjwOJQOvSUjYHE3oJAmudtjpFq7dJVIRpTQYU7HDO7Te44SmLmpmg03yPGXj+gadfsnWxuBltgXSk0QdtjYHzIYVq+sDkoXdJEoTZ5Z8rk/sc1Q+z9Uraww2ztLrJ6xdD0lRc7vnSaTipfNbdPuLCKd4w/33cOrUS8R5j1k54+mnPkMx2mZpuc/V1U1qA1EMaTTPDbft57EnnuSzL3yKLDnE0RPH8ZUjarXDvuMabhGSSCpiDUqWxF5ivYIoDFcFUHuDFRBJQRxp5E50tg8MK6UkSjTcTG9xXlAbQywDCysRCpVIqGukjwgcwVdUvGF4YnBWo3VGokNUOY3iyRvb4CUaCDgRlgojKoQIya3eC7wI2nIhJU6GfS6Y1ZsJ6pf99epD53/rK5hSjTGUsxnj8QgpBPfe+waSrI0V4CYjfv2DH8OJhAOLR/ixf/Gv2NXv89XvehfPfPoxfusn/y2ukyGVYjAYEkcJw60pH//oJ/ipn/05Pvgz/4Zf+5Ef4uFn/gzjBDIC6SuiJEURc3DfPq5ev452NVo2SnBpuP3ue/nAd/1P/PFv/TwPP/0sf/RHf8g73/UefvH/+jH+5/d9J1F3joO7ulxa3eIv/+ITPPDOB2j1+9z95jez/8hxrr10iknk8alCxhHVTDCZFmxNLnJw9wEur7xEFUm+5h3v4FN/+WFqp7C1pSi3iRLPoUPHOXrTHUjdZv7oLdy5NuAfv/8DPPORj7Dupjxz9jKHnnyCn/jBHyHZt4+WT/mF3/s1Ln3mQeIspfsVOfdMtzl/cYX1tQ0iPceFtRmbVc1P/x8/w/KRm/ngj/wEb33fN/Hs7/0+73zde/jxD/5rfvmHfpCNQrG+volIc3AO5wwPP/kMtx07xNEbd/HoI4/w8IP389hjT/PkySusb1wjzjt4n9HqG4aDYVDnOEecaKqqQhrB+7/nB1ioJM+dOku31+aTn/wIaSwZbM4ougnf84HvoL+3T+QFUxcU7N4GAYDxFetbA4bjAfe9+U2sbIzZmmwzMkNa3TkGq1d54Kvewnd/6weQ3Q5V4fnjj3+Kt7/lbfz1px/BCkXk4cyps1xavYBRQamodYJY3scf/smDfOoz5zn0lvs4ffk6F1avMRpvMK0ty/05opbjtjv20112/Og//z4Eiul0QhIl4D1avDplbaf104TRWIOtDfv3HWFtPOHUygrHd81z4fo6o+FV7rjpVuY7y6/YYz+nW/S375ov1of5wiyinbb8zq+hGV7UjrW1K9ywax6VtfB4sjwlcnFY9zy8+51fw437lvDC4B3gJR7XBBHIndnNF7x6nRxR98E7hpFmvre/UV2pv/tJ/L2vHabR326Yfe71hV7Ez/s3jYvEN0or2ZjeRutDfFIE1RWEPeRL2AT7kjeUnDUYYUllirCOmoj+XBtvS+pIUVaWSIeENakFcaxAOLwzqNjRascIIRjUGtdgG60N5BOldZPI5jEGauNwjnAQRRILBcIGe1hdESct8sRhjaesa/CeJAoRhkmSEgFGiiAPLi3bhUBkEZF3SByVC7BmYQTTegw0FjIlQEmk3vG2O4z1dOLdxDpFVQ4poa63UHHcWOMsSZohnQFvUXikCz53HaWoKAuSdZcgIXAJvCCWCXU1bRLiFEqnAdbtLU4C3hPvAMttTZ6lFKWlHymWOzmFqZlNJsz3lpiOx+StFF8HdYbHgwlYcuMESZIS6wD6FA1nKFIBHy4gyJ/xARrqolBo+CZ1xnlqW4UOvAwH7iRSjMqSaDCm129TyZACI4SlMpYkTul2OrSyEeO6YFLU9HoRnU7O5nCbygalihAQSUtd11xc2WCunZLZjKr2TKqSophRDidsjCrSOGJja0avm5HEipbRaBkgySrSaARZq0u9PcP70BSSzmOsRMo0NHYapkZZWjqdLijBDYu72d6aIoRHu2BfcgKkVzgswgcbVaQjFrsd0iRmOpuiZIIUcZg4CslsViKtDxHhkcSYGiegNFVozvjAYkBIJA5la1xpqZVjMBiAgLTTpp2mpCpFuYrhYEjS7zItms66tCilQ+T4eJtO1sLXkKnQULG+QhQarUPijDcGLyWRkDgTFiE81N5hvUE5hbUKrEDIkIBgJHhTkWqBcFWYuvjQTLA+FKCFLym9D4BtZ5EugP0cFdLWSAFV5THOkQrfyOhppvKhsaRQeBtAo8IHNVWaxGitcd6CFygdUTlDFEcwLQJ8PEjOCATTBt7dNMWQQSnjXYiyVjvpks20QohXNjLYsQpEzXjWNUlFQSHYBIyHxVTsNMtqClOhIsL7KEFoibcBxBuadk06jybAxp2jspY4Sjhy6EBQMCnB5vqQbtdw7MT9WDxpLIi0YDQccW19naqasG/fYdqtPtJBnmqSJCPOYpI4ZnHhAFdXVtiaTDl99jL7l/eQxx1sVJN1IoxxVJXk3LlVZtuf4sTRw+zfvcQNywvsfsfbOfn0aZ5/9Fkmfkp3qceN+w+xtT5l74H9pLqNKWsEktrXXLhyhd3LyyzEEb4cUhVhGrhzqFF44rYkUhHHbzrCrl27OX/pPO12hhQ5g+2K2fQ6K9fWEU7hKk+nkxMlFZvDq3z4z/6clStbJCrlsy+cZjAYgrdMpjNe89rXMN9dopjNOHvlNP1ei+m2YK6bkMkc7QX7Dx5ClFu85fUP8NgLD6FkxK133MHGtQFnL1xiffMktjbs2rOXEzedoJ21kMYFrpeqMXjOPv8c3pUk8x3+6qGPkeRdTp95iVaacuzwYTpZymg0YWtrkyOHjzI9OSLTlvlOzt4bDvDSyZMoVbFw4AZynXDms+e5ev48r3/z65nrz7N1ZSOoGyMNoqauDHsOnqC3vBfvLYsLS3T6XdqdeawpcNZz+PCdbE02OHf6PPGeFOEVsvagDVrFqDzGyCnJZBpSyeaWuDq7gqkkp09dZG5Xm3vuvIfVcxcZDyuidoWzMw4c2M/1a9cYDgZYU9LL+1y5dBXhHLFMWbmyQWUU/cW9GN1idfV6AEVbqLwNRDYdM94q2LtnF/sP7iISgrm5nNjHaK24tjlg9foG83MLbG7NiNOYtNNjdPka1gi6nR7OWSZFgdSe/nyHJMvIW210oZAqJ0qgdoJJNUMRAPxaReANUZKENU5IhNYIEyxHlajDbE4nGOtCwlYcY6oamUu8tygpiSMNInDUdtg/O1HvntD0Q0mkrVEiRnuHEpZICqRwQQ2yw4qUEqEk3r6iOnI4CunJYwFURCINTQalguVSeZytw1LmQnDEjkeptjWymCDjEA1vRRX2dWcxrmlkSR/6Ul4QIZCuQpga521IP5ICb8I+66XBOdOsjQJfR0TEjS3Oo6MAlw7NtcDy8c2aHFJFa1IpaecJQkakSYRIIuI4QSiPt66JoRchut6HmiLqtmhlLdppi95cm95ChnMSpRVVZUiiFGcK2kuL7Dp8glkxYd/eW9DUOCe5euoFtibbdHYfZDTM8LS4696jvPDc85TTks+ceZEszYko6He6PPbk49ResqQThqN1Lly8hKsEUTTD1QIj5jGTYD2Z+Ih3ve/bMW5CHOWYypP1etTlLCiyqprJaIwwjiqNEdc3mBoQOgl7mA2HoZBgmlBUJYPtEXFZ0qkt28MRBtmAzT1xHDf7WlAYWu+ZeY2KPIUt8D6iqiY4VwYLtREhNdkbaL7Hy2oipajLKuw9lSeKIqxzKA2xihHKk+gkDEelwhJqcekdCeHQuwPi/fKHcn/eY3vVKelz/mSn7v2HujyN2lqhW21arVaj8gjpuLGUzGTMD//SzzIZzVjYvRDqeRscD6996wPcfuftfOjf/FuurF5kaX4PKME/evd72d7c5Bd+8oNc3Vhnfj5lz64W+WCNTCsSrRlPPJWccGnlHNgYg0CjqKcFshaYyYhf+7mfZvXyRYRIGYwG5K0u3/7VX8+u4ydYXohwqebErcdY/8wL/Of/8nF0K+G1d9/BG9/8Tu66+37+7CN/xh8/+SLjahrOrA5aeZcTdx7h2sp59u87wHe85908+qd/gm23sKZEhi4m+w7fwoEb76GVZbR1l/d/9/ezvLRI3NL8ys/9PEZp/voHfojlQwexpeOP//KjjFdOMhKOOx74Sk498kk6e+Y5+R9+CVOX7F1e5B//L++nn3U4+n0f5Dd++sd54A1fxWBzkxt37ec97/1HfOIv/oi5+29j+LvPk6UtbFi+WFjcRTvNGAxHpC2F14onn3uOk+evsLgsKEyB1CnjWUGcZszNLbGxvolEUJoSpVpkrRYdZ3nDm9/Anbed4Pu+9/vJb/Bk9QazvE9pC3Sa0haQqgg/BiF1sCR6gSkmmMpQVYKf/dlfpRWleOVQacrWxia97gL/2w//MJ986jTv+7av5aE/+0vuvftuXnjwSVbWNsjSFITg4J6D/OYv/xpq9y6Goymv/br38eMf+AD9dvvlj+X5Tz/J+YuPMdpcY/eewzhv2RiOOXP6IuPMcWa4yQ1Zl4dePMnthw9jkoipqzjSnqOJR8DjMUCsFFmW87rX3Mfvf/gjtFLD6297LeB5+vHT/PanH2H/88/xA+/7TrrtLonaCaN5eWX6e68kX0wJ2XgYmsfVfCU0Dz55mtUrT/It7/0WDh++HSUUiQwRSl447rv9NpRs4i/k3+9R7Kx93nvSNEdHkrzdpt28vq9Omftiz+HVKXSvBoh/7vP7/Gb9F1pzd5por/5++m/3oJrz1Od+OwEGIjkJa7ttmLdfwnX9S95QinVMpDyRArTGG8+sGGJqS5IktHJJJKF2wYMqpcXJkAAijcKLADjTiMBpVIo6dF7CREeFj2WuFc7FVMYjiEAoYl+FDSMGXyjSGLyvKUywqbQ7EtlwjLRu+nYu2FgylWJsACYmeootBZrGHmSDXSt8rkxIPlIJ3nqK0mG9RcdA5PDeoDSUlWFtuIXUEdL6oG4px0TCkmuFRlATomMj49FYJBIlknCeVjXOCGoB1lYhBlZqnA9KiZDGEWw6wgdbkhSestgCrWirVmgsSMh0B2cN3V4AoNlGKaG9REpPEMZ6pHU4DCJSlNZQOYt1gljEbK6Hg4RDhcaBSJDSYwlqtJ0FxzaJbUhJUYO3nrXBEB9ZsIo0CeoP5wS2qPFO0kljtmdjJrMps9k0NK7kTuJYSJjBCbyr2Li2TvfAAqBI05Q872N8i2lVMptWzIqSUTXjyuY2eaSZayVMp9DuJkRCYmSwbkWJxhEUObauaXJtmsy/wKWKtURGcbBldtpUdY1UzWdOiAbqLAi0aof0ktpUjKcTFuwczgmcN1gzQdgIbRJ8PUMIg1YJjgJrwzM0zuBsmIhbY4PcUoERgtq5hhMkA2TcgnQBlG2dCfZOW9LOW0HhIyQRmlQpIiEpigpTlGw3FkotBWniSb3CuBBv700d1G1Zird1cze7wD5qwNYe8EISSRnscTI05FRlsJFCCEddmmYxMxBFoWGjQDepa9ZblHSYqEYJ84pFwBTkBJuR2+GENak13nucteG1wWGaqO7wcwiPzbjAFN8p3hr7imz4KN4FpdJOWpIn8MacadL/Gvupb+yreB8ati78fdV8H17mmzXLsAgbhiRwVJwMMczWGmrnQjFlPZIwEZEywSr9/1L35kG7nnd93+da7uVZ3/Xsi3RW6WhfLMuSbYyDFwLGBAZawIHSYQltSFtCmqah6bQpk2knMEk7DRQITElCQ4Jp2O16xbaQLGRLlmRLOufo7Nu7L896L9fSP373+0ryQkzxkOkzc5Z5t+d57+e+ruv3+/6+Cx6RRUYfhdmjIQaD0pq6LkjICK7k1IlDJHmCj4peu8NgOOHK1Wu4smB1cwNtS6Lfi9UdYgyk7RxrEmyaE6Mnyy2nTp1hbfkWWkfqOEAZx9rqOp0sxxoYlTW2A4cO7eHShStcvnCd/cf3cWT/IQ4cneOOe45x7tJVLp5/hac/+zmmruTRBx6lNd9hu9jm1tIq124s8/BDD5AkhtXNDQbbrpEpB4g7seGevN2jk+XcffeDjCYDZnpdjh4+Qqc3z4Vr17l4ZZ1ev4PSCcsb68z2DL4IHD/9AJ994jnmZmb4prc9SnCKL33xFabDbW4/eYzUBLZGy4xGE86dPwdJ4L4zD2DzjKATzl28yPGTJ0nTBG3hTfc+Skgtvo7UzkGoOHXsODdv3uSFl15gXA55013300ozjB/jsDhdk2YtDh85ybkLL3PlhbOAxZFjk70ok3Nl5QbPff4pynIEzrM+2OLkqbvxzjDYDEycwriEMB4xMSUXzr7IfW+6j5m5Waab2+SLc5IqaMVYud/tE5SiXXqK0RBfaTKdEMqikYg7Kldw4ewFxuMxvc4M2lh8VBgdibFCBUuatCmTmvnFKdvjIfOzh7jt4HFu3dpiWkzo5zmdE2c4fNtxVjeGlK7m6q2rLCwucNvpkwy3R0wmG3h3ifWtIXv27iHsq1i5vsalsy9y95vfxUy/S0IUTx4vBs2JTTh++gDHjx+lqqfUE4+pIJiStY2C8WSMVnDz1iqYdWb783Ra4vHXSnNicIRQk6UKm2lcrJmMRyitcT5F65S0ndMvAg6Frwu0FlGR+GA1RvsII0hFWe/WC4hM8EQjQyVrLM5VBOfQxjTm/GC1bgzlhb0Zg+w7NQLAqChnqGxAgUQriEIr9w14tBPRLkMYOWeDcgQcRhmqUDaSd/E9id4RtKYuxdtIK41pZGhgMViMFllebsRvUKOF2WM0+LgbDe+io1COkDQBFSgsAZOIZFhF25h4B6wOVLGSOscmKBNA1STaY7TDGAkBUUo1qbJa6oLY7LHRUbsSYxw+tFGIj4wGXPQEBzRnsIpRrndZ4ayBLJV0o+DRXnz7TDNgqOvIcDzg8tVr5J0e+72ilbZYXV/nzofewpdefJ7t1VsYPP3+HGU5IbGg0czP7qHf6zAd36KYrmNNC6M7uKokUHH42BHcpGS6NWGzmrIw06M/3yJD0eq3eeWFj3H7qcdJtebsC88wf3Afh48eQ1mLSXJMWmBIlYaDAAAgAElEQVTzFlm3Q2dmhtn5vcLI1QJiGpsw3RK5o8WgjCZNM5I0RSWWNGvhXS0pfipK0EiIVEVJHRVTE0jTlOm0ZnlpCasixqbUdUGWptR1jXMwLQOoktoX2O1V7EpORGNsiqsrlBWPyRgj25MRWkXqyhPMkGg3SRKRzFtryTs9UAaTiNTbGGk8QpBp/v8vH38JpCWlFNa+sa0Ss/pGWBMiWbdDdIHW/jZTV2AHE175xBPc9d3fRqgcup3y8ONv4bef+ATLW9tcv/kkR287wqm77masHLVRtNpdhiub9LN5RsUGSdYnySNGW+rSY2zAJsLmDlHhVMmrVy/R7y8wd+Aoy+df4W/+1H/DvpDiklnOXjzPpfOWv/797+ULT36OQaXozi5y28kDJG24evNVWguz3HbiNq6ur2OjbfwkPdbApfOv8B3f/R/z1kfv5WMf/jB3PfggT73wp+TZLMEF6mnFeHtAq93COc/Jx9/E7OI+Ln36U/zbf/UbqDzFV549hw8xGE44c/okn/no7/LsEx/mlcsXeVexwXve/4PcfP4LtLRmJYH9B3sc6Le5dv0yv/+P/wue/9I5rly7zl99//vpZClPf/aT3H3mMd52/5spzl7nl/7od7HtFlan3H3mLpZuLbO8XRCnnqzd5SMf+zj9/gytWDOdlERaZNpQlyWlC3T6+3j0He8ltZpPfeh32d7c4ud+/p/wz3/ln7O9PSTttImDCbQ0VZwSK00VZRxf1hV5npOlKWVZ4VWNNYFISmos3W4HE2qmxZRxpXjLY+/g2Sc+zmc+9UVO3X2U7/i29/P0732UP/jIH7O1tEGv2yFqTVlM+eznniF2U/7xT/zn3Lp4jre9/T1o5ZkMR3ijSI0m9HImg8DP/dwvoU+f5Kd++AfYXlvGm4r3vuWbONGbwwHvfPDBZql4RrWkHcvCET9aFSOFLzHRMNdpcWpxnuvXV3nsftkn7nvsPt7+jke4cPkag/GEmW5/t1jekbL9mcvw68Ct34CdxB3sWHrOSbC8fGGDpZVljh27g6gyQEvAg2vCgfSOUfjXYkB95ZoG3hBE98av/7M3lq+W+vjVPOi+/LXI17zxgghjTDcKiZ2+LH4VltEOU0n+qyKgYTC6yWZcYzzYJnZmQQVa38D9/BsOKGllm2KiBt1GG090oCyUakoVFMaLYbHRMsIJMcj8OmqqKMycpJ0zrRytLBXJiZF/nZsSXIny4u/T67exKhNwz3lcvUWJp9fLiEyogiJJDXkrRwdJatG6YSU0lD5jDDP9LmU1AeWw1uJ9KcbTtktQAaNSKY6iIjEpVgGkZM07qU0k1Qmu8uigSFJLr9sn0YaqDnjlSFJNqCsCUEZAJQJU+UZmZGTKp42hrIcEF0m0mNzWwZEqsCrgcThKrNIolROpReajkUmHhzKUBAUmajHBVL6h+Mn7VAehOIOS5hZP9FN8iCiVQZKToHEolI5MiwJVlYRo8USiHkIUeaGKYlLsEc8J7RXKSSxjmiSsD7ZJNh1tnVBMxEvLIhNYH8G7SGpShpOC7eGQbruNNZJvtzNviwg9vK6mDMcj8qxFMR2jlCHJUvIkpTvXx9WBspoyKSqKKnBzY8zyYEw3T5lrtdCt5n1KDBoBNZ2PYuwePUnm8aFiUmuJ+G2a/bX1dUbjKZYEpyqMUYRaJsw6Sgyvto683SMxCVYZTISgHLXThKpiZe2KJPuZJkIaS13VoDPquiLXkipUe2HlxQSstUQPq5trDKYj1EYkt2NUYqmKAUGljEclR/szVJUAqta0MDYjzSzz/RbTaSG+Y9FRDkuG9ZjKe/r9PknWxgdHmEzIYkJbpxgiQXmC8xS+JIymZNY0zVkjcSTSKjOKUUHca4lBwKIIjSm1RBdX0xLnC8ZGi4cIEYKkEY2GhZjke4+JRnzGYmzMv1WTTuQafw71Og8l05jIS9NolMbFiNsBd7R4dcQAMrnQBKPQ1mISMf6Wybpu4pajSHaUyDyiUs33CzPSRYnQfu0hMGJUOzkJMjXwTXITlbCrdAiooMiUQTYoj2qYh4oIxoKPBFejE4PzCqsCVlmsNqiY4IwmuhpjUwkiiJ6Z/gx13WKtWGOmt4BWbWloVUD7QKYTkmjRSoq0xYW9qBg4f/4ysXZ4oCynEEqMSZhb6PH4I49w+4FD1Ccrrl66wdK1LVbO3aA72+KOu05z9PhJjhw4yLNfeBomQ1760qsk2VV0ZhlOJmwOBpx79TytrM+rZ88RnMPbiqSTkuSGycgRraKYBI7umaPXa6O1Ybu9RZq0WLp5nYsXXqEKDkvKdDplz+wcNlEMxmPOvfwqrcxy953HObT3EFXlSB6xvPj8cyS5wegUqxKKyRSlIt3uPFfXRqS6RZpobi3fZDzdYG5mD3Nzfbr5IrryaGPo9zrcd+89aK24cPVVsIpXL1+g27Lcf+Jepq6klaSYaJnfM8vZsy9z9cZFEqPxNGbj1jOejrlw/hWWl26gCGAMWavHzZXLKDdLt91hPJwwrqcMx0NWLi/Rnzfcc98Z8bIjknZSjE4xpISoBVeuPV55HCWpjoJb+EDwqrHy0mgbUapgsHmJ7r7TaCuecsaKl1mIntSCKhMee/RtDLciefCcOryfykRatss4jhmOJ6ytreANFOUa6xtjEhTD7QlZp8WehT0MJmNurW5z5x23Mdud5dzVa2xNP8ZoMiRo8FVJcBGdKPr9HgcP7pNBjlMQHcFYcp0xrQeUrqY/1yXpBG7dXKIox5gkwVUVeZqyNdzGe0d3po0lQwXFpHK0nGpCNVKiN7RnZ/FWs7F6i7qqMZ022mphO8ZGppZovBYDfWeCmA0nVs5BhXjjNEyCupY6QKnXVrxCPBljIyNWUXxCCB5jxdA/GqipMaFFJJG9qA6709AYQEWDDwofJSo+eo0JBudAqUR+jtKoIEA1MeBjJAaRhe/4woWgxGMrRlQ0uOAxiaUuK2FTqSYSHkuiEqZR9isfPT4EAdCDpMeGIKaxWilJA7UWguxXWqcEJW6QKshwjyZ5M4QgAwAlrGqb5k0kc0MFSLTUFlGeL+id6bT8bZSRNLEkEhPfYHyG6Cpi1BRFQZalKK2oKsd8fw+9/izlYIjpKlr9lNn5BfJOh1Z3liuXv8BosEK5PsbqLkoNmGl7cuMInQVuLt0gyaA/M4M2OZmpGS1vY2zGtKzwGJLMY1zg5uYadvsmr1w8z+kz34wvRjzz1Id58ze/lwNHjlHXNUU5wdcFig6KgMG+ZnaqNMqDTSSIIkRHK8vIsowsTcjSlLzdJm91CLXDB4c2mjgVn8Y0S0itIk3atBLDnrk99GbnpWpTYJI+wUc2lw1Zakn60G51yLKM2dl52r1+w8zOUThaeZfl5jycafcw1pK1c2yisc0Q2CYaY4VRQIh0ewJQS5iM+oaASf9BDL1f07D/pT9ea6KjeI8B0cp1zKOF2Tm++JGnSO+/h7aPtBdmuO4KsoVZ/NY6ymZcunqVVy6+hDUaYsLFW6uk0VKVjhMnHmJcFYw3NsijIvgpedqhc2SB0yeOc+7zn6YKgcXF/eRJh+3VFfI048xdJzj31Ev8+E/+Df7+z/w0x+9+iE8/e57zV5d58NHHqDY2ePJPPsvs4hwXr6wyHg9ZWVrn6NFj3Lh+jclkBHmH4WjIX33Pt/JDP/I3eOo3/iWt3n5yfZl9c3vxOrI9qCjrCc99/k/57X/xy9y6coHSwV95//cSuy3avTnKrW1ihO3hkD39PqNig//xf/pH1JQk3ZzZ85e4d+U61y5d503f8l6GT32Mjz7zIpeHIy6fu8SNwZDczNLv3sYn/vAptkdD5mdn+LGf+Ads31zibd/zfbx05RxPXLxIHSJPPPFpJtOSH/jxv8mFP3mSF0ZbHD55N6dPnOGPP/KbkCV0rAMS5m67nbUbS3Tm5rj92Emuvvg5imJKVJ5TDz5EgiK9cYP17S0GpSVPNLPtQ2xsX5PUzU4XVdUszO1ha31ZQPc6UHtNYiJGR+bzlNGoRqmMu08c5Wg/49Ztp9naWkOnmjN3P8DRg3v58f/sJ3nm9z/G0198num0YmkyYeHoUb7zP/lhjh0/w7UnX+SXf/E3eODkab75fe9kWBV84VNP8OTH/5Dbju3nB/6H/5qDvX1Qjvn9T/8xN65f4cDifDN+icJgNgaFZSbdWaNyjgYiL62ucH5tjXtnu9y2eIiDJ89wIEX2e+Bwpy1nTmY5ML+HytUkmcAMuwCQZteX6SsXy79/f9j9TIjsmOEqIE8TDszP0c9yvvD859izuMji4gFaeV/qfO/Isnx3TUKTWv069tDrP/flrB85TyV507n6dcDPzhd/7de8m3T3OnDpjc8XdlVBr/3Q3V3jNeIRUX7vKF7M8ks0pAv07l6zOyBvgDzvSvEB3VglzkwF51Ai8/tGPr7xptyhxIdAVQViqMV3wEailsJOeYVzgWhEquZCkKk+MI4R7SBLPK6uhKFEJAZHHadCvw2uMTtUVFVJoi3aeFRI0Aaq0on9ig0UDpQ1mKiJsaYBKKWpCwGCI+LROpWpKo2BbBSD8BgjSZpBLNFYVIDaQVmVOGtRscQHofVjxEMgaiWx7FlGr9WhLCZ08gxlUhQOnSbUfkpVO7QOkuKihKFSR+h1umKM6BNcLKm8wzST3kSLrYKkvaVC61eiZ3WhJrddLDnaamonqWo2kSbckDYNuwftiDFDIxG1MVaoCInJxYhRW1AJUXmCCuJ6H1O0iSis6E9l5osKrpEVKTRCkbbGMzUWG5GpMYqiSsiTWqaTKqBwYkSpIEsic62U7WnNqKhIk4YWymt/vK9JFCwudul3uiTKUoZIUXsmwwKlSmxa0G536Pf7dDoB5wJlOWVSlNSlZ6mYYLZhdeUK3Zk27Syn3WqTRE1wHhdrap/gvNwjMTpJaVEpt1Y3cVFkBkkjmTXWiB+PB1QCKlBNHKurWyzOzEkjUyOpCkkkyXKyJEOpjKIYYnRCaQuSpEtRTFHBoHEkWpFo9TpKpqfVSugUXbS1tHszVPWUVndGwBZtwEIxLIg+YI2RpDtlCCGSWksna6OThHquJtaGsqzQucZ4x8raFNXOGZcjyuCoA1htMbHGu0qm9T6SpgkGjXMVCqjUlKgdIz8lM310DHg1xcaEVDeG9wiz0NqEoORgMlpBsNgkaySVgNZUsUYrkZjIvinyux1pSe1qaufxrk1Qsj6jEpC4nWX4uhbvGVc3lFiZ2guDCGE7OfHAct7JfuUcqmGnmaZRi5jGEF6Q/TSx0BjeByXykR2YUwz6ISq53jpqNMI62iq38aWnziJBDTEYsryF7bSJO+eCkiIzhoZtqGpoUqBiI6WEtAEKU9odQyRhOt1msdhLN2/R7baaqO+Icw4Xa5ybElUXbVKSLOf0ydNYLJubS1y4chUrrh94F0msot3KqAJoWiweOcSRY8dZGazx5B9/lKXNJfJ0jj0H9rA23ObwgcO08xlu3LzBxsoytatQMXLl8k2KyWXSVCQxSotEOWsl5EnKyvYAYmA8Lbh+7Rp52qPfMGrSvEWWZYQismffAsPJlPl9+ykmY4bbF5lb2MvenuPQgYPcuHqN/sIC1bQgb7Xo5mIIPy0r5vYtcjqF61fXOLQ4R6eTs7p8gzopubZ0gxtLGxzaP8uRIxEdNEYpiqqi1+0yrSpwnjtvP87V6zfZ2FhFnRJQcVQNaMcuz33xGZaXrlGVjgP7D1NVI7q9fQwGjuefeRrbyjly5CiDzRWwhtmFI+zZN8to3TM7M482CmtTvvDclyiHmzz+9scph2OKEnwNNmpi0ESlJRkwRkICJknxW4rhdAS+InFtHI1XWB04uHiQfjuhmwvrSmuJmxfQvmGNRkUspwy2p9SVJwZLUpaU1uLtlFCXRA2olOlkm7qK7N+/n4ARA2IX6LQyDiwusrZ+i0tXrrNvZpY3P/Jmzl+8ytr6EsZaqsYwWvavQB0cgYjzkcrVTGpwqeXi6i2q6QRWYTQp6Hba6KBR0XPgyF6s1pg0MB5PCAaiBhcCaPBKYaIBDCbJyLTB1R5tNUmSiLSsSUSLUaTqpgH5rVHYoEmThKKqCVpi3nemnFH0X02R3BSeaicEIxC1bsIjG3Znk36K9uyEQKKR9EusJJE2RaFS4t2kqFBBhlYyllAEFN4FVAzCat6R6aKbwA8By40yGKuxxmKTBB+UmD5Dw8IUsDw43zBSEHAxCFNq57X4EEgsGCXpiz6AELcCsSwxSn5PbRJC0LgggQI7Fqbyu0jSWIwSruJ8wJKTxDZEK2C6EmPz6JWA9EpMqQNBACVrMSYl6hQbNNZqokpAaWpdMRmPCdHRaSX0+20mkyG1gq3VIZWr2XvwTs7c+wAvP/8lXEhoZSmbgzGd3FEU68x1O3jvKEaa2c4cVYwMNjY5uL+DJWF1e5NabeGrmjzp0dY5w3GJtQnr6yPqSvPSi8/wtne+l3vuvZdWO+XWzeuMJkO0qRhWA8arI44mR6ExwNYkwk5VEHUCTsCaqHakGg3TtelDQgwybGn+hODYSVWKeLwPwspzldSb0ROcaeRxcubXjRF78CkKAT3jLo3WNFL2iCbIkDW1ZNaQJjKUS5IEa1OMEaawtZbN7S0xpqcpPr8eCsG/5/EXB5O+XN/xxs+9HjbaYQa+1pz9BZ85vp4N8PX+Hrur/3XPHyGASizOR/6jX/hZPv5rH6Tut4mm5PPPvsA0VEQVcbpCW03LdokhMhmOsMbg0xa5zbh8+arIi9OE8XSA0ileRfYdPMy73v3tJDevUOgVTAHveve7SYbr/NaH/oi/9WM/wf0P3MXSh5aZ3XOApZXrbA1ysnbKxbOvYHWNTjSb20O2hufIrOVN997H+o1bPPzQg1x4+YuMlYbNghAmPP2J3+XbPvABjpy6g2tnX+B3Pvjv+Mj/83sMqAHFS2df5bf+4KOcOnUHb337O3FlxWBjjbSVor0jTzP6Mz3e/+3fwe//21+ns2eR6TASlOPlV87yK//HL/L4I2/nkfvu5dbSJdY2t3jq2bOktoUKmu/6kR/Gb094/B3v5pd+9h8wJHL2S6/w0MMPo194kZWbN0nzFJMo6sJiKvg3v/6rxKrip/7Lv0urB3Oxxyf/4F/R7fbx25vYmb1cPnuBv/cz/4gnP/Z7fOnpJ3j26T9BWcNca5Y3v/U9HDl4hGf+71/h85cmjAZj7nvHd/B97/vr/PSPfRdh7x78aIzOW8wdPs2NS69gOjlaRTKjCa6mBpbXNynrgu7sLNdvXuHy5cu02n22RwN+6L3fz/7ZOVSSEMaOzz/zDHq+z/75WYZuyF/7zm/nx7/tO3GjKW9937fwfYePUA8K/uX/9Qe89b1v4UO/+htM04rRcMKHf/3X+Ht/+79HhxYPH72Pqy+dY2t7Ha2gLMRLTZsMrW1TPdNMZg1VcNw5N88Tv/dJntm+xv/yd/4O9504tJteqpA0MR3hzNEjxBhIdLbLcdoBVXz82jbQUcm5pKPaBXu+cr9o9kmtX7eqNYlV3HfydrZunuL+B+7m0MGTEDU3r1/E2Eioppy445E3sCy/mhTtDY/Xfcg0W0lVe3YTub7sC7+a9E0p1QA9f9bu0wzbGnNvIX80F7VJkIvNIIgoyhTxf2pqiub30VpYiZJSJ0xxgpwno+EYNVrGdQLVaEydO5QGS/5nvK4/3+MbDii5MlDHiPJN1LzVGCvmlb6SgzEGRWos6IiLIkVxeKyyeBeoMNTeibl2w0LQ2uBjRahFn6wEi8D7igor0b8+UvmIUxFfC6CQBNVMBaLIrCJEHUg9WNMki1Q108kQtEOrIBKZpilUFGKkhm/eUJHI4QMuNPQzJyBWVXtiVAJKuQkKTXCBoAspLrRih4qviU0qucjGYogNiFUTVENFb+jeIiWLlK6iqiVlS+uUOjSSM+VBRSo3IQQwUVH6SmK7nUYpiw1Bmmah5BC0GMyFIH5GWkVslhKj3JwqOmpf4lyk3SR8BSPFqCbF7DQqBohhNyFNqUhqFIlyRO3QtdDOx76i206wKkgqHNKcozQ2Tdm/dw8zlRh9VsVUmFZREu90s7BauWWm10ZHTYXHa41J5Dmd91RVSagdNklJkhSbGjqdjF63g3NQ1DVFUVKUUybrQxI1QGcJnTSl18pARUyZ4jOPTmuUFrPNcVUxVCmx1SFVBldOmj3WCFAS5b4GzWhS8NKFq7jo2bd/ho7PpKiONRqLDoqoKgFSlJfmp0l0qaOYcUtSnBR+WgsIpW3FQBdkucHoiiSxVHUtiWahpKwU40qYNFobtDUE5YCazICxHrTFRo2LCbmRRJB2npNnJfP9DpVLSU1CUdf4SlMU0iCoKMk3TkUyE8WQPSgyozHREKuAT2uCjZgGbY9IU5M3scM2abxBohfZh69Qzoi8UClqFEpZlDHiA6Ll+zXgazHBT7QST6foMU3csUw8NMoYrBEfK41tGGAyJddaUm2gKSgVeOeE4aCEnae8w2lhIKBDE/f7xsjT0BTTTV2+c0qKRMEmaCRFzgdFsMIeWZybxWZi8myVQlndAAFyXSWlRxgJPkZsc5Du2KaCarydlOxVHkKsyfOc44eOC0PQynWyaGyWYfOW+LaYiGrkfgrFqeOnqOsjmKTFiy99kU6nzexshyyzXLx4iTuOteh0O7SsEW1/WdDtd7n/rnsYFwnbW2u0kg5L11dot4YkaU6/O0uInsGoQGtLZmsCkclkm0SDm5YM6saoNIJNKhbmeigi3U7G1rjEqci+AweZlFMuX75IiDWLe+ZxlePGpSvsm5/j0P557rnvQaqixLUrbKZJbI9udpLJoGQ8mVL4EQv797B/YT+rK0Oef+ZPeM+730NiLfeeuptJVbG1Oqb0A7I0opwlWENqJAp+dmaON93/CK1en7neXuYWuwQNKhhptLRlYXYvd52+i/FoQG9mlul0mySbZW1tmypUjEdjpsNNuu15OgsH2DO7yJFDiyynm+Qtw+zMXsbTFepqTHemx8zsDGXDxoiNIbxSjVymAWGCCpigmA6H9HLZt2sdCaUnGo3Wmtn2LHliJPHUpMSohe2BxiopZYLymDTH+JQqTPFGmL/BQ1UXxCB+UQuLCxzID1JWI9I0p3YKk7ep6wJPxCrL8UOHubaywuWr19ks1tlz4DY2lhOm07q5fw1ExWA04crVm7SzTUJdUNVTVjYC0ackNuITORfbJiVrWRJS2p0Oi/sWSIxicaHH2voGZVngnIDAaEW0mojBxBTQsgYS04AcFudECuy8I8aAbVL6JOXKoBLdzEEacNg3ax+IMQhw1TSAwgASH7AQES8gJI0yeI8LXiRsPpIog46W6B1KeWya4l0tXxtFQi5DFSNr2kfAEWNNolJS3TChFUQlAQCaRMJJEW/DHflvCB4fk8Zn8LXiOxDBB2EsGgEBnG/qCCI1ihYCyEWknpD3y6O8xQUxGI9KUteCU40huRMPSUJD/5f9VgzooqSbuhqjWgSqRvItBa1vZkNqx0tKXCQJeqfZTtFBi1xRJlhMBwOSdoe01WZ95Sa3Ll1hdeUWra4Ygx/Yc5LFPfs4cvtxVpZvsTVYJwTFxtoKyii2hyV9k7A6HKFMF+cUkQS0otdukZnI7OJetidjWqlmy6+jjOPy9assLC6ijWYwLtFZh2vXL4FO2XfkLl5+/immMXL00DGoYWtjg6AjBxb2yXAiiOwwRCOm7dQYq1AxaQZVERcDTkestuid64fG6ERYRUbOrRhFvmlMRtCaNBFW2I6fl2m+Fy/hEjtWAYlN5LxV4onJjpE8sZFvVxAVIXqci8J2NFpQ26ilWVONdyHqddN4SQr5S+YXfeVjp3H76pjS6/77ZzRxDevqz/N4Y8rUn4dtJcxH8XNjV4ICYIzC1/DuH/1eKSlc4OTeg7zw7NNsWdtYBUQqX2J0St7ripIhSairwHTo6C20mI6mKKOoxlOszenEwDedPk36+Hs5VXje8sibue/uM/zaz/88Lir2HzzA2VcvkmiDsl7kou0OK0tXWQlbzHRaJImiDpoQYGZxjnK6wdpojRcuXuTBBx/mgfvu5w9++zf5yIc/wwfX/g3Hj93LgRN3sHj7SX70p36aVlXxv/3rf40zKXXtMInhAz/4Ac7ceTfnn/oU/+v//j9zcWuEtZC2Ut7xjndx+OAJFlptpklKf89eXIws37rBC9NzjF3NucvPsXeuw+K+Pre2xuw/eIxiuM7KzVUYeravrXDsxH3UScK1517msUceZ+PqLeoANZLMHPwUfI1SCTOzPb743Gd46qmnKGqweZ+i8nTSHpujAc5mfPpPPsRHPv4RkjRDYzhz5gHe89g3881veRMnTxxh+rEP8ryCVt7loQcfIFa3sGkLrSKTqqbbbXPx3DM4C66UFGu5KzS1c8zunUUPFRvDDRKTUJWBu++8l+l4ysqFsxTjCbQsx24/yemTx1ii5ur5V9Ea1tdvcP3mZaYvXWTh1DFMEXjpQ0/xgz/+PWzfXKbC8+7v/X4+88Hf4tve8S2EGEltygMPnuHVK/dy7uINHrh3zOrNFVaLDU4fO8Vspy9eusRdENwEOWNarRZ/+3t/FBpWk4QnSY9QVAWlq+i0WjJoUIK9KB93B+Tma2DSO6DTtCjI03SX0RPjDgD0Gk+HKNL6HbarSNMVBw8d4MCxY+TdDlmeUdeRJz79JC+ffZ73f/d7OMEb1+/O4/VMpa94Xc3HjdZkWd4MyG1Dqmj+/hrf+/rfIcBuvd+MqZrPQYwO7wVf8K7G1QW+wRVqJ6modV0ynkwoJmOSXNHpzqJsC2Mt/Tyn3ek0DCzZn11dMRoO2FxfZ/nWVV46d47By59n2L6dyWiV9sxeUqO/6uv+//r4hgNK0Vlh/0SZiCRNspfHIZ6S4l1UOjH/083UwwN4SWmJAVwVMEpTTCqh0NmItQLY1DViNomDUFNjwZUNJQ1CdJLSg8EHcShYNLkAACAASURBVAiqfVMARvA6NAwAaQ69c1R1ENlLIndpIMqUc4farhsWTmJk+qNAO5n6CvXbQzMNFcqg2p0USkMq0hyZUsoN+Xoi287hUnmHUoHgnaTBWJm8Bic3nFaiiVaJNKax8dpRyhC0JInULkJwFC6QJDk+1qAiyiaSHBKFneV8QWjYGgpIU8ny0MjiNFGKzOh9Q7EPeKYkZKioUUp+DiGgmqS9qKQZ7uUJWmuqWGBQuBioqxKdaHwpMcwuBvJWzuzMPJ3eDG3vqeuSlfGYujFRRyGO9FGK2bqoMZkR1n3TACgtHj3I20EkULlKDNUxpGmKtgntzJK3MqhbOO8pqpJpXbM5nKJioHKe7XFNUZX0O52GkaNQ2tLqzlI4CMmE2vlGNpnQzXPqumIwFi8QpRXDqePc5WVMplnozaNNwHtJRwihJhorzbWWZsUqj1ZONpUAMWp8dNLkKGkqMyX+GGiFsRkGha8EELXGyZTbV1KIGoNRkiDmvaEkgtckBlztZEJgNDgx2SvrKSHUKBXRJqFlNDo3tFNNWWiClnvGB0cZFFWoUF4xNJ4qGLbGFXnMsFWgKgO5FeaHjZHKO1IgGk9UIiNUBGxQKB8JRiZ+ppmc6xhEXqF2UmuiMBJCTYxBpvtKKJ3srOHdlaReaxCDoPoBaUK99+K9lAjw57wnNVYim4NnZ3Yfo5JpehCgieioysbstgGQhJr6mh2gbHwCACoU0RiM0k3aS41JhNmlkWLdIM8BoQGVm+YxirHgDrjsBC0TFmP0Ddjg0MriY73r8SIMDN1Ib2Rdeu9ljSIHobg4eTILxw4dYDgYMplusHeuT107NjbGvHz+Je48cQczvQ42t9x88QY27fLqpWs89MD9nLz9EOOtjeZ+n7K2NSSzkiblqkgVAiYznLzzFH/62c9hE4OfbpF1OoyGDhsNxinaWYfZuTkGoy1WV2+Rd7sMB0MGgyExGtZX1/BLy6RWc2TfQc48cJr53j6REhuRNxsioQr40tPptujPzTKtHDFosjzltqN7uXr9FSb1lG6/xd6Fg9RB0WtvcvbcmNpFOq0USKl8QSSgrObggcMEInffdSd1OZFURDytLEcbxdHbj9JOuuyd24d3jnarR1EXxFDQbbXJ85S638IVFeNRxXhzmxuuIDE5zsC0KklbHarJBof37mc4HDIpSwigE00ytrTzPu3UCqihhJmAV+R5StZtoUnFcyL3AmIqQ0xy/ABcLCXRhCB9IUHYNjHiI7Q6bbJWQpppbNLBR0+qIqoWL7w8QFkFknaLmf4M07LEVQM6nZwYUhJrceNAmjtOHj/M+YvXGW8PSe0NbIzkiSJ4C1HjfMBgqGtP6UegPJEKbSytmS46K6hdSjEt8CRkaU6ico4ePkRqUwiOVrdLNZ1S2MjmdsV0OkWrSJrahg2omlRIaXajj42EXthZMYpHXl3XuFrYHFobUpsJQ0A1jBzfDLsAlMLVdfNzaIBtAXx3z2wV0Sh8HZhOpygCVakk0j5UpDFtWGEygiE4ielt9pCyrJkWpTBFvcZEhTY7E1oJKlBElBI/wYikvNZ1QVUVgMjfjW5qE+G578qAlWnkkNHjXCCE2Nwrsl8EdgZcunme1yj0rknNFKNgjTIR1Zy/zcvfre3FyyEIC8nsXHM5l1CpSPxknEiwwt5UsRnBBqkpJNYkEFUkGi/UTe9ZX13itjvv4drFsxSuIO91OXDoNpwT2YFH45XsP1VZ4upagltiQh014yowmmakCSSZIYYptYJMp7JnzMzQabe4/egJXKjwbszm9iajSYnJclKVM7Nnjq0r1whdw9L1y7TnZ9l39DjXl5aEjZ1m9DtdTNZBJR1CMy7U2uyGm0RM837KtTNNLSk4TxNV3ez/jbFXc48ptE5RyLUSy4xk9302VhOCAFcBYX8RZZKuJdYUg8g2vZdBaFQRbWzzfy3gtdHYxrdSqWagonaMpOHLGzC186FvIKr0jZTBfdWW8auBR3+O53vj63vt+76+17ybR9s0wTvVCrsft4nG1w6UAJLtPONQ3uLmdIqOVoaXrqbb7hKCZ3N7Az8doJSi2+lx8ugBXvniWTAQU8CXxLpgEiMPP/wI73/8TQQFbrvgroceZvbJj7HpHJ08pyxHlGVN3mpR1VOSLGHPzD6OHT7CK+e+BE72g7WtVTa2b0LM6GRdHnzwYR666w7+3a8Omdmzn83NZZ5//vPcc89dWGsYD7bRnZxer8t0PKUONVtb20zGY8rhgN//F7/G9fGA2kXK4HnLvffy3/53/5AP//Iv4DUcWNzDgX17mU4KlleW8QouX7rKtUuXSTPoLy5w90OP81/9rb/Lb/78P2UwHPHX3vu9/JV3Pc77vutbmU4d5//P36aclnSKAYNJgYuOrYEwv1SQ4f94OuITTz4FJiPr5NTjEXuPnCEdbrF66ypzMzlPfeoJFg4eoZqU+MmE4fqGgD8Lc1z9+Kd44uxF1lfWefCxt/KpP/4kv/Lc5ygygxqVKKUpJgVlPQCVorynba0MIqym1eqxf34f1wYTEpUSMEBFPt+lNztHd3Y/zz77edoHF+lay82b11hTmq3hmE6nzcsvnuUP8z/iyp++wPf80AeYfew2HvyR98HqNr/ws/+E+XsO8zM/9iP8zIXz4BWZAmUic4tz3HHnHXz2uadZ21ona3WogmNtOqLf6srQQEmCtUKTWEAl/PAH3o9GAmiKsmRaTEFBURcsbw9Y3VrloVN3kWctMpNQOSds42YftEpLOMCXyWd39pU8k8RggKKY4L2n1+u9ce29Xh62M+QFWq2cI7ffwVyvg1EZZJHF0/dyQGWcPP4mwPDVVLtfDRCKTT8vRbbs2+1Wq2nXxTOY2LBL487+HXf3hZ2E6aoqKIqCoiyoqhLvaozW1JVjMi0oyrKROyuci9RVga/H4AO+jownE8bTMUorkqRFkmWcPH2cAwcO02nPkiQJIXhcXTKdDNhYW2ZzbZXlm7e48OoFXj1/keW1NTa2h/R6PYbDfYxGY/pljUnSr2P/+vof33BASdQ/CTtGdMp4TAx4r1B+pwDRhDpAbAzslBGZWdP8hdpBtLjoUcFhrET7KSem2S44MaVWMmHxTgAkFyUcR6uE6IS141GNN4EmeicbelDy+eAE6YwQlcJ5BZUU4s5FjAkQHaYh6O0ARMKgFbZRDBFt5bm1bowLnUxFXeWahk/hvYANBKEFRgQUEUq5QkWND5HptEJrYXGYnSmUkXjYaCTxSkdJfAnBEXb0qFaBh9pVGC3GY64MeBuJocKrSB5TIgEXHKhAFZwUPVpjVUKMCWgt3i5RCaVOeXwU8AelCb7Epl2hT2MwoZHdRWFAoawAXEbAsNJGDBNAY7BYa5rj1WOba1ZWNcOVWzJNdjW1rzCpJQlSUEflsI0Y1EdLWb8m55APR0nRaUgrxigxGY0CFlTVFF1OMWmCsSIBaqkWedaiGzxFMSFNFaWbMJ5MKYuC7daIVpqS2YQ8y8iVpd3uEbMOiU0ZDQdoa5idm8Hi2b40wiYp3U6bYioeTkvLQ/b0R3SzlLqq8VQURsypUSUmS2T6HAI6KAhOpJWACzVeRbmvCaQN4GGQejsxitQqvI4YFcXIvImxlimwQaepFCgu4nwQ35Xa4cuamMh7HJ0iwcoEIeiG1ynJf8KYqMlsQiuxaG1BW+o6YzqZgvZMByXOG4YjTaIMZV1T6YjSFUnaITpwJpIlgcQIQGiilMw1XiZ1SsBcYk0Mye7UOkYlniWIL5hSAuTuMg2V2p3MR0AZaWK10Y3abmfDF/aAb5pGY+UwJAqjqooQ9Wt+JTsNePAip/OhMbgNIk2Rmy7uFqkqIj9XVHYYq0lbOcbIlDf4GmOyZs00XmNNI+tiICgt8/oYxU8tipcJseEUBDmgdJCvUapp+JpWjRB3tdLKiM+bNJCyJi1y0+gmuWrvwh7uOFXxpXMbXLpygduPnuC2244Qg2F1aYV60mHuwD42N26y78AhLl+5xsUrl3nwgYdodVp0VZvZ2UWOHjEUZUE0lvWNDbZGE145+yXWN1dIUlAEfMzEEyhW+CpSoJgWjk6nx/WlW6xsrhPXthiPC+raUztPMZ2yOD/Lo48+Aj6h3+qJtj+3VGXJpBhjyojVCb3+bBPlm5DYFI9hc2WJyXTAkUN7mI6mTKqK7fISC3N72d7cotfLabf6jWyqCYXQiEwpCCiuvfjSKBzRNIyxKMw8T8BEg1MC3m9srHPt1kV6/TkOHT7BwX17SIzGVZH19Q1Wb91gMg0cPHGUweYGc3v3cmuosGmb4fY2m9vbdLodli+vsrayyZ7FvRw8cIhO3hbg1ygIBh+lINPBkwdNdGKQHLXHKXmvNYbEihcb0NxH0tTbmFLEJt5eC3wgwGuUcFQrQwljbaO2UmytLeODp6wkGMJ4MYyuHXSzDvP9nHU3ZTLYlEFI8LJLGbk3Z7o5hw4tkETPpJxS1IqKmmG9CqHETXfAW0M7s8zOztJqd1EugjICFtSKSEpVSexyK+uQ6oQYhP8h7D5PWZbSJDaGZ0ppWc6h+VBDt68bU/7oA857ATu8b1iJyNoKEuXuvEdbAzt+QaEBWSTWDKMNWZKC0dhiKg17lImn1gaaEADfSJHFzNqLh0YjSQqqydGJsucGJUMs2SmkcI0BnA+UVU1VVyJVVwkaRWLsLjMzeCdMoEgjv5Oiv65rAcuj2fVO3AWbG1AnIomUMQa5lhGir4kxkRN7R1qnYMcIQwE6KspKwLEQZX2kOpczDfPa/rzLXg24UFKUY3wtA7QYfONhJcOQ0sPivv0QPc899Qm2yzGdrMNMr894UBDjiK2Ngu3JAGdTMRn1tXxv0PRn9jIZ3mr8ETXddouynnJg/14ym3Pk0Cli7SjHm9RVQSTDYrHGkuURbVOOHz3K3P4+flqwsn6dZ57+JNYkPPrN7+HEnZGNm9e5dv2LxNRJgEasm+so9g54D/a1Kb70H413n0w+0cbADoOoqYyIEe8CeI9OJbhld0ipaBincj0lKMKjQCTlO6wkLV5JwmxrgMRmLYBugCqD1nKuqwaAArMbVCPeYg0NQL22pv7DP3apCV/z01+BKP0Fwaq/KNi1C0W97sfsvMQYhWVvbNJ8INDZu4ejB4/w3OUBeavFwsGDrF4+z/bWFp5IMam4/cRJ9uQ5D5y4n3d861v5+z/5E5i5WUbbQ5IsoZPm9ICqmBBcA0iurZItLNLpddhYXxPw0yQEHzAmI++mYGe59847OXPmDl5+5UXq5v5x0ZBnHe67+yH85iZZkjNc36TfmuOd3/4+PvHBdX7n936HarRM9IGNrTE3btwS3zQv1gKraxssL68yl6TcGI+oYk3lPcpm3Lq5xCc+9Lv8s3/2T5nMdNg4+0UG23u5tbpBHSLGBZSBynlmFw8wM7uPb3rb21lozXB8/xE++soXGA82ef7J59E51GVN/vBpWpnh/LUlyl6fsLYGNiHUYtuQqMZmotViuDXkze/6K1z7089z/2NvZ/3Jz3KjvUS/3aJ36Ahvfcs7eOKPPsLZ4SXWlpa4cfMGn3v2GbaefZpVv0lnIePVi89SRSh9hdUJSgesTkC3qasSVE1uc7TJgCm+Ljl55zGOLBxk+cYNcqM4c9e9XLp0jh/8T3+UG184yyc/9RFeefkXeee3vo3t8ZgH3vQ4H/rER2S/9JHZvE9mcu55+1s5dudd2MaupRhWXL15jfvf/i4A5g/Occ+9D1M7J0bd0TMz2+XeUyc4ML/IrdVNTszPs7y5TNVbwFpLapPd+1SxM8CUIVf0kas3lnjis5/Bx5Kjxw5z5PCJJsxKmMzD8ZRbq5ssLV/hHW95HO8dkxDJrbA3X78WVPNEpgnBCEEYO1U5Jna6O1skb1z8ryHbCiFCnD56O7Y5fzWBb3n8Md752KO0jWnO99efR1/5eH3C827AT4NH+whFXVFXFdH7/5e4N4+x7LrvOz9nufe+vbauXthkc5VEiuImWaJJSvIar7FHsZXEkZfABuxJEAzgIM4MgsCZGcHjzAyyIAN4MnY8HsMex7HikW1JlrVZi0VJlijui8gm2exm792113vvLmebP37nVVPeY8vIA4hudlW9qnrv3HPP77vinZNYlXnN/nSKCx6jxNLXeY8LkaZr6XyHUlII4roOlSL7e3uECP3hhNWVZcbLywyqimJpleGgR7/XF5AqBoKKjCcTJqMJ1lgW4pXoYXNzgzOvvMyV82e4cOEsL7x4kiuXrjCb1tRtyOUZMjfZGAhtx3w6xbc1rve1s7vBXwOgROFJJgjAkAxdisKWGIMioILCFhqSwQcvA2ABWiexUflICIZeaeg6OSKrKHY1D2gjqo0A6CRFUKRESNJ8Fn1BkUOPRZ4e8BlsILO5Ia9Mk/LPpCMm56OQtKgZAsK6KTmkxxhFFUMkhqwWQIY+57M1SGmsVnRNx6CsCC6IVagLuBgoCgNEvO8OLgytLbawYncLkeAdWieqfkFVWZwD7wNd24kUWuuDlPqkxYZGZqBiEptfsiYHeCZqP5fa8wgFBV5HOt/iotgNJa/HkFTAhxZFQUgalCVpcCiSlqawXmnQqiAojcZj0+L8LkCaQZDaQgtYFeMAqwyTcUl/PCG1LdoI655iIPhIO5/Rzmo8ovqx1rIyGbO6YnEx0DaevZ0NCqPpDUoUIWdTZCn34oLXSvylgLZSlSwZQgAGFQMKD0Fyu5IxOOepehWmZ0EFxr2CfmmJOYy56zpp60qevqpolcGXFcXyIeqgSL5m7jWrPY0pDCHJOlpeGnJpa5fLG/soznNoZSy2i8251C8nTWETy5MB/f6QcV8yr0IIGFXglQTUKxIqSFtQjGLpQtTuuc3H4F0NIUkGV/AolQSkVIrSSitkJFEYhQVqlQh0JC+DShE1wcmAE0nQdbLpapWtqBGdJGg9pYgyItMflBprBzRFzfqkR1A9kjIUU0cXInXdMMTQdjIwND5iioA1mlKL0qZJgTJqQkgENKhCArcFdyXFPPTmAVFbYVblZ0sZUFISWJu3H5mDr8lXjVYQdLaQZmb4NYf3KNO2sOU5MDZB3iciMSRMUWQQWEMGbkhJ7BwpyT6mNXjo2ZLSVBS2QqtSAGQv9s24iIdSi5u0AGJyXo/ZlZBIWotdMcUDUINkslRWBk+VD/2KRNIZmVagoxFLcdmHqA8Y8KjB5GuBlOiXJeuTQ9i1w9xw/BYmS8sopdm+ssPG5Uvs1R2j5SEoy2hUcujIMS5d2kAnR6ms7ImIvSKGjtFoyJUrlyDVvHpqB2WSWGWMotmvwcesuFOcuXCV5UNn2NmZ4WPB7tUdDIoj1x9l1O/zwskXqPoDjhw5ynS7JvgOH1razhEIGBXp9Sv6VZ/93X2iyg2g2jDvHFc3LzLt9ml9w9PPPS3ZMCjufdNdbG++xNraKkVZ4p0XpakUoy+wRECDzuCC0qBlmFMxUqZwTUWHIoWGpt7j0KE1XnfbXSwtrdPVDSp09IeWyfIKh4+s8dRTz/PiV55HKY1rZgSX8K7lpjtex/E20HroWhgvDem8Y3tvg6pYoyyGxCTrXuuSFBQYUZwlFWT9pKyoJQoYYXso71BRcnlAcnJQuY1SGUwqMCkJkZHES68KOYAlJC8soajKHgEB+sjXntTEVxhlWV9ZZbI0YRYMp06eZOYyo6lBBXDOUc9mhKKQsoR2RjCQuiCKHS3AndYFxhQ4B3vbDYXW9HslShlW1w6hjGI+DwwHA2647gbA0tSisAhBLERd22RlTL7OjQINtigEEFaeFBu886IgVHLv0EbJfTSvgZib2WxRYqw9yFuQ5aAkO08ZiA0xeSFJrJJfRwVK00frvJehM2u00HrI+9GvepA6vK/pGjms+3CteUby2RafnUBpykLsgCk4uvksnx8yQbMgxhY7W1ywqIqqqtBWbPCSDSdq6cV/Pn+fBWNstKGsyqwSlgr7FEQ+tWi0DJE8AcjrZpWhSKJ0SQapqhd6B1TOdMrhUkKYRFxIQn5FeXkUAVJBDGALqbX3PrCyepiRSWxf3USpgsK0zGf7TGc7eNVypDvBqVdO0SWYrKyytb3FYDxhUG3SaY9JBZPRMq31HF/qsT8rMLYipcDGhdPMfWB57TjgKIs+wWsmgwFLwwG+8agq4UlsbZ9nPFji0vkz3HbT6zk/m+G9KF9nsy1W19YxSpFUtvMpIQZSvpf5rsvWB1HSSUufzmy7gG1C2slZVlS4HVpXeKVytlfOItXZ/ph8Xh/pYG0plQRcUlps1QtcKGc6XVsrSdRtGfBKWqzJNq8PrSW7S1bUtbHytX98rR5/luXkT378WT/An/88f1QR9VoMSn3V7/unPsFXoUN/McGW+lN+tAP9EnHecPHpk5ze32bWH1Nk4LGb1xKJoMRyOKtrvv9v/z3KS1t8w4PfiBoZyuVl6gA+KgwFW/Oa/++DH+boeJm/+U0Qp45HP/QRvrC/D9oyb+ZoBUZZ+mXCFop+VfG93/tuTn3pUT74wd+i9uJuMMZgTY/15cNcf/w6Ls/nqBiZe/hH//Sf8OaH3s66m/Hppx7j8198mO2dHZRZ4sFv+Gae+8pX8MYQXMd0OufDv/d7fLje4ZlTL9F2mqNHj7G/vcvWfJ/3/dovcTl5ep3DGzh74RIuiQAhhpagBZjf29th4/Jlbjh8E2E38MVTT2Nt4snnn+TX/p9fIfU8mshoPOa2G1/HY4//IYW13P/1D7CxcYVXXv4KpleKgqRx2BCpTOS+29/I9NmTvPrUY5x+9SssHVqhmc94zw/9GN9630P84W//LkduuJFQT3nqicd59qkn8HGPLjpiDMz2PJ3K6lI0yStRjMeaUlkiEWtKgq0gdlitOX54nfFwzGgypFKJ4+uHefMD93J8eYX73/39/Mov/SJb3Yz77r2Dp195jtUj65y/cBY9HNPWDZc2LhO05+3veCfaajZ2tjDlgE8+/hTXPfRWXvf627M6VbM938QMl9k9f5nrrztOWfZ59ewFHn7kD3nppdPM24Y77rmT1eVDHF1elTwjpfJZKKGSwigrLtsQuXxlk89+4XMcP7HONzz0IK8/cSuT4TLD/oCoZE0bW7Ax3Ycks2xIiXObGxw7tMag7LHIjlu4TmQ7FJuYURrXTqmbAYP+0lddfungGkzi8BE5EZXNbhXkZx+YPE+pr77eFvtAyn923omSaD7FAk1bs727z9bePp0TN1Rdt2xsbeGahhQDpiywpaXQBm0MRVnSs5ZSgSlLqsGI8WRCf9BnMhkz6PUBJarpbs54aZXReJniINMp5q1FlNdik5VTXOsC05199qf7XL6ywebVK+xsbvHq2dO8/PJJ9ne22Z3OqbuIJlEYmSFtfn1VCui8xzfTPZp2SuEmf+6u9V/y+JoDSrEIudJeJNfCdoBSAtzIISgcHHZSlJuZ0YKgRS9DVgjugJmPWd0TlJJ8JqUpgBREwRGzzUqRiAFMWZCSVPrGGLG2yCtQbtZojaVAJU1KHlDEICooUeVkdlMpOWDqXJuabXMuCHCitRzIYpRhyaIwuZbXZBVRSomiKvBtDtRSOXA3K7USohzRUW4pIUZCZhljSFllI01k3nv6vR6mKHBe2M2itGhraLuO4KK0bfmFuzPhM7Ck0bSpE8AuStNUigEXQBeAMfRKqSU2i+wYGaExJhKUpnaenu0JcGYLVJDNYaFW0nGhqtBYJEg8uIalyQBVKJqYpdhJS4ixiWJ5Ikv+FfRGQwbDESpKy8nedE5wNVY7yrI6sDLJASTkbfs1WReQh4rFZiOVxcoksRxqK4qO4AV0cxJ+naJYG0otgCUL0hC5kWkdGWrNvk/EZBiN1pjvbzJPhgGRw0sjLm/tMW/mHJ2ssjoZsL9Xc3lzC1soVlaWaX2inteEriMEuLi1T1X2mIwsPauIRcloqCmMBnUNQCCBSx3ohEkRvNQJ+9DhOi9AZOskkD4tsEoZShKy1xoljKUuNNZZsQyEiLaGQMInLa9fFAuk1gJyeRfylaUpkkKHvIEnsZIquUCwPXlPqGSw0EGzOukxndbCaluNTx7XQBM8XkU6p7HKkqaesigoisydW2H+U1D5W0WR7C+u8phBFCT7Q3JFxGwQU7h2s+GamEip/PPlQRkW3mYBD1GyblSW2iqt0cnQBrlWgs+h3CEJm55kvaUM4lqjJJi3F6iKsVxz0UjQNxBiwEZF0K9RT2BQSWLr9SK/iSJTyYg0OC2sCm4xFciaF9+fgG4KEqLyjOg88Iv0IaREpeW6UzprA1VkMBhw09ETaKsY9kcYZYkhMFka0+uVnL9wiRPHbuXM2ZcpysjVy3vMZ9u87qbrKftDer0eTSvs4nw+YzqfMhz3efO99/DkM8/SzKYQI0k5XBtEeRYV6Mj23iaPPlPja2nHWl6ZcGx9nWM3XM+5Vy9gbEnbtLx67gpljLhmTq8smKyv07d9GEgLXjywFCvZ27UGPIePHWbjK+fZaSPzWXtABqwsrzCfRo5ddwMqpmzHllB1o2JWwBmxEetwADLpIERGiFL7LUCfDMtaGw6vH6dtggSMo7IFSRraUhsZTZY5/rqbWd4YsP/MZQn/1gIKDocTmuRJeG6+6SaOHD7EtHYoFvloWuxLMaCUZC8cKA9yOHSeJzEq0kUB3FNUArjGHK1ZZBJFW1AOks1KNlGx6NKgCw21FysqYj9dWj1EXTe0TYf3OUdQJWzw6OAZD5a58fgNdLrgzKkzKNXKAJKE5DDGUpQ9atfQZhBcxYDrwkHBQDQQo2a27wiuo9Jg+iW2GGKtouxZYgoMBjscOXyEnu0zq1tICa0jUYFViZDD4bVWFIW0lGmR+rIgN1O+J5P3SKU4CLSW9zsROo81mkGvL2eZ1wyBC+VRkg1FGs4ykxljR9QlUVvSa0olxFakDkgoUU5JhloK2XaUEIWlVhmnSfIcUZ7FWLFI6czeGi0B4YLEr2U7wgAAIABJREFU5O+V8+lCiGgRVeXcB0NymjYhg0UKLHLuFhPugXFYi0XKFpYIqBgwStaqQefzSPyqYTqqRK/qEXuespBXUmlLTIARtTYqN3Vm0s4ky6Ascb7ANzUpdFi1dPA7xejQSuO7yNd9/Tfhkufsy6do2jkaTb9U9MqCarzM+qGjvMgL9CYjVg6totot1iZ9dvolnW+IIbG+voyrLSWwcekCWzvbJBVw3R6Ng+39k6TQMGsKRoMlDi+PKY2i9g2j3jLXn6jY3XkFbXq0+9tgNMaWjAdrmASm7LE6WaKwFpVt1KJsS+g8SPrQyT0pCeFpcp5UDF5IMUUG3gJtN5fXLVPjMUZURBpCM3mgcpNf03U0XZtz12QtxHz+S3gsUs6hUqJtGrx3FIWAtahssQwRaxbrYRHoGg9uoH9W3fXX4vFfBib9eQ91bXlmIvmPC5bUaz9d/lhUuaoMKqWDJ/gTvsVXvwZ/lVdEZRVpiBHfdnzpox/nU3uX6OuEcZ5+NaK+egXXtGhrcW1gPBnx3JNPMp52vPLyec5snGbe7wHS7jxra85ePs+XnnmaH/7bf5dwdY+nP/owH/jSF3lyd4PLl68Q8SStKHRieXSIw8cOcWVzg+eeeJqvvHCSrbmAAEUpwcoqRqab23z0gx+i7hqeOvky1x06yk/8w3/AdD7nO37kJ7jjlef4hX/5P3L33/ge6r05t9x2G1ErdKHQTuG84w8efhhUh3Nz3nTH6/mhH/wxfuVn3ssb3v4gIxKPP/oltBnRNTWDfgGxJLgpCYWPCRcD9cYGxMSzJ5/kscc+x8aV87jgefqZx5g2jv6gpdKRui75zMMPowuL7Rf0D6/ir1xgMChJRtF5zd1vewerSyM2XnyGD77/l5ntOvppTjFsGB+6mbve8RBPPvo4Jx95ljNbVxmtH6GeN8zqfYpSo0uYt4477rybF594ilTAaLxCvb/N7a+7jxjglQun8kHUcvzIcW6/405OPv8UVzbO8/STz1H1x1RLyzDb58knvszXLT/Epe1LHB6vc8PNN3H1yaf45Kc+y23bmzz72NM4W1L6iLKKV06f4bNf+AQPPfAAS0kznc44e/Us/fWKb3nP93Pr2iEUmjfe9VbapqNXOD772FPc1QYeeeTLPPyFP+T5k8+Qguf8xQsM18ecuP5GrDrEQaD04rp/bY19gsGgz1veeh/vfODrufmG2+ScNZkwyFYqYwcM+wOOH/1mfHASGB0Sm9N9jq+v5ytTns9Fj/OBixfPMxz0sVpyR6veEFfvkXrjrJCUPXFh1V7cwb7qWsz/G6IQ5Snm2JEY2Z9PSTFQlSXGFiStCSHS1HOuXLjI5oXTkGouX75IC4zXjzOaTBj1RiitKZuSfr9kaTxhaXmJwWDEaDhkMp5Q9Xoi+kBhC4u1uUktGZkpMgZCXESgiEPp2k6zsKAr6nrOxYsXOf3Kq1w5f4nN7U22t7fY39tja3ub2O6jupam69hvPF2IQnBp6Rq1SQ6GioQLnhRFSVv7RF076npKf9j9FXauP/742mcoAWoRtJgH85QPZTEvhBSEfTcGfNAYl+TQl2SIU8qggpdhM8mC0PraQrEmy3ltInUC8igFRVGJnD0JC0vylNYeyNWMMRKknSQvwEUJuU0kTAEu6MxGFigt9YlojaiKc+2fysqgkEgpZPIxH8aiDKVVWaKUxhgjgdJ6cUsTClFqWa9dSDFFCepE2C0J8Na0XSAlR1lUjCZjvPO0bUAZCQ31QdjyIhV4FwidWHvUAuwyJqsjBK11SYKrfXAoUwj4kA8TEgCpZAiKHASHlmjwUertiaTkqIoeMSicjxTGHAw2UauD7JxSQWGgtYpKK6JzVIWhVJZghI2K+chjFBn5FrCvaVsIkknROcdo2MdgSZSZ0VYZxMoqjYWU/uDQKsCCVDsHUbghA6MpCgENFNJEpDS2KlApEjqPVmS714LzhyKDMYXSWBvZbBLWlPR6Q2oiyfRYXVlmY2dG8LIejx1ZwQTY2Ztx9comw6FkkgyHPWL0zGYN9bxlZzZja5qojGJjr2Z5tMTayph+T9MvK0yh8MlTagF5UgiSN5TtFhFPlwJlzCFuIZD0om2vQKFydphYMUptaZQFrSVHxkqIbWGF4bTGoEmEPKkmnwhKYfOmq1Qi6IiPARvksBmCpkgBqwp8MkTvaLoAhaIoJWDYVpWAjSHhvaLznu22xptI01maeYdPe5L9ZCK9Xsmg18dqfZB1YiuLb2LOSwnYosS5jtJbQg698z5QlfL7iswzo/6oAwD4NXNUHjIFLFpkTMg5Muet+Yj3nhATRWHRWQ0JUYZPL2pMHcRCo6Ncc0F3hORwREqkCjzG7KtSYlHTKuEJBEJWEwmj45XKtZ8CmKXkZN3reMASLSxYi+O/StfGQ2MsqjAo5Q/YHkO2hCpQaApT4cseMqWQ2SFQKVBYw/HjR7G2oOk26fWPUFQrpFCzcekK58NVRpMJVa9gd2fO9tYOjo6VlSWWqlVuf90befqpR0En2sYRQ6Qo1EHGFLqDWHLddYcZjYYcP3Idq8vLbE9nnDr1Kq2PrA4H7M+2ONRfYTKZMKgsRXktL0dlAFpyeaStM6VEWZQsLx/illtfz+DiFrMVy6mXXuT1r7+Ro2uHWe4/wGCwjG8aIgGlCtACxImCIufS5cOfSmKHksEvELXcI5ICm8BpsFUP17Vi30kag1QvCDER0bakSoqjt93Oyy8+z07XUurEkSOHqGceHRL90hC9KL/GlUKrCpIR5W1wqGQlO0hDymHuYAjkDBtYlCrKz6x8XseBsuzJOlEaHX1W3Wpi9PgkTC7Ivc45L5liCiEXTInCkTDU3YzCmKxUXOQfVhSVhEFOhssEd4UuKmI0KJMoq4rJ0gQ1VYSuoyIxWuuztbvByuoR9maeWTunc5HkhFnbYUZ/OKEaTIixJSRFTBpV9FC2omk9nQvErDyNKdEry6welqyfoihp5nOUlkxAOSPk980WhBRyjpkSK3BuRxMxdYEP2bYUr4WJLuD5GK9loRgjFjardN5nAo5IXydU7PLgKUoPuVfJvUUZYWJT1Gil6JKj1ENUFAVhivm6zLa36ANER0ouN6QpFFbuhYmcTSc28xgkw8gYw7xr0NGhfV7bCaIKYqNUYk8TziLkbAkB6FLed4JKEl5uxCosRxahA6NagB2SPSchUIlCl6goVfSKNs/e5hrzHDNRlkTlGDLroTRQ5ANaTDJ8AL3RMjQz+oMRLnQUpaWyY4rSsrO9z7lXLrB+5DjWaDZOv0DPOi5fehmvA8ZoCluytjLhaguzrmNrusOkNBxaO8retmHt2Cq9quCVF14gxMCkaugXiaatmTYdb7rrIVQZ+dLnf4fZ/nnOnV9h9fBNmKpi2BtTJMdgvExZVlnxITZrKTaWYczaPmW/QO/J+x5JJJXovKdQko9p8mChtaIqerRaQOmoDGQSI+WbocrrT6ns011csxlwViK9zCrWhZUxv+06ZXBS1k5IKee1Zds38m9N14qFPg+UfyXU5L/WIw8NClhYXf6UTzp4pD/2sT8b7PrzP+Mv8FBA9ESjuBJritGI4+MRz79Q8h3f9m30ZzPe/4nf5dxsG50ShYW1lSW+6zvfztb2LuopOPmZsyyvLDHd3idaRdPus1fvUvT77F25QjEyDI5OOP/CY0znc0xpMDaDx7FjY3OTpp7xxFOPMJ83tAEqWwgJS+KBB9/JiZV1Pv6ZTzPd8ly6epEbT9xCW89YOXSI9/+L9/Lf/Oy/4Ppjt3DfvffzO7/6y3xh3vHGu9/CS6++yOZMFI+z+R7eea6/fp2+hofe/k08d+cH+IEf+Qkefv9vYpJGGQ1dQA3GFMbQKSXAFAUpRbro6Jc9tIqcPfcqkYBzDkVLv1xmqeiwRcFsdqDdZTrb5Yuf+yRxNkVpyacpqjHBwTu/6wf56P/1v+Gtx9gWH2qqYsDuhQ0G04qPfPLTUBTUzYz6wjlMWVKUfZp2G+8T1WDAxbOv8p4f+4d84H2/JORdmHHPfffy2c98EZMCTou4ou2m1PM9cbVozfLhFapoeeODb2VUDfjCJz9F19bUW1P+3W/97+x3DS61XNneZv+RR9ne3KAsC6IPWCz1bMb585f47Q/+Lm++535Ov/wKX37i89zz5vu46873sLq6itKGm265hf5gxKSsuOXmG9jf2eDMyWfZuHSByk+wOjIejXH7M9ZHI4wxNG1HWRUHSvprF4koaG+95QaOXvc9HDt0mFkXCL4lRodVuZFUG4xWuACv7m4zqfoM+hVvuP4Elb2W3xOyJXg2bfj4xz7G8esPs7t7iW9/+/1cvNRQFC23lkOi7eE6z3Q2ZTqfU/YrlpaWZSZNic61zOsZ0+mUpBWrK6sMe30Kle+rSVxAVVnRq/oHs3ayiVJr4soKlfK4UGN7lvWjhzl63evp98cYVRFioPPiFipsgV00zS0I+8U8mlQ+D8icHrwT9W4WxCysw9okQoR6usfe1hXOX7jIpUtXuXT1KhcvXeLq5Yvs7uzSzVu872gyaWaAiY2MBgalLLsxCPYRhQFN8uQURghT5xxd50gp0DnHtG1pGylJ+Vo+vvYKJeQmli3xclMzJttWdGb8JJ8ApVCWPDQpjEmoEiSJSUwVMWRWRueWFbJuRskNsD8sUF2kVEqUQ8Q8YAiza4zG+YBRwmijNI5EVA4PMgxqhS0SurR5MRhCBxGLscIu5Ts4oJDrQBh9ghLPalAoVeC8QxlDUJC0onWe2EbCQv68QA0Xdq0kU16KEWUMRb9Hip4uL47CWkxZktCYUhGaQOfzQvWe6Ltr2RExUVhDDAnvYmZ9JJg6AKGRFrUQIylIAkuhjKiagqPpZhilKUIipRGBDrRBK0MMMkQYFQi+ps22AG00OiYWr3xEUWTJZ1KGEBUhBYyOQqYmsZeE1yxACyw8/CmI51OyY5TYdExmj5SXML3MuCUliiyNzuojJesidKTo5L2MkZAiaEsd5owKyQHSRoayRB4itEaVVgb56EWpYExWNIl1UStpxznUT5zfm6JsyZJORB8YDEb0ez1mdcN+3bJ+aInxuGV3d4+mTly+cIWesZhCYWzJ8niJpUmg6xyz6ZT5rGVja8rV7X3OXC4Z90tWxj1WV5awpqJfjbDKExQEVcg7GgI9behCpJnNSMGhVZLMoBRo6ym7qqMqSwaDgbANUTKMkvIZpNW44JnNdwQcivKOSAZYDT7QLyusXmRWSTJPF8TiWNcdl7e3GXUDKt0xnzcoFemajunOlBACg2EfjewHSWuMtVRGgyuonUOrQNSWIkFE8tGm+w3NbE6ZcwX6vUqanDSkbLbWWjMaDAk+cPzYEeZNQ/Qe7zp0IRZBozRdFFuAycDngUUus4xK5+aVfMgHCe3uXEvjHN53TOcz+r2esMaL9aIU2lpsQpRIWqEo8F0kFfmazmH1Icn6N4gyRKUISdh/FuBQVGCFZSi0QUVL1AEdxZ7jxSwhIEUS9VHMbEdhNAGTVRCWrnNEJRBOyl+zUGQsAJmY01SsFsWaj5IN1KbEsFfRuA6jDEfWb+TLjz/P7W+4iUlVsb/fcnVrl93dbUKIXHfiMBcvXGQ2r7lQn+K229/Ii8+X7M1a6rlmMrYYq6n6hs47YkqE6FleWWG5P+Hq5R2aznP29DmWVsYMguXOO++im03pjQt6WKqiRAWxL7kgsl2hTfN7li0ask9orjt8M4cmhykmR4mt487bbhUJfG9FjPAZeEmUGZwRYMborHhVCqIXq4k1okoDPBGlPQNl6QKgDIZAkQPcUzSSbxd83rgcVvXR3mDLHsarDFBFUjQo39EbC1mgUwZtUwRdSCZd9iuG3AQptqMon5tasakkRVKemDSWCqtKnPICRqREUoEYBJwwRFS0OSwZCIGkugP1yKIVLIusUDpSGAEsB71SMgS1prRWmtaiQ0ePSYnhcMTO3iY2KaJSeCOggDWGtaUlLLC9dRXf9jm2dpz+eIjRDcaUdFGjo2J15Rht2+FDgmz9dc4Tk8Loirp2OKcpihIfJHtJq4g2Bpvvr1XVE4WgNvnwpdAWlDKM+kNqnSvZkWr00HbYwsjvbTTKFrDIOMyMaM6QxmiBeWOSjCizUAxCvv7z/S16AXvEe0ZK+hpYrcjXJBClx02RKI0maRmcYoiSa6VsBsBTbj4V8DdE2btRoohbAAwp5gwsJaSLVppoSoK+BjKq1M/XiVgNYwwCoiJqqZQUKItR4IPI7o01YLIK2F5TNeVdM4NKZCucztlMHkUlCymZ14R6ByTEW+7hyiDZP+maKlB2JoM2QppV2mB0LjHwToiesuLwkYJLV07Tesv9X38/G6deIJk+s70pylYUzrC0fJjd7SnNbM7l3Sscu+k27n/bg+xt7JBuvJmmnvHiiy8zqxWHVgsGZsr2zg7jJclaG68uU886xkWfcztzzl59mZ3ZlLtufyulMmjdx5oe1hRYI617MUGKnqRl/RgTUVoRo0ZhhfzKLGlSXs4yymTrfsoNpKISq4yAhBiTyQ9zYG+USKRFwK2EahuVDqxMKUWSDrmYIaBNgTV9aQBGZ+ucnGHVIsspRay19Hp9elWFrLQ/Arr8NSmV/roffxL4ow7UUa/5fdIf+8vBw7MYnK49m+gl/uTP/4s+UgTXBV46/Sov0/FKvU8T4ZUzr7I2GhGNoTKGVCq6dob3iguX9/ju7/p2Xp1usvfRDxG2W0zVz/EYCvDoyvDK8y/wn973GzyzfUFsvIVkdc725mACx25ZpR/hMnO6aIRA2GsJwRC6SBM6Hn/0ES6vHcaUlqVhnzCbQpxx/vwZJuMhr25e5Gd/6qf4qX/2P/Pwb/0GTz37HO3wHKtLq8x3Z8TgIMgAP17u0+97dmbbvPef/RR3v/V+fu3nfp6PfOxDuH6PemcPqwNHj59g2Ovz2ONfImLkbGQkU8ygOXvmLG0S9bxVBXe86TbOPf0SrrVs7nX0zRrzXNKUgqFtHTfdcBtVSpw5f4rkHFcunGG2exVT9fjhv//j/Op/+DmS6rFxrqNzLZ/40PsJKtLUu5jCEHWgNyip5zXf8m3fQ+imfO7zD9PGhgtnT1PZgpYaUp8P/M5vkoKhN14jzveAgItSrrC8PMaFOXtbu7zjoYf4lvsfIqSCpx97mr/zPT/AJz7yEU6efJ6rm9uoQjHv9ti9usWwKiWwmURIjuA9Dz7wjbzx3gc4+dxzPPbFhzHB87nPf5of/5EfFxJVRYbGkFzDYGWVN1x/Ax/71Cc5csMat+3fxny6x91f93V4B+dPneEDn/kM73jgQW5dOyL3/NwsSZIZ3EfPbt2wNhmzNByKylkrSl1ycWePni1ZuH0g0QbP2mBIPWvZaFp2tndZmww5enQ9z6Iaqy2PP/4ETzz9OCsr90Nb83sf/hjPnzrNP/7J/46tq5eZ7k+J1lKHyKyZMxiPGY9HQtigpPnbGAaDAaP+kOFggDU2t0/LvlWWpRDnmQzx3tM5h+sahuMhy6sTnA8Mlw8xHPaZjFewpsqXtmWQc4cOSoNUBqwX+2EmeFKS+63RFlMJLgJQ1zOuXDrPpQuXOPfqq2xfvcRsZ4sLV65y4eoe864jBEeZPCWJ1suMqpJnoBJDq2l9YtZ45r7FqBKbJBM6ofBBclO7CC67iBYWTJSci1pX0zUNXef/0vvVn/T42mcooXBdInSFbLE6ZsZToq2VFgZY5eFVLYakqGhJFEqL3Bmb7SilsD1IKKWPMhBaqykKTfJJ7qolRO9xwQnCrQRVr32LUH0KpSPRaKL3KPqQ5GDpQqJz2SamAmWhDxQQ0QsAJHYUuXFrYyT4N1poJQfAJal7V0oG9Og6Wu+kic3khjQvMmidmb4k3cC5vSMv0BgPmr20MSRt6UIgdCH72YH8+olVJme5xHxjNPbgxhZjkPaIIL+bDznMnCT/ppNI5pMlBoeLQb6m1IzSAJ0PKCLLCzgVaCL42glDqjVtbFEEWh/QqoCkKIuEiYplbQHNtKnpl5p+VZKUJQZFpeW9N0ogwkVmh0izhbm1hSFED8YSUoHNVh4XxcGRkqhDTLqW8r/IERAwc5HdkKWRShi+EFtSFKWDtKJFujbgncgFtTECMrhENOHgEJ+CR+c1s2ICQ2YURclm22AYMBlVzOoG7wK+kY0+EjEmMR71SEahiJKh4mtUCZWpWF2dMJ5A07TszmY0jefi9j6X96b0r+7TLwpuOHaYqogs6REu266MEjCvACbDFS7t7Et2AhqroV8V9HtyNI8xkYxkgmFidgRGolIYHSkKublZBVYX2ELTVYbUtthFaKpJFFbsnrZwqDik7JdMJmvU86lYc7TFW6nRDinSho4qloQouWM653JJJpHY2KzS6CphEGAjogjZBqC0Zm93yryu5XPLkv7qgKLIuTdaceHyBQaDIcaJQs9YAZO0UdKAgNR7m0JCjIQhz9lb5MN7blFbnNRDCjgXxP63uKZUfh1jyu1Ewgg3LpB8S/IdM+VI2mC1pt7bZTRYIaZIoUtCUqQQUKbIwb0R5wXfMEkjQLpFRY3ScngS+WuAmCug8hqPhNyIGQ6anrRP6CKhi3wAUJJHsvCWSw5QykCWKHzm8318dKBKohPVpXfS+PLCiy9CscN0f8a999zN0qBAuUCxNmDt8Aqdc+ztTiV0+0TJyZMvcHH7Mk2TBNAKicIWaO3xbaBQJcYCpqBzLS+dfAE39xgj+Ue9qs+Nt91M0+xy5tzLnDh6IzvbgRNHSqJSWKwwY0aAgmgSLhnms0Ch9ykKm9/riE8GFRRpf5c33nSCY+vHhWBQBcnV+BCwukCnQPKO/XmDdxGlG7QWe4g4TjyzvRlLSwPJ2XNgYkmrJXzRKkWboGklMFlrJCslQjAFqAZle3S5gUoXFh0iRhmOrR9iUo0I1hDrgE4agihNfSEB8EYbdIygO5KKRCNrO2Urpk0KFyXPLgaHS5o2t3Ja1EK8QMIRY4tkaBnJSrEG2wW5X2tHvbWN6yKtd+gQsEnYNxc6EZ/4Mis+kbUVvWhVigJlDb2eFRm3knuaSZpCWUyUbMHKFEyGk2xdLajKIZPVAWuHhyTbg6AZj5YBTT2fZVWxJrRyTaYQ2d/bp5nP6Y8GkucUFRhLF6Q9zhaWsqqY1TMGgwrfyHVtbYUyYKuSVHcoC1hF6BxBOwa9keRE2ZLGO8kp1BqCz2pjLXXTKRKDytlKOaMvCeAbnBZLa1L4rCxJIUEMpIC87kaC/iNKrMZaSjKU6eM6T78Se5ktLK5zWGMJiIq5iwI6yH05yh4Q5P4fU6C0pTTB2kU2m0HkRZCSIgVNtKLQlRhCYZlTisSg8++T98bUybkkiRrKeUVMFhk1F09rDpRvIpSRptAQWkIs0UlhTE82Tp1/4yjgmVFRMhczEGU0EBQpFXJGUAFtTAa7MtAZBWQrTEkymtg5qYm+bczVSxt4F7jlnnv44sMfxaHQJjGfd9x59wn2dq9y9upZQkq89fa72d/YppvPWD56jJ29bfamGyjdssyQbj5g7dgR6s5z+OiI4AOkjstXd4m6T1kmxis9bKlJwRJsj2QtCU3SFpWV6TE5Qog4F/I6yRBcth9rW1KUPYw2B5l45kBt3ZGUB1UIKJrfNzFMCOHaRQGZdM5KUkosmDHbPkWAp1lkEKoMhiYl+WI6W+S00ZILpnU+h8aDspYF4/5fBzzKqBkLMvePfIz8sT/2kWugjjr4+J8O96Q/+rc/BxMyr/0f9dV//Us591LKJUXw9K/+Bi+4Pab7ezjnuO7Y9dx7z1089tlPsD3dpLBSStPrj/j9T/wuX/jcZ/j19/06Z/bOSWaeyzmcnYdckvLKqdM88fBniCeu494T1/P5z3+alRO30E+ad//A32G1srzp/rexf/EM//af/xRPeyuAtQpEHei6Oa0LXNy4xN7+DiRDr7KM+5YrZ07xeK/Pz/0vP0M8vMJLj36B8XXX8YH3v59dq+nme9RtK+cro/Ap0R9ain5AmSn1tGPPH+H5555k5cZb+b73vIt/+3/+e3orR4ndPm+45Q28+fa7ePwPP49SlrZu8aXc/zoczBTRGJaXD/PWt9zD01/6JJOlktOXN/nlX/8wv/jLP88jf/A5VN9glcYUBfbIcVyKpO0rpKamaXf5xX/3syTXceHn/g2bGxc4tH6crpmRTMV0vonSHVUhysMQFPvzXcmaMz2+9V3v4s6bXsevvu//5bEvfQpTWGZbDd/6be/moXvv5l/9y5+mf/gwS4du5cq5lzi0fpSbb30Dn/2Dz9Iv+jSh5fBNt3DyxZd58J3fzFsfeJALV6/y3d/7t/jEx3+f2klejw4Bow02WUJu3dSUGG1odve553U3sbG3g31xjRcee4Q7H3wLVVXx7Iuv8Oi5l7h3eZ3lwxM+8PGHcaXmhVOneM/f+nZuv/EOXjl3lrvuuo+UFB/6/Q9w/51vYmVpjYeff4nlSnPfba8XYj4FjJJ25PFgQOM9g6JAJ02fwNZ8xssvvcyFyYjbb7iB3miFnXpGpQz9fo/3f+j36a9oru5M+bYHvh5SonEBayKPP/E8X3rkS9SzhnOnz7E2tnz+sS9z31vuwDVTXNextLLK0vqtGUyX7MyyKHNUhbS/jvr9g0trodaUfVf2xgKFD57ai6qtspaqLKhK2WPFshwZDMZopSRcnRwgkoTogqzhyJE9iwS7xX6qjSYlS/COSxfPcv70S1w8e4mvnD7FmbPnYXqVpV5iUk64dHWL/S6x1XrmbUsiYkm4lJjHhFPi+KkMlMbSBsd+2xGcwUZpBGxcpM1uo4ViXSIqRQgwKi29oUZZg9YRFRLJeXxujP1aPb72CqUOClsK46cNPgZMIfIvsZvIi784jFkMUXl0sphoKTS5ccCICieCTpaANEFVFqpSWBSVsyRMkXA+4ZVG2yq3lhlCCFhrUcljc66BMYmCAmKiaQJ8ibfIAAAgAElEQVQhAFZYbVLEGg6e27eOrpG8gRAU2mYGU0FRlujcOuVzfb3RWhj44IkqZTCmy+G0kiMTU8C7mPOY8vrMDJEmB3Nl9RXJ5HYWK/a1ELGlhCiH4HG+k6pepSFmQCZ0SGpOJPis+lrI61DEqHKziPzMIUTJntDS+OFiwHnJ1skxzigT6fyMFCvmyqGUlUyPFNAm26CUIiWPAVoXIRmqXh8fIzvzKZt15NjyhJEtSMHjVRALTMzrJiVSVm3JbmFECq88itxEYzQhKdAJHyTbR2h0sSrFHOJsch28qJUi1mqSTlTGkrxkJ7U+oKnwKeEILGSxsink4Ew0Ielsd0noLLU1ytMimSPTmcIkzV43J7YdJkJXN1y5usV0VqMNXHd0nfX1Vcp+hSFJfXUVqV1L41pUJzkLVa/iWL/ER8ds2rEzb9lpGvaahq39Kbdcd5idnZbJcMbyoWX6OuJii8u2YryozTLRSb8aMKh6uKaTkNYga0XUQgYfWiI1PgWsVhArMB5tJPhQR0Onc/OTErA1aE9ygeQNGIdSBaNhH+KUQmlS6KhUSWcsOhhwotJDJ4LOQF3U+RqDstSMBkUO7c+gqNYoXRKiojKWyhY0bUfnAylGdvb20XmYj9qwu7eHVorDR45Qas3e3r4MLdYyGEoInoQaa5IW+0BaKA6VkgwznduvkOGoQMOgwhayIWttUEajiRKib+SmoiP0h2Oi7/CpQ1ux32mgdj0SLTYNBZA2HDQoyhAgPRdWLbIyDOIwjZQaiJZIRxc1VjpbJY9OC8KpkkZjkcIpjy5kkB0O+5hCQnFtzidzAWzMlj8l4HdS0B+PGI2GhJgIzhNjSYoDtE2sHj7E1e1zjFfGDGyPwlb4NKevLFBgVcAXiWJQMWbA5K1v4fSLL3HqzGlCEjlvWSnqTjEcSfOXThNed9M6J44fp2fH7GxuMFqW5sSqVwmPoo5SFH3KcsDGlat0TjJkDKIcNCHiCURlZDgzAW0UyagMrss+EgpDih5daArTI4RE3dXoEEQVt2AigpHXU0nAso8RFSOVUkTl6RUWbYYiW+9kPy8KK/tLEgtlDC3NvMWUCm0sEjXm6GqHR/IftAHbt9jaMhiOGY+HdMFTpD7BN+zNdgmqxqQSZRQuSbC2VNYHuq6RoEtVkWIDeHSU7DopcXQ0tRMAMnmxjydFcp6oA9OuYbk3kpt+odGFoZnK/u2mU4wtccFTz+ckVVCWpYRNYvDzlp3pGZbWrsN7x+7eLl2oqFLJ3uY2tt/Rxg5jEjog4bFA0JGgczR4jHSdY2UyZndzh2Q03TxSjBNHjh1idGiZyWiJWV0LKJo6rIJoFZ2LDAcDKm1YGk/wymNiwhSJ1jn2dmuC64guMN3do+3mlFZly2zCNQ0+JFzb0bhWAP9+R8ShSpjWU6b1lP5oBatMDvaHwhaSrxU9wXt8QgK4kxYyRCGWyViKytoakpKGS0FJIPpscUWy7BJiP7UYKiVh3iYZaUiz6cCqXpSlAIPaEHUpVlYR94hqMXZAKRk+Ws47IjJRFKbAx4QpDMmLopGoslJqoaxY5DhErNH40BBCVgdFORoWgCYQSXRB2vEkO05lpRFiY1aBiATTmhhxbkpVjXCpk/k8Z1kI8xUO1IWKgEGIp5CcWB+iqKpVFFtiUCkr2xXeCQtbGgHnyrLH2g0nqKcNCsWVixdw3tEfLgMKVU5ZXhrw0qVdZvWMXn/MC888yfXXn+D4jSdYP3ICpTS7lzdIXUcz3WYeYLS/zY03nGBWd1w/7vO+//yvGa2uMTxyPc3ODg+85ZuYbu/Tdi1Wi55eBGceUxRy/srWPoh4H0SdrZTkW2RFkNiXC7GfRSF6hLHPgI+BqA1WC3EZYyAmKEsjSkZEie4JVCbnCaIxqhTdWwoE2YzRMZFyTIJ8naghlVI58ysTD/m8qLQ0CL4WslkAS38ZgOnAOvcX/4r85wJYuvZPB/97kIf2mi977ee+5vv95bVDX/2k6qv+7a8ItCWEODNw+fef5LdffIamFluLcy0mJcJ+gyt6jFbXaWY1gRkqBrpuxna9w6XpFQgyB5myZDafY1BErzl97jz/x3/4OXpln3/03/4D7r7xNn7w3e/m9LkNip7m7d/8EFpZtFbcePwE3/Xd38fjv/ALmKVl7GxGCB4ftdjXXcKFhsoalCroG88tr7+T/+lf/iv+47/+WX77Y7/NcFjxT3/yJ3jk6iXoTTC2Y9Dr085qUmHE+l4kxr0+JTAPNd/zne/g59/77zEnnqfXNZiiR+xqYtJ89rOf5g8+9nuYcQmdR8dIdHJUSsrgfGBlNOL2e1+Pqvf4x//8f+Uz7/8V/v7/8N/zzJc/y5cf+zKD1QHzrqUwosg8fv1xfvSHfpBHf+lX+Y8f+SCbsxkKj1GGKxtXiF3HxoVXSUUkzmeMxxWzNuCUEEIJcB5c53n2qad485vv5bmXT9Gf9Gl9i3OeN73tft770z/L+tqAw2fO8ZvPP8Ejzz1Lf6lHu7vB1pUNuq7h8s5Vvvkb38GLTz7Gj/zwj/Px//Sfeef3/g2uP3yE9/70z3Bx5zKjYkJKgZQkU0gKdAIoQ0TOPBcun8e1NX/vO76Tv/nQ2/nwr/8Opy8/gwJsoXjbm+7hsU9+jq9bvps77riNzz36KPe8/VtwruDE8RM8+5WnqMrE5q7j6PrN/PKv/N9s7bW86+/+CJ/40G+w/k9+Et/AYFBx9uImn3/iy7ztvju557Y38fK58wQdefnSRdaWxhw6dJjLe5tUtjqIL+lnFdP3fec38eKFc9x+24D15UN85FNf5HNf+Dg/8aM/yq03XsdXjq4SmjVC2OXlV6b0ejXvetcPoZ2mm51hVl9hxd6MtcN8pvj/mXvzaNuuq7zzN1ez9z7N7V6vp16WLcuyOje4cAMxPYGEFBQhjJgCChgBKqQCjAoVkhBnUBkkReMEqEE3iiYYcNEUNthgwAbj3rKwkW01lizp6T29vrntafZeXf0x132yAsHGuAZ1/nn3jTvuOfees/dac835fb9Ph856K1UlpTz7tixF9ywNClMHghGh9ZZR5YMhz9zj+wMkZ+0nPZGokAMdEsXatLdmP0SoYCgMIXPm3AVOnTrFmROPceLhB3no0Sc4ff6CokFamDrD+orl6UsDw2yLxhTmAUJOV/E0znll41JojcOQKRH2lj3znBkK5BhYNcJqZ2kkEvuidSeCF6ERHTxtjIS77ljHrh7j0VM7ODOqza5clfSfvcdnv6FUqIVNRiRifWUXFIOgliVrVG7m1UNDIyNyzhrcaoDidCKaXaWSZ025EoORQCo9GU1iEzI5amFvijKIBEORjGsdrvFAxJYIUXDeQ6NcnETGisN5i3cKOS7VK2pMwXtP27bEUG0aRZPJpKZzeKc8iRxrA6popLAeCJ3CtbPBenMVqImpQOySyaGQY66qnMo9MsqL2Afu6pQo4Tud1FpjMY0lFUe/rAwVUxNY6vMqerPgW+VGqYff4ZuGFAd1fOhoUUGAJUEpWCw2etpmjKQaR1uT0hq3wpBBSlBfvTXEqB5/ayrjASqsNGOKhZA00Sup5Wx3FsjdgsZ5PFItSRV2W1CmFVURJQEx2miAqBDSBEmMcl4sdbIawBhtDFb2TImBYgFqcoAVYsk4E4mlR4LTIrn0ClwGFM5OvSb2bUKWmAopa7x8yAHJmUQmiaXpjrC5d4HRdMSlnSU7s6Dwt0HY2gqIMRzaOMD6gQ1VwdTpKi5RysDINFQRBPN+oN/dAxGm3QoHD6xzYB0WiyV78zm7uzNKLpy+fIHHQ2B6umN1OuLIxggrHZNOkwikNgrF+Hqw1QaclUw29X2xFTgvY4iJRhySCiYHxDpcsWoDdAYZauFbVW0uaDJh6zKNH5HLFq1LJDdRC10pULllxg20Wehq4mLICuTLKWFNYYiZlC1dMXq9pawcH4OqgkphSOiEWAqNdwwxMhp19H3PldkMYmboB86ev0A/JA4eOchoPCLlxHw2Z2dnVw8YXYdrWrpRp5GnknUtEkMAUp3CqmPD0PgWT6IfLHHYt2kpm6Rrujr5QBUpvTZELR5ypCsNQ1miseYKWVW7XkZKQNk4esBKAKJqjiyZEos2yqIWq5JFI6nTgDClUAj7fi+pU8TKYKMkCh7rrCarbUBG0zFbI2QKQfLVSbmviXKNHZExBBeIMSAuInRce/wwW7sd3ma6tqGkXgsYHGIgOV17rDO0TUubJoxeeCenzlwgZm3cTDrH1qzgvGVnd+Da629gY3KIG697Lv18YGxampFhNJ6Qi6lWn0I0LW0zYdLu4uqhPUrBpEA2EQy4rPDnkW0YTcaI101YfdJVmWMblvMZxlhSXGLKkpDBmoaSBooT8MKaHzMZjRDf6rWQ1SicsyoOMUAqVRmaVZuRS5V1C+24Y31jgvct3o10AGITw3zOeLxGWCSGYaAxIyg7mM5rQwoLOWPalrUDa6yvjqHoZxhSIsWo63QamF2KeGMoplp9xRMQpERVKtqWlfWG9Y2DeNuBNVqAR4gxsLpyiOXmNsYmTYKzlvbwAcpQcIstGFSlOXKOyWSEs+4qGNkbOLJ+DQMNbeMZNZ4Dq2tgYO3IYbxzeGmxCIOFnHQ/kmyuysIBuqbF+5Zrj69w5OhBxhs30IxWmFeezqLvCcMAObG32KXxXGUOZDQcopu25OzZ3NlRBkJK9P0cjCoLrTOsjw/oNTssWfQz9hZLQsjszBdqTZfEYtEzlB4pPaVXlkGMCd9HuqkWmsvFUsGaYtjdndGHhOs62jYSJNCHaq0mQIpsLbaYREe/hJA9EW1oGeNVVWnrwKgoY2LoB7Z29yhiGJmA3W25dPkCkoSma/DNhJwHTQkLfWU4Cn0MSLC0TWAxLAkhEHOPs5amaRjSoOb/PqqNqlqhLVpTWbHKazK16ZAFbyq3KxWcEfZCIJWIF8GZTNNY1DtoKiTeqo037/fmhZS1geJpIWrR7rwqYeIgiLWVb6dK0RgNFI+xDY1vEWex1lxN0HR1TY6pEMOCrm2YL9Rcj9EayQm84N67eN/b/oS2W8NGw97WjGY8wfYjTnziSU6dP6ccROe47sZbWJuuMNvr+cgHf59+MeeGm2+icYZLZx7n1NkLHL3mKIvdGRvXHsbahuPX38rpJx7lS/7ht9M5z2SyQX/lYQaBFAdMjmDUimaMEC2qni8D0GkjKKsyF9HmkLAP1R8AXSONs2TJuhYWISaLkxoIUXTvzbHHKPIMk1Wlv89JLHVQU0wGEjGqlVasDkjZP3zlVD/PupdUFau6BVCFaym4aon723vUZpJUS1n5C9991hf7Z8nyST/6N/4NSqkKhE+CffPM6z3r/3/tphnVPgjnH/ggo5uv55u/5Ev4hZ97HQcOrnL23El+5g0/p3y7PrFc9pS0wBuYx0QyUOZzGmfpxhMkJ1anE4Z+IMaAKYYSFrzspZ/LKz/nZRy99hglJ25/qXDl997GO3/s1/nS/+0bca1lmM/58m/7Lt7/pt/hD2c7dE6YR1VLG2lIdfDVh4FJ1xKGXV7z7d/OB97yJj7w2P2kFnb6yPvedx+2sTjbU4isjiybs8Coa7Cux0rGx0DEcejANfzi//VrnA2Z8eZ5tmcJxi0pKmrk9KXzHD+8TprpoDoVqdgDTeP23vL8593M//C1X8N73vJb/MKP/ziveNFtzHYv8Ka3voEDB6bs7i05fOAm9naucHB9lS+6+25eccc9PPfzznDfB97H5Qt95d1C6HtMyYSYERnU2TJETNYgBCQTam2GDXz9a76aK48/xKmnH8aKpTFTNmdXOCwHOXjgIHl+iS//N/+Sjf/0Ot735++hk0NM11o++tE/Y3d7i1vvvotbX/oyPvzH7+E3/uiNPPXURd78Pd/Hoeuv5cRDH2fUTrm8e5m1kSo+ZdLoQLQvmgjeCuISoe9577vex5d/9TVsPXGBx0+c5OLJJxhS5qd+4zf4ke/55/zwH7yVW55/LYfXLL//26/n53/mFzHF8PGHH8F7z/mz55lORmyfOc0jj3yMW593E+OwzaFrWh6+7494yx/ex/rqlHfedx+v+rzP552bj/LEqUt8+GN/xrf/o6/jC+58EWcvXmJZCnetrzNuOj5x/izPOXaMXM/9zhjuvPlWHnjkMc6kwO/+P7/D7Xeu8/BDD3H+7EledM8LuPWmESNXePcHPsLzjx1ixQjbwyYba0e5vP00abGJWxnpumTN1TVPuc3P3JEpZ0Xr1PvSWluTyZ+5R5+1tlUm6RAC2zuXmO1cYdR6JisHcc2InISUE6NuhLOwvbXJJx58kMefeJzZ7jZXLlzkyTNnOX3hAnkx0Ira3EwjvOw5GxixnL24zanzu1zaSnjvONw2dK0gTmiyYXPek2OiNQYszHPCo7a3WcisNLDSGi7uBYKoKt2KPscxY1kGIRNwDkKCkoXr1z0vvPEIW6PDPHF2h1EbyRalt5bEZ/PxWW8o+QacteSkBxhKVjknhWwDGCEHZZpIlSUn0QlVLMoZIgLZUbLK5SmZkgzeOrxT603Og0bTGw8lqYrCt+RB4bW5ZJZD0o2exMhZGqtsIm9bclEot3EKxV2mJc47hapaFXOL0wtPVUmFHAvWgfUW1yhoM4k2qax3GNuCRELKuCKMx51aL0yViBuLlkcK5C0OtVVQpxRoQ8naKnHvI7hSeTi6ueekc3URoWkaxGriTEmi1r/Kieq6RqV79XlU/KP2nq5tNHGpoPDeCoO0ueCKY70dqZTPCKRIN245PjpWp5yZYhUG7YpGyWRitYgosNwCxiQkGnbmC66/ZorJUxIFK5pKZ0xSwYZRlUZC1MaYI+IyEUs0et00VWKZEUxOCHUxIWKdqs1cMQrTLhUIr90NhEAxDl8KpcSrzcJG2qp6gxIKsTRY8fr+ljrlFzTFJxssHmFOMhGPZ9Q17G7vsrvMTMeaYGFRoCnG0o1HjDqPb1v6IRJTZuQcoezRVB6FMRYvQm6Eppmwt3DEPtAvtknDAmcb2qZleuQQB9ZXaJqGZm9GLLC3O2dvd86Ziw2tgWsOrROW/TNTuZxIkinZELIw5EJbCjYpBFWAnGektMIy18QsDJIixe1nJgpZMjbtgzmVw2ETmKbFNQVJkRyUg5FrdGqOjabSyUSvbRKNVf4KuTCIgnTFQCix3otGWUQiZCm4op2lEoMeGipA1KLgWaGrgGDPqNvi3NlLxJQ4ffKsJocdWMdYTyoDMUV2dnbJZZfVjXWs8+zs7BG7EZPRQq2MaGpPKblGhyuHyhSqOjARkq5PGe1zmgK2NCzjNrZAFIOzLWUAa8Y4u82iBNZsV+2AqqjMNpNqQpQhazqbM5A1KZJiEDyFqAB2ooL8a9Fv9ycuFUStkHYAQ4kRL4Z5DmhDSiHeJltsjjhbSOj6SAFb1KbkaipUEYspQjYZouc5x1/ASneYFIUYLJKzNjSsAUkUSZAKJSinxpuWps3M+kwxQhwCY+fZvBwR03D6iZO88PhxbMlYJ2RXEOs0dSg7ZmnBQMLlBZYJ2XhCHhhZS6QwUKf3OotQObOJQMAmtcskNL3T1MZZLoZl0eaZaTuGsIsM0DUNBm3G9wQslpFtAMGURDBqb0omIcOCEHoKGglrgMEqgtmVgE8FGwErhErFSYMjR0dMqh7xFNpG8N0UX1pMaXV/zL1eS0nwURVnKaOfndFGdy6GIh6fhVAizhhCVrvvUNQGU0qhEY/H6ZAiLQklEgYLJpHiwGAGBgEXdFghQ6YtmaE4BpOqZVY5TwVL9JomKr4l9Lb2MasSMmW8E3LqwbS6h6WCtcoVikRCGogpY502jFrT0DmrSmDbYjqHHY+QXplEEUPTFRIDU+lwVodRNI4cBEqknw80naZhmlJIMRKWS5wpjLoRqytrIII1hp4e6yyjtsOaTMZryl7SoYt1gi1qfx63IxrnGUJghX3FjzLfjPOsb3i2d3dpx1O6yQiXAv18F2MKZMfYerK0bKyuYK2wurrCgY0jDCkhplzlrpWc2HKbpKhBEo3paLuGkfc0o0bB2ig/MsWFwq6NKnQEIQyR3e1d0kRZGivrLbO5rv3Jqq19vrfAe0dj1EZb4oJ+b07sB4Y0EDOUZoyxrRb6bWJZIuNRR857hBQxObGzCJidOSsxs0iRZDvEtDTOVau+qxL6TAkFMyR2thaEFctSBlYu7zCkRtU2VQluqy3U4Ag5sJzNtJFihI1mYHtnS9mJ3iJoqAM5IM2YYblFyoVlzrTesdcPLJeRsROuv+FGPvHYg6Qy5r97xct48P4/JzSe06eeZDpew9mOY0cOs721x4XT5zh27XGuu+U6nG04fPgIOxfOMATL9TffTFmCWMuodaysH2TaHObG50153m130O/1XNm6TLENUTzFOhAwjce6FiMWK5lAofMdxqoCGhMpqG0vRR2A7gPfrfV432qNVgMHbAHJiZYCWRRyLgVrGwyuBi5pfemNpvPaOmnXfpxGfJMzhUC2DSbqMLbkkSaixvbqvqqDXkOxYJo6VPOVE1JbJ5+KnfRXff8zSXIr+4qDv+njk5ran8lDnvVL/NV//1/68/+tRpOAWF0X7vrub2Hnvvv5T697Hc3qGt4IN91xE48/+lEubF2hXyZar06LKIZQmf+paL3cz2ea3rsINM6ou8OqYPeaY4dZO7Su95+zlHnPoS/5Qlbf9Vr+7//yG9w4mfLKr/97bJ78BJtWAN2/jTF4HMOw5ND6IVZGLXv9NogORz74/vt572/9CpfKNh2ZbB2ZGaa6NsIyceHiJXUOpIAzib4I80XGSeHy9i7RFPCRzV2IWbCuQ1rDcjnHe2Fz6zJZRsRQh4teFX3Ge0IfsAFOfvwJ7n/gMWa7lxkfeSnvevsfc+7yDocP38Atd97DD3z/a9k8f4af/8kfIw+RxlpO721zOWoET0qBRKYdrxAC9Lu7SBZa17BIheWQGWymk4am6bBhgWsMP/XTPwbS6PlOPKZfMN9dctu9z2E8LZTxQd78kz/L20+cZrq2Sshw5vxZ5rM9No4d4lu/8TV81Rd/DY++/Et54MH7+LLPu4l/8i3fwCu/9Ct579v+mGBhdbRCn2eMV8cYEcarG8y2dxhmC5wYusby8EMf5vSpp/n5N7yexe6cg2tjvvYf/mO+9Au+gGtvPMqDDz/Ih/78Pn7ofz/Bt/2Tf8qocdz/4fdx+PiNvOu+D/LG330L3/1t38TlrTNcuvwkN9x4kH/53f+KZYi89M572Z1vMlo9zjvf8yFe9ZVfxJ233M3FzbPce+89fPGrXsZaN+GRj5/g7IWTvPiuF/LAn38U96IXcf2BgzijKa1Q6EaelIXLm5ewo+NcKQN7YeClL34Zu3u3sNF2XBkb5pcvs9GMeM7R5/ILv/gT3PP857G+fojLT51nNsy4+8Vf90n3lg6thhAIccBZi3dNVeq6Z69Hsv8zV+/YT7qfVa0bc1K9kW9IxrEcBmwN7jh9/hz3/cnbefLBh3j09Hl2Lm8TS2bsnQ5OjKtcxUKUzM2HJtx8aIUrezs8emqbK30Ba1jpHC4WhtgzxIA3LR5htSnsBBXl5KRWWFOT2rLAdOTwJbFnDW2GocCF3TmUQmeF1bbhhsNTjh2acnmWOXFuC0QToPc2t8jzQAkGQmaxWNJtb/+3F73P4PFZbyjZRjunBYu1atNI+74mLCUrsBsgRlXmOFPAZO0jlYAzDiRWu4haS5wXorVk00JJGCLF6iFXGt18Uw6I34eqQhMg+zpZYB882hFFVUEJZRtIKZqyIR7jleNjbE18yqAxtvsdTaBo+lNBVQ3WOHJUZUPslXsSQo/B0DZe+Uk1AnbfZWONxsvW9ocmhe/zC0S9n+1IIeYp5ZpoJFWVrB58511tUhkiuVLeE5hCGCqzwwiNtxT2mTGGtEzKJBBD7nUCbqq6L6dIaowWtWVAnNH0KgZlHJhCTgMm5Wo/U8C1K9pAjDGp/NsW+qGnTz3jskKJAXFGYdIxanoRIHlQEVK1/ZHUjpM0CkUXi/q8iaJ8k9zrZ4EnFp1USlHGQpGiEPGsh10jnlRTvCwWa5Sxo5yAqJ9no9YlBY8Dxak1oKLDnRM8AfGOnAwpC3u7S+XomMLZ7V26tuXY6oTQ9zjraRoHteguYUEYClkcxqlkNfQLVsYrlOlUuVBxwDs9UklpoFhCCsx2ZmAsa9MVWus4fGhDIy77wGI+Z7bsCTlz4sx52sZXRV+hGK8JZC6SBohEGpcxtqVU1k7JyiHyLlHSHn1JVV5pKMUTa4ffI8SQKCZS+obUtWolAfCZZECk0SOkqLWzazq6yYi9tKefW1UZWCv4qFasjEN8REsWQAIx1SSGVo0QpWiylfWOMCRCihAGnHGVMVZwfo3zlza5/QV3cOLESRZDpO8jSME4SysdVizzxYIwaINp6Jc4YGdnu6aQebqJ0HVjKAp6tlgWe0uF6iJXGWj7FtNcMsn0akciYU3HEAuZHpctjQgtDdEoZNtZD6ZRgH9VxMV9mW0VvGSTSTbXO1qbzJq6XdOmElXm7OrKEXDGYEykjwk8ZCn0fU9kIOaRKmEkAEaZOkXIGJJkTZ6LpULJVJk25B4xHQ5PI2NEHLYRlsMCkayH41ywURv+tha13liGfkZjBScNqRSsFZYlYlyp9xUUK8SigNkiudpuQQiq4BJN8CqSoAScWWBkrJHtYsg2IakQSkJMUuiiHeGMVw95ES0IjE7em4JynGIkFqHJmeUQWYjFugg5qB3N1iZfqeqrJLTFsIwOMXpdumak6Yp5IOZIqVOgKJlsVFFhizwDY7cWYwuZGTFNSFkwLuFENGkvDspOQuHNwahapBWPNULKrU7nbWSRIr1XVd2cHm8NMS+xJiNW7YzWCrZtFGodHbYIzmWysTTGsr2t6uEsBS9qdcopI96p8tdpplsnQjaqVshBPx+c6IFVsiahFUGKw9kWY00Nophf1jIAACAASURBVKjsHhMxWW2pNhdyVjumo/LYMJhUMMYRe+V9CQ7nIljLuB0z7lYJ/UDfR1LIlOmIMCxZxiUujTDeYY3HhoGhBKxp6LoxxjaE2KvN3AjNeMJ44lgMA8vL2+RZwjTgO19tRnpwqrAiSgrkPNC4qSp+xVQgtnINnXdaqKYMxVOwakMVo83eIdFOV5DGUqzgq3R+Pw44VzXyPqum7Qy+bRhPJ3S+YzJRi2zjp0jR1132u1W9nLE2szIdM1lf4cDKCpPpGoeOHiaEhLWa6DIZ6/2yWM6UxeYS7ZplPJmwvrLK6uoh1o4dQ6w2VbVWg5QH9kRtKSZ6Ng5ssHZglfXJBtOVVQ4eO67KYWqdV+Hwxugi1qy0bKyvMepWmLQd07VVpgcOkFJSVlRVN8/djCyBEiKOQkkDmDHJC03bUnpNdSm5NikKxBBBYAg9JRSWxpHdFvGxj9GaEe3hQzUteEEcDIshEW3kebcc4+mz2xy/7jBj3/H8e+5ivjtj8+IZLp66RDMZqyphuaQZt8wv7TCTOddct8H2pU2ePnGSg7fexuGNQzx98jxGCilkiDDMl+xubSN9j/cO263T95G2bVWFaqEtHmICa1imwDz0OAKj+YjlfF4P7jWqJGeGode9OwRoRFN40Rp1GAas9crKqhiElFQ9b0qsg4I6HBKtXQs6SBQpZGM0MdI5rGlwLiNWlNVV0ywNz7zv+6m5+49PpVb62wZ1P9vgJs+oleSTvv5rPqFcjc989qs8u8n0KZ7mr2imiQg5BeLJc/zBT/4sMh3TX7zI8dufywuffxsPfeg+Rt2IkU+cPX0W61taYzE1nbePkKIenmMawHpmy8AQoe0aUrbs7PYM84HxeEIuGTexpGR45T/7Tt70znfQ3H49//zrv5YPPfIxtk1iKIauaeka6JcDd95+B9/xz74H89QT/OjP/QjnZzPEr/LOt/wel3YWRCP0xZJtT2MsbQPLQZgFMDHjXWY3OUzfsNJkWlEswHSyziIu2R3m9CHq2pKWignJQu4TC2cooccYoesa2lbTTYchMvYdDz91go+//vWMXKKbTvilX/oVnnPbnbgy4fKli3znP/0XXCOJN//gD3Fe5hxdOcyDf3Qff/SxR7nQL8lDoJ1YDJnZzmle/YovZyv1vOdP30HXTpTnOrFY68llSS4D7WSMbQy57+mHOSkJQ9jjNd/8vbz1N3+Nd3/o/bznI3/GteOO/+P//PfEaw5iO0dIDaEXkni2L23y5jf8NkNsefUrX8EHf/w+fv9Pf5Aoe7zxDb/MysYKr/uJX+Lfft93sZSOfm8L6yfsXNok5cJ43IHAfNFjjGVzsUve3sFbz+W05I1v/A0ub19kfmaGsYGutVy6tM1H3vMeLpy5wIlPPMzf+dxXs/qVDadOPsyv/+7v8Hlf+CW85pv+Z04/9RCr4zWOjlcgJ/oQMGS+6Ku/mFuvfS4b3RqYwvbODptXdjl47SovuO0WDh87QDcacf7SJY7vXODgtc9RtwI6qNJQJrjphhu49frr+c8/+K9ZW5niC8hyk4/c/w5CXPD2d/0ZZy5d4qd/+7dwTrhw+RxfcPfnEvZ6nvjAh9i9sMONt96NnxzCNh0Fg3MN49Gkcjs/FfftmdWg7J//K+x9bC3jtqNwVFmdSesrgMl4jbf/we/z2IlTzGIgN0JnGg62hpuOdGTUor5YOs5vzjl5dpPHT13AG8UkjDV5iDhP6pYiszeouwCbWGToY2EoBYmJiRdG3iieJxbObwa8qKJJMJgctG9hDNjCMic2d3c4tOZZXz9IM4PSDmwcv5nU3IR76l3IpGG5DJCgz/8/ZyilaLQ4sfqBFYI2Q3JNELFgG/2eOhca3UgFXCmVRRCJUX2Eufq5Q8lI6rEkDEIsUkGXhdKUamHSaaOCLEUPBRgaNN0qSQaJxKAR52RhmYIepqzQxx5JauFKFGV2x4RIVssSqGqilDrBLUi25DgQlolh0VevfyYOmRCDNlpKJqRUN/j6NCjPJtdemzEW7yzWWQUSlzo9tQ6RtD8fUodE0iIhpEAZIkY0tackTfUxZErSRpEU5Ynsx4prbCwIhWJFJ41SY+hrQSMpkTKYbJEUabwnBa+HQiVqIEhlXRliqlHDtuBMo9BCa7Eh0FqBqPZBKihSE2BqCHOBIgVnFUxZKhPKUjBYUtTGkNSJc0xJbQ5Fm5G2butRCtlqsp+C0hRWnLIyHLLNlTlTiAyAI5VQQZWGEmtEd5Eaoa6Wv1IyLtfEGqOAzFgWjLuOxSKRc6abgrEDxk1pjSZ3ORPrQd5DslUFohDZnHX6vD2b02UHNlXQaK42KkspVi1JWDKW3dkOfT+jazuctxxeXSH3U5Y5sbW9zTBEUgjk0CNSWC5mnDmbmU1VPmpMiw8ryr1ohZgL2er7F+NAFkjFUqQQcqGYiCsFkxNDru95TpS0SykJ340JueCNI4QFQxYkOyQnXAaJAtHU9MOCzRFrfJX3R03LSrFaFizieuKQiQVKKriRvgeUiBH9XcQUkgNXG4F9SoiZ0nSFna0ZO5u7iDMcWFnVz0sEyoj5bIbkjGu0obecLQiLgVnRxnGOiaZrOXvhEpOVFabjEd76Cme3lJKYzwdSTrQdtAHEqXIyp6isFdNAdtWe02JKpKTE0gk2elwRiiRteJoCKSNpP/WnxonW5rkm8kR8ZQQ0jdExY9biVtlxFUBeqpVC9MY2SUByZWIYionEVMjFYyWyn4IJQdkJRcgpqoJELI4IzpKL2vxisbTFMotReXTR0aBT0mQHjNU1XlJBGDOdTnjVq76Yt775LQQTWFZLbEgRMEiJ5ByvKgBFPGI9uVoDLRbNN0uqqrEwRIcrlTtjIGWL8RbfRxYlMBBJZYDaKDPGaJJhymr1seDEkFFwYxaHlyUa4a6Nk5YGKQ37YHZJogc4MoGBRXB45+qwQ5suyv1KmOIJBYac0TDSymIzMKTIKAmUlr6P7PU7xBSgFeapx2kP/epBsJRSD4GqczKlUPKAyZmpFN2fyLj6t3gcofRIVuix9WsYcTg05S8ZVB6bclWYOcoighsIXaPAeO9wg9AEbejaoiDfXLQJ0phCM/Isl5pcl0omEYklIDFTUk3cLJDQIsnlTKSaA8VWfkWVwRkh50jSGQDGaVMukbG2oQwLisnEfkGY9cRYGEIi9AMpDHgMKQ/kIaqSQtR6XYg0Vm3zQ4oKHxYNj9BE1sLIN1xJm5hskFIb5iLkkhDyVTabZOXMaE2aMGI1bCPrQcZ7tbVjNfnPiWEQtXirWt0hUflUOWUEV693VVxoMqFaKBrrsFjCAN3IYMUCSe2bon+XLVW2Vvl+uU5Tc0mkHIhh0P9nKjcHcs762WfBFEtJDWUQwnyg7yIhlsrTyxSJaDrnnDwYDa0wmlRngtrPUs7EpLgAZRj6WjvUVD6jn62zDZAriN5Wy+C+/VEwdl8Bqw1N01jGo4axb+i6lslkSvZFG5wJnG1YznfYFx633jOdjulDoJHElVMfpDOWFV7CkAeag1MuXnoaNzIQG06eHfDNiNWx4djRozz0kftZLJc8deKjjJsxh4/fwI233Ey7vsEwJIZl4ObrjuFXGtbWDnHDDddxcHaQGJc03QgyhOWMS1JovGU69jQjr8y+1QnddMyo7TThdLSKsarsBpCcGWIE0zDvl8xnO/i2pWlH2jQVPeA4EZYlIwswMsMYR9/3mKgKvphy3SeUSbpc9sznAzkV+mXm8qXLTKIOw4wB1xokBVIpLJcDi3iFvh2p6jlaRpMFqxurZKsD0nE7oXEta+vrqtDnU6uT/r99fLKS4NN5XK2Yn/UMn9Yr/Vd/534/af9J9r/1l/1Gn67l7S+8BgZ3/RFe8qIX8uEH/ozBw4MPP8YD938YsYYmCxbh+177Wt79prfysSceJ/c9znmi62k6raGn444r25HFLOGdBp5kybzjPe/kxfe+mC/7ii+sOAFNwM7XHecfvOY13PejP8R7H76Pwzc9h82nn2Zl7Lnxmts5f+ZpNsMVbr3tDu65804eOfUkX/eab2X3yhXe/Ydv4/GzJzGNoU8RT2YRM6bRwW1JkdCrwyNlwbWQZWAvCZ01tK5wZe8ctjSYwdLkxGwIFBKd9SyHhBTo3Ih5v4e3jiKJeYgYpym3ixjwQwslcOe9L+Hi3jle++//I4999CHe8va3cOH8Zd75gffz3Fuey5krF3gqL/iO7/1OvLeEMiebwmjsme/sMZpMGPlVZk64ePoiK92U7KEUw97uLsZ5VlZGZDPgjHBo4zjbVy4wXyzp+4Jv4Fd/+T/TjVd58MMf45u/7mu46eiUyy4Tz52jsxOGxUBxhWQGxHoe+NgDPPbUCd7+5rs5eOAAk6Zlbe1ati+fo52O+f4f+F/Z297CtoWYHXnoKTkzGY3IRZgtZixDwkvGD5EiDct+TlwOPLi9zVd97dfx5l9/AwfGG+wlQ7+9y8+8/jd53guex5OXZ5y7dIG97T3OPHmWx08+zdr9H+GRRx5ma2ebX/6113Pv3Xfwb7//P/ITP/U6PucfvJxXXf98DrRTcsqElGlHDTetHNNzI5nD0xWMtez1Cw6ONzS9tq1IFvRaMMBtN91IzpFjGweIw8D580/zyKMfZPXglGPj4zz/fM+HHnojN15/jMMHx7zktjuYlSXjQxM2xkcYHbsRGa/SjVfouinOeZxz9V76i17XT74nRW9UQK7ul3ovqgp4P5lO73UdgOiemtjdnnPixCku7i1hWJCc4dC44brDq9x6zQoHxhMeOXOe7b2e8ajhylzPmuudoTEZjLCb4cw8sJ16vDEsF4mVUcsQtC46OnVMnAogGgyzEBiCchBb7zgw9hgpLAWG3jKPPRSh7Qov/pzbuO0F97BxaIOjx67n1aFDOsNzbnguH74ywn70Ka49usFkss7KwSnT6cqnXKv+Oo/PekOptZ1OZnOs3kZ39QP65CU+UdBE3Or5LiAS8KUBUSuG2jliXaQzahhWL2YqhUjQxJFgSEnjuxMKg82StXGSVSrWp57OtwqHjJHksvqCS8JEh2kETaQr+9dSLdA01cvkgKh8BUSIOWusPVaTf8TR56C9KacTyWyzsqSqymk/fULVQvpaivvQw2CMBt94pHZXY9bpJEW9mEnjepQdVKdHYPBOG2IWhbYZr7I7EVOn3JBCVg5BrlGGzlIqG0qh4AYvMPQDHofxniEmoiks+jkQ8RUGrAlxavnbm+3Qp0wkVlp+S+s8Eg0SDWVI+K5TcK5YktEEspJ1U9B3yuEwYDMDWmzbauXxRjfEZcm0eYSRuqNL2ueKUyiVBwOaJmjJpYJgUUaUF1EYZRas1zhdR0OpNrVoE27fSlMEU3xtemUFI6aAawwhGXxqiFF5FKsTw2rnCMB41BJyg7EKXTZiyAVdCPSKQKxOumxuSMXSx55OHMu0wBpVksWU8a7BesvItmhcqv5Niz4ii4Gw2MO5Bj8ZcfTIQULMbF26wjJnxCob4cylS5w8l5l2LUfWVziwqmDCWATTOFKMpFCw1nPu/IxshdZa2q5gTSCEyHK2oG0F22hzw9uMiwnmPSNruLQzo1k7RCeWUgZiDkhqGUgslyrFNEaPv4FAQfB0JBZka2hshrykiGHoM6EYxHfEWDlPzuh0Q9Te07aOpnUMoRBNorWRRT/gx45lnpNKxLWGuMx430FUa6VYp9N9b2lGLU3SKfJoPKXkzHg6oRmNiLGwu7tHvwzErEECbdfiXEPTOZqmIUshxAAlkgmUHMk4PUoLZCOQHLltsSZhinqVizQUlPtjjCWZomk7NV41F1X3EaHxDSllnBW0521V1WUSgk6LrLGaNGnBFqHgKSZhGJHTJibrumSkTr9FKgA3U4zDmkFBuMYSxWPEkKxgogObSSRcycQS8d7jUqY3kVQis3nixIkzzPa2uOWW61gbN5V7Ymhcw6idEtOSGBd6mM37qZIOjK2pHLmqRw3O6mF0mSypWCwBolo0DbnCFzXlzWKIMYJEyLZallpV/9RBhh6zVF1irasNBE33UJ7YGFcNhX2p72spSNLkRFvUJjqUgJgOI82zko80MYBqA9a1w4lafa1zIIUUEp0UTB4QFzn12BPM5gNjq/wmmxNUW7Ku66pEM4jaYUToGchFVRzJGbwppLxvP0HVS0UIuZDQxkikgKitLKcEkumzJmqaFMm27quprqVFwFqcDbQS1GJkoJCwYomlaPCEpdrXTLXsolZXCeBHqpSTQRs4GEpSjWc06HVjlBk1RLX/Geu1YEsFa5RN5Rvl3knMDMvAMqgiQ2uGTCiJRMYVTyKyCIYUgibsWWHUjSp/MZOiUautUagsqZCMqBzeForC0yrfDLLTdBfbeIak+z6iLD2MrYpXZfyUlBQ8LgYjNd0uBox4MpBFQyN0n65hE6YyJovuyZEKCs0JYwIjv0pBG3wpZIpRi6WIoVhlmVixeDci+oFW9Fp0ztdmi/7++r7WGiIGJAeFMSfdy4yAtwYvWRN0faMq6xSqNa3eL1nXKYwmt1kZqzqnJL1/s1q2NGHMI2JpnScOCW+VTSnG1CFbZZrlrGK8rAoE4wQjTg8eWZCgdVUpGiVtrqaUOWIoxARU62BrDCujgxx98W2cPvEAf/7B93Dd7XfSuTGXzz6BFUfUsRTbW3uk4Tmcu3CGUgKln9GZVY4cvoU777yHA2uH+cSDH+HsqUdYXV0lhkAbWnwRhjhgayRz6qPWajmRRZVDVhzetTirWIbGK0DdWEtGD/pIJsfAxnQF2ziaZoRvW0ajEa7VCbNrDL4dsdiZUkzDZLKGMYaucVgrTFc29D2VFltT2nKOOAuT8QjnW6aTEV03Yn1jldHKlKZpiTEymq6wWOxiraVpDN10TOsd3XjEwWuv48jxm2qiqSoxqOmo+03uv72kt/3HX++1nzGx7H/x6Vve/qJFplbudX1+9rN/dt4Tk1XhuDFa42C7wg0338bJRx7iyeWCphvxrd/xHdxz+Chv/tVf59TJc4TQkyQxWyRCn2hcy7hrsaFQQqZpDdY4RpMVlvPADnMeefRxXv7KV7B+aJWyM4dpx6kf/SmOfcdreOR99xO6VTbPnGGY7eIcTHzLt33X/8LP/od/x33vfzc/+L0XuHT2KT7/K76Mv/tV/4j3/v47+eGf/hXMzhm+7198FzIZs7M7Y1mExQxytggRqvo69hkrBRkL7UrUYIGlMAyJmKFpNLBnHgJIwRm1u/azhaq7S6JEh/XQiWcwPV/7j7+BJx/4CE+dfJpDPXzDN30PW6fPcfrkZb7vX7+WX/2VN/DAu97BEy//Ah7c3WROohm3SAk0WZM8l4tdvuZ//BZmZ87y3g+8l+fe/VIWp7a5YDKNM/RxwX//jf8T91x/Cz/8Iz+AH48YloHd7V36fiDEQnY6ap+OV1UZP0m04tlaWmLJTFdHLBcZ0ygD1rUrTEcNy51NXAvrq563/t6bWDu2gfTw3Nvv4t3vfj9HDxm6kePel3wOf/qOt9G0Y7yNxBC0EYKy+A5sHKH0A8OwIFnDcjbnns95Ceujlvs/9ii/+dO/zM7eJt/0Ld/Kx977Xv7Vv/s33H3HPfRD5m0P/CkPP/5xjhw9zN5yk+fcfQ/3rt9BSAPT1TXOnjvHS19+F3//3r8PUZX1YhTPkLLjqVNn2Nrc5MTJp5AGPv/lr+Lm48dpm1ZrBLQiA8VWFBLnz1/k2LFjlKxii904cM0tz2elW+Xy0xc4d3GGyT0vvvNF3Pa82zl45Di33HIXk/E6qysH/tK1qNQ79erdWcqz/t3/mYIyl8u+wCOhQRDiwezf0Vd/cT1uiqOIYe3wAa6/7XaefOhBop0S8sBQhEee2uH0xR1cSViXuebwOpd2EvM+UIxwaVlYhEIrnlmMypM1qiy0rQA9o9YxTp4mFyQUpivayJ9Fbb6uTwwHR4L1A7u7wnIRma40fN49z+fw82/lebffxR0vfDl+dABNbleHTcyGxmWunLhIG6Ftp7Rtp8IJ/Gdl/dp/fNYbSs5kJaBXi8Yzx/rKTaGQU6nKkAIELYopSHFALeCKsn1NUeOHQpI1wqoQaY0gyRCDJqSkWIhSave3QE2vyDEqyNkabYQEnTJq6krCloJEhRkX2Y93VTA0tk7R6+RtP2rc1IZQyVBEZXJGLGDwnSfPZhQLOdaDjaiNTYee2lhK+1ud0Qs8h6Q2KwHXOG06BWWdqCJKr299mZrC4RRC3aegdgcDSSCmSEka1QzQDxFn1d6UaoEwDPuScj00W98QC5CFfhGYeqdWF1E45NbOBbzxWAYilpC0GI4l6uEao4WVzFgZe5w1uFIIEunTDIMl23R1mu2KxcRCrne0HkarjU6kFt6pxjgKJkUGq4enkiOmoNaK2lFORa8jEauHSLcfS58xOesBAcjJYsqA0Nbo9pqKZwQjpUbCPrOS5GJqY9ATK/fHFvCNwTXCbK7TntF4BesaVVcUtVsJNe0PwWRHwetUukSsKxotjHKOLKowU4ZTIqdAS6fNQUvlI3jEi74vRQjAMCwxWErSibsRVZEZHIcPrDMej3QRT8IiC0MKxOJZ7O2BwCIkpp2wjEJMhWXMpNkW8xAUGJ+gaUc0TmWhs3nPbLlJHgLZFEw2PHlmm5XJmJsO63RitGoIYUEO+xODhhzVttr3c86c32K+u81sueDw8aOIW7KcOZaxcGHrMiuTVY5cc0hlpqmnOG3CxRKgFGKGYYg1IjmTgPGkY3t3m1HXQu5VAZMGrHXkFPGN18m+ZHzXMEqRyXhC22rjMMdEYz3jRoi+wayqamLZR5ah58rWFUoutCPP+voa464jx0BJltaPCCmTjDLbStZGbI6WkSsUSz1YqUorgzJuQCPhVbKA2KhqOSuEVFTkGfXQljLEojY4YywlCakYJCdsMWAsOQbIhpx7GuuxrlSFpR4ic0wktNkvuRCHgnhVpfVFY7ytqQon0cbXgkROvdrkCizDgs3tPf74Tz7A2fMXEZOZz2a85N67GLcN2RaKOFWhFF3LrTf40tAnQYza+HLMlKTqlhxTbaKa6hfPpOQoJeGkaHR7zqrsyRoTLCYriy8UtbrGqI1UMTgxqsqyCgO2RVM4JEaQREhVEVkSiCcVBZQr/0yuKr6SlArjzLhUMC1kxWAjueCNIZlCMtAZT2dHJFFFYYrKI4o5YYmUIkwPrOCfMMyGgQ5LdkJOqlQpOWEAi1EhSl2/NEZcEy9zVtBkJtIUYSiZVOGKmEyKPalfqoWxKmNjGJCaQIiA83VgImo6pQgxJhpRaHyyqXLEija7clb2lNVwBmMMKUS1lYWEdyOkNKSojMF9tkvMuud5EU0yE7X1hapCEhO1USKFFHeRZhVbof45LImLGcNiTt8HVtbWEafcsuysNuwMdL6jZEO7OibHJ+maThU11uhBHrnKQCpJ11lSoekcOSRM4ymusvYag8mQJDFqW6x1Vw+jOssUvPPI2GF9oymPJallMVdGZC71ujektNB1X7SpQG1uXrXe5KzA/axKI2PU2t14r5ZWsVjjddhUBv15FBpqLfhGSKLXw2K5YA0ql04HSTp9VQ6crerFUG3mmWp/37fzxEhyYIzDuYaSYhVEWsgRa7QJlET3Sanqg6sK6FTqZ4cy3CYjBFOvVaMg6ZJVbWr0UC4V8Gudq9HMFW7vO8iaHhvDM8EHzntCHIihYKLQYIjOIU44euwG1jYOcOeLX83qNdex2Oq5OH6cxz7+NDfccC3jbszKnmc+nGXNH2Pn4jbe9rzs5S9l4+ANTFY3OHnicS5tPk0p25jQqHIxFgIWcY5sIyFHrAwY05JQxmAkkWyp+73Wd4Lu1zEHDFLT2XQeOoRAYw1O6qCj1lc2WyQKpgWTe1xakvKczoxQUHrWA3W25NJjjUZjl1K0eYWq6kqp4TEla1M1KQ4g59q8j1o3mozaQrDaONSyS5ubYnQIEgcWizmsTT/tZsxn+vhMYNaf1vPWf8vf8PffZ5h8ss3tM3nG/b/zLxyIjd4Hd/ydV/MfvuLv8v8y96bBll3Xfd9v7b3POXd6Y4/obswAAYIkBoKDOIiSLCWaKEuRpfIU27GcxCqX7Dh2XLErTvIhTqpcTlJR7JQUJ66KpSiO4pguSZRFSbREUQRICCAogiAmYmiggZ5ev/nd4Zyz9175sPZ9DcmWI9moSm4Via5+r+9w7j57r/Vf/+HWOy/w4mO/yd/5b/4WL/aRcRc5c/ZW/u0f/CS/felFdl7fI3UthIrxqEEUdg9meHXU4yEpd6TFnP0b10kCd1x4Lx/44IOMJgbaKBmXIX7oEf7Bj/9d/q9Xv8bQCx/75Pez/dpFXnj2ea6+9RZf/Gf/jINuzvrqCW65/z5+5K/+Re6/8x4+/d/9OPe991E+8PDDPPXTT1KNV9iZLoABK+tn+IE//H3Uixk/+b/8A7rJBIf55jpRKpSRWoBNpxUqHgKsDIfI0R6+HhCzI+ae9zz4EK989RkWWYkx00yUeujxavHvLz7zVfZ397njwu089+KzPPaX/gqdwM8//mXOrVR86G99hJ/88b/Nf/Jjfw4JZuTtJOMFqyeSUR2++qUnuXDXg5x/4BP8wk/+BClADhVdmwjO8eLXnuS3fvVXaIajcq8rBzt7RO0Y1A0nBkNmbcuinxOCDYVm8zn7R3NalGHvDKQeTBh65Z4H3sVf/As/xm/8ws9xtV3w1uXL/IW/8mO89967ee31bf7Lv/Gf8qN/+S/xv/7d/5l3f/A+/qP/+G/ytS/+Niu3nMB7z+Urr1jnNI2sjTZYaUbcOJzS9SZrbUYD6mbEs1/+Cn/5R/997rztbtbPrPPtn/gYv/3Zn2dlJLzx5iWG1RAvyqgZ8PrFVwnNrXzXx76Nu+94FzEnVlfW+fQ//UW2dl5FVGiTJeGm2PONV97kSy88zS2TTX7+536GB97/CKlVrt/YJuZ9Hn30YVQHkrcO4QAAIABJREFUx96fWOeMZmjqAhAqVFXg/KlbiF0iBHjh8CWefOZpru1NuXLlMj/yI3+Nzc1bDUDHPIXNRccXoLvcW7pECpb4sRzfc7/zflue6AVsEkGdMa+dLhElOx9zOdspkr3KKR/94Ddx/Wuf5+LrV9jdb9lrK5wX9lur80JQtg52mffmDUyC7U7wQelTR+MdJ1cw5U9UNoae02tj5ilTDRr2jloODuac3hyzPhny+rUF/c4Bk1Fg5ew6Z2/ZYHWyStg4yf333M17HvwIw5XbUFdUPlHB2+DPl+AMJx4JDaMqMBgUybN3OP/O7rnvOKDUl8lSVvvKlsapIQSbG6vpxVnCKgaTHX/pIpZ95ATzp3BW9CzlICnbZlxpImSBbB5CIpko0czaAF2a+FaB3CmuNsNl7Og1SU6RgmUy0it4PZY8WMKa0fRUIXW9TSMdJSELfFXZF5YdTl1piOyzpz7RdibXSmUztWmn0ZQzNz+3846qMu16zuZ/ZGltYokwFGqzGIAkWoC6aAbdLiuZSNdHNCveK1WojeVRWFYpV9aASTBnfOz6Lr+Jo3ZGJY7aV8xzZIQxefrFAsmVxd1LZK6WuGaTP6FC0MofG+BGzezFI6rkGLsazYFpP4NeDBjINVIlGi3Ty1imEWIxjTadBPEV4kx25rUi9onaC5oFKQWqulKYlvGnpe+BE4vU9GAm6wi+hj721FXDom/Nd6Qg5s4Xb6viTSVkM0SjJK7kjM8cN3i9mvlaTB2j8ZrF/EpF6m1Kbn4aHajD4wqjLKJO8dlAMCfm12LFeioIfpl5iYkaFv2CWgf42hl1PffHdE2tawbZwE4PqGRmthqOvaaaIDTFk0WDsdOceLxrTcuLQszs7R4gYWBFP4GczYw6iwPpyWlOkgGVD5xYm7A6HrC9f8R0fkhyjqM+cXhjh639HYZhhZObc06sDDmaOXwFVW3SDVHPzhtXOdi+we7hIYuUqNfPsD3b5a03rrO/u0vMiVvOnEX7jo2zJ2kGA5IK4+GApDOms0PiYsq8XRAGA1wI7BzuUdUN8/aA9fEptF3gYyTUlgLYtgtc5S2USw2QS8kScGwqBt6b3w+aEY1mrO0rJsOKybhmdVSz6Hr62NO3C2Yp0rc9+0dzUhaGozGhCYx8g5OeelhZ0lKMiG8Y5kzVO6gzWpnM1Ger03Myrxd1JodMQO0MUJFgzLYkGXVjS/Qp49LgIkmESE/Q2qanUhp+J9ZU+LKJZkvZNIAaIOLKz0SNJRWqGl10eDHJbK/GXutTRjWSUiZ2cP3KDXZ2thkOPV0Pr791lfsfuJfh6prtvQpVCCabXcrD64rcWuxq1GwTe+ywDt4TvDPZVu4L0FP8ZkQK08Y8rRKRSiBpYbe4AU5naDGv1uxQ542hmRUVwbIjjY1ZB5MoxaR2AKfevJqyN/Ao2jAhqbFR1Cmxb3HjCb0mqmDyKXWW5olaUmbCgiGSYEOKbJ4xGfN9yckxHAyg6qHvmZzaNMPUhMnaKClNGRAlu0TWQFShoqLTiOItitzZhCvnbGdP9maynIWu7Uldaz4r9kWQipSw7421m5KWdQdUntz2xGwafVVL2BLFWFjOivAUewNA0CK77HHSGAhfgImc+wKgOzIJh8PlhOYeJyUZ0RWQQw1kSbGnDhVOzIDexVTOGmM2ZY3M2yn1YETWRBU8zaAxk2ZXkfpE1RjAJL4qAIQNV9Qpqe/QFEmUgIpoXolSzGq1N0DAZTHAORdpeuG4adlj386hN8DfZntOzVw2akK9JwUjNmlKxTcugPSlyM03vfq0BAwUY2ZHQL2B9tp1eIx9Z02sw2Wx9+SlyLE9QXzxynHHhXXOGRds/ccshNAsaWSoGtvTbAbMFsCKdCz9BiEh9NojYhIG57wlsQWLuzeSsgH8ORdvx5TwQgE/7TmayhjmwvL3rVDPy5h61ADPbKw7Ld57GSjdQvk9G70tfadwZoSvOKpQQ1LaxZTR6gbtoiVP56yfPMn+9JCQXycmGE8mnFldpZdt3rz0Gkkiq2sj8vyAjdVVJiduYX/3Bu0rc5BVRqunLdSlnbKS0k2ZrpbvLnVo1+MzVARjYGUDltWbhD5qRsSXQaihNd4be7kKtQHdpW4VFEIie5P9Jjy9N6a1VMHO+ABO6lIXetBsskQMAFe1gBOzUgjU1cjAIpHjQa5gXoY5Zzw2tPU5mwzFe0IB0VyyQWMfTWXwzsM8/+Lj9zSzhn+t119yh/5N37uWfcr+3y0dK24++e8iKf2/gWK/5+f0Vl+effBeVGG2t8/+cy+zyJ7JcMAsCpOzpzh84jG2dnbJLpOd0KhJrRaLGakzgDouFjiUqq6gNuXB62+8xi996he489xtXLjzVnRlRL8/5e5PfIRTn/00fn3CYmufr3zxCc5urjMceFwjnLxwgR86/UkO92Y0R1PGfsjaiTOcHQ+ID7yL1LZcObhBGI3oZpGV9Q1uP7XKn/3zf57P/MTfp/YCPhCy4Csbfog6pKsgdzjNVK5CYyTOD5nUnmkSZtPIXXfeyV//sb/GX/zTf5S0MkE6ZTRoGIcK3wxYW1/nlo2z6Kxn6/ou124cMhqOWD29QrM4YCYDtt+8ytO/9Cu4geLinDAMDJsx88PFMXMwquPajUvE2HL/uz/E0YVb2LnyOnUY0ZFQmfPGy1+3JNNQE2rzoMnJ2FckZfdogRKRYYX2iT62BD8ATFmyOztkGGrwC/oknD59ikce/Sjvfc+jVFXF537501w5OELrddzBm4wmE37x5/8po7HjlrPnuXVjk/HGOh//vu9l9FbP33/+ec7degs/8MPfy+UXX+Pprz3JHXffyeXLV9jau8Ed77qXE+snee61x3n16qu87/47GEwz/8GP/Airo5pXv/YU525/iEv7R1y/fpUudow3Vnnvw+/nlYvf4OT6SQajCW3bsjXdY/3sbca8Cg4Ljwpsrq/ysfc9zDO//TRbu9u89cabXNvZ5fS585wdFyk8WL9SWKeKcn1rm5df+TIffPSjpCSkHHnsiSf4wmO/yg/9wL/DJz76CY72F/zDT/1D3tidQvC07ZzhYGwMdReO70ld2gSwBGt/5z35L2dVmm2NM7YIoMTc0bWt2RqEClWl71rmsz369oi6GjBsVln0HTuvP8t3vucs22cqfu2ZS+wcJt7Y6xhVQhfV6t1GWB0ItReqCmZdpus9XYqMq4qzq5mqdkyPejZWRwzXGu48ucFgdcLlrUNefvM6LRb4cM8D57lv7T4u3HqKe9/1KIPRKQZNYDBZox6dxrsx4pIl6QUBDWhhz8Ly/FUOZ3MUtVAj7/BSU+Rj79jjnQeUemvUc/a4IKhaEyziQMJNMEm1xJkGm8pLBhKOmkQ8Nq0lGUCjEsxPJ1vySvIVSyjSgtzNiDCj9Nn8jYKIpTJ5I8KpYFPsbGwYSKgDp1ZsamnsllF6DiuERBx9MiNnXXoTFM0l2aZ9KSfISt/3BYSyIimmWKR9Utg0b/sCi7RNjbRCwqaVItjvakYqXybKzhDTZPG/IOAFF0zetJwoi2RUPV1vfhdLWpMXgVCViZiWkZQ1XU4EV66fOcLb+3XO0fYtw2zve1h5qlSRXEYk47NDxdPmXHx9lSCehCeRaEuhOW0F8Zm+zzjpIZoPhjE0lE4MQQ1SfCEEqmqA5kQj5jvU4hi36VgOkrXCDEEjmuzG9SLWIGOfR2U5dQXU4+sxmczIDez3iqGU2VmYnEaKB4YL2Qo1sXQCX7yuDLE2cLQeiBme6sgwbF/KDm3s0PSCOPPUcnhLZUIQX0PMSAg3WRBATOb7RK6Ixa5ZNeOieRgZO8UmHcF7gjiqMMRppB4Hrm/t0at5TWVVhoMJk/Go+Nfk8j+lqsu1xxfg7JQV7JJJ2dvBQaZLQuzNnyP2C7rOEIKV0SrnzmzSpxUW88x0vuDw6JC+TXTtlC5GgiivX9rixOkVzp1aw4sjpMzG6oSTQ8/Fq47RyjrNZMIbb7zBwEMY1sy6xHR3l/3JiN3tPfAG8K0OG7RfIM7kaZPJmNoP0AxHu0dUVcN0d4rbPAOpR1LEqaeNHVoAhLaPDJTiG1SaAxzO2X3vCwgdnKApobnF+GWBYVMzmYzBlQSGrMhE2DzRM5tlDmdT4jSy1e8hPjDpJhAzs9mcZuSZzqaQelbW1xlvnCiSlyVTSYyklBSRVAzLW1tTKua5VNgkS61RdtGkdjnjQ20eYsnWrjjb2cU4Gsam1Mze3jbaGzPEOaHt54yHY1wux0AGDSZ9Aagrx/X9PXoX2d3tefWVN5kfHTEcBW5ZG3Jtd0bOwmzeFsDdGEnOKWJhaQhClJ4gBhhlhOm0Y3v7gNi2qCrD0QqINxZUqMzTKYJkm0Tt786pGjNEd2GI6xJZW1KOtNNDfG0SLfMRMmmU8ya5ilGZzVv2dg/pZoeod+R5S84B8QFfB9rFnGZlBMvmrjAwvHNEX5PVc3A0R4IBaLmPpNxbcxbNW0bLWvJiQKDmaMCS1LZ3u8S8VYKfkNmnGdR0s47Um3S5jRmIxefP1pwrvlFJMZleShwuDqldjUhDzJngQbuOLkbavmfgoc9zYjKPBudsgJAWQEy0MdOmlpSUrAHajhg7ehVSVvrszUeOfGwUD2YMn/tkjU8ZGomaDJXliESNPaVa1pNTepQ+CfRKSiAl1jRHO2cdFOn4cu/M5gVIog4ObRqTN9WOA03UzaAwLyJeHUIidR21D2TtqdzQ0vSCNwAiGicn5YyoUgdvIJiY+LMrPkbHey0GPDfOTON9sHNhWbgaia14QeUIBEtsTNH2/AiiCQmDkoa69GGSY/aQSJGsY4mLgWASQZaieEGzMxa3UPysrMYwllogE0ipx7lR2e9NQm/sByWlWLxLElFNHlo5m8471JKlXBkeOIokuMiTpSZlc2ty4m2YJ+ZZokUiqdnWI0HMA03LlLd4TGpMuKoqkocCajiOU21Zgm0YGOh8WRvOoVq8yTzEmJACrpfcEFStqRlUFVW9BHRSYb3D9vVr+EYJ1YB2ccB0Z8y5d99HnU4zPXqDD3zimxkOVqhCQxUGxqTSxHj1NJoCPcrVnVcZrwx5/bdvMDhzmgtnz3N0NCdFZbqYQuztmifQ3sCf4MwYX8o5nbNBEFb7UUBELOEXh68r7E8CWgajxU9PUypDUWNp+RJAg1oKsJll27U1iXsJkVGghImIs5rSbl7IsSPlbPJGUdNnizdGXRmYKcmuZylTU1p+Yf/fPP5NAKF3Agiz+bKd0TbUdWU49vZXeWeaMiHYgMHDeG2Vu7/143zLy89zbTLhQ+9/iLC/z1c+/2vmfSqOzkN20fzlUm91gUCKamnRNabQ6B19E/nKi1/llTdeZfPMKRazA/Ye+wrhve/iqa+9QH3yNu5anfL017/BxVdeYnNtyHg44Pv/yA8RYuKJxz5PMxxy4sxp5tMDYvCcObnJ81/6ErtxH6kdaysTXOUIPjHd3+Jwe5u1zQ32UyY5pY3JhhiiHImaNFl7gq+IzluoRcgQe9rYcjg94s/8h/8urFRsrAUqN6GpJ9x1593cuHqZy29e5PVXv8F4ssJk0PDQh9/PidVNnvjcZ/kzP/xDvOtdd/DS81/nWtxDgGY4xNfCfe99CL+A57/+JL52VLM5fTfn6puvs799g8N2DpLxssBXFWiFr4TgYHV9zN7uEV2bTHbmlMlozHC0ytFsG/FC6jyOIYgxwp2Y+qQZj0DFwhzmR8wPdhivnYCY8U3D+TtWededd1C5AR994CGup32+eu0a3/2DP8hn/+E/4Y03XuXpX3+Cl3/rK/hhDaGiycJbr1zk277ju7j3nvv52f/9p9g/3Gd/Z58TH2z4zu/+Hpoa3LTnxv4uGyfX6buOn/3Ur/Bt397z7gce4j0P3MN9d92F1Mq9585zbvO0pfqdOsNzL32dH/6hP8KoGprfoG9s3/CeM2dOkN7see65l9ndm/LEF7/AD/3wH6MKa1y/+spxmrRgNdJsNqVtZ/zSL/8cf/gPfy+zeTTPvMEKjz7yIULl2dnf4/VLL/LwBx7kvof/a1Qzq5OTeGmsL9Glub4rw/XCDC1ey0ZiuQkwHd+lb2MFKm8Dn8pgYzE9ZOfGZSNVVDXeO7q2Zevya1y79iJVcEyGG7zw4iX+yS//CrfUgRMjYWdXuTydMRnU3L7eMF1kpovI5kBYa2ywsOjBF3/HWzYDp9bGnD+5wav7+3TpgGkzIjRj5OStnLlwO2ffvcL94uj7BSc2Jpw/fweDwQp11eCqCTnB7vZVFv0+Y21YWQ1AQ2xL7GNBvV0w9YENyTzz+QLvWiIw8UbooG/fkb1r+XjHASUhoBpN+54BSVhKih2U2cXypVuShEl0Mtkl8xIhm1dGSeoSAqjRcwkRTR5HTVRLCMrBWTpY3yFByM6ZfEAA7w0ldd7qSs0EEs6Z/EvFwBgDFAqTpGiZncNYVdkm1vWgsola1xtTxdnn62MyMEZt8l2ro++zTZJzmXAC2UaNx5ToUhkdF2j5+K8smUpwxN4K9xTjsUTGigtbDDnbJLmUuUXf7W9K4kSO5X0qAt48F7R4GjjnymSkTARLY0thTYmYfC4S8bUl2SVNVFrhVGkVkhq7iCA0QY4L95ShdTaJzjhy39oET8w8tk8dTozBY2IbpdeIgYrQRSt824Kukj1dnJp0TxNKTZ+VXArY2mGSPF/hcdRVoPZG5zZIIuF8TdIej3XdwVWkZC2oSyZlcUVip5S1gHlp9FmMVaECKZnHjIrFwWtnjXS2wtnQAfNcWH6xUWIBIRWnVixmMrmz65HK2ljSqp2AumR+DymS+gEJR0wLkwAhSF0Tu5ZBCMxmkYwjakdORrHvUmaeerJGGkzSJz4UFN/RxkxdVSDmAQODshYyToRh8ETv6IMn1uk4ojvmlqwOV9WsrnhWJgNW1wbMpnPmbY8GYdZ1XN3eZ3t2yNWtA86dOMXa0CNHB7TTI5IqG00gzfc5PwmMtWVl/QT705a3buxycOMK16dzYlbqEDi7tsrJ8ZDBuKZOSt9FYlywvXPAvJ2jFeCVOtTM26ld9y4xX/R0XURdz2LeMl5RAzHbjsoHcsrFY4hjea6ZLUvxI/FkhC4mghMCFq3sXEkeQhjWgfFkA80ejckMX9/a5szmmMnqkBMn1pjPZ+S+xzWevpjeK+b7lbOiLqNRGVQV7aI3kETKz3KRhGhh5OUMai24k+OcSPCQiARXmAvlnvYFxB+PGnKfqX1NMxhxtKjsuXKGGHHOmJMiFS545rFnMBiweWKNz/3GF3jxlYu4CtYGA27bGKP5kKwmN6xHnhCW79eYH0IqBblJMGtnoEWolNFKoA8mu7O2J5Z3GyyZkd6MxtUzHo+gqtAKvDRUoSJlh2qLSz05g6Qir1QrOsD8Wbyat1kYDA3815ZF5S08QAJ107BoF2WCpuTcFd8YKdN+8CjBlfTJhLEK1OSBSUxeIt4jwfaNPvdI7owdqcF8mXrlqN2DYuw8alZoZy2aMm22gIBRZYwVJeAJ9MV/AooPYGyJsUFdV2YiiSiWBhK7BV3bE0Igth3e1Tg/MMq1OEKo6DNMp3OGdUXrEyFZ+EFazJGqJiIsFgu6vmOEsSpEhNh3LOZHxBhLYITgsuJSRnwkigHeTl0ZKLhjlg8aWUwXpB7aecc4FPZONbD9UQwgR5w1GGKS1lSaXdtzgWzvY7C+UfwBPTH1hbHb09SORTvHNwMDU7DzTSWRYzb/oRAKpF4YgSnT9z0VikSTjyXt7Lyty1qOEV9S2lJeyvaL1xmZPndMFy1oT0zZ1ocDJxVeHZVbWjS4Yx+xXBjQbd8Scw+lWV0GVHRFombG3Rgrx5kJak4GVqq2OLzVOVjRqBlCFdBkY1pRIcaWJAUQQ83fzVsurBSgTbNYOmAVCCGYt3a22iNi4RI++LfJdcxL0mHTz0Qmps4YagXUtQ01Hr8/MfMMu7fKwC0mY2SLX66XJZwmpCxFFufKEMdSGJfSrJgT83ZGNapZtFPyAbRtZPvKjIP9twiTNarg2JzM2Nm9ytbWGsMw4OzJW7j77nezmHfEmEoynrC6ss7a+iYHu/uoi0jVM25O8vSXn+aDn/gORIR2dsjezjYra2uEpiEXHyWTEWeTHkvxFivD72X6Xck+xLkK54z5TAGWlKWgwsDAZYOkSVGNiNRlCFC8zbIxtQyoWxJlykjQOcCY8vbLyyGZFHDY5HhJTV7tRAnOmO+aM04NDPMFSFniJsrynPmDPn6/gMvylf4Ar/G7fZH+AD5Jv++XgONm1WHKgKVK5O2Y0jvxsooWD0Cr10/cczd/4m/8Z/Rtx+qZE8juIR/61u+C2+/gqX/+aZ7cvgEpEbtsdax6nM8lIMF8Bg1nCnQpsjM74uKVLWa/+Ot89anfhNDz6j/+R3zu6S/z5/76X+W1Lz9B0hcIowl7i46Da9f4u//932EQKl5++im+9Xs/yZtXrtC3PY9/5WvEtTf5Ez/2Y9w76nh0a8aHv/t7+L///k+wurHC8y+8yH3f9GFOPPXPWUx7prMWTRA7szKRUYUEcNmRokBOhNrRNIJOHRuTAfODXVbXBgxC4KPvfw/t/jZvbB3wjZefI8ceH4T1JnDL+og+2SDlPQ89wG99/te44W4wfWOOH7T4uYOU6GJk0lT47og47/nExz9BhfKFJ36TmU9E17NYzIz8kASqOWnR4atAO49QD9jf6ZnNFmUA4wguMxgOuO++e3nhxSNihsPpHEoAAS4yKMxBxRPbnqqpOOqUiy+/yns/cJo+dXzgYx9j+9KbXLl0iTtuvY1/66Mf5taPf4Sf/PEf58Unv8rXPvd5qpB58Stfxo+VIHDt6g1+9p98igfuuYsLZ87yyCOP8NLjX2L74Bo3draZzqe89vIl3nrzEjsHe/hgxIOjo0O2dg/59C/+Kjd297nzzntpmjGvvfUSKUd+4wu/xvf/4B/j0pU36bsZo3pIjB113dgwyRnfMauyeWqTP/nH/yh333Mrn/nlz/A//b2fRLzn9nMn+LN/8t8jT9aMCKGZpqm5ePEbPPjQu9lcP0/ft3ixfu3E2jofeugDHBztEpMxkU9tnmc8WrEhDGWjc8t78ibQC/bnlAszAzlm7WoZUKUc7dxy7qZ5tyopZY72trl08QWuXHyZxdEemhacOLHC+uZJptMZwTmalVVu3NjnuYsvMl0knr22YFQ7plFpXOBkHZg4syGoJTGoa1wlxA5849mohpw+O+GRh85z4tT9qFac7Duq0Zjb7rydUxu30YzW8G5gAwAxdrArHpZHU7suw0GgrhvOnL+DuhqRciZIKKEiCeeDBYppJhdMI8VoypppS5WVSj3kBdqPrMZ+Bx/vOKDk1RLYxCVydOBr1FmCCmr6PlVvnkJiyWyuSBzMsDUaAurshtScy6Fph5sv+faKN2+HyswyxQcyCY+hp4hNc9Rhpp9kg/6xaZSWJB7NUqYOdpOIlAhhDDxQtcYEJzZhCu54WqnZjKGX0fbmG15o6x5ESpNVigoAi6Z7++uIsYvE/DRSLF4I5FL0YiAGRlDQwmASkSJLWP4+Jd7UGQpegCEDrCgVjsV7Q6n3svkoLXmCQrZJoDMPolIx0sdEzJk+G2VRijG2VzOCVRW82A1tIKCW6fbSH6Yne2N9JLHI7GW/0ZfFrwjqTa5oQI6SxfxiyCaiiMkkPuYJk1CfUTW/qV5MdkJeQAafKkSyJYipJ2siuEBMPYNQgXhcGuCyUAXzRHHO43KmLxtX1owr/w5viVwqghDNmNe7EgVvm2ytQp9DMSktQYdirK9MOpYvOmeR01GkFG5F4ujNn2kJNjpnIGYWZ75kKmUDDSyiGexK6NBUQa7os3lteLHUQ586Uqv2vYkn+IwvNFTNiT4K3WKBqwxkUCwxzKJ1giXVJfOECiLUlYfa0s8Uz6LtSV1HdplhLTT1KuuFQj876hhNBrSx5/ruEdd3p1zYHLMpiRBLAl9sceqJ8wV1O2URE3UW+r4lSs9kVDPvlbXJhLMn19gYmM7ZazI/HAfTw0P61HJ0MOP2W8/Rd5muy9TeEXORMBbpqhSj9KauaOu6JCjeNN/LaimEIt5krqpvk2vYlKUgJDYIKIWk10SMBnyqOELw5t8kQzTNydikWQtktZSpOIIx3FCWLVoqPidBK0R7lgrum9RhDDCM6eY+gN3GSY1VaQ2NZ8mztM4mHMejG3Hf9rCs5oVGn1Bam2xWNXVd08sMXMBroCHSVOYZ5HygWt2kuzEnhgHDzdtwYWyfMWcD9ZNSSQkESBBdtvcnwqhy1G5AroQ+tlB84lRNTmSFmJb9C+rBChrs72rf4CoP1CgGqMZuaklV6hE143FLajQGoWvMKNzVsewDFc5X5oXjKsJ038DbIpNyFg9JLsAu3jEcDWwvFW/JdkWqHTL0XUucL8q6yEhWBE9wniRCdoIEM1WP3hq7k6dOsLa2UZLglNRmYjs9XouGJXhUeptCLzKTyQprK+sE3xAFNEeCM5A5L2rm0XwpbFBRGLhL+VoBrTc3Nlg/uYZ3Db4eotrj0hh8RVLhaLrP4dHcsCBna64KgcFoZABMTMxnvbFQBfAByYJoNKkldnyrTToIIoyGNVUd6Fp/fMZI5YhqBqWyPNeCsZH7PtF2Jtu0ib0BpKNmRFM1ZoyehKYyGbd4qFymT5HghKaumE21SJGFKpjMOJdbPSUrOnM5VD1CzMtUNYcP7pjJayENxg7SnOyc0kK4zwopo30HBUiRSqBIoNRJqSGWE9KbkvrUzYldh8RY5J4mRawQUBsyCMYMsgbfE7uO6eE+7XxOpT1SGUtJ8qAwXAqru0DJxsJMzI9act+VoVRhAqqC2vPajiPk2KI1xLhg0fXk1Jb1Z6l+qewoSC4Ah7cGuEv07ZyunRk7LxUTbqz+onDIS0GBF8h9T9/NQeLNOkuMPW5bcjBZ45Kxg9LHBX3fWXknEZFfMnruAAAgAElEQVSKGIXN0QqKI4wHXN1+C+8dw2qIOuHoKDBZH/DKxRcYVOt89CPfzGI+xUljbODgWMwX7N3YtUEBEHzDufW7mAxWeOC++7nt9ru59NorHO1u8+Kzz3DruQus3nKerusLg6fURVokZpgPmXPGzLYk0Ezf9lYvOTEPLim1FGJ7o4pdK7V9yuSm2QDNXBGqQIz9TcwlawHw8zHTPBcWzfLMcJpLqleROhdEzpdghCQ334v5fZYzRYLt58vakz8Q1FMe+jv+fHP08TZw6hhHkreddL/PV/rdzdA72hwdF2KUk6DU+rl0Iq58ht/1Od7+DMuGV97+fL/343f4M5UCcrSxhoUTKToZ8/A3f4IPf/8n+emnn+YrR4ek/RkJR/CBYV0BHdO4wCHERUIlWMI0wkHc4elnnmJ+9RpPPvcMjW+ZtZHRiRX+t//xf6DTlnpQE3tLrm3jjM8/9SUGvkL6jp/75U/z8ltv8dAD7+Px177BBz/xHezO56w2Z3n44Y/zwfd9kF9c+xR3vOsenv36c9xz7m4e/uC30T/5OJe7uXm+deArj1ZapJ0Oindu33n2+hbnK8aVI1Obl1pc8Mwzz0PKzDUTmiH4nrxINBpYYcZRn6FNfO7Tv8DmrRe49d4LnFzZ5Jmnf5MbO7uoGyCqnNw8weZaw6k7TrPmRzz2+FMcTnsGKwNgQbewayUMaGpPcj1diix6SzutRiNWXODy1nXEByQp0+mMl577Gm07A23I2c4gdYnFIuOD4CpH7juSKoPVdb7vT/0pbr3rXvq2ZXd/l8FoxPbrV/jME7/Jnbfdydb+Nu9bX+eTf+SP8xP/7X/Fm7uXccMBaW4DhSgGPt566y18x3f8IUZ4Fkd7tN2MnJXFdMZ4soavj9jvjlg/cZLdgy2+57u+k3/8j36GvdkBV69vkTXx7LPP8vxzr/Gu++/k3IVzfPcnf4BKGl585Rs8+MBDxNQTqroEVebjPcGJpYRXwZJ63/fw+1l0Pffcdzddt2/hVLpkOwreV0znHQ8/9CgpJapQgTr61LG7t8/Va5eZrIz58m89ycbmKt/0Td/CTcjd7h97bbNqWMqmU4p07aIE5XQmq1ZH17Wkfo5TwxBCqPGDAYPhBNQxnx8gfebG1Ve5cfUlHB0H812ODq6zaAfsHl4mZ8ct585x4cIdrE0WnHvrLU5c2mM37XK0iDYIFpjGyO7cwPggwrDxnL9lAx0GhmsrnDh9Oxfuvos77ryflbWziB/wYD3EO0fdDGxApSbwTn0ipc6usbOk9ZzBDxrqMLRUW4WUe6QkfYp4qrrGSSj19DGn0sg7mNqm8oVk0gsy6JH8L9m4/g0e7zig5Coh5ECWSPC+0N4sMtc7temKukIFL5TeZN1QJhdqb4bejGeNlmaeNloqbfNhMIAoiLeLtzRdgkL5sqbJCrJi5lzSQiRgkcEp3Tx4vZjPQNJj5NIVM2DxlkwDS9qcO05iE3FGJ07Gioq9yYs02w0g5iyOGSoWZpD9xBZDmUpavLHpjHMBiVzxOnLOkaP9ezM1z1jCCvZMZfEsgSF7CMXIqqBH5WdCeW92rOflZD5lzHfIUmOyJlKMZYpm/65PEfXLl9GSKueIKEGsgDcpRbZUoByoAibrkJuoMcnRx3B8zppPhb1nxZcCHGuzxRponKCphpTM2DcreLEJnndl4yoHfM7WyJER6aGkObnYk8jMYiRkR+/mhNJAqJpMQjGD7yDO/Fgks1INCSHQtgvEOfo+0mjDAE9w5mMiwFFvReTIWdOTBSQIMXkUwfkOyc4SLbwjZ6H2kJNFttu+ax/ero05LUSFPhvYJITCPLPuKHUZZEFOc8gmWwzOJvttP6PvnXmrKPiQzasjp+N7JQFBYAY22RJB0ttlg5G+FJaDUcNgMKbxlh44GniyE6btjK7rUWlwLhNyxaCuuPX8WRZtz2wx43A6R7C0sqYWnCqxz3g1WUCjynS+YO8wcng4Z7Q6QD3ghap21JW5Q6UUyX25pyVTB8G5iqNZx8poxdgIRS6SulSAPWMHNJUZX3rvCSEcM/1iNMYYxTDaynrb3FWE4MTuj2Lat0xeNJaITUCMUWC3eswJX8zzHXVJNCtMvWzyJZOClAm0IRk4TPIZBJMOiXHrlt5qSqbrWuazI1CoKn9syKrlfrRkM2NeWniAksSMuTWbzNEslcw82Do/k+D0KSFeWKsHBixqJrhELZ7zG+tsXb/GXoJ6UNNsniDuOFI9YSsNePnKgkHTM6qcFY54otj+5ktDbVI9cNS2P+flFFyP9xQpYLpkR8KTRWnpjBmWA8klS9X0ybBzZ633EkzTAqZ4cTa1FY8mk48ZmlChRGMuqjHTRIO1ON7jfGODjdQbOCd2D6YMrjCTNDskLEXCWjB5JWL3naKFSaIkEapcAT3TgyNjzaTGJsjHQFqyRrMMJpax8HaCmczXL1l0ameU90KM2EAlObudI9SN7T3kVCRE1vTH7ICERgNFMxAkI0X6qZJQcdbUuzJMQArTwc5GKesskS3a3iWSVCQSGpU2HtlZrebpJCqY+Ys7vj+MueFRMWNlxJU0QQtsMEagMw6QM8+lqrZIdvEGYuRs91hlyEJhddigKKWevu/JZFJM1CL4EEiJImt1JHXHUvEgddkF7WwWxTwaxM5dV+gmS49H74zBY+BxJvjAZLxGzpF+PjO2DZBSexNgLU8shengBYbDAU6VRVWBmJeUE5P3+axESaQymNGkBF8TghACaOXQTshS/NRUbCWKIyex8yzbUKn2lXkTqu19CUhObI0XUGhpFg6lHkFo6gFdn+hmthxySgV8d8clvs1KbAoqAlFNlp3FBnrOhd8hOzCZm9VCqBKkAE0RpKrI2Zjf5aZk6Z/k3c3UOpXEtDsi1MKpyZhh3RDbxHB9QlXXhKbmXe95P0998QkygSgrXDh/G7v7b7I4gGbcGHBYmXVAu5jxjeefoZ3PqEJAY0SzYzxcR53w4IMfJsbIYj4nVI7DrTd48+AtxouHTX7edUVqr8d+i0WniBNjA6c+0ncdsevJ2pG1RrQqA0BZVmvlTFG6GOm6YhVRmKlRbW8WtWGQ8SateOoWnQ0FJCOieM2lhrDn9r4Y/HuYTado7koKrf1sORxZrtWMmXOnGMvAopja/mv2Hkvi0BJIkrfVqLq875av8f+bx9vfi61FEUckU6q548Hf7wUU/YtGwP/qxxJyXb6m7b9yDLalxdw8r9Qh9Rj6bIPelNkYj8gxkRFELa0qOEvmzSWhcxY7Hv/Nz9IuWuZdx6Ku8K5h3re4YBLSyXjEwUHPtFvQJwPQazWD+X66zxe/9BhPPfEEfZ/Y/bXP8sqVN2n3d/mjP/qj/KOf/j953wce4d7Tp3npzVd4Y+syr12/zmw2Z1x7uj7ROsiS6bOj8sLIWXrxoQqqje3fydiZZ2/Z4NzZMzz15ae4ES1pOYtnRcueqcJMHVvTjBfhlS8/y50P3Mvf/M//Fu97//v42z/6p9na2TLANEV8JaytjDl3+hS3njrJC195hteuXaHVTJrPCLG3OkyASvGNSZFz1xNy4NTZCzz0vvex9eqrXLl2DWkc6mDR9nR9S+WN0BBQJiOYxczZW26nmx1wOD3gwumzXLmxy3g04oF776NpGrr5glffuMi5k2f4+Z/9GT77zBf49u/8Pi698Apb812ubV1j/fbTXN6/iqch6Qz8gD52VGQmkzHPfPWr7Fy7xvozX+WZV7/BvI2MRyM+9amfI+VMFTypaZmMBpzYXOXBR9/Pzv4hB/uHzGNHe22Lo6NDXnrlNeZd5OJrb6HxiEce+TiDZgRqSaQ2fDDGaIolEVSEzc1NPvyBD3Dq5TXuv/duiI6d6VsMhiPjcKixMy9dugwuMxxMjPW86GnbA3Z2d/nSlx7nzLmztN1JPv/YF7j77gs8+OCjDJph8aWzQUNe2tWUvTJGs+NYLGZo7iFF5oupWXV0Ld5nBuNV6mpsddZsn4OdG+wcXGdr6xKnRxsomRMrY8LmOoNBw9VLkdwfsr+9x9G8Q3PHaDRhfeM2PvbRj3L63F187Znnuba1w9gdsDoZ082PWG9q+i6xv0hsbq5x+913cPauezh/+11snryd0eRECdrwx0O0rEtnNi1+uIKvHFEKocF5QlUzbG4tdjhLhZHtJ10f6eKMUFd4Z+m1lLoVKdhA2cO1i0b8IFrfny1I5Z18vOOAkumMl4BFMn8eV5pl1eMkHikt63KCR7aGS0RRepMRFM8Q5wyMWDYddpAuD1fz0KEU9QAuWBMAuRRBqRxcVuyKFnNsL0t0x7AXL1bsZgW8UbkXCemLD4KX4rNkJoZS5GpajMLrpqJtYwG3ik9CVEvsybn47RgzKGclSaHn+ZsAlQ+V+feU1xFnP7OJdJkW5ZsFqrjfebAd/xH7XEKZNJe/X9Lhbz7KdI3CdCpU7KTGCEKsqMk91uA6u1ZLk0xrdM0snSxoLyS1xDtRCI2jnS8NJCF4ew6LUrZpfSYhEvE9ZFGSlnWBxXc7kiHBatNVJ+BspEeUYsaNNURmdQkq9t4Va95dNnaFFlPUVq11a4vvAq4kKikGHBQATMksQm/fQd8bEwTFL6ZUOOoQGNbWCC46ZdJU5MYAqIyZwicdETNUrkdya0i/c+QSAa6ixQWsyED1ZmGRgJiF6KxgF8zDw/YTAzNUxFhZlHVHGZ63PSm1tgAUsjP/p5gzTpIxqmBpWWw2upqRbIwqUMQLWT2pz7SLjrQqxFQzqMo7ViGIUvna5tcp4XKmEo9IzWAyYn1tQJci0is6X3B56yqp7xgMDfyaLVpmu3NmPbyxP2VnsWCqkQaPG1Qs2o7FYmFGkd7or7mPJFW6rmNjc429acfO7hG4QBPMm0JRi5dXDPBS7DvNGAupxJv3OZKd+VZ42zYMZEVJyZIRWfqT6BKcWa4lK7qJCYIBCbGYWPc5UbuGnLPJenTJN7KmwBcwAusFkOzw3qQ1BdUqe6gx7DxCVVX44AuLxx1LaJfSEwMlCyyWQXwgxcxiMSelxNAX4EoVjXZgVfXITJn73hhSqTNII2eys7Sp9RMnGIwGhFlPmyKXto+Io3WyBvbazJMXd+k084G7TlFVlQEqRbZT+m+j3YqaO5iqfV51xC6zuzhkOKzwXggu0JUJsE1jbL9zhTl23JyLacNdAcYSyRI/nTOIQIvMNBtDNjhProV521IRELXUGe8Ep3ZNk5okcsmwkJIsUjdDSD25b8vG6qgKWzEixa/P9kGNCVJPTMZs8Y0BVi7U5Ngh6hkFR+M8PZFkzutkMdauqrPwieU2XgqySmy9vJ1tZ1i/FXbee/ygIgxqUpvKHqKkaLKmnHtACTgk2/nVp5754hAnnqoeoqnM3p3JeZdNrHeOvusRzbic8G5IFRwSbTqWUkT7vsgFzOtnKSE2Hx5BcsY5tZj7HA3ALvI4KcBNzmWqthwqFB+jqq6JyRqogN1usW8xzNnjm5p2Z58+mmx6MKiZTqc4X5mhMh4RR9VUSNui4iwNdZneqrokMOODJ6V0zP4Tbp7xUnzWzCdHylrEzNtzpjKKI+YMmWwaqLl8bzfPXJPPWSJuJY4YO1LfESjXordDwGXr9rWARpXzZAedJPt7MVAHMXASsYGO91a0Ou9p6gFF4GTeUeVcNHX8EjAreGs2ALuqKvPTU7vnsmk9be3jrU7Q5bXw1NWIuu5YiAA9rqSQLeEDEUuUKcQ+XFXRDIbErjUdTDZfwqWBtVML/lCSrSEnDOqGtpuyt7eDoJxaP0HwHh8888UCJ55zt7+bUzHy0rMvcn37gPPnb2elGjM8eYFL0210seDo8IiqycyPpkynB+AdG6dOQ7fgaHuXSsxbkBjJ6mnqivN33EXXTnnk49/BwdYbrN52G3vXt3BE6HMZVNjU3gko0ZgXYvdQ8ObDIjNj8aFyfI+UiZ/VYWKsvhC8nfeaiVEL+cbZ95HVEn6XYKrj2A9yKc+yf2BR2JbmZkBpu5gRY6RgTjeBOtsqSp1uw1acN7Zt2b/5vbGT3+MhN98Kx64nUIBp+6+xUvsuM2iat/3+v/hS72zr8/t8lHudAiJpAcbsZ3/gC/Kvfp2yFI49YLT4NWo2+w0HMqoRL1x4z/00zz2BVjVtO2N6OCej+Mqb0EMt7KLveqD0Us6zs79jQJE6JiGAV0JUsiQefPDd7G7dYHF0wH7s8c5bEnGOJYxIy/lk9/HWzjX2n9xi/cyEn/o//h5Hl7f5pgfv49rXHmdeCc9+/WUuX99jFByTsdXzofH0OZFmmc01z6jq6ZNwiKNxNUlrUlVRS+RDH/kIgwzPv/AidfC0Xc+N3SOq0FEXP+F56ri4m1kbrXHbnbfy7kce4cPf9GGubN3go3/oO/n133qC0dom6Jy+gq39bZ575XVefO5VXn7pBfZjYXlkZZGF0qbgAnR9xaJbEEsgwn3veQ91VL7xyuu4ZmADIzK9mDeqU5NtVy4xWRmSDxb8zb/xX/D5n/kpHn/zNR59/wf5zGd/1QZfbYeOlJ1r13jtha+z9m7h6uE17n/vPXznd38zj8XMr37uF5isD/nox76dy69eZDbPdOUW90Xeev3aNbavXUFz4qDd52A+sz1FHYtFx4lTq6xsnGT3yjWkdjz+xON87OPfyq9/5ldZrI75+Ld8gteffYVvvPQaRzPP5379MfZv/DO+73u+mcY1zPpEu7vN7ffdQZCK4WSD4WiMaib1kcPpEadPn6Ui4+MMSR2/9JnPc+c9Z1ks5oxHq7bfCLx59Q3uuetufvuZJ3nfA+9nd/cqXbvL1tUbfPGx32Dj1AmGowkPPvIwV966zJNf/jzf8vHvoaoqFOjamZEcMGZmFzvadkqOBtY7Z4zi+XTGYjYDTago4WCOk4q+a+m6I46uX+XitdfY3rnBuhvinEeD1YzTWcf0YA/6Dhcy1chR1ZnBpYtognN33M/5Ox7i4Yc/wtVrl9HZK6yPN7h27QqhdsxnR6xunODkhfewtnGO1bXTNNX4WClhQL+WM9H2ZF3iEctN2O7+4omYkJzo2gVKT11PipTaem8SLNoFPi0IoWIwWDG7oeWxUmoXdZnuYAdXkl+zs8HKMev4HXq844CSFdtGrV4yLVTTzcNAlwknN72ETDtMaebtkAuV4EJJ2nKOoMtJjh2Ux8+pQPElEL9kwdiheuxbpFooYEa8JmFJXG4JRFkDucRmivTcoo8rAwtcOeVUBEoak6jgjgEvb8/trCkxyV4uE7lcpCg2IdWERXyjBTC6eUiZ4feySLRVYaobR6iXxuRFSiZyLC1Yprwcn3G/a3E6isGntwZAMDAtZ6O3+sr8jCTb5/POYqeXp7sxmaSAb0aRj7noVgtFugoVXjJRE8EH0B6CMJoEvDZUUpFdMt8k7QlUFv1OMfnWm+RGu+EMEBRdXgejLnpRNFc4gTkRRyC45XWCUKQaWUs0s4Cq4L1NjF0WOux3tExFzYiyXLzSaCqYdw0Y6WMAZGscfDB/pqygjdCmzCA4nA9E58zoEqhyKBIkAWp66VFnnjje9yaXE4/PFapC9kahp4CrTqDK1pA58dYISAHhMMNd0WBsu9jblD7btQLB+YYi1EAkQPIo0SR0viKQSGLpPcsULVEh5Bokm+xQQYM952LREnOiCx0KVN7R93OTIoVANF0TSiRIiUAXGHgDcKSu2blyla3tPVYGQ06uDLl444Ar1w9AGna6noTjaN5RuxkDHUCKTEJCU83KeMygMdvq6aJjZ3+fNke2d/fZ2Z8xHk84e3KVYe05eWKdg6M5XR8JTU3XWdKXpaqboXyfwdc1+KVvRDEhLbKt/P8w96ZBlp3nfd/vXc45d+ttumfHAJgZbMQOLgAZUhIlkqJMkRAlMpZcklKWolQqpUhOOSonVXFc5VTFH1yRXZErlksVidZG2RblaCMprpK4gSJBAiAw2AYzg56tp6f3vts5593y4XlvD5XETpQwtu8HVKGBvrfvvee8z/P8n/8iLpKZHUj2mEgYXaALgzeJ4c6Qvb0h0cChpXmR30bxeep3DToGCFHOnAwqkySZURo9YRsIf8gQcOgkBt1ptvXXFm00VlmqTkX04rkSYsTNAAYSXikZxEKiLDS+duxs79Lvi2RprirplpYCkaKhDabqSHR4jFibZWJJiXG8Lrm+M+Tc2hrDVtIT69oxublBGliiFrBzt645dzVw76klFkuDUYnoBTibkQ6ksSWfISIkGE0nvHDuVdY3dlk+ssDDD9/Lwvw8UcUDWbKZGS5mQFQpJcCCzr4uJAIC1AiRXuOjx3RKmlquU5d8HvZKClWKoXmSYAfZyRtSzLIeXebGXl4n4sGKBESZfG8m8fgLKeBTQmmDVQKmp6hJXti2Ril0YXFJU/YriS0uLJ1uh4A/kPNaFfFKggBUNlI22W/FKoPTssnSaEKuKUmTwWEr7ChTYMsuxnQIpkWhMUmJrEUlvHNEW8g9GQO6FZ+VGGXDq5XCpIiJAZUCRgtjU459gQa0AhUDTkU6FGg8hdKMWy9LAhSoSKEF9NZWhuqQghjGym5fFI1aS8OUpG7K+S0svZRBzyC3Ihqoqg5pxkJWs2CGiEL8obTWjCcT2qYhGYVzHspK6r+SBFFlNLbU8noZvJotHsQfQvwTQwaVpTHhLzM1VJY0I+dyShJzH4kZ5BygTJEHL7nmZ942JKnxMfcSAUVHifQPJdJXYQ5J3ZjJZnxwzMQ2KoEKKdfjGciTPdZyTzHzOgrJS38E+FnLk4yk087YZ5AXWghAkX0OZRkjgKy2JksnxRdLKUXwkHICWlSRFAXcA6lVM2BcZXQ85Qb3gAkVk0SYR3kO1EyBH1DKorB5gZCb4pgotOXwwgqDwUBASq3pzQ3Y2d3HqQpTKPr9LkcOH+aVi6vMLx2mYxwXL22xfPQYPnjG0ylVEKnX0qFlVo4ew00nrF+9BDqhU2IyHtLrduh3uhSmpFUeY3ucvOthjt1xN8F79re2UdYQfcQrQ6ENWGE1qKjwJIwV7y6sxphsRyCc09wfzhixIfc5pfiR5fQ9pYwsHrR8jsYUIofQCtHsKYrSUhgr107K/qTZB1JlFpvV2XTdSh8gJShLsZXOvhuC4mojvVJRdTDGfocwk1kPJ/6aJgsIDvrv/HlIaM+tF5Q29pah7r/bhwC0fFu/XWABJ8NqZhrMunf1f/jVgz5czcC7f8tLzQDn/I8D6O2AHQmmU6K9+J6G4Zgjhw+z1BvwxW88g4qyrFjo9VBpQkSWWAZweenrvZPzLEHHGtzePh/+ib/Json8xm/+c9785rfwsd/4dRovyZzWFtK7qFmPL2fxzDDeNZ4zZ8/y3ie/lx6KQa/Pn3/ik7xw5QadDqxvbIKZ5+E3PsjuhedY9wJWC8FAGLTGJDyRbinhB/PzCyRjGE/HXHntKiePLXP3XXfRRzGNiXPnzhPE+JWIw1jodDq87Y2PcGNngw9+4Ed46ktfRPX7vP3Jv877nnmO6vbTPHfuK9x2+wmef+4ZHnzgcfr1lIsvvsyhhcO0k11q1xK1AQO2qEhG09S1sIiN9CFrV1dhbp7b77+H6898HaUNKmp6gy7JT3OIj8mpouDqxPHlFVav3aA0HR5/69v47Oc+ja0M5849y/jYKYY7O+xefZ2NxQGH7jzKE088SBxusjhING7CoFjg609/k9tuO8Hz33qVslMwqR1GSRDE3nCXxYU+t586SUng0tV1XPAUpiK2jsXeHG9//K189k8+x85on5devsCpY3ewv7/HtJ7wtS//BdeuX+O2+85QFRVPf+Ob9IuCbz7zEnUTmVtZphmu4c4PmQ4nHD5xB6dOHGc4mvDq6hXmF+apJ7tM6pZJu8/84gqJhjNnTtPr9DOYrTj30ovcddddvH7xMs89/3mOLcxx6eIFOlXBc3/xHK+eP8/h4R7Lywu89fFHOXr4CPvjLS6svoq2moW5ZVJo8W3L3miHaesk/bUsWJxbEAuIGNnb2eLct55je3OdntUoI/UzNB7fNvR7huNH+sxXJZskNrZvEoOmaVr2JxOcD+A1rY8sLmnuOXmSxYVDOAdr1y6xfPIUiyvLLMwfZTB3CDeepzIFS0dOoYuK0d42MU45tLTCwqETkrI6UzTJDT1bKdP6gGun9LodQPwXZweGzynUkniamNYjtBEwtyoH2UM5SMpsWRCDk0VwkJktpURhc/3MOEI7HUkaa7QkxA/SxP/AASVplmSrbpS6RdNK6SDhI6nZASyqf9GuCxMFDFFZObB13mgl0Mmjc0qcShkcESqKPFcQ+rawhmRjRwKdn0MlocyXhkzrzSPOzOOA/FVGiUYMUYxCZxfCjAmkZv5ESRo3HaV50EGKS1GUmdkS8vNnbwUl/k86p4gl42d2SjIwxkCMklo2AxOSFm1xSrL5Sgd/ay43aYaXxdygBQ6kfnm7J9triag/SBuZsaGKzDCKM3+eDMYpjU9BhmmFbOuUbN+VlybXoLMBt7yisWWWIAjnpVCy3YwxURSa+W4PS8LHPiEolHIiH1JCf0eDVqWQ6VNgRsBQgDJSxpOK6JmvUipwyIZd64rCzqo2WJMyPTPKn6O0DIUqZQNmaWhrJ2DOTCEZkNSWGagYooAiRimszWHYKdEGkVmREkZFKgM+JAoVSFahkkibUlRivkkQWZGWgyIAJii0yw1fFL+omL2WNBE7A44yKyPGSKEjRqf8Hd/ypJD3ZgRU1bMDRH6fGCVCOxn2hi27+yMa70gElvo9jix0QEOhIcY2Swtv3TcisxMg1CNa5dZDkaUPwgxpUGlMZ2ERFRM+WazSWKMpqjz0OE/wicZDMBrVKbg+nbI1nHBxt2U0EYlX1BZlDS42tC7STFraaUC1I6a9gjtOHaXqatrWsb03YnN3j+ubW+yPJiRKlLJcX7N0rOW244cpKmnQO1UHhzCWopLhqtKKxjVoND4GqsJII6ULil3EzMkAACAASURBVLKgM+ixNxzjgzBOpK9KVKWhLEu2NzZYXbvJhYtXGU8mJG1Z6JecvvsOZqi0Uq3cl1plzo0AzwoBYzXmwNdMGdGcB5WymXwGsFPAGHuwHTVqdr1IIpJOCkmOyiB3ivjoKTs91q5c5dTpO+l2u8TaQazx0eNqmLae6AK12sOmfN+g5NrE4FuHKQLPv3Key2tX5MpMoHVEuSGdCUS7QCy6gGJ737O9O2auY4VdmSPog0boNylByqC6vGPWNrZ47uWXmTSe19du0OtWPPbGhwhR/GBS8NgiEZLFpywVTSoD9fk2wBCjBDDozLnDQGE6tHHmhRdRdPI5AymJ5NTMzscYxF9J2TwkJ6y2pCigT/KALfGxhZAQEZuX7y5TsFUQJlHQEI3InUXyKDGuhYFKF0ysAmtpg6My/QPPlJQ3iyGJfEtTUJgKay1tG4R+P9uQK5GdqsyAkcNSmg+lVQ6oQJYZSkDwGERG51PEJE8ICq0the5QFBbd7RDdNA/2UitsZrAkozLrSxisAsTN6rwBBByXBDVNMApNgdGF1LDsL+ZJOAVlIsubpBZL+pX8LKUo/41EWQjDwrmWTlmiQiK0jbB6rCxylNI0dcPS4mGm9VBkSKVFpUiMsmlPGSDTRup7QnwOdesxNlvCp4Q1ihQD1hYYW+QeQvqAKJcsMz/HnAkI+X41MdfKBMZWzPyWVAZ9ZJgWI2xyUpnOUvxUFCIzysNbmknQlCw6gvfCEkoix59tJ2VhkqRxTkl6nyiBEEopkXflZYxKMyokZIvofM1lSUFeyMhCLEtPlYFkKW2ZG+K8dMrXQQyyQAtJH/QeOqUsnQSVE1F19qQShnn+LLUA4irmVKrc1qhM1Tda6mjwXupnTCgsc70F5jtdYYvqJCyMJN57iQL8kMlkRFlVDHd3uLGxTtktuevus0QCK0eO5usyStKn0bT1CBRMXUvwkbl+n2Kuh7YFKku+pQ4aqsEi+ztbMmBjScojroImSxQlKU1ZS2i9mMEn8aGKiHeRzjYHWpnM9CWzdfK4kSWjSmtUIRYRSct3JQtBYSiaKMwI6avJ9ObZNjyb0sacHqwleTahIbOcEznkJfuBzVrLQgvDXM+2lBm4vwV1/D97JMQGQZYyYt6vZn06MJ1MsFVJVVYH98nB785QmIMh7N/HIx18L8E5bCm17cAPKt0CgmZ/Y4wxA8i3Hv+3eFgis4JTlqamg9AdY+UaoZCwjHj5Gp956ovc/+i97F+7QrdjsaWhNxhQmZZKJ5KNbO07kpYeHKe47543UKqWVy9dYjgect+pu/npn/ppFirFl/7gj/iTT/8xdSuBKz1bEnybr1GFUiWRQGG1LB3RmDbwEz/+E/zA97+X3a0bXLv8Ot/3oR/DfvYzvP09f40v//G/5u7H38bxao5/8fw5pk7u4bKrKIsin/GG28/eyXPPnudDP/5BHn/iYX79H/0S67bLd7/zCfZWL3P/u97J5uuXmbjA8lyHZ549z27j8VGRgmNpfomrGzfZ2NpgtL0JRcHb3/wWet0+P/lzf5vf+a2P8PP/9d9j89VzvHrhIvc9+Cjp5Vd40yNPcOzBB/nCn/wul9bW8pkuS4oumqRayqJi3LbEGFm9/Crq8Am6/XkKJWfsoeUVzt5xmmurr7C1vw/B4Bxs3hzTthW/8Hd/gRvrV0gJrlxdRYeWxcE8//Jjv8N3veO7uPeO05S24stPfYXjd93FA2/8bm5cusRD3/W93H9hlaNvuIft9cv84Ls/wC//4q+xOd3lLW9+hD/73BeYO3yI9/zAu3j/X/tBrr/0Kp/81CewRcH8/ByTyZTbT5/h2upVXlk5x/zcHNvDPZqx5wtf/gKT6RBTlrz8yivMLQ34oSd/CD8NnL/4Ar3eHKdOHOH+R97CK9/8Bm95y+MsHL6d8d4O/UPL2AST+hr33/cYz37jaV785tPcd/8DjKZj+nPLtPUE3ba8+OzX6XQqXrt4g5ubFzhz5ixf+sqXme8H/sn/8vcpO3MQC3RMvO2Nd3NzZ4RPLceOHWducBSATreP0ppO2RWPuBhYaA/nOhooioJuJWnPKUS2BmsMt25y25GupDKnRNvUTLf3ubL6Osv9irNnboc4wJSW1clFfAMkL2exT+gAXWM5PuhwoppjIfWwSnNjd4/R5iZLR8TLd2FhAdc9Q0gR2zR0+n36CyfZ3bkhISAH6QwzzEAOAa3IywKN85HWScBL3dRIuqAVVqCS1FWUpddTOOeYjLZRXU9UHSJOejrVUJY9ClsdnE0JWUbMVoEA9OYEZEoRG/Psr76zJ+t3HFASZrQVOGXWHGXMgKgkTULNPlsxz4lJ4VWUdkQnUsyFNsfVC74mH0qcFes8U0ek8Orsp5OiNLDS7mnR/mMh+zaozLhQWZJmcrM8MxwzVmRkydqsh88eGeLgmbfteZsTsv4xKoKPqMwuMhmQMUqABlQUbWMSiUrMPhzp27aMWqs8dGbulqAIsplLkWQM3uXiFm8xpuIteEmeK5vaKhWybMKBEjPUEDWkWxRyi4XsrRIzVV/nlXSK6aAB9MELuJQEzFAxp5og29moFW3wEmNsIoUtxXBNqiRFUYo3gU552pJGQzau7mAwdMlBDNLsaUWMLSqDjEZrgtL0SktlE8YkglfoIF4apZYY66QjhVZ5GxhuDT8xEXTARmiUQiVDYWW4KpJEgoYUM8NDqOaFSigk3cxEmzfIIlWaMQUNCR2hSB6FbOlnzKWYtNAL803deNn6yuZdTG1N0ngx1smXh1A6UYYyQaTF56ZT5glHaWTTrXPSEUlJMmIS+WeKVt669qjQQkyMp7C2vse4cUgaHEwnLY2rOH5kAD6zXIImJhmUhWwkrAgVxdi+pUEEJJmd4oWSGWIi6AmdoiRoRbIRE600u3m42B/us7W+xng4prQlQ1dzfegYp5JGdQQG1WVOkRB5o3eRGs9m65iOxjJQHV2i2xswmkwYTxucd6SkqLoVJItWGhciO6Mhy2qALhT7ey3V3IC2buTacR7fKEaTKe3U4WNDci3O1ZRFV7YiK0tUvZ5A3SFydGUZmob9nR22rq/x3IuvsrM3ZXdS41IixILhcMLuqObs2duY63ZwnYIuiUIL0DhrmD0zHXQeS7MsRkYU2VprJXIrFaWZTyGSvM9eJZkxQUQlgw4xg/MlKYrsrp2KPKvb6YoxcSH+IFZrojEEnwhKE1pP3bQEDaUtKCuoCjl7XBNYX79JrD0+b7N1PuhNPaFbJBoCSVfUHjZ29rnz5GHqNmBMAaGVMz9ljzYtAKlKSa6mpqZxngg413L+wipHjx5lZXGeejpFKYla0KpAaaEAF7YURmGIRB8xRUlRlITUEAg0bU3ZGaCtoSwsNnf+moBRlqALvG4heWIULxmMEnkIMwry7FyVc1KpwEyKo1OkUpaoo9x/BIJJeCV/X5yFDPgkFPoo36NOBpeBGpXyzkOJHFdHLWBiaqXGNQFtPZ2yB0CrWoJupYmIkcLkOhEPuK3E4PFOzByJMxAt++95D84RjdyPjhYdEpUpKEyJtjkZMMqmLCYBO0IUUHNmxzcDBgIJp4KAG0kkOSEG/Ex2k0RSrHU6SLoCL1K7EAmlsP5CFJPuTr41Un4fB1LlzFAJUeTzxhp0TFhrWej2JKlRw+GlJVwTCb7EhEiRoGs1hJbClNR1Q7IVVhvKsoPRBVZZ2jil1NC0DkuktFrkImTvtBnbM8lglWZhIQliaoCAV5HCFfio6PgKHQ3JiwRNgIJs9o/OwK8AyQpZuqkUsEr8TEgak8B/m5wmeWGRxSjMiBmjWalZkqIMZkqJF5TRhhiFRZXUjBmVcqCJbDCjJi+cxDicJH0bZDADYZfOTP599CJfzU1xTLKNN1ajG3kPIn8PkIfOUlthGh1cP0bCWuKsB5PXscZgcs5omDGtoyxitDGEVqSUM+mWQqO1MHrb1jHAsnhomdFwj6aZsLE/5Mq16/Q6Fetrq/SLkkPLK9SjEd35OTqdCq1LfGhJyCARXGK6PyZ6KOYqep0OJmgosgcmWWZgIhmPoVR5yWWEcVxoYQnJeS79YpidBwGiJLXIZ6xUDj/JTPakZk2D9CZKiVl7Aq0KfJT04xRCTvyMeV80+zyVXLNZGkkSEEmrgpgcMVq5/lI2HdbZa4ts+p2XVtpasXMwsuSVeSPXqZmc7q/yiIHN9atoXTBYOESn0xW2fIJ6ssfu7h7HTpzK2xNoGofSUOQkpv+/eUnfHif+f/65fI0pg2oxJ+O56DEUTKcNWkVsFSl0V0DnCM10StntHDBr85By8Lzf/q5mDKTZsjo1DarTyVYh4L1nOBxjy4K1i6/TO7zCi5/6JC+O13nPiSd46rXnWZifp/ZgTWQ8bfCdAl83+CbivUJFRa/b50c+8MPsfuvrtBPHNEQmkynDesKffeLT7BUtN2/sY6ouc9rhmsi4hWQ0Sotc0fvE1DVEIoUtWOgu8JY3PsHK8nHaSQ1lnx/54Pt4+6OPUXX73H/sCHNLR9lcvcxc1SG4lqqEZD1OeYat9GrX166zN2kY9Ax3nT2MTYknP/Qe7jrZ57Kf561ve4w/fPU8vcGAt559hI0r62ycv8JP/s3/hHe+8x38yUc/wheef46FhWP84i/+Tzz+prdyaG6Bhx56jNvP3sOjDz7IcH2H8+de477TD3Lv4AjX5/f4yZ/9BT79x/+K3f0xgYCyHZq9EW964rvZWl9nevUyKXlKDMtHV2gmQ0btmKvXVumXhklIFGXJyZPHmeyuYTpdNtfWcUkWoUW1xIULlwjRkaLiNz/6z3HZImEybLh8/hJnjt1OqypeuXSB/+wD7+f0HY9w6vgDhKT58L7n4Xd8D1t76zz7qc8y3NtEdSt63WWihqoI3HFihXIS+OzH/4gXL12iacfsjcb8wPe+g/d9+Gf4+Z/5KY6dOsWN1Wf44Pue5GMf+yhjp7jt+AqnztzLV7/4NIfmFljqDdgZb3Ht8iq74wmblw9x7coml155AV0WvPraZ1i9eJ53fd9b6HQrmsbRuDFXr17jjttPsb+3x9eeeY71jc+zenmNx9bW6HQtastw5carvPVNDxPaKR968t3sbezxxa9/jrvuu5OH7nszVfYJeu3SKqlIHD98krZxzM2tUJYd2tZJ6AoGoxWDqkdKDtB41+KGOzjXopNC12NW5rrE4GWpExI2KGw/Muj3sFWH4b4DtSP3Ybdg6hrUXMGR5R4hyCIqhcjNvSHnv3wBm2B5YMUuYTKiRXHi5BsoB4cxRVe8EJ0Dbel0+xypepiiQpvywJ5EpfSXAHmdFKXVmEEP8vJHvIs9bRO4cuU8Vu9x26k3UHYW0ckSk2M82kWlEVX3hMy3MTKpd6mSw5gVVEo0rqZpa+b6A8qym9dfmqapmdcGZRSGnMCqzHf0PP3OS96cDOQhyNAvVNs87KeQh1/RqCqTZWJJthe9qmIcfAY7Em0y2ddGpGohL0dLbAZIMj1eQcqNi2tDZqlnuVlGkqNzCEU1+5bkx2w0E4BGPmxiHqQtwlhixgRKt7wEUiJGJRIJHyWtKgoINDPwRSXwgaQy0d9E0c0nQSd1IX+ByqalMQMRWgllPfiAsrkhVLnhSLekguShR6xZcqHKvhoHmy4EDYtJhp2UUt5gakItdBytVXZ7L0SqkGTMDVG8lHSQYS8khcSlCygmCX0aYwzBZ6PXpDFEXHLUbWbU1K0AJiqh4oioPNobki5lYxwtxMg0NPgUKctCkndcLRvrmGhVIiVLx2g6pqTqapHYJUPf9LAHFHCwKhteKoPR+TO3Gq3FF6P0YnrWIcmAEMCYhAotGk2hLBgBoVTMvkVFIpmIVh1sCzFAUGJOG4i5cYYC+TyNUrTBEVUHwy0fgjYkiJpSQVBTEoYiWZEnRJEbaBWyJESGGhUFpNIkjPF5CFHZWDMSjWHcjASYUgpUbsBtiS3FdLbe26d2MkyIBDPik+Hmbs3xoyvYjiTBgCRXoQM+U/J1EpPXkAJVUpQ6YnUiRgPayAarFWaFCxGVAj46kR2NwRYF2pTU++vE6Q5GFxhlGY8jzmtqF4i6IBlJtlEy3RDyoKORlLtJCGztjeh1KpZMlzbK3RuRQ9t2KhbmjtDpVsS2RtspprR0yhJjIt1el6oqaRpP2zR0TCK1LePhPkp7TIwEJ2aUjXdsbm/Rn1+g6pWYqCgax/q1K5w8fIjTR1bwe0OG/W02x2NWd0bsTT1KabwLTEZDODRHCIoQPMZqnPcix4wg/hrgaURHDcJksuJbkAAXVJariGRJkFGRQMzkY55AMlp8FlDsDkdcvnSFvd0hG2vbLAy6LK6scPLUbahUI8bMntJUNCpSj0Z5UBFvo2njSNHQmStpo8Hqgv29XUpVUvsmb1cywh48ZaqJukOoOhAi6zf32dnZJSqReqRWgN3oHBiRE4lPjpjBH+53uGOpx839hmGCG9v7fPJzT/Hgffdy730naeqW5UEfWwgQrcoedRRzfes9KnjqZkoKHt+0BDy2aBnMWS5deo3R/g69fpeyU2LnF9FRmKFizCtqhug9Te0kZcvaPKjJvWRQuGkjJs5Bgda40ODyVKkyi8kHT+scKkZMzCwSQOFxvia2Ee08Vhms9QRqyiQSYbT8PSpaCTUIHh3E98jZbAOb2UU6GkxImELhgtzHJIVRmlIXFGWXgMVqAS908hJNr1P2BwSdIkFFQhRza0eidB5b5MamLNClnEfCOAFiIAZhIKkgyxMVKkIRsv+bsBltynLipEGlA/9DhbDpksnSYqVJxgABk5OnFBGCgNQmp6QaJZ4yMSYKo0ltwKEZj/a58voVDInt3T1ee/01QozcefIE04UFvI90qpLgGvbHQ6rBPC4qXAxoU+Kiz35I4nXjmzrXX2ExmlJquEjhpQMIUXzFks6eNhkswluiFrP7VBiCTjgCxoiMXHZGOehAZaPvzNYWb6FZv5D7neCwSiSxM9m8sG4VCZvl9CFL0yQwIBHxPmKtSC6VVuKVEltZ6qXsBReESWWNAKcJYUvNHAB09uZK2bPNRS99lm+JSoC+AyYSM+kf2GwSnQICAPW6JBUlBc1FUHKfCOhFBpkMSVvZNhOJObBCLjsBp0IQZp9rpR6Kt1XCR09I0LMWU2qcb7CFZdxO6Qw6zHX7vP76Oo+99RGWOprB3BzeeYa7Q/Y2trGdHm3rGU/HaBLN/ghVVnQ7FaUywqpMARMcrRMGojDEIsRSAOMsgxTDeel/fAxYo0TuO0v0TRB1yqsCk88WAW1MUYhUPYfOhJyqGqIXz01tpd5rBclDCoTYSIIoufnLrFafFEWm26sDvz4B/tQM5E0ayMmiWmqJ0rLe02rWB0uDOWPGZ63iX+mRv0LcdMz+zSsoW9Gbm89+j4mmrRmNtpmOx3jvsYX0ndpIiMC/K4nbv+l1ZjIRuUMCrWuFURw0G5vrhJhoxw3j6QjnG/Z3x5x/5RnK/jyHFo7zrvd+P3O9Xn6u/D3MgKQE3kec9zjX4r2naSacf/4FvvzxT/PwB97NxadfYOnEUVYvXGRvb48TJw5xqFPxjvf/EMfuvpPDC8d48flvcGN3j/6gy3Btn8WlAXWcsDN2lKkj54MK9DpdrI587atfYfW1V3noocd4+YVnuTre5R/8j3+faxcuEsuC0cYe7/7B7+LZL36aFkOKkdJaKq1oc4DkocMrzHW7rF1d513vfDcnT56iHu6xs7vLf/T296B1ZHnlMLYs6KrTrN/Y4+TJOxhUJbYp8L7F5VAhHTQTNJOxZ3lxkdO3H+b3fu236Z06wrFOYlB1OX3yFL/3yx/htd0R//nPfpBnP/MZ1jZu8sN//T/mJ//Gj/Pa019m9dWrmNSlUl3uefQhLr76KhcuXuKO2+9gbnGBB9/8DlavrPLkh36Ura1Njt11hkZD1e2yfXWH/X1FUAXJBebn+lx89Rx48cTVuiKamuhaDi2sUHUHjOs1yk6PTqxJ3vPS17/Kz/6d/5anfv8P+fjNLeljg6hqpkoRnJjfb+9u0zEFr164RDv1rF1b55lvfIMzj7+ZO++8l6OHTmJVSWeuRzOecsehI5w8fIL9V1/n05/8HLvR0/WOO8+eom5aptOG3/tXH+PTgz/mxvoNxnWLV4m5/gLnzr3Mc6/9HVZOLvHNr32V7b0x3Upx7NQKk9GEra1ttnaeheiJzYTzL36VBx9+M3/r5/4rPvXxT/D5L/wZL52/wqRueOUf/1NCgIDj+s3LFCby7vc8wQ+//8OM9vdZPn4PTZt44IEHefqpb/LnfJm3v+N7uPv2s3zpzz/Fj/7w+9FtpK1HJFNy+5FTONUwP9fj6KFFghdWzv13nSaElnp/A+c9frrHpdVzPHDfI7jW4wl439IrFmhTzaC/QtNMGA5vMhnty+LMeSbTfdZuXKXT63Dn2fsYHD5DmwLL9zyCsiW4MUpN6N+mOHP/Y8SypDR9bNnJQQqRtmlYvXCRT3/yT7n4+mX2h4mFgeIzf7rPcGeb9/7Qh1m6/WFKVZFI1PWI5IYMFo5TVAOUMuKbqlpJ6rQSjhFjEhkxUvsFN5eZvSorSQmnZTBYBB+xpsSYQqYcrfCpoHURbfaEpJE8h1ZO4r2onMqywISE1kFUNEgtSBHCaJfxeMTO7iaqN6BMNa4ovqPn6XccUDp++Aytc7SxFQf20BJajw/SwJqc9CGs6ky8thofctoLSnx1ks5NMZQG2jQDRxQqiDt5QqGjMD1mBsHayMY3aiXTQk68UrPNOkjqINlPQAmAJHThAFFoyyoXaqOkWRcquD5gIUkjB8nlhjNplCnQ5S0amSKhbE4f04mZ/0fO65EmIgE6SlcWJXUtMjPlVrfMvFCZvZXZSSm/P9LB55miGDTOFFEi/xMW2EHhTJkZIWZEeSucZCgpbFZvJImCVNIMJSXynKSDbPRnpqtKoSiJQWW6fRKZYlS0OLx2uGTEWDfOaH+N4HHWopTP6KkMPFHJkBxSEJPkQsnADAh/zTONkWmaoCcWoyxWwzhOJc3GllS6Q6kVDpevsZnPSMAohzHCBmiiosTRAMHr3Mxk2noEjKKNiaQslRW2EKol6oLQBkLUebBO0mgrMcXVIvQQoCdzsFK+/vwBpbmiDhGP+OCg8nDjBYwJgj0SMBRaHQwwUYFyEsuugoFC5HNllO/PZ78fkxvdkCLRyz1Wt16A3Xz1zQwyg4c61PiQMEaeVySAgaDFOFkJjpE3oTJsBK8IUaGNJtICGuOEFi4gVMKplnESQ7h+2aMKNXsYlC4IrScSadB438g1qzSRIOBmnEk2si7digvjyMHGyKGrhOkO8GmXJoIPkThq2NpepSxleDl9agVbVGgjRsSj4QilDWXVE++oBvo20Tk0T1UqNjaHzHU1c915tsfb3NxrOLQ0x2jccHR5kdQGji/Mc6iwpP19Ti11GXePYtY3KFTF89c3qb2j3+tTqIJhGyhHLXNz4rcgW0dLSBlQSQUxlbQpUOiCFo/FZM8LA3hmkqDZ1lm0z9kEH0/ILvni6ZZ46dw51jd2qF0gNRN29iumX3ua204exqSEjoWwQbWVjbZNVFVBt+oTs7m9TuCVZ3/submxQXSBRnkB1JPP3iuZTRIlBcOnhE8NO+MhvUFXtuumoNAlIU3xKRuOB5Fn+ehpQ0s16HHi8BKN22AaE21MjKcTvv7MC4zbEQ/cexexsPhUo3VXmAsxYlUgeIcTF2lMZTFK07SyQf3j3/8k1zY3CCpgjWHpuUUeuu80j77xITQBb5wAvdHReE8MLcaBShXGFMTgc11QON+gfIuPmuBbTLL42IgUVhl88Gxtb6JQWGPxzuFaT9Q1XVsRWkOFY9o00LS0kwipIClh17QOxqFmd3uHTq8gRjmPirgPuxpVdKjrEUv9OdnCAXJFx8ycivikGLnAcH2LzqghepHqhSLhfYOKLe2wZmFpHnHZQhYSIaAAj6P1LdpaJnXDaHMHUqBbdVBRGDLYRPCG2ifcZIjyntAWmM0txnVDELtsZgsQpcTzy2iLT57UeqbREaKjUzZ0tzYpx2KKunTYUliNaz2ta+kVHaw1hKahqWu6vQ431m6yvrbJyWNH6ZcdpptbXFp9ndX1dYyGowsL3HPiNhYWF9jd36bf7zJsamLTYnsSyNG1HabjBkOFJdGEkO+5gGtbQgRbVihb4Was3JTEoP6AtSuADQE5s5VH6QLvAqPJlDJLnOq2JU3GMrzbQiStCHVd2A4iTXXOYzsRFz3jtqYfAmPXio/k7BxQmUmSl3VKKZwTZqgFvG+ZdR2oSAgKW1h0EnlVTB6fguB8KmIQVoywVUSWliFryKxH54LIaqWqYOjkRVaQZEvIS7U4c6cUeY3WsnCxwsQsbZHThgpSEjp/inlxV1iUDxhTYq3GFKCNlSVjUpgSgs9M8SQJW1GLJDJpsEUJ2S+i1xvQ7XYxtqLXLVHdksF8hU2Oqqwou13xrOr3SSlhS5i3faqqZFIqfL3PelJoW2BtH60DldaUVcQ7qekuJHQhAI2PAR+cMJOKEltYiqKDtRWBQFl28NFldmqD1uDI3plZslZYYWk0yR2wl11MJGMxyWarAoM2pUjnfJLrE4XWIfcXIo2Y2TMU2Q/QkAMNMDmYRZgmSkkPHD1QlbI0jQI4ylo1f80KLDNw5/8KVPrLbJuZub2w6XLKnzYMlpcpqnmqsiO+f0BZliwt38bSITJzToAtSUSd2TakbwOz1AG769/2+Dcxjv7fPFKMNNMRTTNGJcW09WzsXGcSxpRYalezeuUyVy9foqM7bG+N8et7XBhfwPvAu971LmyhaKY1EUVT12zcXOf69de4dmOLnZ0Ryk0pioojh+bZubHGKzef59Jvv86dZ+7mqJrnwTe/gac+8ycMR4a3vPmdrCwscnVrh/e/9308d+FZnmmuELUnVInd8ZjYGpQ14j2nImVMhLalJvL0c19jKoEGCQAAIABJREFUXE9Rr81z4vQ9vHbjC3zlq1+lP9fBjScUgzm+8qkvSdx9W9PpzhMCdPqLVDGwu7fL8eV5KiLbKbByaB6lYbCwwom9PQwWawJbG6tceuUqf/DRj7KvCx5442O8sDuhxWJLxXxVEhI5NdnQtp67zt7Gb/36/8bq1VW+78n3Mp5Y/vTjX+XMPXfzxae/we3338/Lz77EH33qS9hjx3ngkTdw7bUXubG7zb3veIKLH/8E519+ie3tbX7iP/1pTp+5A9sp+NV/+A/4sf/yb3Hp2mucvPd+jk4nrK++zid/71/y1Lee59rGBrFwBBSlSQysWE/0+nNsX71MiDWnbj/C/s4m+/tjrC7QuiCiMWWPY3fewWA44hO/+4e89PyLaA1zZcWwDmjj6EZP6Btc7ajKeZxrWdu8gYqWgTW84d438QPv+kH2Lr7OYLAgfmwmUU9bnvvCU6w8/lb+6S/9Iue3rjGe1Lg28pH/9Vfpd0paF7l6YxOtG7xRnL3vAfavb3JjY5350/dy192n6I4Vj7ztCf7uf/P3uPOeE/zsiZ/if/6Nf8bV7W06uqbQJRv721zfWufD9z/C6kurvPLyi9TO03qHVV1GwaNjonY1V9Y26Vaato1cu3qd5YV5pptrxJBYMCUXz69yaGkOXe/zwle+xPbGRW5chMn+CNvrcPjY7bx87nVubq8zNzjJ1bXzLMytkLDYwjBuW7Qbsri4zOc+/XkWj1R85RtfY2lhnjc9/gNMmqkElKSEthW93iK9xRP44Egp4L1jpZ5wYrTL/PwCS4snUbo88GtECXklRmGKzbwGpc7OZKxCvjh68j6uXd5k7do6u/WY0W5ga5Ionr/BW5+4ytLJ+whhQhs041HNC5ee4+TJ49xx56NU1bz0CpmBaLRYKyQ1c0IU4okxtwAdrRWF0qhUUJUWrwq00cKMVhpjLIO5RcBxY2uTva1rLPQVc8vHaGpPUSZi6GK1ot/tS1ptkoVHiJFm3LKgIov9eTq9AabTofH+O3JWzh7fcUDp3rMP4aI09UpBW7dSeF1L61rxkHE1bVvjfMvU1dKko6inSUwOg5OmKAnN3ZH9ihDvFm0SGDE+diSKpDA6CWiVJPVKpYTKtH2lIOosqctgwczkUVt9y0soZX+dPLQkl0g24LN0Dp3pnzMPkFxPJe1JUlfI8g5FApPQURFUJHoDhbBU5H8T34WUIsEjoNKBWbZCGQHHULPo4eybMGvvjLCpRBKXQSab6dQxoctMX0+a4OLB96OMvfV3kw76AZ1ZL9GL/tuUOZY2M4TQiPY/s4BicjkSOgjDCnlPPieWAdnDI7OzNCJJNAUkSZFS0aPMzLQ6CqaWYztF6uIF2LMaG5XowUPE6jx06YTDs1e34h+joNQ9Cl2K1wca5SEqkZEpHB1boJOBpGhSjVUQgnj3dBTC0MKQlBTllDSpUFAYvHJ4H2maGqJQwp2VGGujNQVCyy+seAgpJaacYhqv8UGkSzE2kCwmBqFuao0qwMaIMTqbnUdSEp+pSlR/tCqgigIbIJmETQqngshKIlSqZJIE7VdKo6OnVD77uQQiU1K0iHG8x0QoSkt/JkP18rlLClcUyn9SB5t6ojBhihllTwWSctK0amG+ldHmtDGNSx4fEsobNFtMW0+wJd57JsnRUmRTd3MgK1GZnaezZDGlnMCW5Pob1y3sTjl+tAtNFCA6Coun9ZG2bainY7pVQWlE8hRcm012YdSO6RVKwAw009hgvGJuaYnh1h7KKMpCsdAfsLHbsHZjnf5gHhfh8uo1Th1ZprswoNnapnCR6Z6jUBV3njhE1bF888IG4+mI19cjL128zqCseOMb7+LuM2cQc38NOmF0wGXgmJlfjFIH91obA4aQQTsRGYJGawG0xadeQCaCpyw0N9a26DVjOniSUXivwTom+1Oa8Ygu8n2kjhUgxlrKskNlCvHjMIraewpbUSTFnFFMyk6WvMrheWvonLH45V7SSlgX7TDRtrXIr+II5PTOZtZZqBey54xKdHsdFk6cpF4bEcKIRBJmn4m8fOEqV1ZvcM+ZO3nssTvp94wwVzC4kI2blaO0hoXBkjTBqc9LL73Eza0NEh6CofGeHbXHuXMXOX3XXax0K4pkxMPOKqrC0OnPU3UqjCkxVQFJHSSteR9I2XMlRU0OmDzY+tuiw8L8PEdWljC2h1YwqvcEnDAdbM+yldYZjTcYtjVaVxjTIbUN0XpUSlS2y9EjPZYWl3CuwYcpSQW0KjG2Yr8eQtOAFnCjSolWiVyndZ6UHJU1zC3NM5hfoHXiuaOswrWO4Fom5R4mGQpd4NsWVSic1WjRuOGSh5DodEqKwTzeO6zRuFrM/mf3vC00i4N5uqpgsNSnPzeQeuyzz4si00wkRSSkgJRPjS0Mg35Fr6zoDeapenMYqymMAPmFMfS7PawxVLbEx0RhDc1kwlyvw6lH72d/a8Tnn/oS25ubDCpLoaB2LZvDPXxqWdvcYHFpgfG0pld2SaWnVJqOLSTxbjKhX1qim9LByhkSkHqhVd4kSu/hfcwg/K10Nyn4idg6CJEYDDZJypJNWVYWI65pSabNUuIASti8Mzm5NuLd1bYjQqwYjgPogr26ZtpMKYxIBMtOgdVavNxMpPUtu+MGlwxN2GFeW+zekNi2WFvinEOXFbaw4AIpiISuqR172/uMh1O87jOvK5JOmGQoqy6tCywszdGGVhrfmNjZ3UcbcPUIW5aYqifMtiBSu05VArJwiskxGY/ROlJQoIct7eEx04lDG5OTLS2qABccTiXGkyHKtZkBq/DFHF5tC8M3+1slLFZ7YhK/pq3tIZNRw9zcgL2dPVRnkBngFa6eYqsCnSKF9RSmy2hrGxUM3aal7PcJTvwxQ3C0TnoR55IYnwfHZLRHcI6yNKjdIdy4SWwCPkCILb1OHzetURFcE5jWE5pWWIqD0T61E2lxXXiSniX7ioWCmwYmdgqqoGoC4/FEWCo5VZAsk/Q+UrsWM4ooppSdLr5ucLVDl5q6aeimkhCkt2mDEzPu4NCmxTlHKAxF0CKVtiKFJmlJXUqRhCNFjykKKLMfnzYHkk6fvCShpsxGy+f+LWjp1k8k9EP+fW/rJru729iqJPgh6JKVY0expoS8aScZ9KxfReceisx+D7SNEylhYfmr+Db9fwWT/lJ9U5EYG6ISmSBJ6ncneHomMjc3YNtWlJMWu9Dh1KF5ru+Mmfgd1tdeZto+wlxxiE7XYouS0C9YXOpz9z23gylwTUM9mVCogqrXZ+fGNb7vyQ/ypU9+mvvf9Ci/8Yv/mCMP388rL73E2QcepZ7WoBWHFhfYenoDvXQbK0fXuHLxOtYUtKOpLDFDSbTi2dgdzPGmt3w3ejLmL/7iCwTvuL51lUm9ByYRfeTeu8/w9ne/nU/9yu/wc//oF/mlX/jveOdPPsnnfudfsJECVSyYNg3douTS+VdZWOwzDQEXa57+7Gc5dvQwz335Mxw/+wZMb8T2+hZeGV7fXuOV7Q1eWL3AMDqUiZhSlg0uRAwaExOL8wtcubKOCwVLhxb5wI/9DA81LTtPzvFbv/KPufPRN/LVr/8FTz//CnXdcvboCp/5/T/gtdcucPree7i4fplx2xCKyPWda/zKr/0Sf/u/+Hkuv7LKU1/6Inc/8QRWF+xt7jDnWv7sVz/CH/35HzIxiahKlIXp3ojewoDROGBwPPKWR/m+97+HI/Mn+Wf/5B9Cd4FmNKaJLS5GSp1YXlpm58pVNtuG27uLjFLN1EdGoaBbiDl3oQyVBr0wz113PcjLz32LEDzO1gzbxBe+8hTPv/gq/d6AaSfxtu/5fu5YPsHCoXmO3PswHe85efZeTgL7W8+gVaJNY5KVM9cUFUprfvTHf4yf/tBP88nf/k026mu878m/wWRnzMd+5yP0iz4Pftf9nFm5k5/673+KYmkelSxtjKjUMh1NOHv3PRxZPsz48D6Hlw9xZW2dqW/wusbakqmvCd6TOgWxtPzuv/48X/nyt3jyfY9z4sgRKt2hiT1GzZjTZw/T6waevniON7z5bopOhzvvOE1ZncC7krQQefJ73sdwUtMtS4y21JOGS5deYeXIMebmFul0Fzj+0JuY7G9wzz1v4PKN65RVD206WFsyC9AgS9LF+3dGmsjMwjz7CxM5ZG+iGa0k5nucg5lDfpDPgJBIYcRwPGWvbdmrA9Epok6cW4v88kd/n//hnrtZXLyfrvV0qg5Hjh3D2IIQIm2Y0in62KL6S2eSMTLrpKQYjWpi2mVp8fCB2ogs49amYtA9CqrKgJenrDosLh3BkJjrL7O7eATf7KKipyiKnEicz9TU5nmjwBrxnvbLy/SKRWwpfT9Fwsb/wBlKIzdFlzN6t6HsFvRkCkCnmM2fwcWAT4Hat+KK3za5YDt8cHjvmUYPTSMO5i7QxhaQraqPThK6EhJtGgN928HolHXjgTZ4eX6FpPL4mGugMH6MyXr9QGZEWAxKUuXQxDLdMvVW4llxYAYLecMn8ojgAn4ypSjF4LgNNdFrovYobG7QJH1jxrwIPtza6AmelKUBSRrVrGAiu7ajvo1qH2Q7JWq5PGGmHDMaEPlANgSbyQwFgVXZ4iJlCm4240R8oAia6ALTcS3SvShvVqPzVlI+A1saJNEmgY6yWVI2L5SEXRWiRackSIW2khwVNZECrYKkbDknz61m34psXbWV92uMFemITqjkEW+TBDmSWekZh8kAJS4FWj/MCVEGlfIlrgSQG/qGLgLc+RDQRuzTo0uM5cqUYV0h8o4Y2W01lbaoaNhrJmgVxaizUJhaTEuT1pjoMCmhjKGoNKW1FLaiU3WoigLlGxSeqlogRYvSDZWyOAVNcKhSmACVtoAUXBC/Em0UpRKDe+3F+4AERssQrNIu5OYV5fFtJFQ9UlVilWapctxkyjTklMCgcRqWKkOns0yjfGazRapUkGKLVRlQVNIApuQP4ihTUtgkctOYrACAKqBCJb4CKhwY7pU4QpsYOtlcu1okL8IyFGEQScvGP+uCU9JogoBK6n9n7c2DPTvv8s7Pu53zW+/atxd1q1u7ZEtqeZEseQfb2DGbgThACAEMBDKZYSABHDOVCQkDQ8JWmQp7GKASnNiEYBYbgm1hZGxsS7L2xdq61fvtvvv9beecd5s/vudeGVdI1VC+VV6l7r763XPe5fk+z+cRISPgiSGwPZry3KmzdAYHMEVJ9p7pbJsYKowRmKQ1Gp0UThtmURxuk9mUZlqxdmVK8g1Vr08IM6YjTwoighQhkBtPTokDy3PMfEQlWF/f4fL6DseWF6mbQDSaytc0TUPXFOim4fL2FJ89N15zI488+SLKZnbrms1PP8psUnPytmulRaK9SKqWs+a0Aiw+N2gyKWmM1ujcATMlRYPe45vkLCJtFs5XVu27p4VP9NpbbuDMxXU2613WdzyXph6fYHc0oxh26bg2iOUbYjMjpUimILV8OWcdCoVH0ahM0HUbS5mhrSJE8SqIuCT/QkeSTqACmIiiIxbpmDE2tWwwaftyxuH9BPJU1gmjmOVMraShLrYOqBAT02lNQ8X9jz/DmQurvOtrXstguNDCgx1QvxTjtQoqyCFy+cKmxC9TkIm7hZXFeb75H/xDek5Tb14GHFG1NezZ4APomPGpwpIpy5Kc2slSlDiRLh269mgvgOnsDClYgkk0GZqmwUQjF3lUm0aZYZoBVUhYu0BvYUA9yjSxQumIyw5jSrQRMUcuVC1OJYlDwZHRAeFdRWlQrXJq3axgnMK0gH/VNrMVumXBKbBOY02fOJWLZpPE82bRhJAlyRcUNIGIkbUutlHwpJjVXva/FEhoYjKU0dLYRK00A2uIKRF0JmuHCgIG10bjjCKrSJMzgUgHjY4ZZyyFKbGt5VqcFrTiTSaFQNMkmtkMOiVNVeF8ZhLWqGYTYj3lyMoSR69aZK475MLli1y6vE2MiQNLy1x3/Y08/cQTRDzz/Q7T2QTXMwzKLrGeYUIktzBnbTRGWbQ2wpPSlhiEUyWCL+25I7Wfr0wacZqsvMxxlKFLZFB2GQwGlKVj+cACneEBeefVHtdPMZtNUNpInMs6+mqefq9Pb26RfnfI3EKfrhfIplIZY0piqORwmoRLtDg/ZG5hAUfk4PIKywcPt8MKXnIWp4bt1t1UWEc56NIdFnRdyfyBeQ4cPoJcOzXGFqikcD3NuumKw1fBcH6RuQMHKKxmcOAI8wcOs48TSJaYPX42xiqwKbO4ME9/bsj8gauoxleYP7CAckNA4ncaS4qZejIh4EkZloaLLC4uop1mfmGBueUlcgrUVUVZdJjWI+JMws3OWYbzC8z1ujhnmRv0GHY7JONEJCottnDMgpwpplXFcHGFbtHDGkXZ66GduF27zjEYWHq9IRv+AlpZOt0e3U5Jt9NHx8DC3AJzcwtMJhNc0sQg7CFjdOvsoq1xlriYNVq4X7YQ5pcrSFpJhC01ZNWA7oEWJ56PDU1oSDHIs9WyMiDhwwyt+3J2iTXVdIavPCZplJJmw5SiuD2rmqw8uTSoUDCaTikLaOoG7WR/SHUgJ0vTNExmE5pcE0hsbmywsChXrElq6A+GxBBZOXKE7qD868agvRmk+qL/Iyu8h8lol7PPPc1TD9/P5dXLJJXoDkpuOXkHy0sncMMCpSQ6b7KRYWnOND6yNRqxtDBHoWUir63mryGb/pZC0f/QsZQRa7bZY1rJIDilyOXz5/jIn/45O+PLvPPtb+WWl99EETXBa0wI9OZLUlAUxmKM4tq5AaOhJjUzdutdXJNITYWioKO7lEbYSlkplHFELVe+nMCWlk45AOSC17t5ntGVDb7yq97G088+DYfnuffTnyDlyNoD99HMJvQXD3D/B3+Te888QXBL7I52cWWgYy0724GkHbZTkFXEe+gouObo1VTjKSdf8wZiNeFzD36eK7tjshPWJT7w6mvuZuU7C5783T/m5S+7hR/+Z+/jmpU5/uNvvZ9zl8/R6RiULSi7C4QQGXQUv/+RP+SP/usH+cEf+2GO3n417/+lX+RUM+MHvvf7uOe1d3Dl0Wd45eKQJ594nIefPE3dBBoPRll8kxj0DbVv2BltEJ3l2Pwi3/StX8/1x4/ye//0n/Gr9/4pzWAOn0psx9EvEkVZ8OLZU5xTmu5gyPPnzhCaWtiAQGcw4NoTV3PttdeRB0sc/KsBf/InH+Trv/of8NiDn2PFWj72yY8y1ZbpeJeya4jW8g3f9K088ulPsBsi1nb5gf/1h3jgTz7MUw99jhBGFNpSOMushphqmqZmvDWiKhyLCwPOn30WH2qaZMhUTJrEcK5DCF6cNHN93vW17+axRx7i9le/ksce+DyHrj7KW7/iLbzq1ls56wNf9dZ3cv1Vx9pGYIP/vfv4tl/8WTZ3N7j7Le/kr/7yo8wvHmQ2ljbjJgR8iFx99TJvvesNTNbWuOmVL+fMvZf4hZ9/Hz/9E/+WleU+v/Lbv8m5c6d5/x//Dq++627Orl1hNr2AtSX9wZDXv/kNvOr2u8mzMaUfU4232N5dp1EGosPkxKvufBnHrjrKfR/7GCdf+Qq2LowZz9aZTQLHrrmFQ0dv4OKZdVYOPsFXvPVdzB88zsmvnOMVt94j93TjcMZyYe0Kr3/dO0k5Mdc3bdAmEvWIQ9fehAOUMfR7Pb7y7jcQQ0BZy/UnbkKhcU7uuXsmBtqzs5R+5f0rr1Z7raTiNIwhUDeREBuMMa1bPraCvAy7cs5EX+NjDbrgytoqXVUz13GMZw1lJ7JYOrpO0Q+GvL2BPrrNhecvc/nSs8wdXuD48dsoO0OU1tLuqWhjvm3hWMqgPIqSsnRMZzO2d9bpd4e4okTrRLfXoSwdOQbqpmIy3aJwPVCGqh5ROE1pBywtLDOrHLN6SukWsWUHrWnFeHFepQi+nlEUBf2dVayfIrK+R/kCFf3fal39m76+7IKSUxCriDMWn6W9I/haeAkp7UMBZRqosbpEuxLKHirIRR0nkSyvFaUSBolG0bQVdya37IcYsSHgQ0PInrr2JF+BMgRf4XKiDJ6QPE0zI7tC8CUtCyGF2Dp/ZH+0zhCqtL9hKnKbg7cynbNyWJcKL4gxtZG3jO04VGHkYSRjjSMqj7OWqBXBQzIC8c45o62SKEAQ1oZteVLikmsFoEzrDmltcvml9ie1B/lLsmFmxNpmnQYnbitjDdYaZhMR4nKSRpO9MYze8zUrcXOpVtQqSyMxCcQaWGpH4yOmFLbQ/kTKtpwM41AYcs77kUZIWKXQprW4KxHssn4pcKVVxhZKyPr7LhULJpOzHDabINNOqVKO7MtbbZQRkO9dGYkKtjXlCDIc7do6Z1rnUA7CplDCdYhKhEGnTBsra2GyrRCXWiBnjSdbj9VKLvdZJs1GuzaiGIlK4OlJC2B8UteouqGYboEGnwTwOyyD/HwElMQ0BCwalFwE5XCaKK0VVhHymZk2AkVWEAQgb1SBRWINnozRBUoXFDqSvRfYrLYUKlEW4uirg2S6nTIsdB3RJpppg0kOCGCCtPi1318gkcJMnEhJ3ovYTuIViahESDPZE7KHmKm02Ii1ETF0Nk5Mq0zIUDWeogClWste6yZrJfr9/9x3+u1HBKzA7SNMQgVhgjKabFvujDbSymdLumWB1oamCngfhVdRlGilmU4rJr7C+YJoIBWZuqs4OljAdTWrq5tEB6XrEVXGdgd05+ZYjJkXNreZO7yAxTILBdlEptNdrr7uaq4t+ywsbnFlFqho6GRNkwIYzVOnznH8qgUWlg8g4G1oNHjVthxZEQPLjPSUpUSjPTYbjJIYRNhbHxBwoNZKoP/aEjwM50oGJG7Wc6xta3QcsxECJkxxMZF1JLdRVpMTycvUX5UiPIQoDU1JxZbRkvE+QaowCiqvBPasIlqLUyeL/ILNmpV+hzuuO8r25jYpZmaVJystrtGcxCoeAyEaaS80jrW1HR75/Cl2diqCThilyUne3hQDgYwuDefWd3j8iVO8+U13M4kzTBCHUVkMUaqgqSCGtpXSAc6wYob0upqd7QkHOwUP/cUnueOeu9qomMLlKM5QnffLH8T1pQjNXtumkahxznRzZjchFWWSVSYrifjqXFAoh8QAxQHgjEFj0DZT4BinGktmmgOWjE5aHGNWhDlDkDYoJVXvDmm+SkZiNqkJRCXbo44Zn5M0Z/rUxlWyNDXGQNyzsXk5sFWxDZQbEUstwswKKaKzNNwRMskqvMqUBkyWwYxUxwc0EZETM0oHNBmlKpRqK9FDapl/an8KN/OZQdC4VvhUJmJdp40LZkptqZM0zrUbGjkmtDPi5E3ieHWlwzlFXY2Y1rBbV8wtzLN0+AAbq2NqMpvjKacvXcQN5/j0g/fjnGOp26WOiujEcj6qJnS6JaqAarcmmEQXR3QWU5biDDSWWVWxJ2/tuRNeYi0K60bOM06GP4APCa9KIoYQGsbeQ9hrPVPE4FtXmzznSSHROGWYV06KEbywRUgNMXiMskCEmDBJXNYFEmc2GWxhZYgUEnqvxlyJ20wcerH9WBOERJwFtC5R2dCEiNIKR4Qg/7w2Fsx8gzEK/AwdKtJsRDE3FG4WCpJEbrOK4uDFtsMbQz2rGAznINWoJE1VOjVYo2hiRhnE8ZGhVI4iCwcw5oBTJUm1n6vSONcFJfG7rGW3aWJCGyl9sWiStuiyxKgSWzhC48nKo6kp3JDCyvPqCk1pHEpltDUY5bCiBLaw70xS4jQunGv3YUO3V9AtSpIP4nItpGXW14ZMRBuFdpqOU3RKh+t0MK7EmgLlFFprStcBpSmcZX6wQK/fp+h1GQ7nGA7miSEQfEQbjS2kgMQpxXy/T3Yd5gZdUobBsEftPLaQvU07SxUrFIpex9EpSrk4zA9YWlrE2S7GCuut7M4x2a1JOdLp9hkO+xQ9Q7c7oN/vY4zFGYNzBSiLc4b1SxeYHD9Kb2VRBp18kQmg/XdNC5vV4rRb3VpjPRvGg2W01hy46gDlylGmBIZazqoGuy9IKaUoC8Ph5QUg77ddWuda4SXtDxv//3ztCUl/o2PJfCmMVqKfK1ddxVv/zlfy3NnncHNd0B2ccTir6HSElRFGGT+ZMa2n6EHJietP8PwDD0MzY77XZ2xE9C8Khy7aPTuDMdI+m6InZ+HHNbMJncGAD/7U+zjy2jey+tgj/PdP3Ef36CGOXXecEKcUXcszzzzPZx6+n7MXT7O2fprRKIGdMn/oCGprjE6y/5UdhR/voCjR1jLyDX/wBx/k9htvoqciDz71BNqKS9hoQ57WYCz/7b/8Z/75T7yXX3v0vZzf2uQP//A3OfXMEyQdpa20q8kh0jQR3RNGXO01udMBpcizhieeOoW5/gi/+HO/zPf/k+/kN/7oj2nKAZPJmKIzIGUlgc/K0+9rjEmEYNCFwuqCi1vrPPa5B7iwu8qR5SH9w4dpmgoXM6YJNDmyO44sHrya0sL6lTWi9OHhjKMcGqJKPPrYg/yj73kPR645zJWtVTZ3N/mzj/w+t95xN0cPH8cfWCCfv0inu8A0jPlffvC9zF54gU9PpUl0NtnlPd/0tVTTitwNHFg5TO2nOBdR2bF04DhvfNOb2byyzqnnn2N9/Qq6SYQcIcGx627iukPX8MjDn0MZw8x7Ds4t8gs//+Mcv2qZ65fmebawLK2s8MJTT3PvH/8B3/rd7+Ghez+Kfu0bmMUZ1193E5O338i5n/xP/PFnP8s33vVGlo4exU8j1jmaEBj0ehw8cpDXvu4kBxYats89zb//+d9gY3aFn/vp9/KBX/8VHnjmSc5dmLC4PODP/+JjnHz5PYw3xjjlyClxxytexr/8kR+j1EOSUfzJf/k9VuOMv/vt38a9H/044/V1fvT/eC+PPvggt117K39h/4Tv+d73UHKQK+PzDKeajdXAzbdczWY/c/PLb2H5wAGa0HDylntwxhJVxrmC3Z0trjpvJk80AAAgAElEQVR0kBgbrO1IAYGSdXZxuMjScJ7TZ89RuBKtZbBptKwRyUuxRVAJ26IXfErkpmbWVIw213DGYHs9UHL/3qsrIRvqeoYPnuQTzWSLsizxvmYwGEjZQkoUpUMZS7e/TKczT1EMeeLIUXrdx7G7Iw73OiyViQu7DReC4xMf+zRvivOsjgIPP/M4r+jezg1lH6UKEbF8wrSdL9I0HMgpiciopbRm0F9gNhtT11Niiq3YJRyn0XiHyXSDfqcAV+CbGTurFxnMOexil521TTY3X2A46NAbVqimJ/egFkWSe0sk5djdWUUnWN88x+FexieFbqt2951ZX6avL7ugpLQViKZSwtrJbS46tTJEa03LyFSnNLoVbjKmV0j7GZkC8EoJvLhw+BgplSO27RbdCF0lmcO9qr3YQiaDUiIWKchNwAdPHWt87eVyrkBraeBo6hrf1PiqJsZA03hS8MToZeLnA0kn4iySCaI8Gr3PDJIsu4gSxEThLGXXEsjEkKhDLUBsLS6MvbacveaPHHNr0JDqXaX2Yj+0h9kIyYsLSem2wlNespxi25qn2g0/txMzYRalmIQDoUQ0QyEXB+nZkMu6gjaPRiRgjEWXhhS8VHZbWpaDfonPJB+gQMu1uBXab1gigTmTMdLSoyLaKnI2rfDl0S38WWJ/GWvEqZGVEbB01mRVolOrekUBVAbV/prsxYFFC57UElVULcMjqdDqEi2zpYXCKrvH2S9QbfOefBoGJcXRmL0K8oy4LxR0VKbKgaxF+S7bGGLQQPaUQGPlW7EgF20ZW4JCILUqyGEaxW41JqtEUEFENiU15KkVEJWW6FxuEi5HSgwRTcxBBKaYUDpjQ4fCZBbKPtNkUXTaZ0M22awCiUhOmuX5eQ6Pp5yejaRPRymsUiwvzxN8otdx1DFCajkWQeChMXt0UsJcyLF9ylJrXFOoKJdfreSzDqqmYxwhGTCKlAKNnzINiSbmVqEH27KEsjKQfes4EXHppUNr2nfCJCAavd8s1OSMoSG1TqkDh4+ws3GFfrcnPrMQiMqgOwXeV2QFNZZkS3QJJRo97LA72kEX4qbbpeLqzoBevyA7TS7n6FqFLoY88fQLXFpbl0f/C5quitgMzgeqGayu7lB2+xxYPsqTjz6Jar9nrRzew85oyjOnz3LXypK8/0qhIpQUhBxJ2RB9Q9GJNMHjjMNmjc9y+TJKkYj7AjAqCwAdESZzDuiyy8XzI4pqzOXNET4mThw8jB+NGXTEYREJOONIIZF8y7nAYpSIiSG1wk+TUBF0qshRi4i050lqSwRAkZTFlEMCXe658xZOHHAiYJuIsY46VhTWUecoa2qyNKnCkxmNZzz9wjnWdnZIWqFblp3SRlyUsa2grwK2sDz25BnuuOMkRaGIydOEiunuDkkZBjmI00tZ5vpdiJEbrzpI6RzV8gK9wYBm3LB24QKHr1pANZVch5oEsRFuEhJpiFFEBaUVwSSiU4Tc4CnlfWoatIOYHYXrkP2UhkzL7hW3QdBEFSmcpvYVSTX4StZqnzQmtO+BUUSdce2anqwgjnNoyySswWQt72UWcHWdAx0tztLISywADSQ/I3iHsl1CjrKGxkRWluBrnPU4FYlZY9t1zmRNZRQ920Vi14U4zJLETmLrktzbx5XR1MrikrhUOqVlagyZBq0dMQr8N2foG8fyYJGdarcdIlhyaAUZklTutnXYuQUKJxTTqiI6aWYNARbnBhQKnj31HKuXrpCzYu3KFU6fv0RV13S6JSdvvpEUNM8/9xy9fp+Td9zOaHtETIGQpH6+1ynZdbvkGDAdS5h6UCIq5PRS4YVzBcZoirKQdSorYvTE1O6XSirYfYoyOMJQpykLZoZTPZzryvOcW15VG2W11tAocTUrZUhKMXCJWE+xw3lS15C1QmthAqIj0SR8UPi9oUnOaAqMKimtxlqHtVbW5HaPVTmjVYFSVlrc0OiUKIwh6YTKHhMixhWyx7ZR0iZrnOrQEFGuQDdRPgs0VoHKkawtRsmeFoIHGrTxJNXgSgUmUbatpbYdOJEzzgCmZcjlKLXkyH6vtUW1LCStRdzOOZJIWGvxjTT95ZgIfiYRLQTiTAs61wgqACy1n6JdH6U6aBWpo0dbR884FJmmmaFViTIFkbbcAOFSZa3BZExWxJAhW+rgccq1aASITYOKQRzYqNax1K6JOaJ0FI5V0SETKHTJLJYkxoQmU7hAjg0+BGk1jEqYGxZhX6BI7ZoW64rkCkKUN3GPiem0RuQkcR0neWlJPuGbCFSgOqACMUn1eU4ZbeQcFYLaf7Z1kjNtMm0ro9bCD7UvRUm+WJrZa3/LStQeazUHjxzl7UeO8va/diPI+78yxZqQ5RApAwZxBoj7X9zwqo2d/m38SF96OfrS/y3vdutI2vvv+aW/qLLC2pJrThzn8FUlMW5DrlG6Q9aKHGpGa1uMd85w5fnTMD+k3xliypK5lXma7UBRztGkK5x/8UUuvPg8R0/cQEhBigY6HTnvOoexHSBQdAf42HD3134j//Ujf8ipJx/jqpO3c/aFp3j+2ad4+a2v4uy5M2yMAknDi2vrFHkOUySiSZw9c4Z+oVB1oDtw+GZG2S3JaUrddDBOEXPD5d0rVNWExgijEw0Lg4AtLbu767zpNXfxGz/+v3Pvww9S+Q7/6T/8FvVoyvZsl+HAEL3ixNVH0MqwMRmj9Ab1RkPZn+P9v/5LfMu3fydveefr+MwDT7NLzc/+u39HPehTV5mi38V7SWiUFuhojFZMQkZncarmkFhYWmF7MuHnfujH+Nlv/x4ubO3I4C3Uch5KmsIVXLl0VgpjCivMRx3pliUul3znd72b2WzE6iOP8NSzZ+kOFjjRXeQ1b3gzcwmOHrueC08+TYWnVI6FQZ//9mu/xOWtbSgs4/GEbmlJNoDpYnLi0vaYrtMsLK5gteXrvvZb+JEf/uesrV7ip3/qX/LJT30aTGRpbp71tTWW5rqceuZBtEmoskO36dC3jq2yYn1zzIc+dhbj5nnqsSdwFlL0fPpTf8mZlSM89MCnKbpdXnfPbcwt9/mGd7ydn/yhH2XUgSImdDODEKGUO2NZFvSKkiOHT9Lpj/j7/7Dit379V/mRf/ELnL20zvKBAzhj2N6+wtu+/ps5dX6VIydO8MILT3Pg0EFyKvj+f/R93Hnnq3nXV38zvcazYlf43Mc+SZpljrzsZu563Zv4zF89yP1feJR/8zO/zOtvf5uIyNyG0hbvG6ajKQ898nkWDyyxvHSEre11jJa2ZWcLLl2+yOGDR6QUwjjZDrVi70bWVDWXLlziE39+L/PLjtfe8wbmF1bIaGIYURQyCJ2OJ/L+CuiV6CtGuzM+8on7qENNszPiwIEe7/g7X8PS4pH2HGfo+IG0WBqY1ofYK9vo9XpoLTFgsghRhZOylCZmLk9q6Bh6XWkN3qoCykXedffNlGXJ2ugFrr/pq7j+la/HFhpw7bA1yXmh2bvPOdEXQmQ23aHX7TEc9vZZc40fYZylaQJNvYNSEr0fdIak4BmtX2K2u8148wJlcRXT2TY+jekPD3Hh8inmti9z4NhNGNuh9jUuW0KYgimx1hLQ0jqXp6QUhHNXTf4aw+nL8fVlF5RMFrhxiJHCGKli378oyuayD8hOsW31SGAMqZrRsX1iO/eWB07oIcpa9F7FnZx/yBlmKZBQ5NQQQ8ZZhcq2tbCBLTs4V2JjB7pyCDFWY3R7GNFQNxUxeLSSuEIMgRREaKpTw6yZUIWaZlZBlKY3XzVEH3Bt3XIKMlUrC0dmSj2dUPlGBDVH68JQGCUAwj02DUa1E3LhRGVlWh4NMqnbmwXthczbmmCp1aWFebdSkkrtRS8ghfYSYxGXFcBezK2FjZJbk08Wa3/rmnFKMWuES0SGUAesE+B2pt2o2whZbi9foMhRE1N78bW0DW3tn5wakspySbVy+BKxSxOye8khwN44KqKMQaHBCRNBOo2EMxSjgEVT3nN9pX3uxUtNYaatcs0Yralzg9ESxekrQ6UiNu9BLTU+I9XROqEMhByJURPQFJQo5QVSmkREydpiMvgscG2rMlonEZMy7fQWcjatQyujUwIrrikdNegom6LJFMnQ5EQEYtJoMg0S/4o64VVsHWCymajsyY1nu6mYcyvtoqhJRUHQidl0gvcjyIrpcMir7zrJXf05ki0JdRamkwl4Dy+eepJCp7Y5p4t2DovGFjLVjb6mbgRgXBSWQWdASgprYGAMMzKl7uFToCAStfzsat+ws3aaPNqW1qrUup68RWeNTl5+3m0NqDBK2meZvUuFuECEJaZwzqCNpakSjZcGteFwHlVXXHv1NZw/f4psNKQZoenjlGaWMt5oYqlR5RC/NcKXA1IObJy+yJHjRznaH3B+a4OOKRguLFJ5jSodq+MZ3iiC01w5c47Ny1c4tnKQsuU0bY122al2ufG2V3BpfYbqWvRuSaQBlTE5M/WR5y+MuW6r5uDBLEBpAqSE05qcGpTV+KRIWrhEMQSyAWPlwO+ytErmFsZvjRWXZFIo5QghsLwyz875TWY+M7fYIfcWGF57LeXCItVoTLHnskSGtEkHmtSQk2vjs9JE5DH4XJHpUgWJ+IUU0Nq2PC3ZKMuyQ3ewgO0sMR079CHH8lIXowsiCaMl/qZNpEkZbRN1SPgGJtMR6zvb+2sJKpMRoLuOClnZDTpC9olRPebpLzzHna++ne2dTVKK1L4h+kRZ9CgGA0prWTh0mE7nRWZaU0dDp7SoYLnq+CFCCKgcaMjyuasESmIpcu+WyzwtfNli5bNXhsbX0oaXPEZrekpi0zOkNS1o1T6zbTQ3ZabVBJ17mGSgaMjK4oqE10qMTgkcAvildSTGnPFZKtRFPGhh0CjKZAR7bS02yL4XQpD4UUzEKAUVBXtif9sSl7IIcdFKV0WSd6tQhln2JJVonIKUSDlS6kKerxTksq4sOYf9+vIq11jTJWdF8IlZVRGybdu+ZCUvMCxYWJqfpw4NDgXJ4KwVFpVSWFOQk2/nGgodVbuHa3SK9Oa6WNvl/MVLnHnxDE899zSjyZTZLKBUQOsOGcXEN3z+2RdQBjpFB+ccg/kFjhw5TFNPIWqBYmvodHqE6LFaMwkVygwpnMM5zbRqRF8xRpg9QSLM0pQipRd79ema0F6INRWRXmeIpmA8nbHQ6wiXTJl2DxY3TMoZHyIhhpahlKktdGxE5QarTQtSji28GlzKOBI2B3Trpos5EtKMJnZJCqKKbTV9lgmwcRIt84GoMjFJZDKbEkUXRSFTTJXallPhRvXKEp8bQqikZVZpcgBrSrEvGyMxBFpumtYSzYxOPLSN7PVV8NQogrKYHNrzTnvmYc/dJ8I6Wr80SIgJnzzWdVrBSSa7zmgKYxjFRE6eGKReXRnExWTE8axVoprV5CRlA52yINYjuqZP0uKoBAs5EVPEmoDKTppHs6U0BtVIBXSTEkWnJ+UCpiPxWiwoizGuZWnK85H2xIkkTYghRgrbEUG3kMGgNhmVE4Uz7UBLYbQitS0cCiVczQQ+tZiAJE151imBcucsjkClSCniQyS26AXdOnu1MZhCBmx7ztCcQJFwNkvsCIPTBtciuL1KAsdWkh7WGWrv2/OL2hdaZX0DEiRkP/hidom01NEOPaRAQFxCiZREEFWIQztE8FWgmo0IjaduGnbHFUVvwKEjS8x1bduwyP7vL0fE/4nclGmjLBFr9b5ABbRnzPySxvWl0/m9+wmWwq2wVY2pqlXm+072PaVIuabxicbB7toVtosrLC0tcfCGW5ib7BDKDmv1hFlSXFy/SCDxyf/+Uc6dv8wtr7yVN7/9nawsHgA8xhbUzZRO0eFjv/P/0j1yjMm04v4P/z7dQY+oDH/28Y8StMWVhfQoBhjNKgoHs5ApnSHR8kSbBLFk0IVO17K+UQOO4AOnXngWpbscPLhCuLyBjw2r6xXOQnG0S5hNWZ1s8ravegsf/sh9XJntsjg34PBil/XzG9xy802842veyPMPP8WV7Qljv8DnN5/mXX/va7jaOu54zev47n/yYzx03/u5OBrz4Q/8Lt/73vdx7wc+xOefO8ODjz7OcK6EmIghtxxCEZGPHj/MpfPn6A67rK9e5pd/7qd45MIFenMdmlnm2ME+vV7JxUtXyNpSaEeRYDKb0e/PsbCyyIVLF/m+7/g2vutb/zF2sMKVLzzEH/z5X/LNf+/d/MB3fCef+tCfspUqSWioLEORxtNkhetqXK+H95GiMHS6Q9bX1wgWVA4Men2CUtx155v5zF98itH2No8/9gAXXniWF579Ao33cn8YjyhKxdOPP4btOjqqg8sFKY8488Jj4uJVJb1un8ksCKolJozq8OijT3BpZZPXvOwavv2f/ih/8bu/y2vf8VYe+dQznHW7/Ouf+Wne/6u/yRObz8jeaC2j3SmHjl7Nm97ydZw5c5nPPnQ/l9c2Wd3YYOWOm7iOHrvVGAy86R3fwptuvI3/+30/w6/98q/w/NPPcP2JmyhUwdPPPcVtd57kzIun+MR9n+Dxi2cZLC5y3Ymj/PZv/S7/2/e8h5/4xZ/k2OL17G5s8a9//P9kSs1cv8N3fe8/ptQlp587zYc+8CHufP1JuqrP8tKA1VPnOHRkmRcvXOL6a45x6dwuMQVpP+3MY4qCwnRx2hBy4rkXTjF/ZJHXv/F1LPUXMFri0UEJx7foWBaX++TMF7VRZg4cVPzADTezPa154tw5Ds51OX74yL4xQ+yw+wsYg367jrVilkyQSl5Sl4XJemBxjr/7zd/KNUf6/NGH/pTLV0ZUzQxjE3iP00OyU8wfOAhuURpIW/e7tlJ0UVU1MdbimMrCHjRGUThH8BVNyIwn2wy6Dq0KsvKk3KCVMKJHkxlbmxfZ2jhPtX2a6669CddbQAMH5peoYqQc3E6zs0apSzKasixa8UwLDqU7xGeHavmFTWgwZU1WBU09+5vX0r/F15ddUKqjJ0ekejZ5sea2rhth3iCLCgAC4QaLyorCiFLonKZuAYIGsVinACa3bVpKGpz3XBZRiRMAJ1Gx4CNYjYoRDDhncVkTgrikUo6EEElNC4dGEZqMKyCFhMkaZweUtqSrFXMskhRYQwvrktrV5APRt/DNULO4uMjuzhY5AEkxGC5KW5DW+KYiBLUvsMi3L6DGlGObJfUy2dLyMJDiPqBb6qmB1NY5Z1F3Nap17xpSEmt+zi1EXMnfk/cEJ+RSrhL7D9z+C4VUZGu0RPpiKzLRtqy0L7XE8vLemRCFQKSNzrQxZpEDEwQFKkXIUvGbohUgbKjRSgDn8nlEOXCrloSfBercmo+k3YxMT0NQkrvFiHXa5PbXkyisTJlClslZBrkwJrlkWaXIUY6UtQ8oq4hJNhjJuGpqxBlnSbQksPbskYlZgObKSJTJ5SCilaxwKCuHNtuG1GJqHWdIpJCsiSR0a2MqCotPnlJbovLUJrWOG4kTpQw6m5YRJdB0nwRKHnPEksm5xOdM9lGm7qYgxITTGUOCGDE50qPH0UMLXHvjTaztNlQ4YqPY2N7g4pXLdDodjMr0S4t1JU5blHZtHllBp0evfWuVNhhXyEVEy6W6o0x7cFRyKdIaHxp6wdC1R7GqpD+a0BnMYR1cOH+F6Ti0z4eFLHD3DHKiVq04KH4eUfezPAupQeIRIUg8xhpM05B9Q7WxSfYeYzUpeKbTCdZKHWhpHcmUaFUQtaff6bK8PE+zvkMeNezkGX3jcKqkmozY2J1x6vwVnr+wRhVBdXuUw0VyU7NVVZRB4q6TXECGS488yfZOhVrogwrigDFKErIxs7G1y5NPnabX65OUBRpxyKW2SjxDNCKEajK5Bb6LgOlFbotf1Fa5DySUty7jmaZI7QtsYUiVZ9xsMg2Rrb/a5K47jkFyAvy3hqiiCHQ0kEsIwoXIKqJNwiSomikE+f5aApu8F1pRWMPiXJcDcxnUDmlWkZtDVI24ZwbdHnXw1HXAQ9tyZZlMA1cujjh/YY3ZeExhwSQtTq0kDriINN3FDNpBThpM4JkXTvOK218GKeEnM5rdKdN2Y9alw1jFoLT0reOZM5epWjbJyqDPyYUlbr/lasa7OzTjXXY3R9SNlDjML60wv+zEWVWmdoqWiD6jcqSuPbowpBiI3hNDpCj6RO1IbRuYUhCbyLSaUdUzBoMBxnQokyPGBhPk0isAYkNIHh8anBMAeI4VMfYYjWesr6+RmsShAwcoFgY4MuNQ0eghRomo1xaNkbQmKSWBNGvRARExUiBEj8oGpzMqBnI0eCzOtpf6wpBHgW6niy067G7vEnyD73RkXVMID4mWHd+ulRopFdBRoUkClyVBkoupDB0idRO4cuasfI8Joos0YY+LJtXU2hi5WLeOlJySHLq0IU1mXB6v8eAjj3Pu4kWUAt/EllHQayHp8nNa3x5hrcG5gFXw4T/7OG978xt42c03cuHiZTrdLlon6b9I0oSlUegc8HUje14SXla32ye0jZUpyzqTUsSoArQhRhH+sA6MxmT5PT2RnnbgEyolYqhQxsk7rQxkmQs5K8O2QjtsAMeQrAoU4tpUea8LxqCsa52asjdG1aBys19jn9MXs5PE7S2JzExhFBaD0lmcpGh0TmSdwBqx+7eRcK0zIdbYLO2JPkS0Skz9mH4q6WrZ98UtCSEGEdg0wujJ4vQqdSECpG7dlBG0FReQkRMdSstwTWdFSF6cf1nhVMIpK0B5CllHsyJmaIKsjeDQpsSZgjZdDzETcoJkGE8mTEc1eSCxvNXVy1x8bB2F5sT1N3DP8kHKot2vWhZdikHWPSfNbSEWOGfRJWDkAppTIgdpVAsp4WMiKSPDT7T8QxphbFlliVmGqiolcVknceVEX+NsT1xZyqLEG4kyFud6wg9Uhpik6S7tMUiNJdUilgVkjTItGiG3TcTGils4h0y2cn4TN5xCq0J+H4R3pxDHlbMGsy8KSfwwZQhNJDQC7Kd1NeUkbZ8xisjtnHDmWqUVa4X/SCsYNY0nZc/mxjanz5xmYzTDqsBsvIUpOyzMLTLo9GmmE0bjMYPlFW48ssR8538C427XJb70r2eJ926urrG9tcnxW45DvYdHyGAUriha4WhveNX+2i8RqTLw6P2P8fTjf8ltt72c41ffxvLycUzZoTuc4+rrbmbr0irTagulDGuXLnDDrbdy8JaTnLj1tXIBdj3KbodXv37M5/6fX+bh9z/F0auuwZ+4jquuvopn/+pzXHPXa9i6dIGnzpxjfrjE8+cv0Z1bIZMIvqY3GDCrAskHXKcV/LolnUEB9QhfGyn1UXKWrqeBS2PIUTEcZLSVOLExHXyInDt7noRBOzDW4jM899zz7Fy6wBvfdJLVp54i9aGaTtjKiW/46m/g0x/+ODvjivGk4F3f8V08+Zef4hOf+SwHB0u8+Z672X7xNB/8tV/nzDPP8+H//Nvc/Y6v4t3v/vs88KE/5dLqJXLc4dCRAY1vqMYNhbVyX1GKZC2XXjwHMbFxaZ3EDo8//AQ79RjrFBSKY8eO0U2GtbNrVLrGR42vJJKeAqyv7VBaxwd+6z+ydfkK7/vxn+Tej/4B3/2eH+H+++7jN97/QX76h3+QjzzwSbwxWANKy1lbpUIwJyljkqfWme2NKwx7Ess2uksMDcuLC6yvbnD40EEunn+RyXibIwcXme87EjUpG7mnZMtgcR4dNRtbG/yrf/VefucX/i2jYkCupaVyMq6wVjilrlsyHCwzmU04dfZFbrvhKg4PFnjL130jl06d4pZ33M2/+YHv57qrbubJzz7FC+de5FVvfjNnn/wCF9ZWuenECRbcAqOq5tLGKv2yh+0tsPbiOhtrqywu9gHN5PIG/+KD/xeXN3b47OcfwijH2toaE2rmFg/w+IOP8MCDD9O9eoHblgcYZ3jdPa/h45/5OMe+4k4+8JsfJIYZdjDHrW97AzWOomNZXjqCirB8BE5+xVdyy8uvoyq79I9dy8Giy9lzp7jtzrtwqiArKLTGGLfPuN1D/Scyb3zbVwiygvbuowXgr43hwsVVDh5YoOM0eY9dBy0OBkLIDJ3hDTfdQGueZB/W3f4ZgnHYS7OovVvVnmVj74Ys4jxSTLC8eIjh4jF80My8p4mG6DW/87kXODg8zbv7nptvvUhjNck3DBeW0NmikRKm6BOZyLSu0FphXYG1XaazbcpiQEwGo8Uxm9Q2s8mMutphfmEJ40rmFpZAK3yYUTKm35+nKObAdpg2DajIcDDPKBesbZxFJU/jPZ2exVlDp1NAjETlxCGYO6QmE82MYFTrGP3yfX3ZBaVMwmgHaJJpm9ryHpTOiuUWafhJOYuilyVKJFbbvQOrNHolhBViEIBeeulRIuQkF9mW17D3e9NykcTskMWW3U7VkUE4TinQloRMqrQzUrWqNXsV3THKI290gXZ23wWkjcYqhe0qdNZMJhPGkzELiyso7eis71L2u8wtLdFzBb35OSbTHZoIvvH4aSWRjbqhbhqSSqAydR1apEwEGsEfZIGzKiJKmRYgJc6gvQmfark7ew6wlDNyx5EXKsX2U2snpdJwp/aNUkrtZeHldQpJE9OeuisH1RSDCFa25Tu1oMGURMRpwt5FQLdNcO0BViETQKuxRrXOBjlcZ4RhkpOGKDFJ4SQJv0W3IoVVSuKAIIJZe3iWqb1puUxyiNaJlyIz7c9Lta1nSYlTMgBWGWFPtO1ZSknzFl90kN8T5HzbNpiziDq0ribJ8xqxvSvEzZTl56CylgtHTpTI1M9naW6LUQ7IOdVYY6hCbAVEeX6zRuDyLXQ+K5lYWxSlUfgUcHLLpsqBTpLoCDlRNRWlyijfYHOgQNPrOHIduLx6kZ3JhAtbFSEX6LJP2e3SeMCUTKYzktZ0rMCzVRtP21tkU5JLBMpjrTinjJEYjjS02XZKKoKy0RpjMpVPVCFiOx2WFoYSG8nr+GqHqg4o1aA2HX8AACAASURBVJOfEwL+hT24nrjVVJaYX04SHW2aiM6GrutAlEazpCLLi3OcvPEa/vyBy/gonJ/ptGbQL1A6krPBZiMAdauZG84zmeywPFgkphobu8QcGTHl8MIhzq2Nuby5KX92VDTTGdqK+D0aTfE20O320FpxfnOb5D3BWOx2pOx1mU4rrM/iHtCK6ANPP3OKROSmW25Aa3lgo5J1zmQRhUNKMk3XkAmgJM6SFML00pYUvMRJtTiUEpnZtOGJZ15kITRsT3cJlExjYuvcKtiC/rzljhuuJwb5frIqSLGROBWKlBrZSHVBThU61NJa5QDkPRUIuwi9ncJx6w2HuOPma2mCYnV7ys7umEG/jzaBZjqTfSBncT4ZeX/6Xctw3jF6ZoeDiwPq0ArOXn7Gu1VDE2U9kVXJEaixuuDy+hZXtraZ7zi2L03Y2thlfTJmZ7rGjY3h5puuZu3CBUb1lCYLcybmADrwilfczoUXnsSPxlze2mB3LA2kCYVb2+TIkaMcv/YopdGELNEilWuZeLcV66mdXvW6XXLwhCQV6spEqnrGs08+z/lzF9mdzDh0ZIVrrzvBysFlog6Y0uHQdFxJVvDZ+59kZ2fMYH6e+bk5VlYGBGV5+LOPcer0KTyZE8eP86o7T9LrdVqQcyQnKFQi5CTvW/aY6CFFsjYE1UaytcZgpKUuZULSkGdo0yGHEkxmWlf4psY4h59MGU3GVE0FVYE2so42IZC0pvaxdTAhEO8yt/upoXROZgwG8Ims2jirknVfjlURrQ3JIA1DsUHhyUm+R723XyTo9XvYFHn2+ed56vnnuLC6ymQ6lZueUZiyS8xa9qxg8KGWrTFE6qpBIxy8e+/7LKtXNjl2/Ci9uQVGVUUuSopel5YQK0wK70UoSuIeKjolKUZMoaRIxFgq5dlz/qYUMC17gaSYzHbZqTz9uYIQM03SxIQ4K9tY/p7ok2MSnkIQjpnRlhQaYhTeXZRJAoqM03l/D5Zd2gLSYpqyh9ROM6Ps4yFGjHb46DFOiSMPSFHEWklwlZANVRXJ+DZmpoheWjU3Nrd5+JEnWd8eMb8wz3XXHmdxuZDoXCscKPbgwqmtbBYOXlZhfz92TmNIWKP3K+llUYsoAkpJXMVog4qQbCQqYUnSnkuMcUTtW1FEhCjlCpwTToWmLVZpeYpZawrXxXW6TMOUnd1tPvvQY3S6JaGOrF7Z4cT1N3DTDde2iAEL2rZsydS6iQ1KO3RGuGAoAahOGpwTV4Vp2R8iEGq00q1bx6KUI7fGK1pHUZMEC6DRFNaJswtxi4seJ4eToKRowWoE3WDl/TOlgMR11uwF9VuTeSvQ7Z3AxcFslAwis/r/mHvTaMvSs77v9w57OOeec+d7a+6qrup5kLrVGlqgGYSFhBAEYeMQzDKJDSaxY5zIiUMcwDErBnstnKwFeFrGNjbGBsmgCY0tJEEjtaYe1HNVd9Wt6c7n3jPt6R3y4dmnqsE4K3H4wOkPvbqqus659+797uf5P//n94fESjMhsrCsLc9qRa2VcOCUDPFmZCEXHNO6pKhKXGhQwVHXBQeb1xjs7nP58nUwgXsffB3HTp7GWvn+h9YhFIOTpFolTdvcXMrpM8c5l2f0spwk7bRnRluTogVo/p9wH92Aa89cRbM24BWdx6xW6S52yPrHSMwcz15/DhVgfW2Nq5c2uPW2s/T7fblmWne+irO/72ZzZVXK297xTr717fexf32T6FJsJ6UqGra39kiMYm7lCAv5KcqqZnfwPCd8ILEdlhYzut0e3juGO7vsXbzC3Q/cTf/6DhcuvogxjsNim4/9u39F5yu/x2iwzWXn+b2P/RZNVOiqIrMKV1ZS1xnQRCofOXPqVobXr9Pr9CmiR3cU13cPKMqSupE606dgb1yXUDVQU1O7hlwn6Oh5zX0PcfGF59maTqkcXNweMf/lx0h7NXnaYVx5qrLgdz72HzgYFCxpzVNff4qLz73AxfPn6R1Zomg8v/UbnwS/z5eeeoLPPvFlxocVr/0zKeXBiA9+9FOsnDnNqTvuYnF7i+cuXGTiAyZNwARCE1nt9Dl32y089vtf4L/9iZ+gHlzm67//FaYNwtEKcLB9gF1e47/68z/Er/7aP2eqM3GapimVr0TYDuDynE9/4REu/ff/JYPdIZ976jluXz/N/IkF5k8usPZkh+26IknkvsjShKATXFGiUXir6CW2HW4r+nNzdDp9JuN9sixjMhwwGk0ZTTd45NOfZzEx7BweEJSndhVz+QLjouSdb349Gxcv833v/7P8nz/396iMZjVfZqoqxju72G5OgqeJBh8tk1JYPm9/7/cwf1hw6cIzrK7fwcXHH+cDP/kzfOM3fpXb//I9vP3N38LHP/Vh/vpf/HH+/t/9Wa5tXuelSy/zqS9/nqe+/jiPf/UP2N7bJO30CA0knbR1I8LTzz9DurKAn8u4dvUqy6tz/MiP/SXuf+ANPP6Vb/Abv/rPGWxfxYRFRsWQv/t3f5bjJ+9kvr/A97/r/fzaBz/GNy5/hQ/8yF9judendpCnsj3klWKxZ3n/d7+TI+vrdDtdIoqqKnnoVa+F2a2qZjiT1rjQ9p+zMywFiFKPNd6TKov3DVuHE/6Hv/W/8hN/9X08/Lr3tvesnFsz96TWUNa1ED2TFp9x434OzHiYEG8cGzMxa/bfM2mpVaPkvAmwf1hRKTA243i/pqMN+8OKSWW5frDPwf5VJsWEcrzPyrFjLC3fgs27+AidbpfGWYpymwRIbI8QNHuDQ3pzU7pzRyiLKZ3cSBqvr+jkXRQigqdpB6XGhNBgu4tMY0InevIsFw6bn+Abj2/GuFCTphlNHSj299EusLQSMKbD7mCferRJ05FNBrQMMlRwf+x5+5/7+hMXlMSjEfBRtVOPKI/MKJNeTbt3HWn5HbQpGbGdTkMVFImeKZDCIooIQFJpLYCrtqspQiBRWganEbGotw99lZhWbZRIdqW5sQNPlDfwM1dO27jqICKAdx7TJrL5CNGFdg5lxBmgwCsRq8pphVaGJJ0n7wSOHTtNd86SpV0So+nNLzCc9NG2i2kby1RMDVRNg6OWPdRxIYpscNS+oioriqZmWsqufeMamRJ5EZ28FyC5DwHvI9ZCmluUVSLSREmSayopE3wjTCAtMJ8WSN66g0TNQ2tL1p0jqgqGB1IEGSMsKQLKiEvH12LZFvuzl+9zy/IIzjMDfYeo0G0ynjbi0FIBUP5GgamkerwBUJxBxpU2xAai9SQ6JdcaqwOByKSpUEATHWlQMqlvVWUd5WIIUYQhE6Uxt0mbgAQy4bNGbPviB4dgsEbW9AKBFpMlYiYiRDZREYNpRTaPB1nhFD89dRSBrfWYyMpnK3Rpo4iY1qkk37eyloLLuSgY8RCJxrfavcBXUYYQnHTx3mO0RQUnriui3MZKJq06gPKeGCpiM6GfR7pkuNiwefkK2Os426cyc+xv7pHlCWk2h/cak3YIKKomQnTSOqubc0IVkc+D2CaVNsQa+WwROaTiDDgvtnzfVIwHBxTTguPLC5w+usJkXLKRBEz0AvGnIczOi/YaMsZKUawtKhqMFnEhKgF9Gy3A+UlZYhLZpbYmcuHyBuNiStSR/f1Djq0fpQ6NCKBOSniLIouavs7Z277EYj9HVxWT6YTJdMCxoysEDZs7B5RVYK7TJdaRpqnxjYguuv2sIQYq5wlA0kmkAdEQm0g/n5MEEiciqFeRMjg2rmyxtLLE2toyOprWEaQFxozGKsEfozzCnm9F2KhwMUrT28L6NZHYwHRa8bUnnmE0nnJg5dQbTKZUThwcqfI89vgFbDQsL/bQiaesauaSDmhDE2oiDQqZroSgSEzK1u42AKFxwnZR8UZjcuLoEq+/73ayCLvjQ3ppStA9DosRITTUoSK1cyjlsNHQhAqlO0xHE65e3sLUDXcfXeD85R32xiUBsNayvrLI1sGIumpaVodMzLXRxLpm49IVzp5cZdoUbO7u8dLuIRUJ0+dewsea/Ss7aCIGL2s+IXLulnV2n/kGw8EmXqdSawRJcUQrynLC7s4mq0dX6PTn0EHJ3nmUYURRjumoSK0iTShoJpqsjkQS6qqm8YG0rkgoWV7OGFdjnr/wMt985hJ33XWGu++5hdpHqgq0Tfn9332UJ5++hM4N6vIVWaG0t7G/fcDGlUu46Gli4PLVq5w6c4xTp04Qmygx1o1H2RTvKmkgfEMTKsq6ptgaUNWehaVFVlcWSZQAZRvv0TaVNVgXMCFiPbhiymRSsD+asH9Y8fKVHXYPJlil6ffmWFxZIM8TlBYAv/I1QXsM4taJXq79Jgh4XQDs9oaQMilLEj3BZxYXSnLmpHgnyjAEJUMoJclwrpIV16qsuLK1yWNf/xq7BwNCCBglcdNg8HULYPci2hidtWmlLVtPSazyzuCQy9c2OXbyJCbPaIqSPE2oSoc1Eas1tr0WYnQ0jSPLUqqqptebv+Gqrn0jja8ClMEkiuAbaBpeeuEC+wcHeBVpiob14yc5s5K1zxbwzZRysI+rHUl/Hk8QcRhJIpTaN4Jq8CqQpyl4iwsCadc+tGWwqLkqBoLyDEdjitrRXW5obdFyTutU3KkkuBhpvDgRZ6t0noann36Wlze/hLU5t952K/ffc5bEQlEUfOEzv8eV0QijLfuHE+YW1rjzVgkCQM3W51soI7SDHeEcRqVpfMRVJanNgISoTQt+n6kfsa1fAk27PxXasz1q2/Iho6wFBoNvQvsslDU7rUR0t6HlZKIIPorAFi2uGuN9pD+/zHS0L0MHpyl9oJNHGg86SXC1a2sQcSiEKO+RqtC662Vo5UMjK1TaolQkeHMD1RBbEdcraaxkvUqQA8EraleTpVYcUMwE2fb+U+Led0oSgISHJ0mOTZB7pNMOalQwNwZ5N9QURetG1+JSsZpoLMpYtLE3ajJ8xKSK2gkonRZlIC2Ubt+7rRGR57ZCBMvNzQ2ypKKT96jqCYOrz/LYFx7l+SubnD5zjrvvuY+mKYEEHwJlWdL4BmtU+4wWFtlcb56sk7f16JRqUjM10Pga1wTyzjzzC/NYI4Oo+Ee4TTfh2rOv/4/rPOTXO1kHZS3TsuFjv/05zj//DH/2B/8LimnDQVPw8EOvI7Ntwpv3N8UkNVOpWve/7mHiOuvHVzCqD1jSpMYFRTGaMBqX9JaWeO6Zp+kcOQ7JPE3QbV0tbv6D0YDHv/kV9qYlp287x3Q64KO//TXufu0bOXrvnZx/eYMjZ85x5HDM9WvbBNvgtWdhdZU7ls/x7FPP4Kz0CasLy9x75jauu5QH3/R6FmzDv/21fwe+QsWGXpaQ2MiokMCDKnCjD1AqxQiKj16W8i1v/BYoCw7PX6BuGpKsQzqv6BCoxoa6cnTzmoOiwquc06fv4rUPPMjnfuc/cG1wBXe4x7BqOBx7/uu/+j/y2td/nZcvblBPa17zuodZ7uf81M+eY+vyNt/+/d/DtRde4N/883/MBx95hMoZUmuJseDht7yJw82rJDajmpTsXz/g+fNPkM73ia4C5xkVY9775vfSdQ15DKi6QmF49QMPMZckXL66wcFwQuUafuCH/wIf+vVfISY9dnae5vrVa/yFH/0x3v627+T3P/0FlnKHj57SRYppibYR75v2TrBkWZeFhYyyCbznPd+NXu5jOws8+dsf5sLFa9jUUgwm/Mtf/VfML/ck6MSD9qBcZHm+w4/+yI/zgQ/8GJ/63GcgNYTG412knpbkczmdfJ66LNHa0J3vs7S+yF/5b36MS7v7RDQbV69z6aV9ro32+M3f/BAf/if/GH3f/fzyv/in1LXj537+p7iwcYGoNI8++mVWzxzjoTe8gcsXX6QKBVXjqBtPats1JwN1XfH97/tevvs73ss//Plf4J6HbufOo6eww5Jj88t8x3u/jxefeIovfOkzYDQf/9h/4Cc/8PPkaQdC4Hve+238gH03//pf/irv+3Pvw4UEry19m1A1js3BQBIajbh3H/3qV5mfMzx4/0MYY9pz0bSDY3XjVo7tM1u1A3sUXLu+zbQqueX4cSZlwUKvy5ve+jpuve0BfB3xuiazGUQo6wprLePRhPFoyKlTp9q/9JVqs/5Dp8QfOjPafuPmB7rxG+2z1HP7rac4trrMagZ3HTP4IvL0lQG3HV/gvle/imiXaPyYa9fPUxWb+PKQzsIxTD7HXG8NrQ1lMSVf7JPYHNAMJzV7m09x5tyryKwMMQiObrePNhl7+5s09YDVlVvY3dtmWgy584676C2clT7TiVYxKWqmw01cPaKTZywsLIMvOH/xItPr18nvANtd4/rGNdxkiHcLRO9a13AgNn/KBaUYBUoqh7RM2mhB2uK0MK1zRt10EhHbgiCIhRxoQsS2EchKK1SbzDb7f8XmrdogG2nuvJdpl/YRa2U1LDUWFSTNTQSqdu2sZTrJNEessjrKCotCYkuFoK2wSthGMbRWuCBQZOUlYcf5VjhopDBYml+j300oygIdDMWoRoRAsdM3dUU5lQmyD5I6kmV90n6XPDFSbEX5s8I9AK8jZV0Iv6mSwsS5mmkxZjqdMh4OcXVBqAPaG7JuTt7tEJynnNa4xtFoh7GGxOYykYzgnCO4WXy5IU8yjq6ts72z2zbNuhUwaoxNIYq7Qykn62lekk7kPpUbN7YEcNU6rJSSS0HUZLFaKD0zHErzG9v4ZtVO9GKMKG8wNsEay6Lt0MtarogPjK1jXJcM6rEkXbWpHVGJ40Mc/jIhl7BrZB8XUDdA6CIUqvazR0AFaXCEayWNs2qdWFoZlJHpnlVaGosgQpZvV15av5Mo6CisTrAqQbsIGrRLCMqQWI1NpdiLCNg9ba2cqLZx1+Li00G3a5HCvEjaVbRIINMJjVLUw6lMBB1EVZOUBX3tWYpgfU2jc3TZsDU6hLxizCFOd1lZOcHwQEBteXeOxpXSyBt14/BX7dQztvfrjfNaCU8sqlkJJgmOykrstwIyYwhR4sknRcnh8ABrLEZLizQj+ng8AY9uVwaUApMYmaB6uV5CEHC61rRR35pEWVbyHrvDAVvFiBdqEaUTBYPhmOUlhzIG5xuK6OllXTKliDqQdXOcbzAhJbWG0WSEqys2d4ZsHxQkWc7qyjJXt/aoGnnPGUwwsQZjRVhsirJ1XRrhX/nQAloD1lhq525wJIiR8bTg6vVter0u3byDDELibLOAgNxb0rtZEXqCbie5UW4mFTAoiqLiwoWrXLyyRVlNUQGuOEk6FGFI1ojqssGFyFMvXuKe229hfa1HrzeP9h4dXSsYebxWLSsk4NDUjQQZuNC681AQ5ZxdXewTS8/W1iaHBwcMKsXKmdvQQVPXsmoVgzSxMlBQ4OQnPZ4ccGa1x6vOnmA0KjmcVKKL+oaTR4+g04yXN65gjYgTKgS01vTnF1hZmZeV5m6Hg6CotfD1mrriYHeLwWhEdAGrhOmx3OuymhhUucexhS6DUc2wrNC6wXkPQdyxdVNSVg1KZ8TgMLFd7/MRa9rUyGAhyn48QdxPMcrvJwYWexnleEhd1JI6Z+HS+SssL3boz2usVRSTkq1ru+S9BOcjeSdhbX2eI0eOceX6NbwKKCX3steS3uWcYzwpsEbjnCeLQ4q6JiehCRWj6YQXX9rg5UtXKcqaIydO8I63vpEjK/OoYLEB8DVN5XFFpAwTMJbh/oQXLlzh0pU9DiZTpiHiorDetFKYSylzecrS6gJHVjrS9gZNEyMuRFIdCMh1r9DoMHMRy5naqIRxbAhVQ3C2nZz7NqFSE1rGmAqxFZShmBaUBJ4/f4GD4VDW4YLBR0mviyESG9c6bjURTQiuHUwgEEyrxP2DYlIUlFVDVXu2d3Y5cuJ42z86MhPRwWMJhLpmdHiI845YV+RZyngylTX3KCsiPnpUkKFFMxqyt78H5YiTy30W8i65hhAbFlePyTUSS0bXLlNcfInxtKG2Fru2jp2bZzo6pHaOqBKCSWTFCeHquIiA8qNMWmNscK4Wx0jt2d48YGNzq4V9zrF68gzOBaw1cka0KVLB1UynU5q6QkWPITA6GPLkc5fZHE2pm4a9vQN803DrqVUuv/hNkjwlLY1MQIGzd91OnqfE6NFovG6Ten3772iZFg1VI8y41FiaWoSUGD0xSFKgjiIG+RCpqsi0aFrnjG2HBSmzcfXB/gG7u5tYlWJSccDNnGdWq1YIvJkEuL+7RzEZ44NjeLAl0HAbSILCBKhb2HYCrC4uipsIx4yjUdUNk8khtWswKiVVELVBBRnwEVouT5LQmcsZ7En9FWKUEI+gJQH2BmdSYW3SVh6KqiiZTicoJF3RmptDPNppu/z0BY9Q1yXESBMCHQ3WCucJVYkDG9sGiHiqqpD3VG3aLupmozTjBmmom5phVRPReA0mNoC4AvWN74S8govUZU2aWlT0dBeWWDRHWV89Tp1GVq5cYXnlBNMwZDgckKYJzonw1ul20ERCUzMZH1A1BT4EqmJMXZVE78jyHqm18rxPunT7PXF/qRnnc+Yk+OMcS+qP+eX2TxuDMVJV1I1n+fhR4qVLuKA4ceY4v/nIB7n//vvITHutacEgxOBQ0QrGQc1QEAlGLbe1lvQXzhuWT50hMZrdrU0whv78Ovfcdx8Lq8tkSSrnk5KVzvXjx3jzu76LJ77ydXoLq9xy+gzv/a4fAjx7WzvccvIax8/dxac2d1l8xyIvPPs0l/f3uePWM1TTSmzhTQK153BvyKNf+goL/QU2LmzwhjfcidUNAUvSBtEUtaObp1RNQ+0tkXZAR6CX58yllrWVFT78b/8pgzpwfP04cybh+Jk1nn7sS0xJqUNClnjSVAZmPoir7OTyGr2uodxpOHH6NB11jTxAPfRQWT7wt3+OS098ndN3v4qrLzzN3Q+8kbtfFZlujpgeTLl+6TI27RBMSuNqokn5yEd+h6YZk6c5v/iLv0jSiSzN59TeSfx5tBwe7vHBf//rpDplRItOiZpTR0/y1z7wt7j6/PNcvnyFq5tXeemJp3n4ne9jeW2NT/3rX+G2Vz/Ehz747zn/jWe5uDWgs5ZReygrDz7Snc/FLds4kqRLcVjwvu99P4Nr+3zzy1/m7Kl7+Jv/24/wqUt7/LPdT9Cd77DTlDTBMCkKGW4qMKmlqaaEPOV//tt/ne3dHaLep3ESxDAeydeY91PO3Xk/OxvbBO143bd/G700YevKZb7rXe/juYsb/Mt/8AtsXL1GNR6Q9j/PdFzyl//iD5EvLtAEz95owNl77+WZbzzHyXPnOHfuTh6+53UUOyM+9Tv/nsuDvRvcYWJgMprwzu98Gz/w/h/mG1/7Bp1eh42L1/nrf+1vcGT1CLe/+gFc0uGpZ7+J7XV58I1v5vXf8kbSNENZI47J2MKb0y6JTciDxWpNXTuasqTa3+WZjZe59c7b2N884Jf+2T/lJ/+Xn2jNIu12h6j3N+7YGCTNWCPmiOm0ZgJ87aUL3HXyGNponrl4iaNHj9E7egtVkfD8/gWaqubVd98DQCfvEIl0OjlJInLGf2Re/P/y+kOfUVG5wO7+FvfftkYnWaRvM8b7hyRLHd748F3c9YbvQpkj2M425WiIUgWm06F2BbaOKLVOVQauX7vEwvztKCU9XuM8z3/zSXpMMP3j2N46vblllNb4ICDzzCwRQ5SB3vwy1nTI0pwk6Yr70R1ybXubgyvPUUy2WV9ewNR34ZqE1ECsp2xcfIosX2a479FKOmHnG0mzDRrv/5SvvBHbOHdt2gbUtQ84TQyeWaS8ah96un2UaWWkYVOy+iTMGilNkxbMp9qpDe36wQ0Ydfv7IkC105cQSdDQTvlkBW6G+Kb9c/IwjbST6tjG3hvRqwOyghaCb9eRZioE4r7xQoZP04zh6JBJMSVLE1SIEgVbOJT1GCNijYpauAVofBDXkfftup5vCD6gg6i5sWUiGZMQWmBikskDlzkRN6zRBB0YHOyzceEig52GuixROpAlsNRfpJPnFLWnLmqaxhOcF+isjkSd4l2Dd47UKPodmRLgA66WpJgkSVno9XFVpDOXQ2ZIcknyAXBVI7C5OBv0tN+fVuCTBD9JIJEbPbQNmLjFQojEljVkWli7Ne3PNyqIhqxRRNfQNI4aQ0DRTzv0e/OEkWZYTUXkQxpzFcWqqFCS3qIiKsrEWhJEpOmJXtw9MdAKHNLUx5YFIrGWgWYmhOJbbhQE37q9aM3/StbmdIxYo1EYEqXIkm4rKimyPCP6SKIkSjNNErRyWJuilSFFWATSEIgQMC0qytq3MdCSwJKYVGCudUE37+C9Yj9MZZ9YRRZxnJgLJEbKy6ZyKO3op4Yq77BdNkQLy6t9lhf6FMMKtEHrjMQI8JWZ4NdyJmSa0EJp4wzICkq5lnmFJMYEif02xmC0pS4LmqrmyPIiR5b7ZImh153jzPETHIwce4cTfBBumXz+1m5qNCaxRB/FRRdCK2QKtDnFYNH0O3OcO3KCcVlQVGMSE1sdWFE1gd2DQ/pLS9QYygCdvqyNxRjIshzfeEbDMUtdS1FUONewN9qhrB3rR0+xsrbOy1e2aOqAThKMEYeTjhrvPFVVAbFNdhD4u5xTgWnl5L7XCh9mKyISfX1ta5fVtXWS1ZTExPYcbM8i+WaitcLFNgFRO2Js0FG3PC5AGTY2tnjhpQ1ZOUWWYnTLsFLR43VsN309rlFcur7HYDjh1Xee5uwtR0i0FgedNbIe5oQ1ZtprfKXTg7CDjxGtIiEaotLkKGJdc/XyJUa7A/ZGU8Z1pAiRfHEFF+RadE3TuhDlTonBY5UmDbDWS6CcsNZNuZYa4baoSGgqjh1Z5+Kly/J1+CisrhB48MG7SNKc8STw/IVtNvdHgMLqQL9rmAxGNE4gz74Vg7PEkqnAbbfcxmiwzWB/JCJG9OICIeKaSFHVHOwecOqW07JGpHzLMEnJ8y55N8FVkCWapmmovZeCyYJvVMv6i8INxkNKpAAAIABJREFUCRG8xGlLpLYwAX1UNF4ERtcCr2er3y40eKUhSQjOoY3cU1ub2zjfsDS/QKc3T4iBTt4ldw02KsrSsLm5zebmLpOqxivFdFpSB0nX1FGcbrVvmF9doDO/ROMbooYXv/4iT714lcIFvPbUyrWiusbjaFygGlc0wdPrHGFtdZ5YlkTlMUSMCbK65GdDIylAJX00QKKxnS7aaMpKUhnT1AoU22iiV63xJmAQ9t/+YB8VI5vb2+1qs6H2jTBrRLmSy78VoOT4Fa7UTFhSrSs6BkdsGorxhO3rW2xe36G/sEaWzmG8wimDrys0mklRMBkXjIZTunNG+I9G470AVIvxGK9l2q/rmnq4i3WOs0fX6GYJOoAJinFw6JgQ8kBTlOxevESxf8C0iQLrbDR3veledlxJJ88Bg7ZGRMuoUJgb0OUwq6uUQmcJabfDZDpl52BIWTdEVfPSixu88e2W2lckVtbhlIYYPQZFnmW4SqDpThmG0xKHJ9VK4LTFHk9+9Q+4+vw8+/vXSWykbwWAvdLpcPvJE+jmgJnYc1M01CIyqUCWJ8yzyHC3FsC1Vi20OrSuU3WjuSWCVYpulkNVMMv6QYEyls3Na3ziQx9hZ38X6zULiyvc/9C95FlKt7/KaO+aPCO0uJg3r17h85/8LNPDfSZlw9LaMjNCR29xmf5cRkMkDYqVuQzTut7RSoJGlHCtUqPRUbVDoVn5IcICqgIV8KFqm0RLYlMaNQUalOogDu9Evta2gZK5qcKkGX7sW9dt0nLGkLXIdoimJC4Ym4hLyLXPX9WWuOIWNkgSY1u7BlkdtVpQBApp4AT4Hlr+kThqo/Lt3ysuVPCt87B1l9MyxVoEQGIMvqlomkCiLcEHjO1y7Phpur0VltdPkSY5TSOg7UQFgi8YDwbUxYS6qWmaEudqst4CvaxDqnPqckzwE5qoSWOXpfXTdNKUGWA03tjlm4XKvMKh9MpO8Qak++ZvxPbXYzvUW1pe5sj6UQaDffZ3d9n45gVC5aAD0TlpupXULeggtWP7HlIBzRouWa9pPAyHE+66/XaOHjnGZDjEktJfWCTPBMSf2RxfVdSuoZxUHD9+lupV8MVHPs3zTz/L2tIKk3KCynOe/NKXiVnGfKfHm77zzZx/9hlSo/jGY48xnE5JbIdQCrOPVLF/OOBwOGB75zKF38FmfZbSQFHvUo4iPhh0UJhgZM3VaOEXBs+dd9zDa+67h6OnTvHlj3+YJsl523d8J1tPPssb3/N2fvrRLzOeyzFVSZIq0qRDN8nQnTlspjHzKToxLM4vcu+dd7A3N8dz58/zMz/z09x/z928ty5ZPHGGTq/H0TvvZefli8zN93nyM5/l0W9+nYuDQ2zeo/FOHHVRkycapRJx+GaGOhimdaT2gabWJNYQcWwOD2lqh48abRRZpnn5/Dep9g+45cytrBw5wt2Ht/F3Pv95fvln/wFb21f58D/5R7zjO97Nh/7hL/DE+fPoXkJZtaEIMdKdsySpJqdLYyqyLGU8POTKhQscDCY88fQTvHjxOt978S8yd+QU7/uhH+TRL3ySi1eHqI6F9lkTEHG70Z5xUXLh0kWCdqgG0nROEAbeo5VhudvjB/7sn6MZNlw+2OG1b3wjd62f4Bd//u/ziY9+nOPHbqHYOaAJDeQJXmk8kf7yOnP5HINml7e88x2cWL6DJx57hqTTY231JKuLyzzwmlfz9d//Xa4Odjl+6zk2L17DdC3Wdti9vMdP/cxPc3HjJTCGRgf23QTtJ8SNZ7n17F2cve8OTt93L+9527u455ZbsFpchs41JClsXL7KXXfcysc/8gkijiRLcS6yu7vL5YsbnH/xPL21BXYGuzz4+oc4feJcK8bevG9juxmkaGt9pYhRs7m1z/WtbZKVJV5z/310TELhPcfmF9m89BLZtOHXPv5Rer0uUSvuvf0OlNHYKGdbJ8+lb/qPDon/59cfTYK8eefLv/MsY/3ISVaz+yB6RsMpMTcsJiucvu+NJN3bJBW+hmk1od9REmqQpJi0R1QwKcY0zUR6QWuIRrO0uEodhFk41+nSWVglSxcELeMLev05rF5iMplSFEOmkxGD3avk3T7aHiexKUtLKxw9dTtNU5AMDSpRjA4P2DmYsr35MsdXO+h0niY6Epu2gwMZrMxCsRx/3Nf/n//6ExeUZuhWH28+nmiTUITp6dvIcWgrwXaFTN10UbfrT7F174hbToOV/ew2EIgYVLuepMSyrGmTGMSlM2MwxTZFhJnjunXe3FhxUzMbjRLAsfcyMQteQM6RVmxoI2Fl652gZf0ECyo1BKOIiaYqxygLWQaJUbKaYhTOO/kzbUNrrGpTtMT9FI3Ca4vzlTSNShOCJtZRgM/y5BeZxIubQCeGNM1YXl2VdbUswVova0LRQmsvNb2cvkmpyhIdFJV3eKXRNiElkhGxOjIsK/YPBvgQsIlEDi4tLeOdYnG5j8pkquxdu37oI0q1hfCsz20nTHUjSToxBojSRNVlIasticK7gHcB17RFDYD3zGWpOM0wMsEMHuck1QJrKLzD+kiuU47Mr6PGA6a+lIYCZF1Mi7Bg27Qw4XgJKBV0u6YnCxJWG0zLXJLtvJZ/pNojKoZ2Sh7RYUYZkMLYRZkmCx9BxLKQBJDLlRgnEBWJUUyckPstYIwmjC3G1GgSlJZYZttyPFR0NMExrRxlLY3vzCBiVCLXdggctCKLiRYC5Fpzpu/pBsWoDEydoomQJp5RIYBYayyFh7ouiL5hLstookQ1ay1JLLT3Hkp+TgJkVzf+mQloAjWVe9YoJZukSkDdSskaglaBxGr63RxFZFJOCQZsYul3O/iyIrh2kqHlXJCiWKLbq2KKUZ48TbFWlrK6acLqwiJLCyssLPZ58/HXsr3zMofDmo3NTYH0LSyQ9ebAWhKb4V0kzTJZVdCSYFdUFcZG8nye8eiAaVlxOC1wUVM738bu3lzVUErjvABrQQRUpaVBkVQnabJABDD/CvvtTf4bTMua7Z0hi/05TLeF8Hq5v4MXB9xs0SVGaGJkWjU4B42TWfJoNOHa5n4LSA2vWBNol9JUJESNUlJYqtYBcTguuLixyUK3y/LiPGkaxPETFd6DMrJ6N51O8VXAaEsTPZkSwV+afMXB4ACz2GGlP8e0DKQpHBwesj04kBhtDzbRrTtT3FJKGbp5yrnFBY6YQKYUi3nKnUeX2BsOuTys2N7a5r7XnJA0MCVfm7EG13jWlxZJLYzKhu2dXbz3JErT72qyOKGcVrSQNaoGjizNsdrPOX5kjYW5Pm4yIMFjopIYYqUpg7xHVXsmo5EIcrFNwGzXb0PUBKfwseHyxlUuXdxgWJSkSjHf73L0xCr5sTUarRgVFXUjTqzEWDCO3Z1d5vvzNLUnIuETup2E52lCluTsVo69JtBEy8SpNmUr8MRzL5Cdzzl58jhni5KjR5fpZB2sluff4eGIx7/+DPujMZUPpEaz1p+jn2XECLV3IlD7gNWWQEOWJly7usUTT16gTjwOebbK+g+Il7MV19FMJmMubVi6nRxFQ2JkhWL2TJ9OpwTlMSSts1V4a5euXePo8jLra0uo6DCIS0UGJ/Js91HWfa22TP2Y4XhMOZpwOBqhVAvdDqGFFLeC62wQpIRnJ25nOeuldpAVvcQa8tSQJrJWnyQJvonoJMNaT9bpE2NkbnGeeFUxnkxpfESnOa5Vc6y1eO8Y7O8SNIRo6ARI64JcezrKyD3mBN4+mER6GxdZvOMWgquYjoYUtfDEvFdMBgd0sg5apyS2FWVQqKhFVAsBfMvmsqp91kS00nSyDrshgGroJpqVXpe1lRXSNKVqpoToIcha5Yyf01QFm5tb1IWIN957VnLLvNbsj4aUowHDg5rNKwGTKFbnF3jgnrv4ytPPcvvJY8xnCYOxY67XbZv1cEMAmg3l0sTiGyvpchEya1qBRJLsxHnSCgWIodja1okX5brEOTpZyoc/+FGeffE8Wa6JDQwGe3QXc46sLLDkapaPniROhhCEOfHCU09CecjZIx2+/swO5cI8RkWWF1ZJjayazyc5PgbWjq0RjMZ5J9dv5IZTXSsrzk4VsTrIHosxLQ9JcAABj3eyNmNb9pRYSUNbwMh5H4MMU63sU9LpdEhTS2hkOKiVOG0lwtmL26plftjEYhMLM5dgYrCpRWlJkTUm0gK5CMGR2pTGlyI6zeovZdontb0xiNJazruIx+rWcY1qf04yOJLvg7gqkjRjsH2d+bwrR2oLt15cOgN2i9B4DkfXqauK0XBKU1XUfkLWyVtOlMckCanO8HXDsK6ZHg4ZbF8mTbp05jK6+QKkPYrJiMQmoGQ9J6pIlndIbU6edTBWUAtowx92Lv2RhugVGlOIimKimJ9fYXAwYLizRz9fEId/+1xVCGvKmIRXMpSCazjY38YbWFxaQ2tL4z3XNgd89vOPMt/r843HH+f6xgZbW7u8+U1v4+677iQ6xzPPPM7mtSscufUsZ289R3SRpSPHOHffA4wOhnzjS4+yPdhlZ/sydnGZenPKj33gf+JIr8v3vP+Hee5rX+by5Q2ubF9lZziSfmYWXCA/WrxRbO8d4js5PVejnEFloCsnwTMqAROp282L+d48Dz3wEPeePcEzTz3Ole0D3vwd72RltceXXnyaK79+QO9Ij+7cEpPJkP2DIZMKjMrQMXA4GkCny7e+5d2sPPs0ly+8xPmN6wyGBb2lee65/34WegvkISVG6HbnWFhbpzg85NL5F3nqhWcptQy2CIHoZGAx9TXBB1LboS6mZEsrVFUDiSGf6xCqAptk+IAEJfgKm6ScOHOKycGU3/3dz7KxcYlrO5t0kxQXLP/H3/vfmT+2iFpcZOuFF/jmc0/RW1ulqRtOnb6V8889Jdey9mSZlSTIMEdV11ir+MLvfY5zdz1M1F1uf+BBvvjpz3FubYW3nX4V+uGSFy6cZ3FhkcOtq1Sl9CZBCdNXRUUdFD5ArqByYyyZcFt9g1GGtazPPe98NaNgGNYVWS/nDW99Cx955PP8zqe/yMQEJuNDOks9tMk4cmSed3/3D/Klf/dB9rIOF89f4cvXnkYbw8r6ERa6K3zxo4/wj/7Nr3Dx+kvUseaWpMNwfMB3f/+f4zXnHuSjj3yS888+y3Q6Ju3N8W3f911svnCFnWsb3Hr2HL1sDrNQ8L63vQOVZDSuQScJ1WTCM889w/bmy3ziM49x/dp1dne2STNH0QSaJlCVBaplK2bXNadvO8efecvb6Hd7SLcz8z623OSZFb9ddSvqGpWl9FeXOHfiKOevbPHCYJcH7rqTT37yC3zxq1/g+s4GP/rj/x17gyE/+L3fR9k02GAwmblxEsw6z///L2EtRSKdBE6fOcfBXkC5kvnVMUtr6+RpxOQrNK6gKUqmh3t0Ukunk4q/JdQkaYZCk9iELO3QVGPquRrjLXE65PjqAt3eKp2sQyfrooxwcIw25NkcEYVNHQsLqyz2+6RpSl3WeH+d/vwaxnQ4sX6UTpqyc20ZP94i0zBPoPELLK7Ms7J6C3l3DbXZYNMvCqNyFq40W3/+E3z9iQtKwhYW4UWK17YgCr41Ds0mVqF1PrRAX6QRFUYKLWjSS4qFmgG0NNaKs2VWyIR2/UNe8caKREDh28+i3I3ZP0q1gK52mj+bmLXGCNmZj7JuNEu2UK0gQSscxNaNgI54I2wh8khMI431DKsRDYauFkixTgWqW/uaaIywnohYJZZUbaw4u6Ii6pspZ1oJ4d67un2YyirYzIEhCVARa3MWV47QX1hjaXEeaz3FYUE5GVJUjqpxFEWNNrWQ9k2CtomsUDmPNYrUKIIrSDMBXnbmNPpAPqu1KVkmEdGhanDR4x14H9HGYnRyUxtW4taoypq6aciStLVfe7LUYpSlLAtcHSWRqJ22edc6mFrhL7GWbpoRPfjGMYkeHzvU0eFCytQrtIMkz1lZWCGMD1ugahSmlpKHqdVCQDJIY6IVNA68r28ka6lXRNzKAsWNTonZoadVbN1xAsZVNmJRuNCuRAaEJxJlNQcdbwRohyAOp0AgKEeNkhUU5fFB1oKUDq2T4GYsvNORkBoSI6JfjLFNXm4FihDQ2hGDxWoRSvpW0+80TIvAVGlcm27SKEXZ1ChlMDajl3bI8pyinFL5htCuN3XmktnRL3b+6AhxNiWUqeqMNRC88E7E+TdzDCKCaQBoCMFRlFOuTyfMd1Lm8oRxUbK1s4dzDUdXFrGThu3BEKUtdXBomwDSaHT7PXrdhF6mUd5RllOsVuSJnAXRwLX9XY4dO8GRU2dZD1AZGI8qevM9vAcXAomWNKrEanwTBDCvZJWotzjPXDfn0Fq0gbKq0WmHykdGuzsCXla6dQ7Glq81axzaSXG79uRdILQ8OBVEgKIthn0rCuoI+EhVTmlcg4/CNCPK6m+MEg2vWjZZwIMzXN3f5/r1Q9IsI80SDsZjxt5A3hdGnBKhWgdp8pugxE6sVOu2iwQnKUD7w4oXLw84UinWq8D8Ypc0NZigCY3HJikb23s8c2WXJiiiTtAaXIwyFTIJm3tDLnUSbl1ZI/GRJjr29gbUMZJ1EwgKk1i08ZLIF2WF0VrLXH+OUIwYTSqcC/STjN0IVfCUkyHWQK/bYVJU4iwEnAtMioLlpVVMtMylGXsBHA5jLdZAGUX4rxpPiJqFXpeV+XmSLCfRsLy8yurBhGGxh81SlsjYmpRyD7TOuLyT0xQTIprgGzSBNEvwZc20KfnSH3yVrcNDiOKqzBNNOR7Rn5vDBU1ROGpXE5XGeVAqUBY100lF0zQCMm5EtNNGiu9qeZWDpWX2NzapJhWuka/D6EDlA1U94fmXL7E3HHJ0fZkHX3M/TVUy2Dnk2tUtdiYlQbVMQjRVVTE+GNDJtQQhRHGCSrS8QVvL8y9cpI4VPighvquAjrOZvJzoCtMC4TXD0ZiDYU0/EbE/KI0L4kryoc1LDUFcbABRMS49V3cHLCx2Cchzz3k5L2KExlUips84cCjKsqIoS5wPrdtTzTQVuOFyaW0bUYY/IYo/TytNVOIqSiKkxtDJLJ0sIUsT5no9kiyVlVqbkXe61HVNtz+Pc9LEOufpdOTzaol+ZX9nn5cvvMy4mBAinDxylLmmppNo8MI5kpcl6oqmLLA6JaYZJk1RU0NiDIVriCGydeklQlPLe8qmjazdS5VExONjlIS2VoiOPrC9vc/lKzsQ4cyxZeZJOHf37VRNhU0ywKK0kzMjgnNT9q+eZ3p4SGZgsjdmvr/IiV5ON83Z7hmeuzCkUQqVKObzhDtPrHLPA/dzdXsXlSSUVU01mdJbmH/FRFcGLlq3KOcoAkiMgSY6Om3FNnOiCydDXEXy1YWWvSTthpENPQiRa1evoFJNOXWoJCG3MJqMOXtqkcMrL3D73fcRFxfYuXQBnCcLgVMr8/RyzcnlDpUOVC4QtGeyN+Dy3pTunIglp/vLLK2t3EQnAER5UnuEV6iUQRtDajMRnIgQLFap1pXYroRGD8FjzAzUDTO8g7A627pAh/Z6Vy0CSRzTWsUbQ4qZG4foUWHGu4riHDaJCB5RSeS2foXja9asKZixw1C0Q9Qo63GSekPwIrJa5dubyUB45b0Vbw5dAYIjz+dJ06z9WctnsmnOeOsqB8UI1zR89dEneeqFK+R5hzO3HuOee8/SScUZWdcFzgfGozFee3wRUaqha2cbAgnN5ouk3UXmOl2pF6ZjkrxDf3GFkM6hVSRVXRFd9WzDILb3xSuvx5sNpbg/A6++53YW5zK29y6ydm6Zzvwym7v7HF1fkxTCG69XOJyIOFczONhl2JQcjA4ZVxXe50xHjmnVMBiN+a1PPULPpDz11DMsLJ3i7jvvQCsYbW/zyG/9Jt/6ve/nW9/6VnJjiEpx7/0PsvvyZe67815Uqvmlv/O3eKGa0BQNb3robZy99RhN7dh98HV87uOf4Fd+418QjMZqSamOOuK1bFz4CFc2B+CE46aMoWMCWE/tE7TNxPFeTpmzCUcWeky3r/G5Z7/CUy89zUGV8ZnPfJ7p4ZBR43j5q1/j7ntOsDM4pJNnVBNHaTXFaIzRhpNnUm69/R5e/7o34f7xL/LYcxeI2oIK3HLyBO/53vczmhQML21z7PajfOGDH+HOd7yVI+fO8q6/8mNc+cWC64/9voikMWCVOKB966zTSUYcjvnzP/yXmFx8kedfushr3/F2PvxLP0+c79PtLLG8uM7VjceJxvLAq9/Ciy9c4MMf/Rgbly/SKBF/jy4c40Mf/U3+5k/+HVTQfPoTn8TbhKosOHv6Dt717u/jEd/g04TD0SFVNZHNh6JhMm3AGYyxTA4G1A5efvkCG08+w2LH8vDrv5WXxpsAzPdXyGLCxqUX8cHhnSGalqEWhMnklBeIvgq4IHiUreGYf/3bv8XDVy7wlu94DycWl0iAoytH+fZ3vZtLv/x/EdNAYiH6hroZc9vJde44fQefHRziFwwvXLzMytIqITZ0Oxlud59Pf+Ij7B9ewiaOpgx886mvUeuK1XyBB++8m1c//Hp+6if+Bi9PCr77B76P9/zAj/DF3/syy82Yb3vLm3npa0/xoY/9Kl959BHe8+73k9mEjeef47O/+3k+/6U/YHS4y8sbAxID2ioWrcJRUxaB4CIhetJUUACD3QG//mu/xoXXvcTdt57hNa97GJQENoGcwyG0XDwvaXu9Xp9enrG1u0dVV3ztyaeZ0wmff+yr7O5f463f+Xa+603fxqPf+AqmrBmMDllbWb1Rh9+8g//j1w2g///rV+CV1ohet0snu5eqnjDcvkisA66ZMB4PWEjXabzwC2OIlGVNNS5xboghI5QV9f4+c9bLOe8DxXTA5qVn0E1DMRxizTW6/RWSpE/UCY0f45oalCVNU9bXjwMK52rqcsRwbxMVavoLJ1Cmw0K3x0YZKHaus9TrkriSk2tr2KxLcArqQDEdQe1okGFKdJ4QLNH9KWcoYYQrogPcBC4Ds5SyqNuipAU4BxFrgkADUBaJ6G2LFYKkY1mtiaGR92hdQ1qr1hEQCWiib5hR4kMLETVtrRxaq530w7MCVAQZRURbcd6ECE7NVuFAxyDOk9a7p6IiRocL7v9m7T2DNL3O88zrhDd8udN098xgInIgCQIgGEAwilEriZZcXNESTUkWtaVVWGvX+8O7Ja9UtldbXtkl25K4Squ4sixKJC0RJEVSzARBBIIggMHMYPJMz3QOX3rDSfvjvD3Wn63yuvihUIMqzEx//fV7znnO89z3dd/kyIRmOqW0IyhBVU+ABCkzvI5FvAvgZZyGWOtBSFSaRkWIDfGSG78gwYXozYX4A3fNBdb5JmRFIVUzvW9qA1PGSOhibEmTEBVKZORJhlAOU00oy4o0y8jzDrqlkWk7ypgFdFqKRFrMTIE3htFkGj8fB0IkJDolWCLwERGhwM41SR6mWbDxZ+ZkiN+fTLAuKjeMicou6yymioDphIAUcfoCTUPGBoSUJErebE5pCRNno8LLQbCCwhsSUZBmvQbwFz+/WK9F5dj+tFY0KSyCqIRzPrIDamOJrQvROBlDI1aLE/PIX48MEwnR+hYswUcLTuwBxAaY32dAOQG2gcKKWIyGICldAB/wMkRLlIomOhnv+gTXfG3n2U8fcKLhYYhoiNqfHjoBQcbLkgsJiUiQXqMSSzdNqBGM8UzQoGMyXO1cY0kDmaegI7C1svH5N65ZWyHGpRdlgRcVUkeekQhN0c9/PhT227xB+iadpUnBEsRURxHVd8556rJic2eIOjDAEaiMo9vJ0YlGeIMSMZ2nmg5BRatEXRUcPDDDA/fdzaCVsHpthSvXVhDBo7VkfWODqnSQaM6cPc+BpR6tdk6etWOTTaWRKyQteVtDiClKQXi8i7YkIQWTyZhzwzV2d4cI7ahsTTm1IDYoXQThi0ZpGKOT95vQsoG2Rv6Ldw3jTaqbv0eGgJNR7SUcUXXS7EFaxGmzIELGHSImWDUNY/BoEZMDvY98lLXVDYraIJMUJxVGapJ21vxMQtwviV83CY7EWYJ3WO8Bg6lLpHFUtWNzOMIIwfbOmHYno99vs9TrMuhqdNrmwo1d1qu6WUOhaYR7cq3JU8XuaMqV7YJ+VjCta9ZHI6alAOVJm+mvUpFD0koTjK1BxL3UK8loUlFORuxOCjZHBTeGBdPa0NKK0d6QuUGf0WSzUe1Fi+n65h7HDi0hQnXTZhhEIE1Seu0MUwXGexMKU+GFYG17xNzcPNvDki25RZa1mOsN2NwZkqQJrgJrAntFEblrTXMkNAlFhHhOlUVJ4mtuXLnMcHcPnaSRdQfUzrO7PWTt4lUmxjIpI3cKv59sFps1hW8u0jbaV2NSpCLt9xkfP85WUVNvbhCsQYn9yNv9gshT2Zrt7R3Gkwk7eyNMZdkbTqiNoXZEBaYQGCTXhxN2q5IDiJimpqMF0bnYVH/59AVOnb2C0wHv4rOmmoEJoVGihXjB9SIAmuA8O+OS7qykDo7Mx7QpHwJZlsYGnIRg9/ujUfFYONEwuCJ3LtYFtmEqRm6MlApnHdY6RsMhtqpIpWz2PBmbtc1e63y0wYrg9hE0+637OEDCxUmckARnaevY9CuKKePxiGUp6LRzyvEOQghavZxiryZJWiilaHc6dLutCOjOE7Y2dnj6qWc4deolgvDMDAakJrCztsqty/McaUlMUeERDKc1tbWUUhCsQ+kcKzRVHbDKUlrLxl6FvXCeo4dm4/muNfuH136DwIf/bGEKzf586fIKz337FJvjMQJLJ1F05vvMzPcJ1iBVyn4qrg8CGSzF9lVkscdM5lHS47xmedAlOM90WHJsaZkLl64yDo6ZBF51dBHtLdV4hJSal1fWOXjxErN5g85uJpoyiCZEZV8S0oRY+NAgChr7r6Cxbcmb4RxxTTWTwiZRyQUik8NZQj1F+Ti4U8GR5inLg1ne8b0/xF/dLK/YAAAgAElEQVT/7q/HGO/Dh1m/fJ4kzTg4O0PhJrSTwNH5AZfKwNBa6nKXsp4yqixOG1yA8+dXePnceU4cOxKbhU2TUojmWSMO8YRKI9C4adII8Z/PPJpQmJiUqWMinNjX7v4d1h3x+/RB4jBYa0h1ZOq1RIZOFCqJSnopJUrJyGXyzWVGxgGjas5/KWMqnRQa38DRZaMA279OieZcAhHVYcEjg4z1sY+qSe/j2Gxf6Sdl0vz55ucrEqIKLVBMh1w7/S0SKTl0693xPZY149UbjGzBpbNX+JM/fwyftAg2cOrsOV44dZZbjx/m8GybIB2dXoc01fRmFxicWCZTYMuKJGvRHcxQ1ROcqygm0XI+Ho/oiXlMMcJXFlNUJEmbRKe0u110HpXOsThV4B0342WJ6rD1y9EGvjS7xLVLGl8GnFZ0eoOmmR9i6mjzp0KAMK2gnWBMxWhvl+2dIWdfPsvLZ17mytoNDh+5jXe+4920OpL1GzfY3dwitAdolVBOovrv0PFDvPXd72G6tkYmFJkNlLtjskGHPFccPHaE+eUDPP6ZLzAOUE5rRtMJX3/iSxw98cNcPXcZEywuiZbXTO5TOSW1rXENX1EGy2RqUSFgvSNxCdKNCEojdWwOJ1LRJufEkaPsXL/Cc099lW63xcLCAh2fUg4rnn/yGYajIS44rl27znAyJCQDWnkX4wK1sxhrkVrH4afOeOXb3s3Bhx/ha3/5MT715DdIbcri/AKhrhgcnOPFf/87/Prn/ob/+fhJDh5c4vDBI7zh2N18/euPM5EpPkxBEDmkQBApWaZJ2j0+9MMfYvq5r/CZk9f4wAc+wMaXv845hjz8yHt51W338sznP8bTLzzPePU6k90dVtfXMLYiJAl+WrN4cpbdvXm+9tkvMdoYU4wLSFIGMweYTEYIpXnVK1/Ls89/E1tVzPeX0QcOsnXpDMOdaySyRZalXLlygbTdYmdzhbZMGBvHY49/hjIYCJ7rV67Q7Q1YWjrE5sYqdRMSZZ3hoXsf4NTVC1Tj3TjscJ4s6SAzzff98Ac4cehWvvqJv2RaFbz/hz6A6PTJpeTKt5/mwYcfZvfsaT61chnlMopiyvXNit/4nY9w2VeISvHgg/fjSsUL33ya1bMX+fjqn3J+/SXmekPSKoYE7A0dnXaHT//1p7l+9hLHTt7O1atXaQ1SfuaDP4bqztJ/0+vpmYp2nnPs9jtIWrPcWF2jGA85c+EMv/+Hf8i3XnyRnWGJUi0SJeJwtYZpER0f3vjmhGr2yQDallw5fYrtG6t8IW3xz5aXOHb0BNESHPZnEUyrCqRkZW2TKzeuc3TQpzQ17VYX0jZ/+Nin6Z9Y5JZ75/ng+36ERCne9YZHKeqK2cGAPMua5r1vUkDFzeZR3CubPfH/VzPp777i6ZvoyGVTSnNtZEhCRWcwSyc/QCsbkKYWUyyzuX6NvbUNqAOqJUELjJFsrq7QblmoppQ7N1i5fJHJ5DqdXh8vAuurGzj5EovHOliXUFcTrB0jZUKadkjzLt4LSltgyilVUbJTrVCXFXl7Fh80bjxia3ODyU5ASsvM3IDUSHY3K4Rf4frqEFeNqEyLnZ0RCIVUbdx3R9J18/VdbyhZGZOHlGrULyg8rlHdRPaKb7xtIQ7jgH0bhYsXchkaFXEEawcCNS6qJBr7SYxEFc2UY79TJPfFXPGC3lxMo6DJN/+nYd7Eej9O7kNMr4pBXfsHcWxoeBnfovQ0hUVo4gqjmiZ4j20OX6cFqYwgaWTMWamFxzsfWSg6RViwlUUgmZppLLJ9vKQqrTGlpdNqoZOG7SAVaaIwPkQOS1O0OedANVBDokS3Ksuo9tGCpGF/ZGmKzjQ677KzvUuaarqdDkF5SDSiFadjQQpskJBEr3jW0wgdAYM6byPGJUneRupY3Fos1texYFRRwi1EY1tCoWRApaCUIkniwk9bCuFttO04E1NhnMFVJl56jcUrh8raeKUoTGymSR9Ynp/l2vYYLxx5AjrLMFXNtK4wPgLMg4jsCdn4cjOZRWWcjIyA/ZSYJG2Br9GJiMloxKLMy5jkR/OkSKFQIuCcbUT+/mbz0JoAWsWGaCPtR8TnXrFvOoryQiNCwwRzEcRO7HIK4dH7ceQi+pVVo4DZt8MAzTQ+RKuTANnwyQof6KQZvvZxJTuPTgITCxOfUDZ2TheigcUR8F7SabVpt/vs7A7RmWVclug0w1qHL0uofROVHrkmolFvRYZSTGERNy+fMQLb+ebdiri+vBeAwgWFCRE+vzUaE1QDrMw0QkuGk4qqqvCN7UU07Kv2YEDwFXk7ZW5uQL+Vc/3qdbyL8OvgJK12xqQqme0cZHNrnVZb0en1yZI2deVJk4Rps26kiva9JEkw1sV9RUGSaobjXbwZsr61TZY3TU8b2NrdRKo0Wu8ilCTuF01jkuY9Sxntr6WtyHWGaNRPdYgpRPvMLq0ihyI21zXFpIysnGZKraRCqqiGUzJ2DYUUJK7CGs/iXJflpQEvnb2ALZpHQzZ8IinwQqFEhldx+hxE/NkJqUhkRpAxet7YHQKSuqoppyWVqtmdjFnd2uV61mLQTZmfH7O1V+BRSGXpoLjl6CLnz6/QTjMS4ZmZ6TJ/8BYmwVMYDy6JkPVEgIwNvbyVo1oaUVVkVlB7S5JoauEpguDa5h7boxG7tWenCtRO0JKCnY1NOp2MPFVUZYX3sQAdDvdiMyb4mGYpYnNek5AJRSsDEkFwGcIHRoXn9OVNXnVgLtoGmoZop9Wn1Wqi4EcluUqoTcnOzg7XV9YY9NvNSWGQwRF8gW4rVi9fR6sEJaEkNmi9B+sFk0nJdlWxVzsqLyM7RQVKEzlKidZ0spxqOqTycQ10OhmmrpDjKZQlYjpFCYmT0TLjfNx5XPw2qcqaIANXr63GRmMjL4gNrEZd4BxV5bl2fYuZbodBNycNKVJokBJTOS5fvUFRm7iEm3/2gyvied3sNfEpIxDVddPxEDPoI0ISbdvEkAZo2B3Ixl4ZzylcTSCjtNFKp5VHhjjwCMGj0wxc3BO9q5lMCkajId6am2oMH6JiVYhofQvG/p1m/v5+HRlVNHvxfhGZZwm51JE5Z0ogsLe+Sekdw+1tRCJJnGA8GtHu5oy2R+StNkJYqqqgtppLV25w6qWzbO0NQQjyNGW8vcal1VWK6Rh5ywL9FMqypvCStNUmn+3iXIlud2nNDrhx5SqFqdgtDZvTwOa5K7TzNBbY0keLsAdUenPIJRplpHOWcy+9zLPPPc/OeIJMUpaO30FLlMzdsgg6Nry9ixdroTXCeVxVUm/dQEtBX0vKMtDvdGhLz+5oyo21dQ51O1TW0U5SXnvbAY4uzrA9qlDFmKO3HuPFz32Nx598ilsPLXFvp0vvwME43Gh0LKKprWKybxwsaLHfrPCE4Jp/o55ESXFzCBf2z7YQE990onDWMJjpowpDnkSrprGB47NdzMYmwRq2Vy5DFiO/g1SkWUaSJ6gGBaBUQu0deZIx3duNASuN4v3KlWt87C8/wavuu5fXPPQAy4cO3hwkeQeB2NxxN/f5xrrnDQ4XA15ktGl6GRukQcrIaRQSncR1IWLQLYmW+BDJj7VxrKxtMSkcB2ZmuVW3ODG71NjuYqy1p7rJ/BRKIWUzdAs0AyCLTFKSNIkgaTwumMj53LefE4jmvPjZxo/Y40VsdvmG39Qk4kRUgdhXVfmYiofAeYuwgrK4wbVz36Q912fjpTNsbtzg7Knv4KTno//pKxROoFxkOG5sTNja2mRtdcj73vdO7rv7CAdmZwhSYpWnkw/wzmC8QcqEVtZHZl2qySajvW2Ge1uxgYGnHu5R1VBMK6ZTw+6o4ODsAo+85xFa3QUEhhAaRp93BJETjGXj+mW+9qWvM7N8hM5gxNWrFzChphNyVrfXuE/eR+S2NjgLAd5aLp87wzNnX+TlMy+zub7JcLTNzuYmW9s7GGt4+aWz3HbrIY4cnCdNAnvr1zlz9dscPXaUXAb+5Pd+n9c8+ABJOyeZGUCW8NlPf4ZTTz7Fuz7wfm6/607quuRzf/LH/NknPsaWLdEE2r0Of/rnf8QtJ47z3GOf4Y7XPUxnps/y8kHaS8u88O0nmZjIn8t0xm133cmN65cptyYYV0HLMRzG87HfaeFcyd33PsDulYuc2b7M+sYq06pkVBne/cjbaGnBVFjWLq6A75L3dzh34QLrexUzMzOsbdfIJKXd7vDIQw+jq4rdles8/sWv8sZH3sC9Dz2E3B5TPvVtvvnM87z7Xd8Xw1e6HSZ/9lF+65Mf54ZxLC4t4kUg63V5/Xvfy4+srfBHX/k8a0bEoUr0lKOUJJEJst/lhU89wTvf+Sg/MNljcWaW9/+9H+X3Pv8xXnv/A9zSmWNy4kEOHD7BY499nCs7OxHuLR3BBH70p/8HHjh5N1kr53//V78SLci6TS/NCQuz3H7yTr7xuU+yN9zj4uVzuODZ3dzj9sE8P/+TP8tv/uovs/zgW/iRD36Ij/xPP8Pzu2MSV1NmkuWFBdr9DisrF0izDp3uDO9417uRq5t87okvs7KxSqvdYjIyzB07gbtyDSEynKnQWRKt1gGee/ybXFpeYXtvxNYXv8htd9/DW173Fo7fdoI3uTdw4MRJ9p46xQuXznNjdYVb7zzGD7z3Q/yz/+UfMzi4hAIunH+Z2+5/A93OgLXVFUbj66hORagdqatJpCJTGUpohrurfOtUwVe/+RXe+8EPMvrWs8x1e1ze2uTzTz9OvbLGO9/5Dm4/fowf/OEfY+XKFX7rI7/J6pVLnLl8hUlZAIGH7zzJV791EZ3HGtZWEuslWkkMZVSmWosXsDMdM5N0Wb22hs8yfuPX/jU//TM/Tz67wFynTZrmPPv8M9x1x71MKsv56xtYH3jq7Bnuv+MEopXw3kcfZv2+21maneHKyhqpMeSZjneDPCeE7KYi8SZLERo23HfjtW/S21fZehKtcbIFlaEjHFlnCSEVmc7ozS7SHRxgZ/sKQlYI2cEYQ9aaY375BMX4OhurFxhNokXu1hPHEUlKMR5zcfU8E7/L7PIJrBzgAox2NjHVDjOziwz0SXyQBGsI3lBWJV7UtOhg6hxTWbqJ58iR43hXxIaUAZ9YKhFV8KPKkScJic5QSpJqhcgU7az7Xfq84uu73lBK0iQqZ2XAq4QGqoJUEi3yyBxJE0hCIyeWqBBl9UZZZkJC6U1Mw7EO7cH7GmumFKVFZy2UiLGcNFYqF6L6JBECY+qbqajG7UOFYxJPaFRJcj89rplsChp2jAhNI6iBACuJCREsrbWObSPvoq+98Z8H4xspssa5+PtbvS7BWqRI4hRW6KjOUgnWOIyJBaQzJSJAnrdJsxSdaOzIUJoJucoIWBKtmiSYGAcbqzDbpKEphExu2tJqaem2ByQ6Wg+DsRgCXmhkK4Fkis4y2v1ubApJjUiiyL4yNTiLjJUMWbcPKiPIQGdxkdoKWp0OItE4a9FlTVlMsHUd0z185ERFIKduipUQeQVSkeUpebdFqiUEHcG0eHwosXUdAeeOhskUbW7Gl5i6pNPq0V2Yoyta9KVCeofXAuMtk8mQ2ns62QCpJInS8dcsiVxtD61em3oyxVrTMJR0tOOlCiF8TNQJinavA0EwLSYY65jpz7K4uMhkssdwd4faxumztw5rHJV1WOpG6SbI85ROklFVJcZ7cHFK1E0VRQ0jawl4jBeoIEhEw6JowMiImPbiRSDVGalOKOo6yreNw7nIPhFB0VaavN9BVAUu8wjnSVLFRARk2iKf6VLtVTGBoVG4BFHjhMIpzczSIlt74yix9BOyNGdvtE0G6ECU9RGwtpn2S0HwDu8DTgiC2ldP7dvAmpmmFxEkKOLBY4qa0gbyPKPVbTEuYwMwBCgrS2UcB+bnmLEgRU7tSqwiPotSsL2zy5e+9k1uP3oQKQUHl5cjcLh2GFuwW1UUOzsYn7A9MVRXthgOt9FJQrerohIuSRq+Sryc2obDVlYGnWcIl7B8y1F2J9OoSislKhVYB0rug92IXBYRm4U356FNwwjnEFbgRCxwqxjAjFY6Kjd8TBOUSkWloYCqKllb3aYynjQtQQhSpVE+INMUEapoAfY+NidmUu68dYHRcJtr67vxvXqHEjXOipgKFiY46RugawS8p60egpJyOm0EARESrlLFzGyburIU05rSVBjr2Jl4buyMKJ1BiUBtHXcdP8jhg8tcPneNbishEZa5hQNxr3OBybBgmgTQkHVyWu0cnKPd7cSESZXSzwQ+WFCKvVHJmcvr7O2VOCfYrSVV0xizXlBMK5I0Y2amxdpqAVrQSlJOHl2ktiVegyM2iRUJpTXUXiBUiko0LWGpa4cL0JYZi/0+0tlGvRjDBkLw0RJpoddu44KhmGxy+oVvc+c9dzE3O4t1cG1ti8m1yAy5fGODWhi8ic0876NaU6U5/cVlVAhsjh1BTlAaitKgrKMz6NPv9ijLgPExLcp5RzEt8ZVh4dIZmFlg0zv27U4+NIENwROISj8p46/ex89qX1HpmsFM7AgFrKk5deZlLl+4xLEjSzzyugdxVYXq5EgtecX9d3Px3FXWp3H/EiGAb1S6wjfY3qg+kcJjncBLqKuC4AcxKTMRjR02kCrBfqMUV8eGkYzWzdCoIxPpwLVwMqCsx9aerK33mf8Ya0nTuH9PRhVaStpZQu0cRV1H0LOIiZFRgRTfnyfctC9FVU8M0EilYJAEBqEmVCUuaBIJZTWlFlAVDq08tgjoJKOaVjz0yOs4sDQHNiqzbO0Yj/YYF8VNNURRlmw4Ry0V14Zjxpc89x7u4+qKaS3oZzN4akglWicsnryVG2tbnHvhFCMXcEFTjUa8dP4ah5a7DDKNDTXaS2xweGGxtUMoj3GGvZ0tnnnmGca1QySa4AyD/hzHFzJmM98kKYLHosnjviRhur1OOdwla6XUVYkCFrodDg66tHTCaFyQtAcsLh+iiyNVElMZEukohiMqE7kxa+sbbG1tcX19h9ePDXe+4naUSpqaKlp5CYHalaBFBJJKhSeuD00TuMC+kiyW/UqAVCASgVbRBqOzFq948AHqy5fotaCoHZOyJpWOM89+ldDWXLp2lRtFTVeJaOHudNm4NGEy2ebcjW3U0kGC9IwmdUyKFToqgyXUAW5sbDN6/AlWVjZ4z3veyR333B5PryZ0wPuAVA6FJkm7BFTEHxiPdYZU6KhuaWzQiYwBETd5bypyi6IVPNrnrq9t8cyL56mqqE5cW9vh0uo6d2/t8cib3kSqfKPaiKpeiM3URKqonhaREyqInLMkybHWNFY1idQ61rUqoHS0yElqvNTRNieIzDIvSGTkUiqSxka2f32STTptbE4rpdkbbrNz+Rzt/jovnX6ZjYvXUDpFdtr05lqMqpJEJ1RTQ5JolAhIJdne3kakfQ6duJdep4WrxtS2QCVdXDB0dY6xlpnZI7HOrg5y49wpdq/coJoOWV/ZZWtvwrhJUq5riw2enavXmOkJXvnWR0nTFoQislRDvPRNh0OeevwLvHz2Em8/cRdpqrjtxCGEtCwdXGZ+/QDHDi/f5FWJRjUnpaMz2+L6hQt855nn8HWFMSWmKullApenrG9s8wcf+W3uuu02bjt8G/cdO8TO+haJLzj70rdYW1nhyoXzPPyG17C7u8cX/uN/YPnOO7jvwdfQWzhAkqbcOP0Sn3nsr1grtiknU47fdicvffsZTtx1Ky984xtsmQmnX3iBq2fOsrm9yaFc02tJvM4wE8OrX/Mmvucdb+H/+Y1/x7aa4kuLzBUqNajQQhNQ6Qwb6zuMxyOkkuyOh4BH6IxnnnuSPJvlbT/4AR54zRJt4/k3v/gLPPyO7+fSM5+i8AN+7mc/zMmleb710c/y7p/7x7T3dvjjX/s1/uyPfo8wnfD3Dy3Tn+vzlg/9A8yNMW/7/ndRVxPy3gxXfcUwyfjtf/uvmL+2jrr9CCiYu/02HnrNw/zt177AuvcIb0kThU40R4/cwS/+4j/nFz/w4zz0tgfRh+Y56eeoKsvmpXMMt1bpzS6TOove3GV1eIOiKmPQj7PoTNNOe9xYvcF3ppZzL7/E1sYqtXP89m//IWvPn6Fz3xFe+PRneezZjzJud2LT3dZMzZiVS6f56EdjLSnOn+JYb8AB3eVjf/5HfOKP/4Ci02b76mXWRlsU0xIhM4rxFs/87Rfo9BfpHzrJiYdfzxe+/DlmOvM8/ZUv0FIJpdA4USH3rejOc/7082SXLxCspzvu8clPfIZnTp3n4Tc9yvFbjyGTFudWd5mMhxgXoLZ88/FvcPLkMdZ29ujOzfDKe4/zhgdfw+Mf/09IOSXJcupaURkJIkVLTzsHMDjhsDgmxSqvuP8uHvvaE4yKMTPtNq84eIQXd/c4fOgw7VbGax94kF/928/z9JPfZDqtqVwAL0iSlPMr6w37ENJMk3hHKw0MyymzrYy5PGFzWrFnawrrOLrQp/KeVldw5vkX+fWP/Fs2Jlv85v/5G+gk5+Tx23DC08oSThw9QI1gpSU5fPAgvd4AKSV3HDpMCJ5c5Xz8rz7Gz/13P4U1FVmWxecZhXOWl65s8Ndf+SK2LqhLw+KBPt/7xjdy/PDhm+u8qdb/C7sY4u/81/5QPxYpWd5jY22LpeUjWAFJsAgnCMZg6jEhBIzxOFcipWbmwAy9uRluXBwx3FkjYOjPzpNKTW0sed5lfmGRdt9j6gKd9ej15hlPp2xunEFRoFROcBGbM6ks02pK3s/QSQenEsY7u+xsXmFjY4WZdkZntkvS6pHNLoJISZVk0w05c/ocOtMxrEkFdJqhEvX/8Rn8172++wwlpbAiIUjIWnm0lWhJOr9Id2aRLE3JWx3a3X5M2HGGTpJjVaTvJzJBaEHiHOPJhNJMCKMR4xsXSYoKJzNSKSirOs5VXQSspqmkmhY4G21Y++k5OHdTDSsaH1tw+zynOEVSIl4ygxLIxr4ik2ijUkKjtMJZGw/voJAyslaUDwSlcKYkSTJw4GpPqlqUdkpVOVSqmmlMjIoXBAzxawQEdV2Tp00hLjydfpfJcI+qMuRZRh3AN5oXkGglGs5K7PAL6WPsqfBo3QDJdRr/vmCQIb5HgiBohUVghaYW4JUkUQnBRJm1UgqtBUIHQmPJS9Ocfn8RU0YLU6olopvSCq6xhklqU+GciakuQVLX9U1AZVVV1KaO9iCvCOhovxEanWqsjeoC2LcQgbE1sjJIdEyU0Smpyrj1YJfJeML23gTtY5TuMAgSGVVBSgUUljzJSRNJZQKdfj8WIy7QSlNMXcfLgnOUhSFJFBNT0k0y8nQO78FVFVoGennK3KCPcjV1NkRLSaozrLGIFDZHBdZp6rogFRmBQCeXJDJlbxLVW4TIdBrWJcaDqy1CSayI1lDVPI/OebzeV5QIskRhrKOoqjjxtFWjGtDkqeTAYIHRZBtvLWnD1tJNge+FJE8zJipgQoF3NS5I0qxFJ+/Q78+Rhwj2Vl7Q7S0gkaRZOxb+PsT1EAAlovWHCMoWIpClCQSPtQaEjpwzH9ea8QF0ig0W18ilfRB0+gMWlheYjodcX91iNCkQCGZmZmi359jZ2CZVirmZGVb3thAuYXHxADOdlHoyZnV9Ex0kxsHi0hIPPvB6Hn/6SbafPxUT3qYTpqVmzXqm0z0GgwG9QZsszclarZjY1PDJnLconVCamn6nh0lqenMDFg7PU1lD3tO4oFi5uoaxLlowZYQI73u2ZTOF9z5gg0P6aN3xzmOFJU0S+rMLGOOwFVRmEtlsaYapPZrYxHUhkCRZLDokICwWTYrAoOIl33ukhPGoYK6VcuTAHNdWx/HC7x21jf7soBr1m1CoQHwemqaWtRXe1hG+LQRKeDItWJibRSvF3mTK7t6E4XBCVRu80hgXIaCpEhTBcX1tg/n5FtaUjIuarb1rzGyOuOXoLfhWTqYF7USTZjAdTcELdtd3olU1SGSW08477O0Zxr4mpJq0l7MzDVgUtipQocYJT1VXWFtxYG6GjfUhCkW30+bgoYW4/oRvVCyCEAyFARcUEwOV86A0Qkmoayoh+PQTz/Hme48xNxPYK6dM6pKqMkzrQJYr6sqilcSZitHqDW60O+xsDXnxubOsb61S+MBsCBxXDucUe84xxFMEKAJU5ZREpbz9rY9y1/0bPPbxz9LKFVIaWlKwPDdDKAyJyEjyHkFsI0kJQJ4m3NfXrNgJo1bKpBzjbASq73ODVICulmStNqNiitQR2q1E3PPx4aaacJ8HU9cGXOCll69w5923IpzHeE9bBLqZ5tWHFvji6R0qleCCic9NvAKjhSdpAPm156blGwJBBgyWIFvUaKzxJEk8TEUEDxIgBptLsNAwzBQOCFIT0oDUEXpfG4MIgjTRiE47Dl3qik4njwol43CujkrJIKL95+ZQcl9R3DBVGluqSBKUDKRYpC9xrsSJHCsAlaDTHLQH1UYJz2hacOSOk7R7PYIxCCmQzmOqirUbN+IZRhx2VCYGTsSLrGZYGs5uFhzoZIzrmtXL19mqal4tMu6+d5HOzCxqMGCKog77fAbD6tYaUmuWZtp0iopOL4mfrw8E6fBB471ldWODrZ3d2FzwmqKqSZKcmfk55HQXmUhGm7tk/Q5CW4wJZEnK7sbLmNqSZpqijiy8TMF4NGRraNiqDOXaNV79wCvZXb/GdGcVLQXFeIqe7XDm4hpaC4zzVNZxYeUq5ssFqYZb77kDZ+OZDzQKn4DxkqKqcFoj0qhlMtY2ivDIFPHW4p3DOvAWTFFRZzmtzgJ1aTl07DjJgUXSdkJtA4kIoDXr58+ghaQuDWZaIJKYZNm75RbOn36B0yt7bBnF8SRH1GN6vRnOr64jmsanAoT01D42es9fu8Lpi+c5duvRqO71gbqeUIQarQJGQ1UVJLWmKkpMXURehpAEG5Be4uua0hokOUlVUZVTEAlKpSgpKOuSNPU8/4q+wY0AACAASURBVJ1TTAqD8BYvFGWomWxu4V88w12vvJ/D8z2siWdm8AHjamxRQyuL1tHEU06mFJMRwYuYnonFVBE3MC6qRrmZ0aoMw+EIlcS90Tc1rTMlQQYqY9FagDRgaqom5dB7h06aS0YjyG3NHWNu4RBoRzWpaA1mWT64zMLhk9Tjbf7iTx6jTFMmw1GEnAtFKhV1PeXFF1/kne98E1mWU5oCGRST3V38eMLpS5eYP3yE61fWmVuY4/rLp7l27nTEV5Q1e5OC0knavT6ddoZEovI2WSfl8CvvQyU5MRgnxQeLtXuk2SIyayOSNiGV7E62ObIwy9LSAkJ6Di4vcuDgMo8/+RLv+Z6HSZVqNhEPUqNaGVvrG2xurNNuZxG7EDy+jimZh+ZnabdyPvyTP8Hc7CKVrch+/Xd4+dwZnn/xWQYiY6W0mLteyV1vfjv94yfpDGZ59f2vYmlxDqUkk4lnIhLGkwqVJly/eIEHXvtGvvWlL/NS9g1muovc89rX85a/90Ns/cEet99zF+dPX6IUisOHjvKBH/0Qg8tXIww76zE1AV9NyPMOrgiUlQKdsLNynplc0U8CBk9lLYeO3s7s0gl+5IP/kPXHn+Ke17+X8fPP8s//+D9w/M67+bWfv871Ev7Rhz6IwPDGRx5ldzrkueevcujV9/PVsy/x8U99hnseephXv/o+egcP8uZXvoIMjbkxZbdWfOzTX+Sn/sWv0D93kT/65Bf5iYfupZ0q3I1Vbjz7FJN0Sr8VGAVJp91GVIH/7Zd+he4LV/nwj3+YpWMHeew3PkK50OeH3v8B7vz+99L62kdZu3AqKrP1lOl0jXG5g9YCJSILL2l1eeHrT3BKxCFkVRrmlg/TbvV45PWvpa52+NKls9jBgLe95x8gtzb4289+lEBgb3edFyd7GGepVq/wkz/1DznQarM3LtmbVHzz8a8y3N0ArSPrydT4BC5dv0iyvoaWkvvf/Cj/9F3v4vd/6V8wme7ytnf/AH/9Z79Ld6bXNGEUVRXAeorpblSUDh1f/+pXeDRTHG69i+W5BfZ2Jnz6bz/J4u0nmTx3iu985wxv+oXvZW/rMuu726S5YLY3x9HeDAeEpup2mZuZYWvjNJWSmDoiCXrtbkyGDhWd1jZioPmdX/2njCbw5HNP8J43v4/Xv+IVvOqOO2knLaTSbK1ept5bo7KW2oEPmjyBnlRs7VToRFCZIVLlGBFYaCUEoXjotiNUuyXODQmJwtSGG3tbQKAnU5zIePrbz3Po6Cyruze4pX+AQjsOZDMkUnDHoUXGtWEml6R5TqrT5j4IISiOLR3gvd/3A8hmOBwaMUVtHLujgv/7Lz7Pi89+DVOZeBBpR186Fr/3++l2BtyUIe7XC82g7L/mZb0jzxO0zpBlgZUJ03LE+dNPcu3s87hgqHpdZuZm6Q0WSHWCd5ZUt9gpDDt7NzgoNMPxJjrVzB06yJFDB2n1+9RWYsIm/d4tCCeYjAv2kook65G3F0lbHWb1AImh12ojRY4ZT3C2oH9oGTfToSMMs7MDsmyW2ikmdYGUgaos8MIiUAil8DJBq3RfpPpde33XG0rF1JClkrzXwXmJISCDJDWCroi08+Ac1WiI1AI/KdmzY/RMi1BM8SJBZ5qiqKmGY6yymL09tPB0tGdjNCTJMoT3WO/QUpApiRSWajoi5lGCRKNFwIQmZ/gmuGvf3Q773vjYgBIRlhtcbDw5G5k3NMoEGXXB1npcDEWKuMyQIPDgNcIoRLCN6kkyKSYkLkVmmnJaNPH0qim+HNZa6qqkTBTGVszPzTM3d4BrVUlZ1RTeRrBnMyveDxEhRCB2EAaBxxiLswbnDNNqgvOOaVGCVATdXISVQgSFD4rKBnZGE5I8oduW2MpgyopWpsFKtG4sYyqllbVJZU6qW1HVZEWMRA1RYdRutUhlivUiNt3wJCppGFOSNMtJlG4KJUFlYuMqxjwrvLPR6uajbD6KXjRaJ+h2jhJJjE9PEnoCru3txgKlqiHiWnAoUtnI8GXAO0siM9JWipIakWiMnOCNpawNXkicEJgQk6AkOU5IghWkWmFE/KATGch0tHglSuHqCEtPGlWOThVpAK0ELQRH53rceXCeK2u7lFNDEWqEEkytIc8SjIEKcVM270NDIQoR9CnwqNAk/gmJsVMILsrdhaTb7rKwtEQrzxlvrKCFwIrGRiB1tOg4H5kLQkcQp5QEC5mW5FmCVAIdmmcaxXA0Je108SpwYGmRuqyQNuB2XLyQRoZ9vOQ0CyfRikynjCcOYyyVs02wVmx7emnROiE4ifAyXjK9xFYOJZuERB/hft1ul0QJ8lYHZxzzvQGTuiRPMw4MlvBmyExvHgGMxgU7u3sMi+scObbHzNwRFo+OmYwKqqpkNNxDq5hEk6YpWZbhXYgHVGMtNKUh1NHm6GrDuBojqinbGxOErxBeYKyhriLz6ybvrVEn7b88TZqgaKwpOIQQ2OhbIZMpmRAEIalEXBPeWDKpydsJk6rAWMPm1pD5A4sgHQJH5T2hHFNJybSuGmaTx7p4GdtLDH054aGTlqTtUKHgda+5l8efNjxxagXrLKUPsXErHPhAkii8A+Gj4kUgkEHiast0sku/32N20GVm0GM6rdnYHFJbR57V9Ds5xdTT7iaMV7ZITEVZWkoPTjjM1hqZlgzm52i1uygL25sb0donBNPJHlpnMdXMGnxd4caCfqvHTH8BfeQk9xy7l+2J5dTT32L7+nkIE5QK+Npw98lbuXZ9yO5wj4OLc7TSDhNXQG0YtDO8qVFSU9eOcQ1FJaOCMo9KUyEFo6Lgxcoyc22V+9NlxpOa9a0dtkZjuv0uWiqsq2NKn/IEX7G6coXO3C205mcpblxD43hVr0O322WYJsyVgT2lKL1hoypxtWFlfZv7KsNtt97K7bcfY/P6Cr6q8NZwfnuDLG9Ty4xuZ0AnGTF1llanx/F776Mut5hVgnaeMiEghEME0YC5NfiK+44eYmtYMSkVwVta7ZTgIrw7V5Is0aQ6wRoTQcp1jUw142nF6XPnWBr0aNMjUOOmIw52U25JLFeDZtycjD44dPA4iPbTxhKnPFgZE6GCD4zHBaasaPd7ZNubuDLat+N+Fm3HSIHwGkUgCEvlSkShsM6gE0lnOMW6LaTwtOcXEUExGu4xnUwZjqdkQNptY2qDMw69PyQK+w0lcfMsj2ViLBSzNIkaGOGikljn6KwHVoAryJRivLXNxqVzyHabkCYMegukmY8NSN9YlK3j4qUrvPTSWbRKMM6SpAk+CIK3DSsuvo/tcYHxgkkxxdvA+LJjYWmbavocZ146zcvnzlPafdt9TNST3rG5vsPq3Cz5TIkIGtPaYerieRmV03D14mWKwiB0tJWpRHL9wmmW0lswezucOXOFPOTc8+bXMRJDNC12wyY711fxRrA53EboBGPh+tomCzM9itozEV20yhDBM3foEGdWLjA2JeNJRV3mbK5tINMU7y1BCIwXXN9a55tPPkW71aY/10emCbVxkYnkFKaqqaYllYpog2y0i5QNoltKpKzBGVyIfJZaSLLeAu2FJdJOHx8cvcEA0RsgrSGxVYwU7/RYuud+ujvbWBOQ5YiZbk6et1m5cpXr19cZ1pIH738FO8MtWt0+V66vsuUUot1FlJHXYbGoJhWwKsZcu3aFna1NaltQ41AixCRiPKmAcjqN4TBaIlNJR3YIDkxt4tpoppWmMtR1zXg0Is1ztPY4KUlli+mkQAjHZDJCCU8QCaYuEVLTyjNaKja0kySJAwhnqCuLCDV1HZP6ppMR4+kEnSRxQOph35Mqic3kRCYo2RisQ413ijRt4b0jSTSmLjEhNpTydhbdAiE2R9tp1ij4mwJTCCpT86qHHmZxfiEqR2vL9nCX4AIz8/M8+ZmPs7lXsbDUjfWpFrSylNlMMybQ7XR47skvk2HZGW8i5Rx//vt/xtyhQ+xs71ILQd5KeeiBB7jz9gMcuecYB+aOItMckWa0VE6nP8NkOMUFjcOh8thwNyWolm6a8mOoU2SeYm1JMRwThiOmW5sMW7Mo2aYoply4eIPLa6t8/K8+xdvf8ttIlUfovYg1mDclRbkbn8vdKWmSU5ZNep4MqFCzvbbLtedOccu7lllcOsyP/tD38ZFfv8j5c7ukg5zV7XU+/cW/4DWu4J4HXs2jb38rrVQxWdnh6tYuh+68lQ//9/8jv/u7/47t0XlsldLpDnCu5I3v/nHM9nW+/ZUvMz23xk//o59n4CuGT5zl1T/2fo5uj7h1ZobLf/M4i9kM//oPf4//4yf+Po9PIUxrJqHCiw6JmjCTJwg/ImlpysIjVaxHfuKDH6QbJE9dOcfixZfZLNb5zie+wtnnX2Zt5XkeePB7+ON/87t8zxse4ewzz/Lps1+k3+4wGlbc//BbuPOOk7zwF49xvDvP8qtvR2nDM89+jef/9Mu8/9//MoPU8bmvfplnP/GXfOBnfpZ2t4Wva7ZOX+TilU2SpIOmppsljMeWV9/9MCePnWDWJaRveYBnP/4ZfuEX/1fe/9M/x2tfv83SwSVOHjjOcH2V81fO8oWvfBnZttgQXQIWReo8dTnBAbIKDEe75FmGNRt87D/+X3z7y09z+10neeaJbzB/y2F+8O1v4lWHjpAN1/jCC09QuoraTPFOkApPafe4vLvBP/nZ/5aJE3TzFKU0vQOH6OqUdzz6Nla21/jUJ/+aB9/6KNs7a9x76ADvfdPbuPNfws/+1C/w1Jf+BpEm1E6SaM9oVKG0QqWQ6wxT11EUkAXaaUpLKRCSuZk2H/7gD3LsgYf5J+97H1/y8Ae/81vk/RlUmlDUln5f0qkcyxPD2UFGNRkzHu2wN5zgSckIFFWJSnKC1BR7U1SiEaEHbsIv//K/pPVLirc++t+QyJRUJ1TGkLeiA0a4DOMLFIGlAwvMEvDb22yNLN1Wi8oZ/l/W3jTqsusu7/zt6Zxzx3euuVRVkqpUkmWNHmQbg20MeEEInRVMvLwMBAgQaPAKq2maQBgMSYwh3UCgg2kD3cQOCY6NGQzdEPAAtmVZRqMta6xBVe883fkMe+oP+5ZgpfMltO9a+qRV9b517jln7/38n+f3VE3DaDpmdSFnUI9Y3x8xdp5psIkJrBRKa5aEwnpPOa248kLgV3/913nL338rX3HPa5KgG5MJZSHXdHWWMDPRz/faAi8CWiluOXb8pXjbjbeUiAJnaw421qmrEbNpgxSKqGBcjpNzds62CnPX8o0yhr/rp9dpk1UncA6cH1KYgoPBkMeefIzJ1pc4d8s5Tp09j8l6NLMJV59+eG5ECRSZ4OIdr0bky0xmE5aWWmQChJbk/WNE0UFGS7SRaloxnVQIXyJqjYqbZLpFDAadgV8+zmwakSpjobvA0oW7QWTYsqKq9mnqGbpxFCFBVIaTiuhS2ZSSoIInSoeIxd/5Wvy3Pl92QUkGQVVWVN5hWm0g4oLG2opqckg13kO2WmRFQW6g2j0EnacHz3nqxqK7HVo6J4QGESPe2/l2N9GtvU/sIjm393vpcU2TDuY35Jcwh+7GBJMF5u1Y4iXAYmqPmTOY5sBUIcRcKEqHSGsbrPeoeStaiKkKO4ZkupNzYSAGT9mUFNogdVrIZ7IkCOjmOa5K4lGuM/QcXA5JFLMuMQYwGV5CbT3OO3RoEUjZfqWSndpF5pWzyTkSBEQtyLoZJhYooTA6o2MMQkjq2mJjg/ERnUtUiFS2pK6nyHyJYAzeVukCBQgq0Myv25HjJzBFwWQ6mLfDaaROTJzQJEHM+joxBAgIDbjE2pFzoSMTZh6BIjm/kPiQNjIxOjKpUbpF42pSiEUQvSczGUoauq2CPMuwIXAwK6kEjEdDlAJXe+qqBqXRWZb4AT4BuZ11Ce4sLOPJlNpaKuepkSgP2hhU9ClC4R218+wf7tJt5SgNApWaYubNM8qnamcpJB7JaNYwiwEjHUYoNIKFrAUestyQFRmuEcgY5jB1SSuC0+lAZqLEzyfeEYgu4VgdDiUVzjbYJkHFQ4wYlbG6usqpk6e48oUn0SqmmlOlqL2bg8nTE62lSZBP6SgUOKVw3uMaS29hhW6vT4iJ9SWzDOsbMpUjfOJ4aKlSdToWP69CtS7Fq0JMgOflxUXyTgfRBKSzSKVTO5jUeA1WSryvcMGCEKgsx7T7tHUPszdFiBqpNSJrMZ1FXKjp9Tr0llusnbyX/fGAotdjeDAFYbB1jW51KYJiMpnw0JOPcvamWzl5/BwbrLOztU673ybXhk47Z2l5lczkNMojlSEqjXASqRIbQhmD945mVjOdDBjs7zMcNRy/aY1qKplOZul9JpK7jxvQdvG32CEkltmNivUbIlMkEL1DZYpMSJywRJ8ejmNtwWKnxdVDx86wREjD4eEhwlcYr1MUTTucEHgX8dGhPAihiHikC1y4Lee2POPgoGEyzXnh8U3qvYybltsEMoQK+CZiEVTeITXsViVKKuxcDZ+/ClHSY5sZYVZiig7dPKN1YgkhMvZ3N9m+fJnKR4aHBzTTCZkSJLRrJHiJC4qD8ZSi36OdG4aDwySAxIbg6hRrdTO8UDiXWG22dkyUIkrPidOnKPI2R6XCvPIBHv1UhT14FuctwTZYHzGZwFvPseMrOJeeD2sCL7/3IsVij2eevUJdVpQ32GzWYoNL/BnnyVswmkzZr5fZG05wQnM4HjFpInnjmNZDXIi08oxC3IjqBGhKXvuGt7B+9TJHux1OrC1ywnm6jSWb1kyEY8NHtvMCu3KMY7ecxwgY7Q9oK4UoRwwPd5l6hfQNJ7r91Ja1tMTZO29j89IVVJYRETyzeciptR4ER5ACGWR6hkkR04Vem167w/YMjKk5cXQVZTq4sqZ2nhgcKnp6Cz1UK2dWWbY3dgneI7xnNrO019romKCAB5t7tHXkxFKL3BRc258wtYLKp8pchMAlHy9+vn5KpTAu0qoCnbU2hcrI231WFpe4NtnCR4uNjiTXSvCRVpFhckEmc7SOFJ0W3bZCqUDW7tDtL2GEJ2+1QEC5Ub8U/Z3UFTYmblJuDAgxd+PF5LydA9m54U4iUphivoHzKDWvrG/1yPpLcDACNF5qRrt77F69whTHoIqsdtu84c1vZjCbQoSyrJlNp2xuXEcqCTGVJYiQGFNEkNHjSIJecJLDZpxScXPW3LSuePjzz/L8C5fQRYEViY2XuKRJrLbNkN29Q15+8TxtAkeXl2ktH6G2M2KEuvE0dU1Vz5BOYbQhRof1nqeuDDhcf4GlfocLZy6ystRJHMQQmdUTtq7vEYSgqQMiTlCmBWsd9lzD1Z0pu3GJXOZMpmNO33qRUq+iqbg2GFHnFtNfoBkNU8Q3pnh2FSIvbu1zMJhy+uZzCGPwdg/nBVJlFJ0iMWSUYmGhz+rKUaTSNI1DKfAeordMdEZvYYWjZy8gtaGZ1RRRUTnL5GCAFpFQV/SOLiGEoLFTJIbjZ85QW4v3glwIbJQMt9Y5stzjtjOnmA4n7DTp+swOD5lOGlqdPs10i6hjYnvF5FgSPjDen1E1HqVyRFBIbWjlhk6nhxaSopORtdsUKt2fOtM00zFaR1ABnWUUpiBXgn6vQ7+/gMk7iDkcPS8KhsMht509xeWr17A6IwZLlilWFvq88t67OXniONHaeTX83N2XCUxsUbRysiKn3evR7vSQQpC3Wmij8bZBG01AkmU5WV6QZSrtrYscqTMyU5AallJJDGSYXGK0ItOKLDP0O10EEqkMCRDp0xcV4rx1SCCCw1qHyQpmoylBwKf/y6fYLxtWibSVojAmrQ/WImLgynPPcc/dJzl+00mOdW7l1IlbeeRjn+L3PvkgWW7S761h/dom3/k938PFey7QN510P3hLRLG/N0KQsXZqjagUdWXZHM04Y1qU9QghBDv714gzh94fsX59g889+CDnL9zJ2fO3U1nJtHJc2riK9IoX9zdYW12kCQ0FxTyaCN6OOLh6iaLTp99Z4mC0R1NVKKVBCWrvEM7TuMiv/V/vp91ucf5lt9Dq9Dhx/gSLz12mu5ihlGC/nPAHv/8Bnnj4Udye4+ve/Go++FM/xYnXvpabbvsW3vA1X83D7/slPqYzhjsT/upTn+b0yjm++R+9g//ww/+M1SMn+NGf+2lW1xZprm3xHe94B8t338WVD3yES1+8zl3v/D5+4R99I2dExuz6IfpID7C0pEIVFmU9lXM4HWm1OgQ7pZ7B9etb/OSP/gjjwYAqOv79x/+cXg5tLel3+8i8xPZgd8Hzkz//M3zx2hOcvO0M7/iG7+J45zhu/5A3fMs38dB7fpWf/dEf55ve9jY+9cd/yp9de5J3/chPs7zYYnlnnUn+at79m7/Br/2PP8nb3v4tNJXl2GteyT+99w6W3vvL/Nrv/S7jRlLXM7z0vO8X/y1HT7+cm4oZn/vsF/mND36A7/22b+PH/8WP84n/47eRrQXuvv8e/vP7/09Ev0VdDZhWUIlU7COiQkRJcBWNh0xn9BYzhFF85k//ENMp+MJTI85/5RtYjJLf+IX30Oq0sTJn5dg5tvauIJWnCg6vc2w1w+Qa0xJk1lG6hmnjEIMDTt11L2995w8weexJ2pOKN771rYwe+wJ/8qd/xgMPvI6jm5Gf/5l/ybv+xU+wcvQY49EOp0+dwdHn8tOPkOUFIcxot/sJ9WIbpr6hwZFryfVf/COe9k8TT57m9Fu+nsUPfwDfa+NmQ4wQNLOSJx69ws5Dv8enx7vE63sMi5IoSqQAZwPOCpSErChQ7VVmgx1arUVGwxmtoo1wDSjS/l+p+bCvYf2557j04jaTWUPeapNpSdV4nh0OWVxZQzU7CCGo6xmB1J4dYk49iwzLmv0yvY8yY5jUFhUF6wcjOu02LZVhgSc+8wXueflFOg+8EeccWun0N80LKELwc/d/En1ugNuVnjuLRHyJPwse5wWziaUqG6yrMPNY72ReVuKjR0U9Lw36/4be/nsb4Ooqsr+zyelTp6F1Em06SKU4c/NFPr/5JIuLC7QWVoleIgP0jhl8VTM62EMUBa3eMkYvIIIgVBXbh5ssdHNkUGQ9jZIZBwcbtMyEl91+jqJl6HSO4lwBUiBlhgqAMjTNjLpxdIIhz7t4H6i1YX9/hAoDinaX7uIC7VaP9sohKlMYPEpmgCBaN4dDf/k+X3ZBKcjUmEb0+GaKihGhMmjGjHYm2MaiW3063S5DX2HHM1rtLtPtMUWnwFYVDAxCCHRmEEjqyYDFtmJKEgKkUeSQbsR5FXztLMGneaUggZTnWZ0E1J3HdoC5Splu1FRdOxdARLL9CqlTUwqgs+RcQc4nbAiCSI4BEeetZsIRXYRZIGqNKYp0o6oUw5P9Lu0io2malFeXAucsjW1wwVPZGmEdg8EoTUKbBqllYueQJn2C5JRJv/VcCJGpvl5FOZ/USrz3NKEiIiCAzlNcIuDptNv4psJjaZoZuskIjUEpUEUqKJbzim5EoN0v8AFKO6OqGoSKSC9xLgleIoaUF5wzZWSIeGfTzxYCrVKVepCpHUIrhWscCnDO4RuPMBKpBZkyuJBiRFrlCO8xUiJCSPweoZlJiZaGoBwiesqqpokBZT1N9GRGIFX6nsra4qKn9lOsS418AUkmDBGbwJcx0JJ5guCKSO0a3Myy2C6S6yQ6gnTUrsRGiNLgbGBWVlS2IdgarwRaSoQWNNZzdeuQw6pGNAFh41yIUBAiWkn6xtDYQO0TbLMOkkxqhAkQHDK4BEhnDn4WApVp6mnF6TNn2Lp+FREsloCWc7EjipeqWBWaPDNpwxwh04m5glQ0LjX9aKPZ2DlA5QUi00zGQ4pWwXgywNua3GiMEYSoEI7UKBhJ969QKFPQ7nbI8zzxFITCuchoMkUIqJqGGOtkRxYSlMaYHCVMikbqHKkyfFDoTh8tLNGXZB3NaDjl7C2nqQXs7+6xeuQERavgyqXnGB3so7Oc4+duYTIYsDfY49iZU1zb8GkhU5ETR4/RKhSZKei1W1RViq8IIlpItEkOpugDnX4/3du2IdbpnjgcDDg4lEhRIGV6FlPO+IZVdv5OkUlETk9WgkNHndyO3W4XYxXtLEdkEjVtGMYZ87J1iCHFVr2gshPKMqOjBTPRkGUabyONEMggkHHOoYkejGSWtfjcxjJ1eYR6dMAiE245usC5mwqOVpGxtShX0kTHfiWorKQWyWk3rRxGqfkBPL1H8B4TJEIrgiuZlGNAJYF2eEDZWDBdBlsTtLzRGDRHvgqBjdBUDdI7qmqMq8d0ugXjSU1EEGSOEpbcFFS2wocIpMKBSd0wvr7B4sIJnFlgsd/h7G13cPXhazhXolUGtsTNKqKClZVFXFODkGQodLvNradPsLe+x+aswjbJKRZkBC8RzGPAUnHhws2cXoh8+rNPkeUF47JB9QSDyQiBZ22xTctoSmupvMP6El9PeO4zH+O+++/j/HiPb9jZIvpAddiwH0dkjaPdFJzKwVc1cnaK8eYe+4MDhteuUNdTpi4waQK9wpAt5Uy295Fb25y54yL65jM4J7jy3NPU4wllLBBS02u3aKYVdQArBLlMou7VzQGj2YQja0t0sz7bOzuUzqNCxLsGSO+mpdVFFpdWKM61GQxHjCcv0rhIa74u+QDjwZCTXUXXRhZWM2gKpk1kUDZMXOJYhZjEEikSZ2WxlXFyuY9gggqrxODQPoACfIFAQ/AoHCFIfMxYXjZcvHgEOxMMm5oMj/ERmpo8Ty1akeQMNVlGXTfcaAitvScXAq01ZdPM4ZsCqRVKpMGLSKNKhEwHici8/ENqMqPpri1h1o7jhUDLgIsNQQeEgnbHkGvFwkJO1kwZjYfIdhs/muADSKO5+fwtLJ84ye7uAQfbO+xsbzGbTAgIjEoTP2NaEAIiWJqYnGVZu8XeaMikaRCpIxt1A7Yt5q2YQuCjZXdnh3FzgayosM5hQnJxxighJFahIA0iIoEYhQAqkwAAIABJREFUoOgsUEvLcFrxmte/jntfcQ8+QFSRvOjyxEMfY3trl9ZKH+s9y2tHOH78CN0jBSpXjNlhtqMoXY3MlnHWcvHee/jSZ/6SqAwnb7oFsoat9XW2NzfRUhFDcm1Ws5rRYIR3f+MGEyKAswifvhMpIvgAIW36Q/QQFd4nXuJkVhK8QriIVA7vLTub22gjsE3J8SNHiJ1WivMTaSpLu4iM9qdpj2cyfGeF0gaEKmjpjOlsyuNXrtI/eozgRjQhUg13WV64BSczbGhQMcGtAxItInv7m+zt7nBiuZW+JyHxwiQIfgYyBIyNRBmSCzUYojBENHgJwSN0QIgEfvfeoaMH4Qlzto+zgdW1VW4/eZRrlUMJRcsITi70WMp02vMSyUwq4tBaYkSOEDUCiYthfv0iYv7+9n7OqPQOJT0BixIJTC/SI5HKZrxPtexSzTmCEUQgbdE9kdQSprOM2KQGKxdSi52QKjEt5+JYURT0M41tt0EINjY3mMbA/qTmyEoHKUEFiVCScjDm1gvnuPuue5jUY0I148rjD/Gxj38S2VvA2zrtu5LCyNPPvchdr74LKQLCzjjY38fVEkGbm2+5FW0yXBS02xLf5Kxf22Zv9wpPP/YgWxsbYC0+CoQ2XHjZy7j77vvJTEZEsbTap7vQ4ulndqn2r3Hs1ruZBIuOjj4pXmO94PLly+xcfRGdCfoLfYJPYv721gZFkYGP9BYXyLsF/+qX/zcW+l0Wu31m0fINX/u1fOG5x7mycZWige5yl6Yd+a0P/Dt+/j0/waStOX71MrWDu+67k6fHAw68Qi12OVkt8Lv/9x9RvfcDPLr5HK9901tYO36EaC3dm89w+6mT4BpGd99BftcFsqMZ55cu8q6vfDOf7Ury0YDYNeiZww8DPgsEIZAhpxpayjJg60BZ7TLrDDDtNiEYiiwjREttK7bXN1k5vcalJ57kjd/9Y+x/9lGePmhz7dIVfuU9v8jFcy/jTa/9SqQw3HbfA/y9E8f58Pv/Lc/mjlaR8e6f/1/50Pt+nYeH1zn3x3/Om+5/I9/89m9ClgE2ZowXMmbjGS/uT5k6RR0snW6Hv37yQWS74Ae/+3t537d+Oz/1n36H73/1K/jp3/otho8/xp98/ON8dv1TfOgdv0+UiyAj3qeESIHACUEINYPhiCg8IrQ4f+sxru8PuanbI+DZHR1y/6u/mn/8HT/AH/7yz3N5sM+po2scbq9z/+vezH/56Edo7BaFdEjfkC90qYNnMGjQJqY9bJR4H7jzzpdx89IyW2tHOL60jI6e27/x7/H6Y9/Nf3zvb3L2tffy99UCPyNLZhPY3z3g1gv3srV+hSxvs9Rpcfb2O3nkc5+l2+piAmxe3WI8mBKPS47dd5ZvvufN6H6XD/7Ye6hyjTsoiXlMCBAJT+1Mefz6Q9SiIg8N2khy1cKWNQ6FLtpkaBYWMiY+w3rDdDbCaagqB87w0z/7bvipBbqi4BWveoAnPvNp3v/v38eLG5cIGmZVyWA6wQVJoeFgOCVIRavQLK4scfOpMzz8159nMp3y5Kym8els622grCqCjDhXUylDUCV51kN4SRA5rSxHI7DRM6lrHrl8iaasufPUaY6uruF9GlganYTCG3uEVMglcRI0kqACUUrIc6SP5Ai0FCx2MrTQzDsq5m2y4r/awTPXBv773EqtdsZwtMfm9ec4dm4Zn3XRus3x42do9ZawrkQJSb58giIErK3wdUPnyHmkVhjTxk8rRrtbDHae57lnnqAlGxYWFmm3Wpy5+RwqW2Kwt4MkEIuCLCo6/ZOIvJW4yE2FVAX9hQX2N7dRxqRCFpEKxi0S13i83WYx0yD7NJVHqgAyxRSDl/gmIvK/e/zvv/X58kO5ZSR6mazAwScgYKipxkPM3DUTmzGHewOyPCNqODjcxUWYuQkdmSHdDC90sqjHiPCOKHIylRGFSwskEJxHqCRG2KZCKJUWqABKmfnhJX1uNFhEmLNUbiQp/yZTGW7Y4V5q1kmeAyXTjXvjz8QbK/ZcMZVy3lw334A3VZlYRS5BNV2MNM6CJNXZWz9n1KSKWtR8oisUTeNSxjEmoUiQprFyXpsdZWp6cz5NR+WNsPu8QVVrUBGUlHhnqav0c4WRiJCmLEoKFhZ6ZLlB1BUyRIiWoOZtc9LM/z0OpTRKa7IionVyr1jbYJuA93NI5DzmUDeWSMRkJsWLrCWEBikT+CtKgTEKJTVOK5qmIs4b/7RR5NKgjEKKiCsrMpkYStpkbG3tsLO3yWJnASEiVe1pbHI8xZh4IkEk8VC3MhYW+xR5l2ub29gQaawnREu3JVjpt1AyYF36b2JDinnFBAwPPiFpRdSAYXswxtoKqSR1VbPc7yGGB6nRRmWsLvapJhXDqmSp22K10OzFGZPK4m2qFw5S0JYKg0DkElV7qlAnh5sCgiL6gJQ6cb1CSIcPlSxMvbUjZEJT7u4RVEC4CFlqQSxkil4pmYTR0jbkusC5gIyBwmgKZZJY6j1be/tsbm1T9JfY29yg026TGUVdTpODx/k5RDo5em7coyqmaXltK7b3DpAiYrKCTruDC1BOKpTOk1gb0tstOoerKg62t8FW9Pq9udsGtJIUWcF42KBURjtvU1vHcHTIdJYszNNyRtFtcdtdL2c4nHB4eJiAzBo2NrcQWZvJdMza8ZtY6LY4feQIL1z+AtaF9BwFn9oRpAEB3nm67Q6T0YBqdEA1PMREx6wqMEYTLBgNwSuMSQe+pInNywZCfAlOztwlkZyScs4t80gfmczGFMOCrNvHekGe9XCixc5szDh6zOISZ44e48hiBx2TM89N9nDBs7R6jNzaxIoxaTIuRXIc2cZjyylyvM3LlnPOnziGVhm7A0fUNUoqcilpfIHMI5uxSsKg1HghEzQVhZep7j45QwNSWJTU5DJFC9xwD1dWqTnRT1FSEaOiDpImWEJIldRGBfIMFlqGcnCA0Mm51W+1GIwntIRH520CUPkE2291UwOGMpqqmuInQ8xiQeULllaPsNNdIpQeXeSYvMtsPGNpcZFcQ+U8hUwNaj5agqtS9A6oGpdcdCGkw69QSUj0nnvPrnLH2RVCljMZTVgOio31y1jrWFooaBcaQcT4tHEro6KuJlSxTX8lpz56EsZjRF0Te4LZXiDXiq4IWCSm10Os5hzubzDcuEI526GpG2JQCBnwITKclLjaUdYz1i9dYmFxgYWVYwwGU4a7h9QxcvLiHWyvX6Ncv07bNRgVcB7ahWZ5qcNiU5DncHCwS1PXtIzB4yhyDULT+MDgYAxectPN55ACNrOcelammK23VOOS0dYW+6tdsrphuZUx7Ths37DcBCrvabzH2khlLXVIQuLa2jK5VAQJVXB0TQZaokyOVTlSpXVfeoEQmkigKqdkEvIiZxQGRCepVKTbXsBFTSENLjrGs4pqOOGFa1eZNhVOpHbGiXXJ9RhlWuOkJNfJBahDBKFwMZJl+bwRViKFRwvoZJKVo2t0j59M8HSXmjtNAJUXKK2YDYb0llJRgTYmuUJJ10mQNq69VotZt8/qkTVuvuMWDrf3ubpxncOt3fk7ez60MgXCRRwWYSQ+alpHj8PGBkJrQtOQ4vGQbEopzjjzlhevH3Ls4hJGt+dOa0UU4BDMnAPlCS4QrAAl2N++Tla0eeNXvop7X3FnKjjRiugF03JCd2GV13zd17J87ASTumZr/TpRWqqRo5xNOHp8mbGdMJmJtAa7ivN33MZg6zp6Y5/Fk0tsPP88D7zpLfzn33gvut9L7kDlqZsZm5ublHWNLnK8CATj0sApaIgaLSNKglYZNnjMHFrtUUgJvV6f0jRMqgn17hjvBUXbYEwHLw27hwcU0iGKDm42Y3V5BW9rGifTsKUqqZs92gurtI8fo57u8eiTX+LFseXiMZDaoIPFh5LxeA/RzvDjGcgbyHYPaCbjiuubu6x0ThEtRKHRQqOjxcQco3MsAoNLnDuh0hR8vm9UIRIri78hBqm0H1FSExGp9c02tJYWiVpxsD9AqogRkEvBNM6b+WJM7y6feFPOlWRG46OnkAnULZQDEZDCkBtBVSUxNypDDCmChxR4qRLuIASUmQ+mQkBpicJib7j7pCQIlfyEUhK0BuuSQ1CmCE5uFCo6pMipmxpbV1jrmYx26C4vsLa0zO6swQbDLUf64C3DyYyysTzz+JP8kQi8/Ktez/nb7+a5T3+cRkoWiEyjppcrTq2skmctlpdWufPifancwhhO3QxKJhdZKpcAESLOAyrwxPPPMtvdYnN3h+evXsOHwC0XbuaeO+7jzpffSyBSW49WbUDTSIi5o3t0kY0rh1y5NuO+25ZxpMhgFWtCkbHV1ByOxwynI8qyTnglCXVVExuHjophKCl6hkZ52uePMDjc5JntdZ64tkkrb2P6OePJgOmLU46dOM8PfN9Pc+kTn+ChK8/w2HPPcvH+u/nGt34Xz/yn9zIcTHj/hz7CWtai+fZ38NtHMn743/06689c5ez5m3jxwSf4+If/hHNf9RUsnTvH9Rf3Gawf8Cfv/jc8eESjdh3qSJtyWuGdTCUUTqKiIDhPZS0xmjSoCh5nBURLWxsKHE42RK2wBg5Glt7xjLeeWePzu7ssL6/xxq/7Nh5+7BFkf5FTd96GVJHV197H17dfxZtXFnnbL7ybdQQyq/nc4QAyuHDrrbz6/nN88o8/ys/+8/+Fg+2Sf/jWd3DTPUdY9xVjAsjIaDakm2me/eLD/NA3vYHF5aO85YHX8iv/8Xd4472v5pff/u1cGj5LLqF36jj7+yVZluErl4oDaBjYwNQGokrPdQ/B+eOLbK1fYWt7xLjydNp9XnzuKX7w2/8BCjh5yylMDvVsyl986D9QNxMK5dlx0LnjbpZdRTHYJg5rZlPDyTNtSmf5B9/xT1hqFyhAqYJee43nX1jnZfe9ms//9eP85daAs0WHf/hjP8GO9vwPb3sbBxvb/MlHPsza2nFkZlg5dpJatohSzzl8DU8/+jn+95/7Od7+Ld/G6ZO3croo2G+m/Ovf+RUe+4rXsL7cppqMQDp6rZyFquHI7Xdx+cqXWFwsMMxoXEOULYKPTKZDjh5dY7i1yVv/8Q/zgd/9XYKd0gxn+EwSbE08KHnXu/4l2k84f/4cm5euM66mlHbCdDojL3q0ui0aG3FNTbfw1AIUkZ7q8+ijj2AyhVQpV+HLBucFWauF0FAYQ11ZrG2YqUieN5Szhu5Klwf/6iFe88qvJ5Oan/vt3+czf/phXKhY7HZ50wMP8K1vfxvnbr3wt9Sf1CodRUoGKJJ4EqxP66mxaOew0bLSKlho59xx23mWFpdT6wN/C3Xz/yPuBrC9s8O4qRkM91hxE1r6CDIqlteO8Kr7Xke/IxDFEp4eLlY4P0N2uvTyPpkqkm7QVpjeModrayysrNEMdyjHBzgcW7sjZtNtxsNDVIj0ex1cNaIqpzS1Z7y/z2QyZHlphe7SCpcuPc+F86dZPHEeHwTj7V3y2RgXM4Z2RrW+walTGdZbIhKsR7XnbL3oEgf3y/j5sgtKk1mZDq8iOYccCTYbmb0kviilKYo2eE1Umu5in9o5tIJqVqLmYM3oPc4HjJBEDwSbHBYyCU1OpqpWoSLdbgcpBYcHA4TO8D5NYm4YupROjB8xd8/cuK1uiEopcJTiZKk1Jc5jLSHdtPM8UbopQSk5V0xDcmJInaJGRGRUiREj0wY21I6matBSEnHEkNqGiAElEgS1VXRptXOkgCzP8M6DTNPIGJItTwuAQNM0zKoaMkWuNASZ4OQIDJALhZCeqBOcN2hFYyvwEaxNEzCl0VGkaxtSuAEPofFEGdF5Rh1CAj7bGt802NqTGY2zFdGnrhZjEpcn8aFIcUQ3r3X2SeSRIl1d71IMTSBQWlGoFtbW8zajZEfHBoiegiyJZRKq8ZTpaEyn06N2NU2Tvleh5NxBc6P1RxNDpJ5W1HnBUr/NzafPUtZNmoBriZYeV40ZllOUKjBKMq0nlE0JMYGw8TWZkXRIrKFMSqQxjKczhBAMx4dkrZyx9VBLDvbHoOBof4mOSILZWHqEalI8Q0naOjF0hFS0WjnTOEO7jFSWFHA3LiBJfII0VXTzPHE71zz/9BMIBZlSpCOHRJqIDMmdhnBAcl6VtSNIA97N72uQOqOOit39wTy6qWgVOf1ul/HhgMbbJND5NA31c+aOD8nxYXSBb1yyl1pLU9X0uj2KosDWnsFkgpSz5FwyOkFrbRJ0lEgOuKaqk1AYE4upEILK5DS2wUtFpjUqRnp5CxMUhc7IhcJXFZkWLC/2aCpLa2mFA7XPcHeXxf4yptVC1iXDgwN0zChMCxAopQgI8KkpLbGEkksAKSmrhlavjbAeYVI9upKKpmmI0TDfbqc69BhSPfPcJp8iN3MmkUxHlOgDS0tLlKqgms1YWljB5dC4iMoKfFVSBsHRpRVW1haRvqEaDYjVBF9XmDzHl2OsL3GNR0aRaumJEDXEgHGO1SyjLQ22DLR7mm4O1jvauWZaDTnSXsDOHKPGQhS0ipzpeDzXngMiSHwQNE0k08ntFklsGBcDdTmjDCVRhuQqzVMDmL/x746JwQXQX+yiNDTO08o0RQSkR6qIjTVKtHF1Q7QBETxLvZyNzQmZkkgZ8WGK9mPqqqTdWWL55GkOXthhudslCM+kqjmmMx7660toEzm6ssJqv4PWCkSg1ynQ86x+WncUfu4cMwikjtx+4TxFiHzF/YvUruGRJ77I3iVLp2tY6vboZC1q2yClxMaIUKC8oKy3iFuRtdvv5qHlE9z32FNYOWFJFkghGMVAkJJSWHY3tihjDX6WZmoqMYd0CASpGGxNMJ0WMQhmdY3dWGdpeQ2ix8VI2VjqrE3v2FHO6Qo/G+GqwMTOGweLFko6jix0qKcVZApBRvCCbquNC5FpWVFWlmo2oZqOuOnUcTau71FHqIkUSjHa2WNSzohqke7iMl2zyGoWGASRmBtVhUUx82k/18kEQSjydkEHReXt/OCcngXb+LR2hICJCj+Pp0cUswpmZWRpIQLzAVBUhCZg0OlQawO1rfj0X36WRx99nNF4iFISKRSNFzTeI+frrhbJgZrET4N1kVwpvE2H6ywKpJRkKtJWQNkQ6oasyFACDBHpoZsZ3HhAq2jjrEWaDlm7y9bGdVpFC1enyGq7lWGioJk0qACTKtAyLc6duYVbbr6dnc1rjCdjJjNP4QI2E2S6TfSBdreFUglGjk8b2iACSs4dj3OnTKBhODgkyBUa4ZOg3SR2mndlmvhGDRLqAFor7HRMt9tla3OX/+ejn0Q7uPn0GXqLbU7ffitnbr+Itg0nTp5gPJnQXujxzGNPMNobMBhN6Q1Kjp48SnssUajkyLINx8/exubsCwwODqiqmrVjZ2jnBcQbDaQSqwRPXXqOi88+y1333o3yHuXS74vzyZ00jxIE4RNoOXikygCPCxYnAuPZNEUS8w4qOMqyZHg4prPSJ5Qzlk4f4+qlbY4fW2ZSjTEilVKE6HAB3MShW4usnL6VRx58hBd2S4QAa2fJBV7VIDN802Dy/rzBTCFCQ8q1B7yt2bi+zq3HF1KQ18e0V4yBts7nslMS/+J8TRRSYH0Ci9toyYVJcX6R3odKpfejNu0knKPZfP5FvnhlnSzrYH1DIxXb04qZ9/iQ3O5GZC+5aW/M05VOHLXcZCihU2uRUPggCN4RbaSVFYS6Icj0pzIB0nuiTi3IEo0286bRKMG5l2DqhBs7XyCG+YDvhpvJ8+ylZzi+tjaf+FuEd6hWzuZzT/Lk00/hQqB0lvrQYxvPar+grgNSGoaDIVeeegwXcl5x72t51Ru/gWOtf43pFJTKceroEr7xDKoxw6nHiGT6kvBSKyoI6qnDE9nYPmDz4IALN5/i/pe9ib3r17h+bJXRwR+xtbXB5eev0I497n/lq6imU0a7+0TdIQrBUn+B0e4u6y9e43WvfD0dYXC1o8lh0hzw+FMP8mcf/wtm4zHjeoSREDTYqqGb5YQYWFzuIWSPcrpLWUY2Dq9zdWvA6978Sj7+lw9y5NgCB6MpxnpE0Dxw8ZW8850/wrnVJT789GXufes7eOPr76erBJ9/z89gul2ORcWJU6dwbopazNnvdPFixkf+4EOcPn877//VX2KffS5UL7D9xUvsliOmswrbVty+cAqTCwiS6aQGL0ALqhKUSc8gYn7I8xG8RxcarRRVWaNRFK02/XbDTDXUZUU+q/mf/sm38vToOjMh+POP/B7Hzt7Ca1/1BhZEGyciSksa79Bf/ZX8RBn44Ed+k09ce4HFVs7lnTF3veIumJQ88EPfyQvvfCc7J8/w+m9+LZ/+xF+ytbFLnhXMJkMyVRBlQks4DZtbV5AdyWc/8VGOjWu+67d/lT9/42vQrRw/CWgKoh1jo02tptJgJAilsdOKk7edQU4q/uKhy1jZQllPJhSNs7x47UVyKWi8ZfNwA4oWs3rMxI5p5y1QkZ959y/xjq99Kw996mHe82PfxnQ5Y3p5zOa6YGG54EjR5qN/8FGqWaB++As8crBJ59w5PvmlR1jJF/m1H/1B3v3e30ScPYrceIbBU5d59OGHuOu+V+OqCdsbV3j+mUvo9nMcP9qhHs+QMtI0gk9+5q9woeKbvv0H+di73s+3/M/fx7/5Zz9O/rrXsPylp1kfTxARait41f138rLjF/nEH38QqddwdkyrXRMcLJ8+woljZ7l8+VG+93vextd91/fzr371fdx+pMuknmJcZBotwk4ZuQ6d3iJPXXqO6cEAH0nfqy6IeFQWaKOp64TiMFpgnCa4kk5vkdF4F+scdVURpSbXbbxzKClwkrS2TgNrS31GBxPuvfMc9d6Is8vH+efveDtf8/0/xB//4Qc51dbUsoVC8uz1a/zCL/86p1odvvOd/5QjJ0/MXZnupeIqiaRqauqq5nc/9HvI3SssLbYwukO/lZO3JcuLC4nXGsVLPG7xX9uT/g6fxZUlXnH/6zBFC6n7+LohhkA7Lzi6tkwnk7TaPWazGdsb60hKTp4+g9HFfHCQ9qZGSZaPnqbTW2Aw2MNsPMnR1WWcWmBn9xpVcIx3t/ETS97JEXmJtzWm26EZDHjx2rOs+TM8e/k6V59+nJe/uAFijc8++iBnjxe86qu+hu7KSXZ31pnOtsn8FC3BRotCEILAxORo+nJ+vuyC0nC4zkQU6CDQhUZJ81JcTIjUtBSUAuspiharq8cwWUZVjqirhqyjaMoSnSXGyczOyEyKn/mYWEfRebyPWA9tY3AyoLXGZDmdbp/JrEQZmRZ/n6bVLqS2Jph7kl4yJsWXQF1CpFrxxFdK/08gkpYkRbI8I+acoIBUIjkVpKBotefspdSSpaVgMmmoyhLnPVlmUuNN3SQgW9pPEiM4lyCQPkSmzQxnfZqwh0hIPwyVGZSR3DhXOpeEoUzreSV8RDhPrAPOuNQIozNMq0CYHEmbZlxRSZLg4xxSBzBJPIv+BpNCEUQCKfsgCTptJH0MeJs2UTEEoidBnqVIG2SV2nqcb1LcTRukSXEpKefxiRCILsG3pUw/Q2pDmMPTfEhAMhMF0qT2o7ryjA4O8XZu544pDgECrZkD3CJaeYT0CJ2qSKuypK5ntNotFhb7CUTmEzRVtrq0o0ogcSE5sdRl/3BGXVuCt0gCeZYlBx2R4GA4nc0fxECQgtmsQgqFVmB1mrpuDCcs5wUxzCibhhAcNoASHh9EqssmgHO02gpjDU2T7P5J+CAB7GW650JMDjQlBMJ7cB6tk0ArVUQHkWDcIiYhXjiIigwDKlKKgJUGLSJKRBSRcjYjN4u0hKYsZxxZXaWuKsrZjHRss4gwF1ZFcvMJoSAmsUubDOcbQlA0dUOZNTifXkwhgLUWZz15u00gRak0Bp1nyEzTOE/t3HxaKpBBJYi8taiySsJaXcFcaA7OMSunCAR1k+IwdVninCOLgsPhLgtLa9jZFFfNkEXG8uIik3KGryMuJlcgyuBJ0YEYfIq3VpaVpVUyI/BZQZYFDvZ2aeUK1yiCUHhvMVoRX2pn+pvPS4hdCTYmxpaIEeUdXoGta+pmhs5zoo+YtqEZVTS1Z/3adXa3tlhpR1qZpi7HBOWpGkflpxiTo6PGi8S0ESJVOhMCRVtw5ugyyxKOLPZQpqAqx+ngZqHb6YJwuAhKpTKrPDNpQjEn+wsR0SLFJ6qqonEJ+KuNRuuM2azB1ulAElRq1/LCzdsub8jwll67xVI/Q9YTFIJMgVKCqglpMKDaTCd7RJkTQoKt7+zsUtaePIsE12E6GNKXGdK3sToxNLQxmE6H3d0BQUqsiuwfjBAGdvcP6OYtlvp9iram2+3Sag0pm4Y8S+4r6wIZHh89tt/jTyear2eCLRSf+/wX+fxDn6fVUXSNJstT5DW98gU+JmB/EAItNHUz4dLTT6Fffi/aTjixvUF7PGVvULMfHWOpmIym1Fs71K1AaEeUAK0dnfk70NqIV8m9qbShcQ4LbL94lZWlFTavvYiWlhgdRW6oGs/h2DMLApkJTNtQ+cjKyio333GCPIyY5V0Orh2S9TM63YKpd6hxnYD+KAaDPU6dOsrtF87ywtVNmqZGtfvs7m4zaRpsdJy64xydIzdx5P5V8sVFspYheIV1lqube1y7fI2D9Q0mVcnaiZM01ZTB9jadmFo6o2Eut6ajaKpqSNcwykhde65t7LG4dJSowUtBIxvaRZYAlCognUQGzcbGBk2dYt5SGJBy7l8VhPn3I2KKLpssx0jNQtGiadIU3jEXLYUgExErNZUTZLlBaU8U6X7wSrG3f8j2YEJ3VcHEc/TCLextbZPdENCDI9qAFanp1eQRlKbwOfXggL2tfcqFFWKe/7+svWmUZdld5fc7w53eezFH5JxZWbNqVEmoVEISkkoSk0AgwBiaFkOrQd0GjGF1220LGoxZtM2y7GYwxjRa0AKB1CAkAZLqgruaAAAgAElEQVQQLUqi0FRVKlTznFlZlVk5RGSMb7jDGf3h3MiyvfpTW/ElV2asyHgR7957ztn/vX+ba04cS6UbrWU23qWuWya1ofUB33WoUl+5b2IUabDWP0F8FBRlQV3XBFGmIVawqVzCWVrbEIynyEuMrRMj0CZg9+blDdYjKM5TaM3C3Dw3v+5mhBI0m7vceMMNNLMa3zhKkXNgdY29cY0oRuzutawdylkaadqmxcaKyfYGh48fJT76ZGoAixCUYm55id293RSBQqCCpJ017G3v0hmTSk3ICFojWofEE0OKM8UYe4VgHzbZQwmCpJm16FwyP1oihsDpC5c4+9zz3Hz7jSitOPXE8xw5eZiqGmJCzaxpiV0SF6xz1HWN04rj193M0VuvJz/9DM5YtNTI6PEhebiTK3Kft+WJQqUClj7KuXFxnc2dYxTCoVVEyIw8y+lkGodHud+imJ6bKJLDqXfvOiIZrh+cputfo7BdR1loOuv51D1fQo7msaYl0wrvBfXMcv+XH+TwgaNcdeJ4QhuIvgBCpsFH7CHhIJFa4ZxH95FCIZIr3cdUhpKcPKnxN4jE20gvVuAFiKCIKHKVYqSCSCF7zmOIV1y3WnAFXL68uMThg4fIVIGuSoKxaCX4rZ/9OS7stNjeBRyCY2vsqK3j4PKQw3MF1k2xoeT0Y3/P5z77Kt75Pd/BD/7Yj/M3f/YnHDu8hPGeTWPpikW+6/u+HalkAoWLvmgkpm1HPlRMd1uOHF1C5JIuSrRQjJaWWTl+ksXVNc6cOYtwkidOPccLL77IgQOHmF9dAT1EZTnPPX+G6XRGbCccObHA4QND2uAICKYhsr4148L5TS7ubCVswCBHyEApJL6zWGfZ9ZaVAyXNTsMorzhxZIkzlyZ85YuPMn/4AFt+j7Z1RG2pBgNuuu02Vra3+dX3vY/dm67n+4PDtx1xY4cXppFm3HD7NText7NHdWCeelrzxKUtRicP8bFP/wFTm4MUmNmY++79LFoXBJHRhRnv+uYf4lXXXcu/eehBxCAwUp4ZGi01UaZ9jhSSSuYcPHI17/yRn+LSs4/wd5/5BNVogf/mv/0lDkbDB3/jV7nkG7SBdpSQFMMjx/mVf/Hz/Mb7fhq7tMrrv+Vb+abX3MbhvCDbtQidMAZCZ9zxg9/BDdescflXfoYzzYjVwvBvf+PXeeJLX+UHf/RHefTMOczqQWZbE7rO0DVThDdopVPjrPZ0XUfs/JWD7vt/87d58Vsuc+sLZxlrx9rKIYzPac6eJi9bOgddmMdHjwgNSmYM1tZ4401XcXAQ+dO/epKu0szqKXog0UXOeDzB2UiRSbqdMc9tfJW4z3UsJTp0vPLoMR74xKf473/yh2lvPk4cR37oPT/Ns/d9iSkdn/nkx7nl5FX81LvfzQf2fptXhSO89vWv4wN/8AF+7F/9OLOtbQ5efxPf+T3v4mcffZAHnv0HTN4y2brEtB0jiyFCROYGGco6lhcrPDN29mp0OeLs5cv8xv/88/go+NPv+iJXveMW3nvzu/i5//hu8sEaKga6xvChD32ESTtDjQqMdcwtLjHII3e+9ht5+Ktf5elHHyQqy2e/8iCffuinWCocO01ighoc88OCuYVlfvZf/Uu+89v+C770qb/k//jffpVLe+uM5kZMxgZpHIOspDENqipRRtE4icocu/WM8TQZAabWkMkMGQXWGXKV41wAL6njHgvzc4ynY978plfz8IMPkeWLPPC1R7nuDbfx0vpF4nQLWy0zyEt8hO1Ll/iBf/7dLJUjCqWQsa/e6MX66FMLeNdafueP/oL7Pv8l9mYTrDconVAn33DDjRw4eKhfY/eFVfl1UZRWFuahPEahF2mtBd2h0Nh6jG0jp85c5OO/+2+4596v0MxqBqXip97zTt75vf8IkRc4s3/WiylhJQOZ1DivcS6ytLpKNVrEzhwYg1LQmo7xS88xKgYM5w5AUbK70aLKlzh8aJ5BtczE16xfepzGX8aZFWZb63Q7hjOnnmC2WrF9ocO1DZ2pMNZQFjleCbqm/f/1+/j/fnzdBSWdDRBAcJF61hJpUhMSIkGNVeqkQAm2J9vEGDi8ukJ9eYPWWJQumM5mZGVBYhuJvu42bRgJaQIdgUwkIcST6rqLoqJYGzI9ew5IGxkhZAIxugTQVSqJWs7141chUnQrpbZ6SHJaXPdriNMWNpGCfEjOJUJINnPhCVEiQkTqDILDRZccQ0IhQsQ2TRJ/sjzF3gR4EXrWQGquiUEgpUoPybSipp+tn+4Jp9BZyg6L6JOlPPoUl5Fyv9wOrRWZ1KndTmQ0AbKoQSmyuRxVZEjnwHW9GyXg96dkUiBkhlCKGGWaAsQ0EQsh7ofbEl8gps1TFKkaODif4kUxELyHPLF1rlgMe1dDIG1sEgAdoowILZEhIKJChoAK8coErTUtk3bab5hyVF4kYKTUWO8TpLJQrC1UFFKglGTaOWobaUzLytoy0RuCE+i8YHFUIaVgYXFIkWdYG5g0hoyCWdulyaoUZLlmbWmRQTVC5gWjOIezHcGD8xEyj5TpN6KFSJlewBIoFCATmDVdqRLXq4e1cYxkjiqKnqUBTRtTw5gu0veP+41qSVDSSvaOuF68i+lalGWCw2eC/rVo8r6NTYjUTFcKhRA+cauixDuBHkTGdYcXjs4UTJsmNTEYw2humCrLQ4pkitgzomSynWYqo0tIJlSWpRacXpQIPTfMxT7q5iNIjc4LikFJXhbExhBCh1I51nnGzQxkIIiAJTKbtcQgMM4RZUauk2MiBs/25maaDEeBFJqyKuhagzUd1WhEJQXCW2b1JDX6eIeNPftMkCbUMeJCxHtFcJZDRw9jph3NbJPprIY8QVOliMzajizTEJMTJkVmXz7Yqv1joU/clkgkyECWS647fiMPfvmL1KZFqxLnO+pxYp5ARBhDHqEIQ/Qo4oXHB4mTiuAMzkeGlQShKbOcKATNdI9Ax6Bc4NjSGkeXRgTv2dybUgdPriXOWMpBhRIZ9uJ5ilxiteLA2iLn1reSQ9CbFGeUlq7Zw8YMj8AGR1ZohLRkrWfbuVRSoCS1d+RaIoTHRYhRUGSSg8sLrGQFwXRIaYlCkhjG6bkmdYZQc+jMo31GEJGqLGjaGdYJWueY1GNkPiBITzEYIaMgK+eoTU07nZLlmqXRHE0z60GQmnHdMa03yLIM71NzUYwR72P6epFEWScVrdC8mA14yTtm59e59+EnWD1+hGsWM4bRUuUlMST2nQ81ynuEi/jgcFLiugiiZnt3B65/Jc+dPsWBmUV5y47vsKpgWSlC29AOc7pW0lgBMqMoPDEogkv8qmAMbdNSlCNULolasLq6zMLiGs5HRm6GcxOmVQH5KiORnB/BB85e2OTI6jKHM0957QnOPP8SVCWtV2TOMSccPpOEUtB5j3SRZm/KLbfdTOsDpraE5cC0jTgxZPHWN3DVra9gsLZEMJFMKIK1aAytabG6YOHgCWb1lO2tbWw7Zf3ciyiVE4Im6gxhNUpBCA6pCqTMQLjeBafAOTY2WuobK4zQ5ESkzIlaI3IQLoG2x3bKxvpl8jJHJGd2P8zpY+giSVaJR1kwmFtgZe0gMgg6H2iCw1t/xdkcXaCOjnUHc7u7LC8MQUhCP0ma1i0XLm0zbGZUekD24llOvOlqLlw8RzUomRHIlEIGlZq36pZyOMC6GQrH3HwOg5y9rRbZWZrLe+SDOdYOH2X5yBEm61t0CPYaj/QaesHNi14cJq31UqTw1HRvhogWFywxgIwCqQqqYsCRo4fZe+o0Sqd4Y/DJFZcRWMgVV199NXd/y9tYqJbRhaCZNRxaOYwMKVrVkUTeam7I8euOs76xSZ6vcuzYMrOdCa4VOAeTnRaR7bF16RIHjh5LpR+zCQurq2xvbSWYfwholVyT46bG989DKfu9BIqoJD46vMhwMSLJkDKx42QMeOex7QyCo5mZXtgOlEpx1XXHWD10kHI4j4gWFSxtO6PrPESBNR079YSNrT2atuWojQwWVrn1ztfy+KNP8uLpF5IDygmCSXuwxHICrQu8Nf0zLa2jXgp2phP2plMWq4AIPa8oBLCBTI3wrknuTBTCped7giJIiGmvFIQi9i5PGcEGjxYF9372b/nUJz8DgxKcR8kMv19jHeH82bN8+A8/xC233sodd76alaXUmKaEJAqBihlaJnYoIZJJjSQ9f32UCJkTQor+CxkTtkC83GUs0Gk46QNCOpR6+XNKCoQUSC1QMongITH7032mFXMLc4QoCTpB9qXQ7G5ucGH9ImE4R7u9RUQRhCMKmHYN9mJDIQ8hJczqjuEo5/EH7+eu17+G20+ucf98iQM6A4NqwMracU4eO5Sinv2QYx9Fse9SqgY5xnmW5+epiixpk1Zx+cVLHCjmUWiWRiURWMwKVg+tIlXB6efOITrL+PI2pmuYXN7hns99kWvvOMGB/AAiBOazkptP3sAjV1/L1qRlvL1HvTvBW089qamKnKrQCJHx0kvn0JliWGYcu+kkpy4+RDub8vo3fQNfvPc+xtMJFy9e4rbX3EnrS/6X3/kA929vMv3yBq++9U7ufO2riNcc4ed+599y9COf4kd++t00s5q//3d/wgOnnuHxjRe5cGmX1qdmT2tDipRGybRukLIDH/nkX/wH/mx3QjEaIMYzlhYWyZspe8YSGoMSGTqCE5HjR47zPW95PV+zDV90mne843t50+tejc5zJv/nr1ELSfCKmet4+2vu5Bff9wv8+vd+N8/YHU5mBaPGc1wVuI0x4cRRWN/l1P0Pc+h1t6OXhlRX3chrj7wG37WYy1MWrlnk3ke/zHuW/xn54RXm5hYZLI2Yj5HCBjrrMc6ivCQaQyBSqALnOvRIcUgscf13vIlrluZZWznCrA6I9jLDzCGFoJIRY9q0GiidHLHS8pl7vsQdd1xLHbfBpbKJudWKlSzn9G6NjZ7oOqoKrj6gqGvLxlhThQmLywd43//wi5w/d5p41TKLsqBaXeT9v/w/8q/f98tc3rrAt37Xu/jsX/wlv/mhD/NXn7+HoRb83efv5e3f+u2cvOpqnj1/hrtvuo1P3P/3/NgP/BP+4N/9LidfextnHnySssoJMjKsIsp1TKOk8an0p/YCES0XLlzAyl40Lhz5V/f4tY/9GUvHK7bPz7A6x5iGpcUFZJHRtg1SCdYOHuM9P/zjvO3Nb+PUM8/yyGNP8YH/65f5vh/5KZ6/7zTPPvkwZtwiOokuoZSaynZsPvsI1Vvfye233MR3fve3cfHyOZ746tNMN88zMTXFIF1DmTaEUKBlZDxrMNaxsLCAcZCHgJaSPMtwJu2/Ykycuhgjo+GAW2+9jm/71rs4uHKSD/2HP+X81pinz5zF+ntYXZpnYa4AJzAiMlwsWVmZ4/Wvu7tvXwxYF/sBt6MzlosbW3z8rz7F1776COu7G1iXDBrSQeMkh44d59Dq4dQkLpIrU8RAFFckpivaUmTfTds7Nl/e4bM/Y+wP/MSgGI93cHunWFxYQ88dRuucYFICYa8L3P/cJT779w8SvEPmkrGJ/N4ffozb7riF5aM3MhgW6HKBZrLDuQvnWZqTlNUKopzn1AsvcdyDygZsbFxmb/Myi0vzTKygmza02RTnHXk+5Oo7buGVr3obXg5Zf+EUm2ceZVZf4PjyKgvLK6zvbNG6TaKfkOsiJZZsGox7F8mHOYgiNYl/HT++7oLS8spRREw19kCCNwuBELJ385De3JjEku2Ny7i2wziH1hJvLVprgrcUWqO1RvYOI4kiQyJ8rzqSLOQyeqRPQLBMFQxGBc10lkQO37st+ovDuwRtFSJFN5x1xBhwPpLnWcqbE/pUS5oOSSnw3kKP1RUhwRCDEAidWEHSdmiVpfY3H1IW3lmGwwEhekxnyRAoIdGCPlYnX25qI7l+jHM4nwQsZIZrWxye2HUooVIcpW0gBrwz7O1sUxYDMpVApSJEvIhIVWFEnkQwnxqZtFJkGrRw+BhxbYsPCa6rlCKKJBxIqeiMxVpLDAMEqm/M6aHkIYUalJZIKa7AUveb82IvnsQIPqQGMGJyc8k+D09Is+dUVRsSvH3/6/D4EJEiRxBpW4tUkjLPyPOcxtVEG9FZjhCWuQEcHEryImNcW4KN2C7QNo7d3RmrSxXESNM0hGCRMsML2N7t0mEhRGJQqTmv35xlCDKtQApGc3NUwxxjLUrkNE2LMQ1106RNZbDJuUTAuABIpo3tGV5Jagje91GHFAYRziIQfSwmEqJE9oKHECm24Xs/ohKC6B0ypofj/v9KEGjo76X0b92+CBTSn1JGlNDkBFqraI2DJrVrLIwGdD4QgiCIdO3v7M5QSpHnZZp4hpDcLN6n9ylTlGVB27YoCdXCfAKniwSNRSURxJnUChORCCGxNtAZhw2BtkvAbikV9d4YWRQp2mU81li2OoOzHSorWVhYYDAaYk2Lj5HpdIbOc5yriXLI/MICWmmcMXSzKfNVBSRXVQLFkt4HEXomWrKBC+Fpm5azZ87ivcXMZgQc1UjhbPo5tU3vQ+y/rt/bpv8ijaOTiCZEP02GEBVlrrn1msM88AWBcx5lLaIzRCmRfS1qiAKLYKuxDBcrRKwYlnNMmxmtmzFXDRkM5pjWDW1MDoOirOg6hw6Rrm651NUM8gxTt7T1hKgrnI8YG1nf2MHqQF1b1puGabtDNpwjOksIDq0Ew0KRaYFPyjrKiQT4tx3jaDERCpVRDgbsbO5iY0DK1GgYBcwPBizPDXDBMRmPISTeTVXm1C1kOgcBQQaKoiAGsCYdiEUUxOCJ1qG8p2smoA1FNyDqEpctsNXWXLp4GSkVy2vzaLXCbDplurvbH2olIViUksyXms5FQu/Acl7SRUfwkTklaYzgid0dtm1G/cZv55AwLO+cJrQThPMU1SBFPjOg8ygZ0b0ALrsJrvC0exOGJ29g/dpX8vDDX0ECC1Jx3cKQapgx7hsyJ21iw4kAc3mJMBajIloL9upk149Zx4gCqRMHbTSqaLoJ6y+eQsYmOTZFjtCJAzUZezxwZHlEbiXLw4JNrRHzc+TBYxuDMx0hGLQMIDJ0JpDSUGVw64038NDjT5NX20xnNQduu57tznLvvfdhG0M1GrFy4ACD0YCqVDjrmdUesgKtNQePHafpajoHl7c3QHqwEPJ0zwcc0Se2Vuzt6QIoCsmgKjATg7QerSNaZbhgsF4QjGFvd8xDDz+Ok0W6y/KcHPpijXBlTSCmqHRRDRjOr6DyCjut0VJSeEEnVAIIRonB0JnAutll8vCM5YV5FpfmX467RkcpHN0sorVl4+yMF585wOpVJ2m7VOkOAiEDKgeZa6SSBBTGOerWUE/WQQ2ZNS3OtezsRiZ1zW1v+S5WvuUEs42XePHJf8Dv7bK3s0PXtNTWEkMkpIdHsuVbj8glrk2sKqLA+haJpNSaG2+8lscee5Iu9C5fAdpLvunuN3NiMePqG1+BzgtEDLigoJ4Rq4iJBcYbkJ5cFyA1C4sLiOhZf+kF9tYN+WARUVjGu3t0MfDAU09xeXsLWZYUKmM63mH14GGeffJJ8l5QQgjKwYBjJ49TDSqayR6+F2Ji9AktIANKp3hfDFxx0AQBdWPY2Z0ybWZcXr9AM+tou4ZoOrTU5HnF1TctMxot0G1v07XJmRUzcLkijxW5nrAxbTi/vknnHuXGG69jeW7IBUnaL8mckBXEaBEhYEwHmSY4m1y32YBoO2R0dO2U3fGUgiw9mwUYZykLjdjn21mHlhkeg/QKZyx1PcE4lwaFcT+iFfDRcu708/zVx/+Gc5fOkQ0GCWFA4nTKK9PyiPOOjc0N1j//OR56+BHuuvNVLMzlzEzipWhE2q/ExEDy1hNF6LfugrqumbRtioIikB4ymaFVRtrBWpDpvpnsTpg0DVWRJQ5nAC81URWEmNyoot/ldk2DN6BFSVVVZDo1HIpcMHvpHDvTGW0wyS0nUuFFuqQljXN4AXjL9qxFZwWXty7yK7/0i9Q7L9FWA8JkgiBjMm2463veyMHlES4kWPj/68MDLuBt7FmECpWnaN9oseLETdewO95k9aklDh5ZBe+4+vbbMZ1hPJlw/nxyPZ67eJGt3V1q7/jbT36SV7zmdn7oHW+lyhO4u11eYnF+ia7pEF4nETTL0JlDZDlGaqpiQF23rCzPs9FZChN413/5Lh576B8YX+yY7jTkQZHNz1EKxf1PPsrpi88RheWVr3szt73hdUjncHXHcNvyj971Th7/k4/ye//+jzgl95CDRep2gvUJzdG1hs6R+J/WJZ4lES8gy0v8XIs3DVkh2NmdEJQjJyOWA4zxzBpHlWt2trd46onneOWb7+a9GxOuvvMO/v4LD/Lpv/0kF+uMzkZQhr31Kevb5/nrP/wQf3PpBVw1YFvVLB5c5blnnsFNal555ysQR5dwywPWHz3LgduPEy5M+Ce/9Rv8hI48+/t/zv/6R7/LppK89z0/wdRZvv+utxKaDrW1STbewYt03rLOkGnFsCr6fZUhFxl1bFheWObTH/owTzx1iuH8kLVsRjk0TFrPtFMIIVFKoXR6rthuhg/w1a+dI6ocjGI4LPF1x0ZscH2trdQFRaYpHDjfcKACpQMHDlzP8xcuYvHQReJiZOvCJrY2fN9//TPY06f4y499FG0tVV0zFzzGR2664RX8wPd9D4tzc9x5/W38xQc/yI98+3ey+eQjnPjR97L5hpv53+//aZCa2DUMlkEpj+08bWex3jO/vMyJY9dx6uln6Lo0DFk6uMr25kWy5Qoxa+lcR6Eko9EcgUBrLSFKbNvxTa+9i29961upBgNuvuZqxucuMzd3iINrx7g8b7n+2lfyxANfQOvkjF3MRiADV99wA0pp5haXeetbvoOPffCDvHj+PEEJFlbX2NrZ4fjaGmUeWHctwaY25iyrKKRCZRVOJMxB9MmgYU26V4VQzGYNN77iGu56wxs4cs1dPPXgZxHCM1+VTJuaqqqYtQ128zKDckBelFy4NOP02dO88vbXMqhKYpRs7e1gOoNrar7y6KM8/MX7UNJweXuDtpkSgkMogZcCIeb4m//4FW55xS284TWvR0uVTmBCpXN9H19+OVicEjgve5d6HE6MvWtC9Dt/wea45VMf/zNee4NkcekwZTGP1iVeegaLayw1NUG9xKweI2NH21l0VnBhb4/GG44fvYmuq9ndPI8qWm668Q46O0YRWH3lGwkip93d5NK55xiPd1lYqlheXUVGzSW7wc7uFqrU3H77nRy74S2ImGO6jky9hNKBg6trLMzlzC+sIlWOMB120CHVgGa2g6BDi2H/UA10VqS16+v48fV3KCmIIUNmOT54yoIrEYnYO46kTzwFJ0EJjXM+LUChjy5pie4rIVXf4pYpgZUJqu0jadoFQMCnUwSuccisYFgMMHVDjBaCf/kAHkkTGZHgXFcESNEf+mO64fZfba9lpuhcP1FKOoggRpWgai4mGHRj0MKkVhNriD4tznmeU+Y5XdsSXCDTIrXR2C5N7wRpohrS5sJ5j/fJCu9jqk22zuIcEEXqpOint96l3LrvPHmWUegsNfLYSMxUUmfzFDmjd1aJkISIaAPOWFzwoBRaq/R7kanhqXMOaxJQXMjEBfL90TME30fWZO+yEug8xzuLNSmiF1wS4oIPCBGuAM1DjEkcEYlHFaNHxNC37ZGmVFIghMJYAyJVNNsQ0MHRtJ5SV2TCg2mRKHanhvG4o8ozpJbJpaAVdT1jb5wTfBJm0kxX4GLXu38yZEwPmiQeJoEsvfNpI0cMINIBONPJQl4OKvI8Y1CNiAJscPjaYruW3XpCdAEXJaFPGMn+2okIopS4ACpAJmWKJUmJ86lGOkRQOk20Y6RvbhOo/qEW+2sY0uZU7bvaYkCT2ANdtODTzy0y0ErhhWLSOBofoOtwPrLXepxxdJ3przmF9wrjwfqOXGfJ2RQS8FtrSZlrbBdTLAPPbDxmVFWU1TI7e3uEoED1h4grlZwCYyz1tME5m+KWIrG0rOsotMYZiw8B23lccLRtzXQyIVca23Q0bU3XdkmgyRIY2zuHrgYEZ7EWykzjXYeUkRg9+22DSqgrDP3k7g8EIjLTID0xGigDroGmi7igKHAMc43x6fcd+onL/kMjRVt7V+P+zDcKcpmxuztj66Un0UqCtcgqMXhTlCatU+nwke6TWd0yP8pxpsaYGukjrWjZcBu03ZQiH1DMr+AkTK3hwtjR7TXcsLLAyaNrNM5BTNGOEAOXx4ZxdOzMLKe3amZorBAIFVBFjnMNmeobHlSg0hqhJD5mZFmJ3drGmtjzvWBxYUQznuKCBREY5slFsjhfkOcy1ZoHm9otMp0imiK5EQRQZqnFq1HJCelt1zsZU9ulcx6cSc9zO8PPdhgtLXP2mXMUq6u0L55j+/I2J08cJMsUQiWXw+54RpFJBmXJcGFAzNM1PhnP8NGCSc2HZBm2m3FqWlMVi+TzOWp6DuFTe1xE0LUNWmsKnVErg3ABLRwxdkhdYOpA3ewxmY45cPfbmWYz2osbjDZ3iVIwdZ7aeNZty9Q7gk/NkjKH2DiqPLnHOm+JQVKFxA0ysw4fPFkucSFHoCiqRSICGyJd45mOp0gURVZS73ZUR1fBGgbzGbmBRigutR2NcASRYOHag84s0dSY2ZiVg8dwj1mefu4lptay9exZnnjs1L5MmprDkGRCoHKFLjKk0uTFkOHCIoOyxDsYri6gynnqeswgy1JBQ7Q4k5hJSkqCztM6KSVXHVzl4PwiopXgJN4LUNB2cP7iFs8+8wjnzm1R2xlRV8QokTH2E8WW4CyImOIQMdnFh/Nz5FWOb00q2yDtGXLv8SE11LStwUdPDND118qh1rK4MEwbyRgQ0TIcFSxoTTkY0O1tkcmrmcXUfCVj8iAqFELlRKkQecFeA9uNoANcKcmaDmszjIi4xrCxNWbhmhoDRJYAACAASURBVIy5o8fh+ecospKrrzrJ3nTM5fEUs782Bo+WGRmRanGZ2gpa7wmzGV3XMqpKgpCMd3d7QaEgOIvGM5wbod2EIi7g2rpfmxyZLNirx6wsryEs6JgioCIzCGcQMTWO2c4y25syygK6czx/5iKyGlJPGrJiwHTWooeC6XiPpbUDqJgGADlp/3TVdVdz5MTxxJtrDZNZTdMaMinRKqKA2DftpoFi4r+FAHXXUJuGyd4WO1vbtF2bxHjvsGZGfPIp6knDq153F4evuYHnHnmQLNe44NBKIXTOcFASnGVrcwtnDUIEpnUDMg1dMlUS4pgQLUIJjOkoyiG+qV8eevWHB78PbFcZ0ntkv3+JKEQmyXSGbT0+OoSHICUu+ATCDf1aHPb3DIrNS+t85I8/zF5rUHlB13X/D6d2ePmei/vrFAil2Nq9zL1f+BJvf+sb0blGSElUaSihZXLhpzXVk5raNEoq8lwRrUfLnhciFFpnWJ8GOkoofEwtcjpTSKFBaqTu0QRin7+QXqLSitFwhFCaUZmjRB9pFJ6IhvGE8zsdcqQQJIeCizaVzIgcVZWIakCsHbuzloOHj0NZ0jVjLuzWbG1OKMuMtdUBUhjuvvtNCCJKpsNdypInVlXaNCY+VVc7vHX4oBgO0nBm9cBBymHGsRPX8Iobr+LSixeYrm9y5qUXePDhJ3nx7CW8he2dCeOmYTLbY2F5yPji86xfuoWTxw+hBFRSE2tHszMjIGnaDhcDxnqq+QEmePbGY7J8jo1Lu6zOjZCd5+F7v4BSgosvnMXXhrteeQeizBDZALt1kTfccSeHl5Z5+msPs3nqNE/d9yDjlWVuP3Iti684ivr95/lascON197E4489jOpFRqRPJUFeXGFaDouMXElmTcd4vEsMlkwpOuNQRPKsJMo8MYUmU3KdSlWef+kZPvZXf8LVK0c4/cILfOSxB7jw7ClwXSp9cBOMGTMYDHjmqSd4/NGH2BEJzSCLglnX8cCLz3PNNSf7SKLk5re/Eeqav3z/b7ORV7ylexMn7jjOte94G//41EP87Oc+S9M2FFXO4ZV5zp85wzNnnmPdTpMzMASc7w/vQqAjlAsLHD94nFe/6xvRl7a47s47uO+RhzDB0wKZlvgyQwZJRjq3WAT4SNeBFAFDB1aRq74AqbVs7U3JdMny0hJra2scGI7YO/80VrapgCkOOfXMI9z66rdjfWSnndC0hs6Nue+Rh7jlllfwxWcf5d4vfJZ3v/uH0fML3PWGN3NwYZRKi7ICi2Ry9hyanOsPLHNrvIv/6dc/wOfvv4fVuWV2mwmDQtK0kayAiCVGjc4r6pnhicceTmUvVqJUjqn3qAqFaWqE1OSZQsoUdY5eIqPqb1nBg/ffxxPfdDevfs03cu5vH+LJp7/Kr/3W7/DbH/4I9QsvYqZj5kaaxniKIsfSsbK0wPXXviLtYb1i6+Iuz7xwBiNJbauNJc8GbHUt2lhmJuBbhw8ehWF9a51yOGJpeZ65smBrZyPhUaTv+cGeyJTXvvkufvD7f5wzT57mM/d8imI4YNa1eJ/OtVmRkylNDIFSCvKipGlnhNgSYsHW7oTf+eM/R4/3aHfO4wK0vmU6nSLMlOg8w0yxMjdgYjxtdFxz/QmWDxy8wq6J9Gf0wP6GPf39ymb+imF0/8ncf13/DzGAkNz38MNcPv84gzteTVatEGVOlD3r2XTgYHdjG2c6Mp2+2EUIVuCMY1ZP2L54hp1LT3Hs5FFUMQUzBSUpZIFDsNc5tte3mJsbceDQYZqmY/fiWdYvXWY2u8Rb7/4+ltduYbJxmd2dLS6ffx47PoPIoSxL8izHeEcMnrwasFYeR0lNM3uWEBzGe4QXdCHiXMvG9u5/QsX5z//4ugtK3huUzBOLSKaqxWTx9r1o87J4IGJqgjEuTY+ESE1MUgggu8I/yZXCkaqAhQpIrcFZZIyp8QOHjylyJEmT72GW4USkmTWJnyR6N1AAVBIsohBkRY63KS8WSa6ply+ul43DsYdXpetRI5ROApNPlk1rHLM4QYkkBASXMvZN2zCoKqrBoN8cC4TK0MQ00Y0gCPhocCH9Xy4kEc06B6QmMTAQ08IqMw2E1F7lU4SudUn80f1kGN+m7xV02kSgUT3rAhzWpwO8i8kOHn2qh9YiojKdoj690JZlGqczlPN9sIsrG6R006U2E6X0FZEu7NuWheBlNPrLIp4PLt2s+1XKMTk8pJAEBNY5vE+OiFRp7nGNJZYZQklynSEyhWs7govYEKlNoNxHKMueQ7I3pijzVEEpInjQMrIPFI6kFg8tIs71HCli2iCKNEUJPgk9+P0WQJ+4EUKQSZ2cXXn/mvvXkraNvYsOKPOSoATRJUcYQiFFjo6WqDNcSD87MWJ7TlXobZpKJSu9EMmyqKVCxJCaZiKomDaMDt9fp5KYZlngIUZJHRW2Fy0lKmVzm9QM4pxAiLyfnJNuEiF6kKxLIoiWhGiJVvWf58r71HlHqVV/TTlylWKFZBKZ5WghEwdCSYILePbdaennS8/59HepM3SUKNsxnBsxWphL0/2+ETCS4j/pXpBYk9qYsqIi0xLXTBPvbB/G1y8GsReRVb8hikJQzY0wwhOlB2XJB0lc1FHiY0yMsVr19emxFxrjPuvyyj2gCo3oIh0eHSMXtra5cO82eZ5jbGBOS3SRpZHwfq62v2+iSOyx1kCIhhADPmh85+jqjjzXhAieyHhvDxcElzvLRtswrCoW2obGWTxQdx2TJrA+7bg8rdlqDPV+Qw4gg4CokeWITLWpCcdHbGiS+OgMeabpGpsmYKjEflKwvLzA3mxKpQWrZYlQkqWlAQMl2K4tBInMcxAaJXNgSgge7y06F3iXoLNNzwdTeQbBEgm44NOBNQi2L2+CaxhPLfWsZfHQEbJyiJmNmVeLbOxssFJKtvYsWgaKQiPxGA9z5YD5hSXOR0G7vYdSRfpeB06Q9/wJs7uFlxnDWFMMBmQ9Ty7YlrzKqbSiy3WaoPoM4XqmSeiwxnP5/PNc8w2voXrzt/Pwpz/BC6ZjZ2fCwBh2omHXOIKSBOvRStDtzfAmgFAYb3AuMd+ssShV0nQdPqSYYmcC490pk56tE1xi7gglURrazjOrDUprQp1cX6aeIZ1grsrRmaDpPE4ITAAtkuvl4vpFustjdrcv9xXuDik1uiwQMabGUcDESBMceIOfTAkeot8inD2TWoOIKX6bZeRSsDdtGY465i5uMqtrEAlmHEM/2YuRpu5o4hRZeWJ0eFHiXKR2gQfv+xpT40iqd07qMexj0SIkIViltUFI0EIxNz9PORhBlIlBICRCiMTKImKMI0TIi2FiHBIxLmJCy86kZTAYEGXEtFMKBZXSzOqOcrDAbDrh3FOnGR07RPQhwcfSokTrkyu5tY6N1jMVGp2XBB/pYkYbE2dFxsClMy8yOnyAqijYmToam5yZu3XH1CZHaIYGoRgtrpAVBQeOH+OxM+c4vz2lGU8JUnDixHGKomCv9qweOYZzaZjjXZec2s6lFleRHCwgCD59XuqQ9kQi4r3FtS2z6RRdZgzLiquOHMPvbTOvPMP5DLs8hzh4iK1Hn2E0KFJszGeUUWEQFHmRhnFZSRRw4fwFTp0+zeLCAirXjObmaOoZ0hti1Agsum/Y6Rf6tJuKEmcNyju0acmCo3MeWeT9PZMzno2ZPvMYKwcPsHbwCJQZne0ggmsMUghynTiK1nrG45qinLKxsY2SGdEFpEqCViQNNawxlNVcWhcFDKuS1eUDXDp7ls56greUaiEdMntRRiDRKvGegnSoSHK+IhJrLtMEm9bCtEZHikzztfseYMd0KQIYXM+e8v2MMvY8vv1Ba3KqextACiZNw3hSs1DGfn0XCJ3EH9GzqJL7Nh0wtYRcaIywgEQKneDnSiFjSHB7mYZVSvXikXBECVrmKCmRImEKYn+fJVFKpgN5HzmJMuI6S1kpzjzzKDvOU4V4hY8ZY+JLRunJioomaLIoWVheY/nEcaSQ5LmG4wIvzrG6vERZlmgR+fM//Sg3/ut/SRETWzH6kNzqMsHs0elZMhgK8iqjaR3egcgk3cxx6cKEO9/wehQBZyT3fO7znH7pNPc//DizNjJcWqNrXRpkR0shBANrOP3YQ5w4eDdCaYJXGNGwurrC3t6Uzgp859FFwbRp6bxFyAyvNJnOmR8t8N6f+Kd88eOfZbAg+NrXnsUcPsBPv/efcejIIeaPnWTz/CUW5kacXJrn8x/+NEM15KFHPsdz045Xvf8XQEnc8grSOc6cfi7xUoUADc4IbGeQKkt7L5lc7nmhaXXGzHTgBFGBCZBLQWEiWZlhXbpWLYmH2rrAAw98gcfynFnTkcmK0aBkNjNpoIwgy5eQwdM5gcolKigimp2dXT72kQ9x+8038/qr35L29dbRbGzx9Je+zB9/5i943nQsVStUQ8fePY9zphyxTEAtD3Gt5YO//++5/56/48ULp9j0EekjwguUSNen9Ypctxw+ehU/+c//O972zXfzq//VezgbJ7Rdm/ZGRjEOEERqTY3CIYSjyucw0/QzGJ9iSqUG41ti67GmQwFaeqo8473v+UkOuoY/+L2f56W6wbaaaqRYWFH85M/9DH/wZ3/O333uU4Rmhz0z5bd+8/28470/znONRK8e4KOf+EtOXncN//QH341dv8xXnn6aBx55jFvwnDy0wjf/4+9HKMGl8SbP773IWCqUVgxKSaYNne3wTpOVGS6zuOixRhGjRwXSXpzIdFKRiZy8hK7umF+SNJ1DyoAxnuhdaiXFEwqY4hl3hpXX3shbbzvGZz7913zt83/DeLKNdDAa5rjOUTeGw3MpZfHCY4+xtng1X/jMPXziI3/EEy88SRCCZmYIMZJXiSFrvaLtLISAj5G5UtNYQ5YpgolM7S5FponGE32kUAKpBIurC5x56lm+9MCX+exHP02nDC4orHCoPF273jukDwgZMGiqYcnS3BKZKqm7wEc//QVmL56mwOCjpXOBzc1NnLEUKrI8LFgpJIVO8d6FQcbdd97BrVddm/b5/XmeEAhe0bsxQKTz7r5XKQ0+ruTgrrAbRS+2163h4ulHWF7ImexNaJpNVDmHsCXbG1uce+ortNMJvtlDK4kgDc0BBpVgbn4Vayy5EqytHMQYy2T7HNVogc41WFODHiB1ztLRqzl+47VUpeaFU4+w+fwpQr0HneHJh5+kODVhOpmgsogJEswuJ6+9Br12kGA7ZrMZulIcWL2GTA1pakNEE5EImzQV7yWPnHqBz/z15/gXv/BL/2kx5z/j4+suKKWGiORKECIg2QdbyyQRCoHQpLYJkQ6SSiuEUumATkRKcEH0h31JjAElXubQBOeJPvT9a7F3EKTD/mw6RscAWqKFhiLHeE9nTP8GJyEnYbBTxCsSehGL/jXtR6/6i+yKo4Ar2UspFTJmCYgYHD5YsAGhUwbc92BFa9JGs8rzBLnuBQspJZq0aCsVUTIkcLQEZEBJjfL7jhRSU0e/wUibJJlA50r00bIk4IUYESE5noQB4STRytRAp2Sqg+3BxInRKwlC4IlJ/POe6D1ZllGUxRWWkZASlWlkTEDt3jCTQOey30z0V8DLh+7eThj25Tl6l0i6XSEdHPrj7pXffSBxQ5K0JBEyHVRCjCgP5JJyfsQgRkLeUEvJeOwRIdWGKy0QKmI6z2zcMKpqlpfmSMpZ//pjajMRMiZwuIsYEfprTPTteuB8SPBooXGiF0NjighKkdhRSoHUEiMSH0yIiIwR1dvAkZKiyjEhyUxCJk1G5XnaMCmBDwJiINepTtR7g+hxt1omq3MqOJKI4PsoQSDIwL550/uQ4gZaEoTrJ8KCTkrq1BmebvgYCT418oWg+qlYOripK+6/tKHVMZJJgZbJlWNNm1xXKuWTg4/U1lHFAFmGbzqcN2idsa+8RAHWe3To2//Yj4elVhcfY7qWZWpGTODvnOWVFRYXl1i/cCG5WCKpjSyG1L6XFUiZXC7zgwHtZJcqL3C+TZM02Tvy+oUjXbP79aHJeeRlAJHsyzonTacFyd3gBbEWCcER94e4SVx8uQES2m5/qpoiVypofJaiXoFAnkmUgCjFlbhvb69EqkgUls44fAw4l5433nuEzNEy6yODfbNRzzKru8AT53cZFooq14xnDbud5cLYszWLTIwhyHSo8CQXnOyfw6ocQZbj4gyRrI+9EC8QIR1SgwDhIzMJ1icBLuAJQdN4KJVkkOfgHSoGyqyicen57kICxdpgCWiC7yiQFFmOzqAzqebetslZRYgUIjlX9rYn7DYt65ubOAJ2NmNpbY1bTwhWc0/II+VCQdO0CJknRpNMcUlhDAePHKashjT1aULTEI+doDh+Ei0VBw6uYS9v4DvHvDdXIr42uHTIVeL/Zu1NYy1P7zuvz7P8l3PO3evW3tV7V292O6uXOCGJieNkYpuQITMkiBGjCA0aBWmk4QUaIpBgXowgBAQCiQGBggacIZmJnDhjO/Fu92633Xa5q7q79r3ufs/2X56NF7/n3A68QUi+L7pVra6qe885/+f5/b4rRaHFtlcmjPdCDjjPtJnS7N7FDA27Vy7zzEd/lje/8OeEtSV2Zw2zpiPWhuWqYtL2+AiN80xcpIs9VVUzdykPK4HGOQyaUJd0846U25Z8ziCQs10+ZyF6OueJwVAPLClkq0lS7O8dErTGFRUhabRSdB7aqPFE9kPHfOcWh9MOHwMRsdjqFETxoKTYIAR5lpMW8FQlOXuTFsuZxAJDHwOqczQJ9puWYueQ7Z0JWiWCElN60vl+DoqDucO1E5aUQZlCyiii5/a9MW2fUDYRg0Oy4+TjQFa2mUwuhRgJEdZWV1lb32CwtExwHl1aSAmfvNy9SmGLCmsrjrIRDdRJUwyHGK1oo8K5yOG8JdhSrBC25HDeS/vg/T02yiHFyNJ1PYW1hJAYT+YsUbO9P2XiRemnraFIUnOOUWigTJrbt94m/qDk2IkzHBzu00WYuZ5pJ/lvCkjKEIKncS2dS/g7d5js7HJrbx8/nVMPBtzenma7kmJl8xRVNWSwtoSbHTJUinpUszPvKA9mLIfEoCwI0TGZN8znc6yVwOSmm7M/HjOezjk+3GRUjxguDWliw/j6FcrlDczyKt/59ptEU9J3LbqqxEpQlxzev0U5EIKmWBoRvONwb8KVd67xvueekRnMSDmE5FbK3broKovRZyVQJERH33WsDSs2Tq7R7W/jHfRB5hHftpTDIX3XsLN9n+nkkEJBMpa+dagEVV0QzTLHjq1z9eptJrM549mM7ft3WV9bwgUHNklektboKASB3JfyzJ85d44fe+E8f/XgHn3f0DXzo+UaRI373pc+IupSSESVUElszirlvKusyuy6hnt37pG8wgVR2YQQUXohkc1E5UIQlDO9UkoYtNRsz2YsFbUsPVoKP5SSZsBFVIKEaItinBiOMi1RRhTlSuzGRgtwqZQhhiAZWGVFDD0URv58Y3Ftn29mlc96yZO69NYPODjo6QD6yCOPnOazX3mVVBZ03mXyZvE9BUg9oWsYH8xYq0oeefQcvmuILtJ0PYPRCsc3NynLEaOBRRcFN959k1e+d5Ff+PHzEnWQEvFgyuywQ1UjBpsDJuOee9v7PHRujXnjJP6hT/R9x8qJY5w9fZZbV97mnUsX+MFbF/EqMG8cPQPcpCGkyKAoKJXYo2sCuzeuodwHUYzwTceVd66TQo/zLmdLSdan83OK0QjfepxrOHd2kxeefY6Hjp3h2PE1bIrMJjM2RyuoDr730nf4+U+c5IWfeAGMZG39/G/9Bnq15vH+gO/e2ReAVsG1MMVFT980NL0Qc9oIkNkpReM6YpJf9zHSSngUiohDygiMMvQEKpVtug4sFcvDJZTy7Ez28C7SlEL2h9BC7wm2p0waFRWJkhjmNLNGzv5cluNjz827V1hZHXDn/n3G4xmT3R2uX7jAF15+iatuTnSOsBn5b3/vP+PnPvW3+dmf+igXXnuRD/zGv8XbX/wcn798ma9d3ENZiXggibUxeFAE6qKnLFqGReCFn/wJvvDPP8OV6V1e+8G3wa6jQoSqpA0tZZkweFzoUNri2zkuiAq1ayLVQKOspZ+3HD9xitnBAfXKCNe2oEpS0lx+9/u00VOzjO8DNiZCGPPZz32Gog2URcXBTsvDDz3MQ08/zd0793hifYWvHk4IwXPm5EM8+ehjTMqa/+BvfJwpiXeuXOWtl1+m7xuWj2+yuXma0x/6EC9/9a84trRMTIGqdGjlaOaKYCxe5XIhJXdS0iXGJHRMtPMJa088T7N/k+FIE5Ki257RO1HhGqsxhWE4qDn/+NNQrTCetezsHfK//OH/zItf+zy2NPh2LjPrzFMUJaaLTGY9h9Oez33lq1y8ep9vff2bXLv5LvO+xUXZs6tBJUdTTLiohFQuNENrKMuCqtL42NM60DEQE/S9pK+VtqALPa6H7738EjeuXmd/OmW4VHF3/wGKAp+CxJxETWUrydELmmNrIx4++yiDesQ3X77Ii1/5ImeWA42xOO+ZTGeUuqBXkcrUpEoRdSApmUlTgvtb9ziYHrC6tC53UtSkpLh04xa7Ow8oS82pU6c4c+IExYL0UOIwWNgH9BGBLIDt/oP7PLFaMI6n2dkD94NXeOanljF2nbt3rvDdC9/l7PFVNk6usjwcUOEJZaKN8OyT5zi2eZbpzjZaTVlePwblkL2tG5iBpe961EqkqCtOn3uYUw+do5nPeHD3Cjdv3OXO3Tvobs6x1SEexXjnLkr1nH/sKfbHkX4649TZ06jhWfppw3i8S1Up6nqV6f6Mvjmkd52ABCniQuLBrS0+86ef53B/+/8b0vn/8fUjB5SkWULYFwnUlgtTa1lRZaGTgVFFuYR95+UyVQllRf54VNOaEjYldJDMIOd6FIXk8QiiQZJ4BVF+JN6rh9WKamjx8xbwonZJcqlKzoMRNiQv6yqriwSwTH/tp5IPmWT8iIVLgh3J3vOMGyi58BcKpxQEPOm7DhUjeSQRdUce0pVWmKQoraIykgllcy6UJaKsMLDJgwpIq4fPWg0lkmhBUkFpKw9FzjJQIRJ9/v+UwRtkqI8LKxrC/kaFRDckQhTGGqWoBjVlUUk4N5J/pBaZMUdsmwBGPoocUvKmMmiAZD6o/L7nqQdyEOl7/1B/LRRNFu7oZRhLKgOGMdKFntprovOk6DBVjUolAwZ0XZPBHU1RV8x6Tx9EzbK1u81wqWBYVFlGnVFpRHJOlNBSq6SZKGT/bO96bFoEw4qyxWfJuklgc/uU0Qqv5DOutSEeNYJJGLS2Rj7LUYLLiQIK9r5nMBxQF4ayc2gSw3rIeDqRbC8f0Boqo7AqHgGfNmV00xjJfABizs4JEWyuzjZacqF6ZfGuR8cgTYMZmC0ioIRVDF5sagLiSSixStLZZG1BXVi0qemTePiV0hB7VKXpgmfumowNeGKQoTR4J591LZY87wMxBPk85MfKIDZDvQhKLRIgCE5d1jjXS/bV4nFU6ihHxDk5N1Ai60dFlCkIfaQuFJ6U2VxpqFMp5eweOZ9iiAI0616Yf2OzgjLmv6/ALdoHyV7slG2bGXiTT6xGJwFUdUqiulKKLiUGRqOUBJ56lVshldjltLFoJeeRDwYPeCfnR/ARXQVINdEZdrvDrLD0C/qEu+MxFx9oloc1rm+ZeMtWl5i7Bq0tSYlR0ipFympQUkSFEqolHBYT5xQmCvuKpp/NMbokRUVwc4qqZt479scTnE/0RBrf8dBoQCQxnTf0ITIYDIhtmwfigPNZkaAiEi2vKWzOo1IebbS0A3rJVUq+pzCJvmtpYsS3c4zRtPt7PPP8M3zwhQGTuxPMsRGxtmwMDV0whCR2PkIE71C94+mnnmLvwZh+7wB1/jlUWQibrVaJJ0s2XYu+t49KkaZpmM47sWFqnU8jgzVG8tZMxMSSukpMfcts1nHlu9/mQ3/zNzFLq6T9GWl5RHMwZaMesLlecf3eDjtdwIVIHyMuapKD3geCks92TJ5WGUYh0vcNISbm8znTaSPPhdGYQqN1orA1ShV419EF+eyURYWPgdYH5q6nMT1OlfTRMu4tbZ/og8fHjqP2ziT15CEKOLw4y4mB4EUVSZKWJbmbMySQRAEU8llolICPqIhPgb3DXdBGgjlJ2KpGxQ6IjFvHjktsVHC8zkrfGDmczIgLxTAJvWAFk8r3b8xXgzzro+VlNo4fZ3ltVUBlo45CrelAaUMZFd5q+iR/no0hszHyPKIibQzc2z/AHCraepNeC4BolOdkOeTu/piwtcOxMxsMh5W0lzaervOUdeRgJsrfsqiJMVBYS1KOojRYJ2d/1025d/ltnOvZ29li7vsMzBt8iDlnTgKRU4h4N2faTQkhUJSWZA2tD5jk8dFTVZYy1NiksLakXlnn7MYKS8OSO/d2uX84xXWekydXmDVT7u7MGHfXGBxfRxca3/ZMDubE0TrF6klcPeKgdbQUXNtrOX18jXcu/hAHGJUVXjkXrqwrdrZuUxQFbSssdWVrzBI89vij1PWQ2eFhJpQ0JumsRdJHd7rK806MAeda2rZjkODkypCDzVUm3RYDDJ0PbHmPygq3rZ0dirIgOU89qAk+yKILLK+ucv6Jc9y/e5/b12+Lri3kJjer6boG70TBllMKUEnAoBLNSr3M2c3jbKwsc3A4ow8Jn1tkY57LVJ5TUiYPYlAom5M3wuKaCouLCaUs+zu7bO/uE+J7wOyioWgx6yzU2+mIpMjkm2w1TOZT1lcG0hqnxSqt8uKjNe8BvzGwmLTkz5O5RSux6WfISuYSEtpki1G24htdkIyVmRAtKokcb5BipNCJP/sXf8L3fnAVFyE5z/teeJb7968KIZkWeSPIOaIgIMHHnWvo6xXJURwYXBJQ2Pc9qysr7Gztc+r4E8yaOTF4/uQP/w8+8NQ/Qgxr5wAAIABJREFUYmU0RFtDt7XL3vaM4cNnmW85XnnjGq98+5t8+Bc+KAHeaolSBWbTGe9/3zPcuPQu3/rG13n9jdfBauYhEooaU5R4DH3scbMZqytyT80mB8y7SBhvYTYfpbl7h+nhBB9kzgreYazBBydAiFV432NSx4c/8gn+jY//Mpcuv0voLe9evcrWziHHV9b45he/zqWbbzMarPDJ849gCoPBwPqQVFpuz6Y882Pn8XcOqZ6s8VVFdOBdh4oQeseJzRO03ZSmtSQvu0ZRgNVif+x8AB2FdPBZkZMMbd9TDtY5/dQz/MKP/xjLXeTC5bf4y5e/gTeyP6XkGQ5rZvOeqBOOwFJlMMGhCosZeKKWc76PPbWykDxXrlzmD/67P2BYLDGfHbA92cVn4H/e9fyT/+oPmM4PWD24iv/hjF/6d/8eH/+1j/Hitet85ep1ITuDMN8pRZLWJJXQVoOOTBvFu+9c4b///f+cN778F1zvx7h+SOgdZWExNhG8QsWIsR4XAzEUGBUZDArJ1HQFdSHPpLYV9+5s89u//Vs8/8L7eeO1V3mwvcva2gbX2sjHPv27XP7263z3u6+wstzRm5YffP2PuLM1ZNoGDqcdn/rFD/M7/97v8Pv/+Pf4yv099sc7DEcrXL55mc989l9SlSUfXl/l8aee5GeefZ5/+Ad/wJ3xHQ4mB/z7f/cfoDc30T7QzsYo48BEliq5dxsnpQWBCMGjVUkMBqMjyhhiP2VlaYXpVkBXivF+S/QaH70846bAFJbnnn2eT/3ab/Dww49w8eXv8xef/xyvvvZltI10zRwVHbZGCje8xvvIdNIQk+Mvv/plhsNvUZVDOt/T9EKUVIU4CVrfoG0JEUpj0YUjhsisn1MUlpg8fdtlBXElzYxKVPk+BipbcGbzBO97/6M8+tQzfOb/+qJkFRs5f8SZIhbl5eEmSimefOoJzpw+TR8SX/rS19nfucFqdUrUSe08iyEShcmuEDzjuWcaW6wpUCguvfsu97fusTJalcOOxMXLd/hXX/gcO/duoiycOvcwzzz9FL/4cx+l1gNZRzKelNRix5Ud8WDa8K1vfY2t29/jFz/6YYYnXmB35xbOtWzt3OeHF6/xnbfu8ma8xt29KVVtKZOhMpqmb/nEL/0S1ixx4/LrPPbsaYrVh+j7Mc4Fbl95g4cf+3Fi1PTzMaaq6Jue65cvcfHNr7J9+wJJd6ilEjMY4oLsrIWyHG7v085bdg8Oefetd6gGe0SEvCk2Vgl9Q3BzIOAijMqCqioZDlf447/8GtPxNtou7qYfzdePHFBaZEeklHBRLlvx0CPgz8J/gfxbKwEdQkoYbRBtRALl8wKniEmUIlLnnCt3lYQYKwXaKkgS+ChqHY0yBmOULAomQmpZ/OVaS6hs5Igmys1af02KlAdd8p+ZUso6mnSkqlH5QNVZRVOVlWSD5MWZlEQVlCT8W+eq1oXCKCaVASkZ2qN3YuULYoswSZRKhS3olZdwwqSIyRDICiMtjEYQFIf8Mkt+QVgoIhRBi3pGR1lqQw7WTpABEGTRT6JUikkUIEVZEIOn71phtL0EUJvs6Y8xSsZXXtAXXylGUXhke1RG0bKtVeX3/ugJzq+BQI0mKWkWSdnukMGrmAJd/j6sSehRPFJIlWWJC56iKFAURwed1om26ZiOp9TrFpFyL3KNHCRpgokpooyoOMh2Nu96Ga6U2Ka0hhJRc2kjbSu1kVBdYy2pigyqgnknWVimzEtW/rm8DxkFl49Z6zrKVGGMpa40g6JgZWWVgLC5OkFhDDYFdJAw5cIarNayAAJdCBJ03bYUVpoPrS2w2hIQr2znO7Fg5nc5BJ8zLeTz7KKAR0IeBUBaCkOQnzkaCF5TFANMEPVMTJpERdIOHyLz2TiHhnuSl4Y7rSJaQ2GkBcIay3wmrYQxyKBbFqUEypouK95AJQmQN8bQdx0hiPIlkm2A+fWezyc417O+cRzneqlCNmJlPQJ9ovzMC4BTAlEzAJ2tpaWp6GIOO9VesiCQDCEMpF4TDRJ4n5EtgYYXB1lmtpO0NYYig1YkDJE143E5iDEQyEJJsaMoTdQaR8yguEhStVKYrN7QNkkuVgTfeUDLgmEj1/bn2MOWQWkpVo/TjHekLRJDSpLZlTLAqZWSxVpDiIZWr5CUxppWwEcS3azl+Z/+efpild17t9C1ppt3dL3FdS3eOU4eG3FqY0g7F0aTqCkKQx0lF0yDWOBUVncFjS4KXHDE1EuAuY8EFzIg6iF5VlAMdY+hYLiySTvZgr7lJ86vsTIEO5oz0Gs0XcdyUVCXiV6XTHuHc9JG2M0blkYVjz31MA+uVEyMpVKS8zGPCsqKoZsTux7vWubTCZ2TM62KmiKaXMyQmSosKckzNaIkpsT9u9fpp1PWz51jenCHYnUZfCCSqI3h+HLNQdvQpgRaYZXFB5UzRgTggMRgVFEXJX3nOJw0NM0M51z+LPH/UKeSFMFFvNYcjMc0sym+71hZWyPNO/anMw6jYdZrOh/wXixmAkAmAkKALDLjQuwFAI0KLz3oR/d2CiqTHTEDPDorWfMdkYPAVFJyp8nlTud6Wu+wPlIYqIwAU32SXLYYPTEaAhbXhZy1Jq9zTLkiPStDFyTJYDBiuLLG2sY69bDOBRaZbIoelfMX1AKg8QJY++QkU4Z4NHuAqCH3DvZRWFS9QU9CO8VSadjtAskYbt7dhrpidX2VGBxN29L2PVUqmcWOoipJIaFij9aVvL4ZMMInTPRMD7fx13tC1+KdlJSEEBYBkPln9aReSUOY89myD2iTnXYBhccWI3RZy9kXEPN60hiViCFlfEpKMpQ3jJvAg+YA1UXMYEBU0DiwoaRt8u9rGupoGR87yyxa/HCJ2ilC6FheXaYwBRooBhXJBerBgNlkivI9GydOUBYFw9HKEWAtYdSBQudzxygiJtuORRGckmI+75hP59TKYYvARmk4NajZWBlxcDhm90DRzhu0LTBK0TUNznnKQaKsrFi2jaE2imJtjc3NY7x78zYpisW7MBatpEwjJlEXeyX3iSoKCi1WNV1YRkvLHN84xvVb95k2Hb3z4nJMQsAlFEEtVDteZoAMzC6QGVFlycxaVxXvXr3KYTvDJy/i45TJs/zPozzRtFBm51/n+0Np2Ns75MzmJj74PJOKzS17N/KdI1a2mNXfEmcgRJ9CAtJ9dBLyvshn1FBocwQqLRAtRSRF+btUJqsSEqWw/eAB49k+pbYMrSZM7mJUL3dnvmNTji1YzJLKO5r5hOWVNW7d2aKdVlTlEFvVdG1LVRUcjqcYW1IWnpWNde7cuMSLL77GJz/xr0nUweYao0LT9jN++M5NPv+Vb/HdN77OVrPDb/3mr3P27DGMCvSHe8ymLZ/53/5XXn7jFerRkNXj6/RRgtN9d4C2SxgFJmp8mBOi4+rtm1TO0rZTqtwaGwLoUshqsfrFPOsoWj8juo5CW+7dusnn/uxz3Lz6Nu1hpDuc4juYzBq2p4eceeopzr3wHIU2AqTsH/DGF7/OD3bv8MPvvMzyiVP8/b/7u1z67Jd5+eI7zFt536uy4tETJ3jk2AbXHtzhYO6wMaIyiF9qjevFElxihJhPShTVnSgPNqoh7/vJD/EP/8O/z52/+hrf+9pXJB/OOaJKEB2+h7IqqIsK54JkMHlHKCxdzEsEWTWuEi72uHbM1mzCs8/9FJE5zWEPMVEag06amWtZO7bGV/7qC3z0H/0+73v+KVRRcvajP8sHXvkWrxwcEFufx6R8uemE0gV9J7uC0orPff4zDJYMZdSkVJBcYlhWYksPoq7tU6SLoHzP6uYm68snuDK9BJWWIAvv0LGkcy3LwxEf/9gnmGzv4npP7wJPf+BnefiRc9y+8CZKJXYPW848dZ7tt28z7a2ULWnFxcs3+G/+yX/CmxdfZN4YUAbXz3nn3bcJFDzx6BM8+WCH8+efZuvVVzFuQmUaRqnnwuV3ee2N1xkNLLNmX4D2NqFrI3NL8jRNQlmDVgarDFErSAWz2YQU4M3vvMqg9oQu0s+85ORlsl9rQ1FqHn/6cc4/+yzjHq5fvcY7b71K8h2pb0g4tBU7LjHQ97nRK1p8SvSuox93FMxQqmB5bYUPfehn6A/HvPnO28znc8lvU5YYHdFLm1mB7CajuqS0Aa0Cznt8LHFRoYlYEiNruHTxFi/89Pv58M98kv/0v/xnlGoJ7zoqq3BdhGTo8lm5NBzy9LM/xsmTZ3n71gEXfvAdjp/YpOkbku+J3mWw19N5ARhLpWhCoHE9da0oKWmbjr53+ZyVff/1N95ifucyRdPQE7jdNFx554c8+ehxnnzkhfeGraMol2yVS6CN4f7ehFOnNzn5+HOUo9N0sWV7e8pf/tUrXLn0FocH+7h5i/Mdx1aWs5UxUpeJE6dOc/Wdi+zdfZfnPvQkRbWOMYa3r2yzf/M7LNtVxnsN3iWm3Zxu2nL1nUvcufkGxraU1mA01FXNcLjE6sYKrg88ONwnHIwxOnLv1l0iD5hM5wxry5OPnoMu4JG8PKMUhdbURcGNO/tcvPIuxhra/Dr9qL5+5IBSyl6hhHjChdKXNwhjM3eFMCyIEkAb8dOWRSkDQOwFGEkBkpGAYxAGKgrCTVTSdlCUkniL2KEkjwWS0gQEWBoMJSzUR8esmZHiwjYmy9Vi6Vz0BKYjJVEGnmIGFN7jggRYWgAeWmGMpShKtIb5dI4CFnHg1tpsldPvjRAqkgiElETO2HmMbvG9J/QOrCH6iC1KYkwYbfE64LwCbQmxRykwqsQUFmMD0Xv5fnKWjtaSwxGi1JoTIWkBgWIUv3bKoNpiRjIIk5diysytxjlHMxMGVWX7oT6SCspwHxeZPwmO8qoWw5LiKG8m/5aMfMniFFNmwo+sSZK5FVMO7w6inIle00dH30tAZh8cS6ORDNdFSfA9tixoe4fLCqeMg3O4P2N1ZYnCWFleorCQKMkeUknq0VMKGEQ5o5JHaRlQtVIEfAagZFEBLWHLSmx0g7pCbaxRtTXTyURaObQ0F+Lze2Jy24BSwhrEJAwFoiZSCIBqdIFVisporDL4Xp6rgCh0og6oqLDWEByUZYUxVVYVQELTe1mg+tDJ75NRkxSjMIkIoKkSiHskiY0tiepqEdbpU6KPCR0jUZkMhGqx9NmK0kZ0MqjYYNWCcZEweGsMdV0yGgwobEFK4P1MmkyUQRUWH4W1DynInGE0tigEiM1B9drkpTNL/Utd5bNfQmJ96FEoDsaHEKUVUJoFDUbke2ikZVFGb2kuhIiLER8VhcklASEbYlVCFxrVy1EWjkqyOXp2VLaEihoTwBOizvOSYtVozq0u0zcHzGbNkY0zJWGao9JE46hswAdFNdB0LsmiojStmqMxJJPQwUotNB6SQfWG1kexPJS1VGUHRdKFKMUo3lNrImoSnXRWDoqSsDcD5j5RMaOM8r598GO/THniYSZ7EzTgu4b9vUPm8yn3H9zAHt6hjofs7ffM5x3DuiQS0Eby6HQhAcFlVdM2Hd5La9J0PMeWWvKJnKiUyNl6YMAHHhoodpselpeYjO/x6LFlnnl8FQ7GhFoGfrfdMqoKWh0pigJPzAq7RHANzeEh559+hrf3eg68gLGTecdIG3yCsm1w00Pafp8QItYYuuBpnaMsCtFT+YCJEaNTVreIMnJ1fYO9q1c4uL/F2fNPc+/NlzHW4qxl2nQsu8Dq0oDT0XN7dyYZVSmgk8WaiFIFOola64knnsK4xP3xIV3vYTFUBlGJkAylkffOeVEF7U9bxn3NZP+AeegYLq+w2x2w1w2YeMnfWyhwUzaYLtQhKii5K414bl2KsmyqdNTWuQB4RJkheS9HFp0kd4q2JgM+YrtNMRBCoA8BrQ0pdeiqQhUV2qjMqIrlXSvoo9j14uJZyIqKlJVKhVUMBkOW1tZZWltiOBhgVJKg9ZToA/g+QJTWGZUt6iHKs6CVJiYBhpU2R2SQUqKSDjESVUAn6JJBGU3nYHrQEvqONiUOmzk+M63TpqftHWUX0KmgMIpuNseaQDJW7FQ+0JIokxEwLnq6gz2KosAqg0sLYBwMQsKYpNDB4Y1ckkonnJHPmkFJCHgUEMB1LdXSilhXQ2aFYyJ4TxFCBtqFVGpDT+cdccsRTUEaFrSFxhRDbD1EFyVd31NqRbF+inFhUcdPEqY38NFg6kJUKz5y8a3vQ1mytLGOqioqq1leqVG2ZNLOGU8m7O0f4HMeYzQlISlqIxYtlZB8pwgxOpqmoQiejVpjnGfZJJ49tc5oach3d/aodKIlgCpoZzO0EaA4ZVuXEHcFKniG9ZATJ08yKOvcrChMuuu9BNtHmS2VgtXjp7DFCGtrYnBsbW9x++ZdTj58lvLtS+jk3ytr0KLOk6ZGjUvSlBqFhYCQJAxepaxikvly+/5d3vjem7R9R4xZdb2IKMgkmlwdKs+UvHef5Nw0pQyHhxOaroFks7pIo4wo6ZxKoIWUUMbmNlyk3VIh5Ry2QBtDWVYonTM9lcJFcpGHlIvEBMrK/WizYlQaZjO4FSOrw4KnT61TGMXpjRXKwvIgJnxUmCiEXspWCsl9TKAjoZ1xcLCDb8Y0hyVnVjZZWluhiy3OGfquZd40WGMpLCwtW9584zt86ld+Abzj9pV3ePPbF7g37rm1vcO1G++QholrVy7x+lvfZv3YMvfuXGNjULG2vE69vIaqhjRdwO1N0dWQqBNaVYz390ip59yZ0zTTAzrfc/X2DfpJz9e+/SYf1Wf52ndfI4Sepm3RSpp1Q0jidwGGo2WSEYfDi9/4FiWapx55iL2DlhLF6ZOb7DUzlk6d4n0//n6e/8ALYoPVmpvffoM/+ot/yYXde1Rly+zOJWY6cf3CZa4e3ia4SNKJn/uZX+BvfuqT7L59AfPGBc49Aq++9g1apVheWuJDH/4gzd1tXvv+BVHfR4cyBTo6rEnYusLYyEPLqwKGP3eev/Ob/zbrW9f40z/+I6q6InY9MZOTyUV0KPHJ4ZJklAqOpNFGMygsDoV3kbKUEPQHt6/j+plwIloyZAOAVwSvObt5jA88/yRLx5bRRcHZj3yE3/71v8X4c3/Km3fugDZUlcWUDh8ThQ2ErqesDMn0RKVpfIcpLbVpid2ASkn2zWDYYUxPRyAFaRSbTyc0Y4/zlQgWEPeG1QZfJT73hT9nb3rIzs0bzOYNN69f5Mz6El/4/Bvcn3WkYpmtA8fe1Y79fUMLzJtDyqLi0uVLXLkfju45UMQI1hScf/xJPvHxX+G5Z59kNBzw9QvfYaYca/WAx376J9iZNgTfYiqL9TUuzQgOIdpioO0U86nDlJaqrimXRpw+cZKf/fAvc+PWVR5+6BSf/cP/iZmDdu4oCgO9WMuq0oLxKHouXLjAK6++wgc+8hE++pMf4FufhQd4ur4llpmkD4EUZE6NWdVrlSUqjfceD5B6No+fQfnIdDzH2ApSh1WJEHq0CvheEZyUN5EipUpYHfLOnVAmMDCKOkU6r6kSuLbh5a++wVtX/mtu3LzP2lJJVcv+GJwjJAUqcm/3Put+FWMr5tHwz/75Z3GzHarVTdq2hdDjnaMP0tzeh4jS9qgUoQ+BMgsj2lbKoRIytzchsn3nDsbNaWczOhyWSNt03Lx+mScfeUGKpkxW1GYUQL40q8OaT3/6k6yMYLiywWzmaJtDpvuOV196iaFxVCpSL9csxVKsmElJ43fyfPUbr/H8E49xds1RFQXGaCYHnncv/oB12zE/OOBg74DJ4ZjpZEIRYby9w/72DqMyYQcVXnlOPPoYZ557gaI+xcF0n1uvvEI33uPJ9eM0XUszCfTjCaaK7Bai6l09+wR26QTlyhrstxhT843XX6V30nyo31vLfyRfP3rLm5Fw4ZQkzFZlmE9EJotlNx2R+sL4R5JrgILhcEDqQw6MljwlAxmwCBBzo1FMYAqRzftIqd+De5TKqCwyIBf1AFsUtO2cLuQK2RTxwUtooVlk6nCESiqtMs4kGU8hX5ZamyP5MQpRVUWPLQy2KNAkbClsPBGKoqCoKqLLS0JWa0XeU0mFFKXdzcXMTOeGr6IUdk5bklIoXZJ0zNYDIzk9ppC8JYGiZbjPgIkIjhYKMJWHuiAtEilmi98i6UChMKho0MmjUxIgLAZU8BAc0TkBGmyRQTxysFlWg/DXgKP/17+FrM+A0eIrLT4ZwnJppTMIJIOaj0pkvikSsu0sKSU5M20g0aCAQTWUhdsKGjvvpjlnR6GswcaE6xz7+4ccO7aO0YulOsnFisHoguh6UgoCOOpA2yeiNYQgB7G8thlQjEFkxAslQRC1WlGW1AqapqHvHSYqUZYZn3MWsvJBC2saXSAUOjeOmaPKWK1zQGZuOinrChWLjM9GvJdB3bueedOJBRKFUYnCGga2YhFKF0IQ1jjbT5U26BxOqrKyLBOtJK0pUAzqGmNlqYgxEJWWgTTnoKUY0IVhNFxhaWCI8479ZnKkDpLhX2NNQVVWlFUlartqgLIdyYtCAZUZbaVJ0WNMIRlMpQSRhpnYN2Nc2ERkGc5POnVdo4yhqgqqssa7FpUKqeNWmhQWirAMfIZA13WkGMSKusi5Uvqo+lqbAu8k7wNboozOOeXCqi1UBgIAiiV1cQlpXcnynjSRwLRxXL69w/5shvMLJRNkCRPORXlObZDzLOmjRj2VRKIsmpVIMiHb4PIyCQKaK+iCYj6doZQSWayW70eAeRn4hVEO6OBJJkDSJGVpGEA3I3aOUg3pOsN6PaJaXaIYWegDw5UxZalYvVrz4AdbdHsNKQS0LlC6wPcBbaBtG7SxstgUlr2dDl3WdLEX6waSN6bRlAmil+aernXsazg3HLG6P8UvDXGd5yefP8vILpo2NY2JdG1PUWiCjhirqA0Eq+mDguRpJvucffJZzj20yfXtnn48IS0PmfetBNG2c7T3dLGg8x2d6+m7Bl8ZBnVNbUHbAh3BRIdxRtQlKhLmc1Sy3L1xjWp1NQOJCVdV7E167DRw/rQER/fuAQdNw9xJy6KO5khtsLJ5jBNnznDz4jVS9PR9T/ACsBRlQV0VAthqJUsw8vpevr3DU5sK2zbs7u+z127zwCnGboBPXpSxGbBXObMPLZ+1EMDFgEnCbGvMUWZRyNaKxTi1uBHS4g8j5dyGghA9OiZ86IkuyvO5oGGSAM+h98yVphgOqKXzS0Cf4Cm0lbzAGPBKLNfKiLV9aXmJjc11ltfWMMUAFOgUIMoSQYK+dwQnBRGRQGksvg9472TJiUFIp6z0AXLAcX4OtMnnqAyzkpmmcA6StlBo9g4brly/w8bmCvPxDN87Zk1AYeldK4rqtFA+egxBhlylsEnyIAyJZHPbq61AS4NlpTQmCUmRbJAmr5jkPY6iisBaklGUqqBSMG970rJknOlMSMSkBOT3URTHKaEJaB+IbSSoSBfnuLmiHI0oTq8RlCG4AFpGP1tHZskLsI2iNQU2GXpVoGPP9u4BIRkKXTBaHTAqLKVWdL7n1o2bTHZ2IAQGw5Km9aRaYYKGqKi1wuXjLqqINnKXdiGwN+8YFpFjayv4pNmdzpiFwKC2OddMU1Ylg8LK585oXBdYXhsSgLZpUL1jbXmZuq4Zd620A1sFQT7PnVJYpVk7fQ4zWCb1iaQrvO/Zun+PV196iY2NFbQqOXXiGHVtCa3DOU9f5k9tkiUuugQqiR04/zddFCwNhpw+e452MuYbL77Elat38EHLTJQJSp3vhsWMtJiTEuQyioVSSEpV2rZl/+CQ4ysF2nYYIkttw3g6lfbczmCtEwV+9Djv6OZzdAoUtmQ8nhKrg6P51xpDVBETA23fZfB3SFdFzPKcyXhOO2/pnaMe1BRFha4tLnieevIcPHoc71tKa9m5fZ/eC1gWQsCqTOjEIM9WnutIiXZySGgKZhMNUbPRjXFKsXp2k9j3khuUPFU9wFYD7t69zosvvUx//x6f/5M/5r5zDJZPkLRlZWPA7HDA9j0B7V7+0ovcu3Od9z31CCdOnOTYo2d5gY/w2iuvySIXWprOsX6s5rHHHmXr3g3m00NicLi+YzJXjA8n/Ku/+CZL1XHevnKVLiTG4zm2EJWqzAyKpmkYjFYY1ku0symjpQHnNk6wcWqVxszpx3NWT53k6qW3WN5Y5ROf+jiDlZr+9hbXX3qTL7/yLRqzzKc//tN89Yv/gnut48++8HlMWdC5GVEljq9t8nO//G/y/g9+kBevbvPpv/NhbKkxDl6/9DopKapyyAs//4uELvD9y+/ikkcjxF5RVJjRiOgjh3tbxKSwxzcozTI3rt9laTjCJ5djMhLJd/hoZPkNjuDEuVAUisJKXqjWHGXraROoypLJeI9CGZaKgmASvoHVJUPba/6jf/Af8+xDZzh26jgBIQGr1SV+4tOf5Nfv3OTK3Ts0RUAXkZpA58W+34dERHIiA4paGdZtge8cmJKRsYQ4JxpHPVAsFTWd63j0/E+zeewhvvqlL5HKkn7WSu6rMtR1xUp1kq07N3nlpW/CvGNpOGTr9nWuvzPlO29eonNzJt7jtaU7PGDs4JO/+mt88f/831k9s8F0OiEmi40JVVoqu8ZSPeR3/t7vcqYvGM5ajp3aBOAnf/Nv8fr+lF/7lY8x+eqX+R+uXGPZBJY3H+JGc53KeJzrmTUJbQTIrWoFWKwq+ejPf4zf/MSv8uwTz9OpwEoyXPjzP+FddUg362QP6qM4WqIlBs287bh8+TL/9J/+jzz8lc/xN37p1zl75lG+v3+TrouoQogJFQTA1kbjksQymMKKK6Es6XrP5unTnHjoEa68fYn98RhnanxIWUSRMIXEwyzIdlKkjy2zBGUsKW2J1ZHCOmIIGBWYtAHDEtdu3MPd3Cb1HRtnz+N6x8HuLYgFKUq2YKumGANf+NJXeOPt27z8la9zcqOknTf4XojYrnEC8tp8o6dEUJGkYy7LinjfcWxYtTwQAAAgAElEQVRjidXVZYwSlarWmqYdo7uGtpvjQkci4nu4cfPKkVD0KJFFLSYgQCVKo3j04bMyEWnDg3s3UV3PKy+9hsJzd2ubk5srFKbCVrU02ReVPGtRcTAOvPTdt3nhyZIPTHZZKvboJ7ucO76E8oGtvfsUS0NOnz6OW11jMhnTTCesFhXat6Ja14FqVLN68jHqwRm6B3dRveTx7t6/S+8jnXMQhOS9FQ4oR0OWN89iVod4p9msB1y/f8jl69dEwes877ksfjRfP3JAqTBWApRVlFBsLxJKn8GDFIPY1NRiZBUU1WLp5jPJUCIvQbpEbGNZVZOl9ypEQhQfbhEihRLABasI3qOjZJAoo3N7hVx0IZFVHBHvW1kwZSWTJd7kizKHywoLq4+YqJREXCybdzpS2pgsI07SlygNZPWAEDzWmtxOpwidI+mELUxWCQmzGEVwJeCVtlAkUcVojYSZqyx5RrJRgspZjsJqS04MYDQpOhIhAwyJpMSyERKgNSF5yXFIC82K2AyElYuoYEi+F0CqyIO3d9hs1ZA87gjR432LceaIBRYUXyAiCQDOL1D2oi5UHSnKeyNKtvfYu/c64FLOwzL4mHAp4pKiSglJdHe4GNBBM5+2GCUqHmNrurbPldH5NczAhveevd0DVtfWqY0FCylEitIetXhJLpBYw4zWUgmqNKsrKxzuH+Y8MGlZiyFIZ14K6JQzqKKEDToVBRDKTYJJSeOSyYopn3MXPInaWrq+h6gJRUk775iNJxR1ydKgRmt5z41OGJ0tVQqwljIanOnQJgNKKQFB/r/03udrYRtUSb6PpBDkVyWppDVWVARaFr+YJGQVbVClJbmOoORZmM9nlIMRtdY0TY+tI4NU42xgVJSkrsMTMQlRAylNDIm2dSglFfGSqWXQppBDSIscs+kDVZlBFSsNLykEUZD0HcYUcujHSGEt1lrKumBpOCJ56JNYv9CJFOQq1BlMUkoAN9c7+lZssOPpjLJYYt7sYZLUUUc/RyeF1WVmvATQJEkulBLpSO4X0LnmOEmweopY8vMaIcbE2Eeu7G7TNx5lLdZqef60lvek84Re0efA+xTBWoXzEWMCWDEZGaXlclQRHXOLn5YAU2VqoioIvoHYi+YnaEySNp+F0kTLti0KsuBRRuObjtGwZqkaUDjPR//2v4OvNjjYn7E0WEIpw4yE1QPCbI/JzXdpJ9uEvpc2QsBai/NiLzU6Mu8bQNG10iRZ5gg1W2h6L2dMNSiIhWE+nzNr5tjSYpdGzKshSxuK6vhpZjc3+Kn3PYWZeFTf443FTx2BAqzCZhWANyVehRxQ74m+IfQd73/uGV750ndoupZiVDOyYt9VbUMXWjQOpaR1sE3SiLbs5DVO0QnEnqSgoI+emAqa8S6+MGxfv8EjH3wBUw9xwVOuHyPMAlOf2Dx7moEVS9PuZEwbDPP5jEkLXmmq4Qonzj9LWQ7ZPdinVU5y61ASgomHNlCaElWU8hn0YlXtvefdmw94fNCxuztmNyoOzAo+5lyurHAkidVRVHNZTZQLEqRpTkBttTiec9bAwh5Jbgc8Oo8TOWi7JRAJIeaQbzKiLsOWUpJ7F5Oj72Sp1vWIaCIuQHARrQyDQcFe02MzEG41rK2vS6j6UKxUMWbLdBJCKcSE906KB/J3ppG7PimEIFJGbD0hCaic7/0FAaKiQiWFi4ufkxwmbFBBWlSjUTQ+cH1rl9bnplgiqZvjoyd2Tu7FmAgEQlrkwkW6mChzu1qMUOWWLh9BByFIdAKNwZZidy1tgfMdw2qFhEKrQDCaQoPxgYjY5RbnQwBRki7G/SQ2AClasBSmJqUpMTl0/h6qzjHwEeO92OdVxGZldsAQrBR82CD2hbnzlEpThWzdGgw5trLMxrBg+/59urYj9B7XNAzrkqaZ4lxPNzfsbEM1GHF/O3B/7FgajEQpXlXM245YDtntA8MEtmvpfcetfY9dPcP6AGad4/jZszz21BMo54kpSXCpNqSgaOc9AD0eY8X6sVBVK2Rx6rsZJnppVjUSXGwqQ9uL6jJ6x4O795ge7PLEw2d57vzj0E0ZzyOdj5RtT0ukmYzpkpznynX44YrMha5j7/ZNDmcdt67fYHt7nyu379G5RaftQqEUsxD7vcFdbG955iQr2ZXOi4xYjA4nU4w5i9aW0or6LIZEVQ7xwWONqDZKbalNgSkryrLCGovWCasLOZsLw6AeCtkWEsNBxbCu0OWQwXBAVRQUVmNHI2bNjN719LEjMURFqEclTXNI183pWkXrWrq+yQppIaKSlww2pRRW2zzySQ5mn6Tl8P7BPtpXmGKAnXkwlta1QvwqzaAcsr+3xX/xj3+P6Dq6+Rxswhw8YLi8ydL6GivDmur4MXbvPOD621eoS8PN6w94cPsuzz7zLL1vuHPvASdPnmBlbUQfD5lN9uiX1zlz4gT3799FEenmPXPA9YnXv/cm+1tbrIxGbG1vM+9aTC+q/Bgh+oRBQFiXVc94Rdc7rt++T9PD2fUTYBWF0ezvbrO5vorSimJthZM/9iyffN/jfGpYM+g7Ln7z87y7v8t83qD6nsIovO75mV/8Vf71X/pZ/Kzj5Pmn6VPPxQtvs/nI0xzb2WL12AqH9w85WO85mHZQKKwrwSesGeFTYFCvU9ZDAhIPcX9/zovbd7h84fu4pDh+9gn8/iGT2Z6QIMnTtzN0ivg+UtaVnO0owEg7aTSS2xqilNQag9KaAen/Zu49o7VL7/K+3912ecppb595p72jEZJmJCEJFQQyQhbIoCCwwODgrEWxCQEDMYkdLGxkEpLglWJjmyR2iFEcsOmCSEIoyGqozKiPpveZt9dTn7b3XfPhv88r2R+yVtbig8+XKWfeec7znL3vfd/X/7p+F8vVgtqNyE6zXjeMx2Ne8fpXQcnM//BzlG++l/UjI9qNdU4dP02NpUdKQA7LYELKJCxaOUpYYm2h9JCUxTVj6qqgSsDnHqMcYZFJNrIqiReeOMfVZpfVKmKyGYRAccnaquZbv+v7ef2LTxPnC77wyBf5xEc+yBceeYR+Ebh89Rp97qEk6towv7HPS9/wJr7/u7+PP/ut3wQ03bzn9jvvYffqC9z9kvs4PTnJn33yU9z/mU/xujP38rpv/2Yef/JJjm4dZePECX78p36Scx/9FP/7R/+Uc90BOq4gTjFYDA2FzCrKWXTcKFRlsWqdYgt/8Ztexzfc9wqmzZg8nbLzb7/EmlNMjaWsK3yfUU3CdhofA+Qaow0xJx55/Em0jqy1LQdhiR+EuEwg9InKWVJJGKuxWkqqTAxAoXYVJ04d54d++G9w/+PPcO6Jh+n6BdP1KcHKOVWrgnWtPHdTh+8T7UjT5UBlFY2zrOuKLQVdznRApSucTnRaseg6atcwGRtOnr6L+z91P1PTcOTIGnvb25LaiBFF5PFHHuKpxx4j546mOYOf7WFKpM8BnwIxytnBGWHQukpc0LooYsoo7TmyPmJtMpZinmH/meopi72O63tzapXZny8pbsK5s89SSqFPnpG1w1H1qw5tWcplCF+AbhWYX3uar3z+S5y98Dxr0xEXLwYmkxalKnKRYYJTiqQUXZG0w87+kk9+7gpfd+ZTfN3LAl3fcfT4BpfP73P+0kV0U3Hi+AqnxHVKCoycZTSZUtYaVmGBVwa8Y3v7Kk89+gBrbYfTR9neP4/vZmQVqWzLKkA/L5x/5iyj0Yhq1rHYPkd17Ch/9okvE1IncUMy+c9ZAvpzF5TiMLEUnECUfzm4YET0kPYrsf0yMFNkjFVrMxxwzRAz0lil0QRyisQY6bw00iiESzQPCWcbXO1QxQ6vo0g5oZJMfXKWiZVGUVeNRLfIFAwpeUoagIXlUNGUqVEIQS4kpdDOysaegjFS254OI3aUoVo+S0X6oRjhRBQyaqjvRTbIOTOwRYZ4QlYUqwScqmRSoJUmobBVJY4oLeBhOcBmDmfCkrEXSzNJIi8xSZCEPDCaBkCjUtIilAa3jR7y82aYmistXJ0UIklpYpBXkTyqVNLmEqEMQkSS2ndrK2FC5UN5qNxsw8u5cAg7P6ylZZiKo8VFlnO56ToxyME9lkEIVJraObqliBrkglWalAPJF7w15ACmdsy7BfvzuQgrg/CQAaPSzWrs7d0DTh3bIuVIXY0Iyd+cwaM0KYnqW9AS7yPh1tbY39uGknHaSbSGhBO/hRy+yuFEUgJGWhuykgjXoagkdlz5nWsUrqoJAzx9ez4DLbGWUqDvAmNrhspc+YyUGdrbZA6NcQ6D1HTmImwnSobsyTHiU7zprMtKBA8FQ0wrC9RaFCu5n4ZIRimZmIeDNQLxliknoBU+LkE7qsqgK0UyGp1rqnYN03lhzpgKV9W4phanm2JoqEEcUsZS9PAbN45Ch6sqilVDpawlRHEqynWURfcyBp0Ma5trTJoJo3FL5WoO9rZlrWgbmbJWLfPFnG6VZKJDGcRFhmZGS7/oOXP7S7l84QX61QHrx27hmWcfpWnl/jy0/ptBEOnLIPzKMjbUJOubmqlMNCVqQ5YJHU0mj6NEzPohIlEGZ+DgUMw49HBfZX3Tz0kq5WbsUiklzros4MFYpJ2yqIJRLcFHUukHMT6QER4dJQuf9RBGnwqZiDI908rx0pe/jNbBwYXzuGbCPV/3clIz5cZOR53nmCrTzz2rnStcf+5zLK49T7/sKCnTxyiitY5oWxNiorFaDs+qJsQFdWuxShF9wFU1q/kKZcXl0KVCDNCphPWBthSud5Hp5iYn7zrDKbVkc1RYLeeE0uNDwfslViVc7cRBmDtqY1lphvdeiKGjn+1yxyu+gVvXHuasTBIG4aGQ/EKeVTESYmTVBVZdROVMt4qMrNzZssJWAzjSoXKPCuJy2bl2jtvKq3FNw+rgAN/NKNHju8JqHjlxyxove9Fd7O5voxrL8mDB7n5knjMrLOvTNa7sHjBbdWA1xoC1hmI0yQ8L0gBW91HRBWG/GeDKLGCWM3Z8ZGbH9HmInhUZIujDA6yS2HlORdqpBlcd2UCBeNOVJF+HYN2cIyT9Nf88uDkHiSmWJM7LQ/ePkmcXg+g8GBRkcNNLe1RA0VUZ7zR9CLSthSxOutrAqTN3MRmvMWlqDBafIqoIByrFIOt9DGQfxFVZDmu0K0ocQK9Z1n2rzc31RpzG4gAppUjQt2RhWqg0PE+scPsQ2LHJwpSJxbI7W0IYHMSdR/lMDomoMspU2CyuYJUjVYlEnLQS6ULSw+8wx+E5aDHI66YsPCWjLUSFSQq7uU7aPyCrjMpG3KGA1g50omgljs/D9zgwGCXaq4n5kPkShcGkFRiJWkcySRWy70hEVGVk7SJji5IiinpM7hKqBEoMFOVQrcJlTWUNR6YTwnImfEA3ZKy0oR6P8KGXoVMWcP/OvKddem4c7GOrEc3mOqZACUmi6RhQLca09CVxMIbp6WOcufUWxpubYDJbJ46x/dRXcHXFsvdYU5gdHKCt7K2yySy7BWWIBeYSMbYi+R5dyXCk0gYdElHLZ1JVtQgFFmzx1EROHd9kOpkyD3NGraOpG5rplMrVjNeOMlGyFwy+wtUNMUX2Dna5cfUifYIrVy5x7aBj3ifQSiLWSPGJ1oaIxOi/+nXofin8+1/iKI+konBVRVvVNC6xNhrTNA1tPSalSNM0hLCikGTgYQXib63DOIN1CoyjcQ5rDNoWirE0TiIZtakkTq9lX/7AA5/h43/2GUBz59338F3v+E42RobiA37R0YeEjnJNohMOR04L/FDiIuuKCJ55EJiLKqiUiSXQ9559Baduv4WTR09w7PgJnNLMF3OptdaRy1fO06UV5Ij3HSUZTI7sLOeMu3WWsxXr0wnRZ+rKsFwccDF4TpzYpDm6xi3HN9g6+mW2jh/lrpe8iK88+GW6hcc0loPZAYvVgrY25Ay7uwfiLs+BB2dzyJl+OKB6H+j7Toa4xVBVFW3TUDlDnzM5Lrm+f4B1iltvvYt6y2G1w601jLbGLLsl07aFsWNy5iRTa9k/f43f/8D7eGh/xnyRZLi77HG1oXY1Jzdv5dzZbc5+/rMcv+cOfvXdf58X9q+LE9MUdnZaSkrc/4Uv4mMgEWirhsVizn1veDVnn3iE3Z1tTpxUfPGBj/Ff/Ow+zz7xNHZrjVW3ja4a/GqJiv1wDquoc6BXnlyq4YwUZTBj5FlgjJO1C4GCx5g40h7jtq3jvPktb2K91rzvfb/HC8sDOjKf/Z0/5R3f/e2Ermfy1leQqpq8M+P8A1/kY5+8Hz9qUMHjc0X0WQax2VO7TPArQs5YLxHILqw4dXxNmK39DBV74srSzRzKGVTRVGuRkm5wctMyahP788J+qDi4seBv/Y138YM/9kOMm5qwP+P4zjU+ExdcvHGVbpmZd0vhAhE4snmKv/MP/2f+zXt+nX/0j34Rewze+YM/yG/8k/8Vg+XeF72G2a7nE1/8BKZ1PPXCE5xqN7hw9hL7R6ZME8z2VoyaCZ/5vd/lrC9M9IirPrE2WaBUou96QtDURvAFJUvKptiIXxY+98RTvOmb/yLWKEZktt54L7e97u3c/+F/hQoBsqF2joOQKT5gG020BZ0jJcHjTz7Hf/rTP8n29iVaaygqSxGIsrjiyKXgMHQpitnC1VilWHUdu9vX+chHPsTu3r7gMYxhtrzGKvdUymCcOBC7HPArz8nbGgyRVDQj1+Cyw2SwqjChos2FqOBGHZinHmzFgV9x/PjtzK5eYWwVJ08cwdlImhbaekS3DPTdipwDVhuwmu2dq1TaomOm6IAPgbgojLZG5CQHTPElBCCTo7AfD2POMcraVDtNdIYrB/tklSkpMD1xHDeZcOnijGvXz3Li6B1Q1LB3Gs4pg7cU8k2h6YUL53jyKx/nuQs3yL7DDiw6XWRA0PuI1YVizVcLR5SwcC/vFH7rfQ/wV7oF7doWO9tXme1fZTqZUitL8olm/RQ7ewforVsYUVP767RbDSxqVqtA6A6obcupreM8fvUcV69eZHPc0nVL3MQyMY5kNXs7nouPX6bPAXt0m5MNPPrsJa7cuIyPyNAvKrD/gTOUmqYi9P0gghhSjGAO42Jw+DBNRaZs9vDhahQhgSsFj8LZiqKEvaFyQEc9WI2lNUeXjDVQUiTkjkLGGjmUaaXQRhTrFKPEqazCmoaYMjZYoKKogE6FHKTdwjo5uFtrMdaRygDk8z0oTR6YRPFwY27V4JgQ27sMg8Ve2PUrKlejdEZXIhIIdBzhDeQoPBsFymowlqSF7pJVgcqRuh7rMsbK5Fc5jRriNqaIVfUm86bI5i7h0c4Sw+CcUNJQQREIt9LidlC5DCygr+E/WYvRmpSTxKn6lQxvh8+wAMpaSko32TFlmP6WklBGchUahTGyuXcVwnxKaWjBkya2PFjCtTHSOuQHoYoiU6AUBBSZDTlEbM5kq9ElUrRY3rLK+JDx3tN7z2q5gpQ4bENDmG8kpYfaXsXu7j4njmygtMIHT5/8TSCxVgZtJKpZjAKr0bZifyabWJeldS/GxLgaE2KgqjQlaRJiDZbPXFqFlBFny+FhJislP/sQleg7EVEygIbt6zc4eeIE1fqUkiLer6iqjDOOogXC6Kw8iFpdS2SrQGXF6VOUuBJSOGwClFBILuIQpIhbqB/a5tKQCz8UOZSxwgNz8nrKiLMrIbBtGVVLa6GuM81kA9O0BEDbFmUD9XiNtMjgLFkbkhYLtXVWeE8HGboebYc4npXfeVYK27SkrsPWlUyataJuKirX0Pc91jnIBe0cRza2AKTavtun7xbMQ8/auGZnuQBlBC5bWfq+DOJtlDr72GFdS8mRqnWcOXMP+1cuE5Rs7vqVRzlQWu6RrBReyUND7k81MOAUDnmIHbLEFEDOKFuYTB2YSEajG3FDqmwpaXBYFCAVETqy2NdjDGhXi05JAUTA9ORhM6+kERPhbill6dKCXMAWYU5hrbiTsALAVnItcCj0pkhdFPec2ORld5+mdZ6rtWdUn6ZPhTYH6kp4VFO1RGvL/PrzXDn/GIvtK/I5hiLlRX2k+MSorVilDpUF8q5VIgdxjIQUsQ6876itJcQVKch0zBlx6vWrwNJ63LExdY6MdGK0adG5J8dArRyhW5CTYlyJ8N7HoRZcebSR+IUxEBZ7rGZ7TBTcPXU8+YXHsCdPELzn2I3nOLHYJubAsvP0XsSJylqquiZqKUtw1orgnoJsrpWiUUhrRhE2wPzaFXRVo2JPv+xIyzkzXzh/6TqnTqyztr7B+qSl9x2Lao11O+dyiOx5qOqWEvfou47JkXXcoStTZZZBorA4+SyD70DJEMSg2VtZSg5s7yXspkUpYeehBtdcTsOlNWyN1FBbngehfJAxYQDsy85nGKQkmciBuCGzfP9rD8BmiDEpxHV56KQtNyPNg9JUIJMwwUBaMlt5VivDdFkx8xqMxjaa204cY3PrOH0USlksgTQw9HKKpBzIJROjcG6UghyCvEochNicxBmZh7V2KIDQWqGHxi20fB8skQRR7umQBBB9ONgCiCnhSoZkSMbSLUSoDyaSdC/vPg/0ryzruimOVDyhOHwRQGxWA+NMW4moxjK0mDFEhArRZvqU2bjtFEFD6rqB+Si/7xADOisqW5FVIBZPiJ5aQQwBMzBsZCiTidFD8OAcWskWTw0DhRJ6KdzQmjLgJ31O6KqiD2qAx4tvKWspRrAlszldYzptubKzjclybWAUse/ZvtaDUVRGfkatKxKZ0fETLHevsTEdk5F4VjEZ006xVUu1sYEyNdkHpl1Hjj3XdrfRO3NaZ5nteOKsI0ZhS1ojB9Bm1A68jMJ06xhHjp/kYG9OThmnIRVNDqCLFANYY+j8ipiFZVbrCi/mDlwDk80tKmfRSq6Fmo4SFE3l0EYJVD2LmGeK8MBm29fpugUpGVSxOFvjDt3fOZJLwhgz3CfCTSyHLujDPQHDNVqyDATRA/8rMxqNxPGjipj8a4etK4oSZ0mF7OdiOdwPigNVA9UAYVXIdWYGN5MqA5/QCt/QGBGPn33mcd77R+8jKcE33NjZ45WvfCXTr7sNMuhscV2iSwanahyW0O1htCLnINdoSoc7YGluLUP0FIW1jnm34uTJY6hGc+nKBREwtSWuFuRUUIPjoAs9ufeyV4gSMw6+o3OK41ub3HPPPVy8dI1uf8V0dIKYA7iGLz74GN/21rfwcz//c/zBH/w2B9uXGY8rFgczHvzyA6TgqeuW/UWiW/Xi2kjitj8UrevK0fdS7lG5hhgzRYkrq+ztDk6zyPFbN9jfWXC02WDr9FEOdrZ56Svu5qHHH+PEmTsx0zFdzDRDCU9IienRCcsnHmQx28G5hLMtvqq49dQWL7/7payva/7wV/8hp1/1Gn77F9/N89uX6IsmJY8LiWsHuxQLtWlQOHJaoci0dcNs3mHamjZl4qrnyuwyz519jqAN5bkgB+p+idk/z9GR4WoMLIvDo8hekbVHa0XlFMZa2UuYwtLPKMnSNC2qOF51z2v4qZ/4m7zi1fdR1TV6MWf85EWu3rrBwWM3+NFf/nvkmLCNpVQ1jkSqp6gjY7Zne2Qre5rkxW1aWSPD22F4b5RFqZYYLZO2pzuY004jXi1xxrLMGZ8tUztifc2iSezM95mMDetVw/kbu0xONnCk4h3vfAcH/ZJ//Av/He/8Wz/Bnd/5Dr7+iSf49r/2wzz60Q/xvg/+EbmqWXYztjaPMztxFLU54tr5x1k7OuG3/uV7eNmrXsvnvvQpfun3/4j/4V0/j21GjKc1r3zNG/nWt38/6xtj7ps0vP93fpMDM+UvfPd3Mz454r3/7PcJquHH3/WfsfvsOUKKlGSpnWaVCk4XSblUha73WDfic/d/lk+85JW8/a1vIzx5lZ/6hb/Nw1ceZ3NU4/sVq+RYLMSpqpwj4kkxUpsarRRvfMMbedtbfoAvfOyD/PGH/4AVoHLEWMsqePwqQGtQAxPJGAOm5s7bXsSp40d5/smHsKoldoKsidkTYkHZSPEaXQJFJUZrlr7zKKWpraX2lonWxBToDIwGA0dlDFZZalNQ0ZLwjBvLbOcGjak42JtjjKci4f0NZl72012A6XhEpQ2zgxVVZXE64LS0jE5HFTXi/DFFRBinLEYnuhAwubDqPIWCtWCM4+Fz19l74SmydtAvuOMV90I1Zb9fcnrzFH/4gQ9wx4teyivvezmV0qzmM2n91tD3PaFETt16hta2nL1wheADOwcdxa/AjdDGMV8E2okhpA6DIRZHGViIqWSsUYzrlsef3+dzn3+Mu88coc+ZmHap2y2cGTHzJ/nV/+X9XL1xkWnbcNfpY3z9nZtsn7/MU+euct8d51nLLZOjR1ntFr706S9RltvcemzExuYam8fXaNt1FtcOWNy4ik8RQ+HExhrzmeLZ819m2cnewWpHcAnFf+CCUud7FBBXKwEqG0PSIqYYhEUD3Iy95SiOHe2k3jv1HqME3q1IhBRQMUMlnAA1uFy6DCZlbBTGqA8RNbZYW8sBVlts3RBUoO97fIzk0JETGOewlabzkRzEIu0qSxpEnkMzeR+8gGt9LzWwOaONoyiZuhol4N0h90ahDEBhS1oIq8VZh8KTwrAZRpxMMQvXRlsrr6akhU02vocbDtkg5txjdEWO4uSxRgvLJw/8ihQhizurZC2T2TwId9GjBpU0lzAY3CVGpbQwabR2aFuh7YAbLwhJP/QQB/7E4HCSqI84r9BgzAD2GlwaEq3JKCWgSmXkm0UXYRKocvPAg9aUlKRRLHqcltrhkg+jF3Lg6Xwv0MmUpI5eZaIb4lDKsL9c4LTBFz+4nDRatBJSyhhjCTmgtZYGgJho2xrQjJ0lJLFrk8FVipzAaXGsKTS5izjkvSqlUaagVMJZ2axm64YJpNi8V30i9lGcGaXgzJA/dlJ5jBmgnFpJa421rGLEhcz23h7T8ZRVt0KnnvXpGIUA2osuUGz5k5AAACAASURBVCxKWVbzGVnpoSnP3NzACmxdWJI7nUDsOIwiFlnc0mGT1CGvqsimL3RLTF3ROoexFmMtMSeZJpMIPqAGh57IdtJaRtBoIkEVmvGUWDI+9KjSUylFjglnDU3bSi2vgqwl3plTISlPJKGTOCuMs+TYi7V+WBtAYbWhGTd432OtppuvSAZ2t3fxoacMTWgHswOMsSy7HmscRdvhvpBWsZADVy5cpLmuGNU1cYjQBFmgqExNwZP6SPAKksUiFtYkxj+sUTJxH6YTeWgDyogzbrxZo3UhKQMpCN+jNvilpmhHQaDqxRT8KuNyYVZWNGuOkJB2QSJZG2lRknonOfQO0Q6lNLGISJspdFmaMlWx8t+rfHNddjkPjhuBBkefQFnq8YTHPv8Zti9f5U3v/EaqI7cxu36A1iuWKbP7zEWsm3H5mc/T7+9SOQN9Zne1FBdbZUgoYb0pgymZRegRALNDmPOByWRMDAdMpi0+OlbLQKVl7S0xkmxkZ39GAOzRI+xsX+F4FfAlEFNPFQpEj1OZVGVy5ynekyz0MaKyhpQIOVEOPP5gl/n2Nc5sblLt7GJK5qh13HnlBZSNrDqPc5YuJJpKYYZWwZwCXTQo25CJoB2kiNGZpJUA/DOgai4+8zQlw7JbUYpCKUe0hrMvXOXMqaNs3nMKW7csckOfMvuTmtwrfN5jVBSqqqX90EL0eYgOSzlAMxphjCLEQlYBfCbnFcqOyMvIMhuiaQW0TgDjUOibwn9BjKAKOZDKsjMI+ZqbKWSJval/R1iS6V8eBCT5w6UgMViQiJceHEkMkVItLtkyuEPNcM3mAjF7tIUUEos+st8nSDVGae666w4m4xEUgeLHIMKxykFaXWJG5SKCinIoa+j7XpoiB6eSMQaSOPGSHq7zIm4uOXaIGEca1qyUOKxUDCVTVY6UgjyPSoZDP8kQFbVO3IB9GMonAFMyDjg1mbCzv0eFRlvFXsikIgwhYy1JLEYYrWStLEVmDmh6xI4fvWfjztvpR2MWGWxBuIWqSGW5SlgS1hrhzhmHspZghB+RlbCjisp0fYf3EhPL0VM5Q8penNd9B+MRWTliqehUphCJCpRx5MqI4yspmnZERcbvHbCxucGRtmWxmNOHFcUoXF0jfQAdIXqkJ8Ci3ZTtvX2SrUHrgWulsVrT9cKnKziMa6nryVDUUhG1paSRwM1LwsdE18+pSsNKZfbCkryK0HnypX1cpRnVjnY6YePUKe4cTdGpY2Ot4vr1PVazjulkDdO2tMYQu55RVWOUk2bCTjGeHuUb/9Kbue/McXavXBK3sDFUukEXRW8UytbokDGqULQ0ahVl2OsV+7HhyMhQ24p5CpR+KQxGFBElDV3IAQ0GmXWIuzHcm4ci7dc6uimK/f2D4fsBnd0w6Bqsi4qvTr9lTkBiiKgaYGg4Vmhy8pTihgSA8LoKAaVGhGLQWdg1R7Y2uXD1Km0zxuqKpqnQ1mD1mMpl9BFLvH4JrxNRKRQJaxt67yVay7/3PhCmpXYNJoHXnr3ZAfNVR+s0xllqa8BoYujpu8BkMuHYpKZjG1ut43MipY6No2NOn7kNW2D/xg2mxuK0QKCVccxXS3b3D7j/c5/jDa99Lddv3GA+3+XYrccZr08I3S52VJFjpF+uKEAfBB+gjTwqtZbnLhlSinLfWxmyTtp1QCrKs4ILF29gtGapEjcO5jz5xDPUGxu85PWvZ96t+OhXvkRTTnD90Wu88d5j3HrfHVz48Kd49OIFXvrqb2XvyjWefvbLqLygXxj253t89HfeQzva4D/5B7/EG7/pLfzqf/MLfObBT2PqilvPvIiXvOjFPPT5T3P5+jVS9mhtCb6nrhvOPvkct999ksXiMko7JhunOD2a8PBTj6BGmpBgWo3ZHDtOH+8weyteuDaUGihFMBqjQNlM0hGnHLoIHiHJAs/m+ibf8z3fxatf9/XSRm0UaTTh7f/jfy338PU57tQmO89fol0/SrMmbnIVApONLW4fTXhgdQFtMrVLpD6z8FCCZX3LUDeGbr+jagwhagpjtJ5jNDhrKFH4f9l5Ol2YLyK2GqHNBsFHdnbWeftf/evMH32YP/7M/fzx73yU1/z4O2nvuZeHr624RWt+8m//Xe572cs5uZzzhU99nBdix9r6Ok8+9xXqf/orHFy/ymzZM0kVfb/HK77l9dxYXuW//Om/ySItqVpL10c+9qE/4dHHHmGC5lSzRj1JnLrjCO/+qT/m3JUn+Nff+3a+/lv+Mk994WHWxyNSMRR6AUWrilQ0aSlNrFElfOp56uEF/f41jC787n/1c3z+2mOYfs4idcwPInYk+5OqkiGiAHELVJBry0tf+2re9LbXcae1PPzs45w79zS5rtAxErJBqQhK4n1Z1Tg0ximyLsTQs1E7VimBCijb0HtppY4r2d+OW4WuI2hNSCO07tgcZRo6wrxG+8y+SujaUqEJuTDKBh0UKxVZlEIfNdW4RS2u0aXI1Db0q0A0CmWHsopSqEpmWjnWNzfouxkqaJQTppcnUWtD7D1tXbOKHjOcb0MSLeELn32U/6n8Oitdcfz0SbZCw3rl6bem3HPPvewdzFnsXOP203cwW63oL1zk7FPneP8fvpe3ftOruePUKRarFWvTMZPJOvXaFKvE1PGSl5zhww8fYTk7i0bjdKZ2loPlAXZU4fueSGHDOYoSoI4YNCJ1bamdZbc3TEcNOSnWgLXKMVObfPDffp4bl1+gT4W9/RnnLl/Ez+9kvsxc2Q3UfWRLfZLRaIrKirzqePJaZGexx8tdwaYNLl67wsH+kmW/oKkzlRM25MUr1zk4mLPoO7S10jWQFaRDF9afz9eff8tbAgy4tpEfWOlhEiLRFZUFsCpTTeTwGKQKMQVZKFO/RDcNfZDaY3G05KENTL4qLbXth/Z3UwqLgwO0cdR1SzNqsZ1MbNGKmOPN+FEKiZR6coFSND56shf2UIyy4TSDw0pYM7JBVtYOGT35GXIaHDVZpr9K6ZtwTKUNoY+k6MnRCTcIhkPeMDnWBmMd1lWQ7RATMAJpzZ6SA9lnrKtRKlFSLygI44TzpGXzXooZoMaQi6aEhBTJisuDQ2ZSESfEYZNaHk7HxRZUTjg5sUrsRimMq25asHWWz5pSBvfEYe08A4MCicEVgSibUsQyGKWxRRdplipK6oNjgRLLVxlXpaCKlqm3UpSUSV3El8TxzXVu3NhmlWWSVeJQ354zXZBZe+uUiEAMMUcQQK3WBG1lIzhs3vZnC5wREc8YOV35EFAIoL2yhtoKiLTEKI67lKHSlNBLXW8EivAOrC6UYiTGoDI+dfQlUZnB+TN4TdIgypUsf9VK47QmlUJjGmZ+mzSDEhUxZo5tTaQlCKmhZxCwyFKhnKIA3WUKKO0LztbiTqoNTTNmMdsTxwwSNctKU3SFjB7lYHN4DylTkaLC9561ccu4rrGqoE3BGD0Iq5lVtxKwa4mYmEEHUrSUWMBWtO1oiMjkQZwM5ODlIKfBWQdIzv2wZbBpRuQUsaUIJ01XWCqy8dTOiTupFOqqYTKZDu6CgFYW3wfmq47gl4QJIsiURBU7KqtwVUtEEfpESCtCWOD7Jb5PpKmlrivmsSN7iQ3EmIf7ddikl4IucoAVuO+AURvYZ26IR+YhUsMA7RcguFzTOULOmrqxGBsJXiLBuiiSLxLbNQaWkaICpYKSNREvkQZlURa0NjTKCCvDWsY2kEJk7iuC0SIcZ5n2qMPWxYI44wbwL8gBZPdgjyvPv8DammNcOZLvUd0B482WC+dvYEsk+h1WVy6wOtgj5cRiNiOnQjQCrjY60ccAyyW2KGiF9+WTBZuZ7XUMRdWUokm5k/WSLGKbk6m6HiB3i+WCxapibXaVySmYpIqy9OjK0IeeEB19Csxmc+argKoyfcj4UEjZka2mX61Y7u9w4+I51janoBTd/oKt7WdQ2ZOpade38PN9NEu6LlG0xafMvMvcciRwqwOlHH2MOArZQMqGGocPGTWquXrpCmvjTXb3DmiqidzT1hDGLQejo1wan8YHzV4dmfkZanqEWEE9PUaoxzTHHMduuZ1+PhOXkXOolKRkQSlSUZhScEWRVeDW206zMdXE2ZLtix5d9fR9oqqsRIkpclEOGywR7+Gw5EEEpkP+3hBBH76MMV893KrhuTYIRKVITL0MKpXVdihRUIMbdNiUpHIzJlfyV5sKtcrk0lO5hhB7bJGmjbXJlNH6hLCMqBpK78WNG4TNk4boXkoJnRU5RQ7D3qCJIWCtEtdhimgz1MrnhLXybiN50M0LEt4QiSlTxPWVZehw6IrNyoCR43kpmhQUplE0FaSlxxuLRg33WeQl99zKZx7Y5u4X38H+3i43LnVkZ7BIvbUtjhQ8lYXKKtq2pfeJ5aqnrp1c+xjKfAU7O4xCwDSORQgYVzNqGwgBjPDBbBJ3iVZGxClAK0Mqhi6FIV4sQyuKxeeAsVYOzIOjhOyhZJJp2NvryHVFkmUBWzWEkNlcX6Obz4gKDmLiVt2zf32f7MW1nUMWAbYUdBJVI+bI0s8JZYXOlv3dXYLR7O7NifNAUJ520nDbkWNsrh8RsTn44f+hIBW8iuQUUMrJs7KqKL1HVSOUSoTeE0pgGRXeWq5dvM7+3gHJaEZNRUwVzfomse25/dRxDBWp7xgfn1JrJw1oRRH6HtdMaCoLKWEKpF5RQiG1GZ3BpkzJHnQmB2mx1W3L2Wee57EL14jVOhdXHhsSB/NERolDWKRonBYunmwO4auFJHl47g1K7wCPJWaUMWQyRzfXqa0DbcTt6SNOtYDCmYSzmZAUJcqfR8n+zGUlgqR2kMGa5vAlyDnRd5F6MkJpGYqNRhWtXWN/b5/aVKw1FWvrU5rWkrLlzr/wTtqm4tip47z3l9+N54CFn2Ndi0T9pcU4Rz/skw3TyQTjLLOVl+h4CVgcKhey71kGeTal8QjnDG27xtHNlvF0TLfc5+UvfhXf8ubv4Lff+x6ev/Ik687QLgvPXzjHtcsz3vjab+D2k6e5vr3LftdhMozHjheeeYonvvhldrev4xrF6sYebvDzZp/p/dCOx+DiLGpwBzO4CGTNSFm4k+DRJbK5OYHa4QmECOPJCdCWfj7jucee5sztd/OOb3sHH7v/fj7xkY9w4CNvfuO3snHPhF/7wAd57Gc+yJXVNfJkg3/+Mz/Offe9hE9+7FOscsv2/nk+8q/fw878BnvnLvLf/+hf4c1v/xF+8t3v5p73vIenzj/LLfe8gtMnT3H2sUfYmc+GASAslzMwkNSSp59+jmMnjpG6OW/+kR/lP5q2/Mgv/CzZblFCQNnE9T4xu+pZLdQwmNW0U03pEq4xKJVpTY0mEXMYhugFnRIOQ2MbRpORuO62Z5SkufrE0/z6v/w3nH7tfSzziq3Lju/6u3+VGDVX/9V7OfnD30t71y180/d9Bx/9Zw9xo7F0XkS9EBTZF2b7HU4nlO7ISZOTofRTlkWhayipEGKglIpiHb0qJAWz+Yppu8EsZb7xO76Hv/fzf4feK34xRo5srfHlX/sNZk8/wbe98x1cfO6LXAot9+bIa976Nn7GGCbr6/zB//m/8YmH7+fxs19CKcvUWVYlo9yEa5dfYLVzjaNHbiFdXTAejeitxncrzj/9BMcma/zYu/4+Tz72JT7w/t+kx7G9vc3JU3fz8EOf5MixCf3BPt1ygbYtJfcomzCqwVjIKjJ2jugVVeP40Mc+wDd/01tJb/pm7vz0db746APElUNrR11ZssnMZitco3BZ4ZzjB/7jH+L7f+wnON6OOPvph/jN/+f9PHflMrUx6NJLQU2VyUVacPs+ihihCyp4DrZvcKqC0XTMtRt74mrulpgiLcA4x9pYUxsNUTFyNauwxOBYLAu5UlRhRRMzy5wp0TCtLa2zNMoR9YxU1ml0QeVd+n7M0nuKDgSnCHY4S9iv8lGXq8ixdYdThWwcfbcUZAIGhyaXHq0TlckcLMVt1fWJFGRYdvnSNtc/9DH2Zgve8NqvR7/4xSzne2ysrfPQ448xbVtuP3U727u71M2I7Z0drl3fp1aOhx96mte/4c3cesvdsn8R2zcAIQTuOHmKsn6aLnya1oKKmemoYtkvUDmTs2Hll1j2UM5i2inRJ3Jw5BxY+sSN7Rmr1SYxWZyq2Vn02OmIdWM5MZ5ydmeHGsV+gAvXtrl1bcSmzRhn2EcRbSLPIrvdiitdYR4V1fP7rNI52o2GU9MJt7qG2WqPVDIHux078xVX93YxBoqqUPSyF9Dp/1PP+f/79ecuKBmlUcYQKShjBvt8xho5WGdlhpiCkqkahaqp5bBoHF1KaC2xFJUUVilp2FIWpZJYbL00mSUlMSWlNEElateidCKUQlp5gt+Tw3fbElYelMR5dGUxpSF4aZ2xpsJZS84edKAEJZDUwQ1QuZqqqYXtk5LAOLPwOEpK5AE0nHOm7yN97+l9IMYg3o4kdvQ8PLSMtbKpzwMQO8kG1SoIsSf4HqPks/Qx4ffnNKMGozShyOQ3xsFx4jOFgLGD0BIiJXjh35QEJQhIuxR8lOmZuFWyNFgNEMWshmaowWUk+p8we0ouAxdFOC4SG5UJmBhgBj5FEmHJaEvMh/0A6ubBQmlpp0uxoJzURgrJSuMM9N2SRQjUxqJ9Yr6/IurEaGOE0nKwsD7jB9ZRLCLw1ZqbVacpBWKRfLZsuBM6i1XUOkdJmeVySZhIg5A0Bg4PrRyw9QijNH2OpBgFjGs0EXmoGSNOO1cMfQo4Vcn7z4BS0rgSesxg/5+FBRwe7nuPrZyIaMPkBwXOVjir0NoSY8/ePKCMoVkpJqOKaV0Pefbh96YQ1URrwBAHy7hSMkGvmxZlLE3dUs3mZLIczqK439Ig4vA1tZF6uDJyKaQojr/p1DCpJ2ijMcYQgme1WtKFgCoZjRUmWnb41KGMwjhwtqGuGnyRbLMdrvU8APlNY3FlyDqXRMma4DvZ7OWM74s02GhZotIA8Y4xEVKiMS2xQDtZ5/K1q3TJM5/P8P2SEOX+aOqKPBl4aSUSYqKPHTplsAlTF4zT9EQWUcRkrQ0ORVWLtb+UgreFiCb0Q5QGsb4ZMzRgFQiDy8to2ZiOpxN8SNiU5JpXGbJGl0LlLMY5Yhw++yxiagpQTS1Oi+VcYkYFmy2eBH1G95FoNH1d0doRRyaW209vsru/Qz/b59r2iO2+RkJ4Ih0Zrb/6/+NQVDUE33PkyBFuuftuHvvsM1w9e5HTr1aE4rh8/gYbW5ssty/R4tnZuUJKC7puRR+FWVeKx6hC9pJTV8phrDhq6rometBlBUBQjqt7exzZmEjVmEm0raUkiEn4c6kT0SjnTBcVxo5p9C6zPuH9klUo5AQ7166TjWF/3tPnRAyJWdcTswIkSun7wO7eddprV9m61TE6ugVV4uRyl4MQsFazmO3TLfcoxaJNzbwPLH3GNpZUBJxuTcJqTUyK2ii64EkEUo4En9m+coXm1FE6n0mxY318knte9SpO3/t1tKOWVbKE3IMSoLh2ipAXGD1BV4Yt2/LKN72NVZhz9itf5ur55/EpkChUTqKeVUrYQblfXLhMVxSbt6+zduYoi3MXcZWVhsoi7W55aDEDiDmCUminyUEEewoCLlVDi2nhJrT+EBYs59xyaIZg+CdZH2ThkIHQze9l4N9tO0QfDn/isM5YklWoaOX72dOadVKQAUzJPVEXbEp4IiEFGlsTkkBBMwqcokQRdeMh00kr0EMzmpg+UYAb7OaogUWm5KdMCINO+B2gCZThmo4Iny/ngEpK3D8loXFYXaOLx+VCMoYQepLRfPbBJ4la89jzFyghUYxG5yJNdkaTdaYe+IcYLZ+bAV1Xcj+mjFMwm+0yvhRpjSPaFq1aKtfguxV9CGhXkbIjZT0038pAJeSEUpqRrqlpmJWVbBhzh1Ke5DXJjXGbG+itLeJyjiqJgEKFhNKRYgzaJ1wpBAKTpuX69W1yiagEJ7e2RACInlgSGSfR6Mbi2jXScknoVqQEVdHUdQttQ0qZdrLB1vgooe3BRdana5zc2sSpRJ+y/F4xEsGmYIZ24JwSsZjBRVPjUKyUOL37kDF6hLENuCQueAoxGXaX4qCpQk1BY2yFGTlK6mlMJpWAqRqa0Yiqrphdv8K5fsZ8MWMVEzlq5iGxbiyryhGSIqcgRRra8pmPf4KPf+oLJCXV0H1O5NgRTSGEJAIqAvNPovbIMAcjw8TC1wi38vwow98LaF1aB+uqJh+6eY24hUPucaoeHKiOUjwM/BBxJ2myNWRtiLFgrSEj4hwoonY4UxGzpSkZVywXn3uOTz/4CF2MrI0atiYOoxR+0WHrmlfccyujtqGuHXF/mysHc44fu4NHLz1FYxoy4u60ODLiJuyWS6xrKNGjiiIqRescR9fHRJW5sXOFydoxJuMJ41FL3YyonMaaxItf9BJ+5K//NPe+/B5eenyd/+s330O0jmcuXGK1qrmxd5mvPHedO0/fRdQLXJWo2paD7V0m62Mu7V5AG2nbLcaIS0odFgYIN1JJ9lfOK0Zg6EoqdbHW0mgnHLJhHVx1S/CK2XKBaydka0BLSUNVG26/504++/iXuHDlKm99y3cyV4EPfeDj7O5f58ixdb7v536WB/7kvSx0zZc+d4WTmyf4C/e+hvf/2q/xex/8HfZUz4VzN3j3P/7nPPgH7+OVZ27jdafv5PW//N+iusB8vsczn36Iq2dewZVrl7l+/Qbrm0c4fevdbF87R1VX1LYwXx7Q2DGPfO4hHjx7nraaSjS2doxshY+wl8oQ5Uk0ztIH4WP2UaNtxmY/rN1yHuuLCKM5dezvXefRX/9dPv7AV5jv7fLps49wEObYjXWOzs/x5jd/N9/7Sz/A0ne88Ed/yj/4P36F//zu23nlmdt5/IuPsmPK4KBXFKKcAxDRs+gVjVonpyUKS5d6xirj+8QqFqyp8UXJeQthAqIUXVrgmnX+5P/+XT7x4Q/hpluMlOEd3/uXeeNrXsXOh/+IReWxozH95bM8e+kWVlev8+BnP8tnH/w0L1x8kiPrW4yrmk4vMVWD6g6wpuZjH/kQ3/aat/DLv/JP+Y13vQv/lrfwT971c7SNoe+W6KPH+Mj73s+lc1+kix3L7GmPjNnf3YODPbQuxJKpKktKPShHToGSlwQizlQYXeN0Ya3KXHz2y/y1H/h29hdLltkTlSE3wrYqwWIUTNYNIyuN3M2o4urTT/DJP/4Qf+lt38Zd8xU/8V3fzqlTW/yLX/8XtGsWi2HUtJhWkYOm+EIIWfhuTtPnFY9cuURdt/ioWYZIDoX/l7l3j7U1vev7Pr/n8r7vWmtfzj63OXOzx/Z4jME4BoxdMNgBgqLGJSiEAuk/VZCQKkjUWxoUKVVVQqWERm1a1CTi1hbTNhWES4FgXCjEgDEYsI3H9mDGM3Nm5lz2Ofu+Lu/lufWP37P2mCBVqUKlbsnS6Hhmn73Xetfz/C7f7+c7bzt2Wkcwmlg8Dnpvzq9FXPSUlDh7WDCtso9pGmYEgkk8HAa6QRWkWRxrs0H8nEnO2On2efTGDUgj5+szQl1MlBq+4LxDxLJJGTZLUlKcjBihH3qc28UlyGIZwsiUEm3JDNOkopWciSLstQ2xKOO0EU+WSDKOL376Gbz3HC3XmAx377yKnzU89cQThFQ42Wz4w88+y61br6/uHXNZ01hnKSReuneCLRloGVJgMdc0OpkUhbNa9ZQ0cvXqVVyKpDhydHSGGM+YA63f5epjj/PU3k0ogVdevcsQhM+98DwzG3l0f87ZaiASScnQtXN2Z8JbvmjBW7/0rfRBePHZP+JsPTK3jjmBxbxjGoTh7pLFzcJ8vmB1FDDzzLAZeeH2IetJE/iMDJreKLDTLv71hzv/Gl9/5gOlbDI2o5YjCzEGXIUNhxQIJarsuKpgSGAkIykyWUPGYpNFCFAKY7LYxmHFg0SiCK7xXN/pmHeOkGGalCHgLcQqY51CZOonhmGsVi9tmXMumKg8FW1wLYXCOE1YkxHjqvpA7Uo5JYb+nC7OtDi3Xg+0SzZOVnVVSWotyIl+HAhhumSqmGovsuIRqwX4FBSYbEvBtxbrjRYuYWKLg80x0rSelDJhTCSHDpQqi2oLUZYAHrWjpTBq0ltOaquRpI2/yqPqUAdVWhmnMcnWEPJEHiONdaSgQ7ICl2yOlNXnb62rF4KCpzU2vLDFOJQSNaWLquZiO8QSdfjXVfkUAzkX+s0AOdE2lmmzYXl+jrOGq92CdsdpwsZYuJhGJAoDyrNJRXT7J5mQBRsL5Hi59VNgcYYEkpIOM7PaJcNmwlhHCCNSasobhSymDr80KSknQ4yFMI2XFrwpRLpZo0DiDDFpg1pyACOYbDDR4IsmxtmoPKYxp0sVgFSriRHBpIxrnEb+WgVR56LDs9PTc8J6zfWDHXYWM1WxNKpe2NnvOF/3daCVseLwrpBiqml7As4SxOqWHsi2kIs+11twusYbm8viyRhVHcQwKTvGaWR63Fo2rULYI5FxHJnNDGL0M9C2VhVNUnDtDDPqcFJKwThwrqNkBX6HaVIGkTcMvQJXjbGMY6TptJgWdIhsikL627ajaxZMY2SxmJG9sFoOhCHStI1+Foxld/c6TzzxOAfXO/rlhSYuURiDY/f6LomB5YWq0nJJ2EZthMZYQoyIq1ydLX9sXiDoRlkq/0qCtteCoRWYZJtMqJujXAxzuyCEgXbeMU4ZpoTzmrLVOCHjCNNEKWo7sIMntQOtnWEMZJMgG3wySCuEST8/sbdsYuLtX/wlvPcvfD33X/ocx4d3mX7v4xwdR0KF42vsdblkPIlITfxLFMncufsK157b5/YrA7dfhadv32P/5prh4oijVWbRBk6OX6VfnTMNhRL1e055UPih02jpmCbyCPN2l66tKT8yUYzQOGE5TnTeE6Mmu7hiCEYHqA1Rk15ELbk2zzi8f8xTl0VV4wAAIABJREFUN6/SZqGcD5yd9fRxZDSW8+GUsz6wzi1TVV32YdB7QVpcsvRTZL3ecHF6zPzqDeZPPMmNPDINa/rimNZrnHVcuXmL8+UF5w9HYjEsFg0UTfC6GNa4FHF+QRZhhaXEqFbhkpmmFWG9Zm/+Ot7w5DNcf/z13Hr6LVx79FFyUsUUourDUBkpTfZYu8B2DdY1TFMgjWcsdm/wxnd9HW5+jXsvPcs4nekCJk2UvieFgM9qgR6Al/74gsXegma+IKdALiMhavKa91vrc9H3Wh9THYZWu7mpOs66WtB7uwL7YassqndE/doOlky90S55YVuzb7XHFd0S6Z2RALEUA2OKdCnTdi2UTBiU0ROHkc7PQAIpTWAceRw0qZVEkURJuhwpSc+SAkgOdMaquiDpIEIHaAWTMzkmtYXbCs2n0qOiXlSaHqc2esw2sECIJdbQD4MvaNMuGZqWYld6ryA464g5szGWbBN9iPXvQdWxCEEKPmeQQrFAyhgaDQcoGesMYWuDSxPTcmRnb6b8PVEmTRFRT78pOMmYkmsgSMDYgpcWUqYvkZAmtZxOGzaxIGME79hbtHRPPclqs9YgjRSJDGTr8dkiGMgTe92c85g4Pj6BlFjsdDTW89jjN3hw91X6MDJRII7alPsdxHkW+1co8znkCXEtjb+KtAtOjo+5fnCVeeM5DxuMaeh8o4EVBax1SEyEospVyYaLzZJcEi5ripUO1sF7YTVEzjcrihQWkjX4JatCsYgQnKrzXIZQsnJ8SJhi0MTYRN+vKBcrHJZihfPDUx69cYO2c6yCELAM68Th2QndqtDHTzCbe7yx3L7/gD9+6S4HTz6qCaFDwFlhTBGbAyRPjIVcRpwUEK+hCWhjKWzvj/qZ0mjYOjB9Te1acianSDDgKotLfKfWDmtIcarszEzKhiIeQ4TtorNE7KVr00OxZAnkQe9d76HILqtxpJkvOD9ZaXJogtV6zZUrMw6u7GFdRymqUuyHwP1XXuCF9Yy/+p3fzQc//J0sHt+hbApFPBAxWQhZQ1+GtNYap6hDIZQCbsG1/Y7l+YqDvQN2F7sYm2g6RVR0szlveuZtPP3m1xNj5M1f8x7+i3e9m0/83mf54R/9EYK/wnLtcH6PN3/ROzk8PmTT97ROrUh7zQLTzjFxxLmWMmXms5Zz59isNVEub4MIRGraJYjZWoW1pm0WLXEUUlTeJ5KgZDonhKmnFIe4BpMCt66/mfXdc/7D7/obfPzZz/DJ517k9uFLNOzytrf/OW4+9gg3D66xOV/yprc8yTf/5Xdw5coV7v/Yj/BPf+qfcOgalmdrZteu8qF/9gF++Mc+gPSJ0liSTciOYza7yuse2ePe88/xxV/yLt7xV97Bv/Ndf40f/4kf52f+x3/IYr7PalwzTYbk4O1veT1/+7/5+/zoD/4QH/zF/4VQ1iSZMNJhs9BvtFcRE7m5Z7h7arFOQR8ihuyyqtyDDrynFDm+uOC//cH/jjFE3vtN387Bo09ydPsjHBzM6e/f5bu/7/v5yL/4MN/yF7+ZcXXI4TQgO44f/Ikf411f+3X83Mc/Sh8yU0xq446KDcBksrP4tmUzZFJuaUqgLwkZdihmRRTLcJYJsSFLrb2MQ2Tkz7/3z3P04gn/2ff9PX7mH/8AP/vhT9I5y4//xP/AwSPfw4PjF/n3v/Xr+fpv+HI++bFPkc0VdtuOrgs8PL/DajXx9nc8w0svfI4cDLeuXGd1MTGYzHIsfOPXvBsfAotXXuT7/u5/wr4xhCB4cdx9+Tb/8uiIbIJC661XNbMZCNER8wXFevoAM+OJRflHBss0JbJVntFibjHdpOrbmZCDZzrPBAq7HnCeOGUW8xlzJ0QmwJBDZG/R8q1f9x6euPIoP/lb/4g3fsdf5Q+ee4753h6ln+htz+YkMdsV5RmaBSaNuE7ILmvaaU6kNNFZwQtsBJJkmq4BmRA/I48DN68dsFmfcXY8sdgzWJNpnGNooS8FZ/RMaoqlY4YY4ZRzeufIU2HHXUEY8c5wvtpgcdq/lEAi4kXr4CkWpuMTHr+6x37T4d2CMSVyMUyScDEQpGCzJaXM2SZSQtAAKlPIYeD44pTo5nzmxbuUWFh4w8xnXnzxDutxSRphf2eX+f4cjOPs/BxwZAfrftCeMKOzgmp1tanw3L0TXvzUb2GTqsYR6HxDEkXkOGfwjSfmyvwTi2RD1ylWRk4zb3/LPl/ytnewPMucnb/CbKahLY9cvY4fM7MmUXIkmYZpHOhmc47PNpyMA7sLS2P2OQ/wpkcbnsk7PHJtzufPzrj/8jlvvTXDTp6Hqw3zhceZHW7fO+Xuw0NKmrB+rk4wAgZh78re/8sJz//z15+95a1oyhgCJVSAZqZaqQwG3cxpbPEWVFwHL6IgxUwGY4lRQDKOgCkt86bjxtUZrUfJCGOAokkVTpSdM/M6BGhah81zLqpsXqo8PIdAGANbaxSoqiPGUIu7urEwCtzO1S6QU1aVjGhRnkHtfVmHTqrb0ctJFT/U38nWAkJ1A5JVGp6S2vwEgRJJSRssK5CNWmiMs4QQ0Y2wxiMbo81CrnKiUrTeNVktgFmKJrxYweCU14SyJMRp8a1WP03/0cSpgJFIIjExqZUvF/1dsqq0RKCUVAsYq9tgqy2FiNHeQ2z1AhVi0u1PTVlnmzakKq4eTKF1rabiOSHGwLofWK17shGWw8RcPM45xmlCxkwvmsiz1T7pZhuSWEyRy+StXHkxlAxisJVFJUn92Ot+w3K9Vu5Hjd7cbqhyVrCx2AoQp61FlRYhxljCVHlFdYMYwoTLhpwS45TZhHD5XitTR4c3JWWNB9OeTA8d4wk5MYWeKUUFKFc2VjTCkCLHpxes12v293e5Ob+BF0OZUo2SjxWYm0hReVFZErFoYpFzRg9GlVChuyGqvZIvaBrVCiPaZZJyYZwC4jTGMtVBXc6iTWJO5JQIw4BxHY0RJGnqF1bo+81W5FA/T5aUSlWaZUKYaHyLRWi9raA/S9t0gELUY060jcM2nhgjbqfFdzM2m3OSKIj+xmOPg1h2dzs208T6+IJbj7+OResYx3OmKSvnQTJDGFj2Ky5OzynF03WOUoeHYTMQg9oabdYufLDK1HCmFndBByqpZLJNOG+RvmDF6mevFDrn2DWGKRnmezs8fPUCpGEcAyULNgW1euTMfG+HuFySQoQIcZWx2VLmUbctqRa01tThbYFkyaHF2omz25/jYz97lxtvuMn8xlt4uHmOYgZSCnjx9fOqRXORrfJEqjDOcni05lc+/AnCsCKVwrCe6FdrxjBy9OCQLj2gH9ZMMRPGTJKJpoP1+URpVBmap8Ki6TDWMw49nsDyYmQ280iJZBlxbUcxQmFEYiaUTD/qAiBEiElwMw1hiKgaYEojffKk9YajiyVHZ8fYxT4nQTg5j0w2EpK+LlNQpV4vidZDyo7kW3KBi+UKu7/PwdkrrIJhnDbcnLW0ixl3XnmJ9Zh1kCTKkdid7+JdC1LwJeAFegptzIx1w9jYwtmdF6AUnN/nK//it+B3rxCHyLQWbNfSeA0uuH86IKIJirlV8LcxWjhdrFbEkDm6d49hc8HF+pxJHMbt4KQg6548jgpu1oNWFZ3WK6PFKAMQjA5BpFpuiqq5MGqrzegwSf7E8Ae23s1LOzrb1FBTtaW8BuPe/ie8NoSiniE6nDKv/blUCDbbxE9RWPs0YmcWK4Z1ykhjMKlg5lJVvEKRxDROVUlVuX2olVSVoArYFylqSM26oFCtS7lUfERyhQ+by6WPFMF7e2kJjbHy4FKqej7dfJuquLJNq2dhyRjfqM20FN0yS1EbwBQwGUzSVLdca4rRFhxGUzSlkOrQ1HiVUXnvNLEz6t9r9apiFKnK6/raCVAyjfjKravKaNHXw1o9b51o8ts6DOzOd0E2GLFECdjHbzKVxPlqjS1Ja4yYcKeBzibk9IJ0XjjY22E6PWNlLK5pMNays9OyWp6wPF8yhKjMKdtijGMaR/Kwwu3uKzsnKzfIGcNmzLTWc+36LeLZOYv5jJIz867D5EwMhWhyvTMjOanqfHu+RuuQHNV6bAxShHG1IuaM83Ns09TnS5EERhIl6TO7qSqgmbXEkmmtZYwRZy3OeYaSMY3Tgao32HmD9Q6bA9YpCHmcYH1yyvnZBbPFHOcs946PQTRxr2sEnKedzdnZbWqEdyHlSOwHDQgJkRAzMU24KVQQdSGXoODbos+hKbVGqZZpEYVnmwJWdAFhcvWb5oytsOdUEuvNOf36TBW5tVYxztcFgrmssa3AatxQnJ4LaZroi+G3f+t3uHN4iPUdZ0OkL56nn3oLs51dYt9zeBIwreH07gusx8jf+of/mD/8tV+gW+yqfbX1ZKOfe2u1hoi5ph5loRhlEvkUaXcWxDhSsqXpOox1OGux1mOdKhw3mw2bcWCv2UEEfvlnf5Ef+sBP8unnPoN4h2sW3Hj0dTz11KM8PPlSXvr8Szz1zNM8dvaQz3zm44RJG6VSHGEKnB2fQ9liKioDU6SGgugAcqvM1ro1sVlpAhhFWaBxVNWzcYYyBbIUXPH0q56veOdb+A++83t44onXc3R0zEd//1mmMeNc4vT0lGc//gf8by9+mtuvPs9f/6K3Mn3+Hh/9+Z/nzv6KtaLfGMNI085Yrc74zLN/xDvf904oGr5RcqF//g4/9T9/gI+99Bzf8x3fxl/5pm/iH/3tv8uHP/tztNYQ+g1DHzFOMFL46Id+kp86eIx3fN1X8+Ff/F/ZzCzr0TKkiX6cSFi6Bvog5ADzWWHZZ2WehogTizcHlFmgWwive8MbaYpw96XbiIff+41fZDq/YLcLDOWInat7/Od/86+zPh+58ug11mGkMZlhHfnU7/0qz//R7zCeTvRDZEqToi9wOGPAjCAQiyWmDV2zIEnAxRnFWii7hPGUIhpKY8wMTGHhZyzmj1KGhve///2YzchXvP3dZCv8/C//OpMrfP/3/k3aKzu87tYTPHj1VR67usfh8QW3XzhE3Ihv5thmxi/9yq9xcGOfmQ/s7S04vJ/ps4XNBR978Q6/9d1/g5/7/d+lO2g5Wk3M2gW3bj4Om57N6kxFAnlBKsIYViSTcDhiMgy179zkjDGFMgwMOdO1jqao42W1mYjVjto4oTNzijdM1tKagfVGU7aHFJgYdclAxpfExeED7r70Cnz6BfafeSv/4O/9l7x6doLJE8kqPmb/YE5hwLiWYZkoFkraxsno0iYFDQCxtjC3Lc5ov9dJ5QvaOccXgTjoIPb0oahFdDcxxEyxGWIiRUFsYI3eISs8fZ+Z4kg5PKbvzmh2PLbdY1gnQlWaJVMQm0ipwUiDz1YV/zbRUWgtBHuFIcFyWpJNS4xBs7YbQ2MdFIszhms3rvNVf+kbeeqtb2MYhP7oIT4NBHFcS8JO5znY3WVn5hVx0sxpm1aX343lsVtX1RSfC+kLSqVQEr1pePq97+el3/0waXPIMI1425GM43zY0LUzxHqMaeinQiwbppTp5i3TFLGt5/r1qySzw/n5yyyXPdf3r3B2fkpMI2YKOKCpNb9tWpjvMHQjDx8sGWLPtUffwJPPPEm4EN7z5e/jouxw8uu/SVq/yJd+2ZNce+QtLFNkHI659xBeuPsCF6sL5R5HFZykFNmdX+HB2fpPD3H+Db7+PxgoRYz42pxqI1+KVWGK0QSLbZx9yiBlQrDEYjGi6S5iFF5cXKF1DdYarPOMcWKcRr00a5z83qzBOx0CpVjYDD0jiX4zsVz1SDbV0Q6IrfJ3tUdV1bgCBK29VNJIUaaCpKRck23STRX9Y6QOGVIFkqr6aav8MeIwJilIWbY2Ai27Y86XAx2VPOfLAhlTVGJXX6NU06DEarKXNdvXUL3gRjRdRi2eqvDYeuXF1u+fzWWBjdGGKOZc7Yg1UjmrGcBo9k3F60iNJQaQOsji0hJHUXuKDozq70+q6iUddIWgBaGxhjhFclKIpIjBN/q+zmSOkInRM7gR5zv6kJimQl8mfA5MJJIUIgrFJRt9LyqLRazGHoekm+aMNiK5bFkX5ZKhVEohTZF+nFgs5oSU8VZtGNnUTbKtKiJrlUmlLwKmqJpHQcKlNvr6/oskjFjCEJjCpOlgVK1Z0f95azGXFqlcmz0wJSKiyr5Q+UrWGChGWThGh1XDZg1cJZWCazocmSkkxGiigak8JGMsVhKdImoYycpeK5mcXyPyaiNZbRglauGFNkA5Fzb9oNYGZ9Uik7MmtyQdduRYyGEiZh0eaqOqw2Ax7pJFIFaqKkAbqxIj3lgkQxhGHSQD49jT+nn9Pk7VBC7TNV21lkYuTk5q3Leq/K7s7dM2LbEk2qajxZOGwkV/QSmBtpnTtTvEkri547nx2HVCzkzDyDiMnJ2esRkGNpueGDKb9UpVQ5KUqZK12MxG7StSmx6jvhOy1clxTqUORGDKgVTg/u1XNZEjqK0jSVKQdVYmm8mvqUSKKbrBCpamKhOkeKaUcWIpOWCcxuXGAHszWLTHxM0Rd37vmBeOP8HYK5zdiRAJlKLWJj2YKzuHrUiwEMYBPBRrKaOGCdjG46yjvTrn7OUNJgnHZyfseEsYJoJk+hA52J1TQk0PNBbnHb4YEg3ZTAxZhxp58uDk0vq7vljTzTsoQszCECKx1OSjoGqxHCNp6EmT5XxInJz3PDzPMPUsp8KQhH6CIUWQrErNejqP0YAIyxXcLIkHyzXXzAIe3CWEiRgiZn/Gg8OHtItd/J5hGAOGwk5n8KKq2rN1wDBwbW+HMSRCTKymQPGF7Bc0ZsHX/7vfzM7BNVVcbCYKmYAhh0zbWVIJjLHQOE/rDe18QS6R9XDB3fsPGDcjd1++zYP795hWF5poaiy+dUQndMWSxWEkIiVX+4gee76bMwwrHVTD5dlatmyvUtge5sqm02HTtqEqZWtLK5esgNoWwnaQ8Se+qvxmq2Ks/1yFm68Nk+pARSrbrR4xmuYTI521zK5dZ7U8ZO/qDq5o8MK06Sli9J/DRNOqkkmyLkCcGIx1iBemkAix3qlbIWwU3fKgQzDnnR5FxlRGEqoEyQXvHH0cdYOVMlWUqhHvYSSILohstrhkkCmyszsnzRtWmxGxRgMEEnjnCMNILBmLIxFx+gboS1Yym6znncNiTbXLSkaiYLwO2o2D1jraxjBlS2P0RpYcMc7TeofXrQ1GjCqvSsK2Xmuparedpgk/63j06i4lG9Zp4PD2C9jzW1hnyFPQ9No4kaaAbxtm1pOsYX2+VluigJupDaltLauzNWEMhPrfjiZgjCpGvfc0RtXV2VTu4qwhbyLdTseDO69yfW+PuXSINczaOaXoIqakQowTIU+MITNOAVMyJmvTcjauEGuwoj/v1G8UqVAKxVhCgXHShENDpuS6JhSwxVKSkJ0u8HTkbIjOYkOCFElFmIzaK52ziFGYdaJBfEdnMkmEbr5gShOnZ0v6VY8Tw6RwIKzRlNX26j6+6fBpg71ywK5xeKcLtnF5zvpiSV6tyERCjBB0aRlTIIZEjJGYgtbGLjGbNRhjcNWyaXxNFDapBjaAsY7FYkHXzergcpue24DVOlLEYBwMfWB/d8HJsS4lXnxwyK985OMMIeJdw5R1MClZeHB8wYPTDU/tP8LBFcf1azv80i9/mr1bb+dr3vUmbv+fI84apFiMFF1gWEuOmlRaaRbkkkgZtQ1PEyebNfMcyWJo3QznjTalxVCSkGzifLXi5FgHkNM08fnPPsvD0/vYrmOMkWla8/j1Wwier373V/KN7/s6lqtTMJ7nPvExcig4L/hiIUdW6zXezSGp8lqM1ra1grw82rZDPCiEMGmgRpwIY8F7Q+MtphQ6awlTZmBNazxvvnqT1ekpn3jxPv/19/9XPPv855hf2eOZL3kH4+oKR3dfwjWeNz/1NJ/42Mf4F+46f/l7vpUH/+SHWFOYhpH5rOXq/IDF4oBNGKqCR8+nlBL3Lh7yuekMmXs+8OM/ys984J9xvHperfMm4bsZLiSys/Rh5KSf+Ps/8HfIWLrOslM8kzFMMTKMuSqpHcZkUszMmhbTTMotDYKQsXKB2I7WG45eeoGcI2IMU4D++D47i8Ki2SCmMIRA210jFM/5wzPEG1ZlIltP58GExBhHQsqEZEk5YKXUutlo2FCCUjqGcWIqiWw27Mo+MRXaRvmXrZnVMAZd4p6dnhDHid/91f+Li7v3eMOXPs36Z17ixkHHUR+YXb/OfmcoZcnXvP29/PEfvsir0wOmssakjoXTUAaZWUII7HcLnv3MHxLF4WNkt7nBT/z4j1Jmlma/JSwDTS6kcWC1PsIGXXzl0uKtI4YlMRtcAdd5hhxo3IwQNqpIjzCKQZxhMxWSzXp/JBiXkWRVDHHVF3Z3LesUld3bF8YAbeyJJK3hc8C3jufv3OEHfvC/51u+/a/xFV/7XmYf/hDv+7fewSc//Lt87sXPYbxnMyR22l1iGIljgBKgEbUXOu0RDImC3rE59MpUdQ3OWGIIrMczFvM9rHXsXJlxdhKwrSXhSXHSqBen7oMUFUFjUiQVRQAOm8TuXqd9YUwQzzVpUryeURIwaKiWMY4xJk6WG570B6R6Bg4bDelJyZDMAKlgbFvTbLes38LDe3f46R/+n0As1lv2dxfV2uoQq9iTQiYadaVgDK0xeN/impY3ftkzvO2r3k0aDatpIEgBAuvDkVf+6FmOXnyVzcP7TKHXujauKALLcaQUHZA6gdN1REQXFCLCfDbDUQiyy+0XXqWEwiuvPGCzOufi7CGvf+aNfO7jZ0z9miEZMp6HDy74zc2n2L96A27cYjXscz1d8K53vpucn+LJp76Sw8NT9q8dcPbwRQqZ9XrNRdwQx4n799fcffBAOdNiCCkSKDTdHN9Yrvn/n0O5yY5kKuSShOAqTFsBeAAiaj2iBIZhZD5f4KzD+Q7nwUnBi8rZCtA0XrkPFPZmHd4W+ikzxsLmotfhS1F+T0qRGBNT3dhma6riJl+qObBWp+RVxFLQbTv8yWQbQeODLxe0hSqVrwk34qrUg6oaKnqRAjFpfK94ryyHehCaClvV1N1tqppUNccXRLWrXwBrBESZHlhPyjr8eu0H0t8tidr3UqZa4TRZphhF4qoDK4EzFThYaqxrAklITYQyVQ4s221OXe5o42DYQncy2wBqbeWMsTinSXmSTf09gqbUIPjGY6xTpkvWzXicBqiMEI9oCsuVA9Jm4u76XLk3xtC6DmcKeYykOlSjWgkQp1vKDEHjl7hMZRaIJdcG22CNY0oRYy0nF2ua+RyDyuYF6jCvaJOB2hZiVt6RZB1oOYEUKrOg6IAEDEbUkjKNUa1ujdNENXRwVACxlrQF3tZB0TZJLaZEkFIZY6psMwVaceSoCV8He3vMTFM5Pqm+3nqIO+cgpnpJAyL41jPrOo3R3Cbrba2A9fkRKYg1mpKlP5Y2nFSORYoYS03sszgcNgZM0O2nWIfzLcM00DX+NRtrlemVOlASFDyqw1N9qEIKeOsw1tGYQmKjst+S6+sSmWI9nQ3gBD+f0bQNpmnJ00gqhr4fGHJAcobiKCYgoiBxi8N4h8k6MBvXqixqbcd8b5f9K9chq+UnBKAk0hhZjxtSnBhWa9bDhjuvPGTaHKvkGnBYGuNZl1FfRwquaAOdYtRhZmOIOZEjSI5Qco1DF4XQux39nJUEWfRz48FYr5vuKdCYhv1uh9YUrHhGIseSWI3CvSVsuhHxE0PbEjfqP08lUcoX2pVMPabqn1X1R0EoMVDIJAtDiFycDWBUkbSJwtnhQ4Yp0zbKRktk8JFNv9HULanD/DyqStC1dHsz1pslUxCkUYm3sQWxEIsjOGWUxUm3YsYWGmuYSmG1XBNy5uR8yRAdaSw8WJ5xmj3DKnC2EUpuGEOhzxpGoCVw0dc0G5Kx9MdrZld7ZG6ZH93h7P4hicxmOXKxKBxcaZhCZkqFSTT1pysOg6EPgYtNz+M3D/S9LQHqJbw2LRfBQRG63QUSA601jALiRZMBs4GmUXB1KFCFm+fnDzk/O+blz/8xh/fusV6uGcYNUmKFuetgdzOMFGuYuxmNLbicsei2rGTBzx0xj+SU9HMSQh3waHOWUtYBk61Lga0F9l/5es3mar5gYbI9n177+lf/fDtE0ru8zvWpyqfLs6WeJa+tacg5MQw9Fyen7MzmzJqGNE6QMmPfa1R40FCHHCLWO4oxkHJNL8xVARspVaHRmEbVtmFASBSxGCOkEGmaVn9u9WLXhY8OsERQ1WPlTeVqSU45oJHDtg6zCjEEdmcNN+1VwnDIVDKm6AJqqsopvX9qrLPzGoiRNeXUiUF0KsZsPle1WwjMdlusBMIU2WwGnHNc6Tpykzk+G2m8MN/ZpREdqs8Xu6TQb2f+5AgxJJwteNHfMaSJo8Njuq7Bzjpmj1yjOz6jnJ2yt39AyYUpBqakXAobE1PS8/U0qIpm1rZYYzk4mDGenZJsS5KqYDUeY3WI0s1aWm9xQLYeGoFQ1KYeJpp5i4mRdtYpp8p7XOuJ06TprkU/uSZFSlDLw2as/x9aP5ScKCZW/2Spy77E2EfCGOinnpAnvBTaCmLPOsmj0UxyTWjLSW34RW3/Yn1V4hlCLDhXlUCmAWPwzpHChHOC2Mw0BDYhYRq9r3PSQbYJhRgtuXRMY2F4cEaeeoxY7LzDtAUXMuINbn+GNQ0L63GzjsXBNTrvWZ+eMGxWFJVrcu3GVfYXM6ZimLKQRfQ5zGBSTb1xihCIOemCwnpdZIrgXR1eOr2DTdFha4ojOSew8Fu/+/uUYmitI2JxRtVV4xD41Md/h5PTc77rP/pP6doGWwrPvOEWX/ED/4C5sSxiYNY1TDEjOerzLTV9yhSIqrRWa2OuCznh6OERt3YadhYdjQOHMgPVqurJMXC+POXwwSFPPXGLT/z2R/joHz3HRQqYhaEze/SbFXc293n08BaNDBxE7xNdAAAgAElEQVRc3ePh0SElGg6PTjDFY7OwWg90naXpZnSmY7VaUYrygqgLaWsbZa4JVQWn55Q4xV7kkjVlrKl4AtGlnSGR64Dm2Rfu0PObXL95lbPhnOs39lnsL5j7wrV5y5e/7R00XtibLZjEcv19X8Yv/9rv88GPfQTjPIbEvA088dSTvPFt7+T/+OAHOV9cYVgtySFzcXzESy88zwsTzA8e5cHJHR7kgJEAQ2F3Z4/Gt1ws18QcEXEMw4ZubhkR1r2GehhTMGitHYL+vjNnWYeeVeiR6Lm2MLTNgNhMmPTZixO4zpKLEKaEF/A24kJgQpg1FnGFIkua7LCtwzVQimE9JqzssVmOivpK2h+JMQi6jDciDNOgS7lRlxzJFRrJgFrpCwZyQ2MMi11h3Qf6MeJnLZ/89Md5aveA898+5pN/8FH+vb/zH2N+5If4yGdfIk2Oq489wv37t/n13/gIafJswordvcKwFh6erunDoMs5G4nThm6nYbM85/rT7+HilTWnqxMa73DJs5jp0nwYCqvzJTPnGPqJ93/7txFi4Dc/9EGuPXYdsx65d3pI30+IjVgd0ZFCRBptxrz3KqhAcHg6LxSb2HEzdYxky6I1LJcD+wctwzoTJu0JjVerd8mRo4sjvvjL38G73/3lcBS4eW3B+7/6G7j/oU9ye74LYaBpOozAuNHaHuPpdpLW6KKhSCkUcjKUlLU+NQZbstZuOTNrCjltaFuDMZH9q4IplnVQoYDYOdaEyu5TxX5XEuKABcQhE6VQpogvHmyC1ECu84CsaqHFXovxEKZAO285HyP7jSfHyNwlVusVILTWYL0lOyGsE1Pfs173WMl4b+hmDd7PePr1j/PMG58kW1HnqiRCzoxjYhgj6/Wa5fkZ167s4pqOdqfjG//cW/lL7/kGfa2+oJgpZJ5/5Wv53u/9WwR6rl+/gkPZWOshcXZxxqJtOD4fCCXRNoXFrCUWR87C0Pc0YvjAT38EJx8mo8O8kgpz77n15IpT5hQT6GY7xOMzrIFh3TNtXuHhnQd89A8+y3d92zv5sneMXLv2GGcnx7Szhi96yxfz8u1PcW+1Yd89BBEOjweef+WMzRDY3dsn2xlpdYHLkHLgYt1z8/r+n6oL/02+/uwHShIULIjBojHIkBGJOKNbQGsdi3mHmMLdw56dnRkLazCdo7Uq27Q4Nv2kypocsMayCZHVJiM212A1QZytcfQZKVYHNKIRhKEU3GWBsVXOFG0865ZVBThGxRlZtypmCxcVMEZj5Nn+e0gFKNaNbs6XsORSFGydqw2OokVKloxxbvstLnlKRgymxlXrw/uaEU/zTC97/EvWU0xJgZ/Oq1VGV69aJNUlrcLDtUBw3tSCHsRWY0LWwduWhVS2q110eKUfI6mQZME5nZwX2Sb71Eu4NtfGVuC2WI15tqKRoZY6yKvbCExVc2XCNEIuNM5hcq52kwUNlosh4otguob5fM7B1auslxvu339YOURaBWQRLWCyyrp1Da5R1VIqfwKo9XLtfASsYb1ak8t1/XdF8KCNS8qXNkXJCnPMVfVDycSiCUo5qRIHLnfyxAxj0EM/C8QIFB2kWLYqjdpglaKWJJO2D0VtjDO2RocLhWyywleTYFzDGMfX1Ej1vZSim31nNGVQQXL62i5mHavVmmHSzZTOTbc/saoI1ZVRk2aq5MEYg/NWud+i7aQYHSyZUTd5vrH4pgVjMa4jScYZB5LrM1JZLTXbWN8Otdc477DWVl5ZwViYLxaEMerTKPpMlJJU+g2EacKaASMFokLBrWkpEmlMIU7pUkFnxCqvohRNhBNLnhLjJlBmgmsUvpzz1vbpoERCSBjXsGudNm7XHiFMkYtV4eToWIcnZB559DEeuXWLT37iD0lx0uegqH1PrGPYDKRpBIQggqsqOh3Y6evRrzfkKdTtqSWJIFMhhYy4hLEdi9l1FsVw88qMeWs5uhg575dsBsO94zkntiGVQj8VQtHBZBELVZVZjFRWnDK92BpGjY6DTVaorSlCzsLm4oJ+jKwOHxL7kdOTNY2DTezJgxYhre+qOlPVesbOoEQwSQvCCN7MWY9LbOt0uF4cXRZ840hTYewVxi1osV6C2n77KZFL4eGy5+GZIy/XrGJDxnHcG1bZIgXGLeg6b68wlZlU3AUXfeT28Zpruz1XNieExnP04IyxDFxZO1wcML7DVflMJFRbZmTKRYd6KTDkQiwQiuFoUzjPgZLBuxXTOmC9UHwgjT3khDcWEoRNIqSJk/Nj8jJxcv8ODx884P69uwzDipTqoFkitVOnpKiWGBFyEvqUwbUU0xBCr69v0vM2lajPdNY7hroASKnecbLl+22taK+lul1a3nK93/J2UJT/dBG1ZSXJdrDEpUXutT957RTcfuWytUnXzyJapA4V3nz96qPYSzu4xt132ZCLIDlha6JhqreTFVSpK/q72SL1OS/V+p6rzVzZJ95ZbP1nHaDqfW+NJaZU+Sk6bNO5QyGHAJJr8qiF4sglEcOIkUKzmOszMo5ATSMlY00hGmXdJKLyjYyrKqWiCami9+jO3oK+HynF8PjN6zS2MKTEKy++iukWdJ1n1sw4ujjCeMe1a/ssvNC1HfgFy9N1TdxTZaS1jaodjZ7fUhWQ6zEwKwY5XSmgOAhNEaZiNaWPrAPKUigp4vwuzeIqj73pgOXRHaZ+zXixZFgH5jueHBM5TVjnseJorKV1jsYrd9CII8WMN55N3zOFkSY1iKOGlohGLxc930IKhKTKv34zkLMuVGLQ+6ukRB7XSDEEieQCY5i0QiqZlANhHEjToDWYqc+OMzQJkikYZxUCnAVvG03wk4T49tJSWEoihQDe6b0Ssi4wrQNfg19swxQ3WkvWOrGYQkyq1kMKpkT9+wxMAiYnZBwwY1bljhFKUfOqM5a96zu0j7yJ3WsHjJ99FtmMyLylbVps27AeI6UMrKeINYbDo3O6LuEcuLZj3jWQI6kYpvps+7qATNslkfW6aDVGb9AizEzHK/fucrHM7OwtWPcbMIGc6/kgltl8xub4PowrRBRh8eb3vYcUdOn05GNv5pGDa7xydKpVRImEpKzMUusatZkVrHpiKZLVsji7wpX9PXLMZImEpKeTNZk49hzef5kXXn6RG4t9/vnP/QKvHL6IN4H1xQWlXZBS4PPPf47hdCCXC9oU6Ga7vPz8S8Qps9NdZ+qP6OPAiGXv6hXaZo9mWBM3+vxcnlRSJ7NG726MMkJd44hRbZjGCsnWUXosykUxUllWhZ/7hZ9nZ2eHp9/0NKdnZ9y4dQ3rCndf/Dw2Zu7dfYhvGxbzHZJv+dCv/wrNzLLbBtpiObjp6Uri9md+h5defglkh1/6td+kGIMn481E22amIPRDZLa4SpmWlKxniG0anBsZpgnfqANjipFSMr0eZ0yTLkWdNyTqICFHZmLBa4S4EdhpM7uLwCYUZQLlCcSTkgbpGHGICJ2DR+c7zHzG5kTwmcGONBj6i54cW4wRdmf6/KWChihlTZlW1f121ZAx1hKZCCGpVdlafDHEMDLzljIJSRrECBRLjonGNVgiceqxZpdAx5te/3r6o7scW0u7cEwDHJ6tWSx2OF9uiGHD2G8Q6/FNpA+BmbV0rbJFxQrOeGIJLFLD0dnLLOY7lNxjnNCaBtdd4fT0DNc4iJl5l/nk7/8OX/UX/m0Ort7kO77zu/jYT/00Z6szYoyMkaoAVnxBQTEwJUZV1pRIFMv+wVX25pZps2To9dmaN5bOw8F1y7JNXDyAMMGUMikJ2UZmXcNsscPZ2ZJf+uc/w7/87Y/xy7/6GySjtX0pEHIg5IitiXm+KxibkOSVExtCff4dxWVCyuQkzKyB0uOaDps7yAkJiQTEFCmSaZ3D+aZyWB1N21Gmic5aplLIURNPZ7OWME6X1tYiDRHFc5TymvBkSku61rNoDbEfWMVA2Zkx5UCX9WyLISDFg7UYbdxwpuCs9mU5C2FIUEYW8xn9OCogPUYwMI2RfkxMIRJCwFcG7rxVJ8bF2QUXmyXzbqF9bu0jCplpGGgYyY3nymJWay2LN7BaJpCASKAQmM8MXSMY22GNYZgy4wxu7s05evgq/VDItjCGAK7l2RfuE8IGrGczjkjjmB9c1TrdiKZ6h8z//kufweOQ7h6NKVi74PN3XubwzoYuwRspGLfHdFEwU+bJa7v464/y0vFIvx6JondLQnjx7iF/ll9/5gMlMRZvLEUyDlul5A5vnU56HcwaT+MVWHh2ZFk0HtJIiXCyDkwhkXHkrB/AxmR2WlvB3knltgLeaEF4yakuSRkDZIrJmCgat5tTlXlSC+RSY0OpQwhUbbLlxBQUXItO000d+GzvH5XXbVUq9Xt+gbXktW2uKATTiIKwjE76sdrEqFKjFoJYSs51MJVrsVMz2ZKO56SycOI4ILlGmCtCAGu0wPXWUyRocYQCPKGKeUSVYmULXa0DDFM99ltLGGwHRuCqFVB83exuv1c9GC+tESJoB26V75BS/SCijVPWGkML2ETOURsKY+pwzmCcxbSFphnZnc3p9vaYLeaIFQbjKmtrC4/VIl2yvq+ak71tnHK19XDZZGwtH8loI9f5BodGwSu3AYo1NFZTUnKKNAZwCtYVkwkx6evqBJzbrvixVFZUBWKLqD1QmQ76jLqmYRsrvx0e1unCZeqCqr6gcY551zGliYI2nM47ijUE0QQ+Z/S9mPmGfhr1NbVUy5MO/iiaKuiswVpDLMoauYTFWx2S5TDqeCopZ0U3eR1d1+CqL18hGa+91wVNUmm8o2ShbZTZY2x5TbJA/TlyrkXba8yCkvXvypebZx0sOKcDvJLT5WAqFlVeZQri6vNrFc5vGz0XvLEkmyk5EGJGSrpULZasP6sYTzENxnZMdRh7mTgpupU2GaY4oclDdfgcE75yKSz63nrv8F2HM46hjFiEkCL7i33atmW12tDMZ2pPMRZjBesc3hoa15GNIYXIRT5jvd4ot0UgF8M4JHzrsHmX0+OJ42HNnYeORecYimWICm0dMgzJqqIjC7Ek1NkYlZ+zTeKqZ0zViegAukaLqy204ASiNRSbGUJklUbun56yXPfsX4HlMuGT0M1VRRKiQm1nO3NyNyePA5JH8hTYDANSPH5nQSyWlAvDkMhS2WMxkbNV62IdgkvI9CGRq8Dl/CJy9yJwcRw4Kgcsx8gyZkAH7bme+WIKJevZo6pPffZCyjy4d4wp8MQzb8T4fe6efprN/83amzxLll/3fZ/zG+7NzDfU3DPYRGPgCIKmBIq0LWsh2ZbkCIU1MKiNw+HwEP4LvFFYG++88VbhcHjh4MJhk5Ydlk3LkkKkKIUoEiAIgAAIdqPR6Gp0VXdVvXpDZt57f9Px4vwyqynJ2ggvoju6a3gv8+ZvOOd7vsOi5CYMq435SbSCqkdaYb9UVj5QUmGzWRHCimnagjZ22fN8nykrD7mR9jM6bSnujG0zY/J5fwO1kmtjTpXHH3/Eu2+/w/XVM3aXl7SWySglOHsWXQ6i/nA+HhEfRKGS2TVlPayQFpG64DHwUzMGijj6+z9cY0fd14tzRu1MEulm2p2l45wx1uRwjx229+H/D/8hx6Puj50B9lLtbDgkVtl9aCtNe/pn04bDHe9JSTvG04g6odRCLSZfLakxbFZdttvTVfTgDWVpoNMy9SbePA3AaNxg60CcHu9s2/v9/utNo/bX3KqdKUezb8sspqaEIriBXvyLfW67CbdaGcBVjdVr6XqO4kz6Kq1LTBt25lXXgVtrQjsGZKO2YB6SznmT12lAvHn6hcFSvRQl+oAPSlZFmkmjxTljrPX34rGLzrwDzVBV1ZFaQfY7mhhzoHhH8DAwWmpkZ76pwKt3X+XzP/6TvHLvFvv9BV/78j/l2UeZ7BMxDubJWE3ibQm9zjyunLfaqVVqa9QhGjM3J4J3VLUUH1FjK1YglcKSCjknUk5Muz25ZqpASt3XKlXGlSMXIe12JntoJgFpzZpdbdmYS0o3o6YzhU3i3ppJF8VHxjHYM68ZLUAQhMKgHpaFEmBYjXRrS3BQtXt7ievmqhaw0bzdE0OzJDDXCpoSDCcMwUPGmM21Up0DKqUaYFNVqdqYpont1TXh5JTn13uunj5HtXHv1i38xszIpyEyRM8YIt979z1Ozm4hoqzXG4ZxxeiEfQIdzsiq7KujZuHmZgc+EscNwQWKm6jSmEtFQuD99x5z68456/XYI9xd9wq1xME1Dl8SV0+e8vzihslPFGwAQF341J/7M/zZ3/sK/+P/+T8biFYU74P5jXSGYhe8dpzfYt5bWvjo6QWbszvGbgqNuqgNW4MdHNvrC/7gnXcpcsofPfwe2+0zhjEQYqPWBR89NzfP+e50w3T9mNAsYXne74jrAdye7BKlJlYn56zun3F7vMez6yf4VIwN3K0BvADeZMSHerGKDWhb8ITNipYLTYuFDPVghFIVVxur0VIXry6v+OCDDwiDh5R5+eWXef7xFRdPfsD28jnOO659oLjAydoRveOV+5/l3njOSh6Spswyb6mrhcvZ42PEqyVj377j8D5zcxUpNRCcInEkZcAX9rsbwu01cQjEADWZh16IkbPoKHNhCJ6rVGxQIRCjwzWxQKDWGIKQauFqMhVJEc9cFd8a0WXzBtNICDA6x7nA2ehNnlsm9qUQVwP7ecfSAnkGNygh2sCzFfMEFddVGc2Ssh1GLojesWz3PZGvEpqynwqb1UBWx8oHVB37qbLfNfzg8GNPbGwLTy+for5wdX2Hv/O3f5U/+KPHfOqNVyjbhYcfXRIDOLnm5bvKlCbi6AhqQTanmxMcM2OIDMOa3bTlzr27/JN/+Pf4c3/pr+Nutnz1j36DSuHW/df4qV/8Czz5xte5/+qrPP3B+3z7D7/FvVvnvP766/zH/9l/wje/+g2+/K1/xrBeceeOZ942luKJ3pFaRRfQbqnhu/S3iULwLIsyLxWtDqeN/RWc3j0x834nuAiuKrlYLbykRoyVD975Lr/yP/z3/M7v/A7Pry/B9x5LG6V0zNTbIDMEBfWkGVwzAoSGYF1zFUq1M0+878O+hpMCOtKYrNaU2tOD+5DAB3JNaDaFi1dTBaVW8cMJmoSV9zQp+GC9r6ZAacYedX3P+WjN7OBOOYsrUp7Z5x0fXDwlDp43bt9BsiPlmWmeGdeOkU7DEOmestKHdRWy5/r6hru3T3HNQUlocKaSaDaUGkO0wTkFhw2Xn188Yb+75NbJ+YsGV7F+ZV645WCWBsHTFsEMZaxHSIupI3KqbG+2uBMgrK3GKUodI9969Iz5ySWjKKfrFScu8/m3Ps0337tkbIVlmsitULIlxYegFn4VPUN0zAj/1+8+4t5tx2q0Ic1+2hGHNWE85fkOdLqkLZ7P/MgZr8RX+MrDmYunD0ETXnt4WWnEf9Hb4F/r64cOKN06GyxtxntWcbCJn6ObT5rO/3I3UeuEqhXeHz27Rh04b8aJ2hSk2iXUKlEM+LDUEzX6OHRGhU37W6exNutPkXZI9TrYi7ZOfT8UmX366vRYCH+iLO/vxsAn5zpnp9PmtV9Kxz/amo1QeWEcegCpFEM46VOk1g6TaSv8Dtwg3yAEQzvBUmgOSVygJkmr/dek0UrCx6EveOkFs21oSxHzlJJYjaOxhJrpOJu9iN7o0i2hDuafHFlbXgTvgk3qS0+isTcD/T0dJqPmk2U+T+ab046yKe+MlVQ7i6t0E7YQxxeFhwRqd+ivoxDuBG6v1vghUKQRmmO735sM8gAaddCuw3YGGfW9UZs9Fvs93yfT0g2KzWfp7OTUIqI7yIbY1KS67jViKwzRRhDXJ+3WFDQU6YbcB2N3VKnFzM6cs5+Zaz3KK8QZi6h1xhYiRDeYtFEDiKfUyRhAwOgdgqem0psqiOOqyxu8UcVFcFFQLR14UoITNmNEFHaSGMbAGAIpJcxqpvUGsUu3fLCGpJovkfPW5Dng1vk5YL+uggE01UxXRc10dhUDKZmlrRw7z9b3SQcdsTVp1UVvbulWvk4AA1xqMwCsFDVpJgY81ZoRwaZLKO7kBM3JUm4wMEgA1dQZhtA6t0F6sVxKNTZgUbSZ/4O2zgxzcoD38HFFNBSGVguuM4zc4RQRoTlht5uM8RUCLGb7XlW5deuMT7/1eS4/esT69BwXwIfIGAZjZQVPCCNzMf+fh+8+5Nvf/AOTI1RBQoO8prSBqhVZKhoiE8JuOxPiaM0wasauFi4KzcDpKg0vvnt39TOuvx89IMAHnZIYkG39kzU6+3lm2V8z5cp+n2lauLme0FKhVc7cSM0wlUIMwvnr94nrFcvVBVqFZblmn2GIgqw35BkYIr7ccDnvGMQxeg9aKYfmozmb+GPWys5HqsI7T5R575mWQE6ephmTOxk4IN3zQvsednpg4xg7cVHlB4+f8tqD1/jZX/h5nl8lnr/3NjctcDbN1JJYDSMpJ2quzE7wg61V0QItswqCZ2D2EVnops2Z3ZR5/NEj7r2ibG/2zLsbnj55zO76iuvrS66udlzfXJOWhSovJGcJM3WOOGu0XTAgo6/Yw3lPZ1hqTUxzZZCAVw+uETC5FmLJJ4cwgVZfME1FBaEDVd2v7wAm2Z483IH//J33AjyS4w14uA0/YdT9ib/2STDpcO/Bi+GEfc9+bzghVOX01BIfacIyzQRnpvAhHlis5mHSVPHOPO5KzpRpJkZP8JGcLKiiHYHyF3Jfk36aHPgQsOEO76nfrSaj6leaWghAmheG9Smuy3IPQ5607PHBAkA4SNicM7YQvRlFcAra7zs/GKjlEFp/T8ts0lv7HDyLVpNFDw4fbfjmwsBmMDZA9J7SEte7HWennhA8Q4gMIZCa2D4T7Cxr5vFnILtDfWQ+2RCGyIiBVa4ae1sSBLfm1vkprTo+/dbn2KzX1CrcvvUyP/+lf5Nvf+OrvPvu24wna26uBCfB/NzUmIXRB2PC9UWjrVKlkquxsq2mUFI2WeOwHkhJKWUhp4X9srBdduz3eyvwY4RmCbA5Cye3T7m+2JJyMslRO5RdzVhCrXs3isnSpGUo3gY/3kFdwEVKWjgZAy7Y3Em04DSieFRgP0/My8L9118mrGxKXedGpfRtYaAiTvFYzZlcM+Cqr739/obVODCsBkIOzB2hWIqYZ2ITEIvgRpRcJubdJTcXkbTfUZuyGkfONmsGD3OtIGqyNoS5ZEJZaCipKUOpRAd+dcp41zPf7MgiTEtm9+77nN+6YhyN8eTGgKOxz3BdwK/u8pNv3uHy2RPGcUVDWFKvKbWxloBo4Tu//22+/W+8x7AUpqpst3uqFsQ7zm+f89LmnGe7vdVMIp2ZZCxAG5LaBya9djTQz+GDN5BGBvCC7wbZKc1sd895+N0/IC8zz64vjEWg4DIsaW/gTxRadLZ/o+JbZXU6kGpjv7+gkWiaWd894/Tebeq+mZQek++7JgZgdfcK5OCopNbsKpa2rNrBZmtCD16aVZWSrS4LYaDlgrTK/dtnjM6xHuBSCpcXT/CqlNTQVglnt5j3mc24oeXKz/6JX+Dtrzzm5PaKeLEn15nSVtQyU3G0AjeXEIMjJ7WV0LCBu3jiAGmfcG7Nm6/f5tnlY1rdEpzgO3t+CMo6WgrllKxmj9EGZUkF7xqjU+YCj68qQwucnQdKTwM+WwvSTBJeq8cHOF0H1uuC+hlcxjfPWXPstpX9TWY1RKQFokZG74kjXORG9lazuGpWEnbwmqS5qTGoTuOKvGR8NEN9hyeiLC13L1Htw9oGAap3XNWMz1f8/re/xemDFb/0y7/M7sNHvPP2u6T9U5ZBePm+MNcMXojDGW2KeCecnzrmaca7QikL+2lmdXZKWBV+6ktf4Du//o/50i/+B3z1d/4BULl++pDP/sQX+KW/8h/ynd/+Tf7Whx/wg/c/4O//2v/CT3/xi3z/e38Ig909Xjx3zhzXO0BiT12t3TfMgpWcL6xWMF1/SK7G5l77QJBE8QP7naPUwvVFIogQ4kB1psbJtVJy5evf/j1+9/e/gmogRnoAhE2ENFuiXlx5Ws3gHbV1b0FtqBjrcYiBOZtNR2sVoVGlMIRATQuDDEhwpApLLjgfcaoWFBXAhTVlyfiWSeqs/3ZCTTOr04AuDZoSR2XOEMaBwB6vFuTTWqUl219+Y0EKrQZwlVqFW34geM+SKzEEbuYJWTJhVEvWrOZXLN46w1IqrSW2VxdovY9lp2dc29hnEIQQInE94kVJk/mBOSfs9zv2u5seZGJ1tfTqp+Y9b37qNnNOiHYbHQTETO2f3uyP1iklNfZ+RkUJztMQdFHGGJldoHnlZllI44p//Lvf5ObiKZ999VVubc7YlsTT5x+yzDPeVWIYrO5yDRdG3r7aMr204dXX7jLPE+PqBDaZx9eOx88bdTbVxNltYdLK9W6Hk4ofHCVltDSC86R6kF/9cL5+6IDS7ZNAyYpqp5Zro9RKWhZSKszVBnjOe4bBG72uFYIOFu/qOXqMeLHm0PckKvOtliO4odjk3YyerYlspuSiNPOK0Q40tF7c0BvbAz3XQCLhyCzqv3YAmGpH00Vc96GxstRojOZfpM2aVZsO2yJTNeEfat4zrru2v/jZHNk6Kop3Vpg5Z99XVC2Z62B2+AnvojjYs9VSuwG6xdUL7jjhN6DCCn/nAyKNUsrx0m9ajywFUfMSOMgFDQAzbxvvowFhXQqo/Tk677pkyYqJqnRmSX9w2hlUXYvetFnT0yriB2tNxB03qojrFFDBrUbGuKGVHV4z835hmvbGfqnQTCcFhwbyMEk/NlJWGBzARsdB2mBgjRNYbdbUZshTO0BPSkej7H1UVxiqGYVqB53sCjQQ7ZDs17lQ3XD9hWmtFTEQnbEnxBv1vOQXcsOm7njgeO1yN4yllHtDtFkNxJVNyyqWEJRrRp0H5xm6D1Nt2iWarUdf2//H4DHoqx8exz5PWa9XtI6EGzgSRIsAACAASURBVJDJkRUwxIgilGy0e2lyTIvSgx5eDBxordie6JN+e+6NgxeX/bgOKIXQG1wDPu3LfLhaZ984Dr/e5ZmtkZaEKIxxMMmkKLUUhtUKEY9KsWRBp92/ydaDgSl9ktFZCDiblGm1WHP6pETFmD1W79hFVXLrn5lDqUgT0pIhF3yzeOqKeTYt08z5yYbhwWuc3TqzaZEIvk+4m0Cu1iish5Ef+dSnePi999heJ6pAFPBuRaqOmvfdaD++YBS1ekx50lLMzwMMcFbsDHD2PKkHKL2DAe6AGtv+NGAPfLMJWNABTQ2XZ6IXVD1LyaRs56wqtH0lOCgqaHEk2VCqZz83fCmkrGzLigFhvTRaAYZI1oSKYy6F2vS4ZQ0a86QKuBXrk4iPAxIiFzuhZQPH1NlnWZuAegS7yL0M5rGCHtSv1ih2aCkpfP3rf8DLr9znZ770s7z93vfZOc/zBmeuopo7O0Uo2cyNxSljENCKd5BK49nkDPAqttZxga9/9StsNiPX1zt22y37/c7AKa1mECxC8/QJr5nuOy94Cd13pIcetD64UH0BWHdwxon0Xy/mCScKbaHVYh55x81shvtmsG8g0gHUVThK2VQORtpyPDMP+89ADvljQNAnjtY/dnbI4f7qd9Unf+94zeon5L1iE9KmShwGSxHDZElFKz4GxC+0nIkro5LnnE3i7JRcCiUvaM2ohzCMODWvJG3VZLO9MTxIC2rTDnQroUkHP7T7ItpgRTrb6RDO0WplCOEIMAsCpTLvdkgciSEQJQCNqo2AmbDa1WF3mXfmZVgx/z45nG/qWPaWttOwKHXXmrEKqUjpxstikdrBG+CRc2WZZk7Pz49JZeIjvg9KXAyEGPEhmIfZvbtMtTCpp202xJWHnNHtwgrzdnMycv+Vt3jlpZdw40BYjeRWaSQkF+7dfcDP/OyfYNyM/ODjjwkhUnIljivwyjDET6y1FwM9FyKazKTZB4txXKbZpFMlMS+VedozLzPLNLPMEzVnBh9NBtOE0jKII5dC3k3kBqtwuHv759qCDRi97wEX+mI41EGMuiwwOpBGzntGGQgK0dMn457UGlMpSG1Mux237ty2AdoBX/DSZbwJVXtdji79d/5Yv6W0Jy4bG4wRzF9QjKUT8J354l4woNT2Qy2ZlhaG1chrrzzgfOVZpj10aaf2/eOdx4dAv5hoJdG63GTwkRwjMQTStvD84pLalOAdrVTUR6Iz6ZFfn/AjP/ZZ2hDwmw33G8hqQN1ISYmWF4ZmMpDx/m3u3zvh/skGbZX9nGghEoLjc2+eE6vyve99yNX1FVfba57fXLLd7dgtM3OeWWqicjjP7CxYr0bWMRKHSBiH4/nQysKcLml14oPvfo33vvtNtrud1d3Z0hWdZhtG1KUnxDk2t0fmy4mKo+aMo1KkwuA4uX0LiQP75do86rypBcS3zuI41MfdcMD5Xudrl2mC1ANg2vd3ax36p6fVZnKrvPnmj3DvwSnPHj1hnvaE0IhDY64Jy09WynzDOEZSU66XiXh+yubuq8w3P2BqjZtS8c1qjCrm3zbNjizQakackGpmu5vZnK4ZZWQ4UaIIZ3de4ma/Z5lvWG/OKMtCqiaDXLnA6C3x9WzVGFeN6yvH5U4ZvKe0RmlWHzZnA72CgQfgiMFb7dQatSr7KkxamOeEA07XQm1KS5G2LOhaLNhE4XTlWPnG6UqZtVj/lK2P885RasZ5xXcAXarg3ArvFXGF1SCElPsdMzCcup6+5XFuoIowrm6xvbjmphTeuPdZ/uJf+Pf5rb/9v/NezWzCiEaQWpgqrEJgDIUlKKF5ihaarywJ9lNGJHDzbGZ1K/K//a9/i+UK/rv/6n/CXVzwjfff5eLd7/Kf/o3/gp/94hdIzx/zy3/lr/E7//A3+dr33ubvffgutdVj3c5+II6CdG89IyJYRRa9gGsMg+fOnYDUyryrqBZCKDSfgMK8RJBGHB3RReYEJ6MN03YzSIbV+gwNjZongmuUUqlmIGsAfHOUpUFV0qoxdAVFbtahnJzd4fU3XufHP/PTfPnLX+WP3n2HmkzKqK0SEapk2zNejOHZrQUWyaaAcGbqIV0GmbLdjaJGCMEXxvVIC5kmntYWnF/IpZjctnV2vQrTMtsZESMrCSxtppTMs6sbPEprEfWe3VwZTu12LbUnI/c6XPp9lKds4LB3fc7V64Ro+z14kyJLD7tyoixLIqfU+yB3HJop8NKDO3z+869QxfPewxukOgszUBv2zKkQQjRigDhKqjhXLPwGwfmGVptsKEKVwu56x377ET/z1me4c3rOyeYezz/8ATllBmf2BnPa49UGZSE0pqS8fH7Gn//Ft/jg2WNWg+Pu7QdMU+DRx894/MFTPn6649F15qbcQKrcOx9RBmqu1LZGGqyOQ8gfztcPHVC6uU4UVXJz7ItNk+kx9KV1E1aRLk04TJdBmnkgtGoTv3ak7rtu7Of7odI6+GEpXu04R+1TU+nGv3Q2UbX0D2PffoK+Jtj36ua1B43ksbzuZAttrfvLGLuFgx+Mlcw0rbTu82IrtvbvRQcVtBeM/Zt2LyQrxPrP4viyesHfjhNV7X4VKmLMBBFqrnYJt2K0b4cZLXeqo/aNBUKtCk45+EVon7DV2j041N7fQdN8ACGk/wNWzKgD8d0r6pgO1Ith4QjcHdke2unwWCNcajUq4sEbqksy2iHtrj8bxIpD8c4mkEVIudCaxbX6XjJWPvEAnXYAzx8BjNYn9IcmAuz1iDbWqwEXPRklcoCc1KRktZn0QA8fyCf+G0GoUIVDEiDdu6I1iwZ30JP4IEhAfcVHd2zg2yHeW14ALV4E9XSZYjHD0CDENlDCmldefoVxFc1fRRuls71EBW2NKA7vR3ANtLCURj74oYgzk0kRYmekHRpA78wvYb4pZpinxoxzWMJgLoU4eETqUT4juP5zxZLcaqW08oL90A4fSweVOnvosPvcUfrWAdbDPjqsjdY68KREZ0WcayZhk2a3WE4LqOm3Uzb/FK3mVSEhIl7whD4lPbCPTCOv0ix1QQ+sjtQB5y6FlZ4MWOV4LuRqzgNVe3CWwLTfsX/0EPJkgESzFIX9fiaXxDyZN1wzJJEqBVHINVFKtclwMWnVK/du83j/jL3SwWugNJSC+ojr+1k7U5PWUM0Gdvem3tZT99ZSodL3hGpne4k19odzqBv2q5h3kwsOgiOXRPVKHAISvLHGJFBbBbG1dQjUKh4+3iYzP05K21c0njOcrnB5S0qKW294djXjknmFlcPaEzOPLgghmPZ+GE/wzpFaIzeFasCMvdaKN35ABzOMZWatgAHM7bDOtLMkncmuFlG+/JWv8pf/6l/i/kv3+PCjD1k/uMfQMqSZhid0iYu6gB+Fs3EgBqEReVqVm9S6H5wlMrkIH/zg+1bcNj0a93OQlvXBQFWTmHrB6N5YIynd2yi3bM1Sb1SP9kRKH1oYGN3Eskp9A03FmLVd4nU8qA53lvZmVoyla9K2F75HxzVw/DqAS4e/+8nf++Tl9C/+Mh0AkU98D5EXd+3h33r4bBqMXYrjqhloDsGxJCGuV9SU2HiTopWc8H5Na42SM7nYvkcUFwwIOph404H8Q2jEMVzjiJvJsSg0AKoeWYeHYUbT2llI3WmsqdUaqqSUCCVTcHbuqc086bKF43vtLGADDardaQ6CE0sNbYVWesqps8bKbuVGKYLzgVpLL3IDuEDJhShiLEcK3gfU2fRWfGTwwWj73jOGgfH+fXZSaNmjOVOKsHu2ZZwzeRxxOhL9yEsPXuHs5ByJ3lI71Zl5KI2hCGdnd/mpL3yR8pXf4/LRR2SfiauhGz+HviR63SMmWXDjGjc5NqcjfojUtJBLxodIKpW0vWGe90wpkZZETcluFXFoKrRi03LnHdvtlnneQghHvydb5HbO+QClGuPBho4W0BGc0CTbPm0V6dIwkYg2uhy8osGDBGopkDM3l1esViMNk1DPrVEFSqrsr286Q7unIDqPugZH+Ucjz5ONUBRLzqyWuLngzYZA7f7xOPw4mhR4mmlNuXvvHq+//IA8b0mTJRhpyVRLU6GmRE4WDoDHJCVqA5Xp5prtbmJYb1hSsvXTh6y5p7JWV5FxhfMDue3Z7oTMBt8qp8Mdzt94nd1ux/7DD4kRVieRW2+8ymtvPeCVu/dxvgPW/b5pvMlnP/c5ts+37K9vuNlec32z5eZqx83VDZcXVzx78oTr58+5eP6Mi2cXXG2vUVc51cCqRVzpktIgODdwdnaL9Wpgt72gpi3eB4oMx/tv3RxRBhwDpQp5mvGLpxYxxmaB6Pu97xxtaehUGdYj6/NTWi0GmmTIecFLwDmHk0jw0VjzrTFNM610ZnozXxZ715aFpRiQ67G9vuTC5uyMeUlc7m/4yU+/QUmJq2tjuVUac1WCr9A86xa4vrni4cN3efMn/zTvf+MrFHmH2maT9wtQezJxT2BGC6uTWzz76IJWhNAKoxPuvfyj3L+94dGThTGesdWR9fgSz27eJ8TASy+9yRe+8HP8k9/4f5Byxb1NhrAwiTHTUrWQoGlpJgn1Zj+hMtBkobYB7wPOLVAqpXkmztic3eXjh99lFR1tSkwls10C0TnunA48v0qUVpgWxcWC+gXXwKkNbObqOqPUBhibGAjBcxrOeXRxgTbBJaUMCSeNokIlEWOkLRWtA3Ue8cGzyzsmyaziyOb2LX79//5Vlu1CKQnnCkNccXUFiyr3ToVYnzOu73Lr9Jwnl0+ZSyNnZ76aNTPtBT948vaKOkT+/t/5FX7hL/51dr/999CPLxjnLc47fvpP/il+7md+nqv33ufrH36fJU288ep93v5ga3W0FlIOVPWUtFgdEBw0wa+UISqrKEQnlCTgK6GZFFp9oJTGVCutWFBPrdaTuWiDlBgdTlZ4t2EVCnNNvS9oDBLJx7u3op397nw0WwwGM7AeBxqezdkJX/zSz/Ptb71DQEyqLYFSC14Cc90xriI1NbxbIUBasg1QQjIAFLvrpEEEhmaAvE+OuPaILhZCsK+UsmdaZlqxHsW3DEFozXOzv0FbZeU9++uJVgulOaZ9IYhCgBAiLQXzzqWxio7RjWb/oRY/I30AW7RAGwzwUsWrosGbN3BuFv6gvX9rjdYyteUX1Uuvh6Zp4aMPvsfaLXzuR1/l7e9dEMTYsqthoKwtPOSgRLnaz2it3Do5YbUZIASkCnEYGDenpO016hw5Tbzx4D6vPnjdLBiCZ1pmzDvJU6oz4o0qY/du887z6MkVt2Lgeh24v/E8uH/K05sJcmO9dYRt4/EMz+dC8JHmIteXF+ahKOafeRJ/uBDQDx1Qui6tM02UompTVC+WBlUBtWV+0MIjao1iny7WdvA9sck73qa6Q2cRKNZ0NTV6t7FwOoVQjPbmMVDGDAGlTw71jwE4R8DpWOoepECHhqX15q43vZ3hYi/Oiv1S85ESbxp0a3hF7WcbEGG/X7VHuh+nwAY+HIr8GCIhWHpcae2F70wH4FqfZBrmUM3LQBuNbHrcWnpTYtIU5535O+RqceeUnjB3AOvM96D1yYPzvSFp7WhqbRc9R3kbmAQRjB2lxdI9EMG57tektTdE9jxCiAQX7YmrGQU7ke4j5Y8siWNjfPR4Abce8TPM6arXkUr1WKHeGWUiVoy7zkpAOgBk3fXxtYjYZ9Ra5fzkzjEhrXVGmoji2mHi6qhaWcdo8guR7unzwijdNazh72Cjavd2kD7xxhghTgWcNzCg2HtrtfV1KcTegDoRO3BawUdPXI8gleDh9OyE1WAXQc2JUhecBovd7MCBBEeUSM5d79e9O7xzxCF2Bl9f/yqdvGVa4vWwoqaFZZoQ7w1wLIV5nonhxEzKO6DmxCalTc0XStVkduo6Q683x9oBvwPrQZRunG5rzNaVgUAh9P1bzFg8p4I2xWtFW0FqwrUKraAlMW0r837H6dkJwXtaLpR+ZpQqBBd6tLc9W+nLwcBFk+cFsRSsejATpRmrA2N3tUKfvCu1OVJeaJqtPxVlnmeefPwx8zGtyOSsQzCTfUQIMZpUQQyMSLmw1MJqXJm0rwMm60E4DWaEuC9CpSBaTDaitcsJ1BIqikWjOq2WaibdnN6gvr7v7NkfWHmIAStyMHEBnDpEIlVsSjd6S+rDz+Rp6fr8Ad+saWr9XG307yGR8ew+Lq4Z1wMvn7/O0w8e0cIGmPAxsKQIwymr28r2ox2+7UxeURreW9iBAqdna9abs+M6E02d89fL96qdOWhrhtY90axUMl+6wxi5M+REDUCrWlDn+PCjj/n44UPefOsV3nvvfZbmSXJCW/ZIBO8rpTRqMzNnrZV9SuwKfFzPKZptai2Hu82kuHYY9HNZeMEIgl7cmTGt57AIba0rBlTKEcCxtdYOqE1vWKxQ64ylpl1EAkNvrI9kIjn86wAmcZgpdMbkYfc7kNLv2vbi79PvwgOYpC8GHZ/8+iTgdJSxfaLwau3wcz4po9bj76PK7du3TRbTfZzGYeD6ZkK7BNyJmLwtZ8bVaGu55i4lt/sweOlAcoZW8cHuEuMPd/D68FjU3ozrgxQO3k5id5+IAZyl9jPN2cAiOOmM3w44tUZLHWhyjVGFQDNfrP4eW5cNe+dN+uw81UHEXl+uFWmFGCNDcLiiVO8QPxA3FhVPKdSWyHj2KbPb7hkHS6QK0hOqnNBEu0m5NwtBbCrqh4CEAXFm8o731BiRfYYlmzQvCLQZ70/RqqRqo699jVZzRdtjw/qUz/70T/P40cfsHz3sNZMZ+GvqHSLGAA9+xbA6YRgbp2cntsZzZpkX4oljnhNpmlmWhZwKpZauQTMpuzZjh7nQZWGtEIYOVLmejqsYG7XZe3DeWfSzeEJn1AavrHxkHAdmMTNhJFIKZLsooFW0dVZ0MSn39c2O09sTt07PQISdgzhErq5ueHpxg4uDLW0f8NEaJLodQBDw2cJjcI4mJp93A7iUaOrR2ojO2IlDXNHSwnJ9w/npCW++/hKnJ2u2y4RTayprE4LrMHltlmpI6KClDR1bbSx5YZ8m9q1R5olBbdgj3uFkMXlQt0yoxZi+S4WrWqnPr9inBvdfZbdLPH/0jPPzSC2nfFQ+5Nd/9Tf53Gd/ih/7ubd4cHe0eh3z3Dy9f4ezl+92o2rb51pMLrbMmWWfqLsd07Rn2u2Y55l5V5hzJs2JZZnYXV/x+NFjPnz2mKL3kGViaWuu5Zp3LiaWUohjAl/RZ5kyCSE2/HhO0BF2A0NabPCggSGOBDJTymw/uCY9mRgHYe0jF9vEONgQzGHPdrNekXUgp4rUYsO2DFQDi4IfUCpxsBChVBpEQXKitswwrFgVeH65sCwXXFxcEfybCFCKIzdPbpglhrfU6aSNi+uP+P2v/BY/8Z//Td6cPb//ne+zH3a05I6MCVrDUYmxIlJJ+YrtfMPoLZmQEDjf3OXWZsWH7RHaHONwxvXzZ2gNXO8T0S988OEF0z4xekfaO4o4fBNO1pG5JHIW1iOcnChJBacjPoyciENKPQ5nVSqnd1/jz/+7v8S/92f+bX7tv/0bfO3jhzx/PlPcigacbBx3TwaubzyLd6jbUXKlNmhFiKKEwTFGoRQoxYY0ZjVmw/ea1RjwEpiSgB8odAP3ybxuXMvUqdBcQTeYGqXNvPf213nt3pf4nd/+JywTeAo5F64XZckOLY67Bca1cL7x5GSgrZ2dwrSYN+EqmI/ZuIr8oy//A/7qf/Rf8oUv/hi/8Wu/yrvvfIt/69/50+y//wF/97d+iy+//zbFK0JhPz3G09hEwUvGazCDc6cMQFz3NOEmRIFVSOyvGtSKuEbsZ05O4EpkDNYb3TuvpEmoKVKqpfD66Ll1+5wgJzy9eNxl2PZ8KYqEwDha0EzTaJ5HHiQ42mxsF6qSppnL5895Nm155XM/zh++9x3qvLczPns0mqxM9xzP6ykt1MXUMgGPBDHGUzUm7okIq2hDx1ZgP3t+5k9+iT/69lcBYZpnI48URSKkCqF1axIq290NO8XOTRVqMr1D9g3agvqCyoiNEeBkXFnqaWks1QzPl5S5nBKtWa0z+l6HeUdRYZkzrTnGcaDUXltyIAj8ceaOKngfuNxdofstcXNmf5YCzZPmhbQU64+byaCnaSHlTMqZs7pmtV6zGlc0tfT33ArrsCJyw1uvfZpcxVjCSyKV2fqfanWsw55v9A1pwhAbHzyb+cZ33mbYFNrmFhdPnvHOw4/Y7S5xpRJOCq/dGuBkxeOLwvVuZj9NRtI49g//kiLvX+Prhw4ohSj46kmlsI7R0tfEdLit0/drN8R2tfV63Giexvc3U0aThdlU0ekBBBJaUST0Yro3EFpNaqZdCdUALWqHVGs4MXr5oZA/MJyO0NJh8iUmcTtkzzjneoIO/c8ZwPNCFiDWrKl2o+naGwk5Fs/ibapOM3d8M55zJgWrRzqHpWoFk6+VbOwDJ+6YOGaMlwODAyv43QHoMbmO9xZf7zpLSiTYZqw2jfXe4ZxNQM2Q1N7DkcEDx81UESQJNWj3JnGdcWWAHgq+VKQ3J4IZyRmbzICnEB3BBzMlXgdCzORkaLA9g84fUXpB3CjSKKkhEnBxIGthv59ATFpQ+2s9St16IyW9FzqsExSTE3hjRQggWjg/O2Nzdmrgjx6dRMygThqtmFxCxejZh5Qk61e7iV5rHaCyIspqvu5hcpR3YcBCl6WJCrVmSq2EGGzribFeLKJTiV2+OMYN0a/Q0cwOg3Pk/plXhZYhSkHEM0slSsB3xtQqroiDJ+e9fb5i0glxDqmY3MZ1eVqzhJvbd+5S6sK03xki3iopJab9ntU4HhlkKh4XMIpnMFPsOMbD0BjwHVQ9gI82ez8at6bUU+6MKVD1RYNufkveJBzYtlhSMiPU0g20+z9opZQFaRZlX6olaNQindHXfbCcHAHLQ3drWFt/Hgg+GDvJdXabOkwm0IrtiNJQKi44fAy0YqBorpUnu0zCfOG8c+ScuXV+ZqkNokw5kXJlAWiFGCKrYTRpXy+ApQVkmhid4IpjMzgGV3guiiN0qYCdE6I9cKAdGnRLp7Tm2d77IX1SVY3w18+t4zP4BJuiczOtOfERFRgwIHkVHMMq4lRI3kAcy9VzOBXi6pyT07usXECykmTNeHaLWhLT1Y40brjOjtvjLVrbcvdHPsNH730P2jWoyVaaipkOx/EF261kk7Io9q5qNQmfCBa+a1Hy7fBMAMTYEhwkzt4jGlG12OcmmYby3e+9z5uf+RFCEG6uLhjPVmy8x9VKCJ47pxvyXBlCpIlQnGOvgWmWDkK0flaLTZApBhY7bPrdXgwt9Hiud4acO9wqdoeYW5T2c8/O1L7F+046rF07a7ty036/YYmAvPh87Qw9nOnaoSXbay+KBtdBGLH0SF6AQIc9/s9L3v5VX/9/f1aP/5Lja9amx/14+84tY1a5AwDVWA8D+/0WKYV5MYZAHx8Z8N9rBlvz3eekluMd61wwSFU6ni5KVez8kBevrM+Y+sPszL8OAlrZdghRCOAP90b3CFwSQiM4k2bXVhA3dqZvNkaBd0eGsPPOXjuCOjFD+ma/HqJJen0HiCTYZ1JTpY6O4Cx58Wa7o5XCSVwRu8ha5FBfQNSDZBucmO8ICE4DQqF6b+fWrVPyUpD93rxDzu5wstkYI0EV3wzQaSlTQmTSwHko5OZZn7/Ea5/6FO+/966tNWdDr9anNypCFU+ogXvjCXVTOYmBZ/sb9nOy+mN05FrZLjNLstS2MERWMZCrMUzi6iAVsDMsxhPOTyBLtelxN68vnfWFVmO2ijf/Q7H9dLpac2dwaM34as875cxSlXEcWK1OyLnhh4gC26sbllLJuXF9vXBrcwsvFU2NJ0+fcLHdk9SSZz0OSRVxBiKFcbAUy1YYQzB5nHpETCbknTXHrTaT8XiPb0IrlbLfs+z2jGenSIgGIg+BYYzM2fd967rsE2IcCWog2oER1nIGZ4EbUk3Wxjji1yeQDIRtYgDTII59mXHaSDqzXF2z7PZMaWKqEyU3lqsLkA1hsyLvr/nNr/0+37qc+bNvvsSfuzcQFWrNeBf55j/6Gn/w1a8ybtYEGXjp5Zd48Nor3H5wl5O7G27fv41/7Q6H1GTzSjFPLUsNVGoqLDcTF7s9YHKZejOxvbjig4dP+PjZR1xd/IDHDz/i+e0dH9085iYv7HaFOJ5w+fyKs80JgczldEFYxKQy61M24ym3z0/5yS/+OKllvla/wnZ/TZoTGpqZpi82IJLapeUlcTKecBJPuLl+TmvC5vQun/vxL3B1fcH733ubabtFBDZjJIxrNm7k+dUlt2+PrDZrvvq1d/DOcXJ2jguZy5sbCI5htWI9rGmpsOyumXc3PH70A9545S1aBh8CRROlWjDJYRBrg+1KrrMNzcSx5EZpA4MLdh9Us+UY4yn77Ye0CiEEpmXPd//oG6Azw6Yxz4rIgHgMJMBxdr7m9rin1MIqKGXy3H71RynzJcvuktYaOQkaT0g3iZ/8qZ/izrax2y64Qdi2DX41GGtmCDy82JLDSIsZrQ7xlc0o0K0DSvNocLaHvMfFjJOBW2HFSjD7jhCNOMCAcyZRDc6GARZy4rn/8iv83J/6Eu/94e/xgyeP+Mu//Nf4P37lV/i93/0GV8+vECKpaGeAwiADwzDw6U+/wZ27r7AaBrJmHj76mFYri5hVSPQB6oppmTlfOa5v9iwp8c++/k1+99tf5/MN/pu/+V/z/e++zbc++C7Ppz0qhRYqz2dLOh6YcS5QpnIkqq4ihFAJbk3OiVAV9oJrhcEJtUSGEzXvoZbIxdiPARjFcTlBLcbsXpZAjMpNu0L9nqaFEBdcgOKV0CJths0aOB3YbZVSDBR1NZBaAwl87jM/xubOPapLPHj1R6lfeYfVIEi2IfHVdSOosJ8dp6fR2EsYwG0lmlDVETqjv2FJiHEQXFTKZCwoCkb4AQAAIABJREFUCQ3KKTF8Gs0fUlKmFiVGwbvB/G279xp9ENdKZb0emBazdqjQQySEpECuSDVAMiUDg703oL1WI5psp73JzqJQCHhVqmYalRBHzlYnpLQYmaB2jctxgN5nUWJjvGEI3Lr7Bt+5vOT105fw3pGy0vLMbpt5fHnDfkpsxmi2CF0hkVLj4vmeO0UJeIaQexleOYvCeP8BIY7slz21JaaWSCVRe0K8djVXUyG3YvtcbTjy4eNnfOaz5+x3lcQVV1fX4Cp375wxninnpyP+Y+Hi8jlkC+9Zet3U+mD0h/n1QweUaslERpNKhBcTzaoWXel8jyVrYlTtwzgXJZdECJ6iPUmlNy+NAEPr8rKCNEPxTVHfvU+0UWqDdkisMKDJ96mlpyc4dBDgIJU7VPBHLwo1xsKxCPeHili674pN40QEvH8hSeuNmQElhy7v2C0g3ppt5+KxGDSJkXbPFiuYVczcV8Weg3R/Har0JgRCjP0109kwZngYobOADibajjBGtEdN20RPofSoyHaQ+HVmkHTfj/7aUs1IsUZfnE3nxIXugWKHdFVL0YPOXurfM4R+0eWCBO3TY0scQ+heWS+m4Ad5lqr5gAxupGgjzRkNjiARr2YuqP01N7QzLQ4UgQPQJMeGVFXYnGzsvTY4P7tthX2tHGUaas+8CIhaAoY4A4RSb95VgoGjzS7B2v15HDbhLrWawVltBtyBFY+IpfG07hkkgSFGM54WW18ikDUzBM80Z2o1Cdf56Yag2v2/DDQoAmhAWcxzzJlXlPl+VXwcjFLvB/xgCNQwDgyrkVQyznl8h1RRNWqnNIbV6vBBmLl9O6whM6ZzrktOOzgUgxW83jmSfELYKe7YZOC8gZMHicHxc6ZLLJ3JFaGb8ntatYSSXDK5KM51M/gOFHgfWG/WrMe19YPBplMuDEgquOjM96Id4qytKT/suUPDeUg8FC99yG7NSBOTfzkCUpRKwseAGw6MmHYELJeWbV2odP8wiDGan8mJyQ9as0Y/hHCU49BKd7PyTNtrpumGLFjMagWfC8GNR+DdwGzzbJE+VWg91VD6fVA6w9ICC3qkuTeZLO3QzPbzGDH5pNhnLGK/Pzax5JTg8YPj/OyM75eMRmfUXiDQ2Jze4s4bn+Hpx9d4v3DvzTc4f+kBy+4V5qsbwt1XGdYrXnJKqRvq1RXRKfHlzP7R9/HphtJls6vgO7ukdWYlDBi7sqpSguBqMUZjU9R5nFNCdf0MOLbT9mhpR6mA0IuLokQX+f7DR3zm828xDJEpLUy64fTkLm7/lFWInEZPVse4WptnSFYuWyRXO1lql4SKgrrW96LiqnuBBDmDeY7sGOx8z5+oUIyN2f4Y00eP70K6hFiPd+eL3Ag7CKoq4RNss6NXUd+3Njs5sPM6S7hLPC3qXHsDIi/OXjgyC/9lQNG/CmQ6SOyMvfJJzyY5DkzAAOb1aiCebqjajilzrSkSbPiwlExKyQD5LiWqpXQPL5ONORdsqNFDFg4okThnkrI+RDqwmVpr/W4xs1ER7NxsB76XvW5RMztfQZeUv2CZaq3kZQYXceOIzo1KpSJkB0W7FN/7fpu2zoriWCQffXG0EJ1j8APiCwHHYLpG5lrYuIG0FG7fOmN/fWOMOIMfOwx5KAZdl9QaE1X6uspV0MGjIeC7/6KPHj0bKfMOlokwBsZVwNMomolVGJpDdM/Kn9BaZkmZWirzlHj28SPmklmpXbXijJVlULy9x7PgEXX46pmXhTJNZEn4BVo9Y5dnkyQ4YVxtelyzxTmPIRKDt886L0wq7HeZswhoI/iBJoWSShcR+X7/Wl2ktaGlh0AEj4wDlUpQseZ52uHdhuiUZZ5oTSit4IeAHwK6CDlXnl9vWZ1sONsMXO13ZoAdA0TbH9LM06RRjyBw7U3OkpM1EnHFahigB0esTu9QLz5GpB1C0CjTggqkec+zlHF+4MfefMXW4DGlVlBJtLomFzPzrbnYYPFYE9jQ0TVIy0xtgp6MuDggxWwN1Ch4VnN5pS6J7fWOm+0eyRXNiSVNoI1WlZjMJHtYnbBQuNld8q2nT3gaGy89uMNbm4FP03jn+gn/8Bv/lA+fPOWDD54yhIFNCNw7Pef+3bu8fP8uL9+5y4M793nw4GVeeu1Vbr18h7P7t9ncPudk7dAzkLtn3FOwkAhBq62Jn8jdsy4vTEtGSyWlhd114Vu/8RB/Z+G3/9+/C+vIh4+/z2bt2F4vPHzyiH1OjDqjk+fm8Q0/ePJ9TsKIG9dcNvj/WHv3mE3Tu77vc53u+zm9p3lnZmd2dvZse9cHMF4MGAoJkDQkgYAKFEKJWrUNVKSqoBGNIJUoFaKERG1SRa1UBVVVqEik1IlkQiGQYMAOtmHttRevvev1rnd3dmZ2Zt7z8zz34Tr8+sfvet6xqfJP61eyZsczu+/zPs9939d1fX/f7+dbxgHnp6RRhVgLkC0mwd72Dj4ETo9PyEVbeJtmwTuffoytyT6fef4jDKnDNVN8MyHmFX2Eu/dOOFuuuTSZMawG9vZ2GJOuUI1r2d66jDGeqTUsTeb4+C2m8xbXLqDJDF3HmC3Xrj1CCJ6j27c5XQ9EcTR+QXd2TEoFI4kQ9kjRk6zyenJUjk1vLLOtXY6PD5DiiFFFqNAWxGdcAy6CmMSYDb6dExysM7joGEbP6anDTG4T45LWOpoGTQC4KX/he/8a3/gt38x+CHzrn/0OXvrtDxFNITTCsOrpmCLiKTZjjA6OvFX4dNtkxgRx8Fp7XwRjR3xjaG0k9oGmTZjgCKHFjkuo8U2RiHdB9/rFYJqGD/zZb+OHfuhH+Mj/NeOff+iD3Ltxj+tXr/PyqzcpER598kHuvfoa3ggXpxO2p7DYv8Sf+4vfx80vvs7zn/scB0c9UizDGDGjDjF2dlr6VUe/ErrQs7W1w4s3bvH+dz7Fr/8fx/zG7/xLhtgp227sMSUTvKIyUnZMgsGYADjEG1x1f5bsWKWAgjZgkEKOkf3tCdvTlrPVQB9HhhjJY8IQSBEmk8Dx0jCmfN5i27QNi8WEdtKwXC6xPqqLzyqyIZdATEva3OCdJ6XEelXAFXbmE5qgTNAYB771/e/j6775z/Bv/s2H+cKnPkrJHSE4xiQYW5ASmEwdvtGhd5FECBCCwzRCNhvmqVenpvWMUujHgjFzluvCMh7Tf+qPWK4iTeMxGELQZmWMYeKV11tkgFJIWEr0jPW4GFMP1uOK1fhWsRgbyGKIRejGkc35w1nPWEZ6MovJrJ6xVE/QlnWNic+mM91xWY06koUSE87qa6EO9ah7BiGz2L2KbbdpJ4G9xZybbx2Sk+OsXys3MSddl42pDeZqWEip0A8js8nAmCak9chWcFx74AKvd1NuHt3l5OiQEnuwlth3ivlAz8lZ1A2VisE7bdYuYliuDRf29vjsyze5PPVcnDcYM4VBWB0tWS97+lNDSl1tct+smaKoi3I+bfuqfH3VBSVXLEMZ1UUg6pRJNccNsOGjbOrVbVEuSe3rOK8ztueizoYFUhiLPpy90YMuood2bStTC6FYcFnIzlSnkS6OelNrta02uBi05enLtuqpZjDMxreiTppSf825RvOsrYQY/TLG1kORq4czS64Vuwbd6Bqjv1pbD+eVT5RztdmVQoyDblByxlpfDzECkrBGc4+u2sE1lldfua3T8tSfCzrqZkgaV/MW51BIo9UGPj2MZm1bEM6dHQhs2uxyjFU40ngQ1tZYGxWop++p1PdFP9/NKSdicJQRTKoMDasOKip8WRlKGzFKG+JSKrSTGTZMcDFC9rS2ZYg9wXl6NrBxdYqcH2bg3MlWahyuIAQMl/b26Lo1yz5xujxhd/EAY3XOseGfGL3eilF3gIJL9BCm4ojB2RleIuf107YeWuui2bQtJabaMgYlRy1PEzDF6qbf+Op2SFgsW5Ndcs7EErUdCOH07ARrMts713F16tzYWmMvHoJHkk7UfU7Y4BEjBKcHQn2YNcolzxEfAk3bkldLdFyi91XTtoQ2nG8mQYUxay0+eHVSGc3VG2vOXWXebj7rTK4T8dC2pBw1liMo08daRYjY6oQIQSdWBqCKI5jqYKrXTb3GU5ba7qZuPrH17xmjr80biuj3c9aeOwed0xpuUy1rpj5EN1BQpJDiSEr1yF8FZ2qtd/2xOI+x1gOhngkztsaOMOpSoNabqxPLENNAt1xjcsGFQMkJ4xoGFMhnUHG35AKp5+TmS5g0cJKcNrsILPvM6Dpa6ygmkEsii0ILregROG3EoSpIe+eq02Rz+DeYWhkrOrJR4cxuYqau/pwR51usM6xPl0xGGN1I007Z2b8AIjg8qXS03mN8YHLxEU7Wwvb+Lg+97WGMm3Lr1pKzk045aQX25zP29rcxdkrYv8zy7l0mxyfI/iX6WwOUhLeG6XRKqPEnjNBUR2YStYAXm+tkG+XUmVTvf8EQlTtUgSEblo4KBjoty1nVGA+cnp1xtjymDRPOxsR6nfEXL+HSCZjC6WpQqH0PyMiQHUM0WKIOOep1klPCoy7anCMWQ7CBYuqzpF4TJlcxOksVEzbixn030cY9eR5r24g8m/VUlzaon73UZ3QpheQMtgrj5+KULkjVDVQdTAWMqS0v1DoBV69puS9c3V8D5PzZqo9t+xW//0rOUhXyzxdD6r9vz0UmfUl6l0wmE3wIlEHvf3J9HXXyWQRKjMrtEG1rUa1cHcfWatxG8n0xVNdVUwVkXwczNeqXVRSSKoDkPGLJGoUUpe5gNFqSS9QBSP0PmCpalMpSzSnSLHYYRLBdT8EySGYi2t5HyTgXar+kvjeGTeRRGWvWwtbeNtcuP0DrYNOzuNjZ4WSVKM3IfDbn0YcvcfHCNs/fe4tiYTRC8uCSRuFVm7bnLELlnhRKEnCBJFmLSTIsGqvlKI0jWiH0A9kYUtdTzMg6DrgiTM0UXGQSE+QVd06WFGtpGsfp0RkuTMnFa3TWC6BuZtdM8QYkWLo0IhaGfsSIozUWCZ6UhGE9EhrHZDalMS2FQiw6rVVem9bLGx+wkknjmjxbVEedFgFkYwhFAfa5tqhuYpLBqAhTciJbYSwqEJYkxGwRNIZVREhjhBAIeabrufNgOig9Z4eHrM8a0jiytzUhmhnWTypQNdSBgF4jg2RiGlkeHzN2PUbUWZusQdKABM/8wj6r1ZEy4ZK2WQ6xJwy63qbYc+d2z4Vty/4kYDC6f7WCNW0dmOleWc73xFQnlx4wvGhNty5YUZlYOVJEmARPsQYbAsRCn0ZKt8T2I2lDceh1LbYmMA4jToR2e4pPKny9/sk/4Qtpzfr0LrJ/mWvvfhvr559jTeLuwQEHYwdjj7XC66tj3OGbbN1yLJyBqG6Ehgnbk5Z5s2BrfomHH3yUx9/z9XzdX/lWZgGmQGgMs0WDt47Jlq7PAmwbXZhNUZfa4088jmkL3/Fd34hMLOlshZ0Gjm7e5tmP/B5vHpywWibmswl9yZz0d1kdHpHJBGPpEE4Pj5i2c5p2m8mkYexXZJvJDPSrFWOKmDAj5cLzn/lDrjz8EN/9F7+XN+6+yLDsGfozmtkCxHD54gMcH79Ot+yYP3IZu+so2VBWRtfq9cD2Y5eZtXv8+b/8H/CZ3/gn/FH6MF/42Ke4/D1PUcoJTYCtyRZHB/dY7Gxx9eoV2qNDDteJYoShz2QRXC5YW2CtnJUsCSwqcJvCtYevc3Bwk1Jm2DYhvvJpcfiQMDnicsDYQBkSR7GwvdhjHDtWXWS7dTTxjDQWhqlh7HWv8773v58f/MHv5/LWAt82vLssePfbvplHHuv4+HN/wHxvi369IudE22bEi5Za9IZxAO8dzggz21OsZy2GKCDJsxxGCnCWEm1rsemMvXkgu1OwrYKYJeBQPMdse8ZDD17DG+HRd72Hv/1t34KsDvkHzz7LOgt/+fu+Dzm5S/aWu7cO8WSu7Lbs7Mx4/aVXeOGFF3nt9lv0o94jgxiCWJq2YRYCYzsyjZa87lm5Bb/5j3+F9O9/D2Vrj2fe9S4+8psfQnyDmIzYwhALLjqcyVy6dp07t19nGgpD1jiuFUMqgSIOazzWFG2jtJZQuZ99WtHHQrEGJ4HJvGWVLYOoy6UfM5MW3HSKsYkxrRiXa8gREU+WlrYZ6XtDF9c03rAaLNvBg0R8KDRhhoREGXqCb9nZarh27Qrf+DXv5XOffI6xrBliYVyusUy4dnWXPCZOTntiHLHe4eyEpm3PTR1aUlCTGEbdcsOYCE3LejnwI//5f8Tl3Uf52Z/5r3n84YdYHp8SmoZoYDKdkfKg19o44gO0YUpfRqQRxDlIKoEkyTTOQB8ho+62zZ7diMagcTgHW2HKdz7zFA9fu8zKbHN8/JbGxVuPsTBtPEYyq64npkKfCmXQ4bGz4Nz9/c3G4W+MZWd3l+2Lj7O7N+X6Y5e5efsG3gqLaUs3DIhELBNNZ9Shn7Z6e7oxc9qPhHxCNpY9hLMovHXzNiEt2Z4F4nJZTTmG1utZPNfj6LKaExIRS4NgOV2tubxzkc+525gcuTBbUEbH63fe4u5Bz2w2ZTkIY9IoeDudwvKMKNpcff4GfpW+vuqCknhP7gZMEkZJatctFbTsPThLMUkr4MeR6nzHFs31bWpVN9wQn3Qj5qxBjJYr51wFGke1Gyi3CWd10fVaeY/RDZWjYEzWiVaROj9Wt4HI/cYXpHzZ+ytV8DTnTQ8aL6gMhbrvti5gbSF4r7XkRnlIFFXKhA0UVUUnqhvCWatZ/NqF65x+FFLFHP2GGvexjScYQUqsIlXGWa9TZ9DomFMrnzMFu+EzUbBFcFarEa1U9pFUELSAKXXCXzfR6npxVdDTmxRURNDzjh74RGplc6noN6dOoQ1o2lSegxTNYOdSMM4SpVTFFdQUVWONrh7wc2LolqQ8Mg49YgrT7TnlNNKtV+fCw+Zj2sCGjVNeRUoKPwZV6o21WGeYz6YcnNxl//J+bVyoUSV98RSUf2TqtFejCPUw5bRV7ZzpY9T5pHu7Ctimtq+Y2v4mYMTppBp156lzBmLWOJU1QtM0nJ6uzkU55z07Wwv2Luyys7PDyWlPipk4jphc8FOPJP3+nRhczjgSU+ewRfksxipyJhtHlwdSycrsMLpp3zgivG/Y2drGGcvRcq2XfIXPO2MxpeAqJFqB1XXDXnSSmGLCLxxDdYSA2pHVEaTKv36qKuJ65xGrhzhrShVWwdiAkIljxnlqO1mon3WhbQJb8znrdcdsOmV7a5tp29I4ow9b0QataLViPOdcHQoFsf5cZNlAuqUIYxrr/W3r/byJT5nKatF7v6TCGCP9yRm5H7HO4c0GbAzObIQnFQSapiHFUd2IlXuWS884Ck4s3lggc3T0Fie3b0LfcToKZ2K1DbDAQCREw2izghS9hShQFLadqDwkLKJN21CjeMVbsJ6Ne0lb+GrQ1yhvSUyNbkihmERxAWthlEjwGTtE9ha7NNsLjK28LGNACnuXrvDoe97LjVffZEg9L7/8FsG0PP7Ot/HEu7e5d+MWz3/847jpnLfu3uDBq48To3D5iceJq4Fbq4HcblP6A5z3+PkMvNXGPGdBNA5CFsak0ZhS1G2arCB5JBcV943Vn0sdgDpoQDh/BlmpTIbqtOvyyJ2jM3zrYVlIaaTLQsOCcXmP+WLKznxCt+oYcqFPlliUu5Zqpn5TuBDLxmGjrYBSX4Oz9nx4IugzZvNnm2kX1dGWczl3sRrOzU3VZq3XYkIfls4YNqakaPT+DhsbVP0XNgy6TTTUGnX9bZpKcinnrp9NA+ZX8I3O15+vdCPp5ujf5VCS/9dvNWpbztdZkSrqVEFJSqFEbUIVEWLJDDHWiCzEcaRtLQnB5Vrdix6gtSzLEWOqr7sKeAZ0wXX1eVWf3aYuNkWI6GsyxuBEWWqlij25fs7eOv0MK9jTomKfWIWb5qybvcFozHsmjlQim/C0ycovdNZhrMeFQE7KTbJVtJ9PW2wZGSTj0WilsQnXCNcv7mGLsL/VslotaZxjGCONWEIxyiLJui6bAkEKvlCjg5UFYh2TYBlHyCWRkq6PxjV4E5C0ZLVec/fkDKwjkmlKpgUmE8sYoQmF6aylF1gPA4ernmgN62HA2ozLhklocH6GCVtIjiyXkRAGZFgx2ZqSukJjZjTTKW0TaJizmAZco66/IQt5qO2x6L06beZk8XSrIxoDgg5LhKhcOwLGDBhTCxXQ0gAjAiVRciLMp9gwJcWCWC1pEevqd8g6OykqFOaYq/MI/bSTtuEaa1mvBma7u7qOOaFxvg7FGsQ6BbA64fT4mJMYiUVwCZwrlDLqOlYyW/sXObv1GuMwMuZINh4YQVq2d3fY31mQS2Z7NseZhPctrc3VRTxQXIOIio+pGHxdn4rJen/V4acxguSIHUdkHBjr/8SCt+pcTmPCG8PVvV2cHHPY9SppWiEEz8w3hLYhb5oynaeZOvrumHR6yMlLz3NycsTLHxQuX77EYuo5G3rGgrqzDDTWsPCCTzrAHWJidB5vB9YpQn9CXB7wmZOXeead7+Bti8Bpv+Zzn/kSlx99kPLCbT7+j/8FJvZcu/ww165d4crVS1x67DpX330FkiFF0YoG8YR+ZOfCHmZ7yu7+Dg8//RhFPFIjsCLCGHsOX3+dU3cGp45/+/u/y52jxKc++VnWo2G9XvLQ0+/ioesXaaYTfv+jH2W+XGHDlNXyhPUq4spF3v74Q8wnjnHwDKcrZuOMeavRs3F5xqQJzKee0+OB7fmUvasPUaLWvR/e/AJ27xqzrS3+05/526SfPeanfvm/5dkPv0RMOtQ7vLfmdJV547WRS/v7vP2xxxCfObx3SIqxNsoWjYc7R0kjMlnQj0tWcY2hcPPmbbZmc856PXgG7xAJnK4yF2ZbzLYGTk9a6HpssExMSxkh0jCbJp663HC0WtNuBe71kZg0wvTKF1/iT17+Iu94/DGMszzyYz/GL2xP+em/9TeZbi04Pb1HTJGZ3wy2CliNMvWj5/RYSMUwnwXmi4xLIzE70piIxeKdsFytuLq7gFywsqJQuLfKeD+lAVKKZGBna4cnHnmMB69d5+Jsi/2HrvDLP/ajHI+nXNmf8/GP/2vWqw7feGZtRkbh5MxzePYan/z8K5x1eqa00zrgAUwuWLGsZGAZDSZF2nZK3yU+98pnWfzBFn/tJ36K3/6VX+HipQe5eXgXaw1jFByOvs/szhpuvXmbQsDZTBOgixaxCeOEYBzBqBM7FUVZHC8jq1XPSRrAebwvTBvL4XpEphMkJsY1ZCwxZYyLBN8qU89C7qu7NlriSpm/rfcQC+IKq9VI8A1h4um6jrg2TJ2jmVr2rm3xzDd8gKPjJb/30Wd5841TMFMmi0tcvDDl7OiAnHpCA00bKiPQk1cwxsR0rvvmbiyIeHVI+UAB2tDShUN+8sf+BrKa8I5veIaf/i/+Jn6yxqBsMmUxRbr1SMyZ3e0dJu0upT/Bh0xMPd5bxtEgTrl6QRoKkJwhOq+mCjRurDNnw/TKZZ5873t55l3P8Acfe5ZxeZMLl1psY4EGYyypoIOCMiCSiEWL8YSiPGM2eyN3vs1pvfDU0+/F2xXv/8DjfPalu5wd3+H05Jib9w607c2iXDAp2kQrgkimz9Af9+xtWdoQOLOG0zdPODl+k5m17M0vqs9L9EzTWI3KewrOCMZaDjKI1fc3GKHvC/245vKlLWY2YDshMtJ1hntLQzpdsTSemGGVCzll2mZGGta1DObfsa37//j1VReUxmVHlAxOIWDGWCzqMGLQw3HOmdbrBHLMiabch3HKuXupLgZON31ZQEpQC6xXALQ1DiRRiiM4ZQ/50JKzuqJq1BzQJiFllhWtCC5FNyWim30MWje8mfwU7XdNJcHGZVQPN8o10imw2uq1rtdgwAjOebIt1S642bx/GbDUghGdUOekG1ChEuJjJHhPjqOC+QQka/Gotl4VdSPUaXU2KnjgHaZU+DNVFa2uFs/9pjnrHd56UinEeqFLKejZZjPNtlTcM8YpXUbNDU7jeaV+TqLuLY2z6WlIW8wsIWyijejhFlOFRYt1vtrv1D0mpfKHkBrJcsR1Zn224ngYlHyfHTFDsEGfHVE5NqpRWrK15zEXqe9xlsTce6beEAk8/vA1PVTXqboxropdOoFxRhNd3jmoFZKMSSuDc0HsoN/TBcQqr8ZpbRmp8nw211RC40uGTczTYYshD5lSLDiL9w0Ew2QSmJpdbRIqhcVsxnw242y1ou9W5GIwPjBtPXhPGrUJzoowYAgpY4dEJGFsZcxY8N7ivKHUyYuzypPKMSNZq0znsxnOwNmRY9MK1fUdTdMQQqPuIPT6MMYgTg9wKWViTGQMqY8Urxta65X1o9bkGsMyGh9RodRinMMHWx9mNfImKgI7owKA9XpPSi60jWF7axtjLLu7F7i4f1EPi95iacipgPcqvuI0kuqUy6ZNYMrHElFnkF4n+npKlvPXoE8KziMxzngt/K0OCZMLiCUHvR+mQRtQhmFUR0wpbO/uMmmnFIQU4/0JhQiJRMrqzGgmC3avPMSbr71JL2tc0QmjMYFZu2CrmTCdtSzXa85WS6yxRMlswrqmRlTJCgvG2fuukSp6bIQVb2skb/MMMLZGGSFI0Bpza6Hx5DxgsrCzt8C3c6Z+QidLioGLO/s88XUf4FOfeplhvWSyM+exJy5x5dJVbr74JW68PHDv3iHX3v4Y7/na93J47xbvfN/7+af/8z/l5sGUlBOzdpvdx7a58cInaJqWNkw0gmUMuUYaxkGZR4g9d+cYdNhgrNM/M6H+NApyNyLn5QtG2Bwd2USZVNgrnBwfElPElpGchOXpCRdsZG97wdb2AlJP03hWvXC01ulvtImqF6h2UythNYpmaqxS/1CkRoero0mAuBGupd4P5w6YSp+r3DAVuFUNyXWqYKyhOEhVHDMbVw2FJKJNRGqUA9GSbjXEo5TIAAAgAElEQVQxFdVRsuBDq+1WVuPg5yaiP/X15VG3LxeaNk5hdfv+6T+3dSqZz6+9jai9EZfOY5agglKK5JQ0qm7ASSH1fS2b0DW2GwaC91rmUXlnm6eF94FuGBWc7UNtT9OBkDOVuWj1/ZZc1zjMJhWNNYFiR9LYqfPTWIxpoPRq8DBKU6JozXhVlRS0LaPC/uNIgxBF3aUqyOb77mPvKSEw3d7FjJl4dkzMhbZp8daTx0EbhbAEZwnNHLoTXafXA0uficB66LSprcwZGbD1ZzRSML42shZ9aOl7kFlsLehyD0WvmwZHkIJIokjCOg/isW5GOw3stB7yoHyjbk0Ilkt+Sp8TjWk4POt1OAik2CvD0rcUp+2MBRX5EiNRMmmw5CbTLzu2dnfZ25kxncwp86SQ3DHTp56xH/Q1xpE0Gk1TF4dIZLHYxkliHFfEuNa4Zto4q1VAtQK+qDvbOY+fTdm/eAG/u8cYE9Fkwu4ejYPujRsIhZhGLFbd80nRCrbGFa2xFL9pQ43MLl4gtDOCgZEExmNMwflAFt1b5aQ/Z67V5kJW1+BgyZqTQ2JHf3SMm3hyTCpO5kgePbsXLzKdTlXAiiNjzhjb4Lc93mpUwzqHmMBh1zM1lpUUWuNwVYA2jQPnsTHp4S5Fhu4Um5VDkotgs77W1dBzOvT4Zsp0umBHlFUaGrAm15hFT+4OSOsZbrKNjIXh3l2G4ZSj0yXjoARdUyKxj9oo63TvZa3XPbuNpLqP7vvE6DPWDFg8PsJgBrCX8Yspg+n57O98mLfW8My3v4OX3nyV51Z3efXVz8MLH8P7CW2yXHnnN/JDP/iX+K4PPMGdm0e89uLH+OynXuTtT13llc+9Qdf2fMc3/Tm+5lu+jmY+xwZ9Ugpgh8CdgzPeeOtV3v8NH+C93/zv8cgTb2M2n3Nwd8nNu0eY4jg+UThx2bnM22/f4vbNG7z+2ssM3ZriIq/eeo0uLilDz87eFqv1KT4YvvT6n+BmwkNbD5KT48r+gwxxQDI8tH+Ztnhunh5x+9YNFm1i7Gf8wE/+Pf6X//4f8uZrH+POcEDDNjJ1hJiYSGDWGvavbOMPB2688kplxKqYLLGQ28IwJqZJn9EpD7jcshrWtI1nbgppFIxMNLlhCuBYLwPJqCA3m1smbeDwJBFC4YnLEE9XdH3A+EwxDrcQJn6HfLrid371H/H2Bx/gma/9GiaX9zT69OYXydZCUfdNcgPWBoaie7zQJlKfyKVlFAsDmJAq79GCE1yxxDxSrN6be/TYC5aTYUIeHKH0rEYd5oZJIBf4zGc+yzNf/41cuv4Qk+mM7/+Jn+KpLzzHJ/7g9/jwJ/+Q2WxOlJ6re8LZceTeccd8N9FOYdmpaByc7v9LyqQI070tur7X9SZ4Mi0mLVmfGF784vO88Xd+jh/60f+Ef/4vbnPr7htY79QhLRbjCqfLyPbWFt4lbDMyrBPiFMMgkggiLNo1A5446IAxl0w2I8W0Km45kLHD2zlgGPqMN4FI3bt2BWyi+AmrcQ1J16teCjIUpqHFkViXiIsWJvuEBOQ1YiJBGvpOOFtlZuYaL7/wRf73f/KLfOqPPsHOXJ+/7cLRnZzpUNdOMaYnJ0sp2lhnSsPejhDLijEVjLSYYjBok2ZMwvJshVlO+e9++ef563/9b/Fdz7yfz//4j/P3f/GXmMwKsjoj1UipOG14S7mw6tbEVKAUQnA0AVLSaJttRx3MD4bWW322S2YctOjHWfjuH/wefuLH/waXL14miuM3PvYCFx+YY8MMG+rAyVhWfVRRLjSEQQWflDLDMNx3+2/2n0b3Z8fHB8TVEbtXtrny6Dv5O3/32/iB/+xn+PQXPk5oLJOgMdQMTLdmdMuOEnWg4Y2jWNgKDW1jOFqPvOvrv42jP7bk5Q3a6QSbJuSVcgeNoOkAMWRvSUUTPb4UkvFEMofrgdXpPb73z387z31myY1PfxjfzDiNkaOhx5jA4NQQ4mUk5pFYlF0VkyX+Kfj4/9+vr7qg1I2DvvtFgU9xrJv5Sl4vBbytjW9WNBKU5LxmOcaeiZ/U30MxXt1NIprndfp3RdDa6ZQR6bXpo7Y2qRU8MhbBm3B/92w1wkSNr5WiE5SSc21dOicrAXoQhsoGYnMoE4WBOo8yK3TqWRCc8Wwqq9lMmTcTX1NtgejCLqJV67rpVgi1Dpq9Omu8R+JYo3R6cMlGRSRxhoIhW08xXhlT4qjmcYx1pHFU+z+2JhCqg8hq85PD1FYU3VSllBmzGvD1ULKJ7+mByW7aogzgnBaeFTl3Cilgu07GK2fHu6BVkNQprygbICMkAcg45zd6m07zcxUUg6VUoW3iA6tuoPFBYckpacTAGApZP4dc6oGqCmOim5tclClhjD7UTbXVb8RCdV5pbM1Yzzw4Fs6Rk1CSHmgzVkWhonPUvOEkWVsjl54mC92YyKPC2KRoHFAB5pbGO20XyINeH8bgwpTgGppFoGkaun5FGyztZELwnqHrGeOIc4aJa5Hi9HrPSVFFRpBsiFJYFoP1BTsOFGMpWIWUisVmff/HOjXy3pBdwDcByYkR8M2EZrqA4LAJpouZbpyzaBaYzUE01/YwxxAT/TCo1TrrAz0Yqw9QGRDjaEKokMH6uUplzyR19aVSIztWa1vHoVeGVNFrwxqj7r1WGMeWbt0zLhKL7W2KZJJo2wVUkK1vII2Y6vgzxWobUl0gqqVBo6FGcM5oJhvlfm0OyuOoU/DZdMqFvSmSCuvlim7sSRK1GdE4UikkI4SsEYVmMmU6n4IIMTWIMXgU4FuKThaNMcRUiLMFj8y2GLNOHo1okLNxnuAaotPDtsVy4/Xb3Lr5hlrEa40xpYLHazsktvZbqUFEIzCb2BJVMCgqNmwWSmuCHi4BciI7z7x1bE2mzK9cpJnOGYYeJLJ96UFevnmKpJ4n3vN2GtdydvMO8fYh04XH5BWL6cjpzbsc7d7Ctw2f/b0/5pv+yndwcHzCH//e7yITz+pkRbPYpTEjmEIZE7EbGeMIpdC2LSZ45dOkjMSMowEN9mJc0ez3xossKvDjasOlPm1q7Ff/Ti4FrOXe4TEpdhirTILV8QlPPHmVx/YXrE8POT09IedMnyxFthDpcaYhywBVhNShgzbMqetrI57qc6YYFZNSdY25qknooMSdq5abRkioLjOjDjRbRSWBWlpX1yWjEytbVIDNueBMva7PqU3Vm1pUqNpE3yLpPM+vP0N1uNWaA8tXikh82e/v/38CqOi0yeFXaROMHh5ERB00AtZUiLwxtSUQwmSqj2lTGGOprxUmITAWFbSdCeQ8khBCDhirzYmavDOUlDAFmjDR4U2uzlHr9ZleFB2as0KbDQYXDNY6ctbmv9Z5hiiMVhvX+tjRp0xjYBxHZhNXnavKyjP1hZqUdV3NI2L0MBG8PpcM6lwL1hHahuAmGBoGViry58wES2in+NaTxjWt6Bp1cucNFHDiiM0M8cLtL76BM44Y9LldorqRDEajAFIYEbzPzHSWxViEk9U9eoy2VhkFawerYF9KJDQtoRQaelzsOTte4lzAzSY8MGm5vGgZvWCTsI5rXnn1FcRmxAniQlUvLbmZkNsJfgCxhjI2nC7XpDyynadMd7YIFqxplP9RIkNOdMPAchwp9WdwPtRW3KRORAHTdwyrHjtztG7B2ekhJQ51z6ERNS+ih2SnDu1FM8OOPfbgQMsTuoi0HSlH0pCxXtc20SkHwYLkzDAmfNvi24DLiWQswQX293ZxNd3dREvxFomWKObc3eysZWdnm265ZN13iFewrBhBYkfwO9x9/TWGktgm4E3DkHU/6RqLn3hiKgRbyBnEaexdcQCQi7ruTRlZpmMomVYsxjvdFzuDxVNcg5sZTAdDhoNlp/ewbbRlzyfa1NLs7LPnW6xriduRLRJmjCRR4SmLtqRiDd3pit3pgnVeksYlKQ/kvmPoO0xK5L3L9Kk/d0YV1AkasIhRN/pYEskpRFm5KQ3RRBXNZjOWpufu7YE3XzvgyR/5boausLu9zc5soo2MGPxkwkG35MGvfZTv/c6nWbSBu89/mtduvspnXv5j3jy8SN8J69STuo/w3MsvceHyJV546XWWpx0DcOPm5zm6e4uyHvmP/+opDz31KF965YtsbV/gj/74RZ5+6m288z2PsDy+TDaF93/tU3zx5Vd49pOf5qknrkLpObx3wKMXt/i5n/053v7k0/yzD/0Wczdn8fAUeeUWH3vhk3SHhwQRLk3nvHJwiPMqHr9245AzM9Jsz/jQP/tH/NL/9A+5sneF7/iG/4qf+Av/J2Z6gXjc47zBFB1cxbLmdHnEI9ef5IUXPs+6NwTR1kgXJuzMF6yOTti5sIMrjolpyW3L1Z1tzo5eYTZpWB6NDP0aaTKSEmenAw9dC0z6jltLYUwtdmLYvtCyWp+Ab7iTT9iaN6xzZOYNpXhsf4oAb7x1wI037vLupxOzacPv/6vf4dNHK6TPdT+cEBrG4knrTDPNGC/41mqD3SCkaOiHqWILcuJ0JVgnSNEUyWqABy5cJLpjDg8jTXDEUfmv1mXSKFx/7GGe+aavZ7aYMqyOWa2POTs8QM46Xrn5GsU6jlZHzJgwzA1DMRQbOTtNDAVyavWaLDp2mrVTpIW+71WonS+I6yOss8wX25yuj3nr4C2MHfn5/+G/geyYNAFnI/u7gZPTjKCNw2O/pt3RJsxs6s9Goq8xbynKOjIlkawnhYam9Zi+Y5SEHQPdeoGfOiQKq0Gjg86qkNibgdBLPZcmnDF0JRKlsDNvMJLoJdEnofGWwKAICelprAWJJGdYD/Cxj3+c/b09todjHgwdzhoWFx5jbXd46J0P8Nzzz7JaH6sZozHkZWa9TuzuNIx5JDkwtiXFhM5PM620hOAZ+0zjLL/5wY/wq//rr/H8jc/xDjdlfmmGPV2TXYM00EwNcYAyCMvlmnGMzCaOdivQ2EjIjuxGyAbrG22+zR4xLaZyBJej7jMevr7Dt37rB7i4exFbtA23CQ3T+S5jLgxx5OTWKQfLY/y0YT6dY53n8OSM47uH7FjL/sVdmmZybvrAaPqnWydefOlZ7n7pD9kNT+MfeJrpLPDDP/Dd/NFHfp25y+fNkCVFjo6O6yAvkCXVUinHxcWULnbs7+wSbCaUjjE5ggvqim4tJatWUjA40YbTJEndpBJpfYvLMEbh8y+9yVpe4IMfeo4ndoTT9TEvnxYynp0gtN4xFBDrEWkY8gprhJnzmjz4Kn599aHceNIwYIxu5lPRB43UUbM14MiIsYgRRlMI6IMEEazzCpitmzgTY2WsFJIMRMBkrVfX2AyQqz03b4QcnQQ7Y8439aBMG8FixJwfjEtRBlBGK5yBunl1CvjMtXobanwLtdYBeYzqvKoKe67W7ZKzHnZT0j14dWyUejhU9sD9DbuvEO9SY0MiWlFYitaq03h101CjcmXD4NDv54zBFIMrmrE05j64VKGMjowezi2CVK6P3cTSNiPrUpGqIsBYowMNrgk479nUNnNusYYN2Po8ZuZ0mrw5tHrX4GqTDPVgUYyhdSqCSS7amoRVAHPO5KTsqLDumE9GmtDSDT19OmF3tkNcdziiTkSrAKWtPtW1pqcvziUu0eYBa5QhpREOoBgVEXOqlnJBxDMUnRSGkoi5kFG208R5fNDrImcVyGyJGDdRF1VRR0RTmUDZCOIr68mKbq5EI4IWFeuMRAUmp6R1nlhWyzPIkdB4vFU3WspFN6h4XFvf41Kjfll0Oj6i6r1Ub0ZWrkIUhSM2YulKqnwPR67g6pjANy3eNYxiaYK6L8YYmfipXkfWnAN/T/sREZ0eDN1aYbWgzKC4EdvU1yaV66BHV73OUyrEmBhMxDmNguY4kvNQ70cQiQRQbo+BElWoLGIRr+wQMQZvGo0oUJgvduj6nuX6lAv7lxDKOaB/cwDesI6cbeoEeMNf0erqak5iMpnShIaUoFC4+MAlrj/5OG986RVWJ706hmI8jz4OOfL4o4/x4NXrpKTxnaZtdQJeLDnr4TQbXaBaKjenbKmFPVWhqeR6jwu+afCi7Jc3bt5DfMALJBcrK83gjLsv2KJCKlRnSX2/9cCvz0A5d8jocy0Hg9jCpPHYIDQuM2nnTPa2aWaei1ce5PRL99ja2sfuPsTb3vUUL8XEyZ0DwmSK8TC9so81lqmdEJpdujTw2p2bSD9w/cGr3Hv1C2xdf5j92S77D1+ju3vAkYmk7jYlF7W8DwPWWybTWX2iaLsXKWHKfb6VlawHThKIqyy5DFKw6PPfCuT7Zi2kCENlpi2Xa1I/YHBc2N5isbPH4uKjLNqILJd0Zs6qrDgpgZWNxFyZTTWipo2lNdJZBRVbW+fEQKncPFeEUiHXtoo61qjYLxs3CVSeX31kZXXyajxMnW2uQhSrtQwrG6cOkCvzzRqK4ZzZZ41y81RUr/7SUuq/VF2h9c2Req1sfq+/3OcqbcYrf1pcKl/2e1vHqvp82DSjbf47+rO5+rmI3G+VS0kHM843WJcpjOefWRwz3htoCoSkTrUKzw0hwHrQ9q6kTVbOaeww5Uws1a1WweM+KMxfpIrwqMusbSaslz05RqwtlNST2okKs1J0nbUGMQrDziUjY8dka4e2nTLW5i4IYMbzQZQtlkkueFdIqcdEvVc9wu5WQ2MzJZvK6Ct0uef45JB25zLrtR7Q33zjHnG9VidsBaoPcSQYIYsKQ5uBmqsNFyIWWyKWoPwkY3EJfKjTLYFxLOQkHJzcYfWZO7TeAYl4OvDY2x/HPfwkNjS0bUM3Ju7dvsHZyR0mHl1bnUeMshCDC5BKFd/B5ZHZZJu8LlxcTDCpMKaEDxCHHu3pUdfzhe0dxAjrbqRvNH4vUQdsjkI3LDlaHmGWSWllQV3Dpl67KWsbEiWTU2HetvisXBk7EZBMYzJmGJHq6hOjldSNd8QhMWIYcyblTDPAxekOk51dhMx60DUl5zrAc0YjDE5j45u7v46NVBgvSR2ACmWsufPE+vAAExrGYhiLNvhiHNPJlNm00YFK3R+W89yr/uIAkxWgO+ZIa4VBImT9LHw2lNjrszJYBqstwgl1c49JsQylGDIevNBu7yjM3VtaGvJMWIQAztYW2IDzgcU0sFgsaLav0F59kJhHrj9yDVuEyXSHk9tv8PLLzyO1dc8UXWBKFsYh4QrYInQ5KxctKCsvj0JoGqbew2CZLab8wE/+KMcmszUMjNMJDz1ymec+5+hLojGJxk/55ve/j6a2U/YLz3ueeYbt/X3+7XPP8ebBLY7PTnAPXeOwnLJ68y77Vx/gsXc9yu/+1m/z7HPP0XrHTjvhg//617nywlUW02129x/kT55/kU+/9ATfefhnGMbEQXcKpfDAA5e4+uB1LYrwif/wB3+Y1XjKu596Dxd2L/K2px8HLH23xgJ/dew5ev0W2fb0Zz2/89v/iqmbc3h8QnpkySQt2J1NaPf32HGB7rRnvrvLM9ce5tmDLxC2psTSMrZG9ybFcXxwilm/Qo6RZDLFpOokjbjGkZ1h1a1Zph7BUuIZYzzBtxYXIrQCa2FqCu3UsrVI7E09XzooGp1edsRiGcwZXhI3jgqNERxnLGSizqKQmdiALT0//P3fz1/6rm/XFboUfv3//i26sce2c3YmicYf0Q9zzs4CfQdNMwE6GqccBh3Cwclpz7wNiIPQCq0Ip72OU5Zl5LOxx6aCCQEZhKFPOlwXQ0qRL778JT74a7/KR37zt3j88cc5OL1D44QvfuKjfOnNOxSf8G5GMVu8+Oo95rNMprDuJxRt30FiYp0iMjUkIjLo2rM322LSzOh2PbnPjDq+ppQBG2E2m1AytbE6c3y4xvtWK+6tYUgO2xeS08r6afAa040aYffMiGNhPhlxfqRIYugNrVNct5hIKRNip2vOOMg5rFyKYRocyWawBi8NsUSS0VKm1OtgvR8TTS0jGtaJtim01iMmQgB3GthbzLh6bZtv+6b38ZuvvIEpjjFmtr72Kf7eT/8Cb796hf/tF/8+v/Arv0RoJnS5o1sXHI5uGUlOcN7RTDPFWXLUgUuyI8Y0eLGshsyMjquPP8w/+Pn/kR/6L3+G8mv75LNDOgqS1NXa2MT+hcAQA5ILfdeTS8fl/T0krgBLGxxOLH0XzxECGlGzmEbPesE3BNcwaVussbx85xhXeoL33D065vU33iDlNQ9fv8jWZJdCw+3DI22UlkxpPE+/+xmuP/iOc16lMjsd8ybw4HyO9/t0Y8GWhPEtk8mUFFqgq2kMiHGT/CkU0cSPaRzBGg6HFTImdvYXfPoTnyJ3I0JkeXSHJi7pRUU6ayFFdeDnMTPmjTMdhnHAWM/xyYhhiw/9y09wFqc8d3tkQJlgBvC1bEOkkKMifgxgcmHMic0A8qv19VUXlOLQV/GgwmytgmZNdeMUI7CZ7EtmlMK8TtcUd6BuHYytGT9ffTe6IbcGnLF4PyGKbjasK6yHtcZbTFJQsKizqVC5OGbT/CVVEKotA2hUx2ymwnUjzIathFS+zQaCWFuWciJGFQd0I13IvsEZFThSzKSo/78Vr0BRZyv3QyfmkvP55r2t39xYdVm5ptFDu60tP1LB2KgYViWuypVQgUsjfdqOdL/60JGyKKC38Yyp0yplDKkISYf6et8oUVajS1brar33eOfwzmCcHspLqbud+jnr4Uonf4ZahWwDzjj9bxmn8RNbhScplUVkaIKv770KPmPU2Nd6fcZZv9Ka0bEw9xbvZsQxgtFWKOVj1UMlFfK9OahZi8Hii7J+mtDgjamtbfWVChq/8jrZFgMlGcQoC0tZPIJDAdfOgDUerDbIqIgEpgwIBm8M2en7n6VgnMEVj7VeJ7FsmqH0PQi2MqiKgC3VVUR9fZWtBTVeoywxBf56ZT5Zr5Nnr+1GpmjMzTgLxRJLwhUFEzfWEbyli0X5OdZShkgUbVrw00BoJ9iizIkNJF3Op6U1kliES4sZJ5KYTCe4EJi16jRS55a6CdQpp5tbFbgUSg7V4VQ/tyyFPMZzMYfqolPfvL7fTaP/LMUypIwUW8HWGWM1xmmsoRDJZWQ+nTGdtIyDkEyqrkSFrUuNJzUh0ISgccvqPKxbezIq/ErOtK7BmkAna6TrdbIfvDpnrAULEwlEY3jf+96jHBbvUbk2gaMKj4Zc1F2izkYVGmy1XIvb3FLVoWLrUV7ApCqYe4vNBptqpfnmPavPAhWuqxBfrzODCvf6+K2ne7LGcpxHaJBS2Gpbxl5oFp6t7QWhaQhiuf7ku3jlpWd5+uueYf7Ik9y7cYd4eszQrbCLKdN2xslbx+zbE3zXcxqu0hXD8t4B1x+6yNWnH+bkxj3+5BMfpZSOZj5jdbRi69IV1jfPkHEkDSMxDdgcGIooDB6pDJLqqrIbWlJmAzsUVAg29RC3cSbKxkYkG7qIPmMdBkvi0v4Ojzx6DSuOB68/SepGuvEIY1uMn2st9VDO2Vol6zPMWr1ei3ocSKjguRGtN0KKtqXlGgNWwaVq3Njqn/oK0LXo39HAGufrp0EFI9GqsC/7rDVipC5dqb+vcoZRIWpj2TZ1baVolDendL5Rqt0F599v83ruXyfC+cl287owG21Cf6Zz4akuB/Wfv/z7S10vRBdvJOln3Ewt3XoAY9Wh2XUKWq7PEms046+CmFYDN41GHX3jiGuFqCenzxqfa3Sy6P3i/eZeVP6RYFVkqvEvP7G4CLFP+u5Lg8ERRWiznBdtQG0Qqwdm8XPa7Rlp1WnUNVmkTZiYKWWsPDWNQUjqcSkzir72gtCt1sqZEN179LlgwxybEkcHdzH/D3NvGmNbdt33/dbe+wx3qKpXVW/qN/TEbjZJURQliiIpKhqsyYodQbGdyLID2Ikjw1EgxE4cJDGSADGcII4hf4miBBFsJbAhyFIceIwmaKAoNSVKpMRukj03e3jze/VqusM5Z0/5sPa59RjE3xgg9wPB7ve66txz9tl7rf/6D5VhMpswdN3GWNmXe58UOy33P2GzkIPaCASXib6n7yt8NgX0UyuA2kBad7iYOD+fM3OWxfKU1NbE6Al9x+0Xv0R68x0W1x8jtw3LIfKVgzusVgNDELKrsNZowo5oMASitZG4ClNXPLa3xUpqJq0jeK/JqQLWZHKIdKse206pnDCsvO7zMSmTNAVNyRH12prXZZ8FBSfEMBRAzeSogFxOG8C1CwN1Xeu6iQGTvLKCvVc2ghOd0kZtinJG5XM5kxN6bQSuPfEkrt4mm8zh6QlZAsGjCblFclyyPhUAsxW2qoksla2T9fwyrsKnQEyJFk3DrY0lx0A2BjEtMVXqe8kYGiHYPA4Eyp5mdehhTSakTFtXRcYrJLEEm2msMMSOMOgEIWdLTgZd/gJZqFyLTwFyQCJUUuMlKDiWtH4xRmsPiZEUHBmHCR19MqzWa0IfqCMcdw85PryNiR5JAVsMerXxhi55QlIGcLaG7IXQ6yCorRrq2QRDwPQnXNt3XK8SB10in66RIfCec7tMjdCh6gZy5IXf+QOOPniN+dRz9Yn38Pnnf49PP/85UhU5f+Vx/O27nC49T+8/wbc98wRPX7nOvXt3+U3TkqXSoWpjWJ50vHTwCt5arly8TTYNr375RV7+0ksYMRwvT4gpcW5/H2cMfuhwIrhmB1dFrDuHqw+4cbLkI88+iZXM1jlDXVkuPHWB1HlunLzF93zPn+CZD7yfOETmWzPanbk+9+LVYtG9/1u/9y/zzmf+CZ97/RVOV4kLs21sY7h79IC33rjFu/Y+ISWmxtFXpjA1HTY37OzM6Bcde/PEnZsHhBxIc4d1C+Kgw5pJA1OXwWXWveX1r2SWyVFbyGLJQ9ABe2XIuSJZy8PuPm5Yscw1kiumc0PvV9y9d4OT40P29/a4e+cu7779Fu/eOuZv/28/y9/+K/8Ou5eu058eqWkznhQmeB8JgwYkaF2YMbkhW5i0mba2XOBO7ckAACAASURBVN3a5sV3jjhdJ1ozcHVrGzHCg9NT+iFRTwVTFbPk3vCJ7/oOfvwv/xhtVbE1m/CzP/Gf8R3/7l/iF++9zK9/8ZDp1g7d2vGj/96f4x/+9P9KJz0ue1SaJjQNGJsIUXCVMKsjJ0k49onl+ph2NmHWVgwm0C88tVhqk7l89b1Mrlzl6M5XuPnuazw4XfP9P/CDvPbqO6we3meQU7q13vdBEq52RNMTvehANWXWpW/sBsuE8n7UEHLF+mTF4DPZWtoUyVQ4E3QAsYZsHIPkEtARWCaPszUTO8cmkByQ6JhMLCKRbIBklNjhLN5nBi+kkKlY8sqrr/A3/ru/SX88IHHg6z/+HH/zP/qPuXJ+l4fLFZ999w2mtSNk2G52aPbXnNxfEquafp2Y1DW4gNDrNYrB+8S0chwtO4TMqvPspJZf+If/iLTb8okPvp9PvfFFJtWMLpxSNRaXFaJ3ybPsIjEbQgczcSy8gWJhE0Ii9QbjXAlTV+m8DtwMl69e5epjVwjFk/O3n38Jk1YcnDzk3Ru3YPC8/7lrNM2U1Try4OCA5aonDBE3n/GdP/BJ/uyP/DC2kByk6OQdhtdff5t/+s9/np244Fm5yuWnnqGdXOHCjuPi9jb5eAklbCXnotQpO7vYYjeQleyxih3zvV3k7l2cCVTbO1x44gmOXv8SRkSZ0lHBKB+FHkOQiCZ8e5LVmmaIjlffPOX6bM6t4xXJao1qEE19jOqhnHMkpgXGgAtCHJOj8v/PJW/WUJzIBSsNKRcPIns21YlWmSw5RCpxWKkQUeaIkbNLMiVNLWRBTIXJgZQCtmwEBlsAq4hofjhILL4Oo/xDzgCcMkMcNZTWWm1ARIoEQD85a8yf/v/SDGzWrFUPgpSoazX03QBKsdefj8qoMqOfRiJlNfjF2k3RrNiaamiHoaeuGxDBOkeKvhQnGUTleLV1GsMpZ0ZfBtUeZ0lU4jYFfRRN88liGWIgSSiLJ23kGaAmn9ooaBKcTm+tNlB2jPQuMgfrSjF+lsxGARxMMUbN5DLBM4hTcM3nIhkswFwen23WZDuDGscOKWtSWA4M/Yrcr5EYaKuG065XGZnxeAJd76lqq0h1cfnOxWw2oybgWYRlCGyHxCSr+auIUGPxkqmNw8eID0ETVdBJv6srhETVWobBUxmVZKUcyWO0bVI0PMWeHNPmZyNnCYWpmHGrMYhKNUJWUME6h3OWBkcw2rAKsNW2nJtuM20nRKMyRxlNiRGWXY9zotPTHLFWGwtXaaSmGKicZd2rX08gEVIoja4wNY6AgMmIBLoQmVYVYjJ149jdmTHf3WexWuNyIJUGVIv2RDRq5r136TLbszl122KNYVgPnC4XZMlEX9ZtSTc0uMLaiWQpWuKkbIxMwtgKJ3LGNjFWAQBrqNuWuqpppnMmIdF1Pc2kJaHNgRpQq/FjTFF18VXNUEx/bVZ2ZIjarltjvwoENcUHLcWRX6Ax62IEkpDTQDKWrlsDWoynuCbhqHHFCFqZii+99EW+5aOfxPsOa1qsGGUNGIPgcYJGN1Mgw6RhAsaIsjJLkp7F4FPc7D/g8b3H4PRZS19YZrL5X5Wq6vUzAgVyhgtIYTSlAlRZMThxalruhN3tPcAwbVuefvppZtMtnK148uuf5SsvPccT3/hRfvc3nmcIK6IJ7O7v89yHP8T5rQlb3X38/WPu31/xgQ89wRdevc85Jzz2+BO89JkXabcn5HXH1qzh/ttvcHq0pmkN7e45+of36fqOmII2M1ETTyQnfPAab0+RUaLR7wXyUMB8BDkoU8dyQCZhA7gZFEysRMghcfHCPn0/8NaX3mF9NLBzYQ9je4z3LHvPyVoYgjIhDMqOHU2uY1Z4Qgw4igdCYeKMCMooBRNR/z+9vuIZJqNV9WjsnnT9jeygDQVZxi+o11EA0fHvSEYZsUkbdVcp0zSnjLGm+Pypcbj3XgH4bPAhYFyl70A6G6R81WeDZJ6dh4+ejf9vn/EMyFm9B0WApLHfudwjJ/r3Ysy4CiqbOY2eEDw5BPXQIG+YWllUOhQTuvcHmO5MFKwnYZ0eJsY6ZbqpsaDKn60oQCiihWcWQlKZu+5p+rPrSUOICd8DoukxVjQBSc8qIeOQPKjc0FqaWUM/LAuoL0R1ylUT9CxI7Nme75ObCf3ikK3GMJht2umMc7s7VFWFlUwaerCGZbb4KNRoQTz0PQfHJ/TRb/yCrDjapmZqK3Z2tpnPGmS1xrYN850Z80a9Jo6qCXlIJGeINpJs1qS1NCBBgZRpBBtXiO/0O8XMe+pdntw+x05bMbFbIDU3/QGvDGvqqqEVtIgtckvjKirRvT8VXziRBClQ1wHrDV2M9N1AiAPkgB88az9wbmtG8KEUi0GNhWMiDhmamslkymSr4fD2DfWtLO+AoJJqI1I8+eLGMH7dZWIIEGHLKmA/xITJmW5Qu4UcdF0bI+oVlIToozLKBXAqx/ddz865LartbY7XL9OdBpLRaPYuDeojSMZlTRwLxRutMkJOFskaXIIbzeIFcTpQ8DEXpnkkm4HBr5lUNZlMkoxDLSGy5LPBIiA54TDYqiq+nAqvD0kZ3hmorMOPPmkiUOmOIUm/1xA8WNG0x6TAauMqPGNdNo4EM0Z0sLRarQr6ITxcromnxzQ50klGfGbw+sfKbPdsEgex+JDpQ6RyVg3pU2ao9B3am05Z+I63n/9VfsFkrj91meF4xcUrFwnrjnfvHTB45X91wwlZDL/2sz/Nv/3Sy3zrh57lyetXeP5XPsPLL7/KN3z9R1jlhMy2uXn/iOVvf47cTNnZu8ztfuDh8SmT2YQ09BwdnXBiDYlIO5+RW0NVtZhJw71bN5UdWlm65YqbNw4xWWjqiijw9//3nyEIfNu33qC9eJEr+7vcO33Ila0dtvennHOWWiqsGLauP0vfdyrrcZmUK/oukvFYk3FVTcjqb/gNP/anefZH/xT/1698nhO/4Np7nmFx9wHP/9Of53h5lwv7T3HrjVd44eUX8cnTTBOGQNcdMWTh5ru36MJ9eqlIadAnmCI59OxMszL/vSX2jmXnOF17XGNpY8bWDtsIdTVhWDuEGWFY0Lo9nOmgq0hVzfEgWCf89m/9Bt/8ye9iJjU/9Xf+Fl85us/1D36Yp89f4I137nDeX2F1OrC1s1Yz8KTAiQ/qR5dVmoEGIDnWa5hULRMMqc8k0XS8PvX0a08YhHWXmTpLO0vEFHGV4zO/9i948VO/yLd++3fynR/7JJ//vd/iV3/rF/imb/lenr12lTeXB1x77Bo/8EN/ihd+61P88u9+nvl0CmJwNmGbDtBE2kkSru3Bl09WtHlCMkt6f0jTNNi8JmSHtTXOwtd960f4ob/4n/CF3/4U/+B//Fssh7t85tO/xrntx5jv7nBij7Bk1idCNXf4AMuk6Ym2UosKsAw+89hzzzJvG9567TVqY1gu1ly5/Ax3D27grOd0kXFW+9psMonIpLbkFDXpHEdl1RvLlYFt1635xPd+H1/50hc5PrlNv07kNBDoOF05jKlpncdMK0KwpBi4e+8GlcxoG8fXfeSb2d49z9/5mb/P/tUrPPnBD/P8b/8y264iiyeIIxkF510lBBtKCqqjqUUTOZOylOoWKrEYl5lMHevLgf/8P/jr/PX/9G/grKWqM7PpNstuRUqWfp0Jg+jAgsS8bVl7T+8DdVNRS8NJv1JWdgHckwghCtga54TnnnqSx68+rsl5RuhW9xCbWKwSnY/sn6uoq5bFKrLuE56MTFpqN7B3fotn3/s0hqYMfMsgLAsima1zO0wnO8S4pHuwoCJC0/Lhr38fl55+inc+f5PWuiIJKMxz0b04ZpTkUVv6YMlZ2Dl3hXuHX6K1a7Zdy/rOLToSMys40YGmLXtPXheMIQUaa2mNsMyZaVXz5dsLPnwlc3lWcRgiKdlSCyu4pOFemWsXz/PGzQf0QZVXMX+t+Un/HwBKIUWl/ZfpsCYJiYIboKlOVouCiIIBKalEzFqVcwmGmIShyFNykbClrMabsUyWjMkQdMIfY9hI1rIZkUWhjDhhAww90mltRvn6z5qcpsCAlCLfGIpR9fhjRBtlYzApEcPZ791UPUZ/r7IodEFJaTa0ibVlkqPaM1uNKW95A7ZY1xDWgYymo0mCkLSRSaXxVH+MYuSKOvqnlIguE7z+rMEoBS8Ej2ivrteQwNiS5mWFJElTh8r3yERE1ESyjLmIJdEtp3FyrZ+zqfSZZMhYRdETiZhGU2/9z8TKxk/EGIstjLQkHgkZXE1Mka1JRddH2qblweKUISVyodfXrtJ7J5BMLqCHK42myhVGWUqOWnhqw3PWRPnkAVNAs0w0wkCmH3rqypYEv5rKxrJuXXmWlknSWHuPI5mEjfqMXaxJIVLqOS0UcybkoFKUPC4SwVY1iQprE9ZWGCNMd+Y0VqWIY5pYQploISSGDDaqpNBZBa2ssVSiIIQtHhuUBlfZRgomtPOZsqe8fm+ViQ6EksB2/fpjPPXENY4XS+IQ6HzYpJeNzaVk3cz39/Y5t7Nd0voyvm6YzFp8CConiEFjURGqWp+L92qwnqICf6offTQtC+KgcroUVPplrCnvsOBcRd1IAWH1eaTyVRFIPtI0DcvlihS0uB374srqtKAYvUCKeK8MMUE2W4KRTOgVsohe5REhRO7fucvDB/fohx5ra52GPHaVxcO7Cj6ngeeefoLQLbBOjQsjKpHIoQBloswJxj3IKMggWZs+CnCXMxhXkbMwEBiIEDe5OyQZARQ5Y7SQ9eE8gkeQyn0Z4acNmFRRYE9CDmzPdnFtw2K5pp5eZmdfjYQnk4bsVzC9yJdfeBnJgdBndpptrHMsDw5pDm9Q5RPWRwv6VWD1R5/l6MDx3Cc+QcyR1VHP1vktLuxchHrg7umKZlZRmUxY9BtPtEwuSY+Zvu8U4DPlXjB+Px0WGNGkGynG3LKRgYVSsBVHIaMSR/XB0b0pJsP9g/vcPzgmS83D7pjTmz3x3BS7WnLSJZa5Ihmr7MGxoR3BfRR4lqyM01jYQOO7IUmlOKOPkuRczoOz80EwGxaVMuIKu0mUjZpGIEkUJpRH2VaiwHDaPPNUWKPK3IO0GTo54wqQJWRjtLhAsGWoMS6TDYD0/9jPx2EBmzU2NshsQMvxz9QHEB49CuHMO089vSoG7zGxpmktRhL96ogYjcpWrRbLOavZtjVaeIUQiAmausVOtKE2KVNZ2ZxFKSlTkZLgKRgtmHJpqFPGh0BMOnyJIW3Avelsyklck3tPSoEQBqSuNbGyDL7AYKtGJU6hI+eelIUQDHivIILoN545IYrDtS12aTVAodnC1i22qnG1njfWClhLOj6ktplglR2Qku5llahhLQnqylJbw6w2tG1NO6k4PDpiWldUdcNsa4KzAV8YYCYN5JRwOGJUKZYLge0MzqsVwVau2TETkMB2EI4eHOKabXbtNqEdaH1FExy+sjgn+JjISRM6K7T+UkloQnzPznxO7AdtwJPWUxj9LsvTJavVkno6Uy+MMjVXNl1mGCIpgY0ZEwKxF4wXBYWcwyYKUKl+h6NHpzJo1euwD4GHfqGAT4I+qO2ADxlTac2gMlQ1Kw+F6aXTZEPjHNYKyyEyGxZ0d5ZU9ZRQDQienDOVq4u8S1nvABIHamsKEFQAGWc00CFmTNLfF3MkFOPsHAP96RGhnRLrliQKtMQciAxgBVM8BpsyINViNKpsGh3cOknobu7wWYNJck7qt1JAKAE9hzJKR0/KdhexWCvoOFbPVZDRipKhXysjUDI+JWZVQ55NGULHNCdOVoEuduRc+FUy0mwj5FzY1WfAtRFLO5nRTOY0dUtF5PDgNr/xL38OCT3BONrJDs32Fvdu3aMfBmBQGWCM7My2OXjny/zm3Rtce/waxw8fEHPN/WXPnYNb5LrGmjmLozXvvv4Oq+M1FgO5xtDgqkzOASqoKsd8d4tLV67RTmf46Dk4usvqZMFka8ZWPWV1Cl03EIYVYnUwa6dz3rpzk63ek7zhC7/668zalg9+0/t44rFLvPLS68Rlz5/413+A0CfeuX3E7taUx87NmU5b2slE3/0m4WzxCRT1GTr/zc9RxTVuZ4udx3b4lt2/wOHpAfvPvZ/zn/odrr749VzYafij5z/Fpfd+My+98GneeusdomQCc7bmERs9kiOLQ5hN5ho+kDLigJzxMRKzo7UGW6sPXyWWCsd/+F/9D/zuz/093njnHvuXr3P/3gvY+ZrBJ9beMDEV25fO89nnP8Pbr73Erf4Oh0cn/Nf/7Y9xbnGfWe2obWZta5VGWUNOHu+jevq5ElyThYBaoXRdTxoyx+sVezMHnePSvrBcL9ibzrFtxRthSYiR4AVTGxpJXLiwxbxp8WGgDwOLOLCk4XOvv8jB6hSTLB/76Dfxf/zPP8mdd0+YNvua1FwlUvBkH6lrrQc7X/GVNxM5OpyFpjb0wyFCizMVOQyImxDimuf/5c+xenBEs3Oeo3UPWCRrknQIPX6dsbaiahKxyxqc0IJJib7PKlP0A1QTPvaJT7J1Gnnt8y/Sh0T2wsODg+I1CpUAUZjMpnR+TWUzjXhN3o1SfJV0UOMkYe2EyWM1P/YX/31+4if+Jw7vH5J8j6nBppYg6mEqURn91mm/HlNm2mRCzDx2/knm7Yy/+iN/nslszl/98f+CaW2JPhAlMalq9vemeBvU+87oMDNkTdysWqteiDkxnxuaypCy4XQ4gW7K9/+57+f4tSMqW/Nw1VHnrIQCq6oO1xgqhG4tYDM+9OxNpyoNT57OZxpXkbNR64OkO2Eisj3b5qknnmQ23Sl1sdAvFgzZ0LdzlsFyKTcEGoxL1Ckw295Blj2hcrTzXS5ceuKsxtkMBnOxH0hcv3yRipqZswyrI6ahJ/iBrdqVQctYIxX7mg1ALyQslTiSeK5f2uWkE7LvCc4wqRuOF6d0faTLMLfqNSZG2UjKTM7MXcW1vW36mJmkyPGyZ9kFbi8Tdc44ErkogxR4UwuTLgSevXaRtZ3yxquvMxrCPMKj+Zp8vuaAkimJO8DofXs2JUcLAfVTiASFG8CodMmKAxNKo5lx5YCTKLSuYhA2f5YF1aJnTcTxI+kmgsREEm1aY86FcYNeDMV3plyjmqpSBsKq8deeT4tvKegkRjZSr0dK7/JYznAr/Wxm1IW2XwoPEcQ6lbElNSCmFDMq80AboKS+K2KdMluyekUp2slogISkEm1cvDMkDmTReyKStRiNA0bUh0d7VimNhn6TbLWx13rl0TSuTAyeMYN+ZGypNE02A/Qz/w02chMpAGIqpuvj1Dpno11X1udqRdS3SSIpeUKKuKr4ZYWB5XJNznA0nGoRStj8PjFCVTkmTcPh0RGjv4YKxUSlKsUI1qPJKiFoSkSWjMSsRqIELceMpZ1MmG5vkYsJ8ax29N2wmRKDgmYGBUCHoB462sufdVrGamMj6P1UmaeUptBgyBjRYtzWja5Jo+yBKIlk7EYSI3mk2GsD60S/j0kWqVTCkbKhFwprR30LInpNra3JlQKf0+mUgUR/dALRkFNgGDKTJhPCQF3PODo65s233mG1WrO9vQ2uMB1KI022NG0DNhdmhgKRIY0MATbsGRFb5Ghq0l5PZuqbkkYPFwHRKVEqGzAV5BiLhEl/a0ix7M+jfFYLZLGjNtkQh4D3mvbj+w6TawRwVo3TR9BYCh01FAA6poA1VsHsInO0JR1RnBrmDt5TVZbd/X3qdkJMkaZWU8bO1ohEUszcvHGPx5+YqvE9DmMEH4QsEYclSiIiSCxSFdG1r4w2px49Y3Ne6+9ORsozVlAhGYGsTXUcgRYK6I6uL8pOUpzMdF8qvt05jcwYBdFsymzXNTkHol/RHz1kUjf0IWLaloc37jDf3+f6E5e4KYnTN+6Rpg3T7TnSVOT5ZfqlpapPafZ3eChXubC7zYO371Bf2OXck9eZzae0O44bb73FbNawvnfIucsXuX2r14Q80SbI+4Hsak1ZtGWNWClyLwNZJc/63VxhLmizr/vPmSwsQ7k/pWkU8DlTxUDvA5KF7a0JVWU4PF2zdfEiw9ExSx/oy7SenAsgkscNTk3gy78fd2yKnG6zP24AXX0PUvkzHWiMMwzZUKNHCdn4HdI47xhnH8XbLhtlKaVRxyYU+WTYgPmSVfbqivRUg/4S1jn8EHCuUjAynZ1i4+er2UejRFzKWsnlvj/yXTgblqRyhojoe6sMUTQAQjSiWVOw9NweOqFuhNlsymIx4JzDFw8a6gqbdTobvCaTte2Era0Zzlr64jMEmmCayjubTfEYLNc+snv11gZiUkZu8AOCKEXfKEg7nzV4axiGnph0Em6dyo8yGalANN2AylhMVSGu0xolWqLT/Ukqh6kMXhpyykQx+NRAELrec9J17NkJTgwpZAwe3y/xQ0dVtYgR+sHTrdf4pOEOTrTINkPmZLVAcmK1sJwcnnK4XoFA7FZMGse6W2NsgyEQfFK2Ws4wwHxwPN6eJ/dr1sGwIw1zWk0HS5ZqGEgmcrpeE9fCljF8oLrI2zIQbE8f15vE3VTVJAt+WKl/2qRl1lTUrQ5iXF2R1p66ajg6OOTw+JC+H5CHRxxPDmnqFhGhD5F+gJgtphJllIbI6TrhTUKMgyAMRKxRWZYU4FkZpLovZHTYFjGcdmtqqeiHwBAT3RDYruoiIC8DzKTJhMpuT0zrVtMEPQynx8jlC1oXLjtCqRcSAjkp26act2TBWcf+/nna6ZR1NxRQJzL0Pf2y03M5Q0xB97oCdsao1gm2Vp9Aayv1eYzF1FsypERPxjpDVTw/x5TJkCIYo/cmlcRfHMbov6+cwaLMXW1tkjJVrSmBLbo+WkbLAq3fDWXuIoYhJWqEGLQeH2zCRkNbWU6LrM5HHXxu9unN6WOQGEg2I7bSc2zoSXUFWb0Z2+kWJnSYpiVKpt2qqR2E1LNOHT4mpk3DfDYlGsdkOmNiM1kGqkrYvnyJg8URh8tT/MNIM+uYTie88MUXCKuBi5eusFiu8ClSA9QV1cRRNY7k4Y3XvsL+pfOEHPGLNTkETg4esl6t6ZYd4iqtOVOibiZYAhf299h76kl+99Of5eDhPf617/4ubDPh5r07vPTCC6xOTsnWsnNul8N79+kWp8jpiupk4H3PPEdTGZ748IfYnm7h1h07u7vkWcPNd98lTKeE8+ewYlh3C3BQ3bpHHRPf9m98H5fObdPbhu2PfZxXbr3KU+f3WDx8wOL0JlPngYRfRmSa8R7WYYJratarXv1Y0V5oPp+SB2XQn54e86f//I/y8e2an/n8Z/lr/81P84M/8v3845/8KX7q//xfiDVIXlCLcHLzVdbPfIhVvyb0A0NV8e3f+GHuvfAC733yCrdOTqkroRsyTWMx0ulJURiy1mSdTwOSErXUmKR+URcngXp7gu8PaVxG8sB0NuOqCCfdmkTAIcwmltqqN+ArL3+Ba1eeYv/8ZQ5uv8MXXniDuplyYe8ce9U2Qxf46Ic+xGu/+ik++P7HCbHnpVfeYBhqhhSwCCFkhpxwTcXcJXzSM6wxNSdLT+VmtCZxrm1Z9Ke8+uXfxU3O432isi1PPf4ETz39JJ/+1G8iuSGnAS862KhShUlCGJLaMwi42rF1fsadN17j5Tdv4BqDqTPVrGI+aTg46MjiMC5y+dx5+nbGjXdfZXsrk5KQNBkJaxLtdM58MmdrvsPB4THDauDHfvzHufPOAZOJJkWerjxXHrvAenFKskWNkaQM3GsaqzV+XdesfSSERN3WxCFy843XgEzvu9KnWlxdQejZ2nIsTjPLpcfZRDsTnMvYrHuNE2HZr/G+RWrH1Bjy7Z62qVj1kWHQQCHJGddkXCNUDqJ3ZAm01nKutuq5KolVDFrTiClhQegwIwuSYGd3myvXr5CL5KvLwoc+/lF+6N/841y8cI5P/87L/OR//1/S3Dlg4T3iHNt7uzz78W/hqWee4eve9z7e+/h1tSih9K1ZB4OLkwVvvvKH6hN4rqXvPCeHp8zW9xiWa1oXydZqEqzV+swUu59I0lRzAs41VKsV3/Bt38iv/9E9JjV0CTrvWa0HNfEGolfswJU+LpZB8tbMgSTqRpNGt4xQO6EPxe8xmw2OMXdCXQnRCqckKuO5fH6f1197U+eb43D9a/j52jOUopoTqxE3Z8wGGR3QtfTOI0Xe6MSIlAg5aLpbYuPvsPEZycqyiCkXOcooYMuqCycpNa0U4TnrIZDyeMidySFyjozBSKMvBHmUFpzBQqPpavmPNg1BimWmnJQxpV4bYK0yYiRr4R830dD6MeVKDOV3WzUpNDL6nii9LovghwGrudQ6YRobqTLVljxKr4peU0pUe06E5Iuvkn4Dax0JO/4QxkxntQ8wo1JjAyrlDaCmn1SkdaMXSUxnfkUb4IPiVpITYEkul4h2lb9lURkgUKbVecO8GSfuaeihqVkvTlkPHUkcPmeyE3JjqH1LPySa2mLJbG/t0tQVx4uFuuJvXo68AQ+SqCSEYqg7NhtiSvGUtLGzWdhqZuxdeIyHD+6S+5X+uc0EPARbzHghEhgxplSeZ11XLFZr/DBQ2RpnHUNWmCSm4imSCgXRZoxEmqqmEX0GFvUXqExFZSu95/lsPZqs6VEiaDpGEmXLGKcFa0kzGl86ixqJ+mTI1jCpGs7vnGOymPJupywiHwNDl2AGKalhcN8PrFYdxEQMJbmlyEYsYHIi+UjsB3q7Vt8pH+l9IChdSTf74k2RYmFqGIvJ6sWViJvUtbGvlcKmQtQjQGzeSF4lqtfIKHzISeVH+k5GBSZNJllLjL74IxU2GcIm+vwRVoUIuMrRiNusYX3NVZ4Uc1Tvr6Cylrqp2D+/Twhq4polkAfY3ZnTe8/QDzSTGX3vcc5g7MiU0EUSJODj6BWl92bc+K11GFMVILeYpPQDGJV6pajszZhNmQZ7EsVYHk2r00mNR5k2jgAAIABJREFUgCiMIknZcMkoUzCWfc6I0ok3rB5JzKYOuz5ldfddbi8e8ODjH6Npt6hCw4N795nNKvzJmvXCM7+wz87eLhcuXmTeTmgmLavcILFmXc9p6/P0Rw9pL16A1nB8eMhyq+bo3XuklDi6/YChW2LvBcgDMXuyRGLQAbwdJzyp7CfjHsvY0I2m0sXgPBcJL4XJN74zBaHJpSAYgZyYIq1rGKrM+d1zRD+osXzVYC9cgYP7mOEEyQMFVlKg+tGDt6xtM7JeKcAPIxLE2V5TzoMxlIBH1vvZIKyYaqM+MTGPyWvCxo+wSKqiUN71szPUlrNhvEIx6lOBKV5KFJA2lrUdYlk7Z9/pXyVle/TPZbye8fc8claOkudxPUIZflCeURaaScPezjb9yhNDoiNjXE3dCKvVUhkq1pFcJvQ9Q1LPrEnbMp1MmU5aovf4oGaokhWwohgCV5LxlbK/YtDIbCc6NUwxa6hGUvn5BiwrT8G6GqaO3veYYiwYQxmSiDbI6r+h3mq2arCywhj1gcySsS6TsmMtVr3cqBlEqFxVcNHM4BN970nGqB2ACDj1DpxqfB9V5UipfMeo3oN96IlpgNizWmvKTTd4fIz0/ZpV8PjgIEDEkwkQAsSMy8KeNzye5+xWNetgmIqh8YbaW0iROqksyebIyi9p7ZSZrXl6ukMOSw6XJxqmkkCSIcdAGJbqH1hSRbOPBBexQ974YPWrBYvFgq7rVZYusFgtWBwdEoeB9bonZottGlzl6ESlFNk5pHhF5qyDFmOsRtFHBXY19lvDQsbwBw0vSXgSXQoaCZ4SPgYaoyCNs6aEHWjarkimsgZrKrp+ja0MD+/c5sL1JxmOTqks+BJ6IsZsUlpHeWom4Zqa7aZm2xhG77Q7796gW3YkCznkDZtO9wqD73q601O2JhMNnAiZaEqCm3nk3ZLRAUxUbmLU/1JlrbCRb5NLwrCCOykWqwU0fc2SlbWZVeLnrIAPYCxdqSet0ZrMkxQIzpmMDq987EvIjiVbHRbFOPrzSQkkMCWtK54N9bIONHMBOeemxQN18VjY2r/EsDgh9WtO7t1j1Q0sFisoNeNkOmdvbweZtJzf2WM23yYMAzJxNGbO/QcP2N0/z/K0IwXP3rlddvb3CF1kOt3i4NY7pNSTa0tta0yJ8HauwqTEhUvXsEBcDBw+uIs4BSn9EPGDJurWdUUeOta+Yz6b4k+XpPUxdfA8uHWDb3ju45yfPMa9J+9w+513eOz6dXa2tplMJpwcHHDMXV4/uMH9t0+QpuYjT11m+eaXefHXf435fIewXpL3dmnOX8KuO+p+zfzCeazAV6oKasMTWzV3Xn+Hrl/jbr2ta9jVuK1tLu80NCYiecEc4c7RisPe4aKCsm7a060zZjFQTSKzeU0aelZdZjq7ykvPP89f+6V/QbryDH/83/oekhi+8WN/jD8jFW/e+iKvfO7TtO2cdupIVSDEI1b9KbvTKV/4g8/y8JVXqSpPBmpjWKVA7QxNVUMe6EMqvngGMZbGaa36gavnybFn3lr6OnHncEndZuY1xL5nPXRUtWdiIslpVLw1htfefI0hZS7t7eGmNR/42Hdw9zd+md1Vx/27h/yxP/MXOLl7RNPU/P7v/x4//MM/yGc/9YvcXy5xpibEjCnjNskDtraIVcPjGC07jWXiAsdxTVNZQs6Y+hx5taZf9wSWtG1DCp4/+QN/kq3tPf7wcy9ysjpBhsje7pzudIXERAwAFU1TkYhqk7Je8ocvfIpMTap1uLE9aVkul4QAbV1jU8fV9z7L7/3Op9meWZCIdRUmNMSUsE5l303rODq+xYOHR1R2Sg4rzu/VeD8Qc2baOrYmE1anB1SMXlYJk4W6tTiXcWKw7ZwXv/AFbn/bd/PElSdZHhwQujvk1BPigA2wc37Otf3H+fILf0iyjug1gCZaA8YQfGYIhqYq/StOz7NoicBy6DFW98zaQNdHDddJDiEQk8Uni7WJiauIIiCDsmyjwRi1/hj8minbJWhBsBiuXj7Pc8+8B1vY/lMRvutDz2lyLIm2meCqlmgSwxBYHa/4vh/6bv7Sn/0r2meM3odj/1i48f1q4Dd/9Rf5lV/+OZ7ag/e+9zE8Ffdu36DaepFXX7zN+v4tHX5seszRRkcBiZGoHoFJY7l/5wEc3WQ+MTTJ8eBkqb5NWYfvVjIuZ/WTJTNkQ8Rw43jJ8dITyeyd26YukuXGCI2FIRlWIeFEmWutCF1RbDW1Ya+ZY4ocL0Qdpn8tP19zQCmLVnejtp2RwYJOxE3UJiejbA9tGrTpT6UwlPIwU6E3DSnTxcCQM9hyXBtTGjal9SYp/hYjMpLzWdGYzsCLnEf/DX3QadT0bq5/bBGK3ne8eqPbz2YqHCMpBnLUOHDrXFmEI/KbilyuXKfRg92Q9ZAvQNDIFkh5NNYu0dJAGDyVNRvQS69MC42Sgq2NVvFEcs6WKXeRPRTJiLhRzocCLFoBaBFQJuQbRAl0wlYmTGYE/lL+qucpj/z9Ryf54zWmZIjF0HQ0Jhtvszb4ZsOSCKk0wU7TaY6XS4YAIVvWcWCrnZDINCkhbU1IA7PJhPlsRoqR+WTG6enp+KjLL9F7a4G+65SsNU71S5KKyMhmEkxVMdnZwlnLtG6RxmGbmrRYl4JZwdAUVcuacizFlCWZSFM1JSJSY4NjULo7pZmsSmqgWvPomnCoJ5XVVcEoDDVS2D0ox8QkTanzCcRaTPGUcc5psVfiHx0JI6NpdEmnQmmP5y/ssbu1xXxnm6qqODo65eHxQ9q2Rs3AdV0uTk8RUXNxXxoawRZwJ5OMNgRIousGmulEryem4n2i019lsun3TzEUJlouU1r1KBLAiT1b1cUgGxH160LYJC2W60tZN05TKIEpJXKQAl5GYjoDTcVIuZdSwCeNUk9lml0Vf7DykNC3SRkOlVFfFmMErC3sQoMmSwayaaA2bO+eo7LQDZ6MGq7GETg2pkwN1CuptqZIETI5mw1YmLKC4Zmskc1nuw6ZTEiGK1f3OT46Zog9Pitov0E1SQqkwEYOQVbvOUClrkbXkghkY9W4LwZELHPrmfuee/0K2a75lV/4Bd73sY+QgqVbHWPbcxzcu8fi9BRTNRBmdCdH9KcLcoqs+wU5CHUaOLz9FjYl1q1huXpAXgz4uGBYe/xigY0DFdAdH5AlFpaYLclVCpJVTY1NyopMScHHERSKuYAhEkhlgCAFYMolhCFD8XQpYAEKNkkWKtews7PNzs55Lu1usVguOV4MiPc02zu49Yr08LgYKJZ7mvOZXMwUHmQBpTdy6BF5kvGpne3rCmwp5C6FTZo2ww59WFLARx04FDANnU7p9lzYQEYHGYYNabnsYTwCZhV4qZyHYnWiNw4lcnpUNne2J/+rQaW8AcDG+ctXT7Zkc6+lhDro389lD9CrbNtGU2EaoVsPSBCMa2hmFUPoiOuS4NX3pBQgq3n3dDpnNp+TgfVqpXK9gt2ZMrSxxdvOkslWvTtiSVuMEUIYiKFXYM1UWFOVIYn6XEWE3i/JPmNblfnkHHUEklUm6KxjkMRieYxMd3Gmpa1rxK8JwWNsTR8i4hSkzmGUYxp6P5AGnTiuUBPlTIKgkuumrfBAH9TzYDqfEY6OFAjOQhLHkDIuZcIQSCUNV0S/sw+eytSkst9mP+BiYMfV7K7h8sqy1WeMDLRAlQSbI40EcgpMJOOc0NjEtp1QVw2VgEuZx3LDbTOnCyfEmHBkct9D1QAq7+1i4GHfsccWJvRsT/dYna7ou44hBFI/YIzT8IGk1xhD2KTmkgMhlkTeBKZ3REFlaTkUlivqAZdlnBqChAIsymYwl2PGNY7KDHgf1C+pDBB9VAAlG1FftpQ14bRqMFaonIMM6+WSuzduUU1m+kxLkIS2G6bUEFpPjJdCOa8QoV/2nJ4uAMFHld7lshYkU7w/lMEtVv0qRYyee1abZmX0CdkWH44ETiCkpPL+nDRsZgxJkYQGYCSVCKWMMQnKOvHpTL4uJFLQ0BdJY906biKPsPxSpA9LUrYMoaNKkIbAkKP6x4WgLERKAmVS+wT1N1EWuCUW1rHKF2e1U2ZhjkhWuSsS8es1635FN5Swk5zLMAma6QSXM7PZBBlWdN0KKfXEvG3x1iC1p0ue9dEhF/b2uXr9OsY57t/eZnXY0DQK7BpncUZoTcPu7i575y/iFz3XrjwO3nPv4D45Q91OaJu8SaIlQ9etyZWjO3rIN3zT19HfO2T3yessTgcWBwu22ymLpsFOGlZdRzudYpOnspkHiwNWRw947LFrVLUw2duh3j/H1tNPcuulLyINpBrWJwvk8C729I56zTQtEiMnDw7xq45FXHHx5rtMgX4IZX9u6HIk9kLnEnmyzXRuaaRCYmayswdS8eCdt+lDjyPRbBkuzbZpJy3nd7d487WXmZoZr77zDk899iTXvvH9fHN/xMuf/wwmTqmYUjcNr/7+r/Pw3ltIiBAH/t7f/bt89KMfYXG0wkgNZqByyqYIvsNa7ftiFmJI+Byoy/s6q4TFMGCZcutw4Nx2Te165qlhMYucxI6TbonkCmcqhlgRBk9Inrq23L13xD/7J/+IOi15eHibygrtbMKXvvAlbvzRlwmTCedm53j8Pdf4yssXuDx9nD/8/RdYJcuYhlzVBldBioZTn1iuEjt14DhYWgciiRQ95y4+jmHGm7deZ2BB3UzJ3vNz//gfcHK8UuA6J3AtH/327+XVX/pn3BOn9XtbMZ04hgGSBPUTrGqOT5W5Wxnh+GQB1lDXFf1qzRPvfYaPf/J7+Myv/RJ2+yKrDvVQitBUwnbb0veRrhpYdYnWttrbmoBlTTTgpgaThRs336ZuTJG6614hqGy5bRsaq9Lsl770Cm+8+wZPXn+CL//8P+eoO4EQSJKomJBzxKYEYhlCBheYbiWcVV/ZlBO1VRC8D4KEGls5Oj+ArYBENDWd79VrNTma1jCt1Sc4W0fdOlyrido+DcVz2GGL4inlzBACxrjCIA5YJzx+7TLXrzypJIlS7459LmJ5eHjAtHbENNC2LUvv+dAHntG+OEfde8ce+JH65vD0kM9/7rPcePs+cj8yl55zV/Z5MPR4//u8++pNZH1aBoCFvCKZLOplZORsAGhTYlk5Du7fZ+4Sk9mEyWSLP3j3jvYyZTAcNxYChZKSKfuq4dBHmkpwzulQKmv/OGBwkmktVMaQkrDwomdMMbnY2Zpi6poc14zzvq/l52sveZMyNUQfymgQOBo3ZmNUjqNwhXpEkNTYV/KGfFuEZMp4yeBDoC9yrDy6updDUJORlAqm/w0guUxKKU0ljFNTUzycNukzeaTrsynCRwhnTA/TsKri/aJdDMpOOpN5OVttaP0ymkcWYEMTqRT4EhnjBBUwi1H9daTo3FMMZTEp28MZ2SSFlR8Om2F33iw4NQgvoE9WyRWb+1PMYsutE2M2zZopBcG4tkaGkv59RUxHcG9kniUUNLRVpSbiqXgwlevaMMmK1uYMEtMNIRe5Ys6pEL30Hvb9msVqjUWZNyJAiPjOU6VAXTlsEnZ2dsDod59PZiyXi3HeUH5fwgBOjLJKUsI5S7Ra5KcC5EQpjC9jwFrWvRqrN64pEd8jr0zXXkoZK66AgdoUOmyhoRpyVsP0JDpNNEllGcoqC+j8uFJ/DKNR5KZy2FwynmJhqBTwA6xSxhGMSTRVRUigVp2ZVIrjGquSwFEIZLSwzikgZNbrNZITk6bl4vlznNs9h7kldOuOPkQimQeHD1keHuuaqMaEIy0xrdP7EHzAx6ixnsHjvdd1b+yZvKo8VymeCimO4HI6A/RSWUukjQTu0fWn7/WjG7uy83LSyZbKSYUQvQJ+WSe2ochmUlQQzll1HoqjYYZRgNQU9pJe59k0OEtW2UGIG5aesUaBwgI3WVOrh09W49RKHILK5GLWJDZftHm1s5hcTATFbFiR6nejnxR9kQwZBcIUI9JpuKi84crFXW5+peHgeAkFnKD4oWUxmyQvKyW+PoFYPUbMaG1BSc20TTEdzlRVwocFKTvU0iXzpZe/yL3ju3SrxHve9352dmbcvvOA2C+ZbU85vn+bJu+Tk+BjwIceCQbjG6TvGBZrZlf3Wd66y/VnnuLBrVs8/vRVbh4/oHWJzghh4ZXtNhqzl8YsodJUZRkpiyOPCW7l+ehqGBMkFVSinCeUPVdvj5ALeDPyVGfTlp2dXa5ducbOrKHvPZeuXCdVE7qQOYqQlZpS1lx5F7WvVyyprMvNfsPZOjV5HGaMTIRxX31UjqifM6BTn2Maf2Y5XpKUn1FYSkGKXHwEm8ZdVUp6aGEIbBrrkVUklkTE1SWG1jk9j8rZtzFvH7+HfPXZOrJsdYsbhwhn5954hsIZmKV/y5Q9QG+es7oT2tpRS2a16AvgEpAciGGg73s9/9Bo+kkzYT6b4pww9J3WE0awpfgzRe6okm4t4GKisHwiIejgZxgGXWvGqiTM2LKHGEwGkoci3clW9z4RixGHcRrEEWLWQcE6MN22rMUxabaIcaA76ZUR5CPBetoYGbo1aRjwdgUIMUR6A3HqyBicAYjM2ynx8lVW3UAujfr58xcQhJODE0LOiKnK8820TaOgAygLBWibilJowLIn90v2qHmqrZncPmVet5giLY/9GkfCmYQTSAzUUiMSIAhZ4TWGZEjJsiXwbL1NJYmbrDlFAy8oSa1lNTOkzPFihUMwy57FckUXvbI8gCiJEBMS0f1VT1B9v0sCYEhgTK3rnVA8sYoU2RokqfdRyqaoGc9ASz3Pk96vLNTWsk4eFXTkEt6RNudLyupTVrmGyurQpJ3UZR8G362wVb15fzFCJOgZlh8Vf+r7oPu7+hgdHh7ikyYVpugRMrbUQso41Vpi7SN9SBrLLsVA3qDPOoE1/zdzb/JrS5ad9/3WbiJOc7vXv8ysbKrJIqtjhyIpiqYhUzJpwIZgQAY0M4eGYcDw0J75H/DMM8seGAIB27Ih0bbKYiOKllgSi1Q1rGL1mZWZlfny5Wtvc5qI2M3yYO0495HyyKiBL/AS2dy8N05E7L3X+tbXeFLbD2YwK2D1WC0txYmKc6HtNS0ABWPvzkz92HXMFplenIFstLoUbECLMZURq79zhbRv/616at7bYLQMCB3TMJDGyfZHcU1F0MpTMalrRRs7tqkDfCBE2wlT2jOWkWm4Yn91TkqWlou3+5DGChkkV/aXFyw6zzBsybuBQfMBcEt5YL9LlGmic57j5YKglby/wseO02Vkf3xEF4TNbmf+XupIDnAGisau5+zOXeo08ez8GVqLgW0qxNhRamZKliBdhz3jxTNeuv8mp3c/Rlz1eBUkdqTNJS+99jqdb2EhWHjCNA54Cr5zrG+cWEKxRD725mdgseD45l3OL54jqzVohhiIix68UJx5rO4cPM8btlfnDOnKGHzOrm0kc7nbsL8cCCHQ9UsWR0ec3rqFD57TG3co+x3b1ZLoV7i2Z9y8eZvl0ZJhs+XopY+Rq+N/+Yf/B5989bOIDvzpP/s93n36nLA6ZnnjDrdff4MP3/km3eIWN5ZLPnb/Fe6e3efpR8/59Ge+wD7BuLvio2dXnHbKRdmRXGlDEjvkU6poFPqonI8D0+S4GDb0/YqPH3WkvZAd7HVDxbXaKQKB3T6Rp8yQR05OjvjU6z/F4/ffpusnuq5Ss7AKS/78a3/KyfIeP/+Lf40/+6N/zre/+V269YIpC773yDhQinmPiRamMeN6yJNQ1PN8B/jKetXRB0efYKEjj6b9ITm05h1VlacfPTbA2nmkCMdvfJL/+j/7L/kv/tf/jcdnZ5TNFT6CDyNdDShHvPTq6zx+/B7UC5woJ4uMFtgVj/Mdz7eXvPq5n+P/+t0v0S1X1DrShUpODWQQh6jnaLWkP7nJVCANTYnhHWlqwRxDQaMjBrHAq+apVT0wKX4q5C7ThYCWzJuvv8xLt++w3w380Vd+j20cuNjtIS4Zy57HH+45v7JgIDXTSPqghKjkybNeQXGZzcYz7h2lJvrJEftILsbi3SZHLdGsILyBURYStOBnfuGLOO9493vfJOSRlLL5Koog6hrI3XrZFjyCCG4RuHvvFp2LVtPPHa3zxg4Fnj0/J64EnZwpcVyTPDvXZHoGAMwVz6HzEPilv/4r9Aw8/P7X+eDBM8aQuHV2l82kfPV7H/LwIlkoEF2TzNlGbh60TYWDDZQrMA47jqNjmx03T4/ggw6pe/t/sGllVT0QTJxRdZvSJ7Pol6SmSlEqmwpjVryDI+8JCvti+jmnCdRS545PViyWx2x3VxwajZ/g108cUGq16EFuNXssHNRW0XwdnNq01zvfaMpG8/JYP13FWATNPKhR91uBzLWUbJ4EW2FxCF8FjPZf1Yy9Z9mCxa63YkBaMS/WWNIK8fl1yloODIM85ca1cVZMFEUaSOZV8WqmjKhFrnvXChs4NBiHor1NP+dxrwE7Vi7MwJfWitZkhuPND+nwyeemvG3QIuBUbYpWC1JaDG0tINrS1QrzIEsBLU26o9aYzB4Zdrm1RbW6puOcDcPn31mZjVgPk+oDiGCeTBY6ZQsDGhOqFRaVpm/BpubavHe8c5Q8GWPHgZZEh5LGAZ0mahRKStw4PcX1nTVe4ln0kcWiZ7vfW6HYnmm7Amprxmg0cTMKr7O9hhVy3mjmadgh2Q702gDRWq3BqcXMYe3aPfNUr6pjKmYsrMWknXh7t+e0llwKRa35Byiaid6Rc5N+teK8NLbZiwQwKxAFL5HOOTwVcUqSSuTao2VG5M15vZW7qkzjwEf7S5Z9z2rR40MkZ2Hc7Q0E7OyNv7i4oJZMCJ4udnQhUpoX1Gq5pBR7L3xj05RaGfYD4ga6bmEH6gweOn8Aa2YG3DVQVNvGOTfiM2tHDFDBmloRCBLMA6Rk8BZ9HpzgMSBSXtABi+W6NwDBJGvet6mMVnI2MYhv6YXGgqI1A/Zj3AtHibRnJeKadKLJHJwxq0J7m827ythItRSCClkqU652ra4dCAfAEw59thrTzPu2jr20hAlv0wcsJa7vIy+/fJ+LzZYp7XEevIsNSC6mhxbBabPuDopTh9TaZMUNoJ9BqhANG4/K8/2e1WLJarVgOt8QIoy7DdvLPRo9u93IwkdeunODk7s3uHj4hHt3Tm1aui+49TFpSpzdOiE/GIixZ3m8ZLNec+fOKR++/TYMiZIyQ04ohaSFNKXmB+QQsSbVBzPyLQ1cAQP07Oibi9IZ07cdyTUQY17vM6hpzC8OwIcKLNeRPnqWvWO5XnN2c8Err0bGnHn7h+8xXl5QWgNorCgrnOZGUV9g0c2OhoqBWDPoYmCztmu4llLPhtoczpxrwH8+xuYl/+IxX9sZ4mmSYzHGqGCsPb22zXrhnW0iQfHUosTYNRaNa3OJevi9vjEm5+ua2byHk/RwzRyuV//Kf5uv/HDOtZuh0g4clD5GM9rHGtrzYWCajI203w/knGy/8c7S6J2jXy7wwVOmCamFGKOxOVoDbUB9Y2p5zzAmaEmRzkdysuQ45zscRhsnmN8SiMlxckbTSFBhajHzulwhzlhJzlkqZcljQ64yXTAD8dj3+KGZh6eEq1BSQlOmpkQuia5k2/e1IJrpvKOLHc5ZfSFFODu7QbcZ6KonRDN5LTfP2G8Hpv2IBJuiXjcTBlSUlPCs8N5RqSAZubhgVQpvdKfcREl5ovoGUCsGBDmF2gAHB+pgkoq3KDRSLlSpBO/pRLiJ4LtTlmHBWySu6kTKk3l2eW9pb7WynyacBIYnl0xpsOI5VRsQqWsyswoEG1RpBecpbW2I8+ADWUy+OpXhsK5TS/atrc7w4hv3ucl+peLUk2dJddeBjIj39o60IaPWcvC8DEAnAhScBGJcUhFC5wnBsxu2EP31e63z5vLCXtPqp9ICXYbNlu12i59ZiA4qEe8CfXBstkPzGSvs0sTVfiBGj2upcaoVXLVBSSnYUM+8FefTvjQZ8OxMkBvzPFe7Hz6nF3wBq035teLFTN+lfZ+6WRZsDahgAzGcMRtyyvgpUzThFbo+UqlEFygKm92eOvsnznth8zAxP6Y2+LLDzpJxvUNdIKU9w7hhyMYszZqhCtFHYhRjni6WxGVk3F7g+1s8vzwnjdk8N1MyY+tSSdPEOIyEaNc3TQPbndIv15QpsQiBGGEvYtIUVY5OT7l69pjt5SVHJydQOuKiI4TOdjQ7WKyOaaCOUHn3+98xwOgtwb/6But6zH7cEWLk8vyCj3/6p0njhG/g4bOnj3n/3bc4f/wEiZ6jkxvUDF3fc3TzBj52nPuA7rcEtToR71gdHbFeHxmQlSpx0aPe4bsFw7gjp5GjtbAfJ/P03JqBtosr1idnuNARQuT47IS63/H0g/fI49aUAClTu1Ouzs9J+y3PLy5ZHq+BwPMJ/vAbf8bVRx/gZeL2Gx9n2lyiXc+TZ+dkf4Jbv8abn/kcv/zv/CpP334XSuBX/6O/Td0M7LYbvvqHf0R2wkePH/Cjd99lyhuiVKgGDpsPa+XRZkRH4eXTJa/cWHIilU1ULtnhVegdRL9E6NkmYZq21FQ4Wd3k13/j7/CLv/xv8ae/94/47rd+h2EYKTVQamRfM3/jV3+Fv/G3/jZf+9I/4Z0PnvDDv/gBi9NThlyb8bWRClK2fqWUEfUeqqdoBzkT1OGYeP3VN7lz71W+/vU/p1t2QCL05nGbJkjVvNCGJxf8x7/1Wzz68v/NW5sd7saZ9bbOwKqcCwQYgN1mB0yYqBSCj+xHZSLjF0ve+eEPePdHb5GCR8tEjJVxzCiCC8K+KEwDl+cfUdPIauEZhox6pUYPKiyPb/G5z36e93/8Q9K44dnVY5arIy52tneXWiipMIpw9+4ZX/yFLyBxyf/w93+b//3H32U/ZHaq9E1hsPAeyRPeWWDP+kgpWcilMSxF2Y8wTSZ/y0VZLE1elVImhhUF80gyKVhjI+iyAAAgAElEQVRhSlDFE3rHJz/1cYbLgYf+L8gpo+qozrezClBPmiYQ85vLrW+/eXbEz3/h821IPe/TRv6wukrheI2rAUJlmipSe54+e277k9YG+L8wqGsDh3u3bvFrv/4bLCXzL8d32W+fcbUfeXb1jG/9+BF/8s132V5NOIXgLQhFZhk2YnJfEWrrXVZOuNkpd1cdk3oWNxe8eXPFkBwfXBpj+LqWtXuKNNxBTErXdf11Py/NDxKlVGGPDVkL5jnYq3WQse9Zdz23b95k8+SB7U8tTfon9fUTB5Tq7J80N5BzVKQYCyTGSBeMhllKxtOMA10lOjuUihaCcxYzeWiU5AAIzB5Arn1vNfSBijVLbZYNDQLRF5oTrTYVm4Eik7W14rm2xqQV6EJLn3ECWBRt1UJw3qY8zjI0ai1EH4jeW/pUayqc881fSQ/NSOsn7dppE16t1JrNlBwBzZSU8c1sqVZLowqNCm0fTQ6N95wyhLRDuNr02abf7T6H3iKO22SrPRxmyZx3gmu08uIKNQPFjOq6vjMT4MZSEm/NcwjBnP4bs0rqPLNr3kgHp3JpbDWjXzMTvaQJjLx5hzjviCESXCClEa+GTscYGcp0kGicnBwZtbQ1P0TPcrlkPwwHfHk24SqidNp8TNq/r60Ky7W2TUDovaOmyaZcOEoaIK+MMdQal1osArtWu157r4x2Lura7HBsEIMBCakq6qp5oMRASGYG2vUtaatNNXNpkb4vsuWajsbLTEf3+MYCqwq+OlKtltJXKs6ZtEpaMR5E0OBJ9mEZdc+035GKgTH7aSJ2C0SNfeRb5GXsuwMQo6otgdDWnPfh4FHTxd5AwEbzr2pgjd1/S0IUZyBN1XlVXsPBOq8ykTZ9n50irn29zBdCm9zMGslZgmjSNQfF3t9c7b0OLnCdStUKW+fJ0sCmaoWN1c1yAJKtibY1If4a5JqhVNsGmkdGuzbb9c2IWaStT6BrHceUC70PINqMks2AdV4aJvmQZpZtz67kBmBwLRMFz8c+dpdhGnj7vfdMsuZjA8vatjEDYzP029ZXuxO2P88sGAcuWvM15spl3hGPwO3Mx2saMuvTU3Y1sBsVJuHW6RFpSkxZ+eDhUxzKMCY8iVQyo8L5+ZbFkefJ99+mW0Xef+cBm/3A2z/6gF2yxMcg1rDUYntv0SZJqxUtrYjQ0sKCigFJagCPO0ySOAwB9PC54DB0aGBSYxC390lYdOZRZskiDu8dfTS5ck0jlGT3cPaBR3G1Ffnt9+tfAVZq++fZi6vOA4QDmMIB4J2xqMNXe8AHEEevQaV26Ye/UgtIoM7m5PPP0hfYk47DXjGfgTPrYf6987pmPn/mi5T5XW8vp75wHS8A9NDAW2tpD1Lyw9XKtVzHNxbiIkTO7t5BtDF5XQEtTHmiVDu71XfQAGShslhGghf2wxap5qeDF1wx42EfIojD+djO0Ha+tCGU8w4tDpyx2SwH1O5FKY0Z2WQzuVRjLoijTCNp3OH63iRa8oKktoHbOkws+siyJBDPVlq8MzbIMalaYwuq1Q2I4sURxRGcgSfiICh0vkNWdq66tg8sFh3HN04oekkX/MEnTSmHukCbT5/gmhTSUcbMrcURt+iRza4xFiechsNQx4xLZxmlw4VAD8QMrhaqb/WNV1IuaJnopeMsLFlUx06VkRGnlmqUUyKKAfYikS44VKKxw+qcsGpTcnUZ9Y40qUViVDGTWFWCg6gWRV9U7J1ovkC1gaiopZL62W/PgUob7uEIKu3+OjNNr8aWCcGTOwfZVpzzDsmVzjfxaMlk9RTv6bqOrvNMmtvaF2M4aQON26ptIzYDUioEUfabDUVNJqLYmZhVWK/XLI+X1PSQKVv6cUojF5fnrDuP63py8zIIc+CEYAmxYAw4DEjHy+G5SwOnjIBl9yrmTC62FmuplrTlABc4VDRSD6BiwWqa2WDfhUAIHV6EKRWihyi2X4Zkaci5JLZppFbzFcN7q/vLIWLDkjvbMLhqxcdwqAO8jziCBeg0ubNU84f0AjEI67MbdF1g2iTG3YZnF+fknAmdPWdHoOTCoBmyJYulcWDc7XHOU/IVw37LlAZ7/7xvLLXKar2ieNg8eYIrtnc8+PHbXFw+JZeJ6HuOT06YpoGSEsE5cJGr8ycEhOXqmHG3Yblc8ey9d3ny6Bm3X3udfrFCUrL3pBZccAzDllQzx92aPgQq2RIvNXP16AnPPvqA9fGa5ekZboSpDUeDD0hVpqqkNFhwh6vUMuBcIU0bq8my0HcrXIi88uZP8eqdmwxT5uLyimG3xwtkHXGamYbMbrsnHC/ZXDxjGjNo5vI8gnimwVIGL588IMaO6fyS4A0MSWIMfd8dsz69wenxDfo3l9y8e5+jowVehM1+x6SV7tYZH/74AZf/8Euk7iklm6fZmEac80QsBv3uuufjZ5HgFCjoYiJmz0vhDldpS4wL9hUu91ucKCKBl1/+OF/8tX+Xj3/qTV5+5ePw98754MG3OL/8kOqV6hJPt4/42re/QZLCcrHk4uI55+OW2BLmKsYg9LIkFWORplpYLwO5ZNZBqGliOxV20vNonFjduMnq9IynT3/EcgFoQVfWA1ztMouTytXDH/Hf/P7vcnV6TL3cgAhDGoElxWVSuuAvvvM1Yil03mS8V4PjJAjiKzVV4tLxrT//13aeBGFK4IqJuoOrqM9sNJvH3li4fbwmqKfkTJ59cRy89MrH+Oxnf5Ev/Pwv8jv/03/P0fqYey/9NN/4i+/QB8hlYhjg9OwWP/9zP8vtGzd4550f8w/+5/+Rbdox7rbETnAuM44Z76qlnBaToUroUKnUydLczjeQkvW/wRd8jCAmxXRdz5iFUqSx+TMek72WFtn2B//nl+goxLrDpQaYqyWcK45BhYz1c77VPtE77ty8yWc/8zOtLrEd+UCWMHNGxqs93itTKuynjPcdf/zlL/Pv/frftR7YzzXXC0O+9tdcC0MZWC48y/4m7zzZ8uWvP+TBkx1Tk1Ubw/7aXkHwSIvMtLPNsWyD4qE4tlPhaB2R6YIv3FvycL/kg91z6pQOteU1daZVc6ptQO3bIPGaQDHXokMtBCwFMDraMMXOUl+UV+7f4p3vuzZIzPwkv37igNI0jcSuQxpSKM3AVNrEmFLw3je5QwNb0JZGUilik0YUNNtm7L0jAskytpkdJoqaEaMPjfXRCmFt5slKo7MfZkkv+ETILJWwyZ9vFPgZ9HGNYTHr3l1wBJ0bAqP4+ugoOZlzf/BWwKoxAlzzVSqlGMOi/ezrxlWaztOaV3EgXg8NhjF8xKiAzS2e9vlp0hhrQK3eKq2JUT0MzhuW05plByFEpmkCZ70JaAND2qvbWBZBW+KSihkcdx1Ui12mSf9mGZD37eVWgHow2TbT8/k5tEZn7u7czFq5nuo3xrsRFUvGtWK5w7WJV+VouaTvOvoYkCaLmE2j16s1m82G/WQN4SyDQYSaCjVlwjKAU2qWA1jnFGLoCF1PGhNBPC6YeZpNFh1ZghW97po9U6s9cwj4UuhiZHTZTOJmTNI5SxOzl9IKrVpwVVmEHl+V6mewzRh2crivrgGCs2TMpol5NoSfQSVnVFV7J1tyi1gCXsEowBa7HJlGYwE4Z3plwZqSkgqOQowGii6WS3tB1D5z8IE8mZ9I7Hpo73bsOmLszUAWM9D2PuBdbCac18+47XoHSY3O3jDeH3yO1NlnNoYI1ii3Zyi+AxcssU3bfWjaIKO4VmNwNBN/Y5Y4a1BdxUdnSWtz0qNryP5hZ5ADQ0QQxGsDUbWBBfbMp5StqBNhaAwQA3DNlN8B1VlaEeIaK802+1LlIO8Q13zmZmCgAVK255QGyNHkSMYwOT72fPITH2O3G3j45DkijtQmN6pq+8y8htrnaoGKTVpkLJeGPhglugjb7YTTRAzgIkgP+13laH3MzVc/yY/ff4ZPIxVhs9+gODbnV6yPzUx2fbRmv9u1RsGzHwppv2G/D9TOk53nckj06yPG7RVxtSCOUOWCTDPcFpBamdJIcPYJTM6jzOy2otWau6ocki3bQMAARts27TnOYLYx6aL4Jl+zSRpiiYnRmb9ZqRnVTFx43M4ApRcM2Q4yunazD++KtveUVuQYN86APNdAbEUPbKS5I/03/IrmGYj9JPvWNpwQtWax4hHVNgwRVI1S7dt1uhZHL67te9mkhCKNFSSeWqa/xEaSxiBtLnh/6bpmBub1xb0Idb1w2TMD7HDWckBLXQOVXv/Ex3n51TcYNkM7Mx1dH/ApUqqxIVQEKRndjcYqdtYc1uKIziPB49UdTDdnyM9JJOXCmBIlTUjwVsxVKzbVgcws25Ip02RJCWom51WBEC3IICVKTYzjQHCOGgLeK1XN80fFrnPcD4jv6V1GfSR0C5SBWh1RS0t1USQGmxjnmbnlrZZo50+m4JrvJFEoVCsCxRi7JycnTAl69cZ+agwfrUb392JJmCa9LpZYhHm9TLKhT5VSR0sFFYHGziml1SneDMBLLXTFCl9JZo4anKDFsc+jbRlSOa+JbR4pacC1M2IsozV63hGdAb1Oq7FUxxZMoUpAUDJooeZCqlBFEDGJsm+BDdVnWyY4QujJpUALF9B2rjpv7CTEJJAi3oJQajOfRnG10oVAGROhgXhBhUlzi1I35qmK1Sqqyr6M+LC0giQ6oxKo2D2eTd3F2IbzoMe20+vMzWEYmxeoHRpBDTA7OTphcXSM3oXnjz+iZNsvxmGgZKV0wQBS2jMGqnpUrB7xDqi1pZ427o/YEMGGgYK0QUnNZp6NmNRPKWgVJFpxYglELwDeej3wdG21RG81ahcdZ8c90UV8gO2TJ1xtLzl/fk5tDY0xkGrbZ2xRaclYXn21d8V74iIQtKJV6PsjEBimgd3mWVMYQG5BFM4pZdyRqrLb7cnOajl8YJoqXd+xH7aESS2FURxd17NeH4FmnCv0wbfas7AbRnJStNjgJ1XHa5/4abwXLp88ZRouefTk8SHsoJRCTtnA0BBwpaISgEJ2wmZzya1bP892v2Ma9mx3Oz77iY9Tp8n2DrW11ruOZVzR3zuh7wKP3/+AOy+/wjjszAS4jrzy5ieo1ROOjklPnhvbWbB04prZXT5jzBN5GnFSiaEwTJXduEeCp49LXIwcL25w69YN+tURlQ23bpzx9PET9mm0z4MylYrznml3xTgNNqDLO2qCGHvG/QXbzYbh6hKVSodnveg5qT0A+9EA4ve++33Kswv6fsXtl+6xCh2xjxSXefbsktvR0/URWS1YxZepaszIiuC7BexHfKnc6ZRNycQucHy6Zrh6wu7pBT/7C7/Ct7/8R1SvEIVVgXE3UMVz8fQpD370FuP5c1759Kf53Bf/fT787e9y5CLV75BTx/e+/TUevv8h8f4Nvv7nX2bvFJkmfCeNpW6WGCdnt3n60fuoLOnJxFCR5Ol9oWolqfDOg4fw4TlH6zU67Fj3Pd4lVJQ0KkMB1czRouPPfu8f8P23P6AuTpi2o/Wn1ZKUb96sLM7u8Oy88PTxA3rXIQj7CYJTSt96qCmxiI5xsnO/NKA3eFBn5uGWuyH0rifGyOZqYlvioVdxsfL8+Yc8fPA2rj9ms73k5Vfe4I1XPseDB4+4uHhGFc+QHc8vN/zBH/wLnl5ekPb/lLw7R1NCROmiZ58GcJCAUI0hPeUMOSJEXBdsyJV7JGX6MOIXwpSEUhKrdcdUt0z5uIVa2Z4gLrLsIl6UNEw8n55y69Rb8pyacgOErFYjjrmpFiS10KqCB5Msh8ZaPpQw0groCiq8+93vonnHbj+xHwpTKnz/O2+x3T1jvbg5N8xW6ci15M0JODzORRb9kn/5Fx/xte8/4dHFDgrk1PyPsTMSKk08T8XsalQqpwtP7yqpKI93yuWUcLvE0iuuOM6zb+eXDVOCVxZdICdjgErzX40+HJKotfU/fmZm4cgUvCjd3AvozOB11Jq4d/c26jpUJrL8/9yU+2pzyWq1NCf/2OPai2EPyZLsa7bYZtWWcOYMbCoNVfTKQdfoG9pQRZt8YAYh1FJhpKkIG5Ch7Rc5o0ZYussBXJgRv/kFsIVX86xF1IZEtCbG0B8Qo7xemxe2A/pQPMsLE9s2t50LZBGCc+R27Nr11uuXVrX5TikH8lHFtL0VwHxGZtTUvCIONKjWeMoBlJolFqgVaEo1WroDpRhrQRw25bTrN2RVLepePHNsomuUX+eCUZ6jSamK5iYDKAaQuGvjcJtKGbiGqBUpTts1tLvs5JBKdjD3rhMlF9J4SR2urECrailmY4GacX3Per0ygzdpzLDWNHYhsFwsmKbJyknBDutGGbs4v+CsGd655nrvsI3IB08MgS4Ye6SUhGI06lLNFFmoDRisB7aZIGQ12dNJ9NbcSpPlYCwCLcUm4CnhFARPlcRiES2pxhsVP/iIjyYdkJlW0gAG1zaD0m4V3qa9VQtdiKbwmEG5Buw4CilXo41WA3FXq1XbzJWpmC45NTq8iKNf9QdzZ0QIMR7WzAykojYNDc7MwMV5XDBzUT+DtofnymEiLu6abSMzqNh6bDd/b3vfaptWGynCzfs8ipKrmVBqY7uZDVmTxZZszZr3TKkBQWLthZOWhtUKvaLV9Nmam2TDH5gBTkxm5rzj4O+jrdkTA46qGNiIGiir7SCr1agtqa1tDwyTbegz8G1MJg4gg3PGXkIrIVjkegju8Nkd/hp0UgMKRO33uVKpmg8sLFe0saiaJKpN4ubtwlXsDdFs7I1ikojUtORRIln3VC84Eq7suRoKi77ju4+uWKw6plrwfkWZPNNuz5QsLn17PpDjks3miuBWLO/eYnv+EWlKHN07opcFm/2GVG0yWz1IbuBMtcZHGrCqVfHR1m9t03fz5AAXHQc5ZbtLdghrY/jMeZK2D9qbUnDVMex37IYrxnxGSQs0OrQmhEwMjqPVgjStOb/csRsGqqst4nvmxNXDJMqg3flEmSEZWjPMNVjZ3nvnrqVlh8vj+nt1nhcwS95mmfA1A8K3hJSqxigIWkwuaWKug6TbVW3v/TyEoaFkMzDkXvh7e7dm5ubha8bkXrja+bvnb1Cjax4+n5l+2l5RKSzikk984hO88sZr9H3PtJ1IZaKURPBCFwJMheIELYqkiZoT2QuZhe2LPhCCmXTGBmQ7NwNiBrhf7XfknKkUG2LhrYFu/mK1MXi0VqO/zG+JArUQxRkI30XKYFLMJNBVWziqmSoOXE+VucnP7PLA5GyyjFozEHAgLcGx7WWutnUZMYDJWy3g20Ov3tFVIR7OTZN1BTzLUrnZBYqd2kAL8ajGiTU/HFvnJVdCzgxl5EICt1xoNgKF6sUK8DaoEAXGRK2FfU5kdYQqDFdb1HvS5Ak+crXbEI5OyD4zMDFMW6Zpj4qY8b9zVAe7VOgbI833nlSLGW2ro+SR4AsSbECUSsHu0AwYV0qN5jEhakzPnBtz2RtI27yNlELR1NaVgRPBB2pVpppwJZtFgKqBid0sg7O9uZRKnsH8A9Orre4qaM4470ipMuUGQs4AUaOvG9jTZBFaWkXnSNnSUw0QN8PwPCVWJzdZLVdUrazXK/J2wf5iCz7QH63pjtfNh7I0kLCl8jZ5oro2DFHzNpLZT7NdS5Zsp0UutNtk73cNh/Vo26FCaYCbCrXJIB2tjmznsmIpr+ocKzXPJ+fVGBB5bCy+AWrBV/eCjYOFiFj9a7WRVyVUmLA1Ii8MkitCzcmuVlu4ghbzAi2CjjtCSqxPTiglc+P4Bnjl8dWVMcnEoZ3DV2NMHJ+ekDQxDgPLdU8mM40DTrzJ1KaB6huQnpTV2W2kjvT9kg/eueTszj3CZsOjBx8SnIdaKTUZU9219apWb1xcPGWTMpdPHvPeW+/x6V/+VaJ4SjXD4aqZPizoj2/x6md+lmkawVXe/ea38LHj9v17EHsCcHrvJXKCzXCJTjtj2/nAmCbOz5+y2z4nlYmaC53roKXCnpysuNpeUHLGKQzTBZvzC+okUEZWN844vXGDJ+//mDxN5FTo+jW37p2y2T2yZMNhNC8ZhNBFXv/Yx/nhD77DxZSJXii+MfHyADkz7MF1kQ8/fI+PHnxA73viouf09DbL4wVlmnh+cUVYeToX2Oz2aBWcVDRAFztzSF0f0TlHorAHkxH3ay7HSnf/FvHlN3gqXyeeHnFydszN8wsmrihVSUx8+Y//ED9m7r/0CreOjrn36s+yP7/Dwo08ffaAxfErvPzaa/zJv/h9hmLTObNVqZb4iCIysnn+I1vXOeDoka4SjzuE5/iuEPGsbt7m0cPnnO+u6Lw9m66a1DBnYczCcfAcH1d695jjZeaqQBfW6D5RUqVfgMpE2p9TRzOSVkBzGxJ5IxcQEmU3cet4xX6x52rw5CxItJ7N+bV5g9YJTcbczPvCsE0MqRCo+LjE7YVtfcqffuWf4LsFNW64cfMeORVeufsGTx5fIH0ma+Kj5x/ytFae//4HON1w++U3efLhpjWOZt3Q/pY6CS6Y7YOlkFliZUqePh6hKNtdZrmINghYQR+v+OXP/xpf+uffJJAZchtQNxDc+YIjs1x0LJ23GpoEYoCe4knFBk+OjsRArRPeWxhSmfZcbZ6j9fVWx8y1u53zV0Piw/e+yxJPSolShDwNXDxVvvuDr/LFn/kN8zJ2/gX2dhsIViN13L//Mv/tVx7xjbceUvbVzqkGwudcWHaVk4VJsp0r4KxOk1oOwQWaK0ngIhditdqxa8SSqzza1LrVgH1wHEXhosihphSB9XrJXEhKaxbF5pTNw9MGVFktbdVVG/d7Z2md/cJ8AUXdob78SX39xAGlcRyoOeNDIPmBvu9xzowtvY+NUVPI4lAv1GRMo6JtmnkoEgviTVsfnKDtEIje5GYqniwFpx5oMd7MFH4ryW2hzvK76xGwVww8ab/zLxXbamgmrVgxqm6xCapixmbSHnqTnVVsIjODPlotkYP2wjhxhAbieIzq33pvk0totYOqZg6mvK3Wd9KaQ70GwgwxLU1S1JoGfy0j8tiUUNrULDiBmsjFErMUd2gwrai25kO93fc6U7uxBiXlhOZ0AM4M7KnNhKzJnBpLaZa21WpGsaUkfG1oxwyKCYekobmJKtNILoWgllSVx4HOeaiWjBNDYLvfc/PslFrbRqEGirh27eujIzOZnpLdiwY2ZREuxx1cee7fuXOQzlVVUil4reDBO2VK5kuBwDDm9j5MxkSZ368G3OUpAY7FsqfrI7kaUGoNjU08Y9Np51rpMDDOi6OPEfEQuh7NCsHAN1VjZhgIN9MdHdoM53GhvZtC1y0O/jHe20013x0rHmd5V3CO6Jv8S+ytHqYB9ZEpJcb9hHOwjB0pJdP+1ooL/tDlHkyRG/Awm8Zpm9z7OTKzYtNJx8H3zLy2bB1C+7ZZX5yvmRHmR2WMvtnAusFFzYuh+b7Mf5jB4tZ+qzFwRKCPBiDN4JgYjbCxPWY6qQGiJVfEqzErRQ9gjGCm3r71z+o8NVj6kKow1cK1KXBbE8VRs02uxJmnEQLOh0MR3dS7JnlpwGPVufkXQoiWlKezca0xUsRDysq4L4fngdDCBeweW3QzZFVia9R8uzeGCjqyWNHvU0WksugrKY0EF0kqDJMiuXB5cc573/k2XX+f/qxnGCpHd0/54J2HvHT3NjVBnjLrGysefPSENO04Pem4ePyUUAr91Y79jx8TV2s8jsvtpYGluVCnjOYmA1LbC4r3qPONKenJZUDVGISdOHKtpAaO6iyBtpeyDRJmho20M3WezitUAzjGaeTy8pLLzQX3bp2a71yZiAK98/ROODk9Zn37Do8fPOL8+XOSZvDSgH5jA8yegMiM7csB9HNtzRwGDlzTlq8HF4fjiHm44JRDk+3nhyuWXlq1NDGmtzXdnulqGei6nmG/M9B2Pj8lmR/NYQVB66CvzfCb+dKBXt3Oyfnd/8tgEvMiaJd+/QG0rVegSdz1cL6c3rjJ/ddeZbk6svvjEqoTu/3eWB7Ok3I6mHHXUsCZv0jXLwh936bJAWlFUh0nW7ctPWuaRtI0IVqZtBzk2/anfWZvIQ/qBOliEys5VAtRTBYuDnwfKKPtxcF5+515RFDzLQgLqjh819Elx5SFsUZgJGhv96JJl71EvK9tT2pJcc7MZaH5YDnz8vPOPGRqcTYIciYjGnIielivAttxMAC+gi9myio1G6A9cUiXC064u7rNagxMOuF9JY0ZOgNwXXUm39FsKZudpcP4qjgXKV5w1RK8csoEhVAySQuxjCzzyFgSRe08il2wOHlvBtI+BDSsCOWKLLUBeoGxwAKTW2ZMZuVpDGk5uCFZeo84uqCWAFalMd5t73ZNslfau+eksXCDZyGeNI4szk6RlGDIlLYWQuzIseLVIWVndU7FpDjFZISJzHqxACo5T+RkZ/xYJlrMSVvSFsRREVTqYQq8H7a2YzurV6tWuqNjbt+/jw8Bl/c4lOWip9/sybNsSx2uTCiVTGhJqQAToubFqILtfbSJdBtgAUSxmsRQ5Xaezp5VVEuc9UIaE9EH6vSi3LUNNptUVAVj+fmA1kSoHb5YKmCZMv16Ra6V5eUVQYPJHFO7M2FmBtswzeEPk3WcIt6TxonisDQogIK9dzm34WmbAwJ1ylSvuCzULAxxYp8ntMLuYm9nCBnnAwsym6cP8IsVR0cnTFcXjFqIvafTnlwqXbeipEopQtrtKHlk5SKjD9x99VOkD35ESpW79+7z/PFjpmkwyZ5AbNJ/54SkhXEaePrwQ57+6C1e+8IXuHP/LiJCFM80miG/3D7Bd8LNxT1SMY+49+XbvPe973Pr3n2ohXHM5OkSOT6i7Pd4F0l1tN5EM8P+ilIMDDLzc2WoFipDzqz8mm3dGYtKKv3iiJNbd9hdPqcMewN1Q5MPFTubQh9gBzmNTNstWaDrItvtnh9/8D7D9opl8NTgWZ+dcff2LdLmgmjtWAoAACAASURBVO35JblOhGq1Q8qZkgvLZc+dT/8U4/ljcvA8e/Au47OM10otJjOacqY6R+gcnQjB97jlAmIH3jGWym67paAsb5zxzlvv4G/eQI+OGfwClpHjl0/pFiu8S2zGDaVkyrOHPNmd84Vf+SX2Fzt+9mP3+cbv/j6yPkJVuHX38+x25+z9lrtnN1l0HQ8fvk31ozGKszSfntaj5BUSd5QuWapi7jl/vmEczHtnfRRYhci4XSJ+oHMZLYnjlaPrK/ss9McLzp8kC7jRSlhC1sJ2SEjaspsUVz1Tri2YClIKlOhxFDoXWZ8InQT0PPH0oRCOHBK9+S25iIojZdtvttuBfco46Rj3G/7T//y/Qljx7g/+NWe37rLZ7/mzr/wzrrZbvvhzr+H9yLe/V3GdI3qlJiF0JzBOJK08fvQDPv7a5xmy8PC979MTEMkGqq4idAWKMOXMNGZevf8x3vzsX6PrVvzwh2/zo+9/nVxH6xUTyPIWV4s7aCrcub/i6tJ+Xs6ZcZqowRZ8wKM1oCQDYCSCh4VbIbkjhEytwr2T1zm5cQLZGP0npytWq94G1cwJ2TY2ii7yla9+h3H7DJWRKSe2O5jGPaqRf/WVP+GLP/ub0JjCIg0OwIY7Ux353g+/zX/39/4+X/nWezBlpknBw1gLTjI//cqSf/uTr/DeVebbH21BPOJmQoyRLFQcU2usiwil2BUOpZpNXm69gGuKIyJZI0XHw75Is+2YGVRWrzm0SYtrUwXl1vs68WYr5Bw+OLwLxrqsk1ntXp9oP5GvnzigFNUbq2MaSZIY0mioonj6vid3LZLX2aETnE2WwtxsZmVXJ7ouUqRR2ksDKmqh5sm8E7DCqpLNtCr6Q8qZvjC9AdpUpIJcR3XPMiTRavIqMc5SbZHM1s81RtDcwAioa7TeJg8r1bw+anXkrNTSDnY1iYvDwxwf6Jvbe5sQaQOvLNLWNrPSEq/MD0PMoFSlsVusEXWK3T88lWCR1sWYLIekt9bUi4CPja6t85TKGvCZrj3L1KAZOGab5omYmVop2aiHsymYuSEjzqRVIhZLXauxrSYtFA8xWNtRS0EyLXrbFpETgWASDq1mXK21mhTCBQYV9nkiOFiEyEm/AIHeBwqpgZSAyKH5OlouSUdHnD+/IM2Jc8KB4XV1dcXxYsXJyTFaDSl2ztOFQC/2/rjoCHFpi10ckcDSR/M2mJt4NRmmlw4n8PLNFfuamcRoykK7bzVTgDEnajb0u48d6ionx8cWVxwCuUxtumfdo8PTB7PbNm8Y8xWwxJYMPhJiRJt3mPhIVYtgrgWc0wPzR1XxIRC6aGaxCi4G1v0J3kemYeI8naNqzVkt5kMQY/wrxtqC4JpPUbH3odrmZAavtuauDYJdw5fmZ04DNF5kO13/e6ttmwdGuPZeMd80K3y1UepBEd/ep8PPMDC65oxH8bHDC7apeyu+fRBqMZ8zVcx8t2TEB2NLxGCTOm17RmvobTkbgJWLJeIBaK7mNaImT8F7ooPamyzHNebSlAq5SJt42GcwNkdtTCfBizWvtRZ8i1cNXsjFpE1OWiKaFPtDRtVb5LmYlGOmwiQseU4bsGmx6PXAYgtScZh849aNE9547Q0Wi2DAZif8IP2Qpx89Z0owTFfcun2bVVzhTiLy5BnrUimPHzINCfYjF+MTdJcRMvt0Re8Db7605rVXb1LTitsv3eTBo8dI3dOXDK5QSsZmhdXMbKt5BCx8oAs9KWX6rqdUmvzWWHQR23/McHvec+bJepMyajX2wOwth4FuzYGF/TTy6OEjXrp7i/ViSQyOiiNGz6Lv0ClBf8TRZ1/io4cP+PDdt8jDxFRtn/WdUY7nPRRo+/O1fNje+pnR0xqnBuAflHT2dJhlZnPG6XWuZJtw0UBztYQs8Y5SEq/cOuPW/RMefXCO9+Yb5tTYDY7GEnVt+tneBQO65kLkRcjIQEmZJwkN9Po3U9wa6CRcr2PmNW0skiLWzIoU6Bx+2dN1HfvNhmdPnzHsdzhV+tWay3rJmLZ46cwvsfpWTBYWQQgBqCP73ZacJ2rOzVMQSmky71IaO7eg1TGScOKb36KidtMIPuK8ATtO+qbvnH1dIillu3/agizqDCbNFcOEl4oLkX7dUy5HRGIzvhdEK50L4DyVyWZG4khTAvxBMt1hGTQGABrQm3MmDQPLZY8rhRIdjJX9+Tl+3DEOJzag6AJxULpniePk6W8ds94I3Xkmh0pXEut+wWun9wi7gUwklExYGmvGVZPuR1/pMT+xEB0lJno6Vove1o8EpBayOnrRZiqeuNs5dlNl7634J3T0XWfASxchVfrVEr+qbLOjeE8Niq/gqg1opBqDjmr7Z3aOGByx8xz1S7qFsUP9JIyjMI4ZR22yOWO5gHkgWjpmm86FYP6WsYcQWa6XpKcbVidrUi242LFYeULIdH1krMq43TMOE9vdgI+BxXrN8WrJsgsU9YyDnYuzDJFqTEGHojUfhiRGZCt0Xc/N9QlDzQxDYthcoZq4fPaQl37qM1w9uKCoYyzFPqM48rBj8+Qxq9WqvUc2WKzeHdaeaIFotbWIhSuoGLjs27liB57Y8Laz+lBrRapDQmPRextlu84dBm7mZ9dSd6saM76KsV9Lwa8tNj2lypgzLlg9X0Mzh0ep1nuRpRJiNIJeY7DTWFy5QppG5OTIfC0pTOOOqUzonHyXlSxNdudD85KqXFw9ZX16hzIOLI+O8GVLrZWBgmRjcA1T5mixIA0jOe4YysDiaEWgYxwcKg7Nwl6gJuHW2Snb58+Jt+8SOofKkpc/9il61/Ho4ftmgp0yy6Mjbr30EsP2iqur57YP7yfUeb711T/mbHnG53/qU/gEdbthUzJl2DHuJ0pNVgcU10I9qqWqPXnKBz/6HjdfeR1/1PH4xw+4d/MYijJUZZdG6qOPwFVSyeADQQIeR+fs/lMyvfesV8rFw0y3PibngnQ9PkLsF5TdFWUa2W/3pKkiVfFl4vzZY4acGYfEbiy4RSSkzCIsuHzyEdN24Oj0lCllVstTkvS45Q3S+RWjVoR82MPWZ6cc37rN0fGaUAcWaUmeCl4TtdhzH/PAmDCT5myjkSITbtqwWnbm1USl5sK2JNLuOcPilGka6K5giiP4jpP1mkW3QHJk6B25yxB7Srfk8YNnHJ3eQPolN179BJuS2V1e8gu/+R/w9OIZcnXJ5z7/GWQc+N3f+Udc7baUUhinQp32SCgHxvu4ieTFMTlU8D3j1cB+vycEYayOUJRFrKgbQQuxC2y1sDlX0ER0S7rs2TFRXKDzxuTX2uMEFr4i0dQuGUhJ2aaBxeTJqSJ9wK8gDR4CrJbtLKdQx70N+WulUthXZUgV7xdItyD2ju9/7cv85t/9T/i5X/pltleZ997+Jv/h3/kt3n/vIf/yS7/N997/Kn51RXCVRd+jPnB0dIvdZsvmSkmp8t4Pf8giKEceki6s9s8DGitSTGXSN6VISpmr83M++wuf4fL8igfvLsibPRgngKvB8+zZhp/+mZ9h2O6p+4fkZAwaEFxccLxY0jHgWo/tJDKVzN/8W3+TEFZo9a2nsuADfGDYDuAdZzeP+aN/+mW+dedtfOxwLlqgRs3gAn/8h1/m/r2XqDpxdEPY5YQvFd85nj7KfP2bX6VqS2rOiS4Ii36Bd5633vuAf/yPv8TV6PiVX/zrlJLI+w1jFXKdeP3WklfPHEV6Ft2em9NzTs7O+PCjCy4urkg5ExZK14IwHEJBiM6TsaJcqnkwhRjZDwmpjeHq48FvdS7Lttstq8UCsIGcNX5CJ47kKoL5/Kq0FEOMMZU14foF3/jOd6iuMNRK+X+dGv5///qJA0qu89bkije5UimoZhIFKYJMhrRVUUIXSDmz32+s0fKOWVLThx7UEbpI7a0g1VzY77aURY+PvU39aFPdaolIITS6sJrcbUYsUbUmuU0GrbhJVK1kZwd00dmfxibeNY/WGORKiDPVuRkxA8xmV86jLiC+MwPjZGZzIdih7ZxHyYi2aFkUpJnpapOANS8ZajUPGe8aIcUc7ikteNNxbRIu3v7gDxHiFfNfUcU+t4NmIgEtGcQYG8EKjEattw/XwIM2XRO5NuzWUo2JEiLQKMMugOvM26ShpgZuKSIVJbapuhptGENEDwlHRS2tpM6ME2MKhWj0SrJ5D9QqeFfwnW9eAoFF3xEIbCaTQJVacSGwXK242mwMoHJNftYGd1WVi6tL1qslU8lWmHlLaBtT+n+Ye5MYy9Lsvu93vuHe+16MOVZVVmUNzR7YbDYpEQQtCvQAiDIowUsvvBFgCfBSWmllwEtvbHitpbyRDWgjA4YhWIZoggYltjh0t3tgV3VN3ZWVc0bG8IZ77zccL853I7PlpXvhBwQqKyLei/fu/abzP/+Bro/QPAtUzAMiY1phdYq3IzTNFcm8irzDSUcQ076qMxo5zfxdFTrn8V3f7kVhcA4tahTgZlQZXGeeIj6YIXY1ltLCwNFF0lCFvjNj+kXOab47BkYsHVvUpCAGipgpNuKYs6WPBBzq2nXzdvgsTR7YtflWWgqgD8akqW3h0mpyuNhFnItomaz+9HbALDb4UGzsvxL/0ABfZZF6GtPDfNBcK1RdSwwKLjTwZClyl1O9/XsBUFEDa0tjByLGSMAtuvPmuVNe/b1Uqhnf+0A/rBCaYbcsbAtp15NrEEwVcLKI8IihsQw8xBjteota97ouJvRm6i/tYG1/vlKyGNCspbFn9LUkGSwVTppvl+HDBDHeSPQOj3Wfkdc6zWJAQWxML+tQQHGY6W0jpYj45smj3Lpzwrvv3meIZrwfu8D+MvHkwSNi55FN5sbhivPdyM3DtyhseePuLSQI+/3ENM0EHO8OkUmtGHj5bMtwo+fjZ1u2uXL54YP2XidW1dGf3uT8cqYbDtHtSxqWSHU2XwzMtnSMrDOht3GVZ4sdL9q8lbDCozYQXox+h3dmvpybfMOATkEIlKwEhd1ux8sXl9w8OKauemjAo2BF5S5VVqsVv/KNb/Pme+/y8vlztpcvoVSePvh586Iy9tD1nqx6bQ6uGEtMwbxjnI3P12o4qmvi2LarF/Racrq8Tls07fdaFLXLlVs3Dvitb95GWPP86QXSws3kOl2zMSqbOWRp0mSL9Frm06sURqvRve0f8mqa/YKn0uvNGF6XyNnabaa3NFTbWEJl3FPHPe7wmKurK6oWhuMjpilxsd2xe/mC6D0ShaA92zJRirDfJ7Q85/TmKXG1QiTgYofoSM0KdAYK12y0+2JS2ayZml6Z05sse2ENF/JcUZ1wbkbBxnjpcBLwqoxacS42VmIFMTmseNsjnUAdJ2MOxMpJiKQp4bVes4hxmaIB32SWtGAGqSYNTlQOWhPHuYiGQsmOnTo6lCJW9xdxxtyaRl5uN8TVgN9m6i5R956+Kn4MxF3GjZmIcCP2rGOk3xdIGa2zRcaLJav66AlVCFIQqUhnYzO63uTBIlQx36nNtEd8IHtHUccLYBsdl+roXM+Yi4Eq2fb3WIWURoLzhHAKFNYD5P0ZoplSEkWFXCzJxtJwwHshhsjRwZpV3xN880TzFScFFZOCpXmmOjFGS2kNuQai5/1kZ6/oiDjGeeQiBMJcSClycHrEuNuz2W7xWHqf1mLyJJSkiuRE33V20J8mQgj2Pr1rrMMG6qIUFi+8ZndZoVSDyLtVzyADY5jYtgabC5GL589Qqcwp47ue23dXxBBxvjefH20+TPIqBEPJOPHkkvGlu94bpIJqMhZSrfa9oqAzFCHtM1JDW2iUpBmJtl/G6qkuUL2jOjX2fJsn0tZRyRUndu51WsnTHqfOGsVj4nxzQZ0SMQj7VIw1hhLUt/fBtXxfqp2RQ7W1lzgg80TJhf00M+1nYnT0occNiVwzpcA4zezHSoyedbdmfbBinECrmfiOKXNwepPx7IKUEnFYcXD7Lruz59RUWJ2c4EXYT5dMecIpTKmAOAt/KJWHX35JnubrYJlaLCWZcSZkWJ/e4q33vsLdd9/l8U8/pEwz4+aCVIXeC5od93/9W/hse7x3Aj6SkmPOW45YE0OAwaPaEXzgrXvvE9ZHPP/ZQ+6++zZdd0S/XpMut1ycv2TejuBaY74UvERcnhl685Cs1dEFz9VVogTPy31Ga0e17hRaJ/oQCCeHbNOOMu4o8x5xleKSFcTzzDhPzCmBt0Tp2K2RrsNnA0bTNHF69zbzuEPzSIxr3HBC2Y5Ms+HxVOXw+Db33nofFeFkfcL3f/w9aPe+Vig5kWvCOzGfSVfN846KpoAET5Wt1Uy95+bKmn3bzRl5Ski/BR85PHmTW8fHbDcXXFy8pIpQNeG6Y+69+S6DeMp25sknj5jGyMHJTQZZITmy8ieUAXzXsd+PnLz7VdzVljqPdL1js9lztduRxkR/fICeXzGX2dgzznF6cpvf+s1vUHTiO9/535nXA3E9kMuePE54XzjykYOh5+pZorrMnRuVs5fK3HyPEMdq5Ugp4bXg/UwXI4TIOFU2W2W/mRl6IfqZ3dYz7wodkXJQGbPtRXNKrHuHR8kaudxPBPEcdx6Y6VZr/u2f/yH/1T/6x7z71V/jzVXkf/jsJ9y+/w67rXL6zlf5W1/9Fn/6x/8rF9sPmccVOTkuLx7hh0Poeua0NUP4/oj95hIRZcYjGvBd4KQPHB8K23GkqCOXifd+9av4wfPZJz8k7zfs54nh1LM9L3z96x/wn/+9v8dnnz5GNxeMLx9xdnHB2csLXl6cUYsyDEeUy6ek3WMDzIqSi/Dhjz7mzbfeZDhYcXh4SO87qx2LMtxYW71ehe/+u+9ac1G9EQCg1VCK950x9VDwniM/UDWB8zx7csk/+Sf/tDXhjZm8KPkrSvCOdTfQ3b5JOU14H6lltiTwZHX9yzKhqSLrnvffP6DiePsdm2fbyz1zGTn1Dpc3HPYHPHp5wYvdlmnOaNEmAW/zQ5yRJZzgfeRgHbi42thZRsyzabPdcnp4YIQarImnYt6ti5g4q6W8oUqtjt0m8dOfP+WzTz/Ft6pyfV1P/XIev/yUt2LAgTohZ4tEtKSlRsVfAAXUTIpVSbNFw9rPrJO3LztjgsyBNO9ZDYFcKuNuR5omVDwSBEczbXaOEBuVvMlHFDOddN4TfGNXYEaWEgTvesQ7VmIspFIrqQE6OWfmnAnOJGtpnuyg7ayQ9VGIMZJVmXIma8YTm7ypNEmLEIMBBDpXqJhhsCREgnXZm8GvJVg5yFOLw5UmCclm8Nc62U4cTbbaWFZGERexBQZJzeiyAVDN50WrmfZafdUYH0vyXBNa2GHCtc4q9j2tRskG+zvOm/xL7LrU1zwzrh+t01WXVLjXmAKLVAnBmE686nqjxj+JIeLFKPGWlNHAQoIlQ6SEW/XE4GEqzLlQMa3z0A303Ypxzk2S1YrOxtDYbcfWTfUmbZBEr137PJ5AAyZcuB6zYVhDNcaIlkLwwVJayGQK2SnjuCfPW0QdXdcZDRwlVmHamMloKQk/dPb6fsBVRxx6hr4n+tAOq7ySkOir2s9MP5XVKlqXsi5uGgZEaHX4YAZ45ttlIJD5EqlFY7eknDKO1FAItaPURNf3DMNgIGxtpvhiXWLnW/of1mX2+opyGWM0cMJHyE1uJNZ1TUaVamo0Axit62jIZ1W9ZpR4XqXT1KoWhe39K2P/VsA6t9A8q70v700uWZv5cL9iTjMhelb9QCqJnGfm2TYFsDFX2nzw0SS4aU7Mc6LvC+vDwdgsDZRa1irTsLbP1zzDxIvNHQ3GImyyVidyDeSqWmrT0MXXwDUaqOtfSagWT6VGX1GpILVxGRwxCCUXM0VvQKkZoRYEK2ZLK65q+wpiuXnoK/mVos3rSxmGnjs3btKHwRIBG/p099YbiO+oFM7OH+M/7MnhFpfzM0Qrq9XapINOKertE+VEGkeoyjwnHnSBtBu5c+cOw60Tbh0PfPoXf8FVAN1dce/rH3D+4ozdZktlNBmhBMYyG+/SR5wTYhjIOZFTtqINQAJeGiPL0XxojLmk7doq9doI2Ym5LDmM0TTOEyF5Xjx7xps3TjjsB4ahYz10LflOWR8cgO9RHO/cfpPbd25TqvLk4Zc8+9knyHDYmAqYv0YDZppW0catcu0Ptqyv3lkha0b03p5qQh9ja6LXhva2MjuCK1Qn6DjRDZF337rHV947ZbzY8nT7Ai+RVfSMac+1wT1WcHvnmk6v+UCpQkuPQl+TkL5616992fryGqEKMNBMRF/zzmtFqGibp66Z2cPZ8xc8+/Ixp8enhOhZH53y+OFT9uOGNG3xvcdLoDohV0cpM6ka2098YBpnxv3e9jaHNYswOfXi1ee9JZEKkMrcUFPzOVvk9KWY0bDTbGyJouz2W3KpuFwoZY+oI1SHCx3RaTPbD5ixf6CUmbm9/v78glU4Zh8ysxS8+CZLrgyxR3YTar0XCgmpHqnGdJ2rsSUExYtCMcD/akzcDmu6mnAe5umKqiPBN+meFkKItj/6Qu0E1URsnh4iHVGDxU5X85NLtWsq6Qa2iuJcbcbtyox1TLUmQujZ68RclTxVhq6nSuWiVh70nsfjjE6JmivFB6ozya8LKyKOYVjjMvjVMWm/odaMpgnV2cAF75t80zGpoLWYHB+5Psy71oEuquSpkFKhZIucLmbRYwb57exnnpQLyzqQaNLQnFEnTDWh+xk3BJPcTDOzOmCkamFMhdXRCSenR3TRJMN7CoMPTPNoTaKUrM9bjBm5nCvA/DZSA7hrtcZadOb31odAd+MGWZSu77n13n0unjwiz8p+u0PEvIScZkQCEFCMmY5mk9i6CDq1EILSAGEDc3NWTO5l4x1VRIPJLfsVUQRCRy2VThOqhUKhin+VdFeUrKnhy3ZGFRfoOui94FpyoFYxw1nvzOA5ge8C4gU/27Wo2B4vuZJpTWMcofOUlBGBs6fPKUU5OF3Rq3JyfMrhes12c0FuEQ2LkXeQZAnAVOaa2F6+pIvBZIJdT51G/NpzXAdihkLl4tEDfNcT+zW73Z5pe2nJvbFHqqXtERxFE3ffuMH99+/z7MFj7n3wFepsY2G/O+f05jEPHj3mq1//NY5u3eDZw4fMaWLMO3ZltlCSOLAZL80fVAKpKMVZ60+ycnl2yZeffA61cvv+O9y5/wFDiPjDjoPUEd+/x+c//Jhv/o3fo/fCxeMH7HdXuA66yRHWEUl76phYDX1jF3vmNKGuw4lw/mLXpGTGAlOELkR2k4UfjDuTnVZNTZlgTbBpTkzJIuiLCiTYpIlv/epf5+jGCT/+o/+T9d271LnSh0hOezabHQdHh9x/4y22+5mUR6btFV98/jOevzjj/fff59mT59RB8GJs+JQSlXrNAnaNNVfV6ggXOjQK+2osxCiCr57NmBi6FS727KcR6R3iPOfPn3P2/BHr4wNqruwvLzl665CDdU8tlScff8zDs2f8zd//u+zKRJ6EQeDxl5+xz4UfXJ3TxYHTW28wHCcutxt8dIg75+jgmJwm3vvKVzh/fsnF8xfMdaLOmfu/+Rvcfe8DPvn+dxlO7xF7j7pMyjMZx50bHQeHwvnLiTEpx6cn7KdCYcfBSvF1j3SRzTgyT7DyHYowTpVeK66rHPeBfr2mlwQ1UxNclcLq2JqhOWP12ODwsXDj5k1SrsjTcyQ7ai4MB4Oxat2af/wP/yG+W/E//rN/zt/6vb/JV2/f4Or0Gf/gH/x9/tv/5r/m4bOPcH3A956oSug6slwxTRNjKriSqXXg5dWOWwee6FcG/o0TOxKHq8jJyZpcZw76NR9//zv89KPPSWVifTPTS8e4D4S8ZRUH0jYzuIqsDgj+bbrj29y9O1PmiXHas8sTzx9MvNw/xzOzT7CZC598/jknt2+xfXLFxZMXVIWLvaU47qcJ5+3sIdVqiy52BhqL1Q6KMs+5NQgE9QUo5gElASfm3WfqG5tHLggiwYApTa1Wdi2MAVSFqWSOD9ZWg3vjHS3JEeICIp4udsRbkcox6z6iVVj1PfffSNxTh+aJ3e6KzThxebXj5csrHs+VNI2kMjPFHceHh5R5xW7cU6USgjBOiXroWEIRzPph6Sia4ksWYon3eDynt475s08vmPdnFCpk4fT0/+eAErWgzqJKs5rnj5dg9EiRVki9ZmzYAKbFYNW70DZnK5mqZrwMLdkmGWClxQqZ1AAR1/xW9rBo8U164FpX1w75Zu7ZE1zzDHCtXHVCiB0SA0480fc4dYwhIbT0rb6zDd4boBSAoM0cVo3aXmoz727dR++FLtogH/rQpHATTTGGiJn+VYyp4+oiWZutGBLrbxdMQmAPM2v0ODKGzKqAbzIeV23zl7p4RLVCvy5MjwqtqNHm/0IzvlwKDEtpARAbo0HMVb45T9RiPjrqyjW7SaDJxDBgQIWck/nY1IpqNhCpZFSVrusai0IaQNHufdOGvwJDinVrUiZJMIwieJxCCJbUpM4Q6dQ6fMPBmqtxT85LlKNdC6lCdYWnZ2fcefMN8Ca7SdPMZrtFvOnfEdOw1uiYa2ZOE049cxZCtLHT9ZY0chBtUZJqDAgXoOqII6ASSY0141RwfQe1cHxwgguebZm5P9xoIIm+Ig5gLA1VpVQseU/EfMmUJjepzcvILn7NtnAaPdIOqqELFO1I09jSYMyXQ0tu8LuNv4N+TdcPIEKas81LpZmXN/BEBAnO4nVF7NAr11APEha2kIE+wdk8DCHYYq0NMFkKgOs1wCj75u2lLd0lGlioSl7y2tuCYV4PJolbAFDnGtgSPSYt8cxpNENyQNXSDXSR5bXub4jOQMJ2vXMpxLb+CHrNSrBpt5iEt/fTPNWEDtQkSNCSjVSbvMUAXx9oaYiOV1DBMmuWIr6BuG7BXE3qZoU6gMM108bazKm9BMQvHkvWZfeNBeOuaSbt/ikUMTkgFEsdGnqOVwctAc3uTa6Fo5MTDtYH7NNELRNXfXpyjQAAIABJREFUT5/y23/7tzl9523+7E/+gm/91q+y3e745AefNzA3c+PeCTcO7/HyxTmPP33AV779dR7+4CPu3gqcvH2Pn373Iw7vnBK7Fbk4Hj16RugCd+69x9mTz8myYS2Bw6MTYt8RXWCeJ/b7vYHhziSErh2aFyefxUyXtvZayIJJ6ESbdMwvbCcDZl0MjPuRs4srnj57ycHBMb4LxGEgeEfVTBwGspiEJuGYtjMldDz57AuCj+brAhYOcT0uFg+idv1pBxQx0FDUAFnFgFbRglMae5BrplYL5LN1HfO+6L3ja7/zdd784B7Pv/c5h72ncMzu2UP225Gshao2fp0s88y3FE+7XtZ0UUrWax+5awaSvj4mX31LVQx8eP13f2HUvvrlpXnk2zgXArVO7PZb9tMeVaHmGVHz4xtWRybncMaYnKbJZLni6foVYTDguXM9vnN0/Yroe1QzLdSKJYre5L2ZlGw/1WqBB6IWpuBca9Kob8+BYXXMlGfGnK4lGs5FVv0BlNkkdRWIxpApanKiqkod93SnR2aAqpVcQKVQo6dIJoSKuGDeHHgURykwpdlA74MjOmfXLUXYX14xzXsbe86k07sxGeCojqlCr5gPjRa6zlOTGVvPNXPQH6Ip4xVy3jJOlxwNayoFr5EogSyVOSd6Z+tusRGCqwZeT2XPJI6aBN/37EJgL/BRnpkVM/UWSyJb9wPTJIgP1BAw0/IZ0ZHOC3nMlDxDMl/Bqm2db3NiYRXiPFINSonBgPWsyrBaESTigrdmnfdNsi8oGbNltOh4FtmVQiTYuQNPmRNpni1mfkmjrHae0azkUvHiWfU9MXY4DzUV5s0O6XtiH0luMqBGpCVq2llNRMjNhF2cWJqvOHywI3U/GMPXIbhs4+cQx8m793j48Ck6B8Y0oWo+oVIzUgsR+1zJcCNqyrbXLU3ZZuyv2hp8LFYHqR1zCxCbFA1czq2FERG10AVEoAeouOrtVCcOreZhJiKsB5Mh5ZwoAmvnqc7S+NBK9gZQaXkVTiPts5v6WgkqaDJAt4+RPCfEOS5enEE5pAwdXW/n+MPhmIurF8R+4Fd+5etEFT786EPGaWcNEi08v3qJ04APYqnKVZmfGhspSkCi7RPz5TmzDxweHZuMnMImbSmpkBSGcIB2nh/8+b/jR9/9Ll1VfvK973O4GqjeNA/7zY5xzmzOL+ijI20vmVth6kNgnio37hxzdHzEreMDzp49oNaZk+ObvHz+hEcPvuD52RlZZ6L36KPKxcUZH3ztaxwdHfH853vuv3efH3z0b0jTS4KvvHj6mLIyQMDXilOT0M9k9rsJCPR9YC4JWxAiKY2oEzx2pkeFO2/d45237rG5OKO8cYt5t+eTGPj4w48MrL3eu2yuOTGG+piUcHSTYTjgd/7g7+BkxdmTh1y8fIZqZLeZuPXmm9y59zbPHz/m4vEzhnfv8+Czz3n+5BHj7oq+H7jZnbIVZS6YckMttVdzsaANJ6RqMt+SHfPsSXMiOkcvcDbtcNHWrlkssCFQuXzxJWOCbujZl8I8jxQpvHj8AB8i/9Ef/AF/+S//N37zP/l97n/tXZ58+jP2Q8/JO3fZ/+V3+PLFCzanN/jgG9/k5ukNnCg/++iveP7wsaUGdz3r4xs8/tlT+uM1YbWG4qH3TFPm+3/5fabLDYe330A0W/EcTqnrxA5hc7Znu8sc3BrYO4HQc6MfiQGOBmE3PoatZ5ssZXLxfct15GSI9GuYpj1zgSiOfRYikbzPjKOAZJICOXB445Q+RnYXL8nZGv9DHOhkYB7PmevMZXrOet3xX/4Xv8/b7/86v/27/yn/9l/9L9Ra+PjjH/CN3/pdfJn56Uffxceh+fAIq8MD1mPlP/v7/4h/+t/993zlV3+V8yePmg9XYlhHTo9X5FnwYgzy5y+f8OLZC2pRC9DoD7lzdINVd4Ore1vee/8e04uXuFzY5QkpG/OupeKdElY9MiaezRu82jzVIPhUufXWm9y7ecRuU+i6U3ZpZH10yMVuR25MIkXJarvDNFuNDVBrJXYd2gB4YxEbdj7WGepkvpNUUrJkyapmweKqWANLm+8vipCsZkzWDDl7lowZW61+KS3gwbCOtlZj87KLgdVqZftN1xFWa45XRxzfvMNpjHgJFISpZvbjnqvLS9I4UqaR413ii4cP2W13bOcJX3ZcrgJH/Qq8u/awPQnWSM44xqzNRsD2jhA6Pv/i59bAzJXOOaZ6yC/z8csHlJzgfQfajENLsUNpM9m1BR6jZvomERDzOHHONxaRsVa8ONs0/ULTV/DOkouyXhdcmEKFRd5jB1szL17Mcg3nSORcCd7TedfAlnZIKJdMKaEoMXRmYDhnoNJ1A8PQm0xEk9HFiho7pSilqnVIvRX4LvS2r0vGqfnA5FDsoK8OV1qCh5j3ghEgFomUVRLOQfCu/ZrJAA1FNWquxaYbM0dVUW+diSrWFfBUutjhCMwtOWTxGqjFjJvNQBXMwMQ1thLX17/VmNeFgyydRNdkJgvZggVUelWUqFpH3ADBBiZWxYVoZsELkAh4H1tRv0TgWscxq2njFxlLTolxnAlxYLfbI9LhpV6DcbUBmAddZDMMlO1IrpkOi29fmCybeeRWyoQYsD1YzLRSpWmitBVi5hPhxCGdt0SqBuScb7ZEB/3xQBDwLuJDB8Ho1LVkYknMRcnO5IIyZ6iV7qYnTTtcTozjaNGqDURbZBqqQgjOpE8IMRrjqGRjQxQwcFC1zS8bR27xPcIYM33XE7ynZNOz9+s1XdchAuN+JIae9dExLjjG/QxuSZoTFhNfaUbPy4CwceBIU2oSMCugfTADTu8DfWesr1qMebSAMbUu/k5LXKVeg1NmkG3eJTa2xBgdDURSWrfYRevae39tPB67cM16C7Fjt99TgZJrQ+6bDK4tI1oKc8pAIKWJOc10ITCnQt+uTxvKDYSyv7PMv0W2Z3HTLV2qzU8XzI9N2n2xdc82l8XzQ9r4Qtprv76Gtm67oboezRirQCJ97Fn1PRf1kuys0+Kc+bLFeu25eX1t1TVgHRpItYADcHrzlOPTI1OuqgGjWmf6VeTWW2/y2U9/Sn84sNvsmC5e4N79Cjdv3eXjf/NXvPH1t7h9a8UHv/ZVfv7hF+zOz3HHhzz+/AHf/Oa7HEfh9lfv0w2Vn3z0IbvdOTOJ3fklMXvu3LzNG++9yfOff0nBIzPI2hG7DlRIyeaG6uKvZjKTgK3pSa1oETHZsXMteStbgZMFajADUwM3DA6vEvAxotuZOWW+PHvG4c0bDOsVoe+JITAnYZ52xMGA36mlkiiZ3cUGjcYu9USyZpbBYrHb0ubkayCMQM5WGGrrnJlviVI0X6chAa/UyQ3LrE4oSXn/W+/wG//ht/Cl58WPH+FKRcKe43Ug03O521OS3Xtpa4MT3+6rgxYGsSQRilsGtx26fnH4yTJ8rnlKr0DQBRCVX2Slth/pNaXSpKA1w9Onz7n/lR37/Q6qsj7oyU4YdxM+GhOuzMX21Cg4IkcnB/QUYnCE4JHQN4DWru08T1YQZdBSjS2cZ1LKXAN7hiqyGFcWcZbmKOZhIF5Y+Qh4cuiBGSnW5JqySen6dQS1w6OrQkkWJJJlRseEHnTMKaFerNit5rs3ldQaHkqqGQ2RIoVQJtjv0OMBvDfzTAlIqfhSrdHjKkUCV7stTm29KcHkXwiUALVGoJDq1Nbsym63hVrxmpts0mTdLi9sz5GkhVQKnXOgnikUgloS16Yk9kW5uTpF1XMlwo/mPfsyUorDxxVSPSqFKU+kMhK0Q1OGXEhareOrUDImoyo7hNwYtw7VbL4dHko1I9yskIudqRIQ+74dyA0MiMOKvJ/IUoniUDVjWsWSIEVNal9QSAY+mT9evWbrDl1HHzrSqpJLYlYoLhgb3AmEDpxFd1dglzOHwdP7jpEdpZTm2Sv42qTXzTfIqdDF7hpMFmdjTQLUbOe+vu/RIKzXa95//wM+/+hTZIyMaTawSK1zLghUG6fq2hjWpSm6JBA1MJ2EUljSUquD4swfT+vCdJzIRRHnqbk21rVZKUhLhzTJvJDnyRp6vqMGR6XQhUDnzZcv5crZ+TlX+z0lFcYpU703eYZie15LVnTt2qjzZFX2szUz5jTSdQObq5dc7IRV6PHRcXx8RK2VNBcePX1C2c/GRIseV4W4OiAMFn6S9qOBTL3j6OYb9P3A1fkzhugo02wGz/s9KW+QIFQMfS5lBhcsOKhmnO7oh5u4WtlPl6AzkxjbYE4TOSfSZWJuTKw0Jbp+YJxmjk87tvsdDvjjP/rX5lFVYb+bmwS+w7uIiqdkYb7YomeXfPGzTwidEIeBjz79Ab0P/Kt//j9beNGqx41XOPW44cAaRz60hD9FC+QyGfPUV6iFGITdvrI+6KnjRPGeRz/5IZeff8Jmt6M6x3674+LsOVodzkMISpDAtK3kbMCVl8AwQNo9N5ZVDCS55ObtE47vHPH0i0ds9zPPnz0hhI6vfPvbfDz/OW++8x7/8d/5u3z647/iO3/0r7m6uDSPl4MOdAuiJAx4Pb15g8PVms3LS/K8p7jC4d3bfPCNr7N5/IyaZh4+fEA/HFnCY2tIdCHiVUk54cKK22/e5Wtf+wYPPvuULx89QMeJ3/j2t/j8xz/i8fkz/uDOTcJcCPuJgDCkxNXFGVB55/7b3Fx36OYM33XUq0sDIXYbpGQ2+0TnhIsH50idEWeM8Bc//4Lo277iAg7XGvEFL2uTG62OuXEMroNcHOoDR1HYjol8eACbgcMhMT2/wkeT/p2sTyhpxzxuGB24UFjHzFgybmXzt5MDTofE011GC/gA+/3E1fMLxpKZpdLFnqrKbrqyYBM8o5vY70bu1JtM85Y//Jf/ggc/+x5dd0reTXz8Fz8kdjNdEMCTipJyQKeCuMC/+Gf/k3nX1ZmDdc9ctnRxTRDHu+//B3z84x8S5oiyRVHGfaXvV3QRetejEgmx59hbUMsT92PWh4fGuRdLzHWhUlptWPd76m5PSpOlETtP9Yl5l7jabJEQmHSPbwqKGAZCmKjiCepwvrY8LUeoBsxXMUm1c46cLQ3ZNz9D7yO5jK0pb4l1lordEshx1zVyqeZBKA5qbkoKtTNlFDtfplJMyIAjeH2tRrBz/zRagMhuO5JKRn3GFVjHFYRI3/f0w8AwRELXc+Nojb95gjhPdB3f+mvfppbC+TgyX+2Y5j0hFS73V0zTjJ8nVm5GaiWn1nZtSdxDtyKXFeNuj3dC7Af8cIvL7QW/zMcvHVC6ces2TgItvZo0zVasO2NNAE0aYg7k2vRb0iQf4o2O7xuF13t/LX9xPtL1B3Zmj68YHS0n6lV3N5fWnDdatCkipCHxplE0SVhtRd6r7vacE6IFXyM+GCXT4cip0KJ1DBzILaq3Gnu46EzfrZnGPSDMk0k3ppTM9EwFmwdKia8KdrEwVTtwiaNbdXhnkqY6T62TYMk3VczAtoqhjkWK0ZDV4RRSLZSUm3GjFdPqjCdeChSp5tvhfCsiWmy9WiqN1Tk2kWoD8ZwsTJWG0DZgwPkGIjiTANX2GkCj/tvDhXAdTShukUU1I92Wb6CNmSW0dBxv/k7SvIRqMbNu1UqZC+t1T0kTBU8/eAiJUBxTqtSSWQ89d4+OSONkB1ba3/MYLb5k5nFk1R0RfANJumidxjaWFmN2cY7VeqBkh0QFb0kzN46Pia6wOjwgV4uIVa22EIriQ6SkguSJvN+SNSMuEKsjikcp5t+UE8N6aIlotrZF7w1Fb2yfGFoX9hrgU7y4FmX+ynS3qCXsWMEoDDIwT6klGLmWfuTo+h6nQgg9uUnAqPZcar1ORlpAqgUQWSSA5l3R5IDe45zinRnlG3BrCTmtlr9+PqJc85JKAsw7yT6XrQcSpP1cXzGFWCilrzGjnEUVu8Y+ER8QrcQump9CiES3AEjmZ9TUmbYe2SW1Dmwx8NVkf4tUyF9f80U+W6WBpE3y59qaAZUQuvbJfAON2nrXWB6L1O0XgAZoALi0LvjrwBPXBZHWDBJBTG55sFobEIsZcletJKkNnLK/1LN0ijEw5To63ta7vuu5eXJE8K6BNVjR0oDnr7z7Dj//+FNqGUF6fvi9H3M2d0QVsmS+/PQhXej4+Q8/Yz9ueHF2xrPzZ/TBMZ+fk0tiu73gpPYcdrA9OETLyHHOvP0r73D2+BkPP9+RthskTy1ks9GNWzpS8IGpTDjxJtVAmVpyZVXzJqraOkqtcHMNYDKQdQHGjaFVBdI0cvViQzdERIRpnHjx5DG3jw5YrQf6YWBPIuU93iuzq4Ts2GxnNi+/YD9eQGjRsCQczTOsQS8L3LLsSThjw1iCqQF7FZq34CKdsbFgTNTFPcXWW1eUDs/V4wu2l5e8d+tNbt+/w/MvvuDO7QMClbfv3mZ+fME4vmjeSzaHrSHjrhkW5o/3ynNLXx+H7aH6qsO2JIn8e7DRdfeN60+j19+XVnTb3DVwuB8GQr+ibjdMuwvz3egDJWU0GTV7Gkdy2qMlUUMgpZlIIamZIMu4awW9Z0nDXB5VK2Wa0FJtnizfr6/Pt4JoJctMri0JU7RJQk2yYpJu8/hJ3hhLuXicDI0VWEhkzAOtwOYFnA54H8jTCN7M71frjlANQDw+PUJR9tuJ6isqlXGc2eWMi9BJRLzwPO/Z6MQ+V4bem7RsnlBmS4hRDGX0Qj2CfJ6oUqiipM6RxxGtE16EtffM2rEr9h4Wm3acAWMlCVPFYuC9IzuBUpmr7fXqA4/3I1/EFVuKCZGyydvynBroqeaPogkIJp1mMP7Qbs9me46TjoIBV0aCUfMTDBYmIBlUrDig7/Hrgd6JSRCCI8tMrZV5mkAruUyIeBtbameCoiZ9rDTvtNWAV6F0Han5LmnKllrWBYaworLGjybRrclMqLV5JolfAN9owR9O2YtYeqbavDWJYL1OmHViiT0+tMaaN1mmxz5ndomqgdJCFnwQuoMV8zjTOSVfd7SbJ5tv6cLZWMXueo6pzV/anlmXjGKTVtu6aECuYN5TFcFHA5PEtcQ38cbWLmYIrE4NALmW9IN0YuzyxqJEqzGbfSUOHcSIVGXtI7kW26uuE2NsXlkQRKCLHVKU4ODg+IDOd0Bimvd0nTGjLs5fkhV8TlycP2fVr673K+c9VRO7MbPbdQy9ME5w9923+Pqv/ToX5+eMlxeM056aM3ncoSUhSZEaLYwjN8kKtg6I9wxDzzhujGmWYcyTee2JsaYW+fk4JYoUY5yPM8FZCI0EA1eP1r2FsHjH8YE1NlJdmjkGKuaSCDhivzJ2ZbYmcKJwfHJImrKxO7yjNtZ01oqSwbdGRBCiN3/YVO1cnxHUemnsBFwtfPHiEfPlJc55Jm0J0tmCcvrO0UVH0kLA0UugX0ecg3Gcefz5RzwXIUrPVdrSqRB7OyNcbUfG/cyDzz/lB3/xp6wO1nz0ox/xh/8H3Lh5m3iwoj91jKWyH2diXBPSnlId3it1HtmkxD6Ndj2co17u2D9+TvTCp48esR460rSjHxzdycD26QavyvHKI/0B2+LQaeT88pzNeIV4x5gT3/uz7/DJJx/zW3/jt/nL7/4p35DM5599BH3Pz88fcXZ5ycntE8ay4aNPHjFtJ6hweX7BXDOhF1ausJ03XGhF9okqjn4aKbVycnNF0dlsC+aMxEh1gYwpVcRB9g7EkxN0vqOIra0SMpvtjigHZMkc3z6w/XmIrA/XlDmxyjNddGwvL9mPG672W2IW1uEWIsIb9yLp2Qt0VrabPS+vRnpxFPcqfEbdZKmpo9JVRyKxjp6L+RH58wRzTx/W7C6foR7Or87pfcTHiltfmQJiNNlW1x1RNxf8td/9PW7evsMnn/4pq+TZjxNHh7fQ/hgfb9OFjm7lOH/8IeI2rI47gu+52Dwllx0n62NODu/QRcecRtgaW1O0AeYYyaJoYXP5gt14SfaFFJRZK/2x5+LqGbm8zTRB55SDo9vkOXN5dcacJlbrW9bAUlptYqmpgpE9JHR4BB8623c8xNgbE9CtCC3llRrAe5KY2sAa9AHv7YynrSZxMVLniiPj3eJLHJi2W1aHB7TIDRbWtzWhC120cUJrkM9aoRSb+zj24540TrzIMwGPqJCdhTeEYOtVN/TE4YDVwcCdG7cQ5/hgeB+cp04Famaatmx3W8RHYhy4eHnOEGGSDuaRrnOkcIur3RVDeE2F8Ut4/NIBpTklHC0tzTmoxo5RIKkVvVBsGxSjO2tLcRN5ZTK8FANIY4jQtPNGqTGE2DUjKqQlINk9dMG1Tf61biV6/ZomC2pOI00+E0JnG2ApBO+JQ8c0TlaQ2Tph3W+1w4xqafHwgqp5MFAiJc/mozRP5FwX4asxKZwZTtdajKaPpWmVJhUK0pmJljSfF++J3pPnqY1vS7fxXvAh4lxH0UAVA8m8OGrw+L6HnJlLRneTIenBjDaLVvBG23feDjFGnTYmineuFQP2MJxrMfpqvkja7s2yYbaiTlk8S14VFq9TL1wD8Zx79X2rERo6XBZPJ6FRc4zOvbheAnPa4cOp6WVrQBwM0VHFurLZOXJJSHBINAqnqMM5hWppdxGLYD6UYzxCEEeaR+bRs1qt7J42E/KyjLYQQE2bWpqZdhzM5yVoZXadxVzHwRBr78i5cpGek1oHsBZLXKOZTosIU5pQOWr/3wpAeZUS5ZcoX7AFDn+dcEVjP6RkYy700RYy57DwhICLlagdtGLV6JyVgsMFYzBU1cYosIXQ8I9metoAHQ/G+hOxaeNdYxo1GEyXsdBkZdepUk3iQH1VmbbFXpyzzQWB5iVjnk12YPftudKMqS1tUHENBFnMwl3nCSFYB4tIqdB1gRBi60xa/HXK5dp4G2dDLOdEjMHML715gy1G5Uv6oRO5BolCcHYd2qMsoI8urI0GaqiBX6VWnAuWikcDt66ZeEJVA2NFDCCiLXHaDPRV8y+wzrxzdDEa6CCCK8ZWi9jv5EZRSY2t12BznDbPJbFZd3p4wL037rLq1g3QcmaKGS0C/M137tMNK6a6pdTCauW4/423efjRU27de4eSCmHlyPOGsi2c3Dwlpxl33HNerjhgZo8lWm23E93K484Tt+++iY6JLig+7/B5yzaZYXDBG0vOLpStt1WbYayt7+ZhZwavC5vHO6FZmXMtDK5mUg9WpC0soCzCrDOH3QGrfmA1rCgps708Z1jdZRg62M94FfJ+Q10Hnjw5o+8CabdtaaEBj4GNpV3npRsFtC4Z1/Oitj3EVjFbJa9/R9oTlnujDUBtMw/U/IXGiTxnioMbb9/ix//3T7h1y8CTdd/jioK2iO6FDSIG3i9LcNWFkm1/2C1MR5b9t/GRrhGk5Zn/PqRkP3rVYFgYQYvvm403VWPg7qeJkgp5TsxpBh+JoUd0j1CY0mhgvzOpzgGB42DMhFKydf9DNLYvDrytG8tD1OLuDUhra4oY5Kr462aJLsC5M9lbytVYymRS3VGL3aQqFUJPTa7JEQVqxrV5lEuhiFJEGSYzf5+0sPJmsn/7cMWz7ZbgPEdHx1xuJnDGLJg0m79JyaAeJ0rNiTdu3mSclLwdcatDfPDcf+dtLp49Z/PinEPXUzWT8bCKcDWhZWfJUaenjPuXIDPZsCHCEPD7iTJntPMUJ7ji6F0kB9eS7hwSOjoiriuMyVFFeJxHfhYy4+4KjXb4iUGIcUAa0A6QS8IHTxci87glDAFNhSlvqAVsy1yuvRXq3kdqzkidDaSg7fFlZh534AOOhI7KNE5st5bERDvQl5osUVGMqVi9Se9tWxFSY5/ZfG/egdWKkzjP1JSpTmx9UJOdhBBAM6m2AkKdpfyIYzvNbYyBVJNzaKnm39XWVxWH921PU5Nd2d7T5nbbszpgv9tx48YhQ3SMMdr60GRjor61shp41Bj63jp9xA6ct1RkiklKaQ0lqr2XsryOd1ADQSG3TpRgQRcWYGvsd60JS890qNoq2rlI16T//eEhcVhBVTYXF6S9Gc1WZ7Kzu3duc6saC6Wqsb+W+2XNEAHfoaXixQBV2wMdwRfyPKNEPNBFIfQrvA+UlMhlIvrQoq4roTNfklXngEwZr/j+n/1fiO+ZrnYcHwX2044p73ES0DnjXKGvfTvHC1Eb6CvBUh0lEMRBZ41WxYBosgFyc2t0ShVK2VojOEZSTnQuIqGzz6yVgKOQ8dVfA0LoYr7vremYMzjzCK3VGoNZCxoqUQoTzhIQpSKpGtXPCRIgz8piERCxNLCuyR63uz0+eKLCydEt3OFNvnj4gP2YcE7oxFMLjHNml6pdi2EgiJn6SrAG2aiVTiujVgO0gif6wHYe8dFxJD3JF8YijNuJMHTEAFdX57iodKMjqbCKjjLP1DyxBIyM00wqoLmScyF2jjlt+fKLj0lzJYXCuSiDBDOb3u5xCn0nJPHsrnZMaobd5eOdNcdLpvPw8GefcOvkkJebMy6ePOPZs4cUlLzLjKVQSmGcLnnwyU/IY6JMZgdSEIZ1bwFKeTIvIRfQgzUpKTVviVK42r60JpK6ZplSKCUgRRlTIVGJK08SUyoUHXEuMjuI0dL5kmCpXSyNr2Aeif2abnVILgUOHaFfceiOCcHRRQt3qDFw53hFqol1P5M0kUol5soqDsRDj+qeMlaiy+QMK6dIneljRfKG9Spy/1d+hx9990+Yi+LKDmgJw9lICzdu3ObmwQ1Ob9/j4c8+44O33yGIcPOv/23+8k/+mOgK0wx5d06aLrkolgwubJFu5vRojYRDcrrC9QaYheg4ODpGnFBrsnW7TNR5tqZ5NRbQOCc2s3C1DcQI8cBS9KLv+fLhQ7qTu8y7c27Mld048eLsOYdHJ3hsT0akpcu+Ou8gSteF5iFrwPngB5CCyIDpjARCNPKCKlGiAfrF7lMIEacRFqA3zfjOMaaMiKfzA+KVslqRppGT01vkrEyTrUMinq46qXHXAAAgAElEQVTv2qruzP+2JtZhzUwlrCviFWENIqxqvk52tyCLbK+3T2yu9sDLZglkNd9qGIgxMqyOCN3AEAInxzfQhqfcfeMWAjx/8ohv/tr7jPkdzp6ekfaRq834/z7b/X94/NIBJa21OY7TIp2Xfq0dctuZuW0+pR0OGhThpHUPHIu05lo+IDQzN5MjGbjxGkjUijFTylg3KTrTi6tvVVrrrC5Yx4JAKQYWObUul3POgIPgqcUQ1dps302m9WqD1trsCKcJt6pEbwcaLyZTKzVT9q1THYU622brqNbBqvbZ7aClzDk140/TlkYwk0F91cGuslCuLQLZ4xtrA4ZhzUHzgNCcKcVsN637ZRpRvH2uxW/KNUBq+VLq9YHAi6eWZFR8tcO7LazN44iyKK9wrShqAqnr66NwzaKxQ5dJ5tw16GQVn3XUFe1Co0w38+TGwvDBW8QsCt5ZVyhXuuDx3kHK5GJaWR8ip+sjnszn5oe0+Ds5Yzbsx7n5rhijhyyMs9GlxTmcOqKzAwLaEpMcJLj2dPG+o8zZqOVOWfcDzg3MWBxj31WODo+4ujxnzokg9tr7NNM5i2cuuZBTvmb+lFJNCyyvfIZqKzJr0+nmYgCe+Qs1VolzNjeczZuilZTmNu4bTbNJh4z8Z51e10zwtRoIbFHcpc0puz+uzUdd5qpb5qU0kFev2UtWXJp0QwREDRz6hZq0LPd9qU302nellEpO9l4soYnr9BlLCLGiudXahBibYV7FVZPdjXlkM43GUGjAdgjeTLCdNNBa7f6LknIhpcTQ9aQ8ITG2NaIZbTt5je2weJvZtVj+vXgVwQLE0ViJi9R0+a/NjOV+S/NzWdgjLEk73gpiMCB2kas573ExGuaq1eafGtheqxq4jqWF1Yb5KhCctHthoNjJ6TH9esU4T63w8YgIJU+UUuj6njffeYvPP/4pQuakS9QvH1AutyTtmbIwDMeI62zjmoU6jpRY2O+u+GQjkHY8e67s9+bRotuJKZ3BlFkFMw6+2uwtgcg5ur5r7J2WGIleg0h9F5qpuRVhUl/p2s2MtPkCaetKYeNU68LM0utCbZwS6hxxveLo4JAYA7tcOa36/9D2Zj+SZUl638/Ocu91jyWXWrqqejRsUhIpDSWMoBcBgqAHEdC/LeldBEiQA3BmRDW7p7urq6tyi8Xd7z3nmOnB7Hpkj/RYikJVZ3ZGRnhcP4vZZ9/C7c0N5f0zWhbG8yOpvmY6LBxubni6POGJCHE2EeB5NCv7/vwMKdrf7tg7u2F1pNLFfkn7lthB2euK2kFA4dI6T58+Yd98x83dLT9+2vin3U1af/rhD3z69ClSbwaJGmuTeA074CM7FuCg9S4Fv74WPlujO0D6j8Gk3VMtnsPuoSf4M7d41in2ngiXy4WPDx9Yt5XDzSukZs5PK6bQdPPmicQslRvJ/Gf2ii9OM/+Qn3ie8xWAd8qo7y8Pa3gZfPjzzde99/KaUryuSM1L2T0GDUouEfcuOLNp+MRxCDVPqBUfdmlHoynyhtPTxKxOTNa52Oams8Gqu3QHETQ5ADKso2kf+ggyOnRFzNg0mqulkKZMGxtJ3JxWpgle3dF/+sBkieaHgbs4pA217pPJqbPlziT+fqYESRsFo6eBSqKqs3lEnGGNCDUlyhAKLr3MA/6U4Q+18Onp4ub9eaKkzGU9MUb4DmVzNosqOoSmF3pvsBo6MqVmEsW/n2sXEKqvmwxzqYwx0cfF99Ho2CZYH4wwN08IrbeXpE08ZjlJ7L2oE1/4cb5evam368AlqTNc9vNcU4oz25lBpUSiDrvUPFGXiVqLDwclhgBxFmFGKi49i9vXrzI1JAULPtap2v7qEk0vaNtAC43EPB9J9oxaCjtrdRbMvsuuZ1nDmEg4u2k3lQcn0PhAo5DwmGkNzzBrCUkjmj33n0pImOVLgDrO2EaVtq2oNnIVyiyRLuxBHKoD7crt61d896tfMd0eqTXzhz98z7/5P/+1p6J13Qt7VxckCcAukUTpeNhN2zZIyYcnpQTDfYLWIQ+gY83X/+F4h0U9/vHp5HuxGg/PZ16/+cLvG1PmIvTtQht36OrKAVNlmqaYWHqdN3S/k8WlxiqQmhuM1+JAWHIrDceuhWSGbg1MqLNLzttQjI2+BduChSkNcoIRtUJGItXRfRW1JE+ZnYvXvUDNFe0Dy2A2sQ4P2Eil0tQHpKId7Ypr1ZQ63yIk9PzEFgOXXCtjrBxm94n77d/+Lb/6q/+WL+6/5t3lB5f6TD7k6t1oKTxSzSgpsdmgSEXEh3GrKVN20F2b8tDOLu0x8d5rFkqH4eZfpArHpfqA8PmMbsL5ckYtudetRrhRSujoXPB0XQPO2+C0NQ8BWQeqwkUb2Tq//MUNNzcZUuGnbdAxSnG28uP2TE7ZZZRjMC8T0yLo8wPLnNG1YWJcNqX1DgzOJ2MuA/rGlM2ZU9MCJWr/dSBjwJw5lHuyrTz3RmNlXAwVpYblypYqCXWPsNEd9DkLkhva/HwdujqLTTNr1HhoouTEUCKlTdGUWEvh9nDgdjmQlnt+bO+xLLz+7jv04RMXSWi6YbTGcjTmdmbtXq+XtAdSDeaq5DLIeaIK9POZj8/vuH37Gjsnvv/tb/jmn/wVH969ZyofeHU/sSzChw8fgcrb5TVLXvjx979Dn08cc2c+vIFUuLt/wzS/4lf/7L/n7/72X7Nd3vN0esZyIi2Jdhlctgvrwyf+6X/+L7l99Q01uY3B//yv/leQzLvf/57/6+//jst68d7LlDbc8/CrX3zFzd0N5/OZ3ja6ddrYGG3w8fkT/eE3PDx94ptvEu/e/cBcKyl9YKpHv+fNw1cYUfumCKoK1YUZLvUWo3UgQpazONFir7izeDiCpkirLSVsZBLYiATpwWzZGU2tc/r0wHJzYI7e+t37n3g6P3J/84o3r78g50TJmc0ESQvaGiVXSjH6aFhVevO7ao50bVKiJl83NjxQTLVHuIq4YbgMVAfPz898/PgxVGBCSRNzrtSlUOfKVBdqmfny9YGpFP7Fd98gCU+b/hk/fnZASWKaThJGd8M3C/BmTyITwQ0uc3J5GZ7+YSi1lM+KW2fdpJKx3U9JShSTym7Mquo6clK6FhLmyEYAWFFkmhD6t0j1cqDJhgMAe2MOjrp6taLX8bLjHnptzHAAFCOhzT/VhsWvhaENiQmW5OoG7IMwKhzo6K5hToV8mOLCXVnbmaEd7Y3HqVKyJ6RgzsoAN+EjrUieSMWLuJoqNWVUHHwptZK1ogEY+CZRUsluoH2d56eYLkcjGlVNyrs0z3+ve0pQVK1plz3Ijgz4YnYcJJru0NJr6FcV9YheLKRxMbEPDyH3fQjqvSlFclRsbiA6RUxzyp7619rGeYWSYWuKJvGppUE9LpQnl1Z4c+ST7JoLuRaKZA6HBTMoKVNL5TDPpKkG8gzLSNTDxOgEkOj62ZKMw5KdGaJKl8x86+av1ZIzT/DG6832Fr1sLj3ImcNyYFkWhg3yXJFa3Eg4JX/44g2qobSmIeHhuq7dPyukVrn6r6PwHd2noSXnAA73IioH4GpISS/T07R7KTio5wNob4hydv+VnY7vjal/HxNn8Fgw59p4kUxiiskLC2sHhXcy04s59S5DjSYRrvsKoJR6Zf7kkq8MiH0C2nrn06dP9O6H7TTNkBLr+UQf3dNtppmbmxumafJzCGEk3/Otb5He6JdyaxuXy5l0d+vx2nGe2E59jPPG9dT+nFQ9hljEmTLOroz9tDfl7M29xtfw6eQOpvnZ4peq75lIUAzbXP+8PcHMqJNLQrv5OjAbdNUQkPLizZSSg3FmqDirc4gyl4k3b94yz0u8rT7ZGX04QyzW+Xfffct/+ru/xyZI40z9+Lfc1K/5/W9/R6Lz4Q/+DEZf0eYG/V17JNQZ5dxpB2OcV94cC2PaOKonCGVRnkbnuStaYAoZ9L5O/HeZWjydM1Gczm1+no8dxUavgNLOjMlxWHvTp6EhE+oAUqZ1946Y5gPHwx3LYUaSsF4ah+VIys7yWJ+eWe7O6ICnx4+M00qdZrSHNAXZj6YrO2l/p/Z7SPDiJqB3PptmuDeUQ1CE8JGACHfXKwfYRGFNXB43ns4nxqVw6o3f/PjMNBprd7mVXX2TvEjaYX3fv/u+2uVsu2vTy8efg0n/7z8ze8H/40sjouF9F2Dy9fOJ/W2slzPv/vQDX7596z/f1rmcT5h1MHN5AMZf5AP/zfKKtz1zbo0fzDgt2f3WsmEyvAmMl2iCp4EO2WcSgcbtwto/x7EdWXdGmSYwwrdJc0hdHJBzMDeRayal7uD2SJ5oJd4UrqYkKpYKqhtg7qlXKveHA9u8MDRxnI/cTjPnhxMpOSNCckGTP/+MUlLC/f8HKc+ouDFnIdO3wal3DjrQkNyLgS4ZmwVLhZGV8npGtmfmFqlfZgxRNjPqcOaeJcPYQorrdRPqYBS28Vgzvx1GuyiUirB5GAYOlrT1hNdbBVO/q7ptEDK00aGkgo5Mrclji1NFpaFsfvbnTC6V0gpptWCyZpQwh04vBvKBCLoht2qsL8WS3wPXOlF3SMlowYAp2YdAipHUkLF7aTnI6Czq4QClOvMtiZuVyjDK7Ob8zWm91CRs7Ow2vxt9Le22Ci5D81osebJvTqzDQakx4KkN5pRhDJbbhTwl0pZoBNAV7y1IML59I+VI+cuRZkQYy5uYp2ThATOjJNo6roCvAFZwc3T8vsgpg3qwjG8OT89KJZG1MpfC8bBQ58qr+9d8+803vHr72q0UkvHujz/wH//Df+C3v/5PfHw6uUepd8fEV3PQFkAGEjXPNGUul43t0tAubgFAIof3YcqAZGS4N0lZCrUK2+p+eUOUZI3L85l5PrA9PflwRYy+PbPMsD59oo2oz6S4qXvNdPWIccnu75hxKagPnDo1u2diyc76UVP2uHG6p8nmyXuDZg3CtzObMtpwe4MBVhNi5Qr4AcGuN/fAnI9XM3pVxdqGJoOeqQWspQAihBkfWHVJbKOjo4WxvZGG11YlVVcd6BbDyOHn2Fz5v//+b7ldKvM8YLi/2EbD8Kw1d9FQLMXwZih5SsxJEKqznmNICRUGLvdNvqdy9p6nD0OaUbK6CXsS5jqRU2JdG1s8Vwt7EVMjDa9/EGcZe9r1BtbRkpkOhUNdIGXO2kGHqxdikGgowxIMPzlSyVgfrKdn8uyhFKk6e6kUnMmTHLDuW6NUJz30UZgYnibIYOhgMsiWwM5YV7BGi+FjnSpTccZZ6TFczCDF6wXJKQKFvDapKaMGS05sOuib1y69eQpqGolLjDxySshhQXJiHYatK7kW7PGRy8MFbhYP9UDQVBmTMJU4I6KNS3Hn5kPhL//JL7mbbvnNb37H8x+PvP32G2YVvvr2l2xqlD/+ief33/OLr95wkyeW5UfWsfL69VuW6ZYvcyHp4Iuv/xnaG/lwwLRw/+oNv/uHX7M9fqCkleOxsA2lbReWaeHx6ZHXb77k9uYVN7n4e58S/+Hf/zt66/z403uGdGoqWCnIGJTitiZo4fVy5DUOYJ9OF969+xOaVkq+4ePDhZESf3r4IylVUpkpU3Wv0uR80StxBCdQoH7PdeSFwICS0nBTeDWsiDeqUSIN3UCMqbriw9S9lbfhgKKwMkxZt41pmemmfHp4oFlnrjPnyydOl0dUV4ZeIFjhQiWJV3hl8r62b8PDu/LESQcoLHFOSHFyh98JPnZg8gH62jp304wPUTKqF0+1HnYlDrS18+nDIyrBSh9CLQnLMNXEMh/C7/Xn+/j5ASXsyqzwVIq90Az/FNshhGh8PmvYVOSaKiYihOmQTzoiBt3jg70MzxZacgLo+MxbwYjp9D5BjWLl+mEWlDCNQ9OFXik+p3eP1PTpmoNQTqoIoCqK9B1G8V9lkkyYbdFcFxBD0gR4VGsiZEG6N/CVnCdHlKu4Afg40sZG2zYkzBFb90QNbWswPxImmVxWJLtO/TC7wdlYV78oy8QwN1gjWF3dnEI7WieXyuGwUPK0PzFHjFtzBpG6VDGJJ7B1VgK5+KyBknhT44C1fRruWv+UEqO161NyMI/wINmbbn8/dF8LCCUVcqS6SRIkTXzx9i2H+1sw9xiQoCObGWvzxAoUTAfDnLl0f3vg8eGZPFXuj7fc3d1xc3fk5njDzc2NU9jj56g5M+VCmWt4TwibrRAeWCmKziHKIQslQTIvLOfemG6OJHbT5oTIYGtnvvziVcTvemExzzPTPKMmVxDAxDWzOxNqn8QK0byKgwMQPZP59CtJcvNyHfTuUa0p/Kcy4jIkcamfxbRRQu+VUiblQq2FQJ4CKIznCi8Sx7SDrYTcLIp6M/pwCWvOXvRiJVhPjrSPKzPK10PK/r6nKFhMlT66T/AjChyVMDF2htK2bqyre2r03q8sBWxfO57SoMMLgzoVaq3My4Gc8pVl6F5KFuwnZ94NHGDSYCxJgHVCeH/sQEecZSmVq6xTKPFeBwvPdsGSN6wOFHjboViYlPuf7uD0jjO5V4q/xyMkbhZ7y5JL6EyEw81MqYWxbfQAVwre8Hrqzv5YLHpoB981zqzb4w3ffPW1czcsIuvFzR73n18N3nzxhlKqT5et89qeqemP2Cvhw8cLM/7Muw1nV2Gk4rKHRUDlDF2w4yAr1FwQ9bTHi8HH50YbwO6pZ5BL8smuugFx1+aysuSyRGepjrgzdh+JYPzYzlICSCEVyWSBUieeHh/pY6DAuvpEflpmDocDSXAJVMlM00w7XTxy/PlHZHrL6dMj6/MzFD//xz6tFU+lVNt9qHgZdFz3asi14yMFvrMDOg6uS5yfO+T40jCbwWDw4cdHxrZyumxIyfzhw0pli8ABI0t4r4gzgovJNb3PBynZQRx8u++31suV+PK7f8xM2ptx/7P98zxIw3QHmV8g1Cv1XNzQ8uHjA7fHA08fP7Hc3fqayYmci0uxNuWv0x2/oDDaxns7ucGwhKxVPPlMitCHkQNYdB/CkGPb7tG230ovAJkRtaTYNQEGyy4x3x1fVF1u0zffA7F7nckpASIbQvcYcklRw3r6YKGQRZhnf+4FRSJd1b+10mNdCB5YkLNLInVMDIMq5ubq3bxRJMymcYm9xP25TZWnCVZRDmpwnDi/vWP78My8bbQxeDc6l2HkujmLWfNOnvFCWxxQUhRNyvfA49aQ2plub9ieN3rfXAZQKxIyZ0nFG3MdUeNlehuUeabkCZHBfDyQ18FmLbyweqw5N8kuxf+l+3pMKNaFXPdzNZ73MCztIIlcywjbz0Z8ae+pirtxnMuUjC7u5bOEhNuGuCeNbs6Gt0EV8QZnP/tHNISlMMnGmqALFNnroTjfRYAYusTeydXDFAT3uUnJh6bWjdPpzHw7UdNgvp24XQqXS4OWwgNEMenBXkguxZLkLC9zD6AUck33BPJQBokm21Td3L267+h132YfejmPCaRMDiKJBnM3UevEPFeOh4NLaEtlmmceHz7xm//4t/z4p594eHjifLlAEdbt4vV4glQrEoymYT3YhP1qjH4BbHRne4vL/nV0nHWbGZsDfqmIMwGB3jPPPepQBkuZaF2Z55muja1B1YlUMxpysqTm5v4Y8+wNWxY3otbqIGKmO6kbY8qK5BmkOnN98u9xfngAHc4em4RSvQm9njcpX3sc0UjZS5H2K8TAzjxdMhI1dbih/NDOMHEASv3Ok+Q10uhGJlMnX+u7Cfw+AK/JQ0Paur2cxRlu5pnTekIuhtRCLSnYGUqeBBmZMYRy8Nc+hnovoT7Q1BiCec1VSQWy7kqC7qlwyeWYe01T9n4hrB1SS/SkzHXB2koPkD+ZD+Wusm9J0Xd5PUh1k/tcE3k+hA2DkhG2JOReHKxMIMPIFTYbThQWcykl0Ay2AXntTHNl21qkCys5EhB7d1VEronR4l4wH99YVtKcyH2w1MKGp/6KZCc9SKFko1b3VRv6UmdJnpgmC/my70XNlaU4i7JMFWsSNef+TAeixUECS/RNWRbDuvL84L6RU8r89P0DQyfK6ZlhMdSpBYmaSbIFUzdjpZINbuvCzXTDNmDUAxxvmO/e8tXdHTJNzLXyZZ2Reeb1F6+oJfP1qzdIEn7xy+9YykyeJ+6XQzD+hJ8+fuLuzbc8bSvf/fJr5jnTrVFT4eHxwun5AdOOMWAYv//tb0hSORwODDM+/vQnPn36wGlsjNFYyoyJ9x0Jcx+2YPGLCKNtPD+fqVV49+5HPn364J7M4izeIpufC8tr79Hjft4Tj+Ok8yGFDUhzDFTC+y7nF1sTc39A070e9ggX8B7ah3KGJFe+XJ4fWEePIcuZu9evOMz/hKRCG51Uc4RFDA7TzFSzewlnqCRa92RmVyaYsyEpFAYEYzabp8Jr9O1SUqzBROuNORWmMrE1H2jnUhw4zcnPHymU2pgPh2AGDve1lUJvK2aDp+dn2mfWAT/Hx88veYuC2g+ez0Ad3UHAaN/lZeppWBQF6dos29DQoweDQv1vNu0eZbQ3ZTuYhB/CrsYJovte0bLX1HbFPvapsIWfiZnr4i1kMnvzvBf2u5RBdmAMbwwVrheGDS8cdIBqFBvJL01GMDECDPJG2oJV5V8jiTNnUi5km6m1eaqdKbmt/rMF+u2Lf5fXuJo+mYGOSIRLtBGUcYZ73URzqWK0FsbiyacjJWjh2+XCtrrsZei4Ag47GclsRAUXxVyk5O1JQSYuPUIitS5AJOcc+OWoaMiuiMYwNta+iFS5ubshT4WlFqZpRiRz//Yr6J3z00dKVo7kiHkVTqNxGm5CCUJOmbrMfHvzDa9fr3z1+jV3rz2SXEQQDY17AAP+syjaB4NEXXIsnUiRk8zQTkKYJDGl5IAAMLKhq5LGbgQMKTtFf85LMOiCuSUee+rT6Ryyq5BWqQNEuwG1Dxx2kE72ToBwiiZfp4IBbKgzndrWnI1VfEJAvD+9bR6NmTNSKqW4Z1DJJQ5Slwv5vR/stCjV3fzaJ306lJLcmB0TTN3Txxlsu0dLFPDhIeAAloRvkLpEwhy49SmpUOZCzs6UWbcOWLAD9OpVhDgDq9bZKe0Il/OJaZpIuXC5nGiteyxnKdTQRhMyPNXx4lnk/6Gtg3XbyKmRshsEJ5mZjw607pK9nZ2RrgDqvm7892N/a2JPX3tyNfZ/fL15g5oCsLqmYF6X/4hfB8AqvidImSSF47Iw14nLevI9Iw6W911qJ35xmfpkF0lkxX1hZOPN61vevL6Pc3Ff/7uHTpyjo/Hq7o5lOfDQH3m8KD89N27LxuH57KC1KdswNhvOUsnu4lGA3N0MWHsAieB+YaUy+uBxEz6eXd6Z8AS+6bB4AaqDto2QGoU3nLYYQAAy4hyP8wVnYRXJvu9q4f7unpu5stzds2Thzbe/5P/43/53Hp9OdDW2bSAqrkE/LvS2xT4vLKXwqa8Ine3xifT2NafHD7izWWIEmOkT493vKIYFOxAcYGdyFOKlQd59AlVjwBKNw/W2iUt0/6XPgxliPH04sW0XvvryDYepso3Oees4Q8QLoR3f383dvRjam20Hcfvu+4U3SNfv/o9ApH8sg7uCSvFpu3yPKAL3O8D/O0iJkB9CXwdb3DcDN6lNOTGa0LXzqmeOBqf1GR2NpzTc0DT52TPVSiqTRwBLAjLaRzALV87rhfVyRnVQUmVaZkh+b/XhshEbyhjdvRIcxXGvNjOwmO5jwRZRGMqpPTPNhXmanV2UBFTRsTJdHtEeA44kDgLUhZR3aXbm4fGRbd2A8OwxWKaZqU5MwWxUc4ZlRlhmjzhOwGbu+6CS2MTI8fwtzpNnCU9K86ljv5n58bRxenygMThp5oDwWmbyGBSRoMS7B91ucj764FwT77VDFUbfuDx5oW/WSQFEFqeR+PkQuIsDKplCogTAoAnyMA9cUb8TRhygax9Mhc+mxT7F97vC71oHaJyZ4AbcscPMQXGxkASYgKj/uezXtQ87hBeWnv/rxtW+WQMYEB+Y9NGpyRm9vnKNdduoUa8W5CopDtH+FQ0eIWtLEjKxnDzyPQlFMi2p13RjIJbIU4BSuXK8v2F6Xn1gNOJHREmZAA8dzkR9r6ruowqCBfoS5LGjuZJioCSZjEv2xwj5rAwkTVfz8ZImRBRLHiqynjvn5yd+3DrtciGnxOvX9+jo5Fo4vrknXyqtn/h4emI0wZIxlYptyc/Q0RnBPNwHsex3X5w1qsOHWbg0f5e0Y5DN8YO2NW+6EtAGlr2x932bSZODG13dvDxHwyTmIHcOpYSaUkp9aThNGX0jTz4kVODN29d88eZL/vj9HylLYWyDZieyCGVyaRM7lhmqhpy8nt3BOqWjF+8lcnLG1dibxmB8t81ZRvv68ddjDiYmTywkZI8+c959AP1dbs2H3NdQhWBebW1Qcc+VpDALqF1o20Yb2Z+1ZKa5kIrQu9BXQy2jDNTcBmKouTzOHDTd/Qolv7CtI5kIQ6h7xHoxZyLidWOajLSpv5hgwjoQOq4m+hZgtBIBP8X3pkS/qAHaDFNG8zOhVh+6+mA3h/Q1PCm9EaNpc1+wNGGakTEQMVrzlOz5kICGjbAjGEY/DzT5WccA3TYfMLSdkeLvU87CXBrbCIaUKcVypJBWRvfURQPqlKnT5FYUgAW7TQceFjNcRj0ETD1sJSOsvcW+j/RxawjO0kYzoyvJOnlyvzPrho3BSALtTEqVy8fBb//m79jyxHlr5NFY339is0xeOsevvqTYRqIChbvXXzLdKmVOvPrmK9bnZ25fvWKpC5f1AlPhMM/88//xf6BKp06Z9MNPpFp4fX/LuDROj49oH3z48UceH3/ylGtJHOvEpTd++PFHtsuZ1jd6azzYJ3QbdDqJQbPN70xNiGaKeCL4+XKm62A+LmyjoZu//8tRuLs5cDwcnd0l4QOMr7Ewv8WS1+WSlCzBLhUJhVRBqiA6SKbXftUHSL4vdQy6BAhVKk+XM7mHfOsAACAASURBVO/ev2dtK1OG482R+jyHUkM43Cyowd39W0pI3BFnQPtdaYhu5FyiHnNz8mFKLskHMlFj5ezrXWMI6ox7H1DPuYBCKc60rKkyTz7k7zGzlgQlVf9FThzmQh9GmYqfaXv//TN+/P8AKLn3RdqTrFSuZtmmO3IIPoPyzSbik9kdsfO4P7+kvWD1CX8p6VqQY0KLQgyC9Rfgyt4g7ePh/TvukX670e6ug4/Pjjdyl7ftBfr+eXItWnyKHEwcXi5Dl5UFeGIeb+tG1PkKPCUTCF3xfkuNmMiLFTcp1/Bt2s0oEw4yZQkcK4CHoIUncXZNxgEhZ0oEyLP7IgVIN3R4AaHOjmhbcyCgVDe13Daf4g9v5HNaqTmHFCnMxY0AyhSxiG7fqYbmpFqX3bh3kYT8yYtFZyH489lBRX8uLinzZLllnsil8Ob1Pa9fveXyfGHJByQNZn3kvlYqialMjJR4T+d9vzBSptSJMmXqPDHVgmRH+zXM+XYUPMH1Nbg0zxPDGM4Iy+GLsU+mMr6pM+naVCESzAhfCZLCeJzQNpc5mr0czc4+3QxpYRQ6vtZcKhXLOAqLF+r7tUkN5o6pxdQNkohfOmOEt45dC8uUcvzdgxfSOYXkU152T6ztHuw5C6BLwq+rRHWuw8L7rLs8DsO04CpXlx85U0AgZ5ylJoze6L2jo4cJvR+OJQDUfY2ezhcHkHa/reSG8imHfCsJh6liBrWEKXq+QcTNeKc6MU8ze+KgT0KdweXSBv/ezhCJxjL28tDBfFgcTPWAqv1QiDMhDnV7AZeuReGOUl+BUm/qJS4TL0qjQY/zYgfYvdj1N/fFUNh/nxCP3WWXgyWWZWY5zHx8irVrznYIp7ooCL0xyld4X6m5MInw1S++INcSHho78OAX4v4bNWOuE69fv+bTnx54/7DxN2p8cwQbna4eP96GelqFQtGdldNpVtCeGDiNt+tgdFh10JtyNgnmnT+TlN04cWwO0hQSNk/uH6YwAoDcJ8WSCjVPlJyZp4njNHN7PHJzf8Pbr99wf/uK483Cm6++5uHde27ffME//Pq3/Nt/++9Jk+9FHc27TlM3Sy35OljQACFZT1RR1vfv92sLFaOoXveOilyPg3iRwagJ0D0WS7ouJ5+caZx3+4fv0Zd7Jni+V6nK89OZNBoluffLpRsmL15o/t7tcsv4bezhlzX15x8vHNv/78Lic5Dpz5LfrnvA9+gubzWNs2x/FAakiOydMod8w7peSJLR1qAbvW3YfMfD84ljb6xj45yUlCemaWI+LhyWI3W5RabqVPQ8IQqHw8Lp+YmPjw88Pz/Szidubm65/+ILUppQNZ7Pz7TWsK25754pTYf/unX62kA3rCcSHbGOWKZt3Rl0Ww+vl/DActollhpN3Z+m5MJmRgaen05YV7beeHx4YrQR76k/mZKFqSTqVD3SOHdy9ajnZfa0LSkVUZ/+awopoURYCQ5cahgMYxHikWDF+EHdp2QicTMfuV3ukPWEmE/XzfY7AyxlktzwXi9uQJwyNhqMgU5+SaXsEgTNk3sR5sEYnmyWspFGw1pjHUqdXT5CDg/J4aDQsME6hHlNHCeXxyaZkNSdSaTCpRs5DIlVlYFSphlD8KBTvyPzzka1EYOViZJrnJd+RohwTfs0INfM4eaG0T0Jt3X3dxw6OJ89FazWmVyqT7Itsa6dpkouhbQbgF+BrNhbGls4S6S7xd6OfZHxoWpPnlzs8j2/v8txCdbXXsd6s2CBhmWKzyHxpJ+UJM4rI6eCjhfAxo25h5+3sf0keWiATIJYpuZ6nbjrUPr6zNo727qytY0MlJxYaiWnzO3tgVITloo3gusTp9Mjz5cT67oxtNNN2dYElGBWB4vNdhnqi4+mJ/OJU0kmT6n1+8Ku54yougfTcAbvfoaOFgMGde+hcXrG5gS9XAGZvq1sXalT9mcZaoMxfABQYh+JZLbu0lBtm4feAKfzienmjlw8ebHWDOHvmuzlWauFD6q4PceIRjQlje8lUc+GTDGBDmE0c88kuNYi3ig6g3eZS/QPwVpU90yJz3I5Li+DOYvPEbqf8VkQ3BzGktB0xP3vq9LUZamoD2lKEbaBS25V3Ps2mPlmOwM+hpgWAGew5vZhBeL+SKXEvrCElExSyKOSS/QLOWGjkTR8O2VAJszAvTFGjExFVEjJA1q64Z53wAjeiD9Q9fPvCvISwTkavWIOxpf3InXKTJNQ6IwNmhISamcHatTuCfexLQhJetSBFslgQkqdsSpQIjBHfRjRdok9LsPKBjmz6SBpRiUHYGdIdkAMddJDj69/aupMspohUqOzZCdMJu+bSG5LAZ09LMZr2gE2GNZ52i583Do9ZVThOCd++sOZp3c/cHNz5PD97ziPxMPpwrSdqApNKrf399jDhgcXJWbJ6LRQjvcsU+f21R3r84nnbWAnhZpI97eUo1LfvmFtRv7iF3zZL9wchLuyXA3drQ2eP31iW8+gxnq+8Hx64uPjR9g2Wh9c2hNJKve3X/Dl3ZdcLo2PH9/x+PTAtl64XB45tRVkMEkhjQmaoMXBmL0ofwnLioaJHP2bYVbdewiXmbs8NQbu3VnTPkz30IUiPuppIUFzJUUhFeP8HGnGn5744u0XHO9uEczB7KjXU6mOc4jRuwcTgCeFKsm9kXKck8P9Dq8WI1pQwiReoPXuXngx+HBvpeb9ZxibD1W6DmfX5krEITmwBczZGAOyGL3Z5/PEn+XjZweUdsRFr//HvtF8QmFXgMcLeYbFhMo8RjAlf2MJX51gRqQkVMWnU5RrY7er2JwBafH9dnp6/Fkwjtyexa5+TAao7BCXTwItpBuy//faUMZ8WvZOMEPxxlvVoCZGBg+LTS/GsPvUKGRHKQk2vAmUAA08rc6ugItZp4ccxyThFt7DfRzEL5Nhxp4clQMJzdkb77w3F0MgeWHnkr/k2vFkyNCr9GgHD0opjFpdWhKvvbUWzbk/AwOuKWNEoSA7G+wFnvMmKphdcSl7up1hfW98ovVQb3glrob9DisCeVPuTHhzvOWQXerz+PzIYZnJJTPXCabKXS680sHj6AxxeZDKy3s5bM9XkiuIZWZXT4T9tWokuG1heGs6SGMfH3oXJWVP3doBCW8yLTlIUfafK856wRP2nIUi16+ze08lglmXErv58nU7xSK5Pt590cSnSEg2HbTwi4eyNz+hLZadSkr8nHwGaARyHgkGKd5lU/UmQo0kPj0Xc8aLiMtDRvd0rl0KhmQKBcmenIfubIyQfgmY7UkqAwtGieG+UFkyaZpIBlvvHtEc+iBVgz5idSWfRmKRiihoeFtdJ5FDKVkQ68Gc8ueveESoiYAUl9iVGcLMvM4LbV1pzUGt3Ux+B1EdPLWrNPblPdmptvsJ7efQC/sQgtvK9Q3YgZx4v1NKV08mlxAJonp9b3Y9da2J5Tjj7DVngiXzAnqIhRG3//29WNeQHN4cFr7++qurB9D+sbNLdgC9SeaA8t0vv+Uf/vgP6Oj88GC8f1YmGaQwQlc1WkjQXLLpz7qFR4YEANNNQOXqz6ZxB7j+3Wng2+nEX/7FdxyPC605wKdjRBjBIKdKzrAcZ17dv0HMWJYD929eMc8LOWfu377heJi5PK4c7299ArR6iMB/8V/9C/7m3/yNrxftTIeZQ50Yrb+cC4BE0qAgbhi8PtPHxQsQTZSraOnlDHw5+2R/96/P9mrBRTSgAeTuBaiwA7N4QbTve+J+U0gJ1nPncln58Q/vOG8dy866EOkUKe4ZcAUnndH2QpPz73FlksaZvptr72Dpfub8GSvpH/2ez75iks/BpNgn+Brcaa1iXgxtz2dKTtjurajOzlGDT/3M99r5UgdqHRWo80y5e8XxuFByAYRtvXA6PZAkU6aFw1LYemcqE+nujk0K03JgWW5gGOfLE/306LXBcDaS372ZnL1Y1DQhcuB8bkzs+8nj6zkZo198CtzdE1FHh1zRw+GF9dxA50zrG5+ehM0SW1tJ5zWYCsXB3lK5uTky1xpDAZ/ey4B5WphLDQmNXA31DcEsxVnvZ+P17iRx0cakAYSKs/6yFJTBm+Xe5SsaoQRhOFokOTuFfbrbaeNE14FmZc4zwzZ0hPQfkAKS9gCQ7M/Se0gawf7YziHjNqwW2mg0/exOtexmwsMtASQbJbuUYeQwTt7vurJQs/sWlVIwg7nOiBmXy4XtciaFhDxPCwhkgto/PPRFckFMKVPi5tUN23mwrZsD8CuehHtpbG2lpmdKqczzgVpeO/Nm3a4+mdehwV7vR73pDYD7XWik+maSf39HD7DhTKcxBmsfHNLCDkb7MM4CLHSfSb9eEiSlhPwB0tXrKxmkkr3WS/ud1+itkcWNx9d2Zm0t5LDOihcxrMPYNtCB1IwhHOYJ7Rt9O3HuuM9VNVo7Y+Zpt71vbJcHtnODru7hR2K5uaFMnn4HnsxoXSNR0mucyPPzWmGXne4eVDG08lYr7qtkIT10NCqnFAPORE7OfpQhkJQpJbJ5guXalVQTObnPoAXIvXs71lTYsoW8EeaSeXj3E08fH7CcOD8+eFL1Dp7HcRel9FUW9rkX0m6+3tX3pZr7qO5AX446rNSodVRDIg417uacPSlwOPkW1U4PX6Fd3ply9DgWbiDiQ3nb1Au37P/bzejq/5ookvQKrLbNw1kye9p11O5JKOqgaEmZbi7X3fsD4p7eh+u7fE2sIAwfyCKYZsbYnKkrbmh8vTGssCzF2f5jkGahVL8janFZjpqvbcnKMnlKWu8g+/A0ZycDqNDcuAgTo5tLUmueISmjwdb9vqtJyHMmi6JdAqhTyt6rxhAcx7KR0ZhqYbThQQji6g8x5bwprcUANlsw4jwx2yz52VtmLDcuo1HLRLKK4aqRUv3ua+phE5mEZZf0DxNyKUxZsB7BUCZcxO8AqQVrjR2QTKl43xRgtfc4iomh2UhZ0eZyuIf1wvP2xHN7oJBibRTetxOny4WyHFnev+fh+0qqxtPvfnD7j2lhvvmCm3mii/G4Xvh4ObOeLtzcH9hOHWxQlpnDrTOGRDpv7w/ev5GZeyMl46v+La1vJBPWy8rHD5/4+nxhSdBJXC5n7t5+wb/453/NX/3Fr5A2+PTxAx/fP/L8dOHxp/f8+PEj59OTEyqs084XLu3MwHg6P/B4esdqm58vaQQTR8kps9sB9O6eXYdp9gADVdbWOF1OpATH5UiuHug1bGDiw0uA28PC3c1f8nx55vf/6YHT4xNG4nBzQ10Wl1DPM3vYzq4sGWME49D3b9eQLIsDYBIglFr31L2d8bdfhuqqm7HXbTa8x+puISGlMLrbTZA9EbukUJ5ch8SOERRJmHXqVD/DaX6ej5/fQ+k6hQ9QYaeBfNbk/FkRHSIl857sejgLHrWdcqFrQxmRTBC09M/AIv++3pCxF8byUrB7AxFaWX9x8fcFV+DuF8znr80PyM9hkl21r1FEIclTHcbwaN5diywaoIszJFJUIJ6eBaTh/+KFGUmC2aChdez+swvOARYD2+WA7gHzAu8EeKfqztQxOTM1UvakFVO/0MxGfG40CslBhiQ4bTOYKwgh+XPj4649ItW5et5YfN892c0botBFS+g+xTX/pN1s22313ZC4h/bX5Yk1Z6o4bV6SRzdOqiybUJ5OvL5/yzS59GH59muP4hQ34E45M0mmqpG2lcdtjcbcX5tIpkims/9eQoa0rzeuU5Acfgg6OiOpp1NIopnR9XPT+D1Jy6VGhl9o/sV8LQ+RK8Cg6pLOcW3c7YoNKZ8BaQBpL2R2iYldARmJggTUkXghmGwvaWEpvC58Xe0A1d6dyAsCzr7QvWjbfUKSgBSBoCmLGhLyC9lpOXNBTdDwcJJ4zT779ibRZWaADjezzdkTeILlpngiX0r5BQzu/QoO+0TRQv4aKktx0+gcR4uYoupfy+Oc/Uibaw5pyn4+OLJfaiUP9bUoQh/OyJM0OcugDXKutNboY/f5ijMhXvcLJuhTNXCwaZeh7iyjXZI6zIsJonHQKI5IL+clFpOwXRsvewqXa733xDa1gYmbi17PuOvzt5hEvBTw7MBUgE83N0eOhxu0ub+HGw1xXdP7gVdTonfju19+y/xv3BxdMU4NHi3Y7PglJZGaNSwuUAs/ITFKyowwCXYw3s8yjFjP/kDNjJom/pd/9T/x1S++pDdoq5uktyhsdRhzyRxub7m7f8XHdx9JKXF3f4uJcdoad/dveH73AU2JaVl4fnhkWjKXbeXbX/6CZa6cGfSuHI834SviwL0qMBSRAIVjEJJQkvo0R4mfTdUZeHFZ9wBedvQoOK3s2CHxPpnk6+ScK/C8A92fg1BRuAvXIce6dX5698zDj++w3btIvChNRbDxGZwZU3sCILkCQddufQeRuDYDvKz0+PMXMOlzEP56bOxAODtg9vJh9gKq7SbBWc0TyJJPq3cDdpPMu9Mzvy43PBYlt8YlV8YyscwL03JHqcnlyE04rxfa5cSrN4Xz8xNtrF40ilGrUDLQV54+PToLrW/u31UyneHJOOYm0D0JOvndWFWwrvuWwNaLN1MK2+aMjByslJIrlCOtO0tGhzetul24lMRF/UwYIS2ToLwX8ZhfiXqgi2DDICemnFx+PBo5ZfowggwR09aMDsGGMtcIBKC6fM0MKS4n76ZkFSqJV3kht1MUtt7QOt9733d+3hzJiHZ/LUCqQutGqukFbN7Pq5Q88EG7AygBnnkYqLnXiQ2SHei9h/cZ0IVmnUvb/OzNAsHiXkp1b5K+YSbXVNYUQ0dngCSmDJIKZoXE5MwgURIuJyHOipTcV2JcVqoIeSh6ubC1TsmVMVX/OurNR8M9bBYSxwla3zw5aet02ffx7j8oVwBWQm5KfwGWMJhzcuAgZxAlJZeWdfGkPB2ddlpjWLqDhi+1lV+Cw72JEObJpfqpFrBO35qHSqinvVpXb+S107KDSHtdqL2zWQoPELc2sOzDhtbcfHg9WbB5lZwzdeqUnOg5kQSmqbjUJmWw1es5FUqZef3mLdOSyXH/XS4rrbm0ZfTB6M6gG80HRAOBHkMZ83olZ0/n82Hmy7lISJZs7GekD6OkFKwP3+tpMBDOvUMMm4kBQRIHPzxtNZgokl2qZlFrh9+b398d7R79pGOvv3wgfAXAcCDP0j6UlJC9ueeORZOvcadbADspF3KpziQLw2A/N53V1LdGa0JeZiTbdfi8P48RtHbHQfx/J8l08TS/PBdSSXTTaDb9Th6GA27dG2dvKBNFCrXuTDr35krhVUUqMRgf7B6zHuziz0VDklmiXxvN2Vm1FmdQ5AxpXM9Sb5orpeb9VmA6+tpSgyyZPiLsRILJVuAw3zIunoqppgHQZbqEFsQvFEpymazbBXTa5ulrBsxLoSQ3Hj816EMgB4ts+J4wFbZgSI9+YW3Z2SLZ6xtTeHoOBhqZkl3zmyUh0wASKVW3ZmkvdV1KQg2GutbM8bayrcrahp9b5h5m81ypk5DEa408TxRRtqKsWyeJyzZzCqBakt894sSAvTe26MXqVP09FCXXiaVvbMMTjal4yuEYtFSx7UIt3mefm5BS5oeHJ6QbxUCmA7fzESg8rquHM9jGwyTUeiCXws3dHW/fvPGeJBmX7Y7pODHViYxRyUjJSPU7rhuU44G3X77i7u6eNozLeuGXv/oVf/0v/zv+yzevHVhTD/wZA9qlc7o0xjaoBozBej6zXk60rrz78MCvf/09nx4/kQe8++mP/PDhB/+87dn3NonVnnhug8EF1IOx1vXEw/NHxuhsN3fc3tyxTAvWQZNxmJzpvdSZj4+PbFujidDUpXKfPn3g1f1rJFcM7wt2dEPMy0WvDxUp2WWcuYC6F9NOrHEK4QvQ60qvdK0pBxY2nh520HkZaqRESCBDgkvz/juK7ZwqtitoCcPvzwaOP8fHzw4ouclc9kbdjER2Y+i9OAb8B7SXZgiuBSuEXICIHyWaOnNkcfc/SdeORHbcwA9bSZHy469njwt+YQRFk6/RaLHPmfdmKIpl2yETApjw1DEvgM2b5ZTjsojmW16SityIzrBgEZn1kKyFN5QFj1FAcDTasN0P0umQSWEomkJ+E8AXBIVN/Fn5a3aN/e7H4zR5iWWzT9TD12RvYAJn2JO4NKawGp4Q3rFq+Nxw/X7N9Iq4vkhmZO8Qr+wKLx52ECIu7eF+TrU4OyXnxKHOvJoXKs6umoq4AZkY0zRzO2WW4vrvnI37u1v34WmO0saQmCUJtzlzEdgk2OGxtjLCSMIQmBTnQ6UXM+C9qfU/8fWRzMh4wdeGy7mGGqYFKTul27uy65rEm2y1WOP+ZKiSXBYXzeRVthjrWJLshKXro7x2o/G6bD+UvJNn18blvdmDK3C4F1X7miboys6S8oJ1L3oI01CJ6afk5MwYM0fRVRHt4fuT6Jj7gA0HN4b6Gt7B4CQS39v3UcLBnxGghwSAlDBPsRsbvY8AxGICbjuHJaRCPobxyXgWugVdNN78Mfp1WrgDPyIglt3osRZvIHKmqbFtnW27cDmfnak3BiIu79QxqDlxPl84HJfr+QQWk7kcMNP+7sb22BtRcwBXI3HGsKvXRnSS/taqXZs8bJc4vZxbfgu5pMmLBX+ul9PG49PzFaS4ApPhH9LjTM1JnMpuRCEM9zdHn76NmLiyy098XanalbmoGG/e3HH36hUfP7wjzz7xIOSdFkmGLhHbATe5AiVi0EPCkjJXEDcFzHpNeRRnOOUM33z9NX/xF7/EEJ6eT1y2RgLWtXnRPjrLceHu1RtUjfP5Qio1WJozonA5X0jzTCKzrp3b24m2de5vZt588Ybzjz+4x1qprG31fdCVUopPLX2D+VmeQLIzO1BQUcQ0jm4HfXZJwg7L7P9+Dhhd8Zbk70e8yfgNRwC5wQCNz3amoO9pzLCS+fXff4/ZtB/NYMrrtzc8P/o6tus/fAYkxdT82sBwZRX9OYD02QLkz37zchd+9iFAyv41et/v7s83g//8tSTm7E2djcG6roxtdbbNuqGa6evgd+PEe1WWMvH6yzcc7u7IxX3VsggdnwbnlGGaWC8XtstGOcyM0UGV0VYONwunxw+MzRvyVCqSPSigUEgFdHVPDSmJtTsYlysYnQJ0G+GpJTAV9zLU7nT4mEbnVGjBtlU2MjO2dWRy2rmIBxX4c99rDZdEmbJvQL9HVBA8WCFdJ5rGdJj44tuvUYF1dG5lopZ8NXbV1Elp8qk/ShudpoNhiUPKpLHF1/Q9nXU4g0jkeh8MM+4QvpTCj7JhZaKN1Y3xY60gwrZtIS0rYIPRfchQcqaIs6NLraS2oZbZ2bXWnd4vZqSSqXX2SbE4QCMpUbOvkd3gepf1iOwJbwN00NqFmitFDCtylUKnMdDWaabMh4UaXlSn0zlk74lt3UjmMspR1UGnHaDA03VuD0emeSZLZrtsXJxqwsCoMW1OwUbVvWYVf38zCXpDUqI1BTW20em9I3V2s9QBeUr01nl+PLu3VwxTkiTS7L6HCa8VNFeyds7rBVsbrTd/DttGizVmIkx1dqAWx4xzEY7LwcG4HTBBOV8unE8PXC5P9GARJRuIOuNpmSZSFkpOTCVsJnrII0hel2r31C0RprlyczxQi7CE2fd68DSxbbvQm0vct3VFew9vpAqTYt1r1hIpsyq7l2eKvzcg7eeWe0L2YB6P5h6ffbjcpavRhjqOIQSTKTxLEpQKOzu71onmaRBeJ4d0SgDtbhmQpPp9SNT6Q9j1FZacsZ3MfbIUUHGA2uEwPzGF6Bvw0BB6eEeJp6GCgYbRs8HlsiEyRWDezuZ1T8mX09TrJ8VN1sfQmM94216DoeQpsX5X70ExfYwYKqv7RG2Z5ZCYpgODgeYcNXMML5NFbZlCIq5e68lnCgsNX77uNdf5fMLMWZe77FPMj7qpVFIWtghNWSY3wt9DUkqpziargo4NUagpI0k9iWv4z68xqHNGdzTTJdO2xtY2JoN1W0kkpmOlFvefO23KRb2+TXj5VdxjhNEMa0YbjZyKv4+Rcm1DYSTaUPqQKzNJ8STNKp5ILBUqwsyAzc+2nAe1OFgwlczWG5fWySUYvXG2Gh1JUWMYdPVURctCnhNjHehw5inxuvpoUbv5e+a+wZkeYGWqlWI+IDlU2Bq43YqxbTHMzePK8ve7slDmmRVjXDrzUEpSPtmZMQqfnh5Jl/f00yf65vdDSolSJqZSwSAvmWmeeH08crMcqfPMPM3My0K9OXC8v+Xdj+/Y2uC7v/zWezNx78vbeeJ2zj644TOGXxU4JO6pHoTkN0pYsXjF1Ab818+NbessCB8fnvj+4Zk8jD99/55syu1c+MPvf+J3f/o997XST4P37z7w+9//A38cf+Bpfcfl9MTz5URbOq1tNBncHo/MxwP97IOltFSWaf5/aHuzJ0m248zv537OiYjMqurue/suuCBAzJiWoZGiOA96kP7o0YNkMplsJDNppNGuoUw0kRSHpEABIHCXXmrLzIizuB7cI6s5er0qGIDurqpcIk+c4/75tzB6p7bqw+qoM14GMIldnZXSzsBzcDoVpdUeDju7ukGjJPD9LZd8VQMN8f5gSR5SYuZhJ4iRUglyg7kkEsVGxYk0DsCqOkP5pe9zlv6PTVH68SVv7PR3by7s02hWiwJ2n9KHOaPLJZzhoSHPqSqoFqcATgXD3d1lR9xi07NPLojh4IZG00b8JFcWk7z8rkXB7i8nNu+X5j1+cUcknJEUv4sZXeJDNdijzm3//XibIxIR9g33GiUezWMfrqXEOpL90OjDmRYeWRpNV+Y6EYvu1heYEJHUPjVPDNQ0mEHiIA47XdiLTFSRSNTyBs9ppr37hr53p6LhA5AcCe/BPhpmYeoaPkuhG++tBlIeqTzJJ3Wu7fe3nZN/lodpZplnDlNhmRNzKRx18o1CBpPAbrCeTCjiANpOl+7DWR/Feb/uEyDu9RDWUQAAIABJREFUWTQFZbcGjNaBGfX0kCHkKOR1Bx12c06za1NtO9vHDKsbLWSMYoIOo8hOUwdMQv7VccRXQRJRQgcra28SXyA+saDQB/DnVOYrIvGyFi3+LmFQvfs8BWAraW/gg68WAIsz+tyXY1+XIx5f2KVtvjH7vxmxiLx57SPu2WDuaWZXnbXha9cL8Z2S7lyPpBar0VsnB6FcgmaxL2zbRm3VWVyteaaymxwgaQeQDZIG6OaNRuvB8OnVk8B6pbZ2bcKdat7JZaLM3nj1IWy1cT6ttHGm4zTi1jttCzlc90Z09Mbd8canyikxBFLODqhdr9w//P89JdIsfOOM+Dx9r9Ed2JVPWvQ4LK8Ad6ynEZ4jukuVPpl27muk98H79x+5/3Af5KigvUezprGM9LpGogwNkO72ePTG3gYpeTHtnkTe3O7+a16cuDzw57/4Ge/ffUsiUnPEpTe+d3rBjDpQ1HTE/d6vK8wwZIQprvj3NMBOSwHIqDCasa6dVGZ2NuicZzdvbgGUTsI0z2RJlLTwWC/0BpqV5TDx/PQI1mNSCr2u1E347OvP+D//9M9CEiIwOtY622VDigOoOeShBlxavSa5+LRPQX0/2tMonUHkBY3PLV+Aoat01Q84Z4rG923sgJHfd8ncN8X5oy5fJOSxhrPBBgNLwrt3F27eqKfQjMFxUn7+zRv+9rlRCc+9TzxersENn5yVO7PCz9AdeI619AkT6Sp1272grtVHNBMBUl0BcHFvFv0EEE/J/YVS9l1BdSKNlS4KbWPbNmyeGeLpJZepkA8HDp+9ctlKUjf57Qm6gy5iuKk6HjrxxezT8K06qHypK9vpzCEf3DNIU9xnCRVo1VmQy1xYh3nyWu8uC8qZ7i7GUegm3nz9Db1tPP/wWwfD1c/sRKfbxKCCdLK5hKz2DcFlJdLdmwESSS0AiTgvxFmbIxhwKNfn7mZYc/Ds9c1rHk5nXwPBbG3uVImqJ3X5Jtg8EGPskSiZ1CvXoATbWcXqzOwAbgRFe+WbUbgAj73StMXr9vPEh0+RrugbhUuxNFGWIztMvCwLTR/Iomzr6tKsSMBSMco0o2XBM/QG1TpDOiUrx5uZEvIna5UyTe4v4p2+s+6SQ7DJnDmlOBDsjByf2oo4UJTVPYHiBaPTDHW4OXXYA7jHIByPC8ty8KYgktRqa9ezqAthtxtAuSOCXjekAJJ7XOPeqDnSrRCkDabj5BJvXFa0duPcB2maIGWXJbZBr5WH05Mz6mujDUV6Z+tukq4lpFP6IjNNSYHO5XJ2IK4TxuqP1G3zXcU8DKNuK2J7qpIPwRwkIM5vQbL7tSQVH7Ro+DMOpbbBVh1MufRBLsXrdnO/0zlNpLR5rZdv6a2zHGZavfB0PmFDuLu54/n5yQGA4X4eistSUGXbNr777gcHiAJsRRUrPoAcrbPW7rWBDDaD2t1nKqmzqZK6x2jJ+/7lRWhSpXcflLkHacJSZ6+iDSNNCc2fGKCbxh5imPUA+gdimTbcrFk1DPI717N0D9ARxdkwJnjInLPuwqoeTEO6pNf91qse9/rT6F9yUTRn7znE+52sSpfBtGWkq4fzuDYVSXiDMlq0Mw5ibWvHGuQ0qCSshldYyUh3SXDdNvfg0r2+NQffYvqREowuyFCyJHpfcR9KrwUTEkwnv/TV0wNIwQ6SIbTaonZwFpn7bmZnFvUBWrDmfopNPO3SJTy+26QY3F3qxunxido31u3CNByEXkpyyaxVaoNzrTSTOP/D71aVrTba5vV7nieXzUbtI7gvk4UyBEkBznvfM5JitZGLIdo5ZuGIUbdBmkow8o217jU8V+Y5IqHYcWZe2ZR5ylz6mSSZ86VjyTjOykXcs8vDatz3KqcAxzSGHeAg1oDejMu2koMN7ri4sm4bVK/jS9SpSS2A14yVgs4TR81sUkndvUqtQN+EMifqeaNtF9q2BrvTsb5h4mnWCiUpv1OhSMKSx/RNmjkuBw7HA6fzBS2FD7/+Da8//5w8O2P0eDI+vv4Jd9/MvLqbKXkXAQfDWYgaydfhzvwVBnMWvpgPXncP4fWXR9529x/9/T/8GUWE26TcXzofTme+mgqlDy618vx85uFx4+OHEx9+eOTxwxPnhwfOj4+8u//I6eGBj/c/8HHcM1jZTk98dfiS364/gJ183mEw5cl9AOkvg1ZcmZPThGlnjI2wbiJJDoCnk9XhGF8ffhYMU8rkYSApwGhXA7snce+GSENz8aGUuCefM5O8Z805I7lQDf+cw7+5jU4pMz/m148PKKmbNZrFFm6+UaQAipp1b2bwhAc3WfTNKgirIEJWZSQ8TQqPDjWGe1rICzouyIusJ/rvqHEDhNlfWBTNOw1EgrWzN4PxrX067MW2BHrsB+4gNvJ9I7CdGSJXsGzgU5Nhvnk7VdsLPi9exL2RcJTbm1UJquC4Nq+aNEA5w8SneDai8b62Jn54CTvq6AflbhcLQiopCqdwlBcvkovtfkH+UDllcs4cDzdobLK9OoCXU6a1HhOm1WVEakit2M5mMLsaiuvkFGZvsAetD47LxJvbz7hdJqacUYHjNHlhaUIWYVdb7xLBXVI0xPX1CaHWDTRRJAcQ6OslkkVZVFly5rxVp9LGdH9XkwzMkx8CAHQTQn/OYX6YEgCTTwpca52TkKYcwJaBKUVw/4hmFDFM/AChd1KSmDSNYMJp4DV+s7sUzAGSl6l+LNrhIEWctX5995vJLKZcmVZrfKZ+nVqPiaO6Tl1V0VRiEtIC9FByStd1axA+Uj7htxGG7cKVaVhb5bLW2LyUUX3dqUIRX/XSa0j+fAPX5M3lVqunL/VKN2ithxlnJ5Xi3jgIJQkpJ0YbaEmeBIIfkKoOeF4uZzYfsxBAvksjspvlglHmmeXmAJbZ2sZWm9NTW2frgxZSUM3JP7va/ObBi0ZScj3/8KJgNJ87Smz2L5I2vTbrIjurx6G6FKbpmgJIxzeQJHIFn3yP2u/XvYCUayKNfy6771slUVwb31cenx64XJ5dCvUJqp5Qj0C1kG76LgYhVUhi3L2+QXKhyC6ri33DeRPsMEcLJpmNwc9//nP+7F/9H+hwj56rbMDhaPaZ7JAdzRrQ436CANfiMhhxLzgQqSNAHFFOtfLu3fuQyXgzMRRKTu6L0rpP/4MxmfOMkKhtMC0Zk8Hl/EwpmUMp9G1Qzb/3v/+P/zP/+X/2zzmvfv/W1nl+fmYpmaLObGrFWbHWq0terbt5Zk+0evFGIehFprj+PsCfgHxc5hQyS/OpAj3OpDBKw6nXsLPTdqboy8AlzhLvAjxXx5QqxmqDufvPGnB785ov7265f/PM7z66t5BQ2dO3TBwklBHrWAByAGH+dCmSaHxtcj3vrnTo61zFZSK+P469qyHiwtjTEMOcI+QZsU+mFA21P65ooqtiJNSUYRuMwbYayxvldTE+bit5yvRUcKPmdr3v9hQco/N4emJZZqAhDNZga6WSKFOmj5XR/WdrHYzuwMNA6VZhVJIONpwBoaqYHjh+kfni7hXTZ5/z8Otfcn7/LWqDKS1kOr/306/4za8fWKXQZVB7R0YjbQkbhSEtWJ+JNnxAgQ7a8D1VowgXBj0JW6tovQTNXRBz5uC2Vqj1Cij3a/xxGB4HwB+wGWZ+8mQZzN0Zwi6h7C7XRJgD+Nbwl8yaeFsK2pW/a8b71uhDqdKAwmQGWbE5gyWmMjk7P1jbOSemVMgl+7Q/zixf+w7iW+/YujHfQkqFPhrPOqimiGYOy8K8FKidwcxxWVCDOpxZIeLNZa0rkEjTFL5mniCYSqHkRMnZDc/HwG78Pc9lRutAuqDJi2rHjzN3dzcsx9nPLfEJsiRDh3oakBppDJfjx/pl6LUeRPBhmEWDF0mTOnxvqCVz9+VnIeXvbKdHzo9PfPzwnlobdXWZlbXN68d4DW5EHn8u6mbivXHZKqN1RttiUOCfw0iZaVlQ66yXZ8ZwkKeZ7+0pXj/htyJAC4hRTGjdfWlK1Oy9+dChj+6gygApXhOYJlozUimMjrNlxoDi4Gink3VGcyKXjDGzNKWUzNs3t2R98Uxa1OHFPM9MYtx/uOf99x/YJLn/Z/K9tq0NEaPZ3hFAoji7WZ0B3lEsO/g0zJv+a0iNeY0pAeR280piH0a22uniA8MeBrlJA5gZI5jV4mtRvQbpNsIGO4D6mDJb1EUMXIWpvv59M42zT+VqQK74WSfhKalx1lytG/DaulF9vaVCa42uCYYxFeGybpxTYs7ODjzmBVKj43K1ZtAvleY5GQxTxrpRg8kwq3CiksxYLys2zWzWoA+y5uvwwNmDvj7Jznpo4o294WduHmHOLhosDEctd39Tvx6RidYHOgfzprsR9aUa8wxNGtOUGZfVz2aCaZkziPDw8Z4P7z7SRCjFB1Nt67QqpGKc1o51YzMYqsxq9OFm30UztQq1bZhBmTLTUtiGUdJMfbqwzJlnq/Tun1U5ZF+L26CURCqZ3jpbW3lVMnc9sSCsc+bcN+blllY378k2B6pEsoeQ2XAJahrczBkUmrnxcj2vWOvYXHh+bKQ5k3Iimw9nky8gPwNHI4vXC2bithwK1nvsYz7emA8TSFjId8BnF9TzGckZKRN9QB1uQTKXhE3KnN1cerWGlsykM+te/9IC4HMp7sCHDtZha+Pax43mAPC7JyWl4mCkdL779jfkXNxEOs/8zV/8BX/353/BTz7/GT//6c/5/V/8Pj//xz/n5vUdN8fZwe6J6/1l5mAM5mcf+wBMfU+6S943HiZPoi4MpqXw6lXhRhwgvTPjK14HZjFoXRnNa3NPhvUAmsulcrofXE6Dd+8eWO/v+fXff8fH7z/ww/3fI7piljidHjltj/RxobHxtD4ywjJEVan1Qs6ZlCeMQdsGeSquYIn3MfaBXqQPZimMOAObDVdD7XujKoiHedhwLCUlV2KNbr7/two26Lg3sYPx6bq3/FhfPzqg1PpLAgK2T/YD5LG9XfHpd5bEaJ4Y1OsgFeWCMJFDziKsDKbjEcuvXPIUzZrTwKKgN5/m7ga6xOzF2VJ7Yf+JXnCXn+ygDDv0I58U2v5YEg297QW/hrGyENN58Qmqo1HocLqZFx67r0Y85W78ZH4NXhhLYdDV8bShAKhEIgkpaHM7UyUqejfdFZ8qlZSZNLGtq09P2P2QgrYZN5vFNCrlhJJCzuZmcClnpzGL8Hx6H14vfo1b81jtHk74DjQ0JhaXBcmtRxceJnLJtO5ytJQVHYk5JW6WG5aUKQkwlzipRQLZHoEbDBwNpgDJX/OIz81nMBIGdPvn6Q1Gb+5ZcCyZtWfO4cczJBhpcen2RLHrh4+Ev5KQNcXPxfQ2JTdOZFDyTLWBmoNE6xhoN7Za3ddIxcG/lMMQ2Qtgn0E4ci8eqs6g+QTVMjLMTSjZX5Kvc3/L3pzuSW0aEgWGMS+HWCr+fpbiIGSNAmtf4s5WyztZgt2PQ3Bdv40WOKsDAhp+Wuu6cjqd3QQWn4TklCmLkqR7/K7sEk5Frp+lcT5XTqdnByLDm0FSdnpmgq2vDniJT5r8gBDyNJOSp0K5oqdhY3DZNqe7F0frVZW2NfdHyE7pPN7csdaN5+cVaPTRruw6yJSiTmfPGQy6uJwhJfXpHL7hD4Q5JzyZbaCS4qB0ADslfbl/xbfQ3fNI1adjeZdipHRlfUjsSrGpBDgdLEbxqOPd7NMZZX7vZYkJJhJAZiGnmc7m8lSuZHyufkpxL10N+RjkLHzx9rVPKEyC9uqsCLOX1xo7OYkJSiINhd4wpjjAnXG1g9t7op4RpuWuqbmCEv7t/WcIfyW5vk8HqRzg/uUv/5rf/0e/x93tMYCJDcho7jydnzkut26WaZ4UNM8zKRVyKZyenrisDtjmeUEYPH/4wL/8L/8b/vyv/pIuimmjMRi98qtf/4qbmwXJuxQ4YVI4PZ+xKKIlZdY+rrIYw4JIGNcprpeO3Vh/v4+d+RXjBvZUUZDrNdkp+zurUD4ZUuzXS0XYM6BkeApWv1Ra7IHL7cRNKXz16jXf35+dsaiKSnb/EFPaNjivG636VIuQr+5s1DbcCfaFrbbvQiMSLWMHGzsgRZyVfubukr0iKYCbADrxZk6L8Or2NV99/TXvf/genQv9ufpaTgdSEv69f/onHMvkTfTzM+W0wXxDE2USn9rVPpBIdh026KMyBpyez5SUsW7UVhm2cTPdcnN3A8Dz4zPrtjmTBwlJQ6evLWRP5hPEsXqyWl44lom3X/6UdPuadXvmNhnTXHg1Z26OC6U3vrw78vTG+M377/YrQLo50AfoOPnz5UKr3gibOEOoD+PS3VdK+wqayCKcLiulzD5oEmFzMw4ubaOaf/YJY5LE1tVBDZWQnsMIYE9UyV3pKdNS51h9fNpCwqKxNn0DKsgYYJ0yMl9L4pjhfRdW9aSjUhY+0HkeG/fV6NbY7CmY1L5X2NYobwpvbxae14k6IOVBjfly65snuSFY+ButfaOZA/pBrMK6UZKzMNZ1JWdlH/mU8HBZVbFhHJLLs5BBzh3IHOcFLeoeOXnCulwBmhbx3EMykr0YbREzXYIlKOrAWUlEDZOovVJKZtQeTESvKydJLptXoZTi5+YOOg1jay4pONfKzehcHh5YpSJZ+eHvv+f5cQugxgcSumSmNNFq41JXLs1B1NrOoMqoPoBVVXrzIIHGQMKf0ocRivXmQxhrnmwc9WOLhiaJUHZbipTCK8iDLTou17zUDZVMGvIJIOP7brNGM9wMewxG3SjzDefW3bNHEr118hxBIbVj3Qetx+PswHI0w1mcta7AMi/kKfP4+BRmue5bmZMzoIY4G8cJzRrDRL/1+nDpmyRIZQqZXCcXZ4U1AsQdR/d/cuM4LHx/tu71RjMfcBrO0O4uAXAvVJUw6w9QO+YnA/fGUvz5+3VPj30z7rUuLvMriLMUK1jyecRujK3qDPtrryDu/bgDBJhEQIEyxdBuOhyp5xOwUdeFLOYyMfF6oYsyhg+1p+IMomHGcnug141tM9q2Ib0zkkFxtUDvrr8azYGQnItf42hUR3bmldfrHmkvCJN6fTWl4j2aF7J+xoQ6JUU4gJfNibG5vNF61CPsCeDiioi9v8MlcXfLkQ8f33N+enCmeU4R7CKcYgBSBozNR2W1NZJAyZnafWBVxf3fxNxzKJcCyZiY/OcPmdoG4xyD1lToawdpzHNiPniC12awkHk1lFkTNwfjOASzxCyFx8uJoonnuGensifpFZIK86QMGpfnlTI5cDe6kadCxc+KNym7TK42Uvja7cNf1USLHiqpG5fLUOxktDqobSVPCtqw5HIyLYK0OKeHwbYh08VfW08BrBc3Ep8y5+HpihvCRZwBW029X4ie2EKN4VzR6P0NP2vUaNbCP2nvcY1T39C+IdV7no+Xj7x7+J0DLpb5bPmMr27f8tnNGz777Atev37LF1++5Rf/6Bf89JufcXx7w3xXyAcDNU7Na9qb6aWrN0bwRxLdJMzYkw8jwgrkpTpVJvb9Tl+UBjboJoyfddQyjS9IQ2i9YSMUD21wOsHloXN/f+H+/RPvvn/Pd7/7Lffvf+D7D7/l3fsfeF4/sp3PSDLq2JiPE4JBydG7G9M8eQ/PS3iV9Q7DwToRHxp7sFDBpCLSgPCrtT0sJeo19eFTEaU3/zxyFnIMn36sr/8fTLndGNrgmoagQYVHQQJl88m7XdOxJDboIm46NqVMCp1sj+LeRgBGyeK5QhgRDctVvWzROftP+UVVB4vMkaBPFhBRWH+K1Bm7J43tr9O8CZAe077QdQuOtI8KzCElkUSTxu7pxH4INSALmeSGjfuEFyMPaMOnljI8PrKPjYGbTlsYOYrnc7q5a0yyaYNaV7qN8FYSGo3W1RNwphIAmINQkgLh35uY3l0KkpS6bayXFYjPI0/MU2aZbqmtscrKvMxMc2FZbri9e8XheGAqk5vxiUSDnhBT6nYmNcgMZslI714eitM+4xxlDFjtExPk7tpfn7jHBGjY1fzZkoNuO1zTRw+N6GDWzOfzDc+6cq4bm8Fm7VrE7AwuExg9fk+8CCRStvyhBqN1LqkyF28UFkteqOGf82aK9eQmyewpJb6WWoNhmzf1KQooDGsXRlYwpbFd1/fuYaQ7yw9PW7mmyJibgl/ZdObNc7eQKJkDEyXtgOu+6Qij1SuAaa6h8vU9+hW8mKYJRHk+PfL89ExrjVTKFbCROBwMj/At4tHrWZNHeatw//DAx6dHZ7CoJxNpMHgkABT3GAKXfYlT/pOwtY5YRZvQWrsalPYAyYbBoDn4iVOK5zwhKXM6PfHb779lW1eW5ZZpnnx61C3SaJxN1EenrRVRoUwZbCLljIj7eCQzLOROAG2d0MUTHlL4O8gVJvev6zRB4vAJ6cr/x8jY/qGUaP+Pj0CJ4kB9EoEXBr2ZNz0YOnzaUIMZNawHwBO/t7+goKabWZiLCrMot7dH5nl2vwJ1rXdKwjTN7L5PntI2SJbpNpjyQlqenVJrdmW8EMX8MK6G4YKDJL43jgCV5Pr+r0lh9JCahqjAdkAF/tt/8b/wv/4Pf8rd4Y68wEFmRhpc+qCuF37x06/54YcP/Af/0X/Iv/Mnf0yaPHK4bSu9VeYpM6Tzm7/9K/7sf/tT/tVf/CXPl4qmHEb7zUHsYdw/vuP+wxuK/oRcCmbC0+mJx+enK3YvAt3OVxCWKJS8TIHgU+4zgCu45lNvnzYPM7ZYT95+RJnTHfj383JfJ58Cy9dHv5qQCu5p4gMZZSrKXCZy8qbWU4F8P2yts61O6fa1FZ+xKzFobM6alQAFg/Ukw8/MOgYq/cVbbS9sxP1izDy6vZu6f8Ko4QXoAIHlzKwenf7Tn/2Mt2/eoI8fOa0nxOAeY8rApfP1Fz/hcDzQW+ex/pKHtTvbZAy0Nfb8xA50625A3Dq1evOR0hkz47xeUFHmeaL3ysPHB86nZ2dZEuCmCsvN0YciT57otFkna/HGqW+0VOipsEw3tPOJ1DqzdN5OM29uD0gbjO++54svvuG33/+OkSdymVju3vD8/A7djEGijYJqdSkJ6jfLdZjh94r04Q2pRl2gSlYhdZdzr+uJRuKQXOrULcyeAZGMjmDV5ExKkMz9l2zOcDiQtwsAOQyLh0DXQcqZkRvSXcKqfXNgXoTbKdO1kMT3mK/7zN9Z53FtdN3BK2/uVbw2+fzNkalvTK8K1hLrqsx03j+eaX0jIcwIau4WbH1DzJu6WdxHkj4YJbtBeCife+zbh+Xg/hQr0DpSynWwYCZMJRKgDK8X8wjvLBCPvokkRKN2YZC9nxrBftJEzsGmNyENN/ktyZ8nH/wsdgVWQjsxwNnYzhdqXd0QveNm5tWv63w8cLtMFLmh9Q1BKPnAYfGhX+uNul5odXB+3ti2SrfKU0iDs7mcRQcOlPUcLEhjxFk/IS4JscFI0Df/fvi9+s4RwFALqfouHSrh95dLYkqfgBkKW9v8HOq+Z2/bxiBsBqh8//47rx1LgWDvZk306pYIqm5O3hu0bmgq9JAXW+xJI4zLwbDW6dvG1juVTsq+21aMum+POrhslaUsNGskpwMzOpSDJwGr6DW+uw1nbosIlna2TUdGZ54SvTdXTLTGlJwpkszld8Oc2eWKhHbdjzvizAgEa0ZOQrWwAMDheJWMJW/exuhXZvxGpOcOpddBWTSea1z9eXwg4UMMT5aNP8d5wgjRa1Kez09Yaw4ijZWkC6hwqStjeP/gjJ6w4hdPXxskynyLToM1XRi1InniEn5I8/WaOBDVzcdaWPMqvbkfZQrbh5yzy1xVo8Z0j8ylFE9e7e6DeLMsTNK5bC5BQ/1krQy0hieUCufaKd09qjSk9V2Ng2a2rXL/dGLtRu2grVKTp5uBcpgyWd2zSFPi5u4zdAj19OzSP/E1XOLw9jql08+d2qH3jakkzufK2oyS3ErPaCQZTKKkzQ3jP0vCXI3SFD2YBzRgaOvcP5wd3OyV3jxUJOvkPmzJWToMvL6tfo3nXDj1DQ4Zaqd1Y710JumIuoTaxq4siPAW5TokGCr+FlFG3UgTHJbCWjfvtZJgkYbY+wCrjA2ePzbytnK8vYPpSMudUpx9NKGs9URqq7NdkpJyyKoEagsQSWJsup9RwZRXPAWRklz+2QwLQkMPgXZHKQbnVpmsI7ZxqWe+PX2L2EA2YyoLvQ9u8sxxfsObV1/w9u4t37z5itevv2DFSEfln/zhv83Nmy+YXx35yTe3NEuMInx1C2d8iysYIwbWelUphJE/LiskthwhkYBE9jrMwgc2OTNrkoJZ59UbQX/PwG4x+4ox/jHN3P+5rsbjY+d8gqeHlaePjzzd3/P88MzHd+94frrnw8MPnNcHTpd71nriUs+4SmdDo25WtSARJJcoi6F5ojdlmnNgAeLJdAHgurl/C6yjYVaQnrnYhR/z60cHlPZJPAbDnAFAyGN8+ukfGD0M21Jy3T/iVED1QrmNDlrY+uAmwByDiE3di7MAdOL5zPbJaACvYXr74lfCFewifmf/g/wb//QpvPQCOEWzEKW941YjaGkdobuEZTho0sYenS2oZgg0dLSKaqb3LSjNKSYiPfq0wCZVw4XJI0NzzrTq8KSFwVdSZ8NocmhFUiKVQq8BTrHLbNwrJpfCUPe3SSQkRxRtcpCv1Q1kcDgeMRNuX7/i9nAb0oFBXTdSTpRpIk8zy7xwjaSPa1/7oMpwZkMyJjI6hESjJCHv5k+44XdOyf0rxM0V99hhb5y9sUsIaA66sfuxuDzYXJYkikki4fI5xZh15jwX7reLU2uHuZwsOxMk2e7bFY1S/LdHd1tyQkoimzDW6k21KpYSefjkdDentOsj+GvP6pRRjaah4fI2M0glgzj9cAg+3dTMbnDex3CdOfvjarB5PLB8Bx6cHecHhz+v06l3Zp3g3iCIS0y2WuOzClR+9BfPiaw8P514frz3SNExKGVx0CV8Yhi9wf6fAAAgAElEQVQ72wLolZQFyRNtdB4fHri//+AHjSYvUCX8U1TDMNglhKN1lBwU3aCzN59WuS9L9cmYuQ9b0kSZEm10ahtkEtO8EBSYkFYI8zS7fj9743Q+nahbh/BmsOF7S55m937IiWee3dNpO7OZM/LW88aUEje3R9bROKQDWFBL96bUJCI8X3aL3ej430zGKkVjmuRH076lgEEAkXuhN8Z4YSV2BzdT0IlRB+bPD0+M7sanqMbU3PcoZ7Dt+xbxb+ACXKV2v/equDm+qjCC6eHrxKdeDEFy4vx0Is2ZeVlY2wUzcVYbL0DR/vp3Nt3LnfCyd17Bkn3vFrzQxv/uLEyhifC+DR4e7+kffOLShyDZC5ffvH9PN+X//mf/CV/9i/+OP/qnf8If/fEfc3u78Ltvf81f/umf8zf/+v/h23fvqNZJh4KEEaglZYyM85E6ORn3Hz+QNXN7fMV2OfH8/ExbtxejyuQ+Fkb7B+eBS2NjuGCffmtP5+MKHhp2ve/8WsU5Zf/gSl3XjHvDfLq0nNc4VNmGe6kUnN03p8Q0DQ5Hlyaft7OnolSPZh/qyT15yhxnYv6QaWOlDF97bRBNubkiw8yHPwSLwaccETXvdGrREZ4lLjEy87+L4lN91L1PknK8OXBzfM1dyfz7f/ALnh8f+O1vvuWX332PHJShC3cZsMSUlN/7o3/CX/1fv+RUK1qUrTlIZb2678pWeX5+du8chG1bMYE55AflZuHp+cL7d++vxpan0xnRzLzM3NzdMnpnfbqAJIbCYT5STbDtjEyGWGc7n7l9Xf2cKZm6bjxJ582ribxM3H//LT/9t/6AX5F5HC75KZK5KUfu5cQyHcllpl0eHMwzZUnKIes1wTLl7PNcGyw5c8yF1v1cDOY+27mSluzpUx1GeOaU1KljINzE4w2Q5h4oapgK65uF+iguw4YYTMSa69Fk94ZICcbB7vEUUpcErRktDc6j0WRQNJFKwmymSqccJzILx/lIf/c9x5v5ajq8BGtc8oFsRtKBZKWL0Wz3YAHtRlsv5LKAVujGul3IA2xk0pyo24WUfXI76MgYbMNBRQ0pFsNZCC3SclN4VPXmLMw+OrI5qJOTomrUrbGNwbEkJGc3uI4UOEpyzxk657pRT+er6W1vnljrQTQud3MJSyJPhfl2orbK69d3YX5cePpw5ryuPDzes9WNer7Qzmfa6GzmsuUhxoazaArua+mMHAfKkrrsIUoKN06OtselEP7v2t3TpkeNkky5Jvuqhgzehzei2YcX0pHiYKWzpF3mWuuKmVAva7BzvP4YXfnw8QOjVd588TnTzexDr9WNuJebowOr1pmXBRLoGLSQkZhVXpeZSZW6rZwxPjw/so4VyX6eYUaTwToak030vvsVGpmXeG5VBxU9AVhhdLatkXNItNUCoGn07o26iEvlWjOQ4nVyixuPOPOHkUp2Q+ZgNuWxy94lvKLdxkMsTPtRZz8gFMmYKDVYSlNyQFCGs0zF9U+fdCGg6sBIa+7tKZICQNrXuQ/orXVXEahCzogMLuvZwT1JTCXz6ph4rhe2gYdPiDnLKYJMRAQnJhXaEEp4qebk4KqLg7mGFDgT2bzOVk9mfrxc2Gpl9O4y5dFIKTvQNipafXiZgyFTcqGbOqvUOgXl3Cprb17zt87NzR21Xki5ULcY/BbhdG6cHx85rRtNjTL70OxSN4rM7pmaM891Q3ulyETaImkyPJhAWA4HJhHqWkGUZV7YbKC9s54rbXPvqIQ6aD+MkmcPmRDjqHCXhWQdJDM0mMoGFLjTzFor2/D1P00zMvwapSx+tq3d72UVptnDFlpz8/0kUyS6Jc5bw5KSUmYqzjIZ5pO82huJTNLiCqExmKeFTYxsRi5OLMgp0QX3p5JMb+6v1xu06nurmbEOaLrC8cD8OtE2pXdB1gujbg5mGeicUZI7pqZBtg57cqKIg/m2V7yDafJ+qOREw+0nTBPCbvPwkgo9MPcjE2HtqxMlWkeSePAEKx954Fff/h32O0WaiyFbFL/HfMNhOvLm+JqvPv+GUmbu7l7xB3/4h6i+5vB64Y/+5N/l7ddH5oNgiZDjO5tfrbudBtGrxn3vs/1gNYvEPpFxDvmeYBiMJzNUBpPBlBK2NF69CRYpM8Yr4OeBXfhrX8/G6WnwdF85fVx5+PjI4733Vu8/fOD+/iOn0z1PT9+zbQ+c2xNGJCymztoqy8FDW7zGzEh3hmXv5ub5wWq10REt/JhfPz6gNHyCqUawR3yzF5EgLn/i02DOCum76W3spyOizrqa08aTU8r6cA8BtZf0mpQ0LESiSApgaaeQRvv2DwEiBD4ZFMZZ7E2efULZj7/D/r14jRKz6WBaieDMFrOrXniM7kyF1hiWPdEMNw0cIfV8iU0NKV/xwz+l7A3WiDIhB8ggSp6cFqvo1ZxDRUglU8qCJCEvC2wr27oCgbRqYvSG4R4YKfnvJ/ED+DAvftNwJOXE8Xjr9OiUnUqNy+tsnq+HqqjA6EHJDWlP6NE7DookOtWMRTqFhcyg+MjQB2HBuOrWEXsxtkVCAzqMFhNxXHhEBDNi3Q9SST7pMQZDHQFPktBkpAEyH9guz+49IeHHpOqePzuoAVeAB+yFcWHCSHj8JoaqkU2o5mypZZmpvbHW7ZrOkjQz5UxJPoHsKiziFHRNhW6DPJzBMbnb+9XY06nAcQ9p+AfFOjBxkFGChh3uWQ4y7lO/aIRdvhbmycOlYvM8OasJc615NVq/cDqdOJ2eWdeVw+FIKZmSF4bB6fRMSuFxZEaZJrDB1isPj4+c185lvWAIUxHmPDOVTJlmevXJgzex8R5w5k3rwToYg2otMJ9I+BHFkjkgud98IvRmblBXCpdaUUnU1ni+PPvnmQuG8fj0RBJlmiaWZcFEnIGCA2u1VVqt8b6fAnSWqxfV4WZxKZUqo/Yw+HyRKhFAX9Zgk7UdSHSGyl6kjfBA672TPilQRTQMbwVVB812ZssIrysRIacE4tfJJaDCpVbuHx/ZPTF2WvjAk+Qm8Um8xhqvfCqXdbmJd8hO4e1jl4BGPPMAG+LFUesMGSzpwKu7O37z/YlS9IV1FMODtENIOwNwf67YIz9lKV0BNZxF5d93SYOJ+6X04b4n7h+mQQ3uTLiBsmFcsvCr9+/4zX/xz/kX/9V/jWpiWyu1D3oWbMkwlK3uzKAwITb3mUpJIB/Jt294/3zh3ePq0cYh+dJcqDaQXtieznGuDZAwvkZCRuvT626NPS6CWAcqu7zI/LGHufeGyFUqscsi9q/9Ou1ssx2EKghb7+QYEAx19mEaDuiWA6RknB/PNDPKvDAfZiyi1Y+H4nIggWqVdV0Y7URrG3X15l/UHHwNUNtvu12eLqTsU+LDYfb1WZw1aWPD6IyhrJfKnmwoYlSdsXRLRXgjna8nZyY99403/YnPX7+l6sLlh++ZfnFLMiNL4+71Zzy/f2AMpbfqrNBaqbW7l5J6amNWv5faVmkBNm39RJ0Gb17f0Efn0leOtzccb98gqjw9PbI+n1DNTMeZaZ5d3nI6cV4rx/Iate7/ViN7e8DaGj+cNr64GCUNrJ959+d/xqYG1rmZbhha/L4WZQqAZ0rqQQY0Pn+1sGS98txePn3h7vbOBwB48lNtjbU2nuvK67sjxVwOoxZMhW6UHLFW1q8JokMk9hG3DHjqkIcwj+b3v2rIWEAkO/Cig2FKGv5YtRkH7dSeWGPNXVAomZGVPHnCrVaDtrIcbxiXJ/cZEuX5+YFSZrbtwrqt1DHYMMpcmBUurdOa+6JMuXBcZtJc6H3loLf0IkynzrlvQOWOwvPlQtKVIcUb8tjWTAhGjGFZOTa5gv+rdUgZGxdonc6gKMioSK/XQr5vjVyUuQk3qiRR/v79e+4vF39sM3pxwAd1n5I8JXKKhMlc2Hrlsl7YemNJkJKDgO+//Z6bt29o/ZnffPtLtnXj/Q8/UA1GS5i4Ubih4Rfla6KIuDHugCEuke/dd4OtWXjyxU4hjT1t0n1VwEZijEoJU3quYLU5Qyy42p5I6omtQwcaA7dee9R5Xtud1rOnKeK+gftwy0h8//jEpVXauvH6szsHcCzUBkvhqDOnbaPWlV/99nfcPz1eB5wfyg88np4RdVDx+bIxcKbvLsnezexrbRgNJUUt5EMxQppbysQeBrJLEbFBs5ezaDe7dhmtA3IpCTKMnDNmLfZ6qHVz9m/4zWCuqiDH+RV4e6+DUYA20JpBGiNcBxISjNFgyfeXZOFpcS8kmvs/luwscT9TNk+q8ykjtpuj02MWsfco4TUrgCU0ZcR8sD2pcWudrTfOa/O9XTzGvaSZc6tkLW52r7vFhwNdJU9odjBiiNfLNoafXfvAc6ucbVDmiZQyp/M5rGB9fW3b5sPiEjJH8ZAU6xL+lwk195TsSdARNhllYjksHG8SicaqyrZ1ejPW9czj6d4H9h3WtjkTSxMR5heGxn7PnLczzRwc2vu+KSXy8EG6lsxWK3a+0Hu9eri3zf0os8JxWRijseTEISXmpNykwd0EWzPOW6McZiZTdDLEKrVu3KYFjoWbfMdTA7OZ5eDAkXUjkZ11H+evM86VPHkybeuJKWVEhFYHWPJ7NKmviRHSrAF9PSNZgE5Pbm6PDcrwHsnY/egEGY1sdmWKD7qLDPtKu8DWzkwDHhRe377m0l2e1evG2moM/pNfPwLUtuhNpxSDQqMZtD6Y1P1nB4PaA/hO3nj5ELlfA6VkFB9siksEF3wvWW1w2laMjnUhbXKVt0gk85k4wPXAiY+XB767/D3/+sNfOCA2zfyn/9N/DJI4ppkvbr7iqzc/5Sef/4Q3n7/l7Zc/4fMvf8JX33zO17/3lru3C9ONUMo+svfDZmDYqGEfI7jlDhCpkj66DVZjytfh4IhKGXixpQnlOTRSMuZbePUK+L0ZYwE+A7w3rRXaZmxn4fTYebo/8/Txkef7B959fOLjuwfe/fBbzqcfeDp95Pn8wHm7Z20P3vNY81CdcoPaRnWRLz/m148OKKm6B4ioR2pqLKJdxrH3FSl0l5r9xvaph0/gJKfr1KUwvHHrLsVQ9WKtmW9+rQUYpVc86gU+sh0pspe/Ilfg6Mpu2pEigz2xzQfIvvvIJ0yfHfjby0Ef4gRnU8yZSGwxyS1Yu5CysJ1XBsPTsNRp1m7o603YFhGQKel16laWgma4XNaQp2Q37wJk+GFZcgkPG3/fbWu0foqY8vBPMk+LmYvTjVMqlHniuBzIJRgSvWEdyrSQSuL25hU762CYU3QJynUOw2T3/4jmqbkpLQJjMo/0rdA1MQ9B8nAJo5/FgHuDmHlCBxDa8RE6a5eU7VO31Yzc3QCxmccn6r7JikT6i70AZupT3ymMym7niYfziabxOfdB03FdA0YAfcCemuUTewel+qictwt36ehrJujQZhtJR3gvJcDTf3LOAUK4/KMZiOVosiMGu7hxnK8+p0jvyKbfK+oeCDFZMzPXv+6HubkJZFZPI9s17wC9dVTFPSh0ordK3S6YmE8CT2cEeD49M80T8zxxe7zxTbB1LuczqrBkdSZS7/St8vHpkdPzs0+xMLatcnNzi3VjXiZUvACx6jRyp5xbNOEvX2awbReW5eDySBwU620EI8Q3by8WHVAew9lcbtB9CaqvUNT177V1hMwyK1OZmKYScsbEVtd43sG2rXH/G1OZAvwxcoDTJTk4l8yTx1rvlDT7+rJ9H3HJUg3Z2Z5OqRCsluERyJ+yTlSv941o5up9FJ41FhTaF6ZTCqblvk+4r0OL6N993WY8+SHF0dVC4z4jJAG6J9EkUQ7Z/SpMsxfEuocU646zR2EAbavOMhmdPM9kTaErj31WJJhFvina7v+0f0mw/UJ3YbYPTnZkfn/fvn/7MFj8YB5eRFcdlCFM4mCGy3YHdRiYp/icxCdjOon70JnQW5gUB+4uozvbQSTkEMLh9WfoceLp/plzL4zh4NWr17feRJFYyiue3v8GSXa9t/bhx/Xzw/egEQDM/jM+3xda+KwQALOb5HM9J/d7e/88r9BbTK01ALocZ0XyCsRZKMWQATfFOM4Z0oxaQ9NEXpZgxSmihTRNmBhlJBgXLj3Thg82dm8LiS59Pys9QVPDr88p1RL7DQZT9j3NC02XTA5xH4CbkkirMc4bNzp4Uw6MeuL9u3e8f/iB2+MdWiFNlf78xGiwLJCPN9x+Vjg+nEEcFEmAlIm0uE+giLP1WmvM0xTMzk69GI/v3/P6F79wtq4or+7eMKy5hOeysejE4c3i0/A26GtllESej5QeAy6d0DSDFBh+xqY0cxrGh3Pl1eT33G/ff2DTBdMG1shTYj31kEB7DaBlInefIqackJxovZJxubKoA4gtBmvNBrROa8a6dfrqZ3mzQRNvHscwmmV0JHa/kdHNfXekk4F5CDc/wCw3VLuw4jKDMTpp7CwVwXQiD8giiA2GZOai1CHMWjhq4aOtVOnYOGMrXLZMFUWo9Aa5dU71mVdlZntaeTp15lv4uFZubm6wLbxQZKKJe8Ls57uokqYjUxZsCI+XJ0qemY9HbJnYLg4wT8tE3QbtsoEoW2g3VZN70YibKbfsDOwQZ6PN/RzPioOnY91PfJLBnCYmA04rfZzIb458/dmRh2L8cBrIlNCcOCRjrQ5QNTMKQq0rl+7DgVorp3Wl1Qb3Sjaf5L/54hXr+kjRgTU4PzYuK5FOVx3ANiXIoxT2hMfOAaUn8Yho8zUnw9lZHfEgCYHJi9frHtXM93j6oPb2MtQVj7fxGiLM+WNgoRJM0ORITq0rosqleXMcc166+ZByZ493a0wpcV7P/Op3Z777vjDNC7e3d3z25hVlekVJidPTR/76r7+jPT6RSrk2Wut2cWAnKTlBtfC2Cv+ekpxpM7aNDUX1gMTZUsQlICodyQnrK2NVT6uSFAwkkOHDtWoe6pHKC4vApUGVeVrQrCHAGS6JmvPVZHuEikAshZeJ16MiICX2dVHf5yF8lLxjTCgi+/DUzyjDa6o8FVJXVLMDzep0iRxsxR4AoahLXHow0G0YRRJbM/IMr2dla9C7+/eoQSZxXp01erPMrLVyXiunxwvk7mqFsZFLCc9Qn/SWVK5nfCnZPZPCa1GSOktHJ7bmZuX+noxSittqDGithvy9M7TRupHniUvdOGN4IJN7ZBneH4xo17T7+zyfnzkcHdgqw+htY7uckB5IXlZuD0cenlZnrh1cfpVC27FaZ5mKD+CmzHltLrkanZE9Ta6OgaaEFjDJpJSp5wsH3Dvz9uaAbRsTmYJEwhqU8IJLCrY16mj+5yZQN8iJCeVwnFi3xjJNNIP5RrmchCkrOQ0umxv5S4HCxPlxoywzW+ss03T1sU2lXNUmvdZI6XUZZe/VQcXhPmjW3Rw/TQvg0vQRliK1Vo7zQquxFq+KBaOJhwSIQVmV7cOJswwuPTFUqFTUBtlkn/jBqL5jFU9IHWE+r+qBHLkUkhAJi92TCnEyRo7a2Xp3Fo91knVnQHavqar4oC9roY/uPljkYKJ6LdDMmVy7SXjfNu8hJNFFKVNBR6NpAql87GfuHx75m4+/RP7WmfIZoVjhthz47OZLPr/9gm++/JovvvqCL7/8hrc/+Smff/mWN29fcfz8hsMtTIt4Wr0jY8FW9LXHeBlA717GsNd9/vOa9zr4SqWIfYlrvWwYSicl4CDojcIXINzQ7Qbkm+u+vK7GeoHz4+DxfuXDD4/8+V/9kn/53/8zxvo730MYrL3Qny5XNuqP9fXjeyiR9q7BpyLqyOWO7CHe+Oxl90tqkk8IJAsZT6cYMhhaPCavB3A0PN3kyj2KAv7KLArUb2cROSh0fUnX3/NnjwksnjxgOH12BIg0fODD7h0Rghc3sXRaEvs0UgZXU2LFGUOj+VijXk5+cPdBb6sngYjvnDuTyVqjmieaNdMrkwJzedlOUZZotMGo2xbyjBReNYFcW6GPzWOHc+Zwe8ebz1/x9vXnPtTIs9MKW7t+JoInHDUzLuczvdVodJwxlnNGs+6XOoCX/Zr4/yhKVxDrbpzIgK6sYsyakIh9zpJj4uTeUKoTzezKxBFxJ6XeCU8Ol4u0mA62PrhId8YOMTkJOaXXmN39g/DNdxFlIXESdemPglzBTgcQ9xJOhqPtRZSCA5sApg4KjDEge8yvgZuIp5kRsbWGm52ZjND8++8XcdzaUnZWFcIklTymePz9c93ldxJmjVzRfsCbjz6QnEJPK0Fb77TaroZsJj51HGOwbc+czydaPdNbp3X31TrOM19/+Tltq9S1M8aFunmccEqZIn5gne9P1MuJ2tyMU9W9FFA4LDNZneY9uhusTqVEwRMVaNwuxgtDx6L4P53OTlLrPX7H782Uc0gS9Mp4RPl/mXuTZUm27DzvW7txj4jTZOa9t6pQBRAgCbARqaFmegqZaaShnkBTzfUQmvIN9B4yk5kGMooGihSAKlR7m8w850S472Zp8C+PvDUvmunAsgyZN/McD/fte6/1r7/h9e0zYzqlLDIyTTGRM6MshYoSbBJw2zbdlxHvBYkWMdpHil4OGnHKAjP30egTytS/mcC+q9E+WDmjR7ZZMMYIWatbMCfjs3GAki5PrjEk1wQL09CjcdezPZLqcgCKzINmnjjSXk618O5y4tMP0tBPE1tlnyqDRZB0FYYGIwCtPGG73WhtZz2vKrZjOhQb6R3gnmPSdvlIzSzezV/99d/wy3/4B2YyFefhwfFHIOGxIwYD59hlj6GC9g4PU+7D90sFzQGkJHeZg0bhaq7JrCPQfNcglRQeY/v9TBB4U1BzkTyLqTFCSJDCS8QPcWpmv+7MMTmtK9tVZrRmlWSPDDrZVtHq9wIzx6eLFE7/8qGPxiq5RxOha5sxFBlxXvghKzxM6KOI43426bsfsnFz0LQ9GGQH+HasFzNqSSylMGbm8WGVp9roWM2KgrZMzTmigXtM2zt4w02TwFS0pglwFBP4OEMSpc+mJK5cV5acmd4k1/LEHIkxqyTsYWK/5Mx/81//nH/2T/+SX/4gBso5vfD9H77j17/9R7aZWGqRUXRK7C+fOI+d7JntOri+vtIjiavWgnsRYyx8TLapPXIe522pLJaZlri9vpFLoS5nLuuZ1nferi+M0am13J/ZKSkNaugN5jY7mclMDZ+F9w8nSu589I2OcV4WHtfC+cMjNhtpyezLI/MtkyiMXBjtjcKZzT6zeec0NxWSHdZcuJweqMuK9f0u41BxqT1ryfJo6AP5J7WJ7ZM8JVmStJUw23fVVvPHybcZIpHrHVVsHSTb8ByAcK5QumQLddGk21Wr1JTVDOKc8hlLRvbMWk4s80be0p0YUZFE3WuGVOjBbrIhJtXsg/O68HB55tQH9XaHtMjTec4FSqKcFpY8wDJpKSypah8fg7VkLg/LnbG9+86+bZRlESM1AyWxuGHJWNZVqVqmYJjH80WgeNvZRydblVeNFf08IHvnlFfePV+wvuvd952fPK789tq5tZ19a9y2K9uQHPvwC6kpx3uxxP2DWcXy27aNdUk8//kHDKOUTKfxtr3idIVFJIE/Kc7JHCb37pLN3xiUWSJFTswbmx7M/BnsBpm3DsviGcihlzZ3xKjJqpWckOsFKGQH60apZWQBjG3fFd+edM6p5KkwCngLY3+xPMtINCu0OamRYtzmYHt75e3tlY8fv2X9lWRa1+tGSZUZkJknJ1WDkWE2mZjnNcDt4/12+axMMbhCk6v0Ylfdbj6pyyJQtikUYpiTimq9I7/UMcYw1qWKMeyD3o197zhKK8OH2D1+pPFqEDDnZM1i/M6sPbikwmjizOaS8F1+cymDD9XkY4p9U7IGi3vvjN7UMySxqGeXqbRk7uGjFNOdlIy9DT3jNhi72LpagfLDypa5zc6705mSRpwhBdokTXnqSBsiuXpZUpAuXQxMHUYS7EzT/oDW2R5nUS2VMZ0+OsUK1oyWGnmpeO/yySyVZV3BnWZNnmWt3cHy0SXbz4vutffBbQ7KlH9Un5OFRElOLka/bZhVbi87o2uY1rZX8A3LHr2B00sXVawnxihiW85OxajLgi9FwNYenmQGp6Wq16NTk5L+Rh+UBLRO7Y575+F0Jg1IVlhL4XRWBHz2SZudl5ui4QsFbxt2OQa7Sr10y/iopIeTWI4F6pLIqXJeCvs24apE2TkHPo1yWpg2eaiVthue9e7WsvB2u2I2qTnSbv1wkbX4JR/d1q7yh835DmSsaQl/4kUqIMtYMM6P+mTGHp4TvL58T1kXxudEWs+8XTu2KwgmRw075xRjL95Ls1AVxXkkm5Jg0w9JkzUcy/ehRrJEXld5Qo3O6HukwWvPU42qdFYssQshZr/7/6hGDZWr/F7zWSCaSj8mQ8DU7OQRNWaWD1opVUzIDN1vXOcrv/n0O/gE9R8LqQ1WqySrnNcL5+UdT48f+OrpK37ys5/x4d1P+ObPfs77rz7w9JP3PLx/4ul55ekpc7pIxm9pBhtM164NLkBm4379B6h0D/SKGtpkfAG6E0BiRhAM977ZKOvk8WTwITE44Sy8+zff8O9//7fU1zO/+MU/4ZQf2bfObdv55W9/zZ/y67+Ih5IHCq8PrSY49kc1AyZ/jkLCNznPmx96SsemXvxD8z19EozM2GRjco+MoyeaAhjRgUeno7+uxs1Cy4/9yOckXkWBWnaM51XgmTaUMQY2k8CUYIQYXxr940uLXQ1ZH000vqF0i5kkddNClwFbyZrMubnkONOpOamQnEp2SKXKYHEMpkk3LumLAJe6yA8km5gI2YzLwwOnhwueHPfEw0W/NzOSD/DE1nbaLVhUJYnua4nWZeSaUyalQts3AQtLDQq10FJ5QolyOYdMl7ERRmHGSGoy9jGZqfPQEjd3LkmeOoa05i3WQkpGGha62hQsA93ngUWjpw2xzS6EfiZysP2b6/4coF5Kos0WwuycyWUpfGoZZrP51WIAACAASURBVL97/Iz4LIe3kZm00iBQsQfinA2qFUpdsFrxPuRNMsFTZpkF8gbFNfXzjLnSzCx7mDlrit68R05dgllEO5zhFcABvHxhJLkf4ohDRhPI9oT+I12spYjCboO27YCztRufPv1A22+sS2FdKuupsK7PpAL9tvHxd7+HFECEyRfKcsL7zuv1xr7diEpLrEIPX5k0QxKZFUluFgkTAqHC4AqzTOSek5GUpo3Otm1ij/RdzLisaelyWjSNHB4S1yFw+aBFt47lKrCpyFzzAKbNtaO1tyYKMxOzkCVEzGxdZbBq0XjnVLjtN9reyMl4SJnrfmOfxnaTh0jNlX3b8cOcwuTrJHbeIFuRieth4lkEiI/R7v4HPlTkj77fGUfzbn6qvcCaivQZxrwJvR/XeaMcHho58/j8hP9jlTyMHoCuqdm8H0KxH4cceOJse+cPv/8DP//zXzC7ZE7uMvmdw8MMPphUAdbuo1Fn4ev371jXNZKnCFPm8GAJAMWS/Qho8Tt4n81CiskdYA3IjC/uH6YCnaZnNp3JYPFgzyAAqgwPkIwA0cEK8WwyTrl/rjnj/XUP9mDo9LOkEf/iX/8bfv6zX/D9t9/x6eX/pd2unIvYUT4mvuz696dxR81lhpvuQ4rDR8//iHX1o3tgIXubYpLl+NQiPB3/xu9n5wF4CUzSvRnHIOb4MxO7oSQZNeecKGnhvGrSP6bTbrskRDn2+FJI1KDTr0wWil/BFmgJ6Ng4EF1j0nCLiRuHK4AzzCEZp/IAdsgIgtXQjLpmJvKH+PaHF95//4m6/oSJ8daNNUOqK29b5+3aOCXg2gW003nbOmVM2jYoZaXPDWNi60odk/22Mdqm5riPex2ga9Z9WU5nPGX2fZJ8o6wLJZ8Z+wteFLBBsAkP6fzwCWMnzc42Tqy1cLpkTpeF60vl9emJ/Hrh67XwZ3/9l/Cr38C1M6dMfIsqu2DZNWzv+HmhD7DmLCmTfbK9vDJLxpMKI0WLJxJiXtXTidYPgMiZc6exM8dGyoM0pwI5tPkgfEkg07DJ4l9A7Z/kwmlc2XwTeOJOSnu8u4k5G9nkaXfKSuXteAzDOktysk1GcupMLB0VwLng5iylsDSHKjB95go5UR9OJBs8Pzxj5nzeb+z7oE7JqqwNHEMYjFNrpqyLmALa9OgeUiR3alYj3FyJYmnJpLXSxpTkaGtsAUDPIRY4JcFMjOtOHwA693PqkCRxtDk08Ex69myTt9sL23WjtCvY5NsfvoWZ2KZYXx0jTUUzW0Qzl6ghVIuE2bXB1z/7mnfvH3l6Piu9cE4upxP71sm5so1JQl52GgDomfZA7EbUwCkFIz9lJbXlFKboOo9V/3LfTVOc5znql+T3SjhqL73Tw4GUNdA0MWXb7LTuLDZ5enrPennQt/bB6/WNl0+vJDPa2BmzMAL8EctxagiLUbSBMUdjn+O+rvptYGRG20lPq0zmD9uEksOOwFhSleSnN+1/HZb1jHt4/5kzR2NaIYUEr29dLKVVZ1IbQ/IrF/PKpzNQQ7rEWTC6EvIwp6xi6Y6uoec4UgyDmrXNSJOzCBjxLsDfjTIAsnrbFPXBSGIpB/iQcpL3ZnjOzRHDoARLmpAiWTgazBTglSfJhmqu9L2TOSRBstRw22jAxzcZjaflrCCVQbDcJZPLtVK8klNnZiXWbXvBrDALeg9EjiTHWaRQpUlrI/xbjxAKWZLsARLVlLlcHgR2XTd6MswqSykhY1ZfNIDZB2P0sHRQmpz6QUkMZwcbN86nE2PT98cmLy+vbK8fuTxNllPh7S2IBz5YFphdA0KvAmZufWj/mIn91snDWDxTlwD9kljkPsRO6e60zQkHec7rwuWyQJtiCKbG7JNqxnXfqXlyyobPjKWOU9i3Hgxwh1yZY2NJN5blPY3Jp9vOtEIpC203Wh8sJ5Nka3O8TYwuT6LhrEvmZTbcCntvLKXQ2o0ZNSimsJwypXqZDBmNm97tnHPYdSz0rmTtnCNgx1LUsBZhBtHkTWd2l8eXDbZ9o6ZKbc7WPF4Hpyb1KuQUEmR9f0yuW7kUAaTRs5QSXkNqZvDZmQNyrkAOk3ul6J0t44s8oSZDYGQkhs8OuWp/n2mCD+aIHiCBjBE0JLp36FMM72kdq1W1Zlb+49h32lCISjIFAMyhelN/33m1G54mn22D7Q+0G5TfJfhPmTwVMHUqZx7qO54en3h+eM+Hdz/hp9/8jD/76U/58NVXPH34wLv3z1yeL5yeM5eHwrpWeVHmo58N3mb4r92Da4zwZNMAG9AQAKK3n6qLg/kEUKMHe3yC+fV7vvnwU/7Jv/pXrA9PJFuZvbP+7e/5U379yQElXAhpsUgsORod7nNV3JIe3NjDm1nYaiXRSdiasLapMDAPAEk+Juk+0YOUJP2wGfySGRP/OFyJw9dBVFU7NNSEMVmU7MOxIsaKrlMP85Dd9X0DV0pCCv8fD+kUThgdquEuVeZ/DOTTsRRG76SUKaXExFkgW8lFcpkA1FTKEy/3oLcZgJmThqhp7i6qZMqsdeV8unA6nbisK+dadIDVQu+7Yg6ZjNtG7+0+3XQSxbNoloMvnh5DvhklVRxYqhIzPNC5PzY9Tz9a7DEtjabx8EWxZNg0Zp24J6wWyVCCilIOlPFIWbFEx4KpNaNRHXhSXPKNTSyEMKQZfSrmNVkc9JAZnFynsDS1ilYeswlkm66kvmiCq3+R7XhM42QRlYKt4aQ8mKlw5xUGY21SFatrXaDdweHmAGhiFD7TveEs5ND8gluGPDFPWlvRW3p4kAigDDAbNd2zD6YZ+95o266Ju4H3ztt2Y98bve3s2w2fg9Op8nRZwTuj7TCd1/3Gtl1Z4z6mqe9hBmN29ltj7I1aMqfTQh+dfd/EiMoyXFyXhVJyeDTJCNbnDNN8Td9SgLi9D0nueqdFfLCjuNN10fS5lhR6aGffdxUKU8Cl+wGuQa41AF+ZifbWmb4zp2I755zUItAolUoOMElsCg+PI2e0G9tt43q76tDN0n3PMbhtG3N0LpcLy/mBW9s5LZk5TabiDGrIN4eBpR2wkIf+iE2WYY7OYov2q/wFwLaIWLXYR1KOcyDkcilYU9kF2m6uQry1hgQdAx8CwVJOtBkcoJC4pumKUE7QYvPd2uDv/uEfYVkEaATzyYCajgaWu1+T0iUSb/OGj8HjwyPf/fAdc5kyfA7wI8EfhVe56TBzIHlXvHHszfNoejyAiNg3PECTJWWliBjg6d4s2VSZ0M20X7iAFTeZshvh/cMIM2N9HrHsYt8//pzEh28+cHo68fHlI2M2xtxoPnheLvJDskwm497oeY0TrCtynAO+iCLMkAdUAEjZ0j35LyFfEr3vRiPo5QEM8aOT8UhhDGdK/ZekM7Oid2va5HFZtBukJBp5FjuyLkkAQc4yMh+wLMZaCqWe8NOJpSQui3HdMiXB6+0mo07Peqa0mCgGUHa/Y5La1eAXH8xBM8lBx5h0umQ7WXHq/9d/+oHffw9/828fef+Q+O6a+Yv1kYfLM8vrJz6NG99/GrzVE48fHrn4pF8bv/3VLzl9+IqbTbAiOdK24aMzt85aVlrfaNOxrIZljs62d3rbMZtcX155+MVX1KVSq2E2GaNwu27MEQziIz21T/YxJD/KJ5aCZDQpcTmvPJ4X9rcH5umB0zL46vmBz69PklAvC+sclGlclkpi8OGS+fX3jYeTJoivqBGbwMt25dwWSUzMkEjXmZEsuc8uf0OM5p2Xlxdwi+m+zqSc5UlHTCr1PHR4jDRwG2SM57xQktOHCvMFTT7dwvg2Z8nPq4r7Skh3o2hVVIjO81Oa/KVX8I1vx8bNJ8Mro23y83FnuZyxeuHp/SOve+N8qbRb5fcfXyRJGLrO1pWymafhKdF9sG2bPOPQoCqlTM0C3f2k5v46OiPSrXpvOJCnYWNiGZYkeU6ywsiSFFEynifeE227xWBORrhuFWzD5+R62/l+f8ED8G9tYD7JvfM6YVLwkBpO0/BP7PbEDAbt6GBWmX7l8vDIh7/4mktdWQbMvFFZuFzOlKakr8rUOMARmAtMczE0zAJsBLOJdqCdSRGCbjGsYWqIlgmeksYHmLz/zMJ31ATS3bcX/UvJH5H8RiRpV30SUgxLLp/LDQEhi1NuRLVOMEDVvvkMDxTzCFIRW8EnWLOQbjl7amKVXmWOvg+ZgeMyaFeStbxFL8uKTWOfAqZwWKyw4tzMJFNqxvVNg6d1CXA7GSlUCtOVXJdTooQEzVunz0mqK94kl8U7UvKkMKwFhurMA4BOKIpdfcSg5sJssM1BG5NTkqdWATbkV5ksgFJ3copkKATC9TlYRwyPgolhLs5k7/ITTK66oyMAy6O4SDZJq0GunMIvrfvkkU572yEkQ7lG9RjDpfNyop7g9aXTRydVedLgilJ/OK+xzidpdGaD3nowstXcu1cB4Tnp3MjQudH3SOYd/YtHKWKMWcqsKSa0tsj7NCfYB6MPhrksOAayKyhaM2nA9e2FHz7/QCnQvbC9Dm5NxXrxDAu4N6ZJAlVSklzRnf3thoKPDIrkjoSXYQ62Xz4G0gadxLJkTglsuzL65FSLZJKeeLneSObUUwU6SzLsXDTYbYWBLBOmZ+paWHzn9e0Tr65BwPOThoE/bJ2ZnGKZEdYtzZ0Z99RzJs/E4+nEte2UaWCNlCtu8gT1Ocmuz6STxJnepbRosACXU+X1ppTEnGKMlzKppqj5gxmXwFpndmMUp9QFXxbm+ZmeMt1utNlhKuV5ZiXsGn4PAsrRE2SHapOZtVfrSwb4at8VdCAbCNld5PDgPPpK4d06DwRG6c9anyxVksw+Ndz2FL5p7krIdqcFowlTbzgPhYA5LAUbk+KFm09ydlJ4Qs6QuCabtDHZ4tsk73iPc8WTUiiT7CVIg2Y3trHx6fMf+OUPE/9lyDljoLnME6f6xPPpiYenZ37x07/g629+ytM33/Dhq/c8f/Wedx+eOT2sPD0WzpdCrVXDw6LBoSWB50ZIvYPpz8F697BJ4GDLg1Vjeyx4O3N6d+Hx+ZlhC+aD5WXjT/n1JweU2hQ922xG8ayNSmCNRdx6uhtsK9FI0xhbVlrr/M1f/2v+/m//PdOFkFITp1q5ls7jw5lkksNMxhHcdW/IZw/plEeih0GpRTQ8J/SdQmln+CWVWsg103bFWqZAdscc9LZFhHqLZnTowR4f+AAB/JBaiL4uz5NIZts3cl5wn5QchmTB5MqlUGqWX8nwQJ2NMY31fKLPzpISuS7UmjhfHljKymU5hb5WzbWNqTZzdLG6xjGpEgCWkpJl5mEGnBOkLJNb07TGYgrvh/wlSbrjxCF7x5MEBB3Ahx0INx4eOrrHh6xPVOud5otSHnB5SbQwJwwjWMnFdCB1h94cUsHGUJExI261FDWTcSD7DPkMSkKT90y8VElI/JIXDE1nB2I85JTZw8BYiJJ9kfBx4JJqqmvKrHWJpj+iMEtRAw+iPM8DQRZDwh0cUbMFBwUoFI10CtbJsQNHPREg0gFrxjs0CSNn2G83ckoyvh6N63Xj7fUFXLrukpyn98+MvvH2+pG9T9ZaOK+Ftm2kJD8Kny6QKbx9RofXtxdqKaynhX3fue5XSs6czyddfUTxghhSZse9MAipZkqZPgbX687onR7yqZwLdV2o1WijBRPOIBJ5yFnPLtCtsoQHwpzMFhHlvcsQMHet1aNxT5nTWXIci+S6EabYftCtTc+qj0lvuqaSC5aM3hqzN2pKYTxeWc5nzBJ1ySpsUyFwoPv7nezH/78fswNFCFtgihHxbPHzJ5I9TVeRe7D+7vsJBydMkk9mieJfMqoxJzaPSGbR3w8W0JwzCjmtt4OFNwAvhe8/v/LPUmEtlUMDXyzAeMI3IpW7oaihtZhz4s/+4s/59rvvwMObKapFj4LiKLoF3Mi7qJvMZYtBE/YSv9TZuIWkylxyTcT26Ife+MfvIogV6XFd8fMlySL89QLQxKL8JmjG2uNIMgD/+v03uGeGT779/gdePn8mLWdNTM0pJu+HMT1kSK6HErLO6ANDFiHge1gweYJZKDRIk+COQLfimnIf7Nbjsd/ZaoSYwb587sMyU8mcxi9m5tucJNXKYfKd4GHJnM9n6Cr4nMYY0OzMkgqpZJZlwZFfwZZ6TFa7PMuz3uE5B3T5FnhIZsxVuILOuumNmgr1XCOGPNFyBiR/oA/2Mfn+beP3f/hE75O3nKkPzvtvvuGWKvbtK5/6jd+8vLH4C/v/8yve/+wbfvHX/5y3rdF++IwGUAX3Jk+3B1Hk2+gMVpWlU3t5nvD28sa+d374/nvOlwfGsrCuVaD4dMgVn03M49GZbnit5FqpVPaGmG85iTna4cO7Z07nyufasJc/UKbz1S/+it+VK3+eVnnmTLC1UAzKdGx8zflypq4nXk4nci6R2DOZEZJx911zPcNPL1dOpxVMjfq+bXz++CL2SM60OdRUj2BUAVijh1QmlRRrS2w/ErA3Frd7rWTjAMv0Ds45SQEmuZsar6Ymdg+/CqYGOj+lkNKFbd5461fJd0LOmRxu10F7TqQ1hymuIttbG8G0MNbzhXx6IKXBGkERuRS2287b9UZeCqkPhmVaySynlTwHe9fn3G873ofYwFnn5xidNJRQe/0sAG5mFWa9K9Je76DMmUtM2Nsc4QOZeOmNfW7UbJyLkpR+83Fjd4H1JE3sDXn2HDVVzkUeIVGLTMTMSMsCPqhpkPJCaztLSjxdnjQMGcggXYVU1AwWzLMZ72KA4eH+nOzwZYzmCz0nnUc55N+ZMTT4EqM8zkATo2V6JC6jyX4akp6N4X8sYa4LaS2QLfYSSW9rXblet9hjE8mGwkaOe5SzJPBT/qjDwW8y8Z0BgPcocvY+7vt2taw2yI02ewwYxSSpS6EO2Ad0OsxOm5JV+tCZmEyNeclhu5Aq00LpkFWLyadK37v3EUKZL3LkfUzy0fuiIUbW5BdPJqmvOXsf4eOmc2ffO6VWMGhDQMyw2MldDBAfTp8BgibVzH0oXSxbAooGjC55yz0RdposMqaTzcmLsfcZsk4103nq+WY3lvWkJjMXtr3x1jtLNzE3cXw0+oA+FC2fnbDmyBFIkRkueX2xCSXhKeOLGIzThxIcTf6KraO6qA/eXnf6Psm23EM2PIuhnZFPDgkUAKJAkPUsqWiyM601psuqoy6qH3JK5Olstx9IpVGXC3Ma171z604xqDXUIwHkzemkWmIoOaO/kAx4WDznGOalIoYbI3Omig2DBrGXNdHfNtVeViiW76EpOWVKlidRWeA6b9x2SHOylioPyrnBrLzeBqOAnU4wxXMcbYR8G9qmwU1x7SvZMpSFSdfQ3yY//fqB0Z2Xj/J78lQkCatgdOTbkxgus+wxNCQ4r5nTArd9Z5ZMLdpjRnKsrqovRg+QWbXAkowWNXheztiyMFJh3zsjBrKOyycwGbM1Zhe7XkQNAY0tgpIO396jn+pjF1c7BvYwSJboNu/hNOne780wtZ/yU52HZY5sEdwkdc4YyZWQSY59M0W1berJJ0cKslGsyFeRGUQIV6BLn4xdgOBwyfnU/5aQDqOKbDSSp0iK1FCq0HDL9G7MphpuL0jOOSb4D/j+O+Zn8N9OHv5+kaVKWjmXC+ty5vH8zOP5Pe+ev+H9h6/5+v0HPnz1Dc8f3vH0/pGn5wtPDwunx4WyVta1spZKrkrPSwksTXrSMMuA6+bsHz/xOj4yb415HliRPNfOJ/6UX39yQGm0pjY4vC/IKTTHYfqXANPUfca0YCJD5lozrF/z3/0P/yP/6//yP7Pbgrc33l0eKGXhtBj2pBuV0jEZkZnx9CytdRODY7rLByiliDfWpiPjWDXvbmIkzN612QW7QKEtSu2xSOyS3lbzgVxkTDuisxl9xvQifI6ikVVjMai1spRM2zYZo05t4r0HzRWlhthJBsopFUiF9x/ek4pSWFSEwnk5ywB6IMGoBYUxQ44kI/cAkwIBMgtE3j3YPZHGFDTtkYMhw+HrclBiAi2LP//SwHMM0NUcRnNl8zDeRMybkOZpd1X0ZaoWrKdJR/TRdFD2XOuk2tRni3Q/j+d5rBUPBcrBQCrJWMw4pUjBSWFSnNR0irXm8XyaPHkA79G0Wgoy0SHXPJgXzpIzqazkonQxWMQ8C4aN+5QEbIg6fcSjB9SmC03i6M3ud5bc/T6bvCMO8GEeTVxcs0d8Zesyai8BZvXe+PzpBUbHEiyLDMFLpLjd3n5gDtFyc83Sn982pQ6Grt/HwcgabHvDk0xk+xjc9ldKyizrSZKDeH+mK03sLnvEwLLWT3jdvLy8sO271iHyD1iXRWwxDyaRC/ioKeNmmpTlElNX9ew+RYcfY3Lbti/fM2t6VZcakskwtDP5+/jU6jqYTQQT5zD2HlPm/iVHwRzg4ixZxolhKs6YJBs6WFOm1hr715d0NaZ/8XhysZfcIE2xNB0iweeQdgWd/ACiOJaghU9S4vA1Cr4KBwNmRMFuJUMS82u61mEC/dwvcI3kA8Gom9MZs3EbnW3feH56uk+FDhDMCWkw6Q7GC+BTk/3zv/wL/vb//g9KmkkBipkHwGP3buQgb4bqIf5MC9y05XNo/g9S32ldeXq4cHt9Zc67GJmUCONINbwYd/nyMRnLKVFzMFchGrFoslwOZMM9Gu3EOVX++m/+BR/ef8Xr7Y2/H39gefogcDEXyR2msfXJaXngfK73vSkduusALO2Afe+DhWPb/GO3vhIAQvfwnyIk2xy+SOGBczzw474FjfsAnC4m7yxnqtDPhjUjsfB0Mb7+pvGbz1d4e2FMw6eSlsbY8VmYTUlX82B8+YymJ8Cx4aSQuo3QxkycNqEi7f802PeBLcZSZFB7pMLlVJhWYUaiqA/q6cTl4T3JMr9k8nVZePcMj9WwbyfbtvEK/N0//IrvPn3mb/6rf8XDwxOv6TWOoQGpUJcL9E7rG+v5DKfJmlZebzdu1yvkrOhrH0Aml8Tl3SPLsrLfGt2uYlQtJ3Jd8EjTceTZNHYVh9nkT9Fub7R2otTKeVkYtXJ5OuFpZz0vPL1/oHiJozDhfVJccv/0s5/R953n9888PT9G8SwJPOh+e9RJM+k5Pb57RE4mLkkLYhiXXEI2rX3K7KDjB4jKjLNcxXKewVpy5+SZmZs8xyzhQQTFJylJ4qBifMY+vEn6OjPZDomC1mOaasz3IR+dcVw7QtbfmJyeHyk1UUrG5MiK1XivamI5nVnPK7WaAKWis+O6Na57w7ctzjQxTdarAP5t37nuG9vLjZQrNSWkjp/4GBQ38hqszkD9395e6b1T68LD6cJ6WvGtx3mggSMJti7GJZapS2E5V/YOf//xRabDc6JUsWPvSkySLAhSouawRJimBEkzebH0zpySp+39xpJlL3BQIabPGCwFu/cACdF6mFFL2EjUJLDFPRw0JjGATHSGQIt7zeDhcxXMTDt2KTBmeE7G9Zd5D06QS5FAs5ILbcoygKn3IaFEvsUyb8Eenz5ib4Yl6qrZe0zKBdiPKf+Q2TvdVA8mTMPknEOqLZ8dHR7pPoXXwHlE8IwDTT6jMxhZVmE2nf11wYuMfftQuthxiM5h5DLJJCbOyEZNYuYOdA5OVN8cpW+J52JEHUGi3ZQ4aR7NbhELEB/k8BjNnrgzTKfHujiUcwOPaHMVnZLwSwWgGvLwjjNGsL0mueg6xnRSsKlbV/CJZaPjLCXzsK5i7ietsVxW1Qs5BsUpcdt3xtUZAXRlM8jQ06CiZ27J1JTeabTRU6RCopBNte/oHYm0ZAQ9esiNplMWyenG7OESH2qL7LIzsczt7cppzWruAyDI5fAGzMyx8PnTD1xvGnzWCq1pyH2qmVoKPgejDaXFWfRhEOypqb/TXAbk2ck5ANTwUctm2FSh0WenrGBLp00NiXPUd+0qwOsY4Fy789I7+UZ4iCZKdmbfuXkhTechHWtH+/g2J6/XSS4yNt83pXqOWHcEMaHFgH/GEs4mQCTnGmdHIpy/wBQaEGQc+X6ZKzwgA6lwOVVa66SSuY5JWiqpLGJhpyF2satVw7TucsosZaGSaYhZlGcKyS/sbWBHT4PWksVE0cfEhySRqai2ma7K544PeKhZnLuX5GFvIX+lw8tYg87ZwuzanX1Xvdfm0GcHJlO+lTHszInwLVInKsPs2IORN+PedlKp9GG04WJZJtXBw+XtlcwwCn3qOYWug+4zwGA9qxQsZHwwZhe41WTb0ZpTSpIv9BQWIh76YPpNyoP2A795+3X0qkpyr7mQ08pSHjktJ57Pj7w7P/L8/J7z83s+fPiG94/PPL1/5MP7Jy6Pj1yeVtbzmeVUyTXzt7+78sN//E+c03f89sPf8/pxo1xO5LXw6Q//P2co5SmgICfF5eXwO8pJL3quC9OGDFGnNmnvjffv39O2nZ/+2cr/9u/+Hb48YL1xOj/TB1zHTtve+P7bP6ieZ0bxYJzqGcsL+74JRQ0quOJGiUWRJDGLrqccLIYeaWs5DjkGY+53ed3dgLLITHkemndMcgpiI14ql4c1NrDMvndyyTC0AIfL4DAVefCkpVCqFug0WC8nHt8/sZQLNS+kmiPKNbGsC5QiCduY7H1QY6rp6CBOB7KDGt1k+d7d9Hl4BREeUHEXbGpKFqirWyG4Ypo2xSQ+vqsAtxxVFWF2e9eTH7Rg/Z4UPiY4bRirVfo03lpnOSQb0vNxaM4zh4zGKFmsil6gdbWeY/boUlOAEXqZS0yPlhRsoOjSx1Ti1XR5GxxgSE45AJto7FwpbcR6TSmxLlXXmQr1/EAq4dI/dV9n/DtzHaSS3Nid+ml2xDnbvUDRRvdFxnh3/Z8jaHaAi0Ez3Rmj0bZNssGp9nPflXDmLs+kGhROmw2zCaPHepc3xXBj39WRm4m9k0qibe3O7spWyEVJOSnS8vLoOmijINVzkfk2Q75eHgCx5Yqb83a73ady5IfrOgAAIABJREFUOUBYu2/ImljWnFjduO07oO+TS6aY/DeU0NWY+4h0mgASg5GTSyGlIlZhJP3lHJ4EXQzC4xCKRyxzvljjyWIqJSg/QCfdrzFCWmuOJRVH+xwUPOjex8vgUZRr+R6pbB6NOQksQK4EpLpwSLpSvEdYsIeiYBMwFElu8R4QEgHJwuTf9vLphd/95ndx/wUmo3rw/r4Pd0ZIIA7wt2CM/UbbVq6fX8g/+WkUz0fRriKBY1LlAu77HNQpH7Lz5cLpfOHl80eOVAp9rmNv4UtDgz5rpC8fWFMwl+wOlB2hCd98/TU///lP+Lv/+J9pnzUmPujPAli/7G1B/VNaS1ISoGjS953t/r+HF1uKJgt3Tg8X8jlzu10Z+9BkuBRSqtTlSdT2PljIvPv6mX37rHfHFVc943nc98aDWswBEh1NO/cJl1JNNcQ4uIfpAJWPvfQAkuI6iYZQP0pTTFLm+2LsHOy1AHm7vC7Oa+G0X/jZV0/84bvPfNwme5vc9k663rCqQs+BvTWdf2i4kFPS/kZ4VrmMdu/G7VNm7VYSZKOkzgimrwrEBixYKrSx8XQ58/Of/4R3j2cupxqAsvNdzqwG72bjL56vfHX5mt++Tf7u843Pt1c+f/xESeUOwgpM0dqpy0pZKqU1zsnYbxs2GngL7D5Ty8Jsm5jty0K77dxe37T/XC6UXDXlnYNSEm8vb6w5sS8r+7ZD1xp9+/iJh/NKfniM5zBIReDNmDuX9YntbQaoOu5nsDmkoWS/EomFAj8OGSj3M2QcgG2amhJnC48kyFXA8Qz2oWMhndbashjGWHI1eFM1QcEEQFAYc6Nk51QFbBx+U8NkoG5pkP0wco6m2AmZqoZo5IwNgdnemxqa+Pl2X/4uaX9O7Ledre2cXSbQI+kv7bcNb0rxzCWzl8p2lzDD+XJRmthIYlp3SX9mgLkvn9+UPlorNYvdWkLmnyxBFguvt07OWYC7Gdu283B6xHJhKUoEwg6eMsxpZMuUapzWzILzn3//e77bbyQv9DmpaYDFmRYsWiPSmfIB1siTo2TjdNLZvN1upGTMAW+3V3JJd3mwcezbdh8qEbvDdMQWC/mrW6R3pSSPtwApjhrGcLA4J9AanBbDU1xyUYKBFv/tSHc8LCmOQVwhcTqt5FQYrhTZVOQ7eTo/8PL5M3V02pA8s7vfzXV9ImpJkpXAjH6gWA5lwrjbOyRzhktyzdTA7xgqZfRO9THYg8GcPcvf0Y3FXKBYeKcOF0iYSzwTUH2Z9Fy8KyjmdEpYNW57JElNYzkvpOFKvUrO3hulFjV95uEN6PQuAKvq4KUTagPUyKfklCRR8BwHACR2QhsR+oCaSA9gL1sS42eI7WKpklOAL36cXxaej2Kp5ZTwLI+7du2MBJ7Bl0SfLVDPkPtNgT1gZDLTMmUt2ve76gMNxnWO9L2BD1KukI0ZAzXLWpMW9fVMADmsO4yaTMCp76oMJlhJDFPq1+gzam/Fx0tSKObzaIk5dw33kYftmAXaAJ9sfcc9U/NCRpYYNh2ShqXtJs85yRwjVbkNqkm5IUmX+pJSEm66H0sxTtmgG7fh7HMnXTJeBDil7iRPYmr1TvJEShXmoNRMzZI6Ts8BlgjEcIe2ixFfmvzBpiVsFtKUNDOnwrYb+y6Gep+DkizSB3uAw1nXnibXzTktJyx19tsgFZ3brctbF9TfpprpLQBY0zu9uTEpnM8nhg9qcsrpJI8jV3+IDegTK46PUCVagVrpMqkkDwHIpKRn2+2e6Abhn4rAIaWYx/rbhrx2i4WNTQxNLRJ342yPtvNevfVIrfZ7ZReAt5n6/dbofVIg7CSOvTR2xKn6eviQXYAPkum74Aq8sazzMqcabEAxwsxl0i7GuwdgPwWEeteeQ1hcWMam0k7laX4MtV1eWhPIiRyJ7NM1vB/oXeoz+sgYMui5hQRyNnxs+P4Ze4P8EVrfNQSyTEnyg1qWlaf1gfPpPc+P73i8fODx8ZH1dOJXn1751X/43/mhbuyfX3l6/Amndw9cHk78+vOE//6/5U/19ac35Sbo4unQU0uKdkgA3r37ipw6//j5RfGK1thdMqnrywv9l3/H31+vXB4/8PDugXfvP7CkRjah+5r5ix6sUxeBGgkhvvukt52SSvQ5R1MslBg/kplcjfwhLpkH3S6owlE0zTiMUz5SNrj/0jRf1UHOhz5VqWy3242aqiR0tWjhnip1ObFcsiZ4ZtxuN8iJ8+VCXVcMGY3NLhYXyUQfbwXLio3MJMkQ4j4fJuV6DQWY2DzAkgP4IaYgYfg8nSMJj4MqaAEiHQfZF1Mg7nHYd2DEjnF6FEKJuw1v/CypYDR1aHNiVHDnGibj2cVASMQ9ddNmg1qsYpM16YCYqGhuMSHPlqmiQkVBBUyLabs+y3BR3IcpBUu0yRINoQ4YS0YtlZoWGYrWJMlX+D3NtCtRLUARMWdCt2pq5i0mfpq+oMPu+BwxndC9mXcWgrsO2S8yGj0HSTWnAIMAmuSd1LUefLAcjD8cxq4NJsNSBS6pYJTh35iiiidTkTNGl3nfFC261AVHTIMZ/wewJCWnzS/6IuZQ02Pmks74FHNqe7u/SylijEsusRbLvWCdEFOJJCN6F2NwTsWejm70LiBNE7FIcUtZhp5dGmj5W+miDJlr4+FvMboKNofDO8cCfCwRaTtdRYuloEW7ptyYSdraOz46fShyttrCtt3ENAsmkX5+CmaHZIySrkn2N2PaKoDo8FaKY3Echf5904yGwO5gQ/7R3tWjSZij8/HjZ77/9tuQd+X7E/NoVu+gchxWFk3ogJDw7ry+vGiyfDyPdHgc3RES7dfzYNIZI4lFyo/2ASV9HBtEgCj3n6t9YljsJT/6sx8DTIYmgLUYo22xh/54zxFL42D1HQ3sjO91pAZG1aG7Z/fbev81jvs6Jj/98z+nLpWxNfZtY3/9zNw2etb7ui4XyqII5LFvvP3wWVKSGc8X+RTMGevIuRvdHld/vPU5rsdjEn5043cGgd8vXcdQPLsvayPEg6Z9ZeaFz0lefmsqkuMlTbAXd55PCU8X/u0//4r/4//8j3x+u+F9su2TwmD0GzbFgulNiVUaMoifoMnZlymf+eRwUcJh2xs9PIiO63SSQPARMocquviHy5l/+a//JW4nedYANo3GoJUzfv7A123nXb7S9o2X1Lj2yeunT5zOF4EKGPkknzVmDzZPRGnvjZe3T/R2i/pCHj3rUnh5+Z5v//Bbzg8rJZ04Xx5JYfw8W4OtyTh4ih2850m6ienAnHcZ+uv3H6kpqwB3yHnFutHT5HIafL4KmB4HfznA2VCi32n5TD0/Q2wzs5CtxYEgtovYYkqJChnU6KRSxVTjKEwsnhtUB8hB95+UpCVTUgmZ3Rs11uDME58BTB2LdHQ10g1y0jtvVrSbWDCVh/bM7hJFCKgI0/uuIY/PweV8oVqn3QZv143L1ri+vvHy+bPY6mOyLGdGqpQyaMUp3VlqpgRzgFRgF3g/3dm2ncwIzym/s0BkFiyQNtciPzHLce8trm+QamXbN/IqAKq7K1G0y2utWCEV+OpceLcM5uj8ww+f+c3boKMkNMtKEvUYGmQ83kvi7FIdOofOoKVm1pPSFeec7LddzYQZk8S79x/wj/uPmPPagy1ZYOXh0RP793QjW6d1kMar01rIZqdCClSPHLud6gNjxG8PH1EiKyOaTY4ad36RX6KQmPVyYamVnF3/PTuWFp7WE68fP9K3F7GoLFH82PcC/E5FcpMctYJJAnd8zo6SyY6dz33GPD6A/wmOpvzjkD5bxt0otmoAMgXglqSB5URBB8mT5HMmz5G8IADdjdEEcqyRMnZ73aEqGXO/vdG7k0uVlAcNA6ZpmFTCGD1nJUq2kKvprTSWYMdnQobu/T4cUsLZYV6um+7ASMFQ6IOSTgFwxfOaR4qqTksPGmkqhiU10W7IU9AHiczt2tiug9O64k1KCTs8LdHRM4c8/XKVz+VE+2kt8mOZXX5wY3bJkSJdlJywYhyxEnPq8ydCktSUoFdTpZQsc2xT/TqG2FnzWB/jqNEbsxutzZDh+v0dbtcm0DOrvl7XcwygdWAegyjvkzEmR2oupv21zWCW44xYe8W4v4OlJpaUsDlpGHZeWCIdcOuDk8nnqNtODbn3Yok1V9IUmSC5k/NKm8Ye/qTKtdD71HxybUNyXOuM101DwGVw2wZ9k7qlzRZDQLHaU1Iw05JgGzukib9lRkvcbruYRAdoPB03MbTcO2Yywq5ZPlBLLVw3/ZuZMmPbKCXHrDsxU1FaozfqWOmb1qtPp6xZNy1bAGoTUqQxR4K4j7ASmKrlY9Ih8DgetkGkF0ouPOPcCwxQg2tXEA1JPYDjodiIvcsCiDbZwSQ6M0gVB9P/2P4siWUtBUmcvT+ie81mpCpyhnyxju38y9B4DFmrgHo1zFgETWMeZI1yDrWHx3A3eoMk0NKdsJ5JnJYqXMlzWFMoLc+CBexpBmtUe+kIxh4jWMJK+4j+rovB5rCT2MaktsR3r+HXhKxfsmlX7e5sc+Nj6nz//e9Z8kpZTpyXQhsJ+J/4U339FwCUxPLIRUlhHiVnLpnH0wNr0A1zrlhOnNczzz/9mvZ6o18H6+WZx3dfcds7eQ62j9/BYnA+M9yCquj3n2VoCrZS1BgiGVsbh3mdNmRhD0dK2jEZ5t7M3aU2RFN+Xyh6USwMw5jzbgZ9pJpt+05ZKp/Wz2oA22BMmQ3Otof/kAOJ3jtrXcTKyFksqpzIdUAZorpOI01N3kuRyZ/nFTdpZM2TDp+IOVM7QBxtse4spCxBHfag/8kp2GOaeifRxD04CoN0/31K6V4AHzfM7o3vj74CaXL0co0DkAqWSx9DFHuMrXdyjsJb8MC98cdTTNc8qNEqqjLaxHPWVOv4mgfgcqAecViLfSEZ3UBmd56M6kUJF7mylEqqVfc5ZbCKWSR3mKjTrrOUQ8Iy1RnEVR0EqzD8swCQAuE2ZKKbMNyHWDOu9BFFh6uQMtPGYzMAmtFEJR4Dm9pYDoPfNIeM+JIkFCUZ58cTYwz27SqAJurIHjKHOZ29bbqGMQJ8qkr3uzc+O4b8axwCvIpGZLqKxqQm6LZvjHFlzEHbd8ySpJRJpnxK2NL0Ug2/nrKHB5Yn0cXHnLR9p7c9DhnRoJcqquaX5Lv0R8wj4cNG3yfN97ivBBA67yCUBWU0hU8S4XNxsMZGXM+ck9EHvXXavlGCvUR28rKSSqW1Qa6TVDRBPcAjDdfvKGs0jx7MkXFnxcyptan7erwypptvB8QS9PPYiY531kDRvwNRv534fDE1jMLw2M+mh7QsqL0jWIfJ5a32+vZG603TttgwPIDDGZOt+9seh3hCzcA3P/8pn3/4lmKiHPuMtERtNV/23KnUqRnvhDnk8BI6GosDXErAu6cnvvnqPd///jvs08uX9/tHYNSXMyb+ICWW8xkfI5r1A636UWNlB+gBFTHk/uk/+ytR3H3w+Q+/4vb7f2CmE35KtNePbHNnkHgZk/b6vQyAD+AnRVHsh1zP7mfJwdiY0wM8DWGfKQHxaDC+JOMFxHj0grHzHvvIIV3Jx96aJqmc8JQ418KaK4+PmTSbdn+Hp8W5PF441cLWldqVQnrWhxgHY4o1OPsew5M4DHA8W/gaHKLESaFASsGalE9g8cHYJ71PcqRO+UiMPOWRtyz02xunywXKmevLmyQiE9qU38evXwavc8FfPjJm5uN1Z+8ry23ndn2lI+nEHEsUd47bZGs3+pjs+05vil9vYwqMSHpGfTZePn/i+vrG49MKyZi7Unq01rN8dtKUTO62Mec1GughuN7g9e2VtBYekYlyWR6oeWVvk+XiEd88xMidARJiP6pRlBaqEda8z3DGPOKWNQw7GEspgCafg9t+Y2s7KVWlUeUYwCTDp93XSsoJc4HoTCStLpB8x2ywjUlJB1NaDZhNF3OoA/vObI3hCc8JkuKoyYeno5L7hsMYG61duY5dUsp5gJ2DP/vqHYmd3tTkug/Gvit5FAEAsxcYE+FlE6eTkisNdx6fQbIeWwoJAek9mM1jjBhKgM9B6/r8tQTrLGqfMeUZVVxJaBkxxZhd979PMTTcWTM8nSrVJ79+bXzX5b+WR4U0Ja1yg7ySDTVkwZYtpVDrSknBDKuDp6cTH54feXc54e5ce+dUKqWe2PrGz3/eSfb5vv9/qT1nSN8cS5llCWAjVYqLCWE4Njdac/Yhr7TuI6RCKYxtDe9O63sMFj3W2girCNUcakpNwSZFCVnMeW+kdF5BG5MlL3SDxGRZVvJVBufdxdZNZuF3c4Di3P1QiL8zDrmvCI4wTcmvPiOYRn5Yrq1dioaDoZAUbOJRY7YO59WYszHIkq252BB2LuQEdTFyFZB/WhLXMei7mJepI/PsWrQWPOrNlMhuIZ85WOTar2u2u1zUpn4vn9dgUFrmCP7wQ3aNSaXg6laOevVohKcraSylGA7nFANryR6PfRiXx6BPpaDmVNjGLc6TjCeYVmCHtGZu+5WAI5Vc7IAHgBkBKJageGFvTWb2ODNX5sHEnWKQC8CR/UAbjRzDBcuyMxgkFBpbyKv8BpOHPYibwMK1/EjiqX/vUwEQStrruobpDJswMm2+UZeF7I2cBmMmWg920ZIZrbNdr+xjUhEYZzUxh0I15pTyxLBg+ohRY1Xm4KtrDLdenkiPD+xt47ZdSWnFqAx7ZU+TW58UnFMaZDJPdaEm9Zk1PEVnOsmzKwFjsHUlDg53Pm2NYZneXlhypnW5IY6uYIvhYiR1Peb7kGpW3atSVrZtY3vdmTZZ1qJ6EqKuUC3R+oTZMBMY5UNDdXoMemMQI1p9EAlDIpxI8pZrg546OUGpsU59hGoCbbxFEntLiN1Nlq+dEwwfgh0kmVcbjZKDSRpWCfIesqNE0ztmsc6zqsJU5LE0/f9j7l1+LMuSNa+f2Vpr73PcPTzyUZVVt6ruq0W3QD0AgaAlJKY9QUiI/xExRM0UJkgIJATdQDe6EvTt+6rKypuVGREZ/jhn77WWGQOzfSJ7ngN8klWZEe7Hz1nbltln3yNUKg65wDxAVsspLOoWCfiTnmKSRt05RKZtRzCBZoKtNqI3jnvjU68KSYbBA9Q6bDKy72y1IBp2NKoaIQ7hSBy11jRxDmdNCedugVypxKrRcjkfD2dInk1jURv/WnIuirAkTawiOm5PnzwLZl9RNsg/Ez+jH7OFZycyjaf5DFwplyeqHxzdn+7rp/dQ8riAPbmvI9kDhYb1Kx+/v3DdJk5hXRfu7k786S++5G/+zd/xx3/2J7x9eOD9uw98+PBb9tcYrB/uH/jFr+/CVM0+0dAAzFpuWQTXkk3Jp8Yd0gDbg5IaKRYzqMMuWfwt2THckNY5gtpXSonBXP3mswMH2BR6SPegqm4jTIr7AK0rlIrURqkrrTXK0vAe1MlSKqUo67lxerhnuVuRGlpen2m26AbUKAYlU+Y8ZGVqx/YiWUQSQFOOW2T7Gv9N4/2KKy0pQQdKnKjrJ9+Rw5kqilTegxzjD7d/dwzBx5Yrh8/jvyXbJoZ85ZCuDDeml4jEZXDMhodGRORHOnaX/JkhXRtjHnu3/D5+a1qCeXI09MmzkRyJSsGrcLeuIA2tBdcSzXI25enOdCtQng2r5UNLXpCSqLt5StxEbkO/2METOYYFwMjGO2Lao7mVuGxy4WxTQq41egxJoweDCAOPLb1qpA62lvMyh5eX8/LxY2zIzZIpVGK4ImJYD3PCpQZV9ZAIqToHHa9AbqiOyHr5dGklG2jsg8u2hxSNoCvf3Z3ztfFpuJEaJy3NdA7mkDtpGjq5vF7YescsfKG0VJZ1CRlbyorw8Lk4tk97H/Qxwvi7BlBVRMHCWN99BnUVDcltCUr7nJPZ43u5FuqyEtr+I4FuBmhgFv4cp4ZvHVyxYWxbZxblfC8pfSV19THki4VXxA3mTj+MeTwLRX8EimnUEjIpi2w6b1KWeL2lhNTo2ELbnMy988PHjwwPmefRJAWF/JOpvklIDDQNixLLjkoxJy+XK5f9ymlJearLjzzELEGMTJszx0uc4QDKjDEHldjAp/VcAKT5DqTiAQdWrdFcJrNEOba6x28eTctpXbm7v4umMkk55pZ+SlGvQiqRz5w5a1t4/PwzXt7/wAwnkPzyW105ytThb/flV7/i/s3CD9/8lsv33/D+69/Snp+YLJxPK9o/8ro/scuJSaSA3fjYyY8N4K/g9GyKUxp8A9SOnxlvgsgnebLlXQRHXZXjFXNAXzGcHD5dlrUUluVEWxsD47QqtTTOJ0E66WFUeLMWymd37G5sM/WGePiwFQvD46yvzGAlzWPgkGAj4M4t+TIlerjHGSBjcj0iuCdCDa1ygLCi1Dk5rS3Am7HT2kOkMWLsMzpZd/jw9My768bTx8lJC8vPf8WvTmeuW+fpw0dOj4/sc9Iy1r57UOlxR2wm3b6weWx0xX8EjJoH3d7g0ndkD08Vl4KUMLkXi2F+9EG/XjGCSYDEEBKBGleefnimuPFYBE532Ar91Vmm03Sw7xl3nBBcUcX6jmQKpadfVUT3xO8eQCy3Le3MlDeTwhCQMSL5bXRm3Rl9Y5jTR9DlrRtjxLJqzo6NnV2U7eVK33ae+s5ffoB/dP8WvV5p2+BeCsUnczpjELK3pUI3bOS2e0xEwuDVdAb7RaO+XUR4sZ19v7LPPc5n1haG8ebNXdzptKgbbuxzixRFnwxi0K+eUisbyBhMLXhK+iMHd4SPElH3pwg+OudauLjdFlWQvGg35OjpchjrFud2znkDQ6uHf91wZ/pgekf3aMavffBDH/wwjbos3C8VMWEpjkqlLiu0E7WknUAyeuRHcn3EGXTaeuLUzsGSLxog3HSkwL5H5Zsp546bteSda8lESyjgYJhYJGaVqpSyoFa51slJI6mHIpgqPgY+dupDYX7mcfamMYl+1szirI+d3jc8AamSM4374YMG+GAbkuCcU3ziJaQ5692CvgiyeTJUYrGpKfMwNL7nzHvQPJinPm596mTm4BTpvkLKty2DejR60ZZ1x22Q1sWIwLQAzrRW6ohnSzTM991n+ipG71m0hocSM+RAOM3hvDb2OdgvI3pvDSlr1TT3TmYx2d+qBfOwW3jRHHWypvVCSFgDhBrTKV6J1n1EfRjJwKmRIneUXZMAd8L0RdIL0GFqsCM8pFTmsO0DNTjfBRNobFv0ot2pZ6GcF0gZ0jgYp5Yf8By4Whg2ayS61aKc1oLQ43fOtLHpwdbiR8sPJeVxFuw/7xOsMroCJYHWZGqa4EzGHvKggqNFftSXK+HRHYwL9YpoC/lSdd59/IG+PaEVltNxDxrXPecyEeaYoYpzYYjd/CXVYsU1Y3cYC924VHFxmgt1zmDcrI2yFEzj3J7vHvFR2PfOq28gce4nhSHCKFCb0iTnEIvvX+oBzu6Acp2TVZ0NpS8Lp1p5HR0x2J7HLTxBckgQaZAJd87k1E6oHKwWwaakt2vMOSTYiORC6Og1RZBSoof1wbRgEK6nE699y1VJnDOVqGVCzEfewJa4r6rOsLfwESwkDzVAkUpZNGxUDna8O8EQCLkpuZTEJlqVSni4ap6n8D7r3BRMGuofSyg6pJ96C6wxC/By5Dt2A3eW8DU6lroRPmP5tsQcXWqA9fN4TZRbb0nKyiLdPH0pzVOVEXLP+L1DzhvWFwJacGmUWlglFkqxVCuUBtYHM89jrZqZU8nAdWHMqC/qZFACAR77TFKwIzeCTPQV5ZiTY7wMNqsEgNRSIn9THRDzXjD4S37OkQJ4pPgKxu4T0UNi8NN8/eSAkk5Ce9gNWyc+JrUuMI2Xp+coSKS/Td/p2zP/5z9/x3bdWNZ3/F4b+76x7Rci2UWZHpGSaDa3EpeNefqF5FYk7rDUedoRzzlvDbx7eJSQBzmpIZmYkIcyjefMO5rRhKHP/jGaCBAP43paeTzfc3rzwHI6xYC8hFHy6byiPqjLibqE3I0xkVD0hKSnNcp5YYgxbEdw5oyNNma03EiLhnFfyeJRSoIherAl7GYGHmyfoF1aNmCR1AGgqb3/JE87TOxi+PzEyAr5zuEL9AnFxaMJtmSwHElNMVQ5qN42sXhsBMM8WSGpqC6KSWXz1JMbQJo3QnrGCGUKXYyhQbNVyRSTw1z8mN5IE3gUywbVJRutephnBg3SeurJ3W6XTLCjEqBLRslIOVRIL4IVFalZ6RfASBwsUhLi303MOsMGs8fWWmtuvjSp1vvOy+sLdu05GK8JmsxMD0hDSLc0xE6ZgnX23lGJdI3teonmU53aCqq5qRdhv14jxWxpLEtljI4QhTguingOyMvfsNuGndweKwG49jHYt/C/8KKxlV2XTJiKBKaDQcSMLVFbYovS+0gtNCDKtl15ebmgWqg1TBtVC3WpuAV4oyXR+hmNi81kCHJsKoO2XIpiU1iXNYxkZ88BPo7e9bqz7Rm3rTXMnTU8IcZ+ofcBKsECbCs1JQpehN1HUKn3nWUPJo+0hdOMlJhyA2DiwgvPC2NZGrMfQ0NHykIpIxth/VH9Iv3VEmiK8YcqyuaOjsFSCiNpzKPvPF+uvP/wnht6J4ccSdJHTdASF3a5GaZzYxg5Tsd4vVy4PD1z+jxSC2du1/VocJxoZNUzzS1MKW1Mvvv9N2FC/6M6eDBZyBZDCB+OUgqLEHLAOVCNgfnGvEFuIMTrPrhejV/95o/5wx/e02dED99cvG8g0ScWZCuFtVaumuCnf3olh9Gt5YIqpOyT+7vGt//yX/DDN7/DxqBfB0Xg8QRv6wvz+Q9ceMO8/xK3SPqUptRVMTkjdNyNviXom7GwB0NRPICNox4eUkeVGsbzhDQJshRyCJCSw3LbwpN+OZr+X5N1eaCuCy3PKARjcWY/qTJ5fKh89rM7fvv77xjbhhIJN0KwSSYWYQRF8akhB1FPL5IEu5KxFLeKHUZ1AAAgAElEQVRdyDpN/GjnYkkwJdL4xmBYBHEEQ2HBZ/gYeoW6PXMZJSTgtbL3iHjv1wBDdi/86b/7j/ntX/4bfvHrP+LyfOXh7sz3f3gfbI0M7+jXDgJbDlhzWkhgPbfo03GPulvEiTSe/Izqio/OPjuSKXSDgaHoDj473je6OOaFLk5VQTKKeds6r88v3L9ZGHJCtaNizN54qPCd79HMJYhofpzHAI6OzYFxGKAHCBKzapyFYCpLLJM0NqieErk5IuHTmMweptnXvrPtG64w+gkZA1fYrXOdnZc5+J2+wRaY1w3pTzxM4UGEhlJMcZRhSrcNs4hjJuUHRWFqoXvUz2czPuJcq/PqPb00ghFUPTawnz/e49Opp7AAGDZxCXN2yyh0fOZwSGxW3RlSA4DIq8eyfks39rFzag0pzs++eMO7D+8YUlHxfK25jFHAjFOpwYrEoU/a23vuaw3Zr0V61bR4Fnw4Dw/3LK3ycevU9YH75R4pFqlkwyjeCfJbTXkJXDzMMoJ5GLWmVL0xW4uUGwPTLEx3975D0VjKfLxENb4hy6EJMuy21Jt05mY3FqwhaKmUxWAmE8J2XITqLXywRBmqCIUqEn4xTW4MUjW9gQVShR4Hi75v7H3HrCNzcFrvgRpDlc/wUoJgxauxns+c1xaeJH3G8y7Kbpog+ORgahdRukwuZlRStnx4kmQfX0UQV1ppAT4SvkOrhunyAaxVAvTp4zh7wj4nhRxaBbSFLOwwXi8u0YcNT7lfAA3rqYbZugVYEWlwEjYERK8b2Ldl719oJRPc3FlKeC32LSwItBT29L6sWDAfkukVASaCVzg0X54L6VI0PdaCRaoJOMd95eABAA0PxrKH03D0OjaD5VUqZjsLztoql6dLLBQPqphKLvkC9jIDs4FpWHiMEcwhr421rezbFoz5HEjnsTAa3BbvUsM2mGP5KAa+c902iihFOnYkos6BezId5wxjYsLGoaHo6LgS0tRWcBtcX99hg0ghLGEnEXNOsPl3j4CVYJIGiKdVY5Cv4Yk2rdO0MQS8CGVRlsU5Y6wODNin0F82uO609ZzhRyFbVG0sbYlb0Co+9pBciTMxllKoTHqPOeJUG/seki5x5bQuiCxQ4++UVpl9MMeIKwGnFg9z6LHjm1PqxKsx+sR9x2WlA2MfeNXAbSR+556/f3R6Ma/VGuqSYcK6PNAd1iVAJnGneo/7yZ2SC0MpMUeyQrETJxSxHa+R9hb+srlU9yC4BduxJkHjaNHSWN2dWlueP0EkklXRoC+Yh0IJidoZt6DjpJIgJWaWn7fj1Fqoyaw0lBLjcaSbirNUDeZtgrOhCvlkl1ApoYiYAzS8h2IJp9TQQgZxhGORaexZo5fi1MQsxGM5NzMUy5NBaRp1x6dGguIk2Vi5QNJMz3XBPRIVSW+2GD3jeRIvmE8Oz9p4P1N2SkjphBJgvYT/ci1hrUO+V+qhDggWVDCyFLv548bdEzVy/sjW5qf4+skBJSvQxCnaItmpzkjBwHi9xFZuKRG/OHzgrrw+P9EEXl47yIHMjZvhqtZIbFE96JKJzmUxmbbhVgh88lOqxOFEEehlNHd2ACnHkiuBnZtHh8bB7D7RkVKsWuOD1EjGaFUTIDpzd3fPup4py0pbIwXKHwM1bktDmGmoF+wJJQyQhYKrMiRTjDxQSWem9jweLkv5jifV7zClPei0B2PqSA87BgFJ+u1tuiYuomMOFD3+f27Ecsh1h1Ib/m/9Xfn09/I1OIf07dipp6zlYDipIMG2pJYA7j59DpH0JdMo4uzEdkBKyhHzu4aLUdK5nTAK9JLIdYBMLpqGfCBFGSoJwJXclnHbA2AJpmmYnR3MjsPvKGiHP7q4rOa2Jw27JeBklZAYqOZ2n3jf9n1PjyLLASspwUWRaby+Xnl6fWUbI7acM7TjvqXZXPA1QyomSSVnMsYeuubccL9cL0hJkz2bmI30SsoUs7nHdq8FY6/3SauF2moU4Tw/AdqEFlpG/PuRpvKi8fts+54DkET8curjUZjeMe/Y7OHZIRHzjCiXl1e23hP4jPdnjhFxvZnI1doS3knTma8bxnERBZOwtIpaQZcAevcekgBDqHWhlthC9hG0T1VhWWKDiVbKUjiVleHRhLrGpTL7Hklu5KWU8qVSQtphffI6lb4NmJ1Zwlfiw9OV8hKg7+3Z0WAoYk5ZF5aWFFSB2TtLHZQaRnzD4wJd60IrwmYjosu7xWZNQxJ32UZKOOJU7vvA5oU+je3yQtPwr5n+ybgPm3ip2T/mjtvirBeiIaxIbEh6Z79eWZcWjA6NuF2345nPfEYdKZ+LlJjpk1/95tdc/3Jnty2Tz+J7Jk6Wz1owFU8tzNZ3BjJjMzvzAi4pyXUEaZXdJ1vv/PyrrzitJ67bRLyHGehI9lGWIT/8v04nPvvsc56+fw83SItsyo80jvCgUnEWlJdvv+Zbe6G7IdaYWvj851/y9r5hr0/sz69o/0DrL9z/0T+E+obdB01/gUnh51++ZfSN/Ydnvv/uD7x7911e8nmhpM/esXqw6L9zkxd/JHhBGUntIUtNnXLCekH7L64oBdES8pq1oWI0LeH75pViJ2y+JCvXuWuNL+/g7y4f8bkhnMFArWOz5BK80PsV6RqvZSZNWxPIL/H6wrsuPZX8AAAl/fZyk6YxaKIhw8YJMMWiKX754Tvk7cIvf/kFz08XtusT+z7o153eOw+Pb/j+99/yer3yF//q3/Dlzz5jf73w5vO3XHsP3zdgjj3p5/HZWgJKFGXO8C5Qz4TWvBdH73z88IHTw+c0WQKsEw8fJZS9v6I97sR5HOBcyJgmG8SBseF9A4/FWKMwdHIZnfXhhP5QwmbXHPWI3TaJOzbWPcqQGA7FjcNVbjDSmL+wz04xZa4l00fherkcRGIgmKqS/kmejWYDZO456AYhXryDDnxdeFbhUpQfesd7MMGKOD4yb0cLvXf6ZmyzB2BZhHMV7k53vFwGT3tnopEcsxSqDU6udPsEK59O95yXikuYBVvxOKtFg00WXIQA6vyQYcWx73un26SVxlC5pWvtdEptUUu3wR++ew9oDOuHbkpL+juFJ2MoxA3fY6h7OJ1DjgHsoyd8J6xt5fT5HZ+9fcO2Xbhi6FqwPrluzuyTYju7BFMVM6o2oGZSWPSTnsOd2WT3YN88fXzm8bGyLmdaJpIWDdnojnE+3fP60uMcJljix3YvVhTR41oA1au3eK/dYL+kB0owNXCn60bJ4c5xfITsRfxWmoPp1cft86q6UGultpX78wOSHk9YZ/hk3zfMjbv1Lt7jGWmrFeV1XKjrSr12ugd7Q6YwkhEehslyC4oQiyXt9Oht3PI5lZDfkgEe2ioydxgzPVON4Yfs7BiblZqbe8WZKjdg2Ry6haQzvI7CcmOm9yMCxTyYtX0G28xDltNaoY/OclqiH7EAieI9iYVYKzVniwDgy1HftbC2Su2DjnIdUV/MRjK/kzlHAEnTYvkZS4DwVzUM9fx5BNh0SCJVlb6HQXWpAfCPaZhWvMK0Tq3KGEqTHn1nXZh9j7NVE0izAHVrKWFBkedl2gwm29YZy2DsEzehOlBmLmot+uPpOew6XUBLAAvZBXNL0da4B67zio1B8fZveffgMxM7HUkWYVhUGC+vr+GFKZXpHWyl7xb9uxH10Z0l7QympHk/KQGzQr9cM8Ev0wMJ8+4mji4VBgxr1OUh4ur3wWBj7FuQFAbcP5w5LQv71pE56VMY6lxsRLrfqVBFqYtw7TPZ2kppymenhc/ulec+eJ07oyz4lXggJRnN4iEDI/t8YGr4lRmOjh3HufTJ1i8MFpYF1EI101GWWjmtFeoCorTTGsx3lwDeqyDLEj6kHmfVPIMeDGbf4xyyoLVR1lhArtrQIoxlYUol0s8GQxpjbjHT5owjJcKCOObYtBBxH3j+GZfD3iXOuJYIVoJIDSW9Kd0tQbNP/q0zZ0A1iRqfQDTEP4/5rhS9MfncPBaZ7mAaCxyinqgY5julluj5k4hRS1rqaBiVL559UYLLSvovD3Af7CPkZ0UbFEdauXEStJaY6XL0nDbYM1FRzCMETApj9jgDpQXukY3jAaS5VyTVOOITkSWIFfWE+cZyqI+yD/MY/gOo8priuGPZC06J2QWjlcrG/88BJTlkQCXYBadSePv2get+Yes71sNDZ4TzHiU3Pbo6PnJrkb5CkpKOqpnmkIZs4QsUb5G7Mr1Gw+KTl+2KHiqFvD5DOxtvoia4ZbPH9nbO0OWrUssSJs2tcLq/Q6VQW6OtC60dQFAcvHVZKO2EagtwIKO8Z9KTg9aqEdcsQqslm80RGwmLBnCqckQDqJDUwQFeb7DKDclNPfwhQTgGGclG+OBp3YAeD1PkoysNCUZo2D99YJ9o+i7GkSzB0eBIHFgVSYAqhzXJiERIVpKE2XBShCU+JsJCOQ3Lj9eYbKloxuLzOYbtcqB8qXWVmppuLwyLYkeJjbm3SGGbGt8DCUaHuiGHgSshayAWZrH1KcEGmR6FVxJIUsimLH8zMSgJbHlQlClRwIoL++x0N3wIY2SamURUKwZuO8+XV64jQBPvM36eObkao7UVMaNY6J3jYx7M2WOL3vdowoA+BrVU1vOJ0QfPl2eO9LP8aPMyOapSxDeTjeU8NtCiQXXMYjd6gGClFsSF59cXet+BAHSXpYVnDkdbYTdAc84AClttmDnvvn/P675RpXC6O1Nr5brvvL68JJgUnlU2YwMTW7Cgi4qPYHFogA+1LagKfR/0OdlHxyX9kTxonFqVfYt0oVYOmrYhWmmqYbSJ0GcYh5daY0Avyno6hfY5ZSPhveRMV05jUndBTvcsrd0usKCkpxxB9JYYVdfKELAtQJhuzqINbUGfdnGkKlIXBtC3jek96PW15fA8mQcophIJIi8fafVMN3h6emXfN7TEdv8wAZ6e7KR8Ng9PjmhYw8PgxuFM36uX5+c07CzZqsdFemQfCYb1ICHvdolhyuDP/vwf8PXvvqY/b8nGiec/zC5jyJJaaMP4L/6r/5x/9l//dzz80Rte3m3hDeYWtTlrlLrzZl356rPPsdl5fX2m1UaYDyb6+6nqpWwuLuplbaynNYEYyYSQBHKOzwoSNBeWtfJ4f2J/fklw2fji7Wc8FGN7/4F9c9BIb5HrE/O3f4E9vKHWM88fn/njf/Tv8fnbR8yc8dnn1HPlu2+/ziv7k1fYAe7bUe+0/Uiaa1SPejVzw2ger/rwtRMRZMDDw4nT42e8XI2lhnG+ulFqBZ/87PNHFMOtYCOMWWsp1OZsM6WiB+uzGyIRhVw05ZqeUte8PgIEjzQnQ8BLNigB/BetuJZbfZkOZWRtz+iqKYPSO8WNN49vkXbi8eGe9x9e6CMYRJ999gV/85f/L1/94iuenp548/Of8fU3f8/5vvHrf/DH/OHvvuH+s7c8//07xrVzfkzvDmmZbmgJMjq9d1oJubl5eAf1EZv7PozL6wv79ootJ6LdEbQQ3j7XyRx73Il1wUwijc1z8z8Md6GWQmudnStt7iArivH8cmV9WDgVp/dgp+4W9GOx2FRPz0h3LGQ7BzDwabeDM5LNA4tZ1H0J6ryasyo0CaC8pDRW/VOvVUtjG1ecinvDcJqG2bC58ebzO87nL7Exeb10nq8b/RqDxN2ysLuz05msGEIX57Ib9vYOOa98AZS6IK3SX1/oo7FnLzKzF7k7nSgysL3jpXFfgiWjogzyvEksWEJaZECDaRSZMILNLm2BVpLMFNvm6cZ337+DZSFWL1HLFKUmuFqXlbUtLGXBRON51JBTjRZAgrngO4gY43rBzXnX9/DKiZNBlQACgh0y4myY4tpCPm3BsDgS9sa0MIL29NuwK7sJf/hm4G/fhAzMJp1GO5943Sfc37FcNup1sFuAMmrcpP5KghiRb/HpTO2GtNisxxo6ZdU+KIesLKeRGchr9H0iGfKRLMJS6fuOVsHolLyP1tqCMbNUzrVwWla+fPwMWovXNYQhOwPnD3/9t7Ry5unjC2O/snun/ahek/2FutMmbHt6iWIsujCY2W8pMqEuldNyovc9AYeQ/6mGrHbYjDOI0IowRpyC9aSM3bHdqDNeo1uwu+ckkpQkosJjeZjSXSfTjwOkKIBoS5AzpNZKDPyWg26kOBm1arA0/JDLgM0I/rjuG9feEa3gwuw/kpRkaEGi8JRkDbk6K+dg02Qf7H6wE4QpAbT0q90W69MmIiGdocbvHJKWAM1H78HKEk8pnX+6Gy3AKUHoo4M7rdbbvEVNYIbB2FPWWcOXDnEKxyxjRBCOM8kFKyH16ePKvIaDX/gVOqPvgIW3DQK5ABWJ+vDmXOnjib1fKZL+NuIwAsjcx7gZNjc5mFUx/3Qzug0edWF0p3gk+/VxRS1S7Xo18BOMyt1SkXWlr402DTndsbaFl/HC8/NLsG+mc73s9G7MeckzqZRl4bRUqgpNKmt17ktluxq9KCaDu/OCyiAdwZjbBiMY4WM43UYwf1qh26TP6E29wm6DxQT6hi4pwXILQLJPvC64B9Pp5dXYRdJeZeF6vuP05g2tnjA37msAxtt2ZdXKNifTB9s+gYa2XHKWyrSCtJX7dc010qSVE5s7cm7MbaOs2UuMgWhDUkmiQizyCZnlsEjqLYX0vQtW0kxihEHKwKNf3c1wTeNtiz+hJRiVtS7sc9DN2S3ICvsYNAoihTEHrYSayEawgFQLRcOQvc+o5VKgiWbCpWIeIPkxCEky/E0KpXj4wSWbT0s9RtcgN9SCMfEZnmyl1Jx0I72REnd+zJCxCCru4DOwhFIoHlJpTcBC1dm3UBeEF5VgI2xNTHp4QAPaTkyPgJmzrGwjQFDTybkpu4UM2SYUai41wwPNfwTAAVR+VJ9+gq+fHFAqOmCuMbDOzj4m26XTivDV5/d8/d1T0q0c0RjmQkKpESU7B61oROlpGLwdzbZrogKSH5YdyQ4JUsygX/cZxoJzxs85EklUKyphGL6cz+GDkWDL6e6epd1RWqO1RltPsWFMn5qETePnM8MyIdFmS+aKoNSy4maMbU86aDQ+c4wwmS8FSzrfTUNvMciIag6qNdhX2XLaPJqF6BFU5NM29QCADhQnt8nJp48hPlfmhxzOyb8uQT+UTG9yJzx4budNbtTX+BnZsBAO+YfXhHs8dIcfjxo3mVxhYtf9FvEukttvP0Q+wQhTL5D+WDXBr9hOOUMUK4WpjVKV0aJZrzSMkn5FlqU7I8QlqNVHnPWwkmbDnjrlNCDNZoGkYWdpwS38F4Y7XTpFg5YtUujWuWxhCBtgXMuBJOjo/XIJaquH34xPZ7fwf2oC9+uJWYWmBTOhSY/BT8NbYfQdmx23QauBOosqS10C6b6OpNOn9GNGkaql0Vowl8acbNsO4iytcWzt57RI85FC3zfO6+lGBX15fmXbrgHkSKGmr5Eexn82g50lmnIkYV1XzCbff/jA2DceHt/y1eMDRQuvl43vvvueaYPTeubh7dv4zPfYFNe6cDtWGKItAN9MX7terrjPkDmaxYarJoDrxNZBoC4lklkkh0rR20ZYSiQOaYk0H3MJAKiF1E5bGO4VL4Hqi4a322nltR7abnJT2FL4aJS1UWvlXE8MIs3k9fIaA04RTqqMblzH4LxEPRnTmHunJ5uqlTNuk0vvuCptrdxXZfSduW+UGnHzc+z47GzPH4NO79GQxM7TgrFAbASblHg9KsiU2zP7afdtyDB++PjM62Wj1SUn2wBdjte2myU1O+ClqTBS+jhGZ1nrQWZKQDBTh/J1vDk1fvmbX3F/v/DLn33JX737e8y3jBuWHI5jmBBR9m3j8c0d6+nM6XSKylCDFRUgZvpskP4ODjXPgUrs+FI0kuOm34ClQngM3d2/4ed//Mj3v32l9aDcr/2J/tFBl/CFGFHTbN8Qf0HefcveBBkL3v+U7fWMuTJ9Y1kaa2287C+YxWoqfO9SnplNgYpCjU30sGjIcaOKZYJfAMKRVtOjBnfjs7e/YH18ZJ9PybyJAak6VFEe784UHVz3PTy9cLw6dV15/PyXPJzfRay0OdTK2J2ykolkErJuNJYBWffId9lzmXDzvDLDZcYWnZAbJkYWviE+mD5RC4bhuRT++Ddf8ebxM/7kN7/hK/+M//v/+Oe0Uvj269/hO3zzu+/xuVN2Y98Hn31Z2S5XXq8b/f0Hug2u1wv34y0QcukpYDNe25wBTlZ1rhINYjXY9lcmgo+M7O5byGRjtQlbjxSa6fQEAspwRjGUGik+dWF3Q2cAJ1Urry8dxiv9K5jlyr4tXDfl8X7l3d8/ozqpREjI8FiwhM9TjWVGejp1P9hGnqC88vzDC589PtDd2FwY18Hzh+dgi5CsNg2JRGq64jPSqEdz9ugh8gZTAjgfGAzB5Z6undfRuV6uATqVQp8dEwUq7pGQ2Q3Wosj9G6oY1YPZfbnu9MvOhJTuBQgpCF98+YBvO0UqVcC1IGXBCC8iPwh84gl2ZMrdwSg3peHUMkEKzEJFGHMw9it9OvdNw0OF8FWqJSwHTqcztUScumihLZX9IizrCT01vnh44PX9R04Pd4zTyNYmJGrn+/uUlyh3d/e8vr4w587z6FE53G9LMRGhq7A4uMxPoCv+I58sDbbOPvnh6cJalPvTCSkn3GskvzEDQFoUHYVmcferpFmsyK0/cndshAddaylMtnzvyzEIRR8Xdhgen/nxcBIDVwD+M4aiuSMVmMrbxzdc+5X+/MrsStPK58tb1lOjndaQPPYAGhZXmha2yytf/voXjK+d+/tHSi08v3zk6fkp6oX1kPDMiRZlvr6CBmh554XNOosEaKhS05S7oqeFMhf2y+XWG/uMG7eIRp8vAfqKdLyfKEOpy87WoVPYZxgI15m0glwqjDQm7h7hG6XUBDRmymSyDyVAChdhCjd2VOSwVK7zQqlhxTD2zus1Qk7cNhat2IikszHmp+WkBihwaCbc7MaMWNeFMXdagXNzRta1olHttcxgnkv6UY2YgQL8j8F4DEdMuX84cbm8gDZm3wlbi/AKmiNYWiUTCzVZMrWkZ6EJdVVKa7y8ZJquNNbcR495WIcEeNRtpERHQCWALY8k2CKFiw+GB3tm7juzW3jduAcDymc+UwVplVInbRGGzwRHDpasMnt41U5PK9kCc48gCoYipTL2YLVdxiW8q0Rwr+js7KPTlsq6NsZmSHVMO8OUp5fJ1MbjmzPUkMHWesJmqF76HFz6Hr2vDda10cfO7kYdynI+UWvhsg/6uMSyls70wsvsXIbweh2YR0qsljBQdwvXx6fX9Px1Z9HJyZY0KZfwwOozZNxaaVLpYwZ4uyiLaHgSIhQzRr/QLfwC53qinc7s9TPevX8XMmIPya+MPe47bXgN5uJkg9rQumIE82ptD/G9S+fSA5Aptod9ikcdMcvewSs2g101k42La9TWSYDYw2+MHccDSAeEmobZIUUexNxXkFt4hBMgTavKNjq1NnzGvThziBDxUESIhSTO4+cUSYsAG0iLuzhmVvu0/LNQRwTIHQb/aA1PsAShuwvnJWqwYzQVFlkhLXF8nxH6cyyc3dP32RkWPQeiwdSyieuSDMbAmPFKqfHCxwxpuEiwwJsu8b0ItcWXn99x307803/yJXd3wtPz5F/99ca/+IuvqTXmvHWpAWB6JPAqsQyUA5w0R7TxU3799JI3iw9hc2cdghR4vV5jGw60tjCsB7BSorkNejpYCaouySjyGYWtaqEhodXXMNTkKPa5oXHPJJLptOXE3fkOqXpjgZSqtLqwLI3zsnJ3d6bWRm0pZ9KK6JIIasjsukXS3MRuwFHQYGtIzBEwwtMnhwiM1IomfdU9Lk7CtCwy7zX1lRqormTMIImcmicgQgIwENI55xO+mGlXxBYVyIf1kzlu0FaIJyuHSiA2KxAbZQ8YNkwMub3mw5fp9of107Yf4r0/DM2DMWaQmw9VDT2pBbOprgs32x6fkQIklYZimXAApBGtMjW8ACgFLyFZoZwQn1xVw1zVBrvP0NXiWAm2hLrG9l/j+lYbIcVLyU5s4ltQgX0mfVlvbvqkRpwa3EklmGxhDNkj2rw1pkeUvc/Bte9cny/4ICVvHZK+WAXaunCqK1I1ElkKyLUz9isyBlfbkfSTOCjPtSpKSCbH7OzXYCbUFp+vzfBvOZ3XYBeVGnRKTT8O/CZpmvM4K+HvcFpbINqm/PDxA08vr0Akv9W2cFpXltbCy2OEzOxgcaHx+9gG1y2Gv+36wts3b3jz858Fk6h3Pjw9MUfn7u4UMbYJPEpKSUZ6FbRWY9OkJHBl9N7j/cZoNaM6JfT8AYrG86gKe88m88a8M6pWXJXZd7Yttrut1rzjYmhumhxGgbosQXsvLbx9auXl5YX7+zdcnj4iS4vkETdaKZyXc+jhzdj2HR/QWuHxvNJTRjgmrOtCUWX4zrg8M7PhWtbKbsa1d3QK59M9kRLVub48YQpv7u7p+8bT6OhyYimnjB4uYSZ7PM3TODRW0x2XlJaap3F4PL6xoFTU4wJ+en7m5emZu/s3N6D5Bh7n841GHbQ+s2ksfP273wHOaW2Mblnw5MZQFFXGvvPLf/hn/G//4//E/eOZ1+cw8VUC8K9RHeO9l5AYvn/3nrs3d5godw+PUdNUYJCMqaiXJp+SNLoNXq8vITnBmHKIZY/ad8BKkWR0enjLx6cLTSpaw7h8t0lxwVvh8Zdf8ub8htO54a1QygBd0QVeNqF8/gueP/Yw8bRKszuW00Mw8jJJ1rxiPhO4jmdV68LYOl989kitC999+x1WnOEh0S4WFHv3iTMoKXH+6osveB0LlStFIxZYHZBJrSsXnwlIOdjAhrJIo1jDrFJqxWqAAXNOGmHAjKfcTrJOtoIMy417DLHTA1BUqSDBAA4ZTsQA7zO8dYpkMhHOFMW68WqDz7985HR+CLntMigPd2z7he6Fj+8/8giHafsAACAASURBVB/9k/+Qzz5/yz//X/8lUuAf/Omv+cP7d3zzN19zvWx89823tPs79svG3gfawl9FJOqZ5Z09xxZNl+TwYdCvM6FTZYqwj0gYVYS+X4MOr40wly24rrFQmMY4gM6i1LuV7d1HSjk8yILVhmv6Smy89sHDWUJ+nWbcRQOsxJ3XMUL6YsEqHDYD9EwNjrvz+rTx9P133D2cmFvhde988+3vef/yAyKFIi3qRrHwbU0TdnMoKIsOdq10OshOFeV8KhRtwbbShkX8C9oW+hGSIZU5leLGrB2Zweit5ni7w4axd7gUUIsFSu/C1o2RKXMtQdO3b07s+44tsYgo3qhSqRpMk5CK13j2kxX9o80VhrB7xI+3aSgXvFXchD/66h/R+Es+f9P417/9hpGshzIn1+vGnBYspVKQslM24eXliTEGC2+55uBxKsfv3KPfMlCXSAKtFbPB/Zv7YGk2p7KEZ8wsuChXOsuMBUQtId8dBAPosGDAQgaNCpVMgTud2K+TssayEUsD6/zfbkTSUw6c9dhE15K9S6SMKoLMeObJflLGHotFVaYWigRTpvce4APJ6EuPveERhyLDuTudaW3l/bffxSCnyvr2HtbK8EigVRca4dE3++Ry2Vla4+npI7oLXXZeXzZkqXzxy19ybnc0LWFW4Mbz+/dc3u788PqMbpNrD2brPi7JGhuUVjmfzpxPC72Hh+qwyTBLxlj4gS4lkh4vhLxkdtj3yaqFSaQg1tZSVgZSNJhHBGu2j5Ayjh7JgmMaNkP2u/tgaRW12Nl7LkoOv0ZVpS4rdyJMGwFyePQKpUpKzuDN/Ylhg5dduPQw+1UtlDIjIGXMkN95skHHpJXC3VICWFrO9H4E6gjd8s7HKYuwJNuh75N5RKm3e2wOJpNtTGqt4ZGXEt/wcDpSOh0SRLNcRI1pLHpibhOxyV1bGdZvK1pwlqUxLCRtMWOEjLW4MywWqy7OqSxc9p3rNkMSlIuOtkgwlYaAz1SMdJZFuVuFy0vnqoN9xmc+kDA4dqcW5fVlDyYMIXGc82g7JmUUMKVUqDO9oiQArLELdSlQlX13HhTuSgTavIyN625QFl4MXAc9peGPb+65W1feffwY0s1ZYJ9s141x19inctK46/ueZvOt8boPhhX6uyvb7My20O7ObPukOYw5glXYlngPPJQ80biHZ6CQSycFFUP3znpeP4EfBFCCh+l0qS2MwuOkBpHjtbOoYx9D+SN3Z+bobJcrfVyjzi+ELKoVNp/UqQgbg0IpC30YXZ21KnMfsBs2QlLqMyTXJTRvqTgwRrJwI9GvhZpAPvnwjtGpNYAOd2WfA9WBKqytIlmrita4b21mfxqAko1Ysoe3XCRda8kgniJ4CfGjiDK3SfcdqeFZZJpsnwST3IN9p5LEEVVsdmAwp6cVDszdqLWytJxrc85WuAFIQH52AYLHnSCJVcSzPGcEclVRItiJG7PQR8xWYxioZQ3LhG8Ukc55Wfj8TvnNz97y5f3Cu5dn/vf/6xv+43985ldfPbA+nHn/8iV/8VfvUIUZJnWhjqrKGDN6zvSYthk9z0/59ZMDSu4DmYKYsLOzl0q1kFNIQnGumjF4Eeu9SWqKo0uMpKmi+G54C4BAWw3tJzO9UzQ0idpo65l1ucOK8id/9gVlabRSWe9OnNZTgEAYrQXl/SD43LZ8Hoyh0dMEUcJsFKIgSqZYSDJ/DiBFPAwQSy0xVmnQlY+tR05m3GKZj3/mkBP6TkJ/6SR11m5tlkjqNuXYA/rtfY50k/h2cV4PmMn/rb9P/p4BB1lOWxoSZmZ8LskwCBo6cdgsKIendQkfizljQ5BsJnPiPRHwOcLgUCI+3syw0TNBQDIGEkhabJFIAQkIrcQgWAutLUgpjJLDJIV5INoWm5HiAXypVo5Ic0hg7di0u0fDdpgIwM0cF2p+pqA+P71XB5AkwZuybliPM9D3Qc+zModhry/s1hluub2xNIBMV5gS4EcrNc5cfLCM6yV8cl6ucLkmtV6CDyCxBccD1LPe2XPDV2rlfA4w5fXlhdYap1NLryaorXGkbM3hqKb+2JMRdxjEagCU333/Pdt1oyZo9LMvvryx3CKa2fIzTE2vCpjSWsVUePr4FAynrdPH4M2bzzFz3n/4yN4H0yZ3d3e0czx7Hpqe0Ol3D6AigdNt23APg9Cacj0/ZB1oKIZqpUgmjbmkwXOjUCPdR7LQA6UslKVwve6M3mPbV0s2PPG8lNx++TS0Vd4+PrKuoTPvFga/S1t5fvmB5y2G5UUq67mw1mBmXC9XzIxlWVjWhdexs4+O2uRuveMwBdz32JiWVtGm6ec0UFHuT2eaCPvYuFxfQeBxWdlk8vz8RO+DKg0fRsBixjY2tEXdmT7SDDF9vWzyyz/5U373178NA9Cj6fJ4QlzIbRhcX6+8PL9w//AmzND3TB0yD7CtBp147kFRf/f9e373t7/lm29+T10r2+sl1yoxHI4fPYtaK3/329/z9d/9HbqsPH/3jGN0cyrCXuKfVcLvbGqkMlVpfPXVz/mrf/1X1Frplw0L6CnQaDL9kpBtFvRTKof/GED6VGUPA/ClVGwO1vMjf/oP/4SFBeOKMWlNOJ8fWduJUgpry7hpU2a/8rob23cf+f3f/i2XcR/JVzIwBrI2QmLoHNp0ycEScrARYXv+yH/6T/8znq4v/Pd/8//AeaX3HmA2NRi9RHOBC6VMXl4vfBwbgy2o1XrIJOIZ6l7p3gNIT4BkkZDQPW87UqIZMc9BawymCa0URhU8jTN9JrNMQ+qgFht5N8d15rMjwezUGF6LK9ODDeH5uUJh0cqbuzNfffUFQ1bG66R1CyZSU2RM2rpyPi9onfzZv/Nz/v6bD/zNX3/Hw+MjReFnf/5r5JvveP/0zNY3tr7FmZzhu+dEszQ92HSkuXlBoMUrcTd2hDviaOxjp9aCtwX3Rp8DUWNR4WIhWQyZU8qzNmdMoavidkW0sfedUsL4v3qj9InNylKct+cT758vQGXre0jfzak9tq9DYmEQMciRfluo7MN489XPMZzeDS2RNPnrf/zv88P/8j9TvIcBNWHOHtJuchESkhgpNZZOHs/wBKaEobKPkZ4/ITOzOQMw0mCbWgkDfd0abluy0grLsqCnAiW8iQoLZVlYTivy9MT+8WN0JAL3d/ecTwvPP/zA5/cn9inMU2GbKadpBR2TjQBgiiTjbUZvox5sUhfH7ULxxi6TxSqLFn7x6694/va3IX2vEfO+SC6gLBYQWiJKHXG2McKw3Ixt2xhjUItwcei2s5tzeXqhEn6D57t71nrPw/nM/eMDTx/eB5gizirK6e5E0RMvc2dqJDFZsqlthrdhJLlOtIQcvlhh3wcP9/e0UnmpRrNYDk6B8nDGn5/ofc/vEY3c6JOxhweQZCS1W6eUhaaKnBpnlKorm3f8FH3QPkYOSuEV5ThopKBa1kHNJUosYwQphY9j52V7pYlS18p+feaFzsP5AdXBLMbUGIafLq883p35+OEHTvcnnu52Pn73Lc/Pr5zPb7kzhTIYrUX6Wq2glc9/9gVfFGHVwvPsTIS2d8b1NRiJz8+0utK3SII0nxHHLaFUaJLJfT39RnrME10tAOQJh8+lSvTnJBjkFqa9W9/oFlL/uhSkcJOlGhOmsutAarB3VMJnsqddgSD49sKyKgXBRCm10ZrSWsP6zrooawtwZZsBVLrle67KooqV+Kz7GBl0MTidzuzzCdMaYH/L+yQZC0Yk21omgCkaht9a6NctmI4t7pp9bNTSWErBi7LbxLzRWmFWY993dhspK08AFGPaFR+T8+kxlnpT0dJiOG/CDFp/0IO8cD7dxVziBj7Z+zWTl+H1OiItzrP3SEFH0ah9JdOv7teFpuln6cbrq6GLULzzUE98mHH/92khf0Xoc8M0PNNq+u68+k4pFaUzh6K2RCLYnBFxPwhbhCUknWWNGbTWhviOzcmHD+9wh3LXWFplmtL7oKiy1BWTHfHKXSbZ7ftg00rZ07t3Dj6+RoCPMem1IVp4PK28MNkwzufG62Vn1cJ1GGh6dhKBSvswxn7hVBembnz+ILhdmaeCy8b1WlmW9MCZxhiE1cNKMLpcUA/g4FQLzMH16WNIY+crm8PYOr4PSlXcn3GrzFGgLGEs7WdohdE2RjtR2srH62C7XNheXkIqPTcg7vFaD38vp489WfUV1Uqf4SNaKMkQNqo4fdupS8vZtrBUvT334csYPczhhVm0sdukSXgPhadrkFHSlIt9GFJasB1VmcO59pn3WSYATk3j7U9rx0iBi5mvj87hU1jST2mMgZawwnAmWPg5W36XUkokDmrUHJ1HBkfM9of5RFWhrXchRUzw6PHhEaHF/bSeAms16HNDJe4An5nO6c6bYfwHf3biP/mTE//0v/xzvD3w3/4P/5r/5p/9K6S841QL3z4JjRLtp6RktoNJJD/WlIObVIamaf5P+PXTeyiR23e/hqRl27mbATyogJcSG1bRGHZzENYSDIageilqHppp04z8U9blxP2bL7k7P3A+nSmnlbqekFJCDkQAB3rzKEiEnvCR6GPewKRgz4bMTDzeVE0NaMgW+BEdLjjGEXMYwE5QG2OgwXpgERKx0kEY8B9jR7eBXZD0H7IbinkkJ0QPILchUfjkIJLK9PyGgUQHg0k4hrpjk//ps5Acco4hS2//hRJJCPGjYxgqGkwPNAxYixb6tgcVOI2UD8CqpOGdkJITJSQJPfDzw3ANM/wwg9UaLBQNIaCrIP8fc+/WI0mWXel9+9zM3CMiIy9V1d1kN8nRUAQBjQABetOPJ6CXgYB5mAFHnCFFcjikpqu7q7oqMyPC3c3OZW897GORxfd+UAJVWVmZGRd3s2PnrL3Wt5LbwCUGdC4OmDMPTHxz7K9lIIlPvlR9/gyedw8wW18MI04Bb37f4lXBr4ciDidMfzVwSYygDki10RmdGflaqQZVldYb+74jY7aPhePDG82UFLyBIYq47T86CK5dPzHq7hZ19QdUwjdzFJ8gC0aa4pbqdATZFIbMvDVx1juez2fcSD3ts/O9NfE/B8a+O1jbN5IOwmytc7tcqNW5RWUpLg4GP7CH4GLvXuvrxvNwrY35ddfaebq8cNtuzmJIPoXerxs5+xR1XReW8+piEF7t7YKSv7chBLePtgYE9r1NSLdPXFP26Wyf8UkJ/vXXOrht1fPMwBKHx3vyCWJExSNjoyv73rleL8B0MbRGb40QAyUnCoKNjRQL79+/4e78QEiTMXZsRGWwlMTtcnOGW/ap2b77RC6VzBIibQy+3ypo45wLpzXR2mBTP8ydQuS8nrnRmVxNTrmwBueHXPZnujZOy8JoO8/bhW6BJDMSibuGshk5+mHMbe79C2NgioVrWvjL/+Xf8fHbH7gNZ13M8NI8hH8RpPd95/npE49vHwnM1oy5LiDiPCf1jcd/+/t/5B///h+IObCuHnHsqbjLbT70nafkG/8u7gL8xa9+ydYHsex89/vfuphs051k5s1c01WUl8Lvf/yBt+8e+fD2HX8/fOM5DrfmBAA7J2zyknR4tNh8TbTpMgw2BS5VDpPoIpHl/IYff+yM2yeWNWLZ+WgeGfktSQaBTK+KiQOwBeMG7Fsm5IItzuwRBWJhWc9gLr47Vzu+wiQFXxMsJmI58el3PxBKIsjCUOcheaZdUfO2HBNlREOGcWuVoQuqXg17QFSJDrgsIUANmESaNIJWNCdaGtS+T4D+FLjG8EN3klmc52yyIN7+MjBPk0+h/7hSdB5Mg4Q5VbS5aZbXQUmYDtaAotKdH7AubLqjRZFY2cXLJoJEam/8+//z3/OrX/yc9z97x+ff/8jDh3e0242Pn6/odz9Qlky9vND7YHu5kdPiGzd+Yief62hXnRu3Qc6FWwrc8EFHr4rERDRvikv5RJ3unsUWUoI1KfvtRpyTfiRw052724W1ZOrN70lV0B4Y6uDq0T8SLFJb5KEon0247Tu5+J4mxuCMvZkPG+otUTqGN4INJdD59Jtvebk9c3f+Gb35xvz693/Lwynxcm3zke38vBQWdrqXeSShZGcFNW3ujDGPyj/fdu7rhXVd6eIctYI3yV2iT4tT9Fpq1YYk8wi5+oxrWCFqoNvu007xhlLLoCm6wxdDh/D+/Tu309fZbtO8mpvqYN0ogsQ8naaCdEd0dwm0+ax2elNnCdEHQyECXg6R35wJS+S6d3JeGbTp1PPnRFBjmfubaIJFh5i+Od1x/vCWlx8/EcvJW/v2TBg7wWA5nyi5ULeKmfLmbiGNRt8uuBs18PbuxPX2TIttRiY7TQJLyiQTQnFGxeBweQ4iQknKKWXu798gAr3tPjwR3KUVZqem+BTeB1r+/I6zbEI7KEq5u+fu/dd8/PX/cEDzqL4DTHBK+cu+N0TevHvD/f2Jl0+fuV43dHgLkAOzp1sZ5wvl4rGv07qw1Suj7mRtSIkEgx1IHRLGS72R1hM/fnomlIVmERHldnshB9D6xEv9TAvCut5xXs8ev24DeXlxF8C6eGWhKlIWHt+9JbwTvv3udy6Uh8E6Cs9TIBJzfioKbbjYRA5YAiWiWgka6PtELzSlSWU9HS2xXile53B4WQJYQru7OoPhop3A6DuRwgiVkDNtDlD9QOv7oeWUWVZvGb5W5/zEYATz2FszpcjCy2UnWGQRoc511Ex8IJsjOow0zwkyB4iNSB/CEHfa+GAxEBRy8gG8TdB5YA7aCYw0WMzdiKrKenYIfRDfT60p4cu/Q+BjypS4OMYpRMJomDqTbwDX284Yg8fHeywoQwNbrYQQSQKSIkMhpeTrGN6at5YTZEFvFYuFq7bp9umcpoBHDOg+vBAgNEwq+6bsmuhimHSieiSsWWNMJESIgqaN3r2d082x7vQYeOOyBLCcyRax5sUGwYxdd0o6kSLkUHlY7j2OHDPEzP2asZhI8sJ2q7w8b7Rl0PbOmtyVLeZDWxXhcrv5uQljbDcXY6NiGjApyHKGPjhNkeTz/sQ1BIKcqbuypFkZvw129WcTOTkDbQw6lT7gfo2ofmKoUWuB6MNPMEafbi/z2LR1j7IK3spZcaF03xo0Q4OQgqNXSh/evKgGo3lgJorHq6IxzPtPbTdEO6NdMCLsV6zfJsPW7yHd3VHVDTeBKC4uHW0wIUGfrtxw7Cu7Q+FrQEenLBllIBYIGlELdLqfhYaRc34VhQFqG4T06s/3M/Ms29ma74dLLNNBPd1R4K6cACku7iAXd/li/nvHmcdM6BpczRJhKSck+tBA5ll09O5n9hRmq3x73VebOHcWM9rE8ziSwV5TWY7iiCx3K6bw4fE9XSMPiycVhhm9mhsWzJuos0z27mPiP3zM8Fedf/NnH/nFz/+YX/7xhb/5x++ofaNqp6Hz2H8AC5gDTmcAmh+MXIcY/EF//OEFJXFYbOBEaL7JMfHspgXnnAQ5Mun4Jqg5PMq0eW2jd5SSU+J0f8/7tx/IZeXx/Zn3f3R2xospfda/utuhEqS8unLUnDVy5D3MzOvbZSoB4XAI6at7xePSR+zoUBnnG2LT4dNtHuImFNvc4XM8rEPgFcDqNtNJ/5jilPMo7BifA7OxhOn28Sscjg3+JN6/7vBnXpR5UIK56ZfD/XQcH/37/qnANL9zBxsf4tKRdxXxlhjDBY3ZukAIDuyeN25KvoFFgDYr3ZluKeYDLyRSjuRZ567qSrSXRXRCipAyTOujzO9X5wQkS/Aq5/k9xVdFeVrtw+QtzUw2GnH4nzNl/H2ZrzGeRY6zWylk0N7m5M7V4VE3+mQCHZ+tt0Zvg703Rt0nfNo3rSLiDjrzBe9tzogE2mgsGLEP2naljR3MJyUlJo/RdUXFhYHRxxQPpzWSn2aMDwfXfLuDs0tCCtOJ4UybJRefdgVvDRijkVMi5QwiXC8Xnp6ffbqXE8uycne6exU8hclusTktJrqaXgr7vrtIJcrL5ZnbZfM60ryQUyKGRFkXh3rWynp3T87ToaYu4sTpUhtjzOrrebDyLlrWdZl6gF9n2g8gv1+wpsb1ulObUltDME55wSSQUpotSc622rcdNeH68uQsjeiMNMHQ0Si5sJbi7Jw1czqdeDifvfI5RiRl55PFwF4by0lYTotPkOciHaML210HtVa2MVjLwjndsdWd6616VX2MnJczIwwue6XZ4D6vlJJp2qn7lURjxZ0D/fbiWfNlhTGQoYwIto/Xr+l8/2ZCvd0mLcEnsCIBVPnZH/+crz98zZ//z3/B//03f8MRj9Ygry4xmaJ1752np2d6q+QQ6eauBtTbW8ZwB8O+Kw8Pd/zxL37Oxx8/vlqb02zK+Wkbh5p6XKspj1+9493XP0eD8PRf/s4tz8lF6GBfAi9mSkg+wRpj8Nd//Z+5frxw3L7R/B7QOU214OsY5sOJMf/b3Ru4i4VANt/Mq0EyI62Zx7sH4l0kJW/pEovU5OPTnBJDgr8OXlTo7qEZm3jzNkCfsOQh+CmqcT4lZwCauxSH4GvR4RidS3GMgVQSj1+9J9qBPTeiOE/JhxQ6hwiBvGS+/uqR//e7jR4C2hpLgWTBa+RTwGKCpKQRMW0uWnDit7/9xD/8P//Ej08X4OTrVFgYYYKPzV09JYR5GBayfgGfywDV6axVhdARcQ7b0c3nxW4ZYmG16HxA9UPt6IHffvcjdnni6RT5N//2V9yXnWiRBSPcR9Zb4He//54WA7/6y/+J/+uv/j2SMywB2QZ3D3dYDPzud99T9226jHzQISbYqC58Dag26GMeEpcFP4zOQ4YqvRkhrwgbut8IudBDZusR5/ltZHa0DggJjQtVfep+9+4td/JAtE4MwikHulQ0GxIr2/U7SnnLwznycIksqdCGPzsUj6RZ1C/uEA1+KOsHGBRavbprRwKS4Ha7oLcK1fknwZQ4nPNWRGgyiCPO9iwlJyOpUdUYMtlxGPvHK/c/OxFsxgSS8XR54WVvnMrCCEKtlSU4k6vJ5L2Y8maJZGne/GNTjPblgZMIlgtBIMXIhw9vGNWoWySxMOqVuikjD6ROcpsVSM6dSMEYeAQgzmFaipk+Etfh4rRHYiuBlfPdmZ4iaUset2jDResY3DCBw7eLeJtePdalAE/f/QAzghYlYSlQW/cI9FAanVIKa0i0uvEv3/2O69MTMcP9hzc83z6xX2/UcaMn5zQ5fHiyM0NEo3MGozhq8+58x7IEznnhfLdQ9xtrWbhVo08mS8PvFZ8Xe4yXCWKe/iIC7uJtPo0irYXryw03YvnrtjePJoWUyDEhXejX5g7dbq/DgSOuXkqZ+3RxSO4YkAqLJrRtaBc+f3rBwspdWggx8nzbiUvk5emJfC4sy5mhgzGeGaPSJRKsI6bsF+P66cLz4s2Ubx/eeQRXAs/X7u+ZCaPdeLlsfHz6zG1vvH3/fvI7hRJnAcj4wj4tudBadwaiCEr1OCfJEQMCZpneBvevgK/Z/pddFMhAMwf5++BSWO/O7M05c00hqtK23V1cMWLiTcNqSm1+BhgGRmRvgywCyYexb86Fqsqb9wtLCjzdBt+/NLqfZd0Vgnk5IWHu/QrbraNBESlIdqyHWHdHnnhk2WN3RytdxLTTNZAwLASHBlcHPWswnm9XukJeC8tSGPM8ZL0yaidGSGVBxeOeiXm4nBXk23ND8s4GtB7IEv19sVmVPuNeXY29N9+TAcvdiRo27tZIre5mEgn0uiOhQ3d0x5q9HGIXKPlMigUridvtI9E6zwOa+CB6tIZWfT2beCOyA5Fj9PvO/UuRpmEynoy9Dk5lnRGowbu3J9YCQZUbjdYHa1pZz4WUFlQrj2HFSqL2nadLZ5nth1kKe3smdmii3sGUMsMStMCaVzSI33PAVp8pi7+uYqDizKZT8kbP223zweGCFxoAo7sgPrqiDSon0EAXJUhyburwmJZ2dYebGqMHcgk0cWREnOzWtg1MIjFmcu3OwWwViQWRxDJfNZ9DJHJ2JiR02jB3KkfY6pX68uzDlnZzAWaaPVp3/qupx2KPtvQxHAOgY9CbTkeYu4e1KaM3bEyWVkrkEudZpBP4icCjx55L/Qwk/vxXOz7XbPFTnWd4ob/seCM7cyipEL4wApmYGpHgA/3gAn6MngLJItgQCKBaiWNyNYU5ZHRLh43JJLYpcMlhjgmvsO9gQpr7kFE3Qt2RmCEGvvvuRpDB08t3fP3hV/zm0/espwUEHt+8583pjq12T0vFzPPlM9/vmSV3/uqfC++/BRZD7n/Bv/vffsXlZWfvF56uO3W70a7PXLfr5KD2WawSpokHL1tIf1hF6Q8uKB3T86BXb/Gap1afaA1XVgW6BKxF1nXh/v6O85JZTidyOXE631NK4e50x7ImlpzQduPT0w0nce1udzQjkdBjUsp4jbWYynStyHQT8epaCjFiQ+cUwF5B0IhwcDvH0JmvlCmmiLdPYNM9pFOU4nVaFw7J4fhkwV1Ar+rPjP0dB8E5YwX4EsPDKfT+P31KMfWoWQzmY40o/pA82EZ2VAYeH2w6oJjuLJhg8ymyDHyfNb07LqRNYcr5Q/5J4xSWgrgkgyi1ubXef9/dAjkKZW6sQkoeiyiLbxI4ILVhckLiq1VRptWbKbwZ5g0g8yY/JpEmM64nM7sr0wMT/Gv3wgkX3jIBgud4DZmU/MgQRatvwLa6EUOmDZsPKmEfyrVWRAcyKtWMPoxkSpHkrBGZzIUY/LUNIHVn1MqiDoNr/eaRzhDntNInrduwuSgdAEjxitLo7gshYr26yAkumuTsk0QA1OtUJ2sBhFqbC5gm7u6JBZHA88sLzy8vAKzrwuPjG1LKtN647RsxBHJJjAmL1Cms5uKTrpfpZuqtouqWXhFYl+wOshhYi0PsdOwOe8SFoqENxJ1jvU8hScFmq8LeOjlnj9f1hkiirIWuDiQPOP8FjNo6+97dZZC9trubT1vqOACALnLs+8a2Xci5UHKcgjdMsAAAIABJREFU7jiDKCw5+6Gp7uQYWdLCm4dHTstCKKtPesSdcjEGxvCpy7u3X/Px999RyhkV50Nttx2AWCJvSqF2Zb/e6AapZE5zzXvZbkgQSlpZYgIa1+0ZRiXNBsGtbjQdLMsDKaUvMFOJxD55SKVwurtjH8Ohh73NlcI3msQArfHhZ19Tbzd++W//hH/6p//GS33BD9e+oe3HejLXqeu1cq2Nu3NGDBZ8bdGAw7mnG+qrr77iqw+P9FYpS6H1wX/9r//Ib37zO+cG+dN0RhSVqM45eHm6YkHZLxsSbEJ8p+F4DhUCkIKQJCNx4XR/IuQzP3z+jPQ6v0PzAgGZOEQxCnBaV+7PKz9GPXzG7gY4xFIVEn6I+HA+cz6v/lXGSJoTKAlpHlDmwz/6+jxskGQypMy8OUNWwhRE9zFgFNa7nRA9JmrBrd/HAENw52SvVzBjWRbuzivp5BNwccWUw02p3v0K6pueLJGFQByGNXcyvTA4B1ijsZYTt9H5sCxcPl+xaIQUSebCRwj4xtBgNyXpAab2FXlUhcVb7TCFNAjDUDpqzdd438US4kKW4FOzIJgsvkGJkbTe4ZVKlWAdtRv7S+WUoJzOhN2vc8w//6ncU0IFOtt+pf6+8eGXv+A3v/4tuu/86b/9M/LdSvz8wqff/8i+b4xW6ZJQySwFWvWSgK4NqkLImAmm1a9u82ek9k7dKsvpgXV1d+c+lHD9jNVO3RpDKyEWUvCvPxChdmxfWQasdydkfGbhxocsnDASg9QaPP2eaxvc/dHPuAsXHvKOphP17hu2ruQ1o3vj6XJh1MpBs3b3s7FIJL19w/c/fnaXYIWHfMfLXcXqlRIKJfk9HpIR8yDsvgkOE0Kdok9F0wDp3tx3v97z+PV7byjV7u5eKfQRvM0Kj5j5XmV+7OqO4EBiJAXydPf6/CeYssSCnN6jH94QY+B8d+bd/Zkffvgt+V5Ia0ZfjFPJvNiOrgm8msNjkcHrlGPw52pXH/jUPiG7hruUekOLoNVYdFBSZpd9RrAjjUHQQXSj66zRdhGc4dDjVAp13wniFfIEd9tKr0gWF1fGPAitC20fDAJyznzz7g2COx0JJ2TszsWaDYhttn85E/BGjBkEH3ACkRVipG4bTRUVOC2BIQWRQhjGbV34/PmJLcqMmeNFNMFdRClmTIzzqZAY7m54ODFUZyuvrzMGYO6UGVYZYzrNujItUa+MjqodS8IiwbeoXVlCRu4ERuJ6feGyvaCfxNtkR+CuLNxuG2ldPTqlO9aFhYUQ8jwwK+2ygSXHU6D01ni6PDEkkPI63fgJGYPQIi8vH7l8/kx5eMNQQULk+XqleccFTQ0hUnzrQ8nFn09jZ0kLW1VC9IFAr8Y5OxsnHoPigO+T51lAgqDN6DPWKqZU7Ug2ikJO86yi7l6w4cMaicK5JNYotN2mI1W5yxEzR2SkDE0Hl0vHsqAK23AnVQhzUGgT+i3u0g/JcQTBhGTltSSGuf9PAZ8oj7nHIbKr/1z7RDbIbBROgU2dOxtx97iqkYKXr9jrQBt32kYXicfwPd5aCmlZnGmVjCqBnFfi7tyVvVYHw8/Wrq3tjDgLaSKodkwTTaZJYCQv0FHFSOQs7kDKC0EapeyMGginO0wXugbqdoHbznpqbBd3NSK42yj4ENzw1scIs/XZnYRpCUSN7L3iFfcGS0dDnoKjsO+NkTqXFtmbEiLsEmgvg9GunJNw9/aeboHLJTJsZ+/PaDCGZopCMnf/S/d9mAVFUuKmO0Od7TaoYJEWBltvmNwj4uzfhrLdKiEKa57uzT681TgpTQshdp7HTh6BNU/DQxuEMt0zo8+zmkPyB0bf/dra2825w+LPhRYADTTt7iKSRAnGkN3FsG6Os4gJIU9sSIKoKN2LZHqdkfxGCoKq7/Mczv1FnM0IKmOaICIinlAIyVMJexsMNXL01FGSRBIHx3dVJM7WNVUkNd+rDY/4RVxE14lnkRgOOYyUBB2OmrEUkTzvo8mD9HbHgGiEoLxygkP04e1kBYtFHxaa+PUjE5Njx2P7y7M7gKNeoldxHiVbYcLpHZrvezbM9+oWnfHo+7vhzizxM9zL838lpUTAWU2XpyfW8z1KoPbK7fKJut/4fHpALHCrGx8+vOP+/MD5fIdJ4vH9A6YPPLZKqzu1NW6XK59fLtyuz2h9pvWKzxOd3Sn2/3tBKZJSItqR3ZYZH4pIjMR0Yj2fON+debh/5P7NA0tayCWRxGGA7jjq/lCcsQgdfjiXEEnBD0mG28OCTVlGDwfOl6/HD+rT8XOAp82Btc6UmbZBjo4imQ9pm04Qv2htfjA/KNjrr4FXcWXqMa+fNzB1pDgdAsNeDUQ2BZvj65u6ibuKDmFojuIPELf95M+pzSnHlIT+lTNpxuuOPysTQGkTBPrKIjF7BRmbTYFLnJPkZ4mphg9mvhXUIikXUgivN1bEozWYIbNFQoIfF20KKEiYwptvKExtCnLzZp5CGfM90BkflDC/WVMkBaz5RAZz4clvds8Ox/lxXEjyKdKYfAIdt6nsG0j0DUtvtGH0WmnTeTEvWrf1ElgW5zikEKm4wht6x9qO9Q0dE3JmgxzThEMLSYpPHMVb84bvcRzAOXbUlJyd+xAMb/Iw38SUnLzGNUQU51e55qavMHRUPaMfjCJe4Xq93di3yu12JabEw/09ZXGbSp814gLc350Y6oDKMbrXc0ogxkirzTP0tflEcAqfKTubyFtC/M/21uhqpOBQahvD3TazpeLYJI9hPsnQQYqJ+/OdT1BqQ9UcUr3faKqTu+DcCEIklxVQWvfJoASPhPYhNHWA3bDNYefSSckz8DLvixDDdCkKQmaJC2uJnJY71mWFFB00LIF18fpxv5UHIygpZ+IyQZOjM3rglDI5QjW4vVxJ0cGIy9RGt+HNLKlkovgE9XK5EGyb94ozXfrNnYHrevK/t9/8/pmTHImR+7d3pBS4Xnb+4W/+llGri9FmDtoN4nbpVPj6/QePd51O/Plf/gX/6T/+R99UiD/8RSaja1aGXW9Xtn3j7nz2qBrzwYvwkFe/bsStxMMGkch+2dmuO7fbbd6peMwEc5ZO9Anpw7ryzVcfaNb5J7XZCON/42g1cwFIOC0rjw8P5JKnHVi8HrlFxugu5M/FbEzRiiVwXk+UcsbUDw1ix1qsc62acVyDd2/f8LOvv/JnirgbNE4J38zm5HyusKqYFCxMmDeRZMMjQL1O96bHr0o6kUKm9yvHEnbwVXzpstl+VlnXwvm08P7NA7/+8RMWvN3SnwUefT0aUFPMPNy/I/3QUfUIztbd3entpXGuz5kuHo05hcJduOfzWLh7uGN9eaa2GyHeOaOAQFgEbkaVzlKKi14BOhHdjS7KaF52zCw2QMUrwYNASHQpnFggCTEbsTTohmmgWyZaJJF4vL/nw1ePKDtlXJBV0H1wfz5z/ViRGPnnv/tn/tf/43/nN//yHef7hee2u4CE8vLDR7oGRhu0bsTkEF+W5Fy066AQqTLQEBjNq6ZdADNyNGzs9FoRWVxQtYbtV3hpLsqs59dmOp0xK22GdSOOyvuu5ADX1hntxugro0MMmfX+kd4bEnYkBe7PZxY9EWwnvT3xm9/cSGFBl8B+uxEksYgRxSHd8f6Oj9uFT88v3EnmbTlxub7wsr0QhnK3FHbZCDnPR7oRTXGXhK/TIXrb4ggKPRClsgCj39Be6UXIsfB4Loz+RI5HuYm52+l1fzN8PVEhqDOATGaDHyDd3Vun8x3npdO7EiVzfrij7Q1acKadQUcYtnNeI/vmXMWuRkkrRRLdhgtdBkkHUeaBAkEtkFKhrwuWE1KNc4YlL1B2aM4bKUE4pn/meRZ0Opt6a6zLwvl0IiJcr1eCwejuGmsYJWSWZaHWPvdpRp2HgsUC27XRRmcnYDkS8olT/7LLGuaDDVNnLY7e2U0JKrRwpWpjs06OiY/bzSvUDVoOlODtlEMi5/NblrRj1ul6RIh9GNZtPrMlg3kLcq9O03Pume/7YhRi4DVKodNFLvE4NCmWPMVBhGwyyymMve9eeR0D1QanN3eUkjit96BGLInr2Cn3XkIS5v5YMmhaOMlCm3EZLdWhvbWBBMp6Yl3fEMOKEbwsA+G0FL774Vsuzy+UULBaMWuo4g5u7XQdM3UyHTq9Us6rD3Y1QRaGDFLvLMkd15XOEvQnrEBB2sTzx7lmHvEeYHQX2S25u0XUIduk2U483SL0wWaNJoEkxpIOx5NX1eeYWRbhclWCFC5VJmoDoqijFAg+JJ2HVcEQdR5SiAtLdHd9E9+D5uguyzaB18OAfjjrIEn29tzob2wS6DFyKolaB1ttxBmJ8vvK5j7SY8I2Jj9K3cFGWOjW5xnEnRqtOn8wZ+fciIK2Nt3DAtV8HRLDYief5RVI3CZwPtvkQYUAJGJJrMs9ySomDimXOsW9a+frh8Lt+kytKxJ3dgZJMgmZfUaFpM64ajTicQLZjZte3CmDlyqgsPfNo/bBqFtnLCfW0+rCQPJzyfPtwmg7d/kEdBd6QkLXK0+bJxdCdyFkTcCMnKpGd6wMJafg+zYTdx+qwPCI+WiDrokB1Iv7D3P0qPjeG7CxloVYEnkRYp581B0Cg0VA2yAf7tehhJAg+DUj5o5zAQdUz2SK4S5sif3VkROCN9d1U5DuUbgU0H5DR6G34tHg5FHGOBSrDWv+jy8nU9Ae7pxSjjPrdFsqiPqQP5oxzIsedLhzP6jvv1Jisj8VGjAOLnCYwZmOBUW7EZO79pKARTA1ckmzGGYaG0iv91lwKz5jVOJMwujEOSB98vbidELNfIgaITqr0szh547oaJ4AMXdsSYgz4u/OqVeesfGKhBE5uMiRV+PBsc8UdyhHwjxfD3JwJVusUaux7Rvh6Qf/f7NAABUuzx8RVfY++PbymYfHt6S4MIZyWk9sdYcYyKVwd77n8f0jcTnxUeBmldo7rfme2sI0hvwBf/zBBSXCSkwLIRhlWVlPd5zPd9zf33E6nb1mcZn093nAVYSmDp/yC75NK6NPbC0690HnhsOBlD4Tl2kDddeLIcGB2Z7HlKlOetTMBZ8wFdXJ3DCZ7IvgUTb8QID5RWWTUyNTQfriApqHl3nIRVz8EI7Dlc6X1wHH/0pt4hCP5n/LsTXwX9jkovgfkTnd8JvhsPmZeMGp//6M72GvLh6xw3E0P6ENn0ZOAcfmQ0YPkWvKbog4sFH8dYkhOi9nMpQOcehwYc2fZoDOD0MHD8enIpNRwny/ptTmEaLjL05ZbPKTXisuTQkHYIwEbThrqXue/HD4uINBnbekyrA+xRtXm5vbl/z3mtJmy0uQMN/rWUEehFDifG0hmueCYzRG30nB0G33xdIGot7uEMxB46hXxY5pCw4TyNhlQkh1+JQTCDlAFPbq9txkzqWKMyZo5o4e5IhOelzsqFlHxN0sGK0Otv3G6IPz+cTju8cpSHjGlxlnO67B2uaij5BSdlEpCLVuXC5X9t1rpZfssbCyTIFxHPyq7kDV6FA70zEbmAK9N7bd4zcmYYq8XskpJAfMjebCnIi3MshgqN+Te90hOGsgqBFzIS+rNzhXb/cQcwaW2kDFq+1jisSYfHrEl1hgDMUFvpz898vC6RQpyUU4qYNcoj+gRUiW2PZKrd1bz4CynPn0/BFSoKwRHcqteiPlelonnNXYujtClpRY1+Si2X5jvz6jrXNeXIzbu8NFSwmv131AWHKgd6X3xvJwT17PJIH9duOv/+Nf88P3389Fweb65ffpMOPh4d4bNGvDgK9/+Ud8+Jd/4fOPP0LQyS3z91/VAHUg7N5IOM9sNkljJjQbBPX7IMy2pSiBFBTtwrHSuS7kYrK3LzqPp/YBKXB7qc78ONY0ETwCc7gPnct0d3cixezg/fSGdTlxu90OqXquTT9ZPF3xANygdaxGOtffeHw6hGSwns/Os2nqzC1T6lxk4wQ7M42hYX6NW/fNZB27x9NMfBM6nVBJvLQgL4WtXuczaU5omRsIc5D2GEZXI5J4c7rjN+MZi14Pfqz7x3dhQJDIw/095+Wz16dPkXy0wd6Vsq5ISaQgtJeXGfHIlLcn+rPf84lAtDbdjrNKGUGSkZcMZMwGvVV6U49FMJxzYvr6Jclcu2cmnBwCQwbnlP2ZMAIq02lpvi70sbFX4WZnRggUubHvV24342XfqJcbQxoPP/+af/jrv+X733/k/u2ZMZTnz8+0upOWwulu5XLZ2FvjviwuPFgktEEwd3xajNNBqd6+9zro8Ejk0A6zrdMP/wNbMpYEDYk2gXiZBRGj9huhNWiFpQ1veKRzOhXieaGKOxBC9latOhp6u/D2w894nz5DjXzu3tpVktBa4H7NxBxZXm4kiyyPD5z+9Bsebxd+98/K89POn/7Zn1AvP/Df/77xnIQlRcrb99NxC2tOviFPLrrmGFklkqyQbOM5eOSy4/Ee6UqLjcRsZRRF1O/FIJElJ3pvxGDcnYvHA4dHT81+ckPgDTgigWWNvLuPDrwNJzR5NHpZMjFE9rDTBtTgUWcLSs4rapWHr96y1UZrc08jkT4HJLmciEy23thIdkcMEUmNlJRvvnrk5anx61+PyRb0SKZEf/Z2FVJwUK2pc+72fWeru++P5n5N5rAkpuSAWZ3V67W6myJ4QUS9DCgegfZYiLdrtuFxjJCiRyMmzwsRaIOYxOMbOkAnk7J1RtvpCoxIDc7HQ0BrpV5uHnoLc28VPQ6Ug7uN7+/PlKWwpMAPvc82IcGO8gwziO6mJDh3KIVEjZ2ICyBNMyMPcsdZcTEjobCsOq8FozcPtfZ88tKTXifLMDLKilmmiIsIirHev2E9P9K3p7leBDxE6GujWSWIcv/2gbSeyZJJyfjx2//B9vzs7CJVUl493t47fe+M7vtBnYfUbN7KanuDGWW5PV1ZJNG0Ya0SgruybnslXS88xEgheqmABQJprr+R87JiauzWGL1jbY5tTZDojVvOV5kYgOADnq4dBdbYsNF53gIxCafFuL1cqTVTj5mtTfVOoruN54FSEY/nes7fxR7tWPLr1sgY3h51MGede5UYdMZo3J3u6Hv3qM3wpsUUF5oNegvznKBY8usichQFeeyOyYh1l0kgpEBtFQkejezN95whJCR4SYfhDWUxRaIGhrb5dfaJCYnovvmeIEbMfB9mNnz9kmkGkIh2b+obHTDHFrRR6e2F338atN3xA1vbkBwIya+t3owyh31mYQKRJ7d2Oi5BHH/C6ueSCWmX0UnljIaESWZZCsvDwt522Da0Bbp0nl8urEl8P0vnjQxyOFFH9CKRoHQJLtBkP+P14+zUlRgNVRdjFGO3TBtCz94oeAgo6XDD4a69psPB4X1wCgXFWVKZgA53E9UJ4Uwx4aeI4e3ZuDMmRd+LG511FWpLGAu9+xnJo7/DEwc6Y8XTaTT6xvUWyPLAmhcXk3UKgzkytskb6uYoCdw80pns13m+NGUmFDxi3OfsUOE10trFxe0svpcf5i2MQZyvZ4Cqmw0O4bz1juAJEBN/HtXm+yU0MLrShzMUDfX08BRymGiaIOJnBXH+sKonPkKYznD5ktLxgaA3eYbgAqSZvgrFQaeBYhozTOa+bw41vZ1efB2wzhh9bqaEGEGyvyohBMf8qO+ymhO0p07hG0oRx3eo+B40xkBKPpTYX57YNKK2sz9lqsGufo3EkFjXFVUfHqvu1MmiM9MZ5fvD/viDC0q/+rO/YD0V7u9OlFLIky/DIcgw7WFTyRbzg4zF6AuCGFH90IZ4xbSLQm7PdvXwEGfcWXRwNAQfXk/PHeBvrGtJfvgCefUiTa0Jm1IHxxEpJA7v0DQeHfoOwKt99PUgcKgq8+2RA1KNvLqiXk1MhwA1HUNHpM71IgeCf/mZL8LOBIK/NmYd/7b5QeH1O3gVy+YnVjNiOEQu5gSeyeWZ3/P8dRB3dRzfWz7ia2HeXPNVP2Bfrs/G+fq6Mmszahhi8gnefF0O19eX1+oQzlwldvaUw14PxpOqzniMuAfYfKKqIi5kuCzsopL6ZK/Nj299oN3YR3NWkofEXQEXt/u9vs7zAeqLSifaIE3lPPRO33fK4u4ql+QOfoxPxC14O6EFfBOpNiHILmqZ+ia9zQdxbAFGJKWFu1RcUjN//2w2jKhN+Ozw9piQZr5921CD0TvbtpNiYllXTo/L/PvyqqyPY/NpXrMu5tObmBzEWOvOVivaL1xvV9aSZzwuTVHV6M3b5kJwkcrFWePgFBB8+tv7Pu8deWUySXCAa4zh1R5qOuZG3gXI2+ZT0pQiYbggkWImLysi3hYRxB0l1pRWHcSc14z1SkyRsnitaoppaqZCCm7JjjFRlkIu0QHbayLen9DgQvbElc54nzfmbPtOa526d2637cuCPvx6yecVEaUN5VZ3pClxXYhLII6O3ip1vzA2h4OXkBm1EnP0NRFB1R1aGjoyBqO7K+z+3XtyXqEqL1vlP/+Xv+Xb3/zWN25jvu6v0qw/rB/fv0NzfBWNlpD487/4S/7Tf/gPr24GEZ+sO3xc2Htz0cY8KhUQrxA3Z5wQ8LpzE3xq6fdiZ17jx8NzXg5eJOzrVNeBpQkhZdZrM+9TO6Y4QoiRsp4c9CjCUKGNwd47xOTwcfFJHDK//xDIufDw+BazPleiuWKIA3KPKIiKA/Pzeuamipj6ICMET3khWHcAIsxNh/lhxqtjhVQKwXyaHIMwxAUeUUVKJmavrZ0q8L+KFos4242QuX94gw3hw89+Rvj2e8wc3Onnj0MlO7bHgXVN3J9OPu3EnJsQAn10JAqDjrWN/fICwcsBXKMTOo1ShLxBswpL8c1sKlAiWYxgnX13908/IlAhT1u2vorZMQDRJ7+GkNRt7LZEb/AM/hVHC8RkXtM8lO5naUSEHBrtcuN282amuJ5p+wtfffWeb3/7A1998xaScX124GtKwqenZ/Y5WUXVBZzmESVvKFuw3rAwiGKvEPpdbyDOTwjibTdtbNOqbxjFD1GmDF0wGjE2dzlqR0cFybSc2b6+Q64/IteNHAo5ZKIaJQ3ePSZGy9QxeAiNnK6k/oTGApKRU8KSEJ9euHt5Qq6Vu/Nb8p/+AtbiTUUx8Ys//iPy8gOinWV54E/+zS/57tvvOb19pCx33gDJ4L6A1UpS5VwK47xTToW9BoIsnM7GmlfWsjuovTeHea6BJRtsF1L0TXBO2Q+wqqQi3J6f6ZsfeJc1024vcL4ngINwA5Qk5LBzSoGcXCy56UahEUugpIyETBVFrLLEM5qUb97D9z8+IcOIFgmjMkwhRLrk2eTqbB3J0UXlsZOa8yeyNX72/i0yrq+DAoJPu5kOcx0N8skXw+qDldvlCsH3e95aGycL0Z2RrXsts+8NlJKzF29YIOQynYz+LFvyl9fLh3G+1sT5/1Eloqw5E7MDz2MpxJhYY2TLgajevCfBq+cRYYyd236l98m4icCMyocYWdYTMb8lRn+P12V1wTx421HtHoEXM2LMHm2MyZ/Vww9AezC6NoLOQ4648/rufkXIMIT9eqUNX2+HdUQrOSTiErl/fIPgrLU2AbarJEY07j+84/btC63XeWCdTWopkFMgL8WZjmIwGkYgns589Uff0LdObZ3ad9rWvNa8tteSk2OPGeYMWNV8wFm8WKOPRjXfeyUThjlzqu4bYylwOhGjD5RE3D0WEBrQupDyGTl3tpdnqErXiKGkxR1Iiu/hrPmRgunEvfVArX5tFIOna58ijFGHx5l0CBYHWMAcODfdZy72xhDo5tevKTT1qLboFJhkYMPju0MHreECkgj15iLpEG9NC5bR5l5DFY8WKcrB5OJIYoQpcJq/jkag6/DYVN8JkwvKwfGJg2zC1tzRIihLiMTg8R0zJadlDnCM0XQC/pXWO9E8lL8jEIMbCcwPz70PdMx9Y915efrkbcopEiyTMUrw4XpM3tI1xiG6+cAnSXhl6WxDGd1LD1L0va+Z79kIEGNit8anW0AaPJycE2rd3dPpfE8SWOMg2JVNO0uCJT4QhryuM+ZwRT/QizFaAAszCdFpXbAubDYgwR4HKhGaR9KGNtYcOa1CHYrt0E1eeX9oh5Gn4AHNAr1HdyXheIsUPQradQ7dg8encxLaPp+NyBwkuQjj7hgX4DFeeUGgWG+0AbG4gWKXRi5nokSKBIYJm7rwvNcbieznFTe0g/iZw9HAghFe2y9dMPGUznJXPG6uxxB+7tUEem/OoTM//9mryUF+Yrjw68bhwu7yjz468abPnBja0OEusDl3dzcSR/QNBMcYaPcCDg+RCiP8ZH+Ii/tH8/SIHvXLCKbdn1e4UNzGRDLMdndD5h4TN7ioeEEZU9yc57NaGzF5KVKM/rotycXuQ//z11g84TH8vSD6e2lqrOeznyM4Y8NjbKm7BjC0crk2evf9fwyRHOf5+tC65jn7D/XjDy4o/fJP/wSfZ/rD2lNEzgI5IlDC0Qzji6TZjGRFe30zD1eQYsSY6NZep+hB5iFddMbdDuvfl9+DA5jlIsertUiYCr0fUJx89OWiPZQfmdN25mTBfwR+Cu32Dyfz48jrBP4nv/mvP69/pukQ4PXjHEKX/GQDc9z48pMHKxgH21Omuvv6Y07f/Er0n2R+ziARESMmXxjDfLCoyJSGJkR23jwBv4hDkFeh7jgoHTf24YoSCYTZPeFfmH5xHR0K6+shcqrO7nuYF7ZMXSkw+5+J83VzRoEg1hGNLgjpZLD0ace3CZUenaMRbR9K1+YA2nF8nQATDjcX4CjzmgzT0aXdJ4ujI3Qwfygh3rShwxdrwzcuDCOFBLE5QNJc8DiEU3sV91zBhyMSmlnK4pDD4AJBn3lfkS+uvRSjDz57o27dNxrmU70xPN/71Yf3UyDzv9VHp/fGUHUBNnrOX0KY9mAX6Wrf6K2xbxe6QkmRx8d7bzQR4WjPM4SYsjNwxpgPjUGMCUO5bjtpMgMLEh+RAAAgAElEQVR84zMICGVZiMHjj2FOuExciMhLpnXP7gczyuLA3969Mjtntwd3NWDQJ+di4G4Bi+54iiUQ15M/3Od9NY51IbnrKOZCKpm0OCheQqTnQA8OI4x4g0xtPlXRAbUPtq3Seme0QY4ZFkHNG0ssKNteuTzf2M1YU2IpCzo6Y29stTLajuqOmINr4xRbw4TguUmo01Vxo5vzaB7ffoCcqepg37/7u//Gt7/+tT9MZ0PDEF9h5VhYxHh8/84rladw0/rgm59/w4dvvuHX/+NfJshT/YD1OlkZXK9X9tEocUKY533hjLXwCjd0wdMPMTEfQjSv65JOmf4ViD+UJS/ssZFSJrTBobsfzCU1Y83J3avLAgZLWLDb7kKAzqrreYh0fdbv5dPpxOPbB55fnmdbqL6uMTOwgM5og4RALiuibnE2OeK9x4ABplzpkHdcjAw5zekVdAuAUG2KZmakGEgBSslTyOVVZJvjCBSvdz+tK+++esfl4zOPbx9ISRjd5nNqFiD85Nlx/Ho9FSyKv2gdfwbGxO2y0fZKH1e0V2/F0Y3RNrRmgiX++Ou3tP0H6u6lGBKLr30BVomodW5xRozaYInhFYJ5DE2CGDllB00e03dxgZjJSFxmnI45aIsl8xAWllzo3SNUMgTd3X3n4H0oy8KPv/2Ncx56ZdTB26/eOXRZGzln9tGci4JNDoq/PirzgIa33BHaHP6Yr9NJWFboQ7HRCTqjM/P6s+BusYhxq53WN3ZVYk6kuGAC1zH4/PE77uPgfCp88+6ecgc53CghEM8LfQilnH2inweNk3NaEDYC7VYpt528DWiVtkCOSixhHjIgp8hXXz1i5vHRu3Xll3/0Dawrqax8HR+IcaBPL7TfN756fM/D1wtPLxvvlkz/4TO//OaXhLcr/RZoGlAJHuUJLh7knHj/9sS352c+PjVEOr354TGIURWqeQxnuw2EnTfvKtdtOGlaFM8bnJwVIhmxwJoDy9uTQ/zNG3DkVDlFkDJI94aFBeIjt+EHPxFnJ6kO5x6Ku5BHLBiDHJL/mk7rxnbZuHsolE+Qojt+mUON47lyDNOOCey2baRTcQEk+nrlMN+f7N98Aubx/eho7Dq8kSjiz3eiP7tKymhtvn7NezrME0tKAVPfqK/rwv2puJsz+j8xZVZ1l3Kd8QkVJaaA9O77YPz56Qfmzph72WRzutwLqLI9PfvakrwWXqYzIaVEiJm1FE7rit07nkDNY+txMkwVkOEsU1OczRVgfXNHGQvXl2e2zQ9MZT1xfvdIyf5MBmdkihlZzIsSRLk8/8DlxxdGdQElzkFBLisxZi4vV0avtBlXvPad3i5I7bTRudRGSSujdxQjBy8GOVotp1+JWBZyXgh51q13QVqbbE6Q4XE+UWPbdmLJLDkRxXc+ZgOT7HH6CJIDpWTQQqfS9ka7bZgWPykHPwfsrcEIr9GW1pzVBAoxOLurJMY+iKYES2y33Zt0TV0MiGkKofO80iCExJgJjN4DIct0nCg6mjsF+/Bf9+HxX4UukbwsxCS0VmkxkENiBHPcyOQrBL40ScfgQwr8kp/CKLNREHIOU/zyNbZ1fJ0OfmfFuf7vo5PFGUUmzAZo5t4tEFLxg705CKv3CuIOMVNz5/fhqpNAbzuXy2dvoItnMH/+kASxjGTDxONdIStjeFxT5kC12SCSqNK9MCnjSBTCxEwIqShlSbO+3l2Zl93ZZr03msHDm3sWCUStzsHSQhyVKMZaMltrSPT3S6c4cYBIQgpsbdDrFKuZUa4QkDCge2OjaxSewonRCAop5dd9zfj/aHuTH0m2LL3vd+5g5h4RGfky36tBrGaTTVIEqYmiFiIFCaCgjcCF/kwtBAJcaaGdBAiQIGghCpCoprrFZg81dNWbMmNwN7vD0eI75vHI9WOgCoXKzIhwNzc795zvfIM5Vhy8aw4ecLkqnCgNw031ctv3qNsJKynCGZzZ5CfmKdEuhLRxyg7ANHvNDvT5Bi4ilY674WyMzXFWCoVhxuydvl8lSdyuYND2DbJSGGcfArujnzvM383V+7hPkmvun3DrLXNSgq9Hr2PMm4R5YDLhPjYHWWyplCswWXOm04PBqIWgDZmxO12pdUnqiOnQppKrc4CAE4Uj5FSlcuhAEqv16FFz0tk8UoreJtINUxik+ww1lMzyh4+oMTNmbwsCww+Z3hZ2CdzCbXpYbPTRZcYfM2FOSX6eCAw/1cQcLoZ2yiImpMSl7cGQ8giymCKPuKZsd6NkYqkpLACk+LJY5PyYXz86oCQWzAwtYaBxZkFdm7dUKztagDDMPiCbnMRgSIfmMGhoMYNANPwjYAk7MLbDyDpo0AdokwLqNcKk2/3Abm/NiBqTkLMd6/bjFR0v7Lgr/AfNSHzf8dreBiz9/SEFuf0IOFhsMSDFwPQDltObpjFAMZ8Qvjwp3q/IR9pkWzCxmPFv4/piRq0ytZO54yERi39z042CmTbvN7MyjpcUzdcPGFmCLeKymN0GS/Db+zuIOcaMBLfjvR1uVreLFGCSXsPb3zjMgY04tOLBPOYZBuyHT9KcjK6NSBs6VGaAmCkQMBUTI1lhuIWfywC6IjetazwXX/N2b2j4HHSPZDtQhfIuxlh2mnfRNucIvyAVkGkBprkOkJrWMF2z2zDrpoMm8Leb1A1X1HTrKjhHS8VUw1zLwsPjAwcL77jxRiRc5VRYlhIAle72fW/yJWuNbbuqQbPM3f2ZWhdGC2+YWDvMIRZHNnkhyRM9RYph4nrdxAgbg8v1OOCMUleWulDLomdgHp+40HnHuLamSPpstNaxVBhzUk93TM+hPjLGPhhDPk2tDwEBUwy+uqwhcQ36sRmkLGDaEm6SJVhNpCo5QCrG1nfGyMyrivboArBTkp9V75PWG30MDs35ZMo8EWhjp10Ge3NIlXNNmE+u2wtzv5Jm1ybLJbkpabKeVlKC7apUSkMHe+9qGnNeON0/cHf/KHZJFyX5//mjP+JXv/ylvAZ8xrErSv7xqEyDU1l5uL8Pppx8Kizp8Do/PMS/dbCjVh73mXN5eWZrjeV0lq9aElilLarRh9hpknaEIWZK5GBevpU7DRoauPT7lpx4eLinlIW0X2+PvMUwiMH5LL+fHOxDn3BeV87ryvOTmJlz9hs70xHAdfdwJ/lPDFMWQMeYGpKP62NHrS5KnMkmUBRH8lQcH/H6DzZOlIKSItHJJyW2SimKY05KZyspc6r1DaCyoxKixgpt4R7u71lr5VPv0byLzWghCb2J8M3jdw76GJzuThqYRgyvVWwdZzL2nefNaJfG7BfstPD9N0887SsKpcjhITUiDUjg2lJ1/cdI0t97owY9fGweLINCNt1xH99/wd4kvXQreCrYUllyogDFC92MUmRMm1JStHwpXF+ubNuV1/6OrcvLYYxgP3XJXpf7O0bfGc2BzuvzEykb9bSy5MRyXrlbF8VE25TZcNxvO9rKYWqYLEIq7s4r7+4Ll0vn9XJh7ht2eqTvXVLlCCPIRT4PNgspd0qW7O2Qv5/y5MOD8cXdmffvkgxaSYxUyCwYm5LYTD5q+zCcV8bo1Mf3uN8DRv74Hv/0PX63cr08cb9kSl3J1wHffk3NifXxA+37Z/j+E2tOlHd3UHb88srl6RPt+8+k18b9w+9xPi9427j+7jfUl431Fz9jSZl/92/8TKzKCS2GxjEmv/36yoefn/jyi8K/+t3GaYm0te48ngvenNctml9P1JoYbWcMgfq6S0vUZz2ruZxItVK9kf2i2puc92vGF5lk3y/G2J1xt2L75PnaGZbZpzyI1LNMZiGkRaoBnUmeCfPG9vLMFz/9GWvR/Z+m3RZmluyNoW5adnjSvTDHoJZMyjV8Kio5DJkP9u0BsI9dqUN7HwyfrD4CMBVjLq8Vf1Xtm6YNekrpBuI7xsxiLJVS1GOYNs+Hee4VJ7uAhcNjMqUqb6Eybj2GzxkpmwIbMwZ9MOdObxd5D6Zgt5iG0rqsnM53nNdKTsZ6WljawhxaMdiAWS3Yu4O70wNlWbBEsJqQ1HWAT9Wn+/dfcPfwjpwLs3VSriRvYloPsXOv24XrvjF2LbtE9Iz61Tqfvv1OzKHRNcD2xtU3+escg5jJxmDsu6Q5cW0PoM1HLCOnmDdrLrxeupY1YcngHObWGqKu244/v+CmVNo3i4RgdZj6sNamnsN3mWkXZod9H+QqDoLPAUNsmsNU3NPAx6DUuG6FGOoTaeicz2akoc8RKzFIC1AkZ7pPSqgw3OIsbRmqSa4dvqHyjxXbw31wsKR8yDNMGgEtHcVEGBxWEJNjKJfUXgD823JX8eYabkFSvJILNrsApFA3wCRN1B9MY+8dG5JC+gh/xOnhlwrede/OZOS6kFweNyVl9q3LGD8Dw7henpVwh4FN9do+mJ4YUeMrx7k6yUWD82zOGFoe3eSsJn8aP9wRZyenzGkVazBbobBQs3q767bRuqwL9pcX8rrw/eVKGYn7MjilRLYLzpCMFIHfO5mRDp+zTu/5lvglOcmkJMOzlrCj5Ui3BeZk7851M3zmkOtrWWjFyYsxw4dob0Osk1ID2G5h5TGk0MoChWeCmmWx4Gj5lFLC90lOk+VU2LbOnEUm2W/6j7dhzQezX5nXRsLZh5FLZuw71jbm5RUfXcstJtUT7ZCnmd/IENkSDLmGefoBWBH95uG9NOd4mxERqwzAclgvTA8ywFTSdNL96CZQJbpIeSoOJ2f5ABoT82OW1H2TSyh+UmKMQetKk6vFBaJaAKIWM6YlAbmhNukT1cqhns6TnheR/eRBm+PX9WCTH/ej+zEh/2DuTgLMUrD8ciq46TwcTbVppkhWdZmjrzmRs7GUpIX7APfEPuGybbRID8cFZuJBPknoGTksX3RIcyi2ZnTrP9bXjw4o6b4Qev9DidbBosimgTDNo+8PvSCxFEBNCYnw7VDTbdMpHih6/C6LZDMxkw4tO7pZsRvb5qDsBPkICEZSPMwe9LUDOAELad2BG4W87VbYAjVx3dZut7/S19u9rBswhpBj0DrmOw6g4GDpHPKrKPxvaLcar+TOzdg2l9u1M+SLkYjBKGsILqUGCnygxzoUBVqFqXbWA5SC7XMAbCXlt88O3v4ORE1P3A4oOJLk3t7/AYyJcXuAbkfS06ERPTgB8h06hkEPOQlTj2KPKzWmDjaxkLqGqt45IruTqoEauqQt4VC7KXrlaAHQgbvMLTko8Elt4ZHCpuPrOMgF1CQTE2Lc7phJHzsM/ew5J1iGSOBJBqlGJH34MSWUtoUAdDGIYmMgdH/S+xAANMLQNydqrdq4LfcCR48CjAz58AAIcZl1T/3tvgtA2radEdvRdV1ZlkLOYqGNpjQtC+D38EaaQxuGfDDn5uR63dmbDmIwllo4rWcN2KZo0BSmoyriKspjDEWyczQpzoyNS7Z8M9oW6CjKcms718ur3l/KlEXRprUumIullUmRjnNIOvU5yQAU+a0Nhz4FSs1Jb5OOts3Mg4GVSdbpfQRe80af763R2y79dAazAksVw6hv0DelXBHyW9RQntdV8sKa8SkjdnbJUUc0YPV0x/nuHcu6So7hShb6wz/8I/7iT3+Jzz18sOLsjy3jUUKSO/fnM493ui+OtAnDaH2y3p/JOfGWKXnwzsT0eXl54Xq98PH9F4gH4rdD0Kf+zXD5I90eb1Oi4K028LbncNRU7m2P6PRBDgYR6Ri9jmpqLMsKlni97hp85uQa92yK+vlDRkFCC4PHx/dYybeh8qi5HMB8gEKG5J3n08o8NvQpqTm2SOUwNcV6YPUMpTB/LyYpF1mveEYNtZRiqEDGyEdfFmea3+qcGHBfffmBJRU1wyE7uNWb4wrG6z9SINuuuPFiMk8maTCdAf57d556xv2EtUZ+t/Jnv/pLfvOauO6dNU9KIWKqVbcsyfB8NCUJLgmek6jXYw5JVdEZbOj999G5NsdNDL+UEp4S1SGbM4saVB+dAqyWOYf/1/X5lT/+09/y15cPYIWaj0CM8OggU+vA0zv265XE4P5upUS6SrtX4udyEjti5ix5U9DttcnURrSUsz63ATUvnNfEKQ2eX3a2ywvv7t/RzW7X20xpkac1MYppM7jcM3vDvLNW+PKx8Ne/Siy1cI34qWnq1DxkwYOE5YFZJi2Z1i7YvtOvG+ePX9LahdfvnziPwXJeGHPSXl44fcjw+srcGuTEqUz2l2d8u9ByIr98ZrDRt1c+Pz2xffc9aTh5f8/j9QRPTzx98ztyn6z7K2k11uuFr+bGb5qR1xVI7C+vuFdSWXh3V6hp8vhQqQm2a+dnX77DxiuvF6O72EK11BvoMrqizi1HJ+WAFbAFy1VpUz3RLdEQMwJTpHqm8u+8N+pr5+sY9jF5bObkmIvJLubvwKazqXvhrixsn77j+2++48PPf87Ly4W0LPhVKaLqLwybh8TfWVKmxdk4mnysRprcLYpjzimzFIW/9ABQxIacYeguaZUngYrTD+N8AWR+bHmP3jGY1GJ+Z+7PJ4q9Lc/G6FjIMaehGhig+q0Sx3LpkLq7gRV5+nlNLMvCYsZEyXWjayg/VjVtKMwjp0SfC9sVLpcrz5+/Z4whP6OpAbTUk2Roa+KuZnJe8KQkMvpk3ifaruVSPq3svZFeL7xcX1hP7/Ch9MceS5fPn7/j5fkZBSAc5dewGbKptGnAH8gFyBFokaCcztj1whyD/Sq/q+5QYkYYU4wHc7GN1ecNKE7OMC67UqJMi+kRbG9H/nz766ab9l6BG8kCIIqTSp+dgA7LiXquzL7D1tX3Hf2vontjkJ1YiSCUkdivkIpjSb6I2Y0lAaWINWLqg1p4YLoLcBmxdK9kEfPNcXIk+BrZ85tvq+mzy8Q8VAAbAeYYM08l8g6t8RM1rCIkrUs5lvrHwjPYtZhmKAECGejBpIrnfcpPTQxfWTqMOI+NKka+O22fZIzdB2tN8rYbThs9ejGd+D2WfbksmE32do3zrmKlkw7QDiXJpUVeROSJNwEh9aS/f5mQKCE1m3FG65ntszPiNS11kHqWZ2+FuzVzymfacC7bJkACo20Xhu+0tnOXEyefMDq7X9j2yv1ygrHH82sUy0rM1AjBHOF7lZyUCunU2VJj7urFc5jBMxPW4WVMmssAOgcAUhad03OftN1uAG22rKASO5NGY3Mxgpal3uablMRAb0OsMh/yJL6/WyjnzuvWFa40R1Rozdg5yddU4KOSX+d8hbRT6qIesF+5Waig/iDlpJSzYGkek6Lqu/q8ZGLKqr722JlpOSc2cxAODsbp0bsm3X+eZM9gGJaP6WbSbgvAzBiNkpTelqa+X+2VZPmlhgVHkO32McSU9E5zGeq7q8fJAfgcfV4y07ORDB8Nm/pMAl5gRohQny1mxgDyjuYz+rwZvfbsx4LHg31/IABqHidONi3+pukcmnOIlRizS8pG2rqW9zlAOzcxqIY+o5s/6dDvPfAJd5dM1I6FBlFjfryvfwspb1E0D9DmAEgO0CUJFCJAEgv5QU45JDnSBY/hNwQPYlvgYdQXBq+J0J3rXosb20J3GAABh02rDnkQWn5ANhbNocWWwAJQOdg3+sPjL97+Lz/8f2bx129r8UO2p68AlW4cm/BDQu8n3dCoAMCCuSQ2hglMMsmu8IM2pw1FytrQWEycHq/HPckEMpDIMXvc6Nri+u1gEOBDJEcdw5tHMynJ1AwQzW7XQb/nANXm7WfdEN7DrGwKA03mmgDi22QI3gLsCqQ8Pt9jiMVVAEafkPxmojZ2HQIHUetw9S/1oJmGHGgObOwBJm34kPTSPe7ToAQThW4e90MCn357oC1leu8s4cHQfdD7ju8CcnI5k3LRFiQJUDnYXqqTx6ZohlRjwBA2X6u0uwCX60bre0jrxKpZ6kqpYmKA38gpMz6vFO+npERaCr115piSMu27JGY5c3daKPUc92d4LGRntPA+mNC6WEuEfC2ZfDFaa2r2WhPCjXN/uhNAnPKN1JdzZcxxM6A7AKWDveWmDYGANMNS1ecwtUXuXdGubTS26ysEM6iWQi4lfJxE2ezh37TtkiWmiBFdTqvAJHe8Sb8+mHgXAFlqhXgdIKaNpyQz8Ju5utHG5SZ5K0UJN/qeAPG2V2a7kl0A1D4b5s5SErlKf15i45GTsTVF9M7RGWOwtU4uJ0kXDPYmfXxvg3/1L/+cv/izPyWNTqPFtYyK5beKAkmg1/svP8ocnQBR5mQko/WdL7/6inU9c91feJPlxm1gYpp9/fU3PD5+Qc2rhsauzXoPf4LphMeQ6rY7lLxyA6Y8PK2dm0lkzmI3DXdt9G/gvpE86M85c3f3gJWV5gL/mME2bC2kaQSYqfph7hSUNCmMpZJSvQ1Xx7WZ8X2OczqdKIcZaQyDifTmF5ftrfbHxT7AOdH139hGyd888VLJYjpGeogfdTFkccT3MwePjx/iMITeN/qYWEiQZ2zp3Y7FR1zz0Sh5kdn31EKg1IKVwpIWrK6QMg/FIQua//zpmT/5i1fOD+/56U/e83BX+ed/8sSlLEw31hKJUBF7rtqxQBr4Lu8SN26N6nD4/vMLnk+YKXkoJVMDF0kz+WBWxECAQy0L2Sbb6PzJn/yaX/z+3+Srj+/oIw6A7Bokkm7atBQYg6Vok56yWJKfPj/x9HzBc9WywCY7sM8h5z4Hs0z3wbu7O/q4ki2TyTyeEsvDHb/8yyf2bcNH51QyrA+SXPUmhg2uJJnZJa/2QcmZ9w8LH9/DeRWo0ptD0UbYPMmipCrlpruTQ2pb04n/4K+cuf7hJ759uSO/+4J+eWZ7ecaeL9i7d/TrlctvfsX+69+S2mTcrfjvfkv/+rfsPtlL5vqNkoycwTaMtlYShW+3Rvv0xPWys+cTtTrffvM7PDnb774h7RslVTYfZCbn88qyZGotPDzoWfj4eKKknU/fGX/ly0fS/h2//W6jN8kSvvzwjnI6UTZnGwMjc3deNZg7WK3yvJhDKWIHPWEalzHJDNZSmd5ZTvAxOd9tapaTYgBIEeFsMfgaktlYFojr7cpDzTzcyYvxt988QylY1rB/SE+SZUmd0VlRamXsTUEiqDkfCZm110zuhd52jgWiR594AMUpvHhShGz0NniaXZISTWE3QChFwzJNfnFLsfBakSyHKYav1MQG2bSQ4+gHjwXj22LVmTfLgxGM8eaTlOC83jG2poQeHFOgtqRoOLMPNt/Y941+eWUfg21EU+qTVJ9ZawF2bA7WvGJF/oVba7y8PrFdnnUm+gZ9YG3QU+LyemXGMOfmwQ4f5LJQhhheNuWFhbu8Eu1YCWoRuE9jrWfyWuVp1Qe+vbI1BR8Uy1pr2NE9C9j32EDv+8bSTzIgjz/3obpuTIE4riF8+OT6+qSFQXoUu9MAJN3GO+5FfoFDALvVSsolwMhYVpIghah7DvKYrHeVmSalJkbb8JmwJUA0N/Ji6nF6YU4NfBZniCeFMcxIxU0+sZnxdSGbhuni8qxyn8yk5+Pwc1G/LPm7/Ig6o+8a0oPJqsWp3RaLjpNsUk19muFMb+SUKVbprmCTEp4uY87bQultftHAfPjERPNPMihLZrYWagIBUcN1lg2HarreNWdqTrQuP9FUErMNLMXSfEBdK6WIRdQVUkymkgokVItKTQxasMgM86KJwIyxXxm7Uc8nGWCnhbUs9HYlr5Hk5VCXgi0PSk7dLuAbJQ+yTXzX4sXSyj5zLDuj7iSYozCO4CcTWLQUPdFl0XN+eYHedTCelwXPztiNbMZlH4wkhs10o2KMPdN2l9yTY1mha3MwJ2o+uq5ITncpbmbrb8wck8nMMOc6Juky2bpjHn6hadK7FtDdldpHDsNwT6TecGv0sYvZ0js5+2120/wBOSd6V0PpcY7PGYvtWIwly1gxrGfcg0GU5Hd2A6Y8pJPJOEIODhWSHtcDbDluOfVRc2gOFHqSSLnohUUfaVOp4NOnLEnaZEw9K7LJcEjCB3Ig+YdpwoFBkBzzxOhDhJaDSW+E5yJgU2eNKXHZo35NiKAx1R31hjGnu15fn0OMsxTrhQB+7dboB0hkcY2x8DQ19iZ2r2cjewGbku2ZJG1MmZoL+5D+z1Msd0MaeATa/FhfP37KGwIj5H3xwyY9oJRDHuSS5iuOXp5KE8dNaCkzDq0DFMpwOKzfQDVz0UAt/8AXZB6f2W14Og6mAxiaAX04cbjf5Fb25pcUUoSDQn+8GeFNb0DT0RAdg88N3TI13jLWGgGQxIARN2/61wy79cCkGNjNZSR3AE2ios7bwyx6f8R79i5aaUlB/4wmO0wZ49HVIIDdfJiSHSNQXAM7sFILnb9Kl8f7uqGp8aEeG/jj9ep/DR+Sc4i657fPYRzMjECSFdOdb9fbULTpGJN9agPTxyFxiILjOuRSuo1sAuDMMTppWvgZTQFKjDBL0yEsgC1zmODO4zUzxTxxSejGHMH40uflffL08onWmrb8NVPXJZDilZKVsHKAVfiIw5XYrOlntaHfemzW9svguu9cmzZ+OZnM7EuVVO/wWbKIinePjZa2rR7Uxm1vSrXZrmyvF8pSFKt+Wm6N9ZjjB2lI4Qc1IRXJxtw9ACOxqnDjchW7aQ55Pqx1EbNmXegtCjry/5E8T9vtNjtba/HIBdDr2oSliOs0ZEA5Brw+f2Jvkiv2qbScZT0RMBpzxvGS1Iz0QPitLPKlqpWlrmoo5qSkErIsJZjUslCXVQB0N1KqYGJlaJOpe3lGVHNr+rP1tAaFdOJzY98ueN/xcYU5FYlaCveLgCzLHrJdxJiazmyd/boxWiflhev1wp//8i/po1Ny5eH+HqsFd+Pl8so3332P9aZhJdg3bw32YXGvWrSUha+++gklq/Ma7rTQWTuDU1lv0k8Vxah7sX3xCd999z3btpFO9XYYJ1fja1OgZQtw6TDSKcuie/JgT5nf2DlzOmVZNeRNsFJiey2Wm7yRVdPOp3sSOt8v6roAACAASURBVLAxbWf7VNrYtEM+Z2ILDNHoo7fVEJZkbE9IEw/t+FGTZOoZwQL+lmB5/Nfd8Xhu7Xb46r3M6YSCHie8q2JrPXDwyb5vYb5/O/w4VhHygNMWcGCSO9hge95krohYGUcCEAiQkfeTggkKiTQn3WQ4SjEeH97x+PCg5BqrfPzJBx5//p7X5xf6d9/S+mdO1llqhfuPPLxfeHmNukUi+5AvhU06k3MMOLsZsxT6nknIN8fjLNMWe97qZyFBUkKQJfA+Irgh0iz3HWeyX3YeTpX3Xz4qrnjq/LdssUGL5VIWxb0kGeMe94o7NM9sQ/f9iK18mjoDTAUMSNw/3PG733wLOLXCqUw+vL9jXZ7ELEuJUs/MVCnD6Kb3OFuT4TmO+SvVCmt2fv4+8/FOsfLNAS+4a4BXms6I5cwQ0Og75AfWXPmH/9k/5K/+wQv/9J/8j3z31/4Gy1c/Vyx430n9Sk5dzfj7dySf5HM8L188kHLhrlYlW+KYdcpweH+PkktXtt7x08qyfknFGQk+f3rWIPLlBx7JDAo9yZS1anrAbXAqiZ8+PJDLK78qL/z044ntRQA4wJIy7z7ck/LK3SkLzLDEw92Z+9OZNBsWqYEFJUNNU8JQBVqc9Z1GppOyUaoDuwBhGzKUD3k10Wh7SlAqZTHyNO4W5z/8934f+rfcr38b611Lx2MTffRu8RzP4diSgwULaaL+LcBeyaLF4PYjycq00FJlnDH0eGyhPWDlxPWyU5b1B3YDRJ0IH0hctX64mECWb559ZoWSRpyt6Cx5axqj/4zFWtTJQ1rb2mDvjVKLUqZq5djOelIfI4AYyFqyDUtQihYrY/xgMHNsduY2aK9Xtnxlt03HQpKxbe+NsQ8aG3Z9weL5r7liw0NqJ9dMS0ZdVtZ6wubOFvXVp2RXOWV6NOLJMiPYI+dSZVIrHRnn9UzOOZYyhD+PPgswqsGaM/seS7QB53pizy/kiCD3OSiLMtJaG+G5qB6ptyttO7GkzMwypC0pFntJQ9d0yEjmkjSYBBtNn21LmdQ2Rh8kExiKj6j1jcUW+uWFa4ecKjVVgRzFqJZoU+lpljLdpmwopkfarWLEmUNZ6i6grB69achzLR3+q7o/RoJcE3NvuEe8ffTVx/k3u4bPkrWANws5WEqkEcs2FKCS8wEEuoAEU886fUoWTvjdxvyTgJQsFp5i5viI/tm1ABGYp2XXHINi8ci7frZbY/qQTVtK5JwUtjJbXGeUmuZa7vbeNU9Zxr2LQWuqByBvM0/OeVmo54X7cyanynJXGS+NfRBWCmKP1XUJ9m6HoTOsXxAoSCEXsKprMUcPeZ6e49YsACNCsjRZUqXmybVP+h5oW5W/ns8hc2evsobI0JP6wzkmPtKt161JoECO3knLfc2351wYU/Oc/DfVv2CSd1m1mF9SsI8W3DZ8HKEeEzs2gGhOC69+MYKiPxs6fAXeeArgRtem+4ieTz2BLv/UZ4ZFaJlCMuaQvC3w82DMGzmX8HqNHmvKR2ogv15ZZMS9PyQD8+zkmSDAGFkAwJFgq/9MWek4Un8coIGYDOSUqBZyM3c8ByXoZomjxcebc8IB4oqYIcKEzuY+mt7YHNxiZw6FVYCNY85gFyrBczSZgRthS5AEfpfwtTJSYAoB4h04Q0rBILcb0CRmr8veMKs3I5YkpKTnezhZm+bb+8thE3EDrn6kr38LgNJh5hwPeSBvHp8XflCJPahr/mYWnMBdH8qgU46nBBmsAbcGQsOMpBiGmAVC7ogDRvSwMQ+tYqB/8UFqIxTFOR6o25e/3Ug/fF8Hg+Zf+3Oz2w1wYzSlQ5YXnjMEzX8GtXtO5jjem5hHNRs2d1KIbSdGKccWuQTjyAV6ZAJMEvBzgBMgcy+wiIJX0clBsz6M08Qmmhp8j2JENDLHzRg3brATNYAGeDDnEUMfGJ0f6W+oqQmt5nAVQlm0S5Y2RjCDojFsQ8W0j8GcnbY3ep+SejmYLeQSbIKUJSs7kGw74h31wGRzvW8fYeio3znHgORvRnnHx2dCkNW5KyrU52HTLlBpa52+d0o2ci28u3vUFjNliMGnJN3BI5Czw7BvInAtBQg2cVKFy2XjZduZ20TRvYXT3Ska3DBBdsIgm4iwDRTJtWHKCVpr7G2ntcF1uzDHZCmVd+/fsdYlZDtR9IKKOoGS/FYtJd0RSLiUQmuDbd94bg2fM/wZqgBNj23vGNpMRoGTh5H0fD4ml+uViT7TUooMfLHY8mTMEmNAazvPz0+Kuu+TWhbMjPvzygxfhIF8mXwq6UM3v7MsYkfUKtmMWaK1nZIK67pSSg0MxZGZNOxbA2TO16cADB+T4V30+tZIGdbTmWVdJCMwp7eN0Zskk3PHfKf3jelOPZ04ryt3dwt9yO+gJjE89iGQz/dd1+L0jrLccVcfuPt85evf/AXP/ZXX5xcO4oYYcA0Lim6aKWSzHgNObL5jiHp8fMf94zuuYcQ5pzNzZpke9VBH8jEwST9/bEq0+Xh+vfB6vbIsAT4N2G1gLkp5sSM+NX7+NMpab9v9YQJNLdiTTCctq95TgrVU9LQQoJ1q0LIsnO9OsaUbUUsX9taP8xLiuTJDB6Il0nTdMy6G2Xo66Xunapiby4QwDuXZ9PmmqL9zCnTaXdsyXcuojfMHld9Vt204ljTwMCVfObz5Ru+K6CHOkAMAJH7m0PD4/PLMdr0yWufT62tstgIYtreUzKMYmxmLCazYhoxKU07QXKl1a2HOxuvlwsPpjt/7az/h/eMjeyrgv+bT540//s2O1zv2VKnekaYAmDIdd1KwEZsGlADevK7MdomB/wcHo4FuzIEELNHcZCOFyeSCsyQYfWe7blyeX6n3d9zlC2sq5Aw+YmGRBZIlJCFw0/Y0WSLH4HJdV8pJNbxvV/kEJG3gDQ1KPhvrqYih03bVy6TElYcTLNl4ulyZ20463Ysh544tBVpj5krbt2jonJyMU0r8/H2h1LCWifqjxjLkwEPSeJG5JmZOmZ1eK/7pyt/7z/8rrn/2Z/zTf/Yr+i9+AR++5G6+8tOzsRb595ndSQLdJEPxn92x24aNhZl6nJkDZoYuP699DoZ3ejdJAXtnGOwzMdfM0jvVjN4ND4PrYs6kM7u8te/vtATI1Xi4Wzgv9cZUfvfhPXenE1hi5EKNe+B0WtivjZKd0zGEHAslPCTHTp2wk279nno61d+xN/KiM7OjuiLQQnHUlgqLZfJi3N9NPn39l/zZ90/87b//X3D3bmF++0Q2xZF72CgccvF9dFaTVOwNvIFqMpAVeJNvi7EZfnwH6KTnLjoZF5N2hCdY9hT9pgy+SYfRrB9dn0IrppMWgS744XMjPz2zYHwyxWgKhqwFwJ8O5lIsLjVI6f4aed4i0v1Wr9SbDY+zI4CHJWcokobP3sglerzwXcoklrqw1Cob2zHw0RmRBuQH43wmxlDiWMEYyUlewAqTLFDXxJicV3k3epE5sPw7JrUUki0CYr1xfy6Upd6YhUtdWU5R//p2S0qyDH59pY9BZ1KRT0/OGSsLp3NlH3e8fH7SNZiDNGW+vFa47MZlE8hA71xenrDesNOJJfrzfdtovuPDOJ3PLEthDsWpW8oRNKL+NR1y3eSQ5NfpwOgh6Rp7sHIyae7MNtgvBqVQ88qMJe70yWywLs7Hr97RrkoG28x1HadDSSQG1RpmSuEkwmEOLxiPXn2pC33slOUkyd8+GS3CKJC023qTJw8OKfyd/Kj9AplSsLpHzFqqd+q5ZeAb0iNLt14EDhWKMdpgrQVPXWmgPWNoQTqDyTHHZEdL+pRMQIKoFCQrlCTZ3H59IdHwu4UxjN5kBdC64+E5KouP28SFM2FCPWWsrKxl4fGLB2qF9vKKkXj//pHRxcRvc8dnh96UCDc20mxgC2kkck062/NKtkFzh2Wh5inwqMlqoQ8tEHNx+VolhbsUn6w18Yphi0A6hhLBMpNa9XkqICLjvetsdS35pnd8GsUEMCaLRL8kgDLbkQRuNyKDDJtjWRizYUI9T0PWDocPZLdGtgK3+qvr2EKaprKeKSXJiNzkZ0kshcP8kVoyHjJpy1EL/Xi/M4IG47NKSWdKqCt07RJLTdiYbE02FiNAlYGDqf9MMzMMWt9ZOOFt4EWEgxr9bEI2GmRTvU1vHmEpZaiwpkJvCgo5JJu4WIJzDklIOc606D+DebV5u923Fr66pSjRMQX2MFKwtKLHmC4FzUyZSdfnc4DmLvxC5MvDJD2Wp3PcAKVEovsAH2JzueR5low2BO43+wFwRiIVgc+miSlUOxGOFKOk7hV+1K8fHVAqDn3O2Pr6bbjGD/RNhU6FBh26c1KqqG59JjzlkMrsMlxNQiRHyhSEwmMCphw9SNkjQjKQIVGAg3UQqL570FCBtwS4t6Hr9nn84Mv/zT8I0OD2jz0GpQkz2ECJA8EMJNEPtpZe0+FRkkza2hxbk8PodXYNDzL0CweilMnLCXJR5GwO+qkTJr6RohQAXsmVenAEETycYhA6AKPDMNluKFwM4AQgFw2IyrWLPheNvvtQipc+UOYQgDS70tZyKRjaArnD3pxcgwXVB3vv9H1Xcd+bGEkus7mUFmpdKVlORZaQ0Vsb+vxcn28ONDpxPGyOjwYM0Vij4XJLwco63itRSIB5mBXGYd87+67GZqkrp/WB+i6L/TU9JJv6uTPM/yzo8u6i7M8wXiZYTpjTZuPlsrFvMoU+rSvvP9yT1wXrknktVpk1aO9TppZzDtbTEqyqKM7J+PT0xPPTZ7ZNaUj392fKqbDUIuS/d/a23/yXDhlfb073Ts4L4CxlkV/LdmVrg7a3QK8Vq3o6raSUaL1jmKKJq6R/bW/Upcpkcnb2vYekDJzMssShGch9rhWGs207l4skecOVGPdwdyL5ZOs7Zc1c906qC7kITC1Vkp2JKbGtKGWOMZneqWWl1CpQKhUdsEMHsCVFi6aUKClz3S7s20bKmbu7ey6vV/brK6fTmdNyYvQGPmLDqzjYfXth9gaRHljryrJU1ruFNa/c3VVeXi/SWSMPi9fX77A5GFS83JPTnWRHp5Xf/5t/wIcPH/n6N7/i8zffsu2veq0OFadP6CaasgYBDTsWN+th7v74xXsNL8PVcx7AchdL6ze//CWty6zx8K764QYHYFw7l+crj+/eYV6OnZDq0ETOQ5YZ0+KZdMqykOtC74MUW3xhWqof98d1rFVMyEjznGhpkE1JgPVO3iaWioa5IaN0YhjlWByBBrL4bdftwhhNdWEE9R3RvHEJQdpx5+Wk9KYRdOsmk8cJt42VT7EINWiq6bYZcj3UyM8xaKNLpjj02oZpIPvhWeG3jUScLSYvrpenKzDZ986wSXGx9QahnY/rnoBTLbQ52a4vjC5mB0VnVIqBklH49OlrLs9fc91eGa9XPn965dIaw+E33zxBucRAIIZDSgkvjry9JTcdXVvLo4MquTBiQXMbbKP5Urs1wTu4yXMj2CLHD/AJ7bqzPT9xbReMMwuDk12hDP3c1PGcsa6FSGLQ8gWAapWZJ0bmUhBAMIze/WYKj7vi7EMOfirG5fvvcYJx2SavF/nokOD69Mzn333NcrqjpsS0onsNSQ/Lov5hUpmWOZ8Sv/hJJcfSqnGNLTPk5LQpsCfnQi0T7y5z0NQZ6cxffvMbfvHhnr//j/8bPn3/3/I//Op3pI8fsZb4O3/T+P2fJRYfXNtg9Mbzs3F3WjBg6xtbm/QBWx+0bdDHZDQlXl5HovVK75O7h8zs8oi4btDJtF54HZM8nL1HepsbZjuzafOfa6KYk0ZlOZ0p7HTkVXc6raxFRrIliTGqT3YwhgxKxST3uOkHMjIXu46sGpBy1eIoZXp39nYi16weJRIjj0Yq5SST/ZQCqGz85Z9/za/7K7NNCpP7emaLoI5jfecBcprDbApv6K3TR1f8fCnQE1bEVvUc6b0TMQFDgkZ0OQco3ZixhIFt26h5ZVn15+YCUMUeDn+6YKCXrC10a1NS2N5ocygxKdiQxHLvWODderIDkBbSH9dB8qOTLaQ5STnTxb3BDKoFsGtK+lpSUXZhMJasrnq/dvy8+B05RwIOSu3yhOdJ865lZ5ev1ziYgKVKdhasogkkX8SicCenorNq9mAWJFI9cT6dcc/04Ywt0tXc4/MfLEdQiTtLqmIbWmLvW0haBP4rlUk+OEvOrJapLJRc2EbjasA22CcsGcZMkJSkuc9J5irPnKcnSsrRW0OzzmI6y5ZcIcWSIAl4DP5F3KUCLy0vpLQoon5R/WLf8CwWS6fAHErtGoPmhw+o7u9sin7fLxcMRcQPnIJTUjCJfFCTEpsuU2bbREKTD50vta6sacHzdtwRUBKFQhtDQy/qiR0xbH1KqnuKIBRpk7Ru8r2T7tTfEQNxXYqYlAdz/fBOtC5WYIc0TexsTImiqVCrafeQEtfew8pCqWD7mJSSSVakyJidNGE9JwErQCkrnUZrQKuMNNnDYzIDFtJYSLhrYbege+f+/E7EIKu07ZnRO2XfMfRsvI6LfG+GA5m5N3JOFCqlG9UGdYFxEdPOzfTeU8LNaZuzX9UPn9YKSczjnid354U5dqwK2F7c+eo+835N+Bz8+mnnpTvbvpM9MwoMbyQinc7FjhxT57MFS2f4ILuuM0Oz23AtBHOW39YB4Pcuhlkfg/vzKkBuhoVMhA6dlkXgxNQi10NJw9C83vadkjTTDnNF08/MkgukY0aUpYTF4l7gj+pnQV6g0/X9OQBw1kT2TGo9Sr960Qb0AENsBvkDo1oWDJItpK1JTG9kXp+nQHFPO2b6jMyOgCKxvKcnzMZteZGTErs9Tfp0BYuUTGta1szbIsRvz42JciSmsIvY4dMUypQEoxxcl5SFaRw1/WD4Hd6mBPiZMEqpWqCiY1Fzf6TNWdRtF3OyONBNZ0me2NCCYyLP4WEeRu1vyX6H963ON0glU8LioeSsGvAjfv34DCWXN0aP7aaadQ9gJHHQrtwTh6H0zENMhBh41CC4NpHxKaWIdh9zhBVPgERAscKkKY0ifoR700Y3qJpiJ9kbXeygKv+AaXMgRBadytHga5g53uCxzTIOHObf3OKmAI6I915+QH9LuAYST8GKcnx2RcF7V5R9smCCJ1Jdo3HLdA+QrJbQeGpjbo42QTm9bQw9pFcHrTFZaOXmbcsgHTUBwh0+JWEWGQyIPuVjQzRNhhpqA3q76uHKGhIFfAktvuxNMhYMM/lMtK3T9o1+2djbDl3yNk9JqUGLkpssIkcVXT9v3gPnpM/GPbyPbN7YDQkPxtKMB0jg4Rg6lC1lyZiIlAG07fIwHtzbK9s+KGXh7vyedV2p5Q2JD6VyxLgOPPxrzJT+4ESiQ484SXN5AV2uXF535jTWuzu+/PCelJdIHpBpJ2bk8lbgUwyk67pSayaZsW+Nl+srl9crL5dn2t54//6Rr778isNALqWDFtyioZcevvVOnzIgraWynldqKWzXK9999x1jiiWYa2VdFvlQFLGw9r2BT2pV42OuRJHDS2H4xIe4WbkEaGHGUuSnpK2wDv5t33l5vXC5KPHLknG33mljm+QjU3JhJEjnBcu6JtoUDoGkdQH0uZakCPtSKhxRnpZorZNzoeR8YBLknBmj87pd2a4XPn78yJzO56fvmKPx7uEd67rIy6h1zJvozT55fX2mbZuo8gXu7s+cTye93pIEIPqJ5JnWn/n86bditVjCl3ekvGL5FDVAg3vJCx8+fsX79x/YLi988+2v+d3vfsv2fKHvI3KVoLjBUAqGKPoBSBvUZNzd35NS0TUcfju0Ss58/fVv+eM/+uOQJB0bJmRIrRKnZ9snLy/PtO09I0/SyFh23EqYphPgs9LnHMVon5YT2+VC/yFonxKWMsv5LDlaXTnVhWGFnHV/KnLbWJcFd4tUHDWGWNbQYn5jH06Os7WI7QeMJhBWenXp4FMAjjZVHxYSsw/cjNUkD+17eL7kpMjhpWgbNN4WAIchrLbUjiWxm2bfxeTZh2q8GbP1aLY9NoUBTh8DYjRXe+uU08J+Ma7bJqNcB+aQ/9UhpTEBW/ePj7z/8J7RJsvpzH7RBvzqk+e+U/cXnr75xMvTC3O+sG2N1RbWpZLqibZf+cW7xMevPvJ///l3NA8GhE2KJ8zFmJDWX8bFB5fNSlJ8bSQixZEkAM5NyuGqISOBBlA/JIaJsp748PiO8ylzuT7xsC48roPL+B3rKXPZO6daSBnK6URrjepi3VhyUqpirzTnxc6sqbIZpHSizI197uRSRMM3MO/01iklcyoFXwuXrXPdV1quLIvAi2ky7jY70bBo3p3kk9Uylz6p7tzlxt/66oG/+7c+8Jt/8VtmhpkCLMAx5J1Bn+SiLfGwCcPwvGBz51/96hv+zj//X7n7u/+If/Bf/2N++d/9E/6P68brcuLbz5P/+D/KPJwmfr2yt8zz9/DwxRnSJ9rr5LJP5lbpI/H6OmhXNdw7zt5g9szrlnj3vmK9M9y5bpNplbE12nT266T3SmsbbU7O64m9/QFu/5ztsjPTYMf59rvveXqd9ClvvZ98rFS7sCRdn1MqKAx1kk/v5KOUxHQts2t7T8JcZsZkgw3ykgO8Hlx2o83K6U5BIck7dBjheZhS+K4YLMj/8evnV+7ywLZJu37Pejf0PGcB0tPVrCdHxrZDgRZt29i2qwatnLCm/spj8QWxUDqWZbFcspApG051C184DW/7aKTTooXSGOTyNlgVJB8WGymzt3a0mMxdYIKkw8egEwtNg5kNL0nyKJfBsA8BSUcwyuaNu3mKpV/AHEnpcjvyq7OSGAYdV+KQg3nGvSkty+zGahAYree+JslPBk6aBdu6FqBxxhID4tBqXIDIIKSCqq2khKeixcdEvUMtlHLH8BIpYgP3Rr/s3N29Z46u5KxcaF3DpeWDPabXW9wkA7XJdd9ZTyvvz++YvnFtk3Qq2GVh8cz0zsvW6N44l0RBA/CwHAyZYMSTmGXFh/rjpZx5fPdAXU4ynD7Mb2O+OHriEq/JfYa/pqQ7KVUN0dWoo0n2ZHtIpjKSyarvHt7ow7BpnJYH9l1Jr17029porOmQDyX2nvU5Taf7rvvWCh7LAZ+dvnXVnSSJ0kO5hyRwqm/y5xk4Npp8ipI+12sEyNg8wAGlZbXtVXNKzhQr2ISaCtN3fArML7UIKJgjzL/FoozbU/Kb2Uk50yLxC2TfgBl5TCkuWmEfCoRYkrFtG9MHZieu7cLY5Y+1I68rS4nRGyVXqTI4ErEquyfu1gdOp5UvHu/5/PyZT8/f8+505nQ+0fpg3weTTgVKWgUGdy2grUP2wjlnpsf5vFZ2dtmPpMreJu6ZbZOqYVk05c9uDEtKWrs4I+94kim7ZGaV0yov028vxsuuGUX3WUiZTMCxDREL5Oc6qesCqTF6gMs+eW3BtM+LFAi9U2uhLkoC23tn67r+wh41A7beqKVI7tXCp3jRTH6wko5AkLJU6jnBzEpIHa5kx9ZYz/K0G2NQs0AKgsxhSPVhWemRYirJ7aqs5TZXYulmvj2bgL0SM5FHSvuKiU1dDYpRJrR9MrMAmdk7M6TENaT303WO4Dqru2/xrEom35Fvc1oyaZrkfzEnDo+IqGCmz3n0lmKIieSawZUqd/hFyX84cIE4xw5wK5MkH3XDs6xVWos5ZQ4tZFNmTKcEo/2YoUtJx47vpnDyG0jkjD4YrqX8alXzbm+kWjSfOngAax796YxnNod/3Yx748f6+tEBpcYkzTAZm5HKkCx0vUYnaJI+bsgZiD68m8mvZAZFjdis+XFYc2P5MFz+PCbKPgBTG2ydppm2x++WkRGHWeJhAOlwWyvfNkTH9f0BSPQDuEi/2wNtjIHh5oBvAsniB0s/iorGCCCHaGIhmPI+w7jQSHkhQFpIlYmkX3NO6pEucpj/auEmpk743TgjBmi94uSHFl1SsUOXT2zhRBfn2FvpWkFotvXza063GNBY393MrXMWP6iPzpwRWTkS5BIG2YXWOtfLlcv1WQ9tpEx4UnLOw92ZVMP3YHSghwHhQvdGtUkx6bLLoobsoF6mABtzLvFwz/DSgmSZvbcoDtD3IxnIw8tJyRj7Lhbc3enMFx8eWNeVm4lbMLKSQ+sqEmN06dZnlwl1F+NHqDyM2Xl+ufB8EV17XR94/Pie+7tTsFf0c3x2FJ2bUCqmQZLE6sABe+tcXl55vbzw8vLKaDu5Vt49vOPjLz5y3a+hQT5orlM01iT5SV4W9r1pYCuF07szOcHz8yu/fXoi58xaV+nIDypmvEexF+bNDNw9GpvRab2z1vMbayEpxa7WhemJ/dLU9FqJYilQ5vn5meFwWu9utG2rJho5znUflOWM10rbmuK8EcOq1BIMOQ2yS1koVWCxu2mrCJRF1OneBn3sNxnddbsyujydHh8f6X2ntY2lZJa7E8USe7syx2C7XlkyOIPr6wvXduVuEQi3rJXz+UQOJsyMhL+np0+0z5+5vn4DKbHzAKlgnnRotA3P2vKZVTJqyFJOnB4f+L3HP+Cnv/d77NfG07ff8/n1CX9+lfdC37lcdO0GigdNKfP4xSOPD/eSRSQB15vLm+T7T8/8i3/2f4E5120noWHD43g5aoTjMAdz36g5a4Ncwa2qobz5rKkegcDe07t7Tg/vePr8PXWiDRPyHMIm6XyiTccvjWu7MvpGcaPgUCqpFu7fPVLXNVIvRA12S2pQDkK7x9rAgzAgFIatD0l6rvL88CFAZCIGn5hQIe+tmbarFtSi5m02beC9DW76M+J3hYeE0g/VHDuJlAnK+LyxAG/Lg7fd09uhEWeJp8x+2Tjf3fP09W9kJO4u0NAEyBxLCg0Mk/dffMG7u/c8f36llMJyhpfXZ4HW2+B1+0xtG7/46ks+fPFX+f2/9dd5OBlf/9lf8hd/+ueUAv/+l4X1vPH/+sB9p41CHRpEzSbZC3nskao0VFpmAwAAIABJREFU5ZOFtvC5FHrftPCJ51yDXgp2l+7BlDKj7W9nBpB8knywnE+UukieneC8OHNeWE6DU7lnKV/gubPUxGw7NT3gRcAS1w6psIfshARtTrpLEpPimovtpKHz/v6e52++YXSndXhtzuenHSdhxVjff6DerWrcm8c5WiQV86GBPpjP9bTy4cuFr1Nix8BqMILD8+RgvyUBb6sV5miU/IS1zDUt/O//0//CP/rZT/j49/5T/suvf8W3//3/zK/Wn/N//mrye384+E/+wYm6LNiWoHdODwn6+1gsXcieKVMMIdJgXhJjyih1TiPnQbbOYeyas2PWsMXkM5E7p31nT86cmQ8//TmXx0n/3/4/fv3NxnnpDJx/+evv+P6p42Pji8ePPK5wrgPfN6g5bF0qu9W49k4ZQxts7yQUqNL9SrJJ/v+Ze5NeW7MtPesZs/q+tXZxioiMiBs3894klZk26UQGWQiJFE4JLBoWGAnkBvRAIP4Af4QGQuIXUBgZaNhCCAkyGyBlYgNWGmdd3Sqqc84u1lrfN4sxaIy5diQ9N26D3YmIe+45Z+211zfnKN73eSWj2c+63iMlKNvZ76QcA6Ke/ujPTqANJQY/p91W3SnlwO2bV4x69jqqKa+PBw4ZdrPJh/RFRZifS0vMjbA3cmrQcnS49nClWiGx2TW9ze3eIbmlwBlKOmtCH224tXZaQfYdi8lDItRtD86P8sXcULd7x1A9jTQl9skBG7g1OMz6TPB/TwRXDZqfO9FcOSmTJRcRkvr5rvMU3y8PmCjrek8IBz90ZnKQzUXsGEYX/30ODp/KkqFoirQJT1bzBZ0nwe5es4sPTPx1+LBjG95kh8kri1J8uBIAMlHnwIPBELcfijZK8MH18+U9o+1ApqaNHItbPGdTdgV5q1U/w6MQ8gHtjdEulHg1OCWvcYNb03yQImQp3KXlZRm9xEwMwtb9cyBWSWRv0tWQdWFZM6Xcgg16q26pV3upoWdVPt+7aUOxiOigBLfvPzw2jgscUiTlxE023i6Jp/PgVJ3nY+aDu2HGGIlaK89P732hHiJthxIXZM2YJEIUtBkP9YK25qiL4IsWHa7Eb2KIeVKYhODphhgiw4cJITjvJcTZH1w/067Ii1OlYKPPhZ3DxMO8i0LAlw19EJdESckDUoo3qluF0CM5Cl0bIpG9DkymoiMu6HCuTJhcXGOQl0SKhbZvbNszxuBwiOzbRu8dxWaYUKKNM0PmwlV8MBNjIqQwB9INJdE1sqy3HG/uaf1C3c4UGq0N1rsFCZmYlHO9YPU8zwLnENXaJ2NOOYSO87Ai7WKEQ0LqTquDsHh63giQi9+HMU2cSYKSbri0C5KMOirgCZtFhKfd+9njMbCGhSU4EJnkZ58qM03PLW46BxQpxrnA8IViljj9GMqyZMyMUQcp+SDJezBnzkZx223X7tbCnF4U2j6PXlhL+Atn3oDusffOGnQm6hjeg5e0+vec82SkuQtGYqC2RhAhR08X9CG0r0TrqKjC8bC4vbaOyS+aTiMbBBLXdDcTHwrlMAc20VWl2Ji8rODnUA5TLHl9PuOLECMGV4dpn5FcptTWyAia/H2SHr0elcDeG3kO6COTgWTGlank4GrvrQPJNdrdGVJi3iOJAGpo724vEyfXuW3PHVnkTK3t5WfXTYkxMHSyu0Z3RdxEkAg+YwghvAzKRGd4BR0k07oPxBpjKqQjUt3eTpi3mPncJCWvmVJK9NpwI0Dgp/n1Ux8oCVffoh9qhKtglHlxOCz2RWEf8ISUMbflch3UeGk+6BAydOZDMn1/6XoJT+8grs5R2/01MH3Booy+EyTN6MbrhPTacAT+YvX/ElHNX1QtfXvBXO1iL4kc12FSnLHy6oOkq9TMNwDT9zw8wcjVJNP/HxOESeO5wnNgplh5IkAqZUYWKjlcP2RXJdSU1Jl7JU3+wqu36/fjxbOYTIl4uP7yC0gXu5IFeBlOmIEMRXTKE3Xa2iZbR/E/K2ZXa/Q+2LbG6eGBre6e1KSADufwlJVUAll8Ch3wrWHrFcFIV4hb230bIuLMjQBBlNo6YTKknOGkRKDWNpubQC7rC7wvBKf+O4x8o1tnP/u0X0S4vb3l/tUbZ8fM90Nn3GYfPiQK84GX4AdgTA4Q9jjYPq1vnctl5/LktrFcCp989CnH4w0EpY8dhhf9vqvx1DMhgPoAzfAzXUf3CTPK09Mjj49PhAg3xwOv7j9DQyAGuOx1DtMyZi7/vQ5c7EWCHihl4fbulrpVHt4/cL6cUFPWZeX2cMPx9uCWyeFbbhsef3tNlyE4q0knLB2MXBb/HM3Pa51pffvThoTIkm5ZlpUhxocPjzw+P2AYMWXW4J7wGBIqeMypegFZDjc+dKzKUg6+RcIwHbRL9USSHCnr4kyb/UIQV8GEqOT1lrpv7LtvvfzluTUvzcS8q3Kv1grqvv8YYN8ubPuFre6sy0prHfrOoHN7c2BNicOaXaodvYh1AH3l/PRA2x7BApJfo7K+SFGR5LBb8+fXQdNjApZ9OMK4JuMZ63EhLR/zkXxMVqGPzl43/ugPfp/T4yOM4XNdU17d3RGBfTvxzVdfc3p6T+uVpRROpxNxAdk7KXrKR/b7ZBbmw4sPC0RJXM4XLlujrIlEYqjbJfvcrorORmUmw2DCsh5AoieCiQ9UxSBFkNowKi0qGeXf+Rd/hZxW/uCrL/nyq/ecNbCkBW1uq4shOkctKDHneYn6QxHEm7FozhgbUSghEIJxprJrQ68FiOmLiv/KeDosq/PBen8582OaywuY2zRvWMZMCfJjONFHJYgPSoMYMox8HWA76dQB5VxhpfPPn3JvwxWOp1r50Q9+yJvbe5YcpvVFp/feZurJtCSHwN3dHRHlm4dvOD9fOLcL2Qb7Y+WTn/2UX/7F77E/dZ5PJ9ZcefjzP+DxJ9/wHDOf/zM/z0+++HN+8/f/lOPhI0q+Je2NFjyu+E6UOAojCBYHUl0JWgzPFIsRUiHIZQ6G5103X58nMzpvSlvzYd4MjEg5cXt3y8988qkPZE8XDh9/wml8xO+9/4qf/OgJ3QevPhY+/zmHyScKu6g3v/sgummcIZEnGj2aDzdkuKUe0AjWvbIIQ1mzEKeSse8u/3YQrpKiP4N5SVjywjH1HUVQayidqr5BBqGa8OU3Zx7f7xxuhf3JFXRXKCbSCcE5EFfFgAv0BlkzI3b6fuZHD41/9Hf/O/7Kv/8Zv/Brv86v/ejP+Xu/9cfUz3+B//EfvuPtYfAL/9zKkgN6hLQGRDt3CFmFmYmDpltCq4goWiMXgx0DSRzWA2c5wy5UFS5qbBdFemCMha5z40ziRo6M09dE7bx7OnGzRlQDP/nxxumsqBYyhXB3R90FDQtxMggtwulx4/Xbe7dXSSNhzF35ZDqYg6MlezpmXME+wBiczzuqBRNn4HVRukw+oESayYxcd5vDMWQOrz9DOaNn45vngQUf44U4xdYw7Z9G60qOnRDKt0vC65BmSifOl2fKTGy9fjnMe/4s1Xx4B4y6+9IAZ8YhUPcO2TzCOQ0Olt0SHANNFdSVQgm3Z3Rz6/EQ/z0O/JrbZpvKyzADMNT87sOTzq7N1Hm/cHi6sJdCDIHT9shaAm3stHGmhIRJ9jpHXaXpbLfmdqLJyfC7yOtORMiSpuXZuOwX6nZhoHQdM810wu/FlwzJ3EIUQkbEcQYxRdQqMbjNvZnfa2KDJFNB1U60/Zk6dpbiEGRVhRS5Ymy51sI62Lbq6ZZLhjDP6CA0VXKbS1FNHkE/hBziVFgEYkrkkDEGVeuEyQuLQMy3kN1aX9aVlAqOzAjYPgMgRp8qBNzSqf7rOjxJiRDo7Zqi5JZG08TjudOzq2zubxrHNdLbymYRnWlZl01fBjUpL4QwsFB9oDDEU4gtEpaVIUKKiZBWQkxIrV6XAtGUgeMfBkptG3VESilIjjxtnW91DbN5Fl+ihqnsQ5nBOH1ancybzK5k9eQvXWTylQxtFR0+MEg5UmunV/FlmTHh8Eos2d/PoT6YI7BEYY1uce0MiMZeL5zPHwixe2KrGhKjJ9UZpJkOaqlgY7BMJUsMgTa6p3jGjBFpFe7v34JEtsszkganJ+X+9R23cePd89f8zOuPOJ0utFaR1ihrpjFou8s+gwxCNkJ2sUEcnXaKc9EUuTsupJJ4rJ0YoWQY09kwmt/xrTn7ZxsXRl+hBy4G6QDnbefSC4cnV1aFKC8L6hQTvbepzpvqHm+spspeGN1ZTUyFWUqLCwSiBzGpmvNBxevzbXM1n8hcnG8Vk0xeygskuohb4FLOoEZtjUabScpuTYwyuVoS2IeSS6LuFZmYlizO9ItcU2nVVanD0w5lhgzFlB2R0XdCzrPx7ASLWHfbWMj5xVHiPLhOCFDKQsdQC3h4tNdYQ+vLosqGkGJhKcKgubpcBE0+dHJbW/L+dgzGEPJEoHTtbvcPboVLU1Xn+JyZ1q3XIavRtPpCyZzDdShlgvDdZRWSw8J16HSvuPjAglses8gUvnj6cxRn8w79lu+Jystd5AvNRnzhBrvtcognsLvt0xOiXWjjTihVIIUXV0kw6HUmUs/BlXb7dn7xU/r6qQ+UimSUjsU0G9RpU5rT8esUKYq34mAs4fpNZ5D2ctkiMx1IImY7g0GJPq2u3UGVNhUQKeDRfql4IcGUec14xetg6NpEvJy5L/8+VUtz2CRcf4v8f34dvLG6/qt/T+Ked/Gt2zB5sWDxF6LOx9zQ2VSkiOBbkbmZu/51mJGivPghtXuxK8K85L996S8vcc7EvIbyQihOj7G/ZpcTKjKbW/FBll7X6P4hjlOWF1KYf55rrLbNN1CHtUDMbHtj2xvG7vykbpxO20vqkSCsxwM5JoJByJM3MD35mBGtYdZYgm+01QZBvRtMWV6UREh5GUYKc5huSkziA7oYsNHpwzcGEoSQ3Jqy7a5CMhyueXO8Y1kOlHVxNkkIE4Ts7xnDB5QxGNobbfqsA+aDFfVpvxm0fWd7OnHZGlYC97f3fPrmLTkn2mWnns4ghkZPZxHrYBHVTkqJpt0v91m4Er1gevzwgXfvH1zCfHfLzc0NpXi0q+rcQDGHjWFGmo7BFaJ+WI7+IzVj2zZ+9NVXtNqQKNwcj9zf35NioI3G6XR2FRZMCLwP1eKUote9TvulTyNyWVhW3/KfzmfcAdBRC6S0EFJBTfni6y95Pj07IG8OfSQkQkpITPTmjK4UArE4/Hl0IaSFw7I412kOFoNEcsocjgdMje3s/KPlcEBC5nI+IyHz/O4dIaUX2W4saQ5GFa6RqlHQ0VmyILmwbxeeny6gSi6FcjwSSkS3zt4Gh5RZc+awJNY10Vr1g1ihtsHz8yNSL3QCmu58G6oNxeNlsUay7qqLF6vBTH1UnTyPjg1jiEO8RRN729BuEDbMAselkO/f+CA+ebJKzpnnx0e6Cd98+SW1nlAq2yVP+InHvYr6f7rSTF92r4krLBAag+dWuRudEeu8eBoyo5bNsqezBbc4RhVulxuOeUF7p2HOF0GIksiHhZgTWOSv/eo/y7/1K6/48qT83PlnOJ8u/N4P3nFZjx7tO9wjXkLxLWbOLPNncx0KeWKJD+N80B6x1oldoDXfiM1zccyEuKj4mbIk2r5h5oyX69B92FQ65plGibhVxbygHxgpLyBGIBLEWMW5FE2EfatYjIQJKAf/vdNEMJWx807YO6c2+Lis3Ly+RUaFWFAHxCEy5vfqn7kSlPP+xMP79/zVX3zN+ZT46qHx4ek9rz56yx/8Pz8Ca3w4n0lqnJ/e8cmnn/D9v/xX+fh05rSd+fju58lL4v3TI/o0IcSjozlSBRKeWCJlpbcLMRix4Qkt4VoaXFUP/hz6sMdVFE07qfn9eIWbRwmsOXJ/m+kaWdfCq4/eUmPkd37/zzjVSiTw+I2yvnri9u4NIShNXVGbUpqcEf/7ow2C9kmrgxE90TWZF3v+8wzUAQWh3Bx4neH0/Ajm27jjMvwzdh5Ym7wHc8ZDiF7MmXQSyYfCcee5LTw+C2+ORx4+PMyABFfQRbIXdRPYnKIPHSoZklKAFo2B8kd/9ue8+Z/+Lt/9m/8h/9Kv/yv8+R//kN/6+ke0Tz7j7/zPP+A/eHPkO7944IYLZX1DswuSnzmGI/0OqEbaKsvug651T1xS5ZgHkcT9wTjkhUvsHFaj9sIlw751nuogjoF03/j+zOtGaE98dhycLUNYSbmSloI06CaEdWVwg0ZXT9ThjCdtRlkTx5IoZh4vHgZhCNeMtGhp8i6ULIJaRcJAtHgzkH1xMJpMy6fXWs52cf7DUCOVSKOT1x39cGZ/2vnd3/tjwmGq51QIE30AzpizEAhEVMK3NV6YuTviCgAdDmDftguXbSOkRIzRmyibAOYrZ0QcLD+G0S0y4lzQdWeonYdbXdWUuvsQJobC0/OZV4sQg7qytBmmrpAL3iu8WNoNv5ZyzoQIcTCVmv56z8+P7H3n7u41a6/s+zNPp2fUIHGAuKIEknnKreFJUwleoNJB5wAtzVN/BlJ4PPVg2x49LMBPbmeBSSAlT4oavc2AA1cBlq0TDgkrOHNHG6bVE7DqBZHAut4AoO3C0ApDifi9HzSjkvw8RSYjZqLNRQkzoUq7p7lhgxwTTQdVhNN2ItiJcnvvqoj1hv3yTI6wmT/b0YxjOXI43BBT9sY+eoNNFHIQ2gTwQmdIcAD1kjCFXvdvC+zZ4CKBpt3xU/gycKirdZHAeTdaUWRbWcx4f6k87tXvWPMkMVVHK6QIYm7pxQJRmi8YkqK9YbGQ+nDcAKDmA7SggRgHooOYnT0YpFGCD7nBbTBEV0CozaRovaZ7+lKki5FDJIeCoWxjcG7VB5lj0Bn0yzNLyp6EFjOMwLYpfXPlQ5Dsdcg0ZIRY6M3VEkvOrAG6dZbo/Yx2Y+/G8/nCpV64Bmokg9AcNCwiHKIrnRBB2yCFlTA6w3c3RIMcOsagdcjLLWsI1MuZqI37u9e0Xrn0jZuyslvjR1//xJVNPbKWAylndP/AIfpybg2JEr1fOqZBXndauGU7Qy4F6Gx1IxGoTZAUKdHV4DJ7TMOHnyXdcFJDuwsnLpuBwkKj0pztVCIxRfZZ94Qc0ToDAsRdNCllMOXx+YlgrvS3oIC7B5IoJA+iCHigQSqRZc2sLbs1DGj7hvaAFYO5vLWhSByoNQ96AOcMRVcWjeGWKGKEtnsoUY6Oa4iDGAspRYzqSp2p4gtBkCjkkFiXQms7or7MvPQKdFJviEU0QFAPiXD8iDHaVak91SZpobXmSbJA60zFhFt+zQStsw8cFS4z7Tv6kLlrI5hQkgABtUjBqMEX+t2MbZx9yJcKSylc9g3ByBI9+MLU5wfDh9Mx+IBPgrrqsA8GbQ7tI6GG6TRx/Icnd5ovroaDsd1OOhnOY7hNOTpbsFr3nkV9RmAm844SbPj3sfeNJWVoSpeBhe7g73kfMXzyodWdOp4keT3QZM4bAipXO+pP7+unPlA6HDJnx64gZgT17UgQnWldLr3V2dSE7ioNl9Z61GdeV5BI3XbSuiDq190iiUMs9LERgR5gmE+YwR/SMcn3fXJ0ormEXa7KpjD7jqnuuaZ6CDPOWK5mEOEFkGhza2GzcJ6DpCuEK6Xs08zgsGq3gwVCTB7FOlVUdlUNpbkZD1NWPFlR2n0z5CkIkd52Ys6U6JBkGTPKfr53quLR3iKzpPOvCbf3aTQuSXevs7wMoNQMCdkPieEy73CFTI5Ka8repm86JcrhSB/K1w+PVH+yCcmn65fTGW3+PpRcKGUlLcmHZ9MrLFwh34Zox7Qiw5Pm1CpEF99J9Pd7DPfyG0K6Kr3EP1OD5v7s3vyyqjO63HyqW3tjv1wwHaR8IC8rN8c5CBneWLV2mRvMK1fhKg10ZpGO5geKa9lJCcgw9s7jh2eeHk/kvHB3+4pPPz6wrgsibr3azo1AJB9WhjX/Psz99qINC4GtVpd1h0iMyvPzmQ/v3/P4/plcVm5fveX27pq65QWrfxp9uBiJmA23egkTGurS4dZ29tp4//BAjIHb2xsOh5WcXW6918aOEoIRxD8XOjq12Ux66zw9P/t7n33YYzZIuRBT5LJtvkmQq7IuTU1B5PnpxLt37zGgLIuDl3PyGF812rYTk/nwZllpfaduOyEmcspuOTufAB+AphD981TKt7alZYEQ2Fqj7idEHK59qmeKuC0thnmuYJQSSdH/m+FJGrX6Z6C35mq1u1taq8Bge3xCtXG8ueX2sFCKP3t1O9N1EGKh9cFlP7k1Uo5YLKh0xBaaddCNoIFm5oc90bkGjG8TK21u6Ofw2cLCeliRAJl70EYKDq4cNhUg4mo2DTNK14xi8Nnn3+Hh+RFt3ZWQ2gDnKtwXdbD28APLz0L3jEdJpBC5v39NMSVInqmYkXhcXAUS5+Al+GBOQoRoLHc3LLdHnvZH97rjTXQOkSJCiQlL0EbgP/tvf4O9Nu5eHfjkk0/5znc/56vuW3ILIDbopv7c90QpC+YifmwCfHUqZIpk7l6/YVmO1G04Z8Ou/KhvGSIE98l/fPeGcnMgIuSYqL3R8KIpzC25LxDcm94wxkwfC+bMO4ccRoZ2SohkM+Kh0FW5u70lhUSz6zJk3iA6SVgiYLsP4nX4QDquiHgTG2cD3IlE67x59Ypf/OVf5tWrV5TlKx6/+pLw/Mht6Owd3r55w7svHjHdSTnQ+8aQQTwu9L7x/idfcTze8erTj+n7xudh4/Eh8K4b2YJfnOIg7aCRDAQWBpUROh0gJSStWNsc9m4JMYfJjuo/h7N4kuMxBGQFumsODktmKYncBksInE4XPrx/x2gnongaVOsXTu/f88mnnzlTKrqahDHotZEXI63ZocraZqKXD/WcEauYOrcjEUhl4XB3oHwIxJvC3e0rFoyP7u+5XB6AM5FGFthnMRfFLeHZJQkeP63GjWWInbwciXja4TURxYIS6AQCPRrdBkkjFga3S2ah0sJCHNBz5mSDf/wP/0/efO/vc/Orf4N//W/8y/z47/yv/PDDE09vv8N///ff8R//R99DDyt2c0fiNaV9oIdO0idYMyUqy/LMvlaO+8LpsrCfzqCF2zcLx3rhKe5su8KaOC8BjZm3N4WHuvHh+YlFOv3pPY8//AGvbhoRpSzCx0fjTp+59MSwSl4Cr9ajhyLoQIn0FCgaubk7kBclTNbjiyI6zgWVCBoiwcByQ2JxUDZCa4GkIFlpMzVIYySaNwvd3JIeY6b3SjQH6Gq70FPgt3/nd1kOd0jK7LU750XEG4uUiNPao8PT88KVN6nGbp37HNmrUffqS6/mwRuDQEq+1NvqzmidNSZau1DVBzQig2AZw0g5TCZN4LQHhgmYs506g68fhHosvLk5kKtbZ8UG1kDEm8QRrgpxV2VJdAtn624XFrzWyTmzromU1BWnIXLeKvvziRAyt2FBQ3eVHJ7oBZ4qS+swkROuOHClnsWBsbONC8d4S1pvEfVUqrpvnj6Fs0V0KCVGLq2zJLeogWK9+10ZlP3SXHFyeYBpBQ4+xqM1r4XFAms5oCJsw8jJeZQofPx65c3dyo+/eaY2Q8dKi/iZNoYLeN0w6By8FOh1Z/vwDUspxMPB1UbmyXKmMkN9jvNkb2SDbfPmcAlCjyCTCxbiHIiTGaMSon+Oem9cU43T1V43vGHrhivlgsfcd1Mk+VD7w6ny4ew1goXgSpsxyKn4fdMrte3s+HDHwbyGpIWaBos4aqHJLBC6M1ax7km78zzMSVzt1xVGZwkJsQgzGXLvm3cKOvmX+OLUgrsURus+NAydJHCXC3UMGoNs4oqYMTgugkmj9vAyBIrR/Hk1wdT5lYqSsq8aSvRldVZfTL5ZF9pF+aZ1Yq+kqcbLIqTszol+9oXQPhx9cVMKPXhoxLIMchHqBVoI7K3RTFiOd8SQOe0PrMcDbRPOpycOh8yHeuHp4QPH9Z6QV3pVGGB7pdUTRSuv00pZAlU2hIXcAsecMTItH4m3C/b8hKwB3RsSFkoUajdPnJOZUhocir3kzPnian8NbndUIIv3GKaFfRkcV1hRrE+7L2FayQDr3heOHWRFpMxaxAdNY+yuFMwLROja3P5rUEqh7Z6S3bvXaklWwuKDdot4enYUtrYjI5NTnK+/zbCrqUCedq8QAsua/YmearSuF6R7b5JzJg5hqONdrEMfjUMORJQajFSMuxDYZQXzZWw0R2p0FA0O7c9hKuoMXxh1l6FoN7AMZozRmHs6TymdTqUlO6vMpt3ZXS+gVxcQcVrXhCUmdnNmq+WVVFzhh0DuDue2AGh0paWBhgm9D4rl4SoiU1R9EGckFyFIBzOEmTZ6nTWM4D0kOnnJbsU19SVJ10EOnvgWQqCLL/nFXJ0ZTQgyGLr7/z9AjJ6Ap+apmYxOx/ycmGouk0G49mrBXlSdg0HMgd7GP9Vc55/266c+ULq9X3mbbmn7hcveGMMldj6CcJCdiA+WZA6XzHxjH2OhpMRle3RwVICkgxzE5dEBttG9CA5xbv4NYyafiat4vGGyaXUShnZyEHL6dpOsV7/5HDaZ2ktqGkwpta9e53cm1z3KnDX5ECjGMAGNfsGMEcDm/9PMlSlzu4VN7+W0k/mk0Nk0PnBx32zvPjlcjzcw5XdB4aqyMvwykXhVD8yLFH+oGc52UDGQNA8c+XaoJQFP0miAb7QiwuiDNhVLInC8eYVhXE4XPjyeuVxOrOsBCfD48IFtPxND5Hg8cnM8eKqEGiF0gnVEgw+rurryyZorMiavQubPYYbbeYFo08udCgDZDL8JvHitbSBE6t4oSeYF36ltePKCmbOZ7u7JS3HO0dxK75fBGNXh0QMkTvujuf7McEXDdbAXJDGNSzw+nHh+emKocv/6Dd/93ie+FRzdL8Q65ud5FgoimHViAFH3kM/ZAUy7z2V74t37C+8evgKM+7tbfvb736Ms2RU7auj/pvTfAAAgAElEQVRwi5vgTVSKcQ4xBmlKHoMItTXef/GNpzt0j86+u71lXZ2P5OwjHzLIhKqbuepKgttDQwhcqvNQyrJg+AWUc/ZUk+HwxZSy/5naHbqog8u+8/jwxBhwc3PvsM7on7fLPLTEhPVw9EFkbWyXMzlnjodbfzbMi94+FNVOXg5+uJrRtgvrupKWhb3Vl+1dLAdEjMv5xP3da0SUHNzeemV9aasvxWNK7iXfTs70yjE5jPuy83h6ZEmR29sblhw4lALa5meygqgP5Zqxb402wGJCYyakRLEj9E7JhRDdDNLMlReGTWXFhK/TfAhn/tw3VXzkMWgqoEoJR7o1vzTUB6EDl+YydCpJ5vDieOD+7WtidEVmCAFVZ5yZ+EXfgiFjIMztbAwOUY6JHCIWvMGJIU0u6/x7otuJzDxZK+REUiFF43h/S3iX5ukYiAx0JHp2G0wQ4bf+j/+bh6/fU22gP/6G9Ls/4u7myHd/4Zf47Oe/7yDEoQT1z2BeM7mkeZ57jHW4bgJEIAWWm4WYjRivD5XMyFf11A3XR7s68GYlisuZZRhJgm8ep/T/Kg8188QPb1RdbZPEG8lqjRjgcDj4MDVG+uTSvb57Qykr9dz9/uHbRcEYiiNi4b74BriwEk0xnB0npt6c0DATPv/Zz/nLf+lnebUKP/6TA7/zTzqHZWXr7wlH+OKPfpv6EAgH52qE4dyQJEK7dE6PjxwPheWw8vDwji/+7Id886zY4Ug1KC2/nLkxBppOGGVwWXkCLAiaItYdKqnJj+EWfGjnFpBIGMrFjEN3kCgSySXPpBP/ce3PTzw9Hqndnx/BbcVLUULwe8It0ztNvQGT4sEAzPdSh5GnMspCZNcNgoOBBUOCAoPQnlhujxzjwiEEvvvpW97cF/7R7z3x/PTAm7YzKP5JtUazOuPO53kROqLCvhX+93/wBb/yqTAyqBjLTFmKIflN3JozblLGRiVqJeKJORdL5NhZP3vFw0/e849/8zf45z/+Hm//+t/k1//kT/iv/7c/RA4/x++lV/wP/9WP+Fv/3g3topRXK6MVwtrQLWFU7KDEmtymIL6dHwo64HA0TBZGHNg3DvO/PYBq4ameWbPw8e0t1jba6cLz44U3ZeFuWbF84DbsfPrxkdOfNdacaZcLhyVwQ0LzDXtrSPCFQllkcnR8mBRQJAyiRHrH7xWLpCRseiSRkHCk1c7WnrF4IIeDD11V6aoviuUUnPMyrBNDIoZC75VeG2KJHBbqpbMcsg89zKkshpHU3AI7n3ENE+Vo0NSQJH5WmzMY26yHrHmybK/N1dDmDEYdg9YbZn4Hx7xwBWG3MRCVuYwZc6vuDTKqtG1w1oXEIC3qddpsOkb1pYCpD6kpaSbjOqS3zWZpYDAESYXL+ZEQnri9vyNQCdH5GDaMdx88tbAsK0VWMgV/CmaNp97wjuHBNd2MrBn2DV3OvkxMmcPtPdYrSQKj7XOIZhyWhaxKyYV9GD0LS1m9LgrKZb+gMoihYxLmchdvYmIijeRBBhN+LLmgbRDKQjUfEPUmXM6dfa/Umf6rrSEKYXL7siQ0OndLdHO1pLpNLna3COZlIYmxNw8IUXFGie39hTOSUHc1AH0IXQNBXTkWR3f1nA6ku+0oTtWl4CpnmX+n2kCCYwH2thNCoJhhQdGpvtfRcL6j0cwHS7nt3GRjTZkiFQmdMWBTb/SOckdIvkQNDIJFH1jE5GmaWsGG20oR9sszQkUtk0JmtwuEjnVvGh2D0Vy1YM6O6BqQaFSF1t0O2PBmXsxYlgWJgaUlLlbpJmQCOUGvys3i78fp1Bm5ILmhuk+lP5N/arS+k0IhSMQETqq0vjO6KzCqVtoYvCm3gCAxM3pzeHo8soZBzwWxSC4Nse5w+ZxoA9birpN931hiJqjy+v6Or9+9o28VcqQsd8SwE3UnLiv7pZKGcJcStylzUwp7rQQKfQQkL7w/V26WGy5nJcXGq2MhRq/hhsAaI0/bxmM3VxdHV+7kYFRtbNqQskzWakK6WxWXmAlp0AI8747TKCEQTfA2rL8IFVzw4wpp0UiU5FKLMYgxTwvZ7gM9m64MlL15OlgICyn52eYJN8kH7OpJcRBJ5cDAa3gxXy6KeF8gIcz/bZCm8tO0Y8kh7lld+S8GteK9ZfSBtlXDLLIb0M3T/qIxgvPhTL/lRUpMlDyRLd4AoBanTXmqkpqy1UGQiiQv1VKIGIExKmWZbMMZ9oVB7YOOq+dSiLS6TyurV6nRx8OuYjdzm2z3gBfBucEKlJI8YU3HHJh534wNZ4wmRz/4cs775t7dqjh0ugEEggkxXWUq5rMKm7XuEmhdp+W0u6tIDDFXm8Qs87wVx0OEOM17fm7ZTCRu5la/IhFRpYVB8pUPIzjw3YVfPhiP4mKaMP5/PlDKZlhrxJx5lQrnS6V2w0tWXkCecbJUDIFcuFkO5OPK+d2DNxIIQTwWb4hBSCS1WWwYrV9BYG7Rce/ildbUSXhx09UbsBiFlAshhNlE64uyB8KLMuYqAbM5eDJz/tF1iyXze0jJY66dnj5HPXLF3fqlJfNykSuPxjwJy1Qc+IwRg1CudrDh/x0npLG3Om1NXiCI9tlTTZ+kCTYbLZMr/8gvvavywQHgNotv/5DblevEbA7Uoed5FmPdFad8880Hzqdn2mTsqA0eHp+wOax49ebVixxQpn0nptnWWUNGd/BkCnTdfV/UB6qDGPLcqvGiDBu4JTEnQYJRu0sTW/OYyKGe3jVmbOTzyaXJdXRCKty9esNhKcTgkZlDfVJ/TTaRJORUaM3hdWG4rmYizFzCjBLED7f9svP09EztUNaVjz/7DjfHIzpj48/nJ2IK5JSmLVNe7GsOsg2IypTSC8N2+qg8PT3y7uv39FYpyys+//y7HA4rmKfE6TVNRRyGrnql83/bfKbkzWndN2r1LSEI67KS80ou2Te2OqjNbTcxRZeTD+e6OBzd/dC9OyTcMF6wYkzIWxCMiAaHoeruYNg2BufLhdPzM12NnAplKfNS9EtHp0Ij5QXBkw1yF0pZiDk7lG4M2l7d8jCUZTn4EDb4CwnBX1ezwfPDB8wgl9XtD8EAl+gHBhlD6GgftFZJIbIsZQ6FBtu2UasXMw7ZXmi1ct6eef3qFTdrZl08cad3Bxb30cF8gLXvldNlsHeYrS5LDA6itUpYIvv+DCEioVCkE5IXgA5f9MuzKyxp8TNmDI7FI9jHaJhEtHdP0RgLkmeqXfEEPFOH1Opw28c1vvrKdSMGB0brlMJ3I6TMCliOhKkWNAnzcvMzpcToG8iw0MbuEcHJE4x6c6VhNAjDIZMhRm7uXnFYDtjljGP5M4RBwsjiTLOb4w3n/ECKhW5G743dYGv+PS558eZKBBsbUTq55BfrScR8IWHe1MUYyWtBbbg1mKnPFIcmNxukWczfHo7cv3o1k1RcFSGSfEAvwohKqxt770iM3EhiTZmrbClJRHHr6O3hZj57vpgoZuy9U9bCeji4xXMGBrji0c9dxLdLawk+7LlZyaJsMDkenjYZJJKD8embVywGn370ivG8sQZP79hOidoqj1+/Q3lLIiBBuPQN7dD3gelOr2fC3YLp4OuvvuYHX5+9oUpHNAhNlAXnqzTxRjjGSBvN32sbQHC1iGx+v0ywVJ8NnmEU9WcgmSAaXU2YvdDygDhP7er9zPPTAyMkUnKrkITE48PXWPtZjMXZWdHhOFJcQRBGpM1htOEN/xht2ljN1cDmTDSGP/shJS5PT3z23Y+x/cI3Xz3wfDkTorGsNzTzQaWaK9uCZZpEgpnHK2e4iLLK4A++vvD2JvG6+PN7XegMn8ZTQoSpGo7mXMEQB4sGkE4x4f6t8H6HP/rBl7z57d/g+//q5/y1v/3v8uMf/6f8Lz/4kuXTX+A3P1T+9D//kn/7rwu/9K/9KuRbVx5kRZ7VVTw2o7fNSCiHFKh2pR4OlrBQD8+M5wFDaMdAoBLNI6NtGD/8ydf85EOl0ri/fcXpAqOJb/9bJZbEeW+o7ViOpDg4JA/bHrJSQqL03WupKIhkhjjg2awTQkNwFV+U7NZehEsdnFtnSCfP+kgCJHW+kPNWzC0X3Uv+ppXX9wubGhIn0zAuWACtzZdSwRsDhsv+lYCq38NDFQlezJupN4XDC+qgfp6EIMhwFVITV4vaUMZUXNm0HshUYQVVr09ims/4NPvNsIpkzrbZ6Xx47Lx5u3xbE1WF7BaiPjrRlIyf3SHMTfgs+mtQOgaXxr5vvHn1dlpAZ6S9RPbuUNV2eURHoxwjQf28EoRmYwadeBNjNjCJ7OMJ44zwFiERVN1+IZFuNhuhyaBTV99YTKQSfQgmgkSo9cJKwKJQUybHiInQgmJ7I9CmlTkQywpSfAE7G9Eyz5z3T098ePClXTgsmAykOxNLJBBzpCxHMOHVq4UQOl7+JVf9mv88QwCNK4groA7FbSWXc3Xvc3CUQ0nZLbYteNplwJWRwSYWYi52U2AojH79vQNJRol+1tmY7ojo90wM/l6lJCiOaIjiPL2QQczVp10bh6VwMGG9WbgxB3Y/jcaGcgJ/fV3oMms1bZP5B6MLayncJeHu1YGYhFgbW33ifNkhFVLw+m6oTAu+K6tMog8XorO0rA4qQojQGZ7sCVgfpJI4yoS2t5m4nIGkvqCO0GpFm0xGmadYaY48nbsvP/PZWV6j0LpyaYMdr4cZgtZA3ZRSYIkRHZFdG3dHIYdM1cronTUJfTeM7hH3McNcrKYQubm5obadh6cnlmXFMBY1llwotpFlodfAIURKDB7a0uHhvJMXRzHH0TGNNDXOrSE58bok5PLkttcBtQnxCMtaKHtnV6E2j2jfhrEsfr/V4eetJ1Y7d7EOcY7a0RO+MQ9UuAY1pei2uaGCRYPkNidGcmXcwPEhITBsQPAF2BjDGUGHG3wS5f1OiD7YqDuIJFSbDz7iDKUyZw/m4mKMhP+sY/All9EJcbC3QLZGoxNaIUf/+wPRe4LpCIoyFzLTFiZh9g5d/TkST3HXJNTmcwKV4Y8ec3Ab3ZbpmD1hTP5fWROI32MhuYIoyKCU4ufhZLAxK8EgYQb2eC3TbWDDfNFrzMPN6z+Gon3CzNtwoQs+l1CbtrNp49c+z0d8wMMcvF/tjqZQyky6Nr97rgp4VD1JV40QEm1053BVSClRx47ATJ1jBqME56WKcU14Fp2BEtNpEMU5n6L+GQ7Z30tU2LSTYvDFbvABlA/yBAtK7zvpGtbwU/r6qQ+ULs39nDZ8wok4Nd2m/NYfAm9kc8o+hBk+3Gjv3zPqzlZ3FnPg167GEgMlBi7gD2MwDscDrblEWWIgS3CJr5hzRsQb1ZiyF47RbTMiYSrrPW7QU2z8nzAVM8GLaYkumzbhBcoZJpizD2e4Myef/k/zpKxwVQ15NeIxozbVWZ5scgWs+WMwFVFhxrC/QLuiQ8TMk0hMwmTK+J+nk0EQYphpbQ6jtiuxUn1kFKbM1abUVGDGWdr8NZffPT4+0bpLJh8en6aXNk1p44GYgwORBdK0Ffbe3BImTHVVI4iRkxddo1X/+TeHUsbg38eVaWPRHLDn9zZxippq7Z4QOBphwv9UlHa+0JpLd5d15fZ4nMM9ARISvWHtY6qahicYDRzebepblN73l+2J4dA/dFBrZ7vstNYpy5G3n3yfZU3k7O99rc5s6rpzPB7cx6sdUGQq5FwF75YS5+V0To/PfPP+HefTmZJXbm9ec3d7Q1lWRAZ9NG8osyuz+rSP2Jy8m8h8Db6NOJ/O1P3C3hzqfXNz4zHsMA8984FKiqyLc43GUOpl43BzpG6Vpq7G2uqMkswO18zJVRnO7fJB7VBh725DrK36RnF3WXhaDiSJpFSIITHGIKTslrU0U5iMCc1cSXNwMcagTshlnN93DJCXjASX8Q/t/lyo+bY8FnJ2a5zMNMh1KYScvLHQjqlvwdbDQk4FNWOv1eXArYF1DmvxeNbRMAavX7/m9d0Nog2sYn1jLcUVWNYYrbLvja0OaocUVn92tRP7TubM6IGHxx2zyuubhbtlmTOxRKuDfaiz1tQ4JmfYgKAFggXf9Itx2h9ow4jDyHmBmGaBYNCaP6PVG6icCio6N0k+SFX1y3egaK1I9iIzZgehYq67E4Q8XLm45jR/3jLPlDIDDebm/XLBCOTFuSMyFY+Hw8L9uqD1TFWhAjrcc1tKIrXK4xffQIyM2rzpToO8Jm7u7tCmxOwWYUMZKvTeWO9uiASqTKm6eOGhfAvQ9ss/TvWlR9Qq6uBmMcIw7t+8Jufsg4l5Ri7Bo8tF3WI36mAtDoFNMSEx0F/mQuopIMsEYeJD42HefHraSeB4d4t88xXMsxxc4dT7QKPbVAdwK8JNKaScMVOSzaGZGIiyHg589OkdKTVy6fzSL33Ow9M3PJweeBDlWCLf+eSef/LlBra6gsISNi7sdbDXnTYuqN7x/PDAw7tHvzOI9NopOdNM2YZQwmB0v0eu8FtxVTeKElMk5uzby2vC4hiEHAnJsNqxxZcEIQYOMXsDeVXOAr01Wj+zXQ4s2smhcLguMAyC+Q9XFf/cRi8KL73SmqdkPe2XyXZxsGzAY4BHcx6NTDl4jpHv//z32Z+f2J6+5nAfuD0YeT2SgkP/Y8gzRWemzKpykIUaB5amhVedV9Baow63soY5vPPh65hLIodxIoOwKlKmPiRmpqYF1SOv7yrn7ZE//L/+AW8+/YxX/8K/wd/82/8mX/wX/w2/++EHxNef8/vPnf/y7/2A/+QvfUz+5CNChNSgZYHWCEVflmtJI3kmmCYCsgbscmFdMnbqnIZyaJ0+Mlmh6sb5fOKrLyrnZjRLpJCx1IhJefvmFYQvPJksBPoGOWwcFth6B1lYJBFlJ6RpTzTXbOfhdooeI6IRkT4rmuZcDiq7nViXwK6KqtBG94XEVCmJzYj1NGPtZ/rlpXZCzGBeZ6WSpwpqhpyo7+bHBNyn4Mr0NOHDgm+/ixRCd/WRRVcmR/XnuMuYKkHnWHbU1UHCy2owhjQ3+J7aE9TTeWxyi8bQF8VVFCCoD83GLHMYXEzJ6tydPga1VkZtlOChECKDOpzZRBRO+4mkvvbbg3Ew49LdapjHTJ0zb7y0d5p2Z1HhY64xxgwWwVOtUqG1M1v9CpHEvS1sj4/kw2GqieOLBWPJEQR6n/S6qXYV9dVbzJFYHH4eLkIpK+VQGFvled8JKpT5nJopvVZydgucaidWt2yveEDLrh3W4jVjvOFUK10rSKNIJtM53L3h5uaWoZ2eHHlQ605UYU2JWhuxZArGEmHJgbZ7Qp9rVx0vsBRhWSL7Fnm+zMTIGInJ5m08gdtmbjdPEWges63qKWdt0IPfozlnXwKZECTjHr2BhOCIjzxVlfsgjERaj/zaz33GX/nuG/7wT/+QuhlnvfAmLzzKgS9Gou0JojJSIdrkowRl1O41VT4gsvFRyqQYuPssYr1zeh48bcrponwYg2FeZwGTiaYEtrkbC0B0kLK4ZTSI22KWEF293HUuMl1xFkzYd2MY1CkQsInO8G97UEp4Ue1HEkMb78+NHTiPSm87bbiqkeSqs9aGJ9Wa16B1r1ys00lENXaBnA8c0sF7uG7sw1mmwvz8JDwMoA40BlIU9r1zsy4sZhyS9xg2VTIavYdqe2fJkTUXmg5Skcm3GWg/O5NnokM0ZT5UdeWsBIaoK8gQQjFq7SzLQhveyGjrnOtOMmi7cHy7osE/ZUF8mb0sgXIoPJ0bdZs2QjOv9RGIHWIgNh9gxBQcGj0xJUOVlIsvVCXMPmKAzQTqJgyqB7KYB1PU3Z0cVxGDg6cbWcTr0OAD5qEesuBohEzt1RVE3SA4PH0M5x6O6TBAncUqBuQEQ1kSPNWKWEEnmFoxYg7kkPwz5mqIF2tdbwNTpSyRWAJD4/9L25v92Jam6V2/b1pr7x0RZ8g8OVRm1tRVHqrtdrfbyAM20IhuCWwZCYwNwsAF+JL/BiFuQEICyciCC4ON5As3eGjkSe62adztGrqrsrKqcjgnzxARe++1vuHl4nnXPgkSN1BEVUpVkRGxI9b+hvd93mfAqogpeafwq3UoZEUgjRQ5GivLCkRkIt1VU5kV/NKqBiBOuJi97xhDlgrm6iJjkMwkux7y04seMJa29GMLBAf6Y5BvUx/yQzLUTzLESAoBrAl86kGgtRRRCu+KPtBIOQlo61KnZMQ07ZrnkZJqnY3Zu3jq46j63jpWQtf+jkE+eV74irDhd6RqrcjaNqOcn87HTx1QGmOwmg7UtXZ67c5SwE34dMkOa5zWKuBHXT3DGikmSp40NUAFX7Qo34lwxKyCT5stBUcNO3HgWkmBPxGlpAg1TJTDjnneC+AJgVEXwoiXCPotDSxkXTA5bTI2vOB2pklwyl0EfOnhm07QhPl0YoOo/G+PapZjCK/TYvx7hzNTIFyoc5tpYkgCvEZrlJzAJ0lEgWYDOc+bm39tyHWMWnCnpcn8PwVFyxrkFMCM9Xzk7v6O83J2ja8KIxXNiUePHhCJTFmTwc23YFgDouJ4CUxzYdRK7ys5+bNvKxE3MeudmBPDDxJpvzoWBjlu0z4BTH10ah3eGMv0bQzoQ5Ogw27P9fWOPE+kIlKfLnRnirn2X1IIgXwE+aoYUVMfqiikNrAOy9qo65GldlLZcXX9kLcfPqTk7EVRozUVJ6VkebqMRhuOiUcVY+o3hthcHdbzwvMXz3n56pYA3Nzc8NYHT2TIXXXIMAR6BjfdU9qXNn03eQjk6FHLXYBhrat0w/PMw0ePtKeCWHi1VtcKK37dbMjHq6swKdPEslRqXUm56PQexu4gs8LmE9DgrK6lVk7nldYFoi51pbeuyauzDed5R7PhKL2RcvY0B+nIoxvTJwc9CJuRt55n7wI7cAlYH76vIsSpyDR0dHJOShbrgz46h70kcQHDmthqKcvYtUR5SvXWON7dUevCPM/sd3sYjV3JjNrkQXS1Z5oyoS7UdqaU4JOQldO6MoYS4dp6Zj2thFC0B8ZCoTNPHburnKvx/s2edx4VpgBmKy97Z12UXFcITCESZyWiYavMzy2wVFh74NzMJQeBkhMpDxJqJk7nE726aV+UUflGJuut0dsiINun4KSEH1P02lziK4Dbeqd2B5X2M+e60K1gQEry/OjWaaOynk5KHJnc8Lw1GfGPzpwy//LPfY3x8hEfvXzFJ7crISeu5z03hx2/5+1rfv4v/QU+Wu45v6j84Hu/y2/99j8X6+f0gtNBMufdFLDRqLUy7yaubh6gXN6VjXc6nCESCcwujz4HB9bB6cVqYmQ6P3j0xpsMB3e2LxomY1WCvEWmrIjpkaLApDGIQUmLMajo2h1mnY9BgwLzSXtr0szvD7NLJmw71rWOY8BCI8YDtTZ+/g//Hk53z+l/o5MCkvdGeWuJFWR8/tGP+N9fPmf97BHTdM3N40f0KTFdX3EVEzVGwnjuDJ1ItZVmg5d3zzn+8J5nT1+Qpx3PP3/B7flEjEYunWWcqBYpFMlfU3KbQAHiEcNdn9xXTqauw9ccfp+a308pJ+p6vshuY8lM2RmBEe5PJz757Cn99im9RFrv7C1iUyfXTgmFXgLruRJHukjwxoBeO+u4p9eV0k0x5EEsJef5M+VEdzArT5H9YU/4/CVvv/82P/neC7g9Uh9Gbl9WUogMa1Rb6S2KuRpUj3S6mxobOVRqT/TY2e0Lh72YcNFlwjn4NNXZDBZgxErpidhl2FpyI8bC0iJjHTy8yrxrE8+erXz/H/waf+DJV9n9zJ/k3/qVD/mv//rf45P7z7m5esiHxyP/83/3bf7N//hn6LsDMU2E1BiObqYpkkZg5EpMgzkW8hRoi9Ka5hhp+ysO9wvn0dhPiTlnXh1nXo7M7uoBN5bIx0Ga5K1wcz1xfvmKw5iI48y5Zz7+7HPeffuKZ5/fEabArsDhesZaJWcjR4EzYUAMQyarbQBidhIjEyeMveQkyytGH5QSmTLsk7Oxg4CFgIZ12x6PQGvQyfQgkIAgn6xqGiJsnoJKvfGhXh80lyt0hnvViZVL1nCi4WyNL1RvW4iIxXAxQw1ugBpKuDAOh7lUzT8Vthq1q0Bn80yzwrDBuVZiUROVLWFN8syMEq9WazKHtkglstrguJ4pKMa8WVMaW85UAoRZd3BfxTzxO8Cie2lmB3G8kWmYEoZsEHqFsBJDJsYD864ACyHO5Figi9Ffkoa83RpEw7pMyi0MYlNVvQuJM0ZdVqkEQqCvldPxXrz3GFndD2aXEzZWzudOqxBLkQy8L6RutBiVXFth2mewBUYlM4jMTNMNuWR2UX5+qQhkqG0l0llHZ1nEBhhIpqb1Aa3qfMUUspLVVdObhtqDQSLRe0WWE/InVJ1i9N6IQ+fcNuEPVaEU5gw5bKEEgSElybMl5kLACKFDUz1qfbAvE/v1xH/yF/88X/+Tf5rIiVc//i6nH/+Af/oP/xEfv3jOP/7xHT941eFUGaEyshrmNAY5DqZD5g+9/yb/+s//Af6FX/klPv3tX+c3f/Of8cPv/y73TzTAHw3ujmc+etG4WwN3Bj2as3pMv2NMWFf7XVvDRmbazaqTRxPgrFKNBaUvz1kAWhtNjE6GmHCjE4lkC1A7IzRa6MxRsvJpl2nLCtaUjrt1Z9EIthBzYKkaGuei+PPlqEF52u8phz2EieVcyRNM2airUiKHy2QFGKnXSi5DKrMGvDkHMYZ6YV0ahsyvR5XMO3SlRYegAWHrRg2JnsWuW1pl5Cw7kFFpTSBtcTVI6wOShiS1gfVOzpljr4Qi5vdo0uFqmN004EgCr7b6Gq+PLWy+WcWJAjDlSHHg3IBGpHdzln7yAYeAvNGjhwwpwj4Gv6tMv2P0VMnh52okqE73O3AMk+SuiX2cI4wAe7L8t7LM6KWcEKhluIdl8J9rwp3MKnMXA9vC5hGc9fodrAkczsgkOwwlxBEiZQo6xxZPnStxIL8AACAASURBVIxJAG8SgNLHoORyGb5nNlBLw3yGCB5TKe6Xp55iiu5tGnxE6QDWQAOzEDTgFJDTHWhyfKOt5DJ5jZnl52QaboQgz82LF3NQSrglDdRzkFqpG+Q8SeLmBITRDZIGAQQcAh9U05DLDIJVsgdbqVZKWGh0Q+oTH2KVBNnM6zX137i9S3Smt5lYUCWX/yco5//Vx/8PgJLkSKMNZ9yIcSJLDvPupzM8+WwEFe1LOxPirJSm1FnXo1PKpP/uvQnVjM76MVy6xcXo2zy+x8zoQlx0SMQAbRD2kZQnhgVnXVQxkhBIlLNrlb057hubCpkkmnpvtijo7bVgo72h13RfjuDPRJQ1obfDgY+x4UlwWbC+BjQpJkCQf050x4LWO3jhE1HjQ5Q3zEbt7t0b+66DoWSPfmzuDXBeuG8L9XTUpC8EplgcuOqUaWYqE/kLGzXg6qMxxBhx9CQnsQL6umhynbQJhlOQbSh5TFpTj9E29IcPSXwEoGpi3PqQuWVtdPcdSjlTpsLjmwdiYYjHrkPvwroSSTP6+zQEJQrQQBcyRNZ6QioFYz2tLEunro1UCtcPn/D2zQ2lZMZomjiumkxhxmupqRgrkexUtK3NNdblRKsrx+OJVy/vqEtlvpp590vvsJs10dOUzi6sKkmH3ETedIkOa7rsHOA7r2fWZSGGyG6eOez27HY71tY0OWtuvGmiT4Ikck52F7DbOqWocO+jOTMqkbMQ9doadW1+mehCWFvleF6oq3wItGd0WeaSHZXPQu3Ta3laiPm1GR2Oc/oBO5zWH0Og5MywLqbQ0LMNTjEFqOtZKUdTZs6TA1byWLm5OhD9UqiturmmiSlX5FN0vL2nthPBBte7HSVBb0d280QJHRJKMbNBbAthdK7mJNPHpguonhaWtkBrhD54UCbFoYZOKp3rbBwmuHlDvksPr+B+iSytcl+VklFsEEuiTMkvd6Xbhdg49sq6DGjGWGUavZtn5hIJKWmKv1TaudOGG2Pm5OxJFQ2jv36+ht63XBStO2LwhBvXqo9OHF2gSYykSUbrxWnDzTxppkNdVta2MO937A576f1b839UVO9L4me/+gZfjVd89+Mb/umPXvC8NtbbF/yxP/4ej/bw7JOFrz8+wLsHvvnlG27myD/5zu/w6Q8+oh3PhLffo13dkGKkzDNKtlCqYQqB1WEznfWqcud5uoCOl+jT17MBDCUg3uwPhKZ1YriWPBhtGKM1BSCkBEkgQTNzBqg8mE5LY5rkH0bATW4Vxzq6mi7o7KeJ3TRzdzy6FFqFgk9PAPj0k2ecv/I2ux1QB3Ga6WGLMJfsqq6D3/nOj/gkwg+/W3j/Wz9LvXqHvHsT2gIx8uLFc1p7zhwiI6x+yRjLeua4nGm98+lPnrLLg0Mybm4i98dKbZmxGm2Sx0cMkaBNgTwMJOJUeEP0KZ5kyQ39TREfEAQYKQNRch1/hjlF5mnmvCz84MMPef78lhQiH3/yOdOAmAfBBikMrnaFOSXqYuQJ1jqITZHAhEBbI2sf9CAWl872SKMKgBjNo341nc05craF891Trh/esG+Fdx7t2cXKk0dGS5GJxKl3+dZEmVoqHaypElsbOUSmWPjg8RVf/9Kej3908qQZ3YbBZVoxBgZOJQ+DNCZKaGL52HCfqkCadrzxsGEh8dHHT3n8j/8mHzx8lzf+tT/Pv/GTD/kr/+Bjnlvn8PARf+vjO778q5/wi3/6a7r/3cetnaozeCM9ZkhG6okYRZHPCWqW7MK60VvQQKIlruZCf+uK+TDx1ssjx+XEi/s7HljmyZuPacdb3n4UeXoKfH40fvzZS/J+T13OpJB4+80DD7OalpTkwVdGoA39/QnIBFponmIWGSjmvtugH886U/KBEKPMR0OVKbpt3pSSrwxLDloO1rWSy55xPpOKpNfoWvMUnc0/JGigiE+amxrcjgnEydtozwt2ZxqOsIFKbs4fhtsUbHt3u/EHAQ1sLrUnatDbUJgAwcQ0MjHXJbw2Uhu0OEijyyssZw+OkU/JMIHJmx3BWFdqjM6MUAJTWCs9bsMtWLVLSSl48M126KnGC+ELxeoY8n1qnf2suPoylddyYdO9byErxdM0BNSR9XrPh415kwxGI1jEcmaMwWjGfp6opyS5j2/WERIB3a+9NxlhjwqjMWd5/e2vrrh+eMVu2nO1n7k9PsXOmTOdnPY8uDqQJw3xzqeF3COKux/QwLoGJPIhiYzeOXVTkI/3Dww39s2Jug7JfG26sNLN5P+DDyIwhVhEt4motRNa9LvHiMnYAYHmHjRdaZSmhKsUVjKDwwxzhmgydn/nevB733rEi9/6q7zYfcbhq7/A9Tvv8uC938uv/Km/gNUX/Mr/9j/y3/+V/4m/+ZGRUmXkSh7dQy4GX3pU+LN/9IZ/8d/+JeYn3+TJt/4Iv//P7bn73j/i27/2q3zve7/Nixefcdqt3OzvuK/GXYMXtfJq7ZxWDQbHyNSqfiqFxDI6vVeSDUaE2gT+jKGE2Ot9EJusrqpfsjzoiqsLxhhYb0qyjlEs4W7MOTPWxjgvJAYhiUGYrRFtYDUw4o6ROnWtXO0m7s8nyDtKmThczezmA/fLYGmdcBrE2CXbH9uADM7LemFjhWgsrbKmLWkL9s4avrBgsvy+osGcNUiNOdFDd0sN4zgi55xpAdbqPmHDAYsQ3PDAh//VsJCo3STdj5FpVs25rgspB3JcWc6DUQc2Jdmf2GCpndFVG1sUySGltM15NMhPAoVwQC5Gj6XPmeT7bPRGq0rcjslI2e1NbJUPZnHvo6haULZ1my2KfIqlYhfQFU1+k/Iaig6iRhJZ3mkhaaCPZGWMrh6sCwRpLBiBGhSy0EJkBPlU6nhyY2wfjA1vjHNKSuluJiuAzfMV7VXreuPkw+yAWlBNltAwIESxR5Oro0jqw7cgLaKed7fAqI3NKznE13eLmfx9mzXv3Yxy8QJ2+4oUEIzivnwOcUhVIhkwIYN1YjIULJKFAzgbewtWioglXkelm5IZQ5BRdw9SdbVuhKCU8XWV5160odcYg+T+vXhvLLl3uPgFR/yeGMJLavtCouVP4eOnDiilVNQYO5rSQvOJh1+2ITgyqAhZTX4Qg6FE+nomtKYEgJCxIHNuC7ivisy8c0iaAARpUmtscqcfHs/tU6tBx1qnp+KXo4CIGAROhKjPJU/eEMVziMEC+px5AlnQ12xA0QaObPF+0SUD+rM2xMltNAgCS3pz9DJeTMOD/65bE9LNkAlFFHtpqLjQQQIypNwWLozeZUI7uhY6WyIOkiedV86nM21dfbI3wFamPJFLdrf87As8ULu5Hlb0RRuD0MW8CdGn+0MMheCLNCYHb2xrbEXjMxuKhjQBjMEkSSQYvVb3b9I0SI9TbJb9/kCZFT2eojOjEMsiOrrXe6fVxadwQQZ5AF5cmhdpbW0srbEuZ6wby1IllTpc8fjJDfNuJyO20elt9SZTB7eZTNQvYIjpvSCaH+ArS+2cz0dPWKuklLm+ecTh7T2xuE+Vg4W5JHl+eFE6zKhN3lAhDlprkqOd6+WgmUrmjcePmafia1drJfvfG1OU7BHY/Cj6GJyXhW6KeJ72OxkJt848z6Kxd732MKfQ4p9blRJ3XhZpenH6rZsZ56S9E2PRRCxszmMOGl3mvxvA5Gu2mUs+w+X9iSESgorl4Qh7X5X6lrO8oraiMAQ1gKkkN5fW908pM9DfCQIkT+c72qiUCIfDnn0pnk40adM0XWbBJLmIOciTYj0x1sbaGsvpTKuVZMYUjYdXe65L4uGUOMyBmM7cTIGrnZLNQmgsa6PVE2vEL2Q4HCbFNlskBlngNzNOZ0XPt25EIg+uZkbOpJSxodjg1o3j0mhDnnIxa8p1wWXBWV3e5BAUBFCyS4GiJtZBTMdelX6RUgFn5GygXx/Nwwqgnhex4PYHDrtZIMnoLHWh1oq1RowZm/esNhHTYLXB3Xnh6e2J+PI7/OCfLfyd3/gOPe+JVzve/vJXeOfLT/gT/8rP8eaX3uTX/u6v09bGup6Z8kR5cCPJ2Qa0spF9AhdvOzSlVmOnaZbpctHZPAAHdEqZKNNEG40cktL3TGfJup6wYezS5EWBGmZtIYHTtVWis1SDbYMM82AFgdi1rhzv7ynTxOHqitvjSeefn9FgDtBU5sOez+9WpvVWErxZf5fmv/q6zZT3TOR2DRCK+kKXQp/bSq0NcVNlyBjD4FxXbp8LaJEZeCXmxJOHhZw7L27lcxNaY1ggTIGV1e8Y+YPlIPo55r5/fjabg/OqUzQe6yMSkuS/s9zXXfrT6XSef/6c73/vB/J2C0b/+AXvPTkQHSS02Hn0OPFgDz/RhaB9ntwXsOu9HT4MUB6XR827PBpzpuuQAfzonbN17PMXvP34IW++/RbTVaK0hcPN4JzErl2dvRptEIqD7xFPQ5kcmI68/SDzzlsTzz6SHDWG4Ywa3CtPayl0p6IXSWJU2BUsqGmZp0ApiWM7c34j8oPvfIeb9/42j37hz/Kzf+4/4F/9+D/nr314hF1hfedt/uo//BGP3/4JX//FN/V+5plYDKuLggWmSGsuZ8+F1CotTgqbmKukNX2hLwkLxhwjc0pYMcrDHfvVmHLn82PgZr/DgONN58t1Yr8kQtlzvq1QCnTYXe0ouRG6hndKXw9KGTJZC4SwDQ0FMg7UlEDlblnpwLRJ9rMRV2f7OXATXU7oF4MGiF2MG2nHgvv9CPiucCnELnHOJgCpyTjIjXSDSyGCzsPWXeYcwfKlFt0svrfabiv+YkqXYU10RtvlLOqyCmguj1BdpvFfsEBvC2nePMUkURu1SjYXFZwQg+7qEJR+mNKWRmTOPm/cH8+EOBHSoFevn0ySJPlwJnYObLZa5R05Gq2dqevKaTmq8a+NkjpXh0wJAtHokGKWmbVbLWCeFBSghiEvJDOqGb0N6q2Rph1xyuQwMXqg7ybORc19NTEncggkg9WZEQLN3FdrNK4Oez744G0eP7zR4DENrvc76nnimDJX8zUfPL7iNBofPT/T2lBtOoYnjek55iSmYHIQY7Tudg6osS5qxNqIArbk/6AqJLoVRURgW1TAiep8gUspyCslRQ2nhzX3twxkEyu6RNMgtFZGMKolzpZIB+NmDlzvAzf7ASw8W1+Qv/urvPGjv8vh4Q2Hxz/D9JWfI1x9g8e//w/yR979G/wvH77kuqhfuJoHD3eBx4fIuw8y4+4FH/39/5J3vvp15puvEA/f5MFb7/ML/95/yh+Oxue/9ff5nX/yG3z4O7/F009+zP16ZrGZV63ycl15ea68vF+46wYhsY5Bdmlp35gjvWGpkELipiSuigAdI0PAmRRddXcMMvlNWZ6vqVMmsQhrrUohDJkQO1PqCr7oMqOWnYGS4VrOnGqjtR1xviFNUWBPbxyPlaWpbs6xuweu++Q23dk2STbU1kZrQQyiXeQ2Q+0r04A57ZgmpUHGELmaEmsT2EmM3B2rmGihs1hkuT8p6MRZe83EzNnqWMzPMVNSFyXJuyi4pccYjGUQS+R8Howms/S1GjDoYxXhIqqmGQBR9SR50FZnmoRIMw2zzFGLGAWOqTZQmiIBDS6TvDGHgyIjSLofoiSdOWW3Awk++A0QJRXudAEgfqiXGCSNivEyDA4bdcr7kUAU28uZEjFEMkPDfgdolrYwlcx5PTPN2UkDMos399PL8XV/INKBBgk+b3d52EaqkNIhELxHi74+9P3NJF/f2DvmljQCrSI9SH7sKMTrgZGZ4xPJpYmJLVFp0Ond9TS2+ZaOze6VQHIJn0C9EfRz7QsXy9YXaSOZBis+tOjDnGulO6Z29SmqydIFBNzOaRzACqGR8vY+yN82ee1qnhyaXWJHSCwOttuo/DQ/fvqAkh/ShlJw8jQRuhsje2O60cFkPo1PVmTgLPod3lSJatuHYuezgzVKXVCxlnN0BZXeyBjSxRRrBDFWYhSKvh5PtOR0YcMb40BkaCozzP0VNkRUv0/0wnH7uPwvXxQbVVafF70VNjd7CEFJAITuVPlwmWLjX2OmMmUEZ0EZusxyEto6tOisb0RtBzcM6F0RoL3Lb8QCx9Mr1qXKI2QMbZI2OJSJabcjhh1lJ/28maIeRYDSexJyIQc3aIyBuqrx1u8qdDv6++ocK38CbnY+lNYGfg7VLv+E2ui90lulj0aMkyLjy07x8KWQU/DCUZsjsLEQEr1VByhlStxH8+anu8+BkPx1PbMsC6fTgvVAZ2AhcnW45ubRjmmaKZNHFrfG6ppU/YE4cIMXkhHiuLwvrTXW9cTpdOK8rNTFIAyuDle8+eZblGmit+4Nj6avYpT4BMDMvZG8ebJOa4ozXtdVzIkAu93E1dWOsmmSg96fYFyYdRuLq7s/WVsrvStZouTMFOP/ZY3F5LHAVeDBZgAOgePRfVhqc6DJKNMs1pOBocj4YKJbCu5/vT5jyG5y2UjuT5U8cn5b62FD8AmMLS2rSkJFkE/GtJslsdIIEWvdk5UUsZrVTWpd9ZWSZvna1IqFRlsq0RoPd4XDYWbKWSkzboY6pBfwmGUj586ynFUgtbPM8MdgPd3xYNrxxtWBJ1czbz6YudlnHsyJvtxjqTDtIjmcOZ+O3J/uOVcvwFthjMDhapZUp3nD41PNpRnrEjg3+SOVFEgFCIm2dnrX5GoZaDIXPVUibrKQjZWp81EeZIlcMkRn3LTgz9UZOe45IlBQADIXgF+A/2iN5hr5adoxTxO5KMhgWVba6pLLELjZH8j7Hb/9k+f8+O7HfO+z53z3k1c8vV2IBK5+fTBuV3Ku8OoVnz39mKc/eZuvfPUrfPODd6l/7A/wO9//mJgy+/2BKU8+cNDmi0EgHwFv9rR+yiRZxuibfMWjtuWp7+kXgXmesUlNdXdfrc3bYD0vkq+OBiNiUc8w+euPVqE3ym6vM83Ejhl+ttsw2ugsa6W1Tp4K82F/MWkNjvYnH24oRNd4+uKO9fYpPSQwTT0FZymQYS56Bn1Urq6uefTkhmefniU7GaKz93XFwhbbHF1urRnEJvWFQJivObz1toqK5x+SesVGZ5juoHU0LG3XWITQ3S9lQJD3nDnAFRiXRmwDAJSkah5h7ZPAoXP5eDxS64LPYC7sInDz7QXefnzDkzce8r0f3DG58tlSYI4CKIcfuMO4yGCD9M9gppjjpAMlpUyrKyFkjqcT4a3HfOVn3uNwPfPqdz+F+AoLK8M2j0FfZ91QQlxgDubmoVBy4uH1xOE6MaXgd5mGXD10SMOZW7pbDnFPiYqC1vNUQmFJgVIyh0PiwX2GHdxH48N//GtcPX6L9M1f5k/9u3+BV//N/8Df/fQFqc7cXT/hr/3tF/xHb8w8+sZDUjbYvfbtSiWQ930jrFF2RQ3H2p09kLGDCsm2Nhqwn/cEJHPsFpkPmUe7TCkzMU98UK548I5xPwarOes0ZFKPvPX2gdRO9ODNDPkyTAtBZbakE5lgdRvFEQjUtXN37F8YMurcLjmRLj6W6fLzEm5uTPD3fxDD8OALBZPESyVm22iCQZcVwTZgc3wKl3r0obTcxmAZHlLxhfvZvPmLW43jjVaOGlZIqrABq/6am2zE7+OGUfDB4xj085meEskml3KKLRQJioNPSlIMANFjsFOkDUnaRWoPrM2N7HsTm2BswIbA3phmyjTpr3D2pI2Vth45tUVSk1BIuWtAN+0Y1uSxYjJQb8t6kbPqTsj+f+S21zZfvi5hhY3BPGYsFtRGqZ7H1OCN7n4kDjRoEj505plkifXKWNaXfPrxc07LmcQgx8ZyqtSWCGkwWePZ6Z6zx2B3k9Sy25DXInqdnLM3i1CiPE5yNpKnhI6xpQAWwN+LZgIrkaanpEiJA1K4yHm22iUgf6wUIqlEQtCgsVc/k1Ikl6jkUYzRAmuH5da4vTNudoNlD7f7yrOXJ958cOCt65knD+958NYz3vr0t2D/kKefDf7Bj+/oGKfVWKtxYrCOJNZUWmgffsqnt4X3v/uMt9/5No8e/m0evfmE/OQPkh59kydf/Tpv/L4/wS/cfs4nv/mP+OH3v8ezH32Xpx//kKfHz7ndB443g+fnxqtj47gGzrXTrBCnSXf8pt60Rs6dlCpWA4xZexjcboBLL9N9anookZIWbht0S8y7orpqyAMqju0aSVQCmU4JkTxllt4g7UmpUFvn7q6y1sapCfSNXXVzCf62DZPnWQxKBKsL0QLdZNgfq1HXzhqMQzDVpiDWWoBTaxrKNRNoP5qGBiFC7KTYGVaJcdYgxKB6LU1wgMEiG8NFxTHE0QkmH6IyFUZCiamm5qIPY1lxYKprnYZItMQkOzlCdFN9l9hvvaksXaLuez8jhnWvjd3UGnlSTjv3QzLVB155q4cMXKTGdNU4xECKTgM1NwAfGqSlWC6vPwKXwKfksuUxjBGbQ+GREhPJVJ/kmGASE3hLIh5hXAbYZnZJhQ6hUKIPcIL2YoiqNYJFQg/U1ogEDzXYlBBehxAcFE+eLu4hHkHD2GiRtq4aiPqQ8DKg9f7A3Fu4D/3/5CxUM68BkxHD8HsreMKhzPvFTdHvPAJiNG/1qQ0GFXPGTXD/Tyy6n6+GhNsaCQS3zwmvPfPM/aGGF8cDGeabK13gNalje8dNwREpfsFXmeH94E/v46cPKGVNvSMylAu1uav+ZmS9+QwFetsWdSYRJTdrYjTJSLk7M0jT6GCOig7RxWSwrYSxzTDLQrj4+LQEIWR2ZXdpmjSBfd2QRp++jguyGhxp1OERLwiumppNOhFMh5joxfjEki9M2bjQ8IwNb4wXJtJwB3ih0pdZtrZ6eI2AD9v8gIKkCWObSmmhjd7prXI8ryynhdOpOqqp74/ZN3aI7K+uBNS5PKi7T45AsewbT7T64H5MAWh1dUQVQvoCa8f/fXBq9ZYytRmejSGjurquYsGYCg0ViIV5t2feXcvMOfvEzImkW1paMDXNwxFz86ell/Tixp/v6JVlWTifTiy1+oGYKLsDD3aZaTfJuNrlcmM40hvEkIvbVAEBLeaGfDrMO+fjkdu7W87nRT8jKq3s5g1J0HKOLpVsEJSeIiApu9QGpdi43j+aiUmznrWOLZJy5vrmmlLShX3SmhgWMci7q/fury8JW2ti1NRlFRKdMzllFbH+PoitpaZtOyR7N3rrrHVhXRdO59WLZB3Uu91MjMmLdl0wwwvqzdzefDqTUiKmfFnFW3oNbgB3ucx8MqrielDP8nWYp90lLj6lzddAEDQxYr16OoW5p4kuyFIkfWptZan3MIz9fub6+hE5eDB7X2FU4uiMoAO8Vfkv5GjUvtLHmePxllZPxGBMEb715ce8/+YbXOfEVWyU0JjzSqiKLp6uZu7HymevnnN3f+Z4rqQ800xpODHCWisjRHqTfKe2k2LaR6KPiRAnUtZTW1Y4rwvnpsTB3jo9S1aYPFls4+oEB1lG7zqnUhbTq4gZkfw4iphLZSFmTc9yiZQkIUNzRts6OuvpLClcykyHPXOZSTY4rwvreaGtlbpUUkmUqyvKfoeNznd++JRPfvgRt8czxyGqfCTwvO148uYj7p/9hDDO7POepx8/4+7pC772jc/5vd/8Jl9+/2f56OM7Xtw1ujUsKgkJL+pUGLVLI5dAVOANRAGfuqnZEAVd5/M07dnNOwd4ogPTxno8MnvCYNkmYBs4D2JirQtzmQTk+LXce9ewwBvUtTXOR8mCCHB1uKbkzFoXL7QhBAHsteqsivs9zz+FkbbUDYEZZsaUM9e7RLLqbLAjr25fEMOOjKZyow9GPTngo3ZPjU8SyBS0VwNwdbXjwZvvkvPM11rmRz/+iZr7MWhDvkWtmHsgwUjaVzFEJPrY7tkgOrppbxcGMRdKkGyy90HImjQrJdBN292YNoyB2iGxCl1VggVPuMqRnDK7qIJqbCk5Ni5AZ/Rp6wYQBIxpmmjjqKFReM0MWdaVn3z2gvc/ecE3rt+ToTjQR2XqqzzYGExph1F8CNE8KREfdhk3V4X9DKlk1k0GELjUHSkI7CLKm6+MKB5V0sAjB929y6kxhc7VVcFaIabK3anz0d/7Vb7x5D3Se3+EP/PvJ3Z/+b/iVz/5nP74Cb+7XPG//q2P+TPvXVNmyX0ZxjifsBgpoWC5Seo+BXLoSgLKkV5cWlsb1jqjZhSUMRht0HOhk6DLh2r0xuEqkvaRXRX7x4aGbTnveHg1s96dWVKgNd8LuHeGeXOypfiGwBYGMkLjtJw5VqVPTbtMcdOIJSUldF5kbxuvyYNLguKfZ5O8ZUqJCg4io3UU8HX5WlptVunWyNGlpsZl4hwccK4WKDjw7LWbD/0dBPFaxweFkvj7YCRstcbwvWb+X1W1YtCZ17Amv7mhpkhySf+W0WhjYEO+IJt0o3ejjsEUs3xv3Aw/xA1cbV49C+QiRKZUXCp/QQFY1hNrXRRMEyJ0GKvu9pyS13BAM1Yb1KZ7KoSkiPHe5dfUJKm1Pphi8lh7Ae+tN6wuXj9D7+ELzPlO9b2qmyZp06NU49o7az3xk49+TF8qFTWUwWBtRrdInRuMe+5GZKQdxc+cYarvpzmzLGdCiC7V7v7oFVKRk+wWau+E2LVG/a2cJxlGR8XZuXeriYmK+8OY1+xoP68D0hDgPM2R1geta91aSy5B7IQQmQvMDHpQ7/NigdtjY0qdl1czz9YTH786cvg88+jTxDu7E8v8nI8+C/z6bSMXpZ6JrDY4rkbvhXUJfJIWnt0vfPLZPV9+FsnzC77yzkseP/4+V48e8fDJVyhv/j7K9c/wwR/+Rd7747/M+dlP+Ow7/wc/+s4/5Uc//Gd8+uwjHqaFu6uEjcxtq9wvsp44HTunXeG4VM7roA4N3fOIQKPHTu1Bvne4v2lIHkYkRckYhg2t694knU+Cni9g1UA177p2lgoWZu2DnBl0og3uTo1tq23+PMPENgvOmEkBb7ADJSZaG2CdRmTWnQAAIABJREFUFiDWTAqwqHzVvWYwu5qkD/WU69rkhQWYdamUQkTOLJ1q8nINzl5tGwAQNGDtzb83babZXb63sxJFbQRCMmJ2MsHlHECfS0HgS5cZfIi6c3saRK/TZQzvHjtR3jfbkDI6S2zDh7b3IiaBWsGChykEZ+r01wbNqNcbJlB9G/qOIRZ6HQJJo8kgPAQjDQ12zeT1Zg5uqyfUwOpCKAiSDRMgJhmgx2gcUnSfSWOsr/utEKB5iMJgk/A2yZpD8XXFRXaoWlD/qM9yzyJ3Itr6VJyNKnbTpnrpNPeUinaBBl4DQt1BmSAgrZqUNwwYcbA2N+F2v6ZNCg2DOARch+QpeAFXkDREeE9idw5ZCFjU+pLPctF54jjFsIB1DUXEYgqXwUs0o1tyHyjAhBukKH87CM5Mkj+r0dksef7vnNz/rx8/dUAplok+Gp2V2roWRut+eXYGnkgWzeOuE8Mkewkpk1Mhtkrn7DQ4tBDctJNuWHTgpQ8ZzodAyFlyGAcmUky0KPQ2xUQcdgF9hPn4bvZPCLH1h/uFKVR0QAE2ap9+RtgWR3ApgDcjF9aQF/Vja1LstTHaVvSbEK3L72BeiKn5F1XbmgrxTCANw+2IOB2PnJdFXlVjsCwnLagBxMR+v2c3K4Y1utFiyROtd5d4KL2DrQDCNjqOkGBZxeuwCe6lFHTIELbC3o0xkedJHzKAlhRNBru9ufN9jky7HbtdYZ5npiJKdUrFn8827VQBNYYKVbWQTQfB2AArPdRcMqMad69ecT6dWVqFEElpYt7dMM2TLp2UKdEE8NgGyBUC0TeWm0X72vCbghCM5XzPq5e3HI8nRlcKxeHwgJwju7mQsmLE9TsNQhQrIcXs3hr6qQEdGCQlS7x88cpRdxU9UyyUXHxPOGxgsEkI+xi0YCQ33LNghCH/g2WtitxMas4k35T/gsA3c/Pg4NTzQa1Nk5/TmbVVem3EXCC6LrtkgkeZKsFEUlOcSSS6Z7hQdWMSiwCU2ickHBl42/Y+6mBrQwwPLDDliXmamaZZzMahdVdSIppo+OYudGLIKfK+ZDESl+XEupzovXPYZR4+uuKw30Fo2NpIzm7UBRcJFukj0qou1GU5cX/3jKXfEWzlwaHwzQ8e8+ThxNvzgUxlTpXU5QGQWGj1hMXC6bjy7PaOp/d38ovIB5pl6oDaZNA4RuRs0c3UoQ2n36YEJUj/PDLnDufeOVWxk3REFEiBFIrOtAs4Hah9yMcISRZiEugUUwLrHhWs2OMQVOjEVIjZvSB8KpODJuCn85nROyUXpqko4QYl9Kznler+a9M0c3V9TSiTT9E7jZWzDRY0DYtBE6mPP33B4299i6sPHnN+8RmLVXZzJ8TK7/7uD/n008958+23ePL4TWzAZ7cr8eqGHDKYEr2sSuo4gBgb0TIpix3XzWN3Q3SOwbY2dXLlw6TnuEniBtyf7im9iznLZgRvPvmR0LmvKylK0y86+QZaiWJ9udjXBn1Q9jtSTjx8+IjD/kCtohFLyCavAcU/G2XK3N7d6h4w5AORtkJwYE3GwRbg9mx88tk98ZCcjTmofZUZ7NArDFthNBT8rCIGb1x3U2aeJ3Kc+MbXvoqR+Off/SEBY7jEqMtIhpREjU8uk4xJ0zu6JCrmBZz5ZReDa/yjm+pbUPxwyRcvGetdcijrEAZ44ZQMRgw8fXXHg/szxEb3KN5uScaoZhe2bjQ1cZXhBbT8zXLO0DI9dfKk8ydNidEj9/cr3//RM770ta8IGDif6bYwXb1BKTvWZaWuK3lOF8n66GIAWmzkZDy8zux2mcM+cboffqaqYZfs1EgDUfODkgVzTDRrhJSIxUi5czw2luf3vPn+NVePI+G2Y7Hzyavn3Py9v87bv3xFfv+b/NK/8x/CX/7L/I2XL5gePeIffrbw7t/6hD/2S+9AVkAHWUO3SKbsBGAlIKfM2A0VmVMEOnknLyMLYLWJKT1DHcbaq0C7qTC6BhJxKgo893SvbrCbkgzzo9HzABPzfBuCCfQPEGQyTw8up25YSty/OtNaYLc7sNvtCUSmGCm9aS0E3fK4hCEEAUWtd0YMbpkUVWybashL/YYYTZfmzIJMgfvwtaOnU6LOzGBGHsY0uhfrusOSyU9pXIZ9PvzZoBtTsqQYEa8Z7GO8loNugxRN4fT6MSXKbsIsMdoKbIwOr/1GZ7QVG2L+TjkRcya4HLv3SoiJbBpe9daV2AZbrgl5SvIXZKvlPAK7y+syOLO9hZUxMrubK0JWoxk61GZUYEU1V9mGYF4zKBTFJf/uVTKsE4KatRYGEZm+khz03p6NaR2ZCdRqQ0bMV1OQfKmduT8psdWCB6TURpXuh7U2Xi1wfXjItBdrNJRAmWTy23u7nM1mhkVnpaRIiIp631ZpKWqkj4tkeKObSxUDh50bj6MzZPUAEnMgO/odY75O2hisFvTzqzGVTB+R2AdzjlTsUgvFFEnJbTtiwnrm45eNT+4qVzOU/cTNNHgwQUuJV+2Kl0vS648I3X1DDWoPvGyJuhqfvjrxaD/zyd0tc7rjk2eDxwd44+E97zz6lDfe+DZXbz7h8Oa7lIcfcLX7Bvtf/BN88Av/Eq8+/ZDPPvsez779m3z/27/BZy8+4dNz47pMzGkmPsmcj51P7irP7ozeE+sxMdbgPjCNZXQCmQJUIudWiDljrbKUQJnFum5E2rrQGn6vV9Ym1YQ6NfcPG2BU8rQnIFZnbCvV/c6COQPK11kPgTjcqiTpvtnNgdoStxZc0oYP4CMWEzUGAVBsjbbRaycEo0aIXT2pgNl6CSMaZjoz16Y+xwR9b2FTYt7p66JJZtRX7e95UuhPDoUQG2ciCi/AvacCMU4y0nZQqq4upd2kXtZIyL9yqGhTnezoaIxGjpDT6wFHDEYY8tOzodT1NHtPOtJrYH5sUt94ITFsypos41zq0LOPNAoBG5k4ElMuSpbsYmdG9/bd9k0d7qWLPLkykTjcsiJ2SoAGLEM9UE5O1nAgP44N4G/0LqP40dUPbxOAGBIjqjePbpBkDLG4hnxfiWKtpgREMU1jccbPyLpJksEQoWLDDEKUsicF9y6NgTEW3UdDIPpwlY7OvCFPMtCgxIcrMSbdDz7RMO+aBVB1kisuVMtHwWBBHsNEpDbophTUIQVGQL3ToBMzuoXT1m5GtkeE95ghiaWGSZ6+mZH/lPGknz6gdDqehVoGT1JoDhIEh5Hh0iy33gkDLAamLMPCgNFWmbuNYRAVcS2fh8RAm8WIhCxz25R0YIQYdfHbNr0RsvcFsZHT9rcCYEOBBLDgvx3ekGjz+ZRUS9y/+gvfZ8Etm8RY2sCo7nTAjcWR4makm1jrKhO4FN2vB0fLRYtrTcCEWaAk2JVCXyvreua0rpyXI7Up4r43Haq5FK4fPmSeZ0KSVjwR6CMQJ1EVmxk9ynuntU4yLTaCs3UcBNjib1MKl/Qja10xrA5oRWehjCFw4tw7tVXWdWVdZJZaSmG3n5mmPSEpNj4FTxLRacGWaqdJuz9V29DexrCVQLoYIqaUaHXh7u6Ou1d33J/PRCamecf+5lqMFcfsNsrjYHhTrlfKKblcI3qywKB7PCsBal24u9XPb2sl7WZ2Vzcc9nt9LyaTvRTd/0Bu+9LcOzC11ZkpELoM3O7vj5xPJ+Iw9rtCmSYBSN5kGe55g4pOvsjSCkLe64DjspKCNM3BAvP+yicUvuaHWE3djOAygNZNaW+O/J/OiyRMvZNyJs97+TglGeyaaU3ElLUvDUaHVGac2fz6ADdTyk8IklsA2HBK7JbwJuPt1ppLizK7vZKxFJs83M9HscXN2SDWm9LMyuyXZ2Q0eSSdT3cMG1wd9jx+4032U6TkQRiLmpsQCHlyXwOlDNbWGD1Qm3F795Tl9IKSG++/c+Br777Pl96aeTQPSn3FPtwLLAP6csK+MHFYW+fZqzNP7xYsT4QwMYjcr8aydGDCYiaVmfvn9/RuirlNiTgCpMIInT7gXBvnBuemKNzonlQCgocnTkA3nwgNfZ9541+mSedgkJdWH/KBaGOQStZEJkWxk4I80NpmKFgFJnUz5p2AkZQywYzT8UhfBJ7UtlJy4XBzzVRmup/l3QLLemJpq0/iNvZfZzlXnn72jG/9oV9gefwWtS+sn37E8fYHLMfGJ0/v+OTjF9w8/Jg33nsTWzItF4gTMUrGcG4LLgphWLoUh6u5DK03Qt8m85c6Q9K43ezNZqDEyPF8xmpV8z0kFW0uoSCaODlNnntlmgX4BFG2196JQ0OMMZTe09aVw/UVU8mUSdPVq8MVr169ctAdL3D0rPbTgdHgcLji7UdXlH0gtM5y7pzrYLfb8/BLX+Z6t9PZWyZq2pGbAzL+vpXdFdYSwwLJgfhQjDwiY8j0d5ozNw8esSuzeB+5iHnUG7EUUceDY1Gu4afECztDXi4CLsHvp4Aa1xDEFHCDTZw5mNtgVzLWBmWeuLm64fzic/oIlN3eGaxGHoNTNo63C7fHriIc1QFxRJfMG4RIDuPimRf8fIsxY11sqmnK1NFIzgoqMat5GMbnn7/gxdNXPHzyJu+/+4hXq9HmRG6F2KHXhdAr2VmYMtFctPZiIk9GngI3Dyae394zUhKTOg4BjGxrbTARiLEQooCflBopR9Jk5FhYzoPzi1sevH1DvJ6IqfKiNb7/4YfEv/nf8s4v/yXSV36RP/UX93z+X/xn/P37Aykf+Du/tfLg5hnf+qMPJTfOYLUzaO7NFbC2ebxIQhUX3c2lRPoGgg4FdhAHKXHxxxhJNUc1seiiVcaSaNE95WojjE6mM0cBPxureai38wI8AJ1GZITOsp6YuebFqxMxZIqnMq5d7MkSlX7Uq9ZVd/dfQ0BGd+8LtlorBo9jV9Fava7r7s2jdKrBnDNzlJFp2ICimC5ynHmeefetJxzvj6yexRyiCnu2YV+AOUVurq+p55XctX8b9pqR7hf89h8ndF+A+m1afiiT/oQ4C2j2Pyf44CjjrEenYcSSYaiW6t4Mh9Yd4NG9M2pnGwgmsgZB/jt160RndeUi7zGVWB2mSNrDUs+MviMMZ2bGTLLoUuFO9eawuXQwpEhymVk/y7x4A5WiJeZdwHbG/X1hnCvNY8C3xkl1XcDG1rAbKRcKOm9CToxq1G7UoUa9RAf4e9JZZaqJIoFeVbcEB3nCQF8DSml22W4fY1PE6v3JSbJySyy1E1NSEEyA8yrAP6CeY6sTRx80c4UEg+K1UTYxz4c1akOeTbpIxKoeGohkApgYYTnIEL33wNIiL05GP535LEj6n8qBMCXWEQg05LWYCEMy+l3J5GCsNM7nwMfnwE9evSLa4PHuyMOrwBv7zpcfzDx5/JKHN5/x6OrbPHrrmifvfon9w/eI+T2uD1/nwc//Mt/4uV/i9336Y27vn/LZ93+D3/mtX+fzH/2AVy9fMXrhg0c73nkwOC2wngIvmySxLZjs1WonJ2O/i5QFbpdGHyvWJuIOSg7klHl57NRV501vnT4q1SSRDN5rSJYPMU4wIrGbM4ICCu0xKS2K++WYD3nSEBPdRFKYimGrEYJqHaeXyAg8an+eh4Yd2YbY6DkwhSIJU4pkm7DYZZ4dqoDGrrAp2Q9kSKoDzJKz/jstBbJ7UwSMsosOTA+m0p3JHxkjyfcRVK81aC4DTJhM9/trhnaIMtQWHiGfMtuAAesXML2NQQPCcG+/EBgxeBDQgBGZi2S11ar6W7ML44Ugcs3mlxqD2Jq5iElEV/rt2o21VaqpT41uwxEGhBhZh1JTY3ptW1Byhq67cD1V0k4WGX00DUGjAJRgAsRlhSG/3GGJkmcwnWltCCCS9YeYlAIJ3TMogcXooQBeO+D2D0P90UhKr0vOXuooFT6EJA+mIVbYliCMy5jDSJfBA4jgofc7ug0LtNbZ9LfR5J2stDdzfEgy+daq2KBxY29Gl+yJAS1/tyzSQ9L7HixfBsyRiOXirEgVI671ogfTPe+MvOipfcJVqhNigp9ZP72PnzqgtNQT0RLVkCmlO+8LgRZ6eLlUTchgDDBapXp8+FLviRYVS5tcN+nAUZrmi1FvAEXyIfmAWj9nuCBTa3lr+BTMi9LNw0lrxSG6rQEduH8HzpoReugwgV9q+nfDLzkVo5u0SChnxySF8PSr87rS6qrpbhRLiyB0cz03alVceylFjIMsdLgv97x49hmvXr4ieJESolFyYbfbs99dAZmc02VqunHyYkpY94IgBU8S0A1bZskSxpB0LW6TQsJrsKhV2joI1phCIOYMRRTt8/Gely9fcTqd5D0QRW+dSuGNNx8KPIpJKXMIMDCi/GUQUGC+iaIbyQaG902vNcPRn1/rjdvbl7x8+ZLTcSWmiWm/49GT9xTfGMJFu2o2nGWlSX5OWe+fu+qH4OkPQ4dqjDrQX756xfPnLzmfTpRcuDocePTgEcyBebcTsAUwmoCToYSMmDTFMZeSmU8UB3C8P3K6vWW0Rp73vHH9mFRME908/Z/MvduvrWl23vUb7+n75pzrsM+7aldVH6rddrptK24fYoeIg4iI5HATcISVCIIQV9wgIfE3cI0UiYAiLkgkhBSCOCrEUkISy7YcBwxx28HtPsTdrqpdtQ9rr73WnPP73tPgYrxzle8QUl+wr6pUtav2mvM7jPcZz/N7qN1s/vawHjeKMA6hcne9alVqKRxyJcZIcoE4zeYucJ7esu1Ih7utYUC3shZKLpRSBr9KKSMCFaaJMOJqKZl77USXsxaCgNw5yOxl4bw1SjhvWxLtnWlO5JyN9yVCL4WODY0hJGuQO9WDJs92czZgyrbRA8UFe9CnaPGOmg/mlIkzwcfxeZ7E2kKuey7vbYnesZknXLKWLBmQWB8iXaxCt2GMnC4V1crLl69YDrdMofHld8740rP7vPNwYusqod8g+8bFXI1646xSVFwz4XTJ5KXy8jrz4iaTQ2SWDXlU9WY8Hqt67t2uv2PueAnWSofiow3Vy6IsrbPmkcMPBryMIdDUoRJwUseG2IbnUvt48Qecj4RgLhTvQJsdGo85U2sjzpMJVFjj2Qlea887E52OhwPi7Pqepsm2RL1SlpWSMzQo2gjTxNnmAg1CkzuJhzkG3n34hOPrW163G/DC2Sby5N2n3Lx9y3G/sFS7NrZxxye9s3+diTHSHVzllcOVcmgVd/bEhEWntKXiu0O0DdHDBk8RsU0iBqxtLQ/Qqgn9Q8NBRNimaKKkCvtlJR8PhGCbuBACEhw6HDoMq3fN1Z5Z0Q93rLlpojrW4TZyTqiu4GI0l6IzV2BMke1uS/DO2n+ww3aXihLAVdaSefTOUx6e77g5LtzfOB4+2djh9vxdHr73ZTYp4iTh/YinNosA6/FI6pMdpHsBiQQgiOfpvR3z2QOurhtXV1e8+/57PHr6AcEFciuUquS8mlthLEzstedtIK0VLR5Jnw+jiKecMjq941wYSxDb0KoTkuPuMC0e0uTJrXB574If+/qPkr77PdqSee9HPuDm+Q8QoGqx63SFtdimzjsb2HDQQ2DC2XumVKBY3buN1wZ4FXvHnu0i4QibKZi4kD2TSzQpHI97Pv3Bd/n617/KN37iS3zvkzd850qYJFC9PR+1VrozXpTTjnORFARIVLXIw+VlYPoYjtpH06iJXTYLNLzv1KZsohLCBGLNqUkcEc/9h5fw2PPJP/0uIUS2j2d8jLQKV68b3/39f87F07/L5hv/PvMHP86f+3d/iVf/xd/iO5cf8Eoj/9tvH7h34Xnv6xc4nN0XR0E9xAl6qSNSZdt6FzLHxZZ4UTzNF7R36lJQjVYdzqkCId85x1sxR1j0ntLLiB4mDCvjrNZ4xMi8QpVO4fOIGJi7pebMcpvZXlb2xwqh4WfAe1zvtvllgL2Ha0BsNTzuYZunnLcDd1IbznEO6c5c1+OCq3WMk2KHrDg5ptDJefD7wKDpAs0J0UcuziO9FdphHYBrNWbTyX2nEJ03cXhZKTLWjydD+90hYzxsdBwgg7NqaDEnrkMGz6UTYrR4aKk0zOkmzhsbMBgLLjZhExJtzUgQZpcIODYbP7QuDyMybn/v2KaJFCNOdTQCD7aSE7wH3zq12wwenbmSxDv2x8UOYFPEnVgdI4JSuolcNmuaOwwBLZ3krcFV1QoJvHp0doj34Ow4YS4uSx2o2OxX0RGX6xwbbGNEfMJPOw7lSGkrtRXUYQdN1RGJEnIrSE9MU7jjk6EnUK/NsL0rc0onbjAVc0J416mrRe5FQAYmIXpzkVeswbf1TskWd0vRSkJOrUuOk/vgczyFaOLEEq2tIF2Ygtiip3foJvhbkYs5oxVzvxkM2dFd5yzOeOkUFzg0T6sB1zsei6rnLki3Nq3NiIM7B0E9+EYI0Ap8dlSeH020+oMp886Z58n5gQfn8OTTW9796JpH9z7h/pM/4PzJ/wXXv07Xp+we/CSXX/1TfOFrf5of/dOvON5c8/Y7v8Z3fvtX+cH3vs3L/cI0b/HnEw+1crtWbrLjuDTeqCUPnBfitJJKhOBZcsftO35K7A8L+/2B2kcjdLd4M63ZYrsbhW4OnoY9d1RtUWqiuSFQzM9kAqCMZERwwuQVKWKODDLiE7ODrB3nJqpWkrP3vAlWyp7KoVXOnGPj1ETZ1sAb46m1imrAuWLnTyAfzS1jIuqplc1jUIVmhoYARE8pSphMvMy5M6UdMVkx1TYpeYGy2jVQRzTQonjV3qtq25tdmsYCsVrDbjMXYXBCSsFMB8PRc0KS0LKVAAx3Sj/9c1UruhBwMtoMu5XB2G5wGDEYTqvT8iaYczj0hvSII6C1EZKJ/dLbiAHbc8Ma1hzBKQ37Lk1WFUKYyLnhXSd0z4o5kY2/N7AmfqBturlsTbgWe+4jIIKXTkjRhJfBB7aW82EUaWqLsLHU8yMIpM5Cl94biJ4hkp+Yqmb8OL2XQLqn8vmCoLQ+mEefP/otEWCCHl1Q5wfKgLukTe3VPudu0bcqVoQ0xTTeH4xFjb3HZTQ0y3CanMql6KcInkNaB9egDE3AO3oXfBg6hjsB88esVA03YI9PM5Q4tbPXD/PXD11QQp1tvcoAtZ0AiWoci+G5sA8LYMB27eVgtfTiEk6FeWvA3xSDUfuj4kIYN08fLohuB68xihiQUUd67fP4WT+5cUb1IUOFVf1jH+nJImO/lSFZ2Rcho9ZRQbxtQfxwQ/VmCnBTc/cYBK2bynk6DArM2y30ZuDlATBFTYU8vzjDSWBZVpZlT2+F25sbesmkmLh3eZ+Q7DCvGMMCzE4n6u/cNzqstmm0dIQYLd9edQgtg1RU7c9lQOYRvfLeolW1EVA74AaBZpT79XDDcmgsy0LLFR88Kc2kFJnTbK0l48AxZL47cYW7Q0xH1WzbztnWqrc64iT2+Zpzx6Elc/v2La9evuK4X1AXubi85PGzJ0PUsEYU50zMCcPq3PqYKnSITM4UbERopdM8+GCH8ONxz5vrN7x+eQM4zi/OuXz30iCg9BH/ssa83qtxDgaM2nk/vuNm2eaxNSmlcPv2lsN+QcRzeT6z20yInAYiBUnWQjCePgZmU8CENnHDlr0UlnXBjQPrvbNLgnNUZ4wRSwGai8T5wHJcORwXlmztEbU1gygPdpiqkKatCXXOHEHOWbQtpnBnV4/ehlcbiMxd5lzkrna027bYOU+p2SzrakwvcTY0OO8pJdNVCcmg0jGY6yZEzLGj9r1ps+tkXQ7EEJjn2RqkRrNd10YvebiNGk8eP2COzlxZtdHKipOAdwn1nqV0nER88ri2sOjC9atPub16yUaVr3/hkg/fuc/TBzPnG6Gub9B1wbnCZpcQJ9Tl5LxyVK3c5pWPPn3Ni9eZqhMuTojbcLOKiUIEXJjMmVkYDrHGvcuZ/a3xm2J0rLVzm22wrs0Z5+z0fQy7b3DBrNciePGU3ijV7LbemZA0TwnnbAOyNNumlFLoCNO8wU2B5L1tSfm8QhdVyrqwHpchhEykZDDoPkSp3srdizAkIW4SHXMOQLWi1Nr5ynsX/OI3fppPfvarvLq65ZBXfD3QA5T6iDfrOfuS0a4cW8fPO3T7hLUcTAxCOAI318o979htGitHCJ4vfv1rTL1Qnv8znl+9oqqnO+XdZxd2PdfC/mZvgpK4kzt4bN2tDph1hSasx1uSM2B5Sgkf7WDkvT2DnDpqzebGi2HsnMYrSu2W9QoEoa+dXDLzPBtzYHDlghd2Z1tCiNS13w1odqBo5FZIaeKz58/5o89e02LicKi01fPk8Y7N7ozddseUksUZh+h9iluklGxrXzoqCyoyQMGd8ynw+Mk9Hj57zHo8sDvbMm3CsGSfanA9ImlENypCsHYqGe+N2pDhaFM+P/CfQJz27vHGDGjgfSIG0FJozWzrbrhjpxB578kDnjx8gEolesfvv/jUyhNwbH2A5mkiHNvCXGzh41McDVWZVhXTNG1eUCzGIN7j1bZtZ+dbmizMs2OtWGuVZpoKSyt8+uKKrxwWLs4nlm/fspYziMocPKITvR2p65EwJbO3e9uixiDUpVGWRJw9Idn3jhQTKgSUZjbz8Ux3YiyvOU4ErxY/9g4ftzz44mNunz/n9cdv2dxLTNPE2UVjua3UKfH7v/Hr/Mn7D3Ff/je49/U/wy/98hv+xt/8X/nk6Zf5KEf+/j+54c/fizx4byaURtGCI9ihQSvUhk8bIh0/B9ajcbKIdjiL3bgfdDHA7zEP58RgyfSAdBufWy0kH4i+cTYLPlY6HpwyecVLs8gtw62HCVc6JqYXr95yva+EnLk5rsi0JaUdxqBUSJC6I2QrE1HLOJlDULFGNAa+QOyAEmNEChDGM9OZ87Wjdy4lJ0KrBe2F1q2YwDbMtmzoqswpENxK74VGMx/6iZ2oxuws3WY3KZVcm63tT6TxsbAawCbGhcDZbsd5bJTF7ks33uluROpPzvUYI6LG3qnVAMDO2UwZQmQ7BaaWrCkn/CB4AAAgAElEQVSqZ5a84uIZLphDI/pA86MhrgM+jJipG4dhUAIde6eu2QCuwUdjDrpk78VoEfhcynjPwCRx/IhCx90tFV0DxnvJljrmdJSidNc43AhdGqWZyKtwx8CMEsz9hYCLaO0UUY5audhd4IKnHjIisJ0D3kerPm9K6QXnhM0cmaZAqXUsBj9nkdofV8xlEkzYK7UPwUmhC/M0mUtDoWpHCJS22mLai7HHYkQkjDIVBg8ShEZKYmD+DscMVcf/A4t7NfUEGZUhakwU742Z2KrFBJ1ag1zq5iAO0YTTQ11NYKHg3cbmHDEelYF6bUmsKmS1c87aHGhD+0otJl6fb6pxxtbO27Zy9dzxnc8cuw083TaePSg8vVh5vLviweVznjz7mCdfuM9GvgkvtuT8lOnhT7D94s/x9Etf44Of/4scb17x5nv/mO/+9q/y3d//Xf7o7Rt2ISA9misRcyFbw/bEdrKW0dKh65bjYmyuXJvFk8UiZr0LIaa7+8/LKNlxHjSzmc6gVhPTqmP1DNd8h2otieYMUibviF0IaaWFxlqhrZnqBXogdMM8GC9TiBG0KwXhgOIDRKfE1pCewTech2MOBlj3UKqzOduBqFDGObGLkvs6GngTKTiLnTsh+Egjn1qkQK1kZh1xejcpSCCxodXMkhsVw0G4MRO3EQfsONbVov8xBrxTJCgnoPZdmsDZctVKYNTES4yrZw6JweA8uVll/PG6mFA3Bp5Ti7QgSLWFdeuKeseyNEQmwyNIJ0yRhjmlS8UWLWNocqOlvKN4jRzXOpiMjVxGRVcYz1QcPkQ7ljcTeSzV58e5C8SHu/IfO50JbZxn1cCbdt48GRJQyrIQHNYo7xRrjU94SfaMH0yo3sWMEWL3vHMD86FQeyN0pTk/BDczEvSTO2xwqMQoFngdyxf8iDEaP1iqOZtrtaQQuVm7uGu4rrZk6N20EWfOK9fsuwrODaOMRTad9zSttr4Ohptp2tAKTYoNQuLs/3syxQzuozn7EiAj1vjD+/VDF5Ra6ziZLO+vdQynDctS21bFiX15OI+jo904NM7erATZWOVsrzjXB2uoobKaYtdPghF3mU27A8bh1wXqAHorjto70bnx+0xRvBOchGFnZaSVBPoJsThurtOkJFZvmPNgBylDxBoZSnc6FI5lq1j29myOhBSpayWvZViL7eKYpw3ee15/9oY3V2+swUo7SiVFT/IT5xcXBqE2SRR1HQhodwbPHFnX4BwE429YY137PI6nindm4xMXzNIfTOVupdJzsY3IyDd350A7NRu0uZfM8bDQvdWL3794yO78nOBtoh577BH1MrHPx2DqajXu0gnZZNuaNtxc1k7nvKnuqp3b2ytevnzJ7fURkcjZ/Xs8fvyMeTJIMCpoGcLFKWIVAh1bVzo1MHwfADJPGOqz4OdEa5WXr15w9eoV65I5Oz/n2bP3mDeJYWwHHCImfrQ+oNNSzHk/xE9VRVvHjXr7w/7I9Ztr8rIwTTOPHzwgpmh57dNDDfDiOdRG8t6+Q7EqYxG1QaIJS14oix1edudn+JA4EbwkmtMrhsiaM90Hcmss++VOTDosqwmDwbYXBl034KEL6U5MVDGwtfFwTIGPwVGHJfcE1xaB2gza7dQRg0VmGBudcFLU/WmDq9SSUe1M02yH0FOEr5vKHr03YVOFqpUo1upHV6Y54IOjU9nf7sl5IURPioHL3ZZpgnbMZiEWJbQAcUPvjjQlclttmFlvud2/5OXVJ6B7vvLskp96/zFfuL9hkkrLV/TrldlnE3JmR6+ZV1dvidOWpsLt8Zabw8onrw7cHjtp2rGZz8jNk7ujNOVYOj5EytiGxmTXWwpm6y/NIltLVUp3RG8tDoTPWRwOE+BjDPQ+QIdqDTutmmDohusvTpavrrVSB6dszdlccyEYH0Y7zQ94aDfujLnd7N7zKRHjxDzNBO84tAyqLMtKK5k1F+ZpAhcpuY1BxMRyc85kdnHiS48jD3aXvHg88ebmwHKcePXqihefvWRd9lx3u95CmIjzJY9/5B5rPrCuxV5y3plbzm/teVwbIUTee7zjp37kHW5+81v80XTOVessbuZyF1mXSs0N1xoPk7KUxk21wyXOxA6ZZ5qbONy+IXp7VhACKrbpDIPDhRoAuGkfzDV31+iCF1ozwH4STxc45IU4gJYNbFAYW7qUZlKM5HUxBh6DGSRwc3XNl35k4rs/+ENqazQqBw/f33deHQ68019z/vRdeo8oGMhbT61rjZKPzClScqL3QG2OqLaPRyIpJeazHcsUTdAlgPcoha62AIgxUAW0BRzOam1HLLd28L3fOTq72HMjjepmwaFeCF7ovRHIFvV0xhBJMbIcV26a0HcT08ZctE4CXsG5SFPornOshUUrS8bA5j5QWsb3YIeOzODm2LhJB99Oex6LlbkmTGliurClgUTl9Zu3lDaq47tCCzi/o/TK9U1mNYylNTWB/bV2tC40geQjwXWkZ3quHN8WNuee7ZnjeLQGq6qQTqLcWCoxoMNJhOSMnRImE4q986TLr/H+z3S+9Sv/kP2LI/e/6NhNW45nHlzlRRJ+67/92/z8v/cYHv8Cj3/uF/nlF8/563/nm+yfvc8/eylc/PpL/twvPmOzO8O1UWs8BRCPykJRPRlMCanSSkBKITjHxie6U5ajMYqmZM/p5DxuDnY8XAqrWtuTV5iJbDaRKSoyD4aKK1CH4FDBdSORtW5qXqHwydUV1V3yyccLvcOcMMEci+atg0EWvLe2JlHWbtd6KdboiQz3A7akOZWleB/wYbwzmlpmU07tc1DWOpzjFsMIztu97DzqG5dnE3U90stq06N3g4VhQkkdB0Lno0U3nB9LUPtcbYYcV46cHAJCOAmLbniLxRY1TcEHwXdHDxalmJrFJp2Hliu9KFRFvI7DjLKsCzfryqoN3i482lo7mnar/u6ihCCEKY4ojbl6WlutHbUsaFtta62N2mBzubP53CnRJ4MH50rN2Zy/seOCR71N1rWMVq5WKSjdB5qD6QSgDdZkaO4Gi++4ESFBBT/OANF7YivMIVo0Pwk9nTOfnSNU3nlwzhT9cAXJH+NRBjbTxLNHl3z04prrRcit0XPHp0DOGeccyQVbJmKuCtGGjHhNGK4o5+zQ6EICDUzp9I5t9NyGE94clr3bYdSLGLO1g3qrTnfe3EIqlghIXsnZU2sh46mlIy4RBKoynDXmlhNx9N5oPQJKCiutC6VVApHmhIrZPYV+VzKB8/RaWceAreO6W5u9g3LPSOtsR4zn3mwpgmOFl2vlxXXhOy86T+5nPngceeewcu/NNc++95rL+5c8frLl4tkN6eZ3Of7G36LX94nv/xkuP/wG99/7d/jg5/5NfuHtc15/7x/z3W/+Gt/6P3+b33vznE9vMqvbkiK0fk0tET8lylgASLdilZDsYFuLUNXEUO11NMparXwKW6Tb2QdV4nbLvF9ZQic3a2mdvZ1l7AAPuTmu98KT88a8bby6FXIWQkq2SKn1bjG3SSYMODE+pA+Kc4Esjv1y5MwL57PQZEOvnbUlGoWgidYdzQ/ekhNzNiOsWdFuJTJVO80FwFtsqxXEQa6V4DwRoTeLfdE6vfuBISj4NAQVFZa84oEUxzmyDrFEzEyhKFUbJZvTxqfIHy+qOD0HQwrU2gnqRitbM4el2jBSWjVkxRC0vR2Eh2AzGi27Z8j8BCye7Hyg1EbwjRCt7U1HpCz4SG2ne6kT/AxOWUrlsBxJYUYQ6LYIP836lqax9lpw5GalSCF4tNtcLhJsiFJQcTQ1McQ7yL3CYFx20TvuqhNrNDZIvKE2sioBRXVhEhN/u/MEjKnrxpLf9Dcr4vCKlQrVNpyudqby3Y9IeSMGA6aLYDB4HGEMMA1j957QIT65wZmUz92etdgM5YW+NkopaLBW2Y49W7UrtRq7OIojRU9rplGcYv+9N6Q1fBxLEurA/gxxyUHEUdSSvX/cQ/PD+PVDF5Ri2Iws68iKikC3PKS1adgL2UdrLdJx0AjqxvVr8lNvi7keutUHon3wkxgvdlNtFPtgVGwrbf2IHbQyXm22dev9Li+oOizDp0ib3ClG9kOYxHr3uwWgWzby1HJ1unG9s6FOtQ82CdR1AXVMm8j2bMN6OPL66tXdTT/FCS8GGHtx9YLj/hZxwhxnBIMab3f3ERrzdkOphTtLorMhuGMArxPIzCSawZ2RsXUJp1hfB8RcIP0E1lPK8UDOdujX3qlrxcVAjGarPtweKbWwHg84FXabLQ+ePrUDUzEYdMFUXSfcxQhPNZJaC63bprYWyyRbg1ez78INgcM71uOB5x99zIuraxqO+w8e8cGH7zHPgdaMTu/qsGBLN6eAU3JdcD4i1TaZCIi3EbC3YfMLNrxev3nLZy9f8fb6hpRmHj5+yPa9HVP0nPpJRKY74Ue7HeiNiaj4sBlMLWud8uJoTrm9vuLV1VtqKdy/d8Hjh09Gjn8MEsHUYCXb4XVkpVUrS6umtTt7oB5zJh8WYtqwvbjAD8DqHahOrOo3iDn8SlFev3pjokEpLKPevTdzFQXv7iywitzF2JzYgCMyYlQKPlhEKJfBcxiDszE4jFkwTzOo3n3nnc4cN3ZQ7UptnVZXkEAMkc2U/pjlEgy2bK2DDBt5a1bRLiFxvr3ER0ethevrK5blhmme2G03pOTZTAGvDVeVECOlKNKEEBw0g1Bfv/6U+/e2rMsLnr/8Aevhli9+4T4/9Sc+5L2HGy5Swx9uWa5e4kNjOwXyegD13L7KthlLkVevD7w9ZK6XyuublR4im+05GifeLp21WKH620O2bUYxR9EcI9GbsyqcNqkhkrPltLvAMVe6KDEkhMAUg7WfjIO1vWRtU+mkE6aEc/G0/yUX29ZKN/ZaV2VKyRyI3kOMd648KETvWd7uWdaVeWvuL+/s38utcVyPVArr8UhfCyKeedpYDt7NwxdZkGr3u9ZCcsr161f8o3/0fT7+6MBtWYkpkJLj1e2BFzcTNTZyPeL9BFrxfUvrFW0VP8oaWmm4JvRJOKoypYmgla999UPa1feJFR5cOGKOPM/2bCndmtD+xBcf8qMfCp9+/BnfebHno0Pnus/0sGFzNrMur4nicWHGTx4Jgbx2IsbHaNoNAFyrRV0m6wY52S16V+Jolm6qlMOB2jLbsw1eGUONbaFLL1bLHQKIs59TsLYT7bx88YpaC9wsPHr0iIawPxwoJXP+5B6PnzyyGGyzoUF7o9bG2hreNw6HhfOz0bJJsu03gkjH+0STLb3onUCZS0H7yqkZapo2eLG+Ne8m23JrN5eSG2LuGCybs6ICdzopjwFEnH1uPjhTfIgGYZfK+WzuCxkRv9JAS/+cQ6ULsSvqCgnPveQ5Sxs+Ph6pmw21gvRGFI/sbBjvUqmarX2w22GzUiEEmlZyLlxczrS8INo5j+fkcEaQxr3dzIdf/Qqb84gcKzHtkCWRs5Ime1+JT2yccqxHohvxUG8OtXmXyIdKOhcu5sStZko3V6alYewz8uNQ3WQ4hYPinG3E52h8Mw1nbL/4p/jizz7n49/6Fv19xbkjmxl0Djx4ecsnFzP/x9/4L/np/+iC7r7Ke//qX+IvfPKf8d/83zfkix2/9Z2FR7/2KT/3Lz0eUSlBwhbfKz1BKIWmBm2ft4Hy9khW0MG7ctGBr1Adyc8UXwljY1+10Jsgbh4zUebYI1Oy+KF3QtkU2qpEnaAXimt3kS5x9hw+Hheef3pDuBforwviNjx88JgpDbB8U6Q1JvGsg1zhRgSuN4O2T16ozlgZkyQaGe0mZBpPpeOcRbT7cN+N1THLcaV2JbjRaOPHtlgVSuXN2zes159S14z4aey57TNyqkQdcakQzFno7Nq9cw3f+WLGQWqMjaKK61ZlbbvQjvhozL4qaDD2hSjgDKTuRajRXEI6INr72z3H26O5YMTTfOR4zKxLxlHQ0bwkAuoC1uvS6M1idK10oofbw5FSheS8LdZEiN7RakVlJo73kk8mOvdaqVqhmHDsxJIAvYOkyVxOpUFVqljpgPMeiqKhmZDmMXeTCAFhEmUKio+JRMCFzi457p1d8nydTLDr1kybyxHvhO0cmXZCijNzmtnMgcuzwidXB0oJNOmo97ZMcAHUeC420xurcYozaQB4c8nk0szpSUS8UGrBJ2t2E9xd7K32TG0Ra54cn5vzZIV1Vbq6EYtVQgLVgu9ioqpLIH2AqcU4sN2DtIGv8GQd5wcUtzbQSK8CWigU/EaRaNN69GmAm9WcWepY85GlmZuLZuKKE08LlSBC6RP7WukVzqLjcue5kB3LurDPK5+8bTy/zZxNb3n/4ZZnTysfHA/84FrY/n7k/tnMw3eekC7/gPXb3+b1N/8m+A+4+LF/ke2HP8sXfuHf5r2f/WX+hfWW/fP/nW/96n/HP/iHv8lv/O4LnvcJZodfYJqFvFzTNFibpPOwVsNPODu7tG4xwAA4P7Gvw5koGVeO5JyJPjBNkbZUiGO5Xx2pV7rYve/U8dmbwkUL7KaF9dDYpIkUMutqqQsfOrtNoJLowZzJot04erkjPlDF8xYTa4+5EWbobG3ZOsYBktJzJ0YThrsKfgpI9OTcSNEbeH5y9GrXlHOBEC29UbEFTe2ZPvAb1mAtkKbPHT9VoTvyYo4WH2xJ3rEoaS2WKFHtuGCRr947Mdk2oWkHbxFmKYqbTSQN0RpzWzfIdIe7tso23NR+LK1DOIHDjX3ag5CzMSZjMMLXUkDcSL2opUCcc4PD6clrR7qjZiuJShEoDkgUsVRKbR1cQdVjhQ+NpsEMJWplAMFP44Hb7VyHUHS4osXbbNqVUxIZtQVZGJCkosX4oT4RtaO9Mk2TlYUN/mavJwuqieNVKziLc5pOEYhzuIuOOS2EEIwnpsYCK6rkfmKDmcM8uDiWpv7uPaXNkCAWk7N4spPAFITSG4sWXBOkOQqF0htBToK5mkMTqNmKQCJKP0WtHcPNbZ9UH06t3i0y6rq5AIILQKf9/11QajQmb5XnSRJVLWfvh6rrnMEIRY0B4J1C79aMkDtNlNRMvXTqRhOIbUtrazi1v8d1q04UsY1SB3WORrYvsbnB43AEsYNX11MlbR/NAiOONdxHfSitOHfnOpJBcbcqS7upUewmEtuYjLSnqeK92X+DxmefvaQsmXm34Xx7RoyR/f6GN1evOBxWSq0WZ7t3n/PddiiObdSq2suwtHGgiNGiW05w3eBfd5EYC3nfgchd8DTfTc3vOrA8eneRLUthXVd8CGxTsmxza/g5UFrl1cvXHA97phA4P7/k4vEZTx49ZL8/sD+s3PZbQkzGSQqe4NQaOLq5pES4q7cU7eS1GujMndg8JiQdj7e8+OglVy+uqR0u79/nC1/+EebdFtd1NEw1e1CJtbqdWvJUBkjOQqOcYJl9wNC9t3ra43HP8+cvub6+AQk8fPyUD58+42w7sxwPgNlDU5qG0+tzh82p8tsmTovmKXboXNcjL1695urlFfNmy8NH9znbbO1Bmm3gCjFYfhmhYTE/EyerWVkbzFNkv79lOeypRYmbLfcePhztd6b0O0mI65Ypb8YmWHPmzSFzfbsnt2zWymLb1XmzNUidtxe66gmSF8FF27CMh3Xr7S7KVlq3CNRwC4oIvQ6GT/D2GY1YlNiigRAm0iZyuL1lGVHOebNltz0zy7kqJRc7qA2otj3MTGBqPRODJ212IAYAvLm9Zn/7FgnKvQfnBHFM3qqxQ60gUKuzmmONbGKk5syrzz7mbLvl3SfnvL7+mJs33+ULDzZ86ce/zBefPmDjj8j+irzv+LrHS8WJcrvcstwemaaJ3ODQHCU3bjN8/DaTq4GQp92OVoW3+5VlKcZe0ErJne0m4sUTponJexq2DRHrnyXQ8S7QGuRWcHG2mIMXYvC2ZXbW5BNcoDuLr0kEr2lwJSqtdsKwq/bxwhB1bOZk4qE3F82xZIgReiN4x/X1NYfDnvN794zz4r2JAqVQqjVe1VrJJZNiYNrssFIA4VS1a6DG0RjYoWnjO1cOKRP77Om6wy1K0kD2Z2weJWqpuFqY5y1TnEEda840bchsTTHaxdq5esdPkRgcX37vfQ77PZ/+wR/xRALTHNlPjvxacLmhrvDOw/t8sD1wdvSsl5e814SLRzP5na9y8GekXliWA2m3oQeLjVEbU7Iq6TKarrQYLybOG6bgzbVjJCyLEYo5ItZ1ZT0cSLuEVsHFaPGW4cI1WH8wC/IQqL1gTs+WOT8743iz8uEHj3h4cY6smX7/guefvkD3V7z7eKb0Zi6BMuYnsaBZPhRr3xKYorXPuO5p/YiXxHa3I00Tt/tbi5h4B30GB6VVSlHm2RMmZc32XGnReF9qAEKiKL0VMh3pQkgTwQlnU4Dk2SNsUyKJPQPxGIumd1IQduczvVSOh4qPjm3cIcWcJiF6nIvsy4HkAr0tFJ+5PV7hUqCX1cDDPVKdsVlad6xrG2wTYe3N7P7Y4Ol6Z9l30tMtpWeWXHj6/mO++N5TJh94+PCcB/e8vcNj4nwbqblzk2+5iOfQ7b3RgLjZELEoXClCmSBgZRTra+X+03NCcrz69JacV1wUmo9MIqw4RCvShDwpqVfaZEuJFjp1uYX1CkmPuPfVr/Lm40948c/fcu/dHfPZjJLJceJpWPl2EnZ/7a/yY//BfwLTPX78L/wV/uX//D/lV24DeUr8yj+55fLS86M/sSWwsYWJ36B+D6vSlwOEc7oeiH6iUugtGostdNKkxvRRIVQh4myJNBecj7TcuW1HwOFa4+3rhY3bELuz8gEaSiFTqV0sCqIW/Wtk3t4eePDgEeHijJxW5nd3xGQLqhhg7dbGtTZolsAj+kSVRnMQxWOYOJtzKrZ5rr2Zq1oEQkJHc6KhQfpd5F98Yppna1CqSlA7LIUuHKXzZl+gb/CzbZd7ddae6QyqK71BqUSfrDXYYZymYA4Cq1z21DAiEmqO29k7XBG02pAv6okePAFcN+7fWvGuM6WAECza5LH4jtga/Oxsx24z8+Lqmq7CJqVR2W2HOhdNQKGPuI8ka0ZUQ0P44VjRaocbgBQ2tpz1ERd3qBRyqWhtFit0nbgLaMNKGFaG4a4j0ZvL3TaYNhs1GcUfC41OxFSQGCa2846oC0EaZ5PjYmtRsuM+49LEBkiTvXdcDUjrHG2kJjroubNUBb2BfoWIcO/sjP3KACRbBAZM3AliC5qcG9KVGB21raj2wYOyGd/5QGuVnO1zlhrws0VabK5sBPMuWNyu1LFktLIfVWNXKoO71k0I6C7gnbEWo7f/XlGldovHiDhzNwSHdnMVd1fsZ8jg4kSthYBQ8mAcOk/TYrFFxJzadaXVYsUmXU1YcYkYMq55WqmUsFIJlGNjzcqaCzt/Q0LYofRNZBHP7eL4vR8U/vBV4VtbePjQ8/6Z48nNJT949R3cxvHs/iMevzuzvfctjt/+HZ7/RqCl93n41W/w8E/+Kzz46p/l57/yZ/m5v3LLm9/5O/zm//xf8T/9vd/kY73HYUpInLi5uiKXzD7PzH4yYLV3lBPT1tmCvIun1WrPUHEsJRPcmcU4XSd5Tx9LYvGdeUqE0NmvleOhoFo5HIQ178bn2xB1zN5xthuR1TqKc9pwtmDOpSZW7tQ9+LiBBnGqhG0w7igCBHs3+kobTVqud6YA3kdyr+zmCe8cay/kwWw7O99xfXNt5SDBIk+tCtUFvBqT8GRjaKczB55WzeUYJov6ttbotSE+0KUR4mCl+kBu3YpWvAc8uWVzJjcdzFghl8qyLGziiHH6zyNyXQX7EkZUTJyJ9u1ugjGBuRs7UuZI6Y21rATXiWpsrBNP2Adv59dS6Q2KZjwdUQ8DzA7gRqurdxHCWB4N9pptt+05aqJLGygNU4xaqXYuCyPWqiCjWMQ7c+eRbBHWWyOqYUua6ijl8pRmUWDrx7SZr4uOdjxFi2kZXsxpWVuh+2hlAaKoeNZmVEdzDJmz2fuxZ+sWSTZlA3qINr9pxQWLrPVqbsShrJGLte9uponoDFVTWmX21sT8eRtxpfvBVnOeKoUoYpE3NceXVzeYf2pwej+KwpxRbFTsO0sn9tYP6dcPXVBSaeTW0SZUtS2CbVTcXfUwY4ujtVrt4GjGEk4wbKvO9ozmHR3ev0EuDw4Uj1NzBjkYbT5WMdkxy1fXZhYwhCyKkwiidzcxzkBpd2JEN8W2tz5s3J0pTYgzKnsXs+G2Wqm5DtdKojZzZog4Sl4tn9oy777zjN3ZhlefveLTTz/h1cuX+OA5P7/Hw4cPOD8/J4XxghRAG71nbm8rzQkpRLpTLi/usdQCVAPCVWsqEvHmGhDBO7UcZmf82ezBgThCSrS80tX4R5vNhrPzM46HI9e3e2uJQUYcTnjy6DEpvGsioNrN9OrqmiCe+WzDjMX0Sm3UVmBECrs0eq/mghruCAlhvPib2XvXGz779BWvXt+Qi7K9uODh+x9wsTuzGBBKXzPeBTbzBrCbpKip0ObQOkXQ/PjeDGjnfSAlz5oXXr6+5uWLl6zryoMHD/jSlz7k7Px8ABw763IkpQTYz46aE0dGxeOoVkO6gjO+QWuN5fCWFy9esCwL9+895kd/5MeYZm8WxT4GkeDvrq9TFEa6AxWLdaA4bRzzwtXrPUpkd/6AeDE2jk7vbKROvA2kNGrp1Ky8ub1hqZ2mK+thT10bzJHt2URyCXHBWCtq25cQNqPiW6h0U8mzCXx+tMCJWhMGaoIkvVt2Odg225KYtglxA/AcR/vD/uYttXYDmZ9fspk3HI8H1uUAwJSm0QSh1sI47B8+Cp4JJwY0zb1xc/OS67fXXF5csNnOeKyt0Gk3J0C3e9/72dx4tbM/vMGXPT/z4+/zyUcf8fH3fpeLXeVnvzLz/oMz7m0EX1/huw6rc6f1he4ab2+OVi3qE4ee8JsJPQaev3jFy7d7jkWJmw3eTez3jR2WEHcAACAASURBVLKs1JzNLqyB2uB8tyHGjb2YQyBzarYJHCsc18LaOmtZWYsBWpN3Njj2zroMyLuYel7HxtW5gBNvYpIRJXFBxjJ+vGQR5u1MdFa7WltDvTDFycCDznH99oa1Fs7vXxJ8IG3SALk3ci3UdRntf90OGJsNzSmzj4yls0VuEXIzJpbSiD4S55nNeWKHAXt77ZTSCAh5zYTQmTeJtJ1HQ2MzsD+Qi8FMDay6kpJjNztibbz4+Pvs31zx6sUb+uUlN9d7vv9WeXV94GG55sOvPWY7C7y95VA6xwI+2jPD+0ivjePtyrw9t3Yjtc0ZZVSPt4ax3MytudnMTPPMyX5tB4HPByUVtRgVIBWydDYbPzL39u+V1a6L8/ML8u0btGe82PZptwtEl3lz8xk+vyV/+oaojXsPHR/85DNefOL4rd/6Pd75yo/x4PFT1jWzltUMt9rpJZvwo4HaCz5E1rLgtRGBWvasy4JIpNdCbgHtVgLRBtC/a+b8bMP+syPEUcpAQ32AXKEX3BDQXHA4Cg8vtmzOPUjm5nbP2TZwLyUen898dH3gtq5477m4eMzTew+4vl1Y8y2KWjPgzly3QUZUbu0cXOcyRtb9npv9lZGZQqItK50FiqNGYcmddVlRrfTRYEhXc6y0jg/w5maBFgibM+KSOewP3HvwhO0E4hq52JCz5E5uUEtGlo5sAC3WdGid8SaSaWYtBy62j5inQo9Aa2wvPBeX93j4zHO87RyuFm6uhVVvQGabHbKyBOVBtDHce4ej0qVQj58SdxXxjftf3PLJ3/+Msle+8LMTbW9lBcdJeRYKv/Pxgcu//Vd5+hf/Y+LjZ/zCX/q3+OSv/XW+Gd/nlST+l3/wmikKz76U2bZ7ds2GGe8z6mbK2vDuEpHFGl9XYyp01/BJkKa4WodLdGUSYXaOGgpLhZQ8eQkE6bS+EsKO0jPRN5LLI+ZwgquPeE49cnUrhPmSJ+92cIHp0X32+z0dg56rmAPjFFePHXTvWHtFAsw+URZbRHlJ0Kq1oGZbhPkwo74TY2RduzWeDe4Szu7ZeeM585fs9wc6C8IAx9Po6phlQ9wF8qZy3N9y7Ob+CK5jqCCPS7BJjTpcCEpH80qBgWVgRDFti342By62jv3bZgey1q1enA6us5FA0WLO8do59owL1vp5Km7REKnHAyk5DvuF882Wy8szksOYQtHz9vaAX4QYFJFIEE/3wpysxMWLRVYnFaL3OBq1F469MqcJ55Xgm72LYzKEgeoAEoN6h5uCpQW6OXKGBkB10J0QujXkgpVy9G6LRBFHdBP+IpGnA67cMjk7dAfvOFSLq/eSeX2rbOeZUgSZHyDlgJaZTrG2NYRm9c8E5zjeWqOcOh27Pbt2Wxdys4KFe5cbaJXaKtAptVmTWwgW5wH8lKDYXFxKIx+XweEM9jOFRMT2MFo7rVRqb2ivqO8oGW18DljP2cSqIFYq1D1xFJxErzYf12ozba1WG948MmK3FcVVi+I1DYTNjPOTrR9ruzvU195tDvSjGrw34/eMeEZA8X5GBJLYXKcoTYQ+zdQCNa/0YvFD8Z3goB4LL3Ll9hh4HiNS/4j72y3zXHDTa7a+8HDa8c6THWeXW/7wu3/It//H/54LmfiZn/pJfvpf+yXu/cy/zsNv/GX+/Df+Mr/4H/4Bf/Cr/zW/+nf/B379n36f7y/Csj2DFGh1MNycOdr62pH2+dmttcZuSrS6UCk4qRSN1GIFI3MMpBBISdgkc7UfDh4RjwRP7Qu0RgoTnkaYAnG2Nkk/dW5rRTQQvLVi1yK05mm94kMnJMfkHSvWFBqdcrZzlKNYyx8wpy2g9JzNzRstmu4ruLSl1dVcvXTSdgNeuX9+wXK7wBxJyVPbapgWgpV9jOOGNXDJ3ZwnwaDjvdsCwIwCVrwQY6KUYuYDN40ImVJKQ3y0e3dVXBeDnqt5j45LY56TtX3iadUc9ojxVe1MaWKHdmMsqjiLuPU2FuX2bI9+Q9BuZ1btNudYUJT9am5vXMdpY04BkWgL62CtnlLsWVTUWXwPT/IJjbY8UIbjxkOto5VsRECbnHyigShCPPGYYTT/Ys9hGYVHWGJHMLfX2hStxXAlzmb2Ps5pHRMDpzkAajzRURDV+sKialzd0Vp9ctcWGYkBZ4gERAwbgzVWWlRVCPNMWRd6zUTvaU1JcWbpCyFZazOqlLKOMi07LW3jRBmikhebAbt0+5yauSF9HIw7TgKbCZNdjdsUgrHvarR3tgIa/7/pO/9vv37ogpIPW7wbtfcdxEyNGFium22rix2e7NRtyuMp8y5KF4dHrSaWQGmK78awCKMNDO3GSRLbI3TssKoj/9lNKRp5XewmxYEYB8GI6SOGV8eBSe3Pg4zGsmDMiFIrJecxXFhdpkXTgrWgiSAuEpIwbyb2Nzfsl8zz3/tdbm9v2M1bHjx8yNd+/CfYzIlWGrf7A7f7a1OXg4k+3jm2m5mcCuKtUjERyMWqjBV7EchkjobWjT2k2mi9msVRLNPbx0GnlMZyPFByYUqBNEeurl5x+OiAqGO73REtBI16WI5HDgHCdgtqzW4pRSROqDbL3Wun1WoAOqurs5Y6+nBEdTOzt4I6Z8LV69fcHvbkm8L24pynz77I5vyC4B29LrRqPI6YDDB5iluVbOJOcIKEoVgzblBt5njxHlXH25sbYy/d3hJj5MnTp1xcntsmpDtKzmZnHjBvq/0c1nVnB3U3qs/tcB3xaeJmf8vrjz5huXnLxdk5Tx4+4vzeOeDotVNbtWF5qMYOa9VT6cNSCXR7kbWycFhX9m+PpHnDo4fvgnh6K6Zmy4QSYAzg0KlVeXtceXtcyMdCb5WlZijKNM1Mu4noBkgabGuKkGKyGKm3YZnioTvWvNjLigHOH+0VfbS2GevCuBfeW82oYC8nUGI0psrxuIxGtcDufEsIiVorb65e40XYzBv7HtVAgwbsLSYsBWOqbTb2Qnz1+gUvPnvOZjvx4Ze+QG8NlTKa0fy4z70Nwy5Sm6PnxnbesDuH609e8s3f/nukrrz7QPj6ly949FjZ6QL1COrQIvw/rL3Zr21ret71e79ujDHnXGvt/vR1qk6dsl1yU7ZjiCIZbAxGJCHJRRBcRNyCxAV/AULmH+B/CEJckGAJHMcB08QxdkyIHeLYcVOn+tPtdu211mzGGF/zcvF+a53cwUVtqVTSqdp7nzXnGF/zvM/ze1rptdanBbziXcS5QJWBU248e7HneCxc7heWLMSQaNVzqpmWM7XkvkWZg2saB1KabJIePA1nG4qPOB+Zl5m5NoiB6B2S7JBMsxaX5gSVSG5mxbdn0Z5/JxEXjHGG7y5DVapor5i1jLhz7jZeTgyBpRkw19G4vrxizZndOFGUDjs3fkQtC62saJ+yOOcYNhtz1olY1StAtefY1kB7x8MwEH2kFGGm2hrUXXfOQSkLiJLiYLHdVlnmldKBpQDDMJo4VSop7GiqfOnNe+w//QEff3LJ5fFAofD9K8chO/ZNKc5zMy+cpYGHk0MOr7iqULw30KcIRRwtL4QBwMCezhmvRLpAJE3vmlMslmuHWgnB4iqu9TXG3JulLJxubjg7O0NEGNPAWuywVGq1bHsVPJ5333jEj50XRhaGlHh2OnJcZm7mK1oLfO/5JSFmWl2494fXPH7nAR98+Qk/+fV3eH515HDYA4G6QtOMUllyYZgCS224oGw2O/Y3lzQJeDKHY4b9gTU3cl6J0dpQXXfi3trr7+12fPr0xoYQYgepUBuaM24QcKFPEW2CmWLkfIhc3VwzuJVdec3PfP1d/p2/9g0++2ff4f/8wyu+O89M48CSV6rOlHXhePDkZaUsJwNTD4kaItf7yoWDzEoaNpxerLiwIS8H8IkyrziEUhYTukWpSwWM69fEhj6DH/BamJeZY154dO+MclPQNVPXhgZPWZTFZZoox/mG/TpzajtsFStI8uhaaC0Tw8B8yjx6dMbji4n33zkjhpVWhHxqVv5wIWymM852Sn1vR7mEw/WG49UNl9czp+YYpVDzht2ZY5sCwkxIIOsVqo9h+ID7X5v44Br+8a/9CRdvnXH2cGC7qQazPcK9x47f/qM/4d9/8rcZf+E/4eGHP80v/Y1f5vn/8Nt8dv6Iby+Rv/OPrvi3vxP5xi974qRICbS+ns1qIhzNmr1WV8gLeI3UVgg45iaobzTx5ByINVMRkjQWUbzPlOIIU2OYjmwjoNmKO24q5dioWRAq16/37HPl5ZpMKBJjx5RSrAHx1tXTDB6+HhcKzmJNgzVGiU8c59kctFTUFWtEa45JzSnnVElpMnfzYQ9q0XOboNu/i48e6soYky1dtTJ2ET4i+EEILlJOJogTlO3gefPBhv2SefbaBjxDcLRsv7ctDfWJorY/Wvytw+vFmJXH08xSrJBFaPig1GKO3VU9PllzT26K5IYsxeL+KeK8FdOclj1Xz1/w5P4Fb729AxFOV0eYF0oNsGbeeOcCr+bGuV4cu2Cw4ttIe9XGssxoq9ybRhxb8roQN1ukeqKLZF1wFMYx0oqSq6LFhrU29BFiNJj2Wq2kJS99ULbb4KM595OLLHNjWbJFRCi4YAKHObsbpdngKAwjl5cnpt1IWTKpu5LcaOKXBgfF+JSIkPjCU+/vWjONcRLFdQZMRMTEqeDNdeCCJ4hjHCIyZ5a1sc4zLjRcC8Z1QXoJRWQ+HYxh6cSA/iH2U5Ht565B015tHu2Cl2KitUKINuApxeDFpbuupV9UbSBraQdu70Gq/X8zZ2urYjEmsfRD0GqODqwmvKmgzZN722d01ugnHdqrzduxrtjnZu11NlAvufI6Z6YkTG7Ah8pu8tw727Kd1NYqhWHccjYEZAjkfMOWiKTKmR8QqczZkZ8XjsfCjUT+5OrI7/0f/4R3f+f3+Maj/5p//S/9m3zll/8Kux/9t/iRv/pf8rW/+l/wtz7+A/7w1/8b/sH/+hv89nde8p3TAL4X97guGrtgNfS5knxCKMRQTHEgU/C4aGdRsPTB/lTZH2yYnAvEAM15lAFxmZBWhhBQDL8xbTIrjjg6wwdoIudGLiut1u4c9ziJnNbCEAaGqYCsRBySEuuSoXnW02IDSS84l5AYjFuHQ2umqFJU2W7PSb6CWEmOdyN1MY6lx4awIh6tzfbfzgBr2WDQtvdqn+SZS8ZFG0A2dTTNnVFlBSqKOYw9jdJxFW1VolhDr+9uH588q1pcMPBF4qP1v8p6FR29BpYxWlu7d51zKUpdIXaHcKPY+VwcQ4h4EXKzVvW5LhjrEOa2EqQzkzVg6TZzG6ooMfUCn1bNOSV0zq5RY4M3WHfTZmex/g7dnksr0kXenp5wPUmEtWxqq3gHy1yI0aKkoqGDzS0yfVsKh1NKo7fLNWs09t54t8WMX+KNa+qluwX19nxlf573WGpK7fw5FwzR48GtK8l7oktdHGq0uhCjdHWnolRctHu99Iio85XkPN4NLOvcXWgQQqSVyNoWQoPkreW6iUUDW7+nODW+oDWs217aXO2S0w/vl/+VX/mVH+of+Ju/9Q9/xYnrE3pTgO3DtijZbV2GdMdCoUHo2pJikD8JiHpb3MUhriG+cVoXXOxuByzyEaRn6LGLsW3wJh6VWu4WdKFBW82umtUmv3LbamT5S9friI1qX5nnhTWvLPPcXRmumysMZibeHuRhiHhp3Ly+4enTTwygK46Hj9/g7Xfe48GDB5TcyGvh9etrEGG72xFTIoTEOE6kIZkNrl8WG0rJ1rjixbLAwdGrwbVb/Xo1bbCDtnhPLgbMXRYD9KLKfDqhLbOulZvXr3ExmRARB4MlqwFqHz18wNvvvsM4Tl9MlWNAWyUEz7Is4MRqhzXjnKdUi88ZXM+hvTUqOMfVzZHnz55xdX2kVHjw6E3e+8o7nF+ck1IkOUdQy1WnYcCFHl8zu5lVzbv+rNAJ+30RrdJI0bPMMy+ePePjjz/m9X7P2e6c9957lydPHjIMwSpdoWdxo/05YiDp/grYd9s1bzssGjhwf9jz2cefcH19zW6345333uHxo3um9JZq9mu4q2yXXquNqE21msUrpT9Ll6+vOR5mfBh5+OQtLi7uYUBdW8xjGmwSIVhUMFf2p5XXh5Wr/cqyZI4nE0e8PyNdnBPDFu8TQQNVPVWagbi9PRdOMCEle8paTMBMER+8saGqxWicE2IaetsJFnsakjGvSra6Smef1eFww7ouhBAZNzs7nGHCrXPGR4ohdpfaFxFCUFJKDENkuxkRBzf7S65vXhIdfOWDD7h3tqPVbOKpt3hf9CPeB2Ic7KDaKqkJ+EreP+ezz/4EX/e8dd/zsx8+5Cf/wn3eerIj1CMyzyTvGZwBMec5s7ZGHBLqRlzcUfzE1anw8sWJp89f8/LmRGmOaXtGTIklV+bTSs7G4koh4lwiDuOdQ7GJs8kHDhcCSxOu9icOa4GQEJeQDtS+PUgUEUR63XkTAw2G1AVqT/DBHCQIIlbVXpuYVRYhjvZ9RWeAx9waRa1xIig8f/mCGCO7nX1H03Zjh/O8UvJKq81+T7MK3+3ujDREfIjEEEjJOCWlVdZqkRPVxpCSia3O3/GDpHNy6BtpzeBjIg7285hAhsGiO7sL+vSpH3oQeDI1/uJPvcHXf/QdvvzwAY8fjMQSeH04sd+vtFbxsnJ2dsGUgPXA05Py9JDJzfggV2tgBcZptOc8uA56hVoKa+lV6dXy5WmcCDGaK0mN4XdbJ9t6e97peMT5QBoGi7n1ODZobyOz6XhrBdHMG28+pp4yH336lG99uud6FkQ8Dx69wf6Uefb8JWttHEvm1cs9WgoPHm0Yw2Cwdy1Iy3ZgrAbpPTvfsayrOXGd53Rcca1YobLfcFwrN/sjeamImnugtMZaK7UKa8vQCq9fXBpryaBjUCpxtIGGcw6nkIJnmyKb5Fn218z7a1SN2XfKlzzQI+++s+F8O3J1KcwtMq8rpSwcb47QxPhPuXA6Lrx+vWfJldiEIQxMZxcsdUT9SBpGCIPFQtTjkiOkQIieUivLWrC+QZv4NUwoqApBIudT4q03HnBY9oybwP7qFeMudPeETQhfvbjizz87cdMmtPPGzISaO9OgoF45k8a7b275ua/uuIggXnGqrPNqB8XSCM0hLTCewe7hyP23LjjfOdzs8MuMj8L9h4Fh40ghMj16E//468jwHiITysSQbmjHSz76g6e8+f65DbpU8ETKWlhD4Hv/8gd85e0B/8aPcO/+Q9LrT/jWdz6H3QVXJ+GPPnrJj799wcV7b+D9ZIfMsFKXQsupe9sLcOuQMSdJbY51zeTFBPNST7gUzAlTentoBrwStHC2VbZb+iU4sD/Z5fLy6jXf/+wHfPOj59SwxU/m9xct1sTnO0exKnMxCKyqoE06XzBydjYSPRhf0MRq3xTxFqUotSHYRXhIiTQONJSrmxNzVnKzC5WCxcDGCob6QUrmycXI44uBLIn5NBsg2TX7zqtyPgk//ZUtH35px3y84epmJXfOTa3mgFqbMGPxuZ4OuVsrpDWiKi0Xc4e2hpeBcRop0qMn0rrzvdgw4pYpVooNNbUZw+i4cr6bePB4opQjV68O7E97rtcTRRMxCeMg3NsIj7c7YkioM75RxTihJS82vMIRgzAlz27n2J5trUU4V9RFcyJ3YG/oTY63juyy2rqhzng+vYbO3sDaaHkhedBqPV3m3hfUd75aqwQxToe48Y7j+PqmUiUyOBii8uTRBmnKWszNI96AyRqMB5WbNd5W7XsgAWkd/N3PaIFGy5miFh109KIWlR6d7HtUH+KorqDV4q6dleZuW/6qxQAVuxtId/uIwDgOiFNiGlhKZc2VoM6iKK4XbKg5jq2YyLGWZu5HLH7jxeFd6DHRZkMIhCFF0nZjQ+J+B3F9f6rFatDBmG105klrFe+EmEaaFJREJTCXQq2OgjNHlwvMq+cqK69PjZc3haubwpIDPgn37zmcX9lO54wsPLwXeOdiw3uPdjx6sOO9t7Z88N4jPvzaA378wzf5xocP+OBRZPCF67LhmwfhH//ZN/njf/QbvPwnf5f4+jsMm4n01k/x1s/8u/z83/gP+dd+5IrD6095nZXjXslF+2dy27CFuai0EkcleeN4OjexCSNTEEYcUTwZpYgVSBjnpDFE43FOk5DSgK6KuIEqMAwmChgIPaFqQ7fWW4UlBhtcFAheiSkzTGLlAN5RqBSTSBGUMcG4AT8IS639ObSWZAnCkBLoiouetVa0RfKSybXg1Vwt4szld2dsaEIu2qHJQog9xaPeTBnaINk5i14uZeus4Pt5UyTY7uh6OgQlt4XSCklt0NmyFTlEZ2d/UWji7pzXVtyjhm0xrkw//zWcizj6+bWL1+Yus7NcwRzcpdpa0FQ6f8iG2R4PTVjrYu+fSHeu2v/Xx4D40HlMAKYPaOumAazVu9TKEEYTstTWPGvDNTemDeH71tfMxSUIMUVc8MQ00PJieoSAJKFQ0ap3Qws7YTjER9Q5rFHYxN2GFdMMyfJT4jAXcO0pn1xZS2VZs7W+VysxogtvvvZSIhFc5/w1L33vK/1+aigfu5DeOqQgVyHX7gprVlZmDkqlSH+ntD934lG5dW7ZPnrLOS6qjMESKa01/rP/9D//r/5/yjv/n79+6A4lwGJC2OHB+dgrxu2yYzDfW8OVgQm1V3OLWDYUsRYoK6s1hR6x3HRt1r4gWHY0twoyEITeqiOIWiY1hmDupBhAzaZtcG+77Ou/8gLbA6bkdaHUjBd3N9kdx56nXc3REofEOA7UVjkdr3l1OlHWBeeEi90FMUUKBrq+ub7m/GzH7nyHl8i0GaySu1YTMZxR+2vtgMemxscQxzg6nBuYl9k4EmLw35isTQBnMa15WWkoTgyCXopN6k7HA+ucWdeM7wLD9t49YoikGJnSyHE+EaNnSEJdV66vX3VxweymtVoL0M3hYBnaBhIU1zzzMiM+sNltiT4yzyuvr694+eI58/FEGiIXFw95sjs3VpUWlvlk0MTYBTQbrZjwgthCKUptFe3qsQcaZtW2XOzMvD/x8cvXnE6Z3fkZ77z3JTabsYtP5uwRHEMcTPgqxeDP4i3+4hK1FVqzVhUwp1EulbJkjoc9zjsePbrP9mzCOwM+5GzgUhfNhkoxx2iMwZg2SIfiQquF4+HA6XDEe8e023G23SE+UrWxlhPOfjJwnlIxZkvNHOaVw6lQc2YpK60oTj1nuwcGPe9igwsmQK7emtYGMeiga2afvbXMhuAZ0tDb/+wdAxDxpCC4znuK3SKu1USHdV3tPXXh7lI+jiM+RhyWwfZiE9bWLCrqg9XZz6cj4pRxHIgxdUHBWiVO8zXX11cMo3BxNjJ6a9hzweFaNJu4BPCBpTi0FmBmiPY8zPMVzmUmOfH+1x7w4cMLHj6ubEPj6viCV88W7u0A51jLwlIyOZtok7N9dVXhsBz4/PrAsxcL+9kOrj4mzqYdh7Xw6vLYP6OES9baU5zgXWBVR852SWsKLgRyU/aHmVyVos5+BufxKXTxTtB+URaxKWStldr0TrCTvhFUVQO5Yow2M3tlcGLfQW/pc8FR1gyizCWTlxWOC+NgMS4F3JA6R65RSiGvdtluueBTIA4TGvshrzZciJyOJ7P89stUQxmm/i5Ar5I3a5SJRHYJXErGxcCQhs4SAtTEJGssaybCVauV9sn4cLTG033m+dMrtruFd97a8eFX3yZ/I/Dt51/mm5/u+eijz6hSOT+feP460w6Bj5/PvNxnxDXO7ivbzcKYzlirEmol94l+Ltn+3fuzX1uhdSdqQ0FtWmnFBl0IVZjnheW4MJxbZFacY22llwTYHrK0GVRoWtnfXPHZD77L5asrrhezJlMK/rjw/o8Lb3/wVa73r7l5/pwabd/63ifXNCc8uDhj8rAsiaxCxsRpdKWsBdesMnnc7vjShx9S88q6nKBWllYpshB9YkgRxfUhi5U1BIm83F9xaBnvG5qtglcdzGthu9maK1Ehxcg4eEJwDA8fU1rl1fPnHPeNl3v47rf+lP/gL3+Fn/uJd/mZ/cjvv1K+9+nCnCvZCSEUQrTJa4qTFQM44d52JLpGJZKXFXWBadqy9CjymII1ZnkDFwe/41SV46s9NdiEmznjasNJoOjCixev0Z/4EtFbjOrpq5XNtpIcrMm4PddzY/RCUnN+zevCGANtNTdXlZVaVq4WOBs9m3RkWQRqxHthOcyUojy4f2ZxBApVLIbhAzx46z7ToxP1+oLl1cx47pguJuL5G8jFh+DfAAZwFUnnhCc/wY/+hUuef//3+fYfPOPdn7vHECJrgO2UWOc9r7aB3/+1X+fnHr+DO/9Jfurnf5FPPv87/O7n17TtjqsQ+PV/8DH/8ZfP2D7ekaaBNl8wncN6gOV4Qh2kWHEysGTHfCz4tjANpiUOUQg5YS1CIzf5xJKBUGkVLq88ZTnx9sPM2c7e++PVNT/49nO++b1XPDsVpCViOXGmiXWZqVqJLaG64HHkHmu181OxljEBJ5XtYCzE42VhWQpZPYM4Wims/QzptzviAEOKjOOGm+uDNZA1OzP4DmeOoVHWRi2OTYKLaeBrb000v+FPPlGSVxwrdTG3w8Um8eHjyMNd5V/86Xf51ueV68XhUiLEZLGgvJBcI2pgkUpVh0Sh5tW6X2gszS5ErvPUxs0OiZGNS+S8cKiN4bYQBhvS2eftyGWlZiW4xvmZJ4XG6fqGq+sDyyrU5hnDSBsm9odrSqlcXzY2E2zP7qHOon80i/6EYWCVhXU+kdeZqrDdDkxeMW60si4L4hxjxAYAzng8IhZTLmoRWakOw/5zB8CW7mDOOeMkomK14aEz8Kqao79W4dQyKpV7LnGcF0LwvHHhePPJjjfvbZnXE3/4+oaahUawCyC35zNPEI8Wo0+KmCO3tJVbUIl3Dvq5RMS4TqdSyOoAY54SOr9FPTE5Sj3ZDAEJ6AAAIABJREFUdbso63wytwjCdjP00qAej1HtFzeFDmaXLohSbUCuXoz9A0gMuGoCkbRgbmonfQCid+wY8WI8ni6MhRC5VSmTj+aUasY7yWrNo6B48YiniwnGP3ROyKIkP5rbXE109wKqSwe127AwuUhuxqp5dqw8Ox35wWXkwVPlyVnj8e4p0+C4uE483544SxdsNtfswsCDqbI7D8RJGEPip378q/zM17/K9XLi+Ysjf/bRFX/68sBvPn3F3//v/zZf+dX/lnffep+BExcffsAnf/Z73I87zvTIS2dneY+g1fhmWqsVhIigxfg7+73w4AFQGuNgg6CYjHcj1ZkQi8P7wjB6NpPiQ+N0KiQ/2P2LlWM2U0LQHtWM0HLFPvZAKYokD6qcjYqkSs3mhFmpNPXUIta2pI6inugca4WqAV8qiF3wa+7CqArkCr4DxZs9y00sbthatUiSQmve7rz+VggCw7I4q7LHEUaD+TfMYV274xPfeXMKUBm9I4hjGqxteC6CaKF6b+d+B5oL3LbO4ahVqWJpAa2tGzuE2hy5mJdXq5k+nPc4Z417Tp0Vwoj2AZslCG4bFe+ML811Mpm1bmdMIG2r9uFVsxY6i9cg3t6r2xm09AhxE09ZM9EHSrOzmfOeFOysbM4/iye32pizDVA8wZyVYgzc02K8oYYZMFJMOLFhYK0Gt3dijkFjLlmxTkieVSyauGqGbA5K34WnEG39K5hAb2UM1mqae4mT1kroDEgtCzGYSFbXRriFtGNsPrpDvjWjzTo1xnOMtjalMHEqpbft0pvjjUMYnK0pgomRxTVC9B1d0YidSZhLwU5ZP7xfP3yGUrUqUdWMo4MPwWCHAmAbkDQlOEeVLuxAt1/1aRPZptdY/EIxkJiKw9regrVyOO2unt6m1BlJzin0dpuqFeotkEvMVnvnKGyUbLXcVjOvpGSHXby5YtbF6ldjDCDC4bDn+dPP8N6ThoHNMDDdvyCGgKixZxS5q7UXEYZhREStTrpfJp13vbXsdsJmUGQTjMVgxj0H6lxD/EhwPSOK3glxQQLzPLPfX7Euq6nGatbcIQ08ePjEKmblCxjzui6UvHSniEJvj7CqRlN+8Ta11GpuA1QoRiszYG+MHI4nnn76KVevr1BtbDY7Hj18zNmXzggpklvpjRQNh2czTf17sIOJk/69i9iGUovF6gRKtoPC4JVaV/bzkVeX1xwPC2nccO/+E959f2cOoVagc5REHBBBq10ig949nyJiUOGuNJsAl5mXA8ebA1phuz3j0ZMnDEPEAU47x8vZlIJmrQSqFpm0O7UtMKrKmhdOxxPLUgg+cf/xY8bRLPilZHStHXpthwAVEyqXZWF/nFlzJldY1ozWiviBaUz4GCiuW4AboImMbbQbF/Cqd9R+7ewpHxIxRVSa5bR7bp0GEiNpijiFIIEiZuFfTjM1L10lt4igvSsVweF9sriMmrX7bnGPXxzuBGWcEs7ZJyOtEoLYxXctrKcbHpxt8F6JPlr9Z604Is0li0KJgSoDEFNi9M4yIcuBtl7S2oF3H+x451Hg0X1F8xW+KedJWO8llmxNIVBouVIQ1gZNPae18ezlJZ88v+LZdWbanLOZzkzQU3j2yhwV3ntzRvmA4vt0WVgqKNaY10OC5KVxWosBCdURhsSQRnDO3iPEDiproXZrsEpFsdpT74OJnnRLNEpZzY4vXQiqzThH63zAuQjOcaqFdVnIOVNKYxgGtrstTYyFsZ0m+gJKztmUNGwKG6YJPwx4wSYsCtEHWq6AOYtanwTFabANsFm7k7UpdcZGy9RWWXPBeYtx5NprVLUasDL39kEPOa8GGA/ehNjSoFY+2Teunq1sWbg/PePLb29QHE8vr3n++Z7TaSG3wLPnkRgT80mZS8V5pRRhOTaSr9y7GCgeNFdCgypil3/nTWTRZow/b9Es524nYNa2ZM1VdphZloVpuzUWmIL62zVb0e7oiRKY88zNq+e8+dYjPrrac6hKDWazRxrjGJlrtsp3ceTGnddaDwvf+cEl86zcezQxbS9sndBKbsbdUvHgKjF5pDefVIfNFhBkMddkCgObabLnh/4a95+L/YEpbJAYWErFS8AFe++bBpIEUnRM08i08STplytxlJhY18z1vjE5+M3f+gFvPdzwzpOJb76oeHGkweLT0zjZ5H1IxGFiHOyz3wweaZDXRgojc17RGImdy+VUCEmgWWTz/tnEcS5cX6/MzgiJrRYyFa9CBl5dzexvrtmdj+TrmUMTro+Vs2TP3pzNvv74XuLmlFAizStZTJiP3nMeJoLLxCisp8r1ITBuPV7UYlK7DXktLE1IVfAyomXFRWuOQpSd39DOFqJ3lFNhZgW5IaZX6LRBggciyoDE9xje/ZAf/Uuf8cf/+yfsnzUeviXsxL6nuPOMi/J83vPt3/gf+eCv32fztZ/lF/+919z86t/n/9nfEGLkdz6/5u2/+y3+8t/6Km6KSIy0OeFj7QMbz3gBtShxbnhnQFGWiVxhnAYk9aKOXBk3QsHB2igtUlNhP1f+/BPDDjRduVoOXC2CG7dsxQYxVOW0N07Q6VgYLjJSDRXg42iuOu3NvyjqzHlSmzHGhEjqlyqnQnYNy2sLKU0MqRE3A7jAYZ77tNbchEjt50fw4klOee8JPB4qFw+U7396TXY7g/Bma7892zq+/EC5tzvy5z+45F9+f+ZqdozbDbuzcxwFtBGqtbSu68zYPEUq8+1gwEb8OInEMBJSIHlrOMIH8IoLkTRXSpk7HwNqyf3Ab5cgBGJ0uLKw389cHY6s1Vp5h/ECOUtoqdS58uK0cjZE5jxzrHt2Zxd3DVLBR0JS1nwk12yOZXW8ul6ZWmQryXw+3W1eekOwNMFLb4IVcN4RaqPWQlUhugDaTChKVnazjYlSM+tSwQVqWY1rsywGzW3V2mHFarY3aeTRm5H3nwwMA7x8+YLPr5RSIhIaLjeaenNsiJXvNDDArXhjBqq5clTVrq5q518fDVXggiUTahPQak4DZ+c8qQbd1TAQnbl3yrJgBT0mMIcQSEPqz5Q58Utr5hDJdiH3fVAcnX12KEixeI0V/WAxmtrbu9RYroYzdZY48NZIZ52vKyoDa12s3bIPhl2INIo5//sa7hR8sOIeUXMyxSCsvXkKb04NHwYUTy0nEMMZqCoSHF6itYY1Ya3w8iCdVSQ8uOfIBXzOPOcVuxBJw5Ep3HB+FjiPkbNhYHMWON9EpouRr//YfX78J95mPc185+Mr/vCjH/Ddjwu//sef8PS1MP+9f8gswtVyxA/REB3OXBSjc8aJas3ihNKYl0peA7sz4WzMtGqDnyLOWqWboBptdKsL4k2g0FgR75jOI23G6ubVs64OR+BicMQAa3VksWeiqDIO4Q5QnF1iEyqwEJMQCIhGTiXjUIbBE8bIqZqTkrKgTXAhEppFPIkeqlIK1LJQmr1P5s40QHNwwYwEufXWcBM7bgtcegyGViopmSCGa0gXPw3Jkowd2wpBIuJ7y6BTshYkKME3XBBba7zaOcOB66xaV5s5SVWtiQzpTZwVCF20M4evYM9/rbfPpA2PbwUQNSnY6u2dta9J6PiZtRov7FaMFemsI8u9Ou8wAwpIszu63La2a7+oq91NK8YJW/WL9EMIJnbbfxwxmEh36zKHxi0LOMSEI6B5xeHJczaWVDMxObfe/qimK4CjOrEWT7i7j4v67p4Vcx+hNkALJvx4ccZfxCDfTnp5QF5oebX7fSvG8S12NncuUl1vJlcT8Zs2ongQQzSo2vlAxXA/ztvz5rURkrHZuHVX9ufaVfusFTVObjCWqTYTzn6Yv374glLPkWp330jPmDZLpJqaKgas1H7xlN5EpR2qrU1QgeRSv7iviNrGF6W3mmEPpmKLqP09xu0R12M6DlqzyNst/A0xy6Gq1aybXU6ppYKjOy28gcmAZV07bNWEj1KtNenRo4eEGO1hNoWHdZ5BqzFSUAiRGDxePKVk2/CcWHYf7e+AKQCuuxxaNiAZAr43YlWbM9BqY50X9jcnswSXYn9u5/9s0sD2bCIOVj29HTeM2w3L2sjzkYaxliRYbEcwF83t5+PEdWHEUUtvEujTRS+9OHxeubq+5ng6knO2HLLzPHr4iPPze4ybyTK/3lQP6XZKLw7nrJ7WgBS3a4B2C7JB0FtTSjMbsVTlNM+8mI+shyMSErvtGe+8/SWGwRbUUlcTkULqDg5jPHFr3VTt+XJzgBg3qeCdsCwzx8PM6bQyRse9+w+Ztht7Xrv6Ky5An0SXbp9s2Mtqz3WjtkotmTVnliWznlbSOPLw/jkhmg16zdbMcmtDbLXeHVqOS2VdFg7HI8uau6yKAbWHHRKS1dLfLi7V4oUxBkZxNNEOEHUdIme/13nbTDSXL54pL5YjdorzA0VsriBNaUcTO7VmcxIO0d4Dtc0sRYtD3fKDxIW7GIrHBAEngmg2B2Ewt7yJckpdM6UutFrYDIlp8FDN/qmtESWibuqgZrugxmhQ1egq++tLLl9+QvIrb2wDP/nVt/nKu2cM2wV/uqbMlXaqOJdJVI77wqFkSjFQfNZErsLN6cTnz695cXnDqTZitDjAaSk0zK2xro1hiAzjQNVAVm9QSSx+ifOID6CeOWeWkllKvYN2pjTaJLGL2IiwroW1ls7cKaxLselGsA3YarNDLwoo5r6sQOUOJk8/aFdvcNi2rt0VYLG77W5rEH4gRGOPlKZ9atmoJVPWTHOCT4kQLOLoxGKSvgv8rVhMuHRr/rCdSDF2V6frjRx0i7RQ1WI0DU/scThbAZpFCdCeFLZnNddeLOA6kcp5VAMxFg4ZXi6VP3t54ve++T22g1DcyPFYWZ1nmBLiHZvR4dcI5ztyq/gUeHT/Ho/ffpPHbzzgW995yuXVDCIEdztltMNaNUtZh/n2gxCYoOEcUrnbF7Q2hntbs2C73v0jjtZ/j30tjilF4tnINsLD3QZdLszJ5Azme5Y8HGfWWiGMuDigZObWKBXycUEvD+SxENtsU3tV1nkhpNidq+aEkL5PFiISHbSCD0oMZksPcaRqITYFqkGtg2NKiWkayLWZwOY9RGtBSg5CaEyjZzdZhn9eCnldEPE8fPSI5Tgj6wlYed3gf/pfPuanv/Em+9PENAVSg7ZmcM5KEsJEjAMeay8Zk7comgiijuvrA/POpk5NBK8FbXa5jc0BjWEc8XGm9nVMu+jgg50TFlGuXu35ygfvcDq85N52S5kz08UGyKQYGcaRsHE8/0Q4NSsnMBaFvX/noyMNBnF9dbjhuy+E7ZAYU0QITJNCNrF4cRBUiSVQEHy1c4uTiq6N9HDL8dk1y+uVuMuEskd0wU6Om75mOnTzdd740afsXx353j9/ztn9twijI60j21RZN0qOC99/+h3Of+fXePxv/Ec8/Nlf5JdePuPyf/un/Nm+cfHO+/y9f/Epu1/9Fj//N7/CbjqnjlukvrZK675mSFC8V1J0DEOCVsmxEYK1fDoPhcaxmljAIeKWxqklc9OuKysRFYdPyqM3Nzx4U9gvhdNSrVK6Jo5aOL+wCHQYNlyvxQZS3i7cTew7E/WAY82Nw7IgktmMGAC3Qa0WUwrnE2OPz8XNlvm4cDqtFq0A+jSHIJ7oCohBkjdh5dSUj/7Z51zVBwz3Ij6mLtibS7Zq5c+/d82ff7JwKNEidcGzztn4OOKMUdEWfBosslb6OUlAnRI8bKYNw5As/qWm8Pp+KXLOkQbB+9Gq6akdlxBMBCgG8FyWmWU+cTrN5NKrnhHunU0WldCFk69QCksx9sjUrI00DNbc6oP1yYINE5fcO6oksBlHWi/OEC9styOqhZqbOVe9QnB3jhgJ5lT12KS+0odgYFN5pa+bAt4xxkjLxispCGXt37Mzh8PpcLBnZIl4lKuDom6kyYp3oxWuqPH8CtJrv83bEJq1HwkB1Jz9WSut2fDDFQN/S8dqqNrf7dUa6m7H1NqfFxGHRNdb5cxhVFuhFuVUFm4fK+c8MUW7ROeKSmHpDhTDXzimYFXiWfoeJybONWxIaFQac03QbBBhMGrFiV1e13ICn1Ay3gWLCvafv4RGyeWulbo1GyiW1qjZ7iLGj5X+XQpLXU1IctY8N3kPrVrUVao54aVh7VmeF/vKnAeum/Bwo0yhcD5tKdGKPrIo16fK9WEhlD3jlHiw3TDu9owxMG6Ei+2Gr757xntPPmQ5VX73n36P//mfP+WjKuQlMMlAbrXfnU288BjuYlY1GkpHSCiVB5vGw92e5iNXx8RpLxxXO9dqNxh4Z5eIZa74VnE+ELwNiyjGIy0VfGh2rlVvde+OPvxsLFqZFWL0BLVoUIiJ1kaGIKxzJfZIm3qhFKta12o8JB+DFTuUTHTG9MJZOUlZreXXBrLtixZxtbY3cL3YJmNFq/6OAVSLtXaLs3Yz0dun16L24h0ZGxqY47q7bnpUatgknDjm00xzHieKdsGpdoHj9hxnf3aj1NYB9krLNnz20VaUtSmSK1p767ML/Q5r5/fgzWxhP2GHwudGSIJEuXP0JB9vb1BUgaaOIhaBFhqitRtCeqOkiQr9nVTsb20MwRhvrv9d0geiDXPhiLNn3nuLztUVXAtotjbPGKO9t3XtRgBLmejt3eM2eieWzmideXprTmjarGSgye1i0XE7JvwECX0o3kwMxxi6mzggxdGqIMEi0hZ/bFRdadXOzd5ZdkUcJiIh0OhD6A6hxwp7MnYHr6LWgussOSA9ZieYiKtq+wrFysp8f19+mL9+6IKSEwNGOwAxnn0TmzzZP7WpAY2utLXOzKE/jL5DusMX09v+InpveWbpgpWo0tQOLCrtDpjYXLXDdo/oGBX9CyZGrZZZp9mi65wnDtEuNa1DDRVOhxMhRWKI1NpIMXF2Fu2CIFBrYzkdidERxONjwLsEzexut2Mz1+1sgoDv4tXtO9JviKWZMFLVcvUitjmdlpVlXsg1M88Hs7T6gSa2KE5DssatEAn94QlpMPcTwtXla0JKtnl5R5CJKkpTc4xYI5stLOvaQGpXnW0DcwKHw57D/si8P9CcRQ69C2x3G7Zn5zgnVumbYo9FNYM4OiH0rLDl4G/V6N7Gp1j9b3dA1KLUmjktM6flRF4yOM/mbMfjhw8ZRxNW7HMzN5d3I6oGbaVPjFDX3UMGf3fOLvXrslDKyrKc7IKelWmz4/yNiw5p7FMmbYi3g4m1E9oUSEUt0uVsMyi1sZZCXg6cTtYWtpl2PH58H/okzUSArqY7aJ1xlYvVYZ9K4XQyUHVejW/gUsJFby1fzRYS700Qak7wIYAXpFZaNR5YUbvkDjGiAkVtwtiaxcyis0mKiMd7xTfIpeHFxLBT7gd/jF9y27zYVPFuMCFKXI8umvVTAQkOh9m6tfVWHioutDuhIgSoZUWrwfnidmRIox38ugW05pUhOupaqA62Kqic0LqyHK+43r8m6MJX39jy1fff5stffsJ2UpbLTzjuXzM2z+Aj4xR59eLA4epAKY11EePp7LYQB25OC0+vDrw8LhRxbLY75pq4PKwcT0dUhd12YjMMEI2flGngIoSEKaEW9ctrY82FJRdWbCEfp8mmqN1xJ2gXGwu5NtZq339pFiuLKRGjVai6zrZAW89GW2PabdzzLi8fLBqH2nqmphSSQiSMo/GAnLfjmRhgMDhvTW5NIXhCDAwx9TYM6SK4OXnWdYHayLWgzjGOowG6m63SrXQBE5vUiggl20FoSoMBCTGuh2IZee3PS62NNc+gShUoa+71sc4OueJw0bEJPRo6TEz3z6i5MD0ZKFoYfSNJYwzCT/7UB0hQnl1eEX1kHCKEyqvvfYer5zesxRssXlu3hhufJa+ZNE52FujrnV2mblvnLMCa82qfZ224YEMGA6qbKH3LH0ALvsycJ4efT/zEB0+I3/gRjlfPKZ0ptH284Y++e8npZE6zYTOxrt1y7ANEjw47MhFpxYYszbOsmXEz0mo1d6ALiB+oWPWta8YnsMvjaBwJr1Clv692wapaOd8lpnDO9c2B2gTvEynalUuckkLm/hRJY+XVfmWZM9psv/TRs502xO05IcEQoMYN334+Mkwj56NwXBvH04qqM5aC2OAH55HmaVVozTEvR6twDlahrH6AlgluAFWS64wqJ6xloWm0S1KP08chQnScuS2qBtlvWnBj4N5F5NXrE6s6NtEYLnHwhObIbsZJNrdjgVayiXqrMxfktENc5dll5sHWs5sSUZUkQhoSV5cnNA049fjdSFtNWPeTff60iCyF8VxYbir1dKLuXxDCFmFAgwc5R2WA8ITw+Bf44Kcrh09/n4/+7+f82M8/sXai45btdIP2Neh73/5jxse/xfk3/grv/8Iv80uvXvD8d/+U5/OB9M4T/rv/6xPG8XP+4l8bCAGII0GV5k8WF2lKS8GeB2ftsCkX0ujMxVsT0+QpN5WlFB5MEzduQaoyt0ar5qgzZ/VgEVuE7RAYBqHURl0dA566igkc3iFs2B/sUk13q4LghxFx0ZoMu7iZRDkVc4GFvqZ5sWhFGiK+wnw82sDI2YXIGBOKeHMYohUfA5+8aOyvMkVGWvRsgmNo1nzpBNbS+OZnC4ej4NKWbRRKE8q64KPQWmAIFj2KMTE6YZ6VXIEg9t9qrsdpiPjobR2p3DEAm3OIBozn7GnZoj61mDDknPELFWVZMvvjSm4ZW5EcadhQxXwApRW2m4GxJTuXeGeD1WZ8DTomIAh4FaYhMaWI1h6ltiXCBH6NNnDwwQZJTo3DW2/5IX1sEgdz3JdiRytVWhGb2NOHFJ1XmiJMm5EH90ZjZi0L85JZ18IUHDkXbk4rn185pPX2NYwVWLX1GLXr038TPFrtF0Zt/TxnSZ+mru9vAlps+IXtabWU7mqwz82JnQnVG1vQOduTVIUUA0uz9IJ3gtIbb9E7ALBH+2ft8USCrMZewzHnbFBm38+knYe1aulMFb0bmNi5qzNSKeY2qJ619Li8igkZdAC5FlpvZlTarWkFUNZiMfvSnTKFTqYVZ64O9K58JgQTn6x0xxiiycO6rnei1+BHnAhXx5XXR2XQlbMxcH9MDBtzEk2bQPSBoUJalHnObA8Du01hmwOtHFkWc4SH3ciUlK++84g/ffqc0hxLrRQcvlRzw2HvsEkkxSJPzphUtWa0bNAM88lxXDxzE0qwn8mJw8wfFR+gtWzf+94RgjBGMZdoW8m14byQqzBKIqNksbKKEBxSi7Wh9WFRagHBolbz2sjZECPWTmb7qfM2aK79fupvZcNW0WCFVJKVIURrENOMF7uXSE9eqFrcj2ZQAHPWte7AsxtOGIwDaPuev7tHoGYEcCKIkfS7eaObJ9SRl0ourQP/LUIXxOJy2sz04b3c3kDtfXEmYot3tBVLZYg58U6znTXKUvANQlJclM7H7G4ih53zmrN/H+mw+lwYXCS7WxCJo7aVlAaWbPd774O5DpvFPKXaXd45d+dYAtt/rMm79KG2uZBd1xi8+6IZTTGHvZmc7J9753Ex3BkxPIk1L6DGjjMHGN2laM3Sta12T+WL8wdwJzIFCTagvAWEd+7arQvVi7kOWxdR3b8S69NgUWXpyQTTTbSvQdobHu0M1/oVl9ZXaTHdwzu7Q4vaOVa0mgGBXniWxO6D+ZZfmMGFPhRWfpi/fvgMJbFIlSdQumJvXy39obAf/FZJNQHJpucmRRhs81aBFq0G3b6LXtgXjVar9pQvXs4i4KtZTF2FUoQmhdasYtMBIqY4+yH1y74t1uuaWde9vUzF3FLTZsIJeO8ZhqE3gak1JFUThVJwpOAtKqbO6v7o72e/pGk/GJsSWrtbql/Ma98sgZZX8rqyLIvFZlqhVYPI+eCZtjuCSzZh6YcnAx5GgvOdfaOsTVnWBXFCmgxEF6ItSCV39w9mrKqqlGK2U8FEvlIqy7xwPN5Q1pW8Ltb2Ng2kNOCDZ7PdkmKyP6cLOdIvJU4cLvpuvevCVLPv24Bs9vcarFKZ58U4JctqGf5msZ1Hjx6wuzjDaWXJM60pPnhisAhWKY18VykpdyLdrXIO9vkejwu1rsynBYdN8c8v7pPiZN+RWLxHnDNh5FZUanJ3kOnHAgQo68oxL5SlsBSDzg/ThrNxxIu5OEwEvRVEsZasAmuunJaF07ygWSjSq4lbJYwGJvfdYk5pBDzqFWdwIsDYXq1a5KO2Bs4s0uYYKr2JzOpFbw/lru9HNG8ibKnM62KqfqukGAjJWqpWbR1oKf3ZS3gVfKt3cVHnLQ5oDPKKl4aLAhSrpfUW6WytMS8z1MKQAuMw4EIkZ707wCkO8YlSMsnBII0YCsfTK06HSzau8uU3Jr78xkO+/P4j0gSH5SXrq0tcm5m8WOT4UDjMleUItTikKptpYAyJRT0vXx35/vMrXh9OOBcI48TcHIe50qpjGDfspg3qPLmsrHOlivTvJRnoDiHnxrpWclZCTMTBIPTW8JBwYI1NNGsdU+WYM8ptY0MgRdvsfGdKtWaOuqoF5xy1FHK2o0YMg01q+2HKBX/ncjKhxuE7sDYEAxu22rqo2XAN5nW1/293tvlk0VCPsRhu96qyZHyFpfy/zL1br2Vret/1e97DGGPOudaqqn3o7t0nt7ttx8iOcSwZyZbxTXCkyEkAmQtkCW644WNwj8Qtl0QIQhCIm0CQsJCJFUESSw7BDrFb6e5t793du/eu2lXrNOcc4z08XPzfucofYF9Q0lYfa1WtucZ43+f5H5Vttt8dRkCuAJbmnWA+/gnQ5VdvOGmW8rJ6f2rr8T5Up3YJjd1U0z3r7PA45uQkX38bEvhaC7033nn/HakbcqZVI/aOuxqxVpxXn73ifHrgh5+9oRdnmhNX777DqSeJsCNSPI78KEuBXjYtU5fl1kw1wJgWNPTO103qtmmXxnkSRx2uFGWOE5syEep2YjufqTgvDhP7vCekzv39I+eXd4RkLNeR0/33Z6WCAAAgAElEQVSJGgN969xcv4sHsetxWGXmXWaaF2IUuLjWBnEizntynhSiOyl/zivQsgJcxx1CNPzSLtQrtfYxnFQoagF5/izx7a9/iSlllt2BeZn47Mef8HB84N2bzHtL4s2xcXzsnGIUwxkj82EvFUdIxDlz2GVulollnigd5g7OxnGuI3x/xzTN7KZZtkhczHQOTHWhWsXqRiuNPJusHqaGV0MgWO/OqTjnVvWcRwjznnl3wFIkh87cTuyWzu2rT3m+u+HmZsdxrZzWzovdFSmqLWWZ4RAqW6ha9GzjbnuQbbgHsm28dx24urmhbYXHk3GssJTO1qW26M0p98YaNsIUOEQFNU3zjGcjtEbPJ/CJtHQeX5+gByy+JKRrQthDqsOGErH0Lvlbv8bP//ot//gf/jGff+/Ms2/PzFMEpCLJJfJwPvP9f/4H/Px7L1i+8ev80r/zN/nsJ6/4n//lK47BSV9+l7//T9+w3Mz8ym++L1VrK5inpzYcpxJSI85GrpFlMuIcNIcYTMBV2BEfVkqt7HLEbcMnwz1h20X/EiGr0bETmIIxRSjmWEucreMhMQcn7CaCw+O2ARWq1pu0ixC0uOzmCQ+wHjfaqeJKWB5n40XpsuP0cEctR2Jw5lEeUVobxEQi5onQN2Xt2DXTMwi9i53tXVaTaSfLWQyk3cwhF1rZlCtXfQRru0jBBt47u5DZ7SovZuNhhdoDOR/A1W7bg8D1i2LHh5q0F4OwYSlSLBOUZI3bOLuNp9mPmIjLgV6ylAljqTydHgjuUFfef3HDbMa2nthdX7F247g6qikW632uG/TKnNXKFC1z8+zA7WnleHK8FZp13WcjT05Nm4p8aFRiGEGF3Udjkci11qQAa9XHHVeASPON27VySjDneRyjK9YbU3Se3Rw033mkh0ykDZVHH5nlOp8ueXQijfWPHA8dguYRHxNnJDwRVgwAqA+bXTcpmRo+MjURoG3DPTNIm17byBaNlB7wKtW51JPtLaPPhZSUu0ENW5C72k27BaK7QLChlEgxvFV7DPfENACljvItLURFGoyyIkUFQDSR8LX2oQ5z2d1Q5AfSNw1gUcpmqeah9cacFTYuUnTMbCZCqCO14DRFrCVKVVP0edtGmHkjxImXx8pnd84uO8ts7GZnprNLgWWXuJqdfdxIU2eXG7vlgatd5Dou3PvKn/7ZHX/2OdydjM2LZtxBILtpSc6TMuy8K6CZrizZ3gK3R2cFvAc8GR46sY+4adeSfgFKpOaf6D2p/XeKpFSwVjQTh84+GJM3NozustBZyDhSKoVBlq10QlPeW7AM9BF6rz0whogTickoZX36XrQhaK4OQVbdlAPVN3JU3Ii+R4ehGr/EZQwMVwCPB1IcZPiweyZPAoT75bzQ7GGjBRr0L5f2cwtR+UtdG0sfav/afQCxan2rbkR8CAwu2cQm611EgJurBKqNFu8YpeYKQXufSMLGZNqQ3AVeBo/00AaEAo5aRxngR8OIVXY6vV8Cj3o1nWXuY37V991DlY0uRDwoC7P3OkQrF5W7PQEx7j5cRVW/B/BgxCSwC+pTbtWSEh09Y713pbEwBCv9ohhzWtAzGu0inAkKGx+zLTCy3Hw8E29FMx40Y0aLKtHyjrmauVsrWIzKa0qX3bzhtT7FDfShmei9jDZO5UU5DWvCGcKYpd2DFGUXsG94h831tlhQrE13KNX4In998QolFEzabQzoT9B6GAt/GBk3cEEAeh+SNTMdmLUSLVC8ajigkzAsTQpx68N4ZHrRzWEbzR69jEVqyHX7UPqkMZhUc13mzcUk9aYfZExSC0yT7HjD2oZr4BfNMVBTGFYgSTVTMGIYobUhyyox/OOXG0yAzXg7xmdRqx7eNhao0+nIdlYyfwyR65srcpj0n1Mm58yUsl42hMLHMJgjR55vVzvH1c0NtVVqr2xto6MlSADsGHhG6BsYtTTW9cT5dFYDzFaJwdnvdzx//oyUJnJS5lBMsl20JntZDFJY9JHPxGBm3HXYBEZAmNmI33fKVjg+HjmelBmEQ4yJw/U10zyRhnUjmA7JFBM5TojP82EzufhsB2Q5/nxlIp2fwLnWpOC6vr5mv1sUiomUdFgbCK/+3t4FAl3+HHcwD9ReKFuhrZVzLbhFljxzNc8KQx0b+dNn4BLY9aag5NMmq9FxgFC1FWYiHmZJD/OstzEG+YgtEZORDUlTA09p/7EjlUoCz5Ew0v3BqG5YyuSglqQYk9R0puWyt0LdNrwWsapBy6dsPk6hy7aUIznpXRVrgAIsR7vKGJXUSDE8xIaApGW3UOtGKRvbdianyP7qipyU4dU7mEnNV6oO40Qg2MYSKtvpc+6PnxI48e139nzznSveezbx/nXnEF5DbYRwpHIizYHQjO6ZjYClzLQ3pnnh+LhSLXLeGh9/9pofvj7y+bERUyBPkeMGD6dK8Mzzm2c4ga05pfioO5Zq0eKEk2gdSmms5zM5ZfbLBCFqOBgNhQ6jbc2wkDmVOkLyZ8oAV2JUI8fFqqqX0gZQqJyj0tUgkqNoWYtRcngzeiv0KvtNHAqiuEykEJlsPMvm8q13x0tVG01zpnki5iwmx9QSggmE9673ufZG9cbzww0xJoHd5iqs6fq7KlSzUtZNZ8A8S7ljl09hhAuaPbU7raNZbtrvZG+KYXzvPLXqBNQUUtaV/V45UBd/e/cBeluiBmOl8eevNx7uCltbyMtCT5nENfkwE5Y6ZM3Qh92tITYvpASDeb5k9F2AZDHxknZPOTOHKMA+yAZTuyTRW91kKeod7wZxJu5mthj40d1Gef3AaY205YYQnceHwM17X+Fcz4RQubrZD5BE+SfK60rjvtEAet4eub4+cDhc6d40VVvHqBDMKSXc1PBnlSe1ZwhiqUIY7XoWca+YTXhMnFbZPfATxze33D08DitSGoN2YD4ceL7T0uHdefHuc7X0hcQ0R5YU2U0TbYSeJzcSmZR0f+U0DYDTiCk+3TcX8MtDxGOgIuuDDebNTUsV5pxOnYdHNaYQJmyOTFmSdQud1O558TxR+onbzwvxWWDJgavrHcfHSo8QkoCq54fE+1ePPBxlU35484r7u1d4h3QVySXyOa957/pn2D8TiKCwVWPrnRgj+6tlLGmB81qY00xjxe5hubnCKCh425jSFa2trHcb6XBL3r/C8w30GawP5VbH/Irlp3+GX/zVN3z/X31C/JFx/bUr0uvKmQRTYwqBN59+xo//8Pf5xu594otf5Df/1t/m08/+Hn/waiXOe85f+YD//h+95nqJ/Oyvv4tNBkzgDeudvkktaHXYnEccQTSVC3iqzDEw+cz98RFzmGOi507zormri92Pg21uVYuNR9M7XZxEAlfwdEpwswcLEw+bMkZqU4NaTs5h0VK8nk88nB9o60otyr3Kc2dOsjqtj4+cTrfUKrVTsEicMq1lammU6tgusUyRtimjKE1ZKi3sKf9vnjMtavEOMapifJqGeh16F7Nf+2iUHCqVwxSZrOEd1t7Z77Xs3la1G2lU96Fg0F281ULoRm8Vn8FIpCibaZ4TbaujKc2Jy448LSL3mqIYpAIVoRrjRGlSCtWtE7dKWvbkPtqNu6CWRKB2Y6uVSicnlUQ0+ahl4XVoI7cxjeykkBKh9+EcGLtfaEPhLcKXKStLVMws3oYFbTQMtW48ro1Wt6ez3ryxnrYxL2cpBgiEqEKA7hdN1LDqO+SQRDJfAKRhC+wmgM+8vc0FCoE+ylvWreqcMKmAbVi4LwKfcFk+TU1/vYvgtWiEpsU3JMhRC5x7VtsdOt9qq4MglaIqZbkuLvalGDt9NBMrO1Xzr7vul9obdJHi3cFsKFKCyLeNRvBMcggjrsEw2Wlq41IcEbgE9CZyTtQtEOmEYGzVqdVxNkKeBDTEPNwbI6/HBQvnmMgxEKZAW1X84W5sPQ9A1Sm9cb8Z6WQkGocZ3nEpLmzqpArnGnisxukEdzN8/4f3fPhj518/FDYCa60jdkLgriIxEsetCLw1I4bIuSpywGLkdKpUmwmxkV0KOwXwZAZ0gveoJrdpGSrsQJ4C85yYknN6aMSwUZthvvBI5dSgh6R5ZhAxnUavPpS/ASNraTcf96uySUOMBNMs0B1iUCC3m6kQoAWsqxylRf28RUp3etKNToSEE9qwy+NSPrkRsjKOzCHkia0p2zEONXoIDFWUvQ2gHkIAMz0n2rpN6q1gmE9kLwNtuDTTRn2CXUq3dPn9AyipTfYtcIWLm5OyypLylIkx0C8OBfcnF4eiPxrmgeCN2hotOSlENTYGqbDkfAkjbkDAcq2NMkQlNlTzFgP4IMAHsKOPQu3x3TvWBuBrmg+latL30lFuU7AwRCqdbo22vo3X6X2AUTZsR6AzHEY8ip7PgD4r844l5VvVqt02pPE1Rn2UCmp8ZCmj9zFKwWRB4hfl1jqtFJGAFsf3UJ7ECykmwaXDLdJoeIjKro2aw2vrEBruQfM2f6k8wMZOzCVmZ4Bcdom3ERD9Rf764hVK+kyFtF8OQze9NLxlHWx46QNDxeJiAGvdSITheRVibA7VIOdpvITy2dd6qZV1ti40tDkkImSBD9HA20YZjH5tyrIxC1rYp5kcI/No6ai14r3pIBnh2rUUUlIDlhQibVgkHOXiCFmOI1NDd+A4sAZYFpIushBUr76uG+tWWc+rgrSHb3M5HJjm6Ym5CimzzztiSuMii1oqYUiRBwtjRshRORvAaT3hrRFyZJoS6+ksNqoPVNyMKSZO542Hh3tOpxPrupKygLWbwxX7w44UIx2pynKKNAR+ya+sGmFAnylChnt7CyylGPEu9UZtlePDA4+PR0oR4BYG2LCb58GQwOWlTFGXDyGNd8EHYt1HTtKgm8aLU2vleHzkeHzEUVPRbrdnt9uR04KFNj6vy4Chz+GCaOvPFvjnA1xqW2HdNtYqiWXwyLxcsd/vUOHQSWDSWIxt+NxrbZzWynEtnGpRK0ottB7oKbJMB2I0YpDtyG0MPyHIrugXa12jdh9BjPLChvGsWlD+wSUgL6REiDOyA4p5Y+ThlFIppUEfMud5IlpkVUCPANaUZP80sZu9q1a+90YKUta4O60XUnCWSXlJIbiGk6Qw7dPxkVo3YjBurvZM08hdujzTRNzCqPQMpBRYwsZ6uuflq08J51u+9hXnW199zjfev+LFPBG9EtY3+AOkmwPPdjN9CdRz4/i4cXpsPNyt4HDaYLfsaFPm5Zt7Pvrkc17dnXlskZh3bN5489DYame3vyJPe049UbaN03l9CkCPKUPKEBJbaWoALJUpJZZZ9rfSmtinAKVrYauuPCsjUGobQwBAEKATA8G1gCg4UGecbKeVhi4e947bpc49DbtEYasbpVYsBZb9gTzsmsnCE2M7EBJKKZIP21AQ5fDEjF8o2zLYXO+u1hozDoeDmovkAcBd57kji3Jpjbqu1NbJu0lSNS7DyZAM9xE6iwbrWirLsoitcR9qBx92aCkjfbQszvPMMs/KfxoV4sqgk6VzrZXmarHJ18+42e0UsJzSCPpuoiFCJFWnh4h15TlN84Sb0QNki/RwuZcGMNQ6567BZtotAmfn/MR6RRsgTYxYzrRNqktV3SdqAFImzgv76Wqw5hUjcbUY25uV/W7HskjNmNLMlKZhZdTPsXWneQWD/WFPCAKHcVczyQgt9pjUOpICbFoqY4zKxsrGZJBTFiPfq77/EFnXjfvjPXOdCG60/QfEBKcEL3ui7ReuFmdqlfNpw7tzdX01GPzINE/MoRNTVuDjYNlCUPaYgFg9425a/FIYeYMWiFGZUDONbRv330WtAU/KgFN1zt2Iyx7aaJSMUOvGfiq8uIJnu0qvyntar5zDkjkszulYuF87YSyoKcGXX8x88vmJH33yCfe3r7k5RJacucqV5xPY8YHXH/4FX/5r3+H5PhEHmOlo+Y1TxGvn8baya42VSroW7RW3lRQNghYhi4npyjm9uqOsZ+LpDWF6BVPWXTTy+brtCM9+lvd+6XNOD6/53p+85ttT5vCu0R80UlWgXWU++9FfcPUv/oD3/q33mL7zq/zW3/ken/53v893z0baH7i9fo+///uf8R/uEj/3y89hKsR0wOxMoFDO4DGQciWlTo4R2xndVk6bQI+eKodpR6+F1BtzSrTS6ZNhVWG+os+7kgmouMdhszG82QA+A5uBhc6exLkFvL9tOZtiY5qd0+nIJ68+5eHNWWB7byScerynRYjzMx4ejgJouOTUOBUFmcb4tmEnJAdTcLH3zpTmEUwfqNvGnKKsxZvUvSkZKY15K4lGSs1pfWGrJ3xYWkufOdeVc6tUd+4eZEltHnXXjoKVMIpc/KIYKZXaO7GuRPxJYdotkiwPVUZRTo4ZMTodnTUTUiKXEZFwcqcECLsdZ5/Yh4lpcepWaWMpjxaosdCokCI2T9SQCAlir2K5iuPbCjHSk+IKvKNcp2gDBBmAxghGt6AcSY2PF1mFjZYovd/BghbymOhx1oLXKnWoBsLINQlRC2PnQjhonndj2DxGhsmw2F/KEgQy+VgQlUnSffweH2G5YbT8Bn1tEGEbRgOpjflvngObBbIFWYcvhHYH4oXo7kMpp6yeyMgXbI0WlHIVo+a1GKLs++OMtgHYxmEJb6OIp/bGU/lQb9iYeWOKNIXtjTNStehRHzPNbDg1UEzIAA86uod2U9Zn2KvC55saDw2YMkw5DODPqB3WVXYazMlNhK13Bft6HEqRUSHWu1Na59Qat2vn1WPlx9PGi51xmCJLjkxz4d1l5vhwx8cvG5+cjXOZSNEVnj9ASWMUjriplMIiATV90dqw/ABZAd69d1YqOUyjWUwqb2U5TgRU/FG2QkzG9TJjZSNFZ5oC1iuVwNEEsFYLA1QVell9EEIDwKXCZpXWZUGMMY3ojjjAmvH8PSmRwiBwjOhS9DcaYRoNlQ1K3QSM4uNnKGWgOU8KccOJ2SEqT/GSCyxRg7IpL46Xi07DERgfx1fXzBj0cx2zFQZTUA6WoggCHjoLRq8i8uDSL6e5tTYjdAHJpEH8uZQ5cUpy5wQbd4Ce89ZVzjByrAXg9KCc0CxVUogjr9iVqNxbp4/WOO2YGh7NlKk7+SQCfbxDmJHMKBVqgCd7bNOu0kYJzEWNRGS8e05pFYsiCluBFhqBOAqi9HNVoP5F7CLwzV1q2+jja3edMS22i2RMQD8MEE4/Xj2j4+fUDe+aq0uQKycF5dUJnxC53Nto1QuaMxmWQ0x/tg0XhALc9QymJIBJsJEa5gZdN5SuUtu31hSLECVACFEzVnr6fr+YX198KDfyC+sJHfFbPlyHAw32wYw5piDD1hSQbRG80kKS7NP1mborrKy0oqrJqsNcUudGHAqkgBGy0Ld1O7OtBWqVImcwzLtpJ9Y0Dl/m8NSD8mgCCijOGYp3WVgsMk/yZ6saVhWG2o4VNu0gu1jbxnqkXzHpOy21sG6F1grlXNg2gVJmiWm3qFp9knUJc6Y8D2ArS2XTnZwufmyT0Ec9ogJZaGoQ67K1hGjEKXM6n3CMFCPzNGFuHLeNh/t71qMUSd1ht99zuL5SdfQka4d551wqu1n1t0J/FV4JCm0NwXRgNqHmIQ7JMBocz+uJh9s7Hh6OnIt8q9M8c/PsQM55WAXbYE0ke0whj8MzyI+LDUWHghgVZNaHKgmOxyMP9w/UWokxsCw75ln2PB36Qm7FaA1lEwL3lD8gFknoLqqdPB1Zz+sA4CJxmliWiWQKm3XvaqxClcvmAhfWrXIulW1rsvKV7alydk4TOe6ZQyRksMESDnIAgo3AXan2yrj4xLInySVHrlIf4JqZEaNCVVNIAhRM4ZytNLay0baiw5umSyIk/f4hOZ8m5W+RZ4Ef3ihN+QDYxJwn+a+9462TgzNPnSV3Wt242u9wAuu6spWCeWW/m5lSIk3xifV0xJJIfilZaMpGr4+cHj/l8fPPeL7b+CtfPfDVL0e+8mzHl14kvEgVGKNaGbbTibbJ/nL3yZHbx8rxDOcT5CmSrp/xZnvkk5f3/PDTWx5Lx/MVFiMPa+X2YSPmzGF/YL/bcS7O+XgkBhv5Q4EcI0Sj9j7a0/SzisGYp0yIgdJGWKiJda+uvALhzI1t3WitaqE1LRspieHCna0UDTEutU8fDEYyWccuViiChs5SNym/RgbFvFtIk4BBDZpoQB+sr9emylV3ZYSlAUr0IbvHniTN7p22riSc/dXNwG99ZGQYwST/jwTKaG/qrRPnBVIcIahBlawjxL2PcMCtbDw+PDItO4H2rT8BZoBUE0PFWc4VI7Lsd4AxxwyM3cAU5Hhhevtge5dJ2QrNNGxeAnsVXzYu4OastbDsh63UjYpr+B8sgAWd1a1W6rYyL5HdTqqw9KQnRtXOLhm5uUJkPQT2h8MTKG4XYDhAaJHaBaJ6k/1ud1hEDiSBhTkphNjtsjAbXsUATimSB7DPuFneDuc622IIeMpMy8LusFdjUIy0XpinScygR2jj7G6N5XDF4fCMYEXMcVKOQUWtlpOD1aKswHUdSyEQ+sigUohqH2eXfk4a5ANSPaagoa3XNgbaEVQahoItJM51lS3XnGoajTqy4p7OR2pZsQaxB0JZ6cD1rnO9dEKAu6NCc7/0pXc4HA4kc9I8cz937h9WpqifU0zw7vMr9tNLHl694tvfesFXbjJxO5MDvNjtePHOc15/9kg4Hdm/cy1lD0GozgBWQwpsj2d6zExxY7/MhBzopdOnqKpi9J6FlLHJ2O5OpHRLmF4T4hVus5Yiy1jcY7wgvveLfPUXbvnowz/iX/7Rj/jVv/ENpoOxnjshRJZkrLnx0Xf/iOXd97n5hb/Jl371t/j3fvIhf/cf/oBPTvfM+3f5EVf8D//Hp/xHMfGNXzyQktP34DHippbRDEzZiFOXMsVldTg/QmgZuwpcucF2guK0FmXhSgZbZQPcg/b2MLG1DtFUx22d1o1ditCDlKPZ2S2Z82mTMiPCkiI5rrx8/ZrT6xM//cGB1uDhWCnNOFej2cZhH6gbeL1EHghQCq1oeI5pLGVjrDa9j51Aafr5pSlSeJupGHOS6qP1J5u+8nm67N0hEENnLYVgkXMLWNiTF5dqeAzpceQYWowidIaa0VzZhiUrANW6LFatn3GfBOqlSOyRGNROt5VG9aqcSBsRs8FIaaHUdaj2GhYvRMcAMnMcBFQgRWhZtdEhR+WXmv6MyR2fFopdCh6k5HdsKAJMM8RQEtMcb1JF5NnZz4GtOKUqNgCQfcJtqJY1l2FqObIQ8CjlrsLhdW4DoyjiL6kCLIzWKG0R7ZIhF2xkPT3BdGOx5+lekNJIZ08c9m0thBf1gS4DgVXKkDyfDeIgAOhYlOqmNRFNF7XqJfvVQlDWCZ1a9XfZSpVlLl7iDdSgF1znK3RiCqNAx94Sy0iNHDwJjA/aHazL8hJyolsmIfKOKkIXS1gyMNe84Kg91kVmRT1IZOKwa6lxayuVaDDN+uRq1ZxYaXSv9LWOnBZnf0hEq/QgxZh75LRV3dFBasetF16dCrcnmInkCZ5dR3gWOD4ceX3fuV87W9PcG2wiJxvAc6cyrIpDRWKmWnsk4iBkWeSlqA0jsFnqvz5QNEP5Ob3DuqoMhQBTiFg5akH3Tg6ZOSFbanG8aamsHZVHoLu6Nc3GrXdOp6b2uWllWqRkDHGiu8LuL+VBAQElF5tMjLJsRuWd0HogBJjmRGWTe2aAm3WowZOKelEnVHhSpVUv2o3aIMBd+TziC13vt4dL34uArqjnU04GG2KG0cbWOi3ohAzex6x4sYrpxZPTRaCURZHZMSaRpQNA6yNDSH+myNNg4/n1t+9mCIb1QPUx37pRmyyKrUkW0q2iYscL8iJQrLtIu4st1qKP87ZQmqygoLPhEg5+mXvN9TNulzst6lzqI5+qFp0vFwCaXkkhDuWkY9HeBqBbEqHQLzO+irviIE9CVHnRRaX1RPq707qUSLKQKrw7R6nOLzEqfeyb0RLtUl4VEu51CCbGh2nDwRXjyCaULTSlSIiOe0KhGD72VSnMqjzbT6IJWXUFkNd+oVz/fw4o6fDVMCjJxpBWPT00o/3ApTSqQzrWqpapMIbq4kqdP583WlVyeo2QieSIajstQNS/1nVj2wR60CulO3OO2LIwpUBKMx6cXgt4JbSRGB8MhvVEBIGqX4UIprFD+JNk1E2+zhDjeCkZVdodgkD9GHS4WxQI1XtnO58o2wifa7Df7ZQxMU8K49NHRRgPWjAxCaVV8rBoYQ1ao5Q2VCw+HhYpIHxIw0K6ZJHIEtGa0+vGy5+8kQ2pKkNizjsO+wPTItVEDPHpZ+dDcjjFScFfSUOLmxRaMQZJwzexZ2koA3DYauF4fuTN7R0P9w+kmFgOB95954boUs7EERpoDq0FrEcNBFEAnZlezCFpwXsdKfsZ3Ni2zsPdLefTEXCWZeL6+iAVUQikfJEQtvFOKU0fj0PCzROqHYaqq5TK6XjkdD7RgZgzecmklAfQFRWuNyo4jUwyWOtGKYW7VfLathXO59MIxZMqJcdEyoGGmjOsdX3fcdRejucQh161ZPegIO1p1JRbdwGJKNugJ1mcYhLjqyY4g9qkqtq6DvyAWk1MlcW1niEv5N3EQWkEUp41AU+hq6ksmkC3xGDoAhCMxcRuJxrzlJT91Tvr+cRuf2CZ92IhRxC7oQGiDV93jJlkK90LD68/4fTmFcnv+eWffsFf/dkv883398TkxNaYbLsgJSSMOCfO68bd7ZHteObxtlE80nzh+vqGsLvmwx9+wp/+6FPW0qkesXTgsTgPp41tq+S4cD3viCFyfDjRujOnKPlvvDCmnXLaWDs00/8Wo2ynMWWqSxVZPeAmZWTHOJ1XXX61Uvu45IOUi/mSgWSBra5iKxs6/5rUczHFEbRopCmAq+5U5FmjG8z7AzkN61o0soehBhmDjoO30UqRM5h+nrviW4YAACAASURBVG2Mx93bYI11xmIKo80xsdvt9HfQPgHD216Lzr7eG6U1Wu/kecFi1llvCn1212B8UZ0WnNPDIzEp+632Pho0L779MK6FYTftnd3V1VhUFNZoI2+gen1qWAlcLJiBOC0alIZy07yPDAwB0kYgxcZpvI+9NZaRddVoIyTdx1lU2dYNDHJeJE1OcaitxBIGXMArdtmPBBZOWTYzeLJ1dZpAlryjAeetkGNiWSbow/6YgsB+U3aXlFABGwB5zokwMiPCCOFU5qATk7FYolSxjPMys8zLWKZgGsxmGDkk3VRnHdzoQUN9I0jd5hCiBmEP+pnIJiL1w0VWfvHit7GMmg27D2q1mrNs2SkJfDezAao7MUuROE1JLVS+5/h41HKexDjjzunxzF/8+cd8fndHjzPVBV5t0r5xk3asDkeXUva9Fy/4qW99lXZ/QjbmzPVh5vXtcfzdEt4Cyz7w/ovM813k6+8nbm5PVDtjW4Ow8cK+xJe/fYNNDWsdssJvw6bvh6g64Xwwzmtjy4X5mCE2Wg5YgZhsfMYKZ85XC+dXjzy+vIP4CTllwi4AL7Cw4D3htiPlr2Ff/Qbf+M5HfPf3/4z/9//M/MJvfEBogTwr3wQPnMqZj/7ZP+I7736F5Wu/wk/99d/h7/zkv+S//sMzJbxh2j3jh2f4e7/3Mf9x+im++os7zDKkQooiyrBOmLQMts0hRwIZfGV9iCRLxF2BkKXUTvpZt270PIJjTTmV/iQra+QsRr+Z8hsSgbUDwVmmhNE5l2ER63qP5wJ/9ZvPef+9xp9/dNLXtcDVLtHzzD5DPuw5HRKf/OT+7bKEQSm0uhIx9suVwq9N9vnaGsGg1ELbChFYchr2NwEF/TIHuBP6SCTwiHcBOzlqBum94KQB9gBm5CTyazMN596H2tIYdqtRcDGWkq1utCqlvKE8HSn+pJafLWBFAdyYlirLyunYTQdqOQ2gdWgl/C9nP0bdT8nw3DFrlFoo1emhi3BCakibdN6rHEb5jUQRPuZB959FFJ5byZPaH61vTCZ2u9Qqu2RKmitcTWQMS5j3tyCVuT/l+nkwLj0GdRB5cahr3RjtwPZEGJQ+Pi/X92mDlDaTqoqgTBy5DlxggykU3obVLMRIjFL3Nmc07CZS65xoyr2kEmNWTIZD7WUoRMZM5gC6n6c0VE8j24cBYoU+cuBiwJLOOYtG7UX38bmSo9qluhYMze4oWDg1xbtYcM103glRVmaz9gQm6PtSu1sbAOS5FHJPw0YLOWbSJMfC+dhYmxN0TavSvjRylrW5lkrpG43IWiCHihdZppJlxQaUTu8CTFtruEExo2KEliiPxvF4Ym6PbF22qLR1zltjdNoRwoQFVXV0Llati2VTZHiOgTTp7IhBi3upKlyKiyyQUhpLxeJNc0QOgZ4q//Z3vs4v/9pv8PjR9/ne6UN+8Ccv+eHdxrkfyHlPSsZW1ZxXWh8WNtn/3HSn9mHxrs3x8zpCnk96aHdXeqZMe0hvVYRzvChU3u4tVmXxtUmq/54maE5iomepx0kCYK24SmiL7lcVUA3Qb5wrfRD42CVO4GKOGsojdywOxYtFWUHpwwo1dt2qFrWtN7qJIPTx+80SnU6W8ApzZ46JU6+6s7icj30ob/T3kmKwXQAA7aDBsGy0KtCJYdOMKRNRpl2laXYYeT+6T+ogyRXWr1lKkSwxwHm7CFIuivnGpTwpTBOmsEwBPUN905saKovqDYmxXzAoLEc8KB8umMFo2dN8ysgukjMoBoMgYKqN78nNhqDxEmWhZyB2fcb6bLUzmnX9+dbV4FgrOaVhVx4umd4gjMwkyWaG0PJizdSbcxFFmMUBPqkh+hLqrrmsj9lMKi73oaQiKsQeExH0Bf76wgGlDnrgBp/NaCK4PNg+FDUgNr+7llYBBY2twnp/4lSFqhtxDKZRS3kwLAqAKrXB2ihVQM2yv+aQEz7PQghao7YiqW8/SYLqssHFoMtcbw/wNABnhX23DVnh/SnbJIyAwl6V8p+iwrx7qViMCk1NE+v5xOvXt2r+qhveOldXO+aUmJaZed6JYYp5hIMq00N/Py1EAmky1i5iSaecN3mehxyYsXg4YmliyJRS8HXjtK6UUmitcj6eaNvKMs0AHBaFpR4O16Qp0aqUFDYGVtWHJxwjRgEtU5zY1pWU9bDW0mluzNPEMi+c1pWXr15z+/o163om5siUZ9774AOWaSKZjZY0e1qwQzB6r4TIYNmGusn0PbauWltAmU2l8fjwwJs3D2LQp8TVs2uWeRpf6y+j5yB2qo88r4vUFrZ1tJGNBpDHxxPHh0fZd6aZ5XA1LjMNJ6CwzTQGAMXaif08HU88bFpIay1s20pdN4JFpv2OOE1kGxkiQEaXoLXAnBMlNhKRUqWM6uaQMmmOJBPafHmnLtHJaZ7USBG1ZOjQirStUrY60G4NifNohug4vlUdvvtrUpoEergArVNXLs+cMnm3YMFJQ+atL1AwX0mhq148B2qRsrA3Neu8984LQp50mDYpDo00DkI9W4mAlyOl33O+v6Vtn/MrX7vmZ37+m3wprjwLd6TjmWmfac04PjzSNliPJ1JtVAvcl8J5PXF8WAlhJk6ZPM2YVb774ff5kw9/wrE7y25H6fB4f+J+a6yls5sWrvcHrHfW01kMUojkZEMBogXjcas6uEMiTRMpz8SsvKutVM6lUzyAJVo3qjfWUimliJ0dUv6LTHmaMlOOpHF56d0c0vRyqX+9ZC+kYZH1YSeqgC656bAw5z05ZFUGj4GUpmyE3hprKWy1EnNmypnqCnfvXommBrgwshTcnFIqhrHsD8NaOxjfNFQXQ2I6BJ20Vt+2YvqldlaA09Y7eVgNYgxs9w/EZdZCYLLAphgp6NLPyC5Yu3LVdvtlZN4kAUjDPlB7FSDiOo9LUxvMfr/X8GQgGXmkuIaNy/KRY+D4eCSniVoaKar100doR0AhrAoKa9StEFIUYJISnSR1qPcRgKqmzuZd+ShAnBaxSzYyQaoPVSBMedh+vVG2wrwsTDlSq78Fv0cr51OILF0WnXmWSixAMpO90SHYpFDNYKxJbY25O8s0M8X0ZFN+UkkZtK4z9pJt1Zv+f3gay9mw7ZiUIBr8ZTmxquZUs47XTholFfLADDZtKKVaTPTtRClGb04emXiEIMtuEOPaa9HfLbkys+aZ0uG4Fn7w4Ud89NHHdDrPn7/HYb+wdqf1QKsb9w8rD1SmtHB9NfPBlxfmqbHimO3ovXOYEscpgRs5BwgKyPz2N9/n4e4W7n9CK8p72tqRco68+vHnfOdnv8R0faCHRClqNWzVWSbwqRNHK5nVQusbzRe6b9jZhg3LiZ5GYGsiTzu4gfP9ifX2jhR/jL03wxQgaFYxKt4DcX6X97/5Pt/+6Vf88T//hKv3Zn7q594lVjVUnYEpzHz+cGb+/X/At/7dTLz6N/il3/ld/v2X/xX/zffPZHfC9Qs+vF35H3/vY353/3Xe+elJBNkUSSXR144VNHwTiAY5Q2fGehH47BM5Bq5CxFC86rkMo+qYgwqdtcnGaL1T1oKFmYlLLtmwIrXKHMDmjJnUrLUaV+/sWXcT33x/4tNPP6aeOn0bZ0Q9Ea92dO/slh15SdzdV25vT2ylUPqwzTAy1kohPVu0ZmyNPE3K9WgCU8tWiHZNnoLO4Cr7RBuNQoSL+dYErlh4CottI7C7wVDkaDa1EImpYeNsKE0LczA17OQU6CMvJIeJmPQZIbGRFsQB7AZzQpip1dhaHTZfLdfuylmsbSPGTHNn21QucEljFcBwWQylLg0I0Li802nWbGcpCjRyLVHNC15lu0vhUsZh1KBwXlw14O5VBE8zqjNAdLWe6iR1pqx7QO1iDJW4AHYb9xy9SsnjbeQeCbAOI9i8j+y3OSaBN+M5JRq92bAxyqyTgyxwffyZMUYWGxXuQ5GmdtNAtqQ8H6IUY6uUdVIySMVn3plSZngg8T5q59s4z5Lu9Tkc6N7YLSoUscYT0B9MZUNSHE9D4SPAQJbmYRocd2XthmXodIICK4etKxGny9sHXi8EqULv12Ecb90p61hQvbPLCYJTXZk+tRtbC8pkDBWLegdq15mdWJQ9eq60mNlWZ8mBHGWr2ZmxVQTmesRNCqzejGmOEJ2745noDvVMWSPVnbWO6OABvIXhSUouu00fAGR3CJOBN3qQo6K5lK4hDDtad0KqYJ1W1QqdTNbb+ZBp2wO/8zd+g2//9u/STg88lkfuP/+Yn/zrf8L3vvtP+N4P/pzvf/TAD+8mzjzD6FjdCL1SDbBMcCmiJEJIChHv6yB0Oo8Pb3BrxDARkWW9+0zOIocJCQtJ827SnJxiHxk50Iaq2eIIVPeC0QkzrFtnPYtATSGMUGYDwohJkEXZh5oIfGQEh3Emmx6qflHGyFKHGy0qCNyAGpQXF4b1sqKIkuBQujMZIs4tQuxMYyaOMdHQHewwsngUIG+WME/DUdRG+60sao7e5TBmRHCmnKGPFtiRXdWbMrRoI9nXlXlpQzHYohCekIbtsXVi0wzUepWwY4CVQRQSwRJuiOiohWodT0YY4h03gUwMkJquAguF62eoOuN0YowzzjpGZUqZsrWBYzjzNCmaIg7wvEkZFOmjcTmO71/K0N6MS1WPD4I3mXKdOsPVNdRVOpxGSLm7BCQObWtq94w2zh3NcY69BUk7WBw4RhS4m4II/3bZH76gX1+85a013FRBXkbWEAwpGj5AkEsQalBw9FpZT2eSGW3IxJck+WgaS1kbF+bWGu10Zt22IRHN7Pc75mfvMM87/HRi3c76ellIZwh9hPk5aTAIYXizFR7t4wUdwAMwzzOlbuPn2J/CRYXOG+fTGYDrmxtCjDw+Hnn52Svu3ryhtsqSZ/a7K8wqKU1MU1JuzUhxtygfvY2lDAkauWTPWdYFGU0AiAW9FL0qLT8kDWyn4/EpU6hsK+W08Xg+UdvGbrenbpsWxWlRaO+y43C1J6SkBrd1FQsepWToJr80piYac8iW1RzmgbIq7G+3nwkEbu9v+fjjj3n58iXeYZonrq+un1rgpjyaEWRQVdBavwSCIrTdpMi6qJJa22jDgtZa4fhw4uHuyPF0ZNlNvPPeO8zzREpGK/6U4yTZ4lu5sr58GNlJCuL0LrVMLZXj6Za7u3uwyNXNMw7DEoj7k89fQ6X+jgwJ+ulUWavzWFba1iil0+sqBixF8n6v3JJghDiRgmw4TsRCVa6URXLQUFZKEVOXlU3SLarZoFd5b4dfPk8Tc5zZTMs6RYql7bzxeDoLlY8JD2qeSOPgapuW3Gk5DCWOfP4gButheyQG43DYM+VZqEE3rBeMSrCCcSZZI8dE787rV7dEjOfvvQPBFWh/qc/tRWG9Xa12Vo2pg/cz59Mt58fXBM589cWOv/KdD/jW19/lsBSW48rN7opeCo+3D/gmwGu9L6ylUEundGerDffK7mbHtibut06i8k//5EP+4vM7pnlHWPY8bI3Xt/ectk7MievDDYf5IMlnWUkmzzEx0ipqTxtSUDcNc/O8w9KsYEuL3J821lJxyxAyW2lspeAm5mCed3p+go/K6ai8saxA0taaLHS9sm6bmO5plookmHJ0LOsSKZW1luF/bkO5k4cqcijPugCq1ipOZzue8A5xmZh2M1p7AoWmTDLQ+wcQAuetYL3z7HBNSBPbhf2ZZMvDJMjrBLay0Vdl3MVhywjWB+go1tYcehTj9/hwj82JOU7UVsf7MOrE+2ivCQo6XLeNKSfyPGnQ6U3V4S5QJsWoE7Ku1FOh9zqUf8pZ6+OGrTQiI8rPjOyNsm2kMJMngWvdndMofAhdw1nvsoS2UphzFkiZjZwFVl+8qRZQicBoaGm9YjjPDleSpjNy3oKNzBRHdcGO98rWCrt5h1cNQjlnYsi0Bt7VWNlNA18platlpzWt1dHmCRYi21mNSxqcxMD3EfIq9aT/pfNLi05zH1aMka0VIylMo2UkjVyzEUp+ofCAKWZOrVB6JQUnx0zvartKdmmnUa2t8jsqtYxzyw16JDAJ5OuyALa24s1Zzyu1NmgrW63gnW1deXX7hrXJxlPWE/NuJtvIqAizcnhKZZ4m3n+nY+XMFIyyRP1MB2u8TIHTunHYZSKdfnamGPnaBx/wwz/+MWcMq2eSz9AqD+czx8cj9XxHDDscNZB1C7LdIabSmpGvob5prNdn9u0a5kJeJ2owPFVSyMTRFMRNp55PbHdH8pSw/WeDbR5M++7rmiDzN9h/5yU/d/eKjz5+zR/+4495990rnn0wlLJxpd85ZWd89PoT5v/tf+Vrf6sSrv4av/6f/id89p/95/wv69fY2Qnma/7V7Rv+p3/wEf/Bb7/P869GsExaoJYjnYa3PWkxYhOZN3UFSJdzox43WpBVu+07hU33tjuzSW3amogld9Ui11akLt0a8ZCxrZLniG9RSsZuzJbwqZEN6unE8+tOayc+f4OUdl7oHRKZkPbQGrE31r4RUydkMa4GzEPhU3vj1e0rln3kcFiI80ytVW6Ucc6fj/fc39/y4tlB8VVDHUmTta20KtDN1NjGODN7V/hqDCp0saAsF49OdSkQc4BqWtpKLSRXxEJxJ6So8OuoFtm4jJmzDhv+mIlJJqCFpGyftlLWQp4SORshZCZ0VtC78kCqQ2wiH4MJVLdOGspnL01n3qXO25x8CdvOieBi5LuQMqZg7GYjBIe+kSPKicqZGNq4vyoRAeatBixqjTNTQU2rktrYyKy47CwiWXjbWNullmzDJt7Q+RQHqBfG+X9RRlObZuMBXAVTOYQspoO0NleLM7LNxXRplZLV3kMn5kntYjlhZWNzvc8RKUJrVWxFHDPxwLFEPHRTviSM/Msu0CAF1q1ph2la8rZhJZtChhowz8Q4qrqtapbHaTXIah/DaFStanmtysayccc5hochl6qKJUgkOrIL5hhG5IVIojRW2YYsdlNPWIIYBaLWpv9+3SqegkDy3ui2UIPzuBXyLFEALivXnPX95yyA5NyUP9XWwmNN9LIyUyA2VPDQqFVtwvQ0QJtLkZGeueaOufaf3gOzKa91qyvREyFFDiFyuDY+uVvBA61JteUwoglWbm6e81/83f+W3/7T/4df+pVf45u/8pu890u/xU/93K/zb/71B07HW+4++xd8949+j3/2v/9f/N/fvePH9ZoSZ1JcSV7Y2igVMKcFhbDnGLF2ERKYQA42ei+UDWortBaJOQ3bJU8zQxwW/xQZsRpSLyq6tNNGgVWKiTkbORjbqoKUOPaZUsa/9yKA3pLmKWCaZtmnuuxWvUPw0TxnNp55vYvBedK25CA1EgGs6b/dvI1CoPYE/rnrfo8pKeMNw2shWFIe0CCcpZDqtNafRA5huFx8qMpFSnaBDtZgvJsXtarmyKjcVgCvI2ZilCiQqK2MeDRlgsaos8lLUfbtsLxZ1OffzHESrVWmHAm9UYOyKt0z27qqlAnYeh2ZeTaUmpUQjK00ppyJGdm/qwEJt0bO4B4pax9Wu4qFrF02DDyhd7A0lKeRbDpH1MKoKIniRbNuF9jsrmchRM2U1Tu46wwXtzkcFYqaEG7i45wVAH+xI16KCNqwDvpQ5KeUiReJ/Rf06wsHlLZNIMw6WPXQ5X317lIg0Tm3M+u5YlXp+TFGiBMhG9GdGhx3yYBble3tXCveCtkhp4mrm+cszw7kKRO2SltPtNNr2nakbUdWN3LY6wK3iCcthMmlTmIc0haDLinzIbcVY26XcNtg4IVtKApwI4bM4fqGViuffvaS0/nMlJIunmTkvGdKmd2SiWkmzTONTsiLNK2OhnvAmoL9zAxKw1snuSqh121T1bL0lHgvbOvK8XjifD4TYuSw25Fi4vFe9jLDuLq+wn1h3Qq73Z797oplmXDEPq2lsT48KhMoZyyCloxLy4JUREK7tTSFkJj3Gcw4bYXvff8H3N+90Ys6z3zlK19lN+/Y7Rd8MPhhePojUCqYVVpJTDnSh4/2onJoXVZF0DlYt8bd7R2fffopaZp45533+PJXv8xumXC3AQ5VhdaZpI8Xn7ykoG/zpdoImAtkttp49flLzqd7lt3MzfMXLLsDrahZJYTBSl0C54YNzT3ysK3cbxvbqoWlNzifjtSy0VPm2V7gokcbX2swcEULsdD7QK8CBE5lpUaFh3tWLk8b0kQrevxSiAPMVBOh19Fi8P8x9yax1q35edfvbdfazTnn+87X3v5Wc10uV5Xj2EnZwbEdTJzYVGThCQgiYEIi5AiFAWKIzIQJImIYYORJkIJANgHJsXCI7KTirpyqsuMyLpfLrrrN155+77XW2/0Z/N99PkvMoAYc6aoaXZ1m773e5vk/z++pmZITuzlRaEQf1QHWKY4Gg6SittEhMviIp9JqobqinJlq8N5yvF0rtNx0C6+AqRPOJKJVGB99mnF1ecGSK6f373J65y44yEvC+aDMAeMQ1yhzIRuF+1VZyPM5bbmk5Svurxqv3z/iuz/+gNceRUy9IJaCdZX04oabp5dMQZsV2lKZ58Iswn4utGKUk7QakOI5u1w4v8k831/x4Ysdw7gmrNdcTMLLi4VcKsebFdvtlug3tCpMKSu/I3S3h1dgZxKgN3mEYDE29uinJ5XCflkoAsYFjPXkBqX2yYL3jHHsEHo9EHpvCTHgrWeZZ90oReO/OVcw2hbTyAQ3aEbaOHJeMFYvfUuuGB84unuMt16nlprX1PhDLuSa9NnNBQvEzQoflJVTpGoLI+oiKUhv9tEpnxPLdrvCRd/hgj3rXrReullLKoU875mniWHoLYnG9CYUXeOtWGII1NbINI2NWst2XGFKw4SgrWpiyO2VZTmVTF4SWMNqtelgcXPbsFEQdTFBn6g2jINaKker485iMLeXECfKwauG28iLVHCDTqo6mUSFr4P5Dm0WollmWxiHFdZZQnBqvTZ6SLFiKDXRWsGFiCkKzI/93z9EuavU/tvoz7N4MFkZp6grEzvgolfeSlUnVuuxDmsM1TUEi/WhsxEqw6CTslY0qlsRPZgbLa04OMCUgaJOSPqsS0x3tbrOt2owDA6hx6cNemBBnXq3mE4D4tRtYbtwZYxRl5sLQOdnGYVJIoO+P0yvGlaadKFf44cA1VoWgWY8Po5a2S7KoQjOEq3DmYoPlpQnLs+EuI7E1bGyZDr70PqGD4657rm5uKS2I3WBiqgrxAf21ws308IqWgY8uRrubo744+rJZmbdIsELBccswpIaZppwWwu5aSNgZ+bl7AirgEXw0mhjY38BsS34XNmsHMEHmvFU01n1rmGJxJMNu3RNulnw8SVmiBizAhMwohdD4wJu+1kef2rHD18Iv/wLX+GLv/YhP/Uz7zHeceAsJWtLJ1vDv/qjP2L8J5EHP3kCd76Pn/67/y4f/Bf/gN/zb+Oro929w5efP2P4pY/4mZ9+jfWpRk794BiixQWNnxkLoyvsrKEYwSPIeqQWXTcGH2ixITWp42EWalCTVbONnBK2BaZaMFkFoZoaw8bhloRYp6B70QtH8I716Nm9fMbbd0dePjvjqjWyCRjjmFtjc3KXcVxRy0RKOxbRmL13+imqoMIIFmnKpyxlxmQFwoNa/qNztLKj2ESpwpL08u0cOC/UFrFVKFVjN94HHc70yb93nmYLRixNsvJanLqU+51BL1tWY3ClVpZSWFrFVyGI0NzhmepsNfSyU6qee6xpmN62ar3X19Q7msm0lEhNY5zQ8IM2m0lzymSrKnIPptdtV4VpV3rTo9HG1pQzpRZaddjRIaVQjbrTvHU9rtfItaIEKh1HqNMs473GfquzhNqIGK52CwUHRtvQatEF9VBgYI0yhMqB+YayX5DS1xFlE2ldfEOKJg7oFe22A14EwKuoiwNbu6zUGqUL/YdLre98KlrnqYhQpeKNskyc1+9VW8GNQ08FKMQ9z8rh0mavfOsyVrOVngVzku7+Ue6iiytlWPXhY82ZEBRALkXYTxNSe7zbG3WVEHAh0KRg6bGtqmKAcxYXB2qr5JpoVfmYWB1EW3H62bd6bq69wEisKH8pJVIV5pz150nFA7kaSDrM9N7TWiEd4u+p860srFaOsQzIoGcCIwLVkEshWy0xCCEjUtn6ESzspFKuZ41Y+pWu8eJwoRCt1XZtdLBrjCE1GFp/Z027jXu5oUexXWRtRlJe8Cbx7qPIp9894pd/54zrBNiOVOkO13GAxsLvvtjhf+e3+Maf/D53/qefJ56c8PH3PsMnP//DPPjcD3H66N/m9fe+wL/2hY84+/oX+eo//yW++Jtf5at/dMbFNFJ9ZDgKrFeZVtSRQ7G633mgFRpNuaXWMAwDtWiUvZSMb4nVuKK4wDZok+xSbXf/FOX/Wj17q1mhQ71RAWeMEWcL0nSvx0BYNVoSpPrO8lx6RK0LSVYHObTeAt0ZnbRGlYJIB+Zbjc/n2p/xLqi7jo2RrExCZ10XnjRVQLPklkF6NI1Xbn4NeejzpZyew03B04PxKnhZT7BCwGiRCK/KaLzpf0ttBO+I3ql23DNnpem/W1vqg1d1TjVRhEhJpQOodWiGQZMkZtCBARnXGjkLiMHYoqU5rTL4qMITdGFbxRZbVfCsNuO8ok4ijWpVNGl4alMh9zpnxArBNqwPGi9rdJFUI2zSVOuITihVy1Jq1pbsapyyvxpaPCKV3MUx6CwpOUQhb31L6jqlD2ZRXeLgdhLrCNJRPFJxVhlKmgdXYbrSOivrO/f1nY+89QUueJ2yizVa4T4n5lLJtcPpgrJfRmtpriKmkkQnHa5aWhbOr2edqkvjaDUyro5wqzXGefIyU29ukJyI1uDWnjh69jiMjRxXx/F2TZaicMKqb4xxUSdo/Y3BdF4S3VbWrwANqM1Ssj5IIQysoiqP8zTz0ZMnzNOeGAac9VxdXjOuB07v32czbjWKUJLyj6xOM+akNbIGg3d62RQRPEmpDAAAIABJREFUck4aP6kNW6tCd53l+Git0b6UWUpid31JFWE1RB7cf4t5nvjow4+4urokxsjp6T3WKz2cO+e5f39FjI5S2y3lfZoTcRW4s7pLyos6MkrBB0cI+p4taUGsKsI2BKIfyFl48eI55+fXLPOC8YY333yHYQh9YXj1ICMQfdCWD+vJeVaRBYsNyoI5qNWtFFpp+KCQyd1+z8uXZ8zTnvV6xSfee4/NZksTobWJ0jLejT3CI7e1rBV9T33P6jd6E1xPnM43E89fnJFT4ujkhMdvvI61npYaOWlzEv1CKKb1zLoKmhepMk9ZGwCDpZXCfndNK4VxM7LabDEu6ntrDk1/9jbj74JeY63xYDR6lKlUa1jFtS7SUlly0kW76udlGC0+dosjnlIaThxpWkglI7Vou5oxXX3WS49UdfH41aBiiTTlZuCoeSHtC+NmYLPdYMLYGTIFJ/33lB3BZoJpt1OG3W7P/nrH8dEJb7x1T+FwQcgVxvWWUhLNQE4LtjWaN1AbyeyZLp7Trp9h8453HkQ+/31v87F37uKAms8wsqMtwK5hKqwfnjLPE8u+MqdEw5IlE+JIPNFYVrOWp0/O+dbTaz66ybz/bM+7jx/wInk+eK7T9zF47m3WuvmbqJ9TYxmHQacG7lWVOUYPdbkKR3dP6CZY5jkzzwoCFuM7l0eXzZS19nbduUOaWzZdGDI4H5TJk1OHe3cOWPDKLxBzG1+owXdujroSEUjGEH1ks92CtXijh5AqopGI1IHrBxaYdxrPc5EqMHid5DV0kj9X5YglNDJcW29dM/S1R5hK6Zluo45no7wMamO12uAG5R+ZbqsV0eaaVivWFI0kLIWyZMbthpwyOEs0ToWQnketnTNQUqbVwnqzxfteL0vng1i9LC6AFXWnRhO4mK45uXMH02MSxmgbDNZ2+3NVZ6W1LNOkk39v9P9zjlzqq4EBB2ejsJSFOS8Iasm2NvS4WiOJwdZETjtaydQ804qKb3fWm7536PfSq42jibLmpIvc19NOmzyHLRhdqwRDado0FKzvbshGm5cOZbd96qnPv3Coou7CMuiUeUn4EPB9T2m1H/lEwa3Oa5lEiBrfGYahizsWZ3XkJbyaYDU0Rq1RD3VoDDGqC6CLZYggpqpjQlwX3lt3RQ1YQo/f6MFGeVdeI2RNa8abGAwRqYsm56zFhshrrz2gLnuuLm9IIsCO/WUD90ydmpuV7p03lfa80Pbn5I99hre/93PdaWIothJCw5jCkrSNyKCDDWMXnl1MbKMhbhYaHme8xpmt4JsgOWPNgJSMDxqzcaKRm+oM0jzDUJh31zR3l9AcrAyFwuAdzq0xIQMWUzIhWlbbEb/dYDcRsxTEnWH9GilPMPGhmmDdhuHeJ3njM0/41B8+4otfeso3v3qH7/2R1zGjp540aoWUhXDa+Je//Qf8+Lt3CJ++j/nEF/iP/va3+K/++3/J5cOHpP2CufuA337/Q9a//JSf+hv3GLdqE5HQkCFg9wXrhRodawTnRxafYNYKe1M8vsBmpbyqaZeZJVOTMFhDqxp5mpbEEEJnGkWM6sTKF7HdgSANvGUbHEdDY4qNzdrwrZuMswFJiR2N4Af8yRHFGawErq53PH92RiVSbDfYCrSamKeJmjPOWkrWKLqRoq2PDnbzxLx/SZuEtlHO3NL3O9fjztI05lC6u0T6c3FwTVvjEZpGJ33VdrOa+yTe97g+mNb0DOIDqRZKacRBzwa4zrFoGofCCqHDXgtCkKauHxHwESOFGMNtnKtWYT2syDmBtSoeOL24QMWbHpkw7bZB85U0rK7R3DJLThhxjHHQM1kfdpXW1P0hDpcy3kIrlUTGuMrpetCLZXZko1Ez56BIuRXCXb/klwp629dLF6JuxlqKsmYOl1N5FQ0XY5FDYgBdh6oov9N2scwMWhjjnTL2Dq26tWWF9GL0otksIhqbVsB4VUeGWHJTB6aPPdLSDLigwxeMNo3mhLvl0GiM0XmvbKQw9BZqEKlM09IHVJ0vhLpSrLfKlrIgrbOvkqVRsaazO72KRcUoH6+JphSkaYGNFW3W7TYUFXPJNKfCrGuB4IVc1AbWulNZgjmgbnQNbnqPwDbEa5EEBsYQEW+YlkywDiExT4sKZQYdPhmjAGWfqaZyOcNN0ljg0coRrAoD1iVqtuynhDeR6qU7XLRl06GD5EME1UiB1hk7Uf++IWhKwLbQXzuNvrUaiKWydZUrnDYSAtbq+b65wFJm0sUZX/hr38MP/uVTWgy8uDjj61/9Ml/6+7+Inbd8/L3v43M/9pO89vm/wjs/9B/yxg/8e/zEzbd4/nv/mN//tX/Mb/3Wl/nSnz7joh6TV1uCX1PShKNSq6HhEPHUpeKinhEkCpvoyUvB1oVcb0g3QikgG8dmPNL3F3UBIxqstdbgUbapmEqlkXXsq4Mwo+LzYEeKS5AMRhRbUWsm5YKXzDjoQD0V3XtpejYQBNMqwQPo0LMi3cVsO4NS2yZqMT0aRT8HqHPQ4NSVeRDxmw5QhdLXsMOQU11P0Aimowm6WGitpTnFOVCz3jt1hQWrZSOHxvK+rONMLwbwHmNg6OzLPOu6ZNEzdq06mAU1sRhrb9eHqSxaruAMs9XGbmcdpjkd0nllsoke9juDU53Nh+OhD0N3YAv7/UJBW+SN1aFalUowRge6oucyYz2mu8Kkt+aVJljR5xtrKeggxppASouunU4dbjZYPXca089TFScRKwpzN7Z1x1/VAZwPZEHXQNAINa3HrNUBb6zubblmfT+MYNshlfWd+/qOC0rZGaiO8/1ewWfTjIgQnMKnh0EZPYdq7FZRm1cW5fi0xLQUXBi4c+eE9fEGZwyuFfJSmS7Pab25zbamtjnv+2QnMgyGOIwKsut22LRPBK+gOBMMJnhVcY22CujUWGGgKvBoU5oQNW5X1S58MydKXtjvdszTnpQSpVYent7jtYcfV+6GqAovBlarVY9E6MTYH9gqABjyUtQK2E9HRoTBR2KMXO+vef7BOXNZyEknsXGIlP3Mt58/4+zFV4gxcHrnHu++/S4nd+4gIlxeXACHSlV1dzTJYB0+DKzWY4drt95WVhmHSEuZPCtcWqy+Rz5Gpv3Mh89fsN/vyClTpXHnzh0enN5TJ5OBcVipuosuNlTpk2+jEHQsDquXyT63pxZArYeFytmLF1xeXVER7ty5y5tvvM7Qq9kPdZCOqBWUVRSSKUU3+hCwVRfEg+25SaHUid31jquXE80Z7jy4z8lmC8aQlkmdEw4Gr8ptqlnddAgpCfs5kaRC9TRrmFPC7RIlV9b37lFcJTiDaepkaP05blJxPepn6NNtp80LGmccGGxAnFMLZ87kTuR31uLjyBiiOsdq7c1PjZxmrncz1VriMPRmJ6v2SWOpRXleLljG7VqZT6bSrNrw56SOhu3dE6ILSKvk5QonDleFwRe8WxCXsaJuu2k/cfXiGj8OvP2xNxi9Rw6VxHpE6KB73UCMg1QSURq17NhfPqXsznnjrufz773Npz9xh+3RAIuKo04m2pKgGUQiqXla3GLrgBkm1uMR15c7losbQgwsouyZb3/4lK9/6ymX+8hVMkyM/PHLSjawXwybVeRkMzCEgIjuEM0Y3dDRuKgitw7w+4aLkSFozXQujVSE3axtKs55hYl7T+2Nkxit+zWd9wU61Si1EuOK2rSat/Q2t9LU8egPjodioamQixgk64ViaZk0L1hnOXpwl9Y3eJpo443ROHERFWaDCRgMYTVibaQa3chzbUiHU2fjCUaFxVwKUjLjuNbNxhj9HQXlMlkh18roI6UU0rwQnMHHgSIN58yrGAMH661aq2vRFr7N0ZE+Z9YwGq31tn8mXlzQDb6lzDgOOO/632P0UicVU1DOhrPY0gjec3V+qaBxizbB9DgovdqZdmjQAEmJUgvDekMttTcTiU5MpeGtI1WdzKacWFLWw4CzGuH0Ci2V1ohWxQ/n1yoG16oONEkKH/ZRGQkilGxuOVQiBuMCpRVEhNUq4oJV3p5mObCm0ppeWFWMcdQMQxyJzusB23odDHD4nPUDl/7xOv3vDix1A+lk7xBDtEUdv2oLN5QsbLZGobPIbRECdKdU9yoFH8lUgq99ymn6FF3djYf9slXT7zyHv1v6zwKMJVdtMJpTUpGz6QRb7d6GXSqwn7SCXQoP7q5498F3Q3Ssj0dMa9xcJl589JKnH51xvd8xm5lxGHn3rSN+8GOf4a3PPeIrv3sBJiLWMahfHNcK82Vh23GJ1nkurvd8dLbw2oMNr60ibZkxJJqLmH3l6F1LsZnmV9RcqUWoVJw0mh8wqDvRtkiUDKmQa8TdNMImU3cNNwqCg6COaFvX+G0gPXvBbByr8ZLx8Sntzn2MCYhdYewRtIFmHjLe/Rjf8+de8sHTmS999Vs8+tiWe+8eMRZIR5mWHKVEzu9PfPF//A1++O8+xt39Apsf+Y/52a//Z/w3/+ycNq5YXMSe3uP/+L33GVrl3/jrr+M2WmkdS6NaYVoywwDGG0KNYB3GJ1wbKSZpdMsZVkPAt8YiGSfgZ8tR9NSamaTQqsX4Fa1kYhwp0wQRrGkKzpXGtOxwYeByf8PDkw2yP2OfCyZ1RpaJJBM58gHqBG0mOseddebp0xuuk7ArQjGO4AynxxuG1THRR5qFq7niXMOZDqP2gTfffIez82twkf00Kai5amMORtvIHGCNo+TSJYQDL6pHhpyWIEg1iK09JgagTpiMMpOsgegFY0KPiC00p9FjK/q8V1HRxxrlRVGFJK23RFko+ZA4JcTY13HDlCZc8LigMQ4fHE20CMBahw8qytva9DkwAL2h1BsVWToTdH8zMaxX2KDxfGP6sEAqEYc3meqgZW1UutllanEaERdhHC2rMZDqgSHZDh4kpIhehJyen30MpGnCu0hr5ZZr6ZzG+29BuBp26WISmGZoSbBBGXzSW0ub0fUbp4KMqYYsWdEPNSlLCt3fMQ5nPaYKiYItBes73xVLtgK56gXTGsI4ELKjlqx7I8rhbK0yDp7aMs56vEApur4aYyhLuYW5N2nUXMBou2TtBR2lZGgFY4IagqpVDlHUz5316kKxcnh9DukFwTaNG3EIGlmtpm8CxutZ0rSG8xpjdsHrkLcIzgUMooU8VUVPjVsGvQOslH2JifrZrK27qG2/lHtMG6jStInO6LNTRUizCqBiR/Clnwln2qKuWOO1pMGiQkMt2kxYOk9qtDpc8EeR45VgWXWHXyZS1d3rAu/vM7OZqaKDGENlvRoxdsCKsOSFMKz55uWOh39kefTolIfrNe/86D2aecDFZeGDb3/Av/jFv8f8D/5b3vz4Z/nUj/80j77/x3jrR/8T3vjLf4e/Ktdcf/P/5Bu//g/5lV/+Vb789feZzGOu4yn4BcoNwTqMHRBTydpZB7Uwrgd2N5kl6Tmm2kKcFiQXhgEWLFOOxLjCGsvR2nG8sZTmuLquTEmfswzqcvMa3q/MWhFPb4o1FuMN3gdCVJZsSZ6UJ+WqBWX3hKDtz/00pCJP+bPth4dCJxWhvD3wfPScYI0yiUwfDveLq4q7+pCqW9P072FFh3mAONNP2doMZy3UvKhDpum9YYgDzRRqayreimjEy3hss3ivFt+GdDaaJa5GaNquXKUq26qLyt2yhXfacBiDAavOutBdTVLlFay7u/YBQoiUnDGm6d9qNGKsn3+9o6rI3xTi36ze2VQOw3rFSkQEyUUHow5oVu821hDMK15zsBYXnA526XtQ1sgtBozT85ZDnz2sDoeDsZSy3JYmSKsUmjpED3uK2P43vnKG092PIQZlTtMYhkAp/z+PvD356EJr/awe3FeDpx4aL+jcJAGphrpkUs20mln5AYzHrTbcvX/S68YT0+UZNPUMaYwIfNBJRLBRhRqBWnTy7nwg7WetGzURgDiOcEhoWgh+INdCSQUHFFH7rOsPxWo7siyJtJ9wMYI0pmnh2ZMPyTkzjiOboxPeOr3LMI7UlDjA+FqDwXttw7GOsiRqSQzDyPHRhmmZqLlQUlUbJcJqjNTauLi85ibvubp43rkuCtZtJbObC8uLJxgcp6f3+NjH3yPYzkcpladPnmEd3L97j7Aa2e9nnah5Txw3uMGyJHVbGDGUpNymECLLNNNqJVjH+uiIVDLzPvPi6Qc8O3vJahiJqw0nJ2sePHqgJPmmLKBSs/Iy6Heb2whRv2jnPUNY6ZRHiq49ThXrab/n5Yvn3NzsOTo64vEbr3O03QKG1nJvY6EDx/Qh1wlMb7wwKny1Dn23Tt/Hadpz9vI5V1c3DOOG+68/wsbA2kemtHQeQkSNmdosQI9jpLlwMyX2tWG8wxfDPF1zkxac86xWI5uTgWy1cVAPMmo9ddaBd4wELKbbpDuTxFiC1ZhVE83VpmnG1Eq1Gj1yg2MzHPcJlh52sNr8NO0zoRnGceiRloAR5TzkZkjLDTGu2J4cK2BQFMxH0WfDYDhabxjigKmLTnQBT2MdKyYmaprw1enhpBVePL8EY3nt9dc5Ot5gpVJLwXgwEhRG3SzIjLeOsjTMktiGzJTOadeX3PEzn/u+N/iej5/y6ETY1Al2O5oLhDBSKFACl2czy9xYUub8yTOSNcRVUBYRI+PDDbsp8dGHL3l+seOjq8TFtELMmhYcmzFwNWeGMPLG3RWDN7Q2d1Blh2OLoWGx3hKGSKqVVLTNYzVusGFAXGC3JI2FFsGYSBwH3VxrozSDWEtKCvGLo8LNrVVHkrHaBig9ztCwVGfJpagjczVgnSMvCePQqJTRWvUmlSXNSC0M2zWb9ZHG0oIHKkPsfCt6NbJXPleaFsIQe+a+4l2gFWV37PaLPpsrB9UgtVKmhc16zRBHiog6djCIQ7P2wGqlG2xJhVorq9WaJA186JwfVQWM6bWxopywaTezWm1u48EBbfGhN2da0clbS9owZ51lGEeMpU/ihUEMi1FkpDeWRRpJKj41TK34ozXVgF7r+7Wvc6HEGg5NS/uUCauRNC8MftBZvQiLKMwz14KVzkRq2njig9NYnw9YF0i9EeogVitE2zL4wNXuhjEGhXE60UMIFofcArfFoa95TVBhc+ekf0YCWHUOtepwmf7iVGoT5pRU/A8KX0e45QCKMRTT3UVNoxmt6WdceQ7ttjnEH1qpkP4ZE2rVNU9k6BW+ymvTGETPOaMidTOQSSx9wi/G6IG62+0F5Ss431tpasE2KNl3F1zGSXjlzgSwvb01F3KaaeLYT0nFvKaOEJNnLlIieMfWX7HZDBwdrTh99xFvPT5hN2WaSbz5hvDpB/f4xBsw7y8xy8ISKhFPaV4v8V7I80yuA00GjLU8efGSi+s9H/vkY0YylqQHZBH2+4Qvlg+/8XWGtz7J5mitB1VjSU3ZeUM1jKypq0zcHnN9dsPR41PGNkEeIRpEEqV6vBsUyDsUPdc8vMPy/AZZgeSkz7QorBUq+FEvDHc+w8M/v+Mv7iZ+5R/9Mb/zz7/Nj55+Eh8dK7+mrjNLaayz42JT+Vc//7/zvf/5p7HzZ3nj3/+v+Q+e/h3+/h9lopyTVkfko1P+ye++4NHbd/hzP7jBb/f47Qpb97gaKDuduvtxQRaL1IEUMkbcrShgrGAGzyaP2FbwsTAtgneFjRvJooDd2RrmnHCmYYowWPTy1au1082OdH7OOx875qOnCzcZFmks3pBKIU2OoWbmWok28v2fPebNxw948uSaP/hg5k+eTNQaOLlzRPBwvZs5ez6xaxanIBFsLIQQuHu0ZrOFq8vG5W5RIdoYbcJy6uw01mN8QEzFNIXEG6ORltaaVj9X9IzRB1AqEFR9+0TACLlqLOMwNtSBZKUumeaC8pkcSFHXU6Wq2GitPjel0VO8Gqu0Xqf2IrQqxCGwlAWp6jhEmra3WXVbNWMR62mhkYs+9w4LTp9qFx3b1UCeM0vJtLZQM4Q4IF30t9Z39qkhDHrZ2ZeZdhDgOzx22ldc9AQPtlZtb6JQc1b3g9N2ShPNbWS/0cBZTNHIUs2d1WmVUWSDlvMYcZ3T1gWkPsDpMJrOoVRnrfKOHM5bSisK265ZI7fN3rpRvfPKNREt56nLQq3C4Afo/ERFA2h7mTe+uzQUGGwt5KRR9tw5MsEYjo+2mJaY29JxQ0XZkWIV42E8BHVFORyuuQ5vV5ZRozDYI+W90hl83RXn+qC220z0/NsUzyC1EdxAM8oNrNap614qwfh+cdQ91DodpgQ3UFuhtIptgKkYWt8HtAHRBk9wOqAsWSMzwQPR6BCgajypGKHkTE4NG0SFqAbiog6E1PitTDlMb8ZtvSjCstl4Ao3SLKOzFNkzNYutC8GPjK2CEzyB003AsGc7wvkuk6oHZ7hZ9oRoWCNsh8Askd/82gV/8fEJ15szticPKMFg48iDU8ebn3qNH/rrlpura/7kD97nX/zDn+Pqvws8+th7fO+PfIHXP/+vc+eTP8MPfPLf4vv/Juzf/6d849d/gV//1X/Kb/7uGf/XhTCFDcO6EZyQlkRtAYyjzDt9r6zBmYpzjvsnkdAC2cxgHJf7GSPqEnl+ZphvBjbriBdH7Hvz6AIl2NuPe2mVlfcIThExeFJbSFQ2w8B6E5mnxLqOpHwY4pvuJhIopQ+MBIPTNahpisPiabkymIBzOuxb+rAQ6VF21DlMj5U66cGqpoJjD1lR2kITjZMhnuiUReV7uQvd8Rm84lRKy9igLqHUCmLRYiorykmx7rZ0xhkFlTtruumhC2JN/dLG6s8yTaPLrZReIKUDOie9tbhPkpOp2A73D9ZhSwVviETWq626vucFK42aNJZqoMf8NSIeXLj93XbTDVWEqWRsc70tV/2h3nWcd98LMBmM65HJQKyWUoTide/0zt3ea7FWHbPSCC7SWmYIHiGQa6NYHRBqfaU6aGsValaxy1lIVs0qDos1AeM8HmFO2mz5nfxyP/dzP/cd/Yb/yy/8bz83jp71EHBBM8VNQArUObPkhZIWqJkweE7unnD6+CHr+6dsT9YEW+Fmz7LfMc3XNDQiYm0j9qp5Z30n5OtEhxgY4pqjo2PGcUXLFaQq1BitsW9WlXI3RJ2hiG5wxkV1hfRa0f1u4vz8nJIXWm189MH7fPD+tzg/e8F6FXn86CGPHj1ktV2h9c2FEAMgZGms17FPo/ViMIyBcdyw211rnKIs6rhyWr+KVM7Pz3j69DnzslCpiNMP3rCNzPPC2flLAO7fe8CDhw9YbzeaHxeNujQR7p6esl6P1KrcJR8tYRgQo46j6+mK/bzowcN5fNCpSi2V9WbDejVSUubJsyd8+/1v8+TJE8Zhxf37j3nr7Xc5Oj7m6Hit3CkVuFnmCRF1Qwl6SNPLl0LlaqsYo9ELMY0QHNO8cPbiJR9+8BHn52cc37nLW++8w4P79xmGqFN3FBimBwWtk+34ekR6RKXbpTWpqJfg3X7HR08+5PziihA33L3/gDt3TgCNhaghxTA3dSKBIYs2xFynzNVuYdpXnZJS2e+vSbsd4izj9phhXGHdAM1ivdFDZbelhnGlkQ6rtmQxOkUDQwgDLg7a/lAbRSq2A/nUVglhvWYcgk4ErNbtpmVhv9uTcyU4ZakoMyJQDLRUmcoCWI62R7SonBil5yhvoqSMt46wWumblgvGNZyHFYI3OwanTYjSGkO0XF5cc3O15+HpQ954/XVWY9TKUhzGqCVZxCOi9bOlWdL5GWW5oJAw00u4fsEbJ4a/9Kn7/PlPP+LeiWfrhaEVZKosS2b/cs9ykbh5csnNZaKVhs2G86sddu0UAl21eeLZy2v+9KMzvvnkkm+fL5ztGyZsMeOGq0UoJrDdbIlrlRnOL6+py8J61Kz7AW5pve81ocq/acYQVmswnqUJZzfX3EyLOgzXx8RxhfrgTH/5CrkolHm1WnWXiTCOI02EUjTuW0phLolcE6kUhmFkXG+I49Cbh6rWPDtHrpnaKkvJ0ConJyesj7aU1hj9yGgtxji12aMHDG2J6/XNK08LGluwRllDzgWWZaE1IUavTZQCeZ6J48hqtSI3nShZZ7WsoGks4uCyqiUxLzOr9UaFlXBou0AvirfzaI15Lvu5w+zBRo81RicqRteaXhZHaZWyJKQ1xtWI6ZcEET1gG2tum+k6dpNoHOeX54zrDSYoELz1dcDQmyON0UNtSsw3e7zz+OhVyHWhb86C6c40a3tbiSgDJjjLehwI/Xc35mB9FprxGmO0Cvg1tjHvJ7YnR+pSaxprcH1yZA0461X0tparyyuGGPFBwby5ZozXgYhWnehlks6Hm+Yd2+O1QuHREoHWgeKHc5ExHbpfYZp2eN+ZI6LiFnTruajN3ZjOrEqZ65sdx0dbQC+9h2he6XWydGu+Hky15WgcR3wYegxCW6Na0yn5AS6J0TW2VAWaOtcPdD4oHLwVWlLm17LMtFZpqdBKxnuF0/rgCGPADAM1eLK33OTC7nrP5csrLq5vuHx5RtktXJ7tOHs68eTDiSdXkeK2GO/BdtcfBusjU2rEIeC9wQ+Gr3zl97nYVz77XQ8ZpnOydJ6MN+xz5t6JhxPY5yPG7ahrvB/YRMsqeAanLUoWcFJx2bPejox3I8Y3ha5WwbqAH7rjx1pcVXhmLdr0Za02tNhhA9Z37a/oPybg4pbNcaJMl/zhH75kHQdOH0YMVQdizSO1UYaBjy4L919+xPp7P00rJ9z/gTeov/rLfK0cYVvBHB2zmxfmJ+e89fbAZrVm3ELsgrsLA0YsLjqcbcpzkoJUTzUZUu5lGRqttc4gqGvZicUbjzhDYiLYQS8SVKYl4Z22IAlQJXP18pqHJyu2w8I3nlxzs6hTzRttV7vew8M3j5QD5hzXL6/56E+e8KffvuLi7JplWki5cHF5w4uzHS+uE9U4jRF7zeqWkljmhmsWauVqp25K6zxgqaVSUuqth11w6UzHQ9zt4Kx09s80avbxmcih0lkFWfqmSk+lAAAgAElEQVSk+bbxrLNBFMOo5QlLH0y9qspWZ59IwyN4o21ypakLZeyOJ2tQLp4I40obufSoJLfMNq11UrdBy1UdIQiU1mNGVt22VtuXmm1Eq6cFaU33ALE6xQcVgEXPMnEIeGvZ16IsItQJevjdRfR1ccZ3UUovf2KEkpXTJRWNOhltoJT+4okat7VdqVW9/HbBiO5ske6OoCgTrlQFFFtj+3lEBRuRhrc9ulpVdNF5huuMSR0oHdy9Ig2aUM1BbFfR3BqrETqn/3nY7fRi5/sAojGuBkIw7OY9DUtpysorTf82I65X2xcVZV3QAt5gerNyIzg99zkf+xqujMCck7o8u5vLovwk2wTbdK9Uwau94lH1+LYyAk1/wfXn29Y6kzMgqEuplletdKADGZ27qPNDB2UGXMYF5cCo40QNA8FGSlu4mTPz0odEtjMpQYdbxvTPlZ6HrbEMzrIKhTiMpO5mm2vFad0a3nqiqQSb2S+O3Bz7Wdgvjnmxyga1BuMtw6BDWqmFO1vPG8ewqwt5V9mfz9Rdpu4r6Xpg2hWWm4yzhkev3eV7Pvcan/juY5z9kD/4jV/hS//zz/PNf/aPcLsLtncfEB9/jsef+Ul+4Cf/Fj/xEz/IX3gsnO7/mOXmJdeTsBcVeqJkmsvY0ojOEFzj7nHDx0qWWVuVadoO1+8BtSVymbhZZmqGlIpmILtDqBplGkkVajXKMXRWY1ZeS30QLVHKuVKzxkNj1AFOM069y1X+zGfbUtuM9ypAS9Hv76zgfcPY3r4nRc+x/QzhukAerOuDpB6vE0s1WkzQum/Mo2VaWGU6tf7+WmvIWYcnrTblwPV1xjpdd3WN1XNU7esyPXZsMOSiukHwDm96Y631WBdeYQyMIl/ENIJxSKnM7QD71+NWtJGCRvWkCVVUILLDCrEaV85lJouKZhrd7K5GUVZpq41SujsK9Nk1DrGO2oS69Db6lrSgpXXTimj80RihVcPStOlSatWoKfTmQxWHmqlIRR2Mrt/ba2ed9mKv1RC0QKano6T176EMFy2uERUZs1TSnJTJZBw/+7d/9r/8fwg5/y+/jIKfvnNf/87f+k+lGfCi1Z/UxjzPelBpAgFWJyccnZ4SwkDNC3m353ra4yj47p4ofRNwRmtJVzGoAmk6p8Z0G5n1jH5k2Gx1k0kFYyu7y3Ou08w4rLUJyAf8sMINa4wVaprpnHBqXpDW2N9ccv7ypQof+4U8J9Z3jnnw4B7bzYYQAimlA4+xbzD6wbLBdfFTFXiaPqhLmvE+EONAzrP+7rX21o1GSQUxQloy07zn4uKSVgvDaoMPsF4fc3J8hA8DyIJUVf2r6L0hhIjxVmtLjbZkmKCXH28DS57UmeUDzVSsCdSccBgGP5KWiSdPPuLZkyfsd3tWR8e89vprPH74Bpv1hnnJpJZI88zgtclFugvJe0dKCSs6tUo59w8wYIy2NzS9bF+cX/DsxTOm3cTmaM2DB484Pb2DM5ZckgpS/bNorOl1mQYOFmiDTkaKbu3Ss7klF66vrjk/OweE7ekd4mqFaYcMqyCdhH9ghKiybagC+33iOmf2S1ULdclM014PDuPIsIoEH2lVP4+V3k5SJ7yPmMETMVorLK0fWjyIA2+xLvYWqoZr0gG8tTNFIsF7VjGQJWtzClBL42aaKVVFOIVwa2ZXKLAUEgY7RI42G7VQi372FNScKFWjHWN0Cl4risgzoRFsYyMJT2KpiRBWeCqX5+fMNwvH9+/w8PFjbUg5xNlcrxaXilSFtOdmmKZLyvUlw7CiUJHphtOQ+OyjxGe/6zUevn5CcBkjhSBg6wKtslzsudplslgoUBzUqbAvCzPw4bfPkOhpzfL8fObl1cK3X+yZsjBlyywe8SuWJpycnOBFnQhpqeRlx2gtq9jjQ1hcHMF7heELtJwpFZ1K+xVTLiy50oD1sMYPQYF/tU9fcqH2BojoA+NqxDpPKVndaHTB0npKLuz2U2dLeMIw4kMkxkGh5wefbanKnuoH6bwk1psNw/GGMmV8F7u1DVA38Vxrr3ztLuTg8E7/t/FaOiAYUmdUSMn9AGjJ84wPgbheq1G3KgBZ28joG5Q+c1MuLGlPjAPWDfrZsQJicaLCgO3MCjBMKZHmBazDRh0kDJ0N0d37DKIXqJwzadLfZVyPOrnt7LEsGiHIUtXJ06BIY391hXeeMAy4Q42r7fbt9mrtSDkxzzNeDMNmVA6RMcr56QcwbfDrl5kuotRSGILHe9/3GHO7p3nvu/3fsEhlZR1Xux3RGsbt2KM8yisJLnSQrHJCcmngKk/ff8HmztFt/r8a8F7fX2rV1iqjB+WUE8s8cff0RFtcCuQO7j7wDQbfo79SyLlyfX3N/Qd39eIkKlDdXriM3EYNcymUUnn27Dmvv/aYV7ETZSzoYVZ6+8ohCt4oS2JzckwYgsZOUI5M9FEv6YbbCtraKsu86GvQdN30IWCdtt5QK3NWISLNi7rirCXVzDgMBOd6tXZVZ7M0mhV8iIShKiD2Zk9ze+LJPdYtsjkxjGiMIplCNFHr0aVBaDw/22M8bL3BhIHf+OJXCbbx9njDMk9YtOwji7AaPe893PDGZ9/g2fU9fHCMg8EHffbX1hPXgnGN6FZ4I+AL84dX3P+uU1bDGitg144wKKvFxogLAWO1+baka5YXCTt4YvCsHryBbE7B31Mx0GwAwdQzyrOv8I3f+Rr/+Je+Rt0Jf+Wvvsej1zZIM0z7xvVcuDkXzttEeFL5a3/zb2A//VPQtlz+/v/K//D3fpGvyDFxs4LVSHv/KT/6nuPHf+wBj9+7gzWJlrIy2cRQ5oWwGlnyTC2W5aaxJMv+cmE/T6TaCCbQJLObMvslMS2NVIWcLLuUqU2Fp/0yM2flwp2ejORSmOdK3SdeO808+dPnfP3DHUkcSSquRLI4Jut469Pv4FpTNpWrCIlWDfNc2C9alIKNVBPJpZG768WKQSykljDVcny85vR4xfX5NdOBpWl6aUdvIcagMTivw8FaGzUrw8cYbif8mO4OFQVZ3wpKtVJKJdeqzXJ4DBoNNVIp/SLdsoK/8Z5qRAV8sdp029cj4dB8loneMURPHAaubpY+Idf2ubxoxKuKroU+jFhvKEnPicqV0ybLVjuj1JrOAtUoS56EUksXnbXB0HudkFvjKC3TTNMYBYZ50jW+NY03OmexTgGx6loUZZCg/MlUtO0MEwgh4EcPFJb9Au4gDHRobef3idH2S60c19ZVizaxWaOuzzknPeM6vaAf1qoq/XeV1v9Rl5MY/fuM0/FHc1o1Lk1fu5a6kI4Kv8H67sRF4ca64agzCUWVG1u1LbCXNxg5IDz0wCpFqCWpwGINqSnnS4s/NWZsq4FcsYPHBXXGGgOt7Mm5qBuji5rGhG5Qbn2QoNEq0zEPuSUGp1B7FTzV2VB6RI4qnUWlQwTfL+hGDhEfx1IWXAgEF5CSWQ0e5xs5K7qidiG0tYp4R02NvOwxVJZJXb8udN5f5+0YsVQr3X0l6hB3GhNCijY7Gos1EzlpfH6MA5SFEMGx6vctbTu7mQ3FWkwQYhzwYri+uGQ7Wh4MwieOt6zGwlv3jjneNk6O1gzrY05cYDs61huHXRdWwTGsI3EzcHQUsBtIF4U/fXbON3/3A158uDDEt/iuv/CDvPuX/k1Wb30K5yLGTOw/+G3+8Iu/wJd+89f48u895Y8vB64kUi2so+O148zcMiVGXLEs/XWbF8tcRhW9TelFD43WsjpUOyYj19zjs0G5fd4yrAdtJUPFOeehlKoinxg9jxnpAqNDGxZrd81pGUiqBVruQmxAmmJavANj1IWn759QMgd6GMbofz+0/WkDuaIDWj8vVQRa6YUaB5i/xoaN0SSQ84a0FKS5LggePiMOa0QH187p3Vw0UmmdpyZ97oLVxEpzKkrn3HQ9qkLovElNe+na7a2y1oq022ZhKUWdkLXpvVIEh6eJIWc1ojjLLcOyomI7CKN1NHI/02rzXZZKqUVbiVHHvJG+HrSiAhcGMTroFTEMOGWU2kA2lda0sVtj1Oo6Mv4gqitrzJjeNm4aiO/isZbuoD2WtwOVItrGSbOdh9k62E7LskrT4QrW8LUvf+3VYff/49d3PPI2pUqWQqwK9HVhYHV0rNauUS9WTQr1+pz5Zk+pmTAEtuMAzVMlI8YwWq/gKGuI1lMErbV0gDkIDk5V/SGypJlaF27OzrBOWPbXSBz7xVGhsa1WhqYgUWWbFHbnl9xc75iXiRgs6/WK6C33jrbKshlXGAc5Lywy46zvjWSi8F3n9eFp2oRac1Mu0kE97e9/WhYF0uWZ6+trLi4uFVbdM8+Hdoo7d08YV2uFhlmj0O/oqLXQsirF0pqKRiqt6Ee429qwr6ZmKZV+IdXFxVqPGMPuZub506dcXpyTlkQMjnfffJOHDx9iw0BFWKbEi2VP8AFaZR2cUvSd1qC3IqQ0I9JYpll/ttPae29db7bI3Oz2PPnwA652O157/SGffO8dzc5WQVpRNkM/UKhabfp9W/qlSC9/rRW9vBuPs4Z5WTg7v+bi8pIhrji5/4hhHTAlQVFeUbOopbzqxqV1nZZ9qVzPhVw07oMFKYmpuyb8sGYcY2fFFF28Y6f3l0QwgttqjFLEk6XootYt0NZYnZKjMEhrjTo6mtCaLtrDEEG0QlUrtoVUEvv9RM1CiKPWSxtADM3AtMxkEe4MA8fbAY8uHN5YxDhKLZS8sLGB49VAc44sDStdSDKFkYSVQpAKQQXZ3e6a84tLtpsjXv/Ea6yOIkbUNtt61l4DNrpxNeuZ88T12XOm6wu2JwNTmngwGr7vE8LnP3XK9kSgOvLNGXOtWIScMg0orVBSpqZCqbDbJZLj/2btzZoly677vt/awzknM++tulXVExpAgyPAQRQhUiEGQ5Qsy7LCgywrREfIEf4C/gD+KH72s15kS6EpJFMRsqkwJykkDqZIgCQmAY1GV3UN997MPGcPyw//nbfh9y6+gEBX1e3Mc/Ze6z9yWhsvnr8mzpmwTzx/tfG9D9/w/M2Zl/edtWc8JkKeebwcOLYxTPWN1uH+bqWbc7Nf2GddPluVQoGUacEEyHRJS7sqKljPZzqBvOzJ8zSYO2ddV85be2D9UkrM08w0JS3hY2F39xG2PXN7dzfyiDo5j7awlCX334rYwBgV6FrbCELVcJj2MyyZddtY5kXZPfEieVVo/yXEHpQFF4IGiTAodjcbNrE2TofAnAPH0z0hJeb9gd4bdQA4aYAUxRWYHVygdDkd2S0zHoYyJyq5oVf98woc1ZC41qKwv2WSZD7IFqW2KKMicLd4F1lQCt2dZVFrZLwwws1JDAbOolg8hq2hOfPVggc1uRG0jPRLCG7XkO2lQnfCPNEZakYexDayEnSXB100vNjmEB+aW2JMIgvG0OQOKcmqPRFGXfCJp289FXtsiZCSWPYOtE9ZKzC248ZaKo9iFCBLJ6VJrLqopMGWSy7femeeFyyqlVJMUsBdQa4MUMmCslvM1N6izDl7qHNXvoZzKfIQsyVAFddzHYKWoRA6nx6/oxpdHzOtjhy3MAYwxIyFIN9+jDbAeNkeotlDTfKlQc6QBaT1Rq1V39cAsVotdDopRg7LomcCBgNrJFdQdk9gzBAr86M9p2PFzkZNsN37CLY1tqpg80jFR9tXSIrtW81YQuQnf+aLvP7TP+J4XHGvxFk12uf1jp5mPny58fh771EPlXZ2kmXmKWJW2VrD7yIxdSydKLayn2dOp8KbUycdNpbgRGZ6C5RzIdcGqRCSEaIxpR02nTkfj9jTJ7R6R2yB2p2Yrx+Yarcd4dH7fOGnN37x1Yl/9c//jD/43ZdcXS8crhJpaUxrYr4qTLed7TH89j/85/xC3jF9+W9y9RN/g7/zt/+Y7//vX+PlsZFzprx3w//1px9xPn7M38qZtz83o7dbteGpJdr9mbwEqFGtel7IE+wITCMyw2vW5xIiUzzz5lgxq6xdQ3f3wj5PZGtsbaWWojkiV/wGnn/8hu8+37itDmjx7V5ovnH16C0mq3rPrarZyhfZ5mZYUiSWPIL+YUoTtclq2r3SurFPezw0lhi5WjrbIdLOKIAWqXiFDo22LK946SObRs9+QC1M3mWNA/33ZrJq1K6Af6nw9L6FQSxdQOkQAzsCpWsxNFeDr0XZkzApCGOMD1mUMQV6j5TSmK9mijXlNlXAO92Q0qY0ZMrVMpOCjcwRHs4wggwb2RPNuyrHQyCe9TtT0NLh7qxFgPOU8wCCp5EDGnHrHJY9mwV6XSlsYBfLWRrqIql/+1C3CBiKbLUSrLGbM31zzmPEC5ZGnXkfFtvRzLtVWmAs3pJo2bjnOq5F8QJuDCt+DGnku/iwPSuXDht2aG/0ogD06AJ5Qkyay2ICBHibBS417AQtym6B0BtT1PwfkpS4boplqMMC33vXz4FBhjkvdO+cS1dzlCnUvzdZ0kkuW1MrmE/aeRDgGUyxEm0oNPCRTyMZKDEkSm8DeAukIPVzCFXfiQUspNHTB0R/aLJqjMbqKRKRereXRkpZQETrpBRYJmdbK00uHcLIV8TTYAHBPXDazlIyWWRtRd97s4f2rxiNlIOeXe+Dc1bW3SEnzmul9DyI0NEU2mEaIMHxXOnB2e2NHZ3Tlmg9Ez2zns6AUa1x5sCf3t9hr8+cOrxzt+dtjzxuxiu/xfrK9S5wNUeurzJPniwcqpT28U2A2nia97z9yz/Dfjfx8evCh9/+DX7zf/s1WnzMj/zcX+Xdr/419u/9PD/33/8SX/17ke3FH/G93/6n/Lv/59f5j1/7Dh+/OfHa1PQXGe2QvdLDTG1dpRsOwaPAgqB2aXOjVsesUMuZ2BJrOxHTzBxn6nZWVmUXedgGYCwgRVllCroeJSXd8BGLkYKssLpvZ+qlzMgrMIBYG2qddcUsjedb6gWzqHYzb6Sc8D5OnPFcBpNjwcJEwx9Cn1OcBJyZky/nYehUAx8xEiGAhSYQCanMbeD8oXdqWQGBwc0GOKoVnBizQOzAmJO1B6dkpDhTWhlqvKE2bLLT6Y3woSjvlKrinDlPBE/07sp1Lg1LnZp82EeHtbZdZk94dFg43W+y23nTjm6jeCZIMRpifGgibmacvSlPt5XRXJpoUcUGUmO5XAUhjCbzobwywyx/2kjfRVj0AZynmKVGNWPKywDaZUXtQfdzAKkko0qDPstfnzmgNBOYYiLuI1dXVxx2V7QYWE/3+N0b7u+PrHXDqKQgeXtcJg6HK063r4lposZAJhFjGI1ERWyJB4InbFzwhi7WmoVUbmXlVApx7ex2N3ow0aHmXTXZ68tbjm9uuT+qUjzlyJRnbh4/Yr9bOFxdKcwxQAoa8LZtY1oUjGjJwPKwLKrmNEWx5Ntp1aVS4f72luWwcNgdeHN7y/l44v54z+2bl5gFDocr9vsbecdDZtkfmJdp4JvhQZ43TTPrdiJlLbrUjsU4bDiDoRgHdquNXpskjCnTe2VKE+v5JJvD61ue/+BjPBhzjrz99BlPn7zFo5vHlPXEaWsCPmIgL5lYnDRaeYijLtWrXjLTpa9AMllWtlp4/v2PefPmDWaJ1lZ2y4G3332PH398A+O7bNXHIhwflAXyz/YHdZPkgCMvZlyC3jr36y0vX35CKZX9/jHvf/GLpCwEu5ftQXotSSakkPEqW9vptPFmPXPaKpFMq473wnk9QjDm3Y55mkblMJL8IrVR3QrRItMUmJKaoro1BXEiNNtCICcpkiR9Vm35VgRcBEeWTJOM2XvFLLFZZ1tXNW3lhTwHvI9wNhQgvvXCMu847BZ2KdHqGYh4jpyrrJQWElf7vax1A71OVGZzJj9jfiaETnBlxd29vuP27pZpOfDjP/Fj7HYZr42wOiktCrDukta3S4BdL7x6/Ybj6Y7oG1dXV1z7yl/+8hVf/XJg2RfabWU9dkLaCLan9Jm63eNb5Xg8UupZwPHWOZ0r9+fOy9PGxx9veM6cWqEbvHi18uLVkduzU9kRpp1yKMLE2mzUAyfW45leGssUSVPm+rAbmT5NA2maOFfnWCsNWXDpEQ+B3lWQPC/6891h2xrrpnwFDYAKxJ+XhZTzg31IZ5AAgNqcrQk4MDPmSaBwSmKqg1dUWw+1rNRtkzVCIg72+wMeIs4IacXIMdHRwrGuBZDCxFFQ65STFC5o6A1jsdlaJZm+/RSc43kl55nlcBB2EU1Apqmq3SLK8ShdIdzHE9OSFVA/GJ9L06BbHYoX3XG1dXxr5CAbRM5q5Kl0MkmZP6O5qK0b3Tt129hdX2NJuXbAw7seRntGbwLs11Y53t6xPxwe2n7SqKoPQkaGusdUt90qU49M06TlygS49AGsAlqmYCgo9e+WY5SaYNgLLiBUw1Uv7QLQkgVu78/spp2W/m5a8lrT39WlbngIzfbG/d2J5eqgJiiCWiS7D+tBwW0ATcFhBC32FFjXTeekI6WBSb0phWSl1C5Wvl8A6mGDG5+qARbCyK8Tu5iTFEwXe/HlbAWGhWfkEriYccehiUF9UMF2o/klv04sfx3ZOT5aV7wVWq3k3UFtma2peW4AUBDIOdO2jWlZCGaUTQqFkOzBiu7elNuDSwURItkUan/KAcsLpEb1RI6V4/iZt/VEbQ6mezOlia3U0aRTCJ5YC5zvKnMuhO70bkRPXM83HJ494VU2TrevRCqFHdFkd0kj30I8jUHN3N1WtkPg/NEZe5pVHhGqljozmul+877g0cA64dk10ydn4tMFWz6ghUCKy1AnSZHtlrHdOyzvVX7qF1c+/KjyR7//Me9/+8SXv3JDtEaYNubiPF32HLfGh73wh//0/+TPv/15wuGrfPFXfpX/5pv/K//g36+c7+6xxzf0x2/xb/7s+3zu317zV//bievDXndt2Qi7it87eCfPQVWPIbLiQKSW+NDWZgYWZzbr7Amk4FQqsezoYSV7Ii0LJxP7W9fC5J3zVni5Vu6scPYzoaXxDHXMMnMMvH61kvIy1BNSZlY3jDwWjoZ5k8yfPgbvQK8CwC9ByDoXMyltTLMqq/FLo6HJaspQeYIs++i6q44ak+THfWhfMkZrISMYGS1DKQ67Qysic0Kk1MqcJoIFsgU6xjJHZZ+ZwCk1g40sStO9EEIk7hPNjbKWCyxADHHMUFJEWm/jHS9IUCw7iYKQo+qh6bJsuMJkA5AvjVIhjHZK/d3busqaGgq7JmtndIdJzb1TgC3YQ/h0rTofQlTI78VxawY5y37o1khTkBp0q3RTLlzQofXgPIgpcVGr95FpZkjt33obQeaZHGYsGtUr0Akm5TRmg8y7BCGoSTW43BK1VYIpCgC0oPoo6PEor4iXBlFEaqtNcRlBzHA0neGh+/h3CwKYXepIC06wTumXaAaH6CPB1ei1joBr5Q9NcR7WnoZTcRfAryDfRPM6QHmBPHXbZGWTo4gpGiGoPGg9n6T4bnoau2l2CKO4IoUge/ZoRXZX3X1AltcpR5JLneIEatnYcmAbLY6MPCnzAZi5CCwnMuVEbytzKNAh1DgaxWTRi+HSm2GUJjiXvomcHu9OXQMkUzapw25/RaBzWtUOXssFKFBbmvVAPW+k6OT9NfvFMV+5L5kU4ZtvTnz4cuPtl5UnVy956/GexolsjSnAbtlxvU/s9x/zeJq52Ss/0a6M5JnbaSLvZn70i5/jS186c3deefHhr/Enf/8fsbd3ePdH/xzv/Owvs3/vJ/ncr/zP/N2/8b9g9SNef/23+I+/8w/5l//mN/jwDdRYCXnj1BI5RsJo6toatBHKztC49wBziIRlxkvDYqC0Fc6FVPMALLOyh2Ie720fUQGDmG9D6XPJQosOoT1YWb3DMs3UVvUMu9G8KByfS+aYQtet61mrXc1zKQVqPSuXKxjZlOOrd3mcPRi9q01RCk+p1M2dKdlQNknxqczwEfNhwwZ7QYx8WLa69ttwEUq0SiZp969lWIJtkIYmMUcwNtT4aIZiYFwAu1+G7su840EA/lZopSo3HNce2MHcJAoyzYJW9B1sdSMH7WfBdM/gsHWBhmYu9WmMA1gdBFsUkNiaZqtgSaUmdaNHkWyBcYAGcK9jF0RzATbaQHWPEZXjlKNaGns3ukfcCxb1/ocYCSmPBnFFyajB8kJ8fja/PnNA6en7z8Q2941yXrl7/jHb+UT3QnTHA6Q5EPNeg16eydNEaCMzIzYSiVb1pbfxkPc2hgBXPV/bikLl9iZ0vVW8N/aL6udjWrB6Yl1P3N2vrMcjZd2AwLLsePT0LXbXe/I8y4qDvjgzHXyqVRR4MyE5XjWjjuGhuw3fsVqceqmyINSi7zgFXrx4yfH4XXQXK7h0GVk2MUXyFJnmWXaAoMVT6bRdVZkjz0Iy2AChY0y0okrRbpJ2dzfZxkpVI4dFtqKckhfH59zd3VO3E9O08OTpEx4/eowFmKYJi4nztmIpERGKq2nGCFnASk+uxopwaRMTw2zDcnI83fPi+SeczidSCuyvDlwdrri+vmLKE+ZJYc5xBLINJkeViPoMjXFp+ZBJmtO62r/O55W72xOv33zCNC/sDo+4ubpmdiRbHosISM5r7oThMz3Vyro1ytYotej7a5Xaz6xbJKfOcn2tGveo3BBntFG1jd4qcQmkedbz1+FYCmYK1p2CWJRkUb7WemlXUpK+0QSCjuDJ0rRUK6AusXlhLY05KCOhDluQLvxOKWdiDBwO12RMFs0oNmzrlfWsbLG8z2TLBJePONCI9Uz2lTl33DR49d45ns/cv7knxswHX/pxDocDKTQFZJKwyWnm43KQbLo3ZzudOR3vKGVlR2UXKz/+xPgrP7/n3Rtje33i5cdnZZMlKMUp6z3rUYqOsp04Hc9AERPaVcW6VdhaYn60hzjx6uWZ7z2/4/a+cSqJlvYs+2uqaxmelongznldqedGjombRwdygHP1MbCoZa+TKNVHpoFCgRuD7ZGyp+kAACAASURBVAyRFBPL4QojsdXOtkkJ11rHm97Tq/2OaZpH48SnNfVSkRZJ06ukv9OyGwGpCuaXbHmoFS8rQR/tFMPWtZsXUpaFMMZMprFE41S1KFCVyVJbo+NMy8QyydLTu4+CDamFOk1DMo61zum8EZKxOxwGyaFQasyYDPJIQCpVbZPr8V7gbsg4Uiz1oVajSR7ffQBpqNHCLutXEDhnAzwtrj+z9Q6lig2rKgKIk352bFgObKiIAHFG47nbCtM0KcNjnBSbjwD8AaCEMTCUtlLaym6aidgIc/20rSma/uzWOj6WjtoV9pqyQNg0cijChS0bv7cHY3KB9pFAmBfOm4JNbSxvPgYb+DRTJcYIKTFPeYBBClC1AZYwAh7N0qeW3ygAu/czKckfb0FsoxHA9GcL6GvkrAHcx+fJYLgcNbU4NrrbB/TosNtPdGTz1YCRH/5+nc2Odyi10EJThhNx2F4hhKpg4Kj2OR/WFYs2FmypJvOsXJbqNp6bhCcj9aHKWzJeO6EpaFch5GIBQzC8x3Gut2FfjJgpCDm5QsRbcTw1bFWTaMcUfNw7vV0ErwoMLwoeo9XC9PRtpv3Mkjo5ZXY58ujxxNXVnt18RZi0IG9r483tmdYj+8OOQ0osi87ImCs5TXiEQ4lsd/f84JuRZ58/cNipxU7ZSRreLK64Z8wmYsq0fKL94Eh89zlhegxeUN7sjJOwuMPtCTbNPHqn8XM//4pvfvuW3/mDFzx5b+adJ4klmsAoM+J15dEW+dMXb7j5J/+EL/3dzzPdfIG/8Lf/Dh/+4B/wr79zpKZMWA6shwO/9m+/x/tvJ372lx8zT5FkmR4a8zJTtkpLXdlIzalTJ/skhfepY4MJtQj7HGGthEmAwP0WiJ6ppdNZyXRCHAqikR939fiGd21i2R05niv3W6C1zJRm5rSwFudUT2o7sqDBu3YBjtozRibIWMd85J4N62+OndoiU5yH2l/McUh6Hnp3AYJDDe5jpjGkMBR0JSA2Jik6a3WsKleoGFhI43wZmUJR7UC9C0ivrWlp7gK5gql7OEQnhmFb8opWq7FEjO2ht840LeSgMN2to3rqkU9EHGTc5W4JylBrvY1yhT5Uj5DM2EY+EYD1SgmBKWeEGQ+bqwVimsf731hrJYVIoxHOTee1h3HGiPCyFAhBM0LzAb545+GfiGqmwlUP3l3zXrgoKVyzbBhRFhdQTapT5YvorNa5H9iI8wX+G/8sNoDxML4fI0j6pLPQUQmOpUEuqJm1+ogiMEU0YJoNYhg2YftUhUHQjXBpr5yziGXDZCcZqlC1q+rstyzFj7mTk7JfpRLtBDfatupojnHkPqGiEpo+c/RH+wUEDVE2pqGmSuiMsuqkZQelYL3iNlRItWNVZ4/nrnzA8f1dLEelddI0Ig0IpIQI3XNkW0WwXVRaMahRW1k7nd5kD0zTnvuj8jm9ag6hFzzOkAxLsjt5Z9hhBXKZd+oG23ElD9tgiAZxIqbItt5RGjQSFjutBrGhtbOfJ2ovai7eGYcUCDXxumn23Ai892hmmjsbjeene3ZzGHmaRgiFuEl5/Pp0z4e3zvW08ugqM1/tubZEopCKsZ1O1Lry6DDz7Oljaivc3/0m//Ff/DrODc/e+zI3P/ZV5s99menpL/IX/oe/wi/86u/wx//0H1Aev8t3/sMf8ifff8mffGfl+XnjvhS8SpVnlzgJG9mQfWOKGojMo5RFEZqr6XxrhRRF/Ic8Y6NeJyS9d1ImDRIwqQgJGwCDBWXbDWV6GPEi5hNcCK1xjrZLZIg1ctR06V5IQZRbc2URx1FCg0vBbDb29aFsT0CLUV7QYZlLo8XM+zgjXCKAPt7hgMtuaka08Tz2SqBBmAdo9UM5dDDa3hRN0EkPeUMXEG3zpjnT5fSZch7B/4UYwwgLF5loXaBN9xGmb8oBq9Y5r1VgWxpEboPWVV4RUFh4G0SlmeZzuo3ZCEJ33WfB6SSso+iJMOvPMc1C3RVkr0IGnXHYaJYLKlnJMREblAfL9OURkOKM8f9fZvSHcPYky2DOn20o92cOKJ1ffUJpFbaNHCOERM5GnHaENLzOw+YUQyBPGiDbUSoc6sheQEhjuLCrwan9rHO7aVmy3uirEUIn5pnuhWDGWgrnV6+5u7ujbJXNnf1uz9PPvcv+sJBnNf6ELsSqh/HwopWzuT8wUZe8DXexY26SB7c+BvbxcIY40+jc37/m/l7Lc0wzh/2B9bTx+tUrDld7bm6esNvvlYtj49JxRqOZDpFOFMAzL8C4DBqALnlZRLUau+ti9QZlbZTTibU0anNuX92SAlw/uubw7CnLvNfyEiPTnFFwc6X2ymx5oMSu7Jwub35tmxqAQqBWKGUj5cxWK+fTPS9ePGdbpYB4cvOMxzePuNrvweJD41C0NuSwjnd58ZWJrYEb68oIGghwa8pjOp+OnI5nzuuZZdnz1vvvs593lFoVdub6vZoDpOiJXUPcsVSOZ333rSg/IHXZALt38jRxdX0gL3EMM+hAbI3WNqnDciLvJsnem3JsAgq+TimS44KKduVNvnyeYSjoPCQsTZgpqE4UjYIYa61SyLiTYyYHBR42eMgSiClwOBzIYWY8dWJpWye0SkwTz3YHSUFxZZT1SqSQ2kpvR6BjLrve+bxyPJ3AnWfvvcv+euGQFgGXYbQmDILLIrTW8FJYtzvOxzN1q5gXrnLhR64bX3478ME7Dq+OfOt7JzqF9SjFxNY6vamiuLUV387E5FiUdPRcXXCbBYiZw2HHJ8fIdz98zUevN16fGiFNhGmhx5m7Eok5k6bAaSv0XsnReXLYPYRvrqVRCbglYkwUFI55WvUsd5y1d/KwssaUHhopWteC0bqUFCkot0LVq2IK1Lqgi6y1zloKx9NpMGbKJZjnRcCwy76lxUf1we5iM2T56kxpIi2zQIdgYrFDYE7pwSrZ6mBUDIhB50WKQ30p0KXVAWZIdkRpRUPFdibmyOGw15katNiDQllTYFicnFSd4/FO550ZrRUao8HrQo3bOHd6V15IKbLEhlGJbfKbK+chEE3Pc+iqa2bI/HfLnjyWDVAIb07gQQHD7lKWrUXP+7LswAUmWRAb42IaHoaK4BDdCHkiTTM2Aikv6pyYEsH1s+Up0bvY8c07eWTOBYsYn4btijVXGyRm+GhJsxSljnto4+ijrMAeiIgo+QOGFI1TUvNjiMOAEGRjNUzDxgA5ey0jG0MKnpwzebD29KFe8IuKSJldbSgnLmMDNpiwcLG82AMQ1Dr0WjmfzizL4eHeVkh85GJB7KMFxgIjcHxYJVoixSBVWLhUAuvy9KEYExivLLhW9T1JZBVljQkaqHD9vbUUrDsxD3VCsAHAK6BcYe76zz4s3aV3POo+ijtZ/ZrpuVZQrevcHiG3RmPOkbJVljkRYuLmC++T0zssU2fOmZ0HhYcn2VYEwK2s5Y61Zqampq8WQd1DPizmiiw9zB2WRxxfrRxvZ5YRHp+mMIb8gHVZdRorgYl8fUN5dU99+Yr4VsBsgTpDzgLfLEI8YGFH2L/mnQ+e8TM/9Ta//lsf8wd/cMtf/HOPORwSljqRzq5nmq/EKfJ7X/smh9/8R7zzK/8dN1/48/zVv/U9fvD3/xW/9/q1gOnDFc9vK//s/37B1ePEBz99YI4mcsfEfNrWdWeboTdKi8CcMy0L2ItVwf++C0zWsSNghWgLJ9tYeyd6pltVY4TPWGtcLzO76Yqnzxqn1lg3Y9u0VNRiai91p5QmhjoIuul2UctKZVgNvA9rdrgw2VL9hBhoQWBAqZXW1cQpglLPbUoCc0spAywYQC+yqxlRSg+DkIcitUqlHYKP906HZDDHvDNPkRilcPdhWTfX6qffA1jGQlKGUZdNCQLYp6HdtWwYUrrWKrVe76Mh2IwYxbC3y797DCInNmXz6ceKtJCU3xbGP8fIqUtJ1puRH4gFAW69jxZZ2frUVCSwuQXZsEJQxllMmpUreu+lrtEn0l0hv8sySXHUOnGeuepGaZXVZWuOIZD8sh/0UU4yWPVhxaEVQu8EgqzVNmBzv4AdYRQ6XObjoUwNAqKThUHYataeogoSehd4Htw1uyXDg4oI+iAIcFdkgnVa2UQe3xeRTGZSTbaqpXOUZzBanrDwAMZ0b8ShmvXWHtqqSndZ/MaZrexDRTfULlA6ZbkzYrNxl3UqujNiSrDWB2AsmgLua9C8iEMvD2JD5W02KYzyNEKtgbappdB9uBCq9pHLda17gXGzihgJIdJaIuYnmDk5b/S6EnIm7vWRZwOqFFhmzjQZ9ID3RJ4CXgqldNIya0+olfOooBci0KAbDYOoRfhC/u+SQK5TqbQa2FwZOFupvLx3ek8crqTqO7dOjE4qnVgqsZ+ZpkReIpGNlFfs48iVvWK+Stxc7Xhy9RibGjE4ZVux7Y45TFw92vPFH800r5xe/z7f/PXf4m4NpPA5lnefcPXsCfsPvsr7n//r/Px/8QWsfMJHX/ttvvX7v8fXv/VtvvHd/8R3fvAJL+427lqn9gSe6K4oAkuIBAFaV/lJTomQVGzSgbpW3Its680Iw/Zp1sGkVIw+AFJEgtequ9lsABAulZONIo6Y0rC8X/I+o575LvVxjIo+0IE3REHYg13VTHO+3EqawvoI8y966AgWyVwa4rQTypoq+51bUIB17jSxexgqfujj58YuJ6arVKsL0HVE6Ol86COTd8xwI8Yg5CA3QRtZU/ECSsv5xLDIhQYEx4Ny3LrJcljDmPHDhf4MrEVKUAaCoY+1Pai8rDt4UJFrkiU3hnhxxUlBNsLJtzbEJYBZJkV/AKt76Hi3cRdJHRmsUgngETOdbRYvsQij7MaUG1xd73AICMT+DH995oBSXe/JObO7uSKmROlO6jy0dfllEE4aGBlJ78Wc/dVjct9Yt3t8a9RWgCZbh2mI773TW6HJZ4BvK/IWb9zfvuL+/kwplXkKzLsDj96+YdrNzIO1VgbEGAFGSEQY+RVhLBEXtkZSRJSw3wbPPZq7dNAZ27rhtbKtjdu7W47nO87nOkILb5mmiWePbvjRH/sxpnlSs8PlTTYbMkRJl7vL1xoGS96q2PEQ9TPW4krtcoFZak4y7u9PvHnzitP9PWWTCivlzNtPn3J1fUVOkWW3o7eORXlUa6lI1hjI0wzIYqYhRmhwisqxcqLqnFvhdDzy6tUrTmeFje8Pez73/vtcHR4Rgi56tf7IH2+hPwwiIZl0uoDyh0YGTY+0XtnWM6fjifNpZd02eoBpt+edZ09JIdGrKmntsqwOlqyjz6U1hQkf18payqjJZgTRafDPu5l5ntUsZUmfbVPDWa8VXAHOIRp55LU0NyKZHo0YZ8k8s5Q1wRmhlpfMLMeDGoF8DDCXAc7G8FKrvMVuSlP2roBISSU1JOY5k5Kqc90bBSngcJimyLK/IUZlKFl3kjvRT1g/Qiu0phDpjnNeN/lxrfPWs8ccDgcsKd2mdgE7CmUYlp8eKFujrkfa8Q3nck/AeZIbP/r2wk+9P/GFp51YNra64nHPtKxUz3iYZY3pgbY1Ym04CywKslzL9sDalgpbga0b/+mTWz58vvL89gzxQJwPbD1RPVFakB2sdNV79spuCuySie3Whi4L6zJT0RB23hql6cJqSqJkWQ7KKEtpgB5JLVnbpkOXyDRF5pzI0yXnxrlUeLob23bmvG4a1ELCYnpQIoXACAiMuqy1StOawp+3TZW/y7SQd7OAvCibcAhRknpvVBe46rWP9jaT2i/Hh0YuRlOOmaxddeT2eNf7oIrovU6rEPW+BKkxLyCJztPGejxRayNNsvYSYAphADg6q+umhru6bSMRPIx/O3T5Dmm9I2DObdQtuxNGns+0KDg2DqXipelOn5uAyNbkRd+OKykpvLCZAk8vIBhBA8alaav3Dq2JBXYtdQr6tKEGHJ/ZGH4MgYJaysZyNtgrM13wGpT6g0LPTCGmcbTA0Z0eGXLs8PCsxHBJNDHOrTzkXOE2bCcwglgeFATBGE0sUjvEYUPRkigQqfU+spl0R0j9JcbcooazQepJPesIFDLDk7KQUs6cS6EWWbm9JywriFfyb4ZqQm2BZdvY1o1tW7XIRlmx1MoiJUcYzS/KUdFnOk8TuAKofZAJNkJx5xToVcNp9YbFJOZ/3Aymj4oQIlOSokyLob655hBSJtE53Z2wuCeN5d5dd6cVAdfBFGofIyzTxDkIEMnzQk6RGBcp9eKoep8mpknWFXNGhkyiNAhkGK0+GxAtkEvGc5cCqkbWdaW1zpvXdyzLzP7ghNSkgjEN3SFcBssGKREfXVGevyJc7whhUVZVK5A65hfSKGLTM/ZPvshPfvnEN75b+f0/e8n1NPGVn9mRYyDZRAgbrU8cl41ja/zeb/wOf+npY65/7r/icz/7V/hrv/J9Pv6Xf8i379/QloX4+DFfuz/yj//FR/zq/n2++GOPtEy0QogBT2e8BkJs5FkquRACNTVsg2aXwdeIScNzSs5SIpYE7vdtZauRHEZ4u8FWGt0bc5hJvZEcdlOgbGe27rQSqS1waoU2o3weA3Km9MY2FF9BpnS9C26y/F8A+BEUHVIiEsgxiYTyPgDk8c4ylJIpasbi0/ns8kvKH/13MWkJkYp+ZHrABVIaoK6PBlBlhtUB2HhSK4/aDwcQPizC2yUHCOUyWdDf1QbIrkyyOGYyMc8+frYQI3PKTClwrsreYGQ2XsC2lHTszHliXTd6h+NxJVz+L6iKPaB7pbVxitlQgpuy2UIXkB2ClrDaZEnpTarFi3qmY5StQdIzUsdZxcgjAtm0jZHdMk4AqQqGDS4O1VX7dFljTH3WH76cC2JNCFLteOgP87WieAcRlyaVDxgE66RhFaqt07Yiorg2NUPrJB7LrxF6GMuuDZGM4WuHKGdF5gKq624yZPUNAdll8FGAoe/Vk5btEJMAxTiaIL3hIShrsnflqg0bmKHm5GCBFJMyvEatk1Rl/SG7JXQjESDyaUNsVT7NZX4IpijhkLOe5+hD1SDViQ+1fxhZmrL7jha5PlQUwzbavBBTJM4zYQrMORJQ4HtpldIYjXyRVpVrozyoRJz2NLYBQhpbqeQ8SiIwYoDgalROk2aHOmzK51WtqWs5URo82iumIs4i3l7cOS/bSrBPwXGzTo7ObDAdVub9TAJC7LRa8LWwf5W5yhvX+40cE7u5MUfj0ZyYF+f0pjIfI1PszHHhvS88orrz/OOP+ZM//l2e/+DINE385I/8Bu985S9y+NxPcfPWT/ALf+dX+KXdxP1Hf8x3/vA/8PWvfZ0/+eY3+c5HP+Dl3S0/+PienhKbjT3EHVodgK/OmxQlOtAt1wVytIYPcvgi2giu56zWMKy6id42QhilLX6xZ+mpvZCqmke6Zo8GKSx01kGIy22hjEbG3uhjL4Xmbajn+oPqs9WC50inK7doELM+Zp3LrKzZRWdfskSjSgXYLwopKZ37UGFHM/IUdff38GCrVAnJUNObadwSyqu8pyaltkflpTFECRb1XvQB0PeRhSc3UqQVcBtEWu+yGAdjq4p8SWZcWsi1V+mOcIAk11FKgVAjjn62xrAjP/y62JV9KN7lRDIDupOD9mUGgIb14ZhiZJ02Ne9hhC4iIEbdX4ov8QF2+Q+dqZ/Nr88cUFrmmZwn0jzL015USx3HMNFb1fFuCCQYl+I0zdAL6XBDSIGNo8KUQxl5H1HMNE7vhXW9p5eNVlXPvHXlVqSrA0+vH3F9WAhpwkYaUbsokIbkt1dlmsQQaSZ5cClVWUSjXaYNZK+7AqR18agRKADbtlHHgnW6P/L69kjOxry/Yn/Y8eTmmZpcHVotbFsbg4SQ5gfGX3pcvdwMu4JriFBgdZUlxcSK11LGkLJxPp14/eZetc7zzGG54vHNDfurK+bRCraVRqlCm5eYiWEeDIUY6JgUTOx9oKYpDzbHefH8Nctu4n6r3N6+VmDsNPH48Q1XV9fKfrmwPC40WCHl/kML3GV/GuG5Q1kQxsJ5PN5zf3fkeL7H3dnNO548e0Ke1CrXm0DEGKLqzOkDcXZqM9Za2dZKKZ1zaxpcykalDWFGlrUwTYQ0cg5cjFQrHbwRieRpVlCc+zgERt7OqLFd4ggoruBbJ7oOg9Kc0KsWZJu07I7mreZiRrspxZ8AMU0ESyMzQQDrpSFpypEpxSHfHsNxN9wSuzkTJ1Mujwe6n9Vu1ivRNzKVEOpoioLzaaO7AjYf3zzi6nrPlONovxvMmVfZYqLYcG8bdW2U0z3n9YiXyiF3PngGP/fFma984RFT3vDNORlMaaVFY14zU3fqDK0ktrLSUmTbdFl1y9Ri3L1eOa2NvCTuzs6b+8bL+40f3BXuz528PIK4V+uPBSqBblGW0AGYLFNiTggQK47HRMyZVhX8e67O2jYcWTRr73gIzPPMbn8gxDzYCKOWSrnU7waFbecIUDVEY6Q84b1RWue8bpQqUChNEzEmWqsCkwYDkmMYjIoueQsKJRXYa+z2eymZki6fZIHpYsGqFfem3zeeQwuROI0q5QCCKkZuxwDDzS+yX7HA05Sl+LSLfVO17sE+bTDrdOiN9f5IaYWYEykq9E8Dt5FTprhaXlrZWE8nnK4GraGwixYe7GqgZzliGnC2gqdEqZXdMpNSJqUw7BmyEjt9KMtc6gMz1vtVC/uoHndUgKCBfcSN2wNxKkCDwWpxsaBdwquHguaB0R4Ch94f7gOBqcBlkTFd4LVJDWddobNuTkrSaoShhvGhpGm9E1Mih/QwzNC6QK4xJF0AZh9/gQUNRT6APQEyCnKPIQpsM/15jGw3qbJcAep0QmjkJFXW0AjprjPTeROUFZIumQDxouYQ6KnFd+S2DKBIbGGkpyRAu6jcQaCNQO+LMjRG9VOKERRJYdawqkUtRCfF8KAAC+PzTiPIvI0SC4KWYwgjSLLreQRC132klqQ2rPOR7c6kiAsBxNFpQU06L8xkD0oRcoK0LKylKBDZ+CE7TsAWVZu3LgVA9AsrnLm/P49cGyOHzjSHh3tB9m8jZgXj+lHkz4uPjtj7E4SJnIyUGs0cCoSecIYtMs/0myvamxO2f0tDpHWcVblb/YSHjMUr0tV7vPvBS37mK/d86/u3/Puvv+HmZuK9t535OhBboJ2dZ48SbV15Uc78v//6N/jFtz8gv/fn+Mpf/i/5yx8+55N//33eVGOaD9iThd/73ne4+sff41f/3p6bz2diMqxr/iCs+JqIUydTYQM2oyeDOkDkCDk3ttWZYsCzlM4X0Blv4841wlhaz2uht4qlRAp1qCVnfDMsOx6dhVmFJN3w1vQs9UgsibNVtjqCVc2ZgJ5Q6DtglsZioDa1FI2E0xojx0sLKZesjzAG7+YPC/QD+TdAXItqi00jBsGHxeKSW+aXGXP8eQAxJkIIbNtKqU7KCXxYuzAY+RseZUtvXQ2fikJAGUpVwG0IERutoAIPwmC5pdC+KOpTUr5eb5G1q/FMCkep7LBMtIyzUQYIF6NylaQgD3jiQQEAPNR4B8CaShwG/KbcqKEsUM7OqLaXX0tKIxcBqplan0tKn94ffbT0XWZHuwB7DyCgtio9J10gShchq8PbpTbH8DA+fFO+p18y3qpyTkMWip8s4THgfcViIFke537FXeRQp9GjUcogL2NQcUfpWFe+SqdJfYEAMaE2soy3KqvgpQSBqFxXMx6CriOyngQ6ybT6XMh2utNaZa3KU5qyPks9407Mgei6J8sg2NxFwiRM7VFhELAGFsZM2XXWq7dASrI4K26kDcw7xE9VwCka0ySlYO+Rso6yIwSGRUuMr5Bl2uNRcs7WndIDHrqAsWiabcf3FFMW8BVkZXV8NCw6rTo5z6TUhxI2kLJUzdtWce+cSmcagdYxqvEqRVjyxC7Aq1JZq6amiJMcAXsLWFbQ8jSKlXqFOWTiAbJn+hS5r52dRdbzid3keJ851OHe2CWUpVjxOwhh5umziV94+gU+eX3P2TfC3ff5/u/+H5TfT+TDu+yfvM/105/g2Xsf8MHP/k1++j/7Hzm9/h4/+MYf8d0P/4w/+/rX+Oh73+Bb3/2YF69WPrxvrJaxnEe0gZ7PXsf8EGzcfwJOvDcSamJU2Lus8jHtqLWOd2lYKE0An3fd5SM0SAA2kJpTq4HLHq8ylo6ZbHq9+VApiZi23kgBzW3e8Qrr1gXGt6HOC41oiikQAaYdBJNV/gKGZYucusid6OOOiUZdB2jrOpuk8NSulkynVR+T0MN+1YZ6CpRV2XXuNO+fkpPjbAluBKTwCWMXFx5gxNCpES6t9W2Fml2N3BaG82YoxOmfnj9dRQnmgV6dnvzTGTwOchMj+5ilg/4F61Bb24NNVXNWHi6hkAbQ1iPeO8W3H5p1NSlPUTNQd7XmPUg6AlzyMz+rX585oPTo6hFBnXs6aG0wPYP9dFNbhIWogQxjzgmSUc4bkcT1zTNe3J10gKRE21bWbaUcz5xOR7bTeaTNR6blinneczhMHPZXxGkmOMqbCZKRKeBVzQoX5ijES66NUEO9QM5WxJxs6wlViGYpolpj21a2rbCtJzG32zYAIGOZd7z73rss+5mYpViI1pW5MSkXwN2YUqSNmuow2Cr3oS6woKaR7mBVrQhdYaTREtu28urVC7VS9cHYeuTpzTN2+x1LTsw5q24QBanRHXJkTv9/Nk7qpkCpqzzUMckK4lC2E/e395zPZ+7PG+dVwN9ud+DqsCdNmWmZ5Tf1T19uG+iR1F96EWAM+F2SU8kqO3Vbub89cX+6V1BizOz2e/b7HfO0wzFq3S7h9oy3fliXJFk/l8ppUwZRrQ3vUrW1IWu0uDAvEymk4fO1AUL5g2w3Rg38yeQHrl3BuoRAiFkSYQu0gahbF6vexr9nCIEpZmLudIuaaAO4acHstRF7U05YEGNpQVkIrVR5y2Mi5wkPQUuGS3rauhatPGVynplSYnUpbfEkMAAAIABJREFUlSavRDrRz8S+Ecb7YA5390eOdytpmri5ecKj6wNzCoPS9E8tfgMwiyNvpGwbvdxTypHtVMg4P/JO4Kfem/mRzzlPl4ifz9y/kaT5vHU1t7Vb1m3FN+POC22F0tXM05sYtuPauLvbWPuZZTdze+986/kdb87Oi7tK8UycDvS0w+PEWpyQMlOI47KEZJkcjd6KwDhTO0YlcXuqGFlBu1WDbbDB2phsVstuj8VE6SPsDw3qrTkxZmKeqXXl7nRmHlXAMU2spbKVjdobtalVLOZRbdqqhmd4YLzlIdeD23qnnFfZgIGr/RW7/SJVTBTbF3+IzcQH4DEuwZTEROXx/LbBTncQk4iGsGSBilR6uymTl+VBOeCtUi5BsyFoqAtGLSvr8UTvjWmaCDEOmzL6vQbNdNlt20Y9q5UyBLUYXhrRQlC2lFR4Cv80l3wejK0UBQ3mTEiRHCIWEha1ePhgqi7tL+dtVUvebtIZYlrsbahWem9cvPGgz7jjYuwuy5Xmecb2gV3OqGG1uihpcpbiRsHdl6tq/Ml+Cbo00pS4v70j7/IDQIUZ1kf2husSv4BVIZgyuDoQbWSzCPyLOatVJclS4s0vRNZQEiRyzsSUZKszsWDhApRdFBZRC321EaR5mZgGaCSWWgOWecCDpPLtAlImgU0hJmTrke04JTF8PerviD08SMaVmydFWa3tU+Y6ToPJM1IKrNtpfO5hgGnh4UtIF6tp6ExBVtPe21DvDqAGf7DtWXcgDlvGaLvDpAQLgXVdCdNENilWohnEgM2MQVnVw3hXy9kliyWM5lLgMsW13ulNpIMHJ5kxL4F0jKzFOZ4by+Tk2knBiV5HTbqG8ylEWszUUih9Yz1mlgk8tAFUaKk3ZG9gsMT5sFBevMLXM77bKc5C1ydO0Z1gC7a8z/LsJV/64BU//vm3+Hd/+F1+9+t37HfXvPcskktiO6z0u4nD1Znt1vnW8+fs/tU/5+f+64nlCz/NL/2Nv8Z3n/8zfuM797TziXRzQ377bX7r2x/y1q99l7/9P/04bdGSas0JtgMvWGuE1B6U0VighkbdXJlAU2LqgQastTDNRjttpADTlFhrG1EBDSKkmDmvmxjtHDHiWI461TdSjEi7FuiWRp5ZZUrDrtsyFhptZC8qUzkQkgLeQ7KHjJsOEBLJC+Y2CDp95xoLRuh9SuP9H6H6+MjbCwJRGQx4lAK71jqa2QbgFPRcPmQoXBQgwYghqMp5MPdt2IQDYYCu8YcUgnXYI8OYXUTMxHFO+VAnGgrLpQ/LRpA1PIegSIihVm11NOXVUWsd47CqpUF4KLPHmwwoeZw7anqLeud6w7Y6lhupXkIKpCwAOIWJ7iawyxmLlTEtme7Kn3JHJgsTKBOGKlr5SAoVrgicCjDswAPQsziGwU6IF6LhEpT+qbopXEBM/HIrKcMzQXIp9N0Nr0NJf7m7cyD9EHhYa5cFzke2i5sysmIUsN06Mev+XXv5NG9pzJb6eYJ+jj5mz16oD4CmclkcB0sjQ0kzetXOO57rNJ5VWTEV4NsFTif9TXko3h5I7/EdXPRZ7jrjbDyHI0GH2jcpIF2LeZoTRKkjPA6lWBkkVFQJkiObmwXXrpVmtZvGkc1jbSjD9H2GEMjDVpWG/NQ9PIBlF6WEiJEs8DTnof5TG18PUtg1bw+fX4iwxCggyUTU5hQIbECn9Mbr1tl8EsncOscue34y59xW1i3yqGZ8K8rDic6WAlc7ZXA2Lxid9awcsa021gjHXFlaZ94y11NmThv7NBOWldwTaQq8dT1T+8L8aCYviVM/cfv6Jbef/IDvfvff8b3f35N3n+fq6Y/y9ue/xOe/8lN88Iv/OX+pbtx9+E0++va3+OjDb/CNP/sjvvPNP+Gb3/+Q5y87b3qkuKIYlC+sd8IQ6qJygDSwoaFeiTO9Ga0rcP+yrzlxgMBj5PMH+Fhjt9v4nvV5a30Lw91jhJE5O5Y0LBpzUhTAqan4amud6AqiDwPMqfjDEDuMQlKUI4eQStBkd59iINDpWUTWVCPbUEVJnT5yscas563Sx3lpDNBrqMFtOD6CORdlj65/ZZqFcWbFrOKXNvaDy+witZ7KDhSPIBX/BMwpKP3MR8snA9gZc2XsTnfZ75QjZw//e+8NQmStfcxkUF1zobzd/SHbTWrEiJEx6yN/i6H4GjO1WBzcKr2LNG0aTIk6qEbT+hgyPqNfnzmglOaJEKDoBocwrFlRFxfuJORbt5yVl1AbpZ7BA/v9nn7/gvN2gt64u73j/PIld6tqVuM8Mz96wmG/Z9ntmOedPqSs3AUPkVAaeZ6ppzvVzFokmSTYalUMQxILeMO6Aolb2cCQXaU767by6vY5p7u7seCVB4Q3WmR/dcX+as+UZ3aHKyxGSlvp3YdX3rCQxjKkILutbMwp4VxqDlXlGwiU0vTQRLU7tFY53p4orXB/d+R0d4/3zn63Y9nt2S17rq8eMS97VIla9TKNyyCEBDlhwSm1kEMSwBUkfRXjGnWIl8rzVy94/vw5p+ORZT7w1ttPefv6GnPI08LhsCOESG3bUDMNC0cfbUDD6iFPuhgIG0yNDqvO8e6eF5+8oBRllyz7met8NRqc9N30XumuDIR+eTlcDMupITXSpnrb0rrAxrIKKMyZZc6klAUaRSXm96ZDTKuKk9OEUcfyqZyVrQmsSllKFQuB4AICvOui7yNI2ZLUEMk1YFZvo9HnUqs60PcQSWlkHWDUokHMQmSZIzZNeuZCpDIcjb3hBPI0qcUl6OjvVMwbyTvZzyjSbST5Ezme7ri9vcNC4ulbN1w/OrBMygvDRyaPa0BvFC2RvbD1ynbcCK1Q2ko7H3l76Xz1S5mf+ZHE9dShFc63K6XIEtnKPaUatXTWGmRRrJ3bpmW9FqO0E15UTXp/XtVoERPfeF750+/fcxpyzJVMSnt6Xjj6/8fam/zatl3nfb8xq7X2PsWtXv34KEqkSEUUVVCULUWSIdmSIEMwINgNAwHSMZy08m8ESCudtNIJYBmBnQgCEiAOIsOIQ8WQFEeyLNKkxUosHvnqW51i77XWnHOMNMbc5zH9d0DgAbz3nrPP3mvNNYrv+33JlQGTb61UO0JzZKma89PEhlzamUBrM44NSs7OIpDBeRmH9MXZOfPuDJPI2vpQ052+lwOATZXbw5WDgoevqpuxHI/D6uA9Z4rOS5HBX4ujgI8jPcvVyQ0zoW4++KrbhqTIfr93APpQmngz4QVtPQGD1Tuj2hrxdH+JjH5XiKPQlLFB1LFZNPV0mpwTKRdvYHC7Zx8NoPeAPsixWlkOB/rm/AVVjy9Ws2F1DdRe6Zuzj3TbRoMl5Dxh3kWPh7Y/lE+xziZKM08s2tT/W6aZnNOd8iYMtY7hvnU7vWfDXhVLurN6RQYXIrjFqjdXevbxzOljEGXB7TQ2HtanAqEP3k4Q34I6OH8ADaMXDqfHqowqxO93L54JiXUkDM3Z4fwmMrgYbrtEnD0Uw6nQUJo2ZxicVFBDPXBaBAQJzhBSG/+GcV3IOAt9o8ypKLl7jUO2Pw5G64qMAAVv9Ef/gQ+K++Acmfr1shyPOPTW2ShuV3MZu4jfvzbsdafGhNGUnFQCJ4WTb7y9MY4xUkp21WNPIK7gCTF5A2WejsIAy6acyeXEJGscj2MBYj6cCGOwpHcNu7+vAU+KoRvTPHF9e2A3TcBQcln0KG33E7l1SwK1Qw7KnLI31GEoBgwanWwOS/ZIA7dXKpDnxMVl49nzxtY9Wj31jDQHosfsCUy+kmqE6IVoDkPxbkNFFcUVMdEHiQmFnrHRJ8fzGX3yjPiqD9U5FfsWQVcfKoYz0tnrvPLGY37+cwvvXx/5xttPeXQx8cLHJ872iYscoAZ2rbBuxvO68lff+mvu/79/wsd//QEvfOoX+M1ffZN3/7c/45tXR3SZiRcP4MHKv/yLt/ixHz3jp37zZawHt6JIJWJIVajJZfVFKVHGfdgJW0NbIKRA7JkpJGKplM23tz24JVOb14IRCOaw3b5V6K6UDihr9IAWT19zGLeIKxTbaMxzVLolLGV/bjYfhPSwEQiUEO7sQMGyD6VDINuwnCa5U9y4unPAT0cDLCNJ7XS/hfBhQiK4WkeDn6e0dscrEgkj9dZryKCMJY4SUmIKzrhrbRT6w7oUh/ri1NipGnXz8JMY3as2ZhP4wMNB3+ADYvpp+NG9WRHcZkmjSGITgcUH7ybmOiQRCMnVXeLsIjW/59FTauiJz+T1WUqu8/CBEqhWgnpdmeOEAmv1M0PNiMktKbV2hDAWEm7bwIaabTRqHajjbA1jKWE6bCsnS4j/s7GBF0LK42zz5tOXBf79bAzeJIjbbW3Yb1W98RnwbMP/TIIvoYN5Qy6iiLSh6PLrztSDGKKd3i//PBAhxQyDbdTH4N+G8iCQOLGJzJSqvjiSHik9QAknkYkPk7qrW+njdxjJmBhsdfPETDxFKqdCi0aJkZQCUpXQxqLG/MyxAfj21GJxdd2AxBPy3bW7Vs+PDlGYckHMPxPVD+2Gp2WZ2Yf9TQrVnzdB8RFMIDa8CQ8eduGN+YDec7rnYDtunmaXfJCVUkSt0cbytW8Vi/7hbts27udEHEMmCYZaIEimtYYEIyfhsHRU8p1So3dj2ZQUlBoChyVzQHiWOkUa01w4m4x5J9RmlHRLihWJmdw9jt1kIceV/UjZ3RVjVypTiMx5YT8Ju5gIc2GfFSOyJeGsdkoRHuxn7p/PbG1hWzuHm7/m+Tvf4K3vBf7yjy9Ju1d48MLH+eSnf4xPfPYX+Myv/z3+5tP3ef7Om3zwwdu8/92v8d1v/iXf+PrX+Pb3n/D2EriRPZJ33v+MGqCu67ClRtAM5GEbHoyhPtSA5kttA6yNa2zUmy5cC2MBMOrb7iqo3gcrk1GbjtFtkLEsxEgkQgrOjtNwN8haa/X7EbsLpxIibSBeRpkG1shBsDjg1iLOYAuD5qd1qO5cMCABT5kNfl3LqOfsdAX0UV+E5H2/MBZlPmQS+ZAVJsFt/dqHk2YoJFWUJIU0hCAmY7AbgvPM1MfYp2v7bmCjw84XhN59gRbFAf3gyrq++blRxs/V/99yEx8k4X1lsIpYQquzLrt4sRJPtWJg1GeDAU304VP3+ipEt486+uaj+/rIB0ohR4Z92Xkwufj2P6bhcfYPLYhAdNuEsXns8bZw+8FbfP9rX+bqdmXdjmjIXMx77r14j93FfULJTGVmyhMJ7wQ6DkQ2gWDOk1gP19y89w5aEucPHrm8X/yCU4zeN5bDlatsutHqSoiBrTaW44G6NZ48eUwbmUN+0QYePHrEbt4xzTMxOygxxODMDRpJIjGLF0nBGwWPare7AUxrfmhP84R243DtVq9YHHy5HQ88v75mOd6w1gZd2Z9f8PDBQ/bTTJn3/vOj37i9bTCmvCkm2pjOW+xYczZAMAc8nlg501QQM66eH3j38Qc8e/6M2jr37z/i9R9/g2nasW6VqcQxGPL42xD8IElJPqTq+/3q7213J3wc9pQgUGvn6ZPHXF8/obbE2fnM2b09JRVyHofAaetmhllFxgwYhUNVDrWiVbgxQ5sitdKWha15alraFXKeiAOCDYxmxF+PmPtUgwk9uBQ7RI9uFVOQBDETSnZJbzs1rob0TjNvTEIpLo+Nhq5KG0kivTsEsEQZVhNvGB1M3FibOf8pC5ISU5mJsfumProtq7ZGNKOkCcnFr5sh5QzRiH2jsDGJobq5lTRmbm+uefbkGcTIo/v3OL/YMRdXZgU8QrObH1A6hijBoG4rrS7YutHbgcO28fLU+fznZn78RwoPsodU9mYOo7Xum/ig9Kx0Lb4ZNCWlzGadXeisW2TRDcE/t+dXnbCLrDXw3bc33r42FjIxlruHf6C4vc3A1g5RqaYeNys+wU/gzcI0+QFuxqFBt0AncGxjwxy9icvTxNmZJw2uW2OrCzqSXgxvJlpvrFsdkHRh3p0xzRO9Vo9P9h2dH24mdywEV6eNwci4dkuZ6c23psfjwlZdxRijJ0ue7c+HFWEMWtWLvmrmCW5jELBtlRA9OruPB56Mwl7UFWbjNiZHBya21pwTl93Kk8Ym21PgPnyoBvFB9uF4oC4LedynJ7acg9kDfasOiF43am20tmEoZd5Biq4ECV4Ex9Egh7H99bfK04aqNi7PL8ZQxeO0/R53foON30XNGVOHwy05RmcEjI2qjoc/ONcnxeHBx9+3Zh5PnCSNAbErVby48i1W136nTmrdY1hDctWLD2gYCibf3ogE5PQAD8K6LIQp+7Ugfk0kcVl5sHG/By+uurjFxP3yDskcBg7nAY7vqWZ3v5sPQjopuDomDlu0D4HHQGU0vWEwB7RtwyJp4z05saJGuT4kV86BGjBfzOG+ePxzbxCkE6P/DFMwFZB0umJ8ABaHHFzc3mH6IevPFRjBf9dRMGGBGN0SGmMchaWO32Lwtk58gsCwjQ7lpDrcVpsPcU8cARc5ubI2EAhi7M9nbg/X9DEQIrjyI2og4tatHLxC7eJWhzQXah13tjVInpKDCnme6HXAeM0fbcGUeZeZVpetqwasu6r6Q/uk3cGdp+JcKtt8UVWrq48jvh204IVoYyhpJSA5EPMljYY+/QB56d4dC8xkNNlDVSDzG8yvdj75U5Wfvzrwf92sfO17z/nUm2e88eOV3bSjnS8cNXPRGwcVbq+Vr/+HL3PvlZe59zO/xRu/+Jv8xvff5dmf/IAnx2tsnkkvvcTRlD/4l9/m1VcveeGzewIJCR3JgWAF65UQmic2GVSTu8/VGVwdIUNoYJGYEzvUN4wBenA7jfMp3F4iEVoDdHP7m51MZT4QlxShBZIqhOTbbZRShNgd3q0S2Hr1BDIRwhQxbfTmCu8cE5kjSISohBg9LBG8+m74gGB8lnQj5+x2u95pdN9cSxqDyMEPUVdL9B6ovQ9gty9MYxoWf4XQg8e4hzFcMK97hDH0HarRU0Pni1j8bNdKyhm0IONFC/57ySjhrSvaVrbQoTl3q0tAumBBiON+rcGtZa03t+aebFUheGKYBpopXaGJp/sWVYaghpNyVXBkRVVlq0NVK/XOanGajsToCXNNbdisjFgy2hplyjDSiHxeErAcfbnQXaFk3ccP4ED+mJxhpl0pwf/9KVHNmiuPxZyFdPpsw4jO1rHkK0HcHnk3qHI2Y21uez8ddCF4EmqjYyGirYIJvbr1xBseATUPmZDoDfr4vZu2cdYKSYwkH35v7f7MUm00jYRY0ZA8bVO7h9SYD8qqv7l39viAD+Gs98F2dVi7BLcspRjcDh18QWp1qElEkWQDXoyrTGwsBMxGenRw5asIU8w+4JFK64IOdk7XzYe9OtRuQIhKTHtUFyQIJeZhBayojZ8fkzOcMGJxhWjdqqu2zLDN0LayOy9kwdMze0NFmKaJmGCRgphbtUMIlCmSCz48sUhMGwXlLB1468nG0nxYkGJkG/ebhOABIJZZeuPm0MgR5hC4qUI5KmXaKLGzS4HdrMyl0RdDQyOo0eZCTpXDIVCyn/270tmlSAnC5dy52ilh2VF2G0tKzDvxFNNhj95PgXnOXNTqHKh6zc3NM9781tf4q68K7X/9J7zw6GN8+uOf4dOf/wU++/N/m/6Lf5cvPHuPq8dv8/73v8qbX/1Tvv6Vr/L1b7/L959vPGemlz0yZR+81RUbTFXDVbfAUIg17thH5v+fnyyARHz3IaRwstIxBAXjOglC6/JDCzBfUvVWsRSYYsbEVWmi0KJfB7YCzSvSOAwIdatuXx28YEP9DA5ejyIB65tbTPGhoIy6wAfwzgT2palr/tE+6lG/76P4YNZ82u4L+uw1j+c86FD6CLX5yeoasJFGFxIalJQKra+oeOCUjHNGu9zB7Q1X9rvtdbynHp/sw7SxVBS6//wQhghhWIer844tCqdQkjgsoWHctyYuqDCMnD3U6bR4DIMH4Zjz4ZxAhpIRJDk6R04BER/R10c+UKJ1Dq2R9w8J24KkTJ4ysRR/480bU0OQkqBXbj54lw+ePubmvacs9QihUc4ecv/RQ/a7wjzvydOOkCdMqwP/VahiiLoM2ohEiyNqXtkOt9xuC5f7S6K5wLObUuuKrgttccWPhMB0NtOt8ezZLc+vbzkebti2ilhnt7+gxMKLr7zGvfv3SCnTBtOoaadbJ5w2A7gdR0w84jhm9ytjWK9Y84ex4g/1J09uAOF8f44E4dnVY957512OxyPTtGOaCvfv3WM/zUxlz1TKKFxd8rest6QcyDESs99EtdVxIOh4dokPJcQP4JwT3ZSnT57w3nvvsB6P5Jz5+Bsf5/79BzSF1ja3oWGUsqM3t6BYt7GNHkqi4GqP3nyQFIM3+4ZwXFfWwzWP333M8XZld75jvnjIo/ML4piQCuLQSD4Erp2aGO1wrI2bZWNR49j07kGyHA/YciCkSDnfU1JCgyCxcOKC9FbR3t0qlL0IdFBaJSYvjLsqvQmX8w6ZPGK+tQ1rdWxf4h18eAqFFL2IdJ6sEXJ0kHrdiAoh5R9iyggNt0i1XokSmaYz0uxxtSn7AR4s0epKb5X9PBOKp8r5weQFesIoVHKqmG6oKmWa2ZaFt995h9A6Lzx4wOWDc5ebihc34QRxYPB4Bqpk651+vKZuR8wqx9sjLwblNz6X+ZmfepXLfCRIRweMPmikF2G/CU0hLUbc9mxpYZsbcfUo3n3YsSw3zM0oc+Zb3zn4j98XfnDdePuDxk2NWIYpJopMiNdzbARXvXSD5NuEHIUopwl+HJBo30orga11KkBM5JToOJ8ihMhUfIsQYmKpY2sbk8uze2ddK7W5DDoEoUyF+fzcP8M+QNgpj/dQx+b6VPQPBUyMri4MLjvuw3JZa2U5HuH0fYsPtqac71Qgp0JOMBjsHELyzVLyos65GN6w90Fg7QKIjWwGT5RRE8o0u3VYAlU7vX8IQE2SBpDbi99tXVgPR3/ohuAD5xjJ2aN6tXX6svlZIQ5bxSAX55B9qEg5MYv8YD/FV2sMtKMPKy93Z+5rF5CubiXyx/soRv1sthA4HG/p3ZgmT/ZxaX4fA1pXAgV82+m/Pf4dYnDFCW6PMoSRNetbRVVvSoM4V6craS4fSqLHEOwEiMWcTWTjc0r4NXq2nwYAftge8DNC/dfhNLYz7VhvQ2HkSoCAD9nvBkq4Oskl1/4zfa4ud1Y3L3w/LAYwtxN68WRIjugCKWW/ZkRQ83bbzDlsbnvog5vmg8PWN6Y53fH8ZERkn5S0RD60rSCI6EhbGsMgHF7pxU3wHj3+UNz0UAjcQZFHNxpTcc5Z8EJLRGit+pZ6q24BDhCk0AbfL4gXYUH8e5vq3VAW60gSdvMemhAmIYWTdXgMGU8bveCA2uGew/rCZoUiE5N2ajCSdah+tosJOgphMSHHzJQM2xomHgIikn3tYUaUjIgR4wQCWQstVHqF403HzoVJ3CJkHWceBJBUCZyuoUDYT2wfXDHdPEXPIWgf3LT9aKyPEO4R9p/g4mMHfvanbzgeN/7jf3ifv/jSU3bnD3ntY5H5vHCxKDoJ9+eGLYEPbo9849/9BT/98ieZXv1ZPv87v8UHb/2P/Ks3K4fnz0j3Xia99AY/+P63+Wd/8HX+0f3PcPHahHQDEsROyBC6+SAuOgQ1hkCPnZhAJNKCK0pzTD4oi54OCkIvhm6gopSYsdCJsbA1QbeNpkpMikinSyCEzNY6Fv3sC0DqkSaGopQcCdo5nhheTRAKkoTJIhr8NbvV0j2QUc2VTlFpbUXEv49aQqKnx97FWUe3wiOKVqXr4mDnImPQN1JrY6DIgBM3ZdNGnn2YqnTisAo758M5kxp1hJiMVFgLiKShRB2DmxQGW61Dc+vwScro6roxacch1bquXo+FPCwkzvAMEcKcEFPqUdm6q07KOL9j8OWXDlivdF/T+vloDO/oncUVHHbfh1pyIO1BT6oGH/jGKCxbc1tiCJR9RE4wScYwpMUxKvXXMeJ2/FxMnm9rqgRG2EPHm6+xKJOhfvNG0hVGw+Dt3wMYcgqyOlsHrZRpZm2rLwRH44n40sNFGWE0cr4cOeEoZKijTPvwWfqhIgxr82jSoyRnrA3VEiPpwYcaRsnOZUoS77Acp7pCenB1awxE9fCUilBpTDGRoi83Yhh8qK1xrNvg10EK/lAy9QdYipEUXUm/rM0j30fgUYh+rs6lkMfrba3fLdld/+vDN4kBqp+qIRpSPaGwqyF7X1ru9jOZkS7XfFgleZxx4sp4T2N0JixFsOpKR4JRNyXiCmkwxBz+TTdf2HOCnUeku52xascEJnX8RTMjkDx1Mrr6P0gaOBBX2LqabAy75omlK8ehgM8rlGCcTZ25Khe7W0oPzAJTyqSguDW3IpowCxQNrM3IKbO0Rj7uCFlJqiybUQ1KVkoypAo5gEQjSeFiToSd8GBfeeUF5fZw4Ga75f3DV/jjP/33/Ksv/j734mu8/uk3+OzPfIGPf+Fv8spnfo6f+MXf5ZefP+fqyXd5/PU/47v//t/y1a98g6/84H3ePF6wzQnLPtCMWjEMlcJqnhbm2bNDoSR2B2wP5gsB+kKVhoQ9MeWhMBw2LfMlk8RhEVcjRL+1Tzo0gpDHInuXC806ed7hQbbd631zVb9zEsQHM2ojKdefAYaiTUk509pKCG6H9tcNbaiJT+xaMUFi9tRvbUMF6S/OVXfu5ImkoSx2Jd00uaKt1s523PzZ3EftFDyYoInD8s06lmwoI52XdBfewEjZ7eaDOHE2XRtJtXHIsE7BMft5ZqkreterBFQYKeniwKM+BvdxOFako+IW/64MxbtzlUCRAY72jsWfx6dwHFEXFthd/f7RfH3kA6W1GtPugnQ+k5aCFiGVgvZGqzo2Mo2b6+e8//67PPn+W9TjSpx2nN274NHZI0/hyjNnu+IWn7wf8k23mhEDJq4c2mz1DZnIiPh0P25EM8mPAAAgAElEQVSKiYuHDyk5OxdJj7RW6bUjbWVZbhy6q5Ht5sDT59c8e/rYLU8xcPngEfcuL8Dg0QsveDEgxmE73HFDRISYhJQExNujlALadEhjvanLOZHKDkU53N5Sa0NMuHd+QTfj3bff5r333yMEuLx/j9dff50UJyQKu8nT6Wpz+We0yLZVYsnMu3OXu7XGsi3uSceXJq1W3E8bkZKYc+Tq+S3vPnmLm5tbkMD5xQWf/NFPgsBx2bg9HDCMeTffybwPy61PNWOE7pv+EITaHP4l4lt9j4WFq5tbnj274vHj9wkG9+7f47WXX7lTGoTow6Ou6k3xXa3i8ry1d27WjeOirJvD4Zr5lre2lX7ozLuZ6fLc4ZYUH+wEnxRrbWBuiZIyOfxNXaVjFojAca2UYJRS2O0KFl162PtKpJFCRC2iUSjZ5fbYGIChpAC1d9bDQsTY5eSAUjN6DGPj02mbFxCXuzNyCahmunSHDKrRdaP2lRwDF7tzmpzO13YHDyyhUWIniEfNh1yotfL9N99mOR546dEDXnz5nqf8WMTikFoM25zKgDEaCJFtObCs17S2cbttnK+N3/z0ji989pwXXzV0W2Db6Bt+sG/GphutbdRjZTsqWxO6bg6r7AGhsXZltRueP12wRXj72S09TVxr5a/fueXxVSDKzFQKsQDSPDlQYEPoMY2kAgfxTrNLinVzNYk34xmJmS6R1r0xDjlwrBtnu0QJDmtMOVPKRMCHZ9vWyLmQc2at3cHaI+WBAGXeuUXOjLYs42clyi7SqsM5SxkMo7FZzCm5BVx8e2Ei1G2j1s2jnsUfFmf7M/bznrKbvOEZKY3BZQ00U1prxBC5XVdKTn7tyoAWC1jrw6rojXHAG/Laqsv3B5uoMyS2BlZ1bHT94ZOGiuh4OHA83noxlxM5+5A5Dnm0qYMwY06shwNr97ZiPoHEwe0XYxPdGakdhCFR9/OvL43d/pyUMtGEkDNpqPdsPES7fOjhXtaVbd3uitqQBxzahnoIv69SPqVheiolQZhiurOLMTZn4cQFmCKqJ+im88/AKDHd9WJpKEcHn3GwIMZ2SvyamLI3aD64cpijYm5DsgjWRqHqfUPv6gVNGFZvICdP83EJtTMrBqhg2Ot0bPs+5AGAoM3ZQqIQ8xj6MCw6wzYSBltkGEGAYUPGh3FCcIsHnnRzfnZJmRJCIKWC/9p+//Tuas6hffCGuLUBdLRhNUignvqDyEi68zc0iDMIJA71zTjn1XwY5FG2o0kJPvBLMZDm4r+rOleMsZlzjpWzMwQjqJFCHlbFSJkuuDkeOY8zMY7IbJx5E6N44klwNoKqsvWVU3qRRFdpRI+ucoFIlDu1XkiA+L0eit9XazeWzaHotVbOVRy6jUIdKqLkDW/tK7ZuxLQjpYmEDUSeM7Qs9TEoM9QKpR2we8r6g28yf+qz/t4PO6PZ7JMB6Wg4I17+NI/+k8iv7TNnBP71v/0eF1+bePSgMF0q5+czi95y3pMz4Ah8+3tv8fDP/ohP/p0XmF75PL/5D5/w9L//X/jjJyv67F304UPOP/Yx/vI73+F//uff4R/+Z68xPZwpjNRKUWJxS34W3+h6glqkxsayBnZUCIVpglQqdQ0Ey9TDQkqRoM2fLZgPDbvbA61ktHZaU2ISGI1kTh6r7kYaHxQnMs0anebPC4NMRLMPI1UgWkc3KOIJb31WTyxSIakQu7IFTzATcFZLTCRVtDpoOkuCGWpzu7knWXbaBmlwMo2hQhQdy8TMsS5sy8KUCyDEOXOSNJl5wxJCQIND3Xuv/lwIXsFKiCDqQ/7hD1RV1rqQc7kbyHrJqz6OSYmUIyyrR8lHT7GN42QICL2Z1zQMUK4qDCWs4waGZSQEkkIxlya4Hb8Thrqs09nUmHOito2UfZFVmxJGKp0ItBruFONJPGa79UYMbiGK2VNFw+AOCl5fRvMQHobdpFMJwVO7pPnApos5xgJXLvv//KwIcSwKToN0cxVXLImSBK3+XBSJLNrpIXDKI3emm9K625WD+FkWx/NOZFy7UegqI9XVoLVh0/YGMoUfChE3t5xtw4YZQ6ZtSi6F3oYCeYzrzdpI98qoBiZJ5KFgB0G6N8gSxVm1LaJs49ryT1pRV/7h1iMVoWqgqz9Lc46IVdKUmHLy52JVSvKFc0mZZVmx1kfjK74wakrvjRiGUkhAop+duq6E1rAW2aJB7Uh0pa0NRbFkHw563e8KY0ORdFIOCRYjdWuYFLo2H76q0vo4U5uHlsQgxLVTl0pTIeXMjW1YrxSJw3IZmEIkCVzMeYT4gGkdllpP9fXBaYDoSYtNA7Umjr1SqvJs6exiZIqN+1NnPir7fSHvMpsFtm1jVWUKicMGuRrnMRBm4UY6OS+UOjPJRpwi+xgokxLSSEQbuIAWBA3eIzzczVxeXsNl5pgqV0+v+Os3/w1/8qUvEn9vzydf/TE+93M/w6f/xi/xxqd/gTd+4lf47G/d8LePN9x8889480//T/78//m/+dL3vs+3V+Fm9xBCIcTOrnVaN6o1X9iEOK69PoYn4z2pRosRrSu5rYQwoUNVG5MrneMIzJChqjQZdmLcoWIDGeHybR/qpOB9syR3dkxnBTB67fTmC621NaZ5Rmu7SzBet5UUXA0ax2sc1Q2bOXpABseN4MNRX/R7EneI7pmV7s6TZGMsNVhwp8ENqmOxP+oChRx8YGP+AknRuXA5pWGBk2Ep9mWhD41sLCyHKj+GoRA0CEbOnuK5rkdchS0I474C75HUe4TWXNFMC16XOFXbVZCDCx0kjtpmpHAOBxbBU+NVvb5xNWIcteRH9/WRD5R2u3PSbqJ3IZ3vwXxrLwjH4zPe+d53ePLWew793RfOH73A7uyMfdljwROQSnD1UdntSSasy0oUVxiEMCZ1+EU3p2m8+YbqytoqsvlKJpUdcZ5o6y1BndURgwOD27bywbvv8fTptfu7z87Y78649+ABl5f3mafsMffiG+NTo5by4Dq0sdHwSnlsehNwUgaFIVONtK1zqCtrPYzEss7N7YGvf/MbLMcDZ/szHjx4yP1H90fynR+0JU20OnY1YTzQBaa5+BR1c7VKKIF5mh0YvW5stTmvYTQlz59e8YMfvEkbcNxpt+OFFx5xsb/guK6E4EMkJNB7RemjKIjsSqF2n9R2baTgcFN/Xb69VjWePLvh6uqaujWmqfDq66+y35+7lHA0fhazp4Ooa5LAuUIiwtY7h824Pa4clori0cnbemRdKyKZ3a5QXjyHHJFuxJBIoUAMLAq1Lp5OZ8Mu03VsNeOdRcQinO/2nKdEjWDNsCHfBU8VC+IgSYnJgblDxmzmEHCawraRzzJFM2ZGMyNnIVqlraAhMO9mZ4rJmJgHb0AP65EyCuDdbkcLSlWPc01RUW3sxdgFbzZO/t3QjPffeper2xtefOlFPvOpVx3s7qgJ38VrR8w3v12cK2C9sVWlHZ+h7cC6LoTtwM+dzfzGb2c+8fFz1usD/cpgVbZlDF1aY10WjrcrrQXWbrQWqW1DNFHjOhQ9Gzc3yrPjynKs6JrY3X/Adx/f8o13ofZM3p0xxUKIXsC2KphkGh1NE8dDJQSY58QcHHAn5oONEBIdoRJpXVjGw3BrSoieEBljJMbENE/EmOmqrK3RuhHKxKYGW3XA9kmGniMx+Wax457mVIoDDcewofc+4JwuFc3DsusPzOBDndEQblXvmvzdfsc0zf56hl7Yk6AivTbasCW13kkpsa0r+5JHEy6kEKmmngoxBqRpcBs8Qt4fbCVlplyc26HQu7Gab9+nFMe01rduujk3CTEPNCiZXDJRnJew1EZtlaor1ox12yAG5vMzUvGhQ7CxtZcfgvyry+hFHLi6bQs5RmQkJpYcydEjqWMMtOAQb1f+CLV3Wq3kOVMmV0A5O81VYYyT9sQcCjEMgAeDlSFj2DPspuPLDHRzy4yXjsK6rkz7nU/dT39pDKdOm8vTTzxRI1btrrw05wv5htmfDZ5ccpIWe1OWQ+a4bm5ZMx+ERZ9MuDpy/MwYHc499mx07ZTogy89KbR0WBnMVVdbV3JIrOuCCtReOcuFYVgcRcIJYO12VGHYeABryrwraFcHF4cw0pa87VT10AodqqjTcNy5dMNqaJuf/8OaBEqYJ7at3zFowhgytdroQdwm20ajn9wWFHEGk1ehHdWN2jcImb4NXln3BYGY3j1fm3T6coNFI/fiysDjNe0ssa0+HMhhpNdFVzEEgywuwSdA0MTNesvlmRfwbi9VQhdInjrTulGprj5sgaUfqW0j7/yMiCmSkg8GY030kAnZz+yAEXaRoDvsuHFYD9A70jO5+BnIFMgW6CmRtg7xGZskaG6d0ac/wF78xFBCNEQ62ATtyGlzVM4+weXrH/BTn7/hg6dHvvL1J3zyjcyPnV2SzjvndYJDYLdTrraVRZWv/OWXOH/lZV75/G9TPvUb/IO//x7f/ydf5HurwLNnbPcfML/+Kv/mu29y/vuB3/1Hb9Dn4slYtSEpkMOeer2SpWJzQpbqR6YqNWbUFt9ar1CicNCKaWdKfq/WptRmbD1Ta6bbAnRPuBLobaPpgpkz35SOaHTblLgaJzNq/9aw2ugaaBppw/4QMKIG2rrRpsi2raxbHxYCb56cyebXF1loZtAEaz74cEW6nzUOTFWmLDhKMxOjYVK94R4W1hCFKSVfLqwb07SjL6svIHOi1ub1zclaJQGTNJQX6uqKEIlJCKF4Spc1Om4D6+tGKiMUBS9dZLDCSrIxOB08PzWaNVcXbT6wGugot5kpdwy4D1Pc5K5JO9W/HrDgz73BdMVMWIMSkyvn7XT2hs6IG3CFdRyyXIGtg/VEZ1hhzAdxljyEZds8sckUeoie+olbmiK4SrLIsFf6s7Ztmw+4CYh4ArDEAMPi44oLH/SeBuJTTqxdx1kZgIaakm0M9Mz7C+3mtpzTwDxEQmIsmCGY0msd6lnoQ0WfkisVtPtgzd0KENspEdlAhW01ZzHOIxQBV+F0/2NPdtXuzWjwtK6AP9PVjLUayYwsEabJ06IMtm2l1z7qIq9fdHxmIXsNHySw3G6EnRJo5HnHcdncWpfycDUkUvbF7rY2+ubP/hQG2zSOBlgbqIcObdWtfrmNJZVEaqs+oDehDc6mD+78vSipoONsbzjjtHdjSsmV6jnRtDPvCuvh4O9tSH7dmxHHkEqs0ENiNfOgEgMsEM2I5nVVyubnRFLaWLusqwe8+EXvAxALAZWJtRvGzFoD1jNPS2dG2B1WylyZUmAKmblkNjuStoKUCPmKOU6UuBJjY58WznJgShPnSYi7wFR8UBlTomSYTyKFKAQOFNsRp0pOwouv7/jYa4XbdsPzK+W7b36Nf/rPv0T9vd/n1YcP+PxPfZKf+5Vf5pUv/BqX/+k/4JUv/D1+9r88sL7/NR5/6Q/58p/+EX/55f/In7/d+UG8IM0zhQBNsS5U6wMS7cucCGjJzmeLgqTTmXkkSHG+ZpgJ0VP/Ykg+QDrVM+boABNPYl+35jb4EFGruCbB6DbcCdYJaeAgRAgj+djU8QGKqwN775TkgQgmQlTnKhUJvlA3d4eYVqaxSF10DKerYlaRIEzTTN+6hwqcfnY4WVozdhp2SkD7RscXwCG6ArA15zN3dcWtl2gO7fbS0pd64W4JOI5H81Rcxjmq6io64TTTHkmg4At7E1rtENzTdlK92+m9MUNqZ60emGHBrcWB8d/BX2swrNYMhroLDz7Kr498oFTu3SNow9rK8bix3Dzl2Q/e48m7H3C8vSHuJvYX93jxEy8wnU0UkkuXCcT5jDjtSCUS60YKie24Ec2LvV4XfNfiF28MbpnylB/BakPripoSe8A0c7w5cGgbJRvHp1c8f+8JTx8/5vnNM+Z5x0svv8YLb7zK/uFLTBpZ18UTmVRR7eQycfX0imkq7PaT2822xdkGNuYQMYyo2D7UR7CujdB9CLYrkf2Uubpaub6+4vrmAOZcpNde+xiXlxeUaeJwPLD1xsPz+4SUqHX1YYEpZVhNmiptyD1PsGta57it1FrZTRPzxSXLtqBq3D57wnuPn5NL4aVXXiPlwtlupqunpO3mHYxhlfaNVDLg1j3VTkmRY6vYekTMk+ZImYuzM0yV9x9/wJPHTzjcHpjnmf3ZnvP9GaWUAUYdioTgzXfvlSARjYaoos14fqhcbxu1+2fZ+8a6HdgOB3Lesbt/wZx3I3EIl/nlRLJIM0+gMutubexePFXxG0o7RCpTTMxnZ0iKlA4H7bC1AYgvdKtECcx57/53q7S1krqzgVq/pW4bMWR//VNkve1U8c1vmQr92EgxMBW3aUoc0dDqkurWGrfrFblM7POeKWWWOhgCKSJSmVGmoqBtgIj9cH/8wTOevP+YRw8v+LnPfRbi2BQhuKYf9xiPaO7hk6D3yuH6hvX2KRqV9Xrjcy8Wfudvvcon3phZDwvtpkIXltvOth3Zeqcu3bdvTbBQICqildC8mG+2sixKs1tqi7z59gcEOePRw4cs55GvvPWEHzyLpOmSe+nkWzbAo2WDzFQaq2aeXFfu7ybOsxFiZx9nSnamgSJIyrQeOK6dtXWaurkoJOfm7Hc7t4OaKzmcG+SbtjZsml2Vbd3G9nskjAEEt3oR8I1XLIh4c78cjmzbSpkmL/C6uqTcb8mx/XB1zLre3sng9/s95xfnpJDv4oGdYQOMRuIUIw1uD/Uth8v7TxDnoMZNX5klEMWogoP/mp9zIQ0GnXEHvCREsjh8XmUj47aELsb17ZVv42KhpEwp2RuIkOhdqePMqBtI3QghsCsz+/0Z24lxc1InjWFOROhBR3qcsK0rqo3p7ByLkf3kwz4ZNo8eRnFvHpqu2liXI2bKNBUf3psNa5c/mAcFyH3n+PvecFhrSWXcI2EAqR3cfFLwiAh0ZYuGNR8ulVK8GRkWhDi8+IwGrIvepQZp86hbrLvdYijSRqjJGNo4E8fwhrTVdgdtrNYoYzJl4wwUkTEPG5ZHuHutJ12Rmg+7Jfg1fSocYoy0uuKHig7bGNS6kfKAYaaMmQx1nSs7VD26Wm2cUXOkTDtvokoY7Cm37Gx1HTV1IEvAJFN1pdfN1UMBT0BLye13wZP8ZFyHbuWz8ZzsWPfBUUoTxBO3SVBpHA4HPxVyQVU4HP3sP9ufuRIwu8oiZ7czZ+/O2Kist80H/u3oRVczcj4D2cgl3UnXXVkjSHS5/iSZVODJsydMUbjcFSQpZZ4IRKaRFLhtlXVxS/a6bUNRkdnNif3FxOWueOrMDFWVEp1BkWInMLn0XGCNgfX5M8IWST0RzRO1QvCkN+mCpgqafHhAIN0vHH/wmPnyZZj241oDxLfC2A1oAN2I+4/z6MeP/OJ14urqhj/64/e4//LESy/OnJ0r6xrZ9cx+jnRdeLoof/Unf8rZK29w8eovcP6L/zn/+Pvf5r/5F9/ldlX6ukfKjunlF/jX33iH1754zi/9neyWqbJH2kayFbko6DpYGKlh14Jm0E3IoVAmI4g//6LsSElouOWib4GlNVb1Zm6tg5NBJ3d/Vval09eFjQ1SQlNDu6udpHU/SwBaRLeGSSIU51QlcTuwaXIbhjWmWIi7BuqNUeuK9UiMfk/33lzVhJEndQYXwtrH8CBkeq88v/ZrNgY/Q7t2PPnQn3PLcnSbVKuOBKhKKg6/t60OlZPPWE6NFwyb6RhjG6OZGMNqt35GJEfnZpgD4EXGth4hkZhjpE+BGJXDuqHV6ObXfk4y7JY+IDANbKOepvfB6/PBVBy2YsUtG2XesS1Hb15CchSAubqwiZBxhpFbTfOojz1Rtw77bxMFy1jH4cli5O4KoPm8uDWsK30k5AW84cIMBqjWZAyIoycZYwPw3dTjw0MYfCMHnp+eGzkk1lapCIelshupo+vNkYgvjIP4Fj+EOFI147D4edLTSaNkmthG0IEEYbfb0VtnW7Yx/DG2zZvzkzWtdedhBrJzSwyadOKQxWoz8uQLQawjfTBPRZjmTDehVrcIWXfstWkbY9NOM5ClE7ND8ynR1Q0IFgIdt2imFGjd0w83cxL+uvmg5vrqQK2d3a6Qoi+LguAWdHP8QzNPQu4aycmtM6ixS57wt64LBhTJSDTMGq36GdF6hRFHHwbnJkv2505f/BkxFlUxKmIespCmyDRH1o2xcHAFX8eYot9ftbriIyDMKXrTLEID6EIzZQFCq6TQySlSrCA67JVJB36j3CnlSz49y2R8loG1BZ6vgSsJnBOYV9ipuVrz5paQYR8E1YXUJm50JUlnnzpP4y1nJZJkJafAlIR9TJRkxDlwlgvnKdNUySGTbWPJ8GBSppBZ24qlQK0B2ZSffG3H5z51n2fXyltv3fC///EX+Wd/+H/w6r0LfvGnf5K/8au/yuuf/zXmT/0Slz/6S3z8d1Z+e7vm8Z/9T/z5H/xTvvilb/Hlp8IH6Yw8nzMFr0OCdlYTtgixF+fIRq/5tFds1NgpJGpr1LqSUiB1X/6GmP3MT6fBh9fLEKh1Q6OMwacHr8ylsPWFARqAGMgk8hgAp5Qo0qi1sq2NPFQ4NtQ/ZhHt1S2qY6B7qhMYJ8kUGFa9RgiRVT1p3AfRRpTB1IuMwffmQhEY4O6R8Bx98KkCqcxUddWPar2zh8bwoSrTGdH4Upc0BkJ++J/+ThRnwp0CoxDG4jR+aMcNcjfojkGopmQLaO136aMSgi9f1B1ATZUkge5JNT4/OClR8Xo4hI9WoSSnmMuP6uu/+x9+z775lS/z3W99h6vDDW2pTBf3ePWNH+PR6y8Sg7Ld3KLmxXUMO/J+B2KUHHyrHYSwbrR1odIJ5oePk8llSLoG0K1WL85EqMstbUzplBUscLxdeHb9jKv3HvP86TNKTtx/9IgHLz5kd+4pcfty5lP92thUXU4fuVMeXVzu6A2unz8bEs4x/Rt7mIBL37sZU46c7faEkFhq4+rqmnfe/h7X189pTUklc3H/IS+98AK7/cxuntjWxuG4cXlx7t7c5jyVXBKtgdaFNO1ofaXVTi55NB4OfIwhMu0m94auK+89/oAPPnif4+2RFx485P79F7n/8AHVlGmXoPlD3gup4dfs3dOocBXE/ctLDre3HJcjZ/tMq8puPiPOM603vved7/HOO2+hqjy494CHjx4xzzuX9A1+UUrzncKgdmO1IykUQvX3+no58vRw60OAALUZx+tbtuMtKc2cP7hHnjKqLoufImwxkspM31aHnumwAPHhwQ+eLBZJlFi4ODujaeTWjhT1P7NeIUZmCk1dBs54mDl4ud8lrbTD4lL42dgxeXLW4IYEcc7Q2htzmdlNk29axoY/jLHzsm3kBBfzPLaEweXIIXphFYwdG/uCq16CU/hvrq547513KWXitY+9xv58djm4Ci10orVTp+FNrXpDrdrZjis318+43q4JdeWV0vhbP/0qv/z5CVKnP1/cMrlE1vVIbY22BurgiFQ9cmwda8KKobYiPRB65boGYigsN9dc185tTczne959tvEXb260mricErl4Mdh7pKtP9VUiRuDZccMIvHC5Y/Zqgn2IbFFR3WirUrtxW6ESUck0jWPr4ZHdu2lmN+/oDIKveDy1mpDyRGudZav01kfj7wduDMI0Tz6UGuDk09aztUrbVmptA64ah3c8oWJuyQrRN+zbylbr8PQL8zzx4v2HrM2LwZQTNtQvMqwEqI3hah+pcmlcB2NBFqL/WausQ35e1ZPNdN1chp4jpUxMubB1T4eIkobpSSFCHIlYYNw+ufaB25zJMbGP2VkNwQf6y7a4aqVWat1gxDjvLi7JYzi5iKfjuFpITh0RvXc//1pjrQu7UEjJLQ27/Z51W8hzdnAsgV3KVPXgAO0dbRtTzqSY/b6JAVPuWEkKzitRL/rcttSIITDHgkkgBR+Onfz7qLdmVX2o0nvn6vkzXnr4yNWG6oBXxXzzHxjLCaPjWy5RodeNHEHyUOKYWyNCdAZOhJHwpWyt+bXQGpfnl8xTIZeJWLKr7YLzqWqthJTuLGQpBn//+kj2wzdaKgOi3k+bZoe4puhnRu+dJ0+fDldbopTCvJuQ0SCZOr+tj+ACM99GrevKvfuXnnQ5GpaU3J63rhWzRu+dZfVrQtUHVtOcmcoOCTCX2d+P4MleKWXfOjdP4tG+sRwXV2LlRMmFlCIp5mE/VrT6e9F0SNWjcDjccrxduXc+M+eMpEScZkrMlJRI2a/naJ3FQJfG8+UZ7XDg/vkD8rxjyidYvrA1ZxKkKEyRYU2PQObx1QdMAR6dnbPbB6ZZSD2TJkFo9E1Zjm6N6L2hVomiXOzPef2VB+QzOEewPCyNedhjgrPLBP/7asLy/EgKxvnDTJ6dyZSTkGIl7PZj6KywKZJ8UN2PjRgeMv34TyJWIE2YnCOSfVEhg1xjG70+p7/3Zb75777Fv/jDb/Ajrz/gV379PsE62xW8d3XL1bPG1Y2yNGM7dr7wEz/CZ/7uPyae/QiWG1/9r/8r/tu/mshZuT2/JM+XLO+9yQNW/ovf/QSf/sIlISakdeh12FCUfjxAhW3dWLfOerOwHjLlEqwLtE7VzrZWjmujSyaQ2erGsihrD2wKy7D2gjiMuwXack3Vxm33RjtYopGofcC5DUxdTXZcFixE8jxhze4SCnOEXRS2ChR8kWWBquqLyhaoCk0robvSKsQJlcLSGxGPMG/9gFljrZ7015uRQ3IYO17QExj6HobFwM9mV2ToSEIsQ9GkwwXnzBq3ZrmdJ0W3gknKrroeytW7v9M8FdS680lKzty7POf8fEddG4fjQtfGtjbM2t32vKsnjCHpLhGoj8G0DDVkSPFODeCpnfhrMX8+Oe8vUHsbDZl/mbliTHCmikTn3tnmnNOlrq6OjYlqPuify+RKQtERjuENmo2FrvxQczZE7W4HHHxB6x3Uh0BdT6qIoVaJiZKcFeeQXFe5BYFtu8vjXqcAACAASURBVCWHTO19NGHeqNkYCoO/BhkqgN4dvRAsnXBSPmwaiWcxe53notWRDGyDOCfBB2KGw6/xZhv14WVQo4XCxcUFOQtyehb3hg0bUeuKavJr004pcoA65B6tpBwxMeY8u1K59rvfBXWVaIwBCZl53tGlcXs8MJXkKj48ve5il9ioWHMG5DQ5KH3bqrNrK6i6erX3Rgl+jVQ9KUyg5Eit/kDuvY6PToaybdiIjDu2XwDUjKqVta+uzq3Gfs7sLiZ69CW91Y1kgbYZlgPnO1ctPnm+YupW4xQ9ot2V+yPUQs1xEtXIKZCiW/W7BJSGNO8HUoBmnf1UyAVubg40y5R8QY6Fq+fvDtvx/8fcm8Xatp5nWs/3d2OMOedauz2Nj+2kbJI4TlxRqip2EpNESqiUEKASBaK4AAJIdclFVOIKISQQV0gIISRKSCCB4LoQEKUqlER1SXAqpBwnMU5jO3FzfPrdrLXmnGP83cfF9891clkSvsiWLFn22WftPecY//817/u8SjABLCk4RDKLCFPIpNkxSWT2niLFYNCiLDGaQjOYfTqqYxLj3/rkuE4wdSAJ3s9EOXIdlvE+CvvJc0gLcfaE0KE7Woa8WgCMiPKiC9+67Xzj5TPee/PIYY186pN/jp/6kc/w8Z/8WR59319Ar78XaSvl+dd599f+V37z7/0d/tFv/yFfuck8S9cwzSQvSO2cakPwiAzHTi14jIXUsSWXKQNt4DZGQsQwmVKyKSLBautuqjrrCQVtgo/Dxu66WcUYCInyIexaokO91YMtmyKw6giKUbPxXvAzYAgCJ1aXdDEgO3WogVBiikiwZXuvWN/YFAawu3dGHWdJizKsczAsznI5A8WYrt0srSI2OLVeYLDmxoBexGpSN9LMvdgieMu25I4+4Lyae8IJIjbAlUvtOwCCTq0GVd9Mrdu6cfac9SfeW+9gjipjNbVxB7Vmn8Wl57gsh//gi1/5UNb///PXd32g9Jd/5l9UBdKDHVePXuHw5Jrl8ABa5fjiJfO8ULdMColl2eGWA0weiY5yWom9ctqyFc/i8GHIrEeTUOoGvbJuG+ICSRxr33AEtvXIdIiU05F3332fD975gOOLW2idB0+f8ui1V3jw6JpWGuW0gnbmZeZqf006zNBtmCLNPPOMBIySbfo4L7ONcXpBSzF1Ujff/LLbsex31Fp4/933ee+9N7k7nnHAbrfn8eNHPHj8mGlJBj+s3R6m3limHWleTDo7hjrEeA8CVrUG1LnBhKgdpDFPM04c27byzrtv8/77H7DenViuDrzxxkd44yMfZzcvHM8bXZRSstm7PHDZjXt7saIbyUooecvkzdQT+8MBpeOc5+Xz93nn3fe5u7sjhMQrr7zKkyePoDdaLhZHr+bzNBZINCuDKs0586qXys2xcHfe0F4tHrEqp3ykbivTtGd32BOmcXGr0kRMbo+j54pvQne2dbZEE2t+O54mAlRSmljiYhwaIOdMu0iaXSO4iXNd7eUnwBhcijhCss98O55wpUNytCRM3VHFYImzCOtZ6VqIy8J+P+Fo1Drgb87UDr1mQIjTzBwEekUkGtQUQVxlEUBW5pRQjOdyPp957+330Fb56Mde5fGTa3o3iHfvHZGO0z6Aj3ZZooUmnlJO5Jc35HxHL40nofEznzrwuR+5Zto16l0ZjaajnDZerCd8NZVBEaXcZk6l41QoPdOrTcRrd/StUF3j3ZvC7bEwRXj0+ClZhS/90R1ff5lZZo+PiSkKXhUGtLB2U8M8Wwun6ni0Fx7sPJGIH9+ddlPiOCpldZy0seFpBDr2/YhTUvBc7RK7aebm1tRBMSVrBICtVEppnLc8BoUMXo9xXCw1xA9Fl8l0bRPZqb1YxHDHhthjI3Yp6FEM/Ngax+PdUC0JV9ePSMn+eUsI9MPu5JDgByBa0d7IObOuqw2TxjbdBYte92KsCBPQqEFNe8PVCl0JKeInU+bcb+LF1CHGIlOCKKfeCKWyrid8nPHelGxuDEO9N2tULo28DRg9jSkEat5wKSBhNltObfQYBoO0G0OqVErNKDrSxoQUo22JRZgWCxNoWs2S2Tql2NDk3CpalVrOgDJP04B+R2JM99LdcUTRRppZrZXjyxu6dxx2e6LzuCkaI0SNhwRWzF+G/LVbA5bzxqOra/OhD+968AF19u/N60qtpgDVUfyXvDEvBkGNwYZw3kd2y3KfUJe3TM2FY9nY1jNBhAePHuOCZ4phyKDtvc3DOnKfTGRaBFNR1Xr//XuxbRJivvpRjo+0pcHx6J333n5mBaqX++Gq8ybXrqXc81ma9vvG7ObFSw7XV7ggpDCxLLP9DGe2il4727axrkdKzZTcyblwuFo4HPYIbgyIRlOH8eha1T/l92+sx1sqnWneEX24bz4NZG+NV602VI3RYMTn88rp7szjh3uudsZJcn6yM7UJ6ovB+ceW/dxs+IUU8t2Z3WFHcoFoqQcUsaGvdDXW2D2EWJBWuL098vrTJ0yuE5YIakBpcQ3nOtupkjNUMrVUJnHMMfDKG1cclsiUElOyba0TG6A7MbWpOON1oUo+bawvM/PiiXvHMnmmOBNEcFeNNGCgUpsNDYPDhUi9g+Vjn8A/ehVkD+4aZL5/BtRdjUat0bevs33rV/md3/wW//hXvs5nP/u9fOpze+pRuXm+cbzJPL+t3G7dFBanzF/+Kz/Hk8/+ddQ/xumf8Cu/+J/wP9cnxL7hrx/TtLN+5y1+4PWZf+uvvcEb37sQJVkhS0V7p2wr5Zyp2x3rOXA+Ztbbxnwdh2Kjkltn2yrbqpTemOOMqKc0OLfOuUBrjuO5UzSbuatXtCjn9UzDttNoMJ6cRDJmAZU+AklaYd3WYRmewZkKJPrAVZq5eXljTE/svStqdoXayqj3AgE1W7aOxLNuyVy5Ko6KSKdgQ6zWlPV0xgkWMW1lFTDsUd3e8TVnvCp5WDS6WtKZd9YMm5qyjaGTDZYsLZTBCYMYJyyd1AYmOhTPqpbCGrzj6mrPsps4nuwcc+Of7X38bIVse1Zj8IzKRww4RMmF0hsSHD5GovOWmNo7c7Tz696u6yK9ZtZiyWd+xDWZtd/CBGQMVekW755Lo1LZ1mIx8ZrwOHbLTCej3dFqHw1R+1Ct2W0x4sa5eWGMqBNc8ONMFHKttny4JG+KgWhxxkWtzViSTaFTCT5Cq3gH6znjolnrvbOFkTM0jAGDYViz8z33z56zwcgTGxQEF0efUi18SC2l2c4dGe22DebEg1Shu4DOkV1KxiItK5KrLba0gJW5BjJWq1MQj0oYTMY2BjKNMBsCoXcxuz9+qCOgVlMSN+3sdhO9FZbdhHOOrXXyWvAIaTLr4LpVFmepucZANVaQtqHm0kHpE0W9Ka0P80It3XiV/YxXzyUR9GJvEzGYdXAB38ZwIdj9s26bpWLpUO7HQFpsc9NrpZwrNLMwu0nYLY7j3cbpbFZOpKId4pRwXdHWCSnYsr11+viuwlAVNTWchylPFNHMHA3vIUHZ7fbc3mzAnqaR8/ac6DvmsLSnsvdOLmecn5gpdAlM04k9HedM6ZikMC2WrE3wIJUkniCKC4GQlL2fkObZTYYjWJISnKlqD9PCPADXkQWfrEcOUyGlSNXIuW7k40ppyi4KW3d87cUzvvrtzHvPMs5lvm//hJ/98c/z4//Sv87VD/84kh4Bnfb+H/D+F36J3/pHv8KvffH3+dJ37nh3vqJFqw0ijp6VLo6qjRSEqtGGtIAbwThoNTVPv6RQRvLW8PEySA9mEWt9nBXggiNGU+DU0U+bcG/U3P6yUBwK7RFKQldoUEslj0WjKgTnic5SZ7tzTDGN976z9kbwik/GG7bfb+ym7kzY0JsJCRjM39qFWpSAErzcM5BExBa9w0cs3p5hqx/tPfc+3CvtAfvueyV5CzLqo7JzgOZGxzAw92ets/fLdZOwXOo+ZNjvxlbTDZZcH0NwC4Dog5tpgzRTxo7eJAQQex+/9qU/+rM7UPr3/4O/qftHT4hTJE0L27ZCd/TtJS+++W3cdGA6HFgePmDZ72CDrXeQitOOdFMwlLH9LMcTmgsaAl7NmuVG6oKOxrrWjbvTHae7W1688x7vv/M+nsj8+CGvvP4aj548xEujVbVJfOscDldcPTRrmTal9m3A/jo9d3wwe1mXwcXw0Q53LAHD6wDS+kD0kXPeeOvtt3n7nbegZ66uHiBO2C8Lrzx9jYePH1JrHTHfla4VH+JoNCz1TJuljTQ1X2mulSkF87Y3bPiUAiFGbo83PH/+nBcvXvDi+QekGPn+7/sUDx89ZpoSHaGuheY6ENBWrUBwNtwxJfG4srWjpVBzMSliiKSQmHYGgP7Gn3yTZy/et4fTwbLs+L5PfD/zLrEd7STvtVHVIKf3AG6xIdC2Zc6lcMzKizWjGIByPZ853t7iqrLbHZivzJLmm8mMNQbEj813N8uduEbEgQa2VkwB5MQSkroyTYndHA081uwzpJq9ZucU8GxkYnO0aKls2rupThDQzlYzpZl31g8Sv+JxYhdyyWdCLuwP9mdWtYLT0K2VKjKGk9ZohRAQKrErjpnqNqQ35qBEzcaTCnYAalOePXvO6e6OV58+4cmTR1xi34MLtsmkDjlyxFdLddpapelGvrtjO71EtbBrmU89cfzc557wkY9HzusZ1Q2/OU4npZ51JPV1+l1j7YXqAqdzxbVMdgrimSQirtJL4cXJcXs68+57K7vDgo/Knzzz/MZXb/ngprGPxsGIMaGuQkjMYcJ1R2m2NVTvmSbPLjRK7yxztC2wBo5bJ/fOqRbK2H5UNRyrG6yXFL1trlKgNytU07wQnOe8FdZqjfu6ZboqMS128QmkGIkxYlgH28ZetmitVnK2wQPa2e339r6LSeyDj9RmNp3T6WSFiY84p1xdP2C37GjF7JfOWwTwpYBkSH/zljmfThaLLIIEjwt2AaaUkGDFD62btLxUSqt0bylIpvwzEL0P3uT+zqSwbliDu1NqzfTcWG9fEncLu93hni+TRHDRk3unbHkosjIO86ejSpgi89WOpm7YuwwwapwvMfvA6WRbb23k0ghTYjcteOfYzfOIY7fvrqmlqzFUMtZkr5xOR5x3hN1s4PQQ8SlaMTCsca2atbPmTO/KeVvZH/YEH4hjM212t04Iflig7We0XKjaeO/5M54crpmmyb63yThCdDWp+0jGK02NXQVQu22TvH3WXiwOPE02qK7ah7WrkEvhuJ3ZTkeuph0PHz8yu0KwARTiyK2a8sIqX1IIQ09mdkzFwKJ+wHF7Z6TijV/a8c4+f1VlXVfe/+Al+8OEE7PapjSbyrc3U9XUci8lr9Xs2DcvbnnyylNqXZmmhXme7dnzUDZLycnbyraead2k5lvOPHx0zX6/R9RZcMbFAjk2XSJiiuE+4tNz5rxueD8xTTZwEafMIViBaLcQOhIJnXM0hdPtkXmO7BaT0NswdIRgAOIg4FnXdQy6FbRwWo88un5IFEcKHoey9Wp2EA0k5/Cuo1IRD7uYuLt5SXCeXfK4MDE5h2AWpShqd9cG6jutbWixM2W/TDx5esXVfiJ4JcbG7GdSGgMrFOfUEpTUvo+7D+5om7J74Jhnx7Iz636cA9Pe45Ij0mg9DvFfBddxdWL53h+C+TEqE+L3KAswIXJhxwjoc/rpG5y+/bt86R/+IV/+0lt8/ude59HjSDkLz16ceHks3J7Vki8rvNIjP/ELf4P9R36MGvaEP/jf+W/+8/+R3959lKCdPB9MYfDiA37yB5/yr/6VJ1w/MhaeqKI06I5y95LcNtopcjxtlBPIXEAjtWy29d3O5FpYV4c2h4+JLoFSm/3vVSkF1mxA1qIOVbtLW7bUyJaFJtBdoGEKk9KMT6e9kbWiteGZTeUZIcaFfZwoObPVs6ES1FEHpFhVBmTf7gh6w4vxaHK1wQZqimL6CIUBOzNKZV0z4sSGqvfqFFOxXDbGfZxjtdr555zgQ2Rs92ypoeOdHc1ErfV+u2wqu0hMyRo3sfTNUgvaO34oFEOQEQjgSPNkQ7nex3KlmtXWDkcLJejYneoiRYeSrBazIzlBg4Ng7503GArabcLRmi20mtj9E2XUMGJqTFPQDBXOSGDpaopBbfDyzhRJh/3C1WzJnq13q2Wa4QtMQWUptcbpEzw67GxuDCrqva1aLqDadukPjBnUyuCdaGcKEbz9WecwwgcETpsF0wjeFkjBBoNBTD1sw0sLh6lDWZbEYLzqAiGBDP6MpYkpeTubgqoP7YYCIeBG3xW8qU8kJlJK7JbJ1JHbNpZWldqKpQUPPIJzgnY3qHlW/6p5ynA04hTv1UTiPTIa14syxGyWFe+ENCdcCsYZrcpWC/spmYCoN0q2pYR6d19n9NosjQtBsX7s3DZbGiC20HBC6w0GM8reiaFGEgGt1iBj9VdnWNCdDermtKfmI24yJXSnEERYTxlttrhCYE5mn1xz55zHi6emGJbacd5U5fcWdSdE54jR2EA5Z7tf1RMjiCtIsxZfBwh/jnt6F/w0czrfYL17HYr2TtNGVRusaLfBV/ALMcIkGzF45nhkcp7rZWf8nlCRhrE7nSNKwqfCLthZHncTTjq+ReaozLuFHcJMoIc7HJHmE06VmU70yhJnU2CGRmBHlM6cKtsKb9/e8GITvvzWxgfPC9M585nXXuPHP/85Pv2zf5X9J38UPz+04ejpGzz7J/8bv/V3/zZ//7e+xpefbbyrE81PiOTRW4G2SJeAd1jin4deFLoNgPzkDH4vntJMQaU9jgGj4EikZTIrenS00tCq1hcHbBDtvfXt3b5TpZu1rDaz0DcFbTQZ8sXBGS3bhjhYDjsLkpDR5xfjjy37+b4ealWhKjhnKIVmi2WziYndQ11o20aaHaWN5xkb1AiM5YHVDjrUp6rcK7J6H0xL9H4pbXYzNxAP/l7ZZM4Fq43EXay1VivW0qBXgotWI44Bs3oZylb/4f3j7HM2NFAb6I9GzY2Q4hBTKF//vT/+sztQ+o/+q7+l7WR2j3K+odSOts7ts7c4PXvG9OgVdteP2B+uLV1GHNUbIyTQ2PIKar72shXopkBx4kZKm3k21+3IVjbaaeXd997j+OwFeWs8ePKYpx95lf3DAykFWimsx8ISPGnZk+YZP1kMYSsrTi2xaRMIDbRnDEo2rHXB4Khl3Si5obTBjmiIePKaeXlzw3k9UVohOMfjx4+YloXrq4dMk0e8p5UCMI7PwQcZgwLvh9+SQOuVWhphjqSYuHnxcqTuCLvDzPH2yPMPPuDmdLQ0g3nPx974OE9fecK2rqy5Eqc4hg+F3irL7oqWN5QhhYue3uwgK8UajlbtAl52O0SEdS288+abvPfiGb1mDlcPePDgEU+fvMJymDifN463t3awd4OvtW4sic5Fbm7qiuPWqd3YELne0k8n7m5P1Aq7gym7jPthjZb3EeeFrW+0okRn8MbuFFG9j85tVdlqxYXAfjoQUxobuU5pmaZK6bD4QG4d5xUvidlbNGWmErFLvQI127PVpCI+4RlKInGUbpu2UhrLnLg67E2WLhlaookV6KdacCiHabLvfsACu0CgE1VAykhuMwnlJbL2dDzz8vkLrh5d8ZHXnrKPE9ot3BLE5OCYHU/FlHROM1nh7nRHvnmfsp04hMQPvur5mT+/443HnXq3sq6FlpWtNYu+RugucndzR8/KRgEieNuwt94pDXK1oYMKvPP+ma99+xlPHj/gtadPuGuVL7258c33N7wEnjx4QIhQh31ZcXixFIxSBEQ5LGPA1oXkC36KJBFKVk65cbNZukrpjSaejmfLJpVP0Zrw3ZTY7RZAyGVYl9RxPJ05ns4gjm0zf7SPidqs+E0xsswLPnqDDIqp/XprrOuKDNBebY39sgBQWyWlhB38Brtct7Oxy3DMabIh5m6h10bTxpwmVMVgqU7oaod9bY3z+QhqhU70xk2y4bmwm2baUOVoHrL2mkHMiiRlI8weDRHvbfAwZFT3snGDCRfy6Y7jNtLcUiJ2T4hWSEdvG961ZPKa6bUjvYFcWDuetNsRQqB0+zsYKNAx4cjbxmmztA2t1gT5EIgpoSIs0Wxeiv3ec2301pkR8Kb8ynmjbZnuwM8TKUSij8YBGoOKWqsNuXofjLfG3emONE3G2MEGPPZOcM8JkiFhzrUMaOvG6Xjk0YOHpGSF+wUeXS82uhHfjZoduNXOdsrsDjsynSlGJjcGPsmSN1ttFDUl0LZunNaVUjJPHj/isDugIsTorYPzwWwTveNEzabiw2iwhDVvtnH0wfhCwyZjg3+956yharJn57i5ueG4buznCe8C80hGBLPLWcFkVre1ZIJ3tFIoW+XR48fDPmKqVzPpWOHTWqXklXVdLfoZOJ5PPH36hCkZ+Dt6g7Tbud9IKeH9SHoqbTDLTvauiMf7ZBY7ICZj2IgT21SKKUjtXjUVW22Vw7K3IXAYtiLf8RiktVHp1XM6rxZG0QtoJrmZeU7EKRC9WaHWrWDg9oaItUHJ2+A2AGhh2gc8jTklHB4/eaLrtFzJm9KDNbi1DJbWBocHM09eTVztIjsfhoXZMY10F+cbSSJoNkvPWnnxcmO/TCzXgWkKBG/P1nwIRN/BRTs7Q8OJDUbKqRIOrzB/9BMIM+ITuB24ie4nnC6oeLqekX5DP3+D01v/L//0l77Ms/cLP/azHyVI5e5m5ebYuDt1XrzcKLWTc+dz3/cJPvnXfhEfryEu1F/97/jP/tv/i2/PD2maaNcL23HjervhX/np1/jcZw9cLwEZASWUTqmN7e4EFGNn3npcVEpuxs9phbZ1aobcTa3UFKw7s4XMXenUUlg3T2mRra1D9eLQulKyMSpzU6JLNIRKoXW731WUMrbXpXSSRHKH/TSzLJHTaaNro/RKd0DDGgW12qQ1N5JzoJRCcB7XC+daQB0aAr07ogSqmCWolk6p2bbWMJQwDe8vVjDbqjs121KphdqGrcHZQgLxQ41t9VkfjUerFgiyZeMNdZsEMc87O/uHWtSJp6oSgic6U2YiDg0jEGAMqF0InI4r0swapmIN2CXFyI9AgNabqVAA9ZdllzOlzX0SpJ1hbigmdUCew1DiEGybnjeDXTsf8M7kQZaU5kz5V60hv9pZre7EhjPb4Jc0tbtbLhv7rnjX6erHoELu1XqmRPL3DVvXbtt454YtRXAp0hn37xguJG9KrFo6N+fz4NtZSio4U6Y6G57IaAB9dGznjFQdVm5MTSpm81LBWLAXi3uuNvgSWwc6Z/+MdFNEBedg3jFPE74XmhZ7ppuxRmu1sB1zshiHxYlBpis2yOqtWvCLQm/GBuxi9ZSIMAVb1OY+kAyjaZ5nYzHGONFaYd4fTCdRM7kaxLw7aLWj1YZUyUUYlqeuCqJDmbqxDL6q+EgvxZ5m9QOF8acGSZfmtxd6N7agl2RMx9IgFCRCiIHJKzEIz56fqNWULTEIKThTzVShdkfyibubG4L35DZskwIxOD58NGU88ma7UrVBg7rGbk6UbL2acwbqFjEuVUgXi6UtGLvau1Kb6Uq0V0o1VaS2gG9maxTvWHwhLQPb0gqiG8E7Hi8TV8kRvdJchm7qSpGKek/3cGgLU1Amb4fWFAphdsxxJopnSabi8iHjXWChs/gdLRnjKq8rTiMhJNbzSg6OF2Xjm995wfsfVPKqfOzJEz77F/8Cn/nnf56HP/h50uExIc2Qv8WLL/4Dfv/v/zL/z29/lX/67e/wtfPGrSwDlO8pCqpupJrJsIR9yAgCU/x676n5wq40V0AKi7lkxOHU0llDsv7feTdqcFPRXyZ2AQhxpvShHnRm+3Ijttcp9+yi3hvRwT2JWrxZQcVqD+cCTmGKxtWs4wy2cEurEWOcWGtBi7GStlrppRODJw/kiMglma2NU18v4mQuAiURZ46dcZaFEKBCSonaKo0PAdmXs16c4T1E7blVhhNBTUXfTZqIqgUdXNSZ4syPKc6eARFFvDeL+kX92q2W/MYffvPP7kDpP/6v/5bm43PaEdZuYCstG+fn76Mobt4T9o84XF0z73b4msmbFeYxuZGK4DieThaHR8Mh1lRq5nw+cXx5w4sP3ufF+y9orXP18BGPX3+F3W5i8qaM0t5Yyxm68ODRI3bTzmSPJQ8/JDa55sPkJOl9WDpMIpxiYl1Xk5Ui7BazZNze3fD82YuhVDjSO1w/fMBrr7zK4XpvEbW9AnHI1w0gznigvAxmi/fgusnCB0jLHnLHum4UrczTzDLNvHj5knfefZPj7ZGYAteHa66vH/D0lac4sW1+64rr0JzQaybOiSigRJQMAyQs4zDs1ThBFz//ljO3t7e8++679K4s88Sjh4+Z5olpgAe3Yhsa5x3JRzskaqN1S3OovY8t5GrDnm7WhlNr9E05H9+B0on7A9PVE7wwGEhj+2U4YbRlOsqEt0Ie8DHhg1C70lezYUxXO9wUmcUj1T4/dUoXg4HXrvepH4HKHHac84oPaSREQV1HOqAfihVxXGwogkJzNBrztOCSyXClVeqF89IwW11pzPPEPFuzpdoI0gkmNCRqJfhODNzbqxTh7u7I6eaGaUl89I03OOwtHVGs47vf1nWM85JV8V2hZppuHF9+wHY+0nvhh1/d8/lPLXz6hxJoJq9mK6xa6EUge6QH1u1IXhvFK+EMJzU+l9OESx3vlVaFUivPXxRe3jRevjjSr2Zwjj9+3vmjtzvPbguPdwsP5oWsFoHqgiBDlbFlJXdIMbCbPSGa2k+0E1wznkJuHHOjaKAOoObxVDgW6M22jzEK0QeWFNjvZpwPlKJsFfCB42nl7u6O3iHFNDai2DBJlWXZsUwzPniaNnq9WGVs03uxOV14NWFACMV7g+Mh5FwoZcO5oVQJkSkamFW6DnB1wAVn1seQQKw52bbtnlsWRnKd6wx1mtjvQyjRIk1d8+Re8b1z3k6ULTPNuiuHhgAAIABJREFUO0LyiFrhLGOb19U2GK5VSlk5bWfq+cjmlGna2zDNJ6IXYoho7+SSyaWgtVpD4C2WXoE071C4BzUr3INapTe2dR3cpI2mEFMyi0SI43Ox5BzvjB+x1QKt3jdRJW+c727xfmZeluGPj0xT4pKqQVfbyHRLauw5I2sha2e5NlVgSpFgEkV6bwYtBUvnGMVBd8Ldyxv2ITLtdqRpRgUmN4Y5whgUjHCHbg3Mum6kNJHmBS8gzoYFIU40EVNFYcPrVgu9dM7nTO+VqwdXTJM1IcF51NuGu+RqyZtOiN3TxDbXrsNx2/DJbF/eMVJE7fPyzlk6h1qBU0ohzhPvvP0OUwym1IqRNFmggveX4edKq4XWy7BpeDuLT7c8uHqMeGHeTaSYzNPfGbyXRquZXAulVLQ1jqeNp68+YUmRPlRq4AzAifGDdABXWzU1Rm+ZbdtoKjifmH0caVQCztS9VjKqvY+qxBgpuXB3c8d+vycEZ99zjKhYhRbEULQds/pupdG2DDXjgrKbd0xTIkZPbnA85TFAVDM7dUVGTLK0jKdx9WiHls5+SqjPhGQKDa/eopNbp6hyLmdazqynM8vVNR99/SFXqTMnU/8mn0gepHeLUnc2PAjiUQrPn9/Ss2N3lVhmR0iB4JXrZYe/qog6xCccKxqsQdet02tk9/GP4x+8hrgEEuluh0jC+QXkYHYd3ejtPfT4Jrff+R1+9W//Lk9ffczHfyCw3TaOa2HbOsdT48VtJjcId/DT/8JP8/Dz/zZBA6SZN/+n/5D/4v98xkvXacsVbndFPx75yC7z8z/+hB/59BWHgylXL6yJ8/EMZeVu7bQjEJRWhNqyDe9zp5ZK6UouxRSkgEpCZGIrjdwr60nI1XOud6Yq6ZZAqMVUims/00u1z8AHmojVJf3SbDurH5qpKkPaMc3G37Cwi0pp9n7VNixQCtoUHyZwFlOudcU5oNvAq4vQNZi9uFusNBiw+aImoasN1hnMntFU+THosNTQaglBMCzKxmTzFpGEiPuQJSTYIlVtSCtjkYl3Fi6hps5U51mmSHSYRXBEv5rNU0xNdrGUb4VSOpfwgj74gF78gB1Dwey1xpsqZrdTJU3BrPwC0pwl0QXB9U7LVrN0N1glo6krTQ3EfbExjfbFcVFgVmIMbOtGro2tY++nc/ZnUBuQ0wu11HuLXuvVYLxicfOMs8iLRySgvdCoxhxRkNbQIGNcJ0RnwzLXGepJuXfS1NrGsMTqczcGVh0bGgZvAxxbdFjjeEmTdFi6s/OmcAhhojuh1s1q7lF7W0hAYHIzXSCmxWoJLdSaxxLKmko3ls9byZR8tgEZlpDnY8I3hlbCGZeot6ECsSVvGZZnh1kBETHrYrIeRRRqaez2O3wyC6OWNmyIjRgE8bCdbcmQ5snCKcQCiVprlFoNxBxNdegdtHMdVmpHb8ZcM56R8ZzmNIE0S63tIAOO7pwlAlZvf7ZddHgHp5OB17WbetjoWA5RjzpPLhuqpnsqCLQNFU+43PVjwGt3iPGeQoi2fBm3SquWbutMb0WVxhwCng7e4MlOvCVpY31IHonbXpxxOht4L+RsQz7BlkkezxQtzTngmGPjYajsFiFF+yyrNoIqFWEJe5ILzHM121gwtXbELNHeN2bvmJNnDqaI3TMRwozsIFKZgrf6wCf8bO+G00Zfj9ycKt+6WfnKu7e8+a0j/XTm449e4Sd+9C/xqZ/6eR7+8OeI82OmZcJHZfvqr/ON3/hlfvPXf4Mv/MG3+KObzvNmqp2GDVDFefDJdp0u0sgE8VaLj+VTbSdimEE9ZdReXuxMC3MgRFPt1+ypRRGvdLEeWZoj3idAyjgrQIO9b5OPtijuWIiKawQNiJ5IU+K8Wl1U1SHVoPAIln4YhOAxpWcZAgwJ1N5BkmER8jYG546i3C8xPA5V6yPrwMRoM0VlG/zN3rvxjpTBfbM5VxcdAT6mjhPMwmqXBOMsNDtrzqYujMGRayH3RppmSs2gdv6hOsR6o4d1jPAaO7u1NlyDtRS+9fVvfdcGSt/1lDe9fcm7v/8V0vVjZH9FjJHeHD1E5mVnMj7niD7Qt0x3nR4cTjr1VGmukk935C1TaPimbPnI+Xjk5bPn3Lx4SS6NZb/j9Y9/lOsHj3j6ke/h8OgBz//k65zPd5Y0p3aoXr32qgHlthWL3rTPruQNxeIareBzNAelWmR28J6SK6qdw2HP6bTy3nvv89bb3+F4OjIlY3688dGP8cbrr9v2F9v8lGKA5konYqwWVehivJ7ex8MyEhKcMwsK2V6Y4+lEihMPD1e8994zvvbuH9F65/p6zyufeI0HV9dM82KNay6s2RQTKVizFLzg02xg5lZBqymrnMlzWy12+YzP4vbulvff+4CXz5/TgVdefZXrqwdMyWIbW2+cz9v9PXS4Ogw4X+G8boBFpG5loyPGCRGzOLVcKOc7TudbvE4c9nvkENGQzB6Es1jW6Mxz3htRhyRczILjQ0SdhyAWSZoVFyOHnSWF1bxxEkdyYUgUxwWq9j2GYckIMnNsBXwY0MdOyxnvhLBcBkx9yMzr/SDKB9inA+IduWTIQ80gQs2WCBiS48HDGcTYTwEhOCvknFaiq6RoC1k1ByNb3njx8iUO4Xs+/gaPHl2Bmvf1QvSnt/stkNJoHejKVs6U03scX3yAw/E9B8cPvbHjc5+Z2S2dm+/cUrNQtpXtXNjKSi2drSlbW9HqaDVSm4Jkk/7XzlmgkVmr0Koj104+VmJY2F095Nmx8NXnnm+8t1K78vD6YAe/dOYYDcynplYz2LlwSIHg3eBTKXNoQ12mnHKlqbASEIkU3TifCznbZ5UWgxK6ICzRM0ePdjXlVFNyhfPpTM4Z7yPLktBuapraKtEJS0zG9tE2EgG56P4HhLoQYyTnjRgj8zyZ3HXs0kSML1XrRvSeFDwpJmIMdpY583VPcUJ8oDXFB/t3C0ovlv4SvTPLQowIBkPVAWZUYUAhO71YmG3ywu22QW/MSzQej3PgLsMkwAT60AtbzxS14ZVzget5oqjg4gSihOihN/K2sZViVlr7Gw5QX+Jq2bNdNhriSCFArfhghU7FtkE0U7kFb4OdlGxY52LAj1Ksd7ORTCFSHCSErWbyVuh4G4A4iM4xpwTelJutWhLSli1prtVKK5lSC8vhAN0GZDKKiVq7wR8ZsuVq/90rtDVb8TlPg11gA42iHXF2wat4ehAb4mY75GKMpoJLljWCOhsSjUuzOdsC5VzxcSa31VR002TDD8SUB85iZLVD8omtZVwd9stxBpeWcaKkAZLuI8Wpdx2RyALeihqnivfBInurxSwLSnBCaxcotNkaRDzIAI6rNaK9ZdBOriteI9onumJ3FGYpB4eTZKqAPhQGo7kEGVyjwJjHWtPNWMqIB9uZAwbldWJQ8i52BxkMMkBnJNqMNKRuQ3aB+wAOHwywPfJPsAiGoR521oT383bfoDg3YJniDPCsBizudIPZd3BRBsTE4btBj0s2S/LWqkV1Z8FHU2xUhYIpeVWrPTspUkfBuTVHqOBio0qlZJiCJbfEkWaUg+IkMB+uePHuDb7U+8akuM6pbKSiTMnYXK4FXDNFH3vQYyV/8AHLFNCQEJmRsNowSeuQxk/AjLgDsrzG/vUf5NM/dcPv/r1vMF2/woPHgUmMTxe7wzvl2e2Z9drzxX/wBT7/6kfwn/5Z6Mob/87f5N988z/lf/i91eDqIRIPe945VX71i+9zmB2f+v4DabYtfuuNsCQDDp9vqM7jY0TbeWxQbYHmmikjPJ7oA8e1WrEdRuqXd5RYEK149dRuLaMA4i2COZSJ7nXYgDJNHKIWWV37xf43oxRUB6C9K70VRMI9cLprNeRAG7YccRTtuGZLK+d3iBr3J03eQNi123PvhtxBHU6UGK0JrsUG7hZ48OGWvg9AuEOYUhqMDVMdo2LNeXD27g6blgxQt4xE21I244/0CjosXuMZusTKp0ko1dOL/cytmWU7Bqu545RsCdZXG6Z1HQnFpsrpzTbZwQlhBIOUbGEDip2N4mTEsXdaV6LauWrAZ7NsMN5RQQZSAjqGiUAMAO5jpAl4CVQVKkLuSqltLG1Gk+0hzoHahHUTpFqtnTTQW7Q/+xgwSPS0xrDBOeJIe9MuuJgQZ6p2aYXejDVYxdHriqUme4JLZnEelhXttmjSwQvsWMAI3qLNXbRhvNuKWWb6AGmrqbS1NaQ7op+shtNOl8GRcg4C9Kogtnz1g91aq9yr1ZzYnYpPyOQGn7NBq1DGUEX+FMDch6GMMAeE9sFwunBr3IffT3Rmf4kBQ3+0SN2MFRXdZEqnSzqgBFwQS45i8FcDlJzBWX3Tu6kuFTX7fSk01+6fHRnKrNoKRY3j58TTqOSeCfghJhFjxDRlbR8OQC+OCJMEmnKjNTsjehdzMgAeG+r3boEluZsrwImYbRQGZ7ANda1Dx5i4DkNh62PA27upQYqabS6ZQkybDptfggDCsKi6gDbuwz5iCITQaBXWCuVsw9cUAje+Ma+VKTmCN4ucLyYE8OHIFALz2VHDiYShUUJ3OFeRWNnNyRAFHhYNTKHi04lZFsQX9jEwT4Vlt7KLCb8IS/BoCkwe/lza8cbDhf5JePuU+eYHJ/6P3/6/+Tu/+mu89vQx3/s9H+MH/uKP8ZFPf57dk0/xib/6Y/zAX0/8G29+ka//+i/zT77wG3zpj9/iqy82XpRCrlbfq/NorwQCbXC+fPCGLsAhwVLiQnDEcV60lmlrwTXHKo6SBeumwMeI08AFfq06wlFU6Sr4LiRnYo7SbcDjuQwoTRWXJsHFBWXm7uaMYApm/AgiKNZfdLH60DPRVQkusm6ruYmiI4iQRwq1QbfVrK32yuElGOuYi9XV7HoXuHdVhVwNF+CErNWUVX1AEMQsdF1tiaa5U2UMjYOnDOeLc5bs6bxhYETsLOzVthHB2bCsj+GSqi1GxNtANAwV2Hfr13d9oPT8nTc53h2R5ZrrOBEnzzlnfIj4yRQ+fSts5zuCF7O30GG945wz0Tlubp6R1yPn44njaeN8vKNulRAnHj58ytWjB+wfPUBbYwk7rh5eE4DHr79GXq8prbIdz+wOe7bzHVOaIBrALtcypuBuFAUMj7vJWfvwbXs8aOX27oavfOXLlDWz2+9IS+LR08dcX12Rppnoo3k2W8PHRECYwoxqJ4mlR9CFEByt9RExO+bqYi9Rb43zulJGGtW2rbz19tvc3LwgOMeTV1/h4fVjlqvJhDPauT3dIdgGPKVol4R2am+W3NSsGbHqXEBH8o8quVSOp5MxjI5HzusR7zwPHj3kcDgwL4vt3rqlg8Dw+3el0zgfzwOA5/A+WsGRs20Ym8nS1/MJstLKhg9w/eQhPk0IzgBjTqgSjH2ixV6OS+F4mZd2BeepYoWXVGMITNeWVGcRiibz7piaRXQMLpw1jl7MTnkpS6MIPZv0XFNkntPw7tt20/dxsHWMWTAZfNZpp2Xbhp3aCfD42tjNM8t8RQy2tezaTYLZO75mnGyEKETnjZ9Vbahxc3tDzoVXX32Vx48e2KCk2fbN6YhJH1Pm1gp0i+gtrbDd3VFvnzPFMx/dB37mh675oU9MTK6ynTbuPjjTSmDLdVza1hRW7bQNYKHmaqklYnbE0kGbNYO1Rray8ez5Bi7w8OEVa4t8/a3Ot+6Eu2Mmzs5UHy6QknJI0QhS1YCUIp5lDqTJo+IQPD5YvHrtjlNpnLsnN7usqxr4rnYbuIUpkbySppkQTO3iRTivG9vWKIN7UYsJ0a2EFYMpqtKqTVxSuqjo8j2r7AKINEuoAaadEw7XeytunDXCyIgOzxt9gKjnlMZAKVghjhUNZqEb7K/BJvNeKTnTNZOCqUhiivexq+LlfjOqYwDachsDkcp6vKNrNcvaiE83yLeMbZvxjS7DqbxmTscjTjx+3pFrIaQ47DeW9nA+nsaFZQ044/d7F1nmnW1mup0rvVv6RBPHeVvtYsqZVgptvJvTbhlRxZ7oosltvbcwLawwa/0iNc700qi5MO1mG645U2wRx63XxbgHY+DTWqfVTqlKmGZ8nHHOVJZu/P+MYWunc7HS2za0cnc6sjvsTDafpqEAsKbNOEX2XtEHDtEJ0mHaDaB/G/YCH2nah0fdilztnegCa7ENZWuF/dViw9OhXhANpmDsirRGE/sZfdgVBdhyGcBqGewVAzBaspIVuX1sbhFjRnhnWyxLKTQO16UZbeiQd/ehjHAG0b2kJnn7T4h+FBR2L4lzo6Cxxk+dFYCuw+YquRYmtQYDGfG3qDFEhkV8+IhsYK+KhGhNtzc2oDT7c+k450VBRyBEL2VYfG3bn2slBG9NPNYwM1QODNVFb4p2GZwqBZdpXTmXRuhwSf50eDSadcc5U4wIDWlmqT6fbqkuoFlJkgiDwxXE/n69BWpvtOZRHYM6Vda14l3g1BWKkELDO09pjeAh1W68oWJFqYZAjcLtuYObqbWZurYrzYsBZ2MbNhHFt4ovnk5jPd7AM0fcBVMkhpkWJ5zboeERhGuzwvUz6IqPkdf+uY/x7I9f8p2v3bHbPWQOEzJBpbJ3E2jnxcvOi9T43b/3d/nz+8780b+Ec6/z2b/xC3zrv/zv+aW3OtvxhiqONF3zrecv+cKXXpJ28LGPzCy+0kultULZMuvmyaUxBSU3aN3sJ6KNIkoTpaoO5iKU00o5m9JiZdhGup1XuXRUw1im2KBSvUc10TXTiqlhVG1Iaypej+LpAwDrWiVEa2rA7Ny2SDEAfvAKKhYaoULVSu9WE1TteBFqq5Yy5C7nhdmhkMEiGkPZPpZJpVkqmvZmAQZjiIVwH3t/sd1qN+VQbd3e917x7sOB0eWZjzHhfbhXYvY6xNTSCQPNEJzZbGvrY/ikcPnZ2nDBU3vDJ49XR96anYnehhQ2nGmm3hs1lARP9HEoCaxexjmiXAYdjHfSDZue9foF43sU84NZ04V972GcU6KCqCVACZ4YBO8vn7GxRAxs26EpXk0BIq2MobDSm7HzfDLFThdFmp3vwcv9c4FTGkqM3gbMQw1bW7U6tHa6t3rUi7kKUjB1UxUbFjoZyW4etFjikxO7+90ykUqlV1sIta73CmARCx0J3tmga5oo9cMEqEtcuHi5hwUb36+h1bhvftTyMqx1Tox/dSrbqOUcnqFGC/4y6R+8SKHYFNB4Us6s7645QzwM6LEUKH2zz1sd6vKwXIehjDOEhyAEVbQ0mlo6qhNLrQIlhnFfxjGwAlPNqanVFGHyYdQ8mSnM5N7GwLfS1Zharirq+mgK7N2UbmoPL24AnNtYZEELwqamhIpDZR7E0cCCGmQ8h27AjZ05VPxYnF/uzxgjToIpCUXx0c4AxVQlPUMXszFaEy3so0f7horjdDabbHRgkVKC4kiTspaCdGv2VRybm+kN2iZ418BXXI/GRfIN35Vzt2d86+OcWSvSHa6b4jzkyuoDq4CfK0sMnM9H9rOnKhzLLe5m5rBk5mA80mVWliWRQmAKju6VV0T4yNXET37yiuc3yjee3/I7f/w1vvD7X+KB/1/42Ouv8+nP/Civf+YnuH7jh/nkv/yLfPpfc6xvfoVvfvEf83u/+0W+/LXv8NV3X/LeaeP53UZujtb8SCHshKD2Pdg43Ab9DsBCcro21lzp6kYPlHHq0ZJxElEvqIZxVpniTIb9tblG94M71IfFXS3IR3tkO1YkemqzROZSxnNBH3yibAuraj1LbdUGTNotGEbMKdFbJXpTUDY1TIh2GcoiU2Ve3kcwlb1Z6caZ3uz4UjF+q3GOdKiwuR8MXYawVuvaO9rrxUZuKBwbIndTwonY2R3kXs2to04z599lMGtzhJD8P8NU55/913fd8vbv/nu/oDULh9c/yjLtUBr1dKTWzNobfjngWyEQ7i+31lbubm44bSfq3cbN8QjHI6tabOXhcMVymJnnGdBBKZ+Y9weevvL/sfZmv7Zl13nfb8w511p7n+Z2xSp2IkWRFNWQoCVZlmG4kdskMJAgQBDAf1yAvOUxSBAEToIgiWw5lmVblmTKEiUVTVIsksW6ddtz9t5rzWbk4RvrlJ4CBOEBigVW3Tpnn73nGnOMb3zNe2oQWzi9t6E0M1MyWN+U5tBGCzPvEciy/j7CBT0nadzPd3ds64Xz5cJ6PrFWbQyurm4pU+H65prlsEsMjuwxHFPJTHP4Sgxnu2xCsstEbVt4YBgtTBSNTJomWpfJ1t3pnrdv33K+bHjbmJcDy2HhnSfPmA8zkGltxV2JSLnMkUTlMXyNBw8VT5KAMDZyymzdyfMio+LTifvLmbu3d6ynC1eHmavrK65vbpjnJei+FUyAlMf20T1orFOOGGVt6nofbLUqMruLXSW5Q6UUGb4uy4xNCWvSLUfXpeZk9GAAhOeMf3IBEuBKS+ClsKTw3uhOBb33u2QvG3PQWDFo0bQZahhE/YXUGge0QWhzYWqd6trEi/pojJSDXlqCqTSwYQxEQ699Y5oPPD4szHnhPLpAqyQzVtrKRGfOQ/KvMDPsrXK6P7GtK8+ePePxs8fMRYVIFrIdj82LIjZ1VtqmWHdvJ+r5Dam+4UtPFn75i5lf+PIVT26c+lZ/btTG2jd6lQ9M7U4bjUtTRGY9Z7wPmq+so4PL2yDVxH1buVs791vj7jLYRiFNCx/fGT94Zbw8K4HtqiSOh5lD2Xh8tUAtnEbirhujabN4ezVznMWoGebkCJxffXCuxtp3vyJnHdJJM0zU6iIK+vVSwoQeLmvlUiPxrElWKePNzDQtjGxhzOwsRV40HlICcEnnUo4t4JDPBM6yHMilqMimPW9BgERt7WFbkVNmnsQSSgxteW1PLunhMTRTq1rVLVIZcW0I5yyZm1sKeap6xhT01D4Eqm4j088btd6ztZXD4UAJORlATmXvE/W8YHivtO3C3d2FOjrTsoAbS4mEm0m+D+t6YV33ZjGeDxPANh8WmZUaD9s/mZYn7u7P2kbXSqubtq/DWeaZ6XiIQaZQ5hmIRBsXsKhhS3LitW1c3txhpXC8uRKVN2cZcaf8MKipt5eMprfGum3U2ri9uRGwHdIiM4tGJLT0AQJstbLVlbZu4M717S1pWZRYU8RU8yFgZ10bdVW8ffPQ9tfO4eaaqeQAR1XHDyHpk1RGAFbHqevG/d0dtVaePH0SrEiZpZeUGUl3lohOI5qosGvxzuW0MoUUb0fTR5P8bt+o5qKzmxylGblzev2Gw/UVKaMoXNeZtD2+t3c8wgFkAjuo7cK6nVmWI6nMLNOkLXn6y3VHDK/e5H+Bw3mVB9PVslDMsBIgjseAE3fOLuuprQawKsmKJ537NJAfSwofizApnsMkUgs53SnJYAlPLcvybrIYvGREbIw26MPotiegyM/NTECbWQBxeJgFq5kr+/caasSG12i5ElNO4SfvzGYQBrjDNCwzBlOCJcMyOxNKrhsZLHWZ+I7BMosZIIZILD8sSeJuJgbGkpln56qUSHlMLEUs426JqYjRXSb5MuW5cJgF4KbJyfMBmzKUmVSuIE8y8ByD0d6yvXnFj773E37/d77P7e2RL335ipJhvfQ4+537c+futPH8deWrj57w6//VP2T5/K/j9lnuvvW/8N/+N/+U33nZqPPE4foJNirlfOIXP5f5G3/1EV94b6L0Sjs11mqcT4113ShL4nQCWDm3TGuSrPZeWfvAN6Nug74N1pYZqXPxwdY7tEzzRm0dH0d8NBqNQWG0EcBjjx7C6A3cRoCpE4xJkldvkcRnrJcKJFpTM+27b1oYPDsCIboPLeVc4MmUEslEBhlDYFgPFkpKOwgs4EIJQFpISC4htp3KYHij7YElWj7Hn7NPPNMwcirByNNrMVdiYPOQwjUlAmkp6pSSuD5ec3VIXC4XtipAfesCrVJOWAwS+1LFuz/0csbOQNk9UJylaHnRulOjZ9W9o/qTERCVzMkxIHbn4a/kkEzMr77rwuL3G/E6QADcvqnvepOJN1U1OUC9XBR74kNpUpYFrsfVTU5AEhDN0JJmZ0b3rvcfUzR8MlPIToO1bnS38CAqzNOs+9EE0u1AmFgRkm2O0cMzpogpYYlBJ7kACY/J0oJtCiEZj1o0z5JOWtYdcalDEus5k+mMKi+yremM5hRU0ITuhBTs/VQ4bytVtCz5rLiLRWzyx8tFi+2tjQA5B6M39Z6WqU2J1SPAT6yLoRWmz9tW2d1fLes14/rcekgwe9Tx4Zuk4WWiIlZ28tgVBXN2NAUy5DjzqSRGN6xbgPEXFFGRSUMszd2XdQyBXzaMjBiqyQSyaJx3LtsmhmFSMt5kWjpOkWyrfUQYag8t8uxhmVOoo5ITTEl3D1kLQ/U+Mlv3Eem63oM1nMPCoJLLJOKCaYHqZAFfdJLBeWuSDU56DjFTIp07tQs4uzlCSRV65XqamScjeSOnoVCVkXGrkI1sgynDlJUOawdjnjJpOFdzJufBITmTHzkejTln5pzJc+OmHDmUTJqMeYKcD4zSyD7wNqi9cV8Toxbe1M4HL17z+sMzuWY+97kv8NVvfJPP/uKv8eSzX+Hpe5/h8OiG/uJ7/ORPfpc//cM/5k++/T5/+oMP+eHL13x8umOlcTYgTeT0hK3VAFTUe4o1p/dbqp4RklJjTgkPqWEOVqck02Jjz2VmnjOePcKUCNBKs8fWtDj0rJ9RTGQWzaS673OAXNXlkem9krxQhzzMyqK+IbkxhpZcKQv01ywQ3qwulpYAAc25Wm7BoDMNsUo3DRuyvghm4gi7B0s7UG/x3qgXs4H6M0LGamJzt94egLoUthHZDE+73FhsyR3xMT3G/Om/+/ZO4fj//fVTZyhNxyvykjje3JK6mCo2TczXB9aPPmR785r5MLOd72Tuejmznt5yPp1ZzxvNnKkceOezX+LRZ9/jUIztfEdvlcPhGPrpxHQ4MC2Zut7roQ9zz95d99EM0SG3AAAgAElEQVRkEI7zuWhrLma+P7APRtLmuI/G69Md96/fcHp7R60beSocj0ee3txwfX3N9fUNZiFZCQd6yKRI3ACZiaakJjcVJRd06kMx6m5hDleovXN/uuPu7i33d2+UrpYLyzJx+85TDsuBaSnMZaZWXeCHeWHPxhkjJBuInZMtYgJ9TxFxzGa6N85bxS9n3rx+w/l8xs1Y5omnt+8qrSei0bcqdJ0AeOog2AXRIJn0m0HCZasb5/XCtq6cV+nfxxiUaeL6eEM6LEwsgOJVR06k1qhW8S4/KXyIhuuKOFVjlh/YGCVllpTZvEfyVUe5UYWtNw4pyYujyD9EoBq4Cwyo2/YgQWKIzu2ueFzfBmsd9ORkQt9fipgmJkPmgTbg9C5zypx4en0QMdagjo3ikOmkPsh0MjU2hnrftrM8Wrx3ro4Ln//sexwPV9Rgw0HUM5PnQB/9wSOg1pXz/VvaZYX6hmfXzq/+/IG//gvXXF81tsvK6dXAW6aezmxrYwswqvZBrYPWXOZxXd4VqQuka2vibr3QcqbdNd6sjXVz+UhNVzQWvv9h48dvB+cBy5x5dph5dEjMS2fOE5cV7jdn65kxZaY5cX0sHGcU751N3loV1u7c18FF7FBaHWzdYoAdpEm045zgMCduDgXceHuqrLXLRDaAMic09FMiT2IuLNOkM5PVnDoWlFFJW5MJxHIEyqZIIdMzJTnP6GF8GZP0DnhY0tnQxy4G2RgdRxI/UWdXNSVZYLG7PzQ8D8lAcWGl2Li5a4DpLu+D3ipjbFhyjtfX5JTVTFv8PqS4HPT6vFX6unI63eNpYjoskGBJiVIkbzAf0oB7/PycY3NiktkdZqxMbH2g9F6HLM+ay1l+ST1AjFyC0l4S11dX0eTqGcxmjABPvUMiMWyEbEjMvJGM49VRwDcuI+407eQkYITsyoLZlrDuHJZDgIOSjqmR14UoRoyoyvvnaHmiWovXWChkUdZdwwAkvIl+LFPz8fBMY/r8sPCoyAVPmRZA0HANPduQZr87WJrkL4LAoB4AZ+sCnwzksZUMN6XkJNPwWYeia0ecQx/ymchZGyUzI3f5/xjQTCBxOR4UTuKZbR1gnWKJ3C30+XvCjoYrff4TFLh0sdta64pJJ64xF/tN2ywNkq3HfdAqW4W56HnLuaqZgZjm1PQonUq+Iwon7wyawgccymYBBiZ2x8ptlxHH69gTSS7B3pLf0SfbQovFhAxxLc6OGBGWJQUpJTFN6eG5lyQkmF3eQqIjFonin00ylzSoRebIa0ia9GdD8hebxjpgbJ2ekoybbRL7IA8NMV1LiJQG88hMNqhZoHDJPW7yzhiZc2tkEqnD8EJuYu+klCEVbSLTYPRM9wAETElLZnrWzHfgbJZXTMpMt4V3Pp/40lcvfOePnvPyUeZznz+Qj/HnR+baM9Ubfmd868evWP63f8lf+c8nyjszV7/49/kv//FzXv33v8OfNBh1w+aFOg++/aMz1398z+PbGx5dZZgMS1XDIZk6Er2tcDCsBpNtc6pn1ppoEcAwPNOy0Rry0OlNiWg9BUCz4t3oDHkfBZu4ewC6XSl6TrQUVGR9bNRe6Taz9UarQ8yUrrO9M7aHedTHEX6awXgcShTa3JnLxLpFIqRLCiXsV/VcEc/x/uNapCDAxluXf0ZW9HxGQ65YPKphCcIXR13M5pvqfNL5wwdme43gAXg3IVLyKOpw2Zy1dtbaae4Mwqy2d0T0sF3/gLmxhvRf2/S9hgpYunQXE9ws+sD+CQiGkoi9y+x29x4lpQCFQ5IE8mPsTnJFjmuQkXQn4QIPDPIUoIBbeJTEQBXR28Ml22q1kYp6/pxntmCupeRxpaUYtiZ5aQ4Ztu+E/dGhJYeh15hsjvolsEFDrT5TnQUNdKpJSDoX9a27GFKqR06PmmcYpWRGVl0bQ35vRA2voiBGb5Gx0vEsg+l5gpzic7gkpbyNHsCZlijDIu3KTSzQqGsCCVuAefpdWh/UADpzgHcp6a5KKJWq9SGPlgedgBhAJe8SQnltjRog2QCbJnp3SkFzEE5iIhnU3vS9cTyAtBQybi1Oyv5vGQ5b73gn2G4zJfxtvRR9YL095MF1Hzjy7UkYlCTJYAzgU87BoJVRfRr9gak3YqAWM0yLBRxJs/f3E2PURisR+96hb2LtlUleijYV2ASOgWLn165YefNB70r8yvMkabbZw5wzF4PU9Ri2uHfTLsUz9e4jcXffydPChjG5ejOrzpwyCYUMJZNp+pzFeJqLAL+tO2lsXPxAyZVpGLNtpArHMDOfJngdstA8VY6zs9hKSU7KM0sewYBuzEvic8cjX/tU5s2XO89fdD54/mP+79/5gPwv/1ce3zzjM1/6JT7zlV/m05/7Cu99+W/zd/7mP+E3Tx/z8s/+gL/49rd5/zvf5jvf+3O+++Mf88Grt7yoJ4VpZLFqNEsrxMSyRq5ESN69yBvXIXelUWaUHowJ9G1m9LWKGXycBMqb+rB9zu8DchdY03aWZI7laSyT3EQM6Oa4iZFHzWGO3cOSoUiOiYB5VVqxoQ3CFLvH/CGgVB6lmqeVVpdljJ/kHZ2D4ELcJTsj3X33fYzFHeMh8ZJIOK3BrtMdKIAqxSLEg2Wvn635XvJreCjSP6WvnzqgZPmAb/es5wt4I+UD85I4n06c3r7lUiUzuX/7hvWiqHrS4Pr6lmfvPmbMmZvDLZ//pV/CtjNvPvqI4c7tk09xvD4GK2dEk67tjI9BRppli0JCly/JvBSUcip0sntoeX1w9+qO169eiJW0beRSOMwTz568x9XNrRKBrg7gMvGUn4NiOHOSES4hnwH5muza7R5uW+taWeaJsiy0Wnn99o7z6cT9/R2n84VlWnjy+DHH62tJ1ywzRsWSpAq16dI8zBOtbkIZA1D13kPypQS87GpgGqLZClzYePH8JW1bOR4Wbq6OLMcrDnMhmaLDh0U0dUhg+tCF0k2a5Naa0Nw2lCAVXgKn04nz+SxNrMuc93B1xbIcsCSqac6JyxiMWrEemvT9Mwp2hLYDFkOUthtWCpZGMKBkyp3GHn2twr1MmUOZ4hjvBUd/pq714aFZioqC59DwD7FWEs7ImaUYpQhEcMt0ktgO3hGVMFMOE8s0S3qFK5EkbBRmGnk0ShqkKE5gbJcLl8uKm3F9dcXjx7fMs5K9WmvhDRJG4lmeGz22Pr1WRj1R79/g64nbMvi5dye++dXElz+b8Vq5/2hV6uHItPM9l3Nl7Rday4zRWH0ocrPL80CSz87YMj03trXzahV7Z1wS09XCs6sj23zFD+/hBy8rH71tNJybm8I7V4VHh0yZJ+7XzttzkgGrKbFoWTJzLiwlM8+DCckdzm1w7nBuTnWTt1ZVA9HdSUXbzlx0lm+OE4cpcamV+7WxVjUz69YYrZNzYp4XNT6uBjKXwpzmKOqw9qrozagXUykh7wnQIOXYysWm2XJsj42wFgpwSkalMmjYvdBgMCIhrGNdRoS7Cau5gJoeYOnAaV0bs1hQxkZaDdHo+/nXVnmeMokDI2dF0yOWhwrsviXXAOLdGVbw442A1TAsdoMaktsxZIg5yvTw++yNu4Xcb+xgE4nqTgFq67QhsKRMMyNMkRkDm2Y2Iv7YM72b2KZjoMfHYvMumdM2JHUs10/wuShlxmHtsI3+EI2ddgmb6XIew0jliJXEeQwmiiQzVBhGDvRqlwjp15L05Ljc4CmzunHaGnlAwui14klbwzyyYmFJDybpno2xDkarGkySk02gSFalkcBu7FIFp5s2V31VMp37SkkTZgXBjrD2XRIrxpA8irJe+1r12dJxwkcIHnx4LMnUfM6F6k0Gjo48SJoAJk9OscySs5KQhs5fD3bSCKn3QGyhyTRgy1MjIXeiiIOOO7K3kIDnEudRDFz3ThojpHcGPZiYmFglsakfwawoyWJgDtAx2Se/WzCcBHyp4doZH/I81JCqmU3Aj7uHvFlNV8kCg0mx4MnE3RINWWzJCXAIU6+AO+byFktFcsced9H+vYeFyW14EqSUY/MsaeY2EmaDnMbDM+oxbArsyfhIIafR5z6GM5jwhmQXuO7HDLTGMhUm032WrdNH0rBCmB9bosiaFU8CORMbZgVYsTSRmfBy4Hhzyxe++i6nV5WPfviW65vCo6eF4/VMorO5cd8SBxJ16fz+dz5g+T9+h1/6TzL58W/w2b/1n/KPP3zO63/2HX7YJPu25cDFO7/3p295fNX5q998wnWR4fJsRrfMNGf6QcDrNA+8SJ5Glc9JmgTuu0OJulbyQvcCZaO7jKLBaSRJXZIHizdBD5+SfcnmarbNJF8jSaqznyH9r3zHdtbD6GLUKaXZg+Ogc5xTegBia2tiGxXdOWl/9j3FWOwP5zIl5EWUIq24N0YS+j3FBnmgu08OaOqLEqbFIh51f2cL6tzubCidMS2Vfai2ENIf1ftgxbjuKcGNAlnoiHkev1cPs2ZIxMwtNp7v7j0EmLlLWsXsSAES9BagiwXDfOjzTHv9Gc4YmWEmkGTwCasAe/DxSwapx0PuCBANI/K5zHR3Lp2QwHSOeaY21Sv1rqor2QJotrAx2C9z3x9NeZGM4aQe4Jxnpdp5EcvNBUxIkhK1ynXXevTfFoAursUSttcFsW7MnTY8ukwV831JhanOuBveerBl5L+a4zRLGm+c6kXAHYTBcPhXdb3GugUi4UnJfRiUGaJ2uBODbCywMNw8gBMNmbmoHkboOZZ1doebzqf8NB7Ad3eDnLRQsfAbi2HX43lofURPJ5CmdbF/UUctuTKx7Gj6rDDjUitLXsRiRel40HFvwcwW63Nnrx6XieaN3uMuoyhR0IKNFz6xUyzh1CJYgIGxuNmNpONOmHLGwytpC/np1hWm5E1qCAuPnZTlzSZAW33cXk9ykrH3QN5YkCk2SKnLDqWrR+zDovbpzOPEEjwzTZnaBx39bCNTirEkY6aQR9NawowyEpsLnFiSCyCslYyk45OdGcAhJ2YzeZ4ulVKcdKnclMyhnCm5UKaZYo2cLhzzQj10Uq9MQ4DY5x4b795ecXLjdK68evGK9//ot/iz3/8t5sNT3vvsF3n6xV/gi1/8Kl/82lf5K//13+Obo3L+0bf58Nvf5j9+/8/57ne/w5//xz/jez/+kJ/c33PXZnqZSXOhWCZHR+LWBIzG8qnksNEwI0faGaPTWxeIaLscfjAsAlu2IFrwyZcIpGIbReUR+aQQoK/hRVK9XBbKlmhV0kMtImCMTd/Mdc5T2kF19Rl57z20CqSkTIUHe4IlWQR+oICqnPYdmxRUjOj9g9keSxBJXi3wB4Ld5Htx1dLNEMwVZ35nwPawWsi5BPvyp/f1UweUlsPC+cXH1Oc/4O7uDcflEbVeuL+/53R3x+VyCn3rRDkcuLm94pAK733lq8xt5cOPXvD4vffI3rj/6AW5zDx99JQ8zXiv+KhCj0cnF/nMFMuxjepk9GB2H1TvrBsPBZ0hSdbdq9ec3txxOZ9JCY7HhceP3+Xq9oblsHCYFlJWqoXFxZljANVAmh/oZyrsSYWgi9a7tQutDa6uFg7zjKXE29evefHqJS9fvsCS8ej2EZ/9zGe5urqS6fQQsyp0ETIitvzQRLTW1LwkXZK5yJS0bhtrqwK61P9yf3fPm7tXnNcKZhyPR549e8JxOYRhq4psG+OTIugaAgEcPTC1dskQwk3//u0954iTPp0vGrjDbF0JbLuEBg2FMVy0oY2+PAE0OGaX4Z4bpKnIvJbEHMyk5k7fmrbkDn1oIMTl95LKzDIveIoh0YeArlrJTWAekwwZcxi0esTWrp7oqSulK1g0ZpKHWAf6kNbXjDJPlNBeZ0LWMQYFUeEnc4p1+R7EoLJuK9tlZU6Jm+srrm6vWQ5zND8qIBYouJq/FgbNMLpR13vq5Q3b5Q3LaPz804lvfuWKLzyD68OABtvaIKIi1yrPnTW8vLats26xo/WhVLyRMKQ7pmTa2nmzdr73wYl3n93y+PEN6TDz8ZvCD14YH7w5cz6fyNl4vCSe3Rg3M2RzzqfOqSI2E5lDdlLeMBKtXqDP1CrkpTbjPDqNQh+DOtRE19oiwUWXt5WMbw07JNpmvDh1zlsT46N7yA8EFpQ8sXWP7aLJKHEoQlqbXqcOJUrs+9/ajWzhs+F6vwiWhJ4Io9OxXGTgKVjgoTgLg4xNZKD9fYh5kgY4IyjsYphZV8OWCediQykzMdxLKRt66qFv4qNDmtjCj2C0RI+o81Zl4rhvGoRJRaJIFtNp+NAlaTIY9JVotMSK1CWm9ImkiVvePi08M0g016tWypBBmuLdkU69jUZPGt5aFShE2tkonbqe6SYjcPMkUNEHY+tYmRnMbOvQe00C66SAXIY3rIX3CPqsLSUNNO7QRcvv7cKwTGpqgTEZZgonzDA+AeB601DlZljX5U5tjNQpIQ3qtrMWJMNK7njVpUsC2qCbk4rRUOTycDUrYgiIF5VsB6V31qj8E7RbkHxsmJGG4pwXU0IV4QEnJI7Qwju9ggY4WYX2aIKJq0YHTIPPFAOdZCKOj/AFcTUaFkw5HymkmINiRfK/0IuYx1kOarabvJ6UopLDaN2x0Rk7mh68aYvt2G4K6xagWDDxzCT52k0sHcd7p8d9zcN4o9PmMWB4ALwWW0viZ/gIyaam6ZC2ldjAaUDOps9kuDxDbH+9RFlAIR0JMWixBLkFYKH3aF9bETLshJhjw51O07nJMmJdTKacrQFh8jon/4QNiJFNYJ/6OJ0lmjEVDfFE3dmp7j7swbPFRLEDQjaSJCPZQdiRNmwYZme9m3nGc6EsVzx65wlf+NqZl//qBX/xvVd8eXnG1XHieG0YjeWSOB4KwxL3afD7f/R9bh5f8bO/eUM5/DK//A/+M/7h8/+O/+Fbb7gbkuPZ8ZY355V//vuveXSc+PpXr8h5YsmNkZNCt46JvCnm3apjU6IjH5OWlVyzWqK2ymQ6G5PB1hdy9FtjVLoN+phpXUyAvUIXM0av0SOEwXQCeRvpNBXvknWnHoCP0dA57xHrPZkFECz26z544mKTD1dsuSXJuj16vsChNIy76mIy0+cQw0ROsbV2DbhbHyG/SvodLe6YYPjsybvpwWfNHzbVOr1xNoJBgynlyvAgjGad++TkTsRIi91KQiy+YOCkiPeOvfX+GIrNHa97lwk5zmiSf6diFDfMs3qX6O08piEjhWRD3zPlPZDE5MV1UGT2dokkNfPoYROtNvWmrs8oBQiSDKx2bBKTpoTXXs66X3sTuzrvCU0hO9zBaYEp8QT1rhflwYQd0EfVnTq6XntR2ivGA0gVkLFk48MDcMvx/ST1Tikkx2kH3CDnqFvBvBK+EoyCCE7YX1ytWrL1Ed6gsfAQU4GH+pWSzt8IVpmOfYDgASJ1tBzaAbX9908hvW9Rl/P+HlkATMNjDtC/J6vHaU3pvzvwn3KYGAca2V0M4+6OpUIf+ozq0Gejz3SAy2ezd/UHvUvWqFTYldpypJ6J8ZsGMHS2pySgfipa4rXeCfIaoygFzF0+mp1OJnp3i59tu4em/B1L1PaH5ytLzmY9TJHLJ4BDa52ttoclRc6STnncQ210dnP0lLrY1iPjY2dNumqTE95pAjktqXdroylMJBhJ3jemaaKvFUZidTjXwTnJu2wqRYyi4iwpMbaG2caSjdvDgTEaKTmjVpJXshdOuXAomXzpzHXlMHUynUtGyoNiXPUzlgaHMqBCqXA6nkkZruzAcSlcl4knKZPfSUxfzKx18Pq08fJ15fmLP+LP/tW/5zv/9sjV7Xu897kv84Wv/jJf/Po3+bm/91/wc+XA9vwHfPTBd/jw++/zg/e/xZ//8Z/w/vd/xAevT7y2iZNlRpoopnqVyr46kGzVUWhWsqxlZ9Sa2gany0byDTkNiF1U0sQeppCZtCgiVCIuwJRk9KT+r3ZXmqPZg0xZ1gMlLGXku7kD+qPv9XnEYlXWOgRQZNFbzsg7eOBQ1LsdclhfED6ysRAwTP3Z7sYU389SQBqAp917WP3Bbrj/wFgy1asdTMsxd3iT9P6n+fVTB5TWu3vS7ZH7j59zvr/j+U9+wumtULxlzlxf33B48oxHj24Zm+RSV9ePmEumnzqf+vSnubo6UN/cs1zfkCdpybfLCegPFwOI5ZGzaLTeOtYlU7pYRH9PEx1nu5y4f/OGlx+/5HQ6ky1zdXXFp957j6ubA8dlpuQJLDNSxooYCQljKnOgjeWhefAo9NooVUavjCHW0vHmivNopGyczmfevH7Fy1evMZwyT3zuM5/h5vaWlKOBtaCo2i73UjEvJk1pzjsLywWIxBDdW2VtZ5KbvE8SvHr1ilcvX3DeVsyM68dPOB4OXF/dkpCvhQ1iK+bkJPbWcF1wowM4vTfReE0X4+l0z/3dPZfTiYEiO4/XNyzzTC7hfQGS9YweTA2njaTfC23lGRmS4w0aStgoWbHXKWtw2LpHMpCapd5FzS4Yx6lgZcFzotiElRkfK5daWduqJgOjTAglNmluB0rDA0ImNWNpRBz3eNgKpGgXlqyEn5KVTNP2IQun2OBq7sx0zHYraHAr1G3l/nwi58zNzRU311fMU4lLd7/T4/J3jdGjowENuGwr9fyWUS+07Z53Ds5f+9ojvv7VwrPrQasr415sj62eaL2wXRrb1hmtsm7SYK/NqR15Q5k28N4lRWpZ7IoPX668Wlem4zXpeM0PTis/+IsLL98o0ePSG24by5K5ZuEwMuPc6FnD6oFMT4rFnunULdEVgMcYjdok0HJLrJ7BNkxPsECiEQi/q6Bvm1K5TmuKra+2cnv/pAKs5neNFLTWlfaRbeA0knU1Xx6UcfeHJnrrleRiaKjQxtCZhsBEH6Qyx0Yzi9Le5VkBRk/7f+Nit+jAkIMBIjaCkwe0BGkIGFETGRTwfYtocfE8bE31TLsZS0pUZFKOf6LJ3r8SaAuL6lDzACJrJDiUgeWJ0RAF30WLTlbow8IvJn6TeNYsfnaOzQ4+HjwnMoZnNTru2tpZMskoXLCQtoYafEYTWEb6hErfhyRTyXL4ycm7wVOwBHfQg8DeckK+Aym08KH/HoOe5a2wJAGU8hGRQTcuE0KyABuDoAYHaOhKjLNUlKgWhq1B/JfhaNKwa4NoDNWoFNvZSDyYJZI8ZEbpYYskREiMN8tT6PhUF5ONh8Y1p4jzZtBwEWuIQVTh9thEANky9WeXWaHUzn0gLYhZlqPL0GZUW+XhwU/YB5Ykdq+YrnEWPTbmQk3kYaddtbzIhoApw5iS/AtykqdRjuVBLvLRyDmTF4KZlOmjisqfJiTZEYiTU6G1lU+Otrw4svuDBCcl/ZVLjgHKHhY8xJZOjBSHkDxpIJXPV56mh9jxKe67UtQjWIrAhjSpCeqNIkqY9kSpMGzCUUronIm4aZntj14lp7qMB6+0uSSuD9qQX09JyZQHJdSMaPRKSmTUr7R1YzJnOWSur6/JZVBKYZ4Lx3kKNltWQtdRNS1T8NTZ3pzgOFOur7BpIuUDlAVs0medDPKR5ImUO3M+8O4XG++9/z1+73c/IM+FL//sEw5HNerHkjgeZHbvF+fcBv/237zP9bMjn/qVieO7v8hv/KN/yE9e/k/8nz9okcAI5eYRH7/Z+Be/94rPf67whZ95hJfOsjr5WOlbplbjcqnQJ7o3KgLd6uasq6LJBxuXNTyQemK9NLbeubRObhMXT1QXO280DZ7ZjToGfTRaa1wujc2hjs7wQsrQW2KZjMkqazOGFyiS+6x195XRuOguUOE4Z9baAtgsYlc2j7ReZxT1YY6893osCnrIS1Pa5YdZIKjJ00fhLwIQPFhCAjks2Gs7QBtQgyV6b2Jv2g6TxoLTBO6qBkFKArxLTrTiJAbT+ERWlAIM7UmLHsLbJ4XsVEw6dSfCutIDk0jeQjClibGzWWRlErW7xRPs+z/U8xfLRSeWdikpMdQShYTlQWU81Hx23xPbmcP6P/IR0pGesu7Q3iMJDshh5OtmFL1J4YujQW4PwTFLePQFWufpvZT0VrHluPrFnMVO3nHzHsvXvRf/y1/7a3ULNuIOjJvBX2IAJJmDPix+99dnKRIhTd4pYyQuW2OrwZxLsXx1D7Arxtauz0dve7xx4X1qllhFXZCfKFIMSMirM2MOVgojinBygUkeAKcFYCm5TZyHQKssIdbagDkJVBzhwUWaKCkAtlRg28ToIjxQY3PhQ+bFvk+9rjrZt0ZNA7cs425iLorPNsegfakt5NiwHCZGrwGeaDmvFMhGMwW65CyvR8fotcXiJbxoiKS+8D8jGb2OkBhp/mvdGW3V6xzgyBdnhKmlJbG6l8lIpgWgx8IpxfC4W5RICtXYzdVhyAcu2L9ug1YSk2XMG8sk39hUB15Va9busOnZLlm9bLGVuXRqPsgoPSf56rGRhlQDpSfEs3ZKd1rI+E65kaoxFedqapTcWAzeTCeeTAdOvWILXFLiqk8si7PIUpIpF66vrrm5ueLz7xqXyzWntfP2Tefl6WPuvvecf/Mff4d/+8+fcrj9DF/8uZ/ny7/4dT7zS7/C53/1N/nmmxe8/vEHvPjR9/joz/+Q9//9H/CHf/pd/uT5az5sDZ8XfJo5WIK2QproNoKxvIPfer+HO9sWk4NrYeI5s7aTklV9otnGKGIxJ485MCe2S5WBfMzzliWx7U01SrOCalUaGVw9Ys7gRT1o8yHpulgaD+yqNKUAriwseMSiynnCR5O9QdZsYVFXxOT3mGc1LZiJhWZZLFeL2ptHqCss2E1jaBkX7w06ivG+xPz00/Xk/ukDSj/58Y95ef+Sy4uXXLqRDzOPnj3m5vYRx6sjY904vvsZbq5m3n70E+arwjQf8NG5evaYZIneJT/LTWa1a6sPDKE9BYag+uas6MfeVGg9Jw7zxGiDVy9e8Pz5R5w+fsFwuLq54dPvvsfVzTXTPJNLYpqnB4NEgDg3ceMAACAASURBVGmadRmWBD3kZd1x6w+AjxB2sSDU5MJxWegdfvKTD/no+cec7+5Ya+XR7SNubm549uxpRGr2h4tbqUGFvEzsaUOtNaYkZL6F7lkymCJ0coygh7qM72rjzauXvHz5MeeLTHw/9e6nuX38SPHSrdHqps2DyYVDXiTG1lSQ98sgxXbX0CB4Pr/l7d0d5/M97onD8cBhPtL7YD4oIcb29DIP4nfaUdJEGVC9icoplyLWy8qcJ9Ic6USx2WrinEt+01GyBc5cMpMZOS/aAhpsMVzUbWXd7nGGGn2DbkLxC455bLqGmuaUtQVLsdVLNtjqJvDAs5r3adLWIWvjWLuAgiVlDsU5Tomce2inkyQrrfLq5SvcB7dPHnN7e8M869EaLiq3RyO3bx5r7ViYJbJV7k6v6O2eMgy2la887fzaVwtferaS71Y+fnXhUs/4vQCj7Qybn9hqpTaDASsdKlxqJ7s8LDaHtas5Gc35+G7j5Wo8WmZGvmK+OvL+c+d7PzyzWpcPj8+klDhOC8+uCjfHwpTEYBlmeDFqFfurtZVSCq2De2LrCR+VU23S1iP2m8elOhAVXpvY0DAH40/NSkyYpkt395ORn00wBXfz0jEgG7U3LICjOsbDRlE+Y4UUEcsydlTPC2oAeu948vAgIwbXEcki8jQY7jGQa1uoeVag4C6xy6ZBIrs/pLCNJgPX0XtsYcGSiw2CQdf3CixE5nldkjG1rvKhCocIRKuN5wQenudaNzFS5iLg2/QcZjOmiGd3N1IzAYt5Z5P4J5JT4iKMZ6t3eYC1eFPMXZHgO+i9N3ioSdplE3a4oiTFw9swRirhO/QJAGNhorizS1IAVMJmdsqvttCY2AiDIVo3YZjsBmEaq340gDf/S1tYPIaNoPi7kl+qiymxM1TMLQYeIzB7gU3RUA8TaFCD7q+YbafEmYMUg1VYlyYemDLJIU1qtPdhY0QD6HUlQ/hOCT497syFJDZUd2eOGG/vTqOTaUxLIpdZ9G6cFN5vKQAlMZP0DPSxS4wzl/vXTN6Y8iyzeYs48UiQUs3q1HqRab43AVDZuL55pLRBD6lqylry+2Ca5gCUJD2rTfKb81qZS+FqOeB0geuMCKvwhwbHPRrqaPJxR72wFkPJoJRCKUpttGALtt7CqDUMes0gPFUOyyyGxNDGNxd5Fy02M4aMtM3FtMgBLmcaa0s0WxhZoN+cEyVrkz2Z2K+jJrJNtOnEsI3rOZMKXN8m5pRYisxPp6UEjT0YBGNQNy3CckosCQ4YyxXcLkf5m+XCMoNbhiLTz2k+kKcALb3hdWO83rCnB2x5BKWQWLC8gC/0IgvlwYyXAeWGQ8p88RvP+Q///od88L23fOqda8iq9alkbm8zfmf0lMh148V24fd++4/5O8+uOX7plqdf/SZ/5x8856P/8Z/x795WRnVSOXB49A7fff2Cf/O7b/ncZ48cbo/Mx0Q6dLzJ/67cVXqbVGcjKWmrg3oJZrQdoKvXs5G59K70NzdOp0b1zH0d1J7xRYamxcR+6GR6K2zHzqV2tlq1zMqJvmWmg3E1FU7rRmvBZswz0wzrFsygkAf3PnGYB9fHics2qGPC+kYpAV4yxfC5R0A7fWyMh8i1vacxsImSC2aq/fJzHMx9RKAEYY2A/EM8JAopiyESkvWBqQdGvjxuSCqKKdEMMauyGVM2RoGRkoIwPEEaYYfgtBIG43cXtq2yIW+bPE1M8wHL6mf2Aaf5CON8sZ7mWaxGO8qbKU1w8IW2BgMzNuEJCx+1FtISsQ2NMEPeNjLOnBOX8A8zr/LSdMh5Claj7sYewE9OCHROAlinofd7RP3EnVaVwFiCAUeSSe6S0wOz1HPicqm6B0OK5vHfFMBGY1T1hZ7kfyMT5x5c3rivXOCbFio7A4a9WZBxeQyFDZdPUAlj4bhClQiYgjlmjLGRaUqhDTB/Z0RZKBb2Psj25Y12ovKYIUjPaZdTWhiyNw3DCIiyJJB9Z0SIoSPwPqcS942sCHowxvBEnnRHWyJYw1oHeTKlVJqRiu7wHu+FpJCyiSjm1CZrDfNPzu4SaX5eElPKsipxeTGlrOdZANrOsJAsE0vUpp6gtwaew3Qf5qzFUxvhnzbCZsOgWKahxV/OHs9T3MWWmCYxTOxhtenBaiOAgjg3iDkmN8GQY6UR/VRMe65PRUx7vdfTVAQEmxjHrQmYdoxSwv8nTbrPiu7DeWefI+9DF1FGzH83PM/6/i1zV7XkEPMlKQjLYEmVUi9arFE454zlgaWqfiJ33JzJKonOPBuPmLl+dGRY55Ab13PhUCpXB+NmOXB9nLg6rlzNB5YsKeLxeuLp04kvpAPbubC+ufAXb15xfvMx/+Fff4t//dv/M89uv8jPfuVrfOOv/w3e+fqv8M4v/Co/9xt/n298/Jy/+/z7PH//D3j/d3+LP/72t/jWxy/54DRxmZzJD5Q8Y9lZeyeeNPXR0QumIcBYz07X/JwTfWwPM3wnkUyBTNlnsquP6j6gGOuoWjLtS8lYKuJK5PMuT97hKRLcBOCUFDYrTcFjpMTWVp2xCmnWmUpDPp0PEtokrUR2sZmTWyztjMvo8mb2wShFd9AQODXqJgbngyWOBh0DPGTJArLFtlKvmB9wj5/W108dUPrud96nzEcePX2X9955wnI4cpgOHJcF907fGsfjgVZXltvHFDeW45E8J9iq6IV9UMcmhgmiQaeBokDDcE1FtNE2aVdzyXiS3OsvvvsT3j5/wVZXDtdXPP3Uu1zfPmI5HsJ/ZaIkNXp4yBByDISlKLGge7COErmoCMlstIb3hdD0rVbGaLx8XXnx4mNev3rJNM+UeeLpzSOefeodnjx6wlYvD024ZSVr0XRplzJJr28yGmvbGUZhTpltVVpUr51WN4opxei0nlm3lctJiXTLceZnvvAzTNNC98HlcgZHZrbxAHSXF8BubtaiIKdSyCh2/Hw6c76c2bZVaVijU+bC1eGG3QRGxc5jONBWzixMwWPQ7F3bRm0DYr1E5+rmhilo38O1uzVTTHB2w0ZXikGeoBTIAocGutQvdWPcbYzJ8IKYRq7o3WEDkqK9JXvSoDstYkfkSHvQW99ZaSg9oMgcMZeHi7H3DqMzl8ScB4cCxznhXvE+yGVibJ2XL5/TzxuPnt5w+/iGMh80zA3wYLc0QxGfwTncwpjztK20yx397hVtNJYE782VX//GkS+9u5DqHevbSm9Qq5hIfW2cL3CqTkvOVlesyvdmq87qrvfInd4TrcNmG3dt8OGHg5YX3n1yhClz1xM/+RGc1g7HmWkInTc3Hi0Tz24DQAuypLxroJOYh3gSYiQZZ19xT7ShTY8VRbXW4YyQfRnyTBixnbVUPklbHDFop92k+y+xeOJMqYkXA8iirRtNDZyGb2CoGYUU6QtiD6huCkxV6o1YftruCqy10CG3TZrzLopRNMLBltoXgSmpf82SMPVI+xgWm+pgULQqILqUSb93EniWoskz21kuzmgtGhY1oDNKWBzZwycuq6n1QXY1F5L6uBrwPGHSLGFZfiajdwEmjhhBHmk9Odg/8em6D7qleN0E+NPJs5KPQLLEaEfJSRvbGJ00kDli/qQCwxnENrrwsN0FtGVk70iVmESY7Us2WZnng9I6TDUhoQ2QI48WXDLHPti9VfX+jcFIinYt8d/0pnOzmDypjsHIwgwbMjl3lNxXctD8TZIpMWCd4Y1jbPYzatxHVp0zc/K00N0oedK5GzKCTMXI4Ss6ZaV6WoKSE5dTo22rpHfupGwsWalrPsLLoyhR1IbkrKMOzvcXHh1vub65FUvB9KbKSFwSG4bOk48AVOjc1QtbPbPkREqNbALQj8vMsiyUoma0jUYpRq0rrSZWWzGgZMkMlllmrGlnj5VCSlkbORu0TcuJ1jvrJbauyO8P9Lxv66YkoWAfmTk2F+QFJF8SAcLyJUgxTE5ZyUXuon+XnHRukbfC8P6QTEU88wIYYzs/OtOk9KICWIY5EeaWzrJktjUrNKI5ZQLzSvZgFxUBT2PKpFXSkb4Z81RY18blkrG5MU1H3fNzDHhWSD2SXzwpMaYYI0Njo5+vGKUzJw2A3TMpd7LNJJtiu5hC0pmYb2+43L+ivnzF/PmneqgoeCqY3ag+echkU8Jthgne+dmv8o1f+T6/99t/wYvnR25un+I401K4GjP5puJvneV2xt8sfPDmzLd+69v8yvWB6dO/yef+2t/mH338krv//Q/5bjear4zpQHr8hH/x3Rc8/e0X/K2/9RluI52y8wYs4wfjctrUbNsg+yRJgMmQ3rqJEZLhUl1hG9dHUr2QzbhrlUqAMBQGRSzELGBJLDH0HnjH26baNiWWKXGYClsdpCkYHUmVz2aTh0kf9AT0RK+JMhuPbuDucpGJq09casOH6qqnpNQgMqUs9C45vGT9+x3RNfwGK/FwOLBtG2YCXltzpqKUMAtT3QH0kaguAGEugmcS46EXJAaWnBOedzDWKBkSYisntEBbyqK+yB1LI4Z3KIeZ5p3SJDktJQXrJFGWGToyE6+DZuJRppSo65kyHaBJWniYC2nAhRI9oFJ4+1Att5QZW5XfTDBFJG8OBlEMOTAomICsoQF7XqYHeW5CbJ4xuliw2ohI7mGwpIlBkxQ4Pt/k8ubCEj2LgTMwlpypvTLNSYtoItEpfEcKmTKlBw+nlCbmKUvm3eSHM4YFmzUWGZGcN1APkC2TouYwHG9NQ95wzOZ4X6JfnSb6aKSykJIx2YK5kWghU+70rqU5KQXTqUjPnRIkJc65B/MbC5uK8OwKpiejk4f6mB7gRhnyLbScNRflzNYqZZq0SDH1agXo3UIKJ2ZtbwL1IJIIo+/33aOuVfqo1OEcD4UpZF11yARf+/Ghmcx0jvc+8JCzUrt7ZypZoTZepEqOoXrvXzw2SD6gNcdmpQaXrOeupBTsWPVOe9qhWWbKU4BGe2M3FJgRi8PaN3LOOv/ZMCv0KvLCFOFBrQV7W54l1NaZZsFQqRTapasfcFmi6C7LD3VhuAeTZJc7Oxa9Tm1iqUpCC8k7c9FZnC3hRcDc1mT6PlzmzylpaX06y15mmMBIvGM+OEyZeYKryRh9kwtt+IcJg5IPVxrOsA0rmXXtHCcFINStMrmzHhKX68rd2ri+VG5L43AwpsPCkoC5k+cjV8vg8O4N1+9OpDZhqfPyfuPNR6/46Ae/xT99//8i+RN+9ue/ztd+49f51Nd+lUdf+Lt8/ut/k1/4u/+Ev/f2Oa+/87t87/d+lz/41/+Mb/3oBd99M/EyFfkumRQOyZ3umcYE2ShxTnaPY7ELtYQw9zhvlwh4WEmjAJN65KHz4K7FY7dVh8z3hNFEylXkhJSoPZa0IeE3wKZMJoN1cp/o3fCkdMC6bdH7ZKYSjHbXcruEDxP+QPpkmWboUmF0A6pTpkIT1VBL2a4ao1k4BUFSIKyemVjU5mDe2f8bmvP//eunDih97dd+jWdPP8Uyl2hIG7c316yXlWEz5ZjAm/Sv0wTlimKdboO8TPTa5AdhLjZOSDJSzlgxqnc8mrKcFQF9Pt3x0Q8/4uWL55zvzizX1zx59pTH7z5lLrM+CIvtQ87MZWL3iTDLYfIcdLQuOmgQAWhVcd4WW9XeG2Uq+OjUrfHxi4958fFLLucT8+HA02fv8N57n8VSZlkWhreIXJ6U+tBlKj0azNOB0YeAoixTxZwMzxPresHKQsmDq8MVd/cXWq2c6sr9/Vu2Njjf31OmmS/8zBdYDjNbbeCdeZJRdSFx2TbmeQ6ZkNGHClXrSjhIlnj95g2tVmx01to4nU+M7kzLzJOrx3r8ctE2wyWJm5Iok7vB7xiSNz0YIYcx3ZwLKc9YbMaKyeMko625fJbQg2tGmoo0+mmiJTXvHoDD+XRPqgObBf7VocG6D21bkpiI9Cb96jzrH0weIGFOKCsmk0bDkwzXZOzdgoYq1D8VOEzGkgfzLEtL940pZwYTr55/zN3dWx4/fsyzz3yaMinlwlGD0fYY1aRtXRrBsvAVG5m35ze09Z40LqQZrjzzK582/uY3rrm+HbRTZYwDNg8sD8gFS85WJo7LRqliwNTDFeu2KTlmNKZz53hcWDdjG8ZanQ8/bvzwZWM+3PLukyPLfORH94PvfnAv9sMyGK2RrVCGjACfXs0cZzSge6ZhVDO2CmNo2O51I2P00WDkSDgZbCBWUMoBMppAH5eBNElAn8MD2yClXQ4WTcsQtVR0eV20liTNTMECsqyiO4bLR2F0lmUhlUmMkWAFpTwFeKCt3UhEMc2SAW4b5XAg5ynkQtIXi/6tzaGPSorzDoNeK70PlqPi1y0XsUMsMcZgNQGdvUhgmj32Sua0rdO3FXL4GTWlbtSkZn9ZZuasFLMWhtOtahux9kY2Zyoyvc1TJvnC1uWp4q2THc5bl99X7w9U3Me3t2oapxkKjNowTxR19WxjhBSiU6tkhFcl8/Gr10yHA9lyxK3/P8y927Nt2X3f9fmN25xz7cu59E0tdasluWXZlmNiW7GTuJyLHZkQwq2SAooHXoEH88YLD5T4P+CBggqYFHEZYuMklQsh2DHBxlfJqpbULam71X363Pfea605x42H71i7/UIBVXrg6KVLfXqffdaec4zf73sVy884E+gC0RwQm1RiWy48ubnh8mInW1ZUyKh8RSaLAZ2tVgHXPRMx8rYxOcfZ5AgpAmDdE3y4bQnzyLpWe6eFkZfQYL8dqL0whcQcPCkmji0TkhdbVQsB1RhrOWkiCJCKJ7p5DB8dN1p2YgyEESguVld3SGsnZcPwoXvY+SQ23DptU4NlbZ2+VULQ++sRSOWsaznzfjBgnegDlk72PUcK8xg+HVvOw+MgQCuFpIwFrz/PwWh8kYIBL2bKNVlkCZ22ZpJFzne78ftkRSytELqnncoKTO8lXYoSZ8Z6OCgbo1XoYSgV+lB0Koy22mgDbJWc67hvoJQN6EzTQkoOQzYuGGet52Pf/1D0iREXoXP6VU+5aL2DaYDWV5FKqzWd/SkmWlvpteBTGorGE4ep/Da6Z4pOzTl98Myl4XeBiao5wzWsafB2JqVkrbJfW+u0ojOqlsZhrZQ18+wR8NJEWBsuZwwv1ZYfKj7niDGqfrgWfFDOR74q5IuA2zouNcraiKdlyUk2r3wSJ4VDTMRPXrB95wnh/l3ich9CoXJBMMOZZh5z50BCqUET4fxTfO7H3+RbX33Id771iE+8fsEuOUpXA94UImdnlauryJ1lpe7hG+8+5OKffZUf+quvkC7/DD/0l3+Bv/Toir/zf36Xj2rBWsbHiT5f8qv/4il3beZLX47KOAwz87HDYngfqeuB1rsAkzrUgRSpvhj2vOFWsNAJNrOx4fAQDwTgUBprUUNrxzDXpCjpbWS5DetQrtSqs12qHmO/HsEirR1w3jgPiVKcsged4YJpEVs7U62cx8BN3ii1jArmYdHOgxABMMON1knZbvzHRIZrekaGosE5TwhavFOKI7S636oggnf4UAX+lE4bKtbkA4SuBuIQ2IbCIY5iGBc6KXpm3zh0aLlByBzqFWnaEUIUDNdlu6vdCLFifZV1ettwpRJngXSyd0iJWi0TTDAENI6b5g8/eVpemdNCSEENSC1rjitS9rvmh0qiUUqm9M40TVrSYqB1h98KoPdj2xq1F3rdOOZGsEg0WTgineMAjEFKJIKjVwWO+0lnX8/KzdGcW2kmW2U2xh1ZhtOgKnqCkSdXoeVK83WY+Ib6pBd6leUrBH3lU/h2Lmqh9F5B84Ya6lyXFaxYpfgG1bH0Rq0Z6wUrjVY6LhqWOyGN/FHrTL5jJI6h0Utm8BMcWx97UVH7Xh+14CbQPedOSl65gV25iH5kkrbeMe853wVm5zishetjFpDlVUoje6IIkugVRM0t8O4UONxlb1c7qYChbg0/JFIpelzwbHUjmKdTmaPsxQbM0eOq5o4tf5ztZ61SRzPgjCcX2cHmmDTrdW6jSKQuEaAXT2UovYGXzZlut1EhdJhcIleF6hsiJmvTmePHbBqjAOrgPFtpbHUcQlNUO7cbOVldTWygFt0yvs/WB4/WDLonbzqLjsei+847Ulz0vTp0a/U+yA7D+SAVcheIp3lYQfBFLC90qTvLADMlCOsfP5dVc56y3aSCbC2w9SKCsXupmr2nbIVSYd2crNrJE2h0y8TSWVqlBI9fKtEnnmyNO65Tt+NQCwu0fb415mb43pjM2PkD884zhUSKjRjO8cuHXLpL4pxYJlmQLUZefGHifMm8lgN9cjx6euA77/xv/O///O/RbeGNT32GP/0v/QRv/PTPcP9zP8ELn/4in/yz/w5/5uaa6w9+n4e/+xv8wW/+U37jj77JH3+057Gb4OyMFAIJN5wBBmGEULs41IBe+UGj7V1GValCW80wPAFtg+biyNwTmKj4FmWShiDiyvkBtg5Qs/eP1YdyNxg21MaG1PtbrwRL42e2kav2b0PzHiMXKZ5EL76Rgr7OOggDnxLrtqkEY6gDu1O+IQ1lL2Ka5byAfIHOaOYssnt+P3993wGl137gTSlK9gdyhvM7d+llkwzSRzGdYR4ZC7rwq20EP8vG1k5SUrtdwFxwrGumVDFp5uFw/Ywnjx/y0fc+5NmTK5Y088IrL/D6Zz/DNC9i070xhYnkI91rIOy1DUZooPYwWNmKNSHJQqT7YHmUE9Ero2UNrq6f8+jhQx49fkzOmd3ZJZ/4xKd48cUXODs/Y384atI1KVV8GmnqTjfaMRc9cNWIIcjb3gqdQh4NLrvzS7o19jc3fPDB+zz86Cm7891oYIAQEp964zXmaWbLVS/KyJjKWXL+0vXAb/ujGBg8FvQj98FY140nT5+qzpvOth6oWUv5/RfvKZS7wZTm8dip6tCHBCZWx5qQ/dpUL62hSRWQ3hwuBeJ4GXMpFNPSbL6NgdxI06LFZChE6NCdZ/JK1d/v9xzWle6NM6/w3jy2ptZPMljoWRLpEBXcrOwU2SoEPoCrRVXzyeOmRQBSk7rESif4wjIZc3Ia1lqBlsF7ok88fvyM60fPuHPngjff/AHZVZDiK3ldbrVtg6HU0pVLZisNHx35cOB4s2HrDc0fmaeFhY0vvX7kJ390Ynv+lOdPK7mogaZvjdyOHKsUV3lV80UDtrbSmsKRn68rtUS25mn9RkNyDrz9cOWDveNsdxdzjmd14fra8+H7j5l2Ahe3A7Qe8NERkuPMPLl2rm5M8nKaPm/vsKrQy4NrzBd3oWz43tkVz9orx5zpvXHY6rBUmTZG87chn2ad3TyTywgp7J1a+mC4pA6LMeJcUFtGkOWntUYyz26Z9XuGDdZS4Op6z+wju91uoPM2lEFqK1lLZQ7KcaldYHBrnf165O7FjrPduZ4RL5DGaqcVsQ+1ZDE/CExdc6XkzPluxzJqYRmMaE3G5BJ+0+Kh/KxKROGjuTc2V/DnCZ8mMW4W8E62nBQia8+S2NaRQzFsBPv9Hu87S5pJ08Q0JV0WgDsc2fJKB7pV4mRsuXJvntgfDywX51ycL8NK4NhGBg99qD1GZoLO50RvG2laePjee9y5uMTs1J5lYF7ghyF7HQIEaKr9rq1wc/2Ui13Sb0dgQUzxlk1UamwVK42qcGWt6MxTJHiTXQmdJyEFVN00sqLGRetMmWy1FoxGGmCc9wI9A1IKhT7yWXqnjhwoqdn0zjozNYs4T1UatlSsTmyoFB8CJrJJAUYdlrQY1LY2sih67Wo6a0PK3E/AZx3NGkORdgIoq1rQHMbkvM5OZ5wkja1k6JWeC5OzEWqsJVZKMDHrcagcalfjlB/EjJi50SbpjGPJ+J6HxWNmmiK9qn6YPoCPqmd8246yVJ4aL4c8u7WGc4FSUGj6UHP1FpAmRzY0Oqz5iPeIYGkQk3KZOhrAQgjkfARk35SirN8ubmoLVUFFbVIS2chFKcOqsdVMwNEYn7tJwdyamOBgTjlYBs5FjutGLrJV6DlzWO1wdOyS4+YoK+zWK7F4LAVJtPoIvvce5wque8KyUA4F8zPX28Zy9BQcORk+67OsQ43TOyrcSMbsFSrf2o62FuqhUeeOK522OFoNOHegFo+rjroN0DoB0QhhR9ndkN//kPj5e/R8xPVnkCaoGcJEZz+WrTOs77B4h8tPvsGf+vMf8g9+5Wu8+9aeH/zJC9zWCKVCC5ynHW02ruhM9cDTmxu+9nXj7qtf45Wfus9070f56Z/7Wb734a/w6994wnFEA/jlDmtv/O3f+oBXXl/4/E8lfPbUtOKrp4YiC8CqJSya0Vyl1SIlW9Bi73ymBYc/ynaw8zrT7rsdT8gn+Q+1K2umbpvedZdotjGniattE2vrh7LMjbnER6nDvCl7zTQL9b7Q6kalyVbQGvtVtq7zeeHJ9TrUR8rsFNF9UiRJoe2HLVx3/ga08dwGfEhjWTwtG8MCY45CobXG8ZhxPhBTJJiBD1TXZY/rjRCMbipMKGWcgxguJII7KX5Gtlgttzkd27ZiUaSFORRy7IAlUZyRc6bXSohSxZRVjJrCvB0pTljLHOpR1oneOW57du6MFh37baV3IwQT2eoNb03B5+bGkiF2XE3Bnc5GbI1eNSNaFUnknFQ6UjiqTVd2NzUS39qi6VgTI1+aEcdhkhvYUPwXKn0rwxrWoZmAHK+YimhRKmSG3Swwni0bbcJgro/WZI8bREIIsnE754gpsG0rvTmSU2zCbbj5OMJ9cxBlt2qWEFdeaA7y4YhbAoFJBFvy5O1INIHupamWPITAgmOtG7kVcq+4rrDqhoAUfSpVpIcJGO210bOpEW1OLFFAtjOJnFqR7Q1OCjtHzY3qig6r7qi9EoJUs32UoYAxT7DmSq1OOUvUoQovUjLHiEtGPm5yYwSRyc5BDApCb13vH3hibRAFtgYLlHXD9dEyHQIuaJnuvQ+l5iBCnYn868Y2LIkdsBgomyrmU4iyKzoHRaD7FgAAIABJREFUHiaLY5YQ2FjLhk+JYlUE5PHIPIss6xYG+an5tTa5V06V9d4nzI3QZhfo3VPKpiwmG+6ZYB/PDU1RJyJJFS/S2kklFcZMZ7i+STFlnZ4nmpO6to7Zbd0EcPmRb1VGGlQ0gSMueFrQ++dcoJWN3hxbKfTWcC5ROmzNOKyGP6jebHJH7vqMb54UjK3B4WZju+nM0eH7RotNsTHWMVcJJeByISyFEI1kE8k1zD/kcudJvnPmK2lxpJh4IV2QZsdy7nAxYAXuTJEf+/zEj3zhLs/3K++8/03+1q/8H/Bf/1e8+vJn+HN/9k/xg3/x5zn/wpc4f/mv8cKP/Dw/+Df2/PVH3+Dhb/99fvcf/0P+19/7Y/7g8TVPXIQpMcXpNn9sa00Zinm8lyFRS6PbaGkcYI4csH3MEEdKtUEeFOWxlYJ5Ry4OC4FaGjHqHXKnxI2qOJwQnLLzWmdrFSNhpZAYBVvRUXPkwifomvGhkavUr93k0PI+0LPevTktyjDEmOM0ctU8PXSOtX6s3mzAcO/UVnGnvKSRTxxCklX7+/jLf+UrX/m+fsFf+3v/81dqq2JlU8T5MFqQVKOXvFiJ0ooYbt/xFrCihPMUkhj/OAmoqBoMJVffeProId/6xjd4+4+/yeMHj5nOzvnc59/kjc98hhdefGG0PETiNHE+7zAg05ldwBA76II+WTGpR3odLWRdIFOM47BpQ45YG9tWefrsKd9++x3e++532V/f8NLLr/KZz32O1157jXv37umybzrQPUbphSlNetlbld2sN5ZlJkSvzKS8qamgFeIUmHc7rFUePn7IBx+8x5MnV0xT4tXXX2eZIilE7lze4+LOPSpQhtKqD6tXbeCC1ErdgY8TuVbSvANkX8m1cP3sOU+fPSOXjIC9QvCRuy+8wLI7owwJqI/TaEHTZ2bDEqKgF7G923Fl2zI+BGJKxBCJ0ZOckwQRY183gofWi6qSu7HMC/P5xDIlog8kCySnF62UzPV+z+FmxVkgBCdg0CouTswmoMK3TqZpEJsSKcnKw0jNdyNPpsu0jUsTljzNSaJdNkm0PY0pwsVsnE+eXRyLpYMQA8frPe+/9y6ezqc+8xovvPSCApu77DWaZ+pYrjVY9JPUrUNd99w8fcS2f45re3yCC3fJF1468Bd+5IZPTZ2HH33E8cnGtq/k68719crz9cDzQybfdI77zr5sXNfCVqAcjMPWuTkascju0Vpnq4UPHje+9tC4qTNxirgQuS6N9x49p1yvnO3UNOBtwmJgiZFmRiWyVni+Nm62QnaObBPdxBRNS2KeZ5wLXGVJbI/Z2OPYto1j9eQiOaj5wSiZ2LcQPFMKnC3K32q13cp6zSsPrONISV+/m+FjYJomUkws0yz1XesCKE02vGPZ2M0TZ7tFC/RQLs0p4ZGFLsZA6FBLppQMrZHXI0tM3Lm8IEbZwJxp1O8Mqazr5JbprbMdV2oRoBSXhTgnARNDdOOjV65ObaxNeRA28msUNA1XNzeyPXkNsXHIzM2U/3bKSDiFhTY6tXaOx41WG8u8U75csNtmxLZJ6n/MCnOsrZOH0nMrGR8cF+cX9GYawpusr97QkmGqPHZ0onXylvHecXj6XCBemjTABz/sVbJmyJY4Wrh6Ux26ddbjEeiczTMOI00z827htk2oN7ayUXMRAKl+Z52dHZZ5FviEhuVTzbxKPOwWnHchYOizW9cN7xy7aaZFJ8VgR4v3AM2cM9IIXu9NTVu1NmquxCnpGUABq2LE0SBio42xVEKHKci6VoeSJqaJFASYtib1Wq5ams0Lk47R4YPdhk0fDgcanWmaceaYUhTTHcPIQOrDV3dSfApcK+sKzlh2Z4AbtgG7fQ6HmB+HnZTO9KY66sN65LgdxrlYcH4ipXAL1jjnhmJCuUnbtol59frCMUbJu80Nub7AR+dGNkrt5E1ZBad8mXXNlFwwM5Zlwg9rRAyyBp4k2H1YGMsIOLbBHJ/eBe8/zlALXssNJ7vJICFq2YZtRSrX0goxySahXAwbAZj9tpkKp2H71IzVGyy7yGHrVAt402fnnJcNpUOMampr/Ug5CHRupWARypYJ00yaAuYq02TExd0qqHStSuUUnBQtVhrmpFyOy0JvkY6egzaIkK3mMSAK1LCghkHbwfr+U9y5w8LZcAcb3U+oYzDS+4rqS6UuNArn50fy8yf84e9+wKv3X+DypQnLjrV2HAo/Pawb3QfWLXJ93dk/fc6rr05Mdz9JvP8FXvc3vP+tb/P4uOFcgthxIXFdDnz3aw/5sTdm0mUgtEC1ghVPpY5cyjIC4aU03jYRKGttbKtX5snmWY/GukpJUprHW1XuYxbo2EznRh3gewhik72PCuTtBZ8iF1Maai9Hq1C7AB/rwzIbjI6/DWzXXerITc//Mkn95sxRm1QhtY5F1zwnZwHdS6XNaKArutNqK8ooMyl/nFcg/JAr0GnD7dxpuRFdwoWOS13Pp8l2gclmWUo5SUqkguywzIEUjXVTiHjOWSrVriINtXYGQkgEF0esgxQNhZFBOjI1jRECa46UJhQk7phDolcpKMq6kctxZHXobLORJScCKWhetCIAoA/7npOSuY7m0j58at5L6eOHOlchylJAryeFl0WUyCkCD4PgE2CsOeNdZE6RkBw4fZ7d/GjtqgQbTodt42RHbL0QnBGA5PU9tt5GqK1hXflarZWRWzKKHsyUTeqkdMz6TQNIOmX7jXfeuY/VbN2wGNhywVejGkyTsg5DCLeNpr2f7thxdrgRzeEV4n1qVtOMIDCotU4McShhh83KFDRtrVBzkaLfw2lxPbUv93ZS+StXq1cpx3E6NToj19Uccai+5xSV89Xk9uhlZLw6x9kSqdWpwc1LudRrJddR+iFpjTL7hhqqGbfq0zp+09o1L7XxiklFKwWKZpEupdkYt3ORGmkrDauNzsh5bboD9ImpCCOMeARZWDvtuFLzyJxxnTRJuuWRg6S1Eazfdd8pv89wbmbyEelA9GqGaCIGg8OicjVV7PFxW6Dy0qD3qvwmyXVFoCGSFRulF95Tu1Nm3LhMZGdU+L734+cC+CgVTW+mrDvfSXGWeKI0ZYuZ9oXSG2YFsyibZq3kprIbCwd2/hx6pviGq4nq4vigja07+rFhWyPOhp88Zgmcp5IV4ZZFJBbXWavhquNq32h941nZKNcbh62Sq3G9L1w/PjCT+KFPvsQXf/AVPvvZu+zTyj/5vd/l7/6t/46v/p1fpb39O7x0BrtPfpp0/wtc/NDP8Pkv/01+7q9+mZ/77B0+XR/jHn3A0ydPeVIqJTR8hJbtFMcvl5JjZGcOJZFT5qbzsqFJ1daHrbGwlU0RB95RapYizkGvq0LvuyzrpziYdTvq/B/zRinjDPJOFv8K08i/rEjJL3UdnHKeeq+sx6znrur7ULajZyuyImtWGll43hNxxCGG8U4WuuAUWXBqEZVq0fiP/6Nf/M//n5Gd/3e/vu+A0j/5R//0K84bIS7ENDFPie3mSuRyF5OpDh+FmtZWMbfgh2Wk10qjsh5vCA7W7cjVzXPe//Z3ePvr3+C9d96l1c4nPvkJ3vj8Z/nkZz4p6W+M4ANpLJ1+DMfWpVIwH5SPVNUI0mrFqnIznDlSVD18CJHa27DVeB4/fsJ3v/0dvvX2t3j86CHOO9749Ot8/s03Ob+8lES6VI7bJtvLqAvGdMhJhq8wrTnNMAIJ8/FIzap2jslzeXHB1dWeD95/n/e/9z0ePnrEYTtw9/I+F3fv0oou5d35OS4E5QM5qQZC1LBUa6M2o1YxV3mr5CJbzfGwcn1zxdvvvMOHDz7g5voKQA1rZpyfX3B2cT4CvWD0Z2AWxHa7QK0axGtprNumS9qMmCLTMot5NQ1SIFm1G+oHTBYbR+UiRXbzjA8CDtxQMPTeuFn3PH92xXrYSF55HdF5MStmJNfZupjqWoy1VZbJc+YDVhl/d8gti0lYC9BoAfw8C+wBHeJFCollMu7uHHcWx9kUCKbLy3vHfr/ng3ffY9s2Xn31FV751Kt4U3hgGBk9vdfhVRUIx1i05ek+cHPzhOPhhmSd6CF0z4/cc/xbPx/50usTk0s4n5jOFqYUcTO05LCQCcFIMTIljw8ybgTzeIxdVBjv3QnikvATLDvPda588KDgJ8+58BcO2XNzfSDOgfP5UlbFk32rwzbeAw008n6naTcCcR1T9FzuIi449sfG1VboI3/meeusubMV47hWwrAthhiG1dGYgxb2eUpD+TBkpV1KrlIKu92OME3EGIkxkqaJeZ5IMTCFIJtdy6M+XYqenjPn88IyJ40JpoE0jAGjNghJF70OY1msbg57lmniYtnhfSItScOK1yJuYxFvJSt/5uZG4dJNQNVumYnejWUa5pgUbu/gsK1SujSdI7k21vXI/nCQzQxura+lCuA15zgcjuTjShtnTy+VbS1c3+zZtpXdssgy4RPeBaaQ6E6192W0LvVaaaVQjg2HkUvhxZdfYWgwcNHfggzaR0brTZMlodbKYV055pWbmxvcIsDDeT+sZRoyVSkvQFCCARvSdCkKl92ZQJo5EHzEhzCGqUbJVedCGxluQ+W0bYU4TezOdvpe9Xrhgiwja9lo1ailjda5MXTXgm2rmDi4BeV0CLVhp5JCqdYyGHABVKVWKcPmWRfy6SIzjfKtKbuErmDQPuyStCawEA0Bznn9N10AYOs2WsZMBIbX5+Od1Gjb4RowUpxvzz4b9oTbtiN9I/TauFlXaG0ETsJ0dsHkIz56aq9074bNQQB3bZWbslHM0dC/e35z1MDbdZekKbEsO7WX+UCMstuZnfK1xgc8AKrTvWZjeHVOyqEQHK0VKfkGINQG6ZDzxrZtLMsiIClOIxNJ9iIzh/fjjnZDPeu4XdjMjJQSIUjdHJ2A31PTG+OuoY2WJtMd5Z3yF0MY4b4mpYj+u8YyT7c5FgKlmpTCQwFXOiKYzNS04g0XdbZ7U+5e7461qP46ZzWylLWSe8fVRj5WBe93j9VMCqPuuOrn1PqoFS+G9Ymbp3sgifnelNvR6zrsNhHnqmS4mEi4ESe52Z76bmf5zD1oZ+ArzUbTX5dq2eoTzN8f2WEr5htn08Zbf/gu33t4xRuf2zFdRrZrR6biXMfXyqFULHS22nh6cyRumU98eoHlZeZP/xCvPPkO33z/iqvtSHFiX6d0xnefPefBN5/yw184IyyByERzWuLH1qhyDBrNqkDw0mnrsHH0NsgZDdX7lrFio3Wz0nJXXg6e2obCISg7xjWVG/Sgs2I3nRF9o9WiRuBBEtJsDOddKmqntp6RIo0b9tGtFOYpkWIXUDwWNR+GldoNhea4931I46zTgF/LWMxvnzXjRMidzqnTe9fqIBMMQpqI43yITnktA7UaAIWCelsrAtOXxBRltym9CaArdbSbChDIZhTEvnuQRXfYgW7/3l7K+bCLhKSQeAuJYNOIOFCGHkFLfa5FOXBVqk25oRVK62MgxhE4i0iAEKW6cT5QS789/1sXmKFl1wawkNm6/AK1d2zYd5NPpDmqpSxLDemjPpNujWp1NJgGzPwILHccWx5KsWGl7bLuJOeYB+ll1lQu4wM4Bf/T+i3Q1YfqxMbPPAQ3MlClBMg5jzylk93fYFhKHbKTVU5nodp+MWVimennK7JCBLRy+QQQtLEOu6E86KeMKVPLZ/BqUvQxyII+JamchwujIsApBM/WR2SFGdY1t50UfVL26yw8FsVlYMovMhvqXQvEaKR0ypySS6HRmJaZ41rouRFDkHbG99s9zwYI2mlQ2m0ZXhs2r1PwfW2NOojS3rh9jjV3DIDVSZnqQiRvVeRDF2hgJiDIBshXq56VTsNHz3K2yLLUG2F2EBUkXlq7zaEpVWpc+igwMQW1e2fM04KzOMLAm+42P6yMJjL6BI66YT1spbPmjToIsuRkYztVuHvvVc409sfWnCINuqO1rCwcExHZah82cGVjliZHzwksrFTtmUGB3LUNgiYywCgbgImWougirRw4ZjiUSjtU1twofY/RSS5iyP7b0Pm17BJnL1zgg752iklqxyh7mwdciMTYeCGe0YJjFw1CIpq+113wTLPn7h3Py5+6y52Xd7g5yMnSCi+cF37q8/f44R9/Gbt/w29/9ff55b/9y/yLX/ol8lu/w4tL5ezlTxNe/Dx3vviX+eK/+u/xL//1L/OvfPENfjTuSQ++x5MHD9hvmR78eBYdyZvIOnMi//SC01plK5syS+kjNsNjTaDRulVaqdzsDxIe9JPSrA6gt96Cjb1JvEIzFTENJ5F5jwUnJ3FXeYHrJsLcQUrag8wHUvKy443zq7XGccsce2OtZbiA3O17atY1cwbNZ3G817WrwdCPXCiL8Iv/wf+PAaVf/fv/+CuhQ/AzPiWsF66fPpKvz8mD2AwBPUBKUuvUXum5st+O7Pc35HLkex+8x7e//hbvfO0tbp5dcX5+xqufe51Pffo1KWnmBaoUOfM8050jmCMO1Jqg0NHT5tGbwpC9jYsr6ELyKd1KVVvNtL7y7PFjvvHWWzx++oRSNs4v7vDpNz7Hp994g3me2GpRkFep45D0I3NFEsVSRgtNrsyT6h/zeqOHtzamOHHv/l3Oz3ZcPXnGW2+9xXe//TZX18+Z53M+9dqn+dybn+Ps7iXrWph3O0IMtNJHy5sWWW9eIFltsj9tAPq7xRg57A88+OgB7737bR48+IApzVzcvcMyqdUnpknLFMjiVpGSYbRDYUPCvRVaGRfGsCTFFD+23JgfLK881rUWrBc1D2SxHzFFzs92mE/0FHWZoPyVw7pxdXXNdjwyRdkOgtdQxgj8btTbKtStVmIKzMt0m92UDFJ0t80anU7wkSV5djGQa8W1Rq4Z6pF58dzbwd2dcb4EDZMmy+O2bnz4/vfYP73mE69/itdefx2zDDXrgh8WlVI1kGIN/AicbgLPjtfP2T97St4y57PDd8c9PP/uX7rgr/zCzG7qFF+IyeGnjK8GoeKyw1IihoUwzbTox4JWWdLMEjvJB+J54vIsEbpx2A7YYead9x/y4YPMxUvn7KbEcZ14cNU5lM5uF5mnmWgJzLOOi9JOIhvnWPOKmfJnzAkFT5MXcNoa27FzKOC6Kkhz89QKeT0qhyYmseShE50jukiMgd28Y54ncl7ZitiYjp6NdTuSJiHzu2XBxUBIiTkElqCqbgEfDCWNHFPRGZfnF4RlFqvQHTHOYE6Sau9wSWqSMgDG3hvb4UiaJpaznQDREJXH4h3beqS1jbaJwT5smZura7FQIRDjwrwsYygUNH6KealudJONvIDW1K5DrZQts5VCmJKyCAymkJgmgYi1iBmevWdOEy0XtrySaya3wt3Le3rnJp2XUwq3igqyJLTmFKQcnNqlnj17xr1PvCS7zxgorCtov2ss1VnSG9YqpY4q0WBcP3zC7vzsNriZ4IlOwIN3HscIf2SEEPaO68b1zQ0pTUPu6zHzOB8FiKOwy9kFsikLI5qpiaXKXnt2dsEck6TF5mhOgbknoEItacpQCD4Qg1FzFoAzJQVE+6BhR85HvHkN1WioD86TUVbQtq2kmJgmhWEzgqK9UzaOmeTwWuKNKSj42p0AK+dv66V7VwtQ5eMgUDVU6nMPPimc34z94Zplt7tlXcUiqShCx5AWTlqjN7WClawcN/PGbrfDG+TepTtpCt8MQWx9YzSbAXkod8rhAMfjICE8KSSBuDHigx9NOn602wHmcUHA7skWGdKMmR9ta4FpGi1hTtbSfsrgGsqIkwJ3d7aQUmS3Oxu5S1JIthGQmkZeFoz9q/db8if6kdN0q8bQ59sHa39ScrXeFYLPaRd0uPG8xjELmDNiclhrhAGU19qo1sl0guv03IneRuaBk5rIpNCKwdEsY06VvH4sRxaMWhwtOvLaODufCclRh33qpJBzTnbItSvLbUmq2O7WObaVsmZ2u0CzjD+FhzvwsSlHyAcs2FggGuRCmBOHpx8RwgX+fIYqfEDuUi+wctg18S9hNuP9juXMc9Yqv/XPv8ns7vHya5dMS2M9dPrWqcloxSgNdjP05rl+vOcswL3XXqHFV7n/+dcJ3/p93r6ptHrE3EQuhTnNfOvxxvb+ymc/f0mcGCpDzWaDfMVcB1PYdjepeoOXEkMKlEbzhVSn2+Xcd1k5N2vj7FII71bViGgm5Y6Pyu3zYWb2Uie5Ac705llzlarCHDq59bXjIFecTVBFFuUmSyxdxSx9qFakZJDSwPmgv6MEypozfaeZwGDGAnlSIJ4KDQZmqw7I3odaSbLXWhu73ZnOwz4y3GIgDgB8y6uAgzBzcbawTKMN2HWiE0i8bVkLkaQQstJ3mJaZ4CMxTFhTccmpJagbCpLuG6WsAt3CRO11WHsbVuVCNnO0NYsgcKdmNhE3Hc3G5dZmKFVPdSI1aq5sJY+5S0w9QHPKBPTOE0xZfbLBFracBc7YUJmWRvSwJJGa3RwKQRbgf2qLdNFIBD0/WeHfzqtF2HXl2bReB9GmociPM6ANK9kJjDmpKKu63lU4YCIubOB+YYDijDtWP1tP7SbVjxNQHdNEcJHgHTIKNnz3Y7E0wuSpaG7t1mSVMQhpqEOddDHBdXwMND+wwSDg01Dou3eeQGcOkbx1tib1Fh2Ccyy7+dZuVVqlNOWUtVJHJktVIdFYunsV4dkayp/qyiXqDlpuipfAk1smOr1XKqBggDQ6W7eacQZpnpjTpPZWf4qu0F3lR2lSp96+R7WJSAHtXCb8D2q/VWVU15mnmTR59UybbkdnAnVrqbQ61Ll9KIUqWO0EJ3upZk9FtjgLdN9UzuATvfnboGfvk3YVg4ZIr1N5DL2Mb3OcYciW6LyRW9F/02Rdl0VK99NJGNG9o24ZRcFO1FZZN1kfm/M4kgCfaLJzEcjHUwC5AAXNNBV6JUQpVehhkEKajZ2HtUZAYKezhee5Kxzcn3FoDtcC+yzgGgeZys0BSl1oFdbrI2tuHFd4vsHVYePB9WP268TTK8ejwxUP9pnnN1dcPc88PzSeHgr7Y+bmuvP8WeH6yUq+KiKHwkQNhf2+UJ817p/t+NKbn+Anv/QZXnwl8Yff+hq/9t//Er//y/8DfOcPuTs35jsvYfc+z+4Lf54f+IV/n7/yb/9N/sZf/Al+9m7nxcePOD58n2c3BzbfCCniTd7RU+umjcZvEXo656kFhsLHG7IPOxW2lC2Tc719XtopgB0RSu2UE9sKrgdcb0M1KPDUjTsnBgHwsk+P9mtDYelec6kNy23tRumN1Dp53IXKJtU86b0UbLp3VbKDl3Jx8srHLK3yi//h9w9Qsn5L5X5/fv0n/+l/1iXFz7QYaMeVZw8ecXnnPn6ZFMDZHbUX8GPpqoiBvn7O+48fcP3oGQ8ePGQ7rpydz7zwwn1eePFl4jTJEpcCfp4F5GBQMt7LahOCciXEog/Z5BjwXa9qBsFpuHeyqKkpR3lFDx99yAfvvIsFx/179zg7u+DOxR2mszMl+ueKDfajAy4GXJxGyKsYr5pVv51iElvQYcsr3gWC14PaWuHb3/kuDx58wPlux0svvcgLL90dA3Dgas3k7aALGs80nWG9UjInEayWQu8orQnAGjfNWjJPnzzmeNizrUdKbaQ50rtj2S2UsqnBKU5qhkLVmkI3/WgrCGOpU5CYipgqaZnkeR7efobEWay28mLKJv9wQGBdioklyjZRUxIjQ8cq7MtGP65S+UyJJXh8q2y9031U4wEB1zr7nOlsLH4ZFdC6+NaiMD3fK1s9Yh0x01HLlXWFBpYtY60TFs/9s4kUK7s5qYK1QvSeddv46KOPuHl+xb37l9x75R7BZnKvJNMyfCgbzQcFjOIJFHpVla1zgeNhz+H5E/K2J0bj7GyHP2x88VXPlz5duXu3c7w5QnUcaLDXgHPNDXaY2OdVUlTbBpjXKaN1IFvhuB7gOLGRwTdCTqxsvP/Bc2o6Y95NPL6C9556HlxvhODYpYUpGBaNbTP2yDpRK0TX2coK3UjRk5yne8cUPCnpUKrZRr5RV6sACOwA9seN2jtTVABz/xPAXAqeKSbMHNu2kmsRE91NOQLVMc+zPOhOiqY5zpg5NR+Y0Z1YsVNIf62V0mGaZ5KXSqq1hvMJNyy0zRKuNwpFweFbIW9HDnnDOceSJmidMCdIgeA8qTtyVTh7y5mSVw7HTctx7cxp4uz8kjaCUL3rBOu3y5sWpU4pkp/3WvS9bivHbSPuFsqWoTaWadIgGTTI1OBxrWGlsrU2AgUrx8PG5cUlIUjx5SY1UHiA06XUFY6ZcxV4AByeX3P3pfvYHLDqcbXRfR9h+gI/HB7rjtozx+1Iq2ITnz76iBATPvmR/QHGRBg7/8c7fB9ggtp52pbZH46cXahlyg3Fnlr8/kT4n/Pjz1fGQymNw3rE0sSUJqydqodlkbF2ksJzK9cNLgpk7o12VMZPDEMJVTulFnqS7DgMawPOCBWyh547uVXKzZ7l4gwX/Wj8O8mN+xi40ADblZkQvSe3jeST2necY0o6Z2prsqJ0Xei1Vpz3hGBjUBEbaR2eXV9xdnFOOa632RxxgDNagkyA0AgDPRyOAqtKxvnE+fm5VHDDD39qjjll0LUO3YZEf2sUg6tHT+hsAKQQwXXOdxciFaYhlTdlaWybCjXqqIHOJbPsdsQ4CZSZJqLTeUEPUg62rMyKdWWrAnj2w7Z8994F86I/J/pbHZjOfe+FnVV0v7Y6bHc668KUmKdFwJA5MbDmhvVVakXdu5W6VdZeSG6oqMKk4T84HFIP95qJ5sfgx1AuZ8xESjQ2JufJrnMsDleUXzMFxxRM70ETA936SjsYa++378PTx89wDs6GnXu361zEmSl6YoLuG6VowL8zOYIpZLrbnn7s3Lm7EM4ndlHnnjPHNEV80sbkTAuXC17WlXIkUzh+O/Pyn/sCvZ9jIWB2SQ+XH4OTFsBnJM8cAAAgAElEQVS/jLbDJ9T1A/LD3+PX/8t/wO/8wfv8G//6j/LKZ87ph5VnjzzHm8YaNp7vM3lrtF457I2XLs746b/2ZS7f/Atk9yLuW7/Jf/Nf/Lf85jN4crOxN50VFyGwPX/Mv/bFu3z533yZMxYtWnljrYW+VfqqwOtt6xwOkIuj586xZQHHgMwAqlsurbNtReUha+YmN3Jz7FfjkNXYF00Kp9IC+3Uj+IV7Z2pzK3Vlqxv12Lg+KHVkmqNs8U3zZKNTm3FzbGytq523688+WypW1Ep7KEbB31reTow0Wu9V+95Xcl3x5qVcGeCoc5PgpC422UzWrtq0mLTWiXECB9FNxORGLpsINjByWTnsj6zHgneBV1664GzyHFeRA5XMceusx0zNheYa3kecg7Qs7JYdnkZtnlwruRecNWrZWA8HainKOeydKSS1QJqyMa1u+GhcHWTF7n1Mdc4zhUiKgWYORomDdVWy++ioeVObW7fb5dkNO1KK8yCbRLrU0UzmTOdrb41SM8nJ3tdGSUC3YfkiqDBiAMxq7UUMf9FZaMDapQIK4iZYwkwuGUuO3Ir+Ls1GHtu4D6rA3WnkFRUnVVApR7wlujWRvqOUoVY1fjUDQ7lvQjzcx+oagF6Jlm4LGVKo+KJigwY0L6dAL401FzXnGkTfsSY7jXmH9xvUxra2oaBRy15HhI+3hpVGMM9h3SSMCGMXMuPi7g6rmd7h6c2eGBy9dA77VYSNG0Ci9yxxovSuwgIH3QLrvkCQErrnPpRXStSzqBKg1sbd3D9WwDo8wYw0Sd2dS6OVAVT2EXfS1Y6seUOAsUKM5dToZiznM/TKtleREk3KtxTDbSyBHCKF0hRDMIeIt0Bumrdwp11qzLpdrbEdE9BNGA1+evasS5VSbeyYvo872A0SrJBzxoc0yLcItSg4u6Gw5NZH+pHO+jws4snrXXLODVtvZqttBDBrnghDIXkKgl7mxHpYybmNO7USUuXubiJnx/W2kTdwvsmqav7WUdK73CxmUp85a+zXjV4LyTecRXo/4FpmDpCmwrIsY04a7aO+MweIDnoIYBnanimonInaOI8eS427KbJ4r5iUyZhT4MyrGCKliAuFaJ7zaSJNxuXssKhd7LAdOT6thDYz3T1QgufbHx352h894ul3r3llucOf/tLP8GNf/lkuf/xnCfPLtBAx5wn1Oe3t3+Ab//DX+c1//M/4X775Dl9dK1tYCOmc4PWu1WIDvD0ptQPHfCS2AK6pYdJBqVIC99EM350bGZYdn8LI8JvYWiEMRVZrAjVPWb99sOSxa6514VR2JcDKbBQ7mAiOaminbQa1sDUwRKx2wNnI90Kq9z7OMO8MLCm/rBbohd/7na/b/0eY5//21/ddofQ//t1f+0op8mRPIZKfHUl37nF255LkHcFPqhMvG2tZOVzf8PjRh3znnbf5zje+zgfvPyAfC/funPHaay/x0isvce/uiyy7c5azc3yK+JiUNWGenvOt/NE5p5wP3KDuFSAYnR/NPkaIUYqEXNnyymHd8/jRI979xtt884+/zsOHzzl76WU+95nPcnnvPruzC9KshTgXtSJIrhblE20NnBhxAVwdPxqM8naEVuh0dvMZPsgX++GHD/jWN9/i4vKCH/zCD3H33l0anZv1yOGQyaMh7OLikuS1ZBy3rNYMHwYAJiamDD9yrY11zTx+9BHPnj8l+MDhcBxBsHE0OUxid3wkzWd0ArUJ6TQXtSTWDohFN6dh+6Tw0oAe6BiliHHzPg5bgRM62yGmxDTPzLuJMCWCT0QfcVENa6U1ainsD0f6YWOKkSlFuhMAqH6gILlhg94a19uBJS1SQ5hjq4XSIDtITj/PY15VfzpFNSZsjZwb+bDRNrFsr9zf8fJl5HI25ikOCXej0Xn++DkfvvcBLhifeuOT3Lu8JJjHuU5wgdIYnuOM66rldOgwF4Owcv3kMVdPHwGFOSUufOSNBX7hJxM/8QXZXcpen8EhH9meFA7bnueHA8+vO/vjDcc1c8yZw6FwczS2LbAdMsfjytXhyNXauKlwOHSet423Hzziw0edyzt3aXHm3Y8y73yY2ZeN893E+W7GJSk+cp7ZRgIOXcx7bZJhp8kzLwEfPDtv7JKsomtpbLmrKaUbW2sK8yuO9ViY/GDXnAYq5SXNTHGWLNe0jOfTgNUlCweY5glnnpgiKc6czYvCGIN+zt4FWpHirdVG3orYg5E547pXHkuIzDEQ40R3URJPUztOpXPYNg4lM+0WlrMzLadR01DvQ9HW1GjSMCqd7j1lWAFiDJydK7gbOr5Jfn9S6LXWKJxACJ07tasR53Cz53x3JpWkgZ8jIUX55EOQmgn9u5yzWCqnPJazO+fMUdZhhm33lOUCaHgBNeyZAu4PW2W+2DHv1HjhvYIaQ9TnZEFSWzcsx8f1IAbV4LDfk1tl2Z1hMQ32VGoj5ySpV2bbCGIeQF/pncPxiDktEzYWs5OlyMM4KzWIdRQW20ulbgo7XOb5lhU+/XLjqhvUAZ1OHP98sg/XkmV3YrDSw6LXrA3dAaNWWYN2G9kMNWd6q8wpYeP7U9DtAAir3u/W6gi7BbxTo50p+8KnpCEMnV0iaPpYsgT4Byd5sfPKFKlVhESIsnGd8rIU1Mlty6h1sfm9cMuC1pqZ5nlIsB3VPh5E3XgOa6t/gp0Puk+ccby6YQrKXMBkm4JOSlHvWe1SDwz747ZtrEcFcx/XA/O8G0pcI3qpz1ptyhEJcZASkvX3wcKbD6zbyrJLTGm+VfOdnouTbTFv5WOrXGdkVSh82LvAFNOt0k4ZV6ccp/G+jX9u43+yWldq6be5EoYW2FPOSSlFyqwopjjQNKAb7Caj5tH20xthBL/ilLlivdJHlXqcPEtsxOSYPMyT49nVnnk+tRQ6luTYRakWJh8ovZKJI1fLCEkqrZo7x+uVeTcyZ0ZW1Ml+rtTPPqzbeiabNUJv7I831Ed7plfOZRtxgBOBhgtgO1mAqFhbteCkhRcuM3/82+/y4cOVT756h+UiEhbj5tgpa8OFTs8COC12Djd7wn7PvddfIU73cfff5E17xFe/9h0el5Gh4z0+KfbgrXeecRkaL35yJjqH+TIM9SKyWmvQRFzl7mhezxHN6MXTncO1iqsCIOlONoOBUbemNtDeG1vpWki8rEt5AEJTMCbf9Xfp7tYSWZua1aJXPpM3LcRbVyts18dM6EbumjXjpP/TmqOTR8EMgwTIOuf7WKZdHBbd0fJZOp2Kd+kW/DUn4P3UEOud1JW1SK3Se6NVKZFOtrStZWyOytEzFRhMSRbY0iGPTDq5Qo04RbZaaBT8JGtTqYXapQLrXeQIrahdrvdhn6603lVE0dqtGru7iHkpSGzMaJ7Bglsb77HeV2X+6WdVjhk3vqde1caWYsIGoKTa+2GHGkuXG8x878rL7PQREt1Z6yBgUGucc6cSBL3xJ6sdfVicUZGCj57QR/18V+Nb622EdCtT8KRIskFKxugIAZKHum0MvwWtVQ5t1ZnsdYCdVLslb/ScGcIHMJWV+Nuf9+n7hOAiKUZ2i2bf/f7ImqVeCZPad0XiMmxpuiDNoc8DKYxTiOwmxW+UqvtZmSmaVVuXrbR2RX/0ttFbxjXdb6Vr1vJOdjlGUVLtCocfn6jsXRLRsmbNLwLAReqLyDc9U03PU1SQ6lAPy444LYHaNnrrTDEQAjSTLb6b1JiYwA8bF0gbWbUMQsuZsQ17tTcny++wj5/uGN8HWeRGK9ogAUttt7lLMQR6K5jY4XEntaGaVN4RVflkAgmVSzP5RPCdYGqmC0NJWFojBil8S+kDENaMwYkotRE3cVK24wbRIWWfeC2pgRpi83wwzkLg3jITggPUEKYw+cIUAqXoeQjBkxbl0pqp0c6cwGxnH39f3dSB7RDB1a3hzfBkYvCsa2dbOxYKLhjdErkpMuOkhO0NiUWc8hDX7aBM0uZpTXlzLUAves7W3qlO7qFe5b7ZauFYMmXr+B7ZKqx546ZkbkZ7qrfAxZ2ZsIPD2lj3lTsh8MNv3ueLP/4Ky0uRr739Fv/oV/4nfv+Xf5nDH/0Wy/qAeD6Bu0P9v5h7s2bJsuu+77f2cE5m3ntr6HnA0AAIiAI0mBJJhS1RClKSJdH2izyEI/wFLD3YX0Gfxc92OBwOO2RrsiRTAyVYDBIEBwBNDA10N7qruqruvZl5zp788F8ni/YzHtQIRDSGrjvkOXuv9R9f/Sqv/PKv8+f+9n/Nr//aX+KvvvWQd08vsOefcLce6bGQ3Sp2mauGSnM89orhhCjeDpg9+8pM5QfBUNFDaQJRW/NZUqUFISSIgxBnRuNlKQyyp+Jq7M4WSwH4XhFiwCruMBnk0Mkh+rkSGEOOqzIcXG1cVP6B5ISpsIy/8++z5e3/+Of/+u/NOYsMs0Sed1y9/irTbqIVgUjn++c8/+xTPvnJB/zk/R/y0+//hOOLe+ZXHvH2u2/z+c+/wyuPH3G4umK/v2bKO1594032+/1F9kwbkndFSQ67afkdvYnqDDrQkwc1DquecA/n85G7u8/45OOPef+7P+DjH3+IWeTd997jy9/4E7z79luElDVwTgJQ+hikGPxL6wU0C8Q0y2s/kLytd6xWRusKYRyySjACH338Ee+//z5lXXjl1dfIOXM+nwGYd3vm/Y7D1bX7mgPLulDPK3TZcgZ4jtGgdnlxS6kcjyc+e/qUT376Mfe3d5zu73nx4jN2uwM5T6Q5EUImzTtCyoQ40QZidiSQu0jQcWAuZbG6F/l28jBzZ482EGmAqwzqxT87zdPF820uPTWMBYVKp64chMM8M+3FZg4HOSy5RLoO7u8WMVAhKVsH1fu21hmeX9NqZdRC7ZLNBnSIb/Jis8icYH+A124yNw+1IIeYXI7auT+f+OmPP6aUwlufe5tXXn/FQ499EI9qHAlIiUEViLes95LA9hOn+1ueP/2U1gpTHhwm47V95i99JfPXfinyxmvNWYNBHyuF4O0fSu1tHjQdQoJkWJNKTPq6Qg2NNVTul85adFDdnxuffPic3bzn5pVXWNfEB086TxZjd3XF45uHXO32niekoVvvjxgUhqTIV7vE9SwP/xwnpmQ82GVZghqeUSFf+dntLGpdFOAYHVyNSZLg/Twzz7IBlVJZ1qLMrboFXQrkPByuXRGiIO6UMh2F+W+2wtqb2OwuhnCaZ+b9ToycBbc4bRJ1D7/rjXUUeuksa+N8OlLWE3OePDhxC88b/hl7Nsww1loVqnxeWdYzYciStdvt9V66rSGlICm2X5x9C+tEKpctj+ju+Qt2+/2lrSHnzM4DxlMMaqnqegtHdUWMDcpp4bA/KMsiqcggOWsRzbxZS0NBa64w643bZ89gVB7dXEPITDFRVoVHL6UoGNKHtO297V3qg9Ya5/OZBw9uiFOWetHZOLqaZmrzUN2BWJzRqaaMqIDJeowvJK5+DdEHv6imnDYEEIqpkQVIfu/sA6AHtPqwiHkrp7+x23k8emP4YBdTJKTguTh4i4/nfQClLPrMfXCpo3E6n5lSxrLyMKIrZba8qOhqIgE2EB2cjDGqGjqoTW4rbtjUsFpq1PwRooAiDYEa1M6n80vbiwc8btkhZpI0bzJlv8ZEuAaBHGmeLy12oQ2mnNl0xjHpa+r3FnjZHgPr+UycwmXBCKZsjmmX9T1iCu6lU8qqkHy3rvQO+/1eSpkgX35Kk6JggoNQpeu8Ghq0DcnE1+NCSonkd5iucLUQmXHJ5YoO/PTW5PXv47IAha1VEC5KrM0Cib9zFtC9YB76OxQWG6MATovOxFojxUBb1XASI8Ta1MwzpAJRHH5lWU1LQRjKnRg6t/voVLdyp0mDf1srx7s7eh+sZbhCU5XZ85SYd2o0Skmf7boaLTQ6Q2rM2Ag90rtADUtgPWKm8OIYBfgNDwsWkCmgqbVGzJ0XH564fuUGu3oNY0UZSiKJhg1URxwZweX8wH5/z1yP/LN/+T676QFvv7tnZgdWqWejluH5WbOfmYn7J3dczws3b79N4IrpK3+Gz336e3z7w1vK6KQ8060QSJQU+cH7T3n3wY7Hb8E0TKpDbytqa4Ky6Psznd9jBI7HwkCqhuC5WRnFCsSg6moLMFqk9khtnWV0B4lk219bpXuDjlqYgKHzAxO4LFWh1A7KwAgEEtK0DUqBEQYRkX1tmGbBoeKWNnR/vwQIYJM2yuoo4rG37o1yAP2lunN7wR0g3JTnzUGhbsqVSaZF02JUDXodWJWyYIzGPCUsDFkvuiyAqhrHM8ESZS20Bqnr3GpDdHeKUeefW/Y6Wq5j3Oq0G610tfoOAe4xTdiItFEv2W/JixLCkBU4TZNIGIO6FH/2ZEdfaiVPs3LhkL1Z+X6NXguxK49Jzl+pU/LOVZwhKpuUSBtQWnW7rd8LiDyQvWlgrhoNQzP8CALgyYnWB1NSLs5geLB4R1tYJ1tgzomI2iOTty4JYJFVe6D7tA/lcuYRCSaiWaStlwu1JnWJKxpjEEjUMHYp++ffVKpTFENRFsVNtFKoa6Uj++UuZIJF9vtZs5xn4O3nSYpaV7RKTem2LtAMMLoAmjEccFbzmjeSEIrIwTBgGY1h3pZoTnLQyJMKMmIIslniLYKj+e8eJlOhgvmS3D3TLbhVLUaBILXJajYuNr/AsnaGZ4q54Iit3GU7y3OMzNMk5eNamC25dVaft1m8qPpGl3odV4TEGC7h48MGtS2+MybfhQIxJaboKsTh2WxD97wU0s2FT2q4U7OzntfkAc/ms0MIQ+DYQLvR5bBwQNkgJJGMw2TlLE0q5dMoYIPRdOf4tkTtVcrZnOnd6FXPGkGqw0anh4B143RSZuU0JRdcyM5J9yzTARHNavrOZLNMcYYRsTBe5kUysdZG6UatkVIrpa6stasVbt1UWMbad1Ke1qLzpweWZeXUCufSWdbAuQ1Oa+fUFp4vheNpsCyN5+fGqa2cTwvlqEbMchzUAvfnW2qFnAtpmqltcPfkSLldePPRjq997XV+4U+/Q34bvvvB9/nHf///5N/9L/87n/ybf8r+/kPCNBjhIbzyHq/80t/gF/7T/5K/9pf/An/xnUd8oRby7Ynj+cQyKmqsDiTT51z76ta0majHwa2mWwu7nEbbHGuhw1hpQwKT0QNQCaZc55iTg1BbjqYy4wRv+5/vbggbspkGD8tP5q26Xu4TtzvEkE2+grn1NUTFw5jJITLo/N3/9r/79xdQ+kf/4B/9vT6K3v6QODx8rMvjeOSzz37Khx+8z4/e/yM+/MFPeP70ljTtePPz7/Lml97hlVdf4SrvudpfMe32CpnLE4erB+wfv8IICiATmooC3EZjRPMwK3lGQ5DFYgAjdkYv1DI4nu558uRTPvjhD/jg+z/m9u7Ew9de5Stf+zLvfvGLHK5uSCRC04u/efEVjBcuknbV0W+eWxnQ9jnTyuq5DYPlvBBy5v54x/l05tnTT1lL4dHDhzx+5VUOV9ekeUfKE9nZ/1KrmgNak9LCXgaftiaFSqlSaNQxOC8n7u9uefLpp9zf37svfubBo8fs9leXzIg4XSm3CCN6gHetw2WnL3+OLeVeF7YWQGxr+9ASaiGKDQqmFpVaGK0w55n9fk/eFhogekX4MlTrTG2kYMzRiFP2lpet7lYBg21A9cDvOE/s98ryiajufZcmhbLSWZcGrSmjJCjUNXqbn1lgSpFXriNvvpp4/eEVEQ26ycMVl1L56U8/4e75kVdff50333yDmLyS1jQYjabWldo6waomKQKFyhgry/0LTref0s6FkAeHEHh9N/EfvD3xl79qvPf64Hy85f6zlfW+cvfsyP2LE+cX99y+OPLixZHj/crtepI9cS2cW2XtjXNplKVx3xeOa2E9D+rJOC4LT5+eWU9w8/prxOmK29vGx7eFPs08frBjng4uDR4+0ERWBFpc7ZIHyXV2OUBv2AhqtUjGbpq8PQ3Oq8DLRmftaug4r0WhvllKBzUrqo1tnjJzlqz5eD6zllXqog50PWvTNLObD2KNokI/tXCqbSqp7sYZXF3ArXfm/V7tX92ryxHbMvzgxTq9asiv68qolXVZOXujVsoKMkw+5ARXI0SfVsoiEKn2xnlZBLpGY97vfTjRYh681WYgGfa21NoGEvTBuizcv7hlniZGMP+9TEzTrJBwb61SxqyWmVYrnc55OStLKs3EHJiCN8FF1RQnhVRQe6WUyvm8UoeAil4Kb7z5DjHtmNPE+bQoY6xvFdYKBKUONbOxNRp17u/uORwO7OdZw42DDEoYQZYzZ/XGGARn75Z1pZZVQfvBteVdw3JMnjG02Zxcqh2GFF61Swkw73YCzzbLGQ4cjM5Q6J1AMGQfK1VV13VdNYy5LD34UqP9TJ9Fa0WXsckSPMZgWVd679xcX1++t83GHIOGT+UX6YwCLtLzFPTMzXlW+8sYF3UTm8ogJWdV9czk6NkmwOn+nhylErgAQ1E5RGlKEJUPgQV/BzZPfqOUlaura/zRlKcfWR47TqD6YtWHBvTtLiznMzcPBOLGJCvybvLQVgdlFWkkwK03XziHxtdpmskeOJxixmK/3H+9VX9StFz1VmlNqqPj/emi7kk5wuiXTCTZGobUWf9/xZHBFqCcPeCcYEwpu4JJg7UwR4XbBs83GK6aaK0rHN0VedHUwhODzptNbVZHp49As+HLQ/WMPKnv6FIydNzmUJV7ZE6w1HVlLfqdraeK5ci6NHJU7kepAo70eHdGVCVwKwpNTm4TwKBZhXuFzKaAVL4pELOW8TFMrVwEQnZryxzIfUfYGeXjM7u3rgiWlVHUDdA9S28YRUuQgVmFaDx6sPLJDz7lt7/9E9586zWuHnRSnampUNasMGsbZA8iPrXO8tPPePyoMb/+Lj1c8/gbX+PmD77JH7xQ5mLIs+73acfT0x1Pf3LLWw/3XD+eBJgNcyWbrK09wmQCutrwGvAmAMdQtpKsxiYWfQy3xA9GM2XTIMtIH8mtuNt5jRp2YrqAxGpKcoJq6O/7aA5BD+KIjLb6RF+xodyu2iqlduWpDd2Pg+h3WXSiMV3KAfQ9bN+PQAm9rJuCkwtRN9jUZzr/aqmkoXG6DtcPmHIEB2qLc1aPKUdyRK1bbficqkavFJIyb3J2VYqyYFuXnbA01LxM19mtZHxfiHVHCJ6XjQ3cWu55NDkmqZNR5lHOiWmaqKWyLIuiLULyXK8hl0GS/U/lLjqrRn/Jxnv0EKVJ/RstkmPWvB8SI7haFOWFSBmMVHkpCiSJgexgpJTH0JHSoNkgD7101VVDUpb6F26Kq4gMYhCoaiP4kqfMvtYa61ocwPJm6NJZlk63SDORlzp/dIGOrsVTYcrKrorZl/vR/OwRkWdIbVP70LzdNCv3IfVdCsoaVa5hIrnVcq2Nsg5vUNVDOLo2X/O7ZmwNPOazSFeW1XkpUtJ6g+JoXdlRMcm+58B0duIkhCCwywHMFANbS+7oyiKyaBfSY/izHjFaXTUbkrAgwqa07vk85iolgavbn2AOVsWk+7LWpmerKV/KhuctiTVk9O6qpOSfnEi54PNAJ/jfe6bocLIjBMd63ALX9T2EoNY3gWnQeqUjMqcOqZ229q/WVMIiZSLgmZIK99bR7PUJCo7PE2yB/75rdjOSz8PBguahoTs/E5mCN9qOQG2NlNSufl5X6FD6Zr8LNDrTpHOstaasqa2p0sPxGeJYWysokScItC1d55p1z+3SLNj956o9slZjrbAWv0/rRGuRpZ2J23uJhpdpzmAzgYwNo1YvZOiykDdU9DNWONugtZVn5zNrrdorqlG6sSyD83kB6+yvJ64fXemcq4OyGte7zFe//Drf+MYbvPaVR/z09BH/9l/8a775v/3PPP2t32A8+QGjHWnsSa++x+u/+Df4U3/rb/Orv/Yf8itffJMvxsB8vsPKSkN22GRSrW7h+yEngZSm9tUwVHxA0O84ponmMRBSaDbqEIk1RtWz0qWSG9F3cZ87zUPZhis4YVwUZpiRgmFxeDyNrKXGSwA4Oemu1jgHNC1Ar9iAv/t3/j0GlP7hP/rHf8+y5PNrXXj26RM+/tGP+Ml3/pAffu97PPnkCVjk8Vuv8dq77/DGm2/w8MGVmtowcp5JeacK+N2OvNsRcoasLIkhfZ0Ol969YlPhtwq9dLVQEMuwnk88ffYZH3/0MT/50Yd8+ukzUpp5593P8YUvvcdrb7zOlHcaOErFQveMn4ZFI2UtcMEbaPQZOlqZEtOkljTzDKLz6U6BuuuZJ8+ecnf3gtPxxNXVgQcPHnFzc0PIqoaMWZdw60KikzPNsnoNRzsTqwf6TjkLCFnPPPvsKU+ffMLd3d0lDDnOmZj1fYakusoRAp1ISJLR1uYZH466M7TMpKxaTy2uRpp14erAbS7jMx94cEahklPkcNizP+wkZ3VmTjLwLk93r4QwOOwSc9b300bajFcXqfpxESuec2De75h3yhnYkVVVmsT0lbVQvaEvTzq4J+RxHiY2+cE+8ubDiTceTVzvovIAeiMN435ZeP70GS+efcb+5op33nqLw26HIfniFqTYavNL2MDBzN7gVBfq8cT9i0/o5yMWO3OCmwxfeAy/8qcmfvEXEtePK50Foi5jtS5Idh2AESpxn3QgTJHcjBQHPQ0xM2iQaK1J+twTT1+c+dHH9xwOr3H9xg2ntXG8T9wug0ePDjy4yj4A47XAnTJwGaVUNGuVZWOK5nbHHV3ECHmS8ux0HhxXKMp5o2yquK6aXrVaBWKKTLvEbo5c72d6XVlrYV26g5bbkq0FYTcfFGqJLKLTnLxhMfqc6ks0CjhfW6e14UG+gV6bDm3E/DpsrGcOAcm9bSBH5f58hBzZHw7sZgEA4QIOmSufpGqspdBq5+50Imf51/e7/SXAzoICn1XJrq8jcETr2ggwWqesK8+ffca82zGCMc0T05SZdzvMW0QsBPmYh8uGQccAACAASURBVNQYvTYqsv4c5pnp5oBFY3YQLASxhzaUPbZWMeutFv8z4HQ+8tabb9K2YMi2YqAWoOS2NRtS2W32ri4w6Xg8knNmnmcPpNUwFUxS8RhcDbAtSXiFcuuUUpVhltLFegVaWHPSGWMOWut56J7TJilx8H92q0ndauLNB0nYFED9klswRqcVARZx0nKyMeoCIRrJ3OJkRnDgfLOllaWwm3bMhz3Z2cuxPUoYpZTLZxnjyzawDQBlDGKelenh4Aa8tPhdwBEcNMKNtQZ1Wdnvd2CS3teuP5cwIOmcVQMVYkN9J1hLodXK/rBjYCSvC94UUgxfVNy+GIaWz+ah0M2BypwT5md9Sh7EbeHCGm9g4pwn8qThZFkW9vuDbNCejTa6hykPE7s8OgNZRxgKiyUkjstC8OdvylKrijHXcGm8XABsoOHZl2yBN5WU1Rql8EqF32LmR53+uT66dzjo8zT/3Wz3WjAYUcxtTgItOt4iNJSPgSnTCG86CwyxsF2Lxzaw9bEt4x2KmuXKKkiv9uE11GocynlToMhalaMRUmAENR9ZSJeFwqJUeMu5ECwR99HVI6q37j7xaxEIqhEnYdYgZnY3M8fbZ4Q6yDcHbES3xWQBEBRGv8M4YuMMo2HtSJgWHubGN3/zj3j+2eDdzz9gdxUIA/IE0CiViw2lGzy7b4yntzx+e2J38yYjvsYX3nvE+du/w/eOC20Mqjdl5Tjz0+dHbn965t1XJ64eZM1NSJllnr+IRZIOd6Yo64TUZQFrsob2TWHg6jVZu7rbv/zs8d9JsEGOEzENwQmmEGYzKVm6W8iag8ljqIGsjUU2PFffabn05zV0tTKNTs4QLIvB/2Nn3EDnznbGYh6QSnf76nDFxXBFg8AUqRy5EJdjdJqHtkdXHTpuRrfh77kWjclDjGt18BzP/zEpq6PpR8jBKEOqkDgCdD3najjc7lWd0abjWguJyV6VcybFCaw4Iy4QIfhCoxyZ7bwq1HJWoPOQbWcMI08TaiJWvtMGqoUY/TywC6jczcPca6OVhnVUxY4UFdHGpZW5D9nOW1NJRPIcEWVB6eGQ/VkAa2lbxpEDWsMYDuRHc5WrW5ZByoIA/p91bofkgNXojNJpTbaV7nNMDH4HeBW5mc71wcBaU7W6q54iBtZ8vhhSmVqQ4qeLOLemnLkU4ktAHaS6rM3vOD8/g6tFe/fMXlc9uDpOzXXt8i7YQAre3imrsriitwp3V7Yqk03gxRhDLXvDPOxdZ90w81Byt6qbfocxRM0xfnZ3z+jaWqqDg6A5TS5KcMWG17ArYkJAWs5uravavWqpbudS3Tt9EHonBM0h06SGue6WZ0ORIWoUjRcVrCHV9whcfh5pC3Q2KxOsuU1W4BnRuAQxB2WqWVRG7+iDGBLBmitOBdjoc/M2265tSKUzARt2UfKGARYyh/2OfcxSdw7PB9NTSBtSu7deL8ILKVeCZ/VACJmcIrtZRTo0f+/1QLiSDAeXNrWf1EA2wLZQfYw6Kg0pOc3vzd4dtO3mRUmZU0mcWqH3M6desa7IDEag1MFSjNYyd6czay0s5czx2DieCrfHhbtj5+545MWpcX+/ciwL98vKuVSOS+P+vPDstHBeC8u5cjxVzveF02mlVxU2LHQ+e/oZpxeVxMoXXjvwta+/xjvvXfPi/JTf/61v8q3/6x/y0Td/g/MH32W5/4xSB+Hhuzz+hV/l6//xf8Zf+ZU/zy+/8yrv7ScOvTBb8xzTVUKL0YkmpZvRvdVYOIX5/GpmTEnW1WFGDJMEJF25gh3l17Va6FXWaY3heg+G53R1l2ob3t7ZN8t/8JwzXRBSpm9PSCAmPVPJyc7NM/6zVCiln9UftP11aoV2+4JPnzzlxYtnvHh+y7oW5hi5eviQN155xPWDG/YxU6sapQy1c+DNJ1tAyJR3hEkZDKf7W2LY8nz0e9rsAOaH1TBodFpbub99zovnL7h9cc/93YkQIg8e3fDu669yfXVN9mG8lXIZ/qVa0ItTR1YjUlkVsD1rTTK2D1ADSfXKwFNZWUvlePec5XjGgpQNr771BjdXjzALnEsXUz76hf0spV0G3qUWmbCGBo8YMufFG3oaPHv+lPv7I/fHe2qrXO32zFcH2IJ2g9D73gfDl20L+r2u6+KHl07M4cNwCL7I2zbz2kWGPVrHQsaGEg9qWcUwtUYMkcPVgSlncg7exFTlge/dTyBlt8QpEXJQHsZI5KAlupXO0lZGa5RzYYSoC2HIo15iw1rHRqTQ3XsrK4NNkTyysm9MVrIUBlczPJgjj/aJw2yEULUMtMZyPHGqR+7OZx5eXfPOO2/5D65FoIFCzfpgqyndpNG9dsa6cjovnE8vsLKSpkjYNQKdt6/h629Hvva5wPV84v6HK+dVQbGlVMoKra6eHxVpvVI5UnqCOtN6wcYEobI2PG+osw6FMz69b3x2d+RHH1euHz0mXM188uJEbDMjBK4fyx63LFp+GsgGMCJL0/vUR1crlGnx7qbLZp9hBNkYlwrrWih10HpQ1kf3SwfJ4Ls/QylJBRaiwgHXtbAUKENAwBiy0CjQMHPzYM+UJ188nWmO22LpF5jbEbbFuA1ZrVLUchNzkj3HB+vWm4O8Yvs7knqX3ohtsN/t2O13bO0x25A//O971zuuuhr0DqZASlmZMXELtHNGy0PypJZB6XhmLmU3eq3c3d+zv74CC8zzRJoiU94Rk0BUWseaFEk0NUVWB4jm3czucAWmjJ6BD7jGJXy7t35hc9VSA/e3tzx69BhClP2zN5pFkityoocmDpq+dhNS2HrneH+EPjwrziijE10iy2azCOZDlsAcc5CjtKaQ5qyspjC2AcUVPU2X65bp00aTHbBWEISr3JqxqXT0NTdJbrSNRZW/f2zWnaFK5WmaiFltnRdVzgCLUmVtz1G0CF25DEuvtN6Yrw+udBOAiz9HUitwIQ0uo2QQmNCGZORLLZ5Z1F35gavUdFMAbhmB5o0+dV3VsEL3M7dfLIQ2IsHPHw32zf97PSu1NlKaiWFCjBVstO/G+mpZ1TgR5GRQcH1v9FG5u3tOjCaAIkZG1/kt4rCr1aiDNS3zEamEVaEuiT+WpSxDn5lihiKDCkHPlQUj9OCg6ATWLwxxCAosT1vovjNxrenzcZhGC1H0z8f/JdfAywwh/cz+63ZwwTDKUNZI9xpvr5bQs1grxYxgykwqdXjwfyeETjSVBlgczCFxrsYa9GDFES4DXB/Qi845SxN1rH62LvQQmBKUc6VPMyEZa2kcbxcyiV2EFDM9ovwYryOmQWQmzCvraWW6yQIOwtB75zXfMQQsDgfVAsoYSFDh8ObM+eMn5FeuSFePgQhjQSB9gLFlO1Y9t70QiLz11c/x13715/mf/tff5Y1vPeCX/sIjUg4cYiCRCaFx+0LvsIVAOgz+6MOnPP5Xv8mX/8YbpOs/Tf/8n+fX//ZH3P6P/5g/OEc+LGrnIgZ2D1/lD1/c80/+1XP+Voq8/s6moIKUoY8d7dwYk5FDI3WpKZPBigb1tVYYapONOTJHzSzWFTA6KvQcORfliPXW2cdBiE33WVOOiAXZvFJUBkhOmpFCGPTQ6cNofXG7kFR1ZlHqjC5rUu2VUANThhg1o+k9kz3cgtLepLBovjQPQMH3ILBJQLpUnJuq2PwIUUuxEyVVgO2IaoiLJpLVYgBv/y2uKhzSoWqVMIGxPahCfJomyJFlWRllYNmwUBXg2rb3REqI6AUcShjTglJXtRqF2JijSnJ61AITo8mSt1asNoKpfIFa6aMSwkTKBxjNCQEF0OJqWQNlVzX9jN1EHI8mC2yh0vqkgOmgpr0piHGvTef+5O1c9EYvQ+HZ0chJ9qeKxtNaGnV04tDuYCE4caSfN41O8lMGc4KRDUgKmv97k/15mmA11r7qfSaACRRSPm5w5Yc5MA6h6TNtvRHaoOVGwJjMoOGWRwEfAcOmCWud2tRGPdmgZ9lvgxmd4kHY+nmlopSVZvTq//bf69gUOFIrxSD1ZRtQGTRFYUFQi9hwu5+5QrKP4C1y3QkE9C4zqL0IMIjKd+lEt+cGv0McPDc/6Z30EMnl6sNYXBHsuXrIwgpa5McwKUeHSknoAqF62Bb45jZZ5V8O07RxKdvoes57d/VdiBc7saFmPXOwSBZcu/zzOMCt2AEYZNba9NkiwnA0WZuoAvT2O2NEY7lv9BGdAnWADQkIShd5El28IFWmQuINnf37mCh1JY+gzKsLC6YsqNqBXkWgJNk0d1G/02EDi52cjXLqLz/3NrzQSp9R8LtNirlOnrgQViGYwCiizjLTzxeGMpIwZe51n8tbVCppq4G9RZ5b5CbtOI9GrjDNs3oNDSnglE4tG1hQHnELld0w7mJndCPHyv0SyPlMNrVzZye5chykIKHDLp2ZMuz3gav9zO6hlFytGM8/vSVa5yuff8SXvvyY06nywcfP+Le/8/fpv/kPeHTzGu995es8+vqf5Y3P/TwP3/0c7/4X/z1f+M8Lf+uj7/LRt36T7/ze+3zn/W/z/Y9+wo+fnfj0eM99HSyu6ErEy5xgoaukAAlfZkuaMaas578U/30PelU5QwheQIKBSb2XHeyLIft7qOwk/Cmo7q7azqkxPAqga76z4GB40zn7/ynL+Rn89TMHlL7zrd/j+OI5x2UlxsD+6oo33n2XRzc35KzEeouG1YYNsdwhRCGyMUPoJEf+yxhEZ3zaUmisjswp0T46atcQ63s8n7m7veP2xTPu7m4ZNnG4vuadL73K4eqGXZLNQEyS8we9i2FJYje2qsjo8rIpSkrZa3OWu0vea4GlrNy+eMb98UhrXVXk+wOvv/YaMU+klCEitUYvknS25oeAswTRxCq4KkmqiU6lc3s+cjqdOZ8XTscjx+O9Dok0Me20ECgoUZ7ZDRkPLkccnlUiEEwe9OHLmcIO8wVxxpmH7aCzLpa5tQp90DyQPEQ1Yh0OV1oU0LIzRr9Y5wQAaJnKFjmykQ0mYOi8ciwVRVg0sd6TBs/Wui+vTYGzQ8xZ6ZWdTcwx0IKRgy58E1HNLg0eXmV2u0QKDaNxrkLq11Pj9v4FfW0cHt7whTdeYcrb5wrDnE3qntHibFMfWvyXulJPZ+x0z9rOzFkob28nXtsFvvRW5k9/bsfbjyJ9FEqJCiUNg2aGpQGtUs5GlSZXgEo8qJr1In0umOkSqK6+m9LET+/u+dFHlefFuHrwGBuNz16sl8bAZp1SMqs1ytqpLdFIl2dqA4MUUO+BjkGKgbw1MlhgrYHTqnyoigaateqfTyk44DrcZ23MMTFnhX2eloXWoVsU6j46jeYWlYlp2inPK0dCSC5z7u4lD3rmyzaQCGQhiMkMSOadXH3X0HKFD0cYrK1TR1PI9PHMGJ3dNDPvZpcOeyOGs4961irrumi5Rb+TUho3VwceXN/QEJPtaM4F7dewqGV9dBy4Ncq5sCxnDrs9BAUVW4zM006LgauhNkCodYUs1yFLZU6Zq8OV1CKi2DCDdRvY3M6zMbdqyYTj6cw8TeymmdqHBzyK0VO9chTbRid0z2/wc2hdF9ZS2B/2WkzCSzuf4WBUcEsQaq6jdVcMyYM9Zz0HJp3+BVgJf9yyW4uzhjpLW6tiBV3lZp7fQdhYQAE6BAO3IWwNSm0o4JMx5PuK6J2y4bOVQG3rw/885UBtnvRaCjFFRpA1E2d6LChfow/PSYoJyxl6k9Q7GnEE5THl7EvhS2JDQbzjYqHb2PvqKsdgUtgF86yvaFLcKs5An2vTcLypfcbYrA6Vup7Z3dzILjka1S10W3V5CMlVDsrF0qzZkEjMlaWl6nxHIGU2VdoKiBFIsQ3dtRZKLa6QkmIhhqShP2SBkybyo8dBH9E/e2ey9ZowZQcGXJr/x5tlt4Y6/w+XOulggT6aZgO3Xge3xzG2hkJ9r6svycECzdyas1liR7zYjjZFQDARICkpmBvLGpaTlAPdn0DrWhpSGNSqWUErpYoaomWqwQiNOStThi41kZQDmfPtQhgOAA21uZ3qgHWQp0asesaLdQcnNkbcONaF+CKzmzM9ugWkuVImyRbcSqePSo6BNIxuZ3K+ol09Z/ngM/gihLgjpAksYWF2UL3AKAjIN2y6YX4Mf/bX3uPb3/oR/+I3f8zjVya++uUbVX7nwBVZ+XQvlBsXzOj7xu/+7kdcvfHPeOsvXJPCVzj8wq/xX4UD/+5f/gb/w7/5Mct0oAJxzixt4g8/PPFLnzbeeW9mXbXk1t615JVKLassIzEwWyAgVXIZUGPwQHQuSouYZ6Yp0HuhlE7vkRZlAV1rYT9Fgmm5hsFSu+rag9tMzbBcsTDpbAhe2WwGlhndiKPTCozhChprNALnondsnrUstN7Y8mowlX0Y1e+LRB3org+RLcBfKIpALCkT7GKPjTEyipq1Sm/U7qpMf28dnpJ9NrtisVc105l+7gjUoQa0EIcs2GkihsHZpDTXOS2lhgXdkzbAsqtwmgLLW+vEuWNn2dirqdXYhqrdo0VlQ5pyQ0I3RsxUqrITG4xYqWctUJMD/wwRXvgsqplF57VaeKUY0Q7daIuACHPCL256DUfiQpBFae0F6iDNE3OIWI7E1qHofpm7ZpVhwZcst7a5XbJ1nQajadkbAdbeiUQsRcKirzlaZxCxPOmM74KBYjbmSTlKy6r8S4uRECdZOFPn2AvNYB7Gpi8YwTwHSZdD70ZIRsiBnHfs5wPBdB7VMjidmpMC8ULQxZigiXiwFBUH4c2z3XNUQpQijaBZoXcNf9GVe5vlurauzzfISrPWdvm+cNBgeNZeHZVojTIaoXPJRhTJlARmD73vwaY/1hI46ATSpIysuhRXUhjRlVZ0B3paoy8NG2qgrnTlqy2DtM8ivIKsY2U4MW1SaPXWXVWorLLe1CqqO8gu5wpDs3Kle2aNbIDdyYraxmVmyebWsZSVidUcHg2BFAR8iVzlYt8bbNK47bwZImsZFwVXiEbqGvT6WjiZ8tH6lmc1lNUowKcJ6G6yR0kk4UqwNki7iEW5DEJWC7LyNwd9hIs9MtDBtvk7wSgisUwGNxuBVn3P9AD8RveiEO1uw1tQQ9TMEG1PtyY73N3CnIwYZw4MoChKNqjsRu+P8utggQSnWIlOrOwtcRcrKVdsrOxSYor6rGKGOQeu4sxxUiTGoQZuzytznJh3xnWYubpJhCZHQDl3kg2++Mae99684n6Fpy/u+N4P/znlD/9vrndv8PrbX+TBl7/BW5/7Kq9+6Uu89Vf/G979mzN/+ekP+ez9b/PBH3yX7373d/mj77/P9z74iB89f8FthxJ1jwW3LvbWPQqm0lHBCSgLzR9AJyn13jXllYBV5GFQoY6FgaUJY3jZjWaqTmeMKgKVJCBR8nG972Z617adZpP0/oz++pkDSrfPnjHNM++89hoPHj1kPhxIux1pAEXKFOuDUs8s5xOdwH7ey3fb/cZI8vvFqDp1amfU6kNa8FBZrT3n04lnz5/x/MkzyvkseeF+zytvvsujx4+YZoEmypNwKb69/CUG9xuabgXWgrIeYiAELr7j0QdTTqx15Xh/x93dHefzGTO4urri+vqG/eGaGPGgW1Q5uwqEGZ7rM6ie0TAuAcUbyANwPJ85n0+czwv3x3sFOfdOaY3D1RXd25rihdVJWlyDL0wOOMpKMi47cE7RwwZ9CAsmhsEPzs62nG/NBkMMT0cBiSZlyLQ/ME2yKHVXJ/SBLncTqh6jgLKBAtowLTrrurKulXZe9YBnBZ7rEHc5K7LT1VVAmMVAnBMp7chRC0lmMAdVuB5yICfYpSQbQysspXLuYtKWpegynicevn7N1fWeMAaldP+6mzJmSEKPs9Oj0epCOy+U8x11PTHnxNUugxVu6Lz92sTX3jS+/NYgpDPHF1BOjbVXtyN1/by1MFpgaYPSC6NHSm3YMJY2WKwSqz7/NqJ/jo3zUjitlfsKjx7tuRmZc1Fl+tX1TK0qVF5HYFTj3IPURCi0TUPa8BmtOxCEhr44NGij4Me1DdbWKEMMXumqTh4EJlefWFAw+ZwiU9blUYYaYJpEXnQKwiwa8zwxz3tlebCFJOISYx943CC1NOjocq99ME8zaXs+MKHpSHmhodJZXDwkf8jLvZxPWKsCPKdZnm1zkMTCBWyQFL/QSrksqp3BowcP2O9nhfui7zUM2ZXw88ccmOijX/77WgqndWGKWaGHQUGMqkqeNEj05iBSc/uuALBaKjFNHK72l6GZLd8MV/3gF4ZfPCF0Rg+clzOMwWF34JL7xvBhU3W61oPHqLy0itVSaE1NizlnSXQdRDe3tMSxsWeN5udT6z44NoFCKQRykq1lDOio7laJgMqao41LK51+KrsAwXmnkGflWelrbNEO/gDrM9vOpq5Q01YUZBiSlgrbpL4D/W6HS/2HgggVvLmxxp3pShlew0wqhBjwyGiWsnhrnLO96WWj0eiD0pvURM1zjjZwzbSERB8GY5TVAjRI5JRciZWkAkPNmHljrxEbvxEMHbG2tRVaU52xMkeKlEjO3DoFIGVnbRd5tblKVKylwmb9mqNH88vfh/kRoUk9uzGeWpwjw+uFY9K9YyFdyItxGX79e8A/D/ClrJOygEjz93cDBgfNF2i3RW6fj3v9BwptVwXzYES9oxajsoeS7rlh/jk642tunQsIMOjDc3bM70ULsrcPY10r+0NkWRbqkEWhR7G/Fjs2yTJhMVMbrLax6wYhkcNgDoFpEujSqyxMtRUsdsJNoi9deTwYPUaWZuRV92zsDULFSIRdYBezL3cTcSqc7k/sD7PKPhKk1omxYSnp/RwKuw3dyGEQwsSgMj284v6j59z99iekw0SadsRZ7ZghBTZXSMCwnGhU+nEh1cFXv/463/zB9/nn/+bHPLr5Od54XfksUzKu9p1WE+PYOa0C525j47d/43vsH/5THn/jgE3vcvgzf51fzmf+xW9+l9/uM6MbY46EGDjVwAdPFr7wU4hWGMUEtFQpeetyJu5WpmlPNjHlV2acRsRKx5JORnpX/8pQ9l9LmZyU12MRYoUaOjlUdhM+Pw5WjNIaS1P2UjfPX7LoS3nDIk7INJpNhGHsgKUZdSj7bgzNcWuVSjoFI7iqRO11sv4Hk1VpWBOj74oTwrZC625Vc5XuKxFaal8NUQqRacrEHimlKKNKvnkHWbUsxBCoZiylQO9uI68QM8qtCYymfJQNZC5VFuoM5GmzQbmFa3j5gGlBT2kQSiUdMqUVzzKphD7oKXjNNhcVcDfzGVP5Q61VxirSWOp4gQ2t+bPoBGfarH1+B1hOIjS7wtzV/gg2lHG2zbUpJm+bdHDJNC8s6+pqZydea78Qqx2BZyEMKXBcdSFrYfCzSX9cG2rdWlsjm9QDfahNa7Tus76AwTlGXnm8YzcHSu08v1t4dr8KxLHB1W6m1aKdwwaBKMLIrY8qVAhuweveOhqYdxP768zoK8ta6MeV0gS8RWv+LOMzl5773g1ljEZCVItnbSs2NJtvvyuzSEgCKRsi2AkC961KvRN2iRQ6tfrZjQhJcyAiuIo49g5dqu66rgwz5gyhQkhSRwyFMkkpswGK/jy0rsydzSJqvgBf9hP//7eh1uHgM04t6x+LNdDPnlKmDoEVytMalxy+1ooIC2SH7WNgI9BbkVLOttnCCedhBByI7CJEgqtLrHcB1U5cDv//nM7d4yvwgHNe5hD53LTNoYaeo9BNyivPrV3KwmKeJDU2rG14aY2PA0FqUu1xkEiclyOWdDeGlFh7xVKWLGi4Urp79qIrpoPJMtrrcFtfI0TB1xbUYNfRvFlrc7VSV6uzn3lSmns7riUgkUxkee0zNRj9DGay3i9lEDzSIAUjB53BoRjWBlMcdKqal62Rit7ZpQ4mzy9KU2e3Qk2N2CN5bRzPantOoZCmzhxuibeJqxSZJjWQt9gpiyyYichrNzOvPpqptXJ7esKHH3/M93/4TX5/HLh67Q1effcbvP3ez/POz32Jh3/yP+LVX/ybfOPuU+4/+B4//v3f57u/9+/4zu9/i+/86Mf84MU9n/WZGqNaBN1lVXshhEQOgbZZ2waMEAWebbzbGFQEwCo43qgsTP3gkHvS7yMkfZYbB87QnTNgsCnMdJ5t58JLau9n89fPHFD64le/wmG3I3jDQ/cfDISeNVzO332wpVJbUS3jNBOygpC3hY2qdgSL5jwz1LLy4sULnn32Gbcvbine/nT14BFXDx8ofydn99viJJAY2+hs6DD323f/RoYrJzDyTk1X5/MZG4FpN7GcC08+e8LxeGJdz+SUub6+4XC157Dfk3JS88bYLtBxkTc7KUVp9aV8f5P5uyVlWRbu7l5wd39kLQu1DXbTjpgirRemPCnXqGuphUBICrCs3Rcvw0EhXcyYEO4QkwJ4lcIpn/qWRTC2F5/L06VlsUBrTPOemNSkECyRZm/i6ZuqZ2OFIEYtHMGzDPq6Kr+nVM7rmT4KvTbHB7K4u17pSBGmumq1xeWkNoE4yXcaBh5ECnk0rnLi4WFmytCqhsh1HZSycHt3y3Je2U/X7Pc79lczuMKmluooLfqBmxY+cOWDXyjrcuJ09xyGkPHrKzX27Ufg8w8jP/+FwZsPAwcbrPf3LEWsW106pyLLWKmVViTZrU2MxGKFUIxChZ45bblKFYzGUs/U1jmtZ86nrlr53TVTM1qZ2e8CaYKlJkiDunZ63EkdNPR0RWe6qismor0MJMzZJJEcGmzX0pAIrfsSGxSK2DoEZf9M0fmUYExTYjdlRusspQqY66rpLkOASSeQ4kROO1QNq8tyqwmXJdJzU3pnLfL8L+sCA66ub5hz8oF2aEizAN42goMRZkg50xu1Nc7nBQP2h50AL88tGH4Jt66shdY7tXlQri/hdQxvlZz8/dBQGDc5aX8ZvLzp+ETeDZbzSq+FPCkDIWblUOV50rA79Fxv0v2t7WX0sCsXKwAAIABJREFUTlsqMQR2+x1hBEbV4owvGrRNUrzljIwLWNKqwJX9bn+R628qaEPnkGYwX3SGg1luqaqlEiIC3XUoEkyfczOdXFJNqmK79+Lgg9tau+qXNzB8MFw1KivUIHiIoD734K9cd2A/50z2Nj9cGqwVa1t2tQDVIOB/XFQ3xdm3eMlpGCHI094Hzc/fzZpsmBSDKWK9E8KJw2H/chD0O2oDtGKIbmnAA30DeA5K7ZIupwHmZxYhXFgogSmyfoQg6bMu8cYWOI3Jgmvj8mFJVu/DQOtd1u3eGb16WUPzEM1EX4uHfm9DgQZehVDXDbJTHpFbxjRHDIVi+2cSLV4G7+HLY2n98rMPB3lal11tSrOKHlLyD9MzFwiX90q/TF0kwwH7EGEsyhWMnr02kEVAYdze2On5MQpH75dsDdlSi3KUgLTdoa4qyxtBZFzylUKS1TCGTlkX+joYUfkd3cGtUsX+11oIoaHXqWAbYzgGob1UNRIESlsS2dSH6nujV4xjnRCbW1JMtccJDldJysyoWuAp6hwOMZLGoJlCwvMEW1RPmhN7m2hjpZ8LdZ4JrWNB76AtQyrgHLEgu21LUo7RKmMY6Qae/OgZZcB8deDqZmJ/E8lzJE0zcZo8Z3HCemTUBVrnjbdf4efee8b3Prjjt779KX/xzz/m6uqgwOzRud5XapfK9FgqKQY+PJ/5f/7ht/grr7xN+uLrYDvS136F/+Qv/hYf/MYPeBImSu3kPLOOwG/83pF6f+KX/tyBxw8yicoYgd2I1ALnZaX1MyGITMIiKUROa1PraPd8kNEBhRhPlthjzNNway3EMbGbE9eHzMiD6zY4F+NctMAUjEQio0BdGFhXxmBtnaU2QiiYRQXoBiM2I1jyqvFBtcG5DIIp1yZ6W10Zmv9cGsFABSKXF93fkUAnRTSPoTt7O8O7Kyu2XLCt1fh8PkOIdKSs2KqqYxjkGDgPWfJsyxRtyuUZMeneoCMJYVRNe9fMF+ugjHGx5FmX+onRsJgIozPl5MUOnT4idM0HvQ1abR4Qu83dm9rUWyTRrNBbh9AcSAtO9ELodrn3zXOkuugEIomwZbTlSK/euBukaErZsNZ0ZznADeZnnzEQkUetqtiORp4yS+kXBb3Zy4Vvy6oyE0kRzKDoDC9rlUoDqaPpIrY2wNwYHA6Jq13CUKzFlBPTrN/NlAPXh5nbF4VpA92cRmlDpIeywQYhJfI8iWxrjVIrx7OaDFs1twpBqVUzchpqWeya+aPPSs3vmBCTgMoeWIuCx3vtustxxVJy67Z/DsFk0RMppWwy8+tM92SgDkVP5JCoQ/lxAkxFEluI9LIyuvnzoIbM0ANrbw52tpfxGXBRofaue2aMdgF2LIpIagSCq4VGQ2qNCKNX6lqJcVKGaa2MoCZEG3JttF4vpMjockNsFtc/dj2/3BcYAo7FVl2UwM1VTFKCaO6sXaUMmv9112yggcDC7vuoz+neyLipWaPPG613KfCCmiy332n3vW1tUhdvwJxWXAfeApLeRmPtQ8o8An7Zifx3IbhyQJXLNVqHroKCEdRyba6UScEcYDXkixRoZFuWpF4HfFSXPbKnC8GVkOIwBmN1obmtnUgjWiPFQU6BBdkyMybgH+0JJXQmKn0o97GhmJAcjbRAMeOczoTVSDaRkpryckjscsTSQsyJQ5hIqbCziZwGMc7EVBksWAluD29cpYkH7+5UQlMHz5bv8fEfvs+PfuefEPIDHrz2ZT7/cz/Hl//Mn+X6q3+Sr/+JX+arv/rr/KUf/hEf/eAP+eA7v8Pvf+tb/MF3vs8Pnt/zhEqNO4Z/X62vDsw2QpiIHdkv9ahQQ9OsFTIpea4Vg9rOWGvUrnepBhH93TZBYN9OQF05uHDEgXojuD33Z/fXzxxQurq50ZDaxuUy0cXjVhXTi769TDnt2F8/UNhUCB4MNyCoent7MqNFzscTT55+wtNPP2U9Fmzecbh5zCvX1xwOswdVxgtotDVojKHFJKAGqW0Y3WAu7RtbDaQu7uYtSMt55cmTJ5xOZ/nxr6549cENu91MyhPR2bLeq0uNA1segpgeydZqd8l6kPWl1EapK8uycLw/cTwJSLooGAaYrZj7aYNX1+dp1sNngC9sYwjtDi5zNNvQ+Q22FlMUQ3IGxi4qiE122ZuW3uHKpJwiaU7M844YA8ltHGVV64mUXShENHqVpAUt/72zritl7RzLAkWLf7LOFphXOwrMM8kxW6l0G6QpC0wyDerZcACgkHPgEI1dNh5cz9iotHWVPW4MXjy/4/b2jpQzD24ecLi6ZpoiEbg0D3qbUa3FQ0FB+SeDAtT1yHL3QrLYGHThYbBW3toVfvEbE+8+Nq5nI4xA7Z2JK8LUqIeFVgtpyWpkWwf1XCkDln6mrp2JSZ/FMJZWSd0oa2TUxkpjqY0XL1Z6iMyHG7pFTndqBbraC+U/sVLpLFU/Tx+mhiO8KagtLH07TrZgRNmPJlcJnetgXQpr7wS3g9TeWFqnjEBMO3IyLT9by5Y3Ux3XsxRk1XOMaoEQKaWRpwPzbiKHQI6zsxPDQyej22D0e2+1XpjcZVkIFjlceZOb110Hv7wcXnfWUstccctRq5VzWUnBZK1z++dmPYqYMiwcTKELzBlwCXbe3dzo6+LnxdDvSw1eUoyEDTk1c1uSmuFaKaQpS7kSpejIIWExyTpaCza24WP4wORAXMzkm2stnDgTGJP+N2Svqb2SLICDKikaAVhOlTnlS6NY8O99mKKdYtfibeiiNw+2Zgik77WSp3QBuKNB99/3FKOsU0kKHDOUl0CgW2X1fCPTASuwGrss+cMH9IpkynRnTfx/tzi86VFfkz5o2yVaO8kEACk/RoxgcwvvWgt5zt44IgXXBlxdbJA6Hi8BhGZSZd4+f65ShCE7hnmuRLNB9yYMDXFidsLFZ7HSYqS5hD84SBVM7GJy6bsG0EBMfhf1jTyIXiihkE2tV/qj6+iEyiUwtbV+YfmVp6dBuo+ur9lfBtCa/zmbPTHndGGegr8sZoNaV7XITLPk0B5AemmCHOMyxOhycJCwKyPusJthY1zrGfKsn8WVr7ABUJpcWiuucvDFwqu1FC4sJjYGbyAJviggkGX0zMCfVYarW6tnhilgGIsEt6fhoO1G25rLuFPS2T9aYWneJBeHyAUHhMZQ4G1yVUYe8aKU6KQLIdZdId2IlGoiXMeW3agw1DCi42wRmwwrxuiLgNc0mKdMtkBMkJKUfBaku8wxgQ1slgIyUwiWSQdld4Sh5sAYZDsJXXdmcEuSWae3QEgDY0ewwbS/4sE7D/noj54ypsb/y9yb9FqWptd5z/t1e59z7o2IjMimUqy+ZVFFsSBSRRISLVOyTMktYMOCZMGeCxBgwL+A/gEGPDFgGR7YgmTDAzcwYVmULatnUaRJio1FkRSpYhWLWRkZkZFx495z9t5f58H69g2NDBioge+oKqvyNuec/TXrXetZfiuE7PGCzmDTjB2PmJ8gF2JsHFPn3TXzg1/9OLeX9/jlX3/G8cr42lci0+wGL9FxyHJq0Byv7go+OH73+R2/9tNf56t/4TNw+iHMX/GFf+PP8+88/8/5a//0wu12oaaETRPPzpWv/86F6zdWfvCrgcPsgKD9MMgNeHe3Qa9E19Tsao3JRyEENrk0GZHk7hx4o1uk5AxdQuGa4TDPHIIqn3MPBJ8JqeNWw2362LTeSRi5g9VxLLagc8lg09Sm81zAxFkyRcM1/OyKQlU4RoH7Syv3z2bzbkR8kGgxzp5DqpfDKQxnU4e+R5Wpw0Tg7rlhzjtSivdA4V3Qv18MPIQUaVvWMuj2VlPtScG64ugmuLxHg45SJE61nNUh6Ia7hN3hW3XG8I4I0L1EWvOUWkY5hYaMfagYgg1rwNRMgnPvq+K2UReatm1ywZhJoLI+hLYx1LEg95appQpzROfI4+zWStH737RX+zE09d7fD3Pv2T/NaBQ5Y5zO/QlYVjl+sXzPZNujuH18Psw0xO27s4UOVedXjycGrUXBBkh5rKfBqRRmWzO0Skqqoi95o7dO9FB4PYh1Un1wTmtgbwULjil6vBk5NxYEYadoOB6Dp+M1XK2VZhu59HvxRbwcxSTbaKb1IRIYZ//axM9qow3WbESrbXDbOmYSHbeS5e4pcqQCw/mjPQszovcCUPvKFBOhl3tn2d5G1lzDtUq0hDeJTiofeD1w3+PY4ojp/XADrzGmkxijAXTs8yF6nR9bl1GgA31/UvWB0NAn3AttWgSMrRedd7vOTq/n5XLP70OnNuJfe6KDDr1UKjqT6U4lEc1Fj3W5SM3LibVzf220VO7cG8cQn73Gls45iUKl3vMR99+r9Xov7DjT0Ch4R/RpiIligoYYx/7oqCWTdfkbr/kYkjrHXmQV8PQeqHXDTO4XrVZjMNjGgKrbaF3fxQpGYYo+By4FSs36e5vid220FJqNtIExnJYaJGqgDWs3sEK0JiHMO+Y+TsTj7uua4RGPVsK0UczYnCLKIdt9GiOGjguV3CKJSOxBzZFuYxstkJEVFzLON5VnhEB0QWzUC+Aqk5t54xixR4lU4a59xPnFL/DLf+9n+cW/81M8evPTfO9Xvp/P/OEf5I0vfoVHX/kan799wdfe/32e/95v8/yf/Sq/+Us/x2/8xjf5lQ+/zXe2SouzQN2I6+hGw2btgsOnqDba1iqG2v9CGHcXdP4INOhFTfNe+xgEjHTPJu1deJ0wzCa1Fnrbo3bfna/vuqDkg9ouQlQMwjEyo35AJ3OB7rB0ZD4+UCzES8RxGs+qUlc3C7aSeXU+8/LpU26ev6TmyvH6mre+522m6xOnw0GT7CYQ535YrbUxT2kcDCuuCoqmae1gNtSug3U0qddFiqi1xvlyy/Onz7i5uxC8cTpd8cbbj5mmWQvJsArmrOo9tbnMbCVjzuQc6UWOIjqHaeJuWbhsK71WtqJq8vPdnWpuW2Pb9GFK80EXrVpwdFKcCGmWZGAOHxOlFNn22eNlWmiU3X9t2+xjM7mn89d2P/lxpkNXWTbB6MaEN8TIYZ6Y06R2uFaUcwZ975GnSiniQxpuKE1vt3Ujr5n1cqcmNi9rcB87uwEtr4oQ9EpdC710YopMSa6SgI1Fp2O1MI3GrdPBMXm1fJX1Qu9q5bs93/H82QuSTzx+6wnHw0wMe6XuLqtE1cVr0KzDyxAVWm/0unJ+eUNbz6pqT3oIrcCbrvCHvjfy2See01zwrXN5tdHzaHjauhovXCOvEgVzkbV8o9Jcp+XOmrUZ5sH6CXNgXQvzwVMvHbPG771/YTpcMU+R26WxlUqKB/CO3KoOmm2iUVmBYDPLpoOJ2100QLeoKZwzUjCcFxQ450Ztw1LcO+U+zmWsWyNOE4cg54inE4LjMEtoWRvU3CgZWpGgWOlUC2zLxuFw5Hh1LVecSSz0AwiqJqnXFuDeoBZBgO/Od0DneJw0ScorfmfCmA2o3BAbxr8v94JYAABXpytd0oMXv2AI1ME7+tiMbecqbBu1VLaS8d6Yjkdt9HsDYkq0kqnmKEWONrm3tBD3rrUqb5nz5YyPkXVZsSiQt0fD37Itsp/XrM2y1AFnlWjszBFPqm2nQnbjYFMLCVP8bVzQXdda03sjN0X16ODSxJhfyDHo9mfU6fVwsO5/V5UtOHcxNixGiTpjquq6oh+1yfqr173RyxDuuhtuOk1JfZruOUlad4D9eznZ97u34dxU/LdvG61U0mHWRYsmvkHrYjj1MfQKNrCymg5va1a04LJCb/hpbJR+d5Dpc+8Z73kXV+j+UtF1Udpy5sHxSDA0+TKw6O/ZBR2HhTGVbDsIGXr35GHRD8Nxt8exei+YaUJPAxcH2HIcQMd8BFojzBGcx/cRDcuqoZXrdBzIx8QQRoNR6uSc2Zs293GT4oxDTNkjA33PyusiuUcD8prpFVJQY2HzpmhveH0ECD6MMgKxGLppuhoGRJaiOJ4b/J7dFQU7bHgXsPT+t6YYiLbK0SoXIz44NELW9BvAxoUHG21sKLJNb/TY2Va1ELkwxu2m/UQX1GGnGpft2OU2cdaHnV7xHB89zgnarR/ix8/RfugR5HgvBqeLfxVdI7nO1gO9O9ZxkQ+mNayakVselzePhSqoce+UxbGVTqp+xPEknBlGsE6wrqGRNVwIGPrcdmvki6DGBCiXDXeYxd72Wi96cVgO+Mkwq1Cr/tlsuK7m2qu3r7k+X7h5f2WNEBfHNHmoFdYL5gI9bLjwCJiI6cAbj+ALX4TnLwt/6x9+k5/9x9/h8THxpc8cmecJCzAlT83QDrBkWC+KtP3Cb3yHz//W/82Dr34vuJn45FP8kX/vL/DBX/4v+amnmVwa7nSEaebZXeMf/MrK44eeL34uiYNkHYsVc41UJvKy4l3BemB1EJsJ2ux3CH2gV4eZmvu0ZnoNPJJcyXMKeDf2Cg+uqMDjENWmtBZFi0LXEbz1SusJF3VyaGvW+xKQAwa7HzKoQEJrj43187I15ikwJ8e26f1uJoeMd3IOqvFRTieJEA7IeKchqgt+4Av8/eVZd2Y57wiRxqaGNJeG+KPonPeBFPUZK6WOc5hE0hgiKkrOahb2gdBVGhDTTC+VUhetp1VujjAJalzrEDXR5VBCkQSWWgW/3yPTpTdKE2/P3IjH+PG5M8MHSMmxnSvOvFyY5u9ZOWZ7RN7LUXU/kDXuo7imC2WmK85U92aFfaDstFaNc6uEeYeFqLKUrVBrIcZIqwUfxDRRCmA4OZuN6CI4r/POvuc4Opdtw5lSAqGa2GoDnVA6nNdKCo5ggxfVGxE9z7dslNYlJlmH5MEbcbhrvY3GXzOx/oZgEp3TWRajOaObhzqKO/xM3cRyzK0JVWFBgovzitUF7Qu1S8Axr8FhWTOu53vQe2+d7jyOTmB/5rSf91FiIgC0WKrOOcVwBy7Ah4AFnV2iCzRfKVuVa78JgN3N2Fqmd7GRVGE/YnudwdPbXUOO1vb9cgwvfNDgPkvI2pkwKbhRuOTltqkqm+h1vLb3XBuHG4UHgoTZOD+V4aR2Y6BlEu0b92cLmZvlKut0ShluvjZag+uArIekPWeIOubGoBTovYxBzHD41a4G0JrlLBrP2loztTISLkOsbGBd9z3njDieOxkcFD8z5/BdOBTXAAsSw2oh0+WK79q/rY14Wh9JG9w+fycmp7geRecDFyQm9Z2da/fMxFEFCl3iResFc7q/tMFLCyGIlzSeaz8+2xj0IhaeuYlCxtWGi41ewXuxh7rzxDHwaxi4zpkqQa17AmIw01eSh0sJJBdZ/UpYEyECbuPgHYu76K5kw/UeILrK7I0lFCE+gieYx2yj9EBeLvSayNXx8IHn3Scnzm7lxctf5et/75f5+3/jv+fttz/NH/zBH+aTP/qjPPzM9/Ho83+Qj3/tT/Dln3jG3Yfv8fyf/zy/8w9/jq//0q/wT5+/4FmHdT4QUoQeoEiobOjOrhtXvx+gtd6xEIn7oLqJOdar2Iq1rzg23CgxMhdIMY1B3RAEu+O7+fXdF5S8H44kPYy+qxkqD7hPDKP1wxwW5URopZB84FIy1TqhFe4+uOXm5UtePH/B7bZxPCQevvkWDx48kNshBkJKpFE3q0acjuuVyTzucKA41ApWoVonBo+P7n5SP80HwpRYLhc5Hujkmxuef/ickjvxOPPOx76H00mA7d46uWRi9GPi0SWoYGx5kwXXgaNBkBPI0ail8OGHdyx5o+NY88r57syyqnlNcZrO8erqPhJH7xzmg9q4ACyQ0sS2rqyjsS0E5bVbrwKS3dPeB0vC9LDt0HLn47D06sC/lcy6qDr4kBJpSngvPlIIskrnrLqH1gXCDQOsHGO6n0h771nXzLaeWRZBjo1G8k4TRweBytoKEYkb27Zwu24c0szhNBNjuJ/Aefo4UHROyTFPamGJiEmwlYJznnVdePr+++TaePzmmzx6+FiRjL73GwtMKctuopWFYzLWUnTR2QrdNc6XV2wvbzAaMSXiEOhiq3zlE4k/8vkDjx9kAY2zY6uO5OQ88hZxdsGbp1Sj94PcGK6Jk+SAimJ7V+CsEZZO4cKaF3wJXGrh99+7ZfUwPXhIc57nZ8XZjqcZaPdA4VYqrTqW0lhqx2N6XT266JAw7/HBkUIgOvBe0cJcM7V52qbN07VK6J4PL2KZzfGgTch2Joqe5zVXllUNIK1vkNvwveycCcPFiTCphaOXsVEHu+fcgO58fVTLduRueHV7y7ZmYkoSKtYFnC5prWcqTbbhNtx3KK62dbW+zPOReUqyQzvDNx0o8oDfNXQ4CqZJnF4DWfPbVgizIJpWG8EFsa/2tqnOYK7sk+NxaTWJTzkX4pQk8jpjPh7opUBIisuyu1bkJsh1gB+rHDAW92mUYjZqVXFoK5eV23fNbXKpLKWxrgtbXjmmmThN7OBniUnjEr+7lYwBdo/3jqpeIbeGmxNX8wS4Ee0b69k4jNPbPU+ne8XpVgTPLVtn9oHJB1m/0aTL4zQpdcNR2TQNjebIZVMEdsuk0xXxNKsQzEYrpVU1WozJVm8C59acSebJLtKKpqNzkoXd72KSyYFmyL2xu3XkRJToXlql1crkA2ma8L2Nqb8NAKoOisHL4ar4bVYF97islVyZpoNAnbUq3mHD7dL03535+/Ybc3JjBSdxZ8sXHXjdLt71IbTauMj2AYkerrzh5nEW6L0RQsRGtXrFVFgwfk6re9vha04ZDHcEpgIEZ7sGo4uFV0TPjem9/r1Oo+nS0Do2IMR9uEHkYBIryvw+od5jHkMQHZ9gHZZH9LCrmc2Z1yF/DCVqraSU9HPveUq7y6yPyZqGHbU31ZwP3gWm77s3cZofAG7n1cY2DusOoxeBV4M3rIkNge2NTo7JB12iirHlwXuywY5yRgyZvHYNB+D+d+29UbLDu5ll7cTJkUZz2xQjgU7eVtalkmZPSForvEkQqQbeKvQIVc9RqJlCw7eZ5ivVGS1XOGeSJeo49PeeNYyKAde8nDytjMiBuF8xTjx59xHlxfusr1bW6lgQE0d/ZMDZDDFDmHDxSDgZb711x/d96QkfPL3lZ//pU/6vX3/O8TDxibcbbkbR6WTEZpxmtYtSG5eD5+/+9N/nJ+IF+8qfI8Qr5k9+hT/+Z/81nv5XP8XXL556OcPpBMfE795e+Pv/6JYn1494+11Pcg1rYr34qw3vJtblQpqOuFJoLeAMgjfyYCUpKKLykGQBvOPijKVrmOOi+ENrM1pdNPC5bKwdWgty2A3zQnByE23VQS1aD7un5ZXp4InOsxToRGpohOGIaRi5MfaWxrJljtFxnQKvyoZvo6rZxHRzPYzBn+Jnet41RQ6+0nuhmMNIDH+SWkCnWS673kh4qqk9cGeq7S4cQ24ynNHWwrZV5oOcmX44Dcz7+4iWIs2DD+MDnkpeMyEGXAhyOeVAXjLh4LisHT+aYbuzsRYL3lyrIjnRtCIYjdZWrASi1980y0rNZgZBjgfrQzyqYh11TWPvHR3DUjmOdtoX5fBRFK9kOW/kqFChjIpkPDbgtG1A/y0YVj25QV8baguTw9YP9svu5Hfey0llleiFxbBa2WpV4UTT0KGWQreuAGbw9K1RykZwEJzYbyrBNGrPg3fZKb0y2aSzkUP7p3N077Hhnu9o36bXEQUP9CbHSeueMCJzzoy2O5z7Rssbxdp9mxfDVey9G597/Y3ePO7gcczkZSVXxSV396lECzeGaR5LaioNGL0ZVprE2uDkeveKFBbb4zpKSPSouHPbCq4beasoTqrIW84Z3GhV6xIMLWon031ZAlAtZTRABqz3e6B8sSr2T25Kg3St47tt2/ZhWhsD6z1eajqLTTEO543uDrvzu5ney1a13907ycZnhS5OpOtGKX3sn4Ldb7lySPNweWuhKWUMNowR+654b9SyD7CMQpUrpTXuq7iHjmc24uwjalmy9m16pc3aX+h6nlsFHxPyiwsgvjN3DX/vIr/HA/QGToPEhsObl+MsqV1wfwa24u7FdPGYbDiz2hjkKdauD7Si+s1B71XCduk6y3RorRADo3hmNI876JbwSc7Eu+2O0GUySN7wXVwl7/1roY9K8sOliCOmTi66P9/pZoDZxuSPHGJn8Rsu6mdOyRNoBAdXznHxhRQjeapcTQeSL+RJovxVmAk+cRWc2khbo6yVJ29MfOLtmVwdTz/4Jn/z//gn8D/9N3zs7U/xA3/kR/nsj/3LXH/+D3L9yS/w6Ms/yGf/2L/Pj774Ni9//R/xW1//u/zcL/wqv/rBC37fJ7IXXzR0R2kbtXmxtTz0Mj6XQ7iMXufg7uIYV3WsbzoPlHXEdwOXsigqOPSB3dz63fryP/mTP/ld/YZ//X/76Z907HyD4UgZMQyDMZWUwubGRuaHO6CtGx989Jzf/+1v8Hu/821evbolXl3x7ic+zcc+9jYPrx9yOl6T5iNhingablwYukmIccETXIQqIDLDdeOGOm+IDxGnaTxUhcvtC56+/5Tn33mfV+db3nzyJk/efZsnj95gnsVv8SOL7oOX5b5BiFEHiCbOC14ZV8W0xb0oA8xcamHZMi9fvuDli5djMhLGVCQQJ/GS3BBoYpwGmyGKm9EdJWdBZndHwL3wsjMcGFwHxQhwjuWyjAapSYIS2hS25cJ6PjNPE9fX10xzuncb2IhPl5I1rXHgYyTGiRCjLg8m5kltnctl4XI+s6wXaO2+icrtk7k+NkUTlDGfN4ozTscTUzpgPo3Jszg+gcZVMh5fRR5fT3jX6fWO2rT5tdp4//fe4/33n3F89Ii33nmbeZoV6fJ+8Fv2xV6HwIix1or3gtJ1q7Sy8erDD+B8i80Bi54pOoIFPnGCH/0cfPVTlZNbqHcb693C7d2Z5VzZzmdut0rfBPfc7la2IsGkFYH+llxk7S2aTExx1BbHTi+dHJT9/vaHC9kljg+vuFsqz2828lbyLsglAAAgAElEQVRVh0vD106txuXchqvIQ9Rh5O4ui/3f0HvsAiFEKerB4WNnoXKbG8vmKVWjlaVuZCRmUY3r04kYNO05HZTV9RapJgixalM11QlT4HC8ppXKeckcj9c0M+YpEsOkzHqIxJjkOjHUzFMbtQ5GVc68ePGCkuV6aCMWVAFMomHZ4dElk7d834q2rAvbunI6nZhCovsR9xyX6x2871wfEbfGVgvrtrIuC1ve2LYsscs80YtPhnfEw0HulxAgaMKZ64Bpd3EoWhcfK0xJzCQfSHNi9gE3RepwTDjnyWullco24N82AM4xBubDzGGeCVOS4BgUlYgx6XdA7/9W84jJqW1xniamwxE/ReLgsnin+FdKUZsjI7MeDWuqvs3bRt42equcTicijmSKs/Yma7sziXbeBf2+NHyKGhB7z92rO5L3XF1dCZwexqR4MClwDIFGa38wNZo1Q9DobhwfXStKhizPzdS06X0YAgJ4Z9RRf1taG0BNXRBOhyNhxOUATW/C/vty7/jpY4ihZrPOensmxAhe0zDbxVPbmQia9NE04WtVfC7BUKFslel41ISojWY4D/Daou+DohY+hGE1lijogPPtRT+3Nk3uhwNIUTPRo0IQF4oRQdwrI27vbjkdZ07Ha72Ww7nWTKKnDfGjlEJpldy0VrThOMvLwpQ8KXpiEpPGnBPDaoBknbkBT5dwqqYQHT7dcGI4r9ctuMDeBiUT1IhXDgdfa1V/1+CZrMuFlCZiDJjFcQjU++OdRJa+R+dMBnoNRfQ5ulwWXYZHtXEMk2IzLgxf2WvWk+mjNZwdIjeu24YFOSq8eQllIzoeSEO40wFLiRPF1zxGCPrnpSiKKCOdIzjHIckhbMGYUuQwGVMwUtCAJMaodfq8cHezsa6Z80UcoIqa8cpwcVSr9CpXSTcx+yw0LK646GhbxSVNkZ1P944+51e8TQJtd0V/rG04Auajqob9xvLRyvHBgdNVIh4DFjsWE94n8IFeL1qr/YxHTlwfAs9fFH7rm8/w3njzDc8c9feq7aeRafhmEmi648Wy8P4//n2+7/sa9epz+GDMb7zLm/ED/tmvf4OXPWhtiBFziac3GVsrn/jkxHGKkIbLLzXcFDDfKKWOCbEfBQMd2bTUStTxtCKWTMSIPdF7YDlXDsfIlBQvrKXgcIRpkiOt6/2MXk6PXMoYDAkq3Eojsw2e2XDNovj2lDyHFJiSG0Uabpxp672wM02BKRmu+Ndro8mRZ+NRM1N8peMVu0JFDbrY2msepnNjwKE1we7dPHKTHOeZ6L34Rx3caO7qXY63bV0IPhLi+JxXiaq17TEV8GFUVfuABQnVrWRarQRvKgjoap91vg/B2g2t53VTUR88R3FJB0zfFAXWLEjOvbaL5zDWA7lDatF727o4L7pE68wj15LwCXsLm/ejfU/+jPvXS+K01qfXVfJiVZkbsSf0tzUlK/WejLvKHsTRC6q/tw7xzprDu3AfIdE/q5Quh8qcPIc5cDyoaGXJeXACUYkJen5bMZrfmWqGQ+43IbpsuHI6wwoyINQarMFwDVMI1kjm6TgxEIfgsEfaXzsV5JQPTmflvVO2tOECHVw7uUnG3tRU6IG1wYNT5Mi1AVAfd4atqIRo339bV6lB8k7MpyY4dR9Oot53v6bW6t7VyIcFwaB3B+CInuuMqqRBrw0/XszS1EJK1evS62jUHdE0bxpY1lbknHdy/6LRDPetpENQrVWiRxjPnDApVa1cNliOjNdE2xjJi11I036pUpndxahkRhlFR24ohxJyu4Y9znDeSIeAi446Bsa1dzUz9zFIabrDtlolWpmclbtztzKieePzM6BlchoNkTLEEefqGv+EEAhhEk+0yGmoqPowfXUjhCZ3NshlNYqaXg/wxrnBawizC3LBR2pR5NAjPEAwz5Y7sCeKNIRqI46HE693PnqmmNh6ZttQi2c3conULrdoHpy33h1UDbsaRoEx1FQ02Uxu/e4qS66sW2etmS1XRtcLDcN3uZVCdKSg6Ju5BjHgWqcTB25lksvPAj42YmiU1bj7aKWsG288SHzusyfe+fSRS3nJL/3CL/D3/of/hQ/+1t9lvvs2j999i/jOxzk8+QTHz/4An/3hf5Uf+omf4Me//4t8XyjMz9/j8uEN51IgeFJyBOtKcRV7/ZlB+05wciJOUYmkENQmaU6g/dr3ohPB5mtRoc9f+kv/8X/y/yrq/H/4+q47lEDiQxstE81kk7Um0FpvEpccWnS2cuZ8c8vT95/z7L3n5FJ4cDjw1mc/zZM3HzKnyOH6DXzbBMTzmjbX0eDlgiNFj+8dX2VB31wZFtXRZmbKG/pjJMTA3XLH5e7Cdl558ew5y7Jwdf2QN77nk1xPSYdWc1Q3dpasyIv1fj/pxyBvWV4CpxNsrZV1LQQztq3y0asPKeumJozeWbcNatWkeUyVaYMvY4IbhqBDrndSXUupQxgZAFSTmuu8hAGz/cAxLOBmbCWzrSuyFidSmMaho3JZFmoRSPPx48cc5gN5u9CLxJbdeQWGi0lCGnuV6g79NepWxYYaTo2SM70LHheTMry+a9E5m6B75dWCxcDVgxMWodSA4Wi2UUoj9M5VSjw8Bk6zIwajtJVty6Qws7XO8/ee8tHzj7h6+y2+8JnPYGUTa8vkxFH9pzYrb46UkmDoJUOTQ6eWjY++8x3y7UuuH13hriZaueCc49QT3/+xxh/+cuA6NTIreI9lWDCKL5AX1k1QzzVn1rrgNrgrmhD01jnXNmpTE9003Y+t8eoSwVbOOG5uN148W+D6mvl64u6yYpZ499ERs8bpGDnOiaXA3R2cLLJ0COZ5fj5z86rSvQRZcQMiLpjipojFcr7tLM3kGDKDVuguUvCspZJS5J3HDziGQHTi/1TfKGWG6iimavWaq77HmDy8enkmN+N4/Yhugbevj/hJjBFr+wblRyxNbTDBRcw77u5Wbm9e0W1UhKfEFCbth17uCUauOedtWDSNXspop4Hj8UoTL3PEkeNPYdLPGwdaGuKgrZmtFJZ1kROlNkIMhBhIPhGCDr0GUBstt8FHaGIk1fr6gGqdsq5M81Hi6+CLxWkimBdTo6x479lqo3kJCCkmWq8sWyaYHD+9VKpXK0oYB9sWHFaNvCzUssgpNWCabXCL5jSJXWZDBLCdfTBhPtB7AY/W3NzIJd/DVZfzxvHqSj6SERlzCt+Ty6aDx8jz7wCBtWwkJ6eadUHA90v21pumnl2XJRq4Ycl1zrHHcD1wW88cr68k2rA7CAUk1gx5HAqiu58sNjrNaeJ0vrkwRfEJ9lY/zAi44Zqp9/ycPhxH+1rdWmOjcTVHaHLeuXGb7INd5AfHBgZPrxas7he7TkoTDj9g914W927DUTdEiAG6drtI1N2INwqwafd/V4MmJ16vVQewPiaQBsEFQUVbVcSiK2Jcx99oXdDfahqiUPvgaw33rDFiIOC8UfJGcF6CA/rsVBThsNaGQ0o3Kk+4v7y2ojY0FzzbsuJ7IbQoBpJefmDEQfYyjSoRTqXpDNOSOA8700otpP4exF3rAMB3Twj2+nLauReJqsARI5LYx+vIEJ0kaoZxYaL2+8tAAbmZ6PeDGDkdithJTTGO2atMxLwN1pjex1ILdEXZe3PUbPRgbBTYKtFPhG5iHAYB7X0Y1b1d6/A2mjBzm4jeyDkzV7iKlSlAMk91EguYPHUrnC8rywvFch/MfYBwO5zgcBT8v1Votwf6caW3RPYZsyAnhOv40OjRc3p4zeW6YcdIOxS2VgklwrZhYcK3M45Z4u4EbCdOD17y8bev+dJnH/GtD+74xV9/xpMniT80B07diE6fC79EYuocykLeJDB8kzM/81f+IX/0L71LvfoR/HzNJ3/k3+Lf/b2n/Ndff5+XA5brQqScHvCPfueGj7258MM/lDgcoPUVNv1++VJZlwb5gjue9Dn2csNtuaMuFEWTrQ5mWmhYF+DVeibYpFYrl3AUrHWiGclvrFZYu8flmW6O8i84Pr1vzF4xriVnyrni0lEuoQqdRh3r8uSgNE/3kSWvnLfGedt4fHBi1dUKLQ0z1BiAOkNpphFpcNCZCSFyudxh3lFbxofp/rzXpFzcx71xje4a3Sqla+Lfd8wAXWURDS61cHt7y5Wpqdd52EobQqpcXL13SstyGDhjipG2bmzrBv2ChUjOBdcWOpV5PlGGKOZsJBRao1W5LdxoecqbgNG0WUDmpmeJLhe3M5hSoBXo3li3TNmy4NsMYX1wZWou4rGWglXD4nD1Ool2wfsR4ynjNRjrVN8vm4P3o1M7MQw3B6YBlw0H8bio7d/AdVM5Rh2Act3ZBzx5OJnME0qWkOgDvhvbpdIdTDHpzjCG6W64oXwX98W6HPit7mJJh5AIFigt03IZgzejli7+TILWOlPwBKfiIlcqAUc2IySHlfxatB8RpOqgNLEPXYfoA8n07NZRXDOFKHpYrfeMwhCdzol0iI2YwgDHS4SptVHXhTQlueO6GJ2tqvm6bJnax53Jix2qe80Q/8xY1pXuPFa7HKU2nNCDt+l8x8eoRFCTeFJrYaXRu8TFySWJu6bPaMttFFIoqji6ghUb3IcILiiRiVc0fAwVuhsM3qZ7WCmCjwPjGdRe40yX9hAcrosZ5p27Z7zSg84JfR8CaVrvgwZZwTm1d0fwIdHqQt+MmVGUs8pdv0ffzSDGKIf22P9bbUNMkNu+t4pl/f9AznydT8YgwwzvZUSgvWaWSlDU4KRbkzszOLbLNlpsVTzRmu4eYVIEzgXF2ErtSrcM97ceQDmaXPfkvLeBe51Pzci5YnhajOJ5Fn0uXIQpRqUS7IADObG6Z9k0dM21Yc0UW6MTm1GdGveyQfSV3qti781TLFCazA7eHL1G7jaJ0dU1lgqhXJhj4eCDnFBpxbnEZB9xcAcsvuIQArdT5uiNGD3XR8/0aNK5de0sH1Vo8PEnB774hRMXN/PeN77NT/2v/wX8lb/KJz75cb72r/wp/sCP/yl4+zOEh3+I+Ae+zB//8T/Hj734Bi9/5R/wT/7O3+Znfu7X+JUPb/iW6/QpEsMVfWsaEHsHFnCuE0zn94Cnm1xlFU+rSZzopiIEaLTmKfse8l36+q47lP7m//63frKOKQpNEYlaZEe2cQFzDi7lwtMPnvI7v/nbfPu3v8W6bDz62Ft8/FOf4J1Pvst0fUUyz2QBmw7k9Y7tskhl651t2fDekYIDy5RtPFCmeMFpmu6V+bKuLDnrw7utvHrxkmff+YBnLz4iHq/4A5/4Hh698ZDTlLA0LPddE5xexwSsVVlm0UOG0zTMHGy58OLFS5bLQq1yI7x6+ZEeSOtc1sxyXhR/CIGQJnETfCLEieAnfBgxMpNy3boNUUych932j5O63sdUxjmp4qUUlmUjb1q4p/mID4F5OtBRxGw532G9c7o63UPMW85M08SU4nA5jYVk8CzEW1J0I6/KaJaiy2IrlXVZWJfL/eFI2d5Kp7J541JWtlyouRAPkXDQAy1+xThQjCjC2w8Sj0+B4BvWM60WautMIfHBBx/wz7/5u5if+NwXv8h8CPdMmBCdXpOibHMITvBvv+euM5e7Be86H718ys03fldskjdOpOhwbcPHxOceO/701ya+8nmPrxfWu4XlwwuvPrzlow8vnF+cuX1x4fKsc/tq5ebmluVSuckrr7YNgLXJoda6IN/4zhwd1UOLnjR5Tm9N/M5vPOO2eZ68/QTnJpa7Ar4zxZngIm89OHE1J14umbvVcG4mu8xlabz34S3ferES/JEpOJIP9FCJk8eFSG2OdTACmlzjsrHmRkie27zSuvHw+prHpyuuUuCYgkTe4Af8XRf3XAHGc2tGrhsfvVwwS8zzBNZ5+PAaC2LLhB6Y5kTwOgDsETJDENHbyx03Ny91uEEL4XSYFJ8Y7VIihRp0bY651SFcZhzGcT4wR7WxTXFG4WodbKrrhK4JXKNSto1127hcVmoeufYUiVPkMB3oYViWg8eNZrk9/kRDvIIYCUGOoW1dCTExp0mOGi/xGWeK4uVKQodNA0rP9FbZWuZuudB7J81pAHb9AGB6AcaHQ7CXlVwEM1RbkXhPuWSuT1f04BRDC+LSdGeyx9tr1wc01lZwo87YvOf86hXhMOOnhI3JlMPLUZcl1ukgoSmrmVpmSu9MIXB394o5HTA/JlJtiApdFuuALrku2OsDTC3c3t3y6tUNYZ6Y5llzLFnR5IwBXdZDgCFMMLL/MBg5m+z7lhJhODtHKBOGYK002V5MAHvExPvA5XLBvGcar/vOTxKo2ZPCaMrQw0stmv7JBtVofSOMPaUNRsG9xaCJCRHG+7H/DvuE3Jtnuei9txgopphGGEOAyYfhMrWxZ4iF0MzAqRnmcrlwPF1hpliiHhEJUC0PYG3fs/EjZmbj8OzhcnNLiEHtp9HfRwNl3df71Kzfg8GdM3rdqCVLEEPQ1L0t0vaXHl0C2tjfu+2CFWMa1si5ULes9tRpHg6kjg9at/f3CXTh8yNyLLeTPs2lZrZ1w3vPPEDyXl3fihiPgYKSfkZ1DIi8YjDbutBLY4pH+lDCJEnKxWqmynTnhHkZ2Tn6cIla1zNRmqNUR2sOFxKYF28u7eKCiSNYnYo3tk4xYzqesGbUPFyOZCYHiUaMcgm44MGP9651trxxyRs5S5hzvuKSJ54O+EmTUx8dPWqwVnvBuQkh7DVNttbU8OU6OS88/c1n3H4E5Ww0a1jZ8G0ZDreMjw+BSu2ZnjfaIs7Ly5eNb31wy8uXFz72ZuD6KspdQqOvY92xyt2qM48Pjt/64CXv3j7j8fe/A/0d/OHEozffon3z1/itFyox8VOiO9jM+Mbv3vCpq8T1Q8XpQK4yZwnvJrrLWGn3DrbeFd+nag12vWNOQOlidURBjHA0nEvjWZVzu/csUapBq27sRTrnuN7xXU5sF7rcm+ZwXsL+lsXHWbfMtlRyfS1Iu4EocOZ1GfKeV2vmEI00G3XTM9aqJu36sVrD6LqQty53n7lIzgspjHjniE83k+C5F6Hs7qIpRryXs2EfCurIKLl+X5PP5zPD2i4Wj5PrsIwmy+iDLrtmOIvEOdB6oRWnUhYnl1DBsWWte4rSjaILG0+WE3fGGqSUOMwH1m2Pr7X7dQOaBiGm2BYdzIKSC7xey6W3D5Ga/RkejBFr92fjfaBoYx3uYx8pRf+5j+9gTXFkXZDHz/KDjWfQTCK3db2Gbm8VHWebPSpoyNWCQ0K160Qvt0kucqrj9D1SkNDhwzhXW8TPYq8R5OYx61CrLtWts2WxrizowuvMCBaYU8LRuL6acUFO/HXLgx0jdxFOqIzDPBH8+N0ZTrCuwHIKnsmrecuN/13yu+JgzotD54G6bTpnUXWO8B5rxuVy4XX7bRkuOMHKzXm2LJfh7rYz8/fNUwInCzxcRoFJrhlPHS5tw/bt1rTH5bwJum1+fB4VO5UbqENVSY3zkRhn8raq0dKrEMSc7ou1ljGkr/RmLEVrgjmtonj9/etaqKWzDZxX74xz7cATlMyWi1zkY6jpxp5mXWcjCyM94MJwBrvhbGuoJ64zpSRxHLichaRYto3WhCFJKdIp+DEExfYypobRmaaoJ77JoBC9pzmZDHasQW2FVhRV1DsmQctHYyuvC2hSmFRWEBMGrMuGdThdR1qHbWOwKyXWqOtDRUZyYcp93nqjlD4GjEM89x7zQSxQ2ogdSmTiX3hZLmvBu8C6OUreiCmJP+uB4Gg90pALu/dA7UbB2Ma6Xiss4w5z3hp9Eb902WDtmW0tErGzWqtrydQi1yZ9FBJYYKHSS8MVuXPng3GIMxaFG3HeaEHJpVoEez88Cjx8+4rjWwewwssPF14+fcGbT97kyz/0mC/8wMd43p7x9b/zc/zMX/ur3Hz96zzIr3jjnUeEh+9g1+9y+NwP8Zkf/zf5sX/7T/NnvvoFfqCthKfP+PDDF9xwweZEiga14lolNyjdMC8xdhTbol4lnTF8Gvuc1774F//if/T/X4dSbYMNUeqwp46pStDh4/zqJR++/5QP3n/O5bxxevQGH/++L/Hw4RUxRkJVsw/WSbtjZr3j/OIGzHOwIBdGEt29tI7vAe86pTlCisynIwYcnBsug4hbzjx9/z3uXr7Cu8TDx2/wzoOTXuChVNbCiF8bgpxqIuHG4blkWSsbcvvkXFjOF1nvkxwWr25f8fLlR4qXlELLhTDNHB8+IKUEHbYtA8oxeh/wIWk60vepk/gXvcsR8C82Ajh7vSC32tnyhVqkdIeYNLEPY7Gis+aV3hreOqfTgXk+DNp+UUuEOfA24Mq6zDXEk3F+NI10tXGZ1+/VeuX21R2X892Iwuh36gDmyG04lkoTLG4S+FwDpB3u56BVjr7z4DBznDtTMHoVfBcnlsnty5f8s2//HtfHE9/7xc8T5yN3d3fQO2HSNGyPW0AhF3DhgHNBlYqsXNZMPp/56PY52+WGhw8fcnjwNt5uWNY7rszxIx83fuj7jwTL43KlDcBdOVx3HC6dLasNp6c7yubhEhQHa0BznPtKLUZvkTwYR7k0aAusDhK8+nBhbZmH77zDdYTnH515dTEeHK/GobdxdTK6bzxfKufeaMlo9cz53PnmB41Ld7x5ugJvzCHRSidNnkxl2zK1QK760LZW6RSCc3QPl9w4zEdO19ecYiS5ziGBa5G1F5beaS7R1krpOlQxnBbLtnA5C8IYoudwmAhTwrpcOtZVk+3ooxZYm3EdEbe7u1vWVZBxs85pmgXhvvcHOVVhJ08pK80bdanDfSHQbToc8Skpwho8pa/6TK4SmFtrjBLVAVbsA24KaYqDQeF0EWuCTboQ9T1qG3D/TuxG7opaSWjRQTyGiTRN4/Ao0bl5sFLVFuVsTGQza9NFOnvHsm6EBsfDNKaJHhcPWFKluNlwKfRGqWoIsx5Ytgtblpj28OFDueW6wJ+uoan3aLXr1mkIfGil0MfkZq2ZdtaaE32AXIflXG1DzRp91MFyf0hx9+yo5AMf3d4yxcO47NiIgPRhtUb8GqeGKk9QzLMDWTyq1jvHmIjjolMYvI4+svteEQ1yHzwI6K5hNRN652XOxEcPB9w2ULsu+n5SbCWVHbgvca/TX8M0uxotHjx4gDNNJfM48Dnr90K6Wce7QKkXMTkwnNX7iXJtml525CiquYvj4B2x6+e0cRhzZveTyOrh7rLoENUqKSjnXvtgW+iv1cVk3IfcWP+pjKjlcPcMuMA+OMeJFbbkTRNC9ki0InDJBbauaXaadPii7SwlsZMwwHQY8qZLp7eOT1pH6QhA3XWJZ1ykWumagOvGNURCxSJUbazPpHkoLVPyPuke0Wp9FJBoOAzz4/2wIdb1ER2NcSJ4wUD7iLc0BFLeT6CKh8De+DbKeuRw8Ilt3VjqQvJJEFoivXu8Tip6XYLhotG2NqbKOlgG9VBr7+rgo+GCY0pjD2LEabaGzXG0yOlAn0YVs0VHaUaIkHzUUMY6pXWiM0LtmqiWSi0aGHk8W+2UnGnB09c6snyKCgQ85iZaN4JM/tDV7lJLExR3moku8canJrZz59XNheYCth1xV5BrpG1qugv1AvEa7y/Ew4kHTxofvzS+9KkL791c8+33bvjFX73l8eGKx486GaPGC6U6rBmTNeYQuL1Urt+44m98/Zv8B5/621z/2BOM7+HwyS/zw//6n+Bb/+1f5+efdfLdRrg+4prxUSj8zz/zAX/+0bt8+tOJFAak2xr+AHOeWbfC5QyJE9GpzWexNqCyAl/bALU6VHFe8EzAaujypwVL0/s5YEvRebVLKNEdVc6JhmPyDu8jB6Akz81SWHPleG3Ulii7KNVHiUOA7Izgk75nN15tnatQORwK53OnWdSZqo+YrxkF8ef87rq0zjxlLDft51GXNlrFh4NEHIPO4P9UCarBReqo896fIW3jxul0JMYogdo70hSpbTgEu/ZMwzOFiYNJpCy1kNLM4jL+4qhbpcUV6x6ixAnDWNdGcwP8HxwMJ/56WbQOmnE4JRWxtD4E4LEIjDW6D5eM1hC5ahqdrQrwHZ2Bi9IFu9YLRVujnI9uOPlbx5wGKdu6jKKqSifi2igA8Pp317IDqsM464vd55oXc7BDsYJvVYKEmRpc2dcOvc4OCZsxRHpZKXWwGmnkrdJ6ZcWrNMJ05vG789KpAXgOEBOsl8ol7zgN3U9Cd8R4pPaKs8p8TOTmWG8WVueZg2MjUFzHNc/khnDnGn6K9FzJuY9LeGXLZ2zSAHJplRAda95wMY19vJGzI7fGOlwkIU4SvZAbpkePBYdNk2JvZcPFidZGeU8r+KDSGjks5QuK0ahWITqianhgXL1aqxxs4FKaxLXuhpA4GFrWg55Op5KIWgq9Fw7OszbDBbmADylxd7mRE4chMtleIiLkwS4M1SZgunNyp3Zf8S6xlRXznYiGOcErEVOrxKvSOrgoUQbHatp7am0055hGtL42bUjOIdcrYhW1EQ1P1rk7n7Uv+opz0FshToFKxXW1TKZpJtciN2CX6Ndaw3lF7iXqNbBC7kJXpEmv8e4i3vpoOq4Ns4p34IlE30k+Qe/k1phDlIPPCqVEnOuULZM3BptohPKN+5ih89qd5+RJXciD7BoFFQcdwkTOEuJ13e1jzdbfNzlwYUSHS+Pl7YVaI9hMXRS777WSfBjQb2jN0y0pTVEz9+kdK6QIdS0cvCc7sT29d4QQCX5FoPorfNvw3gYuplOGAHfOjWP3bFPlXDPznbG2RkwrRxfxVjnEA4FVDN+wkc6Jmxswt422bJimQGbi/fc+5Bu/6Thd3/Cxj13zpT/7hNrhd3/zW/yP/91/Rv/L/ynf+6Uf5Kt/5k/y+Ef+JXj0SdYn30v6E1/mj/74f8gfu/nn3PzsT/OLP/03+T9//pf5tWcv+ZZNlCRUR0DnwNo7tfp7B6q5NpyYAaNQuwwO382v72ZbfcUAACAASURBVLqg1LcNglGcgNy1K3Jx9+EN77/3bV599Ap65MHjJ3zPFx8zT4lpmjAXCaO5I5jpANoFgm4lk+YDLioDDJ2ApkHdKq4Xlk3W/+18pjq4fvAA3zsfvv8B73/nAy63t0xXJ5587F0ePnzANE0sy0LeMi7Ecfh1dBPctNaGdUeuG2W4H7Zch3NoTJJaI51O5Jz58KOPuLl5ybpo+jPNM6eD2ESyeq5cLgsxJeX3LY5s+44/1pc5Gw1Wmr1AH2qi7Jet22DOqHkNc8R5Yp/KeK9ca0eX+LytHA6nkQdNYn8Ez3Q4jCkZbFkZYXMenxIphKHYN2pXE4bD2JZF07GS2baNkNS01Eela0eAuIZhMTEdoqZwXoDFZLC2jo9G7JkHp8j1SR/uMKZpzWRtPr+65dkHH1J64eOf+RzzcWK5nNluXgkGDNQ1K2+NgIl0h3MT0c+s23o/8bh9+vvc3T3jcPWYt956lzeuI5e+4W8yf/STD/jhrx2ZOXP3/MJ62VjyHetd0WSpaXZwKYpwrsVTrZGbx5fOYg3Y6C2wdujVyPnC1iAblAa0yLI1bm6f8+DBI96+foPbc+fpy4WFzPXVFUvPzNHz5EGidePmrtAt4Gok18J3bjLv3xRSnLh2ATc1jkHUoZ5g6XBeHL37+7piZDKnoLxyrpUHV1ccToGj77rWd0V3trZpcpihtk154hFn6s64rJmbpTIfDlwfjhwOh+EEGZ/dpsyxM2V0Sy2Uranidt1YloWSy2CowOFwIAW1FaqBUDXfZbjZalXURBwbRVOOV9diDAVFk/bpVuuVNCVKzQJqVjkuSmvcrQvbmgkhcnX9AAtRE01VZMCIKNEbvfTBrqjcravYSbkQ50Sjc5iPTNMMGD6JHdSaqA17db1zjvOWaWXEQf2I7ZXCaT7incfFpLYrL3edc0YIaTi6xEXwHmpR3K61wul0Uqa9j1rfIf70sZmr+tZorZLLqrV3OJusw5o3rh89pLnBO2K0nDTZt11HglCTZ6iPaDFOUbJeKi3pktXrfllhNBXJSYMTS8SK2HW9KQrWWuf6cMJbYKsQohFHLLM3fU999tpgb4w1cUTncs34FEhRgHrHsEwPYaYPUUoW++Gecooblda4u9xyOl1pvWoSt9wQ6bvb9xREYd8b6XYHEI4QAyVnNZK2wdvAiVvhZCNOYRwo/c6lGLGyWtW40do4ANpwAKGfM6JfdEW0rQ9H1v/D2pv22rZmd32/8TRzzrX23qe55zZ1q6GqbMBNbLAxdix6AhhhBEpQGpIPkIhIkfIR4APkTV5HeRNFiogSKQIEKAJEwGDTmMaxcYztomzXdVXd9pyz91pzzqcZefF/5j5WJF4kqS0dHd179917rbnmfMYY//FvTJsxcPZtVxLRcUaiHbJiupVQKklDUNjAaLLy8f1FKTkpZCzFsdXXRvYwvfbeh+RP3geSkBgeXN48bRvvT8BWCErQGjtmMaHCwDpcElv5akQSWU14isCR9sho+MJIjIK+i3183Yq28IeU14wc82M9DE2gcxjDRZgU7mGM4QxJFHxs6OmSEJTLRi3OKStpL4GkTzGIMdsbZesky/QymDAufyN3YzKBpGUkI3oreDGKOXVvTDZjbeMUFYgQ0oQNKUK9XlWTHJ4+P3POkNNEGvT0kCFZo1b5n4gpkTmdmsyVHVIOkJz6cKFGiC1iORKSJLk+3er8C04wvW6A4IZbJp0Sz99/wvpyZV13ptOFJdwSQyW0QiwZy4ro5vZ90vo1OJ148pbzlS884cvfLHz8ycrP/ZtXvPfixI/97rc5pUjLkXJ11iKPKyLM2am+sz9b+Kv/67/gz33uBdN3/yksvsXnf+D388f/8G/y4d/4p/zqutKvBqcTZ7/lV9bCX/0/PuM/Pb3F888lTkEpZtYLLUeWMBG8spedakofnHYIG6yzgydy0cbZJ5hz58witlaY6Dc7ocDaEmtzvHRankZEttKAu0d6aOxdLK84QeyF0vIAeTK0B/ZN0lTDqCbJWZyc4p3tUgkuVkhYJlrfebgat9PEzSlw3RuYTG5rFfARzWV0P5g1OU/4YbbbV6iSFlkwaAXGcBRSwtoOUcNpa5JXip0ZH89qwmASmjHPE9u2s6+VeVnIKRFiZq8ym66+c46RyBGgkYjeyXeZ7ViWtso5JMnCuxOCnpu1NWKElCSvClE9Sd2uj5LtXitbuRJzxJjwDnPWeWfZqVWpWc11PkayIsh7G8xCxiLAmIPOgubHtZOxcQcsJPJy1rmOGMatN7ol6PIkMXywQxXgQ4ykeNIp27vAE69aFNJIAc6m5DsbVhDNK97hlA1ix1qmjv609471Riu7GFI9MuWTen+TN6Axw5Ixc3JwfJnZ+gatUke9spDAO0EnBla7mLZDxrvtm85dgJYoQcDKkhbJekIfA6SxXYq8X7r6PYsC5EII1LozpzzMmwVQLnnWZ7jvtAZxylgFaiMHLaTDWEDGPEEdhtc6utj3XX0GNoC84bObBqs0qH7EHJg9gkuadV1XugVKUV2OXSDzkbZ3ms60spNjYLq5oW3O1IQCTjlR6pXWN7DMkTQaxsKi9kLIadwzM9RCtMjeqnzIzKh1lednd0554pSivKC8E5iUqtYiXju1djZXWngRRZfgjc0LuH7nzc1Mo7L7xrxkehsLz5ggJ9q2iaGKbFD2WuWNE6KkoUE+iclMOlFRAckhU+vOtl7lvZSS3uug+JYWFMRAJKdILeujH5uYf5G2FkIQy7t5I6cTORt4o7dECA0PzrbNFO9Mi/p1BkSxl0JzCCUOqW4nxUywwM0cqW1nWSYu1zFrIBZNCPId1YQrv7CEziILATn7i+ndEQMnAr4bZjs5JFLoWBoG/H3WbNyLWJNV9XF3p1CZouO+0ZpRTCy/Pb5SOMju5Ljjm6TvFjJL3Im+k3JkyYn7KMA6vXKWurOcgVSZw8RkKzl3JS9aIk6d7I2TZywI4FxeJJ69d6JV59v3H/Lhz55JHvj8V97me/+zL/CwF375F3+Z//G//2mW//a/4fu//4f4wZ/8M9z+2B8inF+w3f12pj/xPfzRP/lf8kde/wr7P/sp/uXf+tv8nX/w0/zMB5/xmxj7KRMSRJcvo5MofdQDQ4u00MaE+J37+o5L3v63v/k3/+Lh6bE/3POtb/0GX/uVX+LjDz6CMPHs/c/xzhfe5enTW5Kpwcw5jw9rIkd5ebgx+O6KUTwvJyw4m1f2Uh6jrN3FHJqnidPdzUCm4f7jT/mlX/gF7j97BeeZL3zpSzx/6wXTlKE32r5pcAqKA2ze2IMo/YolV9tevbJ3uNbO2jrrvlOatLpbLXz62ad8/NFH3L++p9TCfDpx++QpHA9Y69RSNQhPM2ZR3goWjp30oPyLQilDUxXMEGU2e8SdX/eNba+U2th2bQxCTG/8olKku/NweaCWXYbbt3csy8yUp8fDFDtAK10/G2itxTgMh8uwEhHduOz7kA6pGJeyi+nAMQBqMCqtDtp0xFKGELA4EesAityZgvPkJvP+8zuenCfM6/Dp0OZgqzsffPBNrq+uvHj3Xd597301T9eNFgLLlLXFwJlPs2iu6MCIJBmht0apjcv9p1w//RYendtnz7idE7fnTFmd9+zCn/9j7/Gjf+KGPO0ykC2FFhunJwvL3Zl8eyLeBNIcWZY78hLoNmPtKnS/R7ZS2auipMteWdfGtcJ17VwujYe98s1vfcJpmnnx1gt6DnzyaeP1QyfOiSlnQp95/izy7G5ivxovr04hsBXj44vzqx/vvLzCkjNLzKRsnLLYJVsJ9DBxWTt4HvG7ksRgQSla7pAiz5494zxl7uaJOSfmaWItnQ3piffi7M1puqKP9+j9ZeW6FfK0cHu+YU6ZGMLwH3HS0RR4p+yFddvZ9sLDw8rlsrJtmwbxJL+vacqSiTlqYEZ8sTcVp96d67bSWqH2TsoTtzd3zNNMTtOg5cdHv5jz+YR1Z0raF9ZWMND92OQLk2b5z3htMKRzMSkfvjcBN94GiForhMDeBYB1BPiclhMpJvIko103G3IZAcCNwSSoGoxXl1zIt8LN+UyeMqfTSSb8IZJTZooTeaSRmA9CvyGqbTDJr1pjWeaxWRsJUQPMG6OCmv7xNEgTLpYIMdD2nZvbG0IKZIvDwG9IM+IwKLWEhUgOGQ9q+PrwLLh/Kalbb8NwETUdR8yrRbHFZIodRtqWtm6lVEqvxJyGeXYkuYb51qqMA1OS7MPR/1OrjDjNBe7dXxRPPSQpTiChRCZzqHWnqisbvlY6T3vXYuB6uY4kQLHnsCGVDfYoI/F+gByNYIGKvIAYoNA8n2RU6840ZMgpHD9Pn0UY6SQ+gJ5BxmLbNlqpaliCQJI+pNtHUl0c10zFXp+quc7eNoahZTlpSLTD3LSNZ1wMTW3DXXLBePh/KOL88vo186TY2ClnDsNymW8mgglYO/5b65JYXh6u9FYwGu5iBx9SFZWSQ7YyZAPjvnl8D6OO79sqU+5FyYQ2Qi4kF1X96AeYCTIPDhpwemvs64VtlQdfTkmDS4r02iBokSHmrYtVOe5RBsBoUZ9DCFGyTBuSsPH5BlPtq8h3atwmanJNZr2BTT48adQgb6SRUHXKs7RytYil6gKSa1Hcs0cx0a5b5Xq/s66Ny2WjtEL1QkZgQh/SRYsQayCGPMDuQqKRkra2aVL8N73iFehRRtaxyLdCGhsCTUl4sRFo5PMMvXF9udNM/hz57obQK+SMJX9kX5hflJJWG9U36uZ8dOl8++MH1mvncy8WXjzJeIVXDR6ulTyA5ZQFPDefeO2d69c+5qs/eIvPX8KnG95+8ZT06Tf42rc+Zd3FDFwpnOLMt1/tTNfOF36bkW06VC1iB3qTh1ZXEEXMYrrFMRQdJu5pMkmv1kacEjdzooVGNsiGglaOwcC6kvZaxTDmMYilgfwKXJ6gi7VeNi0aSul4DHhw9qagh3XfKLXjTUCpWGxG2wOt2QD+d85zgOHx00ZoQiA8mtM2V5prtCxWTOhDHjLCFhj4t8nEvZRdJv7DsuBYSvauZ2s8jRrk/EhGKqNGlpH6pXP/kOAFCzAWrZuXsSRAQqQ6gILrpu13GvK2sdzr3fCmnwnqD0Z+L70aIXRu7ib2vbPVXQuo1rEgICAmyWdDUCJhHMB2jIlalMp6yIrCIftDS4xgsLcqYKxV8khqFAvM3wQh6I5RgIQFrEM1ONjS45LR8SHPsmGroc+bVkkpsZf9EbMzKj2IMXcwYY7ErdoHGzcEugdad6VvWsJSwiwxz4mQuorXwTJwSYp6EwsqDCP1UrViKF2s3OCu5KxxhjKuSev1UcotcD0wxcCS45BAOVvdiTkIxJ+O9a4AoYINjyGHCMWcbSv02nQWY6x1l4FxjPKF6naMNfQ2pOMuLlttHZqzTJk5Bra2K6U1KCDFRDdl2wp5OtFHTwEd6+VRAhmi0oQJkWVaOHz0lpw5TYFlVm+Tp4laKr12LR2qFo+HGbkhFu60TCOhLBCz5NIpZfYGBCcGMU1A/UIzE8MG17UxhQL1IU/NSQqBYAj4P0XchtQ3HsscxvuNtLEUs9EXr8Ms+liO5STVhr5/vM6YCJb1O4LCTEIKjzX50QUegbzulTBe59GHWBgcOzcxfGon2KLAhdoHceGYBwOl1cffkdD8uLdGH5L3oxc5LB1q11L4dsk8vbuBJAkexjD5NkKOjz2PM6xyjgZqNBJhnO3imUZZzlim94y8jB1HtXzKiZzUL8tFQx6HlgK7N6onLruzFtiaU9x5qIWNzrUZaw1spVN6Y12drQVe7TsPl8blunH/qnNZnctWeHW952FvPKzOw1ooe6C0MO77jYpSD3vPlADrNbBdrqx7I4VMuIFiOx/+5mu+8bXP8Ffwhc8/5Qd+7H3e/t6nfHj/AT/zN/53fvGv/C/4v/4Fni6F+fkt7hPb8g7+lR/my3/kP+D3/cd/ln//x/8dfuzJwjsPr2kvX3G/XWlBy+bI0Gr2Th3WCZXAX/jP/6u/xHfo6zsOKP3lv/w//cWPX77kg6//Or/x9V/j4f7K02dv8f5Xvsjb777gNLZ7aVpYTifmKSkdKQSoI7mg+6OERsUoCsBwWGYNYN3H0Fkb5yc3OJ3L9cq3f/2bfOPXfoOH1xeevP0Wn/v8+zx5fkca8Z50bSR67cPMaiiGTYkF+17E7mnOZXNeb437rXF/3WkDha6lcf/6nvvX91weLhiBPE0spzPuzr4XvbfTmd5cyVdpGukJUSz68b6cQ+c9tOUhjs2Stg4WZG64l0rZ65AoRPI0y9flSNMKgbJrg7OcTpzON5xOCzlLHuTujwMdQeks8mHKOsy6Ir6PhCzvGo63y4VaN8q2AybDvmhDq378sRFAoQZgWhZAcr7QO9mdxMbbc+Dzbz/lxe0J8+1xY2O94r3z0cef8K1vf8h8vuGdd9/TMPbwQM5GmobO3HWwp6ANUfPhb9XUWDtOuV549eEHeLtwevKE082ZU57JyXjWNv7Uj8z82T/9jPPNyvpr9+yfNtb7jXbteHG2h537hwfW+5VybawPjYf7lYeHSuuN2gwj0ULF5+GZM5K2XFMIMQe2daPeF959+wXLkxMPtXH/4DQC881EtInTKfP0fMNNWnhYYWtCka9r4IOXhd+43+hh5pyzikBO5CQ6qVkn2sK67RyJESkquQsTy8Idlmnm6d0T7paJ21m+Y2FIJkpvNGv0stOdRwJ3CEapjdfXjdYj83Lm5jTLQ2CwBEsppByUQuKd+4cL13Xnuq5s1zI2mTshGFPWMz7P03heMnlK1KJo1VKlG2+9s+4brVfoxrQsPHv2VEbvB5AyzGBDTEzzRK+VjnyAaiv0Iv+hOkDKJUvShum+TSmTJgE0rVXEnnZqb/rTKuu+y9zcjBQjp9Mij5wpP55J5jLOV2XT9WitjYYFijcoldPpzDJPnKaZlBJTysx5IU95mEuqcG17YxvePa1s1OvG5XJhXhZizJiLxXVIDA5Dbj86fVe0Le7juhrbujIts4DL0eDmcR74YCg9itZR0gsDII4xsF9XFeVW5dEx2GSEobsPMhiPA1zCTM/iAGP29UoH0jSJheXHRkqU7RTk3eUD+LTgpBBHD9QoW2VdN6Y8KT0ySKbmBiHNkihYJ5uSOBmNlUy7nevlSrROyIpWDt6VBJqCPLtGU4e/SSTzPs7dPuLJSyGdzmDxcQPcB/ATLNBc3l8xjSFsNJkH6LdvG2ZvvCKC2eNr7UPKdmzsBOL18XraALo686ylwHhzQ9aWxgBo8kBpVektOckMdNwj1Sttu3K+mYdnkfw8xILSPfX4d4BadrZtlRdQrY/msbVUGT1aVNAAGuB+a9MnQKyDt1HXGr3JEF/N/Yx87frj9elj4JVZqAACMyXUHYNfsMC2XQnD2D8EAcKlFHKI1MFaaL2JsWW/1QxUku99vVLaTp4FpvbW5R3infuqAUH1sKNo76ZlTRhMBIy27zSDzTvB0uPZ76GRYlSyjyVIUYboGCEk0mQsp8C0GA+XVb5SVab5wQ2S4oiXHEhTYmTeDE8tnY/365WHh4rvMC3yPczRiCkTJx/x0xq+QpwI5kCmxqb73jKkSJob67dfs1+URhaTkRbDWoMUBZ4sb8H0DPOAbff4OtGLcX+Fl9fKhy/vSR5458XC+WRcL8527ZA7UxAC1JF3plvgm/evuHv9knd/59vE8DZ28y7v3iXWb3yd3/jswtrkUWZAiM6vfbjxTgs8e2diXiCmBtGJNmPI7Hlosskh0D0SvRM9QpS5q5mzXndOy0S6cbLLW8qjk6PuMQZoHUzLButOSE4OYtjMMULWPR+jgRUCCkWZQsBDY5lOTGkhhUnpmETFhA+z7ND74/NVWqR0x2icJg2GhASeJYkZPWF3eSmZV/mCcqRcNh6ZMyEOWabOCDHQbXh96iw7QDiHN/56zmC6DoahCezI43wPQTIV9yoGjen/TSEMtvpgBxFhJLQaI4HLRuDA8RUEphgadk/LRG87vYoNtMxHYtUwEO4KQPAQCKZhGdMgLiJxeFzg9O7y1hlntY2lTusyrx6jNxhMOY2Ie8hB0nIOk/sgEM5iHLLfwdBkAEDIqkHdpQayYzHhTR5RBCMe/kxjMYLDkhOnReoLw6DbCDPoxCw2zAG0WI7E0JkQyFM7Mv8mEKUeo9bCvitwpw3bjGbj2QZZVAzA0Xuj1h0PgYbYCCGKdQlQisIiCloG+5H8liJv0sv0t6RiuqbZ5AUbxuAfoxg60cJI0+tsLtsMLb0cXAmbxxLbA4+s7uayEeHwJwqBvfbHwAaGt44NYG6cqnSHNCWBG61jVb6PKUXW66ql6qiLB+gYPI7nR89A706wKHNxd8zkk5tSJEYZ1095Ik3yHzzCoGprkrz1IWtvAttykCw95qRrCZIrToHNBpDVZW3i2PB1SgPYci1Impao5gN865rXQnB6L7omfQRsuA9bFPUfjFqd0vCAC4HSdxipoXkK5BSZ3FmymGV1XNMjITyGWe/PO3JGa+SoPn+rBQ9awpop2TTEyNb6mDekMDl8yba9sLt+1lZWnV8WKKUoc93kqaz9k1FqpzQ9cwfbWXYEWuAo3fHoscCDKfDCobVEbYnaXUFMrY/nUGzv7mjx2KEW1QIdEMMz07XcLnUw6AjyxGpVsuDY8ZAewUMtPEeflSZZEwxQMFhk9chD6VyuxuuL87Je+fRaeKg7bXdKCdTXG+su5vX5NnLzPNPzzuuPL3zyayv7Q+G9L97xPT/yRc7fNfPBB1/jZ/76X+Hbf+2vsf3Kv+BZvhJvn+DdqOkF/sUf4kt/6E/zo3/mJ/mJf/cH+X1vPeVz143tes9W5G1MCqQB9jav/IX/4r/+jgFK33HJ29d+8f/iWjq356e898Uvc35yZk4HOh2Jc+R2yYQUhq75SA4byFk70tSa/CVMh2ZvnVY6YVqYz1nxfWVnazuffetbvPr0FddSSOcT73/1K9zd3upnoEIz8HbSAJHytEAIlOEx1NGmZ+/OZatsFfkYtI4PA7e9drZ9o+wbbdBvoyn5gN5pe2FeJnywekLo5JTJaWIvKhwxJXmWeBveC2rGNUjJvb9boI2Iy23TdqF30cmnedJwaUqEM+zxe3NOzMui4TFID+69aRuXhj9LlbG2tplJRttjgws6/Lzr99WyU/aNFFW0DSNMGQuMhgOxTOLwfIkqIH3EpkZL5ABP5sBbd0+4u81QG+v2enyf2Bf3r+55+fKe5XTDl7/0VYxO7WPjtqhI7bWRZ2mehy2u4mdbp9mQqWz3bA+v8b1wvrkjnSeSOcELzxy++vzCD31X5P33rnzrFwreNqVdbCttMG1KbTy83Cm1gXVJEeZID5mH1w/U6jJW7Y3LBmsT/d4b7B1tW3H2647ZxDvvvk0NzmWVFOXdJyeq6f5NUyDNioO+uvxBskWue+GzS2XdOu/MC3mK8pwZLNc5z8ypUZoO++dPlNhUeuByrbSiQt8tcDqfuD1lTgmmZCTiI5BUu0GTmfneB+iom5G9NNbipPlG0bg5koNJ2nNIpUJgXVfdLyOefC+VWuX503t79HPpHXkLdIEa+76zboUUMlQBI7U2rmUfBr/G6WbhfPtEkd6BwR7UQZ5GMmJvSmTZN5lFrmVjilFsja4hSwV2gBQGIcdxj44hft8oRc8aXYXr5nTWQBpMDUWQebYGPMkB+wAyhwWCmpqxHd5rY8kT5/ffxivyTUn5MVmtY6TRkDJ+VmcwQ8ZAX64bN3d3GhIH8MA4K0XbHl44ziO7JcSI906eJ/Z1U3rLaYHaVeRNCWAumh9mSUN/b49sEg0AkboVStEWNuURgd5HROs4ufJYBhzyXaV4qMnaSsExTlMe62PR1EmBiPw1GoE+KPs6pwO4/A48BO73C/MyianSEVsimHT+teps9j78jSo2sLVaK6V29vXK3e2NQK+YxHSIAlPcArUNz6mxtbbOI7DoNrwrUqKHODxaFAPM8E7baxXIM4CRQ6J1DG+6F5TsN02ZbscwxjCgHnHXY6g7nkEfK3f5xetwrq2QklKi1AzorI5BTDumWTdDO/x/JCnw4sMsVH8cI4XR+wzKf0ySSbdesChTZaWuaDSVPK0P8CsM7Cg8StZ8pM88srM4Bj8e/f/2bRUb0Dspq8bBoPM7MhSm6fsxhmJLfUOKzMuJdatMU5a0a0hM5SUkBuwh2zgwLrHHwFvDcmJ72DhXbevcEmsPBDKL+UjFa5KQJAguA3KPw2Q4JmKexajMeYByQFVtr15HOEAgBV27PEXwSugaOFMPnE+L+oVe6T2wN7jZEy0H7GYmBfn5FGRwjFXyknC7Yb1fuZhzc6lMkyazc3Ji0obeB0bhtuOWsTSkBEHPefCJ+fyUZ5+/5/Krn7LuK/mzRDplbqaOlYgvO5TPCMu72Ax+d8eT/TPe3WZ+56uFj17ecH9/4Wd/5WOePzd+7/e8IJRCnhoWF4I7c3DahJgsQf5SP/tzH/D0/b/Ll/7gcwhf4eZ3/ij/3h/+Tb7x+u/xcx+trJeddHPGG2xL5K///IUXzzLzD06cJjEfiJXDH6Mhx1V3yFMihCypW5W8wcMAVFLDqpJuDwFYjE4k4/FKi2Bh5hR27PVK7YE9BWi65xcSK04LRooLZhOxdWqU11opK94TiaCfGzruO8E6NNiKghXcE9kS1TO1Ba59Y5q0GCkhsLch5nLHUEBEoxHHQH4+n9nrxr638bx2ko14cZPRLy4WfYgZs6Tz2o7FqQb2gCLq3Y3THGlNCcTr9cI0/AmnKBDVQyD2wTwacjNHEqQYRv/SlYZqiEHUqs7knGSmHEJgniYNvTFQyo53AV3X62Au58y2r+ytk8MsWWsacmzL1D7jXoeRuhLptlK0+HQpBtwlzxf4Kxl6PPzkxiJ5Sola5b9Sh0eLAHkBSCnLw8wOeg2D/WNg3Yl+xDJUPEQEBTJfLgAAIABJREFUXUZSN4GoHqGOeG6DWjt5hMOkkJnOC/TG3lbSnCSvwrXUQhLtFlxn9j56d8t6GaEMCbwW2+47xcWUtWmiRVPSVe9YynhvTGFS2mtXwJDqmlgnMQgMyKaF8JyjrKG3FfcwPOn68BkcXlmWdI+aMc2T2CStKYHP0U3WnGimoIwcMMukwWRNMSqdz5wWFOOeYhIT28W8LG0XkOIBTIvT3kYyXlKaXO1idLbrBtGIwQQEpolWneaBWhuhy+soDBZSsyaFCOpRukOtV1rPECQfx03zUus4lSmp7+ujJ/EQqEWySvlDiQnuDpt1ArImaI4CS9Ikf6aQZCTuNvo4XSuj6724ns1iNsBtLe/2thG6s+/yWIxRLDRDIKI81HSwHUBqGgu22ivzNNGqFoBq/gaTO1TSZHBVf87oI1qTT1FrDQsNo1FbovWq2bYlppRISQDVtnfVcJBfWCtkEwjWTUs3esfTzGVtBF/1+SEAyxFTqZtzmvNgxxcaApUChtc+1H2qZbXt8t9skTRkea077o+DLD04U5ZMuXYTqNZ9eE1GvG8DBJWxtnsiuM64vRll9HARJ5SCFYFMMepZXKaGb8IpuN+JKRGsEIKRQxLwGuTLRehD7rvAVPmYxmk5cztPhL5zU2a2y8I8F06nwO1tZnoiEPDVhxe+/bWXnJaFr/6OW77nh5/z6v7KB7/yz/jn/93f4q36Bb78gz/AF378x5m+63eznT+Hnd4h/dB/yPf+yJ/jey/f5M//q5/mV//e3+ef/9y/4B99/Zt84+HCx/3K5PO/Hcz5//D1HQeUXnz+q5xuz9qS2vCUsEOGMOjy3mnVCV0FpptjrdFaFTrpYN7J06RUl2Wht0KLOiT29cLlcs/rV6+4f/mSxsSTp0/40pNbpmnCQ6TXyhTD0ASLcgdvHrh93+T/4ZK3baVxf9m4eqDJOAcsUlujlsK6PsibpXd6KwRzpnkih+FofySLBWk9U15kXlslwUo5ydNkv47rIHPVMGLftUFy2r5re9mbthYhMk1pyEJ4RJ1jVApGrTvQWZaZ0+msphLJDywKLbcxOB1NdojylyjbPrbhGi70H02MjypqZs7adIpKfaDynRAkxbMhSbCuRsoMctLm4HaKPDtNnMUtp1w3bdmi5IDbZeP1Z68wcz7/xfdZloXtctGNOWc1900DibYQ7ZGZsm6XxwGv9p319WvadmWeZ27efiJJlDcWOt/13PmDv+cpbz2fqa9WLhfwvnO5rlCc7XqVPKnAuna21jRYlcguW296ybS2c6k3wuzDxlqOoc+47iuv90JtgbuUOd3dEpYT+965PDTMMst0xsyZp8BkzuaB+3s1M94T1934+NJ4+VoeCE9OmZwB60weKCT2Ci/LzketslgixE5fnb0JtG0NNjQQ3pxm5pzIsRNo0OXd0KJyX0qRbGutdTDkIh6NvXZqj+SzTLPjYI2EwUw6wNS9ijHXm7PXfSS67aJDd8Wcy+tHaVpiL4iFtJcNrGMj6am1Rtl2qjdyjtw+uWPJJ0VLD0mRu42NoX5mH9vCPqSiFhNzVOT2vm0afl0DfO9qkIOZkoLQJrSNuNHU46MPxjQvYi26E9Ikc+cs4/zHuPc+tNmuWPVeZZDaxrYqp4mn776rRiJrqIyj2Y2mRK/mjvWq7UirWFGy0rquPGwrbz19qu1fd0Xe+huZqo+zrAtN0jPeh2ShqyCv28rd0ydqEMa5FxD1X4+6sVWZQwYTyJhcgF3tnet2ZTmfCGN77eM8b334rQ3JXXcNEm4myQ8aXErZ5b0xpF5mRg+SDXDIkWAAARr8H5krdPZtk89PniR9TIFokgYo1UUMiH68CB/eAqatd103UtA2lYOhORgjYhq+id5Woo0GJwbYHQL0UmG+kdyOymSqF8fgNyWBHW7jZ6vMCfx0V90xI055NMyTtpkHQDgkd7VouxXsSDgbyFiOlK0wzXqWWheVHBifhYCJ5BHaIROUIbkHqNbHdjMRSRhpgC9ighg8ekqIoTVkMQwy1EiMqsPk3ULTEsEOE1Ub70UhEQdw6L0NNgVK3LHI9XrhepH0TbaRHRsMLMbnKU+S+Mh+AyXxdJRQ0q+76peNuO4BqgaGf1/T8N0RGBA8MMw5WE63vH59lWdIumEKmUSjmeKvA0q4TJboo8mnVkIPw6w3aPNcd22HMYo3whhgiBqirMk7LCdnLwLVvUU6gVoj3hPNK7c3M3MwyTGnhC1dyXrIny3lpA7NI/SJZRILhVpoySl9ZbIb9ga+dtKkgTiS8B4FzlnFbNL5GYfvG5Hbzz/j6ScXXn525fW0kl8a8a3IUir1ek+aOrQL7o24nIi3D9w9vfLus8SXnp345O07vv7tT/knv/CS5ZR5a8ks50zyKuJQN+Yw06adqTnJM3s0/vFP/WuevvdPufu+Ozy8y9s//sf4yY+/xau/84t87XWlbiuWZ1p1PvbEX/2Zl/wnz2/5wpcWLGkYDKGBN1KCnir7uhPiQp6zJBceoETVxmDMRIymVEKPkgGGjrmxTBNYo5RGyieCG+u6Ezqsw4jHBogs3z0E3BuQA6lnybXWSrATHiCFCGSKV1Lisc4QBiPwSMHtkbpFpiliVocAh0eQVGx2bQxinAjRBR7EznrdCWmw58ZzmKeEeVESYW1M04yFPliPw2Ovyhw7mp6t3gWcpCnRSmNfV7FBkwFRDLPuRFftbBEoMnAO3SGFwSpqrPuGswOSmLXaRxst77h5zjQvLKfM6g13LSlK2TGD02lRylRv7Fuj7oEQBCLZAN4N+cNE14KmyOyR3uVdZV1nWUiSb00hj3slUotkNlpOOHgiRec8L5hVIk7MifW64UfSspkSQN0pXfLVeADNOFtr1DDA9NZl8RADNljPe9HsERCrKU1Z15yJHo7KI6ZGhHEWakFhIYiJZlGMlRiJOdJa5XrdyDHTvRHTBDRCMmKY2IqY5lS9xmmKw9uz0tpYvoSg2jY8GU8xcZ5OtKBAndIa171hxclTZM5xeK5WMcaA0hUiMKdAsUrozmrywKl1sJo8ygMOG/2PQQL3SvUjYW0sYmKgFS1otNhUMT2WE3jVmZ6MNKTMMSRidKZsBAIP247vG3mZpSQwI5mDNy3yOYDCwYbyYVfgjVq06Hd3ZjuRshHICofqfdRjnTGl7Rz0t5SCztXmeNuovSux0CHHSDWjVmOOid6LQBEc7E26GS4GotdOMCW0BTPOKdFSwr1gIeoz9EIac6cjFq+5PybM6sQC6/2xN8UCvekzuFyvbGkwtUzG+DZSxfEu9vEI6/DBzsFWYhrhTd5pLUCKrNcqC5gmv7MYA0vIuq7H0lFavGH6PyxdTJJkJYIjCXqKo5+ARtb9BniFzZzgSqK01vEe6MGwdvjCRdyK0tY84F3L9la70g1BfY91wjzT2wD1TMFRxwDsIEmuXgYtGJubUhup9AJxBLC0kvDGSDQU+z0OFmU0Z86FbEoUTsGIIdH6hm2GJzH+KIWYAv3keNJsVq8itCxz4DxnpjmQz5nL5YFv/PqVtgfONzOf+8otX/n+r3LZP+WbX/8n/PL/8Pc5xWf8tt/+A7z7Q7+H+cu/m+np57CbZ6Qf/nN834/8Wb53/Tb/0dd/ng/+4T/k//z5n+Xnv/7r/08I5//X13dc8vZ3f+of/MVlbJSXaSFPs7Seg6Y2ZLgqgsMQNCBQJRJGylGT5CJEbm5OpBRopXB9uOfVxx/x0Ucf8urTl9CN26fPuXvxFnd3N8QYyQgpjiYdOgdN2I/GFVHiBhvqslZeXVYue+datOlo+06vzr5duVxesr38jLYXcG1MYpKsQDRWhvRkmCUKvn9Dt0NNiUzelNiQUnyjFnOnlJ1SCvu2qnEfD3MaPhh5GL5KGzoNE1enN8lapmkRXXlsiGNMAnwY7vvu4/W88c3otT3SDBkjemsqJn0MAkp6GxKXPiJUR1GIh/Sj7thw3F/mzHkOPD0l3rqdeH4KTEGIcBP/n1IL18sDn37yGZfLlWfPn/LinbeJ0ajbKiqedCySkcU4ZC2QTMG0vhZq2ynrA9v1gfvXr0jm3Dy9ZTmfiNbJtvP2cuEHXhR+13dXUn/N6w/v2R92tgr7Wqi1KNYydeI0EybDsrZuYRJtNQwZSx/yEKIkZPOkAtbc2VfnvhSBpJ64vbslTDOX4lyb00Kmx4y7DYpyZd0S1+asRHoPfPja+OizyscPO81lfttNqUDNI6Untmbs6IA7ivbe4LJ3qkV2TfaczhNPb8+cc2DJ8hsyHzThUXRqc3pXsiCDmguB++tO6UY+nQnBWFIkRd0bbQCdmA0/AxWl3obcqxf2sumezZMO2WGYGWIYvikapI8kvxS0cZIkRtvUZ0/fIqc8qPQHmNQfN6BHIW9NoNZeCq13tm3jcr2yX3dyjHiEmJXoEkbDEhSLM2RH6sab+2PiRVqyhtAQmJdFwFieCTkLdGjakFofzIf+BpQovbLtOwG4vb2hesVaw/zNBikMb4qAWB171zVtTQDVw+XC9eGB880N5/MZGKyeMbj7YPmklIepJwNhUiKdzpvOfr3y5OkTgeZhJJOk9Disexf4pUHeh+xX3xdi4LrK7DAQB8PEBiMpE1MmRFG6Y4jD0DU8NmOl7Dxc7rFgLMsiT6UBZAWTB5UNs+8clNSljZ6GHu8yPr1cHsZrHgB60t+Wk+TD3Tl6AT/8owbw2Vvjcrnn5vbMPE1vpGAmML0OcM4IQ0Z3mHAPOYVDik4tneXJM4IJIIqm9+FhNMhBTM+DmXSkmtoY3vd9Z6+VZV50bo8zGecN4wxR7N/4DtkAeYZ5aDvich3C4Y8xfA9koIcFnY1tMAd9sHxikPTWemeakxgIY1iJY4sp8GZgcq5Icu9tALWi0ueopjlOeQBIebC7R/SwiynYj6XLuL5geNM/r+vK+Xwj3yyTZ2AOCnXg8VyQBFOPuIDGQxLe+xuD8pTjAKcZn6M25d1dS4gYxsintjaaEmAvlxVMjIlo8m6wIAagBTW0Pu6r6CaPCGMYdDdSdHq5DN+qRDXHXHLKGLS9tBEBHVxnao5B/lWTfI1a7zzcr1wfdiXLpk4dG9C9dKo3ycYMyX5TIGb5tobgI7a8kRzyMkzgLekYaO1x8YSZ/IeiYuM1aY80qhix7Hzy6y/pUQNzihrwvHRi3fCoe5sIIU0ka8weSQbdI2s1vvnxS7758YXTeeLd25k5B/npuFJ6QkiENnqMELhuO9tHn/C5L9ySnzzH0zPe/vzbnD/+Ol//6BUvryvBsoblWni5wqsPV774jnF7N9hxDI833+V/0w1vYysW1DsZWpZs1858l5jTNNJlBbb2sd0w2ljwZVrVmT5nI+Qh2RlMk94r1g0Y7MXRf3kRWxGv1LqOMzSN2hLYy4q5JKIW5F0XQiAQZf7tgBdSaPIJGtOuEuf0PFowybLQUgQLWEhahtjRK4kxEkQToNc2/MXCOCv6eI4kka6DGXOwFvEj1lssFkMDe+sjjSoyJDGyoshjkdiHtCukhA0PT0lYB4Ri8jWxIXHCwbuMjW0sEVLIj4z3MBYOtShq3IHempYuaKmky6GkVJ0PQ3blkguZ2zCTluxRy4E0POb0bNTex+fg3J1mljQYw8gXNIgcAyaQNmWxomt3Ddo4BckOI/J4evTSC1pGHJ+lPh9JtGrpAnxczCYBh5KPhZhYUmQK8nraSpMhdzg+80BM0/A4E9ATUqDHgJfhaxcVWHL4CQ1nMc0+XfMNPuRliJF7eDKFZSLkUce6D2VEU9067kUbwQ2j3phJlqV7tWFp+PMM8+je+6MMzr1pfrJR9fxgVDeqD3l3h1YkkXLG5zBeO46kcUECxLF5kfwwRjzIf1dMpqNn9Mfaqi5H91wcvkQ2ninVrAbhYNdNMpQf3k9uWuqFAfIdya9HSGrvYrf21h69keJ4Lt3kexhNjLnedaa0MY+24W1TSx3XYQBdQLKkhWZUz2WHP94gDrgND7DxvBr+2IcEOwJKFrDCNGXKvj36ZtbWpLjoIx21q1oGU60xbMgW1XvVWsRkQjM7wxahqnyLBed9zBJa4KUYiOG3SPpEix8gHlr+uc5t9R2jZ+jjHkbPXZh0ZlscTPtgWjbiwwON3wLUGSFqeRrtTY9SR6+0D0uBGHWuWkCJkxb1LIYwAol0DQJ1JPgyLB+GLcy43s2d1gOlNTZZtNKbY83p1VmbK2m5ucA3OyTFgW2V3PTaAvdbkT/TWnm9bby+FB4eCi8vOw8PhYkAk9QOl33j1YeVT37zE6zC2y+e8bnvfkJ+Fz757Bt88C//MR//859m/+CX6Oun+HrVIn1+Cu99Hy9++A/zO/74n+D3/4E/QHjyxb/0bwV0/l9+fccZSjkEWlA0Zm+IctjVjJkf1PFBXw4ahHoT9V09kKKvDZhPE/t+4ZOPXnN5uHLdrjSHPJ94/vwF8yRPjuYN631EYWs4lIfKIeOw4Qnn7AWuuzxS6jDF86hHYGs7Xpy6XehFLB2ozPNJ/h9Bj4Qf4MIYKKXx1kMe4qhEPvyKxsHoYwvfrbPXQu+DrdU73nyYamd0ttjYsACDypyXRXK43kYSlTTLOc+knMlZdOc+WAqM39lq1YAxaMGtNQ1AMQ1vFHnG1KptxfHGLIgB4WP7pCI94lZdPiuhw5wnpmlinjJzNqbYWLKTvOBNJ1NKkct6Yd1WXr16AJybuzvO5xuiyavJxn2h4j9Mg13X18ag49bHwHphvf+Muu30GLi9u+F8ngjdmb3zZC588Xblu19Uns9G+7TwyW70vrGXxlY6Vp21rGyIotybUbyytQY9sbs2+91lMFmrydsmGIRGjI2+B8pWqaWx5IWnTzIhT5SaeP1gSuvJgeSRaQrMcabXzmU33MVS2fbKRxdkxN2bno0UCdEHcy1QW2CvjTI2Et0FaHS3Yf4pH5BpStzczNwsmURgNgjRHn38JS2FWp21yW8kEQgR1t65bA1CJs+L/r055o3eDp+TN3TefYBL3gblGaeWXUVmGs2dHwO0Bp7DH6aUghtM88x+kWRuILHc3Z7xXqi7rp8n1PwDcTSndBkmHq9BBpWNcrnSgGle9P3DlM+GlNRMRaabaL2tVbEaqryX5vNJw/80EZKA4JtpUbPUFASgDdrBAhxNzmgyJfcr3NzcYN4IxQh5AASmbZV8thKtS2rqIwXK0bnwsF453d5wPkn6Ic7IGKoHoBCGx5AaABXwvYpijTv1euXp3Z2aPucRWLYBHtnY7oUBNkd7Y6QdLbJtV460tRSSIn+DJEY+0i1jiIMFY0ri7Erz3LdV3mG1c3c667Pvoh9rCBCVvnV583gQ8O82Elya7oNWJOUKU9axlATSmEWdk4f+/WBlMdLJTM2VJMAyZ0xJcdy1S/5TWtMGd6SF6ORmeLKpJhmIrj4vhDxB6YNdpQZFZtH1EewTrqfPwyw+Lkz21gdTIIz7bwyFWkc/NuxHA/U4g4wzmCEBFLg0UMnR3PQmRk71PtSbPmqRfszhbdWaZDNm2oo17/ITGQAlLmaj+nbdZ72roTXdJOPZNVplNMVqHmuVZEHDggD2ftDoGKCSGRYTvWk5YmYjMa/T3fSLESDl6H4iIJDMdZ+3WjUgjoa5lU4P43W6U00b2t4O9uHQ9B2vJyrd5nxz4nJ/pZ+dZorGNhQlHw0Nc2aP5r6OBmkG4OToXK6lEBdJNmsIw6heINI8z+p5rD0atzffsWbMKeJno+6BT152oGGvGjl1PE2EZNzsmfQsKe64NWo5fL3aYAlXmhvX3ggvDXuWyEk9hfcuP6aQSNagxyFxHZJSBwbLbLk7MefK6092ljlx/9rwWjk/NUqBtO767NIN8XTHYoG3+ZhwcvIcxhJo4ze++Qn/6usXXtze8KV3nDkGag+0FpmANoHtgeoBmzP/5oOPef5T/4Dv/4ln5Oe/i/7kt/OjP/ETfOuT/5m/9ksvebVeiGf1Oy0lfv7Dztv/aOdP/pHMkxeHGX9k/HSxzC+N7dU9IZ3I8wS54T2wxMDNNBFSwZuYGgOB1rky6lwInZid6hB7Ilphs6ukG93EimIDAoks43cdIeSayMFYQ2VrFUc+hdG1dKktMHuiNKM3Y2+6xyIOdRtymk5OnRQapcvouI1nx7sY3iHEwUQIArEy7NuV7kH9KRq0UpqIJw2Pl20jT1leOe23gDIhqldEzNnuknpr4WJc9yqmzbHQ6cY0QBn3TgyBOpZ8o9sUeEhg912g2/AWMrMheQmPYJZM1BscOXJRZ39vWmzM85FAqsO5mwvUNUlbY0x4c5a0sIVCrZUpZ7pp8dUr1Aj0gHlg3ffHfodRi+uwiThq3bFYmC2NIVGg8nGz9C5ZVRkMFHN5KymoQM/5AYrIWkAeRQwfKjctlvuYc9wNghaKZk7zqoZnLCUYDJPqCgWwAQhCJE0zUw60Vtga1HXFk7FWZ45GTmPpMbyjmkvCqUFe4SceA5ZnBRy4fBKVACamVx7WGm5REqUutDJEXRNzBZ/EEHRdo8IS0gC+S42jNondp8/8ABSMEKfxuSqVuIUOpUO3R7ZLq1XSJhd40od8U97Yh5WJ0S0JUEPfV6v6qpACW9l59F6y/tgjwOjdNLkRTUtcC1X9STT2TUmuxEicM6Vu1Lor6bULzOl0zAogH75e1bNpB1GJSalnjN7AB6iEhzc+X83JYSJ6H/tBMWfK8BINIQmk7YYFAZCSqPMGgDInHYjyb1mQ9MFoyjnR+0xg+Hu6DSTomJN57DPrUAO0UcfFetL1A4aBv35ObbI3CWMZtYc6+u0x0+HDJmIsm2AEU43aymgBXCCbPh/NyzEIuMsWsNgHi7jRMYF91ccyrA3/cflaGepTzOT9dMjxSxUG0bpTmqv+mhhuOYWxoIx4jEAdy4I4rCsTu3W8OnM2CmUwCwWKdZQw7d5wg53AbojdqS6AU4qspVDiCV87kyV6CLReyNFZQyXHxFyNKXYusTKlwBIm2tZIS+ccA/N54uZ24hQnWu745cJ6EYv/rXdO+OfvKGvj25/9M37zb/9TTvkZT9/+Ejdf/X7u3vs+4vtfgOfv0t/7vUS+c1/fcUCpj8jk3puazWNDYgGLYt3EpBQN90bdG606+Sar2Xbp7WsrfPTtb3O9XCilMZ3OnJ48Z5pnsUYY8g3UPPlx+FIpXRQ63GmMoaU7a9m53wrrJhaU9dFgGex157q+hl1R6jln4ulETE4iDD8N3bBwPGJKYQkh6f2FOI4H0Y/Gjx7fL7+g6lVmcy5ENIaoA3oMfKKtpjGoONNykneLK51ARdaY8yyT4Dw9AkWMPr4f4FWXsV2vGtiFcseRMqFErlbqeF99DBwC11o7AIQEjIHzeO+mCMrzzYnTnAneSMHICU45MYWOEYbspXL/6p7LesWicb59wvn2pAJd66Oho+YhezwE69iOhUEvrHR8X1kfHmjXV1gz0jmTzzfMQRTYu9T48k3jK882np8VhXx5vdF3WAd1/Fo39r1qGKvOq6bNiKQjisz1JrZR6dCaCoa3xFr6+HcFzKmbIp/P5xNPzieIgXWFHhLTIvO2GLRNiyFwXQseJA3aWuNycT5dCw/rJFnoAYBEU2y7BVrVBshHEa5DLqoIchXhGBKn+cxpiZxnDUY5KIa4OcOcV4d9bU4lgrWhXzbWbeOyN3qcldQRXP4HjIQ4xsAeZNbYWmO9bjRX4mDZizDGEEh5kgTSsoDM8ax47+Cy/O5D6nR5uMfr8IGJifl0AlRwkympysMAa4/hrveR/iZjRBrstQjMwpnyhCGJXRp+RTaiMsHZqyRmZSuPn3nCpJNHUfQhZ5nYTkqzI9goYv74QPfBmjCXPKy49l/zLA8zi0F/THp2SxFPY9MYDeuR7vIA6tUpRcmN0zxxuj3jtcNgPahJAqIYZj6A7WBOBKpwLTKSmeVlhpQUbWuDkcQbhqI2LCryNuRXoZt8AIpYinOQ/FFNsbaEHbEIdYEzYcr0Btb78KBa2ctGqZWb5Vbmngzi82BmGUGAAZ2YJxmaeh+sIN2n0QLXeiWfFppFFuIApEzFeywM1KLpvgvGI6BE75TWmPJMjvmwLNDQc7yiEKBJkmZj69RHo1n7AN9qZ7o963x0Xb9mWpIw/P8OdszxnHgQI+VxO9j9Md1N/P7h+1OrGJ9J0kF9VmH4pNmb+31XdG4IUWaeostQzKHp90UbDKMmLyGZMRzBDpJIeK94e7NsMDqELCPPx/2hPKh0tuhMqk31qrZyFDK9T3f1rcAw7cG7DSANGDIx78KVg03qzWD4qo0FSlBLbWPx5Mdg1pAJP7r2rbZHplCtFfPh/RR4lB+GGOmlDgZvf9MM+zB/bY1pmfnww1c8vZN0o3dTfY//N3Pv8mvbnt13fcbvMedaa+9zzj33UVV2yo5sKiQ4JjIJjygJiUIQKIoiGiQdFAn+JNogoE0DRAMJARKYRJCQYCcWcZynnIodx+Wq+zjn7L3XmvP3GDS+Y65T6VeD27Drdc/de605f78xvk/d0TkZMxveFP4+hgXwIDKoz47VCtuVnAaLV53TTLohhXTYpJKZmF435lAjVymJaomHx8LTVUrUFCqiZlCTs+3Odm3UWqAa+3Nje+700VFWhJ7X/Zwwdkq5kdLCWheyOe26kd0YNQVLL6b/CDiNA4yUMp///s/Z/tET24dN89mUDSi/dUbbsQLJKqxvSOtb6qsbb8bkZ747ud4GL7fPeb5u/M4Pnvknnz3w6nTiO2/P2DpgzwxLpJmw1WBM0kzcpvEbv/5bvP3ib/HdP/kFdvoufPff4N/78/+CL9/9L/zVf+Fst4aVxEjOllb+xm/tfPKrH/gTf/w1r18vyIWUSGWS0oj3tdD6puzFnKFouTUmucS9E6pDqUm07Iof65AHpUK/DeXmZGffB8M7xpD9LJY5NQTXaOSbVHNKZDhdR6M3oyyFYlJDVIe+TPZbqDAYWAoLsWe6V6w+etrxAAAgAElEQVR31up4nrI9di1tisYNi2kK0KgaySdG5XYbdxtJypXEZKbJRJbOGSCXhRrAZxfo7fHeh3qXrgXbALOiZ31qHlfWyNBaFKoUd6ntJ074YrCUKeuJPpx97yhpQeDYrU9qOkANLcMlcQdhJDALJRZqcTzUmiPItENJdeSvQNwJYW/PNc5PZPMbkhHjcM/Fma4MFdxCnRPnxJxqBnTYm4jbktJ9Kd4H+FFm4EkB5kEiT3dyEgHX52AdsBYRfdsItUUx8iI1nQ+de1KGzFCSHSpXF4Hlg+FGijwtm1qGlWXlTNOMM31C7lJg5CEwa7iAB5PC0LdJm2GZN837DOhIdam4L2ebg7EP+kw6uF1n7EGG2wSmKXgZ5ZlKpSHius8pBbFN8lKZTSClj12L9vRQB4aFcvhdIVqsMIrUWblPWbhjrgL+JWX3AVT6dAbgrcHsqLVUChDQ2Tu6CDASd+XmAaxOUuxKQ2QXauCDxH4L0m9qTrpxk708FeWzDYEF02D0rDs37mhZoMBNgHebMXNMC4IvlIekA9/W3Wth+7euG9k7scrKXmX6rO7KJD9IMH2xww8BAKSS2cemtynLLo4T+3co1JJAKMUnSCE+4nlNsbPmaEA95jiL92k49K1FTtqIuSBsfEFiNv+okNP8nOKZFThMPJMc6kUQiIPIEY84GDM14ykjLVMsfVQRMtl7zMXusWto/7Ws71Mj1X0QlDIsRB24Gt369CCFPMoJdD5NDQcSO0ROm6zCUmFOP/LDOtk0Y06Txd1CaYZbvCm6t4zENmFaY45F5wdHnujHjE8Rh8bukxuTPBPmg3FF9vi1cU5n7FLIBeXI7o35LBDx1ZsTfAb7uPLVh1/jR3/z/+W8vOX1F9/l4Tv/Kucvvsf6x/8CP6m/fuKAkthdI4cN081l2SpV8tfR6Ner2pBctdLny4WcE7frC88fPnB9unFrCvlb1hOvXi1cHi6S+wcAkYI58LBnSWKu+r9hkdM0nOuY9NkYE15ak6phhBR+dObtytx32miQtFAuy1mAjh6NYFE/MuFarpMu5ZSjvU1Pt8fOOQLMsVDV9NZlR8i6IAgbh2GM3kMuXxT2abrE1CgFfY9MJSNCEyt1lYxwhLVuuAeI4xwescN6cP/Z0PLLHFrA3e8HmwlCjrBhDZsHy56tqq61K7/idD6Tk7NkWCsUN2rUutacGb0rdPnlyst1Y07n/HDm7ds3JDf62Jlz6KKKimY/6D6TamoE+JDKQrttXJ+/Zlxf2L1zOp25rK+YZaM/P7NeKj/3yYmf/6zz+153TtnYNmOM+P4mCtn2RGsw9sGtQ+udvSmcsY1BG6oCni0zPOomh1rQvB9qgMFz0yH8+nLik09eUcoaQdSyzF1q4VRXLCtr57nB3pRt1Pvkae988wwvm9PRIp+SUavp0CQxgD7Cr0zkofQBUwxt6x620sx5XahlYVnCihYIXXMtxwNdin1o4EtjUFLmuneetivT1ApUc47mrbi97JB26tU+lrLb1rlteyiVbiQEJMkP3xW0HX8vBj4HjS5FXalSXG8bozcNgSlTFzUS2mHNQpLteSyccdDPLjWdAIXE1ja27SbZeVV9aglQTmHFAnk91EAjauwnYRkNKfOyrMxSlGWCs6YzaVmw5Hgfkc2jQYrpWOtRGzsjQBJqXViLfn4NnSPeL6nILGvomD3ALCQ3HsNp+4bhXM6Xe5BpzRZ5R7KaKlvA7kCWm2yXcyobom2bDvW6RK6QGs3MAD6qqu7tWi51JC5/OgbX21XghaQlegcjD+CwDFsSwDzj8jZCAZoT7eZiyE8KuUxIOZRrvQOUliZLUuPLDHvQ4blPKdH3JiZSDw+prJHloxDNmbhLwkMhHQC8hrOX7Ua/bqxvXt3BTEPHS0SGiNBA9pccGR8Klz+UOXr/qsG4bQwmNo+crJDN3z9bIuRUQ7KyI1B2XpaNUCo5WUDGnDSXzemUT9wzI5DlIx0D3hjcbjcOW4tUBASg5pgnpgcgNrVYeNi0IHI05ozGvck8shEQ2FRX3TXuEb56yPlDSWe2sncVM1jc1XaAkx6KJ1cosFnkfQQIKjWpRfaUchJH3D/nrEWx/Ji6SxalsFGrmFykC2Ix7wCopXgmYyg/Prjj2Yo/wwJAOWyEjgopSq3x3Q7G0kkj061TyrFYm5TBAVyOqL/W0jtxMimtWO0CzvNCdhR0O6HhYT0JFVs/bOIe1n8pa9IZHh6uvDw5IzuMwSzGqa6kou/MeyatiVQKqU7cC70fweeT55fBUhPp3RWrDs0pVe9pGhvlrEIB0mD4JnCZGvfsIKXKm+9+Bzt/ye/++peMl0p/3Hl6D1bPnN4McnO8rNA/YOUN+eEtJzc+c+d7Q/fZy7Xxt//+b/OP/9nXvHr1lsdL4nKunGrmmieMgvWBJ/DusBaeXzq//jd/g4cvPuXtL/wpyN/h8of/LH/+P/wRP/zvfoVfeycVWi2JLTtfW+GX//6NN+eNX/q3Cg8PWWSNi1bECpYulA+NHSmq82ikqVjr2g3mRpomcNxld5s+2bsr78fUeteZuitnYS2wbYPEwkyi0Q/VjIjEACPGgNG4GNBh32/0rZDzCVCTlg+pgZYk0GLMTA/rRndwr/gYlCybus9+VyJKoSY1fDIBY8krnuM7R0CKUSB1DGV/ndeFbR9cb7tA/ZTCKmYKS7fCMC17KV4mEaVSG7ody6oz0PmYS9V9nLQUllyYNn5MbWAsQZBuU++8u1r21C586JFVuOIHaZW1gGWTeqqPrn+Gm8ou5h7AzHK3J82pnLSKxfKovCxHRNXoMyysASSEEmMO1Yu3PuJ80X82DwKE+E5GADnzGFUnFk2r2eS0+HF7zIw5+6U1TjGK+3Qp2phqpPTYaA3y8ZnFuTxGj89Dz3BxU1ajXGr6vV2A57Z31di77r8lg6Up1Zpl0nBqcXKxAHL0j1XWTpbFqincuLWGjQXbUUurRWxGgqUqh3DbdxEE03WRukAYn8rPSYiEmQURLznr58elRhpRAHTYl3sLUAWyFYotcuynfleVHzZ6fUbORxfF8XuHwap3AYUhYAAprscQEYVPzThu2kOTh6NFc5FFUUpCYFDbo2wFvQcpDa7XRrLMelKBxdHa2pnUmRS+nEVkeDbGpmywEUpI0xBNRjOhLpYYc7IHmHu0dB52Rddz7o2UysedLev/55mkUmKEAtWEzE6JOsYMm904sqkyvescUz+VMods6jmR6k7zkjiwQzGln9WHAFRlWmYOOfS8K4E1T8lLIFXsIEnp54qLScgWapiA77C7TkKF73DYNucMZZMLTFFzaSInJ1dZ3Jx0B5+nT+wovIpyEs0NOrclcowZIgfB4lKOKcxdRO3IB5FrJKtYcqpt4AlD72gfRYT9jEKtmERECrpC6Qkb3gT3zBbP0j5u5CQCqZMCdJ3kZux5Z9/zHdBailOXwT7gvKz0PFmWxrV13u+d5aWxWqKmQq6mJjozbr7hT1c9H9nIFS5vCs3e8aNv3vP1j36NNX/GZ/9/BpQMfWnLstC28KBOp+1X2pxqemkNIytDYjjv37/j937wO+y3PSTblcv5wvJw4bysOgxQsKEGdh0CIxDsj+65SffJPiZ96+wzsc0ONhlTFYCzNcbW2NpG94a5vPK5XlQVaUl2AA5Jnsl6YloYLDzwB5jEsfgEbwUagHtrEfS2CZxJsmrMWG4MtdwkS9S6UGvFsj6TWioT2LarEGVLrKdzsDU6BHobYUPw+wE7htQX5EPWLK899nEwaPuun9dMNrn4+46B+UD9tRgoIT9l1cleLq+07LTGqSbO1VhTp4TU0pPx/PLM7fnK3jayZR7ffMLptFASZOQXNReKi1nIf4kAVydFsGtJCWbj9u4rPrz/hsxgPT3ycFqo6wqtkYbz+z5d+YXvZX72bePVglimrdOzM3PidL5oYBob3gpeoVXDN8BvlG2heZalrBU2GptJml7Q0DU9Y9mYfefD1zszn/jWt97w+lJxzzxvG7UslHXltMCSMtlgG7Dtma0Pbj54ac7tCk97ovVELiPq1i1Ye71DB4vjM1RJ4dmW/Dyx9U5eFh7XwpqKnps8WUrFUebSmFHHqteP1mTzyQjd//By4/m2YVVV4oaxZOXGSPYeS74kCGFp6ux7k/W0D+aUba1kTbQpVHElr0yP6mOIhXeGpWjS96gIdliXhVpXLBf5tUsOi2awUBCMnaS1rXd59udk3xsDAdbrulAs02a0kpWCWb6DwXhks/nB8il3QblKSZLmpIwdBYVHXfGULQ4jvisxxvpjPUIuO7VWllI5LVUgRakUnFwrHMUEliNPYEQ7nSpJn68vpNGjtjkzk9QynqTWEbQax4x7nIF6btQ0mdn2nb0PLtEiwwG6HGhg+pe/0yOo1Q4w1zL79Upjhqo0cuAOJil+31qlcnTjx2p9j3wPo6b8L4VNW9HnWimyiwbjjWXm6BEcqefaXCq2bd+hFLxNNS9GKPOcg8UjQyuATsOD5e2AsTs8vX9PqUUhzvG7Eio4NT8BMWgdWu95qHhiYM+eGLXghwwqBjnZoJxS1dim9zVk/Hz067tPMdylhuomgJWQiHef2JIDTP7IZB1KQinxunKHzufIuyIso12gpR/2zzifPN0Z2kQw2Tlr+Kti9bMZpBo5Lx9zm+7PisX7koIQ0kZLLQkJM48MkwOs8fvNr7wIdI7bkVOj/4m5gMh5PDMQ91N8ModwhhA7+IwcKO5tN7jA3+lTyo+sn0FZT9HGF4PuAZoayvY66M+cFIL8cr2xrgXrTZaVEVlxrvpg4bdSFO0cXXeRi+KJtS5s+w0vq86SwOpGcrw7xsRssm/6fXOZLKsUnrJ+OA/nwtM3z2w5s9gknypWC8t56Ew1qeHWmikPZ8rSaXtD4XAw9kaaCS+D6zcbPDRyW8PK3rj4AmvHukWA88CLgAkR9lKFP372is9+/8Y3v33Dz7DPydM3znp5iEwRSHPDvWH1NfnRuKTMt5DF8KV19n7jH/yTb/gHv/mB75xP/OxPJ6mWzekm8iiTaKbvdXnI/Na7dzz88l/nj7555PLdP4nlT/j2v/MX+Y9/9EO++Z++z28OV2PeYnhzftQX/ve/d+PhrfELv/iapQjUUHxCWCy8MD5ElgmO7868deYrV1uWdXJxNbiFzbPPTh+yoU/XHeEDfBfwejqfuLWOzQJzCARPI2x3cZZZYiDbi3cRic8fGnWBXAv7bHong+lmzvjZZRnLofabLuKoJKeaFtNbF2jSRw8AItRGM4MVkqllVTl0TXftJNR/haXKrn/b1Ta3LkuA1un+zstkEmDPHMwmFfaxDPU5YsH/SKrmkj4qDFOCJjjmyDw7SLLZE6MpryXjypNCGZXTB/Q4g+SREZhqjuWsJsShxaxkBWvfrs9YqVLs+qDmJe7DOEuzgIa0CxycTEbv3KK0QCrYHPYrpEg5rApBvukI1GLmszG61C8ewHnORarDUM6mjJbfCTZFCF53NUYRhJgxaU0gWkqV3VsQqQo/l0MgPvJxHJzAUIvwcCm9Sj5UXiNUx4WcIRfd2d2luJEl+UbJ2k3clDtlc9KZYBML27BiCzYMsS7JJl4G0wc+C7hiTHp8Zu5ylvih8iAIIRdhe2/bS3GWu2G5ysZnasc29yDd9YuPrpmkmJS3HnZks0NhfoxyIpZKURbXCHVciu/URqj57Mjn+zgDamaKmfKYjeL+ciQEcIwRivu7WthmWBZdDoEU30PS55UtEQGwYSsf5BqgbALzqV3YFFUisfKMizD2wTnx2SOgPOMjrJ3mUnemTArFmGW9754UXUBsxUc+qpTaKvDIpnsHnDZG/P7zIxjnHJ90zIJhz0Nnqgg/5UB1l3J4uvIVLWU1983jdzyy3zzuYJF8h7MGpNgP860+3yKSpOsH0ecQe/0xYSRLIj+jkGPvCvPOkfGJawPPBlZFco3458+Y0y3Awo8NjgHsHqBa/O5uM+YQQWvZCsmijdAdnxWQYtvDbTSlq2MmF/k8PeZrqdrLTMw02PLUjqWjjiWjnDoTEV6SU0ridtN3V0om54btg3OGhzIgd06nSo33OSUnl0EdC6k4w5XDRh5kg2IqDZkkTmVjSQu1GudLI6Vv+En+9RMHlNwib+UwDZse/poKeQzGbpJB58y23Xj+8IGn2ws1Z+p64nI+s64nypIFhoRf4QjlPVQ441isAuH26Vxb49oabZ9sXQ+YFEsDm53temVcX/DRoChTIeeTEFWTDFD7faa7kYaTh9NzYa01Qgs1GOtPT/dchQlSIfWGh9xW1gr9/nMMZu+y1IQkMiejLgulStFRl1Ws9HaTFadUiihHWXfM6KPdJYfxCsWhHIthoO6WIJWk5rxN6geF6R4KEOhNwdTCdmIANyG/S7TK4RZNY5BNNZyX1bicoMydc8ksJXHdbnx4unG97iz1xCdv3up7NMNMeuY+RzTnHIF2Ye+Kl12zjeE2aNcPvHzzHsbg4fJAWSrLuijjYrvxKg3+2C8av/DzK28ukJouuLY51o1aBqwu4nCF5aXyMgoxS1LyZIxKrivDbxTTEDYtQxqULsve5tEe8fSC58KnX3zO48OF88kki55Dg43BWkJV4vDcOk8dtpHZW+F5bLx/2hl7xhapiU6rgjnVgHBcsAqPG2iQH+1QbkjJMpks5zMP58KaprKOaqVmKar60FDxMVPF6UOMvCWDMXj3fGXbJ+V0IlnRd5RgrZW6HJbHYzAMsHLOyB0SAHA8y+Yop+s+1OdYftUumAIEI8nL3PeNPtW6cjo9kmuJ8Mjw+4eKJxWBL2NEi1vvtNYFKs3JaI2l6pnIVYCFoVD0nHMwVSEhj/NhzK5sh1CTLMuiC9Yyw5215LiYMozBaBp29tH1tjv4FCAwxmAfO2OXNSWXHFWqlZlNQdVkLfc5RQiC5O6SKhvE5zF6o9QSAIUsttMSjVCsdbCsmuiD2bnbNEdXgObLjdPDWTWtPukTllTiIs/3S/5jxtCPsXkpcR0N3xtp0dByyOJH73c1knKWBGjDYReODDt3xtZYSglbrVGxqGcWZyxZr/7sPVhvGz0AM9kh2nbFINQmwITGIJnHUDy0dZnAJ3OXugMtrFvbabedh88uUsq4AkEx1bd6KK/EhukcmkOD8xyN3poGS3fS6RSDTSIF+CYFaNQIj3E/10rOQXII6L9nGSSdDVI2dVqfkY+Q4h7RHXkEo3pk5Mwpm9fosqbOMSMjaHwMFzfoJBEIhHw9zoEDgJljUspCqUMhlAfDnvNHAgGBO8cQeM/7G4Nt22C0qKE+1Oih8pi6c8yD6LAU3MZxO2lwVE6UMgrG6GExAUKJZKLvIsMkBZOu88OAwxMwuv73bd8plvCun3mEclZFE67fMUzx6WB7LYBEN87nM9f372j9QmIn5TM2iuwx3ciRiTZDxaZfR8uKa7an5sr1+p4xCqd1oXuHWUkVZeeEtcxKwRmQMx146U1tMzg1L3z6qfPVlx/glLGt8649YeNMOm0kO3GKxigbCm32FJL+BGXVcmg14S+DzvnOuqaacav6rLtyHmxJTI5Wm8K0gvlOnhdef/dT+vVHvLw06snYd2d738mfGuX2jFdDTWkrZq/IqXLG+Nbs/KHbpN0aY5/84+9/yW98/z2v3sLnlxPJJkvJkFQ7TUq4aRmZj5V/+Ftf8clf/b/5g3/hc9a3/zqzvuHn/9xf4i/94L/kv/qVd3w1GnPPLKWy984//eD88q8+8/Zh4bv/ygnPsq6lWPhYjOWx0p82TmXhfLlSqqrby5JpFfZe1PDY1L6VZ77ne6SUWRT+KYBwOjXvlCVxe4Fmsu1kLwqzxQEp90iFgWaLglFfZ/ax4zVzHjDnwpgJr50ld7o7NlIoQAOMOd6xKfLTLFOL3oMRxFKfkHPVPJykOvY27iHKepcqh72nFBcokDK3fWOMRk4LJUlJe885igpvd2g52keZwerHP39KbZ8STAola3aUJCUFqTRibon7zKRwaaOHqkA2NUtGI+MzPoOiIgNs4jFHp1xkEbdD+ZjwvnPbNtSdkTDPYemclFwjM2rKmuzgSUDy1l0OBTdGctZ15ciMyREkooybAMDRte1egyhw9hlitNlVYd8HrIXiUvQPE+DHcf6CQKwxST6YOarqZ1hxuu6QnJY4vzM1Tfase/Vo1ZpzMlFEQWvOoebvCdw7tSSpMrSgEB1e+i4SdyBspCiT8I+EVXaHyCjsfQ+1qhqqPYGuAD3jI4jgnBIjKTg9eQEUw44P+i61mSfBAQeYIMW48jZLMjqDsSmcWLlOImFyTvf7P9a7O8nokkgpH7Pod+xBiKcD2AgbJRb32oj8u1C9mMX8FSBTaJSkeg9bKNzhRd1lZpEpJoFBn5M+9jjfYesdtySLZNKnQWQjDle2TXJlQB2gssfepbzCCH0fjVI0T/ZQ4eu7a5E5KNVN8mB87KNqXNdmvhczKMBcKvRpstn7lEraEtgcYAIqjzQqBcALqHef1Phzuw9sZgpqCdQuLCCn95glOcg7CRhSijnraM4AZh9Y0aybZWngSANYkprbVN4buY13wDuIu6SzWvNA/HskLkk5yC6T3a9G7lQfAq3HjMko/n6SrKv6XWOmDYUzx/41lfGZ06T1fOjMRIJZ+6iyjrvCceaIBucY7w71lsXzNo/srH0ysnOjUZek8oqw8BUmJcXMZw2zxrLAxZv219RYV+OUwglQBoudKKUHYagctaUYS4JqmVOR6ndLsO8dboVt3fhJ/vUTB5QSqHr8urOUhbwkTg9nfAyuT+9gNK7Pz9xeNnxM0pJ5fP2Gh/Mj9aJ62+RiS3IoWITOR50ieglzfFHtpiyV57bTgL7JatLJAnX6xu32zH7dsDlYl0o6n6mlBot/vGD6oi0XumfaGGQm62nRoZtTZFxEroxlQOyvlEEtGHu/KzIPqwkeNr263AGwlEsEaufYKVK0UAh0WepCyWq6ak0hhzmLmRqhIJIE3w9FMinLn3ow2RCId8pY8jgQYbR2zyHBiZ9JL2MpRcttF2P/6uERS06ywanqAT2lRKbzcFkwc57ev+fr9x+Ylnl8eM3r128AYnHy0AqLEVQ7nBqd7gsDWqhIxthe2D98SbvdsPOZ9HgmW+b0oPpy3574U98z/tDPrLx5mCy+038P9n3QaYymTJ/Wd/Y+FcjXK2NrPI8r3jQUkDrFE7sShVSlWwq5T3rYTlJSyPyP3t84nS588ukjJZ0YA15edh4fTpxPhTz1Go1p9K6B73kOnjrc9smH22TbJmNWSk3UJbEuorSSVa6t0T3UKCMEEdPv2TiZxBbKs1fnlbXUCGy3aPCBPqBZHJ7H4T3i+TMBEq3tXJ8aMxfWiyxmKZ6VZSmspxKNDImexWQcleFqdDNuN4WIrutZ1/CRdXRssZY5ArAVkijQovcuJVwoYM6XB3nRTXlRlyK1nIVkds6JDacAzRU8vE8xcn3fyClxWhasaFCeJrDilKMZDQ0sNWUNHBZtiTkrX2Qo1NuRiqbmjM2hwQ4x/L0faoXJPlpcueBjsg0F+y8pcaoL9bRSPFoJk1FcGUQHgKPXcTDbhD4VZtgbbd9YF2UulaONzqXKsAx9TIane/Mc5nfLk6ypxvXlhXpeSYukvHSoqcIdeJ5EgRrHxnCorY99+fn5mUtdKW73/85xrAp8WEJG68ETTFfoocfAsF9vzN6xUIel6cp3m86YO55VOECC2ZUfxRwkv3+ozD7YrjszVxgK5dymhtRTXdXMiTb6jImdQ0DsxNmT87JfKWtlrYsUAQnoTio1hgANdO6xgAZAqjlDIcuWxIDlKaVrJuqSXYRJJjOaWOvhMwYTQuos4JQqVtCCNRdg1MU2Zi1TRw7gDGDLf8xyPI6Wph8bpjxk1blkNdzlxEyZNFssYha/s0Cl0Set3bAZEm13yhE4z0fGUGqaj/eGE82Nm+yk7oNlrVCqLKiuDIExnJkUuq6g+BTDZEjqLZGL31nwz754C65CiaM+2MagVmWbKKthxN0KB3SZk/KFWqTUp7AnLFVBrAKl9Xyfzqtqgu2wH4ZFLDIcJs7rN6/Ynt+xD4HShwKR0fCZ6UXExjRHbWoTH8Ywo9nUMJ4KA+f53Tsu3/6WTuk5KSg7L8UfaUV2RDNVBxPWOJsaINdzYs1Q18LjKeq4N2ecC7ddNdnLKc6FdBAEnXbrES6feNUrb8+Teh3MU6HmUIsSq7sVPI0AY3VWS6kBmOro68i8+ulHrn/3K/birGVye3JS2VnPRk2JPFf8DOQzlh/I587p7c53fl4/T2+Z7Tr4zX/+gc9eFx7+YOJhLSyJCFBPLCPpXc8KF306n/hbv/Z9Pv32X+Xbf+YN9fQ9/PFn+CN/+a/wH331X/Df/pPOc3MGygp8Gc7f+9L55P/5hv/g8glffKdQApBOCdnUTrI5j1tjqc76WFiGAzukilUF8PZRFBNTZb1uHVoocXCYkX+V98ysRi1XtutGHoXsk0RnWJBQBmaRpbnq/LI06XPyfmu0+PfTOtWgZajDaSh011EDpqUkAhTFVfd9V219mCd6H2EN7QTcgIjFUOrNjnsKe1wUrTi4FXKdXNLKfmv47KS6au45sufC6sKhGrAdBQcLuMoIbN73Xe9UtLvm5cgzSwFGZHI1/awzAN5EzDyhvsnHHVJhCTIxq2lNSv4CY0oRkaUKtpSx0Um1sGJqRm6HBWyGYjHm7sMOZ4TSOVN8YnZidOWRjZlCBbDE/DZUGqDplThSBX5aIvlgnbAlhVEP9M9OQxXoRJV7Lok2NDvpnx9n7RBpoLY4fTaW1aYsoGaB5JTsrIvcEtsmYC3nTJq6f2vJMBOt69KZrmgFkrEPwfRLMhg7TKc3WeRkVRahXyzyX4GcpRaR0nVSQmmuLMsg6pIW72OnKaWSskgKOe51X7cAIcdwxr6LaDHNgYZHu5/2kYKImd610/VQ5nEK7zMAACAASURBVDpS9RH7zUGGHaCFgcLUM2xbo82BD6hEVmio+Dzmoxpo4fSDeNF8bwZ7/6ioc9dbpSDnGQ3EatLuPRwqJmVSG7CNic+dlI2tS6m+lCgesYJH8pnnUBXGZzCTog4sub6f2TEPO2w5Wq0nqSognfiZCXVgHwIlDWfMTZ9XUtGSH025KEvVCQtaWOHWVe17beiMSQnmPKygjg/NnXgEnBMRC4bAe7Ftcb9mZt+l+E+HdVMZZVIXxg0UQiyJ7uRSwaEfWWjH94HQQAtgmomyRAkOMcti5giIn0nh8iUI6RFuSEP3twgqATu5VPZdlkWp8TR3zdGltPS7vwjnaPSUGEK2wELvnShOBF9IqZDSVPvqdFI+jLyVOfbIFVMu00wZxmSMxJ1Im0q5s6RIFBtSxiaEgejH2iH+jNLgne9A4pJP1OeNtGxxRhfW9ZlL3aGeSaVSbHLKM0K9C3sppNyouVHJLBejeoDfP6G/fvIZSn1wvrxitPf0NHj95i3t+sLXP/ohX371JS/Pz0xPrOuZV5+8IZ8qp6oDtSQdIiM5eaR7EOdoXRagpBBan1G1d2tc985Ah4hCiyU53vYb/faBl5eNmhPlXKn5pFCzROSPHBkYOTKXWkhdnVNdcRtYUpcIoHpHkuw8fdKj7nuMJkVQFtOkYS5yWxA4UMpyB8Rqrfr3cdAd/u4x+l0GOXGutyuzDS6Pj1qyQ7KrcLAj0wmpmEyHslikkPKNca9Gl4IqpLuBlirbSo+AeSDwBvvtxuwbbz99y7IkknXOCZZFS/7jsgKZ27bx9Vdfsu8758sjj68/4bRewIzmuy6bsLQAwQQMqAkC95ixRLW2sT890Z6/IdXM8vhKmRcTXr1+gH3wc68b//afuPCz33ZKyzA6/abguumDMXb23dhvOnSv7Znbnsk09j5pN8mA964l9MqVl6txswF7oo8b3eVVnZ6gJWYvfPLpFzxcFvp0nrbBbHA5n2AabdMC1ZrTor3lOtSi9tKd55fJtQsWmjmRVjHvcxiWKluf7DMzklgP7JD+6mdoIV8/XU5czhVFvU3SSHguzFShSznULIJgU1gJ/RjaMs/PV55vLzxcXpPLokDPYNHXdWWpCvqEAj2Iy0TIuwXwvH9+YkTGg/tkKaeQAUcmReTRlKyLWMCWANExxUqXWrmcz9RU7z77w2ZZsxbEbpPUZ4BSIw5iSXS7q+FhSYXWd7U07QY1yyYV4Fd3qQQbQ01wQ0DjDGtU77r0lnWhJDEqE+d8Wui90ZuaR9wHrYeVAamVRu+0fWddF16dHzitC2U9qTmmNY7cpFJkscpW8KE8h+7Ks/HZadeb5M9F7ShWMtUzjWiumINZkO0ExHjOj++R+6RtO6kqD8cOi9CSqSQN4od0Pt7xj81eLkCzFp6fnjC0LFdgKbo8a8q0CC3P8f0ODrBaZ9QYk227sV1vrOsqj3zS4LQzsTZZqgJeRb8Y0xUQrXw62f+KGfveIoh5RJvOILkCk/euxpzp4w7eeJ80Cz5YNTjM1rg8PEqFaeCm9yVbJZPuP3/NEVqeDtvUjAaZxAhbsDexu2YiDTyk4GnIsnkMv3TC5mJx3jqtbWpWOVfScGUkxIKSSfGsT5Zlie/1YBUtSAvY90Y9LSwPJ6najgzBku+KogfL5LEqDBVlHrQ54/NWSH17eq/v/wB7fSosN5iPQx6vR0JDfe8CXGdUpA+XfbMuAupSUrBwMkLmnu8h9B5gVYlwbBB4ejqtXJ/2WIKPu1fs4JhOdgRMHbkkAXQZarE6bpF9b9SlYkl2nuOzmK4swhTKRPdQDJgGbHOph5Zy4lRPsE/mGbIf+RmJ3gd5KpdhRCMTOdGThR0jSdHpk8vjJ7z/5gd0T5xtoXNlugJgOx5KpAAKJh/vwelxFjRycfJSGJtj50mmUFcjL4ntpVNJUh9k8Fa1xHunpcStdUaH9tSxz8HtypvbG+YjGmBNkne3jo/EIJMWSDMxLWMkcibyOE5cHgpvvv3MV7/7TDrpuRxdVhnagPoBa0nvcS5YrZTHN7y2nZ/91zr1XCAN/s9fafz6P/3Am88Kf/D3vWUx8GrQnC3IMMsTz5N1nXzt8Nf+t3/AX/ipz+AXPiOXz0mf/CJ/+j/9y3z9n/83/M9fXXi57fipUrJzHYlf+53BZ3/nHX/6j7/m8c0SYBsB4ifyKQb0tLOuJxgv3F46eQVjkFIhVwRGJak/BxO/TYwiy40PfGZGAeakrAuPw7heG51J9olZoR2sWFYWjHvT80YlcaMAm8HsDVKSYsGhT5F406YUROYCKEzfn/dJKoW+T9ZzF3CF4Z7UUGTtvhgZMxYrp8+IjgjPiBSHYd9JcD5lWtto7UpZTrgHEKMNUlkuQS4e6s3DvuIY5Ip3tXQylaOV7+G5sqHBpGYjzShaSDMsc4nbmOyja2YO0jgXvSPTpD5ZTyK72hDIl3D62MhLlQU27GWVRh+3AOUKFWPusoyrlcqAQUrOec10V+i9df39vTVwo2TV3StPSWeBAntnfBYLY2pBzEOg3owMyN7VWkvOLLlgqISl0yhJWZB9HmxK2LzIFJcdahDNy8mpOVGXwTZcKsQsFbvydbQ8n1fNKG00ajkJTDxynoLxmUEW5Kw4hzmHMqbC0kUuEAt8DqCi5KJZGs0s+4h/nbXwKyNQdJ0HqVBSURNvCp/GyJiJWC/JWIoUQm7xu1VjjgjSDr1HSgLWUpJCiOmkGuBj2+/2Te1OCiK3pOc622DmQylooag+nnsoJ5XM+BxsTQSNRM0eUQ9ItJDCOmemrMScBRjK3425IgvAuXapdCZQSuxXhmaILMI/0fCSMU9QktSQWfutcmkVSn+okzRv63fMYVEk7nSGZs8xhnZkIv/J9XMKcNPvbKiIJ6NM2zIEt5tDTYVkUItRlyoF9IBI19LLb7L0zSQLZc6yg6vt2SF5RKVIrZWzsmiPOWL2w0YmUqXPwbB03/WSBQGYTKVYcX557L2WlId0KINlh3QOKNGbdpzenSNQv++NlIRQOyZFXvzZKR3OCViWEDCkqs9pSBUmpfVBvvpd0a8yB+ht0KNcwJ177MFwp+1FRDSJPBMlcp0mGY//vAc4t6QFsiIK3FQg1vsArzjb/d1KLsC3hp2W6SjvzanVGWXg3liSs8yF3ndqGvQ9se8J1gY0kjk1KVPzbJlUoawrZxucTitPvXCanZ/kXz/5DCUrlJLIq6rsv/znv82PfvBDnq430lo5v/mcy8OFZa0hN0ss0+nAHLuYmiG6UUei5GvmCp192QfbUGNBHyEX7B2yMW3y9HxlPD3Rr43lZDy+OinHxCwyAwK5NbE2HECSQS6LUO6sam9Pk5Qdn1mLjoctoc/74H0g5/cMiDmihUkS4Tk6tZ6ARKmVXD4Gbx+osSPb0N4UYD3HgJRYTyceX19g6tLCDuZVKG4tMcC77DxjyM5jyWQDSNFawIzsBQFOpSSFdcaw8VFlouDIxOBb3/mCU0nxUK4sabIUw61zfX7Ph5cr2+3K+fKaL958i2VVy1mPHBSLA9ZA6o5gz2W1+JhfMNqV69M3ApKm8fj2U9oY7C8fePvmxPJw4tO88YvfG/zSHziRt6+5vSvUuTNaZ7upKna2RB+JaRs9NVp3cl44r0bbnY3BXMFGZqmNPgqPLFzqZNuNrxj0rZKtMOaNd18/89njJ3zrZz4TI9Er755le7NTZSbnUjOlZtpm5ORcp/G+d64DPjxnnl6aDqZUoCzkOsmpRuOP03pn6ykAmaxcBwefClR/ie/8zeXCecnY0ACQcsKtkeyEN9lzuk1SSHtvY4S1SM/h+3c3nm+NfL7EkORAIhctO7Xogp7DwXZBVmPQ2lSIdd95fnkSkBmqlJwVsr/vG7kWxpxkMq1/RPenT0bf6b1DLiynlYeHR2otskKFgsYdAZv3PLAAiKeybY6LqE/Z3lLOakyKARtgyYVkiY4GtjFkk7qNztjUAtdmj1MFVdAnKeZSLrF0QttvtNnZ5sSGslqU+xXAbzC0S6lcTmcptwwNLD7xfed0OmnQGloIWlOz5OwbbezMMdifnzGrLKcTNlRvO1vnOocCCA0pCpoUNZIbGGnCYQmYAYicHh4VpOxiLx2UgSH4/c4aj/kx4BKDtNawiN0UGD7Fns1ddag1FzVPBKuUEOs0mwbNPqda88bkVAt5ke1vosHAUebExDm06D6mjFDzsBQJAN9aY4zGcnrEExQzGlJ1zAiGNNfClv1gIh0bQ99jhtE2LlWZXLtPSjwdbkkKloOVwslTjOOMgF6xtx3zye3lhfXhQSC7FTXM2fmuv58RJlpXZfXMPlhM0vcU39tsuqhHF6h/PI9rVk7Y7JOlLrL9RqgrHNCfBhZ3Z02F5JKHH6obO+zXHgrpKpuWGhh3khsVPd/ddbcWy6ylBCMnO7kfA5unkPuHSiHQZOWYRKykO8mmGuxQzkA9iX0sp8KyLtTlRJuDfdsiXFMW3YQGo1JzqBjU/HNX8HIM03Yfio//ezSWHssnpPgznFr1fN+tnB52AsRWe1jpCIBOn65+j+V85umbbzi/OZN8BCF1sKyJFsRCyk5xZzHVkNthu8A5LSvnpbBfG48PsrSNIevHzI4NY1k0ZA9P4InqUnukRY2bPjIPby/8zm//COqJ3CfrWRlleTpbbpwQGLqeBZiwZ+wDWK60DN07774xTifj5fpEefUIdaoAIQBXraPOmAoANY78xbDgWsHmzqufecu7H3zD5fGBV5+fKWeD0wpWmNlI3rC5wdzUvFUzdX3k9Vuw/kTfJi9b46//ne/z9//hBz5/PPP42cq6SMHhQ+oEKFhW/sTrU+KHW+dv/A+/yp/51ndJn/9ZcnHqT/8J/uJ/8n3+2X/91/jbs9C3nbTKFvHlLPxf/2jw+Zsrv/THEudV1gdmZwyDtpMGJHbabpS00q3T33WWy4qFJaNk8GGaXZZMnUWlAD7vz+ScUX3dCvVc2cYHbPNoIJUazCILqbvA6+k677NLybkW2JuAFK8Ck0fK0Xyou40ZrWnRFpSLM2ch1Z2SO3NeSawKgs1SROMFvCngtThHW6ZGBX3OJRvmCloeniB11nXhtj0zbzfy6UxKUgd+NABxb95qU4sOHmUtmWieEtOuK8k51PJ6ccMmXQQq9yGCQsSn+sH2vpPKpFiib1sQMJmeMmX4PSrDPGNManX2ltiHMWikWkhzUjMiDLdBsxdOa9y/6LMHw1OCUJybJC5st42ZjH3fmbUGySrlxd0qT2T40MlkOqqTz5bIlPsekMywoTnc8gxlcw41jLKyWtSJp2xq7907acksOVFdtiZLmbqscCOUkLuW+Slru++d62yQjbJk8ME+O9MSGSNZAJKu82EmKEgB2rrKO0qu9ADpLKmKvmN4a6zrImCkdYElE7be8OmcV5Wm9CEVnKdCSRlPkzY7jrGUlemdkYYAqqYEutmVO0ZRLoxNBAQVzZp9Rp5oStQl0yDys0QWF1vizhu0MfEuccGaKmU6s2rnoSqKpE8XaMMIwrXjKUWUArJhhSV6ROU8KcCByAQtQZDMObUwYuw27+HoORuWRBsUgHy0bQ92n7gvXHImNcNcIMKS1Zqzh13eqrGg58lcGbctFFsTgxzJRtPhUP2YkdzjGY8WagvAZsApsgTHGLS5k+P9AVnyH1+dgcm2JW77IGfBKUfBgYVVbaKoDHcnpxWzQXa9/HufUCNns4T9LpTKxP7gBvnIXmpDxMmcRCEcKX20ghkWuVtqVC6I3PNDOGHHQy0CvywpFE+HwkbSpDZGKKikDuv+sXhGFjtwEyHlU+9mCqB7xrgw5pASyQRcl1KkLm1SebvB4EryMympFb57o09ZFnPETpRUD7EnwxsbIlIjeSDkVKHSItF7ZE5ZFDjh4B0bSA2WBqkZpcHuN0pJ+NwFjLqT06CawUsDm8rhy7DmwkvaSDax9MJlrTy833n3kHksBxvyk/nrJ5+hdKq8e/+B3/u932F7eYFpLI+PfPvzz/SC5kyKD3rGNnkzGGOjhpVsmqSvnrKsS7Nxa412VSOHJOMWMnZjZ7I/P3N79zXJM3m98PD2TCm6IA2x98mFVDe3QNivspqUMyTEFqaCD7WwVTvfVUZ9DFVmz6klQJNvPBORcI8Yhx6DaS6ZWlbW8wM5BZCUZQXS0hvVpmPEQ5xYT5f4jFLYezq9N4Xhpo+LAHBn92efkjrG4jVb/Cw+2W43jgkh58SyriSc0XsAYdLwiVlNPF4euJwrry6F/fbCmldeP1wwGzw/vef5+Znr8xOpFN58+gWvHz+hlJWt7eGVloVuTrEHPbJaSikkJi97Jw0tj+3lhaenL9lvNx5ff8Z6eWC/XuF25fGTR+p64vfXZ/7dP5r57NVgf3oKj27l3d6Z185tf6LfJnQtuNfuamgZWgpGd17mzrY1Ullwb9z6zmM50Xhiro+kVDl7huF8eLoxb4k/8N3v8PbxNVeHd8+d59vOtJU1Z3LKPDyuLHln3wcvabKNzPuWeNqd68vk6ebMXFhKxUqW1JSmAznJFjd3WM3wVLhNqRPa2Glj0rouq4eHlbLEcOhiF0pJjCG2aBtNTBwZsjNt3BkNs8T11tgwzm9e3QNwj4aJZCXaiLSwZiu8bBt9dK63G23bmaMHA3dcFmJ0FdXQSamAJf1MkY1y6xuFRO8bwzvuCipfk4KY9znpc8PMaD7DnoaCYpMyCvqQTU5NS3pHJk6uqjPPSblHJRtLKdRc2AMQHaF86G2w3a7sTXTkaV0YU4HIOYs5bHMwd1mjynBG3rW0zYlbohksdeWoax4fnsTyLFWNQaaLiDEYwzlfHpi5yN6eKu5Oi2KAGeGe1w8v5AQPnzyS60pKLjXESCynBUtiZbGM9SbfeSxfrLrw3BJj38jrimPkPrCSaT4V1msKzzdX6OwSCoxhAntzUvZae7lKpVUr4JRSWepKsQJrxYbsXmPqrKJ3Ulpo+yDNPUKeTedK5ERJcJlgODnUZ6q3F1gKKRrH9M5uozFbYz2vkFFwKPFuIC5sjGghC5VVn1IslZpjBXJGh1Qrxexes43JpgvGPhXWO+dkFrG8dxZaoxQerGSmxPkYqr9jkDUC8BNrmSM82l35FL01sXbbjbGurHOyo9aaIx/syJSQ7S0IgHE0cOpZtSN8MxRf3bsAslTV/DI6uZaP6hyk9Eldtct6v6VykCIqU7JaEI0sSX5kKEyknKuhZi15YQ5lWeEKjp+t3OsjZwrp+1Ro6HpaSdrMqTYZWc2HA91LM4BhDeAHaKN3p/VOyYHV+QwmWjkME0LVFcxgrsouwdn3xrKc47+bkR+cVE6ZpGjMkXnI8WejJaF65nxe+PJ3d2aHlgdlLKQ0WVKmjanQcloA3sHQR0aJm4Jvsxvl7FzfvyO/PVFbptqkpC4ABlcQcUrKD+FQwelflzKgdeqycjkZ171xWRJ9Gk/XwasMmRM5WWQpGKflREmT6TesGZeS+Wa+8OGpcf5mI3+eWL66qPiiruSTi8V2qWDck9jskK9qAT5REImVqbz+ue/ww3/0Q7bbK86XIrD4dRUJeJI9g+U1yRcsnRgn5fA9ls5PtY0//PSK3/3ygb/3D5/4O7/xQy7/5hf81OWBkpzTOrG9MlOoL92wOXjzOvF3f/CB7/z3/yu/+J99ysx/jIKT/shf5q/8+7/Fl//j9/nNPuhDSuqB8zsN/o+/2/n0sxs/972T8kyG4yZQ29nwuYLdME8snri5iLu8nLUwLI7bYOwiv0qGkSZzQI7sLgDvhmfoDU6nR/bbN1IJ1cpHv7fyTsYwJLnQ8rK6mrTsJODEPNMsgEkTcLKkHOrEWPIHeKqs1pm5AJUxPggMyR6tlxFXMAOESk2Qx67n04dUmj2Cj2uWbYtQ36VUeNk6aVw5XRZStKwJzk6h4Mhx1ups6LjUQCnCcOdg7k2gfU4sSwm7rZrsPMt6u+QF4wiM3kkpU0KV37vyjPpQhLZ1LdC1JNmqJP0CTixLU+Zhz+Sq96q1xsl2Ecw4bb9JjWcJz1V5U+aUU7Qqz4bTqTWacxnsbeKjMmaiZLXgyjKTce9QZKNMm2sRnbKOeZnkfaO5U61T0qIAdXftCQqrUWZUtkh/EBNgVQTLtE316J5hijg8la4mq1zxoQr64brzrvugVJHYpViAeI6H8ndGE9Z0J80UGbFG6lJG976xlkXnvOm7lionYUNzZz5V5u0mux6Z3VV2U5Lf7wAfG551PudDDTKhnlYYnXmE0ro+Mwm9HbMmlDvyqiQeUJxIrcqMrSa1ldVC6QIprk35saessg3NecovNBJlyUwKySd5DsaEW5fSda2Z2YeU+RFuPnuX58R01yYrDExg6UQWpyRaKh1V99Nj9jVGc5KPe7mF1HAZL1Gy0Ru4imysrlQgLxU3o5SVMl0Eo0nTkkqhUrHh3NpOEyLKGq1pzPiMTfEs7tyBokNn1HySYobQjhjvszVpigY8P1+lwBoiG4VbKWvQJd9CikcpL9yc5B1sZfogMaE6RmLJlaPh1ZPmsuh1U7GBN+V6rpUj5/GIHkjJtIuOcWBFuqyns7uKNnIxZpt3Usty0jkLMUP9f6y9W69tW3bf9Wut9z7GnOuy9z77nDp1cdnlimxUSYhsARbIAiMhEnG1nyzEI3nmA/DMh+AJ8sALD4BACAULYhCGEEVxSCSHKJCUHTsu23U5l73Xmpcx+qXx8O9z7foAZ0u2ynV8zllrzjF6b+1/1W+v7MqQmEH/jb4Xk0q49saSs5RLObhcLvNc18/DxAR6jFm4ICWVVJMLL/ZVYyowTQHYDcwbzH2leSN7ImHUMaA7i8mWr6b1LhXgTdYaoWiVRST5mg2NZf2Dqj8UZG8zEqVOtWL0YMmFvjcOuRObVKojBjnzot7eh96VZSkc0kK4c7bBoQ+exoGv8s9XDij90T/6B1wvlfL4wOPHn7CUAyRllPiQTG30qoN1NGoPihlHKzRXDaVHsMdg3zbOtbLtMHrSpT7GDHjt9LqxXU5cnp813B6PpMPCutwRo0KInZkAJSk5173SutqllmWhpDyRSsSgoaR0otPCqLvq4BMKzIukwye6mG6b9iIminyrJi5lJeWinJo02QqT3POG+poL2bUkxZAloay3TA+bGuSUFwUgu9+KCrQY16oGjCnpE5IOJSuM83I5T8Wmhhv3RLRGPiykfKS2Hfk5g8fjkeNh4XAsrMnp+zOfvn4FKbGdLvz4sx9zfn5PJ/PNb3zKw/2jlFEYdbuK0XIgbguRGNicVzzJAnLdOq1vtLZx+fJPiPMJe3zF629+WwdwvWB24e7Na752D7/yzTPf/nSjnDpfPi3Ec+PU3jPqmVM/YS3BvnOOQYzCxa5CiIcCZ7fo5HxgdOcaKxZXSj2SSub92Dm0T9guG8/bBSL47Mtnih/5xZ//lHI88u4MP3l3pk2m4LAkXUy1M+rOU62ktjBGZ9/gdAl+8r7xdHqGASllWkocF+fyfmPvmXcMXucDmzdGJPYxAzOTlEbdjH0uIQ8PTs5aXCzU9lPrxj4KdQTDKynEDqkxrKoljUStjct1A5yUCvvlIuAzK+cnu4Yhhc0m6qYg4G3baF1s33XbuNUJ6xKePvYezGAsACIWMYlpSKIM7H3HgWKF9eGOw929ltysITDjhBu5VTFlgYIcI/DUuZ5PYjFI1FbVtpMWxlQYunWWsrCmg+rq58UEzCapYPRdUvExOBxX1qXQAzwLnM1LoYcWepvVzD7VUFtWVs7dYWUbnUbn/PTMfjlzOB4ZZvSiAOuSM2N01vs7LdilaOEkse8XLJzoG30M2nlnxODh9UccDvekfGs7c1jEhGOmprRhdNezEb1BLljSGVovF0rOaspBl3G0hpkpQDNPabQZeVkmANWm1VfKma1VRkgdIrWOntlUsto0dNK+qGZGlS2q9U7tO7eWsWQf5MthM4dkdIpncpmqxSG20Ke6RHe4FCn0QVl1HouZ+imWHOgYvsoDb2asKU/m6BaE3YihJkPP+UXtwwvLPP/XcDwkyW69cpPH6UcIbAT1uolsS4NBI9mCD+Vx3ZbwTHCYSy23N8GkqfGsAOgx9EyNpMY4ZUUZbX5HCSdax0ueSrypmBoC/nyWJNyIBQZ06xPc+iCP7/2mOrOXJrU81G4UJqVjuJHKqmdjKMCY1vDJTN7sFL1OFhM1Oo6uyTo5tH6lOCQPKZomgCVAx2h7f2ldu4XhRw96rRpCUyKXlZwPAlmK1ladH1Kp2bSAx02NN7NhFCKt7+pmgx+9ypIUN1XYDBZmzPvWpnJwTMn4fIaZaomkbJD9eiUdsmqeu9NnMHCb8nnVA0udICvi0Hlsyli6/+gNT6cvuPTK6iKUhrtsTnWwpExkU827afknxCoP79TRWHd4fP2WP/vTL7lbdI7vwCmMw6jsW9EymT8sqIf1SEqN3jYOx4XLpfL5lxu2gtUD6f4VKXWOYxV4sKBg11MQy8pyZ2AdetLn6AkrB6J/yeuPjpw+Kvzg959587UDx7Vwj5MeE5aDiIrbCSvBIGPlgYhM6Y3XXxv83LcHv/L+27x79//x/X+28/WvX3m1Jt4+HvEE3eGwjaluUfvaYR+8+cT57b//x3z623+dT/6dt1j6OYjEN/7KX+U/+OP/lP/s78EXW2dnAmzAPz4Zv/O7Gw93iU+/oXBlz4b7QixOeTpxd8h4ktV+LIXt0klNoHuJhI0sBU8PzBpXAnqlkcF2zBZSEti2pyu0yv0r5+lpEDgZp8aVPjLGSrHEXgctZm5KIJWbd3oOrvuZbPc4Tg3DbJGV19X+Reh8qvtGHfp9sGnFiwZW1OrlmTaqAKaYFo482wrbiyZNyqGhUhi1jclSva4r3ZxWK/u2sawiPMMFOFsICF5yYTAVobON1zKyN2GEifRpbaf1prw619urcouQYmVJtKfGmpLmFz4ENl+uV5Z1pWQBa91UxOHNJbyh7gAAIABJREFUWPOq/KRp1e1D3y9jWkmWLAJxVNqYRqppq1I8hNrM9stluh2kOvNcWMqgXjcYshhHC1oy+oykyMmkdHOp2Mp6mIqbHas7JSDyIkDWVFrRYt4MHd0REzBvY0zbIi8W8jY6rTlWpGxsaPYal400EmXmU7YZwm2uQoDWpsLQG4vLEl6bYj9KueUW6QZtc1FfcmbMJrXRBAKaB6MHnla22slZcQwlZ+6Pd7S2c9032eV7ULuaS80gUsKLz7ZtyFPdet13kUgvwts0HSTKHTK0kHW7Kc/196050W22vuIc1wMbV27FEWlaha9tkJQGj/sN+FG2n0gtnza9rvyoGOy7Zqp+w18mgXTLcZUiUURW2KDOanibyt0xlFh2iZs5et4vhIqSGJNw1W/sphbEGGApTwKmse2yzifmHTIJJS95tjsmBopeSTOcurab9W7mZvYgYqOZwEIBIszPYOgdsKJ30fUeu8uiCUZrmWvb9C4UJ5krCxjZ1Eqe+cDSeuEYYYoESSnTI82WvtuHKYLWzLH+wcbniyt78Ab2TMDQPWEj0VxKeEtOsaRZc3R8yLaux0SN4CpIGKQQcDb5dTluTCrqmxoRF4l50y41YuZGBjnNIpMb2Bt8yFqzmAr4NOeK23co54eZQPLaOqWIos1ZilubcSOjLS8K7gjNMrt3SnQSUjstOdOSdtbeZStvrVKjkkae7+3yooZ2y4xxU5E3nePObHTbGWFss21Rdk+j0hg92HvHTXdC3sDHiSUlsnVWDx4ebtP9V/PnKweUhh/4xne/w1KKMnQQYih5ux6UaMwkeqcUZ0HS0jEcRuXpeqE246l2tq7DJLc6cy6Cujcuz+/YT89YNg7395SyMG2k2Bgv6LeuYT38Y5OP9XhYFMQ3LSOWlRPQxoDcSSPRr4O9nakWLOkApgPR+8AbVB9TvSQ5/egKzb5/fGRdDi+MqqfJQoyZF3LLqojg1vaTy6Ia6DkUxwwsBfmab86emyKp1U7tFUK2CZgscpfP9XrdaXuVasBm2LQnZesQs3HuTErOmhNvXh15OK54NOp+IS2ZV3f3pATvvviMn3z2GbV1Pv3Gt7i/f5S1qHW1OMzKw1v4o0XMsO9BzitRd2rrbNuZy+XE9vSe6/VLDkvh9affJd+tbLVR3Ik18zo/8hffNv7cmxOHcmL7IqjWqNXZKoyU2OuZ3QxPg+0Q7FsQcdVS2zPv9y5QhkL0zu6dsQWXsZF6cNk3he4xqL7RhrJjvvXJp3zyySMx4M/eV378+c5aFpbiHA6ZWo19GzzeFero1Lry7hJc9s6XV+NHp8E2Bnd3r7grmbw6USDjLPFIpESkjcd14XR2rl1qqqAzOly3yrV3Hu8XHl/fSSHRjWK3Gushm8NQLSnd2OfiZxOgKV7Y98bn755Ylzsd2DOHI5jMGzZBsAvLstC7QvuSJXIp7P3CpZ4mT5n1rNqUTjct2pBwz5Nl3Nn2E540DL/kdHlivX/gePcwWTKButl1idQqS2CMIQbQnRzwVDdd2inYWyWSFuYWUJas1p5cKHnFciEhqeuSk7IZJgC2dSN65f7+yHpYuNaGl4WeMmlRO0IKRSW2WgWO5IJn524G+9baMIK6Xbi+P0mOngpLKiRLrOtKdvC7O3yydOFJTVGj02rjerkICNx3rtuZx9dvOKz3jN5giCUb8zvRKZm51f7eWmgULKqFuu665G9s8JhgRibj2VhywUdQ6cpQqE25RY784zkR+8Z+2ZXTkDM2K0rDElsfjF5ZLRF9/gwmabICN2fT5Nzm07JIWWLKqLmx1e5OjduQW+htJ2J8sGPObKjUA1wWxz5DENMcvMUMzXyDEEBUe8Oz8jA0MjutymaVU1a9K+I9DSg5c9k3KcZcn/DoAmcEahmqFpGyZcnLPHdnvpKNF4nyDQC8hVoyL/3QP1YNNa0Jq3KpcZnAEDdr1g3xmuDaqPVleDJkzyvHZVrwlPOhpVL/o/xVU93YZOumSEufhieFqzPv2Wk/q23MyuEZQIIYyZwX6F3veagtqnVlbRiJvIjZ97JISTjbclLSItCnpXkMI2UBW4M+VbELFgrlTa7Bb/RKZCllYla255vSbwbCjqEMCJiWbHdabdTrLkDc9HPcSgkwte+Epfn3qumody3UZVb+WgQkZ/HC3as79suF++UB642Ul5mjKOVOSombHHjMhe6WhzZs5oKUlce7zPlp4/7NvbIVJ05qttKG0RscYkAemlFsYNFYTHaRMaAcNLB+8UXlcGesaTCWlXLulNJZzVhw1sVJxak2aB22XQdAvi9cL43zEyx2Zf2yUPI9e9opKehXx7zT+0ofynzMKUPqpN7oE6yyOMAYfPKdRz7/w895f7mCBcdzYj8ObEg9bc0IX/ABNk70shD9QHoYfPrz8ItjcK4/x+/8X/+Yf/j9xDffZF7fFdaUYKmcOlM94ZRo9NxZo7B84vz3/8P/y1/9zm/B936dvPwMY/2Yv/ib/xG/8cO/xn/9A+d5q3gy6oDNg7/zZ4OP/+7Gv/avO6+PC5717OboMBZGlmrxmA3vDS/G+VpJaZmh2g18kOlUM8pi+Mi8r1fyOAg4j6kucyOWROpH1sed07uL3nvP0/489OxPwDVImK0EdRKiAmX2cSGllbWInxmmdMQwFWxYmkUMGUDKQj+s1FumTKiFVDXwwFSuny6XaTUrUolP2/MtKF9ZbPNZxliTyKvrtXO+DI7Hgv3UMmQzz0x2zoHq1hs+mBYNprLN5iKv/397UX2G7Fok6lan0i5mluFcgk2E2b5t2K7zS5/l1AgOsDjgi9E6RHRycq7XneKFASz5SIxCbHV+V1LVGkaMqvQ+N0ablibvpJyp+05KhZwHh9XZ9k6bVv+C0+gvWZIeDkXKiOV4INxoV6m3GSruACNazOgJIUleTPmInqF3shdqlvUnZZmL0nDiqgzVej849Z1Tg2QFz4kcGZsWHZvLtxSQG9mdkpzRTLaoQKSuJZ3HzNKUHiQvynTqjW2/kjsvQLlyD2ZI+Ni5DNlz8lJEUs2cyTr6BDR9tsIhYCM7NpSx0yYIcsvju93GNnexW9X76ArnxgYtBssk0JzE3ipG0pzQlKc48Jm3lCaDrc/BXef04obfLfS9ipzZVaySkhFD+Uox9KzGVL67K0bCzBXWziSxpgggl8JAAOdit7vNKJ4oJodM3GySxpzZBDDlw4rtld4qzQTM9NZf7kR85lDVeMmospQ5LonT01WgSWtSdGEcykKyqgzIJoAtkLrIbwQSNkEhkTWdbWaCSgWn91pRMbXprszFKWlluP6eMRRcjasRfM0KcI/ZKlfjVgglwru/vMuOe9bcNhusS1mIkdjbpu9rKNNsWcrcc5URlCwzUhNxjUjKNrqIq64MrjoEzJCnymnOOZ0xCWhesgq7zf8b5X+ZK9OJcL2Lc/Tz2RAgNfWc5fTlvKieRrgsiAYprYDTmkqnHBXqhAUxibKBlG6jwSCz1UR0tdPuBYovYFfCnHJIyj7rPsfMAT40W1vWPjX6JPV9fo+Z/Tpw3+TWWmVrHOOqmWgwyfOZ6dxN8T3uDDMsVroP9vqCsH4lf75yQOlbP/9z8kqDFuV666HQgpFKQjmzldoG2GCPwVb7XPQb1w6VRJ+1wNF2und6PfP+6R3bdSN5Yn11JC2FYZlmzkrCIhNWFczbO2FGbZVsqgkv5i9tOT053dWYo+C3QrsMLvVMjkHJB4Wb3Xj6kDWmjduSBb1VUsocjnes6/pyUN6sRa22KRucJPmYMlt3SjlQShETygwrG7M5I68ANFcWA0211oFR96taH5KahHrrLyh967psy/HAsizTI337dmSXSqlgo/Hm1QOPd4lijbY9UXLi1dtH+r7z7v2XXJ7fUdY7vvb2U5bjPT6reyM+BOSKKJ/NAjPHyUw2lXp9oge8f35P1Avb5z/CKLz52jdZ7x9IEcReuVsWSg8+9cr33n7B24cn2gmer53L9kxriWsHbKOOFRvw3jtRtbS0+cJEg971c4yhZbakSvTMZXRad9wqLWeOCawNelu4v4Of+cbXWPPK50+VnzwPrq3z9s0dS3IYK+f2TEmZh9crWx1cnzKn0ThtwY8ule08uF6vlDUpHHwkos3WltS5hpoJPO28H1rgLo2X5X2vlT4cPHM4rmzn9zhGNnvJLehh9CaAJxeIHUYWDh9dF3KPYK+Bl0VqoeT0OmjJXwK03UzDDoH7md47KS2AJOhbvSpEdz0qlN5kjewRWM6YrWJkZlh37QvJltm6sqMKl87dm9fk46MupTR930nDUG8Vd6N3ONzdEwb7dqX2TrSK22BrCtDGEsNnaHgxUloVap91cbWQEiJ6p1uQU2E777JsroVyKJzbIC8HiqkNI03QaQRY1wV0XA+Qlf2Q52dhyYjWuby/SCG0rpSlsJZCXgophdSDvbOsB3m2h5Q7l8tG3S4zkLtxfn6irCvrYWX0KySnDyP7IkU/srK1ac0yOgzVtZortLO1gVUBnT5VRYLj0wQtpF7UGXS7kAVkZBeA0nrjdDmBiaW25JTDKgBo72JIRKzKRjgZxUBsWavTgjgGeV1JpmskBmrIwm74slrApoLrBqyMWWUYXfkGFqFsublcTBHRBDvyVNYEPQ/GaFO1IzXQGAPPieu5YSULmEdAQikF7DYA3zKkNPgsClXSsjSaPvvopFw4HO7IeREwlGZoZiALRddN3Sb9mufyY0wSEeOyB9kLqRTc4oMkHtmg9X2kF6BOhRCSaN8+n1a3CZYJ+HHLL4ol1YH7i4J1IvqzFU5gk2VXvsH8LPpsf+xtkPN4+ZlHQNuuYp0nKhWjEV1h7JZWNXNG0Kq+kxHzvQxj6xduIbQ5G8Qy/31SfEUMcrL5zxVz33Y4HlZaV2OJu5R5ZhKB6dfzqeYSVnlbWpZlpZQsde682BICiZJpyLNFz8UGc0lSO6TyDmf1b4fXjw/8yR/9kMeHRyyr1luqNp1ToMVeuUzKQEquZk1mWUDbOr4Urqcz718deMgZH520+AwQVr4XEw/EB8MgK22fpczvzjuPbw78yQ/OpFKwIZZ0s+Bd2VgbvHo84JpYqUPnZ6ROvwaHSNSHwZdb8NCdp9OZ5RE8H15ATPeElwHV2S5BPzTyVMLZyNhsnyEHqbzm69+r/P4/+oLlW8HpUsjvM9kqdn8QOJc7cbiD7VnLdnqNlR17vfD1bx35pfoJ+37id/7Wn/F7/3Tho7cHvvnGSSVzVw2f5+TmmZ6AXvnoYeGPr8F/91/8LX7zP/kW9dM7ir8iffLP82u/+Wv84X/+v/J/dmXJpZJnNsvC3/yDykePwV/6lzbepHU2BReMJxIPYFlgdcqU1EgpiHom5QNNxywNtOzGoK2ZEotaOWMG6lP1nvSF5jtrTYx143pps+XojFEYc6xeMPZQGLr6KS+T2JFl+LLvas/MU2U4tNhkT2RfsOSctkokJ2eotZLdaTNLMS9Z1dmhfKBcVqDho+o8m6ZNpagJzMoOPrToq5SgkEfnuMD1urNfjLJmUtJ21kcXIWWyFqv9Si+kQqLVljwmudpemilNIKwby3FVIULdZt6L3vmI8QLajiEVQ2sdtzEdUVKVGEHbr0TT/bPmAnYrC9ECug0FzN8dj2zbld4aWrGC2qpahYWmU7KTl4LZh4Y3rIJ1lkUgxXXXPrKuapElXGR27NgAjztSvsfud8bo5H4g10qPIEqw1w2fJJFIGWPkTDGn3B3gWqnRWVxFQpixFqPkTqZS2iC3xEbDfEzbXp5AkuxFEcg648rOMz9M0ieRcqLP8HK/BaIjm3ZtO5YLHsFWqwie0cEqLQUkNSrePi/GYFkPmttqeyFGRgv0YUxbpRf9u8b864MPgMHMEUxJG5UW+7jh9QIJzKhV5Gpap30rqQnYEvR9pxyTcpCi0y4VD7XltYBlWSUOaCLmtmsVoe/KgDQy1pSfiE0i7bYZpcQ+ZHGGCQox1br7hUCNwwUjFYFSI0zzniNQf1rTJKbVZ1g3EZLrokKdvQfeh0ADE+Aac3fDbJa2iJg63t8RYVxOZwX5u8nyvcza+TFVwMjt4CFA16Iz6qY4AoJlPbDcJ754f6IGMyBfAfKLKW7Be9D7JpuhS+nVZs20u1FHk4jBoQ6RgjmrTMuYgKLNqJOZo6Th1PQ9W+CeSTlm6U2l1wpeSCXRo+l7d2Od+3lLymAagVS+Y7DmIqA4SRnHLFW5NbC33l6aiof7y15dLFGRaybZ4KdxFHe1mGYvgoJsNu32GVZfivIOp0ooQnMKFixrYbSYBVQq8epjquwSyssaavVzDsod3RoRlZIK7p2cVOThUScvKwW+HEAds8RyMEBq0t4F6rcWrPmIxU7sUv13d1qHQ3KKTz7MnR7BuSpyYGuKoynN+eLc+Sr/fOWAknuiOdCq2PSuMLmUDfei4Ku5MNVoRDeudedpq9TQl2Z9QDRaF8sflzPP754Ezhzg/vVrMZ8uubGR6FSFAVvDSqHu1zkkDQ6Ho4blyfCmKZfOqA7SBrp0qhj3Q3FaKMzaQgxwG1UyNosPh5BnDuuRZV11AIdL9TQXoCE6hSkvesl1WFYBDfbiLxYQkIsuAsHUMavNO2O2sy1LkZKgFFLWi7RfN+peSa6WnePxACFPZnLYLxujD+4f72m9sy6Zw5I5Ho4cisF+pRTj8UH2m+268f79O56fn7i/u+f+7p7Hh484bbuyB8rMYdD1hNusuO7yvC8p4xH0tnHunXfvfkw8f0nvjfLRx7x5/ZEWmd7IB8N95SNr/PLPXfhLvwjLeMW2PcJWOV872+mRvQftHOznxo+vZ+p5w56NZlD3xjY6hNrTenRGUxjtxs4IHbDJ4RoC/5YRXJpxOl/4zs++4e1Hj/QKP/jizBenSi4Lr+6P3GdJUb98d6GUhXVdOD013l8bz/vO8yV4d+m834NC4+uvHnmuUngp5DLLu++LWpLyQkuOjTvqSDPrRlLeNILzVhkGPTvFZe1qBOYZTzGtamlaJ6CXoTaTSFJM1IrRMesc5rOSspY9wtlbIy/phT2JLhVAyvYCfEoVt3K8fy1bhad5USIAE8nq02TsPSmHqO2V1lQXezq95+H1I7msuJdpzxkKxPSpKBnKlUlLYQzjfD3jc/HzXGi1sha94z2LAV1cdi1Pakx0jL3ujNZoTZa94U69njif3nE8HLi7XznvA3LGLbOUPPNabk0wGnhKzuyj4/Mgr03229Z23j0/0SI4HO+UReMTHLeYCisBcnXfMDfOm9456s7elVd2vpxxd968fYsX+c5Hn+HEPtnelNjrdVpvdWvFZL8HnRqyLbgbHGZL3hg3ATYv4ddoEa57kxIELeURhtFo207fGo93RzE3SZB5C/Al01unmBoGp7RUWRJLwlpMtY4AmWR6vnwqUEouNJN0egwoZWUMqTf3vc/w9glMjMHeKs2NtZSXYFNLUosmk0WMonc7mVNtWvOyAh5tBHXf6NvO4U5+8DHGrMrVolKWRY0miD1SeKmO5damaiVU8T3Qf1+r3k0muMUMF9fwJLDGkgoSbtZnH135Om0np0JGWSieb21DU5ZsCrPUM+SMJIZZxQyNVnfGECt1d7zX7yI7PtEbB19mG9F8TmC2+enHTUm28Y5yIkAqLcnHb8/BzAcxNc2lJOZr32ceEZJL11opOGvOBD5zZTRM996ptbEsChXtbX5g3fS72xyUTKDciE5n4Fn3qc6Tm3VWQ5JAMi04Ar5vt63YxVIWLBtj3xW2xgROeyOZ4DebdppiAsHVwOSMumPD6Enhmcv9HeFiPa02rKzkmZmihTcxTPlSEbCmpDpuC5qJtYwaHJcjpy/e83zpLEcjt0H1IOWdFFIeZ1eLVO3K9hiWp01m/vZj8PjmjuPnV3obdIcSlb0FdtowS4yeGS0prLZ2tr1iPdPaprv/ULBtcD4NcilcPh8c7rpAKBK9Z2WTdOXaWZflJSykVOiwJANf8Hblzbcf+PhPTzx9trHkZ4FpS+K4VtJS8N6JtoMfwNTwxvoxOYLjp8E3WuFfaJ3TZ8/8/T/Y+IOPT3y0rDzcO1G6MpICMp0Fx/ZE8+DTTw/8/h+f+N3/8rf5lf/4gXH8Ndydh1/4t/i3//IP+JP/8R/zh5dG7MhiOownX/iffu+Jx4cjv/DnxwzpDrwu9LG/KB3xgBVSOLXLop38QGAzX6VDhzQGaySu0V8q2TtOVMN9toUGrKmw5+C6VwW706VWIOmkCZXP5HRH+MLzeI/nwToCemJcgzh0xR3otZtzoRT8y3pQNkq7UlJW2HCqXK/7BCqNzk6E2O2cD2yXToyO+a6GNlONOSEb9Jh2WAsF5AuoLhzWhfNl53KulLWT84qFPrNOE4gxgmjK6lCduqyYt/O49iq1Y3c1MaK8Qy3QK8wldRgzKmIqM10ByQmB9nudtp8JFpu5LE+j06oUFjmXF5VJcmXC7dHxIiVcH1U5OSPYWufgjtvQPT0MVqZtVu1ibivJOtk7hyWxT9vhaD6LdYJ9V/ZiiZ1WO14WRVKURvMFG5pFipJ4CUzh6a2y104tmeVZ8/kBWdm8O14SNQXlmDiWzn4J6FfCE4MFG8rvA6Aja1eVYj2igiWFUlvoux7OIOG31rEZeDxCKuw+pK4opTBqm0DUBBcmYWOz5fJ2Z7ndgKpZ4OCNHo06BiMMmyTEGBMo8lDpybiZjyZQcyNSTGSeuwvYChUjRTTqdcNLnqc7L9/RS1xZBCk59KE2s0VlUOfzGYpT9xsIZ/jQPTSafq7e9Q7ss/Je+Uu6QwqOub1Y0pJP0Ge0qdCZOTsmxb8hcGFxg97xVBhtZ7R4AY18Bi/HzM5bioLL9y4VHQCLlLQ+P2tzkUVmwd260KoD2+Rq52c4fGZCVr0/y/Iyr4UF+CyTqkEbjfu7I0/nK2s5ylo3OoNKtsRe60sDsSLzRbzGFA2k5FhaGdbJHtTa2fcqK79Pu5rNNjf5+26i9RdBQyk6f3Jy3ERI1qpoipznPDI0d+Q8FV8zEqBvm6izmWV6s8qKTLWZbSnST5+bcg8/NBsPPBnlsChjakrKxwQ/+1QX3hoEU3KSxySzhs6DOtSu3udMCOz7rru5zXm9T2XgfD6SB6urbTbQoWc90fZOIKV7ba58y7zQW5HX0W6/a8gB0qR7SqmQyFgM8qKZr1Z72X8ZO56DfajZ1W6AfTCJadNzKciOJX94N7+KP185oASdcd3lyeydxQphXZkqo2G1Y3nlGoPTVtl24zrE+PoY7FvHA3x02UzevRNSva6srx5Zsk8W06eYl/nCZoUSj0a9qkbwsC4adJkosgnxjDkw197Z2mDUTvIFL1BsJsPPnASLTrtlI/SQxDsl8rKQi3zWN++tz4yj1hXSXfdN2iNP9N7I5Qg+5Yn+4Yv0jB78ecjFaC8NcWYJd1gOh3mwBRE7163PRS0JRGLWLw69GPu2wwjKknl89YjReLxfOBxUEb+6kxg8fvyKbIPr6cyPP/sx756euDse+fnvfBdPmeveOTdl6RwOq1L0o1GKmiNa3RSCW2asbQT1eubp6Sds50F7/45yWDh+/KkybOqFY7kj3x1Yx5Vv333Jr37vyMO68+4P3jOaGPu2dc610ZMqVj0ce+18/bjw7riTGlzPzl4P3Bt4KOB1GwIL24D7trDT2IcxauPohfN5Y3/OvHqT+e5f+DbZOu/fD96dds6jc3f3iuOysnhQVtnD3r69x7N+99GD+7xy+nzjXRucgLtj4nD3Cl8yb8qrmR0ktL12+eSP5UDtF65jpfaj2oBSkq95BHXvLA9SA5QsKe1N5ZaK2OyYVfGtaulmLuDXy4XmCm00jOPdoxrQZisUKIQzl1V1o0lLtWUBU603qYC62qtePT6QsoDJlNIMrs9iK0yyz8HNsx0ccmELGOz86Ec/5JOvf43D/VEHXBf7lFKZAeCzxdCgV9VfbpuynpaysNvGdt6o+0bKhbwaC1piCZ/2LJGyz9eLFgKMca1UKn0YdXSWtVDuFs7nRlplyUpZg1vyJOvorgFMgIgk2wkxQp4kua7bTt2r8pHcWHIizwbL1fNUCJkGhjGoHaw39rbTm9SV1/1C9MGrtx9TDgcMozXToOzKzroFdNqt/lXTC86i956Gt2A0ONzfz+8gZtuMTY3gTa4L++hikGJMJdTQ0DE6+/Uyl3tpIxlDVt6sAa14ojaBjrKld6J1rn1n305SBUSQ15XDcX1R5ixLmdk6QUXKLVx/bXQNrY69KHK2beNad5aHO8xv9jrwSVs2bu1dH0D8BTW1mBnDBUxv+yagKM+KXBfI42by/E/bHjPM3Yhp77oBGVLftKpsBjM1joyQWiBqo6ehM9/shf3WNSL2lzGkiEX5Q+EJjwmiMhktv1XkKrci5/zSnKhiBhELY1qeS1lkGx5i0fIM6DazuVzcrARaMHpokAtkERQ41ZTN0ars1Uuew6occymVOQAq93ZMlSOmMMib/as2LZgg9VAiyL5Oy+ytaW+idDNwXYvDtFNMsNOT7OHCjcYMT7/lH81nBV4WIJvtdjaJoBujjRvbvrOuR1nRp33Rhtozc1ZI/41hjZhy77AJmLlsle48X3c+fvMgkHCRTTyGLAliL5XfaBE0k4pFXlORVDkVDsfE9nyml3tZjppztEE3LdfKgRqQjdaY77TmEAMYGbfEm9cr799txDC21kml4yPRhtOr0ZOWGsgKcbVGPx7mEO68eev0qwDV0+VM/jyTPz6y+sCt0psp16XCmc7BilporFPotLwClVQWrAaPP3/H0+9t7M/Q86A+XVmS0X0T6OdO5DvZEyNDSrTlNdkXlo9+yEfPR375V77Dj9//Id///jNv39zz3UNiyYLBc3N6D0oELTsZqY8+/fo9v/NPPudn/uf/g2/+u9/B83eJdeHnfvU3+PU/+Wv8V3/7c364DYaL7IjW+ZEXfutvX/iN9cDPfusXyX5WAAAgAElEQVRIvjvRTApjMwEUnkIlDmXhws71dKVHeyEbuxciQdTOlQqR2IZC5vMwLHV8GDHyzPQJSjmwR6G2oX8fYEPh6rcGSYtERGbJi9SW7BSMfVd2mXXZZJnB8y2KxHUWeBiJ8jIPjrGxLMvMbtEcp7apxqDjZeX0fCYvssLrsFAMxJitcDaVz1jQp22oj8Cz0bbOfg1GqcqiCf8pRbMTRUBtScbICVCpzLCbGnVWj18bJS9E20lF51NZMm2rWLIXlSZhaoXCwKHkBUPKyn2vL6QH8yz1medo1kTujI7NBrXkt+Brn0HixlJUOrFtFxhSJ0e6pUA66bAQM0hbai/Dh2xFAhKDGsp2kbtisI+mpXFs9FHIaWXxqdRPtxlJBQK9qXlNiERj3xvZjnRTw2+yPBvLEseUWGxgXtmAXvUs9qSs1JSydgJzUoG+V+KWz9ravOcqlhblBc7F56YWMlP2YEpG3ytp6LMaaK70PBh9B69YSXqnzQWqCz2YZKlUIckXhmuHuu4byUUyxZwdxwRobGZTMlUsOrPjxSIG8PqjN3z5xRfa/8zoW2VMMDGmKib5VAmHVPY6C2X93s4bFrDvG6MbI+n+NruRnzM82kQIymGids+2X6dKSCBri1uZAZo/3GeG7SDnorBrOWF1L7RKWVZaay/EL+iuKEmqQhUKmYK5w1jSQmuDWncpiE3/vD5EaBrBsmRiKmZ8yVJCRtCqBApjEvsxoDYVg5R53+2jYbmQXXdU264US9Oi5Vxbp4fsjXFT7iIl2+iNkkQYpywbf8pFwI8rd7eFwR7cyh0kSJpZQ12KmVLyVGTHy456a4o2VyZt6zutV6LpPLMkIsyzpL3RgrQujCa1n9pdREYwCWCbd7ZuVlOERO0vxGL3WT4EgIoUblmevasJLqY9naRZ2tPNeZbl/MnaeWNMkgphCeMG2KKZPVzgkvlUNI1AR0nCUqKOjbJmzJy6G2NUxgR9etsFgKYk4GgMkh0107cu4mygbLcEjBVPOvl9TvSjGZYEmhLx4g7Bdtb5/cQMrx83VP8r+vOVA0q9SU1zk+WOmwVk/uc+VGt6HXDeG9tmU77WSG0Q9cr1euFyPmPDyYcDD+s6q24nCDPzRJhf6s1mclsA1gJuCzA9iUmI8wgFcrVpERMLlMjLqnyaoYMzJylfIBE+Gf4h5UZeD9iSJwusF1eNc0GLRm9tWvlc4b8zzf4lWJt5kTO5oqFFNAjazBgZc1BLDkuel1ptClmLGVzsutwdU8DeDB4N0+JsFixL4vHVA8UBEg/FWRdINrhbCqMN3n/+E9598cVkmhd+9pvf5nC441ob0Trmyp4oxWntqkvagV4xmpQ0GNah7jvvn95xeT7R+5k14M2nX8PWA9v1Su9w//jAAeP1uPJLv3jmz3+zsF0+43oCP66wGbEPemz0NtieGud9Y2uJ0U8w5LNdfKVt8knv4VzHWZarHhyylvA24FIrvQ4e74+c3105vW987597y9vHt5z6hS9PmR9/AeV44NXjzb7TufRO3TXsby2z1wvvts67ffDu/YVaZaE8LIX8kLBUwINCobhhXqdlSHWqEY0RBzxWIKhtg3bLwRiUDMmc46JsHnexI4S9BCYPEqRMQ1XtW1XAPKahMq0rhyI7wi3MNk3Lo9gWp9Mna5mgG7VKbjtGE/j4+KhGIktziLMPaL/ZvEhng9I8i7a6c73s/OjHP+Lrn3yNw/GoIEIzwhXMnU22vYjBdPYRo9Jan4ubqjXfn07Up5MavxzlByVRHZGMuCHudTbQxWDbG/12KXVYloX1cE/bg7wWUsrkpbCuRfkAbVp6XMBcYg4LoYM5LwUmq/n+dOL+eKf3alH2krvTY0jZgOGe2XqQYtBqpQ2BVQHUKkDq/tUrlrt7InSK5RQ0UzhpspiNIDZ9Uwa1CSpwyVRHD7bThfuHuymx1msoRkvg+k0tU6d6pXfJ0/XwNKoPqMF2bRzvZYXxJGY47GYBU0A0M9g4ptW2hfKgWtdgmXPmeFj178Ww2byy9U5KGU9M1n0iADfm7QaKT8JhjEFJSSBjSK01pgXgNmxK7aT8u5TEQEWXypWucgKBrkzljWNlDoPwkgeQYdJY+uzGDGEXgCOwVuGzDVAlcp+LelpnucIIedDl6xVbOd8H8zRtZ4NuQYuu5Wfa2VSPPF4AsVoruSRJ9CcAM0JMtCcxo9iUpy+rarKTw6yXjmEaDsb8+xATN0JgV4/BVndyUj6Wcxv+ut5HV6BoSmq47KbAy7AJlt0YtZjLGRPs1OQEpvpoc+UPBA42B+NAgZbRUEIYtDaIZlPVVvTkzIEwJZ1ByhDQMy12lxew6abmBYFate7Ku5kqphH672+D3a2Zymw+U+juVHlX0MJZ7lfa9Ur0e2J0mjeKf2D01QLYGSZrymgxwTCbAdxq3Ll79Zrnz5+pqM3S7ZYrk+bQabQuUDeb02unzwylbBPUbIP7+zue3ldwx5MWj0zC52yyj6acsCULah6Z472AAfcMPnSmWyO1lefPn1leHZSTgRFT+dJ90HZjjwZZgHZOQWUjpYXwBgRv335C/Ubnct7xR+DO6EuTIrwFcT1hq5TdYYtUeuVeA/mrtzx+3fiZMfiXf6Xyv//NH/D7//RL3h5f8+nblYMncoHUKmcGaXFyrzR3HsK4/szCX/8b/4T/8Nt/g+Mv/zqev4l/9LN876/8+/yrP/5v+a3vP/E8OqkUumm4//3d+e2/e+XfO8CrXAiuWJqKiTSgDdlVrGNZy8m4SC1nlqUKE6pN2p2GVKdjG1wxlkgK2zbH8lXPRa2UksDK7CkIAcM2BC4iy3brDU8Hhqt9MxXn6LpHthqED2Ls+h7DaEPnRI8+WXa1suZ1URhtWrlerzQJqMAT2QRkHw4H9rbJ3mKdvVWB01NZ3KftR2+L7MYpt1lKkdnqTtvl17SlyC5XiiyzSQw8FuzVXt7T1rU0Zl+IPF7iHYzGZascDgda3zB3Sipcep2NmRN86FJTtikB8HmO9N6lVApknQtZVkZooS5TiRJ9YClEyhjYzXY8pPo8lpUYxuFY8BRs7QqhXEgRvJNstsCLwrvdCxGdvTXllCIirZnsJWvLxKhc6TdxiEAEc0ZKNABbZDEcg9FkMTtfr+Sk5qtcMiMSxkI0ZbklBsdknEYTOV+nWmKqYMpUvd/swJJ7iXQLlFtkM9vpRpDYtBqaJanXV8OalDXDMil39i6lt3WpXHwSbvPQVQTA/C48KScsmTNKYENzUwyp2BUGFgz8BUgwmKpTm/8Z6Mo4evfuieiDPhoxyShZ5WfLoOuZE4LMi9o9+qBuyhnqXU3A2ODuuGK9kpNL6YnI5kjorhuacxJ52rPQ3e9OtJjKRJ+g1AQRTI6YQshhM8UJAk5u9m7Ye6Uk57jIFpZskaoWqfqGQRtB6yi+YebbRnTtwmmZoMft2azYWASsJQXee5q8Rprtt0j0sLeuZtGcNSObiSjMC9HbzPZR/lqPjlnGo88MQ2UBiQhTY1yvCU+Zy3bBG8roFMRNTx8sYG7ag/vMpwSRBcx3IyZZpr6YmGHgMxw+rUAXaLQLgPWmYcNne6fsHLLAFmZxxlRsaaYWQRRzhv1pVfgtqJupcHLPKrrRSIOZ0255SU3faRo+WxT7bEEcIveS1JhxE6sEyuWKD/EIw4LoPrMhgxKDylVknKt1vm4/pXgfRgypoyMkIBh9DjWxYybCLidXIcLt94+rSIQ0SMg9JKOmyDxP9gJy5ZcRzrHRIKUXZftX9eerVyiNToGJakPrlcbgusvnyZppdWPbG1vrelDqxn7ZuJyfqfsVz6oiTkvGrSABfRKJJEh4XrhIokzDrHCc8jj3gnlXmKpLJaH1zNTy1jrZjeRZLwFDGTzRJbccOmTCgsZGSfccDwepCG4s+xgM/MXrOKb0ttdKXu+UNZPSHConATavb5hLTFcDTsyDA5PNI+UZLBtdB8I2mWi3uTBMpZR9uMwtib1InihrIqfB/f2RFHC8KxyWTInxcrm+e/5yWhZ2Ull5fHzL4XCHpSyL0PyZU5qV3fNyMevE0JIgYCHRauN0uvD07h37diUBrx4/Jh0cb9DPO48Pj9w9LCzxjm/njV949Rn358Ef/T9V4YZ7Z1MHI27OxpXaVC9ck9HaBqtPJVLmXd9JEZzGFR8LYyw0jGxBq4Xr2OlVtsttG4Q17u8e+KVf+JiFwufvdn7yHNQOdw9HjndFCo9oCj+0lW2DSxuc9o0vLvD5U+V0GjzcL9w/GIzEclhYc2LJCZIaxYyGuzzU6ygUM4Yl9j7Y+2DbruwN6DqUHh8VLL3kBbOguPRI8iVPdtCgzobAYk536DlJCUNwt5SpGlNOiJ7Fjhd9kVq05LvOs6VJSj89U4fDgVevHmftti7vZP7yjIGGO0LeZA13xrbtPJ+eOD09862vfzq3NyRztpt0VP5mgSZT7o4UBbkspFGI0ThfTlyeTywlsc8lXHIkY1lWARVoId5rY9s2ATatzSBw4/FYeLy/o3fj8HCvwzZnlrSwmEIxY3SG63Ni2KyJ7VQGO4NRG9TB559/xuH+yHDJ3FcvarObC7cxh8sIUqAGF2tTni0FxeV65e7uyOHx9VTohFRDDtEH2cV+YB+YDprCOUtaaQxSNLbzRiq6THpv87uZQ/D8edJknTX8TttDBGnMvxZ6hh4fX2FZgA3Te04ADRyBiBGV7bwREWRzwqFedvZdFonj3aKcHqZn3tDCjMt6FFJYOlJzBDEHvjHPv8FoTdbbvHCrrzW7PTcKPyY5kRwr8/QMU0bUfCxbDEYMDmWd7V4+AVKpU24K1hsL6iYmfhgvIPyg0+P2vWVmzxqZRHUxg2m23NxKGORW1j3kU9vt5rI4jiCPDlUZTGQF0fYBZV1egIoYN0JhEjCjT1WQ5NDKIWlkN9z1eTGB4FsAJrONBvRAWthcXDoMDW2HtXA4rCRLM2Bfw2sPBTO3Pu0EhhabYuQcuEuxmFIol2FW8ibzFxWTuXLr8syA0GIjwiOmqlD5gTfrmeE2Zh6YGMm4/cxDy6qZqm8/vGUCYVsftLrrV53Aspu+rfFTz4TZXIb8BrjOw8vEjKZJvBDO48Mdn51/pH9PKNw/3f4h48M7ArdcGOUtppTn56LfMy+w2uC6bRwf7mFaAJIFNfRMbKYKbj3Gsk4khtjoocyKlOF4X7heB4c1S9GZnL0G52tTuYYpqyybC7jdFXJbt42dqda4NPK09FyfN+4eD1qQ4zaBIEXlJRHHzsGMlgqpqhXQkDWGGHz05x64/N8/4fM/OlHfLuzryvEYHN9kSus4AlEpR3q+12fthZIWeFN41Vd+sb3l+XTl7/2Dn/D9P4ZD+ZjXrxJ5GSyhrJEFF5jRpDL5ZC385DX8L//N7/KXP3rN3Xf+Dbx8wqtv/wV+9d/8AX/67n/j7/ywse0hZeGUZ/7DLxOvf/fKv/ivOPeXxuuuFKNuCm6nDdre6fugXxQLEFlZHjkZh0hcTeoNS86+C/ypl6D1BLmTi4giT4kSUvPl1GBrdHO6BaD8HVkcp0pEEyFrFsPfDVIbZGvso5Py3VSv7ZpjQwx/DwW99qamzpQz0Y2ywvW8UXDGcLQ7GcdlEWC8K/fETTl8usqdGFIcG0m5MzHVOkUkQylHnq+VvbYJcBe8j5ktJlDVDLX2DS2MxW2SVwPLurvbXqlNapJWGzknyswNW7zor0XA0BKsAoHb+6kspS55DebB8ExMkFn7Yab3W7uXlticfD7DPttRpZJpwxVIb8bpfKI2MBe4kpMUgkRIhWpM9YPA8OxSg28jqIyXn1U5okbKMMJw0+c64lZyMM9mn6B8dNro5K68lVp3RjJWlGl1BU4WtKnKSqity/ZJcDqYd0UomEoOHJ/WNCmUb4BNdFmEp8xGocFjUMdOLtpPLE0rjUsptyajhRQe11318SUpJiB6w7OKgKQclsVJX98gZ6M2nZ+t7rIvTuBGCp+bOnQSG1NRf6us72PgJZPGJLZCSnrZkgKivdgkb/+8GLfSBjRTJdnTkhUOZSEvU5FMewHeXuzqU5nem0K1A7UfprDZuH0Dz2Shtu5KmTPFiVjX5zOHYd3nwUwd0HfVep3kjopvSprK3zG1RVOlLtWw5r5s/v+z9ma9lmVbftdvzGatvc850WRz8+atxlVuqbJskIyQOwxYYFvIRuIT8Nl45AHxgATID5aMMUI2mCoMRgW4kaquq26XNzIiztl7rzWbwcN/rB3JMxkP1dybEXli77XmHOPfksSsSEU7Jpbtruz1oXmUOdTmG88ohxKbsFmOyPFxvadHZIAA24ylybrkyEYMABcnFadmtebuY9K8U6eyeWwMvCuvNR1Ek3HfEcTgieCaswusM93l5jlU5KGCQrt8QRllaKVi5hwuHQ8wZ2BZd24kijOMKC5AGZUcpJHApZxS/H6QYluk1egjdrTYn+M+TAFWaq6bTKWTa/eNfzambI0SZmHdKzHnWig8YTRVkR3zqSdj9iiKyMbwJtLAEjUcF63pzoQcZ4cIQ/eB+RKzb5f1MrkcHyXRGrJS9k63EVlOiRCI6e8adtHhRsuTWhVsPvthffv+fn3/LW+daKGCrXV8m1y9cxvh3euNtu+0sbNvG2PfGJcXPAK5Hl6/1jJhEVRNoH3xfx/hFx4PRSkVTMzMwEPNNuJygNn0kbXWArWDUtTudCy3B2OZkmFTrOC0nbKuPK0nqCe9/GbYEINsrgyaORUK57jyeZaFelKmwIyqVg6E/t5gI0Z6zk/5EWaJUktkXOjL1qFDIKBT2Qoh5z9Ap5RU8VlicV+WSl2MbIOncwnmPLHYYO4bl8sLt8jucTdOpyfO5ydIRYqKocUql3jQXF5ruaMCkZ/jMNfw/PLCh28/st02fOqyWZdKqok+d7IV3j4VvnxtvK2/4E+8vvKmXrleXrjdTrQmCfB139iaMq3cJtuYtLHSfQNMdjMHn5tQ2JkwG1y8490YrspRUCDa3mFuEzyT15WvvviK169X3l8u/OJl8PEyWM9nvnwUm2FpMoZyRAbQR+a2D573ybfPk2+3xu02+eGbE68fF/ayYmUREJQ6axmQnG0KGPAB3RSMOUi0Drd9sG+dfXeW7KRF1fVRyKDwuBSBnknMj6cU7YWZ5B2bQyyIQb9q6Xx8eAB0eNaiw9PSwSKaLhyTte2wUh1oujN5OD+wrCujhy3qAEwsgqs5FCO6wFMMStfReffte3rv/ODrr0iWOK0nSdPDXnRIi2dI0n10VWSOw8es4eLl40eutxfWUtQWUqQQWbPCAwVaGK13+r4z9p227Yze0AKbONXC+fGJzY2Hzz4jmVGHKss55Kejwzy86pHvkkP+6qF82RrPHz9yfjiTaqWUwmMoRDC7AzCeogUCvZ/dpPhqbYcx+fjykVoXHj97cx9M51RtrHePUPCuYMnj855aAj05wztmk9um9/V0PgfgF8ttkJLl+JyZd+k0LgAJxMalXGitU59OYqE6Ar/syNVxhAQmgRl7j3YRATreFAKc1xLVsiX887rIVYTmoYhTFs6crkwL/ZhhERCQ1oYWqNdPj3rm4mI7su7m6DGQawhNQ99Xi8Bxor3sGChTkeAXAow9ntu4OTQafgLeBkN5a2jInmOI4U81RF0TspOmmjYmQFTIHoGdEEvV3R6mM5tjaXUpxGqEeysQ+ljE9POKCXVJ4gMIPJAwXVF678aU3YWud/uefeIIdIsgSFyS+TkFGDM6JS/KHcsZm0eMpthEpjINTC4CWWzitewJvIbtcUpmb5YhZXwEA+mTUpbI3Ii8iQO8cdeCmVRnPEPdcBvOvvcY2COP46ho1UHIoWSbdtjF4ntO6Q5K2pTt+yBUUigpZbETq+5IDn+A4weDqKzZwen8xOg/UcBl1X3qyWOZ1ZCdsjHM0GjpsexEDs3MQTJN8jK4vFx4fDxxSkc2lyQEA31PUsNr8OyjhwVJ3HnJhlN4evXIbfvAtkWeXFJOzTYS5w41rIXTnFlUHtD3zrUNblvDuxQvC8bTq8zLh8ZDXTiTsaqcCvOETQUNuy+06bHMGzY65FUNgamwLCuvfv3MT/6vF3a78XDqPG4TTw88vWkiYbxhwe6bnZlJ93Oeg9PTA59/1fmtP/MDfvmLK3/wk43Xpxvrmjg/JPJiLJ5gz+xWGLNRst7bLx4qf/Duwv/6X/4j/uJ/9kT94V/D6ht++Nt/ib/+l37KN3//n/Evvh30aaR1ZW6d4ZN//EfO+CdX/uxXnS8mCkkvA4+6+bo02MFLws7O7aVj3rGcqeaMlrFFttBhjcVOlDlpTe9ursSzoGevNwG1NRnsHRtDAFaOds2upq6dCTOTfCGzB8ApkFM9SjIvHPkluEgvj2av4Vp68jQxzrmyLiJmj3NWYKyxpJWRDR9NTcrzxiCr5QwtZjMJZEyWpISdhqUFd+e0GgPZvkfWe12KRxj2MStEQEooluSwO5onB1TTYiRCHCdKZ7IA/Tyk1JCtFS3oOohwks7WJEuPcldvQJH9FxfBnNL9/pIqW1ZI3QSLzq+8ci7G3m70/Yr5IoCiDTw7bUzWrJljut9B6RlzsqUCNsh9BFkTxIglFnLkGuVQdCcpqXY1nZnH0u8eJJTuzNve4mePUJYiK8tiiWpgRa23Yzo9qTTAXZ9lnze8yr682qLPM0b23hUE7ZbY266ogazvxNxIQ/fUTLIsHuBczoWxjzsoX1zEQfdJIcEUuTxTVJm7lnqbg5qLGt6KqtRbk2tBjc/x/YU69Ihi8FBvHq1o8wiejl0w10I/7sQ0qaH2JRdKkBX3mz0IHkswZ8ZT5tZ2Uli1eg9AIBmqMtOzIv2Uk6zSbTBmIw3DI8M2hz1rHGAn4LYzyKQjt8b0bB5tyIfDVIp3V+6Zx2QS9u/piZk+2cRzMnwoUzauGXqXkmjMiXvTmQ2cloWoiQqgIwmiySVyzrqsTFPBeO7QZ8HHptkXcFS+MtseJVNyMOQsd4sPDXcNPc/Dh8g4h+YCteYYFJPF06KMxSahaJuUrF2o+6Bo8aAN5QFiakdLrh7KaR1POlOOfNYZymeSf5qtJozZKaEIyxbTnfk9MynH7OmMODvCun80QB8EZwBEh8Xdw/l0lJ0cc7aI0D0A+9gTDNjl7qjx91V5ie6Gu4VtSAXvZHwm9jEpRU20htNdCrpctPf61NyhGQoE0ehzv4fsZ+XJVQf3BbJibswyfYaF7xCbhODF3SWiQe90jrDvcQ9m+35+fe+A0m1r3KZJMjsUeN2t0GcnzRu97dy2jXmLBiSDVCrrw6pBazFsCtxQ3EuiuYOpHtGQzJ8cqgs7vvTwiydl1oxh0TAwI6xXC3eyGDynfp+kyR6uGnmrrRTWWhSsWkugqWIjcjKFUPeudhCzO8CTj6YcF+vsh5pK+KmscLEIHDa3ZKD0+8IcaokYXXK9XCqtabEbQwP4EuHQytcomKsuvSRjqYVSjFoTT0+P0K4xyO88P7/Qb/r8PS08PDxyOj2wLEVhtKPfLzuIh3oEMuyS9dEbg0TJmbbtfLw8c7ncFC6dLPz2G4VOceehPvHF48pvvpn8yc++5bM3O0uD2w1KfqQ1Y7/VAKhSBDVOfCYWiT1gZHzXC7y7Y16Z1rSs9sllygc9GLS5QzdalyKulMJXX5z4wQ8+h5n46Tc3Pj6fIMPrzyoP6wPb6KpsTWoLc09sQ0n4t5vxzcfBh+tGWQtf/eDMuZ7plimlUK1gvlPSUY86acNJLAwybU7adPbZgMLLpTFHZynwcFpo2SlWyT6DbcvUqLTFEvuQTaS707osXaMPxjRurVEMHh+fIgRRdiFIwV4cbEmwU8GUGVLIqPI38fj4qKZBV7OR6ke1xKkZJFQriH08JL6X241vf/lLTsvC68/ekEqRjDSWJp8CkI7GMUwZTz0k1QDbdiO5cbtdebldqFXqkKWUYMEk2y1FYXJ9dEZv7NvObbuqJSJ+racTr88PdHfqeoZp1FIpRc/V4ZYl5wgJRRY6EECZHeti9q/Pz6SaOa0rqRROi/53ClbrUJjkksNOFjax3uAICd92Tqny5rPPOCxp2R0rFUUAjPsQhSlXylPSIGJSG84hhmTfG29fv1ZLGaqCzlkDSTqy3kzL+4jhr5sAkoQul70NyAIHvItRIRZxi88D5w7SeFLzm9rjKj1n8pKpLy+UHC0UycmkezbC8RG7m9QZbqHG8liMWig6Jm3bWU8nLTKhUAFVwwa0xRFOmZKGjmGyY8y9xWfnXPYI70w5cJhJGzPO+Qg+rwIUD3ZL2Sdd4EfkFfS9UZZTgBf6eww/aqdnLDh6jwyppHR2a0j0obrwEW12h3XRzCIPQeqx0ZqyAYLRswD/xozsJRfTm8iRDRgqNKUq0mYPi2K+t4eBgCVlMM1gCJ1tdFrr0Rr36V0Rgynw6DZU5a32J7GBx3O37dv9XKg5a3AMtaFFyHdOyv84AjKlIPQYnD8xfz4nKSfmUHbhyR1Dg19OJRaeEfe5FIwz/i73RTEnoOD0wBQHt9vG06sHfdYBvpkfn1pgqx65GSYg/VA4O7KFL+uDlJGfPwEuO3CAxtqRk8CgAOPnmCFRh0TW9+Lw9Oo1zz99T7t1Hs4L4q97ZI8YPkNJcCxQQ5PFsdB5qDNKzZzPlZeXmyzlrVCS0ffJxTaSOXXJ7A7VNRA31yFX1xNX29h7Z79NnJ1imctFIc62JFUuTyNn6KbzXM0yiVmM6TfSVD29gGDjzdev2N47795/ZC9GieyypZ84lczcp1Q7c5LyivcNTxWrj+QKtU8+/6Hz23/mS959/CN+/yfPvHpd+Xqp1JxYSwTP98GpZDbfWceCrZMffH7mn/70Pa//7j/iz/+nb+HNv0159SW/9Vf+A/7m85X/5nd/wu+/BHFXBQhcZuZ3/wh++bONr37txq/9sbOsfSZCRPFZC9mkCks5s39QmlBOasgxmx1NzLEAACAASURBVKyhyNmm7vVWlKdzNFmJ1VRGTG8Cw73C3if4DjbiFV0pi8DFthc9B7lSuEmJMAUW97aTkiyHPjqJEz4b7lUzEhEwHIs5Efw6gyA67toDyyxV5Q19v1HyoQaS4n9yKEYChLIUYNLEvLNYYkyd9VvveHPZxJOrvnoq14zIbJFF6VAqSFXlc4rksiJSsA3K/b0cCuo1orkohcoALGnhmlM5b2T92SPCkdUUOnRSRGuVRE0KW/fsGCMUQyJcU+4kEpkTJS9yRuw3qSlcVuYZWShHlXiO73iYky0yAU0NlK07I5ZdC2I5y0VFtsxaK21GgUOQ1oGqETiLltmBHBUUAUyWyEsmpYgDMJ0xKQdoEu4M746lSUkDKzkyizRTmkWb9OgBwEMqcQ26zs459BnrG8uMNMmnqvbkIIeKDSBFmyBkL4xQ4HiEFqtZbOJp8lgrJSVejtY3b7Sh+WYeUSAKXcQio9FmEI4jwNYogeF4FuIs1v0GaQzZK3vYNtOnfCYrmTQGlpQbmpNx3TbGnFKDmLLP5hyyhptII1Ko4ZC6LWN0m3fr6uiflGpHw9tBNszYG9xEZAhYGJRSZcvGWEtSLmVWYdAI4EI4aOSXhTvDXbOylMRoXtCjjs+GeWTuTKlusinQ+rh7RfTIWnde1TQuu/0geRIhPZ0ejGTGIoZCxN4YIgamGcU0pHSXOtpcBDXhUuhBnptFnmdSSYvW3sQ21bQ3J2TzEFjIEjdMpU6K1vgESLkJqCzxZ3Tvn7Iw41mQpVPkquzlurNTEE+Eo4Y4Jz3pc5RWqt3vf+3qBOgUhNRU7lE6ihIOK+CMnEbTeTddwFvgX5rDQk2uHTrmN4+zLCV6hz7VKjd8yPrWZxDw+ncKs9KMpv1Eh/m0ipNkZSZFc5xUjyo/MqApgiDugjk1CamsSoBZssRaBH7dSbzv6df3Dih9eNl5mfpQlYwvS0fbLvTrhbbtasHxRFlOUmhY1SU45aduCUgFG3rgjZ3pkyUpJ8COIEYLWWTkd7hDb1pgh6tOVPYh/Skz5D/u+nLTFNgz48FcalHodwmWGlDPhEMsc1t35rbjo0HK1OVMjnBuC5kuMQzPyB05LHGSfHqEc32yIYEJFJkK8nJUw/lyfcECvc6RH3JaFi2yLunhea08rPrZay2UKiXG3G7k2emt0frOy8sz9MHp/Mj68EStygRpfQeiJjmYGWL4T2bY7BGWtjBdMsjn/YXr8429XTDLrGsh0Rj7BRvPvC4LX39+5qs3lT/9a52vTzfSuHF53niZ+o5ag9EvzDQpS+JcHphZignrzkyZlAelZTagDGOkG5TOuRjWCjeHsk0u22SfNzDYdpjd+MFnT3z+xRPnXPj4/srzrVBPC68fKnWFXBTgbq5ML4aS+m8DPjZ4f7lx2+VTff30oFC4UmlpYTEoFp5UK2o5i/yAjgalMZxrn7SmlqDR9gjGTqRlDYZbl3NC4XSnUlRTOmEfMbCMaPvrIT/typo5Lyt1tciWSYzumNV49kYwfrHYhU1jMvXPobC8h/NjLHIBiuajvSyFelwqinlHkrT4fvj4nuvHF87LwtObV5K3fyfk8AjJvf8+E/A6AkAZQ8CSj8nH243WGktd4t+doypWjWFrrVhK7KNxizav275FTa3YkHU9UcqCLZW1apGryyLWR3I/DuJTv+IsCNtJCUZ1bzu3yxXPiVyr1Iy5SAESB8IdUEoCCiayF7VdTUJ9dq77zuyNz7/4QqHHLnZZIeZJjQ+mzAxzE/vgakNpMfiMIUn4dr1xPj/EsK8Q46Nd7FMbiMXNo8/bXA11PgWw4E7bds6PTxoW3SHa/2Q54s7SjBk1ogF4KHZJCos5B8UD9IlzLIVyLBJ8tLAEE2doIB9zqPEmasI1jO+8fvuK6ZFsGb9/G1NZL3E5V4uzPZncXn1KuUUMmKOznp4CWDvwlcOaNL/T8nEolfRLA5mW+tZGMHQHQmP3P79YjmY6tbBJ8aIFTJc5kY/R7/lPn5pa1MiZLB/C+Bj403fCuTuTeVewHXjHGF0lE9GY5DE4S0mlv4N71mCbBEjM+Qk4dgSIgc6P1rsAX2RRHGEVdCbNZ6hnNLiYaWnUDyTwJC+FupyU94HpOzqeESKXaIRu1Wd83sfbFvPwHUDVQlNimbw3//gBROlzPVRu884e6p8dfrTOaLGLHfrT/7wDkUH1xn81DTyWqOnKi8rA+emRy7t3KqggS/2aUmBKyuJTkBfgRuJggqVwSxlokJJa4F4uO4/ryjQTYISWsonF+wCYlmdPaDFHdpxs4N14OFUu16vUk7EopOnsu9NyoxYx02OmYFWVszO73wmzbpnn287jw+Cy3VjPidxLtHYqdPxY7hmdUWfMMLGrO/holAK1Lnzxm6+4/rMb12tnqaG2vXXWtTKtMPsk9Ybni77X9ADnBdqF8tB5uG786Edv+RO/sfN7//wbfvyHH3lYn3j76hSFB1F/3WHmopnNJ6cFzl8Ufvf3/pC3/+P/xB/762+Y59+ifP2n+Df/6l/k57/4u7z7vZ/zzctgpEJeFvZt4yVnfufdC5//g8nf/ltf8eWPFizPeAaKFmxgIUutdXLGNrDSZUV21Vlncy1h7pAnNpzZRUamAFFnNiZSrdekd/5yu+g8SIZbB0/KpKkVulaOmYJtFi4TNp6hoG5DgGAxxOboBEsxZ95BEM/xzhzZZyJgEpM8O5YLXlcFy2awaIWaEVE7RrTMuWxMRxkNHo2ulkh7Ytt3veOmbBw3i8KDY/HRzzbu7+JxJrvyB0l47zBTqFy0pCk0X8CbEyHKfqjNPzHouVQYOe6YuO9MEbxjdHyAM0hWAZ1hbi3OnUTNAUAsBWuTpWRygetV6vo29si8glpCNYXhbmG/0tmNERmQHYbmyC0NNS7mqYa3ZOSSyCk+yxFkqTn+HdUELlLocCt0N65euc4ZcRg93m/9dUcsp8k+hVO32WB25QjGuSiAL8GQKhZTdmyyEXellDdzDtm5LWyDKZHSvO8lRCg7U0ohM2f2XTlVOZowc6bmREr6OcdxNyZ9ZpaNMYy+ezgvBCKY3x8T2c+yMmuP713ZeooYmCOKlSIr1+IpiyWNVFTYMkzEtlnCdzUJlwKji0i1qRwj3TUCbDCkdCN2M1Mu4GmuOv971xxFADxBxhwE3AzVejkml6T/TCCXbIXJZMsNxxsLIt18zhArEDojv2ewHTrohEmVFcLCHPfn0dp65KF5gCkKg4ayLKQyqSmUZW7sYxdwaVKM45qrcknkHHd0fIclQtW7DzL5PtuMmDCsFH0vc8KwEAM4JRWeTovCvC+Dbvr7CYwJgixmBJFwjlUppIicrWRERvFkoUpskEIhHk10YzY01UmYYWFRz6YZZ8kldukec0F8mkGGeeQsHfEId+2Hy/EgTMZiXiTITovZS4RCH1Oq4xzPKyIsS+SHplC1Sx0df8dpd8B0+iF8MZZpWICj+gzskwPAA10iiYBz/flj6ilJUwCwz6w21qgcUD61x7PhMDU3TJe6PB2Mwvf063sHlK7TaQzqFPK3bc9sl5CZTrVRrKczOcnakhwyhZEkkcuWIwAzdDKHNC6nuJgzCv+0CGVTNkUfThqd2ZoWWya5rLKm6SnRIGkeQ7tei6VWyAUWWHINGaQgR0cKE9UbblJceWJxI52OkNRVtwtIpeCyEbWjxjwWtGSSsx6+zUkMxyH3FUNjEU47IxNm8PjwBFNyw/NZDTTLoqFwrYW1JmrqPK7RfhWy87ZtbNcrzx+/JdfK69MDpVTW0xmvJ6kd/GAiHPMWQFIJRYGaDsa2UWuhXy+8tJ3bpeMMvDUey4L7YN8+0ueNt+vk13/0xB//+pGv3xif1/c8lEZ/Tmz7hdu1M71zu9ywmbmOG+4FRmLrneets29iezZ3hhuVzLDBaB4Bh5M+E2ue9IYUX/PG2Ae33TifHvjii4XXrx7Yd+Nnz40eKPjpvHLKidPibE11jMMzg8LYjcs+eW6DD9vkdnNKLqxrJtWK58pS6z18bsmdYoNrG+x9MlEg5Bb2lb0Ntn2yd9nSshunddUhmwy8srhIxlNKnGsORY/RB9y6K7yvS3Z/28WWlJo4rVKeWCr47BhZ1s+70kOg1oydUIi9LCeGsZ7PrOvK0VxhlhQ8bOkewGiGwqWP5dCU3/Dum2/ZtgsP5zMP57MaL8JumZAqxXMOaame/T4mHS3DY2+01hhzcLvdBFKUQi2FdRHImbM+87WECWB0qX56p92VdIanTD2fSKaK01wWSJm1LKxZQ+UMBkLWOLQsHarGJgC3J6fdGu/ffcs+BmnRIprDtjMi70D5OWK0xhQo1nun3za2fWN0ZxsbozW++PxLci3KfHBJ4wem0MkA+hwNnNWD4fOJRSuFzUG/vnBeTyy1MGa/ZxXp+zzChxHb5Q4zmFD3ABE1fbx8+IinRG+NYobmCV1i2SSYtliwZ1w6eX5q47tdNymebjdOtdyBBMzuAJtABItMIv39BmooGnuL5VRLyvV2YzlHda0e1viuNB7MGJhJWQNiADR97+x7i0D1xN7VIpRz/gQomLIVcI9mmcycYUc4sgb8YOZ1mY+uau0U7LGATYGNuogNkFrGAhglPucetisTi6Hwy6xQ+xTMYc6ZQ/jkZvLUz4mPwegtWDd9Z4lofWtD8vKuoThnsW7TubebOjpANAMdYM4BqNidxcR0r4xQaCXXMpgjMDUlSbBL0l1pSctyzjksH9zro4ePUOao8OIYvI6H8TgrjtyqIydqhiIXswgw1vKRa7n/zJ9Gm1jCZwCJSXe/FMuFNaxv6+nEyyUylaZHXgdxn4biAmJoFLCsAdzjfjfmcE6PKx+/6WGjtzhHHWzel57j7yNlyGHRGGERSBEs7zw8LDxvG9t8xcK4n7mppHhfju+HUFUMctczWdZQTrYhECNlNUWekoKXJzQm1x1KcdZVjGvJRs0rg8mSBADnh8SHvbO9wOU6eHgUSN2HoC0t4kkAVuS3ZNdQTgycWZsoo2tYPT+d+PxXX/OTf/WBsRv9Bv02aadOOSV8Jnx/xuqK2wox7HtasdqojydefeH85p96w4fLjZ/87CM//gVYcZ7WE2aZxYDiLK6ZcBmT7Auv1s5LG/yTf/yvePWD/5nP/sIbvPwGj7/5b/FX/71vePf+v+cf/v4z73dlXNblxNZubG784389ePqHH/ibf+Mtj28j5waPNimgJopPzmh53bedtAyWfobxnVw1E3GUMPbpjBa14aFeK5ZIaWF23U8nc27PNzApZtL9vR2RuZIwpCbJAWQUn0xzLq3T3LFlxabufQvloZsfyLnmSnS4lPs5jNjnUHf6nOR6xmyHvmHWwLWdWoAvx9kdp2i0I0uzYTZY10SKrLvbtqsgoZpURcHE63467L9OyQJAPOmeS6XgtcLwyEKb1CSgJWeFH5fIR54D5tBiTpAUIlc1I5kwDt1jZjqep2yGJCO5GjRzOpbXzLRCsokXfWY+jLwseJyzvbewykwYTrGs3JaI1CDyUPTTJNZa8TyYPhXC3ME801wAY0HZTMdMdSzIHlktycAiFFcRqpMUVmUn6zk12WNznLfC+QaOzp4ZCuQc4PmcXTNZODDyEVQcT94cIk/MMkK5jvPbmFa1o+T45+MMmCPsP1M/qE8VEKW0kPpUEP9EeTx9qNQl7FmyJgHZ2MxoTOWYuSsg+gB/cHKVdd8QudbHLtXmPFQnRANsEN/4kd5wBzUxkQ0ewdDrskCToidboe2NgUeoskCYGaTacY+1mLUc4owoek6nSkTUMKhGRA6Q4q6GkZrWgNkbJcliNFLRc2VGsURdBPpsTYCSB4gzpxS2x56Yc4LhPK6FpdhdGTvN8DQiF0u5oilIkOkzinU0D6WqDNnTsmIXV6B9gPXmyoxKR85WChKwDwqJm1oGpKAx5bhZ5MAJ6JIN/l7m4lBrgq6fp6Qc4ec6m/IRBJ2SCgwczAZ0WbildNJ8QHy3KWtGLcK/lE/nk+QhLDlIJ9dMlIGaBimnUDAFKASUuHeZBxgfQJP7fX46gHBld3FXPd8psvisBPDroekHONX7HbjS/Tkib04lHW5AVl4iJhcLZhLDDO0WM55tEybLoXIS8mGKGPCB22C2kMsk/XOySQcoG4B4yi7iyAnV1Azdi0vl/j3++t4BJfdJ6hvj+iw2rW8allIhr+W+ECWOGnKhi5QEPST7nqVUMPlbc10YUX+ZPA4BRz5Pl0Ws+1RDQjDjyYzsrgMXiyE3WgByYSmFXBIlq4ltJn0BYwYDFA/pZbvgzUnxgNZaqbkqYyhFPWEcskcOi9raNK2kOODS8WCmIJtGIN+OXsgpV+wcslKUklmWynqqzKaq27Vmas0sVf9dTbAU41RPLFkWtev2wsfnC7M3qhVePb7h9HjmcV2lQHABKccDe2QzFYMliwnv8cL3puD0by8v3D5uWK5hxRisi+H9mbk982Z1Pn+d+Y2v3/DHf/WRL9ZG3t9Tbje2y8LztcHY6B0acN0lbb+1SZ+NNibXq9Q8AmE9mIPEHI3r1DJ0G7rI3DIfxo3btULvvFyvXFvi7edf8vbNK2aefPthsPeKlUdOJ72IaWbqUpmzMYcynq4OW5O97OU2+XAddAdDPmxyJuVVyrUpVjohxcal35ipwBRyPdzY++TWZX/rfZDcWctCzRbtgLrIVxJLzQyDmjQIvHTYhjNa59oHLaTSramSsy6FZU2UunzHLmVayJICupNlDp92OvKQXM9arYXT+sCyLsH+iBY9liJ5pQ9r6Ccrj56JzrffvKPvG49Pj+SlhjKpBpLueJ+QioAYZjQdSJHkOG3f6bsu9+vLFfejHUsKqbIswZTI+jddjS+jd5iKMx1oIU9u2HnlICsPcCVnga2JaFqLvwNTQI6Z3sURGS61KOvs/bfvmW0wE7LMhJ/oyJlJJRQLU77nEVlQo3fa3u4qpdYab778jOV8EmNNABslcySm4XD48CyFZJURecaS72+XF061sp6kZuM7FkQC/PluOHgfCvfPCAgU+O5sl429NZbTgjM0NB55FQFM+bxvzWIjk2xoJZhOv1x42V5YLFPXhRy5Oyk85+5ETk78MKFM68jnPbrYNBsKdr7dNr5880ZIaUr3z0SfiIaGXIoyqhBYkDG8dRhSFR4FCKfzWRai+Dz0EU4tUoz7QDGjKSlbVKwGyNBbo4/BsiwxxEXOhUX210FEOPHuovY20Xn3BSH5wRqKLb2zcfGAJgwC9DjYRebUmRLgj5s+xz5kjy6l0FvTs72otdSCVUyhJLFs97vnAIOOdyJBSLD1TklJxn2QtvieNXjozE8M5XwFIWSkaIvr5L7zMB91N6KlMcX7q8ucUBR4sOefcgL1M4kFPaqEj3a2OxNrkXvifn+hj+HcDsY32/3fV6ryutxlYcop33+meaeevzub6O9o5lhxqRYHnNaFXBZ665xOJ0BMbRb6c1dleIyPh9ZNCotoY3RIVlmXyseXC7fWWE/pDtCIBT7eV7032cKCFMslpnrhOY3eM+fzysvLxqH4mz4ZTC49k3sinQR8kiBROS1OzwpDXQyW4fQkldLrLdGizSydMqWaMiDjnBgM5jWR1xP51LEAClJ1oEd4bOHNV2+5vmu8XG601ekt0VqmjIpnIw1n7jdsSZhpmbTyAEnnzrl9ww/7mT/98hnPL40f/+TK+VSon2XdkSRSMU5An41kheHGYpAeFr79duN3/sH/yV/58gvOf/wV5C/44s//Nf6j97/gF//d7/K7P93Zth1qYVkqV09sy2v+/r+48ObpHX/53/2c86M+77sqwzNko1SnTkR27ZPltENbELM8mQW8JRhBSDZjb5PiQ+1iKVNIeDHaNllqxh8yt2vX0mQbS9R296xtwYZhaK5SptsEBvU06G3gXfNO8smh4wwkV7aMmKH1Ds0DZ9Lzbh4V9lWDvi80H1xvnVLU/paSFFVjTjzmBQH7UrrrXtDilk35PZ5QpICrjlyKEOKsm3cCICfoltl9BMheaMllM5xqqQ1pBmtVnuERHN8jIyeXrPtjWuwOcYCZVBVj6HMyS5SkXJU51VA4pzNmkuKZzh45c8lNL/5MTFeMRM7GnpI+8+POslDh5k9xGSOAaLJhHmTAFPFpYzLC4d/7zpxF1fW5BNEB1VYOi8qMxtTuItDpyvLb5lBOjSUsLxhSymId7Dv7w5yoAjzF8q1Fm8hhIVuQlwKyskspuo9Bmp3kM87SY4aaKkqwqKWIYGrirljWVSrtNhQN5+GqQLl6KofNeNf8V5JHCLXRW6eaaZlmxr4z4zuIttd1OXAH7vXyY4a63gMsEpjhQcDpXBcx0E1KuzakCj3Vhb03PNRBNhO5LKHCDqX2mByhesOl7k5HJiEWz50UuRMUBu3CR1Im1KCaCaYL3LdS8L1LuegimKWK/ETizZiJqh3kqFTL9p0oFqINPMWdPvqhUNPn4ByfXQBmnkkucEnga2J6pZiAvZSN07pQGmx9MEw2/elhqUcEbA5bYp+deajwk7Ikk0mBE+PMnbyygxCbAjx1hxk2BU7Fi0up6b67KAbABTC5350Vbk0uhyAck4vsTibLeC7RTjeCzDTXWTpFFnmo+7tPWRJNKkhmzBg5KYPzUDDPjC1ZkRce6myICBG9z3JBDYE2U+BRyllzDpEPmrj/hjEctfCawMYUGmXXrJBTwVInhQ0eJz7XpDMk3uUScnRpsjxyFqPgpRb6VH5wCscFAWByxANpIBc4ReSNWaEPKfVCf/C9/freAaXLL3/Gvu1UnJkTta4aclO9f6h4DsZHH67jzKbK5pIkWbPDU2mSEZakwcxd1cutz1iqoEhgKfbDEylS7MccWNGBN11Wjcf1gZSr/jzT4qC5szDD9zj64HrbtcjisuVVef2zadHxYCz7jIU58g886qePZR24V6xi8rV6ZFR4hLxN+v3PqEVJ+MtSZJm7XTmdFh4fTpTsPJ1XsMFaFAZWkwCJj+9/yfOHj5IjT3hYTjw8vuLp6RVuRtu3e4bEoSIQ0q+Gh2piB677EGo+nQ/XZ+bs7GOnmHFeCsWc2+VCGzce887Xb+A3f/TEH/vVt7x6yOT9I3m7UmyDmrltXWRYWhR4xySdCn2fzJHYW6M32NugI5WD70JpOztzDC590PbE5js+K8xJY8r+dOmU5ZGvfvhEOa1cdmfvYldKzVK9JKe1DZsb22Z82DeMhX1WGonna+Pj7rgXpkkmmWulridlUiUxPUtp9F2h4Ftr9KGwz5kKe3NubbDPyd70fZacWU4K3S5Fz9ka1pIlhXXJxfq3aQKR+lQ71JhhMbyRk3E+nXThhsXIzIGqQ23qME6paMAIF1EqCrBre2eplfPjAzkveq9KyHRz2E7gnm10tMoMF/vW98bz8zOjD86PT8xFiqi6LGJXU4oGCsmOMb9nJwmgEYiTSsbGYH950WEZoI0VBer7GMpgirYVD5WWj3kPu56R83I6rwwzLEk5dj6d7zaXnJwxNyCCEpNjuUh+TWTiQMjRJz//2c8FqqbEqYhJzalQIluq1hJzldjL3jt7ZCVNl83NXeGVr958xvnpNTXJfugoIPSwqHWOUPAALMag+SCF/54J18sFm7A+vWb2XaBbeOvNpRZImmMFJMTwM0yDvdQjGoI/fnymnk+hfJoBsMt65xFqeQ8rjitHzI1htTKaLnfrsD6excooPEgAhaOBIligcRQOoJ+ruwbPORS4eXm+cHo4fWIYQ8IuhduxiASzEmBoMoXy9wBiDsWLpULKyq2zGBrugJ2Qeil1DntB/N116DqzD/ZNZ4yF3D/nfGf6kpue2YmsBEEe6OcScaEshillbbCeMz6PHBL+A7A55OTH84d7qKkEHo1YDGZvpFwC5LV4303viqXIKXGSS7ZifgBfh31Ei2qesvF5kky9WsIie0pLZxbIMafY4RigjwDLI3B+zIk3Zbn5HHc7WG8TWW+CRbQjW+CYBvyuDjpALuUUJPa9B6gt25p+aXAOBfgdjHOmfq4YAA/Rwul0wv393fYuTEzAZsmf2EuC1Jl34NTDeq7vqtrC+njmct05PUauYZyDssYNchfoHdOKMg+7BrIRkgp3J5dCzbBfrvT1gWzK3ciu8y0djHwKG3JLFCUmgxcYuywNJM6Pla112hhalrtjOdMNttk5DWOxDJ7DQjRxZFFve2c2WTRvPnj3srNUGbRyqiGbj0ySOKv2qBrPq2z2Yt0Tua53IP78kHn79YnLv7xyuw1KnayPg3nr2BLA6NixkfX3WT7TZ5OuwFvqq8Hj3vn6R4Nf+aMXfucnP+UP/7Dy+rHw5vEk4iQl0mLcJrRdDVrLmCQyT0/OH/z8Hf/3P/hd/vzb38S++Axb3vKrf/Fv8rd//nN++Q9/zP/z4UpvCSuJfU626djywN/7px/5wdsLv/0XXrGUjGUpQhTWYWSHWsG8MMbOtu+Uc2HpyhM5TWNPhX2fVHfmOtScg9j8PEOhmQepwN4TSz1hPrnceryrjWoe6vxE9yL1WU64dWa7krxQ08QKbLdJy0YaiWzKVjQTMDDucLcUlyXp7A+pOm6Jvcs2lSzLkpJWrIJ7x8Z3VLuizjXb+kHChvqVA6QOx0AG5mTbO1QRv5ZEljpO8nR3D3hYKg61dO9N4bMMSsmM5szmZOtqLDSBijmluzUTZNVXkK2FWiIIkZQFzIzIJAVmFMu4SYlwWGlTGjqTYxFNNumeZGssicUKOXVG67q3prPF3JxrYZruccdlW4+z5jBKT6YasLoWz30Oem7UWslFTWkpKbBbO2sGOiPuqzF32jTygL13uiSaSO1TZX1MUkNQgsBDuVJ9fCIRR+8iHeJMJO7UFFabHAA13aAPWLJUJThbH0G451C/2P1eJgnA8Kx7xYBt7tp5ujHjP/PYm7wp14z4d9YstdOIvxZTz4t2J1OI9/S7mtyPzC/0bHE8mwfp7t3EcwAAIABJREFUk+xuiZ7meJtRmpRZiyx++9Bc50EkiZ2IZ2fGucuEPEmuM8aGMnXVWhuz6JxhSc3UEneI6W6ao8efp79L33cpTTxURyDlmF4hhryZAsxiD/P4M6zo90lEGuBRioauPmk+WCQrZUQjc0k1VMOmU2FqrztcL6VoV923jZpKiBzUIDdmlJdMNZJZgjZHBFvrjBF4KoC5BMA25tRsj+4FBqEmjp03J/rsbL3TZqemRc94l/1b55FH5pKIRnnpQqHnUopNs6BltZNaXM5HO17CVMZkUjilJIvhCIBrDpFSfSifUdbVovMKzXjD4nMu5R4TYN+d01AkjeYV/f8pXEmOQMUZ/88BsOGfZnNlQql8QbbkGdlUytQKjRHHtJNM4oTppqKZaIK/E1oGWFaxkbmUa24RYRAA1Djyng5VvkBQTNZ7S1kKLvtutub//1/fO6DU20atq3JJalRzu7y22SZmClCVGse1XCU9WCUlMTFZw/hR71wsMbrAmDa2YHEUrqvnd1Cs0Lzfg1RhYLlgqLmhrpW8xGI2dSkWCNTTsWlc56BdJtuuVounpUJKLCct8dmMmRJzBOo6h6wEY8TQL+tKidDVVCtz6HAffXzKkBB5G0yqfn9KJtCqqv52365kM16/eeLp8UE2JxO7s6RCMqfaZPbGT37+c7bLhWzGw9MTj4+veVhP5LrQ+47C3rSEKP19BBBgMBPFxHg8f7zQBvRufLhc4/NxHlPi/JBh+8j7D9/ydJ784E3lR1888md/9Uu+flvw/pHt8pH9+kLfd263C++3yYcLbFMgn3XlX7zMic3JtXW2vlOmBtqehtB8NxiJWyCGo4st3WbFosZxH4mXj4PPP3/L48PKdkts+2SbG3VdWJdMqc7gRruJqSmp8K7tbB362Gh75do23vfMsmRmhO2VmqlrjufScBsaAsagpAduTc0tySq31tlG43KbynTpqgmttbKuRaHQKYWVJbKbRsdy1QEyJ7tn2pjMNtSAFuHe++wsdWVdBWaWHINijAV6r6aYDJ/a+nFVR6bMHn7188MjdSl3P3oKJVrKeueO3JUZS7iUYVJFbfvOx/fvSRgPjycsF5ZSSaUwcE4lkbPR9lAHrQt9u91ZiZwLMwfj0jvb7abD/VAaJA0nteQ7cFuyACqfA5+dPgf7aLrwHOoicLoknQFqxJn0Nnisa4RKjjuDBoF/uC5LBVvq6H3381/IS110NqzrqjyAdSHPqHnPWbbYYLQ8LsZD+XXdFcK9vnrD02efCYywzI7aJiUqOdhRBQQr80Z5HJguHUYwj9uNt59/xmRQlkWXSDIYM6TbMQT0uGjjKqopJPDTsWL84pt3eADUuWSqm4LIk8UJCsftPYeyvohsmFoSfe/cNtn5TusC9gn0sYMuNoG/oSnUihNqimopalMzo4n5a3vn1eevI+Dw/7ts5JAF55IVguoeFfWTvm9Rxa3/rLedclrFwiL2XJk1+hwJkGU/QlznpMZAIVA99G6uoayWqpaTlD4powKgKinJsjlj2Ih3JWHkJDviAQ5Kmh1Wh8gLIN7/wzIHBBMWNu4xWEtRXo4rTDvVhZx1H5DS/TtWZpU+MDcUBmxq9gin333gPr7XMYYCKQMEc9SCN0YnR2aJrJihdIsGN6zfmflEMHZGgNmaesVofwKBSs7KygoFk+48gGBgXeq2LZa8OVV164cNK5SR6QDiTMOsMjWAkIT7cJa1BtjYqcM0MB3Amin0Mj5sDtUyhqT5oXgqNWF98uqz1/zixz+nv33AcqEaDAviyAS23fMKcroPaJYTre0yAVjHUaD28+XK9vqBNU3S7FJcGGTPlCV2fnQWQMM9Y2XgVIbvIaFJPJ4rt+ukK7mW0hMLk7kbvUauCDMyFVSHbUPBxJt39tnIaeFyg+vWyHln7UkBnSkAYJuUtMCamL7jW2WsPXJ2TArBrO/TbfDwxSOPP7vx7pcXjI31lFnPO6ldqKdKmjVIOtfLaBlm0xm8njk/PfHmNvmNP9H48S/e869//MLrNwtPv3bmdFI4+6RQUqXEcrBIb8fjmrk9Or/3L3/Ol//H/86v/OUv8fIr2MOv8Gf+1n/M3/nmv+A//986P/u4sw9YTk9Yrgycb5dX/Le/c+HVq5Vf/62FGiC9lD6JYpVZdtmlToPhHbYNq7KdLSmD91DTF52la2c0ixwN2RLwSa6Jkgv9ulHLmfPDC5frZEyjpEmZO9iCfGFSd2h+zDAmJVc2r4waKh6kMFH+54RZwTvuUtVkI/I3DuurgIecSsQZDIV541heYEC73CApW01n2sRnqPLinRWRq2dvuDLh5r5TkhTTrUnNeD6tQWbEsjmQvTd+Fk+GewOTJSOnTHPVoZsnrrdGrcqYyebkkjhyR1IqCil3Acs57sucC6p974ykeUEB1Docp8uyfZRQ3J0HyCLq7Diymaecwgovmw6EygiYOd2zAtUUJQK2Wyx8KVHijHYa7oOx98MBTR836nGeJ2XQ5WQUc0aKYN0Ift73jeyJ7baz3YwS2ayOVBHFKjkHOZIE3tvIodSMqIyDqN9FTORSNC6aas6XmhlDuVMpT4Z1GINqCyVAodk7E6eozpBhYbecAFkW/iB78Enrjdl3TuuJ6UPKpJRpXRlNliMYGAgYQuHXUdQAkUdjRy7XlOLJjZIk4XRcCkt3stAoAR6RIaRMWKnhb72Fsi7f1eUi0iNjd35S4ifAppTsKSU6Am633lmymtoWyZGoGc5n3Znb6NpVpitwOwhxqTtjDgqlX00Jn3o3zEVIHO6CnIKYIM6PGVX1x50Vc8rdmTPD2sbQvXiQTlmEhrn+vBnzxd4U1GxTZ6llqflS0s92ADP4iAzXLpLKNXdm0/uQiZzU0elj3AtZDkVxTgrHTjjdYRtTqsCUGDZIROFU0m5v2XBTpX0+CKcZQKHHHOuO96FiLqIdGbC1BOlBAPNEPMHkCO42t3sz3xxSjlsQ+KdUIgMPFjuAGg9KlZi1iuINovHOTAISAT5hFyxh2Y84hbvq2iM3KSfGVLTNdKRsDVWwu2ZLfFAKOofMZY+ecXa4qF6bOkObayfvbRMA7ws2ewxmiVxMd/uxK7ozLXOvhLYE6Pv1JELp+/z1vQNK9fxImiF3to7FQ2glM/MKuBDsoeEnLzWWlJB7uTFNMmTLCs7eN9k2pMDNwdCH19YmDNjnhtmk5FX1iwxySawPDyyp0Am1hMnv7jZoc6qUbyQuW+O27eSROD1UHaZVMshmhehvozfXAtGHwhld9pIU2Se51kCeI6B46L/fIyBOuQ2Syc+24Tjr6czpvIJPNXuY8ebz19RS+PLz1/SoWS1JwNeSxUJ8fH7h+eN7xmh88fZzns4PkXsR4EgoZWo1DZ5x+NeoP59jMJOxtc7clSOCT9p2w5raf+pizHFhe3floXR+/bXxaz944Lf/5Je8fqicfKe//yOu24vUWped7eOVl9tOS4nLpTB9o/VJH8bWnNt4YXTYb0bP+jvhmd09Aq4bc1ZGh2m7FslR2GfGrDH2zKvHJ374oxOnU1Uw6Umy2lKTGvpKUf1oXUjL5KFkcjAjIzUe7IGfbheeW+PhVLh1x6qskLJDWgCcg1ISNSko83m/0SMEc9rktjuXPugD2pDMd82FNermHVitcKoZs67nsq4ky1ybQki32dibc3m54cA+FTT7dFpZiiTD2aouidEpdQngr6sBzRsE02HhQe9bYw7j6enpzqxLNSFAYC1ViD0CbVNcTH20uEwG+3bjw7v3pJQ5PZ1JuVBLxXJSHkEueC74HCzLSTlOCDAeLi/0vg/aaIy+8/zxI2PqPy8lU0vBbbJkAUkpVZILzGpt59aH5J/hXWdMWZOSfNxYweqqRdQVIKoRY8CYbLcrpZbILAoL29QA4jjv3j1z23asrqQKp/VEBpZlpZain3Opamsyw0dI6QN48ynpcNsaqRTevn0izaGh+xjCI+tAl42G0tb051gE/Rc/6jsnz88f+eyzzyim8L0ZNgBlWjilVJQRMwIcUXjrmLIHt32npMz1RTll6+OJuqwczZglat8x7XkDfa4C7jp5GL131vzA/vEjz9sVtsby9IClGd+TQESFE6GWj5ykPokhY5hsAzXQhrRWXt5/YDk/3gsADqtTjhymqRv2DrR2lw+/h9LChyTD+1RQ6VoybTQNNZHhxWG5ug+7cTElwsoKNWd6ygLzEbCaZHi/Az4aaCbFk2yXWc+l3iUL6bd/0jx5Y+87nnKEMoZFrua7v/9oGD0CwktKjNmlOnCt0cqNGKzrORSAHkYW5QgaYCb22EqOiugU4coaxsdUK+jWNykXEdJUImjbUPi7JOYWIJC+j+F+t1imKZVssvjPhrIBi5X4rKQwmq5/byJa/fR036HOQ2WVyVJGxCA7Q4a+LBUYlFziikqkGQCTm9R1x53qE0ZTRlJJpFzx5vQKg86apUZ2Dpm9fp+aVJzD2usH2xlB4o/nM98A29U5vykQ8vsDwJOyOooAMBgwcpBiKcPsMUwap/PK+48f2ftkWQttOkuWemybzmiwHM9rhq05NTsFj8VIQfA+QwlaWth2hobgpKWlNWcrnVOpzKTg1MWcljUWPy6VcZv6jJhcdufhodFmZYRqLg2B3PlE/Lsn+0tXMUPp5FqZQ2fREXhc1sbbX33g8u0mld+1Ma4rIzt1JniY2K3hjx2fG8wNk2YdK0/kh8b6eOOLLx74N379S372h3/AP/9XH3n1OvHDfGKdGVsmuFHN6cXJ3VjM2VPmwZ0P73b+6f/wT3h4nXn75/59LP+I/Pmf49/5T/5DfvbNf81/9S933rWd+vDI+fUrxvVCx/j9Vvh7/8t7/s7bt3z1I1md1OonRe86V2yZeJ2Uh8rzN8/4LZNOidRCEWMDL4a7qe3MOx75F4eKLsV5vJwX+nVjfXhgzMat78yxk4bIIE9a9nIE91telYnCjVpQjpIn2i4VMzZwL3ew21EuaCJFftjA6aGsCPVQLow06L2zpIpj7D4o5zPW9HswmPOolhfJZFPLDGE3KamjXFOjFBEANVVuu2yGiy9yBFgo/EzFENmUm+IG1SrmutdLecDrTrdGKjXY/PSdpPjIdLSJpQq7iYxR9YlmlgElL6QMYzRmmtEkNT8pG3yCZYyoqbeJW8HSQrEtFvVwOWRJZ/KS1arUj1zCgZkC37032dHGJJcsi08QCqWqIMWLYyMWySEVc6qyZXmqWM7/L3Pv0mvJmt55/Z73EhFr7b0zT55bVblctrvbXcaNkQGBe0JPGIAYMAGpJRjwCRpagi9QfA8miO4BQgiJMQIJo8a0sSUMhgbs7q6y617n5Mnce60VEe+Nwf+JldXzMyBVUpWq6mTuXCvifZ/nfyUGPx+blAuEyKhwqzt97VjzvKiISAQH/4PBaE2Ks1aUFxuCZz2GuypT+Hqj1U7sUVGvvmfFnDQTtaK/d29sZSfnhRRh60VqyHZUkivHb4RALwoi7qYWwV6rluRtZ982WoQ4VHbSh4pqLDS3LqK5jcAYUqlOeZbNuxVa29RS2/W8yFId5FyJgcU/770NApFeOiNUppDoXtZRD4XNXslZ9iNAZ6grdnCbuQQvroheMrRKjpGyddIkkDRVV1JZo1skJtmeYjDtkDmTogDoYa4TKsrfhUGy5GrCRkCN3Ieyd+9VMQNRd571I6lHM+ooA8zPHD0B7tJzlfvQTosJxA3eNBCTZigbcGuVFAf5yFU6WtYQyDIFt26ZCCjz2bO5dfBuI/cA6XbY2ce4E6UDFdgsMTlJHSn9Rk5Gj4oW6HtnnpOA6G33fMZBVziuCKcDoIL7mYG5MvxwqPXB2IsCp03qymNus6OZjeB7gs9eDsJigdoGt7ZrHvD4gJzUZlmHPnfzbwFT7tFIeDZecKKQe24VGjnvRFNzS2Dw30UGJc03ypKEWgrDEm3ou+i1E7rUzCEe/4xmy6O1rjvQWWsDtO/O86A0/T370PkQXZmk4hjZGYclPx+6PhMbtJpp/euFgL52QMniiYCQ0hYS0XTx7G3I5jTUIDbliI1KG1XMQRSC24dYgjagbzul7FrEQyDECTh8gI1oOqAa424poRXmtBDmWWCVHW1BBlGHjQZsySBva6Fe5Sc8L5k0LeToSHAKnEakBmPUo2YzMKr7b7PnIBAwt4yBAv5i1FA6mvJ0krN9akrQFnY6n5kXIfr0SgzGw8OZKWceHk/EYLR65WGaCdmgVlptvHv/FS/XG61WHh+f+PjNGw2ztdyZ4iHNPoSkw1d/fZakBq1trwoEG4gha5Xr5ZntujJb4rOnmciNUt4zTztTLHxygr/xW9/hO59NPKZOL++pl/fs159T3l15t2oYjinw8OaBNJ346NaoVvjiyytbSHyScJCp87xW9tJ5vhb2UtjLzlY6I+rlTCFQmpDcVx9lru9X1m3iW7/6hjevZ0aRimmtm+Twy6zK7RgpRfXxIRujeE5OaWy7EeLCl2uhxZmP37zm3eWFMWXIicRMsiGrgjXPwIhcto11bRATpVYpXVwtu++VWjs5GlNUuPQUEzEMUlLgtfLAjDEyWGRtgwLsdcd6YN82eoC96AB8OE/KFYgJTNarWhpYphRXAfhhG6L+94FkukcI8qtXD2L4DSwmQtYSo/YD87wkHdoH8t6LNC/X6zPP774ixMi8nIlRAJlltbUo1FGB0ikZIU2s65VoiW1bYXRudWVft3vOVRuuADkY9WDEJAtenCYdfE1S71Iaal6p4PlEMSW2XokxE6cs5aH7qUdWFfK+KwNg23c/lT/kj0ixITB33zb2rRCWE9mMeZ5IJv+7eS6WeUbD3Yk8PuQ5GYNSi0LXg/HpJ2/IITLlLLbEWSmpwXzIaF35agGpVcbwd7wRLfLu3VcskyrCj8a4o3FvtH5XDBxg0hHADgIEe9VFPBhslyuvn15hySDGe1NkCBqqjmYx/f+VLxWisV9Xpmni/cszt/XGervx+PBAyEn0UW9SS+GZTj74m2VVOkcpOWa3uPbRaL2R88zleuUb3/kOYzQPixUIElCey6F8OvKEWkfVwF3gTowKW6Y0H3COZgtXWdF0lwTgGAjQ0MIY91DzYca+Kzy97oXT49kHA/NcCrurW7ZaSW7BsxDckqXnILlkJ8ZIr4XSuzcUatjo1lRJrA9JeWkpErpnK3hxw+hDg1Gr0JVr0s3VMCP6AAAmyMGBYS1/IQS3PgxGr/ocqgDm1mTLaAxOMUGHFg1JHYChZylGBfcSNHxItp2IkwfK90Yp6x386mN4BbOLtc30fTqbJuWRM+reciKgS/kkUwykJqXtcJDNyPQAvRUfKB2osyOjSTL9FCKtSaGbUuLhvHC5XlgeFuLQnyWF8HCLZPtwBvpwZTgIO3TOE5T9Mc8zt8sz+ZzIeRJ72/GlVuxjtADdKEN2TppbH0NijOpgaWZZEmXdCacT5hXQhEEikLsRutpwRuy0NkMYnguh9qnWO610khlpimzXjSlpgbEYITRuLzAFVa9bUEZbDpE8NXLNsijPi1jxYLS9UzbYr9BiIE0KmlZ4ut7DEhKRStiNnhIBLQ2VnWSJ49o5f3LizTdu1K3z6psL8ycz6eGM2czIsm3aesFyZ4SFkWYIC+aD/fR049Vt5Tu/+ch33z7xP/+vv+D/+ceJ828nxmQsA/0ceVJDE6B2QZgjnB8bf/7zL8j/3R/zr716zfIbD4zxSPjO3+Lf/Ld/wI//i/+N/+WtsdVKv1yYlplyW+kE/q/3C6/+8Jl/42898vhq8m4mXD0RGC1A77TbzgjG9XJlmhfyEmGHaQazTAga8JsFNjZupZKQar7ZIDtIYaeJvlbmNNFKYR9BrZ7DA/KREnCg5zZOro6rlSVP3PaVFF0JGUwse4cpGFvzrCNXVrQuosJap+IAkFt5pzQJgLZAdnXvilH3TW2p0QHh4WCZ9iNle9Dv71Iyr/5mMFJgjEzrxrpXMgKFU4xuMZWqxYZb0eSX0xndir97Ug7UAXuprKWL2DvasbIs8Rak5B42FFI9pIAzV1D2ITWFQG5vOXKVxxjcc1tsdNq+6RxdFipVZ36pUkU3NekKfBYxtO+rZ64IWJncfjZGw1zhZVFRApY67A32yqA6wG1OaHTCGKx1Z5mzL4oeixEiliutdIF0BWKsxJEJMdHNdwcLalgDLCXmMlz96a19Q3Hqg3G3iHkih6zbwbxCvVF3D6lvUovsrdwLGHrtEIOXQLjtKwiMonWy24WSq9VDmlRBT+Oy75SmHJc8ZZI/U8VJHS3lshC2pnvLxmAMWdFGDq4sdmWdK51KL2yl0UxFNkYjxkGpalEuOuXprWo2MEEzMSXNWZjvjr4fdc8BRPmRIU2uTGssIUsdHQ5SLzN6ZVt3J7GkfG5VaqRoImoOh0odcs5Ek7tgVA+2duVudauebND69z7wbL3I8IDn1it4uYuA5H7P49R95oAZJgVPSByJa40mBc4wSi0Q5KSwqGdIb04jp0Stei1FhA/CYsRNSqXWmn8/TtYBICdFGkYOkSVOzJN2ka12Cq7sLzsziYfXyrXc+qZiGDOo2snNjpHN7Z1x0PsuF1xIIpprdZLuwydQm86FXnGlNfffa4xwd+L0frT4Dm+6FDAlcniwbrvPwr42/NLMgO/SZnZX/x8ZycNgVMWbKENWz9IYQ+pFP39SSo4TQE6BTV8sW+3MSQKQ4ERo3w/XlpeRCJ0nhST1oe8S1iPbtusz6YM+CsEyeQq4A5YjOqI6+A0eH9DVLJnzzNf562sHlBIoTyENQsyUVliLhha1JhwDmi4CzbbOiASoVdkMte5Y6EwRYvBsJDuCq3UWNDuYiIR1yCnTHxKWjNSDe0+Vr1D9ok0xU9ug3FbqWohAmlU93gJMeRKjHqIOJoM5ilnc206vjRwCFiaK6XJIDDH1qILazNj33dVLUaFtrUpajbEs890v3vYdRuPhvPD69QOPOWMeVLakAM045cDt+o4vvnzLet1orTFPC9/47JucH89gQyCCH1y6DGA5LdzK6va+46BtPL8oNDQFARb7trFenun7Sgqdcxrk+kKtv+BsO58tgX/+25/x6ecPnKbOY3xPvxX6fsPaRqSzLBNba+wVyjApTL5YeXftvNwGsQ/qeOEXI9CmQtkGo57YPSA8hkHIEwm0iJbGtleWUya0zs9/8I5PPn3Dr/zqEx14fnej7IEbnflxIVpiTsY8RYJNtDlyG53rvrFtnRJn9tWwpvpbyzM5zXzxfGFMCxazUvTN8xCAUguRQGoJmGiTKt4HmUBgs8Zlq9RqnKfEMmVOU2TO2SW6jckg0rCQ2baChchaKkQxRIbXNy8Tz+90sJ8WIc3d2XQOzPyI5PcDeM7uhe863kfrbPtGSoHz+UH2vRDcGhrAJBHvfWBxsLd6D7LtbgXDGut15d2XX5GmzMPjIykmJs8B0AKpVrY6KnvZyA8PbLeVbVvpW/H3oHDbN47WlJwnJssMc3unDgIPxIxE8CXaGzhQq5yNQatVbR050WvjlGdOyXN4hmTZKSRWHxL3bZcdKaoevbbmCoumYbJWLpedOM2EaEx5FusfI2YagkYMkqPncJfeyqqqZp+OgInQB08fvybkRJo0+BJhFOWRKPxYCpA2FGY5upZ/MYOS97Zth9Z5eHjw+2vclSVYYC8CqUdQaL7yenQZBtOQMUolz5m3v/gZDw8PWPTBPrq9ypniOwsUUGB4959pDKkSU+Ld84V125hS8mXdAcCQMNMCa8cgFtRSGcfQIG2wb0WDQmu0FLi+e8/j+YE86TwEyc2HZ/qEoMID3GIx2sBGVDbEkAWilUoz2LeVp1evxJ77mRrsOO+7W6Nka/L7lxSSLwCDrRRaa+zrSo5BocyaD+5KKQGATZL900lDqR2AhZim2o7ge31f2aJCcquaoTDH4FxjBB9AJNXHwlGLPfqg1yNsXeeyFhgtSbIpNrGp9+8cycmpriLSzz1cYt1KRRBkd7uenoEj7y+GSEyR6hdqCmIXe1Owbm+ykA86U87c1sK27Sx5cnk5DtRGP8MRgIua7caoUtAFVwaMpO/WrSIxT7IL9Eb0tqsQJk2EqnASQ+gqvCM/JnpE6vAa9H3dBPo1iYWwD4Nv9MbJewvM0HuTg2xsmEA1OiwPM1/8/C2v+fi+yDAqnei2u0APLqv3Z+xokNEUY658MPJsvFwu7K8emVwZjP8+HS0QI6McrDFR6pU5Z3qvnkmlnJXuC2gdhborBzIFgwlaCNz2ArfIk52JsdwH4tPDTCqVmo3QGpYH1EpNja2urHsgngIpDgKLWzSM5KrDrYKxE5hleeidbkWqgAaByJtfe80X/+hLtucr6VbITxv5BDZm7E0k80gYDYs3zB4ZvTJCgziIpzPLRxufbsZ3/0rnn/7gHf/4L9/z6evAb337Y0baYJ6JozClyMogFQ3lOTZSNuanzJ/+4Es+/Z/+mN959Zr86b9IYCH+7r/Hv/+jn/LT//b7/HnTvNi3Sp4mbvtO64E/+mHgG3+y8Td/L6ldzwHMxs4+GuMmi1bqkfSw0352I3wrkkKijsiUOz10kmdjRovEAaVASmgO8xaiPDrttDDWlYdXr2iXi9Q7Vao9ldBIRdwcLLIYyQFaaczzRPE2WeuRHjuxAyOI+aYQySSyL+aRPqoUADbJ6tP13oVJxINUGXo3pjgpyNhzRYf//xkf7M1HjpLFKECCQAwTex/kJZItcr3dWNebzvUpEaO3vtKpGNO8eBNUJLj9LaBG11qV8xTIlH1XCUCIBK/jDkH17wOjh0wv3S2onTyOBXAQY5ZqsEHv5W6zChjU4cu0tyOGiF2lKB7HHJaiKzF0nueUCWNwXTc1zbadPqRWDlMWYN+LK7496iJGUhIgtcSZvaqdtNtg9EIaytRsrSgryiIpGlM2ck6cH05s9Znb2yutFFKXwsdiJKbsNiGpjduQRXCEwN68gezKlFQiAAAgAElEQVQAUnp3xYmf001qkJAiocmqmJHlurnNOMYoG3W7abvuMyNFFbCYyHyGmrSaaTEnqJ0yZ5VnxG6U2Fm7V6o3lan0ImXYsAFR+yEDRq3UXqh0v6cDrW2a1fogoGWfPNGjafjqUkhZCCSM3fSd5+7qs27MfocG38cY0KPOr8YxN8h+FNwiVJ2Urwx6OQoRBj0Mz+HptE2RFlPQ3ll2AUfNLcQ2OvM0kdLCuhXtDKWRk89sA0pplD4IKXmcQr3bTGVx6nf1TKXdm3ytOTjWlW52zB8YDsp2Qo+UKlJqihBzVKNxFLnYNj1TrVV6nrgN6PWmHLFJIfuMwXUrJJvYR9F8hivjQ6DXxqEGx9BsFhM2Te4sqkSkkLLk1n3b9L6UJOVeDx754PNdFQRDMO3rQQRCCCI31IKsHOUPILEscfrkpHw/SK7eHWPIWcAwrm5qH0C4mCL7tt3zrPpo9N1Dz/3cUTSNZ3cNKKW4EkpzRfR8UsUveH6Tg0M20JzdPOScIatv62xd1tC9dSL9TlKFkQhB2VB9DFLS/FX8M68+N4bgqkovYgohy2bZlD0XjXv7Ywr6mUo9IiAEVClr9uv7Fb/3ve99rb/h3/sv/5vvdYvso7OVroE8JpY5O9gRnZOWlacZtJFIPXC7CkjAGjG7b9ZUX9rRodh9QB8IbV7SiTTPzOcFW5Z7BbnFBFEf2j7qPctpuxVuz7vYu2UinzPznNXEFmdmEsEvhzz0JV/XynrbFWQW0MPpw64RULeSfo2Bqq2R+qLVneGBYCFIwi52NdK7hqXPPvuEjz96ZA4DY2UCKSbCYMnw8u4tP/viS8rtynJ64OM3n/Lxp58zL5MDVQr7lM3LPapNXtgQAjnJWjFa5/ndM2N05nliTonL8zOX9++gNR4n49ViBHtPrl/xK683/ua/8C1+77c+49ufnXj90QLPb2lvf0FbN+oYUnWYEaYT+eGRMCdZEHonLhNrD1iCumyq67QO7ZEd2LZGaQqj3vqg7IflItD2gEXjq8uN6x75re/+Gp98duJ2g9s18HI1apqZHmZyiNiIxJB1uI7ApQ5eWuG2B+qYuFx3brdO3xshJr5ab1xqZH44cX5Y9H1RyElZR8HbdvYeeKlwbSvDBpe1s4/B3mFdC9TG0ylzPs88nhcelkzOOiBykDquErjVwdrg2geld/ZWuVyuEBLb3vjiy2emlHh6nEgpEtMsVsqX7+PyMJPqye4nlWT2fcC67QqoPp+xYco6yhlLSf9ug5iMHMVKdf99GbLvxBhZbxs/+clPmZeZh9evCDkxLwshuWUM2Qj3baeWleW0yLu+7QwGtRdu+04tym1gGPm0cJonFg9JPy8zlgQmOR1AH5I8awhq7LWovbHJxx6S2Mk8zyzzTMpJbRpBDQdlXTEL1FYl7fdcpe4qHPslZcm2VeKUOc0zy2khpkxOAXIkue0xpMSIQeF1KTDaoPQPIZOjdy4vLzw+PXF6ekJpfQpFbxUt/UFNeKNzryQ+LroUkkC+poH35f2LgKlgH8I7Q/TGNs/6MMnqGZ0cozdfmN7/BkyR2/Mzda8sj49MZFkx3KYTcGuTKUT6YGstGD1oyEoWZee1xPN65fPXHzPlTE6JaZq9PWnSz0nEkmx5dIEHilhw5Usw6rrzcH7gix//hMdvf46Vdm9LO0oJDr+5ZMtHwyMU98unaJ7VE9jLRhpGnCeIQS0YUe+DgIfgodHBQYgj2FusfCuV6oDStm5Yikzn5W67O+TW+lwFhKac7kqs0T4ou8wDYcXCN6lfo4DXlKKzwWKJKkNDuzn45aCKWtk6rRVltqDPLUfdl9CldkXTuxmeGYay74731z78/AqKH+zbTiuVacqkKWtw7Y3lYJRd/dNa9VBd76ExY3TZTlvv7LuKAUrZeXz1it66SiOqh5C2Sr5bvc0Bz0rdFXh+D089lEdJWV8hnXSOOXAeHAhK+ZdUZ0cG0/DnhAMwk+x79MH1Wnh4evB2SQfW7PhMDlDewajozYu/fGGjsyEk46tfvOXh6UEANq5mCkfjVSULdXRwXs8U1HvgpZaASgqJfd+JeSZn8cDZAjErXDimpKw7pFyCIKDGlCnSinmLbZUabXT2tXrQZiTPCVKnWGOZEhaOc05KRBsD5sSUgpq96s7YBnWttNKoGDlPLDERJ4EiVgE6fY7UnjGMOQnUpHfCmLDgFk0C0zlTW+Wnf3bjuRi3X6y8ffeCtchMxPKqRZRBTwvDMlYvWL3K4pEi0apsEfvKn/2TL/nx+8GbN4k3SxJ7muDkxFqNjdoCdTS3LsPaIz/+/ld889XK4+eP2PQJMLN893f5jZ/+AX/y08J1V86WxUx128RumS/ernw2Nz761AM1rRDjwEaCFLSgTIFpzmLF3xXiQyRZUsvlMHopvoxHLCax4q6IDEdOEBBaYJBo+43zlOitaHYaA0y23RRkSSeqtSrFRgjy6YvY1EKXzefm1hnB2JsWsfMSiENKes0EOn+OcH3XmTiwGuhkLETwdrnmDcM4aTLuy9m439PBFUbWfbFLAgVabTCaqxjxDLl4zygNJjIzBtnHjwKxw1IapywFICalRh+UrTAsKkcI7tIDCyJzbBzWnOZAmoho/T1dwzH6fbYJblHy6+VupxrIVphRG5usRPoOpUw+rL0iYTqVMQRJdDtUUPg54wrS4OeZmYNiHaosWTFJGdP7EBcYwEIm5cQcBeSXqlyUMbRDtSFSWjb34+gaUh/7t3tkpcr2pulO5R9OWh2BKQO3g/vdFSJhPtGHihf66KxNy20c/kWh+WCYZokxjHzkMx3ZgNFIKd8JgN4Vo9C7mk33Q/nV/bs8FNJteJacq+XHILv6tYWDZAlUg+6NabjCFBSYDCLHlqTMJ7Vo6cPtVXOfBeVkNQ5V7fH36g4MuJrcDgs4NIxkgWrtl98geoPJQ89rUQthHQIXLJhnewExeUSELHD3Uo4hsi8HYwrds/okgOh4LhIm5WHXGVLk79RMZEFEk99l3S2Cchv43GRu3DpmIqK3AzZKGxATdQwF9yOQPqICrF4r+77flcv6wHVuDDRzxqT5+Lhz+tBmvpadvVZSiJzmrPOl64za66B6jL15sLmaXP3nG7pbNCsfBJHLbXCV8P35BgvGNGWC5zyNoWfFnFCWxUz7N06CGZrRg+dyzcuMVJmRYFIb7h6s73GVUoqZ9tMDpFFoujsGTDPEcOJKpTR6z8wVSphmLDMIychRpUYCh1yxNfiQPafpQ+/tUP7Uce4zNPvoPD5EA/7P42dScFviUBzLATbLthzuKqz/6O/8J/8pX9Ovrx1Q+s/+8//qe3uvtGFqK8uzD7qClc1w2d4HD/i+rdxumz5ElxLeKzCHYVXhtTaGKxwyKU4spwemxwfSHAmT8hSmqEGQJuWSjv3EthVutytjNM5PE0+PJ5Z5JsQZYib5cnsZO5iQ6ue2qebcYMpiby1EoQ3eBjaAYWIC6q6F+MiR6L3R606eMnOemafE+XGR/zw0nh4XPv3kDckGfV+ZrHCaEjkYeVat6E9++EPeP79lmjPf/vxXeP30wOk0EdzfbCjsNkTzw7Z5RpLPAKGz7S/06q11MXuQ6I2Xl7e8PL9jTvDqbES7wPYlE8/8tU+N3/3ux3zyGHn91Li9feH29ktsbEy5sW47L+vOy9sL16823l8K79fObWvsvbKVxsu1sl1lzymbebvdTG1gfaPHwd4GwzK1S/GAQS+N1gr72vnWZ9/gn/tr36SunZ88X7neIq0EpqeFOAnPTySGJa/JDFxq5dqMWzVqnyhrY99U62o58K4O4vmRaVYDW22NZTTOGaYk2XKpRi3GdVewXhvGWiU53m+dtewsi/HqFHhaZuZT1iDvS7rmwkgdE1uD0gelKzi81cHmAMjzWnh5v/Hq8YnzIqZK6o/o9hpx31rKxHIf/CKEe9h72XeW8yKAJ6gqlxg0GIegnKPkC9AIbC5hBeSTH4Pry4W/+OFfcn448/j6FSkkNcvlrOVz8GFgYPD66VGX67bT9k5vhToaJmMzfQymRc10pRaFdTIodDWQ+KASuoLmwuj04OBUV/B19CGu9c5pObNMs3z7w0O5HZSJIRIn+flziNSuxVu+fQ8nroXbVonzxGmemFKmJw2U53n28H4xLAYcbXN997a5rgaPdVu5Xi9M88TycEL5AsqkmizDKERXUMWk4s9Wuy+LHfG7YgyHwfPLhfPDWc2CKcpTr01AGU7N2UkfrGOIfgkfjY2dkBJ9GF/84hc8ffSRzgegDDW7ESOtHRHeXgOI57FI/81kerbGaeLy/MJkkfygcPLhgEj0jJowFJBp4HUj8toGcPVLoPVGmjIv759pY/D46onoQ20t9b4kBP8OdOHKWlfaYVnAh2Gd5LfLC3GeiL8ceo0z1DG44q7fLYH3uvlgH8CgPljLxu164/T4IDBkHHfPsem4Aui4p/rQn3lYNo4/cwhM27ZNz2TynLGk7yvESC2H/N7uQJXyeMxtf3gIvv774aq03sUi2xAAFc2VfcGtnD5AAB4oKSat9Srb6uWCYZyfXpFcCXiEc6eUWfJEKdWl0I6tDFkbSt3prVKLAIjuKoDH169YJtVHj26uHPBnM6lJRwyr7u+jda53V2eNzpwT++VCmk46ArqyDUZous8cVGUMz0fQMhu9rSZ5JtBAionLuyvL6USIH5hFLQzhPpe4xsJVENGZXf05KQ1sKDPk+au3hLwwL5NmDYv3nA1GQNFt/s+3TkjBA2uVSaE2+K6lpGzUMjidk4JpkxGzkcytCWMoA6ZruK6tYmGijU5DKtTQAyHrs69Vz2AKGdBg20qjthszRmqzh+xKWTeuG23dqbdKuTRaGZThn2Ud5BywSXeLVS2/3TIQvO67E+Zj2NazlkKWrSdGrCVOnyxsX17Y24BFLXIhQTwHpjYRl4SFjJUN0kxIrxm1CBwlMvadujfKNvGTd5V/8oMXSsx89ibzmLK/QwoiDm41ZgR/viDkjed9591fPPNr38nMb75NSI8MHvj4d77DR3/6D/iT58jaFdRrwe/Mge7ma+VbryMPJwF81gMKWTbi5EG0YRCzUa0TNoMHCftTCFgyqf7qkUmiWNp7s2E4lsPupRKLmrRyoBbPSekCK9UGDIP44QwYeDuu1HLTrIyefQzSdBZ43AZzziyTBgYLkREaISpLcJkXBkFZZK5Gra0zTPmlKcOcIcXOaDuTSaVCHK6CdRTOAvSK9Q5xdjVdw1plCnrf5zTTG+xlA1ccH3EUyRdbAR9ozh5whKMEBKSbL/VhGJtb2AkfVCa1qQxHx193QAmOhuU2ZGmKMSjzLynncd835pRdtWP3Mhzl+kRGzNRSwYxG013sqh2ANEnhTRdAqebIRkKzXkCKLBuH69oXQ1eeJM/h22olxsnJl0lV6FEW/lOGQGPfTSoUV6OqjGTS5+fV4Snq/dckeACADhaZ+xX7Mcf4D3Is3P73GsFY8ox1Wbjz7KBUF2gx/PeNpqiEMHx3CyZSkEBB1upQpeTpvRO7kS36OTHuCtFMFBHSO4wuMswVrQwpyXQdBTXi4pkwQPXYESyocGlI1U4QHNFqvYcMmynovvmznqKIr4ZKkJLPC9WVeKO5LbC5UjOYzv8YdZ6jQPRRPbuyCyDtA3RAB3KSIrH3TrOosHszWt+JQB7Bbd/9vuvGMcjR9N63QcILdlwRc6hcehBAkj3vqHnrrZTOH7KGjmc6u0IuDDkSQky0Kjt1DBmSohm62w0jx3cicOe2N7YG9EDISQUefqcO7E7a6a52ACdFHh7OnB7OPD6+YvSqkHgq5CQVZIyywXsOkOBJKdzaqCLzowNGQsU4bFsKqHcHlLfVHRb9MQYhmUcDCAQ+cuaOXVjzLjCkMJc1EZRRO3kRmJ45879XCB/wNMzBZQv0qjNEdtbD3mZ3kEaKed2Z0TND1fo2HDyKqERBLpXoin8fg++/B+Bzv5SPIoX/WRJ2dBGIDIeSuluUfUoq7fjZ9F4Fk2CmD50jf/c//I///wso/b2//19/L2WIWfXd1iVnwzHIRqd22Qn2bWXdmiNnjirb8QAJWVb4mFc2WiLFzHRK5FMmzbPL9iVRNLRctiZU1Yax33b2mxjYx6cz56cH4jSRYyZaVuZAyLTeqFvF6KzbruA5jJiyqnnNHL3WYDr8QaJVuBb5gA0f1rWU0gfTNHE+n0g5kKIRRmWZA0+PC68fTqTYYRQez5mHZSaMQdk3fvSzn/Czn/6Y0+nMpx9/ysdvPiFN6c7i17orn2fKYjrqIJsx5XhfEjqDy+U9OekwaUUL1bZvbOuFvl548xB4czbGyy/I21f86seJf+VvfMK//Dvf5Nu//hG5bdxevmK93LhervSvXhh1o/XO++vG9dK4bfCyBS5b53nduW47zaQu2dvC8/VGaZExD4WjhUQ1Y9u7mLJuakdrQpCPS/Q3fv3bfPTqkXcvL1y7sa6RmCaYjJ4aoSXiiCzLQm/q7Fh3uBXjZe2UNbPves4asHZjs4mwqLWlVwENOQzSHFjb4LI11lLYW+NWpYhrLpWue+NyudFr4fWjmveWHORZdiQ7dpdmB+iWuTYoHUrpbJtLsDtA4OW6Qx988vHpXgmrBVjVqq0eJxkfFBcOAsQYBBi0Sm+Np6cn5vyh+WCYluvJgjKCJrWV7W2wD2VnWK/UurOXnXdfvuVHP/4hH3/6hjcfvSbFmXmeOM0LU0paqM3IMSqc2hfB0ip13dl7Z99Wtm0j0NgcRbcj8A+IWW1jU548g0XtOMOg7jsEuK6rFp99g2jKfSiN5XwWK5Mz+XRS2HrQEAkwTRPrvvpAqcN+21bBJ36J19KYljPL+cyyLExzltInO5sa1LCI2796la66d2WIaJmRna+1xsPDIzlNhEkS82SJWm6Qgi9dWcxS0SLdfajT16jD/LJewAbLsqhRsHn4oA8ZwS/v4GcLbnPTQ6EAwJBlG3j/8y+Iy6G4yhidyRJhiiTTwCqPt5odE10R5p4rZX0QQ2LfC+/ev+XVq0cMl3FHSZ7NjCknWtTyU0cR+4/YQ9kmBtU6/bpxOp34i+//BZ9/65uSv0/pAzssukiDUs5uTxLDH/xiHUF3RW+dOBQafz6dvLbYnDUP9/8MyhHrPhREv09KFciyFw1Q66ZA4cfHR53v4ECNhoAQAgwxSzEEoofYh+iqJN3RaiMczS0b+rNATBCe7xScUUvhQ+300cYXumwtR4MYTYzx8Jwzs8A0Rd0fyRnhwL0S+gCa73/yGBwta9ttI8ZImiZyFgMekD0njOSZV1FseNfix0DAW2++wCmwvQ+lU5xfvfL3+RjWdEbFKND6aI1TOGRwsFXs3BjDVYudfV0JSXlszdnhGLqreWU/EAl1DK8+qHlrSjAx5jllLi8XRogsp5nhAFRK0dk7p32cagxB/3v0IdWCVMfBIMTE5fZC78jmaU75Oqspu4LWtxiyrHsOrOncMTXCdKONjdEbL5ed0+lE4qhodgk9kVrEThJkKYmmzwCU3TcFzVCN7kN9J1pnmYyg3nEigbrLLj7liZAb3eyXrC4ClWs3ZYFkFYulMBi7KeODhCVXzzlwrSrnjJFIJwVy59jvqg4LAfmuOnkafPXDCyyFuHZGjMQwyK80uCrPMRNbgTAEdozuxvLOaFBo1FvjRz99x09/tvH0YLz+dDClmRi6g7d2zx5pzSiItFiWzI+/eGF5d+Obf/0Blm9pYY/f4Nf+ysL1f/8/+Ke3RBpgbbBkKX+Ldb66wFx3vvmNyOMp6R4m6F/+bHTUWBljYt1WYq+yqoxAJBM7lKCMJ8wopsywDgy36IwIk0UttF0LyTIttP6BWB3hAOyDilVskJJm1VTVqmYoLJgOwzIpycJxmhKP5wShM4aa21JMpOSLW3T13DBOp0zMWsymFIkMzHQ+jiCVr/mcoTMmsdfuFtGOd2y55Uhg6BHS00dnylKGlFIJTuwIUlMOE77ohKDGxtY0WwTzYpu7mlAZhNdV6t6cZA1S/IVpEQ5OSAypPvTt+aLnKqN0B5YDte5Sbvv9INBHYKDssFKshOCATh8cgdTmjbL0xkgOeK2bW2gEIgZXVvgNfQggNZcFpPbqUmjKStogJN2BKfCYRSSsuyrvCVlnPVDLTjTINshJM0GMJqu4ye4t0ERKiuH3z2iDXw4atmP57MqzbN0IcSKHQAqD85LYS2WMeM+ebK07KNRcfTGwDpurUg7QKaDzbNRGaU0WcFeICs/qEPT5OucAQbNRipO+gyBL06G6UEbWoWBG6EDVM1eLlEfRjJCMaUrgRODoTcBYjLIqx+DuE50jvTZXfgfZoE1EYk6R3VV4Bxh6rOiOX4m0asp8Ouby7s/LAKyrOXavq/7JoVs/BJ/putGbQvfXJhJGGgo9j3WIYDiIMDsepvFLyrqgHNQGTuAqxymNgZnb3uu4N+lNyUFwU7BzCkFqyxCZciJE2OrOuu8e/KysVkv6/EOIUnfhAgZXotWuZyLGzNYqt30lp0QpO9u6Qgjszc+9rl0qDAeRY/IIHG+m64cDQ2fJ6N1BlO6AaZeFb2hmmLPal0fod2W46Qu6K+W8tJEj2zHnCfC97l5z1p0c+4BBmEY4nc8O2AVcgWowhhOaw0gxKEcJV9i7qCoeswyuEMIcoUJ36AGI/RLYexBhTtfqjMBN2cN/tnGU2bjCMniBgc+lhwUYx1JsyEsl0NDzGi2Adf7u3/n6AKWvPUOJqJfwkBAqJKn6BWD00tQE0DaGdVI400NnWFcrl4ePtWM5cRvDnLIWpfOMJYVsYZFqatiII1FGYwpZoYVbUQ6TBR4/fmROk744Uy9PN/Pw1EGrG+12kac1Ds42YVOiq0CSYwk0b7MhOHO+Qb0Veq+ESZdODh8Wt3ha5A29XehAnjPLKXHOQXLmsRIqLFMi0rhdX6il8fbdC92Mj958wuunJ149nKhtp7bGId2f58lBDrFuKi4YQAXrrKuaYcKkF7s1CKWxrhu1rYx64SEPprbT3r7nr36y8Ju/8eu8fhV4/WC8/+kv+NH/+XNJe3tncnZ4K5WXFWqGWzV2ixQSl9J5WVe2Xtj2nXVTePFq7xh7p46J/ctIyBB7YWOFlqldQYDJw6zrXng6zXzjG29Y187L7cbWMl9dK+enTzAbDFZizDwuM9t6Yy2qtC0N1tLY9sA2GmN7TwiS05bSGdMj1TK0QQ7GOcESjZBhve5sTUMNIUr62yRv3gliUctGiJGHxzNTMs7zTK0Ni6baewucs5aFW9tZ98JejVo6valVL2Yh/O/f3WT/OkeGZ4GlmBU8WAZ1HKBoFxAZZYmQ/WFQWqGuCticHh5UzzkkNddE420FwxgWJQtunVYat1vhul4ABX3v28a6rXz+K9/i9dNrXdzq7xXgUPzijYF13xmmIaVsG+By5trY9hUzY207wxJzEBCVoqp4Y5D8N+WsAaU2sgUu+43YJTOtQDC9+8u8sK0by+NZP1OSFUG2reLWF2/lKDv7dpPKhXi38TRviBttEKeJeZmI3voYu8Khzc+C2juLNzHsTUN59CzqJc50G+zroD1XHs5StqiBcJDzRKlFmWlJ9rfeHPAbuxbd8WH5H6NSy85eCqdlcUuXhsfuuQClN6Y8UVvTkO+ZUL01cPDHgkE3Xp6fGQQ+ev1GwY7TRC03BVqGRIiB2BQkboiBLZ7PEjzotBqUurG+XDjPs194nWmeNVS7UkQh4uaDTL+HdupnUwvI2AukyE9+/jMt1KcZdmVGVAcBNTTq8qy1kTwEuDa132HGqI3gIMG2b6oxdqVXylJTmIP7Cs12Zs7ctuYgoIY4WVH3LvtBnvI9QPPYAgQHfWC+kv+evQ8I+vyOX+bsU2lVg4mDdT3EQ7ClOtx70LfUK30I2E9pUhNnVzbHMM/D4AMDhQ0sJy1gptphQmCUXaBk/HDXHlW7gPKZTJaQKUYyhtV2l1xXkyR7L/pzp6ilJprRo1FKg5GVs9A7eVIDUW9agPSz9buiq/TBqJ3elYpp5sxphz4axQNG9Vkn9t7INEKYlXESZGERS6ymSXOg72g2og+v7fa6bl8a42SsZeexn0nxWEJ8ifQGL6mjNWRK+KwhPQ63QDDRKHz08Ru+/MmXei9Qy50+S7VXHpbA1jcUnK8lwMyH9aFnBzNsypgpQ3J+UE+s9YHlTswGU3CG0khh0MpOSmJ6lSenzzo0E0CTjBgzp9cTOSQm8xrnrfO4TIQZCJnUBsEybUROU7szyq0rEDSHzukMt8sVSiaUBNOZtEAInUChsmDW2fZB3CtTGrQuO0mKqn633mljZ/p45qOPT/z05R3TQyRsg/XLxnJaiT3KYj0vChLu3FuBegrY+TXTGHxed/76b37Gj7+48T/+4V/wx//3hc9eLcy/shNOCykPyI3YB6kE9tiZxyCMxCU0Hj/P/MH/+zM+/4M/5K/+69+hP/w2gUb7zr/Fv/vv/IAf/f0/4o/eVVqPjC5LdO07a478w+9XPn668K/+3mseHpVhGcaGJGeZ04hc+gVLg/mcWN8qJ8jypCVS7kOSDerw3LIUZKtorqIYRssQayDlQe0BRiWeoF0zjBOt+dkXkBXZDFV3zNhJz9VISdbacGasm9Q6DLoFRlhIMbJdr/RQmNJJS4gpL7P1QcHYSmOaI9YqrfhiTqAPhd2ToO1VwEOtrrgPWJQybgzNBb3irXYKgcYbluoYKuogUWpn34srYRXoXPYK0W0uDlrRjBF079dRtRDT6WZMy8y+bYzbyjxHvcddyq42BBbPWda7WquW4e5gUfiQX5qiEcPEut501wTZcfDFdcRGCEmqlRCZ4kLMCvjuo3sYeiFOmTPKNElj0Eul1gKhkqf5vjgaylTBPAsGKcdtMkpr9LE7eJ7vSoe1VGIyYobYgp+fgTjNUhlX7RupdeY8a6bIAvXzNFH24qRYZ5RGj4E61NTGEKtntogAACAASURBVAgkZeQQMVgDI1TffTKVgI1ETov2nq7vt7RG6YU0DAtSW9ehpdjcDqj9WAqi7v+d2ipVLpBDZO+N0QZzDmSLavS2IzRcJ/FeGgvKFa3jw8yiR1PPoEWppGPTjDnCoAdjSROXWl2hAooV6yLqerwrtpTFJJVPHfr5LEvVtLcqJRRa6vfRySYwrEfc4hUZ48N80VuldrdWImC37CtmXXmBJkCytKLnI5oL/hzIGm7P6h6sH5Faqat0px+2rS5oUL9H8Jbyrvymw16OYc2w1lXEM6AcCjHfh251UN0yGDAHQhVj0A+VtIfwD1lKlA2YJvZd5VAh6rk+zvVSlJ/ae6NtX0qR2bryoEKitaJ3zeMzfFK/N7gOE7PTmoQY2Yz1dtO7D6Qs4rBsxecjIGdCMtKQemk44JeW7MpoV7dLLuoEXCVmc9BYEPRAWED0DGIRT/GAh3zeAOwghrkrAo8WZ7rA8GD6/iyEuxqyODA7QlDRUw53AQVVGV66J1yBN0xK83EorfX8jyOzLx7qKqmcOkNCb/85JXYJjIhn3g0HAgXujtbuTrGv89fXDiiFqL9IConenK0GgRp7VWWqh2J2i1Tzpbcr4Ky2lZTwtpxEsInllJkntzO41zB6sF4YshnRjdwUnH286K9fP7HESIudMgpjSJrcrdDbzqidQabsDYLJ3hObWEQ/RAJQmtaMOlQBXmuhlp2xd6YpY/MDREevXUrfmw6U7WXl6fGB8/nE48OsbJ3YSQGMTo6JlAKX68r1euNyuRDTwutXb3h8fMU0GdfbCzlPmNetty7GHoyco9sDxO6ut1XDTlWY8YJxu+2M1rUMlQvcLjzNjdfDeLMEfvO7v8rnH2eWU6ftN37+/V9webny/t0z2aM39tLppXOeZ0iBfWs8XwulGddVqf4D+fnLpou+NqOUTpwe2WujjJ2pn9lGYe9GLY3YJX3fSgfb+fa33/DmzSvCCGx15ydvCy1E0mmhjJ2AKnbnnFlbpaFg11stbHtirYFL7WytcAo7OQa2i7ek3ApbnLEIMZ24RdgGnPOkCmtnPUtreoa6monq0JK85AkiRK9ivV4H0BhNqo4cO+/fNdZiXnmvxbh2Bc2PMYg58Xy58XA+0/qN52ewIEl/HJLsNlNQNLUQo7E36HWVTL+KEepVKoSUM9vtdreiDcad0TJndXY/VEPvpBTYaqX1TrLIR0+v+apWvvmdbzPl/M/YhPSODWf9kBfdLTWhaDGuwNpWrBpTSNzWlRAX5VrFIQY0OqCRMylGec6Hlu21bEwhcW2bgI7Rqb2wnFRHPJ9PYipDIKWsEG9XZbQiMCmGyMv1Gdz/POfMjt5p9h0zo1Kk0jKDUT0cN2Bd10mMds8iqK4m6b2xr0WWK2cV18tVqsXzogwf02Ase08gZr9ofLlWo5vUWgzjSKJqrVG2jYdlubevBc+xIcje0hlcbzc1rYR4BzpEu+jCCRg02F6eefXmYzUN5pktDOZwwoYx50nPzFhJKWjIbUjyGo4cHQ16t3WjtMarxxMWg4InQxRIjs7r7HeQFKfKx7AQCNn8Auxs20Yk8O7tV3zrm9+ibwVSoBdlIald0O6S7hhkGWi96qMSqiKKqEuZZaOTnh5UsRoUgB4dSDqQoD486NKzM7T0e7MnIi6G19jm5eQ/h/8spmf8qLk3Aj2q0SaZQhKHeZhhHQLbx5EfMtyCKWtHSPGuGrKklklhIrLjjRDZtu2eJ9HHkXXiwaZD71y06NXBRkkC3URB6dyvAw/KFfA5EBBjQfvwsEqnSC1nh+xZaoHu1rloH4YK/XyuImjDGcXBEia2plDu06LcjBD03TXP5RKB6t5+peK6dceYpgnRdnpfz9OZ0QqqUj5sQYned2wcXHqXDcnpvtEVsI8rlQ5LwOuPnvjZFxcYyRn1IwtLA1Mf/nnRGb1owUluw/cgdRvK6VkWt8fUQXb3vABgV0sNo9emPItodCI5qJU2AiM0aikqILHIw7nStp22KGOqtU4okXQQU57tUtmZlol6uykfLpo3jSr8tlFJU2RfK9vesPmwdxrpIdGSLHSpZaBIiTYv2Dx4OEXO1hjDrVxUwlwgFraXTUP5fGVsE5x8we8rMc7U7Uq9TdjcMTZaQnmFZmCZhEEvvPmtj3j/Dwr73OHUsGrsz4XlqUKTAjkEsB4hPXywTlkhTQ+cniqff6Pz27/2TX7wl1/wpz+48Q//7JlpPvOdj3eYlU0Wrd4z33TmNB5rYJon2kfw+//99/n0G7/P07/0wJh/g1g3wu/9B/ztv/wxb/+HH/PnXxU1tPpS3Au8Sw/8/j+qvH5a+e3fMR7mBCRGMAaRMMM0BFiwDPjozPqsTM0UZ6o15hEYXYC/2DtjpEBdBd6M1rSYTYGxV53RZcXoLIux1Z3m7W+tK7srjEBvM2FUGkX5I7V6Q5CWsfW6U1Y10ratMmqlt8ptLZSpEG0mT4ZZ4xj3S5V6KsXo7xFYi+ShIP1uUAxGr06oVBLTXV1kZh4OLMC4tkBgMCWjj0YYYs1HGCKlaqN1z4jCA8tzplbNJVPMDsq6YjYI6O0mLZTZ4DQn6r6zFwhRGWUMBZkrcsJJr+G5QfEDKB9MyH7zQPBlWjAb7H0/pAdqgXJbYQrT3Z5+ABODAMHt6nnBWidNMALcDgVPKWxmmOeZBl/Wj2V50BlxUk5W1Yy8t4HVRpiP5dQjDKpCi1VgooZOqb0GrRWRlH0jWCL2fi+KMCceDGPEKNAh6DsXGX4Xa1KHYgZCr4xulNZVFkO72zfDMEaIRCevay2KIMmJIRYbq02CgJSwCqOrIl1N2p0QdXb33pnSpJm4NsaoWFCJ0v3XgPN0poXObaglNR9ranLVLx2zjpVC8MxBI3DrxnXrlC6lVp6lpo+e8dO6K9FaJY5BdbAvDj0jpem+7TSmkNhQ9MYcdOd2a5o38NBzk6pbX5zegTGMbfj5G0Rk9a6Si+QKxBaVHzx6J85SS1YHFEMSAARqGK3IOhvdOp5yYrSDLENAqM8u0bPWZH80d8lUNouc58zjlKQtjEYbC5ddbXbmqtuu+iAp5oJAuD5kxR0Dn2Gk9k9B4EY9FFluleuuoK+9u1sHN6fLvknrWA2EMNi7NxzmeFcPH0UAvQ1yTrI7m2aA7qpPUK6mwKLueIBaZw08L7i5UgemKTMG7Jvy/yDclW0WTLl5JuKwdpGsMQYn5cOdvAoYLYhcPJRzwnTGXa2ln0MzQ3DAtXZXd0Yp0wYDdlkn4/D5r7W78gxTKLlELxGLnpNqmkNCiNS6K0vTZzbiQDFfvkMMEY2KEug61yy6aqnLHsjO0aD7df36+hVKjuSOIOZ4rzuleMicfQiPOqqhwzjkXE3hsc4uhjyTl5k5qwZ0uIxNKR3yzzYPruutEXYNm2GZWM4zecr/H2vv9mPbtp13/Vq/jDHnrKq11t77nL2Pz7ES20lkOQkgEEbCCgJMIiESgXjgCcS/xTMPPPFCBAgkUGLicIkTLhaXmDh2Ejnx5Vz2Za1VNecY/dJ4+NqYtXnfyzrSlryqVtUcffTW2te+ixr/4Bv7nLTZcDN62zVozpDiRTz7kkS/bzhmk0mJBIQd66K3bxEbnnNivaxYrTHYWBTnEfHPk8fzwvq08tmnb8hogZN8wBzYTCzLivvgZz/7iuePV8pS+PyLH/BweRQV2jvbFpG/pm/wSgMkZEOSpIyhRiLXpGJvk9v7F/bwrhrbjX175s15cnkcfPGY+DM/eMP5KXE57fTnn/LTH3+k77ukL9HA/+TLjX3ruHlEyr7Qe4Kc2EdnG3DbxS7ZutE63LozvDMpMGCtGyXBnIWv/Jm273QKjcFDNfLMfP/dI7/082841cr1NvjyefCzDw2rJ2pZmG70rePWOS2VfZcZZmNlkNkabM25jsGtTTwVrhRediddTlIuNMm/tHFp7JvzuCx8GDs9Gos2ZlzuE++iZJacOV9WedoUUQulhzXGMHpTX/Iyj7BOXcqWdDHmlMRImZKyXR6fSNnINcfPY0BmqSu5aOCyQMp99khEmLy8XEnF6LdNzVPJdIeaEj20v2QxScY8qMn6iRIayvsIyq2JXfGyXXn3/c/IER+pizSL2TXBipD/OQ5gZTL7lO6/VGx06lAcqE95yKQoANWCfpqB2Fi11oO+KqQ/mUw6a6603ijTWc6P2iLWwlIyJVcNWOEb5Lg2TjlhJbNPo6yPZB+Kt59OZsebEg2utxvrWU2khko1VfeJzSwYIElbFhcYOxDw4H3wfL0yx6CuC5enN2QrSCzgkjcOJXetSQEC+xSbIwUbR2yLoQSVCfu2U61Qy0ou8cyicI3W735FJA0YB482uQwc+7EdscSHn33J6XKGpGbz2nfdRTnjXmhti4ZK9TSHOaQaD21OkhvbGPQu0+ZMgjGjUeJefAuvWvBDfptMsaUA5oO97QyD64ePLKeV9eEcDTQkK6++T0HbNuwuizhqyAhfLKYSRPAOSOolGrsGPoE8arpSNDUCaEy1aCqRRWkxGUYmj0GfzuP5rM8XxbnfKckBHEhWNUlJu9M5JWsZLrZNx1+NL3NimuJjrer9TSTa7NRoSEkC8sSf1ybKTZv+GTJUpuRwM3wUiikZzQjQ8WDtgAZcxACSL1BhHuq9OVlylhTcXs3BZTKaYXZqJIlls3vK3Qya/IglvzbzCQWpFHIqYlUlY7HyCra4fqpsKUBoGVWKHR7vqgkoxTpJRhrknOnNGW1QbETDHGax7kErPyTmr1vDNJ3hMipd7UQZ77ntG0tJ5JLurDYBgyMYH4i1YFPnwyNC2I9NXaHkB6wa2/OVy7qE5EGfRThN6ah7wqY2pjudKuRYd1o5xSZx55zOfHj/QuuT8xoee1Ng594Mr420LuRS6dPpS2a45AjcU+s08BYrbH3nZ3/4kfV0okRdNUusi5GH8fSmcV5X1ZbU8X3SbrtY1EmN580b85tJtY25Gx+uK3kFz07qJ3zRfTPnLoCtTbwmnK4jHBtVy2j0HIOlFj79xYU//kc3yjLptdHd6H2jtFX2B9zwvOsdqw8wNvAFq4l8eeDhYeOHP3fhV/7ij/jxV/+Ef/CP3/PF45m1PPLJ285lrgzPDDtiuyd5Jno2GIO3586fXAf/w1//bf7KZ0+cfuEdVj/D+omf+2v/Mf/+T/8T/tP/9T1//LLr3kk67711/tCNv/HbG+fHxC/9mcQpJ4YNsneclbwslHlTz7gYfWn0jw17lJFrrs7KFDsRxwNwzkXDoy0J6zJQ9sWZZWfNC+3WyHknZbFAOk6ZRp9KYJxpoTNhVC7JuCX58Ix5A5yygG2dwqSwQe7MJZGmGAE1Z2iFezrplDmz5cJglVwtGcyQCeltpS4r7RZed9XxsVMMMatSLK7MApCODj5SlbStIFh+YmP0fWdmyRt1J9RXr7Epr5oxB4Pw9ollfRBDVeuSGMm3vUt+SQTRdA1+hzxN+Ph8VTAHIw5TIptqQiJTaPuOjPcXRm8yUZ4bc05S0ngkVqMFy1RL0DlhboOCcSqF5lpctd7CsDzYKpbintc7lXCwSS4GvbKUyUC+f+7t2J1gnjEiScsEah3LhDmSFrTDqYW7b+u4M3/FIPUsSWiyrPuku5ZpIaEZo+FDvYoNA7YAL6vA6iGzdkfPoOSKWYnzN2GXpNrqIuuCYM8IoHDyIYc7/M5yFnEMI6Neq7dOb5NlWfB+w3JiNKPkhYxAkeHxrFMAtj6ZJtuF83mJNDjDtoanAj4oSUvXJcsbNzn0tslvxp1hkuKVFGzJAbtPjE7Nlb13LRNyvddYO1glbpI9hTcNwYTO0Uc6EQzhmRIySIoCiI7ljYeeqkafMtPEh2qDI8PxHqya6YIlK1qqDcQIrikdx4MFpRK7W5wlARbdoWZBhB2xseXpqFTBYWGZMubd1Fs+i4lcDJuJ2aXUMXcuGbrveE4Uk9KHFEy8OUhTzzdQFS27pnxVx1DYC54CpE7h33h4bIVZv8OwiQ2YyQWqjASIaWfGPeBkxPK/NXkJWxJQXlIR+IkHwzgKuAnok6WA7ieP/npaAJW8hpvofTIsyTuUgQDFZJQCY2uvVgEQOAfqLSzfWfdY5oDD5pwk6YVDttgjNMijT0lhXVL1d/G7ZPlI/iOV+4I5mcGQfO0A0HXHqhcT1BmywaxFSvLDnqLyXf75zgGlFsPrrXWsdbbZdTnmFFtMDZva1r4qeytJfiQ1K3WtZMlu0JbIHW2HCdr9UCPQh4wHa07USyWvi4pKIIFy1vegBWsYtoh1nsVkWuySbcyhiysN0c7H7CETeqF3Yx/Okgt1zZRlRaNWJseAJErpYF0yp/XM28cT55owGmtVE0/vgUB2vvzyI7dtp9SFz773OXVdKAbbtosyF3pKDy+I49MogSrut40WOtsE2HRu12dt30mc08T7C71fWVPn84fOu0vnl7545EefFVb/SPcX9q+fSfNG7ZOcjLxkXp6HPrcJbVT2/UYniZJdMvs+aH3SWufWje4wqHSgG+xeaMNpeye1LW6qoPomGG3C7JyenvjRD9/yyduVD3vjq+fG18+VvRXS+UFR1N2ZU5Hn58cT7i7QyjS09H2y98E3zdmtkJdCIeKbLcVgI5lBzcZj1plrNui7WBezyzTzoLD2Mam1cqpFg1eJxmDCnJI3Tpx6Wmlh1p1cngTTjTYiLLd3ttsAKyzVqWv4UpRKqRrm5jTW5UxdMpg+4xFeEYrZnWzXK33vet7RrJAUJ59WMW2E6Bex5+ZR2tDFTopCo0vtAHYfnx5JRcP8siyUuoglNffQxMsUdPi8G4ALjIro0DGwEQbNS6KmMwTYk8KcJJvOb58amA3CEDwrOaHLk2i0Tq2F63ZlqRWrBbngSoaYjsSM2XXBJhlRJ8QeHGOwTsVy9tFkgHm9kmuW3hg1ERpuD2rr8bk4I+kT2/cdy0nS3NbYbhu3NqinyuXxkcUWXdZxlzEHORXqUvF0NFCT2RtWpXGvOLOLej96p/vgFOl5lpQCdk+TykbxcvdwGYI1QgJBbClEhb6+XLk5nFNh7APPk8qRPjM1MPjExyaAZKhw+RBNew4BkX0MrrcXxRjXSAD02CbHAoDQoOMzgP1XE2RFq4pCLqAfPrx/z6dffC7NPGi4NxVBi20yLuNyLIoxGiImqLka8vrY9sH5Ik+ntNSguZsMmqcARie8CsK3KIz5tOnJzk4DCh+uV8xkou9zhlTOj0knPNw8Fh8hMRszTJNDNx8m2AdLpuQSz1JDRDEV8BR0bomVuZ+5Q/oo8HqQE+EdElG1+RUcS4eRrXP/nnb8bDhlyeSa2T/IM2xGhZ3he5ZIMBN5qfg40noSnQC7QYwPD6p0bPyO2Ine/E7dXk+rfJISeA+/r3kk7oCngxVQcDo+e7xfx7nr7BOGjzsIJdcPbexLLuogZmgGIRpROHwBQgxGcmPvO7lWnMFoGzOtpEi5iqtPcH3WoD9iE3jcobEg1J0wIZM5nR/ZXj4w3j1oOFQXKRsQ1/cVzVx/EpFm5T2kcQQDB5h6TtvWOK9VIO6U5LCPqc96bdDDaDSvtH3j8PlwpsyMu7aZ65r58PLC/qKU2rUszLHTu3GqiZctC2QIjw9cd831urHddpo35pCU5VThcoaPH7/h9PQOb5maO6k4KyuTIQPdklTUl6o+Z6awRDHIJ6BiW+Px+29487MXvv7p5PHTxO1jZzntZJ7x1MFXMdv8+W6S6jhmhbSeOX964XsvjV9++R4f/7zzt/+33+G3f+8bHh4TVp8olx3LYoR1Jm757rdYs2rBp59mfu9P3vP5f/W/86v/0Q9Jn/4aXpyU/jR/4d/9D/i3v/zP+C9+d/DN7swqOd4t2NX/8Lnym7914/KY+eHPFQ1sPhHjq1HXqn6wwnpa6fsz46VgJ9WTkhOrzwBntFjFJTcZTQa5KWuJ1WObfJtw3RvZCgUtO8cmtk2bg7wYMMl20nIwDeqxAZ+TSzX2tVBqYq2VvjdKnqTVeGnGoEFWMlBJK6eM6lySn6cfsrOsd1L760kfSfV3OG2flKz7HyB5ja23Nt7JjkVwJVATRgqWRO96HyvMsXG9dUo+Ua3fpbp3E/4UoRBD/bqZSdYc/j2EKW+NPp50eLTps7QS98kMUivHXGvBiImkTWDfJ3VdWE4n6GKlK8jEiVQgWWZEwqzbwMPc+phgatKSIWcxkXrv6k2Y9KF+KZXKtE5OskQg+g535IXlAVwEpXTMQR5K3tQFc1QOdEFPFCBUnDLlx5mC+eFxn/iiobPMox4Wpk2xTrxQigDGaZNcEy+3K9lW1T/AhwJUSrCG53jtiVWPxFaeSUnStJ1UKqPHPU2OAn5YSMjTx12egcR/E+9XssR2u1FTyNJLZngLIF0DNncWdQQ7Zb37DVOi7whfuNGDQaO+Xr59WhKnXFBu27f8pOIO94Kk8HPS203LJwsJ3xBbP03NsDhYCnaci4mev0X0OJjJ0wfXSEM7khMzNYCqAI4CaEhDcvWZ7ist1aqsHqx1kSHMA0QyybLyERzjAcD44ckT9hi9S462T7oMnCQlnQJ6M2IbRtxB+O+LFTj78RIdCazEQlhgvCxY7L5Ycew1eAT9/TEVBlFLDauGIabfDH+iYK6leJfvPeUMo2oETPsQm60N1bV6FHYz2Rag312+uJ2c9NkZSXYEJgB5zBHAW7AP7TUgIEXPq975AGZiEeUIBEoZsxlLVIXOeDStxyJMd4Tu/76LBWQ5666IXpIkJvd0zUqzjyDjHz5b6U5SkSpY8rgJAaiqhz6uBlx3kWW7LxpJx9eEQfkUGG0lrCaC0fVd/vnOAaXt2pmtC4FlktISEoYeBSkRgXwqGJZlHrhUljXfYxEThRFbxnkcHhdAdNs73iMOtGbqeaHkgplkcEK1jwtZIAZz6LDFJl2sAIsHBO6DHlIMsxTD+6TtGz4TJSXWFS6XkwoOMVio56YkWEvidLpwWSqnNVGTs7LFweyKNN8719vO9bqRcuHx4Q2n85lShR5P5j2ZawxtNdtoZJP/gptJsz2afC4C5BhtMFoTc8Mn7hs2rlj/hk+r8clT4kefrHz22crb3DjZV+zXjWSdyzpJE1o2sY22TvbE4+OZujyy74nr3nl/7Vy3hienYGx9skznhPG8Dz5ch5LucHweCRlivMxsVDdm1nZnKZlPHx/44efvyDXzx+93tuaMrTItc1mUgrEUw6jgnfUkv40ZuOtshZdmPI/OhzYxKyypUJKxZhWyMhUVu5thefLmkkkBIt1u4SfSBnMKhT9ShR4eVpaalXyVAsBBZ2O4muliioA/9NQWGvIZ/kftdvgHFJblJIPcIg8hsyzz4awbocRQ3UePZAoBJL01RtsZe6M1+eikJWLHTUadbjIjrHVh9nE/47rU/L45kgwk6J2WqQ8XdpxlTDFPTEbfh6pqeqRMEd4fDimJVdXGYNs3ehe6/nh5gjjDofjlKNcFsQjH1P/SUDFq48qtNY3AU0mKrTvldI50SEKTLHZSMhWWo7HA26unjWXafhN44LCPxt47S86c1hM5TJ8Pz6Qcn/ve9rt3TOud2dRUtyZpzHa9cdt2rC5cHh6wUmgmbb0j0CNHupRZoreN/dbwmcI8sOtdngLR+9R7WhaBimqmLZJhkmKm7dVMeprA9ukCgQ6miQ5LZ/v4zLKeAjzJ8qNMcY8Ga8LQOQW7b4L0r2kbbUyd/wBR7+aXCPTp8XwkD9A9bMa9EdBddXx/nb3ry5XT+cxyOmnrUooAkWOplyMZhBSUam1iDtbaYbp6NH9GJtVDsggk/fw+Ucx6nE/VAT2bdLwEkWqXRDGifbxST6veH0t3iYAJcRQIFBrzFFvH7l33gInNlY40udHZZqTs3WG2I+4+2Gvm92VKPu54D9NN1MgMHwcJ7f9nuFiC1mzJxMSK30zm1UEP31sk7cXXctwfTlklWUy5ME1A1Ta7nr4eYpSwoMub6OiOkzyALARg7vvOtt84lxOGtmhi/nadKdfW9A5qeTS4xxYwgEdJNTV06BOTPG5Mw1IJRoDfmQYeA5eH9Ngt/IoQcOJJQ2xvO0steCrMeUTxejSrSlMp4QXYA0glqQnOSWdmdDifzlw/fIxmPNicIWFM6LmWCnhioBEl5SGgBZi0O9Na5pcygW9jjXcrPBDQ4mDuTqlK1sqRLGQ+wSo+m8xBkbx0OS2cH1Y+ftjZdwFGS+yqWJw2JW0/jHqxwpJhX0702+Ta5CeXzOn7pJ4zaet8/c3gi88brRceJngdFJzRTYubUViqAIRxmN06JC94sISyXfjkz37Ch9/6GdtzeAB92fBWuLwzOBn5YngeMK5ieaWMs5FwyrLy9K7w8z9/Yh/v+OOffc7v/N7P+L//0cp6WVjLytNp6oyVRORyM8mkNCl5cJrOu09P/J3f/SPe/Xd/m1/+9z7BLv8CyQrL5/8cf+nf+Tf48X/+3/Mb/3TnQyRHLoccqhZ++0vj/Fvf8Fd+7YHPPjurdkW2++yDkhf63Mm1UM8r24eus7VGEhiJmRIziTEyLOM50fad2RIpD+oC1SsjT2zJVFZau+n4ZIE9c6KlaAMrYuf0PAVij4MdKmcMrEl6ZDKGHe4CnmphbC1kHQs+dyU8oWCJmQozhfluGIEPh0kYqQ8jlZU8jNZ2ctFGv/l+T/E0k4eOfg793JhJ5uSdJcmAPM2VgbPddva+SzaSM0aDnCUJSSH3Kcfq1O4myL2r18UtUs6UZNi6CsoBSr0OxQESm4BsecwN8oh7I7zvwEi2UKpY5wyZmuf7dJk4osMsCzS0KW89XAnAOcEprYxstN0lrRpBxfIuvxyUjDaZZNSHzLAXwAOYcMM9cxub+gRM5v8INE0pM9GCJIecyaaY78xJ752c9MzJRxIsEGm4yXRPJyTrr2UhJad0ye1Hn2STDMmQHDFbuS9nUlYfMWIZglkwaKHvjVzkKVaLAh9IiRzGbKFSLgAAIABJREFUwMdQfrBEO/M+1PpBs3HwqUU2WXceE1JZwn5C6gylfR4POgm8w5kps+07S1aasXyNNG1iBwv96IP1c8Tahj50T5MziqHWmqO4WD9WY4nifIsF5K+AQ5IcyueU1Ak/1JT6fOaQPypD5/z4nP2QTaGF4vF5BVA2iVo15XGZUpwfEHMo6ola+2BMJ3FvuksK2UIupQRYJOV18DYgFqB3CVcqYakSvj7B5oLDBkOycqWcvQJqR2qg/CFVe6vnY+Tn8NpMIc30bK9LRQL4Qx5mY45Y6E1yMK1mgGjl/jW6I/BIebQ7wgQuGf7kCN4QaN361AxxBzv1M4jgIf+ue39qAa4eC/Cj7wWqhTLBjpQ3op4LCBar2e8L0/ti0bW4MDNMOtT7PC+Z37jPa9Fwq80/1AixSByBGHmRhH2OiXXiXRJzW++93TEXyzr/Ypvr32UalrTE/a7+fPcMpR5IbKCUxIejNCcT9Tao+yVl8mkRmJQzyTLdwljU5fJ/pOHMGTrl4eQpb5a6viYgOHHh8S26Xx93ppIGqoTlHJsQwvhTX61+eDB7gxEskR4JUudFg21VVLNe16mNMp2atR18vKyc18xSRbMvBsk1xHx8vnHdBnPvYJnLw1senp5Ea55dP8/BKBgux/gxud5eOC31vl3BsyKBmeQl4dO4vuz0vWMMqWDHDbOPrPOZH3zq/OiTB374xZnVBnm5YR/f056/1hapFsUWjyieKUGepGFYz5IO5owtzvIIz9cbt9Z5vg6ySx9tGKeUqcuktcLz88azA8m4LGeSG903xj4Z28LDk/HZwyPnhzO3CftH2GehT6V61WzMrFjjWpRuU3NleMM90b3yshnXPnjZBt2VUlGSmCDVDZuD3aEHWyHb5N1DZQynJWffZX7XwtyxCwPiVDNL+DWkqUvnSAngoBCaqctzR6aZkdzkB6XReX6+sTfpgGX+G6kV9+QKlAZoKS6PSe/6eosVW993euvse6P3QSqZWg7eABDgaV407OyjfQv9Pt6DKP6xWXMz8lJZzpdocPx+OWJC9xPa/iiJxO5JCLUIsO298fHlmb13TnXh6eEhJFFKSTukQ/ciZSrCmuu1LRpDAOscN4HKy8JwZymVUhdSrZG6ERvQFO9xD2+WMUTJ7h0rBXMlXEzX9uXldmOplbRUFRS0pUxpBqCmwiiSREQSx7ZkIrnC7XZlb9pSPj6eWdYVN/nDlSTK7JiwnFa2rcPs7K3p/ktqaJ0SAKQYGFsTbXfNy91M8mChuMtTjfi9x1ByBaOrKTc9P0N36ccPHyEnLueFtGQ8pWg1siixvb/i3iGbZAr2cKC7CtgIYGlZVmqt+DE8xz0p8KMp4cZdm6MAne7D+bEVCUDo+uEjn3zxOWUmJeKEB8CxMRbbNKKkTb9PcoE0eQRokuTxgEOtC26mJiVZLEDtvlVNATBE7xjjiN8BHh9d7MgxmS8vnH/ucwxnJoGDHA1vwHVHxEcKE/Hj900maZvAGwsvO0kPxarRZpoSe0ZHTaeLQehhaE6AFNEJyvh16n3NAYAoEe74TYivs/D10M9nrn8bRK3v0XQrqW2ynE6s64lSyl2i51nSm5RzJN2o/s0A3EYfUeOSBgqU4DbHoG87nUTJizZgh6n9cS/qdYr36nhGxwbT75+hpUIfmxh9B2A6B3Sl5h0An37N47wZEW5HsqSEI5M847SskRqq36PEGdbdHIMLBxgbCVYBlEkGFAEcc8gMdji3bWddKpYqlsQYVKK1Pq8R9PglGr004mx7CqxKvg613Hi+brxsDU5KmEqurWEfxtyNYU63RsmOWeW2D2rSBrlPVxz9BEvOm8cL7SZvSqcprIGFdlMCVq+ZWRvUVe1XcVZPPD6ulL2EHEuM3eLO+ji5vf/I7ekN5TJoLZEK2FT8/AxgbN8Mo0CxO2BqUw17sYXeJg9Pb/jen9r5o3/0nnxyvHdsXJkpcxkb68gsjzdBjrYo0dDF5E3Ia+vNQ+d77x74c7/wA3785TN/9JOv+Z0/qLxdtXy8lEQtzhwFI4aYYGAovMG4flL4W3/7H/P287/Bz/3rn+Lll0i58vjLv86/9W9+xU/+6/+Zv/vTyd5hWRLZMm3rjFr5O/9k8mbZ+Vd+NfPJKUleNiZzNGpZyFaYebIsmXGa9H3TNjwX1ZrszOykUfEyqdPpZWGb8kh8GBXm0P2WIdWCz5Xewy9vWTBPjGn0kENJNhrmwpbvnnrVJ4moaUkpRW7Gbk4dQybYfTJdPmrd2yvTzxOtyYT3YN2kuDGTO2T1EZfzmb1l2t5k/1AKZpLNGVpoHouxFIxVSUeyWDA5PGfGZCmVNpzeG0s6aszUz2ix5LCM5ZAJH929KRUrJ5M8uBRSFau1tcYaVgRHaImlANdCXppCBquh1zGTFLHWld4F3FiEB/S+08euvzNmgGRFvblJHq1LP2muHZB9coQVPN9uOIitNI00Q2GR9a64KT79CBsYBJCF413AcprBIgmPO0nnMmGUcvfcU40zpZbOqRBNXFKb6Dlz+DM6JS7moXSvUsKLRZLwaWIZaymj4VlCKVmKm8ERp24enzPBLvPG3DfJrr1pIHd5alkAP1iwUWMwVxiKAMdyDP4mOVNrOqdLMFsOKY9FyqklLfHPJeMjsbuWPrlmllQCDJSRusWC5JX1KpTyABQsjI/NB3YHuY0K2BwMm0y6wrYCPJVMesZ7EEQGQuKJrE/koROyfU/sY5Kzgw/o+lnSvXfQjzaPfnlOluiRpmlhNnNI2ob6CU/QY/ZQpeO+FBoHQHLIvYCtNVKubNtGD8uCpQikOkJFUg5zcFdSLtFzJNNdMKNXK0UeozqBMWvc3aqNkgrZnNaRHDzAKKYA6xQyTrHuQj0QPeIcIi1swaTyFHYyB/McLfQFxAVTO4Amgzu7247ejy73hpzAk6RtxPezo1ZzZ7OJ2/86ZeWUxKw6etUAgi0WqZLh62SJLRWAUvhIOcibM75nJgXInQMcCl86P55ksIrQc29jRIJbLBIO9v1Unyq1RwCbHCCX3RcPZgIfHRhu+Ii7PnlYDn13f75zQMlzyNJ8CP1DDAI1nZJplFJZ68JSK3mpeqDRyuekDYvkBmIitDbCf8NYamat62sBi+Gkc3guAC7N9oE6piwDU4tBJHFsksM0dCrBYPYBbXJY6a21Koklv7rS+zRR210Rvo+nxNNlYV0FGhQ7HtJkutDzl+vGh+dnzAoP50cu5wu5LDHsDSz7fXDWNhg1tgkgs2+3SPkyxtx1lrpMAPveGHsjM/F+ZY4XTqnx/XeTHz1WfuEHZz59XDiVG9cPL2xffmDxHWzS2+R23bg20fZvs7Bb4jYG+zbYRmLbB/ue2NrGNid7m2wj06a2Rq0NtnbokvUi51JYq/Oyd5acwGHNRnlYeHw48/QmQ8tcr4PNKwkBirUYlMmSVSzG1L895gDLjFToo/B+c95vzm00fBpLKZRMxC6rKRk+2WfnFI2FkWi3wfveaVPNcu+d4Ynug5ITD+vCeVn1UruApBSFldgo4BpwAidUgz3n/YxvrfNybTSH9eHMaVmopYR0SCyWnHJse9QI2jEMczQJ2j7t2y3izhPL+UQtYjX5FAKfkgwTzT1MKWOL4xomRwyyx8XoDst5ZblcOLwGTsuqbU4wFQ6w4bi8RkjncimYO/1243a70fad0/nMw3qKWPXC8KNoF8LpFnDaCO8lP0ZKXcY2kd9HSdy2Tiongas3Ud59WeN5xmYomEmOhx4/GBzHsA/M0Xm+ftDGpGrYBDWelmLDmY6tINRogInBWtvQzrZtbLeNZInTw4XT6QFLlVQDYE1J7KRUmVaYaYKHsd4Ui2L44e9zFAdtreu6xnItBnlccpuItR1TzRGxxZkBJKW4v0iJ7XqjtcbpfFYx7KK2dHOZQXaZ8Po0ukki7C7WijEDwHD9/7s8VmqpwbSKuzgfeavHhsXvxoEHYHD3p0DbmKUWvvryA8v5zFoqbko4Of6OYRGvGvcp+h4HsCmDa/lE+dSW97Y31scnPbMi1k+8LGJzJRPl2AQSfKsa6dyg7e3A+fDygVQzZSnYkGzKOWRakn94LD843st5FGYTUy+MPeacbHsLY+UA85KFYWc0OM59QeLB8nK4N7RaTUKxRRtVF0A9HDGi3Dmii4+GJsdAOREjJWV5gwgInUE1l1nnEXdNNKfywbJ7vSm10LoG3RRb9wNkPbw5JqJ7jxGg0nRKtbtxdD4ac2lfiIeq36/ofjzYRTBiGxuNo2sr6SErsCRGSCyy7wy4u7tVMjHe9JFqgB3Ow+XC3q7Ia0qJQ2qujOSvDL0DmMKU2Ordo5kkNuix1KgLzx9vLJ+d7j3F4WF4yAJKLsyppDY3mRQzIc8DkBcwulRJU/fbznmpYlNNRZ5nlERJm1jWsq13Y87CnI1T1RKjpGBRjcFaK6c183GfEcGsoWlMLSNaFxjd4t5OyTmdjHXJETPt5JlkuOyDXAdz/8D7bzYBkBPYYWQNTsUieY4iVnRSkz2DMXlIjnKWD8ibH73h/Z+80K+T9UnOxd4dNoHYlq6ksWNlIfmCkfGieyW/fcNjWvne9iW/+MO3/LOvPuf//L/+gN//Z9/w6VPm4fxEffAA7C3SibRpB6MUeaK9IfP1xfiN/+b/4a9+/ps8/cVHjB9gpwtf/Opf5q/87Cd8/Rv/L7/7Xj1MqRFO0DptOfG//H7jYb3yF/9C5VLF50y56qwmJ8SLSoNlMG9Nxsq5sprRloQP07PugyVDL5Vtj1TJopSq6UZOhVJUa23mCMeAzZ3dXZKU8PmSdDNDglKMljo1DKAxfZ+UGmby6dhSp9RM3zvJSxiBxZ0JND/GWp3HRIo7UcbiKcyGbRZYBvsNUu94rgEoh6/nARIcgEPKJJSCmrLe9VQSPXUyWWe0S+oblrUByAX03DuWsmpQ3J1HWMMxdFlWT1LRorr3kGje/f00pMq/MGqNgcklQt1OLqzlADs6yyik3dhvWxjtih0bcXFEo4dnvQMlFjZjhpqnJNZ1krvpPQ7g4EgKPW4Fxa3Zva4kM9WU0SMZVnI2NwGtHsuOHL45uO6+HomvAvHllSPm7mA08PCvPGTTuvoS0wXETpLOtHfZCQzdCT4Ou4REMi0pLSXJvoLxznSxhUYXwOIw59AcE7PAOBYq0eAeg/Jrw2YBdoW8qKQA1PQFYwgQyCnfWeVJk7HuvUz0lpA8U01pmCRZl0x32hFeMgVain7AvbZr/tRiKZukbzNmAIAWSyJBnLJhcHutJaqbOlMzpVCGVsnY4onnnDQXhhetT6X0TkKmd9Sl+Fzx8CE+ZuNS2KeYLN3FdmLqGY2wobBg27YDIwig+DizbTpizJVgq4XX7ZSvaE0J70ePHdKvmG0OP6H7/MPU+4lFrZU87pDGn04r5EHZG6MZe3OBcVnG8j4n3Pua+PzDwP9Y1LYpPyL5IQamE7Svg0EkI+4IFkDfN7Bd9acB9d07Qn+9o6Z7LK/S/T0cc+pcm0J4AnGMu2fGaxuhAwGgeZj3J4u79P6Rq59JMa+nALEOnEL9S7rL8w/WvT7me1eNlr/HzSF1EKazO8frGda/qc/mkNUeyz7Q8tLwezKmFAN3tsR38uc7B5QWT+DtbvDFATKkhJVCXU/UUtUMGEEdjehen/oFXZvWbd/i0irkXKi1knOk0gRFvQejIIWreRs6ELNr4LZM6KK/hVm6KJLmKmqj7extwy1RU4WcWVaZC5NhelH8oQuzrwke1sSbc+bxslKLIoFxF3ruzt4GH18+cH25kteVh6d3PFweWOuqf9MPui0h8VB8d0a0033Mu156Hxs24qLokzFCBtVuzH7Fxk72Gysbbx+dX/j8ws99lnksk08uTk1X9vdfkYJSP9qkz8TmxrVN+m0ypvOyN7Y5aXOw74Nbh+26MYeG420M2sjcdrh1AX5zzABcoA247to+Y/CyNd77lUu68O7tO5azjtvLFulpLJCKtn7JpE8uFl4yAvG36XiqzG5snrju8OFlZ4SkKFUops9Mpo0HJXCwEJIW12W6dfUEe7x/PhRNW3Lisq6stQa4E5sdgzZ3oesmw9k+jXGn6ApgzJaZyfn48sLzTaj9elo4nVeWXKVFP1go8Brx7VFuZiDaqCDPAb03tm2TYfWRTjZD109SE25o0+lEJCcCluQay5EEITNm+RGUsoAWYqw1Y27qk0w695qTfJEsBcKuYrK1nb7t7NcrjvN0eWA5AJ+SQ46DzPMJFoJPMRsO7ySXxHVGlKWiX+G2b5BCi5w8PAm464FnfI/j4p5xYapb0ra/d905t5cXHFjXlZQzNeU7jfWIqT+YLTWFEWmkdI1gdfVdvkl9DN68eSupW0jaEsgLAW1HcNi3pkE+/FmOxlfzubxP+pi07UZeis7tsbFJGnjzUeSC8XJQs5OrCUsBsLrpd70+v7DUlWVZNeij9EOlF2VGn1g6i3GZJJebYdqpcya25xhKbzmthVxE6XcEWhxnDgJoMUi5AokxxMQ6/GgEQmqD/vLxI0+ffiKAJQdTxI8AVpmwS+4Zjb5PbByFNv6XAhxpnY6zWJIEh1dtPXcZ3gG2HOU3ZFYuZuucXRILJreXF94+vdG5yRbyNYsC7NEYHqkjWZ53x4eGngVmwQQazC7jXYtG3e8RsmoKjNcNqM7hvTDei/30EQOFzkANM1elI77eHdpkpW8xgUwpPTnTe0hS52vzYTg2prZTiftwZB6Ai8lLbegwY0ETt94VWTuVyuouQP9g7skwMn4Vn+FBYHfg62iY7xu/uI/mfI2WdosUOU930CsngfhzhC9FSvFzxmY2XuToQXWOo+9Y1xVLt2im5EV3PPsxtDn8tszkaLiI3mRM3R+WjDQL58cHXj68YEnsx2RidwnRC9lsqniSTNhsaiBjINelGFR8UhfVlrYP5jQtS1xS1lRh2gRTAq28+0V3d8uMlEkzWGQEnd2Mda1c9yuQ5cdhYhLNJPB02zOsg9P9ucQ21iVv9d4YLe6TzWGB24cb109OPK5nsZNiGYQ7pUgmd9vg9gJ+MZZ0gAiDZDqHcyhV53u/+MAf/84H1j+98HipLJcAQzyYvB2SNbzFmUtnMpNUH1neFT7Zrvzw+41f+dOf8M379/z93/+S3/0n3/D5w8JlSVzCo8+QPGl2mZZ6MmqWfOjNY+LH7zf+x7/+d/nLn3xG+dGvk+yB+uZz/tyv/Tq//pOvef4//oQ/uMrXrubKSIPWd74phd/8h1fG2Pnnf+XM+ck4lxrDS9N5tExOlZwSL/vG7IN6mRQTuzbVxNaN7mLar9kYSbU/ZwHQ02YouQyzC913GDsly0Q4zUGeBlalYDSDXIMFV5g5UdNgyUYtSX6GSff4zLBg7HOIXRyAiAfL2RALDqvMuIdTeIpYTvQJZh4JRFDSwjmZ0nK3QVnCczQdKoHj/Qw5ihEyOai5sLsMbPucAcrMMM5NYYatePF83LNzxLsf3y5nqQ7Q++0OOVfchpIER6PfZDicU6LTSTlFQrJk5xsaFpNDOXqWqYG+j0Zx9XklZ/UUMUCazTDJl9wlhxF8TRkrmVtT6uX0Qa2VYplGp9EF3ASLIrnewRSKg5LTnd2dcidnp4Xv2nCO9Z5qv09yqcEkkVTMpgJUSllI1kJ6rmfh4RVj0TeUtNw9DzMK8Ei5UIoCfnrveErMsRPiGzhAteQBaKY7m9VTjj44KnvK4WM1VNNwfIohat+KW896fCFJ0nBOAIlHfS05DKT7sSAVoy6lWMwcgGgfeDByPOpYLgF21Eq7NfYxKSmCaVzfR9jaK0gCTjHJF/X9YlnmFv171LJ+SJHk7Xn0DQc49W27gKUsNEWXk2b0hun13zvagWOjoU5L8+PhQxTBfEjuZa/9LwfgNgRGHZ5AHtYpwW6bITlzD+aOpbs1AEnM8z6CCRRnn/gcid7fTKqDHKzBo8tKhCVA9BE52R1RUC0vnB8L1/e7/MWiJqckr94+j99YwMrBeg0U7c4g621AJE7ng4lzYJJJNfxIYU25gMn7Vzul6JWImU2xune/uBz3HRzLfPUXWACfBsfyGTyISno7Sqnhm/T6DFOyu4+VqYnUbJfsblavuU13IPGcjl8oHQtNffyY6d2c97PlgXcEOBszJEPg/hj9W71iHG4V6VDR6S7HAySbr+f1u/jz3ae8hTGlz13NMIW0rCxLEvqcahSggwGhDwUL+ZEP2tbj806c18PsNkUUYYRFHyhqUNS8xVAYD9Nzug+mRweq8zTvG/G+N1pr5AmZQjmtMThmAUnRPybX619sclkyj48Lj+fMkj3iHKOtjSb9+eXGN9+8x3LhdHnDw9MT67qIztm6hmZ4vdSm6HDJp2i6fVBQklPfBonCvsmrI/fOdv1A267YvJLZeKyTH7zL/OB7T3zxxYnvPxXO/gzXjetPP/AyGiUNtjZoA9pu3FqiJWgzM5Jza87NPYypk9ISUqOcF3kMjUrNk94Ts2nIKsm0uOldhd6NU4a9N6Yl3jxeuJwKuT6QvTIG7A2aZfJSyWUhZXklSXvtFFcM7ZiSQuzd6UnGzR+2wXWTH8spJ2oW4p9I9y3HtE5Nosa76gfb6IxpbNPBRW3tPkmp8rQmlpriZ5FeXAuOuMzzoks2mo+DGXfoXUvJdJ+8//hC2walLJRqrLWyBhvvAAtwo65VQ5lVRtfL7h4x8q3T9kYfg5eXjwE+Kfq0d9jGIKFB19JkdouGf74CVC4fh+POdUxGpnVhWSop67I3D3rxmFjJSmxDW/ucgqKsW4s5Bvsmll1JicfHR2oukfQTg8f0YCaqCR1zxLZAQGxO+W7kqVQXg+7crhsekebJEXiW5OGQ5hTzcAysDYYPBgJ+QMXvSIcxYHvegMT5vEJKFNJ9EEuWozbEoH0YhbcG09kjmfG6b+zbhvfB6XTm8vhELRWS5IYWJqJKvNMmsfWQgek7M1xgyOHxM5hs1w2zxGlZScizSYbIIQMzMUBGFH4csQ2wSBQZkVgJ2/VGSZnL40Uxr9GAZDM1MCh+OrVBG3v4Ivm9OfepjVvrg73JZ6qUCiUrtcNF+S2WcCLqNkAFS0VNDIeUkQBuRFv/6puvqctyT3QpRjyv0M0brxujo5+Impeww0ohPLKcvTXOT28opVJ9MKzExlayMw/QlCnmnmRkUQsCXDXX/9rY8X1QPluxMSFpCyWo6micpGQ4GsQ5RtzTYq06KvqvMdVah/UYEg9vKAtw11KSvAk0mOccCSXpDrTKcJIAQwTSlUB8BI17POMAYjmkqmIU2NRPN0YwiowA5QfbfmOpmZynjDEDOMqlcmxal1KDjdTv4LlwhDAjjX8zWQkJXNSvoPUMZgx4hwxcoP7we98XYI6A6pzEsLSUYzNb9OYE0FxSlgfSsQW2g3mc8JkxG3E/xwWPjNWzVoRq3GNrZ3ZI5e9LPA1wwQwlADAFYABDUt/T5cI3X78wQMbAGfwwEA5PKoClrFjrsWQQCJ+yKdnGDpp+kSz2ZaNPOCfJ1zCwMskjmt4UKVK14MN1lo1INgzphSlKezlnysukjcREDCJLib47LU9uy2R5KAwzii9a8k2n753bdZf5c8Soz9nJp0YGPv6k8fbhTJmyCyjrAkk+PNiQTKaZANMpyYVNJxUh6MkSc+88fPLEw9sbX/3Tb/DPTiwfMuW0kpOxrol6SpRi4fMm5p/Sdq5QM8vDyvd/tPJnOXHrP+T9c+dP/ugDv/sHK++eKj+smVzE6D6lQk2FbSpyngnnfMbm4OmTwu/+9D3f+y//Fv/yf/h9xpt/iZwqlx/8Cv/iX/rX+PGX/y0ffv+Zr3ad/1wzt97JY/KTVPmffm/wcGr8uV/JPKTEXAzrxkTpQgIkO7Z09peBj4ZfLqSSFMhghWUMrFfMBvYgiQ6O2Cc5zPgPVrYVrtddhrzmWNvAKwSbTSyIjJVKnwdzbyebUYuG/zkLiUqqSsPLw8K/ZNK3SRpOymJBGQKPIgUm7mMNnslngGGZvMIS/jDbqdC3xtg3bFEaFGjh4sHYMpfH47E0mHYs0OL98xmL1IW27aov4Wxs2YJVqcWiBzsLIFnlSLHyGfdTAGQkY3QBOHYHAsCnLCLEXCrB0BEoMMfG6D1qhOqRoaXfmCW8H5skjQfLNCmliljKlFJYc9bZC+A7B1AycGYTMDSmGCECRiZH8i0Byq2LQmNG7xoy52R2p6cWM0mmz0byhJnmqMzEs4x+vfB6945B8ixvtqjV05uCh5Lu25QLJgGS6lj0IFCZs+tO9imvsrgr5zT1QFm1Z/YhhsUEMEbTcqsU+Qb13iFYUMeAO2cAa2YwNGhL/iYpjmokIfWT0mP6xEcnR/ooOUuaP0J0FQP1Uo+zA24u36uUxbQ5wLGwlDg8cA6GXsqJ7mKyF4L5k4/loDHDsmL0rv7DURp3LIqnC8xd88LeO5GNJmVNSLCHJ9KUsfOYctQi7BzMPXyljuWm/u1OzK5JEnSfRnb9HpLfC9RzRLDwgCEtEqbxEX2SOoc1l7vHVzL1op4C8AtZG/7al8rGglc/pFjgjR6Ab9TzkvRZ47LrWFMFyzTvkLSAMzJzJtxGLC1zeDUNvetmrwBL3BECo4OJq91L+CDFhJNiAaMfXGc7mIlqEWQlYwGQ5QD11TrL08mSPt9DhnKw1g8Qh6SlVgrigwCwEQtXeapNhGLl4zyjOxSmFunRBx7WAhNxXZTm7Xc1g3w749xFtzzirLprNvUUBBl/ZUoRwDu84lXEJ6J5wqNHSqFgCdD4O/zz3UveLIdpXJU30nKK2O/YxGnsF6Mogl7chuj+N5mbWnbO5wX3RE4LM8nrQhICpR4dFMkxutIUqCQTBTMnk8FnHE6PgaaFh8jsPRhAU3Thg05ZcjxMxfllHB8N3Hl7Krw9r5zOmaVoWxnEN71jKXF9eeGrL79k2waPb97y9tM3LFneHyO2xMlE154tjgVHAAAgAElEQVSur0tBqSMedm8Tz4U1ZfbR+fD+Q6DxzvX6wrh+JM2NPG68PTmfvy384NMTP/9F4bQ0zqcNnt9z++obTquGq+3a2bxz64OeM7fdeH52hjnNZITbhnPdpxLJYqgaBJXXMp5hpoWajDel6uV1Z29TQ7lP2kSAj0E9nUjLGpIjgS5jJk6XyqleqOb0nKAnRuhpS7GYBSe1QqeT08pth29uG2M4a1koS464ZKR9n3rxxhQ1N5QbjJnp3oPhsjC94ynTdjidMo+nlZzlU5PI9y15sdBHJ7FYtGAPzW5sUkrI17a28+VXLwx3al1Z15W6yksh5xqXpgd9uzC8h7RR2mh3pRVu+87Wlbp1fX5BRyrfLzggTLHV8XSXz9Xh73IkAmFqKHJcIqlkyrqy5BUr4VtTFp03U1ORco7CMylVVGiB3lHY0CZkMePp7RvyUjE3aqny4ZoeMkDXJu+45Cb3RgGD7IoxPZD0l5dnSlnvg1deq/5uyZFwBoQxeYoBLC4ZDdZJkbLkxO3DM8OhnCrrsgiMC7lMis2aEdKxSC7bt/0OejElre3bzuxKgHnz7lMohZkkrcu5kBB7YoxJ6zu979Ry4nhKc3ScSUmJPjvYlESwNzGdDr2WBRAdjKkxQjYQWwufYn70scfWQhuutjfa1nl6eogmWD8POdHnq3Fmooq2norkY2hLOcZkb+NbW6kppllQb8eUGSQ5GpBkQcWF6SHTvP+f2CTyCJq0Pni5Xnl6+/a+YdONH7JJUkjTJKEbPuVxd7BZ3ENyZpHwNqQ0qoU8OgYsWWeeJKBt4gGCAbxKAw6ISjmvBsl4+bhxqovkYsH+8tDtH0BXQBvacn1ryCH+/sG00oati07fkzZAaZKGmn/LmRym1nawgbK8FcwIsEpDzt47M8c+ohyJkpHQZorBPaJrPWd61DYmWI5N4owmCA1SvQQLYA6wEU15gY6YoPi9iTkWL6lkWhPwaJYpOdFHbNQC1Dq8A7pPkquzMz+GvaD+Z4OkqN9jiFR4QQnQu5NMzJ/ZdpRmLCaz++t/37e9sRK1kJZh6c4mshHeAFN32egdX2q8YlksRw4WZ2yJhzNs0kanjCx/u+JkK4gitVGXBZKx3W6c3zyFBaju8uMHSgFiLcXYZ1Z08dEM2yHRS0BhOZ3I28bWdh7PhZLjDivouYb0IycV36VmLDm0dmewjKlkoT4lc7qcC19/0xmLkdHzmqOxjQa3zHoerKko4jmvuO+kniVxyfKPut0GfRr+fvLwbuH5652vvhyUT51cDaPhpeiZkrAlsRQtOhJ79G+G20pCjXlKlZmdz/7sJ3z8e3/MH368cnqqXNbG+bzAu0XvQkvxvauGiFrgtkmisy5cPi388PbI/ux8/PM/4Dffv/B7f/gN33934enyxNuHfGc95QDyJ3qEB1Pp0hO8nfy9f/ATPv2bf5M/81cvzPznSfXEu1/+Vf7VX/sj/ujDb/H3/mTyYUzq0PJj75Phg58tC7/x9z+SL87pFwqnyp2RmAxSnqRcOZUTqXa25522bSxWsZJYUiMtBctTkpquobkPl/ejC8xJwMgDK8ayXLhevxFTJlXamJjv+vyp5GkCh+IdkZGvTG89IauFGVKKpHsrEyw/G3jfVK8th6FzD+mFMTiGtUIySTwktdzFyiuFmhyjMEpmu+34bOQsP02fFjUn3ucBM0dDNmPY90mJHni6U9cCM/OyN2bvrOtCsSSWV/Rf3R1D97Fbll+Lw10kktN9UaTg58m+K2FLAQRaUJc08GAtClRXQI+MmCGFaX8ms5DY7Ygmn+G7pjoxW1M97krKkyJDIHUaQNKzrJbJadfXj0mfkpq3dqPaSq6r6gCOU8UeWU7UJgBIJM8RbARYluBvzaFnFMu4nBNtbJKg+pAXrTt902KfYB67dWqWWbOu1kRaC21vwcI32gQXoqKfdTjZICXNbof+sBMLpRF9XvgjzemxYCHkcWIsZUuxpNKWPrvfWdeeBNgll9QqeabrG5PTvId39NkYiCk0CcYuYnwxJOFOwHXver9KYfGJx7wppophAQgpbS4AF9Mz9jHxAPAsgfdOTuqZzePrEYg3ghWUcqbPwZoSrW/MqJupZEqteG8cjCb1QQPPwb47aqSJVODTRFKNGldMS51hDlGDLJL3CDClJAUDzRmSZtNdMNLQZwOHylKsWJdcMiX5EjIsGHbAlAdx1sAqIMgEik70rEtKSouM3j6FZPkI0zDPbFsnVw8PRMlYCdar3kqkiAB21xnwKYAxW45eTuxb+RGLScyIJaI7vSsZ2o7e8WAGoXvWo6/zPpjJGKaAhFrK6zIpZhW+ZU+Rj+8VYJ8fKZDSoQoUNoHnycJHNJjARqQiR1iIwieMMjXv39rAcgl5qWtZlfReE8u8QwYr77iDLJACJBrBNtIiz+6rztcFnv7rMBIKUMw0dyREAjhIEd/ln+8cUDrlwlwypyoQiXRsPC2orMEy0qfGrW8aVh3yUqjLSqmBarrd6XqaTHT4dGn1++CVDTVfYag2TVGLM77wSCYYc9D3Gz7htJxI60IqoZ30SQmPhMUyrXcSjVNJvH1z4c15JYffkWy3FOnuKbGPnY8//poP31w5P73hiz/1VhThXDjiItdVhqwpoMn6/7H2bjuWrVl+1298hznXioiMzNyn2lXV1VWYbhvZavnKVyAZBMIIbngOkDG8Qfs1EA/AQQgJCQmJCzC+gAsLNwcJsCx3t910V/XeO/fOzIhYa87vMLj4j7myHmBH3VRlZayMmIdvjPEf/0NOelnCX8JI2sAavFwaL/OqTdrpxMv2nuvLR9ifqf2FuzT4+rMTP3td+PrLyuePRrX3GBd4meQBp1dZUb9uTEu8PDvPl8m7S1NwpmWaD/Yuw+femwbGISPDNgY+dxwZtlk26nKHlareYOrwsWTUJfNycd6/dFKpLOcT5/MdG4mty0trWOH1wwPn+4GnzOKTmTOjyStAR3Si2Qjq48LwzPue+P6yU/JCqZPVYclhVGk1Bi1RX1PKrEUvYJ8CEJtD8kJDfkneOm/vTzL8Pi3kkEJ6Fio8fNwOlJpkSLsFQj1dGvVqYud8//GF7z8+y1Q6FdZlpYY06jCrHUC2Qq1Z0Z/uWDEu2wV349I2+i6GW993nl9eSITRrtmnpSFOXpTYVGplbzvjuscGTpvDXMTsEvvSyWWhrivLsiidIatBy6mSGNT43wdl/TCslAG+IjDbFMDlfXD3+EguVaXAPoFNJcyJPXYj3SWDPHTXc3Y1hAGgzOm8fHwi10p3Z0nSXM9sNwZQNjH1dJgSTbPHRiTizqcatOu24WOwnF9RinxTigXFdEaSnjuH948aRBWghPx8Bq7zJOlneHx8rcM3ZDzuPfyHdFT3ppTFUmTcKWNNJxeLd1nyr9Eb7bpzf3cmZx3tOYUJJJ/AADvAJNN1Gz5xVxMxwkPDfHJ5ecGKMUxN1VF4cspYKYob9iT6ehZbzUfXfW1DW0MX8Dp2sZMMgXhmSWbGdrBDEkaJhuLwVkKHsakFDlyZbMYP7z+wVhl7i/0jxulB20czRdjsOP34+VNCPVY0YZYpJJ7ahbKesAnN1RDkiJTuTZIo/Rtq8rJFd+vapAZKFL4Ag5eXC68eXqkBOryTEGXbjmcsKVEpB7B3gKolKXUzQVDOoW1XAUQBMFgkyljJjNgaBWGCWvLtnZo+1VxNpw+BmspSnIqE9mj8juck6P/uiEETjKlwHqHPnZGIONwsyv7Rz0xj7tpk7vSb6buTIB8ShjA1zlCmEiPHFECcuzM2AQqpZKjxfQGIp1QwHwLpLMXmTPHQM/mN5p1RyooASX0fQNt37u4WUbXriU9XzW9btpRKSDXAbNwGPDMxRNVoZUot7NvG6XyOs1P1L+ccDNCpi2IHBU2eYxa5Oc6ijW+qlCqp8f6yY69icLJDyqZ7XXLiOrsGzZxIXZ5ph9xghFQoxTCakzH2xjam2AHulJHJNdFj2Zmt6vVKiTm7/JnQ9xccH5k+nOaD9e6e5XJhv3YsT5bwlRhpMlvi8tLkL3MWWlkTLKfE7sDLZDSnzcEcqvvbs3O6y3z8zQfuH1+T2oZbJ/vCSAUXJUhnU1NqbyrO1LaJMJHAUiFlpz5sfP17n/Onf/RrtrOx5BO9w94HS0/kTeljdu9q1Nezdmv7xE4rdb3j/o3z06fMH/xu4dt33/NHf/QN/8+ffsubN5nT6Z6HteIMLO9Un4olmQKql5Tx7MxaaI/O//S//BPefP6az//V1+C/wM5v+Z2/9a/zb3z7Hd//wz/m/33f2XoXsBWeVJfW+PPyyP/8Rx95c9f5ye9UHqoYBnlNYlhYxzixsINDe9oYWzzndiLbZC1wHQnLYeg/I0J9QJi5UdCQBlDqHft2ZTmd8f3CdSPq0yD7HgurldR3fB7yUVegCYPhhbEbyfR33WV0m04FD28js04OttT0DR9Fm3SSwLkAh52J98TwMGCm6c9zoXjicrmQh5aAog9E+htJYMH06AXEHDmSGW0aFoA2ExYyzy8XLt4odYWqha9YC5FEhYDs1jPTjT4mtYjNlIIZLHBHoF3rO7UW1mBgz9H0nqVyq0uyc5Gc7vCAgRjmDx/BGfHmOaRZUylpc4p5VE9V/QoasoV7i32SU9SG1BGNSXPGnDtyVEiUFEmtyRkBTJdcxMRKCkaZc7LvWrwqbXLTMEoJQK2Q3GM5qnrVWqO3hvdMsgrZaKMJkJNrtP5+MCjAsGKUWTA63YrsBlzm35YT0429SUZusRDLqcD08MHT72HAskq2NoYYKLspPCUBHgCbpWAqhRzLEsw8WFOmIKDmzdsH+q4aTnKuW6ONRjssFlzA8r5b9NmV3idlkeqg9xYsE3mXeUi7VJ8mHhYkSnS0UAFokZdzUd83xWpJBICWArg4ajPG7GHQFQBen+B0gb2uediSlnMHK/9gzugTjNaHmLElR7c5ZaYMOnOx23KnBpCkb5dtRcm6BilZ9Av1yESjmquOmBZxM9jSOckSwad6nxpg8xyNw1+S+DPzQV0KORv7mMGOl4+qGGPhFxbnUq3rDezShYzlXFZd6qMHCKX/+/AuOqT9Wqxrjq/LQmuNZAG6YfSua5cNzD6lFPpspJKjv452ME0YWowmRGA4qDxmFu83sbiKZLQkCwg1k2IefZLfR+JbNLhjiB2V/LcWpTmeKSzOVL1TJeudmTcPLM00B6AEn2SRJUsiOGanWrn5cY6puTfQhJi7Qr0VAKiZ5NBaUmctRHFmhnkz3Ppxvn50QOn8eGKWQrVCG11mjmMEowVmihdwTEab5Fy4P51YU6KnFPIDDakDGKMHFKXzr7emwRS5rudUg/EBIyWyQR/OCEpDm4M+urTVPqlpId9X+R8glNGJVLWQ58yx8ZDgzZsz96dCWSo+uwaWo1n0SffO+3fv+f7dR+7XV/zsr/wu9/d3air2K3Ps4Inz3crWriwHnc+0FXJUAHHDLXEZGrKetk0mhv2Z5BsvT++ofuE+bfzkTeFnbyq/+mLh81eG+YVsV5JfKe60fdBeJh867LvxtO+hWy/sBtd9cN2HjP+SWAWj78wxaUMjlgz6slK45sQH5KWy7zvNB71PciqSLdjkug+2niAvPDw+UtczT9vgZddmoNZCPd0zsnEdmbk11vtMQZT7kit9dl7GpI3CtMI+Mh+fG1vr1OphhSfd8kCeKsON7tp2lDu4S4nWjA15nOyuvwOdvU9OJ+P1+Z40M8uidBNqILxTW/NklWxQi8CGOZxqU5G6Qwa8jckP376wtc5az5g75/MptsqJcAwlp6JBP8FsGzkltt5p03Ayl+uVPobAr+sVuoYUHayf5B5HUoYHDXYLuqrDLRqUSHUSAJJgydTT3c0Q3E3MhVwkKVuSoqnNnOmdnDOtdUqRWbG5c+0bl5cLYzgPb9+Qi0z45JvleJqMLsCYPPVuoEF4tB5eQh4AYTDXDJ4+PjGGGHJ1rdE4OaTEuS7B3Bj08IU6+Cai3B7Dvmj128uFvTfO93dkBsH75vBcOfyqalZSouiuIaGZB+0Y5oB2vTL3wasv3kIurLWIXZIsgGElqc2p9wefkuAkvwGFjqRwHo3pfrlyKlW0/GD7ECwGD8DbQhtPAEkQjLuhRLBU5HtwuVzYXzYeXz9oo3rz14kGcgpMGUMsEA8GSfPG9ILlhWJOTfr53RJryDzlY3IUdKBrWDz8aHKWZAN0bQlQ+TAtHHOyt43HN2/kB2GHJ9GnRsnid1EzoM2amrjwiJliLhGNW9s651ePN5npIcswzyH9Ekf0UxRwbG7JNzBhjkFr8pZIA8rdKUBGNWtH4hpHkxmMpCMpbcwh0KF/AhNBBX/0HsblIZcbAsNLygJfotFMKUnGkJLSa5BMeHZtbjnk2z6xcmw9jxCAGaabEdk75SE15rzFyXY07OAhP0PJIDdT+Gi+6tHPmVJpPBdyqWQfajAiFTCFn8ZoTTUxJUotrKtMuRVEqmeNomuvWuBRc0PqGqAcfLq37h6+UJmyHu2Hrm0tRSazsdnFBSrNyY2lN8JfCAtQ2ePdM5li98uI3ZweTTGJ1ATmKS8SS2J95ZRoUwNOeBVzsB18d5aauXx8oo83ArSYt50fpmFWlBgj18zem9grOUtakg9uPhjyxdq3na0NAbljkD2LAUxnH5DyoKRFEoAp2VD2TMbBZjCu5KOxlMJnjye++2Hn4a4obc6PgWRwvR4RxmLJfPLeysyRuW4arudwPFf6Nrn/qtCvGx8/NNacgcIyG62KUWDZYERdPRiphwSMRqKCzWCWLNx9Nfns6xPffOj002CORN+gn4xaE3MflDXjayW3id99gb98KzD0dM/p7eRxa/xknPibv/81774d/Mmffcc//eMPfH6/cv6iUqukTWNxKkjy5Z3dkAnsHTxs8H50/sf/4X/n3/vsNXf/yt/B7AvS61/w1/+1v813v/meD//nN/zzFxh9Cpwcnbl3su38i37iH/xvL/zbD2eW12dOp0U1yAS2Wu0wCue7CdbYn3fSPsXUR0beJyPeu8QoHZuJZpPmV+oUiJ5Topwms4mt0sfG6bziabJ3DWvuRp6TZI2ZoZY4g4dYUDUnJWaWxBjBUsor+75TFshloTtcto1s8tQyGWqAVXKRLJ9YVLQeo40fI44Gt+LgNXOyM9eXCzm0OZPBpJPyIOeFpSSSFUZIgPBBSWdGOgJoJp6n3omceH650LcrV5+0kG3hIdtLCbdFdWKI7dpmALZFfkI5kutmdpg6j85ZxtOjC9jI8VbO41waOrcnOvdvYROx3GImqSFGj/MIWrsyLcuKwyBNv/mh6eebWujkghUjz0TfCTPyRG9NqdV55TQl0cc6pVasabFisfT1McITLpg2q/peHzJ8x8Sud+pN6pWDvdomqvdMTrZqvm0dapaH1tSSoyQB23YsVZIx26CUSsrqFXvrtH3Hh0lRwMFA7QpoSOptu2temjYpRXV2umRwM+T7RtKCCYtnS1+HIuDx7h4zgU6jaQHzcHdHH42xH140HvYDuqYv247VAxAsxIWM1OyQqccS+mAm9zDckWeR1Ae9N8qy3HzxZrB0Dha52O5w2EwY2lU076Qu0KCFhclEQTyyZkwcXkvxKfpMD8VKrOxwLXUtw7UNStI7RCzcLYV36ZG2NyWzHEiCttqiZ2c2SiZUC+A5M1MmJ6f7rh4nwI6UEqkoxVlG7od8zqmWaYdEfw6aTe7PK6sl2uh6B8ZQMuUYNybX2AY1x4xdi2py1qLOJ2z7Lt9c15LPYtF13B/IYdBv5LxCTtRq9GuXJYZPiFoeHaDuR7DvZ5vhp2TQXQMIYiyJiR6hPCDj7KLlpixBdB4Q91gJ7a6Uyz7ItcKQeX7S4KMzdIbPUSgkBi7/1yyQuE6wU5VVTCMUi/F3pkeCnogmJWUOwW8tCUaipkwbceYUU3p21s+fA0DDLOZBC5uKQw4dWzqTwmQeLPAf6Sv/4R/+4Y/6gf/5f/vf/eGpRI5MKkLsDupW1Czf1ZTWU+F0XoSw1UU3PRnFoCfwMYKaORmtaei2QMVzhio2EsDwoCfikAO93Hf2ttOvm5LAzpV6XpQCkDMzWQBd83aaLQZfPpz5yVeveFhEByzmlOQMH6j1dD4+vefP/+wvuDxPfufnv+Lr3/0pSy2c0oEVelAMnTl3llI5zIA96Hz4jO2R0drg5bLz8XKNmz7p+3vm0zvu6xM/uZv8y19W/urPHvjV797xpmyc2ntq6ZSlULPQ6tHgcj0+a+fSna0lLg08F073lWUJinkylrWwLoVlXTiti8CfJGmBJUWLZqu0MWi7YdkouZIsc7kOni6NvRt3dw+8ev2W3ZU+s3fnfD6Tl4WlrmL7ZOOUzjw+VLoPrgOaT7aeaOMkWZoVni6N7z/utJkoGTUkyZgJ7k6r5FapkBOcTpVTUed4vQSjYBjNoxDR2Xvn8W7lzatF4EJasATLuspQG22CkwmQrDVHYyRArFvG5865roy98Wf/37fMZKR1JdfC3VnXLeUUgEGm5MxplffG6DuKiO60Nhnd2a8Xxu70bWN7uQQQI98vi6HkkFR4bII05MYWGhVlJ4ymY1viQFoq6+ksECBJH18joSYlDRbLEoZysdXuvQkoaZItPb985OXjE7MPXj2+VjEgtPmAGCoGLn38GDr055j0kKHifhtKFS3vXJ5fxKKqShTDJCtMJUMuYhbhogSPcTs7Ru/6/WP7V1Li+eWFOQZ3Dw9YzqxRVA5/slIyKcvYPqVCKlWAtoeBYVIDSwB1Ty8vvP7irYaA9YxHQzzdKUgqoi1NvxWRhIZSAQfaSI7RqVZ5fhYLbVkWgT85NvjB9FHtCd+ybLQxwufNb4ChNNva5v3w7Xcs55W1LBrMU0h6k1gzPUyOLbZIHJRlc1KRnMcsxcAwBbAXAeYpmQx/49qBjD5jdhZ4OCc+5Tch0CfEbAYf3r8np8q6Lkr/ytpCGpJIpZwpJYwb54wwhdjb6DDUM4Dw2OfLhWVZuLt/iI0gLKUcVg3MIdBb5VbX65bARciqgD4libpsG94G58d7JaQE+6/kfNumCX9wGVmP4/wI946pDa3j9KEG8/npo1h9YwSQBW4pNn75lt5xNI97a4Cz7fsNCAmrJElOhpqUZVlwSze5oGKcLfqAFD5TMxY0U8PlFAsoIfbu8/OFbbtwd1cVIZ0yqRYsV4HmWVKsI34c1OzfGHkhOexTqUPrSb5TuWRKXugcEsZgAzQxh7rPGBs/NSrmn/wYUkqUGiblQx6Gy7remjkBc50DcJ0BSKUUxtLpMGYd3IzUCVbf6Dw/XTifzgJJY6vp8Z+ctUCSIX5mWQo1WRgTdzJTlPo4e7Zt5/0PHzjdrSx1oRbd2xQ08+HQprbHvTWCuy6W5hyhZA8j7ZDp7ntjYtyF7C25hsKg+2JDzXQ6pCEhg4DBNF2XMZTaSIPTfWZNmfXtPW/uF8q6UE/qO8iS/Z7WFYjlWwQhJAqlrJxqCVN0pzyunObksy9f8fG7Z86nezKSaJOT5EMuBsqYh69SCWPpYJD4cpNPZ5NcqT7A+19vzNIjuTaG1lwk+cPgVMneoJ5I+QFzhT+knDQUmnqz2Y3v3n3km++vnE+FN68LazaqG3lIsiu0IDE8U0MelWRWxHfPO+03T/zyr32O331F8oX06mt+cnrm46//nD//2Hhq+vdKXfAppgsl835PpI+dn35dWE9GiuHEMnjquI24z3o//TIoScxgSwpNGJY1cMQmvSOGl4+I2jCoZZGUzpw0C3PsPKz38sebkWhllTEGu08uL2GAe6qS/+Ss7WuRD9CYGbMCpeCW2aZx7cY2M96nWE2taRESPWqyrCF/pmDURh0I5mcyLUIVuANrKcy9ccicUxj42m8zrdMB4yjYxGMJksOlOYfnUY7zZ46uM3C4rClMJ3AbjUxi9CM0ZGoe8Bmeb4RUhTBuT/Q+6T1kzFFP+pBsTUls6mlkK/BJduJzxlLLj91ILEqG2IMxbLp3zAfedS6JjOVUE0t3To/hMAJeQtazzQmlkMtKXTPZJTfaemg6JswxIKTm2Iw+LFjfJqnWiOVMzhUtOLSoKalgrvNqOgzvDB+03tRn2WSYegwlDsbCO7ZLyRK1JErU7TmngHQ7fE4FBuVQd3j40625kpcEFv6wwagQcHKc5VlspQQUk4+nG9Ss2ckHk8lyWm6hHYfXqCPWzJiTtoux0lqDYnR3KQ98YEUwA/Hvu0uyKmLTb6csJwURDIEJS5aexo66fzQeqR6IGzcvPgS2k8Tcja0Nh39nsUTrLZjJEVKRUngiJQ6/sKSVpYDXEssNDMslmDGSMJobpZbbwk7WFWKtWygOLFQftS76PRAL33OiTWefG55QamfU8INVSzDjPd7ZWpZPaiBdhWAo6d4dvWzvM9QthMWJ3o0+ZV1AvGulyFvWMWYKr8sRLKLoV8cQ0Gigv2thPzP1Z4OBxcyVj5kh5v+b0CsdFg2uBDuLlHdLLLnK2zJJ+jePJV4k2c4h8HQGiEwAYDomNJP1od4s5ZDFGozmca+OZTowtNzEVbcPM3PLwUIzxzPUJRQ3aCGWYzH7KcCJ25LZXc9IjpTbI1CGg4xw9F6mnvDWjZnOk1TkK2Y583f/g//o7/Mjff3ogNJ/+d/89384hgwk3SU/mSnR++C67czprOvC6W4NKmHCa1a8aLz7l94IWTjEprSNQYq0t3TIMtAAPoNFYJYxT4zeGB/3kBgkHu4Wcl2BirNwMwGNYpR9knzy5vUdP397z6s7w+cmSczhyhbUvmvrfPMX7/jw7sJnX/6UX/yVf4mHhzPeG3k6LYpOMpg2ORcldOUiyVMuBUdvoWWj98HWGh8+PLPv0qKbb1ye35Ha99zzws8e4Mu18dd/9ZrPXy/c18Ypdcq8sl2eeX7/xPX9lY/fXnn3w4VrH4qv33Z2l1ykjcnWdi57F5bXW+sAACAASURBVBOlGsspcVqN+yVzdyosYQpYSpYuNSWWkrg/GXfrolSwnEmeuTSYJJbTwuPjI/evXnOZiY/XjpXC+f6MW6KWwmkt1NVI7DycV3xOni8JswUwRs90zzTgw7Xx8SLTs6WaDIPzSrLEOWdm7xiJ0+Lc3ylprjVosyo+eyghr5uS3WZ33j6euLtb6dM45TusOqnEZiTBmlay6ftzNXI+ItONVNdoeIz3zx/4s998oJ7vqeuJ81kRvzkKi6MDdimJpS7MYfRro6TEvk8BXUOmz/u2sbcr1+vGUqo23j6ZhG/BTaohZs8MNo2jDQFHsTpYC5boe+N8OgsoK5mTqbicT6uG0pJZc+Hu/kTbrjpcpgpcb2JKtH3j+eWZ/bqR3Hj9+Wec1oXFxOA7UtwM8GSUKv8gCy8uj8KnuGG7+b/UlLlGQtyEYDvBulS9Z1XDbZliSrS9yezQndmHDncjhhh5m+xzsD7cUUwAc0IsGMtJYJ732+daTmGQKTbekcSwt52P1xdenl44PZ45nV8F+CEATt4FYmjI8yQG/QDiMMkTzTKGNiYejDPvk1d3d6RsSp2L6ybMWybh6j3CF8id2fotBEANsYywP/zwgTEG94/nCCfwT94jSe9i72rSlqWy5IIVsY/UHAe8kxLdYey7GIJxPT2lkEMRErQzqVT9FMEM8qMweaJHC8uceJs8/fCB83llKSu1SqKk5Ye8YUrK8bsI/FSzIS1++q36UVAxvX54Jr+6EzgxJz1JXifD4xmMLF2HEh4+pR4Dyyf69hiK2n3/4SOndWWpa8gQwtRwukCBqOIpml4INYqZUpbgJgMkAL59a9RFyTmlVpZSybXGRjciY/mt70XAbSklho5EXmUInkzPxbqeBPK4zKAVOCDmiRo3Z0ZzP3oAm1oRB/ClRuby8YXZGnf3J+ppJadCyZmS4p7mpGEDp7ddPgtTZqMTNVJzKLZ3tkExo+2SA6WiZ/lgQyYLDzDTdlSxwun2fAdZ/wYoGWKgjd7wPlhDogb+W4BgukneSg7w1rT99MNc1dRgydfJWJeFy/OVuqyRxhS1OEU0exWDYY4R0gwNz33Ip6iYzonk8uNpo/PDD+8ptfD68TVLXbgxR0lgYlOPSfQJYqbO4O633sW4Ut+vKOK50/tkWRcFG9jkZTc6eu5mNrI1McfQ57YAH5kBBoUP1LYNlpNRc2ffjLScWHKl1sT9+cz5vHB/uifVwvGAlRXWWik1k9fMeYFcjVNeWEwyxuWcWbqz+eT+VFlOelZyruTkpCXBXHAreIbRS6DQk1IO41sT7TNnMVnyxvW7ht1pcePDYIoBk4pjRUA34wrLCuk1yXcob6lpYGnHZqaMyXPbePf+mcsOb9/e8fqcSUOJaTkWYQ7MtIFr8TRxbBQ8Jb759plX7Zkvf+8LSJ+DZZavfsFPL3/GX3zzge+eOmMIuM13J8mHu0CNb54S62i8/RzWLOmbEUsOg8miwdkqM3Xm5aJzOuWQlYrJNcPTZYYnxwH85yxQNZnSkEpx8pR333qe9Gsmp5VJx21l88zHy6TmwuksSVn3Y6CbpLRyePykHMbUJKwYRqOUSh+bAlY45EtacqZUDsuQkP8gv0DNw5CDQRyI/8Zgj/7MYzjMOd/efx8C7i0nCgK9xLwIFu6UDD3XTElG3xt9Vz086uRgsKTKHE0+dsE4Ectj3kDuQPUEJCcjl5CBB51EvqpxPhxhEcmih1EwyTHMySeOYFamGEyikJsFYKt7YHEmiqE5Y84Ib56ke3te6g3kSZYha/DPxUK2alo8BnsqlRrMcgT2TEmn9QvGMI1qGlH7DumZx/JtOZ1YTqdgTwr8l/ynkb3QmySIYw6BvHkRwI2WZrND7xqiDcmsBDip5h4JsgfgDuEJFqot9yzLiZskSbV3ILlnmgf7DvoYSjk0J1fZftytKykZH5+esEia68FkXtZVkkBVFuJyCLwb6ldaPMinACdmnNs+oaRyey7ylOTKo275mMw9PJUwrJS4LqEk8HGTtZeUww5CM+mRkNaZZEdAzZCX1JFmdiwaJOBRPbKiOlktk60w+mAPGxJ5HqUARkMuap/kTm5BZkiHzCoxLUFGsswmUKZaCuarzNI9FrfJLUCa6N9TBAFMB7SctgznU8WRF3IK9lnrPbwMwwjdLNREYj2PMePzC1trAupcwLklPTf+W++cWEeJQTCncxUjFMlc53CFAgHjWHQey6ukMzQffpRV9T4bkXg8b+Cp/F9nAOXBfA/5pmOMDqMfPpt+I5+oHwtz85C1uR1/L84YdzD5Zba963t7WPaY3wD3nNONGJADbJzxTB6/4wFyE7NWzZUZNgIH6+g4lvBPs0a0VDeQSwv74/rC3/0PfzxA6UeXvE3UOB1IvW+d1nZSNu5PJ1ItMkYmUU03eDrSrrvRppG8sHvHhjbiE6jLSk6OTzULOQfarTOFxqDNnXHZ8KtMNtdzUBanUU2o6zShkWVq+3lX4O39icfXd6w2GfMq/b1lxjwMD6GkyV/++ls+fP/M3eMbfvf3fsXdqzvm2DWM98nMRWwUk6Z0rRW6syw10pbOIcUZjH1w3Tc6k+161UZqXnj59gewJz4/Jd6+6vzi9YlffFb4+dcPLGunP79g24b1jcTg9Lhy/dDYXhrbdJ73wfU62MakT+c6dy5Pz+RyImUlJjAHbcLWtk8I9dHX1MwcS4Azg6eXK9ddhdA5sdzVm17fSaS80Cm8++HCPo3lfGI9nQJQgCXB7oW0dZZl5WV8xPpCyqIwt1kYGE+b8+4ymOacTordtUBjscmS9ZLXtXB/l6kO7z9eucwaG7uNORPNnY3J3gE3Pnt95pw1gK3mwEaeWWkENlnzWfT91NRzm8R1cvzvzNnwWfn2+w98//GZu1f3gJhTNjM1FW1n85G6EWDisUksxmVvTGAfcL12rF/F3HN4+/hakpYkeUOfjbF1pZe5GFc5mbxVdqXrmYWkCRkoD5QOdnd/z7KsN3N5sRsq2QqzFnLJrJaZbVcENkAuPF+uShhrjdZ2+iZ53tsvP6OGBG13vc9iV6thSLVqazU6MG/mvTkiPkvSs4Y7z09PKuIO02DsjYeHh5sBK67BeBuSix0SvAnMLg+g5DCbZJhtdDHgyJQUssWUo8AeW8tCSfU2TPfeaBbpdnOwXa88PT3Rto37Vw883r2CmklpwUjUKlPRtGjjbLi2oofUxZXAkZOrwW+D7BNG5+n6wt3jK7oNTstZcjR+iy6NzKZ9Ot6aUscQSOexbVvWhTEH7brzw/ff88VPPpPfCWoSj/h1czXR2RI1q9mfLnBzuJOpdAYdRX9fXi7YvpNKaPCDScOU75sHAG7BbHJ2NcmD8IAYkj66Y1b4ePmBnBPLchaLSH2RJDYBMFiSX9cx8JRkVM3fShsKHfw04/n5iWFKHzsMnyshcZiqE8kFVRxLATOYfcdQ8yNd/WTbNjVwe2N9/ZqaEz0d3mbE8KcdTo7WcCJmRzai+ZxhaqtmHZOk85A+ebCtOpI9SLV3NDfGYVaZUrqZoc6pZ7xPtdezB8gJt+ZGDFY938V0HqScaa5NYS0Fs8zuTWDGGAL64nMwpaIxoSzhk2GuZBG6pL/BnB1DDEUlQOqfzynFhlySxoEYh6dl4vVTMl4LyV6N52iOGQ018nJAvhUp/v90MImB1sRS8vBLtI7uaxIImpOH1GDS+5EYGLYEaIkkySRhxCzvtboQw+zRhEkKKgA2iyW6XxUggprPPTsMbZQPgKqsC5fLxjYkUcpx1qQAQtdUKe5cNnlQaRsMRiZnmMn1vsRzvJwXth82xi7wqGXHyyCT6UiWeM1VA1ZNAZIvip2egAs8twrZJ9s18fiwcnnZSDww2042SUysKjnskOvntYhVMYRCriWDnbBlp107H1vHbOHyYXB+TFx+uDI+WzDO+Oz4uOJloYQB63VTXLsNp2UoXhk28KXd5DKGk2ri869f0X4YvLxM5n1nJuO6b9hTp54e8L0z7EKqZ+zyA/74K6wlOJ3IfM4yLrx6zPzs5zt/8/oVP3x/5Z/98w/88R/f8/beeLwrLC7AFN/I+RWLJZonciRlredGy86lJ/7RP/5nfPnLf8Tnf+srzL7G0yu++jf/ff6dX/+n/Objxj95t3MdEzZ4uD/TPv7Ah8skP5z4X/9p5/70wh/8gfPqVGOoVx9TkhjoiYEtiRerMBp2SgKGHCyX8AmEUlb6fnhWStqVboYfSuGyJfG8NRZ36nkwLxuetFhKblSXVD7ZrnfQMm0YYxjMjZLD76VNajEFCbg8Qt0y2GAfk5xXhTb0K57EYLZU6SOxpLPkZXQoshaYFGqujLFRirEg8+Drpcs7LBnJlPJ1+IQKNEjyc5KuBRhKpIohvU2lXi7LwtwkV943WVZYLuwEs8i1sGq9QQ4GgWVqlWSE8BdRYEQn5QUfjX3fqLVS8+GFgoZ7k4wL0m0pkvady+VFTIc5MYreQZAFgoWvUa1aQKRYQFvIG6fjM5YmkerbgSEKNd52LcaZssjIWUzW6PtUS9XT5Fw17FpWn3b8+wNKGUwEJCUTE7KU8J5L6pfmlEHzqVY2gvlQTDKtnBg2xF4O2bmJrqOTugr8qkULHaYYX8MJ4Et9mk3DLXyHkgKIPHM7exOV6UNkjTFo+05KzqkujHCnrW7MIaHP88vG3OB8ymytMayKPdxlht+HWHGlVnJ4fzbvjDGoycUAHGLFJKQE6Unn9CElH3O/LZAHnzy/Wu/cgrzmYM0V2OQllpNsI9D19aiP5eh7zFjM1DfHbDzGiKWSyBGje4QdBHjlwTx01fClLgLX5mSZiZkP1ktIRd1VX2OZGnjUrVfVDBCJ4UDfB5YK5rG8jlSsYqJYjDFvtgdBOxOXYu9B2HAtJ8y5bpsWxUuh9cm1NcnPgnF2yNG0tNH1KSmTa2ZrQ6+CCawcLuCueSdX/Xzy1+rcktbMGLZTk2Sao4ld3qN3XNIiGPCw/UjBQHSTnUUAOClAVgPGYRKePvUKY8wAetPNB9OP3nUOFPChhM9SBMCn/MnDt1hlzJAlcvhe6g6f8iIJv44b+tZZ1uB8JRMD3OIZDJA+8CjKqrBFn0eanIUdwMHe1iI9xX3sYbidzG4gXTt6J4PFE62Hf9mP+PWjA0q7NyCx752yd8wKy929tNvGbdsN0FEhS46kLglSC33t7LoZpqIxk4NLBpTMgw4rltFwJUG1XdHN5SGR1kxuWUlYyegTpncG8mBKDF49nPnis3vul4zNDvFyQsLbIFUgOd9/+z3f/OYdpZz43d//aywneXPMtoU/TYLikCf9eWc9nUmnFbN522g4RqmJfXe2lyvXvjPd2beN3i6k/YLvH7hbd96uzu/cTX7ns8pP397x6rXh1/ds37wnrYsKxL6zDef7dztb2xnN2bqxueFF12nuXUX3fFaS2Obsu4bztje6GzmFSXBJjAnbLorz9Tp5ujTwypdvtfEdFL77uPP0dMXTykyJ61NjPSdyWXhYziG1MrBBTZlicErG2IUw0+HaYNsqwzubGc8t8bINUs48rmv4RTjTKrMIwS4pU6s2PWM0np+HDPR80ofYTX12RXK78VArD/crNRvrAskrnZ1lOUGDXDVIW83Yvss+gJAamD6r+2TbB99++45rd+4fXsNMrGuh1ETOmZoXHdKmgzwni4PcgMneZDg3W+flutN64/p04bSceLi/k9cJTs0VC1rlelqFovdOYopOeuhyMQaTaQnLAh5KUaKimZg+YqEMclnwWmRmTAkq9Yb1EQfS5Hq96ABzp/fGdrliZrz58nMZYvZ+050TWxXcWWpl611bjFKYfRfVOElmZS4U3oCXy4Vra1jJkVxjnO7XGLBFIe69gQ9amBUTzMPeFM9b8qdzY7/u8jUbruhphOqnnPGkgVDG/FWHbUoyo55dgESAA/v1Su+d08M9r14/4iVRl5Ujjrj1AQmqR5zvPuhd/jS36PRpdPx2wLsLsFlOJ0gljKNH0MF1zh0btOHaYBwGgQdN3925u7sTK60Y737za+7fPpBKYY5g14RxnBgiMtTMWalwWGEOF6OvNTy2nR3D+05/fuL+/o5cJGklJW070AYt5RpmjIShuQrbGF2RxQiESa5zuF13yrJAdjbTprEcG7gk4IxgZTqwj0YNiemckuphUqrsbWe0Tr4/yQ9ldEZEFIM85vIh6eiDQZJ8K7bIYtwJtBhNtWW/Xjkt6281/YfRuhrO5HpmlEYSUo9DipSQ75MjujpqZJqLrmwBH5QUMcYppLOHmWu8Z8ksQOYDWFHhZ04NGnBjkTnhBxKf7sOZSb4u06AMNQ/uknAkBDL5aHjbaMGuWWqlrkoS0qY6h1fKIFGoVV5JrXfdq9hH+xyfzpygw3tsxu5OJ1Hzs3x9RNaxYCUdMp4piaiZpK1B0Tlal1ISrU9yrcwJZVnZr5vuXdaw5h6CPydAxfAsImje0yPSWR4jYiwfHmbjBl4d0sLbdg/HZ5zR7nFtE+4CpkjGkWNYF8nAL8/PXPeNdV1u1Pgcss4+O0stZCtce5fvQc06l3OG2bWwMIGNS644L7y0Rl4Sdznkh565K8aeOmUOekqsE8wKK4XuiWERfz0aJQ1sMRgCE3Pfef+b37DWhbo6qxeBzfedak49r0wSbWbydBhD0cfH9nxOSne29sT7qfttPfHyAqf7Jjm9D7zLAywVKEtidCDL4zAlxWhn17OghYPjabCkO776/cRf/l/f0fuEKnlu25z90qjrzhgn/HGStp3kv8Eevsaef02viXy64/zwwut+z686fP/0wvc/PPEnv37Hl19Vfv/nD5yS05JTbKWkXUbwWfKyOqD3E0vZyHfw8v3gH//D/4O//fPfofzsjKV7xunn/I1/9+/wb33zX/HuCt9fJ20fzGqMfIf75OW6YQ9n/sH//Z67e+Ov/VXnXJSkl2fBcyOd9B6U6x3n1539h0kuAQxMw7phZChixpcaktgBw5uODdElIZv+zjlzfUncnzMvY2fpFVLn5Jk1T85pUs1ZZ8FtYqb0wpESw2AMmKUweiPlQe3hFeMdy1ISOCOSdp1uWhr00Ul5DSNn9TfGpIQ92HBIdWVO51Qyg05JjcvlyujBSLIkZijxXBFb9Vg+mSVGjyQ001k+YtgpVeyZ3nbwBSuNVCujd1KaLPlESonWJNHZ905DfjfTDSJV18yY3sLcX2baexssVXUlJUWMwxTrPOvP13Uhl8z15cLs4c+SwzA/F1rr2GxYmqR6oiCvmRTSM70TaBkafUnrjSVkMqflLM8mEnXJYgjlimXV1oMt64kAnjI7kBfwLplRJQmQHZO8LEyTb8oMjy8xgVVX9tFpvTGmgA1rBLNJda6PjqUssNNM/z2nCCCK87hk+r6TlsToYUkQ8kFPqvvdJ7Zr2bWUBa+yonA32tCz1h1qVTrkNibDdxYv8rSr5beYcc7LrvCgmPEl7XfjVE/y2iT6q1xYvDJnZ/YdvMkT8Ejqs0yaWiaXYzILJrx2FJIiWVPSX5sNXPW4mINFCrJ7sPXVN5Q5GH0ICJgENAbVxIwPMpvCn5JYWMlScKEOcDiAgSF+eg8JeUkwU6whh1OWmO9ckj2RwOSFpGUb4XHbsKz24mC7zS6fVCfOaL1udB+4ZWpK5Gn0NNWX9aEF9ui6v9HjiiEveWEKZo27zrHZhQHYcMkXk9j9PpzdAwCM8ABtoaveH5N/GRiUymiCGK0QTMOKmdMMbC2kIZPxHt6tFfnTHdfF0oIBS4XWd4wUZugAHglv89M7ZseyXH2BsHnDpggJlg2fR3Ktzo9kAupTzjFD6nqlLEn3HH7zTPNj2WjQprPkzH7dlZqd8w0s9HgXzdItHMaxQGuM0SKVkPBUdFOYQ8jszfVOcvSPhkB8wNDZuhssWcqxH/PrR5e8/Wf/xX/9h/5xp5TCenfmdP6U+kPVAGMksWd6J6dEraJuXUJnaoxAtwslS76jQqaDaHpYhXlntM71ZScn4/5UKeuCJaPOLJlEFmo9GJh36nTers5P3t7z9vWZc06kMIUlSbs75oRqPD8/8y/+5E+5PjW+/tUv+MVf/SV3ayFDaGwFLOVkUIpQ4qVQ7xbGaOS84KUwe6Omhe0qMGG/vjAvF/anj/T2RPUrdf/AQ7nyqzcLf+P3H/n9n7/h9375hofi7N+9IyVjWRfadePlZefj0+TDpfHSJ1c3dpc2vnGk7ST8oN+n2L6smVzkZF9KpZ4iOnE6e3Na10b9sjvb1lnrwuPrlYnx7fsLf/ndhedtsvXC7pmH+we++slbyrqQ8sr9wwM1YmVPNjVwuAY2UqXNzvTEbpOyVsgL76+Tj5fBuRTOtdyisg1jZMg5cca4PzspOy+t88OzMWflMpyLG91HGP5pBHt9v/LqfiGlSa5Q8qLPmok1STY5TH9uQ1TlWjLrInkirgHp0hp/9s17uiWWuztIhfOpULNMinOt5KKiDUj3nZIOJVN6gTv0Nnl+eWGMzvXa8GSs0VBoA6WBZ9s2aiqRviBAaUSEJ2hbNQ1IGubHdNZcqaXq0D5okwbrunJeVk51xUpVMt2cbKPLvK812r5TqoznemtcXp7B4fPPP4doJg9D7RSeTgn7BLqiLWsyacxlRIxAkqYkkf26cblcYitbOdXKslZyKSzrqne6T2pKbAGyLlnDnTZEMos0M2Yynq8XgYAl32i1onIngtZAn4NaFtHpU9j8zyMRTQPrtslfrZTC46tHqEq/I4oic1A8yV8FRZL3kOM2g5TERBCVXcUyOVwuzxjw+OpBgExJZI+mLTTz5aCEtxHRnWo0Us43DyUPc753379n3zceX79RO5mOuPIk76OcA6TXfSqlaiPUGz0bs3Uuc2cbE8zZX66sixKx5J1ikZroAg1Skt6cwyNi4GFIejQTSpdQ4Xu5Xhit83B/r4TDENWnAPjMCrUuAhdCMuhWboCLhcfE4UXRZ2d7uXB3dxcqnU+ac/ngxA7EJYGTz4UM0gMuUJPkYtOYJV6eX1jPJ2ypHGaJlYNt4tFs5ZvJ+egjkt2yInuDLixWkbbq18uzQMyU5dNRQiZ5NJDx+x5UY/WCU8+pekg1X7MrQW4O3BLldFITkQTUVkvU+sngXwClJH/awh8+H0qqGd3xYUqLLHB/vhOwVMQatCp2xAzj1CPGWkmB6SYl1DZVaXH7vlOSAEUviXKSXL1GfLp8QXRd9Hmi9R+Cv0PikEO+CR7eh0MsllJCgqJwDN3HiSvEnGQpZILhO5GzBspIqKtVtbfWyt46vU/WddXzpYcs5DLE72mk4rE4iCCBnEkH080PdqWz7Z2PHy6cTwtLrjEYHOl7km1O4xOAGE1cRdBgCulMztrUpiIz3t4H66mGtx2YT6YZ1cClI+PwR3OTmEPMs2B5ALODzWChVWgvg2GwWuJ4GzpOScbsjlklpxVbEktRatxoO9et0d3la9PFfuw2WApsLzvnN69ZcnhzmEsmURIMnYmjQ60aOMoxWGSwJGNpgolYFjEFL+8bZQ1maUqS2Jw8Ysszdl5g7JhlfD3hfZM0Lrlkf7Wz2on2nPjzb9/Tcb54vGO502fW6KJTGqQpkMPmwqRT/PDg7Hzz/TNre+bLX/4UW76SdPTxl/zy/J5v/8Vf8puPF16m0hwf7s4aUKcxKPS0cv1+5+dvFu7PJYy3J5S4PilpkWNGKwP/MEi1S2afC+GepAGHwwNR79twDXmH1Ms8PG6S056upLXK1iE1WoPr3rk/ZR6KmpCO9ps+9QE1BtxmMLORIkzBS3gQWRKj3GT+mzmYQzPeS9X7OXdAsrKU1G/NkUJCKlbLdA1PicK+yxA3E4DEcUK74y7gvU/1OMlCojWH+r+sPrCksCBIibZv6o0CaJ1TUhdnkFPRxj0LaM5Hgm3Ue/McHkLtJslqbQ8Zs4UvngsQIZKaAMLsuEQy0wRSEjMUPIAxhSwkn+HTIo9Y4TTymLScZfbvAtGoaAg0jz9bOdUqsGEmuhdmBDuQsmYTwoOuCMxPIfPrvWFJDNY5Z4QxWLBVApQx6EmSwhT3SmfSUdvCmBkxndyULHtYHeclfbonOd3AmNEk+VJse7ktDRIWfqZO65LJwS3SACCY5U6P69Rdz3wqUpRoNRVJgchk2WNpWtbwB426sdSqAI7RxbiigIvx7MibTobuoZxJfjMkdrQEJc7qXcVZig0isStpeTPgmNDVKybVKfdJiX7EQko1jqUaIeUL1UJX9FssFOEw5xlO3B/kUWaQPHyZcjyTqUSKsmkJdjQXQcrIlm4sG6MwpoVsXnJGgkXLiIXowXZB/evpqLOzc7+uWE60SCTzUE3UVGDCWhc9s7moV0Jy8XT02abApYSTpzH7J7ZW67ueLJeczA0Ycqw8ACpLOUJA9PtNxIYutmJzhDwz4TnOi6QZIFedI+Yp6s/AswCkFL/1IQ8Foq8y9Qm40NsACg9PptkFsI8Rr2QsdY9ze/qIn/kAJxNzHM1f3GabzOzkJfomNy1zOFhxRN+oHogRUlsOQ/sZnmayLchVi9XsmjEOe4XDm5lYUDME7AbpScFPZpIMWuI//nv/yd/nR/r60RlKfoXzq1d64bMOkMV0oO1zCPWckF0b9DEG13al7w3PGgCz5ZAh6IGzISRuuAYEM7EprpeN4XC+WxR/7MjnhawGKQyMa3HSHNzXytv7yutXJ0p2zHs8PAaBls4El5cL3/7pN2yt8/nXX/Ozn33NWpebWajldGOPpFLjFxcoUIDcxcYZc6BkpMSHl2f26852veJjo86dU76Q5kaZV754hJ9/ec/nD4mvHo21P7H98XusFE7nwnUbvH//wrXtbMl5fu5cnxsjT7xqCzLGpE2gO60LfNtGD7lAo49IghqC0c0SfRilnKRZjgd6612pZWuhDfju3YWPm5OSs54qp7Ryvr/HrbB3GaDn5dBcO9UOSYpegpEynhOXtjGm6GELbQAAIABJREFUzLefd3h/vdDn4NV54f4wE8xqybuLiplSxptYCU+XC20mbfum0oBmk4yvT4FSj+eFsqrZz4TRXklsvfFqOQfTpnNfz7QxmBZSyOl0l6nmtMS7H575i3cfKKVwfz5RlpOG2SkqcS1r+NOI0VVLwQfhLSPjwo5xuXS26xNj7lxfGtkTd4sSs8wm0+SnMkLf39qmZ8k1HGtozvFshV+JOZc5WUuirIv8NsIM2YZRl0o5VUm3rFLTBJcs5Hy6Y3g0CC1TrbC/PAdjbfLZ288U0T6VpIfZzQ8JQ5TupDQLUAS0JRV99mBcjPAM6J3LZQsvIrDkWIFTXW+yk2J6Bbexk4bGzzZ20WynCvlug+JOv4qpdF7EuEgpRarBJHti5qmidaoMQz5gDntXnPN0lzynD66jM4DH+5OeExaOqNESoCJVqVm2O9dtMNKkBmPJhzTZ3cOwr+9Yl0Hk5198jiE/DCV2xZjp/Na5NlUIpxLAUi2SCs0Z8cfG1jb2pwtffPVTecelxAyTXDUpSvlIB7szQS6myaEUvA2uDK7NqOa0NnDrrMuD5HH1RA5Ke0kCWQ/de84rjgpRZ9eQYfK/McSo6nPim6R5y7rIlwej4dhS8SY/huu+s5SC5ZXepmJrvTOnABBJ9iZjdPbrlXpabowe3AR4emIlqxnLCbKJyj26ouqdYMEI7BqmFDT9b2c9nSSdTtLFd+anJmc6VgUY9anf70hG7B4DckQsAzhKT6GspNapp2AoToGCS4pWLdgx8goIA9UANi203pmknz8pTcWGWDrF/3/m3q3Xtmy77/q13vsYY8651tq7qk7VuTjOsQGbxE5AAQkQInmKEFK4y/A1eMgngO/BIxLiIRIPPCABIkJEYBKISXBighKSOLGdc6u9d6215hxj9N4aD/825ireC4lt+Zw6VbvWnnNcemvt3/4XOLitRyJdJMAmdpW/JQIWqB5sUi/T2XWfbGK4atbaB/M8y8OhCoCJPRlkNb+acZe2CZwdEB0zQTuihcvXoe9KERzDk0WRA5RpCHCHYkp6dCTTGhjhhjGI4ZSMxR5pvu+945PMnNMmSh8qzZ1bVYqNYczzCXfnNLdM3zTMg3muXG+r5IG5lZW0saeEtjBPk9giJNPANBTHlH2HOyMqVOdybkwtGNsGCOAGnd1zyAi1DKO2B4Fvru1zTdmjMWiW54AptGBagu3DCzHeY02A2bBdz83QtroT1OJpJC4Gle2Dlk19pAdZd2fynYfpwv442Db5JbVqUMBvnd0mvG7U0SCqZIXV8Foxm2kNrtsq74smT76Jmd4DG51Pf/CB6cdniJllEaPRI9N4izFqZ7s1bDbKE5QIOoNpVKgTZXSGNazC0y8/8M3zK7eXnXg0anPaMLgZXFQ7mztjOcH2gVi+pLSGjQKn97QnuPjG9/8Y/Mk//QU//fCBn/yTV/7+Z888XT6nPdh9KF0jGbt9UGLQUG2POphL4bUbf+tv/AO++mN/hR/9y5/B/GNsdKZ/8T/gt37/7/HT187f+smV54D19ZXLufG6BqwbzzHxf42JL373lT93OfPV54XW5MMVDWqfmErjVgYPZeZl2xjPGzw0SulQKzODMgU7CXjM8oPsu1QSw2ZgpUXN861hpwtjfeZ0nmGH0QRKYWfaxfD1Cr5BaOAMv5Bhn1TvFKYcSmf2sRLc9CzT2E3JxaUWWkzsAaVlrLXf8tksDAqlHeb4hpVJ0puAI5F5XipRKq8vN/acwszhtExYSfZFVx/XWmVLlmirswCiSFPulF1b1Xl+u3bapE18aRPy83Pcr5oNomnZQMhTz+WBQspmahrYlmK0NmtpFVpUTJNl8EcoKbHInqIla7EtM2OV71u1BMenXAhGYb0NWr9S58qwTgkxKtLpG5ItVqxgA4o1gTfRCdvVuxejVmeJym1EziaRDLKiPg4tv9tyYfcbUyhpa2Sd8j2SwSyWyAhZR8zWcoaqlMUY3mmlsm4je/VBjQRSeuAFykmzlSGzdPnKDPqkma6eNXuwFrEnycHc/Y3ZbkPJwSYGr5YQAnsojVMaIy/Fcpk2uO46j8QOTlPsDO0Yt02yb9MCrqNELCuFxkwNSb/bXLHdJJVjMKeSXEEH9SjnidyaDPRdErPDL3KaG/VcWTepH8IDXIzYEU4flnL+QqEn8aFqlqqFcMmN1GE2rkP+wI6M1ntkxlopzLVpWZRsbGuW5uSWJttaESzJTuo9F0u4auwwjtCqTlBwybmK2HlTM4qr5oYdz1WBKDRLIKsE+yYwv7hhA+aSYTfuWJn0GdOf04O0vBDIQj6zfcjgP/adORlVYfEGcFnVggpP+xMtibVcHXflA21WH9ilChi7knJjNspw3F/Tn071CIsEGQ/WVFBKYzZ57I2uO7vtg8O903PWKrkIVBel3iNy6W9TY8eoxbNfSYZ4ygTldDEOOzy1qK2w7TqfSzFwAYBjzQVRkf1Pd5fXVPbiNaXDViydNQT8tCbl1DAt7CKCqS1SgpRKhJL2LNlI+m5p5p296NS0ADPEojosfb6rX985Q+m//K//8n881arGBA3Gh6eFd4ghRNjdGX2jjy0RehXgsJIPXONu7GkZU4zoovu+Q89Um2XWJt1liIXpIOthVBsstfM4Nb737pEvv7hwuSgJQ7254fa2+bvdbvz0D3/G1z9/4d2XX/Grf+Kf4YvHR0mRhuf2LXI41I3I5UGeo5VWmjZ87myjs207/XZlvV5ZX59hf2X2Z+r+NWX9yGO58uPPC7/2g4Vf/h58dR6060fqWLksQqRfrhvPn3bWNXi+Bj//uHLbgt2M173zcr1yvd64Xm+8vOy8PN94vna+eb2xriu9b2hNWLIhrQwvrLsKYR/OdRvctmAbYj5s0fjwvPPhuXPdBlMtzMsZ5jOn0wOny4PSg+r9ywNo81m05bc6UcrEHoWvb4XNJtZb8HE3Pq6DpVU+Oy8srSpBZpKedbjoysPF/Hg6n7heN8xm4o5ZFLbe8xlzllp4fzkl00AbQgt56HR3HpYn5trAjWlqbF0UT0MbIDPYCDYPfvbhhZ9/kJlmnYpoz5n6sCxTbp1bsnYspQMZXzqOZmnndlvp+y4W3XVlmqfU5mvDtZxkvDr6zugyYgXuQKhkHKgA3odZHWLnaWG6vNMQP81UD5ZE56c23ZP6Ci4vmO7afo6dvq1KkxiD27qy7St923m8PJAqYck6SEDJcrOUh9YRcan1gjO2XcOgD3zfqVaJYjzfrjBLk9+mxmme5Qkzy6uhFn3HI/Lcx2Aqhdu6Ym0SS6g1Skga5RHMswzaWz7L09Sgigk0Qubx1YylNgaFdd+IjFA3Fyvo+Xql33YeLyem5STKKWKORCh5olTTQBgq3oHEz2/xytkUhDPGTjN4/vgNj++faDmYHuyuemyAU4ZDfueIXEfj8gzLc8UtmKbGz3/2Ne+e3jGdTxx8j1qUnuMpQRLbQ9umaukptKsgDwdL+VIUY73eeDovuqZNm05L9qLbsb1MEVed702VrCbz+mRq1ojBtq1s+8b5fKJN7X5fjs1uQSb5tcLclBTJ8RSHUsQCT0r1oI+dvu8spxNza7pnZPJMSR8lk5xppOn28ctdRdMt5RFDzda2ifV2ergk8FbUfIW9bT5NwLC8iw4PHJ31R3NhZooyB8a+Ke7Wyp011GqjZpMFZN07fMXEjCk1G7NyxN3r51LAXVvm0pS4J1mf3r2aP1OATZ417in3FhNhHz0bKQFo19ebjPlPJ/lBFW27jybLMpFSx3YS70P/v+89AW49o9smSYH3TpnOYpxhmVBCbtRMje9dOtjuJrViH+jZrBXJy0Mlab9e9ZdtSpBYaYqHqbtZvUszLY2vixU1b0m39/SDsNJoxXh9Xe9MuThuBrmtTzlihBJe4KCHh9JkfOS993vC0zefXunDefd0YZ5nSqvyVzCYp5OMU3PRtO67ZHylgLmaS0sPwoBAPi0vL6+UOnNalkxtIesobAhonk6N6BknbbB5Z48h8CyvVRB4hUHnPMNYG5Hn6ty05W+TvC5kGF+0yAjTu1z1vyUVV33Z15skBQHzuTJuK6f5wtTEDJFJfJfIwqBM8jH0lE/VMlH2htWAFoQdILlT6gzDeX1+1fAD2pbXoE1iFDOfwTKsAYex6h0nU7xMJvaXZaJ58JOffOTrjzsPDxc+fyrawNbCnAsuyUD0fSIKFhpMW4Wvb5395x/5/g9PzJ/9AGtnGBPnX/81fvCP/yp/52vn05rXnZnWKmsf6QnW+PjizH3ne+/l32WW0dh+JN/Jh2S2Jln3cKLpLBXzsjJqlyQhARnJjTRIN9e7NdiSrVaS9TNRa+O2b1yvhTYtPJ1hrsHYg9EFQkYJKDKBLlYZDBkw19AAGukdl8leMbJu1BB4MbSwMZvxY4CNoJTpvviyehgxuzzPSF+8KibsGDc8dkqp6VHjCqippnsVWXcsGUImhgDkkJob/DZN1AY9GdsH8yJKME0Zrx0Cc4/vG+nD2qqAzuM46OG6V1mnx65UUDuSGk2pY2JS6lwLG5SimgHIgPho/rNX2oWSMbV2vz7He323eo5MIy0CJTx7K4oxo3dlz8AFqwZF7JFGMlhKETCD2OIjP2NkoIlRFAjgWy4d4u7pctTSsGQphOdZ68noNQ5jcTvUAsm4x8CSTUwcaW1aKgSFktIcAY3HYJ8cDyspmcpequizz/OUTN+i3nQMTWdjsPnQz/B4M43GNGelP1Qp8rpzU8+KCbiYI+9rDKUmm/wP+8Gwy0FbgJc+3zF8lyq/0ePaELLBOK7Xva4cXIQDh4hIIJiUUL4tkyR9j4yl1/lUSj4Xod52RFCzH8QEmEggGrmIjGTRHKCMGDV+vC0R9/tTTWqPZkb3XbYIJOerNqhpr5DvY0EMp7lKcujAtu+sSOJ81EotEwTouumTdN9x08JrDH3GI3X5clpYplkMm+GMBJAiCRxlFEnKSkmWVVGqnRnGlM+Ky+sRy1ArsUbrlObfwb2PPfrrmgFex/sSiUWQKdeGSVap7a5qMG+y/CNhVmRxPatHkA6W3yHSty0fiEh7Es2oUpCIzSaAyfJ+t9aEZ+Q/Pn6VPAvDXYs6HRrZt5ikrZFG/aXSlgnvnagh37P8/i3yXTtYdHn21GJJeCzJ1NHP/ov/0V/8T/iOfn3nDKV7CtPoaXpWiDSZGwCl4tFFkw2YmKCmITGDCSGXXvnWCznupsGR23zaRN2dhtgsVpBzgw+aBUsNlqnwvacLj08XphCK7b3L0LBwH4y3bfDy8Ruenzcevvclv/qbP+JyWRg3+RsZQLho4/XQQlZa3hgZjmkY3d3pRHqBbPi4sb3eGPsV61fK2Gm88lBf+epS+ePfO/PVFzOXJZj7M3yzST8dzvOLc+2VjzdtlvYOV4drh+utSw/tXd4Sru3rtgdjFP35oQ2sRxB9hyamyTQveChKOuKIG284lW0E47bTO7zeNto0scyVh8cnrEo+tcwz01R5ve3y8GiVZdaBpWQ9w2qjW2P3ynUNSneu68LHfWNEZ6lwKqJ06jDtxBDV2sNwNyjO43ni0+2VPoo2ZKZmG8SEKBjvLyeWc2WeKhXFZLoLIS4NHtqZPjrrHswtEf0is92lnriOldu2c93g9XVj60E7z7SpclmmHACV5FQMIjRZWhFdkgQbRh8qrgmO4BqSX29X5pR9qkgpZWcfnX1dZSA4TTL3TWO2o2gfuIF7h9Qwl9ZYHs4yrpwEqJYo1NagKPng1gdzk0eUj0FP7XzvMtDer6vMZvdOrY3T5cLhKxMuNsDB1xyh57+irfghUYJk32RBtPA7HfO2bUzTwj60mccKO0rZwKBMlTGC6B2GUhzH6FxvIX+oNFIcoWZq3aU19hJ5oMtxdmQZ8H2XJMWkXR5A33d8uDYPxdj6zrZu+No5nWbaKQHIZFBG13rBrGbqmoAYLA0LI3D0/ERJuDv09z8+P7M8nLA0rI2RRbo0DsmpQTI2EshwpabUKs8Bxpuc7etPH5XEcZlVwK0muAg1ChPGsE5Pur+qTsE87l47RFBjYl4Kw1eWuXFeTvLhmVrWHzFw1EALCJus0k0JgmPvyVKR0EYsKfkfXPcdr4VSleBRq8lcNvHlYtJpt9o4RgS3NKp2bbcKKdkCYox7Etw25M9F0ogrKAUpwcWjMa3tGBT1rB6JWL4PrFa2b145PZzVzNvRFasLNI+UqdbcBI37thEKFmITEZIAUgVI+OpKhkEa91q0eTLPlLKUUVg+V2LvpF/CsdlCvy+yGRpjUFue11S8FMmZ7DDhTFAipU9jKBK6RlK/NZlDDEmosjmybFhL+L3JPJIa5flW8T7uz6S7ZxqTGGNH7C73c0HAWctmpxV5XUTqAq0oVSZyWXk0ecWK2AF9yJsK+aaUYmzboJ2qaulADVIcjVQQgmvURpsa2jC/y3tLMdwF7FEnrMB6W6kPZ8mFTdHmrShwoRxScCSTUzrMSH+144zLJm6aeHg48fzywtY7p9Mb0L8PZ4pBnZqe5ZQp7vumgS3S96m4Gu7cMlMrp4eF6/WV29OTztZ6DDZGycCFGGBlsNGZclnlfdfSjJAtQBXo1vvK3IxiG9uuoWUZxmmpBM6WHivWh1giJgaemQb/h+XC61gFmMZEf3b2ApTOaWl88/GV6ekC0amewHikFKfCcipsN2O9Dup5h/RnkrS0YjHB2AjrXL73yMPLlX0N5piYqcRtxucp5YMbdjnYazobWlkwX8E67bRwZlBa8KfHD7l+uvG//O4/4vd//6e8f/89fukHF87e2KdgCqNP8gKzyfAuw9fJKjYZT0+Df/jTr/n+//jb/Mb777N8/1/A2iM7v8Sv//v/IX/h5/8Z/8U28fF159UHtUzMc6NvnXUbfJhm/vrfX3k83/jnfqPwNOl61DLpfayVUzW6r+zzzHjVWWzlIl5BKZyjwESykQNiYnOHlsaqPZhMwwNAOU30qyaNaS40k7nrPmRiX5pjY9dwHIaPXQswW7AC+9QyldHxBsVLsigqlcLruhFDhu4jdhhGjBut1XxmjE5XSIMO9ZSSygNI55zqdm2F8njhen2VAbNPKS1OcKEEZgLng4nhApZqHtfHeyhmjDPNhWm+cH29McYGyCfFGXfgXRL9uFsOUKr8YVpNH6VB+C6Tez+kqhu7D2wMSpkpZaJ7Zx+dSmVucKRtSmJTGeOWn6/eB71lWRj7xr5uinbPGstQDYdy92mRmXLQ+0ZET5CxsO9r+jEWiOlesyTfghjOsGBeFthu1AJTq9ws8FHx7qlsshz+0yOquO5lhUMg01ouIwpMixIKA7BWGd7Ze6jG6iJrWI6D+SJm8VQamyspdRs7NXJBZibbDZdXWLhlQlsuYYdsDSIC67lgM4EKK8ZUG913+YHawBMIVqKmSARgbMmqq60kG2uCPmhhWJMUzg1GzSclEqQMmdq/9Q85SMYxe6pHLcniOGq6DLX7vbapJqOkTh337MkCGuQy2INpFvC4ZYiE1DHqkyLnhh4D60adjZGssxZiDx2/WX5EDmhhoXAWASLFtMTr+8bUjGlW7dOipKWUUb4+x5L6MHJXcpxIDGNsCVQKfCxR8Fj1zHB4OiGfJksAZXguiKX8Ce0+8z00+ib2X+9bJuHqc/cMyCppoi7MrnKkgxhVSgHTrFhb0Ewm7PLB9ExSzHelhNKWY2TvonewZGquR2B7R0luhZjFyvUuNvGxqIKc9UCLAHJ5O08ChT2f47QMsOwv1dsfbOzAbOQ5IdaUp98YrndtmuTxRbKO4S38IyKILrml54Kq+5DHZlrUFFPv1Ed60KV5u0gVAnQ1dunn5OYu34Hgu/z1/0HKm5rS3rvuo0tb7y4ENvqqLQyFIwJPXxl2YDs2DN2zWYa+yai1tMrUZhUrZO55bOvEVNmYp8qlFt4/nnn/dKY2x8cuqQHakIaLFnrdVp4/vbB+szM/PPDLv/4jvvzivcyLv/mYDXzJgixEWfQ1clsvE2K3Rh87o6/40Asz9ldsX9m2b2i9U33H+ytPk/O+Db58OvHLny18dTbmxXFXStTpvLDH4Pp85XZdeb4NepnoEfQO100HdzQ1/VbQEO8TPkRRDjcWn9PErrD1nX7bue2iDa9bAIt8lgxRDieZ8q7bYNsL5vBwPivitU5Qz1zOZ6JU5lZYd3lHnQ6UF8jRg80NqHQv3Hbj1p3Xbny8vjLXwsMkiqWbgCSLjGtlxygpv9GB/HLdBCaWCl2shb6lLKQWHi9nLqeFUgfFnGLyfKmpq211Yrs5NOnqt71zqgvbtjGK87q+ct03tr1zfR3UtvD0cGJaJqwF5kpYwtRMtVqIGKnR11a2IKlI96G46b2zbiu3bWW7bSyT0HVAjC4frLcuA8Qx7tssH+MOvB4G3NXEdgtUaE+nmTkNhil2Zx+UjCIvCYBcLifJyYazuejjkX4pY8hLZ1s36rJwvjywJ1W0tspMoVZFqWoodjANirUURpfXiBVJhjw9rM7LIhNkH5wfHhlDqUXaBmUT0pSCsveOY0y58RuhGGybqijlddZ2zSXXmZaTNk25LZyrGDEjtyulSlcvlkjFd223BBgJeNJWr8hQ/TQptt0qo6iJHebaurkkWZIM+r1hkBSm55Suppre2a8CnefziSii+7akr7uhQpWxyINgdw3VMTzBDJM5YIIZ19uVl0/PfPWDH6pfD89N7dFhSxIhIDuZTda00LB+Z720qbDjzNPM7eXK08MDVkwGuvctpHyoqiUCUPKQJOi9az9mLhBHkWp3JtrYN+bTciwv9atASxZDFG2qamvsm+rACPkIHHGr9/8LbXQsU+YsUaljq2NBMi+kjwfuPkLl2AIT2AiKG2FFKZrunJcpfUH0FsraSJ/aShWrAsM8we1xbKX0+937fZNkwPWqdLAjLjc8r5sdYJXlxlVeGgPubJ9yrIU845ON+3tZQw17LrSzGTh+JijNLDdpZpKaYccSjFqqvD62XQBMetRANhj35uEAvLTxGunjFcbdLJcgv4Nq+TTb27YvnA1japVWD9aipXl3HI+PhuMcQpSWF1AkmwExIJWms3GKYwg/EoH0SVslgaYEUIrOxJJRz2pw1bjLNBiqhRJeuOS1y8/HtxJR8hqqUdU1UUOuh7jUBMypPL27cH19Zdu0OSyl3BlUo++4DUqZKFaZm2GhQdFz43rEB5c4NuIT5/Mjt0+fGLvDkh58AUGnuZrksa/MpxmQ11P51lA51YUSjlfD+g3c2LtxPg+uv9gp8yMQnJY5nx2x78KD4dBmAee6DjqjFN2+U08T/XVjN+f1pVNrShc+dj7/3kJ4vftWaCjq1FD9YZ8Zi4DVQkA3bMpwDZp8X85nvvjBF9w+vjI9Vk6nieUyUR8eseVMqYtA+f6q9zWm5Akmg9CMOs+cwvjse/Drv/Elf/CLD/z+P/7A3/u7lYdT4/RZo01GDKO5009G7bIQGYaAZIOHpeDvJv6P3/sjPvul3+bHf/aH1MuvMFHwL/8l/ty/+wf85D//b/hv/3Fl267su2LKraqGWZv4J9vEX/s7K48Pg3/2VweXR/WK3TVcjdW4hcMuaW18MjhvYiTr5aC1DlbZ7ViYBH3LPqBNhAdLm7ERrBFMl8J2VQLWNFXJ7ZZCX2ue6br2lI7XgdlOwWne0osxhzVXGldLYN8anJlYt0EPo7W0ARhBR8MrMdOKkpW9O9NUOUxgD3P742wDo9rC42XiG39RWE8NQAa5kUlXbwwMnWUl2beWw3ipWY/S9XiZJ9ats66v1NqoU2WYQJtqVaAzjhX1CcIEVMeHOeYBtck81yteGhYuZidd92ocgHbISNreBrU+ZPAtU9uMGD/AFhOD1kanpJyqWIGeqWnuuViXREXnU5dcfHSZKMtQR+zOrDECetL3sIjlPLUGXSzIOhfW3vGi2pyQg5Kwi9IvhwXmkndRNaxPwFxmrtuu57b3BP47o1Sur3mNaz5XRLJOBUYMYKGxR2cxSdQtcilq3/KaSX8qXB6bh40CteYSSsDXyMAATPHwia5lql0QNCzPgSgGqWboVvDdGWVkmmLBOkylMcyoXlRDAUnudR56VXNRc2Fj9lZzOfoFlydUSUaytax7B4skAg1gCViY5FJm6T01l1yecWfIBPKEUo9zsPAzea7LgqLuwV4bNdnRlv2fgJZ8ZyJ7mfS9ym0i+76zzDN1UiJn74Ol1vuC6s5yyr7AMV73PfGXXMJEoxeddaeTzqERRfW45SL5YBQalAZlyFtt92DKa3HNZLdDVtYDGDtRJYUuyW72ZJ8ej0s+9YyuZ602lICa74X4bMnq8ZFnUALaVrO/SnR6OJSitMiWITpdFh3VjMPf0RLJPrxt1Q6lVUGRpUKr8jeUFL9K+sbx/oOVyD8353RkJRE6Lu7vhcfRG45kgenMPBiFYnbrudhdS0FnUGpnjDfGljO0SPY3/zd3PTQ9358wgVdjG9ghr/3/O6AUI1NjeDOgO+h89010agMrkoQQ8fZyIJNl23ehiCiOuE5FDJtasTwqHfkuYDBbYZ4Ln7278HA58TDnNq8PagxSUcoYzu164/nlhdttY1ku/ODH3+fdZ49MrbLdbuz7qgOpTUJ30yRLFOy4D9pWZPTZxybn9O0G3on1G/z6AXaw2glzWjUeJ+Orx8oPLzNfPTXen4I6blw/vbCvO6ep8LztXLeN4Z21D677zkrn5XWj1YWtkx43HSueiWg6UEYivR5q5Etur4sZp2VimoJtGM83S+khGSXu+G3wvIKbWDlLmqM6xtPjmTpd5OpfjH1ogyyzWDXoe6RMjcIo2pbdtuC2d65dKXuPp5lagtkqhA5bD8/NfqamlSYQ0cnE+fRqoNDD6X0QFU6L8XBemIuMcDGllpiJOtQmAVZbd/nT2K6faYX9dmPf5XG0bjvr3rmtO6f5zPl8SlmavLqsVkrS7yONDCP0v9XgDJySmZwvAAAgAElEQVTCrStpr3clp63rChZMrabPwMEwSCS/5iBfSQaKnu0xxpvnjqUBZRah87RweXwUlXyeiOEsh3dVDr+ByTDcJKMZYwj0Se+WUgrbunHbO6fThWmZZDjXZBrZquRyOhwjQauajWTk9kGgQgzPeM/BNC14wLqttFkpKrXU+4EYqhr6bt0VDVsQ6ypmphosjzM9gli0DRrryrqvTNPCMp90/VJmZFUyMrOJQVDnljT3wtj0vWWPlttAD91zD+ppSsNjsUx8UwMcRQRjS/BhDMd3ydrkX6BDXgvYwWFm3t15fHrCStOzHNpMW1Jta5PsQWxAv6e7lfwu4drE10meQB++/pqHpycZ36JB8vizzeSb4mTalYnWXDLKuJuYNd6DbR3aJH9zBXeW0yR2mcY0/Xd8i5ZtxwYRNcOkp44rbaMnS2v0zrhtWFjGRqtBjprb5KZCKS+cmoanMiC3ZPRFekGoUXb2vjGGMy2zrkvNISA3RrVIX9+T3VOyrpSUJ2gT5mkWnpbOe6dNE22e86zhDjhIonMUraQ2Z0EXRZ87ACjzU7WA4UrIbIu0/YcEJBJMP/q9SENFODZDbw3K8fe9d4rnFm0gya9eM7H3zClj0EZ6EA2xYHRWqAqWkok4mbYVIfqzhrKUoiTdx517k+sR2lrG/R/npYgEn1WbR/dsvEueeRu1F6w0wqtYggcrIdC7M1LWZ/LEO3jgbtn8VYG2hNFOE7YeoKA2ljKgtjtDIfLcKWVK2AdqNUbXOVTzjDoap9oK19uN7gdbK6neJpDv8CM4CGuR8tOSMjWBLAkiY8zLhbnNKAVO1ypwqmZHJS1Ng+h6F/3ww3LnSL8R/V3vRIygtjPT9Mr1+szp8l6mqwZW8rtTWMeuxVo5NpYpwHSIUtKvT+u4Ugpj6zL6bje8O93UHy3TjDdJLiKGlnxosAicqYmo0Vynwskqe2u8rDtuM+WDc3kafPrFyuO7E3Pp2N6wOSjs2Gh4C+ap0tlzq9+YWoYcRMNM3i2SYXeWxxnfNqhOLIG3kma0iocuo4E7pRvMQbBqMG5n/XW7YC04j8r3f/SOX/vV93z4eOP3//Aj53nm/M8vPDWjTIXFjdZha+oauyH2lwcLRjlXfjEGf/O3/zaf/ehL3v/JJ8ryQ8oA+41/h7/w53/CT/+r3+F/+ieVncHeV2iV6M667tRl5h89z/zVv71ybvDjX5mYT4H1Al0AvrVZ7MruxNLw1x27FAEEyTjFihi4Vf6Qw43SWw58Cs+oZizWGDhtOVFvQYyrhr826Yy8duoUOLtYd4GYWZY/p8xaDAmhwBmqWYBZVWjEVBjbKoC7OhaV6pJ8jdAZ2xiy4wy/Mxw1HZJeOJA6XYpVLuczpdwgshZU9Srqc8RQyJfrHvwxyN6EnqmU6rEAhaiAJLmWRrTV7nHfdvTtpjAG/X1J0EcVA+Juqm9GWKUtZ8a+sW2dWgUyWEkflFQnhCdQUBJwioPBnNWjlrvsJcJpDEmMss7ot2lJSAL4ZShooKbX4bbn3LlL4t2SRWt06iS2cljAGDKDdqjLiXaCdb8Se2HdOgwBPCM0TFvIMgF3YhhzqwSFqJW2GH0ovQ3XQO5dSwzVgJZAD4kfqCZWM0ayM3tTsFF1iKIEyuH6vb4PPHaODZTVhtVGm2YxbiLYfAgIOhbdGNEHu6sP85TqFMvv4wkqeWBFPjs6IkPeaQn4FIOO2E2h5vZOGDBQzcttk6HePLJHcLO0XEkGXM6xVmpuckgAbceSZDCU8Y48jZIFbIdcjfsyRL1F/hgrd2Z5z884htPplK4kzVbu2xqRqUouBrPTUG8koN+jcutdXqg5T+w+ZEMQ9Q5gWHY9b79UeKeqWlWsMBaBr71n8Ic7TCUl51r2WBFzqYRp6Uxh6572BOqDai6ghqt42hjpFSTw35B3VLZlut+HVUToPogRrqVL5D0dPZ+B7GuCxNVqPqUJBBN5PUO1ejRJfP2QmOU4HeT8g+W91T+WFYYrydHzqh+gIIAJn9AiL9GjcgDa3/LQPGZAH3TPXv740pE/E9TLH+zmIst8PIjd2QzqEAmgpKyeyj0NN/KMOPw69SoYpSogq2Q/+13++s4BpTEKhMzFCOh9z4Jo+SpZvkB+b64DIY3Vg9voRO+0DqVV6jQLCQ+/32k17kn5rcbjZeGzxwuXJdFgQhIzE/BhYYzobNeNb56vvFxX2jTz1Vc/4un9A/NcNCBK25MHpUAgy22DtLQkKk5usgc+pL8e+87++hHrK+Y3ySWmKod+d06l8sffT3zv0fh8KSysbM+vYjW5Ii9v4dxe9wTknJsPdoK1d3q/KdGiNEoJbAz6vrP3jPLkSGo6tJT6LrUacwOisPZg8ZkyNdZNw1nfd15vK7feaW3h4ZxeG9U4z5O8rZYJq0bvrs1/17XBjL53Sq3sYXSQb00PAWG7sTtYWXisRitBlEqtpBmvvCgMvdyTKTp3CxmEH3RBqGyj092Y58bDqbFUS9N39HvMBVoglJwqgCt5zSo8EexjZ9s3jMYWwbo66+Ys85llWTIuVsCPACoNwKVUrDV5UPWegHew7dJp3/aNsTvrJj8k1Rl5XNRa7nrvbaigtpIx1WjD4iFjvUP20ExNSh+dZordPT9cqJMkh6UWbFZRqMk0u+9UPPCuiHL3oKZR3+7O6Dvb7crcFsos82or0r8f8euEyfw2qa5qjhIE8EENRYOOoca3pWH0y8sLrVT6ulOmgCIKtwZA/XcMAXACZ1WkJXEcRDMYwbCgeuDbTi1VckGDMrfcZBp1bimXNOY6UbBkQg4Oamcpk9ht3tnWVdvRaWY5LUo5rBPbtouVZKEUyoxWNyRXtHBlLqMUJmEXToydAvS9c3p4pBYBcdqAlWxMjmWEEjZ6SiIdvyfQBYd8RWyzn/3sZ1ST9hwfjPSOIO9TJPjajq4Mu/+VI1NnI+9/39lHh9vK+elyb6BHAoIlPxuliaGTDfJw+X4djJUokVJOMRn2bWfddubTSVuuUmRAmZ8mIiOHkQxyuDZntUjPD8n6CYEV7oPbtmorVitLmziiaY/z3seAWnQrqu5RiW9thULFUQlpkuJcxwun0/wtDyJL8CjvTNYHvnUVI89NRQzb/Z3yEDvmdn1J9kVKA/K9G8f2yY5G+01+cUgALWuBcNqiAWqM+1byWML4/bvHvUE+NqYR8VZPcxN1+AmRG9e+i6U2zzqv1MTUlLW8JfFAvDGH80JYNrTH99CtTUkHZDz0hXma75KzuDO1JP3WTbM8RY8zIO5NfB8h43urLPOZtSg96s6+CktA2gSsyQRMMuVQAqw+u66HGs1jswzzaYHnK5ANeBwCD9WA3jtmmVp5AIJx3HcEuoxs/s0obWKaK9v2yt5PNNN2GRO70LPxdetQD7kpCaDpz0mViXrjfAamU+Plm2eenh5kdt1Uxw+kK1xsUCbV71IMpkrs4/7+1jg8JzIKPBbO743nTxutLey9KzmvCCQnWcG4jNk961irhbXret3CGZkG1NFS6NSDvReeP66cf6Dknti6kjMiCGuUFtSpySQ5WgKuQfdNBErzRMQMmyfqpfHy0xeuHzam0+B0XqnLN1gTsNxqo50WohrVGlEabwbHC2VaqAzevZ74E3/q+/zipzv/9y++4e/90c/5/Isz//SvPrG0Ti05CONElSdJGPiQ/KYB758KP/n5yu/+9/8rf+azH3D55X8Nq+8JNx7/7G/xb/7053z9l/8uf/3DYO8p/50qfR9s2wZz4/d+Bue/+cppeeSHPwrmpagmtM55rexWKEWGt8MCX9OsOjfFqhuV1QVK1GrsJbAuKU0t6tlqstRKhWk5Uab0g7SgzobFiX7bmJfCvjs+BL4Oc8wc90Jlyt44lyQ0oijJ03NwWubK2FRTWoFRFE1u48bwSJmrYdYppanfavXea0W+n4ckp7WZSy3s287ryyvVNNhoXZ5RDybpvlWxR2McA4/Sj1obDC93dcA8TdRW3+aM0RnJmCha/xMMDtm9R/oChWV4iIb4QOzZaroHI1UW3NMbM+3WMl3TxMw4gAeFSByegMgrJxS20qNncqql11uCArl8IPRd3Qf7vmei6rHAKIzSkiUeGSkvD6FD2u2FBMNTIlkKURpTkUX6SPnVweRQ6psCAm5Z+0oCZpWWC6muz5aeexHcwwCsIPuJ6BQzdrm351DrmdYoZt0YJmYWYlmQrAlAS78RyeCW9LiGpIDWZrGHvOv89cpt36CoZomEkGyMiDsrSlY8BwNX9dizTwgTo0hJZGnanOXqKFkW8XZvrECm3GrOENoQuUA6GCQyaM6f39M0/WClkaAIqo+OvXnq3Stk9hkmX2BMgSOKgRAD171riRA7ESmz9+wOysGkFtCW3twCV3phTzDCCvR1gBVu+y5PPFSTa4KDHiHGWEgyWqzIf4xMpQ7jMJo/am/k9ZX0WmzikpYi236PRaM2AZrFjj/N0Fr/27LDvEccs77dwbfeHcvwqFoEXEb2xS29ANVziUUEoec0ZxlL+vfhXTXizRaAKuBoJFgtyZvlMkvXaQzNetmaMbem2Q3/1sx9MH8zmIj0eDoaWZI8UZLxZsdi/Dg/jmdX1zUSILFS8RJ34HVk0EHPPsurEoETOsyFoSuBGt4AJxPWMLV6D2j5Ln999wylROsj9Z36EgfocbyG2RibZWFPiVxXgStTw86KBQ+MbmkAh7Z+YRpYHh8X3j+e+eLhzFyd3ff7BvZ4cEbvPK8bz5+e2a4bZZr54ssveffuiSW3FJZAUXJLmVp6fpSiVCjjPnwdDffond47va/47RW7XZncMevpwDXjY2WKnc/Og195Kvyxz1UQzG/0seJ9y4LrjD643QbbUOHyPtgR80cEgIl17YnKigVhhiiAURhdbJZ9qGkQa0dIsI/cyFuhJ/CzO+wedBcIdTpNLFMa3VnhsjTmqTHNM+5innjA2FVI6yl1pLUy3NjD2ALW3lm3YB2Bo3s4VaVwmTklihI0UkPiIa+EVo0+jEEnrFIQKOkx2Lq2ME+Xyuk0MxVJ8jwbMMwoUfWCI3ClJ91Qac0aXLc+2LsOwXXvvG4b+wbLaWFeFMmuX4Uj9lLbsyySJQ/PUuh9Y91WelciyK13tm3NYeUw3FMjXIqamFKqEg9TO1xcg/HY5ZlxyGYMhLLn1rzNM9OypByIOxugtWRnoMO8ThWLTu+Dvsl3LBmY9N55vd7oe2deFqpVekEAbMjwuByHSynUMukzuUCkcTCTDlbSLhbd+XzGSuF61QB3JFaQqH6YDrPDl0UFUJ/H+05xY4/BFgN6bqZCvl42zSzzxHSX89UEQPL+kAkO80nm+mPcfXk0kHV877yuV7ZtYzlfWC4nWl2oRaEBNeN8C0WAFYWxK372nrhmkrBYyYXrcAawvl6VcFZlxB/Z4ASVSNDhkG69Dex29xcwSz8mtKF4eb2yvq58/uXn+Xyo4TgiTbWJUlGMZJ7oz/OMpPU7ALGNIcD09cZpatQqbysnvQoQUBg5OBqS//kYHMbb7gnkaF2jzVl6cWEwTTKQLrlRIrdteORSKZLpglKlRkr9EujUdkRSpdGd6aRtdfgBwhxSWL0vRLz5duSm6gCUpHWPe/GMpPo+nhalxNyBotwcf6vAHxHrb9GtkQ1oNgkp6TQK19dXFXv4f4G4ng3ukTDikekf9+bUjr6VY5LQgJIbtdTqtwy0OKSs1Q6Z3gE2kckiJdlFR2Mn8O9YdBhi7RwxxrWlT1ReV72rpATRk2J9SP2SoZg+Sge7cvSdssh3KccjcL834XeczqB7zxExly+htjmVZJl6pqCLuUhGXKqYfAczCTJufAxsanc5nrkWVZbLk0gmgOQS8vw4zIvVjOUEc9znkOx3pGyFfF4c1dLA70PAYaS7nE+8fPrEvnZOFW1V78BfMq6sQjn+jZRTpkTneA4ke9S5PM8Tv1ifeX1dmVsVCFuTaVmkhCs5SOvsEBCtQV0AHJbm6OFEFNyM86VxvXau28Y0VyJWliNm20gZ464hzgu448m8kkdcvge14PuuMI3VWd4bLx9Wnt6duTxMWG7QSyDPr5QjVpOB69Z3lrLgZWMaM1EyhpDAojCfF9Zl4+NPb8Rt5/SyMS0nTnOF7/Vk3a7yD6lTDjqq8VgjTP4w82nii6/e86d+s3P73zf+7vXK7/3eH/L4dOKXvl+oUSnFE8RU5Pnmxs30vQ3nRPDufeP//INPvPvv/gp/8t97z/TZn6GUC2Gf8eM//1v8W7/4T/n5X/sFf//ToI+qQaAWbn1n3wY+T/zOH3VOv3PjX10aP/zBhVa1Ea6xaSk3TZQJ+jToV2fbdmxKc/SYaIP78O9FXpJbsm9qFEnYWtC62PHWJAuaqtFMzLC21DwnCo1gHxsY7N6pMTNsI8LZAT34RSCH6azjGKpdP3MQjK1TFwVlOOBxw2zSGc1+Z9XpEqecxMTuiPQHKfLwx6aJvkhWFy77CtlYqFkJl/+kQBMY3TnCGyAkt/MhsANXX3b4N93ZI/lnJWgTSgC5n22Gvw1dhBi2nozPOukscdkSeJdpfFiIdZeH/iFBLnmmlgTt3V1/7er5AmfdNp2b2ftUGkf4wOabfF2PmhNAOKVIQqXh3RPgfwtYqGF5D02MA1NdHLvjnbvXoAG+DxlUWzBZw6hKoXUkFSpSghTT94sm2eDBSI4R+BDDsYQYOIeXTg/dw+PcC0uvRVNvSTPC91SmSGlQAyJSXlUqPhTmgimJcaqmviwlWpjT4mCqStocOB47Y+jBUuKy/GTcvjWvoVp5yK2O1debr6HAowiBruqOVNzDxICy9G6UDP1NFiU/Palq9BgJ7PQELfNPyt6MZF1nrbEE9kxyqIL+rEO10axCFfC5J0tVaoa3/qMkoHl8Js3TUqUc8fMWmjOWU+O2yxhbi4VV4Ecks4lMxk6AbqA+rlhlyjmPsrMbFJdXk8fBxlF/N4bq3EHYKZlQfTAVJw6z6ARhPPtaE/Px6L3uTB3s7tNkpp69RKP7yO9mmNudYRn3emlZx7XEOJaXZgggzHNK/XXRe0w2ASbwsJRkqeljSanh+dmR323kPa7pz1U4eHvZ74vKx31xXKtsKqSj1Jx6D6v5dljLMYve/1HO9DqyDzYWSFodLhBakm7NHTXnBDctKI7xdhCpxmhHQ/qd/fruGUp9TZOq/KymAYt8iCO0odZQIb8RNm1GWpthqpRJQJJIY37M2eDO3OByXnj/cObxYUmwYlckNoqxDR+4KbXtmw8f+eZ5pU4zn33xBU/vnpiX+Y6PCmHOopCHSKnl3gBbxN3HA8RI2HxjWzf69orfXqi+MbfCfJnouwZl3+Apdn70pfNPfQafPwSncF62G1vf8DSc9BDQsW2d2yq/m+5B3wY9r2lphlsTUJWRy31w11Xvu7NvvPmc4LxsOxFdMo9ZsrGtO32sjJh4fhVTojbji6eHbDzV/CxTZWmFvTvDNVj31GtOTSBRLUbvOgw2zwatB9d1cNs8JT+Wy6dIcEtDlJvrgDYlLliV51F3v0sjY0jWs47B1Iyn88z7paaPTqE0Y3MNg61OnGqRlAWNxxFCqu/3LpkZfQTXq0DG2hptrlBdTWtRAZsyHcDyED88UMZIAM6dfVvZt85tveKhZy0iDnyL+2RVjiQPWGxiniYGjhsstaXRZmruj+E5D5VaipJ5llm+Dflj3f0uJbXs0uqsqG0b2jzte0/mhsxvX16fCQ8eLg/YtBAuerXMNNOHhwMwSxp07BpUXBGn7s627tiQJ9P8cGZ3x5KRNRDAWYvhjG+xffI5TmPNg388HCV7DaWR1JiwuWBU5sfLt5LDBPaWbNrsSIBCzQeT4T0H8+Kive8adLd9Zb3dWE4XTucHrFVqnVREIyTPKjL9q62CG+u43aV9AVARAJpaZyLY1hUvRUDf0blmEy1sUEaiY8ijZuQ5UrLBPVgyxzu8hfPhFx95+uw9bZoTFHf5PKiz4fDAOajzJZuPIIvxkEn1ds2BYV9p4bRMLNF5w7for0g37pbKQH3GfZccgYxHHa6t1hhdbK9t53y5UNrR3BZALJaKUat8v5od7BCZTY7ewQ/G0tG4Ic+sBD1kimgpL0p4TH0HDRSZOjSiH6mbngbq+QTjwPXlpud6PhKo9Gf4sZrwQ7ashscO6XWoKTmagAO0sx6Yd9Z1ZT6d1ZyHKCdxbFbt+PcOD6D7+iT/U2BQiaOrFFB2l8BmA11SDis2XkbzHo06hxzk+MwlgQuH0gTa5WcmwSqCNDbNZpvs52LgY0/Tfi1iqtV8rjTEeDZ+pTT2sRF9FxATLoD7OKvyuaREgiZ6nu4vv3Hf7B4gqFb7BadTGdQ6ZZSwvH3gYEYGVoOS3g+eZ1Gt2poeBpruQ15JLm+Sfe8s86JG8WjQiiQxkefauu1Ms97hEcGUP+tgTuv7D+Zl5pnC1iP9swQgt7Bky1WcQfGmIbwfDDex9PCBu6TchRBIViCK883LlfPlxFxmvFbMZWpdp0bsPUHtyCGz4nN9o/eHASOlkDq/Rw8eLpWPnzaeXzu9NNwK86zrEmbyGWlG0MRKGV1+fZGU/ZD3SLSQvwVGqYPrOvj49Y3z6QRFQwRWqGPQ3ShL+qAwcK/p11IYRf55pTi4TO7NGuenM9cPOxtOm05i9Uxij5ZlhuJYn/E2U8qMp+zxYKqW+R3xBBdr/MqfCMbLxkOZ+Rv/4A/4O7/7R3z+8BX1cabVoHkjqs6XOtKUGYhiTBjLXFjfOf/b3/p9nr78H/jxv/5AWX4TSsPe/xq/+W/82/yFX/wl/tLvfcMfroNhxtQaEc7aN1opPNcT//M/vHK+dP6VeeHzd2KraEBRqqu1DWem+qDuga8ODfk/FtNwEs6wlDig/sVp1OoJ3FXoxloHkw0uDS753bwE49zYXoOxV6yZGC8J9HrItFb+OhMjdkpIPu0ucFIMUvUBXtJ3aCclYOrb5Z8yIfmk2PNa1otpRBFw4DaIUYjsaC0Gp0Vnx/W2cnjbRb4vEYcs3JO9DPigVGPbt7t3naPgnzGkVvA4fCi7ZOam3qgkWiyT3mQZm1LVihnRNwEl6SdoVTnQk03Evmvx2wc9VvroGkA9n/Oe8l5G9pxCsbWMeVsgDHd232QTUZskUMVkN+Ge4JPYwIdX07qtTHNQa+MIDzn23lqnSR5GLjxJBoZShLUY97w+sXd5KCWrKdjuvoyW7OvbpoQ4L4Wp2F3tUGu5syPunnG1sFthoqRfjFMmpXJNIYneMAF3Zp09pUtmQRlaCmjWDzw6gZgrxQx8hzJRpwmzmnHsB1hdqFXKC7PG3qUKONLaCCjphyV8dIClZYPJv/MIWqG8sZwjnDqCgX5vz2fquM6Q53YuOo6ULhtBlPQMywYwON6BnEUjg3qKwI9x1D4OuZolq/pNDWOm31O9MCpModro5Dmf0kuvx3VVjT3CZPwQqqQB+Ail9aqtG7nIUq9y+Aoe3ZMYtQKjerj6fXTWFSGQkLJ5MYsSMMlrPtJvTQsMfdbi6oH2etg/eII0b0xuK55elQKOjyWbHf5/DAiZy1eTR2cEYu0cigBtlGkJaEImGRYtYDc/7qtT3GQbahkehmvWPm5PLoGqJchnCbx7T38k9a8jFysHYy6SCXxs2MJyKTwSuCxFCZFWEActF53JsLL7nxd3SwKB1FqglvKtfiahqxKSynrTHC8mWPb0OpHE1vb8njUZT3mNv6tf370pdxw3J79worgayOHwU+hd2wl5jkCblKolh3LeVgsunyAjeDjNvH+68HRZmPMwjr6zk819NvF97/z86088f3phWU68e/85T58/cp6XlHX4/cdriNJm+J7QFSoGoxitqOCOLs+Ubb1xXV8Zt1fmuHIqldPjA21ZGLcX+vbCZ1H40Rfw/Xnn6fGVp2khXjtXM/a9s+8b3bQl3ffOPpx9wE5h82A7khWKZGZCkyX5KEfx5UgFk6lha3rPRW+F+TzYe9M1bo1xWyEkNfv0eqNEYWrG+8dHnh4f8t6JCfV4Xhg+WPsr+66GcZ4XgsJyPkME+y5ztuGSuL1sznULUfCKEs0OpNjDiVIpLfAOtQanqdI7oqBHbtYBj8o+4j4oXKaF88kkZ7Smpr8aESpOFZiaNsQjnGGSIpAsIQtjHfJPum6Dj883sV/alEyA41BravhqI4pTS9MhcNClw9j7YN2VmDZuufXrzrZdCedNFklAvgPDOza1HF4h3Nl805/Zs9jft2QCHEY4tRamNmPLzLTMTGVSMZ3SeLo2kkYnZMAFEPZ1Y7utSisBRt+4vjzjw3l8emSaZ4FFpWGutKpSYc8D/vBXcd9w3yguEK6PUPJDX4kBTw+PrF0pakp6kknvwXBrk4zCD4Rfw7refYaGv0Pbv3VtG0rrtHrm8v4zjlQzI+6gbwQMdI1qbmNaafjeiXGlp0wrhsMY3K4r622nzScuj+9SonOg8jr03YMomZy17QyUuHYwEo9t0bE18EiJqwfz5aShs0jPTXCXGBWafm94MkYS8ChHsSJZBqKlfvjZz5lPC5fLCasTzRpBxzP9qWTjEZbnYXp6FZBU18Ai7kBiD4d957QsKkDH+ZtQOhEpA8miHNzP6GFB6YOWRt/DtV/pfSj62gp1WrSdqwJpcBJA9AQttNmOvucZ8BYB7Mm+GyEJ7XBnmibmdgBp5W2zWAyXO+qdTVPJP/cAPNCr4Dmoexi3lyvzwwRocLKaTEaMMQS+t5bbMxeT82hoD7k2x4CTgISPjdF3KI+4d2oWZMcSPCj3f+8AUCz/GUFKz2Df95SNbrQqALhmHL3Vt2bkztbJb3FcExD4nlAjhxytlKJtsuu52sZO29/o/fKFK2kkq6ZqX1f2NHAtdbpv0oRTyIOP6GIstmQIjrf7Jrzo+M9sUBJwKemFEZHM4jwb5UVHLgYq3aCOkcOgp++a3rdIOrl+TvoaJSvrLRo3Elj0O49d0SoAACAASURBVBgn7y4XA7PkM59MPI+uDjsbbfYucDJlwe2+2T6YYU5bFBnft53eUwJvCbaYwNtRwLpkR0TXEsOhmt+vASH/BY/OvnfCBp8+fODdZ08slj41PnR9uwzorWuAIgMMisn09P9h7t16LV2zu77feA7vO+c6Ve29+4w3PhCZYBSCYgFGOMRCSBFIRPkAuchFriIln8H5LLlGIoLESRQUxYaQA5FBssBJmgSMG7t7n6pqrTXnfN/nMHLxH3OWue+LlNTq3tW1V6015zufZ4z/seSiuyYZFpXmZk7rzlKMmnbaVsgVzucdKPG51701Lz0W1CwbRoC0h0VWomTGyAtr2AWWZdB75/mbM28ej6wPGR+JXFCAL5OeEiVsaYlE7xvmGRYB8baCoYICspGPleNnK3zTOb5N3B1XyvGOclyw9Y5UK4wCyxHqW5I3MbTuYEdsMcp6Jt+9Uu5/n186HPjen3zi4e9V/pff/pf8s9878Gd+8UGAiIOhzC3PaoBrY9LDclVJ3B+cd4+T//V//iFvv/dbvP13HjF+FktQf+bP8ef/+tf85N3f4Tf+0Pl678xpLHVV+cXpwjwceFmO/L3ffc/93cKf+7N3PCwHUpZ9M41Ea4MlZ8aqBX6/dPq+YzbxokYnG5klDQXbhvWzRxmIXUk4ptqx0rydxcake+dIJi+FMQd1rBzMePVJG7uCdrsa0CwrdPt23iXZij1MMBmRrzlPLDrR3Tu1GHtzCBWNQBpZTXwSljXdM5mrslp3aTaBEGtNpFQ1rySByDkIXZIUYCmsfD61oKac1D41lPEyUo+cSM1RJKOHqpoAtYY3WU7jnFhSxnNmuEJ4pY68gk4CnjxlUiqsa2KUxH7Zsd4YbSgXDlcQdHeBs/bxTLJUuNJo/5p61kWq4iZsg8n0eC+nRcZb45qD6mhfSCTIV5O15vTZmmyvqeBRdJJsDduMogJ8qEQm5Rn3SdipZ6hLxX6opbJLJXtVmbrqFqVOxZSXSYAy5gLkssi9mstthjBQ9o6HLUn/Np1Q7RP74JTK5brs3lS2LpC+T80vZiOW97BMRalN713PhssmP1yRAhQpNvVJMCyIV5KFIlp3s+VQ/Ez9cx5EDo5TcmbvnbZv4vSiNbcQpEmc67iRmbQswOP6elpEb0TyJn1oZjKPMqKwc9v1TjC75R5mM9LULiRVPrjrHsi1hCpGL/YMokG2Vdf8NeR2YcqxgBmj73jKbJt+/mKZgZrYrs/oDCVNivnDXaH8PieeK+Oys9Swc2aFc9/U8qH2EzCsoWVE26+woRnATLrtemYJAqgKFFqf1ys0F3OdR2vtmGFV80GycsvjNHQ/OxEwLlOufgY8MnAFotxefwTujB6q3qRoFcdv8wPXuSYFAJX1eymr/a+3eXu+/QrAg4gfk21fYI+UhQKM9TP0UP3PsDar5VeqaIs5/mot9RsxqdfRUe6cmwmkQE6wsGZEH+6VqPZooNDr2op2VEsqUri0jVL/f255k+xVPvXmg5wcG9HyFpYHa+hFz7CsagkQw6ZvaQ4hhaM3wLk7VD55uuPpfhWINHcFg92Gaw1vngZff3jhq598oByPfPKdb/PJmydaO+vf8ziE0CEGGsq0BF49hymqvGtUme8KbOuTrZ/pz8/M/kqpxnJ3z93dW1J29pdvKK9f8UsPk5//vvN4l0lj4CxYU3vd3mQX2fbB3oMpmk4bk313LruWsL0L1W7XTKckSRsj7APD6AI2hb6DAs0SeAmvbM/kBcY+6WNy3jp9JE4X5To8rImnpwfyclA2klu0h60Ml698olDyVAqeC607573jJF2mw7i0wXkfXIbRp0STdanKv0pZ3tar3JDMw5JYj+Hf7pXdZoSpKjS9jcE+nLUuPKwrZYFjqdQ6MCq4y0ObM8lRAx3yDSfLpGD05WlPXMIn+vr8yrvnVyiF9bAq6NoVSpiXRMk5ArevjLeqNa+2m9YbY3ZO5zOz6//zOXg9n2Q9u9pL4j9XgFsVnxpQzu2Mmdi9NmV/HM4NXVczzCTnwlJXcqkCk9aV47qKPbcFSqYg5sIw1UUXZ7ucsenUKnZob43TduF0OVPLwnnb2UINMkzL7rQO5vQ5Oawr7kb3joePf7+yUn0oRBjn7uFB2WF9spQKDkvNrMuCJ1WpJ5MSpLV2y3wYc0ZdKXS/BlB2slWSOWPuPN7d69JCijBzVeSa0vGxmTiUUBctCw60/cKc7QYotK0x953z5UKuB+4fH8gpUZIueV0CH33lpagVzqYy3CyYB5MIP1RJ82ad7HtjPawapJIObEOsECRKraQpbeUYTaHhYbuaNxVMDHapKE9iGG8+e4wmC7XILJZoON0VyGjuzLC92ZUJjaBsQn7bk5ptelNQazrUGzsDQWwkXfzXk1ADAfhAQD+6n5opP2OY4xEy2S+dw70AaHfjI7tMgAmoMcr0WVIgt+TTHoyWUtEC5HKxpjnOGM81AIAShQIaPGeYrJZYXOCqyEw3ywQkUpHawn1Q6lFWi5gPpodW1pwRg4dsiTHkagS5XcDKCtB7W7Lx/LrjV3XQVdw8J4Ql81Yj7B5f42qtM3ZkDcoTLqFC9e56hvBba1gyKXOv+UQlWQDbocoIwuPa/JjCcjEDsPE5Y6HSZ8HRW1+vHhCUNdF2qQV6a7S9kcsiwNSG8ogIK1TkpGFOPazkWqmLGiRl81NWScof1RQGtwXzaiVPjj4rQHKpAUc0PboZe++qO59+k/W3tsfSlGmtBYmgYO2rteX631dAiaZw91KltFDLTgAz6GxaAiTC4p6bU4GtGFbCPuFd6jtgTi23x+PK6+nEy6lSkpMts8/EXFxMfV7w2SjTYDqZQfdEu1JAoWa6krAzZdZ6oO07z68vHAw1bJphAa5Nkyk2pyylDIVMigyUQSqJfSZWHO2VAjncE3d3idfnxPJ4ZLFJrQXiebKU9LmzTC7GuhTMB3k6h3RgeKfORD0YVsCbkesg18bzVxvfvHvm0/Igq3rvpCiXSCflUuU1VBxZ5/5VmTlHAJJdi5eZs75daefBbIP0ppAPyiskNay8Ya53qPJ+xSlxtlxZ+YKlFfI9uawc7z7jO9/5kl9JmQ/vNn73h1/z9njk+58fOOSMJacWZNObUvS4hzWAQU3wdMw8741/8F//U/7qdx4pnz+Q0qdYKjz8yV/l1/7qj/jx3/k/+Htfw9YamFGXle20cWkv2NM9X7Hyd3/7mTdvjF/6Myt3CQXo1oItk7EbZcKSdf9sL0mZPQFe+5RiZrHEoAEp4hxKkKdaYpPpZBzB2Gcgz2Dls1NXw16NnJyHDC9zkPd7elnUEji6wMvw76bpcGXs3cCy7mbCro4FqT445IV9akG2PDFaKDbRLRuKpZwrM02SFyZdSiR3llooeWjBnV2f8dgdEh8XZUz2MLOEaaunxHJWc6G1pnsPFQpU0xyl/ChVjINTlxWSh6rMYCZ2n1CqlCzuYTmN+S1iE5IZy7qIrBw9dgeBKymL/NMdFLEcoTadcQbPabh1KQIs1J+9KXzYMyktlGXFfAcrlBKh/jbBB3vfST4pqcKceO+ah0BWqZx1P5oAektZ2XwEaW9OWWUZ7XuXjW0q7NeAlItm56s1WH4ySrQTT59c4s974rbL2Ziyseb4zKe4Y1KUhJSi79UzudQA+cCK7urZZ5ChHy3ffYqssMi2SgH+gEnBXCo2C613thG1BNlIXqnxhI7WRGbHvUkSUaqGtFB3u/5uu9nj0k2N20IVW3OlMeihjB0pxbkedwUflciasQgSipit41nNRY4XuwIME7N6Azhd30CArxYNjHov0pRyJal7ULvqGGr6DiJ0XgPhc6j4Q93KlL0tWeSgojuvlEJNVZ8BZNeTi8Lp8X4MpNhVQJeyPL0F8JoE7l6tzcqZ6kyuz4hRkwLzu8sxENkFApTzR4XftZGYeC0nH4GylDRbaMyUK8N7j4YyqdwIdR2u99FLupGZEDlWCa6ZlHpzdL4x520XcBRRgOm9vjp/r8r9K5GXUgerrHmhbUF2JUjkW8TBTSBwndlMM6clWfjnGDfQ0azQWyOlFI3XSPF8BdfM40OQVPRCglBy55hNPclCfiuJ8+s8LiI8+bypzpmKyhmz30jqn+avn36G0pQ8nVggVI2e1cgyplQRWfaGVEzV7m5AVTUuA2iMnjgumcenyrceH1kT9H2njYZd3/TIkeht8O79V3z48EqyA5/94Ae8+eyJu1LwvSmRfcBEdXkegc4pfNUzDhp9z5Dd2PfGnI3eNsbeeHn9Em+vrG1yvHtgvX+k3j/q/PzwNd/mK/7kz2Y+eduoNkjjwmhwunSqTV66461zHoO9T7Zt0LtAoxYZR62hnJ85GKOz74MxL7hX8IbPqCmdsrD1rhykFkzrxBnJOBwKc0LNib51WeyaBvVcVj57OlCtcDge2Ybkeut64Hh3x5hwjuaz+7ujJLIG24gGt6GhftvVGHdpmXOH5vnmCS9Ke2NZBDaUmdjYqQ6HRSzUa4d9Ti6+gyf6VK7T3p3lkHg8VpZsqsF21d1uU9WJBcl6O6bg5GTUpOWfIdVKx2kue8KX37zj5UU1vTklkg1SXcXAlUotsuyI4ZblZ6a4wOPrnC8n5tjprdM9sbeNdrmw1HIDoOpSZNuLZgRzh67DcfeOR3CkFoSi4S0raHTJhd4U3liXA6XKHpeWhXVZA1nOskCVSrXMkhVobI6UZF5IJTNHZ/glFFVSH+xz0C5ncqqYJZZDIWVltHSfLLXelISg738fXUBdTsGkwP3Do0CCMSlLYfPJui4sptrsMWGG7W9Gy4auLV1QObIJzA3v4idK0uetHI7s28bik2FGbx+tHGOoKnzJhiUnpYVMYYwzPlugOdfga32OpiXePN5TSw4Lt1GzrCnuWtxyXShLpVtTS+PoiOcYYY0Kf/NU4PHYdg1QSxGXYmFjs0yJmUL+exNIeh0yQ5pkoWEViW80d7bnF+6fHiUntiSpu2WmK78mlVBiRJZcgttdcwVBuktu3Zrj48J4vbAelwjLtvDuW+QCSc2SYuG/Pr8zgrelHXMKBWaXimRqufeipSi6iIPdEg85TOxTtkRG+XXXRf96weFJOb5RgNFbAzdqKrKNDbA5iAINWtswFNLZXUtzH1MAegxvHkrEq2rtfDmTSibloot7CqDQnyEYwvRHlDD2R5RjGjBKTpAF2MwBmc72eiaVhZqTVrz4/KSqjIGrmkgXocP4CEh7AGdiZh2SS6UarNMt9+9qF7kOOi4lq8UgLOxw3jJ80nVBucqr4ZZ7MfbKJe2qlt53ygKQaPvGHFJbejS6+WyMNCLHgJuaQ0qGKVZtGo9vV1JJssj41D1620MKlnI8oa7h0jRMXsMmW+9iBacsCyUpVP/D8yuzG+kKHqP3wtDnPSdj2wYl2c3y1vddz1DX91xrZdhkv+icLTHQp6slwInvWTXTWA7bmCwqpMkkh7pqEQiVYrjzwbIeeHk96dlUsA+enZSlrMupsB6VRaeXsUvnYTrjs0/ZDGeNbAcVGwxLXF5eGfd3Yi3LAiYFXfawQiaBt5pXhgKbh5Gyy6ox4dq0Y7Mw+iAf7iinsy7FrAWo5PjcZ91Pbi6pfVYwP54EJtVCOoUacDRmKdRp2F3lcm68/+bEUir3j5nkApAYg2aTcUksWc9A0m9TukKP1WoUoIkblMKSDtx9B/af7PinQ+e5T3ze49sJO1Q8JRiZmf2j2p8/MhAbWLmHVFnTgU9+/sSv/Hs7X/+t9/yjf/IjSv1jfO97hQMVLwlSYkmuIN1pdBIWbPhS4JPHxD//5j3/+L/6bX75P/4W4+nXwoL+hu/88l/j3/+Dn/Djv/+v+OEHZWmm9UC5O3J5/4HX1xPL3ZHfGwf+9m9+w5sH52d+7sDd+hCAQzDlw5klkYvsCJcNetccs7tKalbBNEw9bYzIc0tXdWtUdEuV6rE/K/A/m3GwxHgonM9nsiUOJTOnAtwXFkYtDB+kEZ/TUNvsQwqXa7ArZOItxSq0rWO+s6aFS/PbHZAwhh31v91lO0N5bsmmZsgJZAHhKncZ5KElbm8d0mBJAytSZEshGtbqKTWW8g1VwiEiUPdLCcF2XDj0oZDrGstkWRbalAqF2Ukpk1j0WfSmTLAgJ0bYTnKKO2s5su9n9rHpvENE2gyr2YxzOXlYDNHsJBH5hKrsT6IleXYtw0eTPSeb7sHeld3kDsoKm7TeBNiWIkXY9Zy67p1Z6h29BxWhfk1LvutnWkOtvO/7LWcv5YU5usgEz6H6mUFw6L7KyVhX2cjaGAJxxiRNEVxXe6EXx5LTGdSSua3VHmqlIqVPuhIdWfufewZXG/GVfCxB3FzJg1yyng3Pygyrmcuc+Izw8LAE9RHKm6R8tFJCkR7vf0IzrhvY1Jwzx9Va5BF2HURZTiJxIxd4DH2mVGQ4bvNZtky2HESMSGSSaYa75vYIqcGJ1sXIAFI+VDwXuADOyItZSpVYIhnJSpQxSAihslypBeNBwUdnhlVxjH5l7hhut4B6cmiAPN/AXyOJpJhDQGQwHnZVxfnVZqXPGZMI5J8ktyicyLI1clU9xSEdO0XKVzUxNxozpoKA5kJ555rPSKZ7ScyUHA9ueC4ivdoIoFuztUjN68/j4f6UvVCKOOXZ3sK63al1xVHguS4QnRsen2eP50YKo/hvd1IaeEqUWuKlD8wjlFQ5ewgoAlRKV4JaijO7WnGT8oPzohB06wLirw4CUMyByG8oy3WuGApKz8rKy8TzFO/P1e3CSPH86Y4zTwwLcMn0+e/X/ean9Cv/+q//+k/1C/6Xf/Nv//o1+NX3iUXNZwlfbqkFO2RmyFlTqhSUYSIAbedQjU/fHPnet5+4P1bS6Ix2kqwsEHdLkz4az88v/MEffsH51Pnsuz/Dz/zsH+ezT+4oDi2CiedsZEONEJFRkXOJQ1OyW8Pkmx1D1dSj0y5nLh/e8cWP/yVlbtSZuX984vj2LfX4SB47n40/5BcevuDnv3PhqWykMeiXxnYxTqed8z457WdeLjvbjgJ/e2drCExq86Y4Gi7/6d71+1JEKPOhN2Puhk+pg4zMnEKthzlWlIe0t87LRQzP66nT9p3zpuF6Pd7x5v5IXqoyMcIfv6yF42FhDOeySRVWaqaUJB//cHZPeF64DNi7cWnOqTmXgSxuZSHlSq2VHGHRdkW9phpRjgcjL53Lnri0jHuipERxi4BweLxb+eSp8nhXOR4hlczjYcVItJTwkViXHDWsYlfWpeBhGh6WaQanMTm3xo+//IatKdR1rQcBXlmB4+tyIFctnTOUQfi1gavE6954fT0xe2fbLgC8vJ5ovbOuVV7jGxv/sbLxmp8x56CNxs2YO6cYOovKeAxKZrRGroV1WVlqpa4rqS4sy0KuhVwXgUE5QZEXNmdINjmdXxltSDnlToucjO31RDufJT83QRGWEpahpsy6rMw5WZdDSN3FJCtXIMCAPvB9x2fneHdgNIWki8lXq09KhRKhz7mWqDIN2Wos2T0UecMm+76zb53eBktZOO9nOC6QMoe6yv8dF6DU+EL3F0usS7ldVj4Hre2SMrvjvdN65/JyorWdp08/YVmPFCvkrFBz4hCGccuhoncufXBpPS5VDevZxDaI6Zv0rcFw1sORnLJaFQJoS0ny+ZzLrTHPx0eg+jZYBJhkrhyLr/7wS1ItHNcjiQwJnZHxPllZb7XoZCmqPkYiX5kYIi9LC/N2PlMTPD7c3+TBa6mMqe9vCeuA7L+EopT4WmE3KFIQXW177p2x7dTlEE2IUjrkGBjMZZGzK4BYir7H+J4Iq8OMz8YcYtb30aBGW48h60AIdzvaI4plpkHbT4A+k9dGkIgKUlB63+k09svGsqystSoMPBjG27kfQ6vaVgxPEdZqmel6UVIOAGSGZYPJu6+/JB+P2IzMgZvyQn+HFoERRQhd2UT7HhJ0FRRPgzZkYxgt1EBu1LreAlf/aKaBGXAd5qaGv5QqJa8ol9ZpMvXJXjFU6DB6FyBRpTrcLjvr4UCphaVUSqmsd0dKrcrDcViWhaVWliIbrAfwXfJCssLL+1cen95wd7wTMIyUZUQeFRavsaWPwZNoTlP+mGuAuSJQU/eUD9WvL4ejmLqyRjOZCIXD4ciyHljXe/KiszGXSi4KmsYyHrbsZbnTspPj+UhV6tMkC85HPi6pwSla4NxDDWbBtE9Hef1/BNxKzuV8xnJhXQTA5xJgT3KWUqhpCsyMnEGdDVrUruqBZFk1xZEl1d3pp42yLtytVWUhKamNstYY942UCzlVgTwpYbaQSNQiCC+h73mMHM/NwPbB5TxYjis1wlW19FzVkjPUj4OSk5TQ7hxKldo3AsRz5NCoYKFzevcK2VmPBZAliySSzIbA5Fyc2SvujVtZgyXVgZe4ixyMKuZ3m4xTp7wBzwvFhthvP4nd5UyaO+YvJG8wG/gZfMNsYiYbrpvOlIenydGNH/7f/4ofv7vw9pN7HkqFAjWFtSxdQf6JzRSsvPCIvCT+xY9e+Oxl47M/9SnTPhORUR54+NYj6xf/F7/3vvG6S1m33q1Qyu3+X+7veb8nfvLDD/zcdw8cDiOsnqG+GQKNs2sWhqt92bGCgpsN/XPk3ZDyLaTaDc6b8/oyWe4Kdc1S/wUp60zZIpLu6LmPKJyZjOaMjm4Rk2VSi24KC1yw4q5FzCzHoqKFuZYqNckY5GJsPVToQlv1Zz3dgJ0bIM51sZdqsYRCIQXBO80ZvX1UjU6/KWNSnDFXNawCtMUEJAt1Vqi858xB5FzJ2nZTNQiQ3qWMmcpdSskYJuUTnqSMtVAyx1LNVFYlWcR5iebAUmQN61dVA5F1Z9fPaYolUouvWcHJ/9rvlWOmlmuWa9L7EBX3k2t5g99evxngh4AssKz2SmbkAsXjMlA+nxZlWXpS0gIrslPuB/fBHBbEJRBK+pnisbPA83xIaRvn5fVZJubGEbNTSjlIrgARUropVGd8xq4WPkzRJynJumdZAFQpibUUWRmDSJmYpoOcqJGF2cd1CiIUPqE8ETohlWu+ZtvoXrnaG6cRqnqdRcNnEIJZIF/JchaZ7gaQQm0iJRpXgMpR4HJWvl0p5ZYRNOMOuAbQC0gSsW4BdN3q5CNHqURBztU2H2MkJadoGxZJomiUdMWg4uWWImkMffb1Eiuwv8dcn+IPezKuH/cZ8QtuEXMwlKGbwq41bUZ0QVyYdjWSh9IKbirqnBWOreckQKbrnwti+TrrXN0CI1RqWHzvAfJcAR59DQV/p5JuFld9PsWwpht4o7np+v2kJKvwldjSSOqkkiP83aIA5epeMMwFxHjMe5rhY2aM4gxHqq05ZV/GPyrV+SPkWLYIdY/zM+ccYHj83BEKfiUKnXh/XCBTIoBKM3JNoXb7GBmU81WxK7VcDgYth2jGPFwB0Zrpcb4a8J/9p//5f8FP6ddPXaGU3GhRae+bGhiWdbkNZzMWL5LsPsPRcj4a1MInn93xdP/IoRqjXZjNwXetKRafjwmnlwtffvWey6Xx9luf8e3vfJf1cCD5xtgRo2qTskBd38gWZHarSFblpirvuw/aLrDKfdBn5/X9e/rlmdO7r0lLZq1Hnt5+i/p4R0mZB3/Hzxy/4PPHjUOeeK942ziPydbUHtbHTt8ml2Hs/UJKVWzTNtmHs+9SPfRgGH06W1RAy4JxPbDiAS96WHPOnPdg/NGFc2pdSqNaOZYiptM60zOHAp++fYKyRlheotaC58zWdrw7pUxSgvWYaV3MrMK1Q8kyKx9Ond0T523QZ2Kiynqy2NmaxWKpgU4M0sNRuUTDOnPA88l4eWnUUhneZa+bqoJ8ejzwuC7c1UqJ9wifPO8vZDuSRyLfL6TRaT5Z60IbnfMmu88kbIPmbNvOh+cTy3JkXXMAXA4mFU/JNVQJYhXqkul9pyYtna1NhsO+NbwNLm0Dd16en8VELyslX0eHSW9Dy1cMQT5HBGDGMB0ZT2aqRW9TlcxzdMwT63q4ta/lZSWVomDSfAWTlmgoEXI9bUpddjozWxP5UGO588b7d+84n84K8A3ZtiVJj0uVDLePzt3hqGa6pAamBJHDI6Z6qC6Euq5sXUG3lhLMwXE9UMpCLovyfKLFZHRZmQpaulvrGhiBGZLrOTp1rbxuZ8oqtdjDusTurAO0ZAWFWgxBJbz6NelA3Xtn6wNGpwQgvW9net95fHzgeHfUAFEWLFVdombM7PhQiF0xY/fBpmkawq4FWcuFOXk6I3IZ6t1B6ko0fFhKWtwTzPBNX+jUYYyQ7yszRu0mhpayifPh/QcmjfuHIyMP1uWI0TErJDLTGiUGyZSjQjldA0ldbQ5hAVnIdJTttJ9eKY/3vG4nclJwpWyX6WYzSlkZOMQQI0wmWjpMYF01Y78O3bORrahJccyQFmt4uUq/leuhnKeUshZ7MzUYBTjRR6e57HBjO3F+fabUlW0btJSZCbUt5qrXc8qCakM2v22MW52tJeV9lVLi4oR92zg9n7j/9FM6akZac8IX5ccMNByXsKn11rGqIEN3WYk0/4xgv5zWLix3hTydTx7eyK5WIXXlFshmxw2Uvja9BG2O10yNnWRjcqyrVCdzME4bxQY17AWaGQKgmUAqTCzyqXRezWkKsU6JPnY05qp1hCkG6v7uyPHhQRk0OKfnF3of3N094GNQD7IM96VSHLa4D5jK+ug9yxqCALOtD5bjncDiLNa3pCx2l9g20GA8LOHB2Iod1+d4zKGhWzCKFqMZz0fOpFwFDGTAE6VcCSCRPwDuRYNYgKHDE6k616ICM6csK94mvZ1o+wvHYwFf4t8XaHoLz0/RsmP5NpApALvFEh078YRcjuSy0PadUQTseCrMPqk1apWTQYelqCxitB1CWQdwzXWRl0GhxceU2Sm8vJ759tM9KRl5qRogc9dzOqB6PBsp433Ixk+04Fqn5I8s6BgDvwxq6Rq7yAAAIABJREFUTZz2ja0dOAZLrHxfNa+mVEimOQ0fUfltzCZAcndY0KKYspHpPN4fOD3unE8726Wzrk5y54KKFSadep5YuuewalFtc8jG5hq+U05YyXgvArTSA/MHxuX3Tpy/aDx9+8JcIBXHmuHX9yfVWPZ2qQdThnzQ2Z1zAFf35DLgLfzir+x88cU3/Hd///f53f/nG+5+afKm3DGKbFiWdB5bnHtL1vcKiUN2xifG//i//ZBvf/5b3P/lT2F8ji/G+v0/xb/9V36VP/ji7/I/bI2v2mQOeHi84/mdk/qktcbD2zt+58sz6Td+wn/0N77Lt78Hy0GMuZusxtOG4kyKYWXoDkoro2RIg5ISfQ/LxwyF6Mgk1/2QaKS5YDNTUwaaFtTpYBVDSpp0v/L6+kq3jVoWZipS9+3gTMa4EhWTkTR3qtVtkW0xB4s/Iyi7LPSx433nkJ02LG6EgfsB9xzKee0F+cpggKQIRLtUqHFzqqyLZqq+tbC/Okt2rHWGN6YnEZdJs0C7thUmw7ueYYlSBPKa5MP47GyXC16V1+ddaqVsKiHpQ0D3DAXjbDPIFikguqt1MKdMZSEt+gy2UJ1LNedM1Co4Y3G73Y0eC7AeOIERQWBjMJspD6xkTu0iECkpd0eS7wRFcyYMSo04gTyhZNoW1r7U2UdXaHUJsHyqAVuKkkmxxGFZ6CVxOV/imrKISnCdLUVqp2smC9Np+64MphTAopsyLMfQPTpCUdsVfjxzIpuCwUuRlX3EbjOYAfhcVbJIaZwS3RvsXVbyUgIkV/B4703k8dDXqWUhlYV9O5NSYm5NCuIx8CwF+JjKx6y53pZyzKSQClAnxazJSLcDv5SshjHUdpfNsFLpc7DPq7pGe+ySjELYf1PSe1OzhNzz2iacGU1ni8hMAcMgQHUOEUEpq7Fwn1NkyIQeqifi7/EpC+nwEWrZjwBwj2xDs7C9h0JlGipXcrUL5zyUk5oKNWWWlKIt2COHCjo5SI9OngGKVPDZSUM2d5Ek6QZ+peQB0Or5uGanllrxMclTaiKcCNRW1mxOAaBedwkLggPtxlfTbUoJn0YqJUo8hkK6Y06/2g81f4o87zM+jyGZvYJMyvFULmmKXbdftgDB9QnW3DBu84oxBIChXCZlNapFzeKOVamRyoFwgcYqHjIp2wgFXgqlbja45T+hvz9ZlEJpdp9dWZmjS6lWS0GJaZPq1+xI/f85Z0V5TI8ZX+dTdqmWRht61G9q8J/Or59+KHdzzqeLgp/Cy5mr2Pbb5ULGWyO7Y72z2OTbbx64f7OwHiu+T/p2ITHIVj5eQl7Ytp1v3r3j+f0rj09v+cWf+z6H+xXGRt/f680shVIW1pyE6KYUCfkaIMM+i5vR2x5yOy1+l8srl5dv6JdnrG0cV2dPxuHN93i4N9Z+5vvpxOefvOPtKuXKPhLeJqN39vOOWSZNYwNSmtB1UO67s+2Z7spNGvtgTtXOa5dVu0AfTsquLBgsmD2Fb3efvDvpYlqygUvaamXysBbWJDDk1C6MuXBcDjw+LAwyo2fSosH9ZZ9YnhwPC28eKtmMy9Y5Rb1pyWpUu3TndOqcW+d1H8xU2LsUAykeWPnd1T4ntnqyLom1JtbkNO+wNS5t4LNyt1Rab7QBm2uA//Txjqe7A8elcFggW+Z4t/Djr1+oudI9sZYDfT7Te8LzgfOuxqE+JeXr7phn9nPjvJ15enyilCKm1RI21SiWUqWjQ35ZZLl0N3I9kCMTQoCcDsdSE2kY7142SiqshwWfnTEFJJS6aoiJA9VtRh5CqCd8hgwb6lLF3Jh+P1mWcuCw6uJZCykrF6kuhVwPoX4hFikt2bPv2OiMdtHBXSXlbu3C6d072uUiKSWJhcxSEnmVLbFEvW1dBLIkPOwzcGu/ch2euLEcDvoZupPzkeyduiystVIOB6k4UmHMRh3QQo21zyFGwyLQMCi80RqPxyOn04sW6VRY11XSXiJbyrgpSuwK/tlH0FWX71BQKJOtN06XjcvpzHJ3z/LwCYUjM+g1953uMMMqVSxT16pzYD+R9x6WRwQCILa3xQW7bWdmm1ArvZ91qYe0tEa9u/ga57TvrLUyTXLqNJVZMHzEZSDV5nm78PaTNyx1lU0m2kRySJOvypwU2+jVquJJ71EeUF0X22ZQ6pHTu2/Y26CeLzTLJBplXdhmDwWegA1zLTHJIfURIfwK9E4pMXAufVNbXlcY+ZtPvyWJbTCyxeTD19Afz2Uytb1MZ3icXSNjSTkJ1RJj7gwbyirIyuSRN99YilqcSpHSbYYk1z1hTTXvxRUiWhZZh3KtIfkvfHi9xGumYObkqoS3LhDM0DA/I/RfWRczPOuiod2RjriLiTyslblvzJmo6wEbO7Ym0i4ANR5CUqgd1UDy0d5IydSJrATTGItA05XE+/3CenzgsB6lMAmGkMgwGtPxLCY1cWV7Q5KdJmlk2X7j/h2uz/K45lNFZg0Or88vHA4H6lJxd5ZlJbXtBiinqGoe2nKlxEJrEslpdC6j002c2fQY8uwj25xz1vuWQqHn6WZ9IBlSl2s4J5syxlysqRW1pZml68GpM9QEcplJnZjLwuh7DK8fWU2tqFBrxlKl75m2d3p30uKy2U4xjeQMOEvSc9cJ1UyodoIaxdzVGJMhJeewFD58845xd8RMTWg2J9VW3DruRWoeBHzKLZduQG32+TGzaRjuhVoSh2Pl9XzhtU/qksg+GdvA7kQIONHQNaIFKKdg5zctRC5A29A5PkOtUA6Vu73TT51+rFSgeATE1sp0tZXtQ1BATk4yWYWyJWxPbDQwp1KlgKjOw9PCVz96x/NzIpdo5OzKafHueDG27cxheYo8K6dtnXSsypZxqOb4EkUiE8qs5G8t7H9w4fwwqP5IZpKsYlvH7g4Qdh67ln/YAqxAxmYCaxiFUe7I+cjxO5Nf/pUTv/8v3vFP//kHPvv2yr/xgwMPWcBiwlimckyczk6i5BXmDnWCr7SHyd/9b/4J/8Hn38F/7hHjLbkc+exP/Hn+0q/+iB/9t7/DP/ra+HA5c/GVxzf3nL56T+/Oy2tj+ew7/MPf/wM+/c0/5G/89c94k+5Z4nlO88pCTzIK2p9bYpaNnBWmuyZZbzaDs086ky1NvDlmg5IGiw3SmMxs1KSvrZyHxuxFgd4TllyCQDyQmnNKQ7bHaWHb0fOfZsepeDH67AGMhMIv6ZzIZNLR6LtDc5ZqdDzcGgPLDXMREGOY/o5SIpfERFqNwVJK5L0JKL5fFk4OvW9q/dw70/S5zuYM00KHT5UaRM6alaIq82FBbE66gVsnFUieOG9nWttZlkVtjYg0xU33GZGfMqeA5RRNklmkpAdIl8yYBaxfSw6khMpZ6ixHjdc5a+aaEPENIwYZzXMl6TU7YNj0aIBGoH0sxMnt9plJtTJGY993lnrUGbzvpLRgKTLvUOg+o+lZRjEeuICpgZTfKeau1gatD1objH3jsKwCDixsOAl6TnippDHUhuuyw/Z47XOcOebIlucmIGQ2cqn03rjli06P5ixQhh5YuhLImv8cp7WdMXbaGKz1IOUPmjf6GORSaH1QSuHh/onXyyu9JHLMsH5TG8VZ2XepyW5K6atFEGyOj0HyA3IubFNnqJmRa6E3FSQku+bdRFOXwWidSddM0geYM9pkRtNtm5FNaUlk2rwSl2FXncoGykUB9uNWJS+ARYB+ZoxGKUVk9pzxNUyZdEN32xJ27ZmS/t545kablCqVUk4WRSRS43qezGsMQgqV7pDrJaUjs3X6bPpMjMwYckcEL4Mnv0UnePfIJLzCMbKBjj0CxVPCm9oSu0/ScOX0SsKvP59SqHKiSdbiXJkiJ1OpjLHf1EKTiJHAFJxPYqTJzFO7/4REidlCM7XHzp9LwWenBwGa4p8nRq4W4d1XZZl8hHMOPEDNa3yER5acR6yPu9RvrUU8+1SmmxkBkoadzQL8KiL/LPIeRSS1+L1QFqGz17IsoOmqTirxWk/NOaPpvZPqLVqTXaQx0+XAcW6KpZ/Wr586oPRyeoflQi4l2NpryKVrKfDJnI0yG4XB05t7Pn165P7+jtnO9G2j9Y1kK2r16jAz0xsf3v2E9+/P1OWOz3/+F3j72RuWbPRtp5815Nd64HhQqKQD7TYgyqeLaTgffcbD4cGTOM8vX9Ne3mN9J/fBUh9Y1iNeEk9Phc/zmX/z58/84L7jF+j7Sjtl/PnC676xn5yzTxg7L2Mjzwd2d5rvdJyezuyJeMCyFB97V9vbGAraRqoG6zutA3Ta0Pe+D9WZplRJNnm+bLTR8OQ8Pq7UvHA+DV7OZ7zBt771wP3dA3Vdab1zOjf2BjYna83c3y0sq1j6l8tGG5PlcKTmzKXB82Xn0pVvc2lSwGyXEWyy8kjqKgk1OGl20nSWmllwijl7fI2XJgBnIFazlCMbOhw/e3Pk7WFlWQrHqmX3ebvwxfMHIMuXWla+3r/G0sJdyTR6fCAzZMm3W3NezmfGbCy1KrMn8hrGvmFpYqViTJ4OB0qBzVVReiiLMl8M6rpSa1LwNpN3L6988+4ddT1qKQeoBXOxpjOW51IrMJS7FaCKE2yIQ12rJIlOWFIKy+FIKStGJa+FNVdKrRGAK2VOcslxqWLg5kVZPy+XnWpATgGCTb768kva3knLgnch7z0PKT1ODcsL0zukQt4aKbzcrbewYqnpqHmkvk/ZkrJllrJQqrGWO0aWXNDboGbY9o3D3T2t90DZr/k8shO2KbXT1hpLKbzuF7Z9siTD6MrH2J1WE611KDq0bVFW0gJis1CIoOqxpe7aesdzYj/tHOvCm299QqlSUWUrymgbCa9FLVopQo/3wXnfGNuFmldG1lkxvbPPDt7A9ExsW5NkuHU8ZzwnllQi00BL0ZoP5ENhvL4qRHR2copQWgybk1RhpgS98XB3FHM3TcNbFsgx52BZCt2VC7NYjRpuSbl7QkyoepWYcyhclcLl5ZU3b9+yHKTIKLlQlghyt0TyFNWy0Vg3uxjxmajHg1rchoL8T0kXdo/MsV6UzeGuPDXhLjOyWyKPCClBW1O72/ComkbDg1sM+9PJ7hzr+hFMLDlUerIxyCKVgwkKi4oZs3dqBlB1fIqxsJhY37u7Ow2OYXsaQI3lrQ8tJ0upXNou21ZKMXhK6o0lZoMQCVNS5ZsvfoytlfPYWDwxdif50BJuBeiCA8MqVnMR0xuA1YSwXInJmmGNnW0wHhIzy8as8PLIW5jK9qlVljMzhTJa9J4aE8uDMjTAekqUtdK8kV0DU5Ikjlor5/OZD88f+Na3v63n0mDOFvEkeg1SDGbTuTXNGKZw2dZhb3jrWF255mPNBCWW89kHVg3CSuJT6hczsdEwY0iUesldA9m2X3i4Pisp3RRHKZ4rmExvkMvt/5tTzGDv/QYmeeBQfVeQ8cSYbvQ+1NByldkDKRtzNHKGacomqqWEZaRgpFgQpY5zdx4e73n/7hvObedhqWJqyTcZP1MV6jOD1UpyKR893SRVGoRjGSll0fO+GNuPXrg8n7HDgZHVbFiH2FZFVoSCJCb1bJpvaFqs8xQRM+gQy8TrRcvw8/tX8kMJ60qlTllSzFzfb3I8ZYqJqU7xms0ymVu83q1jJTGb83i3cno4cvngbKvBISkfyToLen+ZcDm9cLi/i89qBm9hqRgMChknj1BLUjkeJt8cNtJPjPVzERqzIhByRH4FUX08lbPjuQJVdeYYI2mohkQ5fJenP3Xh1/7dF378t36HH/7uN3x6d8/yaaEULQTTjJnsBhrSnFkrtcMsG0/Hwu+9PPO//81/wF/4T97gT38Z0gN+910+/7N/kb/y+z/iy3/4Bf/stbBvzixQ3j4xn1/wZIyz8faP/YD/6Z/9v3zyW6/82q+tpMeFer0jp2O2gg1K2hnJmWep5d2dliBVWRqWZuyjMzuMma/6BpbDynHRvOgUso2we2QsL5ifBSqNjnWgXchLoe6DNFeIcpQ5BWBglX1KyVZN4dUWxGyeUFhIyWk+8aI8TJDV1L2KyPHCnBuwSvc0MlLXqF1X33uWoqJUQKG7zmBZBe7srbHkxJI9FsdojnXdhVdQkAC4LVfMlYdEUiPTYomRdBYdYhe57BuLZdYi4CnlEmBMgav1RI+fvv5VTUEKdsywDkuotLa26dnrO2U5iCTG1dqYZK0plql5gSnr7yyJ0UXgdDaqq5Vtbw0jY7nq55wD834DQmSDjYiOhP4sHwF4jV+yuc3R9ITEzzIh1GEBwKdCSYN9Ngy9B71Plmgs9qLz04dHRupCwigmwkJtxcphS9mYvSkH9aqodmXHpiVHA5aazBTkLgWxMrA6Fs2DxeQa0XGpu5Q48/sI25g7s3dyzuyhEqs5lOOjwTCVvXg03rqUNGMo1DxbpufI0nKpuUaPPSZlvE2yOzOjLEkmaU2316Jk6KF+suzgpia8IR1N94Fq4hXAbBHdk02Zeh73SQ4QYSK1ZO+dYQJgFEiquQhvtClV7+hSHKUk+7IKGxKbRzsojo/OshRKdzaqvmd30myaCcKaOqeTq/63guqlyMMTlEQlKevUEp4Wko8ArUqo7zs+dsylzEsWuZIxH05dX9SZMR+MJBBSz5f07WP0uIvkorCPIh3l55pmHoXBK7Sd3jQnjKb9PWzqoK+BEz2VIQqM51Qg88Rcak8wRqw6fs1kQgH3srip5Kg1fb40z8uBkpIU3HM2KZiXHGCwJtJsCUVVJbpD2lc963RuzY+zR/ZZYrSOJSnMnWtjpLIqcQFUGm6i0dJ02o/Wqb4CQ8rT2TS3hBoQBJB67A8DhepjahD9af76qQNK61KgyuO55pXmepGEBir064Dz9LDy+HTPw2GlGLTtQ/icTRXFs+tJcHg+fcNXX34DvvK973+fTz77JGxYk953Rt9JS+F4OCrTwSyaHcQ0Jne6SdLahyTFPl3D3VQ95OXlC/b9hTSCTbUDT9//LocBbw/wx+uXfP69r7nbnXebWEeY9GHMg1OWwlKdPBO0wrwktjaZzWnbADrnOWnN6Q1Ol42+NQ2K8fBlZVjTHc6XRNvPdBOYpGa1wSSTUmcfusDfPt1RlsJln3zx1Q4MHo73vPnsgVwPtAGn0866ZB7ePoh9AR5q4ngo7H1jbzuW4BgBxafz5PkyOO3OPmHMwjCjTSH1WCYvlZKrAI1Ijs8ls2aTOqM3tvPOpQ3aqGyuClybCyUnvnw9s9TM/YMUSn1esB3Gblz65LS5BhLTg//V83sOh3vy6HzYG6TM1mTH6q2ztcFlcw5HqbJKydSqxgww1iI7lQ8BOSS47J1lrdRadJmkjHlmtJ1JY2+dDx8+8PW7D6zrnYKzk7Jvyq4loM3J4rILbntThX2fcdD4za89Qj7eRmNOLb3r4Y675QhrVlaOVVJN1ONRjSRZYX1mBq1RhrHvO300Ltuu0PNYLW04X33zgWELbz59E4xL0gImaJ80dVC1PjiWhX0OiCyN5XBkTlX+HlNiReqieWmMaDUrhwOpHsKWM3QJpELfd4GN5xct92F3KmY0lJFCV3j4YVlprbEudzw9fiL1YqgBlvs7tgmPEGySlsdhej29SRKap9RSnidtdGqVsmC5qzy+fcOSMznUMQr0DsbPDe+qB20IsGijQy4xOGqIb0MXXfLEsh45X868+fTTUNLIjiYAQpYxN7Ht5GjpWw9hU6tx8cHMGqKSS8mU3IEmK4xd8w0KcygoVLL1RWGp0UqEmeTmYzL3XXLW6WyXCwbs40RiUpYqV00ppEU5Qnk6peiztiYt4a0psFTAg5GHKsunT/beya1ynjujDT57On60hSVYglV3nyymBXS6KzvJMzkpV6mas7VOCxWFBsHBmI2tNdbDQQRDXlk8Udpk5ivMLzbqkGRzmsHUppjr+ww5vumi37vyIuphjXdIB2pNwYBOCY8oC6cAvESKRW9LTlSrylKwSA13mItxef9CffMk+2NxDlbpNlD0qKTwJULKzdAwGIHy2eA8B0dT+2JKAt66x1IwZ4S9RzuQ38zO1JLJGGU4OS9SvQXoW5csgCclfDSBxmNSp7MP/bzFEqkasxS8LtAbz998jflkPR6Un2ZJdtesSgzLWZ8pWrQUTjCpHUffcQZbuygDwV0qsDhjlJsV1eEezUuWIkhcf6ImLYfJnW3AuhxYlqoluSStdCHRdr/mhDlmNZg62XKugJZyUhoqr/AAWKMOfYrNtlIoSQoc5VaFzTEsy3OKQR9TDXDLNdsgrIvDRZDkYqQ86NuFkY2xO1719y2pUlKm2yTNleSF4mpqswzZphSzdoTW2MvOWoyx75RpWF14OZ952e45Liu5DdFLxZEyJ+z6IyvMtjjmi1jM0SkJmkHJC1akdrGZIJ3wNDm/dFYyZQ5snQGAgVkFEr1LOn+1gtme8Ao9LJ3msCxSO7h3jp/e8fIv3vP1u2c+eTpyWI9wMbgzrK5YcsYstH1Sj8qoySNBKpEJVGTfKjtsWsbMC4dPBvu/OvF6OXNfK2nK+nINHSUWG2ziPrDZ4x4Ue5zTiqeDBuqaWe5/ge/++Wf+0o+/5jd+8/f5nR/+mId/64/xyUOFnCiemFVqgh45LYeW2BnUlGiHnR+ke/7xj9/xrf/+H/Mn/sNPmPmXSWkhfe9P86d/9Qv+0le/wbv/85kvp/F62rh7eIvVlb694g2OGIfvf87f/u0f8fbpyF/4i4V6lOKGCGlVFltmqege2pxqmeJOL5EfY3DYFxbLvKQdTvEcFOXj7VPZdCmyeWqxsD0cmXbikI5kg8u+YaNzyHBh4l7J7lRrdNvBEtUWPM7C7FJ5TApuiX00KRp3tffmkujtAj0KD6bTfSNZYSSpcklSzpgPkTKuTBhckQ+YRT6fgPW0dil42q6cu2K32vRC0hmSlZMyIztqOFpWU5bqw2QVuWXHjMzom7JJ+8awVbayMalF6VOyIGqH8Xld/K4qy0memYpyYGcd+KZlettfqKlK/YqUjVeA2zrMrPiDFA1b7okeIFCZsq62fVdYO1CK7jW72oyj1MhyDRsZynlMKjpoffD/sfamvZJl2Xnes9be+5y4Q2YN3exuTiJFimgCkiBAEmHThgz/Bv9YA7YBD5ANC7IEGtRISLQmsjk02VWVmfdGnLOH5Q/vipv0NwHubDQaVZ2VFTfixN5rvWNN6zF118x3fw1oSbcQ8aBcHgGv1zrYny/MaVhXTs4YwX65EBPcZ8Yz6LVjwWRgVC7VOdfBtjf66phXbCSx4Hm/cmMehvkmcNN1t91VybOHCNVWOcdJDT33ukanCMQxpaYwp9jKuVFg+CStQWNhzallU9tv71D8rfUPT3v2ku02EsysXijLKXRmVFYWFU0XMDJMYes2VgJCAjtaLv49nFl0L1dEVoY1jterbEl+UvJcrt5SrS7lEUjVu2bqeOtGzE5fN/bW6GtwnosSp9wNU7X3gUL9TS0j2XTGWxbVVi8ca7DXXTOu5Z8flVr8TTFWW9FMkSCDI3v9jPFZha9JTyHZviBEBkQgACgJ73MstWaDbHZ64ighkm1aElRh3GKxF9csV6tmjKX9LIqC0VfOvF3LlGDjamDx9vP6nbwY2vZnAlDK81Ie4Lor4Cwo+R/mkrBEo+T9ywHzBGo2vAaEPqvapBQmCv3siiL4K5lmtRmh7gvNJZaRFMIy9XNeck6SPUqfVwKRQoZlwzDQzh8izLBsAk6QmDmoJlXnDEucQ1PSWF17bkTGLkzqXY2lN0z3Qikicd4yIn8+v37ugJJlGGcpVYej6fCwAXUuLg+Nr94/8sXzheqLMa+cudRIR1SygWByOw7+/Kd/we0KX375fX70Kz/i6fFCrJkVepItlu1C3RQOabmcYRnWBUw3Zq/4HMlYAqszxyu36wvnx4/MeWPeOjPg6fGRd7/wBV+eix++/8iPf+GFL/bJnIXb9eTjx+9QpE4eDJH16ild67Mz6cz+QI/BGUH0jcWQRcQaWznxFmmjltXtbrEOnLbv1G3jnPlFLZ7ZNpPzuLEV5+FxY98rwwrHXFw243F/5vLwHrPCbcC+FfYqGxUO1z748vmZ3YPr7chWBIhwjul8+zro07lO4zqRvQ3DirI8WpVssJlkrhWxo29f3FK4noM5Jn1BDziTla5UbjX4i7/8TpfTu8r1w4n54vRgxMzLdCpcexVGnJgF7g98ur4yxlReRQBRWGMhjLDz/ov3bNuWajg9B72fmZeSYXFN7XbX6yHQxtSQtpZxniPbHBZnv/F6vXKeJ5eHRzDDW+VSBZ8/bA/Uhwd53cvG3qqAy9E5e1dA7xBYOdLzGhG0DlvbeXh4Yn98YL8oUNa3DXApUrxKQjvltWZ0KY3OjvzChS+enllzcRsHn14/8OHbK14q77+44EOLsJRC90rye0hs4ak6xRsPGYKXx75Ys9UZo7Ox0/uN6/WEJnn4OA58LmUZFWdvW/q1xXQoHFJB1WZayO4tHSv0OtZYXOpGe7rIImW5QO4b1dSYFS+vtFo45shcJ8ldR6QlaAU9GZuRuR/rdvL119/DtgbeiFDjnBUNuIGQf6vIAlOLrD7KwWbOgY+ZzJi9BTnOPph9UjdJhVueyCvEENVauL28MGNiD1UBkZMcJruUaF5wCmsMVm2SxJoTW3uT0lt6wGvZchBLOf626flhKoz7nGlnWTjOdUyuQ8qK49OrbIwoNNVTduvcZbm6PObIkPg5iZ4e9aJaXKuNGAOKwrj9dgpM3GrmjKzPy90KmleqafmyVigmZc0Zk7VyKDHLUF81iAQClWqtKZ/XIDFMjWa17vrsDA30pX3O1or1xrAUv7daoMFjXHFbb2GqdQ4wqaksVJ2NVY5pLNtp1bB5yFLlKyXTAqVWn2mxXGzhvJwn33/3lHOHAUHzolDEzAZRLoGnvUAh5I4xvPDgRXaM6JS0l5zHJ/rLK+PdI0TXcoCk7dRN2QTLYA2Wp1cL0KFQAAAgAElEQVS/1remp9lPhdqy3lQ6a036OOjjpKyWQI4yiB5aSxvA5Hy9crudeNvwLEdY6zOj5V7wMgmrSPmvgaUfJ70fYtpD1d0jmWpLmZBBgkr6/gtEyc/E7LOcP2QnNlf21Mzvspody9vAbQaEls3lnsOl7rPP55zyOUo2qqlf/CRmx+ZgY8ugSp2/Y0QuZZXzPHnYds5xLwooueCKCDAUbmmo+XNrT3y8HvDuCw3qswjsdLF/bgpeLwMo2XqXdiTpQQNvxmXK/m7euBTn+fk9t9uNaz/ZV6U05QEWVCSicHd0XyQ72zKvao3CuU5KvWRm205M5TdZa+xPjevLjfZQiH7yGJXqldZa2lz1zB4MNnIQpWDd8OrMdcNpcnduRi+N7WHy+Nz42cfO47tJi0N5eueAZ6OMSVQjTmcWKPtiTBgMyl5Y82ANsFbxdsIYTJvs+0Z//5GPPznYfvUjbXR8r4woNHaCSfiOzY5ZI2Jwt7rJNjwxnsFO4BFq4fn7f4O//bt/yZ/+0ZV/8oc/5d9/8YmH3/qad82lvJzQYmAjl7XS2c04T2NzONtif7/4R//0D/jBr33J0+/8kLBfBnYefvy7/O7v/jl//vrP+b2/MF7QQlW//xUffhrQTz6NxdPlPc9/7Tf4n//Zn/H+eee3/lbl/aV8LudYQS2yYTwWzUrHPASkcrfoQHEF1rZVBPC5sRlsrgyx8zyhZgspK1spF3up9Fj4JajeePl0UuvOvgXn6Ix0EWAbFrLLicluUqJaI7BcUF0qoFawQwRMqTBvB5TAPevRgTphUggOtq28ne0lQSUyq8zS1uJFRJwvZ68brRVGH1yProWxKBNwhUgDK43mjTkL1Uh3gs7lu10pgNGzarvuzHVgLK7XVx4fH4FJnwtMy6JTRQrlMkxIJauisYlRsCYLS9lkX7Z4SOAq7dSE1CQmUNlnMD2U2+4K2W9UYqmJt2bO5iLVr31ltooyyiIDjD1MwJXJIscc1KoClxUjpUh3tbN2FFKlYZ4L/wqFchdj8/3tDJ8+mcdJrMX5epP6uMgVUaxISYTmyOKN4rLAnuukxCNRlD3Uj85ap56nSBVxjFSlCvALE8HjtWYOqMAd7YJT+UN6p5muHB/PAoZbV7ZcrVLKXG+vmnOm3hsrhW0r2SK72GploMIMK0ZYZUbgS/f8eapgQg3NcS/U1NxVjBiRSjjDqqyacpLpLC5WmNb1mq0QNiiXLaNdRFgRWRBist2XzCwkSFXu1KwfqORmiYx2gxHKUfNyr2QxgR1dAEyp2X6X9+EYAmOnQwvjHJOaDccLEcljTd0toaIFCFY6EgzH2kbBOZiK+kjCaDapr+QOFHhyN8f32ZlKdcdS2SX9k70BxwK7ZxLsAjk8yT0iozbyr2XXEqhrtdAP7XMFAy+yv2a+Yg1oS5+NQKTsm11SJ4bfYZeTkmdpc4H5i2ykz+9OxahVWIbappVXqWe+EUyqNykTPXSPlaAvZWhh96IXI4rCz0kC1Lwx5qk5KxVycwT3yIs543OQeYoSPPGmlapE/X2nmlNagbU4x9SfEwKUPCNExtnf5uy3Vt6ArVWO8/zPQHX+83/93AElWrYELKcKqSDWZN+D9/vOu3dPqttbpxpx5iAyhNSRimPcTr795js+3TrPX37NL/7KVzxdLpjBcXullc8tOJhlnkq8qVH0S8uGGIqu9pt7ANjsHC8fOD5+YPYXYg767eTlunj//pkvvnjHrzx+4u/84JV3jws7BtdPg7U6t9tCOVtG78YxBufsnKPj0fh0PRlj8RKT1k7WEstx3G7KTpmdHq/QnTH10ER+OQQ65F+bcU6jVaMV43p2sUYe7M8PvHvccOBnHzsf+6BW5927dzzvDwSF221wedhp1dl2x5pxjsHTY8FscPaJFThPLSdjGdexeDkWg8q5CrdpWQtaM/gQwqusbq1gq+MxEk0NAWss+lBp6bLCSYaUuQJUX1+uPL97z1fPlcetCuwpS8qBKJw5wI/b5BhBbU+SWodzRkHSx55fqkXMbCsxxB6b2DIvWkxaa9S6MefIFqCNMTqXy4V7iG4fg9vtKvUaYi+utyvn+dn3rcpVaLWwbw+UfSfMeWhSxI3etTT1yexD7U5n1kKvbEeIYCuV9nih7Tv75RGL862tqGTl9uydMB2k0U+ykVl1t9Xw5hSCmLCOzss3L3h1Hp42OIeWFDMZClYm/LtUB94eqBXm0PEVKVUP5OtmKhfkOE+uLy+piJAvuu367GkKoo4abNO4rUkJY3vYZZEb8vbGXERXu1sU5xidNSbvn7+SFcqbWgBbxc0TdOk6H8yoMyuCQ81V1UyLWaoTqumMOa4HX3/9FVTJ5EsIQJslwD19413KxcuF0hcv11cpjFZaYqeyKXosDQVzMIspk+nySFjTgGFaFhQyKrn1dXYe9x2bgQ9dgiVZU7GaRriGn74UCG61vrXtxcrMIHKg90ofBw+Pm8JxUc7CGAdMvcZ7YP2MSW3OOA9mKKhvEW+Ld60VW7JM1Frp/WRlBtUaAuZrq7Kx1sroHcFfhRMNY4+XB9ZcNJM5eK2Jt5KV6AatvinCYi36PJljiMnMcNi5ZKWKpSr12xHUtif7osbLFYtWJ/M8lYuE8rvGUuCt6JVIewzK5lhT7C2D4+UVN6fUhpvCDEsOFfr3CO3YSuWcsqNVb6lR0XniiH2N0Ou+7I1+/cRyp9VGdFkYtP/dwdrMYcqqlXsWBCa7QCXoY+BmHGfHamOOk9sh8LKG1Dxx18VPnaVejGjBMU5aawmgDMygFb3napuRp36trHReIdayd8jBtbnu5WXO3i487w/6XlUhquc5cBDzrlch5VJJNjYD5fupYH6novDdwLYqRWTayWYsAbOkQtP083kC3BYKHW21yZ4FsmFtG15rMsCTe3OmpNvZlkRAqhririaONABGKNy3pNJhK9joSfAEkTbjOZRRAMJVa90ELsfnmQHujT/ZWEN6dMx5/uJLvv30EwGjC7Za8K2yvGBty8YpzSIlINKiqkgW5Ri2mEQZ1AmjKCfhnV/gZ4NxBNFh1VzGCkQdeC6wAjVrNowavsNrTyvA20RRwI3aYLnxsDfGy8H5etIeN14j2GonitOKMlGkqtS51XxjbrA+TUY/aZssBfNU/klZhVqMd19tfPvx5LsPB5cvL7SLc0Swvdxg36l90U1L7raKlCkz6Oei7Ap+WEvhphRlo+HG/v4Lvvnj7/jwkyvvf9Wp1wF9Y14cj0XsXQPylPzD6obFRvh+RyATVHQiTqJUnn/pe/zt//JH/Ic/+cC//sOf8v6rxq//4D17kz2iVKfEYHbZUiaLfSvYyKVoq3xok3/8v/wL/ttf/iH2i19omfF3/PDv/wP+629f+cvf/3P+46sCqtuctK+/5i9/8sdsBjVO/P0zH9ov8D/+/ke2B+PHP75wuTiUrsW0a/nsDDwqW5et2Xkk1klD3/WyJsOVOVZ9EB5SQmDJSGtGqzM44qb72E0hv5ZV6w87n24HxSolF9GFbMkrNI/7hL7OtwbcwNjKRo/B4gFn4DWDlYdBuXCcavjTkRUKxLVCC6mWaZ1qqGFsWn7/pQwMpKJ1Zp6H+hjDGqsHc1w5DyAEMsr+tFiurktdtyKX5RpSOLqhx8JNy1grVaCSGS8vL8qxtKb20RJZi57kdAIJhOzceGHMQZyBb1UgVVrGah1qI42Fl8a21STbVv4RhbN39n0TiWRTjddkqQo640e2XMUcIgRaVXvTX2k9K0jlMdl5qw03WAnM50ZFq1ku4CKWSilYn0wLZTqhTsCtGpet0Wrj29dP3NZgC8Nn0FenuJPxSFL1mMjc1nZ8VMI63Q9WKPB89Tt5IHszkYqNyIYyXZdyXhSjsUkl44aHgr5DqM6bLX2SREDANDW+mhm47FnHlCqtohZCb7vycWLBcpoLVFEJjeaS+/0y5+ScCdi1pkBuN7jb7FwShZE2upI75grN4pje33CpUDxUJkAYMwaLSTUVAq1UjJS76tJkwYZI65xcKeD0fmi2SFAmvEh2nXM9SWTXop/DvbBtlU4+J8XYH595PQ7deWlflZJlve3LmpOSjCcYa3FDpO5Yk+GalVtpAknirtWP/Jz0eS3WW/yB6jcGzVUYtJVNIOd95nK0N+U9XotIr5XqY5bu/0JmRuWuEJkLpCwwzZ1j6d9bN8Wd9FBje0sSN2KyootE6Zmr5NoxSpVtUCHdwYghxRH8FftYxazh5YBQy+ueakGPbITL0Q/QfJ1/4Z45T5aFQ16o7gKxASvQp4gyqb0yL9eD4sGqen89ib+SykNzk/2uFOWLzaX55l6Skr9n9ZG2QAWe43I9tNb4ef76uQNKrTZGP7R0Ndhi8fiw8fS0c9kaxmSNQ0ypWfoNRSrezoNP333k08uN/fKOX/z1X+Xh3c6GMnCKiy1LzVPWiIo9dA+2JmRwZtDUymV5JWNxrpNxvfL68WeM2wtlDCwOzAuPl8IPv9r4jV92fnw5+er9B/z4yPUnkznh1dRewIpURsA4nGN2Rodrr5zxyrVPbCw+rBv2YphXjiF7BX3mpdsIFn10zp4PXHG26hlUuBQSaoXrsdhq0DOEdN8LpRifbovj9QB3vv/+SVksZaOPIBg8Pqkh5+GhsnwxxhUP57LtzLm4XQ+OCWXfOAZcBxyzcJhzLqevwnIBPhRJA1tpbO4UBmUNLAbVdI1HqWDOOZQn1Feh55A4bHHrnXEePD880orz7sFpFnlIFzXWrMkeReIJD8ruQGWVTQNAf1XwGQpKG0OWneIbb/LKlsFypi9LLTujLy4Pm5oLHDEofVBK4zxP+qmWtEXQz4Pr64sUK66L73K528+CfX+QrNdKDtkTD4FA5+icx6kWoDlzwVlYyigutbE9PvL49A6rjVIEgFB3wrPtb55Ahu1ZwU0h1G7lbWFrpVEWTDv49ptvoFWen8SOFSvUqqHHitoQbMl2U2iYBbMHPbWYxv0QjGzkWvSj8/Hb7zI8UwBf3cQo0WrWiitYD3NqACUXuilr27kG5zg5ZqeWgncxbpd3jywPqut5iQj25ycupUnNJWc2667YSwZkebx5qHtXi00QvL688r2vvqLUplaRUvAMVB8El9ZYqcJzCv04uL28Umrl6PJxrKUwy3NqqbYFrVW+e/lIuzyooj0DnEUMyWk9Mydm35qUCvBWE+q1MI9J2x4YeXFHcaKfysjatpSzS+mzIqjtIuZ3Tuq2S623FJYoRlDBn5vGbcm456CitpbyfGG77FKQYWA1ATkpQ8YY9NuhVpaZPm53KTbnkiJlTG7Xg27GuJ3Uyw61UahMCykUXRXScwWtaBEQRyS7Y1uLagINxl8JvxZYBXMNxlpsTck7oTR4LaxDtsbpagg5jhvT74o7NGjl5zVup1oqfDGGztOnhwfdE8X1Z+awYkuWm9Y2mJMtWWBHlpCZrx/Q99Utg+qNj999oF4ub/YFN8tsIjWR3CcIfY80oAQZEmlk+0gCgUCXH4w5OqUqpBOU+aC6m7SsFA1clgY4ixwCSuVcUkzGGFKktJKV3cFpjTk6axMwQ8iaaC6ps7tDMdqqZOcfy1eGaooh1NIgyynh7AF72cF1boLaRt9sfSVb93I+lUpJoda2LNUICiJdq8vSM8QWgwJgR+9YFaBV6ufGOb0uLSf3auW7esnJcNFsCGJJ4VFbw1zg03meUhe2xucKYeNz7GmCgDmc3tvaJETTtB+mrB2W8Xh5oMxJvx1YaxCTB8Up5/OkPBFzWP5matHIbqLl1cBl0E/GcdJjKQibRb++MniUsmu5FqCAhEYJZEtlFWqttBIijdbGZTOO185Shzp9KUx7a8Z6hNczmJuyE0SOSxd+bxCc4YSrirquxbDO9dNkbYW2Gb1JaREuUuPh6Ymvf2Hx05+88M3Td2zzHft+wVaDc8quXE7stdFx2kVg3joW3TrEoG4Fa41KJXDmccPM+OLrCx//5IXydPD+/QWPwUiQ02zA9qQvWOJHAtRKIhAzAcKJ+Q7tHeXdr/HX/hb8zh994n/63/49f/iHf8EXT4Xvf72/fY+LOc+l8HFKvUNfjCI7XivO5Qn+6OM3/Mv/8R/zN/+7r+Hd39MZ/vRr/Oqv/wq/9Pt/yB/92Uc+3Ba37thjsFrh5cNH5hi06yvbw86fvQ7+z9/7hu99v/LLv3RhK7LDEjp/ooHF4Hhw+iu4nWxN28o6u5QuyzCb2Aoad5uQ7pmzyyqzikg2s0kZ0CNt4/IBM+vG9TxopbB5oa/AoxBemNHZtmxlnHq6m2VNfThbZn+oV+6KmzJSlgWjn9h0rJoWo3GAOcfQIdpZLG5Y2fB83oIikgzAZK+ac9DnKRTDGzEmm0/okzluDKvUtmcGzfaWtyalQ56rdwW9/rbm1SRHLtuF3s9Uskg15XkPTGnz0+JvQoZRPk1MgRIxJj36myKgWMH2R+Y4iJhv9ekrlU5BUXh2KPh8LeXnrGIJQEipVV07TiRobEvFMYvPQNecd+W3XnfvXQBSk7olbKVN5k5A6S48u9QoJe8pwtisUc2UN2WDtu+MTrZr8QYURGQ2TqhlWHe+cmecmnmKgc2VGT+6g/tSEUxZCzORHbZtb3ajWMF1nrQsIhE4U3WPWFodAWKx1qDH1B2ZBSGWgOmWs0kfJ14SIPQqq1R+t9Y4UMuosbXtzuFwzy601lizU10zVmRMwXxT22RotQ5jlO8fjOkcLP3slnl3ZgzmW8SP4gQOvW/5DAoPq2BVjgdXK92aaZXORsFYMwEq1M7IPfdQwNWCt+zDXUnnCm3GcHMeto3bEkC0CEoo5ytG5N3srMxeBJVAsPT+u+ke95ZOoNC5qznTWWOkXU6aJDPPXM+7KjkzJiNnbDFWKn9K6POeq+ihYHt3Bauz9Nl7vu6V5IcswjND+KW+gyUgsFSaSaEdJqtcpbJWpx+L6pWt6fsgclUFGqX4GyaxsixrLanyFOcwKXXD7A4gObWaCM+utrmF9sb7vZQfvJoKS+GcK0PoRZ6IxDK2kjlWIJGN3umcSfW/K/OTemThjzvmoZbxosKD2gQ6CchUo5vn/KgMUkULLcDavX725/Pr5w4ojVN103sNHnfn3X5h2yteg5hqyrE7IgmwoPfOd9995Lh1vG788Ff+Gl++e6Jk7gggWXTWdiqoSgd9yQ8f1zCimk/l6oAkdmtNbi8feHn5lnl9JfpJHs1sXvjqcuFH7zt//evCb/wWPM5P3GYhxjP21FnXK2UY8zY4Z1BOOM7FbZz0c3LtJ6+3yREvjCl2n6NCDF7XSVA4k4UbK1hd+RyzT2YXcjrmYg4I1+KIV2q22tzW5Ompsbmq6L95WdTS+OL5CbzwcgTeJ/suH3hthcddKe49Dm63k8Lk4eHCnIPzlEye4ny4Lm7LOKfL4jbFYtMkS10YxfTnbhWaq+rYYqKKZ7HhfcERcE5jeeUI49aDcTtUCVk2np4aD63w+LCDdTGs93E7JMvuqWYQmLXTrDKZ9NXZm3Gek9Kc26dTyHVKF2tz6iYVSW1q+asuq9ulVi6bKysp7Vi1PnBmDeqMxTkHr7dXrq83yXATwCklg92KsW+bslLqBhFsrRH95HY9GOeNl/OmWvY+k4HIoSSCfbtweXri+fk91Rv7vkl+uV2oCCX2qqV/zhNbQ5ljaeExk5qllIYTfDxv/Nkf/xG48/z8yLVPKVKaU3yjbpuk1ssorupVfVEEJJVQZoaGAe1Nbs643nj97jv91lAgb2uyJu1tUxvZguv1ldqKXid5+IasTOeaajYbOshYajW7PD5QS6WsHNLcuXz5nkvbGMeNMaaArwXFs2WDoEw1I8mOOOlLDOu4Hrx/embbNik9Uhc6/X6JF2UNxcr2PHi93gi3BFR0uTLeeqg0bLtx3k62utHaLjCHeGsCm2kRgsm2aZiRL152wFKKwslryaymhRenHwpVrNXULlX1z0mZpAXT/PMirUGuZliuBm2PzKSKYNxONeKdJ45x2XfK3tjaJtm86bMqc2LhYi4zY45sHYFgjkGpheO104esZ6tPnSmPj1jZNOybmL6WLVGGApdLMoS4KT9sTabr0i+ebNI5KKZhc5xH5u+gc3hp+bYmdu1e5jATjMqZRQN0An/RZ9oSxZLNPnCv1H1XVk5RmD8m+5ScYeLLiqPFKJSHpoSAtCW5DDPBwkOKwG+/+8jD03vmnOxJ0araN4iq743KAeMNdFJWgMCYhQBGj4p7Z8wMfzZJ8dcalLYTXlhTwZ1eqiwGpHQ6GTqBX6b2mFpYPYGJFRnird9T7nWwpiHFzLF7Y01VRXLs0JbAWykQ9EyYZTB3Liza1vWsqIpXrOsZi420P5oRaRPT+VqlCl53C6Ke2TsfWuu98aimNVXn0aZ/DYTYey0l+fqz8WVlBoF+3myfuqtzikB4LaSw1VNZe7beWF591wSyrUW+VyI3Vr7GEqF8CO544SQYzDBKbeyPF47rwWW/MHqn++cl9OIK//cR0O5GCVXEhy0N3bGUjRNq9DnmlM3IK2vdOG6Dy6Upi2ImgGkZ9JlmpkDLfQlnqxvz7BQvXJpqklvdmGvgS3fH87vg+rNXjtuSnD+gnx3fXO93TJjGnHel90l93HjwnXWcRGY59rmYZuymxerdlxvf/elHvvvpwVN5xP1gKw7L1U44IUpndNOd6ZV1TAauBjcU+u6bAqrPdcI09i/fcZ5XPvzkqvnjHbR5ELXiS5aJ/OLp9Zf3CaJr9oKOWSCQ6ZGyf8nDD42/+d984k///Ft+719+w7/7cuPp8WueH3fZS6sz3NRGOQbRKmUWHpbyNx+X0x8X/+zf/Qlf/6P/i1/8B19iD7+FufPuN36T3/3tf8qfv9z42R/f1PR6m2oyfXRejisP7JhN/OGZf/3Nd/zS73/i6aHx5ZcVr1A2MJ9wLtimsoKWcZ438EK55zXeFtSGMxhrCmwtagza3HEaxzqZK8Pis8HqOSZnN7qdbMsYtdJn5xwnVhp7NfrcGJNUF6YN7e4BMjDrUJoCaE1THBSGR1ozCuGbZjVOWlsCm8hcn3ahnwMonOdJKTNBak8kWuDzWgOYVAOrQe2Ly7snrp+urOZvSoYxBpNFJe+3kkuvJ9gf+tm96KxWA+Wk2c4ad9WA7O2x1KxZctdYcb+/pYgpbm/kRFBhTopvdAbhIhf1HqnkpfeO+2dQ/C0822SLtbT8E4h8SeB4pS0lzUC4pxXOXCTMzDvAEgxww0tjnINjdWXS5awn7Y6UWbESCDNnJllhq7CtBB4ceqtULzxVtUAeZ7b8uc4gv2dcSTAs9VssSAC8YawW9OowUXDzcJYbhHJ0i4msWHkve1UeVmRY953UwaBQcV/0bK68Ex/kuzmXbMbX2Wme5605fQyqO9Yn0ZWhE/JRYVHe7unlSF1uzppqGK9F2ZthTslmZLLAZqHvhED+O9CB/hmcsorAN3TORqqzS9HcNIfmWs+MqXvOorueK80sDRuTc3aFe9/P+8xcVJDzSuugQMcRQSJAHO6KXwnYABn+sgHUVIgSYVikoiqPUX3Fi+YfXbPcmbE1J/062B4uAqQWeOZS3Zv/lPE3Mi9TxR9nvm8AHlOmZDPlgUn69Wb11F0ubVaA9nyX/VTXu+xdGaWorDPudvf859XeovnNq1reTJ9b84pViU36SPIvbW4xIhWn8fZaSBDLTOTijGDNU6qltKAuZGmzVt4addVyLPCeCM0lLiFDvt1SZaI2NhFmkSS/J+CjucsDbOgfWmtk/qAUSirqCWKpVAT//CwFunuxz5bqmbtGDZjVsP5XabX//79+7oDS+4uG5Od956EZpQReUPhogN8tNLG4HYPr7cb1VQvGVz/4Rb76+h2lCOGGeGtZEX2YmwVOTam25QM/l8JMmZPeTxznPA6O28H1+pFxe8H6FeYhWbAXnvaNH71f/N3feuSvf+/KhaC/Dq79YF0Xtyk7zTmGLEemxrWJsUqBfWHb4GHutH1yRJVEbhnXvhRGiV7TbXbGzZW15JNYCgJertwed8e3wq1PzjN4aItzdrbMC5oj+G50xjLePz6wty3tdE5rkmvXWmmt4MU5h/KW+prsm/Ow7YzpvN4WYxXWcK7L+Hgb3CacAdO2tI94MgSLFsFWgsdqeIzM9Oh51y3GEFhwzGC4M63Rh/FyDMbSg/14eeD94xOXpkaHLVna6ilDXDBmcM7JiCpFRDGq5WFB5VKDsTau1w9vNijPgbbUxrZv6fUuqmG3okEONUFtGcpcigKjr+chJVgsjt65vlx5PV7FuJVUZpney1oL2+OuYO9SWaE5tp83xu3k9Tw5jxvzPHPpW3cjB14qDw8PXB4f2C+71BMsbreX9HJv1CIQL94gbdL/m/YVU1vRjGAeB8ftlT/9i7/Am/G8Xbjdzqy/lFy+1kL1wkJ2Ly+FZZJy+lAuh5E2qzV0oSOv7c++/UAryluaSDlXW0tbVHqKhxpU1hxvOTEz1LomWbNsY57+35kV73VrySTId/74uKvVaQ18LopXIgbhxjnOZGF0yK6pmvljZKvZ0bk8XLg8P0gdVRQyWTOrBrMM5pOqAQTozLQvrJThz9lxC/pQTXYxsUMzFuXpQcOlRS7ZKwEhKd3mmFqGMSLb+AQmdYVHG9kcI0myqjxdEuzlzEPVo7FUm3ocB62lpcw860uVTSbGVSB0iczMWao8HV2B5hsC69aKv1Jdb292gjGkuptrZdVxSnFDduAZQV9aFs/zxr5v7PtOFAFfWwa9lrzEm6uFK0x+boC+xKAsuXX036GBdJkAsdfbDfMLx3Eox8DvpICx7fru2rznBICn5N0IbnPAGGKJszXGDBULJJhxV9zdJehu6KJ2Le8DBbeuUJilhcCfQGHSYSYFSdr3+jF4/4Mn/ayphBlLjBBpc1srpcb62icIFqoHdhPwlxkSpklAgFSeE6wJGZ54D5OP/PnXHalBzOeMaxMAACAASURBVJWWx8yHcOU7WNhbEO9cSxbHLVtW7yogPC0RA7dGKxVj6TMrd1ZLeYeWTKCmbuUPlFaYpxpNcC1AEcqSwj/ndJlZZva5zoD8edcdaPO0arkpS88y+zCVhZ7gzJqhJbtW5UkgtcFMZhw0pFdv3KEqS6JJdt5cOZZq0bHPtvJSFLrpfrfP63UWE8tXTM+j4dyVnB7SiYfBw/M7vvvmG2RbkMqvZX4cMVk+WCab111Jba7P2xbUEAA3WHgtNPTM2YMsWrfXG5d9o1VXhsjS86SvWaSrS+euO7gXjnnl9hq4ScEU07EKMwong1oL+1a4nZPZ9byMWqlL9iRWkyrAYJ5BvRQKlYfHSdTKWPndMuXijQQ/d298/aN3/Ol/+sR331whHnl+FrBWt4Y3E+FzB/3mACqrO7FBuO4mRiVsMo6DLZ4pz877779nvv6M1+8O/GKUVfHyyvJHfJxQHnQAAMZBWLaFvX2mC+wOMDzjl8VXv/Rr/L3f/Y6f/ORf8G/+w0e+/PqB3/yVnb1lUPRyLqsxbXJj4nXQWfhRKb54tzkvA/7vf/Jv2b73Bd/72xXffol49wv85n/xN/ivPn3g0zH5139yckMLZNmlajuPwQpj+GArz/yjP/jIV+8+8Xf+zjPvvqh69qvAOItCtaVhoznn6BjGXhvxDGMONnfe18bjXmgl87tY7Bus7kzLOIh81qMudt9gKcx2B1ZseT90trKDi7ghG51kXNNJ5RmIa2vg3nRXeaGGTJ5DKzW+jOtmnP2AM6gOtQgAZt4ovrNQ3k3EovhKdl5KGEgAIPJeyGWdgPKwE7OrFS0DlL1otsSVQyeLrHo61TSrcFoBN8qX8aH8t/QtsYqUpLFOKSgyAwVSgeCya26bgNAeC6aIpVIM1QTmOVZ3zCv9lBsjllrGIhfYSAu0Qn2bQBLX6/AkOwSI6bwaS9/5Why3itWCr0YfXcop17Iu6/yi95usqa45liQluZ+USeo7ajcMc6IkeRFXWtVMXlFD2dmVuxihXLlIhYrAmwZhXLaNWZvO3tcOQ6UHnoScGhULZiJrNDeqLOk+Q5ouMAEaxTW7LqldvRQNAyuwO5nAZ7VWaQqKnix2K/kcoNn3TuyRUQQRAuZCZ/3K3CSvmgEj7tZiWH1wWJJb9zkGf1PLeD5n5lDmwqvLumtSsdcoTJe6xJg5m8fbfaUspvyzELA2igiVanJG9DGlwl9SKnk+D1KbTJVMmMBcQi29ETOVhjodRWikGpUEIdG9r3FJM4lA+FTjpZPIMaIUStU/z1qctmirUF0/h+X8kf8CBspG1rPvSbB/nseZirJwr29K57tS+H4n665ThMlMx5HQUQE1bgk+pspf4AypFM8Zz5NwiqlyCTOsZstczm4xnWLKV8XGGyF3VwAG2ntKgoBzjiwE0CxsJZv6uM+uAq/1PJLPHxh3u2fe3aXw1oSb4OO9ACDQLBp6IakEbyJ+bNHaPW+60GPgVpWZrsGeyDMfzz83VWhzTnDtJvdc1Z/Xr587oPTD94+UWnGb3DPHVnobZZOYnH1wvHZut47vG1//4Id88fyEoyV1Dr1ZnuDR/QGV+1AoulLXdfASIRQcfdCxgpeXT/TbR/pxZfUrnhWM1SqXVvj+886vfB381tcnP3z8SBzwKWBGZ+Jcy2Lzhs0rpQV1bNhUbsMakzk7Y51aCM+KjwVdCpvr0bjayTyVqN/LUODw5tTe6Og9qWb0IqsTQETh8WlXsNYKzkN+ZGJxPRd7qzw+XqhWOboqLJcZ22Wj7ZXqws/nOul9sLXCu4cdr4XVF7fDWOG8HME54cOtc1uLiQsxzoBiZ1FcwcIXNxqqmyUlum6qeY1Qa8dcJhtfVMaAW5fnfts3WjXebRceLjW/ZMbuYgrc5aGfKxgYy7dUExSMypJBlopxHgffvlz1XnlQa6MWASeyEMmK43YvsNbws9XKZ0JDweavtyvnnMwxOY6TTx9fiNHflA01c3fwJmXO5UItd/BKeTzG4nY7GGfndr3Re1eW17wrP5xta7x7fEfZqoDRmaHjXpT8H2DrYG3BLApJrXmIQ+SwQ5aJLW7HJ14/vvByHOAKXj7HkBXP8+f3woxJpOQ3DT80M/qc9LhLYHVorSiETc7rybfffYs5Cv5z09BVCt4aVuVdnkty73JnXCCBMKjpVW5snFP5BDMtYfvlIQ/gbN8rYnyKV27Xq977EMA/l3Ko3oCBBDzWGNhazKPTivPw+ChlDGrLqrWJ6Q4BVj2BIRA4OU7J2meG/EVIbRczxECR6rgILk+PDNfSd2eXhGl7DnwrVRBa+skleMwuZkK3Afd36OxSF9qsCu+d/U1REpa14yZa584GrqkshDKDNWUTMgT8zFhYE8NrpWT+QlDDNLgUKQ7ul/ScKwObz3xvIxlBSYLHlK99BIxTwMd+uaT6RMDVnVkVQyKgU1lfeu1zyOYYQ0vyvQEjEAjlBvOcjAF1N3oycPfvVXFP65TypiLiDXCamUEmC4IAVjyzg6Ykvw8Pm4K6XTkHd1n1yqVfgaY6e2Z8ltBbLvOTZORQK9FuhX6TMqBddj37RWocQ8oslSkkEDfn/W3R+58KqxUC0laIgT7HULD2nKk+knqoaIcTMJYDhnI2kuVMcEzM7r0Vi8/AGwZLz6y3xtbUOlYgQyA3dlewauCMoTNoDtWvL8DSShYmMFl131BczyV2BxBFyoBYwrsOJyAb7u72rMgf6N7ad7/FZd8EZIvDWPPE7nZSfThin+fibov2bFfBxD4an8+I+68I5UWZGbVUzCQPV+uJfn8pM4ErDc4uz8Qb0OV/ZQiN+3Nhej+C4PndM9/9xV/Sx4ISlFVkWx6yu5EqsbFS7Bsap4PMx8rBz6pTZqW63mdbE6+F43rjdlxomcmkvCZZTlag7AcfrFCgtFfD24XjdrI1ow9lfDTXLMYqTA+2BzUpnSPyjPfMEUFqhxQnxQypqwKabdAmPoJYp+7sDK9dDNrWeP/9C58+nnz8eFD3QmsbdYO6Cu6TujtusqZa0bkxx1ArHFXkUVosHr54R4yNiBPqzuMv7Nz+dDE+nbTnAscAXnW++quWSgObu5QHqVDRlu0QjfAgokJ5oDy+5xd/+1f5nb//gf/hf/23/MG/+cBXj4/88AeFli2LqwaxnBKaG5dXZom0VBmXB+Ob76788//99/i72+T51/8GtGcGk1/90ff4ndc/4dOt8e++7fQI4ryxXx4Yt4MxOn4YURt/7o/8w3/5ysOT89s/fuDpob7NKk5llcCaauSJBWfgHgl+Ba3AVmGvzm4CjMXAOzsmK/CEc64sNBBDXpYqy9s6GeE8xANHnPSlzM7V4Ez1DutODoxUO1bMRvK7lgrEqvcms0Kogx2AB87emWuyxQ2zyppG24xFY2GqpM/vh1lJAgMBovm9NwOl4inI/2Hf6adRbTJP3Yme9/mKKdDFM+w7/M1CNpcIRnCFh0cAhXsjEmaMOenjRgk1AJvd16SQYtBE0BkuO/ZKRWsubqqCz/NngzU0M62cLbwqCkPf6tAH1hoxbnlPq5zC/R4JoR0nJQnQlGnkJUFobdgCZR2Uw6ifaU5YKI+lZElHZG6LsqYEQnqTXXkBT/uurMbVCYzSgJiMsVhTGZ2Rc+Jl2yi1Yu1Ce3qC68FxfcWKU6K+ZTQhOFAWvBY652OljU8EV00FiFrSVKSxZghwagZr4Tlfr0TmDX0PPBU0FmmLSrTrDsytkTESkRmcSYCQalHNEiJyW4INcy7WKeU9lmAIIlrX3U4fes/CQ2UcqbDZioCclUqce8FHhHZgc0/wLvJjvW8NkZbpJpClGWsaW1qUvRhriJR2M2YWVElGKNAmRXKaC9YiXLtioBiBdVd+5zPtBhECdsPmG6DJcqiLe0j6XCuLQ8CKYVMmQB2JWXYVqBjDF7Gacizz55z5MwYBRZ8B877glLd5TZBnNhHmfB1Aae1zVuV9ruCenziTfOPONaW6Wn+prNjPSuY7uKoZrUASvcSdyBSoJuBRc6JM7amoT2Lv/i/QXKpn7k6IuZVUBUqt5AStJFm21Jq3+hSwaSsBJ83+d0DpPttNPhOsd9JyRRYHjVQ1xf39zbnQ7srjeAOnSMW4rJaeu+rP79fPHVDat6wpz3CvJGsZa3GcJ+dNYaRulfdffY8vvv6CfS/EPIk+1EBkBWPD/b6UeKooyBlBKOFa4zODEgZn53b9xHFemZ9e5JMduaC1yh7Gcyn88tfGb/7g5PsPNzYbXD/KqtGX/K1nX6zluE3O0WG4Gkmqw1rc5mJEYa0MlWxLFoUNvCdRsYJllc3JoVJNWTMgBkxzStUVWTOrJ9YdaIk39rpUZysXyqYv/RjO0FbHZd/Y9pa5QULVV0xqEYv18Kjaz3MaxwEvhwIzX66Ll3Nxw7C68ZYFgmfomvGQSH3zRZlTfl5bb4thuFTZ4Q3Vb1YGTu+TWMHjpXJ5aOzN2YpjnBiFaoUVUwPAXMwZ9FC9/DKnmOeX5b48TK63gw9XNda4F7aLmNRWBfTc0/hZn0O5962wNbGtbgr9PY+T26lA8nOcvHz8xO16yApUihqaaqWUSmv3i1JZR7L3FHxNKovX1xdergezL0Y/6Wu+nWaGlB9bU5C5hRQqbkb3InY6eBtUxloQXYMGYlXHUpC359J2vd349OkTx3HDSslgex0OpbjAlMwTK5l9VfamPStUN+sB9+aDZQJGWYvb7cbHDx/fZNPkslhcHnWqQDp9B42yb6wVAoVmiJXz+zcdxnEyDfoYlFbZLpc3FYocTGIPCDjH+VZXPs+uxWms9HOLPbQZjK6gyjUmmxWe3j3jNXOiwqi6fhgrqGhosmRxJsFxduhn5imkJTEvkJRO6JIci/p4IarClkvzZDnvmFEyfRGfQ3xXNmYUqbEACDU6zWR4Vih/qNwziDJQuOQSoGIwFQaAQpyLS3LuIy2yuczO0ZNdkSKkPWxiLjOoj2IMFnXlIDRUfjDmYM3PDZkgcO3og7kSZJmTdR5cLhfKZYNianFcM4GKexZMEEU11ncJ78ocJA+IsSjuUmh5ydBI43q9ohAUAYdvV6dl8GZxgRt2Z1ruQN260wlvSpxSCl6balwt2PZNINcCDdgiNDEp2N6YzE02LZ+5IAr9050SZOZZUPbCx7/8hG8tX2teyvnuCevXMhNIHXln2O4MsHCRYHhQoopdbBqwsKDVwt0nP5YWjDDgTcWTpIprYLQIBsFcI0Pp9ese5WSpHrsrAT/bVAS09bneQiZLsrzkT6QcWpNDyDwHTd7OGQjcdT6WWjIngjdb29T2krkOvA0rYaSyq9xnnpRzy7Li6P9bcXBmxpilwtDR52Ymq527yb4bCRbk8hVxb2as+YwIiCy1UFz24bWWziyUrXK3U8ZSZlqEM8c9APw+tOaQl8sDKIvgsl0oVrndOvWpcoYKIrC7IEzKN8+lKZx8bvUfSn5OALVL9UZhna9pvZC6+vlxZ9WMo72TZ3NJZdr08d6Xr1Yv2K7Pa0sApxUjppQOHoXLduEsiyMtLpvn4DzA6sRNVqZaK0VypDy7BD4vK1IZrAHTGCZZfSk7P/jBMx+++YbrS2dvjYdNoFHcHHc1Fa61KG3Dy2L54jz5/7wf7k0gxbYSBDT2y5fE+49cvz2xcrDbrnws7zBvUocahG0Ep55df/f2eQVD1g5zoOHtPY9f/4gf/71v+U//8af83h/8jH/z/2w8PP4CX365oXg/o6xQMcRU/sbVJ1YnaxQ1mj0H//GnH9j+4b/ix+cLly+/4PxgXL7Y+Fu//YTdXvjv/9Xkjz5KJbRssl0u9OPK7LI/+7bxH247/8c/v/LwZPzGr164tExGKYZnK6Sl1XyWxT2SUzmyg8KkeuBlJU7ijJiMgmy2dRGnwzKVTiBlrc5JxQZsOUPN40zl8WKvzuj6PgyLv7JkjXwOBXR9zq4yNdBFZ1gkE69Q6DHUslbrSnvrK2EVsx2LXIqscG84FbMlkJdQtbzIF4HykeGyqwZjCKbuozOn49VYDC3rVnLmuJ9yel2uUV75qxb0qewWpmbcOUMqhPsPYVIx4K4iGyX6EizZ0pdly3GG5aJ7Rapxo+dZG/k9vCsbYqm8opairMZTBTHCkLQoGrzdh+cYlCWLsjAE//x7czZfCzV8hsCpe1hyTJ04c8y3+8fJbMiqVivsrjIdlG1TTqIHpQS3wzmOk1jKHLVSqdsO4YzryV9+/ASRtiIT6GEWTJcqy0MLZ9giSlCWrOozrXnhli1pnipchVdnT2ZmWGqgD7PMN8rP0rRzzVgUg8fq9DUVxp0g1f0Z0IxNqkmVtdlaw9Z4U0Nh4LVqRr0dqYQBK4rnwOxNeeKpkBQBpj2koBysc6n1bC05dVjkgr8SoLmrKXWJR55YdxWJVZcFfq18bcaqzhrnG1hmCS7kEKa/dx9QkkiblvM/U1nDea8upMy9g49+v+8QaFjDU1Wne3rNnGryWQ6/A0UJiHm29pqBV97qKabAWGVgTT2ncQ8lj7fv9B0w+AyC6M8uXt9UUPfXEvchdt3J0UT3lnJX8chswFSyAT1S3ZvNcCUJJzNjulPW/c5QFAARUidGwB005t5snedAGLUIkDWhklgq3sz1+ZGq77uaVsHlzgwpnWLCiC7COomuHN3SXqoDQLlacmOYSSUu9XSqu4p9fv+WXn/C1voz8n2PdZ86f76/fu6AkgQBd9Q8lTZ98OnTK8c5qW3n/Zdf8PT4wL5vWlZGR2bbHAJLURhZE9uqmsm8oDJUVBYDKWbCg3Ec9JdvuF5fGWuyjUOXXQ7ZD83461/Ab/zA+NHDycVP7FSt57mGlFNTUsXbXETvhAVHLKwrpb+hmvTrbXLORZ+6mGcMzpTNnWPCctZwTrvyuoSch09KtLff39HBacUSzBHqeY7J6+1kK8a7pwvPz0/UtnEueHkdkj6WytPlwsNeKdUoZQECckqp7I8bBTiHWtyu3bi9Gh8HvHQY0xmWUnyXVS6fQvZauNRCsc8fqBspPTUpAmLSV9rbloK4j7E4p17D89MjD5fGZS/sNZQRYoEC+jSkzsxdGqtoQcoFw0qCF6Ycmtfrlespr7nC1xv7dkngZ1Owr98PdKilKLMrB6Cx9N7czkgwqTPG4Pryyu2q4EQvRSHZXnCv1KY691KbWPWUE90rWq/XV25n5+hSY8wMjVPWh5ajUusbGq7DSZeRDrZFabJ/3W0VnqDPyuyA+8Ay1+Q4rrx+elHy/2XTgUwugfBWT+nV9frrlipB5/9l7d1+bduys75f65cx57rsfS5VtquMcVlAuEgBIuJASKIoiVDwQ0Qe8icmbygPyVsQIpFRJIIiErBsQGBuxpeyXafOOXuvteYYvffW8vC1MXfl/WzJqiqfvfeZa44+emvta99lMnXhW4IIcXpGDQpwe3nl48sLYcYltfNW2l26Usv5Mwlxt4z4LZ4/SQHiHoZ9jyyfU1rfk+UC57ALoL/XfdGqmudjTYbL0N6DpCWLhj+PlVT0Ra2Fp6cn2uUEnE2XuK7V1Le73rOkTTMnMT09bRLQCn3+lUDg1hpvt52H6wM1advULj+0SHN1O4uT/hNUGGLFp39mMuo7K7qFMY5DTYGVOyvIEgFwSGadNN9+DLGN8i6cx00doom2P8Oz0S3MY2RaXKV03YthxjoGjXKPUfGU6olOq++RUhhzMab825TiOjGfWDgPlwdJAFvLwqpa2NJHqtaS8s9PW6dIwDGq3RmpkIBTMdZ+4+3lRtkuOZzqTIGAtVa7mj/EEiilZHpYyfjmU/6l77qlXElJL3zyzOBTo3curNb6VFzxEyjgfjazbJ0jB72o59hfb7SHq8DJUhKkPOnUcX/vSwIG518fJtDE7nRy/buWmYDCMQQEnOZmRXet3rVPWz9RpE3/PQEv8ueU3wYpYRCwQDFaM/rWaK0qgch0L5D1MEhD7bm0iKmqQ3WiSyrfi3vEsclz49zKWj05oCfIlHU27wkBgTWbmvQH0zJY0re8A8r5nEoRo80hQkuXZgLFzrtdd3+CDnfALj0ISrkzmsRaUfN01qvAmfMg4pqSLhl4VmuS1RQNWnMMDYjRdO9bMhdDtdlTuhIhL5Xnzx755uUV3n2pcAiqNoYsLH0brAvI8LzzjJoXt5rrymmeC1aNUfV7Si+M42Bfk9o3sWTy/WtF0cLmAgdXOL00mjulNcY8lHiqJ6533U7imAA5vw2xU0MPbM4pX42i77M0+VvNpeGthACg8EwHcm3OvaBUL2s8Pl354nsX/virncfnyTTYirMfGgpKO7e4KVevGtDnhDIzoactPQNk3lqsE9W4fn5hvw1uPwmujw3uiVgOfhDRwd8w65h/AB4wmwmaTMzkhSfTs0rZNr74xe/xV/7aj/jDrw5++99+y/v3V/7c5T2PD/LZKJM7uytyYXAfssK59M7tcvCvfu+nlH/2nl/4UedC47P3wWc/+gt89v4rbm+/zd/5V/Djb3del26afrlw3Hb8GEwrWN/4lz+F9//4hacGv/QnrvSt3A2dxUrQ3SBgZ8DI+yWXqWFKA4oa2QdCz54jQmGq6jFbVq3FQnewualnb8ZG5fY2sOXJ7tNWHhNjfUwxh0nj3JNBE3fAKdmLiN1XAkpkwqhX5lz0evZmUzJ3DxTrIG9HSpPMm3Kvk5KMyI7BYrDMsq8RGzNGIGsAiBlpp6BaFJF7rxOswqh1aaiqGUpxaMgacXIjdLTWmMkYkaSoWJNEKfwTQ6h4Mn9S9pLDmpXzvjRar8whqWlMJ+pKIM7EIGpacHg1+uWBdQyOueuuqSW9QhPAWs4KJXhasig48W+Th0qqksFgptTHkaxn+dL7nH9Hqxu4FtHLJ6N3mjUurWJzZd0xegiwX75oXUla++1g7JIIli5AcBHJgrf7IqGcPVOQw69qYm0CE1esZMwqYVDY3lT9C/WtAhcQ6NULncDnFAiIfHkohUtrPFaZi8+AEbBy4I5iMp13T48bfWetFGYAOaCT/anfmWcJwJjTfmYUPyXTApMkfXO0sC1+MrCyN/EloNTQd+MrlypZ2Ktq4+kLCMABmwYHgYeu8xSufpCzxuaZVU85kWxNdbtYxZmEFXps+bmVJOZOSiP1Pnsu5eV1lH2qK2hiRtzTZM8m50yvbikhXzFyxkk5YdZvCnQETJsLuG3Z51kuq08QRt6Aya4nwUh9OhzJtKKIK1xQr7DcNUO6Epgh8Mq9j5T/VeH0Mw2ftNpyHlB9x9MLKp+ncVpSoB6gIP9jU021YiJXBcQUkzVsEdU5U+vLvWmL7L3yPUi5ISEm/szlGnayjTzvswTiSBCpnL2T3YHSYqaFRZxzSZybJk6p3Vkr72fZLEki3y2s9N0DSqnN9Aj5F73cOPaB1c67z7/k3fsnrlvVixuJTOeQYFa49I6MWeWP5Jw+B+X+4E90d87FGoPbvDE+fmDeviFoeGZFVQqlO+8250ef3fjTnw++eCz0GBxjffINCVGtw9M3Ji7sfoAdhAW9y9DZ3NhfQz5IMzfm6p6pvogJPjTQ+jKGLcYslFUYZVE9mAtmhApXDiQVHda5YC3jsl14eqg8bBeWNT7ujlvHa+X6JMPi60XJLRGT8Ml1M1oCIGMauxv7CF5uxsvbZB/GWxRuiTi3VpHlTlBNB0FS/WCzlcSNkNwCGaOtWIxoYJ1BE/PpUFrPdMAaj48Xvvz8md6DVoNLdfaRBQVtjsaSHn5hkrqhDXOpabheRNV+fd0ZMzINDVpVOlarndY3JdsVXbQl08161dUzkn0zlwCVcShC8jZuvH58ZR2KUWwJ/rQmc9C6NXrr0qjnRVSR98oag9eUt83cSPua8DN0w9K7AKkmD6dSFeNurWCt3ROS2nYhgK3qsmDpsjtplGaSEu1vb7x+/AgYl+sFijFDZqw9h71SjNpb+h1t9HYRG27NNPHLyTMkt4gpFsy3Lx95e3nRIN803Pba8XIynpQgYLnlWa53Voty+1TgQ5fWfuwcYyeOA8K5Xh+U8JaytLMO19yqndTL8CVjzTnSJFMX41iDuZSO5XNg4Vwf31ETTDoLpopCUllDFFLrTVvGKWZOwUT99ZlFR1KPc5syfArMzOeFdRlsY7klk9nvyb4sJtNDGQyqiMq7UKkjvkQzP46DmFMAYtN1K+NfFwslh/FTPlRzO9FqgzWI4VBN9HNfHGPoM/niOIa8CO4pOaIy12StFUMeC3ZuluwOKAQp15oTlqRlsFhzcnm4cumXPP9ZcFPacI+Bz0bvTKvyocbi3CydW7sVQTOnFeObt9c7yyTSWyhQUdamVNvus9E+44DNzq21aOVBoSaDMELPuNyb8wRZkYGxPqyAtbOYa9N97n716wSDT8DqDr4u5/HpSQy4UugJtFqxe+N3bn6yAGKun5sTcEowz5PFFxHsY+jM5t/X7uwd/YwWGaVe7h/w/w9gGbCclfed5WbLasV6o15kzN+tJTiV9/gJoIVTuyLWS2hjeLdpD/W+Ywa1nPITPd+1JuO40fomNqP+tpTuSXIrbyudnUD3j4XSVtec+fOq5sknDy6t85rMKU7ZXSn5HXNv2pOzlHK5gpkTjE+/J50MLHIYT9+L4zhSzpqswLA7ACkwUaemFu7nWI/rEwOgYMw8QysWz58/8dNvv9WmGZIdGUxzsEV18NU14EVAWUm4E6Mhw7zlc7JUDWmGLdh6F/v1tst8fUGtQTTyhKuhr6XhMXNb63RTf7G8ZCCE5Ou12f0ZXi6V234Qa0F0Aa4eyVwcbNuW3o6FYyHGwTxlgKl/KvJCOQFEHxNa5f3PbXz7zSsfP+48XTv9s43SCmMKiO+t0GIRUahR03ut4PtibZIzyGQ4sNrF1nKD6wOXL4P9Jx8Z+6I8dygdnbCDwjXfYXUyHh9SqlDBRtbokoDeFeo76vvv8yf/4uSvfz34O3/3X/Jb/+Ir3j3DL//yZ+ojmgzr2QAAIABJREFUSqdRGQ3KrNSYhDe2uhhhVJcP54c5+P3f/QqK88PvvaP+8HO2L/4M/fvv+G9uhdfjX/D3/vVi7krRDOu065Xx8gaH7vO3rfEbfzD4/Lde2Z4Kv/C9C73LALnWwrBdII7BFouwBfukxGTDsDmxJYBkCSPmstIPpsK1wzE1vApDlpmvUygNypKcequNuAT72yBCnn8qGgmAFks/s5rguc71KcuN5CeWYrSmIc8JSjSiBWMFcy35RroLYE6wFCSFunuspKfSXTp9UuNB/578O5Rmq6EuYqKQG6M2XcZhTe93MnbEvLSUI2tR0FolXOmpmLjAeVEKWIhkQbDSskiSZgHb5x2lBWBY4y6VyQu1WJESMyL7nQW1EtHoNLFc3e93vEgwsmgoSIVwDqQO8p7xNHYu+Z2D5inPCp91Tz45zmAxCVjpVWQVX5MyK4Z6LYGWety4eqTeKrVeaDi1CsRwX8xjZ45DnlRWiF1efKN79hMpYa+S9wn0SmCHuC/naiuScawiZr4fycCKezhHWC7ILBk1Wb+tNiiq6WZKXGtF1lYljKZdGcs1E04XwGl5LjGUXHt/hnpGnuBgLLFao5z117X4zOfdQn2DEsxU0+ysmSd1KgG/XjYWSu3UmUjrCZeXHlPn8/xZrGuBchSx8XotnOES3Rq+CjH1LFbI6kX3pRYw6s8/LQPXWqw6KGknchJaPNL751xsAh6V4gWKU9BdHyWybslf6+T9hC9J+4pYyloWC+45E4HvoIhZvkdGpzGWvAY/gTiRZt363vSkyEVUMvx6Jk4HCXgb1Fx6kMbgCdKdfk/C1wQQkiFIPwu6ngxz4TQJNLYErpbYhZLLVqXIhcvCArvP8VbVP5yspSDyLlZoy4KUKfq9p7UE68h/Vs4PdjZipJdaLoft7D9cihkxvJxSA/Nkfhf71L/ce4VkK6LPVs3u/d53+es7B5TM4DgOPnz4wNvLDY/KZ198wRdffMa2KdnG+HSIyC9cfjkrfRtIZqNeiqw1OXzK32OMwdwPjrcPHMcbPg5qSEalRqfwfA1+7vHGLz6+8eU2KG+Tr18CH8FeDjVeq7K7Yz5wT++GKNymY3Hg1rhUsDYw7+y3wdsxlAJRK6tPrt14qlfWHrQjOMriiQc+vE0+zo3nujF5ZY2Cr8IcxsfD2JfSmUoYx5wQhafHq6IsS+EtGTVWVFBaVwJSbxULZ4zJdStctosuptIYE27LmEfwzeviZXeOaIyovAFe5FWyCvRitOJc8sCb52Z1Lax0FbxSGI4Aj9JZ1plRGBi3MRlLF8fluvH4+MjD1tm2QmXQ8pKAxfTGwDCvHGvi1kThNF0ZPYd5zHi77dz2mQZq2rSXWtj6Jo+j1iQfuvvaqIGqGJxa9eXMdcgbZmkL8vHthf12Y86DWjqUSm+NLZk9p4FxLVVsJYPLpqFm7ZOPtzf2PQcTP1k5xmnEvW0bfdvol+3uweER1C7Wk6FNXTUZlAoplzeO4mPjXtQNZ3974+OHDwJYe5c3zBxJ/bRkOInRh0HfNkrfcIrkF56Dv+mSjmTo1Fr48PXX7Leb5HFdz9lKYXelNIaHooVTarGmQJ+CEgbDlHpjZgJW5mTfD2YabF8eH7hcrz8jW9XguG2dWqtiYC1YI5NakjmW0MJdPuMr7tu/y8NV33GteFXSo/y2dPGuNehm0Ap1q/jrnk1m0k/jvOwXtpY2YMm8aP1yH5BFp5WMrlpNBoMozOssWvbJcLAYKelqWcyUQrfW5Nh3LpeOdT0rT6q+TC2TX1Kq/tlaCaYJfF7zhqFEuhmuiPhsrvZ917bXCt4Wcxq9FEqY/K7Sw8fIaFZL1tAYkuKdzatlHDDJMCqFx3fPWJPGP8JlvG6WRvWfAL97jPt590MWyWQHZaH0cPzYeXm90a+PmbKT0bpp2NiaYqhLDiYUbWS0NFyaYRMo1Du/UUph32+As10f9Hkhae8pUUoQ8GfBIz2D8kljnwO/wSdvG4NjP/A8szKflqywUrSyKKRENxurfL9y3BHtO9LEs0iGYglOyDPLZICfQLplo32+K+emnDzhK0Atre4dcpgXUKbNOOczsJRWJchidm6u1HCuswnyRW0FH2qOtE3lfhetlQOQy5li3Q5iDNrlmrQuS5mi6O134K9UZkiC6p4R2ZM8Z2Ih1toxOwFB/Qzuixp6NmutT+aYrjNaI1lVkWAV2rKqzjiWw1wQSoVt+n7HTeEXkYNOuLNM0QnTlUbpTkrvlr5s0hciWQ3nAimxQi79wuP2wP42eH7Y1JTirCn2pxv42pJRlqtMczJ6RTHxQOtdiTO1yqeI9Hyxg7fbjcfrRkFMJMMpZbuzwbBMqplOIbIQGisKRuc2Dpo5tasOA/TLxtOTsx9pRJ+NuaVsYIZTPWXMBaIF06AV1RwfcaflOxmc4gfHXDw8XvjiFx75+scHt9fg/WPQnxslZNgvkDWb/ZYnu8BaFVtga4rdYslgrOeBrDw9P8Ea3H76SukLHifNu6xvPOCSzNCsh8ETYZ1SNohN5DsWuBYolcnD9wZ/9j9+5as//Ja/+3/9W/7xPys8P218/8srpXQ6G2Ezz0C5M6vCd6LpZ7k+GF99+w0v//yV57/8A36+f5apPz/P57/6N/ibb1/z+vbH/PrvvfDNTQyCSslo9hvHnLRa+dAe+Qf/+iOPjze2v9T44rNGNGi5rQ5zShVTqjaoR8A8NKRtDujeO2W/pch/sgbYhqTQw3ibn1KM5JspJmKLxjLoGERnHJMVwdY0uC5H/XhVMpjq8kxvl4ZbTbApgC5qapM823zpWYa81MYY8vw0sdJHBIWWy+gJmJY46YMUZvjUkjiia8te9P2vkYmu0uyyhqQtzSe9aTC2is7JHUoo+T35/R6T3CvyfV2fmB4eBJPwcwGU0hxHkeqVZIEEZmmOHPKLrMIxNbTPPNvhHOPGnAJ7SRaJUWlNTBtfUwz5Fdz2QXPnkoEV90yCWJLd63hQM5AkklHmQCLkApEizYSxu0zWjFyMTXKnlUOr+rZtk3+mx5SNR6C705fYEb2pJyGXUksLvYWlIlnPTEwKydkFsuhs2XIB62cqaamsKMxx6FnFkowsvfNKVS8SIeaylM+VsoyylNRX7DwzmnPmEDvlBAY1U2pw8Dyt45jqBz27yUi5VJ75WqvumIBZsgdM315DfcCyrG/a4GDW8PQaO4k65PcGrrOGYeU0l866NrQEXEvL7EB98JEWGBb6PEWvEx76s8uXQK3zvan67CU9ekslAdeCr2S8JINvegbqGCIZoKQ6SegMi0LnBJALtiVDO33BPN+jc5mv2lTFfs+ehFTjRAjnX0huS85PZN1OwuQdLDFyTsx5avkgQvOKWNTpHVfOdVPW6Zmm26myoWmu0XxbWK7v+VROBHYHlzxBqWJa6hlkEIEYWr78DtxY4hQKl9A9e2+3zNJRIxl7FnczbyKDEs7rJ2cpj7PHJheJnqzLiuWf90HKIs8F2ydLkbuMMeX25/se2T9Czign0PId/vrOAaWffPUVbx92zBpPX37B8/MDD9eLzA7XkAzFNAgGGX9oxoXC4UKkS9Kx7qZXqKFc7rhP5n5jjRvHPlnzDVsHZU5o2vA/Xgq/8mT88vfh+4832prMFYxbppKtxRxi1awphs0xE8V2ONKAzI7JLQZzfcB94EO0yIVLB+yWRSRNYAeKFq0bJXYmGlyPHtS2MHN620Sb7tAfH8EHx9tOicbD9QptY/ciSiNG7QlcOTQLHi4qeo1CqxtbBTMZ5h3T+XjAywH7bfGyB5PKjYKVJsT81Kmfmuuz2Hle7JatczH2IDWblVI6TuX1ELo8AvahYfB67VyvG9drU08XkxUyKPfp7FMFY01Jvabp3y/JR7KMqhIG3l5u3PaD2jYlYoXLxLkLpLle5K/RWhbDZBSsn9EAu+czXmKxzeF8vO28vL4oPQ6BRv3xmv8dGSlaIdDQaBhbK4w1eH29MfbJGFOmhCtjN8laPRf9cmG7XCnbxj1GHCCHmpNh17cmGQyKQe1JIxeJKO4AyjF3vv32I7U0+rZpo5iXOSga2M24NIFf7SLGFtbEkOATDdaKJZtKMrpvvv6Gl9c3aruw1ZqDjqnhKglo1E7EkMRjZixxDvUETB94SCpxAkpjDswKj+/fiZHTmyjMAIiKXYv9jHeO3UGZOWcCGOQKTo0OaxFz0i+dh8dHGfOVZBeZTNh9TaJMGpVaZXq/xmIehza07ukNMrXBWysvbfkR2dbFEpARBwBhMgK37AaKOVYaNefCCTKKXgtmJl3VTP6Y2k7e3na27QHrhXbp+MxhNk7IQcBOqfr5P+1NYM2DOSVtHEvbNXO/g237cVBLEyDtSzT7aNpklaYG2ipmg+M48Lk4Dbilf19Z4PR8zuXFw9MTl37J0INPunBDTTOxxF5L+dEJ/K+1sJW+NL0kSJDFbE0+fvhA1MYsAji3y4VWSj6bkszDlFMVy/daLJczBdMJ7Nrls5BbmmMO3V+t3CngauZkSn4WdUW8qpbcwbU4RXn5IutE6s2phbcPH2lbP/8QEZEgag7x+Y6Jtg5EbsJI9l2CVGfy2PSzlskjK38jSqJM9ppzZxuV+/0rQOP0RgjOQUCU+XMbFctZGQ5Qzp+vSHaZfzC3YUGh061SMiFEEsHQYOSSfpXTtwc1jL5ORpbAeA+lDlmrbNcLZzoJaJit+R4TFcZShHDWGjsTDOcSg7AJgFxrcb1e7yyh89dcSgqctlMsI8aJBKFPuGdpsYLYY5LPN8V/L23SFaiQcrxYnMaUHjlkJuvsnljDJ5DwPCOn6WWvhc8eN775cGM9bDSD1i6ED0EtsTCb5NoSX2JUqUad50OwWHWFKfTWeBtvkjk1GeTvu3O9VPByHx5Pb4w5F6UVjiWD5mDdjVuJUMoPavYLNbfRwXat7G9TLO+mc9gywSdsqYb4EphqAiLCgnKBaE6sme4YSwuLVnnblQb75feujG/h5XhjjMqDBZfHR9oxwGf+AB08GXgmsKKMDTZ5aOaLo7uyiB1aMLbHC6/fvPHx929cv5xcHir9cpWvU7nlkB0CldqR/fLD/fyfCXBRDKuLeoF3P5z8xb/6wr/7vR/z//z2t3zxXPnLf/kHfP5wI8qVVgXAbmYcY+LUBJYFTVyicLvChw+Df/9vvuaHf+pzti9/TNt+kbj+gJ/7z/4Wv/byv/Dt/7H4Bz9+47YWY0zV9d7xfRAGiwt/FBd+/Z+88f6x8R/+hc7T45URi7X0b3MsTfBLyhplut+rZMHMCV11cOqFp3Q9/x7gjUwHk3fP/b4qJbfqS8NkSw/AYxLr4NoLR94Dy1cydz89oxXqOM7BxUpFLKbxM0yg0+tEm//hi24yl68pFtLbK0ZRqedDk9S+tdC5KxulwFoHHpXeKh7yHAQBqr4fkoXOmey+TqlOlJb1pdzvWZJxGMnkY6W0tshU90y0ne7YmnfbDVXzXCiS6Geciz4No2KslDsL1EohfKkne7sRsYiqh9D7BWLmYjUyRRPiGOz7TrgCTu7yH/f7wjDI/p7I73vpbjm9bhLosPRE2+ei95psbaVt1Wul9soMSWVVdgprKkDCdVDy/jOiFerqXB8q+/6mwZ3Te0avMaZADWuGVc13bduINVhrYCzReczElKPQXL0LM/vhIvQuEGCoc+ewBGwvTsCiYFGViOf63MfZD3hO55GBJkV3abXCWieApDRNtdk6U3Zf5vjdqqCWkyUni4pYqpuWzJs7+23pHYNIBmzafjifZNuhLr3nzKFn1QFnjklHy7cwJ4buaSXr1gQy1H81OlYt7QvE+jtl9z4HWKPXSgPmOAjE/OqcSb1Z80pkvdAZLSE7lBPvEw5bqZhmqxCLOdZisXI+sfvC3ZINroTfSmkVTKzLgr7TEk3JfuG4iWkTKIG3WlXQVvZctSAGnql+czJ+Sv4nYqYZArCiBLFC3nFThuGSCidQbHn8kIQtgrtfmdjPWph5CepSzW1FCe0KdvF7X+lr6XssyRYkErOAXlt6ROWSMI1HTwb1CU55qoPOpe3pJXtXFGOwlhZOgZ75VK8s8mbcGU5mZ9uSjMC8v0uCW2utJA18d7++c0Dp9ePB47v3fPG9L7k+COkvEbAOLGRntbLeC/WV8a2hpKogN9KREpsEl/Zjx6eDT+y4Md5e5Y9jYg7Uy0YtjR88B3/2/Ss/+IXBu1ADNUymqqVOSv/kZWDLoU0eZ6PG4s2DayzaaowlKmQP59gvjDl448A8o0rN0xw1GL6r8KzKmpW1gC751Ticn3z4wEFkk7YTLjZCM+fnf+6R733xOdY21jK+eR1MTylPkSfQGJNrbTxeKg9NJ0vmy2pHjynTx5dj8nEY396CfUDQmen1YYhubkD1RS9gsSR5Of1hkLQNM6I2brdJVA0O7iNj7jv7PHh5u3HZOk8PV54fO9um9rKYNhXL5asks1Qh5nXrHCvwuLBVMdNOTfucwbHfWHMqnahvYDojtTRqu3C5Vlq1jGnWRV2KjGpPfw9HAFwQeFR8Lb7+5pt7WlQF+uVCqxu1NgbOdr2w1e0TU2QOWnXGPHh53XnbD+7FoOj789yyrnAeHh7FStq6aI+ooNTe8vtUY8Fy9tcbj5crYx5QYB9K3SqlcoSzz8Eci3m7afPSdHl5hb5dtOUqNYGiKn3+9QpUphvV1OSeZnbnpcaUkebXX39NLYWnzz6n94ukJ0siUXd5edStp1xIDdDICPATkPD0RVlTLB1HMlBq5fokD6K2dRnehRFTzVTvqb820pNLxtkxJpe+CYScB2uG/MnWYK3J1hvP79/Rev6dGFG00VNiBtSoydSS6eTaXzGbvK7BRk25i98BA8JVAFxym2aBSmJhs06wsJCMINYSiGVO0/oum0hd+KuCtZL3lhgUL7cbdbsQrcsPrmyEHfRWtME7t1tFpoCSFsl81+eejWIWEwObYoqUWrndjvtmhRJ4CbZMIaRU6NLAz5CMcI4DPFOVzm3J2cDrQ4il1RrXh4f7e7mm2FC11pQCqgErVd4zJ0sult9lSucqruZnx3WP72Nw3R5Yy2kVtiYD/qDc44EjJUqlnrIKdFZKE6vNp7bQZnpHwjnG0N1SqhI77FPSCXAHA9RIn1zi3CT+zHLmlFR5Ft1ajLfXG61tki8m+8wClowoUrKnlDiPuJuTY5apdJJFt7rdC7mYSUqduW6bmsutQZwm0S6gsHzaoJfQ+9cSpBJlOn0VINltEx+LeQzJyiLraz5bFdYE5rIJjZC775qDujXWCCqZVOnch6O1JMvZWmOfC+0lKiXiLoVsrg05wV3uQKxMycpEE9Nz96XvbS3PG0oCdTPnZz3KxFJCbM98jvIUERNA/lva9kY+9HMLd/o3KbRAoN8YO7MLzFDdMGTgnQBgngcNy56sJQdzghzucmOKAct4fvfMh2//mOWRxsbn/ZD/t0LGxsUwW/hdQif2VUyNLGsr2K0QFS6XjbHv1BLs4+B1H2wPXf5l2SDa2fSid5kaLA59+nahJli1PKihoAaBjDuwaO1Crztzd8ZDxRlcoqrGrUp0KCsoSwDSigrWGFO+d1DEMACYITNmX9jbRnvYePc9+J1/80f89GHj4XmjPxxpvN0oJYiqNN1qAvxKNW7Hol4q9WERpdAiz3h6dUwU8tDfb7z+0UfKzdgeOtQN6kYUJ2lPyGfowMqBzME6UbZ8+QMrHYsn2ILO5Pv/wSt/9a/9KX73x7/JP/nnX/P81Plzf/5z3tfK6NCsEgOsGWU3DhpuTi0CGJ4Ss/q9P3jh3/6Tr3n35Y8plw/YthHPf55f+m8Lf+un/yM/+UfBv/ijV16XZJj18YpFaGkw4LFd+HFs/L1/9C2fPb7nl36l83jt1DoFgvlGWGFsb7T1QG0Dazu1S04/5oTDibJrEKLSIqUXVUNHBLym9KjRwMS4sATFp64UtgplqzidtQ62lPJMHwm2JnixFj4KZpLzRxGI0AwqnZ1gFYXPrCV56iqSQs1jT+P8HBBTZhMmeZ4AwXKXq5YqW4iC3oGeDOjpIfzKja1dKBSFg8TK+18Lj+k7zZoCceYUI2YpVAcPoiiMx0M9A7k89HKhpCR8jJGyJEkorUnQEqH3USlqVfdJWPbXBVpjJDtto8GmlN41xWh2cfgFUtQEhy6NrRilGvvtjTEOtktP38+TjXmym5MhCjQapZLs5mTRFvVAjrxYfDlbF8N728SwPPYpZltt+BzcQrKvkxlr5+WXtannuVq9sfsiaHdW1zHn3dzf18qbXjHxpW+0S2O8vVBL10w0B8WnFqRRoBkUVxCYncxGLX2UtqvVCOjeHUugYCT5IKzKG87ka5dXOW4nwA5RTt8gA5ftxqe+FqLWTFMNqWoQ+6NayqFctcd9Ehhb23Je0vkskWqGmixV0jjdPiWUn54/GPo+i3ykatvk05qb69YaLVldXtKLsGgpdHp0Xnpjne8zEHOKAepOWQZrynMs/cfGnPTa7gsK+SVyB9Im6SNoxvRx9x8S+7cn4ymgddwTYo80zF9atFnV3+XuMAvYFOutiGhQqVgJxDZeTBcj/VQWlNJUj9P3FFOIk3laR4DARU5T//RFK0Zxk5fL6R85taBbSwQN7dfkg5bIpHqGohm8tKL5yQ16UETRUxDU1GLIEkTETeb+KzS7NzGKbKV/K5J0ivWl5znGSDN89WRGly8tkWC9elFMc64+q97n2ioznNYqIwHfllZBayZhI2X9QDLgcrkIWhqf/+M7+vWdA0o/+jN/ikJhawa+cyxd2vKEWbnV7KmtRUwTjKBC+p2QdGpw5qGt4tjfwHfmuBHHnoJEacQvVa7+v/T+hV/+4iu+uDxg+yuv3hm3G2NoC3u4mlhzZ47K9MEysQzeVskYa4MFRyyOmNRRmLwScaV1Hf4IONixMK7xAOORxeK2dipdTBYDohI+mBglDkrpbBHc5uChXfjhz7/nei28vB2pra0sr2LAtNwmuPNw6bx/uHCpUNypm9G3gq/gZV+8zcXHN+fbV+d1GMMai84MoatqE0U77gU2czyTkXqDlq7/pXamGzMqY8AgjYSLthZrBsTBmAdPjxc+e/fMtWlzZIZ8XZZ0yqKWpmlgRWalFtRm2KqUIm+qYsa+HxzHotaN9vQoad9Jj8XS8E3yCLNPgKOM2mWot5WSRsx6jh7BGJOvv/6GfU62XulVseKXras4mfH8+E7I9OllY9q6H3Pn5eMrr68Ha2WUpk/WWtJUp8nbdr3Q25bG5rmQL6ZLOjW5JFXWXKj37faige7wpOwWjjmUuPXyyn4cWDEuXebqttSQxHS61ftQXwDbHtLwTybFvpbSAiJ14KFL2tz59sO3zOX0h8ekO6qB6iU9RaKyGWn2qE3ATPr0ZpXSO7tPxjGYa0ijXipvQ9LNh+uV3s5hTT+3UgiQn5QFUcudfRhrJUVZxSyWs47FbT9wtDWsBs+PT2ylJgBxsg9AlKr8blFax/BdfinjIKouY0uGuk7TGZlp7NPpjw/5zKBYk1xM3IwEUYxiTVu8MThwpi8Z0J/b0xqSl6nWUOck5uTy7hm3lHKh71k9UZrj5eVuZdOl74feoQjMnErJs5cDb1Gyiy9FE2+tccnY3pKbT6bkk6VrW2XZ+HpBqXgrOIY2S9OX0jDCKXPw9PSkzR25HUk5pYw1tT1bSM6tLajfwdzlLllegK1IyZDO2P76hrvRrFNNTZ+vxRg7tTV62e73TClnjHn6Jek15XZ7pYaGMA81Ol5gHIOH6wNGMngsPXJS9nX+Gmuq8Y/UmcMn0DTvAvcl2U3Kft7GwfPjI9c0qT8lcmIW1nvThapXbrbS1yI3nQKtcutHNlUeOfDr/VmY/KDO7a7p63PQ/9/051orWG5VlXqnO+lMbTuOQ6EDKWfEwUomiSEqufvEWufj2yvP7YFBJXq/y7nKaRCKAOtTLrnWft+aYc6+72ylJvtx6MyWwlY7LRseSG+eZKOWrWHHaY4v8LZls5sWlvj0u5TyjDYWm4hsyHWHyoA+G+D49Hs8N4Jm55+BnvIAc93jZHNXaqW1zgynIImKkongdrvRyyZpbugJO6Kdu2sJM3C2hwfKFfw2sKcmmn1tkvrRmOpiqSsB4KqxipYLgJRtVILDgpiLSmEUAc+FNEJeDr2yXJ+/ViWmtuJ4HAmwbflOSh691o1aOgWB54Wa21W94+2h8vVP39ieFe4xTad0Q4swt8BjshVPJnRjjUyqDd2/xRZziFFQO7zswYNNHp6N68MDH78Kxi/Aejuojw1WvUu2JT00wpaWXQV8wdqNdk1goSRbZcnsthanvw/q7cJ8DeJ7DboR44b5jei5Ba5XsFvWi4mV71FouN9y4K0C99igPXB5fsef/ou/xF//d9/wv/793+X//a1veHz/wK/8CJ79SoTTemBUjrprKJ1VNdcmqxaeL843s/Kb//QP+fkfXvnBu39Dq4+U9kg8/Xn+9N/67/kfPv5t/qfjwm//ZGe4UffFtm3sHvjhHLHYHq78zjD+93/wxt98Z/yJX7jSLoa5zNKnD7rBaoutH/Tm1EtHqX6FcZsatM9hBDiZseZB6/BIYy6SEinQcU6ZrlcTA8AxWrnwuAW3G3gsrl1MRbdB9ZpAgVr2FQfQkDgmxKazjWLOpUpicr10xn5DiXznezAp1cWC97xnw6B0zoRHK4U5BMKegDO1sYZ+xlqbGDVLda20xqUoRcz8NCHX4sZOs/CU6eX1mMw/Y2qWoxcjmi6RcegdtNoYPuRhlJIiGyHmxdTCJ4uQAGXLLwcZ5m/W6JeKz4NWKpNdHmM+7klknkue2lT3rSR44sHb6xtrTa7XgtX0m7Kin/msLa1qmG6FCxtlTMYSC1UAyqKWyhgKAbq0qsCZMFrtMvV/rDjygYuhmexk4KgPEyvW08NVPqwJhmEpZdfdvO5+uZLkuwXFLgC0bcNvRzJnDdDNg6KtAAAgAElEQVTdKQNvMUSsWN5flp5rOh9e1EsmFiGrBgphDUw+bJv1VJE0akCMledB9UTmxk6ErBbmmeYaKxlfqG9wp1b1CJWUTIfY1SDm1eHOvh/01iCfmVnNuhvp5VxxO6XXAj/OAxiRIQ0JBhnG1jfGkhesTzFtMJfqoWpGdsDinoVHKQIbli8ZVk/9rF50loNg+GLsO5fWiNIkpz3BB1c6aqxgIHBqLYXsKB2s5pzhubRS6mytWmAUK+BS19zlzdkOlsR/wsSYr1ZYJT2cfoYlVeiUEsy13wGqtRZnSEczY5Vc/iyHBL1Ass4gaPZJdbBiKcWxGtY6YxxAyWfqn5jbRUub5VrKr/sCJ7IXbVgzFiMXqpXh6V2aPfP0JRA0E3VrkwSTqd649q45IOWAa2l2O896MwFVRLK2fVIyjVW9mOt8Jw7pGKUq9fn0dRODHiAZ7oGYp57eTn56rH1asn4Xv75zQKl3p3ohxs6MkTRR7nHTZiTyLMlWscpcO3N9JPZMe8lI7WO/sfyAuZj7K8zBXAfhO80q3eF9cX5wCX7x3cEX246/TV4+fssck33uHMuxpTSjD/6miwgVjUJheGBLB87r4EZQ52LOgq3CLXIQ9Z25JjHH3RtixqKslzT8qhBXbrzhHeoSUmwPj2y3QcuLLV4Ofvj5M5998Y5xm7zeJLO77VNFqRZaCdbYqQHPT1feP124dFGsa9uIWLy9TV5vB6+H82EXkHSsykHlGCYpYXqKVFNxhMAygvzSLNP01JgPh/2Q+fd+TEYYT9cnPCVZtmAdkuFcL43nxyvX1uitSCJhAgWPTL7D9LwjFs0uuKtAmVVa18blWHDsSq7rl0pvlxyOM/mnantE0hEBXl4+0utFA2/K3gqKn9+n4yag7TgOvvnqK/Bgu154vHR676czGmXTlqe3pqSR9FqpBvMY7DdnP6biOf3cAomhc7iG3W3bBOwko+NMArFSWEUbfZw0pIO6KUnIQnRJCvSitK41Xnl5OxRD3TqXuuXGDPn5FMuNTDIA3GnXB21OQe9Wa9Q5UifshCsuPsJ5+eYD+3HDeufj6wvPl439OLhcLrxVgQ6tXolasJiMqWKpNJCFp+9PHEoBa+RQ4DIOf/fZF0nB13aguAuNLyqKpegiPAs2cym+1vgZJuLi7aaz5C723PPzO+y6sVLiJ/PEUBFKk1XDJdtZkiUyXTGpFIobc2kz1kQAoUbh5eUjl8tFcaStMJGnjdmixOJSJAXUtqBo0xPBGDv7GESaIZ+eMGoAgALfvH7k6f0zZk4vpthR30V5TYYIFFqTD1CJxZqHLnqVFYEyGXBwboO3y4Vvvv1G92xr1N7UbCaAV5Ph0nuX35IHx1KCpa/UZ6cM1JF8N3yJodIL23WTX1mrKo5b03amBFHEXGm5GYRIHXoySVDBDau4DQ0kQ3HWcx20x0f2NSWZWWDDmfmcKE4PfVdWKmWJzTOT9bS/3eQrkCywmY3cOgabw9P1grWqd+p8EslU6V0+Mk6I8UCkwvOTgXcgnwdKZVbdUT4mPTeOP/v3WZEviUAfv0s/LFQoTjCOhBtl3q2UwRMcI5uYIkKdpMbpDUgNrHr61YQWerVqCTIzPKK03LZqy7jSNy6QH9ApTQAo4Xd2Zrg2xqVXWm1MK/TSKa3DvCXpRoNas5WsT6UxeTUGopVH6QIA0vC7pvfV6XeibVtucWsoPSeCFgbm3EwpUkdMSvqxnUbXfgRjXmlN8VSGwMeSAJP7Uj2wmk2WNswUKCXum2GPEBU8/TIiJBPd5mBr1ztoFhFce4IWRUud4Q5tk/SjtuxHMoHHyEhigSHDBpfHKx+/fsPePUAZRBWo3msHs/R7yWY/ATBCqU+lg1OoNRTocFsc3qlVy4a+wRhvTH+k19OjKj2xwtldMvqSJsm7L5qlzHEVtk1GwU4wkYTghNUvlycers58Pbi8e+Rwvd9KCVvJtjDKAa1nIiaSvZYIJukzcQ58Bqsu3m6V9hR8/oON3/vtb/j4bWf78sJ1N0oLzCtEwUsmYvkGFVoLmhlzQWXiXGkJcEw7odvCpT3gXzgf/+Bb9q8X24MjKpUaa+YLIP8SiyesvoP4KcQzJTrEG7BzMlc8FlYvPHz5zF/6T3+F3/nxT/n13/gp//dv/ITvX3+Z7fsHVh+wprp1aRUbzipi5hYKDXDbeH+dfD2Df/j3f5e/8fP/lKc/+xnNOtRO/f6v8pf+uz/k1779u/zt8cAfvOxKNPRFu2zcbgeHT2IPHp4f+I3bzhf/8JX/+r9afOGPXC/GaIVmYAesWli9YA22KrlnM8MuxjqUchQu1ibhDBO4HDaVIBcXjkVKiha15Zmbkf5eSvSaZVGfrvC6Y7dX3j9U3kYwoxLNsZWgnZPSLy0xzTJ9UWgIJdBdUCtlHkyrSvkaxhqhdKSSO6KwZCcmeLDkoeSu8BkzeQeVVgWcW4dk89ZWKMtZ2YP62CE/YyVNgSMZPS4m1fREkUx3o58KCex+p6gOy/vGw1kzEy6L2KkWsqqoXX2DUZhrpk9Sw4/4lALZCnWlR+OUqbbHpMQlJW0aVjXITlhGXBqUzjyU/mauRehcnh50J9Jb1SNW/SzXsvGQPfJcaVruQd2MOFBPWuSNOedBuzyAk3etgPMCFC/UKESt8sOh4K4l1dWMdnFW+lrur3vKiP2TzIb0qlmhVD7X56xR2ErHSxDHSF8vWK5QpHMBSCiFLpI9YqgPEEskjazPRekSEFRqgaY62GlEl4zT0My3mPc+dKrIn02BnoMV5gp6yfO11GctM86oITUHThe/l+O40brsJ2Yyqg2BSxKepPccKpYL+USdiX2R4ElPcKMUeerNtUQ6mMHcxfQuVV6nUSVWNZq8PP3QErVqntqo6bckZjzVsZo+n4enxUGDZP4ud5rJrH24anlJZYysBAobWmJGFIo1tir5oRLPAo+BRXC42MmViod8i3DDmrMoEFVgj94urFZaYh1WOqdks/fKPgepzsWAeb6DjoDBAqdcXcC37AsspIQ6Dc9LUT88p+aJ1uodVHI9LHVw6eeWHWIyg8WWvNRNS+GedhanmsB10g3JLvXyRDKWWsoBNXuXJXbuGKftQMkWNmVzpj6LpuAby9X4CgXqOOqn21YyfVKpkRbIS8xOpVfN8BvN4eR9UfIUfle/vnNAqbix5k06Vav01pMirYG7kdrMYswxMYZ0g9MyCtAVr5k+H4zJ/vI1YwyIRUNoeK3GLz7Cf/Rngh8+LexYzBfj45zchuJ1nR0HjjUJOtWvREzWhBGLMW8cIxv1VThW4/AJ9WBN44w+XkCdXWyjIg04yd6bLIoX7VfnwSyN4/WgxMHhhVo/4BbUWdis8Cd++XMu18JPv30BK/RL4e2D4a2xPUquUyO49spTKXz/557pqXdcc/L2Jr+d6cbrDj99C15XYdHZ3VhWKZuox1RFwRsyPVa8Y+PSjS234FHEZJgBqzVdKl542i40a6yAD/uNcTitFj77/JnP3j3hVc2CAM5ybwCW109GrIU0NcvtQrX7C3kMeHtTYbxcLkpaqIXWZdArf4hJqRtrgc8Dw+n9qgugGGsapRfe9oO3YzFQczNvNz58+DoLjLHlBbOWXuRL6RSrlL4lCwN5I8zJy37If8mXfk/GTvuatF7Z0zS7PV5ZDhWh4JYxxhcTXVjDttLETobYOmQCHUXb8gjjNg7GvvP28ZXDI71kFO17xhSXWmhFHglWK/2yYa1qOAiHaVC0HVoxuQvB18J88fLxhdt+w7YuRhKZ+NQqXqBHMu8MDfEj40hNLK9W9T3cxp4NqqWJOPzkm295/Pxdet9o4CmYhv5S5O1wDr25rVtjSoaVm6aIEHNmP/AY1NpZAx4vzzw+vdfqsGx3f5dqgS1JIHyBtcY8hmiz0zVYWGPEIJC/xMXszlw49pvYLFtXY1c6Zp1mAlunGS0ideJTaQhrUfxTShUJIEycGopY7/3C7e0juBrMGDvYhYgjN2spjWqnJ5FxeiVoM6nGsLoYXT7TDHY6pXd8OWMs6rZRy0YrkrL09NCqtdCqto2+31gzuB0yO99Ku3thRbImWPJGiBU8PlwFNhS7myZLqmR0jOJqhOSTkw0v8upaOF61QYeVsSrBtRfG2AmvPFyNMUWTpqTvAIWtybeh9GQoIap7AI2URRY1h3NOfA5R2XFu+01NT9mwcEqCKtYF2tWWwFBRbHpYRfKoRY9sKnKTT9HiQ8lT8OHlJ9wMnrOupQXCvUnxMdla54xdX0teOXFKET3NcCescppF5s4ogoiJldBCw4JuYk6VqMRSSIWjTVxMNaWYvA5iuvgyhv49Rc1wJFh2qY1r6xr09aYLfC1NIIk7tUPvT/JfKM4aAl7P7ycw+dL4oFC4WKNaEx197lhXellJDb6bmEOQfnYBh3uGDKDmZ8lLYpqAiOZVm1HQwFkqa+g7UlMoQKnm64ZLKpBYWbIZoDT5hcxDA/VK3zRKE3DROmaF8XYQTybGrPUcEA2i3Gn5JRTgoGVnGvongy1dLtRbuJo5vPF4feA1XjnCuUwll7WSBvJNdP3wYLpxzIYZbCUoVQHbM2REXJthW+eyCg9hvAWi7OzBmMFDyPRbyq4cjFcBU5PoMYljE2henGmDsQ/atTIp6Rch1uJCg9FnX155+enBMZx2ga12DWelsSywuDFdAtlSBXytUJMcU03vGaU8Dy1kfBnHDK7XZz57t/jDf/81l3ffBwaP24ZnAl5JOUtbh5gjpUoCPPUMqjleD8mbczNOiHW7XTvX543xUcEH/SEos+B8ALtgyNw9NjHkmUWSuHKFGFjsRBxETIhDbJztHd/7k6/8l//Fj/jd3//Ib/7OT/nBu0f+2q9+wePloIXYd1GHPFECqhequWLHm3MpG4/vFn/wx6/81q//e/7Kz/8m9fMr9vAI8UD7lV/jP/+1P+aP/+d/zP/2+5Vvbs5YlW6Vy3blbb9RfDFfJ0/vH/k/f+crPv+HH/hPfrVitlG3HJBKobXFtRhujW4bl+KsOrFamCyOsWhcmBhWnL46URbbuuLhzDpgyX+n0YmAjur7Xt/EiHJo0QQIXxov3hhrp28Nr5N6FHkAJjNiLcPT7HaZ7htJwdJDSZiNZE/Liaj0XpmTZP0oKj4cSnTCUYBLWempCfNYaQrs+Nxp9ULenpLWhrLlQL48VjZ8n9TpHOvAP062S+faBtOciK5acTImOI2Tde+KTSzj4pLA8wrJx4/bQdsu3Obk0rukrOVC8V2sTAyGEVXLweIOteWdXik2mBGUlMDvfiQoZ5jLx6m1jWZBn4Ve4Whwe31hP6D0SeuN1i5y0yqqySUn8rkLtOylKbm1tnxO8jn1UqBcmPtOBDw8PzPnTBZXLp9CbJBSChT5wFGNoAHylHEm0TtrOt2M7dJ5eXvVzNEaYwlI7iZfG3CqQ43QEqNXpcU9XLAxqLXk4G1YPs9z8UCUHNwT5DP5elE7VXReTisNj2AN1ZHFVNDQVtMLTHdV1Jopl3p3jPQUgpRNB9OH5tsu1mkJKakczVzk58CgFdXJEU5LGZpqWtpFZF1L9ItSlGLtS/eqEmWrluG1pESvEq1omVsvrJGyPC8wyCAUBw5mNXrVmaEkuGYGOF5kXdDCOC5FHqJnL+pxBzvNg7BFqxcRBtZkFYOU9tVziWNinIEA3ooxS9bLVfEib17h/QJDZsj3sE71v1ZU52qyutQmpT9UFVCiBcYUo3kGXhzGKdE7vajEIPKl+YDuUkct1TN3ElyC1hqrKOAoQKBsiFV4sr5KUVoyNT1jPdHuKQbbXkKqhpVLwctGsWDzVJykB5cwERjVc2md5xgZ8K+QLcJMT9bT0sXN5ZfX/j/W3q3XsnS97/o972GMOedaVdXdu/fJOzuwbcshjrERBAVHQAxICHHBBV8DZL6C81H4DAEplghXSCEHjO3Yigmyccj2cbt3d1WtteYc4z08XPyfMVdz30vqrm5V1VpzjjnG+z7v/0gU7Ai0UgZcpoUAIlci0qHfZxpHFtCcdT4YQQ4eN6Ky34YImG/wK//Gb/zGN/oNf/M3/+FvTJ+kXFlqUe6LRYWmAUWH7dljITGl0E939r2xbU/sz0+8//AB2k57fk/zjWqDzKDkwrvTwr/z2eCXvrPzWemM7cZ4Np6uG9vQTTGn0UzZMFYesAqLncirkapxypk1L1LZ1AUriVM9hZ1DktAjLd02Z3aj9croykXYe+fWJnuDthl7c67eeOk7G53r0BDZRiPvmc/fvOVHP3rHrb3w4Xkwe+bjdWffnFoW6lKlorBBTc67xxPfenuitwGumlFlkTgv2+QnHxpPN7jOzOYLbVbc9E/KQqlzMlqXPPpSM5e1suSCxQK6ddg6pLSQ68rLNni5ddZ6prhsDl++/5KSJeN/+/YNy+kCKcUQLDbCLLO3SevO4PXgMyNAr5ZVh8KjfnM4Tx+fIuS1xIAauTxDhw135S1sW+OoVTTEhrvPABwT1+uVfW/M4Yxt53Z74Xp91sZXE+v5wvm86vvlyukSwdnLCtk410pqnTk7bd91D15v7NcbrW3so0VmgEIq6+XCqSwCBXKmkFhqsOk5keL+trA3OWq3SEui1MKpFpiyenkf3PbG3jq33qinFVsqTO7tT7nmOIRnSFnDw7ogTgt8NJZamSjA3EfDiLDbaF673W54UVOchiPntCwa9EJZlWth7zPaETp9TFrbKckoizbI0cZ9TdqH8/HpicvjG5aicFEfM15Hj9cXwZQBwNwDvrf9vqAeDRH7tgnsqIV53RV4++6NQuOK2lXEsw/lQ42BeWK4MXeph8TcSYbrcyi4HqMcbNOc9DHYbjdqACiXqtyrI6gOn1jNuGvj6V1Dp2o6I8B3akoulkLCOyNjZ/D+p1/x9s0juYTFsoQVK0mlYHXBLFHLok/Qo4p97NQcbGMSYC12Qu1Iy1L5+PSEkVlqhezKnU2ZslQ1H1YpitydPsUI771F9saM/ATZ1EZswsoiMB4fHtXUVZUjJkupxeFYeVXZZNsrB2t7kLru98+TroBPS5Cz0257DIDKpVkjGyhZplpiXVceTheWqpbHXGqoESV37m1ne3lR0H7v7LNhqXAbjbHtnC9n6nm929FKUVujJ6IGPdaNpVKmRjkfAh5wi5riCPw8WKlkvHz8QM6Vx8sDbtwtrZhUhTXqjM2jkdDsnt8EYb+KY00IiRAkZ/RYZ5bzRVuiCxS1GJRzjgDkyAI4rq0lC9bXIUfjTvjsS1gtfEyWWjlfzgJh4idjwTgnqYmKZVLSXtCnx3U4mtCOe3y+/l13rvuNl6cnPv30nZ7tnONwYAG6q2LY4d741Mak7Tv4pJDY6FIpTu2tKRFMvML6ry9XfablsCMKdE7xDJWaqFWHolp0kLSoIJA7V/eWrvkh/XauT8/K8TqfZb9NRi1GrTUORKqWd8R6Zo72lLAwxs0u9cQ8jgTM0alL5unjM15qWKH1ygVKOdXEV8pNr/ssJyNbudvy0vHZAzXLAChiQ2SGOdR6uq8NOYkomiWDraRD7ZOcMXZlP5hx3Zr2PpfKQjeoFBbZBku0/LR9YumMsyn4FFfY/9A9nIaOj/lQAExjukByH1Lyjja1biWHBjk5+aHz8QvZQC4PZ1kwS8Y9h/V0Skl0NKvWIsVZnvT9JhuQg+W4F9PQny2rgvPfP8OYlLWQmbBLka6yD6k15GPtMJqICc9Mf8bmBGTHsTmAF7DB8rbysMEf/vEX/Olf7jy8q3zrk0yZKdYChf56guFdjVaIJNjT4GyJUit/9OOPvL01vvUjI6VPoV5IVOrnP8/3/F/x0x9v/OTauU2pLkutVFejajLlxNnbd/zBv37mO8C33hVKDeY6a9/8+NxoT4U3336kVn9l/9Pr+mTu+n/rQBHg4zkaKkPFRIQt4xxBwYksy0XQBpYUFTAjN6WY2oWPEFsd+ucry47FvZrE5tvErGOemTakbkHqkWQl3AqySmXXIb0H698G4MqaI2ktM3OWclK+pyu6wYVABRCkA+y9oRRdW8uhNvWkvMFYWw3lFNqhUHDuAcwSDSTu7U9JTopaCtu+ofO/38Oej71kBrdn7pScaPuOB6EXTCmWXK2q6OA80CGzxxniAHXco/wCp1SpdUbbBabY1Oxifif8BL4Qn/Gk9Y02Nsa+se+bABRTztKRR9e6VFx91zrQ54xWOQeLGSIhMCxNmMoxdFcxy4wlRo18mvXb0GHcJ/dZ/lCCDCITZkyWXO42dKYiGzQL2OtWlBLdZxSV6KPus4dqxO9NgpYz02TtGu6YKb8mu9ay8EkHuGl3B0TO+Z616O7UUl+bXO3IQBSI4jlaHz1yswJ0mwxqlXoXi+IajN11X+QAkuTsDFt/gD4HGVtS1hmlzdhLEm3s5Kp9Pnm6ZzYOPK5/0zyAyOyJ1LctuWxP07TXxQtTPk+iZs2kY8ZTPFoooaUEtHBnqJAmsc8pJ4pPBpqNzUX2xKuNPU4ATM5qxpxuuGUBvy6gTg+H5qLZO8lFlsk65iJUTKpAS0V7WcpSbHUBPyMiMIwUsSJOWQpE1h9Etlvv3Bv+0DMh5bZ+NYLUzJGL5s7RKm8HixUzb8qFqVo/qSSTslYThOJV8xHJFbqfdG/q9p73fdhCjdRHBPW73DupRgxJfrW8HpZG4vpqT49NPWZEnZ31v9P13Pg8sio1Y1lW+cZSFbj+6//dr/99vqGvb1yhdLRB1aiYxlVx6KbNaowZTLGTI+/l2iZ+06YFN7aX91i/0nYFW61AwnhcTvxbb51f+O4L3/u0k6bq8/oOjSurVa7tGXNj7y+MYdg2+dif2Mdk7pPNJ9ML0680d2wqY6CbaiCbJWY5kQacmORZ6OedNDN9VDxL6jp9Mmdj9krfFVi2DalO9pnJPtmGczmd+Bs/+y1Syvz0Jx8ZM5NsxS3z177zKTkPqmfmzNxci9+bFc5LgrGL/Z6wjcm+D542+OnL4LklYGHzRPMk2Xyt9wyUyWQpxqlWSoJqTolE+OvelCWVK6kUbn2w3RSSeTmdeTgt7NuVl+sHIa515fLmLblU+pg8bxu5yO7F6LR5DHnR0pUiQAyjWpEXO6lB4dYa+8uz5HcGMzUYidGNelJ49+hEtpAqu8uSaPtgxsakBzHz/HyN4X5yuzVut2swRIlSF06XM25OrYV9Duq60Mw41cQphqXryweeN9kqW2vse2P0QW9HC8pUNXsqnB7eUmplieHDkrzE66KGtYHCpHEPtq2wLpkclqq+N1qwdWNOmqudz905PzxqwA5Zqax/YLmQclWzlhmpKjzSUHtCLoUWOWVHK1RravVqrbHdbvq76FRbc6FexNgvkUpcUpGnNqmqmynGdSD703SFKtac2FtneOd6vbG+eWANy5+PFpXDRYHclgOwiLDjGU0hTczexKW8wWjbzkAqi/70jFni7btP48AqUabNRnbZ4/roAh5N9pfeO3hjLZUx+6tn+84CoCpTnO3WsPMj9byy5Boy4cSCDv4pJfaXjeV0pu2bDnMDRVhPDZM5ScUyXLY+BRZXvvjiS9Z1VZB1qQyXomqYcgeyJalWcmzqwaam8J3v0cSWTL7rjjO3wZoLszVma5SywFQbVEmJ5VRZl0X1vWYC59PX1mJUA92yLJNEwLiyljSEXM4XbTLJpL7oU2pBEjkO5x52CjEfkREX9pgZAxs+7qGfaWqt+fhyJaWKdYE3e9IV9ympMrG2F9NhoQ8xZq012t5ot02Nccnw2TjVhRbtd+6T9XRSHoal4JW0iyTRZxBqqzpDnWFJbYjaaUOdJKn10VJDUkvgejkpuHQRWHqENE8jLBNS1HrUrc/IjRB4FgN9NvI8wr6dUjOj75TTSsnK5qqxlo7IhOMoFTABjjrDx1AdQx8YtSa221A1NsTnJcVV77vu1bA1By4jtWYM1T4aZSmMAF4mkQEwhkB+V61vMVNAfYoMkqL8lTKGVIEeUxaZHR1iFIAvW0AyhdDvdH1uU2vYnI2p24ZSEuti1ErkK0YOm4XK1VyqBpNVFLMY/ruu0xTT6eiwZ/dubdnWlnXh6XpjBFBTinL5WmukmpXBUhMSmh25FHbIKxRE75oRci5SQXQpqWcySl3pt51cMtc+7p9zqgU7rZzqWdfR4r1FcGfvg1oTe58CkkoMhlNgeSoVy1Jtt22jZKlMpw/wTMIVdJySGm2m46kxx6qfVQr73qj3Mk+BKB5z2NaMvDba0416qfiQarXMIIWQTH4ghre7VLb08ao2WRPeYI7EtEIqERI8B8tp4c3nnQ9fNh4/31naQjMo3lSikLIUHCRGyvi+kyjQjXY7ka1SzlvYJRxG0T2XB8ujsT4Onr/csOr444W8Gnk3fHnG7RNS2rA28bTKsuZd95y/weiYTXw2yDflW1jilCc//Pd+yH/4x+/5R7/7Y/75vyh88nbwo29/TzNrMfKSyddJnpWcjM0G2ODkhVEnZ58snyf+2T//Mz79wZnv/gdvSKnA8jOk5Q3f+tX/lv/yL/5H/uqf7vz+l7A7eL9R1oVOo/nAu5O3Bp99zv/8u3/Om0+Mf3s98+4kINGS1IaWN1h3OEgmnDQ6eTH6bVNo+dxJthKaTAaNzS3WTlW1k45IAEhULEmdbLvThjFbp+MsttLawAn7SrRKTpvkogONzbAtmprU5pQ1kGhIS5FZM/ZGKsrNSaGsmL2TTNlIiuNrIiCKAJpJurP8czo5n5hz0rpcESnF2jx7RCwOwCgl0UdlH405R6igJkup5BIK5qTK+zkFzg108M01UciMnNi2jdlT2LUTpWR6n7IeuQAU7SPHQRDmhO3addAdXfEKMdt7TqSufbMY0GUyEzknxQI1gsgjx4ieuJzfcktXXrYn5rbhebBtk2U5xfs/xB5JyogsWc8U7Rt2ISmueu9EZxwvt6vyoUg4at1OrpneMgpAHirIMJLAuljzlXuXoEhBmwrfDykAACAASURBVEsJYPxrSuzZ6dHInFH5R46SFtn3FTSckjLhpjuejzkyIIsp1etgkkNRUzIU97t9MSXNatmK5lN39r6HdXp+7aDtAaRO2hAwUiXYYQbYdtjpfKjRUG8k2suysaZMj9D2bGq0Fimu97TvVywv1LpqHUgTEGBoboTpBQYsh2KpdSnBXM/sqVQi4QmWAjOTx2COA1QMZZ/MewwyL7tIhr0PNRxOgXTZ5OIZYzCz4bkwTHljw1EOojdKh5wUFO0zhfPAdL53lRNNc9yiqc5hj3zZNjUTZJO1uGTj1hXBseQksCjmakzA3vDOmImC5rZcFp11hvLdkgmkmXOS1yJSY4hqlsI/3m9Ta3SaoRpHpFRvTQ4RBZNqFg0HlRz5r6/FPTKjouLusJl2JimcEAORtqelMqfTPEFScRAehKYFUGcdHx6gV4SHx31Uk86jOSsqRwXFDkMA2ER7O6FaJM179IT+rICmw86nmCWPue9A/MPi1o98s1cg6pv6+sYBJZBMsi4RQhbBYc6R9TA1ECQN5W0XQ+TFwXfswxNl33SDlgzzxqUYP/P2wi99Z/Az322cqtG3yqThaTCT4bmyLY21KnBrzW/Yxo2yJN7Mydj1UJW50xv0doKONoWmDWT0XY6NbTI8MWikvikg051mL9i+Sn43DzuBR4sTqDoNLg51ZH74buV73zvx4avJh/cNyoVrsEOfvXvLUmaEMYdccHE+u1gETE/abaO5MSzx1ODpufH+BtusNF/pFKbnABwkZcSEoPvoJBKnmmJbEMBw2xuWsq4TiX0EwDSdh/MD67rw1Yev2F6unM8L796+kS2ontiaGk3WtGClCkSyhFEiS8XujPpd0nkMBqPx8nLjdm2UKuZIFg+opQZaGot7kpz/dKqYSU4/bFCXwrZLsfTyIoXTnJN9v7E3dXStRUGr63ISELAkZnPWZQUrrGaslhlz53Z7ZmsKW2xzsLfG6E1KmUCRc6707pwuj7JGBQjz6bt3uKtLwVJijMncdzJQctEmm5KyT267GgHHVGjkvuM26a3pMH1apPKYxxBiASoJ8Cu1YOjX7i2USVoQPRDqI6dlRMvTtu/s+0Yp+b7Yl5SptbDWiommC3BGYZLZ5Cu3rENlRrlHI1ra+lR0/Mt1I9dMSco9SCZWuSD5ZU5FDSpwD0oeXWzWQZKnoU3o2vaQZmYNv0we3r4Vuo8sKDaD/SPUJm5srdGmNm8vAkO2trOUqkN/HAQtSxGmuXfIUnKqOvS4DnFYVWhzMjGdS2X3RvOu3LIBk8HRPJaKLC8+lftTa+Xl+sK+71weTliRJdVs3kEqVQibghxHF1BjgzSGQAkXlVmTMk4cGHvjett583Dh/VcfxGImCxYlsZ5WLuczta4c20LPjt3UyrWNoTypkskBaOwBqjSf9NkoZmobQ8ySz0kKa2rySXZZPoephbAiwNP8yKt5VZ+pHUy/D3Db5K+3oqD8Umqwx1O5bOdKXldGKKjUMhwAZo8GwABuSqlSVR6h1Xsnx7MxI2RTM4LdQXWJZ3TNzIGSSFRZQ9GgL0IzLKAYKTlj3xl7p7wVQ9enwBE7DkYpoea5Yx2LIepre3NOasE6mhG7sCUyTm+T05sHTudT2PyiVSSGGKnTZ2jqY9gJdlOtRQYzK8zxOFjpGKmhKNZgZWAo/+EYVqfJSkuKDAYb0DvuTYMuI5h32a8n3J8ndzXSmBWp+ZJsKrI/aIAnsgXNyx2QmSD7Bs6GAGsxjpoNcgz4aoWMprYpUC7nRMpLMHWRORLms6Nhxy0HONnjSI1swSY1677fIKfY4we+ICtvKliCRmexKra960NMmEDrLGVDGGAUtkkMw/NoShk8Xh744v1HZhHIk3KO0FYnuewUJS93NlwKQFnIcl4w1yG6pAqzk6uzVPB9sCzGtjW21lj9JLZ9QMHJw0mu4g/LNUDRE2Yj2o1Ms0qWIk/2PT3rfUDKOk4ZRnuG8i5UiynedRKYPHqnW2J1RWDuWQ2n7s5qlZQqpBmZh4bPTs+Fsk0eHjLbVzvvv3hm+dbg7CfZTJAlRKrjBj2R4npOM7BM9x0bq8oF2GIeWPFhWF64fOcdzz/5gp/++MpnP3DOdsFsYtcMt6/gUkhlYeYGfoKZseUTzCr3Lmx7gHzDc8VyY/FP+fZnk1/+1Z/hT/7qyv/5b174nd+ZPP6dK58/Zmw3UlHuSB/OMsE80wJIti4Q9NPF+embyv/+v/wx/9njhTe/eCanwszfpbz76/zsf/Ff8Z9/+Q94/y9f+PHHnW2ApcFaT9yeNrZl0LcbFzvx54+f85v/5D3/zWMif79wWTU/jay1Kc2MlYniMYyOUSwzV6O9f4FF1sjEIjt4kTU+I6KgTbChWU5cgZQDJGfxgjFoc6eQmEOgYb/tpNxDMSSL2pwea7Dy1mqoXT3DsIRxIg2FcKsZc94PO7ixmTGTQQQKZwSQ11wZc6cbZDtpUvQZinbdQ5DCslI4gvcP8N1DWbTUE+6FQQMXwL3vyslUxgnQPeYGhCpE45qha7iUlTE7PavwpJasz390fGr+03qm1taSk2zh7vS+q7AF2ZWGsH5qoBXTkkC4maQG/JpatSQBS1hhtJ3WodjKBdj3G7131nWNnJiIH0hIsYOe/5wXBh1DGTo+BriAwBkgfFlXtu0a87tIcg+FMX3qs0vKzWka2jXHluW+P/Y8RVT6VCbj7FgPFYeBjY6PwY6pYXSoXa4CuZZQAmWKFfbehFwHyCAVlkjC2Xb6rnXTU2K/xzXEnp4SJJ0/rZhAFyfUvrFZB1E5g3xyBI6t6ypiuAg0lajkCCO3V1V2AI8Gyk01e1WcdH2vUkU09rYx2iYSNkAqcLVdmuzYEBbSIGfiX2SkWgOdW5MZuRaWGq2yfYZqfwCDMgTgpORUU8NizWvM5RYEdVX5C1MKxOk4OhOMMfEls+9biGIOG7riNfRKtRsfsR7JFMPgoPfjEXcSRNd5yYwuq+0cBnnIqnfcZ47WmuFMEmPvocqdyoYKZdNaV4YPetN8MbzTPZogMZZUBNiIChFBh0C0Iy+3HoHarjHLEq/AzdG+OlxkmqZTRoC+bhNKojhk017vY8SWUuOnHWo8UwFZZHjNOZlDnydh2/QUjp7RmCnFCUCkq4pBBPeS0l2cc5SWjKGcp+NOOTI8j36A1/VPILn+O8Ve/c1+feOWt3/4P/2D36hrveckST4/Xj8oQbv0trM3sV3JB377wHx6Ybt9RSkncu5gne8/nvi1X3rg7/1C5a99N3NOBesTZmPeJn0bzFvBt8a+g+8KShw3Hfqero3bnukTtr3Tu6TWnSkZWpKdq2Y1DXlNlMU4lZW1LixV3uSUnMojeSxi34NB8tFow7m1zrh2+mY81sIv/OgtD+uJr34yeGlOz8517+ALl7rAPthunR15f988LHz73YmK0bvTwnpw65P3z4O//Ljz3BPPs7CzstuqBpRSlD1QUuTBQM3OeamsS4YpYGH0nTYnuRRmyrQxubbOrTXW08L5svKy3Xh6/qgH8nTi8vBAKoVaFgYpVEgLZZXnHEv3m11LXkjqUhwEUiIlZ983qYnGICcn5UqqRd8rLyxLIZdAWU0DTclZlp5S5Jl3DShjDG67/N5zaCNtTZa0bKo3T3VhmpEXw1JhXVZKPZNLgpoY28YcO1tX2GHfBca03ujR4CMZZNY1vjwI1Al2+nQ5KTMmG3lA3/ZQDWkRLTmUAmNIKTQUCDtmp43GaAJ8HON8WpXLMOXhXUpVc9yyUupCXuR7r1VRfrMNBWZnSTTdp8JsJ4yhIWnbNq63m2pGc1HQdqDXR3uSTbVVEQCFwKWmA/CI7x9eXo9GtjkHt+2F0aXaKCljpYZkWrX1Fgujmlckad/2HQi/NU6LLKIRLW85Z7ZtY+uNd59+ymVZSbWIYbAUklrZf7rLQrP1hifnXBcxPHOwrAu9zwhEPJh+FBwYA8FyuXAqlTXnaJ5TiPtI2jIUwpzx1kI6PqMGXSqFlO98wSsAZPDFF19yeXwkVyOnRfa4ETa5kLHiYuRyWOvcuxQ0cW18qgkGNOC8vDxzPq08Pz1z3Z1S1VyzrIVlXXl4fEOpAlqOe2G/bQIxR2dvaiisWQ1i274LTEKtT77trKWyns9qJqw1DhMpbGMSByvuoYTCQoclj/td1e4jprAIT84aJJ6fn6hJuRLmqlUvKd9beE6nsyTcOd0HkEOqf9Sl9rYrg6F3hVK60915eXri4XShrguEQCYfNtNg9CDAy5TIKSvI8x56qCwpMUUKCSYsWNvLM/vWuZwvzKyWPuDeaARilS0LSCdANf0y7z943hshUY6ebiI+fngSoLQsWKqBwuZYb9IdPCIGtWP4AzGisgGpZU5hxWol9GRs+40cIOHxrOdYjxJCm3LWPVsjaH9vkdWVBB4acNjeem/M0elz8ny9sT/fuLy5yEZyhESnHEoAp3uXcsV0ECLyjMYcUrIG36bQ3gBo0ZBuGO+/emY9y9qlQ0bUUB8AmYf9xl7DtyWpyLon0wGmqZSgD7XE9t54+fjEcrlwuTyoJTXuMULJ5RYmrCFrrCNVl0L39V4UcK+wUTGZCQVMw/XlRYBv1T5U9cowV/FDKgWqbIYQgcU5K2sheQCq6f6zZiieh3FXeCx1IT4+WVJjjcJNIIkuKO5duVAWhyg7tCl6TxysrSVyLpQ8eLl+IJ9PWtNSKBrjfrCkrMGcJd13r3gPRfGUxWN6p3jGhrHkxH6VfWYm5V+8fLVR10pdZHtLJd1Vb6UCXuONiWwwE9M+bWK2yw7LCSGj8XScF5JPPv7FExQoa5RXRIlC+JlEOoQlSVxRk/3AjWRVwLOZnqu8wXxhdRgM/uzPnvijv/ySsyXefStzqStpSgWOZc0tblpDTAoaR1l82ZyfPO+8/MnOD/5ap7w5k9IbLK/kh2/z2eON+ac/5s8/DJ73IzeyMpdE3zeWIDvO9cL7Ufj4p1/y6bcTp1X3/dNXg/1D5u3nF4WxH8oKVy6hm6tQ43lG+chkpk4dC5CUBzdlqWIexxhdO4u8SLcOGdpITJfVP+WCJzX6Jguja7gTBlLQzTlwH1oTU4D9nrDsApxZWNMS9ozXGfLA00efd8uphN9S2ZtrHcAHOS2kPHXt0XNwrJ362R7gRKiapsjKMZUxcmTC+FR2XNbJGQ+15zEbpZQkqLBELikI3AjRRXmwqeQ7kVYiO7KEPbqY+OZDETBddewtLLLJNL/PUHHOMTT3pHgNKMM0Ybw6X4IkDDtf7x2QAjdlzTbZctyTxDWR0jbFGqRcMql79VaU6Zey5ldCaSogPJQrU+psn2HHyUcmpJSeJSknzsNGl2qmWKVGGy3za2DOlOWf9Fpoc6iKj/r3I6bBQ4WiVtkRGTclCBh0vsxS7c8psNr7EMEUf4YkSCRFnAAeuYtBzPQZzW/JRPiazos5CneGv6qaDhYvmUBEraf6b51jghjOJdRQEZMQc4lOwykUuBYzUAoAU+B0iSyv48+MsDHmAK8PqETLvPYDRSzo56uYScCsGsJ2SMbsI0AsWY0FNjvZHE9qkVV0RijPppRZhtaYQro7EO6E3fQA61KQ8q8z/4xrXgwwkZYpZwG3morjGXSB2hbR9Kbf1RlTZ4XjftC9EeeaFHPkHFIZHQS9i571AKCdCZFx2qfKhaZN8tevXxBWum/0/0cT7UBglM+gfbOKf2rsHxzPeIBphB3QzBRDYwKqcylSoQbJaKHst7j3suk2mIcDQJv6fb6cWrr0DKdApe0V5ExfA6U4yL5jfZ0CAd0ESv36f/8//H2+oa9vXKFUao4qwWCuiUq+eGCmT3l3ZwSR7lfa9SNteyZHMFceGyUnvn9J/Ohbg5953NgXpe13VBPbc2GsGfPKQqdfz1zGDdszj/aA74M2Fr4NrG6wDZ72Qbvd+HAdvHS1jM2RuM3OratyeZjBjFBUU53lsITZgmUnJ9UBXywkmkMMdqMz2853v/UZn37qfPH8EX96xCzThrN33fC16AA0KpRaeVwW3p4qJRsfn2+y/kxn35zb8+Slw9MVbl7ZKDRfGLaSohJRGShOcrVVZHNqMrLvWHeut5dYZCZzaHHa+rwHp+rAM/nyq/e0IXasLGfevPsk6g/F/symik8HJdej6sHA5eg+wzqRwY3eB33sMCfPL1eMFN5hSf4nCg7MIbk9QovNoFblP5WiQwWWaePG7XbjtrX7YWe/7UJuw/+aawmrmMCCWjJlXVlswZeVlAfWO+W08vFJQaS3fSMPqYdm2L30PQWG1pR1yG07Sy2UZHgPm0EwFYOQH8hzITXS1D3e+84+j+sdB6umytjTquyX1nYup7PCYGshp6xGnzhoEraSGa1FtcoO4d7vjIGYj8a+b2zbRlkrKRWFcFuK1hHVlEsNJzubo9pKm85SakiCa/AtGiizZXwYt9tOn4n1cqKaNq3RRjR+BOhQ8usi6OpGSoe8czitB/PhztYk0++jsW0bn3/yKefTOQ48sakOIK7xdGefPQYQOJ/O0NRgsKaK7c5aFw0NE6pVhg+2UFicTg+UvFCzwFEBD2JprDWpDZaF3gcprVhveI/hNZU7OzXcGaG8KCXz1ZdfUcuFlIxlfaDWheutcV7VPjaRd9/HiEYdMGbI9wP0wPAa1kS4h6G36Txdr9TzRS1sVeHbqh412tAgWUJCPcbAR6e7NOVLXcQmt0FJmT0G/dE7WOJ0vkgWngXkqrHwaOxydmQ1qh42gJzu6q9jE4uLos94ToG/+86SC2MQG3LHkkDC06Ka+qUu1FLopmHwYFYOhGjf9nuIqLemQ8kQ0+Z9sJ5PgIWFQrk3RyZBJhi4yOdxdyrQTQo/zYOGW2L61zZfS4zWNFBHXolyHuzVR28BQBzASYRvqojC7gOPhQrTQoadphR/Myl7Q1X1rhR2AlAfOo7anZ3X2DntqOQt90ufreDBNg+U9+VxCBlzSi1AgBEWAZZaSmgDqjslVWqK15kT++w6XFqGJJn6dIv1y9nHoN020nRO60lsa3YdMjmY7C6gxqJhLNbobCIc1Pgz4ymIO954ZfFbp6QcwH5njsj0Mr3PWpdYyxT4etzzo+/so2lPgyA7gsEL2Xfb9ri9jgwLuPuvcdKcEawOe2sanKfaalOKw6qnO4kSokVOy8rlHM2ZqcoKHs91Kk5Kk5Qm1XQYsGKSzWMMQi3lyC6bq+rM944ntRN6WhgYWx/kYhHKrgNGCpUqfihJG5ZWOjqAYx7lAvrK0ezlkTFCH8rqS0UZaGWSKTjB/MfZaU4F4xIKppI1ekPG90nyhTk7S9VAXtZE61Bz5XSB919+yVc/vfL4eJIqcwpANXe13STVsQ8zyJXZF6wMvCfGUOBuTp1UdJBS0Ixz+s6JyxcPXD9OTm9kh621MBhwTWChvkt61tyfMC4cmT+CH+IZSA3Vpq2c3xR+/uc+4Vf+n4/8r7935Z/8wU84PRR++W8WHm2BpNyfgiy5FWfzTGayJYHotRgPb1f+75++8Ok/+nN++b/+V9TvP5DtR1h+y7uf+4/5u//JF/zk9nvc/mTwvmlmWJdKXU5c90ZeKm175vRw4Q+eHzj9087f/TuVb78d9NmxpMOWz4onx+JnkyepSzncquPbDStZ9paC2jTnQjGpCJpB88hgGVFJP3UgLxOWMikl0TZnt4HXC8kaz9uL7tMSOTFziISNYGVmoo2JFekuzEV8mikEOBUpdPr+mmM2A+i1oRD5nAf7nKRelTmEVKLuN8yzorIwKdtcq6lUSx5oipRQVgRYLZbJ9cy4bTRvmvMC2JKsZ8ScOWW9D9zaASKcfpmFlkQ+Le70OV4zPodypjxClJZSaAESpaTyjB6RFsqR3ildbYnTDXN9bszG9BYNt1qmzINoiMDokRKnkzLztn1n7Dt1cWXxEdYdn2HfUo6NIRKgm5RKR0SAVKqOjUReK1vbtfbNEcC/Pr8xnWENY5AJ8g/NDh3XWnKALvO1yKPmAkmNcwM1Jfp0UuvYSIzstKS/n75G0KRwAeRUsDQjrFmqMzAVtEzobcOijXZrnZoLexuknBSIPg+i0+9EiAXYY1lZpEzpjUox9tZ0XhnK1sTs9WzrWt9nH7SkSS4nEdSWc8wzdldfeUx62GsWojKhCBACARAzRcGKFDkWGZTugyXAMg8C0pmHDEXxYiWRp+bHZinAFbVfD5dqtU0VFhBkqc0RkRrKhZt9U2HETBQKuzeMRDHNaHtvjJx1D6LXJ+IiLLehvtX+jVQ2caJwnJwL3SMGJfZmka4llE+TbItymeZrI1lOakL0qdmim0Gs325xFi6F0YeskAEuj/u4OiEb1gNwN/29o+fscJzILRC7QpE9/z6LBQjVp9PR6DaZWA18gzu/CJ4xIi8yVXob5My9rVh53/rsBGRGDIRHm9uMY2VYGQUI97ivIjw+nm2R6v0+o/o8Wg9N128G2TAHOYei7XjB3+DXNw4oaSE50Fd9SDYn297wkNsnUyVkv17Z92f62FgwsnceDD69TP79v/4J3/nOB+po8NUzT38+uW4b1y5rlY+NORJbCrbDYO9OGoXx8ESeTq1nlkewh5WSO5daWf1zvtU3ZhOw1YbRtivvn6+83Jznm7PdBu1mPHsWENQTfe7K0HOHmfB4QOcAhvHmlPnsO5+QlsIXHzYaJ1pStk+tZx5qYmYo6aALlNCeyuDaJvNWmDa4NXjZVF3/fIXnZuwzMSzTUeaAmBOBQ8UmtRjmU4cqU6J+b42JrAvTZU3w2PQUZib/Jwa3bcNq4u0nb1jrGXxl98pA1q3mU+yJawFhinUS66OFLcegOaIl57bv3K43bVJVeS9mCTMpj86nk5RVrgUnpRLZNFL4LDWrZr6rce3ldlMr3ITRG23vuCvA1owIsJYFZilrAJsKXIUJc8MHtLZx3RrXvbH1HW+d277rINRbsLJijwUgqdrWayGVGsOF/MqjTwVGB6ijB/8A2yatNcZUK58PLUb77UZNmdP5rOajLMufWWI5L+ScKSVTo7p2+sBbVFb7kGQepyTu+Uv0we36zBiTbdvVEObKjEl6O1TTpp/zwhFaXHKitx5MiGSsyWR3Oyo0jw3iuqm58XS5iK2LxaNktRphQtVHV3C2KoNfWYLeGz3ypQxj72rRu91ujDG4XC6cHx+ksjepe2ZsyNxZDmU71dM5GIgJpVCybCLKJyliQIpaMmafOqCuC7nIz62CCn0eM1V8KICy1pWX61UZR0N/192hSPGWOBZi+d5LLjw/P7PdBg+Xlbos1NMZS4VzriRU21lw5uh4SIHvwy05lEv6/Zrz/bndW+NxXfnpl++xWnk4nzivC6VmaiphH5M8OadEQarGHA1ywyClQUkCCcSeaVBlOGkixr8UgVyxqRPP9KFKmGMEOPAqBzZmZOTJWnuwKgYKp+wK6HQ3qe8sntNFr7mY3ncK4AOzCHXUBjj6UI4PshP0rsws3JQz13oE+C4B1oTaIuf/X+jioUrS2VMHtVRLXHsLtdO8h0O6ifFqW2c5n6SwSZNMVZ5BMtkOjwMGfmc4h8sCmHIwSePw80c+xZikvOCjU7NCe2dYftq234cQAzALafYRZq7HoGTZlMbwuHZdNb5zksbAt412u5HXyvCwH+QAMFOK66J16gjJ1ICRoA/sYOhTDCAe+pRcsDnIJHI2LpeVEkGeZanCnkIBkOPneEB0x+diU6HdZh5gnzDjMSSaF7NZyaUwRtguOU46iDnH7vfm6J0Za+UA8Bly83wHQzWfewTynliWwmhd7/kIHC0l3nMAqTbJNWO93Q8NlpXBMcPOmUrm6Pjx6exzsCR4PJ9oN6lnzee9fcYDXLc5sNlFpuTMSDEstqo8Q9O+6iM43ZwZtkNOWGr0bccynNYHHZY97Jym5rXRiCppsdJjl2IrlRLh27Kk+ujKysnKYehARiTM7eOVWt7iQerZkQMhBJPZQvWWEq1KMZhTUg34iMFvwja6DqrJdUCaiYeHC9eXKy/PbzmfKmnV90k5ha0u8t2SMbxhLcwUZTLNpChJCUfKJsegOSWvvP3Bmdv/9cLTT3b8Oycs2s7yUfs8G9Mq1rdYIwbGCUuLft9fgI7vV6bfZAWrKw+XR37+V77HH//0md//4y/5rX/5Ey6PhZ/7vvGQPe5nvZ5uExsTz7KFru7ccmNZMm/eGv/HH/8Fl39c+Ru/diF/fsLrgl0+47u/8mv82hdPvL/+Eb/9V42XmWl7py5FbaZbo54Lt+cX1oc3/PZf/pT1917423+zcLteyXMJMKRz1AIUq3QzUm5kL/jJuc5GbrvAs9QEbCJCaB6f80whAMv3w15CNseSp9R0JUf21wJUTnbm9gIgwCIM29pDMRzZSugC7HxCQ3OUYVK+ZsNrwkKJlEzFEXsKm8/s1JRJLjXLGE2Auunw70fQOIXhKbiOjFnsL0lKbp+NRBY47EZZVrbrzjRZasaczLFBLpREND8ReyMQWWIpE+rgQSlT7dX9oCXCWht/sKO5202g+0SK3u6hZvNEdYVvy8Uh8MFjI7eUScXwMRkpyR6NVAdSx0EqK3U13Aq32wu9bxiaN3S4nQEYSfUz51SkggkczykHeBP5n6ZDrpWEDQu1pB+SS0T2DRKZDgLiyyTNKMUYxN7o90Uhoz1+uIsQm52xSaXdI7cxTZFZo4aKFcJqHkHo7srfMmeksKKjEGRvatzex5TtOkmNndD8fZB1I1RhGUJhrjnEQxHimTsYVKNqfvrEmr7DzIc9XrPNdGRrNp19iqXYaQUuWC5hgZOK7gj2xo05VJSSLWOufJ6C4lkyFrN8rHVZxrKcioiZmNBTDA/zALlMCpjsxt4F+u0BfnUHK5k2GhSnlqSmvU5Y1CMH0qU6zDkJgLHXzMs8/T4XzqkLWUgoT4l7fqIllNmkDVHq+CarWLaF5JHLeRA7QTL14Qzf4nlDz2wyPMq7Uqif9UgGRUpKoAAAIABJREFUyBmKLeXPyIamnLR5z3UmZxjRnBukMFOVA3rONPtYAL4e70dB55ByomKRV8VdlQ3KVZYwImJQ4jHxGOqUq2b0vlNKZikp9h3uZOX0EcHoIbhIr68zBTZLKM10jpLaStdukEu5q7yOnLAjn0qqZ7sr9MzCZeWvqvtv4uubz1DSRCy0z53eBt6lpKg1k2wwbi+Mp2f62Jmzkccgm/PZufLDz5xf/L7x2cMNK4OeJp1MX4zSFh6mDr63Phjdqcnxpg2j3hz6RvaC0bhuxvWrnS9d3mZGwqZx9cGYxu67bipLvOi2YrqRPNN9aOBbMjkbvWUsN2r4xEtR1Xq7Nd6+W/ns7YmrN56uWhLa7lx3h3LmtK7Uh8niletN37dY5lQL2Sd9GHuHrTvXq9PmpO+Jjze4hszWig6SuRSpZ2bXIbgYJZtsPXi0TlkASUZZMmPCPp3eNOipbjlhVVLvN+8eKadVwYxUcr5Q6wOtCy3u/UpBrRIpgfm4D61OBCdOsUpjTOX39KE622x3X3IuUi8tdZXnOgKd7QCq3NValROjd9oQEPl8faa1LmuVJ7b9FofYjJXj+0iNY6UoCHUR+CMiwmA22j65vrxIJdSHwhG7QKrpknlPxPqWUtXkVpOUK5GJUmuRHhvDql7/Eof43iNVy5WPtMehZE6n3/b74a2uK3tk51TLWvzjn5RlGzssVRyHkZTIlLAEyZI3vd/DxG8vmw7uNYeyN8X6YZQSqsGigF6bOlyoyntEpaThvUdLmEUWR6hx2mD0SVrK0adEjpwrm84YkKKq3SVJYfqgtbAdxFqmQ6Fym0opASZ1lnXl3du3gDZISe0lL00p1EnRoGBJDIvYOUinytwntVah9UeelUsV1mcHg7UsoebLFMKmkBMlLYyxYWvlJbKtQMGa5gOrheZEledhZvA7DXG73ljOJ2aZUpH4pCQ10KlVS7kLKRb26TGoOWJgXZ9RLcqdMkvst2fWlGhdeVgPb99wWitLtPSlYEvmfB34j2wU72rd2Pdd6ow41ouh0DibXUPI6XQSABUAtecjC8AYSD6l5r7BRMGa6Z7RFhJd99hYLaTEhtnR+FXvLPVaSgDQxCEybGR2DGfBIbksdKPr/hnZ2a9bzBb6c+3phcdP3pJrwWI4S7FJHoDGPczTA8IbOuAfWvtDZVVMd51k46rRHbNT8wVIMKay9BIBUujam4llSoRNj/hZkRNWwpckW1VCtjSYszOmc3u5UsKKpzRYvV41mBQdHGriYJglQvHAIj0sJc7IqgvWgbzRths5O9Mvd5BKs7HA/FKyBs6Q6JtBLkZvU+G6gKVMd9UkHwxXShHgmxN5XSmpYkEUgN/tDyJaiCHXggU7gEjiviUk4zNIBg06KVtkEomt1M8ngJ6wMsW1h8hlC1UeM8is/GrZ9cMukNSUeXl84OPTlT523BdlpqR0R/LcxGImjFoVfDmbcqQGM0BstTDlJPuJx2ucrqYxkIJvCdvYXaKPrGsWCop6gJrDIFW2OahJ62Z13es5xcF3BqjlnW3v3NqgBInkM2OZIAAaPnM8Wz2y0JIUZHOAd7wcmTtaW8txADDdb/vWeX65cVlNNoc6SC4V6KTQ3ChzhsJExya3BImwqBt7tIHOobnA3BkFyiXxfO188ZOPPD6cWM9A1v2ix1L2QYuDEymafvYutUUuuj87kDxUxMptOr154LMfOF/8v89QG3ksLJfOXBJ+u5H7ZJ7OZJ9gCZ87ZhvUi+6T8UKa437YFxBqvH1T+f7nb/mVX/ohX3xo/MkXT/zW735BscTPf+eRsrpAC+IZgSD2kG3GCl4cWxu3afz+v/hLHj+98MO//Uj69AHK98if/Sw/9x/9p/y9j0989Vt/xh9+CVd32t45Lwu3sUvVnBPtxTg/vOV3fvyEsfPZxfnMZXGekcf2KqTLDJtUF6l2fpjcnqSWq8lgGotFs2sKANn8TghpSTZwtdD5NJhSlNWkQ2lmUrJalfoe79tEAMyp+Uu95ZFx5A1zlWeYhRUJ7dE5R1MfM8qdjBkkwZxTCEzp2BG27Q1HP0P5hmHRp4etNOCduMdlwRVcM8KOnopzWs9SLsbcYmbKltOnSnYpnwRERvC3SVW/roXeTLmKty418JCr4SBx1ZgZh9qUtf4NNVaP3qWsXWJtZN7tYEdbnlkoc2J+mVN5VDPed0qy6OaSqV4xzty2q5qDS44GOiI2wnGXWrHmoF4c5CCQm0KEWsctkYIUmu603l8JiFgDO1K6HTMXqVNMr/VQX1jsI4MewJzJAp8LlMm2i6xkTIFFWaBBD4udijRENMzIw1TWo57VkqSSmRh04yEZzTutD/Y2WafWTgIIx/XaS8mKcnApHsdQVo6Z7F2tNQE6w7Esa3FQMipGUG0NcBSBqFBiBnl8ECxj9DtxCALP1Ywb+ZBhcz9yS8lJz0GSeh80M874sAQEW6jqRG7q12M+FSGl2Jkp4LLH/hWEnQDexphJ+XpzvAIzM5S1JJpLXdX3nRLqPW9qXovMZ4GJSUSOQrNFemUSJYuUjJWbvFT6HMwe+ZuWSCnUWhg2pQqffpBNOmXq2qsVdcwJa7rvx51QEZJ0xij6vZzCRVSkdBxJZ5/pghnzoVDjaxlDQ8/4pIXKSvt8CtXzUfrUxyBliRA0CxJuDAFShzLx+JJbA81HQwVFR/6nkQM0Oii/A6jyOyl7kFfzrloPoDxmIkWgaA6P8ej4yREF8PoeD5dEtkNy+c19feOAUhy1tTl1tUCUpcJs9PbC7frM3BqMRvKNkw8+vRR+8OB897HzncdG9cnHDx3fJtdhQpz7jTESYzhtNkZI367mpPHCjlPsQpk7lB2f0HOhFhhFsrbkxvRd32ObPL10btvArLK1LiZ5ljujPVx19H06e9JhrM5Ga07Jlc/enfj+99/ybqk898FoC9t+43k34IItOnjuPTNu0CdkL6xr5pQzuTjbdG4Nrpvz0hYFZLfBtSX2BnVZIBc10ZhC9qY7qUCJIS4CROLgblK8xMNuWQ1pt5542hrNYMmVUlfWdaGcV9Ky6JBrmVrOGFXAQhzol1rJ7rLMGUBlJmdv8q4rDDYzMfYpS12bE0fBzfJWp8iskRqp5MxSaoTIyuMuq6Sztxv7vrO3wW1rytpyZ2tNCfmu3CtyihwmWa0sZ/KykEvVwcsHJNiujdYbz9eblBPTBabMeV+09KVWm7IurEn5QzXnYEVhyTnyQQ6gKkUOijNbD+WdAkzn3FUBOyZt22n7zvnyILbeVD1uVshF11fqIf1DiXBJn2ETjEPL4M40td4Zs9G2xrY3tt45P15eQ4P1FOq6piJ2o2SBux4NZT5oeyeXGiCVBqA2Qg46dbhvbVCWRYfJIt+1zplJLETcd2NIETOn0/amQEYICkeQfp/z7k9vvVHXhbcHmOSTRMGKcoeKhdIgFnGC+UlKHlWz2TxsWhlLAsdmV3ZVD/XZ5XKRsmdJGgB0pmOpaxyyT/QRNbVx4EuRjWPuLGgzYBwtIwrwfP74kZqLmltMw7SNrt93YxxSZVrg7BpC7syLHUBEsB5JrTxza+TTwscv35PXyrnWYLC0IYykzSYf9oCp17e3js9O601s5hT4O+GuBlLWlpQty6qsp3yAmO7UFID1cXjPsmWNPu6DIZZic/Vgho7cHSlQ9+sGFDF+cg+Qcw1FoWTJRybBa2ZFgKeE/e/YkJNhJbNYYRuN5GIYHx4fKWZR+vD1vTtYnen6+a5w0Ixr6EeArBRbAUxEBk0yo+03rUklU2R4wmlMT3fwW0DCa4bPwRi9MoTa2g/ZNAfwloxtHwKt9h3PJVqz4rCSEuvRghRDvJrpxM5JcRVK0xEDwTRySdzaYG9DQF4cLAkwSvkUr4CduTNMoY3K/JAlDzOSm4BN1Lxmocpt9AiLzAxSNF1GExlOiwHx68+ppSzrlk3MGzZHDKvHwUCAx/156McQNaWGDRl7yXEdTevDwcC5HyhQv9sSsLABIHVNluSRmuH8eOHD+2e2bbCvQ/aAHoPZ6GrjNFdYfVE18c2b1oN4dmcoFJkprDF6npJBqpX1srD1Ts6LVGYpSc4Q944eHCkXcjqGVF3jQdLZu2Zm0+xUUxUwXSpLabyMxtN1Y62FNYN5h5nugGlGWVIHeJ9zwmYH9e/BNA5FYcpOmlKMleTYSVbnl4+dXqIowjrZGjmfGAxswtac6uluk519kkyq1pphXmU17LFe9TlZzFiycXlz4cP7G199+cz5AaxMhmfKeFWU2W3GuiQFsDdloXQG2cCTUUixRxnEfvTw3Ue2W+PDF4283IAzk0HxiW+NPB2v0X63pLCHVbAzxoKzczDplgrpXFjGmU8/nfz8Dz/np3+r849/61/zh3/+gfNj5c2p8PknJ9Z8ZE6lyORwElJbnSzh7HhJPF6crz40fuef/Rmntyvf/ndPlIeK529Tf/CL/K1f/St+8uF/4+X33/Pjp8YmaTnntfC875GzmNkZjPWB3/4373lMN370xvn8K6ifegTug2Wjetbn/f+x9iZLlm3Jed7nq9n7nIjI5t5q0bJYEBsRpIwDGkVpIMqMpDTRQO+gl9GzaKyBZJrINJGRFClRIEVAAAwEAagKVXWbzIw45+y9Gtfg930iOb8Jg9WtW5mREWevvdz9978pAnSSQT6v3PYXsmcsVRomdo1ltgB/PGvzfZiuW+xP5DfmkCG5DJTN5dBiGDeyEgWTajZFm8Y5XdLClOX9YTuJkxY0BsmKeiXTUqZkZ5iGw3XIh6rnQt9BARta3MmnJZHYeeUKSKJrU8w9yeIAz7jtULIsIrJA8OHGuorFv3VIppCgihj/npN8ogKAd8JfzTVyA+SSGKOQSiyKDm1c1sCr9LMMruAfS0qWbd5pLtVCCoaozHlbMKOypPkEK9WmGMX3mtnFrG9T/ZCJ3Z9c/f/1dmPfJ6VYSGfi+kkC41MkbuXwgUylhOcNePReM2RVno1kJWYk3a85WMkpLAbo4MUY5mBDP0NU5RE+unKnCtATqGGg1nqPZWVk8rXGrFWy3aihYPefHeH9AiuJRPEc9c6Hzrcl9n0Pe4pDyiS2JHF3aJkavoRWw/9rj7AXjccpC9g/Uuxk9aH6L/llPHO1iHfmklhenWmumhhAjYAkpQkeTFpLejBzuJYYNgm1lfoMZBAtjyXD72ASd/kcBu4ht7dYrBiUtDBz193qI97FeAAmcoUlLf7kvyTvJx+CyVLcJxaDiCelNDLAwuCa6DMNLcUOJlyfhx9t+L0Si5Q7jydKu8Xdb9xj7Yke8zDUj5Oo3xNz0Ay2dm89WPlJdTfOy4xeMh0p2D7B832RO10AqRZD6W57MmJ5mPzoZ6Lfyfr7Syo651ML3uHQxqS45s4jWVerRH0NklhfKawhxhDAJJYVWF7kRRfpd4YIIlqEBkuwRRKilfCUPHrwfu+NLHncecfyU337sR+Yc+jOcX0+3+Wv7x5Qcg3rCYfeSbUyfafdPtHbldl2bEgf/O6h8OMFfuOHid96b6wmucQ2OnM4t9nZ5qD38AAYhT46re3sw9knXMdkjCu2GTffqe6MmdnajqdPOrZJxWG4NJiH3OswDpvWsZnwyG7sqTNSmIvOTC6iydnU1u6hJr54t/DD7z2yrIlrG3zY4FfPnefbQkqKQE45It5NutbHx4WFyWKZgXPZJ7c9c9lk6v1xn1z7ZDRtIuppIdfY2AUbBCYpSfJk922GpD5LqUyXN5SplnO77Gx9sjfoRUkD6fwgo+psESmfFJ2ai4bePijmsBxMlcGhjxtoy9HaxEM7DbBvncu2a4sRBVSGsDLfXBYZkx7SIsWEqgnNychrkvlrH3cwae/67200Zhfgl1OmmORsSibTZ5Nrlj9VKlG0lCAy9snWdi7XnW27CSmeTduoiGY8Nq0kMUVyFmuolBoDkoCelLN8YaYYWiVs9M1VJPvsijsfuwzEp7PvOsvns8CkXOrdMyqXTMkysLecBZCkKMJ+eGMcUiPANYj1qc+kXzf2+MzW8ymGWXkmYa9pYBabGR8uiu0Mc+OhIcNmsC3MxQoZaAgcQRcthSW8qXLK5FjL5ypDbjy2N+7hz3OYOXI3WSb+M7kzW+O270zg3fsvdLlXnb8lVSUvFLSt9tjOFQFVcwz9e+L5zclSCqM3JWS4JB2zdfZtYz2tLDVrGxlV/ijwuVYZu/cBo6kYxvc5pgwRR5cu2RQ1J1lTkWHqmJNlPWNMgaHD1bQeqYdW9B5ZbCFGR9RvnblDPjddW4TunXm7sSwLH55fcEs8nM8CTSzkWdidJTVj05qiU8wBaDKn3pcZ23hzbVWT6TehJLsUSYQkuwMj8v6xO3BCmImbHRp4JOkISRSoiONh/OeT1kRfF7NLXyenwvBBDTP345eOUtCe8diWigmYLViAJplv69A+vJBzoa5rfIXXzcsgZJAIMCW21uDyzgjw+jDg1GsmIOv47/veIqQhk0vSHTIOUNfubBrj8J+LvzvYZsfPNOOfZCZKgGOJ677jKROJ0/etoyOAKAUYI4AqfJBCUmopxZIm/pYkICCJQiM28CSYLWICWWyxUmw1LRpoH4MOeB/UOE+JLNbb6MGgO7AegTyEga+PAblEbLOHJDKew3yNjnZMYPwUm805tqsZT37fdh/ywzGkmerBmizBTlBraOA5Om99L+4DJ87wQYOfQ55w1qO7j3QUEsuiGOp932jjTBmSxNiUJNdsvZ8lsQpMfnI+SZaxKZnOmJ0+jg9ID725UdxYzwvXT1f6VGNnwyPxVudRn7/qrGVJcMFo+wzAVYOwZAdGKWLeKh66st86t+uN21opa72nck6hpxo+W4/3UhvLFPddd22VM4rFVu8DWGY0pe+mnLHU6duknI3WCG8LpVZpITMxz1FvwpC3GLMjk/iaoQ/oRexPd9qQmfHTw5l2eeabr154/8VKrpHy9hm47g6W1ENmHFsFqLY5aRuURZIGT8T5cg3IJfHm1x5pl0/sHzdOp0GdhncxzHw3+QVlDW2pJt376YTbimUjeWJSoE5yOoF33o7Ovif++u0dv/r2R/yrf/MX/L///hPvHk/8J79T+PIpsxQCVDJKVu3yAbk4fQgc6KWzPjl/+fHK//3P/j1/783CFz99Ij2c8PSet7/zn/Kf/YMPfPz0L7j8yeQX16khpZo8obbGvu3MmahWGPbAL37xNR++vfHjH904rw+UCksK49/iVM/4lAHvniVLqjkzr2DLZBaHZoort0ROzo5xKzr/l6ZhpqBzqfdWNcjMg20hllotMvlvTVKWkpzNnd3VkCa63tv7ksVjIZnv93/KGnRk2KvlUJ6S4BhKTx2zAcHMdEVxSwri5Ki1xL07XQOdqclAnN0AYshhcbCzroXNBn3X3zWaU7OTvUXctxj/jliNLYfhdSwZc0p4OeRJjRJf+4i5X0sFlwedpPy6vweZGhKjMSQHPIb96R7phhkbU9HtIZ1KAZhMn4cXPDXrHZcsW8mYt9sWYS0TyxayqUjlOu7bKdaaxX3hR9c5xWKXWXG6M4kPhsUshZoFHOSc6D4oMduUSMG+X0RhKH4wmlVPFdqTcmZJSn1UwI58Wp1QF2SdsRQJuCK/HkN+gFVRX1NSXUhTTI1aq0Awn6QZCWg21Nem4PMk9Sfu2o6atPexzBVjzoKRJjwnGL3AzPpe9ERDmWMarLsPyfiB7LrnD9Y/aBF7eH5ZLNMsBzPHh7w8LWEhy0sHCDLV8yWPumgpvk6wsu0ACYzumoMmCosYw8JjjTvYIAvMYPm64y7Tei2aAhvNOm/DXQvoIXm21CXBepry40oJ9R8xZ5vFwhS7z06ObBkOoJCDhc1rn3kYtEuc6DFLiEWZjzRW475NPBbgFkzEXIycBbJNV792BMiYSX6vOXlGoxMm3CEjm8OZNoOtGH2LqQef0VN5LWLWmcEwmgvsyof0MZj3WvAE6EewAGNpmjy6LOd1Fj0+mpgV7t6r999zMIz87lF9KIHM4QhAO36vWFivPXbiUFx8t7++c0AppcxaM77duPSOtSuj3bBxI00Ns6eS+NET/OQH8Ntv4aFeNSDuiX0Mxky0OdlTpkmMi3mkJpRMTivFnTI7ZcA+H9mtsQ7ow0izsBbCUDvihQNBnwNGdzYfNHemJwZBQfNMnzc8VRqdjDY8NYMPMVse3zzwG9974uFJheX51vjmxflwS1ybPCVqSSyLwShkm7jBm6czj6fC6ButOZ/a5NMG29V5aYPrrs3fTJm0yNyvxgWWkr4GQTOfc9yb71JrmCtr2zJdEbC3rbFvgzaViuRUyvnEw/khYpqHDH6Dlp9rkbZ6NIGB87NmlEl3STbaQFpv1BS3rhS36+2mKHCTJ4I2TwJF1pzF7rEcaH8Y1sVGu1aZjfUx2Frjtu20Nhij033S9w0cbQLjYvfQnaaSSctCWispEkOyCWxrrdP3zm2/su8CpWSGLETX4hIZ3akhkfOSgilUFHeaCzktgZwbmAx1c4DOPps0+1MytzEGbUh2NDZt/x8elRLXp4vhk0ThzTWT86qBJQdMMARcpCgUg3lnixGF/JDT7dvGXa4Ul6ouGQs2m76GEPYj6l6botla0CVnxG92skHbmu5wj4JeM3mplKVQorp4PLcjOvRQjczPQK+72WGg/dqeOHvveOtkS7z94h2Pp7OahVoVDdwD2ElFQ+yMCzXEW2o6BUiIRj1ft2jd7wy0tu+cl4V1XVnKIgp80savJhmSp1rYt4uii+Oz0PWvDU4Pdggg0IzwCwFu205eagAM8rUi2IE5J/agAWeLxsyDWn1sFY5tSQzjrXXaLt8SscI6y3riXFZtNuK9qrnEhi4ovx7m3lOfVWu79PqYIpvjWS11pfukujNz5rSeORJaUhSmQ2opGm742mRTlHFO2Ahd9xCQfcSWCpPRlmXf9bnMXKgmiXPNHmkekvEcz+7Yfs8jvvRoPMxksrjUO0vKEUC/P194eHoUwM5nRTb+efixwbFoPsKjIDTldwbR8Vg/A5NA8s5alxistUTQ5Mp94IfwAAoQYwZ7K3QFKu7wH4BDoNb0dr2SlkXfd1Ia4pFKdnw/c0ZzfDQi6fisg5kTssMjvc133Tvde9SJFABh+GGlFN+XGiJtAgNIw+j+mnqjK1HA0oiklzvjiwPUla+Jz66fM9hmPgeHdNmCGaB7QV4GPkNuMv1usp/j8zoanpQyreucWXDfZZ1zPLAZg0bWRtx7DG5+B6a43xQBGEbze3zNfbvRemOdmdHFkFBNnRGdrT+XUyanGlJEA+txFmQmKo+4OK8T2j5Uj/2CNw2tKVv4GuT7Z2fFmSi5VGcqU6SVClmzQCLvM7yu4mdOhSUVrqNzeWksMQhpyV+iwfdgf7nYnpEAKt+nkBsGyGghvc2p0NpGNtX4fE5sHwb1VPB9kNZCZTBGJtj3AtRsyoMlObPrzPbeyLkwpvGqDp+02CQnoJ6Mlw83Pn6zs5wr62kyiwC0o2H26YwyqbEM9JTxtnO9Fc5mlHTEURNnLsOEZT3z/rcav/i3Ny4fmnrRkJZ6kj+d5bOAiD4xdkg3bZRTwq0KjPSFlMTIsnPmiy/gt6/v+Pg7G199feWP/vwv+b0/+obHpVB/knj/UAVmWKaaka0wrOE5k+nUgRj22eBh8me/vPD4T/+cv3t+5PE3z3DKeH3DD/72f8E//PYbvrn+Af/8zxvf7p3hmZoqaUncXi4akCz67PXMzz595J//8YWHh8zffDixFMNLeHrMwpwJ8mAdK/hGGStbVypsWiDlSnMtKnEZx88kUGf3wd6i76t6L3PJpD5wE1DSwuNxJPBSME/0fiMzqExGUkKvEtUkhZalLUw0WM37s9QgWRBAOpK83nL0oyP6YB8NTKlPYxx/cMT5JqTIRF3O915TtiMBdlswDgPYqFUMlhFM5OlacKsjcPpUT+UWYTxxNmeK9K4kmZC5ZLEk2WUYiezQ+s6aEy3S9KZPCpk1QyJjUz2H2PUecvEZUhh1Jhb3aGOIyR01+fC+xI7ACCe7s65O3+Ud6mMKhEgBYhwsv1hcbJtSoAXOhWVCjiWCH9oTv/fXs3emi7XOdHJJ+JTkjalh/JCI3xcawg6Cren3+jSjdqgeytezdyhkWkjGUjaOJK50yA6z7rMZsmqsh+xKwUBitzpz6ueZfdAP+McPE+ToFQIQ8WDOpjFj5kI134/lld29EtWDjZBQaV6y6GVmMJk8xb1q0GaA/R7z1TE4H8CIK2kWK7SuHn30Lrlv9DAH1UxPQ56gB7c7JzRDEHmtB3aZ9HnNLDDBh2wj5pQ3JLEUVxvjAknSweY/+rP4YiZQjgQ0Mast2FU2B54GnnX+tVOSJ+kBc43odT4HM457/2DSJB1U9TLhC2Q2uRtPO5/V0FhWk45NHjOCNnKAoGmqh7DsAazEbBT9inAWzTMzZg5hdMGYw4GQtzl4HvcF+xxiZdnQ9yLRUMCpx33kmqNnDylthiOwhWCSuUsye3xOGpG1DFMfmClVIVozJIoQ5vvJBIgeicvRw2h2ki3NsdzMqcTPmAi37+/s13cOKJ0eTox+Zbu+0G9XkkveBpNTMr54zPz6Q+dH7zpfPgyW2ekXMS3mJnnbPl3DIdpu2Oj0fvhiqMC00dnH5NZv4DUINA4FKglPQQlLmZqNMTM+nD7U2PVpMKF1sTC2lpgTGtCG8eQL1zG4jEHfnceS+f4Xb/jelwtP58KtO99cOt9etcE2MuuaqWvW9mjuMp9OiXxKPJ5kzPYyjUubXG7GZXcuvfOya+uzVqV05KoGKB0Icgq0Py6zqLoCg0pBkclynt92Z+vO1iYthoA+ndPDmbyucbich1o41wphlO5TQMVQpeMw2z5UG7dmTJJ+VtMm6nbd2PeN3vd4OUSPVBHVCwRDbCIkeXM7qMqTZS3UVHAG27az753bLonbHIPWNjXTcVmYm27leMdSLtTcQpodAAAgAElEQVS6UkplKYsuShd1eA4BLvLp+dwnST9fyjm0xYOH9UStC14Mz5JakQyrlZoLShGxQLxD4ztkbunDab3R+05rMuC26Yx9YDlzenhQopMlSkhZkintIpdMNyhBTRxNNGELediMgU23p9hFHv/frhvkTLHQQLsYNYlDOx3+IFnGtqL7Rt2ak+MJpVRwUwS4j6GhwpW2U08reV1Y60JJMg1UVHK6021Fx43EowBL9C2LrTDiDLnDDENcc3j/5g31/CBgx8TQat4FUlkSZd49ADXuDKcDv9dFa8HiEtK/B1C13S7kknk8P8hzrORoGjOlLGIm+WTedhnl2uvXPoChjEvVYjkKFjoTlmjXG6RMKVVDUGy6cq13U+xSFwHW00khq/QwJvYw9ZvJKBRt+0YLLXri+XKllEKple7adHAABQQZNs4DLlmUUiIII2ljzQstgDLJQzPenbY1+bHVGpLOcn8+rxIkU4OYD019U3NvAeb1Q9n+ep6O595aY314w7QcjcakmtFcBp7pYPkcaMPxzxzHRveGGfpc2qDNBhOubXC9Xvner31fRTi2vsleqfVHI3A8S6XqWLDq9HvGjKIbANohXxuj09vg9LiG75GImZbklGDxHh2MpBzyVczwPu/bYz86ghSNjonZCdBuOw/BrtIWrN+ld7HLkpdCNDkCsnWnpByb5WgkhQmJ5dnC9P5o1dTgasjJKYyULdFGfCc+WajsphOlbzeGhpyj0RgBgriUBUeD3FsYSh/mqjPAm0lHcqRsHPSr+FrxDnh4WdkhV9RZTikrejllxrzdN8jm+r6G67ucSQ1SnrrBLGrhHPJpnAGAqUGcd5PLMWR4WWpmv16Y/b2kscUOwpDuval7Ug4g0SjGO27xgCTPOYYsDWiSGgboUyptj1qTkDxfAiE9x6k7vLu2r2baujImMymMImfYtpsa39YU8jAFCGV39n3nssHjuSrdNQWTa+pzT9nu7EMXTiCAoYO70U1yl2JQTMPrcJ2rpS7cxoV9UyKTdSWyehvkKgasBiTHY3s7UshZR3qtM4hNOkySvG0ManGBX6nz9VcXHr9YyEULD63FtDwwlwH4zE6yIY+wObleO5blK5GCnAZDyxZ3cllY3zzy+L2NT3/xwvXpDU914slJM8EudpuVSKUC5nzBOGGcMRtKRK0rjESyFdw5584PyfzN7R1ffbzx9eWZX3595V/9ybc8PhWWH585p6reJxmpDCoCJwqZmY0lHkRenIsN/ujPvuHpX/4Jf+P0wPqjM1YX/PFH/Prf/yf8ow8f+HD7Ob//y8bzlFlvSom6rOz7Bq2xM9hmo43Jn3yzc/qDC49vjL/225lz0TIgIcP1iYbKPHZuq1GTM24eS7bwKBOGAyTJ1hI8ZeOanNumxN/RE6kejAU9L8zwNiXXjJ4isdL7jVQmy4RBCvaJQZJXkFnEyzPvw6zYgLKXCNGHpHcij5B7FyBwlyu3AISDhRYsWQHKsWSIxDqNzIYHq073/mtvlCzJVygZ3gfuPZiu4DTJd6d6swOE6HMyM0rkjSuSJB9WJxZGTvRYKTxijNsUiz7b0OeVkwDgoXM++ojFjpaf3rvkb2ngHoO6y6zaXD4yxyR5LDGUJlewqjo2WtM9GXcBx3uccvSEMCOJakQ/T457L+7+FH2S5yQSYgStlFIklx6TFD6eNsADpHRTX31I6gzu4Nhr+mgM8kiO24Zmq5RyDOadFGdV/djB9AwQwqaebRBOZhiy17SE5+jhk9jFlJH+RwvNIB4oGVDA4DTVvoNSpV7w1SLjYJ9kN/2dQ+mw02TObSaQnOl09P4cA/zBRtaQH4U+gA+LGa2EpFjfpixeBMDlqHNHn3EApFEjP6NKZ8SaUkvnZE93+amlCNFxnefj7OjN0rOZw8U49Xj+UdLNYCaBqQJPjvf3UEFArloGHrUfxLa699Ku+UALvvRZByTQixygXPSjFkzhYy4g1Bw20edmYb9wnNHAg3Oa0SNOctVP6NNFdD4WmzE7albShx52X/dZWNh0GIPPqG8zFn3IEqUNhbwkJI2c0+VTOBGTeB7UdC05exMJo5sLzOzcPwOm32dB3bPR26RX2wUFgAc7yedd1XL01gdx49VPVL2rW7AF753zd/PrOweUnn/1Fb1/ZLRB6Q1SJxfnqRR++j3nr/4YvjjBaRpzjxjcbRXV0BPernh3bDtihHe23pnDmN1CUpXZGOxDHkiiYO40M2YaLPYUSo0Nt0csN21EplK43FwpDVZIdWEpMj+8GqxJl8L78+TrX37kw18O3r955Ne+eMfjE/Tk/OJ548Nl8twT+BKpPZmZgamo5LpWFjeeHgqWJ3sfPN/g2w1uLXG7wcs22JoK2GkpUdRHbDbqazTw8Ni6BO5siVIqpejya32wdbF79pHow+gYovjCulTOD2voeidPpxOPS72/0HP4XU7kgWo7QpJnsAa6hwm3ZVobbPuN3rtiOlNQD81jEJbcrdaF01K1tSshZQoJU60JvLN3SaRu151t01DUe2MMgQ+YxyViShNIArSWWliXipVESWoOQQP77I3b7YXr9XrfPHNsbhF40oZYVuvpQQbcKeMxeJUkv6FSlvtlfbCSZu90Ey2UpiFytqB/mhq4FoyL9fFBgBTa9mmL7NS8gAk1xge40cNsUA1WsBSIAdlFkzWDfU4ut5uYJzkzkuSjKdLWEukzQ1qLM6WCK2lKZXawRfJRM6O3XeaBXSbWfTQZyZ8XSf+SDDXJJVKlumQvU8i9JC2Au1Ju4D6YHWDMIW+cY3I6nanrGlRtF9ssou7NDFokLwRYoiE4TLiPwuOSiuWaBJS0Rs6J50uk6D0s2BJeGKUE0CDdubcdGMwe3/cMkAfu0e8WzZMMStXAKOpXhu5lKaIIl0wpCS+JNRcSYWjtQ1tVH8EwU9qcSEmxcaFI6tEn3mXgfX254WRO6yLzS3tVOQvIGDJOdcnz5sFg8ymPmyRDRyUETvksjcmlSS7Rrlee3r2l5By+ATpHR4Ibpo2jzYktJdJqNOYtJfxZjib5uCgQ62fb2r0QZxMjLpnAvEyAUQF+HQDekWTx+jOq8fMpyrfNSds3KJWXjx+oNVPW5V5QD4NKjHuzo5lIIE8Jmnw72GfjFfwiNs4HMjb2PRrx/MpMQ42j4Xe6f+9iItw3rn6A6en+zuo/1aR43Nupd6x31pAQ+QiLe2k3mTkzplJvUtwBuYSfgAWgjL/6OI1gvvVdyV2xQLwbQbu2bTManjHl3cDo5LKEDdBhvhsOF2bhPaR/7sGjtnjGYw41t1YZDKW9+XHO1cDKYk6G/ObylSEXOjKrx3XWU0pKgTHiM9d53K4X9usNn2qqMb8zHX1oU1xzAJOmd3vMhnm/G++OKaNipdvJ0HLMSV0qnz58GwDTZImzguks5qoEq1Jy2NXFYHU8Z0chEBX61AZZPhmReopSO29bk8wjiWlck957Ue1THEFt+3sMi/TJNIFgcxhbC3bXiBAGiwa+yN9muGFJgKB7j2G3SKZnJguFFNIIQ/4w5kSMkULZos4Uy4RJn+r1U+bysnF+c2J3gQ5luKwAUsI9S96WHRtEDVa9HmNSspjSSjmc9F0/R1+1TDmdCtdt5+OHndN5oSRjZA33YrwmfHTSZtjq6shD8r9vlX5y8kjBIhT46aj3ypZ591uV519Obs+dNz9YsXQKo/cAfXPRciojw/LkHI4SNgtWzrhdoJ5I9Uvy4zNv1q/5dRv87vXGz37xgW++vfCnv/zIF3+68uah8KNsnEw9hKFgD5+NnJwyJzNP8pB3mU/j0gd/8Adf8fj23/OT83vqF2/xXLH3v8Nf/y//a/7xt/8Dn/rgz341+OTqAfNSMWTY7XMw9g6W6XnhD/9y492/Mb54k/m104nFJVMrWcuROcXAWl0Stjkn/gJb28hWFSVesmqGO9uESWKxSSLThnFrHe+ZlJ0jbzuD5B0tIBuHdMpYr+xtkrzF+6GauQ/dnzgCPk330ByOxxdN4YkzY4iT4ly1qvf+WjsjjKakxGjyNvK4zw8Wgt8Z2lOsCQ92WwBWWDCvLIC7aYxkzJ4YHSZ79AHcJcWYFikdyFa0VAswQnL6fLcWUJ1LlOwkL+xbg6HaOJPRc+FUEimdsOFYH6TUXxkxsbDzqX+Xc/j5WWZaF6hySI2zvBINAU6WAYwlZVrOtNuLlhAj/OWmQwTbHEzZ4eEgk1RfBwpIOeThdq+y4Tvo8tk8z1V9ZT9YJC45omtu0DUb5s0IGPGZ9PNWw4fAotYaywloHt6WnXSwnlyUj1Qks8ol7oAEJS+0fWCxYLR7SqXRXIyknBDbGn0tAZIwPZOsiA1r+c4YVk1K9zogNpW/Jn1Fvc/mtKnzOsYQW/dgRk7JBu9JitqCcCSA3WVO8Xcc1hA+9Q5YMZaptFjX49SfDzDXeAUTHCW8HT6J02V07jPYtFngjMc7l8sixtvowfQX40W+Z7EAGiA/vRTeQMGtMtUiD/bgsaRWCVZd8amZwuPuPpaUZkevkO71cN63gvL2Oxa1Hvixe8Z8kO0VaPLwYiyh0olNZJw91Z+wGfwM1NJnJj9T42DjWfhPpazv24i73FBlmAdrj/usbBZLPPmEUMMiwu/zm/qL6fP+jvbhSmOdSJb/mddazp8B6/66MPMAGiEA8HtPGnLhEeqMO5Z4P5z3GfLoHQ3CSuG1f/+ufn3ngNLl0884RwyxMznnzG88On/l3cZvvJ88LGp899nwSBtr68C8kJKxrA+cc2ffnZpgcOZkmcGg0mUC2jL73un7ZN9uXC+T7da47Bu3W+UyBnPAJTn7vCoi0TRszzQYNjAK0xsp7TCV6sTUYM+YfJUmpwo//vGZN+cnluo8b4Pny+DaM41CSas8TpJMTJmTgvH4uDKssaSMe2JrzssVPm07n7oLTNrF9smlsiywUEJbXjgiEC2M10hCXUHb5pwKWKHPRJ/yLNla57oNmicsy3jSTUlObx5O2ibnyTkMpxmD1kUBnwTNL1ncN4nuRjt2CSnrsm4erJ9dIE0AyzmKVsqZWovi02tlqSsZXt32gaXofx+jMUIHu+0ylm5Nl1rvHbnpZ6VAhRa1BKV0rZVlWZTClmT6mpKrce2dy+XKdr3Su+ilIzYRuJGWEmbixsNDwdDXEBvFlDIXaWtHZ1KKpIG9t1ftcZaZ8cggDyExUa7XjX3fqeeV5AiBxu5skrRWNS1EBCoexf145qKHH3TIOSLNqWt4fLk8i46eKiQBN4eBbwlA4SiCFgNOjg2xPHek6z5YWq3JsHA0+T9te+N0PrMuCyVXNR1wL6aWczQ2Ts6Ztu/3s6+xebJ1/VyHn83BzJm9s6wLT0+PeMSvT3fyFMurxHZeD21y3a7sU1uJJeLeCWDJA+gfvYtpMJVsNnrn/PggyWipeKCdyY9tdlCUkzYGNmbQ4MOA8i4vERBzBxZMAMbteqExSDOLsVZSDMcHAVlAYXKxT9wHBFAndpZLXpBFjVfSnqLEex+M1ijncwym0bhGM2c9/BOC+XIkmRnaJHlv2HTa2GEIfOumc3drO9fLhXNdOa9ngUnpzucOIEw09YK2zCXJfD8vNbyCjsY2GtZ7C6FGaNt0L/h+Y0FgdPed9VxDJ57vMsI5JSs8ACQNcjrP++ikaCRb70Ci1Mq+77x9egrZXPzNQcsm3ks9qtjKoUHHHVIt9DjrYj0SIHMULoNtV9JPTfkOMmUTz1J/p861BfigjZEaY9xDenz80YOjqW8u5cJsO8lgqZLyZUeSbNe5sznu0lc1PXZvWo8m0MekBCtoegAt4Xn02qDFUtWIBlyhAfbK35efQJoCtxFL161oYRFMCp8djkEqNoLHAqLEvYBr21lE8ZHpffz5RI4tLq+AIWK4zagtAliOzWuiloJ3Z7tt+v1laFBKjpskIj5cm+ssFoDPkPxGuuCRHpXjXpyBiCkZSLKsPg9XJ+QHZsFXMd11c/I6hCrLXPKAOcPfJSPe2fwMtFetOq8nLpcrpVaWUllCvj6TtrpmAnokB1H9OM7LwcTsXcackt7Yq08CTl0SvsO0fJfF0NUA56xT17oFWOhMMtkLGJTi7M0FmmDIB0B3Y0tTTbN3Tu8K7XJl9BO1aCs7clJAgfmrebtp0UJ4KrnJ+D4DZcnydJkD0qBPY3anJGNZKu1y5eMvr7x5V6llssaQMofSXmc+k72JkTGm6tFJ8ifMQpIJtRSsLErDmZ3ZjVLf8P6vdj79xQu3Z9X69LhoGkhVdTfNWEQ5zJ1ZV0hP6gnKl6T+lU7uupBGIpvzxo2ffHzmP/7xA3/yp8affr3zB3/xLV+8yZzqW/KbxFITRiH0Rdr4W8cLSssdYsKc18HLZeMP//XPePrizI9+9y3p8YSl99gP/w5/95/8nI/P/zP/Y3P6t4NnxDhf60pzGNcrY1cftj4+sV+u/J9/+sz3vzSe3lS+fF+wopj6IAqwY2R3LGcWWxhTPlmzKw2JCZ6dGYu7PJ06xXCoJVFTpXXXWWmFNO9wB2ZOVclim2IyO+oX1lS4+a6BfYRPVrx7KZKgzGS7IJ/8YxsvEDZN+TRd2pEoZhAA6Yy7MOVE3weTkMZhYt+bGA0HwJ9zEcjhHp4uiTmP4AywPHGRYqV0ILzp3ClVdduRuW5OJeTHVTdayuQ0wQUmD8SMS4eROOBL/G/bYLppFole+uBRqckxGJM+mrzSAqj02XRX+4x+3V+Zv8dUfpcwS96f5wx/tkm/bVp0zBFsbb1PyZI+L8ROPtgzOYZ+xcKHJ54P+eT4DAacyzYhqVefrctzNpnuyBGgQQKY5LIIcJnyt6kkRlagCRFJ70jSSDpkvBaLxBSm1VFVphhF2Y2cKt3UE+aUJa8esreYFnOOEVLJjhPgfJfcLockPOccbhHyzUshbYKjj3wVbFmwPdzEKJfMObANHMxZU2akrLCiAIpU4ILvlAhZmEdvLQXK3ejB9E5YSoy2B6h5hD1E/xT4gBbbKd4d+fcOn3dPIgvbBkx9sSeoOXqK1kKCOu+9nQAknZ05Xut1SmBZs1NWoyoZePQvE7GwVDN1Todrqe9j4rEM+lx2pbYsQJOhwnSYTFsWucCIeQn9/cm5S1TjKKufdS3rpun3yNha7F/LxzJQf6vGwwAOg9EVf5z4EeIeP0D6HI8vWLUzWE4zPrejnwjW5Jx6DvjBGpMXrsc7FI6l3O0Xhr/2tfePZ8ZiiLh/gg0bS4wUIFXvYojdv1YwkQTo6v9GfFbz+D3f0a/vHFAqvjN3IbPvH52fvnd++qPGl9VITEZrjH3Q+sZtl5Ho2DuXObBWMDo7k77rYUzPzFwoayHXSc0nlgXq4yPrF8ZDeeDtMM7+wHU+Y23yqb8wn51fbZ1Lc8bLjeueeNkmHzcxAI6YyzwMm52ZBrfembtzfjjz5ffeADsjL1iafLxMXl6MLZ3IZWUpiVoLliI+cU4ea+Htg4YvS1XnoTtf3W7sL4VLh4975nnXZmY9ZbJF4pdPRR57wrwgzzeDIqDCfKdkHYZcMp4yrU/FhPdJ21/TnHpvlJx5eDhzXjOPp4Vsk6UmbQKbPC1Mzsca/OOlHTMuhZJJ6aQX24zWJrfLhdE6c+936qLoyfJyOp0fKElm1su6Bn0U6lIZXcBdtsK+aXvd9satdXrb6b3RumQJZlCSEuFq0lBSshrIuhRmWvFcg7mEDJjnpO2N7XbldruJ3RBDhE0PKVvl+eXG+eGBmjNpJnJxpu3y5Soy4i2HUSxiZSzZBGguwVbKJzXUNklzctufcR9cLhe2W2NZFpaUZKJudjfwTjmiVIcK8uFZM2ILcJcexTZFYPuMoRpePn6i5ADAii6/U13UFMVlkTByLYGEhxxhwOgwZr8P8GVZZJ5vKmSzD26XK6eHM+tpZS0LJWnzNsNjy6cz2yaKb84y8ju2GWh7L1aJiY2QxZ7zAJssJU6nE1bEjsndGVkmslJ89GBpZXaXtlu03wBWXLpwbbHF5tiHElH66Izrztt3T+RVyX9MkfRSgJ1KQEiQMmnIo4up1JYZXiiqBZI+YksYMzrJnI8fP3LbN9bTSY0NsPWG0XhMC44xc4pERG2+RheDsrcjhl01YjjQd9zFUJjJaPvOclpZqozhtW5KArxwrCRGkoGiTTXW8k+f0OYdsDqkOhPJc7be2buip9++f0dkD0TT5q+JjoT0EQ3NczSKOy1o7QcritjmqBofPlkC9FLO9L1TijGT/N5aM1arzNFC7sR9e/J5PXN3gXhT30tDnlWGAFHbB48/eJQuPrbOidgqf771c+5MvYmTiiS3xzbGDk1abLrCQoDWB4eOJsW2KucseVUS6DDaYF1XgbDEndkHMyQpd+Bkvi5/UpL87Pl6Iy0VIkGlpAI+mck5wv/M/b51lMSSkO3JUDSVomae2Gx6CoZaRAHbZ8PYwZDbGzNBySvDJ0tOdI94Zx/4PBhNMokfHl2mKW5crsaNNUzK5xgy54RIglHS0QwgyY7ONrohDY8BoMDxTQpU9DB7DrakZaMDt9awWmJb+dkiJc/4fHR2RttJuaqpyoXZD/q9gL7kQwOTv25xcdha09Bpajxba3jqmFVtP70HmMurx5fLBHiLZD6xXwWEW5zrAZzOlYdtCX9ASdJHPpgSMUTHvDhzwWwoycoibtkyPU2WqmH8blyM7kb6xEZj3zr7OFGSBhgtR7v8htxpu5LacoaW4p1IRk5H/ddnOwxu3smWNTw3hWg8vcm0/Up5PAnIzhqU3Qk1Y8a7IVXYpNsRM65NfS6JhcSciTEL2Ka0thlD58n49vKRNx8rp5OWUSyISe6Q5s7Mhu8jQIvEw4PhmwzAKTBnpg/5NVnNGBnfdyaJx/cn+rXzzR8/k3/rTH2zYenMYcbKcGYO9oMlkndIK6S3zPJ9jf/jAkyKV7w8UJ4G73/zC/7W39n5s68+8e3v/ZJvP2z83h8/8/a0csqF/JBJeZBRIMk1ts1nL3SDWQbJxBqZJ+Nn3z7z8C//nMf3b3j7kydYT2APrH/lH/Of/6MPfPr0v/M/9cHt28EsAJO8VnrfaL2RXUb26fGRDx82/un/c+PXv9w5/+2Vulik8W1kc6pXug1sTBYyPDglTbbboLUdmzvmFbOFEwNq9BWmIUSLgcFlQHa9G0o10nutZKQeMlX1F2U5sW9XHs5JzL1ypvdj6TLvTA1dgtrG92Gvd8XQXTfZKFXsuGQ5Uh2BYlIwmFPWQu/a0B8yF710YsonaxgjTIKHgMVmr4Ix1z1SysSzizXTF9wm7o3eXYENczJzGDfbxE2eJmN2JaXOzLQIh0jyN209CgMJs0IqTupOJsu3r+14hemNmow+NKTmiUygk848wTjJMbTnnOWLEou1UgT+kGRRoITTCBayEzdLtH27L6Dk5SgFQc4Vn0MyTf/cK1GLm5TF2iZ88A6PlkpiaJrF3SNtLmxHMGYYsmecxbOkd5aUxBrSNI/gn6WemDTqsmjhE5LqEUQBMyd1MbJzLhAJz/vYWVIRzH8AZDGUH0bW2CExEzgxJrQ2sFyUnDaagnJM9WsM+dliB+NJ7LlaJGcdsWgDLTByeG32QxqFztatO5ZivWSSSh49u7uTeoAgvd/nJg/AB0fgX8l6njEHTlO/hNoA5kQWIkPLvBRYOZ8BLCPqWDLJ9Pwo1TppUKJO3tm5OUAiYjE6KEcPkuXJeKAvBhFxfxAhgLuawTi2dwe7vFm7m30T/47wyHKSFi1T7GWZqWfIETxTpLA5mESHXcOxfJWM/ljsxTuH7k7H8ZHDYFyzyGgjFlHHDB7P3zXPHN3MGCPe90Jx9Z6HgmOOwxcubq5jKTheGV2DeFY+CLcyzQlTKXzZ5csk9t5gdr+znA4T/qBrcfiaeZeSqRaBgilVeQYfC/24XwU0amlQqrzrUnkFRr+LX985oLR243tPKz94t/OTLzvfs40l9XhxG9ZEFd867F00x+eppAEz2KzBblgpPO87vDjPGCkAg+6R2VISaRSZcdtKm5mUG2UYo4hBZD65pg3vWQjwMllmSAiyUN9anFM9M1rn4y83Hn/wBes5TO684iPzzYvT+gpLxXIRMFxzaCQnS8o8nVce18bTw+D5ZVLSicvlxrde+PpW+HhLbLvRXMDCWpw1jTByO6BIpZaVuoALbJtt8rAknvIDmGQg3eOzGM5tV5rTDFNAq5mHsrLkxNObR9ZloZaF7E3DbQC/TsJTeAW5zM6EWsqjSjirUcrK7Xrj8nKN9K4AkXLBijTwOZlYLacTSxaYVOtCKZm+b2yjsbfGyYxtDnyIyt/6AJeEQqhqJL6ljOeEI/Pqsq4sESVpRT4buWY6kzyA5rS+cblc2febtgMOfQ66D23hffKybZTzKi8nFwg23ClloZTC+bTE9l9Fp5ZEyWA2ZRJZssZuk3a/N2e/Xtn3zvPLhbbvnE4rh9lxqVWNasg7imUVzdjGzz5kEgcqCuZiQQUQNRGDxXE+fvjIWk8sy8osiTQ18JSU/4PUrFQLHpuLEo1mCznMjD+Tc4IxaXNQJkwzLvvO+nRmXU8CkpLRbIJl1rKSU5YnAnB4JvUwENdkd6Q0GVqOvHr8JDNmH5zOZ8qy3hH9gQBXXXjBuplQwg9EUfOTnEIrHA1MSYVDG5w86fuczrv3byEAwZTFH6josh8B5LkPXcpT3jrDJ7t3llzElgoDaDc0cKWCz8G23fh03ViWgo0pNhjgA+oS8hhz0lSiiJ6ain2fGkDHnEH1VrGcbkp2AfZtMDBOq4Dow7DThzNTIqXCICRCPml9yHw7zA63Ocjx53ScJC8SA3HSrjfermdOyyLpYEn3jV8KsO7wBxPoJiDDpmRGwwd96hwlV6M2EvTRyWZcn5/lIVQkK2lTSSgl5zD7ncGECA+EAEvAQlrEXSap6tdZSmK0iZfC9flFzIWHk74GWumKx5HDjH7gIX0pyTDPzEh99H0LZoHg0uwAACAASURBVJvfPY5kRqpJvA99pqdl5fDlGnNSLce2rgTNOpofrR/pQw10ssPbIW7OJLmYm1gpic6nlw/UvAIR1ZyVrH1rsXlLBUoNsEhNwyAWHkgG7Qf1KJ7bKED8fXo5dX9q4BpYyjTXz+HBcunAEv2ZENyggbuk5YaCKlIwfWYCUhIw7SCPG7VpBwu0KFrkvnHHi5Jnot/sonUxR4+vEd4CzmdSgKTo6jv4qzZTEpzYSE61o2P0MMxWQzZnl0w855BMqZnEstiSriGohlzt8vzC+6cnum2kZUKv2LJIghENqF5EbcZnl+TMDu8LHSCMGk110jkZnVuCciQo1kX3T/QtFsMOZmQ30jw2ldo4j7mTT5WUOmkkilXI4W3HoMxC75NaMnsb9L2znCvuclEpLiZFj/epjUnzSbZOsoOxoSWUVtopTF0Tiw362HjeBkvPrOuJ7fqBdC1KccPoU95F60mDdU9il6S+UX3FcE7rKtDLYvhKcFqDzZKGhowM5/OJa3/h+asrXz490B4E4Bbd9GIaeMXuEppOShM/Jfqs5F5Iq4fsz4CifmYtsO3gifP3ErefrXz7/IHTyzvK06J351guUMEynh+DWPdCtgrjKx3cvGJ0RnGsV6ysnB8e+PWfvuMffPop3z7D//EHP+f/+9VHfv/frXzxtFBLJq9QEJM8Lxv5pppSU2GMXUsXSzwsYuT8u599zZf/1x/zN96+Yf3BW7z+JikVHn73v+W/+vrnfP2//Bn/237hm82ZecIwbFkiEarTr1ceHgv5zVv+7NMn/td/9pF3j/DTv3bm4amwWqZb0TnoJbwKd4onbK24Z2bvbG1ndC28pju3GTLrltlHh5751K+0m4MLgJyziUxjmTQyczb67OyzSzLmiW3XXfv4fiFvO1YSfZt4yiGVDeZbXdVOEGweN/ogWJhGoVOS0dPOSM6hYBZPyrheXthu8ifTFj/eZSoy+wZizJ/H8AiSRFmRhykms22tVvCo0zZaSDkLy7KAN3l9udGbU7KYYH2o5meTfcLBFFhKZt/lzaQhdVI8kj2nwDdzZ5qxtRmsejEkS030Odj70BrFQioeTKRcQnY3nbZ5LD207NhaeIiF9CblWEpPl2wXY+xXLAukzFnP+vhsSFqoiYhrjEiYy/QgUryCf9HN0vaG54G7ar2ak8n0TirQU8ZaVr122XqkpMVCyYk9GO34YFoKMFDA1BzAzNEvd2xkLPxNNwTkksLMPReMiiXo3tWjdrDZ6MBIpiHeJec2Kn02sivptftGmwo4UHmdYtCpiIU3DcikQRYDxeSFNKOSEOAGPRYRjuT/sZAzFLQxfEIxuk/yPIJvoleJ7ca9f0RqioHOeo/lcO/6+efsdxNzBWAIDMkp420wg62kZl1/v5apOu+W1fdmM1ZLYTUT7FrXMmLMpnnREsWUeugmIKw3eWbO7rE5iR5lDlosL6ehz9YylgKAGsjcGsR2HfJczUmAiJuLQenqudMI24eQ0RtK4fOCanJXTySfIQGrE/CuhT5j3hdGPrtms6ylY/IgFrjf37c5xUYkbGhKSoyxB4kkyf6FYPcN1WSBsrIrmQFUpZTohCdV4OnZVIszMG1g5fWMGV3bqpQ4pKnVZG/DzOQxGSWFx/Akr5k0XMDvREB5MBw7nUGX9NS+WwjoOweU/tYPK7/+w8GvvZ8UNsy7/HBuibY7rSvidfTObUza1bjOK5e9gn9kGLivNJ7JwxhoiOkUsIqnySiZbhmSTAmf/YrvonVPnE+fOtkr9CH5VhpKizPu0fRTYqwoUhtfvDnz2z/5Abd9QyKlzLbDp5cE6wqPC9YOlN9ou7aFb07Gm0fnYVEjvjcNBh9a55u+8u2nxqeW+XBrlHqiJFiTjC1jf4t8kQzFIssQuE+42uA3Hk+8OydsON/eOt129iHz0zYnbcC0wnJaNOsk5+m8cl6qgJ6SFbNoiVQzt10bTLNIbzoa4aSGzy2RZjTQffDycpGUpU9a07C2lFWI6LKy1MqyyEA4p0Sp8lDKoY2f7vRb42Fd2UdnyQlbDLMF5sJtu7BtzyrYVikmqmmpmXU9gydqrtRIs5twH2ayW/gtDfbrjdvtegcdRFOeAgr6YG+Nt2/e0NHLWJaKJ2OtC9kyy/nEsizUXO5xqoZAkT4GtWZ8TDWbLmnebdsw69y2K9d953E9i7VW4kK2oqEop0h3ywHFxPBsh2b6kPz4Z7RKk58UzodPn3CMdanaNA+ZV5dS2RissZVYliUGmwB5klE8M4DuTi0L9hmjoViiMXj59OkuUbQsacZyyC2R9rrPwZHkNYjkiZDRHXTTMQc1Z+j97peEGbe+syyV9XyS18LkvtHXvjMGnCkPrjalyx4u9osHIJTNFAebCZhEUbj9tvPm7Rsgs5aqpIsiuYlZZpiT+4FVZG3QrUt2ObQhdAPSCZ87hIljzoPed5IZHz88U8nUw9AzixVV6yoWVQCAKRveBKLlpJ9lEo3cwVcN5ZFMNcVO7PtVvlLuDAujTItoepxc1K6B31k93lsMQQKE9jizacLMsE35Us3eGXsjf/9LsW8sRRGLuHbCN6hWLJgQUYOYBcauqHBCpjaSGDmgLYyM6bs+k7mTIivXHTxnLpcLp1wp6wKlyL8G9Q+Jo2ASiRxJCWIlhkN37FR5/tnPefjyHadlubevuJqNQ/KpH0qsy27a6pnLA+KwYZ3uatiCAZYR2LtvW1CzQyef9GfCVe4+FBxGnAmj+4hEFb2zByCOW6Q9yicjTbXcrTXWujDdqAGQp5RYUg7asphCFl5MB23+AJfEu9ZdLQmzsV1GbEl1V5UkcC0lg1qYJt+gYzNln1G0h6M7Lb36iTjOdDXHbtCzAh9yhWqDVDL7mHcvk1QyqSgkA5SolWLzbMkpqWCW6LOFgSsxOAXb1pIA+9iCF8uSMOUYtCC2oiETDCbV7I6H7NeHcyQVHoDSnAbxzJUaZOS8kvPg8eHM7baREKCSbYkXUkyyOfzOpfIkI3xPyAPh2CoPsWzTQTUyPyyIGM2ZlhitcQ72EAco5mLMSXZgcU9IzpOqvDgUjFAYZqRTZp0NvCsllBPXMZnWsbbR+4oP1fOxO61UUtF7Men0Li+MnozFXICcFbF7sjxMRpdcc/fBPoxuG70NajlRcuN5a7x7eGKOjWlQqyQnDZMJaR/kMRi+sZRM33ZIme3SSThpDApOTztWE+aVUhJWjJqMy1cf+cuvCz/kRNofWPIOGGVCz1d6SPcSUA5wa6oO1afEshZKcUpymIlUkyi5N7HUHn/txv6rjdvjA+fHjZQf8W3Tu1BXrKwwb1hZ9Mb3C/Jaqsz0Pax9S7IB6VHP/m3h7Zz8Rz8d/L1f/IBffrjwhz974Y9+/i1fflF5eAtrPWEMlmQs6DN76WA+WHNhc71rI0/OXrj0we//21/w/ss/5Df+/nvs3QPTvkdKC0//8L/jv/n6v+cXrfF7P++89GCm+qQsJ8Z24aVt2LXy8PTA47s3/OtffcX3/wW8fV/4zVzp50axhWYDKx3PK3V/wOxGz5CrYQ8LJNMyoBcSg4cGvWcaTk+JTOK0nngsWWEJOTPaGwabwJ3pdE/kmSleMe/AyrYN+pR88ZQTe+t4MW69kZj0KbuGNDzY6VUeRbNTcnh8+SGpEdjdfFCS7oecFzEXcqXbLnA+OUuWvJBcgiknC4NjkZBSpu1DLJ6kwTn0IlpgerCvY1hus5N8MG43aj1rMZn4/6l7l1/bsiy96zfmY629zzn33ojIyHRluYwLGxuQjUsgjEG2LINMB8lI7lgyHXr8OTRoQ5+XRA9ZIBo8/BIugwx+YIyr7HJVuTIjMzLu45y91pxzDBrf2OeW+9mAK6UypIh7HnuvPed4fN/vw+NkTjRArPeltYbS4SNVfRqSZ+RSNu3kfS91E0v3ezU13Ji9qmIMV42bf/+OO9D30hLcXXfo+VHBJBGuZb277I/ahsro6aHarLXk2em2U6paBcsUrVESa6FFk5ZRqouLC/Yd90WRxeti5RyZp2aGeaUWKFZzIK4hg4fs1iuXibVWFoIgC/mg97eUVC+Hy/Ici9JbKpsmLQxWxVpkn6BFCaYlu5nCIkpdsjQuqT99LaxOAZ/TtlswWoHbeZMQYZ5EcTX7tXAHH3t4Wo600G1sRDgjAi6V4istnZ2KwpK0B5VtSipXg3DVPNm73GtJP9drYp/eW93/UJhr0EgWUdRcCiZ7NvV2ocIF4TUslS+FshdKqsHuSaU1ER8+hp6brAvAWclQEoNUtaAQCOQAyVkm1809HbhWfY1WG+HO8EnDlFaXqmbdzRqIKckwqKXlIFSKvFrF9hMb0aBUSEW6hl+wzCEahaJaNyvEkgrce5DQDNUbnOL0tZ42bdfgSu9NDvjuj7vV11pgrcW+f8aEaBlqtNrTqeGpZM9hb82vH7JsbjUX27msLFmhQLJJMVhLaZpZ7ZfkexgaMM74vDQea+nfu1AV+jvqMQw+O5ASvC6Ok2oWqbdVQ/88//zcB0r/6h+cbPuNdtMk81gCUscazNFkcWNw2uJgcLQgTsHlqhU+nkaxF45x8mJBOYOyNDxZY7FK0FrK6hxWdJwLNZzBC8U7W3vCJ3h/xlBIqbm8sVGaGp/bZIzJ07bzS7/4Ndde+Hg7cNc29ePzJ+Z8A/sFr409gtWq+Ci34NLhze68u+jDej4r4n4ZfByF7z5deH8cPL90bj5p20YxY6+6UAVqk7RSD8imYUsZRFlcqvG2GU9XOI6T9wtehqB+N6YSEABvJatd43K98HDtbE2gZiUV6RG+nUO8KlehGdagm+DUpqnqXJ7bqc8y/3MsxnnPkCjktEcPpVlyUDItpGkYcx4Hq2ireLvdlC50LqJWnMlxeyECznPw8eNHFfd1l6TTclMRxjgXpRU62lBFzdjxWugBz8/PPB835ji0zSgF2RLuPmSxm461eHr3VhLHUnm6XDVMap0guOwXam3EXJzLWUUXeHHH7xJnM9YYvMQND7GDWgnG6bw8HzxdH8Vgah3LwVdvOsBbVcLd/aCZayRbKqG3qFEt1Ffbktg6zsunF6lgLlvGh2oDvbWNCXRrUAv7tuXeLdjKXYIhBQ6BknmW4z5UiTM55+Db99/xsO08PjzSrTAslHhnii1XTGYCCIum7euu7ODu+RYAt5bCimAiaWwvajpbazy8fUNUgfMKmYSVSgXsblWSEnEtDaZiTXGQVlB7pqaEMcaCVP98+vCRr9691UDT0kq3dbwm28YK1QOvQWSqGWYMDz7NF2qpdNMlv2JmBw8wGPOkW+PTpw+Mc7I9PuK+qFtD4ReN2vWaXWoTSHGFWGhjcluTOcer3LrlQGndVUQu5cGncVD2jWvbxNgAaq/stb+q29RkCk46p+OnktdmZNR5gGeqjxlMDzaXnfH28cbj0xMPlwdtkppUbZ5FW9wLVyQDbqWwzoBWWPPAXZbEu/y6RqqLXA3+ObWhiSzQaZCOAknzayVqzWQomGG0uF96uigtnDAlXG5lk8zeAtsKowbn8wtf//AXVWSnoq3V9srsUbKIWAeBUnLMxLugJsAVNGR1FeFrfgY/zjEggr1vqU5dVNRARcsHH71OUZK1RwqWPLAskrRRX69y49IKaw1Ad87bNwoAaFXF+T3VrmQh9cpjQMsGbbClaAGk6KviENxuJ+daHOdUSAJ59hkJVtX7Zctfh4jLXZa7UrVlLSZofWhL7/kapQ6IavoMueUnt3S25NZZgTHug6SWUngSuqptZ2mNcAOkAmTCuV7ypNKmXRDpyMSkmkWkIu2taPCQuE/ZYlGTt5Zeh1ceF4rE1ThUr6nuVhX8VqHvV65PD7x884FB0GJRYlHLzpontmnY47mNvEMvrUYOmSt928U988mxJsUL4bcswFOeXxoMPQdb64Sr2XKCWCcgRtms4nyNIDkqXUrJsjHWieHUSNKhL1nkWoc5qTG4HS/Ui7Eh5s1wpwwtHIzCuU5ZPJczswGXVUEm1yGXCtdaWPPkWE4wGOeN87kRfnCOjcenoIeGOTYHKwrOVG0QxtYKF9MS56yLvXV6KeJ/Xxrn+8HHcxHrZKs7L1PDttKMZcY3v/Yd9Ti59IPrBmE7ewuia2BZLbj14GJBNRXrxSv2KeA0pU71RfWKP4D1oF43armyv73iv+x8+n8O6jeT/av3cDPCBtUqpW5a7Ljnsu0GbERsOsSqoSQLJ+KGlU5/+wO+/CX4o39s8KP3B999+if81o8/8vf/wU/5fV9Wvv59D9gVrAx63TnC2T04cHbfqNX5lMuYS3PiGnz74eRv/43f5M3Xj7z7l95SHq5QvsDiC3745/4j/vw3/zHvzwv/+Ccf+G6GlPFv3rIuOzELt+LUMXi8PuBfGH/113/C93915/FPL75sj6zrpK2KrZ1mC9+CVjbGnJw22UvVgiYgrOLRGMsYt8Ea0Fc+d8soG8y46vO8OcRFjWXAMSPVcSdGp7HztKveu83B4QfXxwYfJ9iFY50acq5FmJbA+iyJqea+iCqbyDqm2DggW/dc9KhaeOH0Dd7alZfbom5Ga4Y1I6LJRpZDot66OGXF6Ze019TCHlKQio0S+TmSRcl8cZyDl+ehBtBv1JYJS3SsbXlqL6o1wgeOiVPkSwoZdywtvpF1e0+Fx/I71U2NpJrdz6y3WitP1wfGGJznSWtSZEYTkNruSp4Q39OKMe+LkKUQo7vq2ELDhKetZUKs5YBKS+1XTmKTqmvOgVsRXxCDXqhpD96sUnDOOaXoXs7Wdmp1xhj6Hbtq7N6MrWnxjxl1LY7jUKhChbY1Si/sppCi4zixc+TdrYHxYuFuufxICHYxShG3j9AIoPUOURn5HvaS8fFW8bJUgxSw3phnaowMSOv3tm0c48RTieI+SZq1apcMXcBktfUcurQQ8oCs/e9Ksr0qoGBl4Ei+0ao3LS2aIVXJ632fe5G7NdSsMWNR2q7lTP79hWHrzufREsXug0mW7uzaksOFhrK9UZZSvwDdNUWJqBSpxmikTbqpXuKz4s6yviACj8lwp3p9/Z0snTe+oJYOfE4uzBKOajA814srZDuvjX3bOT35ibmFuitzSimfe4ccqFpJfEETzmY5mEvFFfDKkozpabWFc3piRhbhQUdfy1PVaA5E4h+qgVXZgvO9wSxVdcJZyKoowYwnd07waz2PxT5b8crr+yRhwd0VQdWyb4RTw6QsREvvmRBxul6TbdtYObR1C4Ipq63rxTXQ4A0opem5TvHKQja5mP8fHyg9NikHlgVrdpiFMgrDbxzj5FxBRCN8g9rpXeDPEhc8nHebLCPX0mnTuTVtpsYaUDZY8DwHZ7zAafiqzNUYpg2c2zN1DGadLArEjVjGMMWctqpCf++FH3xx4fHLa8KIA2fj4/Pger3wC9/7gqh6ED6+OIcvejG2UuiPja3B24fOeTrvX05m7LxM43bAsYz35wsfDj2g+7XJv06RemAtrMBi4qZiaM9c65jG49PG1oKX03n/YtyOwm043gtjHKwJz2jDe+mNh31nq42H64V9M1iTcbsxw7g8PohNtEJqiTAojelwjFPNBTBPpXbp8FLneLsdHMfJPaazbTvbtmFmYiQ1+bfLfXKfsDdBRdNmtQ5WGOcKDZteDuZ5kK2LuEhtS+icZVPiKZVNW05BzabJ3vB8e5EC4OUmhVJ69WVp0uCQ0jiXhjLXh0c1wla57BfMihQCZpTemUVy2RpwzlPnVxU4rdRC24ouFneGD85x8tA2Pn76jm8/njztD9TexElKZUVPZlIpJZPeVBBFRA5aBpYx4K9NX9HhOtfC1+Llkwar14cnQKkb51q86Y/cfKb1E65NgPI5py6+0ME1phOWX3cMbU/uLJ/pfPzwnv2y8/T0JJVHLWx3i57JWibQXW7WI5u4sZKR4K92Oilp0ufuShA5Y7Fwvnj7Jd0aLZS4pmY7U4s8YATrnGyPO8fQduxlnPRWmfOklvZqG5MQRYqocwzePT7x+OYxX98C5R6lW2lPuwbLOG7app15+M816FSsBuc46KXf5SCwTllbQjDpb3/6ie3xCUxnQLOMde1d6hE3RvKf3NMG6PKW3i8fXQHaPNSE/xHGPE5x3HrHDWZRkSBfuZRQQRb5WWCucKXQOFTXkEoF533w5MTUwFxyYOfNV1/SMcUA59YkTAVVNVlLxRYJNQa1UtzhXOxVg/SRwNU7Iqfkxvg2dY61jJsPl6qj7Y37KGAgXz7nFDxaog7ubpW19JqtfI9lyYFli+cPzxQz3j5cmak6jOWKuS96zQXyl/RZAQAFNl3adQ2WBeb2OsA3q2mtcmJNztsNy9fHXc13rbKPxnI9V5H2zZWozBwaiVXBK68NUxpRZiPmgCpTO5rOnY2K90aMk9aapPlFRV0ht5SGZPmQ8nN9u3Fqq3xPQhQwv7PmSPulorQj5eSsO5g6h50p4TZUe495Uuump7MWxTnnWRU5jHLTcGDG5Ha+cBxXettfz8nWtlQc6XldQA29lhFK/pqRgN1eNHh1fe2Vr8E98GCZU+d9C6mNpBIIVfC6r8+JMwQC0OYe0e57P6kCf3daTymF5YPHx0d+9s13zJhMr1yicp4v9LZjJTiOZyWSmikx524/XIcG2vPIu+vgzBS2NUeC0ZLD4It5O7m+fWBYYDPyXNUZFa7PjC1j2qbmaE6ZDcPppUt5yoDQa7PVtFvWYNSgNuPjT3/M+LTT9o0aqTyOyV47y1JtVS0bzkaJITtdNcq6qy/geTqtOFuCNfZ24fDFORrjdvL8MvniqavINQ3Ox5qyr5vxfAygM05oFyN8Ed3pRe9ZqcFxc86bU9sLtcBZGr1N1l65jYNZr2zf23i47lAabXMaLaGxsBu0vabad0m1EUaxTt1WWhUq2Ebpu/5d3yjrEb8+Uv653+T22y+0DWrrEBvME/dnor8Bn5gPaBc9c6Vi/lEAZHvAxk/yvT2ItrO9e8sv/NIn/o0/8kN+9tOD2+3Gb3145v/4tWe++urG77tsUK4Yi90qRzc2F0sGK+wW2FQCVd+NNzT+0YcPfPnX/gl/7Hv/N/svfYHVCuWJsv0L/It/4S/y5//T/4L/cj7y8ZtnTsTN6A9X5jnx48bxIiVLf3ziuL3jL/3Nn/H1u+/zr/3xg6fyhroFk4P7yJotG+MqVmRekixCIPcSlLpxGweNxVpXNmCWGxaLZdDizuxrVDTwmZFKwZiUtJdbd0pt1BYcx+Dd28bPPn3AujhYoQtFjY9pKRpLQ0cVk0bbL7DkcKh59rmZVMlLSv7eHC7wchwQGpyX4kTZ0gkAsZbgvKWylhp9X4tWNRSopWJMGmqw74ua624UL9xugxkTvMkQXUIqCTMl3dapFObIZNkCtvQZnmOy77sYPGuyTPV5LQrZiRL5sxSpl2pnjVN33HKaFdq2i23oWjrU+4IGzyG7hlq9yEAaxehAq8IgaKgEZi3td0ZZp15nLK03SlKLdByUWLin8mEGVrt4Pi0VLLszEu4+xwlubNvGGJN5DNpVgzste8FMjogVlUj+UwylVfqmZcK2i015HxqtOahbT9A0wkbkQMVDdS2u8Jw1JvvDVWy5kPWngGyaCE5d0npoHak3vKCAAS02S0LX70OU5U5iscSLDChLSxrZAhu+Br32tPBLXZQ7PyhaapuXHOprmNByGHFX7o7jxnbpuINTadUy2IW881W7gXojy+V3KYUoQdwt1oFWQxaCna8MlShZh6fCNrjjHfwVJq+R7sq6FqzaqzoJPTHS8qTHPDyfLcTaLLlIsd4gVUrVNOjzuV4X/3dIuIVBiBPk86alQe3JdHR6DpDF1MqFKLB3hYkoIElDr0JaVmNhORTDYQ2TZTgsHSe8ukIitCIXg/IO/leCbEkLfG33wJYcauV7AVqeoh9VAxw9MaoxIwc4oSVVtUKgz9NcWs7ZPY3PSBYXmXanJfSdrTWXUirPGNwFj8Td/jep26Y+PhWHFMsFa3KyfL2Gqvid//Bz+vNzHyj96Hd+m3Ps2iKNyq3f5Ic9TRYMnxDOMmdEodcLdTbMJ7NMjgrNNoyN63XyNPQGnaMzV2F5ZaydGTfWWXg+D2YmnqyzMpemdWvshBdu8YGV6SKHL0pxvn7c+T2/5y2XSyVeKmNVbn7y6fmFN1+84+uvr8xh9GaccfIYG30+4uXki0tnb41irgcyFKX+fgbvj+AY8O2nF1q78NicqIXGYstmlyaveqldr5EF9MK+QR032kOFdeNlGjV2fvYyWDTa1nkeJ5+GMV3KoMteebx2HvqmyHKRjfFw9stGWOWcS6lbtWkKGtquD9dG1OfknnoUnoDg5SyfHLeDFc7T9SltIiLub1uTBaLrd7f7AWfkxFXbEfKAOI+hKfRYCTVLK1mqV8QcapTSKGFsvdP3TR+8ool3L4b7ZIxgq42XWGwPFy5+YayT23HTh3xNaq+8HCcAj09PUjGUyrbvUjbsu4rmPXlKtTBC0Pit7jq05nyVVd6OgRgcBR+L2is/+/SBl4/PPF4f6VWbtL0ruSIKaQHsisy0f9Y2M0OR7AB1Sd2z3FOaqAbsPE4mi8vbJ2rpWKl0Ks1PMKfNjEw3mGMokrx95nrFum86dJlYTvLvyUcfb89s1wsPj4+yqERk8pDOywq0VhkzrVnMHBat143ZbZy6wNZnSwoBM1BkqTvvvviC67Zlw5f2LbNX772FUqq23lPN0jnHwGqV7aJ2fUbyMPZsRFcM9mq8++Kt7HymC51s+LmIr+IroeBj5SRkKvp86ZKZU83snLpcY0k1VAu00vjmxz9m2y9srREmRlCUwlabFICBiv5837ZSeRkHK5bSOKxk2qNA6Jp7im805uI2lNrVMga9lUptqWarxtZ6ViLJq0kpfrNC7JsgxH7ngSTg3ICxaL3y6dsPPL17w9a6LJeRsMJQKs5dcVPztbp7vq00fCSk2eDMC7FC8qzUL/wZfwAAIABJREFUJK55YvOk90ppna10sdNwztz0VitspgCCVsorL+8uBrMskgI9F2NNqkOJoOzw/NNvub55oyTG7Mc8gmnls/8dWdUCPUOy893jnXX+Sd77+WJeS6kyc8y0b3V9LSN/jvU5ojkBkIEG34JRew7QVASEh/gW9c7Y0ftWSqXPxbGCh6dHijWsNg34ti0LirT6NsUjk2dFuW+x7sORUHNWLYfevdM3peb4FKsJLIsTxdl7wJoj7WlNDUmpRGusfD3W1Aurz2l83p6t0BD9vHEOAYAjZE1trb0qQWUp1DD/fh8I6qpnbs4T3UAOGcvsPsXHAI61ONfi+TiJqYKp5NZWTrLIRonXQlTpOy23hJmKFsklyOHu5/82ofUoMr354tNPviMersyXF9WHqbiLNVlT1viazL5SqpKwCBLXAATHulFKZS5FUh/Hjd4ry41L24kzP4+ppDM8bWda3FgJotwwmpQKRfwSfZ6n6ows4D0E7yxFSixn8fLywhHwwy++R9vFdinDlezUtYSIali/YlGoPsTw6dpol6WkSjcNyGrR4L4i1UdZ8E9++zt+9u2N/dJ5YLIXmAS1dWbxtLXtjFNMjM0HdpEyzV+M7VJ42i58aE6gxpiapC7X77J157uPz3z9VcNXpbHoq2MmpZXVzm4aSjdXEANVgGYN48BW0d3IYk3YmiwiYUE9P1K+/338u9/g5dvg+sNKtUz5Gjdq2XUg+TNBpfgFq6lYdcOsQ30Erphlqu/jEw8/qPzyv9z5U4fz/sPJX//7v8Pf+sff8cPvd754+yVfdgPrRJv0UijTON3poQGywjVclqAG87rx93/9Z/zgV/8v/rnLlfpVgy4FX/nyT/HH//w/5if/+f/Ih6PzT797ZlWdqfvDA8/rxOfJGg1ug/a9t3z7o0/8V3/tJ3z59mv+8B8ZlHCsdgzZcYmFuWwzqxZsG4BzHItSdA95rWwGtzOYZfDhNN4a3Bwi1ECPVJhanlFuhVZcoL0AmuOlwvnMZka7GMc5efvwyPNt0c3oLKlHAkaVYqfWtF9jhDdsBmadmn2sp33EkUVnAiOgbJW97BzHoQXQEtPE7rYrXzo/rWLW2Frh5ZDacgGTSa2phrNKtbtJe6NdNso6WMcNKIwl7lEZk60lR2Y5a0zapu+xQpddrGDru1S94VSL5Kh5DjtCVpvQmayrQKqmEoVepUoQDkBN4hjOWifbtqUyQl5pNZABVlkrz3ZftNpYtqRaJ2DqLO6tsZacFgrqKbTt8qoCqVUrMQ+j1KJFbiprIwsHMw3JeroDgORIwXm84Csb2k2s0b11LIy9NY5xgzDZd+4JYMC2bTngmcxirwqatsB64ZxDdrgiq/ccJ5femQTPLy/sFNXbrabVKKDcAcaqASyZb5FLtUIogXUpCTVssYo4m5az2JVJd5FLjxbAGuI1uZYr1QpDEBs8YUAVpem5O9MHniyre50htVxlDNeCOlzBVH1nnSfKxFOhsny9KnCsZBJpqZh12bfy2YkIWuRA7Q6zryVrlmQ9hixUGiTlgNJky3SmVGA1WVLJNhIL1F/7G4NXRfSYqejKe3ffd9Z5aHldpEAuaNDpqb6/A85fOYXm9E2qM5+eSASl8Vp8FiBQ9H3vBWqpYNPAC8wgmu7Qvhlr6hl0V4JwJEg9JT363SNeh8gjv64Ve1VC1VLwkQtFUsFetMyRMGvlYjE/HJZzqLQuluyPwoT8KHfDWiuv/RGZdCy6qj7/FrLq+Zh4lNc6q1mRK4BgnbKgxp0jl+ITVKHl8t3TCvqZv/vz+PNzHyidqxLrhedhRExuHw/W7Hxai+eYOu1nvkTmvB8HIqAfTHeabZQSLCpU50rjkxVadGp1nJMolV7esLdCe+wMFj4mK96w20dOl0KZceF5Fny9MF+c0ze+/PrKu7eKS/10wlmcTy8nz0fj2r7kNgu/9U9PtvbEqi8q8MPZ28m7q+JmR9GDfU7jkwffvhgfzsLzDZ5Pp/cLe5PE1vwOv5Kzdcai7hp8WMgvvg5dNHXTmzsIwjsfb4tFYS/Gx9snDmQPKOE87Z0v3nQurckqh9QeGtjIMuZpQQKBAj0nsMUqrWgDPNeATMKRzH+yYvHycqNnWtmKqa1gLbzGfK8pFdqItBGVV9mmkto08XXXJNjPwTnlPR05ne6bwIGtNxKMQ6uN636RT7sYl8uubbEHL+OkUPl03F5VCQUwlxJmujZJt1QbPF0ftO3tDZog7Y+Xqxrjbce7NkBrTErb9futTPcpDUPfAy+MtRjzxgznfH7BAr731ZfJV5LNrXQpDagtlVtGWbrUS7JmAAF90UBF96tpCIcSlm7HwYjg8viO0pvSAEyfp1KMNZx22bE81KcvyR+XDidL9VC5q1H0dKRP27g93+i9C5LtUJplU+TQGrEC26qiRs0SPKyibfiUNHsttnzPbNtwjDGHrG8hyeabL75g3y+Sp5t4Gb5WSurvm3wYMcTxiY6vEzeXfDcbfOcO+DbukEcfwdObNwINpszVmi6V2jddtrebDkwDa+iCm7IulNo12Cg1bWoCWApmrWHT+0/fca7J9fGCBfS+68IxYy/iSrgFLV8fzHg5Bltt3Mak7VsOTMTqaKUyI1JWLCg6JYHVVuikmq0Wam/sKVFupXKOA5kJdUkoNhaiBTYXK62CpVXO8yRQAt0iePjirdhYrqJyxl32LpVOCaVVpJaD6FVn9zxTBhwppSa3nVXARHdut5Pl0C+dy35hf3ggHNqa4qrgsgZmxPc9Hhl0wc5wYi2lGcaizLQ9Ysx5sF93jg8v/MIf+ENY7VCdGbnJs3hVq0lDnXG5BMPvXB5ntZ7KrYOiCVNuiZScMfwg3HncdjaTn55IrlxIRVlxqbde7f8antbSWMXxkkVjBJa/a0kbDbXw7aePbL0z3Sgx2YpYET6nBixLRcWYKrTWfaO1ZsId7ZVbpK36mQoA0yZwKXWlV1mg5xKTYuv5DG06n/qWPDrT4FLNluExCFRgTR/MqUSukc92M5MSYTnjuPH8/JHWBj4/xwirIJW9t1IRBDYVZwnpNau5FfVkkG36zLtzDnH6zttJJP/jnmQi1hzJsNLvFCYNmAHV2utdt5bnsNBeC9RXdSNiml0eHtmuO/t1U6x3daiVbd8EiE1lnZWW9YmK/C1t3eoPlfwUBiSc/dP7D1gmX5oHHz89c+VKN9l4rHVeoeKtsByEsh4KfShVKrd76Ve6QKKHlLMWfr9qpQK8bKwpu+CbvbH3xrSp9yuVJ60K3mpFQ0mfhp8T0iqDT7GrpuLLvYDZZA9nMvjBm8Zv/eiFcZ7YpWOtUTJyu3clNk2X6s/N8+9nbGlv+ozH4N0Xna3B+/diA5pLxYfBdg1ePt746ft3/HA7WCiltD11Yp2UEswqDopjRPFMYMyUsFBDYJAKWcO9Um8veC/YHrRV6T94y8uv/YjxCepVX6ucJ2Yf9fq3N6+DZ+YnPas5Ci0J7raiwUmsRXn6iqff+4k/tL7gT373i/zOdwd/5ze/4a//vZ/wSz944ukXO72rqfbiSkf1yapOXYXdNWyyKVvp08PimxX8jf/9J+xf/QO+/69Uti8vUH8R4oQ/+Bf403/mx3z33/5d/vtx4cfPqhV6NR6e3vL84WMCkWGvlXdf/SI//p0f8V//Tz/mP7jC7//9T1zeDA0H0mLl1dKoFRCmc2FVht+obYOjsi7Bg1VOFs2V6BcMvC1iKXnQVypdCLwotYg8j9ZyjMGWyogXdE+PMXhzvXC7nZRMaGskbqBWJk02n3lQWyMl/krZRQ2azuaa3DanqfCkVqiXxvPtoIQSfIOUiqzFK8S9qCG97BrWjDGkPpmy8fcm9XWuE6jmPD3sjN74+PE78WeGs5iM2JLjJlbZPO2VebIs2LfOXJNt69xejs8NeKsc5+31rKpFau6VlpRA5/xMy02582AsbaNrMadSs6SsqXgMuAeb1JpsOzFTFT7i1AnWOnKmBVvbaQTnebA8OONFWAWDc0Yu20ragow1My06OX6vNuQQLVS0hAwDiGCOU+rFgN43Bgq4GNOhdP39KMQcsu/WLtWs6b5XOEAhRrzymlqtnCOX1uH6+YcsZ1UAKsbtwHYt1VrZiKJaWdZEw1uV6qrMvN+lvild6jBMvUkp6gW8SAGywumpiH5NvsboqS6LkE1wjEFVOaKzv8rdQdH5S752lZJDwc9WuPDy+iy+QpwNsKwlw6khlW7k77vWQhboCU0pu2MsnZthnHOAy7J317apN0nxzdQCp6B6+1J3xpqZ7ikIfEwtl1lCXNh9wYpYY5TAmlAU7i57VbEMEsh+yExDybRizRB8X/B31cfljkExhUzU2pQmSGH4ieOU0PcuEw3QdHXCWhRrnOuk1EIMo5aAFCrcuUfFdfb5ysGfhz4rqRgknB7qM2rVQKcU2XEVxBz5aikdsbziDO62tnvNe2fRZrAQ5dUdI4W7rhxPy79wBPHq+ulWiFOqJQPwyCRq1UbqYSxfQ399vyh3UcFdjabXy7Nm+nn9+bkPlF4+TI5luJ9MX3x0p2XqQTFZNVZrRDi3NTEaxxyMOSnlyowbz9Mo9cbKQ0OSrppxxZZDERVUxQK8q/Arn9jqzqo3qQ3smboZ1StPj5XH7RG7NG7DGbNxnMGnG6zyhu3SoUvWb+2Bw42H7YmC88VDwa0y52JReT6Dcw3WbLzcjA+n82F4xtcGl3I/QJX0MXO/muRf5goutSqaFOfLfeOyB5+OyTkasRpB5XIpHMfkw8sLwzaGF3oLvnq6svVKM8RZicEcakAwAT5HGOepLQMFzDVUuAO+CiXZKC655BDAcy1ZiXQhivNTq6W6QvDeMG1jCU856D0aXt/biopBn5PjOBlpB5g4jJPaNmptv2vL7dS60bdNsLSiyNXe0n7lU0BXn3z38ok1nHGemoiv3E5VMSRezhN647pfAWNv+6tqqPdNEuHLJjlxWLJ/Ks2cdU7a/WytMIcsgjNmMlomt0P2kOvDRelJ+06rndorW9VzeJ83Wxa5gRK+/hluDcnDGVNNqyzEPD8fxFjs+6Yp+stkFI23q1V9jhpKEyO05Q7jOCVXbqWo4caYOVjQwaFm4vZ8owQ8Pb3BEWei5NDgUorS+PaLYLkRirOcU5doWn1G2ouI4Hq5ptzSseXstfEyD969fcvWd5rrtQ+TtLNGKtpQw/08XqSEmc7B4Ol60WDDBQuMVAfgSp6ZEYzj5OnpAbvbFpFtVP1Xo+8PitythRLBPE/wRQ1XbDOCSuKZFGXI8x6ViCHrYhjPn565PjyyQtbSy7bhtePmHC67ByM4/NDAMJUqN9c2p6EBWmlq4GWdUeO/7hdGCUqt1Cj0LJzuR8U0xBiYg5iLNdTAr4Sdhwm2fvdj22shoUuV2+Dx7VtxD1zFIECh4kUqE0GgtYUn/y1L7UWvhSjGGCp8S62So6fnba3JGKckQ2OBTW7zvQrU46TsKgbHEijxUjsxB3cJiaWlakw9y5XkIqDEk97k218B1zePzDhVqI2QB9wjWRa6iB3w9NoH4EP2KcrEomIR+rzXqoEJUmqdLweFwn65vH5etiIFWnjQ04pQ0Psm2KmKoZXVXVlQ0uK1XJaNaioEeoXzkOrkvB30WnjOv19LSdizYqXzp5KMP4J1nLLn9sac+ozbkvS+1nvih5Qv157pTZGRwbVgtVKzEG6bzlvDGCH2z3Kwusni0BoRizWSSZS+RMuGKEzPw/PzM5fnR6qNPMcrrWmAvu07vW1svYuJF8Z0F4triP9VCrx5fKMF0nYBX8w1xfiYwXFT7eArXtN7yPNC6pyNAFmdWqbvcd8W6rUr5R5HDBH3OG2BeWMLLm8e6PuVhzeP2FAUMktqQ9vT1pV2Baxkcl7T0CSGPm8RzKXRU+sXpjkrrdFrCCh8nge1Q+sX2UtwJoAVYg2FfISJL2dIsVVkf7izF3pT0IJYdVLHtbJRCR6uD/zTf/oNx/ML50OnKzyQ6UFJVYLFojcNdpYrMMJ9Ueei1JWIAp1FPqX08JWqPy9sG2yt8PH9ydNWKT4odrdmFlmSi4babTMmWlDhOoObadmx9UZ94/TtynFzliuFq1XETOyT99898/btA1sveBTah0Hdg7hNrvtOKVqPlGWvXx9bUpJ4KELehtS2ZegZmg3vG/N2Ui6V65cPnN89M4uSaGMEq59YKJTA3Fjlp1i8pXg2fQTLtlT1pBG1nlAaff+Cd18d/OE/cPAr/+gDP/rwkR/97IW/8n9+y7s3hV/4Ch5CQ1bVOzAmnAa0yebGwKVys8q7h8V3H174W3/5N/iTX7+hvT0p5QXKW+oaPP2J/5B/+6f/Cb/9l3+LX/2NxotnSlbvXN48cn56z7yBcdCuhadf+D383d/8Tf7S//wtf+6h8nvbI9cnNVr3GOqo/qokKCxKdcparNukbFAOY7WTbg7dmaexzSunHYDR0/Y0vbBaqLEohZHqOilLHQtjWqXHklIvOmtNrs1o7oxqrFTwmbkUxQRsnTHEFpuWEGUWK6YGuGPlECCtHKZm16ywbxeej8E64bJ1qLm1d9lnS3qJ1xSfbe9qCmsqMFdMpTGXxlqWKWBw3RrtzZWPzydKt7wPwTX8vnN0rKQVLm1ItTUIqZoiFhNnoypEBJfSwAT8LcU0HDd7VSrVVog5tVDxyGGnkqxXrOTIyaKtlMryaqMGQ9SPIKxSNiWJ7X3HXQvlrRql73x6fqb4YLiU6KXUXDYkzycKpW+p7ExbruXZgjFKUUps3IMewGpjrkXcBr6gNy3BeuuqNc21fF+pwgzhOFqr+r2WJ+syQcMotW6zxu08CVNCHFUpx+6TmRaucg6iOdEhgzmZ0bSkX4Owk7oawzI9jsATTTGnBh2CLms555Z96HKS8EmgmnOVwK2qh8o+ybn3Fnkv5f9KBl+0VK2U2l7ZO7Lvuyxnd7ZPcgJ018Md8RhWkiGgwdtwVwDEEkqAtNOdU4udNQPb9JOUkKqZrDskVC4MnJbTkPYaqBKvdrlYIRWvFynvcxhSqziL4cHyA0xJuqVWqBr+hmftaQqhGWvql0FD1a11qXOspPBBKI47DkA1ifoiS0yEnnAt9CiqVVVn7LK/bVrSech5UaxQajInI3uJ8FTq5+QubZrHHDChbFoYzXlSqwQUrSW7CC2jtezL+rRYOjnkBMLi8yAnnJ69ttIEP6fKVYscENbXpa6Z/vlwoSOEz1j0ftHQst3V90M/R1r970u/IFiumrqm1fDn+efnP1CaN6arYD1WsGZh+jPDg+qV4zw5YjLDdC+HVArNNmzCy3KiNMaR3saiqeFyZ8zgnLJlWEhWTgTVT5znbP5PZnG8GG/Nedh2vnj3wPVxJ9w4XowjAddrbdTrTrcCXrFN0lYnuOyNS3PeXBoRBrMwW+V2G9zOxvNsvEzn47G4HQO3ybY90a3QOaUIMaglJ/Vxt4EFLaVwrVYe98KlwMszHLfKMEkhnROovD9OFo0TeLo2vvfU6cWBSistveM1AXwAhTANNTzTMMzkZW8FiGBMFesR8sTOoUJzLec8J5bNVa0ZwV7sddOtftdoXRan8Dss7s6w0Ict3DjGZK1T4OCQzeXy8Ib9ctWWvBhkQVlqp9augiCCVvUa3Y4bhYqFM8egl8J+bUTrrDkZcxAm9cpxyray7RdNi0tRU9Qbe24Net9YVUkWXuDSugY/a5BDW3mB51DjvgCfzHVyzMn+8KS4Tgu2/kgr0C87ZkoQiRWUFpxzKLrctBWzUgTi0wpDhcfQ9qlv2jwdhzgMj9cHgs9b7oX8w5OZF/kmLzU5td420j6c/lr9f9wVGLGY5yB8UXAe3j4pke/SBfu+Q3uPyeV6pRc1MDOcsVYetpJnHkMHlS9o26aoy6Wvf3+2rg9P4j7FeoUKKompsopsk7AypVCviXvw+PbpVWUSVdsYNT+oiTNZrB72nX3rma+pgZ2XSmmyqZVkC0QJjjEyOckzPjV0oSG5MiGbUq+N0190cJfgp9/+SMq2ZOJ4CWjZpHpwNwc6au6t3KPO1eTXvIS9qOgwQrYi4PTB8fLCPE/9PQttr9NiQG4T21qceTmP5XiUV4igh9JbtBlJe1A1jiG+ThTjOA++fvsW6SoLZ0QWL/KOzzWpeamPOV/l78V0dizPLSgq3MIFwSQvyuO8sUYqxObi4/iQ0OKN6UEb0KK9prfdlVMtIfex1ITCZ5ikEakxCrZL5yfffKsmbBys20Gpxhjz1cpESNWnTZf+JqVSoxJFg7FWJB2WrFuchsjPjrvz/PxM1II3edUla1ZRUmtN5pJg2/eLGXI/wOfCQUNnz0F8WvBCm6rzdvLw7g2Xh6s+q0VKLPE69CzdlXH3zTMgTsrWKVvDx5TaZw5F1xKv1qu+bRwvBx8/jYTm8+q91yWfA0XX4oBaOMeLFDNto193fA3WcSpuVgchLYzwLHwLuAe9KhFza7KyXq9XbSotBIrtjb5d2frG8kVdk2XiQUXcaLXQesW8sl2uxHJ6LLoP5rm4XK98eP9BilnUfS9ksb5bCt1RYlEWYLWqySJqDpRyKptb4rsvQwWX+GfFnVZrJhBVPFamyGnbyL0wTevwvQUQf8ohtOXHXRtAM6kZvDDxVGil4tHuCXr62e4WZDXYqSR1x2JhprvunvCzTABwNf8a8pcKpRcudcM249tPn3h4946nS6M1fV1hp4LVgZDNfZ5iyOk5CkZbWjiNmUDTSVlgQxt2RZQHT+863/108ukWtGuyJExNVcXYijHqpPVM3lnQasFTlRmxdF+0xrtLZTwMjiOIWTScapW9P/HdtwcfPnS++t7OcS7mGFysUFblKCcPWyOi4lk7kGdfWC67XGyRWKH7wVTjsCbYwF4a7alyfOu8vD+4vr3QewCytBk1B8QNdvGY1CZ2ggX2AOWan+srFgdcHulfNX7xDzj/5s9Ovvn2I3/17/wOf/sf/oRf/vqRL3+lsT82tgJsTiegBDGkYJ5mRCuyEnuwbYOnN5V/+NMP/OB/+TX+6Pf/V/pXf0LpdfUJsye+/+/8Rf69n/1nfLp9x9/75uAWwRhLjKLtyvFyYkNLhn690r//Pf7Kb/yYH/7qC1/+mcJ23SmtUVBIiC+pyT3IKGoB+NdajJvLku1id3kUusEog+oKwOhRaC1YwFjBYfFaV2gIb69WRnMjfOPGgC0Hej4RFSK4M1ksDFva+seEzSKT4tDrF+hnHCuH92qgdD+DlSXbcis81Y3jWbb91qBV0/fDIKZcKZFNfHxWUFjao6V0Ntrm1JkMOJzSd64PGx9e3osxtGTjJxeZpu0YUe7Dbcezxm+tMYcYlGeGA7SSIHvsVfFRc+EVkEpNDXC2y6Y71XVDRwhGPJEN+d5AliL2XSz9DLWUVJEsJNaQGkRL+V2Kl924lkrcbhznLbl1WW0ULdwKkSoSfebm1Pm5bRvESkGAbPLu93tdFtvwEBNoCYhcVhXnTQc2Znp9Z6Z9yaqmu7f3hhd/XUbldlK1PHDDmAS46gpP9WiUgi29H/f3tSa0uVqh1guTkYoP06IKqVPqJh7QYGiAV0zW+6UzaCwX78YKJD8nvwWlNua80eqG+yCWPmdaNqtuuCNDdLeIPKkFvhwJBWMNMV1L8pY0nFDAjDiaWq5lec1WK6WKKeruWNcAs1bLhZdhsdJJIpt5SKWRSmBxTgcJiPbPnw8rGoqK/ZPJYSZel9RrUvyQ9rq429GIHOJWSkv1DonvaIJFiz0qVVwx2TBVvOUgECcimVOunx3LEJZC/r/u72IKVVpxH9RUCnfoP4kRgNYz9MiByJ6Z+1JJr8typdGd48gkynyu7Q7ml9sDs1xY63kvdleUaTH1CryvadV/VVGrllTCs2etUFTjZBlCqvJK7awxSPpUYnRkgcR1jtqr2CMFLbW8MjEJY53iRP48//zcB0qzIlr70vZ2cn/YnYmksmM6Shne6FtPK1Dg4yS8qhj6olFeBrxk8ZXWCVJlE9OYFpwsDtPDv5ZeUPPKXp13bx756qtH2rVye1msVTkWrCjs+4bVnRcvFFNx7iDlTw32mHzv4ZIyu8LZFs8jeF4bH2fh023ycqgh7lZordDN6WUB2i60qmFLSWiuW2ErxqUHD5uiZOdyPk04Xf9NmOL9Pq3FOWHpE8v3HxvvHqp8tHNoIFDu0Op8cFAsaJg4JQJuK17aQ8A0JUko3nlNZ6wsAM6RF38X6LE3SlWBXCz0AcwDr1Zt2Ty5MZ4e4ZqKhzUX4zxYc7IWxNQE9/LU6f2RS98x7nGgsh6VQNGmm9hVrVjKRPWIlm489XcZ960N8HkeHOPGcR48Pw9636VqMH1YKZWydS61UcPY2sYqRTbbnOS2O2zXLe1+zpxKJlhT6QWB/vnp8pa+dRSApaKq9kZYU4E9pw4A94zDvj9VYr28+mZdG49wgWr9PHj58Mz754988fgmB6ag5kWX4nDZ2KoJChslJblZHOggET9E94zlcCPVZ2Nh7jw+iOFS+wPUnHpHw4F+vdCK1FQ+9DyodlMRMceZPKBQMVorY47XodeKoLfG9XplzZmMHqQKKyUv0BywRTCOF0VrtuDxcpVU1QOvKiJLbnWmizGFO70Vtktnq0rWoJVs+iZzFWIocjpKw+eSH9mW2G0u6yDB520ECKA5lpQlrfLNtz/jfB5sD5cEPwd+Gp/mRyVEkANBD7xkY+ZLg4Khz2/TioIgGGUm0FyF5bkm61y0Uukt1UO1ZVNu9FAayTJLu9t6vVwc+erVHwesmbLdyjgVJdt74/m7b3nz9EApxvlyUxHaKjZOScwNKePuMHXX0FfdoDZxd/h3zQ0fFjQn5beLeTu0ybWWm46iZ/1eTxBp+aopW+dVnaaCyBHVVywVNRTaMXtItfDhJx/44gff40xG2j0lpPQGYTSTdF1gKBWCYq+V14GK5aCqWU1oN7hP2miMeeY2WnfAXer5/bRYAAAgAElEQVQseLlkg9EkwW6l5PB65eBLz6ueMQ1h3SwHCZ8VYe4wh/Pm8YmtqOhhgbWuRqYk+6cWVKYb9wTNjUbraorLfYjEnQtEqt5UJNRa2TYxx0oOwEpdjHkStWHnTQUXRUrBVBBsTMyl0GE5yxPcT2BFQ6/mYGlzcp9slye68QpzB6mCWpHEv5XPSkQLAZRbLdi25WAyz67fxaWqpeB1UTcN7KWdCeIetutF9+FKmGfVttoILWwsG7FUMhFakoDORTc9ZFbE/xnPJ8UbVjXYrzmQtHIvspI9UO6y/PK5/kheSEHDoxga8vVtZ86T3hrdBdpeczD71AIIU5Qydwu8PicVye7PMLaeapFiqZITZLZkUVxKoWyNOhccOw994+XDB8514nPDe/7s6QSdtWqI7849Xc+tsKjJz9HzNn3kENQpvWJjirWE8fCw8+FD8N1tcb3KpKeGRNDvveg93ClMMzU1+tSBaZA+XE2OmbNdOrWGnEcuGHKvledPN37842ce3115ujj2ovuoWGCns0qnZYBJiWwuTJB2z1pJSY8CGNsmFk85TqJW4ND58PaB8yfPxFOBesFsw2wHU21DfZACtXUsZg7yG4Vngj2fqSB4gHLBrhvb7w3+kDt/5nbjJz+78Td//Rv+yt/7MV9+XfiV39/TLqTFSKvt80CckLCgprLHK6UH86vG//brv0P/H/4mf+TfvcLX/5ZkFWWnPPwy//yf/ff5sx//G25r8evfTWzpteqXC0HhOG4YNz6VRd8fse99yX/3dz7xw6+NX/nXK+/eCLZsVZDj2hy3VNiY4NItOucxmCdYdepqUrL1VFF60z3hxqo6s+pabC610OkVb3r/vchl4EXK8jZdoOCmRerwAaHPrxnMlYNcn1Jn52ImqNiQQtPHpNfCQQ4cXKlWE6QQScVeeHB53Hn5dFBn4KWnvanIsoe4pph9vp98ZRKm6ulSmpTYocbvnpi1X4xlD4xPQwPhWPg4XmHnZbXX8xGPz/cDcEal2KJGJi+5BoszFTjm6D0KWZNCB6HUDq6f4c4qivvgOuBu9YUcJpgacE9lpRUpL5W8pZrsPpDAKsULzcD3XYrH2yG1TjaqC3sNLbKlurmJfk2Emvtt67mwFhsr4v7+3YfqWnwSzhhOVKeWymmL6ksJoSU/4+5MB6tSM1HaK54rQsnItRtzQY/GUsQsZ0C9P0bhtCWqTJjqGov7cihhzDR6CUoVOLrm7zdNIu7qUg8FQUf4kBXOSlfCmJOyJZNyJkagyFo51wBKqjrTJhVQTMFGVnSf+tRg8c4piqxbSi7rPRa1dVm4AiKHCLqP8hWuMIvzSgP/XUsPM3tNG5ReXcl5K3+eWEo49apaMLL3W/gr6whTzXEfegLqLdOSXSz7uyosRpQli3qkKtA9XQUaeN5ZepaL3zD1tuEadPd1V343zjGz3EunDWSfHVnbyzFjaX8bvgT4RncPrRAzcvAj91sgRIhbDhIzVS9Sjd+KUdCiVtkYuS001ZL3Ps/yuVYCYNNrs5bEHVU/pxppgLTvuQZzQehZLck+StWVbGz5V0JJhIWmh9qkvp6R/UbVMMvs82wgN6mYi0Y5V55F/P/A8han8XyeTIczL8w5RhLqK2PxaluLtTgWTB/ZbC4NQqIxf+r673STi6aPCrsIJ7ZIrsNd0fP/svZuTbZtW17Xr/XLGHNm5rrss0/duRWgAVglKKCIQhQPBN/G0G9Qfgi/hk9qhBqhPEAIFAjFXbC0CKw6cC5773XJnHOM0XtvPvxbH7l4dmfViThn77UyZ47RL6392/9SoqHrLCXx7v0Tl3Ulp8L90+A2EvuWaSnz8HShrEmGw12XYErwuGYeF2PNimbHnOfNuPfM511+Sc87vOx3jk2bfS2JJUcBa8hE1UJplgqeI83LRVdbS+fpqghLd8MP6WtVBA9ag63BTuLIxlITX19W3l0zJVfFCWZjyULIu2lC3x08ZwbazdOwb/SD1uQrMdxOA0GGsTVnP7oSLtzIZQkz6RRIuBrwlFIg12G2Ojqv7vDBvCL8Mnpn2ze27SbpXKD5biY9ed847psu55ypVZMa+UuIbrhtnc3Flupd7BWfSEkZeDfyMLb9hePYud8PrKy6BJJRFslVZEw66b+KfsVCKlmyUP9NDJxjdIbLNDxZZnTpsD2Q6nq5wFI0KTf5d3hOlFJP5plNg80cyHKwKLJpmn60gdGQAtDpfed+NLaPn7ndblweriEV1H6wNG1sNSWxYDwJiNPl0t1ZS0wlhov+TRTYQ2kh913r6/pwFfOnyhSvepZBYAKv0/MqdLwY2aPQyDBaoxYVCJOh0Ubn3g5SHycobGtlP3Z6ShENr8lUI1IA+6Yiujf6cQiNT0VMnilhYuAhc2xD31vSwM71zVOkAuY4NNPpDVZSZm+7gDaLoXQWCEOPNASD5Jo0QMWtM3rj6GIr3LaN5w8fyeuqhJMsardIHemUldqcSAzEeHJdporpTMFqghifBR1awE1vjZwLueoCnUBsrYosnxKfNjrDXcl8rkIsum8A+QMgursz2IemT58/faTtB2/ff6V/34aAzySArqRMbG7mhF/G5ypI2640jhG/TzJjb9LIC78YLGZstzgChgwxHYcUfy+MqYe7pH9NZvZjTM8j/TmxhOLFoCnYGHomz5+eqSXx9NV7ioOHd0tak+jd0QgL0Eghw9WLmYyQ/GXj5kNMKBtBSxeINUZjXZ5en62JMTQsGCUBeuy9nVNfd8TGMDsZmqkI2AfOwi2b/A/EPEsB+mal+YUcYU7BBMJFkRHDgiRaaUgq1UhYFOaTOeUIlErBSBQFEoii1lvDKpRcFZubgAyDVxA0RerO0GVxFnRJUwQOPxRNGwBjzvn0m5uFWS45wDGBUpJYh3eRJXKJoIKQHY4AJy22yfzdcg4wK5ou7R3HvUUcsM4j42DE3mnNQpYB2Ax9OJjG3KAhQzKnH6Lm79ud1iQvHgHkpaH7lZC8aQEApmetOO8ws0W/lyFT62z6/mIHJHwY3Yz7sVN6x6Y/0qGgBiXXSX47eE3F6iORyqrnZ5lOwdIuRCakCcnEXNvvO09Pb3n+8DPuzy/clgsPS5ZBdVf4SelGScEwq4v2/BAzY4xGtqEJZ81kq3p+4c+RLJICEzw9VD6+3NlboqRCsYzRSXFuPATr4+J6HprgBwOKTN91z2CJUgtWOpb9bF69Dn741YXf+72PPH/bePylzEgL2+cb43rQydSrgCyfiwYjjfDaK2Ga7rG/EVO6JHl8eMh+E5nLV4+Mu4AQ0lxUYlAaBU8LIIkNqYIfUeB2SDJo9gDQMcPSSr7+kKdf3Pi1X2/89Md3fvx54//95jP/+2//lHdr5o/80hveWKWmSk9Qqhh4+YiezLMkfZ5IqXMtB/428w//8U/56qt/zC//5Sfy9dcg/0Cm/r/wp/n1v/KBb1/+Z57/xTM/epY/oTms60objaPtpCPB2FjePLK58d/9rQ9YzfzpXzfevlnFFCiqCZOZDLoZVLvSrLPYoN0CxKiZ6omxdHo2fI+hoielrmX5zfjQPilDjbiPuYsHVJT6aiuH3dj79IuTv5TY0NpDaQ4pognLKekML2JJW67Y6JT4zKRovlyR6TYqPrrARpzrw8J+NOjydHST15yu0q47MM44/XTHrbMjqVYxgX5iiYT0eXQudaE8Zu43sXl627GRZflkjTSKkmTDmmGgoeJw8CavMnO9r1zXAF10JgXcrNpbl5vApa6ggcizOgc9KScIKV8OgFRnSbCNR9RqKUnJcMaftWCbOG2fRsuQi+FLph8D92D7xN6aA0OLQZuZmNZzyEFKWFVdqbN4NsbaOslnAnFhG05KPRCgV+8pC1CCPlSrtkaOIW62hdYP9VJDzNGUUKgHWUx952T+zGTUEsyUngTYSCalz5iXhbH3uSjwDHkyTofq+slgDgJaMNI97nh510iCNSF8O2vVQY9zXAAC6B6U1Y6fsi6RJEJ+NgR0Kqk0ElNjr6sGELOnRd+XYqDJ8DP4YtaPY8yB1DhlT3qfUa2G9cJMAnZXUIqlWA9fWHdA+GmZYd40CCn6gznLO02deRYrzfvp8eqeAsyJXmU4uYTkO37uHDT10aAW0hBJpcc66qOdjDsPZptFvzeCpAHBeBsjhkIGOe53++IuT7Na1+cvrt6PIeuT7urvZ5p5d9UGKfo/TeQEnMqXVSC3Z6WrOSLaEICP7n99HA1m9VkVvBF1Qfgx4UZyvXtDAF1i9uMaumEIAI/+wOe+TFEnzyszThU9swk8fz9f3zugdN9f6AccLmBCbLX2xaIRJTCZDKTk0aJi+mCw98xoTovoUr3bWaDo76thdowMrmL4khNrrayPK9eHQqrG7dD38SPzae94qazLgnvhZQ9MsTprMR6WxLUmUs3y+2nGp8+Dnw1j3517d563wW3foDuXBKWogC0ZobzeWTKaWJqSyVKqdGvUDJe1cy0e01Y1KPdopro7L63x3Ix9yJfizbVwXRNPubIWY1jmwqDmilskVxgCW5gG3DqE5qLs/QjWzWBYZniSFK01jl2MJTDquoQPxkSNJTmzZLHoZDA7JPBWcaycXs1xkw7v+/3Gvt05dvmilJAXGYl+KL3Js1FLItd6moaKFihPo3Y0WngvbU2U4N4kuzpckadLLuz7xnE06noB5CezDMP2DjUoi63jqZAN9tzp+6BaZp/A0nCsi7o7nf6TSZt8HDKAX5blNNyzFLGXKYrqHJdmHEqTWRT/j2JRESBnopO7y6T8vh/c9zswuD49sBTFhPYwMhQqnklBcU5ZjVtpQp4HOzPpASRN7P7qvWFBxczIcDLnxFIXCrpwlrIAkKvkT3q/AnZSiglU07NIuYgllEyeW72zDXmf9dvGm4dH1ndvpG32mESTlGxjurAsaaJoQPOFx6fMw/rAPgasmdQHaXRu/WC0JtPTXfG8Izm5TJ+tdK57lUgZlqLJbypqgIckHJPyCUngkWtKV9Irbd260lwGzseffcP68EDJmXURnd9dkriUdD64RVEbJvB4CvlWlzY8YuYt2Efzch5HYrgmFTXS2wwZAQOsyxra+RmJarRtEyUaD3NbTYxsaI8THi/HvnPsB807fbvz/qv3WFJijgWw2uN7TizYooDQNrYANYLl9uXkYvh5IXk0bFOulnJ59dqJ/4MArTBKqWABdjjnz+ijnyy9+aNsqAEdUSl9/viZxx+8E/AbU68e9YA+9+sExpisFK3NV9PBkC198dlHb8AQrbgLrCtF68YBdSoJ5sQVFWGtdwEhOgE1EQ6JlIoFFRACzOxkm4qxkil1CZlWTLebilsLaY7APRX/YijlEwzTZ9f5ZMF0nBM8hT7IjLX3Qa3EOcYJAM/fe2rsh6m59iQJmccdVkrGvUSKUKwD02dIQfu2Iq8m701Mp6FJ5YiExPk7L3WNqblgZXcV5YmQkTPCXLyAt3h3jWJih/XuMW3Xs7Eo6BgzOY1zbylyWL/vmVrpKpXPw3i4irQhOvl+bLy83Lg+vFWzQ8gishguJej8PRhRMtTUup5kPpnfjjB4D7A5imd5s0h6cnSnVguwzaJ+SeSQ0ii8QmC3d3lVaVt0FcnBjpt+ViU2sRXn8lB58/bC7cNnXt6+ZXF5NM6kGJoMiFMpUbzq2c/BALNoHpAXZNhPjgm6JC0ZePNY+PwMt1vnoSqdScW1psopG4xBz5mCUgb7ELMsxfp2L/K16BqKgBqYYshyIF15ej749t98w/uvfoG8dPodtpeDbJV2GP0STjkDPMvAnx7PP1XcDwVMGPjujDpN23v4cTppNS7vF9qt026HGLdpx72T7BoguQMbeNHz04KLpqQQ9Iko0jOWHuDhB7z5lc/8mT//8/zrH3/mf/mt3+X/+v3v+Nv/fGG5Gr/6w3fUqnTDhLOkEhInvdfFC2TJkQqJxyXzfGn83b/9u/zlrxe++g8fSOsK6Q05X3j81f+U/+QvfsOHz3+Hv/6vb/xsk29IRgOkz587x0unrAfcMvXhyk+eG//93/3MZc38iT+VefOQ1AQSa7pAHmI+VkrUZk47IG8dvwQg4aqBc89YaTLe7gLyewyQcoqWzk2+Ms1JTczrYYeGZE3gcTHDSmE7InmLeby/NsA+JGtvGaqBiMfyFs1ukZjp8k6MgmykrM/qYhSX5ULfGqMpYKfMu6fE+eFZdcUcHA3I1jX0RPtHw4pIV/KEk8kLLGPhaDpLNAg/AsgR+8vc8NaxvIhp5PLBdEsMD4bWIdNn/3caRs57Rg2y7r/ULUB7MbHlsyMAJ5vgeI+hgK60qOnntp9ARxer3iM1jB6MqRgqZVPd3lqws3xK1Cdy7HG2OLRDMvUu1u30abRhr7XMiLsPyRSPofqeQ2vPswlGizrNoq7THSIQ182UipUqYygVOGdJ3HISYAMxnIj+zGP/j/BOzSnOreQoHUwetylpqKx1mkkdNe1wngOq7/Wr5/kILMcZgmpPspQXPhmNI96fBWtLv5OjvmsOQObZrGFJCyAjB1NdXo7nnzN9iJkGajFYMiaYK7BI8s4vPrQFT9bC3ylx1m8tEm6VpBr/fC6alKLeiOEgGpqboTClqAESCYr2lpQQzpFcgPUXjKkJJpkpICTaSvVnYQdACkZk9GjaXvrvHuCVITBFKby6V+bQrUcgFGOyfGWlk0LxIpxBa2Cqisxdvpgm8DGT8OTsok2RLHNvjTWZ/p3QOtVNiSAAxMA2anrVqDprxThWrelmIqPg599TjTGiftKJaC5f4x6g4PSr6nF4m6tPUGKgCuUeNVGKM9Wmr5IP+hdl/vfx9b0DSl9fHtiTcU+ZROPozs2NMVb6gHt3Ofp3MVp6GpFqBr0XICmBYqmMIQnHGKKOdXcdnN3Oxi2lQsFYr5Wv3j6CrYy0sd0Gx0hsB7hXyvKGeilarFRqSuTcWKuT6Syp4iPxsg+ObbD3wktL3L3xaT/4dDtozzeSd11kudCzQU7cTUZ3KRl7sYg63ahpofUkOuJFjJTnu36H1p37cbDtHUbhee8cZGy9sK6Vrx5X3l2FxJaUaCbg6mlZGePgZTgt6fCb0GqJ4rp30fJ7bwEAKaHgeWtsrdGG/EPctaFSypSaYzJj88wAiE0y9b2xqGPRa2quIsTje+7bzrYdSixImR6H9FzMboO6XuWjkpPivAmDwj5oR2O/bWqORwvAscMYtDCcJSGWTzPqelHaSymKXw/JRZaRhA6bWihJ5sDEhrQil/9j27EES16wkvEkY+N+HFgulFxZ6iKAsFRyMfKlQnNKGL/OWG88DhSz0Jv7eQi4C7zrfTDaTjsOjm0D4OHpLbkUlqVqjTc1EK/It1GiEfQmiqPYE8s5VcjxwvpkpoWsZAC1Vq7rqkY4idZ5qQVqJHxFzL2m1vJK8oDIJDZ9pUYOl7+HxhaDdttwdx5+8B5yojqQMjXXc9JhUVxpSil5Ty6ZUit4Yi1av5YFAhR02W3eT9M+S4n1eiXVCilRkkGKpCcvupx7w8JkO1uO318AaMoaLHtMLdoIwCqKqFITP/7RT8gYdXlNRGEopjNFGyHKuprkZpLUJAYegGRvXQwPjDxEOR5JHgXJ1XCtdcEivSEn+XoRUyWzkILhSj871Dz3Ef5b4T3XCalEUsPY7gd4YxwHS60sDxftFfSs5wWcz6lnFB9xYacoQFob0ZgHuJqzgLNTbia5xvPtHutJAJfurmhyU1ZaXdDqLYsOngPE9eHMXl+eVNorIwoUS4n92LnfN776uR/QPAzt3TEU3TsBaMyieJznFVoX/sqmZEJKUXDlAPAn+JST0rtUUAsuErBOHHoqVHIVCJhNaM1M2pjNQ55sIU8n0GRJ765UfX+lMEIO0b2bv56NxPOItT0/+ZSUwevvrEIjfATCqLi1FqycRXvpfC4KFOgDsisdUEV+OcHw3nYZvgbbbhxHzL0RpR6PgY78DAVWh/QsiVkmk+Vp7Bv/zlLIcSVboEzD6fB5G/PPxTTaCD+G+UDCx2oMAWkOFma1akZyPLf8BQia4vuk8DiI4tqn0EyMGrPBdnuh53SCw1I59nN9jTh/5/eeBf/83/JrKOHhMkICoEbOzIIlYPRt50iZVMTQsPAm9EhfocUQwSSh9SJZwpKyVofHe6drCj2UkscAb87j+5Uf/+gj+yHpky9iKgwEajqAjUiQy/H9EmZFrMQ0Tr+jNWdo4Ch9UOtrsCyZh2vldu/sT05KgyVYCtqDAtO6w0jh08YIxpUYlb0N8lLOZEvCs/JIxhLJnu9/8MiPPvyEb/7tMz/8A4/kxTjujfttI5eB5cK6CNCygRgQnkiehf0kAaZpyE+r9xGME8V/j9HIR6Y8VMbhjHvHHwHL8hTptwAsn/BcYRxYfsKZUebBN7U4cdzAIqGvPrC8+5pf+sOf+Ut/9g/w459+4m/8yx/z27/zHU8PmXeXKz98+0DKTnJJsoqFae6R6MlZmBu/kDo8PhZ++unO3//r/4r/7M0Tj3/sEVt+FeyRtL7n6//gN/gLnz7x3f6P+Hv/5uBTd+hOTYXL4xPHhxf6vdHzDdx5eHrD730y/qff+sDDFX71jz/wYCmIjaqvSkmnT02Khj4l+TFaP1Q7JIPa9a5HgKApUZLjbYaGzAEBek6eJUcaTSztkeKa6nEfqNY5xiAn3d7YvHsHg0TyecZYMCIBxHwo0cyOIa+qhGuoTQopjfhO9SrvpeNo5FXsm5k0pVQlMetIBXOjMtlLGXexxCHWWc4y9E6JelkoHe7ImyoxAelx+kTVIoAp+QQdxKL20cEzBwcJARopZUhx3iLZk7vuHDWosed6pD2FDMqGzjp5icY003XA+XzPPpkNOm/d9HzG8BiGxPnmYs3ULGpr7w4t6t1pjm3zM+q87K0LiPOsuhp9f0suNvuYEiPHE2RysFac3g9syA+tpwFpaL+c7FsLIKFHLaC0WkOfdSB5VLZyMoBaV/9gqAhxd0YWSJZdLJhcEs2cWqvquBznS854GzEuex3mzFFVDtm5Egd5vbc95PjRtmQDMaiiuRJ9JO54CwKZE5krAo8sUbLMsOd4xAD6HPyNE+yZg/0pMxtjQE6kEfe4BxttslI64RMmNGyOMEgylxc4KJaS5QAeh4JNUsoarkeUCu4yQlchL7BoKEhH71fA4+IhQUa2DSOexytIp5rbJ9AVg4cSRuVWF1JvAZ7OAWAAUriGY3hg/R7Wh660weRnPzyr1RGqlbOSc9UZoZHnbHy7WE6v9WAKk3iFAQThDcsCW+feHNnOAZfx+ugJkMlGi6UQIFIAZJNFNkkOunFUf/dIju892N0evCv3k5lfgsHfxgjAcn6vyX47K0q+z6/vHVCiXhjLYMnOaHDpRuGKD51Bi0sC5z1hhzwc9m4cvbO2g6XrIWyjsfqiSWU0o2MkfCSWnri3xmaDZS28fbrw+LAwSuI4jHZ3+ljYMFJduBTF0OccB4dlLksmpYj364BntgYffLDt0vbfd3jpnU/3jXa7c6FTs/SUtTilqhgfwLpkliKZ1ZoVcVjMZD4OkIzWVsaQgds+Grs7uyeOYdwTrMuF948rl8uFh8tCtcmAUJHtDG774N4PDooujQG4xeRb+tQ2Bkes3D5g9EEpmZf7nb1BXS5YrtSk5oekBqAkFYanHhUV72ryMpMPIlMvAhUP8MGd/djV0LhjKStms2ayahRylT+TEuOSpD6mQ/M4Dva2s287+30LWmcXKBbj4IJ8NVrTAbNeV8kJa2VdVkot8qXJixg83imWxJJKTqVC6yx1VRE7OnjFHBkZA1swpHoYMualkJYM8XnzkgVoW8JTpqHmcB7AgVPzJVvDRxcNustP49h27vednIyH66NAvZwZ0dyVV/ROtMmkGNrWm6LVc2K0KMiHJgmzcZ3HwxhDSVulsC6rgItkWKmkRQlMnhJtE+MrRZOU0fse8T366KdEKblMt3swlG73G6M13r1/L5CpwVKqCiUMPJ3ab+K6ymkWKEiTnzKLFbECTYVSxmVJb8YRF+NlXVnXRbIaBBiRKj1lGS0eB85BN0lCLPYC8XvJYN0noqGDe8CwTiqJzx8+ctzuPL5/y+gOS6aNiLw3ebfZvExcz6Xjcb/ERRyvrdQaBYDr3WMkV3GXcxYTKcCGXDLTYw0LE8loWo9DEgxdMD2mOKJRR4aNMMzWTzbGGIP18Z0uSwQqYqYmFM7P6yeoEODSbODnB2HS+jlZXWP0MwXxtt1DQkT4pegSPnpnzdNTiddENCTPnFM/fd9X4FL/icldMj59/ERdKktdBSAirxoLgNZOpgDB1BlnQ3KCOcwCbHqXDcleXefmCKbV5XKVP9+IiWmy1yno3MvBfprgbQ8QKEXDnkIi1sZQYQ6n9O/Yd/JS4u/HJxqRTmnlLCbVIL1Oj8VKUoT5WXTGWsspCxBgvoCYWE65csqSQ/g40xBznOEpl6BgI+jHVeS3eP6WJlNqFloCTo+meOPR+9lLqxuUyWVrjd46JVdSqhoWmGFjmq97TFYHuaRgr4yz+JpA4/QnmQXQZEmNiULGun89Y1/Pl1hZKmRnfTwn5L0HizeeZU7c788cfScPmamfWtYhJpFkHCFdiAnjTEsaozPTUMfIZ2Gq4cX8+Yphv+8v2LFRvGriL7EhkF8bK9e7gwDrhslra7yeL6QiGWOP9U5ia41SMzkdPH/8THvzNaUF4JkHeAmHWEWp0+Ux4ykMXocYWZKMNsyKJtsJvCVsMv6Ah2vh9u3Btg0uReehhXRo+vYVJE/MdcH9IBunTM0SgOSzLbh+Ps9UN3KBh4fK+19+4Ge//5GH9xce14yXg2Pv7LcYPjxBxygjkYeYV161tmzIj4OU8bFjaHiU0iKpQG+QlJxY3lX6dxt960ozy4UxGoxG8k3FeK7gu0DLKZmQE6PeoanRVvfZSOUN16++4o/+yc5//u3P8eNvd/7J7/+Mvx4rX2QAACAASURBVPd/fsMP3lz4c38S3pcLUDCcJavx6QM2Os0HS3xvK2K1Pr7J/Ojbz/zD//Vf8GfWC9c/tEL9Q7hdsHe/yK/82b/Cf/HtRz5u/zf/5NvOvQuwXXKFN0+0zx/Z7weLFXpOrI9v+J3Pjf/tt56p18of/AOF9WpYLhqQyGAPUmdYprq8ND0ljrvJYT8VPadg9etQMCWPpkTuCrhgOIduP8hiBAvYWRhp1286ihg69Ii4j3jxoAiVLHZ966qttW6SmteCBiA+4q3IWsN7f/U0cXmPMCT39NIpS2Gli5FSCyXYPPImjIY0zTpr3qfjC0mSFvQELNVn6oxfLgtjs2BtDiVLBdOj9Y73xKvYRGxn7/LsJHUKBTMxOS1YyR41RDKljk72g+eEuzEUOq8zNDyWhk2p1kDef2LUY/kE2iXTD1AiZ/oIqbdFLRLnJZ5YcuVAabtz+BIHavwZlUi9B8O6NVKc40GrEkA2JhwTZ2yC1AVedIx9ND0DF9A3JfRtTL5Ri/sRPE1QSY1/KRkbemcneNPCdPocBlj0lfL0giIAIaueknm7UvfcwKpYp3k4Pe4iASnxBFKKocrrWx1A73ICzElD5vmMYA4neDXIJgYd2TBzmdfHUIaid5Tj/vJCPJsvgRCxtvp8dx5sKkYkHgs01meffxOmZDFgSsbgHJ6qBhry3RlDrPAB50DScqSPWfjYBWjRYQoBJUFXUmHJCnswPWgBnBafyYJgYAJPksM01/bJ3Lekn5k1mJ5sHKUrQm8DseUDD8JC4plpTXecmL5iGgvISufZZVlJtzqg7DxXxP4hfC4JObXMylOYaOvFRK8cPSuTrRT9Rgqf2DYtTFL+wij7pCDASVQI/DTq2cnq1cAs1lH0UzlqhBJP3hOy5ACmtG32hx7r1M9/8v18fe+A0o++u3FYYxvOMhCVuyCjT0/ktIAV2tCE4fBBT43mQxpJkywqd03teh7UostlGYUUl0p7MVgTb99euFzEIHnZGseeaG3FayVfMqWYQIVqlLqwmrHWIcPsUdias41MH4nbsfMyjJe7it3PR+M4Ohfg/VcXFpFpKHUhF+SXgCbwtRqlOKWs1JHkZ5ISg0xjMHritje2Bt0yUJTy5Z11hWstXOvC02rBNtH01YIWKMaOc3ejUyS7ieJ3AqedIePtYC2JeqvJ2c8+3vCRWOuFXBIlZXJI0WRiO0hpGsmGrDA2TUoyNEsmKZ9HZXg2wZa4bfJG8uZiJ8SkKccUfVkCXCorZVmY5nHJCJ+fO9smZpKAjBGpSdp8NRnHMTS5Wh+UilISlqqmZzl+p5xlfjcUN50QbT+nTHenLAvuznbsogJONoXBse/s9zvDjVwXcsoqOpYq5N9KSL50+HkADB6maxYTutlsqRbqbPtOb2KL3baN+3ZnqQvreqXWck67vEleMCVN2cTAGWZK9OoyouzTC+o4JA8xTbP7CHS8DbZ9x5JRg9HjWWBnqgXLBeud1ncVWb3h3ii5RCOlovbUvRN02a6m/OiN475xHAdPb59Ya2Uchxg9pUoqknSb5ZGjGAjTzGi4SCnYGFkgF5neD2ra2CnUrFSy7fMz12Xhcl1PMOm1wHLGsZMtcbQNGLSsC877Ad7PxCvGBMzAPTxt3GQIehx8/O4Dy9Mje1PiUCHec5UHQQrPIQ8fGRvBVBnOMUJf3kMbbWJTpjjULclkez8atq4nw0MsNF1s4OFloAnjTCBznKMr0Q3EVtTLDzo2KPGoJPrWWdeV63plVqsWLI3exynzs2Th9/JaBE4gJWftSzfIpdCOXc1Ol7dFrpXb/a4o1sirlbfSTMfxk6KrglcX3gTGlNYyE+8mAVwbZgwV5m04tw+fefq5H2hN+iAZSqszZw68DTXdYxYA43X6J3q3CldMHk4+XMxJsyhQtH+X9RppL5OKLpB9TnotpDxmdrJAMQQomE6y/AVjp/sINq0aw/v9zuPb93yB0uhsS6Z1RgD0k/IeQL+AnqbPGWdND3YKc++o2sBRAVmWSqlTUmbkKhCzmFGXVeVKJLQwPHy+1KSM3pnSEotibPpuTHQmzc81ZuBEPllNs8kyVPRZLmeRPOJ5wgQAX+EfUd31s1vr57/bDrGqRKKLygrHUySgzGnnCYBFEZYGEFRx0yQVE9+qdxnxMmBZFp5fnmnbi4pCCGmymjcVtha1ZfoCPH4FpYTXRmMbsGhCZ4OildMJ6LVY8/IzKjR0zqQxTcfj3DSt89ZbyNAHDDENvWugsHdC8iHJnFF5fLvy+dNHXtoPKTW8XkLmProo+5Zc8jKTv1IukLyTm4ZKYlSIhm+pMRinJAc6y1KoZfBy67x9qJCCkQu4WwBiUcAOqKWQLJo8JNMf3tXcxKPs6DwdGU22vfDu7Vs+ffsTPv74zvWPPvJgC/e7sw/H+sB2Z6lK1JO8Qb/vGCWSp5PY6DlP2Fnn1JKwseoMT1DqCs+N/mknrQdcLsjYI4M3krfzTPaxQ171vQyw1zPDLONj1TorN/LjD3n3S41f/9M/5NufOt8+f+JH373wt//xz/jB28qf+MOFp3Xeg1CLqi55H6J3Q8KLU4Zx9UR7cn7n33zL49/45/ypv/pE/vkVyi8Dlfr1H+Hf+4t/ib/4+QPf/fY3/M7RsADM81owe2T/+IF9Pxg5cy0JHt/x2z/7yPW3vuWvVPjlX7pyWXQOeSpkQk6IywjfYDWwS+bYu6RbIWk3jzgBLxhHGCHL36/lhLUBLQCO8E8bLrnvOAh2tJ9mzymJyTSHH80VLpMHDAR6tdHi7uza830KwmYrm3SmlukhOu9EremRlaRoPth2DZZyQl6DrjXsHazAyBbnz2RsyxQ+JYEL03PO3BlJ632JVLBBw5OHn8+sH1vITdRUJEs0FPedfSZpfcHU9HSem9MvSQE5SokFx492MujkXxMeTZ0YoujyyUX2AOQpZ0xnPY57+OP1AJo0qDzxwqFzrywxBGz9ZDz0Mf0DI2QizvfWWnzOkACljBy/R8CzSebcUSMk04D86AP6UOokGiKbBeCb55UU4E4aISEbYXugD9yjTsolGOIeATLDQ943SIh53HsjWaKb1tVkaWfEZrKkoVOKml9Pej7bPosScNjDN1HPVoCA985IVQmgJsZSCobdTIeFVwaJBUg1b8qcs9Qas64aYg0HSi84NFhEIk3mE2ixnCQ3zfm1rmHe8SOwo5lW9rqPZloY4UHkPeEuP9UZ+qNc8HQChKr/pog3hu9xVs4AmR7Da/cgLNgESyYD7As5JSHj/aJ+8kEML6adgp81BGMOKFQDSM6mIXl3malLWTD3Tj/fm/sEWYx5hOiuEmjpo+MpwNQTnNQz6yjpD+/nMBaXF2r26NUnfmMCwXNVXzHCKyTPAZ9PhrXqFoszVhK3ABqzRT0/f9QkfxAqk8DKxqzFJ9N6xDpLr8Pw7+nreweUttud2xjsvfHikiUdgexXr+cD3uOy84gP75aEVvqORRKbj0QrTs16WDVrjHPbC+Pyhq/erVxWY0nOy8vBdpOembRSHzMlJ9bkVIO1GnVRobeUDB3uHV6OwdYSW4eXHfZmbL2ytUan8/UDPK4Xlmy4daUoRdR3rZmHWjgiVr0Uo+aFYtJ09yJGR+4LL8fBcxfCvruYcq2JtXNdM9dSqblC6jI/BlI1enP2TZtjULBc8d7l3TM3rYcUqTttxMbpOZ7nwfPLhrGSC5SygikZJ+dCySX8H5SkwtT0CrqOO7SKaZJLsHMkV8T0319uNz6/PGtyXQhgRxDqMEhpIa0L1UxG6bUwxqD1g96Nbdu57zv7cdCOQ2DWbCKGs5bEvt3oo7A+PYlaXBJ5WTESS1bscypVTX4fLLWSS1BRkSeHtwbZOVyFW0kCjgzox8H9dtc6u15ZlivXy0oNH6mGmC+6zIKTygjDIzUWM/p8DD8BjP042I87bXS2+8H9fuPycGFdlUjXYpLjX3yf3ptkEknvo7tANzwSRoaMpLPJJFzA1evU5Xa7iSZaZI7I0ARuauhFNZfsxIbyHUoR86q5gLwetOEU3jqTKru1g+No3LeNd2/fcr1etP4CHOw+aG6U4Ur9KK8yIElUYEp6ooQWYJghxRSp5ATN+eblGcuJ9SJZo+YJU+KSONqOGUoojCa8YBzHHhOCcUYxyxTZkbmhLkVLYgd9+PYbrEgC6AEojeGU8mpC3YbFFDImU0leU0oIDPlZAGbHlL6gpi+ZsR+d7RiUlQBjSlzIcYHHBKLtB+bOtm2M4Wyjn1OqaZDdhgAEXRCiFLchc/2v3r9lGBSK5KbzEnFCXgo1ilDJulIAu7rpSgnT91xiqhnADEp3TCnz8vmFbPmUK6kpijjWrMKnhITMhlNz0jPpkuVqf8yGH637AA4sGbfnG603Ht8+crTGmgU45CnjCgaQYtZR3xDrDzgZQ3287imPd2FZDX5qBMAwWJ+WL4qf8BOINaPGWBNzywn6OOtGhQV4FAcxEe6DU7KI07rYKsuqZMvpRWVYePXYOTFUoxC8G1Nx0oZk3cIxJqVZa+K02rTp4zRYliu1rudZoKSh8PwyxSm30VhRSh5Z6XeEV8L0b5sFJgE0WfgmCFzUxF2szI5T4/ed615PM38BwMy4XMK7QCq6eDdRmJ2MyH6QkgDz3poAPZ+Gmq/ApCEpt5vFFFisrUGLNRKFrgVI3g6OvekO9SEvvPYR2h3PC8O0L3JR83MW+d6ZEfTTG2Iy5MaYPgz+CjRGIz3XgOljCbBrRs8dDsdSmLxHNLZTAiTr8j0rmTYadXqv9BTAZjTlvaMw08rWDh6v7/nw4SP3l43HywMp2Ky9NzU1lk+QUHdjIyE2udugmOTv3gupEpT6gbt8JnLSYO7NW/j0aWM7OpdKDFZi8wX4ruMkgTV8aM/KCsBPY3WLJtaAI+QFZTdSkRfeu1945NPv3tjvD1yvK50Nf2lsOVKasljnKRqpKc8Z08Oud2oq8R4H43jByoKlBayfcoj8NtF/ttGPG1YV7YwfklOToX8msTJM8dVGBWvgl1emCmLjiS34Fmqivj34+V/5hj/3Hxd+/5uf8j/+rY1//bPP/J1/+jPevV351Z+D5QyYcKiCSmpXA1RMoEp1I6dBu8iP8Z/9zjfU6z/h3/+NB9LXD1j6AVjh8su/xn/0F37Ctx/+Ji//8ht+9Ak2d+w4uCwLPL1l+/yJfRMIVHPhuD7xD37vE49//4XfWK58/fMHtQqoFhCrQcOgY10WBrUUzBMbd8YBlML0Q6kukKARZ0uHngvVYKcx0pANRUrQdjIZS4fCViwj/zoHG6RqAhCOdvqAlKz7SaAbWICTHmOvaSRMyJFTH1SbGqLEEf58EwSbA9s6Em0bsMQ/y4ne1LAlukDFuG+xYJOeXnYpwFT5zwkacpYKUNhxNY9RL8qLpUUNreG0Bh1gxQSq9x6A6MpElaQSmLz3uK+SQHUjUUpIowI4MQt5mrsAhBh2+CHW6pTQgUJ2clbfMYbq4+5+EovAT29DIoE3Vch5cByyuDiBpd6QVUL8ZRezbQZVSFaZVXN25+iNYnB4p0+GqAUTuCMz+ENnODWYl/N7xxlMyAlf3UwR+/2a467SPdSHS6LZNZhxkKfdUErdaA5ZdahUCrBaiuAVw1uP8AnCm+hVluQ2AjQPgAIiCCTWLRrIduRP1ZOkSZmKSHG66+PTq7asheNQHd6PYJvxOlTrwcbxAM9SoKatNdWYrn0hQE7sOAvGjIUXmAaMOrsZwXYbX6IfAn+XXOKZGMObeio02Jp9aI76LtpD1YAdhi0a8iZU+0WQjMfWzMlioDVBZJeHa/Qfhx/qqSzsBM7h4fQ7CoCkdYbFHrCZpiimd7Z0ht4xxNpPxWLAOJhBAu5QApQRs3mqdkokckp6O1y9rg3tC6Wdh3l+1Ak9HquaLf1eecSdadFfx4KfyWs92OtMkG/2TfF9uoW8NRaBTWAuasEUNZKGxAEVB9lgDkDiU8WA7fv7yr/5m7/5vX7Df/Q//Le/SYFLlVlaKppiFRPLhUCAp4GoC0dC8xCgS5WJGwOlL7UbfN6Nz0dn9wvL9S3v3j/w5lpZUg0w5SDnhbVeKA+VpWYutXLNC08PC09rJRenRgdy74nbMDY3bs15vjsvW+JlyN9oBX7waDwtYZyYnCVlLpcsr5hSeLgIOFpSCYp1osYBsfvg077TGjy/dD7dnUYRgHJ0XrZOXSqPS+XNZWFZNZVMubAUgWHH3mk9sY8SWvMUk3SwrPQyw2g+OFxO9GNK1HCe7xvHXVTtUvUzcBlUL7VQ68pSc0z0JKxwU2tMMJVSKmdSUsqL6J7YKYn69PmZT8+f4/LRNLyWAkUyrrosWM2Kkq4LuVbJeo5GO3b2fed2f6H3Q81YE2vKQ36SS+G23TFzLo9vyFlmk3URg2gthaVUlqrLUFTVolhxooGz0G1neUJkVCStyxIKh8HLp2eGJfKDnslluVCXMI8PCUzqMUWuin2WV046I8qnj5SmH2q2BP4Mbi8vtH2jLCuX9YFLLXHBayKE+3kJyaxVE1xH0gof4c3TlAKXIAoMHUBqsDu3253mSnkp8bnzRWBlqatSUfpg84PhQs6TAZZx02RlFhWSimQZnLqzhZF42248Pjzw9u2baHAJlkoUY+OLJl4d46ntFf0zySh2HBzHFr4VjbbLQN574/PHZ+63O4+PD6HX1uXhc/ZhRhuajRzCuuTRE43m6I1uarqTv5oPaiKhiz3VwnfffMP9duPh4UFR30mxtCkKOHlC5HN6k3OmJgELeExKXL8zaC0FR0OFXJjjffz8GVsqtZaTGWQpnSbGfSgZxF3JZ92HZI3xVWfSicf7Np2bx944joO2bzw+LDw9PukZlXxOGM3ExFIdl8KOJRh+MRnycTrm6CyYxVZzliJvj5qK2FyfJUezYB6RUsipgpFQigILzMLkOd6Zu5r73gSQEuCEI8muiQL86eNHLpcH3r55E3RenT85SXYxi5fpX2YBbE82lKFir1tsq5hy11wocypj0NvBvh9cH9+QkzH9wnTpno9CRYTpwp4miQxnGj6aW0gCpwRSp2kyw1vj2BuP797GzlalpW0xKDkaWHtl8LxKv8Z5huWUggEV01XUuyYzeYrYYH85uDw+UZc1GDVzasg50R5NDK2cC3jHlirY5QQ9Qt4YwQ4Ckhujw9EHH779hpQTj28eaEcX2GuJ3nf68GCkCOAXu0xnWMpVU8YC3o9T5unO2ZRpPTS2vbHvB07n4XoNandMAl1+ZRLRyEw9pXw+Iz9B9wD2TM3h6IO2HwKbY2JJSbx895HLu6cIKJjm0X4Cf8R6mIWbwD4NE+aaTgFATFBWoNLgaLuK/wS97Yyjn8OY2aTl2MjJHUs1mtLBlOyOo510+BR/T0Ce/uOtMRr0/WBZC/Qbo2ee3rwlTRAITYlzTJWHyb+pomRTyzq1CglKwg4jjguxfM1JNkKKAWUx+j7oHZYlgEaLVLgBliWNHcljb4J1AfRTJTacSTg79/9wV/GejGzOtVRe9gM/nMcfLKwWXnDReGQrpGXKiYlnGHfODBpQ908cDpLfJA1ZNBDadZ836C9N+4BdXl9NgFrPTe8phaxNPy3O8UWAOnq2/054QSqUsrHWnTej8uEnn/i972789LlxXSq/8tXKejFydZnbk6a6imzOgWOj6nMm6C4T4XszPvzkmbd58OaXLlC/Dqb2yvruF/gh33L79qf820+DW9xPCeOyrLIR2Hcxzky+kC1nPnxz5yE5P/xhYVlcVmdJHmBzCOR5vO4HT1g3emsB5hS9ByE12s+OGh0b4bPn53sf7qTukX4cZ78V1fKCo3QmF/2nNdkQkPRn3Ap49AwIHBw24UmdFSnOBJni6udkmoChIWBV03yDove6H9sJEKhJldwd18CKIaYfc50htqG8CaOhS/UcPOUshQSTdWHBfAkWVEqF62WV0e5xaGWlL5gDAVQwPRRt1kFahZaSQPcA71WrWwx5vliLTqQPm36dASlY6CnAFphlqO6oJGpTAPMuBjZz0NLjM5ii7oeCbXyI6T5iQIq/BqXIizjWU9zZZgJdvjhpYZgSJC2Ao1MaNksgf00iA4XVBOs9BbAoL6HJiEpneEgqAolTSqe00ryHJYv8Sb23GChM8DHUCMYXMjo735HDaSI9Ylio9achW29NYNxZ13sM8WONaCKmoWvsy+EOKWwXLHH0Q9LosFJ4TeANdvFEcDwMwFOC0VkDuOnkcy8SvLC5Nph/1exkPQdB93wvOQZBfQ6/PORlILDeY3gSoBJoz81B0LwbB0NJeFGnTQ9Pi7UwWXKSfI/Xs8JVS/lQHWghOxztYHodickdKzmb7qsAU84aIT6HBTh6/nxPUrYFu2kCqSUAGW3hMR2jTl/HHP+8JNU9emiGDRln71owuodGgKOzhncLEgKSNoen67wYU9KAd5rxi1Fv0XeEzNM4hxqTqQST8RQST/RnUvRiOK/Sf+C//i//q/+G7+nr+5e8fdjpKRZ9GFUe7lSu2JIYqcNwlmpYy3QzjuG0DnU4XqP4uS8MfyYNo1Fom5HrIw8PTzwumUri2DLP7aDkQl2/olpSxC5GTYWnBa7LQsud7gfJMy2Muj+OLtZAy9w249OR+NwajwWuS+ftQ2EhFlU21nXlUhcsD7ZA4B1RM53OGEZqCc+FjY3n54Ynob0+OrmoYRzN8AMeL4nHa+ZpWalFRd5wAXHeOy+HUNA+E8hMPhVb2xWb7kp/ORrsUR/1Q84ejvPh44Zj5HoFM5YlM/ygXAbVlVBmWQXTkCbovLTsRFlTNLBKjJOkRwv63jr352e2+02XdlCfcwBJVgqXWsP/R9PxEZfp87HR9p3edo5j1/Q4JFuqNoUm55x4fnkh58LD9RFqEZBUZcybU2Ip9aTTVstYkiwHptdTRDeWFI4F2kRrrRxD8ZvffviO7s5aVlIX0n/3TQdnDsnE2CkE0Nf9FZjKSZfJcUieYnbGvfemNL3PHz/j+8HysJCXhevlwth3yPk8MMwspm+aDIwU83if8F1nuAzKU1bCnvdxHog+XLLDJk+YnJXYZCWHQa8u8N5bTPVgznJyljdE641aCvfb/byojuMQA6d12t447gdvnt7w8HAFCBkHlDWMpe1VhoJxHuTAqxfUaIpi95Dz9M5t23WpHDKr//DpI48PV5n8IcmXBYrfW8dzNCtZaUIZvWtwWjzP6QvVCfqoiXac3Kl1Ydt3np9fuKwXUXhjnZjpmeRl4QjaM0mMtnReSCkK56Aex00mQs8rM2gtlW8+fMRMRugJzv1iZnQfHCPi209AQaDe9BjJluLS0vSkZhUeW2tqUvtgIfP24Q3FBHY6Ki6zpQDg0gliSS9vQQdX0Te1/zOJa5jLeL6ImbUMyVY/fPcdZMXeu/ocJvqSTeBqdkiInXjMwofCSA1vTkd6ekfFXo/CtkRaxeiDr7/+ChyWpeLDxapyMTWAeCZ+MhkFZqYwz9QzHpG8R4AAcrOK6FoUK29Fzhl+tBM00PtRAexupD4YYR7c2qAmGYjmpCSsHFyhOTnLJnA/YWytkSzYaEMeHeeU2QPAkejni33yWixPoXzr7XWtpVfwxa2ebkiOiijMmdK1EUk7KhxNBc3cg6bnVQuUy0LbD5I3eboVMVMk/dB0dZramuls84EA7pnol6fB6JRfj1lf6ROmHEwZTQxjXHlK5iDsyU0SXtlieTQm2k89TDo1RT8Y6yC7UnImQOU+wtQyPCZ6fAhTTHZyTfTMMiUv7LeGXSevIp0/K7qXABg9nqu+15wmyoyWkxGhd0IETHTMjbKKCXy0O4NMDqZoGpGmU5NYHnSGy9ukRxBFzQqKGF0JRoaSczW51/+uJPake+vND5/49ONnXo7GsoR8M8ee80zxyowsTpZJo8HQ2bbdlLjnHAxPAuiDtmYurzrPunfXB/j2mzvL8sTbR901jR1IjG6q8eMOG8nY6Fgwqy3p3DPP5DH9Jwbene5NRqZFoQ5vfuHCx9/dOX7JWa9AKtjujNHY951lT4x4dkbCczQpR8PLQucgS6/yOv0f4RPkWYOBvpOfjP7jne3jnaUO0vtBSav8BdOVzouaS1/wLIaT+x6L+0GAVbCULAmkdT/Il1/k6ReMP/5rN/7qN3+Qnz7v/NMfvfB//POf8gtvFv7M8o73aWEUAYuZxIWD3WVDQG44K+YbKcEzmXLtfLp1/v5v/y5vvv6a97/+Fax/DGyF5R1f//m/xm98+Anf7P8P/+BfNT45HF0m09e3T1j/yHE/aHTMYL0UvvEn/uY/e+HN28Sv/8krD4+DWjKZlV462TqpG8fYqd1kLp90Vt3vOvOokoSQwu+jT8lPoniXxLdmsYat0SMhcTQjp4ojry4FbhnKFhTweb0U9peDo+9MEmROQ2dTr2IlmADB3g08yyfFnGSNbI1kkueYZV5CTp7owUroYnFT2bdG6Z0lGzn3AK2S/HX6rKM0uGwBqLXeSEulT2ZISgwrKPDDwCr3Hdwbdanse6MPkz2AGbksjLVxbDJOJ4l1SSRpRtGBezsBh5wT235XAEpKmHUUgoBMhOO8JYjGNhkNBlIvRFgDAcYigKuH5+ocPAl8TVipYtgMgRbD+wmiliVBzxzHiAGnnWd8844NI1ukqiWx7rE5DErh3yZfKc8a3E7PuhyAVTvAXP3LYGA1x90S7GmXpNfHlDJxft6cFdYyhlPzABN41FyM0uGdZC7vLZdXkmDiyr1HDVMq8kmc8sEJwwn0wOUTJjZ1jecmr12xyCSMWpIY8COkSKfUPcBySqFalrrF7GQZJ6AFOyunpCTjAMyqCSQYSUOjMC6gjaYzPNgpKXpWDO1VC/UBAicnOLaU6V08GS4z4c4ZIWG0qL1Eculnzd9b1MwBJLVg68z1JLaeCAyy1dB6n7XdDPKJ+YH8T7OAtiUVtm0TM7eqR5Ff6ThrIPJ8KSht0F1su6n3xQAAIABJREFUpJlcOnd03OGqJcVq1nBQgEye7zUFiGngJMiJNg7yRQw7o9Bm3YgsSXpU4WaSZB8GaYgFPv2M3J195CAvRd8XVgITH8SdWrX+ZyDT9IQU8BY9ub32P46FQiMGV194eo6oz6bc7d816P7///W9A0p5JGrb2FrDx85xJJo799DzDvOYtiU2Om6ZRkZJbo5lp4+D2uA+4GODwwalrLzLBdrObdy43xdyeSBdL5L2kKXB751cr/zcozbUsR+id+aV5s7LBrc2+HjA0eF2HNxa5fnlmR+sxlNq1FpYs1hOBV2CtRSWBLsHlXQ0Mpk8RKPL2fBu5GqUUbDHRMuVbX+Gy8rnTwefPnVqNd6/X1hWeckoFUCo8FoSx9E5+uC27eR0UYE3D0eiaYocge0YHM3Yexy+Ztz2nefnjZyrvJiSySh5HNRco3FbGNaDyjcYIyYjcVG5DXwEip0KOU1DVHAyt+3Gh+eP+BispZ5mYNRgQtWqqXsySs5Y0vSxlkzfGt53ARQH4GrmxiBMsgdr1eXw+dNnTcDXlbwsQv4jMWJNhaVWZiJQjUn1RPCnee0YMvRro8vPqUjycrTGbdv4dLuTUqWUTM0q6lIppKXQTN9nGToQSx4c3cGbfi8zLDv7vTPjVpU8IS+r0Q5uzy/sR+Ph8YHl4UJdFs22zeQxEpfy1EenUhlxOIgdYAIAgnmTcgkAyGJyLB37y8sL3p03bx7JS5XeOAAecwQMoUa7hG9A70F9djXZyTLbdgfAaqbvMtndj4Nt32j7xuV65frwqPayq4AhtL4ppmVrqZSUY1/qS+daADoJosvDHFG8HbZ+I/fMt999ZLlU8lJVOA3iQkpM02ZQc9faQbKQanXn8KbEmeGMagT7XNMMjSCYuvYP332gLJV6vQj4ypVU9L1rTIqsQCJTVoGUbfSY3iS8HzEBMP1OHj5ASHqxLpVtP9g2PbeaMsuivSHWlQUA8cokOraN0Z29HafswIKNEC6DSBwlg+lm+h6XZcXKwj4cip0+ZinkaB6gWLZ8TqZm4sVwSfaKxSRFvTOOs9ZEP0JOhJh29eGqiyjZ6V8DyJekFHIpJ2sox6V9P3alq40Zxepn/KnYE9LVb7c7l/XKelkYFo27CWDDCNYBk44WHkfasyMK8ekXlXEsTRlAnF+mIjUl42gbKS2SYkVxN5BcJrnjLfxhXHH2liQrICQGxLv3OSWKAjKWAjkn2nFQ1/WsPCd4MouK4QLQ0/RwmtPKicTMAjHQkuiltM7MQj1aKDnp3DWtl8Tr3593xwQQDfnN3D1R0Ts8PKLc43MKPArZiAV/OBnEsGDvO9YEKIdiTowCy+TKCdz5iLCHjJoXU+DFcRzyd4vKqRSx30CF7UB+KnPfToBnTgZTCr+3tqtoZICLYzYncu7pXOPT2F7vQR4jbk6+XmgvB/aLCXYwcnhnTGkiITeT7M1sep6lKNYQLtYtGksxU7OpQD72nZO1mIumpiH9sjgDOjCSWJGMNkdVMiJORj4T1+SFNzyA3CQHENZMZaH3wbvrAx/Hj9lfPpOWt/SDkPW4/EgUW0RzRbtbLrTeqRHSMA41A61JcpA8YFiHdQ5PcEqtZN95vu2s10TuhbqsHM1ZXJKRNt8ZOgd6kyTSM+SO2NQJpfa5vCY9QerO/8fc2/Ralmb5Xb+1nufZ+5xz743IyMqqandjLGMjy2ozAPFiiYmRQWLCV+AjGANfoJEYMPScmfkEMELAAAYtBBa2ZVvGRqIp01XdXa7KjMiIe+85ez9vDP5rn5s9LyRHqTKVmRH3vOy9n7XWf/1f6txIa2E9r/il8u3PP3H5y093eXutwJi0WyU9gAX4yT7wxeipkXs8zymRemPkkEj0DvkUMi2Ym8Co/NWF2y+u7I+Z7AP/MDFOWH0ReyY50xbuwKOtOih1BeF4smxifmHOL5B/jD0Z7377lb/ybzh/7budX335v/nld1f+t//zIx/enVhK5nIuWIqzwxSgMansJgb5bUychcWusHTKTHy5vfAP/s4/5t/56h3Ln/+A5a/BFzj/mD/z1/4j/oNP/w3fv/yKf/rPJ8Mk87Q2yR+emN99otfJ9EYlc15XfrU1fv8fvPD0mPgL/8pZiytvAnZcz6bgRmO2RfdSyZxa59Yb8uDMsHeaDTzHELZPiEXpqcHukyXD1Y29GnMWRtswCxZbPSRAWi7UoXu1lBNed6Z13MR4xCs2IfsCIRdVddYias4RTDml26YkX8BkxjCnDyWqOUXAQy7YTLTbRiMMlZMWyY0pubQbJSl4Q2EPjZSKwheOOhRgNAhcxYu8i9jovZLLSqsN80FZCr02lrKAycuTvZJdw3gfkhtxDJ1oyDxkLXEa6NzuR1CABXPEsVni3tRZKhbOYAyxEuXVFx6cS3gVuRJf0/2c0zJs+sSnWMaLHbPIJGXH0yLJbg1wZNRgBOn8ba3RzbHhrMuixUCP0BcTO7kY9OEMU1qlTTE8Dnbk4S8zphYFJYtBpsVMpGIGa0Sg3yI/r66ertEZ2bB+MMki1Ws02n7VvW6J6Z06WgA3XQFCI0Dyo692p1OxYeSlYEx6b5SQYPY4B4cLsMS09hCzpUCcWQe756i7NhqNWCAyyRPNBHFnJ2RNYQRIMMRWPthBlgTajmOZ2JQwWJJqfY+lFxyLnrf0SotnrkePpP8ugOXooVJyLeMsks4M7qbYNvXvtIlhEsu5OXmzbOj3BecRgCKT6LcF52G/ciwDR/z51trbketaALoHQ2oSC6m4/5NATDfDhv6AMRk1Es79WAi3+3d7zLkHUDbnDySC9gPAbV3oNeTcycnRh9ZQj8gTbdLGpE4FLWnprJ9/sPSm9btUbkwxVe8LxRlWJYZm8GzMWRmj3Zcjc8aS0Zpki9G3jLtGjrsv1uHBeCwk///49RsHlB5/u7CWM6nJQJneuW2irmUPo9rW2Xbjtk1an1z3nVobrzvUvfLddXI6Jb7cOnZy3i0Ll3Xlkos8gsYZLys8LJTV6NMwk6azrPD+4UrzwqePjVQK2St9T1z3whhKbrvdBi9bpjFI88rvnBtGhyWTc2f2V8grOWfWw2Q6wZolNWs9kbyweKKOjeuYjHPh895oDfpYqbdGnyf++ecbLze4XIzHx4VLypHHgKJmTUPS3ju1RgKLZeqIlIxAVJna5LTeubVOa6Ih1t6Y3nm93rjuncvpASIONGXU7Tvgk8USw0JuErIND8YDM4FLLuQRP+qJOxq/j8br6zP15cqSnJlT+NMklnUhr5K72IzNqEXjPAWEjdrYW+VWa9D7oO6dGv4vjsVwYHx+vXJ6eMKStOnnsxgxdXSWlFhT+EGMQHxtBGXzSKrjLpvbNsnp1odHDZxzsO0bn798wXMwazRxy8Q2JzBp3VOv9CT8+/XaKUmMn9k2SJnWOlQdSDUAhtEabatstyt133l4fCSdZPw7usHUe7U5WYpMwkc0K2qHdLD3oQJF1yHqliBZ+A9PFndubefLyzO0zlfvvxKYVETDd9PgPuYkpxSMrkztklSlsgCDvYpRVesNppGXhb3Jv2Qfk2sASg954XLOYqwlvxcJTNImTCh9zgL/cg6q80FvtcMrKuqBHWa8wcAg8eX5s7a0p9Pds8scciqQsnTVtgss7U0bpyxAa3TdGz1pG3T44dzZSUPDYc6Jbz9+Sx2N88OZsi7M4XheosEMzbGHIXnK4b1icWhq29JskPygscvXypFEMWWBhN//+lt5CpVMKSXAFv0cgTem4uBGb1UpYQhAw0SNJej6M6QEyRJ72/G6U+Zk+uTpcpIk0gY+EkuetNCb994julfMldY7yY0cIMaYkxL+DMmiKR1qPNtw+mg8nC9899239Dk5xXYjxT1lrtQxS1nyItMWMgdDobcu+6o5KGWVXHfKz6BYZkbqpRn0rXJ+ONNd/jhurnHcXP5F7vdB380YR0pemCRGTRUTDzUch0dPH4NiyBNoUVT05emkozUkDDn8hjrC74RG6rtY4myS/0djeoqFwOEHNd/Ynib2Saud9fEc6ya9oxHMncEkTUVujxlJWjZjeyzKeK1d0svZYl+vxvqI2s0phfmmZG+zVRnCuwe4IfjQ0VZYxvvazpUJmQ59Q/46MuPXNc14QimLPhhjIRfnfD4z20a9bmSTP41SBsUgK3kGaBmyAO3G1BTZCClYe3sGYihxky/evuv8m6PTqhhhyWXMLvZRxz1jlhij3r9vySIDFBxvUtF5+DQNgWwe0caWndt24/Swcn3ede0D/Jx0SZa7ttY5vQ1Fx5A651vTN0OG2faddtSAMWhd0v6xT/CpZB6PZavB4Wk03e6yyuPeBvlIqn6OQ7EFExnkmu6ZbmDZSXti0hkz83A2nn/9LV8uC0t54OTydTGLTsJHbKyNnI2xJ1oDWzJtG6SlQGsCepKkWxZ+l550zpclkS/w/LJz2Z3lIhZTMydPgcvQ7wy/nKPJTWJgHdL8MeURlImlXLDKxiG7GLD8+Mynf/aRr16Mr56cMuSNpftKg2Qy9VMDyDORZw55hhjrI4BexdZLwmC+MMnyPrlV8ldnyocr7RXm0hhXw95NrJ3VY/RgsHJs928YMejPfp/qpiWMBfevmPMV7D3+4c/w9Z+/8m/91Z/w819/4X/4u3/CH/zRR/7OP7nweMn82Z84y0XSFUsZ8xvJEssO1/5KMaPajjfVBi+dTubnv/rIj//eP+Evf/NjeL8yrQhHfvqL/KW//u/z73333/H9befnX65Uy8ymBDF/956Xb79nvz6TUsfahWVd+KcfNz78vRvvLyd+6892LKV7FLgCKdDicPZgAif2DO6TsVd8zVAcmp43yb0y9J3FXf41yVnHYMxCn4N9dGqbtAk2CiPARaOzzSGbiPBXPK2ZT5/BfBNYMQ8AdAte7gEuNj37JqmPT9XtaoPzemKpO7c5MFvodUbRULiL5wHrQt8r0wcyGc+xpINxKBL8MMVW71fyKcKHmgbwYPr7aErCSkbOC9fXSqdDkg/YaGG0W+GUClZgA0bT4pIphkhKzvR5X9KAgPjR32wGDiNoLSxcCk1tYu4+dj4H3WtUk2CvDC2NetfCZdSBx1JauyxJXw9gXowigeY9hm6GUdZCSo3temPgIWe28MA8zj0Fx5CTbDzKwhiN7EUWWovpPIq6MKbmA6KH7EPWGHk6s2+4maw1DPZ2eJ/p8BhzB3O64EDZcADEZxXtcrIzIZ3pvWtp3OSb9bpvLKVAG8wscGbEwC8Zm3oOZixUUkin5mFKLVVGJkXqd5XeoDdJAecElxIlm2EuyXa3OPw8sYef2j41tzUkLfNgSDMPqZnY3tmS2HSx8Eqpxz8HQ9oOXx5X4ALazyXPzDnwrLOYWDSPYNynJNbUJFjSM0zPfcYy1aJnFkjLwTyNBdwMWaRnpW4eqy5AKopYOM87mBZ+pMnxPsmWqdS376xNJk1+gITRdrwvgvnVe2Mc8siD4Z50HvTRmE0M6WOe0dJOs6/CsYINHn25Nm6TWQ1Piz5jH6SiNe+hlDjS7hxIY0RCXvCH4vuSYsDoQwl8PrWQkm+tft9eK4cc0NByRtethUVGMI2PrzIIhQepUa8zSUN+cDbBDo/B+/rtN/frNw4offz5Rs3PLLPTZma0Irr1gujmXlhYtMk4SX5TTgVvnZJ2vv2j71nSE3nJ/OhcWNbMJSvmfd8721g4Le9ZTs5aDGgsPrCScYefnLRF+/Sl8S4/8jJ39rbQq3Mdg+sYvNzkaeRj8C4bp8WDinsipYIXOK+Zh4xuPnPSEdRthdoaHukMH7crt93Z2uC2vbBPY+uTyk5nYb/u7KPy9dMT7xanOFjWTWRWwpRt8LrfuFY1445Th7FekGyqG2tesankm+d902Hd4yKeEt9//szWBsvpwrDYaAxiqDMsq3hlM/baYIi6Lp+dHDGmQZ9cdFsofUyHUm2N59dXbZZTYhaxMvJa1MwXba5773eZhk8VjpLgtlfR9rqSM3pTylgbEzyTUsXd2Vunvu48PT5RSsGyTJnrUJzseVnlzxG0YgupQTLDsweTI3xZRmd0ocSn85mWkhqc65Uv33+G88o0Z02Z7FnG60VeCD4F1GGONTWq56L7kFYx11ZpjHkfyjuKem97ZXu90rad8niipbcox46SmNpokBLbaBTtfvSdMfX9jE4flT72iF4VCyOFMa68ICav1426Tz58+AY/qSFa1jD6TDIkZHRGRCf31iRTcw9fgihSrbEHk2bbN3m/jM5tq4ytcfbC5XLGzGPAMnCBCj3olckT67pgngSEjCMp5E2uMpj0PUyFJ9y2TayD3pWCuHcen05kz5xTElPKDk18ofYap6VMx+8Dbdd7GNFAORrGfcg4MBRqLLlw3W48v76S1wUzpXCtvjJmiwQkdO3JjGBWaBgVxXofSvFyM8ltYtBU8yQGSs6Zz99/YU4o68p5WZWyFwCcgBGLoVJG3a1qU26mgtqZlOEw+/3nDgSqbrXRXemPp3XVQjRYQ0y49Yn7oI4aRUdnxRQmeR9aR/giuJvSw+JZ6hzbP5ccw+H58wvnx0dtUOL5ViPz5kmQPIDkMehItopp85tCamV5YbSgNLeqZyofvguTh8dTeCIcBpdG7zvFF9VMC8NpD018gBPycg0adzSycwzeEl0blhZSmtRamU1nXyjt1biY/HasHJ5VE5KTLQcgOt8AvjkBi41pPL85wTQx51zb6nL4xs2D6RlFXKhYGM6qufakvx9l/p4W4h5yVWIzOelm9zTCNiq3bWfmgs2mbRThnZF1PY9ntfiZ2SXdG3PIJwuT/99J17ZHlO3MSqVp01iGsaZMvV3BnPXhTF4XSk6YFdpshJ5L8oakYT25VqjzMIiNFKSyLLTamEOJpNpYinn69HDh+WXnHpscm0ii2TsMXnUW+L1BGxM8TLdTXKPDV81yQVymoa2yn6g+6S+v9L1HEpkG0hZGrinuB2Ljf1y7w0fmGBjmaOFdqDHtqIGWnOKZnIw9HUbXpgcRpanaVMqRm4up44NjFZq8MLzCaAEayvPj7l8Rg3jvTQ3preKPKy+/+J6t/xmWtDNHYSSXtYBLJjFSQmbzLWrmJK9GfW24r5Kr0u4A7pxZ4JIFOXA65/OJL99/4XotPJxP2LA7KFlbJ5u2Ad0TJcu/xAAb4VXmE7pipPtsnGbUQ3fSzOSB7sll0p6MX//sE5ff/UDvHlHVhFmu06eRPF5/b1DEQEoY5EEH2uwsXV6Q1puiwAHzSUsDf9nJP3rP+Pw9fWuUZYO5wPoemy0S3sRyJQWjcNYAWwxjwcgBUk3MFrCG88AoP+H0TeVf+nNX/uq//jv84ttX/v4ffOQf/exbfvIh8XAe/NgupLNh3khzYbROK4PsC3UfepYKrH0qTMQH1To/+4M/4pt/9A/48b/5iC2Zmd7refjtf5e/+td/xrff/+/899vkl3uDObDunLLh7x94+fSFfqs0a6zpAb84/8cvPvLNP3T+7ad3/NY3zqRFXHbDrAQr/wG4QmqUYtSbBn/bqpaLc7LTwDvWJmUa3hNtMZhKu7qUHdIUk7Cu3OqV1/bKnKbF8VTfJWlnYuBK+T0lrp8r6yrJfMkrbmLUuCF2BRrse8iTxFwQAJOG2FN0hV6QJUsZEbyDQ5pZvnH7BoiF6ZYV0MHAwrv1oKOaTVq9kZaV3sW+9YNdTA5TXHmyrGcx+eq105uTipFGp7ukVyUbKZ3Y90bdr7F6Dh+dAIE8Rw0Cgap3H6IDUP/TdWYiVgvIA/bozeQXJfBLxuKyRSBYJkYUnKgleA7Pv2DBzsmtbnqlqLdrybgVblfJBWcMvlo0pFBBcF9wzS75eY/50NwpVqQ4OWRdST2FFpRGH+oJlmAdXtsuxiv6iG7yqqQHywQtHo/ZwDyRo46PPiiLkYZzvQ0W6whjFHBKH8zkzLqRchFgFKqNdCyBDOpQctixALEkRrtN8EjEJfx/+h7s2lA64NDonFLWEmjEn21a/MpD55Ap6ZorSj5CIpKJwJEyE+6hFQRw51NLhGOZhRkzyffsCBQ5kmlzTlqUz8OTzI69GnfvIYJBFCihJKoejKmQQ8Z/K6Uw+xDYHtec0QTe5D+9CEpZMlGBZGKoj0hlPpzD7ml0Uwua3t8S8uaQFYzszsQCZhq1ivGfk4xPeu/39LNDZpaSgjBGyB3nHBGScTxbUnWkSMqbUzxq7RfDrL84Pg7fWViAkSVb77WpBpkkbCnAR1IArDMA6oO9Hmegk4NNZaEyakp3/UEyoqFnT6PYG0h0WHHoujkJLR8Pb6v8G2Yr/cYBpee+szcN+UqpuArhvg58SOv+aTZmBbcG3Xi9GdfXyVwrjw9f86MfLdilsM6Cz0xtldY72c+cLo+SOmUH75zzhWmDwuBxLVgavGzapH1sV0aCbcDeJ/tW+LIb15bJs/P14+BcwGah+SOeJsm0NV/TZKORWqalQWZRoWuGjcLNGi+jszd5dbSW6dPp9aaEpLqw152HsvCj85nHxTiVJSisxpIVb1v3xvevjWsHT2s8XIPzxXm57ZIOJWfMdpcOYofuNTHS5PPzF4yF85ppqGiuBdLUgDtMfiq9q8i7ZawECOOuSNUxtImMIoVpg9jn4Hrbef7ygruz5pCihH/McQM7BEglUMPCe8bNeb7dOCivQmI7s23sbcSBNcie2Gqn9s7juycZJOfMsoQOfWormYuMS1PIltrQwKcz+KCBBlV6l7neuq7MpWCW+fL6zPfPn1guJ3wYuRRIhWGJmsJQbhgp251CeCSF1bYJbGiTZenUaUCVGXetjK0ye2W7Xmm1cjqdWbKMsJ3J3ne9l9FZTUTr3XVw9j4Yrk06TYd8a5XWKhNJA7IbxIbJgevLzu268fj1V6RzImUNY6NDyQuejT3SzAqOj65rsGRGG+GBo2Gs33alzt02fRYzRQP3CjQup0cO2dkxpK3ros1aSGkWxZqQPSlueEQCBhpYWmvB4tIWrW+b0u9qZfbOy8vOVx8eQjM/2Ptk8ZBGub7/o3AR9GPRzglPlUxJ0rD3w1UwaLuzVQ2k0/jy6ZPuH/eQ2VhsaRtpVaqKKbtdG8C9CZyrLVIZUsSAjruOfTQxJ+R1lOht8nK9Uk5LNDoWw5gkaBZJSL03xdKOhjnyq4rNkLlrCJuutC8TaNNapH6gTdJSErZEOoW9+fPIfNqCeZPD08Tu0iLgToFWgdXn7UMpi90mc688Pp15vl7pc7KE5LR4fjOfTGL1uSc10sPC2wu6Gc113zSZIMSAEHRjU3T9nPKAKymRzCQHiCbChgB1YTGDfSKwORqRA8Ca0aQcSR7OAfCg5iWAnxw+YeQsn7sRnmKzQ0bNerAya2z+ACXAhByuu2NjEBmSvJlKV2YfJMshaddCQmyww9j7bcMoIBZd65CfHgllIPBAjBgNCclF+87hYeDorOq7jLTniO8sGi43hQnkos1dygs9wBibHUMyi+krVow8O3Wr1Nm07WpDW8PW6aPJE+u0crlceHx4x7oorVJNYqQQTbG4xiSAxJA6daONyml9uLOstDXWBvFIplnLSlk35uvG3jrK8jxA96NZGhxU+DlD2hggosrXG4tu9PCoGDoTRhPra29VW12XhIMSqYvRsMt41kIep+8shcl87zB5S+XJYfDau8xDB3FNh9jZ3SEvYD2RljMlmZ57WzFkQusWrz8str8DS03GoNJGMEMKPQY6i22y4LQls9WOMzg/vedcXvn87TOX3/nA3gfZVGe9QfZOMajTaS0FM0gShnVNzJBH7zYpyBORJP8Wn2J60uFUCsta+PICH945p2D/doxsAlqPDJkyjAUkU02T4sbYJF3Zp86z1zEgydC4WcfGTi6Zta88fvUVf/IHHzn/8eD9B7Ggki33wIXWDGsycO77zrDBeUm4xVKqFPq+01IjZ6ePhPUrngpmZw0220ZeIf/0xP7zF/hzRqudkhrTsyRec2WQYUiOrLS3HWaASjPr3rHBTAXjLPKSvzDyE5ff+jG/+5c2fvnr3+HXH2/87OMrf///+pYfPZ05/YXK+wXcFqZt5DWRNuN1gi2DtMm4u6+D3hI5Q03w8vnGP/m7f8D5wxMP/+pZywd/FMHhd/9j/sNf/jG/rD/j938xuI7EPmDrArqXy4n9+sLwlc1eOZ8W2vLI//KPX3h4v7D8lcHX79dY2oX9gjspV0iddpPpdFsrzgO3rVFcTPOEQetYdqoDPijdSTnDvDFa4pyd/NDZrFHSBd929prIfqSbDlKeVJv0NskzQZ/kxzO3fReANBozlhcT0fBaxLh7ikTI4TALs1koaZxTKSTbpJIYk5k6bQDD8GAQjVq43Ro176xjUMpCtszeq8JU0OIyl0JtsuvISX1QDwAkJ9XM6VrslAmkzg0BxG2OYMxNFi9Mg1ttWpi1XXXHCO+4sKfoMeibBlv196oTx8BNnK0Bf0fvphqaQ0nQovZ6CVNg16LaCIlyeMoICAng3pNA2Wzyjjp8Mw1aFfvKU+a0FLa6y1fOobtHjdZ3lmbMCgmKn7RsDsbNEaow48xQmEX4JhK2BW5c9009kCdqrTrnZmJYjV5NwFBnUvcWEnpjOZYSGF5W5g7MnXPKVA+ApMN1VB12xzXqwVALMMXifwKmdT+03vEEZChTjKje5dekuiwWIjbZeyNPgQudyd5aGEc7KUznt9pYmKSSJSkj0aP2+4g6OqBkJcrqERAAVUokE4dssNYAQHpldMlFD+8k0LJu76rDdKSOsDfJmqHEz5JyLKRbnDeRPnu/f9Qfmzm3elOdnAG02kEyks1HylnM/CkrEDxxeIdb7wqLCApO0o8FEzHB/JBsdrBEnz3qGbGcg0knF1dwzjxSWrXQHKNHzZ3BvjL9eULhYCNseCIowAyfChRzApBC7C6LBZGsIHQv2JhYb7LHsIIS9NTv1DbelECuHvLwvpohZTRzzAUs2YTEUjU5AAAgAElEQVQaViEMAVuy8dD3iUlSKLazwFMLwFhs3Xl/vtS3J7HxfoO/fuMpb//tf/1f/h47+G3wet1pN7Ectm3n+dZ4vik++2WIybN3mdI+LA989c0DD4+PnE4OlqAlRYaas5y+4vT0wGV1SsoMBmVVGMdTLnx1MsjGy96pdfJp01D2ZZ9cbwufXuH7lwm9s9rO46lyOcOCEOKSK+syOZWFtYjGfkor01HKG/LyWHJmq4gl1E2a9OwMKr0PHr868+vnndc2ef/4wLvLynlJ5LxQowiUvOJArY3nrXHrkIoOeEwxxKLzJVJewMTc2buYEbVXUhIt9OW6k/Ii+iRqnEoaJEObAs9Y0RY/uczdcANT6kEfAXQmpT6BWD+thzfP9cbzy7MkUyWappRIZJbTSlmKGpOysIRHRLYo7BNubdeA3CQHAnmXbPsVYlBOxdhqo3a4PL1jLSunkjmdVkAFIZVCKqtkccHEGGNQkszAzYh0QPSAddHgl3UVzXU41+sr333+XjLI5JzSgucFT5JbzADA1qXovc2B24pZB3p4QXVOJbHtk2QN9s7r7UqvnX278fL6wrbvLOcTy7rSzegmg7clJwEhMQiMZGGGKJ39RHIj64PtemW/brQqb6Z1PcGQDlzD6+T1+ZnzV++4XB4osfGnd9Z1lUFkbfjokjM5WHiHuCf2fVPk9hjUVhWnTqDiwLV1Rt0ZTZHny1own9rQzBGRzSl8bpyclAaY0htGPaYApdY7e2sBDMvQsW6btiKm5JeXL8+8//qDtOVD5u4jOdOhlIVpOejEAiFLyrQ+uNWNUZuen2FUBkfCyjRtIW3CcIEhL5+/sN1El89hyHw6rfiEUuRP1YNi6inRB5xC9sqxBZlDTLKDocIM00UNq+up8Pz6ShuD9aTErbIUcpEhLqbvS/KOGQkiAmJ++J2aG2suIQUNjxD3YA4a1jqZxLKKWclhqhhbvJyUKnIY4RJUbBBzIkdB6kMadE/pHsk8mGGcPsinhX/+i19Kw21GKpm8nsjrSioyyfcka+pTLvcN2J0FNya3bYsmpZNsYmPQa9X9PZAM+vMz61pY1pP8Ozhox4SvDfJpAtquRkeMrxjEkTzRPKkp0TuhTm28ZFAspsmX7z5ByizrGk2yh9Gkoc2s2CfxUIQcLcyGhzbnltRIegB1x2aJKdP+WiutNd6/e7oX8cNvyWZI6uK1sfBiCv8gM5e0tjf93BlsC/r9GUth6k9K1L2x3xrlvETCSVayY8qULGnJfauHGGltTJZIX2um7ywZzNHprQqgD7+Nuu3srfL995/IBum0aqNXBGILEMtipDmSNrno3x7SgD4UY588sSxrDEH9Bw2vxzMmr+Rt09lX0pvM737/BnDqKePxc9ApLYl2MMHGUNy0JGgyhh4zkkyBPitfvnzm/PDI+XQKOZ0HEzPrmfWMIQDP0sGIcnIu9/eUy4IRQGvyu9GobtqQY0Zj52WhrCcFSpTwaszoNYjNvQmUPzybxpgaVM1pBulUSIsYeqWslLMWFAetv9ugfqk8fviKhDa/js7DQpJ5bUTSK07aGJbuUdMKTFgo8VwXAqSfwaz0gbvCNL77/MqyZJbVcZ8yRT1k7+E5Mo7CHLHT7oneFYEsxq1p22tdLLJY5uSQ0JYM1/3Gd3+8sT4mpd72kP31Se2TWqFfM7fXyX7VMDwx1mTkE1Bk/J1wrMRwFECvjx36oL1Wyocntucbs20sD6vuz1L0wLrH2XIY55eYEmPRYQNmZdKweQlpSAPLuHemTfL6zLu0UveNn//RC7/6fgObfP1+4fFB1/Xt5y+YNXLXEtUcPCimTjxbCT4/X5kvL/zopwvp4RvMzuBZrJi/8K/xZ7/9X/njT5WP10qnhBEz+GnlNiZb25lxrc7nldc2+dWffOGb94kPD05eJWt2DsPYLm+eLIlKsbfN+NiazNEtIrttfxtshs6B1HXdbE72sUdfCSk7y2qcFuNS4CFN1tJxH5SSSGNwKQvrsuHZuL12Sjkz5gjg+hjuowmJhcBh4JzMaXZM9nresisMxeIaBkykezkTgTFaQMmrlGC36CwVI1v1X8wFgfcHS6MdwQsx3BqASz6EGctyxk3AMbHkWtxxAoiPpR9xbg9mRN4HM2mEJ8xx5iJgwA4AZqqH1qIsnjeT1M2OZcyckuPPFEA/kSh9SNsFSB+fcQwlGNODDUbI8tBC1i2zy6XtbWk1opb1HotA3QOtdWpTP+iesRkyH9dyXYswsYcNnSsewGZ8qdQ5Yzkqpo8JSdDy3DzY5ASTJc6bCc1M0mKH6UqA029wCIuWFvJ5ol+Ndk1lzky1LunZjBtAzJn2p8GYkovmMCysDEQWEzsoZFIuRuu0KSAl4KoeQIAYNV29WfQAnXj2xps0qgTTRh6NMkhvpl5zMEKSbMEcsgCE1GPZockG5qgxOyS9rjk5FYGX+H1uYIa3Ve9vgIUlzRZud9sUsao8erMZC1J9v+4/WBDxg8vgKYArtJDExHieehZ6NH/j7hEpzyeL1xMESUhiRYboo6kmRn2d92fizc9KS89576nvabv6Yhiu7/9ushT3ptLkBOi00dRbe4pFfsMINpmZ6iRawmnmcLqQMvlRpjcfyCP5zg7/qZze+iGXimPGwfd2TX4wp8TvTSbp59473Yz/7G/8J/8Fv6Ffv3FA6e//T3/r906PicenhfzOeXqX+epy4endifcPiafVWQbU686X18maL/z46weevj6zLKIJtyFvoL1nbLmQL4+UpWjItCTa5dI4J8jJOZ8NkihlX6px3Qt9nPhSnduWeX6tfN4HiY3HPHh/TjwsnexO9oWHh4XHh8l5KQwUd5xNw/4ptgubNZm4MdgxLuXMuhiXi1GWya0NKmf+5OOk1cE37x5Zl8nT6szZaOY0R3RWnzxfd573yjahhSdAcg+dczy4xRkDrjVMzYnksOHsG9y2SlmWSP0ySnGWMlkd1pSDYi6zrzGMxQDEJBhj0rqGW0syUzXLzKlkpNo6Xz5/oV13lqKtm4xjoSwn8lpYUyFbCh8PFe2SsyQzPQb7Mbntm4CAZPTbK9vtig2BKqUk6jZgGI/vnvBUWJcTpZS7x5Q8jZzpiVIKJRUV6qninE0Heps6XBRla3jRQNAYvH7+zMfbM+v5zDkKr7mYA14KzRPZFtYsE/IZ1NBJD3TaAgWetH3Q6dQ2tA3tjf165XbbGKNzvpxY15PM8bL8nk5ZgMhYF4pllqKNek6JlIokDHMwauN6vdGCRZLdBXgQ+lkkF/z46Xsev/pAWS4hXUzQZRpc1rNowXXDGSH9GzLHdee27SpEvakIaMqUFxfyM9pur7ReyXmRXMuk/2eowGuQ1XVeloWUBJrYvRQEGDAHvfWQpfWQJR3kVQ0cn19eePfuK87nVcCRKZGnJMdyFkjUj6FwBPtA17/2zlKyHBfmZPFEa7tKcRTgaYrg3G4bn7//guHRlEeanhsnL2EMHYfzHbhTcQYx3w4wbwY76vAoIg57ycQmL69X8rLiS+KynkjrQi6FaekusxpBzz0S3sbU2SGa65TRo08siQ00PeGWGCMiW8dgOa/ygJoDHxOsKZlwKp3yOFOI15hRtET1jtSIAFGY+rl9KHWs10penEHiu19/h18Khcx6OrHkhbKcgokUKSs5pJCGmGHBpBPzJIXHFEp2DB+1A7iYIXV9eLzQTc1NwSVbYoaviGjzBDBuk3ss9F3Xr0eUGPvDBD6ASBMbyZh8/vgdj+/eqzAHEN0Js3zEPlHxBpJYnPUY1tHnqkMb5gP8E7DYtH1Nb8zP9WENDwL5ujB4a96iqZDnlj6L3xsSGaBiBKtJPhcG+r4tGhgm2/Uq+ePlFPLGA4QRCK5BQANIyZnedjWaBNjRRNvuXd5Bo+tzMPQcba2qJrw+M3ujxDm2LjlAFd0zOWkoMpcxf8LE2gpT0uLc/cxSsAkPGZvHBk9G9871JkZbKQJG38Ckt+1yylq6eNIz1aJRu0ui4D68zBmNnyklU4mRlb1WEoXHd+/0vbqGR4GZM4a1aO7DH8o9C1CSSaFCAMZQMzgP9orfpSieCkZm3yWXzvlCSZlyLHpMIFpDN+5Muq4HEHfnrJlqtc7HQjbJNZYBlcmoNaQD8PzdZ07vHjhf1li8GDaVvNqLYVk/MwcA1Jv8Uw6D8QWd1SfPd4+nGUZOkponLsvC9nzl8zYpp8QFwDLDmmRfEMA05Jx0T1giu+OmwW90DVeHlHaaQMlpjs3EzJLjlMeFz59ecArny0k+H5qOSWkRUD21aFwvhfN65nSeeIKSihhnddBruwO6yQ3Lxww0YNuwZizfONc/2pnnnbwkbCxiMY095OQH6HMceapokGNolw8ZdmLSYitvmHVS2smnG6U7z592/vBXL3x63nlXMj95V3iI4IZhjpVJGadI19P9nzHMitI0Z4ktPHz+dWOxytNPT/j6IQCzDOPC5S/+JX78z36fP7xlvrzonKtTgTLrmuC2Matqa8cplxOfX298/vXGT79ZeTgnygLT9by6N8gzktyM6QIqBDV1Wt2xHBLopDPGa6ORSNOYLQmeaDs2oc2jB+6EHSNGMAOnwDNjspTEGI1lOClNlmTUuotVGoCHedwX91/xzzYC9JoC3afqS5+dZId6rd1tGyxYA/I52iRN9qipSUkERiw0hnpsjvt9HJ54fj9PmI7+pvOtdwXVrKtA2xGyJCfB0NJ4DD3vBDg1on4dNTqMITnCQQ4PJSCGW8nMOCB0IwCGKS9BPyTOAneShZwnhmosRc91fJPzDmiaCdAchxx3Dojzb47DPS9SqYTIEPwLhkF2Jbj2seNTTC6ddWJdeHhWetQ8AVkBC8QiSbtl+a3Jf1LM8sNndFrU6Qgz0If4QbKYkIiQ1eVgnAqAUdKrQgDmnOFdJBm00nLl0dODRZziM6SU6a2T88roIVtuAzFaAxxzAZeS8TmGSAQ+JrJ/P15PPnf3uhZYaI/rM477nUgDDtBlxOLVAig61BbJU4CRAaIQMjGPXmuOey9zLAV0sun7mag/xMTctenBfo2a7G9gkP6dPLJmH/c+K8TapKRrd9xqcwT4EbXdLZLrTGntYqwFKBT1Z96BEgH9xz16sOl8CEwdQ3VfDDc9F5KIiX3mYdVwGIjPWEKhaieVVXj9zSHwVvif/2C20YfrAUpJuaNFT0cp2osLADyA3xnnmrlm5TaRf+V8ew6Tq584kuyIXm6Gz+BRuw62vlkwIuO7ikoV31mAfS4fwwb85/8iA0r/49/+r35vtkRrE28y3e6mTWlaYRRt0VZPfPjwjp/+1iMPF2152ii0Dq0uwMJ6eqIsK546qQTF3wenDO+WwrHQnBRer/A6Mrc9cR3wfO18d9143iWdeUqDry9wuXROi7GUC2U9cbkUcoFshTZknJZnDCkeN7Hrwek4o6sZ7TbAnQ3j+9fOt983Pn7eOJ0K799nLhnWdLpHFKfFKNloTR5O19bpU54JJUkmpKKgmzqZ0Q2ue6XOQasbNgetTV5ed5l3ldgqp8SpOOfinEthzWrqfciIVSatem2S/BosCqO2+dHABVthv1VeXp6pbYel3M3Jsi8s5xWzRPHJWhJrKQEo5DtKrTNB4Mf2eoXeRRNtlV43hgkASyWzXXeGO4+PDyw5cS6FXDSYZBcbrZs2JMdrmButVjWl9+F4MNtQYtiYSn2LAvXy/ExrsJ4vWHGZ/5XEMmCUFF4sLioy2qDYXdKnE3zMDqPRqvxEGIO672x1p9WN7bbBhOV8Yj1d8FRwL5QkkG26M5IYZDLbrdrqo2Q/bUGMWRtt30Pu4Lr/c1ZjnhQV//3nzzy9/4rlfGHORi5B4w3EWtGeW+iJNShIJw3btt+T2+4a4imq8l4rvQ22Vhm1wTQxa1ISC4Ew+D6tIdky1tOZlDJrVjT2mFNAWmzpW6swelA4o5R4HPbubK875/XC+d0jCT0H7ih9zh3LKjhK0Rr3FL8xY+BlQGwZUgrTvfnmu3IMn7N1Xr58lgn9qkFwICPeJWWKF+oUvVYDvXTP2fSe2phgKgYHZzdJwA1MUaRtkEvm9eXKcFhPGkLSYqx5IQfwFn599FElZZmTOjq9dYEmSfJFbRwjRjzknLUOeoe97mCTpQjAnUPlRqAfWDJ6bJxzypFO43dPK5UWFfAe5w5D4E/UfUbtvPvwyKdvv2NHWvg7eMmkWJIXg/pm/fyO5B5TTU2tjdk7ve20tqtZGWIJtC5vEzNj33YmndPlcgdpAjkJ6QCk6ViOQhzngIU5f7o308H0GTK3HESM8ZzBWqzU/cb+unN+fCRKtsgHFtKqGCbG6AJfhgxeBQKOSC+0kE2muJ8HFk2TRcjB7fqqwf98UZpVDEfywpL5dUlJDJiDOXro5wMsM+F8+v/Qn1Esr57b4WpCt/ZKa4NlWdXYI9r+GGLk6NnT+81Z/h/mhenGZiOGAiWFeICBeo50pte9Ukfl9v2VNCfrmpXouJ60RPAUwEhsLT3SDDl+1sCTzvzRmmSXFtKtGBTU22ur23rntl3RtnW5/17uPkJxvyWnnE+RZEJMohoscsp3ZlMK9pSZWApuhAWS0243Rq+8+/BB23A/wLcJpqCJ6YmUVpILvBKz5WB1NpyQFlgwI83um0DCTyrlLEq8GadSmDaV5pMM80htskQb4CRsCgiX3E2tYMpJkcnWcaqu8RRLoO6bnqkJ2TOjXtk24/L0wDIHqYSZn8npJ82AQUYn+aRNo89EXhaY8uVKFgNeACKANvdm8v6zSaPz/adXymWwpIT5rv4D3bjDxVTN0xTFHqDpMQhpQIyh1WLTb5OUFEKSugxNl+QUnJfnyuXdhVImaYqBu2b5iaXUKe6sX03O55Xy2EkHI21qoOhD0lBDILwA6YEdwF3f8csDzk792WQ+djFSvOqsJ6TPU8xl+SfNGNwBU02wYDxMqxgrk47H9rkYlN7otfLLX73yx79+5bV3nh4L7x8WTqv6FL8buRusnTJkOTAZZCOYBWJOvrJx/e7Kh8fE+etvsPIIFPAB+Wt+9Oe+5uH/+Yf8v1fjNfxtDFgsa3FTd2YwAcwy58uFX37c4GPlJ98Yl7PYkXcQ3pJYV6q0erZcg/ocRmtd5+rIOJl+Z7gMRtJzahFSsbqTs7Fk3RHJEmU6Gdf9hNgraTQN/jbJPrmkE1amgNq0iBGHx7UwLORvsw28Q/KFORo+PBaTiqDHJb0C+aOO8F70ZDjtLgnrdRMTsINlLXyOWPRkwcE39RWGKekpWNkmhbOYFdPoLXrzdSHbvEtyNURHPYjaejBdjDcWcnLJvvqx1Ih6dAA4d2A8EcyUAyY6ZMIzgOwZQ3fHrDNp9NHorTGalkB9yF5iuuT6B1tI06xAET0UKapV5whX8FjaHTIc7VKcjlIdD6myMWC2qHVdSocY0rEUC71joFbDIkBJ4EBOwdAc8p3V+Z7kK9RngFc6/5NJKqnFWxi3uxjxuSite0wnz8KBFR/sZSJQQGyXkG2TkUuCiSlSpApwjusWucZzxHVDbOsAliZTz7MpmW3E9m9G1mAJT1xd+8N/8wBbBXCN8NnxkHylqes+ezx50QP5wSyy+BxH33Qw1UFS7dEF4BKhTlHT5jiAoMOPygNUPwicIY01AXWyD9CZkSIdcQ4xuObBoorPcywtLAnsHnNSAk2shwl8ALIe0pRk4Z8XTLDDbmH+oHeyiUgCUyd4judJ6c4eAGbU8HhdJbUY0yQ97HNCEoAncFkKD3eFtRyLsaBD3Re1I8Ax9ZfpnjruJkhsgkKhxuBY8B29+Z1hNA+QbQY4K98zSdFneJUStRSO5ZOZ3s59WcPEchIjDl3W//Rv/M1/cQGl//lv/63f08Hl3OpgDGfv2pzV18b23LG58O6rJx4fH6Gv1G5szWh7orcVS08sl4QXGMPJZcFnpqTMwwpPEuNjU9vz2za5jsR1d760yecNXis83xo+Kj+9ZL55mjw+ZJZcyLlwOhXOq7OWBXnOmjZiY9IIc9ShOMzeJ6/7YB/O1oxbg9c6+f7a+NXzxqcvjTmd909nTutJQ7FpKJghJZvAbdu57YOtz3gtY8lOyUH1I8mxfk72Addt5/l6YyDWSB1TW1tPLCeZU69L4rIYJclMLQWDovVGZd4Htt5jQz+1ZdW2Ng4BoLbG67Zzu+1st5sealdzlyzHd7boNbO+nyMenGhoPA6m3gQg7Ncr+75Fat1kbDuQ2GlYymzXSimJx4ezCnAuUGK48/uaCvfCkhPLnzo0YoMxwOeEOahdFPyUnFzEBnh+faH2ztPTI8kRNR+jJFdyyqixsHLMVQiVERHR7YBNpQ312PD3IVPUXnf6vvN6vdHmEHPjtAa9NYcUz+7DVs5ZRrgB6B0HV2/S3da2czvAJDdyyWKpqMOj9c715ZXycGFdV9KUP5QnyQ7nUFrIaI1Wdw7WzIziUHeBSWNOyXaShvN+T3TrYpNtlTmG7rGsdDJPSpTIpxPTNcCv60mvfTrpkGw9EhMy+y7t/AjQYE79ZYSGGmDbN7a9snz1eG/AUloIYgCWBKosRUbfMkkP1kXvSgYJ5oBPw0ZILg4QKceWbg5eXl/Za5VEM4fxY5Ln0lvMLqS1BBXdFX1sTnewnLmHi9oPDudoaNwNXxbarfLadk4PF9a8Sg4aTXgKH4JsJv+aoTSpvfX7xixFMp5eyrAx5J/kiVZl8F9HZbTGGvfHRFICkl5D/jo6R87LSfdP0hl0sFoYwRw7TBonMp8cQbNvjfO6si6JX337ifV8hmGsiwDlZVmxnGNwNLKroZYX24SuVLIZW0I7wBr8zvjoQcHuwLbdSNkp6ypZVEr3ZmzCW0rM/VrZD7Y8AtMOZs+MBr5PPbM9gMgD/Lq+3BgkTpeHe7PPAfi4PkvgmMEm0/uZcS6kg2of78NC/iA5xwh5j/F6vZFXSYGXkFJJAjg4wgRyzvKVSwYeZpOI3TBDckg0PxzbvB8AU3QBa9frldo65XRSkxd3qAZbo5REWfLboEViWlPAwhhUh24ekoVx37ITZ1Stlb3Dy+dPzLqzXuT98/TuHUc4wtFcWpx1oocf3kUh0xidfdfnMkR3by2kEDRGUMTrvrNdNxmflnyvUxbMgxTMQHkdpOg47Aeg6VASUUhgjwFkhmno8Z3OMel9cH298qNvfqymLRYo2vQ5YzpjajBxV11JEdvd2s7ho2BGyNYPYMHv5+EhR5mjMWpjPS13WY7xxua0aNSPBl+bHh2IyVW3kou9NmskI/YuwK/JR8tzUWOf4fnzlfXxiWXJCuAIEwotdaNBr5HwNKH1QUmJOvcAIw/jXl2DhAe4Iu+vQ3b+8vnKYHB+kEH2yWWKLcgoGnYzsB5KwBmb8DDojqbYkzbCKckM3ZORcrACkmOL8/zlyutVIBitMVAdSG7owR14F2jTaSQrHKmqo1Z6nbTadHJ4x+iIna2FxERmtf7jB9rHj9TnTnnnd6HGYJCG2ArH59LTJnCJ6LmwjNkekdWNaRvGVVtx7yy2sXZjvw1+8atn/uTjM3MaP3laeTpl1nNSGMzUUgM3rOseSzjdK2MokTZPybCerzv7pxtffe0s734K6YTZAqPD47/Mb3/YGX/0B/zhtbPXwR4sj5y17d/3XQu1SPSzx0f+8NtX0mvnm28Sl2XFFwPXdt8sIbRcYKphUYeyJISt3WEMgOEDemaa+q4WqJb8L3swRJUkFg+U7tUYvsZ0sK5I9NkUL58GqXfaHkBe1Jhhx3I2gWWFe5iGWXcl9A4d/LoLg7nB9B/Ebjexo4YCUVoXPpdSJIFOSZkOObo6I10rDdUCkGwGYOrhlddV10F9iCcxlXsL2Zcdf0bPyIwBst1V1QKBCBaSBvLEaIODh6AwAn2+aX/6XvVoslRHBWnkcqRp6nr1w0h5zjuQcwyrh5qXYEwT123+8Owa3EEyonZ7LB3nAZYjhhOu2mpJLFWf/c1z0iW1L+kHtT+kxwLYLM5z4rOKqWNM5mixZFQvZwHCWdwbR4/sOszUs2MwxEziMEk3LdIkFdZwN8Ifz9zjWgdLL7yfRj3AuPCCCraxzhACOND7yHbIqWJZ1E33uHl8xyPYYwcsGNwTC/9YZkiV5QPkLu9XMy0UB9wXYFEJwaTw0b0Q4JwfptS6Vq038HKvZ/LL6lILRR83ok9QLxSgnsEc+v2yfwxLiLiuh+wetFA97ksmYfp+1ActcplBfoj+LofMLMV1v/dH977neM0fLFCnlltjHuxzC3Au2MBm+vnmzB8sRCqDNjoliA0TBdaMYx5Juvd9qp9lcl9MQbC+wmd4HNdvaiHg2fASyw0LS5r4dZfOcW/77r2J+jKiV5h30FPV1kM+mu4y3uPnHclwB5gGk7/5GwSUfuOm3DcbccA5I5DuYYnZjTky68OJx8cH5jS2DtuY1DbZamasZ4rrsBgug1n5+nTOS+KxuBIlOuwjsffBNiav3and2W+T12G87Erferc4Xz8sfDhBSWtQp6UBjfRbPdRxD5YJIztnM8o09iXH9q+RF8JAuHOrUJuAoVt3TjnxuBZ8TooLXJokkiuGurXG1ht7081qNrRtD9nNVvdoPohUMhnPtiGU9HbdlHrRB54LKReBFsk4ZWcNra+FBrVHXLSUl3pg5J6vNKE+RhzUgMFeO9tWud1CajWPn+cky3jJnNeL4kGj2Tu8JoR+6pCnd1rr3LaNtm+0vcpwEBi74jKHT9JIbHvl8eEsadghPYv0sEQKZsVCKa6iH5GZR+O9lKKGF31fYyhGMS2J7JnWOy/PL3Tg4fEBS85iKYAENRwK3hVDKSPXf20HosGNRocx74Nx7zIw3ttg3zfq6xXDOJ/OnC5nHcYpfFBiQzTjOjCMfdb70FsPyrRPWt1ja68Hv6TMsmjjJvln5/r8wul0IpcVPMsLKmdGXNNc/E7jVA0d9/ffQrs7gH2XAfesUyDcFGVz3FwjfIYAACAASURBVDbmrkNyeThTIpkrubyfSpaMZTisZYmo+BSNYQ8dudH2DUW9Ng7K2jwMKgGGNPTPr69c3j2R0MCf00KblSMmWxv0EzMtzNqRq0DE2TcZ1jM99NyTnFaSN9LUcJ+GkP7aKtfrjXSXh0KewdBDyW9jgKVCtsIsSTHDB91uSuJ4B2OPQz68QI4El9EH19uVy+WsIbIsOlgCgGi9y9S+SZYyRiTf9RENtNP2Xc1KEmhGNJOMwdYE0LVWKQbrukiqWmRkfUSKghh6bwWIGKLvpfbepOJhajjHPVFtzkFncHn3wKePn9ScWeJ0XijLGU+hHXcVYgtWjRo9GWDiKcxE7Q4wpoMmbNo+CSIY2oT2wXo6h3xMz+nREA1+2KzMOwAYGCVH8slActB5sNgQoHXQYHqrLMvCdt3w9Xz/WYesLnOkgMQgghhJqgEH+P4mbon+A218CYaRMfsMZtS8b+D7IS3UvEIbnbVECEMM1nMGgOU5gAzJnHVd7e5vdu8fzJDzbgwQaIuW9JbeGvlp+Mx36d/xfkB6fVHiBXRP032NzTvdvTHZp7bJ5MKsN0qRZEQb8fSDrdr/x9q79Wq2bedZT+u9jzG+b85ZVWutvbxxnATLSIkTmZhDDBGnEAkhJAQXueBnQPgJ5l8gcYe44AbBHSJCoERATAJ2LEAbG+XkeHt7771OVTXndxj90Lh4Wx+zkutVW1urqtZcc45vjD56b+1t70G3e7RGD3NLxsBsYKjBHH0+u8FkNvRedT1D52fvNXw64ow+iPICgnrINQfOGBVGkhn0TMRBa6Q1vS+TLTl6E32d+dy0Ru+3yr7fWLdNTLOkCHePFDnLkKZbLYNaB8RVDVdbk/NCH+14F1T8Rwc4pmQ24+1GrTuFhVTUkO17I5VXXxUxCHQOeshzolIV+EmCrrSjMaCOFgCOUVxr6WE78ZyfaZcr/nSSpAWdaTqjFCDgZuFh07COhhFZDb6nhX04lsexHvrIlDRZRfJ3e/PuxLff3GjvEssm2cj0CkwWaYQpJr949BFOKiHHiHDY0RN139XwJCB3liyWTU7G46nw9G7hj3/8QlnOlKcCO1xtYGdn8cJY1PRc642znfFTolhmjDskPX/vepd61ju70CArMpuhhjH1leWXn7j+7gvthxVbN0qvB5tMzCSxZOe+OplK0T5o/4mABRGXMtiC5YXl7SM//OUX/sXnL/jDrz/yN3535/f+4Jkfvv2G0zvjV94UTmmhmENS9HxfEil3vEMeizbAJs+sNScenxJ//PV3PP3t/5c/8/YLtl84xVT9AUaDP/tX+bf+yld8/T/8Nv/rjwf9qga5DcjbSayp6519H3hq5Lxibz/nb/29b3h6Kvzrf3Hns7JyWsWiA7DsWDcGC5YGixmUxnIq+K2z352RAlZqpmS4KhNiKw1vRi6DcduDLZspadCQ0W5ypzRJ2ksymf4OMVnqrjfsVBZq3bntkFbDySyKBtP6Tko4qj6OppkYuohJNhg2YvAH1joVsf26pWBmdU4PJ24XDf0ykkfuXecdRkiqYyWYmrkSgwKB4dNsV2lLA44muY4RQ81xSNFHVy2u9yaRy0LdVcfXmYYV+00phMcbx9/5mEbDmZT88JqbAxn5jI3471wyTSoKwIm01ImCmAABG0aL+2YBmln4/0ybAW3bFuwNjp/ZXUBl8OjVaKf4/u6MXoloBz279BqIQNQXknEHDcSM1uP9i2FDC6NlcJZgg0gBoo1/mCntFgG1NerJGFmR8kn3uxg5b7S9YkvGRmP1lWyDkQZ7bfQmZnnKWWm48eaXVJieW/rsUpKMISuLhB1Mdw3YBOY3Zmx8kkF170eQC0PAfxvzGRLBSQHWBZgxgsmccya5cXOPXi3q8ABwkusEk0Q0R12h03PKzwztleCk1LFUWBZZo1iS1DVhjCa5Py4ftJxSSPY4Bhd27JkCE92C5R9vzKzjxGgWqFSKyA9T8q9rjcCNYAr18CczF3Aajz7qnUh5DeApl1egqPf42tk/z+vLmaSiV7YKkdSnoeEcHEohM6LgMVD8aRu4i1ku1rV66HDB0n3xEb2L+sqS5BPoeVY4E7gVG0yg1wRmow4ekkCP6D1ciCyA+vSoU0h2BBl1l1wZm8nHwWz6Hn9974BSyYOUHW+ZOpBBH1C2E4/vHinLRh9O7cbeB7fmDN8o24JviWlcaa4kgnMqrGnw5iyw5nkf3Gqm9Uz1xN2da1Ws/e3uvOwyff3sPPjh08ZSqqbxpkWt4lsPukYjOsgsCZZ1sFjGR6FWpyPfjH0knmtjr4Pr3XhuHcgUG7xbJfvaFkkjsEExodspq1G+N6XBOdrUUnr1LWjupLIe6Oxw+bqM+y0mhpJZyThcniXFZDS5bZlTLiH30Msgt/xoHF201nywYnSA5pyiE0u02tn3nf1+p9c91qwSztwirWPbGObse+Ph4UTPAf4cGz3YkAnty/XG/X6H8O0ZRU2y9MIzvQHevn2jtL68hKGwNpqJouecWYqm52N0SiFYU9ooRFtXgSqPKLAillPrg8vlhcFg3U4ME8LsGLk7Ly7TQyKdSsCFH88gSl5FzrpooIZSqGqtMq297+z3G8mM03oKKaAkWiklxTomzTRTTImGC8jD/aAfe3e81/Bikv48h8G2GAiwt8715cqybizriVzEwEpLUbMRU+LWutIbotAo4UciUDciMAOJd3dokjPc953RBqM3zJ314cx22kQ7L8vRkC8hvSvB+hlDfmD0rsNuVaEt75URJYMzdf4lZxLQPPHxw0fSslDcKB1GavQw35OJeMHyRkoLvbWI8Y41VDs5onhlyqfJSveB90ymYZ7wURkDnj9ccCwm9xZeNAL7ppm4lUiqyJlski9ZyIXaaEzjPExpi0SDfXg8GLx890HmrahQGb1Twsi6Rgz7vd7x1kO+2sUmi3eleYDGGK1G7DnQMEaDtveYhMByWsP4WOXXiIN7TvqHax8iJj82YhRs4Zcw5YKTLTPNO0um7Xe2dcVy4sN3HzidTuTeyWvQw5mRsnGCBXrRA5AIZOOYEI3aGHulrBZ+U0oS6TFlafdG9hSJS69U+ukJAWKhaEuTuXIMUaPJndOviDIOcMldUyOln40AIpT28vBmPQ7gOfUcAVSUSN7oQw3h9IMgZMIcn1qbRYrnJCTaVLz2ruZk0d5GnucOYllGAQPBFIxpVLL0miBjahC8KbrY3KMYj+rGtLeUko5UOmIdCHjyAPvz0djbomGPWaK5BhieDA8D0RlHLGZYlzQoQG7VJ7NwkSm1R0GlBsHVvEYxOhhR5KtMHWMoqankaFTGAdIRLD7Q86lVIRc+HOvRvGSxCyYwmrL8oYKDHwzZ12ZqdK1Ld4Ho7i3Abz3nZGKbyNi5cb1U1k1DAehB/xdzw0YCKmZZaTHKET+AJ0lmVMCP1vAASs1U9BHFOzGVrbVKDjsIiSh4jeI85M4WZ/CUgaUskGVMJlPOMET3n2zNZD3WwkLKiae3j9yfX2i/8LlA5/heHqCUpwSLUb1q6py0L5VikFZSWeh1NnkBisXQpZkaGcbg3bsz7983Xp47n68LbdQwU5+MCI79wmPv8MCMZN596IFIlhmuz5UYtAhWMB+kBZ7ebpSfXfn47Z2HLXFyGLcbqRq8PVGQDMlboq6NXgdKaoLe5WVBG0pU6optX2slbcZGVlDEqORnyOcNe3rh8nXnzdPA15WcNeQQiLDgLBiLhoI47jewR8wewNqkI+DpEeeOecPzGdueePjizp/+5Tv/6j//i/z06yv/zz/8ht/9+x95+4OVL09vyF8Yp1WBK54SK4kaXkoeUUjDnVESNgZbAn+Ef/iP/4jH3/4d/tRfOrF8/he0VtKCdePhN/4j/t3vPvDyN/8//s5PnQ/3EaCDcTqdeOmNvd3xCg83Yzmf2d8+8Td+9J7TNviNf/lL0ttMlG2v4Jo5gxQshAEmNUBtV/Z6YzGxb3PRnnAms45BX2QK35aFpVZqb+H5IgPtkp2O3qlCYXTVzYMm1kZXQ/+wGKUM7t6YgM7QKwJ9CMAaaoIVfKAIbg1QgxExwJNr4OxGG1nyHBu4DZbs2Jb5eLtpOGkFd7GYUhF4EtuYBk+zSUb2E/J0id7QYwyQ4jxztJfl2WQHWys2vZyjBlkF+ursD5PzFACVa8hlwcKU/EdyvQPMjVpcu7hA38OHxiSbH2GUnEOpwayP1bkDAn8wDRzHGIe0jQD2SU73pv0sbkxKUgaUpCHW3nbtIQToEQzp0X1WyAejdzLDUzC9sXZ8TU4WyZsCiiwGfXvrEt9lZ4mhVB+Q5KgcXmghcx4Dd/lXKXEzk0isYe/QPdFqJRWDDgsZWpyhDqN5rJFMbZLTTWb1gWJYOurHmPcIGBoCzEuw6+guw29TTYwl1vn5o+bBxyH1Hj6CQCDPJYZUMJ2QX7p6ujstMAU/2FAJO4ZqEL1fMP0P2ZcP+uAwmk+pICuLGJwvwUCO/0z+VbHGJoA7zd7mOXB4mnHUb5NNRNT6KYIVVC91LCuxtk1vzGQsnsKHU983ByvKnfBPzQHsqZY7jNIns2zKv5JYsI6HgtNkk+BGMUl2s8t3iCGjcc96diXN9yRkZsfv/WBFWeyVMrEHwmPQA9x+rXuj3oK4PwFRx9/1WOhz3c73+mDaxRk7hh/WESo1tZ9o0KOzY9bR39ev7x1QejyfNLUJlC+7sT49sW0bNoy9OreRuO/Q0oKnhbIUrGgamDyTvXPaVtIKm8FDLiQ6H3a41czohdYV6Xwbxr7DbVeaXEmNX3xz5rMnUbK7K3EgRRynJGBCBzFt8tOUGu/4KNwGmmR049tr4/nWuI+Ej8S+V9wyT2fjcTkdG1zJkSJR0LTXneGZ51oZXVOwki0M7VLQ72JGH5Ckd8VG3upO8871LjAh5Ry+JUZZMqc1s2ZYc2Zd5CvUeqe2JlnW1MyihdVbJaHpgCUwL3Sg7ju325X7/cYIrXNOS6CiqAGP6MXaqvwvMFbSEbs+vDNqZ687t9ud231n+uV072omQ5tbW2VZzzy8eSTlREoLOWfWmSpgBDvBsJxCspBRoowmfkRzM9zDRFqT9LwsWCli8lwv0Eck5zlDWbDBLBHDpo6hxR8yujGk63cXE0Vl82yVZVA8hryO6l7ZbxfA2M4P5HWBoiZtgokWDA5JVjSBSjkrJj105Amj9V2H91AiSso5zJv1w1tr3K5X0pJZN5mr5lxY4uDeLEHEt/oYZJKa6JjgTckQhGY8GDrdNQ3bW6e1wagV743Hx0d5TRmsy8qyaIuY6VOjS0qi9SED+z6GdNMDAZdxz2YMJohSn7M8RD5++57q8GY7hWxSjUdll78VmSz0glZ3aa1NBYakm4TmWKfXkWySVLANEjmMiF8uH7nvd8nyTJKwqbl3k9XnPppoq67oUH1iZ1KYk81J0xANvOv5HfGc6RWkW0OyZsNJ4fw6mtaUlGAyIe2t03ul953exzHBIQBS93F4UdW90nbH2mBYZ10Xlk2So9Yb1vROtJC8paGzWwYcAR5+shYk9RkC4IRiqwBNiv0G4/HhzPtvPyhR0jbquOO7pHVlkacW0dxLqjWbQYFG5iPkRPLrGkMMqGkAP4bkqW04tTXOkfqlA1ZT5RgSzRr4dWLmHmyeYCwR9dp8V9EhO8Eg/WxNlOTVBGUtx/Ua8l2aP1OSUNfzqZoUywQ7RUF68FtirTMvUAc22p80JdSBLvPLETZJStQBPWtNdT/xiDCBxBbT5APhDobICFAR45XCHICl2TQOR+Aj5ZAHj5jO42pCPQXQEcUGB/0biAZuJqlYTBbJiXvt5CTwr/dI8skp7DHEBhjTx4MoFAPM691ZtxxFVlDg4wmm2O8dTX49WLkjmAkyCeVVVgTQUVz0Oq/fX4ccGuEi7CXhrtCAMSqdaHSy9iYricvlhS++fPc6cWR8AnCq6dNwOK6XYONZEjiU1dxain3QQu4X0kUxAURzr3tlPZ/1jsQizxYgZXptBAX7RH3AxCYC9MuaXFqcf8kS+bSIDZzkA/jw9pFvL89cr3fS0wkLjyq1cOkIJWi7vENSludeby6vKBOwk2OP9sk4djUQaWi9P2wL775YeP/thfPbjaV0cvjUSEajN7Tj1Hh/yyywgzncu1hWec14xJ6ag3VX4EaslIfTyhdfPvKzn3zkuw933qxiVdyulce9s5wWlnUoNatlWOQnl4vk+NeXO96dfndGb+TNOD8tnE/LEROdwnQ573D+cuP68yu3ny+UX1zxJmN1xo4oig3PS2y6gFfwHUeR2uQVfIe0Yf5FsDIaY9lZniqf/8KdP/srjb/482e+ff/MH3575Xd/9MKfOD3z5/MD6bPEsk52uIY1NRspDQaJ1FXrZjMaBifnPnZ+73f/MUtZ+KV/7Ux6+jUNHPICvOMH/+Zf5d/58F/x8rd/yv/9VaV36EMgzPbwgF+eabWxpzsd43x+4AXjf/q773nz8Myv/dojb99kvPQYWoRHEJLT25BkuVsjr5ncCr0bZYsaOA8oUPoiH6EhYK/Uhb2LSVJDXlbTUOOfHRuZzZ3imVt3hjmVJc7LQu5AG1xaC5xZbM/EZJlnXUdSUzVM77RkUxllhHWt9xwsM3kTSAbnmSU727axt453oyw6BdJw6oTWHSyPV8g82AweoHcbQ4B7nBnTW6fVYNABr1U8EH502qwVQFDrPewD0lG/EwOUOew1j0S0+BlqVvNrg5lS7FUBZHwiXZ51jmS4n3zPozlXbTsCLO/Auq70JtN3yfgK1nswYziUD57Cr8cTdW9Rtyp+XQAlApiiXrAJvkU9kJL6KQ2NLGRa+qeS0DRMGUN7Vm3qLbL7oczornouSC86Uzw8nbLqthxm+B7DGdYSAF465N2jD2oPj79k1N4OZUJuMnxnDDGO4lZO4MUCcAzUUecbQJFP16cMrx4H0IQYRgzs5lnoAd4ztKacGHq6wCqPNTb5lNNEX3+c8jCdK6OP2K8NeRJqQJqSyBQCbJPeC47HcqyHwWTXaR3HaoszFTzgrx4y4xyg3QwOwQSmzJRMmWg7dN3DNNddgIQ2iGHW6zszz9LZXye3WOshbzaBXE4ETvm8J6+sPzex2VJKAiGBEmDfGE3Xk+KBOoeVwehTiqehklIKDdf8Re99km/Z9FRLSWBPChDZZ/0bT3zW7zOV0Tx8paMf1vISMG5ux7rhAAsnaUI/x0zplt/nr+8dUHq5GyWMkdOy8PD2TEortUGvzq3C3RfIhbKK6XI4vafBWpxzypzWhC2xkXS43BKX4bShuOM24FbhpcLHXX4kT+vgzSnx9kl6W8wZfaWUFUsN4Z/SOA9Cw2hJCwvjXjPmiUvbAXiuO5db4jYMH2LzDDOeHlbePgStrxkg066yrLjJBPZedXj0rgmxpaFkLJdfyggamsB557Y3Sfj2yv12Z+8DWbnIfyeXxLpm1pIo2ShTz+rarGvv8lExASFDMCmZpEI75Cm4KWK3d67XK/vtig/R7XMk5YgOu2AlUX2wIK8Pgh2khhz9rDa43u4ye+4NaaLFzGoxhaTrnqznEw+Pb4JtkEIOIqQ65yKZQtJLIvNldO0WpsQmc0o1iq8Ga6kIwLnfdm73u5hAS3hXlHjpYrM/Eq3iRXQf0p1PWmZMKEH7xJiNYDJal/dCrTtY+MmEJMxiw5CZsoC3CQ7gkq0Ml+QDJOeS15HMpX3M9DTpWsdQYXC7XsFgXeUjVZZPTDFToqwLbd9JSGpXqxqG/Ali7qDnhczLswnZ3+tdhuCt0nvl7cMjp8eTGhNLSuXJWdNNU8qclzjEkpKmnEgcUb+nA8nsOKwmjVRIJjy/f2G/7jyeTizLKzNoxhGnvFBQ43e73xVtLCiLGhI2S9EoJgFCKd6R4Q6pY03MhVYbz88v2BomgXRGd4F9BPgwlLKQTetV4FYOTX14Po0ATuH4elAzRBzW15cLedPINudCyQKVM68FmnnorN3xVmlt18SEaIDjn1P7vKyrvCiGkb3SEHX78eEUiWoIgM+hLw9/lTFGpBVOyz8Og/oJxs5iZjjR9AoUTO6UbWNZM1//4dfYskpOM5wURu85n7CeDpDBdJIzfddSHOCzMJA8yQNwUDE4pVBeG4xG2SK1CTXTul9E86ziwP0VADmmwPMPHiyYAB5nYSb/KfnV5aXw/NW3lG0LaWCwY2aRamrKJu8KDy+CEkzD6ZUUoJsAkxR17jSc1WSudRn3Jnv1Xjka5KNIDBBuDCZCNGKPTgyIRB8Hhv1TDErX3j6IaOgxKGWbPCEme8qjYMgRhCBPDQeT35r1WKPhG6CJqhq37o4LoWXJRc1jStTRJYXxeM4mwGr6STEZmiZ5snkOfy1/bW402dC67H6ADjlPmXDHvSty26dkzo41oJQ/ya4m0DgOFp7295xksprS9EXSosi5MzxFqpnqj+W0st/vMUkN7xGC+Rd72kxVEdG3h0eEmAPDA5xISk+ZkswcXiytBxfaMttp5fJy17o2pzVYlhzUe4LWb8Fa0Pee1y9gyo49WMSCRLIFWtPzi/3a3NmWQl6c28ebBgWjq1pJkNaM5znV1VQ1LDsCbHUUXR4srblCXRKHkbSfzeL13ecbl49XLh86pyVRfJCy9oARTaIawDhbR7A7k0zvSSmGb4NpBt8b5KIzpuTM6FqLn3/xwOXjzu2lY145bWDduF8bvS/YQKEEdSc9rBrmuWFWZDqenHVN1MsdS8a6rizbQlqz2Cqr2MsshdN5wfiOy4+vLA+J7V0Jr7sCdodxD8DoUSA+jo2Cjxc8veEw5GaBciYuDqPCaJzf3fjhL9359T/3JT/76gPf/M6P+Xs/ec/felw5Pzb+ubLw9q2qVg8fmZSMPOR50xFDAw8Ggyfebs439cKPfvcPOH32f/KDX38Dp18R+Inh53+WX/7L/yF/5fm/4fJ3v+L33nc+DJOHUS5s5yful4/s9zvFYSR4eDjxfnzG//h3vmNbC7/65wpPT1nrxGQKnHAYmRzvdC6AFXwz9ttdA5usWmLkHMl1g3VAb4laILfMaJCqk2OAda+iGnUX8Ik7JzMai+rNGgzlYBFYb/Q+yBmyLaogjj1E+1KiH3v0HLIcTn+WxWSK0IPS0yTdQk4sLqPkPbyo1mxg+2sKqYtp3FPMvdTxq4E1giGsfSsfzd9k6ThtSBaUdGDEmRdDOhtKLmRVml6cwoc8be6BoO419iCbeOcntAR3MaFxDvBGg3bVMVNeLFhs7o3HT4mfIz+sWuX/5N2PZlxnaDq8ZXy8gguOsS6r6vk+vZv8k2vV9ZQkwMwshfRIZ6Rm8/JL6n3Qk9isTibcRFhsSn7089sYAaZPZiRR2+bonfpRG007BYwYqkQ4UAwSZjc50iBZo1axzEGf00GG6XHOu73WNu46r1LcwskQS0hlopJZA24TbBxgwuszsgCt0kSojoeq9ZVMI6+OB+vNWUw9HcFKx+e5GYBDyL8IUOlTICIf6ylASPw451Tui8faY2CQk1iTc/+f127zzkWNJuuH8cr+dg2/iWCfkoqGruH/1KNmbiFN7zYl7kwV6yeeYQpUMjWvuieT/TY0ebUUvpshJTtAGTNG0X1QrTMHmcb0ttTZrmedZtUQoDHG0atZXN9IYkVbV78WVKhjaMTgYJ2Bh2zyn2QtvbLkdT2YvfZ6eimP5zm/zuLstRjMqbbx4zN8X7++d0Dp+lL57OlM3hJw5o5x3zvXZvSR8LzIBygvIasS/3rNznlbSOysKfOwqlDfu3HtnUvLXO6dvRu7G/vIXG6KA16SgKSnbeGUwSwmN0l+AqSQkWn1Cq1MkbCDJpwVeQq0uvNcG70azz7oI9ObYQy2JbGcEo8nJY5UNzzn2VIxbHDf5e3gpdCb2owcDX4fXVTxru00ZwFL9ypW0r01rrdKrT08khbcEttWWIuxFQFlJRemofNe+0EnzMGAkZt8gTTYloWmwC561cK/7IN6v1N3TevzIoNvm4CLJ9KyHAdMNsVhljAXlTTDqf3Oc8RV9ypjVR+dUeWd5DnRayMPZ103Tmd5pCxlUdWaZwMxX1KX2do0TctFUxxyTJ1DB+qv21xelJB0u9/Z73fRN8Mrp6SM5xSpbVHUhjeKT0O4oY2+5DC9PDZYNWXmkm7UMfQzrleBVcsJYjOLwQPzJY2eB/GhJPPqPkL5NZtipcSNMK100/N7BdOM2+1Gx1nXk/yL1i3OEsNKIZ9WyUf3KspvoOEjzpfRe0j9/Cg4siVIJl+vHfnytMrTaeP8cCZseY/nTejbtV3BErEzI5k2x6qI8ZQQK6HLr2kwtHHPCYzDy+XKy+XGsq1sW5H5chjJJ8S46w4k3RvdxKIQ1T6k5XZHXkaJSYoGVLCMQR33iEY1vnn/UZRXxGzI6OMktJGWJH19ztH8HtTTWfBFkdX7wbYZsfIycyoc9xnjlAtlWfTE3YL5qMNZxvjijYze6UNSDtGNlXhCNI7yGpLcsA9Epe+dlAvbIuPa2fTmkklF8sdJC1/8dWrhPhTyYzJyVbSuDuRkaapMFKNcMlYbD49nri8C/k5vHqD2g5ZLNIQeY1QdvK8TPDeHjhhLRU2A0jsEKPZ4r7IlusngPufEWiTdHXEQKwEDmGyro7SJpvQT9tvweeBqLaQAHEdMhHoUuKkkLi/PvPnyF473/mAEmaZOk2Gs97FEMo8fjb2A/DiMQz4RlbL2vygoWw9PMdeELvwbj6Jgfg6CSTf940Z3rEQBHkVN6/0oAEvSHjg/c55FFMHUC5ZBOorvdEjr5jPvASr3oOrDK1tkjE4JsHg4mFcSsC0L0rCLoZfMw0A/1l96fW4pC4w+mIBASh6swQD8YupnTF8Ji2eXAiA13ML/qHVYw6jTXunq2aYBaBRJn6wTgi2V0qI9cVQ1TyVTikDjHSjRNJ2fztwvlXurSnZcQkY41ORpqmevxaLLq45oT7xr0DXDBZjDMcRIKwAAIABJREFUEYKVZmr0F8twTtxf7kp1XCMlML7vZDQJtNSwS8k3doCVQWpnxprr/A+PHpvFIwfouzwUri8Xnto7XZ8NyEhyMCKkgcToKfbJANa64VlAUGviRWQzhotBZpZJSeCPufOQV959fuabn+9c3p1Yl6idssA00OcYGN26/BnNca/BkBBzvHsjswpsQwV+4A60qkZy2xY+++LMH//4I7s7D+vK9mZhK4ihtBl5GDYEYJQHyehT4bgHMqBXQ7ucFpaTaqYU/ifmd8aygHW2z5/oz++5f3ulPD3CqJEOVdX0ecOyg29aisnwPtfCFoyzjLNCfhfvXYOlkR8Hb39p8Cv3xl96/pP8/OsX/s7f+44f/cF7vnhTeNpW8umBBy07vCRN2dOIAYT2reZ6XgkxeN48wfPLCz/63/8B/9LTW86/eiLlPwUo5GL5wa/x5//ye54//nc8//6V+0fnGizKZSnY6YHr8wv73sj5zpqM9fTIjy87f/3/+JrH8o5f+TMnTqeCFTGeDROIWLRuimuNuBtumf3WaVcnnSWl72nRwCh1rAxSQ15tKZPzwK0zmtIN++iMzKQJQi6sFEbfaW6MagwTo+S0GqUO7j0SB1NSs+PgSX6FEIOHDMPDb8XlNSRsQ9L6bmInl5GoPkEo3fOyytOpV12rpJ06n9JQs9pS7OU+pWWqoxouthbz+wUwaChhNk9fO2MOipOJuT/6AE/kZWPUfjBddA6KHTuHHx5U31cRmR+1rifHe9S6MYzCpmF2gP5MhiW8Umu0xnuwT3UOi91og1Cc6PzBTD6Lsc8wz/Coy3pSeu6Y+3dYJEwmJe7ygpoDqhFDv2SR8hyqBmLY59r7J1FgBk8MIlinA2OwJLFMW8iSsxUsiXXVOrgFM94NCzuGY+BhjuVI2nNJj8w0BBpjRAS8zqk2/AhbMs+zoCFmvAcneA7NBECZbCpI5Khh52nzyTRN/oopHf3FZLykUEf0ga691TDy92OoyDwyZ03iqrNl3mzHcNC7/KNSjoTZeebOTxEAhaVCFM/as8eQfxgayA8mhqPPZ1P+Gd9qHEiKkTzJlyk50KLfhdw82NYc4K3WVqyv6PMm8KrBmx1rvg/VebIhEdFhwjPgr7839YjyakpxfUPpjohR5PHeTu+rbIfJB0dinXP82YfTWljPJP8nB7umfsGJemPEPhiHeWIy7yZ+IRBV6YwCO53pzxaHZXRtmdfaRbWYBQNtQqvf36/vHVD6/PM3rGumuXGtcO+Jfc+0UWAt2JrjgXZwJZ+clsznG6wlU72zlAWns4/BrSdemnFtxsdqXKpRh9F7JY3O42I8nRIPi8z0UjJOWSya7k0TUIMaerecM6e1sGTYe6VVp/rA0i5wZ5/+TtDTyvNtp5jx5rzyuGW8JDpw2WNjtq44XqDtkttMUqBoIbEZZumCBykaBT30271x653r3tjvjdZcjbwNSnLWdeG8Bn0yiUWkRRYJPIFoGrxSKU0T3hKIrjOorVObJE6X6xWa2Aq5LGKJOMefc7AdCBPdbSm4zZQy0ezrvXK/39jrXclGQ0yb1ir4kEHpPmR0uxbKtpLywrYsMlozI5eVYgbJce86hBOBGpeg37oaVuIc84jfNOe0rlQbXK4X7remw3bqz12U5YncyggtbCTjxRTIGCa/E4RxFfMjNh7HaLVzfXnhermwlphwBqBhIQnzKa30HOh7EEhNaSODpmJ9yDSw75U+OtWczSCXVaBDpEDd71VMgFWmy2VbyUVI+rIspKWAGy8vL2RB+iFRNhVeXV4zw4fAs0CnLSWubed+i5SvVjk/nJUWlxLLomj7kmfSyNwUtdjkE6Dnstcqn6bR2XtMuprYcnV0eSuZ5CZ763x4eSaVlWWL1KNgJ7o75CIgalRY5EVh7vQWqVkBjuDB9ogFP42aR6ss8U5YMT589xFvnWVZMSvkw3JRJ9P0osnR/LbWjwSH6aWTAtycRVYJOqoH11EMtMHL7c5yOrOu2zGlKIsK0ZES2SX7mYwmD4BqTi2ESkkKkOPeWs7srdK6/FbutztrUvKGil1neKSjNMe8U1IJICLRAmSYZ0uH8BWIxg41qrM4SbYw6IwE65b44x+/J5UcbKqhBBhewYtBADB5Ah+E5t8FLHowfawL7JrArUUiHa/fZ1s0+ZvMoKSRFh4yGAFs/gogJv2c2YTbfN89aN6E2X0UaK01Ss6S3nWnnE9RfKQoPAMISXq6CTumXFEqBQAEe++Hn8ykm3sUDpNRBYZ1KNui3dc9/HhCahHFeQruvWjds6BWQWAJvKtDyCTR3aPRyCMFEy6o50MNW8rRdHiAR0lpRA7h8yB5FITF1ZhpSQ5e9ZelsD2cApuJiZypgOou8+w2uszzbY3hRYAsITPIKUcijTGZXcm0p6acVQCanrSSRjoz9Wf668jvxxi9hal2pGbGIMCHQ9YabuHDsYbvXE5ZkiaXT9Kc9jLEGBqucA7ViYmcjfN24v7xA23fKacNGJQi7zL9PE0wRf55pajvvZLHGvfUJSmLlS3JhR8DLA8Oe8qFvEjOu5xOMkyHYBBmyQB9FoYxoOjBKprv9XxPDO3LHsa6ZodfZEdm66fzmeuHZ24vV07vHtUEZeTlEdMmm6bCHkOOZlR3cu3heeXs3sl0pgXj6B1bgDDnrt55+3bl/VfPfPxYefv5wn242EE42ZG3XQBL5iE/ib+j96iRpkSxYikm/cNIbTBypu5OWeHtm5Vvt8T758ayoNTMYox+xXyTXHuTxw0ew6XxGsqhzy1PS4bjNOjOGEYqev+837FlQIP1n9m4/sGd55++5+0vOn0LtkMaOC1ApRVLT7GXNcw7I5+wccYyaigNSCvGF3hpuC0sny/8oMOv1c533974ydc3/v43F37772e+eDIeHzPpC+dkSzQFHe87eD4CEtKI98f0mU6e8MfGV8/f8aPf+hH/wtOZ9CfPeP4SbIWycfrTv8Ff+Le/5puXv8nLP2r85FKJNpV1k9/p7XLB73AzMSK3hyf+0Uvnr//tj/z7j86f+tMnHrISsATQaH/PGMkH3SpbsKcyZ+6XnX6XFK63Nv2ssSQwp40BJsmoudZNGZKnaUssmN9ktJ6NNIyyxCCtSy2wdVhIFAZ7q6SykSyYnhFJ7sPi9Wl4DFO7BywRDAclhKWooh0szI1N0lySs1qm9iGmZWryWyslaibVmx7nlEXDOJkEcVyEx6hq8GLpYJS2kORgn8paZE7dPM6rkmgNhu9RFwFjpsWqoTd/ZeTqtwHYJIFkx+CHAJeN1/M4GDW9h2fOAWhYnFcphlnQRyUh2T322ldMfz7VV3FMhjHxUhI5WFljTJNtnX1S4nvs22FCPM8b0oFGDCZzz0PGFvIpHBsC3ASCCYxzNylABqQccHfSPtt9el92+SXF0z9qYZ/hQPEZh9oly43Uq+Rve8dGEwubjIQJAysJjQ/jc0Sd4eF3OaKOPpjrQDJ5qMkbSc+kdSkaZvBOMoei/W0O3IYPMUFb15ofYuZamkhSfLs0a6jEQXt1F7vXFJCVTJ5JZSlMTy/HZ1bSXHR6Bv4Jc8k00BNAmI/zCwsZtIsVJeAxxdeqvTW3w7JApFkPA/J8SDxHbweoeIQQ8U/VhTnuU/iCyYN3Cv+kctDgtR8yTnOngPa0FGEmzFo3RbhHyLft1fg66PS61rjNMsnuejYIQEo5sddOifdQSFhI7KKGV+1I7B16J92i3ouBmQcAR/Qkw3sMoOIdCylq4rW+7SmeUQRSfJ+/vn9T7lJ46bB3uN+dvWVg00Gbp/u4NtbzklnXxLYkHooYPiP0z7cGt93Yx8JLT+wDbsN5qUKqH8vg9ABvN3gshZSE5qacJc+ygXPSARHFw1IS5y2TDF5qp1aZd43YCPfmWFrpPfO8N25DiRUP54UtZ/pQMWOW2XJizZog9u7s02uCSUfW4VVmgRgN5JISlqH2zvO+c9kjvnWXkXDOmqAuGdZtMrn0wpUiZpCot5rG+JwqwHHgKCkJcKPT6aNx2xu1du73G7XJlDon0fUFGCRSWfDwCikpif1jFpHjxlYKo1VutXK/d/aqHX80pbu1USVfs8J+b5SSWYvkjTkvnLaVkjOLBZMq7pdP2VjX8QcREzxE5xwmxL9aYyHTfPCwiMb88vHCbb9HikUirWq6zXKg35JC5Yj+nNRmxyVDcMd7p449NkMZPrq52D+1cXl5oV4Va54fTtqEh7NlsXVgvpdFFNeUWILpMkJ+UShxqCki3B4Sp17gelHDhtNqxyNGe3QBZmUV+6RkJa2VZQk6aqftDR8yoDySe4bL4NtngyJmjBLNjL1WeXQ1h1Y5LwuPJ5m9r9tGXlfKtmLDQzmiXTGFh9AYHU8h6QkjwNbb63QrNOyMSWE27ved55cLaSk8nTbWYPJosKBGrbbGaO3wepkHUnyRqJ+gabAh7qhNucmAqoJzWVY+Pl+4vFzZFsXa5yV8ZGw2uDkkC2E66WpMS1mCNv7KUhtBZx1I1qNEt1kYGtfbTi6FdRFLYwwxiWbi0uCVwmrJ8BbeQkPeDEmagGi67ZCdjABC9trZr1d6b6znk1gfwcZwtD5TSmSXVDOXEm/QLBCl9bac8fAAE7CoQ8p6j+vUe/zweOJ+u3G93Cib1qzlRFkUc55KEfMngFiiADfLAeBqneizNiybipApUR2aGLWYJK0pcdpWgZ4WptcuDtXeZ8QsQUm2Y3+bOvKUDkHZAYLEb5gJbcMHZd14/vhB7MWUgjUoEEMpKrqfw16Ba6IglfY9kUjUABoVbDCX4SzcXwuC7s5WJvg/00wmIBZyrbjvEyQhFWYypPDbAOhiMCGfCBHj1dR/0qhMEwi1m0cx52gPbG1EOl/cu4DExgSj7HVi5sjjp4eXyeEFFACs23wh9A7kAFqZnyMmnY6pWE2SmbhFJHCs99YlFzVLwQoYUehZpPnoa2ttASpE8mYf4I2cijx/Qr7Wk5PzCB+2ycOLJi0nPK7XNBbXRBlIKbMtK7hxfbmybQu9EXJpCwlbju6GABdjLzclniace93pBksOaZrH7YgzQN9Lz6OcNm7PN5TAF/c+Gi+GHWfCHKUckfaxl4CaC6WH9XjyAtpyVsFrKUWDeialF64vFx7fPFIwyVKS1piFaankDolBiYJ8qGEiM/1mIFiQJcu3y2M/NMno15x5elv46bc36rs3JHZGSA2Gg+VFkosmuVMqFs1jeOu0innBE2Iw0XG6zmNCiuiG98G6Fd7+wonnf/TMy63FGVqouzFaIr11TinTinO97ZxywHAJLBW6V7Hu3BTkMBseu9OHwOA+GskzjFhvn+3cfnZj3wrbl1kJxiO8ksaAvjO2VSAfg5E+4uOGJYd+5phcgUAl/1xP0zLbZ5Uvf/nCr3+88ZOfP/PN//IP+Ol3F37r91fePRb+3PmtBksN7DRINdF4ZZourjO7hpcIOJut8Oj84599xeP//H/x5/+DN/jnv0HKb8BWbH3k3a/+G/wr77/mu9vv8L/9xPj2zsFy3x4eJfe+7LRUSQlWg9PTE7//8ZmH33rPv7ckfviLJ8kOLVqb5FhB6X0IbCrL3GcL9TagQlocry77Ax+kJKnlIGwBTL6Llow8OltGthcJNe5h55DGwNZBqQACjEYwQwznpV7wYmRbovHL4INuMQIanTEy3WrsVX6AGuZiHzRTXZhd50F3I/m0u8jyR+l31pKgN4bNJjD85CK9VMCUBpfZ9e65EBi9x8On5//BWCJArB5ojNimA8sD5d+Wgy1ektF6haQ0NQt58TwdBKwL7FFzqjHK7MzmuERm/+OfAMWV3MsB2LgPyBpiupvqnyWpLs1iR01ZsUCIuIpgcExZfJoDki4Auu4t7r8a9gkS6F0hagQld6Y0lRn5qA+mVO+oIbXwNPxIOXpCYhAOVpwcoJvOr528hDQt1ogGcPn1e5lhWZ6sOGyspF5JtdHSwG/XYHDJMqF2J43GUlKci2G9EEmpbfQY4oudmyzF/mOEwV7cP8hFXk6kuIfuUYFEOq6QuBj2D5aSOXQXs0SK2bv27xgeOqSouYuFBMzDiziAa9rAm0yxx5SWzerDp6wqWqOc0GVmeSqJ+kYcn+r/PJQMxFDMXSEm4cWprzF5eiWj96a105tq3Vk+mYb6s17Kn/QMqv0dj2eB6XNpkGla/z5h407qeu70IYZiCRn5ZC1pShX9LjGujvcCaKMJ3GFK3ggmZpKSxBdKAGQ5B3I6ghU1QeM49493IICqV3YYAZC9hnHNIeDk68kcnSNde0wLArK8ACPs5vv6lX/zN3/ze/2G//1//Z//5r0610uijhVfF85vFt6dM4+bcVqMbck8bguPW2Ipmmrse+dD33m+ZT7cM/dWuPTM84Dnm/N8S9zqYPHGu9Pg3cl5ezJOJbNkyGkhp4U1r3jWdG240eIhnFh581AYo3Gtjb374elgWTKw+4C0Lbz/cOf51nnYTjyeFpYFcKMOgRlrXnjYTMyLPrg3497VtHrSAVYxOoabvmbGOpolrrvS0K6tcbnttBrmsFmg15uHlcfzRskJxlA6VtJBgxt7nxNSTVlbb2EIF94vCFjq4clUm3O73Nj3K4NBWU6S5sTkIeVCXhdyyWylsG0y485Zf+c427rS951Lq1x2Rdy3SN+qo8k/KWQWba9iM22L/GTWlVwWttMDJQw4KQmmKRnBlMIO8GCi74cRLwGYBbBVW+fj5aKmMxklL/oeuVDKSskLhoyAU4biHOV5SmEAaK9pTr1JPjlap/VOq5X9euf6cuF2v7EsC8vpTC5Ct9dFn010NMOS0r08JdIS+uDY5D1kZ5jhq0AOErT7ndGqWHS1stemxL3bXQyaHMVZMKXASKWIcXe7HvIT751SNIG43+90F+BjLtbIlJ31LuDvvjdS30nJOZ82pVHlTCoL2/ksUDMrMW/kKNhCtjXyBDBDaxzTtJwF5qal0CwAtaUwhnO93LCUeXh4ZF0XrWvUHJ2KDMjvo0cRy/HfmUfEZ0pBmY2DJtaElULyYEIBo2Rab3zz828EMGZt8EuAPTNKPJcoCsIrqfYeOv3BWsqxactEWgCC0jdGsG4s7mfj2hvLsrGULRIp0qs2GsmUUk4BXOkZ1FaZ3j/aF+I4ns8qGszLfud+vyllKRuPDw8qSplfLvAhmZLFMMk5KFkabSMkLWEI7yPSnWAyvpT2oYMUjDePD/zkxz+lNtG7lYaX2U4nJS+u6yfAgAYIlkqYT6eQJjax/IQnKPEEJjP6oC8T/g3n7RSHc8xwx7w3MZWNImQ2yFM2YBYy0Vmh+CdFeTQULdLsTueVr3/+LevpzPl8giFGWk75AIW1iPygfDoj1mSOyTgxUSOm1H48C+b0OOu6W+0s5y2ktNqT06T/x4ZmJhboIQ8rBcuFNPxI+7OQxGHzZ0tmIEP5+feD5+cX1lU/L5kFuGNx5sx76ixLCVo8AmJ600S8FNrwMKKNQmSuEYO9N/ZWue13rt995Bd++APWpbCUhSWAlxysQ0v5MHG2Sc2O/0leKi+6OflWIkoAkfbKCm29c7/veEqHXDpnAYTdR8i0y+ETlZKRlyljmyCbzZuEPJv051lom8mfzt25Xm8MN56e3hxr38NjYU7lY3UhBpK8ACW37cE8DG8JYos5VmS8s8gTJKfMy4eXSA3T9z6CMT7ZDyaL0EBnQpfJ6wGixplofYQ0Zf5sw8jhOdipdedy3dkezyy5hNwkM1LGQ+JFMAByWQXgpSz2ss3ylOMsyTkH4FolwZ3PrRl56Xz10xfK4yMPZzDvpJFpM6trJNqweM085AdKLB0pQQoQwZP+n+bZICPW7NNHErYtsb/cuVwH65IOL73ujVKU/GTN6SYD8bxMYDQkVM2RRVwkbY0pIRxioVgJpq3jdZAf1MjUD85yNsiOjzspwG33LopgDmnLuEYl2AMQrK9rYTaoAZphjZTvLHQei/Px44U/+KML3+7OfU98+VR4+87ZtrOA3eH4yBEBTTC64/oJHm1SY9JL5Wc//cjjpfL2VxbIX0ZzXEjlxOO7d7zZf8L7n7/n23uiBhiaDNa1MHpl3PdXFnQp2OkNX317ZTzf+eGXmVORYF7yrblXx/vifjR1yQbkHN6PibRmAWCuAJXZEDcG1V9Zu57HARRXjGKLAOic1fy6mHcToD/evWh6e5sDvemhmtWCpzl0HsEk7vQhIDnFGrXj7Iw9wOJ5joElgb0lvFT3AcuySLLD4EjJsBi8oGFGizCNtA6yD6a/p6W5x/jBypkyJnyoZiwCEdXDTGRAe1X3FoCLap4S73ZMSQ7ICAhvIoEZc+AGKDTHJqvIX2uf2P98TCPvrn3QXEydSPJ1wnQ7vXrIzGvUAI+QAI6owcPfMfqBFL/XnjP32kn4Us82fMrqCKZ6BBQEO+c4HuNdO+pLm/6POqPGJ0E8qUSyNca8bcP9kIBNWEw+TiMAiFg7AeJEoUCeOF587UB1Uwo0xWPtpWQaMqXJsorBXJzhOWd6j+bfXUBlvFPHIGJ8+nMtUDeTZ7SJGa21lecVSyp7SBJVAWothfoiBsLHfpXyEcCjAjh66FIOdnUiCA1Rp/uIYAuLIRdEr2B6mhZ4X0qv12KOmaS38/zrQ0b5siqYwxDt5cmD1TefyyQOMI7LFwNX9V41D5mizkhsyg4nm/wVvJMETUBPjuFKzolsUHCRP0qaPuXHO6wAF52dh38gHDVt6x62VgLfCQb1HHbO8AuL5zvQ4CDZ7O9tLk49T1c/lmKNjsA9pt+nwNJx1JKZYICT+Gv/8V/7z/iefn3vgNJ/+1/+F795uRipnMinxPl84u0583QebMV5KIXTWliXTMrG7s7H6+ClJl564u7GzRPPzbkNuN7gepU87TF3PnscPD0Y52wUc5Yiev2aC5RFRYsl9g5QcO+sKfN0ziSH2ovAjaVQNrEOajU+7sZ3V+erjxUbhaeHEw9bYjXjvK5K4HJjXQsP54VcEvduXJrYWGtSoskYRq+DfTi3NmhjNkpa2x8uVz5edm73yv26414oaWFZCttp4eF0Zl3UQGc31qWwFsnl2gDPmTaEQpacQiY0gtmkOE1pZju9Odd758PzFR8VcMpy0iEe63GCSaMYS4n7aAap0BEDhJzYW6PXJmPFWum90UajtnqY9o3W8TZYlpV12wTqpIWH84nzaWVdNjwt5CLQJ3vSy0j4SXXJ1pxI1DDCX0aHWnHTtfbOd89X7ntXus6ipMCSdf2RqktvjWzGFs300YARJrNDHi4zkn46PTQf7LVSdxmNl2VlPW0CVVCEpFs6tM8kJbt5MpZspCjomncl7rQmZgOw3670287+8qKmHaHQvTnX/c7H24vuZW+0Wun3ikVE5nY+UcxodT+a6hbgxBg9notLJnQ038HsiCax7VWyjgTb+cSSCzktlGVhDXCJIaaLLZK3DRd7xWLT1VBJseGYWEH76FHMDMn+Ykp+vwiQe3g68XQ6Sc8e1Oe1rLTW4j5kfc5SxLpDAOosirtrWhzHvoySfUaZhhfRYnz71Td42/Xvs4AOM03iDn+ipIOrLGsYd8Y0IMDb6a2z77t046Mf5oweIJ4Z3O53trwKbCyF5iEjTfKfmoeRDi7HxmDfb4qttikPmjIr5uALzLjVO7VV2n1n9M67N29Zt+3wbxDLKrOkxLIUSkrRzEdxMA4usoq8KO6PZwgBQpueVWts5wfqvvPNV9+wnB40iVn0vk6pqiUL2a3u4wSBJsunR0M9utgSKX5Q64pW7UNNQYr9IudMWbYo+l5lXzlAJHyIXeKvFH2IvSv8aiyevwqmKFydYN/E+siDr3/6DU9ffB6W707JrxJiM0JKN8GiTw41s+PP2bTGl5RjkhQ/c9LQ3Kj3Smud88NZIF88WMsC3EC/lz9CGFhjeDBylFTSmJ3iQeN2iVF0zowoolSYPT9f2B4eIn1Tz7vkLB8+dA/AKMtC8/EpbhZrkNhndd9UpEhuMAbca6N253q/cvvwzGdfvmNdFpZlkTdxDgZmysHOIxQJeq+maWbOqiBfn2dM6IOVKJkYIdvt3Pa7JCwPZ9a5jo2DaVSClZmDmZwSlLJFYMBrQQmDPtoxCZ2AmZmi0scY1Lqz3xuff/6OXFyJQwFEWZzhAk7G67tkSXI+7zrv4rr6EakcoGhMULOZPMniLPCcWTdda9TOwWqMpLEk+npKkpxPEHkCXOuSPmGuqWAtNo3gDdJg0PBeuVxupFw4n1eWKWnO6WBQODPhKJHXEtegc9VDdoBP75OZ+ql1X+KzpV3M7fpy5X6tnL5YyCjBsJkHI6uoGQ6wMgf61sPn5F6HlHjetUcNha5IsC5WYVlkGJ4K9DR4/+1NksDS9VWt0iyxGhpMxPtmsYdZGgHQxXlmKZJ51Ci3vccdHFgftGryghqF8qZQv70yaqac4n1NIVWY/FA7YSHlV+O6xfopr0C0daDhLmmcJTUmy5Y4LcbTCX724w/85KsLH1pn9cRnb97weB4slvGUNcSIm2gB9niAMIZFIuvgwTLVGn/04yu/tDnbL73B7AfR4y/Y9panN4+cP/4hP//2wvubGIEppFC2JG5NXnBOpljUAOuJP/r6ma0n/sTnhbwaaZnv54j3xcGzACwPQIVBzom2x55eiGRUeWmZR2qs7RgzakDSpuSJkQp77Ct5DDwtr3tD7NPaaPW+z4Rlyd+ipsElwSbYbx5rLiKjZlqkVpQavTISKUs+Q1oYaULlagyHyTtnbwNLUJt8E2cDmOYwqnVqdXlI5kFyyT1nKuiIQYKlCah8sraSxdB1Dq7UkJZSDmlLay32Cb1bczB5cEkMzCUjVg07v+888iLaPFCkGdQyh7ATyJnfzEfXUIQRVgE6ACaTW+AC0VVz/Meqyy3qiPmM58zVPhkg6bCyOTAPMETvlr6lGCrhyVkWnW8u1QH+6gvohI9OfPgR+7mAlYCMzCinVf6P2Pp6AAAgAElEQVSFiNGxpMwwebjq3LQoNUJeZzHANWHK8iEVEz+VTHKdE8PF+MHtACBwgSrZLAzKIaVyfL7J5GpjyhMD7J8M/py1Pw47ALpjIOHjGJSlGPKolnWWlF9N4UPamVwG2CMZueh8WcuC96Y6yxQ+NJfeBAjnopjSSIC8qN9mOMsiSfqshzFiYPk60JtsXgFj6oP9daExWYMe1KScC81Vs7Xo8eZZLSBGIFoHyJkeAI7YSYkW70LEv2utI8ucYZL5ixw1SCZpebZprWA6nxkKAXMnucgCHv+f5t461KV+2XtnDdxCyZiT2fTqbXXcl08YRCmuz+K8ngMxDZxmDRWf3zgIJ2bai9T0S24uZrf+/J/+J98foPS9S95GM/LjCV+VkNC8cbkmvCceT0ZJKlAHg3sffKwdHwt761TfqF3/rnbjUhPXe6Xg/GCDH75x1m2l9oGNRt4WctkiKlwFdx1O63DKic2MXDZGkYfSCFezYZW9ZfaRue6V273z1Xd3Xppz2jLvHhcosblYog/98/FcZK5mzu1uVBc76lw6ySvXvTLI7Bq20t1wOmsRTf/9rXKvXVHtvZPzQsqFtRSeViMXobdLCfQ7BThUlTDlybjfGzkbOUONJKGyroFCG62FVKsZl33ner3H5DNx3h4kt7Eug9dlwVYZq2YgD6PboOQTJWQxyeB+27ned9p+575fMVLIwWq8RM5omjRuJwFUIIpkOW2k7UzeTtqvRsc90eqdkhYceVjkMUhD04KRCB0iXEeNg65gi1Fr5/LxI3ttpG074rxLMshOHVUTivoK4s1NVb4cJTS0neY9UrvicBlxwLUBYXR4Ov3/zL1Lq23Ztuf1a/0xxpxzrb1jR5x7zrk3H6CQpIKKlVQR/AIWrWVNEElISDP9CNevIQpqRUFQJK2IpGBqRQsmSJIKeRPv1cyb595zImK/1ppzjv5oFv6tzxVaPoW7IIgde8dea44x+ui9tX/7P06wFwpZtMIJJQzjCYZBzjI+yyZ5Hx7NSzsEuHUZnx7zLWEtu1NM6T633qWV787z5R0pZyoWtNjEVir70xkSvN6u7JboHgAc4GPquacwYQRNqGbEhU6ZBB+3O703zDtbOWl95yLT6Br35dD9a9OxpvQdoeVC71tvjKNhpUg/bdDuB8mMhnMfjXQ0MnC0xuvXV06XnfN+0oZp63BPSsSYULYL5ERpB8foXPLO601mmiuRxVxAHv52UCST5xpmlH3j4w+/4Thu1C1jVT4+GUiRqPCQlEThMkZn9kndFH+ec46EDRPIiEz05lSR5H0+GI0ySs6krZJOG2Cc0olcs4xDSyY3pWoMIS2MpmKg5MqaE+ZoxL2PKCIS13bItLt1fE62urHtJ71TcwGELs8RM+iwEkQKRlvSwyJaf16pdmuKjqJtxd6UT4VNp54Kv/p/fs3MxjEOtm1DkqsRbCbtCQIO4tCK+6lyV8BvLQUfat7X+lz+YIvyfLSm+xrAm9LQeGjGiUY8b5ve72jq5xRg3YOVZtGwrEhVna+aUJbw+8Lgdr0yxmQvVSwC+8k6ijUV31DXGIXsinu2KETdojBtYbodIOpK//PpCgcIDww8dP8WAFp839kHlCgaBmJmOA8ZMp7pU7JSkJF7jgnVDJBN0jtnDB5gqZIyQ6u/pp5Lhmoyjh4zmrzkMBKkJPByhtdGSsHymfK1YHKMHr5Ji20lGdTwwWbyACKaEJvOtKE9eYgF58iHq/cboEZ7cdWXkbkYfDNSTZXQWUqhH/LnG+akXFXU5hRVZdzb2TErGBseMndJwJbU0mUkDSFXXM2dnpEKOAcXi7PWnRgkC6pcrLgl54z/Umpdh7jvAnsF6i8QL0WBm4Nh0KeYj6Ukjtsrfjnp+ucqiKPAdhnIapKszwZIvhgF54hGJP6CaO0m5uDR9BksV3I9Ufcbx/XOGEZjYjVkRT5DMiK26WiNWk7sZQsPqzfmWEqLMatiOW2SvvUABG07IA3e/YUL//cf/MjT6xPnS5Yfhavhe+wUCeaU0at5x6qkk3nA9QDPHSNTKgEidrFbTVIaL2JIvPvmwvvznU/Xxr7ByIaVRL9PbnlSUvjx2IV+j+se8oLKFXIV29yHPH3m6Lhn2jGpkQw226RbUkLZTGw/P3H86ZX2XWIrGe4pAJ4Dnw3LL1CeMDZw1UNuU2vFC+GKDsHkUZ8ksGJ7Snz75xv/Qs/8m58O/vF/+ff5k9udv/eHH/nlu8qlXLBvE5dvNESdaDBhQ2vEk1g8JTnHFHv1tnUuOJ/ajf/tf/4D/vWfPVH/8gcs/XmcgVll/91/mX/+X/ue7z/9d3z6P1741TVxT8YxG6UWnt8/c325akB2T0y/YaeNcv7A//oHX/idb4x/6V88U8sGNYAAH6QpIF3yf4dSKcNoeZBOjX4blNOG1Q5NzVmjkemceqEnIFfs0Po5cqPYwEcYD+cKyGT5jV1gVCvQUgCqg8pkL4nb/QrprOZ1diUsdtlHMGpIvKJrjf0Bizp3NpJ7gCua+B+WyXUyO9ScIatmntPUiLP2Gw2mpmcNJJPuy5Z3Zr/i3hhzPtaEh41BKdr/+4CUapwBYtTnnEl1i8GyGKY5VfKQx2LC6f0ecrsanlDh+WNdISgIHJ8+SKXSp/xXl72BO8EODH5YMnIqjNFCOmcB6uvfcwQ4Ggyat+FPbNbrvE/5MfgBgskhm4DiYrGMsWxSllohzuSQ/ol5I7bp4/slx2YoEMJDMMXZglrAqPfEaCtWaf1gYg8T85wy/ZjUvMfOmsKTTuth1WxYMJkwPDkpi+SQctyfauRcubcbyZzcFYzSXWwadwVTCIvW/XobBmtQMueMYVWcDbb2elRzuHEcqlfNFBhVKQKfgmU6Wo8JktjKeckC23hIK+WrZ0yrqptnp8egeM4JObwcUfDSZoaVGNZHfbL2shmAUEbyeYGcwYJzSbRyXsE6b9JuAVS6x82HzvSoi4EI+glWjztjNg1EMAE9YbDuIfUSS46HrDulIu80k5dnqYU5BqmEdYfGMGL5SbSMR1TjMY0SXpDbXiATibeSoqZcxcxfgGf0izYDEA4iUybTpnr6gvad5pIamll4XtljD/BYczPUOx7SP5ZFhzuE3HPpGMXugknGKVprIdmfODUXzs8Xar3w2/z6rQNKPO+iobtTZmwqu/Sj7ahYtZBiJb5240gVH5JH3o9JI/F6TF4bjH7wvCWenxIfnp2nUjDvmsDmE1WALC0ZG5UDuA7n5+fKpTjNROu/vd5phAeST66txcQ1c/fJl+vBHNoIzvuFrTzh6U5Lg9FFm34679gmJsmtDfrMTDqWnOYwu9NI4J1jdswz1RL7BkcffP36wq11jiZEvm67gINauGyFwGAC+JBM4uiDNu6MCW4JH4rjTSXR7pJKKZdV599xH6LSTufrywu9ezRt0rAqvlubYD2fVODGtCMTqV7F2EsYGG+J2/XKvMsUWPH2Ai1G6zLZxCQfZPB0fqLWTftCSpyezrG5ijkV7YY2mTlpo7Hvu9DeaABISeaac6jAInGfky1Bb4OPn79oWlurDJWrmGElGR2BkdMlZczR8B7jTtqqmnYEhDlxb4gzzpT41XvnOO6M2clFm3GyTOudlIzTfmL2ycx6SYdNNtsZCfbTLjlGyNDG0HRzzkELNogmAC6ju5y4jjvZC/frC6fThXyqbPuJ5Mvjw8lVjdVxHAyfHH3EZMwf05uUE3TJVyZxSKeoWQOc6K0zZqfWBGi6eS4ylL0ES2RmgVX4IMchBioI0tB0pRkCM/pg9EOTRTJtdO59UKua6ZeXFy7PF87PT2o+ShEjwcUsaPdJOp9I9USp+u+TV8zhtBWufQSdPPFg3+ag6w7j8A7AuW68vrxw/3pIzpGytjbvtK7EGklYwiw9ib2kAkyTcVajFDTRsbyFxgw/rS7aLbGm2sDqrnXoQafdq8Ats5hU6ADPqOFyj4mkkBjRYtEGL2DjJ1M6DzPwCefnkw6EYElZFBRzToFT/hOqK0q/mEnF2LZtPIabwQJ406xrCjhbZ9s30py8fn4hP52YvWFMeh88X95JumXBliimpi4mgDWL8uwOp7yjpinMQm+HClsrD9ryiALdE/IIS/IU0nJTGl3NhRwAzlD3GowKe4BJKrbemFhKu3ibZkp+AudSef36kf18EjM5afokOpTo7CkGY3EcywsJrYMSRdwkhX+P0kssrn8V2qPL6+feDrbThWUELfp43HvplUjkN5+mOSXrZAF0SpRZ/hOToam8OynlMHmM77NVxq1TS4lGP4VXjZrqgctwNilVaUZRnU0JmkqvWdPMRE5b+K2Fb5JLnrGKs9Y1pWxzUtskn8SezUtiF3ex5BoSP8e9K4VyHFqj7o8my0NiMtckPCVaU7LbHI5o/iMAUnuYtIuVK/+xJS3WEo2pfLw/a6qnYWLIduOer/e9d4GfpRSgxZ+tyaDuhfkCNVRm9gHuXf5ABU1/S+wty6vJYqRhSRKu9VGSEshO+879y+c4hyzMWCWPn3iwDMSyzmGYqrRJj33ZaPTwcxAlP4oI+cltG3k6bQ7KtrOfd75+vXE9GvmyUT3pLBqObZKughrX19vBXi8CesLvxd3ZFpCcU0h+Ep4GPu+6h9mYFJ5Oxulc+fTrGx/+mUJiRZ9HKpw2uQD/dA9HgIylQB3OQFJ/I+MzMbjHeihiag/JbWpJfPeLC1/+0VdwpQcDeJocczDnxhzG3F9hFHpLIXM0KLuucZO3pg3Dj0SuYkL7VDLSdun4Pd6pa6e+S4znwvFpUL41fB4Uz4wJ3A+xk6xSUqeng+w7sANbrK1BjDtw296kMrER1feDb3/vzr/yV/4Cf/pHr/xn/+M/4uOXg7/7Dz+xPR2U85nz8zeUrCZbvjwaKmzFaF2SoXNybnOwY8x05t37yZ98+cjf+zt/n3/123fYLy54/jYGEjvnv/xv8Fc+/TGfvv49/u4fHvzq1khm9GNwyhtpn3z58oV+AJZpOZFL5dfpwt/53195umz8pb80eBeJwD4dymR2gSAP4+cN7OhsuTBPkv+n54JZYbSVmFQZ1sAz2SF55jY7dWasO/UM7V4ErgyTtQSymVheKDkhLxTE2tirM1wA+rY9MSIQINuuBjZ3tqSUL2mzQ1qGpEZWEv3w8FOEghhvo4Uhdta7UVLleHWKg23G0Sd9Sh4z+6DFe3Y6nXRfHBTqoqNaoHZV3YNpcIlYEn32x0CkT38kdGIEC9Yoddc9nw3SZEx/DCKARzR9TlUgjUnZqX5B6dIxpo91GoyhlB8gkzu6z1P+WGLAqNdYUnMjJNhzsYp4mCjjYCnCb7oHWzUG2R6ypZDUuSf6GAEw6VzNKePofJLEOmlgSiab2LxeIrHMwTwp9QwPZqsAw1yMUi60PvEmWWdfAccm5hdJIU8eYJmvgdZ6bqnEc+hkClbKwm+YZbJZYs5KiyGLBi2qDZorDMBMAKRARZ3HY06my6NNrLWiIV8AYiwwJqReA7gPyDYEsM5Bj1NhKVlWXYuZABiXBGq60l4TTrdJzhvFB+0Y8tFNhvmksPrJto4b9aIBDnqAfobYUqWUAHNC/h4AYxRp4fcXe/YU+cLivJ6u+p1gFHoCb+qvzQOwQftm5yf1setdKAE2+RhxBg3lWalQfoCZSsgGy1Fvusn/zVLYj0mGN5pDybxcG+4WYO/yH1StZRNSB2YEzQSzzTyArwTep76fK5wj1y1q2PkYnKZ49X0RESzu72LeG8FgTg/pqo4PDTlnNLcDSO44jbJtfPez3+PDu5/hdcfsxG/z67cuefvb/8V//PvaEnfwwfO+s6VKsUQxo3njem+83I37qMyR6KNzv8F1dl7uhddbp6bJL5+NX3yAd7uRkxq6hPxdcpE/kmEMEscBYyZ+5/3Gc9kY80qPZt7Kmdcx+HozPl2HfnZ3Pt8bX14GxeFn58o3z2cu5zM5y4zbOuw1cS6FbdPGdW/GrYsql0rlaHDty9zPoEUhuWXebRsv1xc+fnnldmjyaVl+JKdaeT5tvD9vnLalZzT2apxq4XZ0jpD85FzVeDnIPE1TXEtR7PtCvSdtOh+/vHC0GwxNYXMWe0mGwZmaN/a9ivbpjuUsP5pto25bTD+c++uNfjto4y421FwSrqEEnaQIapLz9O5ZL0Wks9XTRtlq6GkzbfojJjhv2vjPeSMH3XQEw2X2lYwAy7zwfFLayPXTFyWzbWICnVMlFcglM12bfEnyjcixIaWUH822jNxiqp3kHyI6thgD7Wjcj7uMB83lk2TpYQjsuUjnWiKuMmVOZSOVnW3bUVJSp3WZa7cRSQQuemROege2nElVO2gi8fnHz6Rth1LYzjslLamKBwW+0u4H2SwmRyE/IA60SBBcNOWjy7QupyTgrsuMvR939qed836m5MLpdKKUIildLQ8D6n2XFK4UpYb5g7ERzeB02hCwO8I3pI/w0XLJ5a7XK+bw9P4d1QKsdMNGeBdMo2PUWmWYXgr9fsgAcIo9ZFlGpx6gwYyC2c1DRmOSQiTj8/ff0+K5Wg7acJiRL48XLGjiKeEmMKKU/KDouhMNnT2Q/9mX4e1bFPxwJ+fCftrJtcCckp2VNxnUAjUWgNZHTBpC320eBtSmacJsep/uo9NG53a7YV2FxxYMhhJy0HXglADIfEoeU5JM8NuM9Zbzg9btCNBYElmBVZKnMSbfvH/iy8ePvL7e4nBztlzIZmx1ewABDzptWcyO/Ii8T9EQ3++3mLRl+lCD3pOJKRNgQD9uZHO2ugPh8ZMipSzOisXYsmxqNqNwlhQnPQ5biIlhFOMe0b3yRBPY8qf/9E/Zni7szxciGVhsI0sMe5ukuqnYFsVZ66mHP8QqilMSypWLjNBFHok1NAftOKjnC3vIZEEMmRyIwiqWCAA9l/BcszVTCCDZsgogf5vg5uXj4AImSIl+3Hh9vfP07n0wwFJ4VPDmZWThIZHCaDOlYLpJciQWlf2keAs/kSEDzpwUjX0/Gp9+8z2/84vfeUiy637CEtRSI8FMMtAcEsd2xOQvSe6arDwKuQXo3O/HQ6qhNRr78fVg9MHptFFLMD0ChII34Ka1HpT6ESya/ABe1CxJZpYWWy8lsmlPSklAUG+N68uNup/Zz4WS3qamKxp7NTD6bRXPy09sxP5p8Ws9njXVnVSgEFN8m5S6cVyvUGr4I6wqajEgJBmZPmN9Si5A+KmYOcxJKTXWDA+5rmRysGikus+TfjTqXtj2XSKeh4WY/KhS7H2ZpCj4WLMpFpRjpBHPIFKuYGA+YGoYZiSKFeoGP/zJC9u+s++CiiW3mALdo0gmDckJ4p0rmwzUfQYDzOWrk8NXz5ZBKvNhCF/PmevHxq0n9i1pkn2GOjN7qqQTlGR4Nmwk8gnZGJgkADjBjIuwEhvUbRMwnjopFzGXRsOrsaWMPVXGpztenbxrH/cM3u/McZC3LXwvW8gfMsYp7ukEwlvEAMsCYx2SnfBSyduk5M6HDxc+/+oT/+Cf3Lj1Trk/8zvvKu8vha1WNTOWyQsIj1kGCCSbHqENabAn8Jz48dNn7OPk5/9sgvrnBBqYYenE/stf8vTyR/z48Qu/+Tpps5DctdflxJGMfu9hG5LwPMmnjddjcPt05/e+2zhfNkqV1HD5Yo00tO/OG54k9zev1FRwm/gBVIsBn9gGIKB8moANN4eRKVmN1F4I3xPth8kzZllsWoL9GACTlYLlkEpO49oOLIyXfbbwQUkPdqOGPnqPLIdMakSKaXFi5AF+KNDDTZ5W6J5PN/o8yDnA6iFJzJKzHF2N837KSiJbdUOAy9qXFKqB63yZQJ8R5jHtoVvet9PbECyaTrFr5e22fIMIoE17Z4QSxa5TSwm/uxzyMTXllkOqkGCl37lPhXPEmh29Y0nrzYM95Ks5jn1L6aUL9DOmyV9WQAcBcEQ6YNIek3P8Gg3/5mJ9hJyOkBVJHsfboCVqxWQCj8RKFTw2lkcarmTOmBYkswAKV30kFk37ySA+eQwZJwGYCIjpvT8AFKZzn05Fw7OSa/QySd5TKQYhU0oGhVto3/fZA3jnUUOCP+Rl823nCA81MVKnafkW7CE/s2CjLwY3JhmeMR4A1AwgTueEQC6LelV2DzPObR713piRoJqM5vOReus+6WsCHeyt/79Vgfyg5vq/Hszf0XVV6/fWvbVVG64hqPFgaXnSmT70oVXrICuDlZz78BqKzweuOj3A0sSbFYQtiZnL5zCtszgnRtJ5/zDF9ym58RCBQDkhAgtTvLuY6f1JmZxqMKd0laqbg4WWpHgpJT96gvWZH1/2xuR7/HoNIeBR7wWHjzdI3WJQrfr/ZBsln7k6zHIm2ca/81f/rf+A39LXbx1Q+m/+8//k94vDeZ98eK7s1SHLuK4y8Qa3VjgoXH1yuPNyGF+G8Tnc48918MvnyrfvZPRXrWDAlnfOW+W8J7GgSExP3G+TtCd+/qHyXETpb+7MYfSUuN4mt7v009c+uI7J59dGOybPFd4/75zOJ3nxmBIKSnJsE6V7TriPye2euU2HLD+RNgZHVzpbHzMmuUty4Hx9ufL53nh5udJ9Uk9PlO3EVhPvz5Wn7c0gLVtmLxUDrrcbt+lMlydETfL6uB4NzWLnY8HlAElah9adr1++PtDYlKeugxRmapWSN/b9RLFC2XZqVVz8dipse8Wmrul+v3O93mhNBT3D8Xvj1sNU0hL3+x1Lme1ywcqOIamCGoqNfavk0AFnd/mrDM0tpok9EMNVbXoTdFSogC05U/ed6+3K7dNnbjin/cJIlT08uMwqzAQhAyiW8NG1kSR7xMNrk9XG1kZnDh2qmEzEZTTemQNqyZxOG56cW0wxaq2a/mWtkZSKQJuthk66wZj01nQojEEx45hijeVkbC5wL8XaKXnjyw+f8FqhnLicd05ZdOg+7phP6lY5joNlwkdX5GiOlB2KEPQF/iw/Kwx6a9yPzv120NqNy/POdjpBytQShrpVjKjVyJ1OZ6abfCSnJkI+JuvHDxQdLtNJgS59OLep+M6adHB/ffnK+28/UIsOkIam3BZqwdadW2u4S9bTRn+ALvd+D8bLEMPC5UuQLY6cHkbnc7LvJz5++sjr7fVB/U8rpjpXUlGceF6HYtJB1buosMzwN8kZhii5fS5gUwWpoYnh8EHJhTGcbdvYThsZFYd1kyl7TikOi0X/DXlR/NqSQIysbo8xxRx7mMHPwfX1ldEO7sed58vO+fKkxjkAFrEK8+PgTEk+SjnJDwIMS4XTVqMYkzH2HMt0VHTiOcX82Led06nyq3/6a/pekEBFSYJWtPckjFK0r+2lkEmPKWldjCif9KAcb7lw+NT77pN7V0EsY/jO/bixFWOvJwYmA2/Lcf8SizpohiQ4OVM8mvbpPEw6wwxTDcWaes03CUA01z/++gfefftNmJdHQRtAKVOebcnskdYBscW6CoQR0z0cKvZIdhEwHSmeAUj2u8yPyy7mQV6MdVeTtPwv5AGRmFlFmadlGjrj3et4b/LFCDAL3oCFgSjbx/3K7d7YLpdHgVKTUVN+SJTW1A5tg9o7sZhcyThzTcXGGLE1uoAytQ0MH7xeD+5fv/L+u/cY8PT8TLJCrRkvG/vThVrkMZjDoyYhWbbnDKk+mht50vlDUrooPK23AAWd1uQNd75sbHsVIzKAxZSK5IfhWZei6MOWF0sAyCx/ppi0B1DkP/FRWmyl++1K2SqX8xkPzzbcAvCLFMZgFnpQ6LEKPpiuoc2cnbVhTpekMkccssXU1U3m4a8vX2kY59MCGvSOC2SwaMjeZGYLdUopnmEbsY9JGkmAuiAgck5njIPZhHaM2w0HzpcnfatgOS/2gIc/IA61bNFED7wfsYeutDyBMSqS9bMfjKysc+H0tPF6feV+z1y+qewpJs8hf7GQn5dqVAtvQrT2HDU/8t5SulhK+qyewhvD5DmSKZRasGz8+OtX2mnix2TD8TTYUqHmhLGpGYm9P5RSpLJkAhHvjOHNsE3yuGQF36IR7GAEE3WveHfGyyCfYs9PanzThHR+1u2wjZQ0ILIwcpesUYwc3TUZZMs0t2JpU22VBzsH3z1d+Md/8If88afJVx9sPvj2XeLyZHq34t7L0LqzTgK3iuUGYRrtycUsz4lf/emV79Lk6c+/w9K3EMEOnp/58PN3nH/zR3z/+YUfXyTLWOBA3jfKOBhD+5lZoljmVDe+/3wnXw9+99uNug9yXvvPIQBjTLCNfIh1kvKg+QGWxQzoWZ4rvhi7BZuEAfdgJg0TatN+eIyDLZInfTqeGmYDm5KJBaykdYfqi80yG5nDnRl1S8kFOa1YvAtiXRvxTD1LZhmYzxyDSsW80zGxHLPAiklW7Hmw5u9tkpNTTT6KQ7woboczJpyKsZUZoMSS5vvbkCEJjC9Zviti7ASDCYH+C0gCyWtq0aArE0wgJw6AAKNi+AhvgStjNLa6PYAKQqqOy9fO41BMcW5hFdCALiWYw6M+Uj2X0RBOLJim4ciE3nVWytcHDZlDpl3LLiZtgFFiHWkQD5LzraGCzsDxGATiAoiSm+SVKhDeAImo7VbzvWqzTEivXB5/NSkpuo2D3kNqN8Is3kKqNsUrV0CEBqqzH9gctOmUaahSX7KuhJM0UBgD0tTg0wRCuEdd40rXWwCX7suUBD2LpVST6rE3QqqzAmdwkQeyJXoT2KQh7XgMlbLZ4zlNUY+VCjcnKylczHcdya2Px9m0+s6ULL6nfO0W2GHRc62hrPvbn0EwcFZN4tGoYCxu+AryEGQWjJ6SBR6GdctiIde6ZMNvxu8aemstrEpOEkLINcX5iDSfLPP3SdmrBs0+SMwAHMEsh32BvKbyAi1jh9UepfPYfcRnV48heZ0AOg/bE4H/Kh6PQbApi/wafTGs7IEhLJB1BIApNqGJtZyWQE9WF2t4vI5hVt3GGr6IqdTaneP1TrIJI/HX/oOvLN8AACAASURBVO2/+mcXUPof/uv/8PffbSfenzdy6njPEJu2kP9K88xtTr4249qNLzdNhC84H07Ot+83fn4xtprYqqBXJaBpMtEsce2V+yj0Du+eKu+fOtuAY3Z8KDXufp98OYz7nBzeOKbxeiSu144zeT4nnk9n6qnIfHAWTd6n0NmcMveZOXpi9JjcVeGxo0/J15xoNpKoicl5vb7y9esr9+a0e8Nypj5d2K3ytO18+3xiM0k0CD1rykYbjdZl6O0udLGWzKlkPn7+yraHvMWcVIvuKYl7c25H4/UaMZVJTVDNMkFzdMCcTxcsC/2sm5JclOC1Jqwd88lxNF6ORrsfTAY+Ovejce2iBfY5aXfp6s/Pz5R6Yc+JWgplO5H3nbqJ2m2mRvtU6wPVTXSSS28+TQCTT9F5cdE9S5gAv16v3G83+qlQ6gly4lQ0wS2lhJWGgU1yEdxmED4XoecOoGLMqQMuGACMSZuT++wwll5bXh/aZ7OKjxKNe0ESwwR4xopooAWlxPU+6G0o8cVEwfQ5QtcubX3JFkbQlU8//EjLidO+83w5UXPIHgh/pGR4HNKiDf9kMxtKodGhL6346A1NxyRtmV3pPu04eD5feP/8jpoyZQ8T7k2eYO6D+/H6YBH1KaPvzpSBNDEJmZIDhoMG47gJCOoyHC9oKvLph4+8/+YbTufTYzqVZ7AlUNHVfXIqhQ54CPlbyFxA6Upr2uMxoXmQ+4fud8qJ6/3Kp9/8oCZlOOfLmeTS1tcsaWSpimPPUZCBPDNSkelf0dhcgIhPxu0OLvZdnyIM9zE4baJY57JzvpzYtrfvtSl+SrTvlPAiY0gLGZsoqWG2V1Z88Hzo5ocZsyZe7zfKVDpZ9snz87uH2aY9Pnt+sFMwUbJr2VRUGvpZCayKUSeW1YzEh7jf421S+Py883J/5eP3X9guZzH0wgNhT5VCwmqGnCWdSusw1v21WkIbL8bdnGJQehcY0HyGf43jvXFrjdkaT5cL08TgeETysgofAT+SwwWYOYNEX7T3uUsG+PAAiOmkR0Ja90MT9Xbl48fPPH33gXMAwctA01DR+lOdPhDFlMU0yoNpokIlxURpBrDxMCwOwKAN2C8XpQtODQvaDPaeqSCqSZKdWtVELvNFM39o5mW0qMJleRlYFEeayjp7LdxeXulHZ7vsSEynAt/TmtrGRaX0kFnmlDltSkibJjmqm1LmMIFnnpacTJ/53p3P1yv90yfe/+wD2Y3LeafsYsqtBM8+x8OseYY8IyenVFVGCwCy2MNn/P8WbNJcKskEavbRuR0NKxt73Vf9rPfdVjnlsU/Zo8HNpayaKgpQyavW+4NNDA0halJd0eekHTfmTJyeJAl/mKY+Jvzrs8fUdUby3GjkWt8aOzOIwY9j0RhqjYxgsqo4ndyvjf1yDlaCPnRGRexYNH7Wg1QjJ3lZiuK/BGPvrRlmQkFU+MMnPU1m7rRx53Z38n5mq1V1DmhKnuLnFqCNMJ3NjFRIRw8PB30+T8GCMGdaJ5tj03DLZJfEvSRIdfDjr79wfvfMeVsgol4WC1+ajECOnCQ1dHf5b5mkMRayqpRzGMfLGBcX2KMJVGc7G58+feH2NVG2iY+iBOAidqcVJ6fwphxGykWeXFls7+lN044xcDLWC2mb9Ka9Ip1KgAvj0SxQB/dXefFZVuGerMb2aQFWbSR6sJQ2zCpuJ0jlcZa6RfNuinqXEfzBTIW9Xjntg4s5/+Af/MAPbXA047lmPrwrXJ4kOzZ0/6ZVSVSy6TpTYqSJ9WASZIM0OPzg6z85+Nk3Rv3Zt5g9gUmKa6ff5efvD+qv/phfvdz5chdo33Rn6JcCTYx1i+TEVAqtbPz44yvPyfnu20zZYk3PXX3AiGY/vEHMjWy7GG4MeZckJeX60JokFXmSbYb3jlVjWgfkq9kPwmB9kvwAXNebDYaAyuWlwpQHopfOKdcIBmlYmnSvUYvGGufNP9B9kBytmTnEvo3GM6eJ1SxG8wIQFpvPO4ZzDO2padO+f+uS3lkpGpDR2cpOb0ewa9GAzcPoVyejQHKAlOkj+CdGBH3w2OMcl+dnVp0wo86w8Dt6NPWoLjYPwD1k4zOkZUFsEYOSxekI+SoaHmHE2euPGiMFS/iNdmlR5wW6FWdYX9JkxBhcqVaSb4UPo0W6lQVRKsksecafzSmGyPKTk3G0fFIFCoi1m5MYtdpL0TsWA7cxnOX7ZMGi0VnecUYkqxKDKg0ugvcS5zaSY0EAr5OULWR5GiLP2enhCVuSQD0skc2ZbTD7pC9QEyVVrkS4FNLNUhT8MDwkc8GCwQOCWQck6k1GDMSSZTGrPIv8EPvOw3bBRgw/osaeLWrOSAlM66wNHyAzJR+b6pt1HqboX3ztbEnybZ/qR3xG0lhJGoKYBpUrzU2BBVHwWYQpRB1C0lupc9kxLwKb0tAQ2QmkUISTxdSOik6fwSc2BYCbQc4aBpFWOmt+AFlvZZOJAW/LViGHqTes0KNlIYG9/T0NnAwPOXcxwldR4HdJ6plqFZC/Eo0HevfXWvYZvKN4GeVVxmOdWPSFybWfroHbo/abb6bptpjnPhXg1O/8jb/27/7ZBZT+l7/9n/5+3gynMdukjUS3weu4cjjce+K1wUs3XkbluE6Yneea+PCU+O6bnctJNzunAsnD60Et65hGH04bjtvgcoJTgdEGrRvNJrcB/Zgcc+K2k4BjJD69Nr7eGnvuPJ0K+1a4bHuge4kAKdm3TabNFF5uzjGl9a5lo7tzPRrX1uiM0P3HuzGV8DNcKQ/yssjs5zNbqVzOhfMmt38f0WgX0THHlFFbiXQCiiYSKcnHaNt2GQvG78+Y1o8O13vndo0YeZM/RrZAPNHBse/nWIyZbd+oNVFSwXImG5rgTef1duPWGgnnlHN47wzmbI8pWJ+T7XTicjmznfZgRygyvpRMKSkQ0vSI5Z6+tLKZ7o0tyfl/En5JrkPBUpG/Ac7r1xdebzds02QxBauKXNhS0MmzMaY23mpGjSQgTULfpmpiNigZafQeLIdoIsaQcXYctMMitYpMMXny5LKRygXLk0Qm10QpGynMan00bUzREB+9c703PeNgE2QZRVBK4sunzyQkcdtPO7VKHoIRkzUezBPJHMJYOumQr5YecpiUE/fjju64GppjDHpr9NEpufD+3TvqVvX8Sw2Zhhr5+62JsZQ3UiRfiU39VvQRxY0H2DCPpgnHmFx7U8rSaefTDz+wPV342Yfn8CaSfMmCak9Koed3tk0JYhlJVtwEPNnqBH9yWMtYnUAVpg4Sh68fP6lgxqnnM6kECBeMnXWtKecHC2J5dS0JkjxhJNk77vcoFTyMikekD8ofBEucLk9xeCYsV4GVLmlhtgX4JHkvDRng64ycMn1tLfwAPHTWTgFe7zdwaNc7vR2cTiexk2IfkB5b0r9SNzGHUmLfNzXi2y6JXVDlS0yE9dn6GxDSO6PLUL8m43I58f2f/EiTThRLRq4q0Lda1GAG8LyaeNF69d6XojSqNXVyF6BzHAcgj6Z4lNznkJcDkEtldE3mFovGTMzERdmeMXnMOUfzLEmrJmDhSyP6j5IAfT78XmZ4SP346+8xyzy9f/dIEkolfkYYOOeQphHrz+Map/lb8t+Mz5HVDIvAtKjFampaFLr1+azmQ+Wahg4pMdD7ugYJZdtk5hsSPI/CXkbOKVic9rgfvkCpMLROJXH7+sowC4nO0Gcb/niD1jC45AIE/T0pyKAPD8p8YvbEqW7BgFUTPvSwweE+O/31yteXL/zi599QSmXfn8i5anIc71P2NWWLCSwCylI0y+7KVbK47703SiTGpRzPBp2N7ei8vlypW2Gr28MrA9b+LnDMVhMeHLNSda0/PQcWiPVTQGithUU/f71d6W1wejoL1HiU0cR12BuYxAJBJTtPhoY8M+SsRMJbFHHYmrumSOXR/v7l9Qt520PCqr/ncz68rxKSYuRSH95UMtnl4f+W6/YwGXWXr5IYHHcl/kwxCLpP7reDmrfwEVILXCxkLdFQ5WT01oEh49CYdmoL8QhJMIorQrmmGtLbYFKFx8zpnPn4443r3Hg+C6zEiXdVIJ+Z4SkxZsijlh/WloNFNrA0qKsJJOQsYZZkSZ5mW07YZvzmV6/k85lqjVpg23fmMck14V0Tb29Osikrha4mm54xGwwSlAE505uTyg3/kihPG7kWmSMXwLuGnXNyXAcMgXBJFGtyclqsC2fAHGJ/WcasQHjvGei/bYFoBIh7VstQjDoPtuK014N/+H99Eru/T765JD5cTuxVQFFGP3eYAxlPA73E9jjT88wkm5SU+HzcuP36lV/+3kZ+/hnYGUyyV779S/zu9sdcv/+n/JOvB68tGj+fFHT2zHHQD/2VzGTbNq4YP/zwlW8355unwpYzpbSw8VtDjZCvTQ1AHGeYhnj9PuIdzoS9m6RBh1gqszft212ovpHpfsfSkgaDeyZ7ZZqaZxkuEyBRwqxyd/kLmcsXtZjSDNdCN5wcn3dEwzcn9Gn0NUyoAmxsqufJpmGiWFu6X7WqVW399rYvDDjG4LSfqBVebi/kFL4/wx8SI7NJyRoQzWi4FwUhGeHpWEi5cBwtJEVvzLAc6YZx5TjrHV3mzxrGLAmxfAM99vOVGpUe+53AtTjO0xtYzwIWggklFlXUb0kqihTnl/buAL9Zci+wJPB41d+rdtHxLoaKuWqmtSevwU+cLI86Q4lX4bHoYput1Fc3e5yr658cibFY+OvF989FMmxcLHUHXYsJZFjAgzDVYE1PJyWx22d8jhG2Fz4Si+WDT/KSjKVMdyUK6rx2OokG9JDEpWBBD12EcHSCgbPQNtMgSSz2FfChkIgaVgYzQB2P+zSGJJUCHLLeowSdxLQpA3kzcqoKCkEesGQLxnewR9OCOIh1pjq4mmqtXMOyIRi1Y+q6Ro+0LFOglEH4IQp4HjHUXvK3GbVPzukB6GjNvZl8L4n4W9LvAvuinjInpUo7ZET/tufa2oYljw3PSy0GDTg8uc5bn3G1ehnEQ0M+Zig1t3sAPu5Sk7BkourNZ0JBKFGHjHgvi8nsfDBDIhsyvVWmhnxvyiOCJdF1X3YQP7FHWPhpFLaL+eem9/lv/vW//mcXUPqf/tv/6PeZTj+SpDAtcR3QPTPmiaNtvBzw5WZc741TGny4GN99k3k6V07V2HNlLFMyg0KFmR/9ZB9CjLcMpxSpRqZ0rZoqY3YOd3LVVP91dH78Ci/3G99cdj5cTuHxUDCDrULd5PiPqUhzK7w2aKgpyTlJMtc6bUUCTwESvXd8dI52SM7UZMhc6onL+cTzufLuXCjmpJh0lJKDgsci5Sg5KxbcmJoWYlCLMNO8gKSJQLXmvL7c+Pr1K6AEqpzLG6ocL8/T0xOeMzXkWeRCqpUckcBb1ZT4ertzb2JJmcH19ZXbIYnBdG2yr32wn05s+0apamAFxlVSlWlhQptmMqWGtS4z1oTiUYWYyxdlTDWUixKaU6GmxHE/uN0OKBsdJWMp3l0TeGE3E6X7OGXTpjUX+yiufY4wN0Mbb2stmHKKxR1d5tLHcUg2VgTa5Bxxl6WS6y7fhpjOWqrUulGA0e9qAkZTVPHoHAFQyQspJDboHMklcX250fsgP1047Tt5i4Y8phn4UORoLQ/QQ/GyYuEwV/R6UkJAV9qb1pKa19bFHKo5BZi06V0qRV4jgGV7AJ+plgBwNfEVjVqTqXYcMitvTSSctJpkp8nwiFIL169XzCc//52fBbaphsOIaTAZT0S6Rg5GmNZKH7EBzvDWcAFHD33IiqE3sCLT1K+fP3O83vCUSFthL4U0Ij0iCVwsASitJKS3RjdOGZyjBXA05yOh5H60oKDL1Pa87XKGO2nvKBE3Ly8BHXd9DPluPQqnRbfXn92uN0br6qlcEbBjrgZr0I9Ov985mqKkn94/s28nHVmrCEEHVg5/qZxEgU+lhGmhfIgsGEJLyqg9ajJa42iHDlRgO53w2fn+N5+xXPXeJIFANYudRzS2WmABaKDJWKqxLsOLpk8Zck/8AYwuTwo1FPJb2Ep+sF/WNa3DWXji2u/mA8TSNEyU6mWsPPraT95khDMmUR7T0+//9Afq5cR+3h+gENMjtnWth/Vuv02nPCaNKgUT5k7dK7d2yHcu0BoBwSoctGbgdD4LiIqiXckcMVUN9KjkBCU/Clwld4XXmDs2ezRbunHJjJ+m2SUSdU+8fPmKm4yGVwKfbqT8d8ZDrqA/GqoqAgzTRJIJadvIwXQENMhBxX9y8Db5cvvK7esLv/i9X7KVPdLYxB7JQRnP2ShF05kFMuYASzwGC5reJUoRiwYS27ZT6yaKeBSN1+uVL19fqKfK+XKKe7CkU/LIWFNMfQXrsMinYgFOeoU8WA/EDQlJ2ZTx/oy98+Xrjef337DHuZ9zelDtV0O1WPxi0gmM8dAXuC2fFJjhTUEUgMmiUDY1jebOy5cvOIl6Osu83wQercJvtQ45x+BmFehhjp8SSv1DBWMiYa4wg+kmLz8TwDxn4359EQvrvJNMUhzG1P7PhEYwaybtPmRUWkyMj4SYirZ8QDqWCjanzg7vwTaQD1FKieM4+Pz5xvn9iVNRLHx3AqKKgU8ACjmMnCVLSQHWr5BmAdpidimhLhHytQxuzulsXD87fhSevtGk+8OHEz4PTXs3AWJlE6hhpTDNI3xh6PNbwXqibFVrMxWog8RG3iqTQ7DlVjDPbJdKvx7cPzqWN/JeSLZBEejvKZHSiZROQMa9YX5gHNF8/QS4NBO4xGKcPeFmpO2VbRycN+M3f/wDf/RD494cZuHplHg+b9Rd71tZw44xFayROmlqPXWGQjfkcg3J+Pz5jr3c+fC7Fzh9wFPFTOEY+c/9c/zu9Q/4/MNXfrh2rkx21ztYc2YmNYRKOUqYTfa68XIkvn668e175/k5U5KGnKRKWmApLt9DxCv3FUrhndkIVoTCDpiFOQ7VjA//EmiuzS0yNpeKCg92iy8/GBJlSTMnjN7U9B9OtcJondkGlsPXxv2xjaoEiYCDsLbQcs9YH+zZUPu9zjCBLOlxnqxBjIYsKRulRrBBObFvmUuG43qPBjmmC+a4NyRPywE26tczUgpnN1KSdD5ne0iWjqMhoDsGlCG1Vmy9VpxqlPGoVeJqH6D2T3D0OJu0n5JUO76lWKo385AamxmtN3LUDsCD7ek+H/9P4MOP8zblTJ8eQ0z/yRBNe2AJlsw6xywArZVaZmtAFOfpmEopDgRDUIOFlIi3a17BNjKMJ+q2ACli708mVcpiosizcQZQ4Q8fKrAgjuuqaipRusUgIlYqWRJ3scR1vpd1Tk1n2vo88uuZHgFCCLzxuPZHdIQOlpD6LZaXLvPoYSSuj/FQaCyPK937onNmdgULpBhMLFAw0s1qidrFYhMOgCLlvB4IWI57luPc48HigZWw91YPxN3Rm7P2rrCa0M8OJl2ksEmCnh5A04wB5lqzGuInIghO9yXWTLaV9pb0DqcU4FIMFeKn2mLIRu8wpgdQtN6fNZQqYHFmkUJJoTpvLAuXOOsS6mOy6TxfZuU5BYCfxfoqlh4pb2QL/FR9YUpGby3YsEkJv7b2SuLZ+7pxj3/lvPaOGMhFejY++Ft/49/7swso/ff/1X/0+31uHG7cu9F8Y7DR5saXnvh8GF+umlA+75Nv3218867wrshUVu78YookD3mDqdGeQ3SwiVDdWrRxL+2ogE/n9jppOUHZ+fFl8OtPQl8/vDvzfMmU9Pbg9pqoW9LBOHOoVozXw7l3ybqSC4hoAWSN2cOYLdDTOR8ytNYFeG175fnpxDfnwtMGZRJNj1Or3nQ1PmvDWwVrRHCWTAlkO5n05gMlqvUBt3vj5eXOcb9jphfEgl0ijxa4XC5s+/7/QeCXrCCZprLLqPp2P7iHzC0Bt9dXjnYwV4qMC2i5XE7sdeNp36llo+wnUhXFOkXTYAF+ZaAfN4o5WxKlVTHwaW3vZJNmuaYsx3ybXF9fud4OZs3M5JxLZk+K552sSYc2nGxGCbqxw4NmugzlQLpdB1p4K80p02wfigi+328kq5SUKUkvuqQXG1YW5T68oMJMLU0VoWPGcxyd1u7AG7U3lRSbmJEdSnKuL3euc1Cenng6R8GJYZ7k04TMEXMqDyDMsg60GWlE2jnU0D98YixkklPyqt6VHPL8/MT5fFbFHQ2emBBOPw4cY9tOmtrG5rsOU2JtLCPnBcak6YzeBCi1g61ujDm5vrzw4Wc/I5PDQFvxqVZrULSDbRI02JxTeGtNjXjClbhkpFlf8KqBPxoy0YeP1yuffvwYIAJspcqnB03Hck6cIrlOl2U/uXfAUKFqrgj7gZILZ9DER7DLHAEQOReGm9Z7gAmrECmlxD4Ve5DFM3NFgDIHt9uNfjSBa8EOW/Tf6c7ROrfXq1KJ2sHzk56bjOz1TOKpqFlHDJpaCjOYdtNl2LmQm+VToOJxBnA3go6dyanwdDnz6eMnrk3AuU0gOXnCtotxWNNGOe8kMnvZSUX3d6uVVH4CrI1onkGg0pjkWmQSX4rO++mM1qPhj74+hY8DYg+UUkJnzqP4NZN33Jzr8NfPW1TpOdeEZsY0UwBPG40ff/0973/+HZtlGTtLI/dgVS1qt9aHPejCa49JOUuWFxOw8pj8hIcM/mCR9NaYCU6Xkw7xrMS25ZNQStHPnjK49JLASxTZMcFyf8hm3WGZZ69yZ/kkLVnsxx9/ZNtkWr08ehaCLc+CoI1PD889GZzPqRjsHJR5Swlz0dJ9FWb2Vqgdo/N6O/Avr3zz8++oZWPbdk0vc2aYYalQ0k7ZqhI4UxErLMA+vIf/WZjMTvEHcBMLNBmWVEC33vj65QsvL6+cTmdOp/3xvhNMCaVBRe3kkoGJJZtDHrDAoLfr0Jeay5V4qGJS+9LterCfL5zOJ52ZQen/Cdypn2eryVKxiyVGeEbpWmUMnzw4Aq7nOYbew5Uud71duR+Tp/fP6rA8QgSYmJUHGD4fRuQgyZ6+n7vAl2SJ0eJ5Rqpcimu2JCCepPNwHJPtfBE4n35yfQ/JQZyzM6mxKNG8DN2DRMaGwDFjRqM2Yn+buHfIE4aRauH1yyuJyvPTRk2E8bWFVDXWeEmkqSaKJLPalDOjT2af+jfBDkharxiS22FUJA9Kl8Gn31x5ej6TR+ZkG6eT0Q5nP2WyFYoVrAoIzlumopROTyGVNMdqljfibUg+xo00dZ/mvb9JN+uGFeP6wx3PhfpcqPWs3w85q56Z9n3zJonbglLCi8ai6fRomDCl3iXf8FIp9ZVTNi7b4A//0Q/8ycvBy6F7+e1l42nfxN7MWZLEaCycqmc7lueHQBUsEocm/PjDC2ebvPvFO3J9pybJEvjG+S/+RX7+m/+TX3+88unqUAs16zPvpUItej4ejOSc2c4nPl8hfYVvvknsdVLL1LpNAswSBU+SCqpZziTPTEu4DYG/wULIblgpVPdgYJSAb1K8BynIb8YcJShF8h/LeEjkMsM6NZpbsVeCWVSNww/amJSy2LYL6jOSD7ILwPUUsuQ+AHkrpaQhbp9T+4KrCfW0mEKw7RvmidY19BqeKDmxFSMVAVj33gQuhJx1SZIePaU3DWYtmDLZGPMgtnAM1YQ55dgboj9SxaE9LoZYKl8mbxsoYd2xGMMWP/Mnwyxb7OC5Vmz8+VrPsS9nASny/tTevPq0FayhelV7WooG3gO4sGDFyDdtgeUegLYFYLd+PpH8mh51gll6hJIYPFLNpi9JWezHOD4tpJchJYsbvliZArHTY/C0PIHSAmd8AW4Ce+YMxnOUQha+cJaclNe1htfQGkhEYpsRXrzuWHi9jrj2uRhRySDLVyitexpHk7s/pPjLRkBA4gxmq8C1hRku7ygxmoe+t4ciIM0YHsLshz4v/pNaaV1jJPeuei76E4foHdefebwLAlBG1ERA+HbFWnv8E9/HF3Cl+/UIOnEXCcQXGG2Ed4GeXJxpfbrwEwdmhFhEPZdSWMg8euS4ipAjEmBWihtWS7ChY52vdSlQh6i7Y+hny4XNHxYN6/t5Wjught8erLhVj+AE0KjhDDEoetS/Qx6SKcV9QNe2wMyffpm9Ddh034zVGM0x+ff/5t/6rQFK5bf1jdaX+5ne4OaDNjNjJDrGfcCtT/poFBu8q4l3Tzvnk7FvUUfNLp2wiapnUsPRiY05GVuQG81lALxlwzLcjs5MWWlWqXI9Ej9+vtNu8u85nSu1oEMiScsf3T7TEr3LqBagHwlS4rxneoduyzBDi8cIy0ObUVDKIPx2k8Rj3wrvLhvnqkOh9x5so2ChPDZUkwt7UnIbXZuBpURZDbgpxt3cadE03m6d2/XO0e9KOQlzS5APUM2ZehJ7aMT3MJeZoiVNT3002oB7H/Sj0XvjWKDZGGqye6MUeWKk/cTT6YwZ1H2LCbQF4guavJkap6SCtOSC12X+BxZoveUEpunZYjaklOg+aLc7t3bgpWDFgmmVmFnJMXuGNifDBqdtpyZjDpmV50CmcX/QUy02Oze4hQfUbJ3ZDmmamxqBnMLYMRrxnHMw1mZoi+Vz5cmhO23o5Ryue8aQKavSCtR0pkCdVx/z8nKlTed8eSZvGS8C6pIb27bhs1OLir+Fxmtfd/pojCnWk08BArd2xKQ/1lSY6rWjQR+cTjt1E03VXJKvXLUWeqQw7PtJzyU5W94Y7mKZzDA7jHjUOeeDLdWOG8OhtaZGKSVefvjIN999S45I9sSb0aKiDYxld4EhI+flITQ7E3tMGdx1aGiCkh7PxMcIXwTny8dPuk/uYf47SD7FJkPvUBsdD8p3isQ37VHyhDKc3g6YYQTrzl437r0LDEC0+lor9/ud7XJRgWM80h5ylnR0urNFwzBcQBt9xBBAUjqPaU6PCceKuR9j0m5HAHedYkY+bYoKt/A4SCZflLQKrWhmq96PFcOLB4Wd5xbcwgAAIABJREFUyZwCqhXvKrB24mLNISNxZ3K7HiQhvtHAqJCvSebx26lyrjuzpDATFiPCXfsN8YxVTKZo9DO2b7SQJxZLHHPQVhqH6e+uSbX2Ze3P3gd125XAMlWIjGCCYY6FaTRRNC2za5szps/S608zXm+vpFp0rVOygbxAo8fULkCCFGsuRTJNPCsF0MhkM4dRQkprQhtgZRRLHsOAamvaOB6AypL3mKmxVxK80sZSXvNHw8Kzo/sQ7BHFT4sETAFaKgoTasJXITiHiicsfu1hhO/aP3vr2g/Q5zIbSrHzzOYdXDGzOTaticCtPgYHAqKO2ZnDyfuaj/IohCbBGG4q3MnEfkDsI+1hhv34N2KT9d6Dta/J2egH9/tdwG/c55XwYqx7ysNjS+wkArBSl+quYtVWYhuwpAhMscsErKmwjKuhHUew0uMHxO9rnYR0KD7Lqv4sS5LQ+kGpOVg60SR1wmvO4hVxGIPDJmU/8eXjC8fRQrKtC3LCiyOGICmkIw8z9wCoEh7nxRtQnZMYjFYnxRPeJXer9cTlMhm3F/rR2PeKk6JJQxKiAuPoGrDUTLtNUklYVqFMc2YezCnm7GoIbRb5fsR0vIfU6vz/svYuTbpt2XnWM+Zlre/LzL33qVOlKutStgvbijAqjNUwESZC4CsNHBB04GcQ8A/Ez6CPA1rQoOsgJBEG4QhjC4xdqHQtlapct3POvuR3WWvOOWi8Y67ccpeTEadKdbQz95drzcsY73gvp8RXv3LmZ+9u3NojrwuieCWU5hjFeEGM45pQ3DqOdajZuDd0bjZI1pWUipMZ0GHYYEPnztPpzPKw8ZMfvuOTr574ybu3PLShOu/zxunBGTlTQoZjA/KpKGWuZsbthudMaRvenXHTu7K8wJPSCIcl+tUpaWD9Tn2onL9yZt8d3zucHc8Lw3dSuwIVLzcsnYAOtjJSIRH+QbZrLYcfJF5Qeb6p+WgFTl/j9Tfe8Su/+i3+9h9f+B9/67u8fb7yuz8ofO3VmadTIX/9gVTFgvcU+6Rnepk+QE7aDaPQfCenzHoy3o0bf/Avf4+H12e+8u0zdv6LWk8Gvv4Sv/T3/zP+o8v/wPie8Sfv4EM+U1yR5+vufCjveX5+i7uxo8n9+pVP+M77G+vv3ikpk4DHdWUsO6nqzKqupLHpFUaG0gRMjx2Zo5dMsi7msi0s/SpGVCn0W0iG+mDsmTIyxTo3q2z7INEZQ95z3Z3sYveUrPNBgM0iWdM5cdsUIJECNHrZ+Xov1SyATcl+ynBuPliFTWmv0qLWf5GD6TCC9VTxDPfNaQPOVf5twzUYfbKVtkUdkDQcJhJNhdCYiigf0XcEWkw0rBOMHOMlnRhCJpRj+JwDzB6yxyiOj0bONUjhAdQH0DQN+/VPAARxdk+cZ96pYn8UydNyJXWQrDACNeLOnCC/7o2Q2UUfrDpQ50vKMehMCTdJIDXskCcSrZHToPfwsk0Bdgf2BEW/zyyPopl+gQo1a53DlzSbefS/Sy66l7JYIyVlmguY2ppAmuGdbAPFmYzwwJqMEf3epUAfO3mot0xxh1kpjJHZW1PasUn+23qnb3oG3gZug5oKo3TaDql3eeQFCIY7bjF0DUAuRFWQxrH/WrCVpmH2EUcfGENvLZ55I3fVsrksDARK+S7G6mSLebLj/p1sctU94eGDgBUz5A0cx1syEQgO1lcoFSRp62Ecby/1lQ7H477tPWrtHvLKYUqhNN0FuGGIOJHTpGjrfmhhMt7HHmBLsJYMNIjRPtK8YLLiegSZIBmsJzxlWtuQrbvCvNwQaJ8QGST25zjS+xI+A3siQVAeUbPmjD1mxiFHihPIsZAZOyBTdc1U4pnb7Cn0/ce7cHsxVjd7eY6e+DfBp/+/X186oHS/w607e6pcHXpP3Hugq6PzqnbenDKPtVDDsM/IBJOOEZO27ECGNAo9FmYpxqnAkrIWeNKxsPXG3Tv3Jorjh815+/6K1crrp0hwsBzoslgqacpufBz0tDRTfDJ4Fio5rDHIuEmek8eC206t2kj77tz3oJymzMOp8LhKfjO6DJ9TyezDGX0nTndAMiByZEsEWNURuj/6gCTwart3mWWPiG6+XjEbMix3TWmI4pVIGZk0+egxdbiquiZboaed+/PG9XY/NmsbO9sWAEl4frR9cDqfWZ+eWOvKknVQpVTY4yen5ODtI0Rei3S4pFBiusRAJ0E3HczJXyQct/3OddvY7jsjZaVLmTwBINE9Emb2jTGcZc0x8BN44ymQXoHAFGJCMgbe23E5btud/X5nbJJ15CJZCknNup7jC0tAIZvh62KDtsnM1SxhYzCa4207DL8ZdlAQCfNkG4PL5ca2D+rpxJqNNRc6MzWrHk2j967nH2/VQayV0cEHbXRImev1Kr1tNJEDMYm2+53RGqd15fSwBnNeIEQhQJnwClgWsbJ8rp24hIvJI2CEbM5beFyZfDV8DLY+sD4o68Lbd+94eHxkXU+x2AyS9tI0i53+UKJvz4m76K9jyMfAcwn8LSi589KKKVaOCNv3b9+pAMw69B8WFYQFAVVHWpfpvXlcZKGiiWmHBeDaKUVU6ylDGb1RcqLvjWVRXDT4kaSmDWuHmX1KWqu97TKeN9j6To9n5bs806wYME2Jmf0p97Zx37bwGRuU08qaioyKrcdn1FE9U9AmU0u+AwJzxxgCCRJ4H9qOwdjJSe8gpTlHlEHl5cOFvQ/qumitFF32p1woZcHaTmamrngA/zILHkcB+NFZE0BoN535jEHBlKyIChZQ0TDTLFIUE4b+PB9RoedXspf4YUfvSB2t4rYFZggonACU5czt+R5ME2Kt+5+Rqx2sMqJWsRfq9WTl9S5wimhyi9m0gpnfBER8sA9OeRHo4WBd4OPcFpO+PZJkTZ4mTXruSy2M/vKJtFcCSPIxgZUwLM9J6SMlx+cWQ0FMRJl6y8xv0HyLYkR3XcpVa9l1Fs/UlujvDwPXHveBQDrFCI+YnqYDNEbhBzQ6jvUwv04uI193sos5qeIz2FNR6E1wcpjSdHpv9L7Tdska3QNgT9Myk0jaQf8u3tcB+pg2u03pSIBGvetnCZQSW2sMyXaspqgN4Hp5prVX2mMjpt6Bhss0espoo3mIGmVZEtdLZ7KMak7s7vQwSNYx43jvwSIwzqfK5/vOvjWWx6pmUDdRrIFopqPRsSHJvMBPAWTTJ0kA9Y4HK4ooQj1PkCZhy4nLcmW/3bGnxxhgjKMIbfG+W+tQi2SQe49hQUP25wWzSd+f0/JEz+OFlTEU1jAcXr1e+eLDhcvzncc39fid5H8XXz29MCJM+6W3naUUNnf599Usz6PQvXmc2d51PlTEUvvq11a++68/sKULbx5WfECug5E6yXfSG8d3WAya7xoeUah03DI24vcqMB4a7KoxRu9iaC4Z2A42Rxrw8HOF64/3YEQ7KSRt9E6fXlHIJy+NhtlZrEpz8D2ac9UgzqYhASmawootr1m/8g2+QeFv/Qd/ic8//8A/+p2f8pPPr/zL733Bm6dMfUwsNZFriabP2B3MNLIZ1snV8V0DL+9OKpmHc+X5w40/+p3fZ3n1xNO/dYb6C3g6CZn69K/zy3/vZ1z/t9/mf/3Dnd//rLGR8GBfnpbMfjfa/S4Zmw/KeMDrid/54wsP3infzpRPNmpascWgdPIcEKdO2mUcruJfbKqdDveh9x4rbSmFvUtmvSwC808Dehq0LnZQLXDLiWtPtGLQK9122n5ntxIYcGJkDexGKWRPrMumYVmfQ5xxrG1zMcDXYeTs3IbYqDaMZkWeYvGzexqYDbKHHCzu3OGDuhYl2m2d0Q0WvRtSIi8Kt7k833Xulhd5DQ6DdEiGIFhh04sr2Jp+SKXjb7Upw5IE1k091Iy6HzN4ZB+kPAF0qTWI332E19px3h01fgz0InyhFDW6hhha078J4/B3mvfXZImlJJ+i6RtVSmWegLLDEPNRCOl8khnMSbmr5kMeWc1lYzHVHqXoPLFcY00FWO8vzK2jFAtqUjtk9haEqKgF4hmWIsWIhzRS9YyGtDLhVl07peKgu60Tcr+onXIqYGIF1Wz0LpbymgXo7ay6C++bitfe5d1FI6XKFn6tJZKsR9zzeqdK+raSZYZ9MJ/0eSbLyj8CLyzkgKMH8JHizhy7PCJ9J1mVB6cllGgZsrDJUDcxXoXbefDWVLcVBOqaxSA4JVINH02P92To55n6RzuqEd1dI6SdoCGR0ml1vuHE+g7mmTemx9cY6jrkZSUgKlclY7cwWPI0ZYVqFjIWLbO+x1LgO56OzxrIDDMoQrZwYlsNRIYx5K3nyaAL/NStLhC0ODF0lE/SeFmRTL9InUPaSylqxt4FCs+69SjEPvq+lzpavYf2dvS383L+Er++dEDp0oybZ1qHy+7sTaaOj4vxcKqsa2IpicVeNLUqhOWBZJ4ifScFMi42SzKjRFLMNqQL3fvgtsHt3njenGtvtLsK6WUtrGuhGKTUaCG1IqmIcUvcuuGmg8mTIjZV7Eux33zQ4nwdUaBi84DVZ9i2Ru/Ouiw8nAunmiimAnWLhTjjui18YAZ+SB+SyeMgeu1j4j6Ho/I0UuzmbROTwZI8ZMwTPjp9dJJJdpNrDhq5gJ9cZXBdqxJFaskkBtfrxuV2oYVnjPchts5wmQgDGCznE/V00pQ4F3ZzsEzbN3ouajZpSl0LiulElicdsPWm5rJLuiNtrQqB3nb2tnPdtgCc5MmSkJ9IzCqoiMlidM7LgiWj9YbPOPiPJt4WABboYG+7pG24M+4btne8D/JpOSZJIxPsrXw0Z7o+IdVVzLIhJoUOjAZdqTnDexhEDowc/hnyg3IfXC53WpPEoJ4WrBZJwlqHrFQ1GSY7re3B2Agt+GRBoKmdjFY7O0oNGbGmtrZzvV5IvXM+nXh6etRnjj2USgnttA6xtBRIKZr0WbDooU3z6Bb+UtLgh1dCgEsCrU48Xy6spfLw6lVQa+OgdZUBOb2YIU6GQQ9ZlhhQKmprUrHlH1G1BQIKN8Bl0nm9Xrk8PwvQcZQGY9o3pS7gTq1Vaz/NQmMcxYKK+uj+EtR80iWy7YwsFkiJwkx+U5nnywfq6VHAR4CmkotqEtP2Xc1UyExGa3jvkbTXDkBrxu2aTe8lO4CDkV3AQFYaYx8W67gHINfDtHeiOPaRN0xMTMYAhrxcXCBk2/cDcLEhwE1eAuA2eP7wjOVMSZXNWzQhKaZU8ooY3RltE9DNnGAWab3NjqJkRNy7D2dkMQPbvtO2JvDZlWYic8h++L0ZdgA8FufHBJqnp4SYGZAGKpaDMZfMRIBzg6QLfDCChWLcLzdef/oGSpEUM00K88udpXt47vj4d33Qwktoeh5pBTFdEPRc57+MZkMR1/kFKJmSWIMaf8/ojd4GpSoe26MAIQA1xdXrXcYRKtbHEFo+PShSthfquqWQm6SjqG24GpYoymUWP5ubkNdZDjZL+CnFpvNgi03DZDH6YC3yFTJvOI3mg5TLi6/A0P4aqbEPIzePdxfRyJ5ofQOISe6ghoVAb/LDG0nPuI+Xs6ndZSRf8nK8K/dpzDmL0Y/rhmDcHOaoHoXgi6SCuMeHK+B5nkt1qdyeN02N5z776N4/gL4Ab0cwRifQVWrRXgVSLpRhJFMSYGsjmj2xlUV6H2Lh3K48PDzEehJzlzGBxRcPijEaw9UkOpIygxgVevcqbocT93EYwatuJvfEqWQ+XG5ctw23GEyAGMndKT4Ylik9QdUgq3d/qVu7PGK8iy2czWjsqqV6pOeYATobS628eVh4fv/M08MrHnKKYRHxmSfbKtF8ULpTywLWaWPQUcpcdpk5tyF2UNAXyHFWtdzJnnh6qLz56omffn7lq58WlrxTS9UQBSIa3LQGkuoFr4l976QKjMwwDfdyTjR2JbktTupgi2EPC367Y96VpnZaWF4l9uvOfrqxpoRXMahJA7xinkMGt6oepekfn6lPx20fNZjY70aB+nVSeuAhPfCtf7fwH+933n745/z2d9/z/R8nvvNQefO6cj4VXkX9QXKsDHITwL6P8NjLkNvCNLPPJTEeNn78+Wc8/LPv8K2n15z+3BnSp2Bn1ea/9Hf49t+4cP3sH/PD73/B24uxd2Pru55BsCnu/ULfC77tnE6P3Cn89u+9Zy2V5d8xPjUjVcITU41VShZDPQ/AzegZzDP37PFeCqM6uRVKFVuvlszo8tTMDFIe7DOxK8IJtpHZ9iRgwDNp7zRrgAzvBZJucdZX1lrp/R7JX8G2xmhpAIltKBZ+McW4K3VM/1hWYlomx10IM6nTiVdOJmdXutc0YR67QJg4v5Y1cb/faXsnh5TPUZ/S/UWKr3MhHez0CSBbqtgYB+PW57DXUhge65/WBKhPmeuRVuVqwMV49Ri0WPjEpaOZtnlvJZsXETORjdGOuosAw2eqp3qW+Pe64ARyDAX1WDbVgnFoyRNx7gs77moz1bGpKjma3uKZNw2xA2wTOJHCPuVl8JBCNjuHz4bSMQUIaSg9FQA+epiiv9iHjLgXBSoN7ttNZ18MVKdlyZTabd5JQ+u9pf3wHpL/mJOsYMOwNGTq3wekpN6sx+Ag6feRx6PewRESMf14oq8uGGaV4U2fJVmwcaP/Gk19pFYSORVmIqB31QQMDZFSeB1m88OTyA99/UtC9eGbFLWUBfUsJyN7isRa9dTmRlkWDcp30LA+FAAf3Xsa7EU9NFSFjTHCj1XejZK+xgUVQIt+3kf13qyncomPPYIEkOXJi/pAxqwnVJN2l4xOQF1YUAQrP1sKmwDdw91HiDKiPkR+qZkc70YfpO+q7YeJBe+jh/VHAJ8qA9SD2ovJfVSMYROh2mzM3zf+o/cAwaIOEovsJbnxkPx+yV9fPqB0N644266mu2TjvCaeHjKnItp9MiC/PACzRLbKCM04JkPdWgzvna1rM/Zm3PbOh33j+SpD4W1A98TzTeBVXgqnc5bcyozNoQ6jZP3MPhRVvw+X+XSKeOqjUjJIOlCaD/pIGA1QElr3wejO7T647UodWNfMw5o41RdjTCem6jEdSKSYMhqdfvgNuY+Qe8SUPibxMxJ73zYuNzU2911NYjJn7PF9k96XtGqkSzYodviLTKlPXSredp6vFy7PG1tX0pO3TYldQZeV0aaRlwVSlpFgrVxt57ScGHSxE1zTPcOxtWriE4W+4REdrcNJ9D/R+TODaoV9iDWx3fdgiBU1OBiVDIHYrkm0cEuDNa9MM7xskufIbT8dl2F3P/yGWmuSNbTG3hs9/rethZQzNWUZ5JlSkFSEqUl2A6tVkZ+tw1ARPbrT9nuwa5qYVtPwMSdykcG7e+d2uXHfBL7U00oKTy4l3mnPbPudksvsI3QQNT/054q8V8N4axujNWqazY0mD/f7XcaqOVOXEx7sL3llRTx4HGbJ5BNlEJMnGc6XYIr0SAHrIeXTREPeN7gMv3POPF+uGInXr55ibb/IfpJpOiJ2VDR8s3iIPSQPo9B6o4KvdYTRh1EesY9SGDRfPnzQ+7OQxSZoo7GEUXbOSjxxVAS18CnLsTYt2JKg9ZuDQSU/2S1OxQzbznJa2bY763IiL6u+ZcTejbZck5k9gDCXLCnAMA8Ghge1/pi0zP2NH9OZtS6YxfQuUt2iVyJOJRWeqvY08QjJHAGQWfz3CNaXB9OALNnjlHHhTlkKt/uN+9YpDw+4G9USNWfJhSyxR/GSloWRNWGkxfTOXorIyfoYe2O0rjSdYOVsYYJvKclDyuy4CEuAVpZmESp5I1lNT5teBRDU6hcaNQFkTaDqsNI2AYiejH3f8WGspzM19qaUYbNY0UOdR78F6Dm9HfrWjmS7BAegN5vfeJnMiafOFj0zj0JM4NYsBEySYPQeZjSxwKLYG6ZJ1cG6cgMfYjNZ7IvYY5g+59Ya60yGS8HyiOevGsc0qMmJXKAPC1mkQ9xDI4AbD6TVgW5qd7Jp6rbmwi2ahd5h3xvVwwfBplRBAI73Tr8PyCUSxzRUmT5bFlNfp5Nyjd7DXtiKATQTZ9R9b9zbnbrUoygSQyhFDWHHu5nnldmMdn6ZNOKTITjm3EPA/OjH+1jWlftFTNalJMQA0DQ+UKqDaZSOhkn71keYIgeQZZN12R3Co+qobS1M30djOVf22+3Yx7O5MUx34wTEEaDaG5Al+zBLYpupklZCUwyz+thibTvTTw0XK2X0C9frjSA7yIjb/bgvFBICjhiW/d4pi9aZPClkpp1Ih/eMTIuhxvS4xzTXcc6PlXeXnXeXzulNORgVeEgr0drEJJcrrQdVbnA+Fa40fVDXAMzjwvSs7x8MdiTvW3Lm53/+xNv3d/Y7rF974KHC+ljJrvO1FCVZwaB5YjQoNXFOhXQq8vEZTqqZujrbGLAbLJIopFJgkQxkDEibUd4sbJcbl589YzZIY4l47ysg9gwZqOB0rF9xKgo9qGARfoCYvJaWQCHkhUl6jTE4f2PhL//1nb//gy/44U//FX/wxZXv/jDzyavKq4fK8vOJNbm8H03154hWKLv8lYpCZykD9gTnRQysP/mTn7D+3/8P33x4Tf3kV0JytZLcKH/p7/DtX/0BP/yT/4Pf3BOf7QKwRwuvyFOibTe2fTvOveW08LY+8o+/+wGriV/5qwtfq4m0VqzIx3C4HQMkJeVFI5QGPTncgc2xRUARQzWI+4j0Jd0Neeh38yHD/tUM9gxZ63TJhZ4TbRi+L5h3Bfj4GiwZGdOfS+G6bXqvSWx0hTgkCk7vklbh+rxbz6p9eQFrcuxgyePtYNAfv2d2lA9RUNbrPPPRuVmd7XY7mLK62z2G2waEOb5B7xpS5VJ1jmWlZLZd7IwjnS3+bh1l+QCXWtsDVNJnaLTjnPIxB/vhTeOT+ZSCSeIH4GXY4RdTSoAcQwOjETYDswYacfc7IT2d9/q8X80RmSC8QkPyKway+P0WteVwP+qDWpLOqyHABAzrSlvzw09v1qrjqPctPsNMAx5tXvJgPR13uWoukRyIcJQeQ459iG1bkst3LTzAdOfnaDEj9j38q5JpoCiwz5Q65mKcli4vsZbF2LWe6N5Dsm2QU7C5Xkysh7145uqOtQDH2rwd4+70AEkG06/NcEmbAwideXfZRPrwl1XBlAWOsN3IsR5Uckzz83m+j2PdHBYdaAgyTHss1SCYzHfL9KjUWmX0F3DUALJk3DZ9jSK9bl4qUc/hL16Sx0A2atRE+Hgl1zq12b8aNu/wwAfGBBeH6k0GYY8QZ0SsQ/X68zaezLDJYg6Tbpv7QB94SlO9qcbzuO21zyb8Gf/pfvSOUlGJZTnlo6Ae+HjXpNkiMIdxsx/pE2n+kr6+dEDp/eZ0G9B3TtZZa6WuFgePkMDiohFm67qYTRM8TAhpSc6aYNC10INq5u5s3bhvndtmNM80G9xbY+twypnzUihF9G73RC6VpWgiZ2NwR+bWI9gYetB+HNjuQkf3MUGe/XgZe+vc987tJvZGLpnzWjmt5aWgN13aE7hVQgzMCbabaSppKRDdjyZl4e0whsCkNga3+5297XgnLgo550/pQwrWymQx9N4pyyrK+ujk1KUD7mK/3K8XtvuVqMLYW6OPPabemb3LQDfX2UgZeSmkWih1TrTGISHcI0ls9M6e9Cx9KIK1B61f02uZZKdhmA1NhXvnet1oXeabJAFyJRcZdAooDrmE4iw9JkGg9ZBt+lQcOxjvMn0dXUyGtu1sbVM0fTQzaanYEF10SviSKGJ4NFkpizFio9O94RGtue+SBuK67NrYmGbXIgroILg839j2RllP1HWllERBE72cM3ZSKlkKSQYWEeXRDDACoQ4ArfVGG52KkpEaUMZQ0llrFNQMWZgIT7rzZKQoxUdTIotiXA2LJGU9GBX7vouJNHog9SjpbQxwHer3rqnhz336STTduujK3AOlIL7Wy6WRcdFvHTXWvctPi/Ak635QsydyMFTRgcHz+2cBZ4T0JQfgWIsMeEnUddEEIi4C7x4pCvGzxnip+HBGTDsc7a/sMngslth6I+XCejrFZRoHedZFPpkemsp0DpPWqVVkUngTM+Z9mhcCtD7kl9CU6mZZyUJE9PackICeiacpS9JaDzWPpAuh/x8BSkyJDzGp3bb74SuQIxXr/fsrLAvreo7pSNHPEE+b1jvn8zmAqCwgLz5XmkbFw8UAHDv7fheo5Ep/E8Cizz5cn6GUgqfY49GfizatYATSXLPz9xY1uoUP0VFwArszg4rCiHIEyJoo2bi8v8jjqlSyB83aUkyUPQwjoyAOIH9W3fJ2U7KW5FR+FEtjfs8BXug89hYU7ZBZzinXNByfQGMui9ipVQkhZhnLfoBkCTVM1iVxbK4YcBmmRipPFNLX5+3wQ1NZCSmKiJIkEzkA1Dk4iTUx5W1jylFdsh6d8RYNhmRL0YFoTbmYn3v47y0OLPKV0XPT4MBGAP+1YMjDzVKjUHGM3gVmpJyh9SjmBIbO5FRJdUJuF/50k7E2v2Yc9QTkcDVxgY/H1C78TCJxyQMIy7HeRp4gYZK5eYLnDxdKlihHoQwp6PYB6PGR+Wcq9JHAcphEz+l8NDzDYXR8bDJGp0s6ktToP33yis/+9VvadmdZFia7DZug64CkQtQ96Z37kNw8F1IptH0TiObTtF0iIVWt/XgeA8NKJS+V7X5jPOh95GgYUl7wIm+aYSMKZ6cFy4XJCov9YK7BUA75fffOPnbJZ4bqvpEGdkqs58zlcuf6aqWEJGQQMvh4VqB9us8QhwJLTQwKbQsmZzAnceEzPTxv5HEmz8DHp4Wvf+PM5z+6cVqN/FTI5wS54E3PtFQ1n+4wmmPLoqGQDQaVvm+QCrlC7oN+afR1YKmRdjvSkRxkktwT9ZPCh+89k6rz8KpBqXg2vEUQeDe8X7HlCcYD7pIkkoHxpDMhZcwqWMHsEeyBxMDHhqdX2JJ4+voz3/4b3+TX/vQtP/uw2A5wAAAgAElEQVStP+HHX1z5zvc+8OnTytO58LXXhbQ6ljpWjBG+ZjIuDkl9abAXNSWeeaqVd/vgj77zfZ4++Rd8+tceKY+/DFYlBeSJN7/6n/Afvv/A5be+wz/5sfHTm9EDTLScWPID2+VK2zuXdAUz6nnhR/cHfuNfXFirU//tBz7xRD1ZnDVqwjpNtVwyrdnRqclJS6J5o+9A0f5lz+TS8CGmUcsCeVIw7BcTcG41mnWHNqBnWWwc57iD9agFTcyhdNLFcrnvQKVnD/mSJLlT/5KicTZamOdKtgRiJE6uhkqOOOMdxs4hB8IQ+1y0BtVeJoA3heG5WojwjACxR0YTDJW1GUU4CMCtG2TdPb334+6Zd/GsLwlgSICDs/dGtqgncgYPJcmhPIjPHE1pykYTxqtG32c/9VInSKYGaaTDc9HifmljD9+Z2by/2HRM2AwTwDiBhDkYSHHnu4+wRYiaCLBcBGKMHmlgaIO52OXZgsll0ytKgJnuKa2/yebZh0zOxapKR7+Y4rwSg3nKofTsBlGLeBXrO8zOs2kYKSPmABitK5GxFGbSc0k5ehFjT528q9YtZtACBBkB6mSxM7MJsH4ZPRjdDGxaIQRglF4Sdh3DTUC97nkNmN3Cr48E3nHv9C6/oR7+S2PWIQdDh8OSwIMNhaVj4K9y66hoSTaiFw8fNLO4VFRHaGDb6SbgZ6oCSKZPO9dLgHLT+sX8RYY+PRrlBWlRT/Wjt51r1mOYNENgSJMlr+8+klVN5wxN9ZOGfDHcRQCPSFKDOWg0BEQdg88YWhIqhWRIKeXB8o5n8nLJpuMkOfZOgFDM8yOIIAL5Au4L+ryA6kEKO4H5/mNrfalfXzqgtA+nGJyLcVoqeSlRKKKHw4hoeDAftB087+ylU21BsYXOBRWatSQWkpqvXY3xTmbD2VwyHHpiXZ3FjKXmSEZSzPzjetIkpg82Ensybq3TuyYL5roEZ306XOkdPgyPAVnzTuuD2z7Yn2WeuK6Vp1PhvFRJtOLQVTOkhK1iiYambr31ONxHNL9qim1OZtAGbE0R6r3B++ud+7aH1EqLJkViiBmRtpQPmZyHl8ZSFCXeg46bzLnvd4EC+35snL139l0yAzdkqFkKS1kZST4RtSxKp4jmSu+3klKY4AXiWtyDzTKxVBOcMGYihSjRI8m3artv3G439jYkxzIdICUncg05hyvxDJLMEMnsbbz4V1gAEK4CqdMlNdp32hATqe+Nvu/sbYOQ8qx5pZkIz+7E5ZGCNjilIJFe1Z3hc6MqHa5t8iky071NoPnJlATnDs/PO/dmLKdHpWVVNblt2ymlcDqdaEk+FZqQT8+b2QRpcmNoGntrO4MXo2sxsjrXy4W+NfDO8vhIqZUpNTQtaProaqzzlBik8OeZ104UaC6GzR4sgXlh7a1rMubjAO7228br129ChwxYMImSLubWdlofmC9C9Xto+qOxdwcr9aOpTyJ1gXK64GMChJ7F5X7l/YcPgnBy1j8pkS1T66o9lRKp6O8yMi2miipU/QD9cg6/GdOlrmFRNOrDWVKmm/bH09Mrci7UXAS4eSAYs+g1TS/1vXpuc4o2n+2cathHRRfxzEdvAiKyHYlpWlJDRc6YqIsfBcysuOaFeSDhUVxFGxtyGfC2h2xR0cclZ4UQNOP0+ChgA01Yxz7wLKZNiWTDtu0RuSwWoZUciR9G7/IM2ffGtu1465S8MnYZ++dSGN5l1OzSreudBkgzI9lzFP+xfwwxY0oEDoiuHXTuFNPOAF/mJS6wS+ul5ML9fmN9OCvo4NCG6R1M4+3Z7I/5viwKsqTUxRTphy1FqtZw2pwYR/M7WX5K4CjksihsAdX2JaVokGQsmhel4w2ffn4BIB5FVUyhTXTmHEy9+Z9dXTopwX270MaugQ1ASAMtFkjCsBzNS8rHeddxarAHe7ArUzQAoxSt1W1nH4MUd2Mf0ZjENKz3nd43ejLcV0J7iNIHF6bvlTAsrRdql8eH6x2eH878m2lCw5WA1loT6BVDCR+SaNQayIjwoePLo5ifKZIjaSo/UxVJhlEEklmKBk17Luq9KFAzuSS2+4VtXygFlhh2WNw5oDUkPwQBzN0d8xZStB7PqMWZIAlszlPqL9TZEnhLnBexFG6XZ8qykIlzpWiqMHyI3cLc94kxtGctxb3HgOYUE6Q4TM1AwdRLDYcmdo0hlur2fKdtT5zOC26ZUuP8mdMcizOsCOCZMgMfMXxB4N7oRsuFnFv4PSbyaGI39IGnzmKJN6fCzz6/8OF64/TJAxUdU8K+Eil1jBpsI1jIamgsURc1OUHj1cARDQ/UyEfjYUrdW0rhG19/4vnzz/n+Dz4wfu4Nr/ad80kyy+rGOdWoyTvVnOwa0EnaLdleao6vhbzO+38CkQ3SAqViozF8p11vpGLUp8T1s0Y6wTklWBZ8a2KCJ0i7kXPF0grjrnS5MScE53j2A6whs8jCIBhV6bXOltMnfPUv/gJ/89ee+eMfPfNb//ynfO9n73j1h8YnrzTkfASWnMkFjEGyTieTXUBmtkYvQ4mkblhx1pPzfLnwe//0D/j2m0de/eVX+PJNjBVY8PXrfPXf/y/4u/t/z/abf8Bv/7TzxQ499k5KiXx29ssF35172rGbsSxnPh/Ob/yf7yml8Mt/xXjNA0sVa8+TgRet+wALmHUkU+IvEJuykLzhm5OqqRHuYhMaLvmli5NV9WPll3I3coHMyj4auWtYNbhJquYyxk1VwSAlF7bN2UCJdGMC8Y1q+p13N8gdO+SY8sBJ5LjCx8HGEHBmGCHZ9kFxw72HOMPIRVJ3y8ZyWpWgd7/qTPfB9Gfz3iRH3ptsFiBA+oyRGU17NIdP6yHPMrFJRzA4j0TVsFwgTaaHDsWBZHyTATTr06PJdceYfqFxRcd5SUiX3ARkKC1NQ/ExmrweP7LYGGF7ognRiES/dJzxh3egw6HO8KShLBN4s6hHZfExa4XRw7+vBStpJLFKTWtsJEh0GCm8P4cGCAn2fTtAsDiF4+yFTIpBobyEJtgyhjNMYU+LGTFSjVowx52qpOhsGUvO5Bu1trEsq4CFBCkkwgp1GAfjWxLBET3MHEIYzRJmoewZ6kenT6sep8UdGL5JHr9RvMseg/dixshFYMXotL4rpMlyDEkFnjgx+E1RiyX97JwEurf9LsA2BroenkeTsRMVK5PllsNbs7kYlfP+ngCJAM0qYkiAeTlq0mFi56mnGbEuX1g9ZjHcQYBi5JcGCONRd0WvE6vcrZMDyLb4+TLFVl1niDEmsNBjYOYByOrZt6hvcvzUyRHDLdboZIlHLTRe1vsEmOwo/8Wum1Ypwg9emH3m6c/0PLOXGObya/oIp/gyv750QOlhgTVlziVDVROgtCxJnaT6HQyyvHoMzCruxjbUOHQzzCvZO3WoaLvtcG9wGcbnz3C9bCQyS3Eooo5KOlHJpin/uspPqLVOG/L2u3VJMGpSHHd3RYXPoq3FlHmms+zBxHh/uXFvzmqJ9Vx5PFdOOUdh4WLUBEDRXNrVlBL3aTpsosnPYs4MaUETdO8HNbM1uG+dy+1O3/bDYG5YJCull8NaHjEyBCYJeSylUIPO+Hh6pI/G5XZlu96I5UvbNvb9TuuOpSLvAR+c1jOlKsHNM2FCXMi1ylfGQWk5gdCbs9TCkjMlq+DUIrcwou0Mm0CTkWslUWj9zvOHZ4ZlbFGaSc1Z6UWlzIR7MIsCvDJ80PddB06uSp3xoYnKBFCGvH88tONt39m3nRYXSV0rVjKqNXTBT9aCDgo1ykRkq8W0vNssJDf2+zZvVEDyN4tCPwXGcN82AVaPJ3mOlEyuYoNRdcDu+0ZjBN17osV2aGExXfDGkCGq22GxYBYGhJcb2/1OH/BwXsUkC3mcpXTQbWfsND6opYaUSNOr1pX8wpjGgjJ6620PQGdGYhrW1eTeLzfePD6yLkWMBEOR1D2mNsCUPbbeySP2vYk2nDB6njr8OCGDTeO+HzRzgqK6t8a79x/YR5eBOXZE16+5HABTTTmGbnb4i3k08zWp4+5pYkHBMkh+NLI55yNl8fb+Aw9PT9Jap0SLlzKNZD38ckCffZpC0uVt0HalRJWi7++px/ow5sAsJU1Euw2e1pPeUzC2ZCUR68L5M9HeqaSYTvAyHYnLL5sMLolLDGDbJCvIWY0lOXF5f6EPpywL5HJMiYqJTdDHTkkTpbAAXWApAaYFnXwaITvRkCAzx31vB+g3hnyDlqK0NWIymESXirQzCwAkHQl/S5IXzdYHta6MFkydAsk7xsvkVZ9xD/q0AIjtvvHm00eKvfiscUyHiCILFRcucGv+sRKFZrak9M+jkib8MTs1iXU551i976S6kusCfVeBawK/cymQFd2aiwoqG/p97XjXSpMceMgitd/1ZywaeE0+J5DW9/1gCLo3uoO5WL7uM8kl4tmzAHkzCyZhYGJoXeaQmezIvDp1AWXM2OMuFnGLJuFFSqGpWMlFwFFKkDO5FIqLdaS+QEXm7vLrE3tsMoZepto2z48koMeMuMM95LTaay8pQS//TLZSsjlJt+N3tJAn4jPGV+CKZJbB/A2AdlkWru+vAoc9DDBxkpfjz8g0WXdLduL39JCL6b7vc6AyxwBZQJvsf8Qq3DClIeXC5fnC0ydvVCMm+wgkjeLxYEXJsHk4kQLklKLn1dtg9mHuiPlg6VijowmAStGgXy5Xzg8rnSH/r5j2jwAdypQDJARcJZMswkOqNqfQY5CLDtimrclgV8pcMCZOD5nTs7M979yfuoByz1r3A3IxzIvGQ2NjdxllpwZWLAYGMJIKeEdSq2GuusdQ7LxLlnd6zPziN1/xr/7fz3n74YbZGboz1g5NMfXlJBnTwNkuDVs7S3lk+EYaha3trDVYSg+Ffun4fYelYK2TFjVVxNCTK5zeLLTnO8+fN8ob1W6MO+MOzECRIYBZMXU3nQDpHsbBA7cdrKoeSCd0GIdNgxc8Veqrr/Dn/8rX+bV/7xf4wQ/f87s/uvJ7P7rw+EdfUM+Jb9rgaV1ZiEFkhhyyQp9375yPJASUFOf0AD99/zl/8L//Pn/19SP1Gw/Y8g3ks5ix8y/y5/7mf87f+vy/5e0/+5z/62fODRnQD2/kmhmnlXHZ6TZotlESnM4P/PiD8xv/9AuW5VP+wrd2XiUnyJpH7QNGGYWhB4X1nWTQSsa2wbjt5Fq5eSO1XTUbMsOeYRGmGwLDKL1DKlh1thapckWsfGs3Ss6SSAlN1ZGXoFYBJ9u2417AFbxRUw3QIB/7U6wEsVyGcAlIkKsFi88Y4TFHcnJySoaalfKVmNyfyZ6JPUfCemFr97iTB/sArMoaIQlUVYMcg0+X86ikQhpAKMMmwDvirAQsmOcpWDEtEnEtGBrJBNgQ30ewiFooEHIWw210xwP0aEN19Yh1plJSTa+YMpnWLRJlndH246z2rvGwUKGElZAWD476K+ZTqvWSSOMigTop67OlqJdqUb2W4r5WCJNYRGkMUql4Uv9gWeoJGwvmg1I1sHz3bpNkbci76IUkob88BViUrLD1LVLr0kGw2LeGm9QcEESGCJVyh5E1zE2TKdsHY2tYUm/bUqKUE0H5ZvcuJl6LwcUY8f/Kh+zdrMimIhMKlACVPkoDVR2aDmBB6Wcj/IGCBcSLFcMYslFQ31SOoe/wruFsSrr7hp7FCBsO3VXqB+YsVgvKVXN7sHdSMP4sauyUyEPrKaFBmyEQMoffkLnqotZGDI/Uaxy+SyPSiKc1jBNAUNQMliI0yJjHz2S7v/TaJsY1ftRrJYs57+bqM/jYlygGyXgMrkSQEEMspN6gXlVUu+hDXXe8f6yaiupBH3kWbOqxw+Re9cgL2Do8xTnw8i02SSZ4sKM5fucv6+tLB5TenI0S9VnPRkKMkpIS1RCKZoPRJw0sMTxxbyquGoPNDO4XmnewwbVXpcW5s+1wve0UN9YKS8l4SaRUKbmzLrqs66JJ5K3tMrh1Zwv/lBQTf7VBURx70+JPBR+KFOze+XC5cLvKT2mtlce1cD5VSixmudXLmtoCmJoX2RiwNxkHSi6SKLXgFvpRhJYrpWWw787leud2uwcoElGahzZEBWaOVCl97vg7syQFHagF1lzBB9t25367M7rkcL139vD/KWmlhRfB6XxiqauAsaWylkJJwRxKSroyk5m6pQYpseYc/15021nCv2iXI4bSLA6szt4az9cLngrLugAyUc4hA7QcWnwzPKI++xC7qKRMQ9RQ0Tj1aHoASB7JQ/LqEADVwvB8PZ8Ao++Nkldqgdac87JEU2yQK6SYWFuPwtro98a23djazmiKU84BDAgEAqyTi3PfO60bjw8nSs0HCDgaMkVeV/btjmmEQjGtFW3yWJOBWAuwigvOZuaPcW+N2/VGu9/ZWuPp/MTptFKXhZQzuVQ11tEHpzJTQF6a6ZSEsENM1BzavkfxIRCqI1COziHn2q7PrOcz9bRADbppLrTRWXJRUohJs5wtCXQIQCsNHaC1VMW7ew9Ap8vbKLKOLZmM/OLnvH/3lvv1whJGeqnI68cMpQG6ns19k2zMZtRrSqQypUzx+yY18T50qZgLwe+hYV/TwhdvP1cjn4KinVXEJdOfUfM0C6UpaZIZ8+5D32PGui4qfgj6rmZJjCyWVnJjXSstJ2yRybVbYvqfkBIFyEW+UHqIFl5R+ZCAvUwtUsiEDJrotO5KmqwTdA0Q5fLhmXJadZ8Mj4ajqKlI8q/KeYIGmoIVJjCJgMe4qpJHglpd8KIPk2tE7Lamc/2+8fT4SmfMNAiOSXSPwiylEsl1xtgbpKopS6yfBlhVJHEN0Ag45H3uBl3nxy1S8+qqhDdKxrrHtKcfZ6fAaLFJVfSrsNaZorPE+6CUWYbNC17yWw9PIBVUjVwecPe4WJPe0wQ9UwoZaVzyHh1Hd4EHKYzpJ8AMKt5dz6CPTkMMGZ3fRm9bMBtdUsMA9nx2Zu7B3pKMetYQ0yeqWEgkjEhts3g3u363KPbMDC9am2MM9qZz+JCaEV4C6L2OMVO8lGSmX8iAQtslnyplIQVIO/1QhO3k489PSUhvOqOn5O7wa3M/kiIdKIiROKuolLOYYwGyO12ygJTADR9i5eLQ2y0aJrFw5nMaMfU1JGtiDEqVuWxOHsOLOymPoxlWwyMJXAqp82g7iU5O0GJ62YOJNHCWU+bybsPbDmnRHgxJpDCGw4FEzUs2nArWqIvOQG+SdYMmkRxFpYry+1DiD9lIIU19//49p1dPpLOMfNNoGFVgYgwbpgx5b52Su6Ri4YEol1mlvI6mJj35jSk3aCHV6DQsdU6Pic++2LhddpYneQa5GbnIlHYapQ4EPuJdwyZLVCRPnB4RbbiaLnSmaBqldT1Gog548xXjK18/8eFzp73ZGCPjrbDnRNk7S7Vo/rvqmA9Gfb1Rc40BR9VeGRUriVw6+9bxpAIkpxPT/4toIPo2qF8rvPujK89955ygpmBHWCGfEt53fH9PKiuMCnkDP+N+wVMDO+t+9Q7eQ82Zcbtj3jB2cso8fuUrfPuv/Xl+8Kfv+elv/CE/vW5850+vPDxcSBi/9NVBskdYnNJCBpU6SdRNfJjWZEbgXnYWc/yV8b0f/5in3/4O3/p7P4eXnwv2SoacSa//At/8u/8pf/v9f8eHnvju253WJTu0kaAY7dTp98YtFSxtWCnUT17z/S8a/8s/ecc/WBLlF41lXfFlxPBZbP8Rjhc5NbEpukHfabXS706/b5RTZlwDXMhCgTJGSTKIGlkgai6xz+bZuCMWelI0/BiFkTfoDbxJttudFnXAkjKXW6NUha707ngBdqe4gRX6MhjNSD32RdyVZmgg6AHydq1pNf4aGFsWm3T6LQlc0W4vVSwjvxTaJqaE9QE0co4+wuuRWDu8hYfNju86P3XOAgyaD7GPhgcIBrjR+i5PyZHDrLspMj7MsC3uCQi2c4Dz5uMYMoAHs8Pmj50YfoAazmQ2paRBYOuNnItsJAaS1Ia3UTLDRjyraOwD09LetyxpVk7ykQn2cEHDjgHkIbbGVDSMEXKnWTf1pvS5FDL8GNx0H2QK7brLaHsAWfe09yAxBErrXWym3gQ2Nhpbb5SeuPldbOCseyFZpSQgao0UAz3HGX1jKVV3aLtT10prnSWfaCHZLl41vM0KvpkprPK065Sse2qkpnU3SjB2XAOPY/Ci/V/iWfQ450vOOq+TrGESagvaITPXWpp1VErp6KmZybJRl5GHDKJdEu8cdTuhdtBgxUNhov4nu/7e7kOkiZTIe0jqourMUXMnpk+UwMzDb8ynB6uAnSz0T+COzwFcj6GLwj886qjp4+ROpNIlSqrqR4DkMYxvPYY8WvrCoWN9egyMDDwVAbrByuq9izUeZ4QNlyT5KB/8BXHzGNyafm6O31cMt2A5k6K5egGK8flsZPA9EOlPuyjRs1RF841+WV/513/917/UH/ib/9M//PW8DKzog9csGdOpFmoWO0MeCzmQNEV0yih7w4de1PM+GL1wazBG4nIbPN87t3ujFjivlWXJ5JpZl8qpVpZTYq1iu9hQwtDz85VtdHpKlKSEOTOC/t/Do0YHzFpE8dx647rtfHi+s907w511qbw6VR7OK2suZMSeqUWskNZHONPr3Gnd2d2oi1FLJpPCKG1QMoHi96DyG20bfHj/gcv1Gg7t8WVxoRVN63MOI8Awiy3FKEXNWE6JdSmc18K+77x7fs/tfud+26IwVnLRGJ1S1mA2JE6nM8siw+iyVPKqpItplBtiEUBT7pITrjxOHfZR4DILqgDDRheYSHdFvW53vrh8wPLCclLqUilVlGXT4VxSfkHTGcemyybaohfHhEaIZRSJT30IHd6HwL9932XKa5BPC3VZ8DZYSsFzxtNgqZWZSlcUO0Iukjd5JCr0vbHtO7f7De+DGiyDPl6ms8OGDM+72GXnx0eWZaWUrKKqNx0Ws7EaI5pqTW6KiT7L/B2jSRtdABwm35hhOvQv1ytjVyz9w8OZp8cnHp/OAnrWNcAHsQ1KLUE/jcvXoVYV8du+ic3RB/dtk+fNPpPBJL2cANQw477dsVR4fHqkZEVXT3YEPqUGeieiDMPUxRPNWU7TyFJyUAz5pyCWBMkptQQ117jdbrx/+05T/Sz2VVn03oyXSQQBMEgWleQ/ANCD1tnHi5beR6wZpY340NTEgbZvPH/xjvrqUUBOiajb7mD5YA1MBkXNOX5XAVdLqez7Rg4QL6ccCR6yx5vllBMsSG8yPMxKIOPjfVYLSyp6X/EOSy4qanM+Jh2p5CNNb4QcaoTJ/rbfGWM/TBpP64nr/cbzhxvL0xvSIlp+zZmyzrjenRSGnSkXzIyKzvFJaU4hRZoSOxmpZtSDhh8A2iP7Ls+5159+Et4BSn5sseZzFgtS0zWYaV3JiujgFVrfSAVSrpJpTZPjkKhpGhMTJDOeP3zgvm+8evWaJaugOrymArAwTOzQ8NOzmGAN02W/96YJn8lfg2DbtB6svz4lhiOkY1AfHjmvD+ShdZYnQB3PZU4kYSZZjpC3RdEeDQg2Dm+jg8Gj0kFr1hu5JK7PatwfXz3q7Bg9vPkitfO4R+w4xZMZ+95j6hVrcsojXT5n5hq2TCnoLL6vbePzn/yU88OJ9VRY11US9rrEX2MRDBADg0jEmewKmf0rMKEG4D4ZSB4+Ywbct519VxF6u+60vfPw9MBpXSj5o7RDCABX7zOXCUZpr8yQi1L1WXAB6xZS2NF3rdImQ3kf8X8P537bqKdVvmaoRgAZ2LpLZqwSciAPNJ25AubSASjlHFII+DNArZJmjNG6Js658/z2mfr0SKlnSrIwNA/Qev63iQUCLvaDqdCsKQWjwSWfcnQvx6SzueEjPLvCw2N04/ndM6lklqrfcKmL2BiRTif2qkC90VV75LLgLrAwZQHXeTiMPXxAwn+vay2WXHFz0oAlJ67XxvDC+bGy1AD5A3rHOhlJjHs0LyUm1ilP/ykxZVRO5zi/MymLHd67y4+nOyUvpPPg+e1gfcp88unK+WGlnuOeqWpQrRs5L7TWFEV+0npKa2Pc1YCXqL9iUB9T/1h3048lQ78N8mNh3wf3zwb1tf6MVWJYsZKKwT7CL2meAfEH2OOEArgJCGWoYbMO3LGxQdsxE0i5lsRnP37P93904b4bvRvnmnlzOnFaMjVHQEsemBUUD4OAmGAPu4nBlNWe0avx9nsf+PpXCusvfAuzBWzRGkuQz1/j9aeQfvB7/OhSuO2NHfnq5GSsdZHk+b7jVjEGT6cnloczP/nZW979rPH1ry4sZ6dahqSfa57i7BPImCKhshYFt1ATFOh3sDWYhlF3eBhSWTA7zeY60aDKiDqkmJKrRlNQDsaLl47Si+cgb0la43vbda6kRE8bhJxfCZkTnAnmEwWzTHOXfUaKOjdl2oDbHU7n8tIcp2AfIoZyVDjxsyfjIdF6oxaBdtNLpo3wRmTKpsQKzSZPPPkEOiXrZ40AE8yQDB8CtAlA2AQqgcl2Yg457IUVqn0Q6VoQ0rmQoBMeO0l/X6Bex/BBd00/QPvWO6lkUpLXbJnS+FkTDQ+QPzqRYB2blWj8p11FIWXdle4ahOZgLkKK50EAfbI6GRajvpywPu0t4j4csbaCcXR4WMbu1N0ZvjfR93QTq1/EAmdaEYiZFsOoeN4ewGGKwBAAGy+m2p0mf6puqj2zfq7xwuZxU2BU1jSH3pwRIT8CAu0AWlJOkoS7hx/vZBDHuxpOMdeALDwaEmL8C1jUzpqEhukzlMI2ZKaH6cYX2aG3JuyDF5DUot4RGDfZ2PFzP7rHVK9I3VFK1RDOjeG7uHA24s4PAdkcMoZ1zUy/m3UJ6N6biXH6W8Zc3h+VSjbRnVh+I9a21s4YQ754eCgBVC/J3mKiqMFM9tjJ/gKw6mxT7ZNi2N/xkM0baUziiwXgxuFdZfPnxs88AHptqhYAACAASURBVLRp68D8C0IvYkTavdZbnuvSRLT5r//L/+q/4Uv6+tIBpX/0P//DXy9Zk/9KYlmKDr9suDoUiM3egW3sotIFosdWeL523BOeCufTwq0Nrlvj3iSxOhddjmaJWvVn1ixYsHfY7o3NO+/evaPtAyxzOp1Ya420rIicd3AX6j2ZQ1vfudxufHi+CoHMifOp8Pq88uq0UMNs2IKur151hEGk6Ky9j2iWE7XGJorDZhrqtq7EI4Zxvd15/3yR+bbPdjPhVY1mDlZCmih9HPBqrgu5LpRagxFj3Lcbz5cbt9vGtt8lQWsjUo88EqscUuHh4ZFaKtRMWU7UUllrPhazEltyIOxKA8pJbDIXRSc2m6YrezQL46MdWkvhcrtxud0o5zPUTELSupSMYpmCiQ6a50ZLMAS2YKYJZArJj+kS733In2oMhisFwZtMuO/3O6lk1tOJkrKYRTEJMtNkNrsFFbZIiuCJXAszIWs0GQ1fLxd5odQXY745And3Hk5K1rhug4fXT5weBMa5oan0kBa5psKgsdSTimcLn5W4YkY02mq4O9u+he5bYFLKmdvtztYbfr/x+PTIq1evKKVScg2j6DBsD/Amx2FVciUlTR8SxnXbdJhhtL3RNnmDtTAyTyaPFNxwS2x90Bs8PJyotWBZBVuOJL5aTvLDGj20u4lBsBQsaRoaT01pHTFxjcvKk5GrUktE3TRa2/nsZ5/RWw+jXHmIralgObGWyloXcl2hFE3oPvo9HackyfI6IySAHmy9JqCvdRid/Xonwf/H3Lv0WpJleV6/tfbeZufc6+4RkZn16KpqQICARoBUQmoxQIIPwEcBqlv9Beq7AAMmCEa8BBISLSaIBqTu5t3dlZWZnZUR4eHu93GO2d57Mfgvsxs1z0F5DiLDw/2eh5nttdZ//R98+eGTfLYerhQr1Kw7tSlB7owDR4VgORLlyGZriuJrRQ2k0nVQU2jaHPV5MCr0Xmp6C+hnpOFtAiO1vDVnxSTFMUuGxSFrdNjzPtm3nbEroc9mMMaGoTPQUxL73cePYiWujYZRTGeBJVumILBmbYUZuVUi8nkttAReD+bQiLeELE/qrmRrwR6Tue84sD5eGbuMREk/luoFjespnYySTbXYKkkkUQPklYKeEcvhKPJzqylQ0lophadPnyheuD486M9ppXbegzqjJn+5yfETXJljQv5sqwISR5cs+WB/zPmmgxcd3FgertRloR6F34GpweRsjjP/xz1k+k1KGktS0WNqA53b3zEkYRwh4NcIqutM+fzxI96M68Pj6QkVIXBfDYg2iT9urI5mbu+77tw08hwxz+1XrU1NsKeUCmP2yb3vfP7uI+/eP3C5XLlcLlyWB9bLBeBkSC5tkfQzkz4lBRv0/cY+dpUfP4C2YNsyjScZU/f7xpwd5qDvnX3beffwSF0qtVb8/C7zV9Zy84LXSJA4P7OLtRgzN7czN5U2iankyjl1n445JZ1ksPedUoy2VD2fWmefKYqlFF1bTNKWWiHq2Ve4N9z0vFgm7Mj7QE1l38XqiaJ78NKMl89fqO2Rh3ePZLHThrUcMhg9g8WrfBIyCY8Q2Hp4mxxnUnhhR3JErOp9WaHkcsKqM7ad/Xbnq/ffqJl1MZ4lcyDvnWDsklaYq7EePWWJboxdPY6ASLGy+5jygkovi2piEQhgC56fB20pLE29Yp48J7jUmSl1yEAQF0AmwFeMRAEGGpKSd4D5yCVvo0xj2E6pK/tt4/O3O+VRo6ADNhzvoYS7UfGyQwgQK77iFcpSYdcGuTQxAQ/GtRYqgdeaSyKBxsMm5WXl+g3cP+54K6zvjbJcKc3VB7cE6QtYXcFbDsnJmM4zUf93QeBCx0LXPrgR4wnmTqmTh8XxmPzqz7/j+6c7t1B8/eXB+LpeqBfJi8pYmDUUkEKRb1QIwA/0ehMIq7hNXmywfdr5/T9q+PUPwSRVtFBNbu9/n6/WH9h++ef88sXZ7vAGhhl2WYj+Kn+3WcG7rtXDhV98+4l4GvzOz0yBOsXF4J8pk7KDWSIgL6zgYdAb0wozduYOZW3YyLSm1lM2CNOOhYGsCXyKhVtjYWcD1/29j8C8aekXjkVK8kN2AyWQ3YNPtq4U5JkMGZXNlHIdhseRcl1HPZSbwDITK7X3wRyFWjhTmN2QgTOqd+F+9k9KPCbZ0JGBN/bW5zQVyurCuT3B7YhTTC3wgcjr/KPBM3uyE6RPpjx5x4Hk8segfdRLLcwyEGFK/iZP2KkbKAdd/fHj5yUzPN/bPjqlNsnEmQqVcU+rD6PvI//q8ZrHQC4wAuxcrosZdcD+AibdjzRVSSePRFIrFcylSMma6TOTP0+msOXrjewdO4mMqC7PIf+fZKwnfK+6TfqJZp0qBrMf1+IAUOSNI9Y0OG/pvmdCqckxxBL5K/kJzS1tVnQNSkLAOSkx5qbFUHXcjl4qvXWzD5KMW4D4+JEP3Wk0fVy70FKmFDGAlISiGa26QLqDWX30GarHen6PXdaxXLYEcnrOzJYM4rAEXKb6QMsFbvQda0WL7jxj9f4GZrJsmT++l+2tZh09gPub/5FAHD+/3+Ctlzj7iiAld/rzY2jGtxlJDDCo6ik8n+eSCgrydTgWvuVYfnOarZdUHEwTZnD0bidQ5wfgZkrETpAzIp/VHwG0x+/7+R2XXMDPPCPsvK88jK6pS/2pwd/5qwwo/df/xX/0pz03OcUnbVGMbkVGjjO9imayA4TMBtZXPCqDoFyM1ozrCs/b4PsvndHhWitLlfGu10Ktk6VeWReT6V4f+KzIeFND4Hp94OHhymWRR8II2Pb0P5lTr4czAm775L7D5y+v7Pedy3WlrcbDQ+OyLixL4UixKfU4POOMMd8juPcgupomRTOPlAFpiGDK7DdMLc3L653npyf27c7etSkvuY05hkAvlVYbpbbcZoll0pZGXS6U0vLBHoz9zsvLndvrptSRAtt2S2+hwEsjZtDawvXyqCaoFLw2SpWUZvFymnCSh9cRpRk4c9/eEPccII8mVlpn4aClOG2pPL0883y/sT5+YHUNm96MtSwspkPUy49Ra1EEvUR6ScBrPx5MDZRzaLs26TLgHoP77Nz3jX3faeuV9XKhub7LiorvMGhJMW0uiVt1NTE6vDpj3xSznobeosFySoxGsiEgNwPFeb5PHj+8oyxC8puJveFeuF5X2tKYYxMLJXT0ex4MXoQax1AhF8DTJUWrYqiUWrnfNsZ9Z4ydZS28/8k3mAXvru8EonihDzEoMLFLJCWoUJyexUsG2XCkZWz3G73vKR0ZWCn0UFzpluDg6DvXy8K7y1XXbRHwuA+4XN4pnrpCrRdtUcyyMOrQohzFD1WaZKI4hSjZ3KjnyqJvfH76wuvLK60uCVypoTrMMs2dVsU82DOSPpw0oy9KRQP2ZCaJkfA2EPYxmAT311diBtv2ShTjd376O9S6sBT9jFLlveWZ5BiQSS+k/jm3KqgBmV0/t3d5ZPXcdo37rn9mM3L78oLPYBYBQ0vKXyzT6yJkWK+tockkOplbRzoV+bwFAlPoPRs8Dc0xtX32Uim1MMfOx+++Z7k+JLPJMQ+WpVLCGGxMKmWpVDNWZGxRimOt4G4stcrhwMDjzTMNS6nPlN/NtnV2C16/vHBdLtqw9bzHxzxZVwdwYmnc6chYeHCkAx6MjgVsMu+b2Bim9mwODQ0CaaWH//z9D7x798D6sJ5t1iCyYQlF0qcHgYq9nWO4mNspVKzlrUk6evAciqsfpsh2fqbL46NkByPT0dxEVQ99poor3cpE2fda3zZLQTYV5GAaTBsCmfM9FvcTjLTifPzuM9fHB5blmgkvakgkueoasFFTQTZatRZ5vdjhf3P46cB0PxcOEZNWSP8MNSSv+53n7z/y4etvuFwuPD68Y2mrWF+aFmil8nC5ULzoMx5LlBw8xkDpmRyMnZLPyFuzKaBEz9rrJqaxXysP12v6dujekVQui0Q2izLP1OBUktmDaQkzxxCIzBTbIUhjWEkce0zJCMdk38TUW9cH1fK5i8GU38Xhr+Ahyfg5YE2dMwoKMMw6MXeFG/R5ys9n+hlQjnqn3mgfjQ8/+UDxZO2Rcs63G/BkeSt1R3T/gxWq+8WIWtkP9sTRpNqxOUb+NO4Mc56fPsPiPD40vBiMYOTipOQ93sdg2qD4FJvJAZ/5+dK4l4EPTrbonHvCw4OYAr/VbxivrzsT4/FysHI0bDsONrkPBZo4Sl0rFhqcDGbXuds82WYpb7ah2uKHGWrVc1zdsbLzm483Xj8b+CBug2CwjWy8G8wo1CZgvDpkTkKeTTqPFbBRdX1KED7E5PIERKdRq1jTXo3yEMSTsXxYBOqsV7w1bL0QdcV9BbuCr3As0+xygg/BosFM3ACclgCDnmFLP9CyVN5fV+x58ouf/8DnUXjdJh+ssj4UHtbg2u5QC+lMxSxD8jQPpU1ZEHSB7aZntxb4/OmV+O6Vb372g6SR5UFNkTXMGpcPv8fX28/54Tff85vNuA2Qx1VmmZXKfr9zn5Npk0JhvSy064V/9KvvWO8LP/uZFActB+4xyXlBPYGILkNefXPSY8e9MtihB6UVRoeaw6i8WVQn9nx+um0JbgMtmMNoZkQJYpspL9zPIAd5ksxknMC6qBZvffBQPsAcMreOoKeMzAKsWtYkEkiyk2Fj3rj3nddbcH24UOoiZpEHZkO9aGhhdRgDHwlW2Fuy1UwpX48iWd/iWVcjLSjSf8q0oD4ZD7lY8PQBau1YZGSNKZE+jSVlr8ci8McMIUGGx4ICjv79+H2xR+x8qpNBVZIxQ0ryT6BLdXfftHyafbDdN507vXMypPx473ae92aZPpknZHX1uxZxyp3dkgmSSWEenGBDcae5GO0CkdSDRBg+32TVZpMxjJKfHbf0GtIVOmCJakZYgleePMoi2WOEAAL3NAvPdNceBxN4UmuhtmQPIf81jzh9evXz4WDnpxOj+ltCCvpkrjEOg/s3f1TP3t+ynz2+W4GYI+cC6Aw8mpYaacVQihY0TCXKHTI1TL12mCfzKVlhYyaj+A2ijARlDvwSM/kQ5abl8HwcmVpm7qT/uv67ezJs9bnqIorpiPHGlM/7TbiOnWw7EQLyUUqATTV9UFI+WfBk6FcOVvmRMvjm86ZZq6D++/BkFc6pJbIV1dwxOp52F5G9nxh0erCOxazbPNVAB/FkRHCYS3heazvu3+PP5TMXU/NRoloISMsuynTNI966iDsDH8Hf+Vt/+68uoPTf/2f/yZ+6BdYEEhQaG8eHTJPPkQdIcaql5nlZMWvpuA+fX4NfP8F3XwCCpTlLM0qD6rDWwmVZuVwKDy1NV32lW6V7UJtRbaG1C16DPmHvQolBvjEjOBv0e995uW18+fREwfjm4cK6Ft49XlhrodlKdCRXs2MIkRnpGDp81OBWDbwOypYiN9+DEZJKiVJbePpy58vTkw5MLMEiz82taOmlOPWysHjBaqWbtuGtJg04jOpBrYPb7YUvX25sty2L8GAfd+brFH3RLL0rKsvSGBilrCeVviK6b8SE4Sfq6T8CCMiH9pA5mdm5rZeBaeTDKhrk8+cn9j24rBcu1bmbsbgMzaupcJVaz2asHcM0Gvos3mJJFZubm4qZ9OChVMA+BnOTgbW3hfaw0tpyelBQFhlimydzZ8FsYSY7rWicl1ljHsT3fWfbRTVvLp+gY/Cbc7DWSmuVj19uLB+ukmNQWFLm4QlqDod905BiU41PS+6kWCFwv98ZsYslsE32GSxNUj1D3k/b/Z6eLoNvvv6GOguXywMjD/OeTqheHK9Nh7kqv6j/ib73vglg98LrdmNPiQc5JMXs9G2wp8557J3LZeHx8RFvegBbW4gY8k9CTKz1cskUFAEBLYfuQ57mxc8o29PQGkmGCmBjZtINfH5+4emHz0r9yyGtpp7aMib7YO8ICAtKlYH3clkFWHfFjtOVgmIpi9yip49A0O+vEDJav43Jz/7aX6PWwnVphCvZMGdVadst6IwTZC1eE1CR/8zcO3sfkuuMySwyx1Pa4OA+Olvv7K8bY7uxrpVaCq0t3Hd5ILRaGaGtREkJqK5pbh4s2UmuZmjEYHQlfuw9m9gZp8+RJc19WVe++/iJOYyHD49i+ZjkpDKhjgT/xBqqRbJgjmY+ZUQEkHRfMTpCjJo50vxwYiHp5H1Mxn1y+dCIXTRqS3C3cFBxj0ZPUoMIZ8xdctrc9Nz2oKxXZghMIpRYMRF4dtCBmzl9bHz64QcuX30lxhpHUdfGaCQYbJYMIrOTyWdZgDUf2tugatoAH75Ahxmlm2GeEkPg+u49rRSm6SyJOUmfYr0mTrifzVdtYh5E/pmSm8hj4aKFqgt4NyfG5FoWXQKMT99/z3J4mh1ecgfL1cRkcTuYwc6yrEo7OrZ1xc9tlTbkyf6ZosErGFLDRo9gu+98+uEL7bJyvVbWdtW5vSh6vpr8kJal6ixjitnKTKDMOBfMDG2n68rYZ3rHSgIauXQhZGj69PzCw/WB61pZmxYrM3StAjHZiivW2Ei5FlpKkKwDQkMbRUuJWg/gVe3JDNW+MWVc3fdJOFyvF2rJKOepMa1UEyvYVYcvl0ItjXsy9WyOBNb1LKSvdQ5RmRQ0B7WJFdGHvE0sOvfnjevX31CLZY9hHPKuOdNQk5IggFHbYWwuqdf93qlVTIY5xUgaeW3IGmRFDXMplWKT2+sLMZ3LcpEvoQVLFLxPog8O3zB8UrrRHJZVzXTfYOydJZPq6IOlkUyxbHyRTG/awDNFb943+gbtYaEt9QTrphs9mccHK0KDqUMZNBbJyRH4Y0U8iuZFUylNpvdRmUV+iGBcy4Xudz59d0/igBY7mICo5cHxu+HNqMuCxZCs62BktpbNeaUs+v6pBfMKo8jT8Rj4rYnV/Trxnzrz87FYGeCrkovqooARWk7dC3BRV+AXJnkmeiWNFAhbMHaBAl4p2rIk2NxZl+Cba+Pp0yd+/k8/c5vByzAul42fXd5JjtpMvh6u5EAtJiTjtXD5kFGxqc9bA0Yb/NmffaL//Dt+7w8HXN9j5UOC+CssDzx+eMf14//LX3y88WnXvTkxLAZLk7+j3+5sEy514fpu4f3jhZ3Cn/3yB75aKr/z1UKzRZYZrn6TZJ+qvhixy/rAUxGABTYGXV8to6ccqEQyDeQXMyNYwzBfwHfqsfAKsCm5d89FqSMWluSAQ/dTMj2aSzL22m/UZRXIAGLizaAVF4vKxOifUwa/M2RaGzHom/Oy7WKz+lBgUeknsz2OwAuHCGf2H7EYEFtrzMCjUZaF6ArRGENMGklkNWuojuUhie77gp/Pl+aW5VySkiCSlp6aA0YG7mQR40BPDg+ZwwPPrYANZuzyMzvApeybj9fXBFQgRp7gyXA2+bJiWmTPLubT8YIz69vht3e+kUg2Uh8y4Y5C9RViPz+TWCFaWg1B7KrNHD+vsGVoU4S8hSxJRiW9hyK9d0cozOYY5w8T8YIzM7iielXPaUpZtlrOBcHsaUMSWtg4CYJzACXOmRydqWGRs6alR45NsYNKUzLlMY9FcYaJkS+cLZ1zqjFMn8GPtAtLhlkGb5D+RTOCVi4KakBAynCpFexQixEQCmmgFDH/3AQ85a1mdiwNs/dJ39YYXcmTU0zh4/Y42fzqSsR4FZqSv5Ps1KZ+2HK+62k/IGxrqo8rlZIM6GlBUFhay3NVFhEnI4ySvqopt5vz9AG2BEHdjx7R6bOfMkjJCSdageY572fFwkOA00z20VvwiBZDph1Igkz5PcjVhSAVVBY09AXpz6Q/UwRbzGThg4z087pivCXxvYGGORBSIihu/O0/+SsMKP13//l//KfFgqU1bbJDA7kXGWhfPGhVjJ9LLWzh7FG5942n++Bl7Pzmy873n3du22BtwaWV9GuotHLhsly5XIy2HtRtGNPZhhFk6ljNiEpTytEc2hh0tPWQr8xg3zv3vfPleeP1tVPdePcgP6ZlabTSqDmIlqrC69WzKecsQGCMUHErVbd+9ariQkiTPNSUjRl8/vLEy+vLCcAc6G2tutFrXTQ015JDtRrmgqVEq9Ba5dIUYXp7vfHp6ZnXTQeoWXAfnXHb8LpQl4XXfhdIsV5wbxRXOkOpGjxoRT4liSIfYEBJJk2EjMeDOAewmR5GM5+E6gVvol9+/PiJ6Y16WSUVdGdpV5mUR+BtEW3QBK54SepkboBjDpl+4/JOwU8Ka1hqS3sXq2gb3O93sKBeGpe66L1ONSSg9z+Q5rWURlTL5koGoJJFaRCxrntj9EFpikaupUKXDKO4sa4Xfnh+4eHxPdfLg5DjpPMbk6hiPbjpJjXgsq753Ykt0eeb3xNTMojetQmdLlrn7J193xm9M3vn4f0DxeV3NacKzH30s7i12qR7N87CYSFWwBjpF1ON/bbx+nTXBi23R6LmzlPCNMbOUozrwwOtNtyrPLt0YmPWmPeJLYWlVSXuOGq624ovC74sWQRlVj0naUYPEGlaPhQrGpN7DJ6//QHMlRoUAjdsKUT6+HjKgwjpjusiI0OPSTs8o4g0CFYRm72nA3Q2JnuX7CSUSvTu/Xsu64Nkgck8O5vZZDGO3K6sbTk19ZLNSWo5xsiiSVK31RiOMVVsRxD75H6/4cDlcqGtKxFwuVyVrGiuZ74IbI0p7y88WQbu5yY0Zsjodcooe4S03WMObtuuoTO3caU0vv/1t1weV67LoqSvZBYJGJ4Uk4Hhkr5NO5KQyfg8n/mYpzngnJPRBSRZhDyHIni+3wX6bB0bk8tl1SmZAMb4Sz5LR+HUBj67AsKl9h59l1yPTh167YMJeaZ7ZbNaauHzD5/p287jVx9Og0lCMuSSAGs13UNlZIpNnpkcEoNsHEoRE+T4zKUUbaKy3o05NBwPeTg8Pj5qyMv3pjNSVOnq8tMYrk1i9UItC92cYmLcTYZMVQ9dlCWdOaWWM8RK6DHxUvn83Xes15V1XXRmHrRAtATwHIYhza4z3exgBx+/Tg+XHBb6VNy9m3xI5lAy2Lbv/PDDRwE7S+N6XXGT98WxCWutvm3+Qmb/s8tYVdtCNZBjFyNiXQr37a6Nvmkze1Do5xw83V55fnnl/fWB6/WSngmWYOfbZ17ayrF/M+1GcJefXHD8OTFAYpLyFPKsQF5f45CLaoDqo7Ne1pRj6t4oVeyQpTXcTEmTZvQ5BVnOQUfMxsPz0N0zClmb0/A4E9Hk0zJ53TeKG18+P3O5vKe1VYlm880AP1JWRi4k9P7PVa9o+Yh1GMkyiUzyPL5PEjCNfP71LE32+5226F5vGB6DaqEEpDSjxSpzOHMEtWkZ0O8jv1dJY2b+XS0O4qyLYcHhduE1MAYvzzutVWp+l/MYsjy5SlXMW8/6DMawTD/ylIq6Zv9atMiJaWlK3/FccFgYwwbLOrm9dPaYXC8ZE+/G7CFPztWIUeAhI6E7+vwxKUsjZsFDpu3uLZ/LIOZdJ9mESLDflgo+sAE8brx+O/AHqLdBLD2voxZYNjf9M03JD284AWwlJSBOUAlaArILYggpIs0Ab8Gy7ry/wG/+/CO//H7nKQQgtzZ4d21caqU+pBQiCiDTV3mS5MDh4o7sKSaJMEbb+fmf37DPGz/7A7D1Kyjv0ChU4d1XvH/X4Tf/hF9/6bxuxmbGCN0v3io7g+W+Mb3z4bryzcMjHx4eeOmDv/jVF75+cD58rVmhHQNxFGo4PVQ/u41kpSZDFLFy+xzEPtTbxaTSVPc9sCGFQg/yOzeGBzMyMTJcJt3KTNC95pJpLr7gCPiXVEaM4m0bStmshs3Df8jOa0c+m2Mey0Oo6Frdx53bBm1prC2IsaeHEblocSpVYBjx5rnpOSQaULX8e6BTi7PdO5e6CIxQ16zzyJVGNbXF13s668ThNfTm23p44RwePUexm2OkrC9lXwaHhOiQAMtPKL0NTf1Jye9Ekl19MQN51crfaqaEtDHmlBF46AxflpqKgGTQkDUfOJK6qjfNDsn8KZ5OUiNTYBOeADGFjrPSk+nkh6eQqUfGHBu6XiMmA7WN2xzIvDl7EEDJYTkbOVLf5HfbDqn9ARIlaceKMQsQeYXmyP5B7yvMTjn9Ef5E8axlMwGFOPAf3CbTXeESObt5KwK0DNmxmO7AI+Chp5E3yTC37IcwkKy6sg+B9F4UaDMD3tJRj9fOfx+57O8CVcGl8JhT/VTaiwiUE2OV2d8+iyhkubhT/yRWmzyRe4JFRw131HNh6qW8yG7CmQKaQwEScwT7fJs1xqb+9EyGnWl7cMwKB1vrQE0PvFLYGTOvocAo9TjkJXIsAeSRS6q3nzGOOoS+0yPkyIxDFXmCopbgm53eX3b2reYC+B07JYLNhG3U7PvmAZKnd9nZS3LUS+EXYno5f+s/+JO/uoDS//hf/ad/uvjKwip3ciZLWcQS8SK0PXQ4vdwKn14HT6+D59fB0175eNt57YoHfFgr7y8Ll4t8mFqprLXyuFTWtlKiMkejB+yhFeDRuFUKMT0p3/o+J4f85LiZVFA+f37hft94WCsfHhfWZUl5k3MfuSVujVKdYoFFoU9t0XTjxKltdlc0qHtRE5ao/wyZwe4dPn36zP1gm3Be66TqGbU2lmWhlMplWZn7xL2xm0n7W53rslKLMbY7r6/PvLy8ct9SyoQ2Cvu+U5re19Otc12vXNK02Zszkx3UaqG2lYtVNRhepBk23oa+sQOHc74+TyQrwBPc8ZTM9d55enrRBm1tMrZshcUWlpqSoSLATvrf9IKZaVqLfG1qKdLJJ+LrXtm2myqZQ+xKXetj434T06atC601fpyWNAy6Vo3gVb5TJhrmkv4VsnkIWimwD56evjBGx1ph9qlBMGUowaRdL3z69Mz6+I62LMiaS0PZRMUlMhmPPLxbDnZnFHUM+n3n5fmZ+xwphRKDqRbRcGcE27axp5nt2hYeL1dKk0wikmH3S3iAkQAAIABJREFUsCzMmNTaZJRb9RwckpByNgE6sG77jfvrPT2aJp4afOZg23axP/J6PDw8sq7yhlkWyUhIdkN0V5rFpVHSH8ijSO4QEHvQX+9stxtj2+mb2FCn/AgVoMMcOJrz+fvvGX1ia1WqRrHTNyUISi1n7Ll+6Tvbx86yKJY7Rjbs0v7kc5rA0hTos293RtJMa1346uuvz3j1A8jXZqJw+HrJ16iqUYtgpvwyxpSs9HwC8zmc+u+gIj4SQOx9o11XrtdrypAaAGtrZ4VpGanuh+9bbgCPrTrlkDjma8xkEYQCCU7jvWlcrlden1/48uUT73/ytZhPoIEyG8cSMuDUphpA8bDuYja4O31Pj7RkQI2ezLmhQXXbd3pM7kP33LbdqWsVpdzsmIM5NjUzG1f5FDnj9JkgQdBjWwXMwZ7eY8eZSQgoefsshS+fPlGWxros8vsC9uiS9Fqmkdnbxlc0ZOMwHJ16UN58ohLcfou3t7fXNgH9o09mKSyPj8m0SRPRbCg8QaGR8qpmurY9YNgqpkKyQ3vf0+zUsDE4hGyJsmeqy2DrGx+//cjl3ZVWGqNHmoZyem+Uk72QTFBD7LbQZzxTH7NhOcAVy0Gb9FcQmL0TEz59/CzZTy20dSGmCRxBm+62tvS00fve75uSRTOtas6eQ4gYRLUVbEyiB9F1tsyhdKYxBaY/f3mhNjFZDg8OUGNVaqEU+QiOkd5WB68dyy28NuFi7pKJp+pD7vcXJZN2vd4BULkrXKO1Nb+7OBlshHwQlmU52WUyzz0GcqUmFm+S96WvZPzIn+SUxMRIYDqoD2KvvL4O1nePCZQqbtstqfcIKNK9tMsHCtj2HUswWh/asCn/rZrDnMWxyZWv1dEwL61yv79wH2KT1OJUXwnAvEq+l0NxlKDvYhm0Wund5NtStXzqY6OZzg3P+n3wPo49s5uxLpXbtnPvAiFLqfQhCZ5zMOwEMY/eIXZJu/xgVqYnXi660tqDSM+KsIl3g5r7blNq0LRgezYe3q2sDaprCGFMlsshAzZ5SU4YsVP8cloRjAispJeIpUxpdg1VtSSzo6n3WgzfKyyG73D/3Il2x+4KdjGvWZ8kHRSWredcOhm9N4F5D5hXrDxg3sAbUT5ADo6WBvGlBtelcPXBz//v7/juroTjfm9crheuj8ZDOGU92A8Ki7Gsj4f8f+TSVe4QkqdPh+9+vXHpgw+/v+PLV1CuSCa0sHz1Ez7wkdt33/Ht0+A2jT0mzZKxva4wBw918LheWRenXRpeVz4+df7i2xd++qHw7lGAV3EjvCuBToQWDbDuOTClkTvpA7erHroJGTKXQ99kTzZSP8+9MIECMcRc7bHTaxwtQ8qkHEPeiXHKv5zZ5f/1uu3J8ir02MFykSd6YgpdxChU7PxksjMG7N15vBYuBZY6xdM2DZNpvsfMGjxDizQNosbwSngjfOV2F5DdKtzHxrSKTNyTkekCz4+5h6N/mHH6EwZHr6PUMEnrD6DFT6nZHCmXzXs2zp5qJsvdzj7z+Jkj6zIcIL/OZc/ziAzFmMkusgglLic4syzy6jK3A8bJuvzmVeSms91KZe+dUhp9znPotpNpe6gc3qR6Z9qWKXX5iJGPXFbOrImO2Han500unY7z/OjToOgsOmps3gHOJEzspmJvrDnL/nQcdTji9DqdeVYf80mtYtCIpJDBLnb005bed2B9pATZziXycQFmgiuGmNVjiuE/0l/wvves1RDbTs8lxuyq0SV0FcS6S/uPozYdvVVKXUsutL1WLRwziMrSwuBI6E1XAC0DcskZ2QTO3sVGCvW8LVnyxl++D095O/JnLi7suBCMuTGSuRdpWXLUInmfHn3dAeslQy6BxhhQq87Z494QwULf5ZE6Cnl0z4NdlbU2f7IfjC3UDx6+TsdrHuDUuRQ8+763ZzSS0uReqCGLBC1Fs8KapRRP1yLePhFmBUdkm8M240/+/f/wtwYo1d/WDzp+RRnsFjA63WWuve8pj4jOsGBMp++T123wfO/s4ex7sI07XiutBg8XaUfXJdHqWlncWKqo/2NMeog1cJ96iBY3fOgAuNddh7kV9r0TFEkd7E0Te7/vPL2I4vjVu4skdZWkg6oxv+B4q7oY4QKveqS/x+Gn5PS+6wZ2T0qdhpCRqGPM4OX1zqcfPuf70aGde/+Ur2ibXGrNWGIl9cxEZ5c0JC5NHkD768be7zI0Nfn5bLserjkG13Zhn3e2DtfHC4s3rC7UZRXSXqF5xYvc82cahp1+JiDqX/qTLK3Jl8l0+B2x8yNZMjOC2+3GdnvFlsraDoppMpwMxVq6vtNyHgyTvmXjkpRSd+gxkjFUsHC2fj9r4djuRB/0vnG73djHoF0vlKUJQLSgmrHgLCZENrthDG0BirlS4faQP4PDvN+4vz6nkVk9/a4KZEQ4LOuF1y+vXK5XRcNL3KKmI6Mk+5ip01Zz6LVALWz7TsXoEfSbBi2qY+ntU3LD4m7E3hnbztZ3mIN1aaztwjYGSy2c+UlFRmslGT3Dpgw3LU3hXFRT6cAlT4u7QK3atF3c+6Cgrds0bZxjTq7rlevDI6XJV2dPJpF7JTx42Z5Ylwu27Wwm4E0Stsm2b6d3V852aqpCxeyQDYW6NGqtfHp+Ytw26mVVcQ/oBLOY/LAitDXBEkwQGNl7z8MW7vtGSyrq/PHhn681pmK+NQAb+33y4ZsP2GXBj42K5TsLFfLw9L0qLl2yO3sCKdu2C7BL2vEh22XMjEWFzfJZ2Tuj7yyl8n59YEnvsmOrNQ6QA8k45tD2BbTZKHBueWzmRjylpgfQ0nsn0jBSXm2d8Ml3330ndiIu35WkKNfDfyA3amEq8LMPCggESEA1Rsc5jP302jOTCPc5iapzwHeZs28B14dG9KAtS36mNInO7YtlYt2Mmc+5GuS1NZmlmieQq+eQGW9DaklvOjOimLzP5qBd5Oszp5iFS4ghOWYaW5oK7DQ1pUczcI4mKTGMoWYqEuQiAbZULmOkVJBIwKpwRCMfSyrDzw1S753WGoFkV9rgQY/GDBObJFOr9nFP/9tg+jgb8Wm61cZ2p/dOl0L33BgeXnRvrKO3rXIgCjZwJr+QDfMB9Iq14ukLcHjjQJhTKiyutsG9su8dqjNGBZKVNAZRJE+dM6i1EsjPKQ4WTyBqvAmwpzjTkrbtqGaURnRFgduEbW4ME+OLwgn++TH9EdSq+qyNo+Xm/DC6l0ziZErtSjyVZHbPYe1gbU1FxA8xN5dl0fbzwBKnsOqDzShweRLWxCLUFoThShJaqkMRqFJsEH1n7pN9qIbNXU+Uvw7WB+f12yde91feL5czBccTJzqMVdWIykh55qZ5DKVQqbAKaCoJIFpEesMIYLNQAu3uxiyFh3eP/PD5hfuU9Lw0DQ+Y0XvQvKU5OLz2wX0AoxA22GMnRoKMS2fumYhz1P4hxkQcEoQhZtpPv6p8/E5M0a0s4HGGoVQXG3SQUlxTMugaArhnTeP5IT+WMSZ1OHVteAyZGjv0PWiuwXMJ+NnDA0/1C9uXjfWnkovUizMH9Hvg7wa+7VAqsxkxGnPubD3xgrEw7kZZpzyUcshkailjVb1EFMnoeQz8dVJ+sjH+n50v03j3Tglha3uGZcXaFaLhY5DCKCJugADhSSf8SsGJ+QomiavOGnJ17lAqfrlw/XrhX/7Xfod/+5ff8qv/5lfcb1/zix/ufPjFF95dHll+r/DTNlnWd+d9qeWDAFOPoNlk1mBMecrA5N0jfBxP/IO//wsev1n53T9+wD88EsUJGtSf8dN/49/lb378ls8vf8b913IpneFsAXVCe/yK2D/Tx8btFbwY76+F/pNv+MU/feZ/+Hvfc9se+IOfOteL01aw3ug25Dm4q1ccMxl4XQEDIwQ8bredy6VR1mCZUF3S1GmDGQuDmRv+yTIrvYwTMLOdZO2iREM7gJaJQhkrPjsLCzE7794vPH8eDNMCgbExp5hekebtzQRCDaaWF6WwvwoIHe7sZEhJORj/YMXAoYyCz8I+Zf3gVk7g2s2YPpl14XUMqgfrurLtOqsta8/MgdlNPpTFckYplguaiXtljD1TXT2ZjGQPqb+bUHr2WgIb1UM1vR83xrhTS01rj5Lnb6OPjYMpaGipM/qkR1d/E8EYd5gCw0aer7MHo+xKFAYOeadkWROmEX3Dp9QWsYmh3WOHor9PLnTku+PJ2n5jOpVStPQFhYhk+jdznNXAc9g391MmHXOwMTgW3N5nLlIF5u1jYj0YdlTeBAES1DkcpI5Zyzzo0WFoAbaUheVc3mYS9Miz25U+PJHhf0HkichrVkphBMzeuVwubH1H/lBKwj3ArMLRP8pTKbLPnCk1nQlUgXpLM5iu+7u4Q9Sjg1ePVPW5cnXOcPChMxIHH2LgjTHZ+12WJy7JoGRcEoTPZNcoy1MLuwOgmwZRhRSWEQmSHUCpvs+aiNPM5VPzFWMSw/L9H31PWkeUqjqltdPJJqqt6WdX1bKYJmlisgn7PtV7CM1Uv2SSFs6Q5Ui2DCdLiuzhldKuc8RdHoV+9rpHR3r8nWQ9zWTnu2bIXXC3OnJ9ZRwsyXnMXPj5esf/8Hj7/7/FX791QKnsFauTVo05CyPSiwcNZrcZfNk7t27MffDS4cu+s1rlemm0pbD6ZM0Y1lpE3/ZS8Ey62KYuSJ8yrHIvrC7zvI1xbstPSQYwZyZWofSol9ebhvvaeH8Ro6PlVi8q4AWfR4S5mE23ngddOKU5i+vh3feBIkKPhg+yfQWb3O7By+2Fz5+fmTOR7FP/rZ/Rqis627UV3/ou/wvAWmUsCgN9XBpjdO4pgToO91qMsW94oqWlVLa+yQD14ZHVK+tSoTYua9VoVmaa11lutefJpvCSmSmRaTku4+NSqoakSOPcONKztIm+3e74Igq7NvSOeRNAhjx3whulKRXIZmi7HyEAMIcdbTh08FkyG+oMSFZW9MG+72y3G7NPMZOWRitVNNuRdNbiSZdXomCrq1hspuFm7CMNeOXiv2+d0WcOYiqSOnSDDiyXCy/Pr/jDlceHqyY5ky/UGLpPg9wk4Cf7BkMgxoTXLp8dG4O+DXqygYrJyLQUpdjdEkyyCFpddPhUo4Sx98AvlSgaqKJPlnURAt410Nl5VkSi8kOskpSgHE3Avu06fJEXFa7tSWsL7z68zyQxY58qnluflCL0vbWiLdrsTJJl5kHvm+5NMmHBVT5H7+nLJc31GKGChnO7bTx/+nyCqSN9esjDW7KGt4O/lEJpNZljJs8KDLzQpz6rDN81gFhI1nLb06A+nLENlsd3XB7fcSQyHc+lnmGBnK0s8oA5trc9PZJCTcYBBDlIQhnzTB+xZA7dxs4ImfG2hwdsbVhxxuhJR38Du2spaqhmyiDQfUYR8Dz1H99YeOnfcNB4Szhrq4wxKWvj9XXj+emJn/7uz5j7noiEMVs9f0YkC6rgMqqbbz9TDAMNhUdmtpfC1jW01lZhdA2sm86Ve0apV5lhsMdgBxl5YxSq7oWA6AL8WlbF6S4gccKlNWLuTDjlt2TRVC8nmWVz436/aZPXmu6ZGWTuIJAU7ursyfQZx/7GPQ3hIzenOfjOKS8bdYVv0a3HljuE5tz7TrtcGJbSr2T+xAHQwBuAagIAQIawvU9G5Hc35LnDIfkCgf92JDBGBkIUxoRihev1MRufydj72wY/wSVRsvJ156RUpWgdptJj7kpgPRrvUrXRjpTyhHytqJXJBiWfizTuH+z0Ibp9rZ05Kn0GpSQLoFYqjYieG0k4zMHdq5rwmal7hnwjaqQvWQ4CJubvnGJxvqVApen+nESp2lQfklMzjrQX0dANL/Nk2Y2xZe1s7LHhPnOQ8nPQaAm4ByU30ggIq2mObkYUQZ3mheiRQ5hkqubOtm90yvlZkgeCUhEn0eHl5U7sd15j8OGrhTFujNeNUZZzg3k0o0fCmUCjkf9MY+rcfFuehbYUSi5+SjH2Gbh0WQksyU8lqNTrQnu+0cZB+U/5QdhpmhpjyGenKMBkbhoGZspVR5jY4bVLMlQqcwp0tWn0ebTsWuQslwt1eWbbdupV17O6A1U+VMeWONI/BNhs0Ko8TkopjOHgKVMKw0YwKlQLbBnMTZ/HXXwRf6h885OFj79+5fYyaQ8LlEpdclAvjkdj32Rz0NyJbTCL7qtmwZgJvngjYslnrusMwZhWZPBsBkzKtcLtSvudjds/7tyvHd9f8ftCo4BvYLuS80bDykog6Q3lna53+QQU5nzW90MnzMUcGk+wf2LuN8b9mfj8Qtkr//wf/YR/9Q8+8r/+8pXPtvN//lnn0ibXdeFSHoAXschFfzsNfBk67VuCFrsbxRSy8Ph44cvnZ/7+//wLrt888v5f+ga7/g3wB4gV3v0L/LN//O/wb33/3/Jx/zX/8DeTpxkUJn0WrgUe331FtZ0Zg6eXV2xUeYi9/4b/4y++5en5C3/8h84/95OF60NQPWhc2MudsW2MfqGUG/fZwDdi95RRGWyVuRjrT8Gq7qHhhRkVKx2fQJ5H06D6Qq8bBbiEscWkWmVLMDNO/xwx9kcPrE5qL9QyeXzvPD3duO8QIa/NGVoETyoR8iKcPgmvGb6y09yI/X7WFUMA6iEfmsDWjZgKNxhxRNMb+9CCwfvBPjGe73ce18g6pj5ooppSkqkgNkNQrLAPedpKwTHE7M9kUxK0KukF85bQpbTp3gf7Lil/8TglVGZiD2rpUjiSv5TSp77KiwJ/xAyS74yYm4P73N4G+hCvY3TVwsOC4Dj7j5+r43Gk0kEG/HNIFj7M5Lc35wn8viV5HQlZWgbNMShTPWaPyT67+poE7zyBCyuZ7D2BBKA8XMutmAnxa5lnQVowaI5yV18SPokyT3VHn0Hvey6EdDbf9xsTSVpL9PQO0mdlJsPGU4pYNXz64GSzNJylLtxHT+Biqi81Y4Yz+yG7Rd8rYkwq4TKN36uSoueckvCGPO5Ky54636vYSlq8jQjqlAVATWPskUwoK1qelrpqwRGaqUtNr64pz0WBVVqSRL6OJJdFDOcitnwtaYSfAJJ6Ocs5MqhGpuQa++iYtRNCOdlJHJjQ4d50tG/Zu1U90wJCxXqrJiZdXQoxxX4NNR1ZlpMBFslsy4WX2Nklb78D1PRcGkkujvMjsEe/P3IhRMjL8tjQ1wQ+S/GUsaYE0I6lqT4pCYwf4R7GG9ngt/nrtw4oLUUH0Jgq+OH6gPs09mG89mCfigx83eFlG1xK5WGtvHu3UttkoeLWGKG5plRDVP/OpLKnPEjdQcHGnVGEHEZ0yd+GtngjukwtY2dO5953tm1jTljXC4/XkpIJyVysGvsu35CZ27TblgZdZpSMJQclRLzdgAf7KR3liw7hbdt4enrm9XVoq1gitwfSdB8+HSUTnEShRKlM2fBHMRYrLMvCNjqMrjj7rWOzU1CDvedDG5HMBpfHRS9FW0sv+OWiQ8gAMzUz5M1quYmPTjHpmSV7yMMhZQ2gprgUJ7r+fds3Xu83fG0CkxIMwyrTC+FpmjkNj5Q0ADE7xQaOgB3OG9x/hOgO8EFsW24iO9u28/r6SvTBslxoi3wuRmpaT/ZtBOGiJi6lirmEwd65ZRKTTbjNTr/veg92bDN0oEQ6SNeHK6+3jfXhysPDAz1GFjmnj0zGOA41IsEVFcWYQ0DPCLbtnhRk3T2RPhZWlF44+6BvO3109tFZqlhpsQgcW9pCWZq2WEOAHl5k9puyNzexA07JVfqWyM8n0wTN6LtM2vHg/rpxxksSfPX+g8CA8Hy21PDMELg2b13bhdHTvFAa4jIFah5Mn+Of2qBkUz+10YghMLHVwrfffqsLlwlOPk3Gh0WsRMst8/GcHPTWyGtda2P0IfAuZlKkeaN2jw4EbtoUzqkt9uXdQ25ujGGe8glt8cSoyOGhqPgo9U6UYM+CK7lbgqAmdpuYcNlH9Sn/h22jmuS7TCXNqaHSPbYPNUREYHOy1pYFRBtow7PpI4fiTh89f46dRa0UBRWUUimt8f2vf8HlcuGhLSp45MYLGcabu8w9szBbeqN5SibdlGxECUbfqCOfTTNKSzDClUBys4AS7M8b13WhDKObBq1q8u/BNVi+UY51fg5GeqOImVoMer7T42gYCaqV/Y127iaQ5/nLflK/j05njC4zeThZbAvOa/T0wcpENbTtemOpkHTyBOzix1ujQ8Zz0OE9wewJ8/DgeSvqXsROLC6QOSC9eJKGPFPgkFLNGZM59BxK2RHHwZKAlmOzyBi6Nd2XpHGqG3N23JuGE9OQsfdBawtMyW6Vhih2XzOhaHOmlMiMwxC3h+qZUWgpLWsPa3rFyNtLJuRZHRLMGrnplzm2vIQmg327Uy+FsiiuvNQjjXLKL8umGHDm0itP3dNjl+Tp8BdTI5ix0IRkR0XnnKgw5JZeYBRoqzxGytpCbMQRYhrJckN+WiM5+LV4LqPGeb5bJkwaTp+DOuT1VTLNZpymuPIJMte9AJ6mrglMovoqb5FClApb8HqTIfDL0wvv3r3X4IOl/C3lu6SUD89gETE842SsdKhpJJpt5Tyivd0Z1pNhoIVSRLDUhbVV7q9PXB4Xhk3FaptY1vp3Va1W4L4piGNaWtOZ/MGqVUaf6bsmRsCIiXtybybsAbc9eOfOdak83Tv72CksFIKdNFKdx3NoGZ+t59+yFjWXXGvuE1sMpr4Ln87sk725+iO6zpMwqg0u7wv2qfDpaeBl4ja5PBTKmsPJZWXuG200ykXJqbYJaOqWfe0d6oBSU9LgRZYEc2I1l1IBEbuGwtp4+ObC9v0T929v1J9cse1OrBNfv8h/Efl7eRGDxnHmumAD5rwTJs9MojLpWFn0fTBhupjJXabg795f+Rv/4l+n/Hsrf/S/fOTv/oNf8csfvud/+4c7l2o8lKAsVy42xXIoAsKYk2ld9+50CoPFnJu5ljblhYevVr794TP/19/7M/71r7/i8gdfQ/sjbelxyh/8m/wrf/M3fHr5n3i6/8A/+dR5DZmIzzHpo0ArvPbO/fMTzxv4urAsC7z7Cb/68pGfboU//OmV3/2qcrl0bLmCvRODY9thf0+3jZ0HrCs1a5+56e9iFrbWwDrVJFEc5szdMJ9pp9gp1biYc6cT1TEfjEyk3kfKm1gEVNrIBY9SZRevuE8erivj6ca0DUphDhcIHXpu4sjDMgHC1Sbb8EylGpQEKTzATX0iR0U3MYSO3rLPndrA+kYpyXAKMY5713NWjvkCT8aRABtDC7Kce08J6Wlb4Zph3mQ5nmfHm9z7sFGIbG7GCGppDOsU05lTvWClvQ3s2at59nKld8JGMlzgSLWKGPJoQ56gAp3K6UN5sHRl0h50NKC3BCTcNZRPP3wetRxqRYN/clc5CAcCpOW1dKToak4T0HjU3KOOeCb10ZX6ZuGnHG3EwJuef9XPCebEGApyiZnsfs/3yTnUL650uH0cvkICNOacFDUHOhsik7os/0SkjcAUOKXleVCbpOh9u6kuTdW0WholFFI13DMwIhdOoT60uL73pTW2qaAThcSQizyBPMz+xoTJ8JgeUqCIRn1IRLWEbd4UQJU+iV40e5HsXoGEYknNVAiNMahekySh5bVHsgdLRUER+v7LUR89lRCnd5l87po1Xdvs32qV4sJ/5Gmk2z6vT+jfZ08mtxnNKtFMzOnsXQ3duzMvued7lZogFRLJEjzu9WMhOYb6hmPBdABF+Uk0B6A6aimXTJUd9Oyr/Jil08MwQoCWu6xtchY9vqfTXzZm+rv99n791gElT53rtNTLz8GIYJtw63DfB/cJLxtsfXCpzvvHxrtlEcW6CAUtFGrlR4ZginwfoQtSZqSxVCYNmWRGMYW592PbOROSmMZt29j2TnGlslxWedXElAlpqYVcg0MOg7cxYMq0uRZt82NIEjQCSA2pNMYZPYkYDM8vX3h5umeq0OFn4YTNvIFkHl5rOdkM/OjGmhk1+dAaYc597NQxGPud7f4q+ibBPjq9y5uiz4HbwvJ45doWRRuGU2qjXhdtGxNRtZgpQZOEKMfWk/qoZvdAe3O4dpOWljhTxba9c986y3rBlxzcZgIkXiRxKyZciInPmU2WKNuHabKYKPpeNEwILIsZSs0KmGOj98H9vjH7YF1XfX+HZAbSm0AD3nATLbYUGZqVwr7vb0al7EJ2T68dPeyHdnmmRMPWhZf7jcUb63Klh9I2DPT8F22zfO8CmRJ9hinfroyu7/sQoh4z71tRrd3EqmBoU7HtOz2T5NbLBVucxRWPScagBrpXy4/iLw+Zz7RIGiZZqFWQJTmVITK7pAXhx4Cpg3OOweP7d1ALHU7TwQOGwJ399ab4c3KzWdQoOTKgIwHIozBG34mx01H6hR9FKSngXz5/ZrvdKOuajYsKYLgGyNrqmfIWx3ee7DfGxtraCSbq3NGhbnGknaHN4RgZdV+4jaBcFiUpdgEy3QUi6nbVWaZr2YF6Pifmji+5ZZjH6ffmRwQH4ADMybZ3xrbpPrvId0XNQrKYXIwHT8Cg751WBfZ6eZOIHjpoIpRil89sSwCl52YmTFKXdWl8ud14+fLE1z/5Wmbvc2a9zybVxMRcFvkOVNPAVjD9Xh86+wAZUuo86n0Tk/GQlpno4AeJiW5c3q9i41V5hvS9n0bAno2avGnsfP9uoRSkmAwXCGWiswlUDE6PoeIyZp1TfhMvr68pgzvHaAG70wXi5vNAZHE1kyF90oIPZqKWA2lMn5vXmG8x9WOM9Hx786KptcIYyQYUdBpxGMhrYChtBdTIeilsQ+fLPEGjUE2Zio+ekRvnoxE2PXPHd261irE0BI6UPH+PRmbQdd64pUxqYFU1TylsIUktU5tdYPQ9N83BsQuLfE/VKpVMgrJ6btgOJqIVsXcPWSCmBouZSUeQAQCqCdHH2/dtMsaNA+4Mxbqf90efpyQ4Qk0qCADDI+8PP/3nYLnhAAAgAElEQVQYDiBn2pTPkPnpsXSacYbrO0mZh/nRQMKMTHYL5MviSrd8iyZWbTyWDz6PpnJypMXMbBi9VI7NZ5nBUlomuyVLYim4PeIP0Gziy8b9dSeipzx0nkabGpwOODYXF+h5Gkgu7qd8pojinzKOmF01vqgP6D1TyaZYS8vS+Pz6RVLG0pJRaNnUwhmgUZ11cfoQeB7JGp/TmKUzRmH6ZBvqq/zY6BZnMDKNJtgsWD9cef7NM/t9ZzbJFIJgt0m3qR4g2a+jiy1bZ7D3YG0abHrvOlMikPRypJ+fUYqS7eZu6WWls+3yaLz8ZvL02mkGlzJpa2XsRlsH0TyTV1doMgdWGZ8KpgjdWyUlu1gur1wSmiiODQirMtWuTpsPPP71wes/ulFaoz0W+YvUouCR2ohSUwasGufLA3CB8UWv0xoeqzgFpSJ2/Eh5M1i80vorbXvicd+5/DNf8fXvfQ/Xxn/5d/8xv/zhM//7//fAV98stEvlm3e6Mf9/5t5s17YsOc/7YjRzrbVPk5nFKhbNRrYo2hLABrJk2YIgwBQM+QV872fym+gBDN9YvpJpSCBBGSQtQqwiq2Exm3PObtaac4wIX/wx1k7Cl04D3kAhMytz77PXnKOJ+ONv7BxQtHaZKevNO7DEQYtKmON2YmtBvAt+9JOf8+4PzvzmP7/Qv9+h/QpKquts/8Xv8w8/fc3Hpz/k+c9v/Oxxclhnn5PnQ1KgFpWPT48cLuaqmdEvbxhz8Bc/feI3fmnyw3cP1HbmvFWsOcODrYsxX6LRRsG6EXMTYOw1GcSAH8RWsRG04lhpeBPrpY3CDvi8UbzRCW5T9WcpTu/a2z4bPiGKgAHtv0bkRefmnCrEu43bfgjkmwkmzWRzItZomHEMpw6op+B0LmxlstGxshEc+DSYuncI5WxNtpTbZL3vN/qpE0weqEw/6OOEDzhmDgG8AMfdiDpcw0czpZSaL6BqDaLATOyjldoW9zo2WUc5+Kq1cj6dsu9Iz6V6Uqx8glStbnmfkt5Jr8PAbXZJviM9C9fv5/r+ZlJHrHp8DCX8jWPcA1PECDeKpZ9Qk2zcQ59Hw6aWNZWMq2U+blK95CCrNoXVvMqCxGZvJgBnRnA9JPWnFrYc9C/w44jjzmI7hkA1kl15j51PRukYN0pafRTT3epLClmMFpXDQ3YJOSQZhJQrFvhY7KzsPRJIDOQjN3PEFQZjCLQJjFaXRFqhC1sJRuuMQyCFVCpxr1rNpGooaICvpq58C6REPdV61q5zsauc0znljlcN9VoV8GRUmlWxiH0IBGmNGeNe9xRd6mm6bSkl1yC3tJJWMovdpfq3UNPvKEN/CCyZgZbJgZpZii1WcrB5B1ot6/e0CQGx8yKHr9MjqfSrj83wiVRT2CDrONMdkE9S9ZHn8OpVDbDsBl731Kv1QsSyYnglq4QXRtZJJQJzDec0B3SteYMRaYkQklNGGpff319aPLBCnP72jPT/9df/Bx5KBY+BFecYhduE5xk83w5uN+fx5lyngVUu5zOfPZw4b41zLYRNZhRt5CIQRmBPwROhN6ZMwVp9pXlFvo5qYk44Kc1wDoLbsbNfdzyCy9Y5bY1eEqX3YAYJAuj5VhM4MRyqNR3yZvLziEgvg/TIgbt2cSbN8nY9eL6+8HK9ElN0Pitr3pBNqgncaLVlulEu3mJZDBpnU9KXmxGHKLljP7iOQyDbnCnry9SLKZbE+e05aZ+aSMhQUAlDG2pS7g2ZiTa5mADVVsy7Fm81UQxLTcVviDYZaSJx23eu+0E7nSitY2kmGIU87BNBF7ydhWFObVkFsBFpZFctkXkEds2xkNRMDLod3PYbw53tfKKuNKqcvtYEeKuJ7osnWl2b3plLelNzUzmTI9O/Xg13VaT7TJ122/BjsJXGm/OFHacXJYjI9CzpjYl+C8vRz5sjE4umc8Si7jZ8mUeuaNWa1GMf7MehdLVWuTw8ULoaEqHURjQlBjar2DJR1k+R/1deiLrQBICuSbJwoYWE68IZeZn2vnHbd04PD5zOD+nJ8m2KsAkIM7icz5DF/h0QRHtJOnNbOGSu+wJW6VlYxP3SmRzXK58ePyo6uuRlhlyEShXoW5DnFfm8FWuqy0qsD7I5Tc1/nqJjTiUxRvrMhIood6j9xNv379m2jc30+4WBW5rbuyiz7kqf8hBg5lMmupJGqchU2mN6LmRDGwsMnZPbkIG81ar46eAOkLX6agJZi6Q1UWYmI+VUDwFwE7EXwwy3gpuLfRNinCluWHs6DKxXvv6rj1SDfhZgVBN86mvaRtA2FX89GTRHCETKsRX79SVlWnqfwyfHsWdjvejrSU/2id92erJX5KIRKRGcd2AdcjKZq3FNiUAMACLSrDpPzgJRZDA906sMAssEsmMezGOynSWnrUXedqUoVVLG4poc7em5Zbx6zPh6Z6ZzMYkxWvdo3+h31Nm3jLoJ6FZZptoqhPPnus6SSHCdAkfKYGea89/jYxeglsDBDHlYaSIlJp9ipQW0zpjUKqB1xStbQ1K0odP2LsuLuDPfcGerLaUDdn8HhBrvqKKms95J6BkTk6iv/+3WFMtb7us+k/6GzGlr698qqFRBlzsgIpNYq7rvKkYrxusKWGC15G4LgNad1zGTZHUVfyUEUGldzXsz4pHBCEZOlwu+L2+1QvgBJgYK4YyUV6xnbY0EMgbRTgJUco+Sd5XGk5Ltuqn4rE2g4zGd0jaWUbiaC60NeudIeWt0NagPpxPmk1O98Iunr3h6fOR0VqP2baNzMebyrkUTcRsh0+I1JIjFpDzkERKruNR+muPA03PyGCrm97Fzdec8BjWSTUrD5itzwYpBNXpTcbvYaYoWF4DlEbiVlNiJrbWA1sjG1cM5ZnDZCrVPvv7yE/3yILsK6WYYJWgmpsxkQMDc5C1SgdGht5lDm0L1AXUDu1FsULZN9+y4u1XKqPwED71wXMRQOEbleQ/sZeJxo5WgXQTa+KG5f40HeZzUHL3VZDeVBlFJ3i4WGjv4AdHszmbDnSgbl+2C/Vql2pn6hRJ3rReqbVi7EK0haYYMxKlvsPI9KjeIG24FuXs2YE38nzUgM/G7rEysfIXVR968P/j1X3f+q38U/OLLJ/63P/oZP/v0wp/81Vs+f7imYbuaxNpNtAEVZ4TNewKseRCz0gjMDmyDl1H4sz//ax7e/jn/yT+5UD/rWPsB2Ab2nne/+y/5rz888tX1/+Lf/Aj+5ubpmRJcbw4VSmvMlytxGHs52E6N7d07PnwY/MGfXPml7cxv/b1GbcapaqzjNfB2IjxHPAFBxc2oNYHhQ8yakR6fy8tG8iaBsgXD56Yzx8RcsmQZiM2qPRPmzDT2tmy6K03nQwXbC1vrYtjEgIK8p4pRzGWRHfIO6plCV4ocYmqRr0ypApz29JyiGM217xqq3wjVO2NujHnkIHFw7icGB0cJOpXrPrjd0o5ipsefyXHNXbVoZmURpHVF1mSvARWLgbFYPcmKdA0vTpvO7Jd93pOXS1pOEFDLqyx5ya81qCh4CYoXyeXSk4gCYZUjjhwaaHhDSDpb0vdFcrPX8zRcvRc4YU2XdqyzMt9x3lkkYLCGJJ7MaBAAr2RKU7ogJpKuxbckR/JoslbujXqrPYcWkuNFXfK4pFSa0SxBzuxuzNEgdq07t/TyqdRmHNPFUste7QjJFK2oHiJc9TCocOGV8QproJLPPsEFTImLaw23YkRraa0wsx4QIyxCdiDFWtZPaXxdMjDBEViEmPqW3qgaXqUOIfsPbNUwsa4f3Zk59PRMzk51pdhpLubs/cPkrsPnmuNkbbM6j3wMeSevAYigtrj3SBiYf4uBt8CW/Oew9T3cvSQ9NGBi6FlaqQKmWetQ5xEhOfmS6Bl6x3cqWv5e+pIXZElG8TIEV1gM9/0H65kpLTGy/4EcBCXpQTYdlr+D6n93neMlFiMxJXhpfG7pL/Vdfn3ngJKoaoV9Tl4O5+OL8WmfPO+Dl5s23LkX3lw2Hh4uPGydreph3WZqtrNhnO5YNGYoKrYt9NiU+qRGBqGYEUlZN7B2l7gc14PjdqVU4+HUOW9nxXe7GkArJrZKeg4sGciISLRbE15NJBCKG4tKGfcFLAChyOj76ZHbvot50mDNeVajLPlOoWUq2h3CJ6ApYe1UG5tV9inDyrnvuE/mkPRvziMbEkuWTxBR6aeu5zIGlK6ivhil62Wvkr2gRrXXnh4VZLznau50wenAzYbXXZRFD0ZM9uuVYzr1fKL2LRHobDBIwKCUewpOQbGFKwLS9YeuvUaghspCnlXTM1GgVnwMrred4+lK4LTthLU0cp2ezXk21VaE1EdOVUq5i1gUNS050XSxh+ackHHLkn3MnHTp8BjzYOudbTtJMleKGqkody8NqwVGenAkU0ZoM8TUVE0Te2Mc6dyyKKEmfbztE993GZ8Xo526JpdFTWNtmViS0/6Sl38ry5ROl+idJZJyn5UUNj0/eyKgYqtMem0cNXh+euHcN84PDzrgit0PurzvCYx+OeuQ2/c8vMq3LpymZ2z6jmIothT5E/WqhneaLoNxDD58+ChD8Vo0dU3grFlNzysBr4vFokQGgRLHPASmDc//P+6R574+L6nhTpZVTTT//efvef/ZZ4Dd0XwjlCwYkkZK85zTqDxnYs5kfxm3BAaPZCLUvLUKxjEGM72wfFcs9Ha+UMryjtFku5Qmw9He5T9SNH1gLsnUK+iiizNyCpeaa4Ju2XAOrYLpknN9eHzi8etPvPulz+jbWZHEtd6bdMtVI7NX7b3F+DmYlN247jfGccs9LFaH+6EzoUoSqvSagvvQ915vnB8ulBn0FVUbKiJbT8Nm4y4JFUgtL6S6PmeyQnXrFbws9pQODDeIeeCHghWO2/UO2FbT3hJwIXbEohmPCA4fpCMURKRkB9ZpnRUnANX0vLEEnTyLjTxVtPXEQCGBQXlgZIrQvWitcIz0pdAZ6h5Ud2DLiaXm2S5dWv7sfFahn3/3LTi05kDreRWNkc7L6y67F9EmE/ZIqXWnypSV3ONFjOKSR9PMabTAlbw1AvrDidI3yibfOgNJ30xF0hhOaZNaz7o/5mus/DLGLqgoTmKoJroE5LQ5qqX8eyGjxj4Hx76z18rGMo6tOXGEYp7suDSURWB+ydQvXNIrsZSCcew4U0mCS1qFfB00sRXLITE4rcEpNrQwKt2LnndHVIip4k2/WyRzp6cspCRj0lm9gKGQAbaLANuiSAPDiRL84me/4P37t2ynTmlFcu0QoOhThaNZECZz3IJ86mprYg8GzOPGcQwsgUvJ2gOfB2M4PnbMg2fXsObp06D3B948vNFZlkanoj4FlJyw4/pbD2YpjPUMTV5bziugahWGjTxfVsk+GSM4qlG3xuPjJ+qtsDV42MSWKk0srpY13UyGOV0DRseYKCDCTf5j04zaG21WTpcCURk3NTpqrjr9VDm/CS4Pk8cPV/bb5GUU7CacrpSdS9vYWuEYAsdLO6hFn0XrN0FOhhrp0J6lNMWC54qSvF1sWcWId7b3lflh4k+D+rZgs+A2qFwp9iA5EA5VYGRjx+t7zD5S5o7ZIWApktFu77IZKmCZSGlNlgjbhcsXN37j19/yj//hr/DTv3niP341+MufPvLDh87DJl85K8EJeZjI9V7SFYugtGRzuMyjazSmGe1kfH298Sf/5485v6t873fP1LcbVr7QgXP6VX7wT/57/tvnR56vP+Xf/nTnq2MpAHKIup0pI5j75MaBUzjXB07vPuMX38D/+scfaNuZv/ufPdCpWG8Yam4t/c/CC1bVEAvPaMnwcB3aO1jrTNtpZUi6GZL4WtG8M0JgFc3xuZKiVavMfI/hQ6yemHhRqqtNDb7OGNa6zJjX7l4Aanb4JWvdUkNpuMnq0BmbvU2NHFBpjRErmXQyQsPyVguE1A5WOxEFa5Myg+MYtFJyT76ytiNKspYSYDGn5sVSssAQyxEBMqswzwZ+MSpWI1+KQYOTndhvCieZpF1IONf9SmuN1nKv+uKe6pwtOUBQHxz3BrqlPFq1m8GS+4XTtoYN+WOKSZX38iEpotshnx6E+64k2UC1Vi2q89yDFhqYj7znW6kaxlpKxzx9qszg1NPnRs9jhobves3a1zWH5zVVLyXtIVSTcweuIu8iES80hKjWspZQj1irMbK3iRATsgy9vmg1kZBXVkslhwVWXr0+cVaqmOfwpRaxkkdKnVo1DjOxZ6bjPjTMVOqFeqHwO5ARkUogK3Rref5oH62hsUzj52svSdZN+b8Sr2xrMQBVHwwj/VQlEtKAVIDnSjab60y1XCOQwBV35rInuiRJZdwtNGRELRlYzJS62beGoXkGrJrQI49BW5WczLaV3KbeFRaDT8qBUrXWwpcBhiWotH7ZuD+H5bW4io/1mRa5JJtEFmRWV/3Jqu+kOlmhNZZ14N0XDEsfsqxhsu8cAY2q9L7/vwNK+z6JAp+OwtPz4MMTPGWajVXjs3Pn4dx4ez5z2todrxN45DILy5XiSeuKLCjDilJGTMXMMXXAOlUNQuQk2xrHvvNyfWHfg/N547RVaaqL4FKPhQjDybI4SLRzuFDaXoyC47YkVbHwwtfXnAWSe3C73WT2vR86CIvlRFkHS7NKT8ontUJt6UORPhzWcJOnRK3G7XZw7Ica03Ek4iipy5iS2VGK6OkY/dTT00Va/tpO1FplEG2kH05IitabDB1t+f5ESj/IQ3OZ+6X8KP/8FRm+ktW204m2dbHKclNZBK0ZkQt+xTxG+kqFvfojSS2ScpQ8wDy0yTV5CnwfXK9XrrcrhiY0JStyn0LaMTVoikVULdRyMr0m7zOmNrwrGek4dk0W5qQlO2X6YEwZuFtpTJcnUd9OLB1ttYK5aL/hnv5UlsbUeRDllJsISQZczdxIw8VFPbZM8RpDv0eknHA7nyldgGCrjd7EZBvZeG2nfp+66fWJbTQjzbYpKdtbdOYEwUplWPpojEPgWK18/PgNAA8Pb3QQWSRbRwd7NSX9gFMSNLCkqFOM4kZvnRziyLelytfAPGjJTLIEkmop7LeDx48fiDlFVyanMKFn204bW22wqNKWzYjr0p7DU2qVGxFjTKU7rsKn5KVcwpUMlIBcf9i4vL3Qti1lDEeeC/mz1n9rpvVblxH2yAlZpARiEOOgWpEgzuXfRJ4jxzzw6y55YSsZ+1rovalwz+LEEnFdyQy1NBURpKn460dUQ+mimvvYKUgPjxfmbQdDxdZW+frnv6CfCu8+f0e1Lv+ZleYISVeXdxf5LtlnFqHLo+kQMFnWyEvnnbsuqHmf9qTc0p2R7LIYAyuiOHs2K2KSJPicII/ZYjNKxjZdWTwLRisL0lxNSHq7+ZzMY+DAft3znpfvlafs1l0Ts9OSCt517etyXzNnFrRyp80bOTnO6eJ0Ma0KJmV0CMCM0vTyIiWQeZ84hiXgFoBbhj94wBivLGrXeV4W8yWBb7I4Kwn46UmrwDiuos+PeUCy1+7SOdO5c5dfzgQ+iumvVu/vH5/MEhD1zq4cua5L6EzpCeQefoj23ytrcr0S4UQ319ChDbHGPFZq4kwz7Zlgn560z1DU9xg6w+4VjmUNpnu69IY/X7ndrvS+Uksku9NdUVDs+mpGjJmgTYtkFsbBPmYCvbuMW+egn3KwYmCRBrIJToc7x+5YyYCPkauyNmpLFtDUtFjY3/pcAgYjihgxayiVZ0fNvbS8GreQd8fE1aCg/fv4s19w6pXewUdFGYVr7Q/Ci7w/bHlBauLrY9Dqia1vHKVAHdlg3xc5flSoU+EH7pRRiD34dH2mfv3IF5+9pdc3aXmxGIKZ355rTamY8t0oh90luz4C76p/jhmca6XQNFxKc21H62O/GlE2MZsIpcm2QhSZnhotJczZ+FulNEmhI2upFgKpih3QJm1rVBFFaKdGrUPvgnEH1a1Ae+iYw9fXJ45jMreUTMzCbR8QRqkTdqMVp5UNm0dKvI0RlTmcfpp5Ti8j7mSPeU/zbsNrSAKXkkl/MxkfrtStUM6qKMMr+JFDvv5aA/knKCdgI2wAtzwX0qahXAgryY4yYGLlDdE+x/oL/fTC57/0wm/+5vf4vd/+hk//5hd8+fETf/Zj+Ox94XJq2gMYfZOBOSUEkAaUqGy1MHAsqqTjXhjb5C3B46cn/uwP/4p/8O4tn//WO8rljFnVGf7+t/jVf/Yv+Rcf/xVPx5f8u59PbrMmSF4oDq2fGfNFlgBlx66F7XKmffYZ/+HLn3P5w0d6d/pvnHgXZ2xLWWkotenAKV7lMRWa/M8ckFYzgY0zsK1qEFsnPmWibKhpLKZ930pNtwC9RzOxHHyGwMts6iaSBJlXrMjX6tSCUiY2IoG9ylggsznuSvnainFYo7BY1WApsncr1BrgCVLfbSEM6jLwhtYr7mJEHrF4C8FSF7RuzGHJ3tGRO6Y6ZkP+RWLkJlgclsnNlsO4PIoTmInV+NYEm0L3SaWynXQmHMeNZaAtoMiZczX8q8Yfrx41RnrYSJKsoYr87Kz6/f4ia1p84k27S32QghtabRxD0jPLtK1YsjB8XSn35j9YIFDHRg4kkH+RtUwStXXXegLaIjlMT4ZyPhfWaE8FjRrXlAlH1pGsuzLvcVjEogQnbC4yk2wD89kI4RNPdAFxngQIvVNBCSOBrob6k1JUB5Ri+Ufoc9e8pwe6h0rwyuD6lhIhECa1AIp8gvehOJ4qF8v6aZ2/ySJbUslXvyC95znn32JmR35o+bPqz1jqgoWpRMx7bbV+XtJBXnvXbzHey0JjNMHKuzxrCRdbsFb5uSm8RD/bM9F3Mdnui7/UTOMVsKyhqmNTw5ZgDQxRr4KAIk9/Kz27uUr3BGbTYD+SvhRZd6IHb/nZ3F/NvlWTzlc2U9F5v2So6w9YElZJAD3fh2peQ8CdBakc+G6/vnNA6XF3bsP58AzPt8F1gltw2SpvH86ce6G3ylY7heBwpYYE8l5xE4Ni5LQOgIxBX7ItmQ8HI9HSmT+jYhwhs+bbywtmweXhwsPDRifuD3VNRddk1ooWk1jkFTOBFCcrxIDDPC8pXg8Hj9xEenG328Hj44umszk1JtFJuy+iSsvJoVNUmJnRbKH1xpZTzNvtYL8dzP12N/x1VxG5TJWhcBx6Xv3UsNZopVGt4bXQL42tNDyMSh6QrYq5REuUOKelsVoVy8Mq7g0BsdgbOsRfbjr8T2/O92lWUO7JcBYq9jwmEQk0FYF2w2UqZqtRQCaCRjZ6HtmsSjoyxiD2g/35Jo193SjdkloYYmCpIgBLxpJl8Uv+7maC9ufkFhBj4GMy58Fy7Z9EyhwzDajIs6FapZ9O94NsHWK6NwQgRTWZZqYx8MRFb3XPhCu9fxmxLs2zisy7sfQYyWQItk0pS2Zi5rTe7z4stTRRfYvYG6uZ5z6JMWglL3o9eyWaJdvKna2dmAkWWDGenp6Y1yvvPvscWtanVu4H/DrYKHnYZyMMCxBQEUC+hlH97jakOGtU+CWaX4qMA58eH5nHwbadVAi1ekfUSyvyNEt2lYqBfG4mlpOkO+XOCFvv0nidpDFdOv+QX5duthXXCbfbFTwB4jmZyCi7msC63lQATvKiyU0frj0xj6FpRzVq15VgJgNkJ9hvO2Mo3e3SOhSTp0it+pnZZDueTDqxL0VJD0ZpgAz6k/7EMn6ONOgHRcbepjP3bEbceXy68vTNB95+/zPidhBNMmS6ziCs3J+V2AcqLErNiVkCvI0CJf1yUvgvhlNNBkbWnlXyF59O285iURwqTHViOdEK1HIvCBbop+PS8qwE3PMsK9jcBUbZigyW/Ct8KrEwZY37Tf/dDHmnlCHN/ciC4ohBmfUVMFyX7fRXEL0kGzNBPPME+6Z8sKoVncHI576bsR8H/dKzyo0s5rQ/ItmYOpPyPArHhynBKQcIi3UIkZOUnHDlmRK+fudVu+SdFYPr80sy5rLxuIN17f7nKTI6GEfJibnAzwNnHAeUbLIdGBmbnB5dTjCKEcMZPvDrDbZOzMG4BfV0YlHaBZQ5xz4pdZeZd0ocLQs/KwLmSlUDc7vtyTgeOVzJeZuZwPTa2XrnVuSLd9o2sEl4YTttzCGGaC1dINEafrjMNVe0ejVj22Sc/TxW0akz7HI5Z7Ox3sdU8TV2juNZgSA+ZfYeTsxBVL2XqNmkxbeGkas2d019NTEtlFboIa6wW9C63Qvy6iFAPA7MKufLQybhGeeHN5J0r7qihnwhqPRtu/swekA7n2RE2xrb6czZZt4BZNQ0BFPx6+7Y3BnunA6nn658dZ18/PjMp9tVcrtIJmVocLDWXyUTMQu0gK3IN6a2hps8K7wVpk2B+lR5u9jCrtWMxhSr8HzpfHy88dlnJ3oTABjeuZzOMA68GHsX2zFmQE9WWsigtUQBKzQOyZGKSZbqk14KwzWxrekFEpkEez43Tm86L8+TmIZNY4RRYrDvle2hC/Q/CqUmg9KHzsS8EqzC1luu3FVOpWSCQYmKNBOWkgunnit+HRwfD07bmdI2SC+QGki6l5dqsGNxJbJuU+PzDOXMghFWDUdIkgKFqG+x7R11e+Hh4YUffu8dv/ebv8xP/uIjX/7FlR99+ci7vzzz+bsTp67VVYpJOlvzvHc1Zt10N7kI3dyKfptTr9jZ+OuvPnL+dz/i7795y5vfuED/VbB3mBXq936Pv/f7j/x3z/+Kp9vBn305eDHFp0fRuVBPJ+b1ytgnsFNKsJ0fOH3xff79139N+aNntg1+44eVN1FgG7jrnXRC+8aBaGKk4hoyp7TkOJxeqmS74VjZKWXL9EAB+DUHFYsB4iFmT28wMsR8TofqkqM5eC2YVyaSfPViYJ4SfJ35TnZxpDRseprBd+RRNbOGVRPYywIhNLBZJs6rYRux/EAHJVkWJWug2ipzaIhHg0HRwJKJW+RalFW9p6eO0g11fjjxOvjLuqcHDiIAACAASURBVPJVSvUKyKwlVzOx16zL6mQfjMM5nU/cpT+R55xHDhfSa7IYFvNeO94b7lbvA/S7p2k27tV09rgV9v2m+zjZLnM6Frv+3udd6iR3hWSOFd0LxErVylonNCyIKkhAvV2qQEggrRTMF0NqiCVnpJwafLGYUM3c0gO4JPAjMDDv8ztgFXdvYFtgfw5BItm1PtM4IGQfoOVi8oTjtQ+dfqAYgvx5FFpV7RrOPTijWsVspueP6i9zsJm9X6jHjaKCLJcjPpTUrbstQS/svj651+ird1/rJmssIoeYdgeetDXs9XHkAarU11xrnvV7/q4lGXyr/1gA0vIjDVvrOCHFUL8ltYxq1EHc/xulCpoAr2TuWE2mlafcF4FbwZSnVYQug8VANyOm3euKxRjKCejr+cx6NAmk3VdB3IG8VT+UtClY/17G7Pm54tWOJPLf+r2ZiW/t17W39F9FSuSWZ+h3+fWdA0rfPMlw78PLYLpS3x5653zunFqVrjYLz8P8jgQ7cKSOF5SwIOWwGj0QZVm0c03Ix0z/oFzFEcbj0xO3A06njYeHxrll2hJgyEi7rEj1sDS+KymxAkpw3joj02AWZZSaSQmJrGe7wTEGt+uN234oJj3RcasLnYycPCuRpwgKzyZcNLZqStiwahSXDOjYd459x8eez6gkS0gggFPZh5LTttqgd6x1zrVpc25Gz6SpVtWouZniLde04b7ZtVzXQnYXW+aIqUMK0eeWfKe0Ls+V/FLql1M9DU5L059VGifb9HNcdHQz6YeryVslOfuS8S1epE9sTnwObrcrvh84oRSljMfEQxdLTZ+GuzFmeQUipdvQ4VQDH871cNo+mX7g84BS5VO1DNpcxamFaRJ/EgiwDH3/1v4zTYvmGPTIRj/SIFgdnZ4PWgdhlkV1Xo6ODrU5CZccwNK8LnzSouEjAbi8uGsxynZSLLpVTfaRdM9nqAGpopHOMUgZtr7WZVfBb1fM0wPr+Ym3797Tatd0rmSj4IoYNaC2Td+X07ZlOjNyHVleQLqwgfCcdpEFlWfyWwUrPD9+5Hp7USOApT+AZcFgnFp656TJI3khlASEygIgsjC4H/bZ0Mwpn6zpU01Ufh8JFnjAfgx5ijEpU8bG1zhk+riYJBECGU3AwwLZ4JXh4nnJHZ4Gw9Lb6t3tikZuVhS7XjJeHAGn+gECxUtrKkgQ262EwaYojBCXWutpHPoc2ck44Me4f86ZwMOHD09i6QD7yw4lxKbzTjGBsa2f0hsjJ7QRHCXYkkFSSiVqUGZhxmSONIsemrhMf724Ew/Fp9MfLkrmMO5xpnplaXCIAIxi9Z6csgqDKOB70IRkUkJgs9JzXt+nIY8sRckP5rETAbsZVp2rT061sHuw1cYKpgvUWJe6poWWoItkX2HGNJn7l1U8p2RyghguZWWH6HZpraTvU254g+kZv+tJY6fd7wTPtfTagI5sCkyeF/46nTVUvPmUn0lEsB8HxxzU0u9T5VqqCsZCMp2KZFC5xmRMmudkOPvciVZksh76k9ZnKJ7rMJ/ZSE1g3zbdDceh5jJ0VsQCJqd+6zEmdUxqFvnlPgWXVG3MwVabvItSMrLAYCLuMsFaa/pKVFqtnM4n3rx7IKYYpg8PG/s+M83JOY7IovAmdkrbWBLmiKBtXcOX22sxx/IKiaBuAnAiOmYFRwXmuO2cL2eaQWnZ+LkAA4FjnkGaKmgtkoUUi41T8GQ/1a5J/JyANTw07JgMAfneJD96OHN+OLOPSe0ntroYFQKjahG40kqhl0qtm2jsOL2ppmhNz7w2rVvLBii84nYwDkVV12nplXHw7s0bfvY3H3l+vHG8C1qQUdiSNUayzooZs6ohM4+UzR6UONPboTpI/HqBuiyjZx0UxTQEHDaxWfn8zYUPX38SC2zrxJzst0Nek5vYV71p/aQ6Rt6OeZ9a013AVJPuJSS/88V1NCIqw9bAg/u+f/9+49ivzBEM09mjAY2z7YGd437OLON3MTQcIxkRQ1Jmr8kSCb3LsJ3hz5LLeXqzTaB02ptgfNg5nm/0dx27G8hriKqr9plZweKExYmIT1kf3yAmlA3zZEYlkh3W8yIGK28o57e02xNvvxj82q9+zm//5gM//vk3/PRp8qOfPPLrX5x4ewlaPXGujZmBKRUNAIYF5ulr1hSWYMMoUekUYptY7PzVT37O+z8+8Z++e6B/r8sz0C5gnfpr/w3/4Pe/4vHxf+Z6FH78YXI1uJoaw6014nRmf7ky9p3dNNA7Xc7EZ7/MH//lT3jfBqd/dOX7v3zirYN13VO9qClX0IlMhM1C0eyYAAdgxk6hKzV3DPpm+C4/1DHlPyl2idjBNcpdShgl64g8txuqqS0KVgV6GGC+BsrOGGLrH2G5/lUzWUBl0NhQQpbWuZJNU/qSjSRFyYVTfFe29OeJeeTIcOjPT+ZjYLQNDSVG5NDS2Idksfod/d6HLIbKGrzoTJw5wIlkoSzjYtVopWgwv1LgzKAW49Q7BeM4Jrfbwem8yRQ85c0CkF5BlyWB0xPWmTxFZ1HPtgaDCJBahYQVBTf106Y+bWS9BMRxhTwbSyu8co/1LNYwu1rBUe1REDOoWjAIyaNCjKRlzu1TQEg1E1MzXpk4JUHRUl6bBEtQbBmIzwha5oIsdv16BsECevRuVylBpnV1/a2Y6ZHsk5b1S/hrTzvTsxXVzK/Mo2Q0ud/ZMSU/e5rMCGjrheEje16j8C2zcrO04ktgKFU+6xy19Ph79aLV10oPnHPelQYLRFlfq6Uwq4ihC8tEfvXbFiXT4Kq8TYf+zFe2UvqnVr0DsXyy9kF1l4JjdOdLvvZacxSDqFVJkCGTc0GLK3GQu+dR+KA0hSiZfQvAy33rkSlwlNzL96dxfz7xLduEBRqtv1oh6zRYKqH81wKVXF50Us2EWFQzJbixamm0Duz1+6QcFDu0BhyL3fQdfX3ngNKHp+BlBLsbD73y9qFybk2JRSbJDqYXU6xSMyK4ujOQjEfysIWqJ2MmaVxkzLgmbXr4JRTb/fiyEx68f3umnjpbF532mOuSlQSi5tvZx8xhdTBixTmCwJWUZtWcoifqOLPod1TUX687t31nmbtZukKLdqoGrXfJeSKZAx6BlaYFkWk/1YT+3sbgOCbHvhPpgj/jEIg207jMxEiI0qlbp/VK1E4tnb5lFHXrSo+xqlQVRA8OnDKnnmtqnSMR2ftCJ00/zQTyWeHj0zM2g9PlpEQt8lAKFR3LVyKK0HpNebMRdHKjTEozFdTpQRMUUWKdbJInHpPhB8f1yn67seSDJGtlutgctomp1muhNjVQxeQRsfyFhitZ6/DBcewQkggGSs/xCMrUQa2I7cZ0AUnbSU0FSVMtxahVdMlI6mugyZ37zCkESZ81Wu13gG5JJfYFjvUOEdx2yYmKGPKSRcz0pnFnzJF/dU6nE4b8Iw4PPG4UatIe9dzm4Yw9TWSnQBxNAbX2amsc48b1+RmrxtPTM+3hwul0FgBZu+SBSIKj+HVkkL7LmYCamJElNdeTGZTF7Jx2N3v1bx/syQLZbwdPj08JVqXxdEgyFTHZtrOKqwQZYvrduH7aagxI1lYWH3PepXCkx5GHmBKi7UqGAaLeWgQ+RjKPgtuc0twnVdgKd98DRwyvu+mi5X7JlDpJK0v+c7mDRft+U7MEnC8Xau0ESgeK4VlYfYuJOTT1ntk0ClzLtWmKo11fMqvMCUMIrFzFwzgOaJ3r4yceLhdqoISIqYsw7zpaMz0T2+/noqWW/XB5FJk7w4LicU9SWh5oSx6ltE2dkzVpzZfalALX2r0YaWYJ/OrsWAy9JSWFbNAhpUBBC797f8kgNYvRvGgjNECXV5ia60qTLKZr7TXT76hkRq2bkmb+M6YYYwkELV8no9yllEc4JaaMi81ovcE+KTi1Gtt2ohc1KKJu6/2XBdCgYnO4CqBGYfohVkjfUBqc9msUSVi0leR3wJJ2RNCbDDLnAbfrjXfv38Cp0ak0a9SW90xKnXs7gcMcNzDHvWSk7aTWk/yvijGOnQ2x8orpfPZ8ID6UDEaF2jQZPm477969S9Zt15Q6uINgKpySEcyS/LnMu9WyiH0ayTQb4/9RjCriFjAxlSz3cVJs0ssEYu73SONl3zaOXc895RA+Dm7HTp8brW8pMc6p3tS5XCLka2hiFIzD2a8H+74TCPzaTU2jzu8MOjjSvyjXi9jLkrhHejCa1bvxamlJSWexO6GUmdIUE0g3BPC+eTjz/LQ8EVedr1TQUtJLz5a8YlJKUJe/kEfe7YVWjemGp2G0rabDggixFWpMzlb4/O3Gzws8fnhk//7nnFolyvLW8ZSS6tDWDS6G45xKZ5zXF0qr8nICwOQRaMmgjdW46m4yE8B77hvbVvn4uLOdTzQv7AH+cnBxefC1LcNbZiSgWqlRsOLUqiZD+1oBAz4Gh3lKMj19hQrejB621HuctsbbS+HpcWcewXFWKIqRPjSHU0/O2Ddqb1DFAtLUXPt6zIOgJrNW+4jYsQPCJuEmlm8p8kvEaP1MPEzGh2esVeppCT4c2s6sWStJs0m0z7DYIV7UWJRl8JsS2Ri5OU5EeaOGqDaifYFdrpzHwfd/0Pk7//kv83f/7BO/eHnkm+dn/uTHjffvGm/PJ3YmbUaGfiD2UDFipKk6hrXKxZ2bJYsDp4Xxclz5D3/6Ex4+v/Arv9Op7yvwQ4HJbGx/71/yD//F3/Dp+Q/4X2bhrz453SSjDIPSNxnv3wb7LaDs1FK5PFz4+Pn3+d//4ku2Bv/0H1faF5NmG2UrlMVG7NwZVJE1iGqTpjUcjbnrkGiXxn4NgUqjyXNlWiYo5WCyps8bsUi8qnNGDhgR8DLZaa4repqay1IrNZ/NFvIr0viPZKsWthpQDmJMaih4ZWTNhlmmNRl70XpTqeBi3I1Jt4Aq8/jJq3eTo2HlLKpdaoMy1FO1pn1s4dRyJmLcAxvuMileLUC+PXi2ZLrqDs5zJwdKK2lNIRcHtzHZbze2U1etEVMhRUVqE5avTAIAhJ5b5Ge8A1iQNUI200LBpISY6g+NoRoA7a8xD9WiRyCCgtYxGQJBGFY3ARnpgeT+KrYyU2KaBmWR4LEl8yVZ8wsAm4PImnn1HQR3f9xVr5Q4WMCHBhF+r+NUJ4sBVVxgSjspvAdLj0/TWT+PyUGoxqnllaUyIaxyuO5OMUDVp5QCvW1rFp/DMARmksoMglLFoF0Dt0VBn2mx0JqSw82TxZSeR4TA5prJd559431w9617vSYIuVLZJKdeAL9qNUsQEReZYUnhSxSYiPWqxZmfKSG5+2CSVN6QNZfdlQAL8C1Itmr5fhMOzmAo1VuyCXBWiudiQM2Z/aInYGvG3Y0yfy9MfWvO9mAkAz7DRWrJpN6sfde+0vDxdX9Y9hR6Bou0oAEN+UnI4dZaa5Z711dvqgehQYrlvvbXWuy7+vrOAaWPtx1mYbtU3r9vPLRCLfIOUUtdFS0IEELUfBpONssYvRXmPO7+GqVryjqcO7ujpqRnhqZY+3WnbY1tq9qIATUK88gLsCw6od0Nnz0EnMxcjGbiL0+Mw5u8Y4r0ozMitdCSDd32G7frznFkkxmvL+eu56yFLTTJ7jWjKRO1LMgcmIAxD0kJhlJhxlSsvWFMz0OIUDEfpmfYglo3Wr/QNjWDrXQtWIMoSo8YETTZ/jHnThI6tCiTapqnqn5/T0+XogOnOnx8eiSmc7lc6LWKjURLE7sEq9LfylaMOEm/C1mMYoVZ07w6fbLuiRgjp4w+FCXqk/3Yud1ueqLVqLXBuXPszqmtCHMZ6dXy2gQmRKxideyYHxxHRm9i1H0w4O5nUYrhY96L3BHOqTdaJkWFxatrf14QK6HJfXDaOg3Y8zgaOb1dPfIYk8OVslbNUjom6V9k9PhiNNSmCXCtjdr7ndGmyZO+Z4yhpI4Et+ZMfXdKZNydYz8ki0wJ4YzgMOmrcef5duUYzv5847ydOF3eYlWmp1EzDcSaVIKuBCU/DuYI6mlTXDKR4F9wPY6UYiz5UmGfQ62GKUlkTe/cJy/XZ7GfqujzS9dfnTQhT+lCpkt4NUknQlPShhE1AZQ5qckSK7UulEOphce8X4THHK8F0L5z80kNpUCywKoihuC0yHStkK+Ia6Lo6W+10LR93xVNnQDnahxniC00xqGCqmyUril+s2AZmazpEiFQSuBByvWOwRpnLblrye9Zl8Wa9FyPQ4wonwIEthMvzzcePn/gi3efUbvhRWHvhsts1NITLgJnCJSOBGPNmMUpDnaqnE0G5fI1MipiCZVMDCFUALRTmue3mo1vMslqTUB5nZOLJp/fn2C2u9gfRZuBPTStd1KKkGB6EXKhdEJD09nplNY4n88yeTXoxejT8JpAkVVqFnHtfMFNe6o1FRGeXgU1zwbJIqAeA8OxQxHvWxj7uGKmJrYkKKYiMc/4PCcgmaFZJJUqgHD99xGaVo5sQmqCdAtsV3lWMu3JsoE3XvaDsZItQ8220aDK94scjtjyCJgNFlWbBL03mCYZU6NTrDDMNbmrhnmhJ5svEsUJC3mGtbMK1qq0VUkIIsu0V5ADR6xa5YDJ8H7KTHX55AXcU1axnEySIDMCCc+98SGC/XqI/WQdY2j6m8wZsTUbtRVOXawrsX8LHpUyyj1hsGSBCCVZX2oUpjvnBM2swmlTGMPLVSb0YrTCiMkWApGnGWfvBAJrB2ClSvYakttYuCSKpRBUDjI23fScw5RGFyP0c0IU/DdvH7g9P7G/3OjFdH8WhVBQxXgbcxmdOx2j9c5+HIxDf/7l/EA4tCKWXoyZPoaWdgNTfkhW6H3j8mCc377lw4dPXJ+fOLdNUutM3QiUCBsUDeciGUfF6Bi3667EMpPnwxykkfMaLCAZfkp8PIKDg1IKn7+v/OhnL1zevOFNL3gxjoB227HmeC0qYPaZ8vlO7Zt+zi6GOJlcRJH0PBZjaDEuQqap8iduFJuUabx9OHO9Xhm70Xc4unHaGj5dsfajMKuMxHtpYuwyKa4aZQXpeZtUiztzK0Jx4KNoeh6mc85jp1qjvzkT+414eiGspTx14qFAlSiqf2084e1MKZccmmTCGwNCvh2SD01w+fNM65K+WqNub4jTRy5fnPk7v/oDfvd3PvDTrz7x46fJX375xPsfbfzw/cbbi7GVBkOHc7TAqpgBHOjuDmN0Y3MXuBa6N6MHn27P/Okf/UfO7wqf/9aJ+uYMXqCcMR54+J3/gX/65d/w9b/+Ec9H8PU+GS5mwLSgbScxt4/J2Ae3slMNHt6/55Mf/Os//4p+Nv7Zf3ni87fQo9BaELGGf4ZN57CJH8kPNw0QZxi1wdwn/Y1xOlX2F6c0o4+Kx8DqkB9TBC19fLSHdUcbpoGLq0E8AjE7hpo2G/IrqxQo6h18wlYaEQdM1XC4YSU4G8wmSXmpgc2JBm2x8BT9PAuBklbYgNb7Pa35gJT5D8ydw52tCbwZA0atEJPn62DMSOai5HJhcIwdSs/5lOTmM1lFSfxhmQiXolqh5EWyfBPvNAozem9YrVxvN/ZdHq864wNoultNp38MyfRigTC4wnmS8VFMHliLlrjYyZhRWqOkD+htN/VRWYeBhlE1wXUj5WtBStuhdbvbjVjJgUUIJKmtp4QyNPSeGqgt9lCtlr1j4ciho89JrwJ6jnFkX1VYsnMpnwQs19LuahBVeQodWEoFjmDrJ8bY5dGXfVvduoKqQmvrZEsIXShUAeo+iHCOkpBfGC+3q/pCM+rdTG8NgGy1gZCf3ecQWxunt87hGnGVlI3N6a8gYPayvpJvXQxtjBwEZne83mu+2zBelQ3JkPP1WxVZd/iYue4EuHkGzixmvuTu6m1YgGQsIDHBztATLghA9AQDJZktmfidg4dc6yI3aRg2iTsQ41NKCNUX6ZdYCvdJLdrHSlpVxWwLY0gsYm3sV69X/X/DFsC0BqaRydeqr5astbX0BvUE4Iz8jHwLpEKeuyYgbgB7/g5kH9Pju3VR+s4BpbDg7fsLnz903j94ml3W1wPEFsoPhxt7Ome6IR+FO7ijiXS1RgxlhsjXwOhVEpkxJy+PT8wRlEvlcipsvTMOTaf2YydCpLIVST3dE5QRQjxi0EvRhcEqdnSYHnOyjFJlmBn4cXC9XdmPHT/WhO1VY6y6OLWaGGVrYnUYWYjqEmqlpInyns3qwe22K8Iefe+YB6/RzuCm9LKtNmppMovuJWPVobQgmuUikfHZEUoLMTR5UcM278j7mgCvc8VTd9x7Z+4Hn55f8DE5vX1gmrGvbhbnFkErdqcNzhlif5CNctLvD5chacFgWiZqiLE0hswd3dWAj2Pgxy6DvUjn/lppW2fu96NG2zHZUEz5IVhrYGpSJGm7pZxnTbaHJAxGUhlRQpSPnFDo3fTaiGOyMyHR7Zb0VatVxoPZDLlPXtKU1bOhItld+xgcx0Fk8oG8dPyuyfVj53D5vZwuim2nnqitUqsi6/Fs5VL3eupd5i0tZWIkiJVTrWNIGUzIHNdTEnSMQa+NHRhzZxzO2HdGqdTbDlWTTbuZWCkkA8BdiSNmWDjH3u/gRaBYaqUFVPkToGnLgdgHPeye1jANbnMw9p1WKmNNXlNfbAW2vikWuGgKWiMbPrP0yEAHbDXK0O88hkDIltOJxYzbjxuigMKpNl5enrGSRr6H87TvmiK2CsNEny8yc9/3NHxMWdRKOBF+oubX90EU55iuqWcxeX8comD3vuF1crqcUwtNFpgpd0oKeKsCyybGqeZkohR6y7RK0ztRaEXcGZrFDEt5KLEmUmInftif+P73f8D79++1H+xEMckCeu/UKIxIsJPQ9N9S4uQCRUdKRagmev++UzPhjbLTMv44phwZtg6PLy/UnryEyCl9kXHgKvxqPouSdOC7H11ouovDzafYA8nK8iAnq9xHT0v65jkt2tLA3vJ8xYNbEQthM2OsBjgv6La8YSyUbhZq2GOxUE1FeMGI9BywCIH9pqnamkLOmv5/eZxqYoWmw1kMij2lJENMEo8RsPvU1NtkJG0lAxEy706A0LwDrLJEGDDE3KvtlFI9JRtZTk8tPKenSOIQMMvQFDzN0snC0y2YlpbPsabILnmjiWVkIP+I40a9dJ3EoXPbGjmFFUt04X9zjGSe7sIVS0lmkpH4ucINckIYZkrsWrK/BAxLkRzRq1FPTU381LrqrdC3jb5VxuHM0pnzxJwz/ZuaTG67pMKBWKan03YH7Fs3fVYEwPc00KdsPLy5cNs1OLLSaL3RFzvKsuGJSIBxoxLsx6T3NYFMSqepzvEpUGIcg+jpw0LIo0t8fN3X1ijnN5T2zMvLVfIRK7hNLnWTx4OTFPug9i4ZglUZ8tqhSBcKhx+StHq+lzk5Ruje1KgFauHUgwcvfP+Hn/PjP/6GD9985O37C+4nIoqAKEPFTEaqk2w+L0FpUA8Nz9q5Evug1I5K7ZlEGxXHMwt9s0IvG9Mnn71/y/bVjccPj2xfnLAitqQ1MTvrCAryz5vH5ChiC5QhP8lSXfWhpxS+nbjtLzQRgqkmxlJzoMn0OlJeWk+Vy0Ph+jgZeyH6ZDZJkHzCC3CJSbSe+ym9XSzkPVLT1+7WwCqjOJQjIfwN81veM1VM4HGDObHtQn0P4xt5k9lZ3m/UBUps8lLzjnkl+okoGzYf0WR7QtywciLsQdeMdWBSQmCTNnXQ64WxXfn8h53f+Z1f47ob//ZPv+I//uKZP//JN/zg/Znzm84Pf0kpw+H+t/x9NHDqTD8w0ngWY9qkx0a0gVvw118+8x/+j5/x998+8O7X3lIuZyZGrReML/jsn/+P/Iuv/ye+Oh75w58ZX0+BNuFBbYVeTxzc8HFw3eXV9GDG995/jy9H8K///Te8P3f+0W8HrRq1dKjrvhITZVqh+pA350DSc0ySxi2Iq2GbUx+gXAMr/Q52HoQYg1GpJgaRRWML45rs2rJYGuHEMCklYqjBiXKXzJxI/0gLti424NcelFPB0rend2P6nt5CRk02qawTJiUZCbO8gioVg1qVRoyeQUirzTSd/YWg9wkOl6I763oAc9Bb5zYPWtYgR/6cln44VnRfCpxYkx+4G4RbYYVN6J9Vp4IAkWrO+SxJ8n4b9C1TrWOFTxiWB5IfknMr0VFD5whoyXTUfay7sJqGIiuJebrOkG3b8EM10hiH1AT2eofemSC+mKFOzF3Dai/387n3jTGldqn1W15OKRmKZO2oxnkV1LkXwqee7aY6JLti2VAMz8RjPa9UXiGVlwJ7CurZ7JicTif2fadYiFmazDLJyxS+NOZkHAr8sZqMRQu2tGg5QkmlXuRJu7CWWSBcKorwwTKFEQHG71I22aRUjoisCdU7jnGkosfw+W0b7eztXOnTpYnAEVkQWvaYvbT0S85aKa1JlOicg7lQsAi2LBEmhtNNYQzmQWldzDdewddF7PAEtCiNOERCkam29o5Xgb8LdLFUNSyVjmwhYIyiwVj2+dNkfzBC0mhtyZH1fcm6qBCe1iEWFJtkcJ7qpCUV5ZWlFckk05ke+ZlyCJ6fxYXQJYNde64kNuA+8ldYjDCdjVnSp0TVsG/lRoxXLd538vWdA0q/9r0T786VU4XeTynHUZHm1XOyOJlISzyi4KVjBSY7eOX5eBHDqECNiVOZkfI2cyIK+zF4enwiwnnzcFYMZ6mMCVhwjOTOJJNBh4wOwhnypQElbUUITW05JfApRB2EJk+Xl9J+c24vV/axqxH6dkGJCnamfvESUibPYyrK0lTwW1PSibfG036j4TRzrreD2z7zQhNzZvhMvWeRmS0lZVQqorZWkn6NkOlaRWkrBQ8lFpxRkxzu0AvW5OHTphJCIsOdlja8tAR+Ivibb75hvOzU7cz18RNGZcew2oTSJ70zcjoNOcVIX4LK0h03eQtEmgACY7wwR+jwNenTxxiKNzajt0qvG2G6emHdqwAAIABJREFUnMdNjY28MjRN2JBJ6SyV0lpKAgRmzTlEwUdNXQf22wEkRTE9SaY7VAEn8xiUVnl5eWJJASoNq41RB15SspfeRZt1uB4cyTpZRmlEan5RYxTzKsAsL58RU/TnGZgNTg+nRLLl2QBwsg2InATLsLX2Ci3jHkei/qURcyq1YQro0WdzXvYjUXYxv2qBuSuW93p9odbKPHb2R+PFF9uiiLG1IlFLIdJw0CKYLZQ+pldHsSYTcQrXlw/01gRGIe8fIlkptbA/P/H8chXzqnUikhZuBR9imNQilkXpDZrovzWlo164s+rM5bOmQ3bqd4gieZIZ+5wc7q/sSHfefP4Z24NkAGbG7kNypWjcCDh2LCatFY70vzGCMsXQkV5bIMkcanZPtYhVt9+wvom5NoK+NfnC9KqksWOw9ZYMQQFsBdJjQkBQKdk4T0lv1ET5nUItO8Clh8q15Eo2IQRUldo4Xm5cavDw+WdgnVI2gXElqEWGn+FBcegpOVzFFJGphUCvG7aShGLQ21mF8SjMKGL52cTKoJnjMTlu/zdr79ZrWZbld/3GmHOutc8lIjMru/riMjJtg7uREcaWLISFJUDIfuAViU9hHvgEzVdBCH8AHnjiwX5BNlLbNGrTwm3RXe5u170yI+Kcs/eal8HDf6wdxXseKVWZUREnzl5rXsb4j/9lUp6embPTWlKLE+SLPLeOOXmsTUBFsncIXeBbiNVnA9wbYw5NlEyT2xVGi6WghDWxObAxs7l3Vilib5ixXGwTs5LeXwKIlzV2E6BWzLMh194VkLM+M8HWos+RhtViSvVYMpsOyQbWMFrT95nktCkiASRRoRPPuasWLdftPNM640wRnfKa436kUiLYvUriIyqJQIiRlIh5ZDOZIBcwYqiASymZpKJiN5mFTE/HoswQ87IUqn2msmsgbPef1UYW0S3w0uR9M4bM2+ci+srrb7KqAHZBYmmWG2lyfdzoxxXfg6090c3Sm0bU7FPGaPkcI8GttimcYIzBsRZtBr3nWZBT8tpkmGvL7jHuKyZjCvjvdJjQ/KKmaXX5ma2Jr42JZFRXErD29M4Yg3HcmOMBKFQkverJiFGNGFBqxk6fxuOSXZyNiZtJst/1PS0iPUJM6zxOb68CVcywvWxsF+f1+sJ2NN61JyqVY0FZnoBhphXRZNbtYFRaONfxxnHccKu4LzCxs/uCNbua4QS6axGbu1bn3btHeKz84mef+PrXvmJ/PPe6hi7BgFVyT4NjNNMeLI83+of0a6gth0vB8GCLkr5DubZWwJrUCt6drTT+8l964l//8Qe2B/heueDWiGMw2iBmERBZta+mkfHeMIpTj6DtCyFGGRcSG8dxUJpTllHLgtl1hmz17m/hGPsTjGtwOw6swvvYWJcE6g+4PUo657MoTIVsbiw00GtK93SvMBXP7TYJbkRTU9uyC7CyE7PDmJTLxmqD9dah6JlFqSzbkv3rRFEi2Fodb1+q6Z9X3QTRP3uQsBD7/6xjBU6G7cS2US8bD7cnfvMH8LeXEY/w6f8I/vTHH/hXP/zArz9feN6N7T1421XrpU9hMWOhlK4WStSV9wjMttiOgtfF2A/+9M9+xpf/Yufh8ZlmG/b4V9TKlYbxm3z/H/y3/P1v/kdej4P/6xedg4uARtJH6HLheAtiDuahTDUuG89ff82nv3jhf/vDDzw8Bv/+vzf4tXmhXiY01cslgr1GehcJVFh9Mu2U+jaMg3EbtP2Rtd8Y44o3Y95yspEDLA0/isC/WdhLYQbQTHJXQuysBA5OwEIPLDSAwlg21c+0SW1OG4vamnjDMdk3gaK3CMleTYyziAFWmaZvupYYCupVjFUXdJjr0Dotxl4LxxGApJYCqQpRxbi4DQ3kt2b0Q6b4raS8yaT82DXH+Ew8Os+rHMZGgsGRrN8zfescwluCamaWNh6dqGDNcJdf2Aky1K3c/T5HGjRryC8AZc7zPFbKMQkQtfpZehYz2LxwlMnuTQOFlAGN0SV1N/usICC9jGbXuVUrBZjHNYeYkna3orNtOdxjeLNOONeGJGmqS5Y5/RB7RKm+Clg5Q30kz0+GuondUk196wj1k+4wjg4eRCls7vQlsNKicCaalyAZ+IGvIpeVDJEqLr/SVgrHTPuYPOtinuSK3L8xPwODBTg7EVsazqU3onwFDasBXVqVJFRLYpprtBQNANb4zEQTkcclh14LXENSN5MfVMrBTlA3QqqcUgsxJq3s3PrttMbn9Akxuy/GZH7nAOz0NppnWE+uY+LOqN69CGyJrEW9ZO0Xue4Xpeizj7GYoTVYQgNC2ZysrFn0ZyPvfEu7C6uW9h2q3WsVnjHXlLfnnFipGlCszwl+M8FWywHmhOyHat4xkrYPS2KHlXv9Rg7jhHeo/hDXbLEchWGsiaXf3Hf19Z0DSu+emui+LXiJK5PKRCi+T4dxMEdwTOfoizUL+27ym43Jy+3g7TrxJhpbC0STrqKKjem8HUP6fDcuTxvYoMUja3jOa4LeuwzbYlBKE4MgLLXJn2OOzwkDXjiy2I8EB9aagMCQl9cXPnzzLbe3673IXRF3NsGdwWBJ+vdMKhiDctnEWKiSibk5bxH0MXCMaimNSpYJLOaMe5pbrc7D9qCpegH3QmsbpW6EnX93uXv4LCbL5evQvHD0rulgEdJZkqEVNhmpuPJsrOrSLPHDt1dmB1qlM9naheaNt/FKXVqwzsGwIIrTrMKIu0fWMKiWxl90TeHnwkqjRAXvQr6PGysBr9vtqrj4thGk8agAXpYtnh52ZpoBF6+aWBNYbXkxrXtRPtN7SGDi5LgdupAsaYApNzuZI+aFx8cdvMojyQSq1KKWUNGmxkNrHGvy+PSet+Mm1sWCPS+LsKamMtm9hWAdVwrQsQT+nGNdiZcPtPIMcyoqtm6EB60lMyUJWZLb6SA+DkWk10z0iy5V/hpdEqAQ/fjajzT7luyrJjNgzOA2rlweN8wzYSQnOTE0odovm95JqNAoWwN0+EboIqJJFiK2XWGOg+kPuDd2Jj5gmAwJS8BtHnx8e8Wt0mpLkErspc0qN5+UbSOyuW1FhvJmKuoGJx1Zcr+5tA5OmYN75fV2TXmlcbxexTYj96k57776nmSFIZZAtY1aGwxjC8UI721T0VKLWB9zcspyZqaEDJNcsyWl1ldA089+vGlPPG0Xrm9X6hnp50nHN7GtVu7VQuRnNLw0ALwstr3lBEjJST1lmQFpUpiU56WiSBfepNXG6+sLz18+a7K+gvDJlr4Cnv5rBJr+xCmxAtCFZO2UswSRXmhjVqYPHtP7bRaTVl8tKLEG15uam+gDc3nk1FJIz+j7M3SMHqFnnHLCOAFDROMurcp7LjRZ82Q5GcERkj72EfQhX56F0kfMTNJbkyT6BN4spznDFpd9U9E7P/tEKF0sPdx+lb5PTtzy+6gIi8+ToZgs1+RVAF0+UzR1vMsFkKQvohPoflFio2SlYukAaA+rYr1bNtLX0CDAxHC6rYk1+anZWvLfKsEYlnR65DUwwf30L1IqZEwDG1hLGdydoeqEZYJefH4GawxipDkRhjfP6F+jd8l+5+zJXFH6UKxgji7TfZd8IyK43q6Mvnh4SHYX0HuHBNLPO7mUymqS1TYr9OlsWyH6G+N60xAoGQTYYowrvIUi3LciBlcsmakPSVCP2TOe/ZXr62v6iWzsDxduKGlursXtNnCXqXUfkxHp63eIqRAPp2wwlFoWxho38C33k6c5ruxBlL4TUEjq++c1YZzmurrjTrl2zAUx8Qget8pxlZRF0/bAfIKXZLyJmWMe+f7y3M11EKG7bKWP1UoZhxi1lrLuyW0ZNQMZnraNL758xzc/+hnffvzEZW/YNEq09FtZ97qjpczXUm5U6oVVb8SxUgo28BKUKTneSjlPLD2/Uh2sp5/dwddfbfz8y51PH1753sM7lg+6g9lkX7pDlUQJzZXu5mVja43gE9jBXjeiT9q+sWzydpO0sFtQwnCakuLWkhxtBEc98HahPrxye13cbsGH6DzZpBeDPnjkwnoK5s2x5dAMn8GMLvC+aWDZ141WdP5EyAPPYmHrxuxTDVfZMmCmi6ny3uk/64xesTmoy2HXfeJlEa1jB+A7lB2sYl5hfUDMBcdsx6IAV4ID7AnzSy64QpgCGR74VkDGbPzuXPz058bPfnnl3/z8I3/05xfef125XJynSxNbYZ5eW1OpUCXuiVZWwJcYO90mHJW9bXw4Jn/wRz/l8n7jt/52o5RHrF2UZloK5d3f4t/9Bz/m7738L3ycjT/5+eDNK8W5D4vK3hivSg17KQe1T57KM+9+/Qf89Ec/4R///pV3rbL/1YPHQ+zyRjCZFCrUSvVg9M6olWN+ImJg0RheielwDOrjrj03pwbCLGYUFl2AlBlbMV7Sl+mxOtcu1tKcYt6VJS8a7QM4TE3hmWS8XDd+6UasQQ0BFZRJA3w5T26YXRmWnme+YBZiySZjIbnwnAMyYCE0Rc+BBDAzpAZS7qhwgYEYm62KQXI91AtcinMsgSq9rDThVyCS2LwpM8XuwOKvmi6f57Z7/oxpEC8SkoaGjc/ATimO2WCO3Afe7qBEjl3wTHg9vRaLlbwXM6QkVgJlwSmSslQdVU/T4tbyboHLZZdaABhzJBvGE/wX0yaGJOHhAsoxKG2jz67zvCdrhEjWSTJB8khfiJUDzrgdRBjXDq0Vmn+2wah11xBlimElQoMAzFIEGLpVxjowq/TZCeI+/KIk2ygEqEUoyGnMDsuTYVwTPHUOv2X/su7AUaQcU/W8n2QXfbbBfS05hleBj7U0WmhYFV5gd2J2bOS601RTw7zi1AbrJmDj7rOJGO1xv3+4g3wBn/2WVGlwNzXPXk/1R2WNA+bKtUQO14X+irkupwWRByZmSaSoG6sfnLalK4IZM9dA2ltkDXIqQ07wqbWmNWAaHB1TbLB735+15OlbqCPX72mAXnO2krYnlsM0TIDyNHlUxgr1coihdZrVn7WgEiRzCpRg2lhL9gi5Hs1zqJWM9mlaLyVK2pEMndvpe/tdfX3ngNJPfnJNff5kWnAbi0nFZlHaDJ3F5JjBPIY24bZRmowm3z51VNgOsWXC2Gvo4QC9V7a28/RuY9+KwBsKVDhuRzbzrkN0Sjs6u6hgMTVZPs18T+DIqCoMXea+mshL87vW4uX1lW8/fOB2fZM5JmpTDB1EtYqlszUBRn1N9m1LyuOFORf71uQjUCWfKxPqvuOxWGjT+SlB80Kfi7ePB1RFBrfWaHgixA1kt6akMytpLhlgBWeKeRCLssTiKaXcwbZYkmFZrESsRYluTZLDTx8+cVuT7/3aF+CVyeDBG6/zYIsvqK6iMqZkYSVTsaKrOMY1Ie5dZnVeC9fXN2JOytMTAK1Urm9v7E0m2R9fP6lAbo1aK62eiTJJ6a4bvR+0uunQIQdApeVmlv9FH5JwCI81mPLScTspGAaz3xFtebwZl4fHlC1m4l5GlEu2VyUnKYUOhC+OEKIeWWhMl+SrNNEr55AR2qBjD5umn0gS8hZBOQoXb/Sl92e14bXy0OzOoDobKytqvsSwE5hkxem3myYjEboM1+RYnTkWx+2aU29dIGMVmMFtdlppXPYneprE7XsTxbRqEmleqJ7pQelXBTnrTMAHb+wXmTwScL1NLu2JOW90L9hm7AmGOYsP337AlrM/PFHrJkqua5o1LXgoG+1hg/BkbIjBuAEWLskVkqGuZelJEgKWi+K9VwStGC+3G7hEjV6M23Wwf/GFQDlvjIWmyun3NZn4VYWkvKGWQD3gtoaMlXN8JoNhcv3AmhIDLTdYmk4/XC5Ub/TxwnaR8WM3NYI7lpeEur17SlSVBxko/azWTMBaSv6QTGwIfE72jAzRyfhvpcS9vL4QffDFF1/QIjTVWmK6HcdBXWIgBoj1mGw/w3VOLplxn9ReUVuCVhplDKZD8R3zldK+FEO5DN5bSd+fnEZZeuastdgvF2LIwwwzsZDMwU0XH8YxJCGUp1Octgm5F/S9wnSma8olg+tl4FWR0KccUMWqC9Qq0LJA3c/ERjTRX1mJyv8o8b9c9CPZSCukZfc8S2cW5zEkly3JiJHkU00ky+5pliqgTn81yyJCBVJJIOaUqkIyyNSPqrAvYuvNs4AVspjeWKYiYw4wJ8LVKIXJVLc45JHXvBKRPnIIJFo5sQszpZYgOdNCDYFVx+L0NWxsrpQQQ88c01Rcn0GfST1FUMrJwNKEzouzu8qOGSNZefke0pPtjLBu+w6rYwP2ObhcKi/f3Li93iRzKY01OzOmpG5zspWNtZx9vzBuB9OUvhrL8GisoSQbM8dsSr5pO6VsMCetVPqhusGsYEX3orskakojbRyDnAZqStpDxrABYhGjyadSGBVYIdYrGXNuCe4MRnptrMg9Nc+z3xQtXnKfz8KaprXgniEKwbCuhjUn2DKKnSnz11epRqya5/iCCmsV1UQYaw5K1Z4JM7YFX375FT/9tz/n9eMb66svtcZQM1KqYSEGzGADhzWH7soOdZtcDzHoauT5aa4BnyeDw7LxmQpAaVYYTBqV7//GI7/816+89iuP1dmLs1YVCDMkhw1Xaq9CpBbFlqRgS6xS0qPENqdOyUEpwTI1cjYCa8FRBVyW08j2sRHHlT4azM5GozxXluus7nNh0QhbRCHltYbHIKKlSygEDSsdTw9LpSNpf3JraZ4uCQK3TtkaaxuMt8nlq0doO1EHNl9lNk/F/Qo2iXHFypMa/HgQCGkulqJltRF7nnHvCbsQ9oKZ1oCMCX7JowU/6F/zH//O4Gc//gV/8Mff8q/+4oVf+/rC9583nuoi9pJx24tgA+uSX3N6kpnOT5/ENIYvmnceL5MPL8G//P2/4PnrR774nS/BnzD7jWzYg/aD/5K/+Z//kJ//r7/Pp1vjJx9DNVZoULDXnbEb8fLKfJU9wW7OZdv56vtf8f/86E/4J39g/Ff7xl/6wXulGW4CUA1oS/Zxbo0yJtjG6EFYF9uzwnE4djtol41xFWBTamVUKSjqkAfboPNswevy+5l9nCqLaXleV4GlpCRtfrYmqCx6mFgO1tOfplHojEwJW2tw2SpHh2WFOXXvdM4GXyC8TIEHYaqlD4O1KnOpRrAoYDMHLmIdBgEx2XyxZet3vR481srmqh0f7YGB331ULXKom2eXfAdzBSVIfcp3tOalFFkJgKjMKWpozei903tnRVCb6vi5xMq0mcbnVWlhY0iOVizTuf30ChLX1qslSKL1Eha0shNz3H2dzqHFWkoC7aNnn8RnMkGC/BGWXotVLJBMUBZoE1A22Z8w1YinL9IdQIsczJtTL/LQux0CN/paKY+yu89QSXZ6ROJ1SEbVWrKraiUIqj8y46obe3aKVZlfZ7LXHUBAzJoxMviqmoC7FSzERjVXuIV8+kijd4FgeGRQjiWza0JRuua7Kl+zkUnK4owYg6YAoZA/5GlDMOYVp1FKpU/VPeQgdGXvDOMuCxxr5M+hd1oSkBQLT6qLnpIwMWs8+0PUaxUnot8VCRo4qX6NZKQ5xrhdKb6nR5ckpX7Wmsi2IgvOHObrZyAB0WhiDo/rEDnENXAr6VF2V4aGgEFLtZH8ckXyUJhV+qGWz8+FHJKD3X/dNFeUkXgOm042tlKtwQklTKf1DTnDVjp13OsvMwFLUlOncus8nL6jr+8cUPrzn33DMqNQlGRjehnVCjXkiTMzWcY8NeJrcTsWZou2GaUEYwQWV7zuWK2s6KxZNZnanK1sbKmPxSbX2w1QoVVcdLuZjI0xBqVu1JP5cQreTiQyUcjej5yuLqAxeuf19ZWPL59wJo8PD8QlsKJGH3Nq28WqMaftW1JCgSLz1mtXfHkMFXujhCaNx1TKTL5s95IpSlrcboPn9xfKtlNLwygyDS5N3km1aOpXaqL3KvXG6LRSxehCaPJCm8OzUXQv9B4ClbKZqFaIOXn59MZtBpenL5VAx9ImOIy9PoFNNirX2ZGfVRNdcwjlpciXgmW4B1GMt9cbW9koTxt7LczaeH37SL/eGKPz+vbKmtJbb9sOLgaHdM86OLbq7KWk+bHdAaeVWvY5puiWIUCg9wObn7WpAZre5uEU6OAea3G5XFREuCQ6ub1VbJvoiWZJofS87LIZMpMEcBPXWpeLfkSUvOECD0xNhTmU6xvruMqjJ5y4PFGbU5cuNVLK4aWyWGylUmuh95FT9MHoAybJ4EqvFBZxLMbthsWiZmMha9SkkWI8fvletHMTu0AJTa6pWdmwcNHLzimOJ/CQzXEBNaIjHWjn5HkXgFhqodFY2XAT8PLxGz69vPH0/I6npydau7BiyOsiTdVb1UTM8r9v/WCve/qmJR03QYUxNK1faco8CaX7LJmjzy7Dc9+aPGm2yuX5iRPRr9VziqufvS5YrYolmcaAM6VOmj7ZHdQI4568p2mgpg3YRn+7slUlBN4+vXHZdmwrrDG5pL+PTP+UcElx+YhYmr9mYeNll1f3CmZVgdJXRzWsLogIgZaj62eZS15kx+sbX7z7grLtzKGmc8wBq9M8CEaC5U3FLsBSVO2KmZRfxZrHgLlueu/lJp+KsRjJelrzgPTKsjkpIbDxlLGFSbY7R67bOe7PMFZK7VDzXkxMuTGSsRDBGp0zgUXTzcHd8DFjjKubCg1zWm33M+O8JiNCnx8Q/bvmRCsL36nLXhteengVZ1nsWdYYKxhmRO86b7IjDmSOCmKXjKVptS0VAtPyNJEh1N3QUgVb/pArwEVrFmtC3hWSiZW7R8/MCeg0+Z/NCDwW00kRAppgTg0pToA8ZmSyabCYxFgsm1i9JDNJJZ64mJJOfuZk6X+XcU/o08/f9N/VOWJRZhHzI6d1g6TThxLqThlISZARqxQ/J8jpsVWM2Vc2yXqDkhzDLBu1VK63zhywVacWGDet21KRh4i5mBhwT+7xulGZrKsYetu+07aNt7c31pxc9otqkdIyFCClwxRa0Zp5vFzoIUZcvy1mWThTU0kPVppwn2QvyVmTRZJnr6oOFf1eJsSkmuNb4xiTdUzG7XY/X7ZNbMoxB9hIVpiaD3cXoJMsUa0OU4MzZRjrbpQqc2w703dOE0+HVgsj2XfRCltOQ9veaMV5/+6Ry9MDv/z5C7/26wf73hhdEteVshSzkFQyglieIR055abiRWwRNSyeybOGIV+XkUzhWhthEx9qOX7j6wc+/NvKt5/e2L94xEwT8rCbLBRwzHXWDFP95eisGHMR15ukP75RmlF3oE+sFEY3bKQnyNvk4eI4VQX7MsrqHG4Kodgks3iKytO+qQHtjXUJfLlAqT2g2H2Q6iVDHFaRZ5eDU4n4AMkKZioh0GrRmTwNnxX/wpg/elMC5GWj+HtW66zxIjZSaWLCkeu8pENPHNrBK2kQ9pxywwblAWOTR1wEUT5CfaTsncd9sN53/p3feMd/9Dt/mb/4xY0f/fwTf/zDnR989Y6nh8mTTeBCK0G0Gz5hRdV7d/neTIKyGjkeBS94mzxdJj96feUP/+n/y9/6cmf/rV2Jz/El2IUoi/13/xv+0599y8/e/oT/fUx+es19j0IDHi8PHGtyfftEXRv9dnApjcd3D3xv/hZ/8OMfc/m/v+DvP1+p2yOXrQpwDjHhRKxc1GqMkOeKfIcEDltbjCNgBf5YiDd12u5G7QJ/RpligDnUQwyZvhllVWpBw2uKal5LBsvSQG0y6L6UuGSAjzsLdYYQr2LB7htXe8VYPG7B2+yYS35WXfWB5DnynVMSs05oFuw0hqUf4wwFK1g21ifbf8GMLc+0FxaFb99uXC4bT/sDn45Dck1yqDLlfzpJxrzZnX0Z6Skz8n4/71tdtWKUuNX/H5uplMLtdqMnCOCuZ23IOw8k+T9N1KUyyJr8pPigAdDKRt8SMFAtsvI9+N2ceK0lnz8zmUsna+mU6J0p18WdNQ9qAkMxh3z/pkCzsaBuWQtO1RNimAjk8pQtqTQIEQxKlVrAQswnPMF/sa7l0XR68NbsIkxBOxYcs0MMzvCEagUOoehhcgRSfT4oObQRiHYqbBa1iBU6YqSxeAiUsgJlfa4bcmim03myN4EfM4LXWFKckKx/hmSepiGqRUrwEUhRrQnYYkoWz9Kzsc8sr5KhErFSVnfChUUSN7WtOXmAe7CVFTQELAkmWVpIFN1JKYTXMVjk+0QqNPba9FzyShJgtFJOCad/lJ+GR16YqQ7A7Z7G3lqT72VeyDPXmoeJwYl8cvU8kkGHEpjdKj2DSe5S0fQli+w1PD93lqoJBp/yulSJ5H6zJDWcfmQkl6lauX+/khJAi0l4Bg0sY9bvFgL6zgGlsm1KcImCR2Vj3uOyWUPTuXqhWqMVUT8DZ+QiFoV50Lwws5l1M3zVnNgVtr1S2kpqrDO7ganpdlchfqoWxuiUttGqZAmn6bWnJGLEYpkmheUEdUrhdlz59PrK9fVGu+x4mmtakQzKveBV/0grW2nu9PyLaxjX0RkzGHHgERyHXuScA1tKjVtJwT4j6A8EeDRz9m2XX1GtgLO3XUafFsmiqTmoFqtqjmCukabdTh+DailrOFN1QlPVIsG9QA6TPOH1dhDVefjikZIxq7Vu9JWsozE46sRCz2B5+nEs8G0DLyxW0kMHxeD6dqVVgyo0d5gTry8c377Qx5Xr2xVbi8fLTmktPZk8hb06EE6Q7paa361UTpciLGmLs2e7dOpZlZC3RsdKFidDjcIiaPuFYy4eLg+0WvIQkCFiZHqVu55nM+l4Rz6/wRDd0gsNXSKOpGhLOI9MxQ22uol+7RXcOW43bi83+cGYsW97RnFn0sVa9FhaY4C3qn/M7+a1ounLi6lPXUwTFRErWRsl5BEEYCazyTkHX77/Hg/bhbkGlhPp09dnWcFrw8KzMMopMDqiikErLeV4OZ2PQ9MQ35ll5EWcJnfVWbcrLy+fuDxdeP5wZ3bmAAAgAElEQVTeO3l+rWCvFzBToe3GEZMasBmspQlhbE4cechnI8TKyV8CxiMvwIjg0SqvIy+L4thmHJ/eePziK0oaHnL6tJi8sbAg1vG5UAE1pWmcjYnxUnGZ+M00aXYxQKZBc+e6OrYJ9LQq1szDu2dGP3jYdqKI1lvtTFGTdKiSBpHJDsFItosaRJbYMC1BLsZKunTn1jt93Fi94wWONTh65+nL92KvRLDiyCJfpoElSlLFYWAQakjnUtLInJ3ado4pv5SIay5oZ1bjSM+OmFDWZMVBq5V+fVORRMYKo6KsuOcFJm+BFdm4k0aMVd5qIAlTrLy8yTWQoL8KNs/fN/E7IBMp+dPaIBZUV2raDLbW8jkuli8l8VR5Bc0gTSwlO7NiVAKjMBIcsz5gaco4esagLzECS2mMIWaV2K5xN6M+pW6nDBmMM7gBzsJb/53tsVgW55qcp0dAfi19bxXvhbqgbU2MmhjUJfCh5lQ57JRG6k5c8/RfWUK1o6QPWR6j9TSirAJ01rx/hmIaQsz0W7K2iW7fWk7xVAgWL5q6lvTLWzLanqc/lW+0TXJm98Ic5W7QTU7exuiMme/DUqJdCvvlmcvjM8RPMTso5Ql8Y/GGu5Kx6pax1XlOmoeYOSGQ+LEaXjceLg+S/oVxfX1j2y70TJXd9o213uSXMM8CWe8vcRJutxutOXurqWQ1ci7GPDqlBPvjrueZ/kIeOvf0OQd4Z82gWpXfiznUSk/29Ewaux3y6SmmtSkmmbyigM/+P0VJlWdzE5HMr7O4zWHSmith0MiBEWLhmlGWivlSCmt0Hlvj3ftHfvGjn/J6vPKFPej7TjE+lqfgZKqYx1P65ItPBCNBkroZjENs1BkcpgEWvqA2mW0XZ8zAG9zm5BHnN37wyE//6Bveni582RzP90E4wz57znk2COGDWirjqNzCcQbeJ1Y38DRHX8koavLw6L3jA9ouhqFFo5bG/hAcn5zLc+MSD9Q68Lpl43UQbCwfrHDdJXa6Qimpz1xNQCwBX2EHxIVFF1hcJpHJduZNoGQs6laJL5z49mA+L9wWlIr5BUOMlKBS4k0msr5j5YFYDXzXPzTMHggaVIHdFksgnldiPYId+Hbgj53teuXLrx747d9+4D/82Rd8eD34Nz/5lj/84cbT8zv+yvef2VtnYFjLejHm3fclEvxW8qqGiSWCS4G2B2ve+JO/GHz1z/6Cv/5ffMEsG2X768gqYof9K97/nf+a/+zn/4iPt2/4Fz+Fb6cRttgIRjh2qbS5Z0BPp6yD/WiUd89cj4N//qcf+eo9/N3nRtkuCgO6uIZuI1hckyFQ8VhcM0nReQGXB+u6vmkY1sS2d08QdG7UWESIoUhtbHmWxiaG+1wwmEpCTGngYBJFoHOdMMxgTZ2xTZq4i11ocWSzDeFV0qwiv74YKTWMU96VHn0uppukLgohkn+oYzPSk3alb77qck+QzrYEs/vGpRmFTh9Xhlcum6RdJdmEPbnhHjo/xurEEphJ5AAwZT3nMEF32tkO5/nkfh96b7vumOtx4AUuTfeoFQU6nA20myXrwu92A2KSqN8jJmZnsha624MEbFYOfE/v1qVEzjnvRtvxK015H51VjDobs9wIDra6EyHAGhZbzVotzrQx/d0RdiITGCJKtNPz0IxHv9DXYK2sX8aUZcqKBJX8V2oC1TARlob3Oif130ts+QR0SvbGuuezZtDD5hhCvcqUNYDaJQ0cCHJ1qoexE1hZSkFUMofUB6UU5ho8FPn2TINSxGJeQ/YFl9roPf150jB8WmBR5Od4Ao6uvz9QDeKELBZytUyyJzWpB2afeHrmzjGYMe994QpJ871UeSYNMfHNXbM9oVHYXPd+oSBmlUXgtWpYHRruxkIWIBap6sgQjvTEPT2vFmImWfqrmRm3ZNw1txwkTH2+lERGqhnO3TBDz43I+3Otew11+k3dR6ImYDeVhBBFnpRnHamFo3WY7FSWfnNAgrqqEwah3m66kk8d7qae39FX+b3f+73v9Bv+z//TP/o9XbCNWjZqaZlI5pSiGN59a1xaYauhqXnID4RwwkORnDjHkKzBMba68XjZ2PeNmua92n7BjMFet/QnUsE20zV/WsXLjnnQZ1LOk1onyXS+8JARKSt4ebvx8fXK9Xbj6eERq9B8U0JRa1QXoLJvmzx2PJHE1Mm2+2S+p4IhDTpTP2lJp4tEx88UqZMW19xpXrBtwy87dWtsrUovmvInHa5wR2JNLBr3yKlqy83itJSBnAtYR73R10ymw+D6dtCtsL17oITn5NXxqJmeImM4t6DWRm1K8mkmyUNJzaib2A59KuEMN3xrAgZL4Xo7+PTxE2sM+vUqdHrbsIsmqcX8fhEVz5QGl4nfKk4tGwtNOsIChnyCUjCSce1DEc1nrLjp8J9p6Nfaxlyw7bsAneKs2rCTvn2XYegZ1HrBTF5ap2bXfWNrkjPVNKqb2RTJiDvlLbVocr4WYxyM109UlJASpeFN02A3Y6zgbdxScla1zlvDStFFmAfOmGmeuxbHGsQ8fU+CY3Smid0hmoslwLV4eHikPW3o8AFczEHPrqSWyhnl7gnqSZyVUh8rSRPdGbMTKcGbiFFWiyLMZcSrd/Hh219y/fTC89M7HmrDU94luqpRpoYkvuSho+m5Y7Uq3l4n7qmS1/RqaJ8cZ6zpCcZO+fhYwGWrrGsnSmV/fsopvc6ZZSXZX0HMg0pq8s3vFOjZ+z2d7vRb05RqEIT25znRyVSK1QdPT8+8fny56/JxJ7yw1yZWSnooiU7+eUIxT1owOd1LWjkhSnisxTip3LEYa0jyKGUV4XC8HTw8XPjqe18lNqVkibE6HrBZzVhyTebWTAPhciYUNlH9V6fRpeVeRh9HsuwOYoyUGk7muOImw+i31zemo8hck0zPE4wrbtQEGWYE3sodPDtvxjMi+YyQPdmjY52m5On3siSFjGQduQfH7QAKdd8T7PcsgDMeXsctxZUCmo4TnDp3X/N+wUs6kvsjgn67aV8dR05sp/Yfn3306sOjWEwz09nslKadU6UERe0EX/Rlpkb6NEZ2u4sR8TzfR/4cZ/qIJ3Dz6eNH+pg8f/ketzOzMr9/Fi+W8jol0Yh1pWHZQvLipGljQotDgN5KZqLnWXxOnYs73ozXj68sC959+SXbfsmkxJpnfA4DloobTSHTL61U8Hp/6drawRpx9xYYcRqFppHuEtOxFnkB/vJHP+Ph+Yl93ynVwByrSuHcLxc8mUZzLsa4ccraI2RGDUYtG6000ecZuOd5lX4NY07GcU7Q06NiKqlou2yUajSWRgjZAyhdM6Vqtdyn6YlJk6ICjM9mncXVKM3EQuVVdtY8kawbEyoVYDjb9pD+Iud7PR9nNhbmMtXPz3zuI/dk35nStDwEWplrvxQcSzr/KbmeIWuCn/3sGy5b5f37LwQaulPKhlsDt/Qu1tqdq1O94lWMrvBCqdzBtBIAC9W9ahIVef+58JXPY2F72Pn4yzduq7BdCrUApql2cWgmANXdlVaXjYiZajJ8YcsZE4WzhGqtWrNR9bPot/TbSKPSZKpZueFjp36vsrn8DUtJaWhXM2NVCVmtGJYGu5/ZfYUIAZUUuzfLUMGGzoNoAvoCVnQx/d43jl++sI6JXwqsNxgdGx3mQXCDedWvOSwv4A9Y+RLzLzF/1D67a2OVjnSaQ6soPSBl2LYWvgaFxnLj4zcHP/7FJ14+XXnadt59sfH46GTuCYOazbqzEpSO5XeGuLy6BssOvYvaGFH58U9e+V7pPP+6Uco7KA/av9bw+p7n7xW2X/wpP/+4+Hg9mSl5h+a6Xn1lw77oMvmk7s/0W/Cjn77xNY2vvzLqpnUnicyCInC95AzdQnWqx57yPcmW+rVjLQfZOnw5WzNLEFprzVK6Im9MTGw/zuFC/qkZxvQAjxxXDkZ0Xt90dl0enVYnrQa9dKpo0tlkIilxyGNoxSnFJdeO3l8syxpt6XOUZJSf+ARa50qaO2U4Sp6yJranL+j6mAkQdbFAXIMmTACIuRhKpFxaP9hZs4RYPvmz6x76laEI2cTmv6+lwezybKYT+Lf8Hro3zzWsz7yy2bezeb6fqqdcXOfWKcNb6+wB1POcoFexrNlOpnkyPzwvTxlm37+l/GcNcjzw+RPF/QAWCHFnjXH/+WSU7/TeNZQhJGk3z3udHEikubnn84xf/bsW9bRvmTKFTn0e6pZVqwt0ADGwJUOerrRBASozmWUJLiaDb5G9scsKZcaSzDDZ38eYKYeLrOtWgubiN1NkLeAm+bRFUIt6frPgTAQ9GV1i1ArkkZ3DGUeUssBYqk9ysQu71t4K194qZtgM1Rr5MuJO0rF8vrmDky1kqYwi11Ct5f7r2KngSc8uyJP8BA9zKMNZFySo5TWVVvEr7z7XRLIE7Q42Zk8TWi/n7y0lY0iz14AT28hUv7xfs8wkIplYdg77ZdNzJtTZr/xDmJ7LykFHDjumiVX73//D/+5/4Dv6+s4ZSrjMqVoJ0awwsS/c2XzjshmlidkSOKOLcRC3zhHBugKhBBYvKoJKLdSiJr+a/BtmCF00L+ztQVS0pVd0PWbSbo2tythVqYRyn4+lQlVrWzswYtIDrrfBx5cbfQTPjw846/6Cz0Qq9yxcEAjVTKlQ4SEPhBPNzHQZ6a2zeVxnI2X4gjGzsBOOochMVKSRTWyrla1WNTR+ejOcizbN2lIOp2ko9Nx4MO/SoLJ0KRHBsSaTJcr50EJ+3Hesq90kZXyRk0WLSXt85F3buK4gjvRHKjr05lq5SYb8bA7FXIefTALn5fXK7fWV43blekiWdNl29svlzkSLWEzi7tXjWcDezfPuDCYdTtVQ9HnIqGwOfd879ZYEHMbIgrCIafDwyHa56BDzwNbICyCBqZUXXNmhbjIKTrO1RmOWjP9Oby/PmNOFJArmJuCEmc13h+NKic6MwVqGZ+LffSI9RGlV0V7uDI/TxE3MAU1aRixuQ7poXE3Q6opPjzUYq2ORh+oURfPh6YG91iw9EADL/bqm1k0HaDKezkYopph1fWkfmU9JftzxZBZZ7rEVyURhcby98fHbT9TaODzg6OxWBB6VSQ1nFJh9yIS5Kha5NHJCMlmjc/RBa5+lSqUWrrdOrZIEzikTx2Mc2mseHL2z+uDy9ZdqhDFORqgaF3lORRd1lwLruIne3QVWjPR8KeaYBavrYtaUOEE0L9prK3h8fuaYwfX1xsO7Bx36qXu+RSbOIRo3a2kd5yUtoEiTnABuo7O1B2LeIJYA0pFnXixF0C5JmVqprBKsfuXya18R2bgZzjjEIDuLDPMg+dr5izoPixsspYgVMurdFnXI7O8sMu+x9cgzQ6mNyXoUJYKInil5jUAGg/sp2dwaFYeazDdTUXGmXpxrnjgbAZOXVql4cWafkEbzJQ/NBVhtkruw5DUXZ3OaxoTpISezyUzlOcFCsacVUBAqQk8ptkBDzyRCyVzV3Bt9DPqc7Ami+1m4RNwngoJrVCT5r0jxzjJWzX7Jgsf43LjokC8JjPVY+fdrLY4hzXy+1jt4hKXUIAsWT4+EMLEtT08qjxtliin0NmWUWeIzUL1IUDkBKlGuT5Aj7+TW2LZNQQnJhhlTgQclgYqZwQC1OGudlO6VHhdT/kFZLpmbJHKeAwqTZKIUp1Tn4ekif4lkmjqNtiUgUCXBk9tagHXOWGk1aFPPj8UcL7AE8I6uz6titBMx7wBYnM1YrZLLIyYBgSTZkZKVqmK9noUwE1sjgRrdndl15XsFOWIWKEPn/xTD1YpTcvoZIZkMblg1er8R0TErup+s3JuknpN3gDEOScjQ4qi13c9O3YufY6fhBBGzqM5moDTjsna+99WXPL174vXDK8dtiN1qhcHpP3l6RwywRm17snAKW2vc+oC1q75akqXXWGmMHSkp1XRdjY/qluN2sLfGb/3WhT/+Nx95eXym2SMbhkIcdeauEZIcmQJelgLy9F5o8spYldshJqnbwZieqZrBnp5Rayp4ozBVFJuxPWx8+tG3jO092/sHSVjDclhyYx6LsD3NzwtG0/lq2rvm8tYZdlCrfKbcG2vd1EjOADsQ7Ud+U5MD7zvbrz/w8q8/ER5s7xyLnrVNoewbtIbViZXAONm2B9CSOfz556AYEWo49ENseDwTMbB6oz5euIw3vp6V371+yTd/44Uff/uJP//Jwf/5Jx/56osHvn7c2b/Q0t3KhNnSZB7dFRRqiO1CypSrKaW3Ejw9wGsc/OE//xGPXz3y/j94otaL6hNzvDxSf/Pv8Dt/95d8eP2nvIxXfvgJDhyPYAeiFMqlyot1BJ+OG2UYPDxSv3rk219c+Sd/9MLl0fjd31nU2LCLfFaXO2XTQLTXoN0qM5xuN3wFbQXXFUwP1qeD+iTmg5Fn+Tr79woMxjSCkgPkRa9T5uzZBKrO1h8K87x7pkAya7CuWCR7dE3Gyh5nysegLzG9a3O8CNWImU31yXRM5kOrkrUtQ+/AlnzYcGYpYs3PoSAUH5QQ53xZ1ZlfDH9uxGvn5QZ1LsomZgpLLJNTomdRmTGTxS2QbJmCQbgDLlld5JlyehWtkDzJXcz4y75zHAe3W6eUwlYLEUP3hdd7De/ZBxHyJDwTqdVDkqqEkBJhaqg1T5a8WQ4EufsriqEetFYoyxlTQQWxgu6yQHBXCipM2rZroad5OJ4hCwlAsFJpE559ZeQ7B48lz9O5qM2ZfWWvtO5Adp+D6OcZApYp0sWVjGakJ2zK5MJy4LhCw1izHJafAChUchhXdF5F+t+pJwXfVhI5VvaeWePOnvevksvDTksB1Sag4coJxjjGsbQ+AvAmYgdTbPc5854wSxZbFbbkfgdLPINrpJTIoYyf5tySd90H3FPrriZjzz3v6Til3JJHMkkWTqa2h2rTPlbirZZMNZ1psZaShBN4NFHRssc9AaZ5X91zneCo51EbORiI+wBLCKjd7wKLHBgbOcSyew04x2B5Apo5aCbS0+pktufeP+Wc5T5Eyu9juY6Dzz/DCci7eIpusgixUJhE/CoL/jv4+s4BJYvBXhoPbXExvdxoMqP2ZZSaht3dGEtJb8eQzGd2Oc5rAzmX5mxbE1ujiX46mUlVFKqnJlTIe5+TI6OlvdZszlZG3ZzmgWp+z5VkiY7OBS/XzqfXQ419lV6zpo+Bu31OEaoltdCaMvacWK8ub4E+NH2KtRhxsKLcp79A/vvKhgY0ZtJCB/BEeyNlTs0r1UQjnCaGR40iSYrFiU1nP6LCVVTDBFZiocMgpwyBCuu56NdOLY3L8yOWkadedx3GS41CcyXx1Sbddu9XYqhJOFFnyc4EzK2uonwVF3Yewdvby90weEwZIvre8K0ybKnRTjDHXL4V98MTvaNLXl5idg1syjOKOZOls+5NYB6HTJBuGDUnfcH+cGF/uKg5I1Hoygnd5q/IRC9cTJuTjQIqEEbvmEEtKngODnk3TM0UFEutiOi5ghg3PCbXISNTL5Xw4MEqfcnnixW0y4XSCrVkExOnnEHrpt8O1hL7a3RF3MdaxNFTw6wYVCkxLEFT5/H5kX1rujhKSdNhHYJzmny6crI71xLrVV2N9kkWRfKkSh+fNZjTdFBqCKPGaMH1uPLNT39M9E592LA0xp0emFUd4MUp43PREDMoWx6IMXETgHepFSzfgwmgrltTYTMXWylcbzdanp8UF1vn6Yl925Ndo2avesPCMgFvUEteSAkWRk5QirneYTaOMSTHYUk6N9I/wEy09Irj+86Hn39D23fF1LaNZnrWc0zcgyEsNBvtoLZMnYslWWRpMqWulRU3GW/GYnQxfXRW6H1s20ZBSXAfj1da23h4fGSsRXPS/FNnSy3yqFqWaRGhwiVCXiEwKWWdQ5b03IFeIiNGB1ihutPHZK0hhmJrjDUVRVrTfHjAr+q9q+sZyEeqCRgK6fHFuk14pQgUxyyjfE/Q0u4NNib541wCRBV97rRNa8TICU8O4zU8U6HnRQmMLEljz0jhcxLleV+tOXCTxKEU55gy2Dc7pYE6o3vGvbplPxiR61v75kyaC/tc6J1F8nnKnOMktzO50HK/ZbGHCqdfNapclndM+hOdAwgPAUOWRtlkIR/ukrii8yocYhpzHOdxB6E1qQPj1PXHnaXkaP+4Ofu20Y8Ba9B7sFllzdCETLcMEZ7FYTL+AKJksSwPC/mBBXOcFHTdySUL9bDz+WgwU93wFtz6jTF3gg0Lxb6LxXYanS8xEBIcm2sxR4jdOAXC1zZZwDGu+DRsORGS5HsR89RCU/QzFrsfQzT8XY2yrAqr/BJLJWbgJSAKxTeBRnlmnmkuXhoRBa0MrdkyrwSLVeQdM+agz9NUVqHnxQvHOOhzSJZpioTWfX5OWk+QWOdwq+1zcW7neY+89Cwgp8wW8uXyLOo9FsPg0pznhwvf/83v85M/+TNePr7w/PzADCV3nvvFTX++3mvTQjPwWpkDjulsrWoIZcimYCLZfYJKyy2ZsoGTibTXxVdff8Hzj994fYXnXWdzLenFkcB+5H4MgGUye2+V2wgx1nxhNAWHnJQDmwJil4DtNU2Jm0WgQC0OpbHqK+ubNz48Ok+RAPGKbNKDsMly4zDJ13yeDF81YJLaGH4EvjlRJmuJCeUziHLVRN8ko5groB/4pVHfT779s294+s1CK0Vmw3OyHQ9s79+n6ueQhDeMWK86iDDMBhEbFhfVfpFnggfLGla+JFAkvV+CLTSL/3oYf6N/n5/95BMfv/kJf/6TF/7lD7/li6+Mv9be8fwOfGtqiJdBE2i9zck0Y63KKAOf2SgmIL1q5+FS+MnLld//Z3/Gf/K08/zbO/GwsPaXdAbW9zz99t/jb/6dj3z79s+5/unkx1cxl3uy+FtrlFV4Ow5ux40SFbOr/DfffcUPv/k5//gPv6Ht7/hrfxXe0diapENhWifbqKwCjx68sTH2gVmweUBfrCNYr5OyC/w2A29BtcURAkSPxd1LspTFPo3uKakyT2DUE4wJgsLNHFsVH5MaBari35UqBtXEHsn5oZhPc1HrPGFyjqQ3RKjxPZ/vCeRUK8wVVCuSfYdRgWlVgM6SLFIeRQtWQzYAk/25YXXx+jJguGSsofcb5yB/dTH5c1AipA1y6qDdXzSwLH5KuVTT2yntifz/DOq2gU2l8JkkWAX1OiyoVeFCYnKv+zDnZM4K2NF5utJHVINUPZtqfr9vzPSzmVsCK5YDrKxz/fTPTaNw13nax3H35cWa7ttYeuamRp5zVHQCCmmtYbEY/eTeRCocFnOoztaA1BPsTw++lSBVgCFfyzWDbZNUjwXDpMog4zXEvCk60wlqqdgqjIAHVNf1rG0JYxyyCTjTMoV/iAkz03TeiTtDWkxG0oIkNPSYwSrSMCzLhPIzDNbtXp/oHj7rMAFv2DlQVe3r2J3tFRbJOgq9wypWT2Q68TmEFbCi/XACh4o1zd57nqzwcvcnTSq5hr1TMmg/WaUrn8kdoDG8VZFASuDWFJRzfptcU2Kkn6Fe+j/O72n5e+Az2KQS9Oyr7F57+tmjp91G5Hpa2dvffw0+k0hYySY+7gop8k7EipjexvmCVaMECgEAiMF3+fWdA0q1bTkIKdRNwNAJRAxz+ko2xYLRF9exGD0y2eQzyLJdHnhsheqhVJPcPvfJrTeaF6qJ0XLtiqFUAokYPXOdBYgS1s4mzlBkc4YqcUx4fe18/PTGmEHdakqe8g0WMWSISDopbG53GrX758NtTB1+QnhzAyz5l5yeGJFO+Cs3jGjteQB6ScNyTZ2KyZspK36xRYrkX8Wcmoiu2E9a3AsVi2sMsWiysZkIPT+bhaMPrDS2d89KVggZnod5yvBckZdbJsRNgRazT1rJyNKlBbrGhKmo5nB9VjFiJ9fXV/r1oN8E1vmS8W4tW7aUpibv1OO7s5J54FazGLZkvpgmJyys65koeSfuh5P8b9JDJk3gSmlqQred9vQgw0QtKNF/lwy/PQFHFcrK7TE7TdAypHkFEYNam4DFJZlmmOdUK2GqGMmyyQS2eWgVF/kqgPPWl6I3Y7Fv8vqS6XrNA0jpN/NkIA2t/+iLZmK83I4rR4KYsx+cJqwrL939+aIpS9ItHUf6cz3TWZZUFbGIVQTCIRr0OmmYASsPsVgyvuZs5Dx/z5InyHUdvH74wO3tysPToyZEWpLZ54qVKNmFQIlaUmONpi1kKqRVg/nZ68VdRt4tgug3vDjHvLGGDMUv+87tOKAWnr74Qp+jpukv6fHiRelwedjaPNOLAmIm7VW+JWc615GySsvmHyJp3tpn++XCuHaidy6PO2NBq5sMaMtJZ1YxcK6Q7PHUwJeCb6ePh5iKEfKk6CQQYEWmwWOwJ5XfliJax8vg8rhRc00Uzzh5BEzPvMROX4J1TqvRHq0GtqSz3lwMhJGTjknS2GOpCMtLrhYVncch88Vl5NS16Bwg3/VWcy3mZw+SIZC+CpY0Xa+4p7m9y87egK2UO7OymnOE0ZDEJIYSMyRBhDPiWBf5uQ/1tAcm348sLoLJaaI584g95ccCErVPIsQgLHkdSC8PMVS8sk7fts9sKwsZNErDrttrRpo//n+svduTbVl21vcbY8659s7Mc6qqu9WtloQRYBtbYIEMNiYIfHngP/abn3AQYSJsHEgosCwkJNCl6XvVuWXuvdacc/jhG3PlkZ/rKBQt1enK3Htd5hjjG99l+d+k5Ik8N3iFk9JYMoGAESrU2eAOEyBWqiLABVIVen7nkmefreYxGYa2QARTjYlamMeguAYFjziBNtWvhXdFDpAZiY2S+PohcKN7elh1LeRq+vnMRe+PbDZnpxQ1zmPIdzCmzPX32ZOZZpLOMAUY5IWdx6DvB4Fx7GIevdx2Sh08tGvWvJ3FAVO5rck+2pGPYDBnMDqYDWYUim30sWPzSL8GDTGzazMZSBraeyayDaWabq073BkAACAASURBVK0opWhMqlcxan0mwOW5OcyXfDWPC/wj1zsBHvVsprXoQT5pQ8ytOXRvtSEV8/IxPmPW5bO1tqUCE/S7Y87cBEsapGvjeU9WOpM8JH2BmwSUIiYtApW+ePMlPy8/490vv+Gr772hNgFK1j0lOjI/x4KwA+uT4Y1agodm3MaROGWBqWWNTcOr2IDqg2YuvuTfV2ZwzJ2rPfCd71z58593DlSzrLjeNFtLm6BOSerDHEout6ar1iKZ3z6mpGlocTWT+u9oINiPSUu5+8zh6vL2wv3rnQ8/f+G4OrXIGL9MeRju3WAXc6miBaC7EWVC9Bx6Lhx79omWA/gUM0XDlJYN59BwAOVg++rC/PF7vv7LzuMXRo1gNiPmgV1vtItsDRh3wifYBYsM5yDyxLNzWAz0PjiypJjlCz0LXjCrbMDbw/j1ffD3f/uH/PRnd373j3/JH//FO777tvF2K1yuD3gzmjm9DKwXwrqA9ZLKgjwHZ9eh6j7YQum5bx4qf/mLD/zhv/wxv3VtPPxGoXAh2g8wNuzhB3z1W/8j//D9L/nm/h/4V3/Z+eVRqFNeL5jTS7B74TaNsr8kwBpc24Xx9kv+9Ouv+Zf/5plLdX7zr0N9KHirRNNCstgGvjMuTr2pfzlaUDy4GHQmxzE4+grwyB49Cm6SqFYvHHR8CrTADB+qq6PDXAb1JpahU7gAh4nTiztuTb8Th5JJYZZ1LbVhI5cbdS1hSmgZgICAkfUtIiPvbeo1YuI+mUO99GI7gKfa3Kgmj59hBqVCB/OBPxif9ruAYHc6uQz37FmS9ZmjPBEJBpuWMVZKHiWr7uhscis55uQUnt+71Y27I+8oU4+xPMki2SwzAj9ZvEEwUr3hJyNMSopMJU5GjVwHUj49jmQjl0ya1edb6WsjzZsj55Y5s89L/yKZHncCyc6D9HbKf8cCsclCrEumEcuBNBnM2gNUukemn61ZPyWxCS6YnMHzjBO4g4tJRCEDfLKezcgUvLV0siRQyKbE5q77P9EC8RhZ113nUNYLYTMJlhUtIsaUf2gxtGweI2EPO+c2YknuJAvs2fdYEVOplgyygbPnm3Mq4CWgVgOXnjaYqfbIYSEVO+7ZVy2ZpYxSdY1IIkC8Ii7uWpbK00kzXsTEZwJW67PMTpjq0mIfhZpRgTknsJjPq88E7ZyYnvYdOlPnVDKy5Tweq2XLOd8sXrsTK/nM6DuJvacZ7HPbAvm8KbF+eqRfVl6aucKWcq4a6kct+0QtXGZOrX4CcuZOiay53/Kfb9+UO1/g1pzrZXIx4xjQh7H34DaHzCaHwqz2oajcPQ6qX6hlagvRhEwa5OCmoQVLQ2yTnvbegz5ljugtvQWqbq65pFjmgWX6zky/Cm33jHvvvPv4wvPzCzMmbUu/J5cnjFcdHMtwdA1Ra4Ovd0rD4liGv3Mw0q9CQ1HqeBG1ek49GD0WuyggX8niMv4urdEuG8WNPRLpDB3jJf8fj3g1q8tDu5g0rFociLJZQkZoknMkpfw2mKXx+PYtW82HzQvMokba4zTBPbrSmZjJCKoNebBkwtMpIZzZ5OoF7XOwH4OX+848dvr9YMagtcrlshHpDRFjqqiZiX5bUy5hQbi2sTEn+ziSMjkVXzkm1lVMFjtp5gZnNfIFI8qmoXKrXB6faKn46TETlJPOeeZnnkj2ZQmqUHilQhqEBdsmzydGsCNjzmKc1z9ymzKOQ34vEYp7L071ylZlrDr6DoRkjW1jaw1q+kOlTGrMzt4PbXFQclttDUNxm72rWPWQj0dRVrj8qS4XLpfLiZhjix6bg2ifydLLdBbLzdsCO1NzHkvSmM9QyS1Pcf1epprIILjfnvn0/pnt4UE+U+YETl2mvZbU1PRhaNcLjmkgIJSuxqSmZ5MVHZ5Wki0RE3qn2CWL2cFl2/CtUZvz8dPB01ffIUpNa5jAvSkcIHXroaqvb2rat4UtGWXJ66TLcByH0gHjVQ5IaMOkiNYgNuPTL9/pGozJVtWgz88YpasY5PhNLWL0SNZpOWDq2mhTFWo885yx0oiSwQCj6mpPeNlvxP1g+94bYgSzriQ6gZruRbRnrQqz0KbMzQzmkT8rAY+1CUxga+RnXyDtkggVq3gouUJyJTubyMhhb8mFtbVMFtDSmq/7kEXX1scJSUMKSjHLVuX8mZih+XVy2/eUkqzBUc3Xem7HHGKUxpIitASbYDF4kqyt+5jyhgV0xFzbovQGKdm7APt+pO9MnODqYlliMMlr4Aua4fSX+BxwWBux1XwES/LGCT5EDi1BnEB42SqrxV5/R0zGolljucNY2vrFLsx/qRjsuYzIAQUrWauQtCS/kfr/XJa4AjRGgB3JiqyV4gI6itkprRspQ28JRq+k1T4OQNK1o4vtZk2fVWbVWkgAjN7Z9519l7/cHCa5jC4oRqf6pnvgzpp8ak1z+ykvJ4DS5OXkpdJKofaK2cY4DnDn+ti4tAvz5ZDhZ/aqj48XPn3cVFtNjM5anNKW54MG81gb2Ri5uZevhOUzUfJ/x+wJ0GpzW11mugdk2tG6z5IsFA+KD8b9hdmfoExmOBSjOukVMfPckA/QRDU7TMypAGyxxtCyQr3QxEthHNqQY8EYGixKNb54+8DjF098fP+1mJ/bg1JM6xRYVaqa6DQDlpefQk6qwwWd8+Ewu/qPmQwEGY7qM45AYCghrxsL7v3G01cFfrFzu8FRg0u+gXOuCPWg+1DCTkhKzTSqK5iDsjEDqk3WmwElZQowPQTMD0lCdD/ByqTVyv2rGx9//Mynp40vHxuPl3J6aBx7l7w0cnOfS5t5jFyWBcwXhhX2O2xAMbHoJ8mI6F3AV5r+WIANo7TC2+87P/vTOx+s8ubSaBRKNeatE9vA6pCfkn3Esu6LtbDpLLY7umITcV4iT6Irbo8keZdpCsPZ3t75at/5z3/z+3z6nZ3ndx/5k69f+KP/8J7vPBW+fFP5qmzUi/zGwjtxpD2AQ1SjJkOwh2mxWdYZ4mwWvKHwJ3/xMy7/qvG3tyv2/TcUuxD1S7CGffWb/Nrv/E/84w+f+PDyE37355NP+S5Nc7BCuRba7MznG3OXzcSYzrZdeXn8kj/42Xse/uAm761fN57qFNPWJlHWAVihDnwqU67Hnt8hIAr9OBgksNyzfvkQG7XMBDhk/t7sKoDUOjebsAscH3kOohlfTIQQS9xCSYs15FEK+2t9iSB1y4S81KlNvlKDXDpNVz2fIVkjIVYYQUcSymowVu3IM4KsLCOlxWYpY7eJVZS2SeW2R/oi6XwbFp8t2ULvm8mLRd0fwIBMvAsPXk2TliPW6kP13BVRFpXaHMExdnqCX/qJ6jXEOMqfl734WpAlRK6ztEhmba73PPLaKn25nEsWXP0Mnkskd7YqtvZMIF5A+aAU3cAxhv49F19s+V8aNRfhyU5KVrlm1JPHlUyZBJYSYDhyUbe82ZbcWl47asJka7Hk9K/YyfrJZkrOHNHpYbnU0gWWjNCyNupnTjNWEMa55My5BrqA4YXM5BRpoRnRi8NUbqfmPPVW6r1cnxPDs09bS6Fa9T6R5At3qWEiJjG0yPKcr2wxpCO0FE0pd2ufJ/S9zjOErv1i6y4bAS25yVEmgbK8FoIk9ZcyM0/291yeiznz2ZKf6a06QaF8JmurWsgNAbk+eyaO24kN2Gd98GLjC5iVjkbhN2u1kb1XXqez//LVjyfg5ZozlEzYcaunl+wizCU8djLfTpApQatx3uNv78+3DygBby7OV282qr/Qj8Gn3XjeJ/sQk0NpWcYx0gsiD5CtSte4XZy2NRbZfmmF3eNkDmkYl+5R1K5FAeWUr4zcDM6xDtHJ2k4fx8Ht3nl+vnG7KTa6lkqrlZZmYqW4oiBNA1/xlO7lw6GHYG09gxj5u4HZuxhRrhQtMdOW6aj+HSGNIUPMUuTXUhpWG+XalPIQSiayWlR8TgDEmF1oOeXkb+nrRZxMLEJEupG00Tnh5a4IzcvTVQ9uBivZ1HBsUSlt+cb03Crml0j0c73sS6p1mughkGXMyX4c9PtOv9+575LvbJcGrSjqOpKoawKR8EIk4KJaVDjNck2HspOa46n773NyTKHftnTECwAxQwYLQCtcL5to7e4M0/AOzsxCM/oCBGcmULVkW2gArJYgeDacJQELG50Wr6kFILBhHB1P7bjo1hWrJWVOlulQIYlbkwcI6ROVFYBAmuWZcq9XcEHeQ/txiBkXI4crDVU9DZfbtkleR2ShNw6kvxVYm7TsKcCxZFEjwYdpYOduO0Ex8voyT1mJNt1GPzpf//QXGMbl4UFKOK+5geHcnhsaZratUZrMonXdeGWWANLyCOSIGIqsz6JqhkxR/YLFlVbgw/t31OsDDw+XZBmlCZ6XBFOyiCZF11h+G/m42IpylQRkmeRKSy3AS7RXfY5+dK7XC2PfGcdOWXLRtaUbM1ly5MbhFajDBXp7S4FhCDippdGH2BCekjlqFdDoBSV/aOM2ovPx/Sequ5gVc6AkiIENUa9LK9kk5f2f2oaWZBgKUNQ/8yJPJMniBOKoQK9rpzQPeZpVZh+i0VZJP+UfwHk+llqzWVajmoo9FutlJJMJdzZbKZ2vRXvd6GXI2NP7LfqU79UxKJdLbnnWIJ6f3+z8ZzOHnCUhW8DWHD194FCDmb5Ug8nIaOIUKhNFjJCZZ9D9fufh4VFLi2zwNJAvf4KUJmWBD5aELEGk0HugV/qzZz6fj7mug+X5GwpsuHedu4/FTtBlxjyZnJaNblY8src5a0LkDSojh/nVl+UmdzFYbDXvkY0hYhoPJjF3xfFa+gOYnRLQkdd8+WVwNkYGvsCdPeugLo5nAx/5Xn2ejjKngiNmaFCIMamXIj/FsdNnsOngS8W3PlMeNHpvc9Bq24XWNtrW8hlTfO8Yg/t+5DKrUW5i85Y0b66t0rbGOPTP3UsajqdJaT8S1MlGMKWRVk0b0HWmf7YcEsNAz1ipVT4oQ1WumGSaK93J3LmWytG7JJgzaFNNbLCGSifsQKw6U8om8rZTMg95zpXPgMi1gc/Beep/86NixbhsxtMXb/jmF7/g/ftPvHnzBXMgllsCuwL3kqWLlmX7lCdSKZNaghGFo8uXbtpMJl8Cy7rpTLQxPkIsjjmC6+XCm4c7Hz/ufPH4hsfQfSFDf6yoTjkawFQnJziMQ2wRzxS1TohkHrpuyytQ9+l1yTT6pE5J+q71ievlA+8/3rill+dT3TBXkIYleGa7lpHVxY7qB9SQJIUymLMyO/LkNsDyjIY8c8WkImZiQpXrV42Hr258ejfov9q4VoOqejjud7g4Xm9arhl4/TIH0yvhSsKClnd+ns/ARKmP+AajakCvj5SHL7h+cfDdI/jbv/UrvH/3zMu/+hE/ff/C7//Jxpsvnd+qF7YiNn2rjXlMYopNqdQv8PSgPPJJiyKWQ7XJFefT4+CP//1PeXxq/LX//sLFLxrU/QuMRv3Bb/Ob/+CX/KMP/5z3x9f88Tvj+ZhSCGA0A7tceTmCsb+wmA21OO3hgXvv/N5ffOLtA1yvD/j3Kw9elKJnMxcgyPj30PJFnnSSmArTLeyHekIFMghcKaViw7K+JHBBx63Q3Oll0kXvpi8pUBpFewlKz/vMYJoYENW2PP0n2MB9ikUzPcHOoGzG1iY7kI2ZBmi0MJtMZvqhmgmgNkvPFNDLMozFHp6jJsCsGuR6pbBpbK0wo3O/d4ZNWi3JfFUYhcfIUaqcfUswWQsrTHHyJRtWMZgSRQrV42WUDGLD2LZhA273nX3fudR2AmGY6z2f41zu6t+UesRzGS1GygIP8rxDAEQtOR+G5lQtnGTcPzM51moAlf24sYJYVi1V7yIZm5l6Z8nmSta9JR+aeBQiE1MiQaRpyR3M+lyqn8TMkYlxxQT5iVW1AKNXUGoFGWgRpjpp2SMV81ziJBjnmaSLUavRp6D6bav0o2d9mTkXZB/iArvNSdWHashMfHMxr1ddjmRWswCemedpLotfazy0ttFn5wRBSeBkBqRVzZrfX/vNNfstEC5Bp1xQWi7PSFP9ddNj3XmTzK7WmliknbLBWBvj7M0s/5P0cVx/1qwOC8gLsEFY4X6XIqQWLarnHDm/2ekrDK9M/Nf7t3qsbHPz2Nd/mxP2FXBmCWDlP8s+lZS4VX8N+skhPD9rcC5sg7Mvz9uU5zLf6p9vHVB6e608PYD74HYMbvfJ827cuqh5K67x3oeADKC0ylYqtRWurXK9FAgnJkzXA9280vJA6D0yCtGEmIaKV8/D3SMwHyfYo4YpmRbAfhw8P994fr4LlPAEc2qRaWwePioAikEWFdrk5RRpgpcMhT5kwB25fVYUsq5HhDT5HvL96POETJWw5Tlw15qJYAXbmh4OSz0+uTk3YfG1VWamw5hb0rrJQjHzd3bJKdLLhAiOo/NyBLM2np6uGPLRWH4MxT1BM/3cY3SEV+mBXs2yxzwT8Zjre+te6HdLS96Pnfv9JRlTIcbV1hie8qdkSFiysty1gSq8boZPwCkGfR0GgHV9p5EvqjS1GrZHiNrsxbAp+WB5vFDDTqRW2OMaLAcHkQZlepYKlmwaadKb1xyijRhDxQQ0IE01IWISqKkdQ1K3BcxoO99kulqbklUSwJyuiHSv8jBa3/sEboa2yVY0ANd8xsY4GPtBR3IUD+Qp5tomXB6ubJkacsZM+7k35TSNzUIkdoedbKa5UJuUEHp6K6moqvkOAo7jHIDf/eLn9P3g8Ys3YCUPf8vocZJ+KsDKvFCrImMxmXCP2WGZhQ81VytiXe/vPBkVLHljvif3D584+uDLH3yPmmzCEWL94Rlnvz6zWhENdin9WP44wjqS35cvspU04x56No4cclsCwc8fnsXGcnmH6cdMPHV+ayhcuvCViFWrZGzrjjsyrDwSULKS71fSpu2Ulk0lju139k+fePPmUcW8SCc9pqRgqYJ5LYh574rXEwCJz1gUix01+8gCZhoY7ZWlVLLJsmLsR89GQn8vI8j8Lu6nuWIfg1r0bhomqSgLNHDcG1ZavpdQZjI+J3S3U07L1EZpyZFnaMs7MzVu9TeB3hFDrAdPGUBBQQhix0Xe64DZea3unL48Y0nR4rPmJwH7OSallnwO1fSUBH0jP0vYatZEf7cTbOOkup+pHjOH69U4QJ5txkrsGquZGBNv5bXZfe3NzwYkCHw6nisrSaAKkdspOwZ7brfOLjvOpyQHEz2V7pGyrzTmnwt4kjywIdr3mAojyFc2nys/v/fsYrWc8kuc6qQfX9bRBCbXs6AzMc7PGMdQ897lXbiPg4ctZe3TXoeYXOaMOZhd7FmSnbma9DEG7pVSK1WEXqIoSXAeA/dGTLT8QumPY+qexpKMZVyzElNzm55M4DFlslus5lE6YcnZV2jDOnaSFVvdiZKmtFMMuo7TLhf8PqDrLPY0X55dw90C70oe27bgStOZoAN41cuizSWLAalQimMMXRMTYycsuFbjy7cP/HS78OHDc7J4m94Bt/RC0jA65mTL2rsGEouZHuQVXOzdBQAXh0qhZy84kGeLvIpIhoHx3S833v/HD7zcNvZL46Flk10Nar5jbpRiWCmcqYilCtwrzqTIJ46Ry6ciOVJSBUs49EN+Su6MexAXxwt88VXj/rMbt2cBAzaCbQuqBSNM8vA+6XsX87SI7bSSA/Ur7sxhhBcZ19qh4Tvv/8yhhmpi2B8FuPLwGzu3D8/c3l+5PnbJ9QfEfmN+cniSR6TxAWyDckGG0Zc8d9agaymlWiwmsSLMHjBeMN+w7Uvq087TGPxaf8vf/Xu/zi+/eeFf/N5P+JMffc31rfPF5YGH9sgb39guhbHJk2UN2JYLj1Kgm1NH8vBtyLi+BNtl8jJv/MEf/ITLY+WH/+2FUh5TdvoI/sDlr/8j/s4//Jr3H/9vnv/omT/rcMsey6YpYfjxgZdxI/rOHmKutsvG5emRTx8H/9ef3HhzLbT2wPe+6jzZBb8Y0GAIuJml4nNSQyycWSqTQY0K07SwzNnDhyZry/OLksmFQz23nrLKcHkA2tmfrmFUg76YDhXP5U/UiZeLjLhVvRQ0VIKo6q0iBpdWqAb3A70zkYzTWEBHJiYiYEUg/Gt/IWaDJVCN/MYy3t08zppnQA1JZe77ZBxGS6+fwavkdo4F7qjgqQfOf2IGJmZYkY2sLAumJPPTOAdtDfTK4Wu1cvRJPw5qFC25MthAjBHNRkBaeMRZRxebZFmOLODpBKHOmmRKzVUzhKMFLRbUulGiMkZk0IFnr7Jmc/Uh5iQjcSZAQDKLxApfXr/LY1ZAg1hy6kvyzPYig/hd9h2zTy2rSzmXa9Vfmf2rFBp5puZcNlEf4rHAtsEcXZU5l2tl9Z5NbKWRcn1Jq0V1tZAn20rItbzci81kpnPj9EjMejPX58qFnGXQgeaY7Ae8IAuRfgJrq76fgRXYOT8DWqaXImP59byXPDfXwirBJMt5Zi3NZMuSaqFkqsWJrLwyxsk+A8j7k4y+WIDVK0Azbd33OAGqvmaLeO0tPIHQfPDP2fV8Hu2znzsDKzPrcpzXGVuzfWQowGpydYfP/wm9P+vhsJxvz+fW7ASa1u8nZPXybf751gGlx0vFfPDh5ca9w74b+4AjfZNOWdGcmQSjiM+tqsHaqkwe+ziIkKnU5iTVUuzgYwwCsYZWSOtZHFN4o0SFNViJ6TFGcPTOp+cXXl5uCWrowCotJW2m7WBxoffXy4bXxpaDQ8+Gq/chY+QxOemDi9bpwTIF9fy99yHPFoeU7BnVk+LshrdGKdqwSO3ueLiMOs3PGOrtotj3aYE1gQee1PU59pPN0mfX/DV0IMWYfLw9M0rl2q4klC32gKmYWDb27iTTIbfM+c72Pk8a95wjqYJpCGry3GBqeLz1O8exy9k/4NIKtAauAcJRDGwYafSmIVVmcEoXYArk0c5WFL8FSMqwVFpkR95H1Zd524SqrYN5xbaNMpUWFynXKKFBM5J+XkIa/WngteFVXkI+pp4JL0TJqNhkDsUYHMunIIuwkekG9zvWjxP82NpG3ZqoyVONbimWQI69+gjlwTQTMFmHkKzqhfpEh34MRu/n55A8oeGl0mdwfXpUtLbLFF0MHzEcJO+2ky2BiXVXS0kAckHaAiRF28zTKLXhTqgBS8nljMm7d+949/U7rk9PbE3PkYbz3JjxCpSZGVtpie3p+e5HZ8w7rT2ml1pIEhh6t+ea9EdGgIdMc7XUHbz75hve/sYP2NpGQ/5D5q/04oD0vHrVKWtwCslEDL1LUwVFrLssVq4hcd0LmRQHtTVebjtH77TtkuC1npvpMlYuVk6a/QKMaq0ynk8wicitVHE6Yu7YkJxXl/zO7IMi/rFkIV54/viRQGDtSuiiJoANbE1gQdgrgBaWCYIgoDrPKjNtYo30z3F9F5yUk1gOaZbeLZJ9jZBEaUwxMGsY9bNGTrr7NNxfwLQvoFKNy1Zq+hFBhLPvOyMOvXd96tpn41ByydAzoc9rRkF/9jtTeHcW9mUkX0jz/pMoH/k7xb5ceLMStvTIK3FM5+scAkSPseMZZ38yY08AjhNQ0b3KxjaCs6dY4Cg5gK0OagFE588S42s9m2MmUdnSSB/9zPDXJsXObYaa3xPI9JQ+6K+4Z+rbtGQsxmsiS16W/F0BsepAAmYR+NoIhUADbcrTaN3hXKnNJUnOreYcuKXk6qyZ6/N+hozl9zRb8oOgtsLzxxufPj3jPeUYc7AVBXGEtXOrSf6uOTr77U5thZd55Hkpk/l9v7O1Skv/pNEHfkzu92eOY8fsEbPGyLp/P3b20SVZOwRwjfQIOlPlXHKq6JIGeAI5EfJY8eoyfHbDy6bExuxVzEyb+dzOjhAbafROa/KM6KMzYv6V5k19jmjvNVN0saAP+fWNlHep6Z14MjcXI7dk0qBA0/RhmQOzQivGmzcbT18+8fzhHbf7jevlkv2NQEVycUGeO3Utf0x4jkJMBtQ0mC/++pwW0RYFsOpMdyvYkM3BOILHh8q1wqd3H/jqzZdsYaqXpv6nODIwNwH1cwiQKi7fNMXCuxhYWavHFJPGp6THswQ+xNguW9Br0A+4bM7l6ZEvb8H7X96ZV+eYh87KGulboWGtH05v0C5gZeq8iGzscxiJmPhWgEqUPCzCc2Ou74EHMQ4I49oeePjhnQ9/fPD8nUptAg3nmHC/Syr3UCFuTH8H5UuIZ2xWsKu+e+heruHIWGzEAv6os2Z+gGZ4fIct4CmCv3b/gt/++z/gJz//mv/z3z/zp3/2gV992nh6Cv76pdHaZGueC13D8vuGAUVMIg8l5N7LZJsuUDgM2wbvX174g3/zYy5vHvje33kkrEL9IcYDXr/izX/5T/mdDz/nm9sf8fInOz++F7o1JpoJaEZ/eOT+8kIfO3GXX0rbLtSnJ37+rvN//OEzb68Xyt8plBpcaqGVi86tcVDb5JiORcXY89h2rEjiU0JeOb4JQC5MouVW1EumzJZkbDoNZxTJbbOQMlhMDLBplFkkX2clsu5A+s/WNPcdq07k+zwhOmzFtVCO9NXSCaCjNgdOD06JePGAaMwQ485StiT51f/vmXCDBcq3EEOawv0+6QPKttjp8nUzVzpqhBZzNlXvgnL2f57bLHdLC1oBppG1eo2+8qN1trbh1jn2naMfVNLPp8uHTHKmNUjnR866fJ6F/rokizyDR1JByqqnnqEZgSp0Ag77kTYUrdG7GChirWdPEXGCZktCNBczBy3rihdGsl8FHopV6BGv35XsN03Pm7fK0Q9i7tnudLZMOoiSYAn679dcyi5m75zjTE9bPevIhUTE5Mjk0MGkVse9qmkIKWlYoFgexD5z6UukOfsQ4OJZM8yyJyhpI5PsZ+OV+bQAmQRlpvTdWk6XcuJAc7z247KPSYZ1gkSLqGGu3zXHKyg4E1yRL9DqsT5jaM/Fh7c04XasujzBxeAFxgAAIABJREFU0nzoNSglH9SskWdy7gILbTHx1KMG6mNOO5EF3JiWq7LgUq0peRmkzJjnZ1sg6GmpcvY8nPXUS0lwOFJJEq/fPxeQPhOYy75TLZz9FZDOU5EQ2eQuuei3+edbB5RGHPQ9uN2c2zE5cmu9j0E/hujUDrU2qjtblRntVsU0YsqEVklULp8DF8J575K8CAQJiMEcq5lZUKqG3OnrJddBEnNyu+98+vTC/a50G6tCgaWDDGrNnV5pGVVcaPVCqSpe932kb8rk6CmFyRs2crNq8CpXsEmkm/8hEoY2U56yolopVSCL5G7GEmUUcygl3fqDZoOLFw3ZQmE4JVAzDbhnP9lA0RVlHQT3/aCPTnu48NAarQQwxYqJHBYw3DTgLFM8RU6/vphzbUAS1LFQGkrx1G/mYfxye+Hl5UVyuQgBHS7tLqiguHv6U5Emm9oUeg5hYtxYNpnOfWZk+hQraSuFvYIPbSa3NFhe3iS2C+jxlJIVL9K6RuRuTsOtpDR55EwN3bU1MD/Nv72004x9xDgHxsX2IUJ68RHMXRI/etdh5k4titcWBXXFmdYEgQruSiRcHkTLwFhymzwwQ827JRtuCFdJCmUWm9q4HVMpdtergNFMgdtI2VnKj2ytFBZNMovhTIO4PrWxdvz0CNNBGvrsI6m2MbDqvLzc+cUvf0mrhct1S1DW5RPmhhWZMpK/ztNgem0clj7cykXPZsm9UG42+tgl8cxDuodSOmbvtGI8v/+G+vSG9vBIy43b4NXocMArNbhkupLLVHeiIuhF7IB9vIJUlnp6ASOvbKOYkqZBcHvZ5c9EUKqAy2oCL3sItPVW0z8B3fO62CVqNCOZNyfjoGtztBKmIiZ938WcQIylWiv35xcBh2mOW/IE6REUTVKp/bfcIHMCCjM08FmMdYXSD2iZ0WeBiklHRtGWLER3pb3tx3E2B6vlWQDjAoHJ5k21Oen+iVaIBSpD9kgvvECpPkrCOCdNnT2mgcvC+XjfxYbyNNJcBTwGjJTx+ALHBCzNMfK5Gul9YmdzZ/N8SLIZSlgjBKj2KXlfKcZx31MqqHdmgVlzNccmGVzJltbyh2mYzmbKxZ6xBH3P0zGvE9kYWbw2yzLyPhIMdTHC8PxdC8FdCSM6z4SfrWY7chMdpzcBESljeG2IhPflMxjzvBaKf+5p3i20YCUohaW/X96vsy6OBPA98L6aN30XEnSIKVaLTzs3ccErZZypzfqlVPp+8Pz+Ew+1ccyOu3F7eaFuVyghZpqBLUZYNoh9iq7f/RCQawli90MAb6taVk353h33G/PaknEXVHeeIzj6oRSgfXJULVCYgmEtG7YZSIYEzMU2Yp6+bGZGH8HmjRE3sv3L3qgwTcOFZIfzfL5LVYiDpKgdo+ZElWwrE3u6tHOvjij3I435D2JOEmd+vRcsNkOCihNGNtAexttL41e+estffPOeT+8/8vbNG8KLhvrQonCmLYBNlPAWPSU9Ot+dTg2FoIwFXNrqmdRsE86w7fw7yTgmrTlfffeJn/zkAx8/Dh63Dat66yrBxbWQ9OJQnIrkg2KSZZOfdbVn/yAQL+UhmYIYpQjYCac8wfgkO4NSjeubyv3jocVGJTf02cwPmC6GQe8VbykJRimDPsS6H55nCYeYfAvEnpHPncGoRM3rYUaZG1987w33X9wZv4T5Vu/0sIPoht3BtxeZL4+JjYqYECVlEXYOFyej0dRDRgIP+BWbKX9rkvlvY/LV9wZ/62/8gL/3O9/wo5//Of/p4yd+/z9sfPHFxtvrC1srbA/qlec+KTYZrrh5MfArRwzVowxssBlgHZPBFj/5+IE//L0f8dtvrnzxmwV7KFC/D3bFrr/O9/7+P+Mfv/vAp5cf8enPJu9maFE4A2Py5uEJp3B7/oj1g+MuKWZrD9ibL/nJ+6/5F//2G+rlLf5fwa/YA3bdiYtR98ocg7HZyTDAXB6ideQUWNVXH0OA25E1Ipe5vdRcsrnetRpsIcuK0cWO7aTHpet8GDHl6RU9lzsG6dEyomNp/r1k+usAjhmMPthaDr9TssI+A2xL4DrE9p1iPJjaHhKv1PlagL5qvP5iLZ8rKWVF4KW7+q6X+2D2UKLlRAvplElHLhaaS1Ivtl1j+bSlKDaB90hZpp21xcyoVhkzPXxmUEplHofMukuVZ9eI9BeN0+tvzteeXH2dQPfWGqd0GuM8RO21v3YXOFdcvkOllKwfWgxdto3jEPhQcrl9ynOzdpnV86S1WCUt2SZTtgWzzGxQ0q8nQealpgDOOdAwRp9njXACLwLu3KuAPFOPtZZm5uVkA+GZ1ByV0+9UFyZ9b89DX/9Rkpn0WYjG8hXF0g+vOJUMK8nPv5hpS3J4ym7TjPusLQkOeQIgXmXBUZKNE6E5a4Ee2RarVyuFrRaOrut2hsqMmUtS0j8sPZeyd1sg1/r9y4/oZHmZmGR/hdnGAvhySDm7upzL4tVSJOaktvLZ8wXhr8u2dX2zw3+9XvbKJJI4XKQMPZ+vTC1zS5VOLmwQoy08ASjPe4aLIWyr804c5ASTdH6IrOB0RtrB5Gf0z5qAb+HPtw4o7cdk74PbDXbSM6mL1j0S4NlaY6vO5oVtKyo26EEp5oqRN0uvIA3O9+UInykac6oZ9yYNrTag0lEeUzT+kgDD0TvH3vn4fOfltuvhLkYrxlYzlSYRUC8FqnGYSc5g0sDe951974wwSdrO/+G1mfRswoeGzwh997l8FkhZkbQ/epHrYgWVHLT18s+MM7feuTanlSpmxghm9FNLHgNFZ6cRnC0gYnaIwb7vSnJ7fKLVisfMhC15e5QSpw/EnCF0mnXW6EG/p7eQA4zjRG1bU9Kb/hi3+8HHj594ub8wDiVI1FLOYZd0wCcLVkHNjJVX6dU0OxkixY0jJmVOpQAhqnlJpNzXv5+JBgK8gpgaeC6XhzPlBWDPaXqaCrx5EYU5m/Y+wDJeXWltGrpmzcYuIr0X8vpMfYZpMlf14+B+u+lsLDLWbqXy+CCmkORTzqXVHHiElhtKy9GGU79XJufJGMkN/xEjzd8yChtNLh7QrlcBrlZ43C75jfOwc8OtJiNJBayVqkFoyXh4ZS6AQDw183nQJQMFK5Qw7tEpkekjGF9//Uvi6Dx+9zt4SQmTkcWw6r0tS87nlLpx6razeNgYbNsTY+6AIsolOUpQAaVBer6T437gEz49v3DfBz/4/vd48kIwuIeYHx5drCFSZ2zy61reQGN0+jwoVT4c9/Sqmrl1kurc/0ogwhqu63Zhv93YvNC2Rq7Es6HTxmTmO+ClnBuBtVUac5wDdj96GuPKu2lONZ+Wfz/7YPSBkkY0gI1Mp9su13NT517USJZIK461IUlelUUWK1sLIG1B1nsZ6HzRZArxGeV2TloWq+nO7eV2eiqoPuncJget1cSVTCxMdEbUbPvcbFxMLrGX8s/a3LunQa82fX3KK2wy2Y9Oe7jm9J4U/MghAFh+DhEQI+jJzqvFEjRMrXx6xnTmCfysJYStdz0Bnxl6vecYtO2q7VEuC9a5EComSj1kNTsz2Y1V37UUxuQ0IvbV1KwGxTzZp/pOHvnvZhMqGefaL8mbaS7fCBk6rX4xG0OxY+YYYg0YNGv0WpjjODel61lQs57ICAZz5u8IjtnpAw6cy7k8qekV0nMQFutqosav5DAU2TBHKOGqj4FTKWWjbcaIu4DFlNStwADV0uU0M8GD68NVwwaBt426bbl0FIASCKQqrVIDPfcRJ0NwhsDMmF1+MJukyDg8XR6YQ8mb5hKhvHl84v5pZ+xdqStFyV7Lx2Rd7TEnxdXM1/U8DfAyz7rBkB+Y14K3ZEal/KHUSkNAuxUlyYwuIctluzKPAf1OlAszwQ5poARGwWSOTqubEl6mfPwkgZ8pP90QcJ++WKMnK0Y9gAF4JRDTuXrhiy+eaJdHPrz7xHe+f1cNiUkrVb5aViiZjBTjIMjf6eCmaHL5WGjqigo9z6PVDptvKJ0MZgai+Ox4a3znB42vv+l886Hz5aNRHwM8stzmomyTVxxR2XumRoUYxuMAq1CoHASlhrycSOZzoDPXnSMqF4zWJuN55yhwoXJ5KuxHZ7tUqotlu6VUHdP2+NgnXoK2aSET0fOabkTJoJAJ9JIScAjL7xyTeUyxicqUR0iFGhe+85uTb/79zssHlyrMBmE7xJCk8HGXHI4P0KreNduAB8wuBC8QJQfgA3DMagJ5iKkUaCqwgcfONne+t09+62//Ku9+8sI//92f8Z9+8Y5/+8eN778tPD46X9U3lCoAZgHWAnM17tasmXXAsCE2V6lsJp+6qw1+9LP3XP/1X/BfPziPv3rBHzasNMwK/ua/4Nf/h/+Ff/L+f+X97Wt+98edjwPcag54he1yBeD+/JG5y6el0ChbY7594k/ff0P7/Y88PTxQ/ia8seCpFPCGRaVGYC0wNrpNpSmHwCFMjLvjMDgMq0BXb+B1UmfhMJLtsmuwHC3BHIUkuMnEOGxS6JgNLBp4AyZmFbdM/bPCMKOsEAS0GhiGsPXslR5KxafxPAKrSm0ullbWMc6lg/q6kNwzOsSyIFhnamcWZ0bJxFSSuSzAopbB9aJKeL8P9ugCBaZ6nGJSFswho28pJqbke6UlKy/r0GcDuBggix2tn7+1jX0/5JGWdf84jgwGEiN4DPUA4ZYKilzQIzJBLTXJsXF+h7lsA2xJ0FTf4jPLjloFJi2vq74faX9Sz4RwUlVgKeXPLvuciaYJjOmh9M+xvKZsseTFDrPsu6ctx8oABqUJ7Chu9EM+UxHB0dPHs0rW6ZGzYqij8pTErX5MGkPHes9kQ+eYgZVKDyims96sUJlpxK45I0bPPqgTqVWMqR5acm+FAmXzyPLhWgxtuQdUncMz8CIpdVgy24ek904RG3VrqhdzaSOyBlmwFSe6ljmR/e7MND7digWQpxfnucsSoFLK6gG1qDfj9EZdAV7MFeyks/+UK0acP5/P5H0jCRd6VmBJMEdiOWYioJQlu9Onzvu1JNZ6l8f4jBGe78HqJOaay1JeSfZkpWiRGLmoKZFLXDQEiNgS6+4ohRLXvJcLhQjy5yyH0G/nz7cOKH3adUjtU8yke+9qhiKo1Wmt0qpzqZWaXimGBnxpmMnGKsAn9+PG2qRsXk8avhdX0Q2Z0xLaNg0M33IjNyv3++B+TD58eKH3Q+BRFUBzqQ2vooSbO761pExL3lYIRtw47pPjOLBwJYoMIapzJuNqjeKxfDsKow8ZziYNzbMvH2gDYNnUXly3YKyWynRA345BYXC5NMmUcvgYkRyWNYjOSUTPRlCNbe9iKt2PTnhhuz6cksGSflFaha60JPkfra30ostCMPsBkZrk6HpQR9DqhpciH52AT/udj8+fuN9fdBBjbNsmxLmUEyQKh+JNOvMxqZuGvOHONDGwJI9uHIGK3lS6n7YQYlHtBGXocxfzU187+oABD9crtunwxJZsLhimjVCd/ioHNL1U1VIXTBCm50LN/yCKPL1KqdzudyG+Y7JnAlzcJ7f9JZsFmWSXTaaVpUoLfWmbBv5FXzw3Jzrw+7EDkmDNNPke42AwYAyOYzB2beQbojd7cy7XC/2A3u9cHwpu6fyJJ70/ZWvJ9a21SN9sTZKQGHkgTrxp0CohZsGcBws991AD8LI/YzFlHt2Mdz/7mo9ff+DNF29zUPGTollalZ8Eiq8sVgS2FHAUXzJR8W7XB7wCvVBrZYwuFsfR5RWVsqWwYN47474zivPh+c53vvervL1egZk+PvrMPYygQzZSUVLzngN3cIg+PAdecpQZHcbg9WhP8CbNa2+HNN0Hg2ME7Xphq0bUejKhttqUVheu5mLqueVzVluyGSKZQozAQibzDqcEAhLsSJAqYlAuG7eXnbo1rg8bzZYHl5r6ac4lgfM+xOocY+r8cQUNLBZHtnfrCKNPaC4QsTj0KTlMZUrf31w+ar2zlaIz04Cpn+YkmFIlaR357iPcR2ED2YBZsmtk5q1MzDRR4aQB1wQ2IsTEPLpAqWliuSHQlbX1W5uhlZ5xruSCg5kbREtgizTzFFtRXhioAXTFo48xOObQsxTa7vQOD9cNNVTlvIcjmxydK+u8Xo2XGppWXF4mwfksaIMHJNtK8qeVGGfZsClBiWTqWW3MOai1ZfqKAJzc9alxJgfsXEqtEIg7ChkQpb3kPVgb+ldQbc7XVNE5BpdsToonxcVA7ujqVfoYVF/m5fOUQRAjmWwC/kZuH+cYlFq1bDDJ7mKOlFsORtfyZc7ORABBM+Px4Q2Pb77gfr8nc2fjcr3q3A6BTr3vzNEpbdISWFQ2tu5Payb/omgMm1xc6aruG/FYiVq4Pl3P9+k6RP/ve5e5KNC2B+oGfR8a1D1OVqMSE9c10qJJDfuuVJ/ZOfpgs6alzgZjdDEn68b18qAExVKYVZ5n7VrYxwtjRHplKI2yxYaz434hvGtYnUap8qocvTMnbK0lYD2pVb5yuBYYHo6zCQhKKbfhDBsUh6fHK49vHvn47pfcbi9ctyttOFELxeR7Mook4nN2SqmMmamRCYK7y8B5HDmcFS0Gm5GAjB6nsKDJHpnuAfPgy4fGd7/3yI9/+ol3t05rzubGTOlYsXoyEymB1YIfkk5DZxymd9UNGw5WsRocXeAMnl2Ya0nSDzGwj+r0PPNbrdw+7rx8Cp6eCntKCwssU5vXxd402hV564yC110nQ3qMrUFTQAKENeCgz0kbudw0h9mhbFzeOG9+OPn0n3Zu/9kD1yZZVRyOfRq0MpgPAcdHCi2B7QalCUmbleDI51/9gagoGrQVJ/ZA2NCQepFf19N37vzar32Hv/vfHPzsQ+d//90f8yc/+prvv7nw3bfGpRnXN1f1T0Xn+DQjhlOQ9D3cTn+ysEOLja5kLYvGx7LzH//857z9/Y2/eX2k1avAFvsSY6N87x/wt/7pL/ifP/1vPO+T/+frzpFb/8DYikNrzO2B/b7Tj8H0O4/ubA9vuPfJ//uz9zz9HvyzrdH+utMC6lUeQzUO9UzNFfRiN0ZZYPEhNlR1breBxcEsV0pX7yJZ3M40Z1IR3qTk4sNIz6fCq5TGmGPDvNBjsPuOeWPL3kj+pLo1Ans1yC5xWvfCbd85WlCaU+ZUvffJPifF9LlKFFhKjZQwrb6eyMXSBvdD/rY29bnNjOhHziWSrxnBpVViqv+hD2qBETtGZauN+yFmEsmgzmolqV2kJMwcvMiyIcReLGlLMMYAq3h15qGERYVXTHpXTZzhuZCTl9kKJOop/FuyHvXR6aOGfoai2VW/LGXslqDDHJM+uvp6VyKsGMR6L1qt6sH7PP0013vjuVAGsWsTb2TMTqvtNcQlwRdD7NhpUFhhIgZzZOpeYZiWJj2CfahPqAkk1Fzs6AsnQDBTqWApfytaPuFGM/3frUhdI9s2T4WBZk+A0iytLQpjJKt6Bm4CPQVYvAZ3GJa9Uvaw58Ldss4L6FpAyQj1gdWcPmEWUgInJcdpwjRVq4ortXy3foZVWJFcLQLY2snCIT2SKVJwMF4DCAR4DfVcnn35CbTmnyV6CnknAgk6LlP0mf2LWM8R0MNpKffvM5+tJT3jVbq3gK6TTZao06vkbYFi6OdDLiI0609kF1StnM+93oA4Q5QM9V6LWYdaNgQ3mqx5SC/RlPLd6RQ+25R/C3++dUDpORMTxpzsvZ/GcVurXFpTUlnTtn5RwyWNCPxir47lAbe9p9ZfsrRl47Ie4MgJZaIDoLrMnMcMXm43JoVPH+/s+6GblxI3eRUliBTpH1OrtoMmap+bsRncj4PjmCnX04E8xxI2lJQ7aGsw0r9IsfFqWscUMtmq0PZSmoA0TwPuTBfRi5tDzphc0d+3Urn3IyNua77WKeuaoeSFqSFqz5hrxfYOrDbatrGZhmRLpoS5syRz1YuAJNM1ib6z/Ef6UJxzya1AhKi1rVaiehqRwf125/n9B46XF23AS2HLIUemyx1I2t1ih6ADw0tNgoCGtZkm2NWClsM/TrKUhqKAe8cvVb4QKCZ0Rif2zjEG9XqhN6fMYPPCDtQJ0+Fian71Mr16wQSD3aFlEtWYhzZMrTJ6p5px33exgwAbHUtW2dwPybFGx9wYI6i16VnL7+smppYlWl+W9jmL/MeX51N3vYbgkVJGm5N93Jk9DWZD98a8cGkb1Ssfbx+51I1r2zTEIY8uS8r0yA11qYVjyX7QwNpKYSuFw43wikVhHsfpaWO25kYZtbccNeacHM+dn/7sp1wer7THCxTJDCMPNwIBU6XQ0X31NHfEHG/OcTvSsNswOq2geOkpw9s9PYokMdT717sO8Pv9TiuNN1++xcoUM0+VIP2xYHLo100N7T0PajfD0pMm8ZzXGdk4i/aYgyMGk0yZI7heNz693AgEANfLlk2IsZWNrRT8fhdYmIkdAsPQ85xSBND3HuavaYs5hMuUU1uEBZIEyNy9Vnp/5nKpMljEF4oiGWqy4fZ9UlrLLZzi2z8vInPq3VwsAbwkO07sBUKgwP1+Y6vO/b7T2oWjD176rm1gslQigaVqhhVjmROqUupZUDNYNHw4ehZCJv/TB8Oq3qWQXKsP+SaMKcmfvECCOCBQKqb6sWQTBZkmVz6j+nKyeVZSjVWHJRlVlVfdYlHZxebpCQ7tyL+qtpb+clAvD4C+msdi35HpbiQjTddGrCQnXKbgpQpNktvUTBry2lNOpguGqultAxr6mxeZHod8zNz8ZKVa1lNLoGq9oxpotIUrJo+uOsnkrWWuD7VmMz5Vy8acaobL2gbqXGqlci+HbqtecDFlIw03s3VykqWHanL0yWWT4a3ZYqIK7CgFgQ/ZXM8p0/MxluF/btJyk13LRm0Fo4nl5Mt7RqDV8ujALLe3uYqyyjE6dXRaAob7uBPjQt/33ArKz+Khqv7aFIDpaSZ9e36mH28orVGB6Y5f1LOMofSsWivkfTVeE1pOJhgJPEwjUmIdsVO3jeM48FozobMoBtoOiEkrzkgA6BDNJdNSN9guWJFcrGCEd3waTMmN5xwwQuEm0bV1zjNrzDSb1kXTYOID21HCJJ2HVvnqqyfe/fIXvH/3kcent7RpWC/ggpItCq/R9VB9w2xSihrlngOZjZFyWM9GPpLdaOCDfQ4mO80SzPYCMfjquxs/+eUn3j9/5Is3T1ySpTKn2E70A7tYvs8mMG5MSsiFcK6BzIt2DGPgZVKGBmSK692cgfeKtcmlObYPRhh23fDrzsvHSW1Biw59UquG1hJOWOPwG/FyA3/gwSvD9DumPehZtI1Jp0xn+sylJkQUyPPXp2EtoBterngU3vzQuH/zC15+cqP++qZ+OTqzO/3lEEjYKnPcML7UIM2nHGINonH6lqG+b/EC1gAGTwLjy4CHgy1ufPmrz/yNDz/gv/v0wl/+9BN/+KNnfu/PfsrDm++zXRq/Vip+VW9/9JQzsRYHOp+pAxt6z8E5ivq9MjvbFZ4/7fy7P/oZj282fvjblfpVxTaH8l1iVspv/DN+65/8OV9/+ne82wd/9txhtjMa3FulxQP7mOzHTuv6z8022tMj+97513/2kaftkf/5eqf8yoUHH9Qt++F6UI7gqF31osvY2XsTO9MPtgv0faMMiBqMPonDOUplHo7RiRhK+Zodt2Ca0iBjFgbBwU6pe9ooVDqVS8Aw+VLi6rVsGGWEllJT88ccCqmw6uz7wfXywMND0O+TqAYdPAZtOj129dZz5jyS0miLPLu1bLuUCzELk457Y99fcJOVwZyqJO6TiEGrxqBwvw+KVyXrEYxMjvYQYCKB0pL6JMiYaxydw7LPGJBMlXGeO8xcfCdbRJ9Zvf1xHMxZVKtygSyPI7Hd7/e7kjpLPf1OVf5zWaXTItnZuerJwdLcia5AoZLeiDNBgTEOajFZTvRsin2h5JrL3CslgofrhllwuHP79Cy2WfpPVqvMBAqtoOuVy0VZYgjsKAa+GZ6kktkX+wmpRDBasoc+TyRbbO19HgyDzcVYlUei+ojjmLr+y1vRpErRZRAjVkxvR7Yyd0oiLqOPXBgclFq1OJ5LavZa21zDL9SSEs4CybYawl0EFM6geNUiP2uEzYp1SZb3vadFSS4KulROJYzjpWeKcPqroubHSp5muhTqwYuWXnPm7GXG3A+xbBl5jkYy9Dl7B9Jiohaxziw0R1aTakB9R0Fpflp2D+YrcEQ+Z/k5dL3mCWQFwjL031fvgis9VEvIVB0w89wmGUjZO8QK/1heFvH6e20BrHmkhAgkQzS2BLm+3T/fOqDUDyHU+z4ZybIobeO6NS5bFT3YkpCVFLiFQlsJ+tzxaOzkXd0ahUQ0k1bg54VlwSsUb0l/Dvbb4L4H7z+8ow8NM7WpyasrzS09XtydZgKSZFp84pr0KVZIJP3OXMNCOeMr1bzvR/oWCQvSn5naWEMG254ShlIoZTvNt/uYmTgkyddWhcr2e6deau6bkf410m/AljxN39+84LOzFSOs8PH2idI2rCku9j53Ll54qKJBt1JFF/UiFk76jByJssqDRAyt1rRN4BgpN4TmVX4MwN4Pnl9eOPaDiCEZ4+Uq5oBmRWmZE2jACi42d6LjCSAy8USbVUQysjviTMoqxeldKHkZ2kAeSQudMySzu1zkeRR5ENvkQqHnFtEppwwIg09zUFDKj1IEJ33cKbaJuuhK87nv+1mAFsi24qz7OLB+sHlRJLsng65VRllMhEgWRmTcNFh+r9FngnxgIcDMpmQIfT9OloRZYRwd89eEnMvlgXffvKO0C+2yUVvLQuzJdoiTAkl6wIQ5VjeI4LrVUws/+tJRZ7OfF3FRekcmy1k+27UY7378c2JO3j49nc93xOuzf2n1TOSrJk+0Gej92zaOOWjIRPNUTwqmk/n9vmcS05JXBtGz4S7O+NT56oc/xGphj0HvneKX3NBCIMlbJLAbCaDgOUwkiyuEIKnAmr9qAAAgAElEQVS4x2dx9ujay99IUoWahu1xu7O5Ua2Jdfj/sfY2vZZlW3rWM8acc619TkTkzbxVrqprbEOBQViWRQcbmw/36NGiyY+wgF9Q/hvIFgghevToABKyhIRBILlhY2yXXWW5qlzU/czMiDhn77XmnIPGO+Y6cfs3pFTem5lnn73Xnh9jvOP9cGfuO1vdFEWecpD4Qi+nqYsmnzX3HWE0C0YNGJ5eXlpo1Yx5ntovSWf1faN3vce63ZbCiZWaiJmicCOySZn0IVCjNcOG/Mgi/52TP4PRj0dO83SGLRmvm9H7iSUocd4f1Gm0liD1mAIW3N5MrKfMfg3Lc1PTqrrp/3sClrieVQxbSimaOd2CzWt6WcnvoHdd/q8vn7FaaOvf54b2WkRdJ/BpCawZJ5JsnfNNlpgwnz57vBkjFxYYIdZeS/ln26pkqElr9/QdWgahmn5CmhGxvKiYk80aV8eWBc1gUK2q2UMT23Vu1Vwjq9idsZJHtDc99Gsuz7jcNSt1RIAp6Ukgr4+Jzo0zBCxOQ58jVKBZSCI5FsDGW1GkkIfASsOxyxTfjFxjcXk4rXCFDe279TqWE10Bp4tJovCEGUUJ5wkueD6/MbqAivUameJ59i6wKzT19ZjMM+/M0DMnh1VYoVWBJ2OeCtY4T3m8JOh53k+ZC08XQ1rIJDOgTyWkjtExJmc/OY/JbWv0CKrtzJCR65oetppU+zVB/GISWUwwotI1I/3YnBIVGAkuFKZXqhUkH9KgRf1CnsVnAkZb5eGD7Zh4OZkMSrulrCB9+woC8zMyHVJWi6aWKqiz5Uu2neMoZVMAcQvnw1c36q3y8fvP/NqvP+he8DiItlHKYhxqsKL9pYm7/AqdOTJhMBtCZgLoNtZRoOHiSCmCVzHMqjMmPG8bv/n1yc+//czL6+D9U8Mi6MegN7HXyhlp0p1MKAQwlgj6OJmlUoeAlFHEGGeCW8F9JMvU6ZHMIef6DAVjf94574P7J4dnYx7a/1udBDLVjfkswO/RqU85NI0FOAB2JLtd4HnFL7KQGog8m23DbvLooRnV3/P1v3Hw47/3mddvnfc/dOZqYh4Hc5M3JfPEzs+w7zC/B3bMb180WsuLwxAlbFz3nVnB2MSYqu+Jdufpw5/imz/3M/6t80f8h989+P7v/BP+v+8f/OPf/44/86duPL+78evbRm0CaaeFjIRnEBXmE5T7qmcjK7HCPrpS1qJzuwWfPj/4vX/0Y/avGz90xz40bC9Y+YqYjfpv/2f8lZ//LV7vv8f/8s+DP37AWP6VQNmcJ9vxj3AcgyNNtsvtCfvmG/r3xv/+uz/n/XPw7/+7wXDnaTa2zSmlck4BwlsYk5OHDShBlC7G7nCg0eOB+aA+Gcf3xpbrpffJAYR1yZmmAychUyyMwZg6A6lQaFg8mCYwto9BGGyUBPWkigjLYB4P7cnZqVY4D9kWfHi38fKx4yLSUEIsoTPewjBW0E5NEEA5hxX3Sp8HpSH5/24wCjMkBx5DfoPmRvGNWxUg/XocNG9s+y6mUoJWIMsN98bZj/Qw07polgb46fVU1h0xQfI33eNlWQdYpiO7Y0O1W59xeQPFGkwk86pmQEfk5xXr400NUNxY17SYGmLellKYp4C3UgRm7Qnu6+eNow+xrvNckKG50uioG4uZ/Plxp9ZKDwE/tVURY+eySUnmlFfJv2KmH1wQw6+a0/ArYdt2E6mhh2SYKOnckxAg+zSx6udEIEMEx9Qzh/z9IYn0mItVY9jIABJf4SbyAh1nsvQdZnRKyMdqBnhtjBWItJ6x6ftyk9TXinEMQYYGtFopgWowAkZnK7uGtKG+1vJssrR86SNZ/Fk8yRbAxRDOHmyF5yxLi8UE/9KYu9iq82EFHU1XXWgB4xxKbTWx3hkyUE/kVUb66L2JyW2qOXowigzy/WKw67NwDYxV89lATCtTf7gsWpRUnyPASK/W65wk2V9KkttrU6qsaZA9Un2zvC8XaKcy09PnWSvTXOzLsbz27FdtyQ3ld37nd36lL/jf/O3/6nd6H8zZqQX2rXLbKrddgJLbSv/wLFo6tUF1UgqUhEnXhK54kWwpEr2LSANs/T5NqTUBOHuX8fbLC58/3kV9KyZDxdbYr2h2gTulFKwVbE0Cjcs7J5CZ9UIsvZQsjnTAEwKXRlezOa+J2xvqN01FzZYu7dZW9HyhtHrFyZpJ1763jW1rlCrApzZJArYiQ2Vz1zMyCKa09Z4eHnlofP70qo3/vIuGPQe3IrZMmLGXhteN1lLqV6o2M0FDLHR5PkFtmuaqyNchtdgvHeN+HLy+vPC435ldaPH2/ExpmyKQi2QztdU0JXaaF5pn0R6apNaU32ALfc0Cd8gsOMmWMJZeWmllRx/wkDRrzFPgY9t/STfu2bAIiVuGxSOjjy29FwqBUrmOqQu3hMwRI9QcLfPRdUhEBOf5ECPmOHDXtGqEUavWV7XKZo6PoNWWoJFAhGtqHxmpXmVsLmnCEEtgZprZOuCHZv9zKlL2w7sPfHz5zDTj9u49W2vUos9uRYfFW+JCemPls+kofczdmea8doEIrVZI2YnW8tpoU95hoQLcW+Hbb7/lpz/9KV998zW3uiXrTkCt0HvXwYzp+68lJZcype5MXu6vAnZt2dGHgKRkxJFgRqnlMrk+7g9arRzHwXbbef+nfk064SmjbCtKu5tL5zMnzTRNucyhx7yMkNf0I/piGMq/aq3H6OlZlWfB/vTE+Tg4Xx7QNijOI2Pt1/5+HHf8zGJMQmu2pHAbedbpmCdIMBe7vvdV5JzjZB5doA7JJrvt3F8PCLilP1epTd9vrMljNoyLRmwg6bDLsNOl6U8/v4tdsfbNUB0hFumQJ8syqyyl8PrySuQZIVbOkElieuSsZVNcRXJNcFuFIoSVbI6ly++j00oTMyvPXy9i+5UIFY1TFN/RBy+vr2xPT5pOrQvU+GLSlJ8k1LBOW6UcF4MD7PINml0MviVBszzzNddMmWJrUBv0O/MMnt49y8x6nc0zyIFumqMjxowDHumV93YeOV/Qk5fqJEGfVQCPUBiCmsxBK5XXlzuvxwtPX39NdRMNvpRsWkPS1vUnz9VSSu77oDSliJG0+JkSt8Vu0iQvrklaxKS0yjg7rRbO8+Dzx4PnD89iLs2QjDnBqzW1rcnCWyp9NygyeeFxnJpi5gVr2BU1r9QZrvNrgWXnqX35+eMrT+8/sN0avd/1bPKeqXXX3ZuF5mooej/zLNQaKl657Xsyb+0aFmFGK0rpIQuxs8sYtp+d++PO/X7w/v1X3J6eqSVN/tPjbRWxpZTrf2t9lOsLWTWL5dCsdz1DsbZGslPTnDZG+mWVLJj1PfeHZHOQQ6F+v545iIY/Sw6szKhF0dFakintTkAur6XLg2ux3ZqF7o2ako0Qg/X7Tw8+fXrl3bt3tCbD8XD56dW6XZJ6McZDY8uis+XRC4/pKactkMas85K/KI2hJ2havOFNm2rDae7MGnz78U7gbE2pXjHsajAbTcln6Z836FqDMZgjaFUsTpXiJ9W05q12aiWB6C3f8x2o7LNwPA68VEmVePD4fKh2TDB8msGcGsy4wMd5qKiq3nA3aoNooYM1Qa3LiFbGKNoX5rjdskaT7NdM9zrv3rP5wcufBO0HIxPzMgnJBrY1ogq8UpOY8maTJHydafrQYpSpH9F6yA4K4o4i75ruivrAY/K+PtEfB3/0Rx/59ui4b/zG14Xbc6Xseu2YeZuHanTzU7XUqEwX488s77zQeaPaxvj25WQekx985dxu4OWZWW6YN3xutD/zIz589w/5/HLw4188OOamux1nhNjQB8kg6D2ByeBd3YlWOY9X/vBPDp6b8xu/Bo0d967v1kySWU8WffhCFJk+xUwYAh3HMWm7zrtxvuCtXInMJEPpkr2kjDAm3I/g48eg7M67245zUofOMIVhiHlYTffHuhcAlN6YfnZZL82p4d7zVuiHwDp53iTrh8UWI+WnWqudmX1UntBRsHJjjEipNglqFNwzPa+Y4u7NCTbF0s/BXjx7EdmG1Kq0t+pxJcQSyI9oBuc4162gd7iab3tzFLrudV//BPlfhlIvV+oWeUctMMn9LUmLdd8n42h5HymFTlYqzBxoxRrAZF1gM/d73u+mgZZ7DrC7BrSWhscxRzKPlLRcUDiK0iCXX1PPYYUY6cWd2vwCIWoqWby8Jb/Wmk6MqWqIU2zQ6f7Ghoms7UNj8xWo0+MU4Bx6Dvttz/P27a5aErVYly4Qc6CPJfl4wOUR5J4ewYg9Xl1DsTk92VUmT6RIplmSRiaDMYeGpAHNapamS1ydgGSAeaSsa3GP7JIyrme1fDmNPMqmABm71gxXLWAXdBJpCykgMi/rywJjScaWp9Lil0QEBxMfSyBo4CntjYnrENN7XTLE9d3nWoysEdZ7E7PL8+7Xh1h9vH7FvGwbRpCTOy6pnNapkn9XrbJ+L5aKiDW4XSfIAu24HiX/5d/4z/8mv6I/v3JA6b/+23/rd0QZL9z2nVvZuG1bUhJ1qCxEdd8KtfjbgRlGH1C3G9XlBB9zibwmjFPJIV9QFFckbz8692Pw+eXg8+OQYVxRwte+S2ZWWr1Ahdpk2Fdro7aNUl3FLipiRQDSAl7NbARpTCxA5+jnZRBHHu6r+MVlxiuDOMe3iu9bAgsq8Fp1ylaptfBUN0V8F+McZ0oWNOEpRbRvHbKKi2cGDHkozTS//PzpRSbG+8aeCP9elSC25d/Nk6FUd6zIvNndkQpJ3Dh3WZ+e/YBpHEfnHJO6Nf03Ebzc79w/3TmPO/088FK4PT/j+8Y1AnAZqJdWcd4iffUnCyQAl7fJIDfWmJxnp9jER3A/Q0Zvc8ojCSHsL49XAYnRKbedfd+yf1rMM+dAKHq9yqhI49VMJpqTUdamE0BX8mKfc+CzK7Ui0yFIQMBnSm+OB9UcT6ppuVVutbKXLaVdecGZ4bWqOZ8z/Zvs0jHPPtK3h7ciK6amUrXgERxDFxYxef/+PedDAOq7Dx8Ehvo6bB0PJ/qp78tIdku++tYExpQdssl9HJ22NdzEjBox3w7AfE7kQRVF8r+f/MmP2Z5uvNufUtedVOzz0AGdncp1SSdwtG3tSqSJMbH0mJL2eCH0kc9mXnv9nIPH4yH/WQsVnb/1mzy3J63jNMez0liG36sQEZCgBst4A1IiT9aF5DsqGNVII8SfoKeUsdRKL87n7z9qn6oTYyW/V4z7cYej63VMk6ExVzplgp05jpeO3hJ46fp3acAtI3pLc1mgGE+3J6YZr59fedo2am35XGXGvZpi+jJdtYvZVEpJzzj0vax0kjSJJLJ0SBDCE+U9j2zG3di3Spjz+fWVtm+0WmnZLDsryUPPvJSi5KdNYMNM1oh5xedUEYCeca0bUSw9dDqYwhJ06PWEZ4JOcJx3Pn1+5fb8LGPFeAt16JFsv9BlTV68fl3uRvSkVocMqGcaJI8EGUfMPEXUdp1d0yt5dnTKDBiDensn0DK/0OKFFd2qVMaSzUVOkxBoseQgZxYtgUBuTdnegI2VsHnBQwnsfPz+E2MGtw/vqaFp7kjvC0y/3xNLHfkKiVvJozDeWBAjzyJ5SH0pe3n748vzbQZbKxzHg9dPD56/epcggM6QutVENDU5LcVTQqhzPX+jirPjpDT5+cnEX2C2z/z8eRec50nv8nibyUT9/PET29ON5+dnTVFdJuVbe1L4wRS7kQQm3zy05B9XS2VrDTNj258UDx3B7faUwJtBLZfMbnYxH3tMjvPO43Gy78+8e38TSFb1DN6m2fF25uWa11GSa8PV4BSX70Eg0L+HpPJ650mxt5Itg6+bDbfgeDzE9HVP2XrBKMQGcZ7AoFCYkYk/Jr8OrT3wqBezUfXvOiffapkwu1JlCAG9ZmIlf/uzj7Ri7FuTpN/lB7kVAYMz3497SVlVoQ/j5T7oUTGXJ5TkrF3F8xVBrUU/AN8Ak49HKcFAyWL90fn4etD2Kq+l6XiVDGQyiHEI0MGV0mRG2WvKPmDfneqdOgVcWzHZAtRVaymlaXbRrPsmxsvjmJStU73y+iK5obV5sThiyktqjorVxfSGenOqG3jFyoSpmHk1CgskmHg05AdSk0H6AJxSdDZLQlnYPsD5+cF5r2zP+udR1pk2sduG+S5gyGrOb4v+Py2R47dpfsK3130rU5GCxQkusKnOQd1hj8n+Hr79+Sf+4E8OXs7B03vn19/vPNeSA1AxCKwUfML0SjNjlsD6Bn4wcUa16/wpVjHv3Mfg87edWym8/0Gj7E5pzwQNSsXs13j/r9x495N/wHePyk++P+moZnPTKbOXSpmT83zoPCoFPHjabvS28fnjR37605PffL/z4YdQu8CW6Qum0X33xp6XH9QsSl48yOHuqe8WA++GF9imE0XDlZZrPCxJk+Y8+sHHT5pNPN1gs0K7JQNu5P3v+j6mDZbfDy62pJiwdjX0XpzHcVIrbDfn7IE1UiokaYsGiUo9tRBoZgmaTdOrNTfmPBMAkIzVTCyfksAj4TnA1v/16jweg2mSobnJUoA0wncZ5VCsCHz3lAWFwEYNmCSnu6i2F3z29kcG3joXFwA1c+gJqnmvdDZb/35e5zBEAv+ruClZywuA032V6WlJeAgsn9tMFk5qh/JELp42JSMB2Ow1PGvJBUoARO/qYbOeLtJlCdh3vgDUlt+UJfHBEugQacIMztAd5UyBNzOu2l8gmN6zmLtGDQFErZZfqukvc+i8lzRwGXlP5vOc5P2iWnGFi+Ce6XB23U5uGlKuFDFvGdU8h4YflmbgUyxZsdJMDCgLLahLxVHyeb2BjUR6EzEIrxcIbSz/rTdATP+5416vZxP518zi/ksGUyTI1CP9VbOHvXrSfOnDxJBn4e6INe6+GHV+3V06Ve2X1vICk67vOL9vrlt3se1MbMQsHpbdz/IjjfweVt+0+lctb33eZTkRaTuw+tfIfmUxlX+VgNKvXPI2dWfSWlOzUYxWM646P3yrGZCevhI9TJ4pHlhTosqR/kCRPgQjm8Czn/Q+39gQKaN4vb/y6fMpajp63bo1tqakhUjU2UwAR6st2RSF3dNo24RezqmDzkoQNjNJpiSYoaJrmXMtZ3wgvzwtKnenFM8mrlK2hqXUrrk8hmqpHDEu1kh6R6pIQcyA0irHmDk9TE3dGDJMzcU+Rufz6wle0z9E0/KtbvimCRVNzVUrlbrtmCm5oFjKC2Yek6HEmTkHowePx6EL3+B+v2NT38fry51+6pBsXiS1spKIqA41eSm8mXxbdTV0pp1tDBkPJphYQsj+PE58DvkEhWlYNjtn6MI+jzuEmE3nHEQx9lYp03iEDIWrGUcMLfBQIo/N4Lwo/1pPrcnrAv0THfRE/pwmF6VsFxX2cvYfk9kPSfksTTu3RksQbRYhxD502Hek51d5Ij+snodKn0H1JWvLgyFp9k+l0kfnPk6IQfQT3xvHVFPx/OE9uLGVKhlF1esu7y5MUwIddCm1GcatPWFeJWm832UIac4Yd+TnNb84dPWeSibRjZj8/Kc/Y/TO7euv1OyhQ3h2CVQsDXinhdh2aXDuRQeniu9gLzWbjlVGBIQkWSONb8GUljgGPoLpxjhP3n/zgXe325v5cNvEQERTHOaAWH4RaWRoOfmaS5/slz9YzEkPhQmUjFSfIU+YnH1RauW4PyjnZC8ykS8WtDBoouA2AsZkblWNZh+0bQO4GEvLC4oiU10Ve854HBwpLSOMl+OOGdzKhreK1cLjcWifpfdKQRfxmDr3FBaQXiWi3IgaDRRm+iNtxIicAKkhmn0xFha75o1BU2rjPB/UWvn8OJnmkEzKHkuKo2mJm8B0tSFim2KSvJi5no8Z3QWQr/OSSOP/xfqxBG5bZZ49z4lODPk4lSLG55wnZ0xaCCwvpqu557QQE+uAyAIkG+QlfU3r6wTup/wXvphSifFj+Bjc3Hg5T04zNlNc/FhNaeRUCl3kPdNIRhYwben5cqWXqc/YE7Bd0gRNgPV+LUFNa47Nqj01e2K79gbYxhtwUlggokBUTQntAllkCBoUxgXGkRPpHukhdRXFk63pfb+y2CsqaF6PE7edOU82E5PYs0iv6DP10RlDd0Sknh+H0nRHaXK8jqYQqNh58xnIaWb1ApyYzVzLC6AcGDfcJFvW83n7PufkmkSqgHZKc7bnGx4VL86+OfFZU31fkvwcHM058a1ipxMPrWUL+QYex4FFEzCUe3vdEWNOtvQu87W+83ybs0uCVStmkgJLFgoxBXqNcWYizkg/MBXlcwGmxdIgVqyJlt4RNlfRPDAfzFOJt3MEYQo7KCZpc3rQX3/M1qQ0EkjPu/o85KuXMP1XXz2x3wrff/qeDz94omzBzpbneIFouttnTw+XZJ6nbHVOFdHT0ITXQ1YBYbT44j41ZwwDE9COw15uzB788Kudj587x8PpOxAn7ZhEKXg1ziEujtmRALM+bNkq0QdxDLxBFBV85vASnXIaNzMsOqN2pT3dYXfjeWt8f3/h9XWjtE7bK4/Xjm+FEs4xguYnweT17NTHRtyMcxj16DJQji4W53xjv0cXy2wxdHEZ2vvIBi9Oztmw9oSfD6Y98Hbj6UcvfPoXB68vxr4pMTRqI44Dv094FvBt9gpzB3sQsanZSLmODMQXgNvAYaXSYTvU9zIbr09Eu7E9vfL8W+/5c/1H/LW//Mof/+z3+Bc/f+H//d0bf/rdwbv2jP9wyLtupkuPgzMZTeBGtBPvTZ6R84HVyhbpQ1kH+3Pw6fPgn/6zH/P8lfNnN8fLjj1XmEpTHe//Gr/9H/0hf/27v8v93vkHP5+EO7NPdpJVsVfO3jjPE14elAmvIRD5+ObX+eOf/ZT/+f/+Bdu+8a/968YP7jpXoho+XbKxCMIqRx3Y2WgjuNfJbTpn3HnMQv0U1H1nmGFxx7ZBHYvx52wBhQ33zmNMbt257cGH7Zlb61SC57IxWnB/PHQHTe0lwy7Ax90pTRYJhNKOnciGsXF/PdhrY9snj1NjVPnx6TzATEHY2Q/RV20oU+nhDt4lTaNy9E5Eu4apEefVwzGDm1X6OPBn5+PHO5u/VyKjS56KaRAz+qBWecE547rXihVmVx+lmizlvnlDroHWlwMBSakEtMzQgFGsHv0XK6XtSzDpYosmiBIXHSW7Nn9j4+ou8mRmy6tmRgaMjPRkCs8aUf6HdEmgNEjq8sLywjE6zSRRssUIJyXA6/fb8mmKq/9sbWdOKQLcXMBCBNQNH4NSptLUh+5Qn+KihdsV4OTJZrHol1Lm6Kfk1p7KGDQwOG0qpAdnJX2PAe6b1l8MAYERCUZmGTHFTD3PSbUte6ihvpmJdePeO61sHI9B2wQwxuySxyXINJNFv+woVsI1OQSdIUJBsAZtBUbnS87v1jaFVunUV1Wb/qiSqqm/C96ApD4Geyp8cOM+R9oY2KqKZcZfNOg4GGJjRXpMOdk3qPYO9xxQqt9dSh9Blul3dIFHeg9XbaDp9VuNag5UzISLVEdKKDNJwtOM3lJ5MtIMPHkNxJzUSK/W5gkq6rVPJrulXPBLBO5X8OdXDii1faNY5ak1qqs4OM5sErLZFnVtMs0YZ6SkLem/4fQu+uI5xCbRFFU+IHMdoEPAR9DpPXg5xNYwK9Rtw13+HmDUpljtag5VTKXqok+XqgId4HiswrMwpiiqFoabtLSlSL7R+2CcYu0saugcWnzyGdKmtVbFkmqbPIumU6unIbOAKcsCeJppGjB02A5T4sg4ui6mlEosutqxoicfndfXO1Ybda9srbIlG2TRCGUeJJq/3Z6YbcP6SYwzG/dEjYcS4o5+QnQ+3R/MkKH4MQbn68nz3ngcD0lcUJO/bTut3QCBdrkVxK4BRpi0dCPTH/wNQLOcSK7mRbKWgYwm5R1W5uAxB2YTG471oLtM49wrt/1ZE711yeakweNNwnOOTitVgEdR0zWT9dVz6jeHJsUNybE6wVZUSI9Avhu1Mk+Zc9cpyY8IDZq8dJ1OtIAYK6UpD5DVnLsmBQqvmhQL+VQlSE/Y1SAeY3AcB4/HQ+sqzVq/fX3h6d0N3Ni9Kr66ukxFp9aQPBmS5ptIqZWqpsN1sB7J+GqbjEMvQhPOTGRzxU6vw/Pl4yc+f/zED77+wUV3hckoQtWj6/ufM2hexV4CyQ9NEh3L51pbFWDQO9HnZeA+z35N+wM4emecpxheKat7/9WHjM8sNCqjlbcCeR7INcayvByCDyZXU7/0/jpTIoGlnNCuCRPpKTMntTmtOMfHEy+W0r2NVl0sKwqliqIcXWbYk0kMNfUzWSxL27xYjCWcmDOTxLqm2l4VqY2nbj0vjjAeH+9sZtSi9bPWHxb4zOlfNSzkkrAkX0uC6kzmOLPoS3lZAheEPIImkl6MU1G8I/2mzArnyydq+MXeMjM8ad7NnGEh4/eqYtRKRiOHgJARaeToXD4/uLT2boV+dmiVFkEUGXZ25H/CnPr3viO5DDlUD47ec8qZl637lYyjAZQ2mCXzafbOGWKYXnLQKQmBLepJJLgyBjFOsI1zTqw2MXDmSrNLMCQZayuifhJUr1STr4WWX6R/UVDDaDPSQyM5rvYm2Suk/Kx3fJIsx5GAp0rgN8+htXQXpd1158XyXPPLO8uLU4EzUmIZIz0yTPHXU8k5SmnR+VWS9VcSFLFkPJIFvM24pmBn5pbiJjAvC+hSmuTwXi423AXrzSUbW2brARz5knFNVL3CcTwS+NIdUZvqh5g9QdEcGpil74WeQfVNwEsyGc0Qu3Z/4p4pmzrn1lmQ92jS9hcL7XG/08+RwRH+NjF/+xJSujyTvaik0kveFLpre++0Jonu8h0njFY2zvPQ8yNHHkEyrJ1933j99ODcOhuDSSV6KDWrD6ZNHkMNrUk7pn1aUw6X4I5eM++GQGxF8jufQJOp8PJRq8C7W+XDh2d+/tNf0BSUrpEAACAASURBVM+TOZ/S18uvzw4mT7BZwRRZrZ7ImEfKwqqCQFp9YpwnhtPnHWom5M3O6J6mq2p63YES3J4LrcG9D0YUtuKMqNd/D40+Z6aIDc6hsUIBtlqImRPykuzROalzY947oxbdzWVSrXGC0m7LpG3Oy3HCNGoJDjuY8Z6y7AFMgQMEHA+4tWRO3gt3C7YY1CMyNTcNclOKjVmmk05miGVvc9JfwZ8nW3yGqMx5MPuDvW7c350cHwvbV074YFiHYsTn7yj1oLZvgI7ZhpUbjkN0lMCVd2V6p6TZgIBdEpm0D5gp0jtuHzCM7euf8xs9+Ev/5m/xkz954X/63/6Qf/mTT/z93/+Wb74q7Ld31PcmRtLUWpxWKRNJ8Wxgs+IMtum4nRwehBfKdJ7dsW3y3ecHv/8Pv+X9hyd+UG/cyjtsfyL8HWUM4k//J/yF/+An/OLxT/l4Dn7/F53he95Jha3uxDvg04sk2+dJrcbm8P79E+f4hj/49k/4u3/vW97fvqL+6cnzWdm8Xd+PWGIySe71QRRje8Dpk+I6HUaZWMhPKR7OOUzppz046mDEoYCYCmaVe51sdlCLs7OxlUHZ4ak0zBvz0YnZJKvymawNT5Cv47J4SXbrAJGRGdH4/DjZ9sKTiSX1sEnpYm/PqXU2h957MdXcYUq4jUDM/RxIKcVtEj1Zr6H6bsyJnBFUz5Q5eXreuN8PzGFrO3Oc1NAd1ZaUsOo+sC6J0DqbVYdmcmkE29Y4M5VqMUdYg3rL82o4+7ZjlgxqE9jjyRSttV6A0ptESOdHJDq9JGRriDbyhFjSSUe13LIm2LadkYnibUvwhcF2k7/RucjKAefZc4gje4Z1t2m4Jkbysv1QKqkS5s6uoWa4YdtOjFP+k15gJues7BQ6tzbTD9BgDHn+hOHNuNg9VR6TY74NCsXISUCsFOp0IkGS5asYCBiRBazuLG9VJujZuw5RrqmlyPvY/LqjI+CME/Ogx4N937KWz75xamAeV7dh62sGE0AYPZhdNYXZpNYczs1QCmkO04IMK7LF/pnJGtNAZuQ+xl1qw2qMrmG21AWqXzYrGSiiJVas5MBBz+4WCqlZWqTNxGKO4pf3WU/Z+KU+UTlHEAKclwyNxR7KIUsECttWobTClMhnb1OpuGUaPUehskJw5Gef0tYVruHJgEsASyEP6mWaiznf8Csd8Vf151cuefvv/7v/9nf2YmIepFngKm7fuA4iD4yLjryQPoRKkgVYyBvHzinaLPoya0oj+uw8Hg9eXu88jvT3aIWtFp62RvHAvOAuXa/nIVPcabVmSoyKq3GGImfnZCRN6OjaNDMZQiNT1GaaAsu4V9M+YTYqONsmEGnbdiUspa9BSaR4RmBVWnyLeUkA5+yXZ4ZF8Dq62ATq6rUxsrmLmNAH9/uBV8kKt6JN4EUCr9aaZAq1UEoT/dgb8wxFIw5J29bEIGLyOPUsz5dDMYTu8vXpM6PHlaY2zgNsSM7mjbCKVTVYZsY0FyA45qUrN4duy7RNm6zn75cWPBSJnWZ2hcI5T3ocNJMnwjg1Ve+ghmB/ptadYqT0Qa9tMS+2xBiiQE9krjtDSXwt2W0W64hIk92cWLpnelamF7g5o5+c8yROeS+M2bHQxeNmtECTXBNLagFaraiwtiLwYYRMBQ2tr0DU5UiGwJyd3gfHeaqxCMmfnp9uPO4PMVP2PddcpdQinyIXOFFdxfqagBCBFQEZpSmmurrx6JIaeCkyqZahR9JCxWro/Vy9K/fz4Gc//gmlNdrzja3uYJIneSky7FYkVHpuWab5yRR/r01pggHTxd7zMSVDmnE1TpFN7kjZX8/91syZx8nzN1+xPT8xkF8N1dnLJhbjecJlfppwQtjVMK8CoyTwMJC0NDtnfZ70UAlQwtyAsjX6Mbi/3i9Ajuq0rVC9sG83LBMsud3wKvP9rW3UvYnBtm+qamZkgab/PYZMf4/HPeU7G5MBM75IiCw8Ruf++c7+/sa2VcW3J4hrbpyIdq1CrumzuitiPvfeAhhGplu0ZI64WUonFY9tOdXo2Sjf0mvu48fPtH2HKqlIFF1mpdSLgltaxUqlbbd8ptrfksRpqnSmjK7U9IExx9K8qflbsTHy9/uUT9394yfa83tut11gkFl6jaRH0QwxdHIaN8ZguhE2BZYAIJPxcXYYQ+vP1PwR5PucV0F69jPNPwePTwfPz+8ITxnXiqyV+F4yPFtmzIIzq2ciaJ4jOhVWsWRfGLfHL/09IRudD3m/vX7/Cmbc3r9nS9AmsRn9lNk1iSyUNEVFQwWD4fqux0KPU81a8rOTLFR9hpwQm8yda5Vx++fPL3z1zQ/Yq4rSknurlnqFDohFqiKulBxyXM2BQJ2SgyY1SzpkrvMwWT1jnpJMjuBxPnh9fRBhvPvwjkjWXMkEV1heYOt5WhZfmmy2HPh4phNa6HyKmFwZyEydeZbNZDENrqb++fF40Mfk3Vcf8CIPopIFsbNA3qSux6p5VCSal2tqPoa8p7CqXZt+KZZ0dveq95/TZv+CfeV07q+fwSqtblen5CEgz6YzQoatiylSixjS1XXuhedUcw6KK21vecu5FfkAuWVzp3Q0SUwLxxH89Gff0nbn3ftn9v2J2y4jccviFjvEDqgmdCrXmOUk3oor6IqU+LtjLv++YovRZSjrWIxnNyhTzPLj7Hz6dLLvhXe3xv6UMkuDWEmCDNwFwlILNRxZUglE0f5dnnFag2NGsrVNwQClMefBzTfsKZinztxSNQgaw9huSnhiQhT5g5xThupPTcy54CRM503xBiUlHilyLDUbpAFkOnKEACDOidnGtLsYI+chA9/aGR8PTce3ZNZNrWufUDbVuLC8kH6A2wBW8lcOV0yG8LovKwuM1V8FMwWFTMv1USu1wm6V777/yB/85Hs+3we33fnhVzv7rVxpUYOh17cuO4VozAJ1uh6Wvi0ilr9eh6JJwcfvD/xe+errQn1KVm75irAbZlB/7bf5mn/E8d1nfvEp+HSqIQb93Tw9K89JDBgj6MVos1Ge39HD+INffIe9TH7tK+fdU6UUdK9BriHTUBQwxLz2OjWMNodyKOkRydfOvBN8rcPQIEYHweD1HHz+PvBt8C7tPbzKGxTXkEEkmqzb8menwt41JM4+YSCwQd/ipFTVK9VcHmBjsX0FoCxHnBxFcBL0PtL3RgCnUQlToIRHvZ5DIT0GbUlnxYyZBq3oRns9Zez9XCrYvMzZy5LlhO6Dxe5Yd12kDFgp1KgWyOGEhgGwgo3e2Ev5PuZMmwRLIOlNSiTQwZLx8fYctO0jmeqrK+Ua5BpSK0Qs1ib5+7JZtyWrU01Tq+qUmPrZNRCaLuaNu86RMeUJS/YGdp2JkgZiquPFrqyUulOqWHkzKuIVD8lJ0XnMFPubeGP2LtLCcLReSxHYFEvW/hYWYVaRTYip71meQua5BpPdawGZlIwtQFO1NWEXUBW8eTQZJn+5kUbrCAuY6OdLqVfFU8oCQbhqcNkWgOT+y1LACMpVuxTjqisSLdT9XsQ+WkNiS0DNyvLR0j67vvsEgWbMy3MTIhmuEMUl2QPt+1zP8mjS+1gJ9TU1rrbW4hefGdJr9IsB4HrPb2vtS7REz9/dM73VOYeSQUfMN0ZVRD4z+6WfjvSh7MwcsGq9OKqR/4u/8Tf+Jr+iP79yhtLzvmuCmfG5up48/RwmY+hLsZwQC/8sMjMNLeCYkwjn0Qdnpmd5FrfD4BjBcXRej5P70bHw3JSVp1ppeYCFFWZOFCkywlTSlFE8iNHTEHZcDbChJq/3Q7F8Y4CLKXCOTozBsQy4cyvMmJRlSldldF3rRthyzYe20Ekv1GQmkIX62kXLJX5FcG+52nucyTARW2DRIY8+wRvtdsOLUR2qNywkxTnOU1I7RMXr/UyTbTXTa0puEcwhueAckpiRXhOPx0EN6eDvry8qfoY2VS16bRXjB8fQgRQlk/u6yZ/IZ5rYFyopSUk03JN+qg0+s/E0fAZHv+dzcYYNhtJcGZbSg+3G1mTIKJ8Iz+Yhkioo80HFdctjxiwg1AQNJj0me6vs3nj0EzqSg5SkLwKdIeZcSOY2x0Po8IpjNvlf7GlAByqKZ+gQw/Wz+66JiqQ79e39THnmnLkOZlIj55wcxyG0HOf2tHM8Omc32t6ow7ntAmhwe5PMBSxTH2E7uphlQl7wGlhUjlNJemVzARpHF7AZmgw5Wh+SPjmPcfLzn/2UPjofPnytdEQS7LGSJq9GDSUq2HqOLqaEuzFsFapFDJQpg8v1masX7o9X5mUGH9lTaNr/GJPt+Znb7R2E00wsBfOWB3SafbpioeUtogtlItC2hC7zkcywqbHHxfRYkpeYkmvWMySdmsbj9RMRpzyq1pQw1/B0ya6iltTra7rvKOrXLZlAYfQsGpcBdeSeDgS8jdHpXVOfiK5jwjZeP90ZDnXbqWm2TwT9TD+nLOCzAk0WwtSEvdbL2NdQtLH49EbL1MlA6VTTAptGR1Mmwylb4fVxiE1TnUqhJhtQzLCh/W5iyjWvecnCGbks/a0YdlT4jNFhdChK9GxWtWcJOIf2aky8w9kfhFfarabfg4GvyG3dK0rNSC1/qSxvrAjP5Ek1U2doRFHQ2UzKlKfNq+BgTPohqv9k0A95n6ghEjNJsgOgx2UsvdYRlu1ZvCXOuCueWHeWJIoLeFj7V0VLMBHw3LCctE1inswmH5Q5svnPgmKlimFcSTryKlCx2a9qMKOCZ55HXyTTzYuhsJpaMajWuTBNLBAFWOjekVfBpsK/CMCZCdCGRco8B26V8MIMJ+apO3qcKjTxDLjIPV+cWhvl3DSdrkHzTZl4XefBGZN9DJ3lQwOEGXFJphYjOiLZwxl17NYSgJv0c9LH5Jx3VuJeBGLh5d70CSWMrTRq3TnOg3Ge+M3FRPV1znKVc+6eDU/K2hYLBU8vPYUI9DGUzOVrVpsx2umLBl1yngT++nEKcDgnJyf9aVBnw6fWrujwnn/XS1TX7+xIhhl5z84cUpWoBBvDDjTMNcmLxklDUe/DNSyBwVcfGs+3xsvHO/ZDaAGjn2ylgG9JAq3MOHEyjMRCrJSqQlb1/2D0mcEYqtkKPcGuSo0ppnIzIgMrosqX8vl5h58/+PwafPVBe2n4xHuINecQ07A6lEC3AOxu0A7O0GTeHHAxpUsEMbLxmEE8TmaTqfa9T7a28/w8OE6wfeMcB5++7ZxHZ29KofVT7CYjOI+T1+K0W6PeC90n3gpn7zRv0Aq22KNTtYAbzBLMLo453pjzpJ93eV26g28wJ/vtxvzaOF9TRjrUQMzDBBzfD+z5hj9ecP+OcfsGY9f03gLLqTshNhwslmSDOHVrlndgT4RJWso0on3L02888a/++R/y17/7s3z/3Z3/51/e+cf//CO/+fWN52enbWBUydSLafAYXeoBl7FyJOCzRSGqmPqlF0oE2z55ncE/++Of8fxPjN9+bnh9opRvoBYBH+XX+frf+U/5K9/9D/zi/gs+/WHn23uwbHiLFZ5uN2wOXu9pMv4aPG6Bt2e2D++gn/xff/jCbd8oe+VPb01m0u6ScJrh3hkjQeCa55t1tj3ox04z4+zQ62Czxj0ODVGjUGyqpqXJWyweFIaM4bdXvSduUmpQCE9wO4cAkh+vs00gqbkLHI1DZ+yQhHrZR5xj0FrFSvA6VA/TQ4NIN5jJGOpnNvUy/fetKiXA8gnGkv5qZUQy1cX28Mu/rwTsBdgLj6PzOl7Z9z0B9jfgZKTcOjRVSsm7BpGew5cI1Sgxu/hCrl4F3vz4dL4KnPfinGfneBwc58m2ggJCNahZvdqs0fVmcpULFEocotbGiEErCWJxfXxWAuaKfV++TO6ypPAQiWL4pAeMhz6fZU03YvKYcdWFUtHrPljhPDk3YPSB+6Y7eE6KN7o3KAKi41AN40hxUG+q5ftxJPEhwcVibBSGl3zOWViYhh9jJjjcDAW2CHQyg4bYOaOn7G+KFae3XXLwdlBqw0YwR2cls8bQM1ZtI2pArYZNhcuMGbrvVquSzKHlE5wknaw1kOTbBAaLeW5UTyAxsYKcxCSYN7WGk9kPqrlnn3itcGpdRZBMeUMejYsVl0bn+QbDk3FOcCLLhg3hGVZrSh7jIkqM0PBJzCKIYjkk4VJoMEvWBXq/y0Yg3mjKCxu71rsuKIM+2KsGpVn50ZJddaTaqrLAQgM8vTtJjvliJiqk5Vf551cOKNVI5QRgNpP+O1OCpclvcdEUjznlf6MSBkv5mgUycgwuaryZX4vqeAxeXk8eaYJZq1NbY0sgw0vDPL/glJis4k6yBy0uUf2XSTLg2fR28R6WlnOMDjjHSKPdmb5JoWbba4Fa8VLToyAjmvsB+6YeKqVHWymZYLbmBCSqKirptYrmoLoJ9R5CFfscnD2j6iMo+8a+b5RWWG8nAGzI88cbrWy45SZZUxbTyeBoE4/ROU6xfyL1vlttvPZT0oM+xRTqYlBtkYyc3JSrWVmH/NKq0sTYCncsPYN2CyUqoYb+S/CFlGpZIu2aHM7reS+jtInTtp2ybSmVWW78cXlfWE7pDYFJxZHxddkgHskOK9js9Eiz8KhMgoYmvF0Lkb2qGI/jJM4D6ydjdM5tY3mUlBA4puevhIW1VZXe0Ahz+hQbYnYRbOf5oJ8nY0xFWPYpM29gntKz48a23YhwjvPBvt3wsrEtEBO+ODyQL1Wi0hctNCTJk5mbXewf1kW9zKqnGHNjHWITmHAw+Pj9J46Xk9v799cEAIoAsTjBG7Wqma8YXpWgV4rnNNSTcp/TN89Y2mRABcZ5vys1MCcDMQXmzQkzBIi9f/9Ea0o5GUNr1c01xB6KQFcsppG2egRDMeNzyLC4qBEcvatJGm8a+sIyBhbbYRrsu0yzj/7ISYPkp41kMFWTbG8EnIU4zkwS04t6dawUel5SxrySZProjAQV3dIgnEjpVF4CpdJjcp4Hz09P7OXNa0BrXs3hXBMok0wkJjJAD6ipvTYTK86KNu8CxqoXlmeU5/5U2pXA/FYb3337kVIlR/G8MAcITJxqytptJVK9ARKSIUqzIuxFcqrz7BAjafH6HCNCMqkpSS2tMIcmOMf9weFwKzII1eG6PvMXk6qcykqS1phzXOmBi/rdalOxQDLWlhQ3p4ceYilVk8+Zlnqec1R9jx6MVRTbOl9T+mu8vV4uhci/VlrNF8PRq+h1R0DRnBkJLiq+maRvx3HQnp6k7/c8a/Mzl7wnBdjZNUkUoJaFfCh5bpiKlrIKp7k4+1MTtRCLkZC/ghRNud6W1NWDUrZkOLxJ/ubMvZM+UaOnSeiazs55NdCrsFvKuBU/bHlublsWZ9543DvUyvn6yAll4TgHHA+Cwq1tyfAUzWMFfIKKOYyL+SSfwA5kumZIdu5k7DwpAxmRHkQhf8itMj+/8Hg8eP9uJ3dzeproHJv5GWpR0MZKlsWRzDu/G2wSNunjkBdaKXk+m/wKIwhv+RsORui8iSH3s+iSD+CmKGUMK1AmOgPrSomy9PICC5dx7szP5FUMF29Klj0ks6gJWFoRk2XJiS2Cfdt49+EdP//Zt7y83tne7cR4xoZ+bp1Lcw6aOcXWdFmMrjYjfdtOAZMFxinWh2TUoaY2dKGNrjXci2M28TLZb4Xnp42Xx8njOLnV4NYkXYmYMqzHknkmkLFtldND/kRMugd1ynB5uIAc+bt0zApzVnoMZnVKVMYI6h6U2DnLyfPzM8f3n+mvB1t7llFvDxlkm5JBX3vgXcbos0+BR0NMJG86W7W1cuC6CXyIeupeCr1eWBBTATezOm7ynLl9Izmx1x3bOrzqd8i/quPzIOKZeHzCyo+Z22/lc90FWrox40xofeVfoiZznVr+pDVQwdiITCF7/1vGn/+LP+Lf+8VHvvv+j/iXP/3EP/jnOx9+cKNtlacnp0yFfeh3bUyDOo2oCeCZMeIUq8Y0vLJQrT6fBp8+n/zu7/6Ep+edP7PduJUP+HOB8g1GIZ7/PL/xl/9j/uqn/5HPj5/x9/+48v1QSIgnKLw935gM7i8HvXdKr8zjLtDj61/j9acH/+fvf8vt/aS2r/jR1872FJKwB5Cerb12+jmxqdooEEBIn5JgHmrcSyvM42Aslo2p9miLuZJn5UCySjFiqhQKLQUxQyDGOEcOlvInXfuxWGCzSYKNAKISTi0p3z9VTzSXj5dYErL5III+glY2GEWNZaZK6k4q6QsjUDZSHzIXkJLKiRhidzsZaW5QfOPzeWecQWkrToPLJiMml0xfIRn9l+oQ3aeTtkkGXAz97rienEqJosRt98LmGqLc749L8gYamOTEUWADM6/KZJpEgkYmS5FSaw5mPc+qlL2Hwo9ihu7yyPPQVKPNORkz2UbzpBZF3luynGL1NHSBuSEiRSzoYtVLOcgc48j6QVWpmCV6/7VKvhjJTMEFwBVrzCHSwZn3ggywZTXiXqF4ysL12iJ5yGs2iWEagKe9ynZzoGYQyZR6ZeSQKz0Lx4RpX9hNAOaWKhIwH/QwmkZ3ChjI+i8SXJt5163aCwTclaIB3hrEC2MNwCneOGINTxejKJ9T6G5cQ5xYQ+TexcZNr6+avdYKApJkr1ysoS/Za8ygLhAs//2MkR6bRUARGabib+/DLvJJSXMQIcCbN06kBtntbZ+QtZrAvTXgFhgcLCJHyuLGSnxUpxMmJuAg5JsUXINNddsLp9aZVL+kaP0K/vzKAaUEr4WQuiIMf4lSb2sTpDTJcloFjCwgRkCZk1qSUjpmJm3BcU5eHwKTMGPbmtLSMmVABZUc+8NE09aGRbrdkC7zigzMJjsSrOip9TcQ9TmmUmaYSWmceYC+fR5dNlXynyr6/4QLgbai5DAZfesLVIKXGBBLT2lwyZOcNAs2o4+pONTZeZyd0Sfb0439aaeVcvkqmRdmXiC4yZTbRJnuczDDaa0K6hgzfQkGx9llZn52SgIQr71zjEk/TxgyyD5nV5pZVbKLITBN5l4hoCz/+W5G92Q8UClWCeti/ZhMOMNIeYWm6LNPmF0FfOh3rqSjMK6Nu5UN85osITV18DZpcMBjXSCSAPZQdPk449Ldmhc2c4YV+jRtVI8sFLTpjUmcU+j/eYfojPHArKpRSxTvSnLL4tCLopFlzp7RyEPPWMmAeaGdb1HsZkUsl1Lp/UAvDG2XBOnjxxdsq9hW2GuVoXgemJBNPSQOFBeopfsvC5Ei81l5yYilEGMSo3MJ/8yopD7YjTPg9fWVz999orUqCZe5plVlIR46oFYD7bXKKN3V9C//NFtNtqd1XoIQ8tAIXo5XRh+ZCnamrDS9ckIpZ/t202RpdDyLXslcjN4fkm+lr4u2uFIRPS/2cIFRvZ86sOebEXdEcM48J0DJXsWJAo/Xh6LlF324FE0C8jPejwet55VQk2tgApLERUJrJHkIRMp7EjiVBEiyxLlAjlqzHtp4vb9I6729JW6o2X1Lhiiu/bjozH0KDPALYO0JOkn+tSY6Zv52XpFsl3y/rSqlZczguJ+UpyeqiY1ICCDmC+34KgRW0543AzE7K63EE14csWKsLX9WRYCn7MUKeVek9GAYt9uT/O8sm6ZYBYMKAhmea++sMIYZMJDhsWk8J+C4FHm9WJ7XWsB6LjNTVJJa3JrMmFt1KAnerUsfFTpm6+wXYJPl7PVcNBGdKTmLlDaJpbMunp5eRDNZqe6mwr4LIOhzsrsYlKQv0rwmsrzRuBNYSgjrKio8ZOKYWBxXKZZDhiUFtTxHlocppmnwnF13ktAJsS22ehVBazRnQIyR92SmyaU8svfjKnZWwa2qQYDDL0sVbhdAcN5Otr1x//zKSL/B8zyo3aG+BRu8gZnzAtJihuSVecfNIQ+jmKcKWpNsuc9Oq5qqziE5RU9Dc3ejVDFTH48jGU9ZgCYLyQrXBPryRCha8WIzqsktCSgZ8llbYF1EYTLlDzcnpe7psbWaD1d67fud188pk09QZoYk3qW4JFVYquESHHajT7Uoqai7imdPNoQXgbnYFKtiDkk6sg7CjVKNr37wjp/++Od8/PSJ9z98pyTYHL4tiZuXSj+DtmUikgwN8DKxCUdMvJa0IjjxmHR3quXziam0uq4zKhiMqdJ7c+er543PP3vh0+cuFjhrSm05GVCN4DcFRhxjQpFszkLNnZUqu4OuDdBKgn6hJslRamr1ypgPnEbZJNEqdePd+5OXjyfHPSRdctVgpQZ+BDNOZi+MbVLmoNPAZbrtZT33xaMNGIVSJNEM69qLyTQd5ykmZS3Z9ELdNviqMI6gvW/ErnvbN1kH2IDYnJkDNFU3YphofyxrVjEgjdU0L9loAQsxEPkAJbDtG+CgPr/nmx/d+Yt/6c/y4z85+Dt/70/4vT/6yA+/3nn3VPjRb5gsUQLKKMxyUqNKml0EgHB2oroSzoCZSoUynK04T0+dj587//Qf/pTn5xs/bIVbqdhtx/wGtlG/+Uv89l/+nr/6+X/l5XjhH/08eO1i77rJPqE8vaeMF/rjleN4obEzcfa90b/+hu9/8RP+j3/8kQ9tY/8Lgx+yc3vWPjGXn1WMDRCoNKfW+fCONSkuqhV6V33YWoNjMDxZpJZMo2i4KQTIbDHJK+5TYG+fNE+gY9bskQS8k3dNJNAvKZq2+jmVvTNDd6ZN1RXuKcfJOyCrIjGGlCshqXsMiGyCs0bSgEmvpxpMdx4WDM9kq+W/BFTPc9CeuB8ns6+ADg2uPaVxRApzLK4kVOaRYItIB713at2YoZplMTivVLNVWaSqo5bK7Qbn0SX7S1lr7/r5VRNh9nbmmV3PZSUbL/bRzLPELevmnkz3mecgXwyvi3OioVUrlcEJBGeXN+Ri4CjBNXuQEBim2nltt5R6z3U5T0ay3VZoCFO2H5FSqyAHwfl+Rkjhop5Zcmb3uHpwgVy6Jx0NyQZrgKtaxVmsoCn2+uUpqLUw/BJsLAAAIABJREFUYw3QBfBbkitmfgYDiJH+iQKpe4aAAHkXZW+uJfNLNcBiHc30siql4PP/Z+7NdizbsvO8b8xm7R1NnqaKrJJFiXSjjgLlUgPZgG34wi9k+w3ohxEMAwb8Br4z4EaCINqmSEuixLbIqlN1mszIiL3WnGP44h9z7aSvz4WTrFPkycyIHWvNZox//I3Of8zToDr99WYCtotRcQ7RY6E/LLaRqcn+pO5I9nTRzxpZz0UWRKql1A9UuzPXhIfaqaxIj5RTgXBOB2OVYrpbZ/bJp/IF0holWIWjlSVa41yrqiOzHkQFmeWeXPXf6n8XaKT6DAxhFhHObiTjfZ0B/z8HlGpdRae0mO6cGkTFiOsHr02O72aK6ptJsZduVy/DAJtJIZzObZ/cbgfHEHun9y0BJSO8SPqQRa+l0VWpOtBa12eY07OJ8/OA1hUKPlOmZCqEBQLMRGH9ZM2sS6qUqqSW2thao/VN+v1axAYojaNqMW+1nhPwGX6mG6yGLuKTAy0QuwBYCQDzdnAMFfLbduXh4SIK/3rulsbATaaQXiX/ElMlVAwWMRbm9JOZpMl9yEskd9yY6d0zHY6hKbIPeii9jzRzW5t0Fa3Wq+RuFMTSMTx9fGCCHxwuIEHNveiePrVRfA7pPSPZWuiyoBRRrYF63SitQ9Q0TuPUU+N3RFcNUDIYjCxCC9hgGbvX3lUwRmiiW4ITJU6wCybHcbDf3ighbyNRaKt8CnLjl7x4SFDJTmacGnkHjjlPvS2gn7cmqyApwNgJ69CsQYd2adxebhQavV+wVjUVM8tDJbL5zdVglsVKotieiRdN3hPM1UTGfU0nOGAlRVifNOnHsfPh62/ADy6Pz9m0h5JXSkmWB/SqyOu+yStJ3kn6jPeLc4EYSnGLcCKpsOEHfuxAMvwi2WuQIJF+fgeZt+dkKLJ55ZAPkwhKObVbl4ru8QTtFFUdZBLeSWHWr3UZjUNSnFYrfkyOt13AWOrwa6n0ZD7OKTaNJ5g1x5CXTGqoV3LI0m3rnovze/oUeNF7Z769qRHKqdlKlhofdy6XTq/l/vcWi2G9dksTP9O50jIR5fQNgJxOJ0qwmnrE4Fi+QVEMj0FDDqClVz68/8DEufQq36FQsRZjShoJ8qShywcmp7HACaKAdPyyyPDzMpduP1LykT4sxZIFGXTTGdxL5eH6cOrP85DAz2t0+cxYgsKrUYNaxKrE54LS7vfMKjAS6I1P3s3aS61tvPqr5LMJ1sYCDnDWaMCS1k8WsWt/5u0ucMwjP/rda8DOvZ8AgC1ZUGgSnOfESspcz2a9f7IBOF9tFv6Qf0Z6VoGWJqmhSsr7mam7J79G5JmQz2qB++ueCk/zyzJhHER6HlQutFI5iiZmUUhmL2JvRDATQNEEOZuCTCpRGs6d/n0m8wRcLhvbdcMt2H1SajAnPETeMaSEkE9+7jPMYoph1uKMmPYIhYYApXhKK3RXWNEdM3ykXDZOOSM4YxcgUWuD4mfzISPXdZamz0UWilrbAlKzKNIAaoYsdaYGDh4JJBIEIxkR6dEROmOenq7M26uerQW9GWNo3QkQ1LO19IPTACQlBeTwiuW1JUZjoTBLAnsGEfXcA0U2hoDRDb747JHrw5Xvvn3PD9++4GF7og4nSvo4FU3UrYgpWLMmWN5ApRp9K9RWsDppVsEl8RWjXAMpSa5XwpL2mY6WweNToX03+fCyc90KWw2uWxU7YKrmizDC1XgPH4xDTU7BqDPw6inBS2CnQpRG7IWWrv4lANLGwXVm2YDhhe1xY39zPr68cXnuNDOaV0oJ2Cq+T6Lo6RIVPw6iNsnfDoFQ2CdMsmEC/Koa2epO1MDigEMmrFaSNWhiR7anDcbOfHPKg5g5tW7QN63j9gjlGdpF+2LFFZSKLTYzh/Y1JRu/hOfMIYaavFjM64qVC6W80h+u/OjXnvmt//hH/OnPvuP//sNXfu8PP/DDzxvPT4UvPitie/aJZcy4lfQ+rEZ4pR8JQhYNb9SwLnBLZrx/8f6F3//dn/J3nwt9e6TbZ8T1R2ANygOXv/qP+Zv/6Fvev/wfvO43/u13gyM0SAhTU3p9vPAaE45X5jGZ3IDJU9t4++wLfv7dN/wvv/8tl8vn/ObfbHxRB1sxtpZDpALuRcMzk8yypITLQ5YcgxzE1kptCnvREysczaENmg2qbTQvtOrUJlmah4zZYxoa/RZmKtOdSQtjuvqoiC6AqAtQTl0aJfy0Klh1Za0akBdGNq7tPJvDBQ7oTj4o1nKYGMzF7IiUCYfM9iUNK9CmgEBXMupWKzOSsY2CMuZQoW4EMRMoyZ8VjLbgTPdlapYsbSNFfAK+059o1WdLZhRwNuG1VnpvvL3dEoS+f+0RGnQtMOrusxQ5VCrEVBKau5/DWp8arpSSQ568H5Z1x5rM1GJ4VI6BesOqvzdnEC5QNljnrwCe6SNtMvQswlFaadHfs7TxWGSIauBF71J35EIRcjib4UWR5+wyic5CCUjj7OxTVJfll0gbArHHSgJOmcQXkhs6eneW96Q8gFVTLO8hT1SjmGMh6wmrAm6Ge4KVkzJc51uGOaw2riS4JzURrNTfSEZ1YFkT6PdEetbZnZ/wrK8Wuyfs5IKx3mCwWGOqFUoOfyJlf+vveyz1gtYKsc5FEjiLBH4sWfqfqAQWa8tWpaWvWSKQ9irB+3WnffrZU81BxH0wOsXIKkU9UsneRF/Bznq0nu88ATu44xxu7Ogdft8A0PfPUJKJQgIkmsSFIbt4UJENiXIuw+6kQoeS2DxjEo85OQKO4dyOyf6WsZW10HoViJMeHiYnPU0SMm2n09h6pzWDmIyZnimnVlKo/Uwa5EpndO4yjYX4HQl0WOgllkQkozWZHNeGF3kVWE7UHeMaZJJJMinGzKnXWtz3xn5m0STE+X6IcTi3mxLXtu2B6/VCq/JmWWjlWkyRxvV4WubNtYx0qPoxdPmNNIdDf7aVZACE6P/z2MWicTUPxTLmuDRNyGrLZjbN1JrJp8qE0k5EwzSMnhRDmYoPJVQZKd/Tu7CYMn30PBDyQJGvVDbC20bfNmm2TeCESGIJgCwwhzxgig4SSQFUlOhSuHtYOTKOLZiolVlErU09fHLc3k502Zb+PKOrS+Tfb8kOS5ZJJHhzFvUJaJDvfbpYY7VUNTGpCfYhI/aYyhu4tM7xtmtqdb2ytY5VySpLNpzrv9VE5Ppb7IhQi1pqSdO3Kj8daVJoyaZbFxg5KRjzEMAzJ2/ffWB/vXF9uJ4RrFELZZOJsqGkq2XDUks9TbrXul9JGTMlMBbzZGBEfp7bbec4nNrUFFoezEpcLDxcNvqmFBpNmjINSov4nKDIlyolTD7Pg3SBf4SkQ5pskaklYpwtM8OREqnFlLi9volt0cq9+a6WchBkZju0o4qV+3MipTylLOgi/ReC8CmgIJL12GpGu+c5mkCPtcrbfrDvB48PT/r6Zuc5dVKF8/mXZFqFGxStz0DR5suEdhUVC0A606f0JM848elBDV3T799/SKlWNry5X2cs8DfSyNJzwqZ1PRd7yUy08VimjSTDY9U8a5qce2Qm/X46fhzMfdcErQgkXXfmAj9KFmzrQbirSV/TptrS9Dvs1OmvSdPaK0AWWmgv5PcRNBCM4Vyfnk6QOEiwdp2yps9e4j7d9LnWZZ4D6cOwmn49qsWsiPPzEgt4tFT3ySvi8OChVA53tirwfhVGd3AxPaggWVD3s2IBfYudl5ZL1ARPBJCtr6X/qZlOo8crmUKEEk9rBWsCO0pr5/ct1U7gbuTemNLh5JpVEVVysrlYNMecn6zRstAxzGCr8NAvev5jz+JtAfecd2bEfa+f+7WAFRH91yTSTImuNT1+xKxT45wPMv9u3memwIO+VSW9DufhKmmbkczU9EZQCVqIkh4eC+DPO0Z7rlMqEEpO8nLgcegszftYdN4AP4ixy0uoBrU1eSn4VFF9qXfJQTaI1ipYxg9DepAsLozdm5nQ3FVTzviEdVrSoFipREpBlATn8XLhiy+e+eM//jNeXm589jgYVd54onXEsuVRUV85GVslOkTQLkarhWq7CuiSUmzPaXGep7rb1/vIO6QY14vx/FnjF18NXo/gs4dK6wLyPYKeBqzHoejrGogp1xphXefvUE0p77BkHplRWoLSprij01cszw4rwAFhlfbUiNdXxlujP4k9WKZhl5JAFrRpeCnMXTKU2pXANELA1Zq9BUO1RnQ1psXw+ga+YaZURtyzpi7gBeud+i54++oV3OgXEQCMSbQrlA3qE2LEHPpZikN6zOjsGDmQqfr9FQUeKahIxrZqzQ24yuZhazy9u/Ab/8Hn/MO/9yt89eFP+PNffsfv/pvO548P9HaR2XWMXFOOXOGMaBpaKPCmrM6HKJI3Vpt0Mx4uAYfxJz97z8Pv/IzLU+WLdoG2Yb1hXKG94/k/+s/4rQ8f+PD6f/I2dn76sTBDoEULw7dGcGV8DPbbwQ40k8XFdnlmPDl/+v49/+v/857LI/zNFrwrNQeXel/FglKCUibhVUASlnIjGFMSTT8GtoyE3fGie2Gd+y20P9wAk+SwoGFOhGFlsm65FeChVOAqT4KWzXsUGV8jqbEhf6w5Ub03J2FOb/pcsToPd4G4pvvC58j1l6xiF2OqJZ9YdZxqS7Fr02c0LOv+CQydpRjXLb1c98kcCkIiFgNlZlNfgFUryd9VAxOXIfiUB9Eq3SQxOs0ElD6bTNZSFrsquF4uHKbQpt5bnnsm3666erT1a90RCcwkC9fDUjqX+/7TexOhMJHvELsbYXtJZlKCNbWqt0iY4T6sSmlJQBYigi0spXBmJBCSzKoiBrcG7HxyNwjUoaoHaqXkAF3ndvh9gBJRaDVZUOs+ijtzKEg7jExprS2VBR5YSV6L30QuwjBP5nXRnVOCDI9IllEE2EFMY86WNZWabc+h55Lnl6y1Z5DNeNZSpTAih0TJolL/nkDPYlYVux/OCbSt2lhXnIYRAo3Wn5dE8N5T5KtJgHJ5uKJWJesL1VvDPdlFqtWKHmCecfqMZL2VbyJ7y3nWm/Ki1I87zqFmllrGJ/XguTzOz6m9n8AnIe9jVv+r/ztfqmr683eXQXfJwd739+t7B5Smk5IlNfPLhCy3Euv9eE5Mfaogr1MTrZFNnBJVnH04+7Eo50brSmerrd4pjGhj9FahVEZM6takCK9V0plx6GCb60BNhDIRZiGwWWj5MmRTU7nYSdXuSQSlVKw36rYJTGIVnZxMFQu4NCU2AdymqPV9ff/yyQTayOIO/V3d8Rxz8u3bG6MVervQ+iULvNxwRdRnaXn1PMdizpwNNohqi+oPnyyqqha1mpLpzjwGY9ySbRHSaBb54dTa1YDmBVlJTfUyXk5WmJsxDJopqnBNGGNqIwwiKaCuZjXS80IialaaX24TPKBvG/ROuAp7HagyLl2AU+K/YNKmL6ab0t56Hrwp/TEgEzjCg8NyLegI0wb0wXy7MceRwJ0u1loFXNaMYm69Ya0xXXIoqwKYahGDbMzJPo6TDXQkQ6zkwbZYZD4nx35gcxIFeq/MMdiPSb9eaZdNnkkni0hXRjX5m/hq/1YDnyCBFTLVpqroIb1M8nIkpw9a/AI6fB5KUXx95eXDd/RauLQm3TPa1x5BmUEvy3zeuG5dHmY5nVu+Vw2x0UjWU4R8pPAEjvedsS/zWb3/mVLK4cH1svH4JF8GknVTqHjUNKJMTzRzTaPXz5VnDbaYgMFxHMQcYNpfJxsmSSQz0oOAECMvpM1fbDcITqNdxLCbQ00wJQ90IRVqyNoC0/OeS/7h8mHzjGLFsvk3UzKkiamIGR9fXpQY17o09imb8WRi5f1+Tt88P0uQZ2ruUTXamTjhfjKHLtbORg0zYtzZW7XLKP3t7Y3HLz7Dx0iwzgW8LT8FD6Ina4pk3ISmbuuCp8ggMBZ4tyrDvBcKKkp8zlynwdxvjDF4/fjCqLoMu0uNrgJyVQF3hqKnLLlkUy6jaL9f/HZnAqZiVgb+rNeovekLdKIwj8k4hsDZhTeEJmyxmEDn17TzQrRPPtf5HCA/e5WUAjF3uT+O06vhZCEWW/M3nTP5NRZLbY3M8hQ8C5K1HpQgJV8MK0WfrUROBiOLQHntrTsJ7GQqaTq2jC5TMueSZetzclL7F/i0WJKYJCPk+jPzszBfwwPuK1kNWJRTpuorsTOGQE3kLdJ6TU8FSXIEji7gJKDe/RBO+XR+LrFgMiUwC8yakfKLiaGPXiVHKYG5hlm9Vz58vLHfbsTTM6eZty3gQQwnrGG1k2NUyKKcWOCz9k2pnVpga0VJoa7wD3IgYkxiHuz7DZ9O82BUget+7BmI0Lj0khKAZAjlMiw55Z2ZHiZwV6bDrRix5O0kWGvJ1soi9wRHF0uoGo3GFz/8jD/905/yzXfv+eGvfMkltvSPCnrefyrs8241MetOM98iYXaJfG6oQZ5T93s1U1JSQVHb2cyLhWeU0vj8+ZlvfvmBjx+D47HKy2Qr+CGD0igaIJgVSZKyrqKkb0X6M3meC9tsmA15pnneC2Eci8URkhJRDGuBHcblWnh8aLx/O7g+qnGac1I9aNdOvGl4UKrhrTAGYjAlG8XrMvKHWj6ZslsnapUXCVewG8TBHDlIXc3UmNTLRt3eeP/zV/pz4eGxKySibtS6Y/aW+7AT9i0WDeoFJSY9IG+kI89VAPlIaT1bnm8TrEN5xNrEecMC2uPgi1+Z/O3f/DF//stXvvvnP+Pf/ul3fP545d1D4/LjJxoOrUm+k2AtBKW5vJmGgk4olsJIgMJw6BbENXiZzh/80S95eKhc2mdc2hdUuxC1ErZRHv4KX/7mf87ff/8N373+AS9/5oxda30CNSpP/cL+YOzHB+a4YVRmVK7FuFyfeJvOv/rqPU+/+4GnrfHrVQnNfQNC4QJNXhVMm5SiujUMJURjKT2dFHPattifsuAwa0w7xM6wRtQbla5Wt3kOZDOJb0j+5Mm2txwclMI53HRpOGlmHMM/8StEITORXpQVSaaPIbkndrLsPXsRsjkG0p501ZO5D0N7Tk25hrTCDQLanXlSSNCm6945jrg38RkEkqNbJN0jWc2GJYRlFrgfOALkVy0Z7vdBkJ233Xn/E0GrBfpKKZ58egdLen7/WS0laIRpuCVUTkBcreeQet3bYrrroq21qa70mbWV6pqo8n3UHp5k6F3e8QvsyKvgrIGW7Cz9zCLfIWKc6a6uJwO7mNQBE5LsoPMU9xO4h6xns7f1IT+j1tbv2gkyrP7BXf+RF/CktcYIiDzPlUinOmEiVp5lY6+QA9Np6qb9EGDUlFQuQKlgZd6T3UxAylxr6fxk8jml5J0Wq1cnrRUykTJll+FrQLKsFtavZBYLecp1FOegDfvkv5MsiS9v3sIphcy6EcQaGgGF7MED+ZVmHb0k92t5+oIwRcRMaz8RaQbJ0P4Ez8gqMEFe9cwVaE09UkmAE8jztJxD0QVqgd2JI0CILqb3+Inv1Pf163sHlAyBSTJ91LPsRcX85F5owieFTf5gq/kLV9N9jMnt5qcnR9kKW8+dkCZWy4RbBmG68C6tK4ozNcvjkJnYSu65SwSAcOKMaNUL8tz4I8GkEtLnVjMxo6pYIqTEDDSh7KVwxFATZoVtu7C1RPbHlFkw90nD8o8hBFThCWoVsGrMEXz8qKSnS5EZY7U0WMuJ3RmRaZZ+URpeqNgXoDA90zoi5W7ZvZ30zSoAZp+TfT8YY3Kk5E+MmJITWAFIa6lby4hCMwEd67lmkRgmo7oxZb4oVpGe9fDcLCVSPjPPKbvq2HtD17qAtKW5LTkJaEXrbcTywNL6qiYJoDZyaot90vLA1QWQpuUUDhKwtPPBqVneb7Dv6XXByboptaQvyD0iHFdjq4mw3QGCfOaniXtqoPFk34QMmXvR1J+QWeiKV94P6cgvXXHcKqLVpFvNS4c782AlXS1AEwNrJdV7TpByR7g3qmjyUbBMWRocczB98P79d8w5uTw+aPoek2oCa/epNdWKAMTeKlvvWM/kuWrn4CXW+zbPKbfnOx8pv5TstbeNeez5s2h/NKs8v3snhlSmRGFFBcg6ZFxADb5orAicSsQfpCefyVIsRiZEBL2UBJGzqU5PpVKMy9Z5ef+iwqvJGJlVUIRYXTNZLAVyauyU0tPHRKy9+wTok4vmEzCgtSapsCkFY4ED3SrH24231488fvas/WN2Xv6FPAM+OVPXr2Vy7KH91yzltlkMLs11IGNFmny8Tu8ZU+FfWuPl618qWKBCpC9AJChMfs1iiuQ+aebrozhn0UJoHdbaE7RJ4DdQwZzU/VVk+Ti43W5MnNf9Rn94lg4935UR1LD0JrATNNAkSQlGhaTGJ5C+wHNbz80WUKOi9f7n1sRoUqtxjKFzseoMUV2seeJpyM/6+fNuC2dNugzSGyzZSUWSiekyQa4JsFjeS8tPz3KtiLEhILqv89bv77zk+fpJLZlMWTEE9cK1Tww1AstPyiLN1de5lcehPNf0PMq6c4saNA+T9KakQXqsI04pi1N4Tsqsapp6C5w5a6FPiqLIwY2PkWatMsSfoeLe0FBm+qEmZAZ1MxjjLAZXMVZKTT+ucrJsxWCuyRxNCXWBS7swjz0bgvXugkUpbVUG7Bos6T23WjjGzsfXj3w2fiCmZ3CyrnUHVWgdL/18J3fvBv2LSLacYxyhIr63yhiTphjU7A01uZ1zZtLrpO1TwObQ4OsYQash689SGAQ1nGBoEFNrXqGWcj6xA0Dv2XcNNsDOBmmmftKW71k2E1ECovL58yOfff7Ix28+8Pr2yrvtMTf8YoDrHUfVjFRgf3rklOUX4n9J+r5kBZ9oN/MzWVL/18WmocLjBT5/avzyuxvvXzcul8I1m989PVunSEZE4fSpm2NIXhKdOQTeV8X5Maeaf/fBQTZXcwjcCZSOVVQb2VRq0MNnjY9vH3l7bfTWAWPuTt0G7aHALg8kDQtNzU9LoAA1tDGTzVsDtykJomswp3u/Y3XibzBip3Qx42I/KHalvqvEzyYffnkjwrhGocUr7H9Baa9wqZTtqvduhtV3ROmU8g7aF4RdIIZqoFA9clKrowA9m5DnlDFJZl4vNx4+c378a1/yk99646ufv/Iv/s13/N4ffc0P3jX6o/FjrnSFGRI+ksUmhkxtpAQqsn4qGJMalV6C4KA3eHyEb94Pfu8PfsnD9af8xkOj1A2uFbPPCLtSP/v3+fFP/gv+0fuPfPv2U37n58GHefe2M69Yu/L4aOwfD7GLYzD7gbUL/fGJtzn53T975fnhA9v1Hb9WxPJrtRAkY6k6b3NiZdKrGEhbhd0L0bMuCQUT1NboM6VKJVn6TTLsXo3WM0yEoRPbUnWAMTwHFjPvpKHaWjLgFQAjQL9VAb6WYImXVIRE+q0UuPbKnuFHM0J1mg8lY2c9F2Fig8x5giySXFU9xwLmJVkPAoyno6/VdIcLRAi2JvBgHvIH9BOwXN5Ofg7E1EjLL84Xe8XqybS3Yif7cN2xCySST1LuJROL57JdOcbOvu/qU5I5TtZ7CySYnnLW6QmixmllsMyjz7s9wbA1hNU5V9D/6q7utTHQWTUdiil1b04//T7Pc+2Tpt4izqCIxbgiUhYbnzClEnxigUjnVRpnT3Defc1gLg8srZsxJyW/B8lSXRWqoR7Kw+EwbjGzDthPyXahMMYUmGSSzZI1TsQd7DNr8v/NDr9Y6lmT6WiECpZVjGZ3WVb+8WK/ez5790QKUroXui9WfXXKIfP7z5mhO5FYwdmDrrrJ7rWVFYFpltJwwEhpYxGZYEYwYuTz17oUaUa9yOpv19fTA1kECf1ToRFi1Lt/whxan4eFjXCugVXf1wxzWX6cuh9m/hh3bzFnYTEJFFqcIUFOJOP+k0Lxe/r1/UvedFuI5hXg88BLT3QRQJTOOeNkuqy55Jj1ZATth3PsLrpqa2yXTVr/aifa2mqltUpvXRe+66C9XHXJKqFtcmQ8unrBdTkKvFqpSMt7Qv2+mrwleVkGl7UWtt659C09SdKcrNZTQpXQOX3rXLoWyZgzmVhVRUqkCe8aSZuK0cWSMnduB7y8HkBVQZv+Ie0TpH01HwJ35GukaV9uOIP9E/NTz0WH5zR8eRo47GNw22+ZvCKQb2uFvnUMmfIGulTc1CRHlZdKNTV0hp5hX0ypfNo9dOAEKtSS0KnY7oCYkRPpgQz5dKgERtkeKb0n1lPPSGvH6NZYSPSahGLyAHBTdLsfO9UF3LjJkG5RvY9sOBaDJds8IiYxdtjfRF3vMvbuved0BqzospFeQZu31YomeGkGOCcxnWPftbkR+KCa3hg4+1BBNmb6kSR1s5TK220QZjz1TYy49OtpBtElJbPQoTUzkXCBbeSaLgYxVLDXovZy+pSG10gmnphoZpXh42xwP374yP7xTSbIXdP/BWLElMG6FT1rihFbZVYZslcTy9Bz34vhFWJjeIKI8+BY9P0IrJ5q+hPgnQ5P7x7ZHh6wshFWsVqJMBmIetCohE1maYm6L38Ul1eSrwS7wI9E9k2ytlYrrVRuPrPQdWyCzeDhsnGbg5e3V6UzcgdgahZbw5LWlIBjmCJue0vQdwUFBOfkJpakcfkOFV01MQWOetLqw4NaO+8/vlBqYdu6ztS8kCOln+UvNamRDCBV7usSrRSKFYYP1rWlBrGJeLUuLXeKT8rymKrBHvDx/SvtcslEEJUCOjuNXrtow2WZr9dzGh8uwO4YA6s6v2LJQvMsWsxOGTvmuZQSnGNKWlVzvz00pU7OspgtKcOxxUjK3ZiIrlHzMpUHxohDLM2Qi0hJNqKvMzEEPBJCWOY8tF5rPeW5FGNIP6u1app6LU7lkjSs6a8Kvzhp5lFWwZqFcibPOSFJ9fn574VGMTUM821QqClpvc/xF6NITJI7VuMJQIrdo+ZAgJ0YYEyxy8yM4kkac56vAAAgAElEQVSvL5JQLGZKOqkmwlToJsedGYNmkuWoiM+Zc67HGkHxOEEFyPCBrBN8yrtGCXYLmNJpHqRUNCY+XH5+OQkuU+tszkmpV8a+6xpwh0y9XP4QsTwNks38/5V3lq3ivuHIrLJScPw0TK8ZOlBb5xJKq/NkI0Q4t5ed3W8yeUYyCncxlWzrWOsySC4lW4dVJMdZP7ZamcehMIDauSymb/75mmElngCoz8m0yb47D5fGjAObB/iFWY2aABIm7xOhjD3XRjo3hCcIP5m1UupG6RfmvmvV+ZJiAq4hh7eSPn01791g640f/OoP+OVXf8zL+4+Mdz+gjMFWG7h8tcK0xvCZsvw0iE6NQEAOIFP6WxGgstj8TMIqJf0oZ7KdClDC6WZ88cXGL7678d3bG++Ojd5aslRr7gHJfaxAu+gumilFXUMy4f+GtbQF8EhpdhqFn2csRO0wg57AtWHwHDy/Vm7fHNwexUy1ERy3Sb9WJhPfwa2ybbr/6ZKgqGsPpVlazSQkSUHVSFWiAiZ5ideB70GNkdLSgsdOuVQef7Dx+kfOh5ehOu51cmwF217ZNmN7eiAuF8yNcn3Dts/0Pkuj1M8JHsFuQAaDpCbZaIQd2CmFawI/2k65OM2N5y+dX/8PB//gFx/56tsbf/iLD/zOv6s8fGk8lc7nrdG3tbYHJRrmxigHdQs4Gj5Tnob8vJajQglFmT89GK8f4Xf/5Z+zPVX++k8eaGWDS1d9UDr9h7/Fb/z9b/hPP/zPfNg/8PtfD9yrfOQwNivU6wWbT7y9vuLhjLdBvajG5/kd77+b/PM/euXpsXLZGj8y53qt1NpVZ5qlHFFyrdZLnvWFipKzjgAfAj9aLXjID63WnVYf6a1xMcldK4UxqvyMbOLF6JFFGjUBfwW3CHwHcRtC3kopN3PP/DBzrAVjyMNsINZ9pXDpnX0cCiHyYCuSO/dimFdiqr4So1zTBUlqgsgo+rPdAaw0bBzYmmhE4OkPFExqU800X2WjkLgMx5INeWCl4V4pMaitpz9fORv/xcZWzbT+PelxVKhyRBbD2TJhrlgSy43bGIxwsfbsRBXWRatzJtL8OiPqcecw3QNBeughUMuKPC6DxL5tfZ77WaKhStN57EGvhoWSPBU4k0PR1RtnneDZr9T0BZrDlJRp9+Gx/p/0QEswbeIaNLtqm2lVQwULpQwf+kzhipxvOVAo5V47LZ9jI4gqQLtmHR/Ia7e0JpacizltVvSDZ90ugE23KYW75G/123MCTb1I3o9BnGBouBQEsT6THjCB1o58roRHk/Xbgkgw1CNM19DkrI+1B4ot0PqT520CWorxSTpz1nI58Aw4fQA9E5KPHCIVOOvwv2SdYOtsdxr5d6aG3rKVTUBLJ6rA+1V3reVpELYAzkiSSHoKFpR26KlKyc8g37tP0wGzF1yoQYCX+/L/vn5974BS741xOD09HFa4NcCilclgVE3EOjDGMMY82I+Zmlun9UavlcvWU2uohdOsUUujbg3r8i1aSRy1mRJJ8tDyoWmLrWmX5wWJClR9tpLNq4oMByVIVTVhpYkZUkun1E20b3ddwmZ4qewZQd7MuV4uXGpTFPFwxdFmAUpRdGGEmtdFtT5yCl0imPvO63SoVx6unVbhGAf9otS2CmCJ8RYBJ2Zp+pt6Znc4lsdEAMgsL2achfdKGnobB/tt53i7CQRxVxrS1oleMZdcaYbYUREocS1crJUEr1YTNHxkXK8arX1XjGYAjIFHwVE6TbgON12QcTaaUQq2bdTedMAvTW+pUMbZ/M6ZkyDLFBvuCPGYkk0qBaPhpEdUKFXDMHYCL6LAh0Nxh7HDcdPF0QXkbH2j1iaqPFqDW2sCJewO9hBJlTXLKajMmlvVmtzTHySmMywYY6d5o6CJaTFJB8c+cDLRKlli8veA2jueRsi1yABaWyllDmcDpWc7DidMUzVJHz0BUYEPYroVRY2apkQ+brx8+55SGm3r2qdouitqN+ezroFMuCmKoTTNGMjD9gTCQ0yoMY80OAdichtK89t6Y+7HeelM18/68O4drXYE4KpJXR5KrVXJzTyAinO/uKYvk1HP97ImXnrfAlQKR1KujyGkfz8OWq3U3vj4/jsBTblXFkCDFaV4hdGEFmGtitGTMpvl5bYuhbXXF4D2qZeQWFoHe0boZsnOkUDv07tnaqnnhOlkpqRHyPLOihCwIlPDniDzurC1H3sW1WJoia1ABHMcAOmhpj3bro2Xtxfe9p2Hx6tot3n5z5h3kP5smmXU3cJUVRnEVEHmxTShLHZ6mywWMO5p7AiHSPwYK9xBqYcW9SxWLd8HWQDa8onKJBZSjqoppNgHi4/hyVgJqyktyOo2J6LTVbDjdzadW+X2duNhu4p5AicwHrbmT5z/DOeUNK/1rLGUPleMAa0ImDUVPWvi6XGnKA/ud2lZwFwX22cZN7urWLx/P921y4xx1UcrgZOp6GEyejiKwIOV9sO6M2IVXfevkRVtFulqnnQul0+eY2IYwJJQiXmb08dS7wyxRHZKa7SMaO6t6lmYJAg2VazLSyOldTXYjxvP9TP5NllkcZnNhi+vRO4DmKLh0BoKaApckmXCCepRJM9bE0LJ57QfSbnfooJ9fHnJvQytaQ/UU1oa8gDxlGCbHoGifnUMWASziPXKmBz7xErgUSQzCA1bSn7/u3G7Vl/kuXwcTrseDL+AGWUemhyXBjOIGDLuLQnyU/BoWEzworVjq1lLtshiKZp+HslIxQKfU950hcqXn3/G1ivf/OJbfvSjH9MvD9o/pWuPJ+ux5PR5Aac+glKcbvq63gzzxmTX9zIxQFstWBT5ZLpOeWFRmqpXgqfHyrsn4+sPNz5/vHAtcHkqVCpvPmi1cuwHc+x4Fdu3VtUyx5y0KlkVdfn3GeMY9KJp7jEGtW3EnMx5Z4AxFxYUbHSefvDM/vrKx5dJ+7zQvbC/FLbhRNdeM/kB6Bq4qTmJukzpg+ITD91Nvu7TaESMPF02aoM4htJZW5fU0h3bK9tnDzw9Hbz/5o2jNqJOYhiXW2HWxjx2ro/PjDKpY6d/5kQPGF8BDepnAqtjzbET/AvOc44EkCkPRP1c51PsbPPgi19x/tbfHfzsFy9888/+jJ9+9YHf+/0HvrxUyuMTX7YnvK4AjQSxTYBVLXdvwC2MUQw1+pVZjGbGZZu473zzMvm/fufPeXzX+eHf0mDD2l9Xc1uvXH/9H/O3/sFXfPPyT3l/7Pzh+2CYaldPts/2+MgI47i9MX2HQ8OK1gqXpye++e49//u/+sDjddL+9jNflgsPvbF1Y5hSI4MQ2NLyoOyTgoyVmxeGifFPDWorhFeYnWad1kKy22HYVk9Z8goECJaZ75LBoNrZCm6T8IOgMXxBPZF1uAa/rg0DqDZhOh6TrYB1OPYbVjYsGs1aDnlzUBPzLuEJW8c3CtgJAdFe8KFeyavkNd2aQHQC5pGG/0oujK1yvIndfmklgwh0Zq9BWaGy571YQIFNqmLUtlmkv20OPYrO2loLh6iGYlBaAj+90U1s8WPfGdPTGygDHGJmXyOEbMY45edm4AkqL0bMjGDkZ655kMvDMZnPWR9GnqUGuMubKEIWI2LV+MmS8qzh1s/kyQgLM2rtCaLrHrKWBv4JKokJk58hUjFSLb2BFlIU6YWUA0kTU3rGxHI4ZbZMt+W5yoSoJtlkWqAUk3XCMdVD9KJgDZ9ZyMUClWbWFOrLFvttMfLFzM7wB0w+omZnLbdYcUtNY6avrWFrJaoMvWGtgWR45p4BMmk7664oGXax1Cn6M+fzJofjnCWMyjUjE/TEA3SzrBf8XB+r+qulJSt6ATVL+pZ8ITvLpyyosn5dNeOactmClxYQ5snuFjN+3FEmltS2mtQNJ3NpeZlCsvaliiqhPToMkSXOwu77+fW9A0pLnrbScoiUWYSAG8vOankmCMSRj8pt37m9KQr88nCl9zRyS1QQWw1qmhBund6NhyoaZkRBSWY5SQpNMtfm04ROi8JrgeHUkyAhsGp8kgxQipJIZPhZzog99xDjyEyx6cC8HfQq3xuKfFo8ApKJ1U3F6hhDUrq1IENNg2KsC7dMeakPjzw8Xri0lsZfD5SqAs1zY1SrMgFPcE4giwiBY470KgrwYHgadCdw1gowJ/sYjD0Nbw/JAntdptVGDC1gNY662lqvWMsDIkQ97HUxvqAhg8vF4rm0KhPktVFSS0uk1Cz0EkoejhHSxlpvSkrIQ26G9OXmahClT05qoK1yFa2PbAScoPZOL52xT9wGq5mu1qQpLhPjSD8Lx+YuT5jasdKoLdFmxHhpKUA2ZCJfaknpI6e8QiScRY3UZx+hRnkMMbEsgmvrjNeZzJG8yJE2tz880vsG6Q2mwk4slpPlFHmwZkO+kqCWcZxYFTJOX5K4rSqRw8fOQt6l44Vjqrh//9179jF4fH5kJYSd/hcLXGotkfY1ubZE/itj6n3LNyovtZgwB9Uggz94O5Kq3C65n5zhYpaZVa7v3nF9fFLKESRFF1a6QxiMlF2tpqvWyjh2oTcnayuSMW4sM7+izknTSXc11vvgsMn18YHbLg+t0pbOe00xFNvMktKUdpcPBjkpWmBPsoQQUGeh72Nk7HvOavc5sTFVnFjhZew8XjY+7gdu8Hh9EFjInQZbz4mMLs5WyynZ23pN6u5ImWZjuPyuLGnZNWU6xfyUHYfB4UeGCzi1dd6+/QWtNVqTjCiKgDRn4TtaRMZflquS578v8MOM29jp28Zpeuhrf5fM6AIBMvNM6gszXt52rCqZpt+dtrX2bV3sOSHiXlh53klWLD0JVACWKrPUPDAyyQRqZLPNuq9nntPB7XbQro9glmy1pNhHYZZ7YaEfp+BpPAolgStR4tezGS5fu9p63pP7uc8sn6cn2BgYdQ5iDFqXnIaahX42GqdHkKsIWftUTDfJLcLASuUgm5SyjDLTKDLfgevquqeFWIrnrGRkdWDpX2eryFoFnQfW7uBSLlrFw6NicZ1P57mSflpitzWd3yVBnW60AnMcarRr4dobMSdazQWsooCFmj+F7ozwBNpNQOiSigdGrRtzHqdHhpItnVLaHYQyNVJLGmx2n+i21hhzcBw3WlH64n6o7rGoeR+6PBYrtCqZk+R3edcx8QHb9UrkWpVRc94dISYEuQZKKbTWFj4PDr1P5u2V+dlGRabWw51rVMpUSiu0lHN0NbvJfDBzYr4x3CDrijnEdJgYpSRj7gTB87Mn6BY4j48bn//wHV//7AMv71943BrWrmytSd2d55OaJ91flk0q7moOSigWOj3wxOAmfdQSeFySqAVi5n1ppUA1Pv/iwtfvX/jw5jz3IJrzeAErzjFv0Kqkb67Bo3BFSyNlNd7XgpBVguZGsU7ELYE0w+0gpmTYbia/zpKFehjXS+fx8xvHV5M5NnptBAf7EdAb2MCiYlPff8ROPdKY3QS6RZF3jneHmTYDMfBRKe0AU9zzbOAj2Y81JYZzQC88/tUrH3/3lbf38PBZSUlrgTI43gqt7cRmlP2V+fERe+eYDcJfsPJAxCDiDeMxu2INhiwu+e/XwXCFmmls7aBug4fnwY/+yjt+8vf+Gl/99I1/+q9/yb/545/zw2vwfG28+/ET9UEsWnOdPyVcDCwqbSgVD0sm4Khgnky/yYVKXII53vjFt87v/bM/4ScPlaftyvb8BPVXCZNJ99Pf+S/5B998zbcv/47XP9z5i1dyhCCmTpSN9qhgiXl7I2JyxI127aq9np752ftv+N9+/8bj5YHf/Bs34uJ4bepNCN2LEdQxxb5DHMt+pJF8aXAEZcK8TC7V2WdhHDf8Uoij6e6I7ItK5OM2phmUSQmx5oap/uhTQOigJakhVEaX7EczHEBNadZHISjWw7ntB9sWPGzwdltnVtYnNpUkVw0bOVyBsw4kjOICBGzCVqrOKzPKbCdbp7QMGQryLGm0ZlzaxuEHxxQTC9P+K2WDODiiZUPvZ9+dPbfAx0AJbnU1PVlz5GfzvAvVxyfo0iqb6Wd8u93wMektSQ35vUqpmUyu9byGp2eSbqxrJeVRePZS6eO6pIYmdlatjTF077etQVSOkQzQArUrQXfmrMV9ZB+6ahMNHH1/o7RFGICSYNaqd8jaZ0kkV01L9kVBpge7i2zhCRCtvx8Djym/ULOs6RW+NA+x/Zmcd85prZFqCqWYuhh065h2AcFgjKEaVOBWwV2BTwuD8kRv5AOctfpI1lYVg9eTcLDsBFhgS9aLq+ayUrQ2MObc9dmKpMQrWIM1dMxfM/uBxY6qlNNse4EySxp6+slZwz3ZTAswyjUi1lM9gS3y/vL0ZRXYI3JH7eXs25feaAFP7vME18MLA4WHBOuZr547zoTvxdpbpJ3F7tOj1GBL9aszTYyt7/PX9w4o0Toci8KnicBZk+Y/TmoYWiT7Ebzug9d9ctku9N7YcpznYdk0SGNsGUXcamWrRZrhUii0lBjl9wmhvse8cczJTEPokhpaFdNVvioxmUE64XM2p820STv11PMyDrFRkoVwTCeOGz2Ma+9caxdghIrhaALSMMnKYroK4yI6tMzGxXS6TSdmoT0/8/Tumd4LrXRwk6dSCHwxqzSrtKoEhohQUp12JnNMrAY2IMZMSrXMyvWzCUnf58F42xm3g2Me6VqviW0AxbW5RmjZOc72+ICTEfWmQy3PepQsowM8Z8Nsdk/z68BhS1q2gIyZB4VuQo9J6xe26xWvmoCaFTzBwoWAhEs+toyxQXaSWmIBczDfVJSXS0vWcDZcJb1v4tDGH1Osg5BBso9DzI6uCYoVIe3rQOtNDLkxBr02+TzVhk/PpkySJsGgeuZjMWNiGQrKqNp3gUk9n1PpVYk0rYt9kyyuLWFvCxPrCVL6UdJ0uLF8YGpO3cOnYjqrLvmAk2kkY8Y0n66FNlxJJQQfXz/y8bv3XK8Xrl0R4GpES2qJJTU9kpYqSahWgRMcY2cgoKm4JJhLWmS5bzrG4YiV0Du1hKbzEYyhFKHWKo/vnj4BCrWVZOas9TLDU1JxB/DcZ7JL8gAO5H8Tk2YCD/TBC2OfMA+86qwJd7au5/Xxw4cEJpRcSEQargtoxoxKE1SwpkylZBpgYcUKB2LDRL57z58lUsazQI/wiaWXFB64VV5e3/P07p0KDDvrKgj5uq3LwkpKfCKbvaq9uOSGEUOgVrJ6ZIq4oqrRhCj8vJxqrbQqv6zXl53+/MBKRumlEQE9YF3pngbIRqbKdePwyUPbeNnfqK2zPPN6bbRSdV6v6ZBJ7y6jZMTgqQIdeqscH29cnp8/oV5rAmUJqOuH0P6WBYtxJD14FadzTbPyzFjnJZFyXeIEZTQpBoqKBDdj98nT9SrAySL37KJ8k7LBbHDTlEhL1u9nXhb+sbzwqt79nFNnbdLuhb/mtGNR2Gthhnxz1pDDLKinyemSBSR4qUnKCTgtLy/5VgWR00ZSFlpKYYxDzcgCp5LKXkJAbSuNqrgLSIDFcqiySMCqLwWGFdBZmtM0qt+TGYcK6JrGp6cXWEoEFyOvFOVml2559xiXbeOb9x8YeXccx5FNsKj42pKW90M+r5xoeji9bxzHwH0y58EYI9MlwWzXeRqOp8mnVUPbbfmTyKdpHM7tdafGCz407KnWFGAxVOf0flGYQOn0/B4z8iJyV9R4BKV17JAMxaW913AulnG1/Bp6snesat9tvfL29gbHF7RiHDGx4VhbQFiCtmgAQ1meUoJOy5yMNRAY0FtnIu8QyDAPNM1fkgczpRfJVK3zo1/9Vf7iT77lZ7/4mi+/fIdPZ+4uECVvQrdMBFyfI8FQW81fpBGxFyTamViRd9mltdUuab82TewjQS2L4IvHB1r9yLcvr3zxdKHPStDw8ZbvUXeV44xhdEtZcFVDULFsEirhh1hl1DR2V+R1EcUUvOE1mVYTeSENsLHz5fPG/u233N5f2H6ouuiYAfvkUo09CrEfbKVDhX2XJ1jbxPA1jOFQ/EjwbkqaXAbum8CQ2mC7ENwYMdkS+HR07vSnxvNfvfKLf/2Bdtm4XgOvUK2BD8Y4KL0TFfy4YfsbdukUfwXfCZnlYZmAtgayatw3SH6aqttHqMmC2z5S5hsPz4/8tV//AT/5yb/HH/38Pf/um1f+xR98x+MPLjy9u/J5Ny7zgWiHmPYrFKK6gFM3Yir8oJYAawmUKAOvzcJ2KXyIwR/+/IXP/8XP+BsPT9S/9kx5epQpkzVs+zW+/If/Ff/JN/8T79/+gtuf7vziJgsORx5j1SoPD5/x6gVur7hN3ButQLtc8fHIv/36Pf1ffkPfHvk7v1GJVyiPktQMy+FzmnNXg1tJKbcH2KR18N04difqxHowD2e+HZTrBlHxod+r0YmUsi3wobie9JxL6nJgDo3OyPS9zRozhgCXSB+aHFxTWrLenIbjXvGb07bKpRbG4RhNrOuivgcPDjVN2dz6IkXoNvOSKcHaS+EOVUC/bQKwzHSONLa0Q4Gtw1NUXnfnNuDSc0BDHlElMkp++SSSt3PVPR05uvG84/KumvMT42QXQfpYk0vArFErXK4X9rFzzDxXTMzmamIR1SpZ8G3f2bYtuwp5vJUcZpXEEZYHMEu5kSDC1i4cx6CYJMnDPQHp+98vtRE2mMdKHZU363Ec8qpKcKInA3cNAAmWo/M5gF5DqN7qGS0vsGHqc4VCZKiGmViqx3GcxI/Acb+doMxt6mdvvtQ+KVlMgu6CStSIlAz1nHjI/w/zu7eltq0Cc9Bd3Gx5DqLnmkDKYNAz7XJGYF45dhmDx2JT2QLf7oANZz33SWJcz1XlCJgKJRF6q9RaT18tgqxmdLcQkaxzX4UEtebv1SKSCLn23SmtpSolP0uCaLawBtPd+akdyanEyX45cUiBhO4aBifjf86ViCgmeXNLP1t5VXe7D/zvlP97DTSy5Pesraqpt2/A/j0DSvW3f/u3v9cv+D/+D//9bxeT6VftnVESQ7RF2VTjNn1w2wf7Pnl5fWM/JGp6vF5ovXLtXQW3STtcU6IVC3Eu2ZhUecmMmRHWcyh+fFczPabMpg0heZGF61pnxxjst4NjFbdNTdO2SV7W+0VFU2kqwKuMPSmKQPcRmAfXS2V72Dhc4JTB+XkrcPhkDE0LW620QHTVEBC0j6Fp17XStyvNNjULeRjoQNcUtVunFSU7+JJmDKHq7jmRnC7KSaK9PnPiU40RwW0O9mNnHAfHHCnlQl45l02TsixGWykcPukPD3iFXgtk1CdIhlYivUiSeaZrBm10gALTAp9DxbQFftzS34ZzY0dr2HZlLqCoNHBNrifJ/nEXpaaCNaWceHwCJIIKTZ+wFSzjSsFlKhdx0lcXfRdzjuPGvL2paUrfJly05jEVM/90ebhPbHMtqRb2c5Lhc9IWqyc8Ab7CHC5wcwzKjDTAlhdJ4FwuV5lGU2iXC301hcWStap1UBHYOsdIvbUJNFqoS1HhQTYgVmui6FCqMY+DpU8U0ye4tMYRwf7xxrdf/ZwocH14oKeMcKHshSIQCDXBD9cLrQnMar2rEfdI9gSUqQN+MZTWpXsc6rZvY7JlQsMYSmPEJ26Fx8/f8XC9yjeFZBrVJrp3pMPI9GR9qEieczl3hSYTrknNMkI8kl24QMjhLqNVgznUrD9uF3xOXj++0i/9PpmrhWaFXhtWtCZarcz0sjHE2hLluOXkvBAjmzNLQz7LAsmVQNiCUx5L0STDemUck33f+fKLLyCCrTWBA6FGv+XUxxHYAJKqaM0gKNwqxe5R7kGIMJYJfDIvlAeO2G1kIz249o3XY/Dd1x95/PKZGsZmNdP69EjWXmul0reNqGL5jEjPLtewqvd+XqAtzfw9pWX3iPuUDE2lTUbVFPd2u/H68Y3rF59xrRfdv7ZM0pMmnedFSTaE1X56PEUhmZk6e2PJnoMTSPLglG2tiWzJgkFsmcLbhxe2pwdqa2ylnV97ASklz7oZ6PNkCUwsKrnWDy6p0kqbFF1eYInBCSgJ/9C/rbXiNnl5eSGs0R8fM5qeXHsL0LSzQNHZu5hn+WdFSRN4l75gZKMwUnIbJ1CYjX+yetoql33w4f1Hrs/PFJPHobVldq0/U6rYszIlSAZlVqI1C61VqFpKdy0bMzetCbXW90nsYgYbwRgHLx9fuDw+Yu40U3PU6pXF2F2U/9MHkUiA6S57m3OcQOb0EEgi5DXPd9UskefvGJNjH2JVj4Pb7ZWHpyuX3gUAJbV/Ra1bVqq1SGIhaXQlqNTWVXwHlOtFjQaTcRxq+CNp+3AmlgqAy4hsNGzodPbbTi0Xeq8cOWHXsK1yEGA1fUAMZ6YHl969BVi9kNAzx3HTvRhL4iZWnec9WcjtU/L+QE3gL37+S/Z98OUPn9i2q+r41oFl6B5ZzPqZbAjas9WMyWQMnYvNEJDjir2uRZ6Op3wDS+Z7+iklc23c4KtfvPDwDO+und4rpRrjNWhWZV/gjiXau54IyZyrGaISNol0rWlFdcWcbxokJGtoSfCKhVKTrFBHwXrlqM7rL9+IZ/l9THf8MKwjedsMMZta5CAgsFVDoXpnzJS8RaGUwMqDah+76XPUlsC11h1dTD1FzDfaY+Xj1zsfxxtPzxexGadhZYMysSppFRRKHFjvCRA6Vp61d0pHc+eU3BCY9Sy2EiEwy3pislLojEEL3Yfjwyt//Off8fXrGx/fF37lB43Hdz1TY9Xok+tD8ptGmTqHS4TAO4vkHSZTLN60fop8zb5+f+NqlS+/7NSnK1beiY0Qhm2/yvMX8PDNH/H+JfjqbeCrboqgsykMoxr4gSPZvTw3O7VfOOLg228G8TZ599i5PhY6xqyTZno+h0GhMsLAJpGmixYVrDJrYc7Oz9+/cLxWnt89ctGsmetFjZ/7jptlravLISYco3CEEchfJ9DQSAmXR9bgVZbeBjMHTL4a84AWpkFC0RoYjpjcaQ9XxgMAACAASURBVIgeKA7+mEquS+yAJR0Xq95yvwicHDEQ2Kw6oDYj0tPLqDls0WcaTLzIgqRglArHobM0itiJREvwQgVGaeX8838JFM8lF2jgo+ttndmWDBHVfpG1yFK5lKr+0bNmtxyEqblXTXiyZvNXKRnEc964pBdfDl5ilYg5gCCHOjUHfuekJQd+Wcvrnit5v02xnIz7f2ddcd9j6/uWs5+O3Jkl1R2gvnZ9NrFN47SpaCmPVfp10ztdYFjoTNdwSUzVcjJ70zPMJ5H94hJg6/nojqvIR7dmnyzP6LRMMUvZZ6xJn3zrslbBtI4s7TtO8kfW62ZFg+N1Ro7BkuavF1EyBRrIAbzMwI953PvMHP6vmq22fAdxt/MoFqdHaW3lfP4zB/tryO5zap0nu33Vp2f4Tt7bQUoWjXO9reTpk11mq5NYazvZTqvljfMLJKvpTtA5+7/zP1qUI5ZXZeRwaP2OCCP/zX/93/53fE+/vn9A6Z/8k98+UvM38geIEC1QTBMZCB9j8nabvL0d3G47ZvB0vbLVxqV15hAaXGulr+K7NaJXSpMUzkyo8Tgmb7ed/TYV45sSr2PsHDGkMVwHSqCp4Jzc5mAcIxtssQpKsTPJq9R2HnStNpkCmimYoggdrgZbgYfLRSbFcV+kZAN+hEvmZCn/cOcYojoGjs8heVwt4Pk9CoDRlhbZ7Dw4S5NfhKUHk4I41Lgovty5jckc80ygAnIqI/R03gbH7cbttjNSLvVYN0rrmhR/itpSaH2jNx1YHU2QdZCYqPJzpqwkzmYkrSq5HUKCYzhjihkTQ4X8MkWDgtVG6Vdqv56HmAAtGXyW0LMYh5hFyaOXsVzo4g00tQ13eiuMqiJL28m5hctHCsmuiP+XuXeLte667vt+Y17W2nufc747KYqixItkUbRIyrIsSo5kR3bsxrYUIC7sGGhRBChq9yEFGtdIX6u+FUUfi/YxfXERt8hb0zZ5yEvqooljx7ElUaJESZQokfzu5773WnPO0Ycx5tqH6ENRVA8+BEF+H/nts85ac805xn/8L1Ys67yjTRNDHm2qGmxiGWK0tdhgiB757Gh410A3dfNW3Ut7+gZaW2NS92GZZgP6HEwqDiaKKmmdIArTrhEP1oQsi1eQCUWsSbODdA/SNqxBN8mKp1jQDyQ7WJvLCqNFN9jzitLdhlBtzDQmrZycnrA7O+Nws2adxwXQEm9Ooti0vmhjGEej8gYh5YFxHJ094gw2FQP0xY3pm8l8aqloLZzvJoZoB13ZTezmrTXQDSStOLp5E6p7FqEQLIUsp0QQscJHILSCONOgFxwCZtidkrHRiptu114U275UivlVCNbkpJxJQ+bs8Ynd45zcfNN9c1IiJDMTFPXfFyUppJwM7KbvT84q88O+XhkGaKme5iHGQmnNZv+1MZWJw4NDHp2cst6MrFYjgssrJfgmYBHntg7sMDcZrYMl2N6zFEVq+6niUyOXsIgfWnZ/G3OdIChNGgerAx4/eMQ8NcbDAanN9wA77ER7sIASUrSkmhDNQBmXhaix0lKIBpppc8moLF5Bxprqd8a89FoQS5MT5eL8nGmqHF6/afth6EDOvujo4QoG7elCCcf3JBEz1TXZk92f5gVM1d7cWtPcnK3Tqr3f0SnX56dnXLt506ZrCARjLIQoCwNQ/SL2BqKdsSQQkhe6gSYm6zNvpw6o2fV0jzxUfb0HMy/NwunjM8gj63HlnjyyX0cOpPR9yJ67S2FdXiTYn7FG2e5NaWbQHYLJCZbkSt9jeq3WDU5LnTh+eEwcbV3mnIk5MZfZvZV0Kci7FMFS12ZbxylZg+2AUk8q7VLO5oCMFZJWYNZq+1cpJvVsrXD8+Jg8mqwohsBms6bHKi8R0+reF/T7icvG8pWCzhoCCftpeGvdF8G9uFqltkopM7tpS9PCPDemXWW1OuDawQb85xYJpJyIKZA9zdC2Q6fsi3sf5mzDjrov+KTO1Dp7IW8sIGEvu+jfw6pHMRl7aDTd0hqMm8GAmaDOYuv7pLF9mwrd5B6JbuYZQBNEY+3amYQtJPXaRM3nMDo7m6pLWl5TpVK4nHbcfe8x12/d4nCzsUFIl0N7kd2lJBbyYc8oiJr8TIVWu/dDQ8T88qyItudvE217d6LXc326DbbP3Hu0A1WuHY1scqIGYwUSzfxdxOSfwdeCBHvOUcU5EA1J3vhJQJKD/GoDwpYKHl1ooLYvIInNpGytMg6RR+fnXF4Km81oMz6FgYFVSv7/4WboCaozEmgecGC3v+qENB/GxnOTBhJt+w4zgeQsGAOREPMv0VqMDTjA+Y8rdaWMq+w+Wg4Qi3mbtFC81uiys8kGEvEQZIQwYOljdn29CYZqoM0yNHTBg072GQqRTEiF+3dPufdo4vS8EFjzxK1rrA8riUwPVrH0VkArNQRUzSzXEgV9OYoiMtv6bLZmgwgXc+D0bMvttXBwcw3DdSSMKM5sO/ow18aHpId3eXwq3LsshDhYsx/VmKjB/d16aiR2/gwxMuQ1l9MFDx9vWdO4eSisVhHqSIuV2CLRmzpbj874E/PbimqMZQS2j7ZMl5WDgxWrtRKmjIyVlIdF2l8xXzfzSGl+XgkaZm8iIciIhh0pZ3vmPdXUa8ZIQKua0kIVCcY4agSfLxZEM6XOrDZrVCdqmclxRW0FJLlHVIeQxGTjPuhvtTlLtlrAS8PsGVolkBdGaj+HcJCsTf6+BtvLq1rCpbgnZRMbfjdxdrDv293WoSkL+zsQ9rWD7BM5WWpkluHxPuagOUASmOdCKZUe064o0eu7PiDp0mxgYbzLAqZ7/SOQUuR9X7I/v/o+F4MpB4KDCuZjLS6zdjCqD5L9TO/DwtAZ783BFweT9tJzRXoK2wLGGMMSP8uCy/gkuV2K4h68LD9rc2sU+/bq93WfdBdcjbAHe9RDXm19pM4cD5nJlRDiUi3V5qFXkGOi+lNp/v2W+vXqsCzsja5LKX52s9wjwYZTZq+B7/EKKia6iwY8dcCsqfk/uYid7p0V3CKnLyhVpYq9f63ta6o+yN1Lyrx2xAHeXjLJvnYwJrjtY90LV0RMWqgLdLt8X/MX9RoMG4bgtgD+Q1DF1qMNznvNFeksrsW6hz6WsyutzQZoU1Mawh/8VQaU/qf/8Q+/utMKTZbpVExmrFzVvIV2U2G7nbm4OGeedwx5ZDUOrPJglOSmhCExZgc3EJKkZeo5iJADIAbU7Go1jx46E6dR52rTkdpAo+sHcYlZN0wu1mj2IjMPdtiGQEyJlDNyxVk/BpvypRRYDYNLzhqrcWAOmLyvb3xiEzTchHlFWJoeamN209BaLJY2p8EkUEnJMTOGyCa6uVprtjjBASaj0oqy+Ag1US8OjZHVmhlqmsY3sMA8zaOHp5l5LktBPQ4DecgegSjL/48Ikg1giz79btWM0Zrflx7fvmhaLarEY5axNLbSWUHQtDI3ew5mtRGpIsgwkPOKPhkVuu+ET2qbmYHGkC29BsA3BB/8U8U2jBzNrDxgxWvwJjgGWweqQlQMaZ8LdbdjCIGYB+JoBrE2tXZzvNqMZRFgrmYijTdj0Td0Xc4AZyp4Q9oAqco8mbRkplEolmBlD5UhZ6a5EoYVOSei9sI7eIyrPwtvomnG5A2dFRD2TaClHyVCzDZVbm1/YHgqX7Bl46BjZdLKbrfj4d37bNYrxvV68WABA3VSCBaHLNYUroaBLqYIQzIjxWQpK7jRKgo2hLeCobrp+3Y275JhNUKrzMUjij3e9satm6Rx8HSyuEjtlpQdVX8HFJMgmJ9K5zVY0WUHWK2zreamtHk2/bE4i9Ipz13uNK7XTNPM+ckpw3ptwGaKpquOzjhyfxgDeMTp7uINtzOYkhXaPULd1oWz1mo1Bk6zSHsR2FVL69nNE3kcmBtsL7bcuHG0b849TajiUxg/uZapiz+tEJwhJU7blT3ToamYpEPFab3Q8RythVYLsTUSAjFy7927iLM1pXYhfWd7spi455SM9WEn6fIOKOyb8lIotZHGgUW+giy09tQLMH8eWoyBsL3YIes1q7U37ZiMsDPHQgi2FnydhmhBCqp7EB5nDS4gAp3S3Pc5u9jOmNzr8yHkxPH5GXFWDo+uWTGUbEDRzcFFWEAbfM8VDCSZ3ddO/ZwprSy0dHF2Rms+dVvuiu9tfmGd6HN5fEkcR/IwWKMgpo3vckLHpHziZV9Nm6UpdlltiL4vGvNpbhXzNrCfPToQYbijTYcT1mynJMzzxMP7j1hv1oQopJSN2XsVuHHZgOAFlBdZSxEmJmWi/54ZgTh7rUvmbd+yJCJrtGurlo5ZKycn57SQSGkgjwYqpeSgv9q96QBSBx1BLKCh6QLCqho7skzFC/Fggw/pMdNQit2PWmdaKSahbRMXF1tCzFy7frgYioJ5KQ55sGtapry+O3khHASi6MJCAaW1mabWnCzMSPe+6u+4ScYMeDVSY4VRmS6UvFmRcibWaJ4oLmtFMc+R4HK5AFMzwFKnYrJ2MZC71IU7RMDqgeZeZxocOHF/G0SJ0ZiGIcGju49Iw4qbt64zpExwVrd6/aL0s8CK6RiCAXkO3pgMFlSK131xWR9m8ApBPXpbGo3ZQQYzHc4r4fE88ejRlvFwwzDAajCJhWi1eqg3XMlIPtQuQ3Z/C99jbM1a0xty8t8zwMZzQ8m9wS3G4EMipQl1gNiE7clEXAViNHlCbI2Y+/DBGhYDUnv8fLCHU5UQkkvdAiE1csgIdrYau9Ob4BAWYCcQCLWxmy8JcyCtIzLNnD1WDq6vzTsquGw2KkImDiPqQxlJkRCyMYPCCmRFT5OUfl86WA2odEtZQAarlymgOyiFQCXkRFbh7R+dcX+75fx84sZR5M76kGFofnY6aGARKnZWBS+qXCJsqUTd+iEAiVQrmUIIyvlFIZxXbl0PpOsR0iEi2T5TI+GJD3Njfgt9fMrdrXBezLdHW0U0kEIkkZgRNzyvvn9EhiEgeWC32/GjhzOHeeDwqHKYAhlLU0NcUug7sAU/2PsL1d5FzRxfnnF2CZvDxOHBQBjMi2udQKNCaSQNaCxIUMYo5GBrLcZAGK3+TLEhIRO1ksX3EHEPTW/exY16SxA/q632y9FYsgUPSKjF6sqUneGYnZEUfVjtZ0tQ93ixkKLaqiXDBa8txIEkNVmVw2sOtJvrY8MA3oinR7dig+9dZRzj0kt4pYcIVkfFLrX2Yb9adW3zGkHcG84sPtSZSvsCZ2G89P3XWTWz16T0esR7tg7K2FDSeSuGEDlTEAdLTEoriHnRiaduoSyBKdg9NLZl8IFJWE76/dyv+XMLy5lufZsuqohlVrS8hrJcm7GUzQsqBKvzLG1ObQ9VXXwrpddKXicbY8qemX2DtgxWaqvvY/70urGoncmdUYVY7dH9lESvnPl0obwRCEwc4aye3qM224Pt2DP5fz9nhCvpwLAf/PWaRsTr6V70eY2mFjJl/Yx4+If/EF6DgXpIja1NFxISgg8cxbwjg4A00CimKOiAkuoCDHXp/sIwQt63Zy4ApQ/Q/GQ1RnztQ6eOqdl1dGl8ddA6ikltLd24y/1YiATL91n6g27sLdbHABlj2v7Bf/r3/+oCSn/4j/7wqzFknwyDePLJVIoZWJbC5XbH5W5LrSaxGganY4dIyImD9cg4DKhPtI29YzrGDtKVanRvrc3+7hegylx2iCjztAWEyRv5VkxvWGpj2wqtmSl3cAlIiJGUsrGREKMDAiIZGcw7JYbAalxBEDO6tlGBMRSwJj+nYTEvi/afUQnGTiqFMu+gmgSutEZKAykPDGNikwdyTqTBUkiCF2Ldt2UxCl2ayGYFTi20Yo2qqC2aqpVxGBzFtI2stMblbkfZ7ijzjCCsVyN5HDwlqGPfAWikYfAiq11hDVmiAxjq2l/A/s5IMBaaiFgUuutLm4jJkmbfgJIY5VoCabUiZtcru/68um8Raql/rdgmXdQ21xj74Wn/lGTFTsamijWYCbFi3kVRFW0TpVocPSh1noziLxCyGasOvvFPrdBKIWMHefN9obQGPrmJXvBK9+oQFtZS96mYS2E7TQu4mKL9XqCBRDtEPYktr6whirgUwA+9lHv8dD/Yg5mb+vqzQyKYvA4DNzR5YaoWxTzT9ky9ELwoN1Bg1srpuw8IKqwO1r5RG1gQg3uW5QyrTIrBWYPWqOU8GEMlRLIkKrMztQK1dSaO7bdzKYsBdIqJUJWLaWeHWTN2wsHBAZvrB4goMXkjqH0qsD8AcR+t5pK4HuFaHKhTtXSeMluxNLdqG3A311eftgRjSKX1wJASjx4e2xRkjEjtzbw9h86Oij7ZDi5H7Nr92IEc3+z36VfBvZIqdZ7dWN8KHWNNicV/C5Az221hIDBuVmak7Swkk5V6k4wDZyF4UW/XY6a9AcjGhPEirPmUKMZkiX9U905SZxk2KOZjFVNiKoWTR8ccXDvCDDi8+cGuWVtbZGUp2jTTUiT2E6Uc9jmf0zyzHgaGPCz+RV4K+Tr2/09nO8Srza/OT85IR0es12vbHryAtCbQeUDuf2UAlhsiCjZRFVjklsHeqVJ7CICBSKhPcvpUJ3iRgTG7dpc7QhPGwyP7/MDicRHUaPlmhi8elGAMSpuGmVwtekEurS6eEUtxGXzyqSxlpiVFKmbcYgXbyfEFeb1iWK/s2flfixmj7mnQSyPeXNKjLluI0UIXMG83rZ0rYnsiYmBjFXsfO+AXQ2AYErvdlrPjC8aDNSkGVqu1nU/SwWufMDroqs0kd6VZqEFO2c5pPwfwAiqI+fdEiXa20vdSS+KhMx1p0Aq74zMAVgcrhnFgyJbe1ao3EGLTaGOPhuXMCOzBmtrP4VJpxSeJ6nI59mkwXY5gjFUzY1UC28uZ1go3n7ixFI5RlHHIDCk72HF14uyQoRfighKCoq0wBKG1rb0XEqjFwG9t5tUoPmUP0SwDtBYzx61CXmWmy0tkPGSMGXGAKhMsch1jlgpizAevScBkdlrMf05x+bIooVpD0cLkEmNAbEgTmoGOYgRgag1oilycnHNycc7tDzzJKg92YMbgLDl/13EAqQPtCNpcOh4qUS3Biu4NJsZGV+33oFpR3wAf5vT6UUjUWLj33hZC5OjayMZ9vroE1xiM1urGYNYJfR+1ppx9Y+RpvzE2cjIZfdNIqEY5CilgKfLeVAeY2pZQYLguTJc7Ls4b4zqRMGAojJE8emPVfNDgtUWIgSEZGyAFk1qkQcij+d/YO1Et2r0JWhLSgk/Hlws3mVUNSMyEm8r2wcRUhPFgNPlcFFpIII2QB2TwObYIpIToDBT28ra2MBxb5xXI4M8HzCjdvT5UoV4SuETrbOydA4Ft4b33zjieJvQy8cSNFYergTAUQsvLfQgOBgczCTNLBxqikTbbJhlLZ6kWhjgSYkGq8uB8JraZ2zcgbzZIPLL1EwTamvFDH+T6yRtcnG15cD6zm3BmlAO7Ukk+DJ2L1RMEby8HS9ut54WHxzuu5cDRgTAmY4dFZzkZ6NhTyoJ7PgpBzZT3+KxwcTZx7cbIYYqMKTIOI+dTcUBRqa2wChkRYwtLBA3miyYEBnFfPbFhShL8+Slz8D01dDaquNzYgZrg8fLuwypUSi00IjEY+Ncwb03xhSmYbM+U4laH9YZ+bmZqHoLaPqt2dmgHb9Xvg9gZ0aTuB1k6+bUFWovGck22v0jDB8vGWmq9yxZodUZbQdtMw8MEWl1QjKugi+Dy+gW88XpJTIEQU3SLjMbgARm2FyphwU/NvN+2TKvNuQKWtFatvl6k9P4eOAu7Xfl3+zwHjsXeqC6N2svBDSRJKV1hG3ewZQ/Q9NrK/t+4//nEwDXBpXXBFAN9zCQuvxcMuOmDlz3zp99D78HwM0OVnmBtQwA7axPBh/bGgMpBaJFFumbDNKu/Wgf3unyrVx59Iq+A7Ifg6kNm+tDMn90i6YthAVIWcKzaPe6Ao6X54Z63dRmm7seeV59l/7UDU2r3OWL3ULFEd/FBkfjzsb5LFzBTED+v8H1S/GwRB8mCy737e8qVvt6uqwONRtzYs40WBY32IeReameoQFtwRnWbg4AwBKuTTeZq3la//1eZofSP/+gffRUNpDi4HNtkaXVWyqxsLwvT1hKYhiExZANUhmGwSd/BIWkMtDpbrGBVckwMw2CNqzTQap/pptZFJ9vcenMgPeo3mFdLEHqse1NdjI7NfNbQbgOSIjlnoy8630GjsF5lp8kPxDQwOeLbN/Iu7RLRKxuJtXG2iHpyAExlpszV9bvGRloNK4YxGQMkJsY8kIbsL2MmpK5FjiRxo9HWQMuiAw7NFmKPQdZgLw/OarAJubCdJ8rlbolQHMYVw2plZuo4GyN4ssiYkGgb7JAjIWamUmkpefpPl9jsX4YmDapL8brsowHSLNWizGho5JgtDS4E4jia/04UYpLlEOs0/SSyj/tumIm0R50LDvBgG5JWZXZwRSS65MtSDErbUSu0YCiilgnmAm1nQIsCTRfUXYptqmmw1IVetOVoBpFmFKdLc9/UKLtm7lmNUVVm5u3WJAJipn+1qsm0QmJcraGa19dwsF4axJyyaYtD2Etm8K2vqSVdBdwjR+y6SzE6owNcIEirSyy4NF0+q9ZC9fdlroXHDx8xX1xyeHRAGgw0itHWhHkBQRqys5vawlYzeahNddX9TUJ3DlRPQ1TbMCu6UGmHFMkhMlcrBJsDGYTAjdu3GIfR6OfVWRw5ICkgHtNr+uGZqAW8+OvG1EHcBFBNVpPE7unslOkKbKeZEO2ZRvebunF4xKPtBfXsnLRek2M0fyVHsSVHxpQX0MFAGTXvi8DSjEqKDCkvCYApZmatNC3u89bcXNnWaG3VDPMlkrOxsi4en7C5tiHFREoO1vjUKrr0sE8gjHmi5kuSrImrwUBxxJg4UaF0Y+4kru2vy2TDwNVqjVGEcbPi7NElZ2Umry11D7V11xzIA/N4U3B5jx/g2qNg8X0oMpUZCWIJZSJLSqadf/152nNopRFqgzaju8K8baxuXscsVMRM4n1/71Jfa++dspwjLagbLEcrYl32I2LrMHf6Ct7gapcV2d7QvJDSpoQhc/ro1MD59UhryiBmxq9igK16MlTonyvQ03VoaixN22GX4jW6tNaKHaVi7MHOS+ry6e5jgDTOj8+Jmw0JA8qjv+dNWKjxi4+SA4O12s/hyhgbVjhYJV51LITPXsx4AdfBLVurmNyJmZPHjxlWG2tAhsRqGB3YAu0N1BVGTtVKbTPd46rVxrybzbcmiu91EIP53ZXZgOLWnF2I2M+BAXMtKMcnF6S0Yn1tZb6CaXDjUpMT42Bn9HSW5uejMaYzIaixHUSobTKjT4ScxGUvnT3hkkR//1opJt9u1eRvBW5cu0FOgzEHkjf1EszPphecolf+hkaj1C4xZHmXazUz6KY20LAzvgDmvdOTNUsxmfc87zgcIm27o8VAXo+EMFqDlYIzMhUkEiTa2axqNYM/4FaNLalBLFZblTSMKJPdB4y5ImrPr4qiwUC+/rNFSWxL4d4PH3Ht9jUOD1aIJrJXxYo1I7G5b0b037OnRMMZEUGuyOEbgiWDihaQ0K15DayjR1kLQSJZCqqZx2eXPD7dcjgcsN4kQjLvSU3GZlERrwGtoWxcGVrgqbgSELIrnwqRRgyDsXn8PorqIoEwbzoQDcxzY5SBvIHHD7YwmO9aGiI5BnLOpGjSd2nZ5IZiMuKQrO5rqrTQGNJAGipNPOlVKwYhQcwZkrEXRRow4+WNARBNyGvIEjn+0RnhMJJHHzpJcIZ4JuQAeWVNDsZ6ol3a7hjWiAxoPQEmAxZkXK4BByts0yi2ZqRgPIVKaI3UAmFVOb/3iB+ebDm5mBhC5s6tyLi2WlAcsKAnDGvcf34zD01xYNP4SVa/m4l0REJhro2z48YqNQ5uQhpHJK6A7ADmDVYfvMGNe9/hZFd5fDazVZY9G5wBbtQEWuk+XYGoibQaqSFy/PiUx+cz1wYY140k2WA2tXe0qifHFaE0pVRj3+20cXxyBqfCsBJ/1o0YK1Ky1XAYeGIKAguQsbmqgf+iFibQ02lLK+xqQhtcykTUQC3GjphaNZRTWdJlxX/dpLFrSmvmJ2UqeKFFE2LTCo3sjCIzzHdBBE2t5qg1WqJtmw3IDytowdax+y261T6EwFwqpdgQvDNVjfxk9ci2FEoxD6UQLZURcdYsyb1/ZowBpgtjozXrj9RrrAUQEZa6yRQdzZUcwmLu7ZtgqTbwUN+rRGRJaTPvR1vitdU9U2dhnvhgp5mPryV59e9h1gvFw2Cas43Uh0Q9zEUI3rdCV46UYsE3odlgoTYPI1jAKfv3qp4Q531YKfNiGt0HSSg2/DJNub3bDgJFsaGjMYv6f+/1ioFStdr3il7n0sESbHhanTnqnYf9dweogpgszZiROHDTb50sPpR2BKp7ItvZ0Lo9g+wlZj1hGq+DO1ij4mQT3GbE70/w+xvUwJZO+MAH9GD9gmsR6TiTgTTOQkMX1nXOzkC8AkZ1D8/m6zKo0kNnvIShF2Fdxll92Idd3TL07ESJzlQzX2YDa8U/R3zYhAjayRWyKOuWz0S6jsR/LMHJOFaj/P5PkKH0E095axK8ILeGoTaYS2OeC7tpxzzN/oIZOj4MZl6bU2Yc1+bJ0mwq1KZiLIbU9Yq2WRf3H6q1uvkwgC4LJLBPELrqmVGDMxp0v+Go68hVEjUaKycHjEEggTRmaojkZCbZFuFrmvMu6VFfeeaZ0rXC/nt45KEXoPNsBtitNvI4GD09O/PB6Ydk25A1BHZSGEIyjkGw5KZ52qHVFmrpqydY86RgtHmcuaPukVEbdZ7Znp8zTcYAGIaBMGQKAWrxZDCfFOfkdjuVmCLb2pBWPeJaF/8dAbTZvbZJbH93TCbRDSfrbkLa7FpyZ52IGMU6JaPj4xuGH1r4FKShaC0I8Qob80qif9qHRAAAIABJREFUgxiyj6p5rzg9Pmtj3u1Mp00h4lrzuSK6Q+cJ6mwUUQ3E7D4GvuFaDW5gXtHu9bRH1Od5XqYdfQdq2D2bS6HNzrTwjWVWXbwYmsIqZ2orzLuZ1cGG2IQqFhgjYlKbHgfZv4V48VNac8NC+3U3A+9SwwSWGOZsA6G5nNE+qIogpbGrje3FJdvjM9brkWE1LM1g026ga02ftkZRZZ0TItEYIsv3UmoQ5u6r48CR2B8GVRIBdzkCAmfzhJRqTMKcaHNlc+sWw3pjWvVmcdIpBjcoN9NJ9cKNpjZx9newVjvEmmBNws4MuiUG2mwMKJKh+jEIqSoazWNkM6zYiiJnE0MelsN9zNlM/aMlK1Y/0EI0GaWgEAO5/zoIIZv3m4E1wdYO6l753kJJT8ix9zilaCBlCByfnBFzIKziIjHsBod7IZP9jAt4HSMhRaZqBzYOIqcA4uycgBWdUm1qQ2cFKMaeC9bk5ihQGycnj1mvRzMVFyHmRFWlx+r2L/PZMpp3qcUlVeIMTdlHfwcD2Jqz1Lzs2E/vvBBvVVkMMktFRpMZa7PDvlYrsuyMsfuG33u8iGooookozeUBaZHpRTVJU4xhmf6ZcbP5kcxal/M/BTMJb9OMHBwQVMkhsPM0ndjUqP3BCdbePDcv+gRba0GsyGiiPqVzEK035IoXdc7IZb9ObCxsZ9euFpN7R3GJfS+qvciUK0WuM0W7r1s/J7RUNLmMRTvh2phl3VsCL7UVxbt525PEDJ3DIoe1varWyiJBQIh+Zim6xNg3VaIzbIoWQuxT0S4LwH05vKiudoqkZOdAUZMK1lpdVq6UNlPmZv4nmHSoOZCtyMJK8W7CYuNDRXK15rXh12umtwLMc3Vbs7JMjGm6NBFN3L8gJYZx4PLsjLlOjDLQWiVhe4U284IRhOCs1yBqUuim5nlDo5bke7kXpV3K2UElu5hFCgreSNDZmMq2FWSTmXYGPqnu3O/M6qKUok9FDdDGAR1RoWLnb5krIQhrf9dMhpUMwHGmnHjlb94QPqoSj84OlSduXeOtAe69e487t6+zisG2ljQs9VENELR6NLoxlNXPp4aZrEYxhrVNp9XYKC0izWSaAdtLmxisoeqsKpR1LnzgyQ3H377L/YcX3D5SxjQSAoySbc/XRlVrgiu2fkTsuccg3sBVkzKFyE5BS2AIgEaqeoCdN1EpZp9gKykZazGosl4fcONw5uRhYfXkQBbIg6VcIoGUPUVpsQaolNhIg6V46WVjCs2k+AkQix03c3RjxRjCCk13xuL2tUlStMzIRWS40zi8Cxd3J1b5CMmNSEUlmbfZPJJCQ6LVqkGUFjJSH6ElEtNT9gzmh4R4DWKwN93rtN5Q23h1JIZrkAttmIjjxHqTePrWDV7+mWd458EP+N6jM/78rbtcuy68ehi4c6Tk2QDlEGckJILONLc0kapkrUySgIKE4u98QUpCQkVzZAjK6fHEd75xn81h5AMvb8jXNqhHR4kEOPokH/7Su/zC7p9zvpv55r3KRcugQpFGbWLg9BouqzLVQtOJhhLDQFgd8PDilPv3z/nxu4lPPJN54vbAOJrkO3WWgA8KFvadKruqHD+65OJMOXhnZB0s2Tdn2ydqmwxAoqLN3kORYHs8zY2rHdSpySLgqZRmAJgKzD0BS7tfqKCdnaQmM9IQFkuGWg30LqpoMUAzBphq8zCxtNSwVlcEQussTZam3QYKJ0hvvDU4k9daW6piGX2e1tXslKl1BgmUamf3dm7uGVMd5+sBQCyAy9I0++/XJr6XOHgg7vPVlTJOMlBsyGNlsPso+ldrzZL36rzI4a4W37KwlfZMGfV6KEh0gJvle3dpk9Uye++x6tYBnZXUWd7yvu+mixdgq37ddFAlLKBHEPcdE69YnPVkKbU99CL4cKYnlFrt5FWKXZfXpcXrgVoc8GvOpvH+pgdeiFjSbei9t1cMgDPRxM0gw3Kv+pfZXqgDbvv6qzPMhd6jy5W/WeqEGAOf/exL/Nt/+xcMMfHCxz7Kt9/8DpeXBoCzx8Pwx7wM58CGYk07brA8fPuHugSt91raDJgPYveCPcNt+eNeg3c6W2fP986tf15rxn4sfpaLDzWC/7k+dul7BfC+76OqlrAbdAHTeu+nnQXduqW41+VXQD/7TVcU9Wei7/uv/7+/fvIeSv/zP/6q9ClhbUxzZZoq0zyz2822YIJNhYYhE2MmxYEhrTyO3YqL1tSK0tiZJmFBf6dposxleZnxaUvfBKTTPD3hSp2K3EpxRJDFkZ4YzGsn2Z/LTumOOZmXTs4QE4NHG1sj0EjNp7fSU0FswQh9A/EH1izmvUwz8zSzmyfT8edEHkaGPFjaRTLgK0UTm3l2yeKVYqwL802Y3SDUKHDuURBdUoelNNTZi/imzNPEbpo5Oz2jTBN1LoxDJo8jISVrTPrmoc0ZLeJtoL0YgWiFdPCUF99keoGJWnMc/BkZCl+90agekW4gXkiB1oSY7B5EMQNBY/e4JKtZkdTZN+aybx1A1+suaUqeqBSSydIkGB3f5jzqIIBQZ0P3A9DmiTLtjP6NpwCJLFMF3zmMsSVmgCzS5TQsL7P9ORagxqQvbjzdevOwZyBoM4mmgUXCNDfiMPg02NatTS+dKt28WfSnkXwdxpSWQ7y2ugBsrQNfImZq6AW/Iku6njWAjak0ttPE47v3SCGwOjxYwKTQ6Zw4Dd0boxwiEkwWEcVMmM28DnoSRBCMAdMbalxygS4IfdWGzMYKSClRVRhXa27cusEwrEwuVw2tz8Ngh3swMDJi+4v2JhEQ3+zFUfqplkUXbxMWa6qiM2yaqpmhOi17XA+cTZdM5zvieiDFSBzMXynFwJDTYv6LsxxM5pUZxoFxXEFKkLJr/XEwIPre0Gy99ncW2e9TwUARxcDAi9MzNtePGNYrk9qFPVChahPxhfpqi5FO8w7JIpFFIkFNWmWP0Jo5FQMZtJblfTT2oE08EYuenXYzjx8fsz7c0KobGjqNonpqZv/Kgyfh+b6XQ1oO5p6MUtWYPj0O3a968YZZihX/TDMOTmwvzojjmvVqY6Ws7GVqvcGnF2RePAli71hw5otLZ1VsFkltZtCLOHvD9k28+TYdfXNQ3qTN56enHF478prNG35/Dvb9nfEqfXpn1xj8Zwr+37uvjZ9aLol04Aibau1p6XtKOxhQcHpywXB0aB5xQE9vi4tppl45k7zg8LUmISDRXSj8eTXMZ0/dh6Bv50n28j+R/bWEEJEIpw8viMNIzsmZpdbkN/dAyNb90oLtoz25riN10s+M0A1EwyLnVV/LXZ6xmHurUutMacVCNbY7tlNhNYyEYPuHdJmedg8Ea2/6uhJwz6fkfks2ma+lUudKKdUp9OogY1xAL7B7UuaZaTdZsEKrnJ1dcnC4YXNwAK0uUdQd+BeXifb1aebWxmoJPgTqcnI7gwyoluAsS610001Zhhpq6aylGqApShqEtisM44gGIcbMEloX+mDB3mERA6BbM2+FfmYgwionrFPwAjz42vYzsE/WF1NUX/KlzAQiD88uOX5wyhNP3WI1Gpu3S2WthrJPDrJvNvrZamvFfYlq8SmtoGqMF9HuQaKLDK8z6mIAgq3/HCMnZ4WTsy3XDzKrIZIyhKjGkIqBqo1dKUgILlNVn1ib0CZ4LdHBTm3VwVVbu8n9DLqpt/r3zwnUyL2kQZABysOZOijjJjJmIasZy8bYKC0gGu3zmoU3xGTMOquvuj+YAskGfxoJ4nugKiLFm/aKVnuO4rVJnRpREmEt7B5UJmaGlTXgKQmSXRrnwyuTBxVCSOCR9BIyhAHRCWEHkv02dAN88caqAjOIJ5FJxQy9K61OSEts50vu3ps4vwxcXgRurFfcPNqQYwfZZZ8Opr57qi4+MkEsqKWGmUgktG5S3v0BKydnlbotHB1VxsORkNcQVoisERocfYRbm8fkhw94vJ14cCHUYO9eFgNa1Z9pq8Uk4RiYnIfI2ckxVDjfzYQ5sF7DuBLW+YBxvWaeLWgoxMQwHoC6TEga24sdbVLyRoiSTH4ZlRDqlSbbpPPSWa6+eZkcUBAGq8V9UBCC7bVjWHNwdJ3d7sxDhDw9TaDH7CgwiycvqtlyxJg4unmTXZkpHrCzWo8cHl5nu5t8FmEHpauclmEWbgEONsw7un6DlAfmuvNnh8MX4inQ9u92Zpuq0ewjrH4j2GDbBhzRQSUbFHulsABBIQSuXb9FT/heBhRLOw8EcWZHP4nZ1wtX/z34rzt4pY1xGLl58ybnF2d+HQb8L6CFirORvBRpfq3/j0Y97JEBPxuW2s2r48PDI3IeKKVydHSN1WrNtPNAo0Vd4OCDD1hZngPcuH7DBsEekrS/Fw52Lft/eN+lXDUdX/Zj9mfCniywB4aawHq15uaNm2y3Oz8H+r3w56T7Wklw9qEPPw1Q8nNB+2DLVmeA5Vpv3bwFqpS691scx5H/5r/6r/nW69/k+edf4D/7/T/gu2++yb279zg4OOTGjeugjVIr69Wap554kubS9kZnQ8ly3oU+4LtyPqaYuHH9Oq2ZCiqlzJ1bt7m43DGkyJ07d7jcbq+8A7bO1qs1m82G3Tw5COsgtkvgbHl3JqzQPU4N/Nnf+816zVMf+AC3bt7i6OiIFAJlnpdhYydT2PvgK1r3z9EuZ1/D9XvX1zrLmwD/+T/4B/8lP6Gvnzig9Ed/9EdfrbWwmybmubGbG7vd5KZmbjScovkEpUBMIxoiOQ7EZBPb2SdlfWJnQFtzQMkXhk88W63WIBpubnSz1ojJ5DmlVaZSmOrstG4DLUSc+h6Cm233TSWhcYDBImez2sEcxBqNqoU+FJJOP5NOHez7hBVErVaT5JXZzMjLRBUl5sRmvXY/kbQwHDqbyibe7nUSAqFZ4T+XGVy6gDidDSvip1rco8IkZ1oNGJqnmToXLi7OafOMVmVcrRjc96Yf1B25jsF01N3oy4BZMyUzfYszPLwj6E1TFIFSLMmp9c+EqoXSygJ4pGyGqCknUl7RPZQWDE6BDo7AotWtOGLcX6ZgnlK+I1oj1tP5pCPw/eQTtOKpgVDmQtleLCh6Bwn0SnMYYkBytLSeaJvCmAe6Jr4n0Fl0sL6vYTLz2OaAZwc2zJAaNVlMzpntbscQR8bVaFKEIKxzYhwGUkpXNtalRwW/hyyNtSPQqozj6JsiS9MmyAJWCCbTLFWZa+Vy2nH24CFl2nFweEhMef++iUklAlbckXwi0RsscPBTvMn2BDpVN3C3X4foR7s36yhm+KmNeZ7IeaRia/nw+jVW48p/JtlPT1y6qT7lt7+MKaG10BV2VT0dxx6C33cDdKvunxXFaDwG4NhhIVk4e3QCMZFzMvlegBQSY8zkPDqLMBrYnBMEW8N5HNGQIGQ0pkWSYV4vHnG9tLRW1FmCiYBLPTSYueDl+TlzUw5vXWclVqR1Tzd/rAs7oTN+FJs6hmBG9D2rKCYr5Eo1iaK670r1dDzpVNhmXjqIGSyPqzX3Hx4z0Qy4LI0hJaInBgbdT+jAUi+SvxO9ATOJrSXTdWP7rjdfaLr+qPTqftH6k59tont5Sd4cMWbzUekMPQPtw/4d9z2oT8kQl2OKS8qaOosIl0jIAtRW7WxPT4HpBUI1CdBu2rE7v+Tw2nU3W3d2aq8BlCsALK7PN2YOantJU6yBDy4ldU+Y5hKuwL6Zor/Xfh3mgQC1zJyfXXJw7YgkBi53AGwpjr3Jx/+9XQGYgrGfF080k3Psgac9gCS2J4vQqfXixW+QRIrC8cNjhvWalBLDkJciyRIv/QzxtBcNe1BtATOCeRkh6t4+Cpjx51Kce9qPFaXme1XKjlLMw+Vyu6VMxvSNIZBTJOe4DAz7B0nfo+wnsL01dDDLgPlSZlSr+bEFWe6jSd2NHdghFq2VsrM0NlU4Oz0jxpFrN64xiBlylz5hVQfylvrViroOKHfAy86/KwWioUp0uj3ONkb7FNUCRmozJvMgmTEn6m5HjBtn/oalhUzJQhrkyk4Sohk+45Je1UYrldTTbVtbzFh7Upv6u9bBrQ6Qd5mEVtg25f7bDzg82nB4bW1sHHod5SANex+6/u7GYOyHirP45snf00bT2fZLMbC5s51s6zcPvYSDVBoZJXGOcvfxGTkPbAaAQsRNf8WaH5q6TDG7DIJlb+1sO2seKxEovo4z3tDF5GeigClYDchOgRCyn8uZpju2xxXxYQUiVApIdAtqA5EkgRnpJlIOSBaTqsdGkDUSyvv2XsHuqX1jX+teE5hcM5kX3a4wHGRqLOzuKbpqjDm6j3MAiocyTGgpyNwl6BVha9LekJGwpunFMhEX6UEMnUUxY5xU7J8SsIRAS7jVEgk5sD0+58HxxKPdJVoy126OHB2YPBllD4A6i8UIN9GY5mqn6ODnavOa0IYNiobCtimn50qelGtHQj5cIXGDSrZrbhG5/RFu6nvIyUMebBuXO+f1u0eO+LthYMWM9cONIIGz0xNaAq3K+eWOVpXrhyte/fQXeOGln+PHP/wWtMatJ5/hYy//PGW65PT8Aa3B7qIwzbDeBB9SVUIc3Ozevre6VMyMlW3zDqF7IypoMNCfSA3iQF7mg888zy/+O3+b1//iTwyY7yei14Dda2b5y9/nDzz1IX773/+Peeft73F6fswQIq/8zGv8wi/+Gm+9+TWm3aUNXSQsKiEf75pvjNq+ThC+/JXf4plnP8Ibb7y+X5/CXpmgiiVxiQNLBkrWWkkhocyImMynqVuV2FvLIq30Pag15e/+3f+QBw/vc3x8TGd6LEnIXk2w31mXf+4lVEvJsJzj1c+9l19+hd/5nd/hj/+PP14GpH14tt+b7av5Z9h5zHIOI93v0T7fah+39e/gEvCbv/mbPPnkk7z99tt86Utf4oUXnufb3/6O/TmXQVnZIfs6wT9XgvCVL3+FlBL3792nOKDUHAzy4wTfJPrmwWLS429wP3u6hEv6nu9/oLVKE7P7+NSrr/KVL3+F119/ne1uu8fHrnzWwmwTZ3wqyxCO4IMs6ef//pmJA26/97v/EY+PH3P/4X27d6p8+tVXefXVT/H5z3+ez33ucwjw5pvf5fz8nF/7m3+TL3/5N3jw4D6np6f8+q//Gn/nt3+L3W7L93/wQyMZOOjXK4Llneg1KfDBpz/Ib//Wb/Ho8SMePHjIx3/q4/ze7/4u/+KP/5hbt2/xe7/7u/zrP/1Tdrud199KzpnXPvsaX/hrf43vff/7XG63CzbQP9hA2bZfGvA+tU2/rNc++1n+k7/39/jVX/kVvvjFL/Lcs8/x43d+zOOTE/PV9FpCvQ69sgz9Aej+McseROpf4crP+pMElML/+//y/+2rlNlYNLVxuZu5uNyabAFLKMvZCj/zLjIqY055McqtVS3eHLzAa57E0ov9fXxuJ1P6W2YTO58kVG1M88y02zHttmYMbJWCmW97lHWKiRCyTVVTImVBktGoxTezLN6UNyv+k0in7bx/YahJ0EozT5x5NqPsUmZml2mklDlcb1gPA9mToGKI7pUjzBpMgubNEO4D02YDp/qGrLWxc9bXxW7L5XbHbrdj2k3UYoXxtN2xu9xysbtkni0OOA/ZAAnfbJbJQQcCYmZwbX3ONtG25A4lImZiLrKP2o5YQVyLSetc2y0oWqv56Tj1EhGmqmiMxGFl0hgJy/PsYEHRRkGNcSWdmIm/cAYwhA5m9EbVi0NpesUwGNMM165lbTZZniZSFEIKJElIiG6q7aV2MAP2lJOxQYI1QMUb76oGZqYQkdqWrYkgPvE3qrGWZhMd3/nNs8sO3Wk3ITkxbtbmDRbFDW73wAU+mV9kKDFAMglVWaYKVxpKkUU62T2hwpUNXdQ9Z5qx1y7Oztidn7NZb0x2iG0+Qb3tkOAyCbv+JOZ7hO69E4w1UJdfSwdXVRdwkmBxOrEX3y41Sim5TKsgqxFJyUBRN9yH6EALi+Z4aboWrRYEtclfChCS7weqS7ofTd2bxsDXXhQFZ7qFIXO2NbP2IY9QXEoqRkkVT3ckmTQ35ZE4rMjjijgMaLJ9LEabBjYxuntvRgxItOKeZL5sDZs21RCYY2CuBrzOuy3j0Xrxaurmj501s8hrg3hcvdg6CdFN7buACWMpVjc0bpWqjbkUA5bE/RCwoswYoSZBVmB7sWMYxqW4CE7/NoDG9uAQfA9fihIHajAAIvTr9nelg4q9bECWWmopcnG9fmtCazOlBmI2zyUrqDzhSfv681JEG2WhSnuh5oDRwhhdkIrObmh91Rot3tmF/ToLxpCZ5tl8UuI+R7D289kL8l4xqL8T3cj66lSoYZ49DQeZ1f0Q/CTre5zSJ6pxuU8d0EgpEfx9qF1qfOWvzursBZl4sebkCZsAI1D9+4XOXNy/6+qFOtoldwYq27NrPvl05qmab5t6cS4d+JD9nhQQhmhn7SIhQ6nVgjpqrVekwQ1rpXQp+vok28g5geheNzllN+J3unqbbZjRz0hVrx+qyygdNGmVniATggFZEtz/yp9ENzjtfhy9uA4u0Q7OYkoiltB5cenAh/k/2B61j6fuwEnH++xc9bo6eCIPngwr+L8HlIzEEXHAo080m4rJYRrWUhdn3w4wz5fm71aLmTNjjGDUwUlpiBRQa9wsJRCT9QY1oAMFqd7cGtDS91WLUzX2rNCWnzPHRJTIrRsbVgcr7r1zj6nYYMv8iNSp/g63q7WJvpljVVejU6BjGtxg3dZs86lzcZsDk7v6uUZksZaPBRHl5vXIOo88OJ05vYTtVNlN1VNHA1kiq5iddSvO9jFWTAfrJdi12pQ5eS3BftjV7RN8zxPFPbCEENWAGGkcPnFIHguX5zPb0riYi1s3FIgYkBRw1rYl9KE2wJFkXqJlmqhF7P2QRGvRvdG8Hg6C5GiBIWq1UBRFcgCN1Em5/sSazfXE/EDYTpUyCfN5ZT7ZMZ3sKBeVNjXqbqKcX6C7HW2e0O1DmB+iJCTc9nNmZ89Gr/JV+6bIwgRIeUNarVmvMpsbAx958g4vv/oUzz2xIaB8/94DvvGde7z7aMfO2dY6KUwOI4jJsUNUUrJQkxgtXGMQT1WKBlRFAoMEDlZQKHz/B4/58TffYXf/+7TpHdBjmhZbY3rI+md+lVdffoaf+2DgyXWzd8ElU1Ejo2TWqzVhXNvgoSi1NEKdSQRSHtmFxNsPKvceZT7xmb/B8y9+lieeeBZtwq0PPMsLH/8MBwc3jMcjxubJoQPrZicwSEU0+l5h6YM5mIdYiN1KoplBt3oAgjRaKETtiWaNo9tHfPyTn3Gwo5ubO+s8GIhsPK49sB1EOLp1h8/83Bf58t/+9zjajDz1oY/wlX/3P+CnXnqZ27fvmL9rEDpJpsfAd56FkdQaKQQ+/bOf4ZWf+bSxCxc5g+0Vi6+s2oCw+6QGIMZMVQiYpUJyoLM6O8VRMb9uA6VCUH72Zz/NC88/R4oswIRcWYfBTxK8TnhfgEWvJ3ttIPb/xGi1+mufe43Pf/7zyzteW3V/t7YMLrqth4EDtgnY/laXz/TKwK5nAWtsnfXr/OIXv8grr7xCSonT01Pu3b9HU9vLbPjx/nbdqhv3C1Ll0ePHbDtrxs8uwYb6aLNaKdhGFQJXBkh2HZ0JLMv3kqWuXWTY3lcMMfGxj36UV199lZs3b9ozEVdUtP29Ufc/MhP1/ZmML4urJtte6KAYSxUJ/OKXvsTTTz9tRAu1wejv//3fZ3Ow4V/+q3/JG2+8QfIh/CdefJHf+PVfB+Di4pJP/vQn+Z2/8zscPz7m/PxiXytpZ0R539nXw3JN8Pxzz/KlL/0iT9y5jYjys5/+FL/0y18ipcBuu+W9996zUKsr179arXjppZd49dVPsdlsroA8+r6/O7O+2x0sDKkr9ekzzzzD7du3+drXv85ffv1rfOGLX+BXf/VXWa3WV3GnXvX5810q6wWP2C8/3f/cXoMrLAz3n9TXT9xDaVdmSqlsdzPTVJaitsfOG4U8LWBST06yJBbf6KsnqPmEv6fdiLKwk+zLmypAVJdoRG0G5szzTHW5RsSipHs8bfRpPpKcGlwtJUwyEaMf52gTdiqLobAISKt0J3grtnxRBrzBtiKr+z3MtZimMifyMJh3Ukw+nZTl5Z1KM/8X8dQBsQlhq2bOF4O4oW9D5+JNFlYcu1NJrc3YWKWwK90EuEBR4mgGqh3ZbKX5pIkFNAkRZ3z5ASBh0Up3cESBzqI1Qz5jiukV6ZVR0quBSvTvCTVmNuPaJHU+uY0hmAxF2KPZdPmE7Jlg4JRza2pTMxCss4t833Z5gL0wFWMZaKuUeYZpYgwBomnmk2TIRqttaokaMUTEYyaT35/e8C2IejB5HQDVG9iq4ABTCOYfJuLxqqEg1cCJ7bQlhszq8JBVGu0+UBlyNumiH4hGxTdt8dxaX2zLBqEO3tRaLbmqFCsqxTzMdNEJC9LM/6K1xlQq24sLLo6PGceBYczLQWL1oacbBZb12ZvjQiN1ZpxeUQqLGEsJjDnksoKOvXYAU4C5KMGn1lMx6eowZPvxqm+CDtjGFA3wA7rBnXrxXLpXTnDwosnCMpFgHibF/X+CJ6QBRv/1w25YWSLjdH5pht21oDhdeC5Ijt6ENpgtGrsD20Hck8qbHkWJrRJ1n6zR004kgNneGhPI5EVWIJW5EufGNO1oDTabA9KiUVcWth6d6twPDCvaVcXXm1nbIl1XjWvc97/uzXdtZtTdJCweO0Z9D5yfnVNnkwporcbG8glT83vf5RES9yxJK1TiMvWkN5DN1nLtDpvsmZ37aaJPC0MwxmFIbC8mJNvzr80GDYs/kniqiOMjxkbyX/s4zvblXlj179VBT/d2087O1Ctnvk2hq7eouptJ42A+b4o1AtQlhhYU7ZP0XqaIFWrdhHr/vlj0PL7HtmbM2r5nLDCX9bYOBNueN5dCiNlBHwcj+rO+QlvXBcASrX0uAAAgAElEQVTqXhF7aQQiPkjwc0T7tXVTcx+euOdT6IAYHQi0BCIJimo11ottnv5s/SYKSyom/v5bmlldpB+1zbRWUSJoYK723kpnA/teypXmKIq4b04j5eR7oQFwxuKty3XY/mdrau91IahWb2giPbghxTWkxm7Xz3krpFuzdz8mZ9kE2Zup+9POOVJ2M9Nui+REwPyjUjAQusctiy2eRcIa3XQ2iA14/GHRlve1Dzn6S2T3ArUnkkKiBQfHCDTNpLxhupyNkasggy5nMdX2vNY9Bxvux1CN7RxsWFOqkqU4iGIvWY+PFpfkGEvIagQrVg3UiaIcjQO3n7rO3e+/w8nJBevVASnu34MORlcg+vAJjShxMfCugOQBnSfUWWOtF8PNzsjUk2axoVNFiY72NKlcGxMfuLPmR+9tebxrrNcQa2UlgpRKjHa+1NbXBnT5VvV6S4IuEjIlmCwJdYNsq6+CmkFrLSZBC2om+XZ9EZ0jqyxce/KAh29PXK4i68GaZkGQwZlqao1UyFi6aHVJbUoGXLmcrTUhxOr7vIPPviYkQIy2H9TSQAIhJ3RdqdtAHAMHH4b5jS3nZ4V0wzxEbWoelhqXGFAttNk8AalbWjlB0gMk3vHXfMYiAx1W8LOmT1qVAZGChhUyHBEOdlwris6BF+oHODlWHv+rwt3zLW+8dcLhZsMqXycfQdLqvi4RgiItIKGRibSI2wjY80maKNETR2MjaWBAWY/K2UXh29++z+og8sFhxXA7I3mEcGDnSn6GG5/5JX729H/hbHuf3TuN+zsDc71yYgiZ9bhhWxtlnpiL2VZoaJCFFDdMF5e08TZ3nnqeVi545oVP8uMffoseHqHOTH3qIy/wwktP0ppw/PgdHvzgm/zUyz8PQfneG/+Gp575KAeH1/nu63/GnQ99lFt3PoykyOmj+zx890fc+uBHODi8BVp5dP8u3/7mn7E5OOC5F19lODziqaeeR0QZhhUf+NCzPP3MC6g23n37hzx48B4ffu7jXL9+h9Im3nzzG7zzox8grSLB6vZXP/MFPv/6n/OBDz7H8899gh98/9vkNPLCxz7OUx/6KE2Vt958g7ff/hEf/MizPPWhZzjYHHJyfMy3v/N1bt95ghu373AwXeOXf+XX+Na3vsZuN/HxFz/JsFrz7rvv8N3vfIsXnv8Yc5347ptv8NyzH+P69ev8yZ/8X3z0pRf5yDMfIcSB737vu3zrja/z8ic/zcnpKd9/601efPElpu2ON954veMw/aDsZSiqwm/8xm+Q0kgHRUop/OAHb/HNb36TO3fu8NJLL7HZbHj77bf51re+xWaz4cUXX+T27TtcbC/44Q9/yLvvvsuLH3+RcVzxlb/1t/je97/HW99/i59+6ad54oknOD4+5hvf+Aa3bt3i9u3b/OVf/iU3btzg05/+NP/7P/3fuH7tBq+99hqr1Yp33nuP119/nReee571Zs2f/Zs/4/nnnuPa0TW+/o1vcHp6upzfIsLl5SWqykc/+jFe+sRL/JN/8r9y584dPvvZz/Knf/qnrNdrXn75ZWJOvP3DH/H669/k/PyM3W7LwcEBP/fSSzz19AfZXl7yJ3/2r9ld7vjlX/ob/Iv/84+5uDjj05/6FJfbnX2PF16g1sobb7zB2dkZL33iJQ4ONkzzzJtvfoevfe3rPP3007zyyitsNhtqLTx+/PjKUM/2G0E4PDjg1Vc/xVNPP02I8M/+6T9jyJnXXnuNg4Mj7t69y1/+xZ+z3mz4whd/gQ4Stta4e/ceX//aN7h//8HiR2XPL9JJDSKBT3ziE/y3//1/x//wD/8hzzzzDF/9L77KK6+8wm63Y7PecO/ufW5cv8nnf/7zrFcb3nvvLhcXl4w589rnPsdTd55groXXv/lN3nrrB/z1L/11jg6PKLXwzjvv8L3vfY9nn32WG9ev84u/8AscHB4YQLSsN+Xxo0ekGPnIhz/Mq6+8wmq14vzyEvm/WXvTIE2u8s73d5bMfN+3qrqrN7VWJDWiJSQEEqCNTcKSWcRigz0YxjfMMGE77DsYL3F9HXM/3OBG3Ajf8cw4xgF22BjMNmDAYhGLsIzEJkAS3epWb1K3eq+u7q5eaq96l8w859wPz5NZDV9HFdE06qX6zcyT5zzP//kvwJVXXsE73v4gMzMz7N2/n1NTp7j1Fbdy/UuvI/cZJ0+eZMeOHbz+9W/g8ssvJ6XE2ZkZfvbEz+Qk0xr1hcOH+fwX/icz58+zYf0kk5OT9HpdXvPq27jyiivaquzx73+fu+68kyNHj3Jyaoqbb76ZTp6zuLDALbe8og1rATh79iw7duxgOBqx1lS/eF8vOqC0OhgRKmGqWEUgnZNIQ2OT0KybGFcFk2RyGUlRmreAmhc3TUEwbXHYSN3kS5FeNDISZIJAoiormaQrQu+dA9sUj/IzSslHqZpyCEYyY+h6SeNIAYKJij5Lg55CakENYSVFKb7FiEOMEVMk1BVlLSlbPvf4LCfvdiS16ZI4bblGNRi3AophJOY9pihMGGvEONckhlUlviahMRmXwjaGWuQ9tQBKIGBDNIlenuHytWakZXwkaQKbJCmA2hhI6vPSNH9yaVL3CmVE9vEQWzmjNEVK1w1N4pg0VXVKWO/oFgXeGCqTxAgz0cwb2uawmTBbtDhTkMmIoyAyhm9IownTNgqo5EaKGhPEHwKURVPV5N5gM0tKHpOMTJu9pw7i79XId4xzbfqOV8+TOq1JRAwCHjVfMcozrEOtzYLFGk8M8jI7DGQ55ajCGUs2NobPvCDviLSqkXRVQQrozEvCRp2kCWjYRQ0IKNI69aPRRtE0BQziW2OaKr6S6xuVFYPBkIXZi5Aind7Y2nQW1CNFDcrl7tJQpVt+Rqv/1cLfeZKmvbWnvEnK8lOjTW0GU0JYa14mrqNRRWdsjNw5sBGMNPMJ2gQ5jFkzaraIX5I1a/5zViSNyYCpI6p0IwadfGuKT0gNuGiJocZbT5blrK4OqEc1ncwRYwUm4Y3Do6wjInVdiozUCZMgGTU+bXyQFJRsGHey7o2YxBOpYoJUi9NAQhL2ABNrshgxzlKVI3yW0807CojK542gjcoai64BCqRfFt+3lGTaiXFQV2Ci9p+SWhXrioZ1kkIiWmmWU4xqpCgT0tWlVaKz4GSW6awYgxusTNSNTgJ1qiU+b2s+CQ2g45wnpoB3EgNv9D1upl2N5E6aRcFLrREZg/eyvl2m12abSdIayyOloOvRqAeKAO6gBs0KojQgnEPT0IzBBhVm6F4ouLgyr3SvTyGSfKKqRoz1xjTlQ9dm0qZJZhGXTNgUNDBrDYnIdZMkfiHXlTknskoFb8wlwF4D3NDse1g9b4CmOdFmtDG0aIcsVsDjZo8wKSkV3yrGF3XyK88h6DnbaPytglfJtLQUuU5lLCXdU10jGTNOgdVm3xWQWWClKGwPbfyaiWiMIlcNCiaDLFlMIEUPVkBWmvNEJ2rGSLpnZSM2yh4bjSHUkZQLs9EaWW8NvNfAXHJLNa1N1wdR9hrnLbbWz6rXapv1rAwnp/tQUrC4MbhNRlJAB6sj+mVJkXlilD9vlIVtrVe8SNhd6DmFSsbWKtU1OVoyArqhcjhRpErCZQNSmhyqACbI/h0w5JmjXB6JlNdkghNorSJScpSZKJJxm6RQ98ZQ1VH3jpKQjMqvVHbcgui2va8CJ1lMM26xUdIek2PL5euYnTrL3IV5Nm3aROHljPTNsMit3WtcIw+3ZA6SsSKzdx6TO1ItdZhJDaAVqaNKsBTMDUYNXZPBOgFHvLVcvrnD4sKApZWSyfExvAOLBKTU3lFkuQDMIRKNerNY+Z4yBVB2SEoEm2StaySJNV4YQrXeK2SPStaJvKthZyQgGNav7zKYrVhdXKXYPE7CE0LE1xabr0lOEwnrBFoOus6NTrVSCMRa/Z5IAkKFtMZascLwMh5iBSbU2KzAdGQwFYeRYrxg/KrA0vnAaCSG4jZzax4ySYFdm2j9JJ2H0IfRHKkwGDsunzUNtRLI9Vq97kkqVU36ebIJzFhFNhjRG4MNG9fxshsjF+ZLVveeZnF5yL7j5xnvJfLrNrK+Z/Am6YDOtHuhKOkjCUnvNSbDWvApUiXlx9qEj4ZulqComVvpc/D5C/huh8u8p5jMSNnlJHpYPEy8nCvumOe1/cdZKVfZeyGxULX/JER5r1KnA6GmrsUM3yQBYL035OMZt995DyvLQ06fOsbmK27AZz01yo4YB5ObLueON76XUCbyfAybWx753H/jhltfx1XXb8c+Ynn1G97FYHWFhdkZ3vCW9+NsIRCzcRw/uIsbX3kPS4sLGGvo9iZYXp7nype8jLvvezdnTx9jbGwjkFi3fpJffee/Z2zdBqrRgOWFBY4c2ssdb3iA0WDA5MbLGHvye8xePEs1kF4tmMTJwwd5+6/9DsbBzMw0ySQ2bN7K7Xe8gS1brgQLp06e4Pv/9h3e+vZf58prr2c46EOCR74je26306PIPC996TYuzJ5n27btvO719zIcDqiqmq9/7cvcfffr2Lh5C5/6xMf4tff8O/I844UXnuND//EP8N5SlTX33vcAn/zk3/HgO36Nfn+Fh776Fd773t/i0MHnOHTwOdrJGmtsq4Swh//Thz/M9KkzAuJdfjnPPXeAshzxpS99iVe/+jXceeedpJTo9/t88pOf5M477+T+++9naWkJ5xzPPf88D3/jG2zZvAXvM27c/nL6/QEb1k/y/vd/QN5b7/nudx9hYmKCe++9l7/6q7/i7rvv5n3vex+PPfYYH/jAB7jnnns4d+4cE+vX8fDD3+LGl23n2muvZWFxibe/7S10O12OnzjByvIybboI8O53v5ssy3jiiZ/wkY/8MfsPHGD7y7bze7/3e5w/f567776b229/Nf1+HzB8+p8+za+9+9fYvfsZVlcHvPvd72Y0GnHZZZexfftNfO97j/JHH/kwR44f5fz5C7zv372fufk5IHHzzTczGo04cOAAU1NTvO2tb+PM6dNsveJyBoM+//k//1+8733v45577uHokSNce+1LKMuSb33rO1p7aM9G4tZX3MoHf+eDlGXJ/MI8P3j8+7znPe/hrW95GzMz59iwYZKvPvQQCwvz/O9/8Ic8++yz3HzzzaysrHBx9iIPP/wtHn30UapQXyoAYk2CtwaCrP3/RK/XY2Jigl6vx7Zt13N6+hRbNm3GWcNLb3gpU6dOkucZf/Ynf8zxY8cZn5jgTW96Ex//+N/yx3/0R5w7d47VlVWss3zj4YeZXL+eouhw3XXXs7C0pP6iUjNdc801/MVf/AX79z/Hb/7mb7J9+8s4euwo586dI9SB9RPreOWtt/Lme+/lumuv48c/eYLf/dCHxFbEWTp5wZ//xf/Jv//AB7hh20s5fvw44xPjHNj3HItLCzQVYLfbZevWrfTGx9i4cQOnpqfZvn07f/RHH+HczFky57nrrrs4fuwYf/qnf8pnPvMZ5ubmeNc738nmjZs4/MILPPDAA9ShZnJyA7Ozs8QY+cvFv+TAgQOXqFxevK8XXfJWDUrqUnyGvLXk3msEuRW6pZUG3vkMn+ciK/IqqTCXSCeMoxkwJp0gBf0R9b+FOVCL4XdVMyhLyrpiqDI7i1EmkhMqscvk8FTJm1CdkQkIYpjqYqTrDZkF0zJvogA0SucL6L8fhIUkDB2V6gSRm4WqpiwFBcx8RuFzxoouHZe1zY9MV3XqZgzGeznUU2p9kIwRKZR3yirSz1RFaVIbGcWorijrSg2xBeAKUWIac5+14EXQ4lzkuCofEyqWptdoO2O1WKcZ2qvmV5ljKcm1GjXblsSrNQlUXes9tVYi0b3DdbqQBFwyCjA4a7QgXfMvafS3GGUGJaDxhyIJDTTqBFr/Lvo9Y4rCUgtJQIYocjyqkSjGVFoFQv/N8kKYQE5iz5u0LDHzldUYtblHGQFYNLVIGUDaqIXYOjfJhM9ZlUYa8V9yhkjN2NgYnaIgd5lgmc608rak0h55Po00J7WgqLONL4ymiICy3VwrO7DGiDzNZdLYsCYlHJZDVpYWqYcl3bFe+04mBRqNkwYrKdjqrBzTrvWnEu8xowaYzjkxOLZOU6oUmE0i/QgpETAkn0ukvXMYL2BlWUZ81mFsbB02K1T2gbIXFdxqZA3tdxYdd6gr+e+mYAfaJAtdK3VK2MxRG6gUADHa0CVj8HkGzjBa7WO8gIgJyJzIvlCZY4gCxqRYE0JFrEtSVRGqUkEaYX9lTUOYjLIfmwZRQM8Ua2RR1qRYkaoRsRrhbSCmmhCgGO/J3hCFAScG+UlBKtNecwtgJIs1GdbL+jJY2bdiErsBBSYiss9427A09a9ra4g1uDwnhES/X9ItipYU0bB3WmkqDUBh5RlZATmEKq00aeu0GFeKc9D0irUHSVsIqpdMYxzt2hQO8cJDP0MDOMualHLD6d9rAGnb0LONnjVom25UTpTWgGD5dmvU5/ZpJdlbnAJUdYwY9X4ThgQtQ8pF3UxjopGwNbScptxpZJfQJFOllmFkUNr7Jc/DGolVb+6lMUaYklEKmktlY81e2TCVZO+XZrThFjXS4KT7acP2kH1eAXJl7VnEZ6d5p2DNJyLUcsaFoHJJ25jPI8mCDmV4NCCdeuup/6HszUaBEQvJKgB4SZGInPdNPDIGNelW4AsBaXLr1GQ/UVUliZpQK0Cqa6E9HnTfFunIJX59CtgI2A2Wxq8jtSBuDIGotYRFzLurUv0YjZN9xDsxeB6NqAmaMKt+YK3XjES+Jy2+G4DOGpFR6GSFpPfMAs4kic/Waa1AqypntgJ2F4XIso13NEEmJCNMS6v7TGpYzmtSBHm+gRRrTAqEUFJXpdwPpwCWAmzxEm+xkELbaTc1BE7OD2PB5XKWbljXY8OWSZYuLjDq93Wtrz1neQFk8IMmhtZB2O1oYABAlnXxLpfasJVligwvRJE7Jj3/5bzU7SpJIs5Et2Dr5oKqXzHfByEpR4ajQL9fMigrfR8FYG6keM15H5WdnWKiJuoAsvFEkfMiJBEJRmOoayGsijQ+tDtLFSwOz8TWHFcmqrLG5urFGAW4sc4IsJokwCEigxKCso69vls1mErPPEU1Y7AkPV7W2Dv6DtTCprRdue+mjHS3dBgf95TLkdFQzp6EDig1hak5qwlgXI5xOYQRploixZECRwHSkJQkvVkK6hxjOhhTgOth3ATYHs5P4MZ65F3LWLfDZRvHuOnGLVxz2RixLpmZW2T/4Vmmz/RZHTb8yqj7tQCsSebCYjKurEfn0FCbiDOOzGRkzpC7RLeI5EVgZn6FI/tnWJo6Tlw9QgpzkJYR/x6Pv+w1vOSO27jt2ozrJy1d3wRPrMmBijyn6PSkToi0csMYEr3eGHfceRe7dj/Ldx/9AePrt7DxsuvaPcgCm7Zcw5bLXsK508c5dmgvW6/exvrNV7Dv6X/FZTmvf+C32TB5JQd2Ps5lW7fR661n10+/yaFnfwpEJjZsIstydv3sER5/5DOs33AZ19/4KrbfchfDquT73/w8z+74PpDodntcc+3LODN1lIvnz7L5siu48prryPKCp37yGP/68Bc4cfggdVULIICA1d/+xucoOl0WFxbY/fMfkwxsvuxKrrjyGp7+2ffY8fSP2HbDTVx2xeVccdVVTG7YxJHDz/HoI1/n9PQJ9u/by/nzZzk9PcXXvvpFZi9c4BWveCUnThzlm1//CrEu2X7Tyzl77ixXXHEVr7ztNbz0Zds5cuQQW7ds5cYbb+UrX/w8X/zcP3LD9pdx4/YbOX3qONdddx2vesWtbNm8hdnZC+1Z1mBKsj/JQCMlyHzB7t272L9/D0tLS3z7O9/i6quv4Y477uCWW17Brl27eeihh5iYmGDbtm1ceeVVbNq0mfn5eb71rW/y5M9+ysXZC+zdu4fBoM9nP/sZDuzfz223385oVPKpT/0j09OnuO2227l48SLdbo9XveqV3Hffmzl06BApRR588B0cOHCAT/3TJ/HOcfttt3H+/DkmJ9fzhte/nuuuu56ZmRkWF+b1Otba8TwvWLduHXv37sUYy/33P8C9997LzMwMy8vLvPzlL2d+foHnn3+ejRs3cvkVl7fA9223vYrFxQU+8Y//wP4DB3jHg29nYnyCRpbnrKEocnrdLuPj4+zZs4dPffrT7Ny9ixAC83NzfOGfv8g3v/lNbrn5Zq6+6mre+pa3sn//fv7hH/6B/fsPsH795C+e2UnOrhtuuIGVlWU+//nP8Xd/97fMz8/zzne8k4PPP8/ffvxjnD9/jrvvuZssL5ibm+NLX/4SZ8+cYf/+fSwuLnDjTS9jfLyH0RqpqSV+IQ1OGcSNnUICzpw9y67duzhz9iwPffUhHvnud/j5jp9z/OQJPvOZz/LEEz/hpptuYnx8gl3P7ubUqVPcfvurufLKK/FZxuOPP86XvvJlrHNcddVVHDp0iNm5Wb75nW/x5X/5CisrKzowNnS7XRIwuWGS+958Hzt37uTjH/843/72dzAGjp84zmc/+1l27nyGW25+Oa9/3eu44YYbeOHQQXbu3Mm2bdu4+667yLOMqVNT/O3f/S0/fuIJOVul4iIBt9x8M3/w+7/Pn//pn3H11S/hwIHn2Hb99Vxz9VX83d//PQ99/WtyRhYFE2PjbY+QZxndToder8ehFw6xe/duzp45wxe/+M/0uj3uvPNOvPc6FLvkTH4Rvl50hhJJCn7vxOi6SUlxar5sXCZyIu/x1tNEPK4tDil+QxB2UVQ9ZgwCFEQtmhv6eEqJsq5IwYg/heo0pei2KqdzWJ9hjSYnWE2lSdJONBHlMUUxGNUocRAJTTs+14lwTKJjbgeyKeGNTC0iUFeVpAw5j/UZWZ634JkkAYmZagNQoQ1eUrlHa4RqpKiIRhoXkQQFpUNLwdNMlolNoljUoZhEgxvnybxS6o2YH4vBpiCuxuu4WVsrKTR1mmwl0Ul01vLrTSvmdOrZvASN4iFFiZwUM2W5TutzXFbIRM/IvW9kQwaZ0htlobRTeZV6pZYnH8E6YhI/pqAGts1EPVqjkhddI0kz3qoa6gpHJCs6JCvMgMxZskIj2QNqTGsVdNB7IZckDXHbUK8BnA0LofH5CojngbMilwla5OZevHXKqqK7bh1F0ZGJYu6EuaAMBAFhQiu1swmwVv1n+AWUXHtkGhPBdhZvjUoTER8GTYJJxlIOBwz6fQYrK2Jk6zM1fUwtG81bQ+G9eqag69O0qXPOiBW88N8iJokeP9N2oblmgpi5ihTJStJhlGK3DoFY1rjkGN+4kbzbER8OxG/D2QxJhcjkGaqpsjwLSbAgyJ+nnY4YrMZxCyOnar04GkAEkzBBRireOVw3Y1iOcMbQHesRSHRdISwv5N1wzoB6s4ECRUgznRkrTKbW68qqubWY8qYknzuFoECEAI4t00OvxTvLoN8ny3I6Y10ikaoBLxuWiBGA09jmSenidF4YYm1DatVXBoK+W83atQr82WajNiKXxYivnc88SwuLlKGmKLrCJEtC7TeIh0mKGsvb7KFO35kkhUpMgWgRUDI2Ue4KSGfuEhALaYSawgCUqSPeGPUI6pDoZbmcKbpHO6ffVyKdBIZOjdwCck0QCRi8Apu6UerWJmdMVCPGBiwX6VXSsAFNozGJURCgwmQ5MUZylWrGFHFYSa/UkpYGuNFmtPn1Rqapj0hYajHgMyepYlZifdH96xeBLWnSLGJKbbVxNwpq0XjupDWZbzvZa86RFvWW669CaM8taWRlPSRljcpebvXz6gACiVePVZCJe0wqNRaWkgCDrQtO+0xMSjgg1kHluJJeFYORtKoQ9axu5JxGwIKqxmQZ3tu2sbcpqYmwwUQBH72RM6oOFXnyCpbI3hVi44ske6dNl4JwOqDR4tR68Xhz3sv+hPomoexIfXfrOlDXGgii+2+eZVgvwzRjjTCYAySbmtesfZ4NKL42jE40JvrQRC6L9L0F9VJUdpyyPvS6BezrYlNJTBIwUMUADqqyptspVNamIFkUBm7zrRNJ9lZdN5IIG3BO99ygLDhxJ2uvwVunylRho5kGPE4BC3hvGTc5l1+1mYN7p1leXGTdunVSA6IS90bS0PyPyo2jrnHIICkD1meQagjCDjVR9nqpCTSQxRrxCxIrY3nWUSRwmzb2OHdxlsHygLLTFf8dsfugHNV4K7Hta++bgt/W4DJLDDJUsto0ibefJJs2KZEpJaLu0amsSbkj1VE9BFWaXkY6vYLu5CrDlZo4HsicZqAmSEZqtjZt16skOag9gDfKym7AFqs1l+4XQWRhyCXoLp/EX0vrklQIGJrHgrS1ZHg0Ua1G8jyRHGAiMVhsnTBFA1wHqSX8hLALYwVhGewkBk9KJZJK5mmLj2QgOYGzbYY1kZRKbHcdPpbkoxUm1hVcdfkEN23fysWlVWZWR0yd6zOez5MXnmsvL+g1iYMxCgssav1aBGKVSHWFsSIxDVZ8Nx2OZKR2LJzDZgJQnzy7wMT+jO54Ticfw+dXQ9YlmQLjxiiuuYvtty+xuPIcK1XN1CIMgyHJTi/PptNlhDDv61jhawN5xg0v3cb1111LXmRMblhPd2wzmy/fTn/5AinJYMIXYq9x021voCorUl1TlcvMzp7m9NRBbnr565g6uo/TJw9x8233UZZD5s+dIruy0K1chn791SUunDpKijW9iQnyTodquMrc3AybV65DJ4N0e+Nsv+lVWO9ZXl6irCpSiqyszHHowB5CSBJwkCBWCRMNRw7t43Of+R8Mh302rt/CzQnyPCcB09NTZFmGs1AOV/nGQ5/jre/4DV59+110uhMcfH4PZX+Fqi4pq5KlhQUu29ojLwpmZy9w7MQR5ubnmVg3zr6f7uJ1r3sT73jw1xkfm2DH0z9hvDsGBvY9+/OWfVJkGc/t280Db3kHb3nLgwwGQ05OTcmJ1tYOUa/Z6s4u6wB9/VoAACAASURBVLU/6BNjoq5LZmZOY61j3br15Lnn9OlTHD16hOFwwLp1E3z7298ixsCrXnUbKUUefvhhqqqi3+8TY2R+fo5Nmzaxfv0k8/Pz7NmzhzvuuJPt27czNTXFmTNnefDBd7Bt20v5r//1q9R1Ta83xpkzZzh65Cgry6tk3nHixHGWl5f4lTe/mRAip0+f0ZS0Zm9phkiyp5w5c5oDBw7wK2++n7GxHl/7mnzv8fEJrr32eq6++mq8d4xGg7Y/6XY7TE1NceiFQ5w+M023122PElIr0m+HBCsrqzz51NNYa7j3jW+kDoH5+XmOHj1KjInx8TEmN0xy5swZzs7MiNm0mDBJ39L0Tghr/9yFc5yaPsW58+dIKbF+3TrOnDnN4cMvcPzYMV52441gDFWomZubo6orlpaXqOuaPM/J8ozWq7Opoi4BkX7Rd0l+rqqSfl+8gufn5llZXaU/WKUcjZidnWV5eQXvM/I8553vfBe9TpfhYEBVlsQYWVhcZHp6mtXVVYqikOFQXbOwOM/cwmzbQ7T3EWktut0O5y+c58KFC4yvW0dKidFoxNmZGWbOneMVt9zC+Pg4zjnued3rqKqKqq44f+ECJBhVJfsPHOD41BTLS4ta50sFeObMWaanp3nDG97Int3P8uyePdx//69gMZw4cYJer7dWO/9SjYH+2nA0ot8fUJYlLxx+gf6gz8aNG9t712ANL9bXiw4oWWU1OOe1P7drjZDNJLI3V0NsrPgAJJXupEioS0ItE/aQ1AA5NOWfFEDtwY9Ecca6kZREjHWSPqWfwWeZpMw4LxGjJklilTOk1BTCUlwU3pF7bUhqaU7rlHBJGlerBVEz0cagxYIWnFEKbkmKsfgsxxW5vCRZ1v79BhSDpJHnAiqhjScKxJESTj+byING1GVNXZVqIu3aFy7qBM0aKd69l4PQ+1wSTBrjOGOliUDi36UZUEAnCkNJ0jUiRNNOs10ztgQ53OMa2BeUTZBIxFoSiYw3IrkyHud7YKz4rGgqVePvE41p5Qcpyn2VIl7ZJtpMNc1TTKldV1IIywzeWi8ld5QpckrKZClLMgNZJ2sbO2sQ4BCVCSYpDI1zimeZtUZD5Vd1CuphY1qpSrQyJRVmjHymiBhhpqqGSsC4FA2DqsJ2eri8I2ljCdDCx4pxlfhCNIeJAm7eCGATYmw3DaPFYdRJuPT9IptqfYISQGNAK/KSsj9gZWEB6y1Fr9PGY8ufF1lm1iTItQ1nUnmmTlKd+O9EI/4TBGnCZf2reaKlNQYXoFZM5YM27lVVsjIY0JtYT547IOKSZPVY54hGwN4YxCdC5K9yP0KopYhMylBEPl9Vl5JSYwUMjnUte5AxmKApgAp2pmAourm0SsslnV4XW3iyBLl3xFrN762wBaJ4GpKUJWiAzDb+b5mY6zth3jUAsI3KmmvekaiFdkytD5x4iSD3rQr0NB7UYampscmugQPykrZ+VjYZZXtIc1bHGotRQC+QrDwL2y4aiCpPs2jDkZKuv4YVlFhZWcEVOVhLFSoKBRZqI0wA33itWYvPnHqj2NbjrtkWG/AkOieBCGskHF1vzc7V7KY6IDAJb8QPzKo0OEUx3M6a9YQkTRpt1Jr91zkr73SMCmhFBap0+hfrVsYJijU2XniXnGHGGFwS3+FyVAKy1q0xIsWhkW+m5tb+IhVbS7ZfvFZ59msGqZY61iJ9Sijw1oBdlwAPLRNE3nNrbeuFE4wyGmLSe9HIw9YYRsbAmtwNfSclOZAYG8Vc+4eNOH6rLErXGIaYajlrYpKUJS+sY9eEGhjxznLGqo+hdrWmeepJgUDdX0mXsBCRoY/WCSlWhBRwqYkXTi1LBpWOhQQOOVfLUU1dRWLReAnqOWXWWCuSKMcaaKV7ubGGUClj1sv+0yREOidndgg1ZV1jrFvzmnIOQtUCeVnmKQel+v01jLkkrEajIAqyGJ1VeUZMbX3UDLvKqsJlmcTah1rejwTRJpKx4mmGeDtGpJYxMRFiIpSJUAWRdIdKpo8asBCNpcYoE0uL8pQIxgDyDjsXqUzExYQxTWqnADwhrZ3NGPT3U1sANz6Ick2e3GZs3LSesbEZLpxf4LKtWzG+wCFyKnkpg+yrKnlv9i4fapyNoAChwZHZjrD7EDm49F5SHzS1komGaMUPqvleAL7jGJvMWbg4YLChIM8kKj3GRD2KlCaIiTnyHlm0Moti0C1JeI5aBxYywBGIzRlhyVhlW0UfSVXABvXCJICRIVgdDK72jG/sUk0NGawO6Ux28QqSEpG6Tt9B4zwmk4FXDLq/aiJiiBEXBAyOtjHmNm3NJs/KkowAL8HX8n7lIJTdCl94xjfVDOYMsbbEHLya1MdQYkYJq/6KsVzB2ISxOSmUEPuyc/tJBHAaQMpJxiOspQpMjUCCOZic5AtMNonvJjpjNam2TEwGtl27kTOz8ywduEC/LDl8ep6JrqfXXcdlGzsUxuGj1K3OOFJWYnF446jVskJqODHVLpH61ltDFWvyzDBB4mKseeHEAhPrPNeMe+zGHOPGwK4Deth8K+tedg+3Liyy3D9Gv6yZWYUqNeCdsAWLbgeXeUIZ1D8xcs+dr2VlZZnv/eAJMicMwYmN17C81BdwOxjxv8Lw5A8e5typacYmDPNzZ9m4cStXXb2dOtSs27SVa65/OaEe4l1O1u1hM0teFArIKlO9MW03cna4zhjWCBUqqSx1ZXWJn/7oEU5NHWXQX2XL5svZftOrMMYS6po6JAH9NDlNAm0sz+74Kc577rz7VzBYCfTB0OutI88LYpSheVlWfPfbD3HrK+/ijQ+8g9MnD/PYd78te0IzopRpBOPrJtiwaSOTGzcwe/Qi01MnOXPmFHff8yaOvfACF86do3fdNsBwzUuu59zsBQzQ7/c5f/4cc7MXuH7bS3n0e49w4uSxtk43OviRIU7juRfafZn2dJczoSwFvNmwYSObN2+h2+1RliVVVfKzn/2UAwee43d/9z8SY2Tv3r3tQKqRDa+urLJlyxauuOJKxsbGWVnps7S0xIED+3nf+97Hysoqzz77rPQ6JDqdDmAoig4Ay8vLnDx5kle84pUcOXKE+fkFnPOXGI//ImiSiPzoRz/gT/7kz1heXuYnP/kJIOFXO3bs4JFHHqGqSk6cPMb999/f1hvXXPMSLI4i7zIajugP+lhrmZyc5MKFC2vFAYaUDHVVr9UqTb1CA+RYRoMhnaLAWUun6OCMlWsztMz55mtifJ0khSY5J4bDEb3eOGC57rrrpBSKkgqS2h/yOZphqZXCpX1ujQS96f27HbmfYqfjJeSItVqs8ZWTa1kbhgz6A/76r/8aUmI4HLG4tLh23TS12yVfSeqExv8yXXJvmmfU63R1zV/611LbL49GAlp94QtfYGpqihACzz//PL/9278NSG+wuChgUuN1KYDSGf7lq18ly3NufvnNbNgw2fYCzhh63S7GGDZt2MDiwgIbJje0Q7D28eoq+uUfjU+lNb90vf+LXy86oOS8w5nGZFTT3IxQvr33ZM6RqyE1SMEijag0WmVZEQJq8qqsjNZHQFdflOMyKIiDhECI5MYYvMvw3mE1WSrRTLRTG1UbjBR2mfVELWkaI1LqmhQidaglXjlIIpV8XmU/YWkkVykk6ghVKcJray1Zr4vPM5xeN40cJEncb1IwyjinzbuwJkyKLehiEgzLmk4mCGxZDamjTkZjwnkD1ghDSw+EkJJEeDsp8rOsQO6OJPKATN5wrp2eKSrUmjyDHkopYWPCBtAQNY0c1yS7GNYQzhQJ6tNjvUP3U7K8gxiGGkwTIU+EqNHtyawZThuDyAKiSmBkAi4dV2rNaa3zUrzK3VewQ+npAYgio4t1CSlivW8LfFtHfJ5R1kKrN8oGagypG6DK2bWktKgsBLm/ojd3DZCGrBNrPbUWc3VZQ6xISXx6RmUQKZrNJPkNj/MWUiAZMaA3tUi1ovrpyF5oMbUU0TEp0OfFkDYaKWmledfD85IGl6iSM4HZGA0rluYXoY7k68cxmTY1BpWbeoz3ko4YIk79RqwxZFZAiGbDS4h5s7GGXEEbSTgSHzDTAFWazliXFTEEaoRlUI9GOOcpxrukVAs7JFiS9aQUiEY8j5oEI6u7aIjiS4bKLkIMGPXnqauKMta4aNtUNWuMFENBQLvQ+qHkmMIz6I+oQ6ToZhjdl0CazpjS2jO2Dpc3EbeQqeeY9VYls1b9dwCfY2uVVsVIQIDXZg4QGyAsCcujyHPKssRa1x7QxERu7ZqkKEn/1aRwiFF9c1ZGUhzhrQKfQcMM2qbaUiFsz2Z/1GGLfg6R++SZZ1CW9AcD8vWT5NoMGS9AsDOWigC5gPTeCMME5xTEEfA9hiQpnt7JRNSJbMPoZw5JpUMhANJoOU3Jawz5BXCvSbk0nVgJVZDP7lqQwGmx53Utyv1NmiKIeKHUwiSsgvhXOYykRLUTGrmRTkEXSTJLrdSlLiuReUoXrY24nBgN4PPLYFST9rV2+OvkVNkMLYunKbj1PSOJt1ZEnmtTWDijctQkE/hmUaQUscmpWbEODDFtMZZoCkL5dsSE9bInmwTJBvX0khSwRhIYiepXI+yi5rN77zG+xncsyav0rmmEtZKQVgKVy5r2npjURBibNuQhgRjjpjX5WMJQawiFsG5AEsqUARnkrKrKSjzQvMUMIdWmTfdM6o2U7Bro1xjjN952wqAWJjXJUpZlm+zjFPC3Rgy0Qy1AtMsEyGxYmI2pvjGGLPOE0UD/XEE06usXI9igQzEtFpuodSP3wVqvnkny6yLlAomdD1qbRFJcA6tiaiTwkvRkrMNFqSWywjAaDKjCEOMKkgKJMSWCEW+KpNJAIY4bBQcgS45UysCqjrWeU8jzSTLYawd8CuY2e1ZqZNgWSJ6J8YLNl2/g9PQSy6tDNhYZ0Vti0Pc1RUyoCbWAIcY5CaWoLdFUJGqM9YQExjusyaD2WNMwyJxIApNpGeY+qaQZR50Q2Zep2bRhnMXZORaXB4x1CjreUiUZQJUBYhUprEFUZRpGYoQJLoCSMCHqEMTLzkr9mIikpN6B0WCsMGpCgOjl/XY1kEcinhQqik4Xu65ksFDSHc+xHVnfVJrK5VMrsTTJEmwiRCSzJgUykzSlqIlSd5gk70+t+40zkmCYsARqUnCkrMBnlfj31QETDcXmMTU+j2vJywryh1qsCrA5DCtsvYQteuqtVBHTrDLZe2AqSH0w41pvJhKVbDwmITK4LsYHTMjw4wW+DIyXjsu3THDTdVs5c27A1LkVlocDnjs+S2/M0Sk8GyccQqJ0SDGK3C+fyKSMar0jax+xdSWgu7FY76hTReYc411Lf5A4cHCWiQnP5ldNkGXr23fKmA5u/Bo23/o6bltaZLE6TxkcF4eRykh97bwjjwmXZVgdIPs6cMedr+Hnz+zm24/+G87B3Pws73rL/ZRTxylruPHWN/H8nh8x6K9w533vpBoMcbnl3/7lY7zmdQ9S5BN858v/jTc9+B945Wvfxs+f+BpFb4wH3/enOJ/RG1/H+MQGik5P1mKGAr6OFw7u4E1vez9/8OcfJ+uM4QzU1TJL87Pc88a3cftgyIXzZzg/c1rXlQAxXgONmmG5pDEGqGsZGMkogfn5WVaXl3jf//b7GGM4M3WSGALv/o3fZtPGLeKB2ckYDfpaKypYQGC1v8KxI4e57/5f5ZWveC0hVPzg8X9lZWWJvft2ceurbmPX7p9TVYGjx08wHK3wZ3/xf1OVkVCW7H52J4PhKgcO7GbzZVs4eewI9WiAUaKB+L42dgDS37XNU0xgVA6rDcnZmTM473jve9/DYDDEWsfJk1O87W1v47bbbsNaz8TEBKurq1IL6iAgpcTyyhIHDz7Pm998H//lv/wXxsbGeeihh5ibm+Opp57mwQffweHDh1lYWCDGyJNP/oy3v/3t3HHHHWzZspVvf/ubzM3Nc+zYcYR9dIarr76aBx54gJ/85MdMT5/irrvuYteuZ1rvQEj88Ic/5A//8D8xNTXFyZMn2bhxE1NT07z2ta/l2muvpapKPvu5T7cAx+7du/ngB/8Dn/jE37Np02Xs2PE0Rw+/wMXz5/jjj3yE4WDIho0b2LtvHw1LqflZ00QUmJAax1nLzp07edc738Xdd93N1q1b8d7zhnvuYdu2bbz97W/j1KkpxscmWF5a5qXbXsp//+9/zdLyMn/5//0lu3c/y1vf+lZuv/02rr76Kv7hHz/Rgh3yc9N0roFZDagm91/rNf29p55+mvf++nt4/etfT56LB96hF15g86ZNTRUm77QwWVqA5dChQ4yNj/PhD38YYwwXL17kC1/4vKyfZuDzy19J9pqjR49RhZqPfvSjPPTQQy3AuGvXLn7rt36LN917LwcPHWR2dlbWjVmrDQ8+/xxvfMPr+cBvvZ/BaAjA3/zN36zVi4Cw6zSZvP01w/LKCl/9+td5+U038asPPMDOnTupyop/+tSn6fV6WGv54O98kBdeeIH3/sZ7ue/N97F1y2Uqu2zq2fZGt/citZ6XL+6X++hHP/qifsPPf+4LH0067ctcJswc5zWu3uF91voByfRVpAbDqoYAVaXNczORJAprJzXyFtPKi0KSiVGDSjbm39Z7bJ61DY/zHpN58AbnhdZu/RpLKil7IMNAqDX5B2GN2ETm80u8P0z7cyJRx5JhWUrzZMDlGd1ul7zbIcsczmc6fV1rYKL+aDxHUNiEEMFJPHtTtMaYKEcjhqMBo7qSjTqq+VxKjKqSuqqp6poYBOzI8pzCd8i8mHAHhH3k1RvGOANevIJItBKvpug1xkqYWlKmEtJYmyRyjZCCyAuDSg+JxCCifuNF2hgVTBJfGul2BIS4BA02ijgTaYxQ14o0bS4xAkgZlPVm9M+q0a4arTeyNABDoKoGpLrUuG+3BojkXryfk/jqNIa9Qt2XQrFhJpGMJsAEgpEyCafMGdQ0PagkU7s/8ZYSsMpbS1WJDLLT7ZGsJTNW1oQBnxuZHiogYrSubKIcjVJJTIPWG2nEjXXC0tCpacMyc7p5hhAp61oYVQlCXbO8vMzywiJFt0PWKdY8Q4QyINI0XYNNsl+MkcyLcT4NoKENVzJGPbAEyCjrUpo2Y9TAusaEyGhUSiy4spNGdc3qYEBv/To6RU+SBq3F4LSBlabLqsyvSY+IUeUZoSbVEjeeRCFKqIU+Lq9sgrLWoglSkDl1sGKGbJKh08mpjWFleZU88+SZx1kvxtoIwNYkgDXX1CQvWCDPMrIi1/tmFY+N4hUVpSnwRCqNRE8NGwlIClY399nlBcOBAGwTY2Py/XQKaayYRxvbrEd5J61xRGOokkqBlP5TJ/EuSqGCGMit+LZINDfkLsPrviNgsfJ1nMHlnpWlFVZWB3TGxoi1pkDou+CMxLS7rMBmuTJtxD+JprFNSSb6Clo55NB1SVkJxhGtER+aWlLwGjBJ/imVoqXEoD/E5jm5zwQcqEVCXIcgZsesUcO9ICnCZgGRENsG/BZJVCLhadg/RkBAbaJk3ag8UH+ulGXaX1jGWSt0ZlKb/OWMaU3Gm6h3DC1NWz6GbZkgre+RceKF4jKaBMtmuioAy1ph07BpZC9IDFYGmKLA5U6uTWnnjXyv9eWzVospI/u1GtuahHp7iddOilFAFmOJUdiZ1hoBnpIyGlpPLINJkSLP6RSW5bkBvtsVr7Y8kzPVyJna+AY5L2y2ZNBETFmrwnpR4Y4+Ay+ZzyLdrktCXatXkDBTTQveV4SqooyVMBCqFQajCmsdReGVCSz7oFWgt3kW1op3Hibi1HPMq/x6VOrZmiSZNaq0M6Qovj4mtcOxxr+nrqu2sKzrmnIwordunG6vi3VoU6ZScyP/dkLqlHaV6N5hjBPDe5LuIQLiW+u1f06A1XUn9zPqIKWRoxuAUOGdJYwq8cfLCllH0LJYGomaAZLTtMRmOGEsBDkXo0rAnRWmoNPBgtW1buVWyp6pgGeKNTYJY8piic5xbuoCfqzDurGORLsbXSMJ8aELlSgo9J0mehKVMD+NbYE25xwpBZKphVWRRN5Pw/JLIkHGilccCqTnxpB5z8ogsLQ0Yny8oFeIAbiJCoHqu+9S3QbGeGT/DDbiklPQ01IHARPFPqBh1sm9U+iD2oAx0uDWxqgMqxZfy2SoXc3y4gBjPEWRKWNNpXtOfO5sA0CnREy23ePqWvyYrNXzbVTrHmIhSiy9ae6x1/2lDhibpDbG07DDnHPYLMIAsjFP3s0xmQGnKXfOY3yBcQUm1RgrEnqsvsdEYfmYLmK2ZBDwCGWpilhT9iY1QKeS/SiuYKvE0AjjtqxHLM326Y8Cq1XJcFgx2euycZ3D5RZrZbHJck9Y/SwxWT03mlcqElPzjtW6YBOFNxhqFvpQLZZsGfMUk4nkJsA5DTLo4brrGZuAYukCy0sjFstIlQQUJ4lAdXFhQSwh9GzvdDo8/M3vsLS4RKhqVlZWOXX6NHt272XhwgyDlRnOnzrIscN7qcqSweIKq/2LTB/ehcm7HN77Q04f38fs2ZOMRkNOHH6GCzNHKUdDZs+f5Pih3Zw8tp+zp45w6uhzVKMKS+KFvU9z5IVnmTt/lhAjc+dP8ezPH2Pq6EFOHX2Bsq5YWVni3PkzHD64h4sz05yZOsHqytLakArDsBoQQsXh5/ZofVDTX12kCiW7f/4EJ448z+rKEkcPP88PHv0Gp6ePc2b6JP2FRY4dPcR3Hv4yz+99lqquGQ77TE8f49SpKaq6ZGrqKKenT7KyssRjj32HfXueIVWJuQsiifrhjx5jeXWZqi55dufPGfQHnJ05w+c/8wlmzp4CLNe+5Dq2XLaVHz3+KBcvntc+Qs5b73P279/L8vKieMIZi3eeHz/xY2ZnL3DxwkUOHDjAwsI8u3c/w+5nn2FudpaZmRm+8pUvc+jQIQ4ePMj8/DwLC/N89atf5bHHHmM4HLK4uMjS0iL79u8lxsjM2RmOHDlCv9/n0Ucf5Qc/+AGrq6ssLi4QQuCHP/whMzNniTGy+9lnWF5eYTAY8JWvfJk9e/YwNzfL5OR6brnlZn72s59y9uxZvPccO3acp556mhgDu3fvZnFxiX379jF18hTD4ZCFhUWefPKnnDx5krIsOXr0GHNzcwwGfU6fnua5557ntttuZ2Zmhscff5x9+/bhvOH7jz/O//zC51heXmb3rt30V1dZWlpm7569fO+xxzhx4gTT06c5NX0KY2QYe3FunmPHj3Pu3AV6vS4//skTPPHTnzA3P0d/0OfJJ59k565n2Lt/Lzt37WT69DQXLlxgZXWFp596iunTpxmWI06fOc2TTz7JU08+xcmTAqJ97nOf40c//hGrq6usrq6wf/9+lpaXeOaZnZw9e5bp09McP35cB46yPnc9u5vl1WUZJhnDjp07KMuSlZUVjhw5wte+9jV27NxBInHm7BkOHDjAcDgkxsjc3BxHjh5hNCo5f/48zz33HHmeMzMzw8zMWXbu3MGFCxfYt28fc3NzLC8vc/jwYQ4dOsTi4iKHDx9hcWlJfm9lhRMnTrB3714y73n44YfZsWMHo3LE0tISBw8eZM+ePSwsLHDgueeoq4qpqSl27d7FU089BQbm5+eZmZnhhz/8If1+n2eeeYYTJ082jWt7LlprODtzliNHxUT9/PnznDh5kv379/Pkz56k1xvj1Klp/vlLX2T24iyf+qdPMT8/z8rKCnv27uWR736XYyeOc+bsWZ555hmmp6c5cvQoo+GIJ59+mnMzM+3Q88//jz//f3iRvsylFK4X4+tX7ntLMt6qkbQki/nMq8bdaKFiaFxqQggMh6VMuMtSm2lBl5NOYcU40lIHASDqRgqnEzOnzTXO0Ms6YgCd5dIEOnBGQC1jLN4kkgkEY8W8D0Ooy9b/w2gD0uo1kWSnZvYq5qWBsqyp68BoNKSuRnp452RjPZ3Qi70pxmshJo1BlYL4+5S1sqWkUNMRssiokEYjVDV1rCU1ryzba0ZlNBU6eo5RponGkGe5+kGI/0RZV8IKiKZtakwmjB1HAvVCEIkPNNIFtMgUrmESOV4IlwBAGs+coI4VKdRYnwstP9F6ZQkS6lqfjMYbSaYKfq2IJmGjeDyRpMkQaZVV7w0pqAFpxvV+Gm+otVAWlVEklENCNcAbQ5F1SMZhMylsOs5TIfIqhxZNZk2G0oA3qaFx6Hq0DRCINBix5WoKSGCcUw8vaWC8tYS6pCxr8m6PvOiSjKXQybhTY+oqBsrhSIpnqxImnXrbhu3XxFfX0kSa3AvwpAl7RllmmYIwdYyM6hKXDHUdWOqvMHv+PC5FeuNjFK6QIjMi/llGC0cja8IYI/4RSidtzIHFyI2WrWe1OWoMab11xFiL7FEn+SHJOxyriioGqlGFKTLWTW7E2wIx34s4kxNNFGaMc+RZ0b6H6D6RQk2oR9T1SFIVNf1mNBjgkpjWDoZD8X9yFp8QWaAXWkcsJQGqGOuw2h9R90e4rqdwjqLTkcbXybPPoiHWNVWMGCcpYiLBNHTygsx7kS5qM5OsoSwDLutgY6AMQ3wQ1mWtPlQpBEJV0ZhWu8wTUmJxYYWxyXV0OmqIbD2F9SSCgswaKx0N1nqs8wQDte59BsQc34pUjFjiUxQ/mCiNXk2QJktZmCYEkvpC2cxiMsfF6XMMq0B33TqIYoIsjbj6bFmLy8V7y8vpgUHWYF0FdOeWgzEJMJ50wpQUMKqi+NxJ8pdVFoiRP6NJcKGqWFpcIe+NUxQF6q7f0sjFl0/M4BPCHm2Am6RMx2QM1DLBjzqNyZIYbAvoJPO3EMOaTFToMMLkUPr8xbPn8eNdLt+ylRAFUTdRfGS89eJHZC+hqP/yeaoJeHlj5Gqkyc7ynDAcir8JsZ3pR50UNkbaIACztzBzZobOuklcxwtD1bq15t2veRFalUYIO04bR93HjJFEQmu9yDJNjSEnpCb1TVvjTgAAFiFJREFUzpBSwKQg0jZj2ukuKeGKjGIMzj4/TTa5nm6W4TJPp9uTqHSjbj9GwgK8c+KbFpVVkQwxilQsxCDniJWUS5maO8qypBwOcVo/kJKC9CJtH41GDIYlncwxHC5zdmaBTneC8YmcyfWTwmBFfWhSQJJdszX5PZFOtyDPMkmzjInhoBRwKFSsrq5qo+KJ0dDvD3Aeup2iZRgPhiPKctSCT6PRkLnzy1x57VVs3ryRPMvwmcEbD1ZDE5xTELORJaGAr8H7jGZeGIPssb45AwnEWGIj4rtGUwckYSzHCmeE1VfXpYBeowpjHPnkevF6s5bGS6vxxZEzTwY2WAOFxVQwqgDjyHNPCqWwtoxD2KL6HiYBolNoEu/U3yuJZNHYSFVHahPZ+eQhbNHhplfexFgvJ3eFgPaxFlZXLCmyTFjB3hPwhFSTZRkhOQxeBx9D6mqIjcJu9CliEQ86mzQkxSo7FQF6U0x4oHY1Z+cqpk8ts36y4OrNXcY78o6VNQqiRjJvcB6yokNmnAwJvdeSVByhq1BhCGTWUniLcwHXgM3WQBQpuo2RInNEE8hixHnH0EZsXUIMLF9cZTQyjF8xzlieyxmWWbJCmONOU+WMScQAyTcSaYu30MkTVvffvLAYVxBMjnXCTnLW4XKt1coRqQ7YwmI7BRZHqEfY2hGzRLlYUWQZ2ZYMYzJAwjCwgM+wvkuKI0xdacSjgrNkmGIzJt8k76nJwU4ogJRIyUKqFEQKmDQk1HOYcokwmGOwdJHVizXzF1Y5OnWRJ3ecZM/RWaoAmbW84iWbufu1G9h25TidrKvr3wKSUoh36kUUJPwlOuqQJLQmGEywBILs3QnK2rA8MCz3A7dcuYHb772K4iXbMcU2ke/ZdUCBqZfpH/4B+378c350eMjBBcsgKUs21Jw8ephEYFRVlKEixzJKpexjDZuOSLm8Si+LXLe1y+UbHJ0isrIc6DrHxOYOLgXy3GFcRgwjWTfOUZVDJIkxl1otiK1CSFCXA2ICnxUysIuB5HJyA6Mkg+V6pNJuZ0nRUcZIrMT7KdaJkEpCIwWKMkx0maccVtqZ1TpELBiq8sC5jDqK/QahwmWWotOhqiPVSPqXQMLbjIClrGpcEuS5CVoZVSNiQ6I2CeszyiBhJ8LEloThzDmqQYnPPa9+zd289zffz8XZWf76r/5fBv1lBRGFbex8R6xQqKWmMY5Op8ugHGKtIbMFZTmS+xpKqWFdLv9da0oyhjzvCKOtquWMignnRPI5Kgfa7xh8JjVRVVUMRyNiLT6ARVHIwEPDIYxNjI2NkaKh1kTTq6++mg996ENs3ryZj33sY8wvLGCd4+LFWfUQ8lRlpWEkkbIsMcZJUEwKVFo/GmPJsgxIXH75Fdxzz9285z2/wWc/+xn+7Xv/Sjkq8ZkjRqiqkfwda8nzjlSMRqSMzokvXVkrEKd1f1VXxJjodQoGgwEgXlpW6+CkvU6C1u6lqdObfi+lRFmWpBQoslykaXXVDmucExa7856orDhrjA5wpPYsikKCUeo1q5s6Roosw3uvAVB1+z2t1sCX/hvNPQMB0LMs0/4+MBqNyPNc+oukARsxinIhy+R76RC2KArp4auKblHQHw6p65puUWCtpQpBGMpWlBEN66nWa8vyXA54YxgOh2RZJtdTixWDNc2wUogxkHR9GrwXlntV1zhjW2ZWCAFvLcNSztCG2VbqOyuelFGY1jGSqUdkVdftAPLCzLmm9Phf/nrxPZQ6kspjvTKTskxlM6ZlyDTMolDXVJUkotE0pzGq1CkoQJCkoa6VRBtqBRCUveCsNr8Wm3mic9g8JxiBrLpqhG2NJQBlFNkEJuJcTRUFOLHI4ktBgnCbtsBYT2P21kyIqrJmMBhRlkP1VjB4bxjXBDHL/9/eGa1qsh11/Fdrre7v23tmzswxHg3BIEhQ1HhhLrzyCYxXQt7De8HnCHkFwShE8hJCVARRc5AkhuA5MeeczOzZu3utVeVFVfee5G6TgCj1g7kYZmbP1/11r1XrX1X/gmblbGPwdiXPBC/iL8FeXJQopszuWfkZWaPdjDIKahumFR2DVhtjv49DU0zpialWQyeLFK7Liqw+yW4AfR5jmS2ykt6XP0IoO2wQ/Frdu8V/tmcZW2S1W2s8zMcX+jiYzDD088xajS4NO6f4zfieavR0HxOS3HwXbE4KPloc6+4VIm6k2qc/8E0O7wnXdsY7/bClFgZHuSYgPjXouF/X5UIti2dhCyx14b7v/j1gZ9WTG2uWU0Q8qpnmaWp7tC3F/wXsc3gFxjyminUqR7YYD/Z3D9qlCS3K9AsSvja+yWwoS21+ncVNnQ//IZEI6MxbWpZlRY9naniLWWsNUzfN1jis732iUa3StfPmp2+gTy63Nyxlie9bolLB/SaKJ6a9os/8HbjExLhY3UHDC0T9nXSjfej77hveHGybH3xcOI5JeFPdq0q9aurZ7Q1rtNf57dIQsipDCjfLGiJDQfGKOHRi2r0V6jy0G9rH+Zk2nWgtXJeFCC+wGpUKIbyt60IVoW+bt9qosivMtrFKi6lTsKuxHOtP8UMnM9YihK7GgtLK4tPYxoyDrIs3S6luYFzEq0FiM7ASPkjVhcH7z95i7Uq7uVCKsNL8ml269IMobqiLCCwN2opglKjAISplxJSCtxCqHLWDFt5f9bT+nXPGfTWWaPkcW+f+7T3Ley9Yrys6uldDiK/Jx6TOWnD/qqg+KmJne3IUkbphsCk9DplajLU2r3grlVkOg0W8+zWEBc9qz9MU+dIWVAclrkGqIHpU7PhaU8vRHhpVrYaLK+CVSnNSC5gOiJa/Q/A6Ki2OKkP36gnRKtYcMNbr1dcdEffiikqnfU5qKzFBi7OSTIggotWzrdTbF82DuFKg756sKF41N+Y4F4+jSFsiOHA9X0/RG/PKEJNwkCvus+QVjce0qsgDGDC99bKUyEhbTOwUvNph4cxWuTk+p1BpFGbxta+YsAjUKYy5U2eHy+JVAvLYYllafXxexY2gTdSTREMRfONxbx+vspPqX8aYLri0Vr3gIto3FW+vdgFnAO6jVJo/k9YUH0DglUiqk9IKo89zzzoGSSiGjckxU0VVvEK0FD+wakzj6z0O9B7o9T48iMVoraLDfYMoQm0FWVYeQsjBog27+mFFSpj5SzkruA5DzVIqNs2HRAD9GBayrr6uqO8Lk8fDRDH1VnH1qXMWwjasMHcPcO8fWOczZFkOPdC/hxB+qhGt55MmCzKgKz4xbkzGPlkWOS0JfJuMRCDqSZ4mMDytpyEwI3q2TwjCr/36+3z/Bx9xd/ea2+vnoMQktbGjvdMWHyxwtEhiXmVTqazibaBTFO1GU0+4gTAQmjWvIg0POCIh5gM/PEFSIiH0/vMLP3m+cfdGuX9Zub2pXBZF6czugdDEfWS0D6/SqQWzAert9yKdpRllNsSUaSOEFM5hDm5s5s+/zMpSvZrmXh+46IXdFpTJ+t6Vu4/uufvkgfp+5RLPSGndFyYrvv5VT9qJGnL16jdPtrqoXqSgNQSWaL3uXRlirAjSBOoKu2IPE5WJrO5HNKPtr95Uxl2hjlvasyvgfprFJiKLC0UCKjtig6ILlBWTCXYH8wbqitDBBlJegFzDL2tHZkyGY0HkGVYfsOWWy7NX2P6afTc+//n3+d3ffstPPn3D9z++Z0f5t//8lN94deH95ysf/MriVUlTsPIAcvFdXhpNFrDGLMOvaxo1qoqKekJS6SxFeHHtzFH5lx/+lBd/f8OXbn7I+qtGvfymP89theUlt7/1R/zOT/+bT7Z/5k4H33tT2MbR6u0xxVIrU4T9/t73hYJXfx0V363w+n7jez9Smq68+qAw9zvmunolXsSspW5YA7ph+w7Ryqf7zqDj3vnik/xMmFOY8w5rCzIHjMEwrzz2eC724KHRwhzTIm34/lHcZ8rjcQUr2O7nMJGBmccKfeynf42OjWqxZtcVHcbDfWet7tU1LHzDpleTF3FPRE8S+QAff3Pd1kPNxY7DJeeIbfvWGeLBxLPrM/7kT/+M5y9e8Td/+1ds+93p2eP7J+zbnVdEN19vzCZ3d298HZvKPjbfO0ZUi4vQR0dCRKlxLu19hyGPVcLi4oG/yu71eQgAFp6C7ttYsUiq2jEUIM6wb9++BfOK6efPXvDVr36Vr3zlK3zzm3/Nv3/3X31vidhDpIYA47YQrT222+1jOw5qEae4aEKBL//Bl/na177Ghx9+yD/+03diujhhwk5U6np8dH//luOsTcRwEG35NtGJD0eIGPvh4SGuP+wjjrO3TR6TE0e7nJ1JukNA87/r3oD7OxW9GvYThrH3PXz3o1UdX4tVYdv6+RkBbyWvsI2dLb6b+KqYqnQNK5O4tnf9qMBj30e/Kk+Sbtt2ij97n+f+vL/z8w+z7dp8/73fNg77m/30RPU9dsTALA3RBvza7jd/Ftx2o/Lw8HAWMdRIIB5xQe/dB5DECWkfj59FMR627fz7vjvD/bad9+qoWPc3C/qRSO3dtVxxH0v05xKgvyC/fEEp2rtaW2jrhbYU1ur1ie6hgAshNrkfO7NrVKwQAaELH0QmbJoi04P+oSNMnP1G18UDsLZ6ifBlvfqBz46R8JUWhwK1TpcWY1YHVynUGHnfSmQMoyJFi7eZ1MhsD3waggfzk+3hPhziB7I0Wruy3lxYFleRmzRQY8env2mIMn10aqkxEjmMSSHEJq9KehgDG56lLxXMBtf1Qh8Pvnh2v1c6Q4QyZS0+aYbmmeepnvH3eykuzkkccnERp4mbWM8S80LMhTXV07XIjR/V/SqmjTAwHacQpmbYtNNPZw5D2kJpFw4DxUNkUU+Ocph3m+G+KBpl6xKtceaVYDVaaexs53AjsVXcn6kfPhzRKoJ44Dv75qaltdHWxTNT4j449+oCzxLq+YKclR0c9wFXdA+j0Y5Sj7L+yMBg6q1k6l4+pp3SqgcA0wPtMTtTJ+1yQxGvAKjVq/SmuQAx7KjIER5UWSUOH9HWgBztKuVcMO2YyGd+Ih7q1UD77odk9w8aLKXS5+DN6zdsr9+4n9elYdVHLvvBzxfAo/98be474S+VtwAWfIM1OWooHpX0o31JRLBjsxHDGi5dzDBYDr8s+qStN1yeP8cQFvWHX+VCr4VLWzFrWF0Y5u1mjxPiYrJUtCSttdJEGOIVi4rQbUIV9gKrFRZAiwdGu/kY4GUpjN7DPN4No2VZmWOyh0hxrGFdB0tdvWqsuMlpbd7r7BUq0VIjh1m2b67VogVMvC1FzOJg6Pfb3x9vV923jXb7Xhi7wqbD1w+8em4yuUTLh1Wv+LHw9qoCUwfHNDnBKzmkih80rCHa0ci8UPzwXEr1dp1SsFqQpXH/5jUDo62NJkD1h6NJZQLbviFreL8ck4QiCJTiZsEmwuhKae4x5x4xGgKTi05q0GKT9D8NM37zwOPInJV2tLcIwxvL/XhYvZVK7WjRefxZcgTK5pVbPSo+XWcpPBxTqMJwWPXI0R5964+m22LCHu/wpVT3rYt/q+Zr64g309tFH4OWAnHA5QxuPAPmlTtM9TXeos3VHt/p85oUN8I2Xxs7/p6rKsuMazY722OLv3ogJdqEYy21aE0xb091mwz/bgYTqwsqK0r3NkaMgh8oVApFXIwUcbHFNAJvnWxjUoZy2wpX9T3TJv5gYue+fsxqMQTE23o0DLrn8KVhmk/eFCn+DMe/V+Sc6kgESXN4i+wxAW9dr1BWLuszWls5AnphRoWU7zdjuBCFRFVKEWy4+I4s3to84omI+6biz5lOr16c81FAXdcLcw4E8eTFsvF2272oMEQai4pbsyNQ9adVQqh/3MPKKTqpeLJp18MjRxBplKKxH0yvphvuTzd0Zx+DJivTYDVDdaePnWVXbprvXSbV11GDirEX5SKFWXwv9sQfjO5rrdQ47IrHBoTHg4i3lxXz9XDaRPugRQg19DDELYhOXr56wfqDj/j449e8d/uSeuP/VzGj2oRh0BYXippgNuixB1ZZaLUw4vPPWSBG2ZsUBvMUSFUUGYMiLQ5SC93eekFNEW5X44OXCx+/3Zi7Mkdz36hS0Dq9Si/8ZNYhbKJcxRNAtQhFhscKcYjyacO+bpXiwk1Y+yC1sits06u61iKUfkXFWBYYE1qD6yrcvd6oNxW7Ni5SqX31akrMM9AINEW0spty20DWqLplRrsguCfdBIvskBi6VaRCKYotHR0rZdtRBlJWFxu6V1hM2RmfvYXLjCEs4r/KA8bubeXDKzKnfUa9PEPKLcyKyluPFwxMPvV9ojZ8dNwKsrjQSIdaEbvSVmUyWF9uvOyd0a988Qsf8KUv3vPjT7/P6115aw/8w3/8mPc+d+HZdeX20mKvvPE1YiyeZKhGKxrVLx530CYDn8hcy8CsUW0wivBshTGU73z3I64vlS/+YUHeXxFpUC5QXsH1C7z6vT/m9z/7CZ++/hF39/BfekzvdYHheBy5rNg2XJAQQAazLrT1ik7l9b7zw086c6ksrKz4vS3LoJWLR9ym6OK9A8UqVE841FmAeRrjlwkLRleBLtS6oDMM9QuUKZgVhEEVqAygMYqEYOxWFoKB9BBEYJQZooUnAh4FgUPJKExXThHxQSd9GE0aZS2UbmHHYdRinsw41v8CooU5xdezECEiUsAK9ClYmYiuIH7O+OyzT/jLv/hzF/HG8NhKjrgVmhgsha0PfIbLgmsDXsXm7ZYgYpiW8JIDQ86KlmMYxiEqSJwD4RCU/buB4kkOe6ycr9XtB3R4POHXFkmvEP+J88vd2zd8/etf5xvf+AZTPfEnEi6wkTj3xHz3ylGLAQAmkTOUU3A4vHdUlW9961t8+9t/5x52MXX7jGlEPFnHI34vvJLzjJ8Oaw0vZ/bpqXYcieXn4iQjbjI6/Jl818aEY5/T8DM8qvvjz89kyikMRswj3mUi8fw9Dq86Pvejz+a5f5p5rBRFCt6y/8512eNnO34vYj/z837GS8izmm4xQQ3d4lGY8vNdfL+ha2CcnRoWZxpMf+bnKsS7dXgw+zRKj6PtfGaQ4/rlsUUdop/Gi0e8QCa+k7i+QyR61/PruO4Rz9FxqDzuqZ/l3n0yfnF+6S1vSZIkSZIkSZIkSZIkyf9vyv/2B0iSJEmSJEmSJEmSJEn+b5GCUpIkSZIkSZIkSZIkSfIkUlBKkiRJkiRJkiRJkiRJnkQKSkmSJEmSJEmSJEmSJMmTSEEpSZIkSZIkSZIkSZIkeRIpKCVJkiRJkiRJkiRJkiRPIgWlJEmSJEmSJEmSJEmS5EmkoJQkSZIkSZIkSZIkSZI8iRSUkiRJkiRJkiRJkiRJkieRglKSJEmSJEmSJEmSJEnyJFJQSpIkSZIkSZIkSZIkSZ5ECkpJkiRJkiRJkiRJkiTJk0hBKUmSJEmSJEmSJEmSJHkSKSglSZIkSZIkSZIkSZIkTyIFpSRJkiRJkiRJkiRJkuRJpKCUJEmSJEmSJEmSJEmSPIkUlJIkSZIkSZIkSZIkSZInkYJSkiRJkiRJkiRJkiRJ8iRSUEqSJEmSJEmSJEmSJEmexP8AlsvZjP5pxXgAAAAASUVORK5CYII=\\n\",\n            \"text/plain\": [\n              \"<Figure size 3000x1000 with 1 Axes>\"\n            ]\n          },\n          \"metadata\": {\n            \"needs_background\": \"light\"\n          },\n          \"output_type\": \"display_data\"\n        }\n      ],\n      \"source\": [\n        \"#@title Draw a Mask, Press Finish, Wait for Inpainting\\n\",\n        \"\\n\",\n        \"if fname is None:\\n\",\n        \"  from google.colab import files\\n\",\n        \"  files = files.upload()\\n\",\n        \"  fname = list(files.keys())[0]\\n\",\n        \"else:\\n\",\n        \"  fname = wget.download(fname)\\n\",\n        \"\\n\",\n        \"shutil.rmtree('./data_for_prediction', ignore_errors=True)\\n\",\n        \"!mkdir data_for_prediction\\n\",\n        \"\\n\",\n        \"copyfile(fname, f'./data_for_prediction/{fname}')\\n\",\n        \"os.remove(fname)\\n\",\n        \"fname = f'./data_for_prediction/{fname}'\\n\",\n        \"\\n\",\n        \"image64 = base64.b64encode(open(fname, 'rb').read())\\n\",\n        \"image64 = image64.decode('utf-8')\\n\",\n        \"\\n\",\n        \"print(f'Will use {fname} for inpainting')\\n\",\n        \"img = np.array(plt.imread(f'{fname}')[:,:,:3])\\n\",\n        \"\\n\",\n        \"draw(image64, filename=f\\\"./{fname.split('.')[1]}_mask.png\\\", w=img.shape[1], h=img.shape[0], line_width=0.04*img.shape[1])\\n\",\n        \"#@title Show a masked image and save a mask\\n\",\n        \"import matplotlib.pyplot as plt\\n\",\n        \"plt.rcParams[\\\"figure.figsize\\\"] = (15,5)\\n\",\n        \"plt.rcParams['figure.dpi'] = 200\\n\",\n        \"plt.subplot(131)\\n\",\n        \"with_mask = np.array(plt.imread(f\\\"./{fname.split('.')[1]}_mask.png\\\")[:,:,:3])\\n\",\n        \"mask = (with_mask[:,:,0]==1)*(with_mask[:,:,1]==0)*(with_mask[:,:,2]==0)\\n\",\n        \"plt.imshow(mask, cmap='gray')\\n\",\n        \"plt.axis('off')\\n\",\n        \"plt.title('mask')\\n\",\n        \"plt.imsave(f\\\"./{fname.split('.')[1]}_mask.png\\\",mask, cmap='gray')\\n\",\n        \"\\n\",\n        \"plt.subplot(132)\\n\",\n        \"img = np.array(plt.imread(f'{fname}')[:,:,:3])\\n\",\n        \"plt.imshow(img)\\n\",\n        \"plt.axis('off')\\n\",\n        \"plt.title('img')\\n\",\n        \"\\n\",\n        \"plt.subplot(133)\\n\",\n        \"img = np.array((1-mask.reshape(mask.shape[0], mask.shape[1], -1))*plt.imread(fname)[:,:,:3])\\n\",\n        \"_=plt.imshow(img)\\n\",\n        \"_=plt.axis('off')\\n\",\n        \"_=plt.title('img * mask')\\n\",\n        \"plt.show()\\n\",\n        \"\\n\",\n        \"print('Run inpainting')\\n\",\n        \"if '.jpeg' in fname:\\n\",\n        \"  !PYTHONPATH=. TORCH_HOME=$(pwd) python3 bin/predict.py model.path=$(pwd)/big-lama indir=$(pwd)/data_for_prediction outdir=/content/output dataset.img_suffix=.jpeg > /dev/null\\n\",\n        \"elif '.jpg' in fname:\\n\",\n        \"  !PYTHONPATH=. TORCH_HOME=$(pwd) python3 bin/predict.py model.path=$(pwd)/big-lama indir=$(pwd)/data_for_prediction outdir=/content/output  dataset.img_suffix=.jpg > /dev/null\\n\",\n        \"elif '.png' in fname:\\n\",\n        \"  !PYTHONPATH=. TORCH_HOME=$(pwd) python3 bin/predict.py model.path=$(pwd)/big-lama indir=$(pwd)/data_for_prediction outdir=/content/output  dataset.img_suffix=.png > /dev/null\\n\",\n        \"else:\\n\",\n        \"  print(f'Error: unknown suffix .{fname.split(\\\".\\\")[-1]} use [.png, .jpeg, .jpg]')\\n\",\n        \"\\n\",\n        \"plt.rcParams['figure.dpi'] = 200\\n\",\n        \"plt.imshow(plt.imread(f\\\"/content/output/{fname.split('.')[1].split('/')[2]}_mask.png\\\"))\\n\",\n        \"_=plt.axis('off')\\n\",\n        \"_=plt.title('inpainting result')\\n\",\n        \"plt.show()\\n\",\n        \"fname = None\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": null,\n      \"metadata\": {\n        \"id\": \"Ug9vfkBHqxzZ\"\n      },\n      \"outputs\": [],\n      \"source\": [\n        \"\"\n      ]\n    }\n  ],\n  \"metadata\": {\n    \"accelerator\": \"GPU\",\n    \"colab\": {\n      \"collapsed_sections\": [],\n      \"name\": \"LaMa-inpainting.ipynb\",\n      \"provenance\": []\n    },\n    \"kernelspec\": {\n      \"display_name\": \"Python 3 (ipykernel)\",\n      \"language\": \"python\",\n      \"name\": \"python3\"\n    },\n    \"language_info\": {\n      \"codemirror_mode\": {\n        \"name\": \"ipython\",\n        \"version\": 3\n      },\n      \"file_extension\": \".py\",\n      \"mimetype\": \"text/x-python\",\n      \"name\": \"python\",\n      \"nbconvert_exporter\": \"python\",\n      \"pygments_lexer\": \"ipython3\",\n      \"version\": \"3.9.7\"\n    }\n  },\n  \"nbformat\": 4,\n  \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "model_cards/lama/conda_env.yml",
    "content": "name: lama\nchannels:\n  - defaults\n  - conda-forge\ndependencies:\n  - _libgcc_mutex=0.1=main\n  - _openmp_mutex=4.5=1_gnu\n  - absl-py=0.13.0=py36h06a4308_0\n  - aiohttp=3.7.4.post0=py36h7f8727e_2\n  - antlr-python-runtime=4.8=py36h9f0ad1d_2\n  - async-timeout=3.0.1=py36h06a4308_0\n  - attrs=21.2.0=pyhd3eb1b0_0\n  - blas=1.0=mkl\n  - blinker=1.4=py36h06a4308_0\n  - brotlipy=0.7.0=py36h27cfd23_1003\n  - bzip2=1.0.8=h7b6447c_0\n  - c-ares=1.17.1=h27cfd23_0\n  - ca-certificates=2021.7.5=h06a4308_1\n  - cachetools=4.2.2=pyhd3eb1b0_0\n  - certifi=2021.5.30=py36h06a4308_0\n  - cffi=1.14.6=py36h400218f_0\n  - chardet=4.0.0=py36h06a4308_1003\n  - charset-normalizer=2.0.4=pyhd3eb1b0_0\n  - click=8.0.1=pyhd3eb1b0_0\n  - cloudpickle=2.0.0=pyhd3eb1b0_0\n  - coverage=5.5=py36h27cfd23_2\n  - cryptography=3.4.7=py36hd23ed53_0\n  - cudatoolkit=10.2.89=hfd86e86_1\n  - cycler=0.10.0=py36_0\n  - cython=0.29.24=py36h295c915_0\n  - cytoolz=0.11.0=py36h7b6447c_0\n  - dask-core=1.1.4=py36_1\n  - dataclasses=0.8=pyh4f3eec9_6\n  - dbus=1.13.18=hb2f20db_0\n  - decorator=5.0.9=pyhd3eb1b0_0\n  - easydict=1.9=py_0\n  - expat=2.4.1=h2531618_2\n  - ffmpeg=4.2.2=h20bf706_0\n  - fontconfig=2.13.1=h6c09931_0\n  - freetype=2.10.4=h5ab3b9f_0\n  - fsspec=2021.8.1=pyhd3eb1b0_0\n  - future=0.18.2=py36_1\n  - glib=2.69.1=h5202010_0\n  - gmp=6.2.1=h2531618_2\n  - gnutls=3.6.15=he1e5248_0\n  - google-auth=1.33.0=pyhd3eb1b0_0\n  - google-auth-oauthlib=0.4.4=pyhd3eb1b0_0\n  - grpcio=1.36.1=py36h2157cd5_1\n  - gst-plugins-base=1.14.0=h8213a91_2\n  - gstreamer=1.14.0=h28cd5cc_2\n  - hydra-core=1.1.0=pyhd8ed1ab_0\n  - icu=58.2=he6710b0_3\n  - idna=3.2=pyhd3eb1b0_0\n  - idna_ssl=1.1.0=py36h06a4308_0\n  - imageio=2.9.0=pyhd3eb1b0_0\n  - importlib-metadata=4.8.1=py36h06a4308_0\n  - importlib_resources=5.2.0=pyhd3eb1b0_1\n  - intel-openmp=2021.3.0=h06a4308_3350\n  - joblib=1.0.1=pyhd3eb1b0_0\n  - jpeg=9b=h024ee3a_2\n  - kiwisolver=1.3.1=py36h2531618_0\n  - lame=3.100=h7b6447c_0\n  - lcms2=2.12=h3be6417_0\n  - ld_impl_linux-64=2.35.1=h7274673_9\n  - libblas=3.9.0=11_linux64_mkl\n  - libcblas=3.9.0=11_linux64_mkl\n  - libffi=3.3=he6710b0_2\n  - libgcc-ng=9.3.0=h5101ec6_17\n  - libgfortran-ng=9.3.0=ha5ec8a7_17\n  - libgfortran5=9.3.0=ha5ec8a7_17\n  - libgomp=9.3.0=h5101ec6_17\n  - libidn2=2.3.2=h7f8727e_0\n  - liblapack=3.9.0=11_linux64_mkl\n  - libopus=1.3.1=h7b6447c_0\n  - libpng=1.6.37=hbc83047_0\n  - libprotobuf=3.17.2=h4ff587b_1\n  - libstdcxx-ng=9.3.0=hd4cf53a_17\n  - libtasn1=4.16.0=h27cfd23_0\n  - libtiff=4.2.0=h85742a9_0\n  - libunistring=0.9.10=h27cfd23_0\n  - libuuid=1.0.3=h1bed415_2\n  - libuv=1.40.0=h7b6447c_0\n  - libvpx=1.7.0=h439df22_0\n  - libwebp-base=1.2.0=h27cfd23_0\n  - libxcb=1.14=h7b6447c_0\n  - libxml2=2.9.12=h03d6c58_0\n  - lz4-c=1.9.3=h295c915_1\n  - markdown=3.3.4=py36h06a4308_0\n  - matplotlib=3.3.4=py36h06a4308_0\n  - matplotlib-base=3.3.4=py36h62a2d02_0\n  - mkl=2021.3.0=h06a4308_520\n  - multidict=5.1.0=py36h27cfd23_2\n  - ncurses=6.2=he6710b0_1\n  - nettle=3.7.3=hbbd107a_1\n  - networkx=2.2=py36_1\n  - ninja=1.10.2=hff7bd54_1\n  - numpy=1.19.5=py36hfc0c790_2\n  - oauthlib=3.1.1=pyhd3eb1b0_0\n  - olefile=0.46=py36_0\n  - omegaconf=2.1.1=py36h5fab9bb_0\n  - openh264=2.1.0=hd408876_0\n  - openjpeg=2.4.0=h3ad879b_0\n  - openssl=1.1.1l=h7f8727e_0\n  - packaging=21.0=pyhd3eb1b0_0\n  - pandas=1.1.5=py36h284efc9_0\n  - pcre=8.45=h295c915_0\n  - pillow=8.3.1=py36h2c7a002_0\n  - pip=21.0.1=py36h06a4308_0\n  - protobuf=3.17.2=py36h295c915_0\n  - pyasn1=0.4.8=pyhd3eb1b0_0\n  - pyasn1-modules=0.2.8=py_0\n  - pycparser=2.20=py_2\n  - pyjwt=2.1.0=py36h06a4308_0\n  - pyopenssl=20.0.1=pyhd3eb1b0_1\n  - pyparsing=2.4.7=pyhd3eb1b0_0\n  - pyqt=5.9.2=py36h05f1152_2\n  - pysocks=1.7.1=py36h06a4308_0\n  - python=3.6.13=h12debd9_1\n  - python-dateutil=2.8.2=pyhd3eb1b0_0\n  - python_abi=3.6=2_cp36m\n  - pytz=2021.1=pyhd3eb1b0_0\n  - pywavelets=1.1.1=py36h7b6447c_2\n  - pyyaml=5.4.1=py36h27cfd23_1\n  - qt=5.9.7=h5867ecd_1\n  - readline=8.1=h27cfd23_0\n  - requests=2.26.0=pyhd3eb1b0_0\n  - requests-oauthlib=1.3.0=py_0\n  - rsa=4.7.2=pyhd3eb1b0_1\n  - scikit-image=0.17.2=py36h284efc9_4\n  - scikit-learn=0.24.2=py36ha9443f7_0\n  - scipy=1.5.3=py36h9e8f40b_0\n  - setuptools=58.0.4=py36h06a4308_0\n  - sip=4.19.8=py36hf484d3e_0\n  - six=1.16.0=pyhd3eb1b0_0\n  - sqlite=3.36.0=hc218d9a_0\n  - tabulate=0.8.9=py36h06a4308_0\n  - tensorboard=2.4.0=pyhc547734_0\n  - tensorboard-plugin-wit=1.6.0=py_0\n  - threadpoolctl=2.2.0=pyh0d69192_0\n  - tifffile=2020.10.1=py36hdd07704_2\n  - tk=8.6.11=h1ccaba5_0\n  - toolz=0.11.1=pyhd3eb1b0_0\n  - tqdm=4.62.2=pyhd3eb1b0_1\n  - typing-extensions=3.10.0.2=hd3eb1b0_0\n  - typing_extensions=3.10.0.2=pyh06a4308_0\n  - urllib3=1.26.6=pyhd3eb1b0_1\n  - werkzeug=2.0.1=pyhd3eb1b0_0\n  - wheel=0.37.0=pyhd3eb1b0_1\n  - x264=1!157.20191217=h7b6447c_0\n  - xz=5.2.5=h7b6447c_0\n  - yaml=0.2.5=h7b6447c_0\n  - yarl=1.6.3=py36h27cfd23_0\n  - zipp=3.5.0=pyhd3eb1b0_0\n  - zlib=1.2.11=h7b6447c_3\n  - zstd=1.4.9=haebb681_0\n  - pip:\n    - albumentations==0.5.2\n    - braceexpand==0.1.7\n    - imgaug==0.4.0\n    - kornia==0.5.0\n    - opencv-python==4.5.3.56\n    - opencv-python-headless==4.5.3.56\n    - shapely==1.7.1\n    - webdataset==0.1.76\n    - wldhx-yadisk-direct==0.0.6\n"
  },
  {
    "path": "model_cards/lama/configs/analyze_mask_errors.yaml",
    "content": "dataset_kwargs:\n  img_suffix: .jpg\n  inpainted_suffix: .jpg\n\ntake_global_top: 30\ntake_worst_best_top: 30\ntake_overlapping_top: 30"
  },
  {
    "path": "model_cards/lama/configs/data_gen/random_medium_256.yaml",
    "content": "generator_kind: random\n\nmask_generator_kwargs:\n  irregular_proba: 1\n  irregular_kwargs:\n    min_times: 4\n    max_times: 5\n    max_width: 50\n    max_angle: 4\n    max_len: 100\n\n  box_proba: 0.3\n  box_kwargs:\n    margin: 0\n    bbox_min_size: 10\n    bbox_max_size: 50\n    max_times: 5\n    min_times: 1\n\n  segm_proba: 0\n  squares_proba: 0\n\n  variants_n: 5\n\nmax_masks_per_image: 1\n\ncropping:\n  out_min_size: 256\n  handle_small_mode: upscale\n  out_square_crop: True\n  crop_min_overlap: 1\n\nmax_tamper_area: 0.5\n"
  },
  {
    "path": "model_cards/lama/configs/data_gen/random_medium_512.yaml",
    "content": "generator_kind: random\n\nmask_generator_kwargs:\n  irregular_proba: 1\n  irregular_kwargs:\n    min_times: 4\n    max_times: 10\n    max_width: 100\n    max_angle: 4\n    max_len: 200\n\n  box_proba: 0.3\n  box_kwargs:\n    margin: 0\n    bbox_min_size: 30\n    bbox_max_size: 150\n    max_times: 5\n    min_times: 1\n\n  segm_proba: 0\n  squares_proba: 0\n\n  variants_n: 5\n\nmax_masks_per_image: 1\n\ncropping:\n  out_min_size: 512\n  handle_small_mode: upscale\n  out_square_crop: True\n  crop_min_overlap: 1\n\nmax_tamper_area: 0.5\n"
  },
  {
    "path": "model_cards/lama/configs/data_gen/random_thick_256.yaml",
    "content": "generator_kind: random\n\nmask_generator_kwargs:\n  irregular_proba: 1\n  irregular_kwargs:\n    min_times: 1\n    max_times: 5\n    max_width: 100\n    max_angle: 4\n    max_len: 200\n\n  box_proba: 0.3\n  box_kwargs:\n    margin: 10\n    bbox_min_size: 30\n    bbox_max_size: 150\n    max_times: 3\n    min_times: 1\n\n  segm_proba: 0\n  squares_proba: 0\n\n  variants_n: 5\n\nmax_masks_per_image: 1\n\ncropping:\n  out_min_size: 256\n  handle_small_mode: upscale\n  out_square_crop: True\n  crop_min_overlap: 1\n\nmax_tamper_area: 0.5\n"
  },
  {
    "path": "model_cards/lama/configs/data_gen/random_thick_512.yaml",
    "content": "generator_kind: random\n\nmask_generator_kwargs:\n  irregular_proba: 1\n  irregular_kwargs:\n    min_times: 1\n    max_times: 5\n    max_width: 250\n    max_angle: 4\n    max_len: 450\n\n  box_proba: 0.3\n  box_kwargs:\n    margin: 10\n    bbox_min_size: 30\n    bbox_max_size: 300\n    max_times: 4\n    min_times: 1\n\n  segm_proba: 0\n  squares_proba: 0\n\n  variants_n: 5\n\nmax_masks_per_image: 1\n\ncropping:\n  out_min_size: 512\n  handle_small_mode: upscale\n  out_square_crop: True\n  crop_min_overlap: 1\n\nmax_tamper_area: 0.5\n"
  },
  {
    "path": "model_cards/lama/configs/data_gen/random_thin_256.yaml",
    "content": "generator_kind: random\n\nmask_generator_kwargs:\n  irregular_proba: 1\n  irregular_kwargs:\n    min_times: 4\n    max_times: 50\n    max_width: 10\n    max_angle: 4\n    max_len: 40\n  box_proba: 0\n  segm_proba: 0\n  squares_proba: 0\n\n  variants_n: 5\n\nmax_masks_per_image: 1\n\ncropping:\n  out_min_size: 256\n  handle_small_mode: upscale\n  out_square_crop: True\n  crop_min_overlap: 1\n\nmax_tamper_area: 0.5\n"
  },
  {
    "path": "model_cards/lama/configs/data_gen/random_thin_512.yaml",
    "content": "generator_kind: random\n\nmask_generator_kwargs:\n  irregular_proba: 1\n  irregular_kwargs:\n    min_times: 4\n    max_times: 70\n    max_width: 20\n    max_angle: 4\n    max_len: 100\n  box_proba: 0\n  segm_proba: 0\n  squares_proba: 0\n\n  variants_n: 5\n\nmax_masks_per_image: 1\n\ncropping:\n  out_min_size: 512\n  handle_small_mode: upscale\n  out_square_crop: True\n  crop_min_overlap: 1\n\nmax_tamper_area: 0.5\n"
  },
  {
    "path": "model_cards/lama/configs/debug_mask_gen.yaml",
    "content": "img_ext: .jpg\n\ngen_kwargs:\n  mask_size: 200\n  step: 0.5\n"
  },
  {
    "path": "model_cards/lama/configs/eval1.yaml",
    "content": "evaluator_kwargs:\n  batch_size: 8\n\ndataset_kwargs:\n  img_suffix: .png\n  inpainted_suffix: .jpg"
  },
  {
    "path": "model_cards/lama/configs/eval2.yaml",
    "content": "evaluator_kwargs:\n  batch_size: 8\n  device: cuda\n\ndataset_kwargs:\n  img_suffix: .png\n  inpainted_suffix: .png"
  },
  {
    "path": "model_cards/lama/configs/eval2_cpu.yaml",
    "content": "evaluator_kwargs:\n  batch_size: 8\n  device: cpu\n\ndataset_kwargs:\n  img_suffix: .png\n  inpainted_suffix: .png"
  },
  {
    "path": "model_cards/lama/configs/eval2_gpu.yaml",
    "content": "evaluator_kwargs:\n  batch_size: 8\n  \ndataset_kwargs:\n  img_suffix: .png\n  inpainted_suffix: .png"
  },
  {
    "path": "model_cards/lama/configs/eval2_jpg.yaml",
    "content": "evaluator_kwargs:\n  batch_size: 8\n\ndataset_kwargs:\n  img_suffix: .png\n  inpainted_suffix: .jpg"
  },
  {
    "path": "model_cards/lama/configs/eval2_segm.yaml",
    "content": "evaluator_kwargs:\n  batch_size: 8\n\ndataset_kwargs:\n  img_suffix: .png\n  inpainted_suffix: .png\n\nsegmentation:\n  enable: True\n  weights_path: ${TORCH_HOME}\n"
  },
  {
    "path": "model_cards/lama/configs/eval2_segm_test.yaml",
    "content": "evaluator_kwargs:\n  batch_size: 1\n\ndataset_kwargs:\n  img_suffix: _input.png\n  inpainted_suffix: .png\n  pad_out_to_modulo: 8\n\nsegmentation:\n  enable: True\n  weights_path: ${TORCH_HOME}\n"
  },
  {
    "path": "model_cards/lama/configs/eval2_test.yaml",
    "content": "evaluator_kwargs:\n  batch_size: 1\n\ndataset_kwargs:\n  img_suffix: _input.png\n  inpainted_suffix: .png\n  pad_out_to_modulo: 8\n"
  },
  {
    "path": "model_cards/lama/configs/places2-categories_157.txt",
    "content": "/a/airplane_cabin 1\n/a/airport_terminal 2\n/a/alcove 3\n/a/alley 4\n/a/amphitheater 5\n/a/amusement_park 7\n/a/apartment_building/outdoor 8\n/a/aqueduct 10\n/a/arcade 11\n/a/arch 12\n/a/archive 14\n/a/art_gallery 19\n/a/artists_loft 22\n/a/assembly_line 23\n/a/atrium/public 25\n/a/attic 26\n/a/auditorium 27\n/b/bakery/shop 31\n/b/balcony/exterior 32\n/b/balcony/interior 33\n/b/ballroom 35\n/b/banquet_hall 38\n/b/barndoor 41\n/b/basement 43\n/b/basketball_court/indoor 44\n/b/bathroom 45\n/b/bazaar/indoor 46\n/b/bazaar/outdoor 47\n/b/beach_house 49\n/b/bedchamber 51\n/b/bedroom 52\n/b/berth 55\n/b/boardwalk 57\n/b/boathouse 59\n/b/bookstore 60\n/b/booth/indoor 61\n/b/bow_window/indoor 63\n/b/bowling_alley 64\n/b/bridge 66\n/b/building_facade 67\n/b/bus_interior 70\n/b/bus_station/indoor 71\n/c/cabin/outdoor 74\n/c/campus 77\n/c/canal/urban 79\n/c/candy_store 80\n/c/carrousel 83\n/c/castle 84\n/c/chalet 87\n/c/childs_room 89\n/c/church/indoor 90\n/c/church/outdoor 91\n/c/closet 95\n/c/conference_center 101\n/c/conference_room 102\n/c/construction_site 103\n/c/corridor 106\n/c/cottage 107\n/c/courthouse 108\n/c/courtyard 109\n/d/delicatessen 114\n/d/department_store 115\n/d/diner/outdoor 119\n/d/dining_hall 120\n/d/dining_room 121\n/d/doorway/outdoor 123\n/d/dorm_room 124\n/d/downtown 125\n/d/driveway 127\n/e/elevator/door 129\n/e/elevator_lobby 130\n/e/elevator_shaft 131\n/e/embassy 132\n/e/entrance_hall 134\n/e/escalator/indoor 135\n/f/fastfood_restaurant 139\n/f/fire_escape 143\n/f/fire_station 144\n/f/food_court 148\n/g/galley 155\n/g/garage/outdoor 157\n/g/gas_station 158\n/g/gazebo/exterior 159\n/g/general_store/indoor 160\n/g/general_store/outdoor 161\n/g/greenhouse/outdoor 166\n/g/gymnasium/indoor 168\n/h/hangar/outdoor 170\n/h/hardware_store 172\n/h/home_office 176\n/h/home_theater 177\n/h/hospital 178\n/h/hotel/outdoor 181\n/h/hotel_room 182\n/h/house 183\n/h/hunting_lodge/outdoor 184\n/i/industrial_area 192\n/i/inn/outdoor 193\n/j/jacuzzi/indoor 195\n/j/jail_cell 196\n/k/kasbah 200\n/k/kitchen 203\n/l/laundromat 208\n/l/library/indoor 212\n/l/library/outdoor 213\n/l/lighthouse 214\n/l/living_room 215\n/l/loading_dock 216\n/l/lobby 217\n/l/lock_chamber 218\n/m/mansion 220\n/m/manufactured_home 221\n/m/mausoleum 226\n/m/medina 227\n/m/mezzanine 228\n/m/mosque/outdoor 230\n/m/movie_theater/indoor 235\n/m/museum/outdoor 237\n/n/nursery 240\n/o/oast_house 242\n/o/office 244\n/o/office_building 245\n/o/office_cubicles 246\n/p/pagoda 251\n/p/palace 252\n/p/pantry 253\n/p/parking_garage/indoor 255\n/p/parking_garage/outdoor 256\n/p/pavilion 260\n/p/pet_shop 261\n/p/porch 272\n/r/reception 280\n/r/recreation_room 281\n/r/restaurant_patio 286\n/r/rope_bridge 291\n/r/ruin 292\n/s/sauna 295\n/s/schoolhouse 296\n/s/server_room 298\n/s/shed 299\n/s/shopfront 301\n/s/shopping_mall/indoor 302\n/s/shower 303\n/s/skyscraper 307\n/s/staircase 317\n/s/storage_room 318\n/s/subway_station/platform 320\n/s/synagogue/outdoor 327\n/t/television_room 328\n/t/temple/asia 330\n/t/throne_room 331\n/t/tower 334\n/t/train_station/platform 337\n/u/utility_room 343\n/w/waiting_room 352\n/w/wet_bar 358\n/y/youth_hostel 363"
  },
  {
    "path": "model_cards/lama/configs/prediction/default.yaml",
    "content": "indir: no  # to be overriden in CLI\noutdir: no  # to be overriden in CLI\n\nmodel:\n  path: no  # to be overriden in CLI\n  checkpoint: best.ckpt\n\ndataset:\n  kind: default\n  img_suffix: .png\n  pad_out_to_modulo: 8\n\ndevice: cuda\nout_key: inpainted\n\nrefine: False # refiner will only run if this is True\nrefiner:\n  gpu_ids: 0,1 # the GPU ids of the machine to use. If only single GPU, use: \"0,\"\n  modulo: ${dataset.pad_out_to_modulo}\n  n_iters: 15 # number of iterations of refinement for each scale\n  lr: 0.002 # learning rate\n  min_side: 512 # all sides of image on all scales should be >= min_side / sqrt(2)\n  max_scales: 3 # max number of downscaling scales for the image-mask pyramid\n  px_budget: 1800000 # pixels budget. Any image will be resized to satisfy height*width <= px_budget"
  },
  {
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    "path": "model_cards/lama/configs/training/ablv2_work.yaml",
    "content": "run_title: ''\n\ntraining_model:\n  kind: default\n  visualize_each_iters: 1000\n  concat_mask: True\n  store_discr_outputs_for_vis: True\n\nlosses:\n  l1:\n    weight_missing: 0\n    weight_known: 10\n  perceptual:\n    weight: 0\n  adversarial:\n    kind: r1\n    weight: 10\n    gp_coef: 0.001\n    mask_as_fake_target: True\n    allow_scale_mask: True\n  feature_matching:\n    weight: 100\n  resnet_pl:\n    weight: 30\n    weights_path: ${env:TORCH_HOME}\n\ndefaults:\n  - location: docker\n  - data: abl-04-256-mh-dist\n  - generator: pix2pixhd_global_sigmoid\n  - discriminator: pix2pixhd_nlayer\n  - optimizers: default_optimizers\n  - visualizer: directory\n  - evaluator: default_inpainted\n  - trainer: any_gpu_large_ssim_ddp_final\n  - hydra: overrides\n"
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    "path": "model_cards/lama/configs/training/ablv2_work_ffc075.yaml",
    "content": "run_title: ''\n\ntraining_model:\n  kind: default\n  visualize_each_iters: 1000\n  concat_mask: True\n  store_discr_outputs_for_vis: True\n\nlosses:\n  l1:\n    weight_missing: 0\n    weight_known: 10\n  perceptual:\n    weight: 0\n  adversarial:\n    kind: r1\n    weight: 10\n    gp_coef: 0.001\n    mask_as_fake_target: True\n    allow_scale_mask: True\n  feature_matching:\n    weight: 100\n  resnet_pl:\n    weight: 30\n    weights_path: ${env:TORCH_HOME}\n\ndefaults:\n  - location: docker\n  - data: abl-04-256-mh-dist\n  - generator: ffc_resnet_075\n  - discriminator: pix2pixhd_nlayer\n  - optimizers: default_optimizers\n  - visualizer: directory\n  - evaluator: default_inpainted\n  - trainer: any_gpu_large_ssim_ddp_final\n  - hydra: overrides\n"
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    "path": "model_cards/lama/configs/training/ablv2_work_md.yaml",
    "content": "run_title: ''\n\ntraining_model:\n  kind: default\n  visualize_each_iters: 1000\n  concat_mask: True\n  store_discr_outputs_for_vis: True\n\nlosses:\n  l1:\n    weight_missing: 0\n    weight_known: 10\n  perceptual:\n    weight: 0\n  adversarial:\n    kind: r1\n    weight: 10\n    gp_coef: 0.001\n    mask_as_fake_target: True\n    allow_scale_mask: True\n  feature_matching:\n    weight: 100\n  resnet_pl:\n    weight: 30\n    weights_path: ${env:TORCH_HOME}\n\ndefaults:\n  - location: docker\n  - data: abl-04-256-mh-dist\n  - generator: pix2pixhd_multidilated_catin_4dil_9b\n  - discriminator: pix2pixhd_nlayer\n  - optimizers: default_optimizers\n  - visualizer: directory\n  - evaluator: default_inpainted\n  - trainer: any_gpu_large_ssim_ddp_final_benchmark\n  - hydra: overrides\n"
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    "path": "model_cards/lama/configs/training/ablv2_work_no_fm.yaml",
    "content": "run_title: ''\n\ntraining_model:\n  kind: default\n  visualize_each_iters: 1000\n  concat_mask: True\n  store_discr_outputs_for_vis: True\n\nlosses:\n  l1:\n    weight_missing: 0\n    weight_known: 10\n  perceptual:\n    weight: 0\n  adversarial:\n    kind: r1\n    weight: 10\n    gp_coef: 0.001\n    mask_as_fake_target: True\n    allow_scale_mask: True\n  feature_matching:\n    weight: 0\n  resnet_pl:\n    weight: 30\n    weights_path: ${env:TORCH_HOME}\n\ndefaults:\n  - location: mlp-mow-final\n  - data: abl-04-256-mh-dist\n  - generator: pix2pixhd_global_sigmoid\n  - discriminator: pix2pixhd_nlayer\n  - optimizers: default_optimizers\n  - visualizer: directory\n  - evaluator: default_inpainted\n  - trainer: any_gpu_large_ssim_ddp_final\n  - hydra: overrides\n"
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  {
    "path": "model_cards/lama/configs/training/ablv2_work_no_segmpl.yaml",
    "content": "run_title: ''\n\ntraining_model:\n  kind: default\n  visualize_each_iters: 1000\n  concat_mask: True\n  store_discr_outputs_for_vis: True\n\nlosses:\n  l1:\n    weight_missing: 0\n    weight_known: 10\n  perceptual:\n    weight: 0\n  adversarial:\n    kind: r1\n    weight: 10\n    gp_coef: 0.001\n    mask_as_fake_target: True\n    allow_scale_mask: True\n  feature_matching:\n    weight: 100\n  resnet_pl:\n    weight: 0\n#    weights_path: ${env:TORCH_HOME}\n\ndefaults:\n  - location: docker\n  - data: abl-04-256-mh-dist\n  - generator: pix2pixhd_global_sigmoid\n  - discriminator: pix2pixhd_nlayer\n  - optimizers: default_optimizers\n  - visualizer: directory\n  - evaluator: default_inpainted\n  - trainer: any_gpu_large_ssim_ddp_final\n  - hydra: overrides\n"
  },
  {
    "path": "model_cards/lama/configs/training/ablv2_work_no_segmpl_csdilirpl.yaml",
    "content": "run_title: ''\n\ntraining_model:\n  kind: default\n  visualize_each_iters: 1000\n  concat_mask: True\n  store_discr_outputs_for_vis: True\n\nlosses:\n  l1:\n    weight_missing: 0\n    weight_known: 10\n  perceptual:\n    weight: 0\n  adversarial:\n    kind: r1\n    weight: 10\n    gp_coef: 0.001\n    mask_as_fake_target: True\n    allow_scale_mask: True\n  feature_matching:\n    weight: 100\n  resnet_pl:\n    weight: 1\n    segmentation: false\n\ndefaults:\n  - location: docker\n  - data: abl-04-256-mh-dist\n  - generator: pix2pixhd_global_sigmoid\n  - discriminator: pix2pixhd_nlayer\n  - optimizers: default_optimizers\n  - visualizer: directory\n  - evaluator: default_inpainted\n  - trainer: any_gpu_large_ssim_ddp_final_benchmark\n  - hydra: overrides\n"
  },
  {
    "path": "model_cards/lama/configs/training/ablv2_work_no_segmpl_csdilirpl_celeba_csdilirpl1_new.yaml",
    "content": "run_title: ''\n\ntraining_model:\n  kind: default\n  visualize_each_iters: 1000\n  concat_mask: true\n  store_discr_outputs_for_vis: true\nlosses:\n  l1:\n    weight_missing: 0\n    weight_known: 10\n  perceptual:\n    weight: 0\n  adversarial:\n    kind: r1\n    weight: 10\n    gp_coef: 0.001\n    mask_as_fake_target: true\n    allow_scale_mask: true\n  feature_matching:\n    weight: 100\n  segm_pl:\n    weight: 1\n    imagenet_weights: true\n\ndefaults:\n  - location: celeba\n  - data: abl-04-256-mh-dist-celeba\n  - generator: pix2pixhd_global_sigmoid\n  - discriminator: pix2pixhd_nlayer\n  - optimizers: default_optimizers\n  - visualizer: directory\n  - evaluator: default_inpainted\n  - trainer: any_gpu_large_ssim_ddp_final_celeba\n  - hydra: overrides"
  },
  {
    "path": "model_cards/lama/configs/training/ablv2_work_no_segmpl_csirpl.yaml",
    "content": "run_title: ''\n\ntraining_model:\n  kind: default\n  visualize_each_iters: 1000\n  concat_mask: True\n  store_discr_outputs_for_vis: True\n\nlosses:\n  l1:\n    weight_missing: 0\n    weight_known: 10\n  perceptual:\n    weight: 0\n  adversarial:\n    kind: r1\n    weight: 10\n    gp_coef: 0.001\n    mask_as_fake_target: True\n    allow_scale_mask: True\n  feature_matching:\n    weight: 100\n  resnet_pl:\n    weight: 0.3\n    arch_encoder: 'resnet50'\n    segmentation: false\n\ndefaults:\n  - location: docker\n  - data: abl-04-256-mh-dist\n  - generator: pix2pixhd_global_sigmoid\n  - discriminator: pix2pixhd_nlayer\n  - optimizers: default_optimizers\n  - visualizer: directory\n  - evaluator: default_inpainted\n  - trainer: any_gpu_large_ssim_ddp_final\n  - hydra: overrides\n"
  },
  {
    "path": "model_cards/lama/configs/training/ablv2_work_no_segmpl_csirpl_celeba_csirpl03_new.yaml",
    "content": "run_title: ''\n\ntraining_model:\n  kind: default\n  visualize_each_iters: 1000\n  concat_mask: true\n  store_discr_outputs_for_vis: true\nlosses:\n  l1:\n    weight_missing: 0\n    weight_known: 10\n  perceptual:\n    weight: 0\n  adversarial:\n    kind: r1\n    weight: 10\n    gp_coef: 0.001\n    mask_as_fake_target: true\n    allow_scale_mask: true\n  feature_matching:\n    weight: 100\n  segm_pl:\n    weight: 0.3\n    arch_encoder: resnet50\n    imagenet_weights: true\n\ndefaults:\n  - location: celeba\n  - data: abl-04-256-mh-dist-celeba\n  - generator: pix2pixhd_global_sigmoid\n  - discriminator: pix2pixhd_nlayer\n  - optimizers: default_optimizers\n  - visualizer: directory\n  - evaluator: default_inpainted\n  - trainer: any_gpu_large_ssim_ddp_final_celeba\n  - hydra: overrides\n"
  },
  {
    "path": "model_cards/lama/configs/training/ablv2_work_no_segmpl_vgg.yaml",
    "content": "run_title: ''\n\ntraining_model:\n  kind: default\n  visualize_each_iters: 1000\n  concat_mask: True\n  store_discr_outputs_for_vis: True\n\nlosses:\n  l1:\n    weight_missing: 0\n    weight_known: 10\n  perceptual:\n    weight: 0.03\n  adversarial:\n    kind: r1\n    weight: 10\n    gp_coef: 0.001\n    mask_as_fake_target: True\n    allow_scale_mask: True\n  feature_matching:\n    weight: 100\n  resnet_pl:\n    weight: 0\n#    weights_path: ${env:TORCH_HOME}\n\ndefaults:\n  - location: docker\n  - data: abl-04-256-mh-dist\n  - generator: pix2pixhd_global_sigmoid\n  - discriminator: pix2pixhd_nlayer\n  - optimizers: default_optimizers\n  - visualizer: directory\n  - evaluator: default_inpainted\n  - trainer: any_gpu_large_ssim_ddp_final\n  - hydra: overrides\n"
  },
  {
    "path": "model_cards/lama/configs/training/ablv2_work_no_segmpl_vgg_celeba_l2_vgg003_new.yaml",
    "content": "run_title: ''\n\ntraining_model:\n  kind: default\n  visualize_each_iters: 1000\n  concat_mask: true\n  store_discr_outputs_for_vis: true\nlosses:\n  l1:\n    weight_missing: 0\n    weight_known: 10\n  perceptual:\n    weight: 0.03\n    kwargs:\n      metric: l2\n  adversarial:\n    kind: r1\n    weight: 10\n    gp_coef: 0.001\n    mask_as_fake_target: true\n    allow_scale_mask: true\n  feature_matching:\n    weight: 100\n  segm_pl:\n    weight: 0\n\ndefaults:\n  - location: celeba\n  - data: abl-04-256-mh-dist-celeba\n  - generator: pix2pixhd_global_sigmoid\n  - discriminator: pix2pixhd_nlayer\n  - optimizers: default_optimizers\n  - visualizer: directory\n  - evaluator: default_inpainted\n  - trainer: any_gpu_large_ssim_ddp_final_celeba\n  - hydra: overrides\n"
  },
  {
    "path": "model_cards/lama/configs/training/ablv2_work_nodil_segmpl.yaml",
    "content": "run_title: ''\n\ntraining_model:\n  kind: default\n  visualize_each_iters: 1000\n  concat_mask: True\n  store_discr_outputs_for_vis: True\n\nlosses:\n  l1:\n    weight_missing: 0\n    weight_known: 10\n  perceptual:\n    weight: 0\n  adversarial:\n    kind: r1\n    weight: 10\n    gp_coef: 0.001\n    mask_as_fake_target: True\n    allow_scale_mask: True\n  feature_matching:\n    weight: 100\n  resnet_pl:\n    arch_encoder: resnet50\n    weight: 30\n    weights_path: ${env:TORCH_HOME}\n\ndefaults:\n  - location: docker\n  - data: abl-04-256-mh-dist\n  - generator: pix2pixhd_global_sigmoid\n  - discriminator: pix2pixhd_nlayer\n  - optimizers: default_optimizers\n  - visualizer: directory\n  - evaluator: default_inpainted\n  - trainer: any_gpu_large_ssim_ddp_final\n  - hydra: overrides\n"
  },
  {
    "path": "model_cards/lama/configs/training/ablv2_work_small_holes.yaml",
    "content": "run_title: ''\n\ntraining_model:\n  kind: default\n  visualize_each_iters: 1000\n  concat_mask: True\n  store_discr_outputs_for_vis: True\n\nlosses:\n  l1:\n    weight_missing: 0\n    weight_known: 10\n  perceptual:\n    weight: 0\n  adversarial:\n    kind: r1\n    weight: 10\n    gp_coef: 0.001\n    mask_as_fake_target: True\n    allow_scale_mask: True\n  feature_matching:\n    weight: 100\n  resnet_pl:\n    weight: 30\n    weights_path: ${env:TORCH_HOME}\n\ndefaults:\n  - location: docker\n  - data: abl-02-thin-bb\n  - generator: pix2pixhd_global_sigmoid\n  - discriminator: pix2pixhd_nlayer\n  - optimizers: default_optimizers\n  - visualizer: directory\n  - evaluator: default_inpainted\n  - trainer: any_gpu_large_ssim_ddp_final\n  - hydra: overrides\n"
  },
  {
    "path": "model_cards/lama/configs/training/big-lama-celeba.yaml",
    "content": "run_title: ''\n\ntraining_model:\n  kind: default\n  visualize_each_iters: 1000\n  concat_mask: true\n  store_discr_outputs_for_vis: true\nlosses:\n  l1:\n    weight_missing: 0\n    weight_known: 10\n  perceptual:\n    weight: 0\n  adversarial:\n    kind: r1\n    weight: 10\n    gp_coef: 0.001\n    mask_as_fake_target: true\n    allow_scale_mask: true\n  feature_matching:\n    weight: 100\n  resnet_pl:\n    weight: 30\n    weights_path: ${env:TORCH_HOME}\n\ngenerator:\n  kind: ffc_resnet\n  input_nc: 4\n  output_nc: 3\n  ngf: 64\n  n_downsampling: 3\n  n_blocks: 18\n  add_out_act: sigmoid\n  init_conv_kwargs:\n    ratio_gin: 0\n    ratio_gout: 0\n    enable_lfu: false\n  downsample_conv_kwargs:\n    ratio_gin: ${generator.init_conv_kwargs.ratio_gout}\n    ratio_gout: ${generator.downsample_conv_kwargs.ratio_gin}\n    enable_lfu: false\n  resnet_conv_kwargs:\n    ratio_gin: 0.75\n    ratio_gout: ${generator.resnet_conv_kwargs.ratio_gin}\n    enable_lfu: false\n\ndefaults:\n  - location: celeba\n  - data: abl-04-256-mh-dist-celeba\n  - discriminator: pix2pixhd_nlayer\n  - optimizers: default_optimizers\n  - visualizer: directory\n  - evaluator: default_inpainted\n  - trainer: any_gpu_large_ssim_ddp_final_celeba\n  - hydra: overrides\n"
  },
  {
    "path": "model_cards/lama/configs/training/big-lama-regular-celeba.yaml",
    "content": "run_title: ''\n\ngenerator:\n  kind: pix2pixhd_global\n  input_nc: 4\n  output_nc: 3\n  ngf: 64\n  n_downsampling: 3\n  n_blocks: 15\n  conv_kind: default\n  add_out_act: sigmoid\n\ntraining_model:\n  kind: default\n  visualize_each_iters: 1000\n  concat_mask: true\n  store_discr_outputs_for_vis: true\n\nlosses:\n  l1:\n    weight_missing: 0\n    weight_known: 10\n  perceptual:\n    weight: 0\n  adversarial:\n    kind: r1\n    weight: 10\n    gp_coef: 0.001\n    mask_as_fake_target: true\n    allow_scale_mask: true\n  feature_matching:\n    weight: 100\n  resnet_pl:\n    weight: 30\n    weights_path: ${env:TORCH_HOME}\n\ndefaults:\n  - location: celeba\n  - data: abl-04-256-mh-dist-celeba\n  - discriminator: pix2pixhd_nlayer\n  - optimizers: default_optimizers\n  - visualizer: directory\n  - evaluator: default_inpainted\n  - trainer: any_gpu_large_ssim_ddp_final_celeba\n  - hydra: overrides"
  },
  {
    "path": "model_cards/lama/configs/training/big-lama-regular.yaml",
    "content": "run_title: ''\n\ngenerator:\n  kind: pix2pixhd_global\n  input_nc: 4\n  output_nc: 3\n  ngf: 64\n  n_downsampling: 3\n  n_blocks: 15\n  conv_kind: default\n  add_out_act: sigmoid\n\ntraining_model:\n  kind: default\n  visualize_each_iters: 1000\n  concat_mask: true\n  store_discr_outputs_for_vis: true\n\nlosses:\n  l1:\n    weight_missing: 0\n    weight_known: 10\n  perceptual:\n    weight: 0\n  adversarial:\n    kind: r1\n    weight: 10\n    gp_coef: 0.001\n    mask_as_fake_target: true\n    allow_scale_mask: true\n  feature_matching:\n    weight: 100\n  resnet_pl:\n    weight: 30\n    weights_path: ${env:TORCH_HOME}\n\ndefaults:\n  - location: docker\n  - data: abl-04-256-mh-dist\n  - discriminator: pix2pixhd_nlayer\n  - optimizers: default_optimizers\n  - visualizer: directory\n  - evaluator: default_inpainted\n  - trainer: any_gpu_large_ssim_ddp_final\n  - hydra: overrides"
  },
  {
    "path": "model_cards/lama/configs/training/big-lama.yaml",
    "content": "run_title: ''\n\ntraining_model:\n  kind: default\n  visualize_each_iters: 1000\n  concat_mask: true\n  store_discr_outputs_for_vis: true\nlosses:\n  l1:\n    weight_missing: 0\n    weight_known: 10\n  perceptual:\n    weight: 0\n  adversarial:\n    kind: r1\n    weight: 10\n    gp_coef: 0.001\n    mask_as_fake_target: true\n    allow_scale_mask: true\n  feature_matching:\n    weight: 100\n  resnet_pl:\n    weight: 30\n    weights_path: ${env:TORCH_HOME}\n\ngenerator:\n  kind: ffc_resnet\n  input_nc: 4\n  output_nc: 3\n  ngf: 64\n  n_downsampling: 3\n  n_blocks: 18\n  add_out_act: sigmoid\n  init_conv_kwargs:\n    ratio_gin: 0\n    ratio_gout: 0\n    enable_lfu: false\n  downsample_conv_kwargs:\n    ratio_gin: ${generator.init_conv_kwargs.ratio_gout}\n    ratio_gout: ${generator.downsample_conv_kwargs.ratio_gin}\n    enable_lfu: false\n  resnet_conv_kwargs:\n    ratio_gin: 0.75\n    ratio_gout: ${generator.resnet_conv_kwargs.ratio_gin}\n    enable_lfu: false\n\ndefaults:\n  - location: docker\n  - data: abl-04-256-mh-dist\n  - discriminator: pix2pixhd_nlayer\n  - optimizers: default_optimizers\n  - visualizer: directory\n  - evaluator: default_inpainted\n  - trainer: any_gpu_large_ssim_ddp_final\n  - hydra: overrides\n"
  },
  {
    "path": "model_cards/lama/configs/training/data/abl-02-thin-bb.yaml",
    "content": "# @package _group_\n\n# try to resemble mask generation of DeepFill v2\n# official tf version: https://github.com/JiahuiYu/generative_inpainting/blob/master/inpaint_ops.py#L168\n# pytorch version: https://github.com/zhaoyuzhi/deepfillv2/blob/62dad2c601400e14d79f4d1e090c2effcb9bf3eb/deepfillv2/dataset.py#L40\n# another unofficial pytorch version: https://github.com/avalonstrel/GatedConvolution/blob/master/config/inpaint.yml\n# they are a bit different, official version has slightly larger masks\n\nbatch_size: 10\nval_batch_size: 2\nnum_workers: 3\n\ntrain:\n  indir: ${location.data_root_dir}/train\n  out_size: 256\n\n  mask_gen_kwargs:  # probabilities do not need to sum to 1, they are re-normalized in mask generator\n    irregular_proba: 1\n    irregular_kwargs:\n      max_angle: 4\n      max_len: 80  # math.sqrt(H*H+W*W) / 8 + math.sqrt(H*H+W*W) / 16 https://github.com/JiahuiYu/generative_inpainting/blob/master/inpaint_ops.py#L189\n      max_width: 40\n      max_times: 12\n      min_times: 4\n\n    box_proba: 1\n    box_kwargs:\n      margin: 0\n      bbox_min_size: 30\n      bbox_max_size: 128\n      max_times: 1\n      min_times: 1\n\n    segm_proba: 0  # not working yet due to RuntimeError: Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the 'spawn' start method\n\n  transform_variant: default\n  dataloader_kwargs:\n    batch_size: ${data.batch_size}\n    shuffle: True\n    num_workers: ${data.num_workers}\n\nval:\n  indir: ${location.data_root_dir}/val\n  img_suffix: .png\n  dataloader_kwargs:\n    batch_size: ${data.val_batch_size}\n    shuffle: False\n    num_workers: ${data.num_workers}\n\n#extra_val:\n#  random_thin_256:\n#    indir: ${location.data_root_dir}/extra_val/random_thin_256\n#    img_suffix: .png\n#    dataloader_kwargs:\n#      batch_size: ${data.val_batch_size}\n#      shuffle: False\n#      num_workers: ${data.num_workers}\n#  random_medium_256:\n#    indir: ${location.data_root_dir}/extra_val/random_medium_256\n#    img_suffix: .png\n#    dataloader_kwargs:\n#      batch_size: ${data.val_batch_size}\n#      shuffle: False\n#      num_workers: ${data.num_workers}\n#  random_thick_256:\n#    indir: ${location.data_root_dir}/extra_val/random_thick_256\n#    img_suffix: .png\n#    dataloader_kwargs:\n#      batch_size: ${data.val_batch_size}\n#      shuffle: False\n#      num_workers: ${data.num_workers}\n#  random_thin_512:\n#    indir: ${location.data_root_dir}/extra_val/random_thin_512\n#    img_suffix: .png\n#    dataloader_kwargs:\n#      batch_size: ${data.val_batch_size}\n#      shuffle: False\n#      num_workers: ${data.num_workers}\n#  random_medium_512:\n#    indir: ${location.data_root_dir}/extra_val/random_medium_512\n#    img_suffix: .png\n#    dataloader_kwargs:\n#      batch_size: ${data.val_batch_size}\n#      shuffle: False\n#      num_workers: ${data.num_workers}\n#  random_thick_512:\n#    indir: ${location.data_root_dir}/extra_val/random_thick_512\n#    img_suffix: .png\n#    dataloader_kwargs:\n#      batch_size: ${data.val_batch_size}\n#      shuffle: False\n#      num_workers: ${data.num_workers}\n#  segm_256:\n#    indir: ${location.data_root_dir}/extra_val/segm_256\n#    img_suffix: .png\n#    dataloader_kwargs:\n#      batch_size: ${data.val_batch_size}\n#      shuffle: False\n#      num_workers: ${data.num_workers}\n#  segm_512:\n#    indir: ${location.data_root_dir}/extra_val/segm_512\n#    img_suffix: .png\n#    dataloader_kwargs:\n#      batch_size: ${data.val_batch_size}\n#      shuffle: False\n#      num_workers: ${data.num_workers}\n\nvisual_test:\n  indir: ${location.data_root_dir}/visual_test\n  img_suffix: _input.png\n  pad_out_to_modulo: 32\n  dataloader_kwargs:\n    batch_size: 1\n    shuffle: False\n    num_workers: ${data.num_workers}\n"
  },
  {
    "path": "model_cards/lama/configs/training/data/abl-04-256-mh-dist-celeba.yaml",
    "content": "# @package _group_\n\nbatch_size: 5\nval_batch_size: 3\nnum_workers: 3\n\ntrain:\n  indir: ${location.data_root_dir}/train_256\n  out_size: 256\n  mask_gen_kwargs:  # probabilities do not need to sum to 1, they are re-normalized in mask generator\n    irregular_proba: 1\n    irregular_kwargs:\n      max_angle: 4\n      max_len: 200\n      max_width: 100\n      max_times: 5\n      min_times: 1\n\n    box_proba: 1\n    box_kwargs:\n      margin: 10\n      bbox_min_size: 30\n      bbox_max_size: 150\n      max_times: 4\n      min_times: 1\n\n    segm_proba: 0\n\n  transform_variant: no_augs\n  dataloader_kwargs:\n    batch_size: ${data.batch_size}\n    shuffle: True\n    num_workers: ${data.num_workers}\n\nval:\n  indir: ${location.data_root_dir}/val_256\n  img_suffix: .png\n  dataloader_kwargs:\n    batch_size: ${data.val_batch_size}\n    shuffle: False\n    num_workers: ${data.num_workers}\n\nvisual_test: null\n"
  },
  {
    "path": "model_cards/lama/configs/training/data/abl-04-256-mh-dist-web.yaml",
    "content": "# @package _group_\n\nbatch_size: 10\nval_batch_size: 2\nnum_workers: 3\n\ntrain:\n  kind: default_web\n  shuffle_buffer: 200\n  indir: ${location.data_root_dir}/train_standard/part{00000..00039}.tar\n  out_size: 256\n  mask_gen_kwargs:  # probabilities do not need to sum to 1, they are re-normalized in mask generator\n    irregular_proba: 1\n    irregular_kwargs:\n      max_angle: 4\n      max_len: 200\n      max_width: 100\n      max_times: 5\n      min_times: 1\n\n    box_proba: 1\n    box_kwargs:\n      margin: 10\n      bbox_min_size: 30\n      bbox_max_size: 150\n      max_times: 4\n      min_times: 1\n\n    segm_proba: 0\n\n  transform_variant: distortions\n  dataloader_kwargs:\n    batch_size: ${data.batch_size}\n    shuffle: True\n    num_workers: ${data.num_workers}\n\nval:\n  indir: ${location.data_root_dir}/val\n  img_suffix: .png\n  dataloader_kwargs:\n    batch_size: ${data.val_batch_size}\n    shuffle: False\n    num_workers: ${data.num_workers}\n\n#extra_val:\n#  random_thin_256:\n#    indir: ${location.data_root_dir}/final_extra_val/random_thin_256\n#    img_suffix: .png\n#    dataloader_kwargs:\n#      batch_size: ${data.val_batch_size}\n#      shuffle: False\n#      num_workers: ${data.num_workers}\n#  random_medium_256:\n#    indir: ${location.data_root_dir}/final_extra_val/random_medium_256\n#    img_suffix: .png\n#    dataloader_kwargs:\n#      batch_size: ${data.val_batch_size}\n#      shuffle: False\n#      num_workers: ${data.num_workers}\n#  random_thick_256:\n#    indir: ${location.data_root_dir}/final_extra_val/random_thick_256\n#    img_suffix: .png\n#    dataloader_kwargs:\n#      batch_size: ${data.val_batch_size}\n#      shuffle: False\n#      num_workers: ${data.num_workers}\n#  random_thin_512:\n#    indir: ${location.data_root_dir}/final_extra_val/random_thin_512\n#    img_suffix: .png\n#    dataloader_kwargs:\n#      batch_size: ${data.val_batch_size}\n#      shuffle: False\n#      num_workers: ${data.num_workers}\n#  random_medium_512:\n#    indir: ${location.data_root_dir}/final_extra_val/random_medium_512\n#    img_suffix: .png\n#    dataloader_kwargs:\n#      batch_size: ${data.val_batch_size}\n#      shuffle: False\n#      num_workers: ${data.num_workers}\n#  random_thick_512:\n#    indir: ${location.data_root_dir}/final_extra_val/random_thick_512\n#    img_suffix: .png\n#    dataloader_kwargs:\n#      batch_size: ${data.val_batch_size}\n#      shuffle: False\n#      num_workers: ${data.num_workers}\n#  segm_256:\n#    indir: ${location.data_root_dir}/final_extra_val/segm_256\n#    img_suffix: .png\n#    dataloader_kwargs:\n#      batch_size: ${data.val_batch_size}\n#      shuffle: False\n#      num_workers: ${data.num_workers}\n#  segm_512:\n#    indir: ${location.data_root_dir}/final_extra_val/segm_512\n#    img_suffix: .png\n#    dataloader_kwargs:\n#      batch_size: ${data.val_batch_size}\n#      shuffle: False\n#      num_workers: ${data.num_workers}\n\nvisual_test:\n  indir: ${location.data_root_dir}/visual_test\n  img_suffix: _input.png\n  pad_out_to_modulo: 32\n  dataloader_kwargs:\n    batch_size: 1\n    shuffle: False\n    num_workers: ${data.num_workers}\n"
  },
  {
    "path": "model_cards/lama/configs/training/data/abl-04-256-mh-dist.yaml",
    "content": "# @package _group_\n\nbatch_size: 10\nval_batch_size: 2\nnum_workers: 3\n\ntrain:\n  indir: ${location.data_root_dir}/train\n  out_size: 256\n  mask_gen_kwargs:  # probabilities do not need to sum to 1, they are re-normalized in mask generator\n    irregular_proba: 1\n    irregular_kwargs:\n      max_angle: 4\n      max_len: 200\n      max_width: 100\n      max_times: 5\n      min_times: 1\n\n    box_proba: 1\n    box_kwargs:\n      margin: 10\n      bbox_min_size: 30\n      bbox_max_size: 150\n      max_times: 4\n      min_times: 1\n\n    segm_proba: 0\n\n  transform_variant: distortions\n  dataloader_kwargs:\n    batch_size: ${data.batch_size}\n    shuffle: True\n    num_workers: ${data.num_workers}\n\nval:\n  indir: ${location.data_root_dir}/val\n  img_suffix: .png\n  dataloader_kwargs:\n    batch_size: ${data.val_batch_size}\n    shuffle: False\n    num_workers: ${data.num_workers}\n\n#extra_val:\n#  random_thin_256:\n#    indir: ${location.data_root_dir}/extra_val/random_thin_256\n#    img_suffix: .png\n#    dataloader_kwargs:\n#      batch_size: ${data.val_batch_size}\n#      shuffle: False\n#      num_workers: ${data.num_workers}\n#  random_medium_256:\n#    indir: ${location.data_root_dir}/extra_val/random_medium_256\n#    img_suffix: .png\n#    dataloader_kwargs:\n#      batch_size: ${data.val_batch_size}\n#      shuffle: False\n#      num_workers: ${data.num_workers}\n#  random_thick_256:\n#    indir: ${location.data_root_dir}/extra_val/random_thick_256\n#    img_suffix: .png\n#    dataloader_kwargs:\n#      batch_size: ${data.val_batch_size}\n#      shuffle: False\n#      num_workers: ${data.num_workers}\n#  random_thin_512:\n#    indir: ${location.data_root_dir}/extra_val/random_thin_512\n#    img_suffix: .png\n#    dataloader_kwargs:\n#      batch_size: ${data.val_batch_size}\n#      shuffle: False\n#      num_workers: ${data.num_workers}\n#  random_medium_512:\n#    indir: ${location.data_root_dir}/extra_val/random_medium_512\n#    img_suffix: .png\n#    dataloader_kwargs:\n#      batch_size: ${data.val_batch_size}\n#      shuffle: False\n#      num_workers: ${data.num_workers}\n#  random_thick_512:\n#    indir: ${location.data_root_dir}/extra_val/random_thick_512\n#    img_suffix: .png\n#    dataloader_kwargs:\n#      batch_size: ${data.val_batch_size}\n#      shuffle: False\n#      num_workers: ${data.num_workers}\n#  segm_256:\n#    indir: ${location.data_root_dir}/extra_val/segm_256\n#    img_suffix: .png\n#    dataloader_kwargs:\n#      batch_size: ${data.val_batch_size}\n#      shuffle: False\n#      num_workers: ${data.num_workers}\n#  segm_512:\n#    indir: ${location.data_root_dir}/extra_val/segm_512\n#    img_suffix: .png\n#    dataloader_kwargs:\n#      batch_size: ${data.val_batch_size}\n#      shuffle: False\n#      num_workers: ${data.num_workers}\n\nvisual_test:\n  indir: ${location.data_root_dir}/visual_test\n  img_suffix: .png\n  pad_out_to_modulo: 32\n  dataloader_kwargs:\n    batch_size: 1\n    shuffle: False\n    num_workers: ${data.num_workers}\n"
  },
  {
    "path": "model_cards/lama/configs/training/discriminator/pix2pixhd_nlayer.yaml",
    "content": "# @package _group_\nkind: pix2pixhd_nlayer\ninput_nc: 3\nndf: 64\nn_layers: 4\n"
  },
  {
    "path": "model_cards/lama/configs/training/evaluator/default_inpainted.yaml",
    "content": "# @package _group_\nkind: default\ninpainted_key: inpainted  # if you want to evaluate before blending with original image by mask, set predicted_image\nintegral_kind: ssim_fid100_f1\n"
  },
  {
    "path": "model_cards/lama/configs/training/generator/ffc_resnet_075.yaml",
    "content": "# @package _group_\nkind: ffc_resnet\ninput_nc: 4\noutput_nc: 3\nngf: 64\nn_downsampling: 3\nn_blocks: 9\nadd_out_act: sigmoid\n\ninit_conv_kwargs:\n  ratio_gin: 0\n  ratio_gout: 0\n  enable_lfu: False\n\ndownsample_conv_kwargs:\n  ratio_gin: ${generator.init_conv_kwargs.ratio_gout}\n  ratio_gout: ${generator.downsample_conv_kwargs.ratio_gin}\n  enable_lfu: False\n\nresnet_conv_kwargs:\n  ratio_gin: 0.75\n  ratio_gout: ${generator.resnet_conv_kwargs.ratio_gin}\n  enable_lfu: False\n"
  },
  {
    "path": "model_cards/lama/configs/training/generator/pix2pixhd_global.yaml",
    "content": "# @package _group_\nkind: pix2pixhd_global\ninput_nc: 4\noutput_nc: 3\nngf: 64\nn_downsampling: 3\nn_blocks: 9\nconv_kind: default"
  },
  {
    "path": "model_cards/lama/configs/training/generator/pix2pixhd_global_sigmoid.yaml",
    "content": "# @package _group_\nkind: pix2pixhd_global\ninput_nc: 4\noutput_nc: 3\nngf: 64\nn_downsampling: 3\nn_blocks: 9\nconv_kind: default\nadd_out_act: sigmoid\n"
  },
  {
    "path": "model_cards/lama/configs/training/generator/pix2pixhd_multidilated_catin_4dil_9b.yaml",
    "content": "# @package _group_\nkind: pix2pixhd_multidilated\ninput_nc: 4\noutput_nc: 3\nngf: 64\nn_downsampling: 3\nn_blocks: 9\nconv_kind: default\nadd_out_act: sigmoid\nmultidilation_kwargs:\n  comb_mode: cat_in\n  dilation_num: 4\n"
  },
  {
    "path": "model_cards/lama/configs/training/hydra/no_time.yaml",
    "content": "# @package _group_\nrun:\n  dir: ${location.out_root_dir}/${env:USER}_${hydra:job.name}_${hydra:job.config_name}_${run_title}\nsweep:\n  dir: ${hydra:run.dir}_sweep\n  subdir: ${hydra.job.num}\n"
  },
  {
    "path": "model_cards/lama/configs/training/hydra/overrides.yaml",
    "content": "# @package _group_\nrun:\n  dir: ${location.out_root_dir}/${env:USER}_${now:%Y-%m-%d_%H-%M-%S}_${hydra:job.name}_${hydra:job.config_name}_${run_title}\nsweep:\n  dir: ${hydra:run.dir}_sweep\n  subdir: ${hydra.job.num}\n"
  },
  {
    "path": "model_cards/lama/configs/training/lama-fourier-celeba.yaml",
    "content": "run_title: ''\n\ntraining_model:\n  kind: default\n  visualize_each_iters: 1000\n  concat_mask: true\n  store_discr_outputs_for_vis: true\nlosses:\n  l1:\n    weight_missing: 0\n    weight_known: 10\n  perceptual:\n    weight: 0\n  adversarial:\n    kind: r1\n    weight: 10\n    gp_coef: 0.001\n    mask_as_fake_target: true\n    allow_scale_mask: true\n  feature_matching:\n    weight: 100\n  resnet_pl:\n    weight: 30\n    weights_path: ${env:TORCH_HOME}\n\ndefaults:\n  - location: celeba\n  - data: abl-04-256-mh-dist-celeba\n  - generator: ffc_resnet_075\n  - discriminator: pix2pixhd_nlayer\n  - optimizers: default_optimizers\n  - visualizer: directory\n  - evaluator: default_inpainted\n  - trainer: any_gpu_large_ssim_ddp_final_celeba\n  - hydra: overrides"
  },
  {
    "path": "model_cards/lama/configs/training/lama-fourier.yaml",
    "content": "run_title: ''\n\ntraining_model:\n  kind: default\n  visualize_each_iters: 1000\n  concat_mask: true\n  store_discr_outputs_for_vis: true\nlosses:\n  l1:\n    weight_missing: 0\n    weight_known: 10\n  perceptual:\n    weight: 0\n  adversarial:\n    kind: r1\n    weight: 10\n    gp_coef: 0.001\n    mask_as_fake_target: true\n    allow_scale_mask: true\n  feature_matching:\n    weight: 100\n  resnet_pl:\n    weight: 30\n    weights_path: ${env:TORCH_HOME}\n\ndefaults:\n  - location: docker\n  - data: abl-04-256-mh-dist\n  - generator: ffc_resnet_075\n  - discriminator: pix2pixhd_nlayer\n  - optimizers: default_optimizers\n  - visualizer: directory\n  - evaluator: default_inpainted\n  - trainer: any_gpu_large_ssim_ddp_final\n  - hydra: overrides"
  },
  {
    "path": "model_cards/lama/configs/training/lama-regular-celeba.yaml",
    "content": "run_title: ''\n\ntraining_model:\n  kind: default\n  visualize_each_iters: 1000\n  concat_mask: true\n  store_discr_outputs_for_vis: true\nlosses:\n  l1:\n    weight_missing: 0\n    weight_known: 10\n  perceptual:\n    weight: 0\n  adversarial:\n    kind: r1\n    weight: 10\n    gp_coef: 0.001\n    mask_as_fake_target: true\n    allow_scale_mask: true\n  feature_matching:\n    weight: 100\n  resnet_pl:\n    weight: 30\n    weights_path: ${env:TORCH_HOME}\n\ndefaults:\n  - location: celeba\n  - data: abl-04-256-mh-dist-celeba\n  - generator: pix2pixhd_global_sigmoid\n  - discriminator: pix2pixhd_nlayer\n  - optimizers: default_optimizers\n  - visualizer: directory\n  - evaluator: default_inpainted\n  - trainer: any_gpu_large_ssim_ddp_final_celeba\n  - hydra: overrides\n"
  },
  {
    "path": "model_cards/lama/configs/training/lama-regular.yaml",
    "content": "run_title: ''\n\ntraining_model:\n  kind: default\n  visualize_each_iters: 1000\n  concat_mask: true\n  store_discr_outputs_for_vis: true\nlosses:\n  l1:\n    weight_missing: 0\n    weight_known: 10\n  perceptual:\n    weight: 0\n  adversarial:\n    kind: r1\n    weight: 10\n    gp_coef: 0.001\n    mask_as_fake_target: true\n    allow_scale_mask: true\n  feature_matching:\n    weight: 100\n  resnet_pl:\n    weight: 30\n    weights_path: ${env:TORCH_HOME}\n\ndefaults:\n  - location: docker\n  - data: abl-04-256-mh-dist\n  - generator: pix2pixhd_global_sigmoid\n  - discriminator: pix2pixhd_nlayer\n  - optimizers: default_optimizers\n  - visualizer: directory\n  - evaluator: default_inpainted\n  - trainer: any_gpu_large_ssim_ddp_final\n  - hydra: overrides\n"
  },
  {
    "path": "model_cards/lama/configs/training/lama_small_train_masks.yaml",
    "content": "run_title: ''\n\ntraining_model:\n  kind: default\n  visualize_each_iters: 1000\n  concat_mask: true\n  store_discr_outputs_for_vis: true\n\nlosses:\n  l1:\n    weight_missing: 0\n    weight_known: 10\n  perceptual:\n    weight: 0\n  adversarial:\n    kind: r1\n    weight: 10\n    gp_coef: 0.001\n    mask_as_fake_target: true\n    allow_scale_mask: true\n  feature_matching:\n    weight: 100\n  resnet_pl:\n    weight: 30\n    weights_path: ${env:TORCH_HOME}\n\ndefaults:\n  - location: docker\n  - data: abl-02-thin-bb\n  - generator: pix2pixhd_sigmoid\n  - discriminator: pix2pixhd_nlayer\n  - optimizers: default_optimizers\n  - visualizer: directory\n  - evaluator: default_inpainted\n  - trainer: any_gpu_large_ssim_ddp_final\n  - hydra: overrides"
  },
  {
    "path": "model_cards/lama/configs/training/location/celeba_example.yaml",
    "content": "# @package _group_\ndata_root_dir: /home/user/lama/celeba-hq-dataset/\nout_root_dir: /home/user/lama/experiments/\ntb_dir: /home/user/lama/tb_logs/\npretrained_models: /home/user/lama/\n"
  },
  {
    "path": "model_cards/lama/configs/training/location/docker.yaml",
    "content": "# @package _group_\ndata_root_dir: /data/data\nout_root_dir: /data/experiments\ntb_dir: /data/tb_logs\npretrained_models: /some_path\n"
  },
  {
    "path": "model_cards/lama/configs/training/location/places_example.yaml",
    "content": "# @package _group_\ndata_root_dir: /home/user/inpainting-lama/places_standard_dataset/\nout_root_dir: /home/user/inpainting-lama/experiments\ntb_dir: /home/user/inpainting-lama/tb_logs\npretrained_models: /home/user/inpainting-lama/\n"
  },
  {
    "path": "model_cards/lama/configs/training/optimizers/default_optimizers.yaml",
    "content": "# @package _group_\ngenerator:\n  kind: adam\n  lr: 0.001\ndiscriminator:\n  kind: adam\n  lr: 0.0001\n"
  },
  {
    "path": "model_cards/lama/configs/training/trainer/any_gpu_large_ssim_ddp_final.yaml",
    "content": "# @package _group_\nkwargs:\n  gpus: -1\n  accelerator: ddp\n  max_epochs: 40\n  gradient_clip_val: 1\n  log_gpu_memory: None  # set to min_max or all for debug\n  limit_train_batches: 25000\n  val_check_interval: ${trainer.kwargs.limit_train_batches}\n  # fast_dev_run: True  # uncomment for faster debug\n  # track_grad_norm: 2  # uncomment to track L2 gradients norm\n  log_every_n_steps: 250\n  precision: 32\n#  precision: 16\n#  amp_backend: native\n#  amp_level: O1\n  # resume_from_checkpoint: path  # override via command line trainer.resume_from_checkpoint=path_to_checkpoint\n  terminate_on_nan: False\n  # auto_scale_batch_size: True  # uncomment to find largest batch size\n  check_val_every_n_epoch: 1\n  num_sanity_val_steps: 8\n#  limit_val_batches: 1000000\n  replace_sampler_ddp: False\n\ncheckpoint_kwargs:\n  verbose: True\n  save_top_k: 5\n  save_last: True\n  period: 1\n  monitor: val_ssim_fid100_f1_total_mean\n  mode: max"
  },
  {
    "path": "model_cards/lama/configs/training/trainer/any_gpu_large_ssim_ddp_final_benchmark.yaml",
    "content": "# @package _group_\nkwargs:\n  gpus: -1\n  accelerator: ddp\n  max_epochs: 40\n  gradient_clip_val: 1\n  log_gpu_memory: None  # set to min_max or all for debug\n  limit_train_batches: 25000\n  val_check_interval: ${trainer.kwargs.limit_train_batches}\n  # fast_dev_run: True  # uncomment for faster debug\n  # track_grad_norm: 2  # uncomment to track L2 gradients norm\n  log_every_n_steps: 250\n  precision: 32\n#  precision: 16\n#  amp_backend: native\n#  amp_level: O1\n  # resume_from_checkpoint: path  # override via command line trainer.resume_from_checkpoint=path_to_checkpoint\n  terminate_on_nan: False\n  # auto_scale_batch_size: True  # uncomment to find largest batch size\n  check_val_every_n_epoch: 1\n  num_sanity_val_steps: 8\n#  limit_val_batches: 1000000\n  replace_sampler_ddp: False\n  benchmark: True\n\ncheckpoint_kwargs:\n  verbose: True\n  save_top_k: 5\n  save_last: True\n  period: 1\n  monitor: val_ssim_fid100_f1_total_mean\n  mode: max\n"
  },
  {
    "path": "model_cards/lama/configs/training/trainer/any_gpu_large_ssim_ddp_final_celeba.yaml",
    "content": "# @package _group_\nkwargs:\n  gpus: -1\n  accelerator: ddp\n  max_epochs: 40\n  gradient_clip_val: 1\n  log_gpu_memory: None\n  limit_train_batches: 25000\n  val_check_interval: 2600\n  log_every_n_steps: 250\n  precision: 32\n  terminate_on_nan: False\n  check_val_every_n_epoch: 1\n  num_sanity_val_steps: 8\n  replace_sampler_ddp: False\ncheckpoint_kwargs:\n  verbose: True\n  save_top_k: 5\n  save_last: True\n  period: 1\n  monitor: val_ssim_fid100_f1_total_mean\n  mode: max"
  },
  {
    "path": "model_cards/lama/configs/training/visualizer/directory.yaml",
    "content": "# @package _group_\nkind: directory\noutdir: samples\nkey_order:\n  - image\n  - predicted_image\n  - discr_output_fake\n  - discr_output_real\n  - inpainted\nrescale_keys:\n  - discr_output_fake\n  - discr_output_real\n"
  },
  {
    "path": "model_cards/lama/docker/1_generate_masks_from_raw_images.sh",
    "content": "#!/usr/bin/env bash\n\n\nif (( $# < 3 ))\nthen\n    echo \"Usage: $0 config_name input_images_dir image_mask_dataset_out_dir [other args to gen_mask_dataset.py]\"\n    exit 1\nfi\n\nCURDIR=\"$(dirname $0)\"\nSRCDIR=\"$CURDIR/..\"\nSRCDIR=\"$(realpath $SRCDIR)\"\n\nCONFIG_LOCAL_PATH=\"$(realpath $1)\"\nINPUT_LOCAL_DIR=\"$(realpath $2)\"\nOUTPUT_LOCAL_DIR=\"$(realpath $3)\"\nshift 3\n\nmkdir -p \"$OUTPUT_LOCAL_DIR\"\n\ndocker run \\\n    -v \"$SRCDIR\":/home/user/project \\\n    -v \"$CONFIG_LOCAL_PATH\":/data/config.yaml \\\n    -v \"$INPUT_LOCAL_DIR\":/data/input \\\n    -v \"$OUTPUT_LOCAL_DIR\":/data/output \\\n    -u $(id -u):$(id -g) \\\n    --name=\"lama-mask-gen\" \\\n    --rm \\\n    windj007/lama \\\n    /home/user/project/bin/gen_mask_dataset.py \\\n        /data/config.yaml /data/input /data/output $@\n"
  },
  {
    "path": "model_cards/lama/docker/2_predict.sh",
    "content": "#!/usr/bin/env bash\n\n\nif (( $# < 3 ))\nthen\n    echo \"Usage: $0 model_dir input_dir output_dir [other arguments to predict.py]\"\n    exit 1\nfi\n\nCURDIR=\"$(dirname $0)\"\nSRCDIR=\"$CURDIR/..\"\nSRCDIR=\"$(realpath $SRCDIR)\"\n\nMODEL_LOCAL_DIR=\"$(realpath $1)\"\nINPUT_LOCAL_DIR=\"$(realpath $2)\"\nOUTPUT_LOCAL_DIR=\"$(realpath $3)\"\nshift 3\n\nmkdir -p \"$OUTPUT_LOCAL_DIR\"\n\ndocker run \\\n    -v \"$SRCDIR\":/home/user/project \\\n    -v \"$MODEL_LOCAL_DIR\":/data/checkpoint \\\n    -v \"$INPUT_LOCAL_DIR\":/data/input \\\n    -v \"$OUTPUT_LOCAL_DIR\":/data/output \\\n    -u $(id -u):$(id -g) \\\n    --name=\"lama-predict\" \\\n    --rm \\\n    windj007/lama \\\n    /home/user/project/bin/predict.py \\\n        model.path=/data/checkpoint \\\n        indir=/data/input \\\n        outdir=/data/output \\\n        dataset.img_suffix=.png \\\n        $@\n"
  },
  {
    "path": "model_cards/lama/docker/3_evaluate.sh",
    "content": "#!/usr/bin/env bash\n\n\nif (( $# < 3 ))\nthen\n    echo \"Usage: $0 original_dataset_dir predictions_dir output_dir [other arguments to evaluate_predicts.py]\"\n    exit 1\nfi\n\nCURDIR=\"$(dirname $0)\"\nSRCDIR=\"$CURDIR/..\"\nSRCDIR=\"$(realpath $SRCDIR)\"\n\nORIG_DATASET_LOCAL_DIR=\"$(realpath $1)\"\nPREDICTIONS_LOCAL_DIR=\"$(realpath $2)\"\nOUTPUT_LOCAL_DIR=\"$(realpath $3)\"\nshift 3\n\nmkdir -p \"$OUTPUT_LOCAL_DIR\"\n\ndocker run \\\n    -v \"$SRCDIR\":/home/user/project \\\n    -v \"$ORIG_DATASET_LOCAL_DIR\":/data/orig_dataset \\\n    -v \"$PREDICTIONS_LOCAL_DIR\":/data/predictions \\\n    -v \"$OUTPUT_LOCAL_DIR\":/data/output \\\n    -u $(id -u):$(id -g) \\\n    --name=\"lama-eval\" \\\n    --rm \\\n    windj007/lama \\\n    /home/user/project/bin/evaluate_predicts.py \\\n        /home/user/project/configs/eval2_cpu.yaml \\\n        /data/orig_dataset \\\n        /data/predictions \\\n        /data/output/metrics.yaml \\\n        $@\n"
  },
  {
    "path": "model_cards/lama/docker/Dockerfile",
    "content": "FROM nvidia/cuda:10.2-runtime-ubuntu18.04\n\nRUN apt-get update && \\\n    apt-get upgrade -y && \\\n    apt-get install -y wget mc tmux nano build-essential rsync libgl1\n\nARG USERNAME=user\nRUN apt-get install -y sudo && \\\n    addgroup --gid 1000 $USERNAME && \\\n    adduser --uid 1000 --gid 1000 --disabled-password --gecos '' $USERNAME && \\\n    adduser $USERNAME sudo && \\\n    echo '%sudo ALL=(ALL) NOPASSWD:ALL' >> /etc/sudoers && \\\n    USER=$USERNAME && \\\n    GROUP=$USERNAME\n\nUSER $USERNAME:$USERNAME\nWORKDIR \"/home/$USERNAME\"\nENV PATH=\"/home/$USERNAME/miniconda3/bin:/home/$USERNAME/.local/bin:${PATH}\"\nENV PYTHONPATH=\"/home/$USERNAME/project\"\n\nRUN wget -O /tmp/miniconda.sh https://repo.anaconda.com/miniconda/Miniconda3-py39_4.9.2-Linux-x86_64.sh && \\\n    echo \"536817d1b14cb1ada88900f5be51ce0a5e042bae178b5550e62f61e223deae7c /tmp/miniconda.sh\" > /tmp/miniconda.sh.sha256 && \\\n    sha256sum --check --status < /tmp/miniconda.sh.sha256 && \\\n    bash /tmp/miniconda.sh -bt -p \"/home/$USERNAME/miniconda3\" && \\\n    rm /tmp/miniconda.sh && \\\n    conda build purge && \\\n    conda init\n\nRUN pip install -U pip\nRUN pip install numpy scipy torch==1.8.1 torchvision opencv-python tensorflow joblib matplotlib pandas \\\n    albumentations==0.5.2 pytorch-lightning==1.2.9 tabulate easydict==1.9.0 kornia==0.5.0 webdataset \\\n    packaging gpustat tqdm pyyaml hydra-core==1.1.0.dev6 scikit-learn==0.24.2 tabulate\nRUN pip install scikit-image==0.17.2\n\nENV TORCH_HOME=\"/home/$USERNAME/.torch\"\n\nADD entrypoint.sh /home/$USERNAME/.local/bin/entrypoint.sh\nENTRYPOINT [ \"entrypoint.sh\" ]\n"
  },
  {
    "path": "model_cards/lama/docker/Dockerfile-cuda111",
    "content": "FROM nvidia/cuda:11.1-runtime-ubuntu18.04\n\nRUN apt-get update && \\\n    apt-get upgrade -y && \\\n    apt-get install -y wget mc tmux nano build-essential rsync libgl1\n\nARG USERNAME=user\nRUN apt-get install -y sudo && \\\n    addgroup --gid 1000 $USERNAME && \\\n    adduser --uid 1000 --gid 1000 --disabled-password --gecos '' $USERNAME && \\\n    adduser $USERNAME sudo && \\\n    echo '%sudo ALL=(ALL) NOPASSWD:ALL' >> /etc/sudoers && \\\n    USER=$USERNAME && \\\n    GROUP=$USERNAME\n\nUSER $USERNAME:$USERNAME\nWORKDIR \"/home/$USERNAME\"\nENV PATH=\"/home/$USERNAME/miniconda3/bin:/home/$USERNAME/.local/bin:${PATH}\"\nENV PYTHONPATH=\"/home/$USERNAME/project\"\n\nRUN wget -O /tmp/miniconda.sh https://repo.anaconda.com/miniconda/Miniconda3-py39_4.9.2-Linux-x86_64.sh && \\\n    echo \"536817d1b14cb1ada88900f5be51ce0a5e042bae178b5550e62f61e223deae7c /tmp/miniconda.sh\" > /tmp/miniconda.sh.sha256 && \\\n    sha256sum --check --status < /tmp/miniconda.sh.sha256 && \\\n    bash /tmp/miniconda.sh -bt -p \"/home/$USERNAME/miniconda3\" && \\\n    rm /tmp/miniconda.sh && \\\n    conda build purge && \\\n    conda init\n\nRUN pip install -U pip\nRUN pip install torch==1.8.2+cu111 torchvision==0.9.2+cu111 -f https://download.pytorch.org/whl/lts/1.8/torch_lts.html\nRUN pip install numpy scipy opencv-python tensorflow joblib matplotlib pandas \\\n    albumentations==0.5.2 pytorch-lightning==1.2.9 tabulate easydict==1.9.0 kornia==0.5.0 webdataset \\\n    packaging gpustat tqdm pyyaml hydra-core==1.1.0.dev6 scikit-learn==0.24.2 tabulate\nRUN pip install scikit-image==0.17.2\n\nENV TORCH_HOME=\"/home/$USERNAME/.torch\"\n\nADD entrypoint.sh /home/$USERNAME/.local/bin/entrypoint.sh\nENTRYPOINT [ \"entrypoint.sh\" ]\n"
  },
  {
    "path": "model_cards/lama/docker/build-cuda111.sh",
    "content": "#!/bin/bash\n\nBASEDIR=\"$(dirname $0)\"\n\ndocker build -t windj007/lama:cuda111 -f \"$BASEDIR/Dockerfile-cuda111\" \"$BASEDIR\"\n"
  },
  {
    "path": "model_cards/lama/docker/build.sh",
    "content": "#!/bin/bash\n\nBASEDIR=\"$(dirname $0)\"\n\ndocker build -t windj007/lama -f \"$BASEDIR/Dockerfile\" \"$BASEDIR\"\n"
  },
  {
    "path": "model_cards/lama/docker/entrypoint.sh",
    "content": "#!/bin/bash\n\nexec $@\n"
  },
  {
    "path": "model_cards/lama/fetch_data/celebahq_dataset_prepare.sh",
    "content": "mkdir celeba-hq-dataset\n\nunzip data256x256.zip -d celeba-hq-dataset/\n\n# Reindex\nfor i in `echo {00001..30000}`\ndo\n    mv 'celeba-hq-dataset/data256x256/'$i'.jpg' 'celeba-hq-dataset/data256x256/'$[10#$i - 1]'.jpg'\ndone\n\n\n# Split: split train -> train & val\ncat fetch_data/train_shuffled.flist | shuf > celeba-hq-dataset/temp_train_shuffled.flist\ncat celeba-hq-dataset/temp_train_shuffled.flist | head -n 2000 > celeba-hq-dataset/val_shuffled.flist\ncat celeba-hq-dataset/temp_train_shuffled.flist | tail -n +2001 > celeba-hq-dataset/train_shuffled.flist\ncat fetch_data/val_shuffled.flist > celeba-hq-dataset/visual_test_shuffled.flist\n\nmkdir celeba-hq-dataset/train_256/\nmkdir celeba-hq-dataset/val_source_256/\nmkdir celeba-hq-dataset/visual_test_source_256/\n\ncat celeba-hq-dataset/train_shuffled.flist | xargs -I {} mv celeba-hq-dataset/data256x256/{} celeba-hq-dataset/train_256/\ncat celeba-hq-dataset/val_shuffled.flist | xargs -I {} mv celeba-hq-dataset/data256x256/{} celeba-hq-dataset/val_source_256/\ncat celeba-hq-dataset/visual_test_shuffled.flist | xargs -I {} mv celeba-hq-dataset/data256x256/{} celeba-hq-dataset/visual_test_source_256/\n\n\n# create location config celeba.yaml\nPWD=$(pwd)\nDATASET=${PWD}/celeba-hq-dataset\nCELEBA=${PWD}/configs/training/location/celeba.yaml\n\ntouch $CELEBA\necho \"# @package _group_\" >> $CELEBA\necho \"data_root_dir: ${DATASET}/\" >> $CELEBA\necho \"out_root_dir: ${PWD}/experiments/\" >> $CELEBA\necho \"tb_dir: ${PWD}/tb_logs/\" >> $CELEBA\necho \"pretrained_models: ${PWD}/\" >> $CELEBA\n"
  },
  {
    "path": "model_cards/lama/fetch_data/celebahq_gen_masks.sh",
    "content": "python3 bin/gen_mask_dataset.py \\\n$(pwd)/configs/data_gen/random_thick_256.yaml \\\nceleba-hq-dataset/val_source_256/ \\\nceleba-hq-dataset/val_256/random_thick_256/\n\npython3 bin/gen_mask_dataset.py \\\n$(pwd)/configs/data_gen/random_thin_256.yaml \\\nceleba-hq-dataset/val_source_256/ \\\nceleba-hq-dataset/val_256/random_thin_256/\n\npython3 bin/gen_mask_dataset.py \\\n$(pwd)/configs/data_gen/random_medium_256.yaml \\\nceleba-hq-dataset/val_source_256/ \\\nceleba-hq-dataset/val_256/random_medium_256/\n\npython3 bin/gen_mask_dataset.py \\\n$(pwd)/configs/data_gen/random_thick_256.yaml \\\nceleba-hq-dataset/visual_test_source_256/ \\\nceleba-hq-dataset/visual_test_256/random_thick_256/\n\npython3 bin/gen_mask_dataset.py \\\n$(pwd)/configs/data_gen/random_thin_256.yaml \\\nceleba-hq-dataset/visual_test_source_256/ \\\nceleba-hq-dataset/visual_test_256/random_thin_256/\n\npython3 bin/gen_mask_dataset.py \\\n$(pwd)/configs/data_gen/random_medium_256.yaml \\\nceleba-hq-dataset/visual_test_source_256/ \\\nceleba-hq-dataset/visual_test_256/random_medium_256/\n"
  },
  {
    "path": "model_cards/lama/fetch_data/eval_sampler.py",
    "content": "import os\nimport random\n\nval_files_path           = os.path.abspath('.') + '/places_standard_dataset/original/val/'\nlist_of_random_val_files = os.path.abspath('.') + '/places_standard_dataset/original/eval_random_files.txt'\nval_files      = [val_files_path + image for image in os.listdir(val_files_path)]\n\nprint(f'Sampling 30000 images out of {len(val_files)} images in {val_files_path}' + \\\n      f'and put their paths to {list_of_random_val_files}')\n\nprint('In our paper we evaluate trained models on these 30k sampled (mask,image) pairs in our paper (check Sup. mat.)')\n\nrandom.shuffle(val_files)\nval_files_random = val_files[0:30000]\n\nwith open(list_of_random_val_files, 'w') as fw:\n    for filename in val_files_random:\n        fw.write(filename+'\\n')\nprint('...done')      \n\n"
  },
  {
    "path": "model_cards/lama/fetch_data/places_challenge_train_download.sh",
    "content": "mkdir places_challenge_dataset\n\n\ndeclare -a TARPARTS\nfor i in {a..z}\ndo\n    TARPARTS[${#TARPARTS[@]}]=\"http://data.csail.mit.edu/places/places365/train_large_split/${i}.tar\"\ndone    \nls\nprintf \"%s\\n\" \"${TARPARTS[@]}\" > places_challenge_dataset/places365_train.txt\n\ncd places_challenge_dataset/\nxargs -a places365_train.txt -n 1 -P 8 wget [...]\nls *.tar | xargs -i tar xvf {}\n"
  },
  {
    "path": "model_cards/lama/fetch_data/places_standard_evaluation_prepare_data.sh",
    "content": "# 0. folder preparation\nmkdir -p places_standard_dataset/evaluation/hires/\nmkdir -p places_standard_dataset/evaluation/random_thick_512/\nmkdir -p places_standard_dataset/evaluation/random_thin_512/\nmkdir -p places_standard_dataset/evaluation/random_medium_512/\nmkdir -p places_standard_dataset/evaluation/random_thick_256/\nmkdir -p places_standard_dataset/evaluation/random_thin_256/\nmkdir -p places_standard_dataset/evaluation/random_medium_256/\n\n# 1. sample 30000 new images\nOUT=$(python3 fetch_data/eval_sampler.py)\necho ${OUT}\n\nFILELIST=$(cat places_standard_dataset/original/eval_random_files.txt)\nfor i in $FILELIST\ndo\n    $(cp ${i} places_standard_dataset/evaluation/hires/)\ndone\n\n\n# 2. generate all kinds of masks\n\n# all 512\npython3 bin/gen_mask_dataset.py \\\n$(pwd)/configs/data_gen/random_thick_512.yaml \\\nplaces_standard_dataset/evaluation/hires \\\nplaces_standard_dataset/evaluation/random_thick_512/\n\npython3 bin/gen_mask_dataset.py \\\n$(pwd)/configs/data_gen/random_thin_512.yaml \\\nplaces_standard_dataset/evaluation/hires \\\nplaces_standard_dataset/evaluation/random_thin_512/\n\npython3 bin/gen_mask_dataset.py \\\n$(pwd)/configs/data_gen/random_medium_512.yaml \\\nplaces_standard_dataset/evaluation/hires \\\nplaces_standard_dataset/evaluation/random_medium_512/\n\npython3 bin/gen_mask_dataset.py \\\n$(pwd)/configs/data_gen/random_thick_256.yaml \\\nplaces_standard_dataset/evaluation/hires \\\nplaces_standard_dataset/evaluation/random_thick_256/\n\npython3 bin/gen_mask_dataset.py \\\n$(pwd)/configs/data_gen/random_thin_256.yaml \\\nplaces_standard_dataset/evaluation/hires \\\nplaces_standard_dataset/evaluation/random_thin_256/\n\npython3 bin/gen_mask_dataset.py \\\n$(pwd)/configs/data_gen/random_medium_256.yaml \\\nplaces_standard_dataset/evaluation/hires \\\nplaces_standard_dataset/evaluation/random_medium_256/\n"
  },
  {
    "path": "model_cards/lama/fetch_data/places_standard_test_val_gen_masks.sh",
    "content": "mkdir -p places_standard_dataset/val/\nmkdir -p places_standard_dataset/visual_test/\n\n\npython3 bin/gen_mask_dataset.py \\\n$(pwd)/configs/data_gen/random_thick_512.yaml \\\nplaces_standard_dataset/val_hires/ \\\nplaces_standard_dataset/val/\n\npython3 bin/gen_mask_dataset.py \\\n$(pwd)/configs/data_gen/random_thick_512.yaml \\\nplaces_standard_dataset/visual_test_hires/ \\\nplaces_standard_dataset/visual_test/"
  },
  {
    "path": "model_cards/lama/fetch_data/places_standard_test_val_prepare.sh",
    "content": "mkdir -p places_standard_dataset/original/test/\ntar -xvf test_large.tar -C places_standard_dataset/original/test/\n\nmkdir -p places_standard_dataset/original/val/\ntar -xvf val_large.tar -C places_standard_dataset/original/val/\n"
  },
  {
    "path": "model_cards/lama/fetch_data/places_standard_test_val_sample.sh",
    "content": "mkdir -p places_standard_dataset/val_hires/\nmkdir -p places_standard_dataset/visual_test_hires/\n\n\n# randomly sample images for test and vis\nOUT=$(python3 fetch_data/sampler.py)\necho ${OUT}\n\nFILELIST=$(cat places_standard_dataset/original/test_random_files.txt)\n\nfor i in $FILELIST\ndo\n    $(cp ${i} places_standard_dataset/val_hires/)\ndone\n\nFILELIST=$(cat places_standard_dataset/original/val_random_files.txt)\n\nfor i in $FILELIST\ndo\n    $(cp ${i} places_standard_dataset/visual_test_hires/)\ndone\n\n"
  },
  {
    "path": "model_cards/lama/fetch_data/places_standard_train_prepare.sh",
    "content": "mkdir -p places_standard_dataset/train\n\n# untar without folder structure\ntar -xvf train_large_places365standard.tar -C places_standard_dataset/train\n\n# create location config places.yaml\nPWD=$(pwd)\nDATASET=${PWD}/places_standard_dataset\nPLACES=${PWD}/configs/training/location/places_standard.yaml\n\ntouch $PLACES\necho \"# @package _group_\" >> $PLACES\necho \"data_root_dir: ${DATASET}/\" >> $PLACES\necho \"out_root_dir: ${PWD}/experiments/\" >> $PLACES\necho \"tb_dir: ${PWD}/tb_logs/\" >> $PLACES\necho \"pretrained_models: ${PWD}/\" >> $PLACES\n"
  },
  {
    "path": "model_cards/lama/fetch_data/sampler.py",
    "content": "import os\nimport random\n\ntest_files_path           = os.path.abspath('.') + '/places_standard_dataset/original/test/'\nlist_of_random_test_files = os.path.abspath('.') + '/places_standard_dataset/original/test_random_files.txt'\n\ntest_files = [\n    test_files_path + image for image in os.listdir(test_files_path)\n]\n\nprint(f'Sampling 2000 images out of {len(test_files)} images in {test_files_path}' + \\\n      f'and put their paths to {list_of_random_test_files}')\nprint('Our training procedure will pick best checkpoints according to metrics, computed on these images.')\n\nrandom.shuffle(test_files)\ntest_files_random = test_files[0:2000]\nwith open(list_of_random_test_files, 'w') as fw:\n    for filename in test_files_random:\n        fw.write(filename+'\\n')\nprint('...done')\n\n\n# --------------------------------\n\nval_files_path           = os.path.abspath('.') + '/places_standard_dataset/original/val/'\nlist_of_random_val_files = os.path.abspath('.') + '/places_standard_dataset/original/val_random_files.txt'\n\nval_files = [\n    val_files_path + image for image in os.listdir(val_files_path)\n]\n\nprint(f'Sampling 100 images out of {len(val_files)} in {val_files_path} ' + \\\n      f'and put their paths to {list_of_random_val_files}')\nprint('We use these images for visual check up of evolution of inpainting algorithm epoch to epoch' )\n\nrandom.shuffle(val_files)\nval_files_random = val_files[0:100]\nwith open(list_of_random_val_files, 'w') as fw:\n    for filename in val_files_random:\n        fw.write(filename+'\\n')\nprint('...done')      \n\n"
  },
  {
    "path": "model_cards/lama/fetch_data/train_shuffled.flist",
    "content": 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23093.jpg\n2678.jpg\n17646.jpg\n7777.jpg\n21214.jpg\n7774.jpg\n26418.jpg\n28015.jpg\n20166.jpg\n3825.jpg\n24201.jpg\n8317.jpg\n14778.jpg\n27354.jpg\n12297.jpg\n24751.jpg\n22045.jpg\n5715.jpg\n16927.jpg\n3904.jpg\n22210.jpg\n19164.jpg\n16728.jpg\n22001.jpg\n29740.jpg\n12380.jpg\n22747.jpg\n5195.jpg\n20352.jpg\n2816.jpg\n5684.jpg\n7932.jpg\n29597.jpg\n765.jpg\n25263.jpg\n26924.jpg\n186.jpg\n29633.jpg\n1240.jpg\n9237.jpg\n25910.jpg\n29842.jpg\n28285.jpg\n29933.jpg\n20746.jpg\n6882.jpg\n19849.jpg\n501.jpg\n10624.jpg\n10257.jpg\n27767.jpg\n9194.jpg\n12635.jpg\n10163.jpg\n26083.jpg\n14443.jpg\n9585.jpg\n4122.jpg\n22546.jpg\n29826.jpg\n23702.jpg\n8328.jpg\n15442.jpg\n13429.jpg\n3246.jpg\n11863.jpg\n15700.jpg\n5302.jpg\n16824.jpg\n13608.jpg\n12499.jpg\n12730.jpg\n4290.jpg\n2139.jpg\n12029.jpg\n29257.jpg\n18063.jpg\n20935.jpg\n27222.jpg\n18024.jpg\n17092.jpg\n19108.jpg\n2908.jpg\n22260.jpg\n2070.jpg\n16758.jpg\n26794.jpg\n4834.jpg\n23293.jpg\n5957.jpg\n2793.jpg\n15851.jpg\n21315.jpg\n16009.jpg\n1251.jpg\n10388.jpg\n2466.jpg\n10638.jpg\n25034.jpg\n15151.jpg\n13741.jpg\n27270.jpg\n4833.jpg\n6023.jpg\n28972.jpg\n7260.jpg\n17444.jpg\n15699.jpg\n23730.jpg\n20254.jpg\n17959.jpg\n21653.jpg\n28331.jpg\n10644.jpg\n23935.jpg\n4600.jpg\n2720.jpg\n6569.jpg\n2528.jpg\n"
  },
  {
    "path": "model_cards/lama/models/ade20k/__init__.py",
    "content": "from .base import *"
  },
  {
    "path": "model_cards/lama/models/ade20k/base.py",
    "content": "\"\"\"Modified from https://github.com/CSAILVision/semantic-segmentation-pytorch\"\"\"\n\nimport os\n\nimport pandas as pd\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom scipy.io import loadmat\nfrom torch.nn.modules import BatchNorm2d\n\nfrom . import resnet\nfrom . import mobilenet\n\n\nNUM_CLASS = 150\nbase_path = os.path.dirname(os.path.abspath(__file__))  # current file path\ncolors_path = os.path.join(base_path, 'color150.mat')\nclasses_path = os.path.join(base_path, 'object150_info.csv')\n\nsegm_options = dict(colors=loadmat(colors_path)['colors'],\n                    classes=pd.read_csv(classes_path),)\n\n\nclass NormalizeTensor:\n    def __init__(self, mean, std, inplace=False):\n        \"\"\"Normalize a tensor image with mean and standard deviation.\n        .. note::\n            This transform acts out of place by default, i.e., it does not mutates the input tensor.\n        See :class:`~torchvision.transforms.Normalize` for more details.\n        Args:\n            tensor (Tensor): Tensor image of size (C, H, W) to be normalized.\n            mean (sequence): Sequence of means for each channel.\n            std (sequence): Sequence of standard deviations for each channel.\n            inplace(bool,optional): Bool to make this operation inplace.\n        Returns:\n            Tensor: Normalized Tensor image.\n        \"\"\"\n\n        self.mean = mean\n        self.std = std\n        self.inplace = inplace\n\n    def __call__(self, tensor):\n        if not self.inplace:\n            tensor = tensor.clone()\n\n        dtype = tensor.dtype\n        mean = torch.as_tensor(self.mean, dtype=dtype, device=tensor.device)\n        std = torch.as_tensor(self.std, dtype=dtype, device=tensor.device)\n        tensor.sub_(mean[None, :, None, None]).div_(std[None, :, None, None])\n        return tensor\n\n\n# Model Builder\nclass ModelBuilder:\n    # custom weights initialization\n    @staticmethod\n    def weights_init(m):\n        classname = m.__class__.__name__\n        if classname.find('Conv') != -1:\n            nn.init.kaiming_normal_(m.weight.data)\n        elif classname.find('BatchNorm') != -1:\n            m.weight.data.fill_(1.)\n            m.bias.data.fill_(1e-4)\n\n    @staticmethod\n    def build_encoder(arch='resnet50dilated', fc_dim=512, weights=''):\n        pretrained = True if len(weights) == 0 else False\n        arch = arch.lower()\n        if arch == 'mobilenetv2dilated':\n            orig_mobilenet = mobilenet.__dict__['mobilenetv2'](pretrained=pretrained)\n            net_encoder = MobileNetV2Dilated(orig_mobilenet, dilate_scale=8)\n        elif arch == 'resnet18':\n            orig_resnet = resnet.__dict__['resnet18'](pretrained=pretrained)\n            net_encoder = Resnet(orig_resnet)\n        elif arch == 'resnet18dilated':\n            orig_resnet = resnet.__dict__['resnet18'](pretrained=pretrained)\n            net_encoder = ResnetDilated(orig_resnet, dilate_scale=8)\n        elif arch == 'resnet50dilated':\n            orig_resnet = resnet.__dict__['resnet50'](pretrained=pretrained)\n            net_encoder = ResnetDilated(orig_resnet, dilate_scale=8)\n        elif arch == 'resnet50':\n            orig_resnet = resnet.__dict__['resnet50'](pretrained=pretrained)\n            net_encoder = Resnet(orig_resnet)\n        else:\n            raise Exception('Architecture undefined!')\n\n        # encoders are usually pretrained\n        # net_encoder.apply(ModelBuilder.weights_init)\n        if len(weights) > 0:\n            print('Loading weights for net_encoder')\n            net_encoder.load_state_dict(\n                torch.load(weights, map_location=lambda storage, loc: storage), strict=False)\n        return net_encoder\n\n    @staticmethod\n    def build_decoder(arch='ppm_deepsup',\n                      fc_dim=512, num_class=NUM_CLASS,\n                      weights='', use_softmax=False, drop_last_conv=False):\n        arch = arch.lower()\n        if arch == 'ppm_deepsup':\n            net_decoder = PPMDeepsup(\n                num_class=num_class,\n                fc_dim=fc_dim,\n                use_softmax=use_softmax,\n                drop_last_conv=drop_last_conv)\n        elif arch == 'c1_deepsup':\n            net_decoder = C1DeepSup(\n                num_class=num_class,\n                fc_dim=fc_dim,\n                use_softmax=use_softmax,\n                drop_last_conv=drop_last_conv)\n        else:\n            raise Exception('Architecture undefined!')\n\n        net_decoder.apply(ModelBuilder.weights_init)\n        if len(weights) > 0:\n            print('Loading weights for net_decoder')\n            net_decoder.load_state_dict(\n                torch.load(weights, map_location=lambda storage, loc: storage), strict=False)\n        return net_decoder\n\n    @staticmethod\n    def get_decoder(weights_path, arch_encoder, arch_decoder, fc_dim, drop_last_conv, *arts, **kwargs):\n        path = os.path.join(weights_path, 'ade20k', f'ade20k-{arch_encoder}-{arch_decoder}/decoder_epoch_20.pth')\n        return ModelBuilder.build_decoder(arch=arch_decoder, fc_dim=fc_dim, weights=path, use_softmax=True, drop_last_conv=drop_last_conv)\n\n    @staticmethod\n    def get_encoder(weights_path, arch_encoder, arch_decoder, fc_dim, segmentation,\n                    *arts, **kwargs):\n        if segmentation:\n            path = os.path.join(weights_path, 'ade20k', f'ade20k-{arch_encoder}-{arch_decoder}/encoder_epoch_20.pth')\n        else:\n            path = ''\n        return ModelBuilder.build_encoder(arch=arch_encoder, fc_dim=fc_dim, weights=path)\n\n\ndef conv3x3_bn_relu(in_planes, out_planes, stride=1):\n    return nn.Sequential(\n        nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False),\n        BatchNorm2d(out_planes),\n        nn.ReLU(inplace=True),\n    )\n\n\nclass SegmentationModule(nn.Module):\n    def __init__(self,\n                 weights_path,\n                 num_classes=150,\n                 arch_encoder=\"resnet50dilated\",\n                 drop_last_conv=False,\n                 net_enc=None,  # None for Default encoder\n                 net_dec=None,  # None for Default decoder\n                 encode=None,  # {None, 'binary', 'color', 'sky'}\n                 use_default_normalization=False,\n                 return_feature_maps=False,\n                 return_feature_maps_level=3,  # {0, 1, 2, 3}\n                 return_feature_maps_only=True,\n                 **kwargs,\n                 ):\n        super().__init__()\n        self.weights_path = weights_path\n        self.drop_last_conv = drop_last_conv\n        self.arch_encoder = arch_encoder\n        if self.arch_encoder == \"resnet50dilated\":\n            self.arch_decoder = \"ppm_deepsup\"\n            self.fc_dim = 2048\n        elif self.arch_encoder == \"mobilenetv2dilated\":\n            self.arch_decoder = \"c1_deepsup\"\n            self.fc_dim = 320\n        else:\n            raise NotImplementedError(f\"No such arch_encoder={self.arch_encoder}\")\n        model_builder_kwargs = dict(arch_encoder=self.arch_encoder,\n                                    arch_decoder=self.arch_decoder,\n                                    fc_dim=self.fc_dim,\n                                    drop_last_conv=drop_last_conv,\n                                    weights_path=self.weights_path)\n\n        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n        self.encoder = ModelBuilder.get_encoder(**model_builder_kwargs) if net_enc is None else net_enc\n        self.decoder = ModelBuilder.get_decoder(**model_builder_kwargs) if net_dec is None else net_dec\n        self.use_default_normalization = use_default_normalization\n        self.default_normalization = NormalizeTensor(mean=[0.485, 0.456, 0.406],\n                                                     std=[0.229, 0.224, 0.225])\n\n        self.encode = encode\n\n        self.return_feature_maps = return_feature_maps\n\n        assert 0 <= return_feature_maps_level <= 3\n        self.return_feature_maps_level = return_feature_maps_level\n\n    def normalize_input(self, tensor):\n        if tensor.min() < 0 or tensor.max() > 1:\n            raise ValueError(\"Tensor should be 0..1 before using normalize_input\")\n        return self.default_normalization(tensor)\n\n    @property\n    def feature_maps_channels(self):\n        return 256 * 2**(self.return_feature_maps_level)  # 256, 512, 1024, 2048\n\n    def forward(self, img_data, segSize=None):\n        if segSize is None:\n            raise NotImplementedError(\"Please pass segSize param. By default: (300, 300)\")\n\n        fmaps = self.encoder(img_data, return_feature_maps=True)\n        pred = self.decoder(fmaps, segSize=segSize)\n\n        if self.return_feature_maps:\n            return pred, fmaps\n        # print(\"BINARY\", img_data.shape, pred.shape)\n        return pred\n\n    def multi_mask_from_multiclass(self, pred, classes):\n        def isin(ar1, ar2):\n            return (ar1[..., None] == ar2).any(-1).float()\n        return isin(pred, torch.LongTensor(classes).to(self.device))\n\n    @staticmethod\n    def multi_mask_from_multiclass_probs(scores, classes):\n        res = None\n        for c in classes:\n            if res is None:\n                res = scores[:, c]\n            else:\n                res += scores[:, c]\n        return res\n\n    def predict(self, tensor, imgSizes=(-1,),  # (300, 375, 450, 525, 600)\n                segSize=None):\n        \"\"\"Entry-point for segmentation. Use this methods instead of forward\n        Arguments:\n            tensor {torch.Tensor} -- BCHW\n        Keyword Arguments:\n            imgSizes {tuple or list} -- imgSizes for segmentation input.\n                default: (300, 450)\n                original implementation: (300, 375, 450, 525, 600)\n\n        \"\"\"\n        if segSize is None:\n            segSize = tensor.shape[-2:]\n        segSize = (tensor.shape[2], tensor.shape[3])\n        with torch.no_grad():\n            if self.use_default_normalization:\n                tensor = self.normalize_input(tensor)\n            scores = torch.zeros(1, NUM_CLASS, segSize[0], segSize[1]).to(self.device)\n            features = torch.zeros(1, self.feature_maps_channels, segSize[0], segSize[1]).to(self.device)\n\n            result = []\n            for img_size in imgSizes:\n                if img_size != -1:\n                    img_data = F.interpolate(tensor.clone(), size=img_size)\n                else:\n                    img_data = tensor.clone()\n\n                if self.return_feature_maps:\n                    pred_current, fmaps = self.forward(img_data, segSize=segSize)\n                else:\n                    pred_current = self.forward(img_data, segSize=segSize)\n\n\n                result.append(pred_current)\n                scores = scores + pred_current / len(imgSizes)\n\n                # Disclaimer: We use and aggregate only last fmaps: fmaps[3]\n                if self.return_feature_maps:\n                    features = features + F.interpolate(fmaps[self.return_feature_maps_level], size=segSize) / len(imgSizes)\n\n            _, pred = torch.max(scores, dim=1)\n\n            if self.return_feature_maps:\n                return features\n\n            return pred, result\n\n    def get_edges(self, t):\n        edge = torch.cuda.ByteTensor(t.size()).zero_()\n        edge[:, :, :, 1:] = edge[:, :, :, 1:] | (t[:, :, :, 1:] != t[:, :, :, :-1])\n        edge[:, :, :, :-1] = edge[:, :, :, :-1] | (t[:, :, :, 1:] != t[:, :, :, :-1])\n        edge[:, :, 1:, :] = edge[:, :, 1:, :] | (t[:, :, 1:, :] != t[:, :, :-1, :])\n        edge[:, :, :-1, :] = edge[:, :, :-1, :] | (t[:, :, 1:, :] != t[:, :, :-1, :])\n\n        if True:\n            return edge.half()\n        return edge.float()\n\n\n# pyramid pooling, deep supervision\nclass PPMDeepsup(nn.Module):\n    def __init__(self, num_class=NUM_CLASS, fc_dim=4096,\n                 use_softmax=False, pool_scales=(1, 2, 3, 6),\n                 drop_last_conv=False):\n        super().__init__()\n        self.use_softmax = use_softmax\n        self.drop_last_conv = drop_last_conv\n\n        self.ppm = []\n        for scale in pool_scales:\n            self.ppm.append(nn.Sequential(\n                nn.AdaptiveAvgPool2d(scale),\n                nn.Conv2d(fc_dim, 512, kernel_size=1, bias=False),\n                BatchNorm2d(512),\n                nn.ReLU(inplace=True)\n            ))\n        self.ppm = nn.ModuleList(self.ppm)\n        self.cbr_deepsup = conv3x3_bn_relu(fc_dim // 2, fc_dim // 4, 1)\n\n        self.conv_last = nn.Sequential(\n            nn.Conv2d(fc_dim + len(pool_scales) * 512, 512,\n                      kernel_size=3, padding=1, bias=False),\n            BatchNorm2d(512),\n            nn.ReLU(inplace=True),\n            nn.Dropout2d(0.1),\n            nn.Conv2d(512, num_class, kernel_size=1)\n        )\n        self.conv_last_deepsup = nn.Conv2d(fc_dim // 4, num_class, 1, 1, 0)\n        self.dropout_deepsup = nn.Dropout2d(0.1)\n\n    def forward(self, conv_out, segSize=None):\n        conv5 = conv_out[-1]\n\n        input_size = conv5.size()\n        ppm_out = [conv5]\n        for pool_scale in self.ppm:\n            ppm_out.append(nn.functional.interpolate(\n                pool_scale(conv5),\n                (input_size[2], input_size[3]),\n                mode='bilinear', align_corners=False))\n        ppm_out = torch.cat(ppm_out, 1)\n\n        if self.drop_last_conv:\n            return ppm_out\n        else:\n            x = self.conv_last(ppm_out)\n\n            if self.use_softmax:  # is True during inference\n                x = nn.functional.interpolate(\n                    x, size=segSize, mode='bilinear', align_corners=False)\n                x = nn.functional.softmax(x, dim=1)\n                return x\n\n            # deep sup\n            conv4 = conv_out[-2]\n            _ = self.cbr_deepsup(conv4)\n            _ = self.dropout_deepsup(_)\n            _ = self.conv_last_deepsup(_)\n\n            x = nn.functional.log_softmax(x, dim=1)\n            _ = nn.functional.log_softmax(_, dim=1)\n\n            return (x, _)\n\n\nclass Resnet(nn.Module):\n    def __init__(self, orig_resnet):\n        super(Resnet, self).__init__()\n\n        # take pretrained resnet, except AvgPool and FC\n        self.conv1 = orig_resnet.conv1\n        self.bn1 = orig_resnet.bn1\n        self.relu1 = orig_resnet.relu1\n        self.conv2 = orig_resnet.conv2\n        self.bn2 = orig_resnet.bn2\n        self.relu2 = orig_resnet.relu2\n        self.conv3 = orig_resnet.conv3\n        self.bn3 = orig_resnet.bn3\n        self.relu3 = orig_resnet.relu3\n        self.maxpool = orig_resnet.maxpool\n        self.layer1 = orig_resnet.layer1\n        self.layer2 = orig_resnet.layer2\n        self.layer3 = orig_resnet.layer3\n        self.layer4 = orig_resnet.layer4\n\n    def forward(self, x, return_feature_maps=False):\n        conv_out = []\n\n        x = self.relu1(self.bn1(self.conv1(x)))\n        x = self.relu2(self.bn2(self.conv2(x)))\n        x = self.relu3(self.bn3(self.conv3(x)))\n        x = self.maxpool(x)\n\n        x = self.layer1(x); conv_out.append(x);\n        x = self.layer2(x); conv_out.append(x);\n        x = self.layer3(x); conv_out.append(x);\n        x = self.layer4(x); conv_out.append(x);\n\n        if return_feature_maps:\n            return conv_out\n        return [x]\n\n# Resnet Dilated\nclass ResnetDilated(nn.Module):\n    def __init__(self, orig_resnet, dilate_scale=8):\n        super().__init__()\n        from functools import partial\n\n        if dilate_scale == 8:\n            orig_resnet.layer3.apply(\n                partial(self._nostride_dilate, dilate=2))\n            orig_resnet.layer4.apply(\n                partial(self._nostride_dilate, dilate=4))\n        elif dilate_scale == 16:\n            orig_resnet.layer4.apply(\n                partial(self._nostride_dilate, dilate=2))\n\n        # take pretrained resnet, except AvgPool and FC\n        self.conv1 = orig_resnet.conv1\n        self.bn1 = orig_resnet.bn1\n        self.relu1 = orig_resnet.relu1\n        self.conv2 = orig_resnet.conv2\n        self.bn2 = orig_resnet.bn2\n        self.relu2 = orig_resnet.relu2\n        self.conv3 = orig_resnet.conv3\n        self.bn3 = orig_resnet.bn3\n        self.relu3 = orig_resnet.relu3\n        self.maxpool = orig_resnet.maxpool\n        self.layer1 = orig_resnet.layer1\n        self.layer2 = orig_resnet.layer2\n        self.layer3 = orig_resnet.layer3\n        self.layer4 = orig_resnet.layer4\n\n    def _nostride_dilate(self, m, dilate):\n        classname = m.__class__.__name__\n        if classname.find('Conv') != -1:\n            # the convolution with stride\n            if m.stride == (2, 2):\n                m.stride = (1, 1)\n                if m.kernel_size == (3, 3):\n                    m.dilation = (dilate // 2, dilate // 2)\n                    m.padding = (dilate // 2, dilate // 2)\n            # other convoluions\n            else:\n                if m.kernel_size == (3, 3):\n                    m.dilation = (dilate, dilate)\n                    m.padding = (dilate, dilate)\n\n    def forward(self, x, return_feature_maps=False):\n        conv_out = []\n\n        x = self.relu1(self.bn1(self.conv1(x)))\n        x = self.relu2(self.bn2(self.conv2(x)))\n        x = self.relu3(self.bn3(self.conv3(x)))\n        x = self.maxpool(x)\n\n        x = self.layer1(x)\n        conv_out.append(x)\n        x = self.layer2(x)\n        conv_out.append(x)\n        x = self.layer3(x)\n        conv_out.append(x)\n        x = self.layer4(x)\n        conv_out.append(x)\n\n        if return_feature_maps:\n            return conv_out\n        return [x]\n\nclass MobileNetV2Dilated(nn.Module):\n    def __init__(self, orig_net, dilate_scale=8):\n        super(MobileNetV2Dilated, self).__init__()\n        from functools import partial\n\n        # take pretrained mobilenet features\n        self.features = orig_net.features[:-1]\n\n        self.total_idx = len(self.features)\n        self.down_idx = [2, 4, 7, 14]\n\n        if dilate_scale == 8:\n            for i in range(self.down_idx[-2], self.down_idx[-1]):\n                self.features[i].apply(\n                    partial(self._nostride_dilate, dilate=2)\n                )\n            for i in range(self.down_idx[-1], self.total_idx):\n                self.features[i].apply(\n                    partial(self._nostride_dilate, dilate=4)\n                )\n        elif dilate_scale == 16:\n            for i in range(self.down_idx[-1], self.total_idx):\n                self.features[i].apply(\n                    partial(self._nostride_dilate, dilate=2)\n                )\n\n    def _nostride_dilate(self, m, dilate):\n        classname = m.__class__.__name__\n        if classname.find('Conv') != -1:\n            # the convolution with stride\n            if m.stride == (2, 2):\n                m.stride = (1, 1)\n                if m.kernel_size == (3, 3):\n                    m.dilation = (dilate//2, dilate//2)\n                    m.padding = (dilate//2, dilate//2)\n            # other convoluions\n            else:\n                if m.kernel_size == (3, 3):\n                    m.dilation = (dilate, dilate)\n                    m.padding = (dilate, dilate)\n\n    def forward(self, x, return_feature_maps=False):\n        if return_feature_maps:\n            conv_out = []\n            for i in range(self.total_idx):\n                x = self.features[i](x)\n                if i in self.down_idx:\n                    conv_out.append(x)\n            conv_out.append(x)\n            return conv_out\n\n        else:\n            return [self.features(x)]\n\n\n# last conv, deep supervision\nclass C1DeepSup(nn.Module):\n    def __init__(self, num_class=150, fc_dim=2048, use_softmax=False, drop_last_conv=False):\n        super(C1DeepSup, self).__init__()\n        self.use_softmax = use_softmax\n        self.drop_last_conv = drop_last_conv\n\n        self.cbr = conv3x3_bn_relu(fc_dim, fc_dim // 4, 1)\n        self.cbr_deepsup = conv3x3_bn_relu(fc_dim // 2, fc_dim // 4, 1)\n\n        # last conv\n        self.conv_last = nn.Conv2d(fc_dim // 4, num_class, 1, 1, 0)\n        self.conv_last_deepsup = nn.Conv2d(fc_dim // 4, num_class, 1, 1, 0)\n\n    def forward(self, conv_out, segSize=None):\n        conv5 = conv_out[-1]\n\n        x = self.cbr(conv5)\n\n        if self.drop_last_conv:\n            return x\n        else:\n            x = self.conv_last(x)\n\n            if self.use_softmax:  # is True during inference\n                x = nn.functional.interpolate(\n                    x, size=segSize, mode='bilinear', align_corners=False)\n                x = nn.functional.softmax(x, dim=1)\n                return x\n\n            # deep sup\n            conv4 = conv_out[-2]\n            _ = self.cbr_deepsup(conv4)\n            _ = self.conv_last_deepsup(_)\n\n            x = nn.functional.log_softmax(x, dim=1)\n            _ = nn.functional.log_softmax(_, dim=1)\n\n            return (x, _)\n\n\n# last conv\nclass C1(nn.Module):\n    def __init__(self, num_class=150, fc_dim=2048, use_softmax=False):\n        super(C1, self).__init__()\n        self.use_softmax = use_softmax\n\n        self.cbr = conv3x3_bn_relu(fc_dim, fc_dim // 4, 1)\n\n        # last conv\n        self.conv_last = nn.Conv2d(fc_dim // 4, num_class, 1, 1, 0)\n\n    def forward(self, conv_out, segSize=None):\n        conv5 = conv_out[-1]\n        x = self.cbr(conv5)\n        x = self.conv_last(x)\n\n        if self.use_softmax: # is True during inference\n            x = nn.functional.interpolate(\n                x, size=segSize, mode='bilinear', align_corners=False)\n            x = nn.functional.softmax(x, dim=1)\n        else:\n            x = nn.functional.log_softmax(x, dim=1)\n\n        return x\n\n\n# pyramid pooling\nclass PPM(nn.Module):\n    def __init__(self, num_class=150, fc_dim=4096,\n                 use_softmax=False, pool_scales=(1, 2, 3, 6)):\n        super(PPM, self).__init__()\n        self.use_softmax = use_softmax\n\n        self.ppm = []\n        for scale in pool_scales:\n            self.ppm.append(nn.Sequential(\n                nn.AdaptiveAvgPool2d(scale),\n                nn.Conv2d(fc_dim, 512, kernel_size=1, bias=False),\n                BatchNorm2d(512),\n                nn.ReLU(inplace=True)\n            ))\n        self.ppm = nn.ModuleList(self.ppm)\n\n        self.conv_last = nn.Sequential(\n            nn.Conv2d(fc_dim+len(pool_scales)*512, 512,\n                      kernel_size=3, padding=1, bias=False),\n            BatchNorm2d(512),\n            nn.ReLU(inplace=True),\n            nn.Dropout2d(0.1),\n            nn.Conv2d(512, num_class, kernel_size=1)\n        )\n\n    def forward(self, conv_out, segSize=None):\n        conv5 = conv_out[-1]\n\n        input_size = conv5.size()\n        ppm_out = [conv5]\n        for pool_scale in self.ppm:\n            ppm_out.append(nn.functional.interpolate(\n                pool_scale(conv5),\n                (input_size[2], input_size[3]),\n                mode='bilinear', align_corners=False))\n        ppm_out = torch.cat(ppm_out, 1)\n\n        x = self.conv_last(ppm_out)\n\n        if self.use_softmax:  # is True during inference\n            x = nn.functional.interpolate(\n                x, size=segSize, mode='bilinear', align_corners=False)\n            x = nn.functional.softmax(x, dim=1)\n        else:\n            x = nn.functional.log_softmax(x, dim=1)\n        return x\n"
  },
  {
    "path": "model_cards/lama/models/ade20k/mobilenet.py",
    "content": "\"\"\"\nThis MobileNetV2 implementation is modified from the following repository:\nhttps://github.com/tonylins/pytorch-mobilenet-v2\n\"\"\"\n\nimport torch.nn as nn\nimport math\nfrom .utils import load_url\nfrom .segm_lib.nn import SynchronizedBatchNorm2d\n\nBatchNorm2d = SynchronizedBatchNorm2d\n\n\n__all__ = ['mobilenetv2']\n\n\nmodel_urls = {\n    'mobilenetv2': 'http://sceneparsing.csail.mit.edu/model/pretrained_resnet/mobilenet_v2.pth.tar',\n}\n\n\ndef conv_bn(inp, oup, stride):\n    return nn.Sequential(\n        nn.Conv2d(inp, oup, 3, stride, 1, bias=False),\n        BatchNorm2d(oup),\n        nn.ReLU6(inplace=True)\n    )\n\n\ndef conv_1x1_bn(inp, oup):\n    return nn.Sequential(\n        nn.Conv2d(inp, oup, 1, 1, 0, bias=False),\n        BatchNorm2d(oup),\n        nn.ReLU6(inplace=True)\n    )\n\n\nclass InvertedResidual(nn.Module):\n    def __init__(self, inp, oup, stride, expand_ratio):\n        super(InvertedResidual, self).__init__()\n        self.stride = stride\n        assert stride in [1, 2]\n\n        hidden_dim = round(inp * expand_ratio)\n        self.use_res_connect = self.stride == 1 and inp == oup\n\n        if expand_ratio == 1:\n            self.conv = nn.Sequential(\n                # dw\n                nn.Conv2d(hidden_dim, hidden_dim, 3, stride, 1, groups=hidden_dim, bias=False),\n                BatchNorm2d(hidden_dim),\n                nn.ReLU6(inplace=True),\n                # pw-linear\n                nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),\n                BatchNorm2d(oup),\n            )\n        else:\n            self.conv = nn.Sequential(\n                # pw\n                nn.Conv2d(inp, hidden_dim, 1, 1, 0, bias=False),\n                BatchNorm2d(hidden_dim),\n                nn.ReLU6(inplace=True),\n                # dw\n                nn.Conv2d(hidden_dim, hidden_dim, 3, stride, 1, groups=hidden_dim, bias=False),\n                BatchNorm2d(hidden_dim),\n                nn.ReLU6(inplace=True),\n                # pw-linear\n                nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),\n                BatchNorm2d(oup),\n            )\n\n    def forward(self, x):\n        if self.use_res_connect:\n            return x + self.conv(x)\n        else:\n            return self.conv(x)\n\n\nclass MobileNetV2(nn.Module):\n    def __init__(self, n_class=1000, input_size=224, width_mult=1.):\n        super(MobileNetV2, self).__init__()\n        block = InvertedResidual\n        input_channel = 32\n        last_channel = 1280\n        interverted_residual_setting = [\n            # t, c, n, s\n            [1, 16, 1, 1],\n            [6, 24, 2, 2],\n            [6, 32, 3, 2],\n            [6, 64, 4, 2],\n            [6, 96, 3, 1],\n            [6, 160, 3, 2],\n            [6, 320, 1, 1],\n        ]\n\n        # building first layer\n        assert input_size % 32 == 0\n        input_channel = int(input_channel * width_mult)\n        self.last_channel = int(last_channel * width_mult) if width_mult > 1.0 else last_channel\n        self.features = [conv_bn(3, input_channel, 2)]\n        # building inverted residual blocks\n        for t, c, n, s in interverted_residual_setting:\n            output_channel = int(c * width_mult)\n            for i in range(n):\n                if i == 0:\n                    self.features.append(block(input_channel, output_channel, s, expand_ratio=t))\n                else:\n                    self.features.append(block(input_channel, output_channel, 1, expand_ratio=t))\n                input_channel = output_channel\n        # building last several layers\n        self.features.append(conv_1x1_bn(input_channel, self.last_channel))\n        # make it nn.Sequential\n        self.features = nn.Sequential(*self.features)\n\n        # building classifier\n        self.classifier = nn.Sequential(\n            nn.Dropout(0.2),\n            nn.Linear(self.last_channel, n_class),\n        )\n\n        self._initialize_weights()\n\n    def forward(self, x):\n        x = self.features(x)\n        x = x.mean(3).mean(2)\n        x = self.classifier(x)\n        return x\n\n    def _initialize_weights(self):\n        for m in self.modules():\n            if isinstance(m, nn.Conv2d):\n                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels\n                m.weight.data.normal_(0, math.sqrt(2. / n))\n                if m.bias is not None:\n                    m.bias.data.zero_()\n            elif isinstance(m, BatchNorm2d):\n                m.weight.data.fill_(1)\n                m.bias.data.zero_()\n            elif isinstance(m, nn.Linear):\n                n = m.weight.size(1)\n                m.weight.data.normal_(0, 0.01)\n                m.bias.data.zero_()\n\n\ndef mobilenetv2(pretrained=False, **kwargs):\n    \"\"\"Constructs a MobileNet_V2 model.\n\n    Args:\n        pretrained (bool): If True, returns a model pre-trained on ImageNet\n    \"\"\"\n    model = MobileNetV2(n_class=1000, **kwargs)\n    if pretrained:\n        model.load_state_dict(load_url(model_urls['mobilenetv2']), strict=False)\n    return model"
  },
  {
    "path": "model_cards/lama/models/ade20k/object150_info.csv",
    "content": "Idx,Ratio,Train,Val,Stuff,Name\n1,0.1576,11664,1172,1,wall\n2,0.1072,6046,612,1,building;edifice\n3,0.0878,8265,796,1,sky\n4,0.0621,9336,917,1,floor;flooring\n5,0.0480,6678,641,0,tree\n6,0.0450,6604,643,1,ceiling\n7,0.0398,4023,408,1,road;route\n8,0.0231,1906,199,0,bed\n9,0.0198,4688,460,0,windowpane;window\n10,0.0183,2423,225,1,grass\n11,0.0181,2874,294,0,cabinet\n12,0.0166,3068,310,1,sidewalk;pavement\n13,0.0160,5075,526,0,person;individual;someone;somebody;mortal;soul\n14,0.0151,1804,190,1,earth;ground\n15,0.0118,6666,796,0,door;double;door\n16,0.0110,4269,411,0,table\n17,0.0109,1691,160,1,mountain;mount\n18,0.0104,3999,441,0,plant;flora;plant;life\n19,0.0104,2149,217,0,curtain;drape;drapery;mantle;pall\n20,0.0103,3261,318,0,chair\n21,0.0098,3164,306,0,car;auto;automobile;machine;motorcar\n22,0.0074,709,75,1,water\n23,0.0067,3296,315,0,painting;picture\n24,0.0065,1191,106,0,sofa;couch;lounge\n25,0.0061,1516,162,0,shelf\n26,0.0060,667,69,1,house\n27,0.0053,651,57,1,sea\n28,0.0052,1847,224,0,mirror\n29,0.0046,1158,128,1,rug;carpet;carpeting\n30,0.0044,480,44,1,field\n31,0.0044,1172,98,0,armchair\n32,0.0044,1292,184,0,seat\n33,0.0033,1386,138,0,fence;fencing\n34,0.0031,698,61,0,desk\n35,0.0030,781,73,0,rock;stone\n36,0.0027,380,43,0,wardrobe;closet;press\n37,0.0026,3089,302,0,lamp\n38,0.0024,404,37,0,bathtub;bathing;tub;bath;tub\n39,0.0024,804,99,0,railing;rail\n40,0.0023,1453,153,0,cushion\n41,0.0023,411,37,0,base;pedestal;stand\n42,0.0022,1440,162,0,box\n43,0.0022,800,77,0,column;pillar\n44,0.0020,2650,298,0,signboard;sign\n45,0.0019,549,46,0,chest;of;drawers;chest;bureau;dresser\n46,0.0019,367,36,0,counter\n47,0.0018,311,30,1,sand\n48,0.0018,1181,122,0,sink\n49,0.0018,287,23,1,skyscraper\n50,0.0018,468,38,0,fireplace;hearth;open;fireplace\n51,0.0018,402,43,0,refrigerator;icebox\n52,0.0018,130,12,1,grandstand;covered;stand\n53,0.0018,561,64,1,path\n54,0.0017,880,102,0,stairs;steps\n55,0.0017,86,12,1,runway\n56,0.0017,172,11,0,case;display;case;showcase;vitrine\n57,0.0017,198,18,0,pool;table;billiard;table;snooker;table\n58,0.0017,930,109,0,pillow\n59,0.0015,139,18,0,screen;door;screen\n60,0.0015,564,52,1,stairway;staircase\n61,0.0015,320,26,1,river\n62,0.0015,261,29,1,bridge;span\n63,0.0014,275,22,0,bookcase\n64,0.0014,335,60,0,blind;screen\n65,0.0014,792,75,0,coffee;table;cocktail;table\n66,0.0014,395,49,0,toilet;can;commode;crapper;pot;potty;stool;throne\n67,0.0014,1309,138,0,flower\n68,0.0013,1112,113,0,book\n69,0.0013,266,27,1,hill\n70,0.0013,659,66,0,bench\n71,0.0012,331,31,0,countertop\n72,0.0012,531,56,0,stove;kitchen;stove;range;kitchen;range;cooking;stove\n73,0.0012,369,36,0,palm;palm;tree\n74,0.0012,144,9,0,kitchen;island\n75,0.0011,265,29,0,computer;computing;machine;computing;device;data;processor;electronic;computer;information;processing;system\n76,0.0010,324,33,0,swivel;chair\n77,0.0009,304,27,0,boat\n78,0.0009,170,20,0,bar\n79,0.0009,68,6,0,arcade;machine\n80,0.0009,65,8,1,hovel;hut;hutch;shack;shanty\n81,0.0009,248,25,0,bus;autobus;coach;charabanc;double-decker;jitney;motorbus;motorcoach;omnibus;passenger;vehicle\n82,0.0008,492,49,0,towel\n83,0.0008,2510,269,0,light;light;source\n84,0.0008,440,39,0,truck;motortruck\n85,0.0008,147,18,1,tower\n86,0.0008,583,56,0,chandelier;pendant;pendent\n87,0.0007,533,61,0,awning;sunshade;sunblind\n88,0.0007,1989,239,0,streetlight;street;lamp\n89,0.0007,71,5,0,booth;cubicle;stall;kiosk\n90,0.0007,618,53,0,television;television;receiver;television;set;tv;tv;set;idiot;box;boob;tube;telly;goggle;box\n91,0.0007,135,12,0,airplane;aeroplane;plane\n92,0.0007,83,5,1,dirt;track\n93,0.0007,178,17,0,apparel;wearing;apparel;dress;clothes\n94,0.0006,1003,104,0,pole\n95,0.0006,182,12,1,land;ground;soil\n96,0.0006,452,50,0,bannister;banister;balustrade;balusters;handrail\n97,0.0006,42,6,1,escalator;moving;staircase;moving;stairway\n98,0.0006,307,31,0,ottoman;pouf;pouffe;puff;hassock\n99,0.0006,965,114,0,bottle\n100,0.0006,117,13,0,buffet;counter;sideboard\n101,0.0006,354,35,0,poster;posting;placard;notice;bill;card\n102,0.0006,108,9,1,stage\n103,0.0006,557,55,0,van\n104,0.0006,52,4,0,ship\n105,0.0005,99,5,0,fountain\n106,0.0005,57,4,1,conveyer;belt;conveyor;belt;conveyer;conveyor;transporter\n107,0.0005,292,31,0,canopy\n108,0.0005,77,9,0,washer;automatic;washer;washing;machine\n109,0.0005,340,38,0,plaything;toy\n110,0.0005,66,3,1,swimming;pool;swimming;bath;natatorium\n111,0.0005,465,49,0,stool\n112,0.0005,50,4,0,barrel;cask\n113,0.0005,622,75,0,basket;handbasket\n114,0.0005,80,9,1,waterfall;falls\n115,0.0005,59,3,0,tent;collapsible;shelter\n116,0.0005,531,72,0,bag\n117,0.0005,282,30,0,minibike;motorbike\n118,0.0005,73,7,0,cradle\n119,0.0005,435,44,0,oven\n120,0.0005,136,25,0,ball\n121,0.0005,116,24,0,food;solid;food\n122,0.0004,266,31,0,step;stair\n123,0.0004,58,12,0,tank;storage;tank\n124,0.0004,418,83,0,trade;name;brand;name;brand;marque\n125,0.0004,319,43,0,microwave;microwave;oven\n126,0.0004,1193,139,0,pot;flowerpot\n127,0.0004,97,23,0,animal;animate;being;beast;brute;creature;fauna\n128,0.0004,347,36,0,bicycle;bike;wheel;cycle\n129,0.0004,52,5,1,lake\n130,0.0004,246,22,0,dishwasher;dish;washer;dishwashing;machine\n131,0.0004,108,13,0,screen;silver;screen;projection;screen\n132,0.0004,201,30,0,blanket;cover\n133,0.0004,285,21,0,sculpture\n134,0.0004,268,27,0,hood;exhaust;hood\n135,0.0003,1020,108,0,sconce\n136,0.0003,1282,122,0,vase\n137,0.0003,528,65,0,traffic;light;traffic;signal;stoplight\n138,0.0003,453,57,0,tray\n139,0.0003,671,100,0,ashcan;trash;can;garbage;can;wastebin;ash;bin;ash-bin;ashbin;dustbin;trash;barrel;trash;bin\n140,0.0003,397,44,0,fan\n141,0.0003,92,8,1,pier;wharf;wharfage;dock\n142,0.0003,228,18,0,crt;screen\n143,0.0003,570,59,0,plate\n144,0.0003,217,22,0,monitor;monitoring;device\n145,0.0003,206,19,0,bulletin;board;notice;board\n146,0.0003,130,14,0,shower\n147,0.0003,178,28,0,radiator\n148,0.0002,504,57,0,glass;drinking;glass\n149,0.0002,775,96,0,clock\n150,0.0002,421,56,0,flag\n"
  },
  {
    "path": "model_cards/lama/models/ade20k/resnet.py",
    "content": "\"\"\"Modified from https://github.com/CSAILVision/semantic-segmentation-pytorch\"\"\"\n\nimport math\n\nimport torch.nn as nn\nfrom torch.nn import BatchNorm2d\n\nfrom .utils import load_url\n\n__all__ = ['ResNet', 'resnet50']\n\n\nmodel_urls = {\n    'resnet50': 'http://sceneparsing.csail.mit.edu/model/pretrained_resnet/resnet50-imagenet.pth',\n}\n\n\ndef conv3x3(in_planes, out_planes, stride=1):\n    \"3x3 convolution with padding\"\n    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,\n                     padding=1, bias=False)\n\n\nclass BasicBlock(nn.Module):\n    expansion = 1\n\n    def __init__(self, inplanes, planes, stride=1, downsample=None):\n        super(BasicBlock, self).__init__()\n        self.conv1 = conv3x3(inplanes, planes, stride)\n        self.bn1 = BatchNorm2d(planes)\n        self.relu = nn.ReLU(inplace=True)\n        self.conv2 = conv3x3(planes, planes)\n        self.bn2 = BatchNorm2d(planes)\n        self.downsample = downsample\n        self.stride = stride\n\n    def forward(self, x):\n        residual = x\n\n        out = self.conv1(x)\n        out = self.bn1(out)\n        out = self.relu(out)\n\n        out = self.conv2(out)\n        out = self.bn2(out)\n\n        if self.downsample is not None:\n            residual = self.downsample(x)\n\n        out += residual\n        out = self.relu(out)\n\n        return out\n\n\nclass Bottleneck(nn.Module):\n    expansion = 4\n\n    def __init__(self, inplanes, planes, stride=1, downsample=None):\n        super(Bottleneck, self).__init__()\n        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)\n        self.bn1 = BatchNorm2d(planes)\n        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,\n                               padding=1, bias=False)\n        self.bn2 = BatchNorm2d(planes)\n        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)\n        self.bn3 = BatchNorm2d(planes * 4)\n        self.relu = nn.ReLU(inplace=True)\n        self.downsample = downsample\n        self.stride = stride\n\n    def forward(self, x):\n        residual = x\n\n        out = self.conv1(x)\n        out = self.bn1(out)\n        out = self.relu(out)\n\n        out = self.conv2(out)\n        out = self.bn2(out)\n        out = self.relu(out)\n\n        out = self.conv3(out)\n        out = self.bn3(out)\n\n        if self.downsample is not None:\n            residual = self.downsample(x)\n\n        out += residual\n        out = self.relu(out)\n\n        return out\n\n\nclass ResNet(nn.Module):\n\n    def __init__(self, block, layers, num_classes=1000):\n        self.inplanes = 128\n        super(ResNet, self).__init__()\n        self.conv1 = conv3x3(3, 64, stride=2)\n        self.bn1 = BatchNorm2d(64)\n        self.relu1 = nn.ReLU(inplace=True)\n        self.conv2 = conv3x3(64, 64)\n        self.bn2 = BatchNorm2d(64)\n        self.relu2 = nn.ReLU(inplace=True)\n        self.conv3 = conv3x3(64, 128)\n        self.bn3 = BatchNorm2d(128)\n        self.relu3 = nn.ReLU(inplace=True)\n        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n\n        self.layer1 = self._make_layer(block, 64, layers[0])\n        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)\n        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)\n        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)\n        self.avgpool = nn.AvgPool2d(7, stride=1)\n        self.fc = nn.Linear(512 * block.expansion, num_classes)\n\n        for m in self.modules():\n            if isinstance(m, nn.Conv2d):\n                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels\n                m.weight.data.normal_(0, math.sqrt(2. / n))\n            elif isinstance(m, BatchNorm2d):\n                m.weight.data.fill_(1)\n                m.bias.data.zero_()\n\n    def _make_layer(self, block, planes, blocks, stride=1):\n        downsample = None\n        if stride != 1 or self.inplanes != planes * block.expansion:\n            downsample = nn.Sequential(\n                nn.Conv2d(self.inplanes, planes * block.expansion,\n                          kernel_size=1, stride=stride, bias=False),\n                BatchNorm2d(planes * block.expansion),\n            )\n\n        layers = []\n        layers.append(block(self.inplanes, planes, stride, downsample))\n        self.inplanes = planes * block.expansion\n        for i in range(1, blocks):\n            layers.append(block(self.inplanes, planes))\n\n        return nn.Sequential(*layers)\n\n    def forward(self, x):\n        x = self.relu1(self.bn1(self.conv1(x)))\n        x = self.relu2(self.bn2(self.conv2(x)))\n        x = self.relu3(self.bn3(self.conv3(x)))\n        x = self.maxpool(x)\n\n        x = self.layer1(x)\n        x = self.layer2(x)\n        x = self.layer3(x)\n        x = self.layer4(x)\n\n        x = self.avgpool(x)\n        x = x.view(x.size(0), -1)\n        x = self.fc(x)\n\n        return x\n\n\ndef resnet50(pretrained=False, **kwargs):\n    \"\"\"Constructs a ResNet-50 model.\n\n    Args:\n        pretrained (bool): If True, returns a model pre-trained on ImageNet\n    \"\"\"\n    model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)\n    if pretrained:\n        model.load_state_dict(load_url(model_urls['resnet50']), strict=False)\n    return model\n\n\ndef resnet18(pretrained=False, **kwargs):\n    \"\"\"Constructs a ResNet-18 model.\n    Args:\n        pretrained (bool): If True, returns a model pre-trained on ImageNet\n    \"\"\"\n    model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)\n    if pretrained:\n        model.load_state_dict(load_url(model_urls['resnet18']))\n    return model"
  },
  {
    "path": "model_cards/lama/models/ade20k/segm_lib/nn/__init__.py",
    "content": "from .modules import *\nfrom .parallel import UserScatteredDataParallel, user_scattered_collate, async_copy_to\n"
  },
  {
    "path": "model_cards/lama/models/ade20k/segm_lib/nn/modules/__init__.py",
    "content": "# -*- coding: utf-8 -*-\n# File   : __init__.py\n# Author : Jiayuan Mao\n# Email  : maojiayuan@gmail.com\n# Date   : 27/01/2018\n# \n# This file is part of Synchronized-BatchNorm-PyTorch.\n# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch\n# Distributed under MIT License.\n\nfrom .batchnorm import SynchronizedBatchNorm1d, SynchronizedBatchNorm2d, SynchronizedBatchNorm3d\nfrom .replicate import DataParallelWithCallback, patch_replication_callback\n"
  },
  {
    "path": "model_cards/lama/models/ade20k/segm_lib/nn/modules/batchnorm.py",
    "content": "# -*- coding: utf-8 -*-\n# File   : batchnorm.py\n# Author : Jiayuan Mao\n# Email  : maojiayuan@gmail.com\n# Date   : 27/01/2018\n# \n# This file is part of Synchronized-BatchNorm-PyTorch.\n# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch\n# Distributed under MIT License.\n\nimport collections\n\nimport torch\nimport torch.nn.functional as F\n\nfrom torch.nn.modules.batchnorm import _BatchNorm\nfrom torch.nn.parallel._functions import ReduceAddCoalesced, Broadcast\n\nfrom .comm import SyncMaster\n\n__all__ = ['SynchronizedBatchNorm1d', 'SynchronizedBatchNorm2d', 'SynchronizedBatchNorm3d']\n\n\ndef _sum_ft(tensor):\n    \"\"\"sum over the first and last dimention\"\"\"\n    return tensor.sum(dim=0).sum(dim=-1)\n\n\ndef _unsqueeze_ft(tensor):\n    \"\"\"add new dementions at the front and the tail\"\"\"\n    return tensor.unsqueeze(0).unsqueeze(-1)\n\n\n_ChildMessage = collections.namedtuple('_ChildMessage', ['sum', 'ssum', 'sum_size'])\n_MasterMessage = collections.namedtuple('_MasterMessage', ['sum', 'inv_std'])\n\n\nclass _SynchronizedBatchNorm(_BatchNorm):\n    def __init__(self, num_features, eps=1e-5, momentum=0.001, affine=True):\n        super(_SynchronizedBatchNorm, self).__init__(num_features, eps=eps, momentum=momentum, affine=affine)\n\n        self._sync_master = SyncMaster(self._data_parallel_master)\n\n        self._is_parallel = False\n        self._parallel_id = None\n        self._slave_pipe = None\n\n        # customed batch norm statistics\n        self._moving_average_fraction = 1. - momentum\n        self.register_buffer('_tmp_running_mean', torch.zeros(self.num_features))\n        self.register_buffer('_tmp_running_var', torch.ones(self.num_features))\n        self.register_buffer('_running_iter', torch.ones(1))\n        self._tmp_running_mean = self.running_mean.clone() * self._running_iter\n        self._tmp_running_var = self.running_var.clone() * self._running_iter\n\n    def forward(self, input):\n        # If it is not parallel computation or is in evaluation mode, use PyTorch's implementation.\n        if not (self._is_parallel and self.training):\n            return F.batch_norm(\n                input, self.running_mean, self.running_var, self.weight, self.bias,\n                self.training, self.momentum, self.eps)\n\n        # Resize the input to (B, C, -1).\n        input_shape = input.size()\n        input = input.view(input.size(0), self.num_features, -1)\n\n        # Compute the sum and square-sum.\n        sum_size = input.size(0) * input.size(2)\n        input_sum = _sum_ft(input)\n        input_ssum = _sum_ft(input ** 2)\n\n        # Reduce-and-broadcast the statistics.\n        if self._parallel_id == 0:\n            mean, inv_std = self._sync_master.run_master(_ChildMessage(input_sum, input_ssum, sum_size))\n        else:\n            mean, inv_std = self._slave_pipe.run_slave(_ChildMessage(input_sum, input_ssum, sum_size))\n\n        # Compute the output.\n        if self.affine:\n            # MJY:: Fuse the multiplication for speed.\n            output = (input - _unsqueeze_ft(mean)) * _unsqueeze_ft(inv_std * self.weight) + _unsqueeze_ft(self.bias)\n        else:\n            output = (input - _unsqueeze_ft(mean)) * _unsqueeze_ft(inv_std)\n\n        # Reshape it.\n        return output.view(input_shape)\n\n    def __data_parallel_replicate__(self, ctx, copy_id):\n        self._is_parallel = True\n        self._parallel_id = copy_id\n\n        # parallel_id == 0 means master device.\n        if self._parallel_id == 0:\n            ctx.sync_master = self._sync_master\n        else:\n            self._slave_pipe = ctx.sync_master.register_slave(copy_id)\n\n    def _data_parallel_master(self, intermediates):\n        \"\"\"Reduce the sum and square-sum, compute the statistics, and broadcast it.\"\"\"\n        intermediates = sorted(intermediates, key=lambda i: i[1].sum.get_device())\n\n        to_reduce = [i[1][:2] for i in intermediates]\n        to_reduce = [j for i in to_reduce for j in i]  # flatten\n        target_gpus = [i[1].sum.get_device() for i in intermediates]\n\n        sum_size = sum([i[1].sum_size for i in intermediates])\n        sum_, ssum = ReduceAddCoalesced.apply(target_gpus[0], 2, *to_reduce)\n\n        mean, inv_std = self._compute_mean_std(sum_, ssum, sum_size)\n\n        broadcasted = Broadcast.apply(target_gpus, mean, inv_std)\n\n        outputs = []\n        for i, rec in enumerate(intermediates):\n            outputs.append((rec[0], _MasterMessage(*broadcasted[i*2:i*2+2])))\n\n        return outputs\n\n    def _add_weighted(self, dest, delta, alpha=1, beta=1, bias=0):\n        \"\"\"return *dest* by `dest := dest*alpha + delta*beta + bias`\"\"\"\n        return dest * alpha + delta * beta + bias\n\n    def _compute_mean_std(self, sum_, ssum, size):\n        \"\"\"Compute the mean and standard-deviation with sum and square-sum. This method\n        also maintains the moving average on the master device.\"\"\"\n        assert size > 1, 'BatchNorm computes unbiased standard-deviation, which requires size > 1.'\n        mean = sum_ / size\n        sumvar = ssum - sum_ * mean\n        unbias_var = sumvar / (size - 1)\n        bias_var = sumvar / size\n\n        self._tmp_running_mean = self._add_weighted(self._tmp_running_mean, mean.data, alpha=self._moving_average_fraction)\n        self._tmp_running_var = self._add_weighted(self._tmp_running_var, unbias_var.data, alpha=self._moving_average_fraction)\n        self._running_iter = self._add_weighted(self._running_iter, 1, alpha=self._moving_average_fraction)\n\n        self.running_mean = self._tmp_running_mean / self._running_iter\n        self.running_var = self._tmp_running_var / self._running_iter\n\n        return mean, bias_var.clamp(self.eps) ** -0.5\n\n\nclass SynchronizedBatchNorm1d(_SynchronizedBatchNorm):\n    r\"\"\"Applies Synchronized Batch Normalization over a 2d or 3d input that is seen as a\n    mini-batch.\n\n    .. math::\n\n        y = \\frac{x - mean[x]}{ \\sqrt{Var[x] + \\epsilon}} * gamma + beta\n\n    This module differs from the built-in PyTorch BatchNorm1d as the mean and\n    standard-deviation are reduced across all devices during training.\n\n    For example, when one uses `nn.DataParallel` to wrap the network during\n    training, PyTorch's implementation normalize the tensor on each device using\n    the statistics only on that device, which accelerated the computation and\n    is also easy to implement, but the statistics might be inaccurate.\n    Instead, in this synchronized version, the statistics will be computed\n    over all training samples distributed on multiple devices.\n    \n    Note that, for one-GPU or CPU-only case, this module behaves exactly same\n    as the built-in PyTorch implementation.\n\n    The mean and standard-deviation are calculated per-dimension over\n    the mini-batches and gamma and beta are learnable parameter vectors\n    of size C (where C is the input size).\n\n    During training, this layer keeps a running estimate of its computed mean\n    and variance. The running sum is kept with a default momentum of 0.1.\n\n    During evaluation, this running mean/variance is used for normalization.\n\n    Because the BatchNorm is done over the `C` dimension, computing statistics\n    on `(N, L)` slices, it's common terminology to call this Temporal BatchNorm\n\n    Args:\n        num_features: num_features from an expected input of size\n            `batch_size x num_features [x width]`\n        eps: a value added to the denominator for numerical stability.\n            Default: 1e-5\n        momentum: the value used for the running_mean and running_var\n            computation. Default: 0.1\n        affine: a boolean value that when set to ``True``, gives the layer learnable\n            affine parameters. Default: ``True``\n\n    Shape:\n        - Input: :math:`(N, C)` or :math:`(N, C, L)`\n        - Output: :math:`(N, C)` or :math:`(N, C, L)` (same shape as input)\n\n    Examples:\n        >>> # With Learnable Parameters\n        >>> m = SynchronizedBatchNorm1d(100)\n        >>> # Without Learnable Parameters\n        >>> m = SynchronizedBatchNorm1d(100, affine=False)\n        >>> input = torch.autograd.Variable(torch.randn(20, 100))\n        >>> output = m(input)\n    \"\"\"\n\n    def _check_input_dim(self, input):\n        if input.dim() != 2 and input.dim() != 3:\n            raise ValueError('expected 2D or 3D input (got {}D input)'\n                             .format(input.dim()))\n        super(SynchronizedBatchNorm1d, self)._check_input_dim(input)\n\n\nclass SynchronizedBatchNorm2d(_SynchronizedBatchNorm):\n    r\"\"\"Applies Batch Normalization over a 4d input that is seen as a mini-batch\n    of 3d inputs\n\n    .. math::\n\n        y = \\frac{x - mean[x]}{ \\sqrt{Var[x] + \\epsilon}} * gamma + beta\n\n    This module differs from the built-in PyTorch BatchNorm2d as the mean and\n    standard-deviation are reduced across all devices during training.\n\n    For example, when one uses `nn.DataParallel` to wrap the network during\n    training, PyTorch's implementation normalize the tensor on each device using\n    the statistics only on that device, which accelerated the computation and\n    is also easy to implement, but the statistics might be inaccurate.\n    Instead, in this synchronized version, the statistics will be computed\n    over all training samples distributed on multiple devices.\n    \n    Note that, for one-GPU or CPU-only case, this module behaves exactly same\n    as the built-in PyTorch implementation.\n\n    The mean and standard-deviation are calculated per-dimension over\n    the mini-batches and gamma and beta are learnable parameter vectors\n    of size C (where C is the input size).\n\n    During training, this layer keeps a running estimate of its computed mean\n    and variance. The running sum is kept with a default momentum of 0.1.\n\n    During evaluation, this running mean/variance is used for normalization.\n\n    Because the BatchNorm is done over the `C` dimension, computing statistics\n    on `(N, H, W)` slices, it's common terminology to call this Spatial BatchNorm\n\n    Args:\n        num_features: num_features from an expected input of\n            size batch_size x num_features x height x width\n        eps: a value added to the denominator for numerical stability.\n            Default: 1e-5\n        momentum: the value used for the running_mean and running_var\n            computation. Default: 0.1\n        affine: a boolean value that when set to ``True``, gives the layer learnable\n            affine parameters. Default: ``True``\n\n    Shape:\n        - Input: :math:`(N, C, H, W)`\n        - Output: :math:`(N, C, H, W)` (same shape as input)\n\n    Examples:\n        >>> # With Learnable Parameters\n        >>> m = SynchronizedBatchNorm2d(100)\n        >>> # Without Learnable Parameters\n        >>> m = SynchronizedBatchNorm2d(100, affine=False)\n        >>> input = torch.autograd.Variable(torch.randn(20, 100, 35, 45))\n        >>> output = m(input)\n    \"\"\"\n\n    def _check_input_dim(self, input):\n        if input.dim() != 4:\n            raise ValueError('expected 4D input (got {}D input)'\n                             .format(input.dim()))\n        super(SynchronizedBatchNorm2d, self)._check_input_dim(input)\n\n\nclass SynchronizedBatchNorm3d(_SynchronizedBatchNorm):\n    r\"\"\"Applies Batch Normalization over a 5d input that is seen as a mini-batch\n    of 4d inputs\n\n    .. math::\n\n        y = \\frac{x - mean[x]}{ \\sqrt{Var[x] + \\epsilon}} * gamma + beta\n\n    This module differs from the built-in PyTorch BatchNorm3d as the mean and\n    standard-deviation are reduced across all devices during training.\n\n    For example, when one uses `nn.DataParallel` to wrap the network during\n    training, PyTorch's implementation normalize the tensor on each device using\n    the statistics only on that device, which accelerated the computation and\n    is also easy to implement, but the statistics might be inaccurate.\n    Instead, in this synchronized version, the statistics will be computed\n    over all training samples distributed on multiple devices.\n    \n    Note that, for one-GPU or CPU-only case, this module behaves exactly same\n    as the built-in PyTorch implementation.\n\n    The mean and standard-deviation are calculated per-dimension over\n    the mini-batches and gamma and beta are learnable parameter vectors\n    of size C (where C is the input size).\n\n    During training, this layer keeps a running estimate of its computed mean\n    and variance. The running sum is kept with a default momentum of 0.1.\n\n    During evaluation, this running mean/variance is used for normalization.\n\n    Because the BatchNorm is done over the `C` dimension, computing statistics\n    on `(N, D, H, W)` slices, it's common terminology to call this Volumetric BatchNorm\n    or Spatio-temporal BatchNorm\n\n    Args:\n        num_features: num_features from an expected input of\n            size batch_size x num_features x depth x height x width\n        eps: a value added to the denominator for numerical stability.\n            Default: 1e-5\n        momentum: the value used for the running_mean and running_var\n            computation. Default: 0.1\n        affine: a boolean value that when set to ``True``, gives the layer learnable\n            affine parameters. Default: ``True``\n\n    Shape:\n        - Input: :math:`(N, C, D, H, W)`\n        - Output: :math:`(N, C, D, H, W)` (same shape as input)\n\n    Examples:\n        >>> # With Learnable Parameters\n        >>> m = SynchronizedBatchNorm3d(100)\n        >>> # Without Learnable Parameters\n        >>> m = SynchronizedBatchNorm3d(100, affine=False)\n        >>> input = torch.autograd.Variable(torch.randn(20, 100, 35, 45, 10))\n        >>> output = m(input)\n    \"\"\"\n\n    def _check_input_dim(self, input):\n        if input.dim() != 5:\n            raise ValueError('expected 5D input (got {}D input)'\n                             .format(input.dim()))\n        super(SynchronizedBatchNorm3d, self)._check_input_dim(input)\n"
  },
  {
    "path": "model_cards/lama/models/ade20k/segm_lib/nn/modules/comm.py",
    "content": "# -*- coding: utf-8 -*-\n# File   : comm.py\n# Author : Jiayuan Mao\n# Email  : maojiayuan@gmail.com\n# Date   : 27/01/2018\n# \n# This file is part of Synchronized-BatchNorm-PyTorch.\n# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch\n# Distributed under MIT License.\n\nimport queue\nimport collections\nimport threading\n\n__all__ = ['FutureResult', 'SlavePipe', 'SyncMaster']\n\n\nclass FutureResult(object):\n    \"\"\"A thread-safe future implementation. Used only as one-to-one pipe.\"\"\"\n\n    def __init__(self):\n        self._result = None\n        self._lock = threading.Lock()\n        self._cond = threading.Condition(self._lock)\n\n    def put(self, result):\n        with self._lock:\n            assert self._result is None, 'Previous result has\\'t been fetched.'\n            self._result = result\n            self._cond.notify()\n\n    def get(self):\n        with self._lock:\n            if self._result is None:\n                self._cond.wait()\n\n            res = self._result\n            self._result = None\n            return res\n\n\n_MasterRegistry = collections.namedtuple('MasterRegistry', ['result'])\n_SlavePipeBase = collections.namedtuple('_SlavePipeBase', ['identifier', 'queue', 'result'])\n\n\nclass SlavePipe(_SlavePipeBase):\n    \"\"\"Pipe for master-slave communication.\"\"\"\n\n    def run_slave(self, msg):\n        self.queue.put((self.identifier, msg))\n        ret = self.result.get()\n        self.queue.put(True)\n        return ret\n\n\nclass SyncMaster(object):\n    \"\"\"An abstract `SyncMaster` object.\n\n    - During the replication, as the data parallel will trigger an callback of each module, all slave devices should\n    call `register(id)` and obtain an `SlavePipe` to communicate with the master.\n    - During the forward pass, master device invokes `run_master`, all messages from slave devices will be collected,\n    and passed to a registered callback.\n    - After receiving the messages, the master device should gather the information and determine to message passed\n    back to each slave devices.\n    \"\"\"\n\n    def __init__(self, master_callback):\n        \"\"\"\n\n        Args:\n            master_callback: a callback to be invoked after having collected messages from slave devices.\n        \"\"\"\n        self._master_callback = master_callback\n        self._queue = queue.Queue()\n        self._registry = collections.OrderedDict()\n        self._activated = False\n\n    def register_slave(self, identifier):\n        \"\"\"\n        Register an slave device.\n\n        Args:\n            identifier: an identifier, usually is the device id.\n\n        Returns: a `SlavePipe` object which can be used to communicate with the master device.\n\n        \"\"\"\n        if self._activated:\n            assert self._queue.empty(), 'Queue is not clean before next initialization.'\n            self._activated = False\n            self._registry.clear()\n        future = FutureResult()\n        self._registry[identifier] = _MasterRegistry(future)\n        return SlavePipe(identifier, self._queue, future)\n\n    def run_master(self, master_msg):\n        \"\"\"\n        Main entry for the master device in each forward pass.\n        The messages were first collected from each devices (including the master device), and then\n        an callback will be invoked to compute the message to be sent back to each devices\n        (including the master device).\n\n        Args:\n            master_msg: the message that the master want to send to itself. This will be placed as the first\n            message when calling `master_callback`. For detailed usage, see `_SynchronizedBatchNorm` for an example.\n\n        Returns: the message to be sent back to the master device.\n\n        \"\"\"\n        self._activated = True\n\n        intermediates = [(0, master_msg)]\n        for i in range(self.nr_slaves):\n            intermediates.append(self._queue.get())\n\n        results = self._master_callback(intermediates)\n        assert results[0][0] == 0, 'The first result should belongs to the master.'\n\n        for i, res in results:\n            if i == 0:\n                continue\n            self._registry[i].result.put(res)\n\n        for i in range(self.nr_slaves):\n            assert self._queue.get() is True\n\n        return results[0][1]\n\n    @property\n    def nr_slaves(self):\n        return len(self._registry)\n"
  },
  {
    "path": "model_cards/lama/models/ade20k/segm_lib/nn/modules/replicate.py",
    "content": "# -*- coding: utf-8 -*-\n# File   : replicate.py\n# Author : Jiayuan Mao\n# Email  : maojiayuan@gmail.com\n# Date   : 27/01/2018\n# \n# This file is part of Synchronized-BatchNorm-PyTorch.\n# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch\n# Distributed under MIT License.\n\nimport functools\n\nfrom torch.nn.parallel.data_parallel import DataParallel\n\n__all__ = [\n    'CallbackContext',\n    'execute_replication_callbacks',\n    'DataParallelWithCallback',\n    'patch_replication_callback'\n]\n\n\nclass CallbackContext(object):\n    pass\n\n\ndef execute_replication_callbacks(modules):\n    \"\"\"\n    Execute an replication callback `__data_parallel_replicate__` on each module created by original replication.\n\n    The callback will be invoked with arguments `__data_parallel_replicate__(ctx, copy_id)`\n\n    Note that, as all modules are isomorphism, we assign each sub-module with a context\n    (shared among multiple copies of this module on different devices).\n    Through this context, different copies can share some information.\n\n    We guarantee that the callback on the master copy (the first copy) will be called ahead of calling the callback\n    of any slave copies.\n    \"\"\"\n    master_copy = modules[0]\n    nr_modules = len(list(master_copy.modules()))\n    ctxs = [CallbackContext() for _ in range(nr_modules)]\n\n    for i, module in enumerate(modules):\n        for j, m in enumerate(module.modules()):\n            if hasattr(m, '__data_parallel_replicate__'):\n                m.__data_parallel_replicate__(ctxs[j], i)\n\n\nclass DataParallelWithCallback(DataParallel):\n    \"\"\"\n    Data Parallel with a replication callback.\n\n    An replication callback `__data_parallel_replicate__` of each module will be invoked after being created by\n    original `replicate` function.\n    The callback will be invoked with arguments `__data_parallel_replicate__(ctx, copy_id)`\n\n    Examples:\n        > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False)\n        > sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1])\n        # sync_bn.__data_parallel_replicate__ will be invoked.\n    \"\"\"\n\n    def replicate(self, module, device_ids):\n        modules = super(DataParallelWithCallback, self).replicate(module, device_ids)\n        execute_replication_callbacks(modules)\n        return modules\n\n\ndef patch_replication_callback(data_parallel):\n    \"\"\"\n    Monkey-patch an existing `DataParallel` object. Add the replication callback.\n    Useful when you have customized `DataParallel` implementation.\n\n    Examples:\n        > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False)\n        > sync_bn = DataParallel(sync_bn, device_ids=[0, 1])\n        > patch_replication_callback(sync_bn)\n        # this is equivalent to\n        > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False)\n        > sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1])\n    \"\"\"\n\n    assert isinstance(data_parallel, DataParallel)\n\n    old_replicate = data_parallel.replicate\n\n    @functools.wraps(old_replicate)\n    def new_replicate(module, device_ids):\n        modules = old_replicate(module, device_ids)\n        execute_replication_callbacks(modules)\n        return modules\n\n    data_parallel.replicate = new_replicate\n"
  },
  {
    "path": "model_cards/lama/models/ade20k/segm_lib/nn/modules/tests/test_numeric_batchnorm.py",
    "content": "# -*- coding: utf-8 -*-\n# File   : test_numeric_batchnorm.py\n# Author : Jiayuan Mao\n# Email  : maojiayuan@gmail.com\n# Date   : 27/01/2018\n# \n# This file is part of Synchronized-BatchNorm-PyTorch.\n\nimport unittest\n\nimport torch\nimport torch.nn as nn\nfrom torch.autograd import Variable\n\nfrom sync_batchnorm.unittest import TorchTestCase\n\n\ndef handy_var(a, unbias=True):\n    n = a.size(0)\n    asum = a.sum(dim=0)\n    as_sum = (a ** 2).sum(dim=0)  # a square sum\n    sumvar = as_sum - asum * asum / n\n    if unbias:\n        return sumvar / (n - 1)\n    else:\n        return sumvar / n\n\n\nclass NumericTestCase(TorchTestCase):\n    def testNumericBatchNorm(self):\n        a = torch.rand(16, 10)\n        bn = nn.BatchNorm2d(10, momentum=1, eps=1e-5, affine=False)\n        bn.train()\n\n        a_var1 = Variable(a, requires_grad=True)\n        b_var1 = bn(a_var1)\n        loss1 = b_var1.sum()\n        loss1.backward()\n\n        a_var2 = Variable(a, requires_grad=True)\n        a_mean2 = a_var2.mean(dim=0, keepdim=True)\n        a_std2 = torch.sqrt(handy_var(a_var2, unbias=False).clamp(min=1e-5))\n        # a_std2 = torch.sqrt(a_var2.var(dim=0, keepdim=True, unbiased=False) + 1e-5)\n        b_var2 = (a_var2 - a_mean2) / a_std2\n        loss2 = b_var2.sum()\n        loss2.backward()\n\n        self.assertTensorClose(bn.running_mean, a.mean(dim=0))\n        self.assertTensorClose(bn.running_var, handy_var(a))\n        self.assertTensorClose(a_var1.data, a_var2.data)\n        self.assertTensorClose(b_var1.data, b_var2.data)\n        self.assertTensorClose(a_var1.grad, a_var2.grad)\n\n\nif __name__ == '__main__':\n    unittest.main()\n"
  },
  {
    "path": "model_cards/lama/models/ade20k/segm_lib/nn/modules/tests/test_sync_batchnorm.py",
    "content": "# -*- coding: utf-8 -*-\n# File   : test_sync_batchnorm.py\n# Author : Jiayuan Mao\n# Email  : maojiayuan@gmail.com\n# Date   : 27/01/2018\n# \n# This file is part of Synchronized-BatchNorm-PyTorch.\n\nimport unittest\n\nimport torch\nimport torch.nn as nn\nfrom torch.autograd import Variable\n\nfrom sync_batchnorm import SynchronizedBatchNorm1d, SynchronizedBatchNorm2d, DataParallelWithCallback\nfrom sync_batchnorm.unittest import TorchTestCase\n\n\ndef handy_var(a, unbias=True):\n    n = a.size(0)\n    asum = a.sum(dim=0)\n    as_sum = (a ** 2).sum(dim=0)  # a square sum\n    sumvar = as_sum - asum * asum / n\n    if unbias:\n        return sumvar / (n - 1)\n    else:\n        return sumvar / n\n\n\ndef _find_bn(module):\n    for m in module.modules():\n        if isinstance(m, (nn.BatchNorm1d, nn.BatchNorm2d, SynchronizedBatchNorm1d, SynchronizedBatchNorm2d)):\n            return m\n\n\nclass SyncTestCase(TorchTestCase):\n    def _syncParameters(self, bn1, bn2):\n        bn1.reset_parameters()\n        bn2.reset_parameters()\n        if bn1.affine and bn2.affine:\n            bn2.weight.data.copy_(bn1.weight.data)\n            bn2.bias.data.copy_(bn1.bias.data)\n\n    def _checkBatchNormResult(self, bn1, bn2, input, is_train, cuda=False):\n        \"\"\"Check the forward and backward for the customized batch normalization.\"\"\"\n        bn1.train(mode=is_train)\n        bn2.train(mode=is_train)\n\n        if cuda:\n            input = input.cuda()\n\n        self._syncParameters(_find_bn(bn1), _find_bn(bn2))\n\n        input1 = Variable(input, requires_grad=True)\n        output1 = bn1(input1)\n        output1.sum().backward()\n        input2 = Variable(input, requires_grad=True)\n        output2 = bn2(input2)\n        output2.sum().backward()\n\n        self.assertTensorClose(input1.data, input2.data)\n        self.assertTensorClose(output1.data, output2.data)\n        self.assertTensorClose(input1.grad, input2.grad)\n        self.assertTensorClose(_find_bn(bn1).running_mean, _find_bn(bn2).running_mean)\n        self.assertTensorClose(_find_bn(bn1).running_var, _find_bn(bn2).running_var)\n\n    def testSyncBatchNormNormalTrain(self):\n        bn = nn.BatchNorm1d(10)\n        sync_bn = SynchronizedBatchNorm1d(10)\n\n        self._checkBatchNormResult(bn, sync_bn, torch.rand(16, 10), True)\n\n    def testSyncBatchNormNormalEval(self):\n        bn = nn.BatchNorm1d(10)\n        sync_bn = SynchronizedBatchNorm1d(10)\n\n        self._checkBatchNormResult(bn, sync_bn, torch.rand(16, 10), False)\n\n    def testSyncBatchNormSyncTrain(self):\n        bn = nn.BatchNorm1d(10, eps=1e-5, affine=False)\n        sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False)\n        sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1])\n\n        bn.cuda()\n        sync_bn.cuda()\n\n        self._checkBatchNormResult(bn, sync_bn, torch.rand(16, 10), True, cuda=True)\n\n    def testSyncBatchNormSyncEval(self):\n        bn = nn.BatchNorm1d(10, eps=1e-5, affine=False)\n        sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False)\n        sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1])\n\n        bn.cuda()\n        sync_bn.cuda()\n\n        self._checkBatchNormResult(bn, sync_bn, torch.rand(16, 10), False, cuda=True)\n\n    def testSyncBatchNorm2DSyncTrain(self):\n        bn = nn.BatchNorm2d(10)\n        sync_bn = SynchronizedBatchNorm2d(10)\n        sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1])\n\n        bn.cuda()\n        sync_bn.cuda()\n\n        self._checkBatchNormResult(bn, sync_bn, torch.rand(16, 10, 16, 16), True, cuda=True)\n\n\nif __name__ == '__main__':\n    unittest.main()\n"
  },
  {
    "path": "model_cards/lama/models/ade20k/segm_lib/nn/modules/unittest.py",
    "content": "# -*- coding: utf-8 -*-\n# File   : unittest.py\n# Author : Jiayuan Mao\n# Email  : maojiayuan@gmail.com\n# Date   : 27/01/2018\n# \n# This file is part of Synchronized-BatchNorm-PyTorch.\n# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch\n# Distributed under MIT License.\n\nimport unittest\n\nimport numpy as np\nfrom torch.autograd import Variable\n\n\ndef as_numpy(v):\n    if isinstance(v, Variable):\n        v = v.data\n    return v.cpu().numpy()\n\n\nclass TorchTestCase(unittest.TestCase):\n    def assertTensorClose(self, a, b, atol=1e-3, rtol=1e-3):\n        npa, npb = as_numpy(a), as_numpy(b)\n        self.assertTrue(\n                np.allclose(npa, npb, atol=atol),\n                'Tensor close check failed\\n{}\\n{}\\nadiff={}, rdiff={}'.format(a, b, np.abs(npa - npb).max(), np.abs((npa - npb) / np.fmax(npa, 1e-5)).max())\n        )\n"
  },
  {
    "path": "model_cards/lama/models/ade20k/segm_lib/nn/parallel/__init__.py",
    "content": "from .data_parallel import UserScatteredDataParallel, user_scattered_collate, async_copy_to\n"
  },
  {
    "path": "model_cards/lama/models/ade20k/segm_lib/nn/parallel/data_parallel.py",
    "content": "# -*- coding: utf8 -*-\n\nimport torch.cuda as cuda\nimport torch.nn as nn\nimport torch\nimport collections\nfrom torch.nn.parallel._functions import Gather\n\n\n__all__ = ['UserScatteredDataParallel', 'user_scattered_collate', 'async_copy_to']\n\n\ndef async_copy_to(obj, dev, main_stream=None):\n    if torch.is_tensor(obj):\n        v = obj.cuda(dev, non_blocking=True)\n        if main_stream is not None:\n            v.data.record_stream(main_stream)\n        return v\n    elif isinstance(obj, collections.Mapping):\n        return {k: async_copy_to(o, dev, main_stream) for k, o in obj.items()}\n    elif isinstance(obj, collections.Sequence):\n        return [async_copy_to(o, dev, main_stream) for o in obj]\n    else:\n        return obj\n\n\ndef dict_gather(outputs, target_device, dim=0):\n    \"\"\"\n    Gathers variables from different GPUs on a specified device\n      (-1 means the CPU), with dictionary support.\n    \"\"\"\n    def gather_map(outputs):\n        out = outputs[0]\n        if torch.is_tensor(out):\n            # MJY(20180330) HACK:: force nr_dims > 0\n            if out.dim() == 0:\n                outputs = [o.unsqueeze(0) for o in outputs]\n            return Gather.apply(target_device, dim, *outputs)\n        elif out is None:\n            return None\n        elif isinstance(out, collections.Mapping):\n            return {k: gather_map([o[k] for o in outputs]) for k in out}\n        elif isinstance(out, collections.Sequence):\n            return type(out)(map(gather_map, zip(*outputs)))\n    return gather_map(outputs)\n\n\nclass DictGatherDataParallel(nn.DataParallel):\n    def gather(self, outputs, output_device):\n        return dict_gather(outputs, output_device, dim=self.dim)\n\n\nclass UserScatteredDataParallel(DictGatherDataParallel):\n    def scatter(self, inputs, kwargs, device_ids):\n        assert len(inputs) == 1\n        inputs = inputs[0]\n        inputs = _async_copy_stream(inputs, device_ids)\n        inputs = [[i] for i in inputs]\n        assert len(kwargs) == 0\n        kwargs = [{} for _ in range(len(inputs))]\n\n        return inputs, kwargs\n\n\ndef user_scattered_collate(batch):\n    return batch\n\n\ndef _async_copy(inputs, device_ids):\n    nr_devs = len(device_ids)\n    assert type(inputs) in (tuple, list)\n    assert len(inputs) == nr_devs\n\n    outputs = []\n    for i, dev in zip(inputs, device_ids):\n        with cuda.device(dev):\n            outputs.append(async_copy_to(i, dev))\n\n    return tuple(outputs)\n\n\ndef _async_copy_stream(inputs, device_ids):\n    nr_devs = len(device_ids)\n    assert type(inputs) in (tuple, list)\n    assert len(inputs) == nr_devs\n\n    outputs = []\n    streams = [_get_stream(d) for d in device_ids]\n    for i, dev, stream in zip(inputs, device_ids, streams):\n        with cuda.device(dev):\n            main_stream = cuda.current_stream()\n            with cuda.stream(stream):\n                outputs.append(async_copy_to(i, dev, main_stream=main_stream))\n            main_stream.wait_stream(stream)\n\n    return outputs\n\n\n\"\"\"Adapted from: torch/nn/parallel/_functions.py\"\"\"\n# background streams used for copying\n_streams = None\n\n\ndef _get_stream(device):\n    \"\"\"Gets a background stream for copying between CPU and GPU\"\"\"\n    global _streams\n    if device == -1:\n        return None\n    if _streams is None:\n        _streams = [None] * cuda.device_count()\n    if _streams[device] is None: _streams[device] = cuda.Stream(device)\n    return _streams[device]\n"
  },
  {
    "path": "model_cards/lama/models/ade20k/segm_lib/utils/__init__.py",
    "content": "from .th import *\n"
  },
  {
    "path": "model_cards/lama/models/ade20k/segm_lib/utils/data/__init__.py",
    "content": "\nfrom .dataset import Dataset, TensorDataset, ConcatDataset\nfrom .dataloader import DataLoader\n"
  },
  {
    "path": "model_cards/lama/models/ade20k/segm_lib/utils/data/dataloader.py",
    "content": "import torch\nimport torch.multiprocessing as multiprocessing\nfrom torch._C import _set_worker_signal_handlers, \\\n    _remove_worker_pids, _error_if_any_worker_fails\ntry:\n    from torch._C import _set_worker_pids\nexcept:\n    from torch._C import _update_worker_pids as _set_worker_pids\nfrom .sampler import SequentialSampler, RandomSampler, BatchSampler\nimport signal\nimport collections\nimport re\nimport sys\nimport threading\nimport traceback\nfrom torch._six import string_classes, int_classes\nimport numpy as np\n\nif sys.version_info[0] == 2:\n    import Queue as queue\nelse:\n    import queue\n\n\nclass ExceptionWrapper(object):\n    r\"Wraps an exception plus traceback to communicate across threads\"\n\n    def __init__(self, exc_info):\n        self.exc_type = exc_info[0]\n        self.exc_msg = \"\".join(traceback.format_exception(*exc_info))\n\n\n_use_shared_memory = False\n\"\"\"Whether to use shared memory in default_collate\"\"\"\n\n\ndef _worker_loop(dataset, index_queue, data_queue, collate_fn, seed, init_fn, worker_id):\n    global _use_shared_memory\n    _use_shared_memory = True\n\n    # Intialize C side signal handlers for SIGBUS and SIGSEGV. Python signal\n    # module's handlers are executed after Python returns from C low-level\n    # handlers, likely when the same fatal signal happened again already.\n    # https://docs.python.org/3/library/signal.html Sec. 18.8.1.1\n    _set_worker_signal_handlers()\n\n    torch.set_num_threads(1)\n    torch.manual_seed(seed)\n    np.random.seed(seed)\n\n    if init_fn is not None:\n        init_fn(worker_id)\n\n    while True:\n        r = index_queue.get()\n        if r is None:\n            break\n        idx, batch_indices = r\n        try:\n            samples = collate_fn([dataset[i] for i in batch_indices])\n        except Exception:\n            data_queue.put((idx, ExceptionWrapper(sys.exc_info())))\n        else:\n            data_queue.put((idx, samples))\n\n\ndef _worker_manager_loop(in_queue, out_queue, done_event, pin_memory, device_id):\n    if pin_memory:\n        torch.cuda.set_device(device_id)\n\n    while True:\n        try:\n            r = in_queue.get()\n        except Exception:\n            if done_event.is_set():\n                return\n            raise\n        if r is None:\n            break\n        if isinstance(r[1], ExceptionWrapper):\n            out_queue.put(r)\n            continue\n        idx, batch = r\n        try:\n            if pin_memory:\n                batch = pin_memory_batch(batch)\n        except Exception:\n            out_queue.put((idx, ExceptionWrapper(sys.exc_info())))\n        else:\n            out_queue.put((idx, batch))\n\nnumpy_type_map = {\n    'float64': torch.DoubleTensor,\n    'float32': torch.FloatTensor,\n    'float16': torch.HalfTensor,\n    'int64': torch.LongTensor,\n    'int32': torch.IntTensor,\n    'int16': torch.ShortTensor,\n    'int8': torch.CharTensor,\n    'uint8': torch.ByteTensor,\n}\n\n\ndef default_collate(batch):\n    \"Puts each data field into a tensor with outer dimension batch size\"\n\n    error_msg = \"batch must contain tensors, numbers, dicts or lists; found {}\"\n    elem_type = type(batch[0])\n    if torch.is_tensor(batch[0]):\n        out = None\n        if _use_shared_memory:\n            # If we're in a background process, concatenate directly into a\n            # shared memory tensor to avoid an extra copy\n            numel = sum([x.numel() for x in batch])\n            storage = batch[0].storage()._new_shared(numel)\n            out = batch[0].new(storage)\n        return torch.stack(batch, 0, out=out)\n    elif elem_type.__module__ == 'numpy' and elem_type.__name__ != 'str_' \\\n            and elem_type.__name__ != 'string_':\n        elem = batch[0]\n        if elem_type.__name__ == 'ndarray':\n            # array of string classes and object\n            if re.search('[SaUO]', elem.dtype.str) is not None:\n                raise TypeError(error_msg.format(elem.dtype))\n\n            return torch.stack([torch.from_numpy(b) for b in batch], 0)\n        if elem.shape == ():  # scalars\n            py_type = float if elem.dtype.name.startswith('float') else int\n            return numpy_type_map[elem.dtype.name](list(map(py_type, batch)))\n    elif isinstance(batch[0], int_classes):\n        return torch.LongTensor(batch)\n    elif isinstance(batch[0], float):\n        return torch.DoubleTensor(batch)\n    elif isinstance(batch[0], string_classes):\n        return batch\n    elif isinstance(batch[0], collections.Mapping):\n        return {key: default_collate([d[key] for d in batch]) for key in batch[0]}\n    elif isinstance(batch[0], collections.Sequence):\n        transposed = zip(*batch)\n        return [default_collate(samples) for samples in transposed]\n\n    raise TypeError((error_msg.format(type(batch[0]))))\n\n\ndef pin_memory_batch(batch):\n    if torch.is_tensor(batch):\n        return batch.pin_memory()\n    elif isinstance(batch, string_classes):\n        return batch\n    elif isinstance(batch, collections.Mapping):\n        return {k: pin_memory_batch(sample) for k, sample in batch.items()}\n    elif isinstance(batch, collections.Sequence):\n        return [pin_memory_batch(sample) for sample in batch]\n    else:\n        return batch\n\n\n_SIGCHLD_handler_set = False\n\"\"\"Whether SIGCHLD handler is set for DataLoader worker failures. Only one\nhandler needs to be set for all DataLoaders in a process.\"\"\"\n\n\ndef _set_SIGCHLD_handler():\n    # Windows doesn't support SIGCHLD handler\n    if sys.platform == 'win32':\n        return\n    # can't set signal in child threads\n    if not isinstance(threading.current_thread(), threading._MainThread):\n        return\n    global _SIGCHLD_handler_set\n    if _SIGCHLD_handler_set:\n        return\n    previous_handler = signal.getsignal(signal.SIGCHLD)\n    if not callable(previous_handler):\n        previous_handler = None\n\n    def handler(signum, frame):\n        # This following call uses `waitid` with WNOHANG from C side. Therefore,\n        # Python can still get and update the process status successfully.\n        _error_if_any_worker_fails()\n        if previous_handler is not None:\n            previous_handler(signum, frame)\n\n    signal.signal(signal.SIGCHLD, handler)\n    _SIGCHLD_handler_set = True\n\n\nclass DataLoaderIter(object):\n    \"Iterates once over the DataLoader's dataset, as specified by the sampler\"\n\n    def __init__(self, loader):\n        self.dataset = loader.dataset\n        self.collate_fn = loader.collate_fn\n        self.batch_sampler = loader.batch_sampler\n        self.num_workers = loader.num_workers\n        self.pin_memory = loader.pin_memory and torch.cuda.is_available()\n        self.timeout = loader.timeout\n        self.done_event = threading.Event()\n\n        self.sample_iter = iter(self.batch_sampler)\n\n        if self.num_workers > 0:\n            self.worker_init_fn = loader.worker_init_fn\n            self.index_queue = multiprocessing.SimpleQueue()\n            self.worker_result_queue = multiprocessing.SimpleQueue()\n            self.batches_outstanding = 0\n            self.worker_pids_set = False\n            self.shutdown = False\n            self.send_idx = 0\n            self.rcvd_idx = 0\n            self.reorder_dict = {}\n\n            base_seed = torch.LongTensor(1).random_(0, 2**31-1)[0]\n            self.workers = [\n                multiprocessing.Process(\n                    target=_worker_loop,\n                    args=(self.dataset, self.index_queue, self.worker_result_queue, self.collate_fn,\n                          base_seed + i, self.worker_init_fn, i))\n                for i in range(self.num_workers)]\n\n            if self.pin_memory or self.timeout > 0:\n                self.data_queue = queue.Queue()\n                if self.pin_memory:\n                    maybe_device_id = torch.cuda.current_device()\n                else:\n                    # do not initialize cuda context if not necessary\n                    maybe_device_id = None\n                self.worker_manager_thread = threading.Thread(\n                    target=_worker_manager_loop,\n                    args=(self.worker_result_queue, self.data_queue, self.done_event, self.pin_memory,\n                          maybe_device_id))\n                self.worker_manager_thread.daemon = True\n                self.worker_manager_thread.start()\n            else:\n                self.data_queue = self.worker_result_queue\n\n            for w in self.workers:\n                w.daemon = True  # ensure that the worker exits on process exit\n                w.start()\n\n            _set_worker_pids(id(self), tuple(w.pid for w in self.workers))\n            _set_SIGCHLD_handler()\n            self.worker_pids_set = True\n\n            # prime the prefetch loop\n            for _ in range(2 * self.num_workers):\n                self._put_indices()\n\n    def __len__(self):\n        return len(self.batch_sampler)\n\n    def _get_batch(self):\n        if self.timeout > 0:\n            try:\n                return self.data_queue.get(timeout=self.timeout)\n            except queue.Empty:\n                raise RuntimeError('DataLoader timed out after {} seconds'.format(self.timeout))\n        else:\n            return self.data_queue.get()\n\n    def __next__(self):\n        if self.num_workers == 0:  # same-process loading\n            indices = next(self.sample_iter)  # may raise StopIteration\n            batch = self.collate_fn([self.dataset[i] for i in indices])\n            if self.pin_memory:\n                batch = pin_memory_batch(batch)\n            return batch\n\n        # check if the next sample has already been generated\n        if self.rcvd_idx in self.reorder_dict:\n            batch = self.reorder_dict.pop(self.rcvd_idx)\n            return self._process_next_batch(batch)\n\n        if self.batches_outstanding == 0:\n            self._shutdown_workers()\n            raise StopIteration\n\n        while True:\n            assert (not self.shutdown and self.batches_outstanding > 0)\n            idx, batch = self._get_batch()\n            self.batches_outstanding -= 1\n            if idx != self.rcvd_idx:\n                # store out-of-order samples\n                self.reorder_dict[idx] = batch\n                continue\n            return self._process_next_batch(batch)\n\n    next = __next__  # Python 2 compatibility\n\n    def __iter__(self):\n        return self\n\n    def _put_indices(self):\n        assert self.batches_outstanding < 2 * self.num_workers\n        indices = next(self.sample_iter, None)\n        if indices is None:\n            return\n        self.index_queue.put((self.send_idx, indices))\n        self.batches_outstanding += 1\n        self.send_idx += 1\n\n    def _process_next_batch(self, batch):\n        self.rcvd_idx += 1\n        self._put_indices()\n        if isinstance(batch, ExceptionWrapper):\n            raise batch.exc_type(batch.exc_msg)\n        return batch\n\n    def __getstate__(self):\n        # TODO: add limited pickling support for sharing an iterator\n        # across multiple threads for HOGWILD.\n        # Probably the best way to do this is by moving the sample pushing\n        # to a separate thread and then just sharing the data queue\n        # but signalling the end is tricky without a non-blocking API\n        raise NotImplementedError(\"DataLoaderIterator cannot be pickled\")\n\n    def _shutdown_workers(self):\n        try:\n            if not self.shutdown:\n                self.shutdown = True\n                self.done_event.set()\n                # if worker_manager_thread is waiting to put\n                while not self.data_queue.empty():\n                    self.data_queue.get()\n                for _ in self.workers:\n                    self.index_queue.put(None)\n                # done_event should be sufficient to exit worker_manager_thread,\n                # but be safe here and put another None\n                self.worker_result_queue.put(None)\n        finally:\n            # removes pids no matter what\n            if self.worker_pids_set:\n                _remove_worker_pids(id(self))\n                self.worker_pids_set = False\n\n    def __del__(self):\n        if self.num_workers > 0:\n            self._shutdown_workers()\n\n\nclass DataLoader(object):\n    \"\"\"\n    Data loader. Combines a dataset and a sampler, and provides\n    single- or multi-process iterators over the dataset.\n\n    Arguments:\n        dataset (Dataset): dataset from which to load the data.\n        batch_size (int, optional): how many samples per batch to load\n            (default: 1).\n        shuffle (bool, optional): set to ``True`` to have the data reshuffled\n            at every epoch (default: False).\n        sampler (Sampler, optional): defines the strategy to draw samples from\n            the dataset. If specified, ``shuffle`` must be False.\n        batch_sampler (Sampler, optional): like sampler, but returns a batch of\n            indices at a time. Mutually exclusive with batch_size, shuffle,\n            sampler, and drop_last.\n        num_workers (int, optional): how many subprocesses to use for data\n            loading. 0 means that the data will be loaded in the main process.\n            (default: 0)\n        collate_fn (callable, optional): merges a list of samples to form a mini-batch.\n        pin_memory (bool, optional): If ``True``, the data loader will copy tensors\n            into CUDA pinned memory before returning them.\n        drop_last (bool, optional): set to ``True`` to drop the last incomplete batch,\n            if the dataset size is not divisible by the batch size. If ``False`` and\n            the size of dataset is not divisible by the batch size, then the last batch\n            will be smaller. (default: False)\n        timeout (numeric, optional): if positive, the timeout value for collecting a batch\n            from workers. Should always be non-negative. (default: 0)\n        worker_init_fn (callable, optional): If not None, this will be called on each\n            worker subprocess with the worker id (an int in ``[0, num_workers - 1]``) as\n            input, after seeding and before data loading. (default: None)\n\n    .. note:: By default, each worker will have its PyTorch seed set to\n              ``base_seed + worker_id``, where ``base_seed`` is a long generated\n              by main process using its RNG. You may use ``torch.initial_seed()`` to access\n              this value in :attr:`worker_init_fn`, which can be used to set other seeds\n              (e.g. NumPy) before data loading.\n\n    .. warning:: If ``spawn'' start method is used, :attr:`worker_init_fn` cannot be an\n                 unpicklable object, e.g., a lambda function.\n    \"\"\"\n\n    def __init__(self, dataset, batch_size=1, shuffle=False, sampler=None, batch_sampler=None,\n                 num_workers=0, collate_fn=default_collate, pin_memory=False, drop_last=False,\n                 timeout=0, worker_init_fn=None):\n        self.dataset = dataset\n        self.batch_size = batch_size\n        self.num_workers = num_workers\n        self.collate_fn = collate_fn\n        self.pin_memory = pin_memory\n        self.drop_last = drop_last\n        self.timeout = timeout\n        self.worker_init_fn = worker_init_fn\n\n        if timeout < 0:\n            raise ValueError('timeout option should be non-negative')\n\n        if batch_sampler is not None:\n            if batch_size > 1 or shuffle or sampler is not None or drop_last:\n                raise ValueError('batch_sampler is mutually exclusive with '\n                                 'batch_size, shuffle, sampler, and drop_last')\n\n        if sampler is not None and shuffle:\n            raise ValueError('sampler is mutually exclusive with shuffle')\n\n        if self.num_workers < 0:\n            raise ValueError('num_workers cannot be negative; '\n                             'use num_workers=0 to disable multiprocessing.')\n\n        if batch_sampler is None:\n            if sampler is None:\n                if shuffle:\n                    sampler = RandomSampler(dataset)\n                else:\n                    sampler = SequentialSampler(dataset)\n            batch_sampler = BatchSampler(sampler, batch_size, drop_last)\n\n        self.sampler = sampler\n        self.batch_sampler = batch_sampler\n\n    def __iter__(self):\n        return DataLoaderIter(self)\n\n    def __len__(self):\n        return len(self.batch_sampler)\n"
  },
  {
    "path": "model_cards/lama/models/ade20k/segm_lib/utils/data/dataset.py",
    "content": "import bisect\nimport warnings\n\nfrom torch._utils import _accumulate\nfrom torch import randperm\n\n\nclass Dataset(object):\n    \"\"\"An abstract class representing a Dataset.\n\n    All other datasets should subclass it. All subclasses should override\n    ``__len__``, that provides the size of the dataset, and ``__getitem__``,\n    supporting integer indexing in range from 0 to len(self) exclusive.\n    \"\"\"\n\n    def __getitem__(self, index):\n        raise NotImplementedError\n\n    def __len__(self):\n        raise NotImplementedError\n\n    def __add__(self, other):\n        return ConcatDataset([self, other])\n\n\nclass TensorDataset(Dataset):\n    \"\"\"Dataset wrapping data and target tensors.\n\n    Each sample will be retrieved by indexing both tensors along the first\n    dimension.\n\n    Arguments:\n        data_tensor (Tensor): contains sample data.\n        target_tensor (Tensor): contains sample targets (labels).\n    \"\"\"\n\n    def __init__(self, data_tensor, target_tensor):\n        assert data_tensor.size(0) == target_tensor.size(0)\n        self.data_tensor = data_tensor\n        self.target_tensor = target_tensor\n\n    def __getitem__(self, index):\n        return self.data_tensor[index], self.target_tensor[index]\n\n    def __len__(self):\n        return self.data_tensor.size(0)\n\n\nclass ConcatDataset(Dataset):\n    \"\"\"\n    Dataset to concatenate multiple datasets.\n    Purpose: useful to assemble different existing datasets, possibly\n    large-scale datasets as the concatenation operation is done in an\n    on-the-fly manner.\n\n    Arguments:\n        datasets (iterable): List of datasets to be concatenated\n    \"\"\"\n\n    @staticmethod\n    def cumsum(sequence):\n        r, s = [], 0\n        for e in sequence:\n            l = len(e)\n            r.append(l + s)\n            s += l\n        return r\n\n    def __init__(self, datasets):\n        super(ConcatDataset, self).__init__()\n        assert len(datasets) > 0, 'datasets should not be an empty iterable'\n        self.datasets = list(datasets)\n        self.cumulative_sizes = self.cumsum(self.datasets)\n\n    def __len__(self):\n        return self.cumulative_sizes[-1]\n\n    def __getitem__(self, idx):\n        dataset_idx = bisect.bisect_right(self.cumulative_sizes, idx)\n        if dataset_idx == 0:\n            sample_idx = idx\n        else:\n            sample_idx = idx - self.cumulative_sizes[dataset_idx - 1]\n        return self.datasets[dataset_idx][sample_idx]\n\n    @property\n    def cummulative_sizes(self):\n        warnings.warn(\"cummulative_sizes attribute is renamed to \"\n                      \"cumulative_sizes\", DeprecationWarning, stacklevel=2)\n        return self.cumulative_sizes\n\n\nclass Subset(Dataset):\n    def __init__(self, dataset, indices):\n        self.dataset = dataset\n        self.indices = indices\n\n    def __getitem__(self, idx):\n        return self.dataset[self.indices[idx]]\n\n    def __len__(self):\n        return len(self.indices)\n\n\ndef random_split(dataset, lengths):\n    \"\"\"\n    Randomly split a dataset into non-overlapping new datasets of given lengths\n    ds\n\n    Arguments:\n        dataset (Dataset): Dataset to be split\n        lengths (iterable): lengths of splits to be produced\n    \"\"\"\n    if sum(lengths) != len(dataset):\n        raise ValueError(\"Sum of input lengths does not equal the length of the input dataset!\")\n\n    indices = randperm(sum(lengths))\n    return [Subset(dataset, indices[offset - length:offset]) for offset, length in zip(_accumulate(lengths), lengths)]\n"
  },
  {
    "path": "model_cards/lama/models/ade20k/segm_lib/utils/data/distributed.py",
    "content": "import math\nimport torch\nfrom .sampler import Sampler\nfrom torch.distributed import get_world_size, get_rank\n\n\nclass DistributedSampler(Sampler):\n    \"\"\"Sampler that restricts data loading to a subset of the dataset.\n\n    It is especially useful in conjunction with\n    :class:`torch.nn.parallel.DistributedDataParallel`. In such case, each\n    process can pass a DistributedSampler instance as a DataLoader sampler,\n    and load a subset of the original dataset that is exclusive to it.\n\n    .. note::\n        Dataset is assumed to be of constant size.\n\n    Arguments:\n        dataset: Dataset used for sampling.\n        num_replicas (optional): Number of processes participating in\n            distributed training.\n        rank (optional): Rank of the current process within num_replicas.\n    \"\"\"\n\n    def __init__(self, dataset, num_replicas=None, rank=None):\n        if num_replicas is None:\n            num_replicas = get_world_size()\n        if rank is None:\n            rank = get_rank()\n        self.dataset = dataset\n        self.num_replicas = num_replicas\n        self.rank = rank\n        self.epoch = 0\n        self.num_samples = int(math.ceil(len(self.dataset) * 1.0 / self.num_replicas))\n        self.total_size = self.num_samples * self.num_replicas\n\n    def __iter__(self):\n        # deterministically shuffle based on epoch\n        g = torch.Generator()\n        g.manual_seed(self.epoch)\n        indices = list(torch.randperm(len(self.dataset), generator=g))\n\n        # add extra samples to make it evenly divisible\n        indices += indices[:(self.total_size - len(indices))]\n        assert len(indices) == self.total_size\n\n        # subsample\n        offset = self.num_samples * self.rank\n        indices = indices[offset:offset + self.num_samples]\n        assert len(indices) == self.num_samples\n\n        return iter(indices)\n\n    def __len__(self):\n        return self.num_samples\n\n    def set_epoch(self, epoch):\n        self.epoch = epoch\n"
  },
  {
    "path": "model_cards/lama/models/ade20k/segm_lib/utils/data/sampler.py",
    "content": "import torch\n\n\nclass Sampler(object):\n    \"\"\"Base class for all Samplers.\n\n    Every Sampler subclass has to provide an __iter__ method, providing a way\n    to iterate over indices of dataset elements, and a __len__ method that\n    returns the length of the returned iterators.\n    \"\"\"\n\n    def __init__(self, data_source):\n        pass\n\n    def __iter__(self):\n        raise NotImplementedError\n\n    def __len__(self):\n        raise NotImplementedError\n\n\nclass SequentialSampler(Sampler):\n    \"\"\"Samples elements sequentially, always in the same order.\n\n    Arguments:\n        data_source (Dataset): dataset to sample from\n    \"\"\"\n\n    def __init__(self, data_source):\n        self.data_source = data_source\n\n    def __iter__(self):\n        return iter(range(len(self.data_source)))\n\n    def __len__(self):\n        return len(self.data_source)\n\n\nclass RandomSampler(Sampler):\n    \"\"\"Samples elements randomly, without replacement.\n\n    Arguments:\n        data_source (Dataset): dataset to sample from\n    \"\"\"\n\n    def __init__(self, data_source):\n        self.data_source = data_source\n\n    def __iter__(self):\n        return iter(torch.randperm(len(self.data_source)).long())\n\n    def __len__(self):\n        return len(self.data_source)\n\n\nclass SubsetRandomSampler(Sampler):\n    \"\"\"Samples elements randomly from a given list of indices, without replacement.\n\n    Arguments:\n        indices (list): a list of indices\n    \"\"\"\n\n    def __init__(self, indices):\n        self.indices = indices\n\n    def __iter__(self):\n        return (self.indices[i] for i in torch.randperm(len(self.indices)))\n\n    def __len__(self):\n        return len(self.indices)\n\n\nclass WeightedRandomSampler(Sampler):\n    \"\"\"Samples elements from [0,..,len(weights)-1] with given probabilities (weights).\n\n    Arguments:\n        weights (list)   : a list of weights, not necessary summing up to one\n        num_samples (int): number of samples to draw\n        replacement (bool): if ``True``, samples are drawn with replacement.\n            If not, they are drawn without replacement, which means that when a\n            sample index is drawn for a row, it cannot be drawn again for that row.\n    \"\"\"\n\n    def __init__(self, weights, num_samples, replacement=True):\n        self.weights = torch.DoubleTensor(weights)\n        self.num_samples = num_samples\n        self.replacement = replacement\n\n    def __iter__(self):\n        return iter(torch.multinomial(self.weights, self.num_samples, self.replacement))\n\n    def __len__(self):\n        return self.num_samples\n\n\nclass BatchSampler(object):\n    \"\"\"Wraps another sampler to yield a mini-batch of indices.\n\n    Args:\n        sampler (Sampler): Base sampler.\n        batch_size (int): Size of mini-batch.\n        drop_last (bool): If ``True``, the sampler will drop the last batch if\n            its size would be less than ``batch_size``\n\n    Example:\n        >>> list(BatchSampler(range(10), batch_size=3, drop_last=False))\n        [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]]\n        >>> list(BatchSampler(range(10), batch_size=3, drop_last=True))\n        [[0, 1, 2], [3, 4, 5], [6, 7, 8]]\n    \"\"\"\n\n    def __init__(self, sampler, batch_size, drop_last):\n        self.sampler = sampler\n        self.batch_size = batch_size\n        self.drop_last = drop_last\n\n    def __iter__(self):\n        batch = []\n        for idx in self.sampler:\n            batch.append(idx)\n            if len(batch) == self.batch_size:\n                yield batch\n                batch = []\n        if len(batch) > 0 and not self.drop_last:\n            yield batch\n\n    def __len__(self):\n        if self.drop_last:\n            return len(self.sampler) // self.batch_size\n        else:\n            return (len(self.sampler) + self.batch_size - 1) // self.batch_size\n"
  },
  {
    "path": "model_cards/lama/models/ade20k/segm_lib/utils/th.py",
    "content": "import torch\nfrom torch.autograd import Variable\nimport numpy as np\nimport collections\n\n__all__ = ['as_variable', 'as_numpy', 'mark_volatile']\n\ndef as_variable(obj):\n    if isinstance(obj, Variable):\n        return obj\n    if isinstance(obj, collections.Sequence):\n        return [as_variable(v) for v in obj]\n    elif isinstance(obj, collections.Mapping):\n        return {k: as_variable(v) for k, v in obj.items()}\n    else:\n        return Variable(obj)\n\ndef as_numpy(obj):\n    if isinstance(obj, collections.Sequence):\n        return [as_numpy(v) for v in obj]\n    elif isinstance(obj, collections.Mapping):\n        return {k: as_numpy(v) for k, v in obj.items()}\n    elif isinstance(obj, Variable):\n        return obj.data.cpu().numpy()\n    elif torch.is_tensor(obj):\n        return obj.cpu().numpy()\n    else:\n        return np.array(obj)\n\ndef mark_volatile(obj):\n    if torch.is_tensor(obj):\n        obj = Variable(obj)\n    if isinstance(obj, Variable):\n        obj.no_grad = True\n        return obj\n    elif isinstance(obj, collections.Mapping):\n        return {k: mark_volatile(o) for k, o in obj.items()}\n    elif isinstance(obj, collections.Sequence):\n        return [mark_volatile(o) for o in obj]\n    else:\n        return obj\n"
  },
  {
    "path": "model_cards/lama/models/ade20k/utils.py",
    "content": "\"\"\"Modified from https://github.com/CSAILVision/semantic-segmentation-pytorch\"\"\"\n\nimport os\nimport sys\n\nimport numpy as np\nimport torch\n\ntry:\n    from urllib import urlretrieve\nexcept ImportError:\n    from urllib.request import urlretrieve\n\n\ndef load_url(url, model_dir='./pretrained', map_location=None):\n    if not os.path.exists(model_dir):\n        os.makedirs(model_dir)\n    filename = url.split('/')[-1]\n    cached_file = os.path.join(model_dir, filename)\n    if not os.path.exists(cached_file):\n        sys.stderr.write('Downloading: \"{}\" to {}\\n'.format(url, cached_file))\n        urlretrieve(url, cached_file)\n    return torch.load(cached_file, map_location=map_location)\n\n\ndef color_encode(labelmap, colors, mode='RGB'):\n    labelmap = labelmap.astype('int')\n    labelmap_rgb = np.zeros((labelmap.shape[0], labelmap.shape[1], 3),\n                            dtype=np.uint8)\n    for label in np.unique(labelmap):\n        if label < 0:\n            continue\n        labelmap_rgb += (labelmap == label)[:, :, np.newaxis] * \\\n            np.tile(colors[label],\n                    (labelmap.shape[0], labelmap.shape[1], 1))\n\n    if mode == 'BGR':\n        return labelmap_rgb[:, :, ::-1]\n    else:\n        return labelmap_rgb\n"
  },
  {
    "path": "model_cards/lama/requirements.txt",
    "content": "pyyaml\ntqdm\nnumpy\neasydict==1.9.0\nscikit-image==0.17.2\nscikit-learn==0.24.2\nopencv-python\ntensorflow\njoblib\nmatplotlib\npandas\nalbumentations==0.5.2\nhydra-core==1.1.0\npytorch-lightning==1.2.9\ntabulate\nkornia==0.5.0\nwebdataset\npackaging\nscikit-learn==0.24.2\nwldhx.yadisk-direct\n"
  },
  {
    "path": "model_cards/lama/saicinpainting/__init__.py",
    "content": ""
  },
  {
    "path": "model_cards/lama/saicinpainting/evaluation/__init__.py",
    "content": "import logging\n\nimport torch\n\nfrom saicinpainting.evaluation.evaluator import InpaintingEvaluatorOnline, ssim_fid100_f1, lpips_fid100_f1\nfrom saicinpainting.evaluation.losses.base_loss import SSIMScore, LPIPSScore, FIDScore\n\n\ndef make_evaluator(kind='default', ssim=True, lpips=True, fid=True, integral_kind=None, **kwargs):\n    logging.info(f'Make evaluator {kind}')\n    device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n    metrics = {}\n    if ssim:\n        metrics['ssim'] = SSIMScore()\n    if lpips:\n        metrics['lpips'] = LPIPSScore()\n    if fid:\n        metrics['fid'] = FIDScore().to(device)\n        \n    if integral_kind is None:\n        integral_func = None\n    elif integral_kind == 'ssim_fid100_f1':\n        integral_func = ssim_fid100_f1\n    elif integral_kind == 'lpips_fid100_f1':\n        integral_func = lpips_fid100_f1\n    else:\n        raise ValueError(f'Unexpected integral_kind={integral_kind}')\n\n    if kind == 'default':\n        return InpaintingEvaluatorOnline(scores=metrics,\n                                         integral_func=integral_func,\n                                         integral_title=integral_kind,\n                                         **kwargs)\n"
  },
  {
    "path": "model_cards/lama/saicinpainting/evaluation/data.py",
    "content": "import glob\nimport os\n\nimport cv2\nimport PIL.Image as Image\nimport numpy as np\n\nfrom torch.utils.data import Dataset\nimport torch.nn.functional as F\n\n\ndef load_image(fname, mode='RGB', return_orig=False):\n    img = np.array(Image.open(fname).convert(mode))\n    if img.ndim == 3:\n        img = np.transpose(img, (2, 0, 1))\n    out_img = img.astype('float32') / 255\n    if return_orig:\n        return out_img, img\n    else:\n        return out_img\n\n\ndef ceil_modulo(x, mod):\n    if x % mod == 0:\n        return x\n    return (x // mod + 1) * mod\n\n\ndef pad_img_to_modulo(img, mod):\n    channels, height, width = img.shape\n    out_height = ceil_modulo(height, mod)\n    out_width = ceil_modulo(width, mod)\n    return np.pad(img, ((0, 0), (0, out_height - height), (0, out_width - width)), mode='symmetric')\n\n\ndef pad_tensor_to_modulo(img, mod):\n    batch_size, channels, height, width = img.shape\n    out_height = ceil_modulo(height, mod)\n    out_width = ceil_modulo(width, mod)\n    return F.pad(img, pad=(0, out_width - width, 0, out_height - height), mode='reflect')\n\n\ndef scale_image(img, factor, interpolation=cv2.INTER_AREA):\n    if img.shape[0] == 1:\n        img = img[0]\n    else:\n        img = np.transpose(img, (1, 2, 0))\n\n    img = cv2.resize(img, dsize=None, fx=factor, fy=factor, interpolation=interpolation)\n\n    if img.ndim == 2:\n        img = img[None, ...]\n    else:\n        img = np.transpose(img, (2, 0, 1))\n    return img\n\n\nclass InpaintingDataset(Dataset):\n    def __init__(self, datadir, img_suffix='.jpg', pad_out_to_modulo=None, scale_factor=None):\n        self.datadir = datadir\n        self.mask_filenames = sorted(list(glob.glob(os.path.join(self.datadir, '**', '*mask*.png'), recursive=True)))\n        self.img_filenames = [fname.rsplit('_mask', 1)[0] + img_suffix for fname in self.mask_filenames]\n        self.pad_out_to_modulo = pad_out_to_modulo\n        self.scale_factor = scale_factor\n\n    def __len__(self):\n        return len(self.mask_filenames)\n\n    def __getitem__(self, i):\n        image = load_image(self.img_filenames[i], mode='RGB')\n        mask = load_image(self.mask_filenames[i], mode='L')\n        result = dict(image=image, mask=mask[None, ...])\n\n        if self.scale_factor is not None:\n            result['image'] = scale_image(result['image'], self.scale_factor)\n            result['mask'] = scale_image(result['mask'], self.scale_factor, interpolation=cv2.INTER_NEAREST)\n\n        if self.pad_out_to_modulo is not None and self.pad_out_to_modulo > 1:\n            result['unpad_to_size'] = result['image'].shape[1:]\n            result['image'] = pad_img_to_modulo(result['image'], self.pad_out_to_modulo)\n            result['mask'] = pad_img_to_modulo(result['mask'], self.pad_out_to_modulo)\n\n        return result\n\nclass OurInpaintingDataset(Dataset):\n    def __init__(self, datadir, img_suffix='.jpg', pad_out_to_modulo=None, scale_factor=None):\n        self.datadir = datadir\n        self.mask_filenames = sorted(list(glob.glob(os.path.join(self.datadir, 'mask', '**', '*mask*.png'), recursive=True)))\n        self.img_filenames = [os.path.join(self.datadir, 'img', os.path.basename(fname.rsplit('-', 1)[0].rsplit('_', 1)[0]) + '.png') for fname in self.mask_filenames]\n        self.pad_out_to_modulo = pad_out_to_modulo\n        self.scale_factor = scale_factor\n\n    def __len__(self):\n        return len(self.mask_filenames)\n\n    def __getitem__(self, i):\n        result = dict(image=load_image(self.img_filenames[i], mode='RGB'),\n                      mask=load_image(self.mask_filenames[i], mode='L')[None, ...])\n\n        if self.scale_factor is not None:\n            result['image'] = scale_image(result['image'], self.scale_factor)\n            result['mask'] = scale_image(result['mask'], self.scale_factor)\n\n        if self.pad_out_to_modulo is not None and self.pad_out_to_modulo > 1:\n            result['image'] = pad_img_to_modulo(result['image'], self.pad_out_to_modulo)\n            result['mask'] = pad_img_to_modulo(result['mask'], self.pad_out_to_modulo)\n\n        return result\n\nclass PrecomputedInpaintingResultsDataset(InpaintingDataset):\n    def __init__(self, datadir, predictdir, inpainted_suffix='_inpainted.jpg', **kwargs):\n        super().__init__(datadir, **kwargs)\n        if not datadir.endswith('/'):\n            datadir += '/'\n        self.predictdir = predictdir\n        self.pred_filenames = [os.path.join(predictdir, os.path.splitext(fname[len(datadir):])[0] + inpainted_suffix)\n                               for fname in self.mask_filenames]\n\n    def __getitem__(self, i):\n        result = super().__getitem__(i)\n        result['inpainted'] = load_image(self.pred_filenames[i])\n        if self.pad_out_to_modulo is not None and self.pad_out_to_modulo > 1:\n            result['inpainted'] = pad_img_to_modulo(result['inpainted'], self.pad_out_to_modulo)\n        return result\n\nclass OurPrecomputedInpaintingResultsDataset(OurInpaintingDataset):\n    def __init__(self, datadir, predictdir, inpainted_suffix=\"png\", **kwargs):\n        super().__init__(datadir, **kwargs)\n        if not datadir.endswith('/'):\n            datadir += '/'\n        self.predictdir = predictdir\n        self.pred_filenames = [os.path.join(predictdir, os.path.basename(os.path.splitext(fname)[0]) + f'_inpainted.{inpainted_suffix}')\n                               for fname in self.mask_filenames]\n        # self.pred_filenames = [os.path.join(predictdir, os.path.splitext(fname[len(datadir):])[0] + inpainted_suffix)\n        #                        for fname in self.mask_filenames]\n\n    def __getitem__(self, i):\n        result = super().__getitem__(i)\n        result['inpainted'] = self.file_loader(self.pred_filenames[i])\n\n        if self.pad_out_to_modulo is not None and self.pad_out_to_modulo > 1:\n            result['inpainted'] = pad_img_to_modulo(result['inpainted'], self.pad_out_to_modulo)\n        return result\n\nclass InpaintingEvalOnlineDataset(Dataset):\n    def __init__(self, indir, mask_generator, img_suffix='.jpg', pad_out_to_modulo=None, scale_factor=None,  **kwargs):\n        self.indir = indir\n        self.mask_generator = mask_generator\n        self.img_filenames = sorted(list(glob.glob(os.path.join(self.indir, '**', f'*{img_suffix}' ), recursive=True)))\n        self.pad_out_to_modulo = pad_out_to_modulo\n        self.scale_factor = scale_factor\n\n    def __len__(self):\n        return len(self.img_filenames)\n\n    def __getitem__(self, i):\n        img, raw_image = load_image(self.img_filenames[i], mode='RGB', return_orig=True)\n        mask = self.mask_generator(img, raw_image=raw_image)\n        result = dict(image=img, mask=mask)\n\n        if self.scale_factor is not None:\n            result['image'] = scale_image(result['image'], self.scale_factor)\n            result['mask'] = scale_image(result['mask'], self.scale_factor, interpolation=cv2.INTER_NEAREST)\n\n        if self.pad_out_to_modulo is not None and self.pad_out_to_modulo > 1:\n            result['image'] = pad_img_to_modulo(result['image'], self.pad_out_to_modulo)\n            result['mask'] = pad_img_to_modulo(result['mask'], self.pad_out_to_modulo)\n        return result"
  },
  {
    "path": "model_cards/lama/saicinpainting/evaluation/evaluator.py",
    "content": "import logging\nimport math\nfrom typing import Dict\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport tqdm\nfrom torch.utils.data import DataLoader\n\nfrom saicinpainting.evaluation.utils import move_to_device\n\nLOGGER = logging.getLogger(__name__)\n\n\nclass InpaintingEvaluator():\n    def __init__(self, dataset, scores, area_grouping=True, bins=10, batch_size=32, device='cuda',\n                 integral_func=None, integral_title=None, clamp_image_range=None):\n        \"\"\"\n        :param dataset: torch.utils.data.Dataset which contains images and masks\n        :param scores: dict {score_name: EvaluatorScore object}\n        :param area_grouping: in addition to the overall scores, allows to compute score for the groups of samples\n            which are defined by share of area occluded by mask\n        :param bins: number of groups, partition is generated by np.linspace(0., 1., bins + 1)\n        :param batch_size: batch_size for the dataloader\n        :param device: device to use\n        \"\"\"\n        self.scores = scores\n        self.dataset = dataset\n\n        self.area_grouping = area_grouping\n        self.bins = bins\n\n        self.device = torch.device(device)\n\n        self.dataloader = DataLoader(self.dataset, shuffle=False, batch_size=batch_size)\n\n        self.integral_func = integral_func\n        self.integral_title = integral_title\n        self.clamp_image_range = clamp_image_range\n\n    def _get_bin_edges(self):\n        bin_edges = np.linspace(0, 1, self.bins + 1)\n\n        num_digits = max(0, math.ceil(math.log10(self.bins)) - 1)\n        interval_names = []\n        for idx_bin in range(self.bins):\n            start_percent, end_percent = round(100 * bin_edges[idx_bin], num_digits), \\\n                                         round(100 * bin_edges[idx_bin + 1], num_digits)\n            start_percent = '{:.{n}f}'.format(start_percent, n=num_digits)\n            end_percent = '{:.{n}f}'.format(end_percent, n=num_digits)\n            interval_names.append(\"{0}-{1}%\".format(start_percent, end_percent))\n\n        groups = []\n        for batch in self.dataloader:\n            mask = batch['mask']\n            batch_size = mask.shape[0]\n            area = mask.to(self.device).reshape(batch_size, -1).mean(dim=-1)\n            bin_indices = np.searchsorted(bin_edges, area.detach().cpu().numpy(), side='right') - 1\n            # corner case: when area is equal to 1, bin_indices should return bins - 1, not bins for that element\n            bin_indices[bin_indices == self.bins] = self.bins - 1\n            groups.append(bin_indices)\n        groups = np.hstack(groups)\n\n        return groups, interval_names\n\n    def evaluate(self, model=None):\n        \"\"\"\n        :param model: callable with signature (image_batch, mask_batch); should return inpainted_batch\n        :return: dict with (score_name, group_type) as keys, where group_type can be either 'overall' or\n            name of the particular group arranged by area of mask (e.g. '10-20%')\n            and score statistics for the group as values.\n        \"\"\"\n        results = dict()\n        if self.area_grouping:\n            groups, interval_names = self._get_bin_edges()\n        else:\n            groups = None\n\n        for score_name, score in tqdm.auto.tqdm(self.scores.items(), desc='scores'):\n            score.to(self.device)\n            with torch.no_grad():\n                score.reset()\n                for batch in tqdm.auto.tqdm(self.dataloader, desc=score_name, leave=False):\n                    batch = move_to_device(batch, self.device)\n                    image_batch, mask_batch = batch['image'], batch['mask']\n                    if self.clamp_image_range is not None:\n                        image_batch = torch.clamp(image_batch,\n                                                  min=self.clamp_image_range[0],\n                                                  max=self.clamp_image_range[1])\n                    if model is None:\n                        assert 'inpainted' in batch, \\\n                            'Model is None, so we expected precomputed inpainting results at key \"inpainted\"'\n                        inpainted_batch = batch['inpainted']\n                    else:\n                        inpainted_batch = model(image_batch, mask_batch)\n                    score(inpainted_batch, image_batch, mask_batch)\n                total_results, group_results = score.get_value(groups=groups)\n\n            results[(score_name, 'total')] = total_results\n            if groups is not None:\n                for group_index, group_values in group_results.items():\n                    group_name = interval_names[group_index]\n                    results[(score_name, group_name)] = group_values\n\n        if self.integral_func is not None:\n            results[(self.integral_title, 'total')] = dict(mean=self.integral_func(results))\n\n        return results\n\n\ndef ssim_fid100_f1(metrics, fid_scale=100):\n    ssim = metrics[('ssim', 'total')]['mean']\n    fid = metrics[('fid', 'total')]['mean']\n    fid_rel = max(0, fid_scale - fid) / fid_scale\n    f1 = 2 * ssim * fid_rel / (ssim + fid_rel + 1e-3)\n    return f1\n\n\ndef lpips_fid100_f1(metrics, fid_scale=100):\n    neg_lpips = 1 - metrics[('lpips', 'total')]['mean']  # invert, so bigger is better\n    fid = metrics[('fid', 'total')]['mean']\n    fid_rel = max(0, fid_scale - fid) / fid_scale\n    f1 = 2 * neg_lpips * fid_rel / (neg_lpips + fid_rel + 1e-3)\n    return f1\n\n\n\nclass InpaintingEvaluatorOnline(nn.Module):\n    def __init__(self, scores, bins=10, image_key='image', inpainted_key='inpainted',\n                 integral_func=None, integral_title=None, clamp_image_range=None):\n        \"\"\"\n        :param scores: dict {score_name: EvaluatorScore object}\n        :param bins: number of groups, partition is generated by np.linspace(0., 1., bins + 1)\n        :param device: device to use\n        \"\"\"\n        super().__init__()\n        LOGGER.info(f'{type(self)} init called')\n        self.scores = nn.ModuleDict(scores)\n        self.image_key = image_key\n        self.inpainted_key = inpainted_key\n        self.bins_num = bins\n        self.bin_edges = np.linspace(0, 1, self.bins_num + 1)\n\n        num_digits = max(0, math.ceil(math.log10(self.bins_num)) - 1)\n        self.interval_names = []\n        for idx_bin in range(self.bins_num):\n            start_percent, end_percent = round(100 * self.bin_edges[idx_bin], num_digits), \\\n                                         round(100 * self.bin_edges[idx_bin + 1], num_digits)\n            start_percent = '{:.{n}f}'.format(start_percent, n=num_digits)\n            end_percent = '{:.{n}f}'.format(end_percent, n=num_digits)\n            self.interval_names.append(\"{0}-{1}%\".format(start_percent, end_percent))\n\n        self.groups = []\n\n        self.integral_func = integral_func\n        self.integral_title = integral_title\n        self.clamp_image_range = clamp_image_range\n\n        LOGGER.info(f'{type(self)} init done')\n\n    def _get_bins(self, mask_batch):\n        batch_size = mask_batch.shape[0]\n        area = mask_batch.view(batch_size, -1).mean(dim=-1).detach().cpu().numpy()\n        bin_indices = np.clip(np.searchsorted(self.bin_edges, area) - 1, 0, self.bins_num - 1)\n        return bin_indices\n\n    def forward(self, batch: Dict[str, torch.Tensor]):\n        \"\"\"\n        Calculate and accumulate metrics for batch. To finalize evaluation and obtain final metrics, call evaluation_end\n        :param batch: batch dict with mandatory fields mask, image, inpainted (can be overriden by self.inpainted_key)\n        \"\"\"\n        result = {}\n        with torch.no_grad():\n            image_batch, mask_batch, inpainted_batch = batch[self.image_key], batch['mask'], batch[self.inpainted_key]\n            if self.clamp_image_range is not None:\n                image_batch = torch.clamp(image_batch,\n                                          min=self.clamp_image_range[0],\n                                          max=self.clamp_image_range[1])\n            self.groups.extend(self._get_bins(mask_batch))\n\n            for score_name, score in self.scores.items():\n                result[score_name] = score(inpainted_batch, image_batch, mask_batch)\n        return result\n\n    def process_batch(self, batch: Dict[str, torch.Tensor]):\n        return self(batch)\n\n    def evaluation_end(self, states=None):\n        \"\"\":return: dict with (score_name, group_type) as keys, where group_type can be either 'overall' or\n            name of the particular group arranged by area of mask (e.g. '10-20%')\n            and score statistics for the group as values.\n        \"\"\"\n        LOGGER.info(f'{type(self)}: evaluation_end called')\n\n        self.groups = np.array(self.groups)\n\n        results = {}\n        for score_name, score in self.scores.items():\n            LOGGER.info(f'Getting value of {score_name}')\n            cur_states = [s[score_name] for s in states] if states is not None else None\n            total_results, group_results = score.get_value(groups=self.groups, states=cur_states)\n            LOGGER.info(f'Getting value of {score_name} done')\n            results[(score_name, 'total')] = total_results\n\n            for group_index, group_values in group_results.items():\n                group_name = self.interval_names[group_index]\n                results[(score_name, group_name)] = group_values\n\n        if self.integral_func is not None:\n            results[(self.integral_title, 'total')] = dict(mean=self.integral_func(results))\n\n        LOGGER.info(f'{type(self)}: reset scores')\n        self.groups = []\n        for sc in self.scores.values():\n            sc.reset()\n        LOGGER.info(f'{type(self)}: reset scores done')\n\n        LOGGER.info(f'{type(self)}: evaluation_end done')\n        return results\n"
  },
  {
    "path": "model_cards/lama/saicinpainting/evaluation/losses/__init__.py",
    "content": ""
  },
  {
    "path": "model_cards/lama/saicinpainting/evaluation/losses/base_loss.py",
    "content": "import logging\nfrom abc import abstractmethod, ABC\n\nimport numpy as np\nimport sklearn\nimport sklearn.svm\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom joblib import Parallel, delayed\nfrom scipy import linalg\n\nfrom models.ade20k import SegmentationModule, NUM_CLASS, segm_options\nfrom .fid.inception import InceptionV3\nfrom .lpips import PerceptualLoss\nfrom .ssim import SSIM\n\nLOGGER = logging.getLogger(__name__)\n\n\ndef get_groupings(groups):\n    \"\"\"\n    :param groups: group numbers for respective elements\n    :return: dict of kind {group_idx: indices of the corresponding group elements}\n    \"\"\"\n    label_groups, count_groups = np.unique(groups, return_counts=True)\n\n    indices = np.argsort(groups)\n\n    grouping = dict()\n    cur_start = 0\n    for label, count in zip(label_groups, count_groups):\n        cur_end = cur_start + count\n        cur_indices = indices[cur_start:cur_end]\n        grouping[label] = cur_indices\n        cur_start = cur_end\n    return grouping\n\n\nclass EvaluatorScore(nn.Module):\n    @abstractmethod\n    def forward(self, pred_batch, target_batch, mask):\n        pass\n\n    @abstractmethod\n    def get_value(self, groups=None, states=None):\n        pass\n\n    @abstractmethod\n    def reset(self):\n        pass\n\n\nclass PairwiseScore(EvaluatorScore, ABC):\n    def __init__(self):\n        super().__init__()\n        self.individual_values = None\n\n    def get_value(self, groups=None, states=None):\n        \"\"\"\n        :param groups:\n        :return:\n            total_results: dict of kind {'mean': score mean, 'std': score std}\n            group_results: None, if groups is None;\n                else dict {group_idx: {'mean': score mean among group, 'std': score std among group}}\n        \"\"\"\n        individual_values = torch.cat(states, dim=-1).reshape(-1).cpu().numpy() if states is not None \\\n            else self.individual_values\n\n        total_results = {\n            'mean': individual_values.mean(),\n            'std': individual_values.std()\n        }\n\n        if groups is None:\n            return total_results, None\n\n        group_results = dict()\n        grouping = get_groupings(groups)\n        for label, index in grouping.items():\n            group_scores = individual_values[index]\n            group_results[label] = {\n                'mean': group_scores.mean(),\n                'std': group_scores.std()\n            }\n        return total_results, group_results\n\n    def reset(self):\n        self.individual_values = []\n\n\nclass SSIMScore(PairwiseScore):\n    def __init__(self, window_size=11):\n        super().__init__()\n        self.score = SSIM(window_size=window_size, size_average=False).eval()\n        self.reset()\n\n    def forward(self, pred_batch, target_batch, mask=None):\n        batch_values = self.score(pred_batch, target_batch)\n        self.individual_values = np.hstack([\n            self.individual_values, batch_values.detach().cpu().numpy()\n        ])\n        return batch_values\n\n\nclass LPIPSScore(PairwiseScore):\n    def __init__(self, model='net-lin', net='vgg', model_path=None, use_gpu=True):\n        super().__init__()\n        self.score = PerceptualLoss(model=model, net=net, model_path=model_path,\n                                    use_gpu=use_gpu, spatial=False).eval()\n        self.reset()\n\n    def forward(self, pred_batch, target_batch, mask=None):\n        batch_values = self.score(pred_batch, target_batch).flatten()\n        self.individual_values = np.hstack([\n            self.individual_values, batch_values.detach().cpu().numpy()\n        ])\n        return batch_values\n\n\ndef fid_calculate_activation_statistics(act):\n    mu = np.mean(act, axis=0)\n    sigma = np.cov(act, rowvar=False)\n    return mu, sigma\n\n\ndef calculate_frechet_distance(activations_pred, activations_target, eps=1e-6):\n    mu1, sigma1 = fid_calculate_activation_statistics(activations_pred)\n    mu2, sigma2 = fid_calculate_activation_statistics(activations_target)\n\n    diff = mu1 - mu2\n\n    # Product might be almost singular\n    covmean, _ = linalg.sqrtm(sigma1.dot(sigma2), disp=False)\n    if not np.isfinite(covmean).all():\n        msg = ('fid calculation produces singular product; '\n               'adding %s to diagonal of cov estimates') % eps\n        LOGGER.warning(msg)\n        offset = np.eye(sigma1.shape[0]) * eps\n        covmean = linalg.sqrtm((sigma1 + offset).dot(sigma2 + offset))\n\n    # Numerical error might give slight imaginary component\n    if np.iscomplexobj(covmean):\n        # if not np.allclose(np.diagonal(covmean).imag, 0, atol=1e-3):\n        if not np.allclose(np.diagonal(covmean).imag, 0, atol=1e-2):\n            m = np.max(np.abs(covmean.imag))\n            raise ValueError('Imaginary component {}'.format(m))\n        covmean = covmean.real\n\n    tr_covmean = np.trace(covmean)\n\n    return (diff.dot(diff) + np.trace(sigma1) +\n            np.trace(sigma2) - 2 * tr_covmean)\n\n\nclass FIDScore(EvaluatorScore):\n    def __init__(self, dims=2048, eps=1e-6):\n        LOGGER.info(\"FIDscore init called\")\n        super().__init__()\n        if getattr(FIDScore, '_MODEL', None) is None:\n            block_idx = InceptionV3.BLOCK_INDEX_BY_DIM[dims]\n            FIDScore._MODEL = InceptionV3([block_idx]).eval()\n        self.model = FIDScore._MODEL\n        self.eps = eps\n        self.reset()\n        LOGGER.info(\"FIDscore init done\")\n\n    def forward(self, pred_batch, target_batch, mask=None):\n        activations_pred = self._get_activations(pred_batch)\n        activations_target = self._get_activations(target_batch)\n\n        self.activations_pred.append(activations_pred.detach().cpu())\n        self.activations_target.append(activations_target.detach().cpu())\n\n        return activations_pred, activations_target\n\n    def get_value(self, groups=None, states=None):\n        LOGGER.info(\"FIDscore get_value called\")\n        activations_pred, activations_target = zip(*states) if states is not None \\\n            else (self.activations_pred, self.activations_target)\n        activations_pred = torch.cat(activations_pred).cpu().numpy()\n        activations_target = torch.cat(activations_target).cpu().numpy()\n\n        total_distance = calculate_frechet_distance(activations_pred, activations_target, eps=self.eps)\n        total_results = dict(mean=total_distance)\n\n        if groups is None:\n            group_results = None\n        else:\n            group_results = dict()\n            grouping = get_groupings(groups)\n            for label, index in grouping.items():\n                if len(index) > 1:\n                    group_distance = calculate_frechet_distance(activations_pred[index], activations_target[index],\n                                                                eps=self.eps)\n                    group_results[label] = dict(mean=group_distance)\n\n                else:\n                    group_results[label] = dict(mean=float('nan'))\n\n        self.reset()\n\n        LOGGER.info(\"FIDscore get_value done\")\n\n        return total_results, group_results\n\n    def reset(self):\n        self.activations_pred = []\n        self.activations_target = []\n\n    def _get_activations(self, batch):\n        activations = self.model(batch)[0]\n        if activations.shape[2] != 1 or activations.shape[3] != 1:\n            assert False, \\\n                'We should not have got here, because Inception always scales inputs to 299x299'\n            # activations = F.adaptive_avg_pool2d(activations, output_size=(1, 1))\n        activations = activations.squeeze(-1).squeeze(-1)\n        return activations\n\n\nclass SegmentationAwareScore(EvaluatorScore):\n    def __init__(self, weights_path):\n        super().__init__()\n        self.segm_network = SegmentationModule(weights_path=weights_path, use_default_normalization=True).eval()\n        self.target_class_freq_by_image_total = []\n        self.target_class_freq_by_image_mask = []\n        self.pred_class_freq_by_image_mask = []\n\n    def forward(self, pred_batch, target_batch, mask):\n        pred_segm_flat = self.segm_network.predict(pred_batch)[0].view(pred_batch.shape[0], -1).long().detach().cpu().numpy()\n        target_segm_flat = self.segm_network.predict(target_batch)[0].view(pred_batch.shape[0], -1).long().detach().cpu().numpy()\n        mask_flat = (mask.view(mask.shape[0], -1) > 0.5).detach().cpu().numpy()\n\n        batch_target_class_freq_total = []\n        batch_target_class_freq_mask = []\n        batch_pred_class_freq_mask = []\n\n        for cur_pred_segm, cur_target_segm, cur_mask in zip(pred_segm_flat, target_segm_flat, mask_flat):\n            cur_target_class_freq_total = np.bincount(cur_target_segm, minlength=NUM_CLASS)[None, ...]\n            cur_target_class_freq_mask = np.bincount(cur_target_segm[cur_mask], minlength=NUM_CLASS)[None, ...]\n            cur_pred_class_freq_mask = np.bincount(cur_pred_segm[cur_mask], minlength=NUM_CLASS)[None, ...]\n\n            self.target_class_freq_by_image_total.append(cur_target_class_freq_total)\n            self.target_class_freq_by_image_mask.append(cur_target_class_freq_mask)\n            self.pred_class_freq_by_image_mask.append(cur_pred_class_freq_mask)\n\n            batch_target_class_freq_total.append(cur_target_class_freq_total)\n            batch_target_class_freq_mask.append(cur_target_class_freq_mask)\n            batch_pred_class_freq_mask.append(cur_pred_class_freq_mask)\n\n        batch_target_class_freq_total = np.concatenate(batch_target_class_freq_total, axis=0)\n        batch_target_class_freq_mask = np.concatenate(batch_target_class_freq_mask, axis=0)\n        batch_pred_class_freq_mask = np.concatenate(batch_pred_class_freq_mask, axis=0)\n        return batch_target_class_freq_total, batch_target_class_freq_mask, batch_pred_class_freq_mask\n\n    def reset(self):\n        super().reset()\n        self.target_class_freq_by_image_total = []\n        self.target_class_freq_by_image_mask = []\n        self.pred_class_freq_by_image_mask = []\n\n\ndef distribute_values_to_classes(target_class_freq_by_image_mask, values, idx2name):\n    assert target_class_freq_by_image_mask.ndim == 2 and target_class_freq_by_image_mask.shape[0] == values.shape[0]\n    total_class_freq = target_class_freq_by_image_mask.sum(0)\n    distr_values = (target_class_freq_by_image_mask * values[..., None]).sum(0)\n    result = distr_values / (total_class_freq + 1e-3)\n    return {idx2name[i]: val for i, val in enumerate(result) if total_class_freq[i] > 0}\n\n\ndef get_segmentation_idx2name():\n    return {i - 1: name for i, name in segm_options['classes'].set_index('Idx', drop=True)['Name'].to_dict().items()}\n\n\nclass SegmentationAwarePairwiseScore(SegmentationAwareScore):\n    def __init__(self, *args, **kwargs):\n        super().__init__(*args, **kwargs)\n        self.individual_values = []\n        self.segm_idx2name = get_segmentation_idx2name()\n\n    def forward(self, pred_batch, target_batch, mask):\n        cur_class_stats = super().forward(pred_batch, target_batch, mask)\n        score_values = self.calc_score(pred_batch, target_batch, mask)\n        self.individual_values.append(score_values)\n        return cur_class_stats + (score_values,)\n\n    @abstractmethod\n    def calc_score(self, pred_batch, target_batch, mask):\n        raise NotImplementedError()\n\n    def get_value(self, groups=None, states=None):\n        \"\"\"\n        :param groups:\n        :return:\n            total_results: dict of kind {'mean': score mean, 'std': score std}\n            group_results: None, if groups is None;\n                else dict {group_idx: {'mean': score mean among group, 'std': score std among group}}\n        \"\"\"\n        if states is not None:\n            (target_class_freq_by_image_total,\n             target_class_freq_by_image_mask,\n             pred_class_freq_by_image_mask,\n             individual_values) = states\n        else:\n            target_class_freq_by_image_total = self.target_class_freq_by_image_total\n            target_class_freq_by_image_mask = self.target_class_freq_by_image_mask\n            pred_class_freq_by_image_mask = self.pred_class_freq_by_image_mask\n            individual_values = self.individual_values\n\n        target_class_freq_by_image_total = np.concatenate(target_class_freq_by_image_total, axis=0)\n        target_class_freq_by_image_mask = np.concatenate(target_class_freq_by_image_mask, axis=0)\n        pred_class_freq_by_image_mask = np.concatenate(pred_class_freq_by_image_mask, axis=0)\n        individual_values = np.concatenate(individual_values, axis=0)\n\n        total_results = {\n            'mean': individual_values.mean(),\n            'std': individual_values.std(),\n            **distribute_values_to_classes(target_class_freq_by_image_mask, individual_values, self.segm_idx2name)\n        }\n\n        if groups is None:\n            return total_results, None\n\n        group_results = dict()\n        grouping = get_groupings(groups)\n        for label, index in grouping.items():\n            group_class_freq = target_class_freq_by_image_mask[index]\n            group_scores = individual_values[index]\n            group_results[label] = {\n                'mean': group_scores.mean(),\n                'std': group_scores.std(),\n                ** distribute_values_to_classes(group_class_freq, group_scores, self.segm_idx2name)\n            }\n        return total_results, group_results\n\n    def reset(self):\n        super().reset()\n        self.individual_values = []\n\n\nclass SegmentationClassStats(SegmentationAwarePairwiseScore):\n    def calc_score(self, pred_batch, target_batch, mask):\n        return 0\n\n    def get_value(self, groups=None, states=None):\n        \"\"\"\n        :param groups:\n        :return:\n            total_results: dict of kind {'mean': score mean, 'std': score std}\n            group_results: None, if groups is None;\n                else dict {group_idx: {'mean': score mean among group, 'std': score std among group}}\n        \"\"\"\n        if states is not None:\n            (target_class_freq_by_image_total,\n             target_class_freq_by_image_mask,\n             pred_class_freq_by_image_mask,\n             _) = states\n        else:\n            target_class_freq_by_image_total = self.target_class_freq_by_image_total\n            target_class_freq_by_image_mask = self.target_class_freq_by_image_mask\n            pred_class_freq_by_image_mask = self.pred_class_freq_by_image_mask\n\n        target_class_freq_by_image_total = np.concatenate(target_class_freq_by_image_total, axis=0)\n        target_class_freq_by_image_mask = np.concatenate(target_class_freq_by_image_mask, axis=0)\n        pred_class_freq_by_image_mask = np.concatenate(pred_class_freq_by_image_mask, axis=0)\n\n        target_class_freq_by_image_total_marginal = target_class_freq_by_image_total.sum(0).astype('float32')\n        target_class_freq_by_image_total_marginal /= target_class_freq_by_image_total_marginal.sum()\n\n        target_class_freq_by_image_mask_marginal = target_class_freq_by_image_mask.sum(0).astype('float32')\n        target_class_freq_by_image_mask_marginal /= target_class_freq_by_image_mask_marginal.sum()\n\n        pred_class_freq_diff = (pred_class_freq_by_image_mask - target_class_freq_by_image_mask).sum(0) / (target_class_freq_by_image_mask.sum(0) + 1e-3)\n\n        total_results = dict()\n        total_results.update({f'total_freq/{self.segm_idx2name[i]}': v\n                              for i, v in enumerate(target_class_freq_by_image_total_marginal)\n                              if v > 0})\n        total_results.update({f'mask_freq/{self.segm_idx2name[i]}': v\n                              for i, v in enumerate(target_class_freq_by_image_mask_marginal)\n                              if v > 0})\n        total_results.update({f'mask_freq_diff/{self.segm_idx2name[i]}': v\n                              for i, v in enumerate(pred_class_freq_diff)\n                              if target_class_freq_by_image_total_marginal[i] > 0})\n\n        if groups is None:\n            return total_results, None\n\n        group_results = dict()\n        grouping = get_groupings(groups)\n        for label, index in grouping.items():\n            group_target_class_freq_by_image_total = target_class_freq_by_image_total[index]\n            group_target_class_freq_by_image_mask = target_class_freq_by_image_mask[index]\n            group_pred_class_freq_by_image_mask = pred_class_freq_by_image_mask[index]\n\n            group_target_class_freq_by_image_total_marginal = group_target_class_freq_by_image_total.sum(0).astype('float32')\n            group_target_class_freq_by_image_total_marginal /= group_target_class_freq_by_image_total_marginal.sum()\n\n            group_target_class_freq_by_image_mask_marginal = group_target_class_freq_by_image_mask.sum(0).astype('float32')\n            group_target_class_freq_by_image_mask_marginal /= group_target_class_freq_by_image_mask_marginal.sum()\n\n            group_pred_class_freq_diff = (group_pred_class_freq_by_image_mask - group_target_class_freq_by_image_mask).sum(0) / (\n                    group_target_class_freq_by_image_mask.sum(0) + 1e-3)\n\n            cur_group_results = dict()\n            cur_group_results.update({f'total_freq/{self.segm_idx2name[i]}': v\n                                      for i, v in enumerate(group_target_class_freq_by_image_total_marginal)\n                                      if v > 0})\n            cur_group_results.update({f'mask_freq/{self.segm_idx2name[i]}': v\n                                      for i, v in enumerate(group_target_class_freq_by_image_mask_marginal)\n                                      if v > 0})\n            cur_group_results.update({f'mask_freq_diff/{self.segm_idx2name[i]}': v\n                                      for i, v in enumerate(group_pred_class_freq_diff)\n                                      if group_target_class_freq_by_image_total_marginal[i] > 0})\n\n            group_results[label] = cur_group_results\n        return total_results, group_results\n\n\nclass SegmentationAwareSSIM(SegmentationAwarePairwiseScore):\n    def __init__(self, *args, window_size=11, **kwargs):\n        super().__init__(*args, **kwargs)\n        self.score_impl = SSIM(window_size=window_size, size_average=False).eval()\n\n    def calc_score(self, pred_batch, target_batch, mask):\n        return self.score_impl(pred_batch, target_batch).detach().cpu().numpy()\n\n\nclass SegmentationAwareLPIPS(SegmentationAwarePairwiseScore):\n    def __init__(self, *args, model='net-lin', net='vgg', model_path=None, use_gpu=True,  **kwargs):\n        super().__init__(*args, **kwargs)\n        self.score_impl = PerceptualLoss(model=model, net=net, model_path=model_path,\n                                         use_gpu=use_gpu, spatial=False).eval()\n\n    def calc_score(self, pred_batch, target_batch, mask):\n        return self.score_impl(pred_batch, target_batch).flatten().detach().cpu().numpy()\n\n\ndef calculade_fid_no_img(img_i, activations_pred, activations_target, eps=1e-6):\n    activations_pred = activations_pred.copy()\n    activations_pred[img_i] = activations_target[img_i]\n    return calculate_frechet_distance(activations_pred, activations_target, eps=eps)\n\n\nclass SegmentationAwareFID(SegmentationAwarePairwiseScore):\n    def __init__(self, *args, dims=2048, eps=1e-6, n_jobs=-1, **kwargs):\n        super().__init__(*args, **kwargs)\n        if getattr(FIDScore, '_MODEL', None) is None:\n            block_idx = InceptionV3.BLOCK_INDEX_BY_DIM[dims]\n            FIDScore._MODEL = InceptionV3([block_idx]).eval()\n        self.model = FIDScore._MODEL\n        self.eps = eps\n        self.n_jobs = n_jobs\n\n    def calc_score(self, pred_batch, target_batch, mask):\n        activations_pred = self._get_activations(pred_batch)\n        activations_target = self._get_activations(target_batch)\n        return activations_pred, activations_target\n\n    def get_value(self, groups=None, states=None):\n        \"\"\"\n        :param groups:\n        :return:\n            total_results: dict of kind {'mean': score mean, 'std': score std}\n            group_results: None, if groups is None;\n                else dict {group_idx: {'mean': score mean among group, 'std': score std among group}}\n        \"\"\"\n        if states is not None:\n            (target_class_freq_by_image_total,\n             target_class_freq_by_image_mask,\n             pred_class_freq_by_image_mask,\n             activation_pairs) = states\n        else:\n            target_class_freq_by_image_total = self.target_class_freq_by_image_total\n            target_class_freq_by_image_mask = self.target_class_freq_by_image_mask\n            pred_class_freq_by_image_mask = self.pred_class_freq_by_image_mask\n            activation_pairs = self.individual_values\n\n        target_class_freq_by_image_total = np.concatenate(target_class_freq_by_image_total, axis=0)\n        target_class_freq_by_image_mask = np.concatenate(target_class_freq_by_image_mask, axis=0)\n        pred_class_freq_by_image_mask = np.concatenate(pred_class_freq_by_image_mask, axis=0)\n        activations_pred, activations_target = zip(*activation_pairs)\n        activations_pred = np.concatenate(activations_pred, axis=0)\n        activations_target = np.concatenate(activations_target, axis=0)\n\n        total_results = {\n            'mean': calculate_frechet_distance(activations_pred, activations_target, eps=self.eps),\n            'std': 0,\n            **self.distribute_fid_to_classes(target_class_freq_by_image_mask, activations_pred, activations_target)\n        }\n\n        if groups is None:\n            return total_results, None\n\n        group_results = dict()\n        grouping = get_groupings(groups)\n        for label, index in grouping.items():\n            if len(index) > 1:\n                group_activations_pred = activations_pred[index]\n                group_activations_target = activations_target[index]\n                group_class_freq = target_class_freq_by_image_mask[index]\n                group_results[label] = {\n                    'mean': calculate_frechet_distance(group_activations_pred, group_activations_target, eps=self.eps),\n                    'std': 0,\n                    **self.distribute_fid_to_classes(group_class_freq,\n                                                     group_activations_pred,\n                                                     group_activations_target)\n                }\n            else:\n                group_results[label] = dict(mean=float('nan'), std=0)\n        return total_results, group_results\n\n    def distribute_fid_to_classes(self, class_freq, activations_pred, activations_target):\n        real_fid = calculate_frechet_distance(activations_pred, activations_target, eps=self.eps)\n\n        fid_no_images = Parallel(n_jobs=self.n_jobs)(\n            delayed(calculade_fid_no_img)(img_i, activations_pred, activations_target, eps=self.eps)\n            for img_i in range(activations_pred.shape[0])\n        )\n        errors = real_fid - fid_no_images\n        return distribute_values_to_classes(class_freq, errors, self.segm_idx2name)\n\n    def _get_activations(self, batch):\n        activations = self.model(batch)[0]\n        if activations.shape[2] != 1 or activations.shape[3] != 1:\n            activations = F.adaptive_avg_pool2d(activations, output_size=(1, 1))\n        activations = activations.squeeze(-1).squeeze(-1).detach().cpu().numpy()\n        return activations\n"
  },
  {
    "path": "model_cards/lama/saicinpainting/evaluation/losses/fid/__init__.py",
    "content": ""
  },
  {
    "path": "model_cards/lama/saicinpainting/evaluation/losses/fid/fid_score.py",
    "content": "#!/usr/bin/env python3\n\"\"\"Calculates the Frechet Inception Distance (FID) to evalulate GANs\n\nThe FID metric calculates the distance between two distributions of images.\nTypically, we have summary statistics (mean & covariance matrix) of one\nof these distributions, while the 2nd distribution is given by a GAN.\n\nWhen run as a stand-alone program, it compares the distribution of\nimages that are stored as PNG/JPEG at a specified location with a\ndistribution given by summary statistics (in pickle format).\n\nThe FID is calculated by assuming that X_1 and X_2 are the activations of\nthe pool_3 layer of the inception net for generated samples and real world\nsamples respectively.\n\nSee --help to see further details.\n\nCode apapted from https://github.com/bioinf-jku/TTUR to use PyTorch instead\nof Tensorflow\n\nCopyright 2018 Institute of Bioinformatics, JKU Linz\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n   http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \"AS IS\" BASIS,\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the License.\n\"\"\"\nimport os\nimport pathlib\nfrom argparse import ArgumentDefaultsHelpFormatter, ArgumentParser\n\nimport numpy as np\nimport torch\n# from scipy.misc import imread\nfrom imageio import imread\nfrom PIL import Image, JpegImagePlugin\nfrom scipy import linalg\nfrom torch.nn.functional import adaptive_avg_pool2d\nfrom torchvision.transforms import CenterCrop, Compose, Resize, ToTensor\n\ntry:\n    from tqdm import tqdm\nexcept ImportError:\n    # If not tqdm is not available, provide a mock version of it\n    def tqdm(x): return x\n\ntry:\n    from .inception import InceptionV3\nexcept ModuleNotFoundError:\n    from inception import InceptionV3\n\nparser = ArgumentParser(formatter_class=ArgumentDefaultsHelpFormatter)\nparser.add_argument('path', type=str, nargs=2,\n                    help=('Path to the generated images or '\n                          'to .npz statistic files'))\nparser.add_argument('--batch-size', type=int, default=50,\n                    help='Batch size to use')\nparser.add_argument('--dims', type=int, default=2048,\n                    choices=list(InceptionV3.BLOCK_INDEX_BY_DIM),\n                    help=('Dimensionality of Inception features to use. '\n                          'By default, uses pool3 features'))\nparser.add_argument('-c', '--gpu', default='', type=str,\n                    help='GPU to use (leave blank for CPU only)')\nparser.add_argument('--resize', default=256)\n\ntransform = Compose([Resize(256), CenterCrop(256), ToTensor()])\n\n\ndef get_activations(files, model, batch_size=50, dims=2048,\n                    cuda=False, verbose=False, keep_size=False):\n    \"\"\"Calculates the activations of the pool_3 layer for all images.\n\n    Params:\n    -- files       : List of image files paths\n    -- model       : Instance of inception model\n    -- batch_size  : Batch size of images for the model to process at once.\n                     Make sure that the number of samples is a multiple of\n                     the batch size, otherwise some samples are ignored. This\n                     behavior is retained to match the original FID score\n                     implementation.\n    -- dims        : Dimensionality of features returned by Inception\n    -- cuda        : If set to True, use GPU\n    -- verbose     : If set to True and parameter out_step is given, the number\n                     of calculated batches is reported.\n    Returns:\n    -- A numpy array of dimension (num images, dims) that contains the\n       activations of the given tensor when feeding inception with the\n       query tensor.\n    \"\"\"\n    model.eval()\n\n    if len(files) % batch_size != 0:\n        print(('Warning: number of images is not a multiple of the '\n               'batch size. Some samples are going to be ignored.'))\n    if batch_size > len(files):\n        print(('Warning: batch size is bigger than the data size. '\n               'Setting batch size to data size'))\n        batch_size = len(files)\n\n    n_batches = len(files) // batch_size\n    n_used_imgs = n_batches * batch_size\n\n    pred_arr = np.empty((n_used_imgs, dims))\n\n    for i in tqdm(range(n_batches)):\n        if verbose:\n            print('\\rPropagating batch %d/%d' % (i + 1, n_batches),\n                  end='', flush=True)\n        start = i * batch_size\n        end = start + batch_size\n\n        # # Official code goes below\n        # images = np.array([imread(str(f)).astype(np.float32)\n        #                    for f in files[start:end]])\n\n        # # Reshape to (n_images, 3, height, width)\n        # images = images.transpose((0, 3, 1, 2))\n        # images /= 255\n        # batch = torch.from_numpy(images).type(torch.FloatTensor)\n        # #\n\n        t = transform if not keep_size else ToTensor()\n\n        if isinstance(files[0], pathlib.PosixPath):\n            images = [t(Image.open(str(f))) for f in files[start:end]]\n\n        elif isinstance(files[0], Image.Image):\n            images = [t(f) for f in files[start:end]]\n\n        else:\n            raise ValueError(f\"Unknown data type for image: {type(files[0])}\")\n\n        batch = torch.stack(images)\n\n        if cuda:\n            batch = batch.cuda()\n\n        pred = model(batch)[0]\n\n        # If model output is not scalar, apply global spatial average pooling.\n        # This happens if you choose a dimensionality not equal 2048.\n        if pred.shape[2] != 1 or pred.shape[3] != 1:\n            pred = adaptive_avg_pool2d(pred, output_size=(1, 1))\n\n        pred_arr[start:end] = pred.cpu().data.numpy().reshape(batch_size, -1)\n\n    if verbose:\n        print(' done')\n\n    return pred_arr\n\n\ndef calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6):\n    \"\"\"Numpy implementation of the Frechet Distance.\n    The Frechet distance between two multivariate Gaussians X_1 ~ N(mu_1, C_1)\n    and X_2 ~ N(mu_2, C_2) is\n            d^2 = ||mu_1 - mu_2||^2 + Tr(C_1 + C_2 - 2*sqrt(C_1*C_2)).\n\n    Stable version by Dougal J. Sutherland.\n\n    Params:\n    -- mu1   : Numpy array containing the activations of a layer of the\n               inception net (like returned by the function 'get_predictions')\n               for generated samples.\n    -- mu2   : The sample mean over activations, precalculated on an\n               representative data set.\n    -- sigma1: The covariance matrix over activations for generated samples.\n    -- sigma2: The covariance matrix over activations, precalculated on an\n               representative data set.\n\n    Returns:\n    --   : The Frechet Distance.\n    \"\"\"\n\n    mu1 = np.atleast_1d(mu1)\n    mu2 = np.atleast_1d(mu2)\n\n    sigma1 = np.atleast_2d(sigma1)\n    sigma2 = np.atleast_2d(sigma2)\n\n    assert mu1.shape == mu2.shape, \\\n        'Training and test mean vectors have different lengths'\n    assert sigma1.shape == sigma2.shape, \\\n        'Training and test covariances have different dimensions'\n\n    diff = mu1 - mu2\n\n    # Product might be almost singular\n    covmean, _ = linalg.sqrtm(sigma1.dot(sigma2), disp=False)\n    if not np.isfinite(covmean).all():\n        msg = ('fid calculation produces singular product; '\n               'adding %s to diagonal of cov estimates') % eps\n        print(msg)\n        offset = np.eye(sigma1.shape[0]) * eps\n        covmean = linalg.sqrtm((sigma1 + offset).dot(sigma2 + offset))\n\n    # Numerical error might give slight imaginary component\n    if np.iscomplexobj(covmean):\n        # if not np.allclose(np.diagonal(covmean).imag, 0, atol=1e-3):\n        if not np.allclose(np.diagonal(covmean).imag, 0, atol=1e-2):\n            m = np.max(np.abs(covmean.imag))\n            raise ValueError('Imaginary component {}'.format(m))\n        covmean = covmean.real\n\n    tr_covmean = np.trace(covmean)\n\n    return (diff.dot(diff) + np.trace(sigma1) +\n            np.trace(sigma2) - 2 * tr_covmean)\n\n\ndef calculate_activation_statistics(files, model, batch_size=50,\n                                    dims=2048, cuda=False, verbose=False, keep_size=False):\n    \"\"\"Calculation of the statistics used by the FID.\n    Params:\n    -- files       : List of image files paths\n    -- model       : Instance of inception model\n    -- batch_size  : The images numpy array is split into batches with\n                     batch size batch_size. A reasonable batch size\n                     depends on the hardware.\n    -- dims        : Dimensionality of features returned by Inception\n    -- cuda        : If set to True, use GPU\n    -- verbose     : If set to True and parameter out_step is given, the\n                     number of calculated batches is reported.\n    Returns:\n    -- mu    : The mean over samples of the activations of the pool_3 layer of\n               the inception model.\n    -- sigma : The covariance matrix of the activations of the pool_3 layer of\n               the inception model.\n    \"\"\"\n    act = get_activations(files, model, batch_size, dims, cuda, verbose, keep_size=keep_size)\n    mu = np.mean(act, axis=0)\n    sigma = np.cov(act, rowvar=False)\n    return mu, sigma\n\n\ndef _compute_statistics_of_path(path, model, batch_size, dims, cuda):\n    if path.endswith('.npz'):\n        f = np.load(path)\n        m, s = f['mu'][:], f['sigma'][:]\n        f.close()\n    else:\n        path = pathlib.Path(path)\n        files = list(path.glob('*.jpg')) + list(path.glob('*.png'))\n        m, s = calculate_activation_statistics(files, model, batch_size,\n                                               dims, cuda)\n\n    return m, s\n\n\ndef _compute_statistics_of_images(images, model, batch_size, dims, cuda, keep_size=False):\n    if isinstance(images, list):  # exact paths to files are provided\n        m, s = calculate_activation_statistics(images, model, batch_size,\n                                               dims, cuda, keep_size=keep_size)\n\n        return m, s\n\n    else:\n        raise ValueError\n\n\ndef calculate_fid_given_paths(paths, batch_size, cuda, dims):\n    \"\"\"Calculates the FID of two paths\"\"\"\n    for p in paths:\n        if not os.path.exists(p):\n            raise RuntimeError('Invalid path: %s' % p)\n\n    block_idx = InceptionV3.BLOCK_INDEX_BY_DIM[dims]\n\n    model = InceptionV3([block_idx])\n    if cuda:\n        model.cuda()\n\n    m1, s1 = _compute_statistics_of_path(paths[0], model, batch_size,\n                                         dims, cuda)\n    m2, s2 = _compute_statistics_of_path(paths[1], model, batch_size,\n                                         dims, cuda)\n    fid_value = calculate_frechet_distance(m1, s1, m2, s2)\n\n    return fid_value\n\n\ndef calculate_fid_given_images(images, batch_size, cuda, dims, use_globals=False, keep_size=False):\n    if use_globals:\n        global FID_MODEL  # for multiprocessing\n\n    for imgs in images:\n        if isinstance(imgs, list) and isinstance(imgs[0], (Image.Image, JpegImagePlugin.JpegImageFile)):\n            pass\n        else:\n            raise RuntimeError('Invalid images')\n\n    block_idx = InceptionV3.BLOCK_INDEX_BY_DIM[dims]\n\n    if 'FID_MODEL' not in globals() or not use_globals:\n        model = InceptionV3([block_idx])\n        if cuda:\n            model.cuda()\n\n        if use_globals:\n            FID_MODEL = model\n\n    else:\n        model = FID_MODEL\n\n    m1, s1 = _compute_statistics_of_images(images[0], model, batch_size,\n                                        dims, cuda, keep_size=False)\n    m2, s2 = _compute_statistics_of_images(images[1], model, batch_size,\n                                        dims, cuda, keep_size=False)\n    fid_value = calculate_frechet_distance(m1, s1, m2, s2)\n    return fid_value\n\n\nif __name__ == '__main__':\n    args = parser.parse_args()\n    os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu\n\n    fid_value = calculate_fid_given_paths(args.path,\n                                          args.batch_size,\n                                          args.gpu != '',\n                                          args.dims)\n    print('FID: ', fid_value)\n"
  },
  {
    "path": "model_cards/lama/saicinpainting/evaluation/losses/fid/inception.py",
    "content": "import logging\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torchvision import models\n\ntry:\n    from torchvision.models.utils import load_state_dict_from_url\nexcept ImportError:\n    from torch.utils.model_zoo import load_url as load_state_dict_from_url\n\n# Inception weights ported to Pytorch from\n# http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz\nFID_WEIGHTS_URL = 'https://github.com/mseitzer/pytorch-fid/releases/download/fid_weights/pt_inception-2015-12-05-6726825d.pth'\n\n\nLOGGER = logging.getLogger(__name__)\n\n\nclass InceptionV3(nn.Module):\n    \"\"\"Pretrained InceptionV3 network returning feature maps\"\"\"\n\n    # Index of default block of inception to return,\n    # corresponds to output of final average pooling\n    DEFAULT_BLOCK_INDEX = 3\n\n    # Maps feature dimensionality to their output blocks indices\n    BLOCK_INDEX_BY_DIM = {\n        64: 0,   # First max pooling features\n        192: 1,  # Second max pooling featurs\n        768: 2,  # Pre-aux classifier features\n        2048: 3  # Final average pooling features\n    }\n\n    def __init__(self,\n                 output_blocks=[DEFAULT_BLOCK_INDEX],\n                 resize_input=True,\n                 normalize_input=True,\n                 requires_grad=False,\n                 use_fid_inception=True):\n        \"\"\"Build pretrained InceptionV3\n\n        Parameters\n        ----------\n        output_blocks : list of int\n            Indices of blocks to return features of. Possible values are:\n                - 0: corresponds to output of first max pooling\n                - 1: corresponds to output of second max pooling\n                - 2: corresponds to output which is fed to aux classifier\n                - 3: corresponds to output of final average pooling\n        resize_input : bool\n            If true, bilinearly resizes input to width and height 299 before\n            feeding input to model. As the network without fully connected\n            layers is fully convolutional, it should be able to handle inputs\n            of arbitrary size, so resizing might not be strictly needed\n        normalize_input : bool\n            If true, scales the input from range (0, 1) to the range the\n            pretrained Inception network expects, namely (-1, 1)\n        requires_grad : bool\n            If true, parameters of the model require gradients. Possibly useful\n            for finetuning the network\n        use_fid_inception : bool\n            If true, uses the pretrained Inception model used in Tensorflow's\n            FID implementation. If false, uses the pretrained Inception model\n            available in torchvision. The FID Inception model has different\n            weights and a slightly different structure from torchvision's\n            Inception model. If you want to compute FID scores, you are\n            strongly advised to set this parameter to true to get comparable\n            results.\n        \"\"\"\n        super(InceptionV3, self).__init__()\n\n        self.resize_input = resize_input\n        self.normalize_input = normalize_input\n        self.output_blocks = sorted(output_blocks)\n        self.last_needed_block = max(output_blocks)\n\n        assert self.last_needed_block <= 3, \\\n            'Last possible output block index is 3'\n\n        self.blocks = nn.ModuleList()\n\n        if use_fid_inception:\n            inception = fid_inception_v3()\n        else:\n            inception = models.inception_v3(pretrained=True)\n\n        # Block 0: input to maxpool1\n        block0 = [\n            inception.Conv2d_1a_3x3,\n            inception.Conv2d_2a_3x3,\n            inception.Conv2d_2b_3x3,\n            nn.MaxPool2d(kernel_size=3, stride=2)\n        ]\n        self.blocks.append(nn.Sequential(*block0))\n\n        # Block 1: maxpool1 to maxpool2\n        if self.last_needed_block >= 1:\n            block1 = [\n                inception.Conv2d_3b_1x1,\n                inception.Conv2d_4a_3x3,\n                nn.MaxPool2d(kernel_size=3, stride=2)\n            ]\n            self.blocks.append(nn.Sequential(*block1))\n\n        # Block 2: maxpool2 to aux classifier\n        if self.last_needed_block >= 2:\n            block2 = [\n                inception.Mixed_5b,\n                inception.Mixed_5c,\n                inception.Mixed_5d,\n                inception.Mixed_6a,\n                inception.Mixed_6b,\n                inception.Mixed_6c,\n                inception.Mixed_6d,\n                inception.Mixed_6e,\n            ]\n            self.blocks.append(nn.Sequential(*block2))\n\n        # Block 3: aux classifier to final avgpool\n        if self.last_needed_block >= 3:\n            block3 = [\n                inception.Mixed_7a,\n                inception.Mixed_7b,\n                inception.Mixed_7c,\n                nn.AdaptiveAvgPool2d(output_size=(1, 1))\n            ]\n            self.blocks.append(nn.Sequential(*block3))\n\n        for param in self.parameters():\n            param.requires_grad = requires_grad\n\n    def forward(self, inp):\n        \"\"\"Get Inception feature maps\n\n        Parameters\n        ----------\n        inp : torch.autograd.Variable\n            Input tensor of shape Bx3xHxW. Values are expected to be in\n            range (0, 1)\n\n        Returns\n        -------\n        List of torch.autograd.Variable, corresponding to the selected output\n        block, sorted ascending by index\n        \"\"\"\n        outp = []\n        x = inp\n\n        if self.resize_input:\n            x = F.interpolate(x,\n                              size=(299, 299),\n                              mode='bilinear',\n                              align_corners=False)\n\n        if self.normalize_input:\n            x = 2 * x - 1  # Scale from range (0, 1) to range (-1, 1)\n\n        for idx, block in enumerate(self.blocks):\n            x = block(x)\n            if idx in self.output_blocks:\n                outp.append(x)\n\n            if idx == self.last_needed_block:\n                break\n\n        return outp\n\n\ndef fid_inception_v3():\n    \"\"\"Build pretrained Inception model for FID computation\n\n    The Inception model for FID computation uses a different set of weights\n    and has a slightly different structure than torchvision's Inception.\n\n    This method first constructs torchvision's Inception and then patches the\n    necessary parts that are different in the FID Inception model.\n    \"\"\"\n    LOGGER.info('fid_inception_v3 called')\n    inception = models.inception_v3(num_classes=1008,\n                                    aux_logits=False,\n                                    pretrained=False)\n    LOGGER.info('models.inception_v3 done')\n    inception.Mixed_5b = FIDInceptionA(192, pool_features=32)\n    inception.Mixed_5c = FIDInceptionA(256, pool_features=64)\n    inception.Mixed_5d = FIDInceptionA(288, pool_features=64)\n    inception.Mixed_6b = FIDInceptionC(768, channels_7x7=128)\n    inception.Mixed_6c = FIDInceptionC(768, channels_7x7=160)\n    inception.Mixed_6d = FIDInceptionC(768, channels_7x7=160)\n    inception.Mixed_6e = FIDInceptionC(768, channels_7x7=192)\n    inception.Mixed_7b = FIDInceptionE_1(1280)\n    inception.Mixed_7c = FIDInceptionE_2(2048)\n\n    LOGGER.info('fid_inception_v3 patching done')\n\n    state_dict = load_state_dict_from_url(FID_WEIGHTS_URL, progress=True)\n    LOGGER.info('fid_inception_v3 weights downloaded')\n\n    inception.load_state_dict(state_dict)\n    LOGGER.info('fid_inception_v3 weights loaded into model')\n\n    return inception\n\n\nclass FIDInceptionA(models.inception.InceptionA):\n    \"\"\"InceptionA block patched for FID computation\"\"\"\n    def __init__(self, in_channels, pool_features):\n        super(FIDInceptionA, self).__init__(in_channels, pool_features)\n\n    def forward(self, x):\n        branch1x1 = self.branch1x1(x)\n\n        branch5x5 = self.branch5x5_1(x)\n        branch5x5 = self.branch5x5_2(branch5x5)\n\n        branch3x3dbl = self.branch3x3dbl_1(x)\n        branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)\n        branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl)\n\n        # Patch: Tensorflow's average pool does not use the padded zero's in\n        # its average calculation\n        branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1,\n                                   count_include_pad=False)\n        branch_pool = self.branch_pool(branch_pool)\n\n        outputs = [branch1x1, branch5x5, branch3x3dbl, branch_pool]\n        return torch.cat(outputs, 1)\n\n\nclass FIDInceptionC(models.inception.InceptionC):\n    \"\"\"InceptionC block patched for FID computation\"\"\"\n    def __init__(self, in_channels, channels_7x7):\n        super(FIDInceptionC, self).__init__(in_channels, channels_7x7)\n\n    def forward(self, x):\n        branch1x1 = self.branch1x1(x)\n\n        branch7x7 = self.branch7x7_1(x)\n        branch7x7 = self.branch7x7_2(branch7x7)\n        branch7x7 = self.branch7x7_3(branch7x7)\n\n        branch7x7dbl = self.branch7x7dbl_1(x)\n        branch7x7dbl = self.branch7x7dbl_2(branch7x7dbl)\n        branch7x7dbl = self.branch7x7dbl_3(branch7x7dbl)\n        branch7x7dbl = self.branch7x7dbl_4(branch7x7dbl)\n        branch7x7dbl = self.branch7x7dbl_5(branch7x7dbl)\n\n        # Patch: Tensorflow's average pool does not use the padded zero's in\n        # its average calculation\n        branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1,\n                                   count_include_pad=False)\n        branch_pool = self.branch_pool(branch_pool)\n\n        outputs = [branch1x1, branch7x7, branch7x7dbl, branch_pool]\n        return torch.cat(outputs, 1)\n\n\nclass FIDInceptionE_1(models.inception.InceptionE):\n    \"\"\"First InceptionE block patched for FID computation\"\"\"\n    def __init__(self, in_channels):\n        super(FIDInceptionE_1, self).__init__(in_channels)\n\n    def forward(self, x):\n        branch1x1 = self.branch1x1(x)\n\n        branch3x3 = self.branch3x3_1(x)\n        branch3x3 = [\n            self.branch3x3_2a(branch3x3),\n            self.branch3x3_2b(branch3x3),\n        ]\n        branch3x3 = torch.cat(branch3x3, 1)\n\n        branch3x3dbl = self.branch3x3dbl_1(x)\n        branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)\n        branch3x3dbl = [\n            self.branch3x3dbl_3a(branch3x3dbl),\n            self.branch3x3dbl_3b(branch3x3dbl),\n        ]\n        branch3x3dbl = torch.cat(branch3x3dbl, 1)\n\n        # Patch: Tensorflow's average pool does not use the padded zero's in\n        # its average calculation\n        branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1,\n                                   count_include_pad=False)\n        branch_pool = self.branch_pool(branch_pool)\n\n        outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool]\n        return torch.cat(outputs, 1)\n\n\nclass FIDInceptionE_2(models.inception.InceptionE):\n    \"\"\"Second InceptionE block patched for FID computation\"\"\"\n    def __init__(self, in_channels):\n        super(FIDInceptionE_2, self).__init__(in_channels)\n\n    def forward(self, x):\n        branch1x1 = self.branch1x1(x)\n\n        branch3x3 = self.branch3x3_1(x)\n        branch3x3 = [\n            self.branch3x3_2a(branch3x3),\n            self.branch3x3_2b(branch3x3),\n        ]\n        branch3x3 = torch.cat(branch3x3, 1)\n\n        branch3x3dbl = self.branch3x3dbl_1(x)\n        branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)\n        branch3x3dbl = [\n            self.branch3x3dbl_3a(branch3x3dbl),\n            self.branch3x3dbl_3b(branch3x3dbl),\n        ]\n        branch3x3dbl = torch.cat(branch3x3dbl, 1)\n\n        # Patch: The FID Inception model uses max pooling instead of average\n        # pooling. This is likely an error in this specific Inception\n        # implementation, as other Inception models use average pooling here\n        # (which matches the description in the paper).\n        branch_pool = F.max_pool2d(x, kernel_size=3, stride=1, padding=1)\n        branch_pool = self.branch_pool(branch_pool)\n\n        outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool]\n        return torch.cat(outputs, 1)\n"
  },
  {
    "path": "model_cards/lama/saicinpainting/evaluation/losses/lpips.py",
    "content": "############################################################\n# The contents below have been combined using files in the #\n# following repository:                                    #\n# https://github.com/richzhang/PerceptualSimilarity        #\n############################################################\n\n############################################################\n#                       __init__.py                        #\n############################################################\n\nimport numpy as np\nfrom skimage.metrics import structural_similarity\nimport torch\n\nfrom saicinpainting.utils import get_shape\n\n\nclass PerceptualLoss(torch.nn.Module):\n    def __init__(self, model='net-lin', net='alex', colorspace='rgb', model_path=None, spatial=False, use_gpu=True):\n        # VGG using our perceptually-learned weights (LPIPS metric)\n        # def __init__(self, model='net', net='vgg', use_gpu=True): # \"default\" way of using VGG as a perceptual loss\n        super(PerceptualLoss, self).__init__()\n        self.use_gpu = use_gpu\n        self.spatial = spatial\n        self.model = DistModel()\n        self.model.initialize(model=model, net=net, use_gpu=use_gpu, colorspace=colorspace,\n                              model_path=model_path, spatial=self.spatial)\n\n    def forward(self, pred, target, normalize=True):\n        \"\"\"\n        Pred and target are Variables.\n        If normalize is True, assumes the images are between [0,1] and then scales them between [-1,+1]\n        If normalize is False, assumes the images are already between [-1,+1]\n        Inputs pred and target are Nx3xHxW\n        Output pytorch Variable N long\n        \"\"\"\n\n        if normalize:\n            target = 2 * target - 1\n            pred = 2 * pred - 1\n\n        return self.model(target, pred)\n\n\ndef normalize_tensor(in_feat, eps=1e-10):\n    norm_factor = torch.sqrt(torch.sum(in_feat ** 2, dim=1, keepdim=True))\n    return in_feat / (norm_factor + eps)\n\n\ndef l2(p0, p1, range=255.):\n    return .5 * np.mean((p0 / range - p1 / range) ** 2)\n\n\ndef psnr(p0, p1, peak=255.):\n    return 10 * np.log10(peak ** 2 / np.mean((1. * p0 - 1. * p1) ** 2))\n\n\ndef dssim(p0, p1, range=255.):\n    return (1 - compare_ssim(p0, p1, data_range=range, multichannel=True)) / 2.\n\n\ndef rgb2lab(in_img, mean_cent=False):\n    from skimage import color\n    img_lab = color.rgb2lab(in_img)\n    if (mean_cent):\n        img_lab[:, :, 0] = img_lab[:, :, 0] - 50\n    return img_lab\n\n\ndef tensor2np(tensor_obj):\n    # change dimension of a tensor object into a numpy array\n    return tensor_obj[0].cpu().float().numpy().transpose((1, 2, 0))\n\n\ndef np2tensor(np_obj):\n    # change dimenion of np array into tensor array\n    return torch.Tensor(np_obj[:, :, :, np.newaxis].transpose((3, 2, 0, 1)))\n\n\ndef tensor2tensorlab(image_tensor, to_norm=True, mc_only=False):\n    # image tensor to lab tensor\n    from skimage import color\n\n    img = tensor2im(image_tensor)\n    img_lab = color.rgb2lab(img)\n    if (mc_only):\n        img_lab[:, :, 0] = img_lab[:, :, 0] - 50\n    if (to_norm and not mc_only):\n        img_lab[:, :, 0] = img_lab[:, :, 0] - 50\n        img_lab = img_lab / 100.\n\n    return np2tensor(img_lab)\n\n\ndef tensorlab2tensor(lab_tensor, return_inbnd=False):\n    from skimage import color\n    import warnings\n    warnings.filterwarnings(\"ignore\")\n\n    lab = tensor2np(lab_tensor) * 100.\n    lab[:, :, 0] = lab[:, :, 0] + 50\n\n    rgb_back = 255. * np.clip(color.lab2rgb(lab.astype('float')), 0, 1)\n    if (return_inbnd):\n        # convert back to lab, see if we match\n        lab_back = color.rgb2lab(rgb_back.astype('uint8'))\n        mask = 1. * np.isclose(lab_back, lab, atol=2.)\n        mask = np2tensor(np.prod(mask, axis=2)[:, :, np.newaxis])\n        return (im2tensor(rgb_back), mask)\n    else:\n        return im2tensor(rgb_back)\n\n\ndef rgb2lab(input):\n    from skimage import color\n    return color.rgb2lab(input / 255.)\n\n\ndef tensor2im(image_tensor, imtype=np.uint8, cent=1., factor=255. / 2.):\n    image_numpy = image_tensor[0].cpu().float().numpy()\n    image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + cent) * factor\n    return image_numpy.astype(imtype)\n\n\ndef im2tensor(image, imtype=np.uint8, cent=1., factor=255. / 2.):\n    return torch.Tensor((image / factor - cent)\n                        [:, :, :, np.newaxis].transpose((3, 2, 0, 1)))\n\n\ndef tensor2vec(vector_tensor):\n    return vector_tensor.data.cpu().numpy()[:, :, 0, 0]\n\n\ndef voc_ap(rec, prec, use_07_metric=False):\n    \"\"\" ap = voc_ap(rec, prec, [use_07_metric])\n    Compute VOC AP given precision and recall.\n    If use_07_metric is true, uses the\n    VOC 07 11 point method (default:False).\n    \"\"\"\n    if use_07_metric:\n        # 11 point metric\n        ap = 0.\n        for t in np.arange(0., 1.1, 0.1):\n            if np.sum(rec >= t) == 0:\n                p = 0\n            else:\n                p = np.max(prec[rec >= t])\n            ap = ap + p / 11.\n    else:\n        # correct AP calculation\n        # first append sentinel values at the end\n        mrec = np.concatenate(([0.], rec, [1.]))\n        mpre = np.concatenate(([0.], prec, [0.]))\n\n        # compute the precision envelope\n        for i in range(mpre.size - 1, 0, -1):\n            mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i])\n\n        # to calculate area under PR curve, look for points\n        # where X axis (recall) changes value\n        i = np.where(mrec[1:] != mrec[:-1])[0]\n\n        # and sum (\\Delta recall) * prec\n        ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])\n    return ap\n\n\ndef tensor2im(image_tensor, imtype=np.uint8, cent=1., factor=255. / 2.):\n    # def tensor2im(image_tensor, imtype=np.uint8, cent=1., factor=1.):\n    image_numpy = image_tensor[0].cpu().float().numpy()\n    image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + cent) * factor\n    return image_numpy.astype(imtype)\n\n\ndef im2tensor(image, imtype=np.uint8, cent=1., factor=255. / 2.):\n    # def im2tensor(image, imtype=np.uint8, cent=1., factor=1.):\n    return torch.Tensor((image / factor - cent)\n                        [:, :, :, np.newaxis].transpose((3, 2, 0, 1)))\n\n\n############################################################\n#                      base_model.py                       #\n############################################################\n\n\nclass BaseModel(torch.nn.Module):\n    def __init__(self):\n        super().__init__()\n\n    def name(self):\n        return 'BaseModel'\n\n    def initialize(self, use_gpu=True):\n        self.use_gpu = use_gpu\n\n    def forward(self):\n        pass\n\n    def get_image_paths(self):\n        pass\n\n    def optimize_parameters(self):\n        pass\n\n    def get_current_visuals(self):\n        return self.input\n\n    def get_current_errors(self):\n        return {}\n\n    def save(self, label):\n        pass\n\n    # helper saving function that can be used by subclasses\n    def save_network(self, network, path, network_label, epoch_label):\n        save_filename = '%s_net_%s.pth' % (epoch_label, network_label)\n        save_path = os.path.join(path, save_filename)\n        torch.save(network.state_dict(), save_path)\n\n    # helper loading function that can be used by subclasses\n    def load_network(self, network, network_label, epoch_label):\n        save_filename = '%s_net_%s.pth' % (epoch_label, network_label)\n        save_path = os.path.join(self.save_dir, save_filename)\n        print('Loading network from %s' % save_path)\n        network.load_state_dict(torch.load(save_path, map_location='cpu'))\n\n    def update_learning_rate():\n        pass\n\n    def get_image_paths(self):\n        return self.image_paths\n\n    def save_done(self, flag=False):\n        np.save(os.path.join(self.save_dir, 'done_flag'), flag)\n        np.savetxt(os.path.join(self.save_dir, 'done_flag'), [flag, ], fmt='%i')\n\n\n############################################################\n#                      dist_model.py                       #\n############################################################\n\nimport os\nfrom collections import OrderedDict\nfrom scipy.ndimage import zoom\nfrom tqdm import tqdm\n\n\nclass DistModel(BaseModel):\n    def name(self):\n        return self.model_name\n\n    def initialize(self, model='net-lin', net='alex', colorspace='Lab', pnet_rand=False, pnet_tune=False,\n                   model_path=None,\n                   use_gpu=True, printNet=False, spatial=False,\n                   is_train=False, lr=.0001, beta1=0.5, version='0.1'):\n        '''\n        INPUTS\n            model - ['net-lin'] for linearly calibrated network\n                    ['net'] for off-the-shelf network\n                    ['L2'] for L2 distance in Lab colorspace\n                    ['SSIM'] for ssim in RGB colorspace\n            net - ['squeeze','alex','vgg']\n            model_path - if None, will look in weights/[NET_NAME].pth\n            colorspace - ['Lab','RGB'] colorspace to use for L2 and SSIM\n            use_gpu - bool - whether or not to use a GPU\n            printNet - bool - whether or not to print network architecture out\n            spatial - bool - whether to output an array containing varying distances across spatial dimensions\n            spatial_shape - if given, output spatial shape. if None then spatial shape is determined automatically via spatial_factor (see below).\n            spatial_factor - if given, specifies upsampling factor relative to the largest spatial extent of a convolutional layer. if None then resized to size of input images.\n            spatial_order - spline order of filter for upsampling in spatial mode, by default 1 (bilinear).\n            is_train - bool - [True] for training mode\n            lr - float - initial learning rate\n            beta1 - float - initial momentum term for adam\n            version - 0.1 for latest, 0.0 was original (with a bug)\n        '''\n        BaseModel.initialize(self, use_gpu=use_gpu)\n\n        self.model = model\n        self.net = net\n        self.is_train = is_train\n        self.spatial = spatial\n        self.model_name = '%s [%s]' % (model, net)\n\n        if (self.model == 'net-lin'):  # pretrained net + linear layer\n            self.net = PNetLin(pnet_rand=pnet_rand, pnet_tune=pnet_tune, pnet_type=net,\n                               use_dropout=True, spatial=spatial, version=version, lpips=True)\n            kw = dict(map_location='cpu')\n            if (model_path is None):\n                import inspect\n                model_path = os.path.abspath(\n                    os.path.join(os.path.dirname(__file__), '..', '..', '..', 'models', 'lpips_models', f'{net}.pth'))\n\n            if (not is_train):\n                self.net.load_state_dict(torch.load(model_path, **kw), strict=False)\n\n        elif (self.model == 'net'):  # pretrained network\n            self.net = PNetLin(pnet_rand=pnet_rand, pnet_type=net, lpips=False)\n        elif (self.model in ['L2', 'l2']):\n            self.net = L2(use_gpu=use_gpu, colorspace=colorspace)  # not really a network, only for testing\n            self.model_name = 'L2'\n        elif (self.model in ['DSSIM', 'dssim', 'SSIM', 'ssim']):\n            self.net = DSSIM(use_gpu=use_gpu, colorspace=colorspace)\n            self.model_name = 'SSIM'\n        else:\n            raise ValueError(\"Model [%s] not recognized.\" % self.model)\n\n        self.trainable_parameters = list(self.net.parameters())\n\n        if self.is_train:  # training mode\n            # extra network on top to go from distances (d0,d1) => predicted human judgment (h*)\n            self.rankLoss = BCERankingLoss()\n            self.trainable_parameters += list(self.rankLoss.net.parameters())\n            self.lr = lr\n            self.old_lr = lr\n            self.optimizer_net = torch.optim.Adam(self.trainable_parameters, lr=lr, betas=(beta1, 0.999))\n        else:  # test mode\n            self.net.eval()\n\n        # if (use_gpu):\n            # self.net.to(gpu_ids[0])\n            # self.net = torch.nn.DataParallel(self.net, device_ids=gpu_ids)\n            # if (self.is_train):\n            #     self.rankLoss = self.rankLoss.to(device=gpu_ids[0])  # just put this on GPU0\n\n        if (printNet):\n            print('---------- Networks initialized -------------')\n            print_network(self.net)\n            print('-----------------------------------------------')\n\n    def forward(self, in0, in1, retPerLayer=False):\n        ''' Function computes the distance between image patches in0 and in1\n        INPUTS\n            in0, in1 - torch.Tensor object of shape Nx3xXxY - image patch scaled to [-1,1]\n        OUTPUT\n            computed distances between in0 and in1\n        '''\n\n        return self.net(in0, in1, retPerLayer=retPerLayer)\n\n    # ***** TRAINING FUNCTIONS *****\n    def optimize_parameters(self):\n        self.forward_train()\n        self.optimizer_net.zero_grad()\n        self.backward_train()\n        self.optimizer_net.step()\n        self.clamp_weights()\n\n    def clamp_weights(self):\n        for module in self.net.modules():\n            if (hasattr(module, 'weight') and module.kernel_size == (1, 1)):\n                module.weight.data = torch.clamp(module.weight.data, min=0)\n\n    def set_input(self, data):\n        self.input_ref = data['ref']\n        self.input_p0 = data['p0']\n        self.input_p1 = data['p1']\n        self.input_judge = data['judge']\n\n        # if (self.use_gpu):\n        #     self.input_ref = self.input_ref.to(device=self.gpu_ids[0])\n        #     self.input_p0 = self.input_p0.to(device=self.gpu_ids[0])\n        #     self.input_p1 = self.input_p1.to(device=self.gpu_ids[0])\n        #     self.input_judge = self.input_judge.to(device=self.gpu_ids[0])\n\n        # self.var_ref = Variable(self.input_ref, requires_grad=True)\n        # self.var_p0 = Variable(self.input_p0, requires_grad=True)\n        # self.var_p1 = Variable(self.input_p1, requires_grad=True)\n\n    def forward_train(self):  # run forward pass\n        # print(self.net.module.scaling_layer.shift)\n        # print(torch.norm(self.net.module.net.slice1[0].weight).item(), torch.norm(self.net.module.lin0.model[1].weight).item())\n\n        assert False, \"We shoud've not get here when using LPIPS as a metric\"\n\n        self.d0 = self(self.var_ref, self.var_p0)\n        self.d1 = self(self.var_ref, self.var_p1)\n        self.acc_r = self.compute_accuracy(self.d0, self.d1, self.input_judge)\n\n        self.var_judge = Variable(1. * self.input_judge).view(self.d0.size())\n\n        self.loss_total = self.rankLoss(self.d0, self.d1, self.var_judge * 2. - 1.)\n\n        return self.loss_total\n\n    def backward_train(self):\n        torch.mean(self.loss_total).backward()\n\n    def compute_accuracy(self, d0, d1, judge):\n        ''' d0, d1 are Variables, judge is a Tensor '''\n        d1_lt_d0 = (d1 < d0).cpu().data.numpy().flatten()\n        judge_per = judge.cpu().numpy().flatten()\n        return d1_lt_d0 * judge_per + (1 - d1_lt_d0) * (1 - judge_per)\n\n    def get_current_errors(self):\n        retDict = OrderedDict([('loss_total', self.loss_total.data.cpu().numpy()),\n                               ('acc_r', self.acc_r)])\n\n        for key in retDict.keys():\n            retDict[key] = np.mean(retDict[key])\n\n        return retDict\n\n    def get_current_visuals(self):\n        zoom_factor = 256 / self.var_ref.data.size()[2]\n\n        ref_img = tensor2im(self.var_ref.data)\n        p0_img = tensor2im(self.var_p0.data)\n        p1_img = tensor2im(self.var_p1.data)\n\n        ref_img_vis = zoom(ref_img, [zoom_factor, zoom_factor, 1], order=0)\n        p0_img_vis = zoom(p0_img, [zoom_factor, zoom_factor, 1], order=0)\n        p1_img_vis = zoom(p1_img, [zoom_factor, zoom_factor, 1], order=0)\n\n        return OrderedDict([('ref', ref_img_vis),\n                            ('p0', p0_img_vis),\n                            ('p1', p1_img_vis)])\n\n    def save(self, path, label):\n        if (self.use_gpu):\n            self.save_network(self.net.module, path, '', label)\n        else:\n            self.save_network(self.net, path, '', label)\n        self.save_network(self.rankLoss.net, path, 'rank', label)\n\n    def update_learning_rate(self, nepoch_decay):\n        lrd = self.lr / nepoch_decay\n        lr = self.old_lr - lrd\n\n        for param_group in self.optimizer_net.param_groups:\n            param_group['lr'] = lr\n\n        print('update lr [%s] decay: %f -> %f' % (type, self.old_lr, lr))\n        self.old_lr = lr\n\n\ndef score_2afc_dataset(data_loader, func, name=''):\n    ''' Function computes Two Alternative Forced Choice (2AFC) score using\n        distance function 'func' in dataset 'data_loader'\n    INPUTS\n        data_loader - CustomDatasetDataLoader object - contains a TwoAFCDataset inside\n        func - callable distance function - calling d=func(in0,in1) should take 2\n            pytorch tensors with shape Nx3xXxY, and return numpy array of length N\n    OUTPUTS\n        [0] - 2AFC score in [0,1], fraction of time func agrees with human evaluators\n        [1] - dictionary with following elements\n            d0s,d1s - N arrays containing distances between reference patch to perturbed patches\n            gts - N array in [0,1], preferred patch selected by human evaluators\n                (closer to \"0\" for left patch p0, \"1\" for right patch p1,\n                \"0.6\" means 60pct people preferred right patch, 40pct preferred left)\n            scores - N array in [0,1], corresponding to what percentage function agreed with humans\n    CONSTS\n        N - number of test triplets in data_loader\n    '''\n\n    d0s = []\n    d1s = []\n    gts = []\n\n    for data in tqdm(data_loader.load_data(), desc=name):\n        d0s += func(data['ref'], data['p0']).data.cpu().numpy().flatten().tolist()\n        d1s += func(data['ref'], data['p1']).data.cpu().numpy().flatten().tolist()\n        gts += data['judge'].cpu().numpy().flatten().tolist()\n\n    d0s = np.array(d0s)\n    d1s = np.array(d1s)\n    gts = np.array(gts)\n    scores = (d0s < d1s) * (1. - gts) + (d1s < d0s) * gts + (d1s == d0s) * .5\n\n    return (np.mean(scores), dict(d0s=d0s, d1s=d1s, gts=gts, scores=scores))\n\n\ndef score_jnd_dataset(data_loader, func, name=''):\n    ''' Function computes JND score using distance function 'func' in dataset 'data_loader'\n    INPUTS\n        data_loader - CustomDatasetDataLoader object - contains a JNDDataset inside\n        func - callable distance function - calling d=func(in0,in1) should take 2\n            pytorch tensors with shape Nx3xXxY, and return pytorch array of length N\n    OUTPUTS\n        [0] - JND score in [0,1], mAP score (area under precision-recall curve)\n        [1] - dictionary with following elements\n            ds - N array containing distances between two patches shown to human evaluator\n            sames - N array containing fraction of people who thought the two patches were identical\n    CONSTS\n        N - number of test triplets in data_loader\n    '''\n\n    ds = []\n    gts = []\n\n    for data in tqdm(data_loader.load_data(), desc=name):\n        ds += func(data['p0'], data['p1']).data.cpu().numpy().tolist()\n        gts += data['same'].cpu().numpy().flatten().tolist()\n\n    sames = np.array(gts)\n    ds = np.array(ds)\n\n    sorted_inds = np.argsort(ds)\n    ds_sorted = ds[sorted_inds]\n    sames_sorted = sames[sorted_inds]\n\n    TPs = np.cumsum(sames_sorted)\n    FPs = np.cumsum(1 - sames_sorted)\n    FNs = np.sum(sames_sorted) - TPs\n\n    precs = TPs / (TPs + FPs)\n    recs = TPs / (TPs + FNs)\n    score = voc_ap(recs, precs)\n\n    return (score, dict(ds=ds, sames=sames))\n\n\n############################################################\n#                    networks_basic.py                     #\n############################################################\n\nimport torch.nn as nn\nfrom torch.autograd import Variable\nimport numpy as np\n\n\ndef spatial_average(in_tens, keepdim=True):\n    return in_tens.mean([2, 3], keepdim=keepdim)\n\n\ndef upsample(in_tens, out_H=64):  # assumes scale factor is same for H and W\n    in_H = in_tens.shape[2]\n    scale_factor = 1. * out_H / in_H\n\n    return nn.Upsample(scale_factor=scale_factor, mode='bilinear', align_corners=False)(in_tens)\n\n\n# Learned perceptual metric\nclass PNetLin(nn.Module):\n    def __init__(self, pnet_type='vgg', pnet_rand=False, pnet_tune=False, use_dropout=True, spatial=False,\n                 version='0.1', lpips=True):\n        super(PNetLin, self).__init__()\n\n        self.pnet_type = pnet_type\n        self.pnet_tune = pnet_tune\n        self.pnet_rand = pnet_rand\n        self.spatial = spatial\n        self.lpips = lpips\n        self.version = version\n        self.scaling_layer = ScalingLayer()\n\n        if (self.pnet_type in ['vgg', 'vgg16']):\n            net_type = vgg16\n            self.chns = [64, 128, 256, 512, 512]\n        elif (self.pnet_type == 'alex'):\n            net_type = alexnet\n            self.chns = [64, 192, 384, 256, 256]\n        elif (self.pnet_type == 'squeeze'):\n            net_type = squeezenet\n            self.chns = [64, 128, 256, 384, 384, 512, 512]\n        self.L = len(self.chns)\n\n        self.net = net_type(pretrained=not self.pnet_rand, requires_grad=self.pnet_tune)\n\n        if (lpips):\n            self.lin0 = NetLinLayer(self.chns[0], use_dropout=use_dropout)\n            self.lin1 = NetLinLayer(self.chns[1], use_dropout=use_dropout)\n            self.lin2 = NetLinLayer(self.chns[2], use_dropout=use_dropout)\n            self.lin3 = NetLinLayer(self.chns[3], use_dropout=use_dropout)\n            self.lin4 = NetLinLayer(self.chns[4], use_dropout=use_dropout)\n            self.lins = [self.lin0, self.lin1, self.lin2, self.lin3, self.lin4]\n            if (self.pnet_type == 'squeeze'):  # 7 layers for squeezenet\n                self.lin5 = NetLinLayer(self.chns[5], use_dropout=use_dropout)\n                self.lin6 = NetLinLayer(self.chns[6], use_dropout=use_dropout)\n                self.lins += [self.lin5, self.lin6]\n\n    def forward(self, in0, in1, retPerLayer=False):\n        # v0.0 - original release had a bug, where input was not scaled\n        in0_input, in1_input = (self.scaling_layer(in0), self.scaling_layer(in1)) if self.version == '0.1' else (\n        in0, in1)\n        outs0, outs1 = self.net(in0_input), self.net(in1_input)\n        feats0, feats1, diffs = {}, {}, {}\n\n        for kk in range(self.L):\n            feats0[kk], feats1[kk] = normalize_tensor(outs0[kk]), normalize_tensor(outs1[kk])\n            diffs[kk] = (feats0[kk] - feats1[kk]) ** 2\n\n        if (self.lpips):\n            if (self.spatial):\n                res = [upsample(self.lins[kk].model(diffs[kk]), out_H=in0.shape[2]) for kk in range(self.L)]\n            else:\n                res = [spatial_average(self.lins[kk].model(diffs[kk]), keepdim=True) for kk in range(self.L)]\n        else:\n            if (self.spatial):\n                res = [upsample(diffs[kk].sum(dim=1, keepdim=True), out_H=in0.shape[2]) for kk in range(self.L)]\n            else:\n                res = [spatial_average(diffs[kk].sum(dim=1, keepdim=True), keepdim=True) for kk in range(self.L)]\n\n        val = res[0]\n        for l in range(1, self.L):\n            val += res[l]\n\n        if (retPerLayer):\n            return (val, res)\n        else:\n            return val\n\n\nclass ScalingLayer(nn.Module):\n    def __init__(self):\n        super(ScalingLayer, self).__init__()\n        self.register_buffer('shift', torch.Tensor([-.030, -.088, -.188])[None, :, None, None])\n        self.register_buffer('scale', torch.Tensor([.458, .448, .450])[None, :, None, None])\n\n    def forward(self, inp):\n        return (inp - self.shift) / self.scale\n\n\nclass NetLinLayer(nn.Module):\n    ''' A single linear layer which does a 1x1 conv '''\n\n    def __init__(self, chn_in, chn_out=1, use_dropout=False):\n        super(NetLinLayer, self).__init__()\n\n        layers = [nn.Dropout(), ] if (use_dropout) else []\n        layers += [nn.Conv2d(chn_in, chn_out, 1, stride=1, padding=0, bias=False), ]\n        self.model = nn.Sequential(*layers)\n\n\nclass Dist2LogitLayer(nn.Module):\n    ''' takes 2 distances, puts through fc layers, spits out value between [0,1] (if use_sigmoid is True) '''\n\n    def __init__(self, chn_mid=32, use_sigmoid=True):\n        super(Dist2LogitLayer, self).__init__()\n\n        layers = [nn.Conv2d(5, chn_mid, 1, stride=1, padding=0, bias=True), ]\n        layers += [nn.LeakyReLU(0.2, True), ]\n        layers += [nn.Conv2d(chn_mid, chn_mid, 1, stride=1, padding=0, bias=True), ]\n        layers += [nn.LeakyReLU(0.2, True), ]\n        layers += [nn.Conv2d(chn_mid, 1, 1, stride=1, padding=0, bias=True), ]\n        if (use_sigmoid):\n            layers += [nn.Sigmoid(), ]\n        self.model = nn.Sequential(*layers)\n\n    def forward(self, d0, d1, eps=0.1):\n        return self.model(torch.cat((d0, d1, d0 - d1, d0 / (d1 + eps), d1 / (d0 + eps)), dim=1))\n\n\nclass BCERankingLoss(nn.Module):\n    def __init__(self, chn_mid=32):\n        super(BCERankingLoss, self).__init__()\n        self.net = Dist2LogitLayer(chn_mid=chn_mid)\n        # self.parameters = list(self.net.parameters())\n        self.loss = torch.nn.BCELoss()\n\n    def forward(self, d0, d1, judge):\n        per = (judge + 1.) / 2.\n        self.logit = self.net(d0, d1)\n        return self.loss(self.logit, per)\n\n\n# L2, DSSIM metrics\nclass FakeNet(nn.Module):\n    def __init__(self, use_gpu=True, colorspace='Lab'):\n        super(FakeNet, self).__init__()\n        self.use_gpu = use_gpu\n        self.colorspace = colorspace\n\n\nclass L2(FakeNet):\n\n    def forward(self, in0, in1, retPerLayer=None):\n        assert (in0.size()[0] == 1)  # currently only supports batchSize 1\n\n        if (self.colorspace == 'RGB'):\n            (N, C, X, Y) = in0.size()\n            value = torch.mean(torch.mean(torch.mean((in0 - in1) ** 2, dim=1).view(N, 1, X, Y), dim=2).view(N, 1, 1, Y),\n                               dim=3).view(N)\n            return value\n        elif (self.colorspace == 'Lab'):\n            value = l2(tensor2np(tensor2tensorlab(in0.data, to_norm=False)),\n                       tensor2np(tensor2tensorlab(in1.data, to_norm=False)), range=100.).astype('float')\n            ret_var = Variable(torch.Tensor((value,)))\n            # if (self.use_gpu):\n            #     ret_var = ret_var.cuda()\n            return ret_var\n\n\nclass DSSIM(FakeNet):\n\n    def forward(self, in0, in1, retPerLayer=None):\n        assert (in0.size()[0] == 1)  # currently only supports batchSize 1\n\n        if (self.colorspace == 'RGB'):\n            value = dssim(1. * tensor2im(in0.data), 1. * tensor2im(in1.data), range=255.).astype('float')\n        elif (self.colorspace == 'Lab'):\n            value = dssim(tensor2np(tensor2tensorlab(in0.data, to_norm=False)),\n                          tensor2np(tensor2tensorlab(in1.data, to_norm=False)), range=100.).astype('float')\n        ret_var = Variable(torch.Tensor((value,)))\n        # if (self.use_gpu):\n        #     ret_var = ret_var.cuda()\n        return ret_var\n\n\ndef print_network(net):\n    num_params = 0\n    for param in net.parameters():\n        num_params += param.numel()\n    print('Network', net)\n    print('Total number of parameters: %d' % num_params)\n\n\n############################################################\n#                 pretrained_networks.py                   #\n############################################################\n\nfrom collections import namedtuple\nimport torch\nfrom torchvision import models as tv\n\n\nclass squeezenet(torch.nn.Module):\n    def __init__(self, requires_grad=False, pretrained=True):\n        super(squeezenet, self).__init__()\n        pretrained_features = tv.squeezenet1_1(pretrained=pretrained).features\n        self.slice1 = torch.nn.Sequential()\n        self.slice2 = torch.nn.Sequential()\n        self.slice3 = torch.nn.Sequential()\n        self.slice4 = torch.nn.Sequential()\n        self.slice5 = torch.nn.Sequential()\n        self.slice6 = torch.nn.Sequential()\n        self.slice7 = torch.nn.Sequential()\n        self.N_slices = 7\n        for x in range(2):\n            self.slice1.add_module(str(x), pretrained_features[x])\n        for x in range(2, 5):\n            self.slice2.add_module(str(x), pretrained_features[x])\n        for x in range(5, 8):\n            self.slice3.add_module(str(x), pretrained_features[x])\n        for x in range(8, 10):\n            self.slice4.add_module(str(x), pretrained_features[x])\n        for x in range(10, 11):\n            self.slice5.add_module(str(x), pretrained_features[x])\n        for x in range(11, 12):\n            self.slice6.add_module(str(x), pretrained_features[x])\n        for x in range(12, 13):\n            self.slice7.add_module(str(x), pretrained_features[x])\n        if not requires_grad:\n            for param in self.parameters():\n                param.requires_grad = False\n\n    def forward(self, X):\n        h = self.slice1(X)\n        h_relu1 = h\n        h = self.slice2(h)\n        h_relu2 = h\n        h = self.slice3(h)\n        h_relu3 = h\n        h = self.slice4(h)\n        h_relu4 = h\n        h = self.slice5(h)\n        h_relu5 = h\n        h = self.slice6(h)\n        h_relu6 = h\n        h = self.slice7(h)\n        h_relu7 = h\n        vgg_outputs = namedtuple(\"SqueezeOutputs\", ['relu1', 'relu2', 'relu3', 'relu4', 'relu5', 'relu6', 'relu7'])\n        out = vgg_outputs(h_relu1, h_relu2, h_relu3, h_relu4, h_relu5, h_relu6, h_relu7)\n\n        return out\n\n\nclass alexnet(torch.nn.Module):\n    def __init__(self, requires_grad=False, pretrained=True):\n        super(alexnet, self).__init__()\n        alexnet_pretrained_features = tv.alexnet(pretrained=pretrained).features\n        self.slice1 = torch.nn.Sequential()\n        self.slice2 = torch.nn.Sequential()\n        self.slice3 = torch.nn.Sequential()\n        self.slice4 = torch.nn.Sequential()\n        self.slice5 = torch.nn.Sequential()\n        self.N_slices = 5\n        for x in range(2):\n            self.slice1.add_module(str(x), alexnet_pretrained_features[x])\n        for x in range(2, 5):\n            self.slice2.add_module(str(x), alexnet_pretrained_features[x])\n        for x in range(5, 8):\n            self.slice3.add_module(str(x), alexnet_pretrained_features[x])\n        for x in range(8, 10):\n            self.slice4.add_module(str(x), alexnet_pretrained_features[x])\n        for x in range(10, 12):\n            self.slice5.add_module(str(x), alexnet_pretrained_features[x])\n        if not requires_grad:\n            for param in self.parameters():\n                param.requires_grad = False\n\n    def forward(self, X):\n        h = self.slice1(X)\n        h_relu1 = h\n        h = self.slice2(h)\n        h_relu2 = h\n        h = self.slice3(h)\n        h_relu3 = h\n        h = self.slice4(h)\n        h_relu4 = h\n        h = self.slice5(h)\n        h_relu5 = h\n        alexnet_outputs = namedtuple(\"AlexnetOutputs\", ['relu1', 'relu2', 'relu3', 'relu4', 'relu5'])\n        out = alexnet_outputs(h_relu1, h_relu2, h_relu3, h_relu4, h_relu5)\n\n        return out\n\n\nclass vgg16(torch.nn.Module):\n    def __init__(self, requires_grad=False, pretrained=True):\n        super(vgg16, self).__init__()\n        vgg_pretrained_features = tv.vgg16(pretrained=pretrained).features\n        self.slice1 = torch.nn.Sequential()\n        self.slice2 = torch.nn.Sequential()\n        self.slice3 = torch.nn.Sequential()\n        self.slice4 = torch.nn.Sequential()\n        self.slice5 = torch.nn.Sequential()\n        self.N_slices = 5\n        for x in range(4):\n            self.slice1.add_module(str(x), vgg_pretrained_features[x])\n        for x in range(4, 9):\n            self.slice2.add_module(str(x), vgg_pretrained_features[x])\n        for x in range(9, 16):\n            self.slice3.add_module(str(x), vgg_pretrained_features[x])\n        for x in range(16, 23):\n            self.slice4.add_module(str(x), vgg_pretrained_features[x])\n        for x in range(23, 30):\n            self.slice5.add_module(str(x), vgg_pretrained_features[x])\n        if not requires_grad:\n            for param in self.parameters():\n                param.requires_grad = False\n\n    def forward(self, X):\n        h = self.slice1(X)\n        h_relu1_2 = h\n        h = self.slice2(h)\n        h_relu2_2 = h\n        h = self.slice3(h)\n        h_relu3_3 = h\n        h = self.slice4(h)\n        h_relu4_3 = h\n        h = self.slice5(h)\n        h_relu5_3 = h\n        vgg_outputs = namedtuple(\"VggOutputs\", ['relu1_2', 'relu2_2', 'relu3_3', 'relu4_3', 'relu5_3'])\n        out = vgg_outputs(h_relu1_2, h_relu2_2, h_relu3_3, h_relu4_3, h_relu5_3)\n\n        return out\n\n\nclass resnet(torch.nn.Module):\n    def __init__(self, requires_grad=False, pretrained=True, num=18):\n        super(resnet, self).__init__()\n        if (num == 18):\n            self.net = tv.resnet18(pretrained=pretrained)\n        elif (num == 34):\n            self.net = tv.resnet34(pretrained=pretrained)\n        elif (num == 50):\n            self.net = tv.resnet50(pretrained=pretrained)\n        elif (num == 101):\n            self.net = tv.resnet101(pretrained=pretrained)\n        elif (num == 152):\n            self.net = tv.resnet152(pretrained=pretrained)\n        self.N_slices = 5\n\n        self.conv1 = self.net.conv1\n        self.bn1 = self.net.bn1\n        self.relu = self.net.relu\n        self.maxpool = self.net.maxpool\n        self.layer1 = self.net.layer1\n        self.layer2 = self.net.layer2\n        self.layer3 = self.net.layer3\n        self.layer4 = self.net.layer4\n\n    def forward(self, X):\n        h = self.conv1(X)\n        h = self.bn1(h)\n        h = self.relu(h)\n        h_relu1 = h\n        h = self.maxpool(h)\n        h = self.layer1(h)\n        h_conv2 = h\n        h = self.layer2(h)\n        h_conv3 = h\n        h = self.layer3(h)\n        h_conv4 = h\n        h = self.layer4(h)\n        h_conv5 = h\n\n        outputs = namedtuple(\"Outputs\", ['relu1', 'conv2', 'conv3', 'conv4', 'conv5'])\n        out = outputs(h_relu1, h_conv2, h_conv3, h_conv4, h_conv5)\n\n        return out\n"
  },
  {
    "path": "model_cards/lama/saicinpainting/evaluation/losses/ssim.py",
    "content": "import numpy as np\nimport torch\nimport torch.nn.functional as F\n\n\nclass SSIM(torch.nn.Module):\n    \"\"\"SSIM. Modified from:\n    https://github.com/Po-Hsun-Su/pytorch-ssim/blob/master/pytorch_ssim/__init__.py\n    \"\"\"\n\n    def __init__(self, window_size=11, size_average=True):\n        super().__init__()\n        self.window_size = window_size\n        self.size_average = size_average\n        self.channel = 1\n        self.register_buffer('window', self._create_window(window_size, self.channel))\n\n    def forward(self, img1, img2):\n        assert len(img1.shape) == 4\n\n        channel = img1.size()[1]\n\n        if channel == self.channel and self.window.data.type() == img1.data.type():\n            window = self.window\n        else:\n            window = self._create_window(self.window_size, channel)\n\n            # window = window.to(img1.get_device())\n            window = window.type_as(img1)\n\n            self.window = window\n            self.channel = channel\n\n        return self._ssim(img1, img2, window, self.window_size, channel, self.size_average)\n\n    def _gaussian(self, window_size, sigma):\n        gauss = torch.Tensor([\n            np.exp(-(x - (window_size // 2)) ** 2 / float(2 * sigma ** 2)) for x in range(window_size)\n        ])\n        return gauss / gauss.sum()\n\n    def _create_window(self, window_size, channel):\n        _1D_window = self._gaussian(window_size, 1.5).unsqueeze(1)\n        _2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0)\n        return _2D_window.expand(channel, 1, window_size, window_size).contiguous()\n\n    def _ssim(self, img1, img2, window, window_size, channel, size_average=True):\n        mu1 = F.conv2d(img1, window, padding=(window_size // 2), groups=channel)\n        mu2 = F.conv2d(img2, window, padding=(window_size // 2), groups=channel)\n\n        mu1_sq = mu1.pow(2)\n        mu2_sq = mu2.pow(2)\n        mu1_mu2 = mu1 * mu2\n\n        sigma1_sq = F.conv2d(\n            img1 * img1, window, padding=(window_size // 2), groups=channel) - mu1_sq\n        sigma2_sq = F.conv2d(\n            img2 * img2, window, padding=(window_size // 2), groups=channel) - mu2_sq\n        sigma12 = F.conv2d(\n            img1 * img2, window, padding=(window_size // 2), groups=channel) - mu1_mu2\n\n        C1 = 0.01 ** 2\n        C2 = 0.03 ** 2\n\n        ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / \\\n                   ((mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2))\n\n        if size_average:\n            return ssim_map.mean()\n\n        return ssim_map.mean(1).mean(1).mean(1)\n\n    def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs):\n        return\n"
  },
  {
    "path": "model_cards/lama/saicinpainting/evaluation/masks/README.md",
    "content": "# Current algorithm\n\n## Choice of mask objects\n\nFor identification of the objects which are suitable for mask obtaining, panoptic segmentation model\nfrom [detectron2](https://github.com/facebookresearch/detectron2) trained on COCO. Categories of the detected instances\nbelong either to \"stuff\" or \"things\" types. We consider that instances of objects should have category belong\nto \"things\". Besides, we set upper bound on area which is taken by the object &mdash; we consider that too big\narea indicates either of the instance being a background or a main object which should not be removed. \n\n## Choice of position for mask\n\nWe consider that input image has size 2^n x 2^m. We downsample it using \n[COUNTLESS](https://github.com/william-silversmith/countless) algorithm so the width is equal to\n64 = 2^8 = 2^{downsample_levels}. \n\n### Augmentation\n\nThere are several parameters for augmentation:\n- Scaling factor. We limit scaling to the case when a mask after scaling with pivot point in its center fits inside the\n image completely.\n-\n\n### Shift \n\n\n## Select \n"
  },
  {
    "path": "model_cards/lama/saicinpainting/evaluation/masks/__init__.py",
    "content": ""
  },
  {
    "path": "model_cards/lama/saicinpainting/evaluation/masks/countless/.gitignore",
    "content": "results"
  },
  {
    "path": "model_cards/lama/saicinpainting/evaluation/masks/countless/README.md",
    "content": "[![Build Status](https://travis-ci.org/william-silversmith/countless.svg?branch=master)](https://travis-ci.org/william-silversmith/countless)\n\nPython COUNTLESS Downsampling\n=============================\n\nTo install:  \n\n`pip install -r requirements.txt`  \n\nTo test:\n\n`python test.py`  \n\nTo benchmark countless2d:\n\n`python python/countless2d.py python/images/gray_segmentation.png`\n\nTo benchmark countless3d:\n\n`python python/countless3d.py`\n\nAdjust N and the list of algorithms inside each script to modify the run parameters.\n\n\nPython3 is slightly faster than Python2."
  },
  {
    "path": "model_cards/lama/saicinpainting/evaluation/masks/countless/__init__.py",
    "content": ""
  },
  {
    "path": "model_cards/lama/saicinpainting/evaluation/masks/countless/countless2d.py",
    "content": "from __future__ import print_function, division\n\n\"\"\"\nCOUNTLESS performance test in Python.\n\npython countless2d.py ./images/NAMEOFIMAGE\n\"\"\"\n\nimport six\nfrom six.moves import range\nfrom collections import defaultdict\nfrom functools import reduce\nimport operator \nimport io\nimport os\nfrom PIL import Image\nimport math\nimport numpy as np\nimport random\nimport sys\nimport time\nfrom tqdm import tqdm\nfrom scipy import ndimage\n\ndef simplest_countless(data):\n  \"\"\"\n  Vectorized implementation of downsampling a 2D \n  image by 2 on each side using the COUNTLESS algorithm.\n  \n  data is a 2D numpy array with even dimensions.\n  \"\"\"\n  sections = []\n  \n  # This loop splits the 2D array apart into four arrays that are\n  # all the result of striding by 2 and offset by (0,0), (0,1), (1,0), \n  # and (1,1) representing the A, B, C, and D positions from Figure 1.\n  factor = (2,2)\n  for offset in np.ndindex(factor):\n    part = data[tuple(np.s_[o::f] for o, f in zip(offset, factor))]\n    sections.append(part)\n\n  a, b, c, d = sections\n\n  ab = a * (a == b) # PICK(A,B)\n  ac = a * (a == c) # PICK(A,C)\n  bc = b * (b == c) # PICK(B,C)\n\n  a = ab | ac | bc # Bitwise OR, safe b/c non-matches are zeroed\n  \n  return a + (a == 0) * d # AB || AC || BC || D\n\ndef quick_countless(data):\n  \"\"\"\n  Vectorized implementation of downsampling a 2D \n  image by 2 on each side using the COUNTLESS algorithm.\n  \n  data is a 2D numpy array with even dimensions.\n  \"\"\"\n  sections = []\n  \n  # This loop splits the 2D array apart into four arrays that are\n  # all the result of striding by 2 and offset by (0,0), (0,1), (1,0), \n  # and (1,1) representing the A, B, C, and D positions from Figure 1.\n  factor = (2,2)\n  for offset in np.ndindex(factor):\n    part = data[tuple(np.s_[o::f] for o, f in zip(offset, factor))]\n    sections.append(part)\n\n  a, b, c, d = sections\n\n  ab_ac = a * ((a == b) | (a == c)) # PICK(A,B) || PICK(A,C) w/ optimization\n  bc = b * (b == c) # PICK(B,C)\n\n  a = ab_ac | bc # (PICK(A,B) || PICK(A,C)) or PICK(B,C)\n  return a + (a == 0) * d # AB || AC || BC || D\n\ndef quickest_countless(data):\n  \"\"\"\n  Vectorized implementation of downsampling a 2D \n  image by 2 on each side using the COUNTLESS algorithm.\n  \n  data is a 2D numpy array with even dimensions.\n  \"\"\"\n  sections = []\n  \n  # This loop splits the 2D array apart into four arrays that are\n  # all the result of striding by 2 and offset by (0,0), (0,1), (1,0), \n  # and (1,1) representing the A, B, C, and D positions from Figure 1.\n  factor = (2,2)\n  for offset in np.ndindex(factor):\n    part = data[tuple(np.s_[o::f] for o, f in zip(offset, factor))]\n    sections.append(part)\n\n  a, b, c, d = sections\n\n  ab_ac = a * ((a == b) | (a == c)) # PICK(A,B) || PICK(A,C) w/ optimization\n  ab_ac |= b * (b == c) # PICK(B,C)\n  return ab_ac + (ab_ac == 0) * d # AB || AC || BC || D\n\ndef quick_countless_xor(data):\n  \"\"\"\n  Vectorized implementation of downsampling a 2D \n  image by 2 on each side using the COUNTLESS algorithm.\n  \n  data is a 2D numpy array with even dimensions.\n  \"\"\"\n  sections = []\n  \n  # This loop splits the 2D array apart into four arrays that are\n  # all the result of striding by 2 and offset by (0,0), (0,1), (1,0), \n  # and (1,1) representing the A, B, C, and D positions from Figure 1.\n  factor = (2,2)\n  for offset in np.ndindex(factor):\n    part = data[tuple(np.s_[o::f] for o, f in zip(offset, factor))]\n    sections.append(part)\n\n  a, b, c, d = sections\n\n  ab = a ^ (a ^ b) # a or b\n  ab += (ab != a) * ((ab ^ (ab ^ c)) - b) # b or c\n  ab += (ab == c) * ((ab ^ (ab ^ d)) - c) # c or d\n  return ab\n\ndef stippled_countless(data):\n  \"\"\"\n  Vectorized implementation of downsampling a 2D \n  image by 2 on each side using the COUNTLESS algorithm\n  that treats zero as \"background\" and inflates lone\n  pixels.\n  \n  data is a 2D numpy array with even dimensions.\n  \"\"\"\n  sections = []\n  \n  # This loop splits the 2D array apart into four arrays that are\n  # all the result of striding by 2 and offset by (0,0), (0,1), (1,0), \n  # and (1,1) representing the A, B, C, and D positions from Figure 1.\n  factor = (2,2)\n  for offset in np.ndindex(factor):\n    part = data[tuple(np.s_[o::f] for o, f in zip(offset, factor))]\n    sections.append(part)\n\n  a, b, c, d = sections\n\n  ab_ac = a * ((a == b) | (a == c)) # PICK(A,B) || PICK(A,C) w/ optimization\n  ab_ac |= b * (b == c) # PICK(B,C)\n\n  nonzero = a + (a == 0) * (b + (b == 0) * c)\n  return ab_ac + (ab_ac == 0) * (d + (d == 0) * nonzero) # AB || AC || BC || D\n\ndef zero_corrected_countless(data):\n  \"\"\"\n  Vectorized implementation of downsampling a 2D \n  image by 2 on each side using the COUNTLESS algorithm.\n  \n  data is a 2D numpy array with even dimensions.\n  \"\"\"\n  # allows us to prevent losing 1/2 a bit of information \n  # at the top end by using a bigger type. Without this 255 is handled incorrectly.\n  data, upgraded = upgrade_type(data) \n\n  # offset from zero, raw countless doesn't handle 0 correctly\n  # we'll remove the extra 1 at the end.\n  data += 1 \n\n  sections = []\n  \n  # This loop splits the 2D array apart into four arrays that are\n  # all the result of striding by 2 and offset by (0,0), (0,1), (1,0), \n  # and (1,1) representing the A, B, C, and D positions from Figure 1.\n  factor = (2,2)\n  for offset in np.ndindex(factor):\n    part = data[tuple(np.s_[o::f] for o, f in zip(offset, factor))]\n    sections.append(part)\n\n  a, b, c, d = sections\n\n  ab = a * (a == b) # PICK(A,B)\n  ac = a * (a == c) # PICK(A,C)\n  bc = b * (b == c) # PICK(B,C)\n\n  a = ab | ac | bc # Bitwise OR, safe b/c non-matches are zeroed\n  \n  result = a + (a == 0) * d - 1 # a or d - 1\n\n  if upgraded:\n    return downgrade_type(result)\n\n  # only need to reset data if we weren't upgraded \n  # b/c no copy was made in that case\n  data -= 1\n\n  return result\n\ndef countless_extreme(data):\n  nonzeros = np.count_nonzero(data)\n  # print(\"nonzeros\", nonzeros)\n\n  N = reduce(operator.mul, data.shape)\n\n  if nonzeros == N:\n    print(\"quick\")\n    return quick_countless(data)\n  elif np.count_nonzero(data + 1) == N:\n    print(\"quick\")\n    # print(\"upper\", nonzeros)\n    return quick_countless(data)\n  else:\n    return countless(data)\n\n\ndef countless(data):\n  \"\"\"\n  Vectorized implementation of downsampling a 2D \n  image by 2 on each side using the COUNTLESS algorithm.\n  \n  data is a 2D numpy array with even dimensions.\n  \"\"\"\n  # allows us to prevent losing 1/2 a bit of information \n  # at the top end by using a bigger type. Without this 255 is handled incorrectly.\n  data, upgraded = upgrade_type(data) \n\n  # offset from zero, raw countless doesn't handle 0 correctly\n  # we'll remove the extra 1 at the end.\n  data += 1 \n\n  sections = []\n  \n  # This loop splits the 2D array apart into four arrays that are\n  # all the result of striding by 2 and offset by (0,0), (0,1), (1,0), \n  # and (1,1) representing the A, B, C, and D positions from Figure 1.\n  factor = (2,2)\n  for offset in np.ndindex(factor):\n    part = data[tuple(np.s_[o::f] for o, f in zip(offset, factor))]\n    sections.append(part)\n\n  a, b, c, d = sections\n\n  ab_ac = a * ((a == b) | (a == c)) # PICK(A,B) || PICK(A,C) w/ optimization\n  ab_ac |= b * (b == c) # PICK(B,C)\n  result = ab_ac + (ab_ac == 0) * d - 1 # (matches or d) - 1\n\n  if upgraded:\n    return downgrade_type(result)\n\n  # only need to reset data if we weren't upgraded \n  # b/c no copy was made in that case\n  data -= 1\n\n  return result\n\ndef upgrade_type(arr):\n  dtype = arr.dtype\n\n  if dtype == np.uint8:\n    return arr.astype(np.uint16), True\n  elif dtype == np.uint16:\n    return arr.astype(np.uint32), True\n  elif dtype == np.uint32:\n    return arr.astype(np.uint64), True\n\n  return arr, False\n  \ndef downgrade_type(arr):\n  dtype = arr.dtype\n\n  if dtype == np.uint64:\n    return arr.astype(np.uint32)\n  elif dtype == np.uint32:\n    return arr.astype(np.uint16)\n  elif dtype == np.uint16:\n    return arr.astype(np.uint8)\n  \n  return arr\n\ndef odd_to_even(image):\n  \"\"\"\n  To facilitate 2x2 downsampling segmentation, change an odd sized image into an even sized one.\n  Works by mirroring the starting 1 pixel edge of the image on odd shaped sides.\n\n  e.g. turn a 3x3x5 image into a 4x4x5 (the x and y are what are getting downsampled)\n  \n  For example: [ 3, 2, 4 ] => [ 3, 3, 2, 4 ] which is now easy to downsample.\n\n  \"\"\"\n  shape = np.array(image.shape)\n\n  offset = (shape % 2)[:2] # x,y offset\n  \n  # detect if we're dealing with an even\n  # image. if so it's fine, just return.\n  if not np.any(offset): \n    return image\n\n  oddshape = image.shape[:2] + offset\n  oddshape = np.append(oddshape, shape[2:])\n  oddshape = oddshape.astype(int)\n\n  newimg = np.empty(shape=oddshape, dtype=image.dtype)\n\n  ox,oy = offset\n  sx,sy = oddshape\n\n  newimg[0,0] = image[0,0] # corner\n  newimg[ox:sx,0] = image[:,0] # x axis line\n  newimg[0,oy:sy] = image[0,:] # y axis line \n\n  return newimg\n\ndef counting(array):\n    factor = (2, 2, 1)\n    shape = array.shape\n\n    while len(shape) < 4:\n      array = np.expand_dims(array, axis=-1)\n      shape = array.shape\n\n    output_shape = tuple(int(math.ceil(s / f)) for s, f in zip(shape, factor))\n    output = np.zeros(output_shape, dtype=array.dtype)\n\n    for chan in range(0, shape[3]):\n      for z in range(0, shape[2]):\n        for x in range(0, shape[0], 2):\n          for y in range(0, shape[1], 2):\n            block = array[ x:x+2, y:y+2, z, chan ] # 2x2 block\n\n            hashtable = defaultdict(int)\n            for subx, suby in np.ndindex(block.shape[0], block.shape[1]):\n              hashtable[block[subx, suby]] += 1\n\n            best = (0, 0)\n            for segid, val in six.iteritems(hashtable):\n              if best[1] < val:\n                best = (segid, val)\n\n            output[ x // 2, y // 2, chan ] = best[0]\n    \n    return output\n\ndef ndzoom(array):\n    if len(array.shape) == 3:\n      ratio = ( 1 / 2.0, 1 / 2.0, 1.0 )\n    else:\n      ratio = ( 1 / 2.0, 1 / 2.0)\n    return ndimage.interpolation.zoom(array, ratio, order=1)\n\ndef countless_if(array):\n    factor = (2, 2, 1)\n    shape = array.shape\n\n    if len(shape) < 3:\n      array = array[ :,:, np.newaxis ]\n      shape = array.shape\n\n    output_shape = tuple(int(math.ceil(s / f)) for s, f in zip(shape, factor))\n    output = np.zeros(output_shape, dtype=array.dtype)\n\n    for chan in range(0, shape[2]):\n      for x in range(0, shape[0], 2):\n        for y in range(0, shape[1], 2):\n          block = array[ x:x+2, y:y+2, chan ] # 2x2 block\n\n          if block[0,0] == block[1,0]:\n            pick = block[0,0]\n          elif block[0,0] == block[0,1]:\n            pick = block[0,0]\n          elif block[1,0] == block[0,1]:\n            pick = block[1,0]\n          else:\n            pick = block[1,1]\n\n          output[ x // 2, y // 2, chan ] = pick\n    \n    return np.squeeze(output)\n\ndef downsample_with_averaging(array):\n  \"\"\"\n  Downsample x by factor using averaging.\n\n  @return: The downsampled array, of the same type as x.\n  \"\"\"\n\n  if len(array.shape) == 3:\n    factor = (2,2,1)\n  else:\n    factor = (2,2)\n  \n  if np.array_equal(factor[:3], np.array([1,1,1])):\n    return array\n\n  output_shape = tuple(int(math.ceil(s / f)) for s, f in zip(array.shape, factor))\n  temp = np.zeros(output_shape, float)\n  counts = np.zeros(output_shape, np.int)\n  for offset in np.ndindex(factor):\n      part = array[tuple(np.s_[o::f] for o, f in zip(offset, factor))]\n      indexing_expr = tuple(np.s_[:s] for s in part.shape)\n      temp[indexing_expr] += part\n      counts[indexing_expr] += 1\n  return np.cast[array.dtype](temp / counts)\n\ndef downsample_with_max_pooling(array):\n\n  factor = (2,2)\n\n  if np.all(np.array(factor, int) == 1):\n      return array\n\n  sections = []\n\n  for offset in np.ndindex(factor):\n    part = array[tuple(np.s_[o::f] for o, f in zip(offset, factor))]\n    sections.append(part)\n\n  output = sections[0].copy()\n\n  for section in sections[1:]:\n    np.maximum(output, section, output)\n\n  return output\n\ndef striding(array): \n  \"\"\"Downsample x by factor using striding.\n\n  @return: The downsampled array, of the same type as x.\n  \"\"\"\n  factor = (2,2)\n  if np.all(np.array(factor, int) == 1):\n    return array\n  return array[tuple(np.s_[::f] for f in factor)]\n\ndef benchmark():\n  filename = sys.argv[1]\n  img = Image.open(filename)\n  data = np.array(img.getdata(), dtype=np.uint8)\n\n  if len(data.shape) == 1:\n    n_channels = 1\n    reshape = (img.height, img.width)\n  else:\n    n_channels = min(data.shape[1], 3)\n    data = data[:, :n_channels]\n    reshape = (img.height, img.width, n_channels)\n\n  data = data.reshape(reshape).astype(np.uint8)\n\n  methods = [\n    simplest_countless,\n    quick_countless,\n    quick_countless_xor,\n    quickest_countless,\n    stippled_countless,\n    zero_corrected_countless,\n    countless,\n    downsample_with_averaging,\n    downsample_with_max_pooling,\n    ndzoom,\n    striding,\n    # countless_if,\n    # counting,\n  ]\n\n  formats = {\n    1: 'L',\n    3: 'RGB',\n    4: 'RGBA'\n  }\n\n  if not os.path.exists('./results'):\n    os.mkdir('./results')\n\n  N = 500\n  img_size = float(img.width * img.height) / 1024.0 / 1024.0\n  print(\"N = %d, %dx%d (%.2f MPx) %d chan, %s\" % (N, img.width, img.height, img_size, n_channels, filename))\n  print(\"Algorithm\\tMPx/sec\\tMB/sec\\tSec\")\n  for fn in methods:\n    print(fn.__name__, end='')\n    sys.stdout.flush()\n\n    start = time.time()\n    # tqdm is here to show you what's going on the first time you run it.\n    # Feel free to remove it to get slightly more accurate timing results.\n    for _ in tqdm(range(N), desc=fn.__name__, disable=True):\n      result = fn(data)\n    end = time.time()\n    print(\"\\r\", end='')\n\n    total_time = (end - start)\n    mpx = N * img_size / total_time\n    mbytes = N * img_size * n_channels / total_time\n    # Output in tab separated format to enable copy-paste into excel/numbers\n    print(\"%s\\t%.3f\\t%.3f\\t%.2f\" % (fn.__name__, mpx, mbytes, total_time))\n    outimg = Image.fromarray(np.squeeze(result), formats[n_channels])\n    outimg.save('./results/{}.png'.format(fn.__name__, \"PNG\"))\n\nif __name__ == '__main__':\n  benchmark()\n\n\n# Example results:\n# N = 5, 1024x1024 (1.00 MPx) 1 chan, images/gray_segmentation.png\n# Function                        MPx/sec   MB/sec     Sec\n# simplest_countless              752.855   752.855    0.01\n# quick_countless                 920.328   920.328    0.01\n# zero_corrected_countless        534.143   534.143    0.01\n# countless                       644.247   644.247    0.01\n# downsample_with_averaging       372.575   372.575    0.01\n# downsample_with_max_pooling     974.060   974.060    0.01\n# ndzoom                          137.517   137.517    0.04\n# striding                      38550.588 38550.588    0.00\n# countless_if                      4.377     4.377    1.14\n# counting                          0.117     0.117   42.85\n\n# Run without non-numpy implementations:\n# N = 2000, 1024x1024 (1.00 MPx) 1 chan, images/gray_segmentation.png\n# Algorithm                       MPx/sec   MB/sec     Sec\n# simplest_countless              800.522   800.522    2.50\n# quick_countless                 945.420   945.420    2.12\n# quickest_countless              947.256   947.256    2.11\n# stippled_countless              544.049   544.049    3.68\n# zero_corrected_countless        575.310   575.310    3.48\n# countless                       646.684   646.684    3.09\n# downsample_with_averaging       385.132   385.132    5.19\n# downsample_with_max_poolin      988.361   988.361    2.02\n# ndzoom                          163.104   163.104   12.26\n# striding                      81589.340 81589.340    0.02\n\n\n\n\n"
  },
  {
    "path": "model_cards/lama/saicinpainting/evaluation/masks/countless/countless3d.py",
    "content": "from six.moves import range\nfrom PIL import Image\nimport numpy as np\nimport io\nimport time\nimport math\nimport random\nimport sys\nfrom collections import defaultdict\nfrom copy import deepcopy\nfrom itertools import combinations\nfrom functools import reduce\nfrom tqdm import tqdm\n\nfrom memory_profiler import profile\n\ndef countless5(a,b,c,d,e):\n  \"\"\"First stage of generalizing from countless2d. \n\n  You have five slots: A, B, C, D, E\n\n  You can decide if something is the winner by first checking for \n  matches of three, then matches of two, then picking just one if \n  the other two tries fail. In countless2d, you just check for matches\n  of two and then pick one of them otherwise.\n\n  Unfortunately, you need to check ABC, ABD, ABE, BCD, BDE, & CDE.\n  Then you need to check AB, AC, AD, BC, BD\n  We skip checking E because if none of these match, we pick E. We can\n  skip checking AE, BE, CE, DE since if any of those match, E is our boy\n  so it's redundant.\n\n  So countless grows cominatorially in complexity.\n  \"\"\"\n  sections = [ a,b,c,d,e ]\n\n  p2 = lambda q,r: q * (q == r) # q if p == q else 0\n  p3 = lambda q,r,s: q * ( (q == r) & (r == s) ) # q if q == r == s else 0\n\n  lor = lambda x,y: x + (x == 0) * y\n\n  results3 = ( p3(x,y,z) for x,y,z in combinations(sections, 3) )\n  results3 = reduce(lor, results3)\n\n  results2 = ( p2(x,y) for x,y in combinations(sections[:-1], 2) )\n  results2 = reduce(lor, results2)\n\n  return reduce(lor, (results3, results2, e))\n\ndef countless8(a,b,c,d,e,f,g,h):\n  \"\"\"Extend countless5 to countless8. Same deal, except we also\n    need to check for matches of length 4.\"\"\"\n  sections = [ a, b, c, d, e, f, g, h ]\n  \n  p2 = lambda q,r: q * (q == r)\n  p3 = lambda q,r,s: q * ( (q == r) & (r == s) )\n  p4 = lambda p,q,r,s: p * ( (p == q) & (q == r) & (r == s) )\n\n  lor = lambda x,y: x + (x == 0) * y\n\n  results4 = ( p4(x,y,z,w) for x,y,z,w in combinations(sections, 4) )\n  results4 = reduce(lor, results4)\n\n  results3 = ( p3(x,y,z) for x,y,z in combinations(sections, 3) )\n  results3 = reduce(lor, results3)\n\n  # We can always use our shortcut of omitting the last element\n  # for N choose 2 \n  results2 = ( p2(x,y) for x,y in combinations(sections[:-1], 2) )\n  results2 = reduce(lor, results2)\n\n  return reduce(lor, [ results4, results3, results2, h ])\n\ndef dynamic_countless3d(data):\n  \"\"\"countless8 + dynamic programming. ~2x faster\"\"\"\n  sections = []\n\n  # shift zeros up one so they don't interfere with bitwise operators\n  # we'll shift down at the end\n  data += 1 \n  \n  # This loop splits the 2D array apart into four arrays that are\n  # all the result of striding by 2 and offset by (0,0), (0,1), (1,0), \n  # and (1,1) representing the A, B, C, and D positions from Figure 1.\n  factor = (2,2,2)\n  for offset in np.ndindex(factor):\n    part = data[tuple(np.s_[o::f] for o, f in zip(offset, factor))]\n    sections.append(part)\n  \n  pick = lambda a,b: a * (a == b)\n  lor = lambda x,y: x + (x == 0) * y\n\n  subproblems2 = {}\n\n  results2 = None\n  for x,y in combinations(range(7), 2):\n    res = pick(sections[x], sections[y])\n    subproblems2[(x,y)] = res\n    if results2 is not None:\n      results2 += (results2 == 0) * res\n    else:\n      results2 = res\n\n  subproblems3 = {}\n\n  results3 = None\n  for x,y,z in combinations(range(8), 3):\n    res = pick(subproblems2[(x,y)], sections[z])\n\n    if z != 7:\n      subproblems3[(x,y,z)] = res\n\n    if results3 is not None:\n      results3 += (results3 == 0) * res\n    else:\n      results3 = res\n\n  results3 = reduce(lor, (results3, results2, sections[-1]))\n\n  # free memory\n  results2 = None\n  subproblems2 = None \n  res = None\n\n  results4 = ( pick(subproblems3[(x,y,z)], sections[w]) for x,y,z,w in combinations(range(8), 4) )\n  results4 = reduce(lor, results4) \n  subproblems3 = None # free memory\n\n  final_result = lor(results4, results3) - 1\n  data -= 1\n  return final_result\n\ndef countless3d(data):\n  \"\"\"Now write countless8 in such a way that it could be used\n  to process an image.\"\"\"\n  sections = []\n\n  # shift zeros up one so they don't interfere with bitwise operators\n  # we'll shift down at the end\n  data += 1 \n  \n  # This loop splits the 2D array apart into four arrays that are\n  # all the result of striding by 2 and offset by (0,0), (0,1), (1,0), \n  # and (1,1) representing the A, B, C, and D positions from Figure 1.\n  factor = (2,2,2)\n  for offset in np.ndindex(factor):\n    part = data[tuple(np.s_[o::f] for o, f in zip(offset, factor))]\n    sections.append(part)\n\n  p2 = lambda q,r: q * (q == r)\n  p3 = lambda q,r,s: q * ( (q == r) & (r == s) )\n  p4 = lambda p,q,r,s: p * ( (p == q) & (q == r) & (r == s) )\n\n  lor = lambda x,y: x + (x == 0) * y\n\n  results4 = ( p4(x,y,z,w) for x,y,z,w in combinations(sections, 4)  )\n  results4 = reduce(lor, results4)\n\n  results3 = ( p3(x,y,z) for x,y,z in combinations(sections, 3)  )\n  results3 = reduce(lor, results3)\n\n  results2 = ( p2(x,y) for x,y in combinations(sections[:-1], 2)  )\n  results2 = reduce(lor, results2)\n\n  final_result = reduce(lor, (results4, results3, results2, sections[-1])) - 1\n  data -= 1\n  return final_result\n\ndef countless_generalized(data, factor):\n  assert len(data.shape) == len(factor)\n\n  sections = []\n\n  mode_of = reduce(lambda x,y: x * y, factor)\n  majority = int(math.ceil(float(mode_of) / 2))\n\n  data += 1\n  \n  # This loop splits the 2D array apart into four arrays that are\n  # all the result of striding by 2 and offset by (0,0), (0,1), (1,0), \n  # and (1,1) representing the A, B, C, and D positions from Figure 1.\n  for offset in np.ndindex(factor):\n    part = data[tuple(np.s_[o::f] for o, f in zip(offset, factor))]\n    sections.append(part)\n\n  def pick(elements):\n    eq = ( elements[i] == elements[i+1] for i in range(len(elements) - 1) )\n    anded = reduce(lambda p,q: p & q, eq)\n    return elements[0] * anded\n\n  def logical_or(x,y):\n    return x + (x == 0) * y\n\n  result = ( pick(combo) for combo in combinations(sections, majority) )\n  result = reduce(logical_or, result)\n  for i in range(majority - 1, 3-1, -1): # 3-1 b/c of exclusive bounds\n    partial_result = ( pick(combo) for combo in combinations(sections, i) )\n    partial_result = reduce(logical_or, partial_result)\n    result = logical_or(result, partial_result)\n\n  partial_result = ( pick(combo) for combo in combinations(sections[:-1], 2) )\n  partial_result = reduce(logical_or, partial_result)\n  result = logical_or(result, partial_result)\n\n  result = logical_or(result, sections[-1]) - 1\n  data -= 1\n  return result\n\ndef dynamic_countless_generalized(data, factor):\n  assert len(data.shape) == len(factor)\n\n  sections = []\n\n  mode_of = reduce(lambda x,y: x * y, factor)\n  majority = int(math.ceil(float(mode_of) / 2))\n\n  data += 1 # offset from zero\n  \n  # This loop splits the 2D array apart into four arrays that are\n  # all the result of striding by 2 and offset by (0,0), (0,1), (1,0), \n  # and (1,1) representing the A, B, C, and D positions from Figure 1.\n  for offset in np.ndindex(factor):\n    part = data[tuple(np.s_[o::f] for o, f in zip(offset, factor))]\n    sections.append(part)\n\n  pick = lambda a,b: a * (a == b)\n  lor = lambda x,y: x + (x == 0) * y # logical or\n\n  subproblems = [ {}, {} ]\n  results2 = None\n  for x,y in combinations(range(len(sections) - 1), 2):\n    res = pick(sections[x], sections[y])\n    subproblems[0][(x,y)] = res\n    if results2 is not None:\n      results2 = lor(results2, res)\n    else:\n      results2 = res\n\n  results = [ results2 ]\n  for r in range(3, majority+1):\n    r_results = None\n    for combo in combinations(range(len(sections)), r):\n      res = pick(subproblems[0][combo[:-1]], sections[combo[-1]])\n      \n      if combo[-1] != len(sections) - 1:\n        subproblems[1][combo] = res\n\n      if r_results is not None:\n        r_results = lor(r_results, res)\n      else:\n        r_results = res\n    results.append(r_results)\n    subproblems[0] = subproblems[1]\n    subproblems[1] = {}\n    \n  results.reverse()\n  final_result = lor(reduce(lor, results), sections[-1]) - 1\n  data -= 1\n  return final_result\n\ndef downsample_with_averaging(array):\n  \"\"\"\n  Downsample x by factor using averaging.\n\n  @return: The downsampled array, of the same type as x.\n  \"\"\"\n  factor = (2,2,2)\n  \n  if np.array_equal(factor[:3], np.array([1,1,1])):\n    return array\n\n  output_shape = tuple(int(math.ceil(s / f)) for s, f in zip(array.shape, factor))\n  temp = np.zeros(output_shape, float)\n  counts = np.zeros(output_shape, np.int)\n  for offset in np.ndindex(factor):\n      part = array[tuple(np.s_[o::f] for o, f in zip(offset, factor))]\n      indexing_expr = tuple(np.s_[:s] for s in part.shape)\n      temp[indexing_expr] += part\n      counts[indexing_expr] += 1\n  return np.cast[array.dtype](temp / counts)\n\ndef downsample_with_max_pooling(array):\n\n  factor = (2,2,2)\n\n  sections = []\n\n  for offset in np.ndindex(factor):\n    part = array[tuple(np.s_[o::f] for o, f in zip(offset, factor))]\n    sections.append(part)\n\n  output = sections[0].copy()\n\n  for section in sections[1:]:\n    np.maximum(output, section, output)\n\n  return output\n\ndef striding(array): \n  \"\"\"Downsample x by factor using striding.\n\n  @return: The downsampled array, of the same type as x.\n  \"\"\"\n  factor = (2,2,2)\n  if np.all(np.array(factor, int) == 1):\n    return array\n  return array[tuple(np.s_[::f] for f in factor)]\n\ndef benchmark():\n  def countless3d_generalized(img):\n    return countless_generalized(img, (2,8,1))\n  def countless3d_dynamic_generalized(img):\n    return dynamic_countless_generalized(img, (8,8,1))\n\n  methods = [\n    # countless3d,\n    # dynamic_countless3d,\n    countless3d_generalized,\n    # countless3d_dynamic_generalized,\n    # striding,\n    # downsample_with_averaging,\n    # downsample_with_max_pooling\n  ]\n\n  data = np.zeros(shape=(16**2, 16**2, 16**2), dtype=np.uint8) + 1\n\n  N = 5\n\n  print('Algorithm\\tMPx\\tMB/sec\\tSec\\tN=%d' % N)\n\n  for fn in methods:\n    start = time.time()\n    for _ in range(N):\n      result = fn(data)\n    end = time.time()\n\n    total_time = (end - start)\n    mpx = N * float(data.shape[0] * data.shape[1] * data.shape[2]) / total_time / 1024.0 / 1024.0\n    mbytes = mpx * np.dtype(data.dtype).itemsize\n    # Output in tab separated format to enable copy-paste into excel/numbers\n    print(\"%s\\t%.3f\\t%.3f\\t%.2f\" % (fn.__name__, mpx, mbytes, total_time))\n\nif __name__ == '__main__':\n  benchmark()\n\n# Algorithm MPx MB/sec  Sec N=5\n# countless3d 10.564  10.564  60.58\n# dynamic_countless3d 22.717  22.717  28.17\n# countless3d_generalized 9.702 9.702 65.96\n# countless3d_dynamic_generalized 22.720  22.720  28.17\n# striding  253360.506  253360.506  0.00\n# downsample_with_averaging 224.098 224.098 2.86\n# downsample_with_max_pooling 690.474 690.474 0.93\n\n\n\n"
  },
  {
    "path": "model_cards/lama/saicinpainting/evaluation/masks/countless/requirements.txt",
    "content": "Pillow>=6.2.0\nnumpy>=1.16\nscipy\ntqdm\nmemory_profiler\nsix\npytest"
  },
  {
    "path": "model_cards/lama/saicinpainting/evaluation/masks/countless/test.py",
    "content": "from copy import deepcopy\n\nimport numpy as np\n\nimport countless2d\nimport countless3d\n\ndef test_countless2d():\n  def test_all_cases(fn, test_zero):\n    case1 = np.array([ [ 1, 2 ], [ 3, 4 ] ]).reshape((2,2,1,1)) # all different\n    case2 = np.array([ [ 1, 1 ], [ 2, 3 ] ]).reshape((2,2,1,1)) # two are same\n    case1z = np.array([ [ 0, 1 ], [ 2, 3 ] ]).reshape((2,2,1,1)) # all different\n    case2z = np.array([ [ 0, 0 ], [ 2, 3 ] ]).reshape((2,2,1,1)) # two are same\n    case3 = np.array([ [ 1, 1 ], [ 2, 2 ] ]).reshape((2,2,1,1)) # two groups are same\n    case4 = np.array([ [ 1, 2 ], [ 2, 2 ] ]).reshape((2,2,1,1)) # 3 are the same\n    case5 = np.array([ [ 5, 5 ], [ 5, 5 ] ]).reshape((2,2,1,1)) # all are the same\n\n    is_255_handled = np.array([ [ 255, 255 ], [ 1, 2 ] ], dtype=np.uint8).reshape((2,2,1,1))\n\n    test = lambda case: fn(case)\n\n    if test_zero:\n      assert test(case1z) == [[[[3]]]] # d\n      assert test(case2z) == [[[[0]]]] # a==b\n    else:\n      assert test(case1) == [[[[4]]]] # d\n      assert test(case2) == [[[[1]]]] # a==b\n\n    assert test(case3) == [[[[1]]]] # a==b\n    assert test(case4) == [[[[2]]]] # b==c\n    assert test(case5) == [[[[5]]]] # a==b\n    \n    assert test(is_255_handled) == [[[[255]]]] \n\n    assert fn(case1).dtype == case1.dtype\n\n  test_all_cases(countless2d.simplest_countless, False)\n  test_all_cases(countless2d.quick_countless, False)\n  test_all_cases(countless2d.quickest_countless, False)\n  test_all_cases(countless2d.stippled_countless, False)\n\n\n\n  methods = [\n    countless2d.zero_corrected_countless,\n    countless2d.countless,\n    countless2d.countless_if,\n    # countless2d.counting, # counting doesn't respect order so harder to write a test\n  ]\n\n  for fn in methods:\n    print(fn.__name__)\n    test_all_cases(fn, True)\n\ndef test_stippled_countless2d():\n  a = np.array([ [ 1, 2 ], [ 3, 4 ] ]).reshape((2,2,1,1)) \n  b = np.array([ [ 0, 2 ], [ 3, 4 ] ]).reshape((2,2,1,1)) \n  c = np.array([ [ 1, 0 ], [ 3, 4 ] ]).reshape((2,2,1,1)) \n  d = np.array([ [ 1, 2 ], [ 0, 4 ] ]).reshape((2,2,1,1)) \n  e = np.array([ [ 1, 2 ], [ 3, 0 ] ]).reshape((2,2,1,1)) \n  f = np.array([ [ 0, 0 ], [ 3, 4 ] ]).reshape((2,2,1,1)) \n  g = np.array([ [ 0, 2 ], [ 0, 4 ] ]).reshape((2,2,1,1)) \n  h = np.array([ [ 0, 2 ], [ 3, 0 ] ]).reshape((2,2,1,1)) \n  i = np.array([ [ 1, 0 ], [ 0, 4 ] ]).reshape((2,2,1,1)) \n  j = np.array([ [ 1, 2 ], [ 0, 0 ] ]).reshape((2,2,1,1)) \n  k = np.array([ [ 1, 0 ], [ 3, 0 ] ]).reshape((2,2,1,1)) \n  l = np.array([ [ 1, 0 ], [ 0, 0 ] ]).reshape((2,2,1,1)) \n  m = np.array([ [ 0, 2 ], [ 0, 0 ] ]).reshape((2,2,1,1)) \n  n = np.array([ [ 0, 0 ], [ 3, 0 ] ]).reshape((2,2,1,1)) \n  o = np.array([ [ 0, 0 ], [ 0, 4 ] ]).reshape((2,2,1,1)) \n  z = np.array([ [ 0, 0 ], [ 0, 0 ] ]).reshape((2,2,1,1)) \n\n  test = countless2d.stippled_countless\n\n  # Note: We only tested non-matching cases above,\n  # cases f,g,h,i,j,k prove their duals work as well\n  # b/c if two pixels are black, either one can be chosen\n  # if they are different or the same.\n\n  assert test(a) == [[[[4]]]] \n  assert test(b) == [[[[4]]]] \n  assert test(c) == [[[[4]]]] \n  assert test(d) == [[[[4]]]] \n  assert test(e) == [[[[1]]]] \n  assert test(f) == [[[[4]]]]  \n  assert test(g) == [[[[4]]]]  \n  assert test(h) == [[[[2]]]]  \n  assert test(i) == [[[[4]]]] \n  assert test(j) == [[[[1]]]]  \n  assert test(k) == [[[[1]]]]  \n  assert test(l) == [[[[1]]]]  \n  assert test(m) == [[[[2]]]]  \n  assert test(n) == [[[[3]]]]  \n  assert test(o) == [[[[4]]]]  \n  assert test(z) == [[[[0]]]]  \n\n  bc = np.array([ [ 0, 2 ], [ 2, 4 ] ]).reshape((2,2,1,1)) \n  bd = np.array([ [ 0, 2 ], [ 3, 2 ] ]).reshape((2,2,1,1)) \n  cd = np.array([ [ 0, 2 ], [ 3, 3 ] ]).reshape((2,2,1,1)) \n  \n  assert test(bc) == [[[[2]]]]\n  assert test(bd) == [[[[2]]]]\n  assert test(cd) == [[[[3]]]]\n\n  ab = np.array([ [ 1, 1 ], [ 0, 4 ] ]).reshape((2,2,1,1)) \n  ac = np.array([ [ 1, 2 ], [ 1, 0 ] ]).reshape((2,2,1,1)) \n  ad = np.array([ [ 1, 0 ], [ 3, 1 ] ]).reshape((2,2,1,1)) \n\n  assert test(ab) == [[[[1]]]]\n  assert test(ac) == [[[[1]]]]\n  assert test(ad) == [[[[1]]]]\n    \ndef test_countless3d():\n  def test_all_cases(fn):\n    alldifferent = [\n      [\n        [1,2],\n        [3,4],\n      ],\n      [\n        [5,6],\n        [7,8]\n      ]\n    ]\n    allsame = [\n      [\n        [1,1],\n        [1,1],\n      ],\n      [\n        [1,1],\n        [1,1]\n      ]\n    ]\n\n    assert fn(np.array(alldifferent)) == [[[8]]]\n    assert fn(np.array(allsame)) == [[[1]]]\n\n    twosame = deepcopy(alldifferent)\n    twosame[1][1][0] = 2\n\n    assert fn(np.array(twosame)) == [[[2]]]\n\n    threemixed = [\n      [\n        [3,3],\n        [1,2],\n      ],\n      [\n        [2,4],\n        [4,3]\n      ]\n    ]\n    assert fn(np.array(threemixed)) == [[[3]]]\n\n    foursame = [\n      [\n        [4,4],\n        [1,2],\n      ],\n      [\n        [2,4],\n        [4,3]\n      ]\n    ]\n\n    assert fn(np.array(foursame)) == [[[4]]]\n\n    fivesame = [\n      [\n        [5,4],\n        [5,5],\n      ],\n      [\n        [2,4],\n        [5,5]\n      ]\n    ]\n\n    assert fn(np.array(fivesame)) == [[[5]]]\n\n  def countless3d_generalized(img):\n    return countless3d.countless_generalized(img, (2,2,2))\n  def countless3d_dynamic_generalized(img):\n    return countless3d.dynamic_countless_generalized(img, (2,2,2))\n\n  methods = [\n    countless3d.countless3d,\n    countless3d.dynamic_countless3d,\n    countless3d_generalized,\n    countless3d_dynamic_generalized,\n  ]\n\n  for fn in methods:\n    test_all_cases(fn)"
  },
  {
    "path": "model_cards/lama/saicinpainting/evaluation/masks/mask.py",
    "content": "import enum\nfrom copy import deepcopy\n\nimport numpy as np\nfrom skimage import img_as_ubyte\nfrom skimage.transform import rescale, resize\ntry:\n    from detectron2 import model_zoo\n    from detectron2.config import get_cfg\n    from detectron2.engine import DefaultPredictor\n    DETECTRON_INSTALLED = True\nexcept:\n    print(\"Detectron v2 is not installed\")\n    DETECTRON_INSTALLED = False\n\nfrom .countless.countless2d import zero_corrected_countless\n\n\nclass ObjectMask():\n    def __init__(self, mask):\n        self.height, self.width = mask.shape\n        (self.up, self.down), (self.left, self.right) = self._get_limits(mask)\n        self.mask = mask[self.up:self.down, self.left:self.right].copy()\n\n    @staticmethod\n    def _get_limits(mask):\n        def indicator_limits(indicator):\n            lower = indicator.argmax()\n            upper = len(indicator) - indicator[::-1].argmax()\n            return lower, upper\n\n        vertical_indicator = mask.any(axis=1)\n        vertical_limits = indicator_limits(vertical_indicator)\n\n        horizontal_indicator = mask.any(axis=0)\n        horizontal_limits = indicator_limits(horizontal_indicator)\n\n        return vertical_limits, horizontal_limits\n\n    def _clean(self):\n        self.up, self.down, self.left, self.right = 0, 0, 0, 0\n        self.mask = np.empty((0, 0))\n\n    def horizontal_flip(self, inplace=False):\n        if not inplace:\n            flipped = deepcopy(self)\n            return flipped.horizontal_flip(inplace=True)\n\n        self.mask = self.mask[:, ::-1]\n        return self\n\n    def vertical_flip(self, inplace=False):\n        if not inplace:\n            flipped = deepcopy(self)\n            return flipped.vertical_flip(inplace=True)\n\n        self.mask = self.mask[::-1, :]\n        return self\n\n    def image_center(self):\n        y_center = self.up + (self.down - self.up) / 2\n        x_center = self.left + (self.right - self.left) / 2\n        return y_center, x_center\n\n    def rescale(self, scaling_factor, inplace=False):\n        if not inplace:\n            scaled = deepcopy(self)\n            return scaled.rescale(scaling_factor, inplace=True)\n\n        scaled_mask = rescale(self.mask.astype(float), scaling_factor, order=0) > 0.5\n        (up, down), (left, right) = self._get_limits(scaled_mask)\n        self.mask = scaled_mask[up:down, left:right]\n\n        y_center, x_center = self.image_center()\n        mask_height, mask_width = self.mask.shape\n        self.up = int(round(y_center - mask_height / 2))\n        self.down = self.up + mask_height\n        self.left = int(round(x_center - mask_width / 2))\n        self.right = self.left + mask_width\n        return self\n\n    def crop_to_canvas(self, vertical=True, horizontal=True, inplace=False):\n        if not inplace:\n            cropped = deepcopy(self)\n            cropped.crop_to_canvas(vertical=vertical, horizontal=horizontal, inplace=True)\n            return cropped\n\n        if vertical:\n            if self.up >= self.height or self.down <= 0:\n                self._clean()\n            else:\n                cut_up, cut_down = max(-self.up, 0), max(self.down - self.height, 0)\n                if cut_up != 0:\n                    self.mask = self.mask[cut_up:]\n                    self.up = 0\n                if cut_down != 0:\n                    self.mask = self.mask[:-cut_down]\n                    self.down = self.height\n\n        if horizontal:\n            if self.left >= self.width or self.right <= 0:\n                self._clean()\n            else:\n                cut_left, cut_right = max(-self.left, 0), max(self.right - self.width, 0)\n                if cut_left != 0:\n                    self.mask = self.mask[:, cut_left:]\n                    self.left = 0\n                if cut_right != 0:\n                    self.mask = self.mask[:, :-cut_right]\n                    self.right = self.width\n\n        return self\n\n    def restore_full_mask(self, allow_crop=False):\n        cropped = self.crop_to_canvas(inplace=allow_crop)\n        mask = np.zeros((cropped.height, cropped.width), dtype=bool)\n        mask[cropped.up:cropped.down, cropped.left:cropped.right] = cropped.mask\n        return mask\n\n    def shift(self, vertical=0, horizontal=0, inplace=False):\n        if not inplace:\n            shifted = deepcopy(self)\n            return shifted.shift(vertical=vertical, horizontal=horizontal, inplace=True)\n\n        self.up += vertical\n        self.down += vertical\n        self.left += horizontal\n        self.right += horizontal\n        return self\n\n    def area(self):\n        return self.mask.sum()\n\n\nclass RigidnessMode(enum.Enum):\n    soft = 0\n    rigid = 1\n\n\nclass SegmentationMask:\n    def __init__(self, confidence_threshold=0.5, rigidness_mode=RigidnessMode.rigid,\n                 max_object_area=0.3, min_mask_area=0.02, downsample_levels=6, num_variants_per_mask=4,\n                 max_mask_intersection=0.5, max_foreground_coverage=0.5, max_foreground_intersection=0.5,\n                 max_hidden_area=0.2, max_scale_change=0.25, horizontal_flip=True,\n                 max_vertical_shift=0.1, position_shuffle=True):\n        \"\"\"\n        :param confidence_threshold: float; threshold for confidence of the panoptic segmentator to allow for\n        the instance.\n        :param rigidness_mode: RigidnessMode object\n            when soft, checks intersection only with the object from which the mask_object was produced\n            when rigid, checks intersection with any foreground class object\n        :param max_object_area: float; allowed upper bound for to be considered as mask_object.\n        :param min_mask_area: float; lower bound for mask to be considered valid\n        :param downsample_levels: int; defines width of the resized segmentation to obtain shifted masks;\n        :param num_variants_per_mask: int; maximal number of the masks for the same object;\n        :param max_mask_intersection: float; maximum allowed area fraction of intersection for 2 masks\n        produced by horizontal shift of the same mask_object; higher value -> more diversity\n        :param max_foreground_coverage: float; maximum allowed area fraction of intersection for foreground object to be\n        covered by mask; lower value -> less the objects are covered\n        :param max_foreground_intersection: float; maximum allowed area of intersection for the mask with foreground\n        object; lower value -> mask is more on the background than on the objects\n        :param max_hidden_area: upper bound on part of the object hidden by shifting object outside the screen area;\n        :param max_scale_change: allowed scale change for the mask_object;\n        :param horizontal_flip: if horizontal flips are allowed;\n        :param max_vertical_shift: amount of vertical movement allowed;\n        :param position_shuffle: shuffle\n        \"\"\"\n\n        assert DETECTRON_INSTALLED, 'Cannot use SegmentationMask without detectron2'\n        self.cfg = get_cfg()\n        self.cfg.merge_from_file(model_zoo.get_config_file(\"COCO-PanopticSegmentation/panoptic_fpn_R_101_3x.yaml\"))\n        self.cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url(\"COCO-PanopticSegmentation/panoptic_fpn_R_101_3x.yaml\")\n        self.cfg.MODEL.PANOPTIC_FPN.COMBINE.INSTANCES_CONFIDENCE_THRESH = confidence_threshold\n        self.predictor = DefaultPredictor(self.cfg)\n\n        self.rigidness_mode = RigidnessMode(rigidness_mode)\n        self.max_object_area = max_object_area\n        self.min_mask_area = min_mask_area\n        self.downsample_levels = downsample_levels\n        self.num_variants_per_mask = num_variants_per_mask\n        self.max_mask_intersection = max_mask_intersection\n        self.max_foreground_coverage = max_foreground_coverage\n        self.max_foreground_intersection = max_foreground_intersection\n        self.max_hidden_area = max_hidden_area\n        self.position_shuffle = position_shuffle\n\n        self.max_scale_change = max_scale_change\n        self.horizontal_flip = horizontal_flip\n        self.max_vertical_shift = max_vertical_shift\n\n    def get_segmentation(self, img):\n        im = img_as_ubyte(img)\n        panoptic_seg, segment_info = self.predictor(im)[\"panoptic_seg\"]\n        return panoptic_seg, segment_info\n\n    @staticmethod\n    def _is_power_of_two(n):\n        return (n != 0) and (n & (n-1) == 0)\n\n    def identify_candidates(self, panoptic_seg, segments_info):\n        potential_mask_ids = []\n        for segment in segments_info:\n            if not segment[\"isthing\"]:\n                continue\n            mask = (panoptic_seg == segment[\"id\"]).int().detach().cpu().numpy()\n            area = mask.sum().item() / np.prod(panoptic_seg.shape)\n            if area >= self.max_object_area:\n                continue\n            potential_mask_ids.append(segment[\"id\"])\n        return potential_mask_ids\n\n    def downsample_mask(self, mask):\n        height, width = mask.shape\n        if not (self._is_power_of_two(height) and self._is_power_of_two(width)):\n            raise ValueError(\"Image sides are not power of 2.\")\n\n        num_iterations = width.bit_length() - 1 - self.downsample_levels\n        if num_iterations < 0:\n            raise ValueError(f\"Width is lower than 2^{self.downsample_levels}.\")\n\n        if height.bit_length() - 1 < num_iterations:\n            raise ValueError(\"Height is too low to perform downsampling\")\n\n        downsampled = mask\n        for _ in range(num_iterations):\n            downsampled = zero_corrected_countless(downsampled)\n\n        return downsampled\n\n    def _augmentation_params(self):\n        scaling_factor = np.random.uniform(1 - self.max_scale_change, 1 + self.max_scale_change)\n        if self.horizontal_flip:\n            horizontal_flip = bool(np.random.choice(2))\n        else:\n            horizontal_flip = False\n        vertical_shift = np.random.uniform(-self.max_vertical_shift, self.max_vertical_shift)\n\n        return {\n            \"scaling_factor\": scaling_factor,\n            \"horizontal_flip\": horizontal_flip,\n            \"vertical_shift\": vertical_shift\n        }\n\n    def _get_intersection(self, mask_array, mask_object):\n        intersection = mask_array[\n            mask_object.up:mask_object.down, mask_object.left:mask_object.right\n        ] & mask_object.mask\n        return intersection\n\n    def _check_masks_intersection(self, aug_mask, total_mask_area, prev_masks):\n        for existing_mask in prev_masks:\n            intersection_area = self._get_intersection(existing_mask, aug_mask).sum()\n            intersection_existing = intersection_area / existing_mask.sum()\n            intersection_current = 1 - (aug_mask.area() - intersection_area) / total_mask_area\n            if (intersection_existing > self.max_mask_intersection) or \\\n               (intersection_current > self.max_mask_intersection):\n                return False\n        return True\n\n    def _check_foreground_intersection(self, aug_mask, foreground):\n        for existing_mask in foreground:\n            intersection_area = self._get_intersection(existing_mask, aug_mask).sum()\n            intersection_existing = intersection_area / existing_mask.sum()\n            if intersection_existing > self.max_foreground_coverage:\n                return False\n            intersection_mask = intersection_area / aug_mask.area()\n            if intersection_mask > self.max_foreground_intersection:\n                return False\n        return True\n\n    def _move_mask(self, mask, foreground):\n        # Obtaining properties of the original mask_object:\n        orig_mask = ObjectMask(mask)\n\n        chosen_masks = []\n        chosen_parameters = []\n        # to fix the case when resizing gives mask_object consisting only of False\n        scaling_factor_lower_bound = 0.\n\n        for var_idx in range(self.num_variants_per_mask):\n            # Obtaining augmentation parameters and applying them to the downscaled mask_object\n            augmentation_params = self._augmentation_params()\n            augmentation_params[\"scaling_factor\"] = min([\n                augmentation_params[\"scaling_factor\"],\n                2 * min(orig_mask.up, orig_mask.height - orig_mask.down) / orig_mask.height + 1.,\n                2 * min(orig_mask.left, orig_mask.width - orig_mask.right) / orig_mask.width + 1.\n            ])\n            augmentation_params[\"scaling_factor\"] = max([\n                augmentation_params[\"scaling_factor\"], scaling_factor_lower_bound\n            ])\n\n            aug_mask = deepcopy(orig_mask)\n            aug_mask.rescale(augmentation_params[\"scaling_factor\"], inplace=True)\n            if augmentation_params[\"horizontal_flip\"]:\n                aug_mask.horizontal_flip(inplace=True)\n            total_aug_area = aug_mask.area()\n            if total_aug_area == 0:\n                scaling_factor_lower_bound = 1.\n                continue\n\n            # Fix if the element vertical shift is too strong and shown area is too small:\n            vertical_area = aug_mask.mask.sum(axis=1) / total_aug_area  # share of area taken by rows\n            # number of rows which are allowed to be hidden from upper and lower parts of image respectively\n            max_hidden_up = np.searchsorted(vertical_area.cumsum(), self.max_hidden_area)\n            max_hidden_down = np.searchsorted(vertical_area[::-1].cumsum(), self.max_hidden_area)\n            # correcting vertical shift, so not too much area will be hidden\n            augmentation_params[\"vertical_shift\"] = np.clip(\n                augmentation_params[\"vertical_shift\"],\n                -(aug_mask.up + max_hidden_up) / aug_mask.height,\n                (aug_mask.height - aug_mask.down + max_hidden_down) / aug_mask.height\n            )\n            # Applying vertical shift:\n            vertical_shift = int(round(aug_mask.height * augmentation_params[\"vertical_shift\"]))\n            aug_mask.shift(vertical=vertical_shift, inplace=True)\n            aug_mask.crop_to_canvas(vertical=True, horizontal=False, inplace=True)\n\n            # Choosing horizontal shift:\n            max_hidden_area = self.max_hidden_area - (1 - aug_mask.area() / total_aug_area)\n            horizontal_area = aug_mask.mask.sum(axis=0) / total_aug_area\n            max_hidden_left = np.searchsorted(horizontal_area.cumsum(), max_hidden_area)\n            max_hidden_right = np.searchsorted(horizontal_area[::-1].cumsum(), max_hidden_area)\n            allowed_shifts = np.arange(-max_hidden_left, aug_mask.width -\n                                      (aug_mask.right - aug_mask.left) + max_hidden_right + 1)\n            allowed_shifts = - (aug_mask.left - allowed_shifts)\n\n            if self.position_shuffle:\n                np.random.shuffle(allowed_shifts)\n\n            mask_is_found = False\n            for horizontal_shift in allowed_shifts:\n                aug_mask_left = deepcopy(aug_mask)\n                aug_mask_left.shift(horizontal=horizontal_shift, inplace=True)\n                aug_mask_left.crop_to_canvas(inplace=True)\n\n                prev_masks = [mask] + chosen_masks\n                is_mask_suitable = self._check_masks_intersection(aug_mask_left, total_aug_area, prev_masks) & \\\n                                   self._check_foreground_intersection(aug_mask_left, foreground)\n                if is_mask_suitable:\n                    aug_draw = aug_mask_left.restore_full_mask()\n                    chosen_masks.append(aug_draw)\n                    augmentation_params[\"horizontal_shift\"] = horizontal_shift / aug_mask_left.width\n                    chosen_parameters.append(augmentation_params)\n                    mask_is_found = True\n                    break\n\n            if not mask_is_found:\n                break\n\n        return chosen_parameters\n\n    def _prepare_mask(self, mask):\n        height, width = mask.shape\n        target_width = width if self._is_power_of_two(width) else (1 << width.bit_length())\n        target_height = height if self._is_power_of_two(height) else (1 << height.bit_length())\n\n        return resize(mask.astype('float32'), (target_height, target_width), order=0, mode='edge').round().astype('int32')\n\n    def get_masks(self, im, return_panoptic=False):\n        panoptic_seg, segments_info = self.get_segmentation(im)\n        potential_mask_ids = self.identify_candidates(panoptic_seg, segments_info)\n\n        panoptic_seg_scaled = self._prepare_mask(panoptic_seg.detach().cpu().numpy())\n        downsampled = self.downsample_mask(panoptic_seg_scaled)\n        scene_objects = []\n        for segment in segments_info:\n            if not segment[\"isthing\"]:\n                continue\n            mask = downsampled == segment[\"id\"]\n            if not np.any(mask):\n                continue\n            scene_objects.append(mask)\n\n        mask_set = []\n        for mask_id in potential_mask_ids:\n            mask = downsampled == mask_id\n            if not np.any(mask):\n                continue\n\n            if self.rigidness_mode is RigidnessMode.soft:\n                foreground = [mask]\n            elif self.rigidness_mode is RigidnessMode.rigid:\n                foreground = scene_objects\n            else:\n                raise ValueError(f'Unexpected rigidness_mode: {rigidness_mode}')\n\n            masks_params = self._move_mask(mask, foreground)\n\n            full_mask = ObjectMask((panoptic_seg == mask_id).detach().cpu().numpy())\n\n            for params in masks_params:\n                aug_mask = deepcopy(full_mask)\n                aug_mask.rescale(params[\"scaling_factor\"], inplace=True)\n                if params[\"horizontal_flip\"]:\n                    aug_mask.horizontal_flip(inplace=True)\n\n                vertical_shift = int(round(aug_mask.height * params[\"vertical_shift\"]))\n                horizontal_shift = int(round(aug_mask.width * params[\"horizontal_shift\"]))\n                aug_mask.shift(vertical=vertical_shift, horizontal=horizontal_shift, inplace=True)\n                aug_mask = aug_mask.restore_full_mask().astype('uint8')\n                if aug_mask.mean() <= self.min_mask_area:\n                    continue\n                mask_set.append(aug_mask)\n\n        if return_panoptic:\n            return mask_set, panoptic_seg.detach().cpu().numpy()\n        else:\n            return mask_set\n\n\ndef propose_random_square_crop(mask, min_overlap=0.5):\n    height, width = mask.shape\n    mask_ys, mask_xs = np.where(mask > 0.5)  # mask==0 is known fragment and mask==1 is missing\n\n    if height < width:\n        crop_size = height\n        obj_left, obj_right = mask_xs.min(), mask_xs.max()\n        obj_width = obj_right - obj_left\n        left_border = max(0, min(width - crop_size - 1, obj_left + obj_width * min_overlap - crop_size))\n        right_border = max(left_border + 1, min(width - crop_size, obj_left + obj_width * min_overlap))\n        start_x = np.random.randint(left_border, right_border)\n        return start_x, 0, start_x + crop_size, height\n    else:\n        crop_size = width\n        obj_top, obj_bottom = mask_ys.min(), mask_ys.max()\n        obj_height = obj_bottom - obj_top\n        top_border = max(0, min(height - crop_size - 1, obj_top + obj_height * min_overlap - crop_size))\n        bottom_border = max(top_border + 1, min(height - crop_size, obj_top + obj_height * min_overlap))\n        start_y = np.random.randint(top_border, bottom_border)\n        return 0, start_y, width, start_y + crop_size\n"
  },
  {
    "path": "model_cards/lama/saicinpainting/evaluation/refinement.py",
    "content": "import torch\nimport torch.nn as nn\nfrom torch.optim import Adam, SGD \nfrom kornia.filters import gaussian_blur2d\nfrom kornia.geometry.transform import resize\nfrom kornia.morphology import erosion\nfrom torch.nn import functional as F\nimport numpy as np\nimport cv2\n\nfrom saicinpainting.evaluation.data import pad_tensor_to_modulo\nfrom saicinpainting.evaluation.utils import move_to_device\nfrom saicinpainting.training.modules.ffc import FFCResnetBlock\nfrom saicinpainting.training.modules.pix2pixhd import ResnetBlock\n\nfrom tqdm import tqdm\n\n\ndef _pyrdown(im : torch.Tensor, downsize : tuple=None):\n    \"\"\"downscale the image\"\"\"\n    if downsize is None:\n        downsize = (im.shape[2]//2, im.shape[3]//2)\n    assert im.shape[1] == 3, \"Expected shape for the input to be (n,3,height,width)\"\n    im = gaussian_blur2d(im, kernel_size=(5,5), sigma=(1.0,1.0))\n    im = F.interpolate(im, size=downsize, mode='bilinear', align_corners=False)\n    return im\n\ndef _pyrdown_mask(mask : torch.Tensor, downsize : tuple=None, eps : float=1e-8, blur_mask : bool=True, round_up : bool=True):\n    \"\"\"downscale the mask tensor\n\n    Parameters\n    ----------\n    mask : torch.Tensor\n        mask of size (B, 1, H, W)\n    downsize : tuple, optional\n        size to downscale to. If None, image is downscaled to half, by default None\n    eps : float, optional\n        threshold value for binarizing the mask, by default 1e-8\n    blur_mask : bool, optional\n        if True, apply gaussian filter before downscaling, by default True\n    round_up : bool, optional\n        if True, values above eps are marked 1, else, values below 1-eps are marked 0, by default True\n\n    Returns\n    -------\n    torch.Tensor\n        downscaled mask\n    \"\"\"\n\n    if downsize is None:\n        downsize = (mask.shape[2]//2, mask.shape[3]//2)\n    assert mask.shape[1] == 1, \"Expected shape for the input to be (n,1,height,width)\"\n    if blur_mask == True:\n        mask = gaussian_blur2d(mask, kernel_size=(5,5), sigma=(1.0,1.0))\n        mask = F.interpolate(mask, size=downsize,  mode='bilinear', align_corners=False)\n    else:\n        mask = F.interpolate(mask, size=downsize,  mode='bilinear', align_corners=False)\n    if round_up:\n        mask[mask>=eps] = 1\n        mask[mask<eps] = 0\n    else:\n        mask[mask>=1.0-eps] = 1\n        mask[mask<1.0-eps] = 0\n    return mask\n\ndef _erode_mask(mask : torch.Tensor, ekernel : torch.Tensor=None, eps : float=1e-8):\n    \"\"\"erode the mask, and set gray pixels to 0\"\"\"\n    if ekernel is not None:\n        mask = erosion(mask, ekernel)\n        mask[mask>=1.0-eps] = 1\n        mask[mask<1.0-eps] = 0\n    return mask\n\n\ndef _l1_loss(\n    pred : torch.Tensor, pred_downscaled : torch.Tensor, ref : torch.Tensor, \n    mask : torch.Tensor, mask_downscaled : torch.Tensor, \n    image : torch.Tensor, on_pred : bool=True\n    ):\n    \"\"\"l1 loss on src pixels, and downscaled predictions if on_pred=True\"\"\"\n    loss = torch.mean(torch.abs(pred[mask<1e-8] - image[mask<1e-8]))\n    if on_pred: \n        loss += torch.mean(torch.abs(pred_downscaled[mask_downscaled>=1e-8] - ref[mask_downscaled>=1e-8]))                \n    return loss\n\ndef _infer(\n    image : torch.Tensor, mask : torch.Tensor, \n    forward_front : nn.Module, forward_rears : nn.Module, \n    ref_lower_res : torch.Tensor, orig_shape : tuple, devices : list, \n    scale_ind : int, n_iters : int=15, lr : float=0.002):\n    \"\"\"Performs inference with refinement at a given scale.\n\n    Parameters\n    ----------\n    image : torch.Tensor\n        input image to be inpainted, of size (1,3,H,W)\n    mask : torch.Tensor\n        input inpainting mask, of size (1,1,H,W) \n    forward_front : nn.Module\n        the front part of the inpainting network\n    forward_rears : nn.Module\n        the rear part of the inpainting network\n    ref_lower_res : torch.Tensor\n        the inpainting at previous scale, used as reference image\n    orig_shape : tuple\n        shape of the original input image before padding\n    devices : list\n        list of available devices\n    scale_ind : int\n        the scale index\n    n_iters : int, optional\n        number of iterations of refinement, by default 15\n    lr : float, optional\n        learning rate, by default 0.002\n\n    Returns\n    -------\n    torch.Tensor\n        inpainted image\n    \"\"\"\n    masked_image = image * (1 - mask)\n    masked_image = torch.cat([masked_image, mask], dim=1)\n\n    mask = mask.repeat(1,3,1,1)\n    if ref_lower_res is not None:\n        ref_lower_res = ref_lower_res.detach()\n    with torch.no_grad():\n        z1,z2 = forward_front(masked_image)\n    # Inference\n    mask = mask.to(devices[-1])\n    ekernel = torch.from_numpy(cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(15,15)).astype(bool)).float()\n    ekernel = ekernel.to(devices[-1])\n    image = image.to(devices[-1])\n    z1, z2 = z1.detach().to(devices[0]), z2.detach().to(devices[0])\n    z1.requires_grad, z2.requires_grad = True, True\n\n    optimizer = Adam([z1,z2], lr=lr)\n\n    pbar = tqdm(range(n_iters), leave=False)\n    for idi in pbar:\n        optimizer.zero_grad()\n        input_feat = (z1,z2)\n        for idd, forward_rear in enumerate(forward_rears):\n            output_feat = forward_rear(input_feat)\n            if idd < len(devices) - 1:\n                midz1, midz2 = output_feat\n                midz1, midz2 = midz1.to(devices[idd+1]), midz2.to(devices[idd+1])\n                input_feat = (midz1, midz2)\n            else:        \n                pred = output_feat\n\n        if ref_lower_res is None:\n            break\n        losses = {}\n        ######################### multi-scale #############################\n        # scaled loss with downsampler\n        pred_downscaled = _pyrdown(pred[:,:,:orig_shape[0],:orig_shape[1]])\n        mask_downscaled = _pyrdown_mask(mask[:,:1,:orig_shape[0],:orig_shape[1]], blur_mask=False, round_up=False)\n        mask_downscaled = _erode_mask(mask_downscaled, ekernel=ekernel)\n        mask_downscaled = mask_downscaled.repeat(1,3,1,1)\n        losses[\"ms_l1\"] = _l1_loss(pred, pred_downscaled, ref_lower_res, mask, mask_downscaled, image, on_pred=True)\n\n        loss = sum(losses.values())\n        pbar.set_description(\"Refining scale {} using scale {} ...current loss: {:.4f}\".format(scale_ind+1, scale_ind, loss.item()))\n        if idi < n_iters - 1:\n            loss.backward()\n            optimizer.step()\n            del pred_downscaled\n            del loss\n            del pred\n    # \"pred\" is the prediction after Plug-n-Play module\n    inpainted = mask * pred + (1 - mask) * image\n    inpainted = inpainted.detach().cpu()\n    return inpainted\n\ndef _get_image_mask_pyramid(batch : dict, min_side : int, max_scales : int, px_budget : int):\n    \"\"\"Build the image mask pyramid\n\n    Parameters\n    ----------\n    batch : dict\n        batch containing image, mask, etc\n    min_side : int\n        minimum side length to limit the number of scales of the pyramid \n    max_scales : int\n        maximum number of scales allowed\n    px_budget : int\n        the product H*W cannot exceed this budget, because of resource constraints\n\n    Returns\n    -------\n    tuple\n        image-mask pyramid in the form of list of images and list of masks\n    \"\"\"\n\n    assert batch['image'].shape[0] == 1, \"refiner works on only batches of size 1!\"\n\n    h, w = batch['unpad_to_size']\n    h, w = h[0].item(), w[0].item()\n\n    image = batch['image'][...,:h,:w]\n    mask = batch['mask'][...,:h,:w]\n    if h*w > px_budget:\n        #resize \n        ratio = np.sqrt(px_budget / float(h*w))\n        h_orig, w_orig = h, w\n        h,w = int(h*ratio), int(w*ratio)\n        print(f\"Original image too large for refinement! Resizing {(h_orig,w_orig)} to {(h,w)}...\")\n        image = resize(image, (h,w),interpolation='bilinear', align_corners=False)\n        mask = resize(mask, (h,w),interpolation='bilinear', align_corners=False)\n        mask[mask>1e-8] = 1        \n    breadth = min(h,w)\n    n_scales = min(1 + int(round(max(0,np.log2(breadth / min_side)))), max_scales)        \n    ls_images = []\n    ls_masks = []\n    \n    ls_images.append(image)\n    ls_masks.append(mask)\n    \n    for _ in range(n_scales - 1):\n        image_p = _pyrdown(ls_images[-1])\n        mask_p = _pyrdown_mask(ls_masks[-1])\n        ls_images.append(image_p)\n        ls_masks.append(mask_p)\n    # reverse the lists because we want the lowest resolution image as index 0\n    return ls_images[::-1], ls_masks[::-1]\n\ndef refine_predict(\n    batch : dict, inpainter : nn.Module, gpu_ids : str, \n    modulo : int, n_iters : int, lr : float, min_side : int, \n    max_scales : int, px_budget : int\n    ):\n    \"\"\"Refines the inpainting of the network\n\n    Parameters\n    ----------\n    batch : dict\n        image-mask batch, currently we assume the batchsize to be 1\n    inpainter : nn.Module\n        the inpainting neural network\n    gpu_ids : str\n        the GPU ids of the machine to use. If only single GPU, use: \"0,\"\n    modulo : int\n        pad the image to ensure dimension % modulo == 0\n    n_iters : int\n        number of iterations of refinement for each scale\n    lr : float\n        learning rate\n    min_side : int\n        all sides of image on all scales should be >= min_side / sqrt(2)\n    max_scales : int\n        max number of downscaling scales for the image-mask pyramid\n    px_budget : int\n        pixels budget. Any image will be resized to satisfy height*width <= px_budget\n\n    Returns\n    -------\n    torch.Tensor\n        inpainted image of size (1,3,H,W)\n    \"\"\"\n\n    assert not inpainter.training\n    assert not inpainter.add_noise_kwargs\n    assert inpainter.concat_mask\n\n    gpu_ids = [f'cuda:{gpuid}' for gpuid in gpu_ids.replace(\" \",\"\").split(\",\") if gpuid.isdigit()]\n    n_resnet_blocks = 0\n    first_resblock_ind = 0\n    found_first_resblock = False\n    for idl in range(len(inpainter.generator.model)):\n        if isinstance(inpainter.generator.model[idl], FFCResnetBlock) or isinstance(inpainter.generator.model[idl], ResnetBlock):\n            n_resnet_blocks += 1\n            found_first_resblock = True\n        elif not found_first_resblock:\n            first_resblock_ind += 1\n    resblocks_per_gpu = n_resnet_blocks // len(gpu_ids)\n\n    devices = [torch.device(gpu_id) for gpu_id in gpu_ids]\n    \n    # split the model into front, and rear parts    \n    forward_front = inpainter.generator.model[0:first_resblock_ind]\n    forward_front.to(devices[0])\n    forward_rears = []\n    for idd in range(len(gpu_ids)):\n        if idd < len(gpu_ids) - 1:\n            forward_rears.append(inpainter.generator.model[first_resblock_ind + resblocks_per_gpu*(idd):first_resblock_ind+resblocks_per_gpu*(idd+1)]) \n        else:\n            forward_rears.append(inpainter.generator.model[first_resblock_ind + resblocks_per_gpu*(idd):]) \n        forward_rears[idd].to(devices[idd]) \n\n    ls_images, ls_masks = _get_image_mask_pyramid(\n        batch, \n        min_side, \n        max_scales, \n        px_budget\n        )\n    image_inpainted = None\n\n    for ids, (image, mask) in enumerate(zip(ls_images, ls_masks)):\n        orig_shape = image.shape[2:]\n        image = pad_tensor_to_modulo(image, modulo)\n        mask = pad_tensor_to_modulo(mask, modulo)\n        mask[mask >= 1e-8] = 1.0\n        mask[mask < 1e-8] = 0.0\n        image, mask = move_to_device(image, devices[0]), move_to_device(mask, devices[0])\n        if image_inpainted is not None:\n            image_inpainted = move_to_device(image_inpainted, devices[-1])\n        image_inpainted = _infer(image, mask, forward_front, forward_rears, image_inpainted, orig_shape, devices, ids, n_iters, lr)\n        image_inpainted = image_inpainted[:,:,:orig_shape[0], :orig_shape[1]]\n        # detach everything to save resources\n        image = image.detach().cpu()\n        mask = mask.detach().cpu()\n    \n    return image_inpainted\n"
  },
  {
    "path": "model_cards/lama/saicinpainting/evaluation/utils.py",
    "content": "from enum import Enum\n\nimport yaml\nfrom easydict import EasyDict as edict\nimport torch.nn as nn\nimport torch\n\n\ndef load_yaml(path):\n    with open(path, 'r') as f:\n        return edict(yaml.safe_load(f))\n\n\ndef move_to_device(obj, device):\n    if isinstance(obj, nn.Module):\n        return obj.to(device)\n    if torch.is_tensor(obj):\n        return obj.to(device)\n    if isinstance(obj, (tuple, list)):\n        return [move_to_device(el, device) for el in obj]\n    if isinstance(obj, dict):\n        return {name: move_to_device(val, device) for name, val in obj.items()}\n    raise ValueError(f'Unexpected type {type(obj)}')\n\n\nclass SmallMode(Enum):\n    DROP = \"drop\"\n    UPSCALE = \"upscale\"\n"
  },
  {
    "path": "model_cards/lama/saicinpainting/evaluation/vis.py",
    "content": "import numpy as np\nfrom skimage import io\nfrom skimage.segmentation import mark_boundaries\n\n\ndef save_item_for_vis(item, out_file):\n    mask = item['mask'] > 0.5\n    if mask.ndim == 3:\n        mask = mask[0]\n    img = mark_boundaries(np.transpose(item['image'], (1, 2, 0)),\n                          mask,\n                          color=(1., 0., 0.),\n                          outline_color=(1., 1., 1.),\n                          mode='thick')\n\n    if 'inpainted' in item:\n        inp_img = mark_boundaries(np.transpose(item['inpainted'], (1, 2, 0)),\n                                  mask,\n                                  color=(1., 0., 0.),\n                                  mode='outer')\n        img = np.concatenate((img, inp_img), axis=1)\n\n    img = np.clip(img * 255, 0, 255).astype('uint8')\n    io.imsave(out_file, img)\n\n\ndef save_mask_for_sidebyside(item, out_file):\n    mask = item['mask']# > 0.5\n    if mask.ndim == 3:\n        mask = mask[0]\n    mask = np.clip(mask * 255, 0, 255).astype('uint8')\n    io.imsave(out_file, mask)\n\ndef save_img_for_sidebyside(item, out_file):\n    img = np.transpose(item['image'], (1, 2, 0))\n    img = np.clip(img * 255, 0, 255).astype('uint8')\n    io.imsave(out_file, img)"
  },
  {
    "path": "model_cards/lama/saicinpainting/training/__init__.py",
    "content": ""
  },
  {
    "path": "model_cards/lama/saicinpainting/training/data/__init__.py",
    "content": ""
  },
  {
    "path": "model_cards/lama/saicinpainting/training/data/aug.py",
    "content": "from albumentations import DualIAATransform, to_tuple\nimport imgaug.augmenters as iaa\n\nclass IAAAffine2(DualIAATransform):\n    \"\"\"Place a regular grid of points on the input and randomly move the neighbourhood of these point around\n    via affine transformations.\n\n    Note: This class introduce interpolation artifacts to mask if it has values other than {0;1}\n\n    Args:\n        p (float): probability of applying the transform. Default: 0.5.\n\n    Targets:\n        image, mask\n    \"\"\"\n\n    def __init__(\n        self,\n        scale=(0.7, 1.3),\n        translate_percent=None,\n        translate_px=None,\n        rotate=0.0,\n        shear=(-0.1, 0.1),\n        order=1,\n        cval=0,\n        mode=\"reflect\",\n        always_apply=False,\n        p=0.5,\n    ):\n        super(IAAAffine2, self).__init__(always_apply, p)\n        self.scale = dict(x=scale, y=scale)\n        self.translate_percent = to_tuple(translate_percent, 0)\n        self.translate_px = to_tuple(translate_px, 0)\n        self.rotate = to_tuple(rotate)\n        self.shear = dict(x=shear, y=shear)\n        self.order = order\n        self.cval = cval\n        self.mode = mode\n\n    @property\n    def processor(self):\n        return iaa.Affine(\n            self.scale,\n            self.translate_percent,\n            self.translate_px,\n            self.rotate,\n            self.shear,\n            self.order,\n            self.cval,\n            self.mode,\n        )\n\n    def get_transform_init_args_names(self):\n        return (\"scale\", \"translate_percent\", \"translate_px\", \"rotate\", \"shear\", \"order\", \"cval\", \"mode\")\n\n\nclass IAAPerspective2(DualIAATransform):\n    \"\"\"Perform a random four point perspective transform of the input.\n\n    Note: This class introduce interpolation artifacts to mask if it has values other than {0;1}\n\n    Args:\n        scale ((float, float): standard deviation of the normal distributions. These are used to sample\n            the random distances of the subimage's corners from the full image's corners. Default: (0.05, 0.1).\n        p (float): probability of applying the transform. Default: 0.5.\n\n    Targets:\n        image, mask\n    \"\"\"\n\n    def __init__(self, scale=(0.05, 0.1), keep_size=True, always_apply=False, p=0.5,\n                 order=1, cval=0, mode=\"replicate\"):\n        super(IAAPerspective2, self).__init__(always_apply, p)\n        self.scale = to_tuple(scale, 1.0)\n        self.keep_size = keep_size\n        self.cval = cval\n        self.mode = mode\n\n    @property\n    def processor(self):\n        return iaa.PerspectiveTransform(self.scale, keep_size=self.keep_size, mode=self.mode, cval=self.cval)\n\n    def get_transform_init_args_names(self):\n        return (\"scale\", \"keep_size\")\n"
  },
  {
    "path": "model_cards/lama/saicinpainting/training/data/datasets.py",
    "content": "import glob\nimport logging\nimport os\nimport random\n\nimport albumentations as A\nimport cv2\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nimport webdataset\nfrom omegaconf import open_dict, OmegaConf\nfrom skimage.feature import canny\nfrom skimage.transform import rescale, resize\nfrom torch.utils.data import Dataset, IterableDataset, DataLoader, DistributedSampler, ConcatDataset\n\nfrom saicinpainting.evaluation.data import InpaintingDataset as InpaintingEvaluationDataset, \\\n    OurInpaintingDataset as OurInpaintingEvaluationDataset, ceil_modulo, InpaintingEvalOnlineDataset\nfrom saicinpainting.training.data.aug import IAAAffine2, IAAPerspective2\nfrom saicinpainting.training.data.masks import get_mask_generator\n\nLOGGER = logging.getLogger(__name__)\n\n\nclass InpaintingTrainDataset(Dataset):\n    def __init__(self, indir, mask_generator, transform):\n        self.in_files = list(glob.glob(os.path.join(indir, '**', '*.jpg'), recursive=True))\n        self.mask_generator = mask_generator\n        self.transform = transform\n        self.iter_i = 0\n\n    def __len__(self):\n        return len(self.in_files)\n\n    def __getitem__(self, item):\n        path = self.in_files[item]\n        img = cv2.imread(path)\n        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n        img = self.transform(image=img)['image']\n        img = np.transpose(img, (2, 0, 1))\n        # TODO: maybe generate mask before augmentations? slower, but better for segmentation-based masks\n        mask = self.mask_generator(img, iter_i=self.iter_i)\n        self.iter_i += 1\n        return dict(image=img,\n                    mask=mask)\n\n\nclass InpaintingTrainWebDataset(IterableDataset):\n    def __init__(self, indir, mask_generator, transform, shuffle_buffer=200):\n        self.impl = webdataset.Dataset(indir).shuffle(shuffle_buffer).decode('rgb').to_tuple('jpg')\n        self.mask_generator = mask_generator\n        self.transform = transform\n\n    def __iter__(self):\n        for iter_i, (img,) in enumerate(self.impl):\n            img = np.clip(img * 255, 0, 255).astype('uint8')\n            img = self.transform(image=img)['image']\n            img = np.transpose(img, (2, 0, 1))\n            mask = self.mask_generator(img, iter_i=iter_i)\n            yield dict(image=img,\n                       mask=mask)\n\n\nclass ImgSegmentationDataset(Dataset):\n    def __init__(self, indir, mask_generator, transform, out_size, segm_indir, semantic_seg_n_classes):\n        self.indir = indir\n        self.segm_indir = segm_indir\n        self.mask_generator = mask_generator\n        self.transform = transform\n        self.out_size = out_size\n        self.semantic_seg_n_classes = semantic_seg_n_classes\n        self.in_files = list(glob.glob(os.path.join(indir, '**', '*.jpg'), recursive=True))\n\n    def __len__(self):\n        return len(self.in_files)\n\n    def __getitem__(self, item):\n        path = self.in_files[item]\n        img = cv2.imread(path)\n        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n        img = cv2.resize(img, (self.out_size, self.out_size))\n        img = self.transform(image=img)['image']\n        img = np.transpose(img, (2, 0, 1))\n        mask = self.mask_generator(img)\n        segm, segm_classes= self.load_semantic_segm(path)\n        result = dict(image=img,\n                      mask=mask,\n                      segm=segm,\n                      segm_classes=segm_classes)\n        return result\n\n    def load_semantic_segm(self, img_path):\n        segm_path = img_path.replace(self.indir, self.segm_indir).replace(\".jpg\", \".png\")\n        mask = cv2.imread(segm_path, cv2.IMREAD_GRAYSCALE)\n        mask = cv2.resize(mask, (self.out_size, self.out_size))\n        tensor = torch.from_numpy(np.clip(mask.astype(int)-1, 0, None))\n        ohe = F.one_hot(tensor.long(), num_classes=self.semantic_seg_n_classes) # w x h x n_classes\n        return ohe.permute(2, 0, 1).float(), tensor.unsqueeze(0)\n\n\ndef get_transforms(transform_variant, out_size):\n    if transform_variant == 'default':\n        transform = A.Compose([\n            A.RandomScale(scale_limit=0.2),  # +/- 20%\n            A.PadIfNeeded(min_height=out_size, min_width=out_size),\n            A.RandomCrop(height=out_size, width=out_size),\n            A.HorizontalFlip(),\n            A.CLAHE(),\n            A.RandomBrightnessContrast(brightness_limit=0.2, contrast_limit=0.2),\n            A.HueSaturationValue(hue_shift_limit=5, sat_shift_limit=30, val_shift_limit=5),\n            A.ToFloat()\n        ])\n    elif transform_variant == 'distortions':\n        transform = A.Compose([\n            IAAPerspective2(scale=(0.0, 0.06)),\n            IAAAffine2(scale=(0.7, 1.3),\n                       rotate=(-40, 40),\n                       shear=(-0.1, 0.1)),\n            A.PadIfNeeded(min_height=out_size, min_width=out_size),\n            A.OpticalDistortion(),\n            A.RandomCrop(height=out_size, width=out_size),\n            A.HorizontalFlip(),\n            A.CLAHE(),\n            A.RandomBrightnessContrast(brightness_limit=0.2, contrast_limit=0.2),\n            A.HueSaturationValue(hue_shift_limit=5, sat_shift_limit=30, val_shift_limit=5),\n            A.ToFloat()\n        ])\n    elif transform_variant == 'distortions_scale05_1':\n        transform = A.Compose([\n            IAAPerspective2(scale=(0.0, 0.06)),\n            IAAAffine2(scale=(0.5, 1.0),\n                       rotate=(-40, 40),\n                       shear=(-0.1, 0.1),\n                       p=1),\n            A.PadIfNeeded(min_height=out_size, min_width=out_size),\n            A.OpticalDistortion(),\n            A.RandomCrop(height=out_size, width=out_size),\n            A.HorizontalFlip(),\n            A.CLAHE(),\n            A.RandomBrightnessContrast(brightness_limit=0.2, contrast_limit=0.2),\n            A.HueSaturationValue(hue_shift_limit=5, sat_shift_limit=30, val_shift_limit=5),\n            A.ToFloat()\n        ])\n    elif transform_variant == 'distortions_scale03_12':\n        transform = A.Compose([\n            IAAPerspective2(scale=(0.0, 0.06)),\n            IAAAffine2(scale=(0.3, 1.2),\n                       rotate=(-40, 40),\n                       shear=(-0.1, 0.1),\n                       p=1),\n            A.PadIfNeeded(min_height=out_size, min_width=out_size),\n            A.OpticalDistortion(),\n            A.RandomCrop(height=out_size, width=out_size),\n            A.HorizontalFlip(),\n            A.CLAHE(),\n            A.RandomBrightnessContrast(brightness_limit=0.2, contrast_limit=0.2),\n            A.HueSaturationValue(hue_shift_limit=5, sat_shift_limit=30, val_shift_limit=5),\n            A.ToFloat()\n        ])\n    elif transform_variant == 'distortions_scale03_07':\n        transform = A.Compose([\n            IAAPerspective2(scale=(0.0, 0.06)),\n            IAAAffine2(scale=(0.3, 0.7),  # scale 512 to 256 in average\n                       rotate=(-40, 40),\n                       shear=(-0.1, 0.1),\n                       p=1),\n            A.PadIfNeeded(min_height=out_size, min_width=out_size),\n            A.OpticalDistortion(),\n            A.RandomCrop(height=out_size, width=out_size),\n            A.HorizontalFlip(),\n            A.CLAHE(),\n            A.RandomBrightnessContrast(brightness_limit=0.2, contrast_limit=0.2),\n            A.HueSaturationValue(hue_shift_limit=5, sat_shift_limit=30, val_shift_limit=5),\n            A.ToFloat()\n        ])\n    elif transform_variant == 'distortions_light':\n        transform = A.Compose([\n            IAAPerspective2(scale=(0.0, 0.02)),\n            IAAAffine2(scale=(0.8, 1.8),\n                       rotate=(-20, 20),\n                       shear=(-0.03, 0.03)),\n            A.PadIfNeeded(min_height=out_size, min_width=out_size),\n            A.RandomCrop(height=out_size, width=out_size),\n            A.HorizontalFlip(),\n            A.CLAHE(),\n            A.RandomBrightnessContrast(brightness_limit=0.2, contrast_limit=0.2),\n            A.HueSaturationValue(hue_shift_limit=5, sat_shift_limit=30, val_shift_limit=5),\n            A.ToFloat()\n        ])\n    elif transform_variant == 'non_space_transform':\n        transform = A.Compose([\n            A.CLAHE(),\n            A.RandomBrightnessContrast(brightness_limit=0.2, contrast_limit=0.2),\n            A.HueSaturationValue(hue_shift_limit=5, sat_shift_limit=30, val_shift_limit=5),\n            A.ToFloat()\n        ])\n    elif transform_variant == 'no_augs':\n        transform = A.Compose([\n            A.ToFloat()\n        ])\n    else:\n        raise ValueError(f'Unexpected transform_variant {transform_variant}')\n    return transform\n\n\ndef make_default_train_dataloader(indir, kind='default', out_size=512, mask_gen_kwargs=None, transform_variant='default',\n                                  mask_generator_kind=\"mixed\", dataloader_kwargs=None, ddp_kwargs=None, **kwargs):\n    LOGGER.info(f'Make train dataloader {kind} from {indir}. Using mask generator={mask_generator_kind}')\n\n    mask_generator = get_mask_generator(kind=mask_generator_kind, kwargs=mask_gen_kwargs)\n    transform = get_transforms(transform_variant, out_size)\n\n    if kind == 'default':\n        dataset = InpaintingTrainDataset(indir=indir,\n                                         mask_generator=mask_generator,\n                                         transform=transform,\n                                         **kwargs)\n    elif kind == 'default_web':\n        dataset = InpaintingTrainWebDataset(indir=indir,\n                                            mask_generator=mask_generator,\n                                            transform=transform,\n                                            **kwargs)\n    elif kind == 'img_with_segm':\n        dataset = ImgSegmentationDataset(indir=indir,\n                                         mask_generator=mask_generator,\n                                         transform=transform,\n                                         out_size=out_size,\n                                         **kwargs)\n    else:\n        raise ValueError(f'Unknown train dataset kind {kind}')\n\n    if dataloader_kwargs is None:\n        dataloader_kwargs = {}\n\n    is_dataset_only_iterable = kind in ('default_web',)\n\n    if ddp_kwargs is not None and not is_dataset_only_iterable:\n        dataloader_kwargs['shuffle'] = False\n        dataloader_kwargs['sampler'] = DistributedSampler(dataset, **ddp_kwargs)\n\n    if is_dataset_only_iterable and 'shuffle' in dataloader_kwargs:\n        with open_dict(dataloader_kwargs):\n            del dataloader_kwargs['shuffle']\n\n    dataloader = DataLoader(dataset, **dataloader_kwargs)\n    return dataloader\n\n\ndef make_default_val_dataset(indir, kind='default', out_size=512, transform_variant='default', **kwargs):\n    if OmegaConf.is_list(indir) or isinstance(indir, (tuple, list)):\n        return ConcatDataset([\n            make_default_val_dataset(idir, kind=kind, out_size=out_size, transform_variant=transform_variant, **kwargs) for idir in indir \n        ])\n\n    LOGGER.info(f'Make val dataloader {kind} from {indir}')\n    mask_generator = get_mask_generator(kind=kwargs.get(\"mask_generator_kind\"), kwargs=kwargs.get(\"mask_gen_kwargs\"))\n\n    if transform_variant is not None:\n        transform = get_transforms(transform_variant, out_size)\n\n    if kind == 'default':\n        dataset = InpaintingEvaluationDataset(indir, **kwargs)\n    elif kind == 'our_eval':\n        dataset = OurInpaintingEvaluationDataset(indir, **kwargs)\n    elif kind == 'img_with_segm':\n        dataset = ImgSegmentationDataset(indir=indir,\n                                         mask_generator=mask_generator,\n                                         transform=transform,\n                                         out_size=out_size,\n                                         **kwargs)\n    elif kind == 'online':\n        dataset = InpaintingEvalOnlineDataset(indir=indir,\n                                              mask_generator=mask_generator,\n                                              transform=transform,\n                                              out_size=out_size,\n                                              **kwargs)\n    else:\n        raise ValueError(f'Unknown val dataset kind {kind}')\n\n    return dataset\n\n\ndef make_default_val_dataloader(*args, dataloader_kwargs=None, **kwargs):\n    dataset = make_default_val_dataset(*args, **kwargs)\n\n    if dataloader_kwargs is None:\n        dataloader_kwargs = {}\n    dataloader = DataLoader(dataset, **dataloader_kwargs)\n    return dataloader\n\n\ndef make_constant_area_crop_params(img_height, img_width, min_size=128, max_size=512, area=256*256, round_to_mod=16):\n    min_size = min(img_height, img_width, min_size)\n    max_size = min(img_height, img_width, max_size)\n    if random.random() < 0.5:\n        out_height = min(max_size, ceil_modulo(random.randint(min_size, max_size), round_to_mod))\n        out_width = min(max_size, ceil_modulo(area // out_height, round_to_mod))\n    else:\n        out_width = min(max_size, ceil_modulo(random.randint(min_size, max_size), round_to_mod))\n        out_height = min(max_size, ceil_modulo(area // out_width, round_to_mod))\n\n    start_y = random.randint(0, img_height - out_height)\n    start_x = random.randint(0, img_width - out_width)\n    return (start_y, start_x, out_height, out_width)\n"
  },
  {
    "path": "model_cards/lama/saicinpainting/training/data/masks.py",
    "content": "import math\nimport random\nimport hashlib\nimport logging\nfrom enum import Enum\n\nimport cv2\nimport numpy as np\n\nfrom saicinpainting.evaluation.masks.mask import SegmentationMask\nfrom saicinpainting.utils import LinearRamp\n\nLOGGER = logging.getLogger(__name__)\n\n\nclass DrawMethod(Enum):\n    LINE = 'line'\n    CIRCLE = 'circle'\n    SQUARE = 'square'\n\n\ndef make_random_irregular_mask(shape, max_angle=4, max_len=60, max_width=20, min_times=0, max_times=10,\n                               draw_method=DrawMethod.LINE):\n    draw_method = DrawMethod(draw_method)\n\n    height, width = shape\n    mask = np.zeros((height, width), np.float32)\n    times = np.random.randint(min_times, max_times + 1)\n    for i in range(times):\n        start_x = np.random.randint(width)\n        start_y = np.random.randint(height)\n        for j in range(1 + np.random.randint(5)):\n            angle = 0.01 + np.random.randint(max_angle)\n            if i % 2 == 0:\n                angle = 2 * 3.1415926 - angle\n            length = 10 + np.random.randint(max_len)\n            brush_w = 5 + np.random.randint(max_width)\n            end_x = np.clip((start_x + length * np.sin(angle)).astype(np.int32), 0, width)\n            end_y = np.clip((start_y + length * np.cos(angle)).astype(np.int32), 0, height)\n            if draw_method == DrawMethod.LINE:\n                cv2.line(mask, (start_x, start_y), (end_x, end_y), 1.0, brush_w)\n            elif draw_method == DrawMethod.CIRCLE:\n                cv2.circle(mask, (start_x, start_y), radius=brush_w, color=1., thickness=-1)\n            elif draw_method == DrawMethod.SQUARE:\n                radius = brush_w // 2\n                mask[start_y - radius:start_y + radius, start_x - radius:start_x + radius] = 1\n            start_x, start_y = end_x, end_y\n    return mask[None, ...]\n\n\nclass RandomIrregularMaskGenerator:\n    def __init__(self, max_angle=4, max_len=60, max_width=20, min_times=0, max_times=10, ramp_kwargs=None,\n                 draw_method=DrawMethod.LINE):\n        self.max_angle = max_angle\n        self.max_len = max_len\n        self.max_width = max_width\n        self.min_times = min_times\n        self.max_times = max_times\n        self.draw_method = draw_method\n        self.ramp = LinearRamp(**ramp_kwargs) if ramp_kwargs is not None else None\n\n    def __call__(self, img, iter_i=None, raw_image=None):\n        coef = self.ramp(iter_i) if (self.ramp is not None) and (iter_i is not None) else 1\n        cur_max_len = int(max(1, self.max_len * coef))\n        cur_max_width = int(max(1, self.max_width * coef))\n        cur_max_times = int(self.min_times + 1 + (self.max_times - self.min_times) * coef)\n        return make_random_irregular_mask(img.shape[1:], max_angle=self.max_angle, max_len=cur_max_len,\n                                          max_width=cur_max_width, min_times=self.min_times, max_times=cur_max_times,\n                                          draw_method=self.draw_method)\n\n\ndef make_random_rectangle_mask(shape, margin=10, bbox_min_size=30, bbox_max_size=100, min_times=0, max_times=3):\n    height, width = shape\n    mask = np.zeros((height, width), np.float32)\n    bbox_max_size = min(bbox_max_size, height - margin * 2, width - margin * 2)\n    times = np.random.randint(min_times, max_times + 1)\n    for i in range(times):\n        box_width = np.random.randint(bbox_min_size, bbox_max_size)\n        box_height = np.random.randint(bbox_min_size, bbox_max_size)\n        start_x = np.random.randint(margin, width - margin - box_width + 1)\n        start_y = np.random.randint(margin, height - margin - box_height + 1)\n        mask[start_y:start_y + box_height, start_x:start_x + box_width] = 1\n    return mask[None, ...]\n\n\nclass RandomRectangleMaskGenerator:\n    def __init__(self, margin=10, bbox_min_size=30, bbox_max_size=100, min_times=0, max_times=3, ramp_kwargs=None):\n        self.margin = margin\n        self.bbox_min_size = bbox_min_size\n        self.bbox_max_size = bbox_max_size\n        self.min_times = min_times\n        self.max_times = max_times\n        self.ramp = LinearRamp(**ramp_kwargs) if ramp_kwargs is not None else None\n\n    def __call__(self, img, iter_i=None, raw_image=None):\n        coef = self.ramp(iter_i) if (self.ramp is not None) and (iter_i is not None) else 1\n        cur_bbox_max_size = int(self.bbox_min_size + 1 + (self.bbox_max_size - self.bbox_min_size) * coef)\n        cur_max_times = int(self.min_times + (self.max_times - self.min_times) * coef)\n        return make_random_rectangle_mask(img.shape[1:], margin=self.margin, bbox_min_size=self.bbox_min_size,\n                                          bbox_max_size=cur_bbox_max_size, min_times=self.min_times,\n                                          max_times=cur_max_times)\n\n\nclass RandomSegmentationMaskGenerator:\n    def __init__(self, **kwargs):\n        self.impl = None  # will be instantiated in first call (effectively in subprocess)\n        self.kwargs = kwargs\n\n    def __call__(self, img, iter_i=None, raw_image=None):\n        if self.impl is None:\n            self.impl = SegmentationMask(**self.kwargs)\n\n        masks = self.impl.get_masks(np.transpose(img, (1, 2, 0)))\n        masks = [m for m in masks if len(np.unique(m)) > 1]\n        return np.random.choice(masks)\n\n\ndef make_random_superres_mask(shape, min_step=2, max_step=4, min_width=1, max_width=3):\n    height, width = shape\n    mask = np.zeros((height, width), np.float32)\n    step_x = np.random.randint(min_step, max_step + 1)\n    width_x = np.random.randint(min_width, min(step_x, max_width + 1))\n    offset_x = np.random.randint(0, step_x)\n\n    step_y = np.random.randint(min_step, max_step + 1)\n    width_y = np.random.randint(min_width, min(step_y, max_width + 1))\n    offset_y = np.random.randint(0, step_y)\n\n    for dy in range(width_y):\n        mask[offset_y + dy::step_y] = 1\n    for dx in range(width_x):\n        mask[:, offset_x + dx::step_x] = 1\n    return mask[None, ...]\n\n\nclass RandomSuperresMaskGenerator:\n    def __init__(self, **kwargs):\n        self.kwargs = kwargs\n\n    def __call__(self, img, iter_i=None):\n        return make_random_superres_mask(img.shape[1:], **self.kwargs)\n\n\nclass DumbAreaMaskGenerator:\n    min_ratio = 0.1\n    max_ratio = 0.35\n    default_ratio = 0.225\n\n    def __init__(self, is_training):\n        #Parameters:\n        #    is_training(bool): If true - random rectangular mask, if false - central square mask\n        self.is_training = is_training\n\n    def _random_vector(self, dimension):\n        if self.is_training:\n            lower_limit = math.sqrt(self.min_ratio)\n            upper_limit = math.sqrt(self.max_ratio)\n            mask_side = round((random.random() * (upper_limit - lower_limit) + lower_limit) * dimension)\n            u = random.randint(0, dimension-mask_side-1)\n            v = u+mask_side \n        else:\n            margin = (math.sqrt(self.default_ratio) / 2) * dimension\n            u = round(dimension/2 - margin)\n            v = round(dimension/2 + margin)\n        return u, v\n\n    def __call__(self, img, iter_i=None, raw_image=None):\n        c, height, width = img.shape\n        mask = np.zeros((height, width), np.float32)\n        x1, x2 = self._random_vector(width)\n        y1, y2 = self._random_vector(height)\n        mask[x1:x2, y1:y2] = 1\n        return mask[None, ...]\n\n\nclass OutpaintingMaskGenerator:\n    def __init__(self, min_padding_percent:float=0.04, max_padding_percent:int=0.25, left_padding_prob:float=0.5, top_padding_prob:float=0.5, \n                 right_padding_prob:float=0.5, bottom_padding_prob:float=0.5, is_fixed_randomness:bool=False):\n        \"\"\"\n        is_fixed_randomness - get identical paddings for the same image if args are the same\n        \"\"\"\n        self.min_padding_percent = min_padding_percent\n        self.max_padding_percent = max_padding_percent\n        self.probs = [left_padding_prob, top_padding_prob, right_padding_prob, bottom_padding_prob]\n        self.is_fixed_randomness = is_fixed_randomness\n\n        assert self.min_padding_percent <= self.max_padding_percent\n        assert self.max_padding_percent > 0\n        assert len([x for x in [self.min_padding_percent, self.max_padding_percent] if (x>=0 and x<=1)]) == 2, f\"Padding percentage should be in [0,1]\"\n        assert sum(self.probs) > 0, f\"At least one of the padding probs should be greater than 0 - {self.probs}\"\n        assert len([x for x in self.probs if (x >= 0) and (x <= 1)]) == 4, f\"At least one of padding probs is not in [0,1] - {self.probs}\"\n        if len([x for x in self.probs if x > 0]) == 1:\n            LOGGER.warning(f\"Only one padding prob is greater than zero - {self.probs}. That means that the outpainting masks will be always on the same side\")\n\n    def apply_padding(self, mask, coord):\n        mask[int(coord[0][0]*self.img_h):int(coord[1][0]*self.img_h),   \n             int(coord[0][1]*self.img_w):int(coord[1][1]*self.img_w)] = 1\n        return mask\n\n    def get_padding(self, size):\n        n1 = int(self.min_padding_percent*size)\n        n2 = int(self.max_padding_percent*size)\n        return self.rnd.randint(n1, n2) / size\n\n    @staticmethod\n    def _img2rs(img):\n        arr = np.ascontiguousarray(img.astype(np.uint8))\n        str_hash = hashlib.sha1(arr).hexdigest()\n        res = hash(str_hash)%(2**32)\n        return res\n\n    def __call__(self, img, iter_i=None, raw_image=None):\n        c, self.img_h, self.img_w = img.shape\n        mask = np.zeros((self.img_h, self.img_w), np.float32)\n        at_least_one_mask_applied = False\n\n        if self.is_fixed_randomness:\n            assert raw_image is not None, f\"Cant calculate hash on raw_image=None\"\n            rs = self._img2rs(raw_image)\n            self.rnd = np.random.RandomState(rs)\n        else:\n            self.rnd = np.random\n\n        coords = [[\n                   (0,0), \n                   (1,self.get_padding(size=self.img_h))\n                  ],\n                  [\n                    (0,0),\n                    (self.get_padding(size=self.img_w),1)\n                  ],\n                  [\n                    (0,1-self.get_padding(size=self.img_h)),\n                    (1,1)\n                  ],    \n                  [\n                    (1-self.get_padding(size=self.img_w),0),\n                    (1,1)\n                  ]]\n\n        for pp, coord in zip(self.probs, coords):\n            if self.rnd.random() < pp:\n                at_least_one_mask_applied = True\n                mask = self.apply_padding(mask=mask, coord=coord)\n\n        if not at_least_one_mask_applied:\n            idx = self.rnd.choice(range(len(coords)), p=np.array(self.probs)/sum(self.probs))\n            mask = self.apply_padding(mask=mask, coord=coords[idx])\n        return mask[None, ...]\n\n\nclass MixedMaskGenerator:\n    def __init__(self, irregular_proba=1/3, irregular_kwargs=None,\n                 box_proba=1/3, box_kwargs=None,\n                 segm_proba=1/3, segm_kwargs=None,\n                 squares_proba=0, squares_kwargs=None,\n                 superres_proba=0, superres_kwargs=None,\n                 outpainting_proba=0, outpainting_kwargs=None,\n                 invert_proba=0):\n        self.probas = []\n        self.gens = []\n\n        if irregular_proba > 0:\n            self.probas.append(irregular_proba)\n            if irregular_kwargs is None:\n                irregular_kwargs = {}\n            else:\n                irregular_kwargs = dict(irregular_kwargs)\n            irregular_kwargs['draw_method'] = DrawMethod.LINE\n            self.gens.append(RandomIrregularMaskGenerator(**irregular_kwargs))\n\n        if box_proba > 0:\n            self.probas.append(box_proba)\n            if box_kwargs is None:\n                box_kwargs = {}\n            self.gens.append(RandomRectangleMaskGenerator(**box_kwargs))\n\n        if segm_proba > 0:\n            self.probas.append(segm_proba)\n            if segm_kwargs is None:\n                segm_kwargs = {}\n            self.gens.append(RandomSegmentationMaskGenerator(**segm_kwargs))\n\n        if squares_proba > 0:\n            self.probas.append(squares_proba)\n            if squares_kwargs is None:\n                squares_kwargs = {}\n            else:\n                squares_kwargs = dict(squares_kwargs)\n            squares_kwargs['draw_method'] = DrawMethod.SQUARE\n            self.gens.append(RandomIrregularMaskGenerator(**squares_kwargs))\n\n        if superres_proba > 0:\n            self.probas.append(superres_proba)\n            if superres_kwargs is None:\n                superres_kwargs = {}\n            self.gens.append(RandomSuperresMaskGenerator(**superres_kwargs))\n\n        if outpainting_proba > 0:\n            self.probas.append(outpainting_proba)\n            if outpainting_kwargs is None:\n                outpainting_kwargs = {}\n            self.gens.append(OutpaintingMaskGenerator(**outpainting_kwargs))\n\n        self.probas = np.array(self.probas, dtype='float32')\n        self.probas /= self.probas.sum()\n        self.invert_proba = invert_proba\n\n    def __call__(self, img, iter_i=None, raw_image=None):\n        kind = np.random.choice(len(self.probas), p=self.probas)\n        gen = self.gens[kind]\n        result = gen(img, iter_i=iter_i, raw_image=raw_image)\n        if self.invert_proba > 0 and random.random() < self.invert_proba:\n            result = 1 - result\n        return result\n\n\ndef get_mask_generator(kind, kwargs):\n    if kind is None:\n        kind = \"mixed\"\n    if kwargs is None:\n        kwargs = {}\n\n    if kind == \"mixed\":\n        cl = MixedMaskGenerator\n    elif kind == \"outpainting\":\n        cl = OutpaintingMaskGenerator\n    elif kind == \"dumb\":\n        cl = DumbAreaMaskGenerator\n    else:\n        raise NotImplementedError(f\"No such generator kind = {kind}\")\n    return cl(**kwargs)\n"
  },
  {
    "path": "model_cards/lama/saicinpainting/training/losses/__init__.py",
    "content": ""
  },
  {
    "path": "model_cards/lama/saicinpainting/training/losses/adversarial.py",
    "content": "from typing import Tuple, Dict, Optional\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\nclass BaseAdversarialLoss:\n    def pre_generator_step(self, real_batch: torch.Tensor, fake_batch: torch.Tensor,\n                           generator: nn.Module, discriminator: nn.Module):\n        \"\"\"\n        Prepare for generator step\n        :param real_batch: Tensor, a batch of real samples\n        :param fake_batch: Tensor, a batch of samples produced by generator\n        :param generator:\n        :param discriminator:\n        :return: None\n        \"\"\"\n\n    def pre_discriminator_step(self, real_batch: torch.Tensor, fake_batch: torch.Tensor,\n                               generator: nn.Module, discriminator: nn.Module):\n        \"\"\"\n        Prepare for discriminator step\n        :param real_batch: Tensor, a batch of real samples\n        :param fake_batch: Tensor, a batch of samples produced by generator\n        :param generator:\n        :param discriminator:\n        :return: None\n        \"\"\"\n\n    def generator_loss(self, real_batch: torch.Tensor, fake_batch: torch.Tensor,\n                       discr_real_pred: torch.Tensor, discr_fake_pred: torch.Tensor,\n                       mask: Optional[torch.Tensor] = None) \\\n            -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:\n        \"\"\"\n        Calculate generator loss\n        :param real_batch: Tensor, a batch of real samples\n        :param fake_batch: Tensor, a batch of samples produced by generator\n        :param discr_real_pred: Tensor, discriminator output for real_batch\n        :param discr_fake_pred: Tensor, discriminator output for fake_batch\n        :param mask: Tensor, actual mask, which was at input of generator when making fake_batch\n        :return: total generator loss along with some values that might be interesting to log\n        \"\"\"\n        raise NotImplemented()\n\n    def discriminator_loss(self, real_batch: torch.Tensor, fake_batch: torch.Tensor,\n                           discr_real_pred: torch.Tensor, discr_fake_pred: torch.Tensor,\n                           mask: Optional[torch.Tensor] = None) \\\n            -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:\n        \"\"\"\n        Calculate discriminator loss and call .backward() on it\n        :param real_batch: Tensor, a batch of real samples\n        :param fake_batch: Tensor, a batch of samples produced by generator\n        :param discr_real_pred: Tensor, discriminator output for real_batch\n        :param discr_fake_pred: Tensor, discriminator output for fake_batch\n        :param mask: Tensor, actual mask, which was at input of generator when making fake_batch\n        :return: total discriminator loss along with some values that might be interesting to log\n        \"\"\"\n        raise NotImplemented()\n\n    def interpolate_mask(self, mask, shape):\n        assert mask is not None\n        assert self.allow_scale_mask or shape == mask.shape[-2:]\n        if shape != mask.shape[-2:] and self.allow_scale_mask:\n            if self.mask_scale_mode == 'maxpool':\n                mask = F.adaptive_max_pool2d(mask, shape)\n            else:\n                mask = F.interpolate(mask, size=shape, mode=self.mask_scale_mode)\n        return mask\n\ndef make_r1_gp(discr_real_pred, real_batch):\n    if torch.is_grad_enabled():\n        grad_real = torch.autograd.grad(outputs=discr_real_pred.sum(), inputs=real_batch, create_graph=True)[0]\n        grad_penalty = (grad_real.view(grad_real.shape[0], -1).norm(2, dim=1) ** 2).mean()\n    else:\n        grad_penalty = 0\n    real_batch.requires_grad = False\n\n    return grad_penalty\n\nclass NonSaturatingWithR1(BaseAdversarialLoss):\n    def __init__(self, gp_coef=5, weight=1, mask_as_fake_target=False, allow_scale_mask=False,\n                 mask_scale_mode='nearest', extra_mask_weight_for_gen=0,\n                 use_unmasked_for_gen=True, use_unmasked_for_discr=True):\n        self.gp_coef = gp_coef\n        self.weight = weight\n        # use for discr => use for gen;\n        # otherwise we teach only the discr to pay attention to very small difference\n        assert use_unmasked_for_gen or (not use_unmasked_for_discr)\n        # mask as target => use unmasked for discr:\n        # if we don't care about unmasked regions at all\n        # then it doesn't matter if the value of mask_as_fake_target is true or false\n        assert use_unmasked_for_discr or (not mask_as_fake_target)\n        self.use_unmasked_for_gen = use_unmasked_for_gen\n        self.use_unmasked_for_discr = use_unmasked_for_discr\n        self.mask_as_fake_target = mask_as_fake_target\n        self.allow_scale_mask = allow_scale_mask\n        self.mask_scale_mode = mask_scale_mode\n        self.extra_mask_weight_for_gen = extra_mask_weight_for_gen\n\n    def generator_loss(self, real_batch: torch.Tensor, fake_batch: torch.Tensor,\n                       discr_real_pred: torch.Tensor, discr_fake_pred: torch.Tensor,\n                       mask=None) \\\n            -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:\n        fake_loss = F.softplus(-discr_fake_pred)\n        if (self.mask_as_fake_target and self.extra_mask_weight_for_gen > 0) or \\\n                not self.use_unmasked_for_gen:  # == if masked region should be treated differently\n            mask = self.interpolate_mask(mask, discr_fake_pred.shape[-2:])\n            if not self.use_unmasked_for_gen:\n                fake_loss = fake_loss * mask\n            else:\n                pixel_weights = 1 + mask * self.extra_mask_weight_for_gen\n                fake_loss = fake_loss * pixel_weights\n\n        return fake_loss.mean() * self.weight, dict()\n\n    def pre_discriminator_step(self, real_batch: torch.Tensor, fake_batch: torch.Tensor,\n                               generator: nn.Module, discriminator: nn.Module):\n        real_batch.requires_grad = True\n\n    def discriminator_loss(self, real_batch: torch.Tensor, fake_batch: torch.Tensor,\n                           discr_real_pred: torch.Tensor, discr_fake_pred: torch.Tensor,\n                           mask=None) \\\n            -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:\n\n        real_loss = F.softplus(-discr_real_pred)\n        grad_penalty = make_r1_gp(discr_real_pred, real_batch) * self.gp_coef\n        fake_loss = F.softplus(discr_fake_pred)\n\n        if not self.use_unmasked_for_discr or self.mask_as_fake_target:\n            # == if masked region should be treated differently\n            mask = self.interpolate_mask(mask, discr_fake_pred.shape[-2:])\n            # use_unmasked_for_discr=False only makes sense for fakes;\n            # for reals there is no difference beetween two regions\n            fake_loss = fake_loss * mask\n            if self.mask_as_fake_target:\n                fake_loss = fake_loss + (1 - mask) * F.softplus(-discr_fake_pred)\n\n        sum_discr_loss = real_loss + grad_penalty + fake_loss\n        metrics = dict(discr_real_out=discr_real_pred.mean(),\n                       discr_fake_out=discr_fake_pred.mean(),\n                       discr_real_gp=grad_penalty)\n        return sum_discr_loss.mean(), metrics\n\nclass BCELoss(BaseAdversarialLoss):\n    def __init__(self, weight):\n        self.weight = weight\n        self.bce_loss = nn.BCEWithLogitsLoss()\n\n    def generator_loss(self, discr_fake_pred: torch.Tensor) -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:\n        real_mask_gt = torch.zeros(discr_fake_pred.shape).to(discr_fake_pred.device)\n        fake_loss = self.bce_loss(discr_fake_pred, real_mask_gt) * self.weight\n        return fake_loss, dict()\n\n    def pre_discriminator_step(self, real_batch: torch.Tensor, fake_batch: torch.Tensor,\n                               generator: nn.Module, discriminator: nn.Module):\n        real_batch.requires_grad = True\n\n    def discriminator_loss(self,\n                           mask: torch.Tensor,\n                           discr_real_pred: torch.Tensor,\n                           discr_fake_pred: torch.Tensor) -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:\n\n        real_mask_gt = torch.zeros(discr_real_pred.shape).to(discr_real_pred.device)\n        sum_discr_loss = (self.bce_loss(discr_real_pred, real_mask_gt) +  self.bce_loss(discr_fake_pred, mask)) / 2\n        metrics = dict(discr_real_out=discr_real_pred.mean(),\n                       discr_fake_out=discr_fake_pred.mean(),\n                       discr_real_gp=0)\n        return sum_discr_loss, metrics\n\n\ndef make_discrim_loss(kind, **kwargs):\n    if kind == 'r1':\n        return NonSaturatingWithR1(**kwargs)\n    elif kind == 'bce':\n        return BCELoss(**kwargs)\n    raise ValueError(f'Unknown adversarial loss kind {kind}')\n"
  },
  {
    "path": "model_cards/lama/saicinpainting/training/losses/constants.py",
    "content": "weights = {\"ade20k\": \n    [6.34517766497462,\n    9.328358208955224,\n    11.389521640091116,\n    16.10305958132045,\n    20.833333333333332,\n    22.22222222222222,\n    25.125628140703515,\n    43.29004329004329,\n    50.5050505050505,\n    54.6448087431694,\n    55.24861878453038,\n    60.24096385542168,\n    62.5,\n    66.2251655629139,\n    84.74576271186442,\n    90.90909090909092,\n    91.74311926605505,\n    96.15384615384616,\n    96.15384615384616,\n    97.08737864077669,\n    102.04081632653062,\n    135.13513513513513,\n    149.2537313432836,\n    153.84615384615384,\n    163.93442622950818,\n    166.66666666666666,\n    188.67924528301887,\n    192.30769230769232,\n    217.3913043478261,\n    227.27272727272725,\n    227.27272727272725,\n    227.27272727272725,\n    303.03030303030306,\n    322.5806451612903,\n    333.3333333333333,\n    370.3703703703703,\n    384.61538461538464,\n    416.6666666666667,\n    416.6666666666667,\n    434.7826086956522,\n    434.7826086956522,\n    454.5454545454545,\n    454.5454545454545,\n    500.0,\n    526.3157894736842,\n    526.3157894736842,\n    555.5555555555555,\n    555.5555555555555,\n    555.5555555555555,\n    555.5555555555555,\n    555.5555555555555,\n    555.5555555555555,\n    555.5555555555555,\n    588.2352941176471,\n    588.2352941176471,\n    588.2352941176471,\n    588.2352941176471,\n    588.2352941176471,\n    666.6666666666666,\n    666.6666666666666,\n    666.6666666666666,\n    666.6666666666666,\n    714.2857142857143,\n    714.2857142857143,\n    714.2857142857143,\n    714.2857142857143,\n    714.2857142857143,\n    769.2307692307693,\n    769.2307692307693,\n    769.2307692307693,\n    833.3333333333334,\n    833.3333333333334,\n    833.3333333333334,\n    833.3333333333334,\n    909.090909090909,\n    1000.0,\n    1111.111111111111,\n    1111.111111111111,\n    1111.111111111111,\n    1111.111111111111,\n    1111.111111111111,\n    1250.0,\n    1250.0,\n    1250.0,\n    1250.0,\n    1250.0,\n    1428.5714285714287,\n    1428.5714285714287,\n    1428.5714285714287,\n    1428.5714285714287,\n    1428.5714285714287,\n    1428.5714285714287,\n    1428.5714285714287,\n    1666.6666666666667,\n    1666.6666666666667,\n    1666.6666666666667,\n    1666.6666666666667,\n    1666.6666666666667,\n    1666.6666666666667,\n    1666.6666666666667,\n    1666.6666666666667,\n    1666.6666666666667,\n    1666.6666666666667,\n    1666.6666666666667,\n    2000.0,\n    2000.0,\n    2000.0,\n    2000.0,\n    2000.0,\n    2000.0,\n    2000.0,\n    2000.0,\n    2000.0,\n    2000.0,\n    2000.0,\n    2000.0,\n    2000.0,\n    2000.0,\n    2000.0,\n    2000.0,\n    2000.0,\n    2500.0,\n    2500.0,\n    2500.0,\n    2500.0,\n    2500.0,\n    2500.0,\n    2500.0,\n    2500.0,\n    2500.0,\n    2500.0,\n    2500.0,\n    2500.0,\n    2500.0,\n    3333.3333333333335,\n    3333.3333333333335,\n    3333.3333333333335,\n    3333.3333333333335,\n    3333.3333333333335,\n    3333.3333333333335,\n    3333.3333333333335,\n    3333.3333333333335,\n    3333.3333333333335,\n    3333.3333333333335,\n    3333.3333333333335,\n    3333.3333333333335,\n    3333.3333333333335,\n    5000.0,\n    5000.0,\n    5000.0]\n}"
  },
  {
    "path": "model_cards/lama/saicinpainting/training/losses/distance_weighting.py",
    "content": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torchvision\n\nfrom saicinpainting.training.losses.perceptual import IMAGENET_STD, IMAGENET_MEAN\n\n\ndef dummy_distance_weighter(real_img, pred_img, mask):\n    return mask\n\n\ndef get_gauss_kernel(kernel_size, width_factor=1):\n    coords = torch.stack(torch.meshgrid(torch.arange(kernel_size),\n                                        torch.arange(kernel_size)),\n                         dim=0).float()\n    diff = torch.exp(-((coords - kernel_size // 2) ** 2).sum(0) / kernel_size / width_factor)\n    diff /= diff.sum()\n    return diff\n\n\nclass BlurMask(nn.Module):\n    def __init__(self, kernel_size=5, width_factor=1):\n        super().__init__()\n        self.filter = nn.Conv2d(1, 1, kernel_size, padding=kernel_size // 2, padding_mode='replicate', bias=False)\n        self.filter.weight.data.copy_(get_gauss_kernel(kernel_size, width_factor=width_factor))\n\n    def forward(self, real_img, pred_img, mask):\n        with torch.no_grad():\n            result = self.filter(mask) * mask\n            return result\n\n\nclass EmulatedEDTMask(nn.Module):\n    def __init__(self, dilate_kernel_size=5, blur_kernel_size=5, width_factor=1):\n        super().__init__()\n        self.dilate_filter = nn.Conv2d(1, 1, dilate_kernel_size, padding=dilate_kernel_size// 2, padding_mode='replicate',\n                                       bias=False)\n        self.dilate_filter.weight.data.copy_(torch.ones(1, 1, dilate_kernel_size, dilate_kernel_size, dtype=torch.float))\n        self.blur_filter = nn.Conv2d(1, 1, blur_kernel_size, padding=blur_kernel_size // 2, padding_mode='replicate', bias=False)\n        self.blur_filter.weight.data.copy_(get_gauss_kernel(blur_kernel_size, width_factor=width_factor))\n\n    def forward(self, real_img, pred_img, mask):\n        with torch.no_grad():\n            known_mask = 1 - mask\n            dilated_known_mask = (self.dilate_filter(known_mask) > 1).float()\n            result = self.blur_filter(1 - dilated_known_mask) * mask\n            return result\n\n\nclass PropagatePerceptualSim(nn.Module):\n    def __init__(self, level=2, max_iters=10, temperature=500, erode_mask_size=3):\n        super().__init__()\n        vgg = torchvision.models.vgg19(pretrained=True).features\n        vgg_avg_pooling = []\n\n        for weights in vgg.parameters():\n            weights.requires_grad = False\n\n        cur_level_i = 0\n        for module in vgg.modules():\n            if module.__class__.__name__ == 'Sequential':\n                continue\n            elif module.__class__.__name__ == 'MaxPool2d':\n                vgg_avg_pooling.append(nn.AvgPool2d(kernel_size=2, stride=2, padding=0))\n            else:\n                vgg_avg_pooling.append(module)\n                if module.__class__.__name__ == 'ReLU':\n                    cur_level_i += 1\n                if cur_level_i == level:\n                    break\n\n        self.features = nn.Sequential(*vgg_avg_pooling)\n\n        self.max_iters = max_iters\n        self.temperature = temperature\n        self.do_erode = erode_mask_size > 0\n        if self.do_erode:\n            self.erode_mask = nn.Conv2d(1, 1, erode_mask_size, padding=erode_mask_size // 2, bias=False)\n            self.erode_mask.weight.data.fill_(1)\n\n    def forward(self, real_img, pred_img, mask):\n        with torch.no_grad():\n            real_img = (real_img - IMAGENET_MEAN.to(real_img)) / IMAGENET_STD.to(real_img)\n            real_feats = self.features(real_img)\n\n            vertical_sim = torch.exp(-(real_feats[:, :, 1:] - real_feats[:, :, :-1]).pow(2).sum(1, keepdim=True)\n                                     / self.temperature)\n            horizontal_sim = torch.exp(-(real_feats[:, :, :, 1:] - real_feats[:, :, :, :-1]).pow(2).sum(1, keepdim=True)\n                                       / self.temperature)\n\n            mask_scaled = F.interpolate(mask, size=real_feats.shape[-2:], mode='bilinear', align_corners=False)\n            if self.do_erode:\n                mask_scaled = (self.erode_mask(mask_scaled) > 1).float()\n\n            cur_knowness = 1 - mask_scaled\n\n            for iter_i in range(self.max_iters):\n                new_top_knowness = F.pad(cur_knowness[:, :, :-1] * vertical_sim, (0, 0, 1, 0), mode='replicate')\n                new_bottom_knowness = F.pad(cur_knowness[:, :, 1:] * vertical_sim, (0, 0, 0, 1), mode='replicate')\n\n                new_left_knowness = F.pad(cur_knowness[:, :, :, :-1] * horizontal_sim, (1, 0, 0, 0), mode='replicate')\n                new_right_knowness = F.pad(cur_knowness[:, :, :, 1:] * horizontal_sim, (0, 1, 0, 0), mode='replicate')\n\n                new_knowness = torch.stack([new_top_knowness, new_bottom_knowness,\n                                            new_left_knowness, new_right_knowness],\n                                           dim=0).max(0).values\n\n                cur_knowness = torch.max(cur_knowness, new_knowness)\n\n            cur_knowness = F.interpolate(cur_knowness, size=mask.shape[-2:], mode='bilinear')\n            result = torch.min(mask, 1 - cur_knowness)\n\n            return result\n\n\ndef make_mask_distance_weighter(kind='none', **kwargs):\n    if kind == 'none':\n        return dummy_distance_weighter\n    if kind == 'blur':\n        return BlurMask(**kwargs)\n    if kind == 'edt':\n        return EmulatedEDTMask(**kwargs)\n    if kind == 'pps':\n        return PropagatePerceptualSim(**kwargs)\n    raise ValueError(f'Unknown mask distance weighter kind {kind}')\n"
  },
  {
    "path": "model_cards/lama/saicinpainting/training/losses/feature_matching.py",
    "content": "from typing import List\n\nimport torch\nimport torch.nn.functional as F\n\n\ndef masked_l2_loss(pred, target, mask, weight_known, weight_missing):\n    per_pixel_l2 = F.mse_loss(pred, target, reduction='none')\n    pixel_weights = mask * weight_missing + (1 - mask) * weight_known\n    return (pixel_weights * per_pixel_l2).mean()\n\n\ndef masked_l1_loss(pred, target, mask, weight_known, weight_missing):\n    per_pixel_l1 = F.l1_loss(pred, target, reduction='none')\n    pixel_weights = mask * weight_missing + (1 - mask) * weight_known\n    return (pixel_weights * per_pixel_l1).mean()\n\n\ndef feature_matching_loss(fake_features: List[torch.Tensor], target_features: List[torch.Tensor], mask=None):\n    if mask is None:\n        res = torch.stack([F.mse_loss(fake_feat, target_feat)\n                           for fake_feat, target_feat in zip(fake_features, target_features)]).mean()\n    else:\n        res = 0\n        norm = 0\n        for fake_feat, target_feat in zip(fake_features, target_features):\n            cur_mask = F.interpolate(mask, size=fake_feat.shape[-2:], mode='bilinear', align_corners=False)\n            error_weights = 1 - cur_mask\n            cur_val = ((fake_feat - target_feat).pow(2) * error_weights).mean()\n            res = res + cur_val\n            norm += 1\n        res = res / norm\n    return res\n"
  },
  {
    "path": "model_cards/lama/saicinpainting/training/losses/perceptual.py",
    "content": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torchvision\n\nfrom models.ade20k import ModelBuilder\nfrom saicinpainting.utils import check_and_warn_input_range\n\n\nIMAGENET_MEAN = torch.FloatTensor([0.485, 0.456, 0.406])[None, :, None, None]\nIMAGENET_STD = torch.FloatTensor([0.229, 0.224, 0.225])[None, :, None, None]\n\n\nclass PerceptualLoss(nn.Module):\n    def __init__(self, normalize_inputs=True):\n        super(PerceptualLoss, self).__init__()\n\n        self.normalize_inputs = normalize_inputs\n        self.mean_ = IMAGENET_MEAN\n        self.std_ = IMAGENET_STD\n\n        vgg = torchvision.models.vgg19(pretrained=True).features\n        vgg_avg_pooling = []\n\n        for weights in vgg.parameters():\n            weights.requires_grad = False\n\n        for module in vgg.modules():\n            if module.__class__.__name__ == 'Sequential':\n                continue\n            elif module.__class__.__name__ == 'MaxPool2d':\n                vgg_avg_pooling.append(nn.AvgPool2d(kernel_size=2, stride=2, padding=0))\n            else:\n                vgg_avg_pooling.append(module)\n\n        self.vgg = nn.Sequential(*vgg_avg_pooling)\n\n    def do_normalize_inputs(self, x):\n        return (x - self.mean_.to(x.device)) / self.std_.to(x.device)\n\n    def partial_losses(self, input, target, mask=None):\n        check_and_warn_input_range(target, 0, 1, 'PerceptualLoss target in partial_losses')\n\n        # we expect input and target to be in [0, 1] range\n        losses = []\n\n        if self.normalize_inputs:\n            features_input = self.do_normalize_inputs(input)\n            features_target = self.do_normalize_inputs(target)\n        else:\n            features_input = input\n            features_target = target\n\n        for layer in self.vgg[:30]:\n\n            features_input = layer(features_input)\n            features_target = layer(features_target)\n\n            if layer.__class__.__name__ == 'ReLU':\n                loss = F.mse_loss(features_input, features_target, reduction='none')\n\n                if mask is not None:\n                    cur_mask = F.interpolate(mask, size=features_input.shape[-2:],\n                                             mode='bilinear', align_corners=False)\n                    loss = loss * (1 - cur_mask)\n\n                loss = loss.mean(dim=tuple(range(1, len(loss.shape))))\n                losses.append(loss)\n\n        return losses\n\n    def forward(self, input, target, mask=None):\n        losses = self.partial_losses(input, target, mask=mask)\n        return torch.stack(losses).sum(dim=0)\n\n    def get_global_features(self, input):\n        check_and_warn_input_range(input, 0, 1, 'PerceptualLoss input in get_global_features')\n\n        if self.normalize_inputs:\n            features_input = self.do_normalize_inputs(input)\n        else:\n            features_input = input\n\n        features_input = self.vgg(features_input)\n        return features_input\n\n\nclass ResNetPL(nn.Module):\n    def __init__(self, weight=1,\n                 weights_path=None, arch_encoder='resnet50dilated', segmentation=True):\n        super().__init__()\n        self.impl = ModelBuilder.get_encoder(weights_path=weights_path,\n                                             arch_encoder=arch_encoder,\n                                             arch_decoder='ppm_deepsup',\n                                             fc_dim=2048,\n                                             segmentation=segmentation)\n        self.impl.eval()\n        for w in self.impl.parameters():\n            w.requires_grad_(False)\n\n        self.weight = weight\n\n    def forward(self, pred, target):\n        pred = (pred - IMAGENET_MEAN.to(pred)) / IMAGENET_STD.to(pred)\n        target = (target - IMAGENET_MEAN.to(target)) / IMAGENET_STD.to(target)\n\n        pred_feats = self.impl(pred, return_feature_maps=True)\n        target_feats = self.impl(target, return_feature_maps=True)\n\n        result = torch.stack([F.mse_loss(cur_pred, cur_target)\n                              for cur_pred, cur_target\n                              in zip(pred_feats, target_feats)]).sum() * self.weight\n        return result\n"
  },
  {
    "path": "model_cards/lama/saicinpainting/training/losses/segmentation.py",
    "content": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom .constants import weights as constant_weights\n\n\nclass CrossEntropy2d(nn.Module):\n    def __init__(self, reduction=\"mean\", ignore_label=255, weights=None, *args, **kwargs):\n        \"\"\"\n        weight (Tensor, optional): a manual rescaling weight given to each class.\n            If given, has to be a Tensor of size \"nclasses\"\n        \"\"\"\n        super(CrossEntropy2d, self).__init__()\n        self.reduction = reduction\n        self.ignore_label = ignore_label\n        self.weights = weights\n        if self.weights is not None:\n            device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n            self.weights = torch.FloatTensor(constant_weights[weights]).to(device)\n\n    def forward(self, predict, target):\n        \"\"\"\n            Args:\n                predict:(n, c, h, w)\n                target:(n, 1, h, w)\n        \"\"\"\n        target = target.long()\n        assert not target.requires_grad\n        assert predict.dim() == 4, \"{0}\".format(predict.size())\n        assert target.dim() == 4, \"{0}\".format(target.size())\n        assert predict.size(0) == target.size(0), \"{0} vs {1} \".format(predict.size(0), target.size(0))\n        assert target.size(1) == 1, \"{0}\".format(target.size(1))\n        assert predict.size(2) == target.size(2), \"{0} vs {1} \".format(predict.size(2), target.size(2))\n        assert predict.size(3) == target.size(3), \"{0} vs {1} \".format(predict.size(3), target.size(3))\n        target = target.squeeze(1)\n        n, c, h, w = predict.size()\n        target_mask = (target >= 0) * (target != self.ignore_label)\n        target = target[target_mask]\n        predict = predict.transpose(1, 2).transpose(2, 3).contiguous()\n        predict = predict[target_mask.view(n, h, w, 1).repeat(1, 1, 1, c)].view(-1, c)\n        loss = F.cross_entropy(predict, target, weight=self.weights, reduction=self.reduction)\n        return loss\n"
  },
  {
    "path": "model_cards/lama/saicinpainting/training/losses/style_loss.py",
    "content": "import torch\nimport torch.nn as nn\nimport torchvision.models as models\n\n\nclass PerceptualLoss(nn.Module):\n    r\"\"\"\n    Perceptual loss, VGG-based\n    https://arxiv.org/abs/1603.08155\n    https://github.com/dxyang/StyleTransfer/blob/master/utils.py\n    \"\"\"\n\n    def __init__(self, weights=[1.0, 1.0, 1.0, 1.0, 1.0]):\n        super(PerceptualLoss, self).__init__()\n        self.add_module('vgg', VGG19())\n        self.criterion = torch.nn.L1Loss()\n        self.weights = weights\n\n    def __call__(self, x, y):\n        # Compute features\n        x_vgg, y_vgg = self.vgg(x), self.vgg(y)\n\n        content_loss = 0.0\n        content_loss += self.weights[0] * self.criterion(x_vgg['relu1_1'], y_vgg['relu1_1'])\n        content_loss += self.weights[1] * self.criterion(x_vgg['relu2_1'], y_vgg['relu2_1'])\n        content_loss += self.weights[2] * self.criterion(x_vgg['relu3_1'], y_vgg['relu3_1'])\n        content_loss += self.weights[3] * self.criterion(x_vgg['relu4_1'], y_vgg['relu4_1'])\n        content_loss += self.weights[4] * self.criterion(x_vgg['relu5_1'], y_vgg['relu5_1'])\n\n\n        return content_loss\n\n\nclass VGG19(torch.nn.Module):\n    def __init__(self):\n        super(VGG19, self).__init__()\n        features = models.vgg19(pretrained=True).features\n        self.relu1_1 = torch.nn.Sequential()\n        self.relu1_2 = torch.nn.Sequential()\n\n        self.relu2_1 = torch.nn.Sequential()\n        self.relu2_2 = torch.nn.Sequential()\n\n        self.relu3_1 = torch.nn.Sequential()\n        self.relu3_2 = torch.nn.Sequential()\n        self.relu3_3 = torch.nn.Sequential()\n        self.relu3_4 = torch.nn.Sequential()\n\n        self.relu4_1 = torch.nn.Sequential()\n        self.relu4_2 = torch.nn.Sequential()\n        self.relu4_3 = torch.nn.Sequential()\n        self.relu4_4 = torch.nn.Sequential()\n\n        self.relu5_1 = torch.nn.Sequential()\n        self.relu5_2 = torch.nn.Sequential()\n        self.relu5_3 = torch.nn.Sequential()\n        self.relu5_4 = torch.nn.Sequential()\n\n        for x in range(2):\n            self.relu1_1.add_module(str(x), features[x])\n\n        for x in range(2, 4):\n            self.relu1_2.add_module(str(x), features[x])\n\n        for x in range(4, 7):\n            self.relu2_1.add_module(str(x), features[x])\n\n        for x in range(7, 9):\n            self.relu2_2.add_module(str(x), features[x])\n\n        for x in range(9, 12):\n            self.relu3_1.add_module(str(x), features[x])\n\n        for x in range(12, 14):\n            self.relu3_2.add_module(str(x), features[x])\n\n        for x in range(14, 16):\n            self.relu3_2.add_module(str(x), features[x])\n\n        for x in range(16, 18):\n            self.relu3_4.add_module(str(x), features[x])\n\n        for x in range(18, 21):\n            self.relu4_1.add_module(str(x), features[x])\n\n        for x in range(21, 23):\n            self.relu4_2.add_module(str(x), features[x])\n\n        for x in range(23, 25):\n            self.relu4_3.add_module(str(x), features[x])\n\n        for x in range(25, 27):\n            self.relu4_4.add_module(str(x), features[x])\n\n        for x in range(27, 30):\n            self.relu5_1.add_module(str(x), features[x])\n\n        for x in range(30, 32):\n            self.relu5_2.add_module(str(x), features[x])\n\n        for x in range(32, 34):\n            self.relu5_3.add_module(str(x), features[x])\n\n        for x in range(34, 36):\n            self.relu5_4.add_module(str(x), features[x])\n\n        # don't need the gradients, just want the features\n        for param in self.parameters():\n            param.requires_grad = False\n\n    def forward(self, x):\n        relu1_1 = self.relu1_1(x)\n        relu1_2 = self.relu1_2(relu1_1)\n\n        relu2_1 = self.relu2_1(relu1_2)\n        relu2_2 = self.relu2_2(relu2_1)\n\n        relu3_1 = self.relu3_1(relu2_2)\n        relu3_2 = self.relu3_2(relu3_1)\n        relu3_3 = self.relu3_3(relu3_2)\n        relu3_4 = self.relu3_4(relu3_3)\n\n        relu4_1 = self.relu4_1(relu3_4)\n        relu4_2 = self.relu4_2(relu4_1)\n        relu4_3 = self.relu4_3(relu4_2)\n        relu4_4 = self.relu4_4(relu4_3)\n\n        relu5_1 = self.relu5_1(relu4_4)\n        relu5_2 = self.relu5_2(relu5_1)\n        relu5_3 = self.relu5_3(relu5_2)\n        relu5_4 = self.relu5_4(relu5_3)\n\n        out = {\n            'relu1_1': relu1_1,\n            'relu1_2': relu1_2,\n\n            'relu2_1': relu2_1,\n            'relu2_2': relu2_2,\n\n            'relu3_1': relu3_1,\n            'relu3_2': relu3_2,\n            'relu3_3': relu3_3,\n            'relu3_4': relu3_4,\n\n            'relu4_1': relu4_1,\n            'relu4_2': relu4_2,\n            'relu4_3': relu4_3,\n            'relu4_4': relu4_4,\n\n            'relu5_1': relu5_1,\n            'relu5_2': relu5_2,\n            'relu5_3': relu5_3,\n            'relu5_4': relu5_4,\n        }\n        return out\n"
  },
  {
    "path": "model_cards/lama/saicinpainting/training/modules/__init__.py",
    "content": "import logging\n\nfrom saicinpainting.training.modules.ffc import FFCResNetGenerator\nfrom saicinpainting.training.modules.pix2pixhd import GlobalGenerator, MultiDilatedGlobalGenerator, \\\n    NLayerDiscriminator, MultidilatedNLayerDiscriminator\n\ndef make_generator(config, kind, **kwargs):\n    logging.info(f'Make generator {kind}')\n\n    if kind == 'pix2pixhd_multidilated':\n        return MultiDilatedGlobalGenerator(**kwargs)\n    \n    if kind == 'pix2pixhd_global':\n        return GlobalGenerator(**kwargs)\n\n    if kind == 'ffc_resnet':\n        return FFCResNetGenerator(**kwargs)\n\n    raise ValueError(f'Unknown generator kind {kind}')\n\n\ndef make_discriminator(kind, **kwargs):\n    logging.info(f'Make discriminator {kind}')\n\n    if kind == 'pix2pixhd_nlayer_multidilated':\n        return MultidilatedNLayerDiscriminator(**kwargs)\n\n    if kind == 'pix2pixhd_nlayer':\n        return NLayerDiscriminator(**kwargs)\n\n    raise ValueError(f'Unknown discriminator kind {kind}')\n"
  },
  {
    "path": "model_cards/lama/saicinpainting/training/modules/base.py",
    "content": "import abc\nfrom typing import Tuple, List\n\nimport torch\nimport torch.nn as nn\n\nfrom saicinpainting.training.modules.depthwise_sep_conv import DepthWiseSeperableConv\nfrom saicinpainting.training.modules.multidilated_conv import MultidilatedConv\n\n\nclass BaseDiscriminator(nn.Module):\n    @abc.abstractmethod\n    def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, List[torch.Tensor]]:\n        \"\"\"\n        Predict scores and get intermediate activations. Useful for feature matching loss\n        :return tuple (scores, list of intermediate activations)\n        \"\"\"\n        raise NotImplemented()\n\n\ndef get_conv_block_ctor(kind='default'):\n    if not isinstance(kind, str):\n        return kind\n    if kind == 'default':\n        return nn.Conv2d\n    if kind == 'depthwise':\n        return DepthWiseSeperableConv   \n    if kind == 'multidilated':\n        return MultidilatedConv\n    raise ValueError(f'Unknown convolutional block kind {kind}')\n\n\ndef get_norm_layer(kind='bn'):\n    if not isinstance(kind, str):\n        return kind\n    if kind == 'bn':\n        return nn.BatchNorm2d\n    if kind == 'in':\n        return nn.InstanceNorm2d\n    raise ValueError(f'Unknown norm block kind {kind}')\n\n\ndef get_activation(kind='tanh'):\n    if kind == 'tanh':\n        return nn.Tanh()\n    if kind == 'sigmoid':\n        return nn.Sigmoid()\n    if kind is False:\n        return nn.Identity()\n    raise ValueError(f'Unknown activation kind {kind}')\n\n\nclass SimpleMultiStepGenerator(nn.Module):\n    def __init__(self, steps: List[nn.Module]):\n        super().__init__()\n        self.steps = nn.ModuleList(steps)\n\n    def forward(self, x):\n        cur_in = x\n        outs = []\n        for step in self.steps:\n            cur_out = step(cur_in)\n            outs.append(cur_out)\n            cur_in = torch.cat((cur_in, cur_out), dim=1)\n        return torch.cat(outs[::-1], dim=1)\n\ndef deconv_factory(kind, ngf, mult, norm_layer, activation, max_features):\n    if kind == 'convtranspose':\n        return [nn.ConvTranspose2d(min(max_features, ngf * mult), \n                    min(max_features, int(ngf * mult / 2)), \n                    kernel_size=3, stride=2, padding=1, output_padding=1),\n                    norm_layer(min(max_features, int(ngf * mult / 2))), activation]\n    elif kind == 'bilinear':\n        return [nn.Upsample(scale_factor=2, mode='bilinear'),\n                DepthWiseSeperableConv(min(max_features, ngf * mult), \n                    min(max_features, int(ngf * mult / 2)), \n                    kernel_size=3, stride=1, padding=1), \n                norm_layer(min(max_features, int(ngf * mult / 2))), activation]\n    else:\n        raise Exception(f\"Invalid deconv kind: {kind}\")"
  },
  {
    "path": "model_cards/lama/saicinpainting/training/modules/depthwise_sep_conv.py",
    "content": "import torch\nimport torch.nn as nn\n\nclass DepthWiseSeperableConv(nn.Module):\n    def __init__(self, in_dim, out_dim, *args, **kwargs):\n        super().__init__()\n        if 'groups' in kwargs:\n            # ignoring groups for Depthwise Sep Conv\n            del kwargs['groups']\n        \n        self.depthwise = nn.Conv2d(in_dim, in_dim, *args, groups=in_dim, **kwargs)\n        self.pointwise = nn.Conv2d(in_dim, out_dim, kernel_size=1)\n        \n    def forward(self, x):\n        out = self.depthwise(x)\n        out = self.pointwise(out)\n        return out"
  },
  {
    "path": "model_cards/lama/saicinpainting/training/modules/fake_fakes.py",
    "content": "import torch\nfrom kornia import SamplePadding\nfrom kornia.augmentation import RandomAffine, CenterCrop\n\n\nclass FakeFakesGenerator:\n    def __init__(self, aug_proba=0.5, img_aug_degree=30, img_aug_translate=0.2):\n        self.grad_aug = RandomAffine(degrees=360,\n                                     translate=0.2,\n                                     padding_mode=SamplePadding.REFLECTION,\n                                     keepdim=False,\n                                     p=1)\n        self.img_aug = RandomAffine(degrees=img_aug_degree,\n                                    translate=img_aug_translate,\n                                    padding_mode=SamplePadding.REFLECTION,\n                                    keepdim=True,\n                                    p=1)\n        self.aug_proba = aug_proba\n\n    def __call__(self, input_images, masks):\n        blend_masks = self._fill_masks_with_gradient(masks)\n        blend_target = self._make_blend_target(input_images)\n        result = input_images * (1 - blend_masks) + blend_target * blend_masks\n        return result, blend_masks\n\n    def _make_blend_target(self, input_images):\n        batch_size = input_images.shape[0]\n        permuted = input_images[torch.randperm(batch_size)]\n        augmented = self.img_aug(input_images)\n        is_aug = (torch.rand(batch_size, device=input_images.device)[:, None, None, None] < self.aug_proba).float()\n        result = augmented * is_aug + permuted * (1 - is_aug)\n        return result\n\n    def _fill_masks_with_gradient(self, masks):\n        batch_size, _, height, width = masks.shape\n        grad = torch.linspace(0, 1, steps=width * 2, device=masks.device, dtype=masks.dtype) \\\n            .view(1, 1, 1, -1).expand(batch_size, 1, height * 2, width * 2)\n        grad = self.grad_aug(grad)\n        grad = CenterCrop((height, width))(grad)\n        grad *= masks\n\n        grad_for_min = grad + (1 - masks) * 10\n        grad -= grad_for_min.view(batch_size, -1).min(-1).values[:, None, None, None]\n        grad /= grad.view(batch_size, -1).max(-1).values[:, None, None, None] + 1e-6\n        grad.clamp_(min=0, max=1)\n\n        return grad\n"
  },
  {
    "path": "model_cards/lama/saicinpainting/training/modules/ffc.py",
    "content": "# Fast Fourier Convolution NeurIPS 2020\n# original implementation https://github.com/pkumivision/FFC/blob/main/model_zoo/ffc.py\n# paper https://proceedings.neurips.cc/paper/2020/file/2fd5d41ec6cfab47e32164d5624269b1-Paper.pdf\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom saicinpainting.training.modules.base import get_activation, BaseDiscriminator\nfrom saicinpainting.training.modules.spatial_transform import LearnableSpatialTransformWrapper\nfrom saicinpainting.training.modules.squeeze_excitation import SELayer\nfrom saicinpainting.utils import get_shape\n\n\nclass FFCSE_block(nn.Module):\n\n    def __init__(self, channels, ratio_g):\n        super(FFCSE_block, self).__init__()\n        in_cg = int(channels * ratio_g)\n        in_cl = channels - in_cg\n        r = 16\n\n        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))\n        self.conv1 = nn.Conv2d(channels, channels // r,\n                               kernel_size=1, bias=True)\n        self.relu1 = nn.ReLU(inplace=True)\n        self.conv_a2l = None if in_cl == 0 else nn.Conv2d(\n            channels // r, in_cl, kernel_size=1, bias=True)\n        self.conv_a2g = None if in_cg == 0 else nn.Conv2d(\n            channels // r, in_cg, kernel_size=1, bias=True)\n        self.sigmoid = nn.Sigmoid()\n\n    def forward(self, x):\n        x = x if type(x) is tuple else (x, 0)\n        id_l, id_g = x\n\n        x = id_l if type(id_g) is int else torch.cat([id_l, id_g], dim=1)\n        x = self.avgpool(x)\n        x = self.relu1(self.conv1(x))\n\n        x_l = 0 if self.conv_a2l is None else id_l * \\\n            self.sigmoid(self.conv_a2l(x))\n        x_g = 0 if self.conv_a2g is None else id_g * \\\n            self.sigmoid(self.conv_a2g(x))\n        return x_l, x_g\n\n\nclass FourierUnit(nn.Module):\n\n    def __init__(self, in_channels, out_channels, groups=1, spatial_scale_factor=None, spatial_scale_mode='bilinear',\n                 spectral_pos_encoding=False, use_se=False, se_kwargs=None, ffc3d=False, fft_norm='ortho'):\n        # bn_layer not used\n        super(FourierUnit, self).__init__()\n        self.groups = groups\n\n        self.conv_layer = torch.nn.Conv2d(in_channels=in_channels * 2 + (2 if spectral_pos_encoding else 0),\n                                          out_channels=out_channels * 2,\n                                          kernel_size=1, stride=1, padding=0, groups=self.groups, bias=False)\n        self.bn = torch.nn.BatchNorm2d(out_channels * 2)\n        self.relu = torch.nn.ReLU(inplace=True)\n\n        # squeeze and excitation block\n        self.use_se = use_se\n        if use_se:\n            if se_kwargs is None:\n                se_kwargs = {}\n            self.se = SELayer(self.conv_layer.in_channels, **se_kwargs)\n\n        self.spatial_scale_factor = spatial_scale_factor\n        self.spatial_scale_mode = spatial_scale_mode\n        self.spectral_pos_encoding = spectral_pos_encoding\n        self.ffc3d = ffc3d\n        self.fft_norm = fft_norm\n\n    def forward(self, x):\n        batch = x.shape[0]\n\n        if self.spatial_scale_factor is not None:\n            orig_size = x.shape[-2:]\n            x = F.interpolate(x, scale_factor=self.spatial_scale_factor, mode=self.spatial_scale_mode, align_corners=False)\n\n        r_size = x.size()\n        # (batch, c, h, w/2+1, 2)\n        fft_dim = (-3, -2, -1) if self.ffc3d else (-2, -1)\n        ffted = torch.fft.rfftn(x, dim=fft_dim, norm=self.fft_norm)\n        ffted = torch.stack((ffted.real, ffted.imag), dim=-1)\n        ffted = ffted.permute(0, 1, 4, 2, 3).contiguous()  # (batch, c, 2, h, w/2+1)\n        ffted = ffted.view((batch, -1,) + ffted.size()[3:])\n\n        if self.spectral_pos_encoding:\n            height, width = ffted.shape[-2:]\n            coords_vert = torch.linspace(0, 1, height)[None, None, :, None].expand(batch, 1, height, width).to(ffted)\n            coords_hor = torch.linspace(0, 1, width)[None, None, None, :].expand(batch, 1, height, width).to(ffted)\n            ffted = torch.cat((coords_vert, coords_hor, ffted), dim=1)\n\n        if self.use_se:\n            ffted = self.se(ffted)\n\n        ffted = self.conv_layer(ffted)  # (batch, c*2, h, w/2+1)\n        ffted = self.relu(self.bn(ffted))\n\n        ffted = ffted.view((batch, -1, 2,) + ffted.size()[2:]).permute(\n            0, 1, 3, 4, 2).contiguous()  # (batch,c, t, h, w/2+1, 2)\n        ffted = torch.complex(ffted[..., 0], ffted[..., 1])\n\n        ifft_shape_slice = x.shape[-3:] if self.ffc3d else x.shape[-2:]\n        output = torch.fft.irfftn(ffted, s=ifft_shape_slice, dim=fft_dim, norm=self.fft_norm)\n\n        if self.spatial_scale_factor is not None:\n            output = F.interpolate(output, size=orig_size, mode=self.spatial_scale_mode, align_corners=False)\n\n        return output\n\n\nclass SpectralTransform(nn.Module):\n\n    def __init__(self, in_channels, out_channels, stride=1, groups=1, enable_lfu=True, **fu_kwargs):\n        # bn_layer not used\n        super(SpectralTransform, self).__init__()\n        self.enable_lfu = enable_lfu\n        if stride == 2:\n            self.downsample = nn.AvgPool2d(kernel_size=(2, 2), stride=2)\n        else:\n            self.downsample = nn.Identity()\n\n        self.stride = stride\n        self.conv1 = nn.Sequential(\n            nn.Conv2d(in_channels, out_channels //\n                      2, kernel_size=1, groups=groups, bias=False),\n            nn.BatchNorm2d(out_channels // 2),\n            nn.ReLU(inplace=True)\n        )\n        self.fu = FourierUnit(\n            out_channels // 2, out_channels // 2, groups, **fu_kwargs)\n        if self.enable_lfu:\n            self.lfu = FourierUnit(\n                out_channels // 2, out_channels // 2, groups)\n        self.conv2 = torch.nn.Conv2d(\n            out_channels // 2, out_channels, kernel_size=1, groups=groups, bias=False)\n\n    def forward(self, x):\n\n        x = self.downsample(x)\n        x = self.conv1(x)\n        output = self.fu(x)\n\n        if self.enable_lfu:\n            n, c, h, w = x.shape\n            split_no = 2\n            split_s = h // split_no\n            xs = torch.cat(torch.split(\n                x[:, :c // 4], split_s, dim=-2), dim=1).contiguous()\n            xs = torch.cat(torch.split(xs, split_s, dim=-1),\n                           dim=1).contiguous()\n            xs = self.lfu(xs)\n            xs = xs.repeat(1, 1, split_no, split_no).contiguous()\n        else:\n            xs = 0\n\n        output = self.conv2(x + output + xs)\n\n        return output\n\n\nclass FFC(nn.Module):\n\n    def __init__(self, in_channels, out_channels, kernel_size,\n                 ratio_gin, ratio_gout, stride=1, padding=0,\n                 dilation=1, groups=1, bias=False, enable_lfu=True,\n                 padding_type='reflect', gated=False, **spectral_kwargs):\n        super(FFC, self).__init__()\n\n        assert stride == 1 or stride == 2, \"Stride should be 1 or 2.\"\n        self.stride = stride\n\n        in_cg = int(in_channels * ratio_gin)\n        in_cl = in_channels - in_cg\n        out_cg = int(out_channels * ratio_gout)\n        out_cl = out_channels - out_cg\n        #groups_g = 1 if groups == 1 else int(groups * ratio_gout)\n        #groups_l = 1 if groups == 1 else groups - groups_g\n\n        self.ratio_gin = ratio_gin\n        self.ratio_gout = ratio_gout\n        self.global_in_num = in_cg\n\n        module = nn.Identity if in_cl == 0 or out_cl == 0 else nn.Conv2d\n        self.convl2l = module(in_cl, out_cl, kernel_size,\n                              stride, padding, dilation, groups, bias, padding_mode=padding_type)\n        module = nn.Identity if in_cl == 0 or out_cg == 0 else nn.Conv2d\n        self.convl2g = module(in_cl, out_cg, kernel_size,\n                              stride, padding, dilation, groups, bias, padding_mode=padding_type)\n        module = nn.Identity if in_cg == 0 or out_cl == 0 else nn.Conv2d\n        self.convg2l = module(in_cg, out_cl, kernel_size,\n                              stride, padding, dilation, groups, bias, padding_mode=padding_type)\n        module = nn.Identity if in_cg == 0 or out_cg == 0 else SpectralTransform\n        self.convg2g = module(\n            in_cg, out_cg, stride, 1 if groups == 1 else groups // 2, enable_lfu, **spectral_kwargs)\n\n        self.gated = gated\n        module = nn.Identity if in_cg == 0 or out_cl == 0 or not self.gated else nn.Conv2d\n        self.gate = module(in_channels, 2, 1)\n\n    def forward(self, x):\n        x_l, x_g = x if type(x) is tuple else (x, 0)\n        out_xl, out_xg = 0, 0\n\n        if self.gated:\n            total_input_parts = [x_l]\n            if torch.is_tensor(x_g):\n                total_input_parts.append(x_g)\n            total_input = torch.cat(total_input_parts, dim=1)\n\n            gates = torch.sigmoid(self.gate(total_input))\n            g2l_gate, l2g_gate = gates.chunk(2, dim=1)\n        else:\n            g2l_gate, l2g_gate = 1, 1\n\n        if self.ratio_gout != 1:\n            out_xl = self.convl2l(x_l) + self.convg2l(x_g) * g2l_gate\n        if self.ratio_gout != 0:\n            out_xg = self.convl2g(x_l) * l2g_gate + self.convg2g(x_g)\n\n        return out_xl, out_xg\n\n\nclass FFC_BN_ACT(nn.Module):\n\n    def __init__(self, in_channels, out_channels,\n                 kernel_size, ratio_gin, ratio_gout,\n                 stride=1, padding=0, dilation=1, groups=1, bias=False,\n                 norm_layer=nn.BatchNorm2d, activation_layer=nn.Identity,\n                 padding_type='reflect',\n                 enable_lfu=True, **kwargs):\n        super(FFC_BN_ACT, self).__init__()\n        self.ffc = FFC(in_channels, out_channels, kernel_size,\n                       ratio_gin, ratio_gout, stride, padding, dilation,\n                       groups, bias, enable_lfu, padding_type=padding_type, **kwargs)\n        lnorm = nn.Identity if ratio_gout == 1 else norm_layer\n        gnorm = nn.Identity if ratio_gout == 0 else norm_layer\n        global_channels = int(out_channels * ratio_gout)\n        self.bn_l = lnorm(out_channels - global_channels)\n        self.bn_g = gnorm(global_channels)\n\n        lact = nn.Identity if ratio_gout == 1 else activation_layer\n        gact = nn.Identity if ratio_gout == 0 else activation_layer\n        self.act_l = lact(inplace=True)\n        self.act_g = gact(inplace=True)\n\n    def forward(self, x):\n        x_l, x_g = self.ffc(x)\n        x_l = self.act_l(self.bn_l(x_l))\n        x_g = self.act_g(self.bn_g(x_g))\n        return x_l, x_g\n\n\nclass FFCResnetBlock(nn.Module):\n    def __init__(self, dim, padding_type, norm_layer, activation_layer=nn.ReLU, dilation=1,\n                 spatial_transform_kwargs=None, inline=False, **conv_kwargs):\n        super().__init__()\n        self.conv1 = FFC_BN_ACT(dim, dim, kernel_size=3, padding=dilation, dilation=dilation,\n                                norm_layer=norm_layer,\n                                activation_layer=activation_layer,\n                                padding_type=padding_type,\n                                **conv_kwargs)\n        self.conv2 = FFC_BN_ACT(dim, dim, kernel_size=3, padding=dilation, dilation=dilation,\n                                norm_layer=norm_layer,\n                                activation_layer=activation_layer,\n                                padding_type=padding_type,\n                                **conv_kwargs)\n        if spatial_transform_kwargs is not None:\n            self.conv1 = LearnableSpatialTransformWrapper(self.conv1, **spatial_transform_kwargs)\n            self.conv2 = LearnableSpatialTransformWrapper(self.conv2, **spatial_transform_kwargs)\n        self.inline = inline\n\n    def forward(self, x):\n        if self.inline:\n            x_l, x_g = x[:, :-self.conv1.ffc.global_in_num], x[:, -self.conv1.ffc.global_in_num:]\n        else:\n            x_l, x_g = x if type(x) is tuple else (x, 0)\n\n        id_l, id_g = x_l, x_g\n\n        x_l, x_g = self.conv1((x_l, x_g))\n        x_l, x_g = self.conv2((x_l, x_g))\n\n        x_l, x_g = id_l + x_l, id_g + x_g\n        out = x_l, x_g\n        if self.inline:\n            out = torch.cat(out, dim=1)\n        return out\n\n\nclass ConcatTupleLayer(nn.Module):\n    def forward(self, x):\n        assert isinstance(x, tuple)\n        x_l, x_g = x\n        assert torch.is_tensor(x_l) or torch.is_tensor(x_g)\n        if not torch.is_tensor(x_g):\n            return x_l\n        return torch.cat(x, dim=1)\n\n\nclass FFCResNetGenerator(nn.Module):\n    def __init__(self, input_nc, output_nc, ngf=64, n_downsampling=3, n_blocks=9, norm_layer=nn.BatchNorm2d,\n                 padding_type='reflect', activation_layer=nn.ReLU,\n                 up_norm_layer=nn.BatchNorm2d, up_activation=nn.ReLU(True),\n                 init_conv_kwargs={}, downsample_conv_kwargs={}, resnet_conv_kwargs={},\n                 spatial_transform_layers=None, spatial_transform_kwargs={},\n                 add_out_act=True, max_features=1024, out_ffc=False, out_ffc_kwargs={}):\n        assert (n_blocks >= 0)\n        super().__init__()\n\n        model = [nn.ReflectionPad2d(3),\n                 FFC_BN_ACT(input_nc, ngf, kernel_size=7, padding=0, norm_layer=norm_layer,\n                            activation_layer=activation_layer, **init_conv_kwargs)]\n\n        ### downsample\n        for i in range(n_downsampling):\n            mult = 2 ** i\n            if i == n_downsampling - 1:\n                cur_conv_kwargs = dict(downsample_conv_kwargs)\n                cur_conv_kwargs['ratio_gout'] = resnet_conv_kwargs.get('ratio_gin', 0)\n            else:\n                cur_conv_kwargs = downsample_conv_kwargs\n            model += [FFC_BN_ACT(min(max_features, ngf * mult),\n                                 min(max_features, ngf * mult * 2),\n                                 kernel_size=3, stride=2, padding=1,\n                                 norm_layer=norm_layer,\n                                 activation_layer=activation_layer,\n                                 **cur_conv_kwargs)]\n\n        mult = 2 ** n_downsampling\n        feats_num_bottleneck = min(max_features, ngf * mult)\n\n        ### resnet blocks\n        for i in range(n_blocks):\n            cur_resblock = FFCResnetBlock(feats_num_bottleneck, padding_type=padding_type, activation_layer=activation_layer,\n                                          norm_layer=norm_layer, **resnet_conv_kwargs)\n            if spatial_transform_layers is not None and i in spatial_transform_layers:\n                cur_resblock = LearnableSpatialTransformWrapper(cur_resblock, **spatial_transform_kwargs)\n            model += [cur_resblock]\n\n        model += [ConcatTupleLayer()]\n\n        ### upsample\n        for i in range(n_downsampling):\n            mult = 2 ** (n_downsampling - i)\n            model += [nn.ConvTranspose2d(min(max_features, ngf * mult),\n                                         min(max_features, int(ngf * mult / 2)),\n                                         kernel_size=3, stride=2, padding=1, output_padding=1),\n                      up_norm_layer(min(max_features, int(ngf * mult / 2))),\n                      up_activation]\n\n        if out_ffc:\n            model += [FFCResnetBlock(ngf, padding_type=padding_type, activation_layer=activation_layer,\n                                     norm_layer=norm_layer, inline=True, **out_ffc_kwargs)]\n\n        model += [nn.ReflectionPad2d(3),\n                  nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)]\n        if add_out_act:\n            model.append(get_activation('tanh' if add_out_act is True else add_out_act))\n        self.model = nn.Sequential(*model)\n\n    def forward(self, input):\n        return self.model(input)\n\n\nclass FFCNLayerDiscriminator(BaseDiscriminator):\n    def __init__(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, max_features=512,\n                 init_conv_kwargs={}, conv_kwargs={}):\n        super().__init__()\n        self.n_layers = n_layers\n\n        def _act_ctor(inplace=True):\n            return nn.LeakyReLU(negative_slope=0.2, inplace=inplace)\n\n        kw = 3\n        padw = int(np.ceil((kw-1.0)/2))\n        sequence = [[FFC_BN_ACT(input_nc, ndf, kernel_size=kw, padding=padw, norm_layer=norm_layer,\n                                activation_layer=_act_ctor, **init_conv_kwargs)]]\n\n        nf = ndf\n        for n in range(1, n_layers):\n            nf_prev = nf\n            nf = min(nf * 2, max_features)\n\n            cur_model = [\n                FFC_BN_ACT(nf_prev, nf,\n                           kernel_size=kw, stride=2, padding=padw,\n                           norm_layer=norm_layer,\n                           activation_layer=_act_ctor,\n                           **conv_kwargs)\n            ]\n            sequence.append(cur_model)\n\n        nf_prev = nf\n        nf = min(nf * 2, 512)\n\n        cur_model = [\n            FFC_BN_ACT(nf_prev, nf,\n                       kernel_size=kw, stride=1, padding=padw,\n                       norm_layer=norm_layer,\n                       activation_layer=lambda *args, **kwargs: nn.LeakyReLU(*args, negative_slope=0.2, **kwargs),\n                       **conv_kwargs),\n            ConcatTupleLayer()\n        ]\n        sequence.append(cur_model)\n\n        sequence += [[nn.Conv2d(nf, 1, kernel_size=kw, stride=1, padding=padw)]]\n\n        for n in range(len(sequence)):\n            setattr(self, 'model'+str(n), nn.Sequential(*sequence[n]))\n\n    def get_all_activations(self, x):\n        res = [x]\n        for n in range(self.n_layers + 2):\n            model = getattr(self, 'model' + str(n))\n            res.append(model(res[-1]))\n        return res[1:]\n\n    def forward(self, x):\n        act = self.get_all_activations(x)\n        feats = []\n        for out in act[:-1]:\n            if isinstance(out, tuple):\n                if torch.is_tensor(out[1]):\n                    out = torch.cat(out, dim=1)\n                else:\n                    out = out[0]\n            feats.append(out)\n        return act[-1], feats\n"
  },
  {
    "path": "model_cards/lama/saicinpainting/training/modules/multidilated_conv.py",
    "content": "import torch\nimport torch.nn as nn\nimport random\nfrom saicinpainting.training.modules.depthwise_sep_conv import DepthWiseSeperableConv\n\nclass MultidilatedConv(nn.Module):\n    def __init__(self, in_dim, out_dim, kernel_size, dilation_num=3, comb_mode='sum', equal_dim=True,\n                 shared_weights=False, padding=1, min_dilation=1, shuffle_in_channels=False, use_depthwise=False, **kwargs):\n        super().__init__()\n        convs = []\n        self.equal_dim = equal_dim\n        assert comb_mode in ('cat_out', 'sum', 'cat_in', 'cat_both'), comb_mode\n        if comb_mode in ('cat_out', 'cat_both'):\n            self.cat_out = True\n            if equal_dim:\n                assert out_dim % dilation_num == 0\n                out_dims = [out_dim // dilation_num] * dilation_num\n                self.index = sum([[i + j * (out_dims[0]) for j in range(dilation_num)] for i in range(out_dims[0])], [])\n            else:\n                out_dims = [out_dim // 2 ** (i + 1) for i in range(dilation_num - 1)]\n                out_dims.append(out_dim - sum(out_dims))\n                index = []\n                starts = [0] + out_dims[:-1]\n                lengths = [out_dims[i] // out_dims[-1] for i in range(dilation_num)]\n                for i in range(out_dims[-1]):\n                    for j in range(dilation_num):\n                        index += list(range(starts[j], starts[j] + lengths[j]))\n                        starts[j] += lengths[j]\n                self.index = index\n                assert(len(index) == out_dim)\n            self.out_dims = out_dims\n        else:\n            self.cat_out = False\n            self.out_dims = [out_dim] * dilation_num\n\n        if comb_mode in ('cat_in', 'cat_both'):\n            if equal_dim:\n                assert in_dim % dilation_num == 0\n                in_dims = [in_dim // dilation_num] * dilation_num\n            else:\n                in_dims = [in_dim // 2 ** (i + 1) for i in range(dilation_num - 1)]\n                in_dims.append(in_dim - sum(in_dims))\n            self.in_dims = in_dims\n            self.cat_in = True\n        else:\n            self.cat_in = False\n            self.in_dims = [in_dim] * dilation_num\n\n        conv_type = DepthWiseSeperableConv if use_depthwise else nn.Conv2d\n        dilation = min_dilation\n        for i in range(dilation_num):\n            if isinstance(padding, int):\n                cur_padding = padding * dilation\n            else:\n                cur_padding = padding[i]\n            convs.append(conv_type(\n                self.in_dims[i], self.out_dims[i], kernel_size, padding=cur_padding, dilation=dilation, **kwargs\n            ))\n            if i > 0 and shared_weights:\n                convs[-1].weight = convs[0].weight\n                convs[-1].bias = convs[0].bias\n            dilation *= 2\n        self.convs = nn.ModuleList(convs)\n\n        self.shuffle_in_channels = shuffle_in_channels\n        if self.shuffle_in_channels:\n            # shuffle list as shuffling of tensors is nondeterministic\n            in_channels_permute = list(range(in_dim))\n            random.shuffle(in_channels_permute)\n            # save as buffer so it is saved and loaded with checkpoint\n            self.register_buffer('in_channels_permute', torch.tensor(in_channels_permute))\n\n    def forward(self, x):\n        if self.shuffle_in_channels:\n            x = x[:, self.in_channels_permute]\n\n        outs = []\n        if self.cat_in:\n            if self.equal_dim:\n                x = x.chunk(len(self.convs), dim=1)\n            else:\n                new_x = []\n                start = 0\n                for dim in self.in_dims:\n                    new_x.append(x[:, start:start+dim])\n                    start += dim\n                x = new_x\n        for i, conv in enumerate(self.convs):\n            if self.cat_in:\n                input = x[i]\n            else:\n                input = x\n            outs.append(conv(input))\n        if self.cat_out:\n            out = torch.cat(outs, dim=1)[:, self.index]\n        else:\n            out = sum(outs)\n        return out\n"
  },
  {
    "path": "model_cards/lama/saicinpainting/training/modules/multiscale.py",
    "content": "from typing import List, Tuple, Union, Optional\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom saicinpainting.training.modules.base import get_conv_block_ctor, get_activation\nfrom saicinpainting.training.modules.pix2pixhd import ResnetBlock\n\n\nclass ResNetHead(nn.Module):\n    def __init__(self, input_nc, ngf=64, n_downsampling=3, n_blocks=9, norm_layer=nn.BatchNorm2d,\n                 padding_type='reflect', conv_kind='default', activation=nn.ReLU(True)):\n        assert (n_blocks >= 0)\n        super(ResNetHead, self).__init__()\n\n        conv_layer = get_conv_block_ctor(conv_kind)\n\n        model = [nn.ReflectionPad2d(3),\n                 conv_layer(input_nc, ngf, kernel_size=7, padding=0),\n                 norm_layer(ngf),\n                 activation]\n\n        ### downsample\n        for i in range(n_downsampling):\n            mult = 2 ** i\n            model += [conv_layer(ngf * mult, ngf * mult * 2, kernel_size=3, stride=2, padding=1),\n                      norm_layer(ngf * mult * 2),\n                      activation]\n\n        mult = 2 ** n_downsampling\n\n        ### resnet blocks\n        for i in range(n_blocks):\n            model += [ResnetBlock(ngf * mult, padding_type=padding_type, activation=activation, norm_layer=norm_layer,\n                                  conv_kind=conv_kind)]\n\n        self.model = nn.Sequential(*model)\n\n    def forward(self, input):\n        return self.model(input)\n\n\nclass ResNetTail(nn.Module):\n    def __init__(self, output_nc, ngf=64, n_downsampling=3, n_blocks=9, norm_layer=nn.BatchNorm2d,\n                 padding_type='reflect', conv_kind='default', activation=nn.ReLU(True),\n                 up_norm_layer=nn.BatchNorm2d, up_activation=nn.ReLU(True), add_out_act=False, out_extra_layers_n=0,\n                 add_in_proj=None):\n        assert (n_blocks >= 0)\n        super(ResNetTail, self).__init__()\n\n        mult = 2 ** n_downsampling\n\n        model = []\n\n        if add_in_proj is not None:\n            model.append(nn.Conv2d(add_in_proj, ngf * mult, kernel_size=1))\n\n        ### resnet blocks\n        for i in range(n_blocks):\n            model += [ResnetBlock(ngf * mult, padding_type=padding_type, activation=activation, norm_layer=norm_layer,\n                                  conv_kind=conv_kind)]\n\n        ### upsample\n        for i in range(n_downsampling):\n            mult = 2 ** (n_downsampling - i)\n            model += [nn.ConvTranspose2d(ngf * mult, int(ngf * mult / 2), kernel_size=3, stride=2, padding=1,\n                                         output_padding=1),\n                      up_norm_layer(int(ngf * mult / 2)),\n                      up_activation]\n        self.model = nn.Sequential(*model)\n\n        out_layers = []\n        for _ in range(out_extra_layers_n):\n            out_layers += [nn.Conv2d(ngf, ngf, kernel_size=1, padding=0),\n                           up_norm_layer(ngf),\n                           up_activation]\n        out_layers += [nn.ReflectionPad2d(3),\n                       nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)]\n\n        if add_out_act:\n            out_layers.append(get_activation('tanh' if add_out_act is True else add_out_act))\n\n        self.out_proj = nn.Sequential(*out_layers)\n\n    def forward(self, input, return_last_act=False):\n        features = self.model(input)\n        out = self.out_proj(features)\n        if return_last_act:\n            return out, features\n        else:\n            return out\n\n\nclass MultiscaleResNet(nn.Module):\n    def __init__(self, input_nc, output_nc, ngf=64, n_downsampling=2, n_blocks_head=2, n_blocks_tail=6, n_scales=3,\n                 norm_layer=nn.BatchNorm2d, padding_type='reflect', conv_kind='default', activation=nn.ReLU(True),\n                 up_norm_layer=nn.BatchNorm2d, up_activation=nn.ReLU(True), add_out_act=False, out_extra_layers_n=0,\n                 out_cumulative=False, return_only_hr=False):\n        super().__init__()\n\n        self.heads = nn.ModuleList([ResNetHead(input_nc, ngf=ngf, n_downsampling=n_downsampling,\n                                               n_blocks=n_blocks_head, norm_layer=norm_layer, padding_type=padding_type,\n                                               conv_kind=conv_kind, activation=activation)\n                                    for i in range(n_scales)])\n        tail_in_feats = ngf * (2 ** n_downsampling) + ngf\n        self.tails = nn.ModuleList([ResNetTail(output_nc,\n                                               ngf=ngf, n_downsampling=n_downsampling,\n                                               n_blocks=n_blocks_tail, norm_layer=norm_layer, padding_type=padding_type,\n                                               conv_kind=conv_kind, activation=activation, up_norm_layer=up_norm_layer,\n                                               up_activation=up_activation, add_out_act=add_out_act,\n                                               out_extra_layers_n=out_extra_layers_n,\n                                               add_in_proj=None if (i == n_scales - 1) else tail_in_feats)\n                                    for i in range(n_scales)])\n\n        self.out_cumulative = out_cumulative\n        self.return_only_hr = return_only_hr\n\n    @property\n    def num_scales(self):\n        return len(self.heads)\n\n    def forward(self, ms_inputs: List[torch.Tensor], smallest_scales_num: Optional[int] = None) \\\n        -> Union[torch.Tensor, List[torch.Tensor]]:\n        \"\"\"\n        :param ms_inputs: List of inputs of different resolutions from HR to LR\n        :param smallest_scales_num: int or None, number of smallest scales to take at input\n        :return: Depending on return_only_hr:\n            True: Only the most HR output\n            False: List of outputs of different resolutions from HR to LR\n        \"\"\"\n        if smallest_scales_num is None:\n            assert len(self.heads) == len(ms_inputs), (len(self.heads), len(ms_inputs), smallest_scales_num)\n            smallest_scales_num = len(self.heads)\n        else:\n            assert smallest_scales_num == len(ms_inputs) <= len(self.heads), (len(self.heads), len(ms_inputs), smallest_scales_num)\n\n        cur_heads = self.heads[-smallest_scales_num:]\n        ms_features = [cur_head(cur_inp) for cur_head, cur_inp in zip(cur_heads, ms_inputs)]\n\n        all_outputs = []\n        prev_tail_features = None\n        for i in range(len(ms_features)):\n            scale_i = -i - 1\n\n            cur_tail_input = ms_features[-i - 1]\n            if prev_tail_features is not None:\n                if prev_tail_features.shape != cur_tail_input.shape:\n                    prev_tail_features = F.interpolate(prev_tail_features, size=cur_tail_input.shape[2:],\n                                                       mode='bilinear', align_corners=False)\n                cur_tail_input = torch.cat((cur_tail_input, prev_tail_features), dim=1)\n\n            cur_out, cur_tail_feats = self.tails[scale_i](cur_tail_input, return_last_act=True)\n\n            prev_tail_features = cur_tail_feats\n            all_outputs.append(cur_out)\n\n        if self.out_cumulative:\n            all_outputs_cum = [all_outputs[0]]\n            for i in range(1, len(ms_features)):\n                cur_out = all_outputs[i]\n                cur_out_cum = cur_out + F.interpolate(all_outputs_cum[-1], size=cur_out.shape[2:],\n                                                      mode='bilinear', align_corners=False)\n                all_outputs_cum.append(cur_out_cum)\n            all_outputs = all_outputs_cum\n\n        if self.return_only_hr:\n            return all_outputs[-1]\n        else:\n            return all_outputs[::-1]\n\n\nclass MultiscaleDiscriminatorSimple(nn.Module):\n    def __init__(self, ms_impl):\n        super().__init__()\n        self.ms_impl = nn.ModuleList(ms_impl)\n\n    @property\n    def num_scales(self):\n        return len(self.ms_impl)\n\n    def forward(self, ms_inputs: List[torch.Tensor], smallest_scales_num: Optional[int] = None) \\\n            -> List[Tuple[torch.Tensor, List[torch.Tensor]]]:\n        \"\"\"\n        :param ms_inputs: List of inputs of different resolutions from HR to LR\n        :param smallest_scales_num: int or None, number of smallest scales to take at input\n        :return: List of pairs (prediction, features) for different resolutions from HR to LR\n        \"\"\"\n        if smallest_scales_num is None:\n            assert len(self.ms_impl) == len(ms_inputs), (len(self.ms_impl), len(ms_inputs), smallest_scales_num)\n            smallest_scales_num = len(self.heads)\n        else:\n            assert smallest_scales_num == len(ms_inputs) <= len(self.ms_impl), \\\n                (len(self.ms_impl), len(ms_inputs), smallest_scales_num)\n\n        return [cur_discr(cur_input) for cur_discr, cur_input in zip(self.ms_impl[-smallest_scales_num:], ms_inputs)]\n\n\nclass SingleToMultiScaleInputMixin:\n    def forward(self, x: torch.Tensor) -> List:\n        orig_height, orig_width = x.shape[2:]\n        factors = [2 ** i for i in range(self.num_scales)]\n        ms_inputs = [F.interpolate(x, size=(orig_height // f, orig_width // f), mode='bilinear', align_corners=False)\n                     for f in factors]\n        return super().forward(ms_inputs)\n\n\nclass GeneratorMultiToSingleOutputMixin:\n    def forward(self, x):\n        return super().forward(x)[0]\n\n\nclass DiscriminatorMultiToSingleOutputMixin:\n    def forward(self, x):\n        out_feat_tuples = super().forward(x)\n        return out_feat_tuples[0][0], [f for _, flist in out_feat_tuples for f in flist]\n\n\nclass DiscriminatorMultiToSingleOutputStackedMixin:\n    def __init__(self, *args, return_feats_only_levels=None, **kwargs):\n        super().__init__(*args, **kwargs)\n        self.return_feats_only_levels = return_feats_only_levels\n\n    def forward(self, x):\n        out_feat_tuples = super().forward(x)\n        outs = [out for out, _ in out_feat_tuples]\n        scaled_outs = [outs[0]] + [F.interpolate(cur_out, size=outs[0].shape[-2:],\n                                                 mode='bilinear', align_corners=False)\n                                   for cur_out in outs[1:]]\n        out = torch.cat(scaled_outs, dim=1)\n        if self.return_feats_only_levels is not None:\n            feat_lists = [out_feat_tuples[i][1] for i in self.return_feats_only_levels]\n        else:\n            feat_lists = [flist for _, flist in out_feat_tuples]\n        feats = [f for flist in feat_lists for f in flist]\n        return out, feats\n\n\nclass MultiscaleDiscrSingleInput(SingleToMultiScaleInputMixin, DiscriminatorMultiToSingleOutputStackedMixin, MultiscaleDiscriminatorSimple):\n    pass\n\n\nclass MultiscaleResNetSingle(GeneratorMultiToSingleOutputMixin, SingleToMultiScaleInputMixin, MultiscaleResNet):\n    pass\n"
  },
  {
    "path": "model_cards/lama/saicinpainting/training/modules/pix2pixhd.py",
    "content": "# original: https://github.com/NVIDIA/pix2pixHD/blob/master/models/networks.py\nimport collections\nfrom functools import partial\nimport functools\nimport logging\nfrom collections import defaultdict\n\nimport numpy as np\nimport torch.nn as nn\n\nfrom saicinpainting.training.modules.base import BaseDiscriminator, deconv_factory, get_conv_block_ctor, get_norm_layer, get_activation\nfrom saicinpainting.training.modules.ffc import FFCResnetBlock\nfrom saicinpainting.training.modules.multidilated_conv import MultidilatedConv\n\nclass DotDict(defaultdict):\n    # https://stackoverflow.com/questions/2352181/how-to-use-a-dot-to-access-members-of-dictionary\n    \"\"\"dot.notation access to dictionary attributes\"\"\"\n    __getattr__ = defaultdict.get\n    __setattr__ = defaultdict.__setitem__\n    __delattr__ = defaultdict.__delitem__\n\nclass Identity(nn.Module):\n    def __init__(self):\n        super().__init__()\n\n    def forward(self, x):\n        return x\n\n\nclass ResnetBlock(nn.Module):\n    def __init__(self, dim, padding_type, norm_layer, activation=nn.ReLU(True), use_dropout=False, conv_kind='default',\n                 dilation=1, in_dim=None, groups=1, second_dilation=None):\n        super(ResnetBlock, self).__init__()\n        self.in_dim = in_dim\n        self.dim = dim\n        if second_dilation is None:\n            second_dilation = dilation\n        self.conv_block = self.build_conv_block(dim, padding_type, norm_layer, activation, use_dropout,\n                                                conv_kind=conv_kind, dilation=dilation, in_dim=in_dim, groups=groups,\n                                                second_dilation=second_dilation)\n\n        if self.in_dim is not None:\n            self.input_conv = nn.Conv2d(in_dim, dim, 1)\n\n        self.out_channnels = dim\n\n    def build_conv_block(self, dim, padding_type, norm_layer, activation, use_dropout, conv_kind='default',\n                         dilation=1, in_dim=None, groups=1, second_dilation=1):\n        conv_layer = get_conv_block_ctor(conv_kind)\n\n        conv_block = []\n        p = 0\n        if padding_type == 'reflect':\n            conv_block += [nn.ReflectionPad2d(dilation)]\n        elif padding_type == 'replicate':\n            conv_block += [nn.ReplicationPad2d(dilation)]\n        elif padding_type == 'zero':\n            p = dilation\n        else:\n            raise NotImplementedError('padding [%s] is not implemented' % padding_type)\n\n        if in_dim is None:\n            in_dim = dim\n\n        conv_block += [conv_layer(in_dim, dim, kernel_size=3, padding=p, dilation=dilation),\n                       norm_layer(dim),\n                       activation]\n        if use_dropout:\n            conv_block += [nn.Dropout(0.5)]\n\n        p = 0\n        if padding_type == 'reflect':\n            conv_block += [nn.ReflectionPad2d(second_dilation)]\n        elif padding_type == 'replicate':\n            conv_block += [nn.ReplicationPad2d(second_dilation)]\n        elif padding_type == 'zero':\n            p = second_dilation\n        else:\n            raise NotImplementedError('padding [%s] is not implemented' % padding_type)\n        conv_block += [conv_layer(dim, dim, kernel_size=3, padding=p, dilation=second_dilation, groups=groups),\n                       norm_layer(dim)]\n\n        return nn.Sequential(*conv_block)\n\n    def forward(self, x):\n        x_before = x\n        if self.in_dim is not None:\n            x = self.input_conv(x)\n        out = x + self.conv_block(x_before)\n        return out\n\nclass ResnetBlock5x5(nn.Module):\n    def __init__(self, dim, padding_type, norm_layer, activation=nn.ReLU(True), use_dropout=False, conv_kind='default',\n                 dilation=1, in_dim=None, groups=1, second_dilation=None):\n        super(ResnetBlock5x5, self).__init__()\n        self.in_dim = in_dim\n        self.dim = dim\n        if second_dilation is None:\n            second_dilation = dilation\n        self.conv_block = self.build_conv_block(dim, padding_type, norm_layer, activation, use_dropout,\n                                                conv_kind=conv_kind, dilation=dilation, in_dim=in_dim, groups=groups,\n                                                second_dilation=second_dilation)\n\n        if self.in_dim is not None:\n            self.input_conv = nn.Conv2d(in_dim, dim, 1)\n\n        self.out_channnels = dim\n\n    def build_conv_block(self, dim, padding_type, norm_layer, activation, use_dropout, conv_kind='default',\n                         dilation=1, in_dim=None, groups=1, second_dilation=1):\n        conv_layer = get_conv_block_ctor(conv_kind)\n\n        conv_block = []\n        p = 0\n        if padding_type == 'reflect':\n            conv_block += [nn.ReflectionPad2d(dilation * 2)]\n        elif padding_type == 'replicate':\n            conv_block += [nn.ReplicationPad2d(dilation * 2)]\n        elif padding_type == 'zero':\n            p = dilation * 2\n        else:\n            raise NotImplementedError('padding [%s] is not implemented' % padding_type)\n\n        if in_dim is None:\n            in_dim = dim\n\n        conv_block += [conv_layer(in_dim, dim, kernel_size=5, padding=p, dilation=dilation),\n                       norm_layer(dim),\n                       activation]\n        if use_dropout:\n            conv_block += [nn.Dropout(0.5)]\n\n        p = 0\n        if padding_type == 'reflect':\n            conv_block += [nn.ReflectionPad2d(second_dilation * 2)]\n        elif padding_type == 'replicate':\n            conv_block += [nn.ReplicationPad2d(second_dilation * 2)]\n        elif padding_type == 'zero':\n            p = second_dilation * 2\n        else:\n            raise NotImplementedError('padding [%s] is not implemented' % padding_type)\n        conv_block += [conv_layer(dim, dim, kernel_size=5, padding=p, dilation=second_dilation, groups=groups),\n                       norm_layer(dim)]\n\n        return nn.Sequential(*conv_block)\n\n    def forward(self, x):\n        x_before = x\n        if self.in_dim is not None:\n            x = self.input_conv(x)\n        out = x + self.conv_block(x_before)\n        return out\n\n\nclass MultidilatedResnetBlock(nn.Module):\n    def __init__(self, dim, padding_type, conv_layer, norm_layer, activation=nn.ReLU(True), use_dropout=False):\n        super().__init__()\n        self.conv_block = self.build_conv_block(dim, padding_type, conv_layer, norm_layer, activation, use_dropout)\n\n    def build_conv_block(self, dim, padding_type, conv_layer, norm_layer, activation, use_dropout, dilation=1):\n        conv_block = []\n        conv_block += [conv_layer(dim, dim, kernel_size=3, padding_mode=padding_type),\n                       norm_layer(dim),\n                       activation]\n        if use_dropout:\n            conv_block += [nn.Dropout(0.5)]\n\n        conv_block += [conv_layer(dim, dim, kernel_size=3, padding_mode=padding_type),\n                       norm_layer(dim)]\n\n        return nn.Sequential(*conv_block)\n\n    def forward(self, x):\n        out = x + self.conv_block(x)\n        return out\n\n\nclass MultiDilatedGlobalGenerator(nn.Module):\n    def __init__(self, input_nc, output_nc, ngf=64, n_downsampling=3,\n                 n_blocks=3, norm_layer=nn.BatchNorm2d,\n                 padding_type='reflect', conv_kind='default',\n                 deconv_kind='convtranspose', activation=nn.ReLU(True),\n                 up_norm_layer=nn.BatchNorm2d, affine=None, up_activation=nn.ReLU(True),\n                 add_out_act=True, max_features=1024, multidilation_kwargs={},\n                 ffc_positions=None, ffc_kwargs={}):\n        assert (n_blocks >= 0)\n        super().__init__()\n\n        conv_layer = get_conv_block_ctor(conv_kind)\n        resnet_conv_layer = functools.partial(get_conv_block_ctor('multidilated'), **multidilation_kwargs)\n        norm_layer = get_norm_layer(norm_layer)\n        if affine is not None:\n            norm_layer = partial(norm_layer, affine=affine)\n        up_norm_layer = get_norm_layer(up_norm_layer)\n        if affine is not None:\n            up_norm_layer = partial(up_norm_layer, affine=affine)\n\n        model = [nn.ReflectionPad2d(3),\n                 conv_layer(input_nc, ngf, kernel_size=7, padding=0),\n                 norm_layer(ngf),\n                 activation]\n\n        identity = Identity()\n        ### downsample\n        for i in range(n_downsampling):\n            mult = 2 ** i\n\n            model += [conv_layer(min(max_features, ngf * mult),\n                                    min(max_features, ngf * mult * 2),\n                                    kernel_size=3, stride=2, padding=1),\n                        norm_layer(min(max_features, ngf * mult * 2)),\n                        activation]\n\n        mult = 2 ** n_downsampling\n        feats_num_bottleneck = min(max_features, ngf * mult)\n\n        ### resnet blocks\n        for i in range(n_blocks):\n            if ffc_positions is not None and i in ffc_positions:\n                model += [FFCResnetBlock(feats_num_bottleneck, padding_type, norm_layer, activation_layer=nn.ReLU,\n                                         inline=True, **ffc_kwargs)]\n            model += [MultidilatedResnetBlock(feats_num_bottleneck, padding_type=padding_type,\n                                              conv_layer=resnet_conv_layer, activation=activation,\n                                              norm_layer=norm_layer)]\n\n        ### upsample\n        for i in range(n_downsampling):\n            mult = 2 ** (n_downsampling - i)\n            model += deconv_factory(deconv_kind, ngf, mult, up_norm_layer, up_activation, max_features)\n        model += [nn.ReflectionPad2d(3),\n                  nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)]\n        if add_out_act:\n            model.append(get_activation('tanh' if add_out_act is True else add_out_act))\n        self.model = nn.Sequential(*model)\n\n    def forward(self, input):\n        return self.model(input)\n\nclass ConfigGlobalGenerator(nn.Module):\n    def __init__(self, input_nc, output_nc, ngf=64, n_downsampling=3,\n                 n_blocks=3, norm_layer=nn.BatchNorm2d,\n                 padding_type='reflect', conv_kind='default',\n                 deconv_kind='convtranspose', activation=nn.ReLU(True),\n                 up_norm_layer=nn.BatchNorm2d, affine=None, up_activation=nn.ReLU(True),\n                 add_out_act=True, max_features=1024,\n                 manual_block_spec=[],\n                 resnet_block_kind='multidilatedresnetblock',\n                 resnet_conv_kind='multidilated',\n                 resnet_dilation=1,\n                 multidilation_kwargs={}):\n        assert (n_blocks >= 0)\n        super().__init__()\n\n        conv_layer = get_conv_block_ctor(conv_kind)\n        resnet_conv_layer = functools.partial(get_conv_block_ctor(resnet_conv_kind), **multidilation_kwargs)\n        norm_layer = get_norm_layer(norm_layer)\n        if affine is not None:\n            norm_layer = partial(norm_layer, affine=affine)\n        up_norm_layer = get_norm_layer(up_norm_layer)\n        if affine is not None:\n            up_norm_layer = partial(up_norm_layer, affine=affine)\n\n        model = [nn.ReflectionPad2d(3),\n                 conv_layer(input_nc, ngf, kernel_size=7, padding=0),\n                 norm_layer(ngf),\n                 activation]\n\n        identity = Identity()\n\n        ### downsample\n        for i in range(n_downsampling):\n            mult = 2 ** i\n            model += [conv_layer(min(max_features, ngf * mult),\n                                    min(max_features, ngf * mult * 2),\n                                    kernel_size=3, stride=2, padding=1),\n                        norm_layer(min(max_features, ngf * mult * 2)),\n                        activation]\n\n        mult = 2 ** n_downsampling\n        feats_num_bottleneck = min(max_features, ngf * mult)\n\n        if len(manual_block_spec) == 0:\n            manual_block_spec = [\n                DotDict(lambda : None, {\n                    'n_blocks': n_blocks,\n                    'use_default': True})\n            ]\n\n        ### resnet blocks\n        for block_spec in manual_block_spec:\n            def make_and_add_blocks(model, block_spec):\n                block_spec = DotDict(lambda : None, block_spec)\n                if not block_spec.use_default:\n                    resnet_conv_layer = functools.partial(get_conv_block_ctor(block_spec.resnet_conv_kind), **block_spec.multidilation_kwargs)\n                    resnet_conv_kind = block_spec.resnet_conv_kind\n                    resnet_block_kind = block_spec.resnet_block_kind\n                    if block_spec.resnet_dilation is not None:\n                        resnet_dilation = block_spec.resnet_dilation\n                for i in range(block_spec.n_blocks):\n                    if resnet_block_kind == \"multidilatedresnetblock\":\n                        model += [MultidilatedResnetBlock(feats_num_bottleneck, padding_type=padding_type,\n                                                        conv_layer=resnet_conv_layer, activation=activation,\n                                                        norm_layer=norm_layer)]\n                    if resnet_block_kind == \"resnetblock\":                                            \n                        model += [ResnetBlock(ngf * mult, padding_type=padding_type, activation=activation, norm_layer=norm_layer,\n                                            conv_kind=resnet_conv_kind)]\n                    if resnet_block_kind == \"resnetblock5x5\":                                            \n                        model += [ResnetBlock5x5(ngf * mult, padding_type=padding_type, activation=activation, norm_layer=norm_layer,\n                                            conv_kind=resnet_conv_kind)]\n                    if resnet_block_kind == \"resnetblockdwdil\":\n                        model += [ResnetBlock(ngf * mult, padding_type=padding_type, activation=activation, norm_layer=norm_layer,\n                                            conv_kind=resnet_conv_kind, dilation=resnet_dilation, second_dilation=resnet_dilation)]\n            make_and_add_blocks(model, block_spec)\n        \n        ### upsample\n        for i in range(n_downsampling):\n            mult = 2 ** (n_downsampling - i)\n            model += deconv_factory(deconv_kind, ngf, mult, up_norm_layer, up_activation, max_features)\n        model += [nn.ReflectionPad2d(3),\n                  nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)]\n        if add_out_act:\n            model.append(get_activation('tanh' if add_out_act is True else add_out_act))\n        self.model = nn.Sequential(*model)\n\n    def forward(self, input):\n        return self.model(input)\n\n\ndef make_dil_blocks(dilated_blocks_n, dilation_block_kind, dilated_block_kwargs):\n    blocks = []\n    for i in range(dilated_blocks_n):\n        if dilation_block_kind == 'simple':\n            blocks.append(ResnetBlock(**dilated_block_kwargs, dilation=2 ** (i + 1)))\n        elif dilation_block_kind == 'multi':\n            blocks.append(MultidilatedResnetBlock(**dilated_block_kwargs))\n        else:\n            raise ValueError(f'dilation_block_kind could not be \"{dilation_block_kind}\"')\n    return blocks\n\n\nclass GlobalGenerator(nn.Module):\n    def __init__(self, input_nc, output_nc, ngf=64, n_downsampling=3, n_blocks=9, norm_layer=nn.BatchNorm2d,\n                 padding_type='reflect', conv_kind='default', activation=nn.ReLU(True),\n                 up_norm_layer=nn.BatchNorm2d, affine=None,\n                 up_activation=nn.ReLU(True), dilated_blocks_n=0, dilated_blocks_n_start=0,\n                 dilated_blocks_n_middle=0,\n                 add_out_act=True,\n                 max_features=1024, is_resblock_depthwise=False,\n                 ffc_positions=None, ffc_kwargs={}, dilation=1, second_dilation=None,\n                 dilation_block_kind='simple', multidilation_kwargs={}):\n        assert (n_blocks >= 0)\n        super().__init__()\n\n        conv_layer = get_conv_block_ctor(conv_kind)\n        norm_layer = get_norm_layer(norm_layer)\n        if affine is not None:\n            norm_layer = partial(norm_layer, affine=affine)\n        up_norm_layer = get_norm_layer(up_norm_layer)\n        if affine is not None:\n            up_norm_layer = partial(up_norm_layer, affine=affine)\n\n        if ffc_positions is not None:\n            ffc_positions = collections.Counter(ffc_positions)\n\n        model = [nn.ReflectionPad2d(3),\n                 conv_layer(input_nc, ngf, kernel_size=7, padding=0),\n                 norm_layer(ngf),\n                 activation]\n\n        identity = Identity()\n        ### downsample\n        for i in range(n_downsampling):\n            mult = 2 ** i\n\n            model += [conv_layer(min(max_features, ngf * mult),\n                                min(max_features, ngf * mult * 2),\n                                kernel_size=3, stride=2, padding=1),\n                        norm_layer(min(max_features, ngf * mult * 2)),\n                        activation]\n\n        mult = 2 ** n_downsampling\n        feats_num_bottleneck = min(max_features, ngf * mult)\n\n        dilated_block_kwargs = dict(dim=feats_num_bottleneck, padding_type=padding_type,\n                                    activation=activation, norm_layer=norm_layer)\n        if dilation_block_kind == 'simple':\n            dilated_block_kwargs['conv_kind'] = conv_kind\n        elif dilation_block_kind == 'multi':\n            dilated_block_kwargs['conv_layer'] = functools.partial(\n                get_conv_block_ctor('multidilated'), **multidilation_kwargs)\n\n        # dilated blocks at the start of the bottleneck sausage\n        if dilated_blocks_n_start is not None and dilated_blocks_n_start > 0:\n            model += make_dil_blocks(dilated_blocks_n_start, dilation_block_kind, dilated_block_kwargs)\n\n        # resnet blocks\n        for i in range(n_blocks):\n            # dilated blocks at the middle of the bottleneck sausage\n            if i == n_blocks // 2 and dilated_blocks_n_middle is not None and dilated_blocks_n_middle > 0:\n                model += make_dil_blocks(dilated_blocks_n_middle, dilation_block_kind, dilated_block_kwargs)\n            \n            if ffc_positions is not None and i in ffc_positions:\n                for _ in range(ffc_positions[i]):  # same position can occur more than once\n                    model += [FFCResnetBlock(feats_num_bottleneck, padding_type, norm_layer, activation_layer=nn.ReLU,\n                                             inline=True, **ffc_kwargs)]\n\n            if is_resblock_depthwise:\n                resblock_groups = feats_num_bottleneck\n            else:\n                resblock_groups = 1\n\n            model += [ResnetBlock(feats_num_bottleneck, padding_type=padding_type, activation=activation,\n                                    norm_layer=norm_layer, conv_kind=conv_kind, groups=resblock_groups,\n                                    dilation=dilation, second_dilation=second_dilation)]\n            \n\n        # dilated blocks at the end of the bottleneck sausage\n        if dilated_blocks_n is not None and dilated_blocks_n > 0:\n            model += make_dil_blocks(dilated_blocks_n, dilation_block_kind, dilated_block_kwargs)\n\n        # upsample\n        for i in range(n_downsampling):\n            mult = 2 ** (n_downsampling - i)\n            model += [nn.ConvTranspose2d(min(max_features, ngf * mult),\n                                         min(max_features, int(ngf * mult / 2)),\n                                         kernel_size=3, stride=2, padding=1, output_padding=1),\n                      up_norm_layer(min(max_features, int(ngf * mult / 2))),\n                      up_activation]\n        model += [nn.ReflectionPad2d(3),\n                  nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)]\n        if add_out_act:\n            model.append(get_activation('tanh' if add_out_act is True else add_out_act))\n        self.model = nn.Sequential(*model)\n\n    def forward(self, input):\n        return self.model(input)\n\n\nclass GlobalGeneratorGated(GlobalGenerator):\n    def __init__(self, *args, **kwargs):\n        real_kwargs=dict(\n            conv_kind='gated_bn_relu',\n            activation=nn.Identity(),\n            norm_layer=nn.Identity\n        )\n        real_kwargs.update(kwargs)\n        super().__init__(*args, **real_kwargs)\n\n\nclass GlobalGeneratorFromSuperChannels(nn.Module):\n    def __init__(self, input_nc, output_nc, n_downsampling, n_blocks, super_channels, norm_layer=\"bn\", padding_type='reflect', add_out_act=True):\n        super().__init__()\n        self.n_downsampling = n_downsampling\n        norm_layer = get_norm_layer(norm_layer)\n        if type(norm_layer) == functools.partial:\n            use_bias = (norm_layer.func == nn.InstanceNorm2d)\n        else:\n            use_bias = (norm_layer == nn.InstanceNorm2d)\n\n        channels = self.convert_super_channels(super_channels)\n        self.channels = channels\n\n        model = [nn.ReflectionPad2d(3),\n                 nn.Conv2d(input_nc, channels[0], kernel_size=7, padding=0, bias=use_bias),\n                 norm_layer(channels[0]),\n                 nn.ReLU(True)]\n\n        for i in range(n_downsampling):  # add downsampling layers\n            mult = 2 ** i\n            model += [nn.Conv2d(channels[0+i], channels[1+i], kernel_size=3, stride=2, padding=1, bias=use_bias),\n                      norm_layer(channels[1+i]),\n                      nn.ReLU(True)]\n\n        mult = 2 ** n_downsampling\n\n        n_blocks1 = n_blocks // 3\n        n_blocks2 = n_blocks1\n        n_blocks3 = n_blocks - n_blocks1 - n_blocks2\n\n        for i in range(n_blocks1):\n            c = n_downsampling\n            dim = channels[c]\n            model += [ResnetBlock(dim, padding_type=padding_type, norm_layer=norm_layer)]\n\n        for i in range(n_blocks2):\n            c = n_downsampling+1\n            dim = channels[c]\n            kwargs = {}\n            if i == 0:\n                kwargs = {\"in_dim\": channels[c-1]}\n            model += [ResnetBlock(dim, padding_type=padding_type, norm_layer=norm_layer, **kwargs)]\n\n        for i in range(n_blocks3):\n            c = n_downsampling+2\n            dim = channels[c]\n            kwargs = {}\n            if i == 0:\n                kwargs = {\"in_dim\": channels[c-1]}\n            model += [ResnetBlock(dim, padding_type=padding_type, norm_layer=norm_layer, **kwargs)]\n\n        for i in range(n_downsampling):  # add upsampling layers\n            mult = 2 ** (n_downsampling - i)\n            model += [nn.ConvTranspose2d(channels[n_downsampling+3+i],\n                                           channels[n_downsampling+3+i+1],\n                                           kernel_size=3, stride=2,\n                                           padding=1, output_padding=1,\n                                           bias=use_bias),\n                      norm_layer(channels[n_downsampling+3+i+1]),\n                      nn.ReLU(True)]\n        model += [nn.ReflectionPad2d(3)]\n        model += [nn.Conv2d(channels[2*n_downsampling+3], output_nc, kernel_size=7, padding=0)]\n\n        if add_out_act:\n            model.append(get_activation('tanh' if add_out_act is True else add_out_act))\n        self.model = nn.Sequential(*model)\n\n    def convert_super_channels(self, super_channels):\n        n_downsampling = self.n_downsampling\n        result = []\n        cnt = 0\n\n        if n_downsampling == 2:\n            N1 = 10\n        elif n_downsampling == 3:\n            N1 = 13\n        else:\n            raise NotImplementedError\n\n        for i in range(0, N1):\n            if i in [1,4,7,10]:\n                channel = super_channels[cnt] * (2 ** cnt)\n                config = {'channel': channel}\n                result.append(channel)\n                logging.info(f\"Downsample channels {result[-1]}\")\n                cnt += 1\n\n        for i in range(3):\n            for counter, j in enumerate(range(N1 + i * 3, N1 + 3 + i * 3)):\n                if len(super_channels) == 6:\n                    channel = super_channels[3] * 4\n                else:\n                    channel = super_channels[i + 3] * 4\n                config = {'channel': channel}\n                if counter == 0:\n                    result.append(channel)\n                    logging.info(f\"Bottleneck channels {result[-1]}\")\n        cnt = 2\n\n        for i in range(N1+9, N1+21):\n            if i in [22, 25,28]:\n                cnt -= 1\n                if len(super_channels) == 6:\n                    channel = super_channels[5 - cnt] * (2 ** cnt)\n                else:\n                    channel = super_channels[7 - cnt] * (2 ** cnt)\n                result.append(int(channel))\n                logging.info(f\"Upsample channels {result[-1]}\")\n        return result\n\n    def forward(self, input):\n        return self.model(input)\n\n\n# Defines the PatchGAN discriminator with the specified arguments.\nclass NLayerDiscriminator(BaseDiscriminator):\n    def __init__(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d,):\n        super().__init__()\n        self.n_layers = n_layers\n\n        kw = 4\n        padw = int(np.ceil((kw-1.0)/2))\n        sequence = [[nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw),\n                     nn.LeakyReLU(0.2, True)]]\n\n        nf = ndf\n        for n in range(1, n_layers):\n            nf_prev = nf\n            nf = min(nf * 2, 512)\n\n            cur_model = []\n            cur_model += [\n                nn.Conv2d(nf_prev, nf, kernel_size=kw, stride=2, padding=padw),\n                norm_layer(nf),\n                nn.LeakyReLU(0.2, True)\n            ]\n            sequence.append(cur_model)\n\n        nf_prev = nf\n        nf = min(nf * 2, 512)\n\n        cur_model = []\n        cur_model += [\n            nn.Conv2d(nf_prev, nf, kernel_size=kw, stride=1, padding=padw),\n            norm_layer(nf),\n            nn.LeakyReLU(0.2, True)\n        ]\n        sequence.append(cur_model)\n\n        sequence += [[nn.Conv2d(nf, 1, kernel_size=kw, stride=1, padding=padw)]]\n\n        for n in range(len(sequence)):\n            setattr(self, 'model'+str(n), nn.Sequential(*sequence[n]))\n\n    def get_all_activations(self, x):\n        res = [x]\n        for n in range(self.n_layers + 2):\n            model = getattr(self, 'model' + str(n))\n            res.append(model(res[-1]))\n        return res[1:]\n\n    def forward(self, x):\n        act = self.get_all_activations(x)\n        return act[-1], act[:-1]\n\n\nclass MultidilatedNLayerDiscriminator(BaseDiscriminator):\n    def __init__(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, multidilation_kwargs={}):\n        super().__init__()\n        self.n_layers = n_layers\n\n        kw = 4\n        padw = int(np.ceil((kw-1.0)/2))\n        sequence = [[nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw),\n                     nn.LeakyReLU(0.2, True)]]\n\n        nf = ndf\n        for n in range(1, n_layers):\n            nf_prev = nf\n            nf = min(nf * 2, 512)\n\n            cur_model = []\n            cur_model += [\n                MultidilatedConv(nf_prev, nf, kernel_size=kw, stride=2, padding=[2, 3], **multidilation_kwargs),\n                norm_layer(nf),\n                nn.LeakyReLU(0.2, True)\n            ]\n            sequence.append(cur_model)\n\n        nf_prev = nf\n        nf = min(nf * 2, 512)\n\n        cur_model = []\n        cur_model += [\n            nn.Conv2d(nf_prev, nf, kernel_size=kw, stride=1, padding=padw),\n            norm_layer(nf),\n            nn.LeakyReLU(0.2, True)\n        ]\n        sequence.append(cur_model)\n\n        sequence += [[nn.Conv2d(nf, 1, kernel_size=kw, stride=1, padding=padw)]]\n\n        for n in range(len(sequence)):\n            setattr(self, 'model'+str(n), nn.Sequential(*sequence[n]))\n\n    def get_all_activations(self, x):\n        res = [x]\n        for n in range(self.n_layers + 2):\n            model = getattr(self, 'model' + str(n))\n            res.append(model(res[-1]))\n        return res[1:]\n\n    def forward(self, x):\n        act = self.get_all_activations(x)\n        return act[-1], act[:-1]\n\n\nclass NLayerDiscriminatorAsGen(NLayerDiscriminator):\n    def forward(self, x):\n        return super().forward(x)[0]\n"
  },
  {
    "path": "model_cards/lama/saicinpainting/training/modules/spatial_transform.py",
    "content": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom kornia.geometry.transform import rotate\n\n\nclass LearnableSpatialTransformWrapper(nn.Module):\n    def __init__(self, impl, pad_coef=0.5, angle_init_range=80, train_angle=True):\n        super().__init__()\n        self.impl = impl\n        self.angle = torch.rand(1) * angle_init_range\n        if train_angle:\n            self.angle = nn.Parameter(self.angle, requires_grad=True)\n        self.pad_coef = pad_coef\n\n    def forward(self, x):\n        if torch.is_tensor(x):\n            return self.inverse_transform(self.impl(self.transform(x)), x)\n        elif isinstance(x, tuple):\n            x_trans = tuple(self.transform(elem) for elem in x)\n            y_trans = self.impl(x_trans)\n            return tuple(self.inverse_transform(elem, orig_x) for elem, orig_x in zip(y_trans, x))\n        else:\n            raise ValueError(f'Unexpected input type {type(x)}')\n\n    def transform(self, x):\n        height, width = x.shape[2:]\n        pad_h, pad_w = int(height * self.pad_coef), int(width * self.pad_coef)\n        x_padded = F.pad(x, [pad_w, pad_w, pad_h, pad_h], mode='reflect')\n        x_padded_rotated = rotate(x_padded, angle=self.angle.to(x_padded))\n        return x_padded_rotated\n\n    def inverse_transform(self, y_padded_rotated, orig_x):\n        height, width = orig_x.shape[2:]\n        pad_h, pad_w = int(height * self.pad_coef), int(width * self.pad_coef)\n\n        y_padded = rotate(y_padded_rotated, angle=-self.angle.to(y_padded_rotated))\n        y_height, y_width = y_padded.shape[2:]\n        y = y_padded[:, :, pad_h : y_height - pad_h, pad_w : y_width - pad_w]\n        return y\n\n\nif __name__ == '__main__':\n    layer = LearnableSpatialTransformWrapper(nn.Identity())\n    x = torch.arange(2* 3 * 15 * 15).view(2, 3, 15, 15).float()\n    y = layer(x)\n    assert x.shape == y.shape\n    assert torch.allclose(x[:, :, 1:, 1:][:, :, :-1, :-1], y[:, :, 1:, 1:][:, :, :-1, :-1])\n    print('all ok')\n"
  },
  {
    "path": "model_cards/lama/saicinpainting/training/modules/squeeze_excitation.py",
    "content": "import torch.nn as nn\n\n\nclass SELayer(nn.Module):\n    def __init__(self, channel, reduction=16):\n        super(SELayer, self).__init__()\n        self.avg_pool = nn.AdaptiveAvgPool2d(1)\n        self.fc = nn.Sequential(\n            nn.Linear(channel, channel // reduction, bias=False),\n            nn.ReLU(inplace=True),\n            nn.Linear(channel // reduction, channel, bias=False),\n            nn.Sigmoid()\n        )\n\n    def forward(self, x):\n        b, c, _, _ = x.size()\n        y = self.avg_pool(x).view(b, c)\n        y = self.fc(y).view(b, c, 1, 1)\n        res = x * y.expand_as(x)\n        return res\n"
  },
  {
    "path": "model_cards/lama/saicinpainting/training/trainers/__init__.py",
    "content": "import logging\nimport torch\nfrom saicinpainting.training.trainers.default import DefaultInpaintingTrainingModule\n\n\ndef get_training_model_class(kind):\n    if kind == 'default':\n        return DefaultInpaintingTrainingModule\n\n    raise ValueError(f'Unknown trainer module {kind}')\n\n\ndef make_training_model(config):\n    kind = config.training_model.kind\n    kwargs = dict(config.training_model)\n    kwargs.pop('kind')\n    kwargs['use_ddp'] = config.trainer.kwargs.get('accelerator', None) == 'ddp'\n\n    logging.info(f'Make training model {kind}')\n\n    cls = get_training_model_class(kind)\n    return cls(config, **kwargs)\n\n\ndef load_checkpoint(train_config, path, map_location='cuda', strict=True):\n    model: torch.nn.Module = make_training_model(train_config)\n    state = torch.load(path, map_location=map_location)\n    model.load_state_dict(state['state_dict'], strict=strict)\n    model.on_load_checkpoint(state)\n    return model\n"
  },
  {
    "path": "model_cards/lama/saicinpainting/training/trainers/base.py",
    "content": "import copy\nimport logging\nfrom typing import Dict, Tuple\n\nimport pandas as pd\nimport pytorch_lightning as ptl\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.utils.data import DistributedSampler\n\nfrom saicinpainting.evaluation import make_evaluator\nfrom saicinpainting.training.data.datasets import make_default_train_dataloader, make_default_val_dataloader\nfrom saicinpainting.training.losses.adversarial import make_discrim_loss\nfrom saicinpainting.training.losses.perceptual import PerceptualLoss, ResNetPL\nfrom saicinpainting.training.modules import make_generator, make_discriminator\nfrom saicinpainting.training.visualizers import make_visualizer\nfrom saicinpainting.utils import add_prefix_to_keys, average_dicts, set_requires_grad, flatten_dict, \\\n    get_has_ddp_rank\n\nLOGGER = logging.getLogger(__name__)\n\n\ndef make_optimizer(parameters, kind='adamw', **kwargs):\n    if kind == 'adam':\n        optimizer_class = torch.optim.Adam\n    elif kind == 'adamw':\n        optimizer_class = torch.optim.AdamW\n    else:\n        raise ValueError(f'Unknown optimizer kind {kind}')\n    return optimizer_class(parameters, **kwargs)\n\n\ndef update_running_average(result: nn.Module, new_iterate_model: nn.Module, decay=0.999):\n    with torch.no_grad():\n        res_params = dict(result.named_parameters())\n        new_params = dict(new_iterate_model.named_parameters())\n\n        for k in res_params.keys():\n            res_params[k].data.mul_(decay).add_(new_params[k].data, alpha=1 - decay)\n\n\ndef make_multiscale_noise(base_tensor, scales=6, scale_mode='bilinear'):\n    batch_size, _, height, width = base_tensor.shape\n    cur_height, cur_width = height, width\n    result = []\n    align_corners = False if scale_mode in ('bilinear', 'bicubic') else None\n    for _ in range(scales):\n        cur_sample = torch.randn(batch_size, 1, cur_height, cur_width, device=base_tensor.device)\n        cur_sample_scaled = F.interpolate(cur_sample, size=(height, width), mode=scale_mode, align_corners=align_corners)\n        result.append(cur_sample_scaled)\n        cur_height //= 2\n        cur_width //= 2\n    return torch.cat(result, dim=1)\n\n\nclass BaseInpaintingTrainingModule(ptl.LightningModule):\n    def __init__(self, config, use_ddp, *args,  predict_only=False, visualize_each_iters=100,\n                 average_generator=False, generator_avg_beta=0.999, average_generator_start_step=30000,\n                 average_generator_period=10, store_discr_outputs_for_vis=False,\n                 **kwargs):\n        super().__init__(*args, **kwargs)\n        LOGGER.info('BaseInpaintingTrainingModule init called')\n\n        self.config = config\n\n        self.generator = make_generator(config, **self.config.generator)\n        self.use_ddp = use_ddp\n\n        if not get_has_ddp_rank():\n            LOGGER.info(f'Generator\\n{self.generator}')\n\n        if not predict_only:\n            self.save_hyperparameters(self.config)\n            self.discriminator = make_discriminator(**self.config.discriminator)\n            self.adversarial_loss = make_discrim_loss(**self.config.losses.adversarial)\n            self.visualizer = make_visualizer(**self.config.visualizer)\n            self.val_evaluator = make_evaluator(**self.config.evaluator)\n            self.test_evaluator = make_evaluator(**self.config.evaluator)\n\n            if not get_has_ddp_rank():\n                LOGGER.info(f'Discriminator\\n{self.discriminator}')\n\n            extra_val = self.config.data.get('extra_val', ())\n            if extra_val:\n                self.extra_val_titles = list(extra_val)\n                self.extra_evaluators = nn.ModuleDict({k: make_evaluator(**self.config.evaluator)\n                                                       for k in extra_val})\n            else:\n                self.extra_evaluators = {}\n\n            self.average_generator = average_generator\n            self.generator_avg_beta = generator_avg_beta\n            self.average_generator_start_step = average_generator_start_step\n            self.average_generator_period = average_generator_period\n            self.generator_average = None\n            self.last_generator_averaging_step = -1\n            self.store_discr_outputs_for_vis = store_discr_outputs_for_vis\n\n            if self.config.losses.get(\"l1\", {\"weight_known\": 0})['weight_known'] > 0:\n                self.loss_l1 = nn.L1Loss(reduction='none')\n\n            if self.config.losses.get(\"mse\", {\"weight\": 0})['weight'] > 0:\n                self.loss_mse = nn.MSELoss(reduction='none')\n            \n            if self.config.losses.perceptual.weight > 0:\n                self.loss_pl = PerceptualLoss()\n\n            if self.config.losses.get(\"resnet_pl\", {\"weight\": 0})['weight'] > 0:\n                self.loss_resnet_pl = ResNetPL(**self.config.losses.resnet_pl)\n            else:\n                self.loss_resnet_pl = None\n\n        self.visualize_each_iters = visualize_each_iters\n        LOGGER.info('BaseInpaintingTrainingModule init done')\n\n    def configure_optimizers(self):\n        discriminator_params = list(self.discriminator.parameters())\n        return [\n            dict(optimizer=make_optimizer(self.generator.parameters(), **self.config.optimizers.generator)),\n            dict(optimizer=make_optimizer(discriminator_params, **self.config.optimizers.discriminator)),\n        ]\n\n    def train_dataloader(self):\n        kwargs = dict(self.config.data.train)\n        if self.use_ddp:\n            kwargs['ddp_kwargs'] = dict(num_replicas=self.trainer.num_nodes * self.trainer.num_processes,\n                                        rank=self.trainer.global_rank,\n                                        shuffle=True)\n        dataloader = make_default_train_dataloader(**self.config.data.train)\n        return dataloader\n\n    def val_dataloader(self):\n        res = [make_default_val_dataloader(**self.config.data.val)]\n\n        if self.config.data.visual_test is not None:\n            res = res + [make_default_val_dataloader(**self.config.data.visual_test)]\n        else:\n            res = res + res\n\n        extra_val = self.config.data.get('extra_val', ())\n        if extra_val:\n            res += [make_default_val_dataloader(**extra_val[k]) for k in self.extra_val_titles]\n\n        return res\n\n    def training_step(self, batch, batch_idx, optimizer_idx=None):\n        self._is_training_step = True\n        return self._do_step(batch, batch_idx, mode='train', optimizer_idx=optimizer_idx)\n\n    def validation_step(self, batch, batch_idx, dataloader_idx):\n        extra_val_key = None\n        if dataloader_idx == 0:\n            mode = 'val'\n        elif dataloader_idx == 1:\n            mode = 'test'\n        else:\n            mode = 'extra_val'\n            extra_val_key = self.extra_val_titles[dataloader_idx - 2]\n        self._is_training_step = False\n        return self._do_step(batch, batch_idx, mode=mode, extra_val_key=extra_val_key)\n\n    def training_step_end(self, batch_parts_outputs):\n        if self.training and self.average_generator \\\n                and self.global_step >= self.average_generator_start_step \\\n                and self.global_step >= self.last_generator_averaging_step + self.average_generator_period:\n            if self.generator_average is None:\n                self.generator_average = copy.deepcopy(self.generator)\n            else:\n                update_running_average(self.generator_average, self.generator, decay=self.generator_avg_beta)\n            self.last_generator_averaging_step = self.global_step\n\n        full_loss = (batch_parts_outputs['loss'].mean()\n                     if torch.is_tensor(batch_parts_outputs['loss'])  # loss is not tensor when no discriminator used\n                     else torch.tensor(batch_parts_outputs['loss']).float().requires_grad_(True))\n        log_info = {k: v.mean() for k, v in batch_parts_outputs['log_info'].items()}\n        self.log_dict(log_info, on_step=True, on_epoch=False)\n        return full_loss\n\n    def validation_epoch_end(self, outputs):\n        outputs = [step_out for out_group in outputs for step_out in out_group]\n        averaged_logs = average_dicts(step_out['log_info'] for step_out in outputs)\n        self.log_dict({k: v.mean() for k, v in averaged_logs.items()})\n\n        pd.set_option('display.max_columns', 500)\n        pd.set_option('display.width', 1000)\n\n        # standard validation\n        val_evaluator_states = [s['val_evaluator_state'] for s in outputs if 'val_evaluator_state' in s]\n        val_evaluator_res = self.val_evaluator.evaluation_end(states=val_evaluator_states)\n        val_evaluator_res_df = pd.DataFrame(val_evaluator_res).stack(1).unstack(0)\n        val_evaluator_res_df.dropna(axis=1, how='all', inplace=True)\n        LOGGER.info(f'Validation metrics after epoch #{self.current_epoch}, '\n                    f'total {self.global_step} iterations:\\n{val_evaluator_res_df}')\n\n        for k, v in flatten_dict(val_evaluator_res).items():\n            self.log(f'val_{k}', v)\n\n        # standard visual test\n        test_evaluator_states = [s['test_evaluator_state'] for s in outputs\n                                 if 'test_evaluator_state' in s]\n        test_evaluator_res = self.test_evaluator.evaluation_end(states=test_evaluator_states)\n        test_evaluator_res_df = pd.DataFrame(test_evaluator_res).stack(1).unstack(0)\n        test_evaluator_res_df.dropna(axis=1, how='all', inplace=True)\n        LOGGER.info(f'Test metrics after epoch #{self.current_epoch}, '\n                    f'total {self.global_step} iterations:\\n{test_evaluator_res_df}')\n\n        for k, v in flatten_dict(test_evaluator_res).items():\n            self.log(f'test_{k}', v)\n\n        # extra validations\n        if self.extra_evaluators:\n            for cur_eval_title, cur_evaluator in self.extra_evaluators.items():\n                cur_state_key = f'extra_val_{cur_eval_title}_evaluator_state'\n                cur_states = [s[cur_state_key] for s in outputs if cur_state_key in s]\n                cur_evaluator_res = cur_evaluator.evaluation_end(states=cur_states)\n                cur_evaluator_res_df = pd.DataFrame(cur_evaluator_res).stack(1).unstack(0)\n                cur_evaluator_res_df.dropna(axis=1, how='all', inplace=True)\n                LOGGER.info(f'Extra val {cur_eval_title} metrics after epoch #{self.current_epoch}, '\n                            f'total {self.global_step} iterations:\\n{cur_evaluator_res_df}')\n                for k, v in flatten_dict(cur_evaluator_res).items():\n                    self.log(f'extra_val_{cur_eval_title}_{k}', v)\n\n    def _do_step(self, batch, batch_idx, mode='train', optimizer_idx=None, extra_val_key=None):\n        if optimizer_idx == 0:  # step for generator\n            set_requires_grad(self.generator, True)\n            set_requires_grad(self.discriminator, False)\n        elif optimizer_idx == 1:  # step for discriminator\n            set_requires_grad(self.generator, False)\n            set_requires_grad(self.discriminator, True)\n\n        batch = self(batch)\n\n        total_loss = 0\n        metrics = {}\n\n        if optimizer_idx is None or optimizer_idx == 0:  # step for generator\n            total_loss, metrics = self.generator_loss(batch)\n\n        elif optimizer_idx is None or optimizer_idx == 1:  # step for discriminator\n            if self.config.losses.adversarial.weight > 0:\n                total_loss, metrics = self.discriminator_loss(batch)\n\n        if self.get_ddp_rank() in (None, 0) and (batch_idx % self.visualize_each_iters == 0 or mode == 'test'):\n            if self.config.losses.adversarial.weight > 0:\n                if self.store_discr_outputs_for_vis:\n                    with torch.no_grad():\n                        self.store_discr_outputs(batch)\n            vis_suffix = f'_{mode}'\n            if mode == 'extra_val':\n                vis_suffix += f'_{extra_val_key}'\n            self.visualizer(self.current_epoch, batch_idx, batch, suffix=vis_suffix)\n\n        metrics_prefix = f'{mode}_'\n        if mode == 'extra_val':\n            metrics_prefix += f'{extra_val_key}_'\n        result = dict(loss=total_loss, log_info=add_prefix_to_keys(metrics, metrics_prefix))\n        if mode == 'val':\n            result['val_evaluator_state'] = self.val_evaluator.process_batch(batch)\n        elif mode == 'test':\n            result['test_evaluator_state'] = self.test_evaluator.process_batch(batch)\n        elif mode == 'extra_val':\n            result[f'extra_val_{extra_val_key}_evaluator_state'] = self.extra_evaluators[extra_val_key].process_batch(batch)\n\n        return result\n\n    def get_current_generator(self, no_average=False):\n        if not no_average and not self.training and self.average_generator and self.generator_average is not None:\n            return self.generator_average\n        return self.generator\n\n    def forward(self, batch: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:\n        \"\"\"Pass data through generator and obtain at leas 'predicted_image' and 'inpainted' keys\"\"\"\n        raise NotImplementedError()\n\n    def generator_loss(self, batch) -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:\n        raise NotImplementedError()\n\n    def discriminator_loss(self, batch) -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:\n        raise NotImplementedError()\n\n    def store_discr_outputs(self, batch):\n        out_size = batch['image'].shape[2:]\n        discr_real_out, _ = self.discriminator(batch['image'])\n        discr_fake_out, _ = self.discriminator(batch['predicted_image'])\n        batch['discr_output_real'] = F.interpolate(discr_real_out, size=out_size, mode='nearest')\n        batch['discr_output_fake'] = F.interpolate(discr_fake_out, size=out_size, mode='nearest')\n        batch['discr_output_diff'] = batch['discr_output_real'] - batch['discr_output_fake']\n\n    def get_ddp_rank(self):\n        return self.trainer.global_rank if (self.trainer.num_nodes * self.trainer.num_processes) > 1 else None\n"
  },
  {
    "path": "model_cards/lama/saicinpainting/training/trainers/default.py",
    "content": "import logging\n\nimport torch\nimport torch.nn.functional as F\nfrom omegaconf import OmegaConf\n\nfrom saicinpainting.training.data.datasets import make_constant_area_crop_params\nfrom saicinpainting.training.losses.distance_weighting import make_mask_distance_weighter\nfrom saicinpainting.training.losses.feature_matching import feature_matching_loss, masked_l1_loss\nfrom saicinpainting.training.modules.fake_fakes import FakeFakesGenerator\nfrom saicinpainting.training.trainers.base import BaseInpaintingTrainingModule, make_multiscale_noise\nfrom saicinpainting.utils import add_prefix_to_keys, get_ramp\n\nLOGGER = logging.getLogger(__name__)\n\n\ndef make_constant_area_crop_batch(batch, **kwargs):\n    crop_y, crop_x, crop_height, crop_width = make_constant_area_crop_params(img_height=batch['image'].shape[2],\n                                                                             img_width=batch['image'].shape[3],\n                                                                             **kwargs)\n    batch['image'] = batch['image'][:, :, crop_y : crop_y + crop_height, crop_x : crop_x + crop_width]\n    batch['mask'] = batch['mask'][:, :, crop_y: crop_y + crop_height, crop_x: crop_x + crop_width]\n    return batch\n\n\nclass DefaultInpaintingTrainingModule(BaseInpaintingTrainingModule):\n    def __init__(self, *args, concat_mask=True, rescale_scheduler_kwargs=None, image_to_discriminator='predicted_image',\n                 add_noise_kwargs=None, noise_fill_hole=False, const_area_crop_kwargs=None,\n                 distance_weighter_kwargs=None, distance_weighted_mask_for_discr=False,\n                 fake_fakes_proba=0, fake_fakes_generator_kwargs=None,\n                 **kwargs):\n        super().__init__(*args, **kwargs)\n        self.concat_mask = concat_mask\n        self.rescale_size_getter = get_ramp(**rescale_scheduler_kwargs) if rescale_scheduler_kwargs is not None else None\n        self.image_to_discriminator = image_to_discriminator\n        self.add_noise_kwargs = add_noise_kwargs\n        self.noise_fill_hole = noise_fill_hole\n        self.const_area_crop_kwargs = const_area_crop_kwargs\n        self.refine_mask_for_losses = make_mask_distance_weighter(**distance_weighter_kwargs) \\\n            if distance_weighter_kwargs is not None else None\n        self.distance_weighted_mask_for_discr = distance_weighted_mask_for_discr\n\n        self.fake_fakes_proba = fake_fakes_proba\n        if self.fake_fakes_proba > 1e-3:\n            self.fake_fakes_gen = FakeFakesGenerator(**(fake_fakes_generator_kwargs or {}))\n\n    def forward(self, batch):\n        if self.training and self.rescale_size_getter is not None:\n            cur_size = self.rescale_size_getter(self.global_step)\n            batch['image'] = F.interpolate(batch['image'], size=cur_size, mode='bilinear', align_corners=False)\n            batch['mask'] = F.interpolate(batch['mask'], size=cur_size, mode='nearest')\n\n        if self.training and self.const_area_crop_kwargs is not None:\n            batch = make_constant_area_crop_batch(batch, **self.const_area_crop_kwargs)\n\n        img = batch['image']\n        mask = batch['mask']\n\n        masked_img = img * (1 - mask)\n\n        if self.add_noise_kwargs is not None:\n            noise = make_multiscale_noise(masked_img, **self.add_noise_kwargs)\n            if self.noise_fill_hole:\n                masked_img = masked_img + mask * noise[:, :masked_img.shape[1]]\n            masked_img = torch.cat([masked_img, noise], dim=1)\n\n        if self.concat_mask:\n            masked_img = torch.cat([masked_img, mask], dim=1)\n\n        batch['predicted_image'] = self.generator(masked_img)\n        batch['inpainted'] = mask * batch['predicted_image'] + (1 - mask) * batch['image']\n\n        if self.fake_fakes_proba > 1e-3:\n            if self.training and torch.rand(1).item() < self.fake_fakes_proba:\n                batch['fake_fakes'], batch['fake_fakes_masks'] = self.fake_fakes_gen(img, mask)\n                batch['use_fake_fakes'] = True\n            else:\n                batch['fake_fakes'] = torch.zeros_like(img)\n                batch['fake_fakes_masks'] = torch.zeros_like(mask)\n                batch['use_fake_fakes'] = False\n\n        batch['mask_for_losses'] = self.refine_mask_for_losses(img, batch['predicted_image'], mask) \\\n            if self.refine_mask_for_losses is not None and self.training \\\n            else mask\n\n        return batch\n\n    def generator_loss(self, batch):\n        img = batch['image']\n        predicted_img = batch[self.image_to_discriminator]\n        original_mask = batch['mask']\n        supervised_mask = batch['mask_for_losses']\n\n        # L1\n        l1_value = masked_l1_loss(predicted_img, img, supervised_mask,\n                                  self.config.losses.l1.weight_known,\n                                  self.config.losses.l1.weight_missing)\n\n        total_loss = l1_value\n        metrics = dict(gen_l1=l1_value)\n\n        # vgg-based perceptual loss\n        if self.config.losses.perceptual.weight > 0:\n            pl_value = self.loss_pl(predicted_img, img, mask=supervised_mask).sum() * self.config.losses.perceptual.weight\n            total_loss = total_loss + pl_value\n            metrics['gen_pl'] = pl_value\n\n        # discriminator\n        # adversarial_loss calls backward by itself\n        mask_for_discr = supervised_mask if self.distance_weighted_mask_for_discr else original_mask\n        self.adversarial_loss.pre_generator_step(real_batch=img, fake_batch=predicted_img,\n                                                 generator=self.generator, discriminator=self.discriminator)\n        discr_real_pred, discr_real_features = self.discriminator(img)\n        discr_fake_pred, discr_fake_features = self.discriminator(predicted_img)\n        adv_gen_loss, adv_metrics = self.adversarial_loss.generator_loss(real_batch=img,\n                                                                         fake_batch=predicted_img,\n                                                                         discr_real_pred=discr_real_pred,\n                                                                         discr_fake_pred=discr_fake_pred,\n                                                                         mask=mask_for_discr)\n        total_loss = total_loss + adv_gen_loss\n        metrics['gen_adv'] = adv_gen_loss\n        metrics.update(add_prefix_to_keys(adv_metrics, 'adv_'))\n\n        # feature matching\n        if self.config.losses.feature_matching.weight > 0:\n            need_mask_in_fm = OmegaConf.to_container(self.config.losses.feature_matching).get('pass_mask', False)\n            mask_for_fm = supervised_mask if need_mask_in_fm else None\n            fm_value = feature_matching_loss(discr_fake_features, discr_real_features,\n                                             mask=mask_for_fm) * self.config.losses.feature_matching.weight\n            total_loss = total_loss + fm_value\n            metrics['gen_fm'] = fm_value\n\n        if self.loss_resnet_pl is not None:\n            resnet_pl_value = self.loss_resnet_pl(predicted_img, img)\n            total_loss = total_loss + resnet_pl_value\n            metrics['gen_resnet_pl'] = resnet_pl_value\n\n        return total_loss, metrics\n\n    def discriminator_loss(self, batch):\n        total_loss = 0\n        metrics = {}\n\n        predicted_img = batch[self.image_to_discriminator].detach()\n        self.adversarial_loss.pre_discriminator_step(real_batch=batch['image'], fake_batch=predicted_img,\n                                                     generator=self.generator, discriminator=self.discriminator)\n        discr_real_pred, discr_real_features = self.discriminator(batch['image'])\n        discr_fake_pred, discr_fake_features = self.discriminator(predicted_img)\n        adv_discr_loss, adv_metrics = self.adversarial_loss.discriminator_loss(real_batch=batch['image'],\n                                                                               fake_batch=predicted_img,\n                                                                               discr_real_pred=discr_real_pred,\n                                                                               discr_fake_pred=discr_fake_pred,\n                                                                               mask=batch['mask'])\n        total_loss = total_loss + adv_discr_loss\n        metrics['discr_adv'] = adv_discr_loss\n        metrics.update(add_prefix_to_keys(adv_metrics, 'adv_'))\n\n\n        if batch.get('use_fake_fakes', False):\n            fake_fakes = batch['fake_fakes']\n            self.adversarial_loss.pre_discriminator_step(real_batch=batch['image'], fake_batch=fake_fakes,\n                                                         generator=self.generator, discriminator=self.discriminator)\n            discr_fake_fakes_pred, _ = self.discriminator(fake_fakes)\n            fake_fakes_adv_discr_loss, fake_fakes_adv_metrics = self.adversarial_loss.discriminator_loss(\n                real_batch=batch['image'],\n                fake_batch=fake_fakes,\n                discr_real_pred=discr_real_pred,\n                discr_fake_pred=discr_fake_fakes_pred,\n                mask=batch['mask']\n            )\n            total_loss = total_loss + fake_fakes_adv_discr_loss\n            metrics['discr_adv_fake_fakes'] = fake_fakes_adv_discr_loss\n            metrics.update(add_prefix_to_keys(fake_fakes_adv_metrics, 'adv_'))\n\n        return total_loss, metrics\n"
  },
  {
    "path": "model_cards/lama/saicinpainting/training/visualizers/__init__.py",
    "content": "import logging\n\nfrom saicinpainting.training.visualizers.directory import DirectoryVisualizer\nfrom saicinpainting.training.visualizers.noop import NoopVisualizer\n\n\ndef make_visualizer(kind, **kwargs):\n    logging.info(f'Make visualizer {kind}')\n\n    if kind == 'directory':\n        return DirectoryVisualizer(**kwargs)\n    if kind == 'noop':\n        return NoopVisualizer()\n\n    raise ValueError(f'Unknown visualizer kind {kind}')\n"
  },
  {
    "path": "model_cards/lama/saicinpainting/training/visualizers/base.py",
    "content": "import abc\nfrom typing import Dict, List\n\nimport numpy as np\nimport torch\nfrom skimage import color\nfrom skimage.segmentation import mark_boundaries\n\nfrom . import colors\n\nCOLORS, _ = colors.generate_colors(151) # 151 - max classes for semantic segmentation\n\n\nclass BaseVisualizer:\n    @abc.abstractmethod\n    def __call__(self, epoch_i, batch_i, batch, suffix='', rank=None):\n        \"\"\"\n        Take a batch, make an image from it and visualize\n        \"\"\"\n        raise NotImplementedError()\n\n\ndef visualize_mask_and_images(images_dict: Dict[str, np.ndarray], keys: List[str],\n                              last_without_mask=True, rescale_keys=None, mask_only_first=None,\n                              black_mask=False) -> np.ndarray:\n    mask = images_dict['mask'] > 0.5\n    result = []\n    for i, k in enumerate(keys):\n        img = images_dict[k]\n        img = np.transpose(img, (1, 2, 0))\n\n        if rescale_keys is not None and k in rescale_keys:\n            img = img - img.min()\n            img /= img.max() + 1e-5\n        if len(img.shape) == 2:\n            img = np.expand_dims(img, 2)\n\n        if img.shape[2] == 1:\n            img = np.repeat(img, 3, axis=2)\n        elif (img.shape[2] > 3):\n            img_classes = img.argmax(2)\n            img = color.label2rgb(img_classes, colors=COLORS)\n\n        if mask_only_first:\n            need_mark_boundaries = i == 0\n        else:\n            need_mark_boundaries = i < len(keys) - 1 or not last_without_mask\n\n        if need_mark_boundaries:\n            if black_mask:\n                img = img * (1 - mask[0][..., None])\n            img = mark_boundaries(img,\n                                  mask[0],\n                                  color=(1., 0., 0.),\n                                  outline_color=(1., 1., 1.),\n                                  mode='thick')\n        result.append(img)\n    return np.concatenate(result, axis=1)\n\n\ndef visualize_mask_and_images_batch(batch: Dict[str, torch.Tensor], keys: List[str], max_items=10,\n                                    last_without_mask=True, rescale_keys=None) -> np.ndarray:\n    batch = {k: tens.detach().cpu().numpy() for k, tens in batch.items()\n             if k in keys or k == 'mask'}\n\n    batch_size = next(iter(batch.values())).shape[0]\n    items_to_vis = min(batch_size, max_items)\n    result = []\n    for i in range(items_to_vis):\n        cur_dct = {k: tens[i] for k, tens in batch.items()}\n        result.append(visualize_mask_and_images(cur_dct, keys, last_without_mask=last_without_mask,\n                                                rescale_keys=rescale_keys))\n    return np.concatenate(result, axis=0)\n"
  },
  {
    "path": "model_cards/lama/saicinpainting/training/visualizers/colors.py",
    "content": "import random\nimport colorsys\n\nimport numpy as np\nimport matplotlib\nmatplotlib.use('agg')\nimport matplotlib.pyplot as plt \nfrom matplotlib.colors import LinearSegmentedColormap\n\n\ndef generate_colors(nlabels, type='bright', first_color_black=False, last_color_black=True, verbose=False):\n    # https://stackoverflow.com/questions/14720331/how-to-generate-random-colors-in-matplotlib\n    \"\"\"\n    Creates a random colormap to be used together with matplotlib. Useful for segmentation tasks\n    :param nlabels: Number of labels (size of colormap)\n    :param type: 'bright' for strong colors, 'soft' for pastel colors\n    :param first_color_black: Option to use first color as black, True or False\n    :param last_color_black: Option to use last color as black, True or False\n    :param verbose: Prints the number of labels and shows the colormap. True or False\n    :return: colormap for matplotlib\n    \"\"\"\n    if type not in ('bright', 'soft'):\n        print ('Please choose \"bright\" or \"soft\" for type')\n        return\n\n    if verbose:\n        print('Number of labels: ' + str(nlabels))\n\n    # Generate color map for bright colors, based on hsv\n    if type == 'bright':\n        randHSVcolors = [(np.random.uniform(low=0.0, high=1),\n                          np.random.uniform(low=0.2, high=1),\n                          np.random.uniform(low=0.9, high=1)) for i in range(nlabels)]\n\n        # Convert HSV list to RGB\n        randRGBcolors = []\n        for HSVcolor in randHSVcolors:\n            randRGBcolors.append(colorsys.hsv_to_rgb(HSVcolor[0], HSVcolor[1], HSVcolor[2]))\n\n        if first_color_black:\n            randRGBcolors[0] = [0, 0, 0]\n\n        if last_color_black:\n            randRGBcolors[-1] = [0, 0, 0]\n\n        random_colormap = LinearSegmentedColormap.from_list('new_map', randRGBcolors, N=nlabels)\n\n    # Generate soft pastel colors, by limiting the RGB spectrum\n    if type == 'soft':\n        low = 0.6\n        high = 0.95\n        randRGBcolors = [(np.random.uniform(low=low, high=high),\n                          np.random.uniform(low=low, high=high),\n                          np.random.uniform(low=low, high=high)) for i in range(nlabels)]\n\n        if first_color_black:\n            randRGBcolors[0] = [0, 0, 0]\n\n        if last_color_black:\n            randRGBcolors[-1] = [0, 0, 0]\n        random_colormap = LinearSegmentedColormap.from_list('new_map', randRGBcolors, N=nlabels)\n\n    # Display colorbar\n    if verbose:\n        from matplotlib import colors, colorbar\n        from matplotlib import pyplot as plt\n        fig, ax = plt.subplots(1, 1, figsize=(15, 0.5))\n\n        bounds = np.linspace(0, nlabels, nlabels + 1)\n        norm = colors.BoundaryNorm(bounds, nlabels)\n\n        cb = colorbar.ColorbarBase(ax, cmap=random_colormap, norm=norm, spacing='proportional', ticks=None,\n                                   boundaries=bounds, format='%1i', orientation=u'horizontal')\n\n    return randRGBcolors, random_colormap\n\n"
  },
  {
    "path": "model_cards/lama/saicinpainting/training/visualizers/directory.py",
    "content": "import os\n\nimport cv2\nimport numpy as np\n\nfrom saicinpainting.training.visualizers.base import BaseVisualizer, visualize_mask_and_images_batch\nfrom saicinpainting.utils import check_and_warn_input_range\n\n\nclass DirectoryVisualizer(BaseVisualizer):\n    DEFAULT_KEY_ORDER = 'image predicted_image inpainted'.split(' ')\n\n    def __init__(self, outdir, key_order=DEFAULT_KEY_ORDER, max_items_in_batch=10,\n                 last_without_mask=True, rescale_keys=None):\n        self.outdir = outdir\n        os.makedirs(self.outdir, exist_ok=True)\n        self.key_order = key_order\n        self.max_items_in_batch = max_items_in_batch\n        self.last_without_mask = last_without_mask\n        self.rescale_keys = rescale_keys\n\n    def __call__(self, epoch_i, batch_i, batch, suffix='', rank=None):\n        check_and_warn_input_range(batch['image'], 0, 1, 'DirectoryVisualizer target image')\n        vis_img = visualize_mask_and_images_batch(batch, self.key_order, max_items=self.max_items_in_batch,\n                                                  last_without_mask=self.last_without_mask,\n                                                  rescale_keys=self.rescale_keys)\n\n        vis_img = np.clip(vis_img * 255, 0, 255).astype('uint8')\n\n        curoutdir = os.path.join(self.outdir, f'epoch{epoch_i:04d}{suffix}')\n        os.makedirs(curoutdir, exist_ok=True)\n        rank_suffix = f'_r{rank}' if rank is not None else ''\n        out_fname = os.path.join(curoutdir, f'batch{batch_i:07d}{rank_suffix}.jpg')\n\n        vis_img = cv2.cvtColor(vis_img, cv2.COLOR_RGB2BGR)\n        cv2.imwrite(out_fname, vis_img)\n"
  },
  {
    "path": "model_cards/lama/saicinpainting/training/visualizers/noop.py",
    "content": "from saicinpainting.training.visualizers.base import BaseVisualizer\n\n\nclass NoopVisualizer(BaseVisualizer):\n    def __init__(self, *args, **kwargs):\n        pass\n\n    def __call__(self, epoch_i, batch_i, batch, suffix='', rank=None):\n        pass\n"
  },
  {
    "path": "model_cards/lama/saicinpainting/utils.py",
    "content": "import bisect\nimport functools\nimport logging\nimport numbers\nimport os\nimport signal\nimport sys\nimport traceback\nimport warnings\n\nimport torch\nfrom pytorch_lightning import seed_everything\n\nLOGGER = logging.getLogger(__name__)\n\nimport platform\nif platform.system() != 'Linux':\n    signal.SIGUSR1 = 1\n\ndef check_and_warn_input_range(tensor, min_value, max_value, name):\n    actual_min = tensor.min()\n    actual_max = tensor.max()\n    if actual_min < min_value or actual_max > max_value:\n        warnings.warn(f\"{name} must be in {min_value}..{max_value} range, but it ranges {actual_min}..{actual_max}\")\n\n\ndef sum_dict_with_prefix(target, cur_dict, prefix, default=0):\n    for k, v in cur_dict.items():\n        target_key = prefix + k\n        target[target_key] = target.get(target_key, default) + v\n\n\ndef average_dicts(dict_list):\n    result = {}\n    norm = 1e-3\n    for dct in dict_list:\n        sum_dict_with_prefix(result, dct, '')\n        norm += 1\n    for k in list(result):\n        result[k] /= norm\n    return result\n\n\ndef add_prefix_to_keys(dct, prefix):\n    return {prefix + k: v for k, v in dct.items()}\n\n\ndef set_requires_grad(module, value):\n    for param in module.parameters():\n        param.requires_grad = value\n\n\ndef flatten_dict(dct):\n    result = {}\n    for k, v in dct.items():\n        if isinstance(k, tuple):\n            k = '_'.join(k)\n        if isinstance(v, dict):\n            for sub_k, sub_v in flatten_dict(v).items():\n                result[f'{k}_{sub_k}'] = sub_v\n        else:\n            result[k] = v\n    return result\n\n\nclass LinearRamp:\n    def __init__(self, start_value=0, end_value=1, start_iter=-1, end_iter=0):\n        self.start_value = start_value\n        self.end_value = end_value\n        self.start_iter = start_iter\n        self.end_iter = end_iter\n\n    def __call__(self, i):\n        if i < self.start_iter:\n            return self.start_value\n        if i >= self.end_iter:\n            return self.end_value\n        part = (i - self.start_iter) / (self.end_iter - self.start_iter)\n        return self.start_value * (1 - part) + self.end_value * part\n\n\nclass LadderRamp:\n    def __init__(self, start_iters, values):\n        self.start_iters = start_iters\n        self.values = values\n        assert len(values) == len(start_iters) + 1, (len(values), len(start_iters))\n\n    def __call__(self, i):\n        segment_i = bisect.bisect_right(self.start_iters, i)\n        return self.values[segment_i]\n\n\ndef get_ramp(kind='ladder', **kwargs):\n    if kind == 'linear':\n        return LinearRamp(**kwargs)\n    if kind == 'ladder':\n        return LadderRamp(**kwargs)\n    raise ValueError(f'Unexpected ramp kind: {kind}')\n\n\ndef print_traceback_handler(sig, frame):\n    LOGGER.warning(f'Received signal {sig}')\n    bt = ''.join(traceback.format_stack())\n    LOGGER.warning(f'Requested stack trace:\\n{bt}')\n\n\ndef register_debug_signal_handlers(sig=signal.SIGUSR1, handler=print_traceback_handler):\n    LOGGER.warning(f'Setting signal {sig} handler {handler}')\n    signal.signal(sig, handler)\n\n\ndef handle_deterministic_config(config):\n    seed = dict(config).get('seed', None)\n    if seed is None:\n        return False\n\n    seed_everything(seed)\n    return True\n\n\ndef get_shape(t):\n    if torch.is_tensor(t):\n        return tuple(t.shape)\n    elif isinstance(t, dict):\n        return {n: get_shape(q) for n, q in t.items()}\n    elif isinstance(t, (list, tuple)):\n        return [get_shape(q) for q in t]\n    elif isinstance(t, numbers.Number):\n        return type(t)\n    else:\n        raise ValueError('unexpected type {}'.format(type(t)))\n\n\ndef get_has_ddp_rank():\n    master_port = os.environ.get('MASTER_PORT', None)\n    node_rank = os.environ.get('NODE_RANK', None)\n    local_rank = os.environ.get('LOCAL_RANK', None)\n    world_size = os.environ.get('WORLD_SIZE', None)\n    has_rank = master_port is not None or node_rank is not None or local_rank is not None or world_size is not None\n    return has_rank\n\n\ndef handle_ddp_subprocess():\n    def main_decorator(main_func):\n        @functools.wraps(main_func)\n        def new_main(*args, **kwargs):\n            # Trainer sets MASTER_PORT, NODE_RANK, LOCAL_RANK, WORLD_SIZE\n            parent_cwd = os.environ.get('TRAINING_PARENT_WORK_DIR', None)\n            has_parent = parent_cwd is not None\n            has_rank = get_has_ddp_rank()\n            assert has_parent == has_rank, f'Inconsistent state: has_parent={has_parent}, has_rank={has_rank}'\n\n            if has_parent:\n                # we are in the worker\n                sys.argv.extend([\n                    f'hydra.run.dir={parent_cwd}',\n                    # 'hydra/hydra_logging=disabled',\n                    # 'hydra/job_logging=disabled'\n                ])\n            # do nothing if this is a top-level process\n            # TRAINING_PARENT_WORK_DIR is set in handle_ddp_parent_process after hydra initialization\n\n            main_func(*args, **kwargs)\n        return new_main\n    return main_decorator\n\n\ndef handle_ddp_parent_process():\n    parent_cwd = os.environ.get('TRAINING_PARENT_WORK_DIR', None)\n    has_parent = parent_cwd is not None\n    has_rank = get_has_ddp_rank()\n    assert has_parent == has_rank, f'Inconsistent state: has_parent={has_parent}, has_rank={has_rank}'\n\n    if parent_cwd is None:\n        os.environ['TRAINING_PARENT_WORK_DIR'] = os.getcwd()\n\n    return has_parent\n"
  },
  {
    "path": "model_cards/requirements.txt",
    "content": "addict\ndiffusers\ngradio\nhuggingface_hub\nmatplotlib\nnumpy\nonnxruntime\nopencv_python\nPillow\npycocotools\nPyYAML\nrequests\nsetuptools\nsupervision\ntermcolor\ntimm\ntorch\ntorchvision\ntransformers\nyapf\nnltk\nfairscale\n"
  },
  {
    "path": "model_cards/segment_anything/__init__.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nfrom .build_sam import (\n    build_sam,\n    build_sam_vit_h,\n    build_sam_vit_l,\n    build_sam_vit_b,\n    sam_model_registry,\n)\nfrom .predictor import SamPredictor\nfrom .automatic_mask_generator import SamAutomaticMaskGenerator\n"
  },
  {
    "path": "model_cards/segment_anything/automatic_mask_generator.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport numpy as np\nimport torch\nfrom torchvision.ops.boxes import batched_nms, box_area  # type: ignore\n\nfrom typing import Any, Dict, List, Optional, Tuple\n\nfrom .modeling import Sam\nfrom .predictor import SamPredictor\nfrom .utils.amg import (\n    MaskData,\n    area_from_rle,\n    batch_iterator,\n    batched_mask_to_box,\n    box_xyxy_to_xywh,\n    build_all_layer_point_grids,\n    calculate_stability_score,\n    coco_encode_rle,\n    generate_crop_boxes,\n    is_box_near_crop_edge,\n    mask_to_rle_pytorch,\n    remove_small_regions,\n    rle_to_mask,\n    uncrop_boxes_xyxy,\n    uncrop_masks,\n    uncrop_points,\n)\n\n\nclass SamAutomaticMaskGenerator:\n    def __init__(\n        self,\n        model: Sam,\n        points_per_side: Optional[int] = 32,\n        points_per_batch: int = 64,\n        pred_iou_thresh: float = 0.88,\n        stability_score_thresh: float = 0.95,\n        stability_score_offset: float = 1.0,\n        box_nms_thresh: float = 0.7,\n        crop_n_layers: int = 0,\n        crop_nms_thresh: float = 0.7,\n        crop_overlap_ratio: float = 512 / 1500,\n        crop_n_points_downscale_factor: int = 1,\n        point_grids: Optional[List[np.ndarray]] = None,\n        min_mask_region_area: int = 0,\n        output_mode: str = \"binary_mask\",\n    ) -> None:\n        \"\"\"\n        Using a SAM model, generates masks for the entire image.\n        Generates a grid of point prompts over the image, then filters\n        low quality and duplicate masks. The default settings are chosen\n        for SAM with a ViT-H backbone.\n\n        Arguments:\n          model (Sam): The SAM model to use for mask prediction.\n          points_per_side (int or None): The number of points to be sampled\n            along one side of the image. The total number of points is\n            points_per_side**2. If None, 'point_grids' must provide explicit\n            point sampling.\n          points_per_batch (int): Sets the number of points run simultaneously\n            by the model. Higher numbers may be faster but use more GPU memory.\n          pred_iou_thresh (float): A filtering threshold in [0,1], using the\n            model's predicted mask quality.\n          stability_score_thresh (float): A filtering threshold in [0,1], using\n            the stability of the mask under changes to the cutoff used to binarize\n            the model's mask predictions.\n          stability_score_offset (float): The amount to shift the cutoff when\n            calculated the stability score.\n          box_nms_thresh (float): The box IoU cutoff used by non-maximal\n            suppression to filter duplicate masks.\n          crop_n_layers (int): If >0, mask prediction will be run again on\n            crops of the image. Sets the number of layers to run, where each\n            layer has 2**i_layer number of image crops.\n          crop_nms_thresh (float): The box IoU cutoff used by non-maximal\n            suppression to filter duplicate masks between different crops.\n          crop_overlap_ratio (float): Sets the degree to which crops overlap.\n            In the first crop layer, crops will overlap by this fraction of\n            the image length. Later layers with more crops scale down this overlap.\n          crop_n_points_downscale_factor (int): The number of points-per-side\n            sampled in layer n is scaled down by crop_n_points_downscale_factor**n.\n          point_grids (list(np.ndarray) or None): A list over explicit grids\n            of points used for sampling, normalized to [0,1]. The nth grid in the\n            list is used in the nth crop layer. Exclusive with points_per_side.\n          min_mask_region_area (int): If >0, postprocessing will be applied\n            to remove disconnected regions and holes in masks with area smaller\n            than min_mask_region_area. Requires opencv.\n          output_mode (str): The form masks are returned in. Can be 'binary_mask',\n            'uncompressed_rle', or 'coco_rle'. 'coco_rle' requires pycocotools.\n            For large resolutions, 'binary_mask' may consume large amounts of\n            memory.\n        \"\"\"\n\n        assert (points_per_side is None) != (\n            point_grids is None\n        ), \"Exactly one of points_per_side or point_grid must be provided.\"\n        if points_per_side is not None:\n            self.point_grids = build_all_layer_point_grids(\n                points_per_side,\n                crop_n_layers,\n                crop_n_points_downscale_factor,\n            )\n        elif point_grids is not None:\n            self.point_grids = point_grids\n        else:\n            raise ValueError(\"Can't have both points_per_side and point_grid be None.\")\n\n        assert output_mode in [\n            \"binary_mask\",\n            \"uncompressed_rle\",\n            \"coco_rle\",\n        ], f\"Unknown output_mode {output_mode}.\"\n        if output_mode == \"coco_rle\":\n            from pycocotools import mask as mask_utils  # type: ignore # noqa: F401\n\n        if min_mask_region_area > 0:\n            import cv2  # type: ignore # noqa: F401\n\n        self.predictor = SamPredictor(model)\n        self.points_per_batch = points_per_batch\n        self.pred_iou_thresh = pred_iou_thresh\n        self.stability_score_thresh = stability_score_thresh\n        self.stability_score_offset = stability_score_offset\n        self.box_nms_thresh = box_nms_thresh\n        self.crop_n_layers = crop_n_layers\n        self.crop_nms_thresh = crop_nms_thresh\n        self.crop_overlap_ratio = crop_overlap_ratio\n        self.crop_n_points_downscale_factor = crop_n_points_downscale_factor\n        self.min_mask_region_area = min_mask_region_area\n        self.output_mode = output_mode\n\n    @torch.no_grad()\n    def generate(self, image: np.ndarray) -> List[Dict[str, Any]]:\n        \"\"\"\n        Generates masks for the given image.\n\n        Arguments:\n          image (np.ndarray): The image to generate masks for, in HWC uint8 format.\n\n        Returns:\n           list(dict(str, any)): A list over records for masks. Each record is\n             a dict containing the following keys:\n               segmentation (dict(str, any) or np.ndarray): The mask. If\n                 output_mode='binary_mask', is an array of shape HW. Otherwise,\n                 is a dictionary containing the RLE.\n               bbox (list(float)): The box around the mask, in XYWH format.\n               area (int): The area in pixels of the mask.\n               predicted_iou (float): The model's own prediction of the mask's\n                 quality. This is filtered by the pred_iou_thresh parameter.\n               point_coords (list(list(float))): The point coordinates input\n                 to the model to generate this mask.\n               stability_score (float): A measure of the mask's quality. This\n                 is filtered on using the stability_score_thresh parameter.\n               crop_box (list(float)): The crop of the image used to generate\n                 the mask, given in XYWH format.\n        \"\"\"\n\n        # Generate masks\n        mask_data = self._generate_masks(image)\n\n        # Filter small disconnected regions and holes in masks\n        if self.min_mask_region_area > 0:\n            mask_data = self.postprocess_small_regions(\n                mask_data,\n                self.min_mask_region_area,\n                max(self.box_nms_thresh, self.crop_nms_thresh),\n            )\n\n        # Encode masks\n        if self.output_mode == \"coco_rle\":\n            mask_data[\"segmentations\"] = [coco_encode_rle(rle) for rle in mask_data[\"rles\"]]\n        elif self.output_mode == \"binary_mask\":\n            mask_data[\"segmentations\"] = [rle_to_mask(rle) for rle in mask_data[\"rles\"]]\n        else:\n            mask_data[\"segmentations\"] = mask_data[\"rles\"]\n\n        # Write mask records\n        curr_anns = []\n        for idx in range(len(mask_data[\"segmentations\"])):\n            ann = {\n                \"segmentation\": mask_data[\"segmentations\"][idx],\n                \"area\": area_from_rle(mask_data[\"rles\"][idx]),\n                \"bbox\": box_xyxy_to_xywh(mask_data[\"boxes\"][idx]).tolist(),\n                \"predicted_iou\": mask_data[\"iou_preds\"][idx].item(),\n                \"point_coords\": [mask_data[\"points\"][idx].tolist()],\n                \"stability_score\": mask_data[\"stability_score\"][idx].item(),\n                \"crop_box\": box_xyxy_to_xywh(mask_data[\"crop_boxes\"][idx]).tolist(),\n            }\n            curr_anns.append(ann)\n\n        return curr_anns\n\n    def _generate_masks(self, image: np.ndarray) -> MaskData:\n        orig_size = image.shape[:2]\n        crop_boxes, layer_idxs = generate_crop_boxes(\n            orig_size, self.crop_n_layers, self.crop_overlap_ratio\n        )\n\n        # Iterate over image crops\n        data = MaskData()\n        for crop_box, layer_idx in zip(crop_boxes, layer_idxs):\n            crop_data = self._process_crop(image, crop_box, layer_idx, orig_size)\n            data.cat(crop_data)\n\n        # Remove duplicate masks between crops\n        if len(crop_boxes) > 1:\n            # Prefer masks from smaller crops\n            scores = 1 / box_area(data[\"crop_boxes\"])\n            scores = scores.to(data[\"boxes\"].device)\n            keep_by_nms = batched_nms(\n                data[\"boxes\"].float(),\n                scores,\n                torch.zeros_like(data[\"boxes\"][:, 0]),  # categories\n                iou_threshold=self.crop_nms_thresh,\n            )\n            data.filter(keep_by_nms)\n\n        data.to_numpy()\n        return data\n\n    def _process_crop(\n        self,\n        image: np.ndarray,\n        crop_box: List[int],\n        crop_layer_idx: int,\n        orig_size: Tuple[int, ...],\n    ) -> MaskData:\n        # Crop the image and calculate embeddings\n        x0, y0, x1, y1 = crop_box\n        cropped_im = image[y0:y1, x0:x1, :]\n        cropped_im_size = cropped_im.shape[:2]\n        self.predictor.set_image(cropped_im)\n\n        # Get points for this crop\n        points_scale = np.array(cropped_im_size)[None, ::-1]\n        points_for_image = self.point_grids[crop_layer_idx] * points_scale\n\n        # Generate masks for this crop in batches\n        data = MaskData()\n        for (points,) in batch_iterator(self.points_per_batch, points_for_image):\n            batch_data = self._process_batch(points, cropped_im_size, crop_box, orig_size)\n            data.cat(batch_data)\n            del batch_data\n        self.predictor.reset_image()\n\n        # Remove duplicates within this crop.\n        keep_by_nms = batched_nms(\n            data[\"boxes\"].float(),\n            data[\"iou_preds\"],\n            torch.zeros_like(data[\"boxes\"][:, 0]),  # categories\n            iou_threshold=self.box_nms_thresh,\n        )\n        data.filter(keep_by_nms)\n\n        # Return to the original image frame\n        data[\"boxes\"] = uncrop_boxes_xyxy(data[\"boxes\"], crop_box)\n        data[\"points\"] = uncrop_points(data[\"points\"], crop_box)\n        data[\"crop_boxes\"] = torch.tensor([crop_box for _ in range(len(data[\"rles\"]))])\n\n        return data\n\n    def _process_batch(\n        self,\n        points: np.ndarray,\n        im_size: Tuple[int, ...],\n        crop_box: List[int],\n        orig_size: Tuple[int, ...],\n    ) -> MaskData:\n        orig_h, orig_w = orig_size\n\n        # Run model on this batch\n        transformed_points = self.predictor.transform.apply_coords(points, im_size)\n        in_points = torch.as_tensor(transformed_points, device=self.predictor.device)\n        in_labels = torch.ones(in_points.shape[0], dtype=torch.int, device=in_points.device)\n        masks, iou_preds, _ = self.predictor.predict_torch(\n            in_points[:, None, :],\n            in_labels[:, None],\n            multimask_output=True,\n            return_logits=True,\n        )\n\n        # Serialize predictions and store in MaskData\n        data = MaskData(\n            masks=masks.flatten(0, 1),\n            iou_preds=iou_preds.flatten(0, 1),\n            points=torch.as_tensor(points.repeat(masks.shape[1], axis=0)),\n        )\n        del masks\n\n        # Filter by predicted IoU\n        if self.pred_iou_thresh > 0.0:\n            keep_mask = data[\"iou_preds\"] > self.pred_iou_thresh\n            data.filter(keep_mask)\n\n        # Calculate stability score\n        data[\"stability_score\"] = calculate_stability_score(\n            data[\"masks\"], self.predictor.model.mask_threshold, self.stability_score_offset\n        )\n        if self.stability_score_thresh > 0.0:\n            keep_mask = data[\"stability_score\"] >= self.stability_score_thresh\n            data.filter(keep_mask)\n\n        # Threshold masks and calculate boxes\n        data[\"masks\"] = data[\"masks\"] > self.predictor.model.mask_threshold\n        data[\"boxes\"] = batched_mask_to_box(data[\"masks\"])\n\n        # Filter boxes that touch crop boundaries\n        keep_mask = ~is_box_near_crop_edge(data[\"boxes\"], crop_box, [0, 0, orig_w, orig_h])\n        if not torch.all(keep_mask):\n            data.filter(keep_mask)\n\n        # Compress to RLE\n        data[\"masks\"] = uncrop_masks(data[\"masks\"], crop_box, orig_h, orig_w)\n        data[\"rles\"] = mask_to_rle_pytorch(data[\"masks\"])\n        del data[\"masks\"]\n\n        return data\n\n    @staticmethod\n    def postprocess_small_regions(\n        mask_data: MaskData, min_area: int, nms_thresh: float\n    ) -> MaskData:\n        \"\"\"\n        Removes small disconnected regions and holes in masks, then reruns\n        box NMS to remove any new duplicates.\n\n        Edits mask_data in place.\n\n        Requires open-cv as a dependency.\n        \"\"\"\n        if len(mask_data[\"rles\"]) == 0:\n            return mask_data\n\n        # Filter small disconnected regions and holes\n        new_masks = []\n        scores = []\n        for rle in mask_data[\"rles\"]:\n            mask = rle_to_mask(rle)\n\n            mask, changed = remove_small_regions(mask, min_area, mode=\"holes\")\n            unchanged = not changed\n            mask, changed = remove_small_regions(mask, min_area, mode=\"islands\")\n            unchanged = unchanged and not changed\n\n            new_masks.append(torch.as_tensor(mask).unsqueeze(0))\n            # Give score=0 to changed masks and score=1 to unchanged masks\n            # so NMS will prefer ones that didn't need postprocessing\n            scores.append(float(unchanged))\n\n        # Recalculate boxes and remove any new duplicates\n        masks = torch.cat(new_masks, dim=0)\n        boxes = batched_mask_to_box(masks)\n        keep_by_nms = batched_nms(\n            boxes.float(),\n            torch.as_tensor(scores),\n            torch.zeros_like(boxes[:, 0]),  # categories\n            iou_threshold=nms_thresh,\n        )\n\n        # Only recalculate RLEs for masks that have changed\n        for i_mask in keep_by_nms:\n            if scores[i_mask] == 0.0:\n                mask_torch = masks[i_mask].unsqueeze(0)\n                mask_data[\"rles\"][i_mask] = mask_to_rle_pytorch(mask_torch)[0]\n                mask_data[\"boxes\"][i_mask] = boxes[i_mask]  # update res directly\n        mask_data.filter(keep_by_nms)\n\n        return mask_data\n"
  },
  {
    "path": "model_cards/segment_anything/build_sam.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport torch\n\nfrom functools import partial\n\nfrom .modeling import ImageEncoderViT, MaskDecoder, PromptEncoder, Sam, TwoWayTransformer\n\n\ndef build_sam_vit_h(checkpoint=None):\n    return _build_sam(\n        encoder_embed_dim=1280,\n        encoder_depth=32,\n        encoder_num_heads=16,\n        encoder_global_attn_indexes=[7, 15, 23, 31],\n        checkpoint=checkpoint,\n    )\n\n\nbuild_sam = build_sam_vit_h\n\n\ndef build_sam_vit_l(checkpoint=None):\n    return _build_sam(\n        encoder_embed_dim=1024,\n        encoder_depth=24,\n        encoder_num_heads=16,\n        encoder_global_attn_indexes=[5, 11, 17, 23],\n        checkpoint=checkpoint,\n    )\n\n\ndef build_sam_vit_b(checkpoint=None):\n    return _build_sam(\n        encoder_embed_dim=768,\n        encoder_depth=12,\n        encoder_num_heads=12,\n        encoder_global_attn_indexes=[2, 5, 8, 11],\n        checkpoint=checkpoint,\n    )\n\n\nsam_model_registry = {\n    \"default\": build_sam_vit_h,\n    \"vit_h\": build_sam_vit_h,\n    \"vit_l\": build_sam_vit_l,\n    \"vit_b\": build_sam_vit_b,\n}\n\n\ndef _build_sam(\n    encoder_embed_dim,\n    encoder_depth,\n    encoder_num_heads,\n    encoder_global_attn_indexes,\n    checkpoint=None,\n):\n    prompt_embed_dim = 256\n    image_size = 1024\n    vit_patch_size = 16\n    image_embedding_size = image_size // vit_patch_size\n    sam = Sam(\n        image_encoder=ImageEncoderViT(\n            depth=encoder_depth,\n            embed_dim=encoder_embed_dim,\n            img_size=image_size,\n            mlp_ratio=4,\n            norm_layer=partial(torch.nn.LayerNorm, eps=1e-6),\n            num_heads=encoder_num_heads,\n            patch_size=vit_patch_size,\n            qkv_bias=True,\n            use_rel_pos=True,\n            global_attn_indexes=encoder_global_attn_indexes,\n            window_size=14,\n            out_chans=prompt_embed_dim,\n        ),\n        prompt_encoder=PromptEncoder(\n            embed_dim=prompt_embed_dim,\n            image_embedding_size=(image_embedding_size, image_embedding_size),\n            input_image_size=(image_size, image_size),\n            mask_in_chans=16,\n        ),\n        mask_decoder=MaskDecoder(\n            num_multimask_outputs=3,\n            transformer=TwoWayTransformer(\n                depth=2,\n                embedding_dim=prompt_embed_dim,\n                mlp_dim=2048,\n                num_heads=8,\n            ),\n            transformer_dim=prompt_embed_dim,\n            iou_head_depth=3,\n            iou_head_hidden_dim=256,\n        ),\n        pixel_mean=[123.675, 116.28, 103.53],\n        pixel_std=[58.395, 57.12, 57.375],\n    )\n    sam.eval()\n    if checkpoint is not None:\n        with open(checkpoint, \"rb\") as f:\n            state_dict = torch.load(f)\n        sam.load_state_dict(state_dict)\n    return sam\n"
  },
  {
    "path": "model_cards/segment_anything/modeling/__init__.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nfrom .sam import Sam\nfrom .image_encoder import ImageEncoderViT\nfrom .mask_decoder import MaskDecoder\nfrom .prompt_encoder import PromptEncoder\nfrom .transformer import TwoWayTransformer\n"
  },
  {
    "path": "model_cards/segment_anything/modeling/common.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport torch\nimport torch.nn as nn\n\nfrom typing import Type\n\n\nclass MLPBlock(nn.Module):\n    def __init__(\n        self,\n        embedding_dim: int,\n        mlp_dim: int,\n        act: Type[nn.Module] = nn.GELU,\n    ) -> None:\n        super().__init__()\n        self.lin1 = nn.Linear(embedding_dim, mlp_dim)\n        self.lin2 = nn.Linear(mlp_dim, embedding_dim)\n        self.act = act()\n\n    def forward(self, x: torch.Tensor) -> torch.Tensor:\n        return self.lin2(self.act(self.lin1(x)))\n\n\n# From https://github.com/facebookresearch/detectron2/blob/main/detectron2/layers/batch_norm.py # noqa\n# Itself from https://github.com/facebookresearch/ConvNeXt/blob/d1fa8f6fef0a165b27399986cc2bdacc92777e40/models/convnext.py#L119  # noqa\nclass LayerNorm2d(nn.Module):\n    def __init__(self, num_channels: int, eps: float = 1e-6) -> None:\n        super().__init__()\n        self.weight = nn.Parameter(torch.ones(num_channels))\n        self.bias = nn.Parameter(torch.zeros(num_channels))\n        self.eps = eps\n\n    def forward(self, x: torch.Tensor) -> torch.Tensor:\n        u = x.mean(1, keepdim=True)\n        s = (x - u).pow(2).mean(1, keepdim=True)\n        x = (x - u) / torch.sqrt(s + self.eps)\n        x = self.weight[:, None, None] * x + self.bias[:, None, None]\n        return x\n"
  },
  {
    "path": "model_cards/segment_anything/modeling/image_encoder.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom typing import Optional, Tuple, Type\n\nfrom .common import LayerNorm2d, MLPBlock\n\n\n# This class and its supporting functions below lightly adapted from the ViTDet backbone available at: https://github.com/facebookresearch/detectron2/blob/main/detectron2/modeling/backbone/vit.py # noqa\nclass ImageEncoderViT(nn.Module):\n    def __init__(\n        self,\n        img_size: int = 1024,\n        patch_size: int = 16,\n        in_chans: int = 3,\n        embed_dim: int = 768,\n        depth: int = 12,\n        num_heads: int = 12,\n        mlp_ratio: float = 4.0,\n        out_chans: int = 256,\n        qkv_bias: bool = True,\n        norm_layer: Type[nn.Module] = nn.LayerNorm,\n        act_layer: Type[nn.Module] = nn.GELU,\n        use_abs_pos: bool = True,\n        use_rel_pos: bool = False,\n        rel_pos_zero_init: bool = True,\n        window_size: int = 0,\n        global_attn_indexes: Tuple[int, ...] = (),\n    ) -> None:\n        \"\"\"\n        Args:\n            img_size (int): Input image size.\n            patch_size (int): Patch size.\n            in_chans (int): Number of input image channels.\n            embed_dim (int): Patch embedding dimension.\n            depth (int): Depth of ViT.\n            num_heads (int): Number of attention heads in each ViT block.\n            mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.\n            qkv_bias (bool): If True, add a learnable bias to query, key, value.\n            norm_layer (nn.Module): Normalization layer.\n            act_layer (nn.Module): Activation layer.\n            use_abs_pos (bool): If True, use absolute positional embeddings.\n            use_rel_pos (bool): If True, add relative positional embeddings to the attention map.\n            rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.\n            window_size (int): Window size for window attention blocks.\n            global_attn_indexes (list): Indexes for blocks using global attention.\n        \"\"\"\n        super().__init__()\n        self.img_size = img_size\n\n        self.patch_embed = PatchEmbed(\n            kernel_size=(patch_size, patch_size),\n            stride=(patch_size, patch_size),\n            in_chans=in_chans,\n            embed_dim=embed_dim,\n        )\n\n        self.pos_embed: Optional[nn.Parameter] = None\n        if use_abs_pos:\n            # Initialize absolute positional embedding with pretrain image size.\n            self.pos_embed = nn.Parameter(\n                torch.zeros(1, img_size // patch_size, img_size // patch_size, embed_dim)\n            )\n\n        self.blocks = nn.ModuleList()\n        for i in range(depth):\n            block = Block(\n                dim=embed_dim,\n                num_heads=num_heads,\n                mlp_ratio=mlp_ratio,\n                qkv_bias=qkv_bias,\n                norm_layer=norm_layer,\n                act_layer=act_layer,\n                use_rel_pos=use_rel_pos,\n                rel_pos_zero_init=rel_pos_zero_init,\n                window_size=window_size if i not in global_attn_indexes else 0,\n                input_size=(img_size // patch_size, img_size // patch_size),\n            )\n            self.blocks.append(block)\n\n        self.neck = nn.Sequential(\n            nn.Conv2d(\n                embed_dim,\n                out_chans,\n                kernel_size=1,\n                bias=False,\n            ),\n            LayerNorm2d(out_chans),\n            nn.Conv2d(\n                out_chans,\n                out_chans,\n                kernel_size=3,\n                padding=1,\n                bias=False,\n            ),\n            LayerNorm2d(out_chans),\n        )\n\n    def forward(self, x: torch.Tensor) -> torch.Tensor:\n        x = self.patch_embed(x)\n        if self.pos_embed is not None:\n            x = x + self.pos_embed\n\n        for blk in self.blocks:\n            x = blk(x)\n\n        x = self.neck(x.permute(0, 3, 1, 2))\n\n        return x\n\n\nclass Block(nn.Module):\n    \"\"\"Transformer blocks with support of window attention and residual propagation blocks\"\"\"\n\n    def __init__(\n        self,\n        dim: int,\n        num_heads: int,\n        mlp_ratio: float = 4.0,\n        qkv_bias: bool = True,\n        norm_layer: Type[nn.Module] = nn.LayerNorm,\n        act_layer: Type[nn.Module] = nn.GELU,\n        use_rel_pos: bool = False,\n        rel_pos_zero_init: bool = True,\n        window_size: int = 0,\n        input_size: Optional[Tuple[int, int]] = None,\n    ) -> None:\n        \"\"\"\n        Args:\n            dim (int): Number of input channels.\n            num_heads (int): Number of attention heads in each ViT block.\n            mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.\n            qkv_bias (bool): If True, add a learnable bias to query, key, value.\n            norm_layer (nn.Module): Normalization layer.\n            act_layer (nn.Module): Activation layer.\n            use_rel_pos (bool): If True, add relative positional embeddings to the attention map.\n            rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.\n            window_size (int): Window size for window attention blocks. If it equals 0, then\n                use global attention.\n            input_size (tuple(int, int) or None): Input resolution for calculating the relative\n                positional parameter size.\n        \"\"\"\n        super().__init__()\n        self.norm1 = norm_layer(dim)\n        self.attn = Attention(\n            dim,\n            num_heads=num_heads,\n            qkv_bias=qkv_bias,\n            use_rel_pos=use_rel_pos,\n            rel_pos_zero_init=rel_pos_zero_init,\n            input_size=input_size if window_size == 0 else (window_size, window_size),\n        )\n\n        self.norm2 = norm_layer(dim)\n        self.mlp = MLPBlock(embedding_dim=dim, mlp_dim=int(dim * mlp_ratio), act=act_layer)\n\n        self.window_size = window_size\n\n    def forward(self, x: torch.Tensor) -> torch.Tensor:\n        shortcut = x\n        x = self.norm1(x)\n        # Window partition\n        if self.window_size > 0:\n            H, W = x.shape[1], x.shape[2]\n            x, pad_hw = window_partition(x, self.window_size)\n\n        x = self.attn(x)\n        # Reverse window partition\n        if self.window_size > 0:\n            x = window_unpartition(x, self.window_size, pad_hw, (H, W))\n\n        x = shortcut + x\n        x = x + self.mlp(self.norm2(x))\n\n        return x\n\n\nclass Attention(nn.Module):\n    \"\"\"Multi-head Attention block with relative position embeddings.\"\"\"\n\n    def __init__(\n        self,\n        dim: int,\n        num_heads: int = 8,\n        qkv_bias: bool = True,\n        use_rel_pos: bool = False,\n        rel_pos_zero_init: bool = True,\n        input_size: Optional[Tuple[int, int]] = None,\n    ) -> None:\n        \"\"\"\n        Args:\n            dim (int): Number of input channels.\n            num_heads (int): Number of attention heads.\n            qkv_bias (bool):  If True, add a learnable bias to query, key, value.\n            rel_pos (bool): If True, add relative positional embeddings to the attention map.\n            rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.\n            input_size (tuple(int, int) or None): Input resolution for calculating the relative\n                positional parameter size.\n        \"\"\"\n        super().__init__()\n        self.num_heads = num_heads\n        head_dim = dim // num_heads\n        self.scale = head_dim**-0.5\n\n        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)\n        self.proj = nn.Linear(dim, dim)\n\n        self.use_rel_pos = use_rel_pos\n        if self.use_rel_pos:\n            assert (\n                input_size is not None\n            ), \"Input size must be provided if using relative positional encoding.\"\n            # initialize relative positional embeddings\n            self.rel_pos_h = nn.Parameter(torch.zeros(2 * input_size[0] - 1, head_dim))\n            self.rel_pos_w = nn.Parameter(torch.zeros(2 * input_size[1] - 1, head_dim))\n\n    def forward(self, x: torch.Tensor) -> torch.Tensor:\n        B, H, W, _ = x.shape\n        # qkv with shape (3, B, nHead, H * W, C)\n        qkv = self.qkv(x).reshape(B, H * W, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)\n        # q, k, v with shape (B * nHead, H * W, C)\n        q, k, v = qkv.reshape(3, B * self.num_heads, H * W, -1).unbind(0)\n\n        attn = (q * self.scale) @ k.transpose(-2, -1)\n\n        if self.use_rel_pos:\n            attn = add_decomposed_rel_pos(attn, q, self.rel_pos_h, self.rel_pos_w, (H, W), (H, W))\n\n        attn = attn.softmax(dim=-1)\n        x = (attn @ v).view(B, self.num_heads, H, W, -1).permute(0, 2, 3, 1, 4).reshape(B, H, W, -1)\n        x = self.proj(x)\n\n        return x\n\n\ndef window_partition(x: torch.Tensor, window_size: int) -> Tuple[torch.Tensor, Tuple[int, int]]:\n    \"\"\"\n    Partition into non-overlapping windows with padding if needed.\n    Args:\n        x (tensor): input tokens with [B, H, W, C].\n        window_size (int): window size.\n\n    Returns:\n        windows: windows after partition with [B * num_windows, window_size, window_size, C].\n        (Hp, Wp): padded height and width before partition\n    \"\"\"\n    B, H, W, C = x.shape\n\n    pad_h = (window_size - H % window_size) % window_size\n    pad_w = (window_size - W % window_size) % window_size\n    if pad_h > 0 or pad_w > 0:\n        x = F.pad(x, (0, 0, 0, pad_w, 0, pad_h))\n    Hp, Wp = H + pad_h, W + pad_w\n\n    x = x.view(B, Hp // window_size, window_size, Wp // window_size, window_size, C)\n    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)\n    return windows, (Hp, Wp)\n\n\ndef window_unpartition(\n    windows: torch.Tensor, window_size: int, pad_hw: Tuple[int, int], hw: Tuple[int, int]\n) -> torch.Tensor:\n    \"\"\"\n    Window unpartition into original sequences and removing padding.\n    Args:\n        windows (tensor): input tokens with [B * num_windows, window_size, window_size, C].\n        window_size (int): window size.\n        pad_hw (Tuple): padded height and width (Hp, Wp).\n        hw (Tuple): original height and width (H, W) before padding.\n\n    Returns:\n        x: unpartitioned sequences with [B, H, W, C].\n    \"\"\"\n    Hp, Wp = pad_hw\n    H, W = hw\n    B = windows.shape[0] // (Hp * Wp // window_size // window_size)\n    x = windows.view(B, Hp // window_size, Wp // window_size, window_size, window_size, -1)\n    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, Hp, Wp, -1)\n\n    if Hp > H or Wp > W:\n        x = x[:, :H, :W, :].contiguous()\n    return x\n\n\ndef get_rel_pos(q_size: int, k_size: int, rel_pos: torch.Tensor) -> torch.Tensor:\n    \"\"\"\n    Get relative positional embeddings according to the relative positions of\n        query and key sizes.\n    Args:\n        q_size (int): size of query q.\n        k_size (int): size of key k.\n        rel_pos (Tensor): relative position embeddings (L, C).\n\n    Returns:\n        Extracted positional embeddings according to relative positions.\n    \"\"\"\n    max_rel_dist = int(2 * max(q_size, k_size) - 1)\n    # Interpolate rel pos if needed.\n    if rel_pos.shape[0] != max_rel_dist:\n        # Interpolate rel pos.\n        rel_pos_resized = F.interpolate(\n            rel_pos.reshape(1, rel_pos.shape[0], -1).permute(0, 2, 1),\n            size=max_rel_dist,\n            mode=\"linear\",\n        )\n        rel_pos_resized = rel_pos_resized.reshape(-1, max_rel_dist).permute(1, 0)\n    else:\n        rel_pos_resized = rel_pos\n\n    # Scale the coords with short length if shapes for q and k are different.\n    q_coords = torch.arange(q_size)[:, None] * max(k_size / q_size, 1.0)\n    k_coords = torch.arange(k_size)[None, :] * max(q_size / k_size, 1.0)\n    relative_coords = (q_coords - k_coords) + (k_size - 1) * max(q_size / k_size, 1.0)\n\n    return rel_pos_resized[relative_coords.long()]\n\n\ndef add_decomposed_rel_pos(\n    attn: torch.Tensor,\n    q: torch.Tensor,\n    rel_pos_h: torch.Tensor,\n    rel_pos_w: torch.Tensor,\n    q_size: Tuple[int, int],\n    k_size: Tuple[int, int],\n) -> torch.Tensor:\n    \"\"\"\n    Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`.\n    https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py   # noqa B950\n    Args:\n        attn (Tensor): attention map.\n        q (Tensor): query q in the attention layer with shape (B, q_h * q_w, C).\n        rel_pos_h (Tensor): relative position embeddings (Lh, C) for height axis.\n        rel_pos_w (Tensor): relative position embeddings (Lw, C) for width axis.\n        q_size (Tuple): spatial sequence size of query q with (q_h, q_w).\n        k_size (Tuple): spatial sequence size of key k with (k_h, k_w).\n\n    Returns:\n        attn (Tensor): attention map with added relative positional embeddings.\n    \"\"\"\n    q_h, q_w = q_size\n    k_h, k_w = k_size\n    Rh = get_rel_pos(q_h, k_h, rel_pos_h)\n    Rw = get_rel_pos(q_w, k_w, rel_pos_w)\n\n    B, _, dim = q.shape\n    r_q = q.reshape(B, q_h, q_w, dim)\n    rel_h = torch.einsum(\"bhwc,hkc->bhwk\", r_q, Rh)\n    rel_w = torch.einsum(\"bhwc,wkc->bhwk\", r_q, Rw)\n\n    attn = (\n        attn.view(B, q_h, q_w, k_h, k_w) + rel_h[:, :, :, :, None] + rel_w[:, :, :, None, :]\n    ).view(B, q_h * q_w, k_h * k_w)\n\n    return attn\n\n\nclass PatchEmbed(nn.Module):\n    \"\"\"\n    Image to Patch Embedding.\n    \"\"\"\n\n    def __init__(\n        self,\n        kernel_size: Tuple[int, int] = (16, 16),\n        stride: Tuple[int, int] = (16, 16),\n        padding: Tuple[int, int] = (0, 0),\n        in_chans: int = 3,\n        embed_dim: int = 768,\n    ) -> None:\n        \"\"\"\n        Args:\n            kernel_size (Tuple): kernel size of the projection layer.\n            stride (Tuple): stride of the projection layer.\n            padding (Tuple): padding size of the projection layer.\n            in_chans (int): Number of input image channels.\n            embed_dim (int): Patch embedding dimension.\n        \"\"\"\n        super().__init__()\n\n        self.proj = nn.Conv2d(\n            in_chans, embed_dim, kernel_size=kernel_size, stride=stride, padding=padding\n        )\n\n    def forward(self, x: torch.Tensor) -> torch.Tensor:\n        x = self.proj(x)\n        # B C H W -> B H W C\n        x = x.permute(0, 2, 3, 1)\n        return x\n"
  },
  {
    "path": "model_cards/segment_anything/modeling/mask_decoder.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\nfrom typing import List, Tuple, Type\n\nfrom .common import LayerNorm2d\n\n\nclass MaskDecoder(nn.Module):\n    def __init__(\n        self,\n        *,\n        transformer_dim: int,\n        transformer: nn.Module,\n        num_multimask_outputs: int = 3,\n        activation: Type[nn.Module] = nn.GELU,\n        iou_head_depth: int = 3,\n        iou_head_hidden_dim: int = 256,\n    ) -> None:\n        \"\"\"\n        Predicts masks given an image and prompt embeddings, using a\n        transformer architecture.\n\n        Arguments:\n          transformer_dim (int): the channel dimension of the transformer\n          transformer (nn.Module): the transformer used to predict masks\n          num_multimask_outputs (int): the number of masks to predict\n            when disambiguating masks\n          activation (nn.Module): the type of activation to use when\n            upscaling masks\n          iou_head_depth (int): the depth of the MLP used to predict\n            mask quality\n          iou_head_hidden_dim (int): the hidden dimension of the MLP\n            used to predict mask quality\n        \"\"\"\n        super().__init__()\n        self.transformer_dim = transformer_dim\n        self.transformer = transformer\n\n        self.num_multimask_outputs = num_multimask_outputs\n\n        self.iou_token = nn.Embedding(1, transformer_dim)\n        self.num_mask_tokens = num_multimask_outputs + 1\n        self.mask_tokens = nn.Embedding(self.num_mask_tokens, transformer_dim)\n\n        self.output_upscaling = nn.Sequential(\n            nn.ConvTranspose2d(transformer_dim, transformer_dim // 4, kernel_size=2, stride=2),\n            LayerNorm2d(transformer_dim // 4),\n            activation(),\n            nn.ConvTranspose2d(transformer_dim // 4, transformer_dim // 8, kernel_size=2, stride=2),\n            activation(),\n        )\n        self.output_hypernetworks_mlps = nn.ModuleList(\n            [\n                MLP(transformer_dim, transformer_dim, transformer_dim // 8, 3)\n                for i in range(self.num_mask_tokens)\n            ]\n        )\n\n        self.iou_prediction_head = MLP(\n            transformer_dim, iou_head_hidden_dim, self.num_mask_tokens, iou_head_depth\n        )\n\n    def forward(\n        self,\n        image_embeddings: torch.Tensor,\n        image_pe: torch.Tensor,\n        sparse_prompt_embeddings: torch.Tensor,\n        dense_prompt_embeddings: torch.Tensor,\n        multimask_output: bool,\n    ) -> Tuple[torch.Tensor, torch.Tensor]:\n        \"\"\"\n        Predict masks given image and prompt embeddings.\n\n        Arguments:\n          image_embeddings (torch.Tensor): the embeddings from the image encoder\n          image_pe (torch.Tensor): positional encoding with the shape of image_embeddings\n          sparse_prompt_embeddings (torch.Tensor): the embeddings of the points and boxes\n          dense_prompt_embeddings (torch.Tensor): the embeddings of the mask inputs\n          multimask_output (bool): Whether to return multiple masks or a single\n            mask.\n\n        Returns:\n          torch.Tensor: batched predicted masks\n          torch.Tensor: batched predictions of mask quality\n        \"\"\"\n        masks, iou_pred = self.predict_masks(\n            image_embeddings=image_embeddings,\n            image_pe=image_pe,\n            sparse_prompt_embeddings=sparse_prompt_embeddings,\n            dense_prompt_embeddings=dense_prompt_embeddings,\n        )\n\n        # Select the correct mask or masks for output\n        if multimask_output:\n            mask_slice = slice(1, None)\n        else:\n            mask_slice = slice(0, 1)\n        masks = masks[:, mask_slice, :, :]\n        iou_pred = iou_pred[:, mask_slice]\n\n        # Prepare output\n        return masks, iou_pred\n\n    def predict_masks(\n        self,\n        image_embeddings: torch.Tensor,\n        image_pe: torch.Tensor,\n        sparse_prompt_embeddings: torch.Tensor,\n        dense_prompt_embeddings: torch.Tensor,\n    ) -> Tuple[torch.Tensor, torch.Tensor]:\n        \"\"\"Predicts masks. See 'forward' for more details.\"\"\"\n        # Concatenate output tokens\n        output_tokens = torch.cat([self.iou_token.weight, self.mask_tokens.weight], dim=0)\n        output_tokens = output_tokens.unsqueeze(0).expand(sparse_prompt_embeddings.size(0), -1, -1)\n        tokens = torch.cat((output_tokens, sparse_prompt_embeddings), dim=1)\n\n        # Expand per-image data in batch direction to be per-mask\n        src = torch.repeat_interleave(image_embeddings, tokens.shape[0], dim=0)\n        src = src + dense_prompt_embeddings\n        pos_src = torch.repeat_interleave(image_pe, tokens.shape[0], dim=0)\n        b, c, h, w = src.shape\n\n        # Run the transformer\n        hs, src = self.transformer(src, pos_src, tokens)\n        iou_token_out = hs[:, 0, :]\n        mask_tokens_out = hs[:, 1 : (1 + self.num_mask_tokens), :]\n\n        # Upscale mask embeddings and predict masks using the mask tokens\n        src = src.transpose(1, 2).view(b, c, h, w)\n        upscaled_embedding = self.output_upscaling(src)\n        hyper_in_list: List[torch.Tensor] = []\n        for i in range(self.num_mask_tokens):\n            hyper_in_list.append(self.output_hypernetworks_mlps[i](mask_tokens_out[:, i, :]))\n        hyper_in = torch.stack(hyper_in_list, dim=1)\n        b, c, h, w = upscaled_embedding.shape\n        masks = (hyper_in @ upscaled_embedding.view(b, c, h * w)).view(b, -1, h, w)\n\n        # Generate mask quality predictions\n        iou_pred = self.iou_prediction_head(iou_token_out)\n\n        return masks, iou_pred\n\n\n# Lightly adapted from\n# https://github.com/facebookresearch/MaskFormer/blob/main/mask_former/modeling/transformer/transformer_predictor.py # noqa\nclass MLP(nn.Module):\n    def __init__(\n        self,\n        input_dim: int,\n        hidden_dim: int,\n        output_dim: int,\n        num_layers: int,\n        sigmoid_output: bool = False,\n    ) -> None:\n        super().__init__()\n        self.num_layers = num_layers\n        h = [hidden_dim] * (num_layers - 1)\n        self.layers = nn.ModuleList(\n            nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim])\n        )\n        self.sigmoid_output = sigmoid_output\n\n    def forward(self, x):\n        for i, layer in enumerate(self.layers):\n            x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)\n        if self.sigmoid_output:\n            x = F.sigmoid(x)\n        return x\n"
  },
  {
    "path": "model_cards/segment_anything/modeling/prompt_encoder.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport numpy as np\nimport torch\nfrom torch import nn\n\nfrom typing import Any, Optional, Tuple, Type\n\nfrom .common import LayerNorm2d\n\n\nclass PromptEncoder(nn.Module):\n    def __init__(\n        self,\n        embed_dim: int,\n        image_embedding_size: Tuple[int, int],\n        input_image_size: Tuple[int, int],\n        mask_in_chans: int,\n        activation: Type[nn.Module] = nn.GELU,\n    ) -> None:\n        \"\"\"\n        Encodes prompts for input to SAM's mask decoder.\n\n        Arguments:\n          embed_dim (int): The prompts' embedding dimension\n          image_embedding_size (tuple(int, int)): The spatial size of the\n            image embedding, as (H, W).\n          input_image_size (int): The padded size of the image as input\n            to the image encoder, as (H, W).\n          mask_in_chans (int): The number of hidden channels used for\n            encoding input masks.\n          activation (nn.Module): The activation to use when encoding\n            input masks.\n        \"\"\"\n        super().__init__()\n        self.embed_dim = embed_dim\n        self.input_image_size = input_image_size\n        self.image_embedding_size = image_embedding_size\n        self.pe_layer = PositionEmbeddingRandom(embed_dim // 2)\n\n        self.num_point_embeddings: int = 4  # pos/neg point + 2 box corners\n        point_embeddings = [nn.Embedding(1, embed_dim) for i in range(self.num_point_embeddings)]\n        self.point_embeddings = nn.ModuleList(point_embeddings)\n        self.not_a_point_embed = nn.Embedding(1, embed_dim)\n\n        self.mask_input_size = (4 * image_embedding_size[0], 4 * image_embedding_size[1])\n        self.mask_downscaling = nn.Sequential(\n            nn.Conv2d(1, mask_in_chans // 4, kernel_size=2, stride=2),\n            LayerNorm2d(mask_in_chans // 4),\n            activation(),\n            nn.Conv2d(mask_in_chans // 4, mask_in_chans, kernel_size=2, stride=2),\n            LayerNorm2d(mask_in_chans),\n            activation(),\n            nn.Conv2d(mask_in_chans, embed_dim, kernel_size=1),\n        )\n        self.no_mask_embed = nn.Embedding(1, embed_dim)\n\n    def get_dense_pe(self) -> torch.Tensor:\n        \"\"\"\n        Returns the positional encoding used to encode point prompts,\n        applied to a dense set of points the shape of the image encoding.\n\n        Returns:\n          torch.Tensor: Positional encoding with shape\n            1x(embed_dim)x(embedding_h)x(embedding_w)\n        \"\"\"\n        return self.pe_layer(self.image_embedding_size).unsqueeze(0)\n\n    def _embed_points(\n        self,\n        points: torch.Tensor,\n        labels: torch.Tensor,\n        pad: bool,\n    ) -> torch.Tensor:\n        \"\"\"Embeds point prompts.\"\"\"\n        points = points + 0.5  # Shift to center of pixel\n        if pad:\n            padding_point = torch.zeros((points.shape[0], 1, 2), device=points.device)\n            padding_label = -torch.ones((labels.shape[0], 1), device=labels.device)\n            points = torch.cat([points, padding_point], dim=1)\n            labels = torch.cat([labels, padding_label], dim=1)\n        point_embedding = self.pe_layer.forward_with_coords(points, self.input_image_size)\n        point_embedding[labels == -1] = 0.0\n        point_embedding[labels == -1] += self.not_a_point_embed.weight\n        point_embedding[labels == 0] += self.point_embeddings[0].weight\n        point_embedding[labels == 1] += self.point_embeddings[1].weight\n        return point_embedding\n\n    def _embed_boxes(self, boxes: torch.Tensor) -> torch.Tensor:\n        \"\"\"Embeds box prompts.\"\"\"\n        boxes = boxes + 0.5  # Shift to center of pixel\n        coords = boxes.reshape(-1, 2, 2)\n        corner_embedding = self.pe_layer.forward_with_coords(coords, self.input_image_size)\n        corner_embedding[:, 0, :] += self.point_embeddings[2].weight\n        corner_embedding[:, 1, :] += self.point_embeddings[3].weight\n        return corner_embedding\n\n    def _embed_masks(self, masks: torch.Tensor) -> torch.Tensor:\n        \"\"\"Embeds mask inputs.\"\"\"\n        mask_embedding = self.mask_downscaling(masks)\n        return mask_embedding\n\n    def _get_batch_size(\n        self,\n        points: Optional[Tuple[torch.Tensor, torch.Tensor]],\n        boxes: Optional[torch.Tensor],\n        masks: Optional[torch.Tensor],\n    ) -> int:\n        \"\"\"\n        Gets the batch size of the output given the batch size of the input prompts.\n        \"\"\"\n        if points is not None:\n            return points[0].shape[0]\n        elif boxes is not None:\n            return boxes.shape[0]\n        elif masks is not None:\n            return masks.shape[0]\n        else:\n            return 1\n\n    def _get_device(self) -> torch.device:\n        return self.point_embeddings[0].weight.device\n\n    def forward(\n        self,\n        points: Optional[Tuple[torch.Tensor, torch.Tensor]],\n        boxes: Optional[torch.Tensor],\n        masks: Optional[torch.Tensor],\n    ) -> Tuple[torch.Tensor, torch.Tensor]:\n        \"\"\"\n        Embeds different types of prompts, returning both sparse and dense\n        embeddings.\n\n        Arguments:\n          points (tuple(torch.Tensor, torch.Tensor) or none): point coordinates\n            and labels to embed.\n          boxes (torch.Tensor or none): boxes to embed\n          masks (torch.Tensor or none): masks to embed\n\n        Returns:\n          torch.Tensor: sparse embeddings for the points and boxes, with shape\n            BxNx(embed_dim), where N is determined by the number of input points\n            and boxes.\n          torch.Tensor: dense embeddings for the masks, in the shape\n            Bx(embed_dim)x(embed_H)x(embed_W)\n        \"\"\"\n        bs = self._get_batch_size(points, boxes, masks)\n        sparse_embeddings = torch.empty((bs, 0, self.embed_dim), device=self._get_device())\n        if points is not None:\n            coords, labels = points\n            point_embeddings = self._embed_points(coords, labels, pad=(boxes is None))\n            sparse_embeddings = torch.cat([sparse_embeddings, point_embeddings], dim=1)\n        if boxes is not None:\n            box_embeddings = self._embed_boxes(boxes)\n            sparse_embeddings = torch.cat([sparse_embeddings, box_embeddings], dim=1)\n\n        if masks is not None:\n            dense_embeddings = self._embed_masks(masks)\n        else:\n            dense_embeddings = self.no_mask_embed.weight.reshape(1, -1, 1, 1).expand(\n                bs, -1, self.image_embedding_size[0], self.image_embedding_size[1]\n            )\n\n        return sparse_embeddings, dense_embeddings\n\n\nclass PositionEmbeddingRandom(nn.Module):\n    \"\"\"\n    Positional encoding using random spatial frequencies.\n    \"\"\"\n\n    def __init__(self, num_pos_feats: int = 64, scale: Optional[float] = None) -> None:\n        super().__init__()\n        if scale is None or scale <= 0.0:\n            scale = 1.0\n        self.register_buffer(\n            \"positional_encoding_gaussian_matrix\",\n            scale * torch.randn((2, num_pos_feats)),\n        )\n\n    def _pe_encoding(self, coords: torch.Tensor) -> torch.Tensor:\n        \"\"\"Positionally encode points that are normalized to [0,1].\"\"\"\n        # assuming coords are in [0, 1]^2 square and have d_1 x ... x d_n x 2 shape\n        coords = 2 * coords - 1\n        coords = coords @ self.positional_encoding_gaussian_matrix\n        coords = 2 * np.pi * coords\n        # outputs d_1 x ... x d_n x C shape\n        return torch.cat([torch.sin(coords), torch.cos(coords)], dim=-1)\n\n    def forward(self, size: Tuple[int, int]) -> torch.Tensor:\n        \"\"\"Generate positional encoding for a grid of the specified size.\"\"\"\n        h, w = size\n        device: Any = self.positional_encoding_gaussian_matrix.device\n        grid = torch.ones((h, w), device=device, dtype=torch.float32)\n        y_embed = grid.cumsum(dim=0) - 0.5\n        x_embed = grid.cumsum(dim=1) - 0.5\n        y_embed = y_embed / h\n        x_embed = x_embed / w\n\n        pe = self._pe_encoding(torch.stack([x_embed, y_embed], dim=-1))\n        return pe.permute(2, 0, 1)  # C x H x W\n\n    def forward_with_coords(\n        self, coords_input: torch.Tensor, image_size: Tuple[int, int]\n    ) -> torch.Tensor:\n        \"\"\"Positionally encode points that are not normalized to [0,1].\"\"\"\n        coords = coords_input.clone()\n        coords[:, :, 0] = coords[:, :, 0] / image_size[1]\n        coords[:, :, 1] = coords[:, :, 1] / image_size[0]\n        return self._pe_encoding(coords.to(torch.float))  # B x N x C\n"
  },
  {
    "path": "model_cards/segment_anything/modeling/sam.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\nfrom typing import Any, Dict, List, Tuple\n\nfrom .image_encoder import ImageEncoderViT\nfrom .mask_decoder import MaskDecoder\nfrom .prompt_encoder import PromptEncoder\n\n\nclass Sam(nn.Module):\n    mask_threshold: float = 0.0\n    image_format: str = \"RGB\"\n\n    def __init__(\n        self,\n        image_encoder: ImageEncoderViT,\n        prompt_encoder: PromptEncoder,\n        mask_decoder: MaskDecoder,\n        pixel_mean: List[float] = [123.675, 116.28, 103.53],\n        pixel_std: List[float] = [58.395, 57.12, 57.375],\n    ) -> None:\n        \"\"\"\n        SAM predicts object masks from an image and input prompts.\n\n        Arguments:\n          image_encoder (ImageEncoderViT): The backbone used to encode the\n            image into image embeddings that allow for efficient mask prediction.\n          prompt_encoder (PromptEncoder): Encodes various types of input prompts.\n          mask_decoder (MaskDecoder): Predicts masks from the image embeddings\n            and encoded prompts.\n          pixel_mean (list(float)): Mean values for normalizing pixels in the input image.\n          pixel_std (list(float)): Std values for normalizing pixels in the input image.\n        \"\"\"\n        super().__init__()\n        self.image_encoder = image_encoder\n        self.prompt_encoder = prompt_encoder\n        self.mask_decoder = mask_decoder\n        self.register_buffer(\"pixel_mean\", torch.Tensor(pixel_mean).view(-1, 1, 1), False)\n        self.register_buffer(\"pixel_std\", torch.Tensor(pixel_std).view(-1, 1, 1), False)\n\n    @property\n    def device(self) -> Any:\n        return self.pixel_mean.device\n\n    @torch.no_grad()\n    def forward(\n        self,\n        batched_input: List[Dict[str, Any]],\n        multimask_output: bool,\n    ) -> List[Dict[str, torch.Tensor]]:\n        \"\"\"\n        Predicts masks end-to-end from provided images and prompts.\n        If prompts are not known in advance, using SamPredictor is\n        recommended over calling the model directly.\n\n        Arguments:\n          batched_input (list(dict)): A list over input images, each a\n            dictionary with the following keys. A prompt key can be\n            excluded if it is not present.\n              'image': The image as a torch tensor in 3xHxW format,\n                already transformed for input to the model.\n              'original_size': (tuple(int, int)) The original size of\n                the image before transformation, as (H, W).\n              'point_coords': (torch.Tensor) Batched point prompts for\n                this image, with shape BxNx2. Already transformed to the\n                input frame of the model.\n              'point_labels': (torch.Tensor) Batched labels for point prompts,\n                with shape BxN.\n              'boxes': (torch.Tensor) Batched box inputs, with shape Bx4.\n                Already transformed to the input frame of the model.\n              'mask_inputs': (torch.Tensor) Batched mask inputs to the model,\n                in the form Bx1xHxW.\n          multimask_output (bool): Whether the model should predict multiple\n            disambiguating masks, or return a single mask.\n\n        Returns:\n          (list(dict)): A list over input images, where each element is\n            as dictionary with the following keys.\n              'masks': (torch.Tensor) Batched binary mask predictions,\n                with shape BxCxHxW, where B is the number of input prompts,\n                C is determined by multimask_output, and (H, W) is the\n                original size of the image.\n              'iou_predictions': (torch.Tensor) The model's predictions\n                of mask quality, in shape BxC.\n              'low_res_logits': (torch.Tensor) Low resolution logits with\n                shape BxCxHxW, where H=W=256. Can be passed as mask input\n                to subsequent iterations of prediction.\n        \"\"\"\n        input_images = torch.stack([self.preprocess(x[\"image\"]) for x in batched_input], dim=0)\n        image_embeddings = self.image_encoder(input_images)\n\n        outputs = []\n        for image_record, curr_embedding in zip(batched_input, image_embeddings):\n            if \"point_coords\" in image_record:\n                points = (image_record[\"point_coords\"], image_record[\"point_labels\"])\n            else:\n                points = None\n            sparse_embeddings, dense_embeddings = self.prompt_encoder(\n                points=points,\n                boxes=image_record.get(\"boxes\", None),\n                masks=image_record.get(\"mask_inputs\", None),\n            )\n            low_res_masks, iou_predictions = self.mask_decoder(\n                image_embeddings=curr_embedding.unsqueeze(0),\n                image_pe=self.prompt_encoder.get_dense_pe(),\n                sparse_prompt_embeddings=sparse_embeddings,\n                dense_prompt_embeddings=dense_embeddings,\n                multimask_output=multimask_output,\n            )\n            masks = self.postprocess_masks(\n                low_res_masks,\n                input_size=image_record[\"image\"].shape[-2:],\n                original_size=image_record[\"original_size\"],\n            )\n            masks = masks > self.mask_threshold\n            outputs.append(\n                {\n                    \"masks\": masks,\n                    \"iou_predictions\": iou_predictions,\n                    \"low_res_logits\": low_res_masks,\n                }\n            )\n        return outputs\n\n    def postprocess_masks(\n        self,\n        masks: torch.Tensor,\n        input_size: Tuple[int, ...],\n        original_size: Tuple[int, ...],\n    ) -> torch.Tensor:\n        \"\"\"\n        Remove padding and upscale masks to the original image size.\n\n        Arguments:\n          masks (torch.Tensor): Batched masks from the mask_decoder,\n            in BxCxHxW format.\n          input_size (tuple(int, int)): The size of the image input to the\n            model, in (H, W) format. Used to remove padding.\n          original_size (tuple(int, int)): The original size of the image\n            before resizing for input to the model, in (H, W) format.\n\n        Returns:\n          (torch.Tensor): Batched masks in BxCxHxW format, where (H, W)\n            is given by original_size.\n        \"\"\"\n        masks = F.interpolate(\n            masks,\n            (self.image_encoder.img_size, self.image_encoder.img_size),\n            mode=\"bilinear\",\n            align_corners=False,\n        )\n        masks = masks[..., : input_size[0], : input_size[1]]\n        masks = F.interpolate(masks, original_size, mode=\"bilinear\", align_corners=False)\n        return masks\n\n    def preprocess(self, x: torch.Tensor) -> torch.Tensor:\n        \"\"\"Normalize pixel values and pad to a square input.\"\"\"\n        # Normalize colors\n        x = (x - self.pixel_mean) / self.pixel_std\n\n        # Pad\n        h, w = x.shape[-2:]\n        padh = self.image_encoder.img_size - h\n        padw = self.image_encoder.img_size - w\n        x = F.pad(x, (0, padw, 0, padh))\n        return x\n"
  },
  {
    "path": "model_cards/segment_anything/modeling/transformer.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport torch\nfrom torch import Tensor, nn\n\nimport math\nfrom typing import Tuple, Type\n\nfrom .common import MLPBlock\n\n\nclass TwoWayTransformer(nn.Module):\n    def __init__(\n        self,\n        depth: int,\n        embedding_dim: int,\n        num_heads: int,\n        mlp_dim: int,\n        activation: Type[nn.Module] = nn.ReLU,\n        attention_downsample_rate: int = 2,\n    ) -> None:\n        \"\"\"\n        A transformer decoder that attends to an input image using\n        queries whose positional embedding is supplied.\n\n        Args:\n          depth (int): number of layers in the transformer\n          embedding_dim (int): the channel dimension for the input embeddings\n          num_heads (int): the number of heads for multihead attention. Must\n            divide embedding_dim\n          mlp_dim (int): the channel dimension internal to the MLP block\n          activation (nn.Module): the activation to use in the MLP block\n        \"\"\"\n        super().__init__()\n        self.depth = depth\n        self.embedding_dim = embedding_dim\n        self.num_heads = num_heads\n        self.mlp_dim = mlp_dim\n        self.layers = nn.ModuleList()\n\n        for i in range(depth):\n            self.layers.append(\n                TwoWayAttentionBlock(\n                    embedding_dim=embedding_dim,\n                    num_heads=num_heads,\n                    mlp_dim=mlp_dim,\n                    activation=activation,\n                    attention_downsample_rate=attention_downsample_rate,\n                    skip_first_layer_pe=(i == 0),\n                )\n            )\n\n        self.final_attn_token_to_image = Attention(\n            embedding_dim, num_heads, downsample_rate=attention_downsample_rate\n        )\n        self.norm_final_attn = nn.LayerNorm(embedding_dim)\n\n    def forward(\n        self,\n        image_embedding: Tensor,\n        image_pe: Tensor,\n        point_embedding: Tensor,\n    ) -> Tuple[Tensor, Tensor]:\n        \"\"\"\n        Args:\n          image_embedding (torch.Tensor): image to attend to. Should be shape\n            B x embedding_dim x h x w for any h and w.\n          image_pe (torch.Tensor): the positional encoding to add to the image. Must\n            have the same shape as image_embedding.\n          point_embedding (torch.Tensor): the embedding to add to the query points.\n            Must have shape B x N_points x embedding_dim for any N_points.\n\n        Returns:\n          torch.Tensor: the processed point_embedding\n          torch.Tensor: the processed image_embedding\n        \"\"\"\n        # BxCxHxW -> BxHWxC == B x N_image_tokens x C\n        bs, c, h, w = image_embedding.shape\n        image_embedding = image_embedding.flatten(2).permute(0, 2, 1)\n        image_pe = image_pe.flatten(2).permute(0, 2, 1)\n\n        # Prepare queries\n        queries = point_embedding\n        keys = image_embedding\n\n        # Apply transformer blocks and final layernorm\n        for layer in self.layers:\n            queries, keys = layer(\n                queries=queries,\n                keys=keys,\n                query_pe=point_embedding,\n                key_pe=image_pe,\n            )\n\n        # Apply the final attention layer from the points to the image\n        q = queries + point_embedding\n        k = keys + image_pe\n        attn_out = self.final_attn_token_to_image(q=q, k=k, v=keys)\n        queries = queries + attn_out\n        queries = self.norm_final_attn(queries)\n\n        return queries, keys\n\n\nclass TwoWayAttentionBlock(nn.Module):\n    def __init__(\n        self,\n        embedding_dim: int,\n        num_heads: int,\n        mlp_dim: int = 2048,\n        activation: Type[nn.Module] = nn.ReLU,\n        attention_downsample_rate: int = 2,\n        skip_first_layer_pe: bool = False,\n    ) -> None:\n        \"\"\"\n        A transformer block with four layers: (1) self-attention of sparse\n        inputs, (2) cross attention of sparse inputs to dense inputs, (3) mlp\n        block on sparse inputs, and (4) cross attention of dense inputs to sparse\n        inputs.\n\n        Arguments:\n          embedding_dim (int): the channel dimension of the embeddings\n          num_heads (int): the number of heads in the attention layers\n          mlp_dim (int): the hidden dimension of the mlp block\n          activation (nn.Module): the activation of the mlp block\n          skip_first_layer_pe (bool): skip the PE on the first layer\n        \"\"\"\n        super().__init__()\n        self.self_attn = Attention(embedding_dim, num_heads)\n        self.norm1 = nn.LayerNorm(embedding_dim)\n\n        self.cross_attn_token_to_image = Attention(\n            embedding_dim, num_heads, downsample_rate=attention_downsample_rate\n        )\n        self.norm2 = nn.LayerNorm(embedding_dim)\n\n        self.mlp = MLPBlock(embedding_dim, mlp_dim, activation)\n        self.norm3 = nn.LayerNorm(embedding_dim)\n\n        self.norm4 = nn.LayerNorm(embedding_dim)\n        self.cross_attn_image_to_token = Attention(\n            embedding_dim, num_heads, downsample_rate=attention_downsample_rate\n        )\n\n        self.skip_first_layer_pe = skip_first_layer_pe\n\n    def forward(\n        self, queries: Tensor, keys: Tensor, query_pe: Tensor, key_pe: Tensor\n    ) -> Tuple[Tensor, Tensor]:\n        # Self attention block\n        if self.skip_first_layer_pe:\n            queries = self.self_attn(q=queries, k=queries, v=queries)\n        else:\n            q = queries + query_pe\n            attn_out = self.self_attn(q=q, k=q, v=queries)\n            queries = queries + attn_out\n        queries = self.norm1(queries)\n\n        # Cross attention block, tokens attending to image embedding\n        q = queries + query_pe\n        k = keys + key_pe\n        attn_out = self.cross_attn_token_to_image(q=q, k=k, v=keys)\n        queries = queries + attn_out\n        queries = self.norm2(queries)\n\n        # MLP block\n        mlp_out = self.mlp(queries)\n        queries = queries + mlp_out\n        queries = self.norm3(queries)\n\n        # Cross attention block, image embedding attending to tokens\n        q = queries + query_pe\n        k = keys + key_pe\n        attn_out = self.cross_attn_image_to_token(q=k, k=q, v=queries)\n        keys = keys + attn_out\n        keys = self.norm4(keys)\n\n        return queries, keys\n\n\nclass Attention(nn.Module):\n    \"\"\"\n    An attention layer that allows for downscaling the size of the embedding\n    after projection to queries, keys, and values.\n    \"\"\"\n\n    def __init__(\n        self,\n        embedding_dim: int,\n        num_heads: int,\n        downsample_rate: int = 1,\n    ) -> None:\n        super().__init__()\n        self.embedding_dim = embedding_dim\n        self.internal_dim = embedding_dim // downsample_rate\n        self.num_heads = num_heads\n        assert self.internal_dim % num_heads == 0, \"num_heads must divide embedding_dim.\"\n\n        self.q_proj = nn.Linear(embedding_dim, self.internal_dim)\n        self.k_proj = nn.Linear(embedding_dim, self.internal_dim)\n        self.v_proj = nn.Linear(embedding_dim, self.internal_dim)\n        self.out_proj = nn.Linear(self.internal_dim, embedding_dim)\n\n    def _separate_heads(self, x: Tensor, num_heads: int) -> Tensor:\n        b, n, c = x.shape\n        x = x.reshape(b, n, num_heads, c // num_heads)\n        return x.transpose(1, 2)  # B x N_heads x N_tokens x C_per_head\n\n    def _recombine_heads(self, x: Tensor) -> Tensor:\n        b, n_heads, n_tokens, c_per_head = x.shape\n        x = x.transpose(1, 2)\n        return x.reshape(b, n_tokens, n_heads * c_per_head)  # B x N_tokens x C\n\n    def forward(self, q: Tensor, k: Tensor, v: Tensor) -> Tensor:\n        # Input projections\n        q = self.q_proj(q)\n        k = self.k_proj(k)\n        v = self.v_proj(v)\n\n        # Separate into heads\n        q = self._separate_heads(q, self.num_heads)\n        k = self._separate_heads(k, self.num_heads)\n        v = self._separate_heads(v, self.num_heads)\n\n        # Attention\n        _, _, _, c_per_head = q.shape\n        attn = q @ k.permute(0, 1, 3, 2)  # B x N_heads x N_tokens x N_tokens\n        attn = attn / math.sqrt(c_per_head)\n        attn = torch.softmax(attn, dim=-1)\n\n        # Get output\n        out = attn @ v\n        out = self._recombine_heads(out)\n        out = self.out_proj(out)\n\n        return out\n"
  },
  {
    "path": "model_cards/segment_anything/predictor.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport numpy as np\nimport torch\n\nfrom segment_anything.modeling import Sam\n\nfrom typing import Optional, Tuple\n\nfrom .utils.transforms import ResizeLongestSide\n\n\nclass SamPredictor:\n    def __init__(\n        self,\n        sam_model: Sam,\n    ) -> None:\n        \"\"\"\n        Uses SAM to calculate the image embedding for an image, and then\n        allow repeated, efficient mask prediction given prompts.\n\n        Arguments:\n          sam_model (Sam): The model to use for mask prediction.\n        \"\"\"\n        super().__init__()\n        self.model = sam_model\n        self.transform = ResizeLongestSide(sam_model.image_encoder.img_size)\n        self.reset_image()\n\n    def set_image(\n        self,\n        image: np.ndarray,\n        image_format: str = \"RGB\",\n    ) -> None:\n        \"\"\"\n        Calculates the image embeddings for the provided image, allowing\n        masks to be predicted with the 'predict' method.\n\n        Arguments:\n          image (np.ndarray): The image for calculating masks. Expects an\n            image in HWC uint8 format, with pixel values in [0, 255].\n          image_format (str): The color format of the image, in ['RGB', 'BGR'].\n        \"\"\"\n        assert image_format in [\n            \"RGB\",\n            \"BGR\",\n        ], f\"image_format must be in ['RGB', 'BGR'], is {image_format}.\"\n        if image_format != self.model.image_format:\n            image = image[..., ::-1]\n\n        # Transform the image to the form expected by the model\n        input_image = self.transform.apply_image(image)\n        input_image_torch = torch.as_tensor(input_image, device=self.device)\n        input_image_torch = input_image_torch.permute(2, 0, 1).contiguous()[None, :, :, :]\n\n        self.set_torch_image(input_image_torch, image.shape[:2])\n\n    @torch.no_grad()\n    def set_torch_image(\n        self,\n        transformed_image: torch.Tensor,\n        original_image_size: Tuple[int, ...],\n    ) -> None:\n        \"\"\"\n        Calculates the image embeddings for the provided image, allowing\n        masks to be predicted with the 'predict' method. Expects the input\n        image to be already transformed to the format expected by the model.\n\n        Arguments:\n          transformed_image (torch.Tensor): The input image, with shape\n            1x3xHxW, which has been transformed with ResizeLongestSide.\n          original_image_size (tuple(int, int)): The size of the image\n            before transformation, in (H, W) format.\n        \"\"\"\n        assert (\n            len(transformed_image.shape) == 4\n            and transformed_image.shape[1] == 3\n            and max(*transformed_image.shape[2:]) == self.model.image_encoder.img_size\n        ), f\"set_torch_image input must be BCHW with long side {self.model.image_encoder.img_size}.\"\n        self.reset_image()\n\n        self.original_size = original_image_size\n        self.input_size = tuple(transformed_image.shape[-2:])\n        input_image = self.model.preprocess(transformed_image)\n        self.features = self.model.image_encoder(input_image)\n        self.is_image_set = True\n\n    def predict(\n        self,\n        point_coords: Optional[np.ndarray] = None,\n        point_labels: Optional[np.ndarray] = None,\n        box: Optional[np.ndarray] = None,\n        mask_input: Optional[np.ndarray] = None,\n        multimask_output: bool = True,\n        return_logits: bool = False,\n    ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:\n        \"\"\"\n        Predict masks for the given input prompts, using the currently set image.\n\n        Arguments:\n          point_coords (np.ndarray or None): A Nx2 array of point prompts to the\n            model. Each point is in (X,Y) in pixels.\n          point_labels (np.ndarray or None): A length N array of labels for the\n            point prompts. 1 indicates a foreground point and 0 indicates a\n            background point.\n          box (np.ndarray or None): A length 4 array given a box prompt to the\n            model, in XYXY format.\n          mask_input (np.ndarray): A low resolution mask input to the model, typically\n            coming from a previous prediction iteration. Has form 1xHxW, where\n            for SAM, H=W=256.\n          multimask_output (bool): If true, the model will return three masks.\n            For ambiguous input prompts (such as a single click), this will often\n            produce better masks than a single prediction. If only a single\n            mask is needed, the model's predicted quality score can be used\n            to select the best mask. For non-ambiguous prompts, such as multiple\n            input prompts, multimask_output=False can give better results.\n          return_logits (bool): If true, returns un-thresholded masks logits\n            instead of a binary mask.\n\n        Returns:\n          (np.ndarray): The output masks in CxHxW format, where C is the\n            number of masks, and (H, W) is the original image size.\n          (np.ndarray): An array of length C containing the model's\n            predictions for the quality of each mask.\n          (np.ndarray): An array of shape CxHxW, where C is the number\n            of masks and H=W=256. These low resolution logits can be passed to\n            a subsequent iteration as mask input.\n        \"\"\"\n        if not self.is_image_set:\n            raise RuntimeError(\"An image must be set with .set_image(...) before mask prediction.\")\n\n        # Transform input prompts\n        coords_torch, labels_torch, box_torch, mask_input_torch = None, None, None, None\n        if point_coords is not None:\n            assert (\n                point_labels is not None\n            ), \"point_labels must be supplied if point_coords is supplied.\"\n            point_coords = self.transform.apply_coords(point_coords, self.original_size)\n            coords_torch = torch.as_tensor(point_coords, dtype=torch.float, device=self.device)\n            labels_torch = torch.as_tensor(point_labels, dtype=torch.int, device=self.device)\n            coords_torch, labels_torch = coords_torch[None, :, :], labels_torch[None, :]\n        if box is not None:\n            box = self.transform.apply_boxes(box, self.original_size)\n            box_torch = torch.as_tensor(box, dtype=torch.float, device=self.device)\n            box_torch = box_torch[None, :]\n        if mask_input is not None:\n            mask_input_torch = torch.as_tensor(mask_input, dtype=torch.float, device=self.device)\n            mask_input_torch = mask_input_torch[None, :, :, :]\n\n        masks, iou_predictions, low_res_masks = self.predict_torch(\n            coords_torch,\n            labels_torch,\n            box_torch,\n            mask_input_torch,\n            multimask_output,\n            return_logits=return_logits,\n        )\n\n        masks_np = masks[0].detach().cpu().numpy()\n        iou_predictions_np = iou_predictions[0].detach().cpu().numpy()\n        low_res_masks_np = low_res_masks[0].detach().cpu().numpy()\n        return masks_np, iou_predictions_np, low_res_masks_np\n\n    @torch.no_grad()\n    def predict_torch(\n        self,\n        point_coords: Optional[torch.Tensor],\n        point_labels: Optional[torch.Tensor],\n        boxes: Optional[torch.Tensor] = None,\n        mask_input: Optional[torch.Tensor] = None,\n        multimask_output: bool = True,\n        return_logits: bool = False,\n    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:\n        \"\"\"\n        Predict masks for the given input prompts, using the currently set image.\n        Input prompts are batched torch tensors and are expected to already be\n        transformed to the input frame using ResizeLongestSide.\n\n        Arguments:\n          point_coords (torch.Tensor or None): A BxNx2 array of point prompts to the\n            model. Each point is in (X,Y) in pixels.\n          point_labels (torch.Tensor or None): A BxN array of labels for the\n            point prompts. 1 indicates a foreground point and 0 indicates a\n            background point.\n          boxes (np.ndarray or None): A Bx4 array given a box prompt to the\n            model, in XYXY format.\n          mask_input (np.ndarray): A low resolution mask input to the model, typically\n            coming from a previous prediction iteration. Has form Bx1xHxW, where\n            for SAM, H=W=256. Masks returned by a previous iteration of the\n            predict method do not need further transformation.\n          multimask_output (bool): If true, the model will return three masks.\n            For ambiguous input prompts (such as a single click), this will often\n            produce better masks than a single prediction. If only a single\n            mask is needed, the model's predicted quality score can be used\n            to select the best mask. For non-ambiguous prompts, such as multiple\n            input prompts, multimask_output=False can give better results.\n          return_logits (bool): If true, returns un-thresholded masks logits\n            instead of a binary mask.\n\n        Returns:\n          (torch.Tensor): The output masks in BxCxHxW format, where C is the\n            number of masks, and (H, W) is the original image size.\n          (torch.Tensor): An array of shape BxC containing the model's\n            predictions for the quality of each mask.\n          (torch.Tensor): An array of shape BxCxHxW, where C is the number\n            of masks and H=W=256. These low res logits can be passed to\n            a subsequent iteration as mask input.\n        \"\"\"\n        if not self.is_image_set:\n            raise RuntimeError(\"An image must be set with .set_image(...) before mask prediction.\")\n\n        if point_coords is not None:\n            points = (point_coords, point_labels)\n        else:\n            points = None\n\n        # Embed prompts\n        sparse_embeddings, dense_embeddings = self.model.prompt_encoder(\n            points=points,\n            boxes=boxes,\n            masks=mask_input,\n        )\n\n        # Predict masks\n        low_res_masks, iou_predictions = self.model.mask_decoder(\n            image_embeddings=self.features,\n            image_pe=self.model.prompt_encoder.get_dense_pe(),\n            sparse_prompt_embeddings=sparse_embeddings,\n            dense_prompt_embeddings=dense_embeddings,\n            multimask_output=multimask_output,\n        )\n\n        # Upscale the masks to the original image resolution\n        masks = self.model.postprocess_masks(low_res_masks, self.input_size, self.original_size)\n\n        if not return_logits:\n            masks = masks > self.model.mask_threshold\n\n        return masks, iou_predictions, low_res_masks\n\n    def get_image_embedding(self) -> torch.Tensor:\n        \"\"\"\n        Returns the image embeddings for the currently set image, with\n        shape 1xCxHxW, where C is the embedding dimension and (H,W) are\n        the embedding spatial dimension of SAM (typically C=256, H=W=64).\n        \"\"\"\n        if not self.is_image_set:\n            raise RuntimeError(\n                \"An image must be set with .set_image(...) to generate an embedding.\"\n            )\n        assert self.features is not None, \"Features must exist if an image has been set.\"\n        return self.features\n\n    @property\n    def device(self) -> torch.device:\n        return self.model.device\n\n    def reset_image(self) -> None:\n        \"\"\"Resets the currently set image.\"\"\"\n        self.is_image_set = False\n        self.features = None\n        self.orig_h = None\n        self.orig_w = None\n        self.input_h = None\n        self.input_w = None\n"
  },
  {
    "path": "model_cards/segment_anything/utils/__init__.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n"
  },
  {
    "path": "model_cards/segment_anything/utils/amg.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport numpy as np\nimport torch\n\nimport math\nfrom copy import deepcopy\nfrom itertools import product\nfrom typing import Any, Dict, Generator, ItemsView, List, Tuple\n\n\nclass MaskData:\n    \"\"\"\n    A structure for storing masks and their related data in batched format.\n    Implements basic filtering and concatenation.\n    \"\"\"\n\n    def __init__(self, **kwargs) -> None:\n        for v in kwargs.values():\n            assert isinstance(\n                v, (list, np.ndarray, torch.Tensor)\n            ), \"MaskData only supports list, numpy arrays, and torch tensors.\"\n        self._stats = dict(**kwargs)\n\n    def __setitem__(self, key: str, item: Any) -> None:\n        assert isinstance(\n            item, (list, np.ndarray, torch.Tensor)\n        ), \"MaskData only supports list, numpy arrays, and torch tensors.\"\n        self._stats[key] = item\n\n    def __delitem__(self, key: str) -> None:\n        del self._stats[key]\n\n    def __getitem__(self, key: str) -> Any:\n        return self._stats[key]\n\n    def items(self) -> ItemsView[str, Any]:\n        return self._stats.items()\n\n    def filter(self, keep: torch.Tensor) -> None:\n        for k, v in self._stats.items():\n            if v is None:\n                self._stats[k] = None\n            elif isinstance(v, torch.Tensor):\n                self._stats[k] = v[torch.as_tensor(keep, device=v.device)]\n            elif isinstance(v, np.ndarray):\n                self._stats[k] = v[keep.detach().cpu().numpy()]\n            elif isinstance(v, list) and keep.dtype == torch.bool:\n                self._stats[k] = [a for i, a in enumerate(v) if keep[i]]\n            elif isinstance(v, list):\n                self._stats[k] = [v[i] for i in keep]\n            else:\n                raise TypeError(f\"MaskData key {k} has an unsupported type {type(v)}.\")\n\n    def cat(self, new_stats: \"MaskData\") -> None:\n        for k, v in new_stats.items():\n            if k not in self._stats or self._stats[k] is None:\n                self._stats[k] = deepcopy(v)\n            elif isinstance(v, torch.Tensor):\n                self._stats[k] = torch.cat([self._stats[k], v], dim=0)\n            elif isinstance(v, np.ndarray):\n                self._stats[k] = np.concatenate([self._stats[k], v], axis=0)\n            elif isinstance(v, list):\n                self._stats[k] = self._stats[k] + deepcopy(v)\n            else:\n                raise TypeError(f\"MaskData key {k} has an unsupported type {type(v)}.\")\n\n    def to_numpy(self) -> None:\n        for k, v in self._stats.items():\n            if isinstance(v, torch.Tensor):\n                self._stats[k] = v.detach().cpu().numpy()\n\n\ndef is_box_near_crop_edge(\n    boxes: torch.Tensor, crop_box: List[int], orig_box: List[int], atol: float = 20.0\n) -> torch.Tensor:\n    \"\"\"Filter masks at the edge of a crop, but not at the edge of the original image.\"\"\"\n    crop_box_torch = torch.as_tensor(crop_box, dtype=torch.float, device=boxes.device)\n    orig_box_torch = torch.as_tensor(orig_box, dtype=torch.float, device=boxes.device)\n    boxes = uncrop_boxes_xyxy(boxes, crop_box).float()\n    near_crop_edge = torch.isclose(boxes, crop_box_torch[None, :], atol=atol, rtol=0)\n    near_image_edge = torch.isclose(boxes, orig_box_torch[None, :], atol=atol, rtol=0)\n    near_crop_edge = torch.logical_and(near_crop_edge, ~near_image_edge)\n    return torch.any(near_crop_edge, dim=1)\n\n\ndef box_xyxy_to_xywh(box_xyxy: torch.Tensor) -> torch.Tensor:\n    box_xywh = deepcopy(box_xyxy)\n    box_xywh[2] = box_xywh[2] - box_xywh[0]\n    box_xywh[3] = box_xywh[3] - box_xywh[1]\n    return box_xywh\n\n\ndef batch_iterator(batch_size: int, *args) -> Generator[List[Any], None, None]:\n    assert len(args) > 0 and all(\n        len(a) == len(args[0]) for a in args\n    ), \"Batched iteration must have inputs of all the same size.\"\n    n_batches = len(args[0]) // batch_size + int(len(args[0]) % batch_size != 0)\n    for b in range(n_batches):\n        yield [arg[b * batch_size : (b + 1) * batch_size] for arg in args]\n\n\ndef mask_to_rle_pytorch(tensor: torch.Tensor) -> List[Dict[str, Any]]:\n    \"\"\"\n    Encodes masks to an uncompressed RLE, in the format expected by\n    pycoco tools.\n    \"\"\"\n    # Put in fortran order and flatten h,w\n    b, h, w = tensor.shape\n    tensor = tensor.permute(0, 2, 1).flatten(1)\n\n    # Compute change indices\n    diff = tensor[:, 1:] ^ tensor[:, :-1]\n    change_indices = diff.nonzero()\n\n    # Encode run length\n    out = []\n    for i in range(b):\n        cur_idxs = change_indices[change_indices[:, 0] == i, 1]\n        cur_idxs = torch.cat(\n            [\n                torch.tensor([0], dtype=cur_idxs.dtype, device=cur_idxs.device),\n                cur_idxs + 1,\n                torch.tensor([h * w], dtype=cur_idxs.dtype, device=cur_idxs.device),\n            ]\n        )\n        btw_idxs = cur_idxs[1:] - cur_idxs[:-1]\n        counts = [] if tensor[i, 0] == 0 else [0]\n        counts.extend(btw_idxs.detach().cpu().tolist())\n        out.append({\"size\": [h, w], \"counts\": counts})\n    return out\n\n\ndef rle_to_mask(rle: Dict[str, Any]) -> np.ndarray:\n    \"\"\"Compute a binary mask from an uncompressed RLE.\"\"\"\n    h, w = rle[\"size\"]\n    mask = np.empty(h * w, dtype=bool)\n    idx = 0\n    parity = False\n    for count in rle[\"counts\"]:\n        mask[idx : idx + count] = parity\n        idx += count\n        parity ^= True\n    mask = mask.reshape(w, h)\n    return mask.transpose()  # Put in C order\n\n\ndef area_from_rle(rle: Dict[str, Any]) -> int:\n    return sum(rle[\"counts\"][1::2])\n\n\ndef calculate_stability_score(\n    masks: torch.Tensor, mask_threshold: float, threshold_offset: float\n) -> torch.Tensor:\n    \"\"\"\n    Computes the stability score for a batch of masks. The stability\n    score is the IoU between the binary masks obtained by thresholding\n    the predicted mask logits at high and low values.\n    \"\"\"\n    # One mask is always contained inside the other.\n    # Save memory by preventing unnecessary cast to torch.int64\n    intersections = (\n        (masks > (mask_threshold + threshold_offset))\n        .sum(-1, dtype=torch.int16)\n        .sum(-1, dtype=torch.int32)\n    )\n    unions = (\n        (masks > (mask_threshold - threshold_offset))\n        .sum(-1, dtype=torch.int16)\n        .sum(-1, dtype=torch.int32)\n    )\n    return intersections / unions\n\n\ndef build_point_grid(n_per_side: int) -> np.ndarray:\n    \"\"\"Generates a 2D grid of points evenly spaced in [0,1]x[0,1].\"\"\"\n    offset = 1 / (2 * n_per_side)\n    points_one_side = np.linspace(offset, 1 - offset, n_per_side)\n    points_x = np.tile(points_one_side[None, :], (n_per_side, 1))\n    points_y = np.tile(points_one_side[:, None], (1, n_per_side))\n    points = np.stack([points_x, points_y], axis=-1).reshape(-1, 2)\n    return points\n\n\ndef build_all_layer_point_grids(\n    n_per_side: int, n_layers: int, scale_per_layer: int\n) -> List[np.ndarray]:\n    \"\"\"Generates point grids for all crop layers.\"\"\"\n    points_by_layer = []\n    for i in range(n_layers + 1):\n        n_points = int(n_per_side / (scale_per_layer**i))\n        points_by_layer.append(build_point_grid(n_points))\n    return points_by_layer\n\n\ndef generate_crop_boxes(\n    im_size: Tuple[int, ...], n_layers: int, overlap_ratio: float\n) -> Tuple[List[List[int]], List[int]]:\n    \"\"\"\n    Generates a list of crop boxes of different sizes. Each layer\n    has (2**i)**2 boxes for the ith layer.\n    \"\"\"\n    crop_boxes, layer_idxs = [], []\n    im_h, im_w = im_size\n    short_side = min(im_h, im_w)\n\n    # Original image\n    crop_boxes.append([0, 0, im_w, im_h])\n    layer_idxs.append(0)\n\n    def crop_len(orig_len, n_crops, overlap):\n        return int(math.ceil((overlap * (n_crops - 1) + orig_len) / n_crops))\n\n    for i_layer in range(n_layers):\n        n_crops_per_side = 2 ** (i_layer + 1)\n        overlap = int(overlap_ratio * short_side * (2 / n_crops_per_side))\n\n        crop_w = crop_len(im_w, n_crops_per_side, overlap)\n        crop_h = crop_len(im_h, n_crops_per_side, overlap)\n\n        crop_box_x0 = [int((crop_w - overlap) * i) for i in range(n_crops_per_side)]\n        crop_box_y0 = [int((crop_h - overlap) * i) for i in range(n_crops_per_side)]\n\n        # Crops in XYWH format\n        for x0, y0 in product(crop_box_x0, crop_box_y0):\n            box = [x0, y0, min(x0 + crop_w, im_w), min(y0 + crop_h, im_h)]\n            crop_boxes.append(box)\n            layer_idxs.append(i_layer + 1)\n\n    return crop_boxes, layer_idxs\n\n\ndef uncrop_boxes_xyxy(boxes: torch.Tensor, crop_box: List[int]) -> torch.Tensor:\n    x0, y0, _, _ = crop_box\n    offset = torch.tensor([[x0, y0, x0, y0]], device=boxes.device)\n    # Check if boxes has a channel dimension\n    if len(boxes.shape) == 3:\n        offset = offset.unsqueeze(1)\n    return boxes + offset\n\n\ndef uncrop_points(points: torch.Tensor, crop_box: List[int]) -> torch.Tensor:\n    x0, y0, _, _ = crop_box\n    offset = torch.tensor([[x0, y0]], device=points.device)\n    # Check if points has a channel dimension\n    if len(points.shape) == 3:\n        offset = offset.unsqueeze(1)\n    return points + offset\n\n\ndef uncrop_masks(\n    masks: torch.Tensor, crop_box: List[int], orig_h: int, orig_w: int\n) -> torch.Tensor:\n    x0, y0, x1, y1 = crop_box\n    if x0 == 0 and y0 == 0 and x1 == orig_w and y1 == orig_h:\n        return masks\n    # Coordinate transform masks\n    pad_x, pad_y = orig_w - (x1 - x0), orig_h - (y1 - y0)\n    pad = (x0, pad_x - x0, y0, pad_y - y0)\n    return torch.nn.functional.pad(masks, pad, value=0)\n\n\ndef remove_small_regions(\n    mask: np.ndarray, area_thresh: float, mode: str\n) -> Tuple[np.ndarray, bool]:\n    \"\"\"\n    Removes small disconnected regions and holes in a mask. Returns the\n    mask and an indicator of if the mask has been modified.\n    \"\"\"\n    import cv2  # type: ignore\n\n    assert mode in [\"holes\", \"islands\"]\n    correct_holes = mode == \"holes\"\n    working_mask = (correct_holes ^ mask).astype(np.uint8)\n    n_labels, regions, stats, _ = cv2.connectedComponentsWithStats(working_mask, 8)\n    sizes = stats[:, -1][1:]  # Row 0 is background label\n    small_regions = [i + 1 for i, s in enumerate(sizes) if s < area_thresh]\n    if len(small_regions) == 0:\n        return mask, False\n    fill_labels = [0] + small_regions\n    if not correct_holes:\n        fill_labels = [i for i in range(n_labels) if i not in fill_labels]\n        # If every region is below threshold, keep largest\n        if len(fill_labels) == 0:\n            fill_labels = [int(np.argmax(sizes)) + 1]\n    mask = np.isin(regions, fill_labels)\n    return mask, True\n\n\ndef coco_encode_rle(uncompressed_rle: Dict[str, Any]) -> Dict[str, Any]:\n    from pycocotools import mask as mask_utils  # type: ignore\n\n    h, w = uncompressed_rle[\"size\"]\n    rle = mask_utils.frPyObjects(uncompressed_rle, h, w)\n    rle[\"counts\"] = rle[\"counts\"].decode(\"utf-8\")  # Necessary to serialize with json\n    return rle\n\n\ndef batched_mask_to_box(masks: torch.Tensor) -> torch.Tensor:\n    \"\"\"\n    Calculates boxes in XYXY format around masks. Return [0,0,0,0] for\n    an empty mask. For input shape C1xC2x...xHxW, the output shape is C1xC2x...x4.\n    \"\"\"\n    # torch.max below raises an error on empty inputs, just skip in this case\n    if torch.numel(masks) == 0:\n        return torch.zeros(*masks.shape[:-2], 4, device=masks.device)\n\n    # Normalize shape to CxHxW\n    shape = masks.shape\n    h, w = shape[-2:]\n    if len(shape) > 2:\n        masks = masks.flatten(0, -3)\n    else:\n        masks = masks.unsqueeze(0)\n\n    # Get top and bottom edges\n    in_height, _ = torch.max(masks, dim=-1)\n    in_height_coords = in_height * torch.arange(h, device=in_height.device)[None, :]\n    bottom_edges, _ = torch.max(in_height_coords, dim=-1)\n    in_height_coords = in_height_coords + h * (~in_height)\n    top_edges, _ = torch.min(in_height_coords, dim=-1)\n\n    # Get left and right edges\n    in_width, _ = torch.max(masks, dim=-2)\n    in_width_coords = in_width * torch.arange(w, device=in_width.device)[None, :]\n    right_edges, _ = torch.max(in_width_coords, dim=-1)\n    in_width_coords = in_width_coords + w * (~in_width)\n    left_edges, _ = torch.min(in_width_coords, dim=-1)\n\n    # If the mask is empty the right edge will be to the left of the left edge.\n    # Replace these boxes with [0, 0, 0, 0]\n    empty_filter = (right_edges < left_edges) | (bottom_edges < top_edges)\n    out = torch.stack([left_edges, top_edges, right_edges, bottom_edges], dim=-1)\n    out = out * (~empty_filter).unsqueeze(-1)\n\n    # Return to original shape\n    if len(shape) > 2:\n        out = out.reshape(*shape[:-2], 4)\n    else:\n        out = out[0]\n\n    return out\n"
  },
  {
    "path": "model_cards/segment_anything/utils/onnx.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport torch\nimport torch.nn as nn\nfrom torch.nn import functional as F\n\nfrom typing import Tuple\n\nfrom ..modeling import Sam\nfrom .amg import calculate_stability_score\n\n\nclass SamOnnxModel(nn.Module):\n    \"\"\"\n    This model should not be called directly, but is used in ONNX export.\n    It combines the prompt encoder, mask decoder, and mask postprocessing of Sam,\n    with some functions modified to enable model tracing. Also supports extra\n    options controlling what information. See the ONNX export script for details.\n    \"\"\"\n\n    def __init__(\n        self,\n        model: Sam,\n        return_single_mask: bool,\n        use_stability_score: bool = False,\n        return_extra_metrics: bool = False,\n    ) -> None:\n        super().__init__()\n        self.mask_decoder = model.mask_decoder\n        self.model = model\n        self.img_size = model.image_encoder.img_size\n        self.return_single_mask = return_single_mask\n        self.use_stability_score = use_stability_score\n        self.stability_score_offset = 1.0\n        self.return_extra_metrics = return_extra_metrics\n\n    @staticmethod\n    def resize_longest_image_size(\n        input_image_size: torch.Tensor, longest_side: int\n    ) -> torch.Tensor:\n        input_image_size = input_image_size.to(torch.float32)\n        scale = longest_side / torch.max(input_image_size)\n        transformed_size = scale * input_image_size\n        transformed_size = torch.floor(transformed_size + 0.5).to(torch.int64)\n        return transformed_size\n\n    def _embed_points(self, point_coords: torch.Tensor, point_labels: torch.Tensor) -> torch.Tensor:\n        point_coords = point_coords + 0.5\n        point_coords = point_coords / self.img_size\n        point_embedding = self.model.prompt_encoder.pe_layer._pe_encoding(point_coords)\n        point_labels = point_labels.unsqueeze(-1).expand_as(point_embedding)\n\n        point_embedding = point_embedding * (point_labels != -1)\n        point_embedding = point_embedding + self.model.prompt_encoder.not_a_point_embed.weight * (\n            point_labels == -1\n        )\n\n        for i in range(self.model.prompt_encoder.num_point_embeddings):\n            point_embedding = point_embedding + self.model.prompt_encoder.point_embeddings[\n                i\n            ].weight * (point_labels == i)\n\n        return point_embedding\n\n    def _embed_masks(self, input_mask: torch.Tensor, has_mask_input: torch.Tensor) -> torch.Tensor:\n        mask_embedding = has_mask_input * self.model.prompt_encoder.mask_downscaling(input_mask)\n        mask_embedding = mask_embedding + (\n            1 - has_mask_input\n        ) * self.model.prompt_encoder.no_mask_embed.weight.reshape(1, -1, 1, 1)\n        return mask_embedding\n\n    def mask_postprocessing(self, masks: torch.Tensor, orig_im_size: torch.Tensor) -> torch.Tensor:\n        masks = F.interpolate(\n            masks,\n            size=(self.img_size, self.img_size),\n            mode=\"bilinear\",\n            align_corners=False,\n        )\n\n        prepadded_size = self.resize_longest_image_size(orig_im_size, self.img_size).to(torch.int64)\n        masks = masks[..., : prepadded_size[0], : prepadded_size[1]]  # type: ignore\n\n        orig_im_size = orig_im_size.to(torch.int64)\n        h, w = orig_im_size[0], orig_im_size[1]\n        masks = F.interpolate(masks, size=(h, w), mode=\"bilinear\", align_corners=False)\n        return masks\n\n    def select_masks(\n        self, masks: torch.Tensor, iou_preds: torch.Tensor, num_points: int\n    ) -> Tuple[torch.Tensor, torch.Tensor]:\n        # Determine if we should return the multiclick mask or not from the number of points.\n        # The reweighting is used to avoid control flow.\n        score_reweight = torch.tensor(\n            [[1000] + [0] * (self.model.mask_decoder.num_mask_tokens - 1)]\n        ).to(iou_preds.device)\n        score = iou_preds + (num_points - 2.5) * score_reweight\n        best_idx = torch.argmax(score, dim=1)\n        masks = masks[torch.arange(masks.shape[0]), best_idx, :, :].unsqueeze(1)\n        iou_preds = iou_preds[torch.arange(masks.shape[0]), best_idx].unsqueeze(1)\n\n        return masks, iou_preds\n\n    @torch.no_grad()\n    def forward(\n        self,\n        image_embeddings: torch.Tensor,\n        point_coords: torch.Tensor,\n        point_labels: torch.Tensor,\n        mask_input: torch.Tensor,\n        has_mask_input: torch.Tensor,\n        orig_im_size: torch.Tensor,\n    ):\n        sparse_embedding = self._embed_points(point_coords, point_labels)\n        dense_embedding = self._embed_masks(mask_input, has_mask_input)\n\n        masks, scores = self.model.mask_decoder.predict_masks(\n            image_embeddings=image_embeddings,\n            image_pe=self.model.prompt_encoder.get_dense_pe(),\n            sparse_prompt_embeddings=sparse_embedding,\n            dense_prompt_embeddings=dense_embedding,\n        )\n\n        if self.use_stability_score:\n            scores = calculate_stability_score(\n                masks, self.model.mask_threshold, self.stability_score_offset\n            )\n\n        if self.return_single_mask:\n            masks, scores = self.select_masks(masks, scores, point_coords.shape[1])\n\n        upscaled_masks = self.mask_postprocessing(masks, orig_im_size)\n\n        if self.return_extra_metrics:\n            stability_scores = calculate_stability_score(\n                upscaled_masks, self.model.mask_threshold, self.stability_score_offset\n            )\n            areas = (upscaled_masks > self.model.mask_threshold).sum(-1).sum(-1)\n            return upscaled_masks, scores, stability_scores, areas, masks\n\n        return upscaled_masks, scores, masks\n"
  },
  {
    "path": "model_cards/segment_anything/utils/transforms.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport numpy as np\nimport torch\nfrom torch.nn import functional as F\nfrom torchvision.transforms.functional import resize, to_pil_image  # type: ignore\n\nfrom copy import deepcopy\nfrom typing import Tuple\n\n\nclass ResizeLongestSide:\n    \"\"\"\n    Resizes images to the longest side 'target_length', as well as provides\n    methods for resizing coordinates and boxes. Provides methods for\n    transforming both numpy array and batched torch tensors.\n    \"\"\"\n\n    def __init__(self, target_length: int) -> None:\n        self.target_length = target_length\n\n    def apply_image(self, image: np.ndarray) -> np.ndarray:\n        \"\"\"\n        Expects a numpy array with shape HxWxC in uint8 format.\n        \"\"\"\n        target_size = self.get_preprocess_shape(image.shape[0], image.shape[1], self.target_length)\n        return np.array(resize(to_pil_image(image), target_size))\n\n    def apply_coords(self, coords: np.ndarray, original_size: Tuple[int, ...]) -> np.ndarray:\n        \"\"\"\n        Expects a numpy array of length 2 in the final dimension. Requires the\n        original image size in (H, W) format.\n        \"\"\"\n        old_h, old_w = original_size\n        new_h, new_w = self.get_preprocess_shape(\n            original_size[0], original_size[1], self.target_length\n        )\n        coords = deepcopy(coords).astype(float)\n        coords[..., 0] = coords[..., 0] * (new_w / old_w)\n        coords[..., 1] = coords[..., 1] * (new_h / old_h)\n        return coords\n\n    def apply_boxes(self, boxes: np.ndarray, original_size: Tuple[int, ...]) -> np.ndarray:\n        \"\"\"\n        Expects a numpy array shape Bx4. Requires the original image size\n        in (H, W) format.\n        \"\"\"\n        boxes = self.apply_coords(boxes.reshape(-1, 2, 2), original_size)\n        return boxes.reshape(-1, 4)\n\n    def apply_image_torch(self, image: torch.Tensor) -> torch.Tensor:\n        \"\"\"\n        Expects batched images with shape BxCxHxW and float format. This\n        transformation may not exactly match apply_image. apply_image is\n        the transformation expected by the model.\n        \"\"\"\n        # Expects an image in BCHW format. May not exactly match apply_image.\n        target_size = self.get_preprocess_shape(image.shape[2], image.shape[3], self.target_length)\n        return F.interpolate(\n            image, target_size, mode=\"bilinear\", align_corners=False, antialias=True\n        )\n\n    def apply_coords_torch(\n        self, coords: torch.Tensor, original_size: Tuple[int, ...]\n    ) -> torch.Tensor:\n        \"\"\"\n        Expects a torch tensor with length 2 in the last dimension. Requires the\n        original image size in (H, W) format.\n        \"\"\"\n        old_h, old_w = original_size\n        new_h, new_w = self.get_preprocess_shape(\n            original_size[0], original_size[1], self.target_length\n        )\n        coords = deepcopy(coords).to(torch.float)\n        coords[..., 0] = coords[..., 0] * (new_w / old_w)\n        coords[..., 1] = coords[..., 1] * (new_h / old_h)\n        return coords\n\n    def apply_boxes_torch(\n        self, boxes: torch.Tensor, original_size: Tuple[int, ...]\n    ) -> torch.Tensor:\n        \"\"\"\n        Expects a torch tensor with shape Bx4. Requires the original image\n        size in (H, W) format.\n        \"\"\"\n        boxes = self.apply_coords_torch(boxes.reshape(-1, 2, 2), original_size)\n        return boxes.reshape(-1, 4)\n\n    @staticmethod\n    def get_preprocess_shape(oldh: int, oldw: int, long_side_length: int) -> Tuple[int, int]:\n        \"\"\"\n        Compute the output size given input size and target long side length.\n        \"\"\"\n        scale = long_side_length * 1.0 / max(oldh, oldw)\n        newh, neww = oldh * scale, oldw * scale\n        neww = int(neww + 0.5)\n        newh = int(newh + 0.5)\n        return (newh, neww)\n"
  },
  {
    "path": "model_cards/setup.py",
    "content": "# coding=utf-8\n# Copyright 2022 The IDEA Authors. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ------------------------------------------------------------------------------------------------\n# Modified from\n# https://github.com/fundamentalvision/Deformable-DETR/blob/main/models/ops/setup.py\n# https://github.com/facebookresearch/detectron2/blob/main/setup.py\n# https://github.com/open-mmlab/mmdetection/blob/master/setup.py\n# https://github.com/Oneflow-Inc/libai/blob/main/setup.py\n# ------------------------------------------------------------------------------------------------\n\nimport glob\nimport os\nimport subprocess\n\nimport torch\nfrom setuptools import find_packages, setup\nfrom torch.utils.cpp_extension import CUDA_HOME, CppExtension, CUDAExtension\n\n# groundingdino version info\nversion = \"0.1.0\"\npackage_name = \"groundingdino\"\ncwd = os.path.dirname(os.path.abspath(__file__))\n\n\nsha = \"Unknown\"\ntry:\n    sha = subprocess.check_output([\"git\", \"rev-parse\", \"HEAD\"], cwd=cwd).decode(\"ascii\").strip()\nexcept Exception:\n    pass\n\n\ndef write_version_file():\n    version_path = os.path.join(cwd, \"groundingdino\", \"version.py\")\n    with open(version_path, \"w\") as f:\n        f.write(f\"__version__ = '{version}'\\n\")\n        # f.write(f\"git_version = {repr(sha)}\\n\")\n\n\nrequirements = [\"torch\", \"torchvision\"]\n\ntorch_ver = [int(x) for x in torch.__version__.split(\".\")[:2]]\n\n\ndef get_extensions():\n    this_dir = os.path.dirname(os.path.abspath(__file__))\n    extensions_dir = os.path.join(this_dir, \"groundingdino\", \"models\", \"GroundingDINO\", \"csrc\")\n\n    main_source = os.path.join(extensions_dir, \"vision.cpp\")\n    sources = glob.glob(os.path.join(extensions_dir, \"**\", \"*.cpp\"))\n    source_cuda = glob.glob(os.path.join(extensions_dir, \"**\", \"*.cu\")) + glob.glob(\n        os.path.join(extensions_dir, \"*.cu\")\n    )\n\n    sources = [main_source] + sources\n\n    # We need these variables to build with CUDA when we create the Docker image\n    # It solves https://github.com/IDEA-Research/Grounded-Segment-Anything/issues/53\n    # and https://github.com/IDEA-Research/Grounded-Segment-Anything/issues/84 when running\n    # inside a Docker container.\n    am_i_docker = os.environ.get('AM_I_DOCKER', '').casefold() in ['true', '1', 't']\n    use_cuda = os.environ.get('BUILD_WITH_CUDA', '').casefold() in ['true', '1', 't']\n\n    extension = CppExtension\n\n    extra_compile_args = {\"cxx\": []}\n    define_macros = []\n\n    if (torch.cuda.is_available() and CUDA_HOME is not None) or \\\n            (am_i_docker and use_cuda):\n        print(\"Compiling with CUDA\")\n        extension = CUDAExtension\n        sources += source_cuda\n        define_macros += [(\"WITH_CUDA\", None)]\n        extra_compile_args[\"nvcc\"] = [\n            \"-DCUDA_HAS_FP16=1\",\n            \"-D__CUDA_NO_HALF_OPERATORS__\",\n            \"-D__CUDA_NO_HALF_CONVERSIONS__\",\n            \"-D__CUDA_NO_HALF2_OPERATORS__\",\n        ]\n    else:\n        print(\"Compiling without CUDA\")\n        define_macros += [(\"WITH_HIP\", None)]\n        extra_compile_args[\"nvcc\"] = []\n        return None\n\n    sources = [os.path.join(extensions_dir, s) for s in sources]\n    include_dirs = [extensions_dir]\n\n    ext_modules = [\n        extension(\n            \"groundingdino._C\",\n            sources,\n            include_dirs=include_dirs,\n            define_macros=define_macros,\n            extra_compile_args=extra_compile_args,\n        )\n    ]\n\n    return ext_modules\n\n\ndef parse_requirements(fname=\"requirements.txt\", with_version=True):\n    \"\"\"Parse the package dependencies listed in a requirements file but strips\n    specific versioning information.\n\n    Args:\n        fname (str): path to requirements file\n        with_version (bool, default=False): if True include version specs\n\n    Returns:\n        List[str]: list of requirements items\n\n    CommandLine:\n        python -c \"import setup; print(setup.parse_requirements())\"\n    \"\"\"\n    import re\n    import sys\n    from os.path import exists\n\n    require_fpath = fname\n\n    def parse_line(line):\n        \"\"\"Parse information from a line in a requirements text file.\"\"\"\n        if line.startswith(\"-r \"):\n            # Allow specifying requirements in other files\n            target = line.split(\" \")[1]\n            for info in parse_require_file(target):\n                yield info\n        else:\n            info = {\"line\": line}\n            if line.startswith(\"-e \"):\n                info[\"package\"] = line.split(\"#egg=\")[1]\n            elif \"@git+\" in line:\n                info[\"package\"] = line\n            else:\n                # Remove versioning from the package\n                pat = \"(\" + \"|\".join([\">=\", \"==\", \">\"]) + \")\"\n                parts = re.split(pat, line, maxsplit=1)\n                parts = [p.strip() for p in parts]\n\n                info[\"package\"] = parts[0]\n                if len(parts) > 1:\n                    op, rest = parts[1:]\n                    if \";\" in rest:\n                        # Handle platform specific dependencies\n                        # http://setuptools.readthedocs.io/en/latest/setuptools.html#declaring-platform-specific-dependencies\n                        version, platform_deps = map(str.strip, rest.split(\";\"))\n                        info[\"platform_deps\"] = platform_deps\n                    else:\n                        version = rest  # NOQA\n                    info[\"version\"] = (op, version)\n            yield info\n\n    def parse_require_file(fpath):\n        with open(fpath, \"r\") as f:\n            for line in f.readlines():\n                line = line.strip()\n                if line and not line.startswith(\"#\"):\n                    for info in parse_line(line):\n                        yield info\n\n    def gen_packages_items():\n        if exists(require_fpath):\n            for info in parse_require_file(require_fpath):\n                parts = [info[\"package\"]]\n                if with_version and \"version\" in info:\n                    parts.extend(info[\"version\"])\n                if not sys.version.startswith(\"3.4\"):\n                    # apparently package_deps are broken in 3.4\n                    platform_deps = info.get(\"platform_deps\")\n                    if platform_deps is not None:\n                        parts.append(\";\" + platform_deps)\n                item = \"\".join(parts)\n                yield item\n\n    packages = list(gen_packages_items())\n    return packages\n\n\nif __name__ == \"__main__\":\n    print(f\"Building wheel {package_name}-{version}\")\n\n\n    write_version_file()\n\n    setup(\n        name=\"groundingdino\",\n        version=\"0.1.0\",\n        author=\"International Digital Economy Academy, Shilong Liu\",\n        url=\"https://github.com/IDEA-Research/GroundingDINO\",\n        description=\"open-set object detector\",\n      #  license=license,\n        install_requires=parse_requirements(\"requirements.txt\"),\n        packages=find_packages(\n            exclude=(\n                \"configs\",\n                \"tests\",\n            )\n        ),\n        ext_modules=get_extensions(),\n        cmdclass={\"build_ext\": torch.utils.cpp_extension.BuildExtension},\n    )"
  },
  {
    "path": "requirements.txt",
    "content": "torch>1.10.0\ntorchvision\ntransformers #tag2text if interfence error, install latest version\ntensorboard\naddict\nyapf\ntimm\nnumpy==1.23\nopencv-python\nsupervision\npycocotools\nurllib3==1.25.11 \ntqdm\ndiffusers\nhuggingface_hub\nmatplotlib\nonnxruntime\nPillow\nPyYAML\nrequests\nsetuptools\ntermcolor\nnltk\ngradio==3.28.3 #or 3.32.2 否则会有Markdown的符号\nairscale\nomegaconf\n#cpython #windows \nmdtex2html\nscipy\neasydict\nsklearn\nsoundfile\npyaudio\nffmpeg\ntiktoken\n#SwissArmyTransformer>=0.3.6\n##语音合成 传输OMNIVERSE\nedge-tts\nwhisper\nopenai\nopenai-whisper\n## 微调VisualGLM\nbitsandbytes\n#gligen\neinops\nclip"
  },
  {
    "path": "requirements_llm_extra.txt",
    "content": "pydantic==1.10.11\ntiktoken>=0.3.3\nrequests[socks]\n#transformers\npython-markdown-math\nbeautifulsoup4\nprompt_toolkit\nlatex2mathml\npython-docx\nmdtex2html\nanthropic\ncolorama\nMarkdown\npygments\npymupdf\nopenai\nnumpy\narxiv\nrich\npypdf2==2.12.1\nlangchain==0.0.174\nfitz\n"
  },
  {
    "path": "themes/common.js",
    "content": "function ChatBotHeight() {\n    function update_height(){\n        var { panel_height_target, chatbot_height, chatbot } = get_elements();\n        if (panel_height_target!=chatbot_height)\n        {\n            var pixelString = panel_height_target.toString() + 'px';\n            chatbot.style.maxHeight = pixelString; chatbot.style.height = pixelString; \n        }\n    }\n\n    function update_height_slow(){\n        var { panel_height_target, chatbot_height, chatbot } = get_elements();\n        if (panel_height_target!=chatbot_height)\n        {\n            new_panel_height = (panel_height_target - chatbot_height)*0.5 + chatbot_height;\n            if (Math.abs(new_panel_height - panel_height_target) < 10){\n                new_panel_height = panel_height_target;\n            }\n            // console.log(chatbot_height, panel_height_target, new_panel_height);\n            var pixelString = new_panel_height.toString() + 'px';\n            chatbot.style.maxHeight = pixelString; chatbot.style.height = pixelString; \n        }\n    }\n\n    update_height();\n    setInterval(function() {\n        update_height_slow()\n    }, 50); // 每100毫秒执行一次\n}\n\nfunction get_elements() {\n    var chatbot = document.querySelector('#gpt-chatbot > div.wrap.svelte-18telvq');\n    if (!chatbot) {\n        chatbot = document.querySelector('#gpt-chatbot');\n    }\n    const panel1 = document.querySelector('#input-panel');\n    const panel2 = document.querySelector('#basic-panel');\n    const panel3 = document.querySelector('#plugin-panel');\n    const panel4 = document.querySelector('#interact-panel');\n    const panel5 = document.querySelector('#input-panel2');\n    const panel_active = document.querySelector('#state-panel');\n    var panel_height_target = (20-panel_active.offsetHeight) + panel1.offsetHeight + panel2.offsetHeight + panel3.offsetHeight + panel4.offsetHeight + panel5.offsetHeight + 21;\n    var panel_height_target = parseInt(panel_height_target);\n    var chatbot_height = chatbot.style.height;\n    var chatbot_height = parseInt(chatbot_height);\n    return { panel_height_target, chatbot_height, chatbot };\n}"
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    "path": "themes/default.css",
    "content": ".markdown-body table {\n    margin: 1em 0;\n    border-collapse: collapse;\n    empty-cells: show;\n}\n\n.markdown-body th, .markdown-body td {\n    border: 1.2px solid var(--border-color-primary);\n    padding: 5px;\n}\n\n.markdown-body thead {\n    background-color: rgba(175,184,193,0.2);\n}\n\n.markdown-body thead th {\n    padding: .5em .2em;\n}\n\n.markdown-body ol, .markdown-body ul {\n    padding-inline-start: 2em !important;\n}\n\n/* chat box. */\n[class *= \"message\"] {\n    border-radius: var(--radius-xl) !important;\n    /* padding: var(--spacing-xl) !important; */\n    /* font-size: var(--text-md) !important; */\n    /* line-height: var(--line-md) !important; */\n    /* min-height: calc(var(--text-md)*var(--line-md) + 2*var(--spacing-xl)); */\n    /* min-width: calc(var(--text-md)*var(--line-md) + 2*var(--spacing-xl)); */\n}\n[data-testid = \"bot\"] {\n    max-width: 95%;\n    /* width: auto !important; */\n    border-bottom-left-radius: 0 !important;\n}\n[data-testid = \"user\"] {\n    max-width: 100%;\n    /* width: auto !important; 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  },
  {
    "path": "themes/default.py",
    "content": "import gradio as gr\nfrom utils.toolbox import get_conf\nCODE_HIGHLIGHT, ADD_WAIFU, LAYOUT = get_conf('CODE_HIGHLIGHT', 'ADD_WAIFU', 'LAYOUT')\n\ndef adjust_theme():\n\n    try:\n        color_er = gr.themes.utils.colors.fuchsia\n        set_theme = gr.themes.Default(\n            primary_hue=gr.themes.utils.colors.orange,\n            neutral_hue=gr.themes.utils.colors.gray,\n            font=[\"sans-serif\", \"Microsoft YaHei\", \"ui-sans-serif\", \"system-ui\"],\n            font_mono=[\"ui-monospace\", \"Consolas\", \"monospace\"])\n        set_theme.set(\n            # Colors\n            input_background_fill_dark=\"*neutral_800\",\n            # Transition\n            button_transition=\"none\",\n            # Shadows\n            button_shadow=\"*shadow_drop\",\n            button_shadow_hover=\"*shadow_drop_lg\",\n            button_shadow_active=\"*shadow_inset\",\n            input_shadow=\"0 0 0 *shadow_spread transparent, *shadow_inset\",\n            input_shadow_focus=\"0 0 0 *shadow_spread *secondary_50, *shadow_inset\",\n            input_shadow_focus_dark=\"0 0 0 *shadow_spread *neutral_700, *shadow_inset\",\n            checkbox_label_shadow=\"*shadow_drop\",\n            block_shadow=\"*shadow_drop\",\n            form_gap_width=\"1px\",\n            # Button borders\n            input_border_width=\"1px\",\n            input_background_fill=\"white\",\n            # Gradients\n            stat_background_fill=\"linear-gradient(to right, *primary_400, *primary_200)\",\n            stat_background_fill_dark=\"linear-gradient(to right, *primary_400, *primary_600)\",\n            error_background_fill=f\"linear-gradient(to right, {color_er.c100}, *background_fill_secondary)\",\n            error_background_fill_dark=\"*background_fill_primary\",\n            checkbox_label_background_fill=\"linear-gradient(to top, *neutral_50, white)\",\n            checkbox_label_background_fill_dark=\"linear-gradient(to top, *neutral_900, *neutral_800)\",\n            checkbox_label_background_fill_hover=\"linear-gradient(to top, *neutral_100, white)\",\n            checkbox_label_background_fill_hover_dark=\"linear-gradient(to top, *neutral_900, *neutral_800)\",\n            button_primary_background_fill=\"linear-gradient(to bottom right, *primary_100, *primary_300)\",\n            button_primary_background_fill_dark=\"linear-gradient(to bottom right, *primary_500, *primary_600)\",\n            button_primary_background_fill_hover=\"linear-gradient(to bottom right, *primary_100, *primary_200)\",\n            button_primary_background_fill_hover_dark=\"linear-gradient(to bottom right, *primary_500, *primary_500)\",\n            button_primary_border_color_dark=\"*primary_500\",\n            button_secondary_background_fill=\"linear-gradient(to bottom right, *neutral_100, *neutral_200)\",\n            button_secondary_background_fill_dark=\"linear-gradient(to bottom right, *neutral_600, *neutral_700)\",\n            button_secondary_background_fill_hover=\"linear-gradient(to bottom right, *neutral_100, *neutral_100)\",\n            button_secondary_background_fill_hover_dark=\"linear-gradient(to bottom right, *neutral_600, *neutral_600)\",\n            button_cancel_background_fill=f\"linear-gradient(to bottom right, {color_er.c100}, {color_er.c200})\",\n            button_cancel_background_fill_dark=f\"linear-gradient(to bottom right, {color_er.c600}, {color_er.c700})\",\n            button_cancel_background_fill_hover=f\"linear-gradient(to bottom right, {color_er.c100}, {color_er.c100})\",\n            button_cancel_background_fill_hover_dark=f\"linear-gradient(to bottom right, {color_er.c600}, {color_er.c600})\",\n            button_cancel_border_color=color_er.c200,\n            button_cancel_border_color_dark=color_er.c600,\n            button_cancel_text_color=color_er.c600,\n            button_cancel_text_color_dark=\"white\",\n        )\n\n        if LAYOUT==\"TOP-DOWN\": \n            js = \"\"\n        else:\n            with open('themes/common.js', 'r', encoding='utf8') as f: \n                js = f\"<script>{f.read()}</script>\"\n            \n        # 添加一个萌萌的看板娘\n        if ADD_WAIFU:\n            js += \"\"\"\n                <script src=\"file=docs/waifu_plugin/jquery.min.js\"></script>\n                <script src=\"file=docs/waifu_plugin/jquery-ui.min.js\"></script>\n                <script src=\"file=docs/waifu_plugin/autoload.js\"></script>\n            \"\"\"\n        gradio_original_template_fn = gr.routes.templates.TemplateResponse\n        def gradio_new_template_fn(*args, **kwargs):\n            res = gradio_original_template_fn(*args, **kwargs)\n            res.body = res.body.replace(b'</html>', f'{js}</html>'.encode(\"utf8\"))\n            res.init_headers()\n            return res\n        gr.routes.templates.TemplateResponse = gradio_new_template_fn   # override gradio template\n    except:\n        set_theme = None\n        print('gradio版本较旧, 不能自定义字体和颜色')\n    return set_theme\n\nwith open(\"themes/default.css\", \"r\", encoding=\"utf-8\") as f:\n    advanced_css = f.read()"
  },
  {
    "path": "themes/green.css",
    "content": ":root {\n    --chatbot-color-light: #000000;\n    --chatbot-color-dark: #FFFFFF;\n    --chatbot-background-color-light: #F3F3F3;\n    --chatbot-background-color-dark: #121111;\n    --message-user-background-color-light: #95EC69;\n    --message-user-background-color-dark: #26B561;\n    --message-bot-background-color-light: #FFFFFF;\n    --message-bot-background-color-dark: #2C2C2C;\n}\nmspace {\n    display: block;\n}\n@media only screen and (max-width: 767px) {\n  #column_1 {\n    display: none !important;\n  }\n}\n@keyframes highlight {\n  0%, 100% {\n    border: 2px solid transparent;\n  }\n  50% {\n    border-color: yellow;\n  }\n}\n\n#highlight_update {\n  animation-name: highlight;\n  animation-duration: 0.75s;\n  animation-iteration-count: 3;\n}\n\n.table-wrap.svelte-13hsdno.svelte-13hsdno.svelte-13hsdno {\n    border: 0px solid var(--border-color-primary) !important;\n}\n\n#examples_col {\n    z-index: 2;\n    position: absolute;\n    bottom: 0;\n    left: 0;\n    width: 100%;\n    margin-bottom: 30% !important;\n}\n#hide_examples {\n    z-index: 0;\n}\n\n#debug_mes {\n    position: absolute;\n    display: flex;\n    bottom: 0;\n    left: 0;\n    z-index: 1; /* 设置更高的 z-index 值 */\n    margin-bottom: -4px !important;\n    align-self: flex-end;\n}\n#chat_box {\n    display: flex;\n    flex-direction: column;\n    overflow-y: visible !important;\n    z-index: 3;\n    flex-grow: 1; /* 自动填充剩余空间 */\n    position: absolute;\n    bottom: 0;\n    left: 0;\n    width: 100%;\n    margin-bottom: 30px !important;\n    border: 1px solid var(--border-color-primary);\n}\n.toast-body {\n    z-index: 5 !important;\n}\n.chat_input {\n\n}\n.sm_btn {\n    position: relative;\n    bottom: 5px;\n    height: 10%;\n    border-radius: 20px!important;\n    min-width: min(10%,100%) !important;\n    overflow: hidden;\n}\n.sm_select {\n    position: relative !important;\n    z-index: 5 !important;\n    bottom: 5px;\n    min-width: min(20%,100%) !important;\n    border-radius: 20px!important;\n}\n.sm_checkbox {\n    position: relative !important;\n    z-index: 5 !important;\n    bottom: 5px;\n    padding: 0 !important;\n}\n.sm_select .wrap-inner.svelte-aqlk7e.svelte-aqlk7e.svelte-aqlk7e {\n    padding: 0 !important;\n}\n.sm_select .block.svelte-mppz8v {\n    width: 10% !important;\n}\n\n/* usage_display */\n.insert_block {\n    position: relative;\n    bottom: 2px;\n    min-width: min(55px,100%) !important;\n}\n\n.submit_btn {\n    flex-direction: column-reverse;\n    overflow-y: auto !important;\n    position: absolute;\n    bottom: 0;\n    right: 10px;\n    margin-bottom: 10px !important;\n    min-width: min(50px,100%) !important;\n}\n\ntextarea {\n    resize: none;\n    height: 100%; /* 填充父元素的高度 */\n}\n#main_chatbot {\n    height: 75vh !important;\n    max-height: 75vh !important;\n    /* overflow: auto !important; */\n    z-index: 2;\n    transform: translateZ(0) !important;\n    backface-visibility: hidden !important;\n    will-change: transform !important;\n}\n#prompt_result{\n    height: 60vh !important;\n    max-height: 60vh !important;\n}\n\n#app_title {\n    font-weight: var(--prose-header-text-weight);\n    font-size: var(--text-xxl);\n    line-height: 1.3;\n    text-align: left;\n    margin-top: 6px;\n    white-space: nowrap;\n}\n#description {\n    text-align: center;\n    margin: 32px 0 4px 0;\n}\n\n/* gradio的页脚信息 */\nfooter {\n    /* display: none !important; */\n    margin-top: .2em !important;\n    font-size: 85%;\n}\n#footer {\n    text-align: center;\n}\n#footer div {\n    display: inline-block;\n}\n#footer .versions{\n    font-size: 85%;\n    opacity: 0.60;\n}\n/* user_info */\n\n#float_display {\n    position: absolute;\n    max-height: 30px;\n}\n/* user_info */\n#user_info {\n    white-space: nowrap;\n    position: absolute; left: 8em; top: .2em;\n    z-index: var(--layer-2);\n    box-shadow: var(--block-shadow);\n    border: none; border-radius: var(--block-label-radius);\n    background: var(--color-accent);\n    padding: var(--block-label-padding);\n    font-size: var(--block-label-text-size); line-height: var(--line-sm);\n    width: auto; min-height: 30px !important;\n    opacity: 1;\n    transition: opacity 0.3s ease-in-out;\n}\ntextarea.svelte-1pie7s6 {\n    background: #e7e6e6 !important;\n    width: 96% !important;\n}\n\n.dark textarea.svelte-1pie7s6 {\n    background: var(--input-background-fill) !important;\n    width: 96% !important;\n}\n\n.dark input[type=number].svelte-1cl284s {\n    background: #393939 !important;\n    border: var(--input-border-width) solid var(--input-border-color) !important;\n}\n.dark input[type=\"range\"] {\n    background: #393939 !important;\n}\n#user_info .wrap {\n    opacity: 0;\n}\n#user_info p {\n    color: white;\n    font-weight: var(--block-label-text-weight);\n}\n#user_info.hideK {\n    opacity: 0;\n    transition: opacity 1s ease-in-out;\n}\n[class *= \"message\"] {\n    gap: 7px !important;\n    border-radius: var(--radius-xl) !important\n}\n/* debug_mes */\n#debug_mes {\n    min-height: 2em;\n    align-items: flex-end;\n    justify-content: flex-end;\n}\n#debug_mes p {\n    font-size: .85em;\n    font-family: ui-monospace, \"SF Mono\", \"SFMono-Regular\", \"Menlo\", \"Consolas\", \"Liberation Mono\", \"Microsoft Yahei UI\", \"Microsoft Yahei\", monospace;\n    /* Windows下中文的monospace会fallback为新宋体，实在太丑，这里折中使用微软雅黑 */\n    color: #000000;\n}\n.dark #debug_mes p {\n    color: #ee65ed;\n}\n\n#debug_mes {\n    transition: all 0.6s;\n}\n#main_chatbot {\n    transition: height 0.3s ease;\n}\n\n/* .wrap.svelte-18telvq.svelte-18telvq {\n    padding: var(--block-padding) !important;\n    height: 100% !important;\n    max-height: 95% !important;\n    overflow-y: auto !important;\n}*/\n.app.svelte-1mya07g.svelte-1mya07g {\n    max-width: 100%;\n    position: relative;\n    padding: var(--size-4);\n    width: 100%;\n    height: 100%;\n}\n\n.gradio-container-3-32-2 h1 {\n    font-weight: 700 !important;\n    font-size: 28px !important;\n}\n\n\n.gradio-container-3-32-2 h2 {\n    font-weight: 600 !important;\n    font-size: 24px !important;\n}\n.gradio-container-3-32-2 h3 {\n    font-weight: 500 !important;\n    font-size: 20px !important;\n}\n.gradio-container-3-32-2 h4 {\n    font-weight: 400 !important;\n    font-size: 16px !important;\n}\n.gradio-container-3-32-2 h5 {\n    font-weight: 300 !important;\n    font-size: 14px !important;\n}\n.gradio-container-3-32-2 h6 {\n    font-weight: 200 !important;\n    font-size: 12px !important;\n}\n\n\n#usage_display p, #usage_display span {\n    margin: 0;\n    font-size: .85em;\n    color: var(--body-text-color-subdued);\n}\n.progress-bar {\n    background-color: var(--input-background-fill);;\n    margin: .5em 0 !important;\n    height: 20px;\n    border-radius: 10px;\n    overflow: hidden;\n}\n.progress {\n    background-color: var(--block-title-background-fill);\n    height: 100%;\n    border-radius: 10px;\n    text-align: right;\n    transition: width 0.5s ease-in-out;\n}\n.progress-text {\n    /* color: white; */\n    color: var(--color-accent) !important;\n    font-size: 1em !important;\n    font-weight: bold;\n    padding-right: 10px;\n    line-height: 20px;\n}\n\n.apSwitch {\n    top: 2px;\n    display: inline-block;\n    height: 24px;\n    position: relative;\n    width: 48px;\n    border-radius: 12px;\n}\n.apSwitch input {\n    display: none !important;\n}\n.apSlider {\n    background-color: var(--neutral-200);\n    bottom: 0;\n    cursor: pointer;\n    left: 0;\n    position: absolute;\n    right: 0;\n    top: 0;\n    transition: .4s;\n    font-size: 18px;\n    border-radius: 7px;\n}\n.apSlider::before {\n    bottom: -1.5px;\n    left: 1px;\n    position: absolute;\n    transition: .4s;\n    content: \"🌞\";\n}\nhr.append-display {\n    margin: 8px 0;\n    border: none;\n    height: 1px;\n    border-top-width: 0;\n    background-image: linear-gradient(to right, rgba(50,50,50, 0.1), rgba(150, 150, 150, 0.8), rgba(50,50,50, 0.1));\n}\n.source-a {\n    font-size: 0.8em;\n    max-width: 100%;\n    margin: 0;\n    display: flex;\n    flex-direction: row;\n    flex-wrap: wrap;\n    align-items: center;\n    /* background-color: #dddddd88; */\n    border-radius: 1.5rem;\n    padding: 0.2em;\n}\n.source-a a {\n    display: inline-block;\n    background-color: #aaaaaa50;\n    border-radius: 1rem;\n    padding: 0.5em;\n    text-align: center;\n    text-overflow: ellipsis;\n    overflow: hidden;\n    min-width: 20%;\n    white-space: nowrap;\n    margin: 0.2rem 0.1rem;\n    text-decoration: none !important;\n    flex: 1;\n    transition: flex 0.5s;\n}\n.source-a a:hover {\n    background-color: #aaaaaa20;\n    flex: 2;\n}\ninput:checked + .apSlider {\n    background-color: var(--primary-600);\n}\ninput:checked + .apSlider::before {\n    transform: translateX(23px);\n    content:\"🌚\";\n}\n\n/* Override Slider Styles (for webkit browsers like Safari and Chrome)\n * 好希望这份提案能早日实现 https://github.com/w3c/csswg-drafts/issues/4410\n * 进度滑块在各个平台还是太不统一了\n */\ninput[type=\"range\"] {\n    -webkit-appearance: none;\n    height: 4px;\n    background: var(--input-background-fill);\n    border-radius: 5px;\n    background-image: linear-gradient(var(--primary-500),var(--primary-500));\n    background-size: 0% 100%;\n    background-repeat: no-repeat;\n}\ninput[type=\"range\"]::-webkit-slider-thumb {\n    -webkit-appearance: none;\n    height: 20px;\n    width: 20px;\n    border-radius: 50%;\n    border: solid 0.5px #ddd;\n    background-color: white;\n    cursor: ew-resize;\n    box-shadow: var(--input-shadow);\n    transition: background-color .1s ease;\n}\ninput[type=\"range\"]::-webkit-slider-thumb:hover {\n    background: var(--neutral-50);\n}\ninput[type=range]::-webkit-slider-runnable-track {\n    -webkit-appearance: none;\n    box-shadow: none;\n    border: none;\n    background: transparent;\n}\n\n.submit_btn, #cancel_btn {\n    height: 42px 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var(--line-sm)*1rem - 2*var(--block-label-margin) );\n    }\n}\n/* 屏幕宽度小于500px的设备 */\n@media screen and (max-width: 499px) {\n    #main_chatbot {\n        height: calc(100vh - 140px);\n    }\n    #main_chatbot .wrap {\n        max-height: calc(100vh - 140px - var(--line-sm)*1rem - 2*var(--block-label-margin) );\n    }\n    [data-testid = \"bot\"] {\n        max-width: 95% !important;\n    }\n    #app_title h1{\n        letter-spacing: -1px; font-size: 22px;\n    }\n}\n#main_chatbot .wrap {\n    overflow-x: hidden\n}\n/* 对话气泡 */\n.message {\n    border-radius: var(--radius-xl) !important;\n    border: none;\n    padding: var(--spacing-xl) !important;\n    font-size: 15px !important;\n    line-height: var(--line-md) !important;\n    min-height: calc(var(--text-md)*var(--line-md) + 2*var(--spacing-xl));\n    min-width: calc(var(--text-md)*var(--line-md) + 2*var(--spacing-xl));\n}\n[data-testid = \"bot\"] {\n    max-width: 85%;\n    border-bottom-left-radius: 0 !important;\n}\n[data-testid 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var(--body-text-color-subdued);\n    margin-bottom: 10px;\n    margin-top: -10px;\n    clear: both;\n}\n.wrap>.history-message>:last-child::after {\n    content: \"仅供查看\";\n    display: block;\n    text-align: center;\n    color: var(--body-text-color-subdued);\n    font-size: 0.8em;\n}\n\n/* 表格 */\ntable {\n    margin: 1em 0;\n    border-collapse: collapse;\n    empty-cells: show;\n}\ntd,th {\n    border: 1.2px solid var(--border-color-primary) !important;\n    padding: 0.2em;\n}\nthead {\n    background-color: rgba(175,184,193,0.2);\n}\nthead th {\n    padding: .5em .2em;\n}\n/* 行内代码 */\n.message :not(pre) code {\n    display: inline;\n    white-space: break-spaces;\n    border-radius: 6px;\n    margin: 0 2px 0 2px;\n    padding: .2em .4em .1em .4em;\n    background-color: rgba(175,184,193,0.2);\n}\n/* 代码块 */\n.message pre code {\n    display: block;\n    overflow: auto;\n    white-space: pre;\n    background-color: hsla(0, 0%, 7%, 70%)!important;\n    border-radius: 10px;\n    padding: 1.2em 1em 0em .5em;\n    margin: 0.6em 2em 1em 0.2em;\n    color: #FFF;\n    box-shadow: 6px 6px 16px hsla(0, 0%, 0%, 0.2);\n}\n.dark .message pre code {\n    background-color: hsla(0, 0%, 20%, 300%)!important;\n}\n.message pre {\n    padding: 0 !important;\n}\n.message pre code div.highlight {\n    background-color: unset !important;\n}\n\nbutton.copy-button {\n    display: none;\n}\n\n/* 代码高亮样式 */\n.codehilite .hll { background-color: #6e7681 }\n.codehilite .c { color: #8b949e; font-style: italic } /* Comment */\n.codehilite .err { color: #f85149 } /* Error */\n.codehilite .esc { color: #c9d1d9 } /* Escape */\n.codehilite .g { color: #c9d1d9 } /* Generic */\n.codehilite .k { color: #ff7b72 } /* Keyword */\n.codehilite .l { color: #a5d6ff } /* Literal */\n.codehilite .n { color: #c9d1d9 } /* Name */\n.codehilite .o { color: #ff7b72; font-weight: bold } /* Operator */\n.codehilite .x { color: #c9d1d9 } /* Other */\n.codehilite .p { color: #c9d1d9 } /* Punctuation 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.nc { color: #FFCB6B } /* Name.Class */\n.dark .codehilite .no { color: #EEFFFF } /* Name.Constant */\n.dark .codehilite .nd { color: #82AAFF } /* Name.Decorator */\n.dark .codehilite .ni { color: #89DDFF } /* Name.Entity */\n.dark .codehilite .ne { color: #FFCB6B } /* Name.Exception */\n.dark .codehilite .nf { color: #82AAFF } /* Name.Function */\n.dark .codehilite .nl { color: #82AAFF } /* Name.Label */\n.dark .codehilite .nn { color: #FFCB6B } /* Name.Namespace */\n.dark .codehilite .nx { color: #EEFFFF } /* Name.Other */\n.dark .codehilite .py { color: #FFCB6B } /* Name.Property */\n.dark .codehilite .nt { color: #FF5370 } /* Name.Tag */\n.dark .codehilite .nv { color: #89DDFF } /* Name.Variable */\n.dark .codehilite .ow { color: #89DDFF; font-style: italic } /* Operator.Word */\n.dark .codehilite .pm { color: #89DDFF } /* Punctuation.Marker */\n.dark .codehilite .w { color: #EEFFFF } /* Text.Whitespace */\n.dark .codehilite .mb { color: #F78C6C } /* Literal.Number.Bin */\n.dark 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.codehilite .sr { color: #89DDFF } /* Literal.String.Regex */\n.dark .codehilite .s1 { color: #C3E88D } /* Literal.String.Single */\n.dark .codehilite .ss { color: #89DDFF } /* Literal.String.Symbol */\n.dark .codehilite .bp { color: #89DDFF } /* Name.Builtin.Pseudo */\n.dark .codehilite .fm { color: #82AAFF } /* Name.Function.Magic */\n.dark .codehilite .vc { color: #89DDFF } /* Name.Variable.Class */\n.dark .codehilite .vg { color: #89DDFF } /* Name.Variable.Global */\n.dark .codehilite .vi { color: #89DDFF } /* Name.Variable.Instance */\n.dark .codehilite .vm { color: #82AAFF } /* Name.Variable.Magic */\n.dark .codehilite .il { color: #F78C6C } /* Literal.Number.Integer.Long */\n"
  },
  {
    "path": "themes/green.py",
    "content": "import gradio as gr\nfrom toolbox import get_conf\nCODE_HIGHLIGHT, ADD_WAIFU, LAYOUT = get_conf('CODE_HIGHLIGHT', 'ADD_WAIFU', 'LAYOUT')\n\ndef adjust_theme():\n    try:\n        set_theme = gr.themes.Soft(\n            primary_hue=gr.themes.Color(\n                c50=\"#EBFAF2\",\n                c100=\"#CFF3E1\",\n                c200=\"#A8EAC8\",\n                c300=\"#77DEA9\",\n                c400=\"#3FD086\",\n                c500=\"#02C160\",\n                c600=\"#06AE56\",\n                c700=\"#05974E\",\n                c800=\"#057F45\",\n                c900=\"#04673D\",\n                c950=\"#2E5541\",\n                name=\"small_and_beautiful\",\n            ),\n            secondary_hue=gr.themes.Color(\n                c50=\"#576b95\",\n                c100=\"#576b95\",\n                c200=\"#576b95\",\n                c300=\"#576b95\",\n                c400=\"#576b95\",\n                c500=\"#576b95\",\n                c600=\"#576b95\",\n                c700=\"#576b95\",\n                c800=\"#576b95\",\n                c900=\"#576b95\",\n                c950=\"#576b95\",\n            ),\n            neutral_hue=gr.themes.Color(\n                name=\"gray\",\n                c50=\"#f6f7f8\",\n                # c100=\"#f3f4f6\",\n                c100=\"#F2F2F2\",\n                c200=\"#e5e7eb\",\n                c300=\"#d1d5db\",\n                c400=\"#B2B2B2\",\n                c500=\"#808080\",\n                c600=\"#636363\",\n                c700=\"#515151\",\n                c800=\"#393939\",\n                # c900=\"#272727\",\n                c900=\"#2B2B2B\",\n                c950=\"#171717\",\n            ),\n\n            radius_size=gr.themes.sizes.radius_sm,\n        ).set(\n            button_primary_background_fill=\"*primary_500\",\n            button_primary_background_fill_dark=\"*primary_600\",\n            button_primary_background_fill_hover=\"*primary_400\",\n            button_primary_border_color=\"*primary_500\",\n            button_primary_border_color_dark=\"*primary_600\",\n            button_primary_text_color=\"wihte\",\n            button_primary_text_color_dark=\"white\",\n            button_secondary_background_fill=\"*neutral_100\",\n            button_secondary_background_fill_hover=\"*neutral_50\",\n            button_secondary_background_fill_dark=\"*neutral_900\",\n            button_secondary_text_color=\"*neutral_800\",\n            button_secondary_text_color_dark=\"white\",\n            background_fill_primary=\"#F7F7F7\",\n            background_fill_primary_dark=\"#1F1F1F\",\n            block_title_text_color=\"*primary_500\",\n            block_title_background_fill_dark=\"*primary_900\",\n            block_label_background_fill_dark=\"*primary_900\",\n            input_background_fill=\"#F6F6F6\",\n            chatbot_code_background_color=\"*neutral_950\",\n            chatbot_code_background_color_dark=\"*neutral_950\",\n        )\n\n        js = ''\n        if LAYOUT==\"TOP-DOWN\":\n            js = \"\"\n        else:\n            with open('themes/common.js', 'r', encoding='utf8') as f:\n                js = f\"<script>{f.read()}</script>\"\n\n        # 添加一个萌萌的看板娘\n        if ADD_WAIFU:\n            js += \"\"\"\n                <script src=\"file=docs/waifu_plugin/jquery.min.js\"></script>\n                <script src=\"file=docs/waifu_plugin/jquery-ui.min.js\"></script>\n                <script src=\"file=docs/waifu_plugin/autoload.js\"></script>\n            \"\"\"\n        gradio_original_template_fn = gr.routes.templates.TemplateResponse\n        def gradio_new_template_fn(*args, **kwargs):\n            res = gradio_original_template_fn(*args, **kwargs)\n            res.body = res.body.replace(b'</html>', f'{js}</html>'.encode(\"utf8\"))\n            res.init_headers()\n            return res\n        gr.routes.templates.TemplateResponse = gradio_new_template_fn   # override gradio template\n    except:\n        set_theme = None\n        print('gradio版本较旧, 不能自定义字体和颜色')\n    return set_theme\n\n\nwith open(\"themes/green.css\", \"r\", encoding=\"utf-8\") as f:\n    advanced_css = f.read()\n"
  },
  {
    "path": "themes/theme.py",
    "content": "import gradio as gr\nfrom utils.toolbox import get_conf\nTHEME, = get_conf('THEME')\n\nif THEME == 'Chuanhu-Small-and-Beautiful':\n    from .green import adjust_theme, advanced_css\n    theme_declaration = \"<h2 align=\\\"center\\\"  class=\\\"small\\\">[Chuanhu-Small-and-Beautiful主题]</h2>\"\nelse:\n    from .default import adjust_theme, advanced_css\n    theme_declaration = \"\"\n\n\n"
  },
  {
    "path": "utils/AudioRecorder.py",
    "content": "import utils.audio as sr\nimport pyaudiowpatch as pyaudio\nfrom datetime import datetime\n\nRECORD_TIMEOUT = 3\nENERGY_THRESHOLD = 1000\nDYNAMIC_ENERGY_THRESHOLD = False\n\nclass BaseRecorder:\n    def __init__(self, source, source_name):\n        self.recorder = sr.Recognizer()\n        self.recorder.energy_threshold = ENERGY_THRESHOLD\n        self.recorder.dynamic_energy_threshold = DYNAMIC_ENERGY_THRESHOLD\n       \n        if source is None:\n            raise ValueError(\"audio source can't be None\")\n\n        self.source = source\n        self.source_name = source_name\n\n    def adjust_for_noise(self, device_name, msg):\n        print(f\"[INFO] Adjusting for ambient noise from {device_name}. \" + msg)\n        with self.source:\n            self.recorder.adjust_for_ambient_noise(self.source)\n        print(f\"[INFO] Completed ambient noise adjustment for {device_name}.\")\n\n    def record_into_queue(self, audio_queue):\n        def record_callback(_, audio:sr.AudioData) -> None:\n            data = audio.get_raw_data()\n            audio_queue.put((self.source_name, data, datetime.utcnow()))\n\n        self.recorder.listen_in_background(self.source, record_callback, phrase_time_limit=RECORD_TIMEOUT)\n\nclass DefaultMicRecorder(BaseRecorder):\n    def __init__(self):\n        super().__init__(source=sr.Microphone(sample_rate=16000), source_name=\"You\")\n        self.adjust_for_noise(\"Default Mic\", \"Please make some noise from the Default Mic...\")\n\nclass DefaultSpeakerRecorder(BaseRecorder):\n    def __init__(self):\n        with pyaudio.PyAudio() as p:\n            wasapi_info = p.get_host_api_info_by_type(pyaudio.paWASAPI)\n            default_speakers = p.get_device_info_by_index(wasapi_info[\"defaultOutputDevice\"])\n            \n            if not default_speakers[\"isLoopbackDevice\"]:\n                for loopback in p.get_loopback_device_info_generator():\n                    if default_speakers[\"name\"] in loopback[\"name\"]:\n                        default_speakers = loopback\n                        break\n                else:\n                    print(\"[ERROR] No loopback device found.\")\n        \n        source = sr.Microphone(speaker=True,\n                               device_index= default_speakers[\"index\"],\n                               sample_rate=int(default_speakers[\"defaultSampleRate\"]),\n                               chunk_size=pyaudio.get_sample_size(pyaudio.paInt16),\n                               channels=default_speakers[\"maxInputChannels\"])\n        super().__init__(source=source, source_name=\"Speaker\")\n        self.adjust_for_noise(\"Default Speaker\", \"Please make or play some noise from the Default Speaker...\")"
  },
  {
    "path": "utils/AudioTrans.py",
    "content": "\nimport whisper\nimport torch\nimport wave\nimport os\nimport threading\nimport tempfile\nimport utils.audio as sr\nimport io\nfrom datetime import timedelta\nimport pyaudiowpatch as pyaudio\nfrom heapq import merge\n\nPHRASE_TIMEOUT = 3.05\n\nMAX_PHRASES = 10\n\nclass AudioTranscriber:\n    def __init__(self, mic_source, speaker_source, model,two_ways=False):\n        self.transcript_data = {\"You\": [], \"Speaker\": []}\n        self.transcript_changed_event = threading.Event()\n        self.audio_model = model\n        self.two_ways=two_ways\n        self.audio_sources = {\n            \"You\": {\n                \"sample_rate\": mic_source.SAMPLE_RATE if mic_source else None,\n                \"sample_width\": mic_source.SAMPLE_WIDTH if mic_source else None,\n                \"channels\": mic_source.channels if mic_source else None,\n                \"last_sample\": bytes(),\n                \"last_spoken\": None,\n                \"new_phrase\": True,\n                \"process_data_func\": self.process_mic_data\n            },\n            \"Speaker\": {\n                \"sample_rate\": speaker_source.SAMPLE_RATE if speaker_source else None , \n                \"sample_width\": speaker_source.SAMPLE_WIDTH if speaker_source else None,\n                \"channels\": speaker_source.channels if speaker_source else None,\n                \"last_sample\": bytes(),\n                \"last_spoken\": None,\n                \"new_phrase\": True,\n                \"process_data_func\": self.process_speaker_data\n            }\n        }\n\n    def transcribe_audio_queue(self, audio_queue):\n        while True:\n            who_spoke, data, time_spoken = audio_queue.get()\n            self.update_last_sample_and_phrase_status(who_spoke, data, time_spoken)\n            source_info = self.audio_sources[who_spoke]\n            if not self.two_ways:\n                source_info = self.audio_sources['You']\n                \n            text = ''\n            try:\n                fd, path = tempfile.mkstemp(suffix=\".wav\", dir='voice_dir', prefix=\"temp\")\n                os.close(fd)\n                source_info[\"process_data_func\"](source_info[\"last_sample\"],path)\n                result = self.audio_model.transcribe(path)\n                text=result[\"text\"]\n                print('识别结果:', text )\n            except Exception as e:\n                print(e)\n            finally:\n                os.unlink(path)      \n            if text!=''and text.lower()!='you' and self.two_ways:\n                self.update_transcript(who_spoke, text, time_spoken)\n                self.transcript_changed_event.set()\n            elif text!='':\n                self.transcript_data['You']=text\n                self.transcript_changed_event.set()\n            else:\n                print('null message..')    \n    def update_last_sample_and_phrase_status(self, who_spoke, data, time_spoken):\n        \n        source_info = self.audio_sources[who_spoke]\n        if not self.two_ways:\n                source_info = self.audio_sources['You']\n                \n        if source_info[\"last_spoken\"] and time_spoken - source_info[\"last_spoken\"] > timedelta(seconds=PHRASE_TIMEOUT):\n            print('判断....')\n            source_info[\"last_sample\"] = bytes()\n            source_info[\"new_phrase\"] = True\n        else:\n            source_info[\"new_phrase\"] = False\n        #if self.two_ways:\n        source_info[\"last_sample\"] += data\n        source_info[\"last_spoken\"] = time_spoken \n\n    def process_mic_data(self, data, temp_file_name):\n        audio_data = sr.AudioData(data, self.audio_sources[\"You\"][\"sample_rate\"], self.audio_sources[\"You\"][\"sample_width\"])\n        wav_data = io.BytesIO(audio_data.get_wav_data())\n        with open(temp_file_name, 'w+b') as f:\n            f.write(wav_data.read())\n\n    def process_speaker_data(self, data, temp_file_name):\n        with wave.open(temp_file_name, 'wb') as wf:\n            wf.setnchannels(self.audio_sources[\"Speaker\"][\"channels\"])\n            p = pyaudio.PyAudio()\n            wf.setsampwidth(p.get_sample_size(pyaudio.paInt16))\n            wf.setframerate(self.audio_sources[\"Speaker\"][\"sample_rate\"])\n            wf.writeframes(data)\n\n    def update_transcript(self, who_spoke, text, time_spoken):\n        source_info = self.audio_sources[who_spoke]\n        transcript = self.transcript_data[who_spoke]\n\n        if source_info[\"new_phrase\"] or len(transcript) == 0:\n            if len(transcript) > MAX_PHRASES:\n                print(f\"限制超过>{MAX_PHRASES}\")\n                transcript.pop(-1)\n            transcript.insert(0, (f\"{who_spoke}: [{text}]\\n\\n\", time_spoken))\n        else:\n            transcript[0] = (f\"{who_spoke}: [{text}]\\n\\n\", time_spoken)\n\n    def get_transcript(self):\n        combined_transcript = list(merge(\n            self.transcript_data[\"You\"], self.transcript_data[\"Speaker\"], \n            key=lambda x: x[1], reverse=True))\n        combined_transcript = combined_transcript[:MAX_PHRASES]\n        return \"\".join([t[0] for t in combined_transcript])\n    \n    def clear_transcript_data(self):\n        self.transcript_data[\"You\"].clear()\n        self.transcript_data[\"Speaker\"].clear()\n\n        self.audio_sources[\"You\"][\"last_sample\"] = bytes()\n        self.audio_sources[\"Speaker\"][\"last_sample\"] = bytes()\n\n        self.audio_sources[\"You\"][\"new_phrase\"] = True\n        self.audio_sources[\"Speaker\"][\"new_phrase\"] = True"
  },
  {
    "path": "utils/__init__.py",
    "content": "import os \nimport logging\nimport logging.config\nfrom pathlib import Path\nimport urllib\nimport platform\nimport contextlib\nimport threading\n\nVERBOSE = str(os.getenv('Prompt-Can-Anything_VERBOSE', True)).lower() == 'true'  # global verbose mode\nAUTOINSTALL = str(os.getenv('Prompt-Can-Anything_AUTOINSTALL', True)).lower() == 'true'  # global auto-install \nFILE = Path(__file__).resolve()\nROOT = FILE.parents[2]  # \nIMAGENET_MEAN= [0.485, 0.456, 0.406]\nIMAGENET_STD = [0.229, 0.224, 0.225]\nCOCO_MEAN = [0.5, 0.5, 0.5]\nCOCO_STD = [0.5, 0.5, 0.5]\nIMG_FORMATS = 'bmp', 'dng', 'jpeg', 'jpg', 'mpo', 'png', 'tif', 'tiff', 'webp' ,'pfm'  # include image suffixes\nVID_FORMATS = 'asf', 'avi', 'gif', 'm4v', 'mkv', 'mov', 'mp4', 'mpeg', 'mpg', 'ts', 'wmv'  # include video suffixes\n\ndef set_logging(name=None, verbose=VERBOSE):\n    # Sets level and returns    \n    rank = int(os.getenv('RANK', -1))  # rank in world for Multi-GPU trainings\n    level = logging.INFO if verbose and rank in {-1, 0} else logging.WARNING\n    log = logging.getLogger(name)\n    log.setLevel(level)\n    handler = logging.StreamHandler()\n    handler.setFormatter(logging.Formatter(\"%(message)s\"))\n    handler.setLevel(level)\n    log.addHandler(handler)\n    \nLOGGING_NAME='Prompt-Can-Anything'\nset_logging(LOGGING_NAME)  # run before defining LOGGER\nLOGGER = logging.getLogger(LOGGING_NAME)  # define globally \n\ndef write_categories(cls_name, file_path):\n    # 打开TXT文件，以写入模式覆盖文件内容\n    with open(file_path, 'a') as f:\n        # 将列表元素写入TXT文件，每个元素都写入新的一行\n        f.write(cls_name+\"\\n\")\n        # 强制刷新缓存，确保文件已经被完全写入\n        f.flush()\n\n        \ndef threaded(func):\n    # Multi-threads a target function and returns thread. Usage: @threaded decorator\n    def wrapper(*args, **kwargs):\n        thread = threading.Thread(target=func, args=args, kwargs=kwargs, daemon=True)\n        thread.start()\n        return thread\n\n    return wrapper\n\nclass TryExcept(contextlib.ContextDecorator):\n    # reference:YOLOv8 TryExcept class. Usage: @TryExcept() decorator or 'with TryExcept():' context manager\n    def __init__(self, msg='', verbose=True):\n        self.msg = msg\n        self.verbose = verbose\n\n    def __enter__(self):\n        pass\n\n    def __exit__(self, exc_type, value, traceback):\n        if self.verbose and value:\n            print(emojis(f\"{self.msg}{': ' if self.msg else ''}{value}\"))\n        return True\n    \ndef check_suffix(file=None, suffix=('.pt',), msg=''):\n    # Check file(s) for acceptable suffix\n    if file and suffix:\n        if isinstance(suffix, str):\n            suffix = [suffix]\n        for f in file if isinstance(file, (list, tuple)) else [file]:\n            s = Path(f).suffix.lower()  # file suffix\n            if len(s):\n                assert s in suffix, f\"{msg}{f} acceptable suffix is {suffix}\"\n\ndef is_online() -> bool:\n    \"\"\"\n    Check internet connectivity by attempting to connect to a known online host.\n\n    Returns:\n        bool: True if connection is successful, False otherwise.\n    \"\"\"\n    import socket\n    with contextlib.suppress(Exception):\n        host = socket.gethostbyname('www.github.com')\n        socket.create_connection((host, 80), timeout=2)\n        return True\n    return False\n\ndef url2file(url):\n    # Convert URL to filename, i.e. https://url.com/file.txt?auth -> file.txt\n    url = str(Path(url)).replace(':/', '://')  # Pathlib turns :// -> :/\n    return Path(urllib.parse.unquote(url)).name.split('?')[0]  # '%2F' to '/', split https://url.com/file.txt?aut\n\ndef emojis(str=''):\n    # Return platform-dependent emoji-safe version of string\n    return str.encode().decode('ascii', 'ignore') if platform.system() == 'Windows' else str\ndef clean_url(url):\n     \n    \"\"\"Strip auth from URL, i.e. https://url.com/file.txt?auth -> https://url.com/file.txt.\"\"\"\n    url = str(Path(url)).replace(':/', '://')  # Pathlib turns :// -> :/\n    return urllib.parse.unquote(url).split('?')[0]  # '%2F' to '/', split https://url.com/file.txt?auth\n\ndef check_requirements(requirements=ROOT / 'requirements.txt', exclude=(), install=True, cmds=()):\n    # Check installed dependencies meet requirements (pass *.txt file or list of packages)\n    prefix = colorstr('red', 'bold', 'requirements:')\n    #check_python()  # check python version\n    if isinstance(requirements, (str, Path)):  # requirements.txt file\n        file = Path(requirements)\n        assert file.exists(), f\"{prefix} {file.resolve()} not found, check failed.\"\n        with file.open() as f:\n            requirements = [f'{x.name}{x.specifier}' for x in pkg.parse_requirements(f) if x.name not in exclude]\n    else:  # list or tuple of packages\n        requirements = [x for x in requirements if x not in exclude]\n\n    n = 0  # number of packages updates\n    for i, r in enumerate(requirements):\n        try:\n            pkg.require(r)\n        except Exception:  # DistributionNotFound or VersionConflict if requirements not met\n            s = f\"{prefix} {r} not found and is required \"\n            if install and AUTOINSTALL:  # check environment variable\n                LOGGER.info(f\"{s}, attempting auto-update...\")\n                try:\n                    #assert check_online(), f\"'pip install {r}' skipped (offline)\"\n                    LOGGER.info(check_output(f\"pip install '{r}' {cmds[i] if cmds else ''}\", shell=True).decode())\n                    n += 1\n                except Exception as e:\n                    LOGGER.warning(f'{prefix} ❌ {e}')\n            else:\n                LOGGER.info(f'{s}. Please install and rerun your command.')\n\n    if n:  # if packages updated\n        source = file.resolve() if 'file' in locals() else requirements\n        s = f\"{prefix} {n} package{'s' * (n > 1)} updated per {source}\\n\" \\\n            f\"{prefix} ⚠️ {colorstr('bold', 'Restart runtime or rerun command for updates to take effect')}\\n\"\n        LOGGER.info(s)"
  },
  {
    "path": "utils/audio.py",
    "content": "import aifc\nimport audioop\nimport io\nimport os\nimport platform\nimport stat\nimport subprocess\nimport sys\nimport wave\nimport io\nimport os\nimport tempfile\nimport sys\nimport subprocess\nimport wave\nimport aifc\nimport math\nimport audioop\nimport collections\nimport json\nimport base64\nimport threading\nimport hashlib\nimport hmac\nimport time\nimport uuid\n\nclass SetupError(Exception):\n    pass\n\n\nclass WaitTimeoutError(Exception):\n    pass\n\n\nclass RequestError(Exception):\n    pass\n\n\nclass UnknownValueError(Exception):\n    pass\n\n\nclass TranscriptionNotReady(Exception):\n    pass\n\n\nclass TranscriptionFailed(Exception):\n    pass\n\nclass PortableNamedTemporaryFile(object):\n    \"\"\"Limited replacement for ``tempfile.NamedTemporaryFile``, except unlike ``tempfile.NamedTemporaryFile``, the file can be opened again while it's currently open, even on Windows.\"\"\"\n    def __init__(self, mode=\"w+b\"):\n        self.mode = mode\n\n    def __enter__(self):\n        # create the temporary file and open it\n        file_descriptor, file_path = tempfile.mkstemp()\n        self._file = os.fdopen(file_descriptor, self.mode)\n\n        # the name property is a public field\n        self.name = file_path\n        return self\n\n    def __exit__(self, exc_type, exc_value, traceback):\n        self._file.close()\n        os.remove(self.name)\n\n    def write(self, *args, **kwargs):\n        return self._file.write(*args, **kwargs)\n\n    def writelines(self, *args, **kwargs):\n        return self._file.writelines(*args, **kwargs)\n\n    def flush(self, *args, **kwargs):\n        return self._file.flush(*args, **kwargs)\n    \nclass AudioSource(object):\n    def __init__(self):\n        raise NotImplementedError(\"this is an abstract class\")\n\n    def __enter__(self):\n        raise NotImplementedError(\"this is an abstract class\")\n\n    def __exit__(self, exc_type, exc_value, traceback):\n        raise NotImplementedError(\"this is an abstract class\")\n\n\nclass Microphone(AudioSource):\n    \"\"\"\n    Creates a new ``Microphone`` instance, which represents a physical microphone on the computer. Subclass of ``AudioSource``.\n\n    This will throw an ``AttributeError`` if you don't have PyAudio 0.2.11 or later installed.\n\n    If ``device_index`` is unspecified or ``None``, the default microphone is used as the audio source. Otherwise, ``device_index`` should be the index of the device to use for audio input.\n\n    A device index is an integer between 0 and ``pyaudio.get_device_count() - 1`` (assume we have used ``import pyaudio`` beforehand) inclusive. It represents an audio device such as a microphone or speaker. See the `PyAudio documentation <http://people.csail.mit.edu/hubert/pyaudio/docs/>`__ for more details.\n\n    The microphone audio is recorded in chunks of ``chunk_size`` samples, at a rate of ``sample_rate`` samples per second (Hertz). If not specified, the value of ``sample_rate`` is determined automatically from the system's microphone settings.\n\n    Higher ``sample_rate`` values result in better audio quality, but also more bandwidth (and therefore, slower recognition). Additionally, some CPUs, such as those in older Raspberry Pi models, can't keep up if this value is too high.\n\n    Higher ``chunk_size`` values help avoid triggering on rapidly changing ambient noise, but also makes detection less sensitive. This value, generally, should be left at its default.\n    \"\"\"\n    def __init__(self, device_index=None, sample_rate=None, chunk_size=1024, speaker=False, channels = 1):\n        assert device_index is None or isinstance(device_index, int), \"Device index must be None or an integer\"\n        assert sample_rate is None or (isinstance(sample_rate, int) and sample_rate > 0), \"Sample rate must be None or a positive integer\"\n        assert isinstance(chunk_size, int) and chunk_size > 0, \"Chunk size must be a positive integer\"\n\n        # set up PyAudio\n        self.speaker=speaker\n        self.pyaudio_module = self.get_pyaudio()\n        audio = self.pyaudio_module.PyAudio()\n        try:\n            count = audio.get_device_count()  # obtain device count\n            if device_index is not None:  # ensure device index is in range\n                assert 0 <= device_index < count, \"Device index out of range ({} devices available; device index should be between 0 and {} inclusive)\".format(count, count - 1)\n            if sample_rate is None:  # automatically set the sample rate to the hardware's default sample rate if not specified\n                device_info = audio.get_device_info_by_index(device_index) if device_index is not None else audio.get_default_input_device_info()\n                assert isinstance(device_info.get(\"defaultSampleRate\"), (float, int)) and device_info[\"defaultSampleRate\"] > 0, \"Invalid device info returned from PyAudio: {}\".format(device_info)\n                sample_rate = int(device_info[\"defaultSampleRate\"])\n        finally:\n            audio.terminate()\n\n        self.device_index = device_index\n        self.format = self.pyaudio_module.paInt16  # 16-bit int sampling\n        self.SAMPLE_WIDTH = self.pyaudio_module.get_sample_size(self.format)  # size of each sample\n        self.SAMPLE_RATE = sample_rate  # sampling rate in Hertz\n        self.CHUNK = chunk_size  # number of frames stored in each buffer\n        self.channels = channels\n\n        self.audio = None\n        self.stream = None\n\n    @staticmethod\n    def get_pyaudio():\n        \"\"\"\n        Imports the pyaudio module and checks its version. Throws exceptions if pyaudio can't be found or a wrong version is installed\n        \"\"\"\n        try:\n            import pyaudiowpatch as pyaudio\n        except ImportError:\n            raise AttributeError(\"Could not find PyAudio; check installation\")\n        from distutils.version import LooseVersion\n        if LooseVersion(pyaudio.__version__) < LooseVersion(\"0.2.11\"):\n            raise AttributeError(\"PyAudio 0.2.11 or later is required (found version {})\".format(pyaudio.__version__))\n        return pyaudio\n\n    @staticmethod\n    def list_microphone_names():\n        \"\"\"\n        Returns a list of the names of all available microphones. For microphones where the name can't be retrieved, the list entry contains ``None`` instead.\n\n        The index of each microphone's name in the returned list is the same as its device index when creating a ``Microphone`` instance - if you want to use the microphone at index 3 in the returned list, use ``Microphone(device_index=3)``.\n        \"\"\"\n        audio = Microphone.get_pyaudio().PyAudio()\n        try:\n            result = []\n            for i in range(audio.get_device_count()):\n                device_info = audio.get_device_info_by_index(i)\n                result.append(device_info.get(\"name\"))\n        finally:\n            audio.terminate()\n        return result\n\n    @staticmethod\n    def list_working_microphones():\n        \"\"\"\n        Returns a dictionary mapping device indices to microphone names, for microphones that are currently hearing sounds. When using this function, ensure that your microphone is unmuted and make some noise at it to ensure it will be detected as working.\n\n        Each key in the returned dictionary can be passed to the ``Microphone`` constructor to use that microphone. For example, if the return value is ``{3: \"HDA Intel PCH: ALC3232 Analog (hw:1,0)\"}``, you can do ``Microphone(device_index=3)`` to use that microphone.\n        \"\"\"\n        pyaudio_module = Microphone.get_pyaudio()\n        audio = pyaudio_module.PyAudio()\n        try:\n            result = {}\n            for device_index in range(audio.get_device_count()):\n                device_info = audio.get_device_info_by_index(device_index)\n                device_name = device_info.get(\"name\")\n                assert isinstance(device_info.get(\"defaultSampleRate\"), (float, int)) and device_info[\"defaultSampleRate\"] > 0, \"Invalid device info returned from PyAudio: {}\".format(device_info)\n                try:\n                    # read audio\n                    pyaudio_stream = audio.open(\n                        input_device_index=device_index, channels=1, format=pyaudio_module.paInt16,\n                        rate=int(device_info[\"defaultSampleRate\"]), input=True\n                    )\n                    try:\n                        buffer = pyaudio_stream.read(1024)\n                        if not pyaudio_stream.is_stopped(): pyaudio_stream.stop_stream()\n                    finally:\n                        pyaudio_stream.close()\n                except Exception:\n                    continue\n\n                # compute RMS of debiased audio\n                energy = -audioop.rms(buffer, 2)\n                energy_bytes = bytes([energy & 0xFF, (energy >> 8) & 0xFF])\n                debiased_energy = audioop.rms(audioop.add(buffer, energy_bytes * (len(buffer) // 2), 2), 2)\n\n                if debiased_energy > 30:  # probably actually audio\n                    result[device_index] = device_name\n        finally:\n            audio.terminate()\n        return result\n\n    def __enter__(self):\n        assert self.stream is None, \"This audio source is already inside a context manager\"\n        self.audio = self.pyaudio_module.PyAudio()\n\n        try:\n            if self.speaker:\n                p = self.audio\n                self.stream = Microphone.MicrophoneStream(\n                    p.open(\n                        input_device_index=self.device_index,\n                        channels=self.channels,\n                        format=self.format,\n                        rate=self.SAMPLE_RATE,\n                        frames_per_buffer=self.CHUNK,\n                        input=True\n                    )\n                )\n            else:\n                self.stream = Microphone.MicrophoneStream(\n                    self.audio.open(\n                        input_device_index=self.device_index, channels=1, format=self.format,\n                        rate=self.SAMPLE_RATE, frames_per_buffer=self.CHUNK, input=True,\n                    )\n                )\n        except Exception:\n            self.audio.terminate()\n        return self\n\n    def __exit__(self, exc_type, exc_value, traceback):\n        try:\n            self.stream.close()\n        finally:\n            self.stream = None\n            self.audio.terminate()\n\n    class MicrophoneStream(object):\n        def __init__(self, pyaudio_stream):\n            self.pyaudio_stream = pyaudio_stream\n\n        def read(self, size):\n            return self.pyaudio_stream.read(size, exception_on_overflow=False)\n\n        def close(self):\n            try:\n                # sometimes, if the stream isn't stopped, closing the stream throws an exception\n                if not self.pyaudio_stream.is_stopped():\n                    self.pyaudio_stream.stop_stream()\n            finally:\n                self.pyaudio_stream.close()\n\n\nclass AudioFile(AudioSource):\n    \"\"\"\n    Creates a new ``AudioFile`` instance given a WAV/AIFF/FLAC audio file ``filename_or_fileobject``. Subclass of ``AudioSource``.\n\n    If ``filename_or_fileobject`` is a string, then it is interpreted as a path to an audio file on the filesystem. Otherwise, ``filename_or_fileobject`` should be a file-like object such as ``io.BytesIO`` or similar.\n\n    Note that functions that read from the audio (such as ``recognizer_instance.record`` or ``recognizer_instance.listen``) will move ahead in the stream. For example, if you execute ``recognizer_instance.record(audiofile_instance, duration=10)`` twice, the first time it will return the first 10 seconds of audio, and the second time it will return the 10 seconds of audio right after that. This is always reset to the beginning when entering an ``AudioFile`` context.\n\n    WAV files must be in PCM/LPCM format; WAVE_FORMAT_EXTENSIBLE and compressed WAV are not supported and may result in undefined behaviour.\n\n    Both AIFF and AIFF-C (compressed AIFF) formats are supported.\n\n    FLAC files must be in native FLAC format; OGG-FLAC is not supported and may result in undefined behaviour.\n    \"\"\"\n\n    def __init__(self, filename_or_fileobject):\n        assert isinstance(filename_or_fileobject, (type(\"\"), type(u\"\"))) or hasattr(filename_or_fileobject, \"read\"), \"Given audio file must be a filename string or a file-like object\"\n        self.filename_or_fileobject = filename_or_fileobject\n        self.stream = None\n        self.DURATION = None\n\n        self.audio_reader = None\n        self.little_endian = False\n        self.SAMPLE_RATE = None\n        self.CHUNK = None\n        self.FRAME_COUNT = None\n\n    def __enter__(self):\n        assert self.stream is None, \"This audio source is already inside a context manager\"\n        try:\n            # attempt to read the file as WAV\n            self.audio_reader = wave.open(self.filename_or_fileobject, \"rb\")\n            self.little_endian = True  # RIFF WAV is a little-endian format (most ``audioop`` operations assume that the frames are stored in little-endian form)\n        except (wave.Error, EOFError):\n            try:\n                # attempt to read the file as AIFF\n                self.audio_reader = aifc.open(self.filename_or_fileobject, \"rb\")\n                self.little_endian = False  # AIFF is a big-endian format\n            except (aifc.Error, EOFError):\n                # attempt to read the file as FLAC\n                if hasattr(self.filename_or_fileobject, \"read\"):\n                    flac_data = self.filename_or_fileobject.read()\n                else:\n                    with open(self.filename_or_fileobject, \"rb\") as f: flac_data = f.read()\n\n                # run the FLAC converter with the FLAC data to get the AIFF data\n                flac_converter = get_flac_converter()\n                if os.name == \"nt\":  # on Windows, specify that the process is to be started without showing a console window\n                    startup_info = subprocess.STARTUPINFO()\n                    startup_info.dwFlags |= subprocess.STARTF_USESHOWWINDOW  # specify that the wShowWindow field of `startup_info` contains a value\n                    startup_info.wShowWindow = subprocess.SW_HIDE  # specify that the console window should be hidden\n                else:\n                    startup_info = None  # default startupinfo\n                process = subprocess.Popen([\n                    flac_converter,\n                    \"--stdout\", \"--totally-silent\",  # put the resulting AIFF file in stdout, and make sure it's not mixed with any program output\n                    \"--decode\", \"--force-aiff-format\",  # decode the FLAC file into an AIFF file\n                    \"-\",  # the input FLAC file contents will be given in stdin\n                ], stdin=subprocess.PIPE, stdout=subprocess.PIPE, startupinfo=startup_info)\n                aiff_data, _ = process.communicate(flac_data)\n                aiff_file = io.BytesIO(aiff_data)\n                try:\n                    self.audio_reader = aifc.open(aiff_file, \"rb\")\n                except (aifc.Error, EOFError):\n                    raise ValueError(\"Audio file could not be read as PCM WAV, AIFF/AIFF-C, or Native FLAC; check if file is corrupted or in another format\")\n                self.little_endian = False  # AIFF is a big-endian format\n        assert 1 <= self.audio_reader.getnchannels() <= 2, \"Audio must be mono or stereo\"\n        self.SAMPLE_WIDTH = self.audio_reader.getsampwidth()\n\n        # 24-bit audio needs some special handling for old Python versions (workaround for https://bugs.python.org/issue12866)\n        samples_24_bit_pretending_to_be_32_bit = False\n        if self.SAMPLE_WIDTH == 3:  # 24-bit audio\n            try: audioop.bias(b\"\", self.SAMPLE_WIDTH, 0)  # test whether this sample width is supported (for example, ``audioop`` in Python 3.3 and below don't support sample width 3, while Python 3.4+ do)\n            except audioop.error:  # this version of audioop doesn't support 24-bit audio (probably Python 3.3 or less)\n                samples_24_bit_pretending_to_be_32_bit = True  # while the ``AudioFile`` instance will outwardly appear to be 32-bit, it will actually internally be 24-bit\n                self.SAMPLE_WIDTH = 4  # the ``AudioFile`` instance should present itself as a 32-bit stream now, since we'll be converting into 32-bit on the fly when reading\n\n        self.SAMPLE_RATE = self.audio_reader.getframerate()\n        self.CHUNK = 4096\n        self.FRAME_COUNT = self.audio_reader.getnframes()\n        self.DURATION = self.FRAME_COUNT / float(self.SAMPLE_RATE)\n        self.stream = AudioFile.AudioFileStream(self.audio_reader, self.little_endian, samples_24_bit_pretending_to_be_32_bit)\n        return self\n\n    def __exit__(self, exc_type, exc_value, traceback):\n        if not hasattr(self.filename_or_fileobject, \"read\"):  # only close the file if it was opened by this class in the first place (if the file was originally given as a path)\n            self.audio_reader.close()\n        self.stream = None\n        self.DURATION = None\n\n    class AudioFileStream(object):\n        def __init__(self, audio_reader, little_endian, samples_24_bit_pretending_to_be_32_bit):\n            self.audio_reader = audio_reader  # an audio file object (e.g., a `wave.Wave_read` instance)\n            self.little_endian = little_endian  # whether the audio data is little-endian (when working with big-endian things, we'll have to convert it to little-endian before we process it)\n            self.samples_24_bit_pretending_to_be_32_bit = samples_24_bit_pretending_to_be_32_bit  # this is true if the audio is 24-bit audio, but 24-bit audio isn't supported, so we have to pretend that this is 32-bit audio and convert it on the fly\n\n        def read(self, size=-1):\n            buffer = self.audio_reader.readframes(self.audio_reader.getnframes() if size == -1 else size)\n            if not isinstance(buffer, bytes): buffer = b\"\"  # workaround for https://bugs.python.org/issue24608\n\n            sample_width = self.audio_reader.getsampwidth()\n            if not self.little_endian:  # big endian format, convert to little endian on the fly\n                if hasattr(audioop, \"byteswap\"):  # ``audioop.byteswap`` was only added in Python 3.4 (incidentally, that also means that we don't need to worry about 24-bit audio being unsupported, since Python 3.4+ always has that functionality)\n                    buffer = audioop.byteswap(buffer, sample_width)\n                else:  # manually reverse the bytes of each sample, which is slower but works well enough as a fallback\n                    buffer = buffer[sample_width - 1::-1] + b\"\".join(buffer[i + sample_width:i:-1] for i in range(sample_width - 1, len(buffer), sample_width))\n\n            # workaround for https://bugs.python.org/issue12866\n            if self.samples_24_bit_pretending_to_be_32_bit:  # we need to convert samples from 24-bit to 32-bit before we can process them with ``audioop`` functions\n                buffer = b\"\".join(b\"\\x00\" + buffer[i:i + sample_width] for i in range(0, len(buffer), sample_width))  # since we're in little endian, we prepend a zero byte to each 24-bit sample to get a 32-bit sample\n                sample_width = 4  # make sure we thread the buffer as 32-bit audio now, after converting it from 24-bit audio\n            if self.audio_reader.getnchannels() != 1:  # stereo audio\n                buffer = audioop.tomono(buffer, sample_width, 1, 1)  # convert stereo audio data to mono\n            return buffer\n\n\nclass Recognizer(AudioSource):\n    def __init__(self):\n        \"\"\"\n        Creates a new ``Recognizer`` instance, which represents a collection of speech recognition functionality.\n        \"\"\"\n        self.energy_threshold = 300  # minimum audio energy to consider for recording\n        self.dynamic_energy_threshold = True\n        self.dynamic_energy_adjustment_damping = 0.15\n        self.dynamic_energy_ratio = 1.5\n        self.pause_threshold = 0.8  # seconds of non-speaking audio before a phrase is considered complete\n        self.operation_timeout = None  # seconds after an internal operation (e.g., an API request) starts before it times out, or ``None`` for no timeout\n\n        self.phrase_threshold = 0.3  # minimum seconds of speaking audio before we consider the speaking audio a phrase - values below this are ignored (for filtering out clicks and pops)\n        self.non_speaking_duration = 0.5  # seconds of non-speaking audio to keep on both sides of the recording\n\n    def record(self, source, duration=None, offset=None):\n        \"\"\"\n        Records up to ``duration`` seconds of audio from ``source`` (an ``AudioSource`` instance) starting at ``offset`` (or at the beginning if not specified) into an ``AudioData`` instance, which it returns.\n\n        If ``duration`` is not specified, then it will record until there is no more audio input.\n        \"\"\"\n        assert isinstance(source, AudioSource), \"Source must be an audio source\"\n        assert source.stream is not None, \"Audio source must be entered before recording, see documentation for ``AudioSource``; are you using ``source`` outside of a ``with`` statement?\"\n\n        frames = io.BytesIO()\n        seconds_per_buffer = (source.CHUNK + 0.0) / source.SAMPLE_RATE\n        elapsed_time = 0\n        offset_time = 0\n        offset_reached = False\n        while True:  # loop for the total number of chunks needed\n            if offset and not offset_reached:\n                offset_time += seconds_per_buffer\n                if offset_time > offset:\n                    offset_reached = True\n\n            buffer = source.stream.read(source.CHUNK)\n            if len(buffer) == 0: break\n\n            if offset_reached or not offset:\n                elapsed_time += seconds_per_buffer\n                if duration and elapsed_time > duration: break\n\n                frames.write(buffer)\n\n        frame_data = frames.getvalue()\n        frames.close()\n        return AudioData(frame_data, source.SAMPLE_RATE, source.SAMPLE_WIDTH)\n\n    def adjust_for_ambient_noise(self, source, duration=1):\n        \"\"\"\n        Adjusts the energy threshold dynamically using audio from ``source`` (an ``AudioSource`` instance) to account for ambient noise.\n\n        Intended to calibrate the energy threshold with the ambient energy level. Should be used on periods of audio without speech - will stop early if any speech is detected.\n\n        The ``duration`` parameter is the maximum number of seconds that it will dynamically adjust the threshold for before returning. This value should be at least 0.5 in order to get a representative sample of the ambient noise.\n        \"\"\"\n        assert isinstance(source, AudioSource), \"Source must be an audio source\"\n        assert source.stream is not None, \"Audio source must be entered before adjusting, see documentation for ``AudioSource``; are you using ``source`` outside of a ``with`` statement?\"\n        assert self.pause_threshold >= self.non_speaking_duration >= 0\n\n        seconds_per_buffer = (source.CHUNK + 0.0) / source.SAMPLE_RATE\n        elapsed_time = 0\n\n        # adjust energy threshold until a phrase starts\n        while True:\n            elapsed_time += seconds_per_buffer\n            if elapsed_time > duration: break\n            buffer = source.stream.read(source.CHUNK)\n            energy = audioop.rms(buffer, source.SAMPLE_WIDTH)  # energy of the audio signal\n\n            # dynamically adjust the energy threshold using asymmetric weighted average\n            damping = self.dynamic_energy_adjustment_damping ** seconds_per_buffer  # account for different chunk sizes and rates\n            target_energy = energy * self.dynamic_energy_ratio\n            self.energy_threshold = self.energy_threshold * damping + target_energy * (1 - damping)\n\n    def snowboy_wait_for_hot_word(self, snowboy_location, snowboy_hot_word_files, source, timeout=None):\n        # load snowboy library (NOT THREAD SAFE)\n        sys.path.append(snowboy_location)\n        import snowboydetect\n        sys.path.pop()\n\n        detector = snowboydetect.SnowboyDetect(\n            resource_filename=os.path.join(snowboy_location, \"resources\", \"common.res\").encode(),\n            model_str=\",\".join(snowboy_hot_word_files).encode()\n        )\n        detector.SetAudioGain(1.0)\n        detector.SetSensitivity(\",\".join([\"0.4\"] * len(snowboy_hot_word_files)).encode())\n        snowboy_sample_rate = detector.SampleRate()\n\n        elapsed_time = 0\n        seconds_per_buffer = float(source.CHUNK) / source.SAMPLE_RATE\n        resampling_state = None\n\n        # buffers capable of holding 5 seconds of original audio\n        five_seconds_buffer_count = int(math.ceil(5 / seconds_per_buffer))\n        # buffers capable of holding 0.5 seconds of resampled audio\n        half_second_buffer_count = int(math.ceil(0.5 / seconds_per_buffer))\n        frames = collections.deque(maxlen=five_seconds_buffer_count)\n        resampled_frames = collections.deque(maxlen=half_second_buffer_count)\n        # snowboy check interval\n        check_interval = 0.05\n        last_check = time.time()\n        while True:\n            elapsed_time += seconds_per_buffer\n            if timeout and elapsed_time > timeout:\n                raise WaitTimeoutError(\"listening timed out while waiting for hotword to be said\")\n\n            buffer = source.stream.read(source.CHUNK)\n            if len(buffer) == 0: break  # reached end of the stream\n            frames.append(buffer)\n\n            # resample audio to the required sample rate\n            resampled_buffer, resampling_state = audioop.ratecv(buffer, source.SAMPLE_WIDTH, 1, source.SAMPLE_RATE, snowboy_sample_rate, resampling_state)\n            resampled_frames.append(resampled_buffer)\n            if time.time() - last_check > check_interval:\n                # run Snowboy on the resampled audio\n                snowboy_result = detector.RunDetection(b\"\".join(resampled_frames))\n                assert snowboy_result != -1, \"Error initializing streams or reading audio data\"\n                if snowboy_result > 0: break  # wake word found\n                resampled_frames.clear()\n                last_check = time.time()\n\n        return b\"\".join(frames), elapsed_time\n\n    def listen(self, source, timeout=None, phrase_time_limit=None, snowboy_configuration=None):\n        \"\"\"\n        Records a single phrase from ``source`` (an ``AudioSource`` instance) into an ``AudioData`` instance, which it returns.\n\n        This is done by waiting until the audio has an energy above ``recognizer_instance.energy_threshold`` (the user has started speaking), and then recording until it encounters ``recognizer_instance.pause_threshold`` seconds of non-speaking or there is no more audio input. The ending silence is not included.\n\n        The ``timeout`` parameter is the maximum number of seconds that this will wait for a phrase to start before giving up and throwing an ``speech_recognition.WaitTimeoutError`` exception. If ``timeout`` is ``None``, there will be no wait timeout.\n\n        The ``phrase_time_limit`` parameter is the maximum number of seconds that this will allow a phrase to continue before stopping and returning the part of the phrase processed before the time limit was reached. The resulting audio will be the phrase cut off at the time limit. If ``phrase_timeout`` is ``None``, there will be no phrase time limit.\n\n        The ``snowboy_configuration`` parameter allows integration with `Snowboy <https://snowboy.kitt.ai/>`__, an offline, high-accuracy, power-efficient hotword recognition engine. When used, this function will pause until Snowboy detects a hotword, after which it will unpause. This parameter should either be ``None`` to turn off Snowboy support, or a tuple of the form ``(SNOWBOY_LOCATION, LIST_OF_HOT_WORD_FILES)``, where ``SNOWBOY_LOCATION`` is the path to the Snowboy root directory, and ``LIST_OF_HOT_WORD_FILES`` is a list of paths to Snowboy hotword configuration files (`*.pmdl` or `*.umdl` format).\n\n        This operation will always complete within ``timeout + phrase_timeout`` seconds if both are numbers, either by returning the audio data, or by raising a ``speech_recognition.WaitTimeoutError`` exception.\n        \"\"\"\n        assert isinstance(source, AudioSource), \"Source must be an audio source\"\n        assert source.stream is not None, \"Audio source must be entered before listening, see documentation for ``AudioSource``; are you using ``source`` outside of a ``with`` statement?\"\n        assert self.pause_threshold >= self.non_speaking_duration >= 0\n        if snowboy_configuration is not None:\n            assert os.path.isfile(os.path.join(snowboy_configuration[0], \"snowboydetect.py\")), \"``snowboy_configuration[0]`` must be a Snowboy root directory containing ``snowboydetect.py``\"\n            for hot_word_file in snowboy_configuration[1]:\n                assert os.path.isfile(hot_word_file), \"``snowboy_configuration[1]`` must be a list of Snowboy hot word configuration files\"\n\n        seconds_per_buffer = float(source.CHUNK) / source.SAMPLE_RATE\n        pause_buffer_count = int(math.ceil(self.pause_threshold / seconds_per_buffer))  # number of buffers of non-speaking audio during a phrase, before the phrase should be considered complete\n        phrase_buffer_count = int(math.ceil(self.phrase_threshold / seconds_per_buffer))  # minimum number of buffers of speaking audio before we consider the speaking audio a phrase\n        non_speaking_buffer_count = int(math.ceil(self.non_speaking_duration / seconds_per_buffer))  # maximum number of buffers of non-speaking audio to retain before and after a phrase\n\n        # read audio input for phrases until there is a phrase that is long enough\n        elapsed_time = 0  # number of seconds of audio read\n        buffer = b\"\"  # an empty buffer means that the stream has ended and there is no data left to read\n        while True:\n            frames = collections.deque()\n\n            if snowboy_configuration is None:\n                # store audio input until the phrase starts\n                while True:\n                    # handle waiting too long for phrase by raising an exception\n                    elapsed_time += seconds_per_buffer\n                    if timeout and elapsed_time > timeout:\n                        raise WaitTimeoutError(\"listening timed out while waiting for phrase to start\")\n\n                    buffer = source.stream.read(source.CHUNK)\n                    if len(buffer) == 0: break  # reached end of the stream\n                    frames.append(buffer)\n                    if len(frames) > non_speaking_buffer_count:  # ensure we only keep the needed amount of non-speaking buffers\n                        frames.popleft()\n\n                    # detect whether speaking has started on audio input\n                    energy = audioop.rms(buffer, source.SAMPLE_WIDTH)  # energy of the audio signal\n                    if energy > self.energy_threshold: break\n\n                    # dynamically adjust the energy threshold using asymmetric weighted average\n                    if self.dynamic_energy_threshold:\n                        damping = self.dynamic_energy_adjustment_damping ** seconds_per_buffer  # account for different chunk sizes and rates\n                        target_energy = energy * self.dynamic_energy_ratio\n                        self.energy_threshold = self.energy_threshold * damping + target_energy * (1 - damping)\n            else:\n                # read audio input until the hotword is said\n                snowboy_location, snowboy_hot_word_files = snowboy_configuration\n                buffer, delta_time = self.snowboy_wait_for_hot_word(snowboy_location, snowboy_hot_word_files, source, timeout)\n                elapsed_time += delta_time\n                if len(buffer) == 0: break  # reached end of the stream\n                frames.append(buffer)\n\n            # read audio input until the phrase ends\n            pause_count, phrase_count = 0, 0\n            phrase_start_time = elapsed_time\n            while True:\n                # handle phrase being too long by cutting off the audio\n                elapsed_time += seconds_per_buffer\n                if phrase_time_limit and elapsed_time - phrase_start_time > phrase_time_limit:\n                    break\n\n                buffer = source.stream.read(source.CHUNK)\n                if len(buffer) == 0: break  # reached end of the stream\n                frames.append(buffer)\n                phrase_count += 1\n\n                # check if speaking has stopped for longer than the pause threshold on the audio input\n                energy = audioop.rms(buffer, source.SAMPLE_WIDTH)  # unit energy of the audio signal within the buffer\n                if energy > self.energy_threshold:\n                    pause_count = 0\n                else:\n                    pause_count += 1\n                if pause_count > pause_buffer_count:  # end of the phrase\n                    break\n\n            # check how long the detected phrase is, and retry listening if the phrase is too short\n            phrase_count -= pause_count  # exclude the buffers for the pause before the phrase\n            if phrase_count >= phrase_buffer_count or len(buffer) == 0: break  # phrase is long enough or we've reached the end of the stream, so stop listening\n\n        # obtain frame data\n        for i in range(pause_count - non_speaking_buffer_count): frames.pop()  # remove extra non-speaking frames at the end\n        frame_data = b\"\".join(frames)\n\n        return AudioData(frame_data, source.SAMPLE_RATE, source.SAMPLE_WIDTH)\n\n    def listen_in_background(self, source, callback, phrase_time_limit=None):\n        \"\"\"\n        Spawns a thread to repeatedly record phrases from ``source`` (an ``AudioSource`` instance) into an ``AudioData`` instance and call ``callback`` with that ``AudioData`` instance as soon as each phrase are detected.\n\n        Returns a function object that, when called, requests that the background listener thread stop. The background thread is a daemon and will not stop the program from exiting if there are no other non-daemon threads. The function accepts one parameter, ``wait_for_stop``: if truthy, the function will wait for the background listener to stop before returning, otherwise it will return immediately and the background listener thread might still be running for a second or two afterwards. Additionally, if you are using a truthy value for ``wait_for_stop``, you must call the function from the same thread you originally called ``listen_in_background`` from.\n\n        Phrase recognition uses the exact same mechanism as ``recognizer_instance.listen(source)``. The ``phrase_time_limit`` parameter works in the same way as the ``phrase_time_limit`` parameter for ``recognizer_instance.listen(source)``, as well.\n\n        The ``callback`` parameter is a function that should accept two parameters - the ``recognizer_instance``, and an ``AudioData`` instance representing the captured audio. Note that ``callback`` function will be called from a non-main thread.\n        \"\"\"\n        assert isinstance(source, AudioSource), \"Source must be an audio source\"\n        running = [True]\n\n        def threaded_listen():\n            with source as s:\n                while running[0]:\n                    try:  # listen for 1 second, then check again if the stop function has been called\n                        audio = self.listen(s, 1, phrase_time_limit)\n                    except WaitTimeoutError:  # listening timed out, just try again\n                        pass\n                    else:\n                        if running[0]: callback(self, audio)\n\n        def stopper(wait_for_stop=True):\n            running[0] = False\n            if wait_for_stop:\n                listener_thread.join()  # block until the background thread is done, which can take around 1 second\n\n        listener_thread = threading.Thread(target=threaded_listen)\n        listener_thread.daemon = True\n        listener_thread.start()\n        return stopper\n\n    def recognize_sphinx(self, audio_data, language=\"en-US\", keyword_entries=None, grammar=None, show_all=False):\n        \"\"\"\n        Performs speech recognition on ``audio_data`` (an ``AudioData`` instance), using CMU Sphinx.\n\n        The recognition language is determined by ``language``, an RFC5646 language tag like ``\"en-US\"`` or ``\"en-GB\"``, defaulting to US English. Out of the box, only ``en-US`` is supported. See `Notes on using `PocketSphinx <https://github.com/Uberi/speech_recognition/blob/master/reference/pocketsphinx.rst>`__ for information about installing other languages. This document is also included under ``reference/pocketsphinx.rst``. The ``language`` parameter can also be a tuple of filesystem paths, of the form ``(acoustic_parameters_directory, language_model_file, phoneme_dictionary_file)`` - this allows you to load arbitrary Sphinx models.\n\n        If specified, the keywords to search for are determined by ``keyword_entries``, an iterable of tuples of the form ``(keyword, sensitivity)``, where ``keyword`` is a phrase, and ``sensitivity`` is how sensitive to this phrase the recognizer should be, on a scale of 0 (very insensitive, more false negatives) to 1 (very sensitive, more false positives) inclusive. If not specified or ``None``, no keywords are used and Sphinx will simply transcribe whatever words it recognizes. Specifying ``keyword_entries`` is more accurate than just looking for those same keywords in non-keyword-based transcriptions, because Sphinx knows specifically what sounds to look for.\n\n        Sphinx can also handle FSG or JSGF grammars. The parameter ``grammar`` expects a path to the grammar file. Note that if a JSGF grammar is passed, an FSG grammar will be created at the same location to speed up execution in the next run. If ``keyword_entries`` are passed, content of ``grammar`` will be ignored.\n\n        Returns the most likely transcription if ``show_all`` is false (the default). Otherwise, returns the Sphinx ``pocketsphinx.pocketsphinx.Decoder`` object resulting from the recognition.\n\n        Raises a ``speech_recognition.UnknownValueError`` exception if the speech is unintelligible. Raises a ``speech_recognition.RequestError`` exception if there are any issues with the Sphinx installation.\n        \"\"\"\n        assert isinstance(audio_data, AudioData), \"``audio_data`` must be audio data\"\n        assert isinstance(language, str) or (isinstance(language, tuple) and len(language) == 3), \"``language`` must be a string or 3-tuple of Sphinx data file paths of the form ``(acoustic_parameters, language_model, phoneme_dictionary)``\"\n        assert keyword_entries is None or all(isinstance(keyword, (type(\"\"), type(u\"\"))) and 0 <= sensitivity <= 1 for keyword, sensitivity in keyword_entries), \"``keyword_entries`` must be ``None`` or a list of pairs of strings and numbers between 0 and 1\"\n\n        # import the PocketSphinx speech recognition module\n        try:\n            from pocketsphinx import pocketsphinx, Jsgf, FsgModel\n\n        except ImportError:\n            raise RequestError(\"missing PocketSphinx module: ensure that PocketSphinx is set up correctly.\")\n        except ValueError:\n            raise RequestError(\"bad PocketSphinx installation; try reinstalling PocketSphinx version 0.0.9 or better.\")\n        if not hasattr(pocketsphinx, \"Decoder\") or not hasattr(pocketsphinx.Decoder, \"default_config\"):\n            raise RequestError(\"outdated PocketSphinx installation; ensure you have PocketSphinx version 0.0.9 or better.\")\n\n        if isinstance(language, str):  # directory containing language data\n            language_directory = os.path.join(os.path.dirname(os.path.realpath(__file__)), \"pocketsphinx-data\", language)\n            if not os.path.isdir(language_directory):\n                raise RequestError(\"missing PocketSphinx language data directory: \\\"{}\\\"\".format(language_directory))\n            acoustic_parameters_directory = os.path.join(language_directory, \"acoustic-model\")\n            language_model_file = os.path.join(language_directory, \"language-model.lm.bin\")\n            phoneme_dictionary_file = os.path.join(language_directory, \"pronounciation-dictionary.dict\")\n        else:  # 3-tuple of Sphinx data file paths\n            acoustic_parameters_directory, language_model_file, phoneme_dictionary_file = language\n        if not os.path.isdir(acoustic_parameters_directory):\n            raise RequestError(\"missing PocketSphinx language model parameters directory: \\\"{}\\\"\".format(acoustic_parameters_directory))\n        if not os.path.isfile(language_model_file):\n            raise RequestError(\"missing PocketSphinx language model file: \\\"{}\\\"\".format(language_model_file))\n        if not os.path.isfile(phoneme_dictionary_file):\n            raise RequestError(\"missing PocketSphinx phoneme dictionary file: \\\"{}\\\"\".format(phoneme_dictionary_file))\n\n        # create decoder object\n        config = pocketsphinx.Decoder.default_config()\n        config.set_string(\"-hmm\", acoustic_parameters_directory)  # set the path of the hidden Markov model (HMM) parameter files\n        config.set_string(\"-lm\", language_model_file)\n        config.set_string(\"-dict\", phoneme_dictionary_file)\n        config.set_string(\"-logfn\", os.devnull)  # disable logging (logging causes unwanted output in terminal)\n        decoder = pocketsphinx.Decoder(config)\n\n        # obtain audio data\n        raw_data = audio_data.get_raw_data(convert_rate=16000, convert_width=2)  # the included language models require audio to be 16-bit mono 16 kHz in little-endian format\n\n        # obtain recognition results\n        if keyword_entries is not None:  # explicitly specified set of keywords\n            with PortableNamedTemporaryFile(\"w\") as f:\n                # generate a keywords file - Sphinx documentation recommendeds sensitivities between 1e-50 and 1e-5\n                f.writelines(\"{} /1e{}/\\n\".format(keyword, 100 * sensitivity - 110) for keyword, sensitivity in keyword_entries)\n                f.flush()\n\n                # perform the speech recognition with the keywords file (this is inside the context manager so the file isn;t deleted until we're done)\n                decoder.set_kws(\"keywords\", f.name)\n                decoder.set_search(\"keywords\")\n        elif grammar is not None:  # a path to a FSG or JSGF grammar\n            if not os.path.exists(grammar):\n                raise ValueError(\"Grammar '{0}' does not exist.\".format(grammar))\n            grammar_path = os.path.abspath(os.path.dirname(grammar))\n            grammar_name = os.path.splitext(os.path.basename(grammar))[0]\n            fsg_path = \"{0}/{1}.fsg\".format(grammar_path, grammar_name)\n            if not os.path.exists(fsg_path):  # create FSG grammar if not available\n                jsgf = Jsgf(grammar)\n                rule = jsgf.get_rule(\"{0}.{0}\".format(grammar_name))\n                fsg = jsgf.build_fsg(rule, decoder.get_logmath(), 7.5)\n                fsg.writefile(fsg_path)\n            else:\n                fsg = FsgModel(fsg_path, decoder.get_logmath(), 7.5)\n            decoder.set_fsg(grammar_name, fsg)\n            decoder.set_search(grammar_name)\n\n        decoder.start_utt()  # begin utterance processing\n        decoder.process_raw(raw_data, False, True)  # process audio data with recognition enabled (no_search = False), as a full utterance (full_utt = True)\n        decoder.end_utt()  # stop utterance processing\n\n        if show_all: return decoder\n\n        # return results\n        hypothesis = decoder.hyp()\n        if hypothesis is not None: return hypothesis.hypstr\n        raise UnknownValueError()  # \nclass AudioData(object):\n    \"\"\"\n    Creates a new ``AudioData`` instance, which represents mono audio data.\n\n    The raw audio data is specified by ``frame_data``, which is a sequence of bytes representing audio samples. This is the frame data structure used by the PCM WAV format.\n\n    The width of each sample, in bytes, is specified by ``sample_width``. Each group of ``sample_width`` bytes represents a single audio sample.\n\n    The audio data is assumed to have a sample rate of ``sample_rate`` samples per second (Hertz).\n\n    Usually, instances of this class are obtained from ``recognizer_instance.record`` or ``recognizer_instance.listen``, or in the callback for ``recognizer_instance.listen_in_background``, rather than instantiating them directly.\n    \"\"\"\n\n    def __init__(self, frame_data, sample_rate, sample_width):\n        assert sample_rate > 0, \"Sample rate must be a positive integer\"\n        assert (\n            sample_width % 1 == 0 and 1 <= sample_width <= 4\n        ), \"Sample width must be between 1 and 4 inclusive\"\n        self.frame_data = frame_data\n        self.sample_rate = sample_rate\n        self.sample_width = int(sample_width)\n\n    def get_segment(self, start_ms=None, end_ms=None):\n        \"\"\"\n        Returns a new ``AudioData`` instance, trimmed to a given time interval. In other words, an ``AudioData`` instance with the same audio data except starting at ``start_ms`` milliseconds in and ending ``end_ms`` milliseconds in.\n\n        If not specified, ``start_ms`` defaults to the beginning of the audio, and ``end_ms`` defaults to the end.\n        \"\"\"\n        assert (\n            start_ms is None or start_ms >= 0\n        ), \"``start_ms`` must be a non-negative number\"\n        assert end_ms is None or end_ms >= (\n            0 if start_ms is None else start_ms\n        ), \"``end_ms`` must be a non-negative number greater or equal to ``start_ms``\"\n        if start_ms is None:\n            start_byte = 0\n        else:\n            start_byte = int(\n                (start_ms * self.sample_rate * self.sample_width) // 1000\n            )\n        if end_ms is None:\n            end_byte = len(self.frame_data)\n        else:\n            end_byte = int(\n                (end_ms * self.sample_rate * self.sample_width) // 1000\n            )\n        return AudioData(\n            self.frame_data[start_byte:end_byte],\n            self.sample_rate,\n            self.sample_width,\n        )\n\n    def get_raw_data(self, convert_rate=None, convert_width=None):\n        \"\"\"\n        Returns a byte string representing the raw frame data for the audio represented by the ``AudioData`` instance.\n\n        If ``convert_rate`` is specified and the audio sample rate is not ``convert_rate`` Hz, the resulting audio is resampled to match.\n\n        If ``convert_width`` is specified and the audio samples are not ``convert_width`` bytes each, the resulting audio is converted to match.\n\n        Writing these bytes directly to a file results in a valid `RAW/PCM audio file <https://en.wikipedia.org/wiki/Raw_audio_format>`__.\n        \"\"\"\n        assert (\n            convert_rate is None or convert_rate > 0\n        ), \"Sample rate to convert to must be a positive integer\"\n        assert convert_width is None or (\n            convert_width % 1 == 0 and 1 <= convert_width <= 4\n        ), \"Sample width to convert to must be between 1 and 4 inclusive\"\n\n        raw_data = self.frame_data\n\n        # make sure unsigned 8-bit audio (which uses unsigned samples) is handled like higher sample width audio (which uses signed samples)\n        if self.sample_width == 1:\n            raw_data = audioop.bias(\n                raw_data, 1, -128\n            )  # subtract 128 from every sample to make them act like signed samples\n\n        # resample audio at the desired rate if specified\n        if convert_rate is not None and self.sample_rate != convert_rate:\n            raw_data, _ = audioop.ratecv(\n                raw_data,\n                self.sample_width,\n                1,\n                self.sample_rate,\n                convert_rate,\n                None,\n            )\n\n        # convert samples to desired sample width if specified\n        if convert_width is not None and self.sample_width != convert_width:\n            if (\n                convert_width == 3\n            ):  # we're converting the audio into 24-bit (workaround for https://bugs.python.org/issue12866)\n                raw_data = audioop.lin2lin(\n                    raw_data, self.sample_width, 4\n                )  # convert audio into 32-bit first, which is always supported\n                try:\n                    audioop.bias(\n                        b\"\", 3, 0\n                    )  # test whether 24-bit audio is supported (for example, ``audioop`` in Python 3.3 and below don't support sample width 3, while Python 3.4+ do)\n                except (\n                    audioop.error\n                ):  # this version of audioop doesn't support 24-bit audio (probably Python 3.3 or less)\n                    raw_data = b\"\".join(\n                        raw_data[i + 1 : i + 4]\n                        for i in range(0, len(raw_data), 4)\n                    )  # since we're in little endian, we discard the first byte from each 32-bit sample to get a 24-bit sample\n                else:  # 24-bit audio fully supported, we don't need to shim anything\n                    raw_data = audioop.lin2lin(\n                        raw_data, self.sample_width, convert_width\n                    )\n            else:\n                raw_data = audioop.lin2lin(\n                    raw_data, self.sample_width, convert_width\n                )\n\n        # if the output is 8-bit audio with unsigned samples, convert the samples we've been treating as signed to unsigned again\n        if convert_width == 1:\n            raw_data = audioop.bias(\n                raw_data, 1, 128\n            )  # add 128 to every sample to make them act like unsigned samples again\n\n        return raw_data\n\n    def get_wav_data(self, convert_rate=None, convert_width=None, nchannels = 1):\n        \"\"\"\n        Returns a byte string representing the contents of a WAV file containing the audio represented by the ``AudioData`` instance.\n\n        If ``convert_width`` is specified and the audio samples are not ``convert_width`` bytes each, the resulting audio is converted to match.\n\n        If ``convert_rate`` is specified and the audio sample rate is not ``convert_rate`` Hz, the resulting audio is resampled to match.\n\n        Writing these bytes directly to a file results in a valid `WAV file <https://en.wikipedia.org/wiki/WAV>`__.\n        \"\"\"\n        raw_data = self.get_raw_data(convert_rate, convert_width)\n        sample_rate = (\n            self.sample_rate if convert_rate is None else convert_rate\n        )\n        sample_width = (\n            self.sample_width if convert_width is None else convert_width\n        )\n\n        # generate the WAV file contents\n        with io.BytesIO() as wav_file:\n            wav_writer = wave.open(wav_file, \"wb\")\n            try:  # note that we can't use context manager, since that was only added in Python 3.4\n                wav_writer.setframerate(sample_rate)\n                wav_writer.setsampwidth(sample_width)\n                wav_writer.setnchannels(nchannels)\n                wav_writer.writeframes(raw_data)\n                wav_data = wav_file.getvalue()\n            finally:  # make sure resources are cleaned up\n                wav_writer.close()\n        return wav_data\n\n    def get_aiff_data(self, convert_rate=None, convert_width=None):\n        \"\"\"\n        Returns a byte string representing the contents of an AIFF-C file containing the audio represented by the ``AudioData`` instance.\n\n        If ``convert_width`` is specified and the audio samples are not ``convert_width`` bytes each, the resulting audio is converted to match.\n\n        If ``convert_rate`` is specified and the audio sample rate is not ``convert_rate`` Hz, the resulting audio is resampled to match.\n\n        Writing these bytes directly to a file results in a valid `AIFF-C file <https://en.wikipedia.org/wiki/Audio_Interchange_File_Format>`__.\n        \"\"\"\n        raw_data = self.get_raw_data(convert_rate, convert_width)\n        sample_rate = (\n            self.sample_rate if convert_rate is None else convert_rate\n        )\n        sample_width = (\n            self.sample_width if convert_width is None else convert_width\n        )\n\n        # the AIFF format is big-endian, so we need to convert the little-endian raw data to big-endian\n        if hasattr(\n            audioop, \"byteswap\"\n        ):  # ``audioop.byteswap`` was only added in Python 3.4\n            raw_data = audioop.byteswap(raw_data, sample_width)\n        else:  # manually reverse the bytes of each sample, which is slower but works well enough as a fallback\n            raw_data = raw_data[sample_width - 1 :: -1] + b\"\".join(\n                raw_data[i + sample_width : i : -1]\n                for i in range(sample_width - 1, len(raw_data), sample_width)\n            )\n\n        # generate the AIFF-C file contents\n        with io.BytesIO() as aiff_file:\n            aiff_writer = aifc.open(aiff_file, \"wb\")\n            try:  # note that we can't use context manager, since that was only added in Python 3.4\n                aiff_writer.setframerate(sample_rate)\n                aiff_writer.setsampwidth(sample_width)\n                aiff_writer.setnchannels(1)\n                aiff_writer.writeframes(raw_data)\n                aiff_data = aiff_file.getvalue()\n            finally:  # make sure resources are cleaned up\n                aiff_writer.close()\n        return aiff_data\n\n    def get_flac_data(self, convert_rate=None, convert_width=None):\n        \"\"\"\n        Returns a byte string representing the contents of a FLAC file containing the audio represented by the ``AudioData`` instance.\n\n        Note that 32-bit FLAC is not supported. If the audio data is 32-bit and ``convert_width`` is not specified, then the resulting FLAC will be a 24-bit FLAC.\n\n        If ``convert_rate`` is specified and the audio sample rate is not ``convert_rate`` Hz, the resulting audio is resampled to match.\n\n        If ``convert_width`` is specified and the audio samples are not ``convert_width`` bytes each, the resulting audio is converted to match.\n\n        Writing these bytes directly to a file results in a valid `FLAC file <https://en.wikipedia.org/wiki/FLAC>`__.\n        \"\"\"\n        assert convert_width is None or (\n            convert_width % 1 == 0 and 1 <= convert_width <= 3\n        ), \"Sample width to convert to must be between 1 and 3 inclusive\"\n\n        if (\n            self.sample_width > 3 and convert_width is None\n        ):  # resulting WAV data would be 32-bit, which is not convertable to FLAC using our encoder\n            convert_width = 3  # the largest supported sample width is 24-bit, so we'll limit the sample width to that\n\n        # run the FLAC converter with the WAV data to get the FLAC data\n        wav_data = self.get_wav_data(convert_rate, convert_width)\n        flac_converter = get_flac_converter()\n        if (\n            os.name == \"nt\"\n        ):  # on Windows, specify that the process is to be started without showing a console window\n            startup_info = subprocess.STARTUPINFO()\n            startup_info.dwFlags |= (\n                subprocess.STARTF_USESHOWWINDOW\n            )  # specify that the wShowWindow field of `startup_info` contains a value\n            startup_info.wShowWindow = (\n                subprocess.SW_HIDE\n            )  # specify that the console window should be hidden\n        else:\n            startup_info = None  # default startupinfo\n        process = subprocess.Popen(\n            [\n                flac_converter,\n                \"--stdout\",\n                \"--totally-silent\",  # put the resulting FLAC file in stdout, and make sure it's not mixed with any program output\n                \"--best\",  # highest level of compression available\n                \"-\",  # the input FLAC file contents will be given in stdin\n            ],\n            stdin=subprocess.PIPE,\n            stdout=subprocess.PIPE,\n            startupinfo=startup_info,\n        )\n        flac_data, stderr = process.communicate(wav_data)\n        return flac_data\n\n\ndef get_flac_converter():\n    \"\"\"Returns the absolute path of a FLAC converter executable, or raises an OSError if none can be found.\"\"\"\n    flac_converter = shutil_which(\"flac\")  # check for installed version first\n    if flac_converter is None:  # flac utility is not installed\n        base_path = os.path.dirname(\n            os.path.abspath(__file__)\n        )  # directory of the current module file, where all the FLAC bundled binaries are stored\n        system, machine = platform.system(), platform.machine()\n        if system == \"Windows\" and machine in {\n            \"i686\",\n            \"i786\",\n            \"x86\",\n            \"x86_64\",\n            \"AMD64\",\n        }:\n            flac_converter = os.path.join(base_path, \"flac-win32.exe\")\n        elif system == \"Darwin\" and machine in {\n            \"i686\",\n            \"i786\",\n            \"x86\",\n            \"x86_64\",\n            \"AMD64\",\n        }:\n            flac_converter = os.path.join(base_path, \"flac-mac\")\n        elif system == \"Linux\" and machine in {\"i686\", \"i786\", \"x86\"}:\n            flac_converter = os.path.join(base_path, \"flac-linux-x86\")\n        elif system == \"Linux\" and machine in {\"x86_64\", \"AMD64\"}:\n            flac_converter = os.path.join(base_path, \"flac-linux-x86_64\")\n        else:  # no FLAC converter available\n            raise OSError(\n                \"FLAC conversion utility not available - consider installing the FLAC command line application by running `apt-get install flac` or your operating system's equivalent\"\n            )\n\n    # mark FLAC converter as executable if possible\n    try:\n        # handle known issue when running on docker:\n        # run executable right after chmod() may result in OSError \"Text file busy\"\n        # fix: flush FS with sync\n        if not os.access(flac_converter, os.X_OK):\n            stat_info = os.stat(flac_converter)\n            os.chmod(flac_converter, stat_info.st_mode | stat.S_IEXEC)\n            if \"Linux\" in platform.system():\n                os.sync() if sys.version_info >= (3, 3) else os.system(\"sync\")\n\n    except OSError:\n        pass\n\n    return flac_converter\n\n\ndef shutil_which(pgm):\n    \"\"\"Python 2 compatibility: backport of ``shutil.which()`` from Python 3\"\"\"\n    path = os.getenv(\"PATH\")\n    for p in path.split(os.path.pathsep):\n        p = os.path.join(p, pgm)\n        if os.path.exists(p) and os.access(p, os.X_OK):\n            return p\n"
  },
  {
    "path": "utils/check_proxy.py",
    "content": "\ndef check_proxy(proxies):\n    import requests\n    proxies_https = proxies['https'] if proxies is not None else '无'\n    try:\n        response = requests.get(\"https://ipapi.co/json/\", proxies=proxies, timeout=4)\n        data = response.json()\n        print(f'查询代理的地理位置，返回的结果是{data}')\n        if 'country_name' in data:\n            country = data['country_name']\n            result = f\"代理配置 {proxies_https}, 代理所在地：{country}\"\n        elif 'error' in data:\n            alternative = _check_with_backup_source(proxies)\n            if alternative is None:\n                result = f\"代理配置 {proxies_https}, 代理所在地：未知，IP查询频率受限\"\n            else:\n                result = f\"代理配置 {proxies_https}, 代理所在地：{alternative}\"\n        else:\n            result = f\"代理配置 {proxies_https}, 代理数据解析失败：{data}\"\n        print(result)\n        return result\n    except:\n        result = f\"代理配置 {proxies_https}, 代理所在地查询超时，代理可能无效\"\n        print(result)\n        return result\n\ndef _check_with_backup_source(proxies):\n    import random, string, requests\n    random_string = ''.join(random.choices(string.ascii_letters + string.digits, k=32))\n    try: return requests.get(f\"http://{random_string}.edns.ip-api.com/json\", proxies=proxies, timeout=4).json()['dns']['geo']\n    except: return None\n\ndef backup_and_download(current_version, remote_version):\n    \"\"\"\n    一键更新协议：备份和下载\n    \"\"\"\n    from toolbox import get_conf\n    import shutil\n    import os\n    import requests\n    import zipfile\n    os.makedirs(f'./history', exist_ok=True)\n    backup_dir = f'./history/backup-{current_version}/'\n    new_version_dir = f'./history/new-version-{remote_version}/'\n    if os.path.exists(new_version_dir):\n        return new_version_dir\n    os.makedirs(new_version_dir)\n    shutil.copytree('./', backup_dir, ignore=lambda x, y: ['history'])\n    proxies, = get_conf('proxies')\n    r = requests.get(\n        'https://github.com/binary-husky/chatgpt_academic/archive/refs/heads/master.zip', proxies=proxies, stream=True)\n    zip_file_path = backup_dir+'/master.zip'\n    with open(zip_file_path, 'wb+') as f:\n        f.write(r.content)\n    dst_path = new_version_dir\n    with zipfile.ZipFile(zip_file_path, \"r\") as zip_ref:\n        for zip_info in zip_ref.infolist():\n            dst_file_path = os.path.join(dst_path, zip_info.filename)\n            if os.path.exists(dst_file_path):\n                os.remove(dst_file_path)\n            zip_ref.extract(zip_info, dst_path)\n    return new_version_dir\n\n\ndef patch_and_restart(path):\n    \"\"\"\n    一键更新协议：覆盖和重启\n    \"\"\"\n    from distutils import dir_util\n    import shutil\n    import os\n    import sys\n    import time\n    import glob\n    from colorful import print亮黄, print亮绿, print亮红\n    # if not using config_private, move origin config.py as config_private.py\n    if not os.path.exists('config_private.py'):\n        print亮黄('由于您没有设置config_private.py私密配置，现将您的现有配置移动至config_private.py以防止配置丢失，',\n              '另外您可以随时在history子文件夹下找回旧版的程序。')\n        shutil.copyfile('config.py', 'config_private.py')\n    path_new_version = glob.glob(path + '/*-master')[0]\n    dir_util.copy_tree(path_new_version, './')\n    print亮绿('代码已经更新，即将更新pip包依赖……')\n    for i in reversed(range(5)): time.sleep(1); print(i)\n    try: \n        import subprocess\n        subprocess.check_call([sys.executable, '-m', 'pip', 'install', '-r', 'requirements.txt'])\n    except:\n        print亮红('pip包依赖安装出现问题，需要手动安装新增的依赖库 `python -m pip install -r requirements.txt`，然后在用常规的`python main.py`的方式启动。')\n    print亮绿('更新完成，您可以随时在history子文件夹下找回旧版的程序，5s之后重启')\n    print亮红('假如重启失败，您可能需要手动安装新增的依赖库 `python -m pip install -r requirements.txt`，然后在用常规的`python main.py`的方式启动。')\n    print(' ------------------------------ -----------------------------------')\n    for i in reversed(range(8)): time.sleep(1); print(i)\n    os.execl(sys.executable, sys.executable, *sys.argv)\n\n\ndef get_current_version():\n    import json\n    try:\n        with open('./version', 'r', encoding='utf8') as f:\n            current_version = json.loads(f.read())['version']\n    except:\n        current_version = \"\"\n    return current_version\n\n\ndef auto_update(raise_error=False):\n    \"\"\"\n    一键更新协议：查询版本和用户意见\n    \"\"\"\n    try:\n        from toolbox import get_conf\n        import requests\n        import time\n        import json\n        proxies, = get_conf('proxies')\n        response = requests.get(\n            \"https://raw.githubusercontent.com/binary-husky/chatgpt_academic/master/version\", proxies=proxies, timeout=5)\n        remote_json_data = json.loads(response.text)\n        remote_version = remote_json_data['version']\n        if remote_json_data[\"show_feature\"]:\n            new_feature = \"新功能：\" + remote_json_data[\"new_feature\"]\n        else:\n            new_feature = \"\"\n        with open('./version', 'r', encoding='utf8') as f:\n            current_version = f.read()\n            current_version = json.loads(current_version)['version']\n        if (remote_version - current_version) >= 0.01-1e-5:\n            from colorful import print亮黄\n            print亮黄(\n                f'\\n新版本可用。新版本:{remote_version}，当前版本:{current_version}。{new_feature}')\n            print('（1）Github更新地址:\\nhttps://github.com/binary-husky/chatgpt_academic\\n')\n            user_instruction = input('（2）是否一键更新代码（Y+回车=确认，输入其他/无输入+回车=不更新）？')\n            if user_instruction in ['Y', 'y']:\n                path = backup_and_download(current_version, remote_version)\n                try:\n                    patch_and_restart(path)\n                except:\n                    msg = '更新失败。'\n                    if raise_error:\n                        from toolbox import trimmed_format_exc\n                        msg += trimmed_format_exc()\n                    print(msg)\n            else:\n                print('自动更新程序：已禁用')\n                return\n        else:\n            return\n    except:\n        msg = '自动更新程序：已禁用。建议排查：代理网络配置。'\n        if raise_error:\n            from toolbox import trimmed_format_exc\n            msg += trimmed_format_exc()\n        print(msg)\n\ndef warm_up_modules():\n    print('正在执行一些模块的预热...')\n    from llm_cards.bridge_all import model_info\n    enc = model_info[\"gpt-3.5-turbo\"]['tokenizer']\n    enc.encode(\"模块预热\", disallowed_special=())\n    enc = model_info[\"gpt-4\"]['tokenizer']\n    enc.encode(\"模块预热\", disallowed_special=())\n\nif __name__ == '__main__':\n    import os\n    os.environ['no_proxy'] = '*'  # 避免代理网络产生意外污染\n    from toolbox import get_conf\n    proxies, = get_conf('proxies')\n    check_proxy(proxies)\n"
  },
  {
    "path": "utils/colorful.py",
    "content": "import platform\nfrom sys import stdout\n\nif platform.system()==\"Linux\":\n    pass\nelse: \n    from colorama import init\n    init()\n\n# Do you like the elegance of Chinese characters?\ndef print红(*kw,**kargs):\n    print(\"\\033[0;31m\",*kw,\"\\033[0m\",**kargs)\ndef print绿(*kw,**kargs):\n    print(\"\\033[0;32m\",*kw,\"\\033[0m\",**kargs)\ndef print黄(*kw,**kargs):\n    print(\"\\033[0;33m\",*kw,\"\\033[0m\",**kargs)\ndef print蓝(*kw,**kargs):\n    print(\"\\033[0;34m\",*kw,\"\\033[0m\",**kargs)\ndef print紫(*kw,**kargs):\n    print(\"\\033[0;35m\",*kw,\"\\033[0m\",**kargs)\ndef print靛(*kw,**kargs):\n    print(\"\\033[0;36m\",*kw,\"\\033[0m\",**kargs)\n\ndef print亮红(*kw,**kargs):\n    print(\"\\033[1;31m\",*kw,\"\\033[0m\",**kargs)\ndef print亮绿(*kw,**kargs):\n    print(\"\\033[1;32m\",*kw,\"\\033[0m\",**kargs)\ndef print亮黄(*kw,**kargs):\n    print(\"\\033[1;33m\",*kw,\"\\033[0m\",**kargs)\ndef print亮蓝(*kw,**kargs):\n    print(\"\\033[1;34m\",*kw,\"\\033[0m\",**kargs)\ndef print亮紫(*kw,**kargs):\n    print(\"\\033[1;35m\",*kw,\"\\033[0m\",**kargs)\ndef print亮靛(*kw,**kargs):\n    print(\"\\033[1;36m\",*kw,\"\\033[0m\",**kargs)\n\n# Do you like the elegance of Chinese characters?\ndef sprint红(*kw):\n    return \"\\033[0;31m\"+' '.join(kw)+\"\\033[0m\"\ndef sprint绿(*kw):\n    return \"\\033[0;32m\"+' '.join(kw)+\"\\033[0m\"\ndef sprint黄(*kw):\n    return \"\\033[0;33m\"+' '.join(kw)+\"\\033[0m\"\ndef sprint蓝(*kw):\n    return \"\\033[0;34m\"+' '.join(kw)+\"\\033[0m\"\ndef sprint紫(*kw):\n    return \"\\033[0;35m\"+' '.join(kw)+\"\\033[0m\"\ndef sprint靛(*kw):\n    return \"\\033[0;36m\"+' '.join(kw)+\"\\033[0m\"\ndef sprint亮红(*kw):\n    return \"\\033[1;31m\"+' '.join(kw)+\"\\033[0m\"\ndef sprint亮绿(*kw):\n    return \"\\033[1;32m\"+' '.join(kw)+\"\\033[0m\"\ndef sprint亮黄(*kw):\n    return \"\\033[1;33m\"+' '.join(kw)+\"\\033[0m\"\ndef sprint亮蓝(*kw):\n    return \"\\033[1;34m\"+' '.join(kw)+\"\\033[0m\"\ndef sprint亮紫(*kw):\n    return \"\\033[1;35m\"+' '.join(kw)+\"\\033[0m\"\ndef sprint亮靛(*kw):\n    return \"\\033[1;36m\"+' '.join(kw)+\"\\033[0m\""
  },
  {
    "path": "utils/conf.py",
    "content": "# [step 1]>> 例如： API_KEY = \"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\" （此key无效）\nAPI_KEY = \"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\"\n\n# [step 2]>> 改为True应用代理，如果直接在海外服务器部署，此处不修改\nUSE_PROXY = True\nif USE_PROXY:\n    # 填写格式是 [协议]://  [地址] :[端口]，填写之前不要忘记把USE_PROXY改成True，如果直接在海外服务器部署，此处不修改\n    # 例如    \"socks5h://localhost:11284\"\n    # [协议] 常见协议无非socks5h/http; 例如 v2**y 和 ss* 的默认本地协议是socks5h; 而cl**h 的默认本地协议是http\n    # [地址] 懂的都懂，不懂就填localhost或者127.0.0.1肯定错不了（localhost意思是代理软件安装在本机上）\n    # [端口] 在代理软件的设置里找。虽然不同的代理软件界面不一样，但端口号都应该在最显眼的位置上\n\n    # 代理网络的地址，打开你的科学上网软件查看代理的协议(socks5/http)、地址(localhost)和端口(11284)\n    proxies = {\n        #          [协议]://  [地址]  :[端口]\n        \"http\":  \"socks5h://localhost:4781\",\n        \"https\": \"socks5h://localhost:4781\",\n    }\nelse:\n    proxies = None\n\nPROXIES = \"socks5h://localhost:4781\"\n\n# 多线程函数插件中，默认允许多少路线程同时访问OpenAI。\n# Free trial users的限制是每分钟3次，Pay-as-you-go users的限制是每分钟3500次。提高限制请查询：\n# https://platform.openai.com/docs/guides/rate-limits/overview\nDEFAULT_WORKER_NUM = 3\n\n# [step 3]>> 以下配置可以优化体验，但大部分场合下并不需要修改\n# 对话窗的高度\nCHATBOT_HEIGHT = 1110\n\n# 代码高亮\nCODE_HIGHLIGHT = True\n\n# 窗口布局\nLAYOUT = \"LEFT-RIGHT\"  # \"LEFT-RIGHT\"（左右布局） # \"TOP-DOWN\"（上下布局）\n\n# 发送请求到OpenAI后，等待多久判定为超时\nTIMEOUT_SECONDS = 30\n\n# 网页的端口, -1代表随机端口\nWEB_PORT = -1\n\n# 如果OpenAI不响应（网络卡顿、代理失败、KEY失效），重试的次数限制\nMAX_RETRY = 2\n\n# OpenAI模型选择是（gpt4现在只对申请成功的人开放）\nLLM_MODEL = \"gpt-3.5-turbo\"\n\n# OpenAI的API_URL\nAPI_URL = \"https://api.openai.com/v1/chat/completions\"\n\n# 设置并行使用的线程数\nCONCURRENT_COUNT = 100\n\nNUM_WORKS=2\n\n# set model an model path \nGROUNED_MODEL_TYPE= {'S': \"../Grounded-Segment-Anything-main/groundingdino_swint_ogc.pth\",'L':None}\nSAM_MODEL_TYPE= {'vit_h': \"F:\\\\sam_vit_h_4b8939.pth\"  ,'vit_l':None,'vit_b':None}\nTag2Text_Model_Path='F:\\\\tag2text_swin_14m.pth'#'weights/tag2text/tag2text_swin_14m.pth'\nLAMA_MODEL_PATH='F:\\\\Inpaint-Anything\\\\big-lama'"
  },
  {
    "path": "utils/dataloads.py",
    "content": "\n\"\"\"\nDataloaders and dataset utils\n\"\"\"\n\nimport contextlib\nimport glob\nimport hashlib\nimport json\nimport math\nimport os\nimport random\nimport shutil\nimport time\nfrom itertools import repeat\nfrom multiprocessing.pool import Pool, ThreadPool\nfrom pathlib import Path\nfrom threading import Thread\nfrom urllib.parse import urlparse\n\nimport numpy as np\nimport psutil\nimport torch\nimport torch.nn.functional as F\nimport torchvision\nimport yaml\nfrom PIL import ExifTags, Image, ImageOps\nfrom torch.utils.data import DataLoader, Dataset, dataloader, distributed\nfrom tqdm import tqdm\n\n\nfrom utils.ops import (DATASETS_DIR, LOGGER, NUM_THREADS,TQDM_BAR_FORMAT, check_dataset, check_requirements, check_yaml, clean_str,\n                           colorstr,cv2, is_colab, is_kaggle, segments2boxes, unzip_file,xyn2xy, xywh2xyxy, xywhn2xyxy, xyxy2xywhn)\n#from yolo.utils.ops import (xyn2xy, xywh2xyxy, xywhn2xyxy, xyxy2xywhn)\nfrom utils.torch_utils import torch_distributed_zero_first\n\n# Parameters\nHELP_URL = 'https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data'\nIMG_FORMATS = 'bmp', 'dng', 'jpeg', 'jpg', 'mpo', 'png', 'tif', 'tiff', 'webp' ,'pfm'  # include image suffixes\nVID_FORMATS = 'asf', 'avi', 'gif', 'm4v', 'mkv', 'mov', 'mp4', 'mpeg', 'mpg', 'ts', 'wmv'  # include video suffixes\nLOCAL_RANK = int(os.getenv('LOCAL_RANK', -1))  # https://pytorch.org/docs/stable/elastic/run.html\nRANK = int(os.getenv('RANK', -1))\nPIN_MEMORY = str(os.getenv('PIN_MEMORY', True)).lower() == 'true'  # global pin_memory for dataloaders\n\n# Get orientation exif tag\nfor orientation in ExifTags.TAGS.keys():\n    if ExifTags.TAGS[orientation] == 'Orientation':\n        break\n\n\ndef get_hash(paths):\n    # Returns a single hash value of a list of paths (files or dirs)\n    size = sum(os.path.getsize(p) for p in paths if os.path.exists(p))  # sizes\n    h = hashlib.md5(str(size).encode())  # hash sizes\n    h.update(''.join(paths).encode())  # hash paths\n    return h.hexdigest()  # return hash\n\n\ndef exif_size(img):\n    # Returns exif-corrected PIL size\n    s = img.size  # (width, height)\n    with contextlib.suppress(Exception):\n        rotation = dict(img._getexif().items())[orientation]\n        if rotation in [6, 8]:  # rotation 270 or 90\n            s = (s[1], s[0])\n    return s\n\n\ndef exif_transpose(image):\n    \"\"\"\n    Transpose a PIL image accordingly if it has an EXIF Orientation tag.\n    Inplace version of https://github.com/python-pillow/Pillow/blob/master/src/PIL/ImageOps.py exif_transpose()\n    :param image: The image to transpose.\n    :return: An image.\n    \"\"\"\n    exif = image.getexif()\n    orientation = exif.get(0x0112, 1)  # default 1\n    if orientation > 1:\n        method = {\n            2: Image.FLIP_LEFT_RIGHT,\n            3: Image.ROTATE_180,\n            4: Image.FLIP_TOP_BOTTOM,\n            5: Image.TRANSPOSE,\n            6: Image.ROTATE_270,\n            7: Image.TRANSVERSE,\n            8: Image.ROTATE_90,}.get(orientation)\n        if method is not None:\n            image = image.transpose(method)\n            del exif[0x0112]\n            image.info[\"exif\"] = exif.tobytes()\n    return image\n\n\ndef seed_worker(worker_id):\n    # Set dataloader worker seed https://pytorch.org/docs/stable/notes/randomness.html#dataloader\n    worker_seed = torch.initial_seed() % 2 ** 32\n    np.random.seed(worker_seed)\n    random.seed(worker_seed)\n\n\ndef create_dataloader(path,\n                      imgsz,\n                      batch_size,\n                      stride,\n                      single_cls=False,\n                      hyp=None,\n                      augment=False,\n                      cache=False,\n                      pad=0.0,\n                      rect=False,\n                      rank=-1,\n                      workers=8,\n                      image_weights=False,\n                      quad=False,\n                      prefix='',\n                      shuffle=False,\n                      seed=0,\n                      load_v8=False,\n                      ):\n    if rect and shuffle:\n        LOGGER.warning('WARNING: --rect is incompatible with DataLoader shuffle, setting shuffle=False')\n        shuffle = False\n    with torch_distributed_zero_first(rank):  # init dataset *.cache only once if DDP\n        dataset = LoadImagesAndLabels(\n            path,\n            imgsz,\n            batch_size,\n            augment=augment,  # augmentation\n            hyp=hyp,  # hyperparameters\n            rect=rect,  # rectangular batches\n            cache_images=cache,\n            single_cls=single_cls,\n            stride=int(stride),\n            pad=pad,\n            image_weights=image_weights,\n            prefix=prefix) \n\n    batch_size = min(batch_size, len(dataset))\n    nd = torch.cuda.device_count()  # number of CUDA devices\n    nw = min([os.cpu_count() // max(nd, 1), batch_size if batch_size > 1 else 0, workers])  # number of workers\n    sampler = None if rank == -1 else distributed.DistributedSampler(dataset, shuffle=shuffle)\n    loader = DataLoader if image_weights else InfiniteDataLoader  # only DataLoader allows for attribute updates\n    generator = torch.Generator()\n    generator.manual_seed(6148914691236517205 + seed+ RANK)\n    #collate_fuction=None\n   \n    collate_fuction=LoadImagesAndLabels.collate_fn_v8 if load_v8 else LoadImagesAndLabels.collate_fn\n    return loader(dataset,\n                  batch_size=batch_size,\n                  shuffle=shuffle and sampler is None,\n                  num_workers=nw,\n                  sampler=sampler,\n                  pin_memory=PIN_MEMORY,\n                  collate_fn=LoadImagesAndLabels.collate_fn4 if quad else collate_fuction , \n                  worker_init_fn=seed_worker,\n                  generator=generator), dataset\n\n\n\ndef create_kpt_dataloader(path, imgsz, batch_size, stride, opt, hyp=None, augment=False, cache=False, pad=0.0, rect=False,\n                      rank=-1, world_size=1, workers=8, image_weights=False, quad=False, prefix='', tidl_load=False, kpt_label=False):\n    # Make sure only the first process in DDP process the dataset first, and the following others can use the cache\n    with torch_distributed_zero_first(rank):\n        dataset = LoadImagesAndLabels_Kpt(path, imgsz, batch_size,\n                                      augment=augment,  # augment images\n                                      hyp=hyp,  # augmentation hyperparameters\n                                      rect=rect,  # rectangular training\n                                      cache_images=cache,\n                                      single_cls=False,\n                                      stride=int(stride),\n                                      pad=pad,\n                                      image_weights=image_weights,\n                                      prefix=prefix,\n                                      tidl_load=tidl_load,\n                                      kpt_label=kpt_label)\n\n    batch_size = min(batch_size, len(dataset))\n    nw = min([os.cpu_count() // world_size, batch_size if batch_size > 1 else 0, workers])  # number of workers\n    sampler = torch.utils.data.distributed.DistributedSampler(dataset) if rank != -1 else None\n    loader = torch.utils.data.DataLoader if image_weights else InfiniteDataLoader\n    # Use torch.utils.data.DataLoader() if dataset.properties will update during training else InfiniteDataLoader()\n    dataloader = loader(dataset,\n                        batch_size=batch_size,\n                        num_workers=nw,\n                        sampler=sampler,\n                        pin_memory=True,\n                        collate_fn=LoadImagesAndLabels_Kpt.collate_fn4 if quad else LoadImagesAndLabels_Kpt.collate_fn)\n    return dataloader, dataset\n\nclass InfiniteDataLoader(dataloader.DataLoader):\n    \"\"\" Dataloader that reuses workers\n    Uses same syntax as vanilla DataLoader\n    \"\"\"\n\n    def __init__(self, *args, **kwargs):\n        super().__init__(*args, **kwargs)\n        object.__setattr__(self, 'batch_sampler', _RepeatSampler(self.batch_sampler))\n        self.iterator = super().__iter__()\n\n    def __len__(self):\n        return len(self.batch_sampler.sampler)\n\n    def __iter__(self):\n        for _ in range(len(self)):\n            yield next(self.iterator)\n\n\nclass _RepeatSampler:\n    \"\"\" Sampler that repeats forever\n    Args:\n        sampler (Sampler)\n    \"\"\"\n\n    def __init__(self, sampler):\n        self.sampler = sampler\n\n    def __iter__(self):\n        while True:\n            yield from iter(self.sampler)\n\n\nclass LoadScreenshots:\n    # YOLOv5 screenshot dataloader, i.e. `python detect.py --source \"screen 0 100 100 512 256\"`\n    def __init__(self, source, img_size=640, stride=32, auto=True, transforms=None):\n        # source = [screen_number left top width height] (pixels)\n        check_requirements('mss')\n        import mss\n\n        source, *params = source.split()\n        self.screen, left, top, width, height = 0, None, None, None, None  # default to full screen 0\n        if len(params) == 1:\n            self.screen = int(params[0])\n        elif len(params) == 4:\n            left, top, width, height = (int(x) for x in params)\n        elif len(params) == 5:\n            self.screen, left, top, width, height = (int(x) for x in params)\n        self.img_size = img_size\n        self.stride = stride\n        self.transforms = transforms\n        self.auto = auto\n        self.mode = 'stream'\n        self.frame = 0\n        self.sct = mss.mss()\n\n        # Parse monitor shape\n        monitor = self.sct.monitors[self.screen]\n        self.top = monitor[\"top\"] if top is None else (monitor[\"top\"] + top)\n        self.left = monitor[\"left\"] if left is None else (monitor[\"left\"] + left)\n        self.width = width or monitor[\"width\"]\n        self.height = height or monitor[\"height\"]\n        self.monitor = {\"left\": self.left, \"top\": self.top, \"width\": self.width, \"height\": self.height}\n\n    def __iter__(self):\n        return self\n\n    def __next__(self):\n        # mss screen capture: get raw pixels from the screen as np array\n        im0 = np.array(self.sct.grab(self.monitor))[:, :, :3]  # [:, :, :3] BGRA to BGR\n        s = f\"screen {self.screen} (LTWH): {self.left},{self.top},{self.width},{self.height}: \"\n\n        if self.transforms:\n            im = self.transforms(im0)  # transforms\n        else:\n            im = letterbox(im0, self.img_size, stride=self.stride, auto=self.auto)[0]  # padded resize\n            im = im.transpose((2, 0, 1))[::-1]  # HWC to CHW, BGR to RGB\n            im = np.ascontiguousarray(im)  # contiguous\n        self.frame += 1\n        return str(self.screen), im, im0, None, s  # screen, img, original img, im0s, s\n        \n\nclass LoadImages:\n    # YOLOv5 image/video dataloader, i.e. `python detect.py --source image.jpg/vid.mp4`\n    def __init__(self, path, img_size=640, stride=32, auto=True, transforms=None, vid_stride=1):\n        files = []\n        if isinstance(path,str) and Path(path).suffix==\".txt\":\n            path=Path(path).read_text().rsplit()\n        for p in sorted(path) if isinstance(path, (list, tuple)) else [path]:\n            p = str(Path(p).resolve())\n            if '*' in p:\n                files.extend(sorted(glob.glob(p, recursive=True)))  # glob\n            elif os.path.isdir(p):\n                files.extend(sorted(glob.glob(os.path.join(p, '*.*'))))  # dir\n            elif os.path.isfile(p):\n                files.append(p)  # files\n            else:\n                raise FileNotFoundError(f'{p} does not exist')\n\n        images = [x for x in files if x.split('.')[-1].lower() in IMG_FORMATS]\n        videos = [x for x in files if x.split('.')[-1].lower() in VID_FORMATS]\n        ni, nv = len(images), len(videos)\n\n        self.img_size = img_size\n        self.stride = stride\n        self.files = images + videos\n        self.nf = ni + nv  # number of files\n        self.video_flag = [False] * ni + [True] * nv\n        self.mode = 'image'\n        self.auto = auto\n        self.transforms = transforms  # optional\n        self.vid_stride = vid_stride  # video frame-rate stride\n        if any(videos):\n            self._new_video(videos[0])  # new video\n        else:\n            self.cap = None\n        assert self.nf > 0, f'No images or videos found in {p}. ' \\\n                            f'Supported formats are:\\nimages: {IMG_FORMATS}\\nvideos: {VID_FORMATS}'\n\n    def __iter__(self):\n        self.count = 0\n        return self\n\n    def __next__(self):\n        if self.count == self.nf:\n            raise StopIteration\n        path = self.files[self.count]\n\n        if self.video_flag[self.count]:\n            # Read video\n            self.mode = 'video'\n            for _ in range(self.vid_stride):\n                self.cap.grab()\n            ret_val, im0 = self.cap.retrieve()\n            while not ret_val:\n                self.count += 1\n                self.cap.release()\n                if self.count == self.nf:  # last video\n                    raise StopIteration\n                path = self.files[self.count]\n                self._new_video(path)\n                ret_val, im0 = self.cap.read()\n\n            self.frame += 1\n            # im0 = self._cv2_rotate(im0)  # for use if cv2 autorotation is False\n            s = f'video {self.count + 1}/{self.nf} ({self.frame}/{self.frames}) {path}: '\n\n        else:\n            # Read image\n            self.count += 1\n            im0 = cv2.imread(path)  # BGR\n            assert im0 is not None, f'Image Not Found {path}'\n            s = f'image {self.count}/{self.nf} {path}: '\n\n        if self.transforms:\n            im = self.transforms(im0)  # transforms\n        else:\n            im = letterbox(im0, self.img_size, stride=self.stride, auto=self.auto)[0]  # padded resize\n            im = im.transpose((2, 0, 1))[::-1]  # HWC to CHW, BGR to RGB\n            im = np.ascontiguousarray(im)  # contiguous\n\n        return path, im, im0, self.cap, s\n\n    def _new_video(self, path):\n        # Create a new video capture object\n        self.frame = 0\n        self.cap = cv2.VideoCapture(path)\n        self.frames = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT) / self.vid_stride)\n        self.orientation = int(self.cap.get(cv2.CAP_PROP_ORIENTATION_META))  # rotation degrees\n        # self.cap.set(cv2.CAP_PROP_ORIENTATION_AUTO, 0)  # disable https://github.com/ultralytics/yolov5/issues/8493\n\n    def _cv2_rotate(self, im):\n        # Rotate a cv2 video manually\n        if self.orientation == 0:\n            return cv2.rotate(im, cv2.ROTATE_90_CLOCKWISE)\n        elif self.orientation == 180:\n            return cv2.rotate(im, cv2.ROTATE_90_COUNTERCLOCKWISE)\n        elif self.orientation == 90:\n            return cv2.rotate(im, cv2.ROTATE_180)\n        return im\n\n    def __len__(self):\n        return self.nf  # number of files\n\n\n\nclass LoadWebcam:  # for inference\n    # YOLOv5 local webcam dataloader, i.e. `python detect.py --source 0`\n    def __init__(self, pipe='0', img_size=640, stride=32):\n        self.img_size = img_size\n        self.stride = stride\n        self.pipe = eval(pipe) if pipe.isnumeric() else pipe\n        self.cap = cv2.VideoCapture(self.pipe)  # video capture object\n        self.cap.set(cv2.CAP_PROP_BUFFERSIZE, 3)  # set buffer size\n\n    def __iter__(self):\n        self.count = -1\n        return self\n\n    def __next__(self):\n        self.count += 1\n        if cv2.waitKey(1) == ord('q'):  # q to quit\n            self.cap.release()\n            cv2.destroyAllWindows()\n            raise StopIteration\n\n        # Read frame\n        ret_val, im0 = self.cap.read()\n        im0 = cv2.flip(im0, 1)  # flip left-right\n\n        # Print\n        assert ret_val, f'Camera Error {self.pipe}'\n        img_path = 'webcam.jpg'\n        s = f'webcam {self.count}: '\n\n         # Process\n        im = letterbox(im0, self.img_size, stride=self.stride)[0]\n        im = im.transpose((2, 0, 1))[::-1]  # HWC to CHW, BGR to RGB\n        im = np.ascontiguousarray(im)\n\n        return img_path, im, im0, None, s\n\n    def __len__(self):\n        return 0\n\n\nclass LoadStreams:\n    # YOLOv5 streamloader, i.e. `python detect.py --source 'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP streams`\n    def __init__(self, sources='file.streams', img_size=640, stride=32, auto=True, transforms=None, vid_stride=1):\n        self.mode = 'stream'\n        self.img_size = img_size\n        self.stride = stride\n        self.vid_stride = vid_stride  # video frame-rate stride\n        sources = Path(sources).read_text().rsplit() if Path(sources).is_file() else [sources]\n        n = len(sources)\n        self.imgs, self.fps, self.frames, self.threads = [None] * n, [0] * n, [0] * n, [None] * n\n        self.sources = [clean_str(x) for x in sources]  # clean source names for later\n        for i, s in enumerate(sources):  # index, source\n            # Start thread to read frames from video stream\n            st = f'{i + 1}/{n}: {s}... '\n            if urlparse(s).hostname in ('www.youtube.com', 'youtube.com', 'youtu.be'):  # if source is YouTube video\n            #YOUTUBE\n                check_requirements(('pafy', 'youtube_dl==2020.12.2'))\n                import pafy\n                s = pafy.new(s).getbest(preftype=\"mp4\").url  # YouTube URL\n            s = eval(s) if s.isnumeric() else s  # i.e. s = '0' local webcam\n            if s == 0:\n                assert not is_colab(), '--source 0 webcam unsupported on Colab. Rerun command in a local environment.'\n                assert not is_kaggle(), '--source 0 webcam unsupported on Kaggle. Rerun command in a local environment.'\n            cap = cv2.VideoCapture(s)\n            assert cap.isOpened(), f'{st}Failed to open {s}'\n            w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))\n            h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\n            fps = cap.get(cv2.CAP_PROP_FPS)  # warning: may return 0 or nan\n            self.frames[i] = max(int(cap.get(cv2.CAP_PROP_FRAME_COUNT)), 0) or float('inf')  # infinite stream fallback\n            self.fps[i] = max((fps if math.isfinite(fps) else 0) % 100, 0) or 30  # 30 FPS fallback\n\n            _, self.imgs[i] = cap.read()  # guarantee first frame\n            self.threads[i] = Thread(target=self.update, args=([i, cap, s]), daemon=True)\n            LOGGER.info(f\"{st} Success ({self.frames[i]} frames {w}x{h} at {self.fps[i]:.2f} FPS)\")\n            self.threads[i].start()\n        LOGGER.info('')  # newline\n\n        # check for common shapes\n        s = np.stack([letterbox(x, self.img_size, stride=self.stride, auto=self.auto)[0].shape for x in self.imgs])\n        self.rect = np.unique(s, axis=0).shape[0] == 1  # rect inference if all shapes equal\n        self.auto = auto and self.rect\n        self.transforms = transforms  # optional\n        if not self.rect:\n            LOGGER.warning('WARNING: Stream shapes differ. For optimal performance supply similarly-shaped streams.')\n\n    def update(self, i, cap, stream):\n        # Read stream `i` frames in daemon thread\n        n, f = 0, self.frames[i]  # frame number, frame array\n        while cap.isOpened() and n < f:\n            n += 1\n            # _, self.imgs[index] = cap.read()\n            cap.grab()\n            if n % self.vid_stride == 0:\n                success, im = cap.retrieve()\n                if success:\n                    self.imgs[i] = im\n                else:\n                    LOGGER.warning('WARNING: Video stream unresponsive, please check your IP camera connection.')\n                    self.imgs[i] = np.zeros_like(self.imgs[i])\n                    cap.open(stream)  # re-open stream if signal was lost\n            time.sleep(0.0)  # wait time\n\n    def __iter__(self):\n        self.count = -1\n        return self\n\n    def __next__(self):\n        self.count += 1\n        if not all(x.is_alive() for x in self.threads) or cv2.waitKey(1) == ord('q'):  # q to quit\n            cv2.destroyAllWindows()\n            raise StopIteration\n\n        im0 = self.imgs.copy()\n        if self.transforms:\n            im = np.stack([self.transforms(x) for x in im0])  # transforms\n        else:\n            im = np.stack([letterbox(x, self.img_size, stride=self.stride, auto=self.auto)[0] for x in im0])  # resize\n            im = im[..., ::-1].transpose((0, 3, 1, 2))  # BGR to RGB, BHWC to BCHW\n            im = np.ascontiguousarray(im)  # contiguous\n\n        return self.sources, im, im0, None, ''\n\n    def __len__(self):\n        return len(self.sources)  # 1E12 frames = 32 streams at 30 FPS for 30 years\n\n\ndef img2label_paths(img_paths):\n    # Define label paths as a function of image paths\n    sa, sb = f'{os.sep}images{os.sep}', f'{os.sep}labels{os.sep}'  # /images/, /labels/ substrings\n    return [sb.join(x.rsplit(sa, 1)).rsplit('.', 1)[0] + '.txt' for x in img_paths]\n\n\nclass LoadImagesAndLabels(Dataset):\n    # YOLOv5 train_loader/val_loader, loads images and labels for training and validation\n    cache_version = 0.6  # dataset labels *.cache version\n    rand_interp_methods = [cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_LANCZOS4]\n\n    def __init__(self,\n                 path,\n                 img_size=640,\n                 batch_size=16,\n                 augment=False,\n                 hyp=None,\n                 rect=False,\n                 image_weights=False,\n                 cache_images=False,\n                 single_cls=False,\n                 stride=32,\n                 pad=0.0,\n                 min_items=0,\n                 prefix=''):\n        self.img_size = img_size\n        self.augment = augment\n        self.hyp = hyp\n        self.image_weights = image_weights\n        self.rect = False if image_weights else rect\n        self.mosaic = self.augment and not self.rect  # load 4 images at a time into a mosaic (only during training)\n        self.mosaic_border = [-img_size // 2, -img_size // 2]\n        self.stride = stride\n        self.path = path\n        self.albumentations = Albumentations(size=img_size) if augment else None\n\n        try:\n            f = []  # image files\n            for p in path if isinstance(path, list) else [path]:\n                p = Path(p)  # os-agnostic\n                if p.is_dir():  # dir\n                    f += glob.glob(str(p / '**' / '*.*'), recursive=True)\n                    # f = list(p.rglob('*.*'))  # pathlib\n                elif p.is_file():  # file\n                    with open(p) as t:\n                        t = t.read().strip().splitlines()\n                        parent = str(p.parent) + os.sep\n                        f += [x.replace('./', parent) if x.startswith('./') else x for x in t]  # local to global path\n                        # f += [p.parent / x.lstrip(os.sep) for x in t]  # local to global path (pathlib)\n                else:\n                    raise FileNotFoundError(f'{prefix}{p} does not exist')\n            self.im_files = sorted(x.replace('/', os.sep) for x in f if x.split('.')[-1].lower() in IMG_FORMATS)\n            # self.img_files = sorted([x for x in f if x.suffix[1:].lower() in IMG_FORMATS])  # pathlib\n            assert self.im_files, f'{prefix}No images found'\n        except Exception as e:\n            raise Exception(f'{prefix}Error loading data from {path}: {e}\\n{HELP_URL}')from e\n\n        # Check cache\n        self.label_files = img2label_paths(self.im_files)  # labels\n        cache_path = (p if p.is_file() else Path(self.label_files[0]).parent).with_suffix('.cache')\n        try:\n            cache, exists = np.load(cache_path, allow_pickle=True).item(), True  # load dict\n            assert cache['version'] == self.cache_version  # matches current version\n            assert cache['hash'] == get_hash(self.label_files + self.im_files)  # identical hash\n        except Exception:\n            cache, exists = self.cache_labels(cache_path, prefix), False  # run cache ops\n\n        # Display cache\n        nf, nm, ne, nc, n = cache.pop('results')  # found, missing, empty, corrupt, total\n        if exists and LOCAL_RANK in {-1, 0}:\n            d = f\"Scanning '{cache_path}' images and labels... {nf} found, {nm} missing, {ne} empty, {nc} corrupt\"\n            tqdm(None, desc=prefix + d, total=n, initial=n, bar_format=TQDM_BAR_FORMAT)  # display cache results\n            if cache['msgs']:\n                LOGGER.info('\\n'.join(cache['msgs']))  # display warnings\n        assert nf > 0 or not augment, f'{prefix}No labels found in {cache_path}, can not start training. {HELP_URL}'\n\n        # Read cache\n        [cache.pop(k) for k in ('hash', 'version', 'msgs')]  # remove items\n        labels, shapes, self.segments = zip(*cache.values())\n        nl = len(np.concatenate(labels, 0))  # number of labels\n        assert nl > 0 or not augment, f'{prefix}All labels empty in {cache_path}, can not start training. {HELP_URL}'\n        self.labels = list(labels)\n        self.shapes = np.array(shapes)\n        self.im_files = list(cache.keys())  # update\n        self.label_files = img2label_paths(cache.keys())  # update\n        # Filter images\n        if min_items:\n            include = np.array([len(x) > min_items for x in self.labels]).nonzero()[0].astype(int)\n            LOGGER.info(f'{prefix}{nf - len(include)}/{nf} images filtered from dataset')\n            self.im_files = [self.im_files[i] for i in include]\n            self.label_files = [self.label_files[i] for i in include]\n            self.labels = [self.labels[i] for i in include]\n            self.segments = [self.segments[i] for i in include]\n            self.shapes = self.shapes[include]  # wh\n\n        # Create indices\n        n = len(shapes)  # number of images\n        bi = np.floor(np.arange(n) / batch_size).astype(int)  # batch index\n        nb = bi[-1] + 1  # number of batches\n        self.batch = bi  # batch index of image\n        self.n = n\n        self.indices = range(n)\n\n        # Update labels\n        include_class = []  # filter labels to include only these classes (optional)\n        include_class_array = np.array(include_class).reshape(1, -1)\n        for i, (label, segment) in enumerate(zip(self.labels, self.segments)):\n            if include_class:\n                j = (label[:, 0:1] == include_class_array).any(1)\n                self.labels[i] = label[j]\n                if segment:\n                    self.segments[i] = segment[j]\n            if single_cls:  # single-class training, merge all classes into 0\n                self.labels[i][:, 0] = 0\n           \n\n        # Rectangular Training\n        if self.rect:\n            # Sort by aspect ratio\n            s = self.shapes  # wh\n            ar = s[:, 1] / s[:, 0]  # aspect ratio\n            irect = ar.argsort()\n            self.im_files = [self.im_files[i] for i in irect]\n            self.label_files = [self.label_files[i] for i in irect]\n            self.labels = [self.labels[i] for i in irect]\n            self.segments = [self.segments[i] for i in irect]\n            self.shapes = s[irect]  # wh\n            ar = ar[irect]\n\n            # Set training image shapes\n            shapes = [[1, 1]] * nb\n            for i in range(nb):\n                ari = ar[bi == i]\n                mini, maxi = ari.min(), ari.max()\n                if maxi < 1:\n                    shapes[i] = [maxi, 1]\n                elif mini > 1:\n                    shapes[i] = [1, 1 / mini]\n\n            self.batch_shapes = np.ceil(np.array(shapes) * img_size / stride + pad).astype(int) * stride\n\n        # Cache images into RAM/disk for faster training \n        if cache_images=='ram' and not self.check_cacha_ram(prefix=prefix):\n            cache_images=False\n        self.ims = [None] * n\n        self.npy_files = [Path(f).with_suffix('.npy') for f in self.im_files]\n        if cache_images:\n            b,gb = 0, 1<<30# bytes of cached images,bytes per gigabytes\n            self.im_hw0, self.im_hw = [None] * n, [None] * n\n            fcn = self.cache_images_to_disk if cache_images == 'disk' else self.load_image\n            results = ThreadPool(NUM_THREADS).imap(fcn, range(n))\n            pbar = tqdm(enumerate(results), total=n, bar_format=TQDM_BAR_FORMAT, disable=LOCAL_RANK > 0)\n            for i, x in pbar:\n                if cache_images == 'disk':\n                    b += self.npy_files[i].stat().st_size\n                else:  # 'ram'\n                    self.ims[i], self.im_hw0[i], self.im_hw[i] = x  # im, hw_orig, hw_resized = load_image(self, i)\n                    b += self.ims[i].nbytes\n                pbar.desc = f'{prefix}Caching images ({b / gb:.1f}GB {cache_images})'\n            pbar.close()\n    \n    def check_cache_ram(self, safety_margin=0.1, prefix=''):\n        # Check image caching requirements vs available memory\n        b, gb = 0, 1 << 30  # bytes of cached images, bytes per gigabytes\n        n = min(self.n, 30)  # extrapolate from 30 random images\n        for _ in range(n):\n            im = cv2.imread(random.choice(self.im_files))  # sample image\n            ratio = self.img_size / max(im.shape[0], im.shape[1])  # max(h, w)  # ratio\n            b += im.nbytes * ratio ** 2\n        mem_required = b * self.n / n  # GB required to cache dataset into RAM\n        mem = psutil.virtual_memory()\n        cache = mem_required * (1 + safety_margin) < mem.available  # to cache or not to cache, that is the question\n        if not cache:\n            LOGGER.info(f\"{prefix}{mem_required / gb:.1f}GB RAM required, \"\n                        f\"{mem.available / gb:.1f}/{mem.total / gb:.1f}GB available, \"\n                        f\"{'caching images ✅' if cache else 'not caching images ⚠️'}\")\n        return cache\n        \n    def cache_labels(self, path=Path('./labels.cache'), prefix=''):\n        # Cache dataset labels, check images and read shapes\n        x = {}  # dict\n        nm, nf, ne, nc, msgs = 0, 0, 0, 0, []  # number missing, found, empty, corrupt, messages\n        desc = f\"{prefix}Scanning '{path.parent / path.stem}' images and labels...\"\n        with Pool(NUM_THREADS) as pool:\n            pbar = tqdm(pool.imap(verify_image_label, zip(self.im_files, self.label_files, repeat(prefix))),\n                        desc=desc,\n                        total=len(self.im_files),\n                        bar_format=TQDM_BAR_FORMAT)\n            for im_file, lb, shape, segments, nm_f, nf_f, ne_f, nc_f, msg in pbar:\n                nm += nm_f\n                nf += nf_f\n                ne += ne_f\n                nc += nc_f\n                if im_file:\n                    x[im_file] = [lb, shape, segments]\n                if msg:\n                    msgs.append(msg)\n                pbar.desc = f\"{desc}{nf} found, {nm} missing, {ne} empty, {nc} corrupt\"\n\n        pbar.close()\n        if msgs:\n            LOGGER.info('\\n'.join(msgs))\n        if nf == 0:\n            LOGGER.warning(f'{prefix}WARNING ⚠️ No labels found in {path}. {HELP_URL}')\n        x['hash'] = get_hash(self.label_files + self.im_files)\n        x['results'] = nf, nm, ne, nc, len(self.im_files)\n        x['msgs'] = msgs  # warnings\n        x['version'] = self.cache_version  # cache version\n        try:\n            np.save(path, x)  # save cache for next time\n            path.with_suffix('.cache.npy').rename(path)  # remove .npy suffix\n            LOGGER.info(f'{prefix}New cache created: {path}')\n        except Exception as e:\n            LOGGER.warning(f'{prefix}WARNING ⚠️ Cache directory {path.parent} is not writeable: {e}')  # not writeable\n        return x\n\n    def __len__(self):\n        return len(self.im_files)\n\n    # def __iter__(self):\n    #     self.count = -1\n    #     print('ran dataset iter')\n    #     #self.shuffled_vector = np.random.permutation(self.nF) if self.augment else np.arange(self.nF)\n    #     return self\n\n    def __getitem__(self, index):\n        index = self.indices[index]  # linear, shuffled, or image_weights\n\n        hyp = self.hyp\n        mosaic = self.mosaic and random.random() < hyp['mosaic']\n        if mosaic:\n            # Load mosaic\n            img, labels = self.load_mosaic(index)\n            shapes = None\n\n            # MixUp augmentation\n            if random.random() < hyp['mixup']:\n                img, labels = mixup(img, labels, *self.load_mosaic(random.randint(0, self.n - 1)))\n\n        else:\n            # Load image\n            img, (h0, w0), (h, w) = self.load_image(index)\n\n            # Letterbox\n            shape = self.batch_shapes[self.batch[index]] if self.rect else self.img_size  # final letterboxed shape\n            img, ratio, pad = letterbox(img, shape, auto=False, scaleup=self.augment)\n            shapes = (h0, w0), ((h / h0, w / w0), pad)  # for COCO mAP rescaling\n\n            labels = self.labels[index].copy()\n            if labels.size:  # normalized xywh to pixel xyxy format\n                labels[:, 1:] = xywhn2xyxy(labels[:, 1:], ratio[0] * w, ratio[1] * h, padw=pad[0], padh=pad[1])\n\n            if self.augment:\n                img, labels = random_perspective(img,\n                                                 labels,\n                                                 degrees=hyp['degrees'],\n                                                 translate=hyp['translate'],\n                                                 scale=hyp['scale'],\n                                                 shear=hyp['shear'],\n                                                 perspective=hyp['perspective'])\n        if self.augment and hyp['paste_in']==1:\n            if random.random() < hyp['paste_in']:\n                sample_labels, sample_images, sample_masks = [], [], [] \n                while len(sample_labels) < 30:\n                    sample_labels_, sample_images_, sample_masks_ = self.load_samples(random.randint(0, len(self.labels) - 1))\n                    sample_labels += sample_labels_\n                    sample_images += sample_images_\n                    sample_masks += sample_masks_\n                    #print(len(sample_labels))\n                    if len(sample_labels) == 0:\n                        break\n                labels = pastein(img, labels, sample_labels, sample_images, sample_masks)\n        \n        nl = len(labels)  # number of labels\n        if nl:\n            labels[:, 1:5] = xyxy2xywhn(labels[:, 1:5], w=img.shape[1], h=img.shape[0], clip=True, eps=1E-3)\n\n        if self.augment:\n            # Albumentations\n            img, labels = self.albumentations(img, labels)\n            nl = len(labels)  # update after albumentations\n\n            # HSV color-space\n            augment_hsv(img, hgain=hyp['hsv_h'], sgain=hyp['hsv_s'], vgain=hyp['hsv_v'])\n\n            # Flip up-down\n            if random.random() < hyp['flipud']:\n                img = np.flipud(img)\n                if nl:\n                    labels[:, 2] = 1 - labels[:, 2]\n\n            # Flip left-right\n            if random.random() < hyp['fliplr']:\n                img = np.fliplr(img)\n                if nl:\n                    labels[:, 1] = 1 - labels[:, 1]\n\n            # Cutouts\n            # labels = cutout(img, labels, p=0.5)\n            # nl = len(labels)  # update after cutout\n\n        labels_out = torch.zeros((nl, 6))\n        if nl:\n            labels_out[:, 1:] = torch.from_numpy(labels)\n\n        # Convert\n        img = img.transpose((2, 0, 1))[::-1]  # HWC to CHW, BGR to RGB\n        img = np.ascontiguousarray(img)\n\n        return torch.from_numpy(img), labels_out, self.im_files[index],shapes \n\n    def load_image(self, i):\n        # Loads 1 image from dataset index 'i', returns (im, original hw, resized hw)\n        im, f, fn = self.ims[i], self.im_files[i], self.npy_files[i],\n        if im is None:  # not cached in RAM\n            if fn.exists():  # load npy\n                im = np.load(fn)\n            else:  # read image\n                im = cv2.imread(f)  # BGR\n                assert im is not None, f'Image Not Found {f}'\n            h0, w0 = im.shape[:2]  # orig hw\n            r = self.img_size / max(h0, w0)  # ratio\n            if r != 1:  # if sizes are not equal\n                interp = cv2.INTER_LINEAR if (self.augment or r > 1) else cv2.INTER_AREA\n                im = cv2.resize(im, (math.ceil(w0 * r), math.ceil(h0 * r)), interpolation=interp)\n            return im, (h0, w0), im.shape[:2]  # im, hw_original, hw_resized\n        return self.ims[i], self.im_hw0[i], self.im_hw[i]  # im, hw_original, hw_resiz\n\n    def cache_images_to_disk(self, i):\n        # Saves an image as an *.npy file for faster loading\n        f = self.npy_files[i]\n        if not f.exists():\n            np.save(f.as_posix(), cv2.imread(self.im_files[i]))\n\n    def load_mosaic(self, index):\n        # YOLOv5 4-mosaic loader. Loads 1 image + 3 random images into a 4-image mosaic\n        labels4, segments4 = [], []\n        s = self.img_size\n        yc, xc = (int(random.uniform(-x, 2 * s + x)) for x in self.mosaic_border)  # mosaic center x, y\n        indices = [index] + random.choices(self.indices, k=3)  # 3 additional image indices\n        random.shuffle(indices)\n        for i, index in enumerate(indices):\n            # Load image\n            img, _, (h, w) = self.load_image(index)\n\n            # place img in img4\n            if i == 0:  # top left\n                img4 = np.full((s * 2, s * 2, img.shape[2]), 114, dtype=np.uint8)  # base image with 4 tiles\n                x1a, y1a, x2a, y2a = max(xc - w, 0), max(yc - h, 0), xc, yc  # xmin, ymin, xmax, ymax (large image)\n                x1b, y1b, x2b, y2b = w - (x2a - x1a), h - (y2a - y1a), w, h  # xmin, ymin, xmax, ymax (small image)\n            elif i == 1:  # top right\n                x1a, y1a, x2a, y2a = xc, max(yc - h, 0), min(xc + w, s * 2), yc\n                x1b, y1b, x2b, y2b = 0, h - (y2a - y1a), min(w, x2a - x1a), h\n            elif i == 2:  # bottom left\n                x1a, y1a, x2a, y2a = max(xc - w, 0), yc, xc, min(s * 2, yc + h)\n                x1b, y1b, x2b, y2b = w - (x2a - x1a), 0, w, min(y2a - y1a, h)\n            elif i == 3:  # bottom right\n                x1a, y1a, x2a, y2a = xc, yc, min(xc + w, s * 2), min(s * 2, yc + h)\n                x1b, y1b, x2b, y2b = 0, 0, min(w, x2a - x1a), min(y2a - y1a, h)\n\n            img4[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b]  # img4[ymin:ymax, xmin:xmax]\n            padw = x1a - x1b\n            padh = y1a - y1b\n\n            # Labels\n            labels, segments = self.labels[index].copy(), self.segments[index].copy()\n            if labels.size:\n                labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padw, padh)  # normalized xywh to pixel xyxy format\n                segments = [xyn2xy(x, w, h, padw, padh) for x in segments]\n            labels4.append(labels)\n            segments4.extend(segments)\n\n        # Concat/clip labels\n        labels4 = np.concatenate(labels4, 0)\n        for x in (labels4[:, 1:], *segments4):\n            np.clip(x, 0, 2 * s, out=x)  # clip when using random_perspective()\n        # img4, labels4 = replicate(img4, labels4)  # replicate\n\n        # Augment\n        img4, labels4, segments4 = copy_paste(img4, labels4, segments4, p=self.hyp['copy_paste'])\n        img4, labels4 = random_perspective(img4,\n                                           labels4,\n                                           segments4,\n                                           degrees=self.hyp['degrees'],\n                                           translate=self.hyp['translate'],\n                                           scale=self.hyp['scale'],\n                                           shear=self.hyp['shear'],\n                                           perspective=self.hyp['perspective'],\n                                           border=self.mosaic_border)  # border to remove\n\n        return img4, labels4\n\n    def load_mosaic9(self, index):\n        # YOLOv5 9-mosaic loader. Loads 1 image + 8 random images into a 9-image mosaic\n        labels9, segments9 = [], []\n        s = self.img_size\n        indices = [index] + random.choices(self.indices, k=8)  # 8 additional image indices\n        random.shuffle(indices)\n        hp, wp = -1, -1  # height, width previous\n        for i, index in enumerate(indices):\n            # Load image\n            img, _, (h, w) = self.load_image(index)\n\n            # place img in img9\n            if i == 0:  # center\n                img9 = np.full((s * 3, s * 3, img.shape[2]), 114, dtype=np.uint8)  # base image with 4 tiles\n                h0, w0 = h, w\n                c = s, s, s + w, s + h  # xmin, ymin, xmax, ymax (base) coordinates\n            elif i == 1:  # top\n                c = s, s - h, s + w, s\n            elif i == 2:  # top right\n                c = s + wp, s - h, s + wp + w, s\n            elif i == 3:  # right\n                c = s + w0, s, s + w0 + w, s + h\n            elif i == 4:  # bottom right\n                c = s + w0, s + hp, s + w0 + w, s + hp + h\n            elif i == 5:  # bottom\n                c = s + w0 - w, s + h0, s + w0, s + h0 + h\n            elif i == 6:  # bottom left\n                c = s + w0 - wp - w, s + h0, s + w0 - wp, s + h0 + h\n            elif i == 7:  # left\n                c = s - w, s + h0 - h, s, s + h0\n            elif i == 8:  # top left\n                c = s - w, s + h0 - hp - h, s, s + h0 - hp\n\n            padx, pady = c[:2]\n            x1, y1, x2, y2 = (max(x, 0) for x in c)  # allocate coords\n\n            # Labels\n            labels, segments = self.labels[index].copy(), self.segments[index].copy()\n            if labels.size:\n                labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padx, pady)  # normalized xywh to pixel xyxy format\n                segments = [xyn2xy(x, w, h, padx, pady) for x in segments]\n            labels9.append(labels)\n            segments9.extend(segments)\n\n            # Image\n            img9[y1:y2, x1:x2] = img[y1 - pady:, x1 - padx:]  # img9[ymin:ymax, xmin:xmax]\n            hp, wp = h, w  # height, width previous\n\n        # Offset\n        yc, xc = (int(random.uniform(0, s)) for _ in self.mosaic_border)  # mosaic center x, y\n        img9 = img9[yc:yc + 2 * s, xc:xc + 2 * s]\n\n        # Concat/clip labels\n        labels9 = np.concatenate(labels9, 0)\n        labels9[:, [1, 3]] -= xc\n        labels9[:, [2, 4]] -= yc\n        c = np.array([xc, yc])  # centers\n        segments9 = [x - c for x in segments9]\n\n        for x in (labels9[:, 1:], *segments9):\n            np.clip(x, 0, 2 * s, out=x)  # clip when using random_perspective()\n        # img9, labels9 = replicate(img9, labels9)  # replicate\n\n        # Augment\n        img9, labels9 = random_perspective(img9,\n                                           labels9,\n                                           segments9,\n                                           degrees=self.hyp['degrees'],\n                                           translate=self.hyp['translate'],\n                                           scale=self.hyp['scale'],\n                                           shear=self.hyp['shear'],\n                                           perspective=self.hyp['perspective'],\n                                           border=self.mosaic_border)  # border to remove\n\n        return img9, labels9\n\n\n    def load_samples(self, index):\n        # loads images in a 4-mosaic\n\n        labels4, segments4 = [], []\n        s = self.img_size\n        yc, xc = [int(random.uniform(-x, 2 * s + x)) for x in self.mosaic_border]  # mosaic center x, y\n        indices = [index] + random.choices(self.indices, k=3)  # 3 additional image indices\n        for i, index in enumerate(indices):\n            # Load image\n            img, _, (h, w) = self.load_image(index)\n\n            # place img in img4\n            if i == 0:  # top left\n                img4 = np.full((s * 2, s * 2, img.shape[2]), 114, dtype=np.uint8)  # base image with 4 tiles\n                x1a, y1a, x2a, y2a = max(xc - w, 0), max(yc - h, 0), xc, yc  # xmin, ymin, xmax, ymax (large image)\n                x1b, y1b, x2b, y2b = w - (x2a - x1a), h - (y2a - y1a), w, h  # xmin, ymin, xmax, ymax (small image)\n            elif i == 1:  # top right\n                x1a, y1a, x2a, y2a = xc, max(yc - h, 0), min(xc + w, s * 2), yc\n                x1b, y1b, x2b, y2b = 0, h - (y2a - y1a), min(w, x2a - x1a), h\n            elif i == 2:  # bottom left\n                x1a, y1a, x2a, y2a = max(xc - w, 0), yc, xc, min(s * 2, yc + h)\n                x1b, y1b, x2b, y2b = w - (x2a - x1a), 0, w, min(y2a - y1a, h)\n            elif i == 3:  # bottom right\n                x1a, y1a, x2a, y2a = xc, yc, min(xc + w, s * 2), min(s * 2, yc + h)\n                x1b, y1b, x2b, y2b = 0, 0, min(w, x2a - x1a), min(y2a - y1a, h)\n\n            img4[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b]  # img4[ymin:ymax, xmin:xmax]\n            padw = x1a - x1b\n            padh = y1a - y1b\n\n            # Labels\n            labels, segments = self.labels[index].copy(), self.segments[index].copy()\n            if labels.size:\n                labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padw, padh)  # normalized xywh to pixel xyxy format\n                segments = [xyn2xy(x, w, h, padw, padh) for x in segments]\n            labels4.append(labels)\n            segments4.extend(segments)\n\n        # Concat/clip labels\n        labels4 = np.concatenate(labels4, 0)\n        for x in (labels4[:, 1:], *segments4):\n            np.clip(x, 0, 2 * s, out=x)  # clip when using random_perspective()\n        # img4, labels4 = replicate(img4, labels4)  # replicate\n\n        # Augment\n        #img4, labels4, segments4 = remove_background(img4, labels4, segments4)\n        sample_labels, sample_images, sample_masks = sample_segments(img4, labels4, segments4, probability=0.5)\n\n        return sample_labels, sample_images, sample_masks\n    \n\n    @staticmethod\n    def collate_fn(batch):\n        im, label, path, shapes = zip(*batch)  # transposed\n        for i, lb in enumerate(label):\n            lb[:, 0] = i  # add target image index for build_targets()\n        return torch.stack(im, 0), torch.cat(label, 0), path, shapes\n    \n    @staticmethod\n    def collate_fn_v8(batch):\n        # YOLOv8 collate function, outputs dict\n        im, label, path, shapes = zip(*batch)  # transposed\n        for i, lb in enumerate(label):\n            lb[:, 0] = i  # add target image index for build_targets()\n        batch_idx, cls, bboxes = torch.cat(label, 0).split((1, 1, 4), dim=1)\n        return {\n            'ori_shape': tuple((x[0] if x else None) for x in shapes),\n            'ratio_pad': tuple((x[1] if x else None) for x in shapes),\n            'im_file': path,\n            'img': torch.stack(im, 0),\n            'cls': cls,\n            'bboxes': bboxes,\n            'batch_idx': batch_idx.view(-1)}\n        \n    @staticmethod\n    def collate_fn4(batch):\n        img, label, path, shapes = zip(*batch)  # transposed\n        n = len(shapes) // 4\n        im4, label4, path4, shapes4 = [], [], path[:n], shapes[:n]\n\n        ho = torch.tensor([[0.0, 0, 0, 1, 0, 0]])\n        wo = torch.tensor([[0.0, 0, 1, 0, 0, 0]])\n        s = torch.tensor([[1, 1, 0.5, 0.5, 0.5, 0.5]])  # scale\n        for i in range(n):  # zidane torch.zeros(16,3,720,1280)  # BCHW\n            i *= 4\n            if random.random() < 0.5:\n                im = F.interpolate(img[i].unsqueeze(0).float(), scale_factor=2.0, mode='bilinear',\n                                   align_corners=False)[0].type(img[i].type())\n                lb = label[i]\n            else:\n                im = torch.cat((torch.cat((img[i], img[i + 1]), 1), torch.cat((img[i + 2], img[i + 3]), 1)), 2)\n                lb = torch.cat((label[i], label[i + 1] + ho, label[i + 2] + wo, label[i + 3] + ho + wo), 0) * s\n            im4.append(im)\n            label4.append(lb)\n\n        for i, lb in enumerate(label4):\n            lb[:, 0] = i  # add target image index for build_targets()\n\n        return torch.stack(im4, 0), torch.cat(label4, 0), path4, shapes4\n\n\n\n# Ancillary functions --------------------------------------------------------------------------------------------------\ndef create_folder(path='./new'):\n    # Create folder\n    if os.path.exists(path):\n        shutil.rmtree(path)  # delete output folder\n    os.makedirs(path)  # make new output folder\n\n\ndef flatten_recursive(path=DATASETS_DIR / 'coco128'):\n    # Flatten a recursive directory by bringing all files to top level\n    new_path = Path(str(path) + '_flat')\n    create_folder(new_path)\n    for file in tqdm(glob.glob(str(Path(path)) + '/**/*.*', recursive=True)):\n        shutil.copyfile(file, new_path / Path(file).name)\n\n\ndef extract_boxes(path=DATASETS_DIR / 'coco128'):  # from utils.datasets import *; extract_boxes()\n    # Convert detection dataset into classification dataset, with one directory per class\n    path = Path(path)  # images dir\n    shutil.rmtree(path / 'classifier') if (path / 'classifier').is_dir() else None  # remove existing\n    files = list(path.rglob('*.*'))\n    n = len(files)  # number of files\n    for im_file in tqdm(files, total=n):\n        if im_file.suffix[1:] in IMG_FORMATS:\n            # image\n            im = cv2.imread(str(im_file))[..., ::-1]  # BGR to RGB\n            h, w = im.shape[:2]\n\n            # labels\n            lb_file = Path(img2label_paths([str(im_file)])[0])\n            if Path(lb_file).exists():\n                with open(lb_file) as f:\n                    lb = np.array([x.split() for x in f.read().strip().splitlines()], dtype=np.float32)  # labels\n\n                for j, x in enumerate(lb):\n                    c = int(x[0])  # class\n                    f = (path / 'classifier') / f'{c}' / f'{path.stem}_{im_file.stem}_{j}.jpg'  # new filename\n                    if not f.parent.is_dir():\n                        f.parent.mkdir(parents=True)\n\n                    b = x[1:] * [w, h, w, h]  # box\n                    # b[2:] = b[2:].max()  # rectangle to square\n                    b[2:] = b[2:] * 1.2 + 3  # pad\n                    b = xywh2xyxy(b.reshape(-1, 4)).ravel().astype(int)\n\n                    b[[0, 2]] = np.clip(b[[0, 2]], 0, w)  # clip boxes outside of image\n                    b[[1, 3]] = np.clip(b[[1, 3]], 0, h)\n                    assert cv2.imwrite(str(f), im[b[1]:b[3], b[0]:b[2]]), f'box failure in {f}'\n\n\ndef autosplit(path=DATASETS_DIR / 'coco128/images', weights=(0.9, 0.1, 0.0), annotated_only=False):\n    \"\"\" Autosplit a dataset into train/val/test splits and save path/autosplit_*.txt files\n    Usage: from utils.datasets import *; autosplit()\n    Arguments\n        path:            Path to images directory\n        weights:         Train, val, test weights (list, tuple)\n        annotated_only:  Only use images with an annotated txt file\n    \"\"\"\n    path = Path(path)  # images dir\n    files = sorted(x for x in path.rglob('*.*') if x.suffix[1:].lower() in IMG_FORMATS)  # image files only\n    n = len(files)  # number of files\n    random.seed(0)  # for reproducibility\n    indices = random.choices([0, 1, 2], weights=weights, k=n)  # assign each image to a split\n\n    txt = ['autosplit_train.txt', 'autosplit_val.txt', 'autosplit_test.txt']  # 3 txt files\n    [(path.parent / x).unlink(missing_ok=True) for x in txt]  # remove existing\n\n    print(f'Autosplitting images from {path}' + ', using *.txt labeled images only' * annotated_only)\n    for i, img in tqdm(zip(indices, files), total=n):\n        if not annotated_only or Path(img2label_paths([str(img)])[0]).exists():  # check label\n            with open(path.parent / txt[i], 'a') as f:\n                f.write('./' + img.relative_to(path.parent).as_posix() + '\\n')  # add image to txt file\n\n\ndef verify_image_label(args):\n    # Verify one image-label pair\n    im_file, lb_file, prefix = args\n    nm, nf, ne, nc, msg, segments = 0, 0, 0, 0, '', []  # number (missing, found, empty, corrupt), message, segments\n    try:\n        # verify images\n        im = Image.open(im_file)\n        im.verify()  # PIL verify\n        shape = exif_size(im)  # image size\n        assert (shape[0] > 9) & (shape[1] > 9), f'image size {shape} <10 pixels'\n        assert im.format.lower() in IMG_FORMATS, f'invalid image format {im.format}'\n        if im.format.lower() in ('jpg', 'jpeg'):\n            with open(im_file, 'rb') as f:\n                f.seek(-2, 2)\n                if f.read() != b'\\xff\\xd9':  # corrupt JPEG\n                    ImageOps.exif_transpose(Image.open(im_file)).save(im_file, 'JPEG', subsampling=0, quality=100)\n                    msg = f'{prefix}WARNING: {im_file}: corrupt JPEG restored and saved'\n\n        # verify labels\n        if os.path.isfile(lb_file):\n            nf = 1  # label found\n            with open(lb_file) as f:\n                lb = [x.split() for x in f.read().strip().splitlines() if len(x)]\n                if any(len(x) > 6 for x in lb):  # is segment\n                    classes = np.array([x[0] for x in lb], dtype=np.float32)\n                    segments = [np.array(x[1:], dtype=np.float32).reshape(-1, 2) for x in lb]  # (cls, xy1...)\n                    lb = np.concatenate((classes.reshape(-1, 1), segments2boxes(segments)), 1)  # (cls, xywh)\n                lb = np.array(lb, dtype=np.float32)\n            nl = len(lb)\n            if nl:\n                assert lb.shape[1] == 5, f'labels require 5 columns, {lb.shape[1]} columns detected'\n                assert (lb >= 0).all(), f'negative label values {lb[lb < 0]}'\n                assert (lb[:, 1:] <= 1).all(), f'non-normalized or out of bounds coordinates {lb[:, 1:][lb[:, 1:] > 1]}'\n                _, i = np.unique(lb, axis=0, return_index=True)\n                if len(i) < nl:  # duplicate row check\n                    lb = lb[i]  # remove duplicates\n                    if segments:\n                        segments = segments[i]\n                    msg = f'{prefix}WARNING: {im_file}: {nl - len(i)} duplicate labels removed'\n            else:\n                ne = 1  # label empty\n                lb = np.zeros((0, 5), dtype=np.float32)\n        else:\n            nm = 1  # label missing\n            lb = np.zeros((0, 5), dtype=np.float32)\n        return im_file, lb, shape, segments, nm, nf, ne, nc, msg\n    except Exception as e:\n        nc = 1\n        msg = f'{prefix}WARNING: {im_file}: ignoring corrupt image/label: {e}'\n        return [None, None, None, None, nm, nf, ne, nc, msg]\n"
  },
  {
    "path": "utils/downloads.py",
    "content": "\n\"\"\"\nDownload utils\n\"\"\"\n\nimport logging\nimport os\nimport subprocess\nimport urllib\nfrom itertools import repeat\nfrom pathlib import Path\nfrom multiprocessing.pool import ThreadPool\nimport requests\nimport torch\n\nimport contextlib\nimport shutil\nfrom utils import LOGGER\nfrom urllib import parse, request\nfrom zipfile import BadZipFile, ZipFile, is_zipfile\nfrom tqdm import tqdm\n\n\ndef is_url(url, check_online=True):\n    # Check if online file exists\n    try:\n        url = str(url)\n        result = urllib.parse.urlparse(url)\n        assert all([result.scheme, result.netloc])  # check if is url\n        return (urllib.request.urlopen(url).getcode() == 200) if check_online else True  # check if exists online\n    except (AssertionError, urllib.request.HTTPError):\n        return False\n\ndef unzip_file(file, path=None, exclude=('.DS_Store', '__MACOSX')):\n    \"\"\"\n    Unzip a *.zip file to path/, excluding files containing strings in exclude list\n    Replaces: ZipFile(file).extractall(path=path)\n    \"\"\"\n    if not (Path(file).exists() and is_zipfile(file)):\n        raise BadZipFile(f\"File '{file}' does not exist or is a bad zip file.\")\n    if path is None:\n        path = Path(file).parent  # default path\n    with ZipFile(file) as zipObj:\n        for f in zipObj.namelist():  # list all archived filenames in the zip\n            if all(x not in f for x in exclude):\n                zipObj.extract(f, path=path)\n        return zipObj.namelist()[0]  # return unzip dir\n\ndef gsutil_getsize(url=''):\n    # gs://bucket/file size https://cloud.google.com/storage/docs/gsutil/commands/du\n    s = subprocess.check_output(f'gsutil du {url}', shell=True).decode('utf-8')\n    return eval(s.split(' ')[0]) if len(s) else 0  # bytes\n\ndef url_getsize(url='https://ultralytics.com/images/bus.jpg'):\n    # Return downloadable file size in bytes\n    response = requests.head(url, allow_redirects=True)\n    return int(response.headers.get('content-length', -1))\n\ndef check_disk_space(url=None, sf=1.5, hard=True):\n    \"\"\"\n    Check if there is sufficient disk space to download and store a file.\n\n    Args:\n        url (str, optional): The URL to the file. Defaults to 'https://ultralytics.com/assets/coco128.zip'.\n        sf (float, optional): Safety factor, the multiplier for the required free space. Defaults to 2.0.\n        hard (bool, optional): Whether to throw an error or not on insufficient disk space. Defaults to True.\n\n    Returns:\n        (bool): True if there is sufficient disk space, False otherwise.\n    \"\"\"\n    with contextlib.suppress(Exception):\n        #from utils import LOGGER\n        gib = 1 << 30  # bytes per GiB\n        data = int(requests.head(url).headers['Content-Length']) / gib  # file size (GB)\n        total, used, free = (x / gib for x in shutil.disk_usage('/'))  # bytes\n        if data * sf < free:\n            return True  # sufficient space\n\n        # Insufficient space\n        text = (f'WARNING ⚠️ Insufficient free disk space {free:.1f} GB < {data * sf:.3f} GB required, '\n                f'Please free {data * sf - free:.1f} GB additional disk space and try again.')\n        if hard:\n            raise MemoryError(text)\n        else:\n            LOGGER.warning(text)\n            return False\n\n            # Pass if error\n    return True\ndef safe_download(url,\n                  file=None,\n                  dir=None,\n                  unzip=True,\n                  delete=False,\n                  curl=False,\n                  retry=3,\n                  min_bytes=1E0,\n                  progress=True):\n    \"\"\"\n    Downloads files from a URL, with options for retrying, unzipping, and deleting the downloaded file.\n\n    Args:\n        url (str): The URL of the file to be downloaded.\n        file (str, optional): The filename of the downloaded file.\n            If not provided, the file will be saved with the same name as the URL.\n        dir (str, optional): The directory to save the downloaded file.\n            If not provided, the file will be saved in the current working directory.\n        unzip (bool, optional): Whether to unzip the downloaded file. Default: True.\n        delete (bool, optional): Whether to delete the downloaded file after unzipping. Default: False.\n        curl (bool, optional): Whether to use curl command line tool for downloading. Default: False.\n        retry (int, optional): The number of times to retry the download in case of failure. Default: 3.\n        min_bytes (float, optional): The minimum number of bytes that the downloaded file should have, to be considered\n            a successful download. Default: 1E0.\n        progress (bool, optional): Whether to display a progress bar during the download. Default: True.\n    \"\"\"\n    from utils import LOGGER,url2file,emojis\n    from utils import is_online,checks,clean_url\n    if '://' not in str(url) and Path(url).is_file():  # exists ('://' check required in Windows Python<3.10)\n        f = Path(url)  # filename\n    else:  # does not exist\n        assert dir or file, 'dir or file required for download'\n        f = dir / url2file(url) if dir else Path(file)\n        desc = f'Downloading {clean_url(url)} to {f}'\n        LOGGER.info(f'{desc}...')\n        f.parent.mkdir(parents=True, exist_ok=True)  # make directory if missing\n        check_disk_space(url)\n        for i in range(retry + 1):\n            try:\n                if curl or i > 0:  # curl download with retry, continue\n                    s = 'sS' * (not progress)  # silent\n                    r = subprocess.run(['curl', '-#', f'-{s}L', url, '-o', f, '--retry', '3', '-C', '-']).returncode\n                    assert r == 0, f'Curl return value {r}'\n                else:  # urllib download\n                    method = 'torch'\n                    if method == 'torch':\n                        torch.hub.download_url_to_file(url, f, progress=progress)\n                    else:\n                        from utils import TQDM_BAR_FORMAT\n                        with request.urlopen(url) as response, tqdm(total=int(response.getheader('Content-Length', 0)),\n                                                                    desc=desc,\n                                                                    disable=not progress,\n                                                                    unit='B',\n                                                                    unit_scale=True,\n                                                                    unit_divisor=1024,\n                                                                    bar_format=TQDM_BAR_FORMAT) as pbar:\n                            with open(f, 'wb') as f_opened:\n                                for data in response:\n                                    f_opened.write(data)\n                                    pbar.update(len(data))\n\n                if f.exists():\n                    if f.stat().st_size > min_bytes:\n                        break  # success\n                    f.unlink()  # remove partial downloads\n            except Exception as e:\n                if i == 0 and not is_online():\n                    raise ConnectionError(emojis(f'❌  Download failure for {url}. Environment is not online.')) from e\n                elif i >= retry:\n                    raise ConnectionError(emojis(f'❌  Download failure for {url}. Retry limit reached.')) from e\n                LOGGER.warning(f'⚠️ Download failure, retrying {i + 1}/{retry} {url}...')\n\n    if unzip and f.exists() and f.suffix in ('', '.zip', '.tar', '.gz'):\n        unzip_dir = dir or f.parent  # unzip to dir if provided else unzip in place\n        LOGGER.info(f'Unzipping {f} to {unzip_dir}...')\n        if is_zipfile(f):\n            unzip_dir = unzip_file(file=f, path=unzip_dir)  # unzip\n        elif f.suffix == '.tar':\n            subprocess.run(['tar', 'xf', f, '--directory', unzip_dir], check=True)  # unzip\n        elif f.suffix == '.gz':\n            subprocess.run(['tar', 'xfz', f, '--directory', unzip_dir], check=True)  # unzip\n        if delete:\n            f.unlink()  # remove zip\n        return unzip_dir\n\n\ndef attempt_download(file, repo='positive666/Prompt-Can-Anything', release='0.1'):\n    # Attempt file download from GitHub release assets if not found locally. release = 'latest', 'v6.2', etc.\n\n\n    def github_assets(repository, version='latest'):\n        # Return GitHub repo tag\n        if version != 'latest':\n            version = f'tags/{version}'  # i.e. tags/v6.2\n        response = requests.get(f'https://api.github.com/repos/{repository}/releases/{version}').json()  # github api\n        return response['tag_name'], [x['name'] for x in response['assets']]  # tag, assets\n\n    file = Path(str(file).strip().replace(\"'\", ''))\n    if not file.exists():\n        # URL specified\n        name = Path(urllib.parse.unquote(str(file))).name  # decode '%2F' to '/' etc.\n        if str(file).startswith(('http:/', 'https:/')):  # download\n            url = str(file).replace(':/', '://')  # Pathlib turns :// -> :/\n            file = name.split('?')[0]  # parse authentication https://url.com/file.txt?auth...\n            if Path(file).is_file():\n                LOGGER.info(f'Found {url} locally at {file}')  # file already exists\n            else:\n                safe_download(file=file, url=url, min_bytes=1E5)\n            return file\n\n        # GitHub assets\n       \n        try:\n            tag, assets = github_assets(repo, release)\n        except Exception:\n            try:\n                tag, assets = github_assets(repo)  # latest release\n            except Exception:\n                try:\n                    tag = subprocess.check_output('git tag', shell=True, stderr=subprocess.STDOUT).decode().split()[-1]\n                except Exception:\n                    tag = release\n\n        file.parent.mkdir(parents=True, exist_ok=True)  # make parent dir (if required)\n        if name in assets:\n\n            safe_download(\n                url=f'https://github.com/{repo}/releases/download/{tag}/{name}',\n                file=file,\n                #url2=f'https://storage.googleapis.com/{repo}/{tag}/{name}',  # backup url (optional)\n                min_bytes=1E5)\n                #error_msg=f'{file} missing, try downloading from https://github.com/{repo}/releases/{tag} or {url3}')\n\n    return str(file)\n\ndef download_model(model_url, save_directory):\n    try:\n        # create directory if it doesn't exist\n        os.makedirs(save_directory, exist_ok=True)\n\n        # download model file\n        file_name = model_url.split(\"/\")[-1]\n        save_path = os.path.join(save_directory, file_name)\n        urllib.request.urlretrieve(model_url, save_path)\n\n        print(\"Model downloaded successfully.\")\n    except Exception as e:\n        print(\"Unable to download model:\", str(e))"
  },
  {
    "path": "utils/ops.py",
    "content": "# YOLOv5_reasearch\n\"\"\"\nGeneral utils\n\"\"\"\n\nimport contextlib\nimport glob\nimport inspect\nimport logging\nimport logging.config\nimport math\nimport os\nimport platform\nimport random\nimport re\nimport signal\nimport sys\nimport time\nimport urllib\nfrom copy import deepcopy\nfrom datetime import datetime\nfrom itertools import repeat\nfrom multiprocessing.pool import ThreadPool\nfrom pathlib import Path\nfrom subprocess import check_output\nfrom typing import Optional\nfrom zipfile import ZipFile\nfrom typing import Union\nimport torchvision.ops as ops\nimport cv2\nimport numpy as np\nimport pandas as pd\nimport pkg_resources as pkg\nimport torch\nimport torchvision\nimport yaml\nfrom types import SimpleNamespace\nfrom utils import TryExcept, emojis\nfrom torch.nn import functional as F\nfrom utils.downloads import gsutil_getsize\n\n\nFILE = Path(__file__).resolve()\nROOT = FILE.parents[1]  # root directory\nRANK = int(os.getenv('RANK', -1))\n\n# Settings\nNUM_THREADS = min(8, max(1, os.cpu_count() - 1))  # number multiprocessing threads\nDATASETS_DIR = Path(os.getenv('RESEARCH_DIR', ROOT.parent / 'datasets'))  # global datasets directory\nAUTOINSTALL = str(os.getenv('AUTOINSTALL', True)).lower() == 'true'  # global auto-install mode\nVERBOSE = str(os.getenv('Research_VERBOSE', True)).lower() == 'true'  # global verbose mode\nTQDM_BAR_FORMAT = '{l_bar}{bar:10}| {n_fmt}/{total_fmt} {elapsed}'  # tqdm bar format   \nFONT = 'Arial.ttf'  # https://ultralytics.com/assets/Arial.ttf\n\n\ntorch.set_printoptions(linewidth=320, precision=5, profile='long')\nnp.set_printoptions(linewidth=320, formatter={'float_kind': '{:11.5g}'.format})  # format short g, %precision=5\npd.options.display.max_columns = 10\ncv2.setNumThreads(0)  # prevent OpenCV from multithreading (incompatible with PyTorch DataLoader)\nos.environ['NUMEXPR_MAX_THREADS'] = str(NUM_THREADS)  # NumExpr max threads\nos.environ['OMP_NUM_THREADS'] = '1' if platform.system() == 'darwin' else str(NUM_THREADS)\n\ndef write_xml(filename, img_name, img_root,preds, width, height):\n    \"\"\"\n    Write labels and bounding boxes to XML file\n    \"\"\"\n    # 构造 XML 内容\n    xml_content = f\"\"\"<?xml version=\"1.0\"?>\n    <annotation>\n        <folder>{img_root}</folder>\n        <filename>{img_name}</filename>\n        <size>\n            <width>{width}</width>\n            <height>{height}</height>\n            <depth>{3}</depth>\n        </size>\n    \"\"\"\n    for label, bbox in zip(preds[2],preds[0]):\n        xml_content += f\"\"\"    <object>\n            <name>{label}</name>\n            <bndbox>\n                <xmin>{int(bbox[0])}</xmin>\n                <ymin>{int(bbox[1])}</ymin>\n                <xmax>{int(bbox[2])}</xmax>\n                <ymax>{int(bbox[3])}</ymax>\n            </bndbox>\n        </object>\n        \"\"\"\n    xml_content += \"</annotation>\"\n    # 将 XML 内容写入文件\n    with open(filename+f'/{img_name}.xml', 'a') as f:\n        f.write(xml_content + '\\n')\n        \ndef save_format(label_format: str =\"xml\",save_path :str =\"runs/xmls\" , \n                img_name:str=\"\", img_root:str=\"\",\n                results='', width='', height=''):\n    if label_format==\"xml\":\n        write_xml(save_path, img_name, img_root,results, width, height)\n        #LOGGER.info(f\"Results saved to {colorstr('bold', save_path)}/{img_name,}.xml\")\n    elif label_format==\"txt\":\n        with open(f'{save_path}/{img_name}.txt', 'a') as f:\n                f.write(results+\"\\n\")        \n                #LOGGER.info(f\"Results saved to {colorstr('bold', save_path)}/{img_name}.txt\")     \n    else:\n        LOGGER.info('no support annoation format! ')\n        \ndef dilate_mask(mask, dilate_factor=15):\n    mask = mask.astype(np.uint8)\n    mask = cv2.dilate(\n        mask,\n        np.ones((dilate_factor, dilate_factor), np.uint8),\n        iterations=1\n    )\n    return mask\n\ndef erode_mask(mask, dilate_factor=15):\n    mask = mask.astype(np.uint8)\n    mask = cv2.erode(\n        mask,\n        np.ones((dilate_factor, dilate_factor), np.uint8),\n        iterations=1\n    )\n    return mask\n\ndef torch_nms_box(bboxes, scores, iou_threshold=0.5):\n    \"\"\"Apply non-maximum suppression to bounding boxes (on GPU).\n\n    Args:\n        bboxes (torch.Tensor): Tensor with shape (num_bboxes, 4) representing the bounding boxes\n                               of the objects in the image. Each bounding box is represented as\n                               (x1, y1, x2, y2) where (x1, y1) is the upper-left corner and\n                               (x2, y2) is the lower-right corner.\n        scores (torch.Tensor): Tensor with shape (num_bboxes,) representing the confidence\n                               scores of each bounding box.\n        iou_threshold (float, optional): The IoU threshold above which overlapping boxes will\n                                         be suppressed. Defaults to 0.5.\n\n    Returns:\n        torch.Tensor: Indexes of the boxes that survived non-maximum suppression, with shape (num_surviving_boxes,).\n\n    \"\"\"\n    # Compute box areas\n    areas = (bboxes[:, 2] - bboxes[:, 0]) * (bboxes[:, 3] - bboxes[:, 1])\n\n    # Sort boxes by their confidence scores\n    sorted_indices = torch.argsort(scores, descending=True)\n\n    # Initialize the list of selected box indexes\n    selected_indices = []\n\n    # Perform non-maximum suppression\n    while len(sorted_indices) > 0:\n        # Select highest-scoring box\n        i = sorted_indices[0]\n\n        # Add it to the list of selected boxes\n        selected_indices.append(i)\n\n        # Compute IoU with other boxes\n        ious = ops.box_iou(bboxes[i].unsqueeze(0), bboxes[sorted_indices])[0]\n\n        # Keep only boxes with IoU below the threshold\n        keep_mask = ious <= iou_threshold\n\n        # Remove selected and overlapping boxes\n        sorted_indices = sorted_indices[keep_mask]\n\n    # Return indexes of selected boxes\n    return torch.tensor(selected_indices)\n\ndef check_class_names(names):\n    # Check class names. Map imagenet class codes to human-readable names if required. Convert lists to dicts.\n    if isinstance(names, list):  # names is a list\n        names = dict(enumerate(names))  # convert to dict\n    if isinstance(names, dict):\n        # convert 1) string keys to int, i.e. '0' to 0, and non-string values to strings, i.e. True to 'True'\n        names = {int(k): str(v) for k, v in names.items()}\n        n = len(names)\n        if max(names.keys()) >= n:\n            raise KeyError(f'{n}-class dataset requires class indices 0-{n - 1}, but you have invalid class indices '\n                           f'{min(names.keys())}-{max(names.keys())} defined in your dataset YAML.')\n        if isinstance(names[0], str) and names[0].startswith('n0'):  # imagenet class codes, i.e. 'n01440764'\n            map = yaml_load(ROOT / 'datasets/ImageNet.yaml')['map']  # human-readable names\n            names = {k: map[v] for k, v in names.items()}\n    return names\n\ndef is_kaggle():\n    # Is environment a Kaggle Notebook?\n    return os.environ.get('PWD')=='/Kaggle/working' and os.environ.get('KAGGLE_URL_BASE')=='https://www.kaggle.com'\n\n\ndef is_writeable(dir, test=False):\n    # Return True if directory has write permissions, test opening a file with write permissions if test=True\n    if not test:\n        return os.access(dir, os.R_OK)  # possible issues on Windows\n    file = Path(dir) / 'tmp.txt'\n    try:\n        with open(file, 'w'):  # open file with write permissions\n            pass\n        file.unlink()  # remove file\n        return True\n    except OSError:\n        return False\n\n\ndef set_logging(name=None, verbose=VERBOSE):\n    # Sets level and returns    \n    if is_kaggle():\n        for h in logging.root.handlers:\n            logging.root.removeHandler(h)  # remove all handlers associated with the root logger object\n    rank = int(os.getenv('RANK', -1))  # rank in world for Multi-GPU trainings\n    level = logging.INFO if verbose and rank in {-1, 0} else logging.WARNING\n    log = logging.getLogger(name)\n    log.setLevel(level)\n    handler = logging.StreamHandler()\n    handler.setFormatter(logging.Formatter(\"%(message)s\"))\n    handler.setLevel(level)\n    log.addHandler(handler)\n    \nLOGGING_NAME=\"Yolo_research\"\n\ndef set_logging(name=LOGGING_NAME, verbose=True):\n    # sets up logging for the given name\n    rank = int(os.getenv('RANK', -1))  # rank in world for Multi-GPU trainings\n    level = logging.INFO if verbose and rank in {-1, 0} else logging.ERROR\n    logging.config.dictConfig({\n        \"version\": 1,\n        \"disable_existing_loggers\": False,\n        \"formatters\": {\n            name: {\n                \"format\": \"%(message)s\"}},\n        \"handlers\": {\n            name: {\n                \"class\": \"logging.StreamHandler\",\n                \"formatter\": name,\n                \"level\": level,}},\n        \"loggers\": {\n            name: {\n                \"level\": level,\n                \"handlers\": [name],\n                \"propagate\": False,}}})\n\n\nset_logging(LOGGING_NAME)  # run before defining LOGGER\nLOGGER = logging.getLogger(LOGGING_NAME)  # define globally (used in train.py, val.py, detect.py, etc.)\nif platform.system() == 'Windows':\n    for fn in LOGGER.info, LOGGER.warning:\n        setattr(LOGGER, fn.__name__, lambda x: fn(emojis(x)))  # emoji safe logging\n\ndef user_config_dir(dir='positive666', env_var='YOLOV5_CONFIG_DIR'):\n    # Return path of user configuration directory. Prefer environment variable if exists. Make dir if required.\n    env = os.getenv(env_var)\n    if env:\n        path = Path(env)  # use environment variable\n    else:\n        cfg = {'Windows': 'AppData/Roaming', 'Linux': '.config', 'Darwin': 'Library/Application Support'}  # 3 OS dirs\n        path = Path.home() / cfg.get(platform.system(), '')  # OS-specific config dir\n        path = (path if is_writeable(path) else Path('/tmp')) / dir  # GCP and AWS lambda fix, only /tmp is writeable\n    path.mkdir(exist_ok=True)  # make if required\n    return path\n\n\nCONFIG_DIR = user_config_dir()  # Ultralytics settings dir\n\n\nclass Profile(contextlib.ContextDecorator):\n    # YOLOv5 Profile class. Usage: @Profile() decorator or 'with Profile():' context manager\n    def __init__(self, t=0.0):\n        self.t = t\n        self.cuda = torch.cuda.is_available()\n\n    def __enter__(self):\n        self.start = self.time()\n        return self\n\n    def __exit__(self, type, value, traceback):\n        self.dt = self.time() - self.start  # delta-time\n        self.t += self.dt  # accumulate dt\n\n    def time(self):\n        if self.cuda:\n            torch.cuda.synchronize()\n        return time.time()\n\n\nclass Timeout(contextlib.ContextDecorator):\n    #  Timeout class. Usage: @Timeout(seconds) decorator or 'with Timeout(seconds):' context manager\n    def __init__(self, seconds, *, timeout_msg='', suppress_timeout_errors=True):\n        self.seconds = int(seconds)\n        self.timeout_message = timeout_msg\n        self.suppress = bool(suppress_timeout_errors)\n\n    def _timeout_handler(self, signum, frame):\n        raise TimeoutError(self.timeout_message)\n\n    def __enter__(self):\n        if platform.system() != 'Windows':  # not supported on Windows\n            signal.signal(signal.SIGALRM, self._timeout_handler)  # Set handler for SIGALRM\n            signal.alarm(self.seconds)  # start countdown for SIGALRM to be raised\n\n    def __exit__(self, exc_type, exc_val, exc_tb):\n        if platform.system() != 'Windows':\n            signal.alarm(0)  # Cancel SIGALRM if it's scheduled\n            if self.suppress and exc_type is TimeoutError:  # Suppress TimeoutError\n                return True\n\n\nclass WorkingDirectory(contextlib.ContextDecorator):\n    # Usage: @WorkingDirectory(dir) decorator or 'with WorkingDirectory(dir):' context manager\n    def __init__(self, new_dir):\n        self.dir = new_dir  # new dir\n        self.cwd = Path.cwd().resolve()  # current dir\n\n    def __enter__(self):\n        os.chdir(self.dir)\n\n    def __exit__(self, exc_type, exc_val, exc_tb):\n        os.chdir(self.cwd)\n\n\n\ndef methods(instance):\n    # Get class/instance methods\n    return [f for f in dir(instance) if callable(getattr(instance, f)) and not f.startswith(\"__\")]\n\n\ndef print_args(args: Optional[dict] = None, show_file=True, show_func=False):\n    # Print function arguments (optional args dict)\n    x = inspect.currentframe().f_back  # previous frame\n    file, _, func, _, _ = inspect.getframeinfo(x)\n    if args is None:  # get args automatically\n        args, _, _, frm = inspect.getargvalues(x)\n        args = {k: v for k, v in frm.items() if k in args}\n    try:\n        file = Path(file).resolve().relative_to(ROOT).with_suffix('')\n    except ValueError:\n        file = Path(file).stem\n    s = (f'{file}: ' if show_file else '') + (f'{func}: ' if show_func else '')\n    LOGGER.info(colorstr(s) + ', '.join(f'{k}={v}' for k, v in args.items()))\n\n\ndef init_seeds(seed=0, deterministic=False):\n    # Initialize random number generator (RNG) seeds https://pytorch.org/docs/stable/notes/randomness.html\n    # cudnn seed 0 settings are slower and more reproducible, else faster and less reproducible\n    import torch.backends.cudnn as cudnn\n\n    if deterministic and check_version(torch.__version__, '1.12.0'):  # https://github.com/ultralytics/yolov5/pull/8213\n        torch.use_deterministic_algorithms(True)\n        os.environ['CUBLAS_WORKSPACE_CONFIG'] = ':4096:8'\n        os.environ['PYTHONHASHSEED'] = str(seed)\n\n    random.seed(seed)\n    np.random.seed(seed)\n    torch.manual_seed(seed)\n    cudnn.benchmark, cudnn.deterministic = (False, True) if seed == 0 else (True, False)\n    torch.cuda.manual_seed(seed)\n    torch.cuda.manual_seed_all(seed)  # for Multi-GPU, exception safe\n\n\ndef intersect_dicts(da, db, exclude=()):\n    # Dictionary intersection of matching keys and shapes, omitting 'exclude' keys, using da values\n    return {k: v for k, v in da.items() if k in db and not any(x in k for x in exclude) and v.shape == db[k].shape}\n    \ndef get_default_args(func):\n    # Get func() default arguments\n    signature = inspect.signature(func)\n    return {k: v.default for k, v in signature.parameters.items() if v.default is not inspect.Parameter.empty}\n    \ndef merge_bases(rois, coeffs, attn_r, num_b, location_to_inds=None):\n    # merge predictions\n    # N = coeffs.size(0)\n    if location_to_inds is not None:\n        rois = rois[location_to_inds]\n    N, B, H, W = rois.size()\n    if coeffs.dim() != 4:\n        coeffs = coeffs.view(N, num_b, attn_r, attn_r)\n    # NA = coeffs.shape[1] //  B\n    coeffs = F.interpolate(coeffs, (H, W),\n                           mode=\"bilinear\").softmax(dim=1)\n    # coeffs = coeffs.view(N, -1, B, H, W)\n    # rois = rois[:, None, ...].repeat(1, NA, 1, 1, 1)\n    # masks_preds, _ = (rois * coeffs).sum(dim=2) # c.max(dim=1)\n    masks_preds = (rois * coeffs).sum(dim=1)\n    return masks_preds\n\n\ndef get_latest_run(search_dir='.'):\n    # Return path to most recent 'last.pt' in /runs (i.e. to --resume from)\n    last_list = glob.glob(f'{search_dir}/**/last*.pt', recursive=True)\n    return max(last_list, key=os.path.getctime) if last_list else ''\n\n\ndef is_docker():\n    #\"\"\"Check if the process run inside a docker container.\"\"\"\n    if Path(\"/.dockerenv\").exists():\n        return True \n    try:\n        with open(\"/proc/self/cgroup\")as file:\n            return any(\"docker\" in line for line in file)\n    except OSError:\n        return False \n\ndef is_colab():\n    # Is environment a Google Colab instance?\n    return 'google.colab' in sys.modules\n\n\ndef is_jupyter():\n    \"\"\"\n    Check if the current script is running inside a Jupyter Notebook.\n    Verified on Colab, Jupyterlab, Kaggle, Paperspace.\n    Returns:\n        bool: True if running inside a Jupyter Notebook, False otherwise.\n    \"\"\"\n    with contextlib.suppress(Exception):\n        from IPython import get_ipython\n        return get_ipython() is not None\n    return False\n    \ndef is_pip():\n    # Is file in a pip package?\n    return 'site-packages' in Path(__file__).resolve().parts\n\n\ndef is_ascii(s=''):\n    # Is string composed of all ASCII (no UTF) characters? (note str().isascii() introduced in python 3.7)\n    s = str(s)  # convert list, tuple, None, etc. to str\n    return len(s.encode().decode('ascii', 'ignore')) == len(s)\n\n\ndef is_chinese(s='人工智能'):\n    # Is string composed of any Chinese characters?\n    return bool(re.search('[\\u4e00-\\u9fff]', str(s)))\n\n\ndef file_age(path=__file__):\n    # Return days since last file update\n    dt = (datetime.now() - datetime.fromtimestamp(Path(path).stat().st_mtime))  # delta\n    return dt.days  # + dt.seconds / 86400  # fractional days\n\n\ndef file_date(path=__file__):\n    # Return human-readable file modification date, i.e. '2021-3-26'\n    t = datetime.fromtimestamp(Path(path).stat().st_mtime)\n    return f'{t.year}-{t.month}-{t.day}'\n\n\ndef file_size(path):\n    # Return file/dir size (MB)\n    mb = 1 << 20  # bytes to MiB (1024 ** 2)\n    path = Path(path)\n    if path.is_file():\n        return path.stat().st_size / mb\n    elif path.is_dir():\n        return sum(f.stat().st_size for f in path.glob('**/*') if f.is_file()) / mb\n    else:\n        return 0.0\n\n\ndef check_online():\n    # Check internet connectivity\n    import socket\n\n    def run_once():\n        # Check once\n        try:\n            socket.create_connection((\"1.1.1.1\", 443), 5)  # check host accessibility\n            return True\n        except OSError:\n            return False\n\n    return run_once() or run_once()  # check twice to increase robustness to intermittent connectivity issues\n\n\ndef check_yolo(verbose=True):\n    from utils.torch_utils import select_device\n    import shutil\n    import psutil\n    from IPython import display\n    if is_colab():\n        shutil.rmtree('sample_data', ignore_errors=True)  # remove colab /sample_data directory\n\n    if verbose:\n        # System info\n        gib = 1 << 30  # bytes per GiB\n        ram = psutil.virtual_memory().total\n        total, used, free = shutil.disk_usage(\"/\")\n        display.clear_output()\n        s = f'({os.cpu_count()} CPUs, {ram / gib:.1f} GB RAM, {(total - free) / gib:.1f}/{total / gib:.1f} GB disk)'\n    else:\n        s = ''\n\n    select_device(newline=False)\n    LOGGER.info(f'Setup complete ✅ {s}')\n\n        \ndef git_describe(path=ROOT):  # path must be a directory\n    # Return human-readable git description, i.e. v5.0-5-g3e25f1e https://git-scm.com/docs/git-describe\n    try:\n        assert (Path(path) / '.git').is_dir()\n        return check_output(f'git -C {path} describe --tags --long --always', shell=True).decode()[:-1]\n    except Exception:\n        return ''\n\n\n@TryExcept()\n@WorkingDirectory(ROOT)\ndef check_git_status(repo='positive666/yolo_research', branch='master'):\n    # YOLOv5 status check, recommend 'git pull' if code is out of date\n    url = f'https://github.com/{repo}'\n    msg = f', for updates see {url}'\n    s = colorstr('github: ')  # string\n    assert Path('.git').exists(), s + 'skipping check (not a git repository)' + msg\n    assert check_online(), s + 'skipping check (offline)' + msg\n\n    splits = re.split(pattern=r'\\s', string=check_output('git remote -v', shell=True).decode())\n    matches = [repo in s for s in splits]\n    if any(matches):\n        remote = splits[matches.index(True) - 1]\n    else:\n        remote = 'positive666'\n        check_output(f'git remote add {remote} {url}', shell=True)\n    check_output(f'git fetch {remote}', shell=True, timeout=5)  # git fetch\n    local_branch = check_output('git rev-parse --abbrev-ref HEAD', shell=True).decode().strip()  # checked out\n    n = int(check_output(f'git rev-list {local_branch}..{remote}/{branch} --count', shell=True))  # commits behind\n    if n > 0:\n        pull = 'git pull' if remote == 'origin' else f'git pull {remote} {branch}'\n        s += f\"⚠️ YOLOv5 is out of date by {n} commit{'s' * (n > 1)}. Use `{pull}` or `git clone {url}` to update.\"\n    else:\n        s += f'up to date with {url} ✅'\n    LOGGER.info(s)\n\n\n@WorkingDirectory(ROOT)\ndef check_git_info(path='.'):\n    #git info check \n    check_requirements('gitpython')\n    import git\n    try:\n        repo=git.Repo(path)\n        remote=repo.remotes.origin.url.replace('.git','')\n        commit=repo.head.commit.hexsha\n        try:\n            branch=repo.active_branch.name\n        except TypeError:\n            branch=None\n        return{'remote':remote,'branch':branch,'commit':commit}\n    except git.exc.InvalidGitRepositoryError:\n        return {'remote':None,'branch':None,'commit':None }\n\ndef check_python(minimum='3.7.0'):\n    # Check current python version vs. required python version\n    check_version(platform.python_version(), minimum, name='Python ', hard=True)\n\n\ndef check_version(current='0.0.0', minimum='0.0.0', name='version ', pinned=False, hard=False, verbose=False):\n    # Check version vs. required version\n    current, minimum = (pkg.parse_version(x) for x in (current, minimum))\n    result = (current == minimum) if pinned else (current >= minimum)  # bool\n    warning_message = f'WARNING ⚠️ {name}{minimum} required by Prompt Can Anythings, but {name}{current} is currently installed'  # string\n    if hard:\n        assert result, emojis(warning_message)  # assert min requirements met\n    if verbose and not result:\n        LOGGER.warning(warning_message)\n    return result\n\n\n@TryExcept()\ndef check_requirements(requirements=ROOT / 'requirements.txt', exclude=(), install=True, cmds=()):\n    # Check installed dependencies meet requirements (pass *.txt file or list of packages)\n    prefix = colorstr('red', 'bold', 'requirements:')\n    check_python()  # check python version\n    if isinstance(requirements, (str, Path)):  # requirements.txt file\n        file = Path(requirements)\n        assert file.exists(), f\"{prefix} {file.resolve()} not found, check failed.\"\n        with file.open() as f:\n            requirements = [f'{x.name}{x.specifier}' for x in pkg.parse_requirements(f) if x.name not in exclude]\n    else:  # list or tuple of packages\n        requirements = [x for x in requirements if x not in exclude]\n\n    n = 0  # number of packages updates\n    for i, r in enumerate(requirements):\n        try:\n            pkg.require(r)\n        except Exception:  # DistributionNotFound or VersionConflict if requirements not met\n            s = f\"{prefix} {r} not found and is required by Prompt can Anythings\"\n            if install and AUTOINSTALL:  # check environment variable\n                LOGGER.info(f\"{s}, attempting auto-update...\")\n                try:\n                    #assert check_online(), f\"'pip install {r}' skipped (offline)\"\n                    LOGGER.info(check_output(f\"pip install '{r}' {cmds[i] if cmds else ''}\", shell=True).decode())\n                    n += 1\n                except Exception as e:\n                    LOGGER.warning(f'{prefix} ❌ {e}')\n            else:\n                LOGGER.info(f'{s}. Please install and rerun your command.')\n\n    if n:  # if packages updated\n        source = file.resolve() if 'file' in locals() else requirements\n        s = f\"{prefix} {n} package{'s' * (n > 1)} updated per {source}\\n\" \\\n            f\"{prefix} ⚠️ {colorstr('bold', 'Restart runtime or rerun command for updates to take effect')}\\n\"\n        LOGGER.info(s)\n\n\ndef check_img_size(imgsz, s=32, floor=0):\n    # Verify image size is a multiple of stride s in each dimension\n    if isinstance(imgsz, int):  # integer i.e. img_size=640\n        new_size = max(make_divisible(imgsz, int(s)), floor)\n    else:  # list i.e. img_size=[640, 480]\n        imgsz = list(imgsz)  # convert to list if tuple\n        new_size = [max(make_divisible(x, int(s)), floor) for x in imgsz]\n    if new_size != imgsz:\n        LOGGER.warning(f'WARNING ⚠️ --img-size {imgsz} must be multiple of max stride {s}, updating to {new_size}')\n    return new_size\n\n\ndef check_imshow(warn=False):\n    # Check if environment supports image displays\n    try:\n        assert not is_docker(), 'cv2.imshow() is disabled in Docker environments'\n        assert not is_jupyter(), 'cv2.imshow() is disabled in Google Colab environments'\n        cv2.imshow('test', np.zeros((1, 1, 3)))\n        cv2.waitKey(1)\n        cv2.destroyAllWindows()\n        cv2.waitKey(1)\n        return True\n    except Exception as e:\n        if warn:\n            LOGGER.warning(f'WARNING: Environment does not support cv2.imshow() or PIL Image.show() image displays\\n{e}')\n        return False\n\n\ndef check_suffix(file='yolov5s.pt', suffix=('.pt',), msg=''):\n    # Check file(s) for acceptable suffix\n    if file and suffix:\n        if isinstance(suffix, str):\n            suffix = [suffix]\n        for f in file if isinstance(file, (list, tuple)) else [file]:\n            s = Path(f).suffix.lower()  # file suffix\n            if len(s):\n                assert s in suffix, f\"{msg}{f} acceptable suffix is {suffix}\"\n\n\ndef check_yaml(file, suffix=('.yaml', '.yml')):\n    # Search/download YAML file (if necessary) and return path, checking suffix\n    return check_file(file, suffix)\n\n\ndef check_file(file, suffix=''):\n    # Search/download file (if necessary) and return path\n    check_suffix(file, suffix)  # optional\n    file = str(file)  # convert to str()\n    if Path(file).is_file() or not file:  # exists\n        return file\n    elif file.startswith(('http:/', 'https:/')):  # download\n        url = str(Path(file)).replace(':/', '://')  # Pathlib turns :// -> :/\n        file = Path(urllib.parse.unquote(file).split('?')[0]).name  # '%2F' to '/', split https://url.com/file.txt?auth\n        if Path(file).is_file():\n            LOGGER.info(f'Found {url} locally at {file}')  # file already exists\n        else:\n            LOGGER.info(f'Downloading {url} to {file}...')\n            torch.hub.download_url_to_file(url, file)\n            assert Path(file).exists() and Path(file).stat().st_size > 0, f'File download failed: {url}'  # check\n        return file\n    else:  # search\n        files = []\n        for d in 'data', 'models', 'utils':  # search directories\n            files.extend(glob.glob(str(ROOT / d / '**' / file), recursive=True))  # find file\n        assert len(files), f'File not found: {file}'  # assert file was found\n        assert len(files) == 1, f\"Multiple files match '{file}', specify exact path: {files}\"  # assert unique\n        return files[0]  # return file\n\ndef check_dataset(data, autodownload=True):\n    # Download, check and/or unzip dataset if not found locally\n\n    # Download (optional)\n    extract_dir = ''\n    if isinstance(data, (str, Path)) and str(data).endswith('.zip'):  # i.e. gs://bucket/dir/coco128.zip\n        download(data, dir=DATASETS_DIR, unzip=True, delete=False, curl=False, threads=1)\n        data = next((DATASETS_DIR / Path(data).stem).rglob('*.yaml'))\n        extract_dir, autodownload = data.parent, False\n        \n    # Read yaml (optional)\n    if isinstance(data, (str, Path)):\n        with open(data, errors='ignore') as f:\n            data = yaml.safe_load(f)  # dictionary\n            \n    # Checks\n    for k in 'train', 'val', 'names':\n        assert k in data, emojis(f\"data.yaml '{k}:' field missing ❌\")\n    if isinstance(data['names'], (list, tuple)):  # old array format\n        data['names'] = dict(enumerate(data['names']))  # convert to dict\n    data['nc'] = len(data['names'])\n        \n    # Resolve paths\n    path = Path(extract_dir or data.get('path') or '')  # optional 'path' default to '.'\n    if not path.is_absolute():\n        path = (ROOT / path).resolve()\n    for k in 'train', 'val', 'test':\n        if data.get(k):  # prepend path\n            data[k] = str(path / data[k]) if isinstance(data[k], str) else [str(path / x) for x in data[k]]\n            \n    # Parse yaml\n    train, val, test, s = (data.get(x) for x in ('train', 'val', 'test', 'download'))\n    if val:\n        val = [Path(x).resolve() for x in (val if isinstance(val, list) else [val])]  # val path\n        if not all(x.exists() for x in val):\n            LOGGER.info('\\nDataset not found ⚠,missing paths: %s' % [str(x) for x in val if not x.exists()])\n            t = time.time()\n            if s and autodownload:  # download script\n                \n                root = path.parent if 'path' in data else '..'  # unzip directory i.e. '../'\n                if s.startswith('http') and s.endswith('.zip'):  # URL\n                    f = Path(s).name  # filename\n                    LOGGER.info(f'Downloading {s} to {f}...')\n                    torch.hub.download_url_to_file(s, f)\n                    Path(root).mkdir(parents=True, exist_ok=True)  # create root\n                    unzip_file(f,path=DATASETS_DIR)  # unzip\n                    Path(f).unlink()  # remove zip\n                    r = None  # success\n                elif s.startswith('bash '):  # bash script\n                    LOGGER.info(f'Running {s} ...')\n                    r = os.system(s)\n                else:  # python script\n                    r = exec(s, {'yaml': data})  # return None\n                dt = f'({round(time.time() - t, 1)}s)'    \n                s = f\"success ✅ {dt}, saved to {colorstr('bold', root)}\" if r in (0, None) else f\"failure {dt} ❌\"\n                LOGGER.info(f\"Dataset download {s}\")\n            else:\n                raise Exception('Dataset not found ❌❌❌❌.')\n    #('Arial.ttf' if is_ascii(data['names']) else 'Arial.Unicode.ttf', progress=True)  # download fonts\n    return data  # dictionary\n\n\ndef yaml_load(file='data.yaml', append_filename=False):\n    \"\"\"\n    Load YAML data from a file.\n\n    Args:\n        file (str, optional): File name. Default is 'data.yaml'.\n        append_filename (bool): Add the YAML filename to the YAML dictionary. Default is False.\n\n    Returns:\n        dict: YAML data and file name.\n    \"\"\"\n    with open(file, errors='ignore', encoding='utf-8') as f:\n        # Add YAML filename to dict and return\n        s = f.read()  # string\n        if not s.isprintable():  # remove special characters\n            s = re.sub(r'[^\\x09\\x0A\\x0D\\x20-\\x7E\\x85\\xA0-\\uD7FF\\uE000-\\uFFFD\\U00010000-\\U0010ffff]+', '', s)\n        return {**yaml.safe_load(s), 'yaml_file': str(file)} if append_filename else yaml.safe_load(s)\n\n\ndef yaml_save(file='data.yaml', data={}):\n    # Single-line safe yaml saving\n    with open(file, 'w') as f:\n        yaml.safe_dump({k: str(v) if isinstance(v, Path) else v for k, v in data.items()}, f, sort_keys=False)\n\ndef unzip_file(file, path=None, exclude=('.DS_Store', '__MACOSX')):\n    # Unzip a *.zip file to path/, excluding files containing strings in exclude list\n    if path is None:\n        path = Path(file).parent  # default path\n    with ZipFile(file) as zipObj:\n        for f in zipObj.namelist():  # list all archived filenames in the zip\n            if all(x not in f for x in exclude):\n                zipObj.extract(f, path=path)\n                \ndef url2file(url):\n    # Convert URL to filename, i.e. https://url.com/file.txt?auth -> file.txt\n    url = str(Path(url)).replace(':/', '://')  # Pathlib turns :// -> :/\n    return Path(urllib.parse.unquote(url)).name.split('?')[0]  # '%2F' to '/', split https://url.com/file.txt?auth\n\n\ndef download(url, dir='.', unzip=True, delete=True, curl=False, threads=1, retry=3):\n    # Multi-threaded file download and unzip function, used in data.yaml for autodownload\n    def download_one(url, dir):\n        # Download 1 file\n        success = True\n        f = dir / Path(url).name  # filename\n        if Path(url).is_file():  # exists in current path\n            Path(url).rename(f)  # move to dir\n        elif not f.exists():\n            LOGGER.info(f'Downloading {url} to {f}...')\n            for i in range(retry + 1):\n                if curl:\n                    s = 'sS' if threads > 1 else ''  # silent\n                    r = os.system(f\"curl -{s}L '{url}' -o '{f}' --retry 9 -C -\")  # curl download\n                    success = r == 0\n                else:\n                    torch.hub.download_url_to_file(url, f, progress=threads == 1)  # torch download\n                    success = f.is_file()\n                if success:\n                    break\n                elif i < retry:\n                    LOGGER.warning(f'⚠️ Download failure, retrying {i + 1}/{retry} {url}...')\n                else:\n                    LOGGER.warning(f'❌ Failed to download {url}...')\n\n        if unzip and success and f.suffix in ('.zip','.tar', '.gz'):\n            LOGGER.info(f'Unzipping {f}...')\n            if f.suffix == '.zip':\n                unzip_file(f,dir)  # unzip\n            elif f.suffix == '.tar':\n                os.system(f'tar xf {f} --directory {f.parent}')  # unzip    \n            elif f.suffix == '.gz':\n                os.system(f'tar xfz {f} --directory {f.parent}')  # unzip\n            if delete:\n                f.unlink()  # remove zip\n\n    dir = Path(dir)\n    dir.mkdir(parents=True, exist_ok=True)  # make directory\n    if threads > 1:\n        pool = ThreadPool(threads)\n        pool.imap(lambda x: download_one(*x), zip(url, repeat(dir)))  # multi-threaded\n        pool.close()\n        pool.join()\n    else:\n        for u in [url] if isinstance(url, (str, Path)) else url:\n            download_one(u, dir)\n\n\ndef make_divisible(x, divisor):\n    # Returns nearest x divisible by divisor\n    if isinstance(divisor, torch.Tensor):\n        divisor = int(divisor.max())  # to int\n    return math.ceil(x / divisor) * divisor\n\n\ndef clean_str(s):\n    # Cleans a string by replacing special characters with underscore _\n    return re.sub(pattern=\"[|@#!¡·$€%&()=?¿^*;:,¨´><+]\", repl=\"_\", string=s)\n\n\ndef one_cycle(y1=0.0, y2=1.0, steps=100):\n    # lambda function for sinusoidal ramp from y1 to y2 https://arxiv.org/pdf/1812.01187.pdf\n    return lambda x: ((1 - math.cos(x * math.pi / steps)) / 2) * (y2 - y1) + y1\n\n\ndef colorstr(*input):\n    # Colors a string https://en.wikipedia.org/wiki/ANSI_escape_code, i.e.  colorstr('blue', 'hello world')\n    *args, string = input if len(input) > 1 else ('blue', 'bold', input[0])  # color arguments, string\n    colors = {\n        'black': '\\033[30m',  # basic colors\n        'red': '\\033[31m',\n        'green': '\\033[32m',\n        'yellow': '\\033[33m',\n        'blue': '\\033[34m',\n        'magenta': '\\033[35m',\n        'cyan': '\\033[36m',\n        'white': '\\033[37m',\n        'bright_black': '\\033[90m',  # bright colors\n        'bright_red': '\\033[91m',\n        'bright_green': '\\033[92m',\n        'bright_yellow': '\\033[93m',\n        'bright_blue': '\\033[94m',\n        'bright_magenta': '\\033[95m',\n        'bright_cyan': '\\033[96m',\n        'bright_white': '\\033[97m',\n        'end': '\\033[0m',  # misc\n        'bold': '\\033[1m',\n        'underline': '\\033[4m'}\n    return ''.join(colors[x] for x in args) + f'{string}' + colors['end']\n\n\ndef labels_to_class_weights(labels, nc=80):\n    # Get class weights (inverse frequency) from training labels\n    if labels[0] is None:  # no labels loaded\n        return torch.Tensor()\n\n    labels = np.concatenate(labels, 0)  # labels.shape = (866643, 5) for COCO\n    classes = labels[:, 0].astype(int)  # labels = [class xywh]\n    weights = np.bincount(classes, minlength=nc)  # occurrences per class\n\n    # Prepend gridpoint count (for uCE training)\n    # gpi = ((320 / 32 * np.array([1, 2, 4])) ** 2 * 3).sum()  # gridpoints per image\n    # weights = np.hstack([gpi * len(labels)  - weights.sum() * 9, weights * 9]) ** 0.5  # prepend gridpoints to start\n\n    weights[weights == 0] = 1  # replace empty bins with 1\n    weights = 1 / weights  # number of targets per class\n    weights /= weights.sum()  # normalize\n    return torch.from_numpy(weights).float()\n\n\ndef labels_to_image_weights(labels, nc=80, class_weights=np.ones(80)):\n    # Produces image weights based on class_weights and image contents\n    # Usage: index = random.choices(range(n), weights=image_weights, k=1)  # weighted image sample\n    class_counts = np.array([np.bincount(x[:, 0].astype(np.int), minlength=nc) for x in labels])\n    return (class_weights.reshape(1, nc) * class_counts).sum(1)\n\n\ndef coco80_to_coco91_class():  # converts 80-index (val2014) to 91-index (paper)\n    # https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/\n    # a = np.loadtxt('data/coco.names', dtype='str', delimiter='\\n')\n    # b = np.loadtxt('data/coco_paper.names', dtype='str', delimiter='\\n')\n    # x1 = [list(a[i] == b).index(True) + 1 for i in range(80)]  # darknet to coco\n    # x2 = [list(b[i] == a).index(True) if any(b[i] == a) else None for i in range(91)]  # coco to darknet\n    return [\n        1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 31, 32, 33, 34,\n        35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,\n        64, 65, 67, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90]\n\n\ndef xyxy2xywh(x):\n    # Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] where xy1=top-left, xy2=bottom-right\n    y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)\n    y[:, 0] = (x[:, 0] + x[:, 2]) / 2  # x center\n    y[:, 1] = (x[:, 1] + x[:, 3]) / 2  # y center\n    y[:, 2] = x[:, 2] - x[:, 0]  # width\n    y[:, 3] = x[:, 3] - x[:, 1]  # height\n    return y\n\n\ndef xywh2xyxy(x):\n    # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right\n    y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)\n    y[:, 0] = x[:, 0] - x[:, 2] / 2  # top left x\n    y[:, 1] = x[:, 1] - x[:, 3] / 2  # top left y\n    y[:, 2] = x[:, 0] + x[:, 2] / 2  # bottom right x\n    y[:, 3] = x[:, 1] + x[:, 3] / 2  # bottom right y\n    return y\n\n\ndef xywh2xyxy_export(cx,cy,w,h):\n    #This function is used while exporting ONNX models\n    # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right\n    #y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)\n    halfw = w/2\n    halfh = h/2\n    xmin = cx - halfw  # top left x\n    ymin = cy - halfh  # top left y\n    xmax = cx + halfw  # bottom right x\n    ymax = cy + halfh  # bottom right y\n    return torch.cat((xmin, ymin, xmax, ymax), 1)\n\ndef xywhn2xyxy(x, w=640, h=640, padw=0, padh=0,kpt_label=False):\n    # Convert nx4 boxes from [x, y, w, h] normalized to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right\n    y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)\n    y[:, 0] = w * (x[:, 0] - x[:, 2] / 2) + padw  # top left x\n    y[:, 1] = h * (x[:, 1] - x[:, 3] / 2) + padh  # top left y\n    y[:, 2] = w * (x[:, 0] + x[:, 2] / 2) + padw  # bottom right x\n    y[:, 3] = h * (x[:, 1] + x[:, 3] / 2) + padh  # bottom right y\n    if kpt_label:\n        num_kpts = (x.shape[1]-4)//2\n        for kpt in range(num_kpts):\n            for kpt_instance in range(y.shape[0]):\n                if y[kpt_instance, 2 * kpt + 4]!=0:\n                    y[kpt_instance, 2*kpt+4] = w * y[kpt_instance, 2*kpt+4] + padw\n                if y[kpt_instance, 2 * kpt + 1 + 4] !=0:\n                    y[kpt_instance, 2*kpt+1+4] = h * y[kpt_instance, 2*kpt+1+4] + padh\n    return y\n\n\n\n\ndef xyxy2xywhn(x, w=640, h=640, clip=False, eps=0.0):\n    # Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] normalized where xy1=top-left, xy2=bottom-right\n    if clip:\n        clip_boxes(x, (h - eps, w - eps))  # warning: inplace clip\n    y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)\n    y[:, 0] = ((x[:, 0] + x[:, 2]) / 2) / w  # x center\n    y[:, 1] = ((x[:, 1] + x[:, 3]) / 2) / h  # y center\n    y[:, 2] = (x[:, 2] - x[:, 0]) / w  # width\n    y[:, 3] = (x[:, 3] - x[:, 1]) / h  # height\n    return y\n\n\ndef xyn2xy(x, w=640, h=640, padw=0, padh=0):\n    # Convert normalized segments into pixel segments, shape (n,2)\n    y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)\n    y[:, 0] = w * x[:, 0] + padw  # top left x\n    y[:, 1] = h * x[:, 1] + padh  # top left y\n    return y\n\n\ndef segment2box(segment, width=640, height=640):\n    # Convert 1 segment label to 1 box label, applying inside-image constraint, i.e. (xy1, xy2, ...) to (xyxy)\n    x, y = segment.T  # segment xy\n    inside = (x >= 0) & (y >= 0) & (x <= width) & (y <= height)\n    x, y, = x[inside], y[inside]\n    return np.array([x.min(), y.min(), x.max(), y.max()]) if any(x) else np.zeros((1, 4))  # xyxy\n\n\ndef segments2boxes(segments):\n    # Convert segment labels to box labels, i.e. (cls, xy1, xy2, ...) to (cls, xywh)\n    boxes = []\n    for s in segments:\n        x, y = s.T  # segment xy\n        boxes.append([x.min(), y.min(), x.max(), y.max()])  # cls, xyxy\n    return xyxy2xywh(np.array(boxes))  # cls, xywh\n\n\ndef resample_segments(segments, n=1000):\n    # Up-sample an (n,2) segment\n    for i, s in enumerate(segments):\n        s = np.concatenate((s, s[0:1, :]), axis=0)\n        x = np.linspace(0, len(s) - 1, n)\n        xp = np.arange(len(s))\n        segments[i] = np.concatenate([np.interp(x, xp, s[:, i]) for i in range(2)]).reshape(2, -1).T  # segment xy\n    return segments\n\n\ndef scale_boxes(img1_shape, boxes, img0_shape, ratio_pad=None,kpt_label=False, step=2):\n    # Rescale boxe (xyxy) from img1_shape to img0_shape\n    if ratio_pad is None:  # calculate from img0_shape\n        gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1])  # gain  = old / new\n        pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2  # wh padding\n    else:\n        gain = ratio_pad[0][0]\n        pad = ratio_pad[1]\n        \n    if kpt_label:\n        boxes[:, 0::step] -= pad[0]  # x padding\n        boxes[:, 1::step] -= pad[1]  # y padding\n        boxes[:, 0::step] /= gain\n        boxes[:, 1::step] /= gain\n        clip_boxes(boxes, img0_shape, step=step)\n    else:    \n        boxes[:, [0, 2]] -= pad[0]  # x padding\n        boxes[:, [1, 3]] -= pad[1]  # y padding\n        boxes[:, :4] /= gain\n        clip_boxes(boxes, img0_shape)\n    return boxes\n\ndef box_iou(box1, box2, eps=1e-7):\n    # https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py\n    \"\"\"\n    Return intersection-over-union (Jaccard index) of boxes.\n    Both sets of boxes are expected to be in (x1, y1, x2, y2) format.\n    Arguments:\n        box1 (Tensor[N, 4])\n        box2 (Tensor[M, 4])\n    Returns:\n        iou (Tensor[N, M]): the NxM matrix containing the pairwise\n            IoU values for every element in boxes1 and boxes2\n    \"\"\"\n\n    # inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2)\n    (a1, a2), (b1, b2) = box1.unsqueeze(1).chunk(2, 2), box2.unsqueeze(0).chunk(2, 2)\n    inter = (torch.min(a2, b2) - torch.max(a1, b1)).clamp(0).prod(2)\n\n    # IoU = inter / (area1 + area2 - inter)\n    return inter / ((a2-a1).prod(2)+(b2-b1).prod(2) - inter + eps)\n\ndef scale_segments(img1_shape, segments, img0_shape, ratio_pad=None, normalize=False):\n    # Rescale coords (xyxy) from img1_shape to img0_shape\n    if ratio_pad is None:  # calculate from img0_shape\n        gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1])  # gain  = old / new\n        pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2  # wh padding\n    else:\n        gain = ratio_pad[0][0]\n        pad = ratio_pad[1]\n\n    segments[:, 0] -= pad[0]  # x padding\n    segments[:, 1] -= pad[1]  # y padding\n    segments /= gain\n    clip_segments(segments, img0_shape)\n    if normalize:\n        segments[:, 0] /= img0_shape[1]  # width\n        segments[:, 1] /= img0_shape[0]  # height\n    return segments\n\n\ndef clip_boxes(boxes, shape, step=None):\n    # Clip bounding xyxy bounding boxes to image shape (height, width)\n    \n    if step is not None:\n        boxes[:, 0::step].clamp_(0, shape[1])  # x1\n        boxes[:, 1::step].clamp_(0, shape[0])  # y1\n\n    elif isinstance(boxes, torch.Tensor):  # faster individually\n        boxes[:, 0].clamp_(0, shape[1])  # x1\n        boxes[:, 1].clamp_(0, shape[0])  # y1\n        boxes[:, 2].clamp_(0, shape[1])  # x2\n        boxes[:, 3].clamp_(0, shape[0])  # y2\n    else:  # np.array (faster grouped)\n        boxes[:, [0, 2]] = boxes[:, [0, 2]].clip(0, shape[1])  # x1, x2\n        boxes[:, [1, 3]] = boxes[:, [1, 3]].clip(0, shape[0])  # y1, y2    \n\n\ndef clip_segments(boxes, shape):\n    # Clip segments (xy1,xy2,...) to image shape (height, width)\n    if isinstance(boxes, torch.Tensor):  # faster individually\n        boxes[:, 0].clamp_(0, shape[1])  # x\n        boxes[:, 1].clamp_(0, shape[0])  # y\n    else:  # np.array (faster grouped)\n        boxes[:, 0] = boxes[:, 0].clip(0, shape[1])  # x\n        boxes[:, 1] = boxes[:, 1].clip(0, shape[0])  # y\n\ndef scale_image(im1_shape, masks, im0_shape, ratio_pad=None):\n    \"\"\"\n    Takes a mask, and resizes it to the original image size\n    Args:\n      im1_shape (tuple): model input shape, [h, w]\n      masks (torch.Tensor): [h, w, num]\n      im0_shape (tuple): the original image shape\n      ratio_pad (tuple): the ratio of the padding to the original image.\n    Returns:\n      masks (torch.Tensor): The masks that are being returned.\n    \"\"\"\n    # Rescale coordinates (xyxy) from im1_shape to im0_shape\n    if ratio_pad is None:  # calculate from im0_shape\n        gain = min(im1_shape[0] / im0_shape[0], im1_shape[1] / im0_shape[1])  # gain  = old / new\n        pad = (im1_shape[1] - im0_shape[1] * gain) / 2, (im1_shape[0] - im0_shape[0] * gain) / 2  # wh padding\n    else:\n        pad = ratio_pad[1]\n    top, left = int(pad[1]), int(pad[0])  # y, x\n    bottom, right = int(im1_shape[0] - pad[1]), int(im1_shape[1] - pad[0])\n\n    if len(masks.shape) < 2:\n        raise ValueError(f'\"len of masks shape\" should be 2 or 3, but got {len(masks.shape)}')\n    masks = masks[top:bottom, left:right]\n    # masks = masks.permute(2, 0, 1).contiguous()\n    # masks = F.interpolate(masks[None], im0_shape[:2], mode='bilinear', align_corners=False)[0]\n    # masks = masks.permute(1, 2, 0).contiguous()\n    masks = cv2.resize(masks, (im0_shape[1], im0_shape[0]))\n\n    if len(masks.shape) == 2:\n        masks = masks[:, :, None]\n    return masks\n\ndef clip_boxes(boxes, shape):\n    \"\"\"\n    It takes a list of bounding boxes and a shape (height, width) and clips the bounding boxes to the\n    shape\n    Args:\n      boxes (torch.Tensor): the bounding boxes to clip\n      shape (tuple): the shape of the image\n    \"\"\"\n    if isinstance(boxes, torch.Tensor):  # faster individually\n        boxes[..., 0].clamp_(0, shape[1])  # x1\n        boxes[..., 1].clamp_(0, shape[0])  # y1\n        boxes[..., 2].clamp_(0, shape[1])  # x2\n        boxes[..., 3].clamp_(0, shape[0])  # y2\n    else:  # np.array (faster grouped)\n        boxes[..., [0, 2]] = boxes[..., [0, 2]].clip(0, shape[1])  # x1, x2\n        boxes[..., [1, 3]] = boxes[..., [1, 3]].clip(0, shape[0])  # y1, y2\n\ndef non_max_suppression(prediction,\n                        conf_thres=0.25,\n                        iou_thres=0.45,\n                        classes=None,\n                        agnostic=False,\n                        multi_label=False,\n                        labels=(),\n                        max_det=300, \n                        nm=0,# number of masks\n                        ):\n    \"\"\"Non-Maximum Suppression (NMS) on inference results to reject overlapping bounding boxes (segment and POSE)\n\n    Returns:\n         list of detections, on (n,6) tensor per image [xyxy, conf, cls]\n    \"\"\"\n    \n    if isinstance(prediction, (list, tuple)):  # YOLOv5 model in validation model, output = (inference_out, loss_out)\n        prediction = prediction[0]  # select only inference output\n        \n    bs = prediction.shape[0]  # batch size\n    nc = prediction.shape[2] -nm- 5  # number of classes\n    xc = prediction[..., 4] > conf_thres  # candidates\n\n    # Checks\n    assert 0 <= conf_thres <= 1, f'Invalid Confidence threshold {conf_thres}, valid values are between 0.0 and 1.0'\n    assert 0 <= iou_thres <= 1, f'Invalid IoU {iou_thres}, valid values are between 0.0 and 1.0'\n\n    # Settings\n    # min_wh = 2  # (pixels) minimum box width and height\n    max_wh = 7680  # (pixels) maximum box width and height\n    max_nms = 30000  # maximum number of boxes into torchvision.ops.nms()\n    time_limit = 0.5 + 0.05 * bs  # seconds to quit after\n    redundant = True  # require redundant detections\n    multi_label &= nc > 1  # multiple labels per box (adds 0.5ms/img)\n    merge = False  # use merge-NMS\n\n    t = time.time()\n    mi=5+nc \n    output = [torch.zeros((0, 6 + nm), device=prediction.device)] * bs\n   \n    for xi, x in enumerate(prediction):  # image index, image inference\n        # Apply constraints\n        # x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0  # width-height\n        x = x[xc[xi]]  # confidence\n\n        # Cat apriori labels if autolabelling\n        if labels and len(labels[xi]):\n            lb = labels[xi]\n            v = torch.zeros((len(lb), nc + nm + 5), device=x.device)\n            v[:, :4] = lb[:, 1:5]  # box\n            v[:, 4] = 1.0  # conf\n            v[range(len(lb)), lb[:, 0].long() + 5] = 1.0  # cls\n            x = torch.cat((x, v), 0)\n\n        # If none remain process next image\n        if not x.shape[0]:\n            continue\n\n        # Compute conf\n        x[:, 5:] *= x[:, 4:5]  # conf = obj_conf * cls_conf\n\n        # Box (center x, center y, width, height) to (x1, y1, x2, y2)\n        box = xywh2xyxy(x[:, :4])\n        mask = x[:, mi:]  # zero columns if no masks\n\n        # Detections matrix nx6 (xyxy, conf, cls)\n        if multi_label:\n            i, j = (x[:, 5:mi] > conf_thres).nonzero(as_tuple=False).T\n            x = torch.cat((box[i], x[i, 5 + j, None], j[:, None].float(), mask[i]), 1)\n        else:  # best class only\n            conf, j = x[:, 5:mi].max(1, keepdim=True)\n            x = torch.cat((box, conf, j.float(), mask), 1)[conf.view(-1) > conf_thres]\n\n        # Filter by class\n        if classes is not None:\n            x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)]\n\n        # Apply finite constraint\n        # if not torch.isfinite(x).all():\n        #     x = x[torch.isfinite(x).all(1)]\n\n        # Check shape\n        n = x.shape[0]  # number of boxes\n        if not n:  # no boxes\n            continue\n        x = x[x[:, 4].argsort(descending=True)[:max_nms]]  # sort by confidence and remove excess boxes\n\n        # Batched NMS\n        c = x[:, 5:6] * (0 if agnostic else max_wh)  # classes\n        boxes, scores = x[:, :4] + c, x[:, 4]  # boxes (offset by class), scores\n        i = torchvision.ops.nms(boxes, scores, iou_thres)  # NMS\n        if i.shape[0] > max_det:  # limit detections\n            i = i[:max_det]\n        if merge and (1 < n < 3E3):  # Merge NMS (boxes merged using weighted mean)\n            # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4)\n            iou = box_iou(boxes[i], boxes) > iou_thres  # iou matrix\n            weights = iou * scores[None]  # box weights\n            x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True)  # merged boxes\n            if redundant:\n                i = i[iou.sum(1) > 1]  # require redundancy\n\n        output[xi] = x[i]\n        if (time.time() - t) > time_limit:\n            LOGGER.warning(f'WARNING ⚠️ NMS time limit {time_limit:.3f}s exceeded')\n            break  # time limit exceeded\n\n    return output\n\ndef non_max_suppression_keypoint(prediction, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False, multi_label=False,\n                        labels=(), kpt_label=False, nc=None, nkpt=None):\n    \"\"\"Runs Non-Maximum Suppression (NMS) on inference results\n\n    Returns:\n         list of detections, on (n,6) tensor per image [xyxy, conf, cls]\n    \"\"\"\n    if isinstance(prediction, (list, tuple)):  # YOLOv5 model in validation model, output = (inference_out, loss_out)\n        prediction = prediction[0]  # select only inference output\n    if nc is None:\n        nc = prediction.shape[2] - 5  if not kpt_label else prediction.shape[2] - 56 # number of classes\n    xc = prediction[..., 4] > conf_thres  # candidates\n\n    # Settings\n    min_wh, max_wh = 2, 4096  # (pixels) minimum and maximum box width and height\n    max_det = 300  # maximum number of detections per image\n    max_nms = 30000  # maximum number of boxes into torchvision.ops.nms()\n    time_limit = 10.0  # seconds to quit after\n    redundant = True  # require redundant detections\n    multi_label &= nc > 1  # multiple labels per box (adds 0.5ms/img)\n    merge = False  # use merge-NMS\n\n    t = time.time()\n    output = [torch.zeros((0,6), device=prediction.device)] * prediction.shape[0]\n    for xi, x in enumerate(prediction):  # image index, image inference\n        # Apply constraints\n        # x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0  # width-height\n        x = x[xc[xi]]  # confidence\n\n        # Cat apriori labels if autolabelling\n        if labels and len(labels[xi]):\n            l = labels[xi]\n            v = torch.zeros((len(l), nc + 5), device=x.device)\n            v[:, :4] = l[:, 1:5]  # box\n            v[:, 4] = 1.0  # conf\n            v[range(len(l)), l[:, 0].long() + 5] = 1.0  # cls\n            x = torch.cat((x, v), 0)\n\n        # If none remain process next image\n        if not x.shape[0]:\n            continue\n\n        # Compute conf\n        x[:, 5:5+nc] *= x[:, 4:5]  # conf = obj_conf * cls_conf\n\n        # Box (center x, center y, width, height) to (x1, y1, x2, y2)\n        box = xywh2xyxy(x[:, :4])\n\n        # Detections matrix nx6 (xyxy, conf, cls)\n        if multi_label:\n            i, j = (x[:, 5:] > conf_thres).nonzero(as_tuple=False).T\n            x = torch.cat((box[i], x[i, j + 5, None], j[:, None].float()), 1)\n        else:  # best class only\n            if not kpt_label:\n                conf, j = x[:, 5:].max(1, keepdim=True)\n                x = torch.cat((box, conf, j.float()), 1)[conf.view(-1) > conf_thres]\n            else:\n                kpts = x[:, 6:]\n                conf, j = x[:, 5:6].max(1, keepdim=True)\n                x = torch.cat((box, conf, j.float(), kpts), 1)[conf.view(-1) > conf_thres]\n\n\n        # Filter by class\n        if classes is not None:\n            x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)]\n\n        # Apply finite constraint\n        # if not torch.isfinite(x).all():\n        #     x = x[torch.isfinite(x).all(1)]\n\n        # Check shape\n        n = x.shape[0]  # number of boxes\n        if not n:  # no boxes\n            continue\n        elif n > max_nms:  # excess boxes\n            x = x[x[:, 4].argsort(descending=True)[:max_nms]]  # sort by confidence\n\n        # Batched NMS\n        c = x[:, 5:6] * (0 if agnostic else max_wh)  # classes\n        boxes, scores = x[:, :4] + c, x[:, 4]  # boxes (offset by class), scores\n        i = torchvision.ops.nms(boxes, scores, iou_thres)  # NMS\n        if i.shape[0] > max_det:  # limit detections\n            i = i[:max_det]\n        if merge and (1 < n < 3E3):  # Merge NMS (boxes merged using weighted mean)\n            # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4)\n            iou = box_iou(boxes[i], boxes) > iou_thres  # iou matrix\n            weights = iou * scores[None]  # box weights\n            x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True)  # merged boxes\n            if redundant:\n                i = i[iou.sum(1) > 1]  # require redundancy\n\n        output[xi] = x[i]\n        if (time.time() - t) > time_limit:\n            print(f'WARNING: NMS time limit {time_limit}s exceeded')\n            break  # time limit exceeded\n\n    return output\n\n\n\ndef increment_path(path, exist_ok=False, sep='', mkdir=False):\n    # Increment file or directory path, i.e. runs/exp --> runs/exp{sep}2, runs/exp{sep}3, ... etc.\n    path = Path(path)  # os-agnostic\n    if path.exists() and not exist_ok:\n        path, suffix = (path.with_suffix(''), path.suffix) if path.is_file() else (path, '')\n\n        # Method 1\n        for n in range(2, 9999):\n            p = f'{path}{sep}{n}{suffix}'  # increment path\n            if not os.path.exists(p):  #\n                break\n        path = Path(p)\n\n        # Method 2 (deprecated)\n        # dirs = glob.glob(f\"{path}{sep}*\")  # similar paths\n        # matches = [re.search(rf\"{path.stem}{sep}(\\d+)\", d) for d in dirs]\n        # i = [int(m.groups()[0]) for m in matches if m]  # indices\n        # n = max(i) + 1 if i else 2  # increment number\n        # path = Path(f\"{path}{sep}{n}{suffix}\")  # increment path\n\n    if mkdir:\n        path.mkdir(parents=True, exist_ok=True)  # make directory\n\n    return path\n\n\n# OpenCV Chinese-friendly functions ------------------------------------------------------------------------------------\nimshow_ = cv2.imshow  # copy to avoid recursion errors\n\n\ndef imread(path, flags=cv2.IMREAD_COLOR):\n    return cv2.imdecode(np.fromfile(path, np.uint8), flags)\n\n\ndef imwrite(path, im):\n    try:\n        cv2.imencode(Path(path).suffix, im)[1].tofile(path)\n        return True\n    except Exception:\n        return False\n\n\ndef imshow(path, im):\n    imshow_(path.encode('unicode_escape').decode(), im)\n\n\ncv2.imread, cv2.imwrite, cv2.imshow = imread, imwrite, imshow  # redefine\n\n# Variables ------------------------------------------------------------------------------------------------------------\n#NCOLS = 0 if is_docker() else shutil.get_terminal_size().columns  # terminal window size for tqdm\ndef fitness(x):\n    # Model fitness as a weighted combination of metrics\n    w = [0.0, 0.0, 0.1, 0.9, 0.0, 0.0, 0.1, 0.9]\n    return (x[:, :8] * w).sum(1)\n\n\ndef split_images_to_folders(folder_path, num_threads, num_folders):\n    import subprocess\n    \n    # 获取源文件夹路径\n    source_folder = os.path.abspath(folder_path)\n\n    # 获取文件夹中图像文件的数量\n    num_images = len(os.listdir(source_folder))\n\n    # 计算每个子文件夹应该有多少图像\n    num_images_per_folder = math.ceil(num_images / num_folders)\n\n    # 如果每个子文件夹需要处理的图像比每个线程应该处理的图像更多，则使用每个线程处理的图像的数量来替换\n    if num_images_per_folder < math.ceil(num_images / num_threads):\n        num_images_per_folder = math.ceil(num_images / num_threads)\n\n    # 计算所需的文件夹数量\n    num_folders = math.ceil(num_images / num_images_per_folder)\n\n    # 创建新的文件夹\n    for i in range(1, num_folders+1):\n        folder_name = f\"subfolder_{i}\"\n        os.makedirs(os.path.join(source_folder, folder_name), exist_ok=True)\n\n    # 使用Linux命令将图像移动到子文件夹中\n    cmd = f\"find {source_folder} -maxdepth 1 -type f -print0 | xargs -0 -I {{}} sh -c 'mv \\\"{{}}\\\" subfolder_$((1 + RANDOM % {num_folders}))/';\"\n    for i in range(num_threads):\n        subprocess.run(cmd, shell=True)\n\n    print(f\"已将图像从 {source_folder} 移动到 {num_folders} 个子文件夹中。\")\n\ndef merge_image_folders(folder_path):\n    # 获取源文件夹和所有子文件夹的路径\n    import shutil\n    source_folder = os.path.abspath(folder_path)\n    subfolders = [os.path.abspath(os.path.join(source_folder, f)) for f in os.listdir(source_folder) if os.path.isdir(os.path.join(source_folder, f))]\n\n    # 将所有图像移动回原始文件夹\n    for subfolder in subfolders:\n        image_files = os.listdir(subfolder)\n        for image_file in image_files:\n            image_path = os.path.join(subfolder, image_file)\n            shutil.move(image_path, source_folder)\n\n    # 删除子文件夹\n    for subfolder in subfolders:\n        os.rmdir(subfolder)\n\n    print(f\"已将所有图像从 {len(subfolders)} 个子文件夹移回到原始文件夹。\")"
  },
  {
    "path": "utils/plot.py",
    "content": "# YOLOv5 🚀\n\"\"\"\nPlotting utils\n\"\"\"\n\nimport contextlib\nimport math\nimport os\nfrom copy import copy\nfrom pathlib import Path\nfrom urllib.error import URLError\n\nimport cv2\nimport matplotlib\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nimport torch\nimport json\nfrom PIL import Image, ImageDraw, ImageFont\n#from pycocotools import mask as maskUtils\nfrom utils import TryExcept, threaded\nfrom utils.ops import (CONFIG_DIR, FONT, LOGGER, check_requirements, clip_boxes, increment_path,\n                           is_ascii, xywh2xyxy, xyxy2xywh,fitness)\n\n# Settings\nRANK = int(os.getenv('RANK', -1))\nmatplotlib.rc('font', **{'size': 11})\nmatplotlib.use('Agg')  # for writing to files only\n\n\n          \ndef save_mask_data(output_dir, caption, mask_list, box_list, label_list,img_name):\n    \n    value = 0  # 0 for background\n    mask_img = torch.zeros(mask_list.shape[-2:])\n    for idx, mask in enumerate(mask_list):\n        mask_img[mask.cpu().numpy()[0] == True] = value + idx + 1\n    plt.figure(figsize=(10, 10))\n    plt.imshow(mask_img.numpy())\n    plt.axis('off')\n    plt.savefig(os.path.join(output_dir, f'{img_name}.jpg'), bbox_inches=\"tight\", dpi=300, pad_inches=0.0)\n\n    json_data = {        \n        'caption': caption,\n        'mask':[{\n            'value': value,\n            'label': 'background'\n        }]\n    }\n    for label, box in zip(label_list, box_list):\n        value += 1\n        json_data['mask'].append({\n            'value': value,\n            'label': label,\n            'box': box.numpy().tolist(),\n        })\n    with open(os.path.join(output_dir, f'{img_name}.json'), 'w') as f:\n        json.dump(json_data, f)\n\ndef scale_image(im1_shape, masks, im0_shape, ratio_pad=None):\n    \"\"\"\n    img1_shape: model input shape, [h, w]\n    img0_shape: origin pic shape, [h, w, 3]\n    masks: [h, w, num]\n    \"\"\"\n    # Rescale coordinates (xyxy) from im1_shape to im0_shape\n    if ratio_pad is None:  # calculate from im0_shape\n        gain = min(im1_shape[0] / im0_shape[0], im1_shape[1] / im0_shape[1])  # gain  = old / new\n        pad = (im1_shape[1] - im0_shape[1] * gain) / 2, (im1_shape[0] - im0_shape[0] * gain) / 2  # wh padding\n    else:\n        pad = ratio_pad[1]\n    top, left = int(pad[1]), int(pad[0])  # y, x\n    bottom, right = int(im1_shape[0] - pad[1]), int(im1_shape[1] - pad[0])\n\n    if len(masks.shape) < 2:\n        raise ValueError(f'\"len of masks shape\" should be 2 or 3, but got {len(masks.shape)}')\n    masks = masks[top:bottom, left:right]\n    # masks = masks.permute(2, 0, 1).contiguous()\n    # masks = F.interpolate(masks[None], im0_shape[:2], mode='bilinear', align_corners=False)[0]\n    # masks = masks.permute(1, 2, 0).contiguous()\n    masks = cv2.resize(masks, (im0_shape[1], im0_shape[0]))\n\n    if len(masks.shape) == 2:\n        masks = masks[:, :, None]\n    return masks\n\ndef show_mask(mask, ax, random_color=False,cls_color=None):\n    if cls_color is not None:\n        h, w = mask.shape[-2:]\n        mask_image = mask.reshape(h, w, 1) * np.array(cls_color).reshape(1, 1, -1)\n        ax.imshow(mask_image)\n       \n    else:\n        if random_color :\n            color = np.concatenate([np.random.random(3), np.array([0.6])], axis=0)\n        else:\n            color = np.array([30/255, 144/255, 255/255, 0.6])\n        h, w = mask.shape[-2:]\n        mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1)\n        ax.imshow(mask_image)\n    \ndef Draw_img(data,draw,mode='box',label=None,random_color=None):\n    if not random_color:\n            color = (np.random.randint(0,255),np.random.randint(0,255),np.random.randint(0,255),153)\n    else: \n        \n            color = random_color+(153,)\n    if  mode=='mask' :        \n        coords=np.transpose(np.nonzero(data))\n        for coord in coords:\n            draw.point(coord[::-1],fill=color)\n            \n    elif mode=='box':\n        #color=tuple(np.random.randint(0,255,size=3).tolist())\n        draw.rectangle(((data[0],data[1]),(data[2],data[3])),outline=color,width=8)\n        if label:\n         \n            font = ImageFont.truetype('asset/Arial.ttf', 45)\n            if hasattr(font,'getbbox'):\n                bbox=draw.textbbox((data[0],data[1]),str(label),font)\n            else:\n                w,h=draw.textsize(str(label),font)\n                bbox=(data[0],data[1],w+data[0],data[1]+h)\n            draw.rectangle(bbox,fill=color)    \n            draw.text((data[0],data[1]),str(label),fill='white',font=font)\n            #draw.text((data[0],data[1]),label,font=font)\n    \n    else :\n        print('no support format！！')\n        \ndef show_box(box, ax, label):\n    x0, y0 = box[0], box[1]\n    w, h = box[2] - box[0], box[3] - box[1]\n    ax.add_patch(plt.Rectangle((x0, y0), w, h, edgecolor='green', facecolor=(0,0,0,0), lw=2)) \n    ax.text(x0, y0, label)\n    \nclass Colors:\n    # Ultralytics color palette https://ultralytics.com/\n    def __init__(self):\n        # hex = matplotlib.colors.TABLEAU_COLORS.values()\n        hexs = ('FF3838', 'FF9D97', 'FF701F', 'FFB21D', 'CFD231', '48F90A', '92CC17', '3DDB86', '1A9334', '00D4BB',\n                '2C99A8', '00C2FF', '344593', '6473FF', '0018EC', '8438FF', '520085', 'CB38FF', 'FF95C8', 'FF37C7')\n        self.palette = [self.hex2rgb(f'#{c}') for c in hexs]\n        self.n = len(self.palette)\n\n    def __call__(self, i, bgr=False):\n        c = self.palette[int(i) % self.n]\n        return (c[2], c[1], c[0]) if bgr else c\n\n    @staticmethod\n    def hex2rgb(h):  # rgb order (PIL)\n        return tuple(int(h[1 + i:1 + i + 2], 16) for i in (0, 2, 4))\n\n\ncolors = Colors()  # create instance for 'from utils.plots import colors'\n\ndef check_font(font=FONT, url=\"\",progress=False):\n    font = Path(font)\n    file = CONFIG_DIR / font.name\n    if not font.exists() and not file.exists():\n        url = url + font.name\n        LOGGER.info(f'Downloading {url} to {file}...')\n        torch.hub.download_url_to_file(url, str(file), progress=progress)\n\n\ndef check_pil_font(font=FONT, size=10):\n    # Return a PIL TrueType Font, downloading to CONFIG_DIR if necessary\n    font = Path(font)\n    font = font if font.exists() else (CONFIG_DIR / font.name)\n    try:\n        return ImageFont.truetype(str(font) if font.exists() else font.name, size)\n    except Exception:  # download if missing\n        try:\n            check_font(font)\n            return ImageFont.truetype(str(font), size)\n        except TypeError:\n            check_requirements('Pillow>=8.4.0')  # known issue https://github.com/ultralytics/yolov5/issues/5374\n        except URLError:  # not online\n            return ImageFont.load_default()\n\n\nclass Annotator:\n    # YOLOv5 Annotator for train/val mosaics and jpgs and detect/hub inference annotations\n    def __init__(self, im, line_width=None, font_size=None, font='Arial.ttf', pil=False, example='abc'):\n        assert im.data.contiguous, 'Image not contiguous. Apply np.ascontiguousarray(im) to Annotator() input images.'\n        non_ascii = not is_ascii(example)  # non-latin labels, i.e. asian, arabic, cyrillic\n        self.pil = pil or non_ascii\n        if self.pil:  # use PIL\n            self.im = im if isinstance(im, Image.Image) else Image.fromarray(im)\n            self.draw = ImageDraw.Draw(self.im)\n            self.font = check_pil_font(font='Arial.Unicode.ttf' if non_ascii else font,\n                                       size=font_size or max(round(sum(self.im.size) / 2 * 0.035), 12))\n        else:  # use cv2\n            self.im = im\n        self.lw = line_width or max(round(sum(im.shape) / 2 * 0.003), 2)  # line width\n\n    def box_label(self, box, label='', color=(128, 128, 128), txt_color=(255, 255, 255)):\n        # Add one xyxy box to image with label\n        if self.pil or not is_ascii(label):\n            self.draw.rectangle(box, width=self.lw, outline=color)  # box\n            if label:\n                w, h = self.font.getsize(label)  # text width, height\n                outside = box[1] - h >= 0  # label fits outside box\n                self.draw.rectangle(\n                    (box[0], box[1] - h if outside else box[1], box[0] + w + 1,\n                     box[1] + 1 if outside else box[1] + h + 1),\n                    fill=color,\n                )\n                # self.draw.text((box[0], box[1]), label, fill=txt_color, font=self.font, anchor='ls')  # for PIL>8.0\n                self.draw.text((box[0], box[1] - h if outside else box[1]), label, fill=txt_color, font=self.font)\n        else:  # cv2\n            p1, p2 = (int(box[0]), int(box[1])), (int(box[2]), int(box[3]))\n            cv2.rectangle(self.im, p1, p2, color, thickness=self.lw, lineType=cv2.LINE_AA)\n            if label:\n                tf = max(self.lw - 1, 1)  # font thickness\n                w, h = cv2.getTextSize(label, 0, fontScale=self.lw / 3, thickness=tf)[0]  # text width, height\n                outside = p1[1] - h >= 3\n                p2 = p1[0] + w, p1[1] - h - 3 if outside else p1[1] + h + 3\n                cv2.rectangle(self.im, p1, p2, color, -1, cv2.LINE_AA)  # filled\n                cv2.putText(self.im,\n                            label, (p1[0], p1[1] - 2 if outside else p1[1] + h + 2),\n                            0,\n                            self.lw / 3,\n                            txt_color,\n                            thickness=tf,\n                            lineType=cv2.LINE_AA)\n                            \n    def masks(self, masks, colors, im_gpu, alpha=0.5, retina_masks=False):\n        \"\"\"Plot masks at once.\n        Args:\n            masks (tensor): predicted masks on cuda, shape: [n, h, w]\n            colors (List[List[Int]]): colors for predicted masks, [[r, g, b] * n]\n            im_gpu (tensor): img is in cuda, shape: [3, h, w], range: [0, 1]\n            alpha (float): mask transparency: 0.0 fully transparent, 1.0 opaque\n        \"\"\"\n        if self.pil:\n            # convert to numpy first\n            self.im = np.asarray(self.im).copy() \n        if len(masks) == 0:\n            self.im[:] = im_gpu.permute(1, 2, 0).contiguous().cpu().numpy() * 255\n        colors = torch.tensor(colors, device=im_gpu.device, dtype=torch.float32) / 255.0\n        colors = colors[:, None, None]  # shape(n,1,1,3)\n        masks = masks.unsqueeze(3)  # shape(n,h,w,1)\n        masks_color = masks * (colors * alpha)  # shape(n,h,w,3)\n\n        inv_alph_masks = (1 - masks * alpha).cumprod(0)  # shape(n,h,w,1)\n        mcs = (masks_color * inv_alph_masks).sum(0) * 2  # mask color summand shape(n,h,w,3)\n\n        im_gpu = im_gpu.flip(dims=[0])  # flip channel\n        im_gpu = im_gpu.permute(1, 2, 0).contiguous()  # shape(h,w,3)\n        im_gpu = im_gpu * inv_alph_masks[-1] + mcs\n        im_mask = (im_gpu * 255).byte().cpu().numpy()\n        self.im[:] = im_mask if retina_masks else scale_image(im_gpu.shape, im_mask, self.im.shape)\n        if self.pil:\n            # convert im back to PIL and update draw\n            self.fromarray(self.im)\n\n    def rectangle(self, xy, fill=None, outline=None, width=1):\n        # Add rectangle to image (PIL-only)\n        self.draw.rectangle(xy, fill, outline, width)\n\n    def text(self, xy, text, txt_color=(255, 255, 255), anchor='top'):\n        # Add text to image (PIL-only)\n        if anchor == 'bottom':  # start y from font bottom\n            w, h = self.font.getsize(text)  # text width, height\n            xy[1] += 1 - h\n        self.draw.text(xy, text, fill=txt_color, font=self.font)\n        \n    def fromarray(self, im):\n        # Update self.im from a numpy array\n        self.im = im if isinstance(im, Image.Image) else Image.fromarray(im)\n        \n        self.draw = ImageDraw.Draw(self.im)\n    def result(self):\n        # Return annotated image as array\n        return np.asarray(self.im)\n\n\ndef feature_visualization(x, module_type, stage, n=32, save_dir=Path('runs/detect/exp')):\n    \"\"\"\n    x:              Features to be visualized\n    module_type:    Module type\n    stage:          Module stage within model\n    n:              Maximum number of feature maps to plot\n    save_dir:       Directory to save results\n    \"\"\"\n    if 'Detect' not in module_type:\n        batch, channels, height, width = x.shape  # batch, channels, height, width\n        if height > 1 and width > 1:\n            f = save_dir / f\"stage{stage}_{module_type.split('.')[-1]}_features.png\"  # filename\n\n            blocks = torch.chunk(x[0].cpu(), channels, dim=0)  # select batch index 0, block by channels\n            n = min(n, channels)  # number of plots\n            fig, ax = plt.subplots(math.ceil(n / 8), 8, tight_layout=True)  # 8 rows x n/8 cols\n            ax = ax.ravel()\n            plt.subplots_adjust(wspace=0.05, hspace=0.05)\n            for i in range(n):\n                ax[i].imshow(blocks[i].squeeze())  # cmap='gray'\n                ax[i].axis('off')\n\n            LOGGER.info(f'Saving {f}... ({n}/{channels})')\n            plt.savefig(f, dpi=300, bbox_inches='tight')\n            plt.close()\n            np.save(str(f.with_suffix('.npy')), x[0].cpu().numpy())  # npy save\n\n\ndef hist2d(x, y, n=100):\n    # 2d histogram used in labels.png and evolve.png\n    xedges, yedges = np.linspace(x.min(), x.max(), n), np.linspace(y.min(), y.max(), n)\n    hist, xedges, yedges = np.histogram2d(x, y, (xedges, yedges))\n    xidx = np.clip(np.digitize(x, xedges) - 1, 0, hist.shape[0] - 1)\n    yidx = np.clip(np.digitize(y, yedges) - 1, 0, hist.shape[1] - 1)\n    return np.log(hist[xidx, yidx])\n\n\ndef butter_lowpass_filtfilt(data, cutoff=1500, fs=50000, order=5):\n    from scipy.signal import butter, filtfilt\n\n    # https://stackoverflow.com/questions/28536191/how-to-filter-smooth-with-scipy-numpy\n    def butter_lowpass(cutoff, fs, order):\n        nyq = 0.5 * fs\n        normal_cutoff = cutoff / nyq\n        return butter(order, normal_cutoff, btype='low', analog=False)\n\n    b, a = butter_lowpass(cutoff, fs, order=order)\n    return filtfilt(b, a, data)  # forward-backward filter\n\n\ndef output_to_target(output, max_det=300):\n    # Convert model output to target format [batch_id, class_id, x, y, w, h, conf]\n    targets = []\n    for i, o in enumerate(output):\n        box, conf, cls = o[:max_det, :6].cpu().split((4, 1, 1), 1)\n        j = torch.full((conf.shape[0], 1), i)\n        targets.append(torch.cat((j, cls, xyxy2xywh(box), conf), 1))\n    return torch.cat(targets, 0).numpy()\n\n\n@threaded\ndef plot_images(images, targets, paths=None, fname='images.jpg', names=None):\n    # Plot image grid with labels\n    if isinstance(images, torch.Tensor):\n        images = images.cpu().float().numpy()\n    if isinstance(targets, torch.Tensor):\n        targets = targets.cpu().numpy()\n    \n    max_size = 1920  # max image size\n    max_subplots = 16  # max image subplots, i.e. 4x4\n    bs, _, h, w = images.shape  # batch size, _, height, width\n    bs = min(bs, max_subplots)  # limit plot images\n    ns = np.ceil(bs ** 0.5)  # number of subplots (square)\n    if np.max(images[0]) <= 1:\n        images *= 255  # de-normalise (optional)\n        \n    # Build Image\n    mosaic = np.full((int(ns * h), int(ns * w), 3), 255, dtype=np.uint8)  # init\n    for i, im in enumerate(images):\n        if i == max_subplots:  # if last batch has fewer images than we expect\n            break\n        x, y = int(w * (i // ns)), int(h * (i % ns))  # block origin\n        im = im.transpose(1, 2, 0)\n        mosaic[y:y + h, x:x + w, :] = im\n\n    # Resize (optional)\n    scale = max_size / ns / max(h, w)\n    if scale < 1:\n        h = math.ceil(scale * h)\n        w = math.ceil(scale * w)\n        mosaic = cv2.resize(mosaic, tuple(int(x * ns) for x in (w, h)))\n\n    # Annotate\n    fs = int((h + w) * ns * 0.01)  # font size\n    annotator = Annotator(mosaic, line_width=round(fs / 10), font_size=fs, pil=True, example=names)\n    for i in range(i + 1):\n        x, y = int(w * (i // ns)), int(h * (i % ns))  # block origin\n        annotator.rectangle([x, y, x + w, y + h], None, (255, 255, 255), width=2)  # borders\n        if paths:\n            annotator.text((x + 5, y + 5), text=Path(paths[i]).name[:40], txt_color=(220, 220, 220))  # filenames\n        if len(targets) > 0:\n            ti = targets[targets[:, 0] == i]  # image targets\n            boxes = xywh2xyxy(ti[:, 2:6]).T\n            classes = ti[:, 1].astype('int')\n            labels = ti.shape[1] == 6  # labels if no conf column\n            conf = None if labels else ti[:, 6]  # check for confidence presence (label vs pred)\n\n            if boxes.shape[1]:\n                if boxes.max() <= 1.01:  # if normalized with tolerance 0.01\n                    boxes[[0, 2]] *= w  # scale to pixels\n                    boxes[[1, 3]] *= h\n                elif scale < 1:  # absolute coords need scale if image scales\n                    boxes *= scale\n            boxes[[0, 2]] += x\n            boxes[[1, 3]] += y\n            for j, box in enumerate(boxes.T.tolist()):\n                cls = classes[j]\n                color = colors(cls)\n                cls = names[cls] if names else cls\n                if labels or conf[j] > 0.25:  # 0.25 conf thresh\n                    label = f'{cls}' if labels else f'{cls} {conf[j]:.1f}'\n                    annotator.box_label(box, label, color=color)\n    annotator.im.save(fname)  # save\n\n@threaded\ndef plot_images_masks(images,\n                batch_idx,\n                cls,\n                bboxes,\n                masks=np.zeros(0, dtype=np.uint8),\n                paths=None,\n                fname='images.jpg',\n                names=None):\n    # Plot image grid with labels\n    if isinstance(images, torch.Tensor):\n        images = images.cpu().float().numpy()\n    if isinstance(cls, torch.Tensor):\n        cls = cls.cpu().numpy()\n    if isinstance(bboxes, torch.Tensor):\n        bboxes = bboxes.cpu().numpy()\n    if isinstance(masks, torch.Tensor):\n        masks = masks.cpu().numpy().astype(int)\n    if isinstance(batch_idx, torch.Tensor):\n        batch_idx = batch_idx.cpu().numpy()\n\n    max_size = 1920  # max image size\n    max_subplots = 16  # max image subplots, i.e. 4x4\n    bs, _, h, w = images.shape  # batch size, _, height, width\n    bs = min(bs, max_subplots)  # limit plot images\n    ns = np.ceil(bs ** 0.5)  # number of subplots (square)\n    if np.max(images[0]) <= 1:\n        images *= 255  # de-normalise (optional)\n\n    # Build Image\n    mosaic = np.full((int(ns * h), int(ns * w), 3), 255, dtype=np.uint8)  # init\n    for i, im in enumerate(images):\n        if i == max_subplots:  # if last batch has fewer images than we expect\n            break\n        x, y = int(w * (i // ns)), int(h * (i % ns))  # block origin\n        im = im.transpose(1, 2, 0)\n        mosaic[y:y + h, x:x + w, :] = im\n\n    # Resize (optional)\n    scale = max_size / ns / max(h, w)\n    if scale < 1:\n        h = math.ceil(scale * h)\n        w = math.ceil(scale * w)\n        mosaic = cv2.resize(mosaic, tuple(int(x * ns) for x in (w, h)))\n\n    # Annotate\n    fs = int((h + w) * ns * 0.01)  # font size\n    annotator = Annotator(mosaic, line_width=round(fs / 10), font_size=fs, pil=True, example=names)\n    for i in range(i + 1):\n        x, y = int(w * (i // ns)), int(h * (i % ns))  # block origin\n        annotator.rectangle([x, y, x + w, y + h], None, (255, 255, 255), width=2)  # borders\n        if paths:\n            annotator.text((x + 5, y + 5 + h), text=Path(paths[i]).name[:40], txt_color=(220, 220, 220))  # filenames\n        if len(cls) > 0:\n            idx = batch_idx == i\n\n            boxes = xywh2xyxy(bboxes[idx, :4]).T\n            classes = cls[idx].astype('int')\n            labels = bboxes.shape[1] == 4  # labels if no conf column\n            conf = None if labels else bboxes[idx, 4]  # check for confidence presence (label vs pred)\n\n            if boxes.shape[1]:\n                if boxes.max() <= 1.01:  # if normalized with tolerance 0.01\n                    boxes[[0, 2]] *= w  # scale to pixels\n                    boxes[[1, 3]] *= h\n                elif scale < 1:  # absolute coords need scale if image scales\n                    boxes *= scale\n            boxes[[0, 2]] += x\n            boxes[[1, 3]] += y\n            for j, box in enumerate(boxes.T.tolist()):\n                c = classes[j]\n                color = colors(c)\n                c = names[c] if names else c\n                if labels or conf[j] > 0.25:  # 0.25 conf thresh\n                    label = f'{c}' if labels else f'{c} {conf[j]:.1f}'\n                    annotator.box_label(box, label, color=color)\n\n            # Plot masks\n            if len(masks):\n                if masks.max() > 1.0:  # mean that masks are overlap\n                    image_masks = masks[[i]]  # (1, 640, 640)\n                    nl = idx.sum()\n                    index = np.arange(nl).reshape(nl, 1, 1) + 1\n                    image_masks = np.repeat(image_masks, nl, axis=0)\n                    image_masks = np.where(image_masks == index, 1.0, 0.0)\n                else:\n                    image_masks = masks[idx]\n\n                im = np.asarray(annotator.im).copy()\n                for j, box in enumerate(boxes.T.tolist()):\n                    if labels or conf[j] > 0.25:  # 0.25 conf thresh\n                        color = colors(classes[j])\n                        mh, mw = image_masks[j].shape\n                        if mh != h or mw != w:\n                            mask = image_masks[j].astype(np.uint8)\n                            mask = cv2.resize(mask, (w, h))\n                            mask = mask.astype(bool)\n                        else:\n                            mask = image_masks[j].astype(bool)\n                        with contextlib.suppress(Exception):\n                            im[y:y + h, x:x + w, :][mask] = im[y:y + h, x:x + w, :][mask] * 0.4 + np.array(color) * 0.6\n                annotator.fromarray(im)\n    annotator.im.save(fname)  # save\n\n\n\ndef plot_lr_scheduler(optimizer, scheduler, epochs=300, save_dir=''):\n    # Plot LR simulating training for full epochs\n    optimizer, scheduler = copy(optimizer), copy(scheduler)  # do not modify originals\n    y = []\n    for _ in range(epochs):\n        scheduler.step()\n        y.append(optimizer.param_groups[0]['lr'])\n    plt.plot(y, '.-', label='LR')\n    plt.xlabel('epoch')\n    plt.ylabel('LR')\n    plt.grid()\n    plt.xlim(0, epochs)\n    plt.ylim(0)\n    plt.savefig(Path(save_dir) / 'LR.png', dpi=200)\n    plt.close()\n\n\ndef plot_val_txt():  # from utils.plots import *; plot_val()\n    # Plot val.txt histograms\n    x = np.loadtxt('val.txt', dtype=np.float32)\n    box = xyxy2xywh(x[:, :4])\n    cx, cy = box[:, 0], box[:, 1]\n\n    fig, ax = plt.subplots(1, 1, figsize=(6, 6), tight_layout=True)\n    ax.hist2d(cx, cy, bins=600, cmax=10, cmin=0)\n    ax.set_aspect('equal')\n    plt.savefig('hist2d.png', dpi=300)\n\n    fig, ax = plt.subplots(1, 2, figsize=(12, 6), tight_layout=True)\n    ax[0].hist(cx, bins=600)\n    ax[1].hist(cy, bins=600)\n    plt.savefig('hist1d.png', dpi=200)\n\n\ndef plot_targets_txt():  # from utils.plots import *; plot_targets_txt()\n    # Plot targets.txt histograms\n    x = np.loadtxt('targets.txt', dtype=np.float32).T\n    s = ['x targets', 'y targets', 'width targets', 'height targets']\n    fig, ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)\n    ax = ax.ravel()\n    for i in range(4):\n        ax[i].hist(x[i], bins=100, label=f'{x[i].mean():.3g} +/- {x[i].std():.3g}')\n        ax[i].legend()\n        ax[i].set_title(s[i])\n    plt.savefig('targets.jpg', dpi=200)\n\n\ndef plot_val_study(file='', dir='', x=None):  # from utils.plots import *; plot_val_study()\n    # Plot file=study.txt generated by val.py (or plot all study*.txt in dir)\n    save_dir = Path(file).parent if file else Path(dir)\n    plot2 = False  # plot additional results\n    if plot2:\n        ax = plt.subplots(2, 4, figsize=(10, 6), tight_layout=True)[1].ravel()\n\n    fig2, ax2 = plt.subplots(1, 1, figsize=(8, 4), tight_layout=True)\n    # for f in [save_dir / f'study_coco_{x}.txt' for x in ['yolov5n6', 'yolov5s6', 'yolov5m6', 'yolov5l6', 'yolov5x6']]:\n    for f in sorted(save_dir.glob('study*.txt')):\n        y = np.loadtxt(f, dtype=np.float32, usecols=[0, 1, 2, 3, 7, 8, 9], ndmin=2).T\n        x = np.arange(y.shape[1]) if x is None else np.array(x)\n        if plot2:\n            s = ['P', 'R', 'mAP@.5', 'mAP@.5:.95', 't_preprocess (ms/img)', 't_inference (ms/img)', 't_NMS (ms/img)']\n            for i in range(7):\n                ax[i].plot(x, y[i], '.-', linewidth=2, markersize=8)\n                ax[i].set_title(s[i])\n\n        j = y[3].argmax() + 1\n        ax2.plot(y[5, 1:j],\n                 y[3, 1:j] * 1E2,\n                 '.-',\n                 linewidth=2,\n                 markersize=8,\n                 label=f.stem.replace('study_coco_', '').replace('yolo', 'YOLO'))\n\n    ax2.plot(1E3 / np.array([209, 140, 97, 58, 35, 18]), [34.6, 40.5, 43.0, 47.5, 49.7, 51.5],\n             'k.-',\n             linewidth=2,\n             markersize=8,\n             alpha=.25,\n             label='EfficientDet')\n\n    ax2.grid(alpha=0.2)\n    ax2.set_yticks(np.arange(20, 60, 5))\n    ax2.set_xlim(0, 57)\n    ax2.set_ylim(25, 55)\n    ax2.set_xlabel('GPU Speed (ms/img)')\n    ax2.set_ylabel('COCO AP val')\n    ax2.legend(loc='lower right')\n    f = save_dir / 'study.png'\n    print(f'Saving {f}...')\n    plt.savefig(f, dpi=300)\n\n\n@TryExcept()  # known issue https://github.com/ultralytics/yolov5/issues/5395\ndef plot_labels(labels, names=(), save_dir=Path('')):\n    import seaborn as sn\n    # plot dataset labels\n    LOGGER.info(f\"Plotting labels to {save_dir / 'labels.jpg'}... \")\n    c, b = labels[:, 0], labels[:, 1:].transpose()  # classes, boxes\n    nc = int(c.max() + 1)  # number of classes\n    x = pd.DataFrame(b.transpose(), columns=['x', 'y', 'width', 'height'])\n\n    # seaborn correlogram\n    sn.pairplot(x, corner=True, diag_kind='auto', kind='hist', diag_kws=dict(bins=50), plot_kws=dict(pmax=0.9))\n    plt.savefig(save_dir / 'labels_correlogram.jpg', dpi=200)\n    plt.close()\n\n    # matplotlib labels\n    matplotlib.use('svg')  # faster\n    ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)[1].ravel()\n    y = ax[0].hist(c, bins=np.linspace(0, nc, nc + 1) - 0.5, rwidth=0.8)\n    with contextlib.suppress(Exception):  # color histogram bars by class\n        [y[2].patches[i].set_color([x / 255 for x in colors(i)]) for i in range(nc)]  # known issue #3195\n    ax[0].set_ylabel('instances')\n    if 0 < len(names) < 30:\n        ax[0].set_xticks(range(len(names)))\n        ax[0].set_xticklabels(names, rotation=90, fontsize=10)\n    else:\n        ax[0].set_xlabel('classes')\n    sn.histplot(x, x='x', y='y', ax=ax[2], bins=50, pmax=0.9)\n    sn.histplot(x, x='width', y='height', ax=ax[3], bins=50, pmax=0.9)\n\n    # rectangles\n    labels[:, 1:3] = 0.5  # center\n    labels[:, 1:] = xywh2xyxy(labels[:, 1:]) * 2000\n    img = Image.fromarray(np.ones((2000, 2000, 3), dtype=np.uint8) * 255)\n    for cls, *box in labels[:1000]:\n        ImageDraw.Draw(img).rectangle(box, width=1, outline=colors(cls))  # plot\n    ax[1].imshow(img)\n    ax[1].axis('off')\n\n    for a in [0, 1, 2, 3]:\n        for s in ['top', 'right', 'left', 'bottom']:\n            ax[a].spines[s].set_visible(False)\n\n    plt.savefig(save_dir / 'labels.jpg', dpi=200)\n    matplotlib.use('Agg')\n    plt.close()\n\n\n# def imshow_cls(im, labels=None, pred=None, names=None, nmax=25, verbose=False, f=Path('images.jpg')):\n#     # Show classification image grid with labels (optional) and predictions (optional)\n#     from utils.augmentations import denormalize\n\n#     names = names or [f'class{i}' for i in range(1000)]\n#     blocks = torch.chunk(denormalize(im.clone()).cpu().float(), len(im),\n#                          dim=0)  # select batch index 0, block by channels\n#     n = min(len(blocks), nmax)  # number of plots\n#     m = min(8, round(n ** 0.5))  # 8 x 8 default\n#     fig, ax = plt.subplots(math.ceil(n / m), m)  # 8 rows x n/8 cols\n#     ax = ax.ravel() if m > 1 else [ax]\n#     # plt.subplots_adjust(wspace=0.05, hspace=0.05)\n#     for i in range(n):\n#         ax[i].imshow(blocks[i].squeeze().permute((1, 2, 0)).numpy().clip(0.0, 1.0))\n#         ax[i].axis('off')\n#         if labels is not None:\n#             s = names[labels[i]] + (f'—{names[pred[i]]}' if pred is not None else '')\n#             ax[i].set_title(s, fontsize=8, verticalalignment='top')\n#     plt.savefig(f, dpi=300, bbox_inches='tight')\n#     plt.close()\n#     if verbose:\n#         LOGGER.info(f\"Saving {f}\")\n#         if labels is not None:\n#             LOGGER.info('True:     ' + ' '.join(f'{names[i]:3s}' for i in labels[:nmax]))\n#         if pred is not None:\n#             LOGGER.info('Predicted:' + ' '.join(f'{names[i]:3s}' for i in pred[:nmax]))\n#     return f\n\n\ndef plot_evolve(evolve_csv='path/to/evolve.csv'):  # from utils.plots import *; plot_evolve()\n    # Plot evolve.csv hyp evolution results\n    evolve_csv = Path(evolve_csv)\n    data = pd.read_csv(evolve_csv)\n    keys = [x.strip() for x in data.columns]\n    x = data.values\n    f = fitness(x)\n    j = np.argmax(f)  # max fitness index\n    plt.figure(figsize=(10, 12), tight_layout=True)\n    matplotlib.rc('font', **{'size': 8})\n    print(f'Best results from row {j} of {evolve_csv}:')\n    for i, k in enumerate(keys[7:]):\n        v = x[:, 7 + i]\n        mu = v[j]  # best single result\n        plt.subplot(6, 5, i + 1)\n        plt.scatter(v, f, c=hist2d(v, f, 20), cmap='viridis', alpha=.8, edgecolors='none')\n        plt.plot(mu, f.max(), 'k+', markersize=15)\n        plt.title(f'{k} = {mu:.3g}', fontdict={'size': 9})  # limit to 40 characters\n        if i % 5 != 0:\n            plt.yticks([])\n        print(f'{k:>15}: {mu:.3g}')\n    f = evolve_csv.with_suffix('.png')  # filename\n    plt.savefig(f, dpi=200)\n    plt.close()\n    print(f'Saved {f}')\n\n\ndef plot_results(file='path/to/results.csv', dir=''):\n    # Plot training results.csv. Usage: from utils.plots import *; plot_results('path/to/results.csv')\n    save_dir = Path(file).parent if file else Path(dir)\n    fig, ax = plt.subplots(2, 5, figsize=(12, 6), tight_layout=True)\n    ax = ax.ravel()\n    files = list(save_dir.glob('results*.csv'))\n    assert len(files), f'No results.csv files found in {save_dir.resolve()}, nothing to plot.'\n    for f in files:\n        try:\n            data = pd.read_csv(f)\n            s = [x.strip() for x in data.columns]\n            x = data.values[:, 0]\n            for i, j in enumerate([1, 2, 3, 4, 5, 8, 9, 10, 6, 7]):\n                y = data.values[:, j].astype('float')\n                # y[y == 0] = np.nan  # don't show zero values\n                ax[i].plot(x, y, marker='.', label=f.stem, linewidth=2, markersize=8)\n                ax[i].set_title(s[j], fontsize=12)\n                # if j in [8, 9, 10]:  # share train and val loss y axes\n                #     ax[i].get_shared_y_axes().join(ax[i], ax[i - 5])\n        except Exception as e:\n            LOGGER.info(f'Warning: Plotting error for {f}: {e}')\n    ax[1].legend()\n    fig.savefig(save_dir / 'results.png', dpi=200)\n    plt.close()\n\n\ndef profile_idetection(start=0, stop=0, labels=(), save_dir=''):\n    # Plot iDetection '*.txt' per-image logs. from utils.plots import *; profile_idetection()\n    ax = plt.subplots(2, 4, figsize=(12, 6), tight_layout=True)[1].ravel()\n    s = ['Images', 'Free Storage (GB)', 'RAM Usage (GB)', 'Battery', 'dt_raw (ms)', 'dt_smooth (ms)', 'real-world FPS']\n    files = list(Path(save_dir).glob('frames*.txt'))\n    for fi, f in enumerate(files):\n        try:\n            results = np.loadtxt(f, ndmin=2).T[:, 90:-30]  # clip first and last rows\n            n = results.shape[1]  # number of rows\n            x = np.arange(start, min(stop, n) if stop else n)\n            results = results[:, x]\n            t = (results[0] - results[0].min())  # set t0=0s\n            results[0] = x\n            for i, a in enumerate(ax):\n                if i < len(results):\n                    label = labels[fi] if len(labels) else f.stem.replace('frames_', '')\n                    a.plot(t, results[i], marker='.', label=label, linewidth=1, markersize=5)\n                    a.set_title(s[i])\n                    a.set_xlabel('time (s)')\n                    # if fi == len(files) - 1:\n                    #     a.set_ylim(bottom=0)\n                    for side in ['top', 'right']:\n                        a.spines[side].set_visible(False)\n                else:\n                    a.remove()\n        except Exception as e:\n            print(f'Warning: Plotting error for {f}; {e}')\n    ax[1].legend()\n    plt.savefig(Path(save_dir) / 'idetection_profile.png', dpi=200)\n\n\ndef save_one_box(xyxy, im, file=Path('im.jpg'), gain=1.02, pad=10, square=False, BGR=False, save=True):\n    # Save image crop as {file} with crop size multiple {gain} and {pad} pixels. Save and/or return crop\n    xyxy = torch.tensor(xyxy).view(-1, 4)\n    b = xyxy2xywh(xyxy)  # boxes\n    if square:\n        b[:, 2:] = b[:, 2:].max(1)[0].unsqueeze(1)  # attempt rectangle to square\n    b[:, 2:] = b[:, 2:] * gain + pad  # box wh * gain + pad\n    xyxy = xywh2xyxy(b).long()\n    clip_boxes(xyxy, im.shape)\n    crop = im[int(xyxy[0, 1]):int(xyxy[0, 3]), int(xyxy[0, 0]):int(xyxy[0, 2]), ::(1 if BGR else -1)]\n    if save:\n        file.parent.mkdir(parents=True, exist_ok=True)  # make directory\n        f = str(increment_path(file).with_suffix('.jpg'))\n        # cv2.imwrite(f, crop)  # save BGR, https://github.com/ultralytics/yolov5/issues/7007 chroma subsampling issue\n        Image.fromarray(crop[..., ::-1]).save(f, quality=95, subsampling=0)  # save RGB\n    return crop\n\n\ndef plot_one_box(x, im, color=None, label=None, line_thickness=3, kpt_label=False, kpts=None, steps=2, orig_shape=None):\n    # Plots one bounding box on image 'im' using OpenCV\n    import random\n    assert im.data.contiguous, 'Image not contiguous. Apply np.ascontiguousarray(im) to plot_on_box() input image.'\n    tl = line_thickness or round(0.002 * (im.shape[0] + im.shape[1]) / 2) + 1  # line/font thickness\n    color = color or [random.randint(0, 255) for _ in range(3)]\n    c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3]))\n    cv2.rectangle(im, c1, c2, (255,0,0), thickness=tl*1//3, lineType=cv2.LINE_AA)\n    if label:\n        if len(label.split(' ')) > 1:\n            label = label.split(' ')[-1]\n            tf = max(tl - 1, 1)  # font thickness\n            t_size = cv2.getTextSize(label, 0, fontScale=tl / 6, thickness=tf)[0]\n            c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3\n            cv2.rectangle(im, c1, c2, color, -1, cv2.LINE_AA)  # filled\n            cv2.putText(im, label, (c1[0], c1[1] - 2), 0, tl / 6, [225, 255, 255], thickness=tf//2, lineType=cv2.LINE_AA)\n    if kpt_label:\n        plot_skeleton_kpts(im, kpts, steps, orig_shape=orig_shape)\n\n\ndef plot_skeleton_kpts(im, kpts, steps, orig_shape=None):\n    #Plot the skeleton and keypointsfor coco datatset\n    palette = np.array([[255, 128, 0], [255, 153, 51], [255, 178, 102],\n                        [230, 230, 0], [255, 153, 255], [153, 204, 255],\n                        [255, 102, 255], [255, 51, 255], [102, 178, 255],\n                        [51, 153, 255], [255, 153, 153], [255, 102, 102],\n                        [255, 51, 51], [153, 255, 153], [102, 255, 102],\n                        [51, 255, 51], [0, 255, 0], [0, 0, 255], [255, 0, 0],\n                        [255, 255, 255]])\n\n    skeleton = [[16, 14], [14, 12], [17, 15], [15, 13], [12, 13], [6, 12],\n                [7, 13], [6, 7], [6, 8], [7, 9], [8, 10], [9, 11], [2, 3],\n                [1, 2], [1, 3], [2, 4], [3, 5], [4, 6], [5, 7]]\n\n    pose_limb_color = palette[[9, 9, 9, 9, 7, 7, 7, 0, 0, 0, 0, 0, 16, 16, 16, 16, 16, 16, 16]]\n    pose_kpt_color = palette[[16, 16, 16, 16, 16, 0, 0, 0, 0, 0, 0, 9, 9, 9, 9, 9, 9]]\n    radius = 5\n    num_kpts = len(kpts) // steps\n\n    for kid in range(num_kpts):\n        r, g, b = pose_kpt_color[kid]\n        x_coord, y_coord = kpts[steps * kid], kpts[steps * kid + 1]\n        if not (x_coord % 640 == 0 or y_coord % 640 == 0):\n            if steps == 3:\n                conf = kpts[steps * kid + 2]\n                if conf < 0.5:\n                    continue\n            cv2.circle(im, (int(x_coord), int(y_coord)), radius, (int(r), int(g), int(b)), -1)\n\n    for sk_id, sk in enumerate(skeleton):\n        r, g, b = pose_limb_color[sk_id]\n        pos1 = (int(kpts[(sk[0]-1)*steps]), int(kpts[(sk[0]-1)*steps+1]))\n        pos2 = (int(kpts[(sk[1]-1)*steps]), int(kpts[(sk[1]-1)*steps+1]))\n        if steps == 3:\n            conf1 = kpts[(sk[0]-1)*steps+2]\n            conf2 = kpts[(sk[1]-1)*steps+2]\n            if conf1<0.5 or conf2<0.5:\n                continue\n        if pos1[0]%640 == 0 or pos1[1]%640==0 or pos1[0]<0 or pos1[1]<0:\n            continue\n        if pos2[0] % 640 == 0 or pos2[1] % 640 == 0 or pos2[0]<0 or pos2[1]<0:\n            continue\n        cv2.line(im, pos1, pos2, (int(r), int(g), int(b)), thickness=2)\n\n"
  },
  {
    "path": "utils/text2speech.py",
    "content": "import os\nimport tempfile\nfrom TTS.api import TTS\n\n\n\nclass T2S():\n    def __init__(self) -> None:\n        model_name = TTS.list_models()[0]\n        self.tts = TTS(model_name)\n\n    def test(self, text, language='en'):\n\n        tempf  = tempfile.NamedTemporaryFile(\n                delete = False,\n                suffix = ('.'+'wav'),\n            )\n\n        self.tts.tts_to_file(text, speaker=self.tts.speakers[0], language=language, file_path=tempf.name)\n\n        return tempf.name\n   \n    "
  },
  {
    "path": "utils/textsplitter/__init__.py",
    "content": "from .chinese_text_splitter import ChineseTextSplitter\nfrom .ali_text_splitter import AliTextSplitter\nfrom .zh_title_enhance import zh_title_enhance"
  },
  {
    "path": "utils/textsplitter/ali_text_splitter.py",
    "content": "from langchain.text_splitter import CharacterTextSplitter\nimport re\nfrom typing import List\n\n\nclass AliTextSplitter(CharacterTextSplitter):\n    def __init__(self, pdf: bool = False, **kwargs):\n        super().__init__(**kwargs)\n        self.pdf = pdf\n\n    def split_text(self, text: str) -> List[str]:\n        # use_document_segmentation参数指定是否用语义切分文档，此处采取的文档语义分割模型为达摩院开源的nlp_bert_document-segmentation_chinese-base，论文见https://arxiv.org/abs/2107.09278\n        # 如果使用模型进行文档语义切分，那么需要安装modelscope[nlp]：pip install \"modelscope[nlp]\" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html\n        # 考虑到使用了三个模型，可能对于低配置gpu不太友好，因此这里将模型load进cpu计算，有需要的话可以替换device为自己的显卡id\n        if self.pdf:\n            text = re.sub(r\"\\n{3,}\", r\"\\n\", text)\n            text = re.sub('\\s', \" \", text)\n            text = re.sub(\"\\n\\n\", \"\", text)\n        from modelscope.pipelines import pipeline\n\n        p = pipeline(\n            task=\"document-segmentation\",\n            model='damo/nlp_bert_document-segmentation_chinese-base',\n            device=\"cpu\")\n        result = p(documents=text)\n        sent_list = [i for i in result[\"text\"].split(\"\\n\\t\") if i]\n        return sent_list\n"
  },
  {
    "path": "utils/textsplitter/chinese_text_splitter.py",
    "content": "from langchain.text_splitter import CharacterTextSplitter\nimport re\nfrom typing import List\nfrom configs.model_config import SENTENCE_SIZE\n\n\nclass ChineseTextSplitter(CharacterTextSplitter):\n    def __init__(self, pdf: bool = False, sentence_size: int = SENTENCE_SIZE, **kwargs):\n        super().__init__(**kwargs)\n        self.pdf = pdf\n        self.sentence_size = sentence_size\n\n    def split_text1(self, text: str) -> List[str]:\n        if self.pdf:\n            text = re.sub(r\"\\n{3,}\", \"\\n\", text)\n            text = re.sub('\\s', ' ', text)\n            text = text.replace(\"\\n\\n\", \"\")\n        sent_sep_pattern = re.compile('([﹒﹔﹖﹗．。！？][\"’”」』]{0,2}|(?=[\"‘“「『]{1,2}|$))')  # del ：；\n        sent_list = []\n        for ele in sent_sep_pattern.split(text):\n            if sent_sep_pattern.match(ele) and sent_list:\n                sent_list[-1] += ele\n            elif ele:\n                sent_list.append(ele)\n        return sent_list\n\n    def split_text(self, text: str) -> List[str]:   ##此处需要进一步优化逻辑\n        if self.pdf:\n            text = re.sub(r\"\\n{3,}\", r\"\\n\", text)\n            text = re.sub('\\s', \" \", text)\n            text = re.sub(\"\\n\\n\", \"\", text)\n\n        text = re.sub(r'([;；.!?。！？\\?])([^”’])', r\"\\1\\n\\2\", text)  # 单字符断句符\n        text = re.sub(r'(\\.{6})([^\"’”」』])', r\"\\1\\n\\2\", text)  # 英文省略号\n        text = re.sub(r'(\\…{2})([^\"’”」』])', r\"\\1\\n\\2\", text)  # 中文省略号\n        text = re.sub(r'([;；!?。！？\\?][\"’”」』]{0,2})([^;；!?，。！？\\?])', r'\\1\\n\\2', text)\n        # 如果双引号前有终止符，那么双引号才是句子的终点，把分句符\\n放到双引号后，注意前面的几句都小心保留了双引号\n        text = text.rstrip()  # 段尾如果有多余的\\n就去掉它\n        # 很多规则中会考虑分号;，但是这里我把它忽略不计，破折号、英文双引号等同样忽略，需要的再做些简单调整即可。\n        ls = [i for i in text.split(\"\\n\") if i]\n        for ele in ls:\n            if len(ele) > self.sentence_size:\n                ele1 = re.sub(r'([,，.][\"’”」』]{0,2})([^,，.])', r'\\1\\n\\2', ele)\n                ele1_ls = ele1.split(\"\\n\")\n                for ele_ele1 in ele1_ls:\n                    if len(ele_ele1) > self.sentence_size:\n                        ele_ele2 = re.sub(r'([\\n]{1,}| {2,}[\"’”」』]{0,2})([^\\s])', r'\\1\\n\\2', ele_ele1)\n                        ele2_ls = ele_ele2.split(\"\\n\")\n                        for ele_ele2 in ele2_ls:\n                            if len(ele_ele2) > self.sentence_size:\n                                ele_ele3 = re.sub('( [\"’”」』]{0,2})([^ ])', r'\\1\\n\\2', ele_ele2)\n                                ele2_id = ele2_ls.index(ele_ele2)\n                                ele2_ls = ele2_ls[:ele2_id] + [i for i in ele_ele3.split(\"\\n\") if i] + ele2_ls[\n                                                                                                       ele2_id + 1:]\n                        ele_id = ele1_ls.index(ele_ele1)\n                        ele1_ls = ele1_ls[:ele_id] + [i for i in ele2_ls if i] + ele1_ls[ele_id + 1:]\n\n                id = ls.index(ele)\n                ls = ls[:id] + [i for i in ele1_ls if i] + ls[id + 1:]\n        return ls\n"
  },
  {
    "path": "utils/textsplitter/zh_title_enhance.py",
    "content": "from langchain.docstore.document import Document\nimport re\n\n\ndef under_non_alpha_ratio(text: str, threshold: float = 0.5):\n    \"\"\"Checks if the proportion of non-alpha characters in the text snippet exceeds a given\n    threshold. This helps prevent text like \"-----------BREAK---------\" from being tagged\n    as a title or narrative text. The ratio does not count spaces.\n\n    Parameters\n    ----------\n    text\n        The input string to test\n    threshold\n        If the proportion of non-alpha characters exceeds this threshold, the function\n        returns False\n    \"\"\"\n    if len(text) == 0:\n        return False\n\n    alpha_count = len([char for char in text if char.strip() and char.isalpha()])\n    total_count = len([char for char in text if char.strip()])\n    try:\n        ratio = alpha_count / total_count\n        return ratio < threshold\n    except:\n        return False\n\n\ndef is_possible_title(\n        text: str,\n        title_max_word_length: int = 20,\n        non_alpha_threshold: float = 0.5,\n) -> bool:\n    \"\"\"Checks to see if the text passes all of the checks for a valid title.\n\n    Parameters\n    ----------\n    text\n        The input text to check\n    title_max_word_length\n        The maximum number of words a title can contain\n    non_alpha_threshold\n        The minimum number of alpha characters the text needs to be considered a title\n    \"\"\"\n\n    # 文本长度为0的话，肯定不是title\n    if len(text) == 0:\n        print(\"Not a title. Text is empty.\")\n        return False\n\n    # 文本中有标点符号，就不是title\n    ENDS_IN_PUNCT_PATTERN = r\"[^\\w\\s]\\Z\"\n    ENDS_IN_PUNCT_RE = re.compile(ENDS_IN_PUNCT_PATTERN)\n    if ENDS_IN_PUNCT_RE.search(text) is not None:\n        return False\n\n    # 文本长度不能超过设定值，默认20\n    # NOTE(robinson) - splitting on spaces here instead of word tokenizing because it\n    # is less expensive and actual tokenization doesn't add much value for the length check\n    if len(text) > title_max_word_length:\n        return False\n\n    # 文本中数字的占比不能太高，否则不是title\n    if under_non_alpha_ratio(text, threshold=non_alpha_threshold):\n        return False\n\n    # NOTE(robinson) - Prevent flagging salutations like \"To My Dearest Friends,\" as titles\n    if text.endswith((\",\", \".\", \"，\", \"。\")):\n        return False\n\n    if text.isnumeric():\n        print(f\"Not a title. Text is all numeric:\\n\\n{text}\")  # type: ignore\n        return False\n\n    # 开头的字符内应该有数字，默认5个字符内\n    if len(text) < 5:\n        text_5 = text\n    else:\n        text_5 = text[:5]\n    alpha_in_text_5 = sum(list(map(lambda x: x.isnumeric(), list(text_5))))\n    if not alpha_in_text_5:\n        return False\n\n    return True\n\n\ndef zh_title_enhance(docs: Document) -> Document:\n    title = None\n    if len(docs) > 0:\n        for doc in docs:\n            if is_possible_title(doc.page_content):\n                doc.metadata['category'] = 'cn_Title'\n                title = doc.page_content\n            elif title:\n                doc.page_content = f\"下文与({title})有关。{doc.page_content}\"\n        return docs\n    else:\n        print(\"文件不存在\")\n"
  },
  {
    "path": "utils/toolbox.py",
    "content": "import markdown\nimport importlib\nimport traceback\nimport inspect\nimport gradio\nimport re\nimport os\nfrom latex2mathml.converter import convert as tex2mathml\nfrom functools import wraps, lru_cache\n\n\"\"\"\n========================================================================\n第一部分\n函数插件输入输出接驳区\n    - ChatBotWithCookies:   带Cookies的Chatbot类，为实现更多强大的功能做基础\n    - ArgsGeneralWrapper:   装饰器函数，用于重组输入参数，改变输入参数的顺序与结构\n    - update_ui:            刷新界面用 yield from update_ui(chatbot, history)\n    - CatchException:       将插件中出的所有问题显示在界面上\n    - HotReload:            实现插件的热更新\n    - trimmed_format_exc:   打印traceback，为了安全而隐藏绝对地址\n========================================================================\n\"\"\"\n\nclass ChatBotWithCookies(list):\n    def __init__(self, cookie):\n        self._cookies = cookie\n\n    def write_list(self, list):\n        for t in list:\n            self.append(t)\n\n    def get_list(self):\n        return [t for t in self]\n\n    def get_cookies(self):\n        return self._cookies\n\n\ndef ArgsGeneralWrapper(f):\n    \"\"\"\n    装饰器函数，用于重组输入参数，改变输入参数的顺序与结构。\n    \"\"\"\n    def decorated(request: gradio.Request,cookies, max_length, llm_model, txt, txt2, top_p, temperature, chatbot, history, system_prompt, plugin_advanced_arg, omniverse_switch,record_audio,asr,quantize,chat_app,*args):\n        txt_passon = txt\n        if txt == \"\" and txt2 != \"\": txt_passon = txt2\n        # 引入一个有cookie的chatbot\n        cookies.update({\n            'top_p':top_p,\n            'temperature':temperature,\n        })\n        llm_kwargs = {\n            'api_key': cookies['api_key'],\n            'llm_model': llm_model,\n            'top_p':top_p,\n            'max_length': max_length,\n            'temperature':temperature,\n            'omniverse': omniverse_switch,\n            \"record_audio\":record_audio,\n            \"asr\":asr,\n            \"quantize\":quantize,\n            \"chat_app\":chat_app,\n            'client_ip': request.client.host,\n        }\n        plugin_kwargs = {\n            \"advanced_arg\": plugin_advanced_arg,\n        }\n        chatbot_with_cookie = ChatBotWithCookies(cookies)\n        chatbot_with_cookie.write_list(chatbot)\n        yield from f(txt_passon, llm_kwargs, plugin_kwargs, chatbot_with_cookie, history, system_prompt, *args)\n    return decorated\n\n\ndef update_ui(chatbot, history, msg='正常', **kwargs):  # 刷新界面\n    \"\"\"\n    刷新用户界面\n    \"\"\"\n    assert isinstance(chatbot, ChatBotWithCookies), \"在传递chatbot的过程中不要将其丢弃。必要时，可用clear将其清空，然后用for+append循环重新赋值。\"\n    yield chatbot.get_cookies(), chatbot, history, msg\n\ndef trimmed_format_exc():\n    import os, traceback\n    str = traceback.format_exc()\n    current_path = os.getcwd()\n    replace_path = \".\"\n    return str.replace(current_path, replace_path)\n\ndef CatchException(f):\n    \"\"\"\n    装饰器函数，捕捉函数f中的异常并封装到一个生成器中返回，并显示到聊天当中。\n    \"\"\"\n\n    @wraps(f)\n    def decorated(txt, top_p, temperature, chatbot, history, systemPromptTxt, WEB_PORT):\n        try:\n            yield from f(txt, top_p, temperature, chatbot, history, systemPromptTxt, WEB_PORT)\n        except Exception as e:\n            from utils.check_proxy import check_proxy\n            from utils.toolbox import get_conf\n            proxies, = get_conf('proxies')\n            tb_str = '```\\n' + trimmed_format_exc() + '```'\n            if len(chatbot) == 0:\n                chatbot.clear()\n                chatbot.append([\"插件调度异常\", \"异常原因\"])\n            chatbot[-1] = (chatbot[-1][0],\n                           f\"[Local Message] 实验性函数调用出错: \\n\\n{tb_str} \\n\\n当前代理可用性: \\n\\n{check_proxy(proxies)}\")\n            yield from update_ui(chatbot=chatbot, history=history, msg=f'异常 {e}') # 刷新界面\n    return decorated\n\n\ndef HotReload(f):\n    \"\"\"\n    HotReload的装饰器函数，用于实现Python函数插件的热更新。\n    函数热更新是指在不停止程序运行的情况下，更新函数代码，从而达到实时更新功能。\n    在装饰器内部，使用wraps(f)来保留函数的元信息，并定义了一个名为decorated的内部函数。\n    内部函数通过使用importlib模块的reload函数和inspect模块的getmodule函数来重新加载并获取函数模块，\n    然后通过getattr函数获取函数名，并在新模块中重新加载函数。\n    最后，使用yield from语句返回重新加载过的函数，并在被装饰的函数上执行。\n    最终，装饰器函数返回内部函数。这个内部函数可以将函数的原始定义更新为最新版本，并执行函数的新版本。\n    \"\"\"\n    @wraps(f)\n    def decorated(*args, **kwargs):\n        fn_name = f.__name__\n        f_hot_reload = getattr(importlib.reload(inspect.getmodule(f)), fn_name)\n        yield from f_hot_reload(*args, **kwargs)\n    return decorated\n\n\n\"\"\"\n========================================================================\n第二部分\n其他小工具:\n    - write_results_to_file:    将结果写入markdown文件中\n    - regular_txt_to_markdown:  将普通文本转换为Markdown格式的文本。\n    - report_execption:         向chatbot中添加简单的意外错误信息\n    - text_divide_paragraph:    将文本按照段落分隔符分割开，生成带有段落标签的HTML代码。\n    - markdown_convertion:      用多种方式组合，将markdown转化为好看的html\n    - format_io:                接管gradio默认的markdown处理方式\n    - on_file_uploaded:         处理文件的上传（自动解压）\n    - on_report_generated:      将生成的报告自动投射到文件上传区\n    - clip_history:             当历史上下文过长时，自动截断\n    - get_conf:                 获取设置\n    - select_api_key:           根据当前的模型类别，抽取可用的api-key\n========================================================================\n\"\"\"\n\ndef get_reduce_token_percent(text):\n    \"\"\"\n        * 此函数未来将被弃用\n    \"\"\"\n    try:\n        # text = \"maximum context length is 4097 tokens. However, your messages resulted in 4870 tokens\"\n        pattern = r\"(\\d+)\\s+tokens\\b\"\n        match = re.findall(pattern, text)\n        EXCEED_ALLO = 500  # 稍微留一点余地，否则在回复时会因余量太少出问题\n        max_limit = float(match[0]) - EXCEED_ALLO\n        current_tokens = float(match[1])\n        ratio = max_limit/current_tokens\n        assert ratio > 0 and ratio < 1\n        return ratio, str(int(current_tokens-max_limit))\n    except:\n        return 0.5, '不详'\n\n\ndef write_results_to_file(history, file_name=None):\n    \"\"\"\n    将对话记录history以Markdown格式写入文件中。如果没有指定文件名，则使用当前时间生成文件名。\n    \"\"\"\n    import os\n    import time\n    if file_name is None:\n        # file_name = time.strftime(\"chatGPT分析报告%Y-%m-%d-%H-%M-%S\", time.localtime()) + '.md'\n        file_name = 'chatGPT分析报告' + \\\n            time.strftime(\"%Y-%m-%d-%H-%M-%S\", time.localtime()) + '.md'\n    os.makedirs('./gpt_log/', exist_ok=True)\n    with open(f'./gpt_log/{file_name}', 'w', encoding='utf8') as f:\n        f.write('# chatGPT 分析报告\\n')\n        for i, content in enumerate(history):\n            try:    \n                if type(content) != str: content = str(content)\n            except:\n                continue\n            if i % 2 == 0:\n                f.write('## ')\n            try:\n                f.write(content)\n            except:\n                # remove everything that cannot be handled by utf8\n                f.write(content.encode('utf-8', 'ignore').decode())\n            f.write('\\n\\n')\n    res = '以上材料已经被写入' + os.path.abspath(f'./gpt_log/{file_name}')\n    print(res)\n    return res\n\n\ndef write_history_to_file(history, file_basename=None, file_fullname=None):\n    \"\"\"\n    将对话记录history以Markdown格式写入文件中。如果没有指定文件名，则使用当前时间生成文件名。\n    \"\"\"\n    import os\n    import time\n    if file_fullname is None:\n        if file_basename is not None:\n            file_fullname = os.path.join(get_log_folder(), file_basename)\n        else:\n            file_fullname = os.path.join(get_log_folder(), f'GPT-Academic-{gen_time_str()}.md')\n    os.makedirs(os.path.dirname(file_fullname), exist_ok=True)\n    with open(file_fullname, 'w', encoding='utf8') as f:\n        f.write('# GPT-Academic Report\\n')\n        for i, content in enumerate(history):\n            try:    \n                if type(content) != str: content = str(content)\n            except:\n                continue\n            if i % 2 == 0:\n                f.write('## ')\n            try:\n                f.write(content)\n            except:\n                # remove everything that cannot be handled by utf8\n                f.write(content.encode('utf-8', 'ignore').decode())\n            f.write('\\n\\n')\n    res = os.path.abspath(file_fullname)\n    return res\n\ndef regular_txt_to_markdown(text):\n    \"\"\"\n    将普通文本转换为Markdown格式的文本。\n    \"\"\"\n    text = text.replace('\\n', '\\n\\n')\n    text = text.replace('\\n\\n\\n', '\\n\\n')\n    text = text.replace('\\n\\n\\n', '\\n\\n')\n    return text\n\n\n\n\ndef report_execption(chatbot, history, a, b):\n    \"\"\"\n    向chatbot中添加错误信息\n    \"\"\"\n    chatbot.append((a, b))\n    history.append(a)\n    history.append(b)\n\n\ndef text_divide_paragraph(text):\n    \"\"\"\n    将文本按照段落分隔符分割开，生成带有段落标签的HTML代码。\n    \"\"\"\n    if '```' in text:\n        # careful input\n        return text\n    else:\n        # wtf input\n        lines = text.split(\"\\n\")\n        for i, line in enumerate(lines):\n            lines[i] = lines[i].replace(\" \", \"&nbsp;\")\n        text = \"</br>\".join(lines)\n        return text\n\n@lru_cache(maxsize=128) # 使用 lru缓存 加快转换速度\ndef markdown_convertion(txt):\n    \"\"\"\n    将Markdown格式的文本转换为HTML格式。如果包含数学公式，则先将公式转换为HTML格式。\n    \"\"\"\n    pre = '<div class=\"markdown-body\">'\n    suf = '</div>'\n    if txt.startswith(pre) and txt.endswith(suf):\n        # print('警告，输入了已经经过转化的字符串，二次转化可能出问题')\n        return txt # 已经被转化过，不需要再次转化\n    \n    markdown_extension_configs = {\n        'mdx_math': {\n            'enable_dollar_delimiter': True,\n            'use_gitlab_delimiters': False,\n        },\n    }\n    find_equation_pattern = r'<script type=\"math/tex(?:.*?)>(.*?)</script>'\n\n    def tex2mathml_catch_exception(content, *args, **kwargs):\n        try:\n            content = tex2mathml(content, *args, **kwargs)\n        except:\n            content = content\n        return content\n\n    def replace_math_no_render(match):\n        content = match.group(1)\n        if 'mode=display' in match.group(0):\n            content = content.replace('\\n', '</br>')\n            return f\"<font color=\\\"#00FF00\\\">$$</font><font color=\\\"#FF00FF\\\">{content}</font><font color=\\\"#00FF00\\\">$$</font>\"\n        else:\n            return f\"<font color=\\\"#00FF00\\\">$</font><font color=\\\"#FF00FF\\\">{content}</font><font color=\\\"#00FF00\\\">$</font>\"\n\n    def replace_math_render(match):\n        content = match.group(1)\n        if 'mode=display' in match.group(0):\n            if '\\\\begin{aligned}' in content:\n                content = content.replace('\\\\begin{aligned}', '\\\\begin{array}')\n                content = content.replace('\\\\end{aligned}', '\\\\end{array}')\n                content = content.replace('&', ' ')\n            content = tex2mathml_catch_exception(content, display=\"block\")\n            return content\n        else:\n            return tex2mathml_catch_exception(content)\n\n    def markdown_bug_hunt(content):\n        \"\"\"\n        解决一个mdx_math的bug（单$包裹begin命令时多余<script>）\n        \"\"\"\n        content = content.replace('<script type=\"math/tex\">\\n<script type=\"math/tex; mode=display\">', '<script type=\"math/tex; mode=display\">')\n        content = content.replace('</script>\\n</script>', '</script>')\n        return content\n\n    def no_code(txt):\n        if '```' not in txt: \n            return True\n        else:\n            if '```reference' in txt: return True    # newbing\n            else: return False\n\n    if ('$' in txt) and no_code(txt):  # 有$标识的公式符号，且没有代码段```的标识\n        # convert everything to html format\n        split = markdown.markdown(text='---')\n        convert_stage_1 = markdown.markdown(text=txt, extensions=['mdx_math', 'fenced_code', 'tables', 'sane_lists'], extension_configs=markdown_extension_configs)\n        convert_stage_1 = markdown_bug_hunt(convert_stage_1)\n        # re.DOTALL: Make the '.' special character match any character at all, including a newline; without this flag, '.' will match anything except a newline. Corresponds to the inline flag (?s).\n        # 1. convert to easy-to-copy tex (do not render math)\n        convert_stage_2_1, n = re.subn(find_equation_pattern, replace_math_no_render, convert_stage_1, flags=re.DOTALL)\n        # 2. convert to rendered equation\n        convert_stage_2_2, n = re.subn(find_equation_pattern, replace_math_render, convert_stage_1, flags=re.DOTALL)\n        # cat them together\n        return pre + convert_stage_2_1 + f'{split}' + convert_stage_2_2 + suf\n    else:\n        return pre + markdown.markdown(txt, extensions=['fenced_code', 'codehilite', 'tables', 'sane_lists']) + suf\n\n\ndef close_up_code_segment_during_stream(gpt_reply):\n    \"\"\"\n    在gpt输出代码的中途（输出了前面的```，但还没输出完后面的```），补上后面的```\n\n    Args:\n        gpt_reply (str): GPT模型返回的回复字符串。\n\n    Returns:\n        str: 返回一个新的字符串，将输出代码片段的“后面的```”补上。\n\n    \"\"\"\n    if '```' not in gpt_reply:\n        return gpt_reply\n    if gpt_reply.endswith('```'):\n        return gpt_reply\n\n    # 排除了以上两个情况，我们\n    segments = gpt_reply.split('```')\n    n_mark = len(segments) - 1\n    if n_mark % 2 == 1:\n        # print('输出代码片段中！')\n        return gpt_reply+'\\n```'\n    else:\n        return gpt_reply\n\n\ndef format_io(self, y):\n    \"\"\"\n    将输入和输出解析为HTML格式。将y中最后一项的输入部分段落化，并将输出部分的Markdown和数学公式转换为HTML格式。\n    \"\"\"\n    if y is None or y == []:\n        return []\n    try:\n        i_ask, gpt_reply = y[-1]\n        # 输入部分太自由，预处理一波\n        if i_ask is not None: i_ask = text_divide_paragraph(i_ask)\n        # 当代码输出半截的时候，试着补上后个```\n        if gpt_reply is not None: gpt_reply = close_up_code_segment_during_stream(gpt_reply)\n        # process\n        y[-1] = (\n            None if i_ask is None else markdown.markdown(i_ask, extensions=['fenced_code', 'tables']),\n            None if gpt_reply is None else markdown_convertion(gpt_reply)\n        )\n    except:\n         return[]    \n    return y\n\n\ndef find_free_port():\n    \"\"\"\n    返回当前系统中可用的未使用端口。\n    \"\"\"\n    import socket\n    from contextlib import closing\n    with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as s:\n        s.bind(('', 0))\n        s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n        return s.getsockname()[1]\n\n\ndef extract_archive(file_path, dest_dir):\n    import zipfile\n    import tarfile\n    import os\n    # Get the file extension of the input file\n    file_extension = os.path.splitext(file_path)[1]\n\n    # Extract the archive based on its extension\n    if file_extension == '.zip':\n        with zipfile.ZipFile(file_path, 'r') as zipobj:\n            zipobj.extractall(path=dest_dir)\n            print(\"Successfully extracted zip archive to {}\".format(dest_dir))\n\n    elif file_extension in ['.tar', '.gz', '.bz2']:\n        with tarfile.open(file_path, 'r:*') as tarobj:\n            tarobj.extractall(path=dest_dir)\n            print(\"Successfully extracted tar archive to {}\".format(dest_dir))\n\n    # 第三方库，需要预先pip install rarfile\n    # 此外，Windows上还需要安装winrar软件，配置其Path环境变量，如\"C:\\Program Files\\WinRAR\"才可以\n    elif file_extension == '.rar':\n        try:\n            import rarfile\n            with rarfile.RarFile(file_path) as rf:\n                rf.extractall(path=dest_dir)\n                print(\"Successfully extracted rar archive to {}\".format(dest_dir))\n        except:\n            print(\"Rar format requires additional dependencies to install\")\n            return '\\n\\n需要安装pip install rarfile来解压rar文件'\n\n    # 第三方库，需要预先pip install py7zr\n    elif file_extension == '.7z':\n        try:\n            import py7zr\n            with py7zr.SevenZipFile(file_path, mode='r') as f:\n                f.extractall(path=dest_dir)\n                print(\"Successfully extracted 7z archive to {}\".format(dest_dir))\n        except:\n            print(\"7z format requires additional dependencies to install\")\n            return '\\n\\n需要安装pip install py7zr来解压7z文件'\n    else:\n        return ''\n    return ''\n\n\ndef find_recent_files(directory):\n    \"\"\"\n        me: find files that is created with in one minutes under a directory with python, write a function\n        gpt: here it is!\n    \"\"\"\n    import os\n    import time\n    current_time = time.time()\n    one_minute_ago = current_time - 60\n    recent_files = []\n\n    for filename in os.listdir(directory):\n        file_path = os.path.join(directory, filename)\n        if file_path.endswith('.log'):\n            continue\n        created_time = os.path.getmtime(file_path)\n        if created_time >= one_minute_ago:\n            if os.path.isdir(file_path):\n                continue\n            recent_files.append(file_path)\n\n    return recent_files\n\ndef promote_file_to_downloadzone(file, rename_file=None, chatbot=None):\n    # 将文件复制一份到下载区\n    import shutil\n    if rename_file is None: rename_file = f'{gen_time_str()}-{os.path.basename(file)}'\n    new_path = os.path.join(get_log_folder(), rename_file)\n    # 如果已经存在，先删除\n    if os.path.exists(new_path) and not os.path.samefile(new_path, file): os.remove(new_path)\n    # 把文件复制过去\n    if not os.path.exists(new_path): shutil.copyfile(file, new_path)\n    # 将文件添加到chatbot cookie中，避免多用户干扰\n    if chatbot:\n        if 'file_to_promote' in chatbot._cookies: current = chatbot._cookies['file_to_promote']\n        else: current = []\n        chatbot._cookies.update({'file_to_promote': [new_path] + current})\n\ndef disable_auto_promotion(chatbot):\n    chatbot._cookies.update({'file_to_promote': []})\n    return\n\ndef on_file_uploaded(files, chatbot, txt, txt2, checkboxes):\n    \"\"\"\n    当文件被上传时的回调函数\n    \"\"\"\n    if len(files) == 0:\n        return chatbot, txt\n    import shutil\n    import os\n    import time\n    import glob\n    from utils.toolbox import extract_archive\n    try:\n        shutil.rmtree('./private_upload/')\n    except:\n        pass\n    time_tag = time.strftime(\"%Y-%m-%d-%H-%M-%S\", time.localtime())\n    os.makedirs(f'private_upload/{time_tag}', exist_ok=True)\n    err_msg = ''\n    for file in files:\n        file_origin_name = os.path.basename(file.orig_name)\n        shutil.copy(file.name, f'private_upload/{time_tag}/{file_origin_name}')\n        err_msg += extract_archive(f'private_upload/{time_tag}/{file_origin_name}',\n                                   dest_dir=f'private_upload/{time_tag}/{file_origin_name}.extract')\n    moved_files = [fp for fp in glob.glob('private_upload/**/*', recursive=True)]\n    if \"底部输入区\" in checkboxes:\n        txt = \"\"\n        txt2 = f'private_upload/{time_tag}'\n    else:\n        txt = f'private_upload/{time_tag}'\n        txt2 = \"\"\n    moved_files_str = '\\t\\n\\n'.join(moved_files)\n    chatbot.append(['我上传了文件，请查收',\n                    f'[Local Message] 收到以下文件: \\n\\n{moved_files_str}' +\n                    f'\\n\\n调用路径参数已自动修正到: \\n\\n{txt}' +\n                    f'\\n\\n现在您点击任意“红颜色”标识的函数插件时，以上文件将被作为输入参数'+err_msg])\n    return chatbot, txt, txt2\n\n\ndef on_report_generated(cookies, files, chatbot):\n    from utils.toolbox import find_recent_files\n    if 'file_to_promote' in cookies:\n        report_files = cookies['file_to_promote']\n        cookies.pop('file_to_promote')\n    else:\n        report_files = find_recent_files('gpt_log')\n    if len(report_files) == 0:\n        return cookies, None, chatbot\n    # files.extend(report_files)\n    file_links = ''\n    for f in report_files: file_links += f'<br/><a href=\"file={os.path.abspath(f)}\" target=\"_blank\">{f}</a>'\n    chatbot.append(['报告如何远程获取？', f'报告已经添加到右侧“文件上传区”（可能处于折叠状态），请查收。{file_links}'])\n    return cookies, report_files, chatbot\n\n\ndef is_openai_api_key(key):\n    API_MATCH_ORIGINAL = re.match(r\"sk-[a-zA-Z0-9]{48}$\", key)\n    API_MATCH_AZURE = re.match(r\"[a-zA-Z0-9]{32}$\", key)\n    return bool(API_MATCH_ORIGINAL) or bool(API_MATCH_AZURE)\n\ndef is_api2d_key(key):\n    if key.startswith('fk') and len(key) == 41:\n        return True\n    else:\n        return False\n\ndef is_any_api_key(key):\n    if ',' in key:\n        keys = key.split(',')\n        for k in keys:\n            if is_any_api_key(k): return True\n        return False\n    else:\n        return is_openai_api_key(key) or is_api2d_key(key)\n\ndef what_keys(keys):\n    avail_key_list = {'OpenAI Key':0, \"API2D Key\":0}\n    key_list = keys.split(',')\n\n    for k in key_list:\n        if is_openai_api_key(k): \n            avail_key_list['OpenAI Key'] += 1\n\n    for k in key_list:\n        if is_api2d_key(k): \n            avail_key_list['API2D Key'] += 1\n\n    return f\"检测到： OpenAI Key {avail_key_list['OpenAI Key']} 个，API2D Key {avail_key_list['API2D Key']} 个\"\n\ndef select_api_key(keys, llm_model):\n    import random\n    avail_key_list = []\n    key_list = keys.split(',')\n\n    if llm_model.startswith('gpt-'):\n        for k in key_list:\n            if is_openai_api_key(k): avail_key_list.append(k)\n\n    if llm_model.startswith('api2d-'):\n        for k in key_list:\n            if is_api2d_key(k): avail_key_list.append(k)\n\n    if len(avail_key_list) == 0:\n        raise RuntimeError(f\"您提供的api-key不满足要求，不包含任何可用于{llm_model}的api-key。您可能选择了错误的模型或请求源。\")\n\n    api_key = random.choice(avail_key_list) # 随机负载均衡\n    return api_key\n\ndef load_chat_cookies():\n    API_KEY, LLM_MODEL, AZURE_API_KEY = get_conf('API_KEY', 'LLM_MODEL', 'AZURE_API_KEY')\n    if is_any_api_key(AZURE_API_KEY):\n        if is_any_api_key(API_KEY): API_KEY = API_KEY + ',' + AZURE_API_KEY\n        else: API_KEY = AZURE_API_KEY\n    return {'api_key': API_KEY, 'llm_model': LLM_MODEL}\n\ndef read_env_variable(arg, default_value):\n    \"\"\"\n    环境变量可以是 `GPT_ACADEMIC_CONFIG`(优先)，也可以直接是`CONFIG`\n    例如在windows cmd中，既可以写：\n        set USE_PROXY=True\n        set API_KEY=sk-j7caBpkRoxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n        set proxies={\"http\":\"http://127.0.0.1:10085\", \"https\":\"http://127.0.0.1:10085\",}\n        set AVAIL_LLM_MODELS=[\"gpt-3.5-turbo\", \"chatglm\"]\n        set AUTHENTICATION=[(\"username\", \"password\"), (\"username2\", \"password2\")]\n    也可以写：\n        set GPT_ACADEMIC_USE_PROXY=True\n        set GPT_ACADEMIC_API_KEY=sk-j7caBpkRoxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n        set GPT_ACADEMIC_proxies={\"http\":\"http://127.0.0.1:10085\", \"https\":\"http://127.0.0.1:10085\",}\n        set GPT_ACADEMIC_AVAIL_LLM_MODELS=[\"gpt-3.5-turbo\", \"chatglm\"]\n        set GPT_ACADEMIC_AUTHENTICATION=[(\"username\", \"password\"), (\"username2\", \"password2\")]\n    \"\"\"\n    from utils.colorful import print亮红, print亮绿\n    arg_with_prefix = \"GPT_ACADEMIC_\" + arg \n    if arg_with_prefix in os.environ: \n        env_arg = os.environ[arg_with_prefix]\n    elif arg in os.environ: \n        env_arg = os.environ[arg]\n    else:\n        raise KeyError\n    print(f\"[ENV_VAR] 尝试加载{arg}，默认值：{default_value} --> 修正值：{env_arg}\")\n    try:\n        if isinstance(default_value, bool):\n            env_arg = env_arg.strip()\n            if env_arg == 'True': r = True\n            elif env_arg == 'False': r = False\n            else: print('enter True or False, but have:', env_arg); r = default_value\n        elif isinstance(default_value, int):\n            r = int(env_arg)\n        elif isinstance(default_value, float):\n            r = float(env_arg)\n        elif isinstance(default_value, str):\n            r = env_arg.strip()\n        elif isinstance(default_value, dict):\n            r = eval(env_arg)\n        elif isinstance(default_value, list):\n            r = eval(env_arg)\n        elif default_value is None:\n            assert arg == \"proxies\"\n            r = eval(env_arg)\n        else:\n            print亮红(f\"[ENV_VAR] 环境变量{arg}不支持通过环境变量设置! \")\n            raise KeyError\n    except:\n        print亮红(f\"[ENV_VAR] 环境变量{arg}加载失败! \")\n        raise KeyError(f\"[ENV_VAR] 环境变量{arg}加载失败! \")\n\n    print亮绿(f\"[ENV_VAR] 成功读取环境变量{arg}\")\n    return r\n\n@lru_cache(maxsize=128)\ndef read_single_conf_with_lru_cache(arg):\n    from utils.colorful import print亮红, print亮绿, print亮蓝\n    try:\n        # 优先级1. 获取环境变量作为配置\n        default_ref = getattr(importlib.import_module('config'), arg)   # 读取默认值作为数据类型转换的参考\n        r = read_env_variable(arg, default_ref) \n    except:\n        try:\n            # 优先级2. 获取config_private中的配置\n            r = getattr(importlib.import_module('config_private'), arg)\n        except:\n            # 优先级3. 获取config中的配置\n            r = getattr(importlib.import_module('config'), arg)\n\n    # 在读取API_KEY时，检查一下是不是忘了改config\n    if arg == 'API_KEY':\n        print亮蓝(f\"[API_KEY] 本项目现已支持OpenAI和API2D的api-key。也支持同时填写多个api-key，如API_KEY=\\\"openai-key1,openai-key2,api2d-key3\\\"\")\n        print亮蓝(f\"[API_KEY] 您既可以在config.py中修改api-key(s)，也可以在问题输入区输入临时的api-key(s)，然后回车键提交后即可生效。\")\n        if is_any_api_key(r):\n            print亮绿(f\"[API_KEY] 您的 API_KEY 是: {r[:15]}*** API_KEY 导入成功\")\n        else:\n            print亮红( \"[API_KEY] 正确的 API_KEY 是'sk'开头的51位密钥（OpenAI），或者 'fk'开头的41位密钥，请在config文件中修改API密钥之后再运行。\")\n    if arg == 'proxies':\n        if r is None:\n            print亮红('[PROXY] 网络代理状态：未配置。无代理状态下很可能无法访问OpenAI家族的模型。建议：检查USE_PROXY选项是否修改。')\n        else:\n            print亮绿('[PROXY] 网络代理状态：已配置。配置信息如下：', r)\n            assert isinstance(r, dict), 'proxies格式错误，请注意proxies选项的格式，不要遗漏括号。'\n    return r\n\n\ndef get_conf(*args):\n    # 建议您复制一个config_private.py放自己的秘密, 如API和代理网址, 避免不小心传github被别人看到\n    res = []\n    for arg in args:\n        r = read_single_conf_with_lru_cache(arg)\n        res.append(r)\n    return res\n\n\ndef clear_line_break(txt):\n    txt = txt.replace('\\n', ' ')\n    txt = txt.replace('  ', ' ')\n    txt = txt.replace('  ', ' ')\n    return txt\n\n\nclass DummyWith():\n    \"\"\"\n    这段代码定义了一个名为DummyWith的空上下文管理器，\n    它的作用是……额……就是不起作用，即在代码结构不变得情况下取代其他的上下文管理器。\n    上下文管理器是一种Python对象，用于与with语句一起使用，\n    以确保一些资源在代码块执行期间得到正确的初始化和清理。\n    上下文管理器必须实现两个方法，分别为 __enter__()和 __exit__()。\n    在上下文执行开始的情况下，__enter__()方法会在代码块被执行前被调用，\n    而在上下文执行结束时，__exit__()方法则会被调用。\n    \"\"\"\n    def __enter__(self):\n        return self\n\n    def __exit__(self, exc_type, exc_value, traceback):\n        return\n\ndef run_gradio_in_subpath(demo, auth, port, custom_path):\n    \"\"\"\n    把gradio的运行地址更改到指定的二次路径上\n    \"\"\"\n    def is_path_legal(path: str)->bool:\n        '''\n        check path for sub url\n        path: path to check\n        return value: do sub url wrap\n        '''\n        if path == \"/\": return True\n        if len(path) == 0:\n            print(\"ilegal custom path: {}\\npath must not be empty\\ndeploy on root url\".format(path))\n            return False\n        if path[0] == '/':\n            if path[1] != '/':\n                print(\"deploy on sub-path {}\".format(path))\n                return True\n            return False\n        print(\"ilegal custom path: {}\\npath should begin with \\'/\\'\\ndeploy on root url\".format(path))\n        return False\n\n    if not is_path_legal(custom_path): raise RuntimeError('Ilegal custom path')\n    import uvicorn\n    import gradio as gr\n    from fastapi import FastAPI\n    app = FastAPI()\n    if custom_path != \"/\":\n        @app.get(\"/\")\n        def read_main(): \n            return {\"message\": f\"Gradio is running at: {custom_path}\"}\n    app = gr.mount_gradio_app(app, demo, path=custom_path)\n    uvicorn.run(app, host=\"0.0.0.0\", port=port) # , auth=auth\n\n\ndef clip_history(inputs, history, tokenizer, max_token_limit):\n    \"\"\"\n    reduce the length of history by clipping.\n    this function search for the longest entries to clip, little by little,\n    until the number of token of history is reduced under threshold.\n    通过裁剪来缩短历史记录的长度。 \n    此函数逐渐地搜索最长的条目进行剪辑，\n    直到历史记录的标记数量降低到阈值以下。\n    \"\"\"\n    import numpy as np\n    from llm_cards.bridge_all import model_info\n    def get_token_num(txt): \n        return len(tokenizer.encode(txt, disallowed_special=()))\n    input_token_num = get_token_num(inputs)\n    if input_token_num < max_token_limit * 3 / 4:\n        # 当输入部分的token占比小于限制的3/4时，裁剪时\n        # 1. 把input的余量留出来\n        max_token_limit = max_token_limit - input_token_num\n        # 2. 把输出用的余量留出来\n        max_token_limit = max_token_limit - 128\n        # 3. 如果余量太小了，直接清除历史\n        if max_token_limit < 128:\n            history = []\n            return history\n    else:\n        # 当输入部分的token占比 > 限制的3/4时，直接清除历史\n        history = []\n        return history\n\n    everything = ['']\n    everything.extend(history)\n    n_token = get_token_num('\\n'.join(everything))\n    everything_token = [get_token_num(e) for e in everything]\n\n    # 截断时的颗粒度\n    delta = max(everything_token) // 16\n\n    while n_token > max_token_limit:\n        where = np.argmax(everything_token)\n        encoded = tokenizer.encode(everything[where], disallowed_special=())\n        clipped_encoded = encoded[:len(encoded)-delta]\n        everything[where] = tokenizer.decode(clipped_encoded)[:-1]    # -1 to remove the may-be illegal char\n        everything_token[where] = get_token_num(everything[where])\n        n_token = get_token_num('\\n'.join(everything))\n\n    history = everything[1:]\n    return history\n\n\"\"\"\n========================================================================\n第三部分\n其他小工具:\n    - zip_folder:    把某个路径下所有文件压缩，然后转移到指定的另一个路径中（gpt写的）\n    - gen_time_str:  生成时间戳\n========================================================================\n\"\"\"\n\ndef zip_folder(source_folder, dest_folder, zip_name):\n    import zipfile\n    import os\n    # Make sure the source folder exists\n    if not os.path.exists(source_folder):\n        print(f\"{source_folder} does not exist\")\n        return\n\n    # Make sure the destination folder exists\n    if not os.path.exists(dest_folder):\n        print(f\"{dest_folder} does not exist\")\n        return\n\n    # Create the name for the zip file\n    zip_file = os.path.join(dest_folder, zip_name)\n\n    # Create a ZipFile object\n    with zipfile.ZipFile(zip_file, 'w', zipfile.ZIP_DEFLATED) as zipf:\n        # Walk through the source folder and add files to the zip file\n        for foldername, subfolders, filenames in os.walk(source_folder):\n            for filename in filenames:\n                filepath = os.path.join(foldername, filename)\n                zipf.write(filepath, arcname=os.path.relpath(filepath, source_folder))\n\n    # Move the zip file to the destination folder (if it wasn't already there)\n    if os.path.dirname(zip_file) != dest_folder:\n        os.rename(zip_file, os.path.join(dest_folder, os.path.basename(zip_file)))\n        zip_file = os.path.join(dest_folder, os.path.basename(zip_file))\n\n    print(f\"Zip file created at {zip_file}\")\n\ndef gen_time_str():\n    import time\n    return time.strftime(\"%Y-%m-%d-%H-%M-%S\", time.localtime())\n\ndef get_log_folder(user='default', plugin_name='shared'):\n    _dir = os.path.join(os.path.dirname(__file__), 'gpt_log', user, plugin_name)\n    if not os.path.exists(_dir): os.makedirs(_dir)\n    return _dir\n\nclass ProxyNetworkActivate():\n    \"\"\"\n    这段代码定义了一个名为TempProxy的空上下文管理器, 用于给一小段代码上代理\n    \"\"\"\n    def __enter__(self):\n        from utils.toolbox import get_conf\n        proxies, = get_conf('proxies')\n        if 'no_proxy' in os.environ: os.environ.pop('no_proxy')\n        os.environ['HTTP_PROXY'] = proxies['http']\n        os.environ['HTTPS_PROXY'] = proxies['https']\n        return self\n\n    def __exit__(self, exc_type, exc_value, traceback):\n        os.environ['no_proxy'] = '*'\n        if 'HTTP_PROXY' in os.environ: os.environ.pop('HTTP_PROXY')\n        if 'HTTPS_PROXY' in os.environ: os.environ.pop('HTTPS_PROXY')\n        return\n    \ndef objdump(obj, file='objdump.tmp'):\n    import pickle\n    with open(file, 'wb+') as f:\n        pickle.dump(obj, f)\n    return\n\ndef objload(file='objdump.tmp'):\n    import pickle, os\n    if not os.path.exists(file): \n        return\n    with open(file, 'rb') as f:\n        return pickle.load(f)\n    \ndef Singleton(cls):\n    \"\"\"\n    一个单实例装饰器\n    \"\"\"\n    _instance = {}\n \n    def _singleton(*args, **kargs):\n        if cls not in _instance:\n            _instance[cls] = cls(*args, **kargs)\n        return _instance[cls]\n \n    return _singleton\n\n\"\"\"\n========================================================================\n第四部分\n接驳虚空终端:\n    - set_conf:                     在运行过程中动态地修改配置\n    - set_multi_conf:               在运行过程中动态地修改多个配置\n    - get_plugin_handle:            获取插件的句柄\n    - get_plugin_default_kwargs:    获取插件的默认参数\n    - get_chat_handle:              获取简单聊天的句柄\n    - get_chat_default_kwargs:      获取简单聊天的默认参数\n========================================================================\n\"\"\"\n\ndef set_conf(key, value):\n    from toolbox import read_single_conf_with_lru_cache, get_conf\n    read_single_conf_with_lru_cache.cache_clear()\n    get_conf.cache_clear()\n    os.environ[key] = str(value)\n    altered, = get_conf(key)\n    return altered\n\ndef set_multi_conf(dic):\n    for k, v in dic.items(): set_conf(k, v)\n    return\n\ndef get_plugin_handle(plugin_name):\n    \"\"\"\n    e.g. plugin_name = 'crazy_functions.批量Markdown翻译->Markdown翻译指定语言'\n    \"\"\"\n    import importlib\n    assert '->' in plugin_name, \\\n        \"Example of plugin_name: crazy_functions.批量Markdown翻译->Markdown翻译指定语言\"\n    module, fn_name = plugin_name.split('->')\n    f_hot_reload = getattr(importlib.import_module(module, fn_name), fn_name)\n    return f_hot_reload\n\ndef get_chat_handle():\n    \"\"\"\n    \"\"\"\n    from llm_cards.bridge_all import predict_no_ui_long_connection\n    return predict_no_ui_long_connection\n\ndef get_plugin_default_kwargs():\n    \"\"\"\n    \"\"\"\n    from toolbox import get_conf, ChatBotWithCookies\n\n    WEB_PORT, LLM_MODEL, API_KEY = \\\n        get_conf('WEB_PORT', 'LLM_MODEL', 'API_KEY')\n\n    llm_kwargs = {\n        'api_key': API_KEY,\n        'llm_model': LLM_MODEL,\n        'top_p':1.0, \n        'max_length': None,\n        'temperature':1.0,\n    }\n    chatbot = ChatBotWithCookies(llm_kwargs)\n\n    # txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port\n    default_plugin_kwargs = {\n        \"main_input\": \"./README.md\",\n        \"llm_kwargs\": llm_kwargs,\n        \"plugin_kwargs\": {},\n        \"chatbot_with_cookie\": chatbot,\n        \"history\": [],\n        \"system_prompt\": \"You are a good AI.\", \n        \"web_port\": WEB_PORT\n    }\n    return default_plugin_kwargs\n\ndef get_chat_default_kwargs():\n    \"\"\"\n    \"\"\"\n    from toolbox import get_conf\n\n    LLM_MODEL, API_KEY = get_conf('LLM_MODEL', 'API_KEY')\n\n    llm_kwargs = {\n        'api_key': API_KEY,\n        'llm_model': LLM_MODEL,\n        'top_p':1.0, \n        'max_length': None,\n        'temperature':1.0,\n    }\n\n    default_chat_kwargs = {\n        \"inputs\": \"Hello there, are you ready?\",\n        \"llm_kwargs\": llm_kwargs,\n        \"history\": [],\n        \"sys_prompt\": \"You are AI assistant\",\n        \"observe_window\": None,\n        \"console_slience\": False,\n    }\n\n    return default_chat_kwargs"
  },
  {
    "path": "utils/torch_utils.py",
    "content": "# YOLOv5_research by positive666 \n\n\"\"\"\nPyTorch utils\n\"\"\"\nimport math\nimport os\nimport platform\nimport subprocess\nimport time\nimport warnings\nfrom contextlib import contextmanager\nfrom torch.nn.parallel import DistributedDataParallel as DDP\nfrom copy import deepcopy\nfrom pathlib import Path\n\nimport torch\nimport torch.distributed as dist\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torchvision\n\nfrom utils.ops import LOGGER, check_version, colorstr, file_date, git_describe\n\nLOCAL_RANK = int(os.getenv('LOCAL_RANK', -1))  # https://pytorch.org/docs/stable/elastic/run.html\nRANK = int(os.getenv('RANK', -1))\nWORLD_SIZE = int(os.getenv('WORLD_SIZE', 1))\n\nTORCHVISION_0_10 = check_version(torchvision.__version__, '0.10.0')\nTORCH_1_9 = check_version(torch.__version__, '1.9.0')\nTORCH_1_11 = check_version(torch.__version__, '1.11.0')\nTORCH_1_12 = check_version(torch.__version__, '1.12.0')\nTORCH_2_X = check_version(torch.__version__, minimum='2.0')\n\ntry:\n    import thop  # for FLOPs computationf\nexcept ImportError:\n    thop = None\n\n# Suppress PyTorch warnings\nwarnings.filterwarnings('ignore', message='User provided device_type of \\'cuda\\', but CUDA is not available. Disabling')\nwarnings.filterwarnings('ignore', category=UserWarning)\n\n\ndef smart_inference_mode(torch_1_9=check_version(torch.__version__, '1.9.0')):\n    # Applies torch.inference_mode() decorator if torch>=1.9.0 else torch.no_grad() decorator\n    def decorate(fn):\n        return (torch.inference_mode if torch_1_9 else torch.no_grad)()(fn)\n\n    return decorate\n\n\ndef smartCrossEntropyLoss(label_smoothing=0.0):\n    # Returns nn.CrossEntropyLoss with label smoothing enabled for torch>=1.10.0\n    if check_version(torch.__version__, '1.10.0'):\n        return nn.CrossEntropyLoss(label_smoothing=label_smoothing)\n    if label_smoothing > 0:\n        LOGGER.warning(f'WARNING ⚠️ label smoothing {label_smoothing} requires torch>=1.10.0')\n    return nn.CrossEntropyLoss()\n\n\ndef smart_DDP(model):\n    # Model DDP creation with checks\n    assert not check_version(torch.__version__, '1.12.0', pinned=True), \\\n        'torch==1.12.0 torchvision==0.13.0 DDP training is not supported due to a known issue. ' \\\n        'Please upgrade or downgrade torch to use DDP. See https://github.com/ultralytics/yolov5/issues/8395'\n    if check_version(torch.__version__, '1.11.0'):\n        return DDP(model, device_ids=[LOCAL_RANK], output_device=LOCAL_RANK, static_graph=True)\n    else:\n        return DDP(model, device_ids=[LOCAL_RANK], output_device=LOCAL_RANK)\n\n\n                \n@contextmanager\ndef torch_distributed_zero_first(local_rank: int):\n    # Decorator to make all processes in distributed training wait for each local_master to do something\n    if local_rank not in [-1, 0]:\n        dist.barrier(device_ids=[local_rank])\n    yield\n    if local_rank == 0:\n        dist.barrier(device_ids=[0])\n\n\ndef device_count():\n    # Returns number of CUDA devices available. Safe version of torch.cuda.device_count(). Supports Linux and Windows\n    assert platform.system() in ('Linux', 'Windows'), 'device_count() only supported on Linux or Windows'\n    try:\n        cmd = 'nvidia-smi -L | wc -l' if platform.system() == 'Linux' else 'nvidia-smi -L | find /c /v \"\"'  # Windows\n        return int(subprocess.run(cmd, shell=True, capture_output=True, check=True).stdout.decode().split()[-1])\n    except Exception:\n        return 0\n\n\ndef select_device(device='', batch_size=0, newline=True,verbose=True):\n    # device = None or 'cpu' or 0 or '0' or '0,1,2,3'\n    s = f'Prompt-Can-Anything 🚀🚀 {git_describe() or file_date()} Python-{platform.python_version()} torch-{torch.__version__} '\n    device = str(device).strip().lower().replace('cuda:', '').replace('none', '')  # to string, 'cuda:0' to '0'\n    cpu = device == 'cpu'\n    mps = device == 'mps'  # Apple Metal Performance Shaders (MPS)\n    if cpu or mps:\n        os.environ['CUDA_VISIBLE_DEVICES'] = '-1'  # force torch.cuda.is_available() = False\n    elif device:  # non-cpu device requested\n        os.environ['CUDA_VISIBLE_DEVICES'] = device  # set environment variable - must be before assert is_available()\n        assert torch.cuda.is_available() and torch.cuda.device_count() >= len(device.replace(',', '')), \\\n            f\"Invalid CUDA '--device {device}' requested, use '--device cpu' or pass valid CUDA device(s)\"\n\n    if not cpu and not  mps and torch.cuda.is_available():  # prefer GPU if available\n        devices = device.split(',') if device else '0'  # range(torch.cuda.device_count())  # i.e. 0,1,6,7\n        n = len(devices)  # device count\n        if n > 1 and batch_size > 0:  # check batch_size is divisible by device_count\n            assert batch_size % n == 0, f'batch-size {batch_size} not multiple of GPU count {n}'\n        space = ' ' * (len(s) + 1)\n        for i, d in enumerate(devices):\n            p = torch.cuda.get_device_properties(i)\n            s += f\"{'' if i == 0 else space}CUDA:{d} ({p.name}, {p.total_memory / (1 << 20):.0f}MiB)\\n\"  # bytes to MB\n        arg = 'cuda:0'\n    elif mps and getattr(torch, 'has_mps', False) and torch.backends.mps.is_available():  # prefer MPS if available\n        s += 'MPS\\n'\n        arg = 'mps'\n    else:  # revert to CPU\n        s += 'CPU\\n'\n        arg = 'cpu'\n\n    if not newline:\n        s = s.rstrip()\n    if verbose and RANK==-1:\n        LOGGER.info(s)  \n    return torch.device(arg)\n\n\ndef time_sync():\n    # PyTorch-accurate time\n    if torch.cuda.is_available():\n        torch.cuda.synchronize()\n    return time.time()\n\ndef get_latest_opset():\n    # Return max supported ONNX opset by this version of torch\n    return max(int(k[14:]) for k in vars(torch.onnx) if 'symbolic_opset' in k) -1 # opset\n\ndef intersect_dicts(da, db, exclude=()):\n    # Dictionary intersection of matching keys and shapes, omitting 'exclude' keys, using da values\n    return {k: v for k, v in da.items() if k in db and not any(x in k for x in exclude) and v.shape == db[k].shape}\n\ndef profile(input, ops, n=10, device=None):\n    \n    #\n    # Usage:\n    #     input = torch.randn(16, 3, 640, 640)\n    #     m1 = lambda x: x * torch.sigmoid(x)\n    #     m2 = nn.SiLU()\n    #     profile(input, [m1, m2], n=100)  # profile over 100 iterations\n\n    results = []\n    if not isinstance(device, torch.device):\n        device = select_device(device)\n    print(f\"{'Params':>12s}{'GFLOPs':>12s}{'GPU_mem (GB)':>14s}{'forward (ms)':>14s}{'backward (ms)':>14s}\"\n          f\"{'input':>24s}{'output':>24s}\")\n\n    for x in input if isinstance(input, list) else [input]:\n        x = x.to(device)\n        x.requires_grad = True\n        for m in ops if isinstance(ops, list) else [ops]:\n            m = m.to(device) if hasattr(m, 'to') else m  # device\n            m = m.half() if hasattr(m, 'half') and isinstance(x, torch.Tensor) and x.dtype is torch.float16 else m\n            tf, tb, t = 0, 0, [0, 0, 0]  # dt forward, backward\n            try:\n                flops = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2  # GFLOPs\n            except Exception:\n                flops = 0\n\n            try:\n                for _ in range(n):\n                    t[0] = time_sync()\n                    y = m(x)\n                    t[1] = time_sync()\n                    try:\n                        _ = (sum(yi.sum() for yi in y) if isinstance(y, list) else y).sum().backward()\n                        t[2] = time_sync()\n                    except Exception:  # no backward method\n                        # print(e)  # for debug\n                        t[2] = float('nan')\n                    tf += (t[1] - t[0]) * 1000 / n  # ms per op forward\n                    tb += (t[2] - t[1]) * 1000 / n  # ms per op backward\n                mem = torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0  # (GB)\n                s_in, s_out = (tuple(x.shape) if isinstance(x, torch.Tensor) else 'list' for x in (x, y))  # shapes\n                p = sum(x.numel() for x in m.parameters()) if isinstance(m, nn.Module) else 0  # parameters\n                print(f'{p:12}{flops:12.4g}{mem:>14.3f}{tf:14.4g}{tb:14.4g}{str(s_in):>24s}{str(s_out):>24s}')\n                results.append([p, flops, mem, tf, tb, s_in, s_out])\n            except Exception as e:\n                results.append(None)\n            torch.cuda.empty_cache()\n    return results\n\n\ndef is_parallel(model):\n    # Returns True if model is of type DP or DDP\n    return type(model) in (nn.parallel.DataParallel, nn.parallel.DistributedDataParallel)\n\n\ndef de_parallel(model):\n    # De-parallelize a model: returns single-GPU model if model is of type DP or DDP\n    return model.module if is_parallel(model) else model\n\n\ndef initialize_weights(model):\n    for m in model.modules():\n        t = type(m)\n        if t is nn.Conv2d:\n            pass  # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')\n        elif t is nn.BatchNorm2d:\n            m.eps = 1e-3\n            m.momentum = 0.03\n        elif t in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU]:\n            m.inplace = True\n\n\ndef find_modules(model, mclass=nn.Conv2d):\n    # Finds layer indices matching module class 'mclass'\n    return [i for i, m in enumerate(model.module_list) if isinstance(m, mclass)]\n\n\ndef sparsity(model):\n    # Return global model sparsity\n    a, b = 0, 0\n    for p in model.parameters():\n        a += p.numel()\n        b += (p == 0).sum()\n    return b / a\n\n\ndef prune(model, amount=0.3):\n    # Prune model to requested global sparsity\n    import torch.nn.utils.prune as prune\n    print('Pruning model... ', end='')\n    for name, m in model.named_modules():\n        if isinstance(m, nn.Conv2d):\n            prune.l1_unstructured(m, name='weight', amount=amount)  # prune\n            prune.remove(m, 'weight')  # make permanent\n    print(' %.3g global sparsity' % sparsity(model))\n\n\ndef fuse_conv_and_bn(conv, bn):\n    # Fuse Conv2d() and BatchNorm2d() layers https://tehnokv.com/posts/fusing-batchnorm-and-conv/\n    fusedconv = nn.Conv2d(conv.in_channels,\n                          conv.out_channels,\n                          kernel_size=conv.kernel_size,\n                          stride=conv.stride,\n                          padding=conv.padding,\n                          groups=conv.groups,\n                          bias=True).requires_grad_(False).to(conv.weight.device)\n\n    # Prepare filters\n    w_conv = conv.weight.clone().view(conv.out_channels, -1)\n    w_bn = torch.diag(bn.weight.div(torch.sqrt(bn.eps + bn.running_var)))\n    fusedconv.weight.copy_(torch.mm(w_bn, w_conv).view(fusedconv.weight.shape))\n\n    # Prepare spatial bias\n    b_conv = torch.zeros(conv.weight.size(0), device=conv.weight.device) if conv.bias is None else conv.bias\n    b_bn = bn.bias - bn.weight.mul(bn.running_mean).div(torch.sqrt(bn.running_var + bn.eps))\n    fusedconv.bias.copy_(torch.mm(w_bn, b_conv.reshape(-1, 1)).reshape(-1) + b_bn)\n\n    return fusedconv\n\n\ndef model_info(model, verbose=False, imgsz=640):\n    # Model information. img_size may be int or list, i.e. img_size=640 or img_size=[640, 320]\n    n_p = sum(x.numel() for x in model.parameters())  # number parameters\n    n_g = sum(x.numel() for x in model.parameters() if x.requires_grad)  # number gradients\n    if verbose:\n        print(f\"{'layer':>5} {'name':>40} {'gradient':>9} {'parameters':>12} {'shape':>20} {'mu':>10} {'sigma':>10}\")\n        for i, (name, p) in enumerate(model.named_parameters()):\n            name = name.replace('module_list.', '')\n            print('%5g %40s %9s %12g %20s %10.3g %10.3g' %\n                  (i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std()))\n\n    try:  # FLOPs\n        p = next(model.parameters())\n        stride = max(int(model.stride.max()), 32) if hasattr(model, 'stride') else 32  # max stride\n        im = torch.empty((1, p.shape[1], stride, stride), device=p.device)  # input image in BCHW format\n        flops = thop.profile(deepcopy(model), inputs=(im,), verbose=False)[0] / 1E9 * 2  # stride GFLOPs\n        imgsz = imgsz if isinstance(imgsz, list) else [imgsz, imgsz]  # expand if int/float\n        fs = f', {flops * imgsz[0] / stride * imgsz[1] / stride:.1f} GFLOPs'  # 640x640 GFLOPs\n    except Exception:\n        fs = ''\n\n    name = Path(model.yaml_file).stem.replace('yolov5', 'YOLOv5') if hasattr(model, 'yaml_file') else 'Model'\n    LOGGER.info(f\"{name} summary: {len(list(model.modules()))} layers, {n_p} parameters, {n_g} gradients{fs}\")\n\n\ndef scale_img(img, ratio=1.0, same_shape=False, gs=32):  # img(16,3,256,416)\n    # Scales img(bs,3,y,x) by ratio constrained to gs-multiple\n    if ratio == 1.0:\n        return img\n    h, w = img.shape[2:]\n    s = (int(h * ratio), int(w * ratio))  # new size\n    img = F.interpolate(img, size=s, mode='bilinear', align_corners=False)  # resize\n    if not same_shape:  # pad/crop img\n        h, w = (math.ceil(x * ratio / gs) * gs for x in (h, w))\n    return F.pad(img, [0, w - s[1], 0, h - s[0]], value=0.447)  # value = imagenet mean\n\n\ndef copy_attr(a, b, include=(), exclude=()):\n    # Copy attributes from b to a, options to only include [...] and to exclude [...]\n    for k, v in b.__dict__.items():\n        if (len(include) and k not in include) or k.startswith('_') or k in exclude:\n            continue\n        else:\n            setattr(a, k, v)\n            \ndef smart_optimizer(model, name='Adam', lr=0.001, momentum=0.9, decay=1e-5):\n    ## param group optimizer:\n    g = [], [], []  # optimizer parameter groups\n    bn = tuple(v for k, v in nn.__dict__.items() if 'Norm' in k)  # normalization layers, i.e. BatchNorm2d()\n    for v in model.modules():\n        for p_name,p in v.named_parameters(recurse=0):\n            if p_name==\"bias\":  # bias\n                g[2].append(p)\n            elif p_name =='weight' and isinstance(v,bn):  # weight (no decay)\n                g[1].append(p)\n            # elif hasattr(v, 'weight') and isinstance(v.weight, nn.Parameter):  # weight (with decay)\n                # g[0].append(v.weight)\n            # elif p_name =='w1_weight' and isinstance(v.w1_weight, nn.Parameter) :\n                # g[0].append(p)\n            # elif p_name=='w2_weight' and isinstance(v.w2_weight, nn.Parameter) :\n                # g[0].append(p)\n            # elif p_name=='relative_position_bias_weight' and isinstance(v.relative_position_bias_weight, nn.Parameter) :\n                # g[0].append(p)\n            # elif p_name=='tau' and isinstance(v.tau, nn.Parameter) :\n                # g[0].append(p)  \n            else :\n                g[0].append(p)\n            # add yolov7 weights key   \n        if hasattr(v, 'im'):\n                if hasattr(v.im, 'implicit'):           \n                    g[0].append(v.im.implicit)\n                else:\n                    for iv in v.im:\n                        g[0].append(iv.implicit)\n        if hasattr(v, 'imc'):\n                if hasattr(v.imc, 'implicit'):           \n                    g[0].append(v.imc.implicit)\n                else:\n                    for iv in v.imc:\n                        g[0].append(iv.implicit)\n        if hasattr(v, 'imb'):\n                if hasattr(v.imb, 'implicit'):           \n                    g[0].append(v.imb.implicit)\n                else:\n                    for iv in v.imb:\n                        g[0].append(iv.implicit)\n        if hasattr(v, 'imo'):\n                if hasattr(v.imo, 'implicit'):           \n                    g[0].append(v.imo.implicit)\n                else:\n                    for iv in v.imo:\n                        g[0].append(iv.implicit)\n        if hasattr(v, 'ia'):\n                if hasattr(v.ia, 'implicit'):           \n                    g[0].append(v.ia.implicit)\n                else:\n                    for iv in v.ia:\n                        g[0].append(iv.implicit)\n        \n        \n    if name == 'Adam':\n        optimizer = torch.optim.Adam(g[2], lr=lr, betas=(momentum, 0.999))  # adjust beta1 to momentum\n    elif name == 'AdamW':\n        optimizer = torch.optim.AdamW(g[2], lr=lr, betas=(momentum, 0.999), weight_decay=0.0)\n    elif name == 'RMSProp':\n        optimizer = torch.optim.RMSprop(g[2], lr=lr, momentum=momentum)\n    elif name == 'SGD':\n        optimizer = torch.optim.SGD(g[2], lr=lr, momentum=momentum, nesterov=True)\n    else:\n        raise NotImplementedError(f'Optimizer {name} not implemented.')\n\n    optimizer.add_param_group({'params': g[0], 'weight_decay': decay})  # add g0 with weight_decay\n    optimizer.add_param_group({'params': g[1], 'weight_decay': 0.0})  # add g1 (biases)\n    LOGGER.info(f\"{colorstr('optimizer:')} {type(optimizer).__name__}(lr={lr})  with parameter groups \"\n                f\"{len(g[1])} weight (decay=0.0), {len(g[0])} weight(decay={decay}), {len(g[2])} bias\")\n    return optimizer\n\ndef smart_hub_load(repo='ultralytics/yolov5', model='yolov5s', **kwargs):\n    # YOLOv5 torch.hub.load() wrapper with smart error/issue handling\n    if check_version(torch.__version__, '1.9.1'):\n        kwargs['skip_validation'] = True  # validation causes GitHub API rate limit errors\n    if check_version(torch.__version__, '1.12.0'):\n        kwargs['trust_repo'] = True  # argument required starting in torch 0.12\n    try:\n        return torch.hub.load(repo, model, **kwargs)\n    except Exception:\n        return torch.hub.load(repo, model, force_reload=True, **kwargs)\n\ndef smart_resume(ckpt, optimizer, ema=None, weights='yolov5s.pt', epochs=300, resume=True):\n    # Resume training from a partially trained checkpoint\n    best_fitness = 0.0\n    start_epoch = ckpt['epoch'] + 1\n    if ckpt['optimizer'] is not None:\n        optimizer.load_state_dict(ckpt['optimizer'])  # optimizer\n        best_fitness = ckpt['best_fitness']\n    if ema and ckpt.get('ema'):\n        ema.ema.load_state_dict(ckpt['ema'].float().state_dict())  # EMA\n        ema.updates = ckpt['updates']\n    if resume:\n        assert start_epoch > 0, f'{weights} training to {epochs} epochs is finished, nothing to resume.'\n        LOGGER.info(f'Resuming training from {weights} for {epochs - start_epoch} more epochs to {epochs} total epochs')\n        assert start_epoch > 0, f'{weights} training to {epochs} epochs is finished, nothing to resume.\\n' \\\n                                f\"Start a new training without --resume, i.e. 'python train.py --weights {weights}'\"\n        LOGGER.info(f'Resuming training from {weights} from epoch {start_epoch} to {epochs} total epochs')\n    if epochs < start_epoch:\n        LOGGER.info(f\"{weights} has been trained for {ckpt['epoch']} epochs. Fine-tuning for {epochs} more epochs.\")\n        epochs += ckpt['epoch']  # finetune additional epochs\n    return best_fitness, start_epoch, epochs\n\n\n\nclass EarlyStopping:\n    # YOLOv5 simple early stopper\n    def __init__(self, patience=30):\n        self.best_fitness = 0.0  # i.e. mAP\n        self.best_epoch = 0\n        self.patience = patience or float('inf')  # epochs to wait after fitness stops improving to stop\n        self.possible_stop = False  # possible stop may occur next epoch\n\n    def __call__(self, epoch, fitness):\n        if fitness >= self.best_fitness:  # >= 0 to allow for early zero-fitness stage of training\n            self.best_epoch = epoch\n            self.best_fitness = fitness\n        delta = epoch - self.best_epoch  # epochs without improvement\n        self.possible_stop = delta >= (self.patience - 1)  # possible stop may occur next epoch\n        stop = delta >= self.patience  # stop training if patience exceeded\n        if stop:\n            LOGGER.info(f'Stopping training early as no improvement observed in last {self.patience} epochs. '\n                        f'Best results observed at epoch {self.best_epoch}, best model saved as best.pt.\\n'\n                        f'To update EarlyStopping(patience={self.patience}) pass a new patience value, '\n                        f'i.e. `python train.py --patience 300` or use `--patience 0` to disable EarlyStopping.')\n        return stop\n\n\nclass ModelEMA:\n    \"\"\" Updated Exponential Moving Average (EMA) from https://github.com/rwightman/pytorch-image-models\n    Keeps a moving average of everything in the model state_dict (parameters and buffers)\n    For EMA details see https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage\n    \"\"\"\n\n    def __init__(self, model, decay=0.9999, tau=2000, updates=0):\n        # Create EMA\n        self.ema = deepcopy(de_parallel(model)).eval()  # FP32 EMA\n        # if next(model.parameters()).device.type != 'cpu':\n        #     self.ema.half()  # FP16 EMA\n        self.updates = updates  # number of EMA updates\n        self.decay = lambda x: decay * (1 - math.exp(-x / tau))  # decay exponential ramp (to help early epochs)\n        for p in self.ema.parameters():\n            p.requires_grad_(False)\n            \n    @smart_inference_mode()\n    def update(self, model):\n        # Update EMA parameters\n        self.updates += 1\n        d = self.decay(self.updates)\n\n        msd = de_parallel(model).state_dict()  # model state_dict\n        for k, v in self.ema.state_dict().items():\n            if v.dtype.is_floating_point:\n                v *= d\n                v += (1 - d) * msd[k].detach()\n\n    def update_attr(self, model, include=(), exclude=('process_group', 'reducer')):\n        # Update EMA attributes\n        copy_attr(self.ema, model, include, exclude)\n\nclass BatchNormXd(torch.nn.modules.batchnorm._BatchNorm):\n    def _check_input_dim(self, input):\n        # The only difference between BatchNorm1d, BatchNorm2d, BatchNorm3d, etc\n        # is this method that is overwritten by the sub-class\n        # This original goal of this method was for tensor sanity checks\n        # If you're ok bypassing those sanity checks (eg. if you trust your inference\n        # to provide the right dimensional inputs), then you can just use this method\n        # for easy conversion from SyncBatchNorm\n        # (unfortunately, SyncBatchNorm does not store the original class - if it did\n        #  we could return the one that was originally created)\n        return\n\ndef revert_sync_batchnorm(module):\n    # this is very similar to the function that it is trying to revert:\n    # https://github.com/pytorch/pytorch/blob/c8b3686a3e4ba63dc59e5dcfe5db3430df256833/torch/nn/modules/batchnorm.py#L679\n    module_output = module\n    if isinstance(module, torch.nn.modules.batchnorm.SyncBatchNorm):\n        new_cls = BatchNormXd\n        module_output = BatchNormXd(module.num_features,\n                                               module.eps, module.momentum,\n                                               module.affine,\n                                               module.track_running_stats)\n        if module.affine:\n            with torch.no_grad():\n                module_output.weight = module.weight\n                module_output.bias = module.bias\n        module_output.running_mean = module.running_mean\n        module_output.running_var = module.running_var\n        module_output.num_batches_tracked = module.num_batches_tracked\n        if hasattr(module, \"qconfig\"):\n            module_output.qconfig = module.qconfig\n    for name, child in module.named_children():\n        module_output.add_module(name, revert_sync_batchnorm(child))\n    del module\n    return module_output\n\n\nclass TracedModel(nn.Module):\n\n    def __init__(self, model=None, device=None, img_size=(640,640)): \n        super(TracedModel, self).__init__()\n        \n        print(\" Convert model to Traced-model... \") \n        self.stride = model.stride\n        self.names = model.names\n        self.model = model\n\n        self.model = revert_sync_batchnorm(self.model)\n        self.model.to('cpu')\n        self.model.eval()\n\n        self.detect_layer = self.model.model[-1]\n        self.model.traced = True\n        \n        rand_example = torch.rand(1, 3, img_size, img_size)\n        \n        traced_script_module = torch.jit.trace(self.model, rand_example, strict=False)\n        #traced_script_module = torch.jit.script(self.model)\n        traced_script_module.save(\"traced_model.pt\")\n        print(\" traced_script_module saved! \")\n        self.model = traced_script_module\n        self.model.to(device)\n        self.detect_layer.to(device)\n        print(\" model is traced! \\n\") \n\n    def forward(self, x, augment=False, profile=False):\n        out = self.model(x)\n        out = self.detect_layer(out)\n        return \ndef get_num_params(model):\n    return sum(x.numel() for x in model.parameters())\n\n\ndef get_num_gradients(model):\n    return sum(x.numel() for x in model.parameters() if x.requires_grad)\n\n\ndef get_flops(model, imgsz=640):\n    try:\n        model = de_parallel(model)\n        p = next(model.parameters())\n        stride = max(int(model.stride.max()), 32) if hasattr(model, 'stride') else 32  # max stride\n        im = torch.empty((1, p.shape[1], stride, stride), device=p.device)  # input image in BCHW format\n        flops = thop.profile(deepcopy(model), inputs=(im,), verbose=False)[0] / 1E9 * 2  # stride GFLOPs\n        imgsz = imgsz if isinstance(imgsz, list) else [imgsz, imgsz]  # expand if int/float\n        flops = flops * imgsz[0] / stride * imgsz[1] / stride  # 640x640 GFLOPs\n        return flops\n    except Exception:\n        return 0"
  },
  {
    "path": "utils/video.py",
    "content": "import asyncio\nimport os\nimport cv2\nimport time\nimport sys\nimport subprocess\nfrom multiprocessing import Pool, current_process\nfrom threading import Thread\n\ndataset_path = sys.argv[1]\noutput_folder = sys.argv[2]\n\ndef process_video(video_path):\n    # Get video name without extension\n    video_name = os.path.splitext(os.path.basename(video_path))[0]\n    # Create output folder\n    output_path = os.path.join(output_folder, video_name)\n    if not os.path.exists(output_path):\n        os.makedirs(output_path)\n    # Decode video\n    ffmpeg_command = ['ffmpeg', '-i', video_path, os.path.join(output_path, video_name + '_%04d.png')]\n    process = subprocess.Popen(ffmpeg_command, stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n    stdout, stderr = process.communicate()\n    if process.returncode != 0:\n        print('Error processing video {}:\\n{}'.format(video_path, stderr))\n\nif __name__ == '__main__':\n    # Get video list\n    video_list = []\n    for root, _, files in os.walk(dataset_path):\n        for file in files:\n            if file.endswith('.mp4') or file.endswith('.avi'):\n                video_list.append(os.path.join(root, file))\n    # Process videos using pool of workers\n    pool_size = 4 # Change this to number of CPU cores you want to use\n    pool = Pool(processes=pool_size)\n    for video_path in video_list:\n        pool.apply_async(process_video, args=(video_path,))\n    pool.close()\n    pool.join()\n\n\n    print('All videos processed!')\nasync def process_video_chunk(chunk_index, video_chunk, output_folder):\n    # 这里输入您的图像处理操作，例如人脸检测等等。\n    print(f\"Processing chunk index {chunk_index}...\")\n\n    # 该函数可以根据您的需求来设置输出文件名，这里我们按照chunk_index进行计数:\n    output_file = os.path.join(output_folder, f\"{chunk_index}.mp4\")\n    # 实际上，通过更改文件扩展名，您可以保存为不同的文件格式，例如.avi, .mkv等\n\n    # 再次调用VideoWrite对象以创建一个新的输出文件:\n    output_writer = cv2.VideoWriter(output_file, cv2.VideoWriter_fourcc(*\"mp4v\"), 25, (video_chunk.shape[1], video_chunk.shape[0]), True)\n\n    # 写入切片中的帧:\n    for frame in video_chunk:\n        output_writer.write(frame)\n\n    output_writer.release()\n\nasync def process_video(input_file, chunk_size_secs=10, output_folder=\"output\"):\n    # 创建输出目录:\n    os.makedirs(output_folder, exist_ok=True)\n\n    # 打开输入视频:\n    input_video = cv2.VideoCapture(input_file)\n\n    # 获取视频帧数和每一帧的间隔时间:\n    frame_count = int(input_video.get(cv2.CAP_PROP_FRAME_COUNT))\n    fps = input_video.get(cv2.CAP_PROP_FPS)\n    frame_duration_ms = int(1 / fps * 1000)\n\n    #创建任务列表\n    tasks = []\n\n    # 分割视频:\n    chunk_idx = 0\n    while True:\n        # 读取chunk_size_secs秒钟的帧:\n        frames_chunk = []\n        chunk_start_frame_index = chunk_idx * chunk_size_secs * fps\n        chunk_end_frame_index = (chunk_idx + 1) * chunk_size_secs * fps\n        end_of_video_reached = False\n\n        # 获取每一帧并将其添加到切片中\n        for i in range(chunk_start_frame_index, chunk_end_frame_index):\n            # 寻找下一帧:\n            input_video.set(cv2.CAP_PROP_POS_FRAMES, i)\n            success, frame = input_video.read()\n\n            # 如果到达视频末尾，停止读取:\n            if not success:\n                end_of_video_reached = True\n                break\n\n            frames_chunk.append(frame)\n\n        # 发现最后一个文件不足，跳出循环\n        if len(frames_chunk) < 1:\n            break\n\n        # 创建任务并发处理该切片:\n        tasks.append(asyncio.create_task(process_video_chunk(chunk_idx, frames_chunk, output_folder)))\n\n        # 如果到达视频末尾，停止分割:\n        if end_of_video_reached:\n            break\n\n        # 增加计数器以准备下一个切片:\n        chunk_idx += 1\n\n    # 等待所有任务完成\n    await asyncio.gather(*tasks)\n\n    input_video.release()\n\nif __name__ == \"__main__\":\n    start_time = time.monotonic()\n\n    asyncio.run(process_video(\"input_video.mp4\"))\n\n    end_time = time.monotonic()\n    elapsed_time = end_time - start_time\n    print(f\"Elapsed time: {elapsed_time} seconds.\")"
  }
]